diff --git a/benchmark_code/results_process/imputation_log/point01_log/BeijingAir_log/CSDI_BeijingAir.log b/benchmark_code/results_process/imputation_log/point01_log/BeijingAir_log/CSDI_BeijingAir.log
index 47437c6..aefc0f7 100644
--- a/benchmark_code/results_process/imputation_log/point01_log/BeijingAir_log/CSDI_BeijingAir.log
+++ b/benchmark_code/results_process/imputation_log/point01_log/BeijingAir_log/CSDI_BeijingAir.log
@@ -2,8 +2,6 @@
 2024-06-04 02:44:45 [INFO]: Using the given device: cuda:0
 2024-06-04 02:44:45 [INFO]: Model files will be saved to new_results_point_rate01/BeijingAir/CSDI_BeijingAir/round_0/20240604_T024445
 2024-06-04 02:44:45 [INFO]: Tensorboard file will be saved to new_results_point_rate01/BeijingAir/CSDI_BeijingAir/round_0/20240604_T024445/tensorboard
-/scratch/users/k1814348/.conda/envs/pypots/lib/python3.10/site-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)
-  warnings.warn(f"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}")
 2024-06-04 02:44:45 [INFO]: CSDI initialized with the given hyperparameters, the number of trainable parameters: 244,833
 2024-06-04 02:46:12 [INFO]: Epoch 001 - training loss: 0.5290, validation loss: 0.3566
 2024-06-04 02:47:32 [INFO]: Epoch 002 - training loss: 0.3447, validation loss: 0.3347
@@ -113,8 +111,6 @@
 2024-06-04 05:22:54 [INFO]: Using the given device: cuda:0
 2024-06-04 05:22:54 [INFO]: Model files will be saved to new_results_point_rate01/BeijingAir/CSDI_BeijingAir/round_1/20240604_T052254
 2024-06-04 05:22:54 [INFO]: Tensorboard file will be saved to new_results_point_rate01/BeijingAir/CSDI_BeijingAir/round_1/20240604_T052254/tensorboard
-/scratch/users/k1814348/.conda/envs/pypots/lib/python3.10/site-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)
-  warnings.warn(f"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}")
 2024-06-04 05:22:54 [INFO]: CSDI initialized with the given hyperparameters, the number of trainable parameters: 244,833
 2024-06-04 05:23:58 [INFO]: Epoch 001 - training loss: 0.5063, validation loss: 0.3639
 2024-06-04 05:25:03 [INFO]: Epoch 002 - training loss: 0.3345, validation loss: 0.3241
diff --git a/benchmark_code/results_process/imputation_log/point01_log/BeijingAir_log/ETSformer_BeijingAir.log b/benchmark_code/results_process/imputation_log/point01_log/BeijingAir_log/ETSformer_BeijingAir.log
index 32ed6f2..97e8346 100644
--- a/benchmark_code/results_process/imputation_log/point01_log/BeijingAir_log/ETSformer_BeijingAir.log
+++ b/benchmark_code/results_process/imputation_log/point01_log/BeijingAir_log/ETSformer_BeijingAir.log
@@ -3,8 +3,6 @@
 2024-06-04 02:44:45 [INFO]: Model files will be saved to new_results_point_rate01/BeijingAir/ETSformer_BeijingAir/round_0/20240604_T024445
 2024-06-04 02:44:45 [INFO]: Tensorboard file will be saved to new_results_point_rate01/BeijingAir/ETSformer_BeijingAir/round_0/20240604_T024445/tensorboard
 2024-06-04 02:44:47 [INFO]: ETSformer initialized with the given hyperparameters, the number of trainable parameters: 7,928,510
-/scratch/users/k1814348/.conda/envs/pypots/lib/python3.10/site-packages/torch/functional.py:507: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1708025847130/work/aten/src/ATen/native/TensorShape.cpp:3549.)
-  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
 2024-06-04 02:45:07 [INFO]: Epoch 001 - training loss: 0.8672, validation loss: 0.3540
 2024-06-04 02:45:23 [INFO]: Epoch 002 - training loss: 0.6622, validation loss: 0.3025
 2024-06-04 02:45:40 [INFO]: Epoch 003 - training loss: 0.5964, validation loss: 0.2689