modules/layer_wise_learning_rate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer wise learning rate settings\n",
    "\n",
    "In this tutorial, we introduce how to easily select or filter out network layers and set specific learning rate values for transfer learning.  \n",
    "MONAI provides a utility function to achieve this requirements: `generate_param_groups`, for example:\n",
    "```py\n",
    "net = Unet(dimensions=3, in_channels=1, out_channels=3, channels=[2, 2, 2], strides=[1, 1, 1])\n",
    "print(net)  # print out network components to select expected items\n",
    "print(net.named_parameters())  # print out all the named parameters to filter out expected items\n",
    "params = generate_param_groups(\n",
    "    network=net,\n",
    "    layer_matches=[lambda x: x.model[-1], lambda x: \"conv.weight\" in x],\n",
    "    match_types=[\"select\", \"filter\"],\n",
    "    lr_values=[1e-2, 1e-3],\n",
    ")\n",
    "optimizer = torch.optim.Adam(params, 1e-4)\n",
    "```\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/master/modules/layer_wise_learning_rate.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, ignite, tqdm]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from monai.transforms import (\n",
    "    AddChanneld,\n",
    "    Compose,\n",
    "    LoadImaged,\n",
    "    ScaleIntensityd,\n",
    "    ToTensord,\n",
    ")\n",
    "from monai.optimizers import generate_param_groups\n",
    "from monai.networks.nets import DenseNet121\n",
    "from monai.inferers import SimpleInferer\n",
    "from monai.handlers import StatsHandler\n",
    "from monai.engines import SupervisedTrainer\n",
    "from monai.data import DataLoader\n",
    "from monai.config import print_config\n",
    "from monai.apps import MedNISTDataset\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from ignite.engine import Engine, Events\n",
    "from ignite.metrics import Accuracy\n",
    "import tempfile\n",
    "import sys\n",
    "import shutil\n",
    "import os\n",
    "import logging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 0.4.0+35.g6adbcde\n",
      "Numpy version: 1.19.5\n",
      "Pytorch version: 1.7.1\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
      "MONAI rev id: 6adbcdee45c16f18f5b713575af3410437177311\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.2\n",
      "Nibabel version: 3.2.1\n",
      "scikit-image version: 0.18.1\n",
      "Pillow version: 7.0.0\n",
      "Tensorboard version: 2.4.0\n",
      "gdown version: 3.12.2\n",
      "TorchVision version: 0.8.2\n",
      "ITK version: 5.1.2\n",
      "tqdm version: 4.51.0\n",
      "lmdb version: 1.0.0\n",
      "psutil version: 5.8.0\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Copyright 2020 MONAI Consortium\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/workspace/data/medical\n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create training experiment with MedNISTDataset and workflow\n",
    "\n",
    "The MedNIST dataset was gathered from several sets from [TCIA](https://wiki.cancerimagingarchive.net/display/Public/Data+Usage+Policies+and+Restrictions), [the RSNA Bone Age Challenge](http://rsnachallenges.cloudapp.net/competitions/4), and [the NIH Chest X-ray dataset](https://cloud.google.com/healthcare/docs/resources/public-datasets/nih-chest)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up pre-processing transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = Compose(\n",
    "    [\n",
    "        LoadImaged(keys=\"image\"),\n",
    "        AddChanneld(keys=\"image\"),\n",
    "        ScaleIntensityd(keys=\"image\"),\n",
    "        ToTensord(keys=\"image\"),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create MedNISTDataset for training\n",
    "`MedNISTDataset` inherits from MONAI `CacheDataset` and provides rich parameters to automatically download dataset and extract, and acts as normal PyTorch Dataset with cache mechanism."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
      "file /workspace/data/medical/MedNIST.tar.gz exists, skip downloading.\n",
      "extracted file /workspace/data/medical/MedNIST exists, skip extracting.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 46946/46946 [00:26<00:00, 1771.12it/s]\n"
     ]
    }
   ],
   "source": [
    "train_ds = MedNISTDataset(\n",
    "    root_dir=root_dir, transform=transform, section=\"training\", download=True)\n",
    "# the dataset can work seamlessly with the pytorch native dataset loader,\n",
    "# but using monai.data.DataLoader has additional benefits of mutli-process\n",
    "# random seeds handling, and the customized collate functions\n",
    "train_loader = DataLoader(train_ds, batch_size=300,\n",
    "                          shuffle=True, num_workers=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pick images from MedNISTDataset to visualize and check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eCo6iKHqp5sC50z10BoNB7Lt0WEyZkVWqVqsolUpYWFiIfV5BEPuNK0qGwyF2d3e9Q2i324ii0+W/ZH8s0FEHEBqXaaAzqT7qKuRN0Hfg5VVpWmtlRZk3jovuQKxjx71nCFaKxSJKpZLXHc4LghidD9RVsn4ECvyhzmjButbyUF96vR6ePn2Kfr+P7e1tPH78GEdHR/j000/R7Xaxu7vrmRbubbO7u4tWq+Wft1QqYXFxEe+++y42Njbw0UcfxdK9BOrHx8d4+PAhWq0Wjo6OcHBwgMFggFwu54v3e72eT68R+Nn6QQtm+Gzscx03K9fJWN5UfU8llcvKLKul/lrCWz8957bMTZIcowUy9vNkPggqlGJnQaKmeCw1DsQLDKeBrqRCZt0kjA4ttDRX262pBI1m9bPWwGq9habvtF3sD101QjaK2+rzPqG6gyTWxrbdfv46qf03Ud+VwVGx/W/ThPo/x5U/XN1E0M5jDRTc6HxgAb0CbTI1/CHoUTCVyWT859kmZVUODw9RrVbR7XY9sNe0loI0TfsqgOL/Ng3HouVKpeI/PxwOY8vLB4MBOp1OsLj+PAnVlt20AuQ3Ud9TSWUWeWN3KA5JyHmqk7dLjLXImCslSF3zN6NVLpe136eBZ2RqCwZ1STa/qw5C01J6DTXgTCnxXqzjUbHRH+/FqJ0MC404n1e/p86BUTaXDJOd0b5SVsouww/VBU1jbbTvQk4hlTNRUGqXe+s42L+Bl3fwBs4YwoWFBdTrdRSLRSwtLcUAvs4BAorxeOxXALJYvdfreTDR7/dxdHTk0zs6B8nwcBWUpn9HoxEODw99OqvdbuPk5AStVsvrKJd5s012U77JZIJ2u42DgwPPSCkT22g0sLKygnw+j9XVVTQaDfT7fdy+fdu3bTKZ4NGjR/jss89wcnLiU7NHR0dot9uJY0OZBu5D/6eSSirzk7cK3ADJha3KgoSYE0aBLAQuFouoVCqo1WreiNJ5KFtBsbuXEojYLez5vtbwKLuihlVXZmn9TxIDYkEF2xVif7QPtD5G+4lgSGskFKjoZm1anH0RcJJUo5PKmSTV3Ni/k4CNvZYCaK4GYo0KHb+moAiwGRDwxG3nnAdBTBcxXcr0D1OYOhc5Nzqdjl9NRX0lQ3N4eBgDS5PJxKfAdNm5TfMy4Dg+PkY2m8XBwYGf3/YQWgVKehAor9vtdj1wYnrZFi2fN2Z2XqTAJpVUXq+8deDGGhybhiKwsLUCpVIJ1WoV+Xze19JUq1W/fTtPNSaI0dVFChh4D66aYq2MvqYAYjAYYDKZoNfr4fj4GOPx2Bce2+fh3yFa29a68LPcnZVpNRZc6kooRsMaVbPOh5E1I3Bbl0EnRoemtQYUraVJEgV8qeFPFmW/tM8oCmYUcNsaHb6Wy+VQqVRQLBbxzjvv4O7du6hUKlhfX4/VnSiYV/DCOUCwc3x8HNt9mKwjUz0UPUeKTKBeiwXvnHf8nOqmBgD6N2VnZwfHx8doNBro9Xp+TvMEcabebMqXK7iy2Szq9TqWlpbQbrfx+PFjtNttv5qK/R1iKkN2yKauUz1PJZXXJ28VuAlFUqE6G62rIZ3eaDR8OoqroHRnVRojghVdXcK0DaNR5063fi+VSi8ZbOCs5oVGejwe4+joyNcsAPD72mgti+4CnPSMWmug7eRy13w+j0qlgm63CwCxlJNdqs6om5unad1CNpv1/UQnO5lM/HMRPNp0YFLNk6b8UqM/XbQ+KqnuStNQdvWfAiPuKVMul/Huu+/io48+QrVaxdraGvL5PLa3t/3OwN1u9yXHrHqfyWQ8uCEgIrtXqVR82wH4gmMAsTQXwTHnHXAGjsnuMFWqz8LvUfcAYGtrC8+ePcPi4iL6/T6KxSKazaafl0wP6xEovCfZ24WFBaysrPhaIGWA2DbV8VAKkBICOKmkksrrkTca3LxqKoOGhUaeRopAplKp+NdoeJTZAM72q2FxpDpqdTjKQvBvRnt6aKEe0cBjFdR5aMpKU1j6PNofWpipn1GgxOW1mUz8DKpZwIXWxtjl5yHmxRY463XsZ1NQM12m9ZWuhgo5V00/qZ5a4EMWhXo0GAz8xnhHR0e+doVAhmyjgn8CbF1xqO0AzrY+AODnGwuPWaBM0GJPpOffumJJ94HSlBEB//HxsU9pRVGEQqGAVqsVe2bO/VKpFAtIms0m+v2+XzUWYia5s7Edn2kAJ5VUUnl98saCGxoJNWIh429pfAB+WWs+n8fCwgIKhQLW19exvr4eY1NYEKn5ft0LhE6AUZ49OoEghjQ4V5Tw81EUeUYkn8/7WgM6jFCtTuh5KGwP+0P7CoCvG+C+HSpanKlRM9+zxca6IoWfYRtshKrnV9nolk5RV7Kog07lVBSYa5/ZORBKiWgalnvW2GJirWHhCqbJZIKnT5/i008/Ra/X83vJcDsBAh4FxwrcdWNL3pftIbjhflF67AdTWc45v0qKfRBFEfb29tBqtXxal9cajUZ+VZfOD+5lwzRbuVzG3t4eHj9+7PU+m836XY4bjYYvMt7Y2MDdu3exvLyM733vewAQY6Y4R2kTdH5Q7EpFnRuppJLK65E3FtyozAJwgDPDoufLEGiUSiWUy2VvrGi4uFdHr9eLFecyutSzoxTIKDNBR8R78n6aSmJBpk09WRB3XtRnn1n7RqN7TQHws+qY9DUtPAUQo+AZyev9bZpJ6yJCzte2PZWwhOpmQiA2xO7p8n8FRSGQzJQkjwxpt9vo9XrY39/3Y860EldHkbWgfnOeEbxwc0veT2tuWLSvz6kFv/q9yWSCbreLYrEYO0BWdVp1kPrLImDW/tjUHA/sZD8xLVwul1Gr1XB0dIRyuYxyueznLBkr3t+ytZat1HFKdT2VVF6vXDm4sSuOQikddczWANuN5gDENpHjniy60oiAg/R3pVLxRZQLCwt+lcTm5mZstRJXg7AQUUXP3CHIsUaZDoSgQhkMUvLsC9b98MRiskbsJ4169W+mh6zYTca0JkNTZcCZA9Tl3nROfKZ6ve77V5ev8zVtD6/Je7BPcrlc7MwffX6mQFQs+2ZlWjrypjiPi7bDPpum/3hNsntAfMNHq3e1Wg0LCwv+GgQXTKv2ej2cnJx4hoZ1ZwDw/Plz9Pt9n77k8m0WDDP1VK/XXzqZnqI1Kry/BglkO7Wgt1Kp+LQpmUyyM61WC1tbW7G5oPtN9ft9fw+bIu31ehgMBiiVSqjVaj79xo0qNzc3US6X0e120Ww20Ww2sbS0hFar5YuJdTsFtpN9pGm5kDCFpQDHMnCW0QnpQiqppDJdrhTc0GEyKtJ8OvPe531fKXYaJhpyLYrV5aukvZkWYlqK4KZQKODw8BB7e3v+u7yXUuwa/SbVmthaBl1VpYdf6v46dAgEN9w5lekwWzthz/rRNAP/1/YB8S329XX9ngIOptHYd/ytu87qMl0FTbZv+IzsI12yy7QCmQAtgOX1QgyDZbSS9OWmybQi0mlpCh13XQ3V7/dj9TI6Hpxj1C0AL4FtsjOZTAYPHz7E5uamZ1tYB8ON+ajLPN6AKaBCoYDV1VUsLi5OfXabftTnVjaEwUQul0On0/HjTABxcHCA3d1dFAoFNBqN2NlW7BMAfhm7PrOmZOv1eoxh2dvb88///PlzlMtlrK6uYmNjw++3o32r9qPT6aDX6/lapCSAooyZjq3O4WlAJ6QX/E4qqaRyJlcKbtTgUkLAwH4ndI1QikWvo2fe2Dy3GkNlUHTlj7IXClas47VpoJCRmZZWUhBFR8SDK7laS0FbyDlaQ0mxjI4tTubnrQG1RcAaAeuzK8giRa+pEX6Gfa59pLqg7N00yn4akEnqjzdVkvSEf4dSfgSHCv4B+BQocAZo9CBXLUKnzrCwV3cX1voqLpeu1Wq+KB+YPkahWiCbQtPnsYdsUpc4R7jSyaawKHxPgyjWk7FPdNdkAH5RAfu41+thZ2cHo9EI3W7X95vaIX6XdoJpWtYt6XiqbVH7kTT2ofmpogsN3ga9TyWVecmVgxvd90V/6OB1pUeosJROlOkRAgI9HJK7rZIKp2gRK5dlHx0dAYCPwoBw4a4FPUmpEkuD6+s2zWLrIOg0Go2Grw3I5XJ+G3pdtaVGXJe/hvpOC03JjLAddB6WwtdrW2dKBgw4W/XS7/dj6UEtUq3Vav67HDsCNi3QZpG1ps/0mWaRaSDzJshFikipJwQtTG/q6dusCatWq2g2mygWi1heXkaxWIytauI46WZ0XAGkYIDsB5k6OvFs9vRk7VqthlqthvX1da9PTFVRQjVANr2q84KvJQUdBDa1Ws1v28DTvnkNBTLsHwK/XC6HjY0N1Ot19Ho9HB4eYjgcotVq4eTkBI1GA++//75/7vF4jOfPn+Pjjz9GJpPx7KUuHOj1eigUCr5eh2zSaDTC/v4+jo+Pg/Nc+yJk5y4KcFJJJZVTufKaG3WiIcCQNJGVdSCFrXl/NYyMnrifhTUejLB09RMddgi0aERoQcosop9LAkkKNBihFovF2F4gylbZ9tnn0+WzCnL097RnUNYmKcKmkVanpt9TNidkeK1z47WnFR3PIjcV2FxEVO8UlLJfOfZkIAD45cxc1TQajdDpdII7TgMvO0fVGa3r4XjZInyCkSQ909o5zn9rB+zfqr9sG/WNBcC6jNvqEHDG3BBEM71aq9Vi9W8E6JnMaT2eBgeDwQCtVsv3PRcFKLukfalsMdlYZW0V3IXSrFZsAKKf12dVNjaVVFK5prQUox8btYVSFUlAQoEIjQlrRLhvDQEMC4O5NLVcLvtD82j0SHPPkhLReo/z2Bt9TRmSaWkHbgfP17mrMI2x1qyECoQpSokreLTPpFGv/qazpOPSWiS9vq6W0jGkg+p2u36310KhgHK5jPX1dTjn/B4q3W4Xe3t73kjb53nTamuSJPQcofZbME3WhSCHuqR7ygDwgCOfz8cKhjudTkxvisWiL4Qnw1EsFv0c4G/uC8Nxz2QyfoNKsop01PbMs2nPSucPIAYGuFNxt9v1bAhTSFxNyB2H9Yw2DRBUL1knxoUG3NqBRypwMz+u8CJjZYuGFUTwerrDMpe8s3+i6HSbB7JeFAKipDlr01e2D6fV8lyEFUwllbdVrpy5sc7Yrp4AXnbK9vs05vasm3K5jGazGYuOGLXpjsMEOaS5NY/Pe9jfodes8VEDayNRRl9JzI9G5qyz4TPQAfFZ2Ffq2Oy1NVrmjy06DUV7FmDa+hymRlS07oHPwG37Gf065/zeQvV6HRsbG8hmszg8PPR1Ddvb2xgOhzPXcGibbzrACbFmVqxjs5E9V5Ox2JbjT4DOtAj7j/Uyx8fH6PV6/h4E+nToBBd6sKTOLV0JyHuQMSG40VTlLGOhbKue95TP5/3+ORqUKJtSq9VQr9cBxIMcy/oR3ADxPZ6YktNdlPl9W0/D4mpeU+vflFUlcCcYAk7rd1gwz+tyTuh8D7E5NvCjDmgtXCqppJIs17IUPARY1KFqNBIy8goU1NFqxMkdf2n4aaTI7BBA6RLVJFCjjp7PoPfU3/zbgh/7vgUhynTofehUisViDNRov2kbdDM0e29lA+xYKO2tbWUNDR2NLQ5m3/E1/W1Xa/E+LOjkrq7ZbBb9fh/1et07DMuUqYT69E0SjbCTUgn6bKyr4VgTrJNFcM7589B4kj0ZGxbL6tEfTL9oDQnBr/6tbApfAxADuNQLLWq1P/o8ChIoCuSVkQTOQBDnB9thWaKk/iN4KZfLfu5wBaICcn6eR0zo/SqVSiwg4BhSfzk/OBZkdIrFImq1GobDoWeiAHi2iPcNgfNQv9lgyurRmzofUknldci54MY59w6A/wHAOoAIwNejKPpF59wSgF8BcB/AQwA/G0XRwTnXikVsQLz4VfPs6iCTGAk6TxojjbK63S56vZ43hOVyGSsrK6jX6xgMBn4Jbb1ejy0XtWDGAh46FwUFChrsc/F1/rZOWw+rVIDFazN91mw2/Z4adGrcNZl9oAaOfaq1FOwzbX+IYdK2cUxouG0hKB0lKXnWI6gz1mMlCGwWFxd9EfdoNEKlUvHb5LdaLZ+GY9tsPYfqyTxlnvoeEps6UGYs9CyantB9l3K5HAaDATqdDorFItbX17G2tuY3zOv1enj+/DlarRZqtRoWFxf9+GQyGTSbTb/3TdJ9AcQAjxYOEzRx9+DRaOQ/q8X9qi98Xo6hniavAI4LBQD4dA+ffzweo1wuo1KpxI5AAcJ1KM6dptaq1SpGoxGeP3/uN+TjMnKm2HZ3d/Hs2bNYgf7CwgLW19cxHo/RbrdjaaYoOiu8VrCnhfQLCwvodDp+nx7us6O1OzZwUiZHn0MDmSQ9elVG53XreyqpXJfMUoE2AvCfR1H0ZQA/AeA/ds59GcAvAPj1KIo+BPDrL/4/V6wxVUOhbIP9vDWSoevqb01z6Y7EdMLKJOg+LGyLLd6z7Utqp0pSzYH9XIi14mc1LcVdjENFlKG+sG0I9a/9ni121v4g2LE/oZRiUhG2vRfHhcuKuVttUluS+tU+9yVkrvp+WUkCHxwPOrxqtYpGo4FarYZyuexX27Gom7U4/G1/KpWKT1Wx9kYLd7WAV+tlQvNCQQb1wP6EmMuQfvJeVhf42nlCAKz7aFFHNRVGJsgWXDvn/LzTPlCbofODDA/ZNTJkynbNWhsTGvfXwM7cKH1PJZV5ybnMTRRFzwE8f/H3kXPu2wDuAPjLAH7qxcd+CcBvAvhb066lzAYQdurq1NSB0yDQ6GhKhnttAEC73fbFl5PJBKVSCSsrKy+d8M17stBQWQAaYDptZTTsZ9TQKj3PpaQhJ6/34t96H9sn+Xwey8vLnmLPZrOxiFnbpSuYtCaD97IOQfuYzBGXA2t7LYvFa00mp+dh2SJJNeCVSsW3m8vaP//8cxQKBV+cWq1W8ZWvfCW2VJkOwzq9acDSpj9eVeap7/MUpjmAMwecy+U8G/nBBx/g3r17/vNHR0fY29uLbUSnjpEgh46fOkh90eXfZIxsqkcLkPk3a+EIqAgqqFO8rq525LUJNHh9zgmyOoPBwLNGyvaG0qlAnPHg/ywO1hVPXBKfyWTQaDRicwY43a8mm81iZWUFzp0WHTMNRaZJf/P6PJfq4OAgVsTc7/dj9U3KVLGdIRZGWa7QPLtIQfFN1fdUUrmsvFLNjXPuPoA/DuC3AKy/mBgAsIlTWnOWawCIn2IMvLwXhjI2tsCVjMHJyYnfF6ZUKmEymaDT6XjQwkJh7vtBg6ZGWp2yOnOb8lBQo9FjCNwo+OJ3rQMOpVcsY0Th3iJcScKDNrndu+03FS2QpMHWfTpCfW6BAq9jQSX7iXuhKCgjw6Rb1DvnfP3B8+fPkc/nsbGx4Z3K7du3MRgM8Nlnn+H58+f+3rYOY5peqVwU4Mg17+OS+j4v0WJwAkCeZL+8vIw7d+7g/v37XieKxSJWVlb83i53794FcDaOdJC66RxwdtBpr9eLbfinQhBD4ELgRWaDBcJctcV7KOhnalh1Xp9R5xN1jc+mqVzdHTskFiTQDihQJ9BioBRFkS8EBuDrwrh3Vq/XQ6/Xw/HxsV+FpjaHc61SqWBjYwOFQgHtdhv5fB47Ozse6NsULtuTlHpSeQ0Mzo3S91RSuazMDG6cczUA/xOA/yyKokOTJoicc0FP4pz7GoCvAfHzjiyVbdkN/q+GTH9zi/Z8Po/V1VXUajV0Oh20Wi3PPjB6ZARJNmM8HnvDApxFeJbVCDERanhDVHyobkefW6+toiyQLbDUSI3Og1GoLaxUYMIVNZZ90bYr4AkVTLM9WkukrI4FZfqbP1pb4ZyLAc9Op+Odmp5hROBod9EN9d3rkHnouz0m4yIyLbXZaDTwzjvv+DOQePArmQ1unqjgUllLijIcypbxXlqTxu/rfCR40e+HWEidEwpMlGEkeAfO9J/PdHx8jMFg4AGOdf5aFK/PBsRrxvisygpSpxkkcfyo//pZ207gLGBgmpugfmFhAVEUYX19HZVKBTs7Ozg6OorNA2WJ1I5MKzbXYOsijE3gmq+s76rrqaRy02Qm6+ucy+NU8f9xFEX/84uXt5xzt6Ioeu6cuwVgO/TdKIq+DuDrAFAsFiMALxkmGlDdpAyIsw78mz+NRgP37t1DuVz2u45+/vnn2NraQhRFWF5eRrlcxtLSEhYXFzGZTHBwcOCjWq4oIc3NKFaZCXmGmBGxbedrBCFqwENFwqZv/W9dcaLf0eW4XIFBcKZRtYIupc65ey0LMnU5K+9DAKKMlKYL+L46Rh0nWwxOB6fsFR2E1jrQeRCs6e6yegCh9v8s4EZ15VVlXvpeKpVeunnIYSWxTtp/9vPOOdy5cwc/+ZM/iYWFBdy9exeNRsPvGTQajbC6uorl5eVYnRYZu5OTE1/orQyFraECztKV1C116nxfx1IDk6TFA8Ph0KczCWjtZ2kPeNba3t6eB8DKJPKzqv/afqaznHOxPYB4BhR1hHVLURT5HZ256pJzLZvN+u9qcTE3DWWKjunAO3fuYHFxEdVqFZ1Ox2990O/3cXh46Ntu7UjIxlB/rF2ZxvrOIhfVd9X1JLCfSirXJedW5LnTmfUPAXw7iqL/Rt76NQA/9+LvnwPwq69y45Dj0YjEfk6jKzr5RqOBhYUF1Go1f4Afr6NFkBqt2b1heA9tw6s+B0WN7auIAgkFO/qjrIqCAxaAMi1gf7Rg0haF8l42og49h13Ro/1k/1ZGB0AMUHE3WD0skykKUv3q6Ai2Qvd6HfK69P0ikqRHZAfK5TIWFxf9yfbsc4JYpj60/2yBLu+jY69OUsdB9VPnpDI99r0kkBlicvRHdYj30GM+FJQo26sMVdL9lB3R9+y1VNeUbeKPMi4aCLCPOU8LhQLq9ToajYYv3NZNRENsk/bLrLbyInKT9D2VVOYpszA3fxbA3wDwB86533/x2v8JwN8B8E+ccz8P4HMAP/sqNw6lGDRiUfqXDo6gJpfL4e7du/jRH/1RFItFH0GRqQGAxcVFv0RZjZGmYfibbVD2AYivuFIDpIYnVCxMEEYDp87FOg5KyHloKkYLKRl9V6vVl+p79P6MILmlPJ0CN0mjw7D0uG2/FQtwdPkvnYLu2WMLJnWJMJfS9no9HBwcIIoidDodX6hqGTBdxm7bMyd5LfoeAoVJEkpJcIwzmYxn7tbX13Hnzh1/TEe/38fu7i62t0+DbNaIEHwosCVAog6QpeB84zgyYKBjp77RKesRG5bttGCZz6+1Psp0hgIO1nNxi4But+sBWyZzttkgmSNNN3HOszZJr5vJZPxr2ufdbhcAYgXVbMfOzg6cOy0oJghXdpFt4vw8PDzE1tYWyuUybt++jeFwiEePHuH4+Bibm5vY3d31q6qsXmhqmmLBvoLDS8hr0fdUUrlumWW11P8PQJJF/umL3DQUkWrEZQsGlfZmDc3Kygru37+PQqGAra0tv98H6wzI5vCEbRtdqvO2TIP+tobDRnk2VaK/LUNi5bx6HAV7TD8xqtUdfG09gKbRoijyq2z4P9MWuuqE37VtCIl9T50Sn5mgKYrOdmW1BdjA2dEOCmRLpZIv+tS6IeqHjWTnCW5eh75fRGz9E+cCd9rmHipLS0vI5XLY3NxEt9vF0dERWq2Wd94EMPaMNY6D9j0BtBa70llr6krTIJqutABfAQ4Q351cgTOLiPmaZVlGo5FPt3U6HVSrVZTLZQBnwJqspAJ06jVZLgA+XWvT4Ew7cdfiUKDDlZi23oZi2aPBYICDgwNks1kfdK2trWF3dxe9Xi9mX3S+6n312vpb78dxuYjcFH1PJZV5y5XuUGwNKhA3HnzNFhEzguTGWAQudPAHBwfY3NxEr9fzB/nx86wviKKzFTyZTAbHx8cxQEMJpWNs/jvEHITAjTpkPqPehwxHEgBSQ2dZEnUYwFl9kl06zahW20qHobvLMmqfRSzrpjVKvIY6UW7CpqCN2/YTvOp7/B36W5/jvPa9anrwJorqDFMc3KiP7EAmk8Hh4aE/p8um8jRFYuup1CFTr2yNDNNcnEecX/we9ZdnX/GeBBYcfz4P2TyusLOpFwWxyuzYdJAGEHoEiO7/w+fQGjPLklL4LGRH+RrbzTowLudWllTTrQRjz58/9+3/oR/6IX9i+PLyMtrtti/0Pm+fLW2H/q9zbM7sZSqpvPFy5eCG0ZWdzMo86Im9NLbcmGxtbQ3NZtPvXXNycoKHDx/ie9/7HhqNBtbW1mIrqU5OTnB8fOyXU+dyOV/Yx+vaaJFtYpvVkNg0FP+2ryk7oSDKppF0FVGoT+x9acxpsKMo8lGg7tiqFDYdEZ0b2a+TkxPk83m/pFWj+1CeX9vGZ7IpPXUsvL/urMx2d7vdmKNh/ZA6K8vc2E3Ykto2re3XLUkrYIDphyIyBVMsFtFsNrG0tATnHDY3NwEAOzs7/oBMTSsBZydZK8DhGIW2BdD7E9gQ5ADwm/3xLLfJZILt7W202+3Ycx4fH3tww/1lNN1ERo/3UjBLcKG6zXQQr8U+IbBhior2hXpDMKVtI5gDXl4xqAX8ulSe+ntwcBCrwQEQ02narH6/j88//xwnJyf4yZ/8Sb80nEBoZWXFn6EVYpwsQ6wBgNYRXjItlUoqb6Vc+dlSISOqVLa+FgIPNJS5XM4bSLt81BZMAmfG07IoGn3NEvGH6iHsc4WAit5TJXTP0P0tyLH3SopG9bu23ocOgYdVMqq3qTbbXsvchN7XqFnHIpS6oEMK1WhwPGeJTm064yaL9g+QDGz4ntY08f3hcIhOpwPgbEUQWQfts6QiYjpSflZ10TpNbY/u+M0zm5g2tgGCsi6qo6G0S5IOW72wAFzndhJ4DOlsko6ozmmqjO1WcGHTZ8DZKjHWkx0dHeHw8NAHdrpyjWlzO/62nZwjdnuBlLF5WezcuqrvvikSmn9vo1w5uKFYYxZFkU9VsIhRc/NkKZaXl/GFL3wBJycn+P3f/30fTd6+fdvXapAloEHVnV0ZIenqEltkrEJjynSPppK0ONMamVAO3KaS7D3YXhUyF9xUTU9z5ndtBMsomxEri4dphMkAOHdalFytVnF4eOi/d3R0hJOTkxjVz+9yI7VMJuN3fFYHbY2+Rs1aV8WTni21H0WRP/eLzBr7n86CToTjxUhYHavdIv86RYuiFXBQlzQ9A8Q3Y6TjIwOSzWbRbrd9DUqr1Yq9B8AfYaGpFS3Q1k30crkcGo2G/6xzDsfHx9jZ2fHMGfew4ZYJCwsLqFarqNVqWF1dBXA6FouLixgMBv4EcgJm3o/jqffS5w2lXHV/GwIa6gEPkyWw0uJn9puygyq6HYKOj56KroW7OqfIAisgPz4+9mk5Ph/TVJ9++il+7dd+zR9qWiwWMR6PY4d5KjACzorudXk+65u4uzTb9rY5ZGVpba0kEN/yAsBL8143jCTjZUGvBlAU2lDaLA3MQqz+TRG1J9MCPDLk1CsthgfO/C194nn9rqJBBvWfmQB+F7jYLtoXlWsDN5RQwaTmvoE4s1OtVrG8vIytrS08e/bMp1YWFhZi31WnyRSHKrR12qEJwHbZ71rR71pGiKITI2RsrXPjdSmMzDWNwM+pEdZnp3Htdrux/USYimNxKiPKbreLTCbj6XX2kyo7x8nuScR2W8YBiAM9OnidAGy7rnI5OTmJLZm1u9fqvfkd7XOt47lOUd3RyFtBjo69silA3FHzmVjz0e/30e12/W7EXEVF4GrZBwAxR8n6nGKx+NJRCTwywDJGBCisfavX6x6klUoldLtdP18ZaBwfH+P4+PiltJPOOwv8tIbM1oMxoKFeWPCowQjFAgBlWjg+GqjoBn7KzNBZcmsFvsZ9b9h/wJlu7+zs4Jvf/CZqtRo++OAD3Lp1y9fwEDDq2LMP9NlUt3UDUrVZs7Cbb4JYexpy0mrnAMTmvYJxfj7EptvrEiDr3mF2jt40YEPhPFfGNdRW6mfoPX1WG5xbAKWifal2h22xWZir6sMrBTdqUNTwAnipEyjsDDq8druN3d1dHB0dBal8NRC2E0OGIgRAQjuGWlZHDZIOrN1m/jzRQU96nf2kUZp9bnWgGrEyCuUz8DOM2hUokOFi9EMno33HtlinzM/wWgriZkkF6Pe4l4nubMxVLraglD+csBpB3AThGNgCevZPKLLUecHP6XEWCkyr1ao/eFQPvNQaK4IP9iOBja6QUvaEv5X5Ub3isvNMJoN2u+2/ozsh2/ms/1MHyVRyZZzqvLJyx8fHMfaP11PAzU0BNUBSXbDzV20EdVuZRTtW/JssGncJ5zy31+JnCXxarRb6/T6WlpZQLBbR6/Vi7C+fW+/LtljnqkyP3czwbRB91qRnCjlUAH6DVo4Jf+v4RFEUm18KBsjYqD153f2r7UsCHdPuze8pEE/6vG6My2urWLbTrii2tlyDCBvMhgL9q9TRKwc3ukJHf2yRsUXnXJ2wvb0di5j0M+qAQ6JKaoGR/q90nUZROsBWiWj0GDWQNeE1KNaJ6W9r5KzzBhBTNG270tdsF7/H9IA+e7fbfUlxudKMKSNGzTp+vKY6KTtufBbtQ3Vy0ySKTve5GY9Pz+bheHI3WzpjnXzqIG8auNG0HYBYf6mB0chIjTX7kYyMsm2VSgX1et1vEsfXGdkToAJn+yS1Wq2X9nDRjROZvtIUD3WKusC0E1OUuVwOq6urqFarqFQqfv8lFsqS5VAWisGKAg/VE7b75OQEnU7Hp6I02KHdIDvE/ta2UxfsMSVqmNkmW0vDZ7fOjUzXeDxGr9fzjI0ad31+Ajk6XgIetlUXH9igQFMFyiLbFO3bJBoEJ72vogEXj7yYTCZ+jzN+h0EdU5lkxnTLCh6iynmgdvx1iQYE+mzWD0yznbME0pzD9nn0/tY/c/EJv6/tYbpZ28V7hIiFq5YrBzcaIQFxZx9iURRIEDRQQUOsxzSEmPSeBUMh5GtrazhZ+Le2xRoqFU05zCIWZNnXbXtDE0SdB52lIngFLQRpBFP20ER1wqGJaD+b9F5I7Fjb64dqm7R/Qve+aWKZGyB8PlBIdyjq9FgbklREr1EdI34dU75Olk73SVJ9swyC6k+hUMBgMPC1JLwn72WZ0BDlndRXNLi6YCBkKNkmAB7IaP8m6QfvYQGWZW1CDCWvrUvtLZDi+wQ5XAQRSn0kiZ1HNu2ggOgHRfSZM5mMB4l0xuwbZRaYFtbv0h9Rf2zh+3UK5yH18TwGZxabbBkv/raf1dWCoawFv2PrH3UeXrdcObhhjQydp0Y7WtgbMmLj8RhHR0e+ILZarcby+DpI1jjpa8DLBoOTROluvkfDpbUuRK3WGU+bEOexCfZ5FSTpd2kYlVrlbzodbTsPA2T/86weRiZMX7DY8eTkBIVCAZ1OxyNzre/RQxKTgGKIrZqG3tUR8bl0CTMdxvHxcbDPrb6EAOp1SBL4UtEas5AzVadGXWARMbc30GMWWEujKUUCjU6n48ecWyVwJQ/byuhNazt0E0kyqSxqzmaz6Ha7aDabsfnBwmeOo36fOsWxtWkAgi4WTetO2uxX1XMCIDo3BcpksSzzqikKy8Tatuh77EdeK5/Po1ar+cCgVqvF9JGpNTKQ+/v7MeZI01w870sZSK17U11XfblJBfTzFF3JZ4NJ6k4+n/dbgzSbTSwvL3sdHY/HMeBPPWy1Wmi3255pmEwm/twvHXNb+/Q6xILVkNigWV/nb4I19pP6RJ0n9vv2M/QFnLfsMwrnKxcJaGH9cDhEt9uNkRhJPvh1y5UXFIeWM6ohSaLf+BmuxnDOeSNyHoNjrxdCrBa9WqekKRyN8tQozjJw06JU+xlVejXK/D9Ux6GOQ+lFUrGaMnPO+ciaBpZUJKlvS8nrNTlR9D0LrFSSom7bP/xRUElwoyvK9Fl5/VCf3DSZ1iZr6JRt07Gn0dWNKfn8mvMmyGFaSg+RpNFm/Y3uDK1gln9z2TIZnuPjY+zv78M556NlOpzxeIxWq4Xj42NUq1VfeGyfQ1koTavyOXj+mO5Tozqn+k0dt6Ksn61l4vsWxITYEQrbxsDBubMtKgh0NN3LWiA6UgIdjqutvbF6wudjHyXpyU0pop+n8Lls+lvfKxQKvsB9cXERzWbT+woCTuqlbpyoKVLqmuq3zjXg9aVWZvUbIT+n79l2qt5T185jf/g9ghqCRxXW9jE4Dtknvb8GElcpVw5u7OoLjUAswFCDZI9fYAeHVu0AYfqNYtGqVQKtd9D39DV1DhqN0uHzfb1/SEGTAJmKKisnntYBaQqBTkAjD2VCWBvAwmfgFDDoPjc02Cw6torJ1yzgYv9oP9kxCf2tfct25vN5lMtl1Ov1WIEz6624PN6uipmFQbtK4diF6qks2NPlyRR9bp5orXqv9yGbqDpIx833+V11pmQ9ON58jWBFAZJGzUpdk0XSe/Lka+DUIFrnTb2lrjBto8aQKQfLZNEmcGk5n5Ggm3qp81X/5nvWIGv/sx1W/xX8qAEn8GCxMPtajzmx9Te8Jx0Fr6/1elZnQrpt0+Zvk6hddO7sHDGe4l6pVPDOO+9gYWHBb3AJwOsS5wxBUCZzuuKvVCqh3+/7OjTWUXU6Hezs7PgSCF5nFmBwUUm6tn12fV3/13nO76gtTArAlVTgXC6VSqjX677mkfOKW38wczIYDJDNZmMLFNjfnKPKSOvzXIVcy/ELauQs5Ws/T5DAYsparYZqteoBTijaUQpXI3t7XY12bHSnzki/r0qgSw6pBKSVAbyEeEOGJ8QwWUWwDpBFb0B8Mz4VOiNlZLgXCp0c78ll13R6tVrNT3yN1jWVwHsnTZrQs4bATgh4lkolZDKnB0Q2m00UCgVPk+ohkcfHx365su0zO27XIWo4kkCtTn6yK/yuOshisYh6vR476V4jdY7PycmJp9ZVqNu6RxLvw/QqAS6dsDKACryUTaNucVm4OvFut4v9/X0A8NsP2OXpNn/PQlnqWKFQ8AdccjwZiRMgcCNKrvrS1I6tGbBRrvaD6iNtSEi/2c+0P5wHbANBCleyETzye9ls1u/uDJwVmTOdSDaBbI0y3do+FQ2m3iax/U8dzufzaDab2NjYQL1exxe/+EUsLS1hcXERi4uLAM6AKn1FsVjEwsICMpkMGo0G6vU62u02Hj58iMFg4O3e3t6eT6/oqlcFxvMU1cUQ+Ljo/bTPQqkv/s3nYx9Vq1WsrKygWq2i2+3i+PgY+Xwei4uLfkEDd7gvl8s+Tcuifns0i7LJrxMgWrny2WCjJ4qCipBj1BSFGsZpisHrhgxBEnNiaTW9hqZoFBxZtGzvMU20fecNvL6vn7NOg23V6I99R6eojIx1ZgRMvG5owmmb+JqNJKwDmaWin9eykTEnXrFY9CddJxl6fm/WMbgKSWqHNToaRZG14KnW3MuJAMKmWHQ8k9pAJkhBCHWar6k+aJDA8dM6KAJRjg1BtXPOL8/msRA6bwmMcrnTQy0J5jj/WWfC5ydbqZ/RQl5NaWuRYwjEcB7Z+X2erijbprYqZIvsfW3NBtNXzjl/Jhjnp6bm+BxJ8rpSJTdFLLCh7tAJ1+t1z9g0m03U63U0Gg0AZ4EzdZO65pxDo9Hw4JKrgTjP+v0+6vW6ryXj6sCrdMyvS6iXtl9pG3jMkQ2gCGo4H2kDuOpRV07SZ1y3/b1y5oZFipYqI5UFxAtlx+Oxz/8xr81ozRovBR/q5EPsgIIs3ouGImRMdC8Q/lajnM1mfbEucOYA7H15L7ZXX+ff+jqfhd8LgUJ1MowaB4NBbPfaXC6HpaUlz3rV63XvfKiUPAuIxqNSqaBSqcT2l7FpuPPGm8K2n1d3QyaBz8DxWFhYQKPRQBRF/iysVqvlo139Pu99EyJZzfkDYaZOQQXHmymo9fV1NJtN3LlzB1/5yleQyWSwubnpi4B1LKgboUgfONXJZrOJfD6PXq/nGR6dB2Rojo+PY+NNyeVyqNfrqNfrKJfLfnfjW7duYWlpCd1u1xdqHhwc4NmzZ9jf38fW1lbMWPJU83K57CO9fD6PRqMRAzdra2sAgP39fezt7fkUqkaQukLm+PgYBwcHiKLIt5H9bQtSQ+A4iUZXsE7wT5tEcEcnSfvA98nmaKq42Wziy1/+cgwkaRpYd0M+T78A+FTtm+58rXDcFBAvLS2hVqvh/v37+NKXvoSFhQX80A/9EJaWllCpVFCr1QCc9Y2u/uTYvvvuu1hbW8Pz589xdHSEo6Mjf9o8t1jo9Xr4nd/5HT9PQuzHPCRkD2dlvu3fqqcaiGvQwNTtycmJ19NsNovl5WWsr6/HMgF8v1wuY3193S864SGyZLeePn3q2cZyuez1UfeiumogfuXgJilVEHJGGukra6PUvA44KTCLGJPYG/2bkalGYJaKVjCm6RnNS6phDD2rAhR19Entta/bZ2DkyLoERrMskOP1tYJelZepHV0izPQVo2JL119ESUNMWZJYRoFUNPdR4eTRlGLofjepBiEEwCzI1eel8yQIWFpawq1bt3ykr/UZ1CWKTaNSqCelUsmzKzYvTl1W48Sl4pqOZLEhd5GmY9B6F67KI4hW5kV3uaZTVgaEz1Cr1fzKLM5/ZRZ15SWAGIgg8NeUmqapz4vEk2yG1hqxLRwvfo73VtCo7SqVSlheXkYul/MBB0Ge2iPnzjaiS7InBABvI4tjgSdBLVPWq6urWFhYwPLyMpaXlz37wu9Sr2gTuYKq0WhgYWEBw+EQ9Xod4/HYA23q9fHxsT/KRPv3dTM402xl6LUkPVUQT/2hzmqaSOvXGFzw+8ogEziSCSM4Yr2SXfzxOgHhLHIt+9wAL+9mqEaWn+WkVfTHvLxSZ5RZO1KjOADegNgKeR1k2x5VGgUXofaEZBpaV1rPSrFYjDlAbasqbxTFawByuZxftUEQRANKxokTnw6LhgQ4O+NKx8xSm6F+Dr3OcbbvqU7QudLB0nnwXJ3hcPhSJb9NrdyESJZjQuM6DRzSeLO+plQqYW1tDRsbG3jnnXfw7rvvIooi7O/ve9CgNV7UAe40TV20hcocF6tnCsw5/vytus6NA6k7Jycn2Nzc9LUKJycnGA6HnnXhcQ2aUiLTQdBqN2Hk3FYanM+jqTPWCykdzmfUtKoCScuATkvj6d/sX01HMc1HFldf05qo3d1dHwVXKhXPxuVyOXz++edot9seINIecQwVwOv/SQWkb4NYVi2fz3smbmlpCQsLC1hdXcXt27c90OG2CKrPvIYNOpkNWFlZwf3793F0dIRer4fBYIBCoYDV1VWMRiN86UtfQjabxcHBAR49ehRbtXcVz6//T7OxIUbYBlEaCOm+UZyHrJvjSkce16NpcGsTAMS2d7EB53Xq47ngxjlXAvBvARRffP6fRlH0XzrnvgDglwEsA/hdAH8jiqJzR15XtyjIoEFw7uwARbIPmhJhMSkAbxBV1JBZsTSeGhE6Ug6gomfN42skq8WEjNRYZKzppWltscyTvmYVOoqiWEGoRpA0vLoTrO4NQnDDFS9au8E+5TlF9Xo9lh4Bzoo4dQwtQJwF4CgoDDn6KIpiTBJ/WMVP2lhBGEV3I70oDTpPfddnpTPUM7C0xgk4S4eWSiV/OOWdO3dw7949vPfee/jggw8wmUzw5MkTtNtt9Ho9v+8PcHZAJlO/1AuycyoEUnZ8tK4mkzk9JJO1IfweCzq5VcBwOMTu7q4vQOcqi9u3b2NlZQULCwtYWVmJOWadRyxm16JgLRpmHYAGHNRvps90F1XtD03d6fxXJiRkiG30rMEEAQzBE1fuMOonkNNxZvuZVl9aWsL9+/eRy+Xw/e9/H7u7uzg8PPQrzsi6ap/pPLftuwSjOlf7/rqExcCVSgXLy8toNpu4desW7t275/uTAFOBjNoo1XWyEM45fPTRR+h0Onj48CE2NzdRrVbx7rvv+rnTbDbxySef4Pnz51cCbpRxoYTsK33MLGA9qd6ROslU3MLCAo6Pj7G1teX3cqLdUnBD36tBpxa/sz3T/PHrllmWkwwA/Pkoin4UwFcB/Ixz7icA/F0Afy+Kog8AHAD4+fMuZCegffiQAdL0iG4nn/T9WST0eZ0U/LGUXihyVLCm7Q05env/aZKkzBp10MipM2c7NYWgtROhIlLLeOiE0UJum+aZZlD1mknPa5/P9jsdJx2hrjoJTdZpuvEKMjd9v4iEdE5ry/S57LPpWOh1+D0df/tZm8riPVk3Qp2jg1cmU+l6BQw0epy3+mPbp99ngKMbTnI5Lp+F9yUg0jOWNECx+mf7b1p/arts39jVXpZN0h+mypJAlgIV208hIKP6Ydt3AblWfT9P+KxkkQl09Rw19Qtq1zQIU9uifUpgynSL6mYmc7oKcHl52W9JkVRWMe9n1mfRv2fR11e5j6ZV6TNCtgNAzI+E7k+/Z/v8uuRc5iY6bV3nxb/5Fz8RgD8P4K+/eP2XAPxXAP7Bedfj5lcKFBit25U5jEBJO2oagmyKpcFCxtICKn7ftksNvxYPAvFTxMkocT8LnRCMIGlsp1GE2l620xp+q2DMbU4mp8vOdZk2Jy+BIJdzky2gs6Dj0N0qdQy0/oZ1BKTLNRXC56PBV9HnswyF9n8oFan6wejh4OAAxWIR7XYbnU4HvV7PM1OcnKEN/l5V5qnvSRObr+tKBAqfgX1B48Ol1dQ7pqX0murMSSMD8LoSRVGMbdGUsGUGSDOT/tcaLu6lww01yUxQRzgGfK5MJoN+vx+7h2VK2Q7uY3R0dIThcIjnz59je3sb7Xb7pbToYDDwhdXU2clk4jcMdO7ls4FUJ1hjp0wiJQT4qMMsuNfvnZycoNvtxgw7++Ho6Mj3O+cd03lqI2wwoc5UnbLON7V/163v8xbt83q9jo2NDVQqFSwuLnqWYWFhwadhtUaMOk7WmuOhGQIuVrl9+zYGgwF2dnZ84TuLu2/fvo319XW/VFwPaH0dz6vBqn0v6fPq+5IAhfWPtpaG20zQ/xFMamkAt99QW877MZ3OXe3pm64T4MxUc+Ocy+KUmvwAwH8L4BMArSiKOMJPANyZ5Vo2OtfXNBrXiJXRoxbo6fWknbHf5zmX0N/q7C3KV8RsCy1pbPRogvNyn9pudU7WqKoQ4Ommatp3Gu1r/Y2N2kOsSuh1Zc7oRMgG6XPYdtK5qpHXz4XqbngtnTh0oqwJYiSshaMUG1VddFLNW99Df2s/aGEtJcQ4KEthGRMVBTb8LnVEjb+2yRpJBTk8M0rbqMEAASmpagWuGsnptUPzQ9NIHPNut+vrISxbR5aETkqXmet9pgUWOt/O0xftZwUVag+40kxBujpDjgHBIsdL78G+1zG0Y2xt02Vknvo+b6E9y+VyPn1Ctoa/dSVUiKHWmhHLwDMto/uBAWfAl/drNBo+FTmPPrcSAikqSYDFAvMQqxMK/qlfDISVNCBYISjUQJ22x27Fof6ac+E6WRtgRnATRdEYwFedc00A/wzAl2a9gXPuawC+Bpw5SioljYE6OVVmFtg1m02sra2hUCjE6Gkg+SBKHVDrRF88U+w322epXvs/I15OHuecp+v5bEA8KrR53xCI0h+2N0RPsuaBRlPbRCBIA18ul30xrjXCWo+j0aA19FRwRkc2qpimwCFgYx2E1sko8AVOI/N2u42TkxNsb29jMpmg3W5jZ2cHvV7POz3uUstn4GS7KIU8T30PrWJRB0Zd5/OyloR7a7TbbbRaLV9/Mh6Psb+/j06n48eZ16ToEsxMJhM7bmM8Hvu5x/oBggEtyiU7w3OQ9MBa1UfuKUJhxOyc805DU6Y8okBPd+f9CcR5enmv18P29jaePXvmgYCyXArcCfZLpRIajQYymYxnt3TuJLG46vB0jOyYAadzu9fr+ehWgwn2K/uZ7AsZ3X6/j93dXXzyySf4N//m36BYLOLRo0eejWNdHMdQRdkarUucliqYRS6q76rr8xbaATrgUqmEarXqlyIru6X2n3OC9pAgk58h68yxUSBNPRiNRmi3276fuTqTaTGyb6/rmUNjyOfKZDKxVV2VSiU2d5VxVGZbAyhdEMJCbS5UOTo6wmQy8cECVynqlgqhYEF9D5lJTdleB9B5pdVSURS1nHO/AeBPA2g653Iv0P1dAE8TvvN1AF8HgGKxGOk+KhwURYEEC5nM6S6SjUYDS0tL2NjYQD6f9/l3LSyeFr2rw9PoUdrnBwc4AzjW2fO37idBh0FloMIB8JQor28dnH1NI0ELrNh2GlECDd6bVKnW4jBNQOXW+yq44b1tRMKJzu/SiPN+vP80pdU0Gz9vGZvQWFBYb8ECt+FwiHa7jf39fb+1P/fm0bFm31kH9apyWX0vFAqRjSYpCmwIIAH4rcyPjo78qoX9/f2YM9vb2/PFp/aQSDICqreMtNg3/BwNUKVSiTF0nFucF9wLiQfi0bgy8HDO+XlJ/aCucgM+6mGj0fBL0e1RC+wjjnO73cbm5iaePn3qr5W0XQSLzrklfzab9QXHavBlnGKBRSilE2Icea/hcOhrijQKTjr1m0XW3NTwwYMHcM7F0lu9Xs/3i7Jr2mb+tnscMdi6jLyqvquuO+dei/dScNNoNLzu0WFz/iiDSL+itp2ghQCdqRe7UtS505Rpq9WKBccsWq9UKuh0Oue0+uKSBFBp03O5nN/np1QqYXFxMbYlA/3DZDLxqXuyUlEUeRZRd0OnnxsOhzg6OvJzjWniwWDg9TSpho1+nQsDeGCuLTW5SplltdQqgOELxS8D+As4LTb7DQB/BacV9T8H4Fcv0xAFH1Q85gO5wsAuZ6bYyEudrk56qzhKTwNxYBNyiiG6T9sAnNH21vDw+6oclhXSflCDq+3Rjfc0FUVUb2uHgLO6Gd34MLRqjW2z9UL67NZRs79s5Kv9kuQ8zos0bRTS6/X8s5N1sLlf25+vKvPUd6WMtb+0jexvCo0znXyn08HBwUGsJo1pGK3T0vsB8X2ZaOwZyVK/dF8KLQZkO/hdO9ZKZWu0aFO4Sk+zjTSoIeqaoJuOxuoGAZqmWzU9qukenQOaHmPbrO4pq2PZ3CR9UlCjYJIbMNIB22Jito3MFfuBq9rYZjIQSe3lmF+UoXzx/Sux7xcVOlhNQamdtiBV55r6AH1dv8d5RiDMuimOAQH9ZDLB0tKSB5V0/rbm0/qxEMvB3zo3tU028CW7yc1rm80marWaX5lng1OylSGbq/ZabQV1MlSEzfbyLCn+1v4mIKdtok6zD/hZ7aeQ2Ll2GWA0C3NzC8AvudO8bAbAP4mi6J875z4G8MvOuf8awL8H8A9nuaEdPCoGJzUVOJfLodls4u7du1haWsKdO6cp3/39fX8YnzIdNNihFJClbFUZbYrAKps1Ltpumw5jsScQZ3gs0LGGySq2Gmemlbhs8ejoCIeHh7HPM4IcjUY++iMQyGROl/Nms1nUajXP5liFJGgaj8ceSNr8NBE/o3qljbUYDTirydCx0ToCq+QhoKOrUo6OjjyTQ1aDUQqjDoqmKy4gc9N3GiUAniVhekIdnJ6FRCDKsfnkk0+wtbWFer2OlZUVFAoFX0jJMQTOCs0teOec4hJjnWdM5/Gk5OPjY7/jM9lBgpxsNuspcO7BQwZWmQRNM5LN0d17Dw4OXkpDEyQdHh6i3W77AIb9x92HOad4WCL1nfclg9Rut2NpKeqn6pwGPXSW2n7qK1MRo9EInU7HM1B0tmQSut2uH4dbt24BOE1BEYyrPdBnmUwm2N3d9dv863WURWPboigK1pmcx6JOkbnpO+fdvCJ155zfI6ler3u2gpuMOudiqc1MJuNZGLbHAh6OK+1sv9/H0dEROp0Onj59ik8++cTPiWw26xmcyWSCr371qxiNRtjd3UWn0/EH+yrA1e0JmGbmvalzHH+17blc7qUzyCaTiX9OMlecy6VSyQOzyWTii9ZZ9KvfDREBCmyYkmP7aCfUJw6HQxweHvqdzWkLOG/29/d9ELa4uIhKpeJTywrAQyBQ9Uf1mr+tHbdBXJLMslrqWwD+eOD1TwH8qfO+H/he4uv2QcjclMtlv4eBKrSlqIGX19erYoeiZ+0oHXSKRf0h5sZGAvo9C274nl12Z/tBCxT1voxuCSz4XToXfo/AiM5D89M2StXIl++pA7KfYxv1fVVg+1kLQrX/k5RTwRC/x4JirRUKTWDb5leRees726agNpvNxnRC04PKSJBaph4oPW8Pi7R6xOdnP+pqP7aFeXXNr7PNFoQC8AZcz4Vie7UN1vCorum+RZqaU6CndXXOOb9hoKaVWQ/BfqRR5jUsO2mfRftH37P6au2Mtott53OwHwjktGh4mj3iHlN0Mho06ApAbZ/Wll0GTMxb319FzgNkNnhS5jmJsbVOk/OPn1GbSXupZQ69Xs8vYFGWulwuY3Fx0Y8P99ti4EhwxWvQXtkAl6yKzumFhYXYfJ5MJjGAxpQUVzRxOTzZbC1M1wCb/UUJsZPqd7WfFNxwkQzBmrKU1ENd/ccATXXT2obQuFvS4TzdOE+ufIdiWyhnDYlSveqQLWgJdZL923ZOEqjh57SWxKZbVKzht5OFfyto4j1CaQQLsGxf6R4zFmhZxxUSfpeRNpVeU2hJ6FiZG0uv8zNU7hBDpr/5utZYhb5DMEZDpQZOmS1+l+/TKGgx5yyT4HWKjr2CSO1zBT00QEl6zfGmAaFYfQ7NK/1srVbzTBCp7XK57M/rIjvG9vKemibQsWDUyV1foyjywIw1KFoTo1EfmQ3W/zjn0Gq1MBqN0Gw2UalU/FlpdAr8m4CAtTU6/o1GwzNWFE3fsj+5jw+fTSNirn7SVU7lctmnJVh/wbOvtPhSz5QKgU+Oha6ApE5w6b6m7uw42nl73br+KsHENCfGvrJ+gP2kAIa2jXrBxRXaV6H0PwE0dyXmCdcrKyuo1Wr+jCUtyuXBsAywoijC4eGh1z+dC9QlAlTOeV2ermwqa3qq1Wos9UrAMx6fbgVB0ETdIpvI51BwQ3DCtmgtDXckzmQyvm5OtwrpdDo4OTnxNWXj8dif2VapVGI1OexHZZE5dtQL+kP1GVYs80uxPjhpLlm5NnBjJ6wqu6YjbDW8vZ7+5veTJpnew3aO0ochp6DtDDkMvU7Sfe1zahvse8AZG6PpHPYhcMYAUMFs32gfUxF1SX1Srt7WT2ifhgxqCBzp++p0p7WTn6chocOMoihmXOiA+J46ShoqUtjXafBDz67smI1qCFzo6PkeRZkOuy2CzpGQM7TAp16v+xWIjD75WS6/1tSi3ldXO9IYqoFmyoyvc3VJoVDwG6LxmicnJ/7YBtarKHBtNpv+nB/W3O3u7qLdbscAN+/PSLZUKqHZbKLRaPjoXKl61W0FN5Y1pRNU5ojOIYrO6mRarRYODg58G3WVmM5Xra/SsdOVoOoEdW4lRb4a7L0JYttqnV3IB+iP+hBeSxkRm6YLMT3USS5YoM5w9+P79++jUCj41Cq/y6JbrrBqt9sYDofY3t5Gt9uNgVWedUUGT/WQCz6iKPJpfp4jx92YCZ57vR4ODw+xs7ODfr+Pra0t7O/vx8AN+0RXQWpfaqlHsVjEysqK/3txcRGFQgG9Xg+dTgf9ft9fl/6HKxiHw2EM3LD2jywVgwWOHYX9ruNgxbJubH8oo3LjwI2KVeYQ4OBE1yJBu/mYlVB+UUXvoQZuWgdaJifkeJKEk0wnW+h7mqqyUbaCC+aJ+R3nzqh4rbnR1VC6coB9Gnpee8+QY1anbOuFrNMI9Tmvl9RHek9N3eg9bb8oGNOiuGnFoFclen+bjrCf0UgrBBYtfaz9Ytmhaf0PnEauR0dHsWW1ugw5BEKZImH6irS4shrq+PlMZNDs+VA0xHQwpLbpbAgEeW6bnnOmUbnqdhSdnSrPuggLHpV6B87SR5YZJuCzzDH1LGm+KfBW8B5i4CwjwHba8Q3p0qwsyVXJLO2xeqk20eqwMtNa+K6pntAPr6fg3qZjlM3mUuiVlRUAQL1eR7PZ9CvuONYMnAhkuYnlyckJ2u02ut1uLAggU6J1U9Qz+rDxeBwLJMbjsdeHXC6HbrfrV4Z2Oh0cHx/737T7llVXu6g+i0EV51232/Vsq6bJGPAw/cXNdBkssg4NeNkHs0/52/qeWfUkpFshWzpNrhzcqKPkAFjKig+hxo8KwAI9m3agslsJpYH4t75m6x1U1HmSAdFraN6ff4eYmJCT0UmsLAQQXznDSXZ4eIj9/X0fdURR5BVQ20AErpubcZLymazzt0yCiqZSrIEIgY8kYKEAKyQ6CZXiVaPFFShqwHhfTvgoimLnTl23cGypjzRIaoBoFDVtomNhWQVdzkkd4rzS/rLtiKLTwze73S5KpRJWVlaQz+dxdHQUq/1QPSawVifc6/XQarV8nj2bzfq6KI4/C775fBw7XqfX62FzcxOdTscvewfOAAxTWVpc2+12/T4/pL65M+podLZHCZ1DvV7H6uqqjzSdO1u6TvbF2gD+TaYKQOz8Ku5Js7u763dKPj4+jq3o4RhwrCzg4z5AXGEymUx8uo1gUYEvRecHx3oaw30TRW1iEpAmUOb+NtVq1bN9BKT6t+o8x46fp87SZ3BcuZ1BFEVYW1tDr9fzRbtMz2xvb6PT6WBrawv9fh+ffPIJdnd3PfszHA6xs7ODbrfrGYt8Po+9vT2USiUcHR15ppHPura2htXVVb+H18nJCVZWVrC0tIRSqYSlpSW/DJtAhPc/PDz0KSotgKdw7mmNEl8jG7O/v++Xax8fH/uUGBed8Cw4Ph8DarLi7B9dQk87UKvVEEURqtWqD34ss2bbPA3whPTjRoIb4HwHR7HUtiLUpO+GWAH9Pe01jQLtNfi+0vcWSdLozCpJUYuCPFuzQUWhwqnD07Yk0dm2D5Ik1EdqUGf5vjW2HHN11hTt0ySGQ6+dxMhZvbpu5iYkoYlsgb0WjOpnKOrY+P+riO7DRANFY62Uv41+Fczqfjhsm674UGCtxccESrrHCFcKdTodv7JP6wVI5UfR2e6pwJneaMqYNoIHeWqBI50PqXvVN+1jDTY0JazPyDnIgItzUVkXDea0nXS+bB/TywR/er9Qcbde3+rQTZeQLUpyblqaQJCsrI3WOIYYaV6b71n7qrUhXCGlDB/nBVchcbNF1pOpLebq1Gw269Owk8nEp3nV3hMcE7gMBgO/ApEr/LiCKsRuWqbRZhT4bNpvancVmBCAaNCuq/Wof+Vy2dfmkZFSUElh3+k8DbHBs+hAko+cRa4U3ChytrSqAgc6cOYZWdw0mUz8mTO6SoGdmwQuLEujrwFngEJrXEJt42f4DDaPq8ZN02eWEbEUXYhysykfGkMuq2VEQiqRbdTzuBgR6pJfu1eE5raVvuRzs426MkXbp4bfRpn2f+1bHSvtazpNfpb5WxZ26nk8oX6nI0wCqlcp04BySBRAs284towwuRSW/acrnPjD8eVvtkFZTFs7E0VR7EwYjf7saiFGxFy5AcT3iKIO6UGb1OV2u+2LI8l27O3t+eWrjArfeecdv8dVuVz2DN5gMMD+/j729/djNQzsK3VWAHB8fOy3UeCyW9UpNfIWeKiDIJPCVFS328Xx8bEvwGYKgrUUZLE0rQEAtVoNlUoFKysruHPnDgqFQszGcY7yHDENpig2cNGaijdBQpF4SJxzfsUs+xOA7/tOp4NOp+PZHTpV9g2Bh3POpyfZl7oUn0wZa7moE4PBAJ999hmeP3/u20mGp9ls+toUMp20PSzAJ6PebDZRrVYBnM2TlZUVrK2tYTAYoFAooN/ve/0hK+qc80Cam/FxLnFTSAumrV9iPyrg4d/0UXt7e8hmsx6EFYtFNJtN3/aVlRWMx2Nfv0a7cXx8jM3NTb+cXuudMpmM35+OwMyuXmTbFLSqTU/ykTcS3ACIITkVG7mTEmShEz/PTrKprKQHTkpJ2Y5ScGWdkoIbTYcpAAHi0bRlU3hfikWhOoAEeLw2DTMAD3C0LzXa0Ep1/mYETTSu71mWyIIWba/Ws+jE0T5VB8G+079VeZVe5z2YguPqBa6eofO1K7b4fU4s7ZNQcfdVix3XEOukYtkS6l6xWPS7+9LxEghSL6kHBLh0zJbp1BQYnTyBAsGSsiYE/ZrSJECgDlEfmG5SZoJ9MBqN/H4crFvgbsSMdKvVKprNJt59911vTPndTOZ0HxNuZsbn53uco0wfMaLm+wT4lUoF4/HYg2VdYaXpKI122QZGuq1Wyzu34XDo6fxQ8MEIn866Wq1iYWEB6+vrHlw659DtdlGtVmNbHuhc1GCJfyuDdRNWB54nNpCb5qioy+xPppu51wpZFOccms2m34+IrAYBc6lUiqUUAcTOqCOQfPLkCXZ2djzgHgwG2NzcRKvV8kvBc7mc38yPeqCb/1HnOL+KxaLfR0pXLS0tLWF1ddXPFTKgnKt6KDKDD85nzm++z3lAgM6ARjd6VbtiC+d7vV6sX3nMS7lcxsLCAprNJgBgcXHRB6bOORweHvq0tKbENbjhflTKslldUDulfRDSnVeRK2du2PEh56gRPmllVm2zQznhQ9QmkLxszH7eggvWd0xruzpjmwPnbxoeNfiWLQoxOKG2aV9QQdhGjYwt9a9AhdfTin1d8h5ibqzx0Z+QKNNl03O2D+zfFqgo2GTkQBpXn12VX9umTuk8KvQqxKYGlR2zzlOFY8exqVarWF9fR6lU8mCVDIt+jmJ1Xtk2BhDcWJBGhlGi3p+6w8/z2qSdLXCjA+e9NbWsS3UZ7emKk/X1dSwtLaHZbMZWOtEIk7khwLMG0DpOtoksFSNpbgzIQIDzip9hu7XtZJvIINq0k42KeQ1lXjOZDGq1GpaWlrC8vIzl5WXkcjns7e35MWTwofbFMps28OLPTSign0VmnZPOOQ8OdEduMhkHBwd4+PChB4R0pKprth6JrzEtRFDPpc4s1uWGlkw3OefQ6XRe2paDDM3S0pIHV/RXZMwVlFKveIbTyckJDg8PfVCheqeAWkX9JD/HeaKstgU3ltGxASLbMBwOkc/nPZtEvdL7RlGEbreLvb29GIPF/YEYZLD+hoX3BO2qC9aWq6+dxuqdp0vXkpZSutCyIMAZ0Gi32zg+PkahUMDh4aFHrWpE+N0kB5wEbCy4sLnDECukbJEOAh2Lsi7ctlufS//W+1tDqfcDTou4uPRP+9EeWqj9qc6OiJrRAYvraEwZndPwa3owNGnUYOu9+To/z2dmX2kUagGQRiF8jqWlJbz33nuIoggPHz6MsQq8pz6fc84zPeqErktonCh8rkwmE1vuDbxcQ6Hpo1wuh7W1NXz1q19FuVz2TjaTyaDVaiGKIj925zFD1FPde4aRsUbCFKXxGTmzAJEpT2Vy8vnTw/yAs9oUHnLKeT0en56Z9fjx49j+Nbdu3cKHH36IZrOJ999/H/V63ef1eZ1+v49Hjx6hVqsBOMvvU3/oyAiyMpmMZ4MqlYrfXZkAbzweY2FhwQNFLjXm+WVHR0c+Fc6UBfvFrlCxKSQtdKXtunXrFt577z28//77+NKXTs+n3N7ext7engddtBt8Duow763zjwGOpsvfFslkMv7Q5FqthkKh4AHH4eEhHjx4gMePH6NUKuH27du+yFWZAs5B6hDHkquPlE3R+ieCCp5xxjobMobUP64cWl5ejs1vAqjRaOTTPdShk5MT7O7u+gUhFmyow7fgWAMk6j2fUZkP2nuSAexPpj15DQKs8XgcW63XarWQy+Wwubnpd21mupUrxHQpOllK1htx7BYXF3F4eOhXY25vb+Po6MiPsdrI0DOqL1G94NzQZe9WrjwtxYe37AfwctGQLq20qZYQ8xGa2EmT3YIgZVy0rby/ZW74HdvxITCl7bDsjTIdlk1SsMCUgU2LsX3TxCJ0BRtJbIIi4/MiLQvY+D0+n2UN7PV0UgJnIJCV95pusvfUa2gEqym0myCWqbGRuQUUOj/YF/V63QMH6oKCx6Sxs2BZQaF+Tt+j8DtKK3Os6AD4vwWd+jndyoG/WeOjdT5MRRaLxdgBsQSEqgsca5v+VXqev3morL6mtWoKUNh2AiYukeePZVZsX4WcFNvL4wRYexNFUawuStutYm0DP6ufnwZq3zTh8zDo0gBWC3l1OwOmG22xra5qOjw89CCD4IbsBI81AM7Gn2PPfudrmtLnNXQneOqOFjzzNRYIcwNIZYKs7acoM6jgxqZyQj8Ufk7ton5OgZQF60xNszaHwI0BJEGILinXgnkeoWIXFrBdod98bitJPtbKlTM3QDIo4G8+nHaUUoBKMVoqOIlNoCQ5O35O20Ejyff5GivISevzt7JIWqBomRv9nG0DEF92TWRNZEyjT4MNILbKRZfc2XSVVsPTIWj9DpVRQZOlBhUUKWjRCFrFovGka6hzZf783r17+OCDD3BycoKtra0Y4teoWJ2r7tlCpuG6RZ+fY2IdMoW6Qr0mA8flmSycJBtSLpd9ASP1ncZLfxM8KGCfTCYeNBFIavpTjeZ4PPbAihtB8jpRdLatvO6QyjHWfWBYOMsaBQC+OJLMEylyrQ3QDddYSDqZTFCv11GtVv1mZ4yUnXP+DCoeMEiGSp1OJpOJ7cLKedTpdPxZV9yGgsEPawm4yovziY6Qn2PkTLtVKpWwurqKd955B6urq35/EUbAduWZBUjsa62F4iGdobn3pgoL1amXHBvWa+m4HBwceJvBWhHOLYJg7Rvqs/Y39VCPK6CtJ/jW2p+tra3Y57LZLBYXF/05YyyAJzvDWhbVL/o2tg8Ib+TK9wnS9PP6XggssA/0Wqw10kCTemWBsrL7yohaVpzX57zd3d31tWWc22RMedbcYDDw2Q0NkCg2O6PPZe+bJFfO3GhHaOOUGWGnK3ND46fv20hflcPe7zwGQsENr6+pE3svggdFuApaQrVBCmz0mW3krg5F6xTUuGazZwcZ8jt0Lkpr6yoKTTcxWiQoIViyfajo34IT9hv7wE66pDG246/XInXabDaxvLyM27dvx4pI7bUUwPJ5+LwacV2XWEYkFGHbNtrPMv2wsLCAarUa2823UCj4/+m4NV+vxoP9QwCh/c2dgalnrEfQ1AsdhO7Fozv4cl6wNkoPNbVOnNsY8Nn4GzhLS/Payl7q4YCM1gHElsjy8Em+x7mStHyVtTh8ZgB+by2m6hi1EsgzKKBNmkxOawSVBSLdz3nIc8HW1tb8eUJ0eARCVmxUTb0go8G26+rMN10I2rgAQoEG6zuOj4+xu7uLR48eATg7aJQ2k/qkIITpU+ecr6lhujWJgVfgQ33SowbY3tXVVc/81Go1DIdDPH/+3C/z5kovZV8sg6rpFr6v/ofz2QacSWLPllL/EHpWtTcUDRiVQdUUltp4bnKon6etIqjJ5XK+RkftC5+T91U7aMGb9S0hufLjF0JpERoEnZz6OgdZnb06VUWntlNC6R69RwgpJ4kyL6r4dAKhz9vn5+ctKFHqXk8BpiJxWakWkJIxYv0EjaSCJV3ezsmuOU69nqJzVTZLhevzK2ug/aj9ZNMVSt/b/iLFyyXBzIFzkiSl4HgvjYbsiq/rEGs4pn0mqb+iKPLLLiuVCrrdrk9T2gmvgGTayjL2Myl9BSHUI2UQbVu1Nss559kc1sUA8KwFdzIm08FCxbt378K5sz017t6961k7Xp+6zzSW7sMTMngAYiCEbBDbpGkxzkMuJ+c+O2SB6PQ0/aNBGA0sx0vZATuGtF28N0+hJhOhq6OsLbKRPcGVLufn2F03mL+IWNvBYxAqlYovHKbu6IaJk8nEgxWmlnRrAGXo7Lxif4VS13YMqFt2MQfnUhSd1TPyurTbHPPQ9ZWxVhCv9lfboM9xXgbC/q3XsP2uheg6551zfv5pgKw1s+xTMrN8VvoSFhkTGBLkj0Yjz3BZG6N9Po2UOE/Xr5y5UZSqBoPGWNEg31PDoLuAqtPWzgfCy72T2BkLhkKizIxVSp0kfAZG1To4ociM32PkOR6PfT6TrxENM1dLZMzIWCcGgRF/qExEz1Q6OiaeuExnojUH6iy1T5UxUaHhTeo74OW6BKUYSbc3m02srq5icXHRs1O6db+tFeF9CW4sS3FdwnZZQBhK9Wk0HproBwcH+IM/+AOfZslmT8+dsc6d6ZEkmlqLiPnD+UP6nCs41FlaeporojgfqtUqisUidnd3/R40AHyKZ29vz29T3+/38e677+ILX/gCKpUK1tfX/QnLPFOHOttut33BMVNanCcKZrXwkMwSl3075/zKLJ2nygxnMqdFlLu7u+h0Otjf3/erY+hAVY/pxEKMmWWVgbMUO9OlvV4Pjx8/xvHxMZ49e+YLM6kPar/oaDg/Cf65ky73HOHcf5NEHST7cmFhAe+9957f74fpp83NzRiwHY/HqNfrPrVIRoA2yzIyBOXUG5sWsoGZvkcbT71icTN3Cm63234Ok2GmnqqNUz+iWyzwedQ/2bacF4zTVxKEqR7aYEDbokys1isBZ2w/QY1zzh8Joe1QwNPpdOCc836q0WhgY2PD173RD7HkQusjlQDQZ04Ce9PkypkbCzCU6kpC0HTgyt5wIDUi1Wsn3Vv/D33Osj1WsdRRha5DQxSaIKF+0ChblzAquNEIQGtq2F5lBTTtw980wlo5rzlNVWC91jTGK/R6koQiUWW6rKNXJ2q/r+Mc0iW99k1aGjsNaOv7ygwo09Pv930ES0aLaZvQdW2fqvC+Sn3TwFAHQ6wNdYjvWSaKY6MMEYCYHpNRYw1RpVLB6uqqT41xJZPqIKM71paw/oGAy/YBdVhtBx2dAgY+A5+HwYNGoXZ+K4i0Ubl1HCxs5/3Yd2TGyEAwmLG6bMfNOkYtiAbCwdObIqqP6gAPDw/9kRtccUfbwADGsqIchxDrSFCctNhgmr2w4MDaeHXIvP+05w1dX38nffZVbZpts22/2k5rPzS41d+afdBnVbtGdlT3ouJ4UX+Z7uU8tRmQWX1MSK6FudEoRJkQ1g6oojNPR6NyfHwM4KwQVh2YshihwbL5PAUsCpBs6oyDwfYQ/fMeulmXfp4FkRw0BTKcXDTcpP8YoZKGt+kBRmdcsspaDEa8jBpI//M6NN7D4TB2GJoyXlrTon02bUIDiAGtkFFWhbVANFRTQGYgiiJPR2sf6w/TYuxrOnwyVUmpoKsQ7V/gzDFaAG1rt1SoO5ubm2i32yiVSrh//z4WFxc9ENbvWpCk19Ox4fc0ImWNSYgRo0MGTsEK2R7q0d7eHpxzMX2lrrZaLezs7CCTyWBtbQ3VahVf/epX8Wf+zJ/xO/rS0HF+0OHTsVWrVbz//vu+P1dWVtDpdPwOqa1Wy9sWAh9eZ39/389dpr3IgNCmDIdD304WXRIMca6RzQJeZnnJgrLYWMEZi5zH4zF2dnbw/Plz7Ozs4LPPPvO2jf2tDJkW6atDKJVKnsHj4gI+x5vE3CgIVB3mhnmZTAY7OzvY29tDq9XyTGW1WvXjQmDHzV1ZS8Pr21pAmxK3bIqCfWv3OKbUDwDeZtqVTPqellKob7Fp82lsjfVl0wJOGzAl2RVN303rE36OhcgK/NWG2eCYbM7x8TEODg48K89VgrpVA1OKupmhttU+4ywyM7hxzmUB/A6Ap1EU/SXn3BcA/DKAZQC/C+BvRFGUvOgc8Q61jAMnMSM74IwKZ1RDEEChA9T6An340ETn51WxFGmrAlrHoEV8fAa+p1GifY9iGRo6YzoCGlnumMniSQAxJ8ODQ/kMpKe54sW5090jWQxJmpL9qM+uKUB1juwrVSSNiLWPdQLoc1sWydKvFjzpfejEdKUY+0wL6+x9SXMqCL2IzEPf2S6rh8qqWUBCp2wncqvVwpMnT/xzaaGqBeu8VmjJso6bPRqASzwzmfgOvbwmgQtrdrgvCw0eozML+o+Pjz0wq9VqfsXQRx99FAPU1PfBYOCdFcF5vV736SsWE3M1CgDv4AkyVB+4eopzl8/GAlTONa5Ao85xnFinxrbp2PJe6tAI/DhX2Y4oimIplgcPHmA4HGJpaSl2lpY6ChuEEeDoae7UsVCx6CwyL11/xXvGnkuBDu0ZAF+fxGDNOeeP/GD/kx0g6KA+KyMAYKq90TYAZ7bKto/vMcAkuNdVpiF2hLbKLlPXz4W+q69Z/3Re3+rfVpc0tW9ZLNohC6yUudG2AGerbamjBPW0M91u16+g4nVo45n2BRALKpKecdp7Kq/C3PxNAN8GwJ3j/i6AvxdF0S875/7vAH4ewD+YdoFQVE+DqO+zM+3KCwA+x7y6uor33nsPzjkcHBz4JZlUNnasovfQ/jq2xgSIb6BGEMFoT42JOlsFVwC8EeQEIEjjYHPZKh0GUa5SrToRNMLLZE6r/rn8cHFxEY1Gw4Mb4KyWR1MJ3CWS+WgFNVq8TaFx1iI5mwvl5/n8uuIlRJPznvqeOlym43q9Hvb39z3w4/Jije51rDSi5n0uKZfWdzp4Gk+2nW2z6cQQtc3rUFcmk9PdTVkrQyfHmgMF/BwzXUGmEkrlsk1qqOhsFcCwjRoV0mgqGzken27jzvqaDz74AKurq6jVami1Wh4g8fnZTp4BNZlM/OZ7rHXgXjHcGI3AwaYoNG3k3GnhMM+x4tLs0WiEg4MDD8AU1GgfatqcRby53OlZVblcDs1m09d/kIUiG+Sc88ua9cwqOgE+u35XHRrrpPjs1WrVr27b29vzAOASNWaX1vVpouy8OlX+JlBYWVlBo9HA4uKitzlMR3ExBf0D6wupa0krxWzAC8TnViig4+sa4OpnOB8IWvhb3+PfygQxMEhiYKz/Uz3Q96c9VwjI2P7m7xADxPc0fWcXzOgzUNQeqx/URSqTycTX3GmfsvaP4F9BX+j5ZgXwM4Eb59xdAP9bAP9XAP8Hd9qqPw/gr7/4yC8B+K8wwwSwkaRSYhwUOkoaR6ZeCDIKhQI++OAD/Lk/9+cQRRH+7b/9t/jss888xRVFEQ4PDz19zv0PCCzU2DOPT0PGaIDLR0mZM8WhjIECEzu4XALIszmi6OysEy5jpFHSfQfUYBIgEZhMJhO/wqVareLevXt+91WCG0Y8NKDcV4ARJPcH4QZS6sR00zTnXIxN0kMA6QxpqOlgdMUVQZFGAex3C2yUfSPKZ4T74MEDjEYjtFotAGeMg+qTTkY1PBddPTIvfaeOqFOzqQw1rtQlgk32D3A6T7haZGdnB8PhMLbjLvf54JJV3QWVy6DV2CmroE5DmQGmblg8yTbSqWjhL+eAbmvPNOvi4iI++ugjNBoNfPWrX8Xq6iqGwyGePXuGfD6P5eVlvwkaU6Y8AXxjYyM2NyaTCRYWFmJghMBXT/8Gzlg8pnczmQy63a4HgaVSCcPhEFtbW+j1ejGQb9lijeTL5TIajYbfmbZcLuOjjz7Ce++953dc7vV6/ngAfq5SqXiGhm14oW8x4KQOjONOMNZoNNBsNrG0tISjoyM8fvwYe3t7sX1YrkPXpwmjeo34gfiS3mw264vMnXN+f5jd3V3s7u56RodziAwNbWcI2IUYgBCrQtHPhYIBit0KQVkdDYCVOWVQrXvVTEv5W9vF62n7FSxa9sv2hQUzSeDGvsbn472ok/ZzGviyLcowAvBp2eFwiFKphHq9jnw+71PVURRhZ2fnJQLCMmhJoMzKrMzN3wfwXwCov/h/GUAriiJ6qScA7oS+6Jz7GoCvAWcsiRUd4IRrxPJ52ezpQYL1eh2TycQDHlL6ipSTUF7oXrbDQtSgdcZaD3Ne1ETkrrudkgJXOk/pWd2dk8yIHpOgyFgnGV/XiabOlfVBVBy2XyN4ZWt0MlqGx/aV7U81MtrHSf3NVAQLPNlXvBa/a8dGr3NJ+fuYo76H8sdJohHgi+sBiLM87B/dY0WZR35P9cGmwOw48u/QOPI1ppCop6HUCa+lqUfWuiwsLKBer6NWq3nwb9tu729X8AFnm7yxtsYacy3iVZ3T9DXvY5ky6wy1f+nUmF6rVCpYWlpCtVr17Ck3GdS0O1kXrm7kfTg/dWfbkI5wHG0aStMxnN8X0P2/jzno+nlynm2nTSebpvWGrDmyLAv7gP0zK7BL0vNpbbUghGOgoMK2wQKYEJC4iFg2xr4+rf0W3NjPJH2OfT0NWFh7H3qfOssgV4vitW+TQOeryLngxjn3lwBsR1H0u865n3rVG0RR9HUAXweAYrEYWSf64h5eyfV/LazTyU1Dy0KkO3dO516n08HW1pYHETRMiqp1fwgKFdAaZwIRRuBkAxjNkVnSfXc4MLrUltc6OjryZ5WwaJi/iXAVfS8vL2NlZQXOudjmUzxvi2mKKIo8GuZ3yWKxToBtIJgiE0SDquwCARWf39KSFBodjocuEVbgx2iFdSK2z2nk+ew8cVlpXG0HDUsSWOYYXpC1mau+s41Kx1PPtJibusXX+Rodaq1W889P9iaTyfjDF3WPI/7QAfM7vDbBLMfOgrAoimJsIl8ja0idVuBhnY4+1/r6On70R38UtVoNt27d8vVhehZWCNQR6MvYIJPJ+KLgfr+P9fV1AMDh4aHXNfYDgx4t8rXPnM/n8c477/hlxDw/SlPFo9HpzuC3bt1CuVzG6uoqVldXsbS0hK985Suo1+veNpExYzqKjM3KyorvPwK+1dVVTCYTv4yetUbcz4dMcr1ej53QHEWRX7KuK+amzYeQzFPXnXNRUnBDhxaak4VCwW/5wCJp7gFEppIAkOyj1ohYJlTtVBJgNX0QBAXWuYaCXE2paukBn5W+Qn2EDbYVNCQtjgmxLBq42GAzlLKyYxICOUmAR1nM8+yqbYf6An1+tUlk4Zi10PpKZfte1abPwtz8WQD/oXPuLwIo4TQv+4sAms653AuEfxfA0/MupI1Lirb1t0ZlGiXSGRIQNJtNDzZ4doh+T1dhkRFhOkorv7WddAB8n99XNoMOl4NAhSS40VU8jEb0tFlOhOFwGNx9l7UF3Csgk8ng4OAABwcHAM62u9ZiNgVa3N6dE5jt4C6fBAgEkrpTrEb8/LFjyfc1etfohWOpxsgqZ4hOZWpDJ4Od2CFDbj9/QYAzN31nW4H4RNcozwIIrSlSUM96C46fMn68rhrQTCbjmQIWCicZcX1dx4z6rkwicLYxJD+fxMZxnOr1uj/YkLUnNqpV5sa2xfYnt3VfWFjwLBDnGVNjwNkOyZrSsH2ezWaxtLSESqUSK1DWcVP2iYXNGxsbWF1dxZe+9CUsLCzEDmLs9Xo+7VupVHx7yTSxD0nFE+xF0Vk6nXaHto4rpCqVit9okAeoJtnUGeS16DqAl3QiZEPYD0z/a1DVbrd9XSLHkMA8ZBeUYTuvbaHXkuaGBQ4qmpZlGlfPWqKNt7rOdltQEfKPobmVNGe1r0MSulaSXbbXC4Gb8/RNnymJRVUSAkDM9yRttTCrTT8X3ERR9LcB/O0XD/NTAP6PURT9R865/xHAX8FpVf3PAfjVWW5IZQl1UEjBKDS2ioR7vZ7PQwNAu9326Qzm8HlP64TVeGnUqM5ZFYX1I0ShulPqtAInpVj1hFndmt05589SYTRAY7qysoJarYZ3330X5XLZb5LGVSiTycTT2ro6QJWHbaaR5N9WcQjUQvSqjo2NGJSFYT/pZGabkgpbrdJalshOYAVeoWvohHpVmae+07lrepT9HqLRCbb5OTIGlqYl4JlMJr5InP1LpoLgX8eK31Ud0WuyzeooOJ4EwPwucBaRa90EWZLhcOh33t3b28OjR4/QaDRw9+7d2InK1LsQS8N2JwEfAgRue88NEOkEl5aWsLi4iFqthuXl5Rgg1vombojHE56pp7xWsVhErVbzJ5Vz64WFhQXfR7pYgGcfcSdpbSNwtpSYNo0LARjxqzPhePL5mIrsdrt+EQWfN6moNklel23nGOmcTAIeHEMuD+bSf+5v45zzdVianrQrfCyoTnptGkszK4PDOaRzjOBGAwybxtc+sUEg9V/nq9oxtbt6LZ2P055f/1cfnPRbvxNiTs4DOLbtNqXFQJt1SAzUGMCFFqPMHdxMkb8F4Jedc/81gH8P4B/O8iWL/HTgVUKGX3Osg8HA73uwtLSEpaUlbG5u+mXUatjVmWjaRivBbWqEbeMAaK0K26EDFmIJnDstyj06OvL7bRwcHHjqHICfFPV6HUtLS76eIJfLYWNjA++++y5WVlbw4z/+41haWvL7PvR6PWxtbfliR9J87XY7to0+fwhoeJAZd/JUZoaOjP3PHz47/9bJo8qvY6ipLE1dacEkcLbTq43GOGa68kDTi/p5pS9tlDFHuZC+U5/IoHEZNfCyjqvBJk3Lz1lwwwLNw8NDZLNZX+fB1SZqRIEzg8Z+tJE03+PntIA2iuKHNfIa3GqAc5LgOZPJ+AP02u02nj17hu985zs+/UAQVq1WPSAgo6XtYZ/YlXG8f6FQQKPRwGAw8AdjEhBks1lsbGzg7t27WFpawjvvvOPBTxSdFvczlUU95lyNorM9OBqNhq8V+uIXv4h6ve4ZZV5PD8zs9/vY29vD9va2ZyAXFxf9/h503hrc8Cw17lKsK7bU+TPoYYE9U4e6QnROOn9h257E5iW1i6s+CW4IbLgKrFQq+fIDAjsNxPQeQDz40noqC7woIQYkyWGrbWSJg+ozHbXaRWVqtS9snYkNuhUEhPwKn8kGdXY8kv62n7fAQd+bVmuTxNbx/2mMP8sVtOB/YWHBZzh04Ujo+ufJK4GbKIp+E8Bvvvj7UwB/6pXuhulUlnWQfE0dFhWWqRUAPld7XkEZB4hGRZ1iUoU58DJDkARkrHIyOuT+HVo3QgVnVMLojhOFxpMORaMVy6Lwf92Lg2iYK540j6kRSGhspinleaLpCqXgnXOx6JrXTFourukqAC/91rEKUb7zkHnou4JDuW7i5xVs2pQgDSLBLB2hFqWqPvAzQPyE5KTxTTLwIbBomU/r1OhsyWRsb2/7lBGBm50z06JAMkpq1JWdtG0jAOQmgQQlvB4jXrJP4/HYp4GBs0MHCW6UHdIgidfgQZvcrA+An9NcyUjnx6BMAy2rGwpOtbZJAysNACygeFWZh66z3SH2JiTKwFGvmbanXpEh4bV0TiQ529B9Q7o17bUkX5DkzK1uTrs/26ilEsrAaFAR0o1pPjQkOibWxyaNjwVPs0gI1Np7qE1Xf6xbVtgFAReVK9+hOEnswHISU6GpEOyEzc1N/P7v/z7q9Tref/99LC0tYTwe+1RUyDgzeuYqJb7GSWR36A3lia2ht3QhnRlTRq1WC8+fP/dGXmn8YrGIe/fu+ROwV1dXAZwtj19YWPDLVT/++GPkcjkfDfKcHgVMrVYLn3zySewzXFqtgGY0GsVSAtaJ6gRVqtsqq0Yyto90B9tKpeKX8XM1BJ3cwcHBS0vG+dumbhQoWOevrId1mtcpNqXC1xSoW1aGn9F+ymROz7Th3h+M8ofDIarVKm7fvu2LiwmGWVdVKpWwsrKC0WgUq0nTCJcpGC04pp4TGHPFGoUpV+qrGlCmpfb29tBut/Hpp59iY2MDd+7cQb1+ujCnXC4n6hCvo3OK92Hbu90utre3sb297fdC0bYw6l9YWMDq6qrf8E2faTQa+b1vqtUqNjY2/L0BoF6vo9lsxvYQ0vbRljx48ADf//730W63MR6PUavV8JWvfAXvvfeeDzr6/T4eP36MR48eYXl5Ge+9954vktVziPQeugiCdWis5ZhMzg6O5Kqpi6Ri5ynWXlsnacED+zWfz/v9ytrtNqIo8jUtPGBR6xSV3VJRBjIEyKexN0n/29cUFHMLAYJXPdsPiJ+qDZw5bNZ9cpsRFqPb1KTOU5s90PZM62t+ZtqY2c9cEigH70+fRx+kG9gC8Icl8znsc76qXDm4meZ0FLHSwPI71slyZVSv18PGxgYajYY30CGmBYija0Z7oTz1tA6dxtqo0BlwaSgLh9UJ53I5LCwsYGVlBYuLi1heXkYUne2Hw5TVaDTy6/+JckejkU8t8VmOjo58yoq0HpcNR1Hki5yBl1dWqGNKij6SRCeW1l8w0mVRNMENnSRrHmwbtPYp1LcKcEIgdBY24LpFKetQpMt+pLPW1SJMIVKYDuJKGuo3gRGLUXkAqwUptr8UsGsBvYJJtsPmxxWMaAE9f/QAy9D9KUkskRpi1t1xftl2kP0kyGGBveoK5+hkMvEMsLKk9Xrdb86nbIK1IwcHB3j8+LE32Pl8Hnfu3MGXv/xltFotPHz40O/Ns7W15R06wUmSIXcufrChnozOsWfANse01KVEWSf9n2J1jc/H87Z4BAhZHaZg+ZNUkzFrUJMEXux3p/kpIL7BInC2k7Wy43Y+KWAn8KFdVn3i51guoYDHzoNZbdysujEvHQq1jSydtp/jGUVRbB+082rIzmMGr4W5sSDmPFSpTpMG4OjoCE+fPvV7TAyHQ7+j7Xg89g6e0aFNc1jKM1SYdR6SDTlQKh6NOQv/ND3AzxQKBTSbTaytrfllnmwXHcrBwUEMxerkZr2D0ppqDPhcNp2l6QrtX/1b/wfi9Q6abgLgI0sFi41Gw6fZmBLQIyO0r+gMuJpEjZcFYXaSJ0VnNwXUTGO5tP6GbdaNujhu3IySDpbLmqkXCjx4ovpwOMTz58/R7XYBIHZSN69ll4BTl7TNynpyzyEW63PcOC/ZVkbZBLTcTXd5edlvwaC6Rf3VtBGNnYJctpXb8e/s7GB/fx+tVssvK1UdJgvQaDR8RM32UY85BnbzQAJD9out8chkTjcuPDg4wGAw8OdF1et1fPnLX0aj0cAHH3yAjY0N9Pt9bG1tYW9vz69uZKE5gSZX2jB6J1jheGWzWR+k2FoEbdNN0fuQqB3W/zknjo+PPfimHaReM0hTEKh2W/XpVQKzaZJ0Dc5L2lHOW9aaKUDhdTj3aIe52aZumMn5Rf3nM+oiDF7LLi2n/VC7rQFr6JmSnk998rSg47z+skBRWSz6JwYpJDDUDlxWri0t9SrKZyPzyWSCw8NDtFotT1seHR1he3vbDy73q1hYWPDGnA5YFY+GQ4HTtEmizjPJiZJq63a7sdNsuXSdz1MoFLC6uorbt2+jXq+j0Wj4ycJ0Urvd9pNbnZmuVNHtq1nTwkmh0YQCOxpKjUL5zOosqYSaMuIPjTTpRCoqACwuLmJhYcEXMdsCbnsv1jc453y6TZkLbTvHxTocBa9arHddokbGbtBmgZlzzkfzCuAIjHXDOq7Y6XQ6vvCSfbGysoIf+ZEf8fuD7O/vY3d3F1tbW341Anei1n6Mosg7XAr1grrG3X0VQFAH9Jwqpl/z+bzXgUKhgJWVldi9NcCw/cZnIugA4I3i5uYmnj17hqdPn2JzcxO7u7t+l2L27Xg8xtHREXZ3d1Gr1XwgwENYWdzOgIAgjGffKMurqQL2Sz6fR6/Xw6NHj3B0dISHDx/iyZMn+OIXv4gf+7Efw61bt3Dv3j2sr69jd3cXjx8/xubmpt9tWMGN7h6uNXOcS1xlxX49Pj5+KSq+bl23kgQwLBur87Tb7foDWDlntLZMl1nzWtRH9pW176/juZQ5UyaG+qJzmn6F+kT7lM/nPaNNRhaAv7YyGXpvAL4/VCd18Uco3T2NkVKiQZ/JfiaUCrMERVKgqWPKEwN0fvGZdcuXy8qNqblRmeXBlBLngXdEvkwJDQYDX6egRlmpQmUiprEz9v9pwEaduAIF+33eU7+jAIu5egUmwFnkkJR+04hbf/jsmhO36R/9jH6OSqfnbTFnzOWq/Dxwtimg5r85LjTcjNDsvXQsNGIP1WUkMX+hCXodQuBGUVYGiAOgkOg4EuBqoTmZHrIUuvcJtzg/OjqKjXcSaxrSKeqmAh++RkDE67Kd9gBDjdi4SolgXO9JtpMMC9umq8b4Oe7zwjQNP8f6JOfOTjJnPQrbbnVMHSznCfXbFrDTuZFBOjg4wNHREXK507ORlpaWPGt5fHyM7e1t7O/v+wUFXP2kIE+Xkdv0EuenziEFxRwbPsebIqHg0IISZapDNs9eT4GNzqlp7EOI3Zgmdr5Yu2vbZP/XlX52mwdrs+3zaNCjv+3zh/rG9sU0e5P0nHofO272O9PuHbqnZeNmkfPG7MrBzTQqTIWTWlcmWFqTnbK1tYVOp+PPIzk5OfGH4bEQUzeoY+qGyzC5PwGNpK3mtu3SqAM4K8jUSIxFn6S39VpaGNvpdDwVy89xd16yURr19no9D4QIHELRBIvdGPForQ9rEUjH62Zxyg5peogOhiwRV3pls1m/VJUGGoA/6HA8HqPb7cbSCcfHx76QmH1EZkLBjPZxUpQaYts4JvrM1y2a7tE0kIJJG6WRNSGAH41GPuLTA1IB4Pvf/z4++eQTjMdjf3r27du3PfjZ3d317bARmuqQBTd8jWCDNQC8Fg+3VDZxf3/fp1e4CqnRaCCXy+GTTz7B7u4u/tgf+2NYW1uLrf76/PPP8dlnnwE4W61079493Llzx4ON8fj0fJoHDx7g8ePHaLVa6Ha7sd1iqbNMXdVqNWxubvrVS6xz4Q9tA/XROecPytUUAMeJWzrs7+/jD//wD9HtdvHlL38ZP/ETP4G1tTW8//77KBaL+MY3voGPP/4Ym5ub2NnZwWAwwOrqqmeTyUxyafjR0REODw99f9D28blZH0TgyLGjvbrI2VLzFE0RAeHaRYJLBpXKCuucpm0nWNcN8YDk9LMGcfQX02zALCyPfZ9tVpaE19E0m841toXFw4uLi1hfX0cURd5OM4DRsgFd5Uo/oylL1ctprIndUsP2XSjQ4f2p//q67ZNpfWjHls+jtoftV39vwbp9xvPkWsFNEpKzeVn7W/+eTCaxpc6c+Fw7zxyoHUgaaf4mTTatXXpvS0OqI1KQY1Mn+ox0Hv1+3xtvADFHQQqa4MNOGPapTXMos6WRDyeD7tXBqJr35+SZBm7o2Fg4bKlGMjzK2PR6PR9tM+LWDdCm9b32G5+Pz6H3ZX9MiyauUmwEqc4oFGWq2CiN7APpXTr04XDo6064twwAf/p2tVr1Y6ES6jNbuK8Rs6400vy4Gks6Ij1kFTjTH54KfP/+/ZfmTavVwtOnT+Gc88W9S0tL3rhyrHu9Hlqtll8hxR2+ydwoSOeJ8tzwjgBADb+mb1nkzj6zhcoAPDjf39/H9vY2ut0u/sSf+BN47733sLi46FeD7ezs4Nvf/rZPN00mE3+MgjJFtFe8l9ZbsH8tI2xBhLJ4N1UskND6CjtHqKtk4myh7bTn1CDSMjOhuZY0/0K2Y9pnQwGYMn+08Zy31WrV6yivYVfMhdLw1smzvyywtWytna8hVtI+kzKdSXIesNG28POWCQp9L1SQnvT5kFw5uLFIOgR2OFBKgfG3GlVODHXg/JuOG4BH/TRWZGtsB+tAhgCEfl6NPR21HvTILe9JRevzUrlLpRIODg4wHo9Rr9dxfHzsl0wTPOh29ZrS0UnO59Zl4dzfRvPTfBalRAlM2CbgZSaK3+d3tYbJ0ve6EoosDSN5GnDgtOBY628ymdNN4TimemKyFQIEzTdz7BQQJFHYVynTALxlUWjI+T3tYzVUOzs7ODg48CuBnHM+un/69Cl+4zd+A0tLS/ixH/sxf37RvXv30O12/Zk91AHem33H/y3TCJxF4hxDjR451vwhy0DH3e12USwWsbKygmq16mviyOidnJzg+fPn+N73vueZylKphOXlZSwuLvoCzPF4jN3dXc+GdLtd9Pv92H5QFEbD+/v7+P73v49qtYp3330XzWbTryALMYPA2blp3FRuOBx6MLW1tYWtrS0Mh0OsrKxgdXUVGxsbWF5exng8xre+9S0cHx/j+9//Pra2tpDNZrG6uuqZYxaI83n29vb8KiH2MesxeOaU3ZCTbdZ2X7e+cx6ex5QALzs7AjQ9i08BYMhnKPCflX2xzjEEfpK+q2JZIZ23fI/sOOu7mCkg+8pifAUgDEI0iOZzcr7oOCel6vVHQY0uoAmBG9vPGsAnyTTAobaEAI330/ma1IZXZWtUrhTc2MmYpFBU6FCEywnEiR9FkWcDKKxzoIM8Pj72AIOOmUyDrlawxUza4ZalUSTNA95YC8A9Pphe4r35XPl83h+2x9UezWYT/X4fpVIJa2trfhk1ozyCG4IYpRotuNGI1Sq4cy62Rb/ufFqpVGKKqoVtbD+ZLka8OlEUgDIK7XQ62N3d9ePDSd9oNPwzMSrX8490VQ3bYkVBjeoUDcBNSEuFog99ncJ26wnxZBr4efbt1tYWut0u1tfXcf/+fa/rk8kEjx8/xve//32srq6iVCrhC1/4Amq1Gu7fv4+9vT1897vfxe7uLhqNBhqNxks1ZwQsShNbB0pnS9AdRZGvgSHY4D5Mg8EABwcHHqz3ej00Go3Y/kZMVz59+hTf+c53/F5I9Xodu7u7WFhY8DvzTiYT7Ozs4PHjx9jf3/cpHdYhKavBQIcpIU1Ba2pIdVgDLKaMeObU5uYm+v0+dnZ2sLOzg0ql4vftuX37NtbW1vD8+XP83u/9HnZ3d/Htb38bz549w8rKCt555x0PVAaDgQemHPOjoyNvL1hcX6lU/HcYHLAmScckSceuUqyzTxLL3ABndUMKbjSAs2Cb19HURug+If9iGZbzGITQNUKLFew80RKISqWCWq3mA9dcLud31ObOvMrGWnADnOmjZgLUB1hWSwGT2mz6OKvvtt/0eiEJgcQQ86M/apcBxBb48Bpqt/V7fH/aWFm5ln1ugJejRb6m74UejH8TXADxbes5sBrx0kHzb414eX2uyrBIku2xE0QLgNXwaIqM7dN6IRr5arUK4KyIkhtVkQXhMQwWXQ8Gg9j1gLij1HSbRfAUThQAsWWxIZCp37XFx0ljy3toP3HFDwsqFxcXASC2IZntL6sXHKvzItTQM1+HaBuUZdL3gZdXUHFFna5M0wiVekTWUCNF5uQnkwl2d3eRz+f9wbLcHE2digIYq+OcM1oUDsQZGzo0ZeA4j+zKL+ecP2aEW+rz+oxql5eXUSwW/aGYPNmcdD4ZFd4rlJ9nf6kekfrf39/39TWcZwRqTGVHUeQ/z7PcdA8WzlPuflwqldDtdvH555/7TQVbrRZGo5FP3bIfCFzH47HfiNHW0ZBRLZfLABArNKaOsA+UFb4JYF4lNIdVrANT+6JO3TL5oWvwfiH7nSTqvKcxD/bvJNBkr0vdpq4omwMgBlSAMJs7zQ9Z+xLqG/ueBRyhOR96P9Q/tm1J4xN6FrWD2j6tYbqsXBu4ARDscHaQ/tbP8n+dyAQTVCYaUa7O4A69FK5coHOIosgfsqdRM++nBpo/4/HYb0bGVSr9fh+tViuWNyUjooPZaDRw+/ZtDIdDPHjwAIeHh97pk5Gq1Wr+rCnWGfAZiewV5BH5MzrlXiTa35puIkiKorOiUa1B0mgiCSRRETlhtX9090n+zd1eV1ZW8N577yGbzeLb3/42nj9/jsPDQ2xtbWE8HseOabB1HhbwWpbNOr6bAHCAlwGaGms+l64eY79ziTI3g2SBLlmHnZ0d5PN5LC4uolQqxZbM/vZv/zaiKMLa2hrW19d9W1iMzEJcjjtF+5gAQGlxzp9MJoPDw8MYcGcq6eTkBMVi0ben2+2i0WjgR37kR/D+++/ji1/8IiqVCgD41OtHH33k77e2toZyuYw7d+7g1q1b/vrKwDC1w6XdtgCReszdwXlydz6fx/LyMm7fvu3ZUaZmmTbiOXVHR0c+bXZ0dITRaITl5WWsr697xqZYLOIP//AP8a//9b/2RczcD2h1dRXFYtGn6rjcvN1u4+HDhxgMBtjf3/cp2WKx6L+3tLSE/f19bG1t+TQz98khA81n7PV6vq7nusQCjVBdonWWDA6p72TgyFiSmQPgbbWmT2n7aPc1bROqMdO2TmMApoGakP3Rz+jcoV3nwa1qt6lPtKOajQgBD7WBFA1ULBFgn5F/WwBpQab+T5ukzxUS67dtX/L7ypLy/rpVCVm70IIQxQM2VR6Sa1kKfh4Ct5PEOReLDK0Sh4ARjbZu/MTOKpfLngKmQeCScZs75fV1GSwHndEUaWNusa6DCJwZWQqZI0a9unsxB5nGu1qt+vsQbFjGRh0885i65bc1CIy86Sy49bcWMmof6LPwvjYa0H5SR8j/abjq9boHbpnM6cGErVbLj4ctaFUwyTG3ACEEcvT96xTtK8s+hpguPh+fW5k49jN1IIrOVtYx1cprsTZEV+tx00iyFRwXft/Ov6S6JY6vOjAyFIVCIbadPNlSvkdgUKvVYsY2mz3dv2d9fd0XEpdKJTQaDb8RJ+e9XQygkb0CHM4BZREPDw/989GRDodDz8JUq1WfeiJTw8CCv1mzUywWPShqtVp48OBB7FBc9gULoSn8n3pPNgiAX72o31XmhmluZa1CAd9Vi3XGQHgpdBJToDaT6SmmQKmf08CIDcBCPiTU5osCnCTRuWuZGzvftXgciO/bw3mngbLt42nPxXvovOFvfnfafJ8F0Jwn2r9q/5TV4r20X2xRtZVZbfq1FBTP0mFWWW2NAieAGnj9LoGKIlVLU3PDMV3uqgZDwY3WIlh6nuwEjTgjMF3BwOV/PICPy/9IwS8tLfmlsY8ePcKTJ0/QbDbRbDYxHp8uCx8Oh2i3235fDbstfKlUwvr6OoDTVU2Li4vY39/H559/7iNJ3USQwIrPx+JnLRjWJfSqjAo4BoMBMpnT3Tm5qRzfLxaLuH//vj9H6/bt2z7S5A6vXLbPs3/ojO2qEKsXKqGJcN3ABgifLQWcFazadBV1nY6s3W77WiSmKXS3Xer93t4ejo6OYszjysoKJpNJLOWyv7/vAT5BA5c/K3jUVXKq09ns6aaNzWbTpxcLhQIWFxdRLpdxeHiInZ0dtNtt/Lt/9++ws7ODUqnkGZgPPvgAX/ziFzEYDPDNb34T/X4fz58/x/HxMTY2NnD//n0fnADA5uYmHj586DfaI5C5f/++r7lhf3D+Uc9v3bqFZrMZW8HYarV8f5IJYWqWNQ/D4dDX2WjA0Wg0PKggW3t4eIhMJuM3ECUgy2Qy/sBN3luPeuCCA7Y5k8n4Az4LhYIfK02FEeDoqjDWq1jG+TrEBjx0qDqHlWkgcKFtVkaBdir0PCFAoism9V7TwAvbyWva10KSZHeUKdZgg2lLsug2iNd6GN2wUvc+0tS9FQ10k/yqzucke2SBThLRkCShPlMATjuiu9lr0EsfafdxUlF2bhYQf+UFxZf5jo3KCW54WB0/w46jk6QCWHAzGAzQarV8ZESjZ3dMBRBbimiZGQqjykwm44sB+flcLof19XUsLi76E4Q1J7+0tITV1VX0+3188sknODo6wtraGtbW1nxenquwer2eT03wvByuuLp3755nhprNJj755BN8/PHHvqiaBXt8Ft3krNvtegXkM3CLcCqVTmAqIiOQXq+H7e3tWHH3ysoKfviHfxjNZhPvvfcebt++jVarhc8++wxHR0ce3GhEyg0YQ9FSqN+TgM1NADchhpJiAbmyDrrPBXCaSmLaFDhbrs/+1/2FeAr18vIy8vm8TwuRkRgMBn6VD/Aya6TGkEKwzEM419fX/eGP3FOn2Wxid3cXz549w+bmJn7v934P7XYb1WoVd+7cwd27d/H+++/jgw8+wB/8wR/gm9/8JtrtNh48eIBut4uf+ZmfwY//+I8DgAf/n3/+OR48eIBCoYClpSWvI/fv30e5XMb29jY6nY5fyURwUy6Xcf/+fdy9ezdWW/Ppp5+i1Wqh1Wpha2sLk8npbuccGz6/FvcS3C8sLPjrcLdg7juln1tbW/NzkAdecp4dHBz4A005ZgwguEw8k8n473BhAldKcq4xINGtEGyt2lWLda7WUerctStbte3KyE17JsuE2PdsWmyanPd+EkNiUy1sC/WwVqv5QNf2Ce2prjolY8Xr0K4mMWOWvQ+1URkYO6/PkyRW3N4j9Dfbx2cje0ufRT/BLAuDOl3EYq+l/v08uVJwYx+cv5UhsYxN6Ps66DTMZEloKDQSBeAjTt1Vl0bj+Pg4VqXOVRwhsU6I7bJol8wRl/2xhkSNpq7oKhQK3lByl9N6vY5qtepBGA3eYDDwEWKlUvFptmKx6MEPC00rlQrW19d9Vb4qjuZctUiUz0hGhikRu6SdjBcVklHwaDRCtVpFuVzG4uKif5bJZIKjoyO0223s7+/7DfxsLU2ozy9Ct18XRT9P4TOQWeNrnOws3tU+5Oe4soaRJNkPRlHUEZ1/Cm4YWWrtAB3w4uIims0m6vW6X9HDlX/7+/tot9te/2q1mtdV1qXxWIh2u+3ZFK6CAs7AzdOnT/Hs2TNv9PP5vGc9CPh1kzHgZdvCNtNWHB0dodVqodFo+EJrtQXAWeqPdoVFynovrXlR+8P7sm1MPXFjw9DeUZqqBsInx3Me6oKCUIR9k2QW5kSXfbNPbFr7VZ5tVmb3omID7lCWgXaTQgAaSvfwc9QhZa712TneOuZJBbivA+SeN46hNtgfPrvaMG6LwvmVdOJ7aI5Pa9NM4MY59xDAEYAxgFEURX/SObcE4FcA3AfwEMDPRlF0cN61lBZT5Kn1EbbhqgzsxMPDQ+zt7QGAX85MpAzAr0RgJxaLRbzzzjtYWFjwBrfT6XgGodPp+IiKjsIiTxo5UoC6YspSslwWu76+jnfeeQfZbNafFVWpVNBoNFAqlbCxsYFqtYqtrS18/vnnaDQa+LEf+zGsrKx4RmV3dxeffPKJP3hvOByi0Wjgww8/xOrqqq/52d7exh/90R/FmJ21tTX81E/9FPr9Pv7wD/8QT5488YVr2receAA8qCHDpFGhjoU6QvY1C03X1tZw//59rKys4MMPP0S5XMbOzg6ePXuGnZ0dfPe73/V9RIehY0w9CP0dEkudXsaYzVPf5yWM+qnLXF3EnaF1B9d2u41cLucPryTdXa1W/fygg+eyVJ2H3EpgMpn43b3r9Tru37+PRqOBH/qhH8Ldu3dRr9f9Pjrf+973sL29jd3dXTx58sSfQ/XOO+/gnXfewfvvv49qtYpnz57h8PAQ3/rWt/DNb34TvV4PW1tbODk5wW//9m/7OUiwwRRtrVbDvXv3vBFk/QxBv4326AjG4zEWFhbwwz/8wx48ZDIZtFotv7/MN77xDa+Xz549g3POH/bKuT8ejz0QVyBDMMM+5ucB+JSS7vVEPed4sLiZZ7Px2XR7CT4X76ErQWl3QsWms8rr0ne150nOl8/KPqNdYm0U9cDW4ulve69ZmN3LiPoGC2gIdKkLZNbIUugKRfoS3TyVOqJMHRmeEPDTDQBfhyiotku0X/U6wFnbeV36qEajgd3dXTx9+tTXyfJ7OpcBxPqCgVxIXoW5+XNRFO3K/78A4NejKPo7zrlfePH/3zrvIlQMiiqIlSQqkA/K4xVYeKdFtBrV8P9yueyXl5K639/fh3MuRv/Rads6CAU7AIITjhNZc/U0qtz3g8CDYIz7efAU82aziY2NDQ+4eHyBFhVyLwwu8+VqDq7w0JUXCwsLnu1hG/lj61oUwCjzpWKLzkgxs5+Z0mo2m74glKtQWq0W2u02Dg4OvFOy/aj3nAdYuaDMRd/nJaxtoo7rvhXUBzo5ZV3IuGmRunPOs4Q23ajsqI4DQRJrdVhQm8vlfLp0b28P+/v7fum0pjaZouHqF+4wrPvi7O/v48mTJwDg61G2trawv7/v96ZhkTKAGKjR1DOdBnVT20EWlUvMDw4O0Gw20W63fXqKz2v7mg6F1+fnnHO+Pk2dNleKkXFVhoIsBVMRXPWlbJsWnKrD1kBQ0w6XnCNXru9qK7U/p7E20xgJm0KxTngeNuS8LIP6CMuKKkCw7A8Qr9uxATPft30QspWvU2YFNiHWRsG4+mtlLVkiQgCUNJ6zPOtl0lJ/GcBPvfj7lwD8Js5Rfo3sLaBJyq3ajuQAcx8YPjQpdgs89DWmTniwHZefDgYDPH36FE+ePPHnyzANwPSSrUVgkaAquS5hJGXO/Sy4wyrZmnfffRfOOc/mcMt8prFYjLi8vIxcLofV1VVfV8PNw1jDw7ohInmieV6LhaX7+/solUr+8ELNvyoi1wlpN3Xj6wr4qKCsvWD7lpaWAAAPHjzAeDzGkydP/K6yWsPEsWENgl1d9ipi9WWOoOiV9X2eog5PV8tQqOM6rzS67/f7cM75eQLAsxNkJDV9ValUYsDo+PgYjx498iD84cOHPjU6mUzw/PlzD1g4f8gwDIdDz4hQZ/b29rxBIxA4OjrCxx9/HHsGBgOVSgX1eh3lctmnd7gEfjQa+ff43Ww2G6tXoX4uLy+jUql4VuTo6AjA2a7dZMLUwdCBsC+YCub8mEwmfqm+vsZUFPuVbdCaOIIxAiHOS9b06LzTeWGdJd+fo4O7tL6H5h77kvrLvuPiA6YuDw8PfRpSASNBvg26LPMfYoLnIXotbQP/5n5MDAb4ozuuU7/YRj1XSgGMBUCcz3ZVKd9/HaL6Nw1AK4NF8M/iYQJ/4GwbFtbusSj+8PDQBzu6mlczC9on86y5iQD8f5xzEYD/RxRFXwewHkXR8xfvbwJYP/ci0VktigIcDpitZVEJvacDSrBBx24Hn3Q7FYjLPllUyZU63Mqdh18yB68FWVQuLQhUw8f70yFx8vLQvrW1Nbz77rs4OTnBxx9/jN3dXfR6Pf+c1Wo1RotHUYSVlRUMh0Pcvn0bGxsbWF1dxdramj99mJOGRpvtqtVquHXrFoDTc32KxaIvKuVzUWlDwIDvaa0BlZiGhxO52Wzii1/8IprNpj/TqNVq4fPPP0e328Xm5qZfrWPTjEyFAIhtXnhNMhd9n6eo8SMVS4MBvMyAKk2ue2BQeLSHFaZddGdkFrhubm4im816kMzNKMl80qF3u10A8PU9w+EQm5ubsXQTHTzfLxQK2N3dxfb2tp9HbAd/yL5EUeTBDdNsGxsb/hwqsj6tVsund7LZrN83p9FoAIBnsAD45brcCRl4uZaOqzzYlvF47J+VNUBaC0g7oWltMplkNp1zsXofrt4kw0UboPtP2SCLwcYlwM1r0XfLUGhfUq+YeppMJlhcXPS7pHNvMl4jiiI/NozsVULsv7Vp8wAAlgmyKTI6do45U5d6bqAGj/Q5DFwUxFhwY/1mUqpvXqKg5jxh+zQlVy6XY2dp6fzP5XJYWlpCNptFv9/3GQrOHQ242M+h0ohpMiu4+Q+iKHrqnFsD8L84575jOiF6MTFCD/01AF8DXt6XxFJ8FpCoWEpOU1B6OKaNWik0RFEUoV6vo91u+1oCAoqNjQ3fudw1lDuW2n0q7L0Iviy4IjBQWq3b7fqNuZ49e4bd3V0PzvL5PP7gD/4Ae3t7aDQaWFhYwMHBgT+uYHV11e/iynNrHj9+jM3NTTx9+tRvCNZqtbzzW1xcjO0zwo3AOKGsY1TaXUGIImheL5fLYWFhAY1Gwxdsc+k6V7EcHBz45azsBzI3esAi8+1a12R06arSU3PR99Cqu8uIXS2geWnVT97XFoBr+hA4CxhCUTCXnXMsNCKjrvLeKgqkNO1FcEw2k6lUBQGsQyNY1oCE84ZFugTdfEYCezKAZEsISI6Ojnxaj0vmyZgQ6JOx4j01XeKcix0JQofEezHomUzOVkwS/OtqEQIU1poAiK2EtKlaghYVZZTU8E8LDs+RC+m76jrbOitzqg5aA14uCKG+6s+0uiILpEIyb9uhWQGOA2upmKJku3UxgP7WsaTNo65FURSrr9I0XdKzzPsZX+V6tEscI/XJutcWcFZE75zzrCcPiVZfowG16sws7ZrJ+kZR9PTF723n3D8D8KcAbDnnbkVR9Nw5dwvAdsJ3vw7g6wBQKBQiPSyMHWJ/a9ExEF/Lz9cYPU0mE7/8mNQu61Fo2JiOevbsmV8lMhwOUavV8O6773p24+7du2i32yiVSjg8PPQpJRoxOmAqnKa7OKhML+mgkmpjNPr8+XN/gOEf/dEfeYq+WCzi8ePHfunrl770JXz5y19GpVLBvXv3/OqjZrOJ/f19/M7v/A4ODg7w4MEDPHr0CIeHh3jy5Il3Hjs7Oz6dValUsLq6itu3byObzeLhw4e+vxUh05hyMymOh9Z20HizfuH999/H+++/78HKaDTC8+fPsbOzE9u1maheV1WRumetEdMYmg5kf4Yiwdch89L3Uqk0twZqER2NHJlDu2qHrAs/Twc+Go18ykhZG/YxwWo+n0ej0YitFNKl/yx2dC9Ssc45Py+YMwfOQJcCEv3NOcW5wmXiCqw//fRTPH78GKPRyJ8YroaRz607YbMIl3tIMSUKAJ9//rkHHjyChc6jXC5jfX3dn4ulqza0dq7T6WBvb8/fyy5r1q0UWJdE4E4HzXnBbR44PgyOtMZPGRuCWn5Ojb7dkXxWuai+q6475yKuKJslqta0FANKLrdXJoP9MhgM/OdC11eHeFWiAJa2stFoYGNjw48Z5wPTwTpGtF+W4QPgl5Dr6iHq9RUFeOeKtcUEXgpM9NgULoJYXFzE4uIi6vW631OKiwoI6gmM9FmV7ZsLuHHOVQFkoig6evH3/xrA/wXArwH4OQB/58XvX521QyzlpsAm1HBFuUmFddqhjBbtNXQjLW5cxkPsuMx1OBz66I15eEaQBALqSCi2hkT3btDoNIoiv6EamY2joyM/qEyHZTKnm4AtLy9jaWkJd+/e9W3k/jkHBwf+EL/t7e3YDsk8hbnT6fj8NWlAG41T8bTmwuY7abjV4NIRcvMxpgX5DHQQNFZM2QFnE5rGio4qm80GFZu687on9rz1fY7teqneghJFUWxeAGcBgTKYHE86SE2/APGVjNQNjonOKR0zghwaHV3ZovuxEMhwzGnw2Vbm6JeWlvw4j8fj2En1BHP8jgYYGuETXJVKJb+EW4EKI0imfZg+Y39xfqtt0XHgPVSH7ThxbnBlm6Yl2CdkaThntJZC0xB8RgU4Crr4+Ys49uvWd8soKtjk+6q30+a/RvlXIdaX0YYxSGC7mUpOWuzCa2gQz88ynUmdmwYcr5K10WfWz6ptCo0b09FMXdnNLZPuf5ExnYW5WQfwz15cPAfg/xlF0b90zn0DwD9xzv08gM8B/Ox5FyJyDYl2QMiJqZMFzlZKAPBRmjpu4CxK4rWpRJ1OB1tbW36XURrVpaUlRFHkC39rtRo+/PBDv1soz6hhBEEjyiiQBprRZS6X80CK0QejWzIcZCy0cpzn0ZycnOD3fu/3sLKygnK5jJWVFWxubiKXy2F7exsPHjzwxcTNZtMba7IkGukUCgU8fvwYlUoFnU4HwFmdEftX91tQsKMsSqgWptfr+bqhhw8fotvtotVq+fsoemfBJCcqN1wjDavUsyp7aPLMom8XmBRz0/fXJaFnYsqPwEUpfgCxs5MIQnq9Xuxz1BVd2cNVTtls1uu4rswic9FqtWI7fQNnm9NpsTi/3+/3fb0K5/be3h5arZZvC6+rDAejY54dxzapw6eeMrWmB9WSfVLDS5BFwEWGiwwi5/nBwUHsGkwfUM+0qJtChoXFxjzCgRsAqvEnO0Q7SVvG6xEEhByjpilfUeam70lzc1ogq8w2V5Y557w9Ozw89IyFpjEtkNHrvkrq4jJCwK3jxTpH585qqTQtydQ9fYAyrTYdyvGsVqvo9Xp+93fVPw14LMietygIC4Eb9r0GK6w5YiH96uoqarUaxuOxZ/Z101crCvQ49rMwg8AM4CaKok8B/Gjg9T0APz3TXV4IkS3FNlKjkVB0qv8z9UIhElR0zOjU3oeUcCaT8acEs96mXC5jbW3Nr2ri5nrcIv7Ro0eeQqPhYsEjNwujwubzeWxtbeHg4MAbNCJV1hFQsSk8QqFer+PTTz/Fp59+ioODA9y7dy8Wne7t7eHJkyfodDp+JQkLTtVQ0IhqgWmpVEK9Xkc+n48VJtIpKuukjA3HkNfhuAwGA7/nyOeff+53Xx6NRrGDQzudToxyBuCXq7MNSbrBe71umae+vy4JOQtlBJRpoWEl6Ob82N/f91sGKAiiweb4qG5Tp/XeBDjclFHbSJBMah2A1wVubKdBAg+2VLDS7Xb9wZXcA4dggBtvUufVfhCcA2erBoGznZ3JnrK+jnNS63b4OToc/mbkqcWTjLCVntcz4/hbTwBnPZ8ywfl8PgY46UhC9RaqB7MwGyGZp77PEunbz3DcCKoJ0mu1mh8rfs4yiEn3vqq0DfWf4IZpKj3NncJ5Ruadz8H0Je0jMwoMILgjNmsYgThzqqURr1s0VaoAh31BcENQzswHfUChUPDbl7RaLR8Qaz+FUlGcVyFfPk2ufIdifZAkIUJUNK7XUBpdO1udrp3oqgg0nqREnXMe7LCq226fzn1Z9ORdXd7HZ+MJ30TlXAkCwOdd9XgBPQEbgI/ciN4ZcQNnyJ5GV/uBf2s6gQyOPbRNCw+ppPqaIvRc7nQbdK7EsH09mUw8mCG1yMnO73DM6UA090znQAeQxNi8qlyUpn/TRJ9RI3ym99Tg6GepW3ZZeSiyphPW1XvAqT4rK0mdtf1OVoFpYOpauVz2bY2i06Lfzz77LObM9/b2vFHTOjDej89i+8Q5588sU4fDuaKBj6aEtM+ov2pcOadCCxaou7ye1hrxutpm2gU7p7TWzDJS2jZ+l/PyVQz/VYq158DLNRoEflyaT53QPqHeqA7aa12F2LQZN8Jk0EggPR6PPVtKJ89DV8vlsl+JC8T1EjgLIgl8crmcDw673W4ssKdOqLyOvgilidTO0kZwftiVjgxGer0eOp2O34DTptus7bZM3azPdqXghg+mHaMKq3UCKqq8moMlMGEKiJ1i85N8nZ0OxA8mo4N+9uyZP8OG0S7PweCmZ4zsVPHYFkawAHybuPyTz95qtfwz5HKnh2my2JDPTkCQz+f9smrS5wRYbA+vxb+1XoATi8srCR4YIaqx1bSUph60D9V5qQJy11kWU7LWgcyPnqUzHA79MmStb9DP0emGwG2S2Mn9tgEbOxZ8PuqfMhU6VzKZTKxYnGkUnhAOnBUeaz2OTTHlcjm02+0Yq8J5offkZzW6Ixjhb+dOdwBeXFwEcJZi3tzcxGeffebZHKabyWIo8D88PIyBNmWV2L6dnR0PqnQFmU1fsx/1errSydojphTogBjYEBwp4FMnpOkLrszS8eVn+Ay6UMFGs7QZlr25KgcfkhC40kBJdZhBEJ8POD38dTw+3TJjZWXFA0CuIlW9Utug+vK6hXNHD38sFArY2NhAo9HArVu3sLGx4XWXm6cS0HAfMD2aY29vL3agKpn/bDaLlZUVv8kkyxx4SC4DD2X0X6fY/tUUFecT206/R9C3sLAAAP6w2Z2dHTx9+jQWACguoD/XWjsF/rMA+Ss/FRx4eSMkzZHaXJ7KNIcVyr3y2uoMeC+bd6cBGY9Pl4wzZUM6mYyNOphQio2/icDVkGo9hLZLo107eHydBpWrwhThK5ulDoX/6/4XdnmrRu0UjUwIcuzz6ne4So21NRpNcizUwepz8zkVqOnzXATYnPf62yrsT3WQQJw+V92zTludo46fghleT3PfOq4KajTFqawL26o1XplMxh9VoGlLdWihlSak9smo6Nzm/CXIBs7qgHQrCRYdK1C0tiikS3ovXfJLVsgu6dZ5qWNh2Urtc3X+1o6xH3Ws3ySxLCELS3O5+PlKoaJt/f5Viw0umHKiU1fwxXaq7nPukY3kGGuRv7J0ugfTeDyOFSxfB0Otz6Kitl3th+o+x5glE0pCJPkH+3qICQzJlYIbsh2aH1bjQAPDz1onrGCF+W415GqAeV0tyiPCpzjnPKuh57jQEdDJ26hJnYOi1iiK/AF9ykJREbnlO3Ovo9HIH0VA6g6AjwI5+J1OB48ePfKRq3Mulh4j2GE0mMlk/GvqwAg++OwKWixlzs/oc2s/A2c1Mrnc2YGMoaW5/ByXN5JZYzEZIzMW4lnHOU2ssX9bAY2CETUuCoJDQBU4K5TldwH4jeR0PPn9XC7nN+fTmg+7IlDbpKCeAJ46wVV6o9HZzrsAYlEqf5PN0ZSQZRFDoID35fey2SwWFxc9Fa7BAPDy/j0EQAQkPPJE78lrU2f1/haMs93lcjnmmDWQ0YiUz0OQb42+9onaNKvvN03/lWmf5vAnk4mvrWJ0D8C/pivu+H39HUprhGzWPETtLZk41pWMx2NsbW1hOBxia2vLM5vAaX3h5uamX/VaKpXQ7/fx+PHj2PElnH9cqFIqlXwhugIe1Y/XBfS0HymW8efcoW5yYQtZzEKh4LdWabVafnNcWxyv1w+1g32tS8anyZWDm1KpFMt3W8Unlc5OI8JVapOvM2ettBwNhiJrG8lStCCQaSUOnBpWNXDaLv4OrdQKLdsjeGEBcL/f9wXH3JeHk1z7h58jiOBOnWrYJ5NJbEdI7QMK26g0t13yraKKrYZCWS4CSNb1sL9ppBQI8mwv1jBxy/1er+dPms5kMj56P0+mRdZJxv9NFX0O61QVZCjrppG/3WfGOedrvWhkSP1zawTV6dByTYJ0585SnoPBIHbmWz6f9ztu0+BzJ1Ke5cbN+zg/+F3qKsEunZumVdl2bgJGu5DNZtFsNv22Dtyyn32nAIEAh21nmkn3sGGKhIEDnze0dFvZJpte5fOw7cAZO6XPSpClkSrvY1+z+nHTZBoLq+/ZbSQAzOzIKDbynzfLo+MGnC1kYQkAHfhgMMD29rZfdXdycoJSqYTBYIBqtYrFxUUsLS3h+PjYH0nD40uoM/l83rOZ1EX1P/SHr5PBsvV4IVCqukn2kvpMm8LFA9xkNglwKmOqov541uLpKwU3uVwOa2trniXRWhbNGVpHq8acnUIHqgwJHS2/ox2l6FNBAWlkGk3NDSd1ogIgBT6an1fmiNQjQR2LKkejkWeOAPhoxT4/DSyvwahSVxLQcenOqWRuGPEpINHo2z4bn1/TRnyNTgaAT9nREGcymdixDgRZNDJkoegYCHbpUDmetsbAynmGPMRevE0ScmrsL3UElmGxzA6X3VpHyXHS1xXU8nVG09oGBb5c+aTLVwkUOOZ0DmoY+bcCVAYfCkyAOOhmO3gvni1ll1AnGVA6IcsSWMCoNuY8elyZZP7W61IsS2PBlwZUdpxvkli7HfptP6/OMynI0t/TWBi9h5Y8vA5Rm7u/v/9SkTr1T1/f3d1Fp9NBr9fzy9x3d3d9HSX1j/6IK3O5IpU+k7Uqmvp8HWDHghl7bc0A8H0FYVwwoIDeXkMzJFqYrz5CGf1Zdf9KwU2pVMKHH37oz5Ho9/vY3d31UaGmhYCzXLhFigp2GIFqDp3XUMYnZKgBxM50skwQDRlRozoGrQug09C6ADVMjDJ1s65+v49cLucL5549e4YnT54gk8n4Zdp8liiKfDuZyiGLQ8Uh8KDj4PPrRoQ2f8n+4zNQ8UJL7tj/urswHQdBHZ0Tow67zF2PYKDxXlhY8N9lJD8NnMwKbN4m1oZCZ6grjNRpAmHjAyC2oy9wqkeMEsnS8PuDwQDtdtvPE84DsnO6JJdzQM8ZI5jvdDr+4MtareavwbosXo+MyHA4xOHhYWy+62pCG/HZlYCcC9TfdrsdC1B0Xuqc0SNA+Dm2kfNX+5aROvuK/a9zRv+30T7boUWTen21N8oE2aDM6obWxF2HKFC2Tjakl3aO2qJYy8BoLct5AEfnBq81bxmPT4/QIIDhsm27Ilj9TavV8vpFpj50ECifc39/3zNC/Bz7Recfg+tXcf6ziM2qqFgfws/zO7T1+hl7LfXfDJb1u5VKBdVqFePx6ZE+r1I4fuWzYVr6IKSAIfQ/S1Q/q3PTa4VYjJAjsd/V17Vgl+/pj0WmyhYxWmVxpF6b91LDZtksTTFoDRM3ktLIW6NGtoeTQttohZ/THzVoNrcOvLz0Xusc1HhplKDR3DTRcVaWSq//NsksE1sNB9lDjZBDNDNFCxsV3PA9vYYCfL0ux5vXocHnuAPhE5X5eeoJr6XX0/eAl8/vUoZR54rVNQU3WsjJeyYBhSTdThIb9SYxCZZJtSmz89rxNoh9piSGbR7Xnuc1CVCoR7rCkCBE7R9wdtCzshkK4KhTTEfp5zknQrr1OiSp75L0eZqvfpU2hgDyq4zjlYKbwWCATz75JLbiR9NSanwomvpRJ2zTLbqSgwqgCFclSRGUDQLOImS2DQiDMaWLeR1NFXHzJburMu+VyZyuFKFBJTuin9P+0J1fFRRwIuhrvLa2TXfF1D7RtKD2uWUI+KMAgp9TBsCm9dh27Tv2k9L9oYJRvYaOBQCf3igUClhcXPQHotbrdWxubr40zm+ihPozVEBsnT8QB8H6ebITnH+a4uH1OQ40pgqalfbnNWjcdUwZ4Waz2Vg0R+H8UOaPbdDCdMuOaEBgn5VtsmkS/T6fT09tBs7SeDYNwt+asgpFyWpf1EbY/reBiwJQvbcNZkLsZsjOXbUkRfghsUFQkuPSNJUdh5siZBrsqi5ri1U0I6H9Zp9Nay/1c5bts5+5KnnV1F8ICBELhMZVa+VelZG6UnAzHA793hM0VEoz0XFpDk6V2zIDNuJXsWmoJLGgRK/LewKIGVGbC1ejxLYouOGgaDRsi3J5XX6OBjJE/Wl6QkVpShXdLAqAp0NDTBWvY0FNaAJyrLT/Q8Zb+zq0/Nj2uz5z0qTRtjM9wdU2lUoFS0tLWF5e9qnGN12031U/rZNTkKjjGZoDlkWxjj/UBopNH4TqpHReaL2ZdeBJzwvgJYpfvzdrYWHouqpj56VzkhyOvY69TwjghK5rgzl7nZBRv6lszqs4uVd1iDfh+ZIkNMemPWPSeyHnb8c/6XvX1T+Xva9NR8763nlybUlajcQUwTJCtCkFRqkaSc6ThlNjpBEWFVZrWzQKC7UTeBlx2+fVz9sIhvK60iqMupModW2zNSw2cp63qPOxfWH/tg5wPB77XWn1mIu3QWxUb3U1NI5JzOJ5qRSVkG6Gxn2WIu5pTnzaZ0NtmfaefbaL2Ipp97ru2pZUUknlfLnyWWrrT7S+g2yCLrPm36SNWbhoV/1cVnitUCpLAQjfCwEPNaJKG4bSPbwWf4cAkr4+z2fVFEMSFaz3PK+mYN6S1Ef6G0CM/er3+34PBO73w1UIb4sksQQhdg2I18Dwb3XMswDXEHsX0gdbcP8qknS9ae2c5TUVy3QkBRTT2kTh3iM2CFA7NkubXleNRCqppHIN4EYL+V7VMapxZ63A6xBlcYA4k6PAJ5Qf12uEHIOVaQCJzxlKIV1Wpjkq/UyIag39PW+xYwAkOwMFfqzP4FblN5nKflWZJY2T5ERtKsvqFNlQTZW8buA6TTj+IZCVJPz8eazPNL2aBqYpCuTYj6+6HHeW1GsqqaRycblScMPCQp38nOSM1MhWJKVkZmETLiLnUeazFrOF6glmARK2fkef/1XSCLMIWTPbvlAbQzlg+/frAF6WSbM1R0lRM4vTWN9xkbqMmyghZi/EsFn90+MPWF/inHtphZCVWeeZgpBZGL5puqZiVy+FPquAzTIz2l+6Ckv7Rxlizjv94XUtOFQ2VVcg6mKBpCLIUCo7BTippDJ/uZZTwS0zEXLoIWNuQcK8DcIszMB5kpSyepX7h4CNrc25rMzquJKK1+z/865/mnYP20/W2djl9m+LTCvyVfAdKny3zvp1rjiZpjPTaraSxsq+rkwpQU3IXuj/unlYCFzobsx2SwG7lF7ZZ/4/az/YdvE5UkkllfnKtexzoz+h+hJg9gjvquRVCxyB8EqWJNF0gKW4ea959cO8Hf+8mZskcKjFs9pfNuWiTukHRc4rDic4JCDg0vmQJKW4ztPfWYDLeezOtNqUUApX51iIFUmS874b2ieJn2ehuoJprSXk3Eoq8k5rbVJJ5fXLTODGOdcE8N8B+GEAEYD/PYDvAvgVAPcBPATws1EUHZxznZf2iUlK41gDa1mdq2Jtkt6bBdhYcHKe2CLm11FrA5xFnrPWAb3q+5cVy9Tp31p4zrZYvWLf21VCs8q89P2qJQSmQ4xfFEUol8sol8uJRcAhQJFUqxJFkV+hpvfQ39qmaZL0vRDQsHYkKZ2tZ2Cp3ofSejb9rOBQ/+fOxHoECsGOXicpdTvv1Z6XkTdV31NJZZrMurzhFwH8yyiKvgTgRwF8G8AvAPj1KIo+BPDrL/4/VxhRJ51sq6yCdcA3LcVwWeOkhtMa0dAy+Xn98N43VWapubLPw/dULlFwPjd9n6dMW+49TRdZY6VOXHcPtj98z/7YnamtI7dgI/TDw+/O+9HjBwhU+bq+F0rl2rbwmbXN+lufUfuBZ/pw01H+rRsNhuaUHackFvIGyY3U91RSuYy485ycc24BwO8DeC+SDzvnvgvgp6Ioeu6cuwXgN6Mo+mjatcrlcnT//n0ALztvNZShIjxb9GcNy2XlInTxNOZD9+QBkpfZ6mvTalfmHeVNo/8vcv95giVlE7T/QoCGn0/ac+jJkycYDAYzd9489b1UKkV3796d9dbntWumz2nfaKrOLgXXfkyqc7O1X6E5wteLxeJLacDzloYngddpwJa/X2UVn37esjNa+Dvtt16XIC3UHgWQti+mAdF5zZ8nT56g3+9fub47525utJTK2yy/G0XRnwy9MUta6gsAdgD89865HwXwuwD+JoD1KIqev/jMJoD10Jedc18D8DUA/lh4zU3b03rVMCjtfROZhlkdTlIthP2d9JwESddNYyfd/3WOjQLEJMZGP6dtvGB0PDd9n+dmbyHQArxclGr7y77HNIpucBhyulqoGwI6fI/vk5Gx7XpVwGzT1fr8oXG3zEloscF4PPZbBPB1ZbK0AN2mmULbVhSLxeDz6Gds+y07fd1zWeTC+q66nkoqN01mYW7+JIB/B+DPRlH0W865XwRwCOA/jaKoKZ87iKJocdq1lLmhWEBDY8O/k1ZGXVdh3mXuGTLMSe/N876zyCyM0TRwM0+Ao4yCAtykeyV9bjKZYGdnBycnJ68Syc5N3+fJ3ADhfgkdtWDBTWiuhM5ySQI559WLkLlRMDdNX6cBn2nfC4GY0BYNIXBngyj7HXufaZ+5aC1XqG3AfOf2BZibueh7ytykck1yKebmCYAnURT91ov//ylO869bzrlbQltun3ch7kOiaQRdlcD9S/hZPZ2Yh9Xx/2w263crnoeEaPrLSJLhvGja6bqA3HXcl7URuiQ3lKpS0doK/k7agfkcmZu+z1PUoYfAgbIquiP2tOslvabMQqhuKZRu0p2gQ+8npb5Cnwn9nwQ4pu0/lZTG0nocy0wpcLF73vB11uNYmXWPK9VTvf81yY3U91RSuaycW1AcRdEmgMfOOeZbfxrAxwB+DcDPvXjt5wD86iw3tMWJFFtIyMJB/q8FyNcpr+Isp6VR3hSxzxD6eR0SckBacGrf53emrZqZReat7zdVbP8l/byKKAtrf+zeQ6HP2WJlW8xs6/T0u7M+43n6YUGULT62Jz9fVq7bLvyg6HsqP3gya1HAfwrgHzvnCgA+BfC/wykw+ifOuZ8H8DmAnz3vIpPJBMfHxz4/T/BCh8V9N0i106gNh0McHx/HwNC8gc55xvxVUkj8zCy59VnveR0yrf2hKH9ewrHN5XLI5/MxQBNykCrq8C7BPM1F369CrI5MS7Wcl266jEzb44b3OQ8UvMqmdropod7Dvh9qn22nAvVpTJBe+7xi6dC9k3T2Bsgbo++ppDKrzARuoij6fQChvNZPv8rNWE9Dp6NOSmsIbO4+is62Tb/M/iVXJRYIXVd6Zx4yDWC9LoDDvVlUFzjumnKx/TqtpulVZF76ftNEx4kpmNBnkuR1O+VQukkllNpSSdoQlO9Nq7mxoMMCEv1ePp9/JXCjcgOBzVur76n8YMuV7lCcyWRQKpVixZ/cDGs8Hvsonb9JI+fzedRqNURRhEql4mtw+v3+3IzFNACiTmEWB6pG+DyDnCShotDrkiSH9zra5tzpCfA2LamphOFwiEwm4+uwtD2hJb9vs0xjalRsrQsDjVeRacAnaTfoWQBm6BmmgYck1japL85jcTRlRQmlXJPAl9YnTbMhDNLsatBUUkll/nLl4KZcLsdy11yOOhwOfaReLpeRzWZRKpX8awqKAODo6ChWxHhZscaNQoZAPwfE004hA26LC5Py+/o79PrrSPucJ0nFpkmOat7gplQqoVQq+dor28dc+UIHHepHu3z3B0n43NMKe4GLswih79ml4NqO8wDYee28aJv0PqHrnVfQnCR66Ogs153XfVNJJZXZ5VrOlrISynlP+96r5rwv2rZ5OcfUeF1MXiWd94MKZKxcZz/YFKGmlN8kFu0m18Glkkoqs8m5+9zM9WbO7QDoAti9spu+HlnBm/8MwNvxHOc9w7tRFK1eVWNUUn2/UfI2PANwQ/X9ha5/jrejn9NnuBkyyzMk6vuVghsAcM79TpSw6c6bIm/DMwBvx3Pc9Ge46e2bRdJnuDly05/jprdvFkmf4WbIZZ9h/rmdVFJJJZVUUkkllWuUFNykkkoqqaSSSipvlVwHuPn6Ndxz3vI2PAPwdjzHTX+Gm96+WSR9hpsjN/05bnr7ZpH0GW6GXOoZrrzmJpVUUkkllVRSSeV1SpqWSiWVVFJJJZVU3ipJwU0qqaSSSiqppPJWyZWCG+fczzjnvuuce+Cc+4WrvPdFxTn3jnPuN5xzHzvn/sg59zdfvL7knPtfnHPff/F78brbep4457LOuX/vnPvnL/7/gnPut16Mx6+8ODjvxopzrumc+6fOue84577tnPvTN3Uc3kRdB1J9v0mS6vvrlVTXb5bMW9+vDNw457IA/lsA/xsAXwbw15xzX76q+19CRgD+8yiKvgzgJwD8xy/a/QsAfj2Kog8B/PqL/2+6/E0A35b//y6AvxdF0QcADgD8/LW0anb5RQD/MoqiLwH4UZw+y40bhzdY14FU32+SpPr+eiXV9Zsl89V3Pfrgdf4A+NMA/pX8/7cB/O2ruv8cn+NXAfwFAN8FcOvFa7cAfPe623ZOu+++UI4/D+CfA3A43f0xFxqfm/YDYAHAZ3hRBC+v37hxeFt0/UXbU32/nvan+n71z5Hq+vU9w9z1/SrTUncAPJb/n7x47Y0R59x9AH8cwG8BWI+i6PmLtzYBrF9Xu2aUvw/gvwDAQ36WAbSiKBq9+P+mj8cXAOwA+O9f0K//nXOuips5Dm+8rgOpvl+zpPp+hZLq+rXL3PU9LSieUZxzNQD/E4D/LIqiQ30vOoWVN3ZNvXPuLwHYjqLod6+7LZeQHID/FYB/EEXRH8fpmU0xivKmj8ObJKm+X7uk+n5Fkur6jZC56/tVgpunAN6R/+++eO3Gi3Muj1Pl/8dRFP3PL17ecs7devH+LQDb19W+GeTPAvgPnXMPAfwyTunLXwTQdM7xZPibPh5PADyJoui3Xvz/T3E6GW7iOLyxug6k+n5DJNX3K5BU12+MzF3frxLcfAPAhy+quAsA/iqAX7vC+19InHMOwD8E8O0oiv4beevXAPzci79/Dqf52hspURT97SiK7kZRdB+n/f6voyj6jwD8BoC/8uJjN/0ZNgE8ds599OKlnwbwMW7mOLyRug6k+n5TJNX31y+prt8ceS36fsVFQ38RwPcAfALg/3zdRUwztvk/wCkV9i0Av//i5y/iNK/56wC+D+D/C2Dputs64/P8FIB//uLv9wD8NoAHAP5HAMXrbt85bf8qgN95MRb/bwCLN3Uc3kRdf9HuVN9vyE+q76+9zamu36Cfeet7evxCKqmkkkoqqaTyVklaUJxKKqmkkkoqqbxVkoKbVFJJJZVUUknlrZIU3KSSSiqppJJKKm+VpOAmlVRSSSWVVFJ5qyQFN6mkkkoqqaSSylslKbhJJZVUUkkllVTeKknBTSqppJJKKqmk8lZJCm5SSSWVVFJJJZW3SlJwk0oqqaSSSiqpvFWSgptUUkkllVRSSeWtkhTcpJJKKqmkkkoqb5Wk4CaVVFJJJZVUUnmr5FLgxjn3M8657zrnHjjnfmFejUollZsoqb6n8oMkqb6n8ibLhU8Fd85lcXrE/V8A8ATANwD8tSiKPp5f81JJ5WZIqu+p/CBJqu+pvOmSu8R3/xSAB1EUfQoAzrlfBvCXASQqv3PuYkgqlVQuIVEUuTlc5q3Vd+dOuycU6GQyGf+Tz+fhnEM+n0c2m4VzDrlczv/OZrP+O/b6vEcmk/H/h+5rXwt9Loqil9o67b3JZILxeOzf08/q94fDIfr9PiaTCQaDAcbjcaxNryoXCRxD3+H9tS/s+/zeeDzGZDK5cn13zkV23FNJ5XXLZDLZjaJoNfTeZcDNHQCP5f8nAH7cfsg59zUAX7vEfVJJ5SbIhfRdndFFWdLXKeowFUSwrfl8HoVCAQsLC7h79y7K5TLu3r2LxcVFFAoFVCoV5HI5NBoNlMtl/1omk0E2m42BmWw2i1KphFwuh3w+j3w+H2xTFEUeWORyOeRyOf8af49Go9h3RqMRJpMJRqMRTk5O/OcIVLrdLiaTCU5OTvzr/E6/38doNMLTp0/x8OFD9Ho9PHr0CJ1OJ9Y/o9EIURT51yxQSgJNoT4PPbNzzgPJQqGAbDaL8XiM4XAYu3YI5PC1TqczbbhfRc7Vd9V15xxqtdq87p1KKjPJ4eHh50nvXQbczCRRFH0dwNeBNyeSTSWVi8qbpu/qlAlE+Dpw5tCHwyGAU7BRLBZRrVY9exNFkQcUURR5QJLJZBBFESaTiQcbzjkPKoCXmRn+8H0FNWzLZDLBZDKJOfjRaOSBwGAwwGQywXA4xGg08oCH31OANBqN0Ov1cHJyguPjYwwGAwwGAw8iKAqGQn3H654nFpjoNch+ZbNZVKtVFItF9Ho9DIfDGDAjCNL+4veuEkCrrmez2Ruv66n8YMllwM1TAO/I/3dfvJZKKm+jXErfbyJrY4VOk3/ztzpRAgwgziLwfWVFmKZKAjDj8TjG7BB8AIiBCH7PtlXbp8LrsC0KahQ8KfAZDAYeRBAsMOWWyWT86/ZaCtJC7bSSxOQ451AqldBoNJDP57GwsIBCoYByuYxKpYLhcIiDgwP0+/3YfbUPZgVYM0pq31N5o+Uy4OYbAD50zn0Bp0r/VwH89bm0KpVUbp5cSN/nCWpCNQ3npUCmiXXIrKvR6yk4GAwGyOVyGA6HsXoUOtfRaIRMJoN+v++BTaFQQCaT8UwOr0sGQgGV3pdsDdmIaeDGgieyMmRuQiDn5OTEsyJHR0fo9/vo9XoevBWLRZ8iyuVyGI/HKBaLnh3S1BdBngVQSWJrjNg3y8vLeO+991AqldBsNlEqlTAejzEej9HtdvHxxx9jb2/Pt5Vjxv7nmMxJUvueyhstFwY3URSNnHP/CYB/BSAL4B9FUfRHc2tZKqncIHld+j6tWDVUaDsvCdVt0JnbNigzQKBA1kU/p5/hawQxVpJqVPQ9y1AkPYMFeJoq0mJie20FQQRsCvSY6iGDE0WRZ3Gy2WwMzGhf2dTVNOH7LNSu1+solUqo1WooFov+M6xXyufzODk5CfbBPIF0at9TedPlUjU3URT9CwD/Yk5tSSWVGy3Xqe9JjmtWh2aLhnV1E6/BIl8W/wJnAKFQKODk5ASZTAbtdhu5XM47YHXoo9EIg8HA1+Pk8/lYnYiumtK6Fsu+2HRPKLWlbMzJyYlPMbG+RguPFaDxc/1+37Mw/A7bmcvlfD9pcTSBTajImWBHa4MsSNNaIh3DyWTiC4kLhcJLBdPD4RC5XA6lUgmDwSD2PfYLC5DnJal9T+VNltdeUJxKKqnMRy4bmWsKhyuW9LosBLZsBR36aDRCv99Hp9Px7zebTe/0VQhoFPSQCVG2Ret2APhrhWppQuCGK4nIvmgqSpkYfp+vK6jR7xDIENywvcBpio3sDZkbLVDm/yw81jZoW+zz8XkIBvP5vE+d/f/Z+9MgybLrPBD8nruH7x7usWVErpVVlYUqVAHExgUEQBIEBYkcSgOjSYKsJdE4M5RBNBvJ1NY91qL6T8vGemTkn5HUNi01IakpSiaKlNhcQQ45JAgSXAAQIApb7ZVZuce++BK+hIf7mx+R3/XvnbjP3SMzMiOy6McszD2ev3ffXc495zvLvZf17vf7SKVSSKfTrm5altZpQhOa0ATcTGhCJ0rqveD/90PWM8PvcX8EMlSy9NbQU0EPjoZNSAoMqIAJlDRBWNtETwQ9C/SO2JCVJu6qx0MTnW3CsHpNtL4asiHQYBIx/1eAwrAcvSgKcHRsFKTwnfSa8FkLbujN0f12tG1BEKDT6WBzcxPpdBqZTMaV2ev1HKhkntCwFWYTmtCEJuBmQhN6qDTOKpq436zi93ky1OOhCbq6Zwr/4sCN735NAKaSZcLw3t4eGo0Gkskk6vU60um0C1EpSKDXg4CGyp9ggYAobkWSrv4h+FLvCz03/LTeGoaSOp0O2u02er0ems2mAwv02ujScm23XW5OYvhIQYUm9CqQUVBFkMM62hBVtVrFSy+9hKmpKSwuLiKfz6PT6aDRaKDT6WBlZQXNZjMCiG1+0YQmNKEDmoCbCU3olJH1wtgQjW+lEBWyAhmCAd1MT3+zoRcLkOx1VaRU2vTc7O/vu3CKXTFkc2Q0AdZ6Hex1Ddnoaiub0zIsoVaBhn73gQEFdHGk+8mopyZunLTeOp6++mt/an6Q7tmjfTEOeJ7QhP4i0gTcTGhCD5FGKR0LUPhdjzKwy53jQI2vDPvd5+FRrw3L0pVQVN4EQwyZ7O/vo1qtOg/M/v4+0um0887oc3yHrlLa29uLADAbRtLEXFXmcR4sfVZXSXEfGyY7K7CxoSy+Z2pqyrXXhtjs6isgui8Pr9k8HPaR9UzxWqlUwtzcHKamppDP590KqVKphHa7jXa77d7F97HfCIAmNKEJHdAE3ExoQidI1ptiz2zyeWJ8gMjmutiQVBxw8QEdH2k9GLrp9XouuTidTgNAZDUR83ZsHg2vUanrKiR6RVTx93q9CAiy/aftYB1s6Iq7++pfHEgiMLPvIPH5UcR7hiUeA3AhtXK5jHPnzkU8Q6zP7u4uVldX3VERzLvhPkJs54QmNKEDmoCbCU3oEVEcKLGHT+o1mw+jSly9MdaDY0GO/a6kK4J8vxOA+EAY/+yeMRpi0bCS5qVYYKfAxwdELLAhQCAI4vsJZDS0o/0UhqEDX8yfSSQSrt7DQlw6lqNypXQ5PfvBB25SqRT6/T5yuZw7e0sBFftX86SsJ47tsUvIJzShv6g0ATcTmtAjIM1j4WokggQqV4ZCdCm2Ag9faMl3je9TsjkaVnlqHYHDK4IUGBEUEAQkEgm3TJy5J1Ts3W43soRawQsTlLkEO5vNurLozWCuiYI8Eve3odeCddjd3Y08y/YTkGh/EbgxTKahMwtyNFHZhqV8fc13KPlWeBH8zM7OolwuR05d17BcNptFOp2OgEENsSUSieM8OHNCE3qsaQJuJjShh0zq4dDwCi1x9SAw5MNPTXD1gRyCCP19FIAZRnbZs95vE5TtUmb7pwmzumIqLofFLs9Wzw2fVTDhWw5u/3RlkQVH9Jho+Iyb87FuNunYghkLgPQdOiZKHFe7eowb+BH0qqcrmUwik8k4IKzvsyBpQhOa0ATcTGhCD5U0jEMFxU96auixsOBGgQIwABFUfLyPv43Km2F9ABxSiAoCtCyS3qt1ZHsYTkmn08hms4e8TvSKaG6NKnB+0mPDpdtMlGUCs/XcMJSkYEaTse3KKvYB+x0YeFO4ZJxeHLsPDt+pXiC74knBDfvJl+xsw1KJRAKVSgWVSgVBEETqwbosLS2hUqm4emreEpe21+v1+2PUCU3obUYTcDOhCT1E0lyPZDLpQg60wqlk+Ru9ODanBcAhpam5L777h5ENi6j3gSEyX04N81USiQQymQyKxSKSySRyuZyrvx7JoPkw9LwQGPEd7CcFKZ1Ox33qqioLbvSUcmAA0hRYKQgg8LGbGAKDFVIMb7HOvkRdAjVfsrTmwVig5cshYr8VCgXk83kAcOE2ApwwDFGpVAAAtVoN9Xo94ulKpVJotVrenKkJTegvIk3AzYQm9BCJipxAJpfLOTDAgxALhYLLvaE3gwDB7hdjc3dsWCouaZh1ocJXJWvDKnG5N/Q0sC3cvE/DaNwoj+9QhQ7AeXi0Tlo3DXcRNGjSsHqt+F71Kmm+jv7ZZGEFhiyLISoCH12Szvboxn4KJFlPXz6UtpP17PV66HQ6CIIAhUIB6XQapVIJ+Xwe/X4fu7u7SCQSKBaLKJfL6Pf77tRyAA6E0aujR2ecBGlfPOieO+rp4tjGAXf1OPJ/G1K1wN0mg+v9ds75DIVh7YszLEYln9t7bGjXtt1esyHYceo2bju0v4a1Q72TAA4ZGKPe4+v3Yf0ziibgZkITekik4Q96NorFIlKpFAqFArLZLLLZLKanp5FOp51XZ2pqCrlcDkEQuKRYn9BQRWoVvlUGFrSo0rb3KAggUAAGIRkmtqbTaRQKBQRB4JRsq9U65FWggmISLD0VVFq+fWz4XLfbRbvddsq73+9H8k64x45uKsgjCvhJTwy9RurlIpDkOBUKBee5Uu8MQz8ahtKQIr1TOvb6qR6lROJgl2cCmNnZWccP+Xwe+/v7qNfr2Nvbw8zMDBYXF7G/v4/l5WXs7u6iWCy68Ws0Gq6P2E8nRcNAw1HLoaeL/KdA3vapvUZFqgCBPMb+Vx5nPRXk2jKURoGfOAAW95zW3Zfwrkn62se8BiBy3zDvnX3XsPv0z+Z02WdpLNDbyWupVCqSKzesvyyQHdY/E3AzoQmdMFHYUDn7/jQUxU8qKeu1AeLBje+7BS4q/HweCL5LQ1U2/4ZAjGEl9Upo3gxBmQVLWpbuKaPCc5hSUSGnv7EMTUamJ4d10SXnfD+Blio0qwgVpGluD8eOZVmyFq8dYw1fqVLSLQG4QkrHS8GshigfNxrm6bAeFR+oifNWxPH+g9brOOkoXiHfHB0FTu7XgxbnLRlGFlTa5x+kPizHN++H0QTcTGhCD4mC4GCTtVwuh2Kx6EJQDE8xqdi6bnWZNDBQmrzX7l4MHLaSbR4Nf+MzrBef1dwYLatQKEQ8Sr6wUbfbRa1WQ6vVQrvdRr1ed+XZfJROp+OAEcNbfIeeiM29aTR8xLJ0xRU/NRG53W67/W70xG8SPQEaerOeKnp5CD7pdQuCwAEmVTC5XC6yNxFwkDeje+3wXZp/pOURGDKHplQqIZPJuPLa7TZqtVoEZGkfn+RqKWvZ+5Sxfvosc5K2RUEgeYB9qtcsYFZet0DAJoErMNYygGgo0hoF4/SJ/R5nUPD95E293xoXurGllqmhXTvv1FsV1+/an8qfcQaSjqnWiQsKNF/NglPfe32/8ZrvbxSNBDdBEPzvAP4qgLUwDN9179osgF8EcBnAdQCfDMNwe+TbJjShU07Hye9UzqrMqdB1TxvrdvUpCmupW0GrYEbLsMKMSpurs/Sddql1EATIZDIupMbVRXrOERNv2+02ms2mSwCO28slDA8290un0+48KnomgMEeMnzOAjgFOEoEhOqp8Z0hZfvVKjcV6uq9SaVSyOfzSKVS6HQ6kYTnMByEqBR86jsVQGoOEvudYE5d+roHEoEX+5btsEDzqHSc/G5BxjCFNYrYFgJrfYeGL2wOjV7zrfhTb6KWx+8MX2n4VsOmR6WjtN3OE5IFQsr/2kYLRLQ9vvr4wA2NF+1j6/nS/lGDbG9vzxlsiUQCrVbLa0jEkQVNw+4/FnAD4N8D+P8A+A9y7ScBfDYMw58KguAn7/3/j8coa0ITOu3073FM/E5Fx+XR6XTaLZ22AEdzQSgMVEATJMW5pykURrnfNYGX3gR1J7MuWg8qVgo99Yjokmm2S49iUIWniccMyamQ1I30qNy5+op5MBrGse1hzL9QKKDb7Ubi/RbgaGIyc25UCHN3ZVqfBF0KdjQXiedWsX5WEbAfdW8dPUJBN+UDBmEp9nev13O5WBaMKXi6j3DKv8cxy/ej1oH1VoVllaf10iiP6v3sQwt4FMgqaNY+1/ro93E9BXFljCIF8pw7Stazq3OU5JMTlCc8W03zxzSMqUaQlT92TDTnbn9/H81m03kaWS/1Usa1xXphFIzxPv1T3h4l40gjwU0Yhp8PguCyufwJAB+99/3nAPwBJuBmQm8DOk5+D4LAeTyYSMxl3wp0VBipt4DPU5nSc6LWKy1+kiYUWwHIOtFDYK1YKnoFEkEw2FGYXgOGfbQMfd56LjS0YHNLSAxvtdvtyAoyVTyao6Kggf1EpabnSKlCUAWngpO/+dqoYIwAo1AooFgsOpc7Vz5xybrmIjF3ip+qYPg8+4SAUfuJ3iHu9MzQGH9jkrNV9uPSw5DvRwE3HAurYLW/7EnoNqdMx1AT4C3AVI+gJqj7PCbWs3NUD4x+6nfbN+oxpRdTPYh8RutIIKFhSTWk6GEsFovIZDLY2dnB2tpaJPzJOWk9vKyfrhbUhOZkMol8Po9isRjZDZweG4J8CyyVbHts/+pYW2/OOAYc6X5zbhbDMFy+930FwOJ9ljOhCT0OdN/8rhvWqWdGN7GznhsFAPZeq6B977O5H+NY9b5QDK+rZ4JeHHpy7IqrOCVgXdtKmj9CbwatS6ug1LJkXbV8rYuvjT5XPYW372gELj8PgsApWZsTwhVbPqWm7ef9VEy2vnbfIp91rmNLDxBBl03WfgC6b36/n/dbEDBs/PQ92l9x74/jSwtabBk6/zhW43jG7icEN6yeWle9V+cqyYamNbFeZUBcLpL1cGq9bH0UJAKDvZoIMI8SIh0FVu63Tx84oTgMwzAIgti3B0HwKQCfetD3TGhCp4GOwu/cwZdLfNUjwuvJZDLiLaGw4ISnlUarjGQtdAp5WqT36uoUNC02dUtTcBOgaI4HLbfd3V23pJqJwuoR4ScFpoaBeJ8u+7aretRy7ff7kbi9BU8aErIAThNrCb7oWWK52ncqUK1CAAZ79NB7RKEdhqHbLC+ZTKJUKiGRSLidhFXwK3hhGdzbSMeQddQETOaasG60mC24oUdJj244ThrG78rrFkzH3B9RYsO8JNbyVw8OvWn5fB7T09Po9wd7ACkotkpcy9Qkc3sfvR4A0Gq1HKi/n/DUMGJZnNfcSkENDIITbQ8/uTAhDEOX78b7E4kE6vW641vyLnkPgOM18qpuD2ENC+2zarWK3d1dpFIpzMzMRMLN9F7SC2pDVD4PjM+bq0BNw78cv3E8lPcLblaDIDgbhuFyEARnAazF3RiG4acBfPpepY6PMyY0oUdH98Xv2Ww2pCdFdxVWBW2XhCswUUGuW/n73PI2l8UqWQtGfJ4OrR9dwVS4uuV/nHWpXg2CqmF5AiQKTf1N81y0vr5cJL5bvUwW2LTbbVe2lhGntFgXKkv2BetLL4mG4TTkZMvVPAcNNQKIHDFBoa27J7NvOK4KbtjPOobHQGPxu/J6KpUKfd4AS6M8KfrdgiH1drH/uC8QQ5GWNxRw8/0sX5NdeV3LTiaTkWNDdO48KOm7FDzQU2hXCPrmvvIQgEiIiv3lC/coaLJjwlWaykdaHjAIw+bzeZd/w7YEQeBAjU2cj/NA+Qwerb8PnI4DMu8X3Pw6gB8D8FP3Pn/tPsuZ0IQeB7ovfufEZSxdQQo9N7RAmUzIPACbWOrLW/EpexVKdmdd64JnWSyX76eQI7ChV0KVtwVLJFVQuuSZbdHVKNbbxCMp2FfWsrNKRb0+esQDAQ7/bB3tCiO7osoqEQIP7gSs4wgMjqtQRWRDANZbpDk3fEaVmHrWtF8tfxEQd7vdQ0rpAeihyXdrwQPxIctEIuE8XZ1OB81mE0Ew2J4AALa2tlxekvWY6aaKFiQDOKRQ1UvUarUc4BilSEeBHV/oS39TYK9Ai2DOLoX3gWcAkVWHBN7kCbu5I+ezfZ6eIB/A4Pu1n2q1WsTw4uaZ9CpmMhmXS6f9rzJOPZskHzCz7x9F4ywF/884SC6bD4LgNoD/CQdM/1+CIPhxADcAfHLkmyY0oceAjpvf6ZFhyICCirsWM9xAEMQwA0McKpxUsWpuhgo0Cj/+aWKtWr68P5lMunoQcHS7XbRaLZdnw4RWlqW5KRpSUuUMDCxJ3sfVTOq9oIJmeI4Ax+Yn2ZwbFb4EYfxTYKYKTomAKAxD585nn6g1qQKWVjzBDJWjflKos6+td0zHSN+pwETBk83NUeXNepDPdJXWuPQw5btV6qog9TfraSHPJJNJFAoFFAoFNBoN52nh/j/VahWrq6su3GLzZDi+yjs+EKEbPPJZHkCqSnVEP95PFzleIKkXkP8rv6sHETjgSfahbu0QBAFyuVzk/DpgsI0DPSwKlpiwTs+MGkfaTvX+1Go115+JRMLJjEQigbm5ORSLxUhoT5PnfeEmH8C1MuvYwE0Yhv9NzE8/MLL0CU3oMaPj5ncVplTU+qdLKDUcpM+xnDjvhQpe/m/DIyzLAhzrFWIZDOvYpcaqmO01YBB+0bb7kqeta932jfaXT0HZNuh1ey+FKtumwMOCEF9f63hoGewjemA4VqqQrPXJ5/WT/WbH1We92nwSflpgOS49avke54HScVOvFzDYoFHDSQqsbVkW5Oj4at9b0MJ5oPyu/TsOyGEdRvWBD/BpX2g51luhnlNfn/pAJOeRgiMNX/N/NR5Yjj5r6xXnlWK5dgNNXaU1jlfMlj3O/aTJDsUTmtBDIgokzadhEjEPzuS+MOqRAeCErA3dqAJT69SCDf5Oolua5VMxMykxlUq53A+ee8Q8Gw3x0KukJ2VrKETzBehVYAiO7WBb6f1gP9DDxeXzXAnEPADrwWEbaXVSYLKPNUzBOlrAx097LpTmvNjwAL09ALCzs+MOvSwUCgDgQlT0eLFMHRMLDAnAbFhSFQn34/F5IGy/nAT5wgj2N6ucFNxyjvAaw0PVajXST41GA61WK3JavM4TJqPTu6B1UG+O1o9eomKx6M4nU4+CelN97Yhr9zh9ZO/X9tDTMjU15cLXbLuSBTsM1THcw3cw8TiVSrlz1KwxoJthcu4yd80aTexfhpP5fBgeLEbQfbASiYTzxDFxmjIsLrwdB0zHATkTcDOhCT1kUrc7AQzBivVU+Kxy/qnbHojuqKsCwmfN+sqw3iQNCeiKI59A0+RNCi7fuxTI6DVVSr7kagV6LNd6LXhdk3X5Z5MwFYCp94r9YN/F96ly8AFLKjwqCo53GIZuhYpV8vZPQ4a83wfM2H7rteKnhigfNfkUjlVKcWT5UAFKvV5Hu92OeHWo8C3faTkcP62b9TJq3xGME4Srt4hljNueo5AP3GiOjE3K960s8tVJPSfWQ8gcMpsHqAnq6rmikeFLDlYQwnlHPrZemyAInPHC7QvYPi1nnP6dgJsJTegESSczrW7mk9Br4Vu6q4JZgZG6yJV8Vg7/t8R7M5kM8vm8s0p7vR4ajQZ2d3fd+UxM0lRLWvNLbH4NlbSuuNAwgwIEVWSaU8Df9TgCBSraF8AAqCg4ZB1arRaazWZEWVhBSovU1pkrQFSh6Nhx/KgI2u125Hd6EZjrEDcWw8JOrC9/oztfFbTygk/pPWqyIZW4eyzYBgaAkTkfAFwOFkFLv99Ho9GIeC7U+wcMgAj7XsM47G/lK+br6EaQmUzGLc1Wj81RyecltL/rGLM9XIXHegBwXlXNEaInT8Ge5u3oooB+v+82oSTvs439ft95aTT3RhcxZDKZiAdN977SLSgymcyhPCKOIevf6XTce0maB6XGlJV3vHcUTcDNhCb0kEjBjYIcTR4e5pYGolatTn4rMDVModYs77ECguCGwo372FSrVezt7blzYVQBMfSj1h4tPgoq3StDwYYCHFVs7BuSBTcaFtL26eokgi62m3VoNpturw++Q3MJWD4FrnrWdJdh1pUKlJ4FYLCMvd1uO/BqwywcC6sY4qxf7SMNUZEnrJcuLu/hUZP2q71uAbj1ViqfUCEnEglUKhWX8M7QKUOmCo55EC1DqZoor6FNjivBO8NRrLOGWRha0b1njtofdp76PCz0EGl/EGSwb9rtNjY3NyNjn0gcrLrkPQwzs+7tdtv1Jb1flEEaaq7Vauh2u5iZmXE7g5dKpUjdAET2yAHgPJNMolfvry5qyGQyKJVKAIDt7W23JxEBqi4u0PweC27UQBpnLCbgZkITekhEVzctev2zit7nmuanWvIsV3/Ta3GfvvKYk2BXGak3ZFjbrJIm+fJF9HcKcvWiaPjKAidbVlx9bHjD9p0N52gfWUVr607lEQckVBgrKBplffJ+fnJFkOUR2498hmXYJNPTRhbc2hwh7SPlQfIIQR4VKcNHtiztOy3fvg8YjKEe76DzJC4Z2dc2Lc8HaIZ5frROGk4CBp5DYAACONYcewXNccn+zE+iB9ZHDFmRt7R+nOusk/W8sf9UdviAiDUMrFywgF/7x/bTKJqAmwlN6CFRMpnE9PQ08vm8212We0/4vDY+sONTBBrGUYVgLWf9rpZREAROyO3v77u8hmaz6Vzw9HTokk0rSHVzPOtSZt1U+aoyV7czl8Uz2ZCWeCKROHSuEOthBTRDNjzFnNYvD7O04I5KQzfm8ylL9USxrfTCMcmT7+ZZVOqi59i0Wq3I+Nh9R9hftLCZ5K1t1vwEVS6s24OET46DfICPn6wzQSdBrFr4TMBWkKF5TRyrQqGAUqnkDoTU0JbOK5aTSqXcGCoIJGDl2BSLRcc7Om/oTdAdjW0b4/7X9seRXcnX7Xaxvb0d8eDQiwUM9qjZ3993G2sytMz+UoBCw4XeLPUmkhfJv9VqNeIpJi/TY9br9dzmffRmAnAeS+1j1luNlEKhgFwu5+aSjr/P02UBKUlzeeJoAm4mNKGHRMzTUM+N5nXEeW58oMROcFVutPY1fGPLUKWuyoPCVJM0fV4bn0Wlbmu6pUcJcj5n+0nDPbaffOEOLUPv1QRltR6tpakhDbsXjlqjGhbyCVQbHrKeG1UyzGHg+FCpsV02idWOo3q67G++308D+axw6yVhm6mcFayoN4IAlIASgAs3Kd/7EqvV48Fy+angieNn62sVr0/hjkPWi6h8oPOXbaUnRT2aBGXqrbHgABjwsd7HsJz+rl4XgkuSGhG66zfrrkaD9aBR/qkninOc48x3xIXttK+0Tm9rz40yhW+/AwpIu/socBiN+4QtEL8CwGehKNIEDu9bcVqJ/aSTQ92s7CdrMQJHb6O+yyoKnSinUUjfDyWTyUjOgF0pFPcHRLcg9wEXvUcVtHUV8z6OHUEIBZkus9YVSpw33J2Y8XmdMzYkFYahsw6DIHBJ1Opa5718Xy6Xc8vi1ZNC1zXzDtRbo7xieTWdTqNQKLjQhc3nyefzbiUO+czG+Un8zRI9N6wvvUOsuybD8l18P+uuISV9ryof3/xSK52Ki2VyqfBJku0/IKrQ2ef03pC3mDfFvCbmkvR6PTSbzYj3j7kavrCRyipe0zOOSDoWmUzGXedGjeQ5Ak7VN0oWKKhusblRhUIBV65cQalUQrPZRKPRQDI5ODOM3kB99tatW1hdXXXeJxoC2qfqyVWvMAGGJhTTw8f5B8ABJ51LquPI19y9mHVUr6YFiUB0kYEFYArkrTEwDk+NAy4fW3DDQQT8Fo0ygA/c6AZG9tlhStaniOwgAeOvxT9J4oSlstPdZimA1GX5IG2074obM2CQqPa4kwU3Kph81mGcZ0IBDhC/UkABjOVPWmQUZO12+5DgpoDlnh78ZHIxBaS+z1pcmlioBz1SWZDHuOyWIalcLufqqm1hzhJBBAGHzzIPw8Euq7pXjgIqutR1S3hdCktQp3uEEGixHZo3oIcP6s6vVJpUSj6Pmo6vbqym132Km+PJ+1knPefqJIhjNiy0oICEfUMlSA9nOp12gIN7pbA8JnsTQFjPinoJdCWb3f5fwz0KbvRkdZbF+acA3couG8bh7wpw8vk83v3ud+PcuXNYXV11oGVubg5TU1OYnp5GsVh0RkWn08Hu7i6uX7+OIAjQarVcAjTDQSqL2acaDuJc4UndDGOxj+ldoSNAt3hgufQMM1SsGyiqocO+4LMKQinvGXq0htowGWg/x/WaPbbgZpgl57P0LPrzeSLiXGL2Wd813/fHgbQvrBWpE1qT2ijYydj2RGQf6ZjEeWYeF2/XuKQg2xeK4j0k6zL3WYs+5aGWj3Xpk2yoh+9Tl7/ubaM7E7M8a13peFlvlIYBVLlZQETgzC3b1ZXuc7Vbwcb+sH2qYFo9Vz4eVFBvd2T2hbQ0p8YqcAp6DfMBiIBa9ZT62mX7S/tKlTjHgP3l80KfBlJwqStj9CgAG0JkH6kMAgbKXPNffMaonUPqbVCvg5X/PkDJ+tgcKBIBsHpAGQKi50Q9oQqcw/BgRRaNPgJj9WLyf10Sz/7UzSv5TgIf1pPP0LhhmWy7hq20/1TW+Awsettsf/vGXvvKGmdxRp2lcUEN6bEFN3SrU9hYjwBRO5lbB0+z8TWx0FqNFr3a7z4BNE6i02kg1t13uByJbmJ1nU5PT7tlfezT9fV1rK+vu/0L4vpA3Zi2Hor+T6OAvh9KJAbn4NDaVE+GFaqavEv+o/fBggqr0HXZqHpJKJTosaE3gRYcD7qjlcgl3apI1eXOulmBpN5LKgFd3cLwEK9pSKdaraLVajlPDu8PgsE+NzY85xN0qujYZu6uSt5SRaOghv3qO4uKwEQ3PWN71IBiv3Opq+7aWiwWHY9rDg+Vjwp8O44KaBSAsT3sJ1rnJ0GUpT7DhXXKZrNYXFxEIpHAzs4OdnZ2DoFGKmFgMIY2zEoQoPsIqYLm+5Rv0+k09vf3sbu765JuuZkd+ZXXrFFBg44H3foUtnrNWE+OL9/Z6/WwubkJAG6ZdqvVwt27dyP8ks/nsbS05PQWvUzT09NIJpOurKmpKSeLuZN4NpvF9PQ0+v2D/YC4LJ4hS92NnHNdT1Rnf/FPvfiqFzWnxjoTyK+sp85V1dV2runY+QCQNcpG0WMLboDDm5dZhaFWZxyi9D3L31XADHv/40xx3ipgkBfBjZ8ymQymp6dd1j77uN1uo9FoOOFqLVNrrfveZy30twOpwKbQtvxmlZcVBpZPSeoRUYtXyT7r87yo50bzamw7fMqX3603Qd8f583T8aYFTYvXeim0Pr6QHOtnQTWBiHo0rIWqdVEDR+eAvoNlaljEAk32J8tTa5t9HgfQ9N22frYv7JyK84ieBmIfaX6H5pBYGUz+VqBjPW+WFODofGA5Ok7qoVFvkCp5/bO5LpbsjtrA4XwcAM6rx/eHYegOpyVgC8MwcryJJsmTn3mdIFsTdunNaTab7j5+0tACDoeN2M64+Wd5y4Ig5X8rI/jpi7AoWY+YTxaNS481uCEDqyuZZK1iHSSdNOqRIfnQo/XSKCka1T0GTjP5mIZKmEmXpVLJ5YxcvHgRxWIR09PTKJfLkf7Y3t7G1tYWms0m7t69i1arhVqt5gBPs9mMXQWiO72+XXJtSLRs1IKnoLOgxrp1fWDFCgmrICm0WS75lwA1CAJnnTabTeexUcVj55F606xlpuErX5KreqtYF66esEmRyn+a3ExQGCcEtU9YBhVEoVBw3ilaqaosOWe5iaF6D6lw1btCS39vb8+FHJgnYr1ziUTCLdXVfVnU6xaXM6jtsQpGla2lk5w7Pj4mj6o3nd4aJmEzD0QNAc1Z0f6nklaF2mw2I/MKwCGQoXXK5/ORPtVcLm5wp54b3b06n89H3kO+Bwb8ol5M9QzSa3Lx4kWcPXsWrVYLrVYL+/v7WFxcjHgos9ks5ufnEQSB85SHYYhWq3Wo38kH3W4XjUbDeXrYR9xVmKSrG8+cOYO9vT1sbW25jQo511Qu6NykEWHBJ0PafKc+p8CH84WnjlsD2AfsKVfYx9ruYfTYghud4OoSt38+lM9OJ0JW5reWkbVQfaSJmD6F9TgQLZOpqSmUy2Vks1lUKhXMz8+jVCrhueeew+zsLEqlEqanpyOKlYpyZ2cHr732GqrVKu7evYv19fXI/htqWas1RMWv1srbgYIgcMDGhpZsbofPQ6E8rGDHWrkW5GiSMIUCcw501QSTilV56E6+QPQ0YV7XVVIsz4YcVfFrW6jQKRhtLhCv6Z9a3Tq/LLCjIGUZTFJmCEDlABUZE6WZs6BWMcGN9eYwTME/jjMPzuS9uj29Jq0q+GN/+mTUML5SFz3ppOWNfb+Gk9i31WoVwKDe/X7frS7TvW9InCOas8RyGVoJgsApdF/OmQIM3sdx3dvbQ71eR7/fd7t2q5JnuE9XcrEeHF9tq64o1HnCuXXu3DlcvnwZ9XodtVoNwEBR04OTSqUcCJuenkahUEC73UatVkO/P0i8Bgb7O3U6HTSbTSSTSRSLRccjmmPDEFS320WlUsHs7Kw7JFRzn9hmLtdmX1geVXDTarXQ6XQihpzP20sjQJ0KPqeE8o3mRtnQ3zB6rMGNunoV+VFYqzC0z/o6x2cd6oDGeW84ACcV734QUqYrFotIp9OoVCpuoyxurJbP55HL5SJbcFsrNJPJoFwuRxLqWq2W2xa90Wi403ZVCGiuyduNrIfFWirAYfe1BS9KPgAfN9EpYPinQkuXYlII6TJkBV92FR1/0x2NfYKPAp0JnUDUK0o3uy6tVjBkw3gEBRq6U0tO5z3lA71RuVwu4lnVoxHUY6WgjvLDenK1v9l2G1JUwyrOyxTn4fVZr1YO2bqcNLBRsuOlfWDboYBXDUgddx1XIDp37JEAKke0LOZ4Ue4oUMrn8w7ckCeZy0MQpLlAlPUM4yovK6BVryYNips3b7pzlRg2st4PegL7/T42Nzfd+VY678LwIB2gXq+7OvGTXhjdt4o8Q3BHgEHQx/kwNTXlEvzVUOC8UyeCHUv2kV5n26irFdQoQGe5mh/L9ytYOgq/P7bgRq1Mtco4mMqMPquIk2aYR0YBkx0cJTLlMEVz2kgVQBAEqFQqOHfuHPL5PM6ePeuATjabRaFQwMLCAsrlcsSdS6udk4bKaX9/H5cuXXITeHV1Fa1WCzdv3nTft7a23OTTQ/DeTkTr3CpK69lTAaDgxyfMWa6GV3jN8h7d/GoR82BMhqQARPaC4dhy+TfP6iEwsiDHl4BLPiAo9gkl9XyQ1/SaJpdqW5X/VOCpR4cCmTyp52jRHc7wB9tAcJ7NZiNtpAKzYMsqOu7QqoBSQ1ysEy1t9WT6LFcdD0vWiwdE8yZOkth+9fBpXZXn1asIIKKMNWypBhS97UyozefzkWfV08ZPJuTu7+9jc3MT3W4X5XIZhULBedx0bIvFIs6cOROphybOcg6xXalUCqVSye28W6/XI2F41mtrawu/8zu/EzkXTftCr1EW0COiMoB8tbu763YyZh/t7e25pGVN6A2CALlcDmfPnsXc3Bx2d3dRrVaxv7/vkqFpyLZaLbfgQPPLdCsGzk3N51GPFjDYtVjD2QRpNDA4npxDBHWNRgP7+/tumwj1cGpS/jAaCW6CILgI4D8AWAQQAvh0GIb/MgiCWQC/COAygOsAPhmG4fbINx4j6aDG/W5/0/+PYgFbFytweOmuLf9xIApi7g+Sy+VQKpVQLBad4KErkcqAwoXMpsuduTqF3qCpqSl0Oh1kMhlsbm4il8u5JMth1utJ0XHzu82dUfBy731jg21PXSNl2N98v2uejFU8/LShDt/qIhuq4vMq9FQIqqHR7/cjoEvza+zKmGFts/PW3mvnLcNWHBf19rDOCjz43QdCdcy0X+O8Or5xYd/Z8bQ0zLszrJ/GoYcp38fxmlugrp4B3z22XE1OVoCrQEE9Acr/LFs3kAzD0AFdemTU8wkMkoK13sovVvmyPjxWge/19Ycln+fPen/J2+RvBebqudHl6myH3Z+G85FGhuY3qfGg331jFEcW1HH+8dM3/zkvLaAZJSfH8dzsA/jvwzD8ahAEJQB/HgTB7wL4vwD4bBiGPxUEwU8C+EkA/3iM8o6Fer2e25hJXVyKbq3bXQeIrsZyueysO11Oq+41Drqia8ZAuRmYWrVaj9NK7Beepjs/P4/z58+jUCjg3LlzbjMpInv2B12qvV7PhZiUEfU0ZVrP5XIZuVwO3W4XxWIR29vb6Pf7zrNDD4KdvCdEx8bvdmLqBFULX4WNekWAqBK2Qo2/85P8yWdU4FgBqSEb62anh4NA1rZF9/sgkfd5thNzXjTXSJUQwwGsJ+ck/2f71SNh20TLms8rONT2WlCSzWYjoTnd5I3vYFKk9h2J8oD1A+DmgypEhmbZz+xz7sxLz6cV5pasYraKcZRxNoKOhd8VPNLTZdMGaPwEQeBWB2l/6blHCj603+142jCIrU9wz5PQaDQAwK30DIKDHL98Po9Lly4hm81iZ2cHjUbD5UeRN7iT8O7uLoDodgJs197eHtLptJONGj7SnDffn9aZnxbwAfFJ9PzN7ttjw7q9Xg9vvfUWlpeXXbK7jg+9TlNTUzh//jySySRqtZrzROk8U5Cj19XLxT5UMEn5oCFCyhXN7yFo7fV6rt+t1/uBwU0YhssAlu99rwdB8AqA8wA+AeCj9277OQB/gEcIbjScoaj1Xj0PKRF+VwCSSqXcEudisYhCoeAEJssBEJmAvFar1RxDUFDqM48LZTIZFAoFlMtlLCwsuBBUsVh0QEaBHScFQZ3uMMoVVrqqgWEHgr98Po9MJoONjQ2kUikX3z0tdJz8TqHi83roGUoa8lQr814dIs/pNSvkNL/Gp+ist0h3iGWuAGPtdvUQcBhIAYgoo36/7/bZoJDTlSvaJl1BZK1t3RVW2826az20vXFhGVW0rC8T3dlWrmrSUJ71OrHfdUku+0vzL1h31kVXhVCOqKUfp6zs+CvQs+28X4BznPzOdhGkWoMwkRispOFOtWqpc48XJgqrF8fnyRnmLdDfmDYwNTWFSqWCVCqFZrPpxnxxcdHJ/l6vF0l3YF1s2UB0XyTmudzrx4gHh/qJZfrmMsu17fSMV6S/tY3WCLB/vV4Pq6urEf7UP8r66elpXL58GaVSCXfv3j2UcO8LC1netEYd5z5DhLrpq66CYrs5T5lDBEQPGB1Hzx4p5yYIgssA3gfgSwAW700MAFjBgVvT98ynAHzqKO+5X1LwoTFPThJ2Yj6fR7FYdJtK6RbwKgi1XG7gBRwM5PT0NIABU5N5Tvpk3qMQ+8TmO9gVLmybKh0KeJahHjIKfbX+yfjM4VEhQ0F22vrtQfl9bm7u0Cqde/e473EeGZ8Xwj7P34FoUj1/0zCPz2JUq0rd2foOXZbrsxx1zgHRsJSCI+0DC740zOtr6zClrcpNAR4tQdab/KfLWZkPoP3EutA61/1LVLawvtZ4Utc67+EcsCB02MoPHVuSjxfs2DwIHZXfldeth0H7WnlLt97XPtc+pVGkz5GXtK+B6FEtCgzVwAUG+Wfkx5mZGXem2cbGBnZ2drC1tYVqteo2uaTxakGzBQUkBWN8pwJv3sMy9H4L5OxctXNeAYOtl75H6+YZv0PPUHavr6+j0Whge3vbne2lOpVEj46CWi3bGig0JnQBDu9VQ9nKAvJEIpGIhNOG0djgJgiCIoD/A8B/G4ZhzQxqGASBd3aFYfhpAJ++V8ZD0V7akdYS5B9DJFNTU7hw4QKeeOIJFAoFXLx4MXJwmY9xU6mUO1eD79ve3sbi4iIajQZee+01LC8vo9lsYnt728sEp5EUbPCPrmHNmwAGlgE9NCq4NC7LhLHNzU1sbW0BGITAyuUyZmdnkUwm0W63XWJeo9GIeAtOAx0Hvz/xxBMh9/NQ4XTvvkNC005qFWKqPFShWkGnc4EAVC1/CiF6zDQxXIGYWsbqiWM9pb0Iw0GioO4VohYfhRGX09olpgoU4ixPvs8zVhFetHvoaO6BnqlDYUxLkgCewjoIgshxIyTtEwVzClooh7gb7u7ubmQlDL1Dqvwtf9g+1nFWUKdhy/udP/fD78rrqVQqtMqY7dQx5gohAN6wKGUFE7u5F4wmddMjyL5lv6nspywKw9CBGPJCGIZ46qmn8MILL2BlZQVf+MIXUK1W3XM6vru7u15vGeuiZ5ex3Ro242+sv+nziFywIEP7k+8CDi+g0frwef30jLV3nnEMarUaXnnlFVc+26X8yt/4uyYvq0dV+0VljZZLx8He3p6LiKjXD4geessw8iheHwvcBEEwhQPG/09hGP7yvcurQRCcDcNwOQiCswDWxinruMnHMHHXM5kMMpmM25yuUChgenradZrmEuigc98BtTjpveG+ArlczsvAp5m0fTZJUq15omWfBW8ZmaQTlH3CCcqtzNUyPi2gBjhefqdb2scXtj99feBTWr6yKCz0XRwfKyh1bK1wtAJJr8WFd229dI7o+6zy97VlmAIYh1hnKgR+WgNIwbuu2LHAUvOAtF3sD7W4faES2992LFVZjCJ9jv2pdb1fOm75bhW2BfBAdNdelat2vsTxiS3Pxyf6rPUkEiQFwUE4ptFoROrDsY1blWPno8o5lY8EDfZaXJtYlv1jufYZ37VRNEwWAYOUDysDNKJh57i9Fhc+VS8dy1fjzQJkLdfy06h2j7NaKgDw7wC8Eobh/1t++nUAPwbgp+59/tqoso6TLFKly1JXPAADRi0Wi3j66acxMzODJ598Ek8//XTEgszn826/Ay5508zxYrEYEVpEm0yIzWQyWF5edksNTzuxr3ieDy2hTqeDer3u8mqUyQFETuzVhG3NaUgmky5spx4KgkgmlfX7fbefjiZlnyQdJ7+Tl6xXEIie6aVC0U5uH9Bkn1vLqd8fLEnWlUocR+tJIZ/S06LLoO3SWnpndG5Zpcy6MVHRekxoJJCH2B/kJ7t0XT2EY4yby7vjclJ6Y7T/aX1OTU0hl8u5ecwwKs8B4u7amkvDe9k/LEfBjc8i5nb49AyxXlyhw7HXPlUgar04yl+W3446f45bvtNzFgSDXYj17CKOp/Ko5m3pIhE1vtj/AFwfKnhWQ4pjzM1GOR763CuvvILNzU239xYw8IioblElrWF366XRdzD0pqkMHBf18Km3W9+jbddnNWHY5qmoh4fvVIM0zvi8xwPuHt6n4WiVWZQTNE4pE+il6/f7qNVqbl6T9+MMY5ZL2cZzsbRPCHZ3d3edsTaOQTCO5+bDAH4UwDeDIPjavWv/Iw6Y/r8EQfDjAG4A+OQYZR0b2c6x1ot2Il3BS0tLWFpawhNPPIEnn3wS/X4f9XrdracvlUqR5X5MfuLZShp7p+BttVrY3Nx0Oz0eFUWfBOlkZc4NvTOJRMLtrUDBHoaDs07y+fwh1E6GSyaTzhri7rA6iTgBeF+323UThcw9rhX7EOnY+J2AQENKJF8owgozq+B8FroKdypyq2wJfjSeTSFMxa8CV1dLMWmcoSxbD9ZZw0zkFY67GgkUeFpPCi/dft8uD9fybD+q5U+eVrc3+0kVHWUCAWG/33enMzORXnMDVGlTuGpZ1pLX8JguUNA5ZQ00O65xoMb3u++eMelY5bv2MflA+5B/uoEjZUW9Xnf5d1RsNLy48y3Ls95I3SuFPFQqldyiBeXhbreL27dvY2VlJdJv6lVQcGo9Bmpo6G/K/wrKlLT9BLeap2nnFb9TjoZh6NpojSXylK5SI6j0eXzi2kpj1cpiGxpUw4dRkVarFdnnJw6o2WsM2epmmxrmCsPBPjs634fROKul/hhAXEk/MPIND4l0Alg3sE78YrGIubk5zMzM4MKFC1haWsL09LQT4BsbG2i325iamsLc3FykDJv/AAwELL09qVQKlUoFu7u72NjYGMvKPGnyKShVetYiUaFphbW1nHTSEf2r4uF1bn2fz+dRKBQiS1xPko6b39m3BI46oW1+iwIeEn/T+y245DUFAwSK9BYwQZbjxxwya5GqEtDcMbUq9X9rXSu/sNxsNotiseiUlAIXtULt6iTtQ5an4Rj1ktj79H/73fYt+4DhUm4RoUvGAUTqq3zNfib4U/e9zU1ThcPfmDdiwUwcwFHZp54fH+AZRQ9DviuPs58UzKpiI88Bg9zGfr8fScCmIuY4aM4Hr1Em0RhVb5Cu7FSQql4y1tvyjk8x+/jL5qZYL4yGPTVso++empo6tKEmwZi+V/tW/+enJmcrn+p9Wn8tV0GJHUsFUlwlqGPh0wEsw3qaVK5pOod6o21btb/Gocd2h2IOIpmCYRV1xQHA/Pw83vWud2F+fh7ve9/7sLi4iL29Pbem/80330StVkOxWMSVK1ecwODSQRUaurIklUphdnYWvV4PtVoNiUQCm5ubh1ZanVayngN6GVT4UhHpxGEoi8IhDEO3PbhaZonE4NA2mw/CMF8qlcLMzIzzEjwOwPAoZIWzeh583hp1t1pPA8eF42ZX8Kg7mZ4Y3q9Layn46bWgd4Zgn4l6nEcUwiT1rqmi0E0e1WOTSCRQKpVw9uxZF/5h4jE9hqwv+8MCFgImAgJ6CbWPtX7ad1YRcS4rr1Hx8BmC7l6vFzngkHXXfWzoMWDohXzM8JgeaaHGA8NiYRi6PlfhHQdUVDnq775rj5pUIaqStTl9FhS0220kEgkHgjXZXfne7mTObSmY90hPkCpSls9znOgxshvY8dPKb53DyvfqrdO2JhIJ54nimJDXyS9qyKkMZohe+3NnZwd7e3uReaEyVY0h6q5hgFf/V+OGz2pOkparnwxvs7/4LPUHQ1GadK+bImqSOT0yaojZlc4+sDUOPbbgJo6sZckDIJk4TGtMXWoWzfrcXmRoPqfC1cbaHxca1W5lMApsaw1bYWUnj5ahlgwZnRNfN1A8SQH9MGiYRW35xU5qApk4S82S5UMVItaLxt+t4o8TnHGeE32nHWPOGz3Aj7/7LHnLT7avlBdt/2lZo/rb97sKawpnCl2GeyrwdAABAABJREFUbbVt+k7b51Yoa9/66qNjoc/GARYtV8s/KbL8pdeB+ERx31grj1rviu9ejpv1HhJc+JYp+8r01XEYKa+pIWDlHgG8emz0/dYwUaJsjOMnfj8uvTOsLCtTdAy1XdbD5NMb2ve8/yjgfJz2PvbghlamdaFzd9QLFy7gPe95DwqFgjvIsdFooFqtotvt4vz581haWkKlUnHCi0mPRJk8miCZTGJ9fR3VatUhcSZQ1et1lzzKc0x8AnjU4KmA1RjwwyBVQASByWQy4sHpdDrudwqPVqsVWdLKw9g0tMU22OXkuuEfABciZGjQjuXjTLToVPBb96vuF0QhwD4EEEk6VYvPJ4QIJOjaplfBJ0g0D0E9Rlo/C35Zrm/Zs01OBAaCmXkP/F2ThH1Knx4m1sPmBmhbfEBJy2S5yo+sL9uoG3DSU0lASWDGkJNuiZBIHGxvwHpYzwUtUq2vgnjtZ6u8bGjBglQ7nqfJc2MNTPubKkENc/Nka8C/LxL7VROyuWKVW1hw7uzv77vTrjXswTlE0KH9PsxQYz30OjdADcOD86bovSaRx3K5HBYWFgDAeQGbzabL8WI+5/T0tNsAk3w6PT3t8jlXV1cjSf2+/mW/63cLJqzciAMm2tYgGGysx8gG55F6k8jjwIGHp9PpOE8WAOel1DmvEYI4sKuyVPeeGkaPPbgB/Ks2uCKDuTYEI1w1woPPZmZmXP6MJmzpsnDupkpLrtlsuskVhqHb7ZLnjahCsgw4jmdCV7k8bCLA0QPkKMTpHtZVLkFwsPqBfUKrJAgC5yamt8HmdFh3JnCQoMyEbs1PeDuAG2AgOKwFqx4B7SMgmo+j1p7PMtQy+Z2Cwq4+s7yowCYuzk2iElLXsrrnCZaUNFndWnpK9r12jrCfVLlYivMCWIGplr32tfYt79NyuXgAQCR/SeUC5Qc/1cABBvueaHjB9rcNKfgAjW03ycrBkyAfvw2TeTqu3DVaPWEKbChXCWBULmu4ieENDZdbrxrrB0QBpI83dcz0Gg07AlkFUTp+3LkdgAP6ykPUVbpyVUO1ALC9ve1diTvM6zGOUe0DpL57dCzUGNJ5zee1Tt1uN5JHZHfzZ/oCx26YN0/l4jgpDI81uIlDmalUynljFhcXHapvtVqO2Whx0fvA+DcwOCVZrTtF5erVIRhiMiJBkoa9bB2VfMhbFc7DIr6X1pLGszVhTBNU+RyVHD1d1gOgE9sqErU4CIzI3MflWj1tpJNSQxucsEx05MSPs+AVDFnrV5fYWnc+lYEqSz0fh2OhSZi+FUB8n1pSLE9XVRH4jwKplj/Uk2h5QYWnVXq+69azFHcv6+Gbh2wvvUhUPGr5q8dLcwwscOEn+0hBljWIbN8q/6iQVzCk4cKTIB9AV2DAPxpCuqJHd7G2AEPnCeeGbhkADM4v4hzy5Y4on1vQFadM+bwF3Hy21+u53Xt1xS7bTG9/uVx2Hhme2VcqlTA7OxsxCIHBUUI0ChRQLC0tod1uY3NzE/V6PfL7MAB8XKTzRNMygAHPKg+oPLIgSp+l94e8oMaxGl66E/44bX1swY0KYxI7J5PJ4MqVK7h48SKeeuopTE9POw8LvQTlctl5JJLJpLM8NdzCw90AOE9PEAxOu2Ycle7JYrGIcrnsvBu+VRCkOAuWE+VhEoVLEBxsYFWtVl1SH+vORD0ma7N+GvbQ80EI5BTRkzmtOxcYKEkuA2eS8tsJ4FjFRKEbhqEDdcDAO9Nut10YkONDvvQpbPWgaHIuE3v1nSyHYRWtIzDwdsZtZqeKVpeUc+zplgcGAFgVtH0f60b+sCDNeltYru+Pv/E+PVbBWuqqtHTJOMfB3qt1UwHO+zg2msiqoT8bXmLipRoMvp2kbUhR66XKlPOO7TnJ+aOhAgXUQPScOYa+2T/W4+WTl+w3etmnpqYiz2kCsgJ7zZP08Yte0112LZjX8Kh61rk8WYn8Pzc350JS8/PzLpGf/NPv91GtVvGtb33LLUohLzANgH9cVcoIwc7OTkSOP+z0BQXSQRAckhOUMwoY1QPFfuWYad3Jz6ozKc98q8VUpg6jxxbcAP68AzItzy/KZrMABpONIRXdS0OVgSoNdZ9Z1EqGV4tA92MY14qKsxgfFVFoxOU1WGJbVej6XOfDPDcqtNlvRPhvN4Bj3ai+fiKY1XwjVZI+a8Wn5C0osP3O+igvH4V07BTYjJMjpd4q32+jDAD7rM+69z07rJ0KmvhdlZvOTeuZ4XgoTxNQxcmkOOHsA2vaL3FeIDUq1LNwUhTnBdHfbL/qJzCcr4d5J3QMfIovzpjkb0dpi32nvsOCaco28owaiwBcrqEmQ+s2CeqJpDGtfPsoyddflu9SqZQDsLVaLWJkWXlm54T2nf7vG/O3tecmzoXJENOlS5fw7LPPIpfLoVarodvtYm1tDa1WC2fOnHFuQrXG9GgABTfM1eE7uEcLc2/4fC6Xw/z8PNLpNLa3t8dqh046i74VSBx3iIpgb3d31y155Tu5QZOCOmtd22WadpJrv5AR6RHSPS84sXlauC6pfdyJ3hhNgLPCXXes5UnJXCZswZ6GnigkNQ9GQ6Xkaw098f0kFca8X8GLVRjqfVCrmXVToc36WOHlA/36PuU1q9y0L2ysX/uWHkPtCw3rqNLhO8nL1muq9WJ7OP+73a6bPwx308Cxlj8tWA3X2ZCgzamyY8K+Yj2ZiN/r9ZxRdVJ5N+Rl7X/9zcoITfTVvlJlrta9AnfNZdK+AqIJ6FqGDZlZAwuIylvrpVFlqm2x3kHtB/WYMlSr8yQMD5KSZ2dnkclkMD097VIoyC9hOAgh8x3MZeM7WNbDAjsWSKghrOMDAGfOnMH3fM/3IJPJ4Ktf/SquXbsGAIeW8adSKZeMrYnf7EvqI+qpcYwVS48tuAEOJ4cpwp2ensbCwoLzTLTbbbRaLXeAnbWMgagXwQpm7WAKKF39QwHGDelsyMxXd9bbCgOSDugwq+V++o2fZDp1/dkVIlofq3BVedg+pQDhZFblwf5Vi0b3w3i7kN0UUQU5hSgVFBOuAbjln1bYW6AZF85RBcr3xnkTrfdBPQKqXFk3vUZPh92NdZgXjvdZz572D7/7vBpx/496xtaB19RzqyENfU7HgMpHAZF6F7QvKC80QdRapKqAVRYowFHSsdWw5TgrSB4maSgaOOw5tPLL5sHEhR+TyeShFU+cGz65aT2TPiNRAQrvVyDDPrb8qe3SOqr3ms8T1Oi7yD/q6WN0gekOygtaBkk9Hjbs+yjIGiraD8ViEU899RSy2SyuXr3q+k9zoFRPs+7WsFLj2vLJuPrhsQY3JO3YmZkZLC0tYW5uDqVSCdVq1Z3/REWrbj92OvdbISixe1qwYzkInU4HOzs7kbg6QRW9O9aq8JEvpKPtIpB6GKTCmdb41NSU86I0Gg23dI/1VG+WWlDsNxVAzGFi/6jnhhObq6SY65TJZNz5Mo870ePn8z5wuf3e3h4ajYYDCcDAOlcwQ2uNitIemUCAA+CQYAAGYFL5Tb0S6sGwnht7v++aTYAF4FYPcoWIJkoCw8+7iSOtq312mBGg9/MeC1z4m97DcdT7mTsBILLRJ40Bgh3+6dipLFH5Yq16Te5WpcnyOXeZk0Cgc5IeTzsmCrYUyCcSCVdnlcVaju6CzpVIOic0l0aBhBpTlhd8AH+YstT5p+CEZ5Jpzo31DKXTaVQqFZTLZZRKpcgRNxYclUold9Yaf7ceIeUZ/e7rd9bhOMhnXGs/KmhJJg82U7x48SLK5TLq9TrOnj2LO3fu4Jvf/KaT6xw/TfsA4OQD5aIuoOA7tE6j6LEHN6owyuUynn76aSwtLWFxcREzMzNoNpsuLEWrit4ZYLDunmec6EBS+KhVpHsorK+vo9vtuj110uk05ufn3bI+G5cHDi97tVaObVuctf2gxPeROXX1TKlUctbS+vp6JAylSV+qOHXzKXqE9EwZAO7MHnq5qKiz2awLxVDZvx1I3atqwQMHfMcE4mq1in6/70ClgohEIuFCDlxVxvOZrHVrPWS6comeIF0mrkCVv/msbvtp+bnX60WAMJW77jzNZGUKebWmdSWXgrxhSsfOI981vV+vK6gAol4mK8htOAsYrJZku1hvFcgMX3HMCeL1/WoB03DSHZ/VmPJ50xjK1LOJThLcqLwjTxIwE3zt7u66+6nErceKCbVhONg/hnJXQ03dbtetVlJgo/vdKL9qGJek42rlPn9X700icbDjdrFYRKPRiByyrACZicQLCwtuvgKD8AzrEgQH25H0+wfnHO7u7jrQZz2h7E8Fgz6Ac9zARr3M5FfqBHtwZrlcxjPPPIOlpSWcP38e1WoVf/qnf4pXX30V9Xo94hHrdDoABjxLOeVbiOCr1yh6rMGNbWA6ncb09DRKpVLEDazCG0AknslJ5XPpKyMpM3Gi6d4eVPDZbNYlLVuBq3X2gZ2TILXwNY6tSXA+5eCzinxl+94VBIEDm8rIduI+7kRe8SXdcoMrWqXaBypU9M+66+19dtL7xseXI6BeGN+z9pp9noo2kUg4bwJ5SFfw6HuGhWJ9dNTrvvtsm+Laq/VV/rdzl22k94ZC2sf3bHecEcPPYeOqZah3TvnrpHJurIy0ZHnMF1rVMVKZpO1WoGc9hbb/bP1G1Z9hoW63644rYV10bmo9tGw7L3xz1/aXhqi0/faTgAcYeA4Jbm3djpusvI/r4zAM3WaDACKgm8aX8q/mC7H944bZxrnnsQU32sHskHK5jOeeew7z8/Nuvxm1BiqVClKpFMrlcmQ7+H6/7ywlDbckEgkXDwXg3Kq7u7tubxguPefxDrlczm3GRNKYLt/hSx5WcAX4E/GOs//YJirhvb29iHeA+/ZoP/sEqSbMWktXLWQVxrTCmLzd6/WQzWZd+OLtQPRoUOEDgz4gD9GLwj5iyIneAXo8mJOkydiqAPmbCn4VmgT21n2un0pWEOszPpDDcad1Tp5SoUzr3RoTNj/FAmofWcBhhXuc98c3n8jPPgHOvgUGYRYaNsyxS6fTbmddq+QpT5rNptfLxnfQS8PrNMAY2tWyCJjpfWN4hJ6ykyINe7Jtti94nd5I9WCovKOVr8YBvXo0CDQMpx4d5ScFqOxjHW9SMpnEE088gfPnz2NrawtvvvmmOxm+0+lEvP3VatXVj+0jX/N/PTWb5WvSv/YN5yu9qtZwUE9SGIaoVCpOtqysrBwK3xwXwImb6yrTNSy7t7eHV155Bf/L//K/oFQquSjK7du3kc/n0e8f7ObPzRq56MbOYfaJfa86K8bRiY8tuAEOC91MJuNOANdkJWBwoFk2m3UuNHpzOAFIZP4gCCJnggADAa3Z3XSjMumt2+0eSihmeb4ENV97+My4SPZ+SAWKKkQKCV+4SZWmgjBtl89CsWUwqVuTslXQvR0oDEMnGCn42c/NZjNiHWqIhG5fzcUYZgGqhahjwj8FQypY46zbYeOnZQMD4E5lzKWtNATsjqqWZwiaSWzHOBQHwIbdrx4ENSYs0LN9YfPu2O+6azf71/aV8rvm0rB8n5eG80nHyjf/yFM8+fphyYpxyMdPvrmvgFwVJQAXDmdOmvXOKGhWOe3789UjLuwaBAdHHSwtLSEMD/ahonfVruriBqeJxGCDRw2pkqdVRnIOE9Bpn2gdWa4Sr7Ef0uk0SqVSZF+Zh0kW+Gt96Y3hOG1vb+OrX/2qO3IhCAI0Gg1kMpkIyFeDzxps2i/6nWM9rofysQY3JHZKJpNBsVh0W/pzqTYZKpvNutg3hQ2FPZGnDiStZZbV7XYdwwNAqVRCEASoVCqYmZlxu02GYYhiseg8ETpBLZOQqMgU0NjvD4Os25vCg9tmz83NRRQBEyl1lU8QRJckkgltkiQ9EMAg9sy4LS0XGxN/nGl/fx+bm5tot9vOk0fBrMtD1SPDvlMrz7rvlVQgWGXtc/vzPXZJqn6OQ9Zjo+Uzb4geOeUJH0DT522bfOQDNT4F5jMmFERaUmVEy9Eueec72Hc0lAhaFajo5nz6vAIm9TbYvmFZ6uUhj6g3Qw2TkwQ32kYbRrXXtU8soOT8oOGYSAz2Lev1elhbW3OeMvUK80+9OTYPKM767/V6WFlZQRiGqFarzmtDAGM9bfxOucdxLJVKmJmZcaCXOTScDzR2FJRub287naLGJstlXpfuvqweHW3jwzSEFXjYxGj12nW7XWQyGVy4cAEf+MAHcPfuXaRSKVSrVbz55ptYW1tzi0sscPUZHPp+Bf+jaCS4CYIgC+DzADL37v+lMAz/pyAIngTwCwDmAPw5gB8Nw3AvvqSHQ9rgTCaDcrmMQqHg9nDZ3993Ln6GjTgATGAi2NG9bCios9mss0A5IGRE7nI8NzeH+fl5F6sF4EBWq9WKMK4yhB08ghtVCA/bzazeAdaRkzqdTuPMmTMOyOzv7zuBrYnIas2qJ4LKWcENVwUQSBIQcqWWPVPlUdNx8nu328Xq6iqKxSIKhUJEaBM4U3jpHjX0hOjGXlbxWbAwLGdMkxHZvwo0feD7qICH99GICMPQfVrPJz8fJNxqAZ0VfGrtWr7UZ7Sd6g2wHjD7Pust0z6zoEU9EQpQOUd8Y6zv0RA6rXsNTynYOapyOy5+51z2ATU7Nmp98zkF3DR4ALhk3KWlJbzjHe9Au91Gs9lEvV6PlZkE9rpCifJJUwO0r/b393H37l2sra25XBb2uT3s1I4DMAjNz87O4sqVKwiCAO12G41Gw+kOrg5lnbrdLhqNBt566y3s7u5iaWkJCwsLTp72+30XaWB4TL1WyveWZx8GabvVe6n9EoahOyzzqaeewoc+9CHcuXMHuVzOGXqdTsfpZuvhYllsv4apLQ+NonE8Nx0AHwvDsBEEwRSAPw6C4P8L4L8D8M/DMPyFIAj+NwA/DuBf30ef3TexsdyqWpcV62DbTc90zwRr8SkqJ3Hy6MnGFDpaNl2GXHnFc0QajUasa+8kLS0qVuZ42Ax9ABHXMZWk5iBwktFqtVa1TxmzDOtdsAmoJ0THxu9hGB7axE950lqBfIb/WwvYAhwfryrZ8BaAQ8qH40KhxWuWL4fxqfWMWD5Qr4J9p5KG4HyCzN7vK8M3r/m/D6Tovfa3YX0wau6OsqIpUwg4bd19bfR5WX2A9Ih07PI9bly0zxQY2GfUU0w5HgQB8vl8RMEq//veZ41J35jpHPSt0onjBZanuWx6jYqe+kFX5wI4dICz73w962UiOGN9rOH7MHWJ5UmSjpX2b6/Xw8bGBq5evYrd3V1ks1m3Ctc3r6gLfLIsbgxG0UhwEx6UyLW5U/f+QgAfA/C3713/OQD/FI8Q3JCR0uk0zp8/j0qlgnPnzrll2UTGXHuvu+DSU+MTFv3+ILmYVgWXJzNhj9ZppVJxp2lTKDOp88KFC9jY2MDdu3exs7NzSMAr454EwCEzzc7OolQqYX5+3oXSNCtfXaKJRMLtRcMYP60M5hzoMQq0kjSpLpvNRo64pyeB/cj3nhQdJ7/v7+9jY2PDbR5JT6D2Az0onPS6d5C1VnwWsQp7VXKcGxxnjoXGt1XwA4NjRcboo4jHQ61w5W1r6bF9VhmzPuq581lsWld9Tr9bAOMD1/xfP7XtvpwyNZb0Xbosm/fa8Iz9Ht7zjjabTSeX2FbmK+nmoFxOTtnCFTKac3O/cuQ4+Z11ZV8qH7APNdzHfidP6qGxlAHMyaOHnDkbe3t7kX3JrNIkr2m9eN2OOcvgeVeU45Z/tM6UhVziTd1AMMN9bpSv2u02bt686XRJu91GJpPBwsLCoQRr3ZeKXiyew0fviIandAyOkyxw1HnEXDL2CfuF9fuVX/kV/PEf/zHe+c534q/8lb/i9vPhQhyVGzomlFlqBMXNq2E0lhYJgiCJA9fkFQD/K4CrAHbCMCT0vg3g/HjddXxEpmU8tlAoRFAzhYNuoqUx8GHuLXYohQfBEi0GeorsgXl8Fw/R3N7e9jLHcTPh/VAQHHi9isWiA4W+3VX5XT0sYTjYEJEucRXeqpRU4VJAW7SuAvwkw1L32nos/N7v990BpOrpI1kPhfaL9p9PWUtdYy0bjp0CI01mVKuLZWm99BrL13ml17Q8FYCWD3iffioPWMUYZzFa8oGcYWTrqV7EuHvt2PiEM/vE9o3Wjb/ZPDX2hQI0fU7rp32oOTf3Q8fF78o/9rqPX/hdV6HZ0B37mAYBv1tw7CM+69uOQOum79KkYMv76p2mHGToVetOWUb9oHqk0+lEwE0YHoRvadhpfymPUX6MCuta3jkOsn3nG0+VKzRob9y4gZs3b6JQKERSEnygzLZb36tt4zwdh8YCN2EY9gC8NwiCCoBfAfDcWKUfVPBTAD417v3jkiK5mZkZnD17FuVy2V1nTFpRMIWoZXaGqSxaZxnccI1x02RysJsuvUTNZjPC0PPz87h48SIajcZYGxP5BKUFB8dNyWQSc3NzOH/+PBYWFpDP5yPhqTAM3ZJHWiasC+PIutqJ7VDmVCtYvQi0eHidyd66HPak6Dj5nX1FQVYqlSIrZtTq19i+8oCGkOK8Hr4cB+v9AKK7xo7Tzz6lTSBm3897LGhRy10tbAt2yC/W8xNnDCiv2e8+UuVr56IKTFsvkq7O0XfS28tndJdZ9VACcF46DZ3bJcMKZKznRy1k2xf3Kyful9+V15VvmYdnFaAFI0EQOI9tIpFwG0Hyt6mpKVy4cAHlchnveMc7cO7cOVSrVRQKBbcDPMv35SYqWOZ9Oqasr+aP0HNowYWWt7i4iOnpaVQqFSwtLaHb7eLll1/G+vo6isWi28iVUQLyAXOFKpUKlpeX3TYR6XTarba15ygpL1IGM+9Idz9X4PEwSPWTGgZq/PJTxyUMB3vfJBKJyPjaOae6WOeIzS8at41H8v+HYbgTBMHnAHw3gEoQBKl76P4CgDsxz3wawKfvVe7YNDQbHYYhZmZmcOHCBUxPT0fADV2MNvdAw0dc1gfg0LJn7qNAYLO3t+dCW8q8TPbK5XJu34szZ86g2+1iY2MD6XQ6shIrrj1WmCrjPozE4lQqhfn5eVy4cAELCwsOYZN5Wq2WAzeaW8MEPd9+E752MZlSmVYPfqNw595EJxmWUjouftc9MHxAQHMP1GtiPSA+cKOg3ZZhl50q2ffwM+77sOf1f22PVSy+smxYhf3kC8OZfo4IWb3u+z6s3eo5Us+J1kVBi9adgJ2yQMNuKnM0oVs9DxbgUi7RI2q9exawajkPKiOOyu/K68lkMtQ+AQ4rIwsmgyBwe4QBiKyeISB88skn8cQTT+DixYs4d+4cMpmMAzd6rw+sEqj4QozKMwqubS6cDbcyufns2bM4f/48nn32WTSbTWxtbaFarboQP+ce52Eul3Pycnd3F7u7u7h58yYAuN9brVZEFrBOKif29vbQbDbdIhebl/MwyS69Bw6nEGjfUbZzPx4Lbqxs0/QHJoOrbmF/Hhu4CYJgAUD3HuPnAHwcwE8D+ByAv4GDjPofA/Br47xQ0ehRfhtWHkNBehqvVQCK0hUN24nI+wFEvBP0UCQSCeed4TP0DKlVygQqhnz29/fRarUiE8m29Tg8NMOsVyoLhqIYOisWi26Jt2VeftrVGPZAN+vSZxl2wllBxP8pjKzn6EH64z756Vj5HRi0URU5r7Oe7Gf1cvEen7Kwk9zH78PCrkPaH3nfMGtQr2ud7NJlX3/YcnTuWd7z1S/uN1sn+12FqvUkAHDLdNU7YhW09jMQDa9RAIfh4KgXOxaqkLUew/pnWD0ehB4Gv/vAZlwoQVdGqYXOLQUWFxfxxBNP4MyZMyiXy+h0Og4A6vuGydO4/tT74vjG3qfUarWwtraGTqeDRCKBSqWCqakpt/pTeYiKnfl3YRi6JGKCFnqQer2eW3Wr/ESZS6JXEIBLK2i1Wu44FzsOyWTSnXOl9bD9xfoeVXb4+ohl7O3tub5IpVKRVYk+OaFgngAqzoiJo3FM5LMAfi44iMsmAPyXMAw/EwTBywB+IQiC/xnAiwD+3RhleV3CvG5RtlKc0uaAVSoVl5gHwK1uIgMlEgmHipVJbBIlY7tBcLD5UK1WQ6PRQL1eR6/Xw9mzZ7G4uBixKslYLLvf72NhYQGlUgnr6+s4e/YscrkcVldXD20IRYqL3dtrKph9v9mljmqp5HI5ZDIZnDt3Du9+97sxPT2NJ5980m16SIYjaiZz9Xo97O7uotPpuJ2L1U1KcKljRGuV9WKd7KTXJX/lchnJZNKFx3SFmnoGhilHC7asJTQGHSu/qyLTJHcN5akLVjeHVFCkoFOtd71H88HIJ3FKxcdrvv7kb8MEnSpvgoO49/jeF+et0rr4AB2f5X1an2EGlII/facCM19ug08uqceJbdXwrZbB3zi+XDoNDFYe6r41Cr5siI/32TrfB+A5Nn4Pw0HeEtvHtil4VeDe6XSwtbUFYLD31dTUFEqlEs6cOYMPf/jD+PCHP4x0Oo18Pu885/RaqIyxuTja76NAVpxStl5TyqR2u41r167ha1/7GoIgwPz8PN75zndiamoKd+/edXk3NIbz+TyazSauXbvmdumlh2d3d9fteD01NYVOp4O1tTXs7++jUqmgVCo5EEjeYf4KDdNnn30WS0tLuHr1Kr74xS+i1WodkhGZTAYvvPAClpaWcO3aNbz00kuRlcMEWeqJ5LPjjL0a7uw7yoNarebkFlcRUwbqIhQS+4+pHv3+4DDNcb2U46yW+gaA93muXwPwnSPfIKQCyjcJ4yy9UeUxB0YzzOPeyY7R2J4VnqpIfbsRc68cChdmyKvrmsvTC4UCSqWS27XYZ13qZLc0TJEP60OfEmBsl7Hi6elpd2wE4BfeJFXMymA2v4L1UmvTgjGdCPyfE4FuezuO2rZxBfhR+elefY6N3+VZAIMQjFot2lcqhOO+k6wwUAUdF8oBDicHjkOj+tHynIIGXz+Q7Dy1333WHD9HWdvD2h/XFpvjovVWMKRtIZ/aXCIFnbr6ib9bUKTlWsNFx1VXZQ3rz3HpuPndztE4HtM5YQ/iJUBPp9OYmZlxuwaHYRjJbfLJ0aNa97wvjufsfZT7/f7BESqrq6tIpVJYWFjA9PS0W95Nw0rnRKvVQrPZRKPRcB50hl40bM+VdHt7ey4XUdMB1MBRmb64uIjNzU1v6JJ1mJ6exuzsLFZWVrz9p8bwUWTtMEOJhrB6RXndFz3xhaRVt44L4h9pcoPP2+D7fRixw4nkeVBmoVBwm/QRlBCB0hNhVwJpbE8Zt1qtotfroV6vo9PpIAgOdiEOgsDlpfgsMPU0ZDIZ5HI5PPHEE/iu7/ourK+vO7ckBV4QBC4xTjcR1P6wxHb4PFwKuPgsd23OZrO4cuUKFhcXMT8/j/PnzyOXy6FcLruwGfOPGOe0wE7HQIGhtcaAgYvZMrBti08g8wBSxqiHKflhdB9W7EMl8ooeuMprViEoCKIQUoHuAzK+fBIl+2wcePDRuL8N6/Nh4MTyFwV0HOCIq5cP1Fvjhe9TcO6rg/0eB9rsMmS1YH3GCIW11k/fZcGpgiQFSr6/00JWLvg8075ngAMPzs7OjvOC3LhxA1evXsWLL76ItbU1XL9+faywiQ+wWFkUxxvMkbShduAgN6hWq6Hf77uNXMMwdGEeKmDqIIatut2uS55mDme/33e7LW9sbDh5y6NZ1FsDHPBapVJBpVJx72Gy8vLyMtrtNubm5lAoFFxeDsvsdDq4evUqNjY2XBRBATcBF/vJ8pOuvrT9qeBf+ZbbYbz00ktIJBJYW1tDtVp1nir21/7+vgNpJHVGAIjsgzYOrz/yzE0fgw8TyD4KgsC5LpkzQpcll+bpDpd0D9ozLBTc6Lb47HweuUDEyz0pNInKupu5UqBQKCCXy7ktqFdWVvDqq69ieXnZMTYTa+2RBLa/lDSub0EH68M+CoKDZDbuZfPud78bzzzzjEvKY3sYq+X+GQRodjWURdg2wUvHVl35cVZonODXLfyZgGbv8ZGPf04a4Filpzs6W+teFb6GJ7Qsn2BWK8e+M87DMQzcjAMihrU1bn7zO+uk9+s9yuO+0NooPvABQLvPifa5jz99fKnWrM+rqPUHDu+Xo99ZjoYO+KxeZx18Xj/1/mh7TgvZPrT9HEfc6X1qagrLy8u4ffs2/uiP/gj/4T/8B7fC6CjgZtyUB/IKwzNhOMib4u9hGLrDk6empjA9PR0J3duVcFpHLprIZDKo1+tOrjKReHNzEzs7O84oZ4hKV58FQYBSqRQxSMPwYEUSD09dWFhAp9PBxsaGO/4BONiL56233nIeQOsJYd5qGIaR87NU3ivps6oLyP/Uh1tbW64NNFipj6mruWpsbm4OYRii0Wg48KeOCo4j90QaRicCbpR8E2AcYnhI95pRxmIiks9FbxUCgQKRLhPClLl1B0lduqmTjO63IAgiy8YrlQq63S6WlpYcA6+trR2y2jVm7FNipDh3nlq8nADT09M4f/48isUiZmdnHTiz+wFpmZahaR2Q2cm8Ni/C55r0jfco5cnEYk6IcTw29vejenkeBalHxva5BTE+5cv+V/IBF997j+r1OCrAietrBTO2LvY3fcYH2o5KcVY5Py3w5nhYHrW8HOfh8b3fzhX1ZljZ4VP8dvxtTo5+PowVlUchnau++ei7P64cyicamVTyccuCfR6Ecb1GCrbVQ2JBJu/l3lXZbDbifbD9QOXc6XTQbDadscaQVqPRcJ4bKnzud8MxpT6iN9tuA2CBI2U2IxtBELgQl8oZ7RM1Nn3AfRTZuaUAnqCHEQW9n6TGwO7uLoDD55TxT5fWn3pwo3SUTs1ms1hYWMDc3BxKpZLz2uh+EhZRKlnB2Wg0sLm56ZZ+U6EzWZi7HCcSg+Vs6qYjoNne3nbtoOvtySefxPz8PD74wQ9iYWEBL7/8MjY3N52FwhitzdtRplMmIVrXiUjAwrNIXnjhBVy6dAkzMzO4dOmSOw6CbkedFHRbEg3rXhz0bOVyORcHZz3VOlEhw3It+FKr1AIjEoVGqVRCv3+QiKYCU+/XSTWuQD1JoqDTZfRBEDhPGftOeZD5XVaZWUXoU8bDyAf4bTn62zjgMs5wGQa6fKRe1qMKXcsfNrSl+WJ2t2RVkD6Z4TsaJA4QKr9bhRIEUbe63mPrr/OTHl8NNXDe8tpJ8bwaQj6Q5eNXqxRJzGHMZrPY3d3F+vo6arWaGzuV3dZA42ZxBAZxSlwNMpVj/OQ+XFbG9Pt97OzsYGdnB5VKxclTLpTg3/7+Pmq1mpPvnM/01nDMmCgchiFmZ2exuLiIdrvt9EOjcbB5NBfNqPxWvtE2ZrNZ9Ho9twcbD+bU6AT536ZmKA/Z8fHxvgJJ1SnkB11oAyCi51h3bj/S6XTcfjjUN7zf1nMcOh0biuBoioighclUqozpItSY/TDBz9+ocFTocUdZDpJ6bhRw6CDT2qbgIYIGgPn5eTQaDdy5cycywAoQbIzX/vmEvC6FJ4CZm5vD0tKSSzTTJdYqgHSS+FztipjVc2CFhG8s4/raRyp4dEn4MOuOnz7eOW3AhqTLPe04KOmY+BSwkvXmaBnA+Pkyw7w14wCcuHKPeo+Pp8gfnCvWAxRXZ71mrVwLii1As+8Y1p9x79RPziV9j/Uy2LJ9oSfLE3zexxuPktTAsTLL3ueTySoDOfc7nY7LfYwDSFapWjnKvonL3fKNueZ2WP5QkEADRcECx4m7EDebTee5sYsxCHQAuNCLzcdj3o0lHX8Ffbq/UjqddoeMWqCm7WWoymc42b7xjbmWyz/d0oO6xSer+H67WsvyhG3bKDoxcBOH2se9L51OO88ND8zkAOmzlvmVwnCQX0LXIRA9O4RhHMb7wjB0y5+JKBkf7fV6qFQq6Pf7EeTPsNaVK1cwPz+PMAxdYtWdO3fcwZo+q1wRNXDYu5VMJnHhwgVcvHgRlUoFly9fRrFYxPnz5zE/P+/u0+XvvncR2QODzQx1khNdK0DygRtrNbOO2uc+RaN9xX14ms2mNyzhUy6nFcwoqVeO7mZaWRoHTyaTkfwn9exYS3WY4LZKwP42SlAdlYZ5MUbd77tucx0ARHhP71XXtuVBKh/+ri5vfXZYfo/9Hif4VQHo/Rout89Tpuh81zlKr4zuu6XKUfP9TnIeWGNNQ99KPmAGDE7W5ph1Oh18/etfx40bN7C8vOz6T0GUgg/rRSCP614pWgfWk2NCGVksFrG4uIhkMonNzU3U6/XIszSi9/b2sL6+HjGi6XHa3993oExDzKwvDThtf6/Xw+rqqvPo00hn3bgVx8LCAsrlcmRhAsGBej35l06nHRADonrFgmuSD0BavaHv0t9Vz2i7ScrjarCz76hT6QFj3TQkNY4X90TAzVGsujiLMZPJYGZmxu1vQ6ABRGN+ShZ5qktPl/AR0NAzpDkqZBJeI/AhuJmennaCSlFnNpvF5cuX0e12Ua/Xce3aNWxsbGB7e9vFGX2WF+sb53IOggDnzp3DCy+8gMXFRbz//e9HsVh0u3jW63UsLy8fcsFbRuVEsRYqhYnu3qzv9ilJ/ma9POod8lnRfIagkoBymKX8OJFafaqwrIeGygqAU2Q+UKPAZphl6qtH3Lj57h3n+rjejGH362+2LT7A4ANQ7AteU0Voed4HbnwAT61jwA+6FNj4PAGqTHwASJNkdZwJYnSVnQ2t8ftJ5tzYOtstNvQ+IJrzoX2khlW328Vrr70WGTebl2jH2vYry9UkX62regHYv1NTU1hYWEAqlUKj0UC1Wo28g+PEkA/nNZNiuXuyHrRMcMUxymazbl828l+1WnXhNxo3rFur1cL29rYzqrlXDMujzqFhTXDJd5M/NFHfjp9vTvNeC9D6/X5kLx993jfXLLixoCkMD7ZPYV6RvlfrQ2Azzv5lJ+q5iROccW52pampKeTzeZRKpUPARoW370+JHTs1NYVCoYAgCFwHxx24RibSTYVSqZRLgNX6cyBU+M3OzuLZZ591We3r6+tuDwTrsuTgcmNBBVWFQgHZbBZPP/00nnzySZe5H4ahy8Df29uLbENugQ3fpe3VutoNxdgmHUff2OqnZVIdJxUyJAJLa4HEWdHjKuDTQApguPcRPYG6MZuOv7q7VZDoWGrY0Pb/OEDG11dxQMJ33RoO9pmjAJyjGD9AvLxgu33hVh/4tgLVti0uFBvXN3Fkx1CVho49+cH+Wa+rDVedBGn/aSjfelN8YR4+z+fUw+njBQs67W8a1rHvtPUFBt4+1p3bgSQSCbdSR9/p8xzq++34aF2HAX69jwmzFgD0+32XH2q9NMDhM9LIFz4ZoO237/f1s/XC6diq0aW8oHOM76KcsjsP+3Sn9qO2x9bVRyeac+MDOCpEtLOUOYIgQD6fx9mzZ92afkXGvJduLJZn81nUki4UCjh37lzkHeoC1U5lCIHJXnwfk9l0e+le72ArbR3sK1euuEM1n3/+eayvr7u9HHRfGSauJZNJB+L02ISnnnoKpVIJzz//PJ566ins7e2hVqthb2/PhZmYFG2Biu7Ro54Bth+ILnO3AsIKnjhLXhncrvIA4FzpCqh4Sq6ieB+dRvAyitj2vb09xxe6fwmTC7nSgt5EAG5XUr2fwAfwh6GGCWGftcbf4q7FKfW4WPo414a9Z9QYDwM2SpZ/yZcaxx92f5wHYhxgo2NBpRWGoQNSdhyZZMq/TqfjPrmikzzA76chLMX28ZMhBiC6+kcBHZ/T8D73A6M1bxU9y7PvBgY70wMDD5saDQyvqz5QjxzDTdevX4+0SXMx+X5rELJ+NndF5Z0CZL6bz5Hn6MHiXFcvBUNXW1tbqFQquHjxoss5tfNeQZ7di0nHhd5j9WLZuiuQskYYgIge4X260WAulwMAl2TNEJTqeU1fYLmqd8k7cTlIlk6l58YyixUy7EDrxlKBbQX7KCFvPTVx1q5adwpg4qxmjUHz2Vwu51Z4nTt3Dul0Gp1OB7VazQk6WuNkEoKbSqWC6elplMtlLC0toVQquX1seOosGZL5GxTiaulZtKyT/SiWqG8c7cT2kbU89X+1/nzj4LMuHjfyeehsKCLumvK5WpLjKnrSKAvYV4bPmh71/6jrJ0HWW2MtXh+Is4BrVL+OA+6skrNjHjfu1gOinydF2pcKHJVHKS+sMWUBhw94x4HxuHus50MNKPuMGmMKZHwLG+IMA+UPHxjzlUGwwGskNfD1GWCw/xhzezTUqWVoyMpHVmeO6lc+Yw0n/q5jbOcP+cBe55hbme6LDmgkYxxePzWrpUg+YKMdy9VR09PTLueGMUYFHLxfk71I/F+ZipZ0Mpl0Ccp2QyYAbnM+PheGgw2UdGm4DfEAAzSqcdNnnnkGly5dwpNPPon3ve99kUFlqErbMzMzg3K57N7Pcu/evevCY0xgJrPR1dtut9HpdCL9rd4xTbqz7nk9zVgBm/6vSpj97lPeGjNVq5VjxxVmHGsFfDpBxpmUp5HCMHT7KjGcmUgc7FrKPqU1xX2TgiCIeHXUOvMlBVqFbRXzUfrNKgWf8NLPByH7Div0LIgb9m6rMIHoSd4K6pVXfe2Nq6etm6++cQqVnwpegMHKTc5XuwScvKHePlrOJzUfVEYo76nstAaMyg/yNWUyTwpXcKfJs1Yh2nHVdzPMTUOUaQK+MeH4cysR9rPKRw31aPu1TVpP/q59YVd/heFg4zwtQ5W/8kgQBKjX625TPgXrrCffXavVIkc88Hm+R6Mb/I26zAIKBd32XQxD2gRtG2Lkkm6ujOIZUuQBBceZTCZyqLPdUHYYnVhC8TBBqL9rI8h03LyPxy7QhQdEN/+xKNLGzBU8UaAwg12Xf1PJAAe5L9PT064MH8LkoPNZCh3ez9+npqZw4cIFp6Q46RR8kTm40+Ts7CxmZmbQaDRw+/ZttFot7OzsYGNjA+l02u2krPVTIGFPm9V62xUFpDAMI0c+sI6sr1qVFujopPR5HHwJkfTK0WryWU6WNx430n1L6GHjJ8c+CAIHbnh6MDBwvSsgVB4HxjtEMe53K7TjFH3c9+MkK+iHAYZhZVglQyFsfwMOgxLfe3xW+zieAgvIdS7wfw1LcYUU5YP16nH+cA6dFJFHraK0ljbby/5X2aDbZ9CAbLVaETCn8jFOR9AIYxiDSlL7cJhMUQXNvtedii0/8ruWZcGO9dIokCNpeMi2yRr6iUTCnVPFe/hO5WvVLXrfKGPFAjUrt7WeCsA0xEsZp1EN6lWGKGmIU74pSFU9SePczpVhdOo8N3HEDmQ+Bk+OJgoEDjO5Ci6LTDkY6qEgEZgQUXKvAsYUaUGzo7lcsN/vR7btViZlaImnajNTXgWCDSuwngoo2u02tre3nVeHGfl8F4Vio9GIHF3Q6/VQq9XQbDYPMXWckI/7zk8KESvMKHR9k1InkgontZoUWGp4yj7/OIMbKjFd/m0Vl10VQ95SYGOtYlUCumpvWMgxDiTYsYr7bikuPBZHdv6p8vJ5RPS9Pv60c4Zk+VD5VoFiHF+Nw2/6vA+EK+DX8WU/qJLnNgDWCFAesWWdpOeGMs96u9VYY/6MPeRY+0rlhq4IGwbcVSawj3XsredXdYAdc32XLdsHWvQ+O/4+IKvAIpVKoVgsIpE4OFiTeXjsH+o65YFarYbd3d1DddQ5AwxWUGlfcFw0KuGrpw9AaN/5QL/yIsedepTRFTVird7guwqFAt797ne7Az5v3rwZ2bTSjkscPXJwE9cxlnxofGpqCuVyGbOzs5idnXVnStnneL+6tyyw0RCSjb8TNHG33729PWxubrrleIo89/f3sbq6ip2dHezu7rrDz8gczOXJZrN46qmnMDs76xKPE4nBTozFYtEdhka0m8vl3AoudTGura2h0+mgWq1GXLVheHAmR7fbxdWrV3H37l3nkgXgkhGVeekdUQa0qJmg0ro7lTH5ncyrYIX3cpL5rFlV8gSV9N5QmSs/aJn2Gss8LeBH+Z39TlcttyAIw8GmkfZ3YOD25R+vaVzdgkRV8Py0IVogmg/g+7Rz1jd/fYBm1Bz3PWuBzjgAx1dfn5Vt//dZpwoYLPnqFkdWQfAdClKY+KreF4IaJtUyLKVgRwW9LhEfJ8nyYVEQBG7hAjA47ZthITVUyOsE9PSKK/ggWKOxpjJQQyz8o5xifwTB4FBiesKsgUVwpXNHgYACXzufWC7nqQ2zAdFVRtb44zuy2SwuXLiAfD6PjY0NbG1tuftSqRSuXLmCCxcuABgkmn/zm9/E9evXIzzsMxT5Rx1E4Oy7ZkEMeVP7Qe+x77Vgm3OQW6WUSiUAgxCZ6hYFbkFwsJr47/ydv4MPfOAD+NVf/VX8x//4HyPhWT47ik48odgntKzyU2bUZcI6WRRV6jM+K1OvWwDkey8HWycrJyU3a6rX625PBHUfc5AZMur3+24vBLpLU6mUAx7A4UQwths4OCZid3fXrYxizoUyYLfbRbVaxdbWlgMmwMDFqn2s4Eb3laHA0L621q4PXJC5rfWsY23Hw449xyPOoxXHT7Yep4l8fK4eGk0eBQ67gSlI2a821KeAhu9TfmYIwPa59pfPc6ifWu5xkvaND3yN87yW4+NL37t8fKxk5ZC95vvdV4ZvLNUIsODKxxNq2Wp5VjGfpOfG9oXWmUDGyl6VFerptm1SOTKsDnzWN25W9ozqq7hy4t4bV49hfEIZnMlknFFNolGsOZ5q9KkMsO/11XWYcXKU9o+S3/adCoBsfRUQcoHQwsKC+6NXK678YXSiZ0sNYwrbCZqEyxVDHGQKAc3PUBep3apZ92Bg+CSbzTpESwBA8ERLigec9ft9ty14r9eLnMjKAaPl1W630Wg0kEgkcOvWLXduEncy5snjbHs2m8X58+dRKBQwMzOD2dlZV19gsNFTs9nE6uqqS8AKgoPl8TxV9Vvf+pZbzkhinJd1oDeAfaQTC4DLbSLD5XI5F1YjaLPAkONGRaxWkIIjer+odJU0LMhPjeXH8Y0FsqcN4CgRaNDStKDFhpbIg7RmyacKIK27V8G1CiXbj9qfGsYdde+4NA4A8AGoccbQZ8Co3FBQMez9/LSGkgXnep/WWctQ40THU729DDMCiCTr220B1Kvj+9NnTjLnhjkRwMCjyKRZ9QgDAxCnHm4acJTJNCT5nCYFA4gAJZapOXvkfx17lVUaOlcvn/UIKUDR8CF/12d5ry73B6LyiYmzrIMCwEwmg+npaaTTaVQqFQdk6vW6k89BEGBubg79/sF+N+vr64eWqGs9bN11Q0H14lvQoiuQleKAlI6v3ktdyb5WPa3j1O/38b73vQ+f+MQnUKlUUC6XsbKyglqt5oBvXApKHI0NboIgSAL4CoA7YRj+1SAIngTwCwDmAPw5gB8Nw3DvCOUdAjpxyJMdxwHWlTTqnqL1yfuZaGw7XcNSHGiCJAARIcRJwH0mWq2WCyVUq9VIspsmM/OeWq2G9fV1lxcTBAEqlQoWFhbQ6/Wwvr6OZrPpDlcrlUou3jg/P48zZ85EGHt7exvVahX1eh23bt1ygCqZTGJ6ehqXL19GMpnE7du3cfPmTVd/enT6/T5KpZLbgVP3pWBfsN+5SSIBDoUEgaEmLvvi0CpU+EklbpPeqECsS5MCzwKlOOYeVymOouPk9zhgoEJeQZ56b1SBaWK4WvUUEOwfBUbK+9r/Pg9JHEgc19K7X/KN17jvGwa6jlLnOPAT1/Y4T49VMhpuYiiKskXnruap6Nj6cmx8XiD+fj90HLxOg4Z8p2E3zQkk+eqrMojASEEEQYNdNKIeLODwMQxKlDsaHtJrWi6/syxbXzU6Nc9H56HVcTZvSH+nNyafz7vzAJvNptsXhvKWK8mSyYPjIXztDMMwAhY5NhoOpF7wORx88jyO5+2zdg4Q6Or71elAPfvEE0/gh3/4h5FOp7G8vIxqteoAs4K1cfPLjuK5+UcAXgEwfe//nwbwz8Mw/IUgCP43AD8O4F+PKoQdoAOvnaJoWScEhUOxWMTMzAyKxWKEKRWZAoOYryJVWw8LcsgMRIhcIcTcGJZJpuD77SZK/X4f09PT7kRbtcDoWuRgzc7OYnp6GvV6HTs7Ow5E6P29Xs/lZZDZ6bFhTFMP6GTiXj6fd3Fa4OBk2WQyiWKxiPn5+QiD6e6g7XbbTQZmtLdarUMhKxVGVLKFQiEibFVQ6BJCHQNrJQEDSy6bzUY8G6oIfVaDAoIHpGPhd61n3G8cHx1zq8B0ZYm9R+9VRWyFjV5T0BIHZMYBBzY/wdI4ZVmr0VrOPqtUy/cBENvnqlR8Bg/LspahDwDa+vreF/cObY8NJ/vaqgDJgh0CCgW598n3xybbtQ0KRriqR/tBgQkAt2GleiIol/inBoDP4BnFszpH4ozpYe2z31l3LcMHkvibnYthGLrjdzqdjstHYRm6GSLbym0kuJgkTo+qnNDfLDiw+8QN6wvLYxas6X0cO+qgIAicriJI4b2JxEFC9erqKgDgW9/6FtbX13Ht2rXISQB8l80T8tFY4CYIggsAfhjA/wvAfxcc9NTHAPzte7f8HIB/ijEmABlUl/X5OgeIutd5jEClUsETTzyB+fn5Q243zU1hh+iOlBw8ghdV0nw/k3tTqZRLYkokEiiXy5iamkKj0XAdSw8GwVYul0O5XEa/38fW1hZ2d3dx/fp17O3tufDT/v6+22k2lUrhzJkzyGazWF5ejoAtXSmhgINhKSp87vnDOrMduVwOlUoF3W7XHUA5Pz/vls/Pzs4eCjlQsGxsbLhDRGlB0UvFpC7GRynA6I2ZmZlBIpFwk49eMDIkl1VyDBR8WvcxTzmnRaRCw/Cnqwff9SB0nPwO+C1IFY7NZhN7e3tubBTIaajJZ70rALIhQM41uxmizWWyz8V5Kyyp8rZCXK/FWcJx97HuFsj6QJqvry3FATbeq3xlQwrazz5go+2xfKdhED38lPfZcIiCV/2zq6c07MVwpa6MOQodF69TlhNsBUEQWchQr9fdbumaJkDeDcPQHRapISqGwK03xBpJwwCu/qZe0TgPT1wf2nlCvQREAYJ6ny3I0LGnvOauvWwbAQF1lcpKekF2d3edTqAx6/OO2TmjYEz7mmFuG+Ky/aj9pfpKx4R8TyBD4z6RSLhzD5muwfYmk0nUajW88cYbaDab+K3f+i1cu3YN9Xodu7u7kZCrTRCPo3E9N/8CwP8AoHTv/zkAO2EYMj3/NoDzY5blyFc5n2Wm92pClc/y8QlP/m+XXAIDpaE5HrqngRU0ZAYOCpWwtTCy2SzCMEShUEC5XEY2m3UDwuTddDqN+fl5t9MkN3WbmZlx++lwGSBwgNibzaZ7v4ITnbyK+llPYLC9eBAEkT1V+BtJvVbqPaInjO22oRENH8VZvCzLTna9T2PAvr0z4oDCMdK/wEPgd5KtrwUtwOGESmvZk+z9tmw7Ftab4JtrD9oe328PUrad16PKGib0hgGAUd6fuHcdBVRof+hYHJUHRvHFEehf4Bh53Qc0OM/ZV1Ry2WwW7XYbOzs7hxY6KPgG4PY163a7Lr9xWB3i+iNO54xzjWWPc82WoQCBoCWXyzmPeyaTcaEiHrxJ0KJyWtMkfN6Lo8hIH9DX/8fh67j76BCoVCqo1Wqx48WxTiaTaLfbLk1ja2sL29vbziFgQdo4BthIcBMEwV8FsBaG4Z8HQfDRUfd7nv8UgE/xf5v0psotDqjo78Vi0YVyuNKIu/gqaPGhfVr1jJXSc6PnXGi9ut0udnd3Ua1Wsby8jHa7jXQ6jSeeeMKVH4ahW+m0u7vrwje0Ui5fvoz5+XkEQeD25iGlUimUSiWk02nU63V3WNv09LSrE5meoag7d+5geXkZu7u7uHPnjvNQNRqNSAwYgHsXY7n0pGxvb0dcgsDB3gI8eJOhK1qLwMESPgAue59t4SaKejosJ6lvOSHHm7FUWp2aV0QQ1+8fLIfnhGdelF3y6hPyal0dhY6b331krSsNOdDCYRvV+vNtmsjyeE1zoQgKFZCqYPCFlIYJ6qMIdusFGuYxISkQ4zPjKm0LCOx3K/THAUAWUFlBrvXXawrcFbQAgzwN9VhYz6bN97N5NeQTfmo48yh0nLzOnCHNH2LCajKZdOdG0Rv7wz/8w/jO7/xOfOMb38Cv/MqvYGdnx7VPk4d3d3fR7XbxgQ98AH/zb/5NNBoN/OzP/iy+/vWvew3WcZTeg5Dyj3qn9P00FH05N0xhuHTpEi5fvoxSqYQnn3wS+Xze7XPTbDaxsrKCRqOBcrnsTgCnp2hnZwfr6+tOvloDRueO9od+qhGsctrXVvVIaflso/YH5UqhUMAnPvEJfOQjH8Gf//mf4z//5//sQA4X0HAVGB0Vt27dwi//8i+j2+1ieXnZ5aJy7x+u5tV0iPX19dixGsdz82EA/+cgCP5PALI4iMv+SwCVIAhS9xD+BQB3Yjro0wA+fa9TQtthVhkNs0LITDxUUXNd2MH6rHU7WoGhwj+dTjuho/su8FiGer2OVquFpaUlF9Kh2399fd0paSongq+ZmRksLS1hamoKc3NzLneHLsFcLoepqSm33XoikUA+n3deF2DgsaEQ7Ha7mJqawubmpiuL7YkMruQMqcuYbmL+FgQByuWym3zlcjkCwvb3990p4ypMGe5iO7SvbX6NkvUIabI1EN1ynYKOXrVhFtkDWK9Kx8rv47zQKkMFahwju+Q3roy4EJNPAOqnks+D8SAKQ4WhBRfWuh3nXePwgPaVBUlx4MrW2Wf9+upvy7NzgL9zrthwhYIVzYfQci1QstfuB9zgGHk9nU6H7CMNq5B/1SuezWbxrne9Cx//+MeRSqXwu7/7u9jd3XX3M4+QfbG3t4czZ87g+77v+7Czs4Nf//VfP2QsjevBOA5SneVbLUUjj+Oi85DhqNnZWVy6dAkzMzN45zvfiVKp5DbnW11dxe3bt9FsNl0epYJfDeloWy1ZQwE47Lkl/6kuUD73GSV6j3VOsP3pdBovvPACPv7xj2N/fx+/8Ru/EdFhTGnQlcvb29vuGCG+j3pRV1cd2z43YRj+EwD/5F7lPwrg/xGG4d8JguC/AvgbOMiq/zEAvzbybWOQzzIKgsDt6sszpfL5vAMstHRUiDMGyQmloR12pjIdQcP+/r5DmCsrK1hdXXXholwuF4kBMqGLWd0cbKJMhqe4M3G73UaxWEQmk3EbXumyOIIrTnSu0Op0OtjY2EC73catW7ccqqc3iW47nWxsi8YnFTzwGQqh3d3dCNNwB2UKJrXMCJRu376NVCqFpaUlANEztaxgpiWnG2yxLvf4DJ1Ox8XZyfTZbHZkPgH73Cq3+6FHze8KYphDoXuCaCKxTSy2fyzPghrrESH5LDof2esWQOl35TOf1WjBi/6udY3z9hxFaQ1rjwJBEvnIks1lUAXmA0oWnNrntP7qjVFDRa9b0MP/NZn4fvj9OHmd85fedPYbMEgKBuAscXrQK5UKnnnmGUxPT+PmzZvY3NxEKpWKhCKmpqawt7eHlZUVdLtdvPvd70Y6ncatW7fwxhtvONnPPtYxeBSkY6384wMS9MhOT0/jqaeeQjKZxPLyMu7evetWzO7t7eHs2bMun2pjYwOdTgeNRsPtb6apAFq+Jeo2ko9Pdc7Z8hS0WSCnhgPLOHPmDJ5//nnMzMwgCAK8/PLLuHv3rpNdCmCpo/WkePYb26fLvwl0dRuFYfQg+9z8YwC/EATB/wzgRQD/7gHKOoQYgcHEz2QyLmwyPz/vlsnxdy6r1LwXukFp+ROZ7u/vRxK0NGmZ3ggm1F6/fh03b97E1NSUCx/dvXvXHbewsbERcSGrQOY1ntqdz+dRr9fd3jXnz593dSWgIKDZ3t52y8i57Putt95Co9Fwu1i2Wi1sbm5Gzp2xAlj/yFQEKHbHSiYe0x3c6XRQKBRQLBZdjlAymXQApdVq4caNG2i323juuedc4hsTYjXxj3tWcOJSALJeDK1xS3HmMGUyGZdQ7FvxRlKhYa8fIx0rv5N0UjOfgMl9wMD7RmVGXlWFB0QPZVWgoN6CByGr9C0AsbwGxIOgOACkXg3f+9hfx0E+EEPhC8TvlOzz0liFoSDEKiFbDjBIDFbgon8KYlTe6dw/avh1BB2Z1/v9PlqtVsSo1IRVJpY2m00H0KemprCwsIBv//Zvx+rqKra2tnD9+vWIkUTZ02w2ce3aNRQKBXzv934vvud7vge//du/jevXr0c8xD7AP8oT+CCkwIY6hu+kblGeoCxcWFjAe9/7Xmxvb+P3f//33SohACiXy3jHO96BqakpfP3rX8fVq1fRaDSwsrISWXmrizF8PKDeLd9vOu98uoLtY7uoN3U3bLaTuuzJJ5/EJz/5SczOzmJ9fR1f+MIX8PrrrzsZTuDCECV1Lo1ezQ8F4HQBQR0XuIwjB44EbsIw/AMAf3Dv+zUA33mU501Zsb9ZkMMVTNxzhbE3jVvzOSvUNTFNAQ1JlQMVML0zPLQNgAtVcTM/WiC6yRbfoS5EepH29vbc0QwA3F49wMHmTVyJ1Ol03FEPtVoNtVoNjUYD29vbaDQarm5cNWEnM6+RYX2KzWdRsg8oaLk6jUJXvV2alEyg02g0HGghcNIJrpNGQ1E+QMZ+VGXH+lm+GebReVA6Tn4f830RQWgnsSpWawQojfLEjEs+D4qOp32XBS6+9sXV0/6NUzdbdpx3aFQ9jkq2rZzzRyXrZbRz2ReG8nnsjsNLcVy8rjKZ81vlD2XU1tYW7ty5g9XVVezu7qLT6SCXy2F2dtaBNyVuWsoNULPZLMrlMi5evOgSUJnH96g8NnHkA1UWnBO0UuZTz6hcDYLAHZ6qK1Xvpy5xZOvlmzc+A1mvJxIJzM7OolKpYHFx0S1dr9VqWF5exs7OzqHwLA/AZpqEzfuxnjDVFVZ/xdGJH5zpcyOqGyoMQ1QqFbznPe/BmTNncPHiRVQqFeemo9XLvAx6GKhoVcmrB0fDN2F4sH9Ms9nE9vY2vvnNb6JWq7ls9Xa7jbt377pkLi7dpbeEirjb7aLRaESEULvdxurqKpLJJK5evYp0Oo3FxUVcunTJnbnB85PI6Gtra5FzZRiWUkanyy6TyTjBx6RmRd20fhQEEFwAOJS3BAD1et3FdXd2dpyFxeXf3IuH1tXq6iqazSZKpRIuXryIfD6PfD7v8ouCIHCeGQWMGoZIJpNumShXEXBMp6amnCCwoJdeIpuo/jgRJ6t6IO2Onja/QnM0rIfElzysLmSf94DkAy0kzhnrifB5YSxp+JH3ku8sEPfVLU5I23paMOgDEHFeIOuV8d1nrV2f54akRpQ1IPSTfaGeGxpNPs8NwxXc+dUmrp4k6RYcPDdPjT/K2s985jN46aWXUK/XsbKyAgC4cuUKPvjBD+LatWv48pe/7MJX3W4Xb7zxBhqNBkqlEl544QUsLCzgypUr+Gf/7J/h7t27+Df/5t/gpZdecu8AHp7Hxjc3dKztkmjqCGAQ+lxdXcWLL77oZCIw2Gut0+nglVdeQb/fx82bN13agyp7zjXlVRvy1Pr6kvUJymkoa/iQc4dtoOFPsKoe1nw+jx/5kR/BX/7Lf9lFFO7cuYM/+ZM/wauvvur0Ep9NJBKoVCq4cuUK+v0+Xn/99chqORrYrJNNu1CZNoxOHNyQLBrk9zAMkc1msbi4iMXFRZTL5cjx9bqhHt10miis5Vmhr65jKvNqtYq1tTXs7OygUCggl8uh2+26k7h5pIIVwMlk0iFrZcJ2u+0AB69xrxyG2+gh4fPM89E28hmS7hlhgaGtm3qv2NcEGAyLqZBmqI27LGezWRSLRQCIJPeyTPZJp9Nxx0oQZFqFSqXN/YP0sEiOnYYXdVL68hdUKTyuZD026pHz3avWPO/Tfo7zgOh9vv+HKYNRYGaUpyTOLW7BDX8bxysRZ2Vaj2Bc++1vwHhJxtYi5zstD/rGwbZJx8z+bgGt/tncHK3fSRG9DlTuVNZ2c8r9/X288cYbuHPnjluskM/n8YEPfADPP/88ut0uvvGNbzhZ2u8f7BtWrVZRKpVcPsfTTz+N7/u+78Nbb72FX/qlXzqxdmv7gagus/KY483EYRqsvJ96ZGdnx4ViqAvse/Rdw67zN+Uz1QnKVxYgKfDW9AvdnT6VSuGZZ55xY/G5z30Om5ubuHXrFq5evepyJynTgYNVtzMzM+j3B8nF5Gtg4NliHax+Gwe4nujBmXGkyUTJZBLz8/N4+umnsbi46HbZVaGqOwpzOTEwWOJmO0MtJB5Cuby8jOXlZTQaDXey9t27d52ngeCCLjTrLlMgAgyW8NKCobXNpKjt7W2kUil3CiyJCc10P1LIsTxNILTK3k4mKzhJ6rlivbSfdLdiJk0T3KVSKXf+yblz5zA3N+c2Fux0OlhdXUU2m0Wz2XRen2Kx6CYJmVwFga5coweMfaHjqueIWSVv+8Fa7I8DqcU+zLNiFZ9aiTaBGIiCC2uZsUwt317zhaLigJM+q2NghbAFNUexsn311XpoP1nwdhRgY+vt4ymdc3GgRwErZYR63Aj4eZ/KEgUwCg401+808LgPyFGuaD1VPhEE0cPzxhtvoFarYWVlxc19EuVAPp/HU0895fL8vv71r+PWrVuo1WoR+TUKGB8H6bzy8a9dMZpIDFbC8gie/f2DTU273a47M7HT6bi8yzAMUavVUC6X8fzzzyORSODatWtYXV2NGHXqJfIZEja5mXyWSqVw7tw5TE1Nodlsol6vHzKcyLsEICpDADjP2uc//3lUq1XnieKmtppDxb7Y2dnB66+/jn6/7/Y5skY6dYIF/OOO64mAG59LV5mDAjuTySCfz+Ps2bN44YUXsLS0hPn5+QjqDIIgsixcE2XVxZtIJFyujm4ExU2DXn31Vbz22msABifYXrt2DTdv3nSTiomeZAo9KiEMB4mxLIODwzoxcYphJl7jCi5N5qXC1zLo8WB4hu2yeUXKDNpPJAIN9rWNbfJ3WlW0yIjAz5w5g0wmg8uXLyMIAly9etUx9e3btxEEgdufgaEqghrdswGICgC2VQ8X5AqudDrtNkJUdA8cDgVY4PQ4EOuqSsvmk6knTBNOCaKtZ5LlWsveZ2EqWQvPKq44b41aftquuHmuYSm9d1yFFOe5YVk+gGPJB2wsKBsGcKxCUXCtwIYyQjclU3DHe/XsKV9ysYIbzbs7Cjh8WGS9w7oi0+cZoEzhNhJf//rX3bJfrqZUPmTu5bve9S5853d+J15//XX86Z/+KZaXl7G1teVC5aoDHga48XndfB46zmOV0dzGZGFhAefOnUOv10OpVEKv18PFixdx7tw5dwzB7u4utre3cfPmTSwtLeEHfuAHkE6n8Zu/+ZtYXV11IaUwHBzpo7KVpHmmCli4K/rly5cxNzeHGzduoNlsHkrQ1sUNOibAYAUTPW1MtQDgdsLXkCT5emNjA8vLy976AYf37lGD7lSDGyBeoKrHIZfLYXp6GqVSCcVi0QEMvceXsBrXCbyfA8Kk3d3dXbedNTBIClYBBBxWBlScnERkZEW7PotX66rgy3ok7CRS8nks7PtsffmpyNsmJQM41Ic6LiqoqHAZ2qLQBeDOS0kkDs4M0Wd53ZJNVgvDwcoubgXAftL6vV3ITuA4Uv7QcRvmTRlFPm/MOPX1eWmGeVaGeX9sOfdLvmd9oIfXOc99ISnbvjhS2RL3jA0jUhnp/FJQ63unnaO2Dic5HywfqsywQN0ns3kvT8cOw9Alo/I5KkbuA1MoFDAzM4Mnn3wSmUwG1WoV1Wr1SCD5uCgObM/MzLjjgmiIVyoVdwwB8264MSoPZeZGprojv5Wbti95zQLyYXOCe8kQKI1L+XweCwsLyOVyOHfuHGZmZrC/f3BEEMtUYMN3ajgMiO6Or/IhjtfHreOJeW40SZJKbGpqyiHKZDKJCxcuOBfkhQsXUKlUkEwm3U673BdGAQVdeSRVwAxzbW1tYX19HdVqFS+99BK2t7exs7Pjknjr9Tr29/ddHejBYKIcj0vgNUXoTPBlUq5aJrSwaV3QO6NImff5LAEVBkEQRJbP+SwjtfbVM6KCmHsnaHIqhUgYDpbvaaiKS8bpaWL8tNVqYX193SV7r62tuRNsOXF1ybhVAlzmR55grk8QBLhw4QLS6XRkd06uGvPR4wh86AXkWMS519V6Z/hSx5r8YXlIPXb833ePz2uj5AM1SvZ+m1hr+VLLVTf7/ZA1CqzCHeYlsuBE26fGgwXh2uf6LhXOmiNDMMVcNyoBJpSr947PaghWQ1anhcfDMIwcz6IgjbmKlIXqke71em73c250uri4iHe84x3Y39/HN77xDXfuHgBsb2/j53/+5/GZz3wGH/nIR/DX/tpfQyKRwMc+9jG0Wi388i//Mn7+538+sjT6uL1acSAmju8/9rGP4e/+3b/rogGNRgNPP/00Ll++jBs3buDLX/4ybt68iZmZGXc2IXVcr9fD0tISisUi6vU6giBwK8l0KbiPJzkuAA7NK/Z/v3+QtMwdgdVQ5/18RssIggBXrlzBT/zET+DChQvuqKAvf/nL+Jmf+Rmsrq46HrUJyBrNYF30N+Ud+/0odKKeG2UGTQ5lI0ulEs6cOYPZ2VkUCgXk8/lDrlgVgnTra/mag6Cdx+XLm5ub2NzcdCsPNL+GoRWiUK2nrmJgnXTDIebnAHB5QFoH63U6KlnBrcKXbbSuPe0PgisFTOpBsgmkLMfeR8blZnvsFybKBUGAer2OXq+HQqHg+jAIorFxdd1TmDPUwi3bOUa1Wg1BELjYrq9v9PNxIQV2/N/XBuuZtELcPqPegmFk52Pcb753+O5TnrTgIg40PajHxvadfT9/ux8vl1qTcffbZGYdIwuoCAJ4nwVBFqDrs6fNc8n6+3IkdGmzlXthGLrfuBKzVCphfn4evV7PgSHyTafTwZtvvol+v4/nn38eZ86cQbFYdH35la98BcDwxPBHQWpMXrx4Ed/93d/tTsbe3NzEmTNn3DE7a2truHHjBjY3NyNGXyKRwNzcnFtEw3QElc8ayfB5zqxM0PnA/ie4tHyqbfHJ1HK5jPe///14+umn3Xtee+01rK2t4datWygUCu6cLP5OY1l5X1MqbGiTc+J+6ETAjRXivMZBy+VyyOVyuHDhAt7xjnfg3LlzLplJk+ms25NxaGCQ88KMe+BglRIA3LlzB7dv33ZeAGamU3mSKff3951iZpnciCqRSLh9atiOvb09t+SNbSCTq9LWwbIWt3XfaW4MAOcdUrIKhaTWHsvnu3Qlgk34Yv+yHzTnhcvp2R49yr7f76NSqbidNum5Yq5Ro9FAIpFweTcsU89CsgidE4OrtQiqmOit1i3brJPxcSJrqds/5XfNS1JPG+PwBI8+ZaNLuofVxQoae3+cBev7XwWYlqlkhbP97gMqwzxIcaBO+cOWry7zUYBO5RhlkL7X5x1S5W6BvZVtrIeCHjWqTlqBK6kcAxABLalU6tDmnrxPFTXn88rKiiuvUCjgHe94B3Z3d13iKZ/9xje+gZ/5mZ/B3NwcPvCBD2B2dhbdbhdzc3PuyJw4z+5xk+WXSqWCj370o7h06RLe9a53uRW4L730ElZXV1EoFPD888+7fqHBvbe3h3PnzuF7v/d7MTs76wzslZUVfPnLX0a9Xsfc3By+7/u+DxsbG7h69So6nU4EsOgYWB70GcTkJdU3Fkjr3KPHqN8/WMWWy+Xw0ksv4erVq3jttdfQbreRyWTc2YjAILkcQMRrw/J5D/nDt4+b8vs4nt0TBzd2EnOJc6lUwhNPPIHnn38es7Ozzv3GhFwr7BX4AAMLimCk3x9s3Xzr1i23f8DGxoYLIXHre25CRLBCBQ/AxUez2SwqlUpk6fnOzg42NzcRhiHm5+cxPT3tPBgqnICoVetT6uq+Y9vYP3quhs8qZJ9YZUfF5mMUdRVy6SFXKTHBj+dg8b0URgQ3wMGk3tvbQ6PRcGPWbDYBwPUFz7HK5/OYnZ2NABeOsbW8y+Wye38QBGg0Gm5VGfuX9XocgY1aKTYHgSEqq+gI5hXAaHhRvSQ+Acff4kj55SheH3u/DUtpnfSZUTQOuLIUZ4WOelZDVHF1U9mjeQHaz9aIs8qH46lJxBpS0TH1raKy5Z0kqaeYcgeAWxjS6/WcAenjD3ommFCby+Xwbd/2bTh79ixu377t0gX47Fe/+lV85StfwYULF/D3/t7fwzvf+U50u10sLS2hXq+7dz1s8nlNKpUK/tbf+lv4yEc+grW1Ndy9excrKyv42te+htu3b+Md73gHpqennezr9w+O3dnf38fTTz+NH/7hH8YzzzzjtiP53Oc+h//0n/4TVldX8YM/+IP4ju/4Drz88su4deuW82D7FhOQx8hP5BlNYic/UdbzWWAAOBSU667/1He/8Ru/gd/5nd9xoVWeep7L5dx+bTQedIk3ED3YWo8xsn3MuaRLyofRIwc3diKqFcmOZ/JooVBAqVRCNpuNCBIfOLKeDgAudlmr1VyOBncKZsIW68D9GDjoujRZBRzBhebKaGKxnqtihZ212njNZ41SAKvA0rqxfSTtT6vIFFANE+r2fTwCgWCzWCyiWCyiUCi45DjmD3GbbE5Q9ofWzQpvtUJ971cByYmhO1vSjc2yLT+Qp06SjupFirP4fd4AFV6+ZzRMMO67xw3XKEix1/gcr8WV6yPl0XGBiH2/D7zZ+TSqT3xAxTcm1rtm2+h7jwr1UV46q6CUz331OQ2kfc1PG0ZTr7GV2fqd+4/R8KSc1tWc1WoVN2/edDmAV65cwfb2NqrVagQIHnf/+Ph9dnYW586dwxNPPOEOSt7b28Pa2hrW19fRaDTcRq1vvPEGbt++7bzY3Ork0qVLbqd6rqq9ffs2wjB0y8YZUaCBrQDYd0SBb75aWanX4gwgGppzc3MoFotuB+LNzU3nqZ+fnz8EkkgKsvi7GnI+I2xcT62lRw5uNPmXHgGGQrrdLgqFAs6cOYO5uTlcuHABFy9edO5MXQKpik+XgZPBac2vra3htddew+7uLlZWVrC7u+v2suGz09PTEaDDJeNEqXxvGIbI5/MoFosup4YAqtvtutNegyBwIS0dDF3Kq14JBVK8Vy1x1ov14Ym57AdgoMh0nx+icj04k+/XcbAuVYKwQqGAhYUFFItFPPPMM27bcy7r5kZ9JO7eTC9ZrVZzHiH1LqiXjUs/LY8kkwfnWO3s7ERCLExMLhaLqNVqSKfTqFarEQuYQmBqasrlPT0OxLFUL5/11gCD/X80l8HeywR99T5o/8dZPj6BEkc+sKxWl89Vrm21AkqNCX2HvW9cL5I+Owzk2vJ9AMYHKMjn6iXl3OHv/PNdp7eG8oOeZTuWvvs4908LoCFpCMNuX6FE+cyNTOkpp8HI+X737l2sra0BgFtpBMB5sMIwxNbWFn79138dpVIJP/RDP4R/+A//IW7evIl/9a/+Fd544w3s7u46+X7cZHntwx/+MP7+3//7mJmZwaVLlxAEAW7duoXPfe5z2NnZcXup/eEf/iHeeOMNtFot7O7uYmFhAT/yIz+CH/qhH0K9Xsfrr7+Or371q3jttddw/fp1B+4WFhawvr6Or3zlK0ilUnjve9+LMAydsb65uYkbN24cWgZvc910nupCE81Ztc/y3ve+97344Ac/iM3NTfzWb/2W22W63z84M+u7vuu7kE6n8dJLL+HmzZsRI0sTlm3YSevkkyFK4xitJ+K58XkjqJSYc8NDGwuFQkSRW+uGDdfBo+Ag8t/a2kKj0cDq6qrz4rTb7cgW/3pKqU1w4sBwkLLZrHOfUeh0Oh3k83kHtDQfQhUJPTo6ePouIJrhrq5nlqUeIFVcet32x1E8GCyXK8M4FjzdXENVCsz29/edBwcYKGEtk/WxOQRsN98LDFaZ6DgQxPZ6PeTzeXfWl4ZPtC8eN/JZ+6M8NaO8Oj4gc1TAoPeNe29c+3zffZblUWiYh2wcABDnCbGWpLUqVRb5wkRxYJGGhebhqdC3lq3OmThP0Wkhzj9SHMBj/6innPxKnm02mwjD0IW2CHyAaDjr9u3bbrXtU089hUQi4RJx7TlVx0U61qzLmTNn8N73vtftXdPr9dzK0Vqt5vJqVlZW3JL1fv9gufelS5fw/ve/362gun37Nl5++WW8/PLLKBaLOH/+PLLZLDqdDra2tjA9PY2FhQXXd2wrV+FZgOPziur1OMOE/7Od5XIZ58+fR6fTceCTMjyTyWB+fh6ZTCZyNJC+0/L8MA9kXJ1PpeeGTE5Ur2GHVCqFcrmMZ599FmfOnEG5XI5MZnpLuLEcJwvDFSoIuH/NnTt3cO3aNacEVXCo5aVJvrQyrBXFa/TYaBIuTyEH4HIkLFEBA4eFsQpEX+hNXdkWVWs9rKVKsGAtTpvYp7/T+0OQ1m633YqycrmMcrnsvCea/0MPD4+b4Blc/F9PeN3e3nbgyIYA7TJYnwcgmUyiXC47zxpXUJGfmOx80pZtXKjEJ1BUOdLzxGRuPTeNid169pZ65PT4Cn7nPZq8bWP0vv/Vk+IDqr728R57v73PJhlbr00c0LP9qP/b+RJHccKU4+Aj2wbOLRpn1hBhm+x81vdaoK91YJnsH3rqfHl7J83nJDXAbGhPiePU7XbdAg2SGjzsR+YBptNpXLx4EdPT07hz5w5u3rzpvPlhGOLFF1/Ez/zMzzhQdP78eQBwZ/7ZOmi9fXUcRplMBuVy2e1bk8vl8MQTTyCZTKJer+PLX/4yVlZWcOfOHZw5cwZTU1O4fv26Oytqd3cX6XQa09PTyOVyaLVaWF5exo0bN/DKK6/g1q1bLqelUCi4HJxGo+EObX7jjTeQTqfx5JNP4plnnsF73vMe/OAP/iDq9Tp+93d/F6+//npse338HOfRJa9PTU3h5s2b+PznP4+trS03dpTt9XodX/ziF5FIJHDr1i0XIeF4a4gwzpjQ+mWzWZeGoEvVx6ETATf0khBchGHovCfT09N45plncPbsWbfmX//IFAzPcECI2ikwarWacwO+9dZb2N/fd8pB3WSdTsflz/BTwZQCG6Jh/q/5JnoPlYyuLAIQUdQWhPhAjVo8/FQBSIGgqxRYnioqAi1lMFUCViko6CC42djYcAKDCdccA9azWCyiXC5jf38fOzs7zlO2vb3tAGMikUCn03FHM3DHTlXArAfd8bRK2H9UzOVyGfl8HvV63YUjFQTe7xLC4yQfiFVQbvmLRN4iqCHIYZ/rnkEKqvkbAb+CHQXCClwseFGQ4QMyPtDC+uu9FqzwHt99Wi4QXX3i85xof+ony+E7fN47/uYDMXYeW9I6KVjUva6sgNaQrNaB81zz0HSOcp4qr/C+0wpstL2sv494fX9/3y3a4GHHujCE/RuGB7u5T09P49KlS7hw4QIymYw7e4mLC1588UW8/vrrKBaLuHLlCs6fP+8MH8urvvoP+93+ls1mMT8/j0KhgCeffBIzMzMO3Ozs7ODzn/88vvGNb2BxcRFnz551fFKtVt185i6+3NDv7t27uHnzJl577TXcvHnTRQgKhQKuXLmCcrmMW7duYWNjA5ubm3jrrbcwNTWF9773vfi2b/s2LC0t4bnnnsPGxgauX7+ON998M2JAaTuVV+18sfzK8Ukmk25fHOpvzoNkMolarYYbN244j/v99jt5nhs6Mr3C5uUMoxNbLWUryP1MGAbhGVL0nhC5U+Gx02idEtywE7jMm8wfhqFDpeq1YIhDJ6MKGZ/iUQCh//tcxXrN/m5dbD7BbZnDR9Z61Wdtv1vrUe+zQEe9H/R6EUl3u12XLEdET/dyEByshJqenka/P9hskeDQMrMCXiodC/6YW6KTT7123E+BoHncCfAwiZPTF4b0Wbc6odnPPF19amrKnUvDU9Pt6ekEPzoW/FTvmP7pWOhmWtabw+9sgwUVtr/jwA3v9YEgnxdGPRfWK+MDXBZg+Dw/LNuX40YikLYAQu9TQcycO/Vc8B7dxEyfpSeXMkzngK5KVOCk7Thp/rZkx0ZBnQI+IBqq4u+aLK33cZ5QHtVqNWxtbbnVU+l0Gs8++ywymQy2trawsbGBIAjcFh9UwMoXrK+SlX9KNGKnpqbcAc4MC+VyOZw9exbT09Po9Xr42te+5pJsqYsAuDCU5nTSYGfStM/YJH9tbGy4NIudnR2XnNzr9bC6uoq33noLvV4PCwsLaDQaWFpawgsvvIB2u+36imcA6kIeDenbea3pHxwLynAdU00x0DEfBr6tEQBEdytWHaTgf1w6EXBjY8pBcHDUQrFYxMLCAi5fvoyzZ88ik8m4E6drtRr29/ddqIOWPhPS8vm8swK2t7fx8ssv48aNG7hz507ENabhKxX4Nu7NjvVZd+od4Ke931qALNeCpTgw4rP6LJLmPWrl6vNsF9+n+wioYLYWMtvEnUUTiYTzltVqNVSrVeRyOezs7CCfz2NpaQlLS0vOWxWGIRYXF/HMM8+4fYQ4EZvNpsupUmGTSBwkCwNwy/KZa8X2cP8EnYg8mHNmZsYBYAqDR7XHRRwlk0nMzMzEgmQKBa5uoOU6OzuLhYUFZLNZZ9Wl02l3/AgPGmQegl4juOH4a3hKPQ20tIDh4SYfwLaeHbbLCh5V8nqPFWr2Pu2nOBe2D3DxuWHGhg+k+Oaq3e9Dn7N14p/PA8X7VWERhFNRcOM29gNXA+3t7bmEe+YIttttN+etNa7z/SRI5VAQBC68bZcTA4iEkuhN5xYfBO5BEBxK2A7D0IVs6vU6Go0GLl68iJ/4iZ/A888/j1/4hV/Az/7sz7okYuYCcpsL9Ypb4nzx8U0ul8PCwgLm5+fxt//238Z3fMd3uHlFj2gikcDv//7v45/+03/qUicARPJrgiDAwsKCSwrf39/HnTt3kE6nUa/XI4YiywWAWq2GL3zhC0ilUmg0Gk5G8j1/8Ad/gK9+9at45zvfiWq1ikKhgO/5nu/B937v9+LWrVt45ZVXsLOzgz/7sz/D8vKy4zc1SHVe8prKUYJE6jqdt81m0zkRVM4AA5ACDDbxU3DLd1ImhWHo9mHT/cz0zMU4w0lpLHATBMF1AHUAPQD7YRh+exAEswB+EcBlANcBfDIMw+1xymODlZigymXgeo4QE4DttuPWImUHcHkgE4k5sQhg+H4LRhSAjEKJVkiyXKvErKvZWn0+D4tVHPoewL8tvg+g2Gd9kzquDK2zCu10Ou12cGYslAqclEgk3E6jPKqBuTba52odaLuU1HOjglDbz2XpVBoML94vHRe/JxIJd/RGXP9rDhXDT+R/gkAFN+xbghuG9NjHOid0hR2/66dabxbEqIL2KW29zwc++Ns44CYufBX3nL7fAjGfNa7XlPfsvT6L0wIbPmtz37Q/bXiNv9tyqLjodWOeDT3WfF5zb1RpWIU0StjH0XHxux0flVO+8bX9S+vfWu8qm/f39538oU5IJpM4e/YsnnrqKczMzLhwiQIqy+sEo7bOcfVNpQ4OTy6VSjh//jyuXLkSmdP83mq18Oqrr6Lb7WJhYQGFQgH1eh3r6+vO2KEnm+0lUOEnx149rPv7+9je3nZGHnmCXpTNzU13evjKyoo7b2tmZgbdbtcdtKk5kspH6sVUOWGNfo4TZSx5mHpay/LxhY9ntM81pMxybbqG1n0YHcVz8/1hGG7I/z8J4LNhGP5UEAQ/ee//fzyqEBt/ZkdWKhWcO3cOS0tLmJubQ6VScUsfu90uarUaer2eEwQq2NfW1txeAtevX8f29jZu3LiBu3fvYm9vz3kEgOhOiWQaZXwV5Lae+glEEw/JCFoWMABRHDCf5eAT2j5iX3G1ki61pNvO7m5qn6fAUWa2pMLGMir3lmAOE5OMa7WaCw3Sk7C0tIQwDDE7O+uAKnfT5OTZ3d11yWJ07dN6YVkU8Jw8XJFGJZ7NZl1yMZeFE+g+AD0wv5dKJfzAD/yAV5BzMlNIA3AAZXp6GhcvXsTU1JRboUZPJQGmhuP43W4poPk1FtxYT4zWyV6zv9nrwHhhqfsBNxZcqOAbBsyB6HJzH5Dx1cnOHd/vtg0E3el0GpY4161h0+12kU6nnfzhRmfFYtF5TWmZM4G02+2iWq06xaKGAseYW+nfBz0QvxNEA1GFxHEhuNP6kp+tLuj3DxYjWIDJP4bKuUp1ZWUFv/iLv4g/+ZM/wZ/92Z85OaxJ9OqJ0zpTJvmM2iAYLBQplUool8tIJpP47d/+bfcea5i98sorjg+45Qi9b4nEYLk7gEgyehiG+PrXv45+v+9yFLPZbOTsOPVi24OK6e3a2trCH//xHyObzbozqfSg0f39fXcoKdtJnagbxKoRkE6nI/2kW5CQyMdxBrTmfSp4U3lOUGrnmybQj+u1AR4sLPUJAB+99/3nAPwBxgA3Pi9FIpHA9PQ0FhcXsbCwgEql4pbS0X3HQxK51wrzc3q9g1Nit7a2sLy8jDfffBM7Ozu4c+cO1tbW3OGLnAzqqWGHAlFvCL1APuFrwY2NhbM8db9ZC2uU0B02cPRSqLLjpKFny1euRemaqGfvUaa3SoPKuNfrudNfa7UaGo2GU7qsI3NzaE01Go1InwJwYUdaI+l0GnNzc847QeFHocCcE2AQ9mDSGYEOd7E+ZjoyvxeLRXzoQx+KjLVa8gTXdOdykufzeXeKMIGsWje6ZYHNr/FZn5rzoaDWhjN9zw4DEnGARMuy5AMoFkCT4kJKowRbHDA6yn0+QGrH0QJJzZOx7bWe4G636yz4dDrt9q8huNGwQ7FYRLPZRLPZdKCfOXAEElTgDMEfAx2Z33VBBjAIU3GxiCZJq6dLeYLykeEWlQG8V8MUNGp/8zd/0+1rRa8Mn6UhCAzkPOcLgQVzNSnzOLY0oovFogMFf/iHf4i1tTUHWlVBc84CcPktGl6ikcal0rpn1SuvvILr16+7chiC53t0XzeG2dgv3LajWq3i9u3bCILA6Urm9ABwq1x1p38axnyv7xo9RgQ3NKI5nuQ/X0iP/c3202hWGZZKpQ7tZKyeI2uYHSe4CQH8/4IgCAH8TBiGnwawGIbh8r3fVwAs+h4MguBTAD7lCopxUdEiBeCWyLXbbZcQpksgOYmYhLW8vOx2gGQoih1OJtKEJyBq1Y6KVVsAw3aMim+rF0jB0qjB4UTXvuJg02vFMJ4lX0iG79dN3XyeG6s8fLkQNpZKQMWJ1263I8l3FBBMjuUf80vYj+pmLpfLh5SKunFtrpMKM/5ReN5neOpY+H1+fj7iNVQlR+FKwEZ+JXhj/1BAK99ouEmFhI+/hlk5w7wzozw19rvv/7h8GN9z1sOjz5N0vvkAkQ136hwaF9zY/+PAjfWAUkj75pNan9pm7Q964RieouHW6XRQLBYd2KlUKm7fKV3ZSZ6vVquH2jkG3Re/K6/HAVS23+YpcX5ar5b2D5/VP/VOK0hWkKLAk/OMpDxkFSf7n+/lPcwVZN4M5ZT1JtHQ8hnNWqdRwF9lnN2ORGWH9jvDWhqOo2zmu9l/FoBonWx6ho1EsJ5WTugY+9pGPqcRoPNK5bmWyfllvc12vsbRuODmI2EY3gmC4AyA3w2C4FX9MQzD8N7EOET3Jsqn71U6tMKNHZLNZjE9PY0gCHDnzp2Ie5XJYRwQIvTbt2+j0WjgS1/6Eq5fv+4YkPfxPKJSqeRADWPaPsGpISsl6zrVZ+618ZBlRsTLZDbeq5nmcQNkf9OwQz6fd+5RJiLSa8JJRya1yFvLV2Bgl4bzfus1YLvIdEzwajabaDQamJqacv07PT3t3l2pVFweAb0rvLa7u+smJdvBJd4q9GhZ8YR2LY88QW8e7221WiMBaAwdC78/99xz4ZkzZ9xvKsgVfNFa0dVOzK8BovktCshVWPqE5jDBZEFPnMfGfifZuvnIlkdQYPl+VM6NbY+PWHZcGb7yfLzhA0M+cKN1Z92s/LDgnBQEgVu2b+ujSpPXqLSfeeYZdyAkFS3Hk/z05ptvevtnBN0Xvyuvp1Kp0MpAbRc/qXg5VznvrV6wgMT2iQX6tPppwKonxWeI6pYevI8HPap3utlsOpnDXY5brVYEFBEAaeI0ywWi+/bQS6EyWnULQQllq4I6PTeR84lAgZ493kuvlBq2YRgeKpd14vsJpNQjqMa5jD3ujbvrz729vQgY0XtVL/C8Kd3NXuujskjBkJ45OI5cHwvchGF4597nWhAEvwLgOwGsBkFwNgzD5SAIzgJYG6cskrWq+Me4MmN4VIA8bZshDCqver2OjY0NrK+vu9UFasENsyj03T53GsuwyYPW02OXkmvZgP+cmvshTiZ6KWwexSjipNCkamsVcVxUoeqzvK5gjf3OCcH8A8aFVRCpZ0XHhm5X8oBuRa9bANCty3dZq8P21/3QcfE7LXIp1ykvzRkiPyi40f5hn6o1owLAAhPt11F9YC0we81SXNm+ey3Ysrzlu08FXJwVqOQzOnh91LOa5AscXu0TVxcFFTpGPk/VsLpQaKsXlcpY+wYYnJ3GfaGYQM97yC/Kb+PSw5Dvnncc6g8Ftbw2zpxlWRoWVBnuMxytbOC9aixoaJG8QblOzwhw2MOgXhEf8LXAdlgb+U7KPq2v6hP1DPH+uDbyvRYY+Oa5em7smI0i2+++eaAGma+ePvLNyXFoJLgJgqAAIBGGYf3e978M4P8J4NcB/BiAn7r3+WtjvdBMXnYIj2+/c+cOrl69Ggkx1Go1F+NcWFhwnhgq1WvXrmFzc9M7WDs7Ow6MqPIl4ylTxDFdnEVLUkbghCGKVUaxmd9x5fksbcYpuXEhY5TBPc9Nq9VCp9NxKwlUAOjSYNaX9xAoaN1trNqCRJ/QbjQaEaWby+WQzWbdCb/cuI/eGSYM0pKwO5Wurq46YKMhK66m0zFhGIyHomoOz1GB5HHyO4WOHU8df9+2CKY+EW+ZBTcWdKrAV2VrAdD9Aj8fjSN84kDDg9Yl7p2+OTSKF6zRYhWvkjVc9JlxABLnV1wZugiB72OeHQ+LZTnAIIdrmHfLR8fF75QlWi+fh4L8yqW+NBzpOaH3l9sjsD/002dAKuBkH3BzQF+d+E5Sv3+QE0hvAsujN4fvCILAbWLKctgu9RgFwWApPEER+UuNNPYBANcn1oPH97IefD6TybhTxXd3d11+D+U9c7dU3rCu1K2qA7RPdWGKyii7kk2fszkxGnqk58quelbe0PFVL5XKMZY3jlwfx3OzCOBX7r04BeDnwzD87SAIvgzgvwRB8OMAbgD45KiCyFDWw9Hv91GtVnHnzh2nyOgSS6VSaLVa2NraQr/fR6lUQi6Xc53S6/XcRn10daqAISNYJaGrcDR8E0c+N7y15Pid9ytaZjvtoPgUjm8SUwjs7u5GklETiYTzbBEgaP8yr0OZzoIbjcOqFUhhqu231hHLqNVqEQTOFU+sE/co4smxnIgsg8c0AECn04kkzIXhwJ3KnCPtWyb97e8fHFaqnp37oGPjdwoJTlBtj41t+8gHWiy/KMjhffaavf9hkA+oDAMvcb/5wM9x1G1ckGsBzjCyilXfN06dVFHEWdgW3ABwCwpYXwt8j0jHwu+W11k3NfB4DRgcfMk2M1xNg0gP++U99n32Uz1CACKgwsp/3VOFc5G5nZqrQ8DFVaIA3MaZWpcgGOxPxHdp4q+CFeZSMWmaz+ieMlZOqFeJRj3D1wSL3JxPwQ2Nd1/iM2WmBVKso3qRVNb4dJrPI6MJymyTLvbQ92k/qiNAV4Cyn+x8i6OR4CYMw2sA3uO5vgngB0a+wZBdM88O29vbc0qQWdz0IBDRKqKzisFm1WuoSN2P1pLQQYrb9M2nFOImm+/3OCsPGB808TonbDKZdN8ZpiOQUzTNPlEhoe1Xb5JlNP3f1kl/o3Wg47q3t+eArJ7fxXfSs6UThuVbK8KOk+Y2aNs4iXntKEpNyjs2flegyP/t72rR231qRpUdx5PjKtdh331lDONVfo+bB9rOcZ/13WfJlhl3jy1n2LU4D46vTnZ+6riosPaVY/uEMorKIq4Nvvoclc/luWPhd/Kv9oHm/+mcZH0teKMRql6DmDqPalPkHpUdNo+FdQcGnh2tn+7lwk/qCgUfdt8ZgotkMulkM59l+dYLEjcPLdgln/T7/chGhSzHph0oqeHLuvtCWiT7Tl/9gmCwZF77RvWLDXcpn6jMJv9bb3Umk8Hi4mLksGQAqNfrsXzwSHcoVgQLRHNimLilneOzOJl7QwqCwG1wpkxgz6HQEAtzOlT56OaAPhomQMcVNlbI6QD7wJIFGsABkLBKxudNiavvOErkqCuMuGvqsDayXAVVHDtF98BguaQVhsBg9YAS86/YF3wvE7pPitg+fvcBRlplKiB0Utsx9f2xrDheUrLzyn7aZ4cBmmHC2PaD/W3YO8cBZ757R/G1lRu+esYBHHufeuKGtccC7Tgw5AOz1qM6TrvvF+Q8KCUSBxtWqnFJee7bikCNTgKgZrOJVqvlVcrAYUPHR+od13wchvXt+HCVlIZqaHyxPMobto/Jy9QjWh+fl0RXP2nd7OndWjed/9oG3sMtAba3tyPt9xnzCrJtUq4FpZYPFYxYwKH15LmCrVYLOzs7Ec+c6iflF/VqEUSqR5LtZsTmQx/6EM6ePetybPv9/tAE+kd+/IKPMYneNN6opIjauvFV+Oqg2fusANAQjD5zlHpr/Y9yvwV3Foz4QI39HOc9497jsxKO8tywftNJ4WuX9aCN815fH1gFcBQF+TBplAfGlz/j89zYtvmUnQ/cxAGZUWWOShyOK8+nvO1vw+pinx+XhgF6+964+/VaGIYjAY6td9y42PePY0D5xtMnK04T+frzKPJqmLdM+3CUvI3rI5/BqDJPy7a6xOYc6tYi5BW2QUGINTrtu3z945sfto18Ni5kNKyf2C7WU3O1rHGs0RBfPVgXC161rFGA1PJ5MplEsVh00QnmTnFFrF1wEUcncraUEjtFwxY+Gtah9NQoylYLnkpDwyIskwP8IBbP/T5LJht1j7ZhVDKVKku2i+/wvWuYxTmMbJ2si1eFkS9WquDG5zXzlUNlMw6P+EDToyYmgPsSyVWIMKSqewP5hI1dlq9xbjvuQDwQiRtzljkMANnv4yjauPfZ6z5gNC7A9pEP1MQ9p4qOv4fh4HBLS2od67u0bI6dCmHlX97nU+oKcsk3LItKTZ9RHjgJoleA9QnDwcovbQfzLTSHQvuBbbR9qG3kcSQMT+hct6ElC4o4bvyNC1NUVmr/qszVuQvgkNxi/biSjR5otl91kI8PbH/avtAkW5UP7Dsr331giNe0/ylHqIMJJCiT6M1i2oi9LwgCtxOzbm0xzHhRYEaPGfX4mTNn8Nf/+l/HlStX8PnPfx6/8zu/47Z8KRQKh7aGiaMTBTdxStiST7hY5Qog4v7zvSPOS/CwlOA4wnnc91oPVdz7rCdAJ2Tcs1YhWgt2nDqNqjvrY/vbB7zilJ26tEfRSQMbkhVC2q/qsle3uAovK/CtS9gmkvpAhwU99neWNQywxJUbd9+w3611OqzsUfwe9y4fWBmHxjV01FKPe7+WqSDJF2YiL/C6vV/nsibqan3uI5n42EgNRa2T8qx6Onx7aFnlC+BQGgP3+uIu5QxH67PsO5UT9h3atwyB2/xD1oNlMaRDxW9XOvLdzD3h8UFaJ1sHH68poOO4ar4Qd4JX8hl9tv12bHwhICZF8yBebrrabrfdog72BVepJhIJt2+Nkg0D+vRuEAzCdwSD2WwWH/nIR/ChD30IOzs7+OxnP4tWq4U33njDyc1xdMCJgBvtZMsc9j4rRICoUOezVoj5Jo2i14dt4cQN6v0QB3SUoLftGzWJ4uo37F6lYWOnZVHoxYFLOzHtd984A9HdP31WymkiBZxsMwWMFfL2OZINFVke03vjeNx3LS5RcNizcd9tvx+1PN84DmtXnNXrezaOfEAoroxx+CquD/jdByx1ywXL81RuwwCqWuInQZlMBk8++SR2dnbcBqw+ZW43lrRGrc+w0f7o9/vOQ8BVtewjYABGqKATiYRTzFq+ygwFm/o7f1OZauWYGhV2bHhMkIZQdPWpgjCbXK0JwvZd2mdab18ISetjiffqu9SLA8D1sd3tmMvctf7aR3GgldeWlpbw1FNPodfr4a233sL29jby+Tzm5uZQKpXwta99Ddvb23jllVcO6T7FBcPoxMCNulfjlKme8KyHbVlEaOOHRPiJRCJyNIBlBD4LjKfQj6IwdYDHAQGjaJgLToFeXJ6RrZPeM8zLYZlV7x/Xg6JuX1+7fL/5UL5eV/7QMbYx35MmtbbJl7xOa53Wz7AJq+M27D4rROJoGKA5CrAZdp9ei3vGpxTs/xbwjFMPfTbuvjgwE3fNPqekyZu+8LpvzFTh25Uz1ghTL4jPy6ZLgE+CisUivvu7vxvf+ta38Oqrr0bkkCo+VYrWCtel04Dfw9Hr9dwRE3yW4FA9w6lUCgsLC8hkMlhbW3N7gvlkJd+vOxqzjppTozsOc+7a88T4XCKRcLu0K3ihl0KNOq60Iu/r8m3lA5UdXLKuslnbPw7xXr6fnuMgGCxLZ7/xnWEYum0+CHosvw5LQ6Befv755/GjP/qjaLfb+Lf/9t9ibW0NZ8+exXPPPYdut4tf+qVfQrVadat/tTz24yg68ZybowxE3PWjlvEgoOU0k2WkozxzP+85ym/388xR7zmtZBVRnAcmDgT4FLrea5/xgQVr5Q8r11dm3P329zggElfOuNeHeUPiyhnluYkDM/qsD/TwdyA+NKvPxv3me85av/Y6n7FlDAOQj5J4nh+NDyprKnirDO+HfPklPuOLFJeLpH04zLjzgVsfn/veTdDhW8E0Dg2bT5Yn7D3307+Wv30A0/Kbvc7vPEOQB3eGYejyCskj+g5Sv3+w7936+npkf5+j0omAm1FJscAgnGHvtYlYRMB8Rt2JRJy+99uBiavD/dK47zgOOmp74izSuGeskjgJoGGFEK0qu1nfqPDdoyRaGKyLzS+yYSlNPNQyeK+12Cks9T4VMD7F67P6h4Ec255hbbXvvx+K47P7KXMcI2YY0PCVNQ5faV/YuWO/+8qj5e8Di+QDu+Go8tFJUKPRwBe/+EXkcjk8++yzqFQquHLlCrLZLOr1OnZ3d7GysoKvfvWrke081CPBOW098ySrDH3pCbowZXNz020CS9K8IJ0nLFNDNARommyr16lfdJyonzTpVkmXmPvC9CxTvV1aV373gV/+pu3xha/sMz4Z5Qv5k+dsHQFEQm/AQZjy4x//ON7znvfgW9/6Fn7zN38Te3t7uHLlChYXF9Fut/GzP/uzaLVaeOutt9Dv97G+vu48NZ1OB5lMxiUZa5207sPoxDw341TOB0xGIf5xvRePQvk9SgV71Hc97PsfBtk6HCUB/aRIz6gJgsPb6itIGbbaxSfQfIBC77NlDQM2cQp42LVxfiONmrMn6XXwgQwLTCwA8ln96s3xJRqPAmu+3/keXyK50jDeeRTU6XRw7do1PPvss1haWsLS0hI+8IEPoFQqYWNjAzs7O8hms/jmN795aN7acArba/vDN06+3EvOMR4JY1dIsXyWS/J5aBRIMiyjBxFbfaTfbSoFywCiRxrY+augJI6G8VVc3YaBIQIWu5u9kg356x9BKus+NTWF5557Dt///d8PAPi93/s99PsHxyddvnwZr7/+Or74xS+6jWiBA4Csy76npqbcBohaz1EYgHTiYakJTegvAsV5Y+z1uJCRFTj2mv3N0rjAJq7Ow+4b9ds4vx/XM3EUB6B8XhvrGfbVRb2fPiCkACeuPb46DfOq6hj7yj9pCoIAtVoNt27dQr1eRxiGEc9No9HAuXPnUKlUsL29jVqthrNnz+Ld7343EokE3nrrLaytrbnd6m3f+8Ifmqti5xUVrSpg7e9R48N7uLTZlzwcp2RtWXFjNapOo9pvgYz11vhC4Hq/lktvjJVD/E5P1KVLl3Du3Dn37P7+Pm7duoW1tTVMTU2hVCohn8/jjTfeQCKRwMsvv+xAysrKCnq9HlZXVw+BdlsX6zHyrTIcRhNwM6EJPWRSZUWlZJMBfaDF/q/CW3+z1yz53PyjgI2vDffz21He8TApDjDcz3O2H62SUG8LKW7J+P3Uk2NpAc5JeixZn7W1NayuriKVSuHFF190izr29/exuLiIb//2b0c6ncbXv/51tFotvPOd78Q/+Af/APl8Hv/1v/5XfOlLX8L29jZu3rx5yIvgC73YhSQAIom3vJ+f/I0hbf3Np2gZJtEd7vX3OBCqnz5PjG9e24Uxep+W4TvSRXOZtD3kER9QtKvEFNwoyNFzGAHg+eefx/d///e7Pmm32/jVX/1V3L59G7lcDouLi0ilUvjCF76A3/u933PnC4ZhiNdffx1vvvmme7+G4jXUpUvo0+m0ez8B1qldLTWhCf1FIBVMVoha4XUUcOO7j9csjQtshnlpRr3jQX972DTKOzPq2ijvyiiPjAIdDZH4nlPS+6zVbkGtrx6PmqiwdPfeIBjsH9Nut92eMmxPr9dzOTE8riGRONioj+cM7u/vI5fLoVwuIwgGq4larZZLVB0VfuF3Oxft/XFljBs6VfDwoKB/3PH0Aaxx0jMoizKZjAMqzDEicRl9IpFwe98EwcFZkFwJxg10WV895LlarUa8SgztxW1dEBfiGyaf4mgCbiY0oYdIaoVZ17Ymh3KyW4vMenj0NwtarDUzTGjbZ+39owT+cf32KGiU9+V+wY8t14IWn7Vst6DQsocpW12K7FsufdIJxQxP8KwmVWjAIGT1ta99DclkErVaDUEQ4JVXXsFP//RPIwgCrKysoFqtYmFhAd/1Xd+FXq+Hb3zjG1hZWcELL7yAT3ziE0in06hWq+h0OnjxxRfxR3/0R+4cQS5ntsvqdXWVhrHYj3G8oZ4Lu4Rc80z0ft22ZNgci/tNl/yzPN1Rnx4ObZsNufnqSU+XJgynUinkcjlcvHgR09PTeOaZZ/Dcc88hDEPXx3/2Z3+GL3/5yygUCnj66aeRz+exsbGBX/7lX46A2ZWVFaTTafR6PWxsbAA4yKEhmFWDzoajNHRoQZnPY+XrOx9NwM2EJvQQySd09LruUGwnrvXaxHlgfC7aYZ6aUcDl7QRsSD6AM+y3o17zARsf+OFvcSAnrjwgupOsJqr7chceNVEJEVwQ7Ci4abfbuHv3bgQwrK2t4erVqy4JlfvTXLx4Eb1eD2+88Qb6/T7OnDmDD37wg8jlclhfX8fu7i42NjaQSqUOndBtedkCQc47q/Dj5oydk1qO9cDqnjbjenuUWI5N3tXv2lYmT9sT2RXUaR217ly2Pzc3h7m5OTz//PP48Ic/jF6vh7W1NTQaDdy4ccOVv7i4iFKphFdeeQVXr151+4vxd4av6E0j6FRgbvtV2zKsvfrMuDQBNxOa0EOkuFVQvE4LygdeAH9YyqfwRnlpfNcelefmtFBcHYeFnh70mvXi8Br/1xUm9l6fF8ha6VQMJw1uqCwBHPJcqPJSpcvfuI8JgX6tVsOrr76KMByseLp58yY+85nPYGpqCo1GA51OB6+++io6nQ6mpqZw8eJFFAoFbG5uYnNzE1NTU5ifn0c2m8X58+dx/vx59Pt9dz5StVpFvV5Ho9HA7du30W63I8aCL9znC09ZIKOKmO2xxzTEzTtV5Jpv5NvVGQDS6TRyuRzS6TQuXryI2dlZZDIZFAqFCNC6c+cOXnvtNbcxX7/fx9zcHJaWlpBIJFCtVlGtVtHv912ib7PZxN7eHm7cuAHgIGR448YNZDIZbG1tuTHVT5LtAwv0OO72pHQL6mx5vMfnbfPRWOAmCIIKgH8L4F0AQgD/NwCvAfhFAJcBXAfwyTAMt8cpb0ITOs10XPyuFqINL2nM2YYTrEXOsnwAKc4bM8xzE3fN9+ywa+P89jiQ7ftxrumz43h47G9WEdpnfO8kENKddG2y6P2MxXHwO8ENwzIK2oHoTvAEN/QqcEM38nO1WsWLL76IMAzdDrmvvPIKrl27FlFunU4HrVYLs7OzeO9734tz587hxRdfxMbGBqampnDp0iXMz8/jL/2lv4SPfexj2Nvbw/r6OprNJl5//XVcu3YNt2/fxurqKnZ3d11buBSZ7dLVQvxdvWg6bqrkmcNiecGOl4IWDRsB0f2LdDl5r9dDJpPB0tISSqUSPvrRj+K5557D9PQ0FhcXnRclDEN89rOfxfr6Our1OlqtFnq9nluq32g08IUvfAHLy8t46aWX3Lu5azoTe1utFl599VX3OwEK38Ext14tG0JlW/WsLpWRdpm9huV07MdZKTiu5+ZfAvjtMAz/RhAEaQB5AP8jgM+GYfhTQRD8JICfBPCPxyxvQhM6zXRs/G49JRRQGluPs1aGARCfZXk/3ht7Le7/uGtvNxrHM6O/8RkfYPGVMSyEFVcPG8JSJWKt2vsco2Phd1/f6W9aX1+9ea3f7x8674grrpTUa0KgQ2UchqE7PqBWq2FjYwPdbhfb29totVqo1+toNptun5VE4uA0bwIttoWHX/rAqhotVNBU+Mlk0p1ezmXQ1tNh+8Mq7SA42ASU51Ol02lXp729PaTTaRcaajabqNVqCMMwcsRDGIbY3d11Sdi8tr+/j1ar5Tw0euRDEAQOrOhYsg0Ebb4xt9eGgXwL7sZ5BoBrH4AIKD10/yj3ThAEZQBfA/BUKDcHQfAagI+GYbgcBMFZAH8QhuGzI8o6PburTegvDIVhOLbEP05+/7Zv+7bwt37rtyKJcCoM1XNzr57eXYwt+bw5owCOT3mOA2jG+e3tTsNkpM/b4rt/2H0KcoYBFl5XRaXnD4VhiI9//OP42te+9sj5PZVKheVy2VnaljcJQnwnVfuUG6uioMGeZcdnp6amUKlUkE6nUa/XUa1WkUwmUS6XkclkUKlUUKlUAMDVr16vo16vo9PpoNFoIAgCvPOd78Tly5fR7XbRarXQ6XRw8+ZNrK2tReYm+7xYLGJ2dhapVAqVSgXZbDaigOfm5pDP51099/f3sbu7i263i52dHVSrVXcA6P7+Pmq1GhqNhmtbIpHAmTNnMDs7i2KxiMXFRSQSCWxvb7uw2srKCsIwxPz8PEqlktcLsrGx4ZbWsw2FQgHlchn7+/vY2dlxuwCTdLNRy38sX/lPx0SX2/v2zdGx47voxVNZqLzAcoMgcJtEBkGA3/u93/vzMAy/3cuTvouGngSwDuBngyB4D4A/B/CPACyGYbh8754VAItjlDWhCZ12OjZ+V4tOPTe+T9+zPhqVPDzqt3HB0FHJF5p5u5CvLdZro9/1mpYxqo9GAVOWoQpAle4oQzWGjl2+W2XoAy++PWV8Co1zxNc2zi8mwOo9PGAzCAIsLy87wMXDRbvdrlvdlU6nkc1mMTc3h4sXL6LT6bgQzvr6ugvxaJs4Dvl8Hul0GjMzMygUCk7xp9NpLC4uIp/PuzDi/v4+tre3ndcFgFtNtL+/j3a7HQEBPNm8XC5jenoaZ8+edUnXU1NTbhl9u912O/tyPxi21R48zT5nvpECTN0XR08A53O+/D7fWKuM0jL0PoIv3mPLsSBfyy4UCpifnx+518044CYF4P0A/mEYhl8KguBf4sBFqRUO47wyQRB8CsCnxnjPhCZ0GujY+P38+fN6PaLg1AVvJ7Ylupi1LC1Tr9nv9j57fZxr45BPeb+dyQdq9LtPoR/V/R7n7dE/DXPYVSdj0n3zu/J6EASx79e6W15UZaieBc3r8HmzSHYZsbX++U7mfqinQfd04S67XOHDcNTOzo4Lx6iXDDgID21tbbmN7AhYWPbKykokzKVemkajgUajgf39fbdPTLPZjOT1AHDvz2az2NnZQTKZRKPRQLvdxs7OjrtfQ3Sj+o736W/2mhpelh/tTsY+QMMy6W1hGbqizfKxjivLVR7nvkkMox0HuLkN4HYYhl+69/8v4YD5V4MgOCtuyzXfw2EYfhrAp+819i+O9JvQ40rHxu/vec97wjhgwUmrVqmNtwOICIdRylC/j3strpwJHY3iwI7+PoyGPWsVkW+liu7lckS6b35XXk8mk6EvbKT1p7LTNhB46DUFKLq0mmXqfLCAQ5NYuTKInhOCFq7Q0tOm9/f3ce3aNVy9ejVST5av3g/OyXa77VZZ6b40nNfZbNZ5feyKMTuuOp4KBDY2NrC2thZZgKBgSXOTdD+YuL5T4OMbr2HhcNaT+U82GdyGD335hHalnPKvGgZ8VgGv5gN1u92R4GbkHsZhGK4AuBUEAeOtPwDgZQC/DuDH7l37MQC/NqqsCU3otNNx87sPWAy7b5zfVMAN8+KMumbLelA6zrIeB/L1pf0tbqx8z/vIWt6jPHhHpePmd+sNUAvcF2oAEAFm2g4fQBw2F/T99l0+r5K9h2BB/0atRqNyZi6PfufS6729PZcEvLe350JiCkrjDBcFPlTqWjcL9uL6zrfCKa4tw7x/+j67f471olleiOvDuHli+53Xu90udnd3hyYTA+OvlvqHAP5TcJBJfw3A/xUHwOi/BEHw4wBuAPjkmGVNaEKnnY6F31UAjLoP8B+GOEwY6LP3472Z0KMntU4fJIynPHMM4cBj43cquWQyGclvsXua8P5+v49Op+PaYhNK6UXhXFJLn0n59KzwOstTD4eGozQ3xecRAaJL1+lNYtjJBwBU4evqNoIdhlT4p7uR+/rRggi2kXXScrSevr5jG8MwRLvdRq/Xc3k7+qx6UBQwqddN36vyze6kbPuE99OrpvLILriwoSp69ljuzs5O5DTxOBoL3IRh+DUAvozkHxjn+QlN6HGi4+b3owALH8Cxz8Z5DMa9NqGHR3F9bJW6BTg+sKPXfBaw/bxfD85x87v13MT9TrJ5NrzH9pmvbar4VRkDg/CMvsN6hoZ5TGxddW8bXzviyrc5Kvqs9VL4yiMI9N1nw3xxdaJcYV/p3lrDPD7W+2I9NlpGXFjUtjXuu4IwXfZv9xTqdDoOwA6jyQ7FE5rQQyTrZdGJrH9A1EqMExT347Xx/T+h46NxvDD2HgU4vmsW9Pju43cgmstwEqSKj3XY29tzdfVtVEnyrcKx/6vngr/pNfXqqGeDwGCYYvXNNV8oit4XLcMHdGw77KaAJG1PHODT+3VhAT99oMd6n+g9CsPQJVEzn8XW07c5YZw3y/aTbtg3rI99ITEAyGazmJ6edsviCWD0XXHP+mgCbiY0oYdIPnCj7msrUHwJfVZYjwNubB0m9HBpHIAT94xv/HzKxUe6WdtJghsgyuv9fh97e3sIwzCSvOvLx4gDN3pN54bvmg0H8ZrtYzsXR5WrZybRm0CAYBW2zlMl33YPvgTwuLHndfXSWKDhu5/3at2mpqbcpojc8JDPMPGabbW8aetrAVVcmM3n1fF55bgcn8viufeOBVLjApwJuJnQhE4Z3Y+iHFbWhA5omHV9HHSc43a/7z9tNG5/3G/dj2LJH8e7ToKGtXFYne63vkd91/2GQ4eVNawO475r5A7Fx0lBEKwD2AWw8che+nBoHo9/G4C3RztGteGJMAwXHlVllCb8fqro7dAG4JTy+z1ev4G3Rz9P2nA6aJw2xPL7IwU3ABAEwVfCmO2SHxd6O7QBeHu047S34bTXbxyatOH00Glvx2mv3zg0acPpoAdtw+h1qhOa0IQmNKEJTWhCjxFNwM2EJjShCU1oQhN6W9FJgJtPn8A7j5veDm0A3h7tOO1tOO31G4cmbTg9dNrbcdrrNw5N2nA66IHa8MhzbiY0oQlNaEITmtCEHiZNwlITmtCEJjShCU3obUUTcDOhCU1oQhOa0ITeVvRIwU0QBD8YBMFrQRC8GQTBTz7Kd98vBUFwMQiCzwVB8HIQBC8FQfCP7l2fDYLgd4MgeOPe58xJ13UUBUGQDILgxSAIPnPv/yeDIPjSvfH4xeDg4LxTS0EQVIIg+KUgCF4NguCVIAi++7SOw+PI68CE308TTfj94dKE108XHTe/PzJwEwRBEsD/CuCHADwP4L8JguD5R/X+B6B9AP99GIbPA/gggP/7vXr/JIDPhmH4DIDP3vv/tNM/AvCK/P/TAP55GIZXAGwD+PETqdX49C8B/HYYhs8BeA8O2nLqxuEx5nVgwu+niSb8/nBpwuuni46X3+1ZHw/rD8B3A/gd+f+fAPgnj+r9x9iOXwPwcQCvATh779pZAK+ddN1G1PvCPeb4GIDPAAhwsPtjyjc+p+0PQBnAW7iXBC/XT904vF14/V7dJ/x+MvWf8Pujb8eE10+uDcfO748yLHUewC35//a9a48NBUFwGcD7AHwJwGIYhsv3floBsHhS9RqT/gWA/wEATzCbA7AThiHPlj/t4/EkgHUAP3vP/fpvgyAo4HSOw2PP68CE30+YJvz+CGnC6ydOx87vk4TiMSkIgiKA/wPAfxuGYU1/Cw9g5aldUx8EwV8FsBaG4Z+fdF0egFIA3g/gX4dh+D4cnNkUcVGe9nF4nGjC7ydOE35/RDTh9VNBx87vjxLc3AFwUf6/8P9v78+CY8uy60BwXYe7w0fAMT08vDGGjIzIZCaZkSSlJCmKKabYkkhRakk0mlgyWXY3zfKnukxlVm0lsuuj2trqQ/qRyI+ysk6TVGKb1KIoUizSaBqLpSRF0pRDMJmRkRlzxJvxMAMOn93htz+AdbDuxrnXHXiY3ou7zWDuuH7vuWfYZ5+1h7PPwbVLT0EQ5LDP/P88DMN/fXB5JQiCpYPflwCsXlT9xqAfAfBXgiC4A+BXsW++/GUAtSAIeDL8ZR+PBwAehGH41YP/fx37k+EyjsNTy+tAyu+XhFJ+PwdKef3S0Knz+3mCm68DeOkgijsP4G8C+O1zfP+JKAiCAMA/BvBmGIb/QH76bQBfPPj+Rez7ay8lhWH4i2EY3gjD8Dns9/v/EYbh3wLwnwD8zMFtl70NjwHcD4Lg5YNLXwDwXVzOcXgqeR1I+f2yUMrvZ08pr18eOhN+P+egoZ8E8A6A9wH8DxcdxDRmnf8M9k1hrwP4k4O/n8S+X/N3AbwL4H8HMHvRdR2zPZ8H8DsH318A8DUA7wH4VwAmL7p+I+r+GQDfOBiL/w3AzGUdh6eR1w/qnfL7JflL+f3M65zy+iX6O21+T49fSCmllFJKKaWUnilKA4pTSimllFJKKaVnilJwk1JKKaWUUkopPVOUgpuUUkoppZRSSumZohTcpJRSSimllFJKzxSl4CallFJKKaWUUnqmKAU3KaWUUkoppZTSM0UpuEkppZRSSimllJ4pSsFNSimllFJKKaX0TFEKblJKKaWUUkoppWeKUnCTUkoppZRSSik9U5SCm5RSSimllFJK6ZmiFNyklFJKKaWUUkrPFD0RuAmC4C8GQfB2EATvBUHwC6dVqZRSuoyU8ntKHyVK+T2lp5lOfCp4EAQT2D/i/icAPADwdQA/F4bhd0+veimldDko5feUPkqU8ntKTztln+DZPwXgvTAMPwCAIAh+FcBfBRDL/EEQhJlM6glL6fxoOBwiDMPgFIo6Nr/PzMyE169fP1IfUhAcVisMQ/cXBAE4T3zKxyiFxP5+3DJYDyWt60GfRq6Nehevxz2TVGf77Kj7k+6z98fdY8sIw9C1W585qXKYVKfj3BsEAYIgwPT0NGq1Gh48eIDNzc1z5/dMJhNms0+ynKSU0vGp3++vh2G44PvtSbjxOoD78v8DAH/a3hQEwZcAfOngO4rF4hO8MqWUjkftdvu0ijo2vy8tLeHXfu3XHKDZ29tDp9PBcDhELpfDxMREZNHsdrsYDAbIZrMoFosIggB7e3vY29sDEF2QFSQp6eK7t7fnXYj5/GAw8JYxHA7R6/UQhiEymYxb6PnJegIAFzQFAHyvlsc22LLCMES/33e/+9qwt7eHIAjcu/r9Pvr9fqQP2IeZTAYTExOunv1+P7af9vb2XD+wLIIFgsvBYODq12g0sLe3h36/j16vF3n/OMBkFCjyAS3fOLOfgyBALpdDNpvFT/3UT+Gnf/qn8dM//dMj6zEmjeR35fWJiQnMz8+f1rtTSmksWl5evhv325lD7TAMvwzgywAwMTFxempOSildQlJ+v3HjRvg7v/M7btEaDAZuUcxmsw7cEAz0+30MBgNMTExgcnLSgRsFQAfviHya97vrFtzYZxRAkDKZTASMkLjoA/sL/t7eXgRIxAEq/mYXabXgxIEs2x4CDoISLddnGRoOh7EgUMv23cN36XsIlAis4sCNb1x898ZZm7Svk+qdyWQwOTmJfD5/BCCeBymv5/P5VLandKnoScDNQwA35f8bB9dSSulZpGPze71ex3/4D//BLYTU+vf29o6Am729PQwGA2e5yWazDmhYEDLKSsCFdNTiPjEx4d4L7C/omUwmAij0eVpxWA/eT7JASa/76q0LOsvT98SVo88RTFkXn97nu6bWmSQgofepq9DXPwoq48j3rK0H35lULwCOf9T6dYpuslS+p/RU05OAm68DeCkIguexz/R/E8B/dSq1Simly0cn4ndaGQhg1BIDHLps1DqjYEMtE74FNY6sRcIHLOKsDRZ0KHhIimGJ+00Xbd89CpB8sUZ2kdf6+KwcfA9BAl04o+qm5ScR30nrCYDIO3yxJxbgDYdDb7v1f3vNjjsBEO+dmJgYCYiOQal8T+mpphODmzAMB0EQ/N8B/HsAEwD+SRiG3zm1mqWU0iWik/C7LmSqVQdBELGqUNPXBdJaXawFYZQVxAISCxZ8sS+0JvH9Wnd9v49Yd989XIR9AdRaB63LYDCIxNBYsKZl6ju13+yzPgAW5wKyMThAMmg5CcVZmI7rlsrlcsjn88jn86cGblL5ntLTTk8UcxOG4b8B8G9OqS4ppXSp6ST8rsGxcTEy+kmyC7mlUZaIpGv6v7WA8H+1oMRZSbTuWsckd5O2L6kNWg/9Xy0eQRAcsVbod/6mz1rr0bgWHdsGBT9JFAcItf0+648N5LaWO3VdnZHlJpXvKT3VlO7dSymlMybd8RRHXKiTLBT8f1SQrH7yu88FZcuxcT1q8fC5pfReXVR9wbjWPaTttPcHQYDJyUlMTEy4+CPd1dRqtdDr9ZzlwlrCWEcNVLagxEdJAE3H0AcgfM+yXr57fS4pghhthwWWBJK8hwHovV4P3W53LJdlSil9FCgFNymldIZkwcao4FX+rmDIavjjvNPurrKAiYu1LrRcTHW3lAVF1vKh8UGsvw1ojqu3AocgCBx4yeVyzt3Crc57e3vo9XrY29tz4GZiYsJZK3zB09rnvi3tvMfnyuP9CgTVHWSDgNkG7WP7LkvWykTXZdI4++rEoHBu308ppZRScJNSSudCcfEVcW4pEq0d1gowjjvLWgH4fgU+tnwbBwTEWz3su20bxwU5umAHwX4+m6mpKRSLReTzeeRyOQdquIj3ej3vQh8HJqyVyPc7Y5niLC7aTl9wr8bL+NyKcUCIwCZud1uSu2mU2zOllD6qlIKblFI6Q9Jt4BrXoguZDRxWd042mz2ycI1KHudzLVmixs8AYq2ruqdskj77fl+d7PbwODCgAcH8HA6HKBaLuHnzJmq1GkqlEgqFAnq9Hra3t9HtdgEc5trpdrte6xL7Utul7VFLjo6Dz8VmY3p8QGKUZS4pbmowGES2/etvdnv8KJCTUkop7VMKblJK6YxplMWD9+guKH0mDpzElaX3jIrBsKDFV9e494+ykviAjb5LLUK6iGezWRQKBZTLZZRKJRSLRXS73UicTS6Xc+XRvearjw+EEEAmuct8Acy+NlqXnm88kixBNt4oLtjZ9pcvDioFNymldEgpuEkppXMiXYh1MVNriXUbMWDUljPKbWJdUhaM+CxBcW4R/qbZh1metdIo+X7z1YWgbmJiIgJuisUiqtUqpqamnIWm0+mgVquh2Wyi0WhgZ2cH/X7fHWcBJCfRi/ttVKB2nOXEZ5GyFAdYfKDH9qsPGGob2Gc23UBKKX3UKQU3KaV0hhRnpVEA4ouT4QLGeAzg6ALsK9vGoOi1OJeHtdyou0h/6/f7EeuCLsK+dzDLsm2b9oMSA4R5/MTk5CTK5TKmpqbQ6XTQ7XYxMTGBcrmMcrnssvL2+333nPanb2u773gIX7/6XIW+QGL7fZzdSkl9on3u2z3HdwyHQ8cb3E2WApuUUjqkFNyklNIFUNJCb2NISBpLM6676ThbgxXc2BgQ3cVDIAEcLsbc6aRbptVV5HMPWUCkW8X1OAr+sT7M6aLnWmm8kA/c6H2++BSbUyapj5QUjFirzrjBzezruB1WcfVhO/T4jhTgpJTSPqXgJqWUzoFsDhMfsPEBEp/bYlzQ4gMQCjbiAIfdhcSFN5fLIQgCVCoVFAoF9wxPO+dW5F6vB8Cf+8W+S/8YRwPsH07Z7XbR7XZd2dxRNDExgUKh4IKtrSXMt6VaA4Gtm4htTgrY1T7UfooDUkntj8uT47M0JcU8aaA6TypPwU1KKe1TCm5SSumcKcm1dJxYmqT79H5+V9BiF/m47wpumFSvUCigVCodidVRd1rce5Pa7AvetVYZ4NAd5tuVFdfmuLgX/d8HDHyWHB8w9JWtFhqtuwVGSYHNx7HQpTE3KaV0SCm4SSmlM6K4eBv9PWnRt9dG/W6v+Sw3vnfrwqsWJgUL+Xwes7OzKBQKuH79OmZnZ53VZjAY4NGjR9jZ2UGj0UCv1zviIiLF7WgKw2hAMRP4heH+idfWPUXiM1qOL5cQLRz5fN7ljaHlRw83ZVJCdVMlbb+2bbLvBw7diXFJ/pJ+Bw55Rs+2UkCWJvFLKaWjlIKblFI6Q7LgJCl+YpTVBhid4yYJ2FjrQZKlwsamZLNZVKtVlMtlLC4uYmFhwS2svV4P7XbbLbLZbNbFf/hAlY1vse/RwGLgMP7GBs1q4LNtn+0zjSXiOxiISyLI0bLj+nqU+yoOxPmS/AHRJICsi01u6CuX1i4CvxTcpJTSPqXgZkzyaWn2f58A9GneKjh9C0CSmVq101Hv9pnURwVMjqo7f0tpPNJEbfzfupeSgAjJB2qSrDpJVpu4++yWc0u0pnCL9mAwQLvddon3qtWqOxYhDA+DjH1gJm5rtc4RxtaUSiXkcjn0+333m7rCGIfD5+NiVDKZDIrFIorFIsrlMqrVKobDIer1Onq9HprNJnZ2diLzzOcm84EOW/c4eaFlkHwuqlE5e+zcP4uDM1MaTUlza5y8S0nPJMn3OMUkpUNKwc0YFOezHyWcfb58Cjbf1lQr9OPcGtTqstlsrBbue39cnSz5wJfuVEkn0XjEfqRWDcCNmY57UkCxkt6nn/a7vTZKMMYt5EqaWK9Wq2Fubg6dTsfVaXZ2FpOTk9jb28PKygoAuK3ajI/xgXPrXmG/EEhVKhXMzs6i3W67IxfYpyzfAhzLq+zTiYkJ1Go1zM7OYm5uDtevX8dwOMTjx4/RaDSwvLyMZrMZsRL55oyCE18f+xYo37xLUpTYFl8Mj/7O8jTgO6WzJd/YH3d9GCWz7Vz0WTlTSqYU3IxJcWBjlFY8zm++CZAETPR/Wy8rTE+TUsF5clKNfxyAqc/ovUlAZVz+THKb+ISv5UFaVQjS6UKiu8cX85JUX73HHnCprifl97g8PnEWK9aZh3IWi0UXFF0oFDAYDNxJ5DZhoa/vfAuTr399948DdEZp5ukCd7Fkx5fflZKsd77yRrk6fUqyfT7li0NKwc0BjeNft2jaFyypPnM1F+v9PuuLJh/z7Q7hosHPcf90B0uc5cY3KXwLw6h+Suko+RZgkm+bt89VZf9P0v5874773weEWQcLHvTAym6362JsaC3QHUH5fB7D4dC5rOw72Ha+S2NFOF8IZnh+FLeXB8H+tudOp+OSCvI+a9nSQNxKpYLJyUnMzs5ifn4eMzMzmJqacvXM5/NotVqoVqvodDrY3d3FYDCIWFnVmhPnrvL9P2qu6TWf1S5pSz3dfulcPD+yFhaV03FzVp/lc1qWPqvg3WfJ1fdrvinmS6KV+KNO8XboAwqC4J8EQbAaBMEbcm02CIL/GATBuwefM2dbzfOhJORsQQMQ1RR9ixEBju9ZDXJUYc7f1Nyuk8cKf1s3Mjn/VNPVusVppXELptWen1U6D36P63vf/6PuS3rHKNKxjONpXw4ZuoR0i7gKbd2mncRrvi3kNqsx36dHDoTh4Q4qkpZl6x8E+7ukGL9TLpdRLBYxOTmJfD7vrDiFQsFd47zx9cW4dBxLmu0rfdcoN2VcuePQR0m+nyYlKZSAf22wFlDKU1o+7Rpg1wG7HrAstZ4+y7L5uDQS3AD4pwD+orn2CwB+NwzDlwD87sH/zwRZJiVZQQ8gAiCUKWmxyeVyyGQyLpbAAhUFHr4J4AMovrJ4PwEVFwPe59M0fe3V92m7fcL3GaZ/ilPid99iZa0L+t1abXwAI+maHZ+ka9TwbMyX5T8V1rTcdDodFwOjpLlwuG1ZedXWX2NsaMXM5/PuT8+LUhA2qi9Iag1lnRjLw6MLbHsVXOlvvjE9Tn+PO1YWTCpv6P3KKz55cAz6p/gIyffTIB75of1uZSjBisrUuHhFldnWOqg7By2AIcinJbPdbrug+5TGcEuFYfj7QRA8Zy7/VQCfP/j+KwC+AuDvnmbFLpLihBl/458NYNTFK5/PI5vNRjKHKnPbDKR2GyrvsZqAAhbN6EqhTxO/gq5R1hafVcknfD8KdNr8bhcomo7tYiXvj/ARcHTr9nHHxHef8hHfYfmTfMT6drtdZLNZNBoNNBoNZ/UAjp4LRauKHgaqfKbmcwU3pVIpYkWh25Z15P1xmqzte/YjY20mJydRKBQiriy2ncAnn8+75/W94/ax1nNcsm4JfSfHhfzD39kPBJInUTw+ivL9SYgy1lotfVZtC2L1N50HCvD1NwBeQMT71aVLXmfgf2rBOXnMzWIYhssH3x8DWIy7MQiCLwH40sH3E77ufMgy4GmUp5/AeIGDvvt8v/vq6yvP3mOfHdXuuHd9hOhE/M5FMo6slYHX7CKn9/l4apwFLe4efUecRYGf1p9PgK/CnWCE28Hj2uNTGGhd0Vw31noZBwbjeJO/qUVJLUvaFt+z9p2+a7acUXM6SR7oe+y9ACIAx957ijQWvyuvj0oj8CzRKGWR5ONV+79v7OL42cphrcc4SuxHjZ44oDgMwzAIgtjZFYbhlwF8GQAmJiYurfqfJHDiAEQSkCC61lgBXzZV1Q5VSwUOY2/4LD+tlcUyOXDoMuM1W65va65v+6H9Po5gfpbpOPxeqVRCuX5k4bOxFHGuJs87Ip/2u+9ee833rHUh2d86nY4DHoVCAYVCwQXltlot9Pt9ZxmxlhvyrQbNs0/oJuJWc1pvGBeTy+WcZsogX2YapkmfZdi6h2HoAoqr1SoqlQoqlUokSaDlY1+ckc7lJIvOuAtT3P0K+Ox4qWzR+XiWxy4k8bvyej6fv7Sy/bRJZat1YSr/+eaz3gscyn1fOSQLaBRI0nqfBLg/qnRScLMSBMFSGIbLQRAsAVg9zUpdJPmEi/7v00TjyokL/vJpY2RwCyKs6wCIT/LHe1gvH8K398Rp6bbdtm1Jvz+D9ET87gv8TNLqkgRVEtg5yTX7uw9UEZQouND4mDA8zEtDPh4noNhaVVge3VEsj2VqEL4vWN7OUb4zk8lE4ngYMMwyk/pZy1fZkDQHkkBMksyIu98HjJPqcwr0zMr3JyWfLFVS3rNA3idrFST5ABLfYQFO3BrAOLaUTg5ufhvAFwH8vYPP3zq1Gl0SslYNTQVvmZf3630qeKi1hmEYiTMg2fgbZW6fP92a9tU1QOIkVI1Tn+e96j/2tcc3iT6CGsKp8DvH1woy/d1nUfGVY7+PAjPjPGO1TiVuBacVxQKJbDbrrCzqTopri76L+WfUJeVbNLhLKwxDL3jSeanfaelhfhvGJHBu6lZvTeDH5+1i4WuLT3lQsgqFr7ykeWUVqjOeg8+8fH8SSlJofXI9SRlleT4A7fv0KSBJytBHmUaCmyAI/gX2g8vmgyB4AOB/xD7T/1oQBD8P4C6Anz3LSp43WaaxACVud4ZGs9PEr9lZ7aF/qoEChwifApyapj7r01hZR59WYQU2gCMxB+oiUFKzvdbxWaaz4ncVWnEBxXGWk6QyxwE3SYAp7v0+cNNut50rSPkQ2DePMwiYQEX5Jm5xpluKFhUG4vvAAoPz7byy9dadaQRChULBHb3AIOhMJoN+vx9RSPRwTu0HO1dVNmi7LI1r1UkiO45xZZxkgfsoyvcnJcvXvr7Xe3y7anmdZ7P5XJ3W+m7fpzzOT1pBPyIW9UQaZ7fUz8X89IVTrsulJitglOmStFKSD3zEkc/fbstXq4rdehtXhgp7u8DoX5Jmqr89ixPoPPk9DnyMq8n7wJGv/Lh3Jf1m+UCtGDbvBu+3AcA2tYAFNvZ9lgf1faOAX9Iiwy3eGqSsz+mp4L4dWEn9Gtdfes3OtaR+5rU4SgJHJwE3qXw/Pvl4w44xyQeGeS2O33zlJY2tnTsp7VOaofiArJUGOLrlDojmsaHvXi0y6nbifSSfm8cHGghUaAHSe7XcQqGAiYkJFItF5PP5yEGGmhSK7+z1ekdOa9YMl3ZrO7+zfrQ6MKDRFyOUkp98mplPI7e/+ci30Mc9m/R73KJqLXU6B/r9vstQDCASv8Kt1nQB8U9jZbQ/rMBXfuJ7WMderxcBOuxPaxnVPuE8KhQKLqCYViUGIg8GA3S7XbRaLbTbbbRarciZWUn971vQfEpDklXHjoVu9U4aV1+dnnWr6mUg7WefdYbXeS/vtxYWWw4/x3Vl6TuPawn8qFAKbjzkE1QUzhYEWQFlwYidAKOEpi03Lt9JEAROG2X2VS4IACJaqrqgCJj0lGqbVZZ18WkU+n7bRymNpjhAE2eR8D1/XAtN0jtJPm2T3zUwkgGLBAbqorLJxuIAs22LAhsb/6I7onQukj8VQNi+UauNBjzr4qOgjeA/zk0b169xFhr7nAU6SZaepPf6KMmqldLpko9vAX98DD/JUzYvjcp7y0dxVhgdY5vPzBcj9lGlFNyMQRZRkyFtgjHVQsvlstNquUWVKesVhPB5zcNRqVSQzWbdmToqtDgpNJsqP/kOCmsCFm4X5Hk8dvKxfTY3iQIzjUuwv6UUT3bBUUuYD+jGLVDWShP33fcZd81XvgWrai0hj/J+8urExAS63S4Gg0EEaHO7eKfTcdmMbcyY1p9AXcE4s8GS5wmakhZxLhqM4ymXy6hWq6hWqygUCu7sKwIaZnn1HfUARNM7HJeSFikf+YBSErGtGmyd0tmSz9JiATMVXStP9Sw28jPTGljwzjLUbarzxSbdHDUvPmqUgpsxSZE1gMgWVTKrIvGZmRlUq9UI03MBaDQaR+JfuJMjn89jcXERhUIBjUYDu7u7kdgG3eqniwJROwW2Mj0Xi2636wIoOcm0bZyoPuuTL3YipfFIBZI1Wdv7xiknyeozLrCJsw7Y55T3yPPktYmJCQfEM5kMut1uJM8NA4zVbWutg/o+8iWD6JXPFdyoNcnXBt6bz+dRKpVQqVQwPT2NqakpN8c4FzlfCHKslUhJLTrj8P84Wvg4zySNG9tLEJgubmdPKlstL9ASSP61qQsIZJgxO5vNolwuI5vNRp7tdrsYDofodDpOESbI57zR98flbPooUwpuhCyAUdOjL9eGXWwofO0haEosi9lr8/l8xC2kCZosqNDrw+HQRdnrYYZWkOoiwffH+fPtn9bBJiDU51I6PiVZaPR7HBgaF8QkXRt3gbbvJq/Z0+bJJ7qtW62KJDvH9LrOHcvHcQs7gZats87buGd9PK+/q4vY3mPBvvZrHEiJs8iM44qKu4d9kLojzo/iZKi6X7kWcNfr5OQkpqenHegmqCmVSkfSgVC2Mwas1+uh0WhgMBig1Wo5hUETS/pcvx9lSsHNAakwVSahW4fmd/rlgcPgYdLk5CTK5bI7X4fCULXMTCbjsrBmMhnUajXk83k0Gg3U63VnYiZyJ6IHDl0BExMTaLfb2NzcjLitdFKRhsOhQ/400+sk9G1r58TRvCPFYhETExOR7bjUJFIaTT4rjc+aY3/zAZwki43vnaPAjtbBV4bNgNvpdFCv11Eul931bDaLMAydBbLf77ucMgxyJ//yfbYO2WzWZSX2gWlrcQzDw63bVA5U4OdyOZTL5SNzUc381krD+2ghVa3Y1zdx8TNx5ANHcaDF9lEcQAMQOcMupfMhn0LIP8rMXC6H+fl5XL16FdVqFR//+McxPT2NxcVFXL161eWHsi5XgpXd3V1nwX/w4AFarRbu3buH1dVV1Ot1LC8vo9vtol6vo9PpuJQM4wQkP+uUgpsDUquNFShkPO6ysMBANVc13SuTAocBxRroW6lUUCgUsLe3h2az6XIfxAUfq1bLxGoU1ARiytjqxqJJVOukQt9nJVCLEhcwDSxNWlxTOkpxICTJihJnXbBl2nJsPFjS/XHj5xtfPRVZARp5xWYX9s0rH1nLp5IGTaoSorxs+8vGxVkwGKfpJvW13hPncrK/WRBr73sSYtkak5HS8ShufMZ9TuW8zgPuHJyensb8/DxqtRpu3bqF2dlZXL9+HdevX3dWfOV90nA4xM7ODhqNBnZ2dpwSrIkst7a2nHxnnKXWjXQct+hJ+uIy0jMBbmwwl5K6eqil+Xzm6gKyu6CY7IvmeDKXxiJkMhn0ej3s7u66GBrdcprJZDA1NYVSqeSOpy8Wi5iensbMzIwL+mW8ALC//bVerzvtVC03jBHwCWG7GPqC3ayGYPuMz6j7YWJiAqVSCVNTU5F3bW9vY2dnJ1aw2gVXBbEuZEnxKE8zWUvgcZ4hr+o4WfcpcMjbNH/ncjlMTU1FTo4fDAZoNpvo9/tot9tuO7dmBI5zGQFwvKwBk7QK8jp5fzAYoFKpoN1uo9FoeK1PCqz5LIX91NQUJicn3bu1HMYmaL/6AODk5KQ7p4qZkweDgYtjUOCvMWu6k1DnjG/nYhx48Vle9FklO091jPV+W5buFuPYppbUZNLFX/mPMlH7PGku+Fz/e3t7mJ+fx7Vr11CtVvHKK69gdnYW8/PzWFxcRLFYxNWrV10cWLFYjKw7dsdqEAQoFouRNAu9Xg83btxAvV7H+vo63nvvPezu7uKdd97B8vIy2u026vV6BOjqSfc+AKyJZ2mt9PHf00bPBLgBotukdfCovel21rg4FgpXMpQmWaLJl7spVNjx2V6vh3a7jSAInEDVtPWzs7Mol8tucclms5iamsLS0pLb8trv99HpdJxrant72y0idBXR+mIFvJL6YHVCa44auzBoW1gG76dlaGpqCgsLC85FwGd3dnZGjo8u1OxD31bKZwnYkNQyFkfWIqHgRvnNbh1V4arHDFy7dg3lctmV3+12sbq6ina7jcFg4NygKmBJlif4fuV/BTfKa4VCAcPhEOVyGZ1OB9vb224+2bLte7LZLAqFAqrVKkqlkqvPxMQENjY2HC/qriYFSDon8/m8c40puGm325Ft3xwbto/gRrel+wB3nOVG+9DytA/Y+L77LFd2bLTMfr+PVquVgpsEIp8DR3ckqfLhU35tORaIkm+mpqbwyiuvYGFhAX/uz/053Lx5E9PT06jVahGrpPKqWvbtuNNNGwQBrl69Gpnza2trePvtt7G9vY1yuYxKpYKVlRVsb29HwhV8KRl8Flfrin1aQQ3pmQA3Kih9jKmxMRaR2kHURURRfdyfvl8FJa0qyjCNRgPAvkb54osvIp/Po9Pp4MGDB9je3sb29razDFHDtABGJ4QFJD5hqxM6afFSIWz7UNvb6/XQbDbdll+6IDhByuUyMpkM2u02Op1O5L2+d9r3fFTJZ32z30ctcApU6MsnQFDgHgQBWq2Wi0XxvV/LJKlLVgGGggy+R1Mb2B1StnwfuFVg7WuvT5HRMtkH5NG4Leh27mobff3h65dxLDi+PvYpWPZ+HxjyXUvdUqOJ/UQga63YvAeIWsWscqGWNoLomZkZFAoFvPTSS3jxxRcxMzPjrI96oKwNfAeSY6+0Dna+FAoFB5pu3ryJMAxRKBTQbDbRbDaxvb3trLPjzBt937OwtfypBjccELUCaMAiQUa323VCl3EjACJbWXUxpu9SBXdSAC6FvQoYnp7MuvR6PTx48ACZTAZ/9s/+Wfy1v/bX0Gg08Ju/+Zt47733IhoxJxO3qLIM1qnf70f6QRG/1pG/2cM8tQ3sF15XxragKgz3XVCtVgu1Ws0FyT1+/BgTExOoVqt4+eWXUSwW8cEHH+DBgwcRoaBbI+ku4aKjgaCjNKenieKAcNy9vs+4xV/LZj/SBF0oFDAzM4PZ2VmnLXIekM/pZm02m0fM0QoG+E6mGGD+JbpXwzCMJO8rlUoIgn2TeqlUiuTx4FwBDnnT8u/e3p4rl/foNme1IFnSPuAW8Eql4iw3ap1hP+p2cGZC9gGvuHgGXosDJUkAh2X7xjgJ2FjQR2UotdzEk/Kenv3HIHXfcQgEx2q91+B4un//wl/4C3jppZdw+/ZtfOITn3AKXxAchihYRaHX6zmXU9zuWuUNtfAEQYBqtYoXX3wRe3t7WFpawu7uLu7cuYOvfe1r2NzcxB/+4R/i3r177lkFwewPVTzUPcb6P82njD/V4EYpySJgXQLWcqPbVXUx9gXexgk2+6fmcZbNyREEAa5cuYJsNotWq4WVlZUjCwvf79uxoVqFtjmufvxdF0SdKNoG+5xtJ2NwCoVCZFEjSKpUKiiXy+7kZV8fxWkiPs33o0LWUhNnuRllsdC+4yKvgbWFQgEAXA4a3/t9iy3gt25oMLu6zdSi57OOxllwVGEZDAaRgORx+lDBuQY2+0C+PqftGQ6HI83ytv76/qS66Xc7F0aVoaS8YJWDlOLJymkgqmDxHl0zNMGelpHJZFySyGvXruGFF17AjRs3cOPGDQBAs9mM5Bzz1QUYrczF8T+Vc9Zlenoa/X4fN27cwOTkZATU+5SsUXLkaZfDTzW4Ueaw+VsogAG4YCpeZ0wCGVrPXFLhrUJ8lKCzjGJ3idB8mclk8OGHH+Kf/bN/hk6ng0ePHh1hJk4s3wIHRE/rtnXQCcqyfLEOBDUaTGbPzwIQOZVc+7vVauHtt9/G5OQkNjY2nDut1WoBgAuitkGY/LNB27w2rnB/2kiFirWI6KfvGZLPJan3EkAz8LbZbLqYE/JMLpfD3t4eyuUyZmZm0Gq1nOVG32EBMS0cQXCYDZcWEPI630FQQbcY68Cge1t/xsbUajVcuXLF5QXhvKbFVU8O1+MUaJFh/SgL+FypVMLs7CxyuZx7t84hXy4bHSdrDUmystjxHYf0GV049X12Xlh5oekjUoonn6zWOBtVLvU3KnYcG7qCXn31VczPz+PTn/40bt++jVqt5sqhSyoIAmeBJ/F3C2ytfLbWHv3k3FDr0NLSEj772c9ic3MTOzs7uHLlClZXV3H37l3s7e05oB+X0oOpQvSMxKeVnlpwo4zgAzc0W1MokjkZlMscHb64Fg2iVRN1Ul2sq4cWDRW61KLv3r2Ld955x2vF8GkWVuDaoDP7fsuQFtxQs/WBDNVaNBhbd8OEYYhWq4X3338/InQZKM0FUK1hdqxUW7LtetYEdJwlJul/Hxjy8YZ9hgJrYmLCHQbJIF8K5Vwuh1Kp5MZIsw5z/HXsgGg8mcba2NgYlkclgkcwML6Hbh+ttwYRz8/POxDE8tQSRECjwIbB+LQgKXCni25qagr5fN71j7Y3KY+NdVOwTkngIw6g6xyIA7N23H3l+ZQablx4mhejsybf/LGgxoJ7XldwwzxM165dw4/+6I9ifn4eL7/8Mubn5yMxZrSM9no9F3jPsgnSbf18Gw8s+OGcI/+zPABYXFxErVZDvV7H9vY2ZmZm8O1vfxsffvghBoOBA1y+gGoqA8Ph0AFlGyf0NNFTC26A+ORXin6VSYlKuWtCQZBlqjhBMy7ZSHjgMDbIZz62QMa20Qota+2x99mFSa/pYskFQdvo6wcLIH3a5GAwwM7ODjqdDjqdTqKg1YmqdX7WycdLce32ARtLvvGnRYVClbv/6N8HELGAUKjxOS78+m6WzfJpuSFPc5cWaW9vL5Jt2B5XwvrawGMF4Nb9qsBL3aHaX4wXYPtoLdXEm1Q47PELSbsPfYBEx8cHcHzj5OMB28dx9+nvVuFKA4pHk/aXWkYUzPOauuu5VpCfr169iunpady6dQtXrlzBzMyMs8LwfpWXChCUPwiWdNeezVfGucE62zr6+IqbCa5cuYJ+v4+NjQ3Mz8+7ZJo2JMH2kVosfXL+aaGR4CYIgpsA/r8AFgGEAL4chuEvB0EwC+BfAngOwB0APxuG4dbZVfVIvY5cUyBh3S4TExMuOIxbWHWLtZbrs2JwgOM0KCVlXA3O7Xa77po9iNKnVaiGaIUumZgTgBNPt/xZczWfVxeUbg3XAwstqvcFvOkhimG4bwW7c+fOEY1H66wCQOvps0hdBJ0Fv7ONce2zC5sP8PnAje0zjhctaDs7O5Hs0uSNYrHo3sMzoBTs63ywfEl+Ylp43l+tVlGr1bwB/LTgkEfYFwRbtO7QWgr4g41pnaGriUkvlS+z2Syq1WrkPKlcLodGo+HcnsPh/pk93FWyu7uLer3utlLHuaKtbLCUtBD4AI0FrjoXfMCJ7+CnXYhOcrbUZZXvZ0VqYSRf+XbT8j491obxNZ/73Ofw8ssv4+WXX8arr77qLDnkX8bZ6Lwj8Ge5nEM8a1AVBu5qzGazmJ6eRrVaBYBEC4ryDg+f/cxnPuM2eHCL+J07d1xOMs2BpSBMrUOXQSaflMap+QDAfxeG4ScBfA7Afx0EwScB/AKA3w3D8CUAv3vw/4WTWhrsn2ZM1ch4nzBLEhIW5PiesVYJwJ+/JOl5vkvfq2QDJ6k98+RjZsjUc37s2T1azzhLkE9btH3L9rXbbbRardgEg9o+H6C7BHRm/O5rqw/E+O73AZu4xVeBre7+Ia+oxYYBh3FBvz5rBcGxWjp8IFv/dO7ZNiiIsZqpClfVOn1ZjIHD41L4x4VHY+i0LIJBCwzG4V3f91HP2nJGfff9H0cndOc+VfL9NEiVL50zvns0ZpFHG8zNzeHq1auYn593GyhsJmz9BI66MqkkcMch/1qtlnMndzodB3h87rK49Yrzr1qtYmZmxv1NT09HdgtreMCzSCMtN2EYLgNYPvi+GwTBmwCuA/irAD5/cNuvAPgKgL97JrX0kA6uTQOvjEsBp4CGZmgfgACijBIHZCzZspRxrHbNSWOfU5Bgy6WVRGMLaInidtcgCI60VZN72cRle3t72N3dddo04xzG2d1iBYK2m8/QGhCn5dr/rXC4CDorfvctWHELZBwA8glPW65a75goktpgJpNxeW8oXPv9Pkqlkht/Hx/zXZxnnU4Hu7u7mJ6eRrfbjVhG+OzExASmp6cRBAHq9TpqtRqy2axLSkn+y+fz7n5rZdU5xPfStG4D4TUgslaruczffK/mYgqCILKtlyBQNXi1ptjx8YEqdWvrOCXJjqTF0FIS0NL6Hpcuq3w/bbJ9rbFhqogRYNst47Ozs3juuecwNzeHV199FZ/5zGdQLpedjFVwr+lI1LXKxJm9Xg/r6+tYX1+PKLxMsUD+n5iYQKPRcIdtTk1NOeVErexsj+U3Av1bt27hC1/4AlZWVrCxsYF6vR4BbSrvGZdKpWfc9e8y0rFiboIgeA7AqwC+CmDxYGIAwGPsmzXPlZRhaepWcKP+dwBHjlBgGaMWcv30UdL9Wr5qpnGauM+CRIFPYb63txeJK6AgJ+ABDjXTXq/nzJ/tdjuSI4cuBLqtNM+ML2jZ9oMPmGncRNLWVLbVd93XPxdBp8HvdqEbBXJ8n3Gkz1reB/ZN3wQ3mUzGnUacyWTQarXQaDTQ6XRQKBScUNV3q4aoQLbb7aLZbLpMv3SDMViZgpkJHaemptyRHRq0CeBIjAHvoQBXaw1z66iLz+bhyOVyqFarmJqacu4pnT+6LdyCG6u92yD9pLmrY2KvWTrOYqHlxb3fZ3k4CV02+X5axDmhCqXlbQWnCjgI6AuFAm7evImrV6/ipZdewssvvxyxjNpzoYBoLh3K7t3dXTSbTaysrODhw4cA4AA+jwUh/zNWrNPpOOsQLZEW3Ph4kO+9evUqJicn8eDBA/z+7/8+crmcS+mhllMNV9CQg6d1g8fY4CYIggqA3wDw34ZhWDedGQZB4JXEQRB8CcCXDr4/WW0NWRO2vaYLCy0JccHDcXXzXWd5o9qjk8jW2dbRlmvLVuChbiYNxLTvJMDTLbrWIqDWJZ/myfJ82mqc+TyuzVq+b7z0nRdNp8Hvvt0QcfQEmjeAaPoACiTu0Gi32+57qVSKgJBisYhiseiOCLFWxoM2HbEs2tgcPdOKuzG4Q6pYLLpjR+gq1XlolZQwPEyQRoFr22bnh7pobVZYzgH7HrrW1EWtbR93fvuUG/0tDuAmyYVxyIKuJ1mETsLvyutPy44a3zxTxdJa6hg3s7CwgOeeew5Xrlxxlk4g2m6fnAPgNli0Wi2srq66OC+CC41/ZLmDwQATExPodDrOwsRwAxKVgjhiuxh3NzU1hWvXruH555/H5uYmHj9+7ACcbbdt09NIY4GbIAhy2Gf8fx6G4b8+uLwSBMFSGIbLQRAsAVj1PRuG4ZcBfBkAJiYmTq2nbPAwkaeNLyBiZhChZg4+qJ+2M/IO/f+42pkyigpmyywEFTaegNdUEIdh6BYlLkxE8j5ww4nHfuGiQeIix/7TADu1vgTB4TZ2m8BN+8GCINtmBVzWdHuZNITT4vdKpeIGW3dkWC3cxmb5BIrtR3uvLnAc652dHWSzWWxtbWFnZweVSgWzs7Muey+tJjMzM84ETmBid4go0ZSv6QEoPMlTbFOhUHCHB9KFyvPTOp1OpC3qYmIuKt5jNUsF7QAcmCkWi6hUKqhWq+5MKeU38q/GPPBP427sXI2b6xYE+RZOH8DR73rNp9jwXlVAdG5pXU+yGJ2U35XX8/n8pV0FLTC3skvTYXDu0PXJc6FeeeUV/OiP/ijm5uYwNzcXsYYCh7yrfMn5vr29jXv37qHZbOLu3btoNpuRXVBck/RcM8rd4XCIRqPhFBCmM6jVapicnES1Wj2i3PI7+aVUKjlr6p/+038aV65cwbe+9S2sra0dSc2gQO+0jRHnTSMDioP9Fv5jAG+GYfgP5KffBvDFg+9fBPBbp1+9ZPIJEl+QlC4gcTshxqGTDva4Wtk4Vh1OHhscrM/HgS91XbCvNEOz1dCVTtr2pOeS3ndRdNr8HrdQ+a4dx3ozjlVBXbD2tGuCEAYXa+B53A4Ju7BaM7+a5q3l0FobraavZSpA1/6yi7jeZ9+jQcoa2MxndTOBuiFsfeL6PG4M7W9xdY97dlR5SeWehC6zfD8Lips3VpEl35RKJVSrVQdypqenj+wysnxKoqLR6XTQaDScG7jb7UYUSJ8lVJVLur/4rKYuGGfsNURjZmYGV65ccXE8evTOKP5/2mgcy82PAPjbAL4dBMGfHFz7fwL4ewB+LQiCnwdwF8DPnkkNY8iicSYNK5fLEWuNmt34HBC/mCQBkThtzscEapHRQF1rZVEmtkct6AJC7aJUKqFSqbjsrzyXRPN4cHKGYehynVizO7VZRtBz6zC1dboErMlW3QTMvqn9ohaiOIsS71OLldWmLhDwnBm/64LM//X6qGetKdxnKVMtjv3aarWwtbXlAA+ASHbU+fl5TE5OotFoOGvG1tZWJGu3DR5nGzQQV7VfbomlIAfgtn4Xi8WIBZUaK11EtKqwfMaFaf4eWlpYLs+x4u6VSqXizPh0mdFVx0WCC4Xd+eUbIzsWqt3GWWviaNSzce/V33mPAkL7+5h0KeX7aZKOq1oiNc2FymJu+S6VSvjUpz6F5557Di+99BKuXr3qjjSwoIY8RAv3YDDA1tYWOp0OHj58iPv377sAesbYcJ1qt9uRBJRBEERcqpxTjJlk/A0AF1NmZab+z7gdAPj4xz+OW7duod/v4969e9jZ2cH9+/ePpFXQMIjLpHweh8bZLfUHAOJa94XTrc74pIsuhSmFG4Aj25F94EY/k94Tt/iMMkfTlM4MxbrgW8GkQW1aN17jpCSoYcp6xtFwgtlU8lxkVGCrlaZYLB5xRcRp71pPdWVZTVitTNp3ViBY7V8F0EXRWfG7XTBPonn7gLWCGYJFu+ODmiNjXQC43RB7e3tuzlSrVacc7OzsROpnF08FplwkfCBYD7gkIOYCwPI127E+OzEx4bRUBkDyN/7PPmCwNI960LgbunJ1B6ECKt1B6OtrJR1DC1J07sZdi+OJpDHXPo+z6J6ULqt8P03SPlLZF2fF5xl5U1NTuHXrFl5++WXcunXLnfxtA5CBQwWbPER30u7uLjY3N7G+vo6JiQnMzs5icnIywvPkb006ad3BmpuNuwYpt+lCA/xgRNeg69evI5vN4t69e7h27RpyuRweP34cCdJn/SnDn9bt4k91hmLgaIAtB4Q7NbgrRAVsnLAmxQmLcYSREieAbme1OzA4GXTS8VleV2YniOFOEtbJMqEuFDaHB8ESJwr9rsVi0b3XBlla87/2o2qOdkLa/rT97gtgfZYozsVjybcAWs1eF1Zr7Uri2X6/78BNs9mMLPzcdjocDl3iOwBulxVwdNeQ1kF325FHyKsMhLSC1/IqAY4mMuP2WnWpESipiV6FsLqk7B/fxZ1dagnyBVP6LCjHmfvjWHNOYvVJqsu4vPZRI/IqXTMKzJXIn/l83qURWFpawq1btzA/P+91pVpr6nA4RKvVQrfbdckheewB36v8rC4mtbir0koZT8DPtAsTExNuPtNCCRzygcpl/S0IAkxPT+O5555DqVTCvXv33Hl0JJbxTFtuLjsFweGhYUSy2WwW8/PzKJVKePToEba2tiJmP0XeSQGwStYsP4r0fg3itbE/fBfjBSjorcWGiD4MQ5cBlgFsXKx0waBmSzO8WlXU7cTMrAyeGw6HLheD9hODszXwmEBLLTUUHlygVNuMs27pp/bR007jCoVx26oAGYhuWSX5tKxWq4X19XXs7e1hc3MTmcz+9uzJyUlMTk5ienoak5OTLmXAxMQEVlZWImdIsZ76x0M6CYTIr5yHzNSqvEntVJOeEYDTBZXJZFw+G4InuqGYt4l8q25QJq7UJH4K4gaDgXuW+Z24sGiuLF+fH2dsj8O7tny19lhlylpukqzKKe2TWgY1Rww3lwDRfqU1+/r165ifn8enPvUp/MAP/IDja1s25SMtks1m0/Hm2tqak6WVSiVilSSvk58VZGu6AhteQfnLucBNJVNTU062qxJM5aJUKkV459q1a/iRH/kRPHjwAO+88w62t7ddvdieOBD4tNBTDW50seXCWiqVnDC1VoU4y8sobSlJKx63nvpMnLCky8pnXfJpo7Y8ZUJOIo3+V7Bir1utbxyz/DhCn6BH/9d8K5zg2v5nSWCfxiJorQfWgqL3+XhcdzdRU1RXEhUDxsNQWWBmX199+Ge3U6s53Qe0LJBleRaoqZk/6U/L1SDiuJ1FuiMrqTzta9817ecki67OZ989Ph7QeW/fk9LxyYJCG1ulYDGT2c8VxtxhjN1KIh0XWsM1rktBlHW92kBiludTYHhNNwpobBqVaJsDh+9WolJTr9dRKpVQKpXQarUip5f7ZHncb3H3jbr3LOncwY1lKGuO10G0MR0kCjKa+ihMr169ik996lPo9/v4zne+gzt37kQOcIxLUmdN+z5XVVxb4v73tVFdPJpEDDhM7c2cBJlMBru7u+58oHK5jFwuh1qt5mIkOEGoRfMd1HgZa8N7uJBpfbmVkVotJ566jXxtseCLv2sEv5o2tW63b9/GT/zETyCfz+MP/uAP8Oabb7qTocMwRLPZPHLCsc886lt4L5ubS9sOHNXMR2lFCkh9i5yaoPmbjWPq9/toNpvI5XLY3t52LpxisYgwDF1g+mAwcLE4Dx8+RCaTQbPZdHNIATEBEwV4o9FAvV5HuVyOZBcmCKcQVyukBTQK2i3v0hJEYa47uxTw53I5l7uHVk6Cr26369Lc0zKpu1nUfavpGHSrbBL/x80Rzne9N2m87YKrlk/lF5Un+n9Kh0QrhJ7QTb4iP1pAfOPGDXz+85/H1atXcf36dVeWVbzUjcR4mI2NDdy/fx/tdhurq6toNBpHgDZdU6wLwZO6llSeU0Gk0s7gYtYhn8+7RH+VSgVzc3ORHbVcD9SKOjU1heeeew75fB6vvPIKJiYmcP/+fdy9ezdi7VLygRZrlfcpprreniePniu4sWg5SSvi/xSCfF4XXXuS8NTUFF544QW0Wi1885vfxNramhex664l36A8ie/aCjwfAPIJQwrmXC7n0mz3+33s7Ow4puS2xJmZGfT7fWxvb0c0TjVfUntQN5JNOEUXHgU4Bb7VMuMQvG+cdAeC7Uf+Pj09jVdffRWlUglvv/023nzzTbfoAXCR/foOn7avn1rPUZa4iyAfr41TRzsflHw85vuuO5darZZL5U5tj/3OA/ra7TZKpZKz8tjsvWppUZdSp9NxZVkLDe/V9AW+PtCdLNp+Lgh2N56CbwIdLhIEduRpBm5qObxHD9DVuidZVuL+17aNy5+Wt+OsPKN457Lx/WUgAuHhcD/XmY49EE0AOTExgVqthpdffhnXrl1ziiRwdOFWuUeraLPZxNbWFtrtNnZ3d90RKNalpbtOCfYVjKibajgcRvKRcS6wDnS9Up7bdYzlsS8AuFxp/X4fS0tLaDQabq1REK0AzieHLFhP4nl+nhePniu4sYs64Ld2jDLfqjDTTqvX63jvvfecJhanWVlgEbd46Pvsu8ehOAFGzQE4nFjc/cRtiDyvhwdhcosrrTthGDptW7fF6sLDd1rNIQgOT1+2Wr5PePoYlgBGBbG1iNn+4v3b29t47bXXkM/nsbKy4spXy8CovhzV5+OCh7MmG5jn+1Oy4N/nLhwX8PBTg3Dr9ToAoFarObDB3UuMP6lWq5ifn0c+n3cWE9Xk1KxOdxfjYiYnJyOmcgIJtcbyVG/uPuFRCeVy2bnE2FZ9hsdETE5OuniD6elp7O3tuQDQqakpVw5wGHvGPuCioVYnq6GyjeRX35jaRYDf7djq/GBZPvdXSmdHumOPY6eKWL/fRzabxc2bN3Ht2jW8+OKLKJfLbsu2T5mjDGXsDrd97+zsHHG1831AVN4q+FDe8cXSKals73a7CMP9c9fozeBOKlphafXhe/Vd+XweN2/eRBAE2NzcdEczaBJPC1BISVaaUQrAedC5u6V8Jjpe13sAf3yH1dSU+VZXV110+ubmphPePvRoY3JGgRYfMLJCy5Jv4IMgGmzL7dz08+ZyOZdgqdFooFwuo1gsYm5uDpVKBfl8HoVCwflrufuj2WxG+s66hzKZTMS/qzlobJ3jgEGc1Un7w5cUSsENADx+/Bi//du/DWA/2FUX4CcBN7qgH8c6clZEPrMARzUh25e+53Uh9bXHx7d6Td0tKysraDabqNVquHr1qnODEshQaN++fRu7u7vubDICFY4VLSAETa1WC81m05UXBEHk7CnlOwZA0l2wsLDgEqVxl6Oa42li14MKWR7By9LSEubm5jA/P4/p6Wln6m+3264M3ZlCLbvZbEaC4jkOVjbo76wP62I1Xc2+beeJD5iOI3dSOhkRvHCHKV3+OuYE45/4xCfwIz/yI7h+/TpmZ2ede9XKEs5JKgudTgfLy8uo1+uOpwimdJOIfhI4UNG1vKeA21o6dRs5n6elP5PJuDiharXqvBS6LvT7fcfDxWIR3/M934ObN29idXUVX//61wEcnsOo63UcaLFyzCfT7Od50LmDm1FWmVG/+aww1Wr1CBPpu3xa7mksfnEWqJM8T3RNtE2NmiZ2RfQ2x4futIoDB0n11d1Pcc9ZDTQJzfu+a1m0Bqi7zDdxFLiOQ7ael4187dPPOIuM7QcVKLb8uLar65G7hGgFsYs2rYiDwcCZrwFETPo24JeAQQ/kszymcQ35fB5hGLodW3oWlI/PWDedH5qcT8uxOwd1QdAFQq1KozRN3xgkkfKiD7yOw6PHAT1pvE0y+RZhEi0slUoFCwsLLrFpXHgCx5FxXBrLRUugxnfaoGC77qjctu5fPm8VNioh3AVI0MJ5DcBZ+TV21RKVkTAM3TENGnOpfeTjY5VXvnV3lAHgLOncwY0dPDvQcQCE11XAAUC5XMaP/uiP4mMf+xgePXqE9957zyFoxm5oDgEGZNFyopagOLRp68X/k4Caz3StViqaPHl9cnIStVrNBRZnMhmXcwTYt3C0Wi1XLgMnJyYmMDU1hVKphG63i52dHW8EPjVK5h7hRCIoirNw+SwhvoWX7baTV8vitWKxiGvXriGbzaLRaLgMndTICdisa8BXZpw/eByt+DxIhVYSEPQ9N841Xtc+8mlaBDYbGxtoNptYXV3F/Pw8+v2+47MgCFzOG2qu7XYb+XweOzs7LjCRweeMy+l2u9je3nam71qt5viJAIQAKZfLYTAYoFwuOzcr+Vc3CNgNBEysplq3HvQ5MzOD6elpTE9PuySXbLtuvaWLgi4EJvu0/au8buOA1M1m5YDKkiRNV+OFnoQumr+fFtIxoRWP4KBSqaBSqeCFF17A937v97odg1aJBg4tQcPhEDs7O1hZWUG73cbKygrq9bqTY0oaQ6OBzD7Zqjym9aZVRuvEecJ1jbFxVB76/b6z4thMxqo0zM3NYWpqCtevX8etW7ewvb0dkcVst+0H++lbN/ibnc/nQRe+FTwJSFjwQ4Stmn42m8ULL7yAz3zmMygWi9jc3HTCUFEzn6VwJDq1ACdOgxs1IL5n48rTXSTAPvMzmRoBCBcaTiaCFhI16kxmf+si27e7uxtbVxXUltksCPEBvjjrjbbN5gvxCXh1vbFcu2VSx0OFgG8hiaPLJPhVUIyiJJ6zQCbued/YDQYDtNttDAYDZz6nj53CM5/PYzAYuLwY09PTTmgys6pNqse4r0ajgUql4pQJ/tHKokoG84IQ+DD/iO4qsnxEK42msCdR67Qnk6vVxpryNQeUb576gLKVSTaxmw/s6Hjp91EW0+PSRWjHTwNZWWZlGC2Vs7OzuHbtWuQ533eN2eI82t3dRaPRiJSryU4p25QHbWiABTp2HuuaxXrwHrr0JyYmUK/XXSZ7tdr4FL5MJhPJlcNYPFUc4uSRrbsFN777zpMudCs4J7guYEo6eJr1VI99HwwGePDgAQqFAj744APcuXPHJeiK05oILiwKHVVvwO9CsIOsdddn9LsNcOt0OtjY2MDk5CQqlQqy2awzdzKuZjAYoFaroVqtIpfLuTNGCPholeHWX+6W4js5wRRJW/O9ry+S+sle8wUU22udTgePHz92fmLdGWCfVb4YB8hcJgHv026Ao4ukz7plnxv3Pv3dp0GRB5rNptsW3u123QnajL0hsKZblAHABBgEPJpB2AZCsr4Mkidgp/DkH8EKXUsAnOVOARKPj1BZQA2bZ0tRNvgCobUvCcjsqch2/JLmho5H0i5DC4j4aZNc6jN2PFM6GSmYptWesi+bzaJareKll17C3NwcarVahOd0DtHqwnjOZrOJnZ0dNJtNF9SrsWKUsUEQRNIZaCCzKt12jHUe0cIOHMpXtfxRYedcZiqNfD7v3GY0BDDGE8CReTEzM4OXXnoJ1WoVDx8+RKvVcu9nX9q+jQM4Phr1+2nThW4Ft4jSB0Zo1bBxKGS2breLd955B1tbW7h//z7efPNN53e0AknBDU3oSVHpti78VOTsa5/+psyr5m2r3dJVQOFeKBRckCYzwe7t7eHKlStYWlqKWLDYJzRBDgYDrK+vY3d31wl5CnROgqQgVyWftmr7xGrpvKa/6f2tVgt37txBJpNxrokkS9IouozAhqT84Nv55CO7wOl9VvDasvR+n/WNIGRnZwerq6sA4KxmmsmUgb20JpbLZVy5csUFD5O3ut2uA6l6UrFaJtTlSpM33VUKapifJggCZxZX9ynjg2i10eMWyuUypqamnPuLridmXNVdMgRlVADiwI3253HGSoOnOa/sOzQLuVqsfAvAeS8KzxrpFmsGgHMNmJ+fx2c/+1ksLi5icXHRZQG2c4f81O12sby8jI2NDZdaQV1NNr7G/hHY6G4k7la0MWEsg/9TdjOYXRVTdXlpLI6mfSgUCrh69WrEAgscyviFhQW8+uqruHv3Lr797W+7uarZx33WzHHoImTzhbulxl28fBoOvzNqnUCADBynRT0pxZURZ/Ww7/YJK048xtJUKhW36BDgBUEQCbzUk2P5LoI236ncluJ8/ifRGH1tiitbJ68N9FQAHLeAP01k+TUJuOj9Sffob3HX4/pN+1gDEQkmCAQ0to28RDChge6ab0YXaa1HEATO3TU5OYlOp+OC5DVQmJ+sgy4IdEepskPibkP+0XpDRcFavuzCk9THSf1s+1rHWDcIaBn85GLF58bRepUsL42qZ0r7pGCZ1u+5uTksLCxE+MW6ZGjp46GVtKjrNnMr660VxyogqvAkKSoae5gUv8frBNdUaqmQhOHhzjHf+sgdu9PT05EdlDYmzVqEfZS07pwXXQi4USsGyWqn/FSLgG6dozAF9re3rq2tuSyjGmSlEehWyAFHA5zHsRqMM2BqMmRbdQFRMyMZstfroVar4Xu+53uwuLiI1157DXfv3kUmk3GnyTLRGn2lmcz+OSN0wxEEcfIykJT+WguItB/sJLTj4JuItl/1zCD+puDFmmLtouqzPKjm6+MTfcb3+0WRWgzsHxdtIBmkaJ+pFc3elyTsfOVTWLdaLWxtbQHYj1uhJkkBt7a25lxU5LfZ2VkXiMlcNcwxwyD3IDg84oMHERYKBdTrdRfETvcX079zOzjnOvmDsULAvtVve3sbW1tbzr2cy+WwsLCA69evO7duEARuC3u73cbOzk4kWJNWX03eZ8eOpP1v+1Pdu5ohOZfLYXZ21iXkpEWMYH53dxePHz92Zwv5FLK4sX0SufRRJcoQjnetVsP8/Dyef/55/NAP/RCuX7/uEvaprCN42d7exoMHD9But/Ho0SNsb28fATMqV7lOWQuMterYsdTNHrTG+O7XOloZSznKGKB2u41Wq4VisejcwoVCwVlJSQsLCy6/z40bN9z5WM1mM7IGxNWBn775NG684WnShYEbH4DwWQxsp/oWLx4boCZmuyCrxsrf7YLo06COa73Q52ygMpmQgtsKTIKy2dlZLC0tue249JVqTAGFKcGNJgUEENnKSIFKzXqcNsVps3Eao9Vg7W86QbX9nOw+5rfjkGT50vfZZy8bqUAk+fhSr/sAja9cn5XGRwRXDIxkYC1wmIsmCA4thRrbRb6ksKS1RA/EtDxNd1GpVHIWI7rCCMRZBt9P3mZsCkEW60SQrNm7JycnXXwPd5CEYRgJtGe9dCEZxTfk0TjLm1qhGL80NTWF+fl5p5QEQeDanc/nXWwgk77F0SirTkqjSa2VYRi63Xe1Wg3Xrl3DtWvXIoowcBhLRSDMAzEbjYY7bJiWRuDoAq4KjFpxVBZavrMWH607SdN+aPvUwsO26nv29vYcyLFpF4DDoPytrS2XVHN7eztSbwugkmSZtst331nThYAbX2f5AIYPLRINK0rWXRsKZhQBs+w4DcySbwHXT9+99hlF2myT3QLKtk9NTeHatWsol8t455138PDhQ2xubmJhYQH5fB7Xrl1zsSlsJ9ulbeTk1TgHzcxKUh+/+m/jgEsSWfBpLWV8nsLflh2G0a2xOnF0nNTiEWetuawUJ4zsPUnf+ZkUJ+bja9+91GLb7bYTYBRqFNrZbBZTU1OYm5tDvV5Ho9FwoCYI9uNyCCjoflF3jFpoWLdisejic4BDPmT8DhMBdjodBwYY70AgABy6XS2oYjkAnGVGsxT7/mxfxwEJnyJGvp2ZmXFb2jlnNfifOxtZDhMSNptNt2HAZ4m013x8ZH9PKUraR5QtcTltrHW10Wig2Wxic3MTm5ubLiMx+ZZKJeck+VXntg0iJlHeEQARyGudWSfbDgXoDJTWNtr7OYe4rszPz7uz5LgxhbK3VCrh+eefd5ZTZpG31kytm8odXwDyRYDzkeAmCIICgN8HMHlw/6+HYfg/BkHwPIBfBTAH4DUAfzsMw158SYek6NQnqC0S1kXXghtlKLXU2EAuH7CwZkUVKD5BFve/1tmWZxdntVoAcEzP80y63S7eeOMN7O7uOgtOqVTC0tISCoUCNjc33S4XWnWssAbgchvkcjl3PAMDLDVmQS1d6tclKVCJI18/+gStnvGSZG7l2Ot4si7q4z4LQX7W/G6vHbwTQHKAelK/etqQeB/nDLMPb21todfrucW5WCw6i8PU1JSztq2srESCwMvlsgPQBDe0umisCdvHwGTOVc5jFb71ej0y7zk/GLxJUDQ5OenSJ1hwQ1cn+Z2xa+QdKkJxCfzG7Vu6xrPZLBYWFnDjxg1MTU3hxo0bbls6g5857xmrxEV1Z2cH6+vr2NjYOAJo2H/2mo83Tkpnwe+XjXTeUKYwQ/f8/Hwk/ktpOByiXq9jY2MD6+vrWFtbc8lHGQBMdyLlmY0f5PutQqY7dlU51x2tJFUaVcEjqLHlW+s8y2ZKEQC4efNmJDu5WlwrlQpeeeUVzMzMYGNjA2+99VYksZ8PePF73HqetJ6eFY3jCOsC+PEwDL8PwGcA/MUgCD4H4O8D+IdhGH4MwBaAnz/Oi0c10DLIOB0Up8X4TMm+v+PUbxxKQqu2ftVqFTdu3HAgRsGHzw/LHVRM/23dcWr1srsxtDx+T9oOm6TNHofi+tpa6PSTgFQzbfosTKdIZ8bvlpfZHh+Q0/tsn/gsf8clxmLp8QiMT9FkdwAi7ha6XDSRptZXF3weFULhSdcTP20gsY6tKiLkUQpoCnDuuOJ5VKpxk8/VBeYDM3GKVdzvtp20XlUqFQcM2Ub2EfuBgMce+OtbEOy4jgIyJ+UDnBG/XzZi3ygvV6tV5y6NUz6oFOphq9ZawT8bWxi3HpFUwbVuq6R2WF5QQ0FSWWEYOishd+Iygarek8lkXDwdzznUWMqkeo4zf86LRlpuwv3aNQ7+zR38hQB+HMB/dXD9VwD8vwD8L0llWQ1fP807jzxHNMpFWS036r5QweYDRpbxfKazOGTq+z/umi5U9l2sH++bmJjAK6+8gp/5mZ9xORQ+/PBDd/J3r9dDuVxGt9t1J83ShM/gOB5BoeZvIn1eowkcODSnWo3DtsdOZi3XXlfyCWZrrtV+4mKo7jZ1Y5EHfEDLjuGTTKjT5HcpM9ImLnja39T+fEkQlbcBRLQ1q+kpINLrljTGhm6inZ0dZDIZl+WXOW0YvL64uBiJPeDJ22qBYkoCJufjllQCp2q1ijAMI66rqakpl3SMgIAxN41GwwGw3d1dl7ODicdoYWLCMl1oOp0Oms2mi5PgIhVnrRnVb6oZT0xMoFqt4vbt26hWq7h69SoWFhYccNMjIpggjplxOWb1eh07OztH4m3ilAlet3MrDiCNQ2fB75eN2C+M/crlclhcXMTt27ede6bVajmLI+ciFcjt7W00Gg3Xx0EQOHc/cLjTVV1PVhlV5ZNkvQyaIkSVGZ33/FS5SesP5bJm3+d7yDf2pPJareYCiVmPfD6Pj33sY2i323j99ddRrVZdXJ4qGtYD4uNNfo6SR2dBY8XcBEEwgX3T5McA/M8A3gewHYYhU+Y+AHA95tkvAfjSwfcTm6esRcIurhZFk8mslmtNfFr+ceoy7n0+oOZzxUxPTztzNjUKTjAKxYmJCRc83O/33SGhdBEQdXNCsL0EUJxArAMQBTdxdbcAkte0L3yAw6dhxI2dmmn5jMZV0UrgCyyP6+uT0mnxO7VCkvYj26t1twGNAI6AeMvfY7TF2y+MudHzlej6Yep2BZMMamc8DPlRNTjWlYKTi4XmWdI8VcPh4ZZc3VZOUKDjr5Yb4ND9RUuJ8o7GPzCOgX9JOW0s2X7T8SNApWLB08iVj1l/PSpFFQmbRDBOubDXTptOyu/K69aKdxmJfMzx4JglWVoYcE85CUQPb9ZnbKiFz4LCcdR1ySotPtL57rPIWFBlrUu8j8HR7XYbjUbD7ajVemYyGbfzkTFjBFQ22Nn271ny6XFpLHAThuEegM8EQVAD8JsAXhn3BWEYfhnAlwFgYmIi1M6O6wzfwqWuFV0UFB3zGj8pXNQlo79pHXwDFoc47YLuQ6tx7fJNEAD4xje+gV/6pV9Cq9XCN77xDayvr6NYLKJcLrudKwBcIjNqIADcPYzDsfXSHVaKvCloreYRZ52KWyi1Xb5+UsFnrRC6ePEIAP3TwD3fbhVbj9Og0+L3qamp0LpaqNmTp5WYUVrbYV2KauEJgiAimOIoDmiGYYher4fd3V0AcAn1uEVc5xUT/DFLsFrUNLCf1kXyUz6fj7i7NNEfx5dWHprC1erJOvV6vcjRJACc8M3n887qA8DNg0KhEHk3rT62b3xKTtw8DsP9Awanp6cxMzODxcVFt0gSkNECyZgbAC7p4cOHD7G1tYWHDx/igw8+cOn7rayx7x+Ht8cBuz46Kb8rr+fz+YvzP4wgyhvOJW62WFxcdCkP9LgBtZwAh/2qAfLM0G2VL92hpN9JPuugAh4rfxXQ8DvL9K2jVmaqNWc4HKJSqbiM4SqbtM2sA604pVIJACJKioIdyrZRxovzBj7H2i0VhuF2EAT/CcAPAagFQZA9QPc3ADwcpwzfwNoOscKaC4Ga4ABEBpuLn2pNVvtXa4a1HPi0Zq2nrb+vPaMWGf6uCwI/v/a1r+FrX/taxIry3HPPYW5uLhKDo1rq7OzskbJ9CZr0uAqNrbDmU3WVWYGvfz5QYbUe7Xc7xjo5eZ3BeVwcNNYGiAJa++6k/5+EnpTfVWCwLbRcqLnZAn7g0MrFe4HDg/LUAkEha8HLKEsavxPcqAVQwQ3rz1wtFLLU5mgCB+DiE+r1uqsXU8Db4F7yCDMPE6jMzs4iDEN3uvLk5KRzm9lkgpVKxbm06M7SYE9eD4LAgRuCQh+oiVO22P98tlQqYX5+HnNzc7hy5Qqmp6ePjDEXUWaDrdfr6Ha7eP/993H//n2srq7i3Xffda4NHw/Y8TxN3vbRacj3y0Zq1QjD0M0/BhQzK7YP3KilDYA7r0zdx7xXjzRQcKM7eH1y1F7zkcpotdDYNdLG7tHyD8DJEeaAooIV936Wz7k5HO7vHNMNAD45z8+LBjbAGAHFQRAsHCB6BEFQBPATAN4E8J8A/MzBbV8E8FvjvDAJAMQBBLtwm/q5rZaac2Dcd/oE3DhkEbgt37eA2zrps5rLplqtolqtRvIRWG2OTGyDUpP+fPf46uRr43GYM65sa7nipNBrdEvE+aPDMIz8nvTukywGp83vSTxC8l2Pc7/pMz4N/7htpzuKW1y1v7mYE0wwKLZSqaBarTpLjmYp5rsVuBHAKDDXP1qzaFn07SzRNis/s24UuDYezwYR2znj6+e4vlOXBtvPRZH9Ui6XXf4fTTrKwxV3dnaOxHCQfPNmHG34MvG71sM3Dqwz70tSlE6LlG+UFxngbUMXtA5qFdRFXe9XWW/H0/I7SUEQr1trkW8eWMXTvlfrwzpYWUmQNz097VI/WGJ9C4VCxH1H643NmaYA0lffOBdd0no9ak0dtSaNY7lZAvArwb5fNgPg18Iw/J0gCL4L4FeDIPifAHwTwD8eVRAnuh0g2yDb+OFwiHa7HUGmyrC1Wg1BEDitURuvyFsFmfVLqjtg3AmXJHT0nQQovgFmPebn53H79m0EwWHeg3K57AIlVXBbwKKxLKyLThLgMDiXk1QDK30LpP2ufefTMG2f2P73LUoAXAwRT+ZVCx35hQF/tOww3w8PE/WBMJ92MyadGr9TuAGHKQy0zzXI0PKYBXe6wPt2/+hCchz3RL/fx+bmptNm5+fnnYWFW0WZuZjKA60r9XodKysrEVcoy6Tg063hnU4HOzs7LkB4b2/PBeZWq1VkMhns7u5G2g4c5pLRBHjkZS5QPHqFmVeB6PlS7FsubvzjXGDfqhuAREDN7K1Xr17F7du3XdKzbDaLubk5zM/Pu/vDMHSHKjYaDdy9exe7u7t45513cPfu3ch5RJZ3kwCOT0aqZeki+Z0Ws+FwGImb4hleVOAo49h+nxvF1w9PQpwjtPbNzMxgZmYmcpyNggL24/T0NIbDIdbX17G+vu5kEOes7jTUXGIE7epd0E8Ajv8J+kkaNqAyTNMKUH5y7imoobUGQETx4BwKwxDT09P4xCc+4SxXdg3hexYXF/GpT30K9+/fx1tvvYXNzU2Uy2UHdnQrvI1n0zO9rGFC+dYnp3mNbSXYo9XYhyEsjbNb6nUAr3qufwDgT4163vNcZPHlNR/5FkgNYiTDkkHtQqdgRQctCaicltbA94xCmBxknuuhQMYGSgJwgiOuLO0zFdYa4+LTJHwC9UlJ62H7VXe/KRProkhhwfHnwqbttxNG6SRjedr8rnWxC6bVtnwgM26xfVINV/nd+tLVDE5tl6BAlQCehab1UoClQJbZhXXLNv8IVIBDYe1b9JWffVY+ax3QuAdtN8v3acK2r0l0l9JMT2DD9zMzMnCodXNHGl1/Ozs72N3ddTFOSfPvOOP4JLLrrPgdQER2+fibdJogJomUlxnw7rMC69yylhsLSBVUWIsJcAj4dN2x1g2fomqVfZ0Ttl9t3S1AVCDB8nigJlMTxPUX45M2NzcdLzPpn4IWn1HCGiJ88o2f4/CuyslxeOZcMxSTuRT5Wq2eW4K1wT63iw4ihSfPV+JiGQSH6c61DkDyeTG+etvv+n7LPBMTE5idnUWxWHRJ0qyWTcS+sLDgkqZpQCTvI7rXhUInozK3AhpuKee7OPF4gKHV/gF4sweT1MrA+3zMbMGV/ulOGNWCZ2ZmIlYAxnLoQstFkcKJfaGLvwWxFhheFGkfqAYCHOabsSZXX9yT7/uTEMvh2LbbbZdEj4dbTk5OYmZmxo0b72MWYLtjis/QgsLARb6P1ha+l3O01+s5MMB4momJCTSbTZclllagUqnkElTyAEPG8nDH197enrPsUdujvOEOJQVIdG3pmGl7yuUybt26hXK5jIWFBXcmFgNSyc/ku729PayurmJ9fR2PHj3CW2+9hd3dXdTr9ch8BOIPsD0OnQbgfVKyliO1KliruS70Pvl8mqSygbzpO8LAtxjzGnnE5ghTGahy2Y6HT06xT2wG7VFWZwsSlH/U3W9d+hYg8S9JAa9Wq7hy5Qrq9Tqq1WrkbDifrNLxtbJO36P3+1yWbIPdech7FMDF0YWAGwUYGpjEzqYrxufLtJoKtUEyLImInJNHAVQckiT54jl8HWkHmOUxY+n8/DweP36Mzc3NCNPSrDY5OYmlpSXMzMy4xF9arjKKTjCLmrU+wL6Q2d7exsrKiltMWH+CQAU3BB5q6rTAhPexnj5gqoBCrS4KUgnqSqUSyuUyvud7vgcf+9jH8P7772Nzc9MdbGef1UWIway8ZjUmCzgvkuz4WKFFDV/bAxzyoA3WI8VN7HEXN51HHCsCCT2yQy2KBDLq5uE1kmrFHGON3+FvBBnkFSYXa7fbTpvM5/NotVrOlUXeKRaLqFQqDoRxgeAi2Ww2Xfl0OdFFEIaHwcoUmOQhHyDnuVXT09O4ffs2arWaywHEeAQe/MmgZk3jsLGxgeXlZbz33nvufCu1bo2rhcbRRQMarYfOQQUtqmyqZYK/qeJkF7/TrF8QBC7WJinOBIi6RayV0HfEgg/c+Cw6vvsJPOyWbDuuvnG2Sr/2G9dX/ukaouAmDlwHQeDA/NbWlktUqeusyieOr1rbte/1Pl2HWU+V29agASACxMbh+3MFN2yIDeKzn/Z+e42dRQ2+3++7ZF92MSYw4LM2mZJlyLhJZQGV/c0iV55ezNOMGb9ADZjCkYGUTMqnQk9BAU2BzDsQB7bshKJP1AZ3xdUbOEwMpcGdOg5JY2IFlNWEhsMhSqUSXnjhBUxNTTlwpwcq2rJt//uSAdpnLpPFBkBk0qrFZHJyEtPT0wDgzk5SPiUROCa967h1sxZLaxEcDAYuER4VCOAwbqXf70dSEOicUx6k5qhgnO2hwLUClmUQJNjt4wq2WJ6N4/JZS1l3yg5rrldrcBAEbnv61NSUi/Wha4qbGDRXTxiG7piI7e1tbGxsYGdnx9VHx5Bzg+MwSvaMGs+kOXEeZOeqb/H3uarilMzTrNc44MZHFjjErQO6MPvGwbbRghBb1yTiGuq7z9bBgnYFDvbdvnpy2zgtk1TMLTC3a7qvbfYd4xgRfGBvXEB/7uBmMBg4xGgXcvtHjU4tAuwsmqjDMIwEKdqkdAQTYXgY/KXlcVCsiVHrbFG21lkXLXU1PHr0CI8fP3bXJicncf36dUxPTztww1ibIAiwvr6OBw8eRFLMUxjwLJpcLofnn3/eaa3KZKwrk5WxnjT3A3DCmOejkLgIdbtd5xLgFl5uCVbG1dgmRetsO/vdCm/W7+rVq/jrf/2v48qVK65Ojx8/RqfTQaPRcAGoXBhZHt9ltV47XjpuF01af44lk+T1ej3Mz8/jc5/7HAqFAt5++208ePAArVYL6+vrbjHUBd0K1CchO990Uee27o2NDQD7JwbPzc053uX2WbaDmbQVyPA3ftJiQo2YQb2axI98yTnQarVcjArbzB1LBD/AYfAis8pq2nz+DsDNne3tbRenRyWAfcJjFfL5PK5fv45bt245Kw2TbDIzMvPb8BiGRqOBhw8fol6v4+2338Ybb7zhlBy1UrMdQNRlEWehG9dyd5HERRc4qkjqjrsgCBxgpQIF+HdQnhaR53gSuCbYjOt3BQJqvbHpF9TKyjnrA/pA1M1C9w553bd4q+tSwyAUvFgLh7aL99AyQoDvAxYWaAbBfnLZW7duYXd3FzMzM9ja2nJKmK6fVtng+OtvrAvHwvK2j6d1DVCeuHTgBjiKOi3as1q+Thi9h88Ch/v5rVkPiCJKn4UhSXjEdaC97tMA6V7RhYnWGi4QqsF3Oh2Xb4SThwJb82bYFNhaFyssOAn4v05Iggfeo5o1Jx4FfpzG4ptQOhltv3JMJycnsbCwgMXFRVeeghkfEytIsuDyslKcdqf8mMvlMD8/j0qlgsePH2N9fd2Nv5pqLc/FWe4s6biNIh1P8hjBscakaFAmj5GwpmIKNxtL4FMKtF9Uo9Rnbf9ZmaBWAQKrOMuBNcn7NEG6xTRRoM5De2aWKhs8861er2N7e9spCFY2qewZJ4YgbmwvG1le0LFW145PfiTJ3SelTCbjrA/WcmNlia9vfUqU/T1uXsYBKJ0vceUCR4Gj8otvjYyrp86dcfqZHgfyey6XO5IM07eeJpFvvvnWYZ/Vxt6TROcObkiqKQKIaKhcVKn1sVNVc6eFIggOg8T0vCVlFm6NUwSpjMHvvt98QscKykqlEtEEBoOBO8NDAdjq6qo7v8cGTNH6xPepkKfbrd1u44MPPnDaNJmaB/bpQpjP5zEzMxOZWNS21S/KYLatrS2srKxgOBxGDk+kZsw6q0uIfUyLBOuq1hW9j+Pd7/exsrISiUPa3t52i4la9dRNpYBXQQ7rp+/S918UUZgqgCQv0mJQqVTw8ssvo1arYWVlBaurq+4eajjkfZ9QsHQcIWCFOcEM5x0BOucYNV4FBY1GA0Gwb+5nhmUCE24nVzDOnDrW/893sA2cC0wdYRUegnBaAtRywz+WSYuRWtHoXtrb24ucEwTA7SQplUqYnZ3F/Px8xJ01PT2N2dlZZ7FhoOnm5iYeP36M7373u9jY2MDdu3exsbHhFnJakNlG64LVMRx3keCnzoWLIutaVIsGx2p2dtZZzhqN/SOtbKjAWRB5pFKpYHp6OrJpRRUyq8SRl7V/KZ9tf9uFWHnVKmlaL/teLYvl6Cd5n59qybHfdSu4gg+m1xgO/btySUwAy7QkhULBWSF1vbH1t4BH11TG//C6z4qj/W/l3rjz40LAjVaUZnDdkkcLAgGMBkCp0KN7ZXZ2FuVyGY1GwwlkvkNdW0TsFhGSSWx8CLVPyxhkSGpulUoFlUoFwGHqd5riWc5gMMDm5uaRPlDm1Ih2XazJiADQaDTw4MGDCLiamZlBrVZzWWQzmcPt8dRAKVyoZTKIki6sbDbrXAvqPmDfcHwscOQYUsDTxKquLAVBBK8bGxsR69Pu7q5bQBS82FwwdHso+awbl0HgK3hnW7josn+LxSJu376Nubk5vP766yiVStjd3XX1pxWNZWjZSse1Yikvk+c4HpoRmOOYy+XceTMkBhwDiIAU1p1tVWsQ+UuzB/P95E0qB7RE6vPKU5yD6joIgsDVn/Xm4qS8xGfJ+6rJcx5NTU1henoa09PTEXM+F0jd/EDlY21tDR988AFWV1exvLyMra2tSNt81mXLv3HjOEo7vmhej4tjofwgaJyYmHBZsAngj8u/x60b68cgd83aHgc8AHjBDcu0z+szOsb2WTvOGtJglW6fsqbfuT6pFV5BpYInnfP0FgBwaxd/U97k3GA+KSoT6nKystfKFuVv2x8W3Gn74sCNbwx8dO7gxjaEZM1lZDSfNsAJQXAxPz+P6elprKysYHt7OxYp+xjNdjrv09+1bkoUljTdKyAiCPORrZ/v0/cMJ6jmFiFoYaZJ/ra7u4tWqxWxHtgU9QROe3t7aDQaDjRQYPOANd9Yad+oC0HBmUXz/M5tuvl83lm41tfXXYyE1Vj4LhUCPorTHi6K7KS0gsbyJYGbttEH5rQ8+z7fPb7rPq1TAQDrSV4mj2u9NVbA19faTl88hYJmnV8EWLqrSeMNWB4BB+vJ5zSuh3zPnCbD4X6eqHK5HFFIFMRPTk46AMNdUNx1SACuGjCwf8L35uYm1tfXsb297U4wt/3tm/tWk48j3+Jr59ZFAxwL2vR6JpNxMUpU9CzfnFUb+B5VlH1KkdZD56Rm77Zzl5+6AcNu6VaZRn5TQKPxKlYxtGDYAh57b9LOI5bPzQI+merrE84jriG277Rd1gqTRLZtvneqIn4cEHzuW8F9vk4ATruxg0QhZc1Y1KJKpRJeeeUV3LhxA2+88Qbu379/JG6Dna5b4nwLtHUJKXNoQBT/GFQFwJnqCGooZK0rjO/yCSVdJHyTB0DkBHBqpzMzM7h27ZqrX7/fx/379/Hw4cNIeXyvDzTyvKlcLoeFhQVUq1Wsra3h0aNHXk1TP2mx0TGOs4SF4f5pyJubm2i323j//fexurqK7e1tbG1tuYBzfZZlkslH8Rg/L1LQk2wdbOwBALeQ6xlMymPM+Bu3S85Ho4CdT6AouGI8DY8O4OJN4JvJZFxeGp3DdqFWMG2D/RmvootNJpNBr9dDs9l0WclpFbTPaZqDnZ0dNJtNl8maO9F4L/m/WCw6ENRoNLCzs+MC+ZmvZmpqCtevX8fCwgLm5ubc1u+5uTm3xZ1jx1w6d+/exYcffojHjx/jzp072NnZcW4XbbNvHml/xY1dEi9b5eyiiGNMuadpP4B92bW4uIjJyUmsra05uUgLocaZAafnplJgw/HTs6Rsv7EvOf/UTa+uTQUyGudFi71asXVt0XZybihAIVi3gNiGCLAe/FT3FZVaupx0QwY9AVtbW25TgyUfAGduJ5VDbJfd9q08bNtmrT2+tZefzGk1GAxQr9fdOjMOnbvlJmnSKhok0rcTncyhi4RN2T9Kg01CfzoIWidbd0XNdCfRVWYFjZY37oIbtzgryifT8qwU1Rh0cvG9/M1nQdDnbCyEtkPr4esLy9g+otAA9t1s9L+zDhbYJAn94/TdRZDPlRQ3B7ibhABWhb6997QEfxyRR1R4k78JWHz5SWwd43jCapV2oaEQ1rnN59Ulq8+p1Ybv0F1quluJO7663W4kMJimd8axESApoFJFh/FJBEr1eh3tdtsF/mufWCE/Lo1z/0XzexxP61xkv9M97nv+rPhbeUdl36h+Ix+qVcZXX+tGUS+AVWT1WVoS4+Rs3DWfFYf1IKlbSp8jUOr1esjn82OBYl1rFYz56mDnua2/bau1Sulcp0wcDAYuvs++L44uZLcUcNSECeAImNHFmGd6UFgB+41rt9t4/fXX8e6772Jtbe2I6coHTvS9Gkxm01CrRqSJlqz7Sv2yJLVQKcq1yNbnprDMrxowTfXlchk3btxwW1T1vblcDrVazWkd29vbR0ykLFczRhM4rq2tYWdnxwVEE/HrOGl7fGOofWvHodvtYmVlBdls1mVxDcPQbe23pNqNjo3v+7gA6DxIQbouinYHEbAfuPeZz3wGV69eRb1ex/LyMprNJt544w08ePAgEuCrdFIgp4BCgYZdfFUQ0nWpsWsUeMDhPFBQwmMXLN/zOW7rLpVKboupdXNqnen/5+nG1KDJ56rdcs7oKey07ExPT+PGjRuo1WpoNpsuQzi3dy8tLTmrzdTUFDKZjNPM+ddut7G8vIxWq4X3338fH374Iba2thzA0R1B2tdqjUziHb3X8lTcMxdNlC3WjR+GocvTxU0DNv7IxmKcVf2s9SxuDoVh6Nyxak3V3zVY3pf5XcGOuoCUT7VO1lrjs/JbBdD3HMm6gvlensMIIJIixVpbtBxmHdf5b0FcnPzlfWpF4vhrDig+y7lbq9Vw+/ZtdDodlyrE5syKowtJ4mcXbxIHgsKS5mgAbtcEF2TgUHP68MMPI4HGJB9osABFwY0GG2ud9T7L4HrNLrKWAXmPag9Wk/D1l97D/B2FQgFzc3OYmppy23R14ahUKuj3+9jZ2cHOzs6RicD77Vk/YRi6+3XBs3XnxPYBC07iOEHV6/WwtbWFiYkJbG9vo16vu51SOj5xmr9vjJIm1kWSLmTsNyDq3gT2ef1jH/sYXnjhBTSbTZf8jS4hHj54GsLf8p1+1/J17Omj5/8ExdYcrkCfwezWbcmy1VoyOTl55B3aP7yflspisRjhQeZkUjCgViGb1ZvHQpTLZezu7qJarbpxYk6fWq3mgFcYhpEsyMC+1fHx48eo1+t49OgRlpeX0Wg0XBI/n4KlpAvPZQAmp0F2seU1AC6MgAukyhXfc2dZPwBH+N13L0MNbMwNEOV3/SOPWeWPz+rnqDrqNd99ej//1GJoZSb/p+s3l8sdSa7poyAInKuacz+unj63E7/bjSlWkWEdKVt4/APzR2m+qFF0YVvBgdE+Zgobn2lLg7wUJdsy9D0W0ABRC4u1yABH87XYuAL+Zhd5+369ps9rvX0DZs2BvkBldedwOy2BnyYdVKbTMqwpdRwgwfJ8/ezTjvQ+DWZTQaCAxmq1KkjsRNWyL9MioRq7T6PKZrPodDp4//33sbOz436ji4UnXVuLm4Ikvue47Y6zoJLf6IKhENY/zVYMHJ5ubIWeWlZ8i5cCHRvgrn3HezUbMBUfjYVQIBRnDdW4PqaZ4OnQ6paqVqsuCJl15txiAD7z2NTrdezu7qLRaLg4JI65b775LJvjUNIYH7es0yY737XdVhtX0GwVw7NQUPRdGhtj5ZO+V8E9Zai2Kw6QWUVRyafE+srzWcT1fl/brJVT2wxELSeqQKoF2cpcazVVa6iVwfbzOPLYp8SqEsUY0yRLp48uBNwo41ghpKY7Lsvn184AAFiySURBVAC+wdd9+rqAk3wLK+9TJqPlggOtz1jTalJ77GDrQsRBsWCAApZAwS6EqnXSlAsgwsjaLp7ZFAT78T8MhCPzMrhM20qkr+hZ45d8/WoDBq3wthPLgqput4utrS0EQRDR1Ol6VBeO/qZ+b6t9XUZwAxxaIenO0euZTAY7Ozv4yle+4s5sodVtYWEB/X4frVYr4vPXPgWSgY0FgHrdglnOR01eR+sI55ie7zQ1NRUJoOeJ2eQv8j6zAMeNHcebW3MZw6IAl0JOD+TkGWm0ZBLIs35xbjfGpxF0ZbNZ3Lp1C3t7e6hWq6jVahEZpICuWq0im826fERbW1t4+PAhtre3sby8jPX19UjQqfY1SRd3X4Bo3Dgm/aYa8kWSat1qodBcKxonpS4dwJ+H7LTqRZnHoH2+065FvEb+oNVRLXbWQmnBjd35ZMtWUK/10znpA02+duluQuvqYt11LSLvM+eUutu0ThasaGwr22Xvs8DNB/5s/VlffirvhGGIQqHgXMaKBcahC03iBxwdNDtBLRJUk54PPfusPLZDrNC3C6T9zdY56fooUq1NF+SkMhhnwKBhomxNCsdyVEOxgpbvtxpIXD1H1UvL0UVWP+MmJRcwjqed2LZfkjS809b0Tot8WhZJtVfuuiPg0WRvGm90nInte2fSPXFaYtw80twk6v7xzT8fWSHvmxf2fj2iQe/3gaY4oUv3FK1TrPdwOHQ7M1Sz5TzSMeAhn61Wy/1xV9txAMZJxvKy8jrgt1QrKYDzzfGzsNro++gy8rk1kt4bp0zF8alv/fAp8nHvjeMLBd1KqkjadYny3+cdUJk6Li/a+W0tLrYdtlzfNZUhPvnBea/tGIfGBjdBEEwA+AaAh2EY/uUgCJ4H8KsA5gC8BuBvh2HYG1GGY3BrulXrgZ7kq2nfwzCMBFVa/50ifxV4er9aBHifz6Wl5GNmH0L1CVXgMIjLbscDotqK7SuWWyqV8JnPfAYzMzNYX1/HxsYGer0e7t69CwC4efMmFhcXMRgMsLa2hl6vh7W1NWxubrqgRtVEtP36PrvV2PadZV67vdUuhtou/s56cJvscDh0i7nVjnT8aLnxaT5xC+KT0Gnw+0E5rv1KrHOpVMLzzz+P6elpZ4mYnp5243n16lU0Gg2sr69ja2vLaWD2HScB2Paamu273a5z06gbiONAUE1rlAIPtTLp1lkVXtbdagGc8hQBn8ZrsL7cmaQJMPkugherkYbh4dlWw+HQHbrLTOi5XA6zs7OYnJx0IKbb7WJjYwPtdhuPHj3C3bt3sb29jQcPHqBer7vDMRWA+OSFtuusrBTHpdPidcpgX5weLQZ6nX/kEfa98s1pkCp9mj9J2j/yWQCRtcZuIFEvA3mM96vc4r26Ftn3WNL5zTJ5r92CrTGUlKecjwT0vN+ua6xDEshke2hlt9ZRW66CIH1e5yWBiyrLdO+qp0WV+XG8KuP5Xfbp7wB4U/7/+wD+YRiGHwOwBeDnxykkLgZBkTWzmLJh6kaiOU2FmS52ukXULn7sSH1GNb8kihs0lm8Bkl18raXGVyd9VoXh5OQkbt68iZdeegnPPfecO4Bza2sLy8vLLj8NsJ+8j0nEmLGZZXLxsjEQKmxtTJFdZKzpUE2ztv0+gc4JwOj3MAwjidh027FanzTGSsfbFwN1SnQq/K7kAzj5fB6Li4u4evUqZmdnUa1WXWbcWq3mvjMzsF0wn7TtChYJMPWPoJcLk2q/ALwAJWk+6Pt0rtrYOsszBCMEW7yP2Yiti01lgL5X0ydMTU2hUqm47d78jabwpaUlXLlyBbOzs6hUKuh0OtjZ2cHW1hY2NzexsbGBzc1NbG1tuUBHrUecVs6/cfg3yUpwinRqvK5yRBdLdU2rrFDgoeN1mqTvVDkSd6/vWbaNbbAWD5VHlq8134/ly7i2jmPhAY4GNauCqHKT2f6T+GgcXqMM8PG6b/76ZJ66wQjICH7JJwRQCm70vePIvrG4KAiCGwB+CsA/Ovg/APDjAH794JZfAfB/HqcsKTNSQfXfq3+dPnYKIgo2FVS6917Biq/zfQuu73eSj7ntPb7rOgF4D68D8cFlSvl8HpVKBdlsFmtra7h37x7W1tbQbDYRhiEWFhawtLSEwWDgkvbRYqMJBuPaEGdVsPVUH64KZb1PJ759Nq5P9BrL0BgM3ZrvWxxHTcaTLvynye8WzCuQpubGOIC4WI04y1Rc2308nPSbHTPLH6p16V8YhpE5aF1GVkBZAO+bAz4lgsKMcT2qDFgQpvEdFtiwDSr0wzBEpVLB3NwcZmZm3JlSdBd2Oh202200m02XaHJzcxObm5vY2dk5Epxqx2VcAJokl0Y9p207Lp0Wr9sFfxRfEpjSRUirxygr+kmIvDYYDLCzs+MSiI7zHK0dPiuLAn3GeukJ95RR5E3NEE9FwsZ5jqoL36Pv0kB7yk1NtaBH8SjIV8+IWh19dWG8EpVlGxepY27539cWwC9P+J31I6ixho5xAP+4bqlfAvDfA6ge/D8HYDsMQ8LEBwCuj1mWt8HUwPL5vEvRTUHDgdrb23NnknARZEPJvBwkn4Bh5/h2VvgoaVFQ8qHvuAFWK4n+Zu8B4HJsZLNZvPvuuxENoFAo4GMf+xgymQw++OADvPfeew4g6oLgAw/6PgWCdiEmqlbm16Bk3msBEHB4Joladwg+9f0qzFgONXQGiaoGb+MttAy7IJ8U3OCU+J3jrAuXNcuG4f7hk0n+dBtTomSvJy12PkBrPy0osAGg6nZS0zqFqGqPdAGoNqvj4wMfCmiUDzOZjIuJ0V1MFLhBEEQS7qlFVOeA7sYif8/Pz7uEfaVSyd3X7/edy2lrawuPHz/G6uoq7t+/j3v37kVAqfZ1EuC3Y2fBOvnY7kqJI3123C2yhn4JpyTbfS7TuPrSAs/FmuOvlofTIgU3y8vLkRwvSjoXWB9mwaYbBogCZLaBMkvDK1RZt+4grnecE+RVjr+tPxDdsWQ3qZDI96wTA/p1PvN9NvmrKg2Wj/b29tBsNtFoNDAcDl1OMp8iYmW8lqfXNA8U+5XPcm3PZDIuoJtrvi+W1EcjwU0QBH8ZwGoYhq8FQfD5kSUeff5LAL7EhsVNPh+QYGMnJycxPT2N4XCI9fV1Z9Upl8sAoufvkDG5Y0L9dL6F4DzIN8j2f9ZN/dM8y4bBw7qgcKFhG+mjJBiwMQZx9YmrSxzF9eE4msdxaZRl5knLjynn1PidFgCtowVfjD/ScZqYmMDOzg729vZc0jkVBEmWGZY97iJnF984jduCD8sDFLrjLMb6jC7qVgBawGVj5lSgst98YElJgTzvo0VI40IoPFVjZdCwHpPh2zET18dPQtYy5Pv9uO84TV5Pcq/4/k+SxU/aV746sH4EFb7EcQCOKEl0mdhDXlUG+9oZZ7mwvK7vVYUxrg1aN2tBV1Dmq4fO9XH6WO9RQKcAbBRf+trhu9cnw3yGh6S5bWkcy82PAPgrQRD8JIACgCkAvwygFgRB9gDh3wDw0PdwGIZfBvBlAMhkMiF3Hqg2aBvDRZqHP3784x/Hj/3YjyGXy2F6ehrvv/8+ZmZmcP36dfR6PXzjG9/A7u6uY5BCoYCXX34ZMzMzePToEd5///1IECQRIHC22yd1IgCI+HrVcqJMVy6X8elPfxozMzPODE7AQyBDgbu2tgYA7ugCay2yFha7MPoYX68rqRZpt7DaRZtlqBvL/uaznLF8BohSe9Z2Wc1GNXt9xxPQqfH7zMxMqAuvdd8AwPb2Nl577bUj+VSq1Sr29vawvLyMer2OTqcTSZynbbakY2GFj89aoH2rWpGNHyBRyKnAZxqCdruNdrsdcS9bPztNztQsaf3o9/sOzJPURV0qlZxlhdmMuXVcD/TzyRa2lYeA0t3NQzIrlYprfxAE7syq5eVlPHr0CNvb2y6WbXd3F/V63Wnu1nKTNCb2f9+CFEc+EGoXoGPSqfF6LpcLla/sImxBhMp7nyv0tAAOrRTk4WaziSAIXG4iBqqrbCWPhmGIanXfoJXL5bC9vR1RKHkeH8eFvKC75hRUkf/CMHR8TSsO5aQP2PvCK6yFRS0ZtK7yoFvtfzu/aTWzAc4WgNlt9D7rjCV93spnC5DU6qP10T8b4jCKRoKbMAx/EcAvHlTs8wD+H2EY/q0gCP4VgJ/BflT9FwH81si3ARGLg8+1QXADHIKBbDaLF198EcViEQ8fPkS9Xsf8/Dyef/55tNttfOc734nsepqYmMCVK1dw/fp1dDodfPjhh5E8G6qhnUTjGYfsxNYB9KFr/mWzWVy/fh1LS0u4c+cOdnd3AcDt/BgOh05jZP9oojfSuHExcczpE7qWMS2y5jNWo/aBKqu1qbC2yRnj7vXV7UnpNPndgjj2l8YSdTodd5K9toVnvnAHlQXKtk/1Wf2Mq5fvPgJSa23yCV3LQ/T3U3hzTtvdi9oOgjU91sCnndLlRQ2aoFeT96nComCNv6lFpt/vO9eWZkkmUFLTPuM0eBwJBbwCsiT54VMqfOOg9T4vOm3ZrjxlFR4LxuPuOW1wA0TjyHicAsdS30VwqwspwXSr1XKhEeR3ymMFNLQM6bt14aZs01ALjUuL20GnczBublvLD634GgNEYKMbcwBEQh5IFjjHJUCM42F7zyily8pAqzwfV8Y/SZ6bvwvgV4Mg+J8AfBPAPx7rhZI0D/D7jHVhBoD19XX84R/+IUqlEnq9Hm7cuAEAuH//PlqtlvMDUigVi0VcuXIFN2/eRK1WwyuvvIKtrS380R/9ER4/fhxZdM6KtHy1elhLg96vlhE9BFCZgb5U9tfExASuXbuGGzduoF6v48GDBxGzq/YxcFRo+LR/n9lUgafGuvgYVgGO1TJ04VOtimPC37XtVuMbBcz0XadIx+Z3X9wHx1f99zZmKAxDF/BIvvbxrI+HfRq9jyxf2broOANRAaXCWi1TBB8aP0ALHAG5xgKwD/SogmKx6BQa3stTuVVOENwoT9m2qxLV7Xad8lAqlVAulzE9PY1cLudia1QLbrVaAOCOvWi1Wm53FM+3GeWO0j5Ostwon/uEfhKdFqg3dCLZTrJ1jpMB+rs+e1bK5nA4dIlf19fX8eDBg0hcJ+UQ+YaKcCaTcWkYFFDzXgARpUDlE91Xdu4A0VQZWs84ee1TSqxstO9nHahQUa5OTOwfM8JDTFkXXZczmYyzdDKfU7vdjoA3rZuvHsrbtq76v/UwKLDxuafGoWOBmzAMvwLgKwffPwDwp47zPBGvL+hIAy2B6MJw//59/OZv/ibK5TJ++Id/GC+//DLu3r2Lb37zm27rs5rBq9Uqbt++jU984hO4du0ann/+ebz//vt4/PgxVlZWnIA8K7ILtQ/c+Bibz1C7oPDWMqlp7u3tHxaYyWTw4osv4saNG7h//z42NzcduBkMBq5POLl9oIX10zqohY311eAuJatpKwhSkyitZzq2jO7n+IdhGNnqb/vJLvJxTO8Tosel0+D3QqHgFlh1e+juPtXmaPLmYuzrH12Efe07aZsVjNj8GfrHuvCPILVQKKDX6zmAQ02Wf4PBwB1hQMsJABcvxrkbBIE7a6pUKqFarToNmsBGDzNkn1iwxwWL4ATY18R5sOzCwgKGwyF2d3exs7PjgFQYhi7WaWdnx51c/+DBA6yvrzuXVNICHccPo+gklovTADhPyuu2HuMoR3HPnjYp33KRvnfvHt566y0sLS1haWnJ8aO1HDKuc3193fGebsJQPlfwTR5W5VGt0VwLdbxVqbdgMK6/KLutJ4S/qSscOARh2WwWlUoF5XLZpVdg/VQ+9Xo91Ov1yBEjaq30jbkqbnEWHgV7cQoZ+8PnIRiHLvRsKUs60Ipu9/b2sLu7i8Fg/9jz3d1dp+0x4IvMyUW00Whgc3MT1WrVCUKfJeAs2+LTjrWNPm3ODqJlWOCQoekv1a3w1vw3DvmYK07T9GmYvmd4n4Ibn2DjPXF1SHrfZSfVQIBDtw81xCA49IEruCTAAA5jstStShrHOnOc39Ui4/vdp4ETwPJ3WnB0t48+TyFrz6GygpD9prs67MKvz/j4R8ulgFT3ltaLZbOfm80mut0ums0mWq2Wc0UReMfxv69P7Zwfxc/nJaNOm5LGAjjsH5+SddbzW3k7CAJ3iny1Wo2MZ5LcI1j27dbRhVgBkvK4DcdQ3oyzcPjaoXVL4idbF14Lw9DtAisUCpEgfY15AeCOgKHFhgpFHNg47voTd4/PWnNcZfXcwY26SsIwjPj8GABL7ZYZSbvdLnZ3d9HpdPD1r38d7777Lmq1Gp577jkMh/sBxBsbG2i1Wm7b5r/9t/8Wv/d7v4ebN2/ihRdeQL1ex8rKSiRPjrXgnJZAUSGu5WrQlrVcUfBq8ButMzqxaCbN5/Oo1Wpusi0vL2NzczPiniODWCuGMgj7XxcuOyF08jC2Qcv0xWRoPAMD9prNZiSeKpPJuB1hGjsEHMYYaX/5tHNLcRPtool9yBgaFfKqCfJPrVgkHQMCo5NabnyCgkH3avnTbd/Wd6+Ai3UuFAq4evWq01zJV+Rrbj0tFotu3PV5mzekVqthaWkpYlpXa5daxKyVT/OoMEfWzMxMJI6C1jWVM61WC3fu3MHOzo5Ls8DgfipZJAV2cYu3T5FRvrD/++71KT623Ce1VD4pqUzQuug8pmxXy64PsJ/mvFUe5Xx79OgRXn/9dXQ6Hbz66qsu87XlIT6TzWYxPT2NTCaDTqeDZrPp5DAAB5j1eQ2iVnDNOnU6HQCHaRSUx7UP1YpK0nnFd+lcomxVpYC8nslksLCwgOeeew7lctkdgsugYY5XJpPB5uYmPvjgA9y9exebm5vY3d2NxLDpmmKtT3EASHk3jl8VHFoZkTSflM4V3GjHqyAADpExcLjwcyGlWXs4HLoD6l588UW88sorCIIA29vbrtHb29vupOUwDLG+vo7t7W13xLsvxmTczjoOWdTO9rB9NubIh/qVsSlQ1aLFwxb7/b47kVjfbQGHbTM/FaCM0h44TlYrtnElahIlIGP0vmrrVmsOw9BNLBt7dRxwc5mADRAF82qeJvE3K4h92q9awiw9yeKm9VLXE0kXKRW+vJ+LAM3tNq06hbt+WgGoix6tNnQN2PfaRVQ1SvbnYDBAoVBwpnfG23S7Xac48DdmRm+1WlhfX8fm5qb73NnZiViAdTzG4TMrY3zf40BN0v+8Nir+57zIyhWSjovO43EWuietj+VRAE7ZnZ+fd9Y4u2OIzytY4NFA3KFnXe/D4eFxMrrI892qRNIqq1ZKXSMsWSupynm+T99DhUT7myCqXC6jVqs55ZLl63ynMra1teUC6vWYElXMdLzj1pHjjK/2k7b/OOD3XMGNVpidrSd92rTYXKyHw6E7oZiMsrOzgw8//NAFe/EIgps3b2IwGKBer6PX6+HatWv45Cc/6XY3cKAYp8N6nTb5Fih9n83IySzMU1NTzsU2MzOD5557zmmUnIStVgvlchnlchmZTAbVahWFQgFhGEbO0+E7leF91hgFUz6gx7HRvrLMq20NggBXrlzB/Pw8er2eiwNSfy4Xt8FggGaz6TQ6Xx/adpB8k8fW7yKJk58gXYGJLgI+37QFi1ZLIrGs02irWm40toDv05gEK8woGGnuVoGucQZWWw+CIJKNmnxGdwFBB+MafO457UddaKhZMycWrZzcDkyNlTtcNjc38ejRI+zu7mJ1dRXb29tYXV3F48ePEwMptf99gFTvjQM4Sdd43T570fwdRz5Arnyjlq+4OXwWCif5gYGy6+vrePz4Me7cuYNut4ulpSXMzc1F6sK/YrGIa9euuQBzAF5eZOyZyitVbtWi3+/3nQLo2ypvwRA/rfKnvGet7pr/LZvNYmpqCsViEbVaDZVKxbmQWXfGCfH9W1tbuHPnDpaXl9FsNh2o4xxWq1ISuLGKnN6XBHpU0dKYoEsHboBD85kNXKLwUuKheBpo2Gq10Ov1sL29jbfeeivSyNnZWSwuLmI4HOLBgwdoNBp47rnn8AM/8APu7JeVlRV8+OGHWF9fd1aCswI3dlHSLL8EN8yNUCqVcPXqVVQqFWcivXLlCubm5rCzs4PvfOc7kRwD/X4fMzMzLjhycXERe3t7bvKSAdQaYBnOLphqSbECh+9l2VywqS2QyRmsdv36dXzyk5/E2toaNjY2nEuKi1mlUnFmT1rULDDT/rPbwkdZmHw+2/MmnfyaT0LrZQWganIAIu4XCzZUaPrefVyiICS4sds+rVtV38H6ZTIZ5/Zpt9vOBadCyQJrggwmystkMqjVagD2NU3yPXdF0oyvRNeUWgpZ30KhgKmpKVendruNcrmMSqWCMDy09q6uruKDDz5Ao9FwKScePnyIe/fuReaRbbcFqz7yLdq+z1F8nQRyLtpyE6elK7ixgeo+OgtgA0TBDQOLi8Ui3nzzTWxvb2NyctKBG2vx4QG35B9g362paRyotKqbXS206nbSuDq1iGpMDvvAunl88T36rN0Zxd/plp2ZmcHCwgKmpqYi8kgDj4F98LW+vo533nkHKysraDab6PV6zqVsgY0F+j5ww3u03knWHcoOdVnbsxjj6EICin2TUK9ZV4QuvMChpkhBqFtPKXR1u/T6+roDToqSz0sY8F3Wv6yki3ur1UIY7ieQmp6edgyjcRAMdqTg4JZGUpKQjbvmEygWTOh9OjYEitxayARpapmzz6rWY7U7XQyStOG4dp2VgDwpjVP/pEkeV17SmB2XVJire4rkW3RVsKqWGVc+gTKVHBXQ1g3J8decNozHsXXj/KFbTOMGbBwE+5qAqNlsotlsunibVqvlUs3r8QpxfDhOfydZGH2fTyv5+EOtab77x9Hgn6Q+agHhuzim7XYbW1tb7gR4XWe0fuQlJqusVqsIw9AlUAUQeY/2Bf+njOR3u8gDfhCgZEMX9DoQ3V7ONhLkUy6Xy+WIEuyTzUyQyXnAvtE+0XoqWf5W4G/l+bjjbsu4lJYbdW/o4qXXVDukAGKKev5O11Mmk0GlUkGhUECn08Ha2po7L2ZychLr6+v4F//iXyCXy+Hq1asuZwbRNQf4tH3Wypy+oEMNBmU+j0qlgr29PXznO99Bv9/Hn//zfx6f+9zncO/ePXzjG99Aq9VyAIfJxMrlMr7v+74Pr7zyCprNpjtLROtgGSOunjaBlFpkNMmTtUSRyuUynn/+eVSrVSwuLkbADRclLkw7OzsA4JA4Jx6ASHIpknWtWaFo26ga0EWR8jIDqDXPk3UZ6qcG0SVZaCwdh4etgFLg3Gq1kM/nI9YbX9kU5oxrYQwY26wANgxDdxI8FxHmpeKuSNuGbrfrLDaa14bHIHCMy+UyqtWqcwtYocndldQ6KT9arRbeeustrK+vY2trCysrK2g0Gvjggw9cADHbOC4/2cVx3HFJstjYcn3PXKTlxoIH9he/23xWWnefteo0QZ5uruAcYrqNlZUV/Mmf/Anm5+dx69YtvPTSSw4I+ABDJpPB9evXMT09jeXlZedJYBC+vovxLRpHViqVjrhYFPzoXOG7KTPU3WoBjq6NDNpvNBro9XoOjFWrVSwtLWFxcRHFYvHIWkT53O128fDhQ+zu7uLDDz/EnTt3HIjzbZdXUGfjTHUtsYaKuDG2QF9xAOtw6cCNoi7fhNSJwd9UeyPDcOLoYWYAXDwKDzGrVCpYWVnBu+++6/yMjFOx6PosLTnaLmoNumsMOGTiwWCAra0t58a5cuUKGo2GEwyaa4GDPjExgenpaVQqlUg/W3Qcp3X6rDP2N8u4PotKNptFrVZDrVZzO6TsuzOZjIvt0EmhLo9RY2EntN5rhcNFk05QS7b/tC0+QGG1oCTyaTe2rLj6EEDbGLi4tgFRU7mtu85jFezkX10AFczxfj0MFjjccamKErMMa8yPvkMzD/OAQ4I4Bg7TcsN0Ezs7Ow4UHXextXLlOM+PA3B8Y3nRPG8VDmv1ULnO++3zZ1UvayGhHG21WlhbW8NwOHTJGQE4tydlHp/NZrOoVqvI5XLY3d11mcQ1M7e+09ZDFS+7BZvf9ZMLuZWPcTxJCybLo1KlsWfVajXiveD71RXO3E47OzvY3d11lhvbJl9742SW/W0UxcmopN+VLiSgWCeAL0iUiNYKP7WwBMHhVtPBYP/gSE4gHrkwOTmJjY0NV+b9+/exsbHh4m2Uqc6qvQCOCGUfsq3X67h7965Dp5OTk06D7HQ6+MIXvoDv//7vxze/+U18+9vfxsLCAj73uc9hZmYGlUoF3/rWt3Dnzh2nHajWoCZYG9ekE8xa1YCoSVXHqlQqua2Eq6urWF1dxZUrV/B93/d9mJubw/vvv48//uM/dgdAcsIxOZ1OXtWm2F8+QGYntQ9gWXB20cS2abArAZ4VCL74Gx/QGwfY+GhcAM88UUyAp2nXOTbkG7aD/KaLGBP2kYbDoXPzaC4cjdfSeurCaDVXxgfQ4lOtVl0wpHWl0TIYhqEDL9vb21hZWXH/93o9bG1t4d69e2g2m253iM89x3Lj+tpqntrvPsBpwYCWe9wF/zLwvF2glc85D3zWG59cPK36WEWP7+z3+9ja2sLe3h7u3LmDN998E7VaDTdv3nTrC8vgd274YCLIbreLer0ekW3qFdBt0z5eIP9rnBuJfE4+1n7VMnTu8XnGbs7MzGBxcdElw4yLWWFyzK2tLRdnw1PUGdzvCwEBDoOldR1R0pAMa7mxCg2tSCovdSzHpXN3Sym4sR0EHA1CIsJlA2nmZuI+ghtadsg8q6urEZ/+YDDA3bt3jyyGLM/6+5+UdFKQ2RSYaT8EQeDOr2FEe6FQQL1ex3vvvYe5uTn85E/+JAqFAv7JP/knePvtt3Hjxg38jb/xN3Djxg289tpreO2117CysoJMJoNyuexSaxNQUAu3OxXIrDSthmHoJpPVtrTfKpUKvv/7vx9XrlzB66+/jm63i6tXr+Kzn/0sFhcX8fbbb+OrX/2qW/jy+TxarZbL26LWLAIf3WbrE3ZWEPqAaRLwOW/yCW0KAd0dxomv4EaFu13wkto0qr3jaFKDwcCdaUZATA1V2wEcghu6pbT+Kgw12FG33VLxsEnRtG80MFj/Hw6Hbpu4Kj8WpNNaMxgMsLa2hkajgZWVFdy5cwe9Xs8lBd3c3MSHH37oBDxj+qypfVT/6qe9rmSBjY+OY/W5aGCTpFjo+Pju9z1zmuSbV1wXmCONsvbGjRtYWFiI8C7LoFJdKBQwMzODa9euodPpROQbUwzoM+R/8hIt9boGMlWGXmPwPOUkcBhLo/3FOaFrWKlUQhAEmJ+fx/Xr193Bsxo0DBzOE4Y6rK+v480338S9e/fw4MEDdzgod3xSIVF5xfbQImbj6Wx/WMuTT0lVa7C6BsflkwsLKPYxG+AXDr57w/BwB4ZdyLTxunhq5Lot8zQ1hTiygzwxMYGpqSm3cNhtwwQm3W7XbcErl8u4cuUKSqUSNjY2EIYhlpeXsba2hk6ng/n5+cjhb7TUkPl8riVfoBj/53NMgkbLEk9R7vV6mJqawgsvvID5+Xmsr6+7xcEeNaDo3i6wSTzBuvjIp+kladUXQXbcrYXCN7F91pokK4EFhXE0DkCiG0e3hFtFRLVOAhSawCnQNP6Af9RifTlFgKilC0AkJkfjIICoQLfAi+9gf+iOq93dXbelt9PpYGdnB61Wy4EcexjmSUBykgXCJ9zjyki657LwN8nHsz6yLhj7zCjAd1xSpVLjV5R3GA7w4MED5PN5NJvNSHI9tT6xXtlsFuVyGdlsFt1u191HBc4miFU3vc4H3d3EuUaArvW0iqaWpb/rOsIAaCq7XFeU//jZ7XZdssqNjQ0H+ngP26cATNun8UVqfdd3WTeftoffSVZ2WBrF/xcCbrRjrC9UmUgFhHYWcBisSA2R5mleY3Cj+tgnJiac6Y8uEGW4syTWk9oCTyb+9Kc/jatXr7rzb3q9HjY2NlyAGjXoO3fuoFgsYm5uDj/+4z+OdruN3/qt30K73XaJCm/evIkf+7Efc66ira0tl5CM8QXcnkgBzk/WT4l9Nz09jR/8wR/ElStXUK/XsbOzgzAMXc6gT3ziE/ipn/opLC8v49//+3/vzvDiYtfr9ZDJZFxCNvq6CejI3BQGlie0D/W6DTLWZ9UsfFGkbeJCzr7g4gtEA7OtCVYBueVTXxyDNfGOIp1vfJbBtoxh63a7DoDTkkqBrGCA8S4U+BpEynHSTK4abM75zXqwLszLoX3CE51zuRyq1SoymYzLQ6MKD+dbo9HA+vo62u027t+/j+3tbdTrdZeGgAHF5G1NJqljaMc2bsztb9bqOA6wSaJRoOGiaBQgJLC3oF75NQkUnrROXBcYbK5HgFAWdrtdfOtb38KHH36Iz372s3jxxRed4saDNZnOgfUsl8sur1qtVkO73cbKyorLQq67+/QMKu0XKo5MYcDAXQbLc74QmPmIsjufz7vsydlsFouLi5idncWVK1ewtLTk6sDNAjZ78erqKr773e/i/v37eO2113D//v2Igkq3L+e8yhoqH2yzL/mnyi8bj6kyjuWpW0rd0xzXUTxyYZYbXXwsuImzpihTWDTO34la1S+vzOFzzZyXpq9abRjuu39mZ2dx9epVp1G22223OyQMQyfEGdyYz+extLSE5eVl3L9/3wGYTqfj8uLMzMw4EAfA5RFRfynby/eoSZMMxz4sFAq4cuUKbty4ga2tLTeJNjc33eGGt2/fRrvdxoMHD/DBBx9EQIsCWcY+aJ9rPXjNZ9GLQ+/KL/p3mTRb1Xj4PylJqI+y2NhyTlo3fvJ9VAD0FGSf5YaLFeeUbjFVYaQ8oJYbXfC0n/g/NU8FQQzyZaAklRaCRC2D/M4t3jwEkJYbnjG0sbHhMhBrv5wGDyUJ4rjro957mXjbR6PAnw/QnFWbyDtqebD5XPb29hzovX79OnZ3d11yVAUBSlxbuJjncjnU63Un4xgKEAduCRoIYCjrOZ/4u84n4GguHLZH3byqTPIYo0wm4/KqWQ8G5wgtNhsbG9ja2oq4xSxISRrjuHCTUcBeFSFt70no3MGNRW6+323sgQpKDULk4q3bTbXzeV1N3DaYyi4O1ppAsh3M8pWxaJonMLBATevJrc+3b9/Gxz/+cbz99tv44IMPEIYh5ufnkcnsB0a//vrryOVyeOutt5DP53Hjxg0sLS1FDgDkorm6uorf+73fcwny2B/Xr19Ht9vF2trake25NNOyPGrG/X4f5XIZS0tL7kyVbreLSqWCarXqzhzZ2NjA7/3e7+HOnTsuYR/7W7fsEajpYml5wscnccBTJ4DeYy18l4mUfxUs2JgOy3faHqsd+/pxHLCuvGndBHSHBkHgXKLMe6F8pXUE4Phfz3QKgsA9z+esBZbAnbscGV9DbVAFsApNCmS+f3Jy0oGc4XDospRvb287yw1PN97Y2MDDhw/RaDSwsbHhFArbR3H/JwHOcYj8qzLOjmecfFSygZoXxfdxFitVmMgbwNGjMoCTL2KjiPNOF2VaUxjgrikvVlZW8Id/+IeYn5/Hpz71Kdy8eTOSzdfHB8zvxRPGeaI841i424hxacVi0cXEUIZ3Op0IuGGMmiYAtX3L9axSqTgr+/z8PPL5PG7fvo25uTkUi8Ujign7ut/v4/Hjx2g0GnjjjTfwta99DRsbG2i325H4Nq0Tx055j5t6NCTCxspauaxrNtd0XT95rdlsuuD+4/D3hYCbJPTmm+CKGBmkqJoaF0uLLNlZQPQsJuCoCZdMb4O1rGXAV0/NOOxzlegiwnrmcjlUKhXcvn0bL7/8Mh48eIC1tTXk83l88pOfRLVaxbe//W288cYbrk7ZbBY//uM/jhs3bjhErXlT1tfX8Z//839GLpfD888/j6WlJXfw4GAwwJtvvnmkfRqsWSqVXA6dMNw3u167dg3lctktdPPz87hy5Qqy2Sy2t7dx79493Lt3z9VBA7RtTIUejKn9qJ/2e9KC4uOjJI3ivIm8q0DXB27i2miBj8/tYOdKkrXT3qeCR++lWRyAAzWaPTgMD4POlbc53zgvyeuDwSByjpNu+d7b23P5ZuhiAuC22+qib8HN3t6eE8IMaGadCZroamIsGLe2rq2t4cMPP3RJ3AiSLCDX/rQuwDhrm6+/feSz6MS56/V+5XEFBD732XmS7Q9fnAjBjbadiyXnRhyvn5TUAkCeUz6kNZ/8vLa2hj/6oz9CrVZzcnZhYQFzc3NH0lWwnoxnoULY6/WwtraGdruNjY0Nt92cz05NTbkErZ1OxwEfBTe0Bmkgr/YdwwaYgoPpN7jZ5tatW6jVam5eKihh3/d6PRez+d3vfhdf//rXXYgE54MvJojP0/qqGZftmXIcY+UR5WH1vCh4osGAVlfNczUOXYhbCogyd5Lmzu/aKTaYyf5mXS8qDGlBsQuL7WwOmM1Ho6QDp8cTaLmMQQjDw6RKHPR+v4979+45X+vCwoKz2FBws15s2/b2Nh48eODO01LwpoKh0+mg0Wi4hYj5QnwBngQmCj4YHMY6MA8Ifbr0LWv9FNz5LA4WtCQJ6o8C2T7xWW+SJrLtT11cxll44wCl/q+AVQGGxt7ouCnIJ2/aRZc8YrVArTdN62rxtJqbvoO/U1MmIOMnBTbPlWMQMWMblB9t/8eBnSSZMM51VX6sVmvf4/t/XHB1mcinoJwlqXUMiKbloNzj4ko+ZqxjEARYX1/H2toaAGBmZgaFQsFZaXxzhn8E8sA+ULeggGcD8v38nQs6FUwNaFalnTFEtHRyhy3nJBVfrRv7Qz0fjUYDa2trePz4sTsmhwA0DmDH9bG9Nop84N43v06qrI4FboIguANgF8AegEEYhj8QBMEsgH8J4DkAdwD8bBiGW+OUZ5ng4B1HfidZFK8I0moz6pZQdK0+Qwo7+15gf1Fnor9msxmJFmfd+D9RK0EB/aescxjuu5g++clPYm9vD6+//jrW1tYc6q7X6/iN3/gN5PN5fOITn8AP//APo9ls4hvf+IYDExogGwQB3nrrLXfWDUEQABd0yfqtr687cMM+63a7bvu8TmRqVDwzZWJiAsVi0WWq5H1qHev3+9jc3DwybgyY0/gauyiphkwLHCelLmYXRafN70CU5+P4OwncjOqPUcqCpVGasQIUAgQe9Mf/S6WS20VHrZPHgqg11WehGg4PD8LUA/v4TgZl+oQkAXun08H29rbTcGnRpVuVyTC3t7exubmJ3d1dvPPOO1heXnauKfYDd1RaDZNzReUL6TQWZivMkwCUbyE4pTrcwSnzu+cdAKK8fl6gTMG35tIi+CDIoCzqdDp49OgRNjY2UKvVXBxOr9dDtVrF7du3MTs7G5GjCtgpv5gVeHZ2Nna+WaWSZOe9BfIA3HrD+BobdKubbHQHVSaTwc7ODjY2NrC6uoqvfvWruHPnDt566y2srKwgCAKXK+08FE4fz1tZYWOMxqHjWG7+XBiG6/L/LwD43TAM/14QBL9w8P/fPUZ5APzaZ5Ilxwof+52kIEfROj/VIqOdRTeNMgqZWIWcghsKfl20yezc4dTv911gr1pulpeXsbe3h5s3b2JqagphuG+mZLyAmngBuDNwdGIxlkjbqtH6VgtQl5Ga+rmYqA+UflQ9BNG3AOhYjLO4+iw3alG6SHBzQGfC75Z84OW4wOak7xz3Xo63tbgAh7zHwEvOOwp5n+VTLYw6l2wAchwfKNBg0DOFPxOR0b3KP5q2d3d3XUAxXW+6ZV3L1/qyTk9CSVadJGAzylp0SnSm/H5aQOxJ3m95mSCE7im17NNVxHityclJl+zvypUrbieobReVOY2HUfccSV1FGmRvrUicaxqPouCF8Tg+IMLy7RrLtYdZuNfX17G6uurWHK4Ndk48CT3J82dquYmhvwrg8wfffwXAVzAG848yc1lGsfdwsHRBjFsMlKHDMIyYyS0S5N9weHggJ60xlUoF169fj2SsXFpawmc+8xnk83m8/fbbWF5edsGJYRhienoaxWIRt27dwqc+9Sk0m028+eabR5id771//z7+4A/+AL1eD61WK7JNkcwLHJ4my0kERIMKWT9OhiAIIvkNdILTgmStX1wkNAU/wZIKYhtfFASBC0pWjd1aK2wdNMrfxxOXhE6F35WS2nmWAMc3x/Q3AmcFGipke70e2u22mw8M/KUmnMvl0O/3XcwB47k0XwfLAuBO59a4GZ2z/Gu1Wkd24XFR4HzgLi9mHN7e3sby8jK++93vuuR9u7u7R6xCcVY1VWrsQvGkY+CzxOh80rGy13zA55TpRPzuIxt7c96k/UX+oaLGLc5AFEhQYb1//76z8rXbbUxPTztgUK1WMTc3F9mookonecY3Rsr/BDQ295Pygv5mFW7KdpWpnA8ETwxkZmDzO++8g//yX/4L1tbW8MYbb+Dx48fodDqo1WqROvmsyeP097jkK5NtY2iE3Vqe9KzSuOAmBPAfgiAIAfx/wjD8MoDFMAyXD35/DGBxnIKSBGvSPWq58U3suPvUMqGWGw4aBZcyCS0m9IeWy2Xcvn0b1WrVdfz3fu/34ud+7udQLBbxla98BW+88QaWl5fxne98B8PhEDdv3sTs7Cxu3LiBj3/849jZ2XHuLt8CwgP7uFDkcrlILh66bFRz1gPPOBE0aG4wGLgzRYLgcOeL9o8GhvK5VqvlwI3eD0R3QKmf2Caa4lj4FgwtTy1gcda0C6BT4/fYF4whKM5jIfD1s4JVrQv5jgCCGqryLF2RnU4Hjx8/dq5Vnmejlk0+T+AzOTnpgtoZQEgwxcypenxINpt1QZQWfDEl/vr6Oh4+fIh33nnHnXBMiw3bqjLDB/ZUeXiSftW+9fV1nOVmnHKf0H1wKvw+TpsVmJ4XcQxtSoPhcOg2gwTBYRCvWruXl5exvr7u8iDVajVUq1UMh0MsLi6iXC67oF6OgQbZqoLns/Co1UXlp8pHlqmy2va1PbYBQATccMs6LZt37tzBH/zBH2BjYwPvvPMOdnZ23E5YluezJif18WmSttECN9Zn1DvHBTd/JgzDh0EQXAHwH4MgeEt/DMMwPJgYvkp+CcCXtGLaAN/3OBq3A33vSRIU1vRmYyC63S42NjbQ7XaxuLiIubk5TE1NuW16s7OzuH79ugvwZaZHMi/PidK03CQKe4vYudBrMBnrRsZTQMd3qUnR5q7RvmB5tGYRyBAs2T7RupEskLF96QM1+ptPGx2Hac+BToXfeTTAOKSLbNK1syA7drYOdmunBv+qlU8XLuYVYbIwxgaQlE91YdDDOuNAln5XTZxB+MwXtbKygocPH7pt4OPENtg+OI7FJsmachyg4rsvzspzSnQiflde98kCW39168fRqN9PQlqmb9Fk3Bd/p5uVsogK7/b2NgaDAd5//310u91Inq8rV65E4s9YlnXtKI9QztpNHnH3JRkG4kBPJpNxR4x0Oh3cv38fOzs77tT7RqPhABbbamW3nXNnIZPs/LPjdJJ3jgVuwjB8ePC5GgTBbwL4UwBWgiBYCsNwOQiCJQCrMc9+GcCXAWBiYsI3QSKfcb+P+m2c5y2TA0f9inoPO3Vrawvf/va3USwW8Zf+0l/Cyy+/7LZjZ7NZvPTSS7h69Sr29vbwb/7Nv8HW1ha2trYQBPuBWX/8x3+MwWCAx48fRyxEwOEWQuaZoUWG4IYByqwf3VZqzeH96uPV5EuqKXEy8Tp3cPGeTCaDYrGIbDbrNGBfPwJHd2ixbCDqFlOQxX5VS5D1XZ+m+f8kdFr8Pj8/H6rQSeJRCjQVbPZaUp/YuTDOM3yH/a5zgHxGfqKmS+2Uu+noOqJ22O/3nVWFGiEtOgp8yKsMfmRQO99tLYJaX1ppADiLzO7urnM9vfbaa3j//fddoj7lUWtVDMNo+gLtC92+7Otv/T8OxNjfR8m9JHqSZ310Un5XXs/lcomMppsF4uJAzooUoGo8oaYuYD00hgyA21HH/EiZTMbtcH3uuefw6U9/GrOzs/jBH/xBXL9+HbOzs1hcXPS6mPgO9ocqkXb+6vqk1wG/fLShDkrr6+t45513XE6yu3fvYnV1FY8ePXLWU85PynuV8bYNp00WzHEe8t0ainEcPhkJboIgKAPIhGG4e/D9/wTg/w3gtwF8EcDfO/j8rWO2yfeuI9fiOlRR7nHK9k0qW44KNjXFczLQDN5sNiPIn0CDQYwA3BZUAJEAYbVc+ILN7ECqVmx3HKgLzmcVsZYoXfh8Ufg+jSOpDy3KH2ch9o0DF51T1kqPRefJ7zHvj/R3Un/ELbBx5Y1Ler/GEdg/4HAXB4GNxmlp9lX6z237lNdoifG1QYWf8rHG5tB9xWzf9Xod29vbEcuSTcJm2+vrtyRQo9dGKVtPCkpOa3zN86fC70nyWJUXX8zkeZDlOQ1N0N+BQxcQF3vez2d4wOXk5CQWFhbQ7/exsbGBcrmMXC7nrPpcE0hxACaJjtM/Vt6zvowFXVtbw8OHD3Hv3r2IJ4HJCdVq6uu706ZRYMUaHY5L41huFgH85sGLsgD+f2EY/rsgCL4O4NeCIPh5AHcB/Ow4L4yr6LjXfBYE3/8+AWZBgT6ji75qGXT38J5vfetbWF9fR7lcdifHEvjcv3/fBWZRw6UmS81Qz+hRpKwWGjKlzSbMNvBeXQysdUaRPxEwk5tx6y2fp9C35wX5ylPmV4HBd2h9fWZX2/+qyVBbogvhguhU+f0k5OPdUcDfB3QsSPLdZ8vRazof9CgTjeNqNBpuIajX6+5ZWgCZLdsm3OQOJibX1C3mypd0/e7t7blgdU0FwV0tjUYDrVYLq6urePvtt128DXMxafJOu8DaNmufjGNhSAIt9jf9jBPsFsydMZ0Jv1uArKDSLqA+pek0SXlZwbq6NG1cB11LtD5TaQUOd9dxkwgPMa7Varh27RpeeOEFlMtl3Lp1C1NTU5iamkKtVjuixPrmNeuq581xPmkfxsVYMQt3u93GnTt33En33/rWt1Cv1/H+++9ja2vLKR9xvMZ3+wKijzNOo0CsVd5YH2u50U1A49ZjJLgJw/ADAN/nub4B4Aujnvc8d6LflKy1IK6MOMaxv6kmaGNVbFnvvfce3n33XW/5urtJtVs9C4eCXoOCLYhgXRj8pUQzvm5l1N0stl68n0CK51PRraATX++judI3IS0g1NPGbZCxBTdsn/W/M7cO++ui6LT53ZQR+fSRz1qj15LI8nQcqBkH4ChfcD5YgKNaLEEzxz2fz2N6ehqVSsVZVRRUEMjwfp6sTLBCVynP9+G7yeNqUWVukmazia2tLXc4Jo91YMAzn0vSBM8K3MTdlzQu41qEnoTOkt8PygEQPdCWf3HtOm2QY+W+/gFRhYzXGINCEM8AeuAwI32328X9+/cRBAEePXqEbDaLmzdv4uHDh+58v6tXrwIApqamvPPYp2jruqD1ohs3zvpI2b21tYV6vY7XX38dd+/exQcffIBvfetbzovAjSbFYjEin/keOz/GUa7GGYNRAIdk1wpahPnbqPJIF5ahOIl8neZj/uOAIX02rnxldmV4WlzI5NaNo2VbE7tu+VPmtWZRxg9YAWrRNctXJEsAYrfv6gJFsonyqJnba1pWXF/qd62LfdYHumzfafzEee6kuEykYOS498RZbuz9SQto0qJPcEPgMjk5GZkj3H5Nq2AQ7O/O08NqVVBbwMvyJycnI+4rAifuXOS84u+a24ZJN3mQrM5Ty1NJAbAqX0ZZWo4LbE5Kpw1qzpJsf9ogdJ/rMe7Zs6qbD2QAcIoZrTTkcd1ODeCIC4e8Vq/X8fDhQ+zs7KBYLGJ1dRVra2vY2trC5OQkpqamXIA9AZQCKt0WbnfpERRSUWD85WAwcLGT29vbePToERqNBt5//32srq5ia2vLgTMqojzHjW3UtYqKro1NO62+98kkWz6VKh2DuPLi6MLBTZwFJekevfc45SctmkTETOAUhoeummw26869oTaoZavVR6PONU6HAlatQyrcqckq2CAT6jW+jwMet6tK44NoWtVkf2pi5TZB3XpuAyu1P3VBsu/S84DYZl8wGP/nX7/fR7PZBHAoKJ4lUqAxjgXGZ7WJuxa38Op7fe8gafwM+VPvo6ChO6pUKqFarTp+GQwGqNfr6HQ6KBQKqFarkQMxbdwOLY/8X+cZd6lQgNMdQLeUunqHw6FzYTIL8dramsvOTR7nXFS+Vf5VRcDnMombC9qPo/p4nPufBYqTF5rL6qI2DNjxVesAcDSfk1UKeQBlo9FwFkvyEOMs2+02lpeXkcvlnMvq2rVrLknrxz72MdRqNczNzWFubi4C8qenp52Fh3KUpPNgd3cXrVYLW1tbuHfvHlqtFu7eveviau7fv+9izwj8W60WgiBwm0U4l4fDIXZ3d12sXK/Xc3FDuVwOnU4nkjpBaRxlbNxxsUqEek9oneXv49KFgxvg9DrpSetA4iTwJU0atUBZwKM+fmv+tIKTE8q6gxQEHJcsCGT91SysdT4u2Tqx7j60bU2dfN5qdBfNC2dJSXwzDojx/eYr25rZx62bb3FSMEAXDy0y1lVE3iIA0bNutDzdEWF3emg8gFo4dT4qEbzwyAYNIFaKs8SMy/+j5vxZAZynBRBZsGCvA5diJ6T7bmWqVciUVxXI6HpgATL5kLzaaDQcoG+1Wpieno64aDVAXz0FnC+sV7/fd1aanZ0dNJtNbG5uYmVlBa1WC48fP3b5eB4/fuzmgSbA0zrHxez4wD/rkMTLpzmmyu8+L824SuKFgJs4gez7f5wyfOQbPIvalRTV251CPGAsCAKnrfoWagUSZN4gCFwAsC8aXRlbMwAHQeCQNK0uOuhcQLQ8rbdu4bMAQjVVjf/R7baWeXQSKwhjuVzEyuWyM+vSEmUBlv5PVwcXqLjxeVrJul+AowtrkpWGZMfeUtI8soIBONRStW4KTnTnGhPslctlVCoVlMtllEolFItFlMtl7O3tn7LcarUiLtdms4ler4epqSln+WR/5PN5d42mcrvwKZ9oe2gpVOtPvV7Ho0ePXFZkzhvGkVkhaRUNFfhqZWVfaT/5+nNc5WMcAOQDXlZmJsnQy0C2v1V+2FiSsyYfgLHjrffS5QNEXUX8LBaLLhaHREuiJj7lBo7V1VV0Oh1ks1ncuXPHWSBplWdM2fz8PObn55HL5VAsFpHJZJyLtd1uY3Nz04EcghcCHuaxabfbLt2CBuUzvob8y/g04DAzssZi8qiS4/TtaZKOz0mtfRdmuUnqlNMCOD4BH4f49He7JVnzb6i/2NfZ1sSpWqteV4FpdxORNA7HAh8rgG19uCjZuukf36F10ftYjv7pDjL2ERmRE5ZuB5oSre/YAknNUEygdBkF9nFpnEVqlLUmrtxR10dpNsoHPt+61ofAlSci8095YjgcIpfLuSR63MnEfDc2iR/jDrQu6vvXOviIlhnOnVarhY2NDezu7rq4OLUwKaBX3qfioFo5y9d6JPGlBa+jyAdMjgt69Nplt3SqEqQKFskHGk+TFLxbZZCk/K7gRmU05xRP31ZZz5AGzgcqjgQ7BAq6kYSWTW4hX1xcxMLCAvL5PKamppDJZLC9vY3d3V00Gg1nkeHuIVpRdQ3R4H2S3azBuVmv15HJZDA9Pe1kd6lUckn/ut1uJDbIJ1N4TdeO0yILbjge49K5g5u4xicJ8qQFfJx3jdMhPqEeB450Uddrtt583gbQ6bMqQK3mCOCIKd4HPHyCexRZ4KP1VasTsL8QXblyBeVyGWtra1hdXT3C1NTYqa1wgdF6+RZ73Rlw3EXiaSAfeLXfR1lrLB/7xnjcRY/32R0SKpTjxsACa3VRVatVtwOjUCg4YG5jF9QNq3EwSmrN4e961hl38umJ5TxagbsLrVbuW0B9Qtln3VXgEzdGp012bCwgusygJonvLqrOVjH0KYmaLV6PtOF9PHOQ8YUsV8vQwOO49UcBkwI/AvRsNus8BbTcKNjSDSQK1IHDdcSGNVDRJLhiCgYtT489oWvMekDOyuI2au06Cd+cK7g5zuIL+IUPv58msOH9XGAtorcarYIbXRTsezlBaKZUFxT/NLmYnuzNd9AUSlKrj++8qSQNkHXz+YpVO7HtKJfLePXVV7G4uIg/+ZM/webmptMctO+ooag273uvvkPdCxQul1Von5SUV3xuKtsnPp63i5sCojgeT7ovLjuqCnS9rjEy1L6p7WUy++dDAXCAo9fr4eHDhy73jS4aXCAYlKlWHd7HwGJaPpkplrugyGM0n29ubmJ9fd25Qm18nM67uHmuPKsLnbqN4/pnHDnjW+SSro8qw36Pi6M4T4oDNhyL4yinp0UKJnTs+b+6b2ilsFZkxpBpvbVcgnHN1wUc8pW1Ytu1LQz3M+FvbGwAiO4+1feo3FeLpIIr3ZDCsrhJhu/L5/NuzmpyQt18Yl264/Y13/GkY2WB43HpUgQUnwdRUGnnKbg4bllx2m2S6c5HFkzwe5IGbctLqg9/B6I7qhR4jRPDYa/rp7bBmvKPQ+cl7M6T1EICHA1q9VkCFFjG3e8DLHF8Z8vwfcaVodql5TP939dOn9anQNwqDD6e0Xf4tGE97HDUroq4NsTxaVwfxf02Lv8mlZf0jlFz8jKQHStrOfPNhfNUZsZ9n4IQknWNaFvVmmLfF1cPCwTUis35oUfq+KytSj6Fnt99Mp7gjs8q8E86X3AcSurncSw0ll9OAozPFdyMWoQt+RZ+XzkaEGvN0eyUXC6H2dlZFAqFyKnbjx8/dsfA6wDzPZbiTNR2EHxC1NcfOhnUfaVt0/ssSuc74iw8fC6bzbqkakT3e3t7WF9fR7PZPKKZKOpvNBr45je/iUKhgK2trSN9q2OSxIDat/oe37PPAgXBoVVCBRg/44CNFU4+cGK/x72f5OtbH6+qxgTsB1QWCgXXDo2tymQOT1rm8xrsyzJozdE4BdZN+4ZxCLQC0nzuOwyWzzHTMf90Z4hP1lBw+/pJt59a+WL7Ka5s279JY5K0UCX9phQXr3fepAtjLpdzljlNfKfBtsDRXFx6ZplvMX6SuvFTZY3msGF9mE3eBz7iLGNUkoMgcC4dnUMqJ+2njrMPUMTxmq2T8jX7PCl2TeOKdL3Tk8d9YMnScflOXYA2xQJlB70CXNOs4m/XzTg6d8vNcTvDtwDw02oIVqtVmpiYQLVadUfUl0olt52OKd997/QJnThB5NMsR7VZn1UmS3pGd7MAfneX/pEZKpUKarWai4znLrB2ux3ZMkhfKxmx0+ng3r17iW2KA2+W4oDqs0gqsHzWLf5vn/FZXOw9x6mDLSduvviEBhcr3a2n7iXrCo1TSDQvEzVSBTvsFwo3ZiomiGJdbJxCv993bjDNbB23KPh2yIzqV99CNArcxI2dvS+urFF18ylQF00ECtz9qKBQY1h4r/6p5eM0gY3WDfADfuV5dQdZshsjlHyxWkpJwFfB+3Fci76x16DpUYqmLxbUAn/fs8epn32nb8xVFqmyxPxrvrQi4/D9M+GWUnOdNV+R9vb2sLOz4yLAuXWu2+0eCaD1ac6WUbg9lpHoXAC4vU/jcWgq1wBb69tX0nvs90aj4ZLd6f16L+vIOnDRYLImTWXPbYUETJZpfILd0rNmcTkt4gSNsxQoz8YtXj5g4vvuK3/U/dYK4ZsH1t0EHM2wTY2PlhzyvP7ZjNzAUWGuv/ssKPp+1kHPnbHabhKNq5nGURwojftfr9vx9ykNdmxsmy4LoFFSgKx8YwPBbbyXti1Ofj/LdJptvux9p3xrQSzXVeUdu7vYWsWS6KkHN6ptWm1BO7Lf72N9fT0y+aj52Z06PteW3b2Uz+dRq9UwOTmJq1evolaroVwuY25uLhLbwpOKB4MB2u22M3vynTT3a301s7Fqsv1+H2tra5GdIjZTsS4OauIdDofY2NhwDMTgZeYE0fN3kjQZK4hIl31SXQQFQeAAsF4Doj79OOFG7SbOGhJHSVYDrYeOKT/t4mvdhgTKGtui7iLm3+AuJp5n1u/3IwubLVvrbjV5rTPrwPgavkuzYI+r/SaBoKT+s+6UuGcsOPUBG/3uu88XgB6naJyFxeM4xHGj4qTjxSy3/F1dgFY5/ajRWbjgLispz3LN0pgi8gbJrtFqCR/V1qcW3FgtKWly6CRTAegTUOO+l4CCGqQKW91JpNf4yWcmJiZcrgOtE2MWNFgyLpHROBoiJ48CIGtB8gl6C17iLAmXfUJdFKkmEvd7nPUgzgoQ992WO877khbVOAuCr362Dcpf45Jd7OOUDP20gclJ7R9lpfFdjytnlOaYBFx81+IAUtzvT4MVR+tOWaZauY/ngGdXniStM89ie+PIjnXcvLOA18bBPtOWG7VWKNqzjSYaJALk5KGJ1G6n1o7U8lTLpSayvb2NTGY/OyqzOzJehX/2UDIN9rIaGskXwa7xL9SC4gAey7MI11qsSDYFvnVx2X63/ZSkAX+UaWJiApVKxR26av84FjaQ3Nf3ceDiOOBG7/eNmbV60NKimnYc4NBnrWXH9gnL1e3WrA/jNZT3CfgV6KvLS3f82Zgc1vG0+JPl2PxTcdYUe833Pc6Cxu/WauqLzUgC0edFqjgqf/d6PTSbTUxOTqJWq0Wy/gKjldNnhXwA+yzA3GWVxRbw2kz3+jvPvqLng2fU0Sr4zIObOHOeXfStaV8Zir8nBXnyPiXNWswYGLUEaX4M1TT1vjhKAgv6Dp9ZPylWgdd8Vixf3FKcZcv+7ovLSCnqlvKNBeAHqHH32msnWbTjFlpfWbqQW17zAQd+EsxbbUsXPf2uwEb7wIJ73589O4fvOUuKs5yMsqbEXbOCPe4d9m+c8i+CtA0E791uN+IOt+5GH3B/2uiy9P9lpyTZR9LM/lRsgEN3/jiuvKcW3ADRTooLKNTffMFIaoUYZ1IlAQ8VyCp0ta4quJXi/reavNWM2AZagqw1RRnB7rCy77NuK11wbN8paEwntZ8Ibuw2fd+ipd99i52lcdwjo2icZ33mYQvOFeiqO1UtmfxNrQ6+4zaUr+m29aXrt5bJuPl7Fgul1lGv+b7HXTuLOXPRoEDBjCpO/X4f7XYbk5OTbsMF54RPcbrM9KRzDhgdjP4sU5Ks040K3NG8vb3t4vhU9pya5SYIghqAfwTgUwBCAP83AG8D+JcAngNwB8DPhmG4dcy2nphUwFhLhBInmy7UJM1rwDLHeW/SwsOOp1BWQazv95n3fVYlrWsYHm6h1Xr4zofid9Wg9R6a1ZVh7JY7vc+nWdmdLs8KnRa/cyedNcHrp1oNFUjEafBPIhhH3ZdksSQvqSWFxDYQ2DD+TA/j03upvesOKl0MgUO3j1qBrPLi0wD5v08pOA1Sy1NcP4163nffKHA0isbVZmPqVMMp8Dv5gLKPfdTr9dBoNFAul93OUj1vL0lhvIx0FnV9mtr/pBQHcLhuhmGIYrGI6elprK2todPpuE05x3HjjWu//WUA/y4Mw1cAfB+ANwH8AoDfDcPwJQC/e/D/uVGcgPMhQp3442p3SSZgBST2elw9xmXeuHstE4xCsHq/dTvZutp2+eof985ndFKeGr8nuezigEzS2MVZd5LKGPXbqL8kEOv7LWnrplVKfGDNPmdPslcaZ+6O075x/0b170n7WftmnPvieOmEdGr8bi03HENuktAcSVZGP0sK0nHppHPzJGvMRZPyRtz4axI/9YIch0dGWm6CIJgG8GcB/F8OCu8B6AVB8FcBfP7gtl8B8BUAf3fsNz8hqZCk8FONip1GTSIIgojlQrVN29lWiNiF3YIbH1PZ08B5v26R1GfjGJN1Y1AqgyktY+h3lkdBwjNCxnXBqeDRIFGrZfsSPj3tdJr8zj7ieOsnx0vjsnzBsPq/Ly7suAtCEjC1/M36aLCqFULWikHT8nA4dLlvJiYmXOZWCi2bbIx9peVz7jJdgbqodH6r5Yvls8/i+knbbvsh6TfbBxbQxD2TdI+vDHtdP3U81A19klij0+J3jh8PTFXe54GMvV4P+Xzeuad4wKSNzUrp2SWVf3FHPATB/snrpVIJmUwG7XYb3W7Xzf9xaRy31PMA1gD8r0EQfB+A1wD8HQCLYRguH9zzGMCi7+EgCL4E4Eus9GmSD3zwOv/iktuNAilaBrURFZi+9+mzdmfJOILWgh4LJAhurAtKy7A5e/Q+DcyK2+nke1br/RHQsE6N3+fn53kNQLJLyfY5eccXJ2aB0rhk708CN7w3bveRT/si2Rg3PqPWnLj+8M1N7oqy7jBLtv9sfX332++jrrF9dkEeBW5G3adgd9T748bwhPL1xPyuvK4gXneiqqtKZRn/Ro1pSs8u+eI+gX0+zmazTqHX+Z9kULA0DrjJAvgsgP8mDMOvBkHwyzAmyjAMwyAIvCteGIZfBvBlAJiYmDjVVdECFhUcVgPlp11obEZEW7YVjPYdvKb/AzgSBOkry1eGkgprPbJeBbg+p2VbixSvadnskziwp+X4+kA16GeITo3fX3zxRXdPnFatANJnEdAx8S1yxwGavvvj+I6WUK0jgCPWFsuLFhzxGq2nCppYTlzbufip1caaqH1Khu5U1Pt87Rx1zXePnouj9ySBG7037h2+30gW9NhzhJ6ATszvyuv5fD7k+FoFiwCViR0zmQzy+Tymp6fR6XTQaDSeVVlyhOL48BlXGCNkZRnnJ1MElMtlhGHo5j3BjcqVcWgccPMAwIMwDL968P+vY5/5V4IgWArDcDkIgiUAq8dr4pNRnKXG97sGxaoAViGh5tE4C4uWwe/2fv5p3EHSYNiFQN+hC4aeXq5C3JZF8r3f9101Luv2GLUg+Or+DNCp87u1JOh1H5hRywyv6T32uXEW7nE0HluWAgX9TQPPfSCbvKe8xQzFPuHkAzZqBVAhZ4OQ6QbTuticLxYEjeqHpH7UuZl037jlKflSO/jIbkxQIHoCOhV+V9lKKzf7iYtTr9dDu91GEAQuwzuPkxnFz5eN4pTKJIq77zgL9rNEqjCF4f4OqUKhgGq16nLJaYZzn7U0iUbOiDAMHwO4HwTByweXvgDguwB+G8AXD659EcBvHbt1Z0QUenEaIe+x1grfgu7rQJ+26ROc4wgqBWYWpMUJZRUkWidb3zjBbOvle19cX+hi9ixOyLPgd1+/JfGIb/L6nvP95is77nf7/Kh3+vh9FM/wHo1vs/NT43S0TF73ZerWHDfqsvLVx1evky5Ivvpr+5L+fP3gA2pqhtdkhdpu22829cS4dFr87lM27W88KZzWm1Kp5LaGP800Tv2fBsB2EaT8T6tNsVjEcDh0xxX5dkKP0+fj5rn5bwD88yAI8gA+APB/xT4w+rUgCH4ewF0AP3vchj0JxS34pLggY2oSPiuNdqAuSHEAKA7QAH5/ok879S02unXbp0nrmVH6qZq2r77aHp/LzF7Tuqlrj2XY4M5niE6F39mnNMOrNUMBqk/wqavQN7GTtD3fdZ+1xI6d77vygC6iXEjJiwwY9ikVvJ95a9TFRA3NWlz4e7vdRrPZdJq/LvCdTgetVsstmHR9qZWI/eSzbJyUb31jMi75QKTPhZw0huwrzkEAkcNwT0Cnwu/kdeY20raF4X529ZWVFZTLZRQKBVy/fh0TExN4+PDhM734P8ttOynpukU5UKlU8PzzzyOXy6HVauHRo0fY3Nx0AOe4WbjHAjdhGP4JgB/w/PSFsd90BkSmiYuZSRLecRqo3me1Vn027n59ly5ethwVVLZe+lySa0nrQsHiq4sFSEmTzQfYbJsU3DyDwObU+V0XV58FJc5lFQRBZLFSHkrq93E1yaSFVK/7eI78poH2Fozb7xaMK/ixi7JaMSwYsiDL/matK3ECcZxFxydX4ub/OKTz2/bbuOCG31UhUYvOcem0+F3lsVUKgf0NDc1mE0EQYGpqCuVyGdvb214Z+7TRs9CG8yJd35T/s9ksqtUqstks+v2+Oyz6pHPtqc1QbDVKXuMCz0h9qyXb520gJH9juT4Q5LO2+DrdauVJVpq4a76t7Sxb261J+bS++hs1bF89tY4+sMi+sDFKcf2R0j6pRU1Bji4APt5UC4avj5OE6LjgxgeSVODk83nk83mXdE1zlMTl79FdMvwjAOIWbZ17+k4AbjuxD+wx+R+fKRaLKJfL6Pf7qNVq2N3dxeTkJKrVqutTWox81ps4xYftGNWXo56J65+k332ByvZ5Wm50p1GlUkG1WsXdu3dj63uWxDFk3ex1YB/c1Ot1DAYDzM7OolarYWtry9tvHyWQ8FGUnT5lp1AoYGFhAWEY4v79+9jd3XXB5oB/80sSPfXghp2jJn8AEfcNO4JWEF0s7A4qvV8BhH2v/a7P83tSnbVOcQPlAw0+gKKDbgGHCnXGLtht6vzk/TbIWE2HPsQdt9CldNivdsce+zkO3DCBFQPJfYszcBQc+7YSJ9UtrrzBYOCADT/1AE3LW/rOXC4XmY+8R90VagG0O7B8oEcXTbarVCqhUqlgOBxiZmYGrVbLBaoSVNm5rs/bz+PERNks4b75ptd85Vmga9/lqy/L191SYRg6cPP7v//7MaN99jQcDp17TGUGqd/vY3t727khZ2dnsba25rVIfdTotOTn09J3PnBTLBZx5coV9Ho9vPvuu1hfX0er1XLrtoL6ceLLnlpwc5p00gl1Ggx5Ecw4rjn+aZkoTwP5eCXJcjDOfXEul3HKjbPaMG5mVN2PS2cFfm1b7c5IfvdZTCyQiAMmltT3b++zAMkCGXsPy9Pvvnbpu2zcHa89wY6pcyML4lNKieQzNjyJLArOcwELgmANQBPA+rm99GxoHk9/G4Bnox2j2nA7DMOF86qMUsrvl4qehTYAl5TfD3j9Lp6Nfk7bcDlonDbE8vu5ghsACILgG2EY+oLXnhp6FtoAPBvtuOxtuOz1G4fSNlweuuztuOz1G4fSNlwOetI2XH47ZkoppZRSSimllNIxKAU3KaWUUkoppZTSM0UXAW6+fAHvPG16FtoAPBvtuOxtuOz1G4fSNlweuuztuOz1G4fSNlwOeqI2nHvMTUoppZRSSimllNJZUuqWSimllFJKKaWUnik6V3ATBMFfDILg7SAI3guC4BfO890npSAIbgZB8J+CIPhuEATfCYLg7xxcnw2C4D8GQfDuwefMRdd1FAVBMBEEwTeDIPidg/+fD4Lgqwfj8S+D/bNlLi0FQVALguDXgyB4KwiCN4Mg+KHLOg5PI68DKb9fJkr5/Wwp5fXLRafN7+cGboIgmADwPwP4SwA+CeDngiD45Hm9/wloAOC/C8PwkwA+B+C/Pqj3LwD43TAMXwLwuwf/X3b6OwDelP//PoB/GIbhxwBsAfj5C6nV+PTLAP5dGIavAPg+7Lfl0o3DU8zrQMrvl4lSfj9bSnn9ctHp8rum0z/LPwA/BODfy/+/COAXz+v9p9iO3wLwEwDeBrB0cG0JwNsXXbcR9b5xwBw/DuB3AATYT5CU9Y3PZfsDMA3gQxzEicn1SzcOzwqvH9Q95feLqX/K7+ffjpTXL64Np87v5+mWug7gvvz/4ODaU0NBEDwH4FUAXwWwGIbh8sFPjwEsXlS9xqRfAvDfA2A+6zkA22EY8qCqyz4ezwNYA/C/Hphf/1EQBGVcznF46nkdSPn9ginl93OklNcvnE6d39OA4jEpCIIKgN8A8N+GYVjX38J9WHlpt50FQfCXAayGYfjaRdflCSgL4LMA/pcwDF/F/rEGERPlZR+Hp4lSfr9wSvn9nCjl9UtBp87v5wluHgK4Kf/fOLh26SkIghz2mf+fh2H4rw8urwRBsHTw+xKA1Yuq3xj0IwD+ShAEdwD8KvbNl78MoBYEAQ9Pvezj8QDAgzAMv3rw/69jfzJcxnF4ankdSPn9klDK7+dAKa9fGjp1fj9PcPN1AC8dRHHnAfxNAL99ju8/EQVBEAD4xwDeDMPwH8hPvw3giwffv4h9f+2lpDAMfzEMwxthGD6H/X7/P8Iw/FsA/hOAnzm47bK34TGA+0EQvHxw6QsAvovLOQ5PJa8DKb9fFkr5/ewp5fXLQ2fC7+ccNPSTAN4B8D6A/+Gig5jGrPOfwb4p7HUAf3Lw95PY92v+LoB3AfzvAGYvuq5jtufzAH7n4PsLAL4G4D0A/wrA5EXXb0TdPwPgGwdj8b8BmLms4/A08vpBvVN+vyR/Kb+feZ1TXr9Ef6fN72mG4pRSSimllFJK6ZmiNKA4pZRSSimllFJ6pigFNymllFJKKaWU0jNFKbhJKaWUUkoppZSeKUrBTUoppZRSSiml9ExRCm5SSimllFJKKaVnilJwk1JKKaWUUkopPVOUgpuUUkoppZRSSumZohTcpJRSSimllFJKzxT9/wGFHLUHDtMoygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(3, 3, figsize=(8, 8))\n",
    "for i in range(9):\n",
    "    plt.subplot(3, 3, i + 1)\n",
    "    plt.imshow(train_ds[i * 5000][\"image\"][0].detach().cpu(), cmap=\"gray\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create training components - device, network, loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\")\n",
    "net = DenseNet121(pretrained=True, progress=False,\n",
    "                  spatial_dims=2, in_channels=1, out_channels=6).to(device)\n",
    "loss = torch.nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set different learning rate values for layers\n",
    "Please refer to the appendix at the end this notebook for the layers of `DenseNet121`.\n",
    "1. Set LR=1e-3 for the selected `class_layers` block.\n",
    "2. Set LR=1e-4 for convolution layers based on the filter where `conv.weight` is in the layer name.\n",
    "3. LR=1e-5 for other layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = generate_param_groups(\n",
    "    network=net,\n",
    "    layer_matches=[lambda x: x.class_layers, lambda x: \"conv.weight\" in x],\n",
    "    match_types=[\"select\", \"filter\"],\n",
    "    lr_values=[1e-3, 1e-4],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the optimizer based on the parameter groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = torch.optim.Adam(params, 1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the easiest training workflow and run\n",
    "\n",
    "Use MONAI SupervisedTrainer handlers to quickly set up a training workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "trainer = SupervisedTrainer(\n",
    "    device=device,\n",
    "    max_epochs=5,\n",
    "    train_data_loader=train_loader,\n",
    "    network=net,\n",
    "    optimizer=opt,\n",
    "    loss_function=loss,\n",
    "    inferer=SimpleInferer(),\n",
    "    key_train_metric={\n",
    "        \"train_acc\": Accuracy(\n",
    "            output_transform=lambda x: (x[\"pred\"], x[\"label\"]))\n",
    "    },\n",
    "    train_handlers=StatsHandler(\n",
    "        tag_name=\"train_loss\", output_transform=lambda x: x[\"loss\"]),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a ignite handler to adjust LR in runtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LrScheduler:\n",
    "    def attach(self, engine: Engine) -> None:\n",
    "        engine.add_event_handler(Events.EPOCH_COMPLETED, self)\n",
    "\n",
    "    def __call__(self, engine: Engine) -> None:\n",
    "        for i, param_group in enumerate(engine.optimizer.param_groups):\n",
    "            if i == 0:\n",
    "                param_group[\"lr\"] *= 0.1\n",
    "            elif i == 1:\n",
    "                param_group[\"lr\"] *= 0.5\n",
    "\n",
    "        print(\"LR values of 3 parameter groups: \", [\n",
    "              g[\"lr\"] for g in engine.optimizer.param_groups])\n",
    "\n",
    "\n",
    "LrScheduler().attach(trainer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Execute the training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:ignite.engine.engine.SupervisedTrainer:Engine run resuming from iteration 0, epoch 0 until 1 epochs\n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/1, Iter: 1/157 -- train_loss: 1.8304 \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/1, Iter: 90/157 -- train_loss: 0.1188 \n",
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      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/1, Iter: 156/157 -- train_loss: 0.0413 \n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/1, Iter: 157/157 -- train_loss: 0.0319 \n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Got new best metric of train_acc: 0.94216759681336\n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch[1] Metrics -- train_acc: 0.9422 \n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Key metric: train_acc best value: 0.94216759681336 at epoch: 1\n",
      "LR values of 3 parameter groups:  [0.0001, 5e-05, 1e-05]\n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch[1] Complete. Time taken: 00:00:21\n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Engine run complete. Time taken: 00:00:23\n"
     ]
    }
   ],
   "source": [
    "trainer.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix: layers of DenseNet 121 network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DenseNet(\n",
      "  (features): Sequential(\n",
      "    (conv0): Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (relu0): ReLU(inplace=True)\n",
      "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "    (denseblock1): _DenseBlock(\n",
      "      (denselayer1): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer2): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer3): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer4): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer5): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer6): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (transition1): _Transition(\n",
      "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
      "    )\n",
      "    (denseblock2): _DenseBlock(\n",
      "      (denselayer1): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer2): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer3): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer4): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer5): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer6): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer7): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer8): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer9): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer10): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer11): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer12): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (transition2): _Transition(\n",
      "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
      "    )\n",
      "    (denseblock3): _DenseBlock(\n",
      "      (denselayer1): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer2): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer3): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer4): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer5): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer6): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer7): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer8): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer9): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer10): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer11): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer12): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer13): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer14): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer15): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer16): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer17): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer18): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer19): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer20): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer21): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer22): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer23): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer24): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (transition3): _Transition(\n",
      "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
      "    )\n",
      "    (denseblock4): _DenseBlock(\n",
      "      (denselayer1): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer2): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer3): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer4): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer5): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer6): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer7): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer8): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer9): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer10): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer11): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer12): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer13): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer14): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer15): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "      (denselayer16): _DenseLayer(\n",
      "        (layers): Sequential(\n",
      "          (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu1): ReLU(inplace=True)\n",
      "          (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "          (relu2): ReLU(inplace=True)\n",
      "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (class_layers): Sequential(\n",
      "    (relu): ReLU(inplace=True)\n",
      "    (pool): AdaptiveAvgPool2d(output_size=1)\n",
      "    (flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "    (out): Linear(in_features=1024, out_features=6, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(net)"
   ]
  }
 ],
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