imprv_AUC_transformer_binary_v1.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8d0a3b11-dddb-4d0b-9eaa-d89c27bf012d",
   "metadata": {},
   "source": [
    "#### Transformer for prediction of binary hi/lo drug response  \n",
    "Template: https://keras.io/examples/structured_data/tabtransformer/  \n",
    "Version 1.1, feature dimension reductions to n = 10, 25  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "00fbb312-5a32-4238-b4d7-aae0addf47ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/tf310/lib/python3.10/site-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
      "\n",
      "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
      "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
      "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
      "\n",
      "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
      "\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import tensorflow_addons as tfa\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cef35220-1176-48b2-8d3a-00e2eab5d96d",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Loss and accuracy plot functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98d5cabc-24bd-44d6-8a76-4e855ad74a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_training_loss(loss_dict, latent_dim, merged_df, feat_type, model_type):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(loss_dict['loss'], marker='o', linestyle='-', color='b')\n",
    "    plt.grid(True, linestyle='--', alpha=0.6)\n",
    "    plt.title('AUC binary '+feat_type+' '+model_type+' training loss', fontsize=16, fontweight='bold')\n",
    "    plt.xlabel('Epoch', fontsize=14)\n",
    "    plt.ylabel('Loss', fontsize=14)\n",
    "    plt.annotate('Samples: {}'.format(len(merged_df))+'\\nFeature dim: '+str(latent_dim), \n",
    "                 xy=(0.7, 0.3), xycoords='axes fraction', \n",
    "                 bbox=dict(boxstyle='round, pad=0.5', fc='white', ec='black'),\n",
    "                 fontsize=12)\n",
    "    plt.savefig(\n",
    "        'AUC_v1/'+model_type+'_loss_plots/'+feat_type+'_'+str(\n",
    "            latent_dim)+'_'+model_type+'_training_loss.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94335fc8-872b-46d8-af37-37b0131dde1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_training_accuracy(loss_dict, latent_dim, merged_df, feat_type, model_type):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(loss_dict['accuracy'], marker='o', linestyle='-', color='b')\n",
    "    plt.grid(True, linestyle='--', alpha=0.6)\n",
    "    plt.title('AUC binary '+feat_type+' '+model_type+' training accuracy', fontsize=16, fontweight='bold')\n",
    "    plt.xlabel('Epoch', fontsize=14)\n",
    "    plt.ylabel('Loss', fontsize=14)\n",
    "    plt.annotate('Samples: {}'.format(len(merged_df))+'\\nFeature dim: '+str(latent_dim), \n",
    "                 xy=(0.7, 0.3), xycoords='axes fraction', \n",
    "                 bbox=dict(boxstyle='round, pad=0.5', fc='white', ec='black'),\n",
    "                 fontsize=12)\n",
    "    plt.savefig(\n",
    "        'AUC_v1/'+model_type+'_accuracy_plots/'+feat_type+'_'+str(\n",
    "            latent_dim)+'_'+model_type+'_training_accuracy.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a45964e1-5f7e-4c1d-951b-641436da3dfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_validation_loss(loss_dict, latent_dim, merged_df, feat_type, model_type):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(loss_dict['val_loss'], marker='o', linestyle='-', color='r')\n",
    "    plt.grid(True, linestyle='--', alpha=0.6)\n",
    "    plt.title('AUC binary '+feat_type+' '+model_type+' validation loss', fontsize=16, fontweight='bold')\n",
    "    plt.xlabel('Epoch', fontsize=14)\n",
    "    plt.ylabel('Loss', fontsize=14)\n",
    "    plt.annotate('Samples: {}'.format(len(merged_df))+'\\nFeature dim: '+str(latent_dim), \n",
    "                 xy=(0.7, 0.3), xycoords='axes fraction', \n",
    "                 bbox=dict(boxstyle='round, pad=0.5', fc='white', ec='black'),\n",
    "                 fontsize=12)\n",
    "    plt.savefig(\n",
    "        'AUC_v1/'+model_type+'_loss_plots/'+feat_type+'_'+str(\n",
    "            latent_dim)+'_'+model_type+'_validation_loss.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21c6204e-321b-49f4-a19d-0ca824117dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_validation_accuracy(loss_dict, latent_dim, merged_df, feat_type, model_type):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(loss_dict['val_accuracy'], marker='o', linestyle='-', color='r')\n",
    "    plt.grid(True, linestyle='--', alpha=0.6)\n",
    "    plt.title('AUC binary '+feat_type+' '+model_type+' validation accuracy', fontsize=16, fontweight='bold')\n",
    "    plt.xlabel('Epoch', fontsize=14)\n",
    "    plt.ylabel('Loss', fontsize=14)\n",
    "    plt.annotate('Samples: {}'.format(len(merged_df))+'\\nFeature dim: '+str(latent_dim), \n",
    "                 xy=(0.7, 0.3), xycoords='axes fraction', \n",
    "                 bbox=dict(boxstyle='round, pad=0.5', fc='white', ec='black'),\n",
    "                 fontsize=12)\n",
    "    plt.savefig(\n",
    "        'AUC_v1/'+model_type+'_accuracy_plots/'+feat_type+'_'+str(\n",
    "            latent_dim)+'_'+model_type+'_validation_accuracy.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bde0593-d104-46b4-a0fb-8c2deae01394",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c1edab6-b95e-41c5-9028-a9e431a9c3d7",
   "metadata": {},
   "source": [
    "#### max diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d9ce4fd0-bdab-4a4a-97f5-e2a98bbc20e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_type = 'max_diff' # section toggle, to plot functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ef9a6364-1fd7-4222-b452-2ad454f4010a",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 10\n",
    "exprs_feats = pd.read_csv('AUC_v1/max_diff_expr_10.tsv',\n",
    "                            sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "bfad2c10-f6af-4f45-9972-3b34cda1872d",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 25\n",
    "exprs_feats = pd.read_csv('AUC_v1/max_diff_expr_25.tsv',\n",
    "                            sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dce9518-69d7-44c2-a902-acd556f0df59",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 50\n",
    "exprs_feats = pd.read_csv('AUC_v1/max_diff_expr_50.tsv',\n",
    "                            sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fa94487-dfd2-456e-af27-703f2fa29eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 100\n",
    "exprs_feats = pd.read_csv('AUC_v1/max_diff_expr_100.tsv',\n",
    "                            sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c4abde9-00ac-4f8b-b8bd-ac938fb62084",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 150\n",
    "exprs_feats = pd.read_csv('AUC_v1/max_diff_expr_150.tsv',\n",
    "                            sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80662229-0481-40c0-9e5a-6e91e4502386",
   "metadata": {},
   "source": [
    "#### UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "610acc05-cdde-4c93-b1aa-1d80c588bfdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_type = 'UMAP' #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "a2d6c9d2-6170-41b6-a929-e7fee22483e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 10\n",
    "exprs_feats = pd.read_csv('AUC_v1/UMAP_expr_10.tsv',\n",
    "                            sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f14828e5-45e8-4d25-84c3-7af6cbade4cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 25\n",
    "exprs_feats = pd.read_csv('AUC_v1/UMAP_expr_25.tsv',\n",
    "                            sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29728de5-035e-40bb-869e-4863a7a2bc7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 50\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1d024ac-161c-462f-aa57-27c817da1dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 100\n",
    "exprs_feats = pd.read_csv('AUC_v1/UMAP_expr_100.tsv',\n",
    "                            sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d9e2938-89f0-4737-9db3-1717763dbc09",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 150\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "959574c3-2361-4116-a149-1654e347ab5d",
   "metadata": {},
   "source": [
    "#### VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "6c67f113-335b-4bb2-a674-c11f40064685",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_type = 'VAE' #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "39c359ae-5075-44c0-909d-aca2d18fcb36",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 10\n",
    "exprs_feats = pd.read_csv('AUC_v1/VAE_expr_10.tsv',\n",
    "                            sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "191bb488-c84d-4807-9db9-6c9e7ffb68fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 25\n",
    "exprs_feats = pd.read_csv('AUC_v1/VAE_expr_25.tsv',\n",
    "                            sep = '\\t', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d5b3634-6175-4347-9f79-6824c5a912a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 50\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fd8c531-1bda-4408-8a9b-db4ab9352bf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3969ef1e-d654-4336-bd18-9c26df362f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 150\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98667c42-0e80-4cb1-a4af-d9dbb7b2c1ea",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Model setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4284dd29-3075-4d8f-b730-e07c9f5b5375",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the y target and categorical X file\n",
    "# Retain in memory for numeric feature iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54006ee8-df7c-4dc0-aed5-eb38ec8a423b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_catX = pd.read_csv('AUC_v1/AUC_binary_unq_pairs.tsv',\n",
    "                            sep = '\\t', index_col = 0)\n",
    "y_catX.set_index('improve_sample_id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "099f8b19-1e66-4f7d-b7c2-e6ed8889baa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_catX.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "256cd43e-ad49-474f-abdf-1f4a577b6d21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2099059, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_catX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "80e7e895-97c3-4c1d-946f-0feedbe0841d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_catX_dwn_smpl = y_catX.sample(n=50000) # Reduced from 100k in version 1 tests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ee74daf-e000-47b7-a23d-3d555afba867",
   "metadata": {},
   "source": [
    "### Build the input frame for numeric feature set n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "36a274b9-61df-437e-a662-af254e52d6b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = y_catX_dwn_smpl.merge(\n",
    "    exprs_feats, left_index=True, right_index=True)\n",
    "merged_df['fnlwgt'] = 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "d5a4d09b-9600-40b2-8745-df87dc252984",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC</th>\n",
       "      <th>canSMILES</th>\n",
       "      <th>pairs</th>\n",
       "      <th>cl_ln_gxp_0</th>\n",
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       "      <th>cl_ln_gxp_20</th>\n",
       "      <th>cl_ln_gxp_21</th>\n",
       "      <th>cl_ln_gxp_22</th>\n",
       "      <th>cl_ln_gxp_23</th>\n",
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       "      <th>2.0</th>\n",
       "      <td>high</td>\n",
       "      <td>CCCCC(C)C(C(CC(C)CC(CCCCC(CC(C(C)N)O)O)O)OC(=O...</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>1.072425</td>\n",
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       "      <td>4.0_CN1CCN(CC1)C2=CC=C(C=C2)NC3=NC=C(C(=N3)SC4...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.410028</td>\n",
       "      <td>1.976111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.253432</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>high</td>\n",
       "      <td>CC(C)C1=CC=CC=C1OC(=O)NCCC2=CC3=CC=CC=C3C=C2</td>\n",
       "      <td>4.0_CC(C)C1=CC=CC=C1OC(=O)NCCC2=CC3=CC=CC=C3C=C2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.410028</td>\n",
       "      <td>1.976111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.253432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.783314</td>\n",
       "      <td>1.091452</td>\n",
       "      <td>1.308224</td>\n",
       "      <td>2.066199</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>high</td>\n",
       "      <td>CC(C)OC1=C(C=CC(=C1)OC)C2=NC(C(N2C(=O)N3CCNC(=...</td>\n",
       "      <td>4.0_CC(C)OC1=C(C=CC(=C1)OC)C2=NC(C(N2C(=O)N3CC...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.410028</td>\n",
       "      <td>1.976111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.253432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.783314</td>\n",
       "      <td>1.091452</td>\n",
       "      <td>1.308224</td>\n",
       "      <td>2.066199</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>low</td>\n",
       "      <td>CC1(CCCN1C2=NN3C=CC=C3C(=N2)NC4=NNC(=C4)C5CC5)...</td>\n",
       "      <td>4.0_CC1(CCCN1C2=NN3C=CC=C3C(=N2)NC4=NNC(=C4)C5...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.410028</td>\n",
       "      <td>1.976111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.253432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.783314</td>\n",
       "      <td>1.091452</td>\n",
       "      <td>1.308224</td>\n",
       "      <td>2.066199</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97123.0</th>\n",
       "      <td>high</td>\n",
       "      <td>CC1(C[Se]C1)N2C3=CC=CC=C3N(C2=O)C</td>\n",
       "      <td>97123.0_CC1(C[Se]C1)N2C3=CC=CC=C3N(C2=O)C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.568556</td>\n",
       "      <td>1.124516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.096508</td>\n",
       "      <td>0.275017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.401150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.368960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.243957</td>\n",
       "      <td>1.431726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97123.0</th>\n",
       "      <td>low</td>\n",
       "      <td>CC1=CC(=C(C(=C1)C)S(=O)(=O)N(CCCCCCN)OCCCN)C.Cl</td>\n",
       "      <td>97123.0_CC1=CC(=C(C(=C1)C)S(=O)(=O)N(CCCCCCN)O...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.568556</td>\n",
       "      <td>1.124516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.096508</td>\n",
       "      <td>0.275017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.401150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.368960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.243957</td>\n",
       "      <td>1.431726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97123.0</th>\n",
       "      <td>low</td>\n",
       "      <td>C1=CC2=C(C(=C1)S)N=CC=C2</td>\n",
       "      <td>97123.0_C1=CC2=C(C(=C1)S)N=CC=C2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.568556</td>\n",
       "      <td>1.124516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.096508</td>\n",
       "      <td>0.275017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.401150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.368960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.243957</td>\n",
       "      <td>1.431726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97123.0</th>\n",
       "      <td>low</td>\n",
       "      <td>C1CC2=C(C=CC(=C2)C(=O)O)C(=C(C1)C3=C(C=C(C=C3)...</td>\n",
       "      <td>97123.0_C1CC2=C(C=CC(=C2)C(=O)O)C(=C(C1)C3=C(C...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.568556</td>\n",
       "      <td>1.124516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.096508</td>\n",
       "      <td>0.275017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.401150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.368960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.243957</td>\n",
       "      <td>1.431726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97123.0</th>\n",
       "      <td>high</td>\n",
       "      <td>CC(=O)CC(=O)N</td>\n",
       "      <td>97123.0_CC(=O)CC(=O)N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.568556</td>\n",
       "      <td>1.124516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.096508</td>\n",
       "      <td>0.275017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.401150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.368960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.243957</td>\n",
       "      <td>1.431726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35058 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    AUC                                          canSMILES  \\\n",
       "improve_sample_id                                                            \n",
       "2.0                high  CCCCC(C)C(C(CC(C)CC(CCCCC(CC(C(C)N)O)O)O)OC(=O...   \n",
       "4.0                 low  CN1CCN(CC1)C2=CC=C(C=C2)NC3=NC=C(C(=N3)SC4=CC=...   \n",
       "4.0                high       CC(C)C1=CC=CC=C1OC(=O)NCCC2=CC3=CC=CC=C3C=C2   \n",
       "4.0                high  CC(C)OC1=C(C=CC(=C1)OC)C2=NC(C(N2C(=O)N3CCNC(=...   \n",
       "4.0                 low  CC1(CCCN1C2=NN3C=CC=C3C(=N2)NC4=NNC(=C4)C5CC5)...   \n",
       "...                 ...                                                ...   \n",
       "97123.0            high                  CC1(C[Se]C1)N2C3=CC=CC=C3N(C2=O)C   \n",
       "97123.0             low    CC1=CC(=C(C(=C1)C)S(=O)(=O)N(CCCCCCN)OCCCN)C.Cl   \n",
       "97123.0             low                           C1=CC2=C(C(=C1)S)N=CC=C2   \n",
       "97123.0             low  C1CC2=C(C=CC(=C2)C(=O)O)C(=C(C1)C3=C(C=C(C=C3)...   \n",
       "97123.0            high                                      CC(=O)CC(=O)N   \n",
       "\n",
       "                                                               pairs  \\\n",
       "improve_sample_id                                                      \n",
       "2.0                2.0_CCCCC(C)C(C(CC(C)CC(CCCCC(CC(C(C)N)O)O)O)O...   \n",
       "4.0                4.0_CN1CCN(CC1)C2=CC=C(C=C2)NC3=NC=C(C(=N3)SC4...   \n",
       "4.0                 4.0_CC(C)C1=CC=CC=C1OC(=O)NCCC2=CC3=CC=CC=C3C=C2   \n",
       "4.0                4.0_CC(C)OC1=C(C=CC(=C1)OC)C2=NC(C(N2C(=O)N3CC...   \n",
       "4.0                4.0_CC1(CCCN1C2=NN3C=CC=C3C(=N2)NC4=NNC(=C4)C5...   \n",
       "...                                                              ...   \n",
       "97123.0                    97123.0_CC1(C[Se]C1)N2C3=CC=CC=C3N(C2=O)C   \n",
       "97123.0            97123.0_CC1=CC(=C(C(=C1)C)S(=O)(=O)N(CCCCCCN)O...   \n",
       "97123.0                             97123.0_C1=CC2=C(C(=C1)S)N=CC=C2   \n",
       "97123.0            97123.0_C1CC2=C(C=CC(=C2)C(=O)O)C(=C(C1)C3=C(C...   \n",
       "97123.0                                        97123.0_CC(=O)CC(=O)N   \n",
       "\n",
       "                   cl_ln_gxp_0  cl_ln_gxp_1  cl_ln_gxp_2  cl_ln_gxp_3  \\\n",
       "improve_sample_id                                                       \n",
       "2.0                        0.0      1.72768     0.000000     2.178468   \n",
       "4.0                        0.0      0.00000     0.000000     0.000000   \n",
       "4.0                        0.0      0.00000     0.000000     0.000000   \n",
       "4.0                        0.0      0.00000     0.000000     0.000000   \n",
       "4.0                        0.0      0.00000     0.000000     0.000000   \n",
       "...                        ...          ...          ...          ...   \n",
       "97123.0                    0.0      0.00000     1.568556     1.124516   \n",
       "97123.0                    0.0      0.00000     1.568556     1.124516   \n",
       "97123.0                    0.0      0.00000     1.568556     1.124516   \n",
       "97123.0                    0.0      0.00000     1.568556     1.124516   \n",
       "97123.0                    0.0      0.00000     1.568556     1.124516   \n",
       "\n",
       "                   cl_ln_gxp_4  cl_ln_gxp_5  cl_ln_gxp_6  ...  cl_ln_gxp_16  \\\n",
       "improve_sample_id                                         ...                 \n",
       "2.0                   0.000000     0.000000     0.258668  ...           0.0   \n",
       "4.0                   1.410028     1.976111     0.000000  ...           0.0   \n",
       "4.0                   1.410028     1.976111     0.000000  ...           0.0   \n",
       "4.0                   1.410028     1.976111     0.000000  ...           0.0   \n",
       "4.0                   1.410028     1.976111     0.000000  ...           0.0   \n",
       "...                        ...          ...          ...  ...           ...   \n",
       "97123.0               0.000000     1.096508     0.275017  ...           0.0   \n",
       "97123.0               0.000000     1.096508     0.275017  ...           0.0   \n",
       "97123.0               0.000000     1.096508     0.275017  ...           0.0   \n",
       "97123.0               0.000000     1.096508     0.275017  ...           0.0   \n",
       "97123.0               0.000000     1.096508     0.275017  ...           0.0   \n",
       "\n",
       "                   cl_ln_gxp_17  cl_ln_gxp_18  cl_ln_gxp_19  cl_ln_gxp_20  \\\n",
       "improve_sample_id                                                           \n",
       "2.0                    0.000000           0.0           0.0      0.000000   \n",
       "4.0                    2.253432           0.0           0.0      1.783314   \n",
       "4.0                    2.253432           0.0           0.0      1.783314   \n",
       "4.0                    2.253432           0.0           0.0      1.783314   \n",
       "4.0                    2.253432           0.0           0.0      1.783314   \n",
       "...                         ...           ...           ...           ...   \n",
       "97123.0                1.401150           0.0           0.0      1.368960   \n",
       "97123.0                1.401150           0.0           0.0      1.368960   \n",
       "97123.0                1.401150           0.0           0.0      1.368960   \n",
       "97123.0                1.401150           0.0           0.0      1.368960   \n",
       "97123.0                1.401150           0.0           0.0      1.368960   \n",
       "\n",
       "                   cl_ln_gxp_21  cl_ln_gxp_22  cl_ln_gxp_23  cl_ln_gxp_24  \\\n",
       "improve_sample_id                                                           \n",
       "2.0                    0.000000      2.434945      0.000000      1.072425   \n",
       "4.0                    1.091452      1.308224      2.066199      0.000000   \n",
       "4.0                    1.091452      1.308224      2.066199      0.000000   \n",
       "4.0                    1.091452      1.308224      2.066199      0.000000   \n",
       "4.0                    1.091452      1.308224      2.066199      0.000000   \n",
       "...                         ...           ...           ...           ...   \n",
       "97123.0                0.000000      1.243957      1.431726      0.000000   \n",
       "97123.0                0.000000      1.243957      1.431726      0.000000   \n",
       "97123.0                0.000000      1.243957      1.431726      0.000000   \n",
       "97123.0                0.000000      1.243957      1.431726      0.000000   \n",
       "97123.0                0.000000      1.243957      1.431726      0.000000   \n",
       "\n",
       "                   fnlwgt  \n",
       "improve_sample_id          \n",
       "2.0                   1.0  \n",
       "4.0                   1.0  \n",
       "4.0                   1.0  \n",
       "4.0                   1.0  \n",
       "4.0                   1.0  \n",
       "...                   ...  \n",
       "97123.0               1.0  \n",
       "97123.0               1.0  \n",
       "97123.0               1.0  \n",
       "97123.0               1.0  \n",
       "97123.0               1.0  \n",
       "\n",
       "[35058 rows x 29 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "555e4e65-3b52-43b2-9c11-8c81708c6e64",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df.drop(columns = 'pairs', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "aead5bff-4327-4e38-831c-8d26d8540433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35058, 28)"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.shape # numeric dim + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "75944213-4262-4c23-b2cf-9aeee676b342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35058"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "5215ef15-c19b-4d87-bff1-e74a16b028e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "81308f66-56ce-475f-b1d2-6432fa826d8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AUC</th>\n",
       "      <th>canSMILES</th>\n",
       "      <th>cl_ln_gxp_0</th>\n",
       "      <th>cl_ln_gxp_1</th>\n",
       "      <th>cl_ln_gxp_2</th>\n",
       "      <th>cl_ln_gxp_3</th>\n",
       "      <th>cl_ln_gxp_4</th>\n",
       "      <th>cl_ln_gxp_5</th>\n",
       "      <th>cl_ln_gxp_6</th>\n",
       "      <th>cl_ln_gxp_7</th>\n",
       "      <th>...</th>\n",
       "      <th>cl_ln_gxp_16</th>\n",
       "      <th>cl_ln_gxp_17</th>\n",
       "      <th>cl_ln_gxp_18</th>\n",
       "      <th>cl_ln_gxp_19</th>\n",
       "      <th>cl_ln_gxp_20</th>\n",
       "      <th>cl_ln_gxp_21</th>\n",
       "      <th>cl_ln_gxp_22</th>\n",
       "      <th>cl_ln_gxp_23</th>\n",
       "      <th>cl_ln_gxp_24</th>\n",
       "      <th>fnlwgt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>improve_sample_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>high</td>\n",
       "      <td>CCCCC(C)C(C(CC(C)CC(CCCCC(CC(C(C)N)O)O)O)OC(=O...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.72768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.178468</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.258668</td>\n",
       "      <td>1.135206</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.434945</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.072425</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>low</td>\n",
       "      <td>CN1CCN(CC1)C2=CC=C(C=C2)NC3=NC=C(C(=N3)SC4=CC=...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.410028</td>\n",
       "      <td>1.976111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.253432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.783314</td>\n",
       "      <td>1.091452</td>\n",
       "      <td>1.308224</td>\n",
       "      <td>2.066199</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>high</td>\n",
       "      <td>CC(C)C1=CC=CC=C1OC(=O)NCCC2=CC3=CC=CC=C3C=C2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.410028</td>\n",
       "      <td>1.976111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.253432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.783314</td>\n",
       "      <td>1.091452</td>\n",
       "      <td>1.308224</td>\n",
       "      <td>2.066199</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>high</td>\n",
       "      <td>CC(C)OC1=C(C=CC(=C1)OC)C2=NC(C(N2C(=O)N3CCNC(=...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.410028</td>\n",
       "      <td>1.976111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.253432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.783314</td>\n",
       "      <td>1.091452</td>\n",
       "      <td>1.308224</td>\n",
       "      <td>2.066199</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>low</td>\n",
       "      <td>CC1(CCCN1C2=NN3C=CC=C3C(=N2)NC4=NNC(=C4)C5CC5)...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.410028</td>\n",
       "      <td>1.976111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.253432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.783314</td>\n",
       "      <td>1.091452</td>\n",
       "      <td>1.308224</td>\n",
       "      <td>2.066199</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97123.0</th>\n",
       "      <td>high</td>\n",
       "      <td>CC1(C[Se]C1)N2C3=CC=CC=C3N(C2=O)C</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.568556</td>\n",
       "      <td>1.124516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.096508</td>\n",
       "      <td>0.275017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.401150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.368960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.243957</td>\n",
       "      <td>1.431726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97123.0</th>\n",
       "      <td>low</td>\n",
       "      <td>CC1=CC(=C(C(=C1)C)S(=O)(=O)N(CCCCCCN)OCCCN)C.Cl</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.568556</td>\n",
       "      <td>1.124516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.096508</td>\n",
       "      <td>0.275017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.401150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.368960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.243957</td>\n",
       "      <td>1.431726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97123.0</th>\n",
       "      <td>low</td>\n",
       "      <td>C1=CC2=C(C(=C1)S)N=CC=C2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.568556</td>\n",
       "      <td>1.124516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.096508</td>\n",
       "      <td>0.275017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.401150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.368960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.243957</td>\n",
       "      <td>1.431726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97123.0</th>\n",
       "      <td>low</td>\n",
       "      <td>C1CC2=C(C=CC(=C2)C(=O)O)C(=C(C1)C3=C(C=C(C=C3)...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.568556</td>\n",
       "      <td>1.124516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.096508</td>\n",
       "      <td>0.275017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.401150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.368960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.243957</td>\n",
       "      <td>1.431726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97123.0</th>\n",
       "      <td>high</td>\n",
       "      <td>CC(=O)CC(=O)N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.568556</td>\n",
       "      <td>1.124516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.096508</td>\n",
       "      <td>0.275017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.401150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.368960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.243957</td>\n",
       "      <td>1.431726</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35058 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    AUC                                          canSMILES  \\\n",
       "improve_sample_id                                                            \n",
       "2.0                high  CCCCC(C)C(C(CC(C)CC(CCCCC(CC(C(C)N)O)O)O)OC(=O...   \n",
       "4.0                 low  CN1CCN(CC1)C2=CC=C(C=C2)NC3=NC=C(C(=N3)SC4=CC=...   \n",
       "4.0                high       CC(C)C1=CC=CC=C1OC(=O)NCCC2=CC3=CC=CC=C3C=C2   \n",
       "4.0                high  CC(C)OC1=C(C=CC(=C1)OC)C2=NC(C(N2C(=O)N3CCNC(=...   \n",
       "4.0                 low  CC1(CCCN1C2=NN3C=CC=C3C(=N2)NC4=NNC(=C4)C5CC5)...   \n",
       "...                 ...                                                ...   \n",
       "97123.0            high                  CC1(C[Se]C1)N2C3=CC=CC=C3N(C2=O)C   \n",
       "97123.0             low    CC1=CC(=C(C(=C1)C)S(=O)(=O)N(CCCCCCN)OCCCN)C.Cl   \n",
       "97123.0             low                           C1=CC2=C(C(=C1)S)N=CC=C2   \n",
       "97123.0             low  C1CC2=C(C=CC(=C2)C(=O)O)C(=C(C1)C3=C(C=C(C=C3)...   \n",
       "97123.0            high                                      CC(=O)CC(=O)N   \n",
       "\n",
       "                   cl_ln_gxp_0  cl_ln_gxp_1  cl_ln_gxp_2  cl_ln_gxp_3  \\\n",
       "improve_sample_id                                                       \n",
       "2.0                        0.0      1.72768     0.000000     2.178468   \n",
       "4.0                        0.0      0.00000     0.000000     0.000000   \n",
       "4.0                        0.0      0.00000     0.000000     0.000000   \n",
       "4.0                        0.0      0.00000     0.000000     0.000000   \n",
       "4.0                        0.0      0.00000     0.000000     0.000000   \n",
       "...                        ...          ...          ...          ...   \n",
       "97123.0                    0.0      0.00000     1.568556     1.124516   \n",
       "97123.0                    0.0      0.00000     1.568556     1.124516   \n",
       "97123.0                    0.0      0.00000     1.568556     1.124516   \n",
       "97123.0                    0.0      0.00000     1.568556     1.124516   \n",
       "97123.0                    0.0      0.00000     1.568556     1.124516   \n",
       "\n",
       "                   cl_ln_gxp_4  cl_ln_gxp_5  cl_ln_gxp_6  cl_ln_gxp_7  ...  \\\n",
       "improve_sample_id                                                      ...   \n",
       "2.0                   0.000000     0.000000     0.258668     1.135206  ...   \n",
       "4.0                   1.410028     1.976111     0.000000     0.000000  ...   \n",
       "4.0                   1.410028     1.976111     0.000000     0.000000  ...   \n",
       "4.0                   1.410028     1.976111     0.000000     0.000000  ...   \n",
       "4.0                   1.410028     1.976111     0.000000     0.000000  ...   \n",
       "...                        ...          ...          ...          ...  ...   \n",
       "97123.0               0.000000     1.096508     0.275017     0.000000  ...   \n",
       "97123.0               0.000000     1.096508     0.275017     0.000000  ...   \n",
       "97123.0               0.000000     1.096508     0.275017     0.000000  ...   \n",
       "97123.0               0.000000     1.096508     0.275017     0.000000  ...   \n",
       "97123.0               0.000000     1.096508     0.275017     0.000000  ...   \n",
       "\n",
       "                   cl_ln_gxp_16  cl_ln_gxp_17  cl_ln_gxp_18  cl_ln_gxp_19  \\\n",
       "improve_sample_id                                                           \n",
       "2.0                         0.0      0.000000           0.0           0.0   \n",
       "4.0                         0.0      2.253432           0.0           0.0   \n",
       "4.0                         0.0      2.253432           0.0           0.0   \n",
       "4.0                         0.0      2.253432           0.0           0.0   \n",
       "4.0                         0.0      2.253432           0.0           0.0   \n",
       "...                         ...           ...           ...           ...   \n",
       "97123.0                     0.0      1.401150           0.0           0.0   \n",
       "97123.0                     0.0      1.401150           0.0           0.0   \n",
       "97123.0                     0.0      1.401150           0.0           0.0   \n",
       "97123.0                     0.0      1.401150           0.0           0.0   \n",
       "97123.0                     0.0      1.401150           0.0           0.0   \n",
       "\n",
       "                   cl_ln_gxp_20  cl_ln_gxp_21  cl_ln_gxp_22  cl_ln_gxp_23  \\\n",
       "improve_sample_id                                                           \n",
       "2.0                    0.000000      0.000000      2.434945      0.000000   \n",
       "4.0                    1.783314      1.091452      1.308224      2.066199   \n",
       "4.0                    1.783314      1.091452      1.308224      2.066199   \n",
       "4.0                    1.783314      1.091452      1.308224      2.066199   \n",
       "4.0                    1.783314      1.091452      1.308224      2.066199   \n",
       "...                         ...           ...           ...           ...   \n",
       "97123.0                1.368960      0.000000      1.243957      1.431726   \n",
       "97123.0                1.368960      0.000000      1.243957      1.431726   \n",
       "97123.0                1.368960      0.000000      1.243957      1.431726   \n",
       "97123.0                1.368960      0.000000      1.243957      1.431726   \n",
       "97123.0                1.368960      0.000000      1.243957      1.431726   \n",
       "\n",
       "                   cl_ln_gxp_24  fnlwgt  \n",
       "improve_sample_id                        \n",
       "2.0                    1.072425     1.0  \n",
       "4.0                    0.000000     1.0  \n",
       "4.0                    0.000000     1.0  \n",
       "4.0                    0.000000     1.0  \n",
       "4.0                    0.000000     1.0  \n",
       "...                         ...     ...  \n",
       "97123.0                0.000000     1.0  \n",
       "97123.0                0.000000     1.0  \n",
       "97123.0                0.000000     1.0  \n",
       "97123.0                0.000000     1.0  \n",
       "97123.0                0.000000     1.0  \n",
       "\n",
       "[35058 rows x 28 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "15c32036-603d-4b5a-a42f-3c2de51b7975",
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_HEADER = merged_df.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "7596011e-4769-4bee-a34e-da6a766c09a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = merged_df.sample(int(len(merged_df)*.75))\n",
    "test_data = merged_df.loc[~merged_df.index.isin(train_data.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "f4379a5e-b373-4438-9699-65be8a302ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_file = \"train_data.csv\"\n",
    "test_data_file = \"test_data.csv\"\n",
    "\n",
    "train_data.to_csv(train_data_file, index=False, header=False)\n",
    "test_data.to_csv(test_data_file, index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "2f7880dd-ec61-4440-b1d2-bec72a14c2af",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMERIC_FEATURE_NAMES = CSV_HEADER[2:-1]\n",
    "\n",
    "CATEGORICAL_FEATURES_WITH_VOCABULARY = { # One cat feat for these AUC tests\n",
    "    \"canSMILES\": sorted(list(train_data[\"canSMILES\"].unique())),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "69759d39-34ef-4ae8-a27d-56106e33d567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(NUMERIC_FEATURE_NAMES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "eb5bbb88-f1fe-47cc-a283-f0f1d5ae567a",
   "metadata": {},
   "outputs": [],
   "source": [
    "WEIGHT_COLUMN_NAME = \"fnlwgt\"\n",
    "CATEGORICAL_FEATURE_NAMES = list(CATEGORICAL_FEATURES_WITH_VOCABULARY.keys())\n",
    "FEATURE_NAMES = NUMERIC_FEATURE_NAMES + CATEGORICAL_FEATURE_NAMES\n",
    "COLUMN_DEFAULTS = [\n",
    "    [0.0] if feature_name in NUMERIC_FEATURE_NAMES + [WEIGHT_COLUMN_NAME] else [\"NA\"]\n",
    "    for feature_name in CSV_HEADER\n",
    "]\n",
    "TARGET_FEATURE_NAME = 'AUC'\n",
    "TARGET_LABELS = merged_df.AUC.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "1afb4586-2e90-4132-ad0c-07b4872fe72f",
   "metadata": {},
   "outputs": [],
   "source": [
    "LEARNING_RATE = 0.001\n",
    "WEIGHT_DECAY = 0.0001\n",
    "DROPOUT_RATE = 0.2\n",
    "BATCH_SIZE = 265\n",
    "NUM_EPOCHS = 15\n",
    "\n",
    "NUM_TRANSFORMER_BLOCKS = 3  # Number of transformer blocks.\n",
    "NUM_HEADS = 4  # Number of attention heads.\n",
    "EMBEDDING_DIMS = 16  # Embedding dimensions of the categorical features.\n",
    "MLP_HIDDEN_UNITS_FACTORS = [\n",
    "    2,\n",
    "    1,\n",
    "]  # MLP hidden layer units, as factors of the number of inputs.\n",
    "NUM_MLP_BLOCKS = 2  # Number of MLP blocks in the baseline model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "5479e392-7276-4f2f-a577-d29c1676a94b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/tf310/lib/python3.10/site-packages/numpy/core/numeric.py:2468: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return bool(asarray(a1 == a2).all())\n"
     ]
    }
   ],
   "source": [
    "target_label_lookup = layers.StringLookup(\n",
    "    vocabulary=TARGET_LABELS, mask_token=None, num_oov_indices=0\n",
    ")\n",
    "\n",
    "\n",
    "def prepare_example(features, target):\n",
    "    target_index = target_label_lookup(target)\n",
    "    weights = features.pop(WEIGHT_COLUMN_NAME)\n",
    "    return features, target_index, weights\n",
    "\n",
    "\n",
    "def get_dataset_from_csv(csv_file_path, batch_size=128, shuffle=False):\n",
    "    dataset = tf.data.experimental.make_csv_dataset(\n",
    "        csv_file_path,\n",
    "        batch_size=batch_size,\n",
    "        column_names=CSV_HEADER,\n",
    "        column_defaults=COLUMN_DEFAULTS,\n",
    "        label_name=TARGET_FEATURE_NAME,\n",
    "        num_epochs=1,\n",
    "        header=False,\n",
    "        na_value=\"?\",\n",
    "        shuffle=shuffle,\n",
    "    ).map(prepare_example, num_parallel_calls=tf.data.AUTOTUNE, deterministic=False)\n",
    "    return dataset.cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "61ed5bf6-7795-4210-85d6-df9b6823e5bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_experiment(\n",
    "    model,\n",
    "    train_data_file,\n",
    "    test_data_file,\n",
    "    num_epochs,\n",
    "    learning_rate,\n",
    "    weight_decay,\n",
    "    batch_size,\n",
    "):\n",
    "\n",
    "    optimizer = tfa.optimizers.AdamW(\n",
    "        learning_rate=learning_rate, weight_decay=weight_decay\n",
    "    )\n",
    "\n",
    "    model.compile(\n",
    "        optimizer=optimizer,\n",
    "        loss=keras.losses.BinaryCrossentropy(),\n",
    "        metrics=[keras.metrics.BinaryAccuracy(name=\"accuracy\")],\n",
    "    )\n",
    "\n",
    "    train_dataset = get_dataset_from_csv(train_data_file, batch_size, shuffle=True)\n",
    "    validation_dataset = get_dataset_from_csv(test_data_file, batch_size)\n",
    "\n",
    "    print(\"Start training the model...\")\n",
    "    history = model.fit(\n",
    "        train_dataset, epochs=num_epochs, validation_data=validation_dataset\n",
    "    )\n",
    "    print(\"Model training finished\")\n",
    "\n",
    "    _, accuracy = model.evaluate(validation_dataset, verbose=0)\n",
    "\n",
    "    print(f\"Validation accuracy: {round(accuracy * 100, 2)}%\")\n",
    "\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "d6b8e4d7-e6cd-41e2-a1a6-bce818e40820",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model_inputs():\n",
    "    inputs = {}\n",
    "    for feature_name in FEATURE_NAMES:\n",
    "        if feature_name in NUMERIC_FEATURE_NAMES:\n",
    "            inputs[feature_name] = layers.Input(\n",
    "                name=feature_name, shape=(), dtype=tf.float32\n",
    "            )\n",
    "        else:\n",
    "            inputs[feature_name] = layers.Input(\n",
    "                name=feature_name, shape=(), dtype=tf.string\n",
    "            )\n",
    "    return inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "0221f407-28a5-4d27-b2df-289b8791256e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_inputs(inputs, embedding_dims):\n",
    "\n",
    "    encoded_categorical_feature_list = []\n",
    "    numerical_feature_list = []\n",
    "\n",
    "    for feature_name in inputs:\n",
    "        if feature_name in CATEGORICAL_FEATURE_NAMES:\n",
    "\n",
    "            # Get the vocabulary of the categorical feature.\n",
    "            vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]\n",
    "\n",
    "            # Create a lookup to convert string values to an integer indices.\n",
    "            # Since we are not using a mask token nor expecting any out of vocabulary\n",
    "            # (oov) token, we set mask_token to None and  num_oov_indices to 0.\n",
    "            lookup = layers.StringLookup(\n",
    "                vocabulary=vocabulary,\n",
    "                mask_token=None,\n",
    "                num_oov_indices=0,\n",
    "                output_mode=\"int\",\n",
    "            )\n",
    "\n",
    "            # Convert the string input values into integer indices.\n",
    "            encoded_feature = lookup(inputs[feature_name])\n",
    "\n",
    "            # Create an embedding layer with the specified dimensions.\n",
    "            embedding = layers.Embedding(\n",
    "                input_dim=len(vocabulary), output_dim=embedding_dims\n",
    "            )\n",
    "\n",
    "            # Convert the index values to embedding representations.\n",
    "            encoded_categorical_feature = embedding(encoded_feature)\n",
    "            encoded_categorical_feature_list.append(encoded_categorical_feature)\n",
    "\n",
    "        else:\n",
    "\n",
    "            # Use the numerical features as-is.\n",
    "            numerical_feature = tf.expand_dims(inputs[feature_name], -1)\n",
    "            numerical_feature_list.append(numerical_feature)\n",
    "\n",
    "    return encoded_categorical_feature_list, numerical_feature_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "e94efa81-6918-4ce5-8ed7-83cc2b12f4d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_mlp(hidden_units, dropout_rate, activation, normalization_layer, name=None):\n",
    "\n",
    "    mlp_layers = []\n",
    "    for units in hidden_units:\n",
    "        mlp_layers.append(normalization_layer),\n",
    "        mlp_layers.append(layers.Dense(units, activation=activation))\n",
    "        mlp_layers.append(layers.Dropout(dropout_rate))\n",
    "\n",
    "    return keras.Sequential(mlp_layers, name=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "b0b3c2f9-eca5-44b0-bf81-7bd5efb90879",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total model weights: 252373\n"
     ]
    }
   ],
   "source": [
    "def create_baseline_model(\n",
    "    embedding_dims, num_mlp_blocks, mlp_hidden_units_factors, dropout_rate\n",
    "):\n",
    "\n",
    "    # Create model inputs.\n",
    "    inputs = create_model_inputs()\n",
    "    # encode features.\n",
    "    encoded_categorical_feature_list, numerical_feature_list = encode_inputs(\n",
    "        inputs, embedding_dims\n",
    "    )\n",
    "    # Concatenate all features.\n",
    "    features = layers.concatenate(\n",
    "        encoded_categorical_feature_list + numerical_feature_list\n",
    "    )\n",
    "    # Compute Feedforward layer units.\n",
    "    feedforward_units = [features.shape[-1]]\n",
    "\n",
    "    # Create several feedforwad layers with skip connections.\n",
    "    for layer_idx in range(num_mlp_blocks):\n",
    "        features = create_mlp(\n",
    "            hidden_units=feedforward_units,\n",
    "            dropout_rate=dropout_rate,\n",
    "            activation=keras.activations.gelu,\n",
    "            normalization_layer=layers.LayerNormalization(epsilon=1e-6),\n",
    "            name=f\"feedforward_{layer_idx}\",\n",
    "        )(features)\n",
    "\n",
    "    # Compute MLP hidden_units.\n",
    "    mlp_hidden_units = [\n",
    "        factor * features.shape[-1] for factor in mlp_hidden_units_factors\n",
    "    ]\n",
    "    # Create final MLP.\n",
    "    features = create_mlp(\n",
    "        hidden_units=mlp_hidden_units,\n",
    "        dropout_rate=dropout_rate,\n",
    "        activation=keras.activations.selu,\n",
    "        normalization_layer=layers.BatchNormalization(),\n",
    "        name=\"MLP\",\n",
    "    )(features)\n",
    "\n",
    "    # Add a sigmoid as a binary classifer.\n",
    "    outputs = layers.Dense(units=1, activation=\"sigmoid\", name=\"sigmoid\")(features)\n",
    "    model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "    return model\n",
    "\n",
    "\n",
    "baseline_model = create_baseline_model(\n",
    "    embedding_dims=EMBEDDING_DIMS,\n",
    "    num_mlp_blocks=NUM_MLP_BLOCKS,\n",
    "    mlp_hidden_units_factors=MLP_HIDDEN_UNITS_FACTORS,\n",
    "    dropout_rate=DROPOUT_RATE,\n",
    ")\n",
    "\n",
    "print(\"Total model weights:\", baseline_model.count_params())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "3bbfae16-c3f3-4120-827a-668f9acb7a1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Observations:\n",
    "    # weights increase from 400k to 450k going from 50 gexp features to 100\n",
    "    # weights at 246k for gexp dim = 10 max diff\n",
    "    # weights at 253k for gexp dim = 10 UMAP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293691fb-3def-47a0-9db2-d3c0ea45008d",
   "metadata": {},
   "source": [
    "### MLP train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "b7a43393-efb3-47a0-b531-ea92fa37d9ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start training the model...\n",
      "Epoch 1/15\n",
      "     96/Unknown - 2s 12ms/step - loss: 0.7742 - accuracy: 0.5151WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 3s 16ms/step - loss: 0.7728 - accuracy: 0.5154 - val_loss: 0.6864 - val_accuracy: 0.8182\n",
      "Epoch 2/15\n",
      "100/100 [==============================] - ETA: 0s - loss: 0.7229 - accuracy: 0.5110WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.7229 - accuracy: 0.5110 - val_loss: 0.6959 - val_accuracy: 0.3636\n",
      "Epoch 3/15\n",
      " 96/100 [===========================>..] - ETA: 0s - loss: 0.7088 - accuracy: 0.5163WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.7085 - accuracy: 0.5168 - val_loss: 0.7022 - val_accuracy: 0.2727\n",
      "Epoch 4/15\n",
      "100/100 [==============================] - ETA: 0s - loss: 0.6991 - accuracy: 0.5270WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.6991 - accuracy: 0.5270 - val_loss: 0.6887 - val_accuracy: 0.7273\n",
      "Epoch 5/15\n",
      " 99/100 [============================>.] - ETA: 0s - loss: 0.6909 - accuracy: 0.5471WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.6910 - accuracy: 0.5470 - val_loss: 0.6969 - val_accuracy: 0.3636\n",
      "Epoch 6/15\n",
      " 97/100 [============================>.] - ETA: 0s - loss: 0.5894 - accuracy: 0.6680WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.5847 - accuracy: 0.6721 - val_loss: 0.3312 - val_accuracy: 0.9091\n",
      "Epoch 7/15\n",
      " 97/100 [============================>.] - ETA: 0s - loss: 0.2348 - accuracy: 0.9147WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.2333 - accuracy: 0.9157 - val_loss: 0.5651 - val_accuracy: 0.8182\n",
      "Epoch 8/15\n",
      " 98/100 [============================>.] - ETA: 0s - loss: 0.1409 - accuracy: 0.9539WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.1411 - accuracy: 0.9539 - val_loss: 0.5003 - val_accuracy: 0.9091\n",
      "Epoch 9/15\n",
      " 97/100 [============================>.] - ETA: 0s - loss: 0.1228 - accuracy: 0.9577WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.1224 - accuracy: 0.9577 - val_loss: 0.5702 - val_accuracy: 0.8182\n",
      "Epoch 10/15\n",
      " 98/100 [============================>.] - ETA: 0s - loss: 0.1128 - accuracy: 0.9582WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.1129 - accuracy: 0.9581 - val_loss: 0.6829 - val_accuracy: 0.8182\n",
      "Epoch 11/15\n",
      " 96/100 [===========================>..] - ETA: 0s - loss: 0.1067 - accuracy: 0.9581WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.1071 - accuracy: 0.9580 - val_loss: 0.5600 - val_accuracy: 0.9091\n",
      "Epoch 12/15\n",
      " 96/100 [===========================>..] - ETA: 0s - loss: 0.1005 - accuracy: 0.9607WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.1007 - accuracy: 0.9608 - val_loss: 0.5791 - val_accuracy: 0.9091\n",
      "Epoch 13/15\n",
      " 98/100 [============================>.] - ETA: 0s - loss: 0.0991 - accuracy: 0.9602WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.0991 - accuracy: 0.9602 - val_loss: 0.5406 - val_accuracy: 0.9091\n",
      "Epoch 14/15\n",
      " 99/100 [============================>.] - ETA: 0s - loss: 0.0950 - accuracy: 0.9615WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.0949 - accuracy: 0.9616 - val_loss: 0.5590 - val_accuracy: 0.9091\n",
      "Epoch 15/15\n",
      " 98/100 [============================>.] - ETA: 0s - loss: 0.0927 - accuracy: 0.9620WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 1s 13ms/step - loss: 0.0928 - accuracy: 0.9619 - val_loss: 0.5930 - val_accuracy: 0.9091\n",
      "Model training finished\n",
      "WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "Validation accuracy: 90.91%\n"
     ]
    }
   ],
   "source": [
    "history = run_experiment(\n",
    "    model=baseline_model,\n",
    "    train_data_file=train_data_file,\n",
    "    test_data_file=test_data_file,\n",
    "    num_epochs=NUM_EPOCHS,\n",
    "    learning_rate=LEARNING_RATE,\n",
    "    weight_decay=WEIGHT_DECAY,\n",
    "    batch_size=BATCH_SIZE,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e09dde-3ae7-450e-8622-2ca02592a820",
   "metadata": {},
   "source": [
    "### MLP loss plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "7935d0c3-d1cf-4fb3-9e80-f480c1aebeae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_type = 'MLP'\n",
    "plot_training_loss(history.history, latent_dim, merged_df, feat_type, model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "bd4477dc-2b25-49ce-a514-8e147f7743a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8TllentJ29Tt69KjL5AcNGjRw2XUPqJ3XYVlTozv/pb2qBg8ejObNmwNQJ6Kwj9dkMuGBBx6o9uNXRn5+Po4fP44TJ064zS7YrVs3t7+W15bBgwcjIiLC421JSUlo27YtJk6ciO+++w4pKSk4cuQIjh8/juPHj7tsnS7rNV4RN910k8v1st5/lXX11Ve7bJmwWCwuW1mdH/vAgQMuu9BFRka6bXWv7rnTZs2ahc6dO+PNN9/E+vXrcfDgQWRmZuL48eNuuzM6/17tkyjYjRgxotxTBpS8T3x8PLp06VKt+quiNl5nBw4ccJloIzg4GLfccku5tdT25ypRTWAzRVSLSu4yVXLXpJL/mZRcv+QxA6XtguZpn3y7mJgYmM2ub/Xansa55ExnANz+c65qQxcZGel2HFbJY0ycv/C+9NJL2LRpU4Uf/8KFC6XeFhsbW+Z977vvPpfa7LNPrVixwrGsT58+5T6OJwMHDnT5He7evRt79+7Fl19+6TLe4cOHux2PUd3XoSdlTY1echbBqij5Jcru2muvRZs2bar9+FXl6+uLqKgo3HDDDViwYAE2bdpUqRP2VkfJWdicjRs3rsLncKrubGfNmjVzuV7W+6+6j+3p8e1KfoZ4OsFzaSe4rojdu3dj8uTJbrtJl8b5s6PkbswVeU9U5T6elPz9l/WZ5kltvM5Kjs1TzqWpzc9VoprAZoqolly4cAFff/21y7JZs2a5HFtScmvD9u3bsW/fPsf1kl/STp486fG5Sh6/4Xy/0NBQXHHFFS631/YU2CXr8bSsqvv8lzzQGUCpB0gDcJtQ4ZVXXkFWVhZsNhsURXE7F1NZyvvLcpMmTVz+av/ll1/izz//dJn4oaoHSPv4+OD22293WbZkyZJyT9RbE69DrZT8EgVoM/HEggULoCgKFEVBcXExMjMzsWrVKowbN86tvtpU2usvLS0NO3bscFwPCgrCN998g7y8PEfdUVFRNVZHyfdgWe+/6j52WY9f8jPE0xa30j4zK2L58uUujYn9mKeioiIoioKffvqp1PuGhoa6XHfeMlyT9wHcfz/FxcUu1z2dSqEstfE6Kzm2ytRUm5+rRDWBzRRRLVm1apXbRAgV4dzodO7c2eW2AwcOIDMz02WZoihu51e68sorXa7fd999LtdXr15d5q5cRUVF+OuvvypVtzP7jHfONZY8d1T79u2r/PiVcezYMcflsLAwTJ06FZGRkTCZTLhw4QK2bNlSo8/n/GU/Ly/PZeuKv78/brvttio/dsmmZ/78+S71N2rUyG23ppp4HWolOjoaw4YNc1xv3Lgxhg8frl1BNaTkluLyJj0pj/NrHFAnDxgxYoSj2UhNTa3w1gQ9adOmjUvzlZmZiQMHDrisk5iYWOXHL/l7feqpp9CxY0fHlrKyzi3WqVMnl+vLly8vd/fHkvdJTk52aV5KU3JLdMn/I77//vtyH6MiqvM6a926tUud+fn5LpNHlKc2P1eJqovNFFEtKfllNDQ01OPxJSV3iXO+X0REBPr06eO4brPZcMcddzhmS8vLy8PEiROxZ88exzrBwcFuX6jvv/9+t+ZlzJgxePHFF13+4z1y5Ajef/99dOjQwW1rRmX88ssvmD17NoqLi3H+/Hk8++yz+Pvvvx2316tXD/3796/y41eG83TCubm5ji8WeXl5eOCBB5Camlqjz3f99dejZcuWjutbt251XL7xxhvd/kJbGT179nTZBafkl6bbb7/dbUtJTbwOtTRp0iRcd911uO666/D888973HKhNyWnuC55Et/qPt6mTZscTcWBAwcwatSoaj2+VIGBgS7HZyqKggceeACZmZlQFAWrV6/G7Nmzq/z4JX+vX375JQoLC2Gz2fDVV1+5TdPubOjQoS5bzo4cOYKbbroJycnJjq1de/fuxSOPPIL09HQA6q5vvXv3dtzHZrNh5MiR+Pnnnx276h0/fhz/93//h2XLljnWc/68AdQZ7nbt2gVFUbBmzRpMnz69ir8BV9V5nfn7+7vtXvzAAw84fqeAehqPDz/8EP/+97/d7l+bn6tE1Va3810QeYeCggK3mYv279/vcd2LFy+6naBxy5YtjtvXrl3rcuJC+4/zDGzOPzNnzvT4PAcOHFAaN27s8T4hISFuJ7b1NCNUaUrO5mf/8fPz83jCSudZrxSlcrP5lZyJqrz7l5wFD4ASFBTkmJUtMDCw1PuWnM2vojOBzZw50+Pvo+QshFUxZcoUj48NQNm0aZPLujX5OixthjtPP7fcckulxlTerHxVvV/J2xs2bFhqzRMmTCh1rDV5gmJFcZ9hzv4ejIyMVBISEhzrVWSWNkVRZzwrOVujPSP8b1Y4f39/l9ucVXY2v5K/5/LuX5nZ/EqecLy8+2/YsMHjDIv2133J2yozm19iYqLb4/r6+jo+M0p+dpSs/b333vP4Pg0MDHT5vHX+fW7ZssVthlEAio+PjxIaGurxNXnx4kWladOmpf4OSv6UN5tfbb3ODh065HKScPuP2WxWGjZs6MiqtOevzc9VourglimiWrB8+XKXmYuuuOKKUg+at1gsbrsuffHFF47Lffv2xSeffOI2GcWZM2fcHmvChAmlnrS2devW2L59u8ctQnl5eW67oHg6IWdFTZgwAaGhoSguLnY72L1v377VOmlvZb388stux54VFBRAURQMHTrU7eS2NeHee+91O2g+PDzc5QSUVVXajFUtWrRAjx49XJbV5OuwJPsMd55+qrJbYV3Izc0ttebqnPesssaNG+e2a1ZeXp5jxsDKMpvNePfdd912H8zPzwcAzJw5s0aPmZKkd+/eePXVV92Wnzt3Dj4+Pnj99dddlpf8HZVlwIABjvOx2V24cAHnz59HWFgY3nrrrTLv/9hjj+G1115zm93y/Pnzpe7y161bN6xYscLtM+vixYs4ffq0x/tYLBa88cYbbsdO2d/75Z3IvKKq+zqLi4vD6tWr3SaKsNlsyM3NLXfiktr8XCWqDjZTRLWg5C5S5e3PXfL2pUuXuhxHcffdd2Pv3r2YPHkyunXrhoYNG8LHxwdBQUFo37497r//fvzxxx945513yjwYvFmzZvj111+xbt06PPLII0hISEBYWBgsFguCgoKQkJCABx98ED///HO1/gPu3Lkzdu3ahbvvvhtRUVHw8/NDmzZt8PLLL2P16tXVatQqq0WLFtiyZQtGjRqFsLAwBAQEoH379njttdfw7bffVurLVUV5Orbn9ttvr5Fd1OLj4z2edPmOO+5wW1bTr0OqGS1atMDatWsxdOhQhIWF1cgEDsOHD0diYiL69u2LevXqISgoCD179sQ333yDf/7znzVQtVxTpkzB8uXL0bNnT9SrVw+hoaEYMmQINmzY4PYHhoYNG1bqsZcsWYJXXnkFrVu3hq+vLyIjIzF69Ghs374d7dq1K/f+zz77LJKTkzFp0iR06tQJwcHB8PX1RZMmTXDNNdfg1VdfdZtxcNCgQdi/fz9mzpyJPn36IDw8HD4+PggPD0fnzp3x1FNPoW/fvi73ueOOO7B8+XJcffXVCAwMRFBQEPr374+VK1fitddeq9SYy1Ld11mXLl2QnJyM9957DwMGDEDjxo3h6+uL0NBQxMfHY/z48aVOmV6bn6tE1WFSyvtTABERVdqnn36KcePGOa5v2rTJ7YsdEdWumTNn4rnnnnNcnzp1Kl555RUNK6Lq4OcqScQtU0RENUxRFJetQm3btuV/+ES1ZOrUqfj555/ddhP7+eefMXPmTJdlJSdBIP3g5ypJVXcnyCAiMrhp06bhzz//xJ49e1zO0/TEE09oWBWRsf3222+YMWMGIiIicPnll8NisSA1NRUHDx50We/uu+92O20EycfPVZKOu/kREdWQfv36uU1z3adPH6xZs8btIHQiqhme3ncl3XXXXfj444/h7+9fR1VRTeHnKknHLVNERDXMx8cHsbGxGDVqFKZMmcL/8Ilq0XPPPYe2bdti06ZNyMrKwunTpxEYGIiYmBj06NED99xzD3r16qV1mVRN/FwlqbhlioiIiIiIqAo4AQUREREREVEVcDc/qCeMO3bsGIKCgmrkfB9ERERERKRPiqKgoKAA0dHR5Z6Pks0UgGPHjiEmJkbrMoiIiIiISIiMjAw0a9aszHXYTAEICgoCoP7CgoODNa4GsFqtSE5ORnx8PA+wFIB5yMNM5GEmsjAPeZiJPMxEFkl55OfnIyYmxtEjlIXNFODYtS84OFhMM9WgQQMEBwdr/mIi5iERM5GHmcjCPORhJvIwE1kk5lGRw384mx/U7jMkJAR5eXkimilFUVBYWIiAgAAewyUA85CHmcjDTGRhHvIwE3mYiSyS8qhMb8DZ/ITy8/PTugRywjzkYSbyMBNZmIc8zEQeZiKLHvNgMyWQzWZDUlISbDab1qUQmIdEzEQeZiIL85CHmcjDTGTRax5spoiIiIiIiKqAzRQREREREVEVsJkiIiIiIiKqAs7mB5mz+dlsNpjNZs1nMyHmIREzkYeZyMI85GEm8jATWSTlwdn8DKC4uFjrEsgJ85CHmcjDTGRhHvIwE3mYiSx6zIPNlEA2mw379u3T3WwmRsU85GEm8jATWZiHPMxEHmYii17zYDNFRERERERUBWymiIiIiIiIqoDNlFAWi0XrEsgJ85CHmcjDTGRhHvIwE3mYiSx6zIOz+UHebH5ERERERKQNzuanc4qiID8/H+xzZWAe8jATeZiJLMxDHmYijxEysVqBtWuB//xH/ddq1bqiqrFagTVrFHzyyTmsWaPoahxspgSy2WxITU3V3WwmRsU85GEm8jATWZiHPEbIxChf3AG19l9/tWHOnBz8+qtNl2NZtgyIiwP69wfuvFP9Ny5OXa4n9nFce60J991XD9dea9LVONhMEREREVGZjPLFHbg0lgEDLHjuuTgMGGDR3ViWLQNGjgSOHHFdfvSoulwvYzHCOHy0LoCIiIjIE6sV2LAByMwEmjQB+vQBdHh8umOLzpYtocjOBvr109c47F94S+4NZ//C+/XXwIgR2tRWWUYYi9UKTJzoPgZAXWYyqbdffz1gNgM2m/qjKO6XK7qsNu5z8SLw4INlj2PSJODmm2W/X9hMCRUQEKB1CeSEecjDTORhJjLYv7jv2tVIl1/c7ZYtU78QOv/Fulkz4J135H/ZdXZpHBYAcQD0NY7CQuDxx0v/wgsA48cDTZsCfn7qa83Tj9lc+m3Ot5vN6pfo2lCRJqSsL+82G1BcDBQVqT+VuVzV+3m6nJ8PnDpV+jgVRX3f6H1ONUUBMjLUP6j066d1NaXjbH7gbH5ERGQMRmpAPG09sH/J1sPWA0C7cRQVAQUF6pfugoLqXS4srPn6ylNa41XRhqy0n7w8YNeu8p+/aVPAx8e9mbl4sdaHXufM5ksNrPO/pV2u6LKK3Cc3Fzh0qPwaFy8G7rij9n8XzirTG7CZgrxmymazITc3Fw0bNoTZzMPatMY85GEm8jAT7RmlAbFa1eNZSh5DYWcyqQ3ioUOyt7hVZhxmM3D+vHtDU9Um6MKFOh0qACAsDPD3V8ft/GOzuV83Cl9fdWucv7/6U5HLFV2vrMtJScBDD5Vf348/An37em5sTKba2wJYUWvXqsfelWfNmrrfMlWZ3oC7+QmkKAoyMjIQGhqqdSkE5iERM5HHCJno9fgcRVH/wvvoo2XvinXffeouM4DnYxsqu6y69y9t2cmTpTcg9vFkZABXXaV+gQdcvxhW5t/avM+JExUbR0iIuvWnNmaTq1cPCApSf4KDq3Z5925g+PDyn+ubbyr2hdeee1kNV3kNWWVvt6+TlAS88kr5Nb7zDtC9e9kNjZ+f2pRo4aqrgJdfVo/z8vSetzfqAwfK/gzr00ets7xx9OlT97VVBpspIiLyehJ2j7NagdOngZycyv3k5lbsi/jp0+rxIEaxY4fWFdSMs2ddrzdoUPXGx/lygwbqrmrVFRNTs194TaZLu93VtVtvBRYuLH8sjz4quwmxWNTPppEj1Zqdx2Jv5mfPlj0GwDjjYDNFREReraZn97pwQW1wKtoMZWer/54+XaPD8qh7d6BFC9djGEoe01Abyypzv337gNdfL38szz0HdOig5mbPrjL/1vZ9/v4bmDOn/HF8+ilw3XVqE1S/vnZbO0pjlC+8gLHGMmKE+tnk6Y9As2frY5dewBjj4DFTkHfMlNVqRVpaGuLi4mDRwzva4JiHPMxEHr1mUpHjWiIj1QOg8/Iq1hwVFFSvpqAgdfe10n7Cw92XJSUBgwaV/9haHHtQWfZMytt6oJdjpvQ+DjtPW29jYvTzhdeZkcai192TS1JnIbUiKekUEhIi0K+fRdNxcAKKSpLWTBERUd2o6AHQlWUyAaGhZTdFnn4aNlQPaq8sI35xHzlSvexp64FeJtMwyjjsjPLFHTDWWKjmcQIKnbPZbDhx4gQaN27MWbEEYB7yMBN59JpJZmbF1mvSRG1WKtoUhYTU7RczI+2+BBhj1x/AOOOws1jkb9msKIsFuOYafX5uGZVe/x9hMyWQoijIyspCo0aNtC6FwDwkYiby6DWTevUqtt7ixfK/RBrti/uIEerJU/W+9cA+jrVrrdiyJQNXXRWj+S5MpNLr55ZR6TUPNlNEROSV/vtf4JFHyl5HL1Pz2hnti7tRtoTYxxEefhoJCTG6zYOI3LGZIiIir1JYqM4E9/bb6vXoaODYMWPsHgfwizsRUV3Szw6JXsRkMiEsLAwmrU9NTQCYh0TMRB69ZPLXX+oJL+2N1COPAAcOqCccbdrUdd1mzfQ3QYCdXvLwJsxEHmYii17z4Gx+4Gx+RERGpyjAe+8B//wnUFQENGoEfPIJMGzYpXU4uxcREQGV6w24ZUogm82G9PR02Gw2rUshMA+JmIk8kjPJzAQGD1YnZigqAoYMUc/L5NxIAZd2j7vjDvVfPTdSkvPwVsxEHmYii17zYDMlkKIoyMnJATcaysA85GEm8kjN5NtvgcsvB1avBgICgDlzgJUr1RPxGpnUPLwZM5GHmcii1zw4AQURERnO2bPAE08AH36oXr/iCuCLL4AOHTQti4iIDIZbpoiIyFC2bgU6d1YbKZMJeOYZYPNmNlJERFTz2EwJZDKZEBUVpbvZTIyKecjDTOSRkInVCrz6KtCzpzpDX7NmwC+/ALNmAf7+mpWlCQl5kCtmIg8zkUWveXA2P3A2PyIivTt8GBgzRp2NDwBuvx3497+Bhg21rYuIiPSHs/npnNVqxcGDB2G1WrUuhcA8JGIm8miZyRdfqJNMbNgANGgAfPopsGSJdzdSfI/Iw0zkYSay6DUPTkAhVEFBgdYlkBPmIQ8zkaeuMzl9Ghg/HvjPf9TrPXoAn38OtGxZp2WIxfeIPMxEHmYiix7z4JYpIiLSnfXrgU6d1EbKYgFeekldxkaKiIjqErdMERGRbhQXA9OmAa+/DigK0KqVujWqe3etKyMiIm/EZkogk8mEmJgY3c1mYlTMQx5mIk9dZLJvH3DXXcD27er1e+8FZs8GgoJq7Sl1i+8ReZiJPMxEFr3mwdn8wNn8iIgkUxRg/nz1JLznz6sTS3z4IXDrrVpXRkRERsTZ/HTOarVi7969upvNxKiYhzzMRJ7ayuTkSWD4cODhh9VG6rrrgKQkNlLl4XtEHmYiDzORRa95sJkSqrCwUOsSyAnzkIeZyFPTmaxaBSQkAN99B/j5AW++Cfz8M9C0aY0+jWHxPSIPM5GHmciixzx4zBQREYly/jzwz38C77+vXo+PV88l1amTtnURERGVxC1TREQkxq5dQNeulxqpCROArVvZSBERkUxspgQym81o2bIlzGbGIwHzkIeZyFPdTGw24P/+D7j6aiAlBYiKAn76CXjnHSAwsIaL9QJ8j8jDTORhJrLoNQ/O5gfO5kdEpKWjR4GxY4FfflGv33QT8NFHQKNG2tZFRETeibP56ZzVakVSUpLuZjMxKuYhDzORp6qZfP21OsnEL78A9eoBH3wArFjBRqq6+B6Rh5nIw0xk0WsenIBCKL29kIyOecjDTOSpTCYFBerxUAsXqte7dlUnmbjsstqpzRvxPSIPM5GHmciixzy4ZYqIiOrU778DV1yhNlImE/Dcc8CmTWykiIhIf7hlioiI6sTFi8Arr6g/VivQvDnw+edAnz5aV0ZERFQ1nIAC8iagUBQFhYWFCAgIgMlk0rocr8c85GEm8pSXycGDwOjRwObN6vU77wTmzAFCQ+u2Tm/B94g8zEQeZiKLpDw4AYUB+Pn5aV0COWEe8jATeTxloijq7nxXXKE2UsHB6rFRX3zBRqq28T0iDzORh5nIosc82EwJZLPZkJSUBJvNpnUpBOYhETORx1MmOTnA7bcD99wDnDmj7s7355/qVimqXXyPyMNM5GEmsug1DzZTRERULVYrsHYtsGpVKNauVa//+itw+eXq1Oc+PsCMGcCaNUBsrNbVEhER1RxOQEFERFW2bBkwcSJw5IgFQBwAoEEDdUsUoM7Q98UX6tTnRERERsNmioiIqmTZMmDkSPW4KGf2Rur664Hly4H69eu+NiIiorrA2fwgczY/m80Gs9ms+WwmxDwkYibas1qBuDjgyJHS14mJAQ4dAiyWOiuL/ofvEXmYiTzMRBZJeXA2PwMoLi7WugRywjzkYSba2rCh7EYKADIy1PVIG3yPyMNM5GEmsugxDzZTAtlsNuzbt093s5kYFfOQh5loLzOzZtejmsX3iDzMRB5mIote82AzRUREldakSc2uR0REpEdspoiIqNL69AGaNQNK263dZFKPmerTp27rIiIiqktspoSy8IhtUZiHPMxEWxYL8M477jP5AZcarNmzOfmElvgekYeZyMNMZNFjHpzND/Jm8yMi0otbbgFWrHBdFhOjNlIjRmhRERERUfVUpjfgeaYEUhQFBQUFCAoK0nxqSGIeEjETOXJy1H+fflpB+/bn0aJFIK65xsQtUhrje0QeZiIPM5FFr3lwNz+BbDYbUlNTdTebiVExD3mYiQwXLgBbt6qXx4614cor9+Oaa2xspATge0QeZiIPM5FFr3mwmSIioir580/g/HkgNBRo21braoiIiOoemykiIqqSzZvVf7t3B8z834SIiLwQ//sTKiAgQOsSyAnzkIeZaO/339V/e/RQ/2UmsjAPeZiJPMxEFj3mwdn8wNn8iIiqolUrIDUVWL0aGDhQ62qIiIhqRmV6A5FbpubOnYsWLVogICAAXbp0wYYNG8pcf86cOWjfvj0CAwPRtm1bfPbZZ3VUae2w2WzIzs7W3QF4RsU85GEm2jtxQm2kTCbg6quZiTTMQx5mIg8zkUWveYhrppYuXYpJkyZh6tSp2LlzJ/r06YPBgwcjPT3d4/rz5s3DlClT8OKLLyI5ORkvvfQSHn30UXz//fd1XHnNURQFGRkZ4EZDGZiHPMxEe/Zd/Dp0AEJCmIk0zEMeZiIPM5FFr3mIO8/UW2+9hfvuuw/3338/AGD27NlYvXo15s2bh5kzZ7qtv2jRIjz00EMYNWoUAKBly5bYvHkzXn/9ddx4440en6OoqAhFRUWO6/n5+QAAq9UKq9UKADCZTDCbzbDZbC6h2pfb1ytvudlshslk8rgcgFv3bTaboSgKFEVxuY/FYoGiKG7rWywWtxpLW67lmDwt18uYPOWh9zF5ql1vY3LOxChjqkjtUsa0aZMJgBk9elz67LRnotcxlbVcb2NyzsMoY3KmxzHZM7HZbLBYLIYYU3m1Sx+TPRP7YxthTM416m1MFcmjrsZU8vayiGqmiouLsX37dkyePNll+cCBA7Fp0yaP9ykqKnI7WC0wMBBbtmzBhQsX4Ovr63afmTNn4qWXXnJbnpycjAYNGgAAwsLC0Lx5cxw5cgQ59rNSAoiKikJUVBTS0tJQUFDgWB4TE4Pw8HAcOHAAhYWFjuUtW7ZEcHAwUlJSXIJp27Yt/Pz8kJSU5FJDQkICioqKkJOTg+TkZJhMJlgsFiQkJKCgoACpqamOdQMCAtCuXTvk5uYiIyPDsTwoKAitWrXCiRMnkJWV5Viu5ZiKi4uxb98+xzI9jclisbjkYYQx6T2nPXv2uGRihDHpLadffmkNoAG6dwcOHDiA8+fPOzJp1aqVLsdkpJzy8/MdeTRv3twQY9J7ToqiICcnBydPnkR0dLQhxqT3nBRFwdmzZwHAMGMC9JuT/aS9ADQf05kzZ1BRoiagOHbsGJo2bYrffvsNPXv2dCyfMWMGPv30U5dw7J577jksWLAAK1euxJVXXont27dj6NChOHHiBI4dO4YmTZq43cfTlqmYmBjk5OQ4DjLTsqu3Wq04dOgQYmNjYfnf2S+9+S8VWo/JUx56H5On2vU0puLiYhw+fNiRiRHGpKecLl4EwsLMOHfOhORkoG1bdUuIPRNfX1/djam85XrLyTkPHx8fQ4zJmR5zsmcSFxcHX19fQ4ypvNqlj8lqtSI9PR0tWrSAyWQyxJica9RbTvb3SMuWLUvNo67GlJ+fj7CwsApNQCGymdq0aRN62OfaBfDqq69i0aJF2Lt3r9t9zp8/j0cffRSLFi2CoiiIjIzE6NGjMWvWLBw/fhyNGzcu93k5mx8RUcXt2AF06aKerDc7m+eYIiIiY9HtbH4RERGwWCwum/UAdVNfZGSkx/sEBgbik08+wblz55CWlob09HTExcUhKCgIERERdVF2jbPZbMjKynLr4EkbzEMeZqIt+8l6r776UiPFTGRhHvIwE3mYiSx6zUNUM+Xn54cuXbogMTHRZXliYqLLbn+e+Pr6olmzZrBYLFiyZAmGDRvm2GSnN4qiICsry21TLmmDecjDTLRV8mS9ADORhnnIw0zkYSay6DUPURNQAMCTTz6JMWPGoGvXrujRowfmz5+P9PR0PPzwwwCAKVOm4OjRo45zSe3fvx9btmzB1VdfjdzcXLz11lv466+/8Omnn2o5DCIiw7I3U927a1sHERGR1sQ1U6NGjUJ2djamT5+OzMxMdOzYET/++CNiY2MBAJmZmS7nnLJarXjzzTexb98++Pr6on///ti0aRPi4uI0GgERkXGdOAEcPKhevvpqbWshIiLSmrhmCgDGjx+P8ePHe7xt4cKFLtfbt2+PnTt31kFVdcdkMiEsLMwxDTdpi3nIw0y0Yz9eqkMHdQIKO2YiC/OQh5nIw0xk0WseIpspb2c2m9G8eXOty6D/YR7yMBPt2Jsp5+OlAGYiDfOQh5nIw0xk0Wse+pyhweBsNhvS09N1N5uJUTEPeZiJdko7XoqZyMI85GEm8jATWfSaB5spgexnSdfbbCZGxTzkYSbauHgR2LJFvVxyyxQzkYV5yMNM5GEmsug1DzZTRERUIUlJwLlzQEgI0L691tUQERFpj80UERFViKeT9RIREXkz/ncokMlkQlRUlO5mMzEq5iEPM9FGWeeXYiayMA95mIk8zEQWveZhUvS2Y2ItyM/PR0hICPLy8hAcHKx1OUREIrVpA/z9N7BqFXDDDVpXQ0REVDsq0xtwy5RAVqsVBw8ehNVq1boUAvOQiJnUvZMn1UYK8HyyXmYiC/OQh5nIw0xk0WsebKaEKigo0LoEcsI85GEmdct+vFT79kDDhp7XYSayMA95mIk8zEQWPebBZoqIiMpV2sl6iYiIvBmbKSIiKldZk08QERF5KzZTAplMJsTExOhuNhOjYh7yMJO6VdbJeu2YiSzMQx5mIg8zkUWveXA2P3A2PyKisuzaBXTuDAQHA7m5PMcUEREZG2fz0zmr1Yq9e/fqbjYTo2Ie8jCTulWRk/UyE1mYhzzMRB5mIote82AzJVRhYaHWJZAT5iEPM6k7FT1eipnIwjzkYSbyMBNZ9JgHmykiIiqTvZniTH5ERESu2EwREVGpTp0CDhxQL3MmPyIiIldspgQym81o2bIlzDzKWwTmIQ8zqTv246XatSv9ZL0AM5GGecjDTORhJrLoNQ8frQsgdyaTibMKCsI85GEmdaeiJ+tlJrIwD3mYiTzMRBa95qGv1s9LWK1WJCUl6W42E6NiHvIwk7pT0cknmIkszEMeZiIPM5FFr3mwmRJKby8ko2Me8jCT2me1ln+yXtf1mYkkzEMeZiIPM5FFj3mwmSIiIo/++gs4cwYICgI6dNC6GiIiInnYTBERkUfOJ+u1WLSthYiISCI2UwKZzWa0bdtWd7OZGBXzkIeZ1I2KHi8FMBNpmIc8zEQeZiKLXvPQV7VexM/PT+sSyAnzkIeZ1L7KnqyXmcjCPORhJvIwE1n0mAebKYFsNhuSkpJgs9m0LoXAPCRiJrUvOxvYv1+9XJEtU8xEFuYhDzORh5nIotc82EwREZGbP/5Q/23bFggL07YWIiIiqdhMERGRm8ru4kdEROSN2EwREZGbykw+QURE5K1MiqIoWhehtfz8fISEhCAvLw/BwcFalwNFUWCz2WA2m2EymbQux+sxD3mYSe2yWoHQUPUcU7t3A5dfXv59mIkszEMeZiIPM5FFUh6V6Q24ZUqo4uJirUsgJ8xDHmZSe5KTL52sNz6+4vdjJrIwD3mYiTzMRBY95sFmSiCbzYZ9+/bpbjYTo2Ie8jCT2mU/We9VV1X8ZL3MRBbmIQ8zkYeZyKLXPNhMERGRCx4vRUREVDFspoiIyAVn8iMiIqoYNlNCWSq6bw3VCeYhDzOpHTk5wL596uXKbpliJrIwD3mYiTzMRBY95sHZ/CBvNj8iIq2sWgUMGQJcdtmlpoqIiMibcDY/nVMUBfn5+WCfKwPzkIeZ1J6q7uLHTGRhHvIwE3mYiSx6zYPNlEA2mw2pqam6m83EqJiHPMyk9lR18glmIgvzkIeZyMNMZNFrHmymiIgIgHqy3j/+UC9z8gkiIqLysZkiIiIAQEoKUFAANGgAdOyodTVERETysZkSKiAgQOsSyAnzkIeZ1LyqnKzXGTORhXnIw0zkYSay6DEPzuYHzuZHRAQA994LLFgAPPcc8OqrWldDRESkDc7mp3M2mw3Z2dm6OwDPqJiHPMykdlTnZL3MRBbmIQ8zkYeZyKLXPNhMCaQoCjIyMnQ3NaRRMQ95mEnNy8kB9u5VL1d2Jj+AmUjDPORhJvIwE1n0mgebKSIiwpYt6r9t2gAREdrWQkREpBdspoiIqFq7+BEREXkrNlNCBQUFaV0COWEe8jCTmlXVk/U6YyayMA95mIk8zEQWPebB2fzA2fyIyLvZbEDDhkB+PrBzJ3DFFVpXREREpB3O5qdzNpsNWVlZupvNxKiYhzzMpGalpKiNVP36VT9ZLzORhXnIw0zkYSay6DUPNlMCKYqCrKws3c1mYlTMQx5mUrOcT9br41O1x2AmsjAPeZiJPMxEFr3mwWaKiMjL1cTxUkRERN6IzRQRkZfjTH5ERERVw2ZKIJPJhLCwMJhMJq1LITAPiZhJzcnNBfbsUS9XZ8sUM5GFecjDTORhJrLoNQ/O5gfO5kdE3mv1auCGG4DWrYEDB7SuhoiISHuczU/nbDYb0tPTdTebiVExD3mYSc2pqeOlmIkszEMeZiIPM5FFr3mwmRJIURTk5OTobjYTo2Ie8jCTmlNTx0sxE1mYhzzMRB5mIote82AzRUTkpWw24I8/1MucfIKIiKjy2EwREXmpPXuAvDz1ZL0JCVpXQ0REpD9spgQymUyIiorS3WwmRsU85GEmNcN+st5u3ap+sl47ZiIL85CHmcjDTGTRax7V/O+TaoPZbEZUVJTWZdD/MA95mEnNqMmT9TITWZiHPMxEHmYii17z4JYpgaxWKw4ePAir1ap1KQTmIREzqRk1ebJeZiIL85CHmcjDTGTRax5spoQqKCjQugRywjzkYSbVc/o0kJKiXq6JLVMAM5GGecjDTORhJrLoMQ82U0REXmjLFvXfVq2Axo21rYWIiEiv2EwREXmhmjxeioiIyFuxmRLIZDIhJiZGd7OZGBXzkIeZVF9NHi8FMBNpmIc8zEQeZiKLXvMwKXo7zXAtyM/PR0hICPLy8hAcHKx1OUREtcpmA8LC1HNMbd8OXHml1hURERHJUZnegFumBLJardi7d6/uZjMxKuYhDzOpnr171UaqXj3g8str5jGZiSzMQx5mIg8zkUWvebCZEqqwsFDrEsgJ85CHmVRdTZ6s1xkzkYV5yMNM5GEmsugxDzZTRERehpNPEBER1Qw2U0REXqamJ58gIiLyVmymBDKbzWjZsiXMZsYjAfOQh5lUXV7epZP11mQzxUxkYR7yMBN5mIkses2jBveWp5piMpk4q6AgzEMeZlJ1W7YAigK0bFmzJ+tlJrIwD3mYiTzMRBa95qGv1s9LWK1WJCUl6W42E6NiHvIwk6qrreOlmIkszEMeZiIPM5FFr3mwmRJKby8ko2Me8jCTqqnN46WYiSzMQx5mIg8zkUWPebCZIiLyEjbbpWnROfkEERFR9bGZIiLyEvv2AadPA4GBNXeyXiIiIm/GZkogs9mMtm3b6m42E6NiHvIwk6pxPlmvr2/NPjYzkYV5yMNM5GEmsug1D31V60X8/Py0LoGcMA95mEnl1fbJepmJLMxDHmYiDzORRY95sJkSyGazISkpCTabTetSCMxDImZSNbU5+QQzkYV5yMNM5GEmsug1DzZTREReIC8PSE5WL3PyCSIioprBZoqIyAts3aqerLdFCyAyUutqiIiIjIHNFBGRF6jt46WIiIi8kchmau7cuWjRogUCAgLQpUsXbNiwocz1v/jiC3Tq1An16tVDkyZNcM899yA7O7uOqq15ZrMZCQkJupvNxKiYhzzMpPJq83gpgJlIwzzkYSbyMBNZ9JqHuGqXLl2KSZMmYerUqdi5cyf69OmDwYMHIz093eP6GzduxN1334377rsPycnJ+Oqrr7B161bcf//9dVx5zSouLta6BHLCPORhJhVXVyfrZSayMA95mIk8zEQWPeYhrpl66623cN999+H+++9H+/btMXv2bMTExGDevHke19+8eTPi4uIwYcIEtGjRAr1798ZDDz2Ebdu21XHlNcdms2Hfvn26m83EqJiHPMykcvbvB3Jz1ZP1dupUO8/BTGRhHvIwE3mYiSx6zcNH6wKcFRcXY/v27Zg8ebLL8oEDB2LTpk0e79OzZ09MnToVP/74IwYPHowTJ07g66+/xtChQ0t9nqKiIhQVFTmu5+fnAwCsViusVisAwGQywWw2w2azQVEUx7r25fb1yltuNpthMpk8Lgfg9oIxm81QFAWKorjcx2KxQFEUt/UtFotbjaUt13JMnpbrZUye8tD7mDzVrrcxOWdilDFVpPaqjGnTJhsAM7p0UWA222C11vyYnDMx+mtPD2NyzsMoY3KmxzHZM7HZbLBYLIYYU3m1Sx+TPRP7YxthTM416m1MFcmjrsZU8vayiGqmTp06BavVisgSU01FRkYiKyvL43169uyJL774AqNGjUJhYSEuXryIm266Ce+9916pzzNz5ky89NJLbsuTk5PRoEEDAEBYWBiaN2+OI0eOICcnx7FOVFQUoqKikJaWhoKCAsfymJgYhIeH48CBAygsLHQsb9myJYKDg5GSkuISTNu2beHn54ekpCSXGhISElBUVIScnBwkJyfDZDLBYrEgISEBBQUFSE1NdawbEBCAdu3aITc3FxkZGY7lQUFBaNWqFU6cOOHye9NyTMXFxdi3b59jmZ7GZLFYXPIwwpj0ntOePXtcMjHCmGozp3XrigEEoFWrE0hKyqyVMZ0/f96RSatWrQz72tPLmPLz8x15NG/e3BBj0ntOiqIgJycHJ0+eRHR0tCHGpPecFEXB2bNnAcAwYwL0m5OiKI66tB7TmTNnUFEmpWSLq6Fjx46hadOm2LRpE3o47dj/6quvYtGiRdi7d6/bfVJSUjBgwAA88cQTGDRoEDIzM/HMM8+gW7du+Pjjjz0+j6ctUzExMcjJyUFwcDAAbbt6q9WK5ORktG/fHhaLBYB3/6VC6zF5ykPvY/JUu57GVFxcjD179jgyMcKYajOnyy9XkJRkwtdfWzF8eO2MyWq1OjLx9fU17GtPL2NyzsPHx8cQY3Kmx5zsmXTo0AG+vr6GGFN5tUsfk9Vqxd69exEfHw+TyWSIMTnXqLecKpJHXY0pPz8fYWFhyMvLc/QGpRHVTBUXF6NevXr46quvcMsttziWT5w4Ebt27cK6devc7jNmzBgUFhbiq6++cizbuHEj+vTpg2PHjqFJkyblPm9+fj5CQkIq9AsjItKT/HwgNFQ9x1RmJhAVpXVFREREslWmNxA1AYWfnx+6dOmCxMREl+WJiYno2bOnx/ucO3fO0U3a2bceCOoTK0VRFOTn5+u2fqNhHvIwk4qzn6w3Lq52GylmIgvzkIeZyMNMZNFrHqKaKQB48skn8dFHH+GTTz7Bnj178MQTTyA9PR0PP/wwAGDKlCm4++67HevfeOONWLZsGebNm4fU1FT89ttvmDBhAq666ipER0drNYxqsdlsSE1NddscStpgHvIwk4qrq5P1MhNZmIc8zEQeZiKLXvMQNQEFAIwaNQrZ2dmYPn06MjMz0bFjR/z444+IjY0FAGRmZrqcc2rcuHEoKCjA+++/j6eeegqhoaG49tpr8frrr2s1BCIiMWr7ZL1ERETeTFwzBQDjx4/H+PHjPd62cOFCt2WPP/44Hn/88VquiohIXxSlbk7WS0RE5K3E7eZHqoCAAK1LICfMQx5mUr4DB4CcHCAgoPZO1uuMmcjCPORhJvIwE1n0mIeo2fy0wtn8iMiIPv0UGDcO6N0b2LBB62qIiIj0Qbez+ZHKZrMhOztbdwfgGRXzkIeZVExdTT4BMBNpmIc8zEQeZiKLXvNgMyWQoijIyMjQ3dSQRsU85GEmFVOXk08wE1mYhzzMRB5mIote82AzRURkQAUFwF9/qZc5+QQREVHtYDNFRGRAW7cCNhsQGws0aaJ1NURERMbEZkqooKAgrUsgJ8xDHmZStro8XsqOmcjCPORhJvIwE1n0mAdn8wNn8yMi4xk2DPjhB2D2bGDiRK2rISIi0g/O5qdzNpsNWVlZupvNxKiYhzzMpGxanKyXmcjCPORhJvIwE1n0mgebKYEURUFWVpbuZjMxKuYhDzMp299/A9nZgL8/cMUVdfOczEQW5iEPM5GHmcii1zzYTBERGYz9eKkuXQA/P21rISIiMjI2U0REBlOX55ciIiLyZmymBDKZTAgLC4PJZNK6FALzkIiZlE2LZoqZyMI85GEm8jATWfSaB2fzA2fzIyLjKCgAQkPVc0wdPQpER2tdERERkb5wNj+ds9lsSE9P191sJkbFPORhJqXbtk1tpJo3r9tGipnIwjzkYSbyMBNZ9JoHmymBFEVBTk6O7mYzMSrmIQ8zKZ0WJ+sFmIk0zEMeZiIPM5FFr3mwmSIiMhBOPkFERFR32EwRERmEFifrJSIi8mZspgQymUyIiorS3WwmRsU85GEmnh08CJw6pZ6st3Pnun1uZiIL85CHmcjDTGTRax4+WhdA7sxmM6KiorQug/6HecjDTDyz7+J35ZV1f7JeZiIL85CHmcjDTGTRax7cMiWQ1WrFwYMHYbVatS6FwDwkYiaeaXm8FDORhXnIw0zkYSay6DUPNlNCFRQUaF0COWEe8jATd1pPPsFMZGEe8jATeZiJLHrMg80UEZEBnDkD/PmnepmTTxAREdUNNlNERAZgP1lvTAzQtKnW1RAREXkHNlMCmUwmxMTE6G42E6NiHvIwE3danazXjpnIwjzkYSbyMBNZ9JoHZ/MTyGw2Izw8XOsy6H+YhzzMxJ3Wx0sxE1mYhzzMRB5mIote8+CWKYGsViv27t2ru9lMjIp5yMNMXCmK9s0UM5GFecjDTORhJrLoNQ82U0IVFhZqXQI5YR7yMJNLUlPVk/X6+dX9yXqdMRNZmIc8zEQeZiKLHvNgM0VEpHPOJ+v199e2FiIiIm/CZoqISOe03sWPiIjIW7GZEshsNqNly5YwmxmPBMxDHmbiSkIzxUxkYR7yMBN5mIkses3DpCiKonURWsvPz0dISAjy8vIQHBysdTlERBV29iwQEgJYrUBGBtCsmdYVERER6VtlegN9tX5ewmq1IikpSXezmRgV85CHmVyybZvaSDVrpm0jxUxkYR7yMBN5mIkses2DzZRQenshGR3zkIeZqLQ+Wa8zZiIL85CHmcjDTGTRYx5spoiIdEzC8VJERETeqlrN1JkzZ5Ceno6LFy+6LF+6dCnuuusuPPDAA9i1a1d1noKIiEoh4WS9RERE3qxaE1A8+uij+PTTT3H8+HHUr18fADBv3jw89thjsD9s/fr1sW3bNrRt27ZmKq4F0iagUBQFhYWFCAgIgMlk0rocr8c85GEmqtRUoFUr9WS9+fnanmOKmcjCPORhJvIwE1kk5VFnE1Bs2LABAwYMcDRSADBz5kw0bdoU69evx5dffgmr1Yo33nijOk/jlfz8/LQugZwwD3mYyaWtUp07yzhZLzORhXnIw0zkYSay6DGPajVTR48eRcuWLR3Xk5KScOTIEUyYMAG9e/fGyJEjcdNNN2HdunXVLtSb2Gw2JCUlwWazaV0KgXlIxExUknbxYyayMA95mIk8zEQWveZRrWbq/PnzLh3kxo0bYTKZMHDgQMeyli1b4ujRo9V5GiIi8kBSM0VEROSNqtVMNWvWDH/++afj+g8//ICGDRsiISHBsSw7OxsNGjSoztMQEVEJZ88Cu3erl9lMERERacOnOncePHgw5syZg2eeeQYBAQH46aefMGbMGJeDxvbu3YvmzZtXu1AiIrpk+3b1ZL1NmwIxMVpXQ0RE5J2qNZtfVlYWevbsibS0NABAVFQUNm/e7Gie0tPT0bJlS0yYMAFvvfVWjRRcGyTO5mez2WA2mzWfzYSYh0TMBHj9dWDyZODWW4Gvv9a6GmYiDfOQh5nIw0xkkZRHZXqDam2ZioqKQnJyMn755RcAwDXXXOPyhAUFBXjzzTcxaNCg6jyNVyouLkZAQIDWZdD/MA95vD0TicdLeXsm0jAPeZiJPMxEFj3mUa1jpgAgMDAQw4YNw7Bhw9w6t/j4eEycOBHt2rWr7tN4FZvNhn379uluNhOjYh7yeHsmEk/W6+2ZSMM85GEm8jATWfSaR7W2TJXm999/x8qVK1GvXj3cc889iI6Oro2nISLySmlpwIkTgK8vcOWVWldDRETkvaq1Zerpp59GQEAAcnJyHMu+/vpr9OnTBzNnzsQLL7yAK6+8klOjExHVIOeT9epsbwgiIiJDqVYztWbNGvTv3x9hYWGOZS+88AJCQkLw2WefYdasWcjOzsabb75Z7UK9jcVi0boEcsI85PHmTKTt4mfnzZlIxDzkYSbyMBNZ9JhHtWbza9SoEe644w68++67AIADBw6gbdu2mDZtGqZNmwYAuPnmm7Fv3z7s3bu3ZiquBdJm8yMiKkvXrurU6EuWAKNGaV0NERGRsVSmN6jWlqkzZ864nJB348aNMJlMGDx4sGNZhw4dcOTIkeo8jddRFAX5+fmoRp9LNYh5yOPNmZw7J/Nkvd6ciUTMQx5mIg8zkUWveVSrmWrSpAn27dvnuP7TTz+hQYMG6NKli2NZfn4+/P39q/M0XsdmsyE1NVV3s5kYFfOQx5sz2b4duHgRiI6WdbJeb85EIuYhDzORh5nIotc8qjWbX9++ffGf//wHc+bMQUBAAFasWIGbbrrJZX/Hv//+G82aNat2oUREdOl4qe7dAZ5jkoiISFvV2jI1depUBAYGYsKECXjggQfg6+vrOFYKAE6ePIm1a9eiV69e1S6UiIjkTj5BRETkjaq1Zap169ZISUnBN998AwAYNmwY4uLiHLcfPnwY48ePx5133lmtIr2R3s7+bHTMQx5vzETiyXqdeWMmkjEPeZiJPMxEFj3mUa3Z/IyCs/kRkR6kpQEtWqgn683LAwIDta6IiIjIeOpsNj9nFy9eREpKCn7//XckJyfj4sWLNfXQXsdmsyE7O1t3B+AZFfOQx1szsW+VuuIKeY2Ut2YiFfOQh5nIw0xk0Wse1W6mcnNz8eCDDyI0NBQJCQno3bs3Lr/8coSGhuLBBx9EdnZ2TdTpVRRFQUZGhu6mhjQq5iGPt2YieRc/b81EKuYhDzORh5nIotc8qnXMVG5uLnr06IH9+/cjPDwcffr0QVRUFI4fP45t27bho48+wrp16/D7778jLCyspmomIvJKkpspIiIib1StLVMvv/wy9u/fjylTpuDw4cNYtWoVFixYgB9//BGHDx/G1KlTceDAAbzyyis1VS8RkVc6fx7YtUu9zGaKiIhIhmo1UytWrED//v3x6quvol69ei63BQYG4uWXX8a1116LFStWVOdpvFJQUJDWJZAT5iGPt2ViP1lvVBTQvLnW1XjmbZlIxzzkYSbyMBNZ9JhHtZqpY8eOoXv37mWuc/XVV+PYsWPVeRqvY7FY0KpVK5eTH5N2mIc83piJ8y5+Ek/W642ZSMY85GEm8jATWfSaR7WaqZCQEBw+fLjMdQ4fPoyQkJDqPI3XsdlsyMrK0t1sJkbFPOTxxkykHy/ljZlIxjzkYSbyMBNZ9JpHtZqpfv364auvvsJ///tfj7f/8ssv+Oqrr9CvX7/qPI3XURQFWVlZupvNxKiYhzzelon0k/UC3peJdMxDHmYiDzORRa95VGs2v2nTpuGHH37AoEGDMGTIEPTt2xeRkZE4fvw41q5di1WrViEwMBD/+te/aqpeIiKvk54OZGUBPj5Aly5aV0NERER21WqmOnTogJ9//hnjxo3DDz/8gB9++AEmk8nRUbZq1Qqffvop4uPja6RYIiJvJPlkvURERN6sWs0UAPTs2RP79u3Db7/9hp07dyI/Px/BwcHo3LkzevXqhffffx//93//h2XLltVEvV7BZDIhLCwMJolHmXsh5iGPt2UifRc/wPsykY55yMNM5GEmsug1D5NSyzsm3nPPPfjss89gtVpr82mqJT8/HyEhIcjLy0NwcLDW5RARubjqKmDrVmDxYuCOO7SuhoiIyNgq0xtUawIKqh02mw3p6em6m83EqJiHPN6UyfnzwM6d6mXJW6a8KRM9YB7yMBN5mIkses2DzZRAiqIgJydHd7OZGBXzkMebMtmxQz1Zb2QkEBurdTWl86ZM9IB5yMNM5GEmsug1DzZTRESCST9ZLxERkTdjM0VEJJgeJp8gIiLyVmymBDKZTIiKitLdbCZGxTzk8ZZM9HCyXjtvyUQvmIc8zEQeZiKLXvOo9Gx+Q4YMqdQTJCUl4dixY5zNj4ioktLT1eOkfHyAvDygXj2tKyIiIjK+yvQGlT7P1E8//VTpgvTWYWrNarUiLS0NcXFxsFgsWpfj9ZiHPN6SiX2rVKdO8hspb8lEL5iHPMxEHmYii17zqHQzdejQodqog0ooKCjQugRywjzk8YZM9LKLn503ZKInzEMeZiIPM5FFj3lUupmKlTw3LxGRgeitmSIiIvI2nICCiEigwkJ9nKyXiIjIm7GZEshkMiEmJobHmgnBPOTxhkx27AAuXAAaNwbi4rSupnzekImeMA95mIk8zEQWveZR6d38qPaZzWaEh4drXQb9D/OQxxsy0dvJer0hEz1hHvIwE3mYiSx6zYNbpgSyWq3Yu3ev6OnkvQnzkMcbMtHb8VLekImeMA95mIk8zEQWvebBZkqowsJCrUsgJ8xDHiNnoqeT9TozciZ6xDzkYSbyMBNZ9JgHmykiImGOHAGOHQMsFqBrV62rISIiotKIbKbmzp2LFi1aICAgAF26dMGGDRtKXXfcuHEwmUxuP/Hx8XVYMRFRzdHTyXqJiIi8mbhmaunSpZg0aRKmTp2KnTt3ok+fPhg8eDDS09M9rv/OO+8gMzPT8ZORkYGwsDDcdtttdVx5zTGbzWjZsiXMZnHxeCXmIY/RM9HjLn5Gz0RvmIc8zEQeZiKLXvMwKYqiaF2Es6uvvhpXXnkl5s2b51jWvn17DB8+HDNnziz3/itWrMCIESNw6NChCp9gOD8/HyEhIcjLy0NwcHCVayciqgnduwN//AF8/jlw111aV0NERORdKtMbiJoavbi4GNu3b8fkyZNdlg8cOBCbNm2q0GN8/PHHGDBgQJmNVFFREYqKihzX8/PzAaiziNhnEDGZTDCbzbDZbHDuN+3LS840Utpys9kMk8nkcTkA2Gw2t+VWqxXJyclo3749LBYLAMBisUBRFLf1LRaLW42lLddyTJ6W62VMnvLQ+5g81a6nMRUXF2PPnj2OTIwwJvvyoiJg504zABO6d1dgtepjTFar1ZGJr6+vYV97ehmTcx4+Pj6GGJMzPeZkz6RDhw7w9fU1xJjKq136mOyzx8XHx8NkMhliTM416i2niuRRV2OqzIyCopqpU6dOwWq1IjIy0mV5ZGQksrKyyr1/ZmYmVq1ahcWLF5e53syZM/HSSy+5LU9OTkaDBg0AAGFhYWjevDmOHDmCnJwcxzpRUVGIiopCWloaCgoKHMtjYmIQHh6OAwcOuMxE0rJlSwQHByMlJcUlmLZt28LPzw9JSUkuNSQkJKCoqAinTp1CcnIyTCYTLBYLEhISUFBQgNTUVMe6AQEBaNeuHXJzc5GRkeFYHhQUhFatWuHEiRMuvzctx1RcXIx9+/Y5lulpTBaLxSUPI4xJ7znt2bPHJRMjjMme0+7d9VBcfBkaNVIQG2vTzZjOnz+PnJwcJCcno1WrVoZ97ellTPn5+Y48mjdvbogx6T0nRVGQk5ODkydPIjo62hBj0ntOiqLg7NmzAGCYMQH6zUlRFEddWo/pzJkzqChRu/kdO3YMTZs2xaZNm9DD6WCBV199FYsWLcLevXvLvP/MmTPx5ptv4tixY/Dz8yt1PU9bpmJiYpCTk+PYlKf1lqmkpCTEx8dzy5SAMXnKQ+9j8lS7nsZUXFyM5ORkRyZGGJN9+dtvm/DMM2bcdJOCFSv0Myb7Ftz4+HhumRIwJuc8uGVKxpjsmXTs2JFbpoSMyZ7J5Zdfzi1TAsZUkTzqakz5+fkICwvT325+ERERsFgsbluhTpw44ba1qiRFUfDJJ59gzJgxZTZSAODv7w9/f3+35RaLxfFl2c7+y/W0bm0tt89IWLIe+7KSSquxsstrc0ylLdfDmErLo6z1pY+psssljslTJnofE6AeKwUAPXqYYDLpa0z257dvwTXqa6+6y+tqTPbnsa9nhDHV5fLaGJP9C15N1VjZ5czJfbn988pIYypvueQxlZdHXY2ptNs91lThNeuAn58funTpgsTERJfliYmJ6NmzZ5n3XbduHf7++2/cd999tVlinTCbzWjbtm2pLxiqW8xDHiNnoseZ/ABjZ6JHzEMeZiIPM5FFr3mI2jIFAE8++STGjBmDrl27okePHpg/fz7S09Px8MMPAwCmTJmCo0eP4rPPPnO538cff4yrr74aHTt21KLsGlfe1jWqW8xDHiNmcuQIcPQoYLHo82S9RsxEz5iHPMxEHmYiix7zENf6jRo1CrNnz8b06dNxxRVXYP369fjxxx8ds/NlZma6nXMqLy8P33zzjSG2SgHq/ppJSUlu+5aSNpiHPEbNxL5V6vLLgfr1ta2lsoyaiV4xD3mYiTzMRBa95iFuyxQAjB8/HuPHj/d428KFC92WhYSE4Ny5c7VcFRFR7dLrLn5ERETeStyWKSIib8VmioiISF/YTBERCVBUBOzYoV5mM0VERKQPos4zpZX8/HyEhIRUaC75umCfi98+Fz5pi3nIY8RMNm9Wm6iICODECUBvwzJiJnrGPORhJvIwE1kk5VGZ3oBbpoQqLi7WugRywjzkMVomzrv46fX/dKNlonfMQx5mIg8zkUWPebCZEshms2Hfvn26m83EqJiHPEbMRO/HSxkxEz1jHvIwE3mYiSx6zYPNFBGRAHpvpoiIiLwRmykiIo0dOaL+mM1At25aV0NEREQVxWZKKIvFonUJ5IR5yGOkTDZvVv/V48l6nRkpEyNgHvIwE3mYiSx6zIOz+UHebH5E5F2eegp46y3gkUeAuXO1roaIiMi7cTY/nVMUBfn5+WCfKwPzkMdomRjheCmjZaJ3zEMeZiIPM5FFr3mwmRLIZrMhNTVVd7OZGBXzkMdImRjlZL1GysQImIc8zEQeZiKLXvNgM0VEpKFdu9SGKiICaNVK62qIiIioMny0LoCIyJvZd/Hr3l2/J+slooo7deoUDh06hNOnT+vyBKVGYrPZkJaWhrS0NJjNsrYvmEwmBAcHo3HjxmjXrh1M/A9CLDZTQgUEBGhdAjlhHvIYJRMjHC9lZ5RMjIJ5yHHq1Cl8/fXX+Oyzz7BlyxZYrVatSyIdiY2NxW233YbbbrsN3bp1M3RjpcfPLc7mB87mR0R1z2oFNmwAbr0VyMkBEhOBAQO0roqIatqff/6Ja6+9Frm5uejfvz9uu+029OjRAw0bNtTlF0eqGzabDXl5eUhLS8Py5cvxzTff4OTJk5g8eTJmzJhh6IZKgsr0BmymIK+ZstlsyM3NRcOGDcVtdvZGzEMevWeybBkwcaJ6ol67pk2Bd98FRozQrq7q0HsmRsM8ZLA3UrGxsVi5ciWaNGmidUmkUxcvXsTbb7+Nf/7zn4ZtqCR9bnFqdJ1TFAUZGRm6mxrSqJiHPHrOZNkyYORI10YKAI4dU5cvW6ZNXdWl50yMiHlo78KFCxg0aBBiY2ORmJjIRoqqxcfHB8888wzeeustvPbaa/jPf/6jdUk1Tq+fW2ymiIjqiNWqbpHy9P+EfdmkSep6RKRvv/zyC7KysvDxxx8jLCxM63LIIJ544gn07t0bX3zxhdal0P+wmSIiqiMbNrhvkXKmKEBGhroeEenbV199hdatW6NTp05al0IGc/vttyMxMRG5ublal0JgMyVWUFCQ1iWQE+Yhjx4zycys2fWk0WMmRsY8tKMoClasWIHbbrvNcMe1kPZuvfVWXLhwAT/88IPWpdQ4PX5ucWp0gSwWC1rx7J1iMA959JpJRQ+Z0OOhFXrNxKiYh7bOnDmDnJwcJCQkaF0KGVB0dDQiIiKQnp6udSk1Sq+fW9wyJZDNZkNWVhZsNpvWpRCYh0R6zaRPH6BZs9JPzmsyATEx6np6o9dMjIp5aOv06dMAgNDQUE3rIONq2LCh43VmFHr93GIzJZCiKMjKytLdbCZGxTzk0WsmFgvwzjueJ6CwN1izZ6vr6Y1eMzEq5qEt+0l5fX19K7T+H3/8gVtuuQXNmzeHv78/IiMj0aNHDzz11FO1WWaNGDduHOLi4rQuAwAwdepUdO7cGWFhYQgICEDLli3x4IMP4vDhwy7rpaWlwWQyefxZsmSJ2+OmpqZixIgRCA0NRYMGDXD99ddjx44dbuvFxcV5fMyHH37Ybd2dO3di+PDhiI6ORr169dCuXTtMnz4d586dq9BYfX19cfHixQr+ZvRBr59b3M2PiKgODRkCBAUBBQWuy5s1UxspvZ5nioiq5ocffsBNN92Efv36YdasWWjSpAkyMzOxbds2LFmyBG+++abWJerG6dOncccdd6B9+/YICgpCSkoKXnnlFXz33XdITk5GeHi4y/qPP/447rzzTpdlbdq0cbl+8uRJ9OnTBw0bNsQnn3yCgIAAzJw5E/369cPWrVvRtm1bl/V79eqF//u//3NZFhkZ6XI9JSUFPXv2RNu2bTF79mxERERg/fr1mD59OrZv345vv/22ur8KqkNspoiI6tB//qM2UjExwIIFwIkT6jFSffroc4sUEVXPrFmz0KJFC6xevRo+Ppe+lv3jH//ArFmzNKxMf+bMmeNyvV+/fmjRogWGDBmCb7/9Fvfee6/L7c2bN0f37t3LfMw33ngDJ0+exKZNmxAbGwsA6N27N1q1aoV//etfWLp0qcv6oaGh5T7m4sWLUVhYiG+++cZxjNC1116LzMxMzJ8/33HiWtIH7uYnkMlkQlhYGGcAEoJ5yKPXTBRF3c0PAB57DLjuOuCOO4B+/fTfSOk1E6NiHvqRnZ2NiIgIl0bKzmx2/Zq2dOlSDBw4EE2aNEFgYCDat2+PyZMn4+zZsy7rjRs3Dg0aNMDevXsxaNAg1K9fH02aNMFrr70GANi8eTN69+6N+vXr47LLLsOnn37qcv+FCxfCZDIhMTER99xzD8LCwlC/fn3ceOONSE1NLXdMiqJg7ty5uOKKKxAYGIiGDRti5MiRbvfduXMnhg0bhsaNG8Pf3x/R0dEYOnQojpR1DolKatSoEQB4/P1WxPLly3Httdc6GikACA4OxogRI/D9999XaVc7++6fISEhLstDQ0NhNpvh5+dXpVr1Tq+fW2ymBDKbzWjevLnbhyhpg3nIo9dM1q8Hdu8GAgOB++/XupqapddMjIp56EePHj3wxx9/YMKECfjjjz9w4cKFUtc9cOAAhgwZgo8//hg//fQTJk2ahC+//BI33nij27oXLlzAiBEjMHToUHz77bcYPHgwpkyZgueeew5jx47Fvffei+XLl6Nt27YYN24ctm/f7vYY9913H8xmMxYvXozZs2djy5Yt6NevX7kTHzz00EOYNGkSBgwYgBUrVmDu3LlITk5Gz549cfz4cQDA2bNncf311+P48eOYM2cOEhMTMXv2bDRv3hwFTvtBv/jiizCZTFi7dm3FfqEALl68iPPnz2Pnzp2YNGkSLrvsMozwsA/1a6+9Bj8/P9SrVw+9e/fGd99953L7+fPncfDgQVx++eVu97388stx/vx5twZx/fr1CAoKgq+vLzp06IA333zTcQyd3dixYxEaGopHHnkEqampKCgowMqVK/HBBx/g0UcfRf369Ss8ViPR7eeWQkpeXp4CQMnLy9O6FEVRFMVqtSqHDx9WrFar1qWQwjwk0msmI0YoCqAoDz6odSU1T6+ZGBXz0NahQ4cUAEpiYmK56546dUrp3bu3AkABoPj6+io9e/ZUZs6cqRQUFJR6P5vNply4cEFZt26dAkDZvXu347axY8cqAJRvvvnGsezChQtKo0aNFADKjh07HMuzs7MVi8WiPPnkk45lCxYsUAAot9xyi8tz/vbbbwoA5ZVXXnF5rtjYWMf133//XQGgvPnmmy73zcjIUAIDA5V//vOfiqIoyrZt2xQAyooVK8r8/bz00kuKxWJR1q5dW+Z6dpmZmY7fJQDl6quvVo4ePeqyzrFjx5QHHnhA+fLLL5UNGzYoX3zxhdK9e3cFgPLhhx861jt69KgCQJk5c6bb8yxevFgBoGzatMmxbPz48conn3yirFu3TlmxYoVy1113KQCU0aNHu91/z549Srt27VxqnTBhgmKz2So0zg4dOihPPPFEhdbVC0mfW5XpDXTW+nkHRVGQk5Oju9lMjIp5yKPHTNLSgBUr1MsTJmhZSe3QYyZGxjz0Izw8HBs2bMDWrVvx2muv4eabb8b+/fsxZcoUJCQk4NSpU451U1NTceeddyIqKgoWiwW+vr7o27cvAGDPnj0uj2symTBkyBDHdR8fH7Ru3RpNmjRB586dHcvDwsLQuHFjtxnvAOCuu+5yud6zZ0/ExsZizZo1pY5n5cqVMJlMGD16NC5evOj4iYqKQqdOnRxbmFq3bo2GDRvi2Wefxb///W+kpKR4fLx//etfuHjxomOc5YmIiMDWrVuxceNGfPjhh8jJyUH//v2R6XQ29CZNmmD+/Pm47bbb0Lt3b9x5551Yv349OnfujMmTJ7vtulfWbmfOt82ZMwf33HMPrrnmGtx88834/PPP8dhjj+Hzzz/Hzp07HeulpaXhxhtvRHh4OL7++musW7cOs2bNwsKFC3G/0XZbqAS9fm6xmSIiqgNz5wI2m3qcVHy81tUQkTRdu3bFs88+i6+++grHjh3DE088gbS0NMckFGfOnEGfPn3wxx9/4JVXXsHatWuxdetWLFu2DIC6S5qzevXqISAgwGWZn58fwsLC3J7bz88PhYWFbsujoqI8LsvOzi51HMePH4eiKIiMjISvr6/Lz+bNmx3NYUhICNatW4crrrgCzz33HOLj4xEdHY1p06aVuatjeXx8fNC1a1f06tUL999/P3799VekpqY6jhcrja+vL0aNGoXs7GwcOHAAgHouJ5PJ5HG8OTk5AODx9+ls9OjRANTj1OwmT56M/Px8rF69GrfeeiuuueYaPPPMM5g9ezY++eQTrFu3rlJjJm1xNj8iolp29izw4Yfq5YkTta2FiOTz9fXFtGnT8Pbbb+Ovv/4CAPz66684duwY1q5d67KVpjZP3JqVleVxWevWrUu9T0REBEwmEzZs2AB/f3+3252XJSQkYMmSJVAUBX/++ScWLlyI6dOnIzAwEJMnT66RMTRr1gzR0dHYv39/uevat4jYj9kJDAxE69atkZSU5LZuUlISAgMD0bJly0o9JgDs2rULHTp0cDs2qlu3bgCAv/76q8Jb4kh73DIlkMlkQlRUlO5mMzEq5iGP3jL5/HPg9GmgVStg6FCtq6kdesvE6JiHfjjvfubMvttedHQ0gEu7k5VsUD744INaq+2LL75wub5p0yYcPnwY/fr1K/U+w4YNg6IoOHr0KLp27er2k5CQ4HYfk8mETp064e2330ZoaKjHE+JW1d9//40jR46U2QAC6oQdS5cuRUREhMu6t9xyC3799VdkZGQ4lhUUFGDZsmW46aabyp0l8LPPPgMAl+nSo6OjkZycjDNnzris+/vvvwNQG0BvpNfPLW6ZEshsNnvctE7aYB7y6CkTRQHefVe9/NhjgN4mKaooPWXiDZiHfgwaNAjNmjXDjTfeiHbt2sFms2HXrl1488030aBBA0z83+bsnj17omHDhnj44Ycxbdo0+Pr64osvvsDu3btrrbZt27bh/vvvx2233YaMjAxMnToVTZs2xfjx40u9T69evfDggw/innvuwbZt23DNNdegfv36yMzMxMaNG5GQkIBHHnkEK1euxNy5czF8+HC0bNkSiqJg2bJlOH36NK6//nrH402fPh3Tp0/HL7/8UubWmj///BNPPPEERo4ciZYtW8JsNiMpKQlvv/02wsPD8fTTTzvWffLJJ3HhwgX06tULUVFRyMjIwHvvvYddu3ZhwYIFsDidq+Lpp5/GokWLMHToUEyfPh3+/v547bXXUFhYiBdffNGx3uLFi7Fs2TIMHToUsbGxOH36NL766issWbIE48aNQ6dOnRzrTpo0CcOHD8f111+PJ554AhEREdi8eTNmzpyJDh06YPDgwZWNyhD0+rnFZkogq9WKtLQ0xMXFubyhSRvMQx49ZfLLL0BKCtCgAXDPPVpXU3v0lIk3YB768fzzz+Pbb7/F22+/jczMTBQVFaFJkyYYMGAApkyZgvbt2wNQJ6r44Ycf8NRTT2H06NGoX78+br75ZixduhRXXnllrdT28ccfY9GiRfjHP/6BoqIi9O/fH++88065xwl98MEH6N69Oz744APMnTsXNpsN0dHR6NWrF6666ioAQJs2bRAaGopZs2bh2LFj8PPzQ9u2bbFw4UKMHTvW8Vg2mw1Wq7XcSQkiIyMRHR2NN998E5mZmbh48SKaNWuGYcOG4bnnnkNMTIxj3Y4dO+KDDz7A4sWLkZ+fj6CgIFx11VVYvXo1Bg4c6PK4jRo1woYNG/D0009j7NixuHjxInr06IG1a9eiXbt2jvVatmyJ06dP47nnnkN2djZ8fX0RHx+PuXPn4qGHHnJ5zJtuugm//PILXnvtNUycOBF5eXmIiYnBQw89hClTpnjteab0+rllUvQ2ZUYtyM/PR0hICPLy8hAcHKx1ObBarUhKSkJCQoKuXkxGxTzk0VMmN94IrFypbpV67z2tq6k9esrEGzAPbaWlpaFFixZITEzEgAEDtC6n0hYuXIh77rkHW7duRdeuXbUuhzyIj4/HoEGD8NZbb2ldSo2R9LlVmd7AoDucEBFp7++/gR9+UC8//ri2tRAREVHNYzNFRFRL3n9fPWZq8GDgssu0roaIiIhqGpspgUwmE2JiYnQ3m4lRMQ959JBJfj7wySfqZW+YDl0PmXgT5kHVMW7cOCiKwl38qE7p9XOLE1AIZDabER4ernUZ9D/MQx49ZPLpp0BBAdC2LeA0MZVh6SETb8I8iEhv9Pq5xS1TAlmtVuzduxdWq1XrUgjMQyLpmdhslyabmDDBuNOhO5OeibdhHkSkN3r93PKC/+L1qbCwUOsSyAnzkEdyJj/9BBw4AISEAHffrXU1dUdyJt6IeejDwoULYTKZPP44nxupJqWkpODFF19EWlparTx+bVi7di1MJhPWrl3rWDZu3DjExcVpVtNHH32E4cOHIy4uDoGBgWjdujUeeeQRjydijouL85jxww8/rEHlcunxc4u7+RER1bB33lH/ve8+9fxSRETlWbBggct5iwAgOjq6Vp4rJSUFL730Evr166dpM1JdL7zwguOkxlqYNm0a+vfvjxkzZqBp06bYt28fXn75ZXz77bfYuXMnIiMjXdbv1asX/u///s9lWcl1SH/YTBER1aA9e4CffwZMJvXcUkREFdGxY0fdT/hw4cIFmEwm+PjUzdfLVq1a1cnzlGbnzp1o3Lix43rfvn1x5ZVXolu3bvjwww/x/PPPu6wfGhqK7t2713WZVMu4m59AZrMZLVu2hNkbDrTQAeYhj+RM7MdK3XQT0KKFtrXUJcmZeCPmYTxLly5Fjx49UL9+fTRo0ACDBg3Czp07XdbZtm0b/vGPfzh2O4uLi8Mdd9yBw4cPO9ZZuHAhbrvtNgBA//79HbubLVy4EIC6O9q4cePcnr9fv37o16+f47p9t7tFixbhqaeeQtOmTeHv74+///4bAPDf//4X1113HYKDg1GvXj306tULv/zyS4XGunfvXtxwww2oV68eIiIi8PDDD6OgoMBtPU+7+ZlMJjz22GNYsGAB2rZti8DAQHTt2hWbN2+Goih444030KJFCzRo0ADXXnuto96qcG6k7Lp06QKLxYKMjIwqP6630uvnlr6q9RImkwnBwcG6mxrSqJiHPFIzyc1VZ/EDvGM6dGdSM/FWzEN/rFYrLl686PJjN2PGDNxxxx3o0KEDvvzySyxatAgFBQXo06cPUlJSHOulpaWhbdu2mD17NlavXo3XX38dmZmZ6NatG06dOgUAGDp0KGbMmAEAmDNnDn7//Xf8/vvvGDp0aJXqnjJlCtLT0/Hvf/8b33//PRo3bozPP/8cAwcORHBwMD799FN8+eWXCAsLw6BBg8ptqI4fP46+ffvir7/+wty5c7Fo0SKcOXMGj1ViU//KlSvx0Ucf4bXXXsN//vMfFBQUYOjQoXjqqafw22+/4f3338f8+fORkpKCW2+9FYqiOO5rbxJffPHFKv0+1q1bB6vVivj4eLfb1q9fj6CgIPj6+qJDhw548803dTfZQm3S6+cWd/MTyGq1IiUlBR06dIDFYtG6HK/HPOSRmsknnwDnzgEdOwJOf8D1ClIz8VbMQ3887f514cIFZGZmYtq0aXjsscfw7rvvOm67/vrr0aZNG7z00ktYunQpAGDkyJEYOXKkYx2r1Yphw4YhMjISixcvxoQJE9CoUSO0adMGANChQ4dq73bWqlUrfPXVV47r586dw8SJEzFs2DAsX77csXzIkCG48sor8dxzz+GPP/4o9fHefvttnDx5Ejt37kSnTp0AAIMHD8bAgQORnp5eoZqKiorw888/o379+gDUL+nDhw/HmjVrsGPHDseX9ZMnT2LSpEn466+/kJCQ4FjXYrFUaetIQUEBxo8fj5iYGNx7770utw0dOhRdu3ZFq1atkJubi6+++gpPP/00du3ahUWLFlX6uYxIr59bbKaE4l8qZGEe8kjLxGoF3n9fvTxxonrMlLeRlom3Yx768tlnn6F9+/Yuy3x8fLB69WpcvHgRd999t8vWqoCAAPTt2xdr1qxxLDtz5gxefvllfPPNN0hLS3N5DezZs6dW6r711ltdrm/atAk5OTkYO3asS70AcMMNN2DWrFk4e/aso9Epac2aNYiPj3c0UnZ33nknEhMTK1RT//79XR7f/nsdPHiwy1YP+/LDhw87mqm+ffu61V0RhYWFGDFiBA4fPoxff/0VDUrMPjRnzhyX6zfffDMaNmyI999/H08++SQ6d+5c6ec0Ij1+brGZIiKqAd9/D6SlAWFhwF13aV0NEelN+/btPU5Acfz4cQBAt27dPN7PeQvKnXfeiV9++QUvvPACunXr5thlasiQITh//nyt1N2kSROP9TpvISspJyen1GYqOzsbLTwccBoVFVXhmsLCwlyu+/n5lbm8utNxFxUV4ZZbbsHGjRuxcuVKXH311RW63+jRo/H+++9j8+bNbKZ0jM0UEVENsE+H/uCDQGCgtrUQkXFEREQAAL7++mvExsaWul5eXh5WrlyJadOmYfLkyY7lRUVFyMnJqfDzBQQEoKioyG35qVOnHLU4K3l8i32d9957r9RdCMuaDjw8PBxZWVluyz0tk6CoqMixC+G3336L6667rsL3tR+rpbcJF8gVmymBzGYz2rZtyzeXEMxDHmmZ/PknsHYtYLEA48drXY02pGXi7ZiHcQwaNAg+Pj44ePCg2y51zkwmExRFgb+/v8vyjz76yG3XKfs6nrZWxcXF4c8//3RZtn//fuzbt89jM1VSr169EBoaipSUlEpNGmHXv39/zJo1C7t373bZ1W/x4sWVfqzaZt8i9euvv2LZsmUYNGhQpe7/2WefAfB8vJw30uvnFpspoeybnkkG5iGPpEzsx4SPGAHExGhbi5YkZULMwyji4uIwffp0TJ06FampqbjhhhvQsGFDHD9+HFu2bEH9+vXx0ksvITg4GNdccw3eeOMNREREIC4uDuvWrcPHH3+M0NBQl8fs2LEjAGD+/PkICgpCQEAAWrRogfDwcIwZMwajR4/G+PHjceutt+Lw4cOYNWsWGjVqVKF6GzRogPfeew9jx45FTk4ORo4cicaNG+PkyZPYvXs3Tp48iXnz5pV6/0mTJuGTTz7B0KFD8corryAyMhJffPEF9u7dW+XfYWWsW7cO1113Hf71r3/hX//6V5nrjhw5EqtWrcLUqVMRHh6OzZs3O24LDg5Ghw4dAKiN4LJlyzB06FDExsbi9OnT+Oqrr7BkyRKMGzfO7fgwb6bHzy19tX5ewmazISkpCTabTetSCMxDIkmZnDoFfPGFetnbpkN3JikTYh5GM2XKFHz99dfYv38/xo4di0GDBuGf//wnDh8+jGuuucax3uLFi9G/f3/885//xIgRI7Bt2zYkJiYiJCTE5fFatGiB2bNnY/fu3ejXrx+6deuG77//HoB63NWsWbOwevVqDBs2DPPmzcO8efNw2WWXVbje0aNHY82aNThz5gweeughDBgwABMnTsSOHTvK3Q0uKioK69atQ4cOHfDII49g9OjRCAgIwPv2GX5qmaIosFqtFXrvrFy5EgDw6quvokePHi4/4512U2jZsiVOnz6N5557DjfccAPGjBmDgwcPYu7cufj4449rbSx6o9fPLZPiPLm+l8rPz0dISAjy8vIQHBysdTmwWq1ISkpCQkKCrqaGNCrmIY+kTGbOBJ57DrjySmDbNu+cxQ+QlQkxD62lpaWhRYsWSExMxIABA7QuhwwoPj4egwYNwltvvaV1KTVG0udWZXoDbpkiIqqiCxcA+2y33jodOhERkTdjM0VEVEXLlwNHjwKNGwOjRmldDRFJ4evrCwAeZ8UjqgmFhYW6PL7IiNhMCWQ2m5GQkKC72UyMinnIIyUT+3ToDz8MlJhAy+tIyYRUzENbDRs2BADk5uZqXAkZVW5uruN1ZhR6/dzSV7VepLi4WOsSyAnzkEfrTLZtAzZtAnx91WaKtM+EXDEP7QQGBqJZs2b4448/tC6FDGj//v3Izc2t1KQgeqHHzy02UwLZbDbs27dPd7OZGBXzkEdCJvbp0G+/HWjSRLMyxJCQCV3CPLRlMplw66234ptvvmEGVOO++uor1K9fHzfccIPWpdQovX5usZkiIqqkrCxgyRL1sjdPh05Epbv99tuRmZmJ9evXa10KGYjNZsOXX36JYcOGITAwUOtyCGymiIgq7YMP1Jn8uncHunXTuhoikqh79+6Ij4/HmDFj8Pfff2tdDhmAzWbDo48+iqSkJNx3331al0P/w2ZKKK3n1ydXzEMerTIpKgLmzVMvc6uUK75PZGEe2jKbzUhMTES9evXQv39/bNiwQXe7L5EcWVlZePDBB/HBBx/go48+wvXXX691SbVCj59bPGkv5J20l4jk+vxzYMwYIDoaSEtTJ6AgIipNZmYmBgwYgJSUFDRt2hQjR45Ejx49EBYWhoCAAK3LI6GsVivy8vKQlpaG5cuXY/369bBYLPjggw9w7733al2e4VWmN/Cpo5qoEhRFQUFBAYKCgmDiWUA1xzzk0SoTRbk0Hfr48WyknPF9IgvzkKNJkyb4888/sXHjRixevBhffvkl3rF/kBCVw8fHB9dddx0++ugjDB8+HGFhYVqXVGv0+rnFZkogm82G1NRUJCQk6HJzp9EwD3m0ymTzZnVKdH9/4MEH6+xpdYHvE1mYhywWiwW9e/dGSEgI5syZg7NnzyI3N1eX00AbidVqxf79+3HZZZeJe5+YTCaEhISgYcOGjpNAG51eP7fYTBERVZD9j8l33gk0aqRtLUSkT/YvySEhIVqX4vWsViuKiopENlOkH5yAgoioAo4cAb7+Wr08YYK2tRAREZEMbKaE4kGpsjAPeeo6k3nzAKsVuOYa4Ior6vSpdYPvE1mYhzzMRB5mIose8+BsfuBsfkRUtvPngZgYIDsb+OYbYMQIrSsiIiKi2lKZ3oBbpgSy2WzIzs7m+SiEYB7y1HUm//mP2kg1bw7cdFOdPKXu8H0iC/OQh5nIw0xk0WsebKYEUhQFGRkZ4EZDGZiHPHWZifN06I89Bvhw2h6P+D6RhXnIw0zkYSay6DUPNlNERGVYvx74808gMBC47z6tqyEiIiJJ2EwREZXBvlXq7rsBA58rkYiIiKqAzZRQQUFBWpdATpiHPHWRSVoa8O236mVOh14+vk9kYR7yMBN5mIksesyDs/mBs/kRkWfPPAP83/8BAwYAiYlaV0NERER1gbP56ZzNZkNWVpbuZjMxKuYhT11kcvYs8NFH6uWJE2vtaQyD7xNZmIc8zEQeZiKLXvNgMyWQoijIysrS3WwmRsU85KmLTBYtAk6fBlq1AoYMqbWnMQy+T2RhHvIwE3mYiSx6zYPNFBFRCYoCvPuuevnxxwEzPymJiIjIA35FICIq4b//BfbsARo0AMaN07oaIiIikorNlEAmkwlhYWEwmUxal0JgHhLVdib26dDvuQcICamVpzAcvk9kYR7yMBN5mIkses2Ds/mBs/kR0SUHDgCXXaZe3r8faNNG23qIiIiobnE2P52z2WxIT0/X3WwmRsU85KnNTN5/X/13yBA2UpXB94kszEMeZiIPM5FFr3mwmRJIURTk5OTobjYTo2Ie8tRWJvn5wIIF6mVOh145fJ/IwjzkYSbyMBNZ9JqHyGZq7ty5aNGiBQICAtClSxds2LChzPWLioowdepUxMbGwt/fH61atcInn3xSR9USkVEsXAgUFADt2gHXX691NURERCSdj9YFlLR06VJMmjQJc+fORa9evfDBBx9g8ODBSElJQfPmzT3e5/bbb8fx48fx8ccfo3Xr1jhx4gQuXrxYx5UTkZ7ZbMB776mXJ0wAdHb8KxEREWlAXDP11ltv4b777sP9998PAJg9ezZWr16NefPmYebMmW7r//TTT1i3bh1SU1MRFhYGAIiLi6vLkmucyWRCVFSU7mYzMSrmIU9tZLJqFfD33+rsfWPG1NjDeg2+T2RhHvIwE3mYiSx6zUNUM1VcXIzt27dj8uTJLssHDhyITZs2ebzPd999h65du2LWrFlYtGgR6tevj5tuugkvv/wyAgMDPd6nqKgIRUVFjuv5+fkAAKvVCqvVCkAN1Gw2w2azuey7aV9uX6+85WazGSaTyeNyAG4H2dnXb9SoERRFcdzPYrFAURS39S0Wi1uNpS3XckyelutlTJ7y0PuYPNWupzEpiuKSSU2MafZsMwAT7rtPQWCgDc5PzZwqNiZ7JoqiGGZMzjXqbUz2PGw2m2HGVN5y6WNq1KiR43ajjKms2vUwpsaNGxtuTHrOqVGjRmXWXldjKnl7WUQ1U6dOnYLVakVkZKTL8sjISGRlZXm8T2pqKjZu3IiAgAAsX74cp06dwvjx45GTk1PqcVMzZ87ESy+95LY8OTkZDRo0AACEhYWhefPmOHLkCHJychzrREVFISoqCmlpaSgoKHAsj4mJQXh4OA4cOIDCwkLH8pYtWyI4OBgpKSkuwbRt2xZ+fn5ISkpyqSEhIQGFhYXYvn07goKCYDKZYLFYkJCQgIKCAqSmpjrWDQgIQLt27ZCbm4uMjAzH8qCgILRq1QonTpxw+b1pOabi4mLs27fPsUxPY7JYLNi8ebMjDyOMSe85/fXXX8jLy3NkUt0xHTzoj//+tz3MZuCuu04jKekwc6rkmM6fP4+CggLHYxthTHrOKT8/35FH8+bNDTEmveekKAoKCgrQunVrREdHG2JMes/J/ge5zp074+jRo4YYE6DfnBRFQXFxMbp27YqTJ09qOqYzZ86gokSdZ+rYsWNo2rQpNm3ahB49ejiWv/rqq1i0aBH27t3rdp+BAwdiw4YNyMrKQsj/zq65bNkyjBw5EmfPnvW4dcrTlqmYmBjk5OQ45pLXsqu3Wq1ISkpCfHw8LBYLAP6lQssxecpD72PyVLuexlRcXIzk5GRHJtUd06OPmvDBB2YMHw588w1zqsqYrFarIxNfX19DjKlkjXoak3MePj4+hhiTMz3mZM+kY8eO8PX1NcSYyqtd+pjsmVx++eWOvR70PibnGvWWU0XyqKsx5efnIywsrELnmRK1ZSoiIgIWi8VtK9SJEyfctlbZNWnSBE2bNnU0UgDQvn17KIqCI0eOoI2HE8X4+/vD39/fbbnFYnF8Wbaz/3I9rVtby00mk2OLlPPt9mUllVZjZZfX5phKW66HMZWWR1nrSx9TZZdLHJOnTKoyptxcYNEiddnEicyprOXl1WJ/fvsWXCOMqTaW19WY7M9jX88IY6rL5bUxJvsXvJqqsbLLmZP7cvvnlZHGVN5yyWMqL4+6GlNpt3usqcJr1gE/Pz906dIFiYmJLssTExPRs2dPj/fp1asXjh075rI5bv/+/TCbzWjWrFmt1ktE+vfxx8C5c8DllwN9+2pdDREREemJqGYKAJ588kl89NFH+OSTT7Bnzx488cQTSE9Px8MPPwwAmDJlCu6++27H+nfeeSfCw8Nxzz33ICUlBevXr8czzzyDe++9t9QJKKQzmUyIiYlxdOekLeYhT01lcvEi8P776mVOh149fJ/IwjzkYSbyMBNZ9JqHqN38AGDUqFHIzs7G9OnTkZmZiY4dO+LHH39EbGwsACAzMxPp6emO9Rs0aIDExEQ8/vjj6Nq1K8LDw3H77bfjlVde0WoI1WY2mxEeHq51GfQ/zEOemsrk+++Bw4eB8HDgzjtroDAvxveJLMxDHmYiDzORRa95iJqAQiv5+fkICQmp0EFmdcFqteLAgQNo06ZNpfbZpNrBPOSpqUz69QPWrQOmTAFmzKi5+rwR3yeyMA95mIk8zEQWSXlUpjcQt5sfqZyncSTtMQ95qpvJ7t1qI2WxAI88UkNFeTm+T2RhHvIwE3mYiSx6zIPNFBF5pXffVf+99VYgJkbbWoiIiEif2EwRkdc5dQr44gv18oQJ2tZCRERE+sVmSiCz2YyWLVuWOpc+1S3mIU91M5k/HygqArp0AUo56wJVEt8nsjAPeZiJPMxEFr3mIW42P1KnhpQwEQapmIc81cnkwgVg7lz18sSJnA69pvB9IgvzkIeZyMNMZNFrHvpq/byE1WpFUlISrFar1qUQmIdE1clk2TLg6FEgMhK4/fZaKM5L8X0iC/OQh5nIw0xk0WsebKaE0tsLyeiYhzxVzeSdd9R/H34Y8PevwYKI7xNhmIc8zEQeZiKLHvNgM0VEXmPrVuD33wFfX7WZIiIiIqoONlNE5DXs06GPGgVERWlbCxEREemfSVEUResitFaZsxzXBUVRUFhYiICAAJh4dLzmmIc8VckkKwto3lydgGLLFqBbt1ou0svwfSIL85CHmcjDTGSRlEdlegNumRLKz89P6xLICfOQp7KZ/PvfaiPVowcbqdrC94kszEMeZiIPM5FFj3mwmRLIZrMhKSkJNptN61IIzEOiymZSVATMm6denjixFgvzYnyfyMI85GEm8jATWfSaB5spIjK8L78ETpwAmjYFRozQuhoiIiIyCjZTRGRoinJpOvTx49WZ/IiIiIhqApspIjK0338Htm9Xzyn1wANaV0NERERGwtn8IHM2P5vNBrPZrPlsJsQ8JKpMJqNGqbv53Xsv8PHHdVSgF+L7RBbmIQ8zkYeZyCIpD87mZwDFxcVal0BOmIc8FcnkyBHgm2/UyxMm1HJBxPeJMMxDHmYiDzORRY95sJkSyGazYd++fbqbzcSomIc8Fc1k7lzAagX69gU6daqj4rwU3yeyMA95mIk8zEQWvebBZoqIDOn8eWD+fPUyt0oRERFRbWAzRUSGtHgxkJ0NxMYCN9+sdTVERERkRGymhLJYLFqXQE6YhzxlZeI8HfpjjwGMr27wfSIL85CHmcjDTGTRYx6czQ/yZvMjoupZuxbo3x+oV0+dhKJhQ60rIiIiIr3gbH46pygK8vPzwT5XBuYhT3mZ2LdK3X03G6m6wveJLMxDHmYiDzORRa95sJkSyGazITU1VXezmRgV85CnrEwOHQK++069/PjjdVyYF+P7RBbmIQ8zkYeZyKLXPNhMEZGhzJkD2GzA9dcDHTpoXQ0REREZGZspIjKMM2eAjz5SL3M6dCIiIqptbKaECggI0LoEcsI85PGUyaJFQF4e0Lo1MGSIBkV5Ob5PZGEe8jATeZiJLHrMg7P5gbP5ERmBzQbExwN796oTUHDLFBEREVUFZ/PTOZvNhuzsbN0dgGdUzEMeT5n8979qIxUUBIwbp11t3orvE1mYhzzMRB5mIote82AzJZCiKMjIyNDd1JBGxTzk8ZSJfTr0e+4BuIG57vF9IgvzkIeZyMNMZNFrHmymiEj3DhwAfvwRMJmAxx7TuhoiIiLyFmymiEj33ntP/XfIEKBNG21rISIiIu/BZkqooKAgrUsgJ8xDHnsm+fnAggXqMk46oS2+T2RhHvIwE3mYiSx6zIOz+YGz+RHp2TvvAJMmAe3bA8nJ6q5+RERERFXF2fx0zmazISsrS3ezmRgV85DHnsnFizbHLn6PP85GSkt8n8jCPORhJvIwE1n0mgebKYEURUFWVpbuZjMxKuYhjz2TH35QcPAgEBoK3H231lV5N75PZGEe8jATeZiJLHrNg80UEenWe++pH2H33w/Ur69xMUREROR1fLQugIioMqxWYO1aYOXKRvjlFxNMJuDRR7WuioiIiLwRmymBTCYTwsLCYOIBICIwDzmWLQMmTgSOHLEAaAoA8PcHduwA4uI0Lc3r8X0iC/OQh5nIw0xk0WsenM0PnM2PSA+WLQNGjgQ8fWKZTMDXXwMjRtR9XURERGQsnM1P52w2G9LT03U3m4lRMQ/tWa3qFqmy/vQzaZK6HmmD7xNZmIc8zEQeZiKLXvNgMyWQoijIycnR3WwmRsU8tLdhA3DkSOm3KwqQkaGuR9rg+0QW5iEPM5GHmcii1zzYTBGRaIqiTjhREZmZtVoKERERkQtOQEFEIqWlAZ99pv4cPFix+zRpUqslEREREblgMyWQyWRCVFSU7mYzMSrmUXcKCtSJJD79FFi37tLy+vXVLVTnznm+n8kENGsG9OlTN3WSO75PZGEe8jATeZiJLHrNg7P5gbP5EWnJagV+/VVtoJYtA86fV5ebTMB11wF3363O0rd6tTqbH+A6EYX9M5ez+REREVFN4Gx+Ome1WnHw4EFYOTWZCMyjduzZA0yZAsTGAgMHAl98oTZSbdsCM2YAhw8DiYnAmDHqlqkRI9SGqWlT18dp1oyNlAR8n8jCPORhJvIwE1n0mgd38xOqoKBA6xLICfOoGdnZwJIl6laorVsvLW/YELjjDnUr1FVXXdraVNKIEcDNNwNr11qxZUsGrroqBv36WWCx1E39VDa+T2RhHvIwE3mYiSx6zIPNFBHVqgsXgB9/VCeS+P579ToA+PgAgwcDY8cCw4YB/v4VezyLBejXDwgPP42EhBg2UkRERKQZNlNEVOMUBdi5U90CtXgxcOrUpds6d1YbqDvuABo31q5GIiIioupiMyWQyWRCTEyM7mYzMSrmUXGZmcDnn6tbof7669LyyEhg9Gi1iUpIqP7zMBN5mIkszEMeZiIPM5FFr3lwNj9wNj+i6jh/Hvj2W3Ur1M8/AzabutzfHxg+XG2grr9e3a2PiIiISDrO5qdzVqsVe/fu1d1sJkbFPNwpCrBxI/DAA0BUlLrL3k8/qY1Uz57ABx8AWVnqZBODB9d8I8VM5GEmsjAPeZiJPMxEFr3mwb8VC1VYWKh1CeSEeajS0tRd+D77DDh48NLy2Fh1Jr677wZat66bWpiJPMxEFuYhDzORh5nIosc82EwRUZny89XzOH32GbBu3aXlDRqoJ9EdOxa45hrAzO3cRERE5GXYTBGRG6sV+PVX9TioZcvU46IA9fxP112nNlC33KKeTJeIiIjIW7GZEshsNqNly5Yw80/9IhglD6sV2LBBnXGvSROgTx+4naNpzx61gfr8c+Do0UvL27ZVG6jRo4GYmLqt2xOjZGIkzEQW5iEPM5GHmcii1zw4mx84mx8Z37JlwMSJwJEjl5Y1awa88w7Qty/wn/+ou/Ft3Xrp9oYN1Yklxo4FunVTt0oRERERGR1n89M5q9WKpKQk3c1mYlR6z2PZMvXYJudGClCv33qreg6oxx9XGykfH+Cmm9RjpDIzgTlzgKuuktdI6T0TI2ImsjAPeZiJPMxEFr3mwd38hNLbC8no9JqH1apukSpr+7PVClxxBTBunLolqnHjuqquevSaiZExE1mYhzzMRB5mIose82AzRWRAZ88CSUnAl1+6b5Hy5O23gX79ar0sIiIiIkNhM0WkcydPAjt3Art2Xfp3/371BLoVlZlZW9URERERGRcnoIC8CSgURUFhYSECAgJgknawiheSkofNBhw65No07dwJHDvmef2oKHXmPedJJUqzZo2+tkxJyYQuYSayMA95mIk8zEQWSXlUpjfglimh/Pz8tC6BnNR1HsXFQEqKa9O0e7d6Al1P2rQBOndWj32y/xsVpR4PFRenTnPu6c8mJpM6q1+fPrU3ltrC94g8zEQW5iEPM5GHmciixzzYTAlks9mQlJSEhIQEWEqeCIjqXG3nkZenNkrOjVNKCnDhgvu6fn5AQoJr03T55UBQkOfHtljU6c9HjlQbJ+eGyv5Hn9mz3c83JR3fI/IwE1mYhzzMRB5mIote82AzRVQGqxVYuxbYsiUU2dnqrnBVfX8rirpLXsnjm1JTPa8fGuq+taldO8DXt3LPO2KEOtW5p/NMzZ6t3k5ERERElcdmiqgUl050awEQB+DSiW7La0CsVnUSiJLHN5065Xn95s1dm6bOndVlNbXL8IgRwM03Axs2qJNNNGmi7tqnoz/8EBEREYnDZkqYmtwSQlVnP9FtyeOMjh5Vl3/99aWG6tw5dRryXbsuNU1//gmcP+/+uBYL0L69a+PUqRMQHl6747E/t54mmSAiIiKSjrP5Qc5sfpe2hFxaVtEtIVRz7JM2lHV+ppAQYPBg9Vinffs8T0Ner57aKDlvbYqPBwIDa6ty76EoCmw2G8xms+Yz/pCKmcjCPORhJvIwE1kk5cHZ/HSoMltCqPacP69mUd6JbvPygCVLLl1v3Nj9+KbWrblVsTYVFxcjICBA6zLICTORhXnIw0zkYSay6DEPNlMCWK3qFilP2wgVRT1uZuJE9ZgXfjmvGkVRj1c6ckRtUEv7yc2t+GPefjswbpzaODVpUluVkyc2mw379u3T3Yw/RsZMZGEe8jATeZiJLHrNg82UABs2lL0lRFHU28PC1C/tjRqpPxERly6XvB4Roe3uZFZr3U12UFiozpJXVpN07Jh67qaK8PcHiorKX++RR3gMEhEREZE3YzMlQGZmxdbLz1d/9u2r2Pr165feaHlqwkJCamb2uJo69ktRgOzsspuko0fVdSqqcWOgaVP1p1mzS5edfxo0AFq0MOaJbomIiIio5rCZEqCiu4h98on6Jf/UKeDkyUs/ztftly9cAM6eVX/S0ir2+D4+5W/tcr4tPFy9j7OKHvtVXHxpa1Jpu94dO1axLUQAEBDguTFy/mnSRD3pbUUY8US3RqOnXQC8BTORhXnIw0zkYSay6DEPzuYH7Wfzs88eV96WkEOHKvYFXlHULVglG62yrp85U7XaGza81GhFRAC//KI2cKXx9VW3gJV2viVPGjUqv1Fq2LDmzslk52kLW0wMT3RLREREZGSV6Q3YTEH7Zgq4tEUH8LwlpLZn8yssLH0rl6frOTmeG7/K8PcHoqPLbpKio9X1tGK1AuvXKzh06DxatAjENdeYuEVKAEVRUFBQgKCgIM2nTyUVM5GFecjDTORhJrJIyoNTo+vQiBFqw+TpWKO62BISEKBudYmJqdj6VqvaUDk3W6tWqbsilmfmTOD++9XdBKV/dlkswDXX2NCw4X7dzS5jZDabDampqcxEEGYiC/OQh5nIw0xk0WsebKYEGTFCnf587VortmzJwFVXxaBfP4vILSEWy6Vjp+wiIirWTHXvrq5LRERERKRnbKaEsVjU6bbDw08jISFGZCNVmj591C1pnAWPiIiIiLyBWesCyDO9nf0ZUBvBd95RL5fcfU/vs+DpMQ+jYybyMBNZmIc8zEQeZiKLHvPgBBSQMQGFkXAWPCIiIiLSq8r0BtwyJZDNZkN2djZsNpvWpVTJiBHqua3WrAEWL1b/PXRIv42U3vMwImYiDzORhXnIw0zkYSay6DUPkc3U3Llz0aJFCwQEBKBLly7YsGFDqeuuXbsWJpPJ7Wfv3r11WHHNUhQFGRkZ0PNGQ/uxX3fcof6rx1377IyQh9EwE3mYiSzMQx5mIg8zkUWveYhrppYuXYpJkyZh6tSp2LlzJ/r06YPBgwcjPT29zPvt27cPmZmZjp82bdrUUcVEREREROSNxDVTb731Fu677z7cf//9aN++PWbPno2YmBjMmzevzPs1btwYUVFRjh89zU9PRERERET6I2pq9OLiYmzfvh2TJ092WT5w4EBs2rSpzPt27twZhYWF6NChA55//nn079+/1HWLiopQVFTkuJ6fnw8AsFqtsFqtAACTyQSz2QybzeayudG+3L5eecvNZjNMJpPH5QDc9gs1m81QFAX169d3uY/FYoGiKG7rWywWtxpLW67lmDwt18uYPOWh9zF5ql1vY3LOxChjqkjtUsfknIlRxlSyRj2NyTkPo4zJmR7HZM/EZrPBYrEYYkzl1S59TFarFQ0aNHA8thHG5Fyj3sZkf4/YH0PLMZW8vSyimqlTp07BarUiMjLSZXlkZCSysrI83qdJkyaYP38+unTpgqKiIixatAjXXXcd1q5di2uuucbjfWbOnImXXnrJbXlycrLjTRUWFobmzZvjyJEjyMnJcaxj3/KVlpaGgoICx/KYmBiEh4fjwIEDKCwsdCxv2bIlgoODkZKS4hJM27Zt4efnh6SkJJcaEhIScOHCBZw9exYpKSkA1BdbQkICCgoKkJqa6lg3ICAA7dq1Q25uLjIyMhzLg4KC0KpVK5w4ccLl96blmIqLi7Fv3z7HMr2NyTkPo4xJzznt27cPVqvVkYkRxmSUnFJSUgw3JkC/OaWkpBhuTIC+c8rOzjbcmPSek8ViQXp6uqHGpOecLBYLsrKyNB3TmTNnUFGipkY/duwYmjZtik2bNqFHjx6O5a+++ioWLVpU4UklbrzxRphMJnz33Xceb/e0ZSomJgY5OTmO6Q+17OptNhuOHz+ORo0aOdbz5r9UaD0mT3nofUyeatfTmC5cuICTJ086MjHCmPSek81mc2Ti4+NjiDGVrFFPY3LOw2KxGGJMzvSYkz2Txo0bw8fHxxBjKq926WOyZxIVFQUAhhiTc416y6kiedTVmPLz8xEWFlahqdFFbZmKiIhwdKPOTpw44ba1qizdu3fH559/Xurt/v7+8Pf3d1tusVjcjrWy/3I9rVuby+1jdr7dZDJ5XL+0Giu7vLbH5Gm5XsbkKY/S1tfLmPSck9ls9piJnsdkhJzsmZhMpgqtX5HlWo+pNpbX1ZjseTj/Ua6iNVZ2OXOq2HLn7zNGGVNN11jZ5dUdU2n/v9dkjZVd7s05lZdHXY2ptNs91lThNeuAn58funTpgsTERJfliYmJ6NmzZ4UfZ+fOnWjSpElNl0dEREREROQgassUADz55JMYM2YMunbtih49emD+/PlIT0/Hww8/DACYMmUKjh49is8++wwAMHv2bMTFxSE+Ph7FxcX4/PPP8c033+Cbb77RchhERERERGRw4pqpUaNGITs7G9OnT0dmZiY6duyIH3/8EbGxsQCAzMxMl3NOFRcX4+mnn8bRo0cRGBiI+Ph4/PDDDxgyZIhWQ6g2k8mEsLAwx64ypC3mIQ8zkYeZyMI85GEm8jATWfSah6gJKLSSn5+PkJCQCh1kRkRERERExlWZ3kDUMVOkstlsSE9Pd5v1hLTBPORhJvIwE1mYhzzMRB5mIote82AzJZCiKMjJyXGb/pK0wTzkYSbyMBNZmIc8zEQeZiKLXvNgM0VERERERFQF4iag0IK9A87Pz9e4EpXVasWZM2eQn59fqXnuqXYwD3mYiTzMRBbmIQ8zkYeZyCIpD3tPUJGtZGymABQUFAAAYmJiNK6EiIiIiIgkKCgoQEhISJnrcDY/qAe8HTt2DEFBQSKmY8zPz0dMTAwyMjI4u6AAzEMeZiIPM5GFecjDTORhJrJIykNRFBQUFCA6Ohpmc9lHRXHLFACz2YxmzZppXYab4OBgzV9MdAnzkIeZyMNMZGEe8jATeZiJLFLyKG+LlB0noCAiIiIiIqoCNlNERERERERVwGZKIH9/f0ybNg3+/v5al0JgHhIxE3mYiSzMQx5mIg8zkUWveXACCiIiIiIioirglikiIiIiIqIqYDNFRERERERUBWymiIiIiIiIqoDNFBERERERURWwmRJm7ty5aNGiBQICAtClSxds2LBB65K81syZM9GtWzcEBQWhcePGGD58OPbt26d1WfQ/M2fOhMlkwqRJk7QuxasdPXoUo0ePRnh4OOrVq4crrrgC27dv17osr3Xx4kU8//zzaNGiBQIDA9GyZUtMnz4dNptN69K8xvr163HjjTciOjoaJpMJK1ascLldURS8+OKLiI6ORmBgIPr164fk5GRtivUCZeVx4cIFPPvss0hISED9+vURHR2Nu+++G8eOHdOuYC9Q3nvE2UMPPQSTyYTZs2fXWX2VxWZKkKVLl2LSpEmYOnUqdu7ciT59+mDw4MFIT0/XujSvtG7dOjz66KPYvHkzEhMTcfHiRQwcOBBnz57VujSvt3XrVsyfPx+XX3651qV4tdzcXPTq1Qu+vr5YtWoVUlJS8OabbyI0NFTr0rzW66+/jn//+994//33sWfPHsyaNQtvvPEG3nvvPa1L8xpnz55Fp06d8P7773u8fdasWXjrrbfw/vvvY+vWrYiKisL111+PgoKCOq7UO5SVx7lz57Bjxw688MIL2LFjB5YtW4b9+/fjpptu0qBS71Hee8RuxYoV+OOPPxAdHV1HlVWRQmJcddVVysMPP+yyrF27dsrkyZM1qoicnThxQgGgrFu3TutSvFpBQYHSpk0bJTExUenbt68yceJErUvyWs8++6zSu3dvrcsgJ0OHDlXuvfdel2UjRoxQRo8erVFF3g2Asnz5csd1m82mREVFKa+99ppjWWFhoRISEqL8+9//1qBC71IyD0+2bNmiAFAOHz5cN0V5udIyOXLkiNK0aVPlr7/+UmJjY5W33367zmurKG6ZEqK4uBjbt2/HwIEDXZYPHDgQmzZt0qgqcpaXlwcACAsL07gS7/boo49i6NChGDBggNaleL3vvvsOXbt2xW233YbGjRujc+fO+PDDD7Uuy6v17t0bv/zyC/bv3w8A2L17NzZu3IghQ4ZoXBkBwKFDh5CVleXyf72/vz/69u3L/+uFyMvLg8lk4hZ2DdlsNowZMwbPPPMM4uPjtS6nXD5aF0CqU6dOwWq1IjIy0mV5ZGQksrKyNKqK7BRFwZNPPonevXujY8eOWpfjtZYsWYIdO3Zg69atWpdCAFJTUzFv3jw8+eSTeO6557BlyxZMmDAB/v7+uPvuu7Uuzys9++yzyMvLQ7t27WCxWGC1WvHqq6/ijjvu0Lo0Ahz/n3v6v/7w4cNalEROCgsLMXnyZNx5550IDg7Wuhyv9frrr8PHxwcTJkzQupQKYTMljMlkcrmuKIrbMqp7jz32GP78809s3LhR61K8VkZGBiZOnIiff/4ZAQEBWpdDUP962LVrV8yYMQMA0LlzZyQnJ2PevHlspjSydOlSfP7551i8eDHi4+Oxa9cuTJo0CdHR0Rg7dqzW5dH/8P96eS5cuIB//OMfsNlsmDt3rtbleK3t27fjnXfewY4dO3TznuBufkJERETAYrG4bYU6ceKE21+wqG49/vjj+O6777BmzRo0a9ZM63K81vbt23HixAl06dIFPj4+8PHxwbp16/Duu+/Cx8cHVqtV6xK9TpMmTdChQweXZe3bt+ekORp65plnMHnyZPzjH/9AQkICxowZgyeeeAIzZ87UujQCEBUVBQD8v16YCxcu4Pbbb8ehQ4eQmJjIrVIa2rBhA06cOIHmzZs7/q8/fPgwnnrqKcTFxWldnkdspoTw8/NDly5dkJiY6LI8MTERPXv21Kgq76YoCh577DEsW7YMv/76K1q0aKF1SV7tuuuuQ1JSEnbt2uX46dq1K+666y7s2rULFotF6xK9Tq9evdxOF7B//37ExsZqVBGdO3cOZrPrf+0Wi4VTowvRokULREVFufxfX1xcjHXr1vH/eo3YG6kDBw7gv//9L8LDw7UuyauNGTMGf/75p8v/9dHR0XjmmWewevVqrcvziLv5CfLkk09izJgx6Nq1K3r06IH58+cjPT0dDz/8sNaleaVHH30UixcvxrfffougoCDHXxJDQkIQGBiocXXeJygoyO14tfr16yM8PJzHsWnkiSeeQM+ePTFjxgzcfvvt2LJlC+bPn4/58+drXZrXuvHGG/Hqq6+iefPmiI+Px86dO/HWW2/h3nvv1bo0r3HmzBn8/fffjuuHDh3Crl27EBYWhubNm2PSpEmYMWMG2rRpgzZt2mDGjBmoV68e7rzzTg2rNq6y8oiOjsbIkSOxY8cOrFy5Elar1fF/fVhYGPz8/LQq29DKe4+UbGh9fX0RFRWFtm3b1nWpFaPtZIJU0pw5c5TY2FjFz89PufLKKzkNt4YAePxZsGCB1qXR/3BqdO19//33SseOHRV/f3+lXbt2yvz587Uuyavl5+crEydOVJo3b64EBAQoLVu2VKZOnaoUFRVpXZrXWLNmjcf/O8aOHasoijo9+rRp05SoqCjF399fueaaa5SkpCRtizawsvI4dOhQqf/Xr1mzRuvSDau890hJ0qdGNymKotRR30ZERERERGQYPGaKiIj+v737CWn6j+M4/vo2c2Gmq8AK+zNxo4iQLtoqo28ibkLQUYI8degoSQRRkBDYoUtB56LoYOGlQ7WVQWvhxUunUIo5E4XKZbPSZSx/B9n319qy8cV0rucDvod99v5+eX+/l/Fin8/nCwAAbCBMAQAAAIANhCkAAAAAsIEwBQAAAAA2EKYAAAAAwAbCFAAAAADYQJgCAAAAABsIUwAAAABgA2EKAIC/xO12y+12L3cbAIC/hDAFAChosVhMhmEseOzdu3e52wQA/INKlrsBAADyUVtbqxMnTuT8bvPmzUvcDQAAhCkAwArh8XjU1dW13G0AAGBhmh8AoKgYhiHTNDU6Oqq2tjZt3LhRa9eulWma6u/vz3lOPB7X6dOnVVNTI6fTqaqqKrW1tenVq1c562dnZ3Xt2jU1NDRo3bp1Ki8v1+7du9XZ2anJycms+q9fv6qzs1PV1dVyOp2qq6tTb2/vot43AGDpGXNzc3PL3QQAAL8Ti8VUU1Mjv9+vYDD4x3rDMFRXV6fJyUlt2bJFTU1NGhsb0927dyVJoVBIpmla9fF4XD6fT2/evJFpmvL5fIrFYurt7ZXT6dSTJ0+0f/9+qz6ZTMrv9+v58+fyer0KBAJyOp16/fq1Hj9+rP7+fmsNl9vt1vfv3+V2u/Xx40c1NzdrenpaPT09mpmZUTAYVEtLy6I+LwDA0iFMAQAKWjpMLbRmyufzKRAISJoPU5LU3t6uW7duWZ/D4bCOHDmi2tpaDQ0NadWq+ckZJ0+e1I0bN3Tu3Dl1d3db1wyFQgoEAvJ6vRocHLTqz549qytXrqi9vV03b96Uw+GwzkkkEnI4HCovL5c0H6ZGRkZ07Ngx3bt3T6WlpZKkp0+fqrm5Oe+ACAAoTIQpAEBBS4ephXR0dOjq1auS5sOUw+HQ8PCwtm3bllF39OhRPXjwQJFIRI2NjZqdnZXL5VJZWZnevn2rsrKyjPpAIKBQKGTVp1IpbdiwQYZhaHh4WOvXr1+wr3SYikajWffgdrv1+fNnxePxPJ8EAKDQsGYKALAi+P1+zc3N5TzSQSptx44dWUFKkg4dOiRJevnypSRpcHBQMzMzamhoyApSkqzpgD/XT01Nqb6+/o9BKs3lcuUMg1u3btWnT5/yugYAoDARpgAARaeqqirn+KZNmyTNT8eTpKmpqYzxX6W3XE/Xp8NPdXV13r1UVlbmHC8pKdGPHz/yvg4AoPAQpgAARef9+/c5x9+9eyfp/4BTUVGRMf67+nSdy+WSJI2NjS1arwCAlYswBQAoOiMjIxodHc0aj0QikmTttrdr1y6tWbNGAwMDmp6ezqoPh8MZ9Tt37lRFRYUGBgZyboEOAPi3EKYAAEUnlUrp/Pnz+nmPpXA4rIcPH8rj8ejAgQOSpNLSUh0/flwTExO6fPlyxjX6+vr06NEjeTweHTx4UNL81LxTp04pkUioo6NDqVQq45xEIqEvX7785bsDABQKdvMDABS0fLZGl6Suri5Jud8zNT4+rp6eHknZ75n68OGDfD6fotGompqatG/fPus9U6tXr1YoFFJjY6NVn0wm1dLSokgkIq/Xq9bWVjmdTkWjUQWDQb148SLjPVPpe/iVaZoKh8PiZxgAVi7CFACgoOWzNbokK5QYhqHDhw/r9u3bOnPmjPr6+pRMJlVfX6/u7m7rX6afTUxM6NKlS7p//77Gx8dVWVkp0zR18eJF7dmzJ6v+27dvun79uu7cuaOhoSE5HA5t375dra2tunDhgrW2ijAFAMWNMAUAKCrpMPXs2bPlbgUAUORYMwUAAAAANhCmAAAAAMAGwhQAAAAA2FCy3A0AALCYWAoMAFgq/DMFAAAAADYQpgAAAADABsIUAAAAANhAmAIAAAAAGwhTAAAAAGADYQoAAAAAbCBMAQAAAIANhCkAAAAAsOE/Qv6bXFYTm40AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_accuracy(history.history, latent_dim, merged_df, feat_type, model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "00e59690-f3a0-4568-a826-d46995cedfcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_validation_loss(history.history, latent_dim, merged_df, feat_type, model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "0e05d901-5700-456b-bfea-2afaa6cd5de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_validation_accuracy(history.history, latent_dim, merged_df, feat_type, model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "2404dc69-9cca-461c-8ece-e1d54550def8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MLP history save\n",
    "pd.DataFrame(\n",
    "    history.history).to_csv(\n",
    "    'AUC_v1/MLP_history/MLP_history_'+feat_type+'_'+str(latent_dim)+'.tsv', sep = '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "54835c33-d097-4b1e-862d-5e7e450f7725",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total model weights: 262831\n"
     ]
    }
   ],
   "source": [
    "def create_tabtransformer_classifier(\n",
    "    num_transformer_blocks,\n",
    "    num_heads,\n",
    "    embedding_dims,\n",
    "    mlp_hidden_units_factors,\n",
    "    dropout_rate,\n",
    "    use_column_embedding=False,\n",
    "):\n",
    "\n",
    "    # Create model inputs.\n",
    "    inputs = create_model_inputs()\n",
    "    # encode features.\n",
    "    encoded_categorical_feature_list, numerical_feature_list = encode_inputs(\n",
    "        inputs, embedding_dims\n",
    "    )\n",
    "    # Stack categorical feature embeddings for the Tansformer.\n",
    "    encoded_categorical_features = tf.stack(encoded_categorical_feature_list, axis=1)\n",
    "    # Concatenate numerical features.\n",
    "    numerical_features = layers.concatenate(numerical_feature_list)\n",
    "\n",
    "    # Add column embedding to categorical feature embeddings.\n",
    "    if use_column_embedding:\n",
    "        num_columns = encoded_categorical_features.shape[1]\n",
    "        column_embedding = layers.Embedding(\n",
    "            input_dim=num_columns, output_dim=embedding_dims\n",
    "        )\n",
    "        column_indices = tf.range(start=0, limit=num_columns, delta=1)\n",
    "        encoded_categorical_features = encoded_categorical_features + column_embedding(\n",
    "            column_indices\n",
    "        )\n",
    "\n",
    "    # Create multiple layers of the Transformer block.\n",
    "    for block_idx in range(num_transformer_blocks):\n",
    "        # Create a multi-head attention layer.\n",
    "        attention_output = layers.MultiHeadAttention(\n",
    "            num_heads=num_heads,\n",
    "            key_dim=embedding_dims,\n",
    "            dropout=dropout_rate,\n",
    "            name=f\"multihead_attention_{block_idx}\",\n",
    "        )(encoded_categorical_features, encoded_categorical_features)\n",
    "        # Skip connection 1.\n",
    "        x = layers.Add(name=f\"skip_connection1_{block_idx}\")(\n",
    "            [attention_output, encoded_categorical_features]\n",
    "        )\n",
    "        # Layer normalization 1.\n",
    "        x = layers.LayerNormalization(name=f\"layer_norm1_{block_idx}\", epsilon=1e-6)(x)\n",
    "        # Feedforward.\n",
    "        feedforward_output = create_mlp(\n",
    "            hidden_units=[embedding_dims],\n",
    "            dropout_rate=dropout_rate,\n",
    "            activation=keras.activations.gelu,\n",
    "            normalization_layer=layers.LayerNormalization(epsilon=1e-6),\n",
    "            name=f\"feedforward_{block_idx}\",\n",
    "        )(x)\n",
    "        # Skip connection 2.\n",
    "        x = layers.Add(name=f\"skip_connection2_{block_idx}\")([feedforward_output, x])\n",
    "        # Layer normalization 2.\n",
    "        encoded_categorical_features = layers.LayerNormalization(\n",
    "            name=f\"layer_norm2_{block_idx}\", epsilon=1e-6\n",
    "        )(x)\n",
    "\n",
    "    # Flatten the \"contextualized\" embeddings of the categorical features.\n",
    "    categorical_features = layers.Flatten()(encoded_categorical_features)\n",
    "    # Apply layer normalization to the numerical features.\n",
    "    numerical_features = layers.LayerNormalization(epsilon=1e-6)(numerical_features)\n",
    "    # Prepare the input for the final MLP block.\n",
    "    features = layers.concatenate([categorical_features, numerical_features])\n",
    "\n",
    "    # Compute MLP hidden_units.\n",
    "    mlp_hidden_units = [\n",
    "        factor * features.shape[-1] for factor in mlp_hidden_units_factors\n",
    "    ]\n",
    "    # Create final MLP.\n",
    "    features = create_mlp(\n",
    "        hidden_units=mlp_hidden_units,\n",
    "        dropout_rate=dropout_rate,\n",
    "        activation=keras.activations.selu,\n",
    "        normalization_layer=layers.BatchNormalization(),\n",
    "        name=\"MLP\",\n",
    "    )(features)\n",
    "\n",
    "    # Add a sigmoid as a binary classifer.\n",
    "    outputs = layers.Dense(units=1, activation=\"sigmoid\", name=\"sigmoid\")(features)\n",
    "    model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "    return model\n",
    "\n",
    "\n",
    "tabtransformer_model = create_tabtransformer_classifier(\n",
    "    num_transformer_blocks=NUM_TRANSFORMER_BLOCKS,\n",
    "    num_heads=NUM_HEADS,\n",
    "    embedding_dims=EMBEDDING_DIMS,\n",
    "    mlp_hidden_units_factors=MLP_HIDDEN_UNITS_FACTORS,\n",
    "    dropout_rate=DROPOUT_RATE,\n",
    ")\n",
    "\n",
    "print(\"Total model weights:\", tabtransformer_model.count_params())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "188a3477-9768-4413-9899-2a84d8f48d9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Observation:\n",
    "    # 440k weights for 100 gexp features\n",
    "    # 258k weights for gexp dim = 10 (max diff)\n",
    "    # 263k weights for dim = 25 (max diff)\n",
    "\n",
    "    # 259k @ UMAP dim = 10 (?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "9bd7b89e-7ddc-46a3-a846-bdda7434d95f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start training the model...\n",
      "Epoch 1/15\n",
      "     97/Unknown - 4s 14ms/step - loss: 0.7495 - accuracy: 0.5248WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 5s 20ms/step - loss: 0.7488 - accuracy: 0.5251 - val_loss: 0.6055 - val_accuracy: 0.8182\n",
      "Epoch 2/15\n",
      " 97/100 [============================>.] - ETA: 0s - loss: 0.6198 - accuracy: 0.6567WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.6164 - accuracy: 0.6599 - val_loss: 0.4354 - val_accuracy: 0.7273\n",
      "Epoch 3/15\n",
      " 99/100 [============================>.] - ETA: 0s - loss: 0.3250 - accuracy: 0.8707WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.3246 - accuracy: 0.8708 - val_loss: 0.5757 - val_accuracy: 0.8182\n",
      "Epoch 4/15\n",
      " 98/100 [============================>.] - ETA: 0s - loss: 0.1764 - accuracy: 0.9417WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1762 - accuracy: 0.9418 - val_loss: 0.5800 - val_accuracy: 0.8182\n",
      "Epoch 5/15\n",
      " 98/100 [============================>.] - ETA: 0s - loss: 0.1394 - accuracy: 0.9534WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1393 - accuracy: 0.9536 - val_loss: 0.4934 - val_accuracy: 0.9091\n",
      "Epoch 6/15\n",
      " 97/100 [============================>.] - ETA: 0s - loss: 0.1238 - accuracy: 0.9560WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1232 - accuracy: 0.9561 - val_loss: 0.4285 - val_accuracy: 0.9091\n",
      "Epoch 7/15\n",
      " 97/100 [============================>.] - ETA: 0s - loss: 0.1118 - accuracy: 0.9579WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1116 - accuracy: 0.9580 - val_loss: 0.4306 - val_accuracy: 0.9091\n",
      "Epoch 8/15\n",
      " 97/100 [============================>.] - ETA: 0s - loss: 0.1032 - accuracy: 0.9595WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.1029 - accuracy: 0.9594 - val_loss: 0.4877 - val_accuracy: 0.9091\n",
      "Epoch 9/15\n",
      " 97/100 [============================>.] - ETA: 0s - loss: 0.0942 - accuracy: 0.9603WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.0942 - accuracy: 0.9601 - val_loss: 0.6387 - val_accuracy: 0.9091\n",
      "Epoch 10/15\n",
      " 97/100 [============================>.] - ETA: 0s - loss: 0.0944 - accuracy: 0.9597WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.0942 - accuracy: 0.9595 - val_loss: 0.6361 - val_accuracy: 0.8182\n",
      "Epoch 11/15\n",
      " 98/100 [============================>.] - ETA: 0s - loss: 0.0950 - accuracy: 0.9582WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.0948 - accuracy: 0.9583 - val_loss: 0.5882 - val_accuracy: 0.9091\n",
      "Epoch 12/15\n",
      " 98/100 [============================>.] - ETA: 0s - loss: 0.0940 - accuracy: 0.9592WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.0942 - accuracy: 0.9592 - val_loss: 0.5483 - val_accuracy: 0.8182\n",
      "Epoch 13/15\n",
      " 98/100 [============================>.] - ETA: 0s - loss: 0.0945 - accuracy: 0.9595WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.0945 - accuracy: 0.9595 - val_loss: 0.7229 - val_accuracy: 0.9091\n",
      "Epoch 14/15\n",
      "100/100 [==============================] - ETA: 0s - loss: 0.0898 - accuracy: 0.9607WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.0898 - accuracy: 0.9607 - val_loss: 0.7122 - val_accuracy: 0.9091\n",
      "Epoch 15/15\n",
      " 99/100 [============================>.] - ETA: 0s - loss: 0.0877 - accuracy: 0.9593WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "100/100 [==============================] - 2s 18ms/step - loss: 0.0875 - accuracy: 0.9594 - val_loss: 0.7448 - val_accuracy: 0.9091\n",
      "Model training finished\n",
      "WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "Validation accuracy: 90.91%\n"
     ]
    }
   ],
   "source": [
    "history_tab = run_experiment(\n",
    "    model=tabtransformer_model,\n",
    "    train_data_file=train_data_file,\n",
    "    test_data_file=test_data_file,\n",
    "    num_epochs=NUM_EPOCHS,\n",
    "    learning_rate=LEARNING_RATE,\n",
    "    weight_decay=WEIGHT_DECAY,\n",
    "    batch_size=BATCH_SIZE,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "e78bc3ff-2ce3-456b-a5cf-48605a2725ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_type = 'trnsfrmr'\n",
    "plot_training_loss(history_tab.history, latent_dim, merged_df, feat_type, model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36cff595-8729-451a-840e-a6e0f8781174",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Observation:\n",
    "    # Transformer smooth loss with no step down vs MLP for UMAP dim = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "4a9cf523-f5b0-4316-b0b4-5d9c50b1fda2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_accuracy(history_tab.history, latent_dim, merged_df, feat_type, model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "39758a76-a981-42e9-952e-09d104070095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ChXJxcbFDfSn5uLi4OIf6O3z48FKPZ73v7NmzzfcZDAbZ39/f4v7PP//cfH9aWlqpfZRl2/dtdHS0XFhY6FA/rY0ZM8Z8HEmS5JSUFPN99evXN99Xv359m6lpBQUF8scffywPHTpUTkhIkGNiYuTIyEg5MjJS9vX1tTiuaSqiLFduSuHIkSMt7ps+fbrFY0+fPm0xTdje67KinyuyXP50O5OyphRa3zd06FCbx7dv395in88++6zUx7du3drisdu2bbO4f+DAgaW2056y+mg9lXDdunUW9ycnJ1vcHxgYaH7/Pv300+btderUkY8fP15uW9555x3zYwICAuTdu3c71ReiyuIZLiI32rZtG1JTU83fN2rUyHx2KSAgAAMGDDDfZzQabaaGuIK9a//k5eW5/HlK6t27t8X39i7abF3Ew1F6vR4DBw602GZ9XaqrV6+ab+fm5qJr164YMWIEvvzyS+zfvx9nz55FRkYGLly4YFP+PjMzs9TnjoiIsMisJJ1Oh4kTJ5q/P3nypHlKUsnphJIk2S3XXp7SphWeP3/eYupTUFCQxTRBwD2vw5CQEJtpUM5Oh6qsAQMGICIiwqF9+/fvb3EmtKzXTIsWLSwqdU6ePBnVq1dHQkIChg8fjtdffx1//vlnJVt/y5gxYxzet1evXubbOp0O4eHhFvf37NnTfNt6imPJPtozYsSICl+rqrRphXv27LE4Gzt69GiLqZ/nz59Hq1atMHHiRHzzzTdITk5GamoqLly4gAsXLlicaZRl2W7xj4o4evSoxfclf66Acia7fv36pT7elZ8rFXXo0CGL7+1dR8/6s7jkmR5r1me0ynqPVEZ+fr7NWXDrdrZs2dLi9Xv9+nWcPHkSACw+39LT09GkSRNERESga9euePTRR7FkyRJkZ2dbHG/AgAHmaZYFBQXo2LEjQkND0alTJ4wbNw4ffPAB0tPTXdI/Ins44CJyI+vpWdbTtwYPHlzm/tZz0kub7mZd7a7kOoKYmBib9QJl/afrCrVq1bLZZv2LcUUHfZGRkTbrE6x/SZRLVBibNWsWdu7c6fDxS/6CZy0uLq7Mx06YMMGibaZqhWvXrjVv69q1a7nHsadfv34WP8ODBw/i2LFj+Prrry36O2jQIJt1EJV9HdpTVln4qiqFbqpY6Yjo6GiL78t6zeh0OqxZs8biotGFhYU4dOgQVq5ciRkzZqBVq1Z44IEH7Fa3dJYz/bAeRFmvtSl5v/X7vmQfK9sOa7169bKYwmX6g0BZ0wkBYMqUKTh+/LjDz1PW+9MZ1p8/9gbuZQ3mXfm5UlHW07Ltfe5abytrKrcz75HKsG5DcHCw3fVXpbW9R48eSEpKspjGmpmZiR07duCjjz7C+PHjER0dbVEttlGjRvjiiy8s/kCRm5uL3bt347PPPjNXOp07d65L+khkjQMuIje5ceMGvvnmG4ttb775psVaF+uzFvv27bP45cP6r9eXLl2y+1zW8/JLPq5GjRpo3bq1xf3uLv9tb52A9TZ7Z94cYf0LJoAyiyVYF4F45ZVXkJGRAaPRCFmWHVqjYlLeBX3r1Klj8Vfir7/+Gn/++afFAm9nimWU5OPjg+HDh1tsW758ebkXO3bF61BUzlxg2fp1U9ZrBlDWOR46dAg7d+7E66+/jnHjxqFz584Wv4SaLhZdWc70w/qPDdbsvT/c0Q5rOp3O4rW3e/dunDlzxuK117lzZzRq1Mj8fVFRkcW133x8fLBo0SJkZmZClmXIsow77rijwm0qi3VxEkc+s0py5edKRVkXcbH3/4P1trIKvzj7Hqko6zbk5eXZnBEEym771KlTkZaWhpUrV+K5557D0KFDLdZfXbt2Df/85z9x4cIF87YRI0bg3LlzWLduHV544QU8+OCDSEhIMN9fXFyM559/3qbIFJErcMBF5CY//PBDhaa/lBwMtWnTxuK+EydO2Ex7kGXZ5vpTbdu2tfh+woQJFt//9NNPZU4bM/01v6Ksq0HJsmxzDZzSigK4WsmF2WFhYZg5cyYiIyMhSRJu3LhR6jVqKqpk8YycnByLaYb+/v4YNmxYhY9tPTBatGiRRftr1aplM63IFa9Dd7M+E1Ne4ZKqIkkSEhMTMX36dHz66af49ddfbc4Ob968WaXWiWfs2LEW3z/zzDMWU8esz25dvnwZRUVF5u9vv/12/OMf/zAXfMnJybGpRucq1p8/1p9ZZ86csZgKaa2ynyuueM23bNnS4vuSFz432bRpk8X3JQcYagkKCrI5m2rdzkOHDlkMlgIDAy0G64DyR7uhQ4di7ty55iIoM2bMMN9fWFhocxYyICAAAwcOxOzZs/HVV1/hzz//xIcffmixj73rtRFVFgdcRG5i/QtrjRo17K53sZ42UfJxpnnpJkajESNHjjT/EpOTk4OpU6darEcICQmx+aV74sSJNr9gjBkzBi+//LLFAO7cuXNYsGABmjdvbnNWxBmbNm1CUlISioqKcP36dUyfPt08/x5QSv+WXGviTiX/KpqdnY3//e9/AJSf3T/+8Q+cOnXKpc/Xt29fi/U/e/bsMd++9957nSq3ba1z584Wv6hYD76HDx9ucwbEFa9Dd7P+i7f1hZCrWk5ODnr37o1PPvkEJ0+eNFerMxqNNhdGdcdUMa1q3ry5xR97Sp4F8vf3x4gRIyz2DwkJsTiLcuzYMfNAJT09HQ8++KDb1pvec889Ft+/9957WL9+PWRZxsWLFzFx4sQyB0GV/VxxxWt+6NChFj+/NWvWYNGiRSgqKkJBQQFmz56NvXv3mu+vVq2axeUr1GR92YqnnnrKvC7y7NmzFn+oApS8TGswFy9ejEmTJuHHH3+0WBuXk5NjszbP9P788ccfMXr0aKxZs8aimui1a9dw8OBBu48hcik1KnUQebq8vDy5WrVqFlWW/vrrL7v7FhcXyxERERb7lqygtGXLFptqWQAsKsuV/Jo7d67d5zlx4oRcu3Ztu48JDQ21uYinIxc8NSntIrF+fn6yj4+PzXbrimDOVCm0V/mtrMdbV/cDIAcHB5svNBsYGFjqY62rFJZWJdLa3Llz7f48rKsrVsSMGTPsHhuAvHPnTot9Xfk6tK6WFxISYq4kZ/01ePBgp/r04osv2n1NRkZGygkJCeb9rCsPlvUadWTf0l5X2dnZFvf5+vrK4eHhNpXnAMgvv/yyQ8csydGqjI7sW161u7LaU95FdSsiKSnJ7mvTXgU9WZblLl262OwbEhJivm39/izZ/8pUKSwsLJRvu+02m+e2fr+U9tyV+VyRZVl++OGHbR5fs2ZNOTIyUr777rvN+5WX0cSJE22OU9rnrnV11PKOXdHPP5OyXpsXL16U69SpY9NGexeTDgoKko8dO2Z+7Lx582zuDw8Pl3U6ncV2nU4nnzp1SpZlWV6zZo3FfYGBgXJ4eLjdn9OWLVuc6ieRI3iGi8gN1qxZg2vXrpm/b926danX99Dr9Rg0aJDFti+//NJ8u3v37li8eLHNouL8/HybY02ZMqXUC/82atQI+/bts3tmKScnx6YCVVkXkSzPlClTUKNGDRQVFdkU9OjevXulLnzsrDlz5tishcvLy4Msyxg4cKDNX1pd4ZFHHrFZcB4eHu6Svy6XVuGwfv36SExMtNjmytehtdzcXHMlOesvZ6cwjhs3zqbQR05ODi5cuICLFy86dSx3uHHjBjIzM23WmbRq1arM6355o1GjRtldZ2Y9ndDk7bfftrlYcG5uLgDgscceQ8eOHV3fSCgFIZYvX25zxtn0funbt6/N+6mkyn6uTJo0yWZaYXZ2Ni5cuOBURcMFCxbYrBez97n71FNPWUy3U1utWrXwww8/2BQQsv5/KDw8HN9++y2aNGlS6rFM19Kyvm7aSy+9VGqlyevXryMzM9Pm5zR+/Hh0797dma4QOYQDLiI3sJ6OVd66Hev7V6xYYTGd5eGHH8axY8fw3HPPoUOHDqhZsyZ8fHwQHByMZs2aYeLEifj9998xf/78Mhc6R0dH45dffsHWrVvx2GOPISEhAWFhYdDr9QgODkZCQgIeffRRbNiwodSBmyPatGmDAwcO4OGHH0ZUVBT8/PzQuHFjzJkzBz/99FOlBnPOql+/Pnbv3o0RI0YgLCwMAQEBaNasGV5//XV8++23Nr/0uELt2rVtBi/Dhw+vVEEDkxYtWti9cPXIkSNttrn6degu9evXx5YtWzBw4ECEhYW5bbG+o0JCQrB+/Xo8++yz6NKlC+Li4lC9enX4+PigVq1a5ippu3btsim+4O1q1aqFu+66y2ZbaZdT6NixI3799VcMHDgQISEhqFatGtq0aYNFixZh4cKFbm1rmzZt8Mcff+Chhx5CrVq14O/vj+bNm+P111/H+vXryyyRX9nPlcTERPzwww/o2bMnQkNDK/ya9/f3x1dffYWffvoJI0aMQGxsLAICAhAYGIiGDRti7Nix+O233/D222+r/r6y1qpVKxw6dAjz5s1D9+7dER4eDh8fH4SGhqJjx454+eWXcezYMZuS/aNGjcLixYvx8MMP4/bbb0dUVBR8fX0REBCABg0aYOTIkdi0aRNefPFF82N69eqFZcuW4dFHH0WbNm1Qt25d+Pn5wd/fHzExMRg0aBBWrVqFxYsXV/WPgbyEJMsuqvNJRERmn332GcaNG2f+fufOnWX+xZyIiIg8E89wERG5mCzLFmeXmjRpwsEWERGRlyr7gh5EROSwl156CX/++SeOHj1qcR2rp556SsVWERERkZo4pZCIyEV69OhhU965a9eu2Lx5M/R6vUqtIiIiIjVxSiERkYv5+PigYcOGeP7557F+/XoOtoiIiLwYz3ARERERERG5Cc9wERERERERuQmLZjjIaDQiLS0NwcHBwl3LgoiIiIiIqo4sy8jLy0PdunXLvfYeB1wOSktLQ0xMjNrNICIiIiIiQaSmpiI6OrrMfTjgclBwcDAA5YcaEhKicmsAg8GAw4cPo0WLFlyQLwDmIR5mIhbmIR5mIh5mIhbmIR6RMsnNzUVMTIx5jFAWDrgcZJpGGBISIsyAKygoCCEhIaq/4Ih5iIiZiIV5iIeZiIeZiIV5iEfETBxZasQqhQ7Kzc1FaGgocnJyhBhwybKMgoICBAQEcE2ZAJiHeJiJWJiHeJiJeJiJWJiHeETKxJmxAasUapifn5/aTaASmId4mIlYmId4mIl4mIlYmId4tJgJB1waZTQakZycDKPRqHZTCMxDRMxELMxDPMxEPMxELMxDPFrNhAMuIiIiIiIiN+GAi4iIiIiIyE044CIiIiIiInITVil0kIhVCo1GI3Q6nepVWoh5iIiZiIV5iIeZiIeZiIV5iEekTDRfpXDhwoWoX78+AgIC0K5dO2zfvr3UfceNGwdJkmy+WrRoYd5nyZIldvcpKCioiu64TVFRkdpNoBKYh3iYiViYh3iYiXiYiViYh3i0mIlwA64VK1Zg2rRpmDlzJvbv34+uXbtiwIABSElJsbv//PnzkZ6ebv5KTU1FWFgYhg0bZrFfSEiIxX7p6ekICAioii65hdFoxPHjxzVXpcVTMQ/xMBOxMA/xMBPxMBOxMA/xaDUT4QZcb7/9NiZMmICJEyeiWbNmSEpKQkxMDN5//327+4eGhiIqKsr8tXfvXmRnZ2P8+PEW+0mSZLFfVFRUVXSHiIiIiIi8mI/aDSipqKgI+/btw3PPPWexvV+/fti5c6dDx/jkk0/Qp08fxMXFWWzPz89HXFwcDAYDWrdujTlz5qBNmzalHqewsBCFhYXm73NzcwEABoMBBoMBgDKI0+l0MBqNKLkUzrTdtF95203zUO1tB2AzitfpdJBlGbIsWzxGr9eb57aWpNfrbdpY2nY1+2Rvu1b6ZC8PrffJXtu11qeSmXhKnxxpu6h9ss7DE/pk3UYt9alkJp7Sp5K02CdTJkajEXq93iP6VF7bRe6TdR6e0CfrNmqtT6ZMTMdQs0/W95dFqAHX5cuXYTAYEBkZabE9MjISGRkZ5T4+PT0dP/zwA5YtW2axvWnTpliyZAkSEhKQm5uL+fPno0uXLjh48CAaN25s91hz587FrFmzbLYfPnwYQUFBAICwsDDExsbi3LlzyMrKMu9jOoN25swZ5OXlmbfHxMQgPDwcJ06csFg/1qBBA4SEhODIkSMW4TVp0gR+fn5ITk62aENCQgIKCwuRnZ2Nw4cPQ5Ik6PV6JCQkIC8vD6dOnTLvGxAQgKZNmyI7Oxupqanm7cHBwWjYsCEuXrxo8bNVs09FRUU4fvy4eZuW+qTX6y3y8IQ+aT2no0ePWmTiCX3Sck4nT560yMMT+qT1nHJzc82ZxMbGekSftJ6TLMvIzs7GpUuXULduXY/ok5ZzMuWRlpaGuLg4j+iT1nOSZRlXr14FANX7lJ+fD0cJVaUwLS0N9erVw86dO5GYmGje/uqrr2Lp0qU4duxYmY+fO3cu3nrrLaSlpcHPz6/U/YxGI9q2bYtu3brhnXfesbuPvTNcMTExyMrKMlci0epfB8razj6xT+wT+8Q+sU/sE/vEPrFP7FPZfcrNzUVYWJhDVQqFOsMVEREBvV5vczbr4sWLNme9rMmyjMWLF2PMmDFlDrYA5QfWoUMHnDhxotR9/P394e/vb7Ndr9dDr9fbHM8e6/1cud00wg8ODjafUQFgPttlrbQ2OrvdnX0qbbsW+lRaHqXtr4U+ObtdtD7pdDrk5eXZZKLlPmk5J1fkUdp25lSxPsmybJOJ1vtU1dtd3aeSmbiqjc5uZ06l5+EJfXJ0u5B9Mhggb9uG66dPI7B+fei6dQOc+Bm4uu2l3W+PUEUz/Pz80K5dO2zcuNFi+8aNG9G5c+cyH7t161acPHkSEyZMKPd5ZFnGgQMHUKdOnUq1V01GoxGnTp2y+UsAqYN5iIeZiIV5iIeZiIeZiIV5CGT1aiA+HlKvXqg2YQKkXr2A+HhluwYIdYYLAJ5++mmMGTMG7du3R2JiIhYtWoSUlBRMmjQJADBjxgycP38en3/+ucXjPvnkE3Tq1AktW7a0OeasWbNwxx13oHHjxsjNzcU777yDAwcO4L333quSPhERERERUQWsXg0MHQpYr4I6f17Z/s03wJAh6rTNQcINuEaMGIHMzEzMnj0b6enpaNmyJdavX2+uOpienm5zTa6cnBysWrUK8+fPt3vMK1eu4NFHH0VGRgZCQ0PRpk0bbNu2DR07dnR7f4iIiIiIqAIMBmDqVNvBFqBskyRg2jTg/vvtTi8UhXADLgCYPHkyJk+ebPe+JUuW2GwLDQ3FtWvXSj3evHnzMG/ePFc1TxhavnCzJ2Ie4mEmYmEe4mEm4mEmYmEeKtu+HTh3rvT7ZRlITVX269GjyprlLCEHXFQ+vV6Ppk2bqt0Muol5iIeZiIV5iIeZiIeZiIV5CCA93bX7qUSoohnkOKPRiMzMTC7kFATzEA8zEQvzEA8zEQ8zEQvzEECtWo7tJ3ghPA64NEqWZaSmptpcl4DUwTzEw0zEwjzEw0zEw0zEwjxUlpMDvPlm2ftIEhATA3TtWjVtqiBOKSQiIiIiInGkpAADBwKHDgF+fkBRkTK4Kjn4NV3TMSlJ6IIZAM9wERERERGRKPbtAzp1UgZbUVHAzp3AqlVAvXqW+0VHa6IkPMAzXJpmuvI5iYF5iIeZiIV5iIeZiIeZiIV5VLFvvwVGjQKuXQMSEoB164DYWKBdO+D++2HYsgWXk5MRkZAAfY8ewp/ZMpFkTkx1SG5uLkJDQ5GTk4OQkBC1m0NERERE5BlkGXjnHeCpp5Tb/fsDX38NCPw7tzNjA04p1Cij0YiMjAxWzhEE8xAPMxEL8xAPMxEPMxEL86gixcXAlCnKBYxlGfjnP4H//c/uYEurmXDApVGyLCMjI4OVcwTBPMTDTMTCPMTDTMTDTMTCPKpAfj4waBCwYIHy/X//C7z/PuDra3d3rWbCNVxERERERFS1zp8H7rkHOHAACAgAvvgCeOABtVvlFhxwERERERFR1Tl4UCn7fv48ULs28N13SmVCD8UphRolSRLCwsIgma5BQKpiHuJhJmJhHuJhJuJhJmJhHm7yww/AnXcqg61mzYDffnN4sKXVTFil0EGsUkhEREREVAnvvw888QRgNAI9eyrX16pZU+1WVQirFHoBo9GIlJQUzVVp8VTMQzzMRCzMQzzMRDzMRCzMw4UMBuBf/wImT1YGW+PGAT/+6PRgS6uZcMClUbIsIysrS3NVWjwV8xAPMxEL8xAPMxEPMxEL83CRa9eAYcOAt99Wvn/lFWDxYsDPz+lDaTUTFs0gIiIiIiLXy8gA7rsP2LNHGWAtWQKMHKl2q6ocB1xERERERORahw8rlQjPngXCw4G1a5ViGV6IUwo1SpIkREVFaa5Ki6diHuJhJmJhHuJhJuJhJmJhHpXw889A587KYKtxY2DXLpcMtrSaCasUOohVComIiIiIyvHJJ8CkSUBxsTLIWrtWOcPlYVil0AsYDAb8/fffMBgMajeFwDxExEzEwjzEw0zEw0zEwjycZDQCzz8PTJyoDLZGjVLOdLlwsKXVTDjg0rC8vDy1m0AlMA/xMBOxMA/xMBPxMBOxMA8HXb+uFMOYO1f5/sUXgS++APz9Xf5UWsyERTOIiIiIiKhiLl0C7r9fWafl6wt89BEwdqzarRIKB1xEREREROS848eBu+8GTp0CatQAVq8GevZUu1XC4ZRCjZIkCTExMZqr0uKpmId4mIlYmId4mIl4mIlYmEc5tm4FEhOVwVb9+sDOnW4fbGk1E1YpdBCrFBIRERF5CYMB2L4dSE8H6tQBunYF9Hq1WyWOpUuBCROAGzeAO+4Avv0WqF1b7VZVKVYp9AIGgwHHjh3TXJUWT8U8xMNMxMI8xMNMxMNMBLF6NRAfr5ytGTVK+Tc+Xtnu7WQZePll4OGHlcHW0KHAL79U2WBLq+8RDrg0rKCgQO0mUAnMQzzMRCzMQzzMRDzMRGWrVyuDiHPnLLefP69s9+ZBV2GhMtCaNUv5fvp0YMUKIDCwSpuhxfcIB1xERERERAYDMHWqchbHmmnbtGnKft4mKwvo108p9a7XA4sWAa+/Dug4lHAEf0pERERERNu3257ZKkmWgdRUZT9v8vffSnGMbduA4GBg/XrgH/9Qu1WawgGXRul0OjRo0AA6/mVBCMxDPMxELMxDPMxEPMxEZenpju03aRLw2mvAH38ARqN726S2nTuVohh//QXExCjf9+unWnO0+h5hlUIHsUohERERkQfbssX5sua1awP9+wN33QX07QvUquWWpqlixQrlAsaFhUC7dsD//qdUbCQArFLoFQwGA5KTkzVXpcVTMQ/xMBOxMA/xMBPxMBOVde0KREcDpV3jSZKUAceCBcB99wHVqwMXLyol0h96CIiMBDp2BF58UTkTVFxcte13FVlWzuA9+KAy2Lr/fuWaWwIMtrT6HuGAS8O09mLzdMxDPMxELMxDPMxEPMxERXo9MH++/ftMg7AFC4DHH1euO5WVpZREf/ZZ4PbblYHKnj3AnDlAly7K2a7hw4HFi5Uqh1pw44ayPmvmTOX7adOAVauUwaUgtPge4YCLiIiIiAgAhgwBvvnGttR5dLSyfciQW9v8/JQpiG+8ARw8qAyqFi9WBlk1awJXrgArVyoXCI6OVgZlzz6rDNIKC6u0Ww65cgUYMAD45BOl+uC77wLz5vGCzy7go3YDiIiIiIiEMWQIEB4OnDuHjIkTUevBB6Hv0aP8gUfdusD48cpXcbFytuvHH4GffgJ27waSk5Wv//5XOWPUq5ey9uuuu4AGDaqka6U6cwYYOBA4ckRp24oVyvfkEiya4SDRimbIsoyCggIEBARAKm2uMVUZ5iEeZiIW5iEeZiIeZiKI8+eB6GjIOh0KMjIQEBFR+TwuXwZ+/lkZgP34I3DhguX9jRvfGnx17161U/h271bWpF24oAwa160D2rSpuud3gkjvEWfGBhxwOUjEAZfRaIROp1P9BUfMQ0TMRCzMQzzMRDzMRBCrVwMPPAD59tth/OMP1+dhNAJ//nlr8PXrr5YFNvz9lQIepgFY8+alF/KorDVrlIIf168DrVopg63oaPc8lwuI9B5hlUIvYDQakZycDKOnX/9BI5iHeJiJWJiHeJiJeJiJIH7/HQAgd+rknjx0OqB1a+C555RS9JmZysDnn/8E4uKU9V0//ww88wzQsiUQG6sUsli1Slln5QqyDLz1FvDAA8pga8AA5YLOAg+2AO2+R7iGi4iIiIjI5LfflH87daqa5wsJAQYNUr5kGTh+/NbZr61bgXPngI8/Vr70eiAx8dbZrzZtlAFcWQwGZTCVnq6Udk9MVKoPfvCBcv9jjwHvvAP4cFjgLvzJEhEREREBytS+vXsBAHLHjspgpSpJEtC0qfI1bZpy9mnbtlsDsGPHgB07lK///EcpPW+68HK/frYXXl69Gpg6VRm0mQQEAAUFynP93/8BTz3lvimLBIADLiIiIiIixaFDwLVrylmnpk2Bw4fVbU9goDKg6t9fKdF+5oxS9fDHH5Vph5cuAV98oXxJEtCu3a2zX2lpwIgRylmzkgoKlH///W/g6aervEveiEUzHMSiGVQW5iEeZiIW5iEeZiIeZiKADz8EJk0C+vSBvGGD2HkUFQG7dt06+3XggOX9kmQ72CopJgY4fVpT19kS6T3CohleoqioSO0mUAnMQzzMRCzMQzzMRDzMRGWm9Vt33AFA8Dz8/JQS8nPnAvv3K2e0liwBHnwQCA4ue7AFAKmpytoujRE6k1JwwKVRRqMRx48f11yVFk/FPMTDTMTCPMTDTMTDTARQYsCluTzq1AHGjgW++gp4/33HHpOe7t42uZjmMrmJAy4iIiIioitXlKIUANCxo6pNqbR69Rzbr04d97aDAHDARUREREQE7N6t/NuwoW21P63p2lW5plZp65wkSVnD1bVr1bbLS3HApWF6DS1y9AbMQzzMRCzMQzzMRDzMREU3L3hc8vpbms1Drwfmz1duWw+6TN8nJWmqYIaJFjNhlUIHiValkIiIiIhcaOBAYP165SLATz6pdmtcw951uGJilMHWkCGqNcsTsEqhF5BlGbm5ueB4WQzMQzzMRCzMQzzMRDzMREWybHOGyyPyGDJEuXbX5s3AsmXKv6dPa3awpdVMOODSKKPRiFOnTmmuSounYh7iYSZiYR7iYSbiYSYq+vtvIDMT8PcHWrcG4EF56PVAjx7AyJHKvxqckmei1Uw44CIiIiIi72YqB9+2rXJ9KyIX4oCLiIiIiLybnYIZRK7CAZeGBQQEqN0EKoF5iIeZiIV5iIeZiIeZqKTEBY9LYh7i0WImrFLoIFYpJCIiIvJA168DISFAcbFSUCI+Xu0WkQawSqEXMBqNyMzM1NyiQU/FPMTDTMTCPMTDTMTDTFSyf78y2IqMBOLizJuZh3i0momQA66FCxeifv36CAgIQLt27bB9+/ZS9x03bhwkSbL5atGihcV+q1atQvPmzeHv74/mzZtjzZo17u6GW8myjNTUVM2VxfRUzEM8zEQszEM8zEQ8zEQlpumEnTpZXCSYeYhHq5kIN+BasWIFpk2bhpkzZ2L//v3o2rUrBgwYgJSUFLv7z58/H+np6eav1NRUhIWFYdiwYeZ9du3ahREjRmDMmDE4ePAgxowZg+HDh+N30wJJIiIiIvJOpt8HrdZvEbmKcAOut99+GxMmTMDEiRPRrFkzJCUlISYmBu+//77d/UNDQxEVFWX+2rt3L7KzszF+/HjzPklJSejbty9mzJiBpk2bYsaMGejduzeSkpKqqFdEREREJKRSCmYQuYqP2g0oqaioCPv27cNzzz1nsb1fv37YuXOnQ8f45JNP0KdPH8SVmIO7a9cuPPXUUxb79e/fv8wBV2FhIQoLC83f5+bmAgAMBgMMBgMAQJIk6HQ6GI1Gi1Obpu2m/crbrtPpIEmS3e0AbOap6nQ6yLKM6tWrWzxGr9dDlmWb/fV6vU0bS9uuZp/sbddKn+zlofU+2Wu71vpUMhNP6ZMjbRe1T9Z5eEKfrNuopT6VzMRT+lSSFvtkysRoNEKv13tEn8pru+p9SkuDPiUFsiTB2KYN9DePYS8PzfTJE3O62UZTJqZjqNkn6/vLItSA6/LlyzAYDIiMjLTYHhkZiYyMjHIfn56ejh9++AHLli2z2J6RkeH0MefOnYtZs2bZbD98+DCCgoIAAGFhYYiNjcW5c+eQlZVl3sd0tu3MmTPIy8szb4+JiUF4eDhOnDiBgoIC8/YGDRogJCQER44csQivSZMm8PPzQ3JyskUbEhIScOPGDVy9ehVHjhwBoLwgExISkJeXh1OnTpn3DQgIQNOmTZGdnY3U1FTz9uDgYDRs2BAXL160+Dmo2aeioiIcP37cvE1rfSqZh6f0Scs5HT9+HAaDwZyJJ/RJyzmdOnUKBQUF5jw8oU+ektORI0c8rk+AtnPKzMz0uD6JmlPuypWoD6CgUSOkZWaiYY0aNn1KT0/XVJ88MSfrPun1emRkZKjap/z8fDhKqLLwaWlpqFevHnbu3InExETz9ldffRVLly7FsWPHynz83Llz8dZbbyEtLQ1+Ja4S7ufnh88++wwjR440b/vyyy8xYcIEix9wSfbOcMXExCArK8tc+lHNvw4YjUZcuHABtWrVMu/nzX/xULtP9vLQep/stV1Lfbpx4wYuXbpkzsQT+qTlnOzlofU+aT0no9FozkSv13tEn0rSYk6mTGrXrg0fHx+P6FN5bVe7T5gxA7o334RxwgTIH35o0SfrPLTSJ0/MydRGUyZRUVEAoGqfcnNzERYW5lBZeKHOcEVERJhHrCVdvHjR5gyVNVmWsXjxYowZM8ZisAUoI1tnj+nv7w9/f3+b7Xq9Hnq93mKbKQB7+7pzu6kPJe+XJMnu/qW10dnt7u6Tve1a6ZO9PErbXyt90nJOOp3ObiZa7pOWc3JFHqVtZ04V75Mpk5J/uHO0jc5uZ06ObS/5+4mn9MnVbXR2e5l92r1buZ2YCNzcp7Q8NNMnOzSfUwml/b5V2v6ubGPJ7aXdb7dNDu9ZBfz8/NCuXTts3LjRYvvGjRvRuXPnMh+7detWnDx5EhMmTLC5LzEx0eaYGzZsKPeYREREROShDAZgzx7lNgtmkBsJdYYLAJ5++mmMGTMG7du3R2JiIhYtWoSUlBRMmjQJADBjxgycP38en3/+ucXjPvnkE3Tq1AktW7a0OebUqVPRrVs3vPHGG7j//vvx7bff4ueff8aOHTuqpE9EREREJJjDh4GrV4HgYKBpU7VbQx5MuAHXiBEjkJmZidmzZyM9PR0tW7bE+vXrzVUH09PTba7JlZOTg1WrVmH+/Pl2j9m5c2csX74c//nPf/DCCy+gYcOGWLFiBTp16uT2/riLJEkICwuDJEnl70xuxzzEw0zEwjzEw0zEw0yqmKkcfMeO5umEJTEP8Wg1E6GKZogsNzcXoaGhDi2MIyIiIiLBTZgALF4MPP888OqrareGNMaZsYFQa7jIcUajESkpKTbVXEgdzEM8zEQszEM8zEQ8zKSKlXPBY+YhHq1mwgGXRsmyjKysLJvSn6QO5iEeZiIW5iEeZiIeZlKFcnKAo0eV26UsMWEe4tFqJhxwEREREZF32bMHkGWgfn2gdm21W0MejgMuIiIiIvIu5UwnJHIlDrg0SpIkREVFaa5Ki6diHuJhJmJhHuJhJuJhJlXo99+Vf8uoWM08xKPVTFil0EGsUkhERETkAWRZmUZ4+bJypkvDlwki9bBKoRcwGAz4+++/YTAY1G4KgXmIiJmIhXmIh5mIh5lUkdOnlcGWnx/QunWpuzEP8Wg1Ew64NCwvL0/tJlAJzEM8zEQszEM8zEQ8zKQKmNZvtWkD+PuXuSvzEI8WM+GAi4iIiIi8hwPrt4hciQMuIiIiIvIerFBIVYwDLo2SJAkxMTGaq9LiqZiHeJiJWJiHeJiJeJhJFSgoAPbvV26XM+BiHuLRaiasUuggVikkIiIi0rjffgMSE4FatYALFwCN/eJO4mCVQi9gMBhw7NgxzVVp8VTMQzzMRCzMQzzMRDzMpAqUnE5YzmCLeYhHq5lwwKVhBQUFajeBSmAe4mEmYmEe4mEm4mEmbuZkwQzmIR4tZsIBFxERERF5BxbMIBVwwEVEREREnu/CBeDMGWUqYYcOareGvAgHXBql0+nQoEED6HSMUATMQzzMRCzMQzzMRDzMxM1M0wmbNwccKIDGPMSj1Ux81G4AVYwkSayWKBDmIR5mIhbmIR5mIh5m4mZOTidkHuLRaibaGh6SmcFgQHJysuaqtHgq5iEeZiIW5iEeZiIeZuJmThbMYB7i0WomHHBpmNZebJ6OeYiHmYiFeYiHmYiHmbiJwQDs3q3cdqJgBvMQjxYz4YCLiIiIiDzb0aNAfj4QFKSs4SKqQhxwEREREZFnM63f6tAB0OvVbQt5HQ64NEqn06FJkyaaq9LiqZiHeJiJWJiHeJiJeJiJG1Xg+lvMQzxazURbrSULfn5+ajeBSmAe4mEmYmEe4mEm4mEmbuJkwQwT5iEeLWbCAZdGGY1GJCcnw2g0qt0UAvMQETMRC/MQDzMRDzNxk9xc4PBh5bYTAy7mIR6tZsIBFxERERF5rr17AVkG4uKAqCi1W0NeiAMuIiIiIvJcFVi/ReRKHHARERERkeeq4PotIleRZFmW1W6EFuTm5iI0NBQ5OTkICQlRuzmQZRlGoxE6nQ6SJKndHK/HPMTDTMTCPMTDTMTDTNxAlpVphBcvAjt3AomJTjyUeYhGpEycGRvwDJeGFRUVqd0EKoF5iIeZiIV5iIeZiIeZuNiZM8pgy9cXaNPG6YczD/FoMRMOuDTKaDTi+PHjmqvS4qmYh3iYiViYh3iYiXiYiRuYphO2bg0EBDj1UOYhHq1mwgEXEREREXkmFswgAXDARURERESeiQUzSAAccGmYXq9XuwlUAvMQDzMRC/MQDzMRDzNxocJC4I8/lNsVPMPFPMSjxUxYpdBBolUpJCIiIqIy7N6tnNmKiFAKZ7DSILkQqxR6AVmWkZubC46XxcA8xMNMxMI8xMNMxMNMXMy0fqtTpwoNtpiHeLSaCQdcGmU0GnHq1CnNVWnxVMxDPMxELMxDPMxEPMzExSpZMIN5iEermXDARURERESehwUzSBAccBERERGRZ7l0CTh1SplK2LGj2q0hL8cBl4YFOHkBP3Iv5iEeZiIW5iEeZiIeZuIiprNbTZsCoaEVPgzzEI8WM2GVQgexSiERERGRRvznP8CrrwLjxwOLF6vdGvJArFLoBYxGIzIzMzW3aNBTMQ/xMBOxMA/xMBPxMBMXqmTBDIB5iEirmXDApVGyLCM1NVVzZTE9FfMQDzMRC/MQDzMRDzNxEYNBuQYXUKmCGcxDPFrNhAMuIiIiIvIcx44BeXlA9epAixZqt4aIAy4iIiIi8iCmghnt2wM+Puq2hQgccGlacHCw2k2gEpiHeJiJWJiHeJiJeJiJC7hg/ZYJ8xCPFjNhlUIHsUohERERkQa0agX8+SewejUweLDarSEPxSqFXsBoNCIjI0NzVVo8FfMQDzMRC/MQDzMRDzNxgbw84NAh5XYlCmYAzENEWs2EAy6NkmUZGRkZmqvS4qmYh3iYiViYh3iYiXiYiQvs3QsYjUBsLFC3bqUOxTzEo9VMOOAiIiIiIs9gKphRybNbRK7EARcREREReQYXFswgchUOuDRKkiSEhYVBkiS1m0JgHiJiJmJhHuJhJuJhJpUkyy49w8U8xKPVTFil0EGsUkhEREQksLNngfh45dpbublAYKDaLSIPxiqFXsBoNCIlJUVzVVo8FfMQDzMRC/MQDzMRDzOpJNPZrVatXDLYYh7i0WomHHBplCzLyMrK0lyVFk/FPMTDTMTCPMTDTMTDTCrJxeu3mId4tJoJB1xEREREpH0smEGCEnLAtXDhQtSvXx8BAQFo164dtm/fXub+hYWFmDlzJuLi4uDv74+GDRti8eLF5vuXLFkCSZJsvgoKCtzdFSIiIiJyt6Ii4I8/lNssCU+C8VG7AdZWrFiBadOmYeHChejSpQs+/PBDDBgwAEeOHEFsbKzdxwwfPhwXLlzAJ598gkaNGuHixYsoLi622CckJATHjx+32BYQEOC2fribJEmIiorSXJUWT8U8xMNMxMI8xMNMxMNMKuHgQaCwEAgLAxo1cskhmYd4tJqJcFUKO3XqhLZt2+L99983b2vWrBkGDRqEuXPn2uz/448/4sEHH8SpU6cQFhZm95hLlizBtGnTcOXKlQq3i1UKiYiIiAS1YAHw5JPAgAHA+vVqt4a8gDNjA6HOcBUVFWHfvn147rnnLLb369cPO3futPuY7777Du3bt8ebb76JpUuXonr16rjvvvswZ84cBJaoUJOfn4+4uDgYDAa0bt0ac+bMQZs2bUptS2FhIQoLC83f5+bmAgAMBgMMBgMAZZSt0+lgNBotFu+Ztpv2K2+7TqeDJEl2twOwqcRiOsbp06cRFxcHvV4PANDr9ZBl2WZ/vV5v08bStqvZJ3vbtdIne3lovU/22q6lPhUVFeHs2bPmTDyhT1rOyV4eWu+T1nMyGAzmTHx8fDyiTyVpMSdTJvHx8fD19fWIPpXXdpf16bffIAEwduwIucRjKtMn6zw8+bWnlT6ZMmnQoAEkSVK1T9b3l0WoAdfly5dhMBgQGRlpsT0yMhIZGRl2H3Pq1Cns2LEDAQEBWLNmDS5fvozJkycjKyvLvI6radOmWLJkCRISEpCbm4v58+ejS5cuOHjwIBo3bmz3uHPnzsWsWbNsth8+fBhBQUEAgLCwMMTGxuLcuXPIysoy7xMVFYWoqCicOXMGeXl55u0xMTEIDw/HiRMnLNaPNWjQACEhIThy5IhFeE2aNIGfnx+Sk5Mt2pCQkIDCwkKkpKQgPz8fkiRBr9cjISEBeXl5OHXqlHnfgIAANG3aFNnZ2UhNTTVvDw4ORsOGDXHx4kWLn62afSoqKrKY9qmlPun1eos8PKFPWs/p6NGjuHz5sjkTT+iTlnM6efIk0tPTzXl4Qp+0nlNubi6ysrKQn5+P2NhYj+iT1nMyVWCrXr066tat6xF9qqqcWt0sCX86MhJ5N++rbJ9Mefj5+SEuLs6jX3ta6ZMsy8jLy0ODBg1U71N+fj4cJdSUwrS0NNSrVw87d+5EYmKiefurr76KpUuX4tixYzaP6devH7Zv346MjAyEhoYCAFavXo2hQ4fi6tWrFme5TIxGI9q2bYtu3brhnXfesdsWe2e4YmJikJWVZT5tqPYZruTkZLRo0YJnuATok708tN4ne23XUp+Kiopw+PBhcyae0Cct52QvD633Ses5GQwGcyY8wyVGn0yZtGzZkme4nOnT5cvQR0UBAAyXLwM1arikT9Z5ePJrTyt9MmVy++23q36GKzc3F2FhYdqbUhgREQG9Xm9zNuvixYs2Z71M6tSpg3r16pkHW4Cy5kuWZZw7d87uGSydTocOHTrgxIkTpbbF398f/v7+Ntv1er35F+qSx7PHej9XbpckyXxmq+T9pm3WSmujs9vd2afStmuhT6XlUdb+ovfJ2e0i9sleJlrvU2W3q9mnyuZR2nbmVPE+mZ7HtJ8n9Kkqt7ujT6ZfAl3VRme3azKnvXuVf5s2hT483GbfyvSpZB6e/tpzdRud3e5on0wzidTuU2n3222Tw3tWAT8/P7Rr1w4bN2602L5x40Z07tzZ7mO6dOmCtLQ0i9N6f/31F3Q6HaKjo+0+RpZlHDhwAHXq1HFd46uYJEmIiYkxv+hIXcxDPMxELMxDPMxEPMykgm5OJ3R1OXjmIR6tZiLUgAsAnn76aXz88cdYvHgxjh49iqeeegopKSmYNGkSAGDGjBl4+OGHzfuPGjUK4eHhGD9+PI4cOYJt27bh3//+Nx555BHzdMJZs2bhp59+wqlTp3DgwAFMmDABBw4cMB9Ti3Q6HcLDw0sdxVPVYh7iYSZiYR7iYSbiYSYV5KYLHjMP8Wg1E+FaO2LECCQlJWH27Nlo3bo1tm3bhvXr1yMuLg4AkJ6ejpSUFPP+QUFB2LhxI65cuYL27dvjoYcewr333muxNuvKlSt49NFH0axZM/Tr1w/nz5/Htm3b0LFjxyrvn6sYDAYcO3bMqQop5D7MQzzMRCzMQzzMRDzMpAKMRmD3buW2i89wMQ/xaDUTodZwmUyePBmTJ0+2e9+SJUtstjVt2tRmGmJJ8+bNw7x581zVPGGUrKhC6mMe4mEmYmEe4mEm4mEmTjp+HMjJAQIDgYQElx+eeYhHi5kId4aLiIiIiMghpumEHToAPkKeRyDigIuIiIiINMpNBTOIXIkDLo3S6XRo0KCB5hYNeirmIR5mIhbmIR5mIh5mUgFuKpgBMA8RaTUToS58LLLc3FyEhoY6dHEzIiIioRkMwPbtQHo6UKcO0LUr4MQ1ZYiEcPUqEBKiFM44dw6oV0/tFpEXcWZsoK3hIZkZDAYkJydrrkqLp2Ie4mEmYmEeAlm9GoiPB3r2BEaNUv6Nj1e2k6r4PnHS3r3KYCs62i2DLeYhHq1mwgGXhmntxebpmId4mIlYmIcAVq8Ghg5VzgaUdP68sp2DLtXxfeKEKli/xTzEo8VMOOAiIiLyBgYDMHUqYG8lgSwrX48/rgzGtFJ22WAAtmwBvvpK+VeDv4hRJbhx/RaRK7F+JhERkTfYvt32zJa1jAwgJka5HRgI1KwJhIUpX6bb5f0bGlo168FWr1YGkCX7FB0NzJ8PDBni/ucndckyB1ykGSya4SDRimbIsoyCggIEBARAkiS1m+P1mId4mIlYmIcAvvpKWbNVHkmyfxbMUZKkDLqcGaSZblerpjy+PKapkdbtND32m280Oeji+8QJqalAbKwyuM/NVV47LsY8xCNSJs6MDXiGS8P8/PzUbgKVwDzEw0zEwjxUVqeOY/v9/DPQti2QnQ1kZd36t+Tt0v69elUZBF25onw5y8+v/MFZjRrA00+XPjVSkoBp04D779dk5UW+TxxkOrvVqpVbBlsmzEM8WsyEAy6NMhqNSE5ORkJCAvQa/A/F0zAP8TATsTAPAXTtqky5O3/e/mBFkpT7u3dXBio1agD16zv3HEVFyuCrIoO14mLl8RcuKF8VJcvK2Y/t24EePSp+HBXwfeKEKiiYwTzEo9VMOOAiIiLyBnq9sr5p6FDb+0xTc5KSKndWyM8PiIxUvpwhy8rZMUcGaUeOAIcPl3/M9PSK9YG0geu3SEM44CIiIvIWQ4YAr78OTJ9uuT06WhlsqbXuSZKAoCDlKza27H23bFGuHVYeR6dQkvbcuAHs26fc5oCLNIADLiIiIm9y82yWnJiIs/fei5iOHaHv0UM7653KmxoJKJUWu3at2nZR1fnzT+XSBTVrAo0bq90aonLxOlwapdPpkJCQAJ2OEYqAeYiHmYiFeQhk82bl36FDEfPss9D16qWdwRZwa2okUHpFw8REbfXpJr5PHGSaTtipk2NVLSuIeYhHq5loq7VkoaioSO0mUAnMQzzMRCzMQwDFxUoxCQDo2VO7mQwZopR+r1fPcntYmPLv118D69ZVfbtcQLOZVKUqKJhhwjzEo8VMOODSKKPRiOPHj8NoNKrdFALzEBEzEQvzEMQffwD5+UDNmjC2bKntTIYMAc6cUc7YLVum/HvxIjB5snL/6NHAyZOqNtFZfJ84qIoKZjAP8Wg1E67hIiIi8hZbtij/dusGaGxKjl16vW3p93nzgP37gV27gMGDlV/Oq1dXpXnkBpmZwIkTyu2OHdVtC5GDPODTloiIiBxiWr+lsetTOcXPT5luGBkJHDoETJxYenEN0p7du5V/b7vt1hRSIsFxwKVhWrrgmzdgHuJhJmJhHiq7cQPYsUO5fbOsusdmUreuMujy8QGWL1dK3muEx2biKlV8/S3mIR4tZiLJMv/s44jc3FyEhoYiJycHISEhajeHiIjIOb//rvySWrMmcPmyZ0wpLM+CBcCTTypTDzdudOz6XSS2u+4CfvoJeO+9W+v1iFTgzNjACz5tPZMsy8jNzQXHy2JgHuJhJmJhHgIwrd/q3h3Q6bwjk8cfB8aMAQwGYMQIIDVV7RaVySsyqQyj8VaFwio4w8U8xKPVTDjg0iij0YhTp05prkqLp2Ie4mEmYmEeArBav+UVmUgS8MEHQOvWwKVLwAMPKBfMFZRXZFIZJ04AV64AAQFAQoLbn455iEermXDARURE5OlKrt/y5IIZ9lSrBqxerUyl3LMHmDJF7RZRRZnWb7VvD/j6qtsWIidwwEVEROTp9u0Drl5VqrpVwZkB4dSvD3z1lXLG66OPlC/Sniq84DGRK3HApWEBAQFqN4FKYB7iYSZiYR4qslq/ZeJVmfTvD7zyinL7iSdulRcXjFdl4qwqrlAIMA8RaTETVil0EKsUEhGRZvXvD2zYAMyf791T6oxGZR3X2rVAdLRy5q92bbVbRY64dg0ICVEKoKSmKvkRqYhVCr2A0WhEZmam5hYNeirmIR5mIhbmoaJS1m95ZSY6HfDZZ0CTJsC5c0rlwuJitVtl5pWZOGrfPmWwVbdulQ22mId4tJoJB1waJcsyUlNTNVcW01MxD/EwE7EwDxXt3aucHQgPB1q2NG/22kxCQoA1a4CgIGWq5fTparfIzGszcYQK0wmZh3i0mgkHXERERJ6slPVbXq1ZM+VMFwC8/TawfLm67aHysWAGaRg/eYmIiDyZacDlbeXgyzNkCPDcc8rtCROA5GR120NlU+EMF5GrcMClYcHBwWo3gUpgHuJhJmJhHiooKirz+lten8krrwB9+ihTLgcPVi6qqzKvz8Sec+eA8+cBvR5o165Kn5p5iEeLmbBKoYNYpZCIiDRn506gSxcgIgK4cIFTCu25fFm5kO7Zs8DAgcB33/HnJJpVq4ChQ4HWrYH9+9VuDREAVin0CkajERkZGZqr0uKpmId4mIlYmIdKyli/xUxuiogAVq8GAgKA778H5sxRrSnMpBQqTSdkHuLRaiYccGmULMvIyMjQXJUWT8U8xMNMxMI8VFLG+i1mUkLbtsAHHyi3X34ZWLdOlWYwk1KoVDCDeYhHq5lwwEVEROSJioqAX39VbrNgRvnGjgUef1y5PXo0cOKEuu0hxY0byqUNABbMIM3igIuIiMgT7dmjFIOIiABatFC7Ndrw9ttA585ATo5SxTA/X+0W0aFDwPXrQGgocNttareGqEI44NIoSZIQFhYGSZLUbgqBeYiImYiFeaig5HRCOz93ZmKHnx+wciUQFaX8oj9xIlCFU5eYiR2m9VudOlV5MRPmIR6tZsIBl0bpdDrExsZCx0pKQmAe4mEmYmEeKijn+lvMpBR16yqDLh8fYMUKYN68KntqZmKHaf2WCtMJmYd4tJqJtlpLZkajESkpKZqr0uKpmId4mIlYmEcVKywsd/0WMynDnXfeGmg9+yyweXOVPC0zsaPkGa4qxjzEo9VMOODSKFmWkZWVpbkqLZ6KeYiHmYiFeVSxPXuUdS+1agHNm9vdhZmU4/HHgTFjAIMBGDECSE11+1MyEyvZ2cDx48ptFQZczEM8Ws2EAy4iIiJPU876LXKAJAEffqhcbPfSJeCBB4CCArVb5V1271b+bdQICA9Xty1ElcABFxERkacpZ/0WOSgwULkocliYctbwySfVbpF3UemCx0SuxgGXRkmShKioKM1VafFUzEM8zEQszKMKObB+C2AmDqtfH/jqK+WM18cfAx995LanYiZWVLrgsQnzEI9WM5FkrU2CVElubi5CQ0ORk5ODkJAQtZtDRERk3/btQLduQO3aQEYGpxS6yty5wPPPK6Xjt21TbRDgNWRZmUaYna2cXWzfXu0WEVlwZmzAM1waZTAY8Pfff8NgMKjdFALzEBEzEQvzqEIOrt9iJk567jlg8GCgqEhZz3XhgsufgpmUcOKEMtgKCABuv12VJjAP8Wg1Ew64NCwvL0/tJlAJzEM8zEQszKOKOLF+i5k4QZKAJUuAJk2A8+eVyoXFxS5/GmZyk2k6Ydu2yllFlTAP8WgxEw64tMhgALZsQY0fflD+Y9XYKJ+IiNyksBDYuVO5zYIZrhcSAqxZAwQFAVu3AtOnq90iz8WCGeRBOODSmtWrgfh46Pv0Qfzzz0Pfpw8QH69sJyIi7/b770rp8shIoGlTtVvjmZo1Az7/XLn99tvA8uXqtsdTqVwwg8iVOODSktWrgaFDgXPnLLefP69s56BLNZIkISYmRnNVczwZMxEL86giTlx/i5lUwuDBwIwZyu0JE4DkZJcclpncdP06cPCgclvFM1zMQzxazYQDLq0wGICpU5WqPdZM26ZN4/RCleh0OoSHh0On41tKFMxELMyjijixfouZVNKcOUDfvsC1a8oALDu70odkJjf98YeyPq5OHSAmRrVmMA/xaDUTbbXWm23fbntmqyRZBlJTlf2oyhkMBhw7dkxzVXM8GTMRC/OoAgUFTq3fYiaVpNcr1+eKiwP+/hsYPRowGit1SGZyk2n9VqdOql7WgHmIR6uZcMClFenprt2PXK6goEDtJpAVZiIW5uFmv/+uFM2IilIq6TmAmVRSeLgynT8gAFi/Hpg9u9KHZCYQqmAG8xCPFjPhgEsr6tRx7X5ERORZnFi/RS7Uti3wwQfK7VmzgHXr1G2PJ2DBDPIwHHBpRdeuQHR06f+JSpIyz7lr16ptFxERicGJ9VvkYmPHAo8/rtwePVq5aC9VTFqaskRCpwPat1e7NUQuIeSAa+HChahfvz4CAgLQrl07bC9nXVJhYSFmzpyJuLg4+Pv7o2HDhli8eLHFPqtWrULz5s3h7++P5s2bY82aNe7sguvp9cD8+cpt60GX6fukJGU/qnI6nQ4NGjTQ3CJOT8ZMxMI83KygANi1S7nt4ICLmbjY228DXboAOTnAkCFAfr7Th2AmuHV2q2VL5XpnKmIe4tFqJsK1dsWKFZg2bRpmzpyJ/fv3o2vXrhgwYABSUlJKfczw4cOxadMmfPLJJzh+/Di++uorNC1x/ZFdu3ZhxIgRGDNmDA4ePIgxY8Zg+PDh+N30ptaKIUOAb74B6tWz3B4drWwfMkSddhEkSUJISIjmypR6MmYiFubhZr/9pqzfqlMHuO02hx7CTFzMzw9YuVJZQ3foEDBxov3KwmVgJhBq/RbzEI9WMxFuwPX2229jwoQJmDhxIpo1a4akpCTExMTg/ffft7v/jz/+iK1bt2L9+vXo06cP4uPj0bFjR3Tu3Nm8T1JSEvr27YsZM2agadOmmDFjBnr37o2kpKQq6pULDRkCnDkDw2uvAQDkunWB06c52FKZwWBAcnKy5qrmeDJmIhbm4WYVWL/FTNygTh3lD6A+PsCKFcpZLycwE9w6wyXAgIt5iEermfio3YCSioqKsG/fPjz33HMW2/v164edplK3Vr777ju0b98eb775JpYuXYrq1avjvvvuw5w5cxAYGAhAOcP11FNPWTyuf//+ZQ64CgsLUVhYaP4+NzcXgBK0KWRJkqDT6WA0GiGX+CuWabv1i6G07TqdDpIk2d0OAEarMrM6nQ7yxInA889DSkuD4eJFoHZt6PV6yLJss79er7dpY2nb1eyTve1a6ZMsyyguLra4T+t9std2rfWpZCae0idH2i5qn6zz8IQ+WbdRrT7JW7ZAAmDs1g2yweBQn0pmImKfNJtTly4wzpsH3ZNPQn72WRhvvx3o1cuhPpkyMRqN0Ov14vSpqnIqLoZuzx5IAOSOHSEBqvbJOg/hX3ue+H6yarspE9Mx1OyTM4M+oQZcly9fhsFgQGRkpMX2yMhIZGRk2H3MqVOnsGPHDgQEBGDNmjW4fPkyJk+ejKysLPM6royMDKeOCQBz587FrFmzbLYfPnwYQTfnFIeFhSE2Nhbnzp1DVlaWeZ+oqChERUXhzJkzyMvLM2+PiYlBeHg4Tpw4YVHSskGDBggJCcGRI0cswmvSpAn8/PyQbHUF+4SEBBRWq4bC2FhUT0nB2RUrcLV3byQkJCAvLw+nTp0y7xsQEICmTZsiOzsbqamp5u3BwcFo2LAhLl68aPFzULNPRUVFOH78uHmbXq/XTJ/0ej2ysrJw+PBh82lurfdJ6zkdPXrUIhNP6JOWczp58qRFHp7QJ2FyqlvXvH7reJ06KExOdqhPubm55kxiY2PF6pPWc3rwQUg//YSwdesgDx+Oc999h/iuXcvtkyzLyMrKwqVLl1C3bl2x+lQFOUkHD6LJtWswBAXhar16CAFU7ZMpj7S0NMTFxWnjteeJ76cSfZJl2dwutfuU78Q6TUm2HgY7IT8/H1lZWahbty58fG6N3VasWIHvvvsO1apVw+OPP47WrVs7dLy0tDTUq1cPO3fuRGJionn7q6++iqVLl+LYsWM2j+nXrx+2b9+OjIwMhIaGAgBWr16NoUOH4urVqwgMDISfnx8+++wzjBw50vy4L7/8EhMmTCi1lr+9M1wxMTHIyspCSEgIAHX/OmAwGJAzdCjCv/0Wxmefhfzaa179Fw+1+2Q6xd2iRQvobxYu0Xqf7LVdS30qKirC4cOHzZl4Qp+0nJO9PLTeJ2Fy2rYN6NULct26MJ49C0iSw2e4TJn4+PiI1SdPyOnaNei6doV04ADk9u0hbd8Oo59fuWe4Dh8+jJYtW8LX11e8Prk5J2nRIugmT4bcuzewcaPqfbLOQzOvPU98P5U4w3X48GHcfvvtkCRJ1T7l5uYiLCwMOTk55rFBaSp1hmv69On47LPPcOHCBfOA6/3338cTTzxh7ujy5cuxd+9eNHHgIowRERHQ6/U2Z54uXrxoc4bKpE6dOqhXr555sAUAzZo1gyzLOHfuHBo3boyoqCinjgkA/v7+8Pf3t9mu1+vNv1CbmAKwt6+7tuv1egT36wd8+y10v/9urk4oSZLd/Utro7Pb3dmn0rZroU96vR5NmzaFr6+vzUJOrfbJ2e2i9cnX19duJlruk5ZzckUepW33+pxurt+SevSA3sen/P1vPqdOp7PJRJg+VWC7cDkFBQFr1gDt2kHauxd48knoPvrI7rFNfTJlYvqdSrg+2eHSnHbvVtqQmGhei6hmn6zz0Mxrzw7Nv59K3G7atKl5UGRPVfWptPvttsnhPe3Yvn07+vTpg+rVq5u3zZ07F/Xq1cO2bdvw9ddfw2Aw4L///a9Dx/Pz80O7du2wceNGi+0bN260KIJRUpcuXZCWlmZxWu+vv/6CTqdDdHQ0ACAxMdHmmBs2bCj1mFqhv/NO5caePcDN+aykHj8/P7WbQFaYiViYh5vcHHChAtffYiZuFh8PLF8O6HTAxx8DpQy4SvLqTAS84LFX5yEoLWZSqQHX+fPn0aBBA/P3ycnJOHfuHKZMmYI777wTQ4cOxX333YetW7c6fMynn34aH3/8MRYvXoyjR4/iqaeeQkpKCiZNmgQAmDFjBh5++GHz/qNGjUJ4eDjGjx+PI0eOYNu2bfj3v/+NRx55xFw0Y+rUqdiwYQPeeOMNHDt2DG+88QZ+/vlnTJs2rTLdV5XRaERycTHk0FDg2jXgzz/VbpJXMxqNSE5OtjkVTuphJmJhHm5y/fqtMtpODriYSRXp2xd49VXl9hNP3BpU2OHVmVy5Ahw9qtwWZMDl1XkISquZVGrAdf36dYtR5o4dOyBJEvr162fe1qBBA5w/f97hY44YMQJJSUmYPXs2WrdujW3btmH9+vWIi4sDAKSnp1tckysoKAgbN27ElStX0L59ezz00EO499578c4775j36dy5M5YvX45PP/0Ut99+O5YsWYIVK1agkyBv6ArT6YCOHZXbpgteEhGR99i1CygqUq7P2KiR2q2h0kyfDgwerGT1wAPAhQtqt0g8e/Yo/zZsCNSqpW5biFysUmu4oqOj8WeJMyvff/89atasiYSEBPO2zMxMc1U/R02ePBmTJ0+2e9+SJUtstjVt2tRmyqC1oUOHYujQoU61QwvkO+6AtHGj8p/u44+r3RwiIqpKJacTlrKegQQgScCSJcoZnGPHgBEjgI0bAV9ftVsmDtOZWq3/MZzIjkqd4RowYAA2bNiAf//733jhhRfw448/4t5777VYxHbs2DHExsZWuqFkn2y6MCDPcBEReZ9KrN+iKhYSohTRCA4Gtm5VznrRLaYBlwAXPCZytUqVhc/IyEDnzp1x5swZAEqd+99++808wEpJSUGDBg0wZcoUvO3k1dZFk5ubi9DQUIdKP1YFU+lMXW4upLAwZeOFC0Dt2uo2zEuZ8yijag5VLWYiFubhBteuATVrKtPUTpxwekohM1HJmjXAkCHK7WXLgBKXrPHaTGRZmUaYmamscTMtl1CZ1+YhMJEycWZsUKkzXFFRUTh8+DC+++47fPfddzZns/Ly8vDWW2/h0UcfrczTUCmKioqAGjWA5s2VDTzLpaqioiK1m0BWmIlYmIeLlVy/1bBhhQ7BTFQweDAwY4Zye8IEm6JXXpnJ338rgy1/f8DBa7dWFa/MQ3BazKRSAy4ACAwMxD333IN77rnHZnTXokULTJ06FU2bNq3s05AVo9GI48ePK1VaTBeJ5oBLNRZ5kBCYiViYhxuYphP27Fmh9VvMREVz5gD9+ilVJocMAbKzAXhxJqbKjW3bAgKV/PbaPASm1UwqPeCyZ9euXZg5cyZeffVVpKWlueMpqCQOuIiIvA/Xb2mXXq9MJ4yPV87ujB4N3LgBbNmCGj/8oGRrMKjdyqrDghnk4SpVpfCZZ57BggULkJaWhrCb64i++eYbPPjgg+aR57vvvot9+/ahXr16lW8t2WcacO3Zo3xgs+oREZFnu3bt1lkBDri0KTwcWL0a6NwZWL8eiIiAPjcX8ab7o6OB+fNvrffyZCyYQR6uUme4Nm/ejJ49e5oHWwDwwgsvIDQ0FJ9//jnefPNNZGZm4q233qp0Q8mWXq9XbjRtqqzlun6dF0BWkTkPEgYzEQvzcKGdO5U/sEVHAw0aVPgwzERlbdoA//iHcjs31/K+8+eBoUOVQZknu34dOHBAuS3gGS6+R8SjxUwqNeBKSUlB48aNzd+fOHECx48fx5QpUzB69Gg888wzuPvuu7F+/fpKN5Qs6fV6JCQkKC86ne7WhxSnFarCIg8SAjMRC/NwsUqu3wKYiRAMBqVqoT2mItLTpnn29ML9+4HiYiAyEoiLU7s1FvgeEY9WM6nUgCs/P9/iosY7duyAJEkYMGCAeVvz5s1x7ty5yjwN2SHLMnJzc2Gu6s91XKqyyYNUx0zEwjxczAXrt5iJALZvB8r6HUmWgdRUZT9PZZoa26mTcBfv5ntEPFrNpFIDrjp16uD48ePm73/88UcEBQWhXbt25m25ubnw9/evzNOQHUajEadOnbpVpYUDLlXZ5EGqYyZiYR4udPUqsHu3crsSAy5mIoD0dNfup0UCr9/ie0Q8Ws2kUkUzunfvjq+++grvvfceAgICsHbtWtx3330Wp/lOnjyJ6OjoSjeUymH6y9Dp08oFkCMj1W4RERG5g2n9VkwMUL++2q2hyqhTx7X7aZHpDJeAAy4iV6nUGa6ZM2ciMDAQU6ZMwT/+8Q/4+vripZdeMt9/6dIlbNmyBV26dKl0Q6kcoaFAixbKbZ7lIiLyXC5Yv0WC6NpVKXxSVo7R0cp+nig9HTh7Vul/+/Zqt4bIbSo14GrUqBGOHDmC+fPn45133sGhQ4fQsmVL8/1nz57F5MmTMX78+Eo3lGwFBARYbuC0QlXZ5EGqYyZiYR4u4sLrbzETlen1Sul3oPRBV0SEUlTCE5nObrVsCQQHq9uWUvA9Ih4tZiLJWlt1ppLc3FyEhoYiJycHISEhajfHvk8/BR55RPlL2LZtareGiIhc7epV5TIgxcXAqVOcUugpVq8Gpk61LKBRuzZw5QpQVAQMHgx8/TXgU6mVIOKZMQN4/XVg4kTgo4/Ubg2RU5wZG1TqDFdJxcXFOHLkCHbt2oXDhw+j2FP/GiMIo9GIzMxMy0WDpjNce/cq8/upytjNg1TFTMTCPFzk11+VwVZsLBAfX6lDMROBDBkCnDkD46ZNyPvwQxg3bQLS0pQLIvv7K6Xjx48HPC0rgQtmAHyPiEirmVR6wJWdnY1HH30UNWrUQEJCAu68807cfvvtqFGjBh599FFkZma6op1kRZZlpKamWpbFvO02oGZN5SKCBw+q1zgvZDcPrTEYlKlKX32l/Kvx6754RCYehHm4iAvXbzETwej1kLt3x98dO0Lu3l2Zbti7N7BypXJm64svgMcfv3V9Lq0zGIA9e5TbAl7wGOB7RERazaRSA67s7GwkJibi448/RmBgIPr164eHH34Y/fv3R7Vq1fDxxx+jc+fOyMrKclV7qSw63a2/EnEdFzlj9Wrlr+U9ewKjRin/xscr24lIHC5cv0Uace+9wNKlygD7gw+Af//bMwZdhw8rU2SDg4FmzdRuDZFbVWrANWfOHPz111+YMWMGzp49ix9++AGffvop1q9fj7Nnz2LmzJk4ceIEXnnlFVe1l8rDwhnkrNWrgaFDbS++ef68sp2DLiIx5OffOiPAAZd3efBB4OOPldtvvQXMnq1ue1zBVDCjY0flbB6RB6vUgGvt2rXo2bMnXn31VVSrVs3ivsDAQMyZMwe9evXC2rVrK/M0VIpgexV9OOBSjd08RGcwKAu17f211LRt2jTNTi/UZCYejHlUkmn9VlxcpddvmTAT8ZSaySOP3Kpo+PLLysBLy0zrtwSdTmjC94h4tJhJpcrdpKWlYeTIkWXu06lTJ+zYsaMyT0N26PV6NGzY0PaOjh2VaQdnzgAZGUBUVJW3zRuVmofaZBnIyQFSU4GUFOXfkl9//aUszC7r8ampwPbtmvuLurCZeCnm4QIl12+5ADMRT7mZTJminOmcORN45hkgKAj45z+rroGuJHjBDIDvERFpNZNKDbhCQ0Nx9uzZMvc5e/YsQkNDK/M0ZIfRaMTFixdRu3Zt6HQlTlSGhCjXs0hOVs5yDR6sXiO9hcEA49atyD1+HCFNmkBnWuxcFa5etR1EWX/l51f+edLTK3+MKlbqe4RUwTxcwMXrt5iJeBzK5Pnngbw8pZz6Y48B1asDo0dXbUMrKycHOHpUuS3wGS6+R8Sj1UwqNeDq0aMHVq5ciXHjxqFPnz4292/atAkrV67EoEGDKvM0ZIcsy8jIyECtWrVs70xM5ICrqty8doru3DnUMG2LjlamfQwZUrljFxYq66rKGkxlZzt2rPBwICbG9uvyZWXKYHnq1KlUV9RQ5nuEqhzzqKSS67e6d3fJIZmJeBzO5LXXlNfEggXAuHHKoEtL/9/v2aPMoKhfX7nemKD4HhGPVjOp1IDrpZdewvfff4/+/fvj7rvvRvfu3REZGYkLFy5gy5Yt+OGHHxAYGIgXX3zRVe0lRyQmAosWcR2Xu5mKTVivfzIVm/jmm9IHXcXFylmjsgZTFy441o7gYNuBVGzsrdvR0YDVGkszgwH4v/9T2mxvHZckKY/v2tWxthCRe+zYobxf4+Ndtn6LNEySlD/s5ecDS5YAI0YA//sf0L+/2i1zjKlghsBnt4hcqVIDrubNm2PDhg0YN24cvv/+e3z//feQJMlcG79hw4b47LPP0KJFC5c0lhxkKpyxZ49yhXo/P3Xb44nKKzYhScDkycp+aWm2g6m0NMcuYBkQYP/MVMmvykzZ1euV/7SHDlXabN0fWQaSklhBikhtLl6/RR5Ap1MqF169qlyra/Bg4McfgW7d1G5Z+TSwfovIlSo14AKAzp074/jx4/j111+xf/9+5ObmIiQkBG3atEGXLl2wYMEC/N///R9Ws7S0S0mShLCwMEj2Lnx5221AWBiQlQUcOKAU0iDX2r7dtox6SbKsnKEaPrz0fXx8gHr17A+iTGeowsMrfXHTcg0ZopyNmzrVtk/Nm1d+aqRKynyPUJVjHpXkhutvMRPxOJ2JXq9cEPnaNeD774F77gE2bQI6dHBvQytDlm+d4RJ8wMX3iHi0mokku/lSzePHj8fnn38Og0bLSpvk5uYiNDQUOTk5CAkJUbs55Rs4EFi/Xjk7MXWq2q3xPF99pVwguDwNGwJt2tgfVEVGinXmyGBQBpLp6cpg8MEHlbNwf/yh9IGI1JGXB9SsqbxHz5xRysITlXT9uvL//ubNymtl61YgIUHtVtl36pTyf6OfH5CbC/j7q90iogpxZmygnfIeZMFoNCIlJQXG0qal8Xpc7uVoEYmPP1amerz9NvDUU8rUvU6dgLp1xRpsAUp7evQARo4Ehg27dXbOdN0XjSn3PUJVinlUwq+/KoOt+vVdOthiJuKpcCaBgcB33ylnjLKzgb59lct+iMg0nbBNG+EHW3yPiEermXDApVGyLCMrKwulnqDkgMu9unZVikmUdkpbkpSzWFouNmGqXvjVV8o13TSm3PcIVSnmUQmbNyv/unj9FjMRT6UyCQpSZra0bq1Mae/TByjn0j2q0FDBDL5HxKPVTDjg8lQdOyoLalNSyr6wLVWMqdiEPaZBmNaLTXTqpPy1tKgI+OADtVtD5L3csH6LPFTNmsBPPwFNmyoFmnr3Fu86iiyYQV6IAy5PFRysXAAZ4FkudxkyRCnHay06uuyS8FpiOsv1/vtAQYGqTSHySrm5wL59ym0XXX+LPFzt2sDPPytTUP/+W5leePmy2q1SFBYqxbwADrjIq3DApVGSJCEqKqrsKi2cVuh+QUEAADkmBlcWLoRx0ybg9GnPGGwBwAMPKFMjL14Eli9XuzVOceg9QlWGeVSQaf1WgwZK9VIXYibicVkm9eop1Qrr1gUOH1auz5WT45pGVsb+/cqsiVq1NHE9Ob5HxKPVTJwuC3/33Xc7tX9ycrKzT0EO0Ol0iIqKKnunxETgww854HKnDRsAANKgQajx2GMqN8YNfHyAJ54Apk9XpkiOHev+MvUu4tB7hKoM86ggN63fApiJiFyaSf36yqCrWzel2uzAgcp0w+rVXXP8iig5nVAD/5fwPSIerWbi9IDrxx9/dPpJtDYK1QKDwYAzZ84gPj4e+tLWCZnOcO3bxwsgu8vGjQAAQ69eOPP332XnoVUTJwKzZgEHDyprSTRy4VWH3iNUZZhHBblx/RYzEY/LM2naVPnDYM+eytnSwYOVaoYBAZU/dkVoqGAGwPeIiLSaidMDrtOnT7ujHVQBeXl5Ze/QuLFy4dzMTOU0vkY+4DTj1Cnly8cH6NEDeWfOqN0i9wgLU85svf++cpZLIwMuwIH3CFUp5uGkKli/xUzE4/JMWrcGfvhBqVq4cSMwYoSyztjX17XP4wgNFszge0Q8WszE6QFXHC+4qB2SpHyoff+9Mq2QAy7Xunl2C3fcoRQp8WRTpigDrv/9Dzh5EmjUSO0WEXm+HTuUi483bKispSSqqDvuUD6/775bOcM1diywdGnVVtK9cEG5cLckAR06VN3zEgmARTM8HQtnuI9pwNW3r7rtqApNmwIDBgCyDLz7rtqtIfIObly/RV6oZ0/lzJaPj3J9xUmTlM/0qmKaTti8ORASUnXPSyQADrg0SpIkxMTElL8+jgMu9zAYgF9+UW736+d4HlpmKhG/eLEY1a7K4RWZaAjzqAA3X3+LmYjH7ZkMHAgsW6Zcp/Pjj4Gnn666QZdpwKWh6YR8j4hHq5lwwKVROp0O4eHh0OnKidB0AeTUVOD8+appnDfYtw/IzgZCQ4H27R3PQ8v69lX+Mpmfrwy6BOcVmWgI83BSTo5SWQ5w2/otZiKeKslk2LBbn+FJScBLL7nvuUoyrd/S0PIGvkfEo9VMtNVaMjMYDDh27BgMBkPZOwYFAQkJym2e5XKdm+Xg0asX4OPjeB5aJkm3znK9845ylk9gXpGJhjAPJ5nWbzVqpFxM3Q2YiXiqLJOxY4EFC5Tbc+YAb77p3uczGIDdu5XbGjrDxfeIeLSaCQdcGlZQUODYjpxW6Hp21m85nIeWjR6tVL48c0ZZeC04r8hEQ5iHE6po/RYzEU+VZfL448Drryu3p08HFi5033MdParMjggKUmZKaAjfI+LRYiYccHkDDrhcKz//1s/SGwpmlBQYCPzzn8rtpCRVm0Lk0dy8fosIgDLQmjlTuf3448Bnn7nneUzTCTt0qNrKiESC4IDLG5S8AHJhobpt8QRbtwI3bgDx8Uq5Zm8zebJS5WrbtltrTIjIda5cUa6dCLht/RaR2Zw5yqU/AOCRR5RKhq6msQseE7kaB1wapdPp0KBBA8cWDTZqBEREAEVFt/4Tp4orOZ3wZpUcp/LQunr1gOHDldsCn+Xyqkw0gHk4wbR+q3Fj5f3mJsxEPKpkIknAvHnAhAnK627UKGD9etc+hwYveAzwPSIirWairdaSmSRJCAkJcawspukCyACnFbqCacDVr595k1N5eIKpU5V/ly8H0tPVbUspvC4TwTEPJ5jWb7l5OiEzEY9qmeh0wIcfAg8+qMzgeOCBW9NaKysvDzh8WLmtsTNcfI+IR6uZcMClUQaDAcnJyY5XaeE6Ltc4fx44ckQZxPbqZd7sdB5a17Ej0Lmz8h/z+++r3Rq7vC4TwTEPJ5h+0XVzwQxmIh5VM9Hrgc8/B+69FygoUP41TQWsjD17lGt9xcUBUVGVP14V4ntEPFrNhAMuDXPqxcYBl2uYzm61bw+EhVncpbU3f6WZSsS//77yn7OAvC4TwTEPB1Tx+i1mIh5VM/H1Bb7+GujdWykQddddwMGDlTumRqcTmvA9Ih4tZsIBl7fo0EGZMnDunPJFFWOnHLzXGjwYiI0FLl8Gli1TuzVEnmH7duVswG23AXXrqt0a8kYBAcC33yqzGK5cUf6/O3as4sdjwQwiDri8RlAQcPvtym2e5aoYoxH4+WflNgdcSqXCJ59UbiclKb8kElHlVNH6LaIyVa8OfP890LYtcOkS0KcPcPq088eRZc2f4SJyBQ64NEqn06FJkybOVWnhtMLKSU4GLl4EqlW79bO8qUJ5eIIJE5T/mJOTb/2iKAivzURQzMNBVbR+C2AmIhIqkxo1gJ9+Ui5UfP68Mug6f965Y5w9q/y/6esLtGnjlma6k1B5EADtZqKt1pIFPz8/5x7AAVflmKYT9ugB+Pvb3O10Hp6gZk1g3Djl9rx5qjbFHq/MRGDMoxzZ2cCBA8rtKrr+FjMRj1CZREQo//c1bAicOqUMui5dcvzxprNbrVsrUxU1SKg8CIA2M+GAS6OMRiOSk5NhNBodf5BpwPXHH7wAckWUsX6rQnl4CtMFM9etA06cULctJXh1JgJiHg4wrd9q0gSoU8ftT8dMxCNkJnXrKtPpo6OVtVz9+ilruxxhWr+l0emEQubh5bSaCQdc3qRhw1sXQP7jD7Vboy0FBcC2bcptrt+ydNttwMCByu133lG3LURaxvVbJKr4eGDTJqB2beUs7IABShXD8pjOcLFgBnk5Dri8iSQpVYcATit01o4dyqCrbl1lPjtZMpWI//RTx//ySUSWqnD9FpHTbrtNmelRs6YykLr//rIvCVJYeOuPuxo9w0XkKhxweRuu46oY03TCPn2UgStZ6t0baNECuHoV+OQTtVtDpD1ZWbeud1RF67eInHb77cCPPyqVj3/5BRg6VJk1Y8/Bg8p9ERFAgwZV204iwXDApVE6nQ4JCQnOV2nhgKtiyrn+VoXz8BSSdOss17vvAsXFqjYHYCaiYR7lMK3fatoUiIqqkqdkJuLRRCYdOyprdgMClNLxY8YA9i5EW3I6oUb/UKmJPLyMVjPRVmvJQlFpf1UqS/v2gF6vlHZNTXV9ozzRpUvA/v3K7T59St2tQnl4koceUv6SefasctFMAXh9JoJhHmVQaf0WMxGPJjLp3h1Ys0Yp9/7118A//qFcq7IkjRfMMNFEHl5Gi5kIOeBauHAh6tevj4CAALRr1w7bt28vdd8tW7ZAkiSbr2Mlroq+ZMkSu/sUlDX3WHBGoxHHjx93vkpL9epAq1bKbZ7lcsymTcq/t99e6l+eK5yHJwkMBCZNUm4nJanaFICZiIZ5lEOF9VvMRDyayuSuu4Dly5U/4n76qTLLQZZv3e8BBTM0lYeX0Gomwg24VqxYgWnTpmHmzJnYv38/unbtigEDBiAlJaXMxx0/fhzp6enmr8aNG1vcHxISYnF/eno6AjR6TYhK47RC55QznZBKeOwx5S+eO3YAe/eq3RoibcjKAv78U7nN9VukJUOGKIMtQJlOPnOmMr1w7Vrlul0A0K6das0jEoWP2g2w9vbbb2PChAmYOHEiACApKQk//fQT3n//fcydO7fUx9WuXRs1atQo9X5JkhDlxLz4wsJCFJa4VlVubi4AwGAwwHBzrrIkSdDpdDAajZBL/FXHtN1gNae5tO06nQ6SJNndDsBmFK/T6SDLMmRZtniMXq+HLMs2++v1eos2Sp06Qffee8CuXaW2XY0+2dvuaJ/K2l6pPskydBs2QAIg9+kDCbDbJ3t5CNsnq7a7NKfISEjDh0P35Zcwzpun/Ktin0pmornXXil9cqTtovbJOg9P6JN1GyvUpy1boJdlyM2awRgRAT1QJX0qmYmnv/a00idTJkajEXq9Xht9eughIC8PuscfB+bOhfzee5Bu/s4EAHKrVjDOmwdpyBDN5WSdhye/9rTSJ1MmpmOo2Sfr+8si1ICrqKgI+/btw3PPPWexvV+/fti5c2eZj23Tpg0KCgrQvHlz/Oc//0FPq2kZ+fn5iIuLg8FgQOvWrTFnzhy0adOm1OPNnTsXs2bNstl++PBhBAUFAQDCwsIQGxuLc+fOISsry7xPVFQUoqKicObMGeTl5Zm3x8TEIDw8HCdOnLCYztigQQOEhITgyJEjFuE1adIEfn5+SE5OtmhDQkICCgsLkZ2djcOHD0OSJOj1eiQkJCAvLw+nTH9VAhAQEICmTZsiOzsbqTfXbPnVrInmAPDHH7iYkoKMEmW81exTUVERjh8/bt7mTJ8AIDg4GA0bNsTFixeRkZHhkj7JR4+i2blzMPr6Ir91a4QAdvuk1+st8hC5T+7OKfDuu9Hkyy8hff018N//ItvfX5U+HT161CITrb32PO39dPLkSYs8PKFPrsopf9Uq1AKQ2bIl0o8cqbI+5ebmmjOJjY312NeelvokyzKys7Nx6dIl1K1bVzt96twZMffcg/B16ywGWwCA8+ehGz4cVz76CDUnTNBOnzIyzHmkpaUhLi7Oo197WumTLMu4evUqAKjep3xHrkV3kyRbD4NVlJaWhnr16uHXX39FZ9P1ogC89tpr+OyzzyzCMTl+/Di2bduGdu3aobCwEEuXLsUHH3yALVu2oFu3bgCA3377DSdPnkRCQgJyc3Mxf/58rF+/HgcPHrSZemhi7wxXTEwMsrKyEBISAkC7fx2ALENXrx6kixdh3L4dsmmKoZb7VMb2yvRJWrAAumnTIPfsCWza5BF9sm6jO/qk694d0q+/AjNnwjh7tkf0qay2s0/sU2X6hNatIf35JwzLlwNDh3pEnzwxJ/apjD4VF0PXsCFw7hzs1SOUJQmIjoZ0+jSszwkI2ydPzIl9cmmfcnNzERYWhpycHPPYoDRCDrh27tyJxBKDgFdffRVLly61KIRRlnvvvReSJOG7776ze7/RaETbtm3RrVs3vPPOOw4dMzc3F6GhoQ79UKuCLMvIy8tDcHCw+YyKUwYNUirJ/d//Af/6l8vb5zHuuw/43/+AuXMBqzOvJVU6D0+zapVyfZbwcKUaZmBglTeBmYiFeZQiM1Op7gkAFy4AtWtX2VMzE/FoNpMtWxwr+LJ5c5VX4qwMzebhwUTKxJmxgVBFMyIiIqDX6y1ODwLKKcPIyEiHj3PHHXfgxIkTpd6v0+nQoUOHMvcRndFoxKlTp2z+EuAwFs4o340btyqHlVMwo9J5eJr77wfi4pRfJr/8UpUmMBOxMI9SbNum/Nu8eZUOtgBmIiLNZpKe7tr9BKHZPDyYVjMRasDl5+eHdu3aYaOpKtxNGzdutJhiWJ79+/ejTp06pd4vyzIOHDhQ5j4er+SAS5yTnGL5/XcgL085S1PGej+yw8cHePJJ5XZSEl9jRKUx/VFHQ3/1J7Lh6O9T3vx7F3k1oYpmAMDTTz+NMWPGoH379khMTMSiRYuQkpKCSTev7zNjxgycP38en3/+OQClimF8fDxatGiBoqIifPHFF1i1ahVWrVplPuasWbNwxx13oHHjxsjNzcU777yDAwcO4L333lOlj0Jo3175pTgtTZnyFRurdovEYxr49+kD6IT624Q2TJwIvPwycPiwci2zMi4aTeS1TBc8rsLrb5H40tLSkJqaiitXrqC4uFjt5pTPaFT+OJmZWfo+ERHAlStAKcs9RGQ0GnHmzBmcOXPm1ppLgeh0OoSGhqJu3bpo2LCh2s2hMgg34BoxYgQyMzMxe/ZspKeno2XLlli/fj3i4uIAAOnp6RbX5CoqKsIzzzyD8+fPIzAwEC1atMD333+Pu+++27zPlStX8OijjyIjIwOhoaFo06YNtm3bho4dO1Z5/1ypUtcRq1ZNuQDyvn3KWS4OuGxt2KD86+D1t7z2um6lCQ0Fxo9Xrs2SlKTKgIuZiIV5WLl8GTBV8LpZ5KmqMRNxnDt3DitWrMCXX36J/fv3q90c17t8GRg8WO1WeKxmzZph+PDhGD58OJo3b652c9xKi59bQhXNEJloRTNc4skngQULgKlTlV+I6ZYrV5S/1hmNwNmzHJBW1IkTQJMmypTCY8eU20SkWL0aeOABoEUL4NAhtVtDKtq0aRPuueceGI1G3HXXXRg2bBjatGmDGjVqwM/PT+3mkaCKi4uRk5OD48ePY9WqVfj222+Rm5uLhQsX4rHHHlO7eR7PmbGBcGe4yDFGoxHZ2dmoWbNmxU9zJyYqAy4WzrC1ebMy2LrtNocGWy7JwxM1bgzcc49S6fGdd4AqnMbLTMTCPOxQef0WMxGDabDVo0cPLF++HKGhoWo3iTSkTp06aNq0Ke6//34UFhbi2WefxeTJkwHAIwddWv3c0k5LyYIsy0hNTbW5LoFTTIUz/vgDuH7dNQ3zFKb1Ww5OJ3RJHp5q2jTl3yVLgOzsKntaZiIW5mGHyuu3mIn6Ll68aB5srVmzhoMtqhR/f38kJSVh6tSpmDx5Mn799Ve1m+RyWv3c4oDLm8XHA5GRQHGxspaLbjENuPr1U7cdnqBnTyAhAbh2Dfj4Y7VbQySGS5duTSNUaf0WqW/16tW4ceMGPv/8c02uSyHxSJKEefPmISYmBsuWLVO7OXQTB1zeTJJ4PS57Tp8GTp4E9HqWanYFSbp1luvdd5UBPpG3M11/q2VLoFYtddtCqlm5ciV69uyJWnwNkAtJkoRhw4Zh1apVMBgMajeHwAGXpgUHB1f+IBxw2TKd3brjDsCJAikuycNTjRqllAROTQXWrKmyp2UmYmEeJQhy/S1mop7s7Gxs2bIFw4YNU7sp5IGGDRuGCxcuYJcH/n6nxc8tDrg0Sq/Xo2HDhtDr9ZU7EC+AbMvJ9VuAC/PwVAEBgGnxbhVVxGQmYmEeVgS4/hYzUde5c+dgNBqRkJCgdlPIA5leVyUvpeQJtPq5xQGXRhmNRmRkZMBoNFbuQKYLIGdkKOXPvZ3BoFykF3BqwOWyPDzZ5MmAry+wcyewe7fbn46ZiIV5lHDxonJBcEDV9VvMRF1XrlwBANSoUUPVdpBnqlatGnx9fc2vM0+h1c8tDrg0SpZlZGRkVL5KS2Ag0Lq1ctsDTzs77Y8/lEp6ISGAExfGdlkeniwqChg5Urk9f77bn46ZiIV5lGBav5WQoEy1VQkzUZdpbY2vr69D+//+++8YPHgwYmNj4e/vj8jISCQmJuJf//qXO5vpEuPGjUN8fLzazQAAzJw5E23atEFYWBgCAgLQoEEDPProozhr9UfnM2fOQJIku1/Lly+3Oe6pU6cwZMgQ1KhRA0FBQejbty/++OMPm/3i4+PtHnPSpEk2++7fvx+DBg1C3bp1Ua1aNTRt2hSzZ8/GtWvXyu2nJEnw9fVFsYetm9bq5xavw0XKtMK9e5UBl+kXYm9lmk7Ys6dy5o9ca+pU4PPPga+/Bt58E6hXT+0WEVU9QdZvkXZ8//33uO+++9CjRw+8+eabqFOnDtLT07F3714sX74cb731ltpN1IwrV65g5MiRaNasGYKDg3HkyBG88sor+O6773D48GGEh4db7P/kk09i1KhRFtsaN25s8f2lS5fQtWtX1KxZE4sXL0ZAQADmzp2LHj16YM+ePWjSpInF/l26dMH//d//WWyLjIy0+P7IkSPo3LkzmjRpgqSkJERERGDbtm2YPXs29u3bh2+//bayPwqqQvyNkpQB17vv8gwXwHLw7ta2rTKFats25SLIr72mdouIqp4A67dIW958803Ur18fP/30E3xK/DHwwQcfxJtvvqliy7Tnvffes/i+R48eqF+/Pu6++258++23eOSRRyzuj42NxR133FHmMf/73//i0qVL2LlzJ+Li4gAAd955Jxo2bIgXX3wRK1assNi/Ro0a5R5z2bJlKCgowKpVq9CwYUMAQK9evZCeno5FixaZL/5L2sAphRolSRLCwsIgSVLlD2YqnHHggHdfAPnqVcB0kUAn1m8BLs7D05lKxH/4oXJtLjdhJmJhHjddvAgcOaLcVvn6W8xEOzIzMxEREWEx2DLR6Sx/lVuxYgX69euHOnXqIDAwEM2aNcNzzz2Hq1evWuw3btw4BAUF4dixY+jfvz+qV6+OOnXq4PXXXwcA/Pbbb7jzzjtRvXp13Hbbbfjss88sHr9kyRJIkoSNGzdi/PjxCAsLQ/Xq1XHvvffi1KlT5fZJlmUsXLgQrVu3RmBgIGrWrImhQ4faPHb//v245557ULt2bfj7+6Nu3boYOHAgzp0759DPzhGmsvz2fr6OWLNmDXr16mUebAFASEgIhgwZgv/9738VmtZnmmpqfTHsGjVqQKfTwc/Pr0Jt1Tqtfm5xwKVROp0OsbGxNh+0FRIXp6yvKS5WphZ6q61bgRs3lJ9Ho0ZOPdSleXi6++5TLrqdlQV88YXbnoaZiIV53LR1q/Lv7bcDVlOXqhoz0Y7ExET8/vvvmDJlCn7//XfcuHGj1H1PnDiBu+++G5988gl+/PFHTJs2DV9//TXuvfdem31v3LiBIUOGYODAgfj2228xYMAAzJgxA88//zzGjh2LRx55BGvWrEGTJk0wbtw47Nu3z+YYEyZMgE6nw7Jly5CUlITdu3ejR48e5RZr+Oc//4lp06ahT58+WLt2LRYuXIjDhw+jc+fOuHDhAgDg6tWr6Nu3Ly5cuID33nsPGzduRFJSEmJjY5GXl2c+1ssvvwxJkrDFNF3XAcXFxbh+/Tr279+PadOm4bbbbsOQIUNs9nv99dfh5+eHatWq4c4778R3331ncf/169fx999/4/bbb7d57O23347r16/bDCK3bduG4OBg+Pr6onnz5njrrbdsrpc1duxY1KhRA4899hhOnTqFvLw8rFu3Dh9++CEef/xxVK9e3eG+ehLNfm7J5JCcnBwZgJyTk6N2U2RZlmWDwSCfPXtWNhgMrjng4MGyDMjyG2+45nhaNG2a8jOYONHph7o8D0/39tvKz7pZM1k2Gt3yFMxELMzjpsmTldf+lClqt4SZqGzz5s0yAPnEiRPl7nv58mX5zjvvlAHIAGRfX1+5c+fO8ty5c+W8vLxSH2c0GuUbN27IW7dulQHIBw8eNN83duxYGYC8atUq87YbN27ItWrVkgHIf/zxh3l7ZmamrNfr5aefftq87dNPP5UByIMHD7Z4zl9//VUGIL/yyisWzxUXF2f+fteuXTIA+a233rJ4bGpqqhwYGCg/++yzsizL8t69e2UA8tq1a8v8+cyaNUvW6/Xyli1bytzPJD093fyzBCB36tRJPn/+vMU+aWlp8j/+8Q/566+/lrdv3y5/+eWX8h133CEDkD/66CPzfufPn5cByHPnzrV5nmXLlskA5J07d5q3TZ48WV68eLG8detWee3atfJDDz0kA5BHjx5t8/ijR4/KTZs2tWjrlClTZKOD/29Wq1ZNnj9/vkP7aoVIn1vOjA00NjwkE1mWkZWV5boqLbwAcoWuv2Xi8jw83SOPAEFBwNGjt37uLsZMxMI8bhJo/RYz0Y7w8HBs374de/bsweuvv477778ff/31F2bMmIGEhARcvnzZvO+pU6cwatQoREVFQa/Xw9fXF927dwcAHD161OK4kiTh7rvvNn/v4+ODRo0aoU6dOmjTpo15e1hYGGrXrm1TyQ8AHnroIYvvO3fujLi4OGw2vdbtWLduHSRJwujRo1FcXGz+ioqKQqtWrcxnqho1aoSaNWti+vTp+OCDD3DENB3Xyosvvoji4mJzP8sTERGBPXv2YMeOHfjoo4+QlZWFnj17Ij093bxPnTp1sGjRIgwbNgx33nknRo0ahW3btqFNmzZ47rnnbKYJljXFreR97733HsaPH49u3brh/vvvxxdffIEnnngCX3zxBfbv32/e78yZM7j33nsRHh6Ob775Blu3bsWbb76JJUuWYOLEiQ710xNp9XOLAy5SePsFkNPSlOviSBLQu7farfF8oaHKoAuosgshE6nuwgXljwySpPr6LdKm9u3bY/r06Vi5ciXS0tLw1FNP4cyZM+bCGfn5+ejatSt+//13vPLKK9iyZQv27NmD1atXA1Cmv5VUrVo1BAQEWGzz8/NDWFiYzXP7+fmhoKDAZntUVJTdbZmZmaX248KFC5BlGZGRkfD19bX4+u2338wDyNDQUGzduhWtW7fG888/jxYtWqBu3bp46aWXypxWWR4fHx+0b98eXbp0wcSJE/HLL7/g1KlT5vVrpfH19cWIESOQmZmJEydOAABq1qwJSZLs9jcrKwsA7P48Sxo9ejQAZd2cyXPPPYfc3Fz89NNPeOCBB9CtWzf8+9//RlJSEhYvXoytpunJpAmsUkiKdu2UMugXLgBnzgD166vdoqr188/Kv23bqr6uwmtMmaJUx/zhB+DYMaBpU7VbROReJddvlfMLGFF5fH198dJLL2HevHk4dOgQAOCXX35BWloatmzZYnG2x50Xv83IyLC7rVEZa6EjIiIgSRK2b98Of39/m/tLbktISMDy5cshyzL+/PNPLFmyBLNnz0ZgYCCee+45l/QhOjoadevWxV9//VXuvqYzK6Y1RIGBgWjUqBGSk5Nt9k1OTkZgYCAaNGjg1DEB4MCBA2jevLnNWq0OHToAAA4dOuTwGT1SH89waZQkSYiKinJdlZbAQMA0fcAbpxVWshy8y/PwBg0bKgU0ALdcCJmZiIV5QLjrbzET7Sg51a0k0xTBunXrArg1dc16EPPhhx+6rW1ffvmlxfc7d+7E2bNn0aOM1/k999wDWZZx/vx5tG/f3uYrISHB5jGSJKFVq1aYN28eatSoYfeiwhV18uRJnDt3rsxBIqAUGVmxYgUiIiIs9h08eDB++eUXpKammrfl5eVh9erVuO+++8qtfvj5558DgEWp+Lp16+Lw4cPIz8+32HfXzd/RoqOjHeuch9Hq5xbPcGmUTqezexq/UhITgT17lAGX1UX+PJosV2r9FuCmPLzBtGnAt98Cn30GvPqqS//qz0zEwjwg1PotgJloSf/+/REdHY17770XTZs2hdFoxIEDB/DWW28hKCgIU6dOBaCsn6pZsyYmTZqEl156Cb6+vvjyyy9x8OBBt7Vt7969mDhxIoYNG4bU1FTMnDkT9erVw+TJk0t9TJcuXfDoo49i/Pjx2Lt3L7p164bq1asjPT0dO3bsQEJCAh577DGsW7cOCxcuxKBBg9CgQQPIsozVq1fjypUr6Fvi/+vZs2dj9uzZ2LRpU5lnff7880889dRTGDp0KBo0aACdTofk5GTMmzcP4eHheOaZZ8z7Pv3007hx4wa6dOmCqKgopKam4t1338WBAwfw6aefQq/Xm/d95plnsHTpUgwcOBCzZ8+Gv78/Xn/9dRQUFODll18277ds2TKsXr0aAwcORFxcHK5cuYKVK1di+fLlGDduHFq1amXed9q0aRg0aBD69u2Lp556ChEREfjtt98wd+5cNG/eHAMGDHA2Ko+g2c8tl5fs8FCiVSksLi6WT548KRcXF7vuoF99pVTPatfOdcfUgoMHlX5XqybLBQUVOoRb8vAGRqMst2ql/Pxff92lh2YmYvH6PNLTlde5JMlyZqbarZFlmZmozZkqhStWrJBHjRolN27cWA4KCpJ9fX3l2NhYecyYMfKRI0cs9t25c6ecmJgoV6tWTa5Vq5Y8ceJE+Y8//pAByJ9++ql5v7Fjx8rVq1e3ea7u3bvLLVq0sNkeFxcnDxw40Py9qUrhhg0b5DFjxsg1atSQAwMD5bvvvtumT9ZVCk0WL14sd+rUSa5evbocGBgoN2zYUH744YflvXv3yrIsy8eOHZNHjhwpN2zYUA4MDJRDQ0Pljh07ykuWLLE4zksvvSQDkDdv3lzmzzEjI0MePXq03LBhQ7latWqyn5+f3KBBA3nSpElySkqKxb6ffPKJ3LFjRzksLEz28fGRa9asKffv31/+6aef7B775MmT8qBBg+SQkBC5WrVqcu/eveV9+/ZZ7LNr1y65d+/eclRUlOzr6ytXq1ZN7tChg7xw4UK7Vfd++eUXuV+/fnJUVJQcGBgo33bbbfK//vUv+fLly2X208QTqxSK9LnlzNhAkmVvrJDgvNzcXISGhiInJwchISFqNwcGgwHJyclISEiw+CtLpZw9q1wfyccHyMkBqlVzzXFF99ZbwDPPAHfdpawnqgC35OEtliwBxo8HoqOBU6eAmxd7rCxmIhavz2PFCuDBB4HWrYESlcjU5PWZqGzLli3o2bMnTpw4Ue5UNhEtWbIE48ePx549e9C+fXu1m0N2VK9eHXPnzsWUKVPUborLiPS55czYgGu46JbYWKBOHe+7AHIlpxNSJT34IFC7NnDuHHCzkhaRxxFs/RYREVUdDrjoFknyvutxFRQA27YptzngUkdAAPDYY8ptlognT8UBFxGR1+KAS6MkSUJMTIzrq7R424Br507g+nUgKgpo2bLCh3FbHt7isccAPz/gt9+ULxdgJmLx6jzS05VLHwh2/S2vzoQqbdy4cZBlmdMJqUpp9XOLAy6N0ul0CA8Pt7hmg0t42wWQS04nrMSb1215eIvIyFuVMV1UIp6ZiMWr8zBdf6t1a6BmTVWbUpJXZ0JEmqTVzy1ttZbMDAYDjh07BoPB4NoDt2unFC24eBE4fdq1xxbRhg3Kv5WcTui2PLzJzbLGWLkSKHEtk4piJmLx6jwEnU7o1ZkQkSZp9XOLAy4NKygocP1BAwK85wLIly/fqhbWp0+lD+eWPLxJ69bKL6QGA/Deey45JDMRi9fmIeiAC/DiTDRmyZIlkCTJ7lfJa0e50pEjR/Dyyy/jzJkzbjm+O2zZsgWSJGGL6T0HZepjfHy8am36+OOPMWjQIMTHxyMwMBCNGjXCY489Zvdi1vHx8XYznjRpkgotF5cWP7d44WOy1bkzsHu3MuB66CG1W+M+mzYp0yZbtlSqM5L6pk1TfjldtAh44QWgenW1W0RUOWlpwPHjwq3fIm369NNP0bRpU4ttdevWdctzHTlyBLNmzUKPHj1UHbBU1gsvvGC+MLQaXnrpJfTs2ROvvfYa6tWrh+PHj2POnDn49ttvsX//fkRGRlrs36VLF/zf//2fxTbrfUh7OOAiW4mJSrU4Tz/DxXLw4rnnHqBBA+V6XEuXAvyrHmmdaf1WmzZAjRqqNoW0r2XLlpovUnHjxg1IkgQfn6r5FbRhw4ZV8jyl2b9/P2rXrm3+vnv37mjbti06dOiAjz76CP/5z38s9q9RowbuuOOOqm4muRmnFGqUTqdDgwYN3LNo0FQ44+BB4OpV1x9fBLLs0gGXW/PwJno9YLpA4/z5gNFY4UMxE7F4bR4CTyf02kw82IoVK5CYmIjq1asjKCgI/fv3x36rC23v3bsXDz74oHmKW3x8PEaOHImzZ8+a91myZAmGDRsGAOjZs6d5atuSJUsAKFPfxo0bZ/P8PXr0QI8Sr3XTFL+lS5fiX//6F+rVqwd/f3+cPHkSAPDzzz+jd+/eCAkJQbVq1dClSxds2rTJob4eO3YMd911F6pVq4aIiAhMmjQJeXl5NvvZm1IoSRKeeOIJfPrpp2jSpAkCAwPRvn17/Pbbb5BlGf/9739Rv359BAUFoVevXub2VkTJwZZJu3btoNfrkeqC9creRqufW9pqLZlJkoSQkBD3lMWMiQHq1VPW0njqBZBPnABSUpRS5C6Y5uPWPLzN+PFAcLBSRttU1KQCmIlYvDYPgQdcXpuJhhkMBhQXF1t8mbz22msYOXIkmjdvjq+//hpLly5FXl4eunbtiiNHjpj3O3PmDJo0aYKkpCT89NNPeOONN5Ceno4OHTrg8uXLAICBAwfitddeAwC899572LVrF3bt2oWBAwdWqN0zZsxASkoKPvjgA/zvf/9D7dq18cUXX6Bfv34ICQnBZ599hq+//hphYWHo379/uYOuCxcuoHv37jh06BAWLlyIpUuXIj8/H0888YTDbVq3bh0+/vhjvP766/jqq6+Ql5eHgQMH4l//+hd+/fVXLFiwAIsWLcKRI0fwwAMPQC5Rudk0kHz55Zcr9PPYunUrDAYDWrRoYXPftm3bEBwcDF9fXzRv3hxvvfWW5gpEuJNmP7dkckhOTo4MQM7JyVG7KbIsy3JxcbH8559/ysXFxe55gqFDZRmQ5blz3XN8tS1YoPSvZ0+XHM7teXibadOUfPr3r/AhmIlYvDKP8+eV17FOJ8vZ2Wq3xoZXZiKQzZs3ywDkEydOlLvvp59+KgOw+3Xjxg05JSVF9vHxkZ988kmLx+Xl5clRUVHy8OHDSz12cXGxnJ+fL1evXl2eP3++efvKlStlAPLmzZttHhMXFyePHTvWZnv37t3l7t272/SxW7duFvtdvXpVDgsLk++9916L7QaDQW7VqpXcsWPHMn4asjx9+nRZkiT5wIEDFtv79u1r0+axY8fKcXFxFvsBkKOiouT8/HzztrVr18oA5NatW8tGo9G8PSkpSQYg//nnn+ZtW7ZskfV6vTxr1qwy22lPbm6u3KxZMzkmJkbOy8uzuG/y5Mny4sWL5a1bt8pr166VH3roIRmAPHr0aKefR5ZluVq1ahaZegKRPrecGRtwDZeGufUvHomJwDffeO46LheVgy+Jf4FyoSefVKYU/vQTcOQI0Lx5hQ7DTMTidXmYzm4JvH7L6zLRuM8//xzNmjWz2Obj44OffvoJxcXFePjhhy3OegUEBKB79+7YvHmzeVt+fj7mzJmDVatW4cyZMxavgaNHj7ql3Q888IDF9zt37kRWVhbGjh1r0V4AuOuuu/Dmm2/i6tWrqF5K4aTNmzejRYsWaNWqlcX2UaNGYaNpuUA5evbsaXF80891wIABFmdPTNvPnj2LhIQEAMo6LOt2O6KgoABDhgzB2bNn8csvvyAoKMji/vesKvTef//9qFmzJhYsWICnn34abUxVpL2cFj+3OOAi+6wvgKy1U7dluXEDMP3nw4IZYmrQABg0CFizBnjnHeCDD9RuEZHzBJ5OSNrUrFkzu0UzLly4AADo0KGD3ceVXO8yatQobNq0CS+88AI6dOhgnp5199134/r1625pdx2rSsCm9g4dOrTUx2RlZZU64MrMzET9+vVttkdFRTncprCwMIvv/fz8ytxe2VLkhYWFGDx4MHbs2IF169ahU6dODj1u9OjRWLBgAX777TcOuDSMAy6yr21bZX3TpUtKxTiVq/y41O7dQF4eEBZ265pjJJ5p05QB1+efA6++CoSHq90iIudwwEVVJCIiAgDwzTffIC4urtT9cnJysG7dOrz00kt47rnnzNsLCwuRlZXl8PMFBASgsLDQZvvly5fNbSnJer2NaZ9333231Ip8ZZVCDw8PR0ZGhs12e9tEUFhYiEGDBmHz5s349ttv0bt3b4cfK99cO6a1IhFkiQMujdLpdGjSpIn73oD+/sqg67fflLNcnjTgMk036N1bqYrnAm7Pwxt17aoMiPfvV67LNWOGUw9nJmLxujzOn1eK8+h0ymtZQF6XiQfr378/fHx88Pfff9tM3ytJkiTIsgx/f3+L7R9//LHNNC3TPvbOesXHx+PPP/+02PbXX3/h+PHjdgdc1rp06YIaNWrgyJEjThW6MOnZsyfefPNNHDx40GJa4bJly5w+lruZzmz98ssvWL16Nfr37+/U4z///HMAYKn4m7T6ucUBl4aZTnO7TWLirQHX6NHufa6q5Kbrb7k9D28jScpZrrFjgQULgGeeAXx9nToEMxGLV+VhOrvVti0QGqpqU8riVZl4sPj4eMyePRszZ87EqVOncNddd6FmzZq4cOECdu/ejerVq2PWrFkICQlBt27d8N///hcRERGIj4/H1q1b8cknn6CG1TrDli1bAgAWLVqE4OBgBAQEoH79+ggPD8eYMWMwevRoTJ48GQ888ADOnj2LN998E7Vq1XKovUFBQXj33XcxduxYZGVlYejQoahduzYuXbqEgwcP4tKlS3j//fdLffy0adOwePFiDBw4EK+88goiIyPx5Zdf4tixYxX+GTpj69at6N27N1588UW8+OKLZe47dOhQ/PDDD5g5cybCw8Px22+/me8LCQlB85trlJctW4bVq1dj4MCBiIuLw5UrV7By5UosX74c48aNs1mv5s20+LmlreEhmRmNRiQnJ8NYiesUlavkOi5PkZMD/P67ctuFA64qycMbjRgBREYCaWlKERcnMBOxeF0eGphO6HWZeLgZM2bgm2++wV9//YWxY8eif//+ePbZZ3H27Fl0K3H5k2XLlqFnz5549tlnMWTIEOzduxcbN25EqNUfBurXr4+kpCQcPHgQPXr0QIcOHfC///0PgLIO7M0338RPP/2Ee+65B++//z7ef/993HbbbQ63d/To0di8eTPy8/Pxz3/+E3369MHUqVPxxx9/lDvlLioqClu3bkXz5s3x2GOPYfTo0QgICMCCBQuc+IlVnCzLMBgMDr131q1bBwB49dVXkZiYaPE1efJk834NGjTAlStX8Pzzz+Ouu+7CmDFj8Pfff2PhwoX45JNP3NYXrdHq55YkyyUuLEClys3NRWhoKHJychASEqJ2c2AwGJCcnIyEhAToXTQtzsa5c8o1ufR6ZaBSyuJVTVm7Fhg8GGjcGPjrL5cdtkry8FazZwMvvQR06KAMlh0s4MJMxOJ1eTRuDJw8CaxbB1Tw2kXu5nWZCGbLli3o2bMnTpw4gUaNGqndHPJA1atXx9y5czFlyhS1m+IyIn1uOTM24BkuKl10tPJlMAB79qjdGtdw03RCcqNJk5QCLnv2KFNciUR37pwy2BJ4/RYREVUdDriobJ42rZADLu2pXRt46CHldlKSqk0hcohpOmG7doAAMyJITL4316Taq/ZHVFmyLKOwsFCT6508EQdcGqXT6ZCQkOD+Ki2eNOA6e1apGqbXAz17uvTQVZaHt5o2Tfl31SogJcWhhzATsXhVHhpYvwV4WSYCqlmzJgAgOztb5ZaQJ8rLy4PBYDC/zjyFVj+3tNVaslBUVOT+J7G+ALKWmc5udezolqphVZKHt7r9dqBXL2V6qxOLopmJWLwmD40MuAAvykRAsbGx8Pf3x++mQk5ELmR6XTlTyEQrtPi5xQGXRhmNRhw/ftz9VVratFHWz1y+DPz9t3ufy93cOJ2wyvLwZqazXB99BOTnl7s7MxGL1+SRmqp8Vur1wJ13qt2aMnlNJoIKCgrCXXfdhZUrV6rdFPJAX3/9NRo0aIDWrVur3RSX0urnFgdcVDZ/f2UdAqDtaYUGA/Dzz8rtfv3UbQtVzMCBygW4r1wBbl4Ikkg4XL9FThg+fDh+//13nD59Wu2mkAcpLCzEmjVrMGzYMEgOVvYl9+KAi8rnCeu49u8HsrKA4GBlSiFpj04HTJ2q3J4/H9DYX7fIS2hoOiGp795770VkZCTuv/9+XLp0Se3mkAcoLCzEsGHDkJ+fj9GjR6vdHLqJAy4Nq7LrD3jCgMs0nbBnT+BmZShXU/t6EF5h3DjlrMFffwE//lju7sxELF6Rh8YGXF6RicCCg4Pxyy+/4OLFi+jduzcOHDgAXh6VKur06dMYOnQoNmzYgLVr16Jly5ZqN8kttPi5xQsfO0i0Cx9XqfPnletx6XTKdK7gYLVb5LxevYDNm4F33wWeeELt1lBl/OtfwNtvK2vxNmxQuzVEt6SkAHFxyvqt7GxtflaSKo4cOYK+ffsiLS0NjRs3xrBhw9CmTRvUrFmTZb2pVMXFxbhy5Qr++usvfPPNN9i7dy+qVauGVatW4a677lK7eR7PmbEBB1wOEm3AJcsy8vLyEBwcXDXzc2NjlcXgmzYpgxctuXYNqFkTKCoCjh0DmjRx+VNUeR7e7PRpoFEjZUrhoUNAixZ2d2MmYvGKPD7/HBg7FujUSRMX6faKTDSkqKgImzZtwrJly/D999+zXDw5LCAgAHfffTeGDRuGe+65B0FBQWo3yW1E+txyZmzgU0VtIhczGo04deoUEhISqubUamKiMuDatUt7A65t25TBVkwM4KbyqFWehzerXx8YNAhYvVpZy7Vokd3dmIlYvCIPjU0n9IpMNMTPzw/9+vVDnTp1sHjxYuTn5yM7OxvFxcVqN81rGQwG/PXXX7jtttuEfI/odDrUqFEDNWrUgI+Pd/xKr9XPLe9IhyovMRH4+mttruMyrd/q1w/gX3E9w1NPKQOupUuB114DIiLUbhGR5gZcJC6dToeaNWt63EVrtcZgMKCwsFDYARdpB4tmkGNMhTN++017F0B24/W3SCVduihltwsKgA8/VLs1RMDZs8p0Vw1cf4uIiKoWB1waFhAQUHVP1qbN/7d35+FRVff/wN93JiRhSYIJQgjZ2H4EQhBk36NolFpqS/1CtURbsUqLAiIoCCpoBXFBsIoVa3HBhVZxqRubEIKoIJsxCIIQEjaRxUxAksDM/f1xmMk2CQlk5pxz7/v1PHky3NzMfA7v3CSf3HvPEWtyHTsG7NoVvNe9WIcOATk54szWkCEBfamg5mF3hlG2EPJzz4lLRv1gJmqxdB7es1s9ewIa3T9h6Uw0xUzUwjzUo2MmbLg05XQ6kZKSErxT3KGhei6A7F3suFu3gF52FvQ8CBgxAoiNFU31f/9b5cPMRC2Wz0PDywktn4mGmIlamId6dM2EDZemPB4Pjh07Bk8wF3/VcT2uIF1OKCUPuwsNBcaOFY/nzatyqSszUYvl89Cw4bJ8JhpiJmphHurRNRM2XJoyTRMFBQXBXSBRt4bLNMvOcAW44ZKSBwF33CEudf36a2D9+gofYiZqsXQeeXniLSRE3F+oCUtnoilmohbmoR5dM1Gy4VqwYAFat26N8PBwdO/eHdnZ2dXuu2bNGhiGUeVtx44dFfZ755130KlTJ4SFhaFTp0549913Az0M6/E2XN9+CxQVya2lNnJzxeVm4eFa/RJEdXDppcCoUeLxvHlSSyEb0/T+LSIiCg7lGq4lS5ZgwoQJmDZtGrZs2YKBAwdi6NChyM/Pr/Hzdu7ciUOHDvne2rdv7/vYF198gZEjRyIzMxPbtm1DZmYmRowYga+++irQw7GWuDixALLHA2zYILua8/NeTjh4sGi6yJrGjxfvly4VM8URBZuGlxMSEVHwKNdwzZ07F6NHj8Ztt92Gjh07Yt68eUhISMDzzz9f4+c1b94csbGxvrfyN9PNmzcPV199NaZOnYqUlBRMnToVQ4YMwTzN/yIeERER/BfV6bLC5cvF+yBNBy8lDwLS0sQMlB4P8OyzFT7ETNRi2Tw0brgsm4nGmIlamId6dMxEqYWPS0tLsWnTJkyZMqXC9oyMDKyvdH9GZd26dUNxcTE6deqE6dOn44orrvB97IsvvsDdd99dYf9rrrmmxoarpKQEJSUlvn+7XC4AYhE8t9sNADAMAw6HAx6Pp8K1pN7t3v3Ot93hcMAwDL/bAVS5MdDhcMDhcCA5OdlXEyBmbjFNs8r+TqezSo3VbT/fmDy9e8OxZAnM9evhcbvrdUz+tl/wmIqL4cjKggHAM2QIHOeeO1A5+cuj3sdUh5wC+bWn5JjGjYNz1SqYL74IPPggjHPfjMtnot2YKtWoe05A1Tx0H5Pb7Qby8uDctw9mSAjQrx8MQKsxeTPxeDyW/drTbUzJyckwDANA1a8lXcdUU+2qj6l8HlYZU/kadRxTcnKyEmOq/PGaKNVwHT16FG63Gy1atKiwvUWLFjh8+LDfz2nZsiUWLlyI7t27o6SkBK+99hqGDBmCNWvWYNCgQQCAw4cP1+k5AWD27NmYOXNmle25ublocu4a/ejoaCQmJmL//v04fvy4bx/vWba8vDwUlbvXKSEhATExMdi1axeKi4t929u0aYPIyEhs3769QngdOnRAaGgocnJyKtSQlpaGkpISbNu2DeHh4TAMA06nE2lpaSgqKsKePXt8+4aHhyMlJQUnTpxAQUGBb3tERATatm2LI0eOVPh/ON+YDiYlIR6Ae/16fPvNN0hITKy3MZWWlmLnzp2+bRczppP/+x/anT6NMzExOHTJJUgEAppTSEgIvv76a18egRhTXXIK5NeekmOKj0dKYiLC8/Nx+oUX0HDSJOTm5uLUqVO+TLQbk8Vy+v777/Hzzz/78rDCmHbt2oVGS5YgEcCp1FR4PB5EAtqMyeVyobi4GOHh4UhU9Hu53Y4n0zRRXFyMpKQkxMXFWWJMOufkzSMuLg5JSUmWGJPuOZmmCcMw0KVLF+ljOnnyJGrLMBWa5uPgwYNo1aoV1q9fj77eS9cAPProo3jttdeqTIRRnWHDhsEwDHzwwQcAgNDQULzyyiu48cYbffu8/vrrGD16dIX/4PL8neFKSEjA8ePHERkZCUDuXwfcbjdycnKQmprqu3wyKH/xOH0ajuhoGMXFcOfmwkhJUeYvHuW34/774ZgzB56bbgJeey3gOfnLg3+ZCu6YjAUL4Bg3Dma7djB27kTp2bPIzc31ZaLjmMrXqHtOpaWlVfLQfUxutxvGn/8Mx2uvwTNlCoxZs7Qak9vt9mUSEhJi2a89ncbkzaRz585o0KCBJcZ0vtpVHlPlPKwwpso16jYmbyZdunSBYRhSx+RyuRAdHY3CwkJfb1Adpc5wNWvWDE6ns8qZpyNHjlQ5Q1WTPn36YPHixb5/x8bG1vk5w8LCEBYWVmW70+msstiaNwB/+wZqu3c2xsr1eLdVVl2Ndd3ubNhQLID8+edwbtgAdOpU59rruv2CxrRqlXickQGc2y+QOVWXR037BzSnAOZR3XbpY/rzn4EHHoCxezfw8cdwDh3qNxOtxhSA7TLHdLF5VLdd2pgcDiArS3zulVcC585u6zQm7+t497Pq116gtgdiTOUvw7XKmOq7xrpuv5gxlc/DKmOqzXaVx+S9kkj2mKr7uN+aar1nEISGhqJ79+5Y4Z1d7pwVK1agX79+tX6eLVu2oGXLlr5/9+3bt8pzLl++vE7PSeWoPnHGsWPApk3i8VVXya2FgqdJE+AvfxGPNZ8QhzSRlwfk5wMNGgD8eUJERNVQ6gwXAEycOBGZmZno0aMH+vbti4ULFyI/Px9jxowBAEydOhUHDhzAq6++CkDMQJicnIzU1FSUlpZi8eLFeOedd/DOO+/4nnP8+PEYNGgQ5syZg+uvvx7vv/8+Vq5ciXXr1kkZY30wDAPR0dG+Lj+oVG+4PvtMLHqcmgq0ahWUl5SaB5W5805g7lxg1SoYixYh/vRpGMePi6UB6vCXKKp/ljxGVq8W73v1Aho3llvLBbBkJppjJmphHurRNRPlGq6RI0fi2LFjePjhh3Ho0CF07twZH3/8MZKSkgAAhw4dqrAmV2lpKSZNmoQDBw6gYcOGSE1NxUcffYRf/epXvn369euHt956C9OnT8cDDzyAtm3bYsmSJejdu3fQx1dfHA4HEhMT5bx4+QWQXS7gPNetBl2Qp4MHJOdBZZKSgN69gS++gOMvf0Ez7/b4eGD+fGD4cJnV2Zolj5E1a8R7DaeDByyaieaYiVqYh3p0zUSpSTNU5nK5EBUVVasb44LB4/Fg//79iI+Pr/Za1YBKThaLzK5YodZle6YJtG4tavvoI6Bc4x1I0vMgYelS4Pe/r7rd+5ewt99m0yWJ5Y4R0xQNfkGBet8Ha8lymVgAM1EL81CPSpnUpTfgV4+mTNP0TSErhaqXFe7eLZqtBg3EZWRBIj0PAtxuYPx4/x/z5jJhgtiPgs5yx8jevaLZ0vj+LctlYgHMRC3MQz26ZsKGiy6M9xcM1Rou7+Qo/fppeU8FXYTsbGD//uo/bpriF+Ts7ODVRNblvX+rd2+gUSO5tRARkdLYcNGF8Z7h+vJLoNKaCVJ5G64g3r9Fijh0qH73I6qJ5vdvERFR8LDh0pRhGIiNjZU3S8tllwENGwInTgDffy+nhsrOnhUzFAJBb7ik50FAuaUg6mU/qleWOkZM0xINl6UysQhmohbmoR5dM2HDpSmHw4HY2Fh5Nww2aAD06CEeq3JZ4caNYtbESy4RizMHkfQ8CBg4UMxGWN03YcMAEhLEfhR0ljlG3G7gjTfE5atOp5gSXlOWycRCmIlamId6dM1Er2rJx+1244cffoBb5gQAqk2c4Z0OfsiQoK+5pEQedud0iqnfAf9Nl2mKBZG5HpcUljhGli4VM7SOGiX+7XYDnTqJ7RqyRCYWw0zUwjzUo2smbLg0VlRUJLcA1RouyfdvSc+DxJTvb7/tf8HrSy8Fhg4Nfk3ko/UxsnQpcMMNVSdmOXBAbNe06dI6E4tiJmphHurRMRM2XHThvA1Xbi5QWCi3FpdLTOABcMIMuxs+HMjLg3vlSuTNmgX3hx+KBuynn4AnnpBdHenIu+SAv2mIueQAERGdBxsuunAtWohFhk0T2LBBbi1r1ohfdtq2FTWRvTmdQHo6fh46FLj2WuCpp8T2xx4D8vPl1kb64ZIDRER0EdhwacowDCQkJMifpUWVywolX06oTB7kUyGTESOAQYOA06eByZNll2ZLWh8jFl1yQOtMLIqZqIV5qEfXTNhwacrhcCAmJkb+LC2qNVwZGVJeXpk8yKdCJoYhJtRwOID//AfIypJdnu1ofYxYdMkBrTOxKGaiFuahHl0z0ata8nG73dixY4f8WVpUWAA5Px/YuVP8Mn3FFVJKUCYP8qmSSdeuwO23i8fjxol12yhotD5GBg6suZnSdMkBrTOxKGaiFuahHl0zYcOlseLiYtklAF26iAWQf/5ZND0yeM9u9eoFNG0qpwYokgdVUCWTRx4R67R98w3w4otyirIxbY8Rh0Pcs+qP97IWTZcc0DYTC2MmamEe6tExEzZcdHEaNAB69hSPZV1WKPn+LdJIs2bAww+Lx9OnA8ePy62H9PDyy8DWrUBICBAbW/Fj8fFiKYLhw2VURkREGmDDRRdP5n1cHg+wapV4zIaLamPMGKBzZ9FsPfig7GpIdfv3A3ffLR7PmiX+vXo18MYb4v3evWy2iIioRoZp+ltYhCpzuVyIiopCYWEhIiMjZZcD0zRRVFSEiIgI+TO1vP8+8NvfAqmpwLffBve1N28GuncHmjQRv0A3aBDc1z9HqTwIwHkyWb0auPJKcanYli3i0lgKKC2PEdMEfv1r4OOPgd69gc8/1/KywepomYnFMRO1MA/1qJRJXXoDnuHSlGEYiIyMlP7FBqDsDNf27cFfANl7OWF6urRmC1AsDwJwnkyuuAK44QZxhrS6BW2pXml5jLz6qmi2wsKARYss1WwBmmZiccxELcxDPbpmwoZLU263Gzk5OWrM0tK8OdCmjfil9auvgvvakqeD91IqDwJQi0yefBIIDxeLZr/9dlBrsyPtjpGDB4EJE8TjmTOBjh2llhMI2mViA8xELcxDPbpmwoZLY0p9scm4j+uXX4DsbPFYgfu3lMqDAJwnk6Qk4L77xONJk8TXEwWUNseIaQJ33CFmX+3ZE7jnHtkVBYw2mdgIM1EL81CPjpmw4aL6IaPhys4GSkvFLGEdOgTvdck67r1XrJ+Unw88/rjsakgVr78OfPghEBoqLiUMCZFdERERaYwNF9UPGQsgl58OXrNreUkRjRoBTz0lHs+ZA+zbJ7ceku/QIbEwNgA89JCYDIiIiOgisOHSlMPhQIcOHeBwKBJhly7il9fCQmDHjuC8pkLrbymXB9U+kxtuAAYPBoqLxaWFFBBaHCOmKZYNOHFCzH56772yKwooLTKxGWaiFuahHl0z0ataqiA0NFR2CWVCQoK7APKPPwLffCMeDxkS+NerBaXyIAC1zMQwgGeeEVPEv/22mDKeAkL5Y+TNN4EPPhAzntrkUkLlM7EhZqIW5qEeHTNhw6Upj8eDnJwceIJ1+V5tBPM+rpUrxfuuXcUsiZIpmYfN1SmTLl3EmQ1AXE529mxgi7Mh5Y+RH38E7rpLPH7wQSAtTW49QaB8JjbETNTCPNSjayZsuKj+BLPhUmQ6eLKQhx8GoqPF4t0vvCC7Ggom0wT++lexeHq3bmWzVxIREdUDNlxUf/r0Ee+3bxfTKQeKaQLLl4vHCty/RRYREwM88oh4/MADwLFjcuuh4PnPf4B33xWXEC5aJHURdSIish42XFR/mjcH2rYVj7/8MnCvs327mEksPBwYMCBwr0P2c/vt4vLCEydE00XWd+QIMHaseDx9OnDZZXLrISIiy2HDpSmHw4G0tDT1ZmkJxmWF3ssJBw4UTZcClM3Dxi4ok5AQMYEGIC4r3LYtMMXZkLLHyNix4mzmZZcBU6fKriaolM3ExpiJWpiHenTNRK9qqYLS0lLZJVQVzIZLscsJlczD5i4ok8GDgREjxHpy48aJS1ipXih3jPz3v2JmypAQ4OWXxULHNqNcJsRMFMM81KNjJmy4NOXxeLBz5071ZmnxNlxffRWYBZBLS4GsLPFYoYZL2Txs7KIyeeIJoGFDYO1a8Us5XTTljpGffiq7lHDqVDHjqc0olwkxE8UwD/XomgkbLqpfaWlA48aAyyXutapvX3wBnDoFXHqpuNeGKBASE4EpU8TjSZOAX36RWw/Vv7vuEk1XWpq4d4uIiChA2HBR/Qr0AsjlZyfU7Ppd0szkyUBSElBQAMyZI7saqk/vvAMsWQI4nWJWQhteSkhERMHD31g15nQ6ZZfgXyDv41L0/i1A4Txs7KIyadgQePJJ8fjxx4G8vHqpyc6UOEaOHgX+9jfxeMoUoHt3ufVIpkQmVAEzUQvzUI+OmRimyTvCa8PlciEqKgqFhYWIjIyUXY7a/vc/4De/AVJSgO++q7/nPX4caNZMTGKwfz/QqlX9PTeRP6YJDBkCrF4N/P73YoIF0ttNNwFvvgmkpgKbNgFhYbIrIiIiDdWlN+AZLk2ZpgmXywUl+2XvAsg7dogmqb589pn4BbhjR+WaLaXzsKl6ycQwgPnzxeWr77wjvgbpgihxjLz3nmi2HA5xKaHNmy0lMqEKmIlamId6dM2EDZemPB4P9uzZo+YsLZdeCrRrJx5/9VX9Pa/ClxMqnYdN1VsmaWlll6CNGwecPXvxxdmQ9GPk+HFgzBjx+N57y+41tTHpmVAVzEQtzEM9umbChosCIxD3cSnccJHFzZwJxMQAubnA88/LroYuxPjxwI8/ijPkDz0kuxoiIrIRNlwUGPXdcP3wA7B3L9CgAZCeXj/PSVRb0dHA3/8uHj/4oJh4gfTxwQfA4sVllxKGh8uuiIiIbIQNl8bCVf6lofwCyG73xT+fdzr4vn2BJk0u/vkCQOk8bKpeM/nLX4DLLgN+/pnrNl0gKcfIiRNllxJOmgT07h38GhTG71vqYSZqYR7q0TETzlJYS5ylsI7OngWaNhWLFH/zjbgP5mIMHw68+y7wyCP8ZZfkWbsWGDxYTKaxaRPQrZvsiuh8/vQn4JVXxKypW7bw7BYREdULzlJoAx6PB8eOHVP3psGQEKBXL/H4Yi8rPHu2bHY4Re/fUj4PGwpIJoMGASNHitkyx40T76lWpBwjH30kmi3DAP79bzZblfD7lnqYiVqYh3p0zYQNl6ZM00RBQYHa02LW131cX38NFBaKM2Y9elx0WYGgRR42E7BMnnhCLIq8bh2wZEn9PreFBf0Y+fln4PbbxeOJE8u+H5EPv2+ph5mohXmoR9dM2HBR4PTrJ95fbMPlnZ3wyisBDVcXJ4tJSACmThWPJ00Sl82SeiZOBA4eBP7f/xOXIhMREUnChosCx7sA8s6dF7cAMqeDJ9VMmgQkJwMHDgCPPSa7Gqrsk0/EbITeSwkbNpRdERER2RgbLo1FRETILqFmMTHir8sA8OWXF/YcRUVlZ8gyMuqnrgBRPg8bClgmDRsCTz0lHj/xBLBnT2Bex2KCcowUFpZdSjhhAtC/f+BfU2P8vqUeZqIW5qEeHTNhw6Upp9OJtm3bwqn6JXYXex/XmjVi0ow2bcSborTJw0YCnsnvfgcMGQKUlIgzXlSjoB0jkyYB+/cD7dqVrZ1GfvH7lnqYiVqYh3p0zYQNl6Y8Hg8OHz6s/iwtF9twaXI5oTZ52EjAMzEMYP58cV/hu+8CK1cG5nUsIijHyPLlwL/+VXYpYaNGgXstC+D3LfUwE7UwD/XomgkbLk2ZponDhw+rP0vLxS6ArEnDpU0eNhKUTFJTgbFjxePx44EzZwL3WpoLeB4uF3DbbeLxXXcBAwcG5nUshN+31MNM1MI81KNrJmy4KLBSU4GICODkSSA3t26fu38/sGMH4HCIGQqJVDRjhrhfcft2YMEC2dXY1+TJQEGBuPR41izZ1RAREfmw4aLAcjovfAFk79mtHj2ASy6p37qI6ssll5T9gv/QQ8BPP8mtx45WrgQWLhSP//1voHFjufUQERGVw4ZLU4ZhIDo6GoZhyC7l/C70Pi5NLicENMvDJoKayejRQLduYoa86dMD/3oaClgeRUVllxKOHQsMHly/z29h/L6lHmaiFuahHl0zMUzdLoKUxOVyISoqCoWFhYiMjJRdjl4+/hi47joxRfzOnbX7HI8HaNECOHoUyMoCBg0KbI1EF2vdOnHfkGEAX38NXH657Irs4W9/A55/XqyLlpMDNGkiuyIiIrKBuvQGPMOlKY/Hg/z8fD1mafEugPz998CxY7X7nG3bRLPVuHHZ5ytMqzxsIuiZDBgA3HgjYJrAuHHiPfkEJI/PPhPNFgC89BKbrTri9y31MBO1MA/16JoJGy5NmaaJ48eP6zFLS3Q00KGDeFzbBZC9lxOmpwOhoQEpqz5plYdNSMnk8cfFVOSffw68+WbwXlcD9Z7HyZPiUk4A+OtfObHOBeD3LfUwE7UwD/XomgkbLgqOut7HpdH9W0Q+8fHA/feLx/feK5oCCowpU4C8PCApCZgzR3Y1RERE1VKy4VqwYAFat26N8PBwdO/eHdnZ2bX6vM8//xwhISHo2rVrhe0vv/wyDMOo8lZcXByA6smvujRcp08D3szZcJFu7rkHaN0aOHAAmD1bdjXWlJUFPPecePyvf4mlJ4iIiBSlXMO1ZMkSTJgwAdOmTcOWLVswcOBADB06FPn5+TV+XmFhIW6++WYMGTLE78cjIyNx6NChCm/h4eGBGEJQGIaB2NhYfWZp8TZcGzacfwHkdeuAkhIgLg7o2DHwtdUD7fKwAWmZhIcDc+eKx08+CfzwQ3BfX1H1lsepU8Ctt4rHt98OXHXVxRdnU/y+pR5mohbmoR5dM1Gu4Zo7dy5Gjx6N2267DR07dsS8efOQkJCA5703RlfjjjvuwE033YS+3l/sK/EGVP5NZw6HA7GxsXA4lIvQv06dyhZA/vbbmvf1Xk6YkSFmfNOAdnnYgNRMrr9enJ0tLRVnvKj+8rj/fmDPHiAhAXjiifopzqb4fUs9zEQtzEM9umYSIruA8kpLS7Fp0yZMmTKlwvaMjAysX7++2s9btGgRfvjhByxevBh///vf/e5z8uRJJCUlwe12o2vXrnjkkUfQrVu3ap+zpKQEJSUlvn+7XC4AgNvthvvcGRrDMOBwOODxeCrcvOfd7q50Jqe67Q6HA4Zh+N0OoMpMLN7n2Lt3L5KSkuB0OgEATqcTpmlW2d/pdFapsbrtgRyTo1cvGKtWwVy/Hp7Onasdq2P5chgAPEOGwAEoPSbvdn95VJef6jnVVLtOYyotLcW+fft8mQR9TE89BUe3bjDefx+eTz+FWe7yWDvm5C+POo9p7VoYzzwDAHC/8ALQuDGc0ON7RLVj8rM9WDm53W5fJiEhIZYYU3k65uTNJDk5GQ0aNLDEmM5Xu8pjqpyHFcZUuUbdxuTNpE2bNjAMQ+qYKn+8Jko1XEePHoXb7UaLFi0qbG/RogUOHz7s93N27dqFKVOmIDs7GyEh/oeTkpKCl19+GWlpaXC5XJg/fz769++Pbdu2oX379n4/Z/bs2Zg5c2aV7bm5uWhyburh6OhoJCYmYv/+/Th+/LhvH+8ZtLy8PBQVFfm2JyQkICYmBrt27apw/1ibNm0QGRmJ7du3VwivQ4cOCA0NRU5OToUa0tLSUFJSgvz8fJw8eRKGYcDpdCItLQ1FRUXYs2ePb9/w8HCkpKTgxIkTKCgo8G2PiIhA27ZtceTIkQr/t4EcU2ybNohdtQrmF18gp9KZyLS0NJSWluKH9evReds2AMCO+Hh0ApQekzcnp9NZIY/yY9pZbu0xHXLyjqm6rz1dxvTdd9/h6NGjvkxkjKnNn/6EyJdegvuuu5D7xhtAgwa2zWn37t04dOiQL486j6ltWxi33goDwLHf/Q4FLVvCuX27kl97uuTkcrlw/PhxnDx5EomJiZYYk+45eWdga9y4MeLi4iwxJp1z8uYRGhqKpKQkS4xJ95xM00RRURHatGkjfUwn6zAxllILHx88eBCtWrXC+vXrK1wa+Oijj+K1117Djh07KuzvdrvRp08fjB49GmPGjAEAzJgxA++99x62bt1a7et4PB5cfvnlGDRoEJ4599fSyvyd4UpISMDx48d9i5vJPsOVk5OD1NRUbc5w4ZNP4Bw2DGb79vB8953fsZqvvw5HZibMyy6DZ9Mm9ceE6vPgX6bkn+HKzc31ZSJlTIWFcKSkAD/9BM9TT8EcP/6ixqRzTv7yqNOY7rkHxvz5MOPj4dm2DYiKkj4m3XNyu92+THiGS40xeTPp3Lkzz3ApMKbKeVhhTJVr1G1M3ky6dOki/QyXy+VCdHR0rRY+VuoMV7NmzeB0OquczTpy5EiVs16AOPPx9ddfY8uWLbjzzjsBwPefHBISguXLl+NKP2uzOBwO9OzZE7t27aq2lrCwMISFhVXZ7nQ6fb9Ql38+fyrvV5/bDcPwndkq/3Hvtsqqq7Gu2y+q9v79RY27dsF54gTQrFmV/Y1Vq8T7q6/2fa7SYzqnujxq2l/1MdV1u4pj8pdJUMcUHQ3MmgX85S9wzJwJjBoFNG9+QbXU13aZOV1wHp9/Dpz745jx4otwRkdX2FfFr72L3R6sMXlfx7ufFcYUzO2BGJP3l8D6qrGu25lT9XlYZUy12a7ymLxXEskeU3Uf91tTrfcMgtDQUHTv3h0rvJMmnLNixQr069evyv6RkZHIycnB1q1bfW9jxoxBhw4dsHXrVvTu3dvv65imia1bt6Jly5YBGUcwGIaBhIQE3xedFi65BEhJEY/9LYBsmtquv6VlHhanTCZ//jNw+eWAywVMmya3FokuOI/Tp8X/oWmK99deG5gCbUiZY4R8mIlamId6dM1EqTNcADBx4kRkZmaiR48e6Nu3LxYuXIj8/HzfJYNTp07FgQMH8Oqrr8LhcKBzpckXmjdvjvDw8ArbZ86ciT59+qB9+/ZwuVx45plnsHXrVjznXcdFQw6HAzExMbLLqLu+fYEdO8R6XL/+dcWP7dgh1i4KCwMGDpRT3wXSNg8LUyYTp1OcnRkwAHjpJWDMGKB7d9lVBd0F5/HAA8CuXWKZCO90+1QvlDlGyIeZqIV5qEfXTJQ6wwUAI0eOxLx58/Dwww+ja9euWLt2LT7++GMkJSUBAA4dOnTeNbkq+/nnn3H77bejY8eOyMjIwIEDB7B27Vr06tUrEEMICrfbjR07dtRphhQl1LQAsvfs1sCBQMOGwaupHmibh4UplUn//sAf/yjO0tx1l3hvMxeUx/r1ZU3WwoVA06YBqc2ulDpGCAAzUQ3zUI+umSjXcAHA3/72N+Tl5aGkpASbNm3CoEGDfB97+eWXsWbNmmo/d8aMGVUmzHj66aexb98+lJSU4MiRI1i2bFm163XppPyMKtoovwDy2bMVP7Z8uXiv2eWEXlrmYXFKZTJnDtC4sfhjw+uvy65Gijrlcfq0WODYNIFbbgGuuy5whdmYUscIAWAmqmEe6tExEyUbLrKwTp2AyEjg1KmKCyCXlgLeRlrThouoRq1ald3Dde+9QLmpacmPGTOAnTuBli2Bp5+WXQ0REdEFY8NFweVwAN7JTMpfVvjll6IJu/RS4LLL5NRGFGh33w20bQscOiRmLyT/vvoKePJJ8fiFF8SEO0RERJpiw6Uph8OBNm3aVDv1pdL83cflvX9ryBDRlGlG6zwsSslMwsPL7kmaOxfYvVtuPUFU6zyKi8VshB6PmEZ/2LDgFGhDSh4jNsdM1MI81KNrJnpVSz6GYSAyMlK7aTEB1NxwaXo5odZ5WJSymQwbBlxzjbiM9p57ZFcTNLXOY+ZM4LvvgBYtgPnzg1OcTSl7jNgYM1EL81CPrpmw4dKU2+1GTk6OdrO0ACi7pHD3buCnn4ATJ4CNG8U2TRsurfOwKGUzMQxg3jwgJAT44ANg2TLZFQVFrfLYuBF4/HHx+IUXxMLRFDDKHiM2xkzUwjzUo2smbLg0ptsXm88llwAdO4rHX3wBfPaZuHwoJQVISJBb20XQNg8LUzaTlBRg3DjxePx4cbbLBmrMo6QE+NOfxPeCG28Err8+aHXZmbLHiI0xE7UwD/XomAkbLpKj/GWFml9OSHRBHnwQaN5czMT37LOyq5HvkUeA7dvF/8kzz8iuhoiIqN6w4SI52HCR3UVFAbNni8czZwI//ii3Hpk2bQIee0w8fv55oFkzufUQERHVI8M0TVN2ETpwuVyIiopCYWEhIiMjZZcD0zRRXFyM8PBw7W4cBADk5gKdO4sZCT0e8f7YMaBpU9mVXRDt87AgLTLxeMQ9jV9/LRb5fekl2RUFTLV5lJYC3buLdflGjgTeektekTajxTFiM8xELcxDPSplUpfegGe4NBYaGiq7hAv33Xdi8gCPR/zb4wHS0oClS+XWdRG0zsOilM/E4Si7fG7RorLJYyzKbx5//7toti69FPjHP4JflM0pf4zYEDNRC/NQj46ZsOHSlMfjQU5ODjzehkUnS5cCI0YAlU+uHjgA3HCDlk2X1nlYlDaZ9O0LZGaK42HcuLI/QliM3zy2bClbAHrBAtF0UdBoc4zYCDNRC/NQj66ZsOGi4HK7xaxs/q5k9W6bMEHsR2QXjz0GNGkCfPkl8PrrsqsJjtJSscCx2y3+0HLDDbIrIiIiCgg2XBRc2dnA/v3Vf9w0gYICsR+RXcTFAdOni8f33gsUFcmtJxhmzwa2bQNiYoDnnpNdDRERUcCw4aLgOnSofvcjsooJE4B27YDDh4FHH5VdTWBt2ybu3QJEs9W8udx6iIiIAoizFNaSirMUejweOBwO6bO01MmaNcAVV5x/v9WrgfT0QFdTb7TNw8K0zOTDD4Fhw4AGDcRMnu3by66ofrjdMNeuhXnwIIzmzWFMniyaruHDgbffFhPoUNBpeYxYHDNRC/NQj0qZcJZCmygtLZVdQt0NHAjEx1f/C5ZhAAkJYj/NaJmHxWmXyXXXAUOHAmfOABMnyq6mfixdCiQnw7jySjhGjYKRkSGarSZNxEQZ/CVGKu2OERtgJmphHurRMRM2XJryeDzYuXOndrO0wOkE5s8Xjyv/ouX997x5Yj+NaJuHhWmZiWEATz8NhISIs12ffCK7oouzdKmYDMPffZsnTwKffx78mshHy2PE4piJWpiHenTNhA0XBZ/3MqJWrSpuj48X24cPl1MXkQo6dBD3cwFiRs8VK4A33xSX4+o0e2dNM5ICornkjKRERGQDIbILIJsaPhy4/noxG+GhQ0DLluIyQs3ObBEFxAMPAC++COzaBWRklG2PjxdniIP1RwnTBEpKxKyJLlfd3vbvr/2MpBrdr0lERFRXbLg05tS9OXE6LfWLlvZ5WJC2maxcCRQWVt3uXRz8fGeCPR7g1KmqTdCFNE5nzgRunABnJJVM22PEwpiJWpiHenTMhLMU1pJqsxQSkUW53UBycs1nh5o0EY3XyZP+m6Siouov5btQTZoAkZEV3yIiqm7zvu3bB9x///mfV7MZSYmIiIC69QY8w6Up0zRRVFSEiIgI6dNiEvNQkbaZnG9xcEA0Wi+/fP7ncjqBqKiaG6PaNFFNmtT9cl+3W8xCeOCA/+bPMMQlkhrOSGoV2h4jFsZM1MI81KNrJmy4NOXxeLBnzx6kpaVpeWrVapiHerTNpLaX2I0cKZqVmpqn8HB50657ZyS94QZRQ/mmS+MZSa1E22PEwpiJWpiHenTNhA0XEZFKWras3X5jxqh/KZ53RtLx4yuetYuPF80WZyQlIiIbYMNFRKQS7+LgVrkU79yMpO41a1CwYQMSevWCMz2dZ7aIiMg2uA6XxsLDw2WXQOUwD/VomYkVFwc/NyNp8e9+J87K6VS7xWl5jFgcM1EL81CPjplwlsJa4iyFRBRUS5dWvRQvIYGX4hERESmgLr0Bz3BpyuPx4NixY/B4PLJLITAPFWmfyfDhQF6emDb9jTfE+717tW22tM/DgpiJepiJWpiHenTNhA2XpkzTREFBAXiCUg3MQz2WyMS7OPiNN2p/KZ4l8rAYZqIeZqIW5qEeXTNhw0VERERERBQgbLiIiIiIiIgChA2XxiIiImSXQOUwD/UwE7UwD/UwE/UwE7UwD/XomAlnKawlzlJIREREREQAZym0BY/Hg8OHD2s3S4tVMQ/1MBO1MA/1MBP1MBO1MA/16JoJGy5NmaaJw4cPazdLi1UxD/UwE7UwD/UwE/UwE7UwD/XomgkbLiIiIiIiogBhw0VERERERBQgbLg0ZRgGoqOjYRiG7FIIzENFzEQtzEM9zEQ9zEQtzEM9umbCWQpribMUEhERERERwFkKbcHj8SA/P1+7WVqsinmoh5mohXmoh5moh5mohXmoR9dM2HBpyjRNHD9+XLtZWqyKeaiHmaiFeaiHmaiHmaiFeahH10zYcBEREREREQVIiOwCdOHtpF0ul+RKBLfbjZMnT8LlcsHpdMoux/aYh3qYiVqYh3qYiXqYiVqYh3pUysTbE9TmbBsbrloqKioCACQkJEiuhIiIiIiIVFBUVISoqKga9+EshbXk8Xhw8OBBREREKDEVpcvlQkJCAgoKCjhrogKYh3qYiVqYh3qYiXqYiVqYh3pUysQ0TRQVFSEuLg4OR813afEMVy05HA7Ex8fLLqOKyMhI6V9wVIZ5qIeZqIV5qIeZqIeZqIV5qEeVTM53ZsuLk2YQEREREREFCBsuIiIiIiKiAGHDpamwsDA89NBDCAsLk10KgXmoiJmohXmoh5moh5mohXmoR9dMOGkGERERERFRgPAMFxERERERUYCw4SIiIiIiIgoQNlxEREREREQBwoaLiIiIiIgoQNhwaWjBggVo3bo1wsPD0b17d2RnZ8suybZmz56Nnj17IiIiAs2bN8dvf/tb7Ny5U3ZZdM7s2bNhGAYmTJgguxRbO3DgAEaNGoWYmBg0atQIXbt2xaZNm2SXZVtnz57F9OnT0bp1azRs2BBt2rTBww8/DI/HI7s0W1i7di2GDRuGuLg4GIaB9957r8LHTdPEjBkzEBcXh4YNGyI9PR25ublyirWJmjI5c+YM7rvvPqSlpaFx48aIi4vDzTffjIMHD8or2AbOd5yUd8cdd8AwDMybNy9o9dUVGy7NLFmyBBMmTMC0adOwZcsWDBw4EEOHDkV+fr7s0mwpKysLY8eOxZdffokVK1bg7NmzyMjIwKlTp2SXZnsbN27EwoUL0aVLF9ml2NqJEyfQv39/NGjQAJ988gm2b9+Op556Ck2bNpVdmm3NmTMH//znP/Hss8/iu+++w+OPP44nnngC//jHP2SXZgunTp3CZZddhmeffdbvxx9//HHMnTsXzz77LDZu3IjY2FhcffXVKCoqCnKl9lFTJr/88gs2b96MBx54AJs3b8bSpUvx/fff4ze/+Y2ESu3jfMeJ13vvvYevvvoKcXFxQarsApmklV69epljxoypsC0lJcWcMmWKpIqovCNHjpgAzKysLNml2FpRUZHZvn17c8WKFebgwYPN8ePHyy7Jtu677z5zwIABssugcq677jrz1ltvrbBt+PDh5qhRoyRVZF8AzHfffdf3b4/HY8bGxpqPPfaYb1txcbEZFRVl/vOf/5RQof1UzsSfDRs2mADMffv2Bacom6suk/3795utWrUyv/32WzMpKcl8+umng15bbfEMl0ZKS0uxadMmZGRkVNiekZGB9evXS6qKyissLAQAREdHS67E3saOHYvrrrsOV111lexSbO+DDz5Ajx498H//939o3rw5unXrhhdffFF2WbY2YMAArFq1Ct9//z0AYNu2bVi3bh1+9atfSa6M9u7di8OHD1f4OR8WFobBgwfz57xCCgsLYRgGz9RL5PF4kJmZicmTJyM1NVV2OecVIrsAqr2jR4/C7XajRYsWFba3aNEChw8fllQVeZmmiYkTJ2LAgAHo3Lmz7HJs66233sLmzZuxceNG2aUQgD179uD555/HxIkTcf/992PDhg0YN24cwsLCcPPNN8suz5buu+8+FBYWIiUlBU6nE263G48++ihuvPFG2aXZnvdnub+f8/v27ZNRElVSXFyMKVOm4KabbkJkZKTscmxrzpw5CAkJwbhx42SXUitsuDRkGEaFf5umWWUbBd+dd96Jb775BuvWrZNdim0VFBRg/PjxWL58OcLDw2WXQxB/hezRowdmzZoFAOjWrRtyc3Px/PPPs+GSZMmSJVi8eDHeeOMNpKamYuvWrZgwYQLi4uJwyy23yC6PwJ/zqjpz5gz+8Ic/wOPxYMGCBbLLsa1NmzZh/vz52Lx5szbHBS8p1EizZs3gdDqrnM06cuRIlb+GUXDddddd+OCDD7B69WrEx8fLLse2Nm3ahCNHjqB79+4ICQlBSEgIsrKy8MwzzyAkJARut1t2ibbTsmVLdOrUqcK2jh07cqIfiSZPnowpU6bgD3/4A9LS0pCZmYm7774bs2fPll2a7cXGxgIAf84r6MyZMxgxYgT27t2LFStW8OyWRNnZ2Thy5AgSExN9P+v37duHe+65B8nJybLL84sNl0ZCQ0PRvXt3rFixosL2FStWoF+/fpKqsjfTNHHnnXdi6dKl+Oyzz9C6dWvZJdnakCFDkJOTg61bt/reevTogT/+8Y/YunUrnE6n7BJtp3///lWWSvj++++RlJQkqSL65Zdf4HBU/PHvdDo5LbwCWrdujdjY2Ao/50tLS5GVlcWf8xJ5m61du3Zh5cqViImJkV2SrWVmZuKbb76p8LM+Li4OkydPxrJly2SX5xcvKdTMxIkTkZmZiR49eqBv375YuHAh8vPzMWbMGNml2dLYsWPxxhtv4P3330dERITvr5JRUVFo2LCh5OrsJyIiosr9c40bN0ZMTAzvq5Pk7rvvRr9+/TBr1iyMGDECGzZswMKFC7Fw4ULZpdnWsGHD8OijjyIxMRGpqanYsmUL5s6di1tvvVV2abZw8uRJ7N692/fvvXv3YuvWrYiOjkZiYiImTJiAWbNmoX379mjfvj1mzZqFRo0a4aabbpJYtbXVlElcXBxuuOEGbN68GR9++CHcbrfvZ310dDRCQ0NllW1p5ztOKje9DRo0QGxsLDp06BDsUmtH7iSJdCGee+45MykpyQwNDTUvv/xyTkEuEQC/b4sWLZJdGp3DaeHl+9///md27tzZDAsLM1NSUsyFCxfKLsnWXC6XOX78eDMxMdEMDw8327RpY06bNs0sKSmRXZotrF692u/PjVtuucU0TTE1/EMPPWTGxsaaYWFh5qBBg8ycnBy5RVtcTZns3bu32p/1q1evll26ZZ3vOKlM9WnhDdM0zSD1dkRERERERLbCe7iIiIiIiIgChA0XERERERFRgLDhIiIiIiIiChA2XERERERERAHChouIiIiIiChA2HAREREREREFCBsuIiIiIiKiAGHDRUREREREFCBsuIiIiCRKTk5GcnKy7DKIiChA2HAREZH28vLyYBhGjW9du3aVXSYREdlQiOwCiIiI6kvbtm0xatQovx+LjY0NcjVERERsuIiIyELatWuHGTNmyC6DiIjIh5cUEhGR7RiGgfT0dBQUFGDkyJGIiYlB48aNkZ6ejvXr1/v9nGPHjuHuu+9G69atERYWhubNm2PkyJHYvn273/1LS0sxf/589OrVCxEREWjSpAk6deqEiRMn4sSJE1X2P3XqFCZOnIhWrVohLCwMXbp0wdtvv12v4yYiouAzTNM0ZRdBRER0MfLy8tC6dWtcc801+PTTT8+7v2EY6NKlC06cOIGWLVviyiuvxIEDB7BkyRIAwLJly5Cenu7b/9ixY+jTpw92796N9PR09OnTB3l5eXj77bcRFhaGFStWoG/fvr79i4uLcc0112Dt2rVo3749rr32WoSFhWHXrl1Yvnw51q9f77unLDk5GWfOnEFycjKOHz+Oq666Cr/88gveeustnD59Gp9++ikyMjLq9f+LiIiChw0XERFpz9tw1XQPV58+fXDttdcCEA0XAGRmZuKVV17x/TsrKwtXXHEF2rZti507d8LhEBeCjB49Gv/+978xdepUzJo1y/ecy5Ytw7XXXov27dtjx44dvv3vvfdePPHEE8jMzMSiRYvgdDp9n1NYWAin04kmTZoAEA3Xvn37cP311+M///kPQkNDAQCrVq3CVVddVesmkoiI1MSGi4iItOdtuGoyfvx4zJs3D4BouJxOJ/bu3YuEhIQK+/3617/GRx99hOzsbAwYMAClpaVo2rQpGjVqhPz8fDRq1KjC/tdeey2WLVvm29/tdiM6OhqGYWDv3r245JJLaqzL23Dt2bOnyhiSk5NRVFSEY8eO1fJ/goiIVMN7uIiIyDKuueYamKbp983bbHklJSVVabYAYODAgQCArVu3AgB27NiB06dPo1evXlWaLQC+Sw/L7+9yudCzZ8/zNlteTZs29dswxsfH4+eff67VcxARkZrYcBERkS01b97c7/YWLVoAEJf+AYDL5aqwvTLvdPPe/b0NUqtWrWpdS1RUlN/tISEh8Hg8tX4eIiJSDxsuIiKypSNHjvjd/uOPPwIoa4IiIyMrbK9uf+9+TZs2BQAcOHCg3molIiJ9seEiIiJb2rdvHwoKCqpsz87OBgDfLIIpKSkIDw/Hxo0b8csvv1TZPysrq8L+HTp0QGRkJDZu3Oh3+nciIrIXNlxERGRLbrcb06ZNQ/m5o7KysvDxxx+jXbt26NevHwAgNDQUN954I44ePYrZs2dXeI6VK1fik08+Qbt27dC/f38A4jLAO+64A4WFhRg/fjzcbneFzyksLMTJkycDPDoiIlIFZykkIiLt1WZaeACYMWMGAP/rcB08eBBvvfUWgKrrcP3000/o06cP9uzZgyuvvBK9e/f2rcPVoEEDLFu2DAMGDPDtX1xcjIyMDGRnZ6N9+/YYOnQowsLCsGfPHnz66adYt25dhXW4vGOoLD09HVlZWeCPaiIifbHhIiIi7dVmWngAvsbFMAwMHjwYr776KiZNmoSVK1eiuLgYPXv2xKxZs3xnq8o7evQoHnnkEbz//vs4ePAgoqKikJ6ejoceegidO3eusn9JSQmeffZZLF68GDt37oTT6URiYiKGDh2K6dOn++71YsNFRGRtbLiIiMh2vA3XmjVrZJdCREQWx3u4iIiIiIiIAoQNFxERERERUYCw4SIiIiIiIgqQENkFEBERBRtvXyYiomDhGS4iIiIiIqIAYcNFREREREQUIGy4iIiIiIiIAoQNFxERERERUYCw4SIiIiIiIgoQNlxEREREREQBwoaLiIiIiIgoQNhwERERERERBcj/BzqmuTfLpRPVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_validation_loss(history_tab.history, latent_dim, merged_df, feat_type, model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "73d6754f-8621-4969-9880-326a15603759",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_validation_accuracy(history_tab.history, latent_dim, merged_df, feat_type, model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86052258-38b9-425a-a001-bde07cc19e95",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}