models_without_spatial lag.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "12f2e921-3255-407c-8e12-9974cdbff0b8",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "- [Loading cleaned dataset](#section1)\n",
    "- [(Normal) Splitting dataset (train, val, test)](#section2)\n",
    "- [(Spatial) Splitting dataset (train, val, test)](#section3)\n",
    "- [Creating new features based on pca](#section4)\n",
    "- [Encoding categorical features + target feature ](#section5)\n",
    "- [Feature Selection](#section6)\n",
    "- [Baseline_model (KNN)](#section7)\n",
    "- [Baseline_model Random_forest](#section8)\n",
    "- [Baseline_model eXtreme Gradient Boosting (xgb)](#section9)\n",
    "- [Baseline_nodel TabNet](#section10)\n",
    "- [Prediction based on test data](#section11)\n",
    "- [Classification_report + metrics](#section12)\n",
    "- [Feature_Importance_RF+XGB_TabNet](#section13)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "60f42bdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "%matplotlib inline\n",
    "import torch\n",
    "import joblib\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score, balanced_accuracy_score,confusion_matrix, classification_report\n",
    "np.random.seed(10)\n",
    "from xgboost import XGBClassifier\n",
    "import geopandas as gpd\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "774d3725",
   "metadata": {},
   "source": [
    "<a id='section1'></a>\n",
    "# Loading cleaned dataset \n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4c491bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164364, 35)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "df = pd.read_csv(\"df_no_correlation.csv\")\n",
    "df.head(2)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41aced28",
   "metadata": {},
   "source": [
    "<a id='section2'></a>\n",
    "# (Normal) Splitting dataset (train, val, test)\n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49d564e1-3c4b-4cf8-8882-97cf2155ad4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split dataset into train,test \n",
    "# Extract features and target variable\n",
    "X = df.drop(columns=['TransportMode'])\n",
    "y = df['TransportMode']\n",
    "\n",
    "#  Extract groups ('UniqueIdentifier')\n",
    "groups = X['UniqueIdentifier']\n",
    "\n",
    "# Stratified Group K-Fold\n",
    "sgkf = StratifiedGroupKFold(n_splits=3, shuffle=True, random_state=0) \n",
    "\n",
    "# Stratified Group K-Fold splitting\n",
    "for train_index, test_index in sgkf.split(X, y, groups):\n",
    "    X_train_random, X_test_random = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train_random, y_test_random = y.iloc[train_index], y.iloc[test_index]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c4d99e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split training into train, val\n",
    "# Extract groups ('UniqueIdentifier')\n",
    "groups = X_train_random['UniqueIdentifier']\n",
    "\n",
    "# Stratified Group K-Fold\n",
    "sgkf = StratifiedGroupKFold(n_splits=3, shuffle=True, random_state=0) \n",
    "\n",
    "# Stratified Group K-Fold splitting\n",
    "for train_index, val_index in sgkf.split(X_train_random, y_train_random, groups):\n",
    "    X_train_fold_random, X_val_fold_random = X_train_random.iloc[train_index], X_train_random.iloc[val_index]\n",
    "    y_train_fold_random, y_val_fold_random = y_train_random.iloc[train_index], y_train_random.iloc[val_index]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "777e9258-83b4-45fa-a84a-9dec12c15c2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55012, 34)\n",
      "(72653, 34)\n",
      "(36699, 34)\n"
     ]
    }
   ],
   "source": [
    "print(X_test_random.shape)\n",
    "print(X_train_fold_random.shape)\n",
    "print(X_val_fold_random.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3022e0ad-b333-4a64-9420-662877e95b72",
   "metadata": {},
   "source": [
    "<a id='section3'></a>\n",
    "# (Spatial) Splitting dataset (train, val, test)\n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e81e412b-5cac-4e74-844b-c1cfb41ee0d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split dataset into train,val\n",
    "# Extract groups 'Residential Municipality'\n",
    "# features and target variable\n",
    "X = df.drop(columns=['TransportMode'])\n",
    "y = df['TransportMode']\n",
    "\n",
    "# Extract groups 'Residential Municipality'\n",
    "groups = X['Residential Municipality']\n",
    "\n",
    "# Stratified Group K-Fold for train/test split\n",
    "sgkf = StratifiedGroupKFold(n_splits=4, shuffle=True, random_state=0) \n",
    "\n",
    "# Stratified Group K-Fold splitting for train/test split\n",
    "for train_index, test_index in sgkf.split(X, y, groups):\n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a8251fb1-1bc8-4c7f-a6f1-2ae4d2063955",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split training into train, test\n",
    "# Extract groups 'Residential Municipality'\n",
    "groups_train = X_train['Residential Municipality']\n",
    "\n",
    "# Stratified Group K-Fold for train/test split\n",
    "sgkf_test = StratifiedGroupKFold(n_splits=4, shuffle=True, random_state=0)\n",
    "\n",
    "# Stratified Group K-Fold splitting for train/test split\n",
    "for train_index, val_index in sgkf_test.split(X_train, y_train, groups_train):\n",
    "    X_train_split, X_val = X_train.iloc[train_index], X_train.iloc[val_index]\n",
    "    y_train_split, y_val = y_train.iloc[train_index], y_train.iloc[val_index]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "460d8fe9-a465-4bdb-a716-697e4fea8276",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34282, 34)\n",
      "(95473, 34)\n",
      "(34609, 34)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)\n",
    "print(X_train_split.shape)\n",
    "print(X_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "247095a8-bf68-43b2-ac5f-b3e5054eef3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set percentage: 58.08632060548538 %\n",
      "Validation set percentage: 21.056314034703462 %\n",
      "Test set percentage: 20.85736535981115 %\n"
     ]
    }
   ],
   "source": [
    "training_percentage = str((X_train_split.shape[0] /df.shape[0]) * 100) + \" %\"\n",
    "validation_percentage = str((X_val.shape[0] / df.shape[0]) * 100) + \" %\"\n",
    "test_percentage = str((X_test.shape[0] / df.shape[0]) * 100) + \" %\"\n",
    "\n",
    "print(\"Training set percentage:\", training_percentage)\n",
    "print(\"Validation set percentage:\", validation_percentage)\n",
    "print(\"Test set percentage:\", test_percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b990d68-6cda-449c-b56a-83900968b03e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1100x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization: spatial split vs random split\n",
    "plt.figure(figsize=(11, 5))\n",
    "plt.subplot(121)\n",
    "\n",
    "plt.scatter(X_train_split['LongitudeLiving'], X_train_split['LatitudeLiving'], color='blue', label='Training Set', alpha=0.3)\n",
    "plt.scatter(X_val['LongitudeLiving'], X_val['LatitudeLiving'], color='orange', label='Validation Set',alpha=0.3)\n",
    "plt.scatter(X_test['LongitudeLiving'], X_test['LatitudeLiving'], color='gray', label='Test Set',alpha=0.3)\n",
    "\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Spatial train_val_test split')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.scatter(X_train_fold_random['LongitudeLiving'], X_train_fold_random['LatitudeLiving'], color='blue', label='Training Set',alpha=0.3)\n",
    "plt.scatter(X_val_fold_random['LongitudeLiving'], X_val_fold_random['LatitudeLiving'], color='orange', label='Validation Set',alpha=0.3)\n",
    "plt.scatter(X_test_random['LongitudeLiving'], X_test_random['LatitudeLiving'], color='gray', label='Test Set',alpha=0.09)\n",
    "\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Normal train_val_test split')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "plt.savefig('train_val_test_split', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b43ca78-389b-4d47-a540-e930caa336af",
   "metadata": {},
   "source": [
    "<a id='section4'></a>\n",
    "# Creating new features based on pca\n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b98a3e0f-cbc6-47f2-b85b-a470c63a1332",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "\n",
    "scaler = RobustScaler()\n",
    "X_train_split_scaled = scaler.fit_transform(X_train_split[['Duration_by_foot', 'Duration_by_cycling', 'Duration_by_driving']])\n",
    "X_val_scaled = scaler.transform(X_val[['Duration_by_foot', 'Duration_by_cycling', 'Duration_by_driving']])\n",
    "X_test_scaled = scaler.transform(X_test[['Duration_by_foot', 'Duration_by_cycling', 'Duration_by_driving']])\n",
    "\n",
    "\n",
    "pca = PCA()\n",
    "pca.fit(X_train_split_scaled)  \n",
    "\n",
    "\n",
    "X_train_split_pca = pca.transform(X_train_split_scaled)\n",
    "X_val_pca = pca.transform(X_val_scaled)\n",
    "X_test_pca = pca.transform(X_test_scaled)\n",
    "\n",
    "\n",
    "for i in range(pca.n_components_):\n",
    "    X_train_split[f'PCA_dr_wl_cl_duration_{i+1}'] = X_train_split_pca[:, i]\n",
    "    X_val[f'PCA_dr_wl_cl_duration_{i+1}'] = X_val_pca[:, i]\n",
    "    X_test[f'PCA_dr_wl_cl_duration_{i+1}'] = X_test_pca[:, i]\n",
    "\n",
    "\n",
    "X_train_split.drop(['Duration_by_foot', 'Duration_by_cycling', 'Duration_by_driving'], inplace=True, axis=1)\n",
    "X_val.drop(['Duration_by_foot', 'Duration_by_cycling', 'Duration_by_driving'], inplace=True, axis=1)\n",
    "X_test.drop(['Duration_by_foot', 'Duration_by_cycling', 'Duration_by_driving'], inplace=True, axis=1)\n",
    "\n",
    "\n",
    "X_test.to_csv(\"X_test.csv\")\n",
    "X_train_split.to_csv(\"X_train_split.csv\")\n",
    "X_val.to_csv(\"X_val.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f514557a-878b-459a-9e60-05eb5acee3b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99312836, 0.00490974, 0.00196189])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "41984767-d231-46fd-bdf9-a3a3a64ee63d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# part of this code is from: https://sanchitamangale12.medium.com/scree-plot-733ed72c8608\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.bar(range(1, len(explained_variance) + 1), explained_variance)\n",
    "plt.title('Explained Variance vs. Number of Principal Components')\n",
    "plt.xlabel('Number of Principal Components')\n",
    "plt.ylabel('Explained Variance')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('pca', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ded75d70-8c33-48af-8b10-bcb92a28e74e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save it for later use\n",
    "X_train_region = X_train_split[[\"Residential Municipality\", \"ResidentialProvince\"]]\n",
    "X_test_region = X_test[[\"Residential Municipality\", \"ResidentialProvince\"]]\n",
    "X_val_region= X_val[[\"Residential Municipality\", \"ResidentialProvince\"]]\n",
    "\n",
    "\n",
    "X_train_region.to_csv('X_train_region.csv', index=False)\n",
    "X_test_region.to_csv('X_test_region.csv', index=False)\n",
    "X_val_region.to_csv('X_val_region.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "389054ff-b1a7-42ea-9f02-a4f53e45c608",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop columns not needed for the prediction\n",
    "X_test.drop([\"UniqueIdentifier\", \"HomePostalCode\", 'Residential Municipality', \"ResidentialProvince\"], inplace = True,axis = 1)\n",
    "X_train_split.drop([\"UniqueIdentifier\", \"HomePostalCode\",'Residential Municipality', \"ResidentialProvince\"], inplace = True,axis = 1)\n",
    "X_val.drop([\"UniqueIdentifier\", \"HomePostalCode\",'Residential Municipality', \"ResidentialProvince\"], inplace = True,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "48c10be0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34282, 30)\n",
      "(95473, 30)\n",
      "(34609, 30)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)\n",
    "print(X_train_split.shape)\n",
    "print(X_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2856284",
   "metadata": {},
   "source": [
    "<a id='section5'></a>\n",
    "# Encoding categorical features + target feature \n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6e94b1e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# part of this code is from https://medium.com/anolytics/all-you-need-to-know-about-encoding-techniques-b3a0af68338b#:~:text=Ordinal%20encoding%20is%20similar%20to,map%20them%20to%20integers%20accordingly.\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "\n",
    "educ_order = [\"Low\", \"Medium\",\"High\"]\n",
    "educ_encoder = OrdinalEncoder(categories=[educ_order])\n",
    "X_train_split[\"EducationLevel\"] = educ_encoder.fit_transform(X_train_split[[\"EducationLevel\"]]).flatten()\n",
    "X_val[\"EducationLevel\"] = educ_encoder.transform(X_val[[\"EducationLevel\"]]).flatten()\n",
    "X_test[\"EducationLevel\"] = educ_encoder.transform(X_test[[\"EducationLevel\"]]).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "29555a8a-390a-4668-be32-0b80b78a2b25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Daily or almost daily', 'Never or almost never',\n",
       "       'Several times a month', 'Several times a week',\n",
       "       'Several times a year'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df[\"FrequencyOfWalkingOutdoors\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "231ac0f6-c2cc-4d98-886f-53f1b109b7f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ordinal_order = [ 'Never or almost never', 'Several times a year',\n",
    "       'Several times a month', 'Several times a week',\n",
    "       'Daily or almost daily']\n",
    "\n",
    "ourdoor_encoder = OrdinalEncoder(categories=[ordinal_order])\n",
    "X_train_split[\"FrequencyOfWalkingOutdoors\"] = ourdoor_encoder.fit_transform(X_train_split[[\"FrequencyOfWalkingOutdoors\"]]).flatten()\n",
    "X_val[\"FrequencyOfWalkingOutdoors\"] = ourdoor_encoder.transform(X_val[[\"FrequencyOfWalkingOutdoors\"]]).flatten()\n",
    "X_test[\"FrequencyOfWalkingOutdoors\"] = ourdoor_encoder.transform(X_test[[\"FrequencyOfWalkingOutdoors\"]]).flatten()\n",
    "\n",
    "pass_encoder = OrdinalEncoder(categories=[ordinal_order])\n",
    "X_train_split[\"FrequencyOfUseCcarAsAPassenger\"] = pass_encoder.fit_transform(X_train_split[[\"FrequencyOfUseCcarAsAPassenger\"]]).flatten()\n",
    "X_val[\"FrequencyOfUseCcarAsAPassenger\"] = pass_encoder.transform(X_val[[\"FrequencyOfUseCcarAsAPassenger\"]]).flatten()\n",
    "X_test[\"FrequencyOfUseCcarAsAPassenger\"] = pass_encoder.transform(X_test[[\"FrequencyOfUseCcarAsAPassenger\"]]).flatten()\n",
    "\n",
    "non_elec_encoder = OrdinalEncoder(categories=[ordinal_order])\n",
    "X_train_split[\"FrequencyOfUseOfNonEelectricBicycle\"] = non_elec_encoder.fit_transform(X_train_split[[\"FrequencyOfUseOfNonEelectricBicycle\"]]).flatten()\n",
    "X_val[\"FrequencyOfUseOfNonEelectricBicycle\"] = non_elec_encoder.transform(X_val[[\"FrequencyOfUseOfNonEelectricBicycle\"]]).flatten()\n",
    "X_test[\"FrequencyOfUseOfNonEelectricBicycle\"] = non_elec_encoder.transform(X_test[[\"FrequencyOfUseOfNonEelectricBicycle\"]]).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6e45ae08-c4c1-45eb-b0af-75a1eff8c05a",
   "metadata": {},
   "outputs": [],
   "source": [
    "household_order = ['1', '2', '3', '4', '5 or more']\n",
    "household_encoder = OrdinalEncoder(categories=[household_order])\n",
    "X_train_split[\"HouseholdSize\"] = household_encoder.fit_transform(X_train_split[[\"HouseholdSize\"]]).flatten()\n",
    "X_val[\"HouseholdSize\"] = household_encoder.transform(X_val[[\"HouseholdSize\"]]).flatten()\n",
    "X_test[\"HouseholdSize\"] = household_encoder.transform(X_test[[\"HouseholdSize\"]]).flatten()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "704dddf4-6cb5-4773-8362-5cc84a5f1bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_order = ['6 to 11 years', '12 to 14 years', '15 to 17 years', '18 to 19 years', '20 to 24 years', '25 to 29 years',\n",
    "             '30 to 34 years', '35 to 39 years', '40 to 44 years', '45 to 49 years', '50 to 54 years', '55 to 59 years',\n",
    "             '60 to 64 years', '65 to 69 years', '70 to 74 years', '75 to 79 years', '80 years or older']\n",
    "\n",
    "age_encoder = OrdinalEncoder(categories=[age_order])\n",
    "\n",
    "X_train_split[\"AgeClass\"] = age_encoder.fit_transform(X_train_split[[\"AgeClass\"]]).flatten()\n",
    "X_val[\"AgeClass\"] = age_encoder.transform(X_val[[\"AgeClass\"]]).flatten()\n",
    "X_test[\"AgeClass\"] = age_encoder.transform(X_test[[\"AgeClass\"]]).flatten()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "10b8a431",
   "metadata": {},
   "outputs": [],
   "source": [
    "cars_order = ['0', '1', '2', '3', '4 or more']\n",
    "cars_encoder = OrdinalEncoder(categories=[cars_order])\n",
    "X_train_split[\"NumberOfCarsInHousehold\"] = cars_encoder.fit_transform(X_train_split[[\"NumberOfCarsInHousehold\"]]).flatten()\n",
    "X_val[\"NumberOfCarsInHousehold\"] = cars_encoder.transform(X_val[[\"NumberOfCarsInHousehold\"]]).flatten()\n",
    "X_test[\"NumberOfCarsInHousehold\"] = cars_encoder.transform(X_test[[\"NumberOfCarsInHousehold\"]]).flatten()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4fc343ff-6ea1-4c2e-8ad0-316df5142a0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "income_order = ['First 10% group','Second 10% group','Third 10% group','Fourth 10% group','Fifth 10% group',\n",
    "               'Sixth 10% group','Seventh 10% group','Eighth 10% group','Ninth 10% group','Tenth 10% group',]\n",
    "\n",
    "income_encoder = OrdinalEncoder(categories=[income_order])\n",
    "\n",
    "X_train_split[\"DisposableIncome\"] = income_encoder.fit_transform(X_train_split[[\"DisposableIncome\"]]).flatten()\n",
    "X_val[\"DisposableIncome\"] = income_encoder.transform(X_val[[\"DisposableIncome\"]]).flatten()\n",
    "X_test[\"DisposableIncome\"] = income_encoder.transform(X_test[[\"DisposableIncome\"]]).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "525e34f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert gender into binary \n",
    "X_train_split[\"Weekday\"].replace({\"weekday\": 1, \"weekend\": 0}, inplace=True)\n",
    "X_test[\"Weekday\"].replace({\"weekday\": 1, \"weekend\": 0}, inplace=True)\n",
    "X_val[\"Weekday\"].replace({\"weekday\": 1, \"weekend\": 0}, inplace=True)\n",
    "\n",
    "\n",
    "X_train_split[\"Gender\"].replace({\"Female\": 0, \"Male\": 1}, inplace=True)\n",
    "X_test[\"Gender\"].replace({\"Female\": 0, \"Male\": 1}, inplace=True)\n",
    "X_val[\"Gender\"].replace({\"Female\": 0, \"Male\": 1}, inplace=True)\n",
    "\n",
    "X_train_split[\"License\"].replace({\"Yes\": 1, \"No\": 0}, inplace=True)\n",
    "X_test[\"License\"].replace({\"Yes\": 1, \"No\": 0}, inplace=True)\n",
    "X_val[\"License\"].replace({\"Yes\": 1, \"No\": 0}, inplace=True)\n",
    "\n",
    "\n",
    "X_train_split[\"ElectriBicycleIHousehold\"].replace({\"Yes\": 1, \"No\": 0}, inplace=True)\n",
    "X_test[\"ElectriBicycleIHousehold\"].replace({\"Yes\": 1, \"No\": 0}, inplace=True)\n",
    "X_val[\"ElectriBicycleIHousehold\"].replace({\"Yes\": 1, \"No\": 0}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f3a41a8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ordinal_features = [\"AgeClass\",\"HouseholdSize\", \"DisposableIncome\",\"FrequencyOfUseOfNonEelectricBicycle\", \"FrequencyOfWalkingOutdoors\",\n",
    "                    \"NumberOfCarsInHousehold\",  \"EducationLevel\",\"FrequencyOfUseCcarAsAPassenger\"]\n",
    "numeric_features = [ \"TotalTravelDuration\", \"DepartureTime\",\"NumberOfTripLegs\"\n",
    "                    , \"NumBusStops\", 'RoadNetworkDensity','AvgDistToTrainStation',\"TripLegDistance\",\"N.OFHouseholdMemberUnd6\",\n",
    "    'AvgDistToSupermarket', 'NofBusinessEstablishments', 'SurroundingAddressDensity', \"LatitudeLiving\", \"LongitudeLiving\"]\n",
    "categorical_features = [\"TripPurpose\",\"WorkCommuteReimbursement\", ]\n",
    "binary_features = [\"Gender\",\"License\", \"Weekday\", \"ElectriBicycleIHousehold\"]\n",
    "pca_features = [\"PCA_dr_wl_cl_duration_2\",\"PCA_dr_wl_cl_duration_1\", \"PCA_dr_wl_cl_duration_3\"]\n",
    "len(ordinal_features) + len(numeric_features) + len(categorical_features) + len(binary_features) + len(pca_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ae78b713-9218-4b0a-bb54-69e78e695386",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler, OneHotEncoder\n",
    "\n",
    "# Define all_features variable\n",
    "all_features = ordinal_features + numeric_features + categorical_features + binary_features + pca_features \n",
    "\n",
    "# Scale numerical features only (excluding ordinal features)\n",
    "scaler = RobustScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train_split[numeric_features])\n",
    "X_val_scaled = scaler.transform(X_val[numeric_features])\n",
    "X_test_scaled = scaler.transform(X_test[numeric_features])\n",
    "\n",
    "# One-hot encoding for categorical features\n",
    "encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
    "encoder.fit(X_train_split[categorical_features])\n",
    "one_hot_feature_names = encoder.get_feature_names_out(input_features=categorical_features)\n",
    "\n",
    "X_train_cat_encoded = encoder.transform(X_train_split[categorical_features])\n",
    "X_val_cat_encoded = encoder.transform(X_val[categorical_features])\n",
    "X_test_cat_encoded = encoder.transform(X_test[categorical_features])\n",
    "\n",
    "# Concatenate encoded categorical features with binary features\n",
    "X_train_encoded = np.concatenate([X_train_cat_encoded, X_train_split[binary_features]], axis=1)\n",
    "X_val_encoded = np.concatenate([X_val_cat_encoded, X_val[binary_features]], axis=1)\n",
    "X_test_encoded = np.concatenate([X_test_cat_encoded, X_test[binary_features]], axis=1)\n",
    "\n",
    "# Concatenate scaled numerical features with encoded categorical and binary features, and PCA features\n",
    "X_train_final = np.concatenate([X_train_scaled, X_train_encoded, X_train_split[ordinal_features], \n",
    "                                 X_train_split[[\"PCA_dr_wl_cl_duration_2\", \"PCA_dr_wl_cl_duration_1\", \"PCA_dr_wl_cl_duration_3\"]].values], axis=1)\n",
    "\n",
    "X_val_final = np.concatenate([X_val_scaled, X_val_encoded, X_val[ordinal_features], \n",
    "                               X_val[[\"PCA_dr_wl_cl_duration_2\", \"PCA_dr_wl_cl_duration_1\", \"PCA_dr_wl_cl_duration_3\"]].values], axis=1)\n",
    "\n",
    "X_test_final = np.concatenate([X_test_scaled, X_test_encoded, X_test[ordinal_features], \n",
    "                                X_test[[\"PCA_dr_wl_cl_duration_2\", \"PCA_dr_wl_cl_duration_1\", \"PCA_dr_wl_cl_duration_3\"]].values], axis=1)\n",
    "\n",
    "# Combine all feature names\n",
    "all_feature_names = numeric_features + list(one_hot_feature_names) + binary_features + ordinal_features + pca_features \n",
    "\n",
    "# Write feature names to a text file\n",
    "with open(\"all_feature_names.txt\", \"w\") as f:\n",
    "    for s in all_feature_names:\n",
    "        f.write(str(s) + \"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a898ebbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "# encoding target feature\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "# Fit encoder on train set\n",
    "y_train_split = le.fit_transform(y_train_split)\n",
    "\n",
    "# Use fitted encoder to transform the test set\n",
    "y_test = le.transform (y_test)\n",
    "\n",
    "y_val = le.transform (y_val)\n",
    "\n",
    "np.savetxt('y_train_split_encoded.txt', y_train_split, fmt='%d')\n",
    "np.savetxt('y_val_encoded.txt', y_val, fmt='%d')\n",
    "np.savetxt('y_test_encoded.txt', y_test, fmt='%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ea0e3628-2c2e-4f17-83c5-a3aa6b39f35c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "40308c50-25d7-475a-be36-3bbeb08aff11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['TotalTravelDuration',\n",
       " 'DepartureTime',\n",
       " 'NumberOfTripLegs',\n",
       " 'NumBusStops',\n",
       " 'RoadNetworkDensity',\n",
       " 'AvgDistToTrainStation',\n",
       " 'TripLegDistance',\n",
       " 'N.OFHouseholdMemberUnd6',\n",
       " 'AvgDistToSupermarket',\n",
       " 'NofBusinessEstablishments',\n",
       " 'SurroundingAddressDensity',\n",
       " 'LatitudeLiving',\n",
       " 'LongitudeLiving',\n",
       " 'TripPurpose_Leisure Activities',\n",
       " 'TripPurpose_Maintenance Activities',\n",
       " 'TripPurpose_Other motive',\n",
       " 'TripPurpose_Subsistence Activities',\n",
       " 'WorkCommuteReimbursement_No reimbursement',\n",
       " 'WorkCommuteReimbursement_Not applicable',\n",
       " 'WorkCommuteReimbursement_Reimbursement provided',\n",
       " 'Gender',\n",
       " 'License',\n",
       " 'Weekday',\n",
       " 'ElectriBicycleIHousehold',\n",
       " 'AgeClass',\n",
       " 'HouseholdSize',\n",
       " 'DisposableIncome',\n",
       " 'FrequencyOfUseOfNonEelectricBicycle',\n",
       " 'FrequencyOfWalkingOutdoors',\n",
       " 'NumberOfCarsInHousehold',\n",
       " 'EducationLevel',\n",
       " 'FrequencyOfUseCcarAsAPassenger',\n",
       " 'PCA_dr_wl_cl_duration_2',\n",
       " 'PCA_dr_wl_cl_duration_1',\n",
       " 'PCA_dr_wl_cl_duration_3']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d57642e",
   "metadata": {},
   "source": [
    "<a id='section6'></a>\n",
    "# Feature Selection\n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f2e4008-846c-46ea-aa42-6e239d21a543",
   "metadata": {},
   "outputs": [],
   "source": [
    "# part of this code if from: https://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_with_cross_validation.html\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "min_features_to_select = 1 \n",
    "clf = RandomForestClassifier()\n",
    "cv = StratifiedKFold(5)\n",
    "\n",
    "rfecv = RFECV(\n",
    "    estimator=clf,\n",
    "    step=1,\n",
    "    cv=cv,\n",
    "    scoring='f1_macro',\n",
    "    min_features_to_select=min_features_to_select,\n",
    "    n_jobs=2,\n",
    ")\n",
    "rfecv.fit(X_train_final, y_train_split)\n",
    "\n",
    "print(f\"Optimal number of features: {rfecv.n_features_}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "507b1c83-10e2-4b6f-aa11-c91a21943939",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_scores = len(rfecv.cv_results_[\"mean_test_score\"])\n",
    "plt.figure()\n",
    "plt.xlabel(\"Number of features selected\")\n",
    "plt.ylabel(\"Mean test f1_macro\")\n",
    "plt.errorbar(\n",
    "    range(min_features_to_select, n_scores + min_features_to_select),\n",
    "    rfecv.cv_results_[\"mean_test_score\"],\n",
    "    yerr=rfecv.cv_results_[\"std_test_score\"],\n",
    ")\n",
    "plt.title(\"Recursive Feature Elimination\")\n",
    "plt.tight_layout()  \n",
    "plt.savefig(\"RFE.pdf\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07c7f8da",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_feature_rfecv = np.array(all_feature_names)[rfecv.support_]\n",
    "\n",
    "with open(\"selected_feature_rfecv.txt\", \"w\") as f:\n",
    "    for s in selected_feature_rfecv:\n",
    "        f.write(str(s) + \"\\n\")\n",
    "len(selected_feature_rfecv)\n",
    "selected_feature_rfecv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddd8615f-867e-4c9f-85ef-0cd03ba660b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('all_feature_names.txt', 'r') as file:\n",
    "    all_feature_names = [line.strip() for line in file]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84928879-a200-4f58-bdba-d7d14cd330b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_feature_rfecv = ['TotalTravelDuration', 'DepartureTime','NumberOfTripLegs', 'NumBusStops', 'RoadNetworkDensity',\n",
    " 'AvgDistToTrainStation', 'TripLegDistance', 'N.OFHouseholdMemberUnd6', 'AvgDistToSupermarket',\n",
    " 'NofBusinessEstablishments', 'SurroundingAddressDensity', 'LatitudeLiving', 'LongitudeLiving',\n",
    " 'TripPurpose_Leisure Activities','TripPurpose_Maintenance Activities', 'TripPurpose_Other motive',\n",
    " 'TripPurpose_Subsistence Activities','WorkCommuteReimbursement_No reimbursement', 'WorkCommuteReimbursement_Not applicable',\n",
    " 'WorkCommuteReimbursement_Reimbursement provided','Gender','License', 'Weekday','ElectriBicycleIHousehold',\n",
    " 'AgeClass','HouseholdSize', 'DisposableIncome', 'FrequencyOfUseOfNonEelectricBicycle',\n",
    " 'FrequencyOfWalkingOutdoors', 'NumberOfCarsInHousehold', 'EducationLevel', 'FrequencyOfUsecarAsAPassenger', 'PCA_dr_wl_cl_duration_2',\n",
    " 'PCA_dr_wl_cl_duration_1','PCA_dr_wl_cl_duration_3']\n",
    "len(selected_feature_rfecv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "46af8e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# not selected features\n",
    "nt = []\n",
    "\n",
    "for i in all_feature_names: \n",
    "    if i not in selected_feature_rfecv: \n",
    "        nt.append(i)\n",
    "nt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7b1bc26e-a841-4aa8-8aab-47b03766faea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select top features and match with train, test, val dataset\n",
    "top_mi_features = list(selected_feature_rfecv)\n",
    "\n",
    "feature_indices = [all_feature_names.index(feature) for feature in top_mi_features]\n",
    "\n",
    "\n",
    "X_train_top_mi = X_train_final[:, feature_indices]\n",
    "\n",
    "feature_indices = [all_feature_names.index(feature) for feature in top_mi_features]\n",
    "\n",
    "\n",
    "X_val_top_mi = X_val_final[:, feature_indices]\n",
    "\n",
    "\n",
    "X_test_top_mi = X_test_final[:, feature_indices]\n",
    "\n",
    "# save the data\n",
    "np.savetxt('X_train_top_mi.txt', X_train_top_mi, delimiter=',')\n",
    "np.savetxt('X_val_top_mi.txt', X_val_top_mi, delimiter=',')\n",
    "np.savetxt('X_test_top_mi.txt', X_test_top_mi, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6684bfab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the X_data\n",
    "X_train_top_mi = np.loadtxt('X_train_top_mi.txt', delimiter=',')\n",
    "X_val_top_mi = np.loadtxt('X_val_top_mi.txt', delimiter=',')\n",
    "X_test_top_mi = np.loadtxt('X_test_top_mi.txt', delimiter=',')\n",
    "\n",
    "# load the y_data\n",
    "y_train_split = np.loadtxt('y_train_split_encoded.txt', dtype=int)\n",
    "y_val = np.loadtxt('y_val_encoded.txt', dtype=int)\n",
    "y_test = np.loadtxt('y_test_encoded.txt', dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5cdabf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(95473, 35)\n",
      "(34282, 35)\n",
      "(34609, 35)\n",
      "(95473,)\n",
      "(34282,)\n",
      "(34609,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_top_mi.shape)\n",
    "print(X_test_top_mi.shape)\n",
    "print(X_val_top_mi.shape)\n",
    "\n",
    "\n",
    "print(y_train_split.shape)\n",
    "print(y_test.shape)\n",
    "print(y_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7dc4a95",
   "metadata": {},
   "source": [
    "<a id='section7'></a>\n",
    "# Baseline_model (KNN)\n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0cbea7b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functions_baseline import Objective_knn, Objective_rf, Objective_xgb, Objective_tabnet, all_metrics, predict, conf_matrix\n",
    "import optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "69e1c9a9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-04-28 20:07:41,707] A new study created in memory with name: no-name-e7cbd813-3ea3-416b-a5b0-1cb57af70e5f\n",
      "[I 2024-04-28 20:07:45,500] Trial 0 finished with value: 0.5606766730631904 and parameters: {'n_neighbors': 283, 'weights': 'distance', 'metric': 'minkowski', 'algorithm': 'auto'}. Best is trial 0 with value: 0.5606766730631904.\n",
      "[I 2024-04-28 20:07:49,151] Trial 1 finished with value: 0.560591154783071 and parameters: {'n_neighbors': 280, 'weights': 'distance', 'metric': 'minkowski', 'algorithm': 'auto'}. Best is trial 0 with value: 0.5606766730631904.\n",
      "[I 2024-04-28 20:17:16,297] Trial 2 finished with value: 0.5546637606314869 and parameters: {'n_neighbors': 311, 'weights': 'uniform', 'metric': 'euclidean', 'algorithm': 'ball_tree'}. Best is trial 0 with value: 0.5606766730631904.\n",
      "[I 2024-04-28 20:17:32,290] Trial 3 finished with value: 0.56374032657299 and parameters: {'n_neighbors': 257, 'weights': 'distance', 'metric': 'euclidean', 'algorithm': 'brute'}. Best is trial 3 with value: 0.56374032657299.\n",
      "[I 2024-04-28 20:26:01,697] Trial 4 finished with value: 0.555814322567356 and parameters: {'n_neighbors': 295, 'weights': 'uniform', 'metric': 'euclidean', 'algorithm': 'kd_tree'}. Best is trial 3 with value: 0.56374032657299.\n",
      "[I 2024-04-28 20:37:49,274] Trial 5 finished with value: 0.5552353743879325 and parameters: {'n_neighbors': 308, 'weights': 'uniform', 'metric': 'euclidean', 'algorithm': 'ball_tree'}. Best is trial 3 with value: 0.56374032657299.\n",
      "[I 2024-04-28 20:51:31,641] Trial 6 finished with value: 0.601283101293663 and parameters: {'n_neighbors': 298, 'weights': 'uniform', 'metric': 'manhattan', 'algorithm': 'ball_tree'}. Best is trial 6 with value: 0.601283101293663.\n",
      "[I 2024-04-28 20:51:52,741] Trial 7 finished with value: 0.5557645373301623 and parameters: {'n_neighbors': 288, 'weights': 'uniform', 'metric': 'euclidean', 'algorithm': 'auto'}. Best is trial 6 with value: 0.601283101293663.\n",
      "[I 2024-04-28 20:59:53,621] Trial 8 finished with value: 0.5642236133166462 and parameters: {'n_neighbors': 255, 'weights': 'distance', 'metric': 'minkowski', 'algorithm': 'kd_tree'}. Best is trial 6 with value: 0.601283101293663.\n",
      "[I 2024-04-28 21:07:59,399] Trial 9 finished with value: 0.5598958074165641 and parameters: {'n_neighbors': 251, 'weights': 'uniform', 'metric': 'minkowski', 'algorithm': 'kd_tree'}. Best is trial 6 with value: 0.601283101293663.\n",
      "[I 2024-04-28 21:20:25,415] Trial 10 finished with value: 0.6050148088343273 and parameters: {'n_neighbors': 270, 'weights': 'uniform', 'metric': 'manhattan', 'algorithm': 'ball_tree'}. Best is trial 10 with value: 0.6050148088343273.\n",
      "[I 2024-04-28 21:27:54,800] Trial 11 finished with value: 0.6050148088343273 and parameters: {'n_neighbors': 270, 'weights': 'uniform', 'metric': 'manhattan', 'algorithm': 'ball_tree'}. Best is trial 10 with value: 0.6050148088343273.\n",
      "[I 2024-04-28 21:35:31,628] Trial 12 finished with value: 0.6052495323350344 and parameters: {'n_neighbors': 269, 'weights': 'uniform', 'metric': 'manhattan', 'algorithm': 'ball_tree'}. Best is trial 12 with value: 0.6052495323350344.\n",
      "[I 2024-04-28 21:42:55,017] Trial 13 finished with value: 0.6052495323350344 and parameters: {'n_neighbors': 269, 'weights': 'uniform', 'metric': 'manhattan', 'algorithm': 'ball_tree'}. Best is trial 12 with value: 0.6052495323350344.\n",
      "[I 2024-04-28 21:43:19,555] Trial 14 finished with value: 0.6057611838526478 and parameters: {'n_neighbors': 267, 'weights': 'uniform', 'metric': 'manhattan', 'algorithm': 'brute'}. Best is trial 14 with value: 0.6057611838526478.\n",
      "[I 2024-04-28 21:43:44,750] Trial 15 finished with value: 0.6059612193059373 and parameters: {'n_neighbors': 262, 'weights': 'uniform', 'metric': 'manhattan', 'algorithm': 'brute'}. Best is trial 15 with value: 0.6059612193059373.\n",
      "[I 2024-04-28 21:44:08,320] Trial 16 finished with value: 0.6059612193059373 and parameters: {'n_neighbors': 262, 'weights': 'uniform', 'metric': 'manhattan', 'algorithm': 'brute'}. Best is trial 15 with value: 0.6059612193059373.\n",
      "[I 2024-04-28 21:44:33,435] Trial 17 finished with value: 0.6062522563895694 and parameters: {'n_neighbors': 260, 'weights': 'uniform', 'metric': 'manhattan', 'algorithm': 'brute'}. Best is trial 17 with value: 0.6062522563895694.\n",
      "[I 2024-04-28 21:44:54,846] Trial 18 finished with value: 0.6068224680116613 and parameters: {'n_neighbors': 277, 'weights': 'distance', 'metric': 'manhattan', 'algorithm': 'brute'}. Best is trial 18 with value: 0.6068224680116613.\n",
      "[I 2024-04-28 21:45:17,028] Trial 19 finished with value: 0.6061549876874679 and parameters: {'n_neighbors': 280, 'weights': 'distance', 'metric': 'manhattan', 'algorithm': 'brute'}. Best is trial 18 with value: 0.6068224680116613.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.6068224680116613\n",
      "Best Params: \n",
      "  n_neighbors: 277\n",
      "  weights: distance\n",
      "  metric: manhattan\n",
      "  algorithm: brute\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "random_state = 0\n",
    "\n",
    "pruner = optuna.pruners.SuccessiveHalvingPruner(min_resource=1, reduction_factor=3)\n",
    "study = optuna.create_study(direction='maximize', pruner=pruner)\n",
    "study.optimize(lambda trial: Objective_knn(trial, X_train_top_mi, y_train_split, X_val_top_mi, y_val), n_trials=20)\n",
    "\n",
    "# Save the study for later use\n",
    "joblib.dump(study, 'study_knn.pkl')\n",
    "\n",
    "best_trial = study.best_trial\n",
    "print(\"Best Score: \", best_trial.value)\n",
    "print(\"Best Params: \")\n",
    "for key, value in best_trial.params.items():\n",
    "    print(\"  {}: {}\".format(key, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6de31ec9",
   "metadata": {},
   "source": [
    "<a id='section8'></a>\n",
    " # Baseline_model Random_forest\n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "20bf6388",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-04-28 20:00:06,416] A new study created in memory with name: no-name-0ae8baf0-8e90-4f97-ae82-b3976be7dcb4\n",
      "[I 2024-04-28 20:00:08,037] Trial 0 finished with value: 0.7205487051068171 and parameters: {'n_estimators': 7, 'max_depth': 36, 'min_samples_leaf': 7, 'max_features': 'sqrt', 'bootstrap': True, 'criterion': 'gini'}. Best is trial 0 with value: 0.7205487051068171.\n",
      "[I 2024-04-28 20:00:08,912] Trial 1 finished with value: 0.7034524853071666 and parameters: {'n_estimators': 4, 'max_depth': 13, 'min_samples_leaf': 5, 'max_features': 'sqrt', 'bootstrap': False, 'criterion': 'gini'}. Best is trial 0 with value: 0.7205487051068171.\n",
      "[I 2024-04-28 20:00:10,530] Trial 2 finished with value: 0.689200729041935 and parameters: {'n_estimators': 1, 'max_depth': 29, 'min_samples_leaf': 5, 'max_features': None, 'bootstrap': True, 'criterion': 'entropy'}. Best is trial 0 with value: 0.7205487051068171.\n",
      "[I 2024-04-28 20:00:11,457] Trial 3 finished with value: 0.7166719833489333 and parameters: {'n_estimators': 1, 'max_depth': 11, 'min_samples_leaf': 7, 'max_features': None, 'bootstrap': True, 'criterion': 'entropy'}. Best is trial 0 with value: 0.7205487051068171.\n",
      "[I 2024-04-28 20:00:14,295] Trial 4 finished with value: 0.7208538086601385 and parameters: {'n_estimators': 7, 'max_depth': 36, 'min_samples_leaf': 5, 'max_features': 'sqrt', 'bootstrap': False, 'criterion': 'entropy'}. Best is trial 4 with value: 0.7208538086601385.\n",
      "[I 2024-04-28 20:01:41,524] Trial 5 finished with value: 0.7043035672019033 and parameters: {'n_estimators': 58, 'max_depth': 29, 'min_samples_leaf': 7, 'max_features': None, 'bootstrap': False, 'criterion': 'gini'}. Best is trial 4 with value: 0.7208538086601385.\n",
      "[I 2024-04-28 20:01:41,908] Trial 6 finished with value: 0.6752218034064809 and parameters: {'n_estimators': 2, 'max_depth': 10, 'min_samples_leaf': 5, 'max_features': 'sqrt', 'bootstrap': False, 'criterion': 'gini'}. Best is trial 4 with value: 0.7208538086601385.\n",
      "[I 2024-04-28 20:01:42,170] Trial 7 finished with value: 0.6298207016384891 and parameters: {'n_estimators': 1, 'max_depth': 14, 'min_samples_leaf': 9, 'max_features': 'log2', 'bootstrap': False, 'criterion': 'gini'}. Best is trial 4 with value: 0.7208538086601385.\n",
      "[I 2024-04-28 20:01:53,992] Trial 8 finished with value: 0.7410304472057885 and parameters: {'n_estimators': 31, 'max_depth': 37, 'min_samples_leaf': 5, 'max_features': 'sqrt', 'bootstrap': False, 'criterion': 'entropy'}. Best is trial 8 with value: 0.7410304472057885.\n",
      "[I 2024-04-28 20:01:55,713] Trial 9 finished with value: 0.7243732684608687 and parameters: {'n_estimators': 8, 'max_depth': 25, 'min_samples_leaf': 5, 'max_features': 'log2', 'bootstrap': True, 'criterion': 'gini'}. Best is trial 8 with value: 0.7410304472057885.\n",
      "[I 2024-04-28 20:02:02,357] Trial 10 finished with value: 0.3308908603867479 and parameters: {'n_estimators': 248, 'max_depth': 1, 'min_samples_leaf': 9, 'max_features': 'sqrt', 'bootstrap': False, 'criterion': 'entropy'}. Best is trial 8 with value: 0.7410304472057885.\n",
      "[I 2024-04-28 20:02:10,909] Trial 11 finished with value: 0.7381232054407313 and parameters: {'n_estimators': 30, 'max_depth': 28, 'min_samples_leaf': 5, 'max_features': 'log2', 'bootstrap': True, 'criterion': 'entropy'}. Best is trial 8 with value: 0.7410304472057885.\n",
      "[I 2024-04-28 20:02:19,537] Trial 12 finished with value: 0.7375279163347626 and parameters: {'n_estimators': 31, 'max_depth': 39, 'min_samples_leaf': 5, 'max_features': 'log2', 'bootstrap': True, 'criterion': 'entropy'}. Best is trial 8 with value: 0.7410304472057885.\n",
      "[I 2024-04-28 20:02:30,617] Trial 13 finished with value: 0.7401117482063024 and parameters: {'n_estimators': 40, 'max_depth': 31, 'min_samples_leaf': 5, 'max_features': 'log2', 'bootstrap': True, 'criterion': 'entropy'}. Best is trial 8 with value: 0.7410304472057885.\n",
      "[I 2024-04-28 20:03:07,187] Trial 14 finished with value: 0.739686208787268 and parameters: {'n_estimators': 126, 'max_depth': 21, 'min_samples_leaf': 5, 'max_features': 'log2', 'bootstrap': True, 'criterion': 'entropy'}. Best is trial 8 with value: 0.7410304472057885.\n",
      "[I 2024-04-28 20:03:16,009] Trial 15 finished with value: 0.7376255716481575 and parameters: {'n_estimators': 26, 'max_depth': 33, 'min_samples_leaf': 9, 'max_features': 'sqrt', 'bootstrap': False, 'criterion': 'entropy'}. Best is trial 8 with value: 0.7410304472057885.\n",
      "[I 2024-04-28 20:03:38,784] Trial 16 finished with value: 0.738811730243971 and parameters: {'n_estimators': 81, 'max_depth': 33, 'min_samples_leaf': 5, 'max_features': 'log2', 'bootstrap': True, 'criterion': 'entropy'}. Best is trial 8 with value: 0.7410304472057885.\n",
      "[I 2024-04-28 20:03:45,005] Trial 17 finished with value: 0.7370215309798709 and parameters: {'n_estimators': 17, 'max_depth': 39, 'min_samples_leaf': 5, 'max_features': 'log2', 'bootstrap': False, 'criterion': 'entropy'}. Best is trial 8 with value: 0.7410304472057885.\n",
      "[I 2024-04-28 20:05:06,170] Trial 18 finished with value: 0.7395621841340067 and parameters: {'n_estimators': 231, 'max_depth': 21, 'min_samples_leaf': 9, 'max_features': 'sqrt', 'bootstrap': False, 'criterion': 'entropy'}. Best is trial 8 with value: 0.7410304472057885.\n",
      "[I 2024-04-28 20:07:41,682] Trial 19 finished with value: 0.7580287761299398 and parameters: {'n_estimators': 105, 'max_depth': 24, 'min_samples_leaf': 7, 'max_features': None, 'bootstrap': True, 'criterion': 'entropy'}. Best is trial 19 with value: 0.7580287761299398.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.7580287761299398\n",
      "Best Params: \n",
      "  n_estimators: 105\n",
      "  max_depth: 24\n",
      "  min_samples_leaf: 7\n",
      "  max_features: None\n",
      "  bootstrap: True\n",
      "  criterion: entropy\n"
     ]
    }
   ],
   "source": [
    "random_state = 0\n",
    "\n",
    "pruner = optuna.pruners.SuccessiveHalvingPruner(min_resource=1, reduction_factor=3)\n",
    "study = optuna.create_study(direction='maximize', pruner=pruner)\n",
    "study.optimize(lambda trial: Objective_rf(trial, X_train_top_mi, y_train_split, X_val_top_mi, y_val), n_trials=20)\n",
    "\n",
    "# Save the study for later use\n",
    "joblib.dump(study, 'study_rf.pkl')\n",
    "\n",
    "\n",
    "best_trial = study.best_trial\n",
    "print(\"Best Score: \", best_trial.value)\n",
    "print(\"Best Params: \")\n",
    "for key, value in best_trial.params.items():\n",
    "    print(\"  {}: {}\".format(key, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e1845d",
   "metadata": {},
   "source": [
    "https://link-springer-com.tilburguniversity.idm.oclc.org/article/10.1007/s11042-024-18426-2#Tab6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78fcf66f",
   "metadata": {},
   "source": [
    "<a id='section9'></a>\n",
    "# Baseline_model eXtreme Gradient Boosting (xgb)\n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d87da217",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-04-28 18:43:08,330] A new study created in memory with name: no-name-cadabbee-ece0-4017-9019-6fd798c1baa0\n",
      "[I 2024-04-28 18:43:25,604] Trial 0 finished with value: 0.7736450897581267 and parameters: {'learning_rate': 0.29132443978979455, 'n_estimators': 698, 'max_depth': 16, 'min_child_weight': 77.24534209102481, 'gamma': 0.042730351134688926, 'subsample': 0.8039352037633734, 'colsample_bytree': 0.3528692177294338, 'reg_alpha': 12.971531129600407, 'reg_lambda': 18.362970750513192}. Best is trial 0 with value: 0.7736450897581267.\n",
      "[I 2024-04-28 18:43:34,177] Trial 1 finished with value: 0.7779973185109933 and parameters: {'learning_rate': 0.19606864782715086, 'n_estimators': 524, 'max_depth': 5, 'min_child_weight': 96.41853885567953, 'gamma': 0.47215091499160244, 'subsample': 0.5402702770785732, 'colsample_bytree': 0.5942690753173899, 'reg_alpha': 13.036682473925929, 'reg_lambda': 25.586745856532005}. Best is trial 1 with value: 0.7779973185109933.\n",
      "[I 2024-04-28 18:44:03,586] Trial 2 finished with value: 0.7688892678611212 and parameters: {'learning_rate': 0.019955080987751953, 'n_estimators': 689, 'max_depth': 30, 'min_child_weight': 9.923208989192782, 'gamma': 0.4115724296773433, 'subsample': 0.5335995463058344, 'colsample_bytree': 0.4641351739602879, 'reg_alpha': 25.629647546313823, 'reg_lambda': 10.33682611851006}. Best is trial 1 with value: 0.7779973185109933.\n",
      "[I 2024-04-28 18:44:24,312] Trial 3 finished with value: 0.7799061974386795 and parameters: {'learning_rate': 0.05003933910543036, 'n_estimators': 375, 'max_depth': 18, 'min_child_weight': 118.00759429186202, 'gamma': 0.5497352866556707, 'subsample': 0.7730888040653583, 'colsample_bytree': 0.8732169779065739, 'reg_alpha': 12.794231407954179, 'reg_lambda': 10.326755142416776}. Best is trial 3 with value: 0.7799061974386795.\n",
      "[I 2024-04-28 18:44:44,213] Trial 4 finished with value: 0.7292129140212753 and parameters: {'learning_rate': 0.015366128971296206, 'n_estimators': 682, 'max_depth': 3, 'min_child_weight': 68.06145286003499, 'gamma': 0.14684336896786432, 'subsample': 0.7096653212601223, 'colsample_bytree': 0.30175970894096416, 'reg_alpha': 17.86753769719727, 'reg_lambda': 26.743099657531886}. Best is trial 3 with value: 0.7799061974386795.\n",
      "[I 2024-04-28 18:45:05,867] Trial 5 finished with value: 0.7723649606173203 and parameters: {'learning_rate': 0.02554465930369685, 'n_estimators': 463, 'max_depth': 18, 'min_child_weight': 183.65206705076557, 'gamma': 0.4822113722270658, 'subsample': 0.5919956714912611, 'colsample_bytree': 0.6671474473345812, 'reg_alpha': 19.259638134196404, 'reg_lambda': 3.377818077615904}. Best is trial 3 with value: 0.7799061974386795.\n",
      "[I 2024-04-28 18:45:11,579] Trial 6 finished with value: 0.7658338724264196 and parameters: {'learning_rate': 0.8195472025259698, 'n_estimators': 148, 'max_depth': 11, 'min_child_weight': 147.41226898407925, 'gamma': 0.8168413588625512, 'subsample': 0.75081235749396, 'colsample_bytree': 0.21305164699093684, 'reg_alpha': 1.5820631503034988, 'reg_lambda': 17.2272457098152}. Best is trial 3 with value: 0.7799061974386795.\n",
      "[I 2024-04-28 18:45:32,064] Trial 7 finished with value: 0.7725288216090889 and parameters: {'learning_rate': 0.5653832496783814, 'n_estimators': 768, 'max_depth': 29, 'min_child_weight': 57.40712010074212, 'gamma': 0.5546050163623439, 'subsample': 0.7750622402124943, 'colsample_bytree': 0.33084823191585655, 'reg_alpha': 6.7932451939645935, 'reg_lambda': 21.671955101757938}. Best is trial 3 with value: 0.7799061974386795.\n",
      "[I 2024-04-28 18:46:10,215] Trial 8 finished with value: 0.7767808742204844 and parameters: {'learning_rate': 0.03881384531450126, 'n_estimators': 686, 'max_depth': 21, 'min_child_weight': 77.70843378894858, 'gamma': 0.4548688649337115, 'subsample': 0.501347416017133, 'colsample_bytree': 0.40161858731107225, 'reg_alpha': 1.7125932241045205, 'reg_lambda': 10.391287996635981}. Best is trial 3 with value: 0.7799061974386795.\n",
      "[I 2024-04-28 18:46:17,612] Trial 9 finished with value: 0.7172747804902453 and parameters: {'learning_rate': 0.01626251215897681, 'n_estimators': 163, 'max_depth': 14, 'min_child_weight': 135.14060944182, 'gamma': 0.4056753017437603, 'subsample': 0.8821703435055186, 'colsample_bytree': 0.4039169502689507, 'reg_alpha': 20.047991843621553, 'reg_lambda': 24.395676782716734}. Best is trial 3 with value: 0.7799061974386795.\n",
      "[I 2024-04-28 18:46:31,857] Trial 10 finished with value: 0.7780016716117449 and parameters: {'learning_rate': 0.06048152219028145, 'n_estimators': 346, 'max_depth': 23, 'min_child_weight': 129.50075097924758, 'gamma': 0.9839250715770385, 'subsample': 0.9859076422209198, 'colsample_bytree': 0.8845904497964316, 'reg_alpha': 28.842167362017555, 'reg_lambda': 0.01813130760794124}. Best is trial 3 with value: 0.7799061974386795.\n",
      "[I 2024-04-28 18:46:45,479] Trial 11 finished with value: 0.7781648384223488 and parameters: {'learning_rate': 0.07174817955233007, 'n_estimators': 372, 'max_depth': 23, 'min_child_weight': 136.35008126576832, 'gamma': 0.9388788775668726, 'subsample': 0.976505505964484, 'colsample_bytree': 0.9383599829024191, 'reg_alpha': 29.300379271355624, 'reg_lambda': 4.659266366550063}. Best is trial 3 with value: 0.7799061974386795.\n",
      "[I 2024-04-28 18:47:10,668] Trial 12 finished with value: 0.7784046045228311 and parameters: {'learning_rate': 0.10066148114495713, 'n_estimators': 987, 'max_depth': 24, 'min_child_weight': 189.59717925686112, 'gamma': 0.7619055602746863, 'subsample': 0.9944372901909648, 'colsample_bytree': 0.9952179185104418, 'reg_alpha': 8.596003271078382, 'reg_lambda': 9.193125468590965}. Best is trial 3 with value: 0.7799061974386795.\n",
      "[I 2024-04-28 18:47:46,068] Trial 13 finished with value: 0.776535844378887 and parameters: {'learning_rate': 0.15363821957729384, 'n_estimators': 950, 'max_depth': 26, 'min_child_weight': 195.42842731081305, 'gamma': 0.6791806619008842, 'subsample': 0.6619856370014858, 'colsample_bytree': 0.8112692608480281, 'reg_alpha': 8.273443067246982, 'reg_lambda': 11.084695427815161}. Best is trial 3 with value: 0.7799061974386795.\n",
      "[I 2024-04-28 18:48:13,864] Trial 14 finished with value: 0.7806901353904756 and parameters: {'learning_rate': 0.11035472913773701, 'n_estimators': 993, 'max_depth': 10, 'min_child_weight': 166.33677387889497, 'gamma': 0.6970463624426666, 'subsample': 0.8682517336083143, 'colsample_bytree': 0.7445228268335462, 'reg_alpha': 7.639114789998363, 'reg_lambda': 13.554907113097677}. Best is trial 14 with value: 0.7806901353904756.\n",
      "[I 2024-04-28 18:48:31,629] Trial 15 finished with value: 0.7784124670623225 and parameters: {'learning_rate': 0.03846479072798426, 'n_estimators': 345, 'max_depth': 11, 'min_child_weight': 163.54770951643005, 'gamma': 0.2653931079738757, 'subsample': 0.8623421958189446, 'colsample_bytree': 0.7146640400216573, 'reg_alpha': 5.45341537450115, 'reg_lambda': 13.901456351447228}. Best is trial 14 with value: 0.7806901353904756.\n",
      "[I 2024-04-28 18:49:13,083] Trial 16 finished with value: 0.7750201012039983 and parameters: {'learning_rate': 0.010412885704509253, 'n_estimators': 833, 'max_depth': 8, 'min_child_weight': 108.09694958977198, 'gamma': 0.6331803117738177, 'subsample': 0.8795382426095587, 'colsample_bytree': 0.7800588856009015, 'reg_alpha': 11.137667267584263, 'reg_lambda': 14.616472309588387}. Best is trial 14 with value: 0.7806901353904756.\n",
      "[I 2024-04-28 18:49:24,244] Trial 17 finished with value: 0.7787630522423383 and parameters: {'learning_rate': 0.10935731604681949, 'n_estimators': 217, 'max_depth': 19, 'min_child_weight': 164.03006104025337, 'gamma': 0.8419266571445945, 'subsample': 0.8363393075970935, 'colsample_bytree': 0.5643186006089458, 'reg_alpha': 14.68915754561976, 'reg_lambda': 6.523519714127614}. Best is trial 14 with value: 0.7806901353904756.\n",
      "[I 2024-04-28 18:49:40,291] Trial 18 finished with value: 0.7781420493075021 and parameters: {'learning_rate': 0.2782353456405603, 'n_estimators': 565, 'max_depth': 14, 'min_child_weight': 113.76044819506149, 'gamma': 0.6251666882581982, 'subsample': 0.9263879302467543, 'colsample_bytree': 0.8536651059240914, 'reg_alpha': 3.769772231460545, 'reg_lambda': 20.00057203021497}. Best is trial 14 with value: 0.7806901353904756.\n",
      "[I 2024-04-28 18:50:22,014] Trial 19 finished with value: 0.7789753930898347 and parameters: {'learning_rate': 0.04485511101902052, 'n_estimators': 889, 'max_depth': 9, 'min_child_weight': 45.798361888146175, 'gamma': 0.33827985922725673, 'subsample': 0.7001495448249743, 'colsample_bytree': 0.7081806588476853, 'reg_alpha': 23.637249858717425, 'reg_lambda': 29.71908124766137}. Best is trial 14 with value: 0.7806901353904756.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.7806901353904756\n",
      "Best Params: \n",
      "  learning_rate: 0.11035472913773701\n",
      "  n_estimators: 993\n",
      "  max_depth: 10\n",
      "  min_child_weight: 166.33677387889497\n",
      "  gamma: 0.6970463624426666\n",
      "  subsample: 0.8682517336083143\n",
      "  colsample_bytree: 0.7445228268335462\n",
      "  reg_alpha: 7.639114789998363\n",
      "  reg_lambda: 13.554907113097677\n"
     ]
    }
   ],
   "source": [
    "import optuna\n",
    "random_state = 0\n",
    "\n",
    "pruner = optuna.pruners.SuccessiveHalvingPruner(min_resource=1, reduction_factor=3)\n",
    "study = optuna.create_study(direction='maximize', pruner=pruner)\n",
    "study.optimize(lambda trial: Objective_xgb(trial, X_train_top_mi, y_train_split, X_val_top_mi, y_val), n_trials=20)\n",
    "\n",
    "# Save the study for later use\n",
    "joblib.dump(study, 'study_xgb.pkl')\n",
    "\n",
    "# print the best scores for each trial \n",
    "best_trial = study.best_trial\n",
    "print(\"Best Score: \", best_trial.value)\n",
    "print(\"Best Params: \")\n",
    "for key, value in best_trial.params.items():\n",
    "    print(\"  {}: {}\".format(key, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a1d6d6",
   "metadata": {},
   "source": [
    "<a id='section10'></a>\n",
    "# Baseline_nodel TabNet\n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0112766b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-04-28 19:06:24,780] A new study created in memory with name: no-name-2f034d02-838c-4481-84f1-8fbc0ec20a46\n",
      "[I 2024-04-28 19:09:04,271] Trial 0 finished with value: 0.6358856440982257 and parameters: {'mask_type': 'sparsemax', 'n_steps': 9, 'n_d': 8, 'gamma': 1.6, 'n_shared': 3, 'lambda_sparse': 1.086682583199076e-05, 'patienceScheduler': 7}. Best is trial 0 with value: 0.6358856440982257.\n",
      "[I 2024-04-28 19:11:26,419] Trial 1 finished with value: 0.7413595149403651 and parameters: {'mask_type': 'sparsemax', 'n_steps': 9, 'n_d': 4, 'gamma': 1.0, 'n_shared': 1, 'lambda_sparse': 5.106617186880669e-06, 'patienceScheduler': 7}. Best is trial 1 with value: 0.7413595149403651.\n",
      "[I 2024-04-28 19:13:43,325] Trial 2 finished with value: 0.7639045971346053 and parameters: {'mask_type': 'entmax', 'n_steps': 8, 'n_d': 4, 'gamma': 1.0, 'n_shared': 2, 'lambda_sparse': 0.0002732149204235589, 'patienceScheduler': 9}. Best is trial 2 with value: 0.7639045971346053.\n",
      "[I 2024-04-28 19:16:55,512] Trial 3 finished with value: 0.665657135601756 and parameters: {'mask_type': 'sparsemax', 'n_steps': 15, 'n_d': 6, 'gamma': 1.2, 'n_shared': 3, 'lambda_sparse': 0.00031028873078111654, 'patienceScheduler': 6}. Best is trial 2 with value: 0.7639045971346053.\n",
      "[I 2024-04-28 19:19:14,329] Trial 4 finished with value: 0.7615212032440152 and parameters: {'mask_type': 'sparsemax', 'n_steps': 9, 'n_d': 10, 'gamma': 1.0, 'n_shared': 2, 'lambda_sparse': 0.0007121589290276896, 'patienceScheduler': 7}. Best is trial 2 with value: 0.7639045971346053.\n",
      "[I 2024-04-28 19:21:40,063] Trial 5 finished with value: 0.7509587448249688 and parameters: {'mask_type': 'sparsemax', 'n_steps': 7, 'n_d': 8, 'gamma': 1.0, 'n_shared': 3, 'lambda_sparse': 1.835136222519093e-05, 'patienceScheduler': 9}. Best is trial 2 with value: 0.7639045971346053.\n",
      "[I 2024-04-28 19:24:57,542] Trial 6 finished with value: 0.5113792189656502 and parameters: {'mask_type': 'sparsemax', 'n_steps': 10, 'n_d': 2, 'gamma': 1.2, 'n_shared': 4, 'lambda_sparse': 8.946887919288342e-05, 'patienceScheduler': 6}. Best is trial 2 with value: 0.7639045971346053.\n",
      "[I 2024-04-28 19:27:35,285] Trial 7 finished with value: 0.6502169690017742 and parameters: {'mask_type': 'sparsemax', 'n_steps': 11, 'n_d': 6, 'gamma': 1.6, 'n_shared': 3, 'lambda_sparse': 2.677219803627618e-05, 'patienceScheduler': 4}. Best is trial 2 with value: 0.7639045971346053.\n",
      "[I 2024-04-28 19:30:40,663] Trial 8 finished with value: 0.6286785738546117 and parameters: {'mask_type': 'entmax', 'n_steps': 10, 'n_d': 2, 'gamma': 1.2, 'n_shared': 1, 'lambda_sparse': 6.438579738228663e-06, 'patienceScheduler': 9}. Best is trial 2 with value: 0.7639045971346053.\n",
      "[I 2024-04-28 19:33:50,826] Trial 9 finished with value: 0.7698261176389601 and parameters: {'mask_type': 'entmax', 'n_steps': 3, 'n_d': 10, 'gamma': 1.0, 'n_shared': 4, 'lambda_sparse': 0.0003665999408830711, 'patienceScheduler': 7}. Best is trial 9 with value: 0.7698261176389601.\n",
      "[I 2024-04-28 19:36:04,061] Trial 10 finished with value: 0.7502812126017602 and parameters: {'mask_type': 'entmax', 'n_steps': 3, 'n_d': 12, 'gamma': 1.4, 'n_shared': 4, 'lambda_sparse': 1.464126797189443e-06, 'patienceScheduler': 3}. Best is trial 9 with value: 0.7698261176389601.\n",
      "[I 2024-04-28 19:39:10,672] Trial 11 finished with value: 0.7677661680390483 and parameters: {'mask_type': 'entmax', 'n_steps': 4, 'n_d': 12, 'gamma': 1.0, 'n_shared': 2, 'lambda_sparse': 0.00013608986947967014, 'patienceScheduler': 10}. Best is trial 9 with value: 0.7698261176389601.\n",
      "[I 2024-04-28 19:42:36,073] Trial 12 finished with value: 0.7515062931322025 and parameters: {'mask_type': 'entmax', 'n_steps': 3, 'n_d': 12, 'gamma': 1.4, 'n_shared': 2, 'lambda_sparse': 8.823478636580354e-05, 'patienceScheduler': 10}. Best is trial 9 with value: 0.7698261176389601.\n",
      "[I 2024-04-28 19:45:20,637] Trial 13 finished with value: 0.7686548432858317 and parameters: {'mask_type': 'entmax', 'n_steps': 5, 'n_d': 10, 'gamma': 1.0, 'n_shared': 4, 'lambda_sparse': 0.00011663533930750158, 'patienceScheduler': 5}. Best is trial 9 with value: 0.7698261176389601.\n",
      "[I 2024-04-28 19:47:56,149] Trial 14 finished with value: 0.7661436151717623 and parameters: {'mask_type': 'entmax', 'n_steps': 6, 'n_d': 10, 'gamma': 1.2, 'n_shared': 4, 'lambda_sparse': 0.0009072296628601196, 'patienceScheduler': 5}. Best is trial 9 with value: 0.7698261176389601.\n",
      "[I 2024-04-28 19:50:07,773] Trial 15 finished with value: 0.7645334556324654 and parameters: {'mask_type': 'entmax', 'n_steps': 5, 'n_d': 10, 'gamma': 1.0, 'n_shared': 4, 'lambda_sparse': 4.834655168077172e-05, 'patienceScheduler': 5}. Best is trial 9 with value: 0.7698261176389601.\n",
      "[I 2024-04-28 19:52:04,868] Trial 16 finished with value: 0.754121355643927 and parameters: {'mask_type': 'entmax', 'n_steps': 5, 'n_d': 10, 'gamma': 1.4, 'n_shared': 4, 'lambda_sparse': 0.0003085773559348786, 'patienceScheduler': 8}. Best is trial 9 with value: 0.7698261176389601.\n",
      "[I 2024-04-28 19:55:59,958] Trial 17 finished with value: 0.6555603667043661 and parameters: {'mask_type': 'entmax', 'n_steps': 14, 'n_d': 8, 'gamma': 1.2, 'n_shared': 4, 'lambda_sparse': 0.0001566341937219093, 'patienceScheduler': 5}. Best is trial 9 with value: 0.7698261176389601.\n",
      "[I 2024-04-28 19:58:10,680] Trial 18 finished with value: 0.7643036983752358 and parameters: {'mask_type': 'entmax', 'n_steps': 5, 'n_d': 10, 'gamma': 1.0, 'n_shared': 3, 'lambda_sparse': 4.879561382328346e-05, 'patienceScheduler': 3}. Best is trial 9 with value: 0.7698261176389601.\n",
      "[I 2024-04-28 20:00:06,384] Trial 19 finished with value: 0.7484680899499331 and parameters: {'mask_type': 'entmax', 'n_steps': 3, 'n_d': 12, 'gamma': 1.2, 'n_shared': 4, 'lambda_sparse': 0.0005314360700098154, 'patienceScheduler': 4}. Best is trial 9 with value: 0.7698261176389601.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.7698261176389601\n",
      "Best Params: \n",
      "  mask_type: entmax\n",
      "  n_steps: 3\n",
      "  n_d: 10\n",
      "  gamma: 1.0\n",
      "  n_shared: 4\n",
      "  lambda_sparse: 0.0003665999408830711\n",
      "  patienceScheduler: 7\n"
     ]
    }
   ],
   "source": [
    "import optuna\n",
    "random_state = 0\n",
    "\n",
    "pruner = optuna.pruners.SuccessiveHalvingPruner(min_resource=1, reduction_factor=3)\n",
    "study = optuna.create_study(direction='maximize', pruner=pruner)\n",
    "study.optimize(lambda trial: Objective_tabnet(trial, X_train_top_mi, y_train_split, X_val_top_mi, y_val), n_trials=20)\n",
    "\n",
    "# Save the study for later use\n",
    "joblib.dump(study, 'study_tabnet.pkl')\n",
    "\n",
    "# Retrieve the best trial and print its score and parameters\n",
    "best_trial = study.best_trial\n",
    "print(\"Best Score: \", best_trial.value)\n",
    "print(\"Best Params: \")\n",
    "for key, value in best_trial.params.items():\n",
    "    print(\"  {}: {}\".format(key, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69687cc5",
   "metadata": {},
   "source": [
    "<a id='section11'></a>\n",
    "# Prediction based on test data\n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d085a8a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the parameters of knn and get the predictions \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_parm =joblib.load('study_knn.pkl')\n",
    "\n",
    "y_pred_knn_baseline, y_pred_prob_knn_baseline = predict(classifier=KNeighborsClassifier,\n",
    "                          params=knn_parm.best_params, X_train=X_train_top_mi, y_train=y_train_split, X_test=X_test_top_mi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e061de86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the parameters of  rf and get the predictions \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_parm =joblib.load('study_rf.pkl')\n",
    "\n",
    "y_pred_rf_baseline, y_pred_prob_rf_baseline = predict(classifier=RandomForestClassifier,\n",
    "                          params=rf_parm.best_params, X_train=X_train_top_mi, y_train=y_train_split, X_test=X_test_top_mi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bafa690",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the parameters of the XGBoost model\n",
    "xgb_params = joblib.load('study_xgb.pkl')\n",
    "\n",
    "# Make predictions using the `predict` function\n",
    "y_pred_xgb_baseline, y_pred_prob_xgb_baseline = predict(classifier=XGBClassifier,\n",
    "                          params=xgb_params.best_params, X_train=X_train_top_mi, y_train=y_train_split, X_test=X_test_top_mi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "12809566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mask_type': 'entmax',\n",
       " 'n_steps': 3,\n",
       " 'n_d': 10,\n",
       " 'gamma': 1.0,\n",
       " 'n_shared': 4,\n",
       " 'lambda_sparse': 0.0003665999408830711,\n",
       " 'patienceScheduler': 7}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabnet_parm =joblib.load('study_tabnet.pkl')\n",
    "tabnet_parm.best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e361068",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0  | loss: 1.21051 |  0:00:13s\n",
      "epoch 1  | loss: 0.76109 |  0:00:25s\n",
      "epoch 2  | loss: 0.63905 |  0:00:35s\n",
      "epoch 3  | loss: 0.60596 |  0:00:45s\n",
      "epoch 4  | loss: 0.5884  |  0:00:56s\n",
      "epoch 5  | loss: 0.57477 |  0:01:07s\n",
      "epoch 6  | loss: 0.57428 |  0:01:18s\n",
      "epoch 7  | loss: 0.56227 |  0:01:29s\n",
      "epoch 8  | loss: 0.55571 |  0:01:40s\n",
      "epoch 9  | loss: 0.55056 |  0:01:50s\n",
      "epoch 10 | loss: 0.54676 |  0:02:02s\n",
      "epoch 11 | loss: 0.54563 |  0:02:14s\n",
      "epoch 12 | loss: 0.53902 |  0:02:26s\n",
      "epoch 13 | loss: 0.54108 |  0:02:38s\n",
      "epoch 14 | loss: 0.5391  |  0:02:49s\n",
      "epoch 15 | loss: 0.53353 |  0:03:00s\n",
      "epoch 16 | loss: 0.53333 |  0:03:12s\n",
      "epoch 17 | loss: 0.5284  |  0:03:22s\n",
      "epoch 18 | loss: 0.52842 |  0:03:34s\n",
      "epoch 19 | loss: 0.52788 |  0:03:45s\n",
      "epoch 20 | loss: 0.52402 |  0:03:58s\n",
      "epoch 21 | loss: 0.52404 |  0:04:09s\n",
      "epoch 22 | loss: 0.51976 |  0:04:21s\n",
      "epoch 23 | loss: 0.52224 |  0:04:32s\n",
      "epoch 24 | loss: 0.52007 |  0:04:44s\n",
      "epoch 25 | loss: 0.5186  |  0:04:55s\n",
      "epoch 26 | loss: 0.51628 |  0:05:06s\n",
      "epoch 27 | loss: 0.51352 |  0:05:16s\n",
      "epoch 28 | loss: 0.51318 |  0:05:27s\n",
      "epoch 29 | loss: 0.51174 |  0:05:37s\n",
      "epoch 30 | loss: 0.5155  |  0:05:48s\n",
      "epoch 31 | loss: 0.51166 |  0:05:58s\n",
      "epoch 32 | loss: 0.50698 |  0:06:09s\n",
      "epoch 33 | loss: 0.50728 |  0:06:20s\n",
      "epoch 34 | loss: 0.50786 |  0:06:31s\n",
      "epoch 35 | loss: 0.50475 |  0:06:42s\n",
      "epoch 36 | loss: 0.50367 |  0:06:53s\n",
      "epoch 37 | loss: 0.50322 |  0:07:04s\n",
      "epoch 38 | loss: 0.50473 |  0:07:15s\n",
      "epoch 39 | loss: 0.50388 |  0:07:26s\n",
      "epoch 40 | loss: 0.50116 |  0:07:36s\n",
      "epoch 41 | loss: 0.50235 |  0:07:48s\n",
      "epoch 42 | loss: 0.49791 |  0:07:59s\n",
      "epoch 43 | loss: 0.49468 |  0:08:09s\n",
      "epoch 44 | loss: 0.49963 |  0:08:21s\n",
      "epoch 45 | loss: 0.4936  |  0:08:33s\n",
      "epoch 46 | loss: 0.49916 |  0:08:43s\n",
      "epoch 47 | loss: 0.49463 |  0:08:54s\n",
      "epoch 48 | loss: 0.49105 |  0:09:04s\n",
      "epoch 49 | loss: 0.49384 |  0:09:15s\n",
      "epoch 50 | loss: 0.4945  |  0:09:25s\n",
      "epoch 51 | loss: 0.49126 |  0:09:35s\n",
      "epoch 52 | loss: 0.49027 |  0:09:46s\n",
      "epoch 53 | loss: 0.48618 |  0:09:55s\n",
      "epoch 54 | loss: 0.49042 |  0:10:05s\n",
      "epoch 55 | loss: 0.48258 |  0:10:15s\n",
      "epoch 56 | loss: 0.48742 |  0:10:25s\n",
      "epoch 57 | loss: 0.48551 |  0:10:35s\n",
      "epoch 58 | loss: 0.48584 |  0:10:45s\n",
      "epoch 59 | loss: 0.48031 |  0:10:55s\n",
      "epoch 60 | loss: 0.48603 |  0:11:05s\n",
      "epoch 61 | loss: 0.486   |  0:11:15s\n",
      "epoch 62 | loss: 0.48575 |  0:11:24s\n",
      "epoch 63 | loss: 0.48583 |  0:11:34s\n",
      "epoch 64 | loss: 0.48558 |  0:11:44s\n",
      "epoch 65 | loss: 0.48318 |  0:11:54s\n",
      "epoch 66 | loss: 0.48391 |  0:12:04s\n",
      "epoch 67 | loss: 0.47932 |  0:12:14s\n",
      "epoch 68 | loss: 0.47879 |  0:12:23s\n",
      "epoch 69 | loss: 0.47937 |  0:12:33s\n",
      "epoch 70 | loss: 0.48132 |  0:12:44s\n",
      "epoch 71 | loss: 0.4852  |  0:12:54s\n",
      "epoch 72 | loss: 0.47888 |  0:13:05s\n",
      "epoch 73 | loss: 0.47929 |  0:13:16s\n",
      "epoch 74 | loss: 0.47231 |  0:13:26s\n",
      "epoch 75 | loss: 0.47313 |  0:13:36s\n",
      "epoch 76 | loss: 0.47285 |  0:13:46s\n",
      "epoch 77 | loss: 0.47526 |  0:13:56s\n",
      "epoch 78 | loss: 0.47334 |  0:14:08s\n",
      "epoch 79 | loss: 0.47101 |  0:14:18s\n",
      "epoch 80 | loss: 0.47412 |  0:14:29s\n",
      "epoch 81 | loss: 0.47242 |  0:14:40s\n",
      "epoch 82 | loss: 0.47404 |  0:14:50s\n",
      "epoch 83 | loss: 0.46926 |  0:15:00s\n",
      "epoch 84 | loss: 0.46819 |  0:15:10s\n",
      "epoch 85 | loss: 0.46743 |  0:15:20s\n",
      "epoch 86 | loss: 0.46841 |  0:15:30s\n",
      "epoch 87 | loss: 0.46864 |  0:15:41s\n",
      "epoch 88 | loss: 0.46794 |  0:15:50s\n",
      "epoch 89 | loss: 0.47329 |  0:16:01s\n",
      "epoch 90 | loss: 0.47395 |  0:16:10s\n",
      "epoch 91 | loss: 0.46923 |  0:16:20s\n",
      "epoch 92 | loss: 0.46496 |  0:16:30s\n",
      "epoch 93 | loss: 0.47007 |  0:16:40s\n",
      "epoch 94 | loss: 0.47017 |  0:16:50s\n",
      "epoch 95 | loss: 0.48513 |  0:17:00s\n",
      "epoch 96 | loss: 0.474   |  0:17:09s\n",
      "epoch 97 | loss: 0.46726 |  0:17:19s\n",
      "epoch 98 | loss: 0.46602 |  0:17:30s\n",
      "epoch 99 | loss: 0.46474 |  0:17:40s\n"
     ]
    }
   ],
   "source": [
    "# load the parameters of tabnet and get the predictions \n",
    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
    "tabnet_parm =joblib.load('study_tabnet.pkl')\n",
    "tabnet_params = { 'mask_type': 'entmax','n_d': 10,  'gamma': 1.0, \"n_steps\":3,\n",
    "    'n_shared': 4, 'lambda_sparse': 0.0003665999408830711, 'device_name': 'cuda'}\n",
    "\n",
    "y_pred_tabnet_baseline, y_pred_prob_tabnet_baseline = predict(classifier=TabNetClassifier,\n",
    "                                                              params=tabnet_params,\n",
    "       X_train=X_train_top_mi,   y_train=y_train_split, X_test=X_test_top_mi)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ea9996-2ed0-458f-9c29-5194314366c6",
   "metadata": {},
   "source": [
    "<a id='section12'></a>\n",
    "# Classification_report + metrics \n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3f600762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification_report_knn_baseline:\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "         Bicycle       0.57      0.69      0.62      9750\n",
      "    Car - Driver       0.68      0.80      0.73     11438\n",
      " Car - Passenger       0.87      0.11      0.20      3519\n",
      "            Foot       0.64      0.63      0.64      7082\n",
      "Public Transport       0.93      0.67      0.78      2493\n",
      "\n",
      "        accuracy                           0.65     34282\n",
      "       macro avg       0.74      0.58      0.59     34282\n",
      "    weighted avg       0.68      0.65      0.63     34282\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mapping = {'Bicycle':0, 'Car - Driver':1, 'Car - Passenger':2, 'Foot':3,\n",
    "       'Public Transport':4}\n",
    "\n",
    "print(\"classification_report_knn_baseline:\")\n",
    "print(classification_report(y_test, y_pred_knn_baseline, target_names=list(mapping.keys())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8a887666",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification_report_random_forest_baseline:\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "         Bicycle       0.71      0.74      0.73      9750\n",
      "    Car - Driver       0.76      0.83      0.79     11438\n",
      " Car - Passenger       0.73      0.40      0.52      3519\n",
      "            Foot       0.82      0.82      0.82      7082\n",
      "Public Transport       0.89      0.88      0.89      2493\n",
      "\n",
      "        accuracy                           0.76     34282\n",
      "       macro avg       0.78      0.74      0.75     34282\n",
      "    weighted avg       0.76      0.76      0.76     34282\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mapping = {'Bicycle':0, 'Car - Driver':1, 'Car - Passenger':2, 'Foot':3,\n",
    "       'Public Transport':4}\n",
    "\n",
    "print(\"classification_report_random_forest_baseline:\")\n",
    "print(classification_report(y_test, y_pred_rf_baseline, target_names=list(mapping.keys())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b722ec21-a39e-4d87-9e03-a6c3ce5b56d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification_report_xgb_baseline:\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "         Bicycle       0.73      0.76      0.74      9750\n",
      "    Car - Driver       0.78      0.83      0.80     11438\n",
      " Car - Passenger       0.69      0.46      0.55      3519\n",
      "            Foot       0.83      0.84      0.83      7082\n",
      "Public Transport       0.91      0.89      0.90      2493\n",
      "\n",
      "        accuracy                           0.78     34282\n",
      "       macro avg       0.79      0.75      0.77     34282\n",
      "    weighted avg       0.78      0.78      0.77     34282\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mapping = {'Bicycle':0, 'Car - Driver':1, 'Car - Passenger':2, 'Foot':3,\n",
    "       'Public Transport':4}\n",
    "\n",
    "print(\"classification_report_xgb_baseline:\")\n",
    "print(classification_report(y_test, y_pred_xgb_baseline, target_names=list(mapping.keys())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "15894387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification_report_tabnet_baseline:\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "         Bicycle       0.70      0.76      0.73      9750\n",
      "    Car - Driver       0.77      0.83      0.79     11438\n",
      " Car - Passenger       0.68      0.39      0.50      3519\n",
      "            Foot       0.83      0.82      0.82      7082\n",
      "Public Transport       0.89      0.84      0.87      2493\n",
      "\n",
      "        accuracy                           0.76     34282\n",
      "       macro avg       0.77      0.73      0.74     34282\n",
      "    weighted avg       0.76      0.76      0.76     34282\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"classification_report_tabnet_baseline:\")\n",
    "print(classification_report(y_test, y_pred_tabnet_baseline, target_names=list(mapping.keys())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1ef46503",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>knn_baseline</th>\n",
       "      <th>rf_baseline</th>\n",
       "      <th>xgb_baseline</th>\n",
       "      <th>tabnet_baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Precision (Macro)</th>\n",
       "      <td>73.81%</td>\n",
       "      <td>78.19%</td>\n",
       "      <td>78.72%</td>\n",
       "      <td>77.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall (Macro)</th>\n",
       "      <td>57.89%</td>\n",
       "      <td>73.64%</td>\n",
       "      <td>75.46%</td>\n",
       "      <td>72.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-score (Macro)</th>\n",
       "      <td>59.41%</td>\n",
       "      <td>74.93%</td>\n",
       "      <td>76.6%</td>\n",
       "      <td>74.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balanced Accuracy</th>\n",
       "      <td>57.89%</td>\n",
       "      <td>73.64%</td>\n",
       "      <td>75.46%</td>\n",
       "      <td>72.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Log Loss</th>\n",
       "      <td>0.883212</td>\n",
       "      <td>0.595334</td>\n",
       "      <td>0.546659</td>\n",
       "      <td>0.602831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  knn_baseline rf_baseline xgb_baseline tabnet_baseline\n",
       "Precision (Macro)       73.81%      78.19%       78.72%          77.42%\n",
       "Recall (Macro)          57.89%      73.64%       75.46%          72.78%\n",
       "F1-score (Macro)        59.41%      74.93%        76.6%          74.23%\n",
       "Balanced Accuracy       57.89%      73.64%       75.46%          72.78%\n",
       "Log Loss              0.883212    0.595334     0.546659        0.602831"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_knn = all_metrics(y_test, y_pred_knn_baseline, y_pred_prob_knn_baseline, \"knn_baseline\")\n",
    "metrics_rf = all_metrics(y_test, y_pred_rf_baseline, y_pred_prob_rf_baseline, \"rf_baseline\")\n",
    "\n",
    "metrics_xgb = all_metrics(y_test, y_pred_xgb_baseline, y_pred_prob_xgb_baseline, \"xgb_baseline\")\n",
    "metrics_tabnet = all_metrics(y_test, y_pred_tabnet_baseline, y_pred_prob_tabnet_baseline, \"tabnet_baseline\")\n",
    "\n",
    "\n",
    "# Concatenate \n",
    "combined_metrics_df = pd.concat([metrics_knn, metrics_rf, metrics_xgb, metrics_tabnet], axis=1)\n",
    "combined_metrics_df.to_csv(\"combined_metrics_df_baseline.csv\")\n",
    "combined_metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "58d2f43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r1vj4+DB//nzs7OwYMGAAAE+ePKFGjRq0bNmSggULoqOjw+7du/Hx8aFVq1aqtpUsWZKlS5cyefJk8ubNS44cOahevXqKnW8hhBBCpL4hQ4bQokUL1q5dS8+ePWnYsCG7du2iadOmNGjQgBcvXrBs2TIKFiyodqteUri5udGgQQMqVapE586deffuHQsXLqRQoUJqdTZq1Ihq1aoxatQoPDw8KFasGMeOHWPv3r24urri4OCQXN3+ZjNnzqRevXqUL1+eLl26EB4ezsKFCzE1NWX8+PFfPN7BwYHJkyczYsQIPDw8aNKkCSYmJrx48YLdu3fTvXt3Bg8ezKlTp3BxcaFFixY4OjoSExPDhg0b0NbWpnnz5qr6SpYsyYkTJ5gzZw5WVlbY29urFl0XQnyFtHr8nhBCfHTt2jWltra20sXFReP+K1euKLW0tJT9+vVTbQsPD1fOmzdPWb58eWXmzJmVOjo6SgsLC2XDhg2VmzZtUsbExKjKfnx08ecvHR0dZZ48eZRDhgxRBgcHJ/jMRYsWKfPnz6/U1dVV5syZU9mrVy/l+/fvE5Tbtm2bskSJEkp9fX2lmZmZsm3btsrXr1+r9vv7+yv79OmjzJ8/v9LIyEhpamqqLFu2rHL79u1q9Xh7eysbNGigNDExUQLKKlWqJPEsCiGEECI9WLNmjRJQXr16NcG+2NhYpYODg9LBwUEZExOjjIuLU06dOlVpa2ur1NfXV5YoUUJ54MABZYcOHZS2traq4z5mmZkzZyaoE1COGzdObdvOnTuVBQoUUOrr6ysLFiyo3LVrV4I6lUqlMjg4WDlgwACllZWVUldXV5kvXz7lzJkzlXFxcQk+o0+fPmrbEmvT6dOnlYByx44d33yePnfixAllxYoVlYaGhsrMmTMrGzVqpHzw4IFamXHjxikBpZ+fn8Y6du7cqaxUqZLSyMhIaWRkpMyfP7+yT58+ysePHyuVSqXy+fPnys6dOysdHByUBgYGSjMzM2W1atWUJ06cUKvn0aNHysqVKysNDQ2VgLJDhw7/2XYhhDqFUpkGq8MJIYQQQgghhBBCiJ+GrAElhBBCCCGEEEIIIVKUDEAJIYQQQgghhBBCiBQlA1BCCCGEEEIIIYQQIkXJAJQQQgghhBBCCCGESFEyACWEEEIIIYQQQgghUpQMQAkhhBBCCCGEEEKIFKWT1g0QPyYbl71p3YR06ez4OmndhHQpu4l+WjchXQoMj07rJqRL2Yz10roJ6ZJBCv1FNyzh8l3Hh99clEwtEeLHZ9tvf1o3IV06NbpmWjchXcppapDWTUiXPoRJftJE8pNmhropVK/kJ41kBpQQQgghhBBCCCGESFEyA0oIIYQQiVPItSohhBBCiCSR/KSRDEAJIYQQInEKRVq3QAghhBAiY5H8pJEMQAkhhBAicXIFTwghhBAiaSQ/aSQDUEIIIYRInFzBE0IIIYRIGslPGskAlBBCCCESJ1fwhBBCCCGSRvKTRnJWhBBCCCGEEEIIIUSKkhlQQgghhEicTCEXQgghhEgayU8ayQCUEEIIIRInU8iFEEIIIZJG8pNGMgAlhBBCiMTJFTwhhBBCiKSR/KSRDEAJIYQQInFyBU8IIYQQImkkP2kkZ0UIIYQQQgghhBBCpCiZASWEEEKIxMkUciGEEEKIpJH8pJEMQAkhhBAicTKFXAghhBAiaSQ/aSQDUEIIIYRInFzBE0IIIYRIGslPGskAlBBCCCESJ1fwhBBCCCGSRvKTRnJWhBBCCCGEEEIIIUSKkhlQQgghhEicXMETQgghhEgayU8ayQCUEEIIIRKnJWsYCCGEEEIkieQnjWQASgghhBCJkyt4QgghhBBJI/lJIxmAEkIIIUTi5CkuQgghhBBJI/lJIxmAEkIIIUTi5AqeEEIIIUTSSH7SSM6KEEIIIYQQQgghhEhRMgNKCCGEEImTKeRCCCGEEEkj+UkjGYASQgghROJkCrkQQgghRNJIftJIzko64+HhgUKh4NatW6nyeVWrVsXV1TVVPksIIUQGpFB830uIVCD5SQghRLoi+UkjmQGVyjp27Mi6detU783MzChdujQzZsygaNGi2NjY8PbtW8zNzdOwlenLhQm1sMmWKcH2dWdfMHr7HdxaFcPZKTs5TQ0IjYzh+ot3TN37gGc+IWrlW5S1oVt1B+xzGBMSEcPBm16M3n5HrUyPGg60qWhHrqyGvA+NYv0/Hiw8+iRF+/et7t66zl+b1+L+6CHvAvwY6zaXCpWrAxATE826FYu4evEcb71eY2RkQonSZencsz/ZsucAwPvtGzavXcHt61d4HxBANvPsVK/TgFYduqGrq6v6nGuXz7Nx1VJevniGnr4+hYv9Qre+g7CwzJUm/U6q1auWc/rkcTxePEdf34CixUvQz3UQdvZ5VGV2/bWNI4cO8OjhA0JDQzlz7gommTOr1dOwbnXeenmpbXPpP5BOXbqnSj9SQlhoKKuXL+Lc3yf58P4deR3z4zJwOPkLFgZAqVSydsViDu7dSUhIMIWLFsd16Bisc9uq6njl6cHyBbO5d+cWMdHR5MnrSKceLpQoVSatupXstm/dzPZtW/B68wYAh7z56NGrN5WcqwAwcfxYLl+6gJ+vL5kyZaJY8RK4DhyMfR6HtGx28pEreCIdkPyUdOfG1dCYn9b/84JZBx8zsJ4TzvmzkyurIQEhURy7+5bZBx8THBGjKvtyQaMEx7usvc7+G5/+HrZ3tqODsx3WZpl48z6cRcfc2XX1dcp0KhncvXWdnVvW8fRxfH4aPWWOKj8BbFy9lLMnj+Ln642uji55nQrSvpsL+QsVUZXZun4lVy/+w3P3J+jo6rDj8LlEPy8o8AN9OrUkwM+X7YfOYmySOdGy6c2Na1dZv/ZPHj68j7+fH7PmLaJa9Zqq/QEB/iyYO4tLF88THBzML7+UYuiI0eS2tQMgMPADy5cs5NKF83h7vyVLVjOqVq9Brz79MTExSaNefb+w0FDWrFDPT30GfMpP61Yu4fSJw/j5+KCjq4OjU0E69+xHgcJFVXW0aVIHH2/1XNm1d39at++aqn1JSUsXL2T50kVq2+zs7dmz/wgAkZGRzJ45jaOHDxEVFUWFipUYOXoc2X6U3+OSnzSSAag0ULduXdasWQOAt7c3o0ePpmHDhnh6eqKtrY2FhUUatzB9aTjzb7Q/GwV2ssrMlr4VOHAz/svg3Vcf2HP1NW/eh5Elkx4DGzixqU95Kow7Tpwy/phu1R3oXt2BKXvuc9PjPYZ6OglC2YTfilA5f3Ym777PI68gsmTSJYuRXqr1M6kiwsOxz+tE7QZNmDRyoNq+yIgInj5+RJuO3bHP60RIcBDL5k9n/LD+LFy9BYDXLz1QxsXRb8gYrKxz4/H8KfOnTyAiIpxuLoMA8PZ6zYThrjT7/Q+GjnMjNDSEFQtmMmnkQBav2Zbqff4WN65dpUWrNhQqVITY2FgWLZhLn55d+Wv3AQwzxf8MRIRHUL6iM+UrOrNo/pxE6+rZpx9Nm7dQvTfKZJTi7U9Js6aO48Wzp4wYPxVz8xwcP3KAIS7dWL11D9lz5GTrhtXs2r6Z4WMnY2GVizXLFzGsfw/WbN2Lnr4+AKMGupDLJjezF69CX9+AnVs3MGqQCxt3HcIs248RIHLktKD/gMHktrVFqVSyf+8e+rv0YdvO3eTNm4+CBQvRoGEjLCwtCQoMZOnihfTs1oVDx06ira2d1s0X4och+SlpGs/+Ry0/OVqasNmlPAdvviWnqQE5TQ2YsvcB7t7BWGc1ZMrvRclpakCv1dfV6hm08SZ/P/RTvQ8Kj1b9d7tKtgxtlJ/hW+5w2/MDxW2zMK1VMQLDozl5zyflO/kNIiLCsc/rSO0GTZg8amCC/blsbOk1YDgWVtZERUawe9smRg/qxZ9b9mGa1QyAmOhoKlWtRf5CxTh2cPd/ft68aeOxd8hHgJ9vivQnJYWHh+PolJ/GTZszZEBftX1KpZJB/fugo6PLnPlLMDIyYtOGtfTq3lmVsfx8ffHz9cV10FDsHfLy1ssLt8nj8Pf1ZcacBWnUq+83e+o4Xjx/yohxU8lmnoMTRw4wtG83/twSn5+sc9vSd9BILHNZExUZyV9bNjCsfw/W/3WQLP/+DAF07N6HBr/+pnr/MZf+SBzy5mP5qjWq95/nolnTp/LP2b+ZOWcexsYmTJs6iYGuLqzbuDUtmipSiQzLpQF9fX0sLCywsLCgePHiDB8+nFevXuHn56dxCvn9+/dp2LAhmTNnxsTEBGdnZ549e8bZs2fR1dXF29tbrX5XV1ecnZ1V78+fP0/VqlXJlCkTWbNmpU6dOrx//15j2yIjIxk8eDC5cuXCyMiIsmXLcubMmZQ4DV/tXUgUfsGRqleNwjnx8AvhknsAAJvPv+TyswBevwvn3utAZux/RC6zTKoBJlNDXYY0zI/r+hvsufaGl/5hPPIK4vjdT+ctb05j/nC2o8uKKxy/682rgDDuvgrkn0d+GtuUHpQuX4mO3V2oWKVGgn1Gxia4zV9O5Rp1sLG1o0DhovQeOAL3xw/w9X4LQKlyFRk0ahIly1bAMpc15Z2r0rx1B87/fVJVj/vjh8TFxtGhuwtW1jbkcypA89btee7+mJiY6ASfmx4tWraKxr82wyFvPhyd8jNhkhveb714+OC+qkybPzrQqUt3ihQt9p91GWUywtw8u+qVkYNCZEQEZ0+foIfLQIqVKEUum9x07NYbK2sb9u3ahlKpZOfWjbTr1J2KVarjkM+J4eOn4u/vx7m/TwEQ+OE9r1+9pHX7Ljjkc8I6ty3d+gwgIiKcF8/c07iHyadqteo4V66Cra0ddnb29O0/gEyZMnHn9i0Afmv5OyVLlSZXLmsKFCyESz9XvL3fqmZMZXgyhVykE5KfkkZzfgrl0tMAnrwNpufqa5y854OnfxgX3AOYeeARNQrnRFtL/d9tUHiMWj2RMXGqfc1KW7P5/EsO3PTiVUAY+294seXCS3rVyJva3f1qpctVokM3F7VZT5+rVqs+JUqVw9LKGlv7vHTvO4iw0BC1v2vtuvSm6e9/YJfnv/t5cPd2QkOCadaqQ7L2IbVUdK5M776uVK9RK8E+z5ce3L1zmxGjx1GocBHs7PMwYvR4IiMiOHL4IAB58zkyc+5CKletjo1NbsqULUfvvgM4+/dpYmJiEtSZEURGRHD2zAm6uwyk6L/5qcO/+Wn/rviLszXqNKBkmfJY5bLBLk9eerkOITQ0hOdP1e+qyJTJCLNs5qqXoWHGzZWJ0dbWVsvOWf8dgAsODmb3rp0MGjqcMmXLU7BQYSZMmsrtWzdV+SrDk/ykkQxApbGQkBA2btxI3rx5yZYtW4L9b968oXLlyujr63Pq1CmuX79O586diYmJoXLlyuTJk4cNGzaoykdHR7Np0yY6d+4MwK1bt6hRowYFCxbk4sWLnDt3jkaNGhEbG6uxPS4uLly8eJGtW7dy584dWrRoQd26dXF3Tx9fJnW1FTQrbc22i54a9xvqafN7udy89A/F6304AM75s6NQKLDIYsip0dW5Mqk2SzqXwjKLgeq4mkUs8PQPo2bhnJwfX5MLE2oxo01xsmTS1fg5GVFoSAgKhQKj/5jyHBoagomJqep9PqcCaGkpOHZwD7GxsYSGBHPy6EFKlCqLjk7GPDchIcEAZDY1/ULJhNauXkl157K0admU9Wv+zLDhCSA2Npa42Fj09NVn+enrG3Dv9k3eer3mXYA/JcuUU+0zNjahQKEiPLh7G4DMplmwsbXj2OH9hIeHERsTw/7dO8ia1QzH/AVTtT+pJTY2lsOHDhIeHkaxYiUS7A8LC2Pv7l3ksrb+cWZjKLS+7yVECpD8lDS62gqalrJm+yXN+Qkgs6EuIRExxH6cPv6vSS0Kc3NqHfYOqkTLcjZq+/R0tIiMjlPbFhEdSzHbLOhoZfwvUNHR0RzetxMjY2Ps8zom6VjPF8/YvHYFg0ZPRusHOBf/LyoqCkA1IxpAS0sLPT09bt28nthhhAQHY2RsjI5OxrwRR5Wf9DTnp/8XHR3NwT1/YWRsgkM+J7V9W9b/SZPalejRvgXbNq4hNgPnysR4er6kVrVKNKhbgxHDBvH2bfxthw8f3CMmJpqy5SqoytrnccDS0orbP8wAlOQnTTLmv/wM7sCBAxgbGwMQGhqKpaUlBw4cQEsr4Q/a4sWLMTU1ZevWrap1eRwdP/0B7NKlC2vWrGHIkCEA7N+/n4iICFq2bAnAjBkzKFWqFEuWLFEdU6hQIY3t8vT0ZM2aNXh6emJlZQXA4MGDOXLkCGvWrGHq1Kkaj4uMjCQyMlJtmzI2GoV28g9Q1ClqSWZDXXZcfqW2vb2zHSObFMJIX4en3sG0XXSB6Nj4AJXb3AgthQKX2vkYv/MuQeExDGlYgM0uFajtdproWCW5zTORy8yQBiWsGLDhBlpaCsY1K8yyLqVptfBCsvcjtUVFRrJ66Tyq1qyHkZGxxjJerz3Z99cWurl8mo5uYWXNlLnLmDpmCAtmTiYuNpYChYsxadYijXWkd3FxccyaMZViJX4hb76kBclWbf4gf4GCmJpm4fatmyyaPwd/f18GDhmRQq1NWZmMjChYpBgbVi8nt10esppl49SxQzy4dxsr69y8C4ifYZjVTP2LXVazbLx75w+AQqFg1sKVjBnan4bVyqHQ0iJrVjOmzV+GSeakD/ClZ+5PHvNHm1ZERUWSKVMm5i5YjEPeT1e+t23ZxNzZswgPD8PO3p7lK9egq5d+b+FNkh/4KpzIWCQ/fbvaRS3IbKiTID99lNVIj7518rHlvPoA1eyDj7jwxJ/w6Fic82dnUosiZNLTYe3ZFwD8/dCPVuVzc/SuN/deBVLExpTfy+dGT0cLM2M9fIMiNX1cunf5/FmmTxhGZEQEZtnMmTJnGaZZsn718dFRUUyfMIIuvQeQI6cl3l7pd02sb2VnnwcLSysWzZ/DqLETMDQ0ZNOGdfj4eOPvr/kOgvfv37NqxVKaNW+Zyq1NPh/z08ZE8tNHF8/9zeQxQ+J/hsyzM2PBCrWfoaYt25DPqSAmmTPz4O5tVi2dR4C/H71dh6ZFt1JEkaJFmTjZDTs7e/z9/Vi2ZDGd27flrz378ff3R1dXl8z/t+aqWbZsBCTy85PhSH7S6McdWkvHqlWrxq1bt7h16xZXrlyhTp061KtXj5cvXyYoe+vWLZydndUWhf5cx44defr0KZcuXQJg7dq1tGzZEiMjI9XxNWokvEVLk7t37xIbG4ujoyPGxsaq199//82zZ88SPc7NzQ1TU1O1V9D1v77qM5OqVQVbTj/wxScwQm377quvqTvtDL/NPccLvxCWdC6Nvk78j7eWIv4K3bi/7vL3Qz9uerzHZe017HMYU8HR/N8yCgx0tXHdcIMrz95xyT2AIZtuUdEpO3lyaB6wyShiYqKZMmYISqUSlyGjNJbx9/Nh1MDeOFerRb3GzVXb3wX4M3/6BGrWa8yCVZuYuXg1Orq6TB49GKVSqbGu9GzalIk8e+qO2/TE13lKTLv2nShVuiz5HJ34rWUrBgwextYtm1RXADOiEePdUCqVtGxYgzrOJdm1fTPVa9f76iu1SqWS+TOnkCWrGfOXr2PJ6s1UrFKdUYNcfpzw8C87O3u279zDxi3bafF7a8aMHMazp09V++s3bMy2nbtZvW4jtrZ2DBnkmuCLZYYlV/BEOvEz5KfAazu+6jOT6vdyuTnz0FfjgJCxgQ5repThqXcIcw8/Vtu34Kg711685/7rIJadeMbyk8/oUcPhs/1POPPQlz0DK/FsbgNWdSvNzivxgy1xGTAnfFTsl9IsWr2N2UvXUbJsRdzGDeXD+3dfffya5QuwsbWnep0GKdjKtKWrq8usuQvwfOlBtUplqVimBNeuXKZipcpoafjdHxISQv8+PciTx4HuvVzSoMXJZ8Q4N5Qo+b1RDepWLsnuHZupVqseWp8NOBQvWZoV6/9iwcoNlC5XkUmjBvP+XYBqf4s2HShesjQO+Zxo1KwlPfsNYc+OLRk6V/6/Ss5VqF2nHo5O+alQ0ZlFS1cQHBzEsSOH07ppqUPyk0YyAyoNGBkZkfezK+erVq3C1NSUlStX0rWr+pMPDA0N/7OuHDly0KhRI9asWYO9vT2HDx9WW3PgS8d/LiQkBG1tba5fv55g4dyPVxw1GTFiBAMHqi/iWHDYsa/+3K+VK6shlZyy033llQT7giNiCI6IwcMvlBse77g3oz51i1my9/obVdh64h2sKv8uJIp3IZFYZY2/19o3MILo2Dhe+Iaqyjz1iS+fy8yQ577qT9TLKGJiopk6Zgi+Pm+ZvmClxtlPAX6+DOvblYJFitF/2Fi1fft3biWTkQld+wxQbRs6dip/NK3No/t31Z7mkd5NnzqRc2fPsHLNRnImw61RhYsUJTYmBq83r9WeqJeR5LK2Yd6ytYSHhxEWGko28+xMHDUYSytrzP69peX9u/gnJH70/l0AefPlB+DmtctcOn+WvcfPY/Tv7wjH/AW5fvkiRw/upU2HH+dJLrp6euS2jX/6X8FChbl/7y6bNq5n7PiJAJiYmGBiYoKtrR1FixajUoUynDpxnHoNGqZls4X4ofwM+anwiBNf/blf62N+6vHn1QT7jPS1Wd+rLKGRMXRfdZWYuP8eNLrl8Z7+dR3R09EiKiaOyOg4hmy+zYitdzDPrI9vYARtKtoSHBFNQEjG/SJtYGiIlXVurKxzk79QUbq2bsTRA7v5/Y8uX3X8nRtX8Hj+lIZV//3/+e9gXKtG1Wj1RxfademdUk1PVQUKFmbLjj0EBwcTEx1NVjMz2rdpScFChdXKhYaG0LdXV4yMjJg1b1GiA8MZhZW1DXOXquenSaMGY5nLWlXG0DATuWxyk8smNwULF6P9bw04vH93otmoQKEixMbG4PP2DTa29qnVlVSVOXNmctva8crTk3IVKhAdHU1QUJDaLKh3Aeq5U/x4ZAAqHVAoFGhpaREeHp5gX9GiRVm3bh3R0dGJ/rLu2rUrrVu3xtraGgcHBypWrKh2/MmTJ5kwYcIX21GiRAliY2Px9fVVW4TzS/T19dH/7P5vIEWmj7csnxv/4EhO3v/vp6ooFAoU/856Arj6PP5qg0MOY7w/xM+cypJJFzNjfd68CwPg2vN36GprYWueiZf+8dvs/5359PrfMhnNx8GnN688mb5wFZlNsyQo4+/nw7C+XcnrVJCBIycmuI0hMjIiwWyYj2WUSvU1H9IrpVLJDLdJnD51ghV/rieXtfWXD/oKjx8/QktLSzVQk5EZGmbC0DATwUGBXL10gR4uA/4dhDLnxtXL5HWMH3AKDQnh4f27NG72OwAREfH/nv7/50ahpZUhZ8glRVxcHNGJXKVUAiiVP85VzB/4KpzI2CQ/fZ0W5WwICI7k1H31p7AZG+iwoVdZImPi6LLiqtri4okpaG3Kh9Aoov6vbEycUpWxGv2Si1P3fPiR/gzExSmJjv763+mjJs9WmwX75OE95k0bz8xFq7HMZfMfR2ZMJv+uL+r50oOHD+7Ry6Wfal9ISAguPbugp6fHnAVLEvzMZ2Rq+enyBbq7DEi0bJwy8dwA8PRJfK78/Cl5P5qwsFBev3qFeaPsFChYGB0dXa5cvkjNWnUA8HjxnLdvvShWrHjaNjS5SH7SSAag0kBkZKTqySvv379n0aJFhISE0KhRowRlXVxcWLhwIa1atWLEiBGYmppy6dIlypQpg5NT/EJ2derUIXPmzEyePJmJEyeqHT9ixAiKFClC79696dmzJ3p6epw+fZoWLVpgbq7+iHRHR0fatm1L+/btmT17NiVKlMDPz4+TJ09StGhRGjRIu2nECgW0LJebvy6/UlscM3e2TDQqmYuzD30JCInCMosBfWrnIyI6jlP/DlS98A3l6O23TPitCMO23CIkIoZhjQvy1CeYC0/i17L557Efdzw/MKttCcbvvIeWAia3LMrZh75qs6LSk/CwMLxef1qrwdvrDc+ePMIksylm5uZMHjWYp08eMnHGQuLi4ngXEN9Xk8ym6Orq4u/nw1CXruSwsKSby0ACP3x6so9ZtvifjTIVnNm9bSObVi+jaq16hIWFsXb5AnJYWOHw76BEejdtykSOHD7AnPmLyWRkpFqXwNjYBAOD+IXo/f39CPD355Vn/Pl86v6ETEZGWFhaYmqahTu3b3Lvzh1KlSlLJiMj7ty+xZwZbtRr0IjMGXito6uXzqNUKrGxtePNK0+WL5xDblt76jZqgkKhoHmrdmxcs5xcNrmxtMrFmuWLMDfPTqUq8U8OKlSkGMYmmZk2YRTtu/REz0Cfg3t24u31mnIVKqdx75LP/LmzqeRcGQtLS8JCQzl08ADXrl5h6Yo/ef3qFUePHKJ8hYpkzWqGj483q1etQF/fgEqVq6R105OHrGEg0gnJT0mnUECLsjb8dUU9Pxkb6LChdzkMdbXpv+EqJgY6mBjEfy0ICIkkTgk1Cucku4k+NzzeE/nvGlB9auVlxalPtxXaZzeiuG0Wbr78gKmhLl2r5cHJ0oRBGxMuxpxehIeF4fXmU37yefuGZ+7x+Slz5ixsXb+ScpWqkjWbOUGBHziwaxsB/r44V/v0JDhfn7cEBwXi5+NNXGwcz9wfAWCVKzeGmTIlGGQKCozPWDa29hibqK95k56FhYWqshGA15vXPH70kMymplhaWnH82BGyZs2KhaUVT92fMGv6FKpWq0H5CpWA+MGnPj26EBERziS3mYSGhhAaGn9XQdasZglmDGYU/5+fViz6Nz81bEJ4eBib1q6kgnNVsmXLTmDge/b+tRV/P1+q1KgNwP27t3h0/y7FS5bBMFMmHty9zdL5M6lRt+EPtYbmnJnTqVy1GpZWVvj5+rJ08UK0tbWoW78hJiYmNG3WnNkzpmFqaoqRkTHTpk6maLESFP1hBqAkP2kiA1Bp4MiRI1haWgLxVwzy58/Pjh07qFq1Kh4eHmpls2XLxqlTpxgyZAhVqlRBW1ub4sWLq12l09LSomPHjkydOpX27durHe/o6MixY8cYOXIkZcqUwdDQkLJly9K6dWuNbVuzZg2TJ09m0KBBvHnzBnNzc8qVK0fDhml7G4mzU3aszTKx7ZL6Og+RMXGUcchGl6p5MM2kh39wJJef+tNk9j9qU79dN9xgXLPCrO1VDqUSLrn788fii6qp5koldF5+mYktivCXayXComI488CXSbvupWo/k+LJo/sM6/tpGu+KhbMAqFmvMe269OTSuTMA9O6ovtDj9IWrKPZLaW5cuYTXa0+8XnvSrklttTJHzsc/5ax4ybIMG+/Gjk1r2bF5Lfr6BhQoXIwpc5agr29ARvDX9i0AdO+s/m9j3KSpNP61GQA7t29lxbLFqn1dO7VTK6Orq8fRI4dYvmwR0VFRWOWyps0fHWjXvlMq9SJlhIYEs3LJfPx9fTDJbIpztZp06dVP9YTDVn90JiI8nDluEwgJCaZIsRJMm79M9cQb0yxZmT5/GX8uXcCgPl2IiYnBLo8Dk2YuwMHR6b8+OkN59y6A0SOG4efni7GJCY6OTixd8SflK1TE19eHG9evsXHDOoICg8hmno2SJUuxftMWjU/mypDkCp5IJyQ/JV2lf/PT9kvqi48XtjblF7v4BZH/Gau+1lXF8Sd4/S6cmNg42jvbMaZpIRQK8PALZdLuB2y5+CmLaWsp6FbdgTw5jImOjeOiewDN5p7j9buEs9LSC/fH9xner5vq/cpFswGoWbcRLoNH89rTgymjBxEY+IHMmbPgWKAQMxetxtb+0+2fG1ct4cSR/ar3fTu3AmDagpUULVE6lXqS8h7cv0ePLh1U7+fMnAZAw8ZNmDB5Gv5+vsydOY2AgADMs2enQaNf6dajl6r8o4f3uffvk3ObNFDPmvsPn8AqV/LMSk9toSHBrFqqnp8694zPT3GxcbzyeMH4Q/sI+vCezKZZcCpQiHnL1mGXJ/5nSFdXj9PHj7Bu1VKio6OwsMxF81Z/8Fvr9l/45IzFx8ebEUMH8uHDB7KamVGiREnWb9qOmVn8LK/Bw0ai0NJikGs/oqKjqFChEiPHjEvjVicjyU8aKZQ/+n0SP4kuXbrg5+fHvn370ropANi47E3rJqRLZ8fXSesmpEvZTX6c6djJKTA8Oq2bkC5lM/5Bni6XzAxS6JKSYZMV33V8+J7uydQSIZJfestPtv32f7nQT+jU6Jpp3YR0KadpxrgYmNo+hEl+0kTyk2aGKbQkmeQnzWQGVAYXGBjI3bt32bx5c7oJT0IIIX4gcgVP/IAkPwkhhEhRkp80kgGoDO7XX3/lypUr9OzZk1q1an35ACGEEEKIn5zkJyGEECL1yQBUBvf5I4OFEEKIZCeLaIofkOQnIYQQKUryk0YyACWEEEKIRCkkQAkhhBBCJInkJ81kAEoIIYQQiZIAJYQQQgiRNJKfNJMBKCGEEEIkTvKTEEIIIUTSSH7SSJZmF0IIIYQQQgghhBApSmZACSGEECJRMoVcCCGEECJpJD9pJgNQQgghhEiUBCghhBBCiKSR/KSZ3IInhBBCiEQpFIrvegkhhBBC/GxSMz/FxsYyZswY7O3tMTQ0xMHBgUmTJqFUKlVllEolY8eOxdLSEkNDQ2rWrIm7u7taPe/evaNt27ZkzpyZLFmy0KVLF0JCQtTK3LlzB2dnZwwMDLCxsWHGjBlJaqsMQAkhhBAiURKghBBCCCGSJjXz0/Tp01m6dCmLFi3i4cOHTJ8+nRkzZrBw4UJVmRkzZrBgwQKWLVvG5cuXMTIyok6dOkRERKjKtG3blvv373P8+HEOHDjA2bNn6d69u2p/UFAQtWvXxtbWluvXrzNz5kzGjx/PihUrvrqtMgAlhBBCiHQhIwUoIYQQQoj04MKFC/z66680aNAAOzs7fvvtN2rXrs2VK1eA+It38+bNY/To0fz6668ULVqU9evX4+XlxZ49ewB4+PAhR44cYdWqVZQtW5ZKlSqxcOFCtm7dipeXFwCbNm0iKiqK1atXU6hQIVq1akW/fv2YM2fOV7dVBqCEEEIIkTjF970iIyMJCgpSe0VGRmr8qIwUoIQQQgghEpWK+alChQqcPHmSJ0+eAHD79m3OnTtHvXr1AHjx4gXe3t7UrFlTdYypqSlly5bl4sWLAFy8eJEsWbJQqlQpVZmaNWuipaXF5cuXVWUqV66Mnp6eqkydOnV4/Pgx79+//6rTIgNQQgghhEjU904hd3Nzw9TUVO3l5uam8bMyUoASQgghhEhMauan4cOH06pVK/Lnz4+uri4lSpTA1dWVtm3bAuDt7Q1Azpw51Y7LmTOnap+3tzc5cuRQ26+jo4OZmZlaGU11fP4ZXyJPwRNCCCFEor53IfERI0YwcOBAtW36+voayw4fPpygoCDy58+PtrY2sbGxTJkyJUUClL29fYI6Pu7LmjXrt3RVCCGEEAJI3fy0fft2Nm3axObNmylUqBC3bt3C1dUVKysrOnTo8F3tSG4yACWEEEKIRH1vgNLX1080MP2/jBSghBBCCCESk5r5aciQIapZUABFihTh5cuXuLm50aFDBywsLADw8fHB0tJSdZyPjw/FixcHwMLCAl9fX7V6Y2JiePfunep4CwsLfHx81Mp8fP+xzJfILXhCCCGESFRqPsXl8wBVpEgR/vjjDwYMGKCacv55gPqcj4+PWjhKjQAlhBBCCJGY1MxPYWFhaGmpD+1oa2sTFxcHgL29PRYWFpw8eVK1PygoiMuXL1O+fHkAypcvz4cPH7h+/bqqzKlTp4iLi6Ns2bKqMmfPniU6OlpV5vjx4zg5OX317HEZgBJCCCFEupCRApQQQgghRHrQqFEjpkyZwsGDB/Hw8GD37t3MmTOHpk2bAvGDYa6urkyePJl9+/Zx9+5d2rdvj5WVFU2aNAGgQIEC1K1bl27dunHlyhXOnz+Pi4sLrVq1wsrKCoA2bdqgp6dHly5duH//Ptu2bWP+/PkJbhX8L3ILnhBCCCES930zyJPkY4DKnTs3hQoV4ubNm8yZM4fOnTvHN+WzAJUvXz7s7e0ZM2ZMogFq2bJlREdHawxQEyZMoEuXLgwbNox79+4xf/585s6dm3qdFUIIIcSPKxXz08KFCxkzZgy9e/fG19cXKysrevTowdixY1Vlhg4dSmhoKN27d+fDhw9UqlSJI0eOYGBgoCqzadMmXFxcqFGjBlpaWjRv3pwFCxao9puamnLs2DH69OlDyZIlMTc3Z+zYsXTv3v2r26pQKpXK5Om2EJ/YuOxN6yakS2fH10nrJqRL2U2+7v7mn01gePSXC/2EshnrfbnQT8gghS4pmXfc+l3H+69t9dVlg4ODGTNmDLt371YFqNatWzN27FjVE+uUSiXjxo1jxYoVqgC1ZMkSHB0dVfW8e/cOFxcX9u/frxagjI2NVWXu3LlDnz59uHr1Kubm5vTt25dhw4Z9V1+F+F62/fandRPSpVOja3650E8op6nBlwv9hD6ESX7SRPKTZoa6KVNvauanjEQGoESKkAEozWQASjMZgNJMBqA0kwClWUoNQGXvtO27jvdb83sytUSIH58MQGkmA1CayQCUZjIApZnkJ81SagBK8pNmcgueEEIIIRL1vU9xEUIIIYT42Uh+0kwWIRdCCCGEEEIIIYQQKUpmQAkhhBAicXIBTwghhBAiaSQ/aSQDUEIIIYRIlEwhF0IIIYRIGslPmskAlEgRsti2ZgVrDU7rJqRLby/MT+smpEumKbUqYgYXJ8/OSETKBB0JUEKkHllsW7PCdYakdRPSJd9LC75c6Cdkmkm+4mqiRPKTZpKfUpP86xRCCCFEoiRACSGEEEIkjeQnzWQRciGEEEIIIYQQQgiRomQGlBBCCCESJVfwhBBCCCGSRvKTZjIAJYQQQojESX4SQgghhEgayU8ayQCUEEIIIRIlV/CEEEIIIZJG8pNmMgAlhBBCiERJgBJCCCGESBrJT5rJAJQQQgghEiUBSgghhBAiaSQ/aSZPwRNCCCGEEEIIIYQQKUpmQAkhhBAicXIBTwghhBAiaSQ/aSQDUEIIIYRIlEwhF0IIIYRIGslPmskAlBBCCCESJQFKCCGEECJpJD9pJgNQQgghhEiUBCghhBBCiKSR/KSZLEIuhBBCCCGEEEIIIVKUzIASQgghRKLkCp4QQgghRNJIftJMBqCEEEIIkTjJT0IIIYQQSSP5SSMZgBJCCCFEouQKnhBCCCFE0kh+0kwGoIQQQgiRKAlQQgghhBBJI/lJM1mEXAghhBBCCCGEEEKkKJkBJYQQQohEyQU8IYQQQoikkfykmQxACSGEECJRMoVcCCGEECJpJD9pJgNQQgghhEiU5CchhBBCiKSR/KSZDEAJIYQQIlFyBU8IIYQQImkkP2kmA1BCCCGESJTkJyGEEEKIpJH8pJk8BU8IIYQQQgghhBBCpCiZASWEEEKIRGlpySU8IYQQQoikkPykmcyA+oGMHz+e4sWLp3UzhBBC/EAUiu97CZHeSX4SQgiR3CQ/afbTzoDy9vZmypQpHDx4kDdv3pAjRw6KFy+Oq6srNWrUSJM22dnZ8fLlSwAMDAzImTMnZcqUoWfPnlSvXv2Lxw8ePJi+ffumdDNT3d1b1/lr81rcHz3kXYAfY93mUqFy/PmIiYlm3YpFXL14jrderzEyMqFE6bJ07tmfbNlzqOoIDgpkyZxpXD7/NwotLSpWrUGv/sMwzJQJgFcvPVg4cxKeHs8JDQ0hm3l2qtaqT7vOPdDR0U2Tfn+JcSZ9xvVuSOPqxcie1Zjbj18zeMZfXH/gCcCKCe34o3E5tWOOnX/Ary5LEtSlp6vD2Q2DKeZkTdnf3bjz5A0A+no6LBzVihIFcpPfPieH/7lHy4ErU75zyWjn9q3s2rEVL6/4PuVxyEuX7r2oUKkyAK9febJgzkxu37pBVFQU5StUYtDwUWTLZg6A15s3rF65lGtXLvMuwB/z7DmoW78hnbr1QFdXL8369b1Wr1rO6ZPH8XjxHH19A4oWL0E/10HY2edRldn11zaOHDrAo4cPCA0N5cy5K5hkzpygrn/OnmHlsiU8dX+Mnp4+v5QqzZz5i1OzOykqNjaWZUsWcejAPgL8/cmePQeNmjSlW49eagtMPn/2jPlzZ3Hj2lViYmPJk8eBWfMWYGlplYat/36yiKb4nOSnjOPurevs3LKOp4/j89PoKXNU+Qlg4+qlnD15FD9fb3R1dMnrVJD23VzIX6iIqszW9Su5evEfnrs/QUdXhx2HzyX4nFvXLrPhz8V4PHuKgaEhNeo2okM3F7R10u/XjO/NULktzRjRvS5VSzuSM1tm3voFsuXQVaavOkp0TCwAziXz0bddNUoVsiWzsQFPPf2Yt+4EWw9fS93OfqM1q1YkyAl9XQdhZ2+vKtO9c3tuXLuqdlyzFr8zcsx4AJ48fsTaP1dy++YNPnx4j6VVLpq3+J3W7dqnZleSVXKclw8f3jNm+FDc3R8T+OEDZmbZqFytOn36DcDY2Dg1u5OiQkNDWLJwAadOnuD9uwCc8hdg6PBRFCpShOjoaJYsnM+5f/7m9evXGBsbU7ZcBfoNGEiOHDnTuunJQvKTZun3L0MK8vDwoGLFimTJkoWZM2dS5N9/BEePHqVPnz48evTom+qNjY1FoVCgpfXtE8smTpxIt27diIqKwsPDg40bN1KzZk0mTZrEqFGjNB6jVCqJjY3F2Ng4xX9pRUVFoaeXul+6I8LDsc/rRO0GTZg0cqDavsiICJ4+fkSbjt2xz+tESHAQy+ZPZ/yw/ixcvUVVbvqEEbzz92fqvGXExMQwZ+o45s+YyPDx0wDQ0dGhZr1G5HUsgJGJCc/dnzB/+gSUcXF06tkvVfv7tZaObUPBvFZ0Hr2Ot36BtK5fhoPL+vJL88l4+QUCcPT8fXqM26g6JjIqRmNdU11/5a1fIMWcrNW2a2tpER4ZzZItZ2hSo3iK9SUl5ciZk979BmCT2xaAg/v2MMTVhQ1bd2KZKxf9enUjn6MTi1esAWD54gUM7teHPzdsQUtLi5cez4mLi2P46PHY5M7Ns6fuTJ04jvCIcPoPHJqWXfsuN65dpUWrNhQqVITY2FgWLZhLn55d+Wv3AdXAbER4BOUrOlO+ojOL5s/RWM/J40eZPGEsffoNoHSZssTGxvL0qXtqdiXFrf1zJX9t28LEKdNwyJuX+/fvMX70SIyNjWnzb4h+5elJ5/ZtaNLsN3r16YuRkTHPnj1FX08/jVv//SQ/iY8kP327NMlPEeHY53WkdoMmTB41MMH+XDa29BowHAsra6IiI9i9bROjB/Xizy37MM1qBkBMdDSVqtYif6FiHDu4O0Edz58+ZuxQF1r90ZVBoyYT4OfLotlTiIuLo2ufhJ+ZXnxvhnKyz4mWQguXyVt59sqPQnmtWDymNUaG+oyYG3+eyhWz5577G+asPY5PQDD1nQuzalJ7AkMiOPzPvdTt8Df4mBMKFipMbGwsixfMxaVnF3Z8lhMAmjZvQY8+nwZwDQwMVf/98MF9zMyyMdFtOjktLLlz6yZTJo5DS1ub31u3TdX+JJfkOC9aWlpUqVadXn37kzVrVl55ejJ96iSCAgOZMn1WqvYnJU0cO4anT92Z7Dad7DlycGj/Pnp268TOvQcxzJSJhw8e0K1HbxydnAgKCmLmtKm4uvRm8/adad30ZCH5SbOfcgCqd+/eKBQKrly5gpGRkWp7oUKF6Ny5s+r9nDlzWLNmDc+fP8fMzIxGjRoxY8YMVUhZu3Ytrq6urF+/nuHDh/PkyROePn2KnZ3dN7fNxMQECwsLAHLnzk3lypWxtLRk7Nix/Pbbbzg5OXHmzBmqVavGoUOHGD16NHfv3uXYsWOcOXOGPXv2cOvWLY4dO0bjxo3x9vYmS5Ysqvr79+/P3bt3OXXqFADnzp1jxIgRXLt2DXNzc5o2bYqbm5vqvNjZ2dGlSxfc3d3Zs2cPzZo1Y+3atd/cv29RunwlSpevpHGfkbEJbvOXq23rPXAE/bu2xdf7LTksLPH0eM61S+dZsGozjgUKxZcZMJwxg/vQrc9AsmXPgWUuayxzfRp8yWlhxZ2bV7l3+0bKdew7GOjr0qRGcVoMWMH5G88AmLL8EPUrF6ZbC2cmLDkAQFRUDD4Bwf9ZV+2KBalRrgCth6yibqVCavvCIqLoP3UbAOWL5yGLiaGmKtI15yrV1N736uvKrh1buXf3Dr6+vrz1esP6rTtV/67HTXKjZuVyXLtyiTLlKqgGYD7KZW3DS48X7NqxLUMPQC1atkrt/YRJbtSsWoGHD+7zS6nSALT5owMA165e1lhHTEwMs6ZPpf/AITRp9ptqex6HvCnU6rRx+9ZNqlSrgXOVqgBY5bLmyKGD3L97V1Vm0YJ5VHKuguugIaptNrlzp3ZThUhRkp8yWH4qV4nS5TTnJ4Bqteqrve/edxDHDu7mxTN3ipcqC0C7Lr0BOH5or8Y6zp48ir1DPtp06gGAlXVuOvdyxW3sUNp06kGmTEYaj0tLyZGhjl94yPELD1XvPd4E4Gibg24tnFUDUDNXH1M7ZvGWM9Qon59fqxfLEANQC5epz3gfP8mNWlUrquUEiJ91aG6eXWMdvzZtrvbe2tqGu7dvcfrE8Qw7AJUc5yVzZlN++7216r2lVS5a/N6aDWtXp0yj00BERAQnTxxj7oLFlPz3vPTs05ezf59mx7Yt9OnnyrJV6v0dPnIM7Vq34O1brww/e1wk7qdbA+rdu3ccOXKEPn36qIWnjz4PG1paWixYsID79++zbt06Tp06xdCh6l84w8LCmD59OqtWreL+/fvkyJGD5Na/f3+USiV796r/8R8+fDjTpk3j4cOHFC1aVG1fjRo1yJIlCzt3fhpBjo2NZdu2bbRtG/8L/9mzZ9StW5fmzZtz584dtm3bxrlz53BxcVGra9asWRQrVoybN28yZsyYZO9fcgsNCUGhUGBkYgLAw3u3MTYxUQ0+AZQoVRaFlhaPHtzVWIfXa0+uX75AkRKlUqXNSaWjrYWOjjYRUdFq2yMio6lQwkH13rlUPl6edOP27jHMH/k7ZqbqP/M5zExYMqY1XcasJyw8KlXanpZiY2M5duQQ4eHhFC5ajOjoKBQKhdpVaT19fbS0tLh9M/HBx9CQEDKbmqZGk1NNSEh8yE5Kvx49fICvrw9aWgratGxK7erO9O3VjafuT1KqmWmiWPESXLl8kZceLwB4/OgRt27coKJz/G2ccXFxnDt7htx2dvTu3oXqlSvwR+uWnD55Ii2bnWwUCsV3vcSPQfLTj52foqOjObxvJ0bGxtjndUzScXr/N9NTT1+fqKhInj5+kNzNTBbJlaH+X2ZjQ94Fhf1nGVNjQ95/oUx6lVhOOHzoADUql6dl00Ysmj+HiPDwL9TzY2Wo5Dgvfr6+nDp5XG0AK6OLjY0hNjYWPX313w/6+gbcvHFd4zHBIcEoFApMTBIu95ARSX7S7KebAfX06VOUSiX58+f/YllXV1fVf9vZ2TF58mR69uzJkiWf1tCJjo5myZIlFCtWLCWaC4CZmRk5cuTAw8NDbfvEiROpVauWxmO0tbVp1aoVmzdvpkuXLgCcPHmSDx8+0Lx5/NUINzc32rZtq+pnvnz5WLBgAVWqVGHp0qUYGBgAUL16dQYNGpRo+yIjI4mMjPy/bUr09VP/1pOoyEhWL51H1Zr1MDKKv9L6PiAA0yxmauW0dXQwMcnM+3cBatsH9GjP0ycPiY6Kot6vzWnftXeqtT0pQsIiuXT7OSO61ePxCx98AoJoWbcUZYva8+yVHxB/dW7vqdt4vAkgj7U5E/o2Yu+iXlTpMJu4OCUAKya2Y+Vf57jxwJPclmb/9ZEZ2lP3J3Rt35qoqCgMDTMxfc4C8jjkJWtWMwwMDVk0bza9+7qiRMni+XOIjY3F399PY12vPF+yfesm+g0YonF/RhQXF8esGVMpVuIX8ub7+i8eb16/AmD50sUMHDwMq1y52LBuDd27tGf3/iOYmmZJoRanrk5duxMSGkrTRvXR1tYmNjaWPv1cqd+wEQDv3gUQFhbGmj9X0qdvf/oPHMz5c/8wyLUvK1avo1TpMmncg+/zI4cg8fUkP6VWfopL1fx0+fxZpk8YRmREBGbZzJkyZxmmWbJ+9fEly5Rn745NnDlxGOdqtXn/zp/Na1cA8C7AP6Wa/V2SK0N9Lo+NOb1aVVHNftKkea0SlCyUG5fJWxItk17FxcUxe4ZbgpxQt35DLC2tyJ49B+7uj1k4dzYvPV4wc+5CjfXcvnWTY0cPM3/RstRqeor63vMycugg/j5zisiICJyrVGP0+Emp3YUUY2RkTNFixVm5bAn2efKQLZs5Rw4d5M7tWxpniEdGRrJg7izq1m/ww6yDJflJs59uBpRSmfCPRmJOnDhBjRo1yJUrFyYmJvzxxx8EBMR/0fhIT08vwdWz/1eoUCHV+gL16tX75nb//w9xqVL/PTunbdu2nDlzBi8vLwA2bdpEgwYNVFcpb9++zdq1a1VtMzY2pk6dOsTFxfHixYuv/hw3NzdMTU3VXkvnz/yGXn6fmJhopowZglKpxGWI5vUevmTkxBksWr2VYeOncfXCP+zcsi6ZW5l8Oo9ej0IBz49NIfDyPPq0rsL2I9dUwWjH0esc/Psu9596sf/MHZr1W0apwnZULpUPgN6tq2CSySDBFPEfka2dHRu27eLPDVtp1vJ3Jo4dyfNnT8lqZsbUGXM5d/YMVSuUokalsgQHB+NUoKDGtUh8fXxw7dOdGrXq0KR5izToScqYNmUiz5664zZd8zpPiYmLiwOgS7ce1KhVhwIFCzN+khsKhYITx46kRFPTxLEjhzl8YD9Tp89i8/adTJwyjQ1rV7Nvb/wXjY/noWq16rRr3xGn/AXo3LU7zlWq8tf2rWnZ9GQhT3ERIPkptfLTsgWpm5+K/VKaRau3MXvpOkqWrYjbuKF8eP/uq4//pUwFOvcawKJZU/i1Rhm6tflVddufQpF+v2Z8b4b6nFV2U/Yt6sOuEzdZs/uCxs+rXCofyye0o/ekLTx87p2ifUsJ0//NCVOnz1bb3uy3lpSvWIm8jo7Ua9CICVOmcfrkCV6/8kxQx1P3Jwzq34duPXtTrkLF1Gp6ivre8zJw6HA2bdvJ7PmLefPak7kzp6Vm81PcZLcZKFFSp3oVyv5SlC2bNlC3XgO0/u93Q3R0NEMHuaJUolqo/Ucg+Umzn24GVL58+VAoFF9cKNPDw4OGDRvSq1cvpkyZgpmZGefOnaNLly5ERUWR6d9F5gwNDb84unno0CGio6NV5ZMqICAAPz8/7D97ugKgcQr850qXLo2DgwNbt26lV69e7N69W239gZCQEHr06EG/fgkX2c792cj0lz5nxIgRDByovtCkV/DXB9XkEBMTzdQxQ/D1ecv0BStVs58AsmbLRuAH9TAVGxNDcHAQWc2yqW3PnjN+/Qhbewfi4mJZMH0SzVq1R1tbO+U7kUQvXvtTu+t8MhnokdnYAG//IDZM68SLN5qvOHq8CcDvfTAONtk5c+UJVUs7UraoPYGX56mVO79pKFsPX6Pb2A2p0IvUoaurp1qEvEDBQjy8f49tmzcwYswEylWoyK4DR/nw/j3a2tqYZM5MvRrOWOVS/7Lj5+tL724dKVKsBCPGTEiLbqSI6VMncu7sGVau2UjOf9dP+Vrm2ePXNrDP82nNJz09PXLlssH77dtkbWdamjd7Jp26dqNu/QYA5HN04u1bL9asWkHjX5uSNWtWdHR0Eqx9lSePQ6LTzDMSuYInQPJTauWn14Fx/3lMcjMwNMTKOjdW1rnJX6goXVs34uiB3fz+R5evrqNZqz9o+ns73gX4YWySGZ+3XqxdvgBLq1wp2PLv870Z6iPL7KYcWdmfS3ee02eS5plNlUrmZef8ngydtYvNB66kSH9S0vSpkzh39m9WrNnwxZxQuEj8oPIrT0+sbT79W3j+7Cm9u3WmafOWdO3eK0Xbm1qS47yYm2fH3Dw7dvZ5MDU1pWvHdnTt0Qvz7Ml/S3JasMmdmz/XbiQ8LIyQ0BCyZ8/BsEEDyGVtoyoTHR3NsEEDeOvlxYrVa3+Y2U8g+SkxP90AlJmZGXXq1GHx4sX069cvQTj48OEDWbJk4fr16/HTKmfPVs2E2L59+zd9pq2t7Xe1ef78+WhpadGkSZMkH9u2bVs2bdqEtbU1WlpaNGjQQLXvl19+4cGDB+TN+30LBuvr6yeYLh4QFfFddSbFx8GnN688mb5wFZn/77afAoWLERIcjPujB+TLXxCAW9evoIyLI3/BIhpqjKeMUxITE4NSGQekvwGoj8IiogiLiCKLiSE1KxRg1DzNC4XmypGFbKZGePsHATBoxl+MX3xAtd8yuykHlrrwx/A1XL3rkRpNTzNxcUqi/2/thyxZ4285uHblEu/fvaNy1U+Pqvb18aF3t47kL1iIMROmfNeTmtILpVLJDLdJnD51ghV/rieXtfWXD/o/BQoWRk9Pj5ceLyjxS0kgPki89XqDpdWPs3hkRER4giv5WlpaqplPurp6FCxUmJefzXwAeOnh8UOdB/Fzk/yUOvlJP+K/189JaXFxSqKjk74mpEKhIJt5/Jfmv08cIXsOCxwcCyR385Ldt2YoiJ/5dGRlf24+9KT7uI0aZwk6l8zHrgU9GT1/L6t3nU+xfqSE+JwwmTOnTrD8z3VflRMeP44foP54gQrg2VN3enXtRIPGv9Knn2tKNTfVJNd5+X8fM0XU/+XTH4FhpkwYZspEUGAgFy6cw3XgYODT4JOn50tWrF5HliTc/isyrp9uAApg8eLFVKxYkTJlyjBx4kSKFi1KTEwMx48fZ+nSpTx8+JC8efMSHR3NwoULadSoEefPn2fZspS/Xzk4OBhvb2+io6N58eIFGzduZNWqVbi5uX1T0Gnbti3jx49nypQp/Pbbb2pBZ9iwYZQrVw4XFxe6du2KkZERDx484Pjx4yxatCg5u/VdwsPC8Hr9acqqt9cbnj15hElmU8zMzZk8ajBPnzxk4oyFxMXFqdYcMMlsiq6uLrnt8lCqXEXmTZ9AvyGjiYmJYclcN6rUrEu2f68wnDp6EB0dHewc8qGrq4f7o/usWTafyjVqo6Ojmyb9/pKa5QugUMATD18cbLIzdUATnrzwYf2+ixgZ6jGqR332nLyFt38QeWzMmdK/Cc9e+aue2vLK+71afSFh8etQPH/lxxvfD6rt+fNYoKejTVZTI0wy6VPUMf6K5p0nb1Kno99p8YI5VKhYmZwWloSFhXL08AFuXLvC/CXxTzHZv2cXdnkcyJo1K3fv3GLODDdat2uPrV38FXNfHx96de2ApZUV/QYMUbs1IVsiTzfJCKZNmciRwweYM38xmYyMVGteGRubqNYv8ff3I8Dfn1ee8f/+nro/IZORERaWlpiaZsHY2JjmLVqxfMlCclpYYGlpxfp/n+BSs3bdtOlYCqhctRp/rlyGpaUlDnnz8ujhQzauX0uTz57u06FTF4YNHsgvpUpRqkxZLpz7h7N/n2blmvVp2PLkIRfwxEeSn+JlqPz05lN+8nn7hmfu8fkpc+YsbF2/knKVqpI1mzlBgR84sGsbAf6+OFf7tD6Wr89bgoMC8fPxJi42jmfu8V+krXLlVj1y/q/NaylZtiJaWgrO/32KHZtWM3zCjHQ5e/yj781QVtlNObqqP55v3zFizm6yZ/00a+Pjk/Mql4offFq8+Qx7Tt4kZ7b4h+NERcdmiIXIp0+ZyJHDB5k9f5HGnPD6lSdHDh2gonMVTE2z4P7kMXNmTuOXkqXI5+gExOeGXl07Ua5iRdq276iqQ1tLm6xmGXPt0eQ4L+f++Zt3AQEULFSYTJmMeP7MnflzZlGsxC9Y5Uq/MweT6sL5f1Aqwc7OnleeL5k7eyb29nlo3KQZ0dHRDBnYn0cPHjB/8TLi4j6tv2pqaoqurt4Xak//JD9p9lMOQOXJk4cbN24wZcoUBg0axNu3b8mePTslS5Zk6dKlABQrVow5c+Ywffp0RowYQeXKlXFzc6N9+/Yp2raxY8cyduxY9PT0sLCwoFy5cpw8eZJq1ap9+WAN8ubNS5kyZbhy5Qrz5s1T21e0aFH+/vtvRo0ahbOzM0qlEgcHB37//fdk6EnyefLoPsP6dlW9X7FwFgA16zWmXZeeXDp3BoDeHVuqHTd94SqK/RL/NIlh49xYPMeN4f26o9DSolLVGvRyHa4qq62tzfZNa3jj+RIlSnLktKRR89Y0+71dCvfu25kaGzCxb2Ny5czCu8Aw9p68xbjF+4mJiUNHW0nhfLlo26gsWUwMeesXyImLj5i45ABR0TFJ+pw9C3tha/XpVsXL20YAYFjCJbFD0pX3794xYfRw/P39MDY2Ia+jI/OXrKRs+QoAeL70YMnCuQQFBmJplYtOXXvQul0H1fFXLl3g9StPXr/ypFEd9X+Hl2+lzyf8fI2/tsffKtC9s/rvtHGTptL412YA7Ny+lRXLFqv2de3ULkGZ/gOHoK2tzdiRw4iMjKBwkWIsW7WWzJl/nCfcDBs5miULFzB18kTevwsge/Yc/Nbid7r3+vSQguo1azFq7HhWr1rBDLcp2NrZM3PuAtXMsIxMppCLjyQ/xcso+cn98X2G9+umer9yUfw6NTXrNsJl8Ghee3owZfQgAgM/kDlzFhwLFGLmotXY2n8asNu4agknjuxXve/buRUA0xaspGiJ+Ix17fJ5tm1YRXRUNPZ5HRnjNk+1DlR69b0Zqnq5/OTNnYO8uXPw7NgUtbo/5qN2jcpiZKjP0C51GNqljmr/2Wvu1Ok2P/U6+40+rmHYo3MHte3jJk2l0a9N0dHV5cqli2zZuJ7w8HByWlhQvWYtunx2i93J48d4//4dhw/s5/CBTz9HllZW7D9yMnU6ksyS47wY6BuwZ+cO5sycRnRUFDktLKhWoxYdO3fjRxISHMLCeXPw8fHG1DQLNWrVok+/Aejq6uL15jV/nz4FQKvfmqgdt3L1OkqVKZsGLU5ekp80UyiTsqqkEF/phX/q3YKXkRSsNTitm5Auvb2Q/oNYWtD5AW71SwlyWjTLpJsyQafU5NPfdfy10d82ACDEz+iZb9regpdeFa7z4zx5Njn5XlqQ1k0QGYi2lgyIaCL5KXX9lDOghBBCCPF15AqeEEIIIUTSSH7STAaghBBCCJEoyU9CCCGEEEkj+UkzuZFBCCGEEEIIIYQQQqQomQElhBBCiETJFHIhhBBCiKSR/KSZDEAJIYQQIlGSn4QQQgghkkbyk2YyACWEEEKIRMkVPCGEEEKIpJH8pJkMQAkhhBAiUZKfhBBCCCGSRvKTZrIIuRBCCCGEEEIIIYRIUTIDSgghhBCJkinkQgghhBBJI/lJMxmAEkIIIUSiJD8JIYQQQiSN5CfNZABKCCGEEImSK3hCCCGEEEkj+UkzGYASQgghRKIkQAkhhBBCJI3kJ82SvAj5q1eveP36ter9lStXcHV1ZcWKFcnaMCGEEEKIH4XkJyGEEEKklDdv3tCuXTuyZcuGoaEhRYoU4dq1a6r9SqWSsWPHYmlpiaGhITVr1sTd3V2tjnfv3tG2bVsyZ85MlixZ6NKlCyEhIWpl7ty5g7OzMwYGBtjY2DBjxowktTPJA1Bt2rTh9OnTAHh7e1OrVi2uXLnCqFGjmDhxYlKrE0IIIUQ6plB83yupMkqASirJT0IIIcTPIzXz0/v376lYsSK6urocPnyYBw8eMHv2bLJmzaoqM2PGDBYsWMCyZcu4fPkyRkZG1KlTh4iICFWZtm3bcv/+fY4fP86BAwc4e/Ys3bt3V+0PCgqidu3a2Nracv36dWbOnMn48eOTdDEtyQNQ9+7do0yZMgBs376dwoULc+HCBTZt2sTatWuTWp0QQggh0jGFQvFdr6TISAEqqSQ/CSGEED+P1MxP06dPx8bGhjVr1lCmTBns7e2pXbs2Dg4OQPzFu3nz5jF69Gh+/fVXihYtyvr16/Hy8mLPnj0APHz4kCNHjrBq1SrKli1LpUqVWLhwIVu3bsXLywuATZs2ERUVxerVqylUqBCtWrWiX79+zJkz56vbmuQBqOjoaPT19QE4ceIEjRs3BiB//vy8ffs2qdUJIYQQIh373it4kZGRBAUFqb0iIyM1flZGClBJJflJCCGE+HmkZn7at28fpUqVokWLFuTIkYMSJUqwcuVK1f4XL17g7e1NzZo1VdtMTU0pW7YsFy9eBODixYtkyZKFUqVKqcrUrFkTLS0tLl++rCpTuXJl9PT0VGXq1KnD48ePef/+/VedlyQPQBUqVIhly5bxzz//cPz4cerWrQuAl5cX2bJlS2p1QgghhEjHvvcKnpubG6ampmovNzc3jZ+VkQJUUkl+EkIIIX4eqZmfnj9/ztKlS8mXLx9Hjx6lV69e9OvXj3Xr1gHxt/4D5MyZU+24nDlzqvZ5e3uTI0cOtf06OjqYmZmpldFUx+ef8SVJfgre9OnTadq0KTNnzqRDhw4UK1YMiA+NH6eWCyGEEOLH8C3rOH1uxIgRDBw4UG3bx5lA/+9jgBo4cCAjR47k6tWr9OvXDz09PTp06JCsAcre3j5BHR/3fX7LX3KR/CSEEEL8PFIzP8XFxVGqVCmmTp0KQIkSJbh37x7Lli2jQ4cO39eQZJbkAaiqVavi7+9PUFCQWkDr3r07mTJlStbGCSGEECJj09fXTzQw/b+MFKCSSvKTEEIIIb5WUvKTpaUlBQsWVNtWoEABdu7cCYCFhQUAPj4+WFpaqsr4+PhQvHhxVRlfX1+1OmJiYnj37p3qeAsLC3x8fNTKfHz/scyXJPkWPIhfg+H69essX76c4OBgAPT09CRACSGEED8YLYXiu15JkViA8vT0BNQD1Od8fHzUwlFqBKhvIflJCCGE+DmkZn6qWLEijx8/Vtv25MkTbG1tAbC3t8fCwoKTJ0+q9gcFBXH58mXKly8PQPny5fnw4QPXr19XlTl16hRxcXGULVtWVebs2bNER0eryhw/fhwnJ6evnj2e5AGoly9fUqRIEX799Vf69OmDn58fED+1fPDgwUmtTgghhBDp2PcuopkUGSlAJZXkJyGEEOLnkZr5acCAAVy6dImpU6fy9OlTNm/ezIoVK+jTp8+/bVHg6urK5MmT2bdvH3fv3qV9+/ZYWVnRpEkTIP6CX926denWrRtXrlzh/PnzuLi40KpVK6ysrABo06YNenp6dOnShfv377Nt2zbmz5+f4FbB/5LkAaj+/ftTqlQp3r9/j6GhoWp706ZN1QKhEEIIITK+1HyMcEYKUEkl+UkIIYT4eaRmfipdujS7d+9my5YtFC5cmEmTJjFv3jzatm2rKjN06FD69u1L9+7dKV26NCEhIRw5cgQDAwNVmU2bNpE/f35q1KhB/fr1qVSpEitWrFDtNzU15dixY7x48YKSJUsyaNAgxo4dS/fu3b+6rUleA+qff/7hwoULak+OAbCzs+PNmzdJrU4IIYQQ6ZjWdy6imRQfA9SIESOYOHEi9vb2GgNUaGgo3bt358OHD1SqVEljgHJxcaFGjRpoaWnRvHlzFixYoNr/MUD16dOHkiVLYm5unuQAlVSSn4QQQoifR2rmJ4CGDRvSsGHDRPcrFAomTpzIxIkTEy1jZmbG5s2b//NzihYtyj///PPN7UzyAFRcXByxsbEJtr9+/RoTE5NvbogQQgghREYJUEkl+UkIIYQQP7sk34JXu3Zt5s2bp3qvUCgICQlh3Lhx1K9fPznbJoQQQog0lppTyH9kkp+EEEKIn4fkJ80USqVSmZQDXr9+TZ06dVAqlbi7u1OqVCnc3d0xNzfn7Nmz5MiRI6XaKjKQgNCYtG5CuhQcLudFk/CohLMCBNhlN0rrJogMxFA3ZeptsPzKdx1/sEeZZGpJxib5SXwNvxDJCZoEhUd/udBPKC4urVuQPlmbGX650E9ISZK+9v80MummzGCP5CfNknwLnrW1Nbdv32br1q3cuXOHkJAQunTpQtu2bdUW1RRCCCFExqfgx70Kl5okPwkhhBA/D8lPmiV5AApAR0eHdu3aJXdbhBBCCJHOpPYimj8yyU9CCCHEz0Hyk2ZJHoBav379f+5v3779NzdGCCGEEOJHJPlJCCGEED+7JA9A9e/fX+19dHQ0YWFh6OnpkSlTJglQQgghxA/kR14IMzVJfhJCCCF+HpKfNEvyANT79+8TbHN3d6dXr14MGTIkWRolhBBCiPRB8lPykPwkhBBC/DwkP2mmlRyV5MuXj2nTpiW4uieEEEKIjE1Lofiul0ic5CchhBDixyT5SbNvWoRcY0U6Onh5eSVXdUIIIYRIB37gDJQuSH4SQgghfjySnzRL8gDUvn371N4rlUrevn3LokWLqFixYrI1TAghhBBpT9YwSB6Sn4QQQoifh+QnzZI8ANWkSRO19wqFguzZs1O9enVmz56dXO0SQgghhPhhSH4SQgghxM8uyQNQcXFxKdEOIYQQQqRDcgEveUh+EkIIIX4ekp80S7Y1oIQQQgjx4/mRF8IUQgghhEgJkp80+6oBqIEDB351hXPmzPnmxgghhBAifZH49O0kPwkhhBA/J8lPmn3VANTNmze/qjJZaEsIIYT4scjf9m8n+UkIIYT4Ocnfds2+agDq9OnTKd0OIYQQQogfiuQnIYQQQohPZA0oIYQQQiRKSy7gCSGEEEIkieQnzb5pAOratWts374dT09PoqKi1Pbt2rUrWRomhBBCiLQnU8iTj+QnIYQQ4ucg+UkzraQesHXrVipUqMDDhw/ZvXs30dHR3L9/n1OnTmFqapoSbRRCCCFEGlEovu8l4kl+EkIIIX4ekp80S/IA1NSpU5k7dy779+9HT0+P+fPn8+jRI1q2bEnu3LlToo1CCCGESCMKheK7XiKe5CchhBDi5yH5SbMkD0A9e/aMBg0aAKCnp0doaCgKhYIBAwawYsWKZG+gEEIIIURGJ/lJCCGEED+7JA9AZc2aleDgYABy5crFvXv3APjw4QNhYWHJ2zohhBBCpCktxfe9RDzJT0IIIcTPQ/KTZklehLxy5cocP36cIkWK0KJFC/r378+pU6c4fvw4NWrUSIk2CiGEECKN/MjTwFOT5CchhBDi5yH5SbOvHoC6d+8ehQsXZtGiRURERAAwatQodHV1uXDhAs2bN2f06NEp1lAhhBBCpD6JT99H8pMQQgjx85H8pNlXD0AVLVqU0qVL07VrV1q1agWAlpYWw4cPT7HGCSGEECJtackVvO8i+UkIIYT4+Uh+0uyr14D6+++/KVSoEIMGDcLS0pIOHTrwzz//pGTbhBBCCJHG5DHC30fykxBCCPHzkfyk2VcPQDk7O7N69Wrevn3LwoUL8fDwoEqVKjg6OjJ9+nS8vb1Tsp1CCCGEEBmO5CchhBBCiHhJfgqekZERnTp14u+//+bJkye0aNGCxYsXkzt3bho3bpwSbRRCCCFEGlEoFN/1EvEkPwkhhBA/D8lPmiX5KXify5s3LyNHjsTW1pYRI0Zw8ODBb67L29ubKVOmcPDgQd68eUOOHDkoXrw4rq6uafZ0GDs7O16+fAlApkyZcHJyYsSIEbRo0SJN2iPirVq2mNUrlqhty21nz9ZdB1Tv796+xfLF83lw7y5a2lrkc8zPvMUr0DcwAKBZg1p4v/VSq6NnX1fad+qW8h1IJndvXWfH5rW4P3rIuwA/xrnNpULl6qr9586c4OCeHbg/fkhwUCBL1mzDwTG/Wh3vAvxZtXgON65eIiwsFJvcdrRq3w3najVVZdwfP+TPJfN48ug+WlpaVKpakx59B2OYKVOq9TUp7t++zu5t63n25CHvA/wZPmk25SpVUyvz6uVz1q9YwP3bN4iNjcHGNg/DJswke05LAI7u38nZk0d47v6I8LBQNu7/G2NjE7U6dmxcxbVL53jx9Ak6OjpsPnA21fqYUurVrs5brzcJtrds1YaRo8fRpeMfXL92RW3fby1+Z/S4ianVxDTxpfPyytOTObOmc+vmdaKioqhQyZnhI8aQzdw8DVqb/H7gDJRmJD+JtOLn68PSBXO4dOEfIiIisLbOzcjxk8lfsDAAfy5fzMmjh/H18UZHVxenAgXp3rs/hYoUBeCt1xvWrlrGjauXCQjwx9w8B3XqN6R9l+7o6uqlZde+2t1b19m5eR1PH8fnp9FT56jlp41/LuXsyaP4+Xqjq6NLXqeCtO/uQv5CRVRlgoMCWTp3GpfPn0VLS0HFKjXp0X+oKhu99vRg0czJeHo8JzQ0hGzZslOlVj3adu6Bjo5uqvf5a9y7dZ2dW9fx7N/zMmrKHMo7V9dYdtGsyRzZ9xfdXAbza8t2qu3b1q/k6sV/4rORrg7bDp1TO+7E4b3Mcxunsc6Ne0+RJatZ8nUoFUl+0iw0NIQlCxdw6uQJ3r8LwCl/AYYOH0WhIkWIjo5mycL5nPvnb16/fo2xsTFly1Wg34CB5MiRM62bniwkP2n2zQNQZ8+eZfXq1ezcuRMtLS1atmxJly5dvqkuDw8PKlasSJYsWZg5cyZF/v2hPHr0KH369OHRo0ffVG9sbCwKhQItrSRP9FKZOHEi3bp1IygoiNmzZ/P777+TK1cuKlSo8M11ZmRRUVHo6aV9wLB3yMuCpatU77W1P/0o3719i4F9e/BHp64MHDYKbW1tnj55jOL/fg669XKhcdPfVO8zGRmlfMOTUUR4OHnyOlGnQRMmjhyYcH9EOIWKlqBy9TrMmz5BYx0zJ40iJCSY8dPnY2qaldPHDzF17BAW/rmZvI4FCPDzZXj/7lSpUYc+A0cQFhbCsvkzmTVlDGOmzE7pLn6TiIgI7B0cqVnvV6aNHZxg/9s3rxjZrws16v1K6449McxkxCuP5+jq6avKREZG8EuZCvxSpgIbVi7U+Dkx0dFUrFITp4JFOXFoT0p1J1Vt2voXcXGxqvdP3d3p2a0TtWrXVW1r9ltLerv0U703MDBM1Tamhf86L+FhYfTq3hlHp/ys+HMdAIsXzaefS082bN7+XX9/0gtZRDN5SX6S/JRWgoIC6dW5Hb+UKsOsBcvIktWM154vMTHJrCpjk9uWAcNGYZXLmsjISLZvWs/APt3YuvcwWbOa8dLjOcq4OIaMHEcum9y8eObO9MnjCQ8Px2XAkDTs3deLCA/HPq8jtRs0YfKohPkpl40tvQYMx8LKmqjICHZv38Togb34c+s+TP8dIJkxYSTvA/yYMncZsTExzHUby4IZExk2fhoQn0ur121IXscCGJmY8OLpExZMn4hSGUfHHv0SfGZ6EBERTh4HR2rVb8LU0QnPy0cXzp7i8YM7mJlnT7AvJiaaStVqkb9QMY4f2p1gv3P1OpQsU1Ft21y3sURFRWbYwSeQ/JSYiWPH8PSpO5PdppM9Rw4O7d9Hz26d2Ln3IIaZMvHwwQO69eiNo5MTQUFBzJw2FVeX3mzevjOtm54sJD9plqRk4eXlxdSpU3F0dKRq1ao8ffqUBQsW4OXlxcqVKylXrtw3NaJ3794oFAquXLlC8+bNcXR0pFChQgwcOJBLly6pys2ZM4ciRYpgZGSEjY0NvXv3JiQkRLV/7dq1ZMmShX379lGwYEH09fXx9PT8pjZ9ZGJigoWFBY6OjixevBhDQ0P2799PbGwsXbp0wd7eHkNDQ5ycnJg/f77asWfOnKFMmTIYGRmRJUsWKlasqLoiePv2bapVq4aJiQmZM2emZMmSXLt2TXXsuXPncHZ2xtDQEBsbG/r160doaKhqv52dHVOnTqVz586YmJiQO3duVqxYofb5Fy5coHjx4hgYGFCqVCn27NmDQqHg1q1bqjL37t2jXr16GBsbkzNnTv744w/8/f1V+6tWrYqLiwuurq6Ym5tTp06d7zqfyUVHW5ts5tlVryxZs6r2LZg9nRat2tK+UzfyOOTF1s6eGrXrJgh+mTIZqdVhaJg+Z/QkpnT5SnTs7kLFKpqvcNes24h2nXtSonTZROt4cO82v/7WmvwFi2CZy5o2HbtjZGyC+6OHAFy+cBYdHR1cBo3ExtYOpwKF6TdkNOfOnODN6+/7t5VSSpatSNsufSiXyFW7TX8u5peyFenY05U8+fJjmcuGMhWrqAWfxr+1pXmbTjgWLKKxDoDWnXrRuEU7bPPkTfY+pBUzMzPMzbOrXmf/Po2NTW5KlS6jKmNgYKBWxtjYOA1bnDr+67zcvHkDL683TJwyjXyOTuRzdGLSlOk8uH+PK5cvfbnyDEAW0fx+kp8kP6WH/LRp7Z/kyGnByPFTKFi4KFa5rClTviK5bHKrytSu15DSZcuTy9qGPA556TtwKKGhITxzfwJAuQrOjBw/Jf44axsqValO6z868vfpE2nVrSQrXb4SHbq7UKGK5pxQrXZ9SpQuh2Uua2zz5KV730GEhYbw4pk7AJ4ez7l++Tz9ho8jf6EiFCpWgp6uwzl78igB/r4AWOaypnaDJuTJ50ROCyvKVapK1dr1uX/7Zqr1M6lKlavEH91c1GaD/T9/Px+Wz5/G4DFT0dFJOI+hbefeNGn5B3YOmrORvr4BWbOZq15a2lrcuXGF2g2aJls/0oLkp4QiIiI4eeIYrgMHU7JUaXLntqVnn77Y5M7Njm1bMDExYdmq1dSuWw87+zwULVac4SPH8PDBfd7+310qGZXkJ82+egCqXr162NrasnDhQpo2bcrDhw85d+4cnTp1wug7Zo68e/eOI0eO0KdPH431ZMmS5VNjtbRYsGAB9+/fZ926dZw6dYqhQ4eqlQ8LC2P69OmsWrWK+/fvkyNHjm9u2//T0dFBV1eXqKgo4uLisLa2ZseOHTx48ICxY8cycuRItm/fDkBMTAxNmjShSpUq3Llzh4sXL9K9e3fV/Zxt27bF2tqaq1evcv36dYYPH46ubvyU3GfPnlG3bl2aN2/OnTt32LZtG+fOncPFxUWtPbNnz6ZUqVLcvHmT3r1706tXLx4/fgxAUFAQjRo1okiRIty4cYNJkyYxbNgwteM/fPhA9erVKVGiBNeuXePIkSP4+PjQsmVLtXLr1q1DT0+P8+fPs2zZsmQ7n9/jlacnjWtX5bdGdRg/aqjqdrp37wK4f+8OWc2y0b1jWxrUrEzvrh24ffN6gjo2rF1F3WoV6NC6OZvWrSYmJia1u5HmChYuxt8njxIUFEhcXBxnThwmKiqSor+UAiA6KgodXV21q+B6+vEzhdJziEpMXFwc1y6dw8ralvFDetOhaQ2G9GrPpXOn07pp6U50dBSHDuzj16bN1e5DP3xwP1UrlaV5k4YsmDub8PDwNGxl6vv/8xIdHYVCoVAb4NbX10dLS4ubNxL+3hE/H8lPkp/SS346f/Y0+QsWYvTQATSs6UynNs3Zt2tHouWjo6PYu2sHxsYm5M3nlGi5kJBgMmc2TYkmp7no6GgO792JkbEx9nkdAXh07w7GxiY45i+kKleiVFkUWlo8vn9PYz1erz25fvkChYuXTJV2p4S4uDjmTB5Ns1YdsLVPnotvJ48cQN/AgIpVa365cAYh+SlebGwMsbGxqu8NH+nrGySaj4JDglEoFGqzMsWP56tvwdPV1eWvv/6iYcOGaGtrJ1sDnj59ilKpJH/+/F8s6+rqqvpvOzs7Jk+eTM+ePVmy5NN6QNHR0SxZsoRixYolWxshfur07NmzCQwMpHr16ujq6jJhwqfbmuzt7bl48SLbt2+nZcuWBAUFERgYSMOGDXFwcACgQIECqvKenp4MGTJE1e98+fKp9rm5udG2bVtVf/Ply8eCBQuoUqUKS5cuxeDfdYzq169P7969ARg2bBhz587l9OnTODk5sXnzZhQKBStXrsTAwICCBQvy5s0bunX7tMbRokWLKFGiBFOnTlVtW716NTY2Njx58gRHR0fV58+YMSPRcxMZGUlkZKT6thht9P/vF05yKVSkKKMnTCG3rR3+/n6sXrGUXl3as3HHXrxevwbi1zBwcR1CPqf8HDmwl349u7Bxx15sctsC0KJ1W5zyFyRzZlPu3rnFsoXz8Pf3o/+gYf/10T+cUZNmMnXsUFrUq4y2tg76BgaMmzqXXNbxV0OLlSzD8oWz2bFpLU1atiUiPJzVS+OvVL8L8P+vqtOlwA/viAgPY9eWNbTt3Jv2Pfpz88oFpo8dzKQ5KzJ0MExup06eIDg4mMZNPl2VrNegIVZWVmTPnoMnTx4zf+4sPDxeMGf+ojRsaer6//NSpGhxDA0NmTdnJn37DwSlkvnzZhMbG4u/v18atzZ5/MgLYaYGyU+SnxKjMT9Fp1x+8nrzmj1/beP3th1o37k7Dx/cZd4sN3R1danXqImq3PmzZxg/cjARERFkM8/O3CUr1Waaf+71q5fs3LqZPq4Jb3nPyC6fP8v08cOIjIjALJs5U+YuwzRL/Dl4/85fdSveR9o6OpiYZOb9O/VsNKhne54+eUR0VBT1Gjfnj669U60Pye2vzWvQ1tam8W9tkq3O4wf3UKVmPfT1DZKtzrQm+SmekZExRYsVZ+WyJdjnyUO2bOYcOXSQO7dvYZM7d4LykZGRLJg7i7r1G/wws8MkP2n21QNQ+/btS5EGKJXKry574sQJ3NzcePToEUFBQcTExBAREUFYWBiZ/l30T09Pj6JFi/5nPYUKFVJN5XZ2dubw4cOJlh02bBijR48mIiICY2Njpk2bRoMGDQBYvHgxq1evxtPTk/DwcKKioihevDgQPxWzY8eO1KlTh1q1alGzZk1atmyJpWX8IscDBw6ka9eubNiwgZo1a9KiRQtV0Lp9+zZ37txh06ZNaucpLi6OFy9eqILY5/1UKBRYWFjg6xs/9ffx48cULVpUFbYAypT5NA304+ecPn1a4z/yZ8+eqQJUyZL//aXczc1NLUwCDBkxhmGjxv7ncd+qfEVn1X/ndXSiUJGiNGtQi1PHj2BnnweAJs1a0vDX+F/8TvkLcO3KZQ7s3UWvvgMAaN2uo1odujq6TJ86gV59B6T5Gg2pad3KxYSEBDNt/goym2bh4j+nmTJ2KLOXrMHeIR92efIyePQkViycxerlC9DW0uLX39qQ1SwbCq2M90tVGRf/+6ZMhao0bhG/aGaevE48un+bo/v/kgGoz+zZtZOKlSqrLQT5W4vfVf+dz9GJ7Nmz071LR155emoMEz+i/z8vZmZmzJg9n6mTxrNl0wa0tLSoW68BBQoW+mHu/c/4q1ilLclPkp8Soyk/DR4xhqEjUyY/xcXFkb9gYXq4uALgmL8AL54+Zc/O7WoDUL+ULsOaLTv58OED+3f/xdjhg1ixbgtZzbKp1efn68Mglx5Uq1mHxs1+rAXmi/1SmkVrthH04QNH9u/CbexQ5q7YmOR1ioZPmEF4WCjPnz7hzyVzsdiyjhZtO6VQq1PO08cP2PfXZuav2pJsX6of3rvNq5fPGTR6crLUl15IfvpkstsMxo8dSZ3qVdDW1iZ/gYLUrdeAhw/uq5WLjo5m6CBXlEoYOWZ82jQ2BUh+0uy7noKXHPLly4dCofjiQpkeHh40bNiQXr16MWXKFMzMzDh37hxdunQhKipKFaAMDQ2/+Ivx0KFDREdHq8r/lyFDhtCxY0fVPf4f6966dSuDBw9m9uzZlC9fHhMTE2bOnMnly5dVx65Zs4Z+/fpx5MgRtm3bxujRozl+/DjlypVj/PjxtGnThoMHD3L48GHGjRvH1q1badq0KSEhIfTo0YN+/RIuUpj7s19SH6ecf6RQKIiLi/vP/nwuJCSERo0aMX369AT7PgY94Iu3CIwYMYKBA9UXKwyJSb6rvF9iYpIZm9y2vH7lScl/1zuyy+OgVsbOPg8+3m8TraNgkaLExsTw1usNtnb2Kdre9MLr9Sv27dzK8g07sft3HSOHfE7cvX2DfTu30n/oGACq165P9dr1ef8uAAMDQxQK2LVtA5ZW1mnZ/G9iYpoFbW0dbOzyqG23zm3Pw7u30qZR6ZCX1xsuX7rA7HmaF2D/qEiR+JkSr169/KED1EeJnZcKFStx4MgJ3r9/h7a2DpkzZ6ZGlYrkqls/jVqavOQKXvok+enHzE9B0SmXn7KZZ8fOXj0f2drn4cyp42rbDA0zYW1ji7WNLYWLFKNVk3oc2LOLPzp/mgXm7+dL3x6dKFysBENHj0+xNqcVA0NDrKxzY2Wdm/yFi9K1VSOOHtjN7390IauZOYHv36mVj42JITg4iKxm6k8/zZ7TAoDc9g7ExcWxcMYkmrVqn6yzIVPD/ds3CHz/jk4t6qm2xcXG8ueSOez9axOrtyc+GJ2YYwd2kyefE3mdCiZnU9OU5Cd1Nrlz8+fajYSHhRESGkL27DkYNmgAuaxtVGWio6MZNmgAb728WLF67Q8z+wkkPyUmzQegzMzMqFOnDosXL6Zfv34J/lh/+PCBLFmycP36deLi4pg9e7ZqPZqP6wUkla2t7VeXNTc3J2/ehPc5nz9/ngoVKqimcEP8Va//V6JECUqUKMGIESMoX748mzdvVi026ujoiKOjIwMGDKB169asWbOGpk2b8ssvv/DgwQONn/u1nJyc2LhxI5GRkaqp3FevXlUr88svv7Bz507s7Ow0LiT4tfT19RNMF48OTb31lMLCQnnz+hV1GzTG0ioX5tlz4PnyhVoZT08PyldwTqQGcH/8CC0tLbKaZdwncCRVZGQEQIKnHGlraWm8sv7xyufRA7vR1dPjl9LftmhuWtLV1SVv/oK8eeWhtt3rtSfZc1pqPugntHf3LszMsuFcuep/lnv072L15hqehPMj+tJ5yfrvlfErly/y7l0AVaslvpBrRpIBJzv+FCQ//Zj5KTIk5fJTkWIlEuSjV54eWFha/edxcXFKoqKjVO/9fH3o26MTTgUKMnLc5B/iaZ9fEhenJDoq/hzkL1yUkJBg3B89IF/++MGT2zeuoIyLw6lQ4UTrUMbFERMTg1IZB2SsAahqdRpSrJR67hs7uBfVazekZv1fk1xfeFgY504fo3339PlEwG8l+Ukzw0yZMMyUiaDAQC5cOIfrwPhbdj8OPnl6vmTF6nVkyaL5Vt+MSvKTZuniL8bixYuJjY2lTJky7Ny5E3d3dx4+fMiCBQsoX748AHnz5iU6OpqFCxfy/PlzNmzYkKYLOubLl49r165x9OhRnjx5wpgxY9QCyosXLxgxYgQXL17k5cuXHDt2DHd3dwoUKBD/qFoXF86cOcPLly85f/48V69eVU0NHzZsGBcuXMDFxYVbt27h7u7O3r17Eyyi+V/atGlDXFwc3bt35+HDhxw9epRZs2YBn0Zj+/Tpw7t372jdujVXr17l2bNnHD16lE6dOhEbG/tf1aephXNncvP6Vd56veHu7ZuMGNQfbS1tatWtj0KhoG37TuzYuolTJ47y2vMlK5Ys4KXHCxo2aQbA3du32LZpPe5PHvHm9SuOHjrA/NnTqVO/YYZaRDM8LIxnTx7x7En81W9vrzc8e/II339negUFBfLsySM8XzwH4kPmsyePVGs32djaYWWdm/kzJvHowV28Xr/iry3ruHH1EhWcq6k+Z+9fW3B//JDXnh7s27mVxXOm0blnP4zT6QKB4eFhPH/6mOdP4xeU9X37hudPH+PnE39emv7envOnj3HswC7evvHk4O6tXL1wlnpNPt0+8P6dP8+fPsb7zSsAXj535/nTxwQHBarK+Pm85fnTx/j7eBMXF6f6zPDwsFTsbfKLi4tj355dNPq1idoXq1eenqxYtpgH9+/x5s1rzpw+yZiRwyhZqjSOTl9egyajS+y8AOzZvZM7t2/xytOTg/v3MmSgK+3ad1TdEixESpH8JPkpKX5v2577d++wfvUKXr96ybHDB9i36y+atWgNxP/9XL5oHvfu3sb7rRePHt5n6oTR+Pv5UK1m/FP8/Hx96Nu9IzktLHFxHcKH9+8I8PcjIAOteRceFsYz90c8c4/PTz5v3/DMPT4/RYSHs3b5Ah7du4OPtxfujx4wd+o4Avx9ca5WC4DcdnkoWbYiC2ZM5PGDu9y/c5Mlc6ZRuUYdspnHL95/+thBzp48iqfHc96+ec3Zk0dZu3wBlWvURkdHN9G2paXwsDCeuz/i+Wfn5bn7I3x93pLZNAt2efKqvXR0dMhqlg3r3HaqOnx93vLc/RF+Pt7Excap6gsPU89G/5w6SmxsLNVq/xgzhUHykyYXzv/D+XP/8Ob1ay5dOE+3zh2wt89D4ybNiI6OZsjA/jy4f48p02YSFxe/dqa/vx/Rnw14ix9Pms+AAsiTJw83btxgypQpDBo0iLdv35I9e3ZKlizJ0qVLAShWrBhz5sxh+vTpjBgxgsqVK+Pm5kb79u3TpM09evTg5s2b/P777ygUClq3bk3v3r1V6yFkypSJR48esW7dOgICArC0tKRPnz706NGDmJgYAgICaN++PT4+Ppibm9OsWTPVOgBFixbl77//ZtSoUTg7O6NUKnFwcOD333//ryapyZw5M/v376dXr14UL16cIkWKMHbsWNq0aaNa18DKyorz588zbNgwateuTWRkJLa2ttStWzddX83y9fFh3IghBAZ+IEtWM4oW/4UV6zarZh/83rY9kVGRLJg9g6DAQPI6OjF/yUqs/33MsJ6eHieOHubP5UuIio7CyioXrdq2p1W7DmnZrSR78ug+Q/t2Vb1fvjA+INeq15jBoydx6Z8zzJ76aR0Jt3HxC6y369yTP7r0QkdHl8mzFvHn0vmMG9qP8PAwrKxzM3j0JMp8Nlvs8cN7bPhzKRHhYVjb2tNv6Ghq1m2UOp38Bk8fP2DMgO6q96uXzAGgWp1G9B8+gXLO1ek5YCQ7N69h1cKZWNnYMmzCTAoWKaE65si+v9i27tNjuUf1jz/PfYeNp0bdxgBsXrOM00f3q8oM7BYf4CfNXUGR4qVSroMp7NLFC7x960WTps3Vtuvq6nL50kU2bVhPeHgYOS0sqVGrNt16ZNwFVZMisfMC8NLjBQvnzSEwMBCrXLno2r0n7dp3TP1GphC5gpd+SX6S/JQUBQoVYeqs+SxfNI+1K5diaWVNv0HDqF2/IQBaWtq89HjB4QN7CfzwnsymWShQqDCLV60nj0P8rLKrly7w+pUnr1950rSe+izPc9fvJ/jM9Mj90X2G9/t0O+HKhbMBqFmvES6DR/P6pQdTDg8iMPADmTNnwbFAIWYuXo1tnk8z64aOm8qSOW6M7N8DhZYWFavUoKfrpwfZaGnr8Nemtbx59RIlSnLktKRh81Y0bdku9TqaRO6P7zOy/6fzsmpR/HmpUbcRA0ZO+qo6Nv25hJNHPmWjfl1aATB1/kqKliit2n7s4G7KV66ebi9mfgvJTwmFBIewcN4cfHy8MTXNQo1atejTbwC6urp4vXnN36dPAdDqtyZqx61cvY5SZcqmQYuTl+QnzRTKr1jFMikLaDZu3Pi7GiRSzqZNm+jUqROBgYFfXLvhewWk4i14GUlwuJwXTcKj0u8V47Rkl/3bH9Eufj6GKXRRfdD+x991/OxGiT++/Ucn+enHkJr5yS8Fb8HLyILCo9O6CelSEpYu+6lYm6Xsv9OMSsnXP7ziZ5JJN2VGiiQ/afZVM6CaNGnyVZUpFIp0PfX4Z7N+/Xry5MlDrly5uH37NsOGDaNly5YpHp6EEEL8OOQK3reT/JQxSX4SQgjxvSQ/afZVA1BJeTKISD+8vb0ZO3Ys3t7eWFpa0qJFC6ZMmZLWzRJCCJGByENcvp3kp4xJ8pMQQojvJflJs6+6BU+IpJJb8DSTW/A0k1vwNJNb8ERSpNQteMMPPfmu46fVd0ymlgjx45Nb8DSTW/A0kzFuzeQWPM3kFjzNUuoWPMlPmn3TIuShoaH8/fffeHp6EhWlvkp9v34/1uM0hRBCCCGSg+QnIYQQQvzMkjwAdfPmTerXr09YWBihoaGYmZnh7+9PpkyZyJEjhwQoIYQQ4geSfp/plbFIfhJCCCF+HpKfNEvyeRkwYACNGjXi/fv3GBoacunSJV6+fEnJkiWZNWtWSrRRCCGEEGlEofi+l4gn+UkIIYT4eUh+0izJA1C3bt1i0KBBaGlpoa2tTWRkJDY2NsyYMYOR/2PvvsOiuNYwgL9L701BihQRFFGxG7tiQ0WN0ajJTWwYO/ZujF3BXlFsscWuibEbe6yJDRQrViyABaV39v5BXFmZVVZYZlne3332PjJzduabCSwvZ86cmTBBFTUSERGRSLQkkny9KBvzExERUfHB/CRM6Q4oXV1daGllv83GxgYREREAAHNzczx9+rRgqyMiIiJR8QpewWB+IiIiKj6Yn4QpPQdUtWrVcOnSJbi7u6Nx48aYNGkSXr9+jU2bNqFSpUqqqJGIiIioSGN+IiIiouJO6RFQs2bNgp2dHQBg5syZsLS0xIABA/Dq1SusWrWqwAskIiIi8WhJ8veibMxPRERExQfzkzClR0DVrFlT9m8bGxscPny4QAsiIiIi9aHJ8xAUJuYnIiKi4oP5SZjSHVBERERUfDA/ERERESmH+UmY0h1QZcqUgeQTZ/Phw4f5KoiIiIjUhyYPAy9MzE9ERETFB/OTMKU7oIYNGyb3dXp6Oq5du4bDhw9j9OjRBVUXERERkcZgfiIiIqLiTukOqKFDhwouDwoKwuXLl/NdEBEREakPCXgJryAwPxERERUfzE/ClH4KniKtW7fG7t27C2pzREREpAb4FBfVYn4iIiLSPMxPwgqsA2rXrl2wsrIqqM0RERGRGhAzQAUGBkIikcjdvpaSkoJBgwahRIkSMDExQadOnRAdHS33voiICPj6+sLIyAg2NjYYPXo0MjIy5NqcOnUK1atXh76+Ptzc3LB+/fr8FfuFmJ+IiIg0j1j5Sd2zk9K34FWrVk1uEk2pVIqoqCi8evUKy5cvV7oAIiIiUl+fmjhblS5duoSVK1fCy8tLbvnw4cNx4MAB7Ny5E+bm5vD390fHjh1x7tw5AEBmZiZ8fX1ha2uL8+fPIzIyEt27d4euri5mzZoFAHj06BF8fX3Rv39/bN68GcePH8dPP/0EOzs7+Pj4qOR4mJ+IiIiKDzHyU1HIThKpVCpV5qCmTJkidzK1tLRgbW2NJk2awMPDQ5lNkQZ7k5jx+UbFUHwyz4uQ5LRMsUtQSy7WxmKXQEWIoa5qtjv/dP6ezuZfxwGpqalyy/T19aGvr6/wPQkJCahevTqWL1+OGTNmoGrVqli0aBFiY2NhbW2NLVu24NtvvwUA3LlzBxUqVMCFCxdQp04dHDp0CG3btsWLFy9QqlQpAEBwcDDGjh2LV69eQU9PD2PHjsWBAwcQFhYm2+d3332Hd+/e4fDhw/k6XkWYnygvXiUwJwiJS04XuwS1lJUldgXqqbSVodglqCUplPqzv9gw0lVNR1Fh56eikp2UHgE1ZcoUZd9CRERExVRAQACmTp0qt2zy5MmfzBODBg2Cr68vmjdvjhkzZsiWX7lyBenp6WjevLlsmYeHB5ycnGQh6sKFC6hcubIsQAGAj48PBgwYgJs3b6JatWq4cOGC3Dbet/n4SXUFifmJiIiI8krZ/FRUspPSHVDa2tqIjIyEjY2N3PI3b97AxsYGmZkcyUBERKQp8juCfPz48RgxYoTcsk+Nftq2bRuuXr2KS5cu5VoXFRUFPT09WFhYyC0vVaoUoqKiZG1yBqj369+v+1SbuLg4JCcnw9Cw4K+eMz8REREVH4WZn4pSdlK6A0rRHXupqanQ09NTdnNERESkxrTymaA+d7tdTk+fPsXQoUNx9OhRGBgY5Gu/6ob5iYiIqPgorPxU1LJTnjuglixZAiB7Mq01a9bAxMREti4zMxN///035zAgIiLSMIX5KOArV67g5cuXqF69umzZ+4yxbNkyHDlyBGlpaXj37p3clbzo6GjY2toCAGxtbfHvv//Kbff9k15ytvn46S/R0dEwMzMr8NFPzE9ERETFT2Hlp6KWnfLcAbVw4UIA2VfwgoODoa2tLVunp6cHFxcXBAcH53nHREREpP4K8yEuzZo1w40bN+SW9erVCx4eHhg7diwcHR2hq6uL48ePo1OnTgCAu3fvIiIiAnXr1gUA1K1bFzNnzsTLly9lt7sdPXoUZmZm8PT0lLU5ePCg3H6OHj0q20ZBYn4iIiIqfgorPxW17JTnDqhHjx4BALy9vfH777/D0tJSqR0RERERfYqpqSkqVaokt8zY2BglSpSQLe/duzdGjBgBKysrmJmZYfDgwahbty7q1KkDAGjZsiU8PT3RrVs3zJkzB1FRUZg4cSIGDRokG8rev39/LFu2DGPGjIGfnx9OnDiBHTt24MCBAwV+TMxPREREpCpFLTspPQfUyZMnlX0LFUOp6XwurJCSpnmbB6W40dEuxCEWRYjvigtil6CWDgwo+FEqpJgW1Ovnc+HChdDS0kKnTp2QmpoKHx8fLF++XLZeW1sb+/fvx4ABA1C3bl0YGxujR48emDZtmqxNmTJlcODAAQwfPhyLFy9G6dKlsWbNGvj4+KisbuYnygvmJ2E2ZsxPQnS1tcQuQS21X3lR7BLU0t5+dcQuoVhRp/ykTtlJIlU0K6YCnTp1Qu3atTF27Fi55XPmzMGlS5ewc+dOpQogzfTiXZrYJaglM0NdsUtQS+yAEsYOKGHsgBJmoPQlpbxZfv5xvt4/sJ5LgdRR1DE/UV48e8v8JMTcSEUfcEUcO6CEsQNKGDughDE/FS6lP7X+/vtvtGnTJtfy1q1b4++//y6QooiIiEg9aEny96JszE9ERETFB/OTMKX7+xISEgQfF6yrq4u4uLgCKYqIiIjUQ34fI0zZmJ+IiIiKD+YnYUqPgKpcuTK2b9+ea/m2bdtkM6QTERER0QfMT0RERFTcKT0C6pdffkHHjh3x4MEDNG3aFABw/PhxbN26lfMXEBERaRhewCsYzE9ERETFB/OTMKU7oNq1a4c9e/Zg1qxZ2LVrFwwNDeHl5YVjx46hcePGqqiRiIiIRMIh5AWD+YmIiKj4YH4S9kVzvvv6+sLX1zfX8rCwMFSqVCnfRREREZF6YH4qOMxPRERExQPzk7B8P7szPj4eq1atQu3atVGlSpWCqImIiIjUhFY+XySM+YmIiEhzMT8J++Jj+/vvv9G9e3fY2dlh3rx5aNq0KS5evFiQtREREZHIJBJJvl4kj/mJiIhI8zE/CVPqFryoqCisX78ea9euRVxcHLp06YLU1FTs2bOHT3AhIiIiEsD8RERERKTECKh27dqhfPnyuH79OhYtWoQXL15g6dKlqqyNiIiIRCbJ56u4Y34iIiIqfpifhOV5BNShQ4cwZMgQDBgwAO7u7qqsiYiIiNQEn+KSP8xPRERExQ/zk7A8j4A6e/Ys4uPjUaNGDXz11VdYtmwZXr9+rcraiIiISGS8gpc/zE9ERETFD/OTsDx3QNWpUwerV69GZGQk+vXrh23btsHe3h5ZWVk4evQo4uPjVVknERERiUAiyd+ruGN+IiIiKn6Yn4Qp/RQ8Y2Nj+Pn54ezZs7hx4wZGjhyJwMBA2NjYoH379qqokYiIiKhIY34iIiKi4k7pDqicypcvjzlz5uDZs2fYunVrQdVEREREaoKPES54zE9ERESajflJWJ4nIf8UbW1tdOjQAR06dCiIzREREZGayNeVKvok5iciIiLNxPwkrEA6oIiIiEgzafJVOCIiIiJVYH4Sxg4oIiIiUojxiYiIiEg5zE/CODKMiIiIiIiIiIhUiiOgiIiISCEOISciIiJSDvOTMHZAERERkUIcKk1ERESkHOYnYeyAIiIiIoV4BY+IiIhIOcxPwtgBRURERAoxPhEREREph/lJGDugiIiISCFewCMiIiJSDvOTMN6aSEREREREREREKsURUEVcz549sWHDhlzLw8PD4ebmlq9tnzp1Ct7e3nj79i0sLCzyta2ClJmZiQ2rl+Po4QOIiXmNkiWt4eP7Nbr59ZPda5uclIRVQQtx9vQJxMXFws7OAR27/oD2HbvItjNsQC+EXr0st+1233TGiHGTCvV4CsrVy5ewcf1a3L59E69fvcK8Rcvg3bS5bP2bN6+xZOE8XLxwDvHx8ahevSbGjJ8IJ2cXWZunTyOwaP4chFy7gvS0NNSt3xBjxk9EiRIlRTiiwrF29SosWTQfP/zYHWPG/wwASE1Nxfw5gTh86CDS0tJQr34D/PzLZJQoWXTPQ/fapdHjK0e5ZRFvk9HrtxAAgJ2ZPvo3cEEle1Poaktw6ck7LDv9GG+T02Xtp/uWR1lrY1ga6iI+NQNXn8Zi9fkneJOYjo/Zmxtg5XdeyJJK8fWqSyo9NlVbu3oljh/9C48ePYS+gQGqVq2GYSNGwaWMa662UqkUg/r3wbmzZ7BwSRCaNmsusMWiRYuDyEkDFcf8BABJiYlYt2oZzp4+jndvY+BWzgODho+Dh2elXG0Xzp6G/X/sxMBhY9Dpu25y6y6e+xub1gbj4YN70NPTg1e1mpg+Z0lhHUaBWrdmFU4eP4rHjx5CX98AXlWrYfCwkXApU0bWZua0yfj34gW8fvUShkZG8KpSDUOGj5T9Hrh39w7Wr12N0GtX8e7dW9jZO6BT5674/sfuYh2WSuzYtgU7tm/Fi+fPAQBl3dzRb8BANGjYGACwa8d2HDq4H7dv3URiYiLOXLgEMzMzMUvOt261SqNb7dJyy56+TUbvLaEAAEsjXfSp54TqjuYw0tXG03cp2Hr5Oc4+jMm1LV0tCZZ0roSyJY3Rf/t1PHydBAAoZaqPTd2r5Wo/ZFcY7kQnqOCoCkde8tPrV6+wYP4cXDx/HolJiXBxKYM+ffujeUsfESsvGMxPwtgBpQFatWqFdevWyS2ztrYWqRrV27rpV/z5+w6MmzQTZVzL4u7tm5g94xcYm5iiU9cfAABBi+bg2pV/8fPUQNja2ePSP+exaO5MlChpjfqNvGXb8v26E/z6+cu+1tc3KPTjKSjJyckoV94D7b/phNHDB8utk0qlGDl0EHR0dLFg8XIYGxtj86b1GNDXD7v+2A9DIyMkJyVhUL/eKFfeA8Gr1wMAVgQtwfDBA7D+t+3Q0tK8AZNhN65j185tKFeuvNzyubNn4czp05i7YBFMTU0RMHM6Rgz1x4bN20SqtGA8epOE0Xtuyb7OzJICAAx0tDCngycevE7EqD+y1/eq44gZ7Tzgv+MGpP+1D3kehy2Xn+NNUhpKGuuhfwMXTG5dHkN2hcntR1tLgp993HHjRRwq2pkWyrGp0uVL/6Lr9z+gYuXKyMzIxNLFC9C/T2/8vvcAjIyM5Nr+tnGDxk06qWGHQyRT3PITAMyfNRmPHt7H+MmzUKKkDY4d3o8xg/tg7dY9sLYpJWt39tRx3A67jhLWNrm28feJo1gQOAW9+w9F1Zq1kZmZiccPwgvzMArU1cuX0Pm7/8GzYiVkZmYiaMlC+PfvjZ3/5SMAqOBZEa3btIWtnT3iYt9h5YogDOr3E/YeOgptbW3cvnUTVlYlMC1gNkrZ2uF6yDXMnDYZWtra6Pr9DyIfYcGxKWWLocNHwcnZGVKpFPv+3IOh/oOwffcfcHNzR0pKMurVb4h69RtiyaL5YpdbYB6/ScLYvbdlX7/PTwAwpllZGOvrYPKBu4hNyUBT95L42ccd/jtv4MF/HUzv/VTPCW8S01C2pLHgfsb8eQtPYpJlX8elZBTwkRSuvOSnnyeMRXxcHBYvWwFLS0scPLAPo0cOw5Ydu1GhgqfIR5A/zE/CNO8vymJIX18ftra2ci9tbW2cPn0atWvXhr6+Puzs7DBu3DhkZHz4IEtNTcWQIUNgY2MDAwMDNGjQAJcuZY9UePz4Mby9sztqLC0tIZFI0LNnTzEOL5eb10NQv5E36jZoBFt7BzRu1hI1a9fDnVs3PrS5EQqfNu1RtUYt2No7oN03nVHWrZxcGwAwMDCEVYmSspexiUlhH06Bqd+wEQYOHoamzVrkWhfx5DFuXA/F+ImTUbFSZbiUccX4iVOQmpKCw4cOAABCQq4i8sVzTJkeAPdy5eFerjymzgjErZthuPTvxcI+HJVLSkzE+LGjMXnqDJiZm8uWx8fH44/duzFqzDh8VacuPCtWwrQZsxAScg3XQ0PEK7gAZGZJ8TYpXfZ6H2wq2pmilKk+5hx9gEdvkvDoTRJmH72PcjbGqOb44dzsDonE7egEvIxPw62oBGy98hwVbE2grSX/G9avjiOevk3G6fA3hXp8qrJi1Vp8/U1HuLm5o7yHB6bNDERk5AvcvnVTrt2d27exccOvmDp9lkiVqoYkn/8jUlfFLT+lpqTg71PH0Nd/BLyq1YSDoxN69BkI+9KO2Pf7dlm7Vy+jsXT+LEyYGggdbflr1ZkZGQhaGIi+/iPRrmMXODq5wKVMWTRp3qqwD6fALA1ejXZff4Oybu4oV94DU6YHICoyUu4zvuO3XVC9Zi3YOzjAw7MiBg4eiuioSES+yB4J9PU3nTBq3ATUqFkbpUs7ok3b9mj/9Tc4eeyoWIelEk28m6Jho8ZwdnaBi0sZDB46HEZGRrJ89GP3nujdpy+8qlQRt9AClikVzk8A4Glnij+vR+Huy0RExaViy5XnSEzLgLu1fCdTLScL1HC0wKpzEQr3E5eSIbefnB1dRVFe8lPotWv4/ocfUdnLC6UdHdG3/0CYmprh9s2bn9hy0cD8JIwdUBrq+fPnaNOmDWrVqoXQ0FCsWLECa9euxYwZM2RtxowZg927d2PDhg24evUq3Nzc4OPjg5iYGDg6OmL37t0AgLt37yIyMhKLFy8W63DkVPSqiquX/8HTiMcAgPv37iIs9Cpq123woU3lKjh/5hRevYyGVCrFtcv/4tnTJ6j5VT25bR07cgBft2yIXt9/g9VBi5CSkgxNlJaWBgDQ09eXLdPS0oKenh5Crl0BAKSnpUEikUBPT0/WRl9fH1paWgi5eqVwCy4Es2ZMQ6NGjVGnrvz3xK2bYcjISMdXOZaXcS0LOzt7hIaEFHKVBcvBwgDbe9XApu7VML6lG2xMsv9b62ln/ypIz8yStU3LyIJUClRSMILJVF8HzcqXxM3IeLmAVLW0GRq5lcCSU49UeCTiSoiPBwC5jsvk5GSMHzMSEyZOQkkNG0EhkeTvRVSUaHJ+yszMRFZmptzveSB79HdY6DUAQFZWFgKnTkCXH3vBxTX3rYjhd2/j9auX0NKSoF/3zujs641xw/rjUREeAfWxhITcn/E5JSclYe+e3+HgUBqlbG0/sZ0EhdvQBJmZmTh08ACSk5NQpUru28c0iYO5Abb2rI4NP1bFuBZusDb58DN0KzIejd1LwFRfGxIATdxKQE9bC9dfxMnaWBjqYph3Gcw+dh+pGVkCe8g2rU157OhVAwu+8UQdF0tVHpIohPJTlWrVcOTwIcS+e4esrCwcOngAqWmpqFmrtlhlFhjmJ2G8BU8D7N+/HyY5Ru60bt0a5cqVg6OjI5YtWwaJRAIPDw+8ePECY8eOxaRJk5CcnIwVK1Zg/fr1aN26NQBg9erVOHr0KNauXYvRo0fDysoKAGBjY/PJOQxSU1ORmpr60TIJ9HN0dhSk/3XvjaTEBPTo0h5aWtrIyspE7/5D0KJVW1mbIaMmYH7AVHRp1xza2jrQ0pJg5IQpqFKtpqxNs5ZtUMrOHiVLWuPB/XtYtWwhnkY8xrTZi1RSt5hcyrjC1s4eyxYvwM+TpsLQ0BCbN21AdHQUXr9+BQCo7FUVBoaGWLJwHgYNGQ5IpVi6eD4yMzNlbTTFoYMHcPv2LWzZvivXujevX0NXVzfXnAVWJUoU6fNwJzoBc47dx7O3KbAy1kX32o5Y1KkSem8Jwa2oeCSnZ6JPfWesvRABCbKHiWtrSVDCWP4PlT71nPC1ly0MdbVxKzIeP++/I1tnZqCDMc3dEPBXOJLSMwv5CAtHVlYW5syeharVqsPdvZxs+dzZAahSrZrcvGtEpN6KW34yMjaGZ+Uq+O3XlXBycYWlVQmc+OsgboWFwr60EwBg26Zfoa2tjY5dhG8be/HiGQBgw5oVGDBkNGzt7bFzywaMGOiHDTv2F/kOl6ysLMyfE4Aq1arDLcdnPADs3LYFSxbOR3JyEpxdyiBo1Vro6uoJbic05Br+OnIIi5cFF0bZhSr83l10+993SEtLhZGRERYuCULZfM6bps7uRCdg7vEHePYuBVZGuvixVmks6FgRfbeGIjk9CzOOhONnH3fs/qkWMjKzkJqRhamH7uFF7Ief7dHNyuJA2EuEv0pEKdPcP9/J6ZlYefYxbkYlIEsqRQNXK0xpUw5TDt7DxcdvC/NwVUZhfpq/CGNGDkej+l9BR0cHBgYGWLh4GZycnUWsllSJHVAawNvbGytWrJB9bWxsjEGDBqFu3bpyc5HUr18fCQkJePbsGd69e4f09HTUr19ftl5XVxe1a9fG7du3oYyAgABMnTpVbtmIsRMxctwvX3hEn3bq2BEcO3wAE6fNhotrWdy/dxdBC2ejhLU1Wvl+DQD4Y8cW3A67jpnzlv53L/4VLJ47EyVLWqNG7boAsiccf8/VrRxKlLTGyEE/4fmzp3Ao7Si476JKV1cX8xYuwbTJE+Hd4Ctoa2uj9ld1Ub9BI0il2aNXLK2sMHveIgTMmIptWzZBS0sLPq194VHBExKJ5gyWjIqMxJzAmVi5+leVhXx19O+Td7J/P3wD3I5KwJae1dHEvSQO3XqJaYfuYZi3K76pYgupFDhx7zXuvcwOQjltv/oCh269RClTfXSrXRpjW7jh533ZnVAjmrrixL3XuPEivjAPrVDNmjEVD8LDsX7TFtmyUyeO49I/F7F91x8iVqY6nESTNJU65qfhYyZihIryEwCMnxyAuTN/Qdd2zaClrQ338hXg3aI1wu/cwr07N/H79t8QvGGHwrnspFnZozd+6NkHjZpm3/I/euIMfNe+OU6fOIJ233QRfF9RMXvmNDy4H4416zfnWtfatx2+qlsPr1+9wqYN6zBu1HCs3bglV5a4H34PI4cOQp/+A1GnXv1c2ynqXFzKYMfuPUhIiMfRv47glwljsXb9bxrbCXUp4p3s34/eZHdI/da9Ghq7lcDh26/Q4ytHmOjrYMyftxCXnIF6rpb42ccdI36/iccxyejgZQtDXS1su/pc4T7iUjKwOzRK9vW9l4koYayHztXsNKYDSig/AUDQ0sWIj4/DqrXrYWFhiZMnjmHMyGFYt3Ez3D+ao7WoYX4Sxg4oDWBsbJzvJ7bkx/jx4zFixAi5ZW+SVfcDF7x0Pr7v3htNW2ZfeXR1K4foqBfYsmENWvl+jdSUFKxZsRjTZi9G3QaNAABl3cvj/r272L55g6wD6mMVKlYGADx/FqFxHVAAUMGzErbu3IP4+HhkpKfD0soK3f/XBZ4VPzz5pm69Bth78Cjevn0LHW1tmJqZoaV3A5TWoPNx69ZNxLx5g+86d5Qty8zMxJXLl7Bt62asWLUW6enpiIuLkxsFFfPmDUqW1JxbqxLTMvHsXQrszbMn3r/yNBbdNl6DmYEOMrOkSEzLxE6/GoiMlb86H5eSgbiUDDx7l4InMcnY7lcDnrYmuBWVgGqlzVGvjBW6VLOXtdfWkuCvQXWw4MQDHL5ddEeQAdm3bf59+hR+3fCb3G0X//5zEU+fRqBB3Vpy7UcOG4zqNWpi7fpNhV1qgdLkYeBUvKljfnqVpNofOPvSjli4Yj2Sk5OQlJiIEiWtMf3nUbBzKI0bIVfx7m0Mvu/QUtY+KzMTwUvmYfe237BlzxFY/fd70NmlrKyNnp4e7OxL42VUVK79FSWzZ03H2b9PY9W6TYK31pmYmsLE1BROzi6oXKUKvOvXwcnjx9Cqja+szcMH9zGwjx++6dQFP/UdUJjlFxpdPT3Z6BTPipVwM+wGNv+2EZOmTBO5ssKRMz/Zmemjg5ct+mwNlU0e/vBNEirZmaF9ZVssOf0IVR3MUMHWFAf6fyW3naDOlXHi3mvMPf5AcD93ohNQ3bFojyh8T1F+ehoRgW1bfsPuP/fDzc0dAFDewwNXr1zGtq2b8cvkov09xfwkjB1QGqpChQrYvXs3pFKp7CrWuXPnYGpqitKlS6NEiRLQ09PDuXPn4PzfL5H09HRcunQJw4YNAwDZHAGZmZ++lUZfXz/X1Z+ErLQCPqIPUlNScj2RTUtLG9L/5qHJyMhARkYGtD6aGFlLS0t25U7I/Xt3AQAlSpQs4IrVi6lp9pw+EU8e4/atMAzwH5KrjaVl9n3n//5zETExb9CoiXeuNkXVV3XqYNeefXLLJv88Hi6urujVuw9sbe2go6OLfy9ekD0C9vGjh4iMfIEqVauKULFqGOhqwd7cAMfuyHcKvZ9Ys2ppM1gY6eL8o9yPEX7v/Y+Y7n9zSA3eGYacP3b1XK3wXQ17DNkZhteJqvtMUDWpVIqAmdNx4vhRrF2/KVeHrN9PffHNt53lln3boR1GjR2Pxhrws8MARcWJ2PkpLrNwPisNDY1gaGiE+LhYXPrnPPr6D0dD7xaoXquOXLuxw/qjRau2aNW2AwCgnIcndPX08DTiMSpXrQ4AyMhIR1Tkc5SysyuU2guaVCrFnIAZOHXiGFau3QCH0qXz8B5ACinS0z/893pwPxwDfuoF3/ZfY9CQYSqsWL1kZWUhPa3o/o5XloGuFuzMDXD83mvo62Tnn49Hi2dJpbI8FHTmMdb/81S2roSxHgLaV8DMI+G4E52gcD9lSxohJim94A+gEH0uP72fe1dLovjvuqKM+UkYO6A01MCBA7Fo0SIMHjwY/v7+uHv3LiZPnowRI0ZAS0sLxsbGGDBggGyuAicnJ8yZMwdJSUno3bs3AMDZ2RkSiQT79+9HmzZtYGhoKDdXgljqNmyM39atgk0pO5RxLYvwe3ewc+tGtG7XAQBgbGKCKtVrInjpAujrG6CUnR1Cr17GX4f2YeDQ0QCA58+e4viRA/iqXkOYm1vgwf17WL5oDryq1UBZ96I53DMpKRFPIz48WePF82e4e+c2zMzNYWdnj6N/HYalpSVs7exxP/we5s2eiSbezVC33ofJ2/fu2Y0yZcrCwsoKN0JDMG/2TPyvWw+4lHEV45BUwtjYRO7ecwAwNDKChbmFbPk3nTph3pxAmJmbw8TEBIGzZqBK1WrwqlJVhIoLRr/6zrjw6C2i41NRwlgXPb9yRJZUihP3XgMAfCpYIyImGe+S01HRzhSDGrpgd0gknr1LAQB4lDJB+VImCHsRh/jUDNibG6DXV454/i4FtyKzb7mLeCs/iX95mzRIpcDjmKI9uf+s6VNx6OB+LFq6HMZGxnj9KrvTzsTUFAYGBihpbS048bidnb1GjB7U5CexEH1Mk/MTAFy6eA5SqRSOzi54/jQCq5YtgJNzGbRq2wE6OrowN7eQa6+jrQOrEiXh6FwGQPbv0HbfdMGG1UGwKWWLUrZ22P7begBA46YtURTNnjkNhw8dwPzFy2BkbCyb79HEJPsz/tmzpzh6+BDq1KsPS0tLREdHY/3a1TDQ10f9/0ba3w+/hwE/9UKd+vXxQ/eesm1oa2nD8r85wTTB4oXz0aBhI9ja2SEpMREHD+zH5Uv/YsWqtQCA169e4fXr17I8ej/8HoyMjGFnZwfzT8yHps761HPCxcdv8TI+DSWMddG9dmlkSaU4ee81EtIy8fxdMoY1ccWqc08Ql5KBeq5WqO5ojl8OZF/YfpWQhpyX+pLTsy+Gv4hLkV2ca1G+JNKzpHjwKhEAUL+sFXwq2GDhyYeFeqwF7XP5yaWMK5ycnDF96iSMGDUWFhYWOHHiGC5eOIely1eKXH3+MT8JYweUhnJwcMDBgwcxevRoVKlSBVZWVujduzcmTpwoaxMYGIisrCx069YN8fHxqFmzJo4cOSIb/eLg4ICpU6di3Lhx6NWrF7p3747169eLdEQfDBk5Ab+uXIbFc2fg7dsYlCxpjXbffIvuvT8MdZ40Yy5WBy3CzMnjEBcXi1K2dujdfzDad8yem0BXVxdXLl3E7m2/ITklGTY2tmjo3QLdevUV67Dy7dbNMPTr3UP29YK5gQCAtu07YOqMQLx+9RIL5wbizZs3KGltDd92X6NPP/nh4Y8fP8ayxQsRGxsLewd7+PXpjx+69SzMw1ALo8dOgJZECyOHDUFaehrq1W+AnydOFrusfLE20cPPPu4wM9RBbHI6wl7Ew3/HDcT+N+LJ0dIQP9V1gqmBDqLjUrH58nPsComUvT81IwsNXa3Qs3ZpGOhq401iGi5FvMPmw+FI14CrVJ+yY/tWAEDvnt3klk+bEYCvv+ko9BYiKqI0OT8BQGJCPNasWIzXL6NhamaOht7N4dd/CHR0dPO8jX6DR0BbWxsBU8YjLTUVHhUrY37QWpiaFc3bhXbt2AYA6OfXQ2755Omz0O7rb6Cvp49rVy9j628bERcXhxIlSqBajZpYu3ErrEqUAAAcP/oX3r6NwaH9+3Bo/4dR1nb29th3+HjhHYyKxcS8wcTxY/Hq1UuYmJqiXLnyWLFqLer+N9fVzh3bELx8max9r+7Zk9kX5d+X1iZ6mNDSHaYG2fnpZmQ8hu4Kk+Wnn/ffRe+6jpjmWx6Gutp4HpuCucce4FKOuTfz4oeaDihlqo/MLCmevkvBrL/CceaB4lHoRcHn8pOuri6WBa/C4gXzMcS/P5KSkuDk6ITpswLRsFFjMUqmQiCRSqWa/ZcDieLFu+IzFFcZZoZ5D3jFiY42rxAI8V1xQewS1NKBAcLzuBV3Biq6pHT8zut8vb+Zh2bf1kxUkJ69ZX4SYm7Ea+ZC3t8CT/Lar7wodglqaW+/Op9vVAwxPxUufpoTERGRQhxCTkRERKQc5idh7IAiIiIihTiJJhEREZFymJ+EsQOKiIiIFOIVPCIiIiLlMD8JYwcUERERKaTF/ERERESkFOYnYZy5joiIiIiIiIiIVIojoIiIiEghDiEnIiIiUg7zkzB2QBEREZFCnESTiIiISDnMT8LYAUVEREQKMT8RERERKYf5SRg7oIiIiEghLV7CIyIiIlIK85MwTkJOREREREREREQqxRFQREREpBCv3xEREREph/lJGDugiIiISDEmKCIiIiLlMD8JYgcUERERKcTHCBMREREph/lJGDugiIiISCHOoUlERESkHOYnYZyEnIiIiIiIiIiIVIojoIiIiEghXsAjIiIiUg7zkzB2QBEREZFiTFBEREREymF+EsQOKCIiIlKIk2gSERERKYf5SRg7oIiIiEghTqJJREREpBzmJ2GchJyIiIgUkuTzpYyAgADUqlULpqamsLGxQYcOHXD37l25NikpKRg0aBBKlCgBExMTdOrUCdHR0XJtIiIi4OvrCyMjI9jY2GD06NHIyMiQa3Pq1ClUr14d+vr6cHNzw/r165WsloiIiEgY85MwdkARERGRWjh9+jQGDRqEixcv4ujRo0hPT0fLli2RmJgoazN8+HDs27cPO3fuxOnTp/HixQt07NhRtj4zMxO+vr5IS0vD+fPnsWHDBqxfvx6TJk2StXn06BF8fX3h7e2NkJAQDBs2DD/99BOOHDlSqMdLRERElF9FKT9JpFKptGAOm+iDF+/SxC5BLZkZ6opdglrS0eYYVSG+Ky6IXYJaOjCgrtglqCUDFd1Uf/VJXL7eX93Z7Ivf++rVK9jY2OD06dNo1KgRYmNjYW1tjS1btuDbb78FANy5cwcVKlTAhQsXUKdOHRw6dAht27bFixcvUKpUKQBAcHAwxo4di1evXkFPTw9jx47FgQMHEBYWJtvXd999h3fv3uHw4cP5Ol6i/Hj2lvlJiLkRZw0RoqvNsQRC2q+8KHYJamlvvzpil6CW1DU/VbTVR2pqqtwyfX196Ovrf/a96pyf+KlFRERECkny+b/U1FTExcXJvT4OVIrExsYCAKysrAAAV65cQXp6Opo3by5r4+HhAScnJ1y4kN1he+HCBVSuXFkWngDAx8cHcXFxuHnzpqxNzm28b/N+G0RERET5kd/8FBAQAHNzc7lXQEBAnvatzvmJHVBERESkkESSv9eXBqisrCwMGzYM9evXR6VKlQAAUVFR0NPTg4WFhVzbUqVKISoqStYmZ3h6v/79uk+1iYuLQ3Jy8hedJyIiIqL38pufxo8fj9jYWLnX+PHjP7tfdc9PHM9KRERECuX3Btnx48djxIgRcsvyMnx80KBBCAsLw9mzZ/NZAREREVHhym9+yuvtdh9T9/zEDihSCUtjPbFLUEucck0YT4swznUk7EZErNglqKVaruZilyDoSwKUv78/9u/fj7///hulS5eWLbe1tUVaWhrevXsndxUvOjoatra2sjb//vuv3PbeP+UlZ5uPn/wSHR0NMzMzGBoaKlUrUUEqYcL8JCSLQUEQT4swznUk7MZT5ichtcqoZ376EkUhP/EWPCIiIlKsEJ8jLJVK4e/vjz/++AMnTpxAmTJl5NbXqFEDurq6OH78uGzZ3bt3ERERgbp1szts69atixs3buDly5eyNkePHoWZmRk8PT1lbXJu432b99sgIiIiyhfmJ0EcAUVEREQKSfI9iDzvBg0ahC1btuDPP/+EqampbM4Bc3NzGBoawtzcHL1798aIESNgZWUFMzMzDB48GHXr1kWdOtlXvFu2bAlPT09069YNc+bMQVRUFCZOnIhBgwbJRmL1798fy5Ytw5gxY+Dn54cTJ05gx44dOHDgQKEdKxEREWku5idhEinvCSIVSE4XuwL1xB83YRJJ4X1AFyU8LcJ4C54wVd2Cd+NZQr7eX7m0SZ7bKvosWLduHXr27AkASElJwciRI7F161akpqbCx8cHy5cvlw0PB4AnT55gwIABOHXqFIyNjdGjRw8EBgZCR+fDdbdTp05h+PDhuHXrFkqXLo1ffvlFtg8isTA/CeMteMK0GBQE8bQI4y14wlR1Cx7zk4Ja2QFFqsAAJYw/bsLYASWMp0UYO6CEqaoDKiyfAaqSEgGKqLhjfhLGDihh7IASxtMijB1QwlTVAcX8JIxzQBERERERERERkUpxDigiIiJSjFeSiYiIiJTD/CSIHVBERESkUGFOoklERESkCZifhLEDioiIiBTiXBpEREREymF+EsYOKCIiIlKI+YmIiIhIOcxPwtgBRURERIoxQREREREph/lJEJ+CR0REREREREREKsURUERERKQQJ9EkIiIiUg7zkzB2QBEREZFCnESTiIiISDnMT8LYAUVEREQKMT8RERERKYf5SRg7oIiIiEgxJigiIiIi5TA/CeIk5EREREREREREpFIcAUVEREQKcRJNIiIiIuUwPwljBxQREREpxEk0iYiIiJTD/CSMHVBERESkEPMTERERkXKYn4SxA4qIiIgUY4IiIiIiUg7zkyBOQk5ERERERERERCrFEVBERESkECfRJCIiIlIO85MwdkARERGRQpxEk4iIiEg5zE/C2AFFRERECjE/ERERESmH+UkYO6CIiIhIMSYoIiIiIuUwPwkq8pOQu7i4YNGiRZ9sI5FIsGfPHgDA48ePIZFIEBISovLaiIiIijpJPv9H6on5iYiISHWYn4SJOgKqZ8+e2LBhAwBAV1cXTk5O6N69OyZMmAAdHdWU5ujoiMjISJQsWVLp906ZMgVTp079ZBupVPqlpaml9evXY9iwYXj37p3YpSi0ImgpVq5YJrfMpUwZ7Nl3GACwa+d2HDqwH3du30RiYiL+Pn8JZmZmYpSqclcuX8LG9Wtx69ZNvH71CgsWLYN3s+aCbWdMm4zdO7dj1Jjx+KFbD9nyNauCcebvU7h39w50dHVx5vylwiq/UGRmZiJ4+VIc2L8Xb16/hrW1Ddp3+AZ9+g2E5L+btaVSKVYELcHvu3YiPj4OVatVx4RfpsDZ2UXc4gtZZmYmVgTlOFc2Nmj/9Tfo2//DuSrq9m5fj0vnTiLy2RPo6enD3bMyuvoNhn1pZwBAQnwsdm9ahRtX/8GbV9EwM7dAjbqN8W33/jAyNpFtZ+OKebh36zqePX4AeycXzAraLLefF8+eYN3SQDyPeITkxARYlCiJek188M0PfVT2+440F/OT+isK+QkAoqOjsXjBXJw7ewYpKclwdHLG1OmzULFSZQBAUlIiFi+cj5MnjiH23Ts4OJTG9z90Q+eu34tcecF6n59u/5ef5gvkp4cPH2DJwnm4evkSMjIz4epaFnMXLoGdnT0AYMbUSfj34gW8evUShkZGqFKlGoYMH4Uyrq5iHJJKfO77hfkp24qgpQhenvtvkz/3HxapooK3d5uC/OToLGtz4uAfOH/yCB4/uIuUpESs3HUcxiamctt5FH4H239dhof3bkFLSwu1GjTFD32HwcDQCADw91/7sWrBNMEagrYdhrmFleoOklRC9NTbqlUrrFu3DqmpqTh48CAGDRoEXV1djB8/XiX709bWhq2t7Re9d9SoUejfv7/s61q1aqFv377o06ePYPu0tDTo6el90b7UQXp6utgl5FlZN3esXLNO9rW2trbs3ykpyajfoCHqN2iIJYvmi1FeoUlOTka5ch74+ptOGDlssMJ2J44fxY3robC2scm1Lj09DS1atoJXlarY88duVZYrinVrV2Pn9q2YNnM2yrq54dbNMEyeOB4mJqb434/dAQDrf12NLZs3YfrMQDg4lMbyZYsxsF9v/P7nQejr64t8BIXn/bmaPuu/cxUWhkkTx8PE1BQ//HeuirrbN66iRbvOcC1XAZmZmdixfgVm/zwYs1duh4GBId6+eY13Ma/xv5+GwsGpDF6/jMS6ZYF4++Y1hk4MlNtW45bt8OBuGCIe3c+1Hx1tHTRo1gYubuVhbGyKJ4/CsXbxLGRJpejac2BhHe4X0ZC+Ro3D/KS+ikp+iouNRc9u36NW7a+wLHg1rCwt8eTJE5iZmcvazJsTiEv/XMTMgLmwd3DAhfPnEDBjKqxtbNDEu5mI1ReslBz5aZRAfnr6NAK9u/8PX3f8Fv0HDoaxiQke3r8Pfb0PmaCCZ0W09m0HOzs7xMbGYuWKZRjUrzf2HT4ml0uLqrx8vzA/fVDWzR2rcv5tolP0vwdykstPWZnYse6//LQqOz8BQFpqCrxq1oVXzbrYsS4o1zbevnmFwPH+qNO4OboPHI3kpET8tnIBVs6fJstYdRo3h1fNOnLvWzl/GtLT0tS+84n5SZjot+Dp6+vD1tYWzs7OGDBgAJo3b469e/cCAJo0aYJhw4bJte/QoQN69uwptyw+Ph7ff/89jI2N4eDggKCg3N/g7wkNIb958ybatm0LMzMzmJqaomHDhnjw4EGu95qYmMDW1lb20tbWhqmpqezr7777Dv7+/hg2bBhKliwJHx8fAMCCBQtQuXJlGBsbw9HREQMHDkRCQoJsu+vXr4eFhQWOHDmCChUqwMTEBK1atUJkZKSszalTp1C7dm0YGxvDwsIC9evXx5MnTwBkX1msWrUqVq5cCUdHRxgZGaFLly6IjY2VvT8rKwvTpk1D6dKloa+vj6pVq+Lw4Q+98O/Py/bt29G4cWMYGBhg8+bN6NWrF2JjYyGRSCCRSDBlyhSF51ZM2traKFnSWvaytPzwgfRjt57w+6kvKntVEbHCwtGgYSMMGjIMTZu1UNjmZXQ0Zs+agVmBcwWvlA8YNAQ/du8Jd/dyqixVNKEh19DEuxkaNW4CB4fSaNGyFerWa4CwG9cBZF+927xpI/r0HQDvps1RrrwHps+ag1cvX+Lk8WMiV1+4QkKuoUnTHOfKR/5caYKxM5agUYu2KO1cFs6u5dBvxCS8eRmFx+G3AQCOLmUxdOJsVK/TEKXsS6Ni1Vro3GMArv1zBpmZGbLtdB8wCi3adYa1rYPgfmzsHNC4ZTs4u5ZDyVJ2qFGnEep5++Bu2LVCOc78kOTzRarB/MT8lF/rfl0NW1tbTJsRgMqVveBQ2hH16jeAo5OTrE1oyDW0+7oDatX+Cg4OpfFt564oV95Do34PAED9z+SnoCWLUL9hYwwbMRoeFTzh6OiExt5NYVWihKxNp85dUaNmLdg7lEYFz4oY6D8MUVGRePHieWEdhkp97vuF+UmejrY2Slpby145/zbRBGNnLkGjlm1R2uW//DRSPj8BQKtvvkf7rj3g5lFJcBvX/jkLbR0d9Bg0BvaOzihb3hN+g8fh0tkTiHrxFACgp28AC6uSspeWljZuhV5GE5/2hXKc+cH8JEz0DqiPGRoaIi0tTan3zJ07F1WqVMG1a9cwbtw4DB06FEePHs3Te58/f45GjRpBX18fJ06cwJUrV+Dn54eMjIzPv1nAhg0boKenh3PnziE4OBgAoKWlhSVLluDmzZvYsGEDTpw4gTFjxsi9LykpCfPmzcOmTZvw999/IyIiAqNGjQIAZGRkoEOHDmjcuDGuX7+OCxcuoG/fvnK3wNy/fx87duzAvn37cPjwYVy7dg0DB364qr548WLMnz8f8+bNw/Xr1+Hj44P27dsjPDxcro735+/27dvw9vbGokWLYGZmhsjISERGRspqUjcREU/QwrsBfFs1w/ixIxEZ+ULsktRSVlYWJk4Ygx69eqOsm7vY5YiiStVq+Oefi3jy+BEA4O6dO7h29QrqN2wEAHj+7Blev36Fr+rWk73H1NQUlb2qIDRU/TsLClLVqtXw78WLeJzzXF27ggb/nStNlJSU/cetsam54jaJCTA0Moa29pcPIo568RTXL19EhcrVv3gbhUUiyd+LCgfzE/OTsk6fPAHPipUwasQQeDeqi67fdsDuXTvk2lSpWg2nTp5AdHQ0pFIpLv2b/fuzbr0GIlVd+LKysnD271NwdnbBwH690axxPXT/X5dPdqokJyVh757f4eBQ+otHDqqbz32/MD/JexLxBM2bNEAbn2YYP2YkIl9o9t8meclPH8tIT4OOjg60tD50Sej+N1LuXlio4HvOHj8IfX0D1G7YNB/VFg7mJ2Gi34L3nlQqxfHjx3HkyBEMHqz41iEh9evXx7hx4wAA5cqVw7lz57Bw4UK0aKF4FMh7QUFBMDc3x7Zt26Crqyvbxpdyd3fHnDlz5JblvArp4uKCGTNmoH///li+fLlseXp6OoKDg1G2bFkAgL+/P6ZNy77fNS4uDrGxsWjbtq1sfYUKFeT2kZKSgo0bN8LBIfvq+9KlS+Hr64v58+fD1tYW8+bNw9ixY/Hdd98BAGbPno2TJ09i0aJFclc8hw0bho4dO8q+Njc3h0Qi+eQvz9TUVKSmpsoty9LSL7ShtpW9vDBtRgBcXMrg9etXCF4eBL/uP2DXnn0wzjFHC2VfvdLW1sb3P3QTuxTR+P3UF4mJCejQrjW0tbWRmZkJ/yHD4ds2+0rK69evAAAlclzVBACrEiXw5vXrQq9XTH4/9UVCQgI6tP1wrgYP/XCuNE1WVhZ+W7kA5TyrwNGlrGCb+Nh32LP1V3i37vBF+5g6ojce37+L9PQ0eLf+Bp269ctHxYVFg1OQBmB+Yn76Us+ePcXO7VvxY/de+KlPf4SF3cCcgBnQ1dVF+6+/AQCMm/ALpk35BT7NGkFHRwcSiQSTpsxAjZq1CqVGdRAT8wZJSUlY9+tqDPQfiqHDR+H82TMYNXwwVq3dgBq1asva7ti2BYsXzENychJcXMpg+epfoatbdG8nzelz3y/MTx9U9vLC9JnZf5u8evUKK1cEoVf3H7D7T8382yQrKwu/BX86PwnxrFITm1ctwv6dm9Cqw3dITUnG9l+zP1ffxQh/z5w6shd1vX2gp29QILWrFvOTENE7oPbv3w8TExOkp6cjKysL//vf/5Qeply3bt1cX3/uyS7vhYSEoGHDhrLwlF81atTItezYsWMICAjAnTt3EBcXh4yMDKSkpCApKQlGRtkTrBkZGcnCEQDY2dnh5cuXAAArKyv07NkTPj4+aNGiBZo3b44uXbrAzs5O1t7JyUkWnoDsc5CVlYW7d+/CyMgIL168QP369eXqql+/PkJD5XuXa9asqfQxBwQE5JpcdMLEyZg4aYrS2/oSDRo2lv27XHkPVKpcBW1aeuOvw4fwTafOhVJDUXDrZhi2/rYJW3bs1pgJpL/EX4cP4eD+fQiYPR9l3dxw985tzJ0dIJtgmz44cvgQDh7Yh4A58+Hm5oY7d25jbmCAbOJ2TbMhaA6ePX6IX+atElyflJiAeZOHw8GpDDr+2PeL9uE/fhZSkhLx5FE4tq5ZioO7f0PbzpoxnxYVLuYn5qf8ysqSwrNiJQwZNgIA4FHBEw/Cw7FrxzbZ78OtmzfhxvUQLF62AnZ29rh65TICZmbPAVUnx0gXTSbNygIANGnSFD927wkAKO9RAaGh17Br5za5DqjWvu1Qp249vHr1Cps2/IqxI4dh3aatGjH/UV6+Xyjbx3+bVPaqgtYtvHHk8CF01MC/TWT5ab5wflKktEtZ9Bs1GZtXLcKOdcuhpa2Flu27wtzSChKt3H+rhN+6jhcRjzBg9JQCqpzEIPoteN7e3ggJCUF4eDiSk5OxYcMGGBsbA8geev3xU1EKemJHQ0PDAt3e+9rfe/z4Mdq2bQsvLy/s3r0bV65ckV0xyzlU/uMAJ5FI5I593bp1uHDhAurVq4ft27ejXLlyuHjxYoHWLlR/XowfPx6xsbFyr9FjVTMJal6YmZnBydkFTyMiRKtBHV27egUxMW/QpmVT1KxaETWrVkTkixdYMG822vio/zDWgrJw/hz0+qkvWrXxhXu58mjbvgN+7N4Dv65ZCQAoWdIaAPDmzRu598W8eYMSX/D0p6Js4fw58OvdF63/O1ft/jtXa/87V5pkw/K5uPbvWUyYvRwlrEvlWp+clIi5vwyFgaERhv0y54ufNFbCuhQcnF1Rr4kPuvoNwu+bVyMrMzO/5asUh5CrJ+anbMxPX87a2lqu8w4Ayri6yqYxSElJwdLFCzFy9Hg0btIU5cp74Lv//QifVm2wcf3aQqtTbBaWltDR0YFrWTe55WXKlEVUjvnGgOxbzpycXVCjZi3MXbAYjx8/wsnjebutVd197vuF+UkxMzMzOGvo3yYbgubi2j9nMWGOcH76nHrerRC09TCWbt6P4B1H0bFbH8TFvoONwJyapw7/Ceey5VDGvYLAltQP85Mw0TugjI2N4ebmBicnp1yB3traWm4iyczMTISFheXaxsdB4uLFi7mGWCvi5eWFM2fOqOyJJVeuXEFWVhbmz5+POnXqoFy5cnjxhfcAV6tWDePHj8f58+dRqVIlbNmyRbYuIiJCbrsXL16ElpYWypcvDzMzM9jb2+PcuXNy2zt37hw8PT0/uU89PT1kfuaPI319fZiZmcm9xLzSk5SUiGdPn6KktbVoNagj33btsWP3n9i28w/Zy9rGBt179sby4DVil1doUlJSoPXRp7qWljaysrL/YHEoXRolS1rj34sXZOsTEhJw43ooqlSpVqi1ii0lOQVaH12B0tb+cK40gVQqxYblc3H5/ClMCFwuGHiSEhMw++fB0NbRxYjJ86GnVzCfb9KsLGRmZCBLzR8/z0k01RPzU94xPwmrUq26bI6/9548eQw7u+zPwYyMDGRkpOf6PaClYb8HPkdXVw+eFSvlOlcRTx7Dzs5e4fuk0uz/U3ZuNnX1ue8X5ifFkhIT8VTD/jaRSqXYEPRffpotnJ+UYW5ZAgaGRvjn9FHo6eqhUvWv5NanJCfhnzPH0bgITD7+HvOTMNFvwfuUpk2bYsSIEThw4ADKli2LBQsW4N27d7nanTt3DnPmzEGHDh1w9OhR7Ny5EwcOHMjTPvz9/bF06VJ89913GD9+PMzNzXHx4kXUrl0b5cuXz/cxuLm5IT09HUuXLkW7du3kJtfMq0ePHmHVqlVo37497O3tcffuXYSHh6N79w+3bRgYGKBHjx6YN28e4uLiMGTIEHTp0kU298Do0aMxefJklC1bFlWrVsW6desQEhKCzZs3f3LfLi4uSEhIwPHjx1GlShUYGRnJhr2riwVzZ6NRE2/Y2dvj1cuXWBG0FNraWmjVpi2A7Dl9Xr9+LbvqcD/8HoyMjWFnZwdzcwsRKy94SUmJcldXnj9/hrt3bsPM3Bx2dvawsLCUa6+jo4OSJUvCpYyrbFlk5AvExcYiMjISWZmZuHvnv6eBOTnByEj5K7zqplETb6xZHQxbO/vsW/Bu38ZvG9fh6286Aci+ev5Dt+5YvWoFnJyd4eBQGkHLFsPaxgbezZqLXH3hatzEG6tXfThXd27fxqYNH86VJlgfNAcXTh3B8EnzYGBoJJtzwMjYBHr6Bv91Pg1BWmoKBoyehuSkBCT/N9GmmbkltP57tHbUi6dITU5G7Ns3SEtNxZMH9wAADk5loKOri3MnDkNbRxuOLm7Q1dXDw/Bb2LF+Ob5q1OKLR1MVFk2+CqepmJ+yMT992o/deqBnt++xZlUwWrZqjbAb17F71w78Mjl7Di0TExPUqFkbC+fPhb6+Aezt7XH58iXs37sHI0ePE7n6gvW5/NS9V2+MGzUC1WvURM3aX+H82TP4+/RJrPp1IwDg2dOn+OvIQdSpWx+WVlZ4GR2FdWtXQ19fX+52rKLsc98vzE8fzJ87G40F/jZp/d/fJppgfdAcXDh5BMMnC+cnIHsep9i3MYj+74l2Tx/fh6GhMUrYlILJf5OV/7V3B9wreMHA0BBhV//F1rVL0LWXP4xNTOX2d/H0UWRmZqJ+09aFeJT5w/wkTK1Tr5+fH0JDQ9G9e3fo6Ohg+PDh8Pb2ztVu5MiRuHz5MqZOnQozMzMsWLBA9gjfzylRogROnDiB0aNHo3HjxtDW1kbVqlVz3e//papUqYIFCxZg9uzZGD9+PBo1aoSAgAC58PM5RkZGuHPnDjZs2IA3b97Azs4OgwYNQr9+HyavdXNzQ8eOHdGmTRvExMSgbdu2cpN0DhkyBLGxsRg5ciRevnwJT09P7N27F+7un34SWr169dC/f3907doVb968weTJk9XuUcLR0VEYP2YE3r17B0srK1SrVgMbN++AlVX24053bt+GlSuWydr79fgBADB1RgC+7tBRcJtF1a2bYejj10P29fy5gQCAdu07YNrMwDxtY8WyJdi3d4/s6+86Z9/Xv/rXDahZ6ysF7yo6xk2YiKClixEwYypiYt7A2toGnTp3Rb8Bg2Rtevr1QXJyMqZPmYT4+DhUq14Dy4PXaMQcDsoY9/NEBC1ZjFnT/ztXNjb49qNzVdQdP7AbADBzbH+55X1HTEKjFm3x+MFdPLibPXJkZG/5z4uF6/fAulT21e81i2bizo2rsnU/+/8o10ZbWxv7d25C1PMISKVSlLSxRYt2ndHqm+9VdmwFRaLR1+E0E/NTNuanT6tU2QsLFi3DksULsCo4CA4OpTF67AS5B03MnrcASxYtwIRxoxAXGws7e3v4DxmOzl3V/7NLGbduhqFvjvy0IEd+mjozEE2btcCESVOwbs0qzA2cCWeXMpi7YAmqVc+eu0xfXw/XrlzBlk0bERcXhxIlSqB6jZpYt2krrD6alLuoysv3C/NTtujoKIwbneNvk+o1sGnLh79NNMHx/f/lpzEC+alldkfb8QO/44/NH+6ymDGqX642D+/exO+bViElJRn2pZ3hN3g8GjRvk2t/p4/sRa36TXJ1TKkz5idhEunHkwRQkTNlyhTs2bMHISEhYpcik6yaEflFHn/chBXnSdE/hadF2I2IWLFLUEu1XPP+6GNlRMXm7wPd1rxgJqkmKmjMT0WHut+qLJaPpxSgbDwtwm48ZX4SUqsM81NhUusRUERERCQyBnkiIiIi5TA/CWIHFBERESnE/ERERESkHOYnYbwFj1SCQ8iF8cdNGG/BE8bTIoy34AlT1S14L+Pz94FuY6qZQ8iJVIH5SRhvwRPGW/CE8bQI4y14wlR1Cx7zkzCOgCIiIiKFOIkmERERkXKYn4SxA4qIiIgUY34iIiIiUg7zkyAtsQsgIiIiIiIiIiLNxhFQREREpBAv4BEREREph/lJGDugiIiISCFO5kpERESkHOYnYeyAIiIiIoU4iSYRERGRcpifhLEDioiIiBTiFTwiIiIi5TA/CeMk5EREREREREREpFLsgCIiIiIiIiIiIpXiLXhERESkEIeQExERESmH+UkYO6CIiIhIIU6iSURERKQc5idh7IAiIiIihXgFj4iIiEg5zE/COAcUERERERERERGpFEdAERERkUK8gEdERESkHOYnYeyAIiIiIsWYoIiIiIiUw/wkiB1QREREpBAn0SQiIiJSDvOTMHZAERERkUKcRJOIiIhIOcxPwtgBRURERAoxPxEREREph/lJGJ+CR0REREREREREKsURUERERKQYL+ERERERKYf5SRA7oIiIiEghTqJJREREpBzmJ2HsgCIiIiKFOIkmERERkXKYn4RJpFKpVOwiiFQlNTUVAQEBGD9+PPT19cUuR23wvAjjeRHG8yKM54WINBU/34TxvAjjeRHG8yKM56V4YwcUabS4uDiYm5sjNjYWZmZmYpejNnhehPG8CON5EcbzQkSaip9vwnhehPG8CON5EcbzUrzxKXhERERERERERKRS7IAiIiIiIiIiIiKVYgcUERERERERERGpFDugSKPp6+tj8uTJnODuIzwvwnhehPG8CON5ISJNxc83YTwvwnhehPG8CON5Kd44CTkREREREREREakUR0AREREREREREZFKsQOKiIiIiIiIiIhUih1QRERERERERESkUuyAIiIiIiIiIiIilWIHFBERERERERERqRQ7oIg0XHp6Ovz8/PDo0SOxS1Er6enpaNasGcLDw8UuhYqIadOmISkpKdfy5ORkTJs2TYSKiIhIVZifhDE/kbKYnygniVQqlYpdBBGplrm5OUJCQlCmTBmxS1Er1tbWOH/+PNzd3cUuhYoAbW1tREZGwsbGRm75mzdvYGNjg8zMTJEqIyIiVWB+Esb8RMpgfqKcOAKKNNaDBw8wceJEfP/993j58iUA4NChQ7h586bIlRW+Dh06YM+ePWKXoXZ+/PFHrF27Vuwy1Jafnx/i4+NzLU9MTISfn58IFYlLKpVCIpHkWh4aGgorKysRKiIiKnjMTx8wPwljfvo05id5zE+UE0dAkUY6ffo0Wrdujfr16+Pvv//G7du34erqisDAQFy+fBm7du0Su8RCNWPGDMyfPx/NmjVDjRo1YGxsLLd+yJAhIlUmrsGDB2Pjxo1wd3cXPC8LFiwQqTL1oOiK1evXr2Fra4uMjAyRKitclpaWkEgkiI2NhZmZmVyIyszMREJCAvr374+goCARqyQiyj/mJ3nMT8KYnz6N+Skb8xMJYQcUaaS6deuic+fOGDFiBExNTREaGgpXV1f8+++/6NixI549eyZ2iYXqU0PHJRIJHj58WIjVqA9vb2+F6yQSCU6cOFGI1aiPuLg4SKVSWFpaIjw8HNbW1rJ1mZmZ2LdvH8aNG4cXL16IWGXh2bBhA6RSKfz8/LBo0SKYm5vL1unp6cHFxQV169YVsUIiooLB/CSP+UkY85Mw5id5zE8kREfsAohU4caNG9iyZUuu5TY2Nnj9+rUIFYmLE2gKO3nypNglqCULCwtIJBJIJBKUK1cu13qJRIKpU6eKUJk4evTogYyMDEgkEjRt2hSOjo5il0REpBLMT/KYn4QxPwljfpLH/ERC2AFFGsnCwgKRkZG5rlxdu3YNDg4OIlUlvrS0NDx69Ahly5aFjg5//N+7f/8+Hjx4gEaNGsHQ0FDhverFxcmTJyGVStG0aVPs3r1b7v58PT09ODs7w97eXsQKC5+Ojg4GDBiA27dvi10KEZHKMD8JY34Sxvwkj/kpN+Yn+hg/QUkjfffddxg7dix27twJiUSCrKwsnDt3DqNGjUL37t3FLq/QJSUlYfDgwdiwYQMA4N69e3B1dcXgwYPh4OCAcePGiVyhON68eYMuXbrg5MmTkEgkCA8Ph6urK3r37g1LS0vMnz9f7BJF0bhxYwDZV34dHR2hpcXnVQBA7dq1ce3aNTg7O4tdChGRSjA/yWN+Esb8JIz5SRjzE+XEnwrSSLNmzYKHhwccHR2RkJAAT09PNGrUCPXq1cPEiRPFLq/QjR8/HqGhoTh16hQMDAxky5s3b47t27eLWJm4hg8fDl1dXURERMDIyEi2vGvXrjh8+LCIlakHZ2dnxMXFYf78+fjpp5/w008/YeHChYiNjRW7NFEMHDgQI0eOxLJly3DhwgVcv35d7kVEVNQxP8ljfhLG/PRpzE/ymJ8oJ05CThotIiICYWFhSEhIQLVq1eDu7i52SaJwdnbG9u3bUadOHblJRe/fv4/q1asjLi5O7BJFYWtriyNHjqBKlSpy5+Xhw4fw8vJCQkKC2CWK6vLly/Dx8YGhoSFq164NALh06RKSk5Px119/oXr16iJXWLiErmRKJBLZLQeZmZkiVEVEVPCYn7IxPwljfvo05id5zE+UE2/BI43m5OQEJycnscsQ3atXr3I9ChYAEhMTi/W9+omJiXJX7t6LiYmBvr6+CBWpl+HDh6N9+/ZYvXq1bM6LjIwM/PTTTxg2bBj+/vtvkSssXJyMloiKC+anbMxPwpifPo35SR7zE+XEDijSGCNGjMhz2wULFqiwEvVTs2ZNHDhwAIMHDwYAWWhas2ZNsX78acOGDbFx40ZMnz4dAGTzXcyZM+eTjxguLi5fviwXnoDsySTHjBmDmjVriliZODh3ARFpIuYnxZifhDE/fRrzkzzmJ8qJHVCkMa5du5andsXxitWsWbPQunVr3Lp1CxkZGVi8eDFu3bqF8+fP4/Tp02KXJ5o5c+agWbNmuHz5MtLS0jBmzBjcvHkTMTExOHfunNjlic7MzAwRERHw8PCQW/706VOYmpqKVJW4Hjx4gEWLFsme5uLp6YmhQ4eibNmyIldGRPRlmJ8UY34Sxvz0acxPuTE/0XucA4qomHjw4AECAwMRGhqKhIQEVK9eHWPHjkXlypXFLk1UsbGxWLZsmdx5GTRoEOzs7MQuTXRDhgzBH3/8gXnz5qFevXoAgHPnzmH06NHo1KkTFi1aJG6BhezIkSNo3749qlativr16wPIPh+hoaHYt28fWrRoIXKFRERU0JifhDE/Kcb8JI/5iXJiBxRppNjYWGRmZsLKykpueUxMDHR0dGBmZiZSZURFR1paGkaPHo3g4GBkZGQAAHR1dTFgwAAEBgYWu3keqlWrBh8fHwQGBsotHzduHP766y9cvXpVpMqIiAoG8xNR/jE/yWN+opzYAUUaqXXr1mjXrh0GDhwotzw4OBh79+7FwYMHRapMHM2bN8ePP/6Ijh07MjzmoOjRrxKJBAYGBnBycip2IUFIUlISHjx4AAAoW7as4MSjxYGBgQFu3LiR62lQ9+7dg5eXF1JSUkSqjIioYDA/yWN+Esb8lDfMT9mYnyin3M9EJNIA//zzj+AkiE2aNME///wjQkXiqlixIsaPHw9bW1t07twZf/75J9LT08UuS3RVq1ZFtWrVUK1aNVStWlX2ddWqVeHh4QFzc3P06NGj2P9iNDIygqWlJSwtLYtteAIAa2trhISE5FoeEhIi+JQkIqKihvlJHvOTMOanvGF+ysb8RDmxA4o0UmpqqmzIa07p6elITk4WoSJxLV68GM+fP8eePXtgbGyM7t27o1SpUujbt2+xnkTzjz/+gLu7O1atWoXQ0FCEhoZi1apVKF++PLZs2YK1a9fixIkTmDhxotiliiIrKwvTpk2Dubk5nJ2d4ezsDAsLC0yfPh1ZWVlil1fo+vTpg759+2L27Nk4c+YMzpw5g8DAQPTr1w99+vQRuzwionxjfpLH/CSM+enTmJ/kMT+RHCmRBmrSpInU398/1/KBAwdKGzRoIEJF6iU5OVm6Y8cOaZUqVaRaWlpilyOaWrVqSQ8fPpxr+eHDh6W1atWSSqVS6R9//CF1dXUt7NLUwrhx46TW1tbS5cuXS0NDQ6WhoaHSoKAgqbW1tXTChAlil1fosrKypAsWLJA6ODhIJRKJVCKRSB0cHKSLFi2SZmVliV0eEVG+MT99GvNTNuanT2N+ksf8RDlxDijSSOfOnUPz5s1Rq1YtNGvWDABw/PhxXLp0CX/99RcaNmwocoXiiYqKwrZt2/Dbb7/h6tWrqF27Ni5evCh2WaIwNDTEtWvXcj0m986dO6hWrRqSk5Px+PFjeHp6IikpSaQqxWNvb4/g4GC0b99ebvmff/6JgQMH4vnz5yJVJr74+HgAKLaPUyYizcT8pBjz0wfMT5/G/KQY8xPxFjzSSPXr18eFCxfg6OiIHTt2YN++fXBzc8P169eLZXiKi4vDunXr0KJFCzg6OmLFihVo3749wsPDi214AgAPDw8EBgYiLS1Ntiw9PR2BgYGyUPX8+XOUKlVKrBJFFRMTkytcAtnnLSYmRoSK1MPLly8REhKCkJAQvHr1SuxyiIgKDPOTPOYnYcxPn8b8JIz5iQA+BY+oWDA0NISlpSW6du2KH374ATVr1hS7JLVw/vx5tG/fHlpaWvDy8gIA3LhxA5mZmdi/fz/q1KmDTZs2ISoqCqNHjxa52sL31Vdf4auvvsKSJUvklg8ePBiXLl0qduE7Pj4eAwcOxNatW2VzOGhra6Nr164ICgqCubm5yBUSEVFBYn4Sxvz0acxP8pifKCd2QJFGaty4MXr37o3OnTvD0NBQ7HJEd/ToUTRr1gxaWhz0+LH4+Hhs3rwZ9+7dAwCUL18e//vf/zg0GMDp06fh6+sLJycn1K1bFwBw4cIFPH36FAcPHix2V8O7du2Ka9euYenSpXLnY+jQoahatSq2bdsmcoVERPnD/CSP+Ukx5ifFmJ/kMT9RTuyAIo00bNgwbNmyBampqejSpQt69+6NOnXqiF0WUZHw8OFDlClTBhKJBC9evMDy5ctx+/ZtAECFChUwcOBA2Nvbi1xl4TM2NsaRI0fQoEEDueVnzpxBq1atkJiYKFJlREQFg/mJ6MsxPwljfqKcdMQugEgVFi1ahHnz5mHv3r3YsGEDGjVqBDc3N/j5+aFbt27F4p706tWr4/jx47C0tES1atUgkUgUtr169WohVqZewsPDcfLkSbx8+TLXo3EnTZokUlXicnd3R2RkJGxsbGBvb4/w8HAsX768WPzcfEqJEiUEh4mbm5vD0tJShIqIiAoW8xPzU14xP+XG/CSM+YlyYgcUaSwdHR107NgRHTt2xMuXL7Fq1Sr88ssvmDBhAtq0aYMhQ4agadOmYpepMl9//TX09fUBAB06dBC3GDW1evVqDBgwACVLloStra1cyJRIJMU2QH08MPbQoUO8OgVg4sSJGDFiBDZt2gRbW1sAkM1v8csvv4hcHRFRwWB+Yn76HOYnYcxPwpifKCfegkca799//8W6deuwbds2mJmZoWfPnnj+/Dm2bNmCgQMHYt68eWKXqFKZmZk4d+4cvLy8YGFhIXY5asXZ2RkDBw7E2LFjxS5FrWhpaSEqKgo2NjYAsh+VGxoaCldXV5ErE1e1atVw//59pKamwsnJCQAQEREBfX19uLu7y7UtzlfFiUgzMD8xPynC/CSM+UkY8xPlxBFQpJFevnyJTZs2Yd26dQgPD0e7du2wdetW+Pj4yK7S9OzZE61atdL4AKWtrY2WLVvi9u3bDFAfefv2LTp37ix2GWpHIpHkuuXgU7cgFBe8Ek5Emo756QPmJ8WYn4QxPwljfqKc2AFFGql06dIoW7Ys/Pz80LNnT1hbW+dq4+XlhVq1aolQXeGrVKmSbGJE+qBz587466+/0L9/f7FLUStSqRQ9e/aU3YKQkpKC/v37w9jYWK7d77//LkZ5opk8ebLYJRARqRTzkzzmJ2HMT8KYn4QxP1FOvAWPNNKZM2eK3SNOP+Xw4cMYP348pk+fjho1auT6RWhmZiZSZeIKCAjAggUL4Ovri8qVK0NXV1du/ZAhQ0SqTFy9evXKU7t169apuBL18vTpU0gkEpQuXRpA9u0pW7ZsgaenJ/r27StydURE+cf8JI/5SRjzkzDmJ2HMT5QTO6BIIz169AgZGRm57isODw+Hrq4uXFxcxClMJFpaWrJ/5xwKLJVKIZFIkJmZKUZZovvUFU2JRIKHDx8WYjWk7ho2bIi+ffuiW7duiIqKQrly5VCpUiWEh4dj8ODBxXbSVSLSHMxP8pifhDE/kTKYnygn3oJHGqlnz57w8/PLFaD++ecfrFmzBqdOnRKnMJGcPHlS7BLU0qNHj8QugYqQsLAw1K5dGwCwY8cOVK5cGefOnZPdhsAARURFHfOTPOYnYcxPpAzmJ8qJHVCkka5du4b69evnWl6nTh34+/uLUJG4GjduLHYJai0tLQ2PHj1C2bJloaPDj0USlp6eLpvX4dixY2jfvj0AwMPDA5GRkWKWRkRUIJif5DE/fRrzE+UF8xPlxE8K0kgSiQTx8fG5lsfGxhbL4dLh4eH4888/8fjxY0gkEri6uuLrr78u9o+FTUpKwuDBg7FhwwYAwL179+Dq6orBgwfDwcEB48aNE7lCUicVK1ZEcHAwfH19cfToUUyfPh0A8OLFC5QoUULk6oiI8o/5SR7zkzDmJ1IG8xPlpPX5JkRFT6NGjRAQECAXljIzMxEQEIAGDRqIWFnhCwgIgKenJ8aOHYvdu3dj586dGDVqFDw8PDT+EcqfM378eISGhuLUqVMwMDCQLW/evDm2b98uYmWkjmbPno2VK1eiSZMm+P7771GlShUAwN69e2VDy4mIijLmpw+YnxRjfiJlMD9RTpyEnDTSrVu30KhRI1hYWMie5nLmzBnExcXhxIkTqFSpksgVFo6TJ0+iefPm+OWXXzB06FBYWloCAGJiYrBo0SLMmjULJ06cQKNGjUSuVBzOzs7Yvn076tSpA1NTU4SGhsLV1RX3799H9erVERcXJ3aJpGYyMzMRFxcn+1kCgMePH8PIyAg2NjYiVkZElH/MT9mYnz6N+YmUxfxE77EDijTWixcvsGzZMoSGhsLQ0BBeXl7w9/eHlZWV2KUVmq5du8LCwgIrV64UXN+3b1/Ex8dj69athVyZejAyMkJYWBhcXV3lAlRoaCgaNWqE2NhYsUskIiIqVMxPzE+fw/xERF+Kc0CRxrK3t8esWbPELkNU//77LzZt2qRwfbdu3dC9e/dCrEi91KxZEwcOHMDgwYMBfHjE8po1a1C3bl0xSyM1FB0djVGjRuH48eN4+fIlPr5+UxznRyEizcP8xPz0OcxPpAzmJ8qJHVCkMa5fv45KlSpBS0sL169f/2RbLy+vQqpKXNHR0XBxcVG4vkyZMoiKiiq8gtTMrFmz0Lp1a9y6dQsZGRlYvHgxbt26hfPnz+P06dNil0dqpmfPnoiIiMAvv/wCOzs7WeAmIirKmJ9yY376NOYnUgbzE+XEW/BIY2hpaSEqKgo2NjbQ0tKCRCLJ1cMOZF+lKS497TnPiZDo6GjY29sXm/Mh5MGDBwgMDERoaCgSEhJQvXp1jB07FpUrVxa7NFIzpqamOHPmDKpWrSp2KUREBYb5KTfmp89jfqK8Yn6inDgCijTGo0ePYG1tLfs3ZVuzZg1MTEwE1wk9arm4KVu2LFavXi12GVQEODo6Cv5RRkRUlDE/CWN++jTmJ8or5ifKiSOgSCO9efMGJUqUAAA8ffoUq1evRnJyMtq3by97qktx4OLikqdhrsU1cF69ehW6urqyq3V//vkn1q1bB09PT0yZMgV6enoiV0jq5K+//sL8+fOxcuXKT96aQURUVDE/ZWN++jTmJ1IG8xPlxA4o0ig3btxAu3bt8PTpU7i7u2Pbtm1o1aoVEhMToaWlhcTEROzatQsdOnQQu1RSA7Vq1cK4cePQqVMnPHz4EJ6enujYsSMuXboEX19fLFq0SOwSSY1YWloiKSkJGRkZMDIygq6urtz6mJgYkSojIsof5idSBvMTKYP5iXJiBxRplNatW0NHRwfjxo3Dpk2bsH//fvj4+MiGCA8ePBhXrlzBxYsXRa6U1IG5uTmuXr2KsmXLYvbs2Thx4gSOHDmCc+fO4bvvvsPTp0/FLpHUyIYNGz65vkePHoVUCRFRwWJ+ImUwP5EymJ8oJ3ZAkUYpWbIkTpw4AS8vLyQkJMDMzAyXLl1CjRo1AAB37txBnTp18O7dO3ELJbVgZmaGK1euwN3dHS1atEDbtm0xdOhQREREoHz58khOTha7RCIiIpVjfiJlMD8R0ZfiJOSkUWJiYmBrawsAMDExgbGxMSwtLWXrLS0tOXEkydSsWRMzZsxA8+bNcfr0aaxYsQJA9pwOpUqVErk6UmcpKSlIS0uTW2ZmZiZSNURE+cP8RMpgfqIvxfxEWmIXQFTQPp40Mi+TSFLxtGjRIly9ehX+/v74+eef4ebmBgDYtWsX6tWrJ3J1pG4SExPh7+8PGxsb2R9nOV9EREUZ8xPlFfMTKYP5iXLiLXikUbS0tNC6dWvo6+sDAPbt24emTZvC2NgYAJCamorDhw8jMzNTzDJJzaWkpEBbWzvXJIlUvA0aNAgnT57E9OnT0a1bNwQFBeH58+dYuXIlAgMD8cMPP4hdIhHRF2F+ooLA/ERCmJ8oJ3ZAkUbp1atXntqtW7dOxZWoL19fX6xZswZ2dnZilyK6p0+fQiKRoHTp0gCAf//9F1u2bIGnpyf69u0rcnWkbpycnLBx40Y0adIEZmZmuHr1Ktzc3LBp0yZs3boVBw8eFLtEIqIvwvz0ecxPHzA/kTKYnygndkARFTOmpqYIDQ2Fq6ur2KWIrmHDhujbty+6deuGqKgolC9fHhUrVkR4eDgGDx6MSZMmiV0iqRETExPcunULTk5OKF26NH7//XfUrl0bjx49QuXKlZGQkCB2iUREpCLMTx8wP5EymJ8oJ84BRUTFVlhYGGrXrg0A2LFjBypVqoTz589j8+bNWL9+vbjFkdpxdXXFo0ePAAAeHh7YsWMHgOxbVSwsLESsjIiIqPAwP5EymJ8oJ3ZAERUzzs7OvDf/P+np6bL5Lo4dO4b27dsDyP7lGBkZKWZppIZ69eqF0NBQAMC4ceMQFBQEAwMDDB8+HKNHjxa5OiIiUiXmpw+Yn0gZzE+UE2/BI6Ji66uvvoK3tzd8fX3RsmVLXLx4EVWqVMHFixfx7bff4tmzZ2KXSGrsyZMnuHLlCtzc3ODl5SV2OURERIWC+Ynyg/mpeGMHFBEVW6dOncI333yDuLg49OjRA7/++isAYMKECbhz5w5+//13kSskdZGeno5WrVohODgY7u7uYpdDREQkGuYnyivmJ/oYO6CIqFjLzMxEXFwcLC0tZcseP34MIyMj2NjYiFgZqRtra2ucP3+eAYqIiIo95ifKK+YnyokdUERERHkwfPhw6OvrIzAwUOxSiIiIiIoE5ifKSUfsAohItaRSKZ4+fQobGxsYGBiIXY7a2bVrF3bs2IGIiAikpaXJrbt69apIVZE6ysjIwK+//opjx46hRo0aMDY2llu/YMECkSojIqKCxvz0acxPlFfMT5QTn4JHpOGkUinc3Nzw9OlTsUtRO0uWLEGvXr1QqlQpXLt2DbVr10aJEiXw8OFDtG7dWuzySE1oa2vj5cuXCAsLQ/Xq1WFqaop79+7h2rVrsldISIjYZRIRUQFiflKM+YnygvmJhPAWPKJioGLFili7di3q1KkjdilqxcPDA5MnT8b3338PU1NThIaGwtXVFZMmTUJMTAyWLVsmdomkBrS0tBAVFcU5LYiIihnmJ2HMT5QXzE8khCOgiIqBwMBAjB49GmFhYWKXolYiIiJQr149AIChoSHi4+MBAN26dcPWrVvFLI2IiIhExvwkjPmJiL4U54AiKga6d++OpKQkVKlSBXp6ejA0NJRbHxMTI1Jl4rK1tUVMTAycnZ3h5OSEixcvokqVKnj06BE4OJRyWrNmDUxMTD7ZZsiQIYVUDRERFQbmJ2HMT5RXzE/0MXZAERUDixYtErsEtdS0aVPs3bsX1apVQ69evTB8+HDs2rULly9fRseOHcUuj9RIcHAwtLW1Fa6XSCQMUEREGob5SRjzE+UV8xN9jHNAEVGxlZWVhaysLOjoZPfFb9u2DefPn4e7uzv69esHPT09kSskdcA5DIiIiD5gfqK8YH4iIeyAIipmUlJScj0u18zMTKRqxHPx4kXs27cPaWlpaNasGVq1aiV2SaSmtLW1ERkZyQBFRFSMMT9lY36ivGJ+IiHsgCIqBhITEzF27Fjs2LEDb968ybU+MzNThKrEs2vXLnTt2hWGhobQ1dVFXFwcZs+ejVGjRoldGqkhXsEjIiqemJ/kMT+RMpifSAifgkdUDIwZMwYnTpzAihUroK+vjzVr1mDq1Kmwt7fHxo0bxS6v0AUEBKBPnz6IjY3F27dvMWPGDMyaNUvsskhNTZ48+bMTaBIRkeZhfpLH/ETKYH4iIRwBRVQMODk5YePGjWjSpAnMzMxw9epVuLm5YdOmTdi6dSsOHjwodomFysTEBCEhIXBzcwMApKWlwdjYGM+fP+dVGiIiIgLA/PQx5iciyi+OgCIqBmJiYuDq6goge76C948NbtCgAf7++28xSxNFUlKS3LwNenp6MDAwQEJCgohVERERkTphfpLH/ERE+aUjdgFEpHqurq549OgRnJyc4OHhgR07dqB27drYt28fLCwsxC5PFGvWrJEbFpyRkYH169ejZMmSsmV8LCwREVHxxfyUG/MTEeUHb8EjKgYWLlwIbW1tDBkyBMeOHUO7du0glUqRnp6OBQsWYOjQoWKXWKhcXFwgkUg+2UYikeDhw4eFVBERERGpG+YnecxPRJRf7IAiKoaePHmCK1euwM3NDV5eXmKXQ1QkPHr0CBkZGXB3d5dbHh4eDl1dXbi4uIhTGBERFQrmJyLlMT9RTpwDiqgYcnZ2RseOHRmeiJTQs2dPnD9/Ptfyf/75Bz179iz8goiIqFAxPxEpj/mJcmIHFJEGO3HiBDw9PREXF5drXWxsLCpWrIgzZ86IUBlR0XPt2jXUr18/1/I6deogJCSk8AsiIiKVYH4iKjjMT5QTO6CINNiiRYvQp08fuSeWvGdubo5+/fphwYIFIlRGVPRIJBLEx8fnWh4bG4vMzEwRKiIiIlVgfiIqOMxPlBM7oIg0WGhoKFq1aqVwfcuWLXHlypVCrIio6GrUqBECAgLkwlJmZiYCAgLQoEEDESsjIqKCxPxEVHCYnygnHbELICLViY6Ohq6ursL1Ojo6ePXqVSFWRFR0zZ49G40aNUL58uXRsGFDAMCZM2cQFxeHEydOiFwdEREVFOYnooLD/EQ5cQQUkQZzcHBAWFiYwvXXr1+HnZ1dIVakvnx9fREZGSl2GaTGPD09cf36dXTp0gUvX75EfHw8unfvjjt37qBSpUpil0dERAWE+SnvmJ/oc5ifKCeJVCqVil0EEanG4MGDcerUKVy6dAkGBgZy65KTk1G7dm14e3tjyZIlIlWoPkxNTREaGgpXV1exSyEiIiIRMT/lHfMTESmDHVBEGiw6OhrVq1eHtrY2/P39Ub58eQDAnTt3EBQUhMzMTFy9ehWlSpUSuVLxMUCRkOvXr6NSpUrQ0tLC9evXP9mWj+UmItIMzE95x/xEQpifSBF2QBFpuCdPnmDAgAE4cuQI3v+4SyQS+Pj4ICgoCGXKlBG5QvVQqVIlHDp0CI6OjmKXQmpES0sLUVFRsLGxgZaWFiQSCYR+bUokEj7JhYhIgzA/5Q3zEwlhfiJF2AFFVEy8ffsW9+/fh1Qqhbu7OywtLcUuiUjtPXnyBE5OTpBIJHjy5Mkn2zo7OxdSVUREVFiYn4iUx/xEirADioiIiIiIiIiIVEpH7AKIiIjU1d69e/Pctn379iqshIiIiKhoYH4iRTgCioiISAEtLa08teMcBkRERETZmJ9IEXZAEVGxJJVK8fTpU9jY2OR6xDIRERER5cb8RET5kbeuSSIiDSOVSuHm5oanT5+KXQoRERFRkcD8RET5wQ4oIiqWtLS04O7ujjdv3ohdChUhx48fR9u2bVG2bFmULVsWbdu2xbFjx8Qui4iIqFAwP9GXYH6i99gBRUTFVmBgIEaPHo2wsDCxS6EiYPny5WjVqhVMTU0xdOhQDB06FGZmZmjTpg2CgoLELo+IiKhQMD+RMpifKCfOAUVExZalpSWSkpKQkZEBPT09GBoayq2PiYkRqTJSR6VLl8a4cePg7+8vtzwoKAizZs3C8+fPRaqMiIio8DA/kTKYnygndkARUbG1YcOGT67v0aNHIVVCRYGJiQlCQkLg5uYmtzw8PBzVqlVDQkKCSJUREREVHuYnUgbzE+XEDigiIqI8+N///odq1aph9OjRcsvnzZuHy5cvY9u2bSJVRkRERKSemJ8oJx2xCyAiUgcpKSlIS0uTW2ZmZiZSNaQulixZIvu3p6cnZs6ciVOnTqFu3boAgIsXL+LcuXMYOXKkWCUSERGJhvmJhDA/kSIcAUVExVZiYiLGjh2LHTt2CD7NJTMzU4SqSJ2UKVMmT+0kEgkePnyo4mqIiIjEx/xEn8P8RIpwBBQRFVtjxozByZMnsWLFCnTr1g1BQUF4/vw5Vq5cicDAQLHLIzXw6NEjsUsgIiJSK8xP9DnMT6QIR0ARUbHl5OSEjRs3okmTJjAzM8PVq1fh5uaGTZs2YevWrTh48KDYJRIRERGpFeYnIvpSHAFFRMVWTEwMXF1dAWTPV/D+scENGjTAgAEDxCyN1JCfn98n1//666+FVAkREZF4mJ9IGcxPlBM7oIio2HJ1dcWjR4/g5OQEDw8P7NixA7Vr18a+fftgYWEhdnmkZt6+fSv3dXp6OsLCwvDu3Ts0bdpUpKqIiIgKF/MTKYP5iXJiBxQRFVu9evVCaGgoGjdujHHjxqFdu3ZYtmwZ0tPTsWDBArHLIzXzxx9/5FqWlZWFAQMGoGzZsiJUREREVPiYn0gZzE+UE+eAIiL6z5MnT3DlyhW4ubnBy8tL7HKoiLh79y6aNGmCyMhIsUshIiIqdMxP9CWYn4onjoAiIvqPs7MznJ2dxS6DipgHDx4gIyND7DKIiIhEwfxEX4L5qXhiBxQRFTsnTpyAv78/Ll68CDMzM7l1sbGxqFevHoKDg9GwYUORKiR1NGLECLmvpVIpIiMjceDAAfTo0UOkqoiIiAoH8xN9CeYnyom34BFRsdO+fXt4e3tj+PDhguuXLFmCkydPCt6zTsWXt7e33NdaWlqwtrZG06ZN4efnBx0dXtMhIiLNxfxEX4L5iXJiBxQRFTvOzs44fPgwKlSoILj+zp07aNmyJSIiIgq5MiIiIiL1xPxERPmlJXYBRESFLTo6Grq6ugrX6+jo4NWrV4VYEamzrKwszJ49G/Xr10etWrUwbtw4JCcni10WERFRoWJ+ImUwP5EQdkARUbHj4OCAsLAwheuvX78OOzu7QqyI1NnMmTMxYcIEmJiYwMHBAYsXL8agQYPELouIiKhQMT+RMpifSAhvwSOiYmfw4ME4deoULl26BAMDA7l1ycnJqF27Nry9vbFkyRKRKiR14u7ujlGjRqFfv34AgGPHjsHX1xfJycnQ0uJ1HCIiKh6Yn0gZzE8khB1QRFTsREdHo3r16tDW1oa/vz/Kly8PIHvugqCgIGRmZuLq1asoVaqUyJWSOtDX18f9+/fh6OgoW2ZgYID79++jdOnSIlZGRERUeJifSBnMTySEU84TUbFTqlQpnD9/HgMGDMD48ePxvh9eIpHAx8cHQUFBDE8kk5GRketKr66uLtLT00WqiIiIqPAxP5EymJ9ICEdAEVGx9vbtW9y/fx9SqRTu7u6wtLQUuyRSM1paWmjdujX09fVly/bt24emTZvC2NhYtuz3338XozwiIqJCx/xEn8P8RELYAUVERPQJvXr1ylO7devWqbgSIiIioqKB+YmEsAOKiIiIiIiIiIhUitPPExERERERERGRSrEDioiIiIiIiIiIVIodUEREREREREREpFLsgCIiIiIiIiIiIpViBxQRqa2ePXuiQ4cOsq+bNGmCYcOGFXodp06dgkQiwbt371S6H4lEgj179qh0H0RERKTZmJ+ISF2xA4qIlNKzZ09IJBJIJBLo6enBzc0N06ZNQ0ZGhsr3/fvvv2P69Ol5altYoSctLQ0lS5ZEYGCg4Prp06ejVKlSSE9PV2kdREREpL6Yn+QxPxEVT+yAIiKltWrVCpGRkQgPD8fIkSMxZcoUzJ07V7BtWlpage3XysoKpqamBba9gqCnp4cff/wR69aty7VOKpVi/fr16N69O3R1dUWojoiIiNQF89MHzE9ExRM7oIhIafr6+rC1tYWzszMGDBiA5s2bY+/evQA+DPueOXMm7O3tUb58eQDA06dP0aVLF1hYWMDKygpff/01Hj9+LNtmZmYmRowYAQsLC5QoUQJjxoyBVCqV2+/HQ8hTU1MxduxYODo6Ql9fH25ubli7di0eP34Mb29vAIClpSUkEgl69uwJAMjKykJAQADKlCkDQ0NDVKlSBbt27ZLbz8GDB1GuXDkYGhrC29tbrk4hvXv3xr1793D27Fm55adPn8bDhw/Ru3dvXLp0CS1atEDJkiVhbm6Oxo0b4+rVqwq3KXQFMiQkBBKJRK6es2fPomHDhjA0NISjoyOGDBmCxMRE2frly5fD3d0dBgYGKFWqFL799ttPHgsRERGpBvOTPOYnouKHHVBElG+GhoZyV+qOHz+Ou3fv4ujRo9i/fz/S09Ph4+MDU1NTnDlzBufOnYOJiQlatWole9/8+fOxfv16/Prrrzh79ixiYmLwxx9/fHK/3bt3x9atW7FkyRLcvn0bK1euhImJCRwdHbF7924AwN27dxEZGYnFixcDAAICArBx40YEBwfj5s2bGD58OH788UecPn0aQHbQ69ixI9q1a4eQkBD89NNPGDdu3CfrqFy5MmrVqoVff/1Vbvm6detQr149eHh4ID4+Hj169MDZs2dx8eJFuLu7o02bNoiPj1fuZOfw4MEDtGrVCp06dcL169exfft2nD17Fv7+/gCAy5cvY8iQIZg2bRru3r2Lw4cPo1GjRl+8PyIiIio4zE/MT0TFjpSISAk9evSQfv3111KpVCrNysqSHj16VKqvry8dNWqUbH2pUqWkqampsvds2rRJWr58eWlWVpZsWWpqqtTQ0FB65MgRqVQqldrZ2UnnzJkjW5+eni4tXbq0bF9SqVTauHFj6dChQ6VSqVR69+5dKQDp0aNHBes8efKkFID07du3smUpKSlSIyMj6fnz5+Xa9u7dW/r9999LpVKpdPz48VJPT0+59WPHjs21rY8FBwdLTUxMpPHx8VKpVCqNi4uTGhkZSdesWSPYPjMzU2pqairdt2+fbBkA6R9//KGw/mvXrkkBSB89eiSru2/fvnLbPXPmjFRLS0uanJws3b17t9TMzEwaFxensG4iIiJSPeYnYcxPRMULR0ARkdL2798PExMTGBgYoHXr1ujatSumTJkiW1+5cmXo6enJvg4NDcX9+/dhamoKExMTmJiYwMrKCikpKXjw4AFiY2MRGRmJr776SvYeHR0d1KxZU2ENISEh0NbWRuPGjfNc9/3795GUlIQWLVrI6jAxMcHGjRvx4MEDAMDt27fl6gCAunXrfnbb33//PTIzM7Fjxw4AwPbt26GlpYWuXbsCAKKjo9GnTx+4u7vD3NwcZmZmSEhIQERERJ7r/1hoaCjWr18vdyw+Pj7IysrCo0eP0KJFCzg7O8PV1RXdunXD5s2bkZSU9MX7IyIioi/H/JQb8xNR8aIjdgFEVPR4e3tjxYoV0NPTg729PXR05D9KjI2N5b5OSEhAjRo1sHnz5lzbsra2/qIaDA0NlX5PQkICAODAgQNwcHCQW6evr/9FdbxnZmaGb7/9FuvWrYOfnx/WrVuHLl26wMTEBADQo0cPvHnzBosXL4azszP09fVRt25dhZOMamllXx+Q5pjH4eMnwSQkJKBfv34YMmRIrvc7OTlBT08PV69exalTp/DXX39h0qRJmDJlCi5dugQLC4t8HS8REREph/kpN+YnouKFHVBEpDRjY2O4ubnluX316tWxfft22NjYwMzMTLCNnZ0d/vnnH9k99hkZGbhy5QqqV68u2L5y5crIysrC6dOn0bx581zr319BzMzMlC3z9PSEvr4+IiIiFF75q1ChgmxC0PcuXrz4+YNE9mSaTZo0wf79+3H+/Hm5J9ucO3cOy5cvR5s2bQBkz5Xw+vVrhdt6HywjIyNhaWkJIPuqZU7Vq1fHrVu3PvnfQkdHB82bN0fz5s0xefJkWFhY4MSJE+jYsWOejomIiIgKBvOTMOYnouKDt+ARkcr98MMPKFmyJL7++mucOXMGjx49wqlTpzBkyBA8e/YMADB06FAEBgZiz549uHPnDgYOHCj3BJOPubi4oEePHvDz88OePXtk23w/hNvZ2RkSiQT79+/Hq1evkJCQAFNTU4waNQrDhw/Hhg0b8ODBA1y9ehVLly7Fhg0bAAD9+/dHeHg4Ro8ejbt372LLli1Yv359no6zUaNGcHNzQ/fu3eHh4YF69erJ1rm7u2PTpk24ffs2/vnnH/zwww+fvArp5uYGR0dHTJkyBeHh4Thw4ADmz58v12bs2LE4f/48/P39ERISgvDwcPz555+ySTT379+PJUuWICQkBE+ePMHGjRuRlZUle7IOERERqS/mJ+YnIk3DDigiUjkjIyP8/fffcHJyQseOHVGhQgX07t0bKSkpsit6I0eORLdu3dCjRw/UrVsXpqam+Oabbz653RUrVuDbb7/FwIED4eHhgT59+sgeoevg4ICpU6di3LhxKFWqlCxUTJ8+Hb/88gsCAgJQoUIFtGrVCgcOHECZMmUAZA+93r17N/bs2YMqVaogODgYs2bNytNxSiQS+Pn54e3bt/Dz85Nbt3btWrx9+xbVq1dHt27dMGTIENjY2Cjclq6uLrZu3Yo7d+7Ay8sLs2fPxowZM+TaeHl54fTp07h37x4aNmyIatWqYdKkSbC3twcAWFhY4Pfff0fTpk1RoUIFBAcHY+vWrahYsWKejoeIiIjEw/zE/ESkaSTSnDfIEhERERERERERFTCOgCIiIiIiIiIiIpViBxQREREREREREakUO6CIiIiIiIiIiEil2AFFREREREREREQqxQ4oIiIiIiIiIiJSKXZAERERERERERGRSrEDioiIiIiIiIiIVIodUEREREREREREpFLsgCIiIiIiIiIiIpViBxQREREREREREakUO6CIiIiIiIiIiEil2AFFREREREREREQqxQ4oIiIiIiIiIiJSKXZAERERERERERGRSrEDioiIiIiIiIiIVIodUEREREREREREpFLsgCIiIiIiIiIiIpViBxQRFTs9e/aEiYmJ2GUQEREREREVG+yAIqIioUmTJpBIJJ99TZkypUD3+3678+fPz7Vu/fr1kEgkuHz5stLbvXXrFqZMmYLHjx8XQJVEREREX05RpomNjUXt2rVhYGCAw4cPY8qUKZBIJChVqhSSkpJybcfFxQVt27aVW6aqLEVERQ87oIioSPj555+xadMm2WvIkCEAgAkTJsgt79ixo0r2P3fuXMGg9aVu3bqFqVOnsgOKiIiI1FJcXBxatmyJ69ev448//kCrVq1k616+fIkVK1Yotb2CzlJEVPToiF0AEVFetGjRQu5rAwMDLFmyBC1atECTJk1Uuu+qVasiJCQEwcHBGDFihEr3RURERCS2+Ph4+Pj4ICQkBL///jtat24tt75q1aqYO3cuBg4cCENDw89uj1mKiACOgCIiDXLmzBl07twZTk5O0NfXh6OjI4YPH47k5GTB9g8fPoSPjw+MjY1hb2+PadOmQSqV5mpXv359NG3aFHPmzFG4rZzu3LmDb7/9FlZWVjAwMEDNmjWxd+9e2fr169ejc+fOAABvb2/Z0PRTp0592YETERERFZCEhAS0atUKV69exe7du+Hr65urzaRJkxAdHZ3nUVDKZiki0kzsgCIijbFz504kJSVhwIABWLp0KXx8fLB06VJ07949V9vMzEy0atUKpUqVwpw5c1CjRg1MnjwZkydPFtz2lClT8hS0bt68iTp16uD27dsYN24c5s+fD2NjY3To0AF//PEHAKBRo0aCtxBWqFAhn2eAiIiI6MslJiaidevWuHTpEnbu3JlrPqf3GjZsqHSHUl6zFBFpLt6CR0QaY/bs2XLDwPv27Qs3NzdMmDABERERcHJykq1LSUlBq1atsGTJEgDAwIED0a5dO8yePRtDhgxByZIl5bbdsGFDeHt7Y+7cuRgwYIDC4eZDhw6Fk5MTLl26BH19fdm2GzRogLFjx+Kbb76Bq6srGjZsWGi3EBIRERHlRY8ePfDixQvs3LkT7du3/2TbyZMno3HjxggODsbw4cM/u+28Ziki0lwcAUVEGiNnkElMTMTr169Rr149SKVSXLt2LVd7f39/2b8lEgn8/f2RlpaGY8eOCW5/ypQpiIqKQnBwsOD6mJgYnDhxAl26dEF8fDxev36N169f482bN/Dx8UF4eDieP3+ez6MkIiIiUo3o6GgYGBjA0dHxs20bNWoEb29vpUdBfSpLEZFmYwcUEWmMiIgI9OzZE1ZWVjAxMYG1tTUaN24MIPsxwjlpaWnB1dVVblm5cuUAQOGT6T4XtO7fvw+pVIpffvkF1tbWcq/3t/a9fPkyv4dJREREpBIrV66Enp4eWrVqhbt37362vbIdSl/SaUVEmoO34BGRRsjMzESLFi0QExODsWPHwsPDA8bGxnj+/Dl69uyJrKysAtnP5MmT0aRJE6xcuRIWFhZy697vY9SoUfDx8RF8v5ubW4HUQURERFTQc7DH+gAA9UdJREFUPD09cfDgQTRr1gwtWrTAuXPnPjkaqlGjRmjSpAnmzJmD/v3752kfn8pSRKTZ2AFFRBrhxo0buHfvHjZs2CA36fjRo0cF22dlZeHhw4eyUU8AcO/ePQCAi4uLwv00btwYTZo0wezZszFp0iS5de9HVOnq6qJ58+afrFcikXxyPREREZEYateujT179sDX1xctWrTAmTNnYG1trbD9lClTZB1KefGpLEVEmo234BGRRtDW1gYASKVS2TKpVIrFixcrfM+yZcvk2i5btgy6urpo1qzZJ/f1frj5qlWr5Jbb2NjIAlhkZGSu97169Ur2b2NjYwDAu3fvPrkvIiIiosLWrFkzbN26Fffv30erVq0QFxensG3ODqWUlJQ8bV9RliIizcYRUESkETw8PFC2bFmMGjUKz58/h5mZGXbv3o23b98KtjcwMMDhw4fRo0cPfPXVVzh06BAOHDiACRMmfPIqH5AdtBo3bozTp0/nWhcUFIQGDRqgcuXK6NOnD1xdXREdHY0LFy7g2bNnCA0NBQBUrVoV2tramD17NmJjY6Gvr4+mTZvCxsYm/yeDiIiIKJ+++eYbrF69Gn5+fmjfvj0OHz6ssO3kyZPh7e2d521/KksRkebiCCgi0gi6urrYt28fqlatioCAAEydOhXu7u7YuHGjYHttbW0cPnwYUVFRGD16NC5duoTJkydj+vTpedrflClTBJd7enri8uXL8PX1xfr16zFo0CAEBwdDS0tLbpi5ra0tgoOD8fLlS/Tu3Rvff/89bt26pfRxExEREalKr169MG/ePJw+fRqdO3dGRkaGYLsmTZrIHvySV4qyFBFpLok05/0qREREREREREREBYwjoIiIiIiIiIiISKXYAUVERERERERERCrFDigiIiIiIiIiIlIpdkAREREREREREZFKsQOKiIiIiIiIiIhUih1QRERERERERESkUuyAIiIiIiIiIiIildIRuwDSTPb9fxe7BLV0fkYbsUtQSyVN9cUuQS3FJaeLXYJasjTWE7sEtWSoq6LtVvPP1/uTry0roEqINF+5MYfFLkEt/TG0odglqKWypYzFLkEtRbxOErsEteRU0kjsEtSSgYp6RJifhLEDioiIiBSTcLA0ERERkVKYnwSxA4qIiIgUk0jEroCIiIioaGF+EsRuOSIiIiIiIiIiUimOgCIiIiLFOISciIiISDnMT4LYAUVERESKcQg5ERERkXKYnwSxA4qIiIgU4xU8IiIiIuUwPwliBxQREREpxit4RERERMphfhLEDigiIiJSjFfwiIiIiJTD/CSIZ4WIiIiIiIiIiFSKI6CIiIhIMQ4hJyIiIlIO85MgdkARERGRYhxCTkRERKQc5idB7IAiIiIixXgFj4iIiEg5zE+C2AFFREREivEKHhEREZFymJ8E8awQEREREREREZFKcQQUERERKcYh5ERERETKYX4SxA4oIiIiUoxDyImIiIiUw/wkiB1QREREpBgDFBEREZFymJ8EsQOKiIiIFNPiEHIiIiIipTA/CWK3HBERERERERERqRRHQBEREZFiHEJOREREpBzmJ0HsgCIiIiLF+BQXIiIiIuUwPwliBxQREREpxit4RERERMphfhLEDigiIiJSjFfwiIiIiJTD/CSIHVBERESkGK/gERERESmH+UkQz4qaefz4MSQSCUJCQgplf02aNMGwYcMKZV9EREREqsD8REREpP44AqqQ9ezZExs2bJB9bWVlhVq1amHOnDnw8vKCo6MjIiMjUbJkSRGrVC//zPSBYwnjXMvXn3qACdtC5Zb95l8PTSvZwm/FBRwOjcz1HktjPRyd2Az2lobwGL4PccnpAICFPWqga13nXO3vvoiD97RjBXQkBetGyBXs3LIe4XduI+bNK0wOWIh6jZrK1p89dQwH9uxE+N3biI+LxfJ121G2nEeu7dwKC8X6lUtx59YNaGtpw9W9PGYtXAF9fQO5dmlpaRja50c8vH9X4bbU0a9rVuLk8aN4/Ogh9PUN4FW1GoYMGwmXMq4AgNjYd1i5fCkunj+HqKhIWFhaoUnTZhgwaChMTU0BAPfu3sH6tasQcu0q3r17Czt7B3Tq/B3+92N3MQ8tXzIzM7Fh9XIcPXwAMTGvUbKkNXx8v0Y3v36Q/DdkOHDazzhyYK/c+2rVqY85i4NzbS8tLQ0D/f6HB+F3sXrTTrgVke+PvIqOjsbiBXNx7uwZpKQkw9HJGVOnz0LFSpUBACuCluLI4QOIioqCrq4uPD0rwn/IcFT2qiJy5QWAQ8hJDTA/Ka+UmT5GtSmPRuVLwlBPG09eJ2H8zhsIexYna1PWxhij2pRH7TKW0NaW4EF0Ivw3XUPkuxQ4WBri5PjGgtsesukaDt+IBgDUdbPC0JbuKGdniuS0TPxx+TkWHglHZpa0UI5TWTdDr+DP7RvxIPw23r55jbHT5uOrBt5ybZ49eYiNq5bg1vWryMzMQGlnV4yZMhfWpewAACsWzMD1K//i7ZtXMDA0RPmKVdCt7xCUdioj28aapXNwJywEEY8foLRTGSxYva1Qj1MVrly+hPW/rsXtW2F49eoVFi4JQtNmzWXrVwQtxeFDH/0uHDocXkX4d2FY6BX8vnUjHty7hZg3rzFhxgLUbegt2DZo/gwc3rv7/+zddVgV2R/H8TddkhagKCiCid1id/zUdd011u7ublQwsbs7VldX11q7u0VFTBRBQEVAOn5/oNe9clGRhu9rn3ke78yZuWdm4d4PZ845Q/f+w2neur1ifVDgB1YsmMmVC2dQV1ejSvU69BgwEj19fQDeeL+me5sm8Y43e+kGChdzSJkTSwVrVq3g+NF/efbsKTq6upQqVZrBQ4d/yeABASxdsoiLF87h4+2NqakZterUpd+ALxk8Q5P8pJI0QKWBhg0bsm7dOgB8fHwYP348TZs2xdPTEw0NDczNzdO4hulLI5eTaKh/+QUubGnEjsGO7L/hpVSuRx1bvhd15nYowwOvD1ia6imtn7jjNs577ilea6qrc3R8bf756j3Sk7DQUArY2tOgSQucxg6Nvz0slGIOpaleuwHzZ05ReYz7924zbmhf2nToSt8ho9HQ0OTpY3fUVHQZXbN0Htlz5OTpY/dkP5eUdOPaVVq3aUexYiWIjo5m8cJ59OvdnV17/kFPXx8/X1/8fH0ZPGwkNgVt8X79Gpdpk/D39WWW60IAHtx3w9QsO1NdZpHb3II7t24yzWkiGhrq/N72jzQ+w5+zbdNa/v5rJ6MnTsemQEHcH7gxc9oEDLIZ0ur3L6GpQuWqjJowTfFaS0tL5fFWLHIlR46cPPHIWD8fPyLwwwc6d2hL+QoVWbx8FWamprx48QIjI2NFmfzW1oweO5G8ea0ICw9jy8b19OnZlX0Hj2JmZpaGtU8G0oVcpBOSn36ckZ4m2/pW4vKTt/RYe513wRHkz6HPh5BIRRkrMz229qnIrquvWPivB8FhURQyz0Z4ZAwA3gGhVHE6oXTc3ytZ0a2GDWfc/QEobGHIqq7lWHbiCSN33CW3sQ5OLYuhoa7GzAPp8/sgPCwM64J21G7UnFmThsfb7uP1krGDulG3UXPadO6Nvr4Bns+foqWtoyhT0K4I1es0ImduC4ICP7BjwwqcRvZj2Zb9aGhoKMrVadScRw/u8eKpR6qcW0oLDQ3B3t6eFr+0Yuig/vG2589vzZhxX74LN29cT58eXdl/KON+F4aFhmJja0e9xs1xnjAswXIXz5zA/f5dzHLkjLdtztSxvH/nz9S5y4iKimLBjEksnjOVERNdlMpNc11OPuuCiteGxsZfHypDuXb1Cr+3bU+xEiWIjopm0QJXevfoxl/7DqCvr4+vX1wGHzp8FAUL2vL6tRfTnCbj5+vL3PkL07r6SSf5SSVpgEoDOjo6ipBkbm7O6NGjcXR0xM/Pj48fP2JjY8PNmzcpVaoUAG5ubowaNYozZ84QGxtLqVKlWL9+PV5eXtSpU4eXL18qha7Bgwdz/fp1zp49C8D58+cZN24cV65cQUdHhwoVKrB9+3ZMTU3j1S08PJxx48axbds2AgICKF68ODNnzqRmzZopfl0S8i44Qul1/wYWPPMN5uIjf8W6YnmN6VW3EI1cTnB7Vvw7CAAdq9tgpK/FvAMPqVNcOaQGhUURFBaleN2wpAUm+tpsv/A8+U4kmZWvXI3ylasluL1uw2YA+Hgn3Ii2YsFsWvzalt87dFOss8pvHa/c1YvnuH7lIhOmz+XqpXM/X+k0sHj5aqXXU6a6ULdmFR7cd6NMufLYFrJj9rxFiu1WVvnoO2AIE8aMICoqCk1NTZq3bKV0jLx5rbhz+xYnjh3NsA1QbnduUbV6LSpXqw6AuWUejv97iIf37yqV09LSxiz7t3sUXL5wlmtXLjDFZR6XL2asn48fsW7tKszNzXGa9iUo5slrpVSmcZNmSq+HjRzDnr924fHInYqVKqdKPVOM3MET6YTkpx/Xs2YBfD6EMubPLzfXXr0PVSoztKEdZx76MfvgI8W6l+++lImJBf+vMli9Yrk5dNuHkIhoABqXNMfdO4glx54A4Pk2hFkH3VnwRykWH3vMx/DoZD+3pCpTsSplKlZNcPuWtUsoW6EqHXsNVqwzz6P8mV+/6ZdckMvcknZd+zK0Rxv8fF4rynYfMBKADwHvM00DVDXHGlRzVN0rDqBxU+XvwuEjx7Bnd8b+LixXqRrlKiWctwHe+vmyYuFMpsxeitPoAUrbXj5/yo0rF3BdsZlChYsB0GvQKKaMGkDXvkPIniOXoqyhkQmm38lcGcmylWuUXjtNn0Etx8o8uO9G2XLlKVTIDtcF/8ng+fIxYNBgxo76ksEzNMlPKkmzXBoLDg5m8+bN2Nrakj179njbvby8qF69Ojo6Opw4cYLr16/TtWtXoqKiqF69OgUKFGDTpk2K8pGRkWzZsoWuXbsCcOvWLerUqUPRokW5ePEi586do1mzZkRHqw4E/fv35+LFi2zfvp07d+7QunVrGjZsiIdH+vji1NJQo1VFK7ZfeKFYp6elwZJu5Rm3/RZ+geEq9ytkYciQJkUYtO4aMbHf7xLetqo1Zx/64vUu9LtlM6qA9295eP8uJqZmDO7Vkd+b1mJ4v67cu31Dqdz7d2+ZP3MKIydMR0dXN4GjZRzBwUEAGH3jrlJwUBAG2bJ984svODgI4wx8Z6qYQyluXLvMS8/nADx+5M692zeo8FWj5q0b12jZsAYdWzdj3sypfPgQoLT93Vt/5jhPZuxkF3Qzwc+HKqdPnqBoseIMHzqQWtUr8/uvLdi9a2eC5SMjI9j95w6yGRpiZ2+fijVNIWrqSVuESAGSn76tdtFc3H0VyII/SnFxYi32DqrCbxXyKrarqUGNIjl55h/Cmm7luDixFn/2r0TdYrkSPGaxPEYUzWPErquvFOu0NdUJj1K+JuGRMehqaVAsT8b7joyJieH6pXNYWOXHaWRfOv9Sh1F9O3L53MkE9wkLDeXE4X3ktshD9lzSC++zyIi470LDzPJdmICYmBhcp4/nlzadyG9TMN72h253MMhmqGh8AihVtiJq6uo8un9PqezUsYP5o3ltRvbvwuXzp1K66qkuOOhHMngw2b6TwTMMyU8qZYL/sxnPP//8Q7Zs2QD4+PEjFhYW/PPPP6irx/9BW7JkCcbGxmzfvl0x9MXOzk6xvVu3bqxbt44RI0YAsH//fsLCwvjtt98AmDVrFuXKlWPp0qWKfYoVK4Yqnp6erFu3Dk9PTywtLQEYPnw4hw8fZt26dTg7O6vcLzw8nPBw5Yaf2OhI1DRUD9VJioalLDHS02LnxS8NUJNbO3DtyTuOqJjzCeLC0dJu5Zm6+y5e70PJlzP+fFL/ldtYl1rFctNv7dVkrXt64+0V1zNq09rl9Og/lIKF7Dl26B9GD+rJik27yWOVn9jYWOZMn0CTFq2xK1Lsm72pMoKYmBjmzHKmZOky2BayU1nm/fv3rF65jF9a/ZbgcW7fusG/Rw6xYHH8uZAyinYduxHyMZhOv/0PdXUNYmKi6dZ7IPUaNlWUqVCpGo4162JhmYfXXi9ZvXQhowf3YfHqzWhoaBAbG8vMqeP53y+/YV+kGD6vM/bPR0JevXrJnzu28UfHLnTv0Zt79+4yy2UaWlpa/K95S0W5M6dOMmrEUMLCQsmRMyfLV67F1DRjDjkQIj3KCvkpJioCdU3tH7oe32Jlpke7SlasO/uc5See4GBlzPjmRYiMjmHP9ddkN9Amm44mPWvZMP+IB3MOuuNon4PFHUrTYeUVrj59H++Yv5bPy+M3wdx8EaBYd9bdn07VrGlSyoJDt73JaahDv7pxf4TnNNKJd4z07kPAO8JCQ9izbR3tuvSlQ89B3LxygVmThuPkupJiJcsqyh76eyebViwgLCyUPFbWTJq1NMFh6lnJ6VMnGTX8P9+FqzL3d+HuretQ19CgWau2Kre/f/cWk6/OX0NTE0NDI96/ixvNoaunR7e+QylSohRqaupcOHOM6eOGMm66KxWr1kzpU0gVMTExzJrpTKnSZSiUYAZ/x8rlS2nV+vdUrp1ITdIAlQZq1arFsmXLgLg/dpcuXUqjRo24cuVKvLK3bt3C0dExwS+0zp07M378eC5dukSlSpVYv349v/32GwYGBor9W7du/UP1unv3LtHR0UoBDeICkqq7i5+5uLgwZYryHEPZyv6GYbnk//BoW8Wak25vePMhDID6DhZULZyT+tOPJ7jPmBbFeOwdxF9XXv7Qe7SulI/A0EgO33qdLHVOr2Ji4+Z4aNz8Vxo0aQGArV0Rbl2/zJF/9tK1zyD+3rWV0JCPSkP0MrIZ05148tiDNeu3qtweHBzMoH69KFCgID37xJ/bAOCxxyOGDupHz979qFzl212y07NTx45w7PABxjvNxLpAQR4/cmfJvJlkz5mThk2aA1C7fiNF+QK2dhSwtaP9L425deMqZctX4q+dWwn5GEK7Tt3T6jRSRUxMLEWLFWfg4Li51goXKcoTDw927dyu1ABVvkJFduzeS8D79/y1aycjhw9m89Y/MfvG52eGIF3IRTqRFfKTWZX2ZK+a9KHdampq3Hv1AdfDcT2wHrwOolBuQ9pUysee669R/zS35nE3X9afjbup98A7iNLWprStlC9eA5SOpjrNSluw9PgTpfXnPd4y64A7Ti2LMvv3EkREx7D02BPKFzAj9gd6nKc3sZ8mTq9QpSbNWsf9f7Cxteeh222O7Nul1ABVvU4jSpatxPu3fvy9cxNznEbhvGgd2toZr+EtOZWvUJGdu/cSEPCe3bt2MmLYYDZv+/ObvwsZ1WP3++zbvY35q7YqHuDyM4xNTGnxewfFa7sixXjn78df2zZmmgYo52lTeOLhwfpNCWfw/n16UaBgQXr3VZ3BMxzJTypJA1QaMDAwwNbWVvF69erVGBsbs2rVKrp3V/5DTk9P7+vdleTKlYtmzZqxbt06bGxsOHToEKdOnfrh/f8rODgYDQ0Nrl+/rjSBIqC446jKmDFjGDpUeRJs+2GHfvh9f1QeMz0ci+Si+4pLinVV7XNincOAh67KY85X9arE5cf+/Op6lmr2OSmcx5gmZfIAKL4g7s1pwsJD7sz554HSvm2qWrPrsieR0RkvOCVG9k9jzPN/ehLFZ1b5bfB94wPAretXeXDvDk1rlVcq0797O2rXa8yI/0xOnd7NdHbi3JlTrFq3mdwqJqr9+DGYAX26Y2BgwJz5i1X+0fL0yWP69OjCL61+o3vPPqlR7RSzfNFc2nbspmhkKmBrxxuf12zdsFrRAPU1yzxWGJuY4vXSk7LlK3Hz2mXu37tNfceySuV6dW5D3QZNGDNpeoqfR2rImTMnBQsqd6u3KVCAY8eOKK3T09cnX7785MuXH4eSpWjWuD57/tpFtx69UrO6yS8TdwMXGUtWyE9lJp9SXTiR/ILCeeIbrLTuiW8wDUrkBuD9xwgio2N4/OarMm+CKWsTf46rhg7m6GppsOd6/J6u684+Z93Z5+Qy0uFDSCR5zfQY3tiel29DkuVcUpOhsQkaGprkza+cjfLmt+HB3VtK6wyyGWKQzRDLvPmwK+pAx+Y1uHz2JI51GqZijdMffX198uXPT778n74LG9Vnb2b4LlTB7c5NPrx/R9ffGivWxURHs3apK/t2bWHNjoOYmmUn4P07pf2io6IICgrE1Czh+Z7si5bg1rXLKVb31OQ8zYkzp0+xdkPCGbxvr7gMPm/hkszTk1Dyk0rSAJUOqKmpoa6uTmho/PmGHBwc2LBhA5GRkQn+Mnbv3p22bduSN29eChYsSNWqVZX2P378eLw7bKqULl2a6OhofH19cXR0/OH66+jooKOjfLcnJYbftalijX9QOMfu+ijWLT7iztbzz5XKnZxYl8l/3uHfO3FD8rqvuIyu9pdAWCq/KfM6laXlnDM89/+otG9luxwUyJWNbedfkNnltshD9hw5efXiudJ6r5cvFJMt9h08is49+ym2vfXzY+zQPoydMovCxUqkZnV/WmxsLLNcpnLyxDFWrtlInrx545UJDg6mf+9uaGtr47pwabyfZ4Anjz3o3b0zTf/Xgn4Dh6RG1VNUeFhYvGEr6uoairu/qvi98SHwQwDZPz3hZcCwMXTr/WWyTX8/P0YO6sXEabMpmkF+Pn5EydJleP78mdK6Fy+eY2GR55v7xcbEEBER8c0yGYIEKJFOZcb8lBzD7wBuPH+PzVdTDljnNMDr00TkkdGx3H35gQJflbHJacDr9/Gv56/l83Livi/vP0bG2/aZ76d5OJuUsuD1+1DcvAKTehqpTktLC1v7orx++Vxp/euXnuTKbZHwjrGxxMbGzQEolMXEZpLvQhVq1W9CqbIVldZNHNGXWvWbULdR3M28wsUc+BgcxGP3+9jaFwXg9s2rxMbEYFe0eILHfurhnuEnJI+NjcVl+lROHD/KmvWbyPvVA1wgLoP36RmXwRcsXqYyg2dYkp9UkgaoNBAeHo6PT1wjyvv371m8eDHBwcE0a9YsXtn+/fuzaNEi2rRpw5gxYzA2NubSpUtUqFAB+08T+jVo0AAjIyOmTZuGk5OT0v5jxoyhRIkS9O3bl969e6Otrc3Jkydp3bo1OXIof6jZ2dnRvn17OnbsyNy5cyldujR+fn4cP34cBwcHmjRR/XS51KCmBr9Xzs+fF18Q/Z8/kP0Cw1VOPO71LkRx5+3FV41MZtniwp2HTxCBocpBqm0Va64/fYf76/QfmkJDQnj9ylPx2ue1F08ePcTQyJhc5hYEBn7Az8ebt/5+AIrJpk2z58Asew7U1NT4tV1nNq1ZRoFC9hQoZM+xg/t4+eI546fNBSCXuXLY0tXTB8AyT15y5sqdCmeZdDOmO3H40D+4LliCvoEB/p+uR7Zshujq6hIcHEy/Xt0ICwtlqstsPn4M5uPHuDvCpqZmaGho8NjjEb27d6Zy1Wq079hZcQwNdQ1MM+hjhSs71mDzupXkym2BTYGCeDx6yJ/bNtKoWQsg7udrw+plVK9VF7PsOfDyesmKRa7kyZuP8pXi/kjL/dXPh96nn488ea3ImTvzTMT6R4dOdO7QltUrl1O/YSPu3b3D7l07mTAp7vM2NCSEVSuXU7NWbXLkzEnA+/fs2LYFX9831GuQCe6ESxdykU5Ifvpx688+Z3u/SvSuVYCDd3xwsDLm94p5mbDbTVFmzelnzGtfiqvP3nPpyTuq2+egVpGcdFihPKQxX3Z9ytuY0mPtdZXv1a2GNWfd/YmJhfrFc9OzZgEGb7nFN+5npKnQ0BB8vL5My+Dr7cWzx+5kMzQiZ24Lmv/eEdepoynqUIbipctx88oFrl08w9R5KwHwef2K86f+pVS5ShgZm/LWz5e/tq1DW0eHMhW/DM339vIkLDSUgPdviQgP59ljdwDy5i+QYXt4hHz8iKfnl+zp9eoVDx88wNjYGGMTE1Z/9V24fdsWfN9k7O/C0JAQvP/z8/LG24unHu5kMzIiV24LjIxNlMprampiapaDvPmsAbCyLkCZClVYNHsq/YaNIyoqihXzZ+BYu4HiCXjHD+9DU1OLAoUKA3DxzAmOHfqbASMmpso5phTnqVM4dPAf5i9aioG+Af5+nzK44ZcM3rtHV8LCQnGeMZuPwcF8DP6Uwc3M4vUozXAkP6kkDVBp4PDhw1hYxP3hZmhoSOHChfnzzz+pWbMmz58/VyqbPXt2Tpw4wYgRI6hRowYaGhqUKlVK6S6duro6nTt3xtnZmY4dOyrtb2dnx7///svYsWOpUKECenp6VKxYkbZtVU+Ut27dOqZNm8awYcPw8vIiR44cVKpUiaZNm6osn1qqF85F3uz6Sk+/S26Gupo0KWPJhJ13Uuw9ktOjh26MHPBlyMGKRXMAqNfofwwfP5VLZ08x1/nLF5fLpFEA/NG1Nx26xQ0f++X3P4iMCGf5wtkEBX6ggK09LvOXY6niDkVGtWvnNgB6dlX+3Zg01Zn/Nf+Fhw/cuHf3NgAtmtRXKrP/0DEs8+Tl+NEjvH//joP/7OPgP/sU2y0sLfnn8IkUPoOUMXDYWNauWMyC2dN4//4dOXLkpFnLX+n46WdDXV2dJ48fceTgPoKDAsmeMxflKlSma6/+aGsnzx36jKJ4CQdc5y9m4QJXVi5fQp48eRkxaixNmv4PAHUNDZ4/e8qwfXsIeP8eExMTihUvwdoNW7C1LZTGtRci85D89OPuvgqk38abDGtoR7+6BXn1LhTnfQ/Zf/PLA1uOuvky6S83etUuwPjmRXjm95EBm25x/XmA0rF+LZ8Hnw9hnPPwV/le1e1z0qd2QbQ11Xn4Ooi+G25wxl112fTgift9Jg7tqXi9bpkrALUaNGPAqClUcqxNryFj+WvrOtYsno2lVX5GTplNkRKlAdDW1uHBnZv8s3srH4MCMTbNTlGHMrgsXKc02fTSOVNxu/2l0W5Yz7ifneVb/yGXuWVqnGqyc3O7R/cuX35X5sxyAeB/zVsyftIUnj17yr6/lb8L123M2N+Fj93vM3ZwD8XrNUvibtLWbtiMIWOcEtpNyfAJziyfP4PxQ3qhpq5Olep16DlwpFKZHRtX4fvGO24IaD5rRk6aQdWa9ZLvRNLAzh1xGbxb5w5K652mudC85S88uO/G3TtxGbxpI+VzPfjvcfLkiT9qQagWHR3N5MmT2bx5Mz4+PlhaWirmOvw8/UxsbCyTJk1i1apVBAQEULVqVZYtW0ahQl9+P9+9e8eAAQPYv38/6urqtGrVigULFigNJ79z5w79+vXj6tWr5MyZkwEDBjBy5Mh4dUqIWmxGnCFQxNOtWzf8/PzYt2/f9wunAsvef6V1FdKlC9Maf79QFpTDMBN1t01GX/fQE3FMDbJWA9iP0kuhG+p6zVckaf/QvzPfvB8i80hv+clu5OG0rkK6tGfQjw9tzEoK5v72k52zKk//jDf/WGrIl0M/rauQLummUJec1MxPzs7OuLq6smHDBooVK8a1a9fo0qUL06dPZ+DAgQDMnDkTFxcXNmzYgI2NDRMmTODu3bvcv38fXV1dABo1aoS3tzcrVqwgMjKSLl26UL58ebZujZs8PjAwEDs7O+rWrcuYMWO4e/cuXbt2Zf78+fTs2TPB+v2X9IDK4D58+MDdu3fZunVruglPQgghMhHpQi4yIclPQgghUlQS81N4eDjh4cpTzaiaOxDgwoULNG/eXDHk29ramm3btimeEhsbG8v8+fMZP348zZvHzU+2ceNGcufOzd69e2nTpg0PHjzg8OHDXL16lXLlygGwaNEiGjduzJw5c7C0tGTLli1ERESwdu1atLW1KVasGLdu3cLV1fWHG6BkZqwMrnnz5tSvX5/evXtTr17G7qYphBAiHVJTT9oiRDok+UkIIUSKSmJ+cnFxiZtf7T+Li4uLyreqUqUKx48f59GjRwDcvn2bc+fO0ahR3NOunz17ho+PD3Xr1lXsY2xsTMWKFbl48SIAFy9exMTERNH4BFC3bl3U1dW5fPmyokz16tWVpuNo0KAB7u7uvH///ocui/SAyuD++8hgIYQQItlJDyiRCUl+EkIIkaKSmJ/GjBnD0KFDldYl9JTA0aNHExgYSOHChdHQ0CA6Oprp06fTvn17AMUDPHLnVn6IVO7cuRXbfHx8yJUrl9J2TU1NzMzMlMrY2NjEO8bnbaampt89L2mAEkIIIUSC1KQBSgghhBAiUZKanxIabqfKzp072bJlC1u3blUMixs8eDCWlpZ06tQpSfVIbtIAJYQQQgghhBBCCJEBjRgxgtGjR9OmTRsASpQowYsXL3BxcaFTp06Ym5sD8ObNG8XTZD+/LlWqFADm5ub4+voqHTcqKop3794p9jc3N+fNmzdKZT6//lzme2RyBiGEEEIkSE1NLUmLEEIIIURWk5r5KSQkBHV15aYdDQ0NYmJiALCxscHc3Jzjx48rtgcGBnL58mUqV64MQOXKlQkICOD69euKMidOnCAmJoaKFSsqypw5c4bIyC9P6j569Cj29vY/NPwOpAFKCCGEEN+ilsRFCCGEECKrScX81KxZM6ZPn86BAwd4/vw5e/bswdXVlZYtW8ZVRU2NwYMHM23aNPbt28fdu3fp2LEjlpaWtGjRAoAiRYrQsGFDevTowZUrVzh//jz9+/enTZs2WFpaAtCuXTu0tbXp1q0bbm5u7NixgwULFsSbq+pbpAFKCCGEEAlKzTt40dHRTJgwARsbG/T09ChYsCBTp04lNjZWUSY2NpaJEydiYWGBnp4edevWxcPDQ+k47969o3379hgZGWFiYkK3bt0IDg5WKnPnzh0cHR3R1dXFysqKWbNm/fxFEkIIIYT4j9TMT4sWLeLXX3+lb9++FClShOHDh9OrVy+mTp2qKDNy5EgGDBhAz549KV++PMHBwRw+fBhdXV1FmS1btlC4cGHq1KlD48aNqVatGitXrlRsNzY25t9//+XZs2eULVuWYcOGMXHiRHr27PnDdZU5oIQQQgiRoNQcRjdz5kyWLVvGhg0bKFasGNeuXaNLly4YGxszcOBAAGbNmsXChQvZsGEDNjY2TJgwgQYNGnD//n1FiGrfvj3e3t4cPXqUyMhIunTpQs+ePdm6dSsQ1+28fv361K1bl+XLl3P37l26du2KiYlJokKUEEIIIYQqqZmfDA0NmT9/PvPnz/9mfZycnHByckqwjJmZmSIrJcTBwYGzZ8/+bFWlAUoIIYQQ6cOFCxdo3rw5TZo0AcDa2ppt27Zx5coVIK730/z58xk/fjzNmzcHYOPGjeTOnZu9e/fSpk0bHjx4wOHDh7l69SrlypUD4u4MNm7cmDlz5mBpacmWLVuIiIhg7dq1aGtrK54Y4+rqKg1QQgghhBApRIbgCSGEECJBSe1CHh4eTmBgoNISHh6u8r2qVKnC8ePHefToEQC3b9/m3LlzNGrUCIBnz57h4+ND3bp1FfsYGxtTsWJFLl68CMDFixcxMTFRND4B1K1bF3V1dS5fvqwoU716dbS1tRVlGjRogLu7O+/fv0/eCyiEEEKILEce4qKaNEAJIYQQIkFJDVAuLi4YGxsrLS4uLirf6/MjhAsXLoyWlhalS5dm8ODBtG/fHgAfHx8AcufOrbRf7ty5Fdt8fHzIlSuX0nZNTU3MzMyUyqg6xn/fQwghhBDiZ0kDlGoyBE8IIYQQCUtiBhozZky8p6Po6OioLLtz5062bNnC1q1bFcPiBg8ejKWlJZ06dUpaRYQQQgghUkvmbUNKEmmAEkIIIUSCknoXTkdHJ8EGp6+NGDFC0QsKoESJErx48QIXFxc6deqEubk5AG/evMHCwkKx35s3byhVqhQA5ubm+Pr6Kh03KiqKd+/eKfY3NzfnzZs3SmU+v/5cRgghhBDiZ2XmXkxJIUPwhBBCCJEuhISEoK6uHE00NDSIiYkBwMbGBnNzc44fP67YHhgYyOXLl6lcuTIAlStXJiAggOvXryvKnDhxgpiYGCpWrKgoc+bMGSIjIxVljh49ir29Paampil2fkIIIYQQWZk0QAkhhBAiQak5h0GzZs2YPn06Bw4c4Pnz5+zZswdXV1datmypqMvgwYOZNm0a+/bt4+7du3Ts2BFLS0tatGgBQJEiRWjYsCE9evTgypUrnD9/nv79+9OmTRssLS0BaNeuHdra2nTr1g03Nzd27NjBggUL4g0VFEIIIYT4GTIHlGoyBE8IIYQQCUrNELRo0SImTJhA37598fX1xdLSkl69ejFx4kRFmZEjR/Lx40d69uxJQEAA1apV4/Dhw+jq6irKbNmyhf79+1OnTh3U1dVp1aoVCxcuVGw3Njbm33//pV+/fpQtW5YcOXIwceJEevbsmWrnKoQQQojMKzM3IiWFWmxsbGxaV0JkPpa9/0rrKqRLF6Y1TusqpEs5DH9sfpisJjA08vuFsiBTA+20rkK6pKeVMsfN3nFbkvZ/u7FtMtVEiMzPbuThtK5CurRnkGNaVyFdKpjbIK2rkC55+oekdRXSpXw59NO6CumSbgp1yZH8pJr0gBJCCCFEwuQGnhBCCCFE4kh+UkkaoIQQQgiRIOlCLoQQQgiROJKfVJNJyIUQQgghhBBCCCFEipIeUEIIIYRIkNzBE0IIIYRIHMlPqkkDlBBCCCESJAFKCCGEECJxJD+pJg1QQgghhEiY5CchhBBCiMSR/KSSNEAJIYQQIkFyB08IIYQQInEkP6kmk5ALIYQQQgghhBBCiBQlPaBEijg9pVFaVyFdKlJveFpXIV3yvbgwrauQLhnqyUe0SHtyB0+I1PP3EMe0rkK6VKbxqLSuQrr0/uritK5CumSkr5XWVUiXYmPTugZZi+Qn1eSvGyGEEEIkSAKUEEIIIUTiSH5STRqghBBCCJEgCVBCCCGEEIkj+Uk1aYASQgghRMIkPwkhhBBCJI7kJ5VkEnIhhBBCCCGEEEIIkaKkB5QQQgghEiRdyIUQQgghEkfyk2rSACWEEEKIBEmAEkIIIYRIHMlPqkkDlBBCCCESJAFKCCGEECJxJD+pJg1QQgghhEiY5CchhBBCiMSR/KSSNEAJIYQQIkFyB08IIYQQInEkP6kmT8ETQgghhBBCCCGEEClKekAJIYQQIkFyB08IIYQQInEkP6kmDVBCCCGESJAEKCGEEEKIxJH8pJo0QAkhhBAiQRKghBBCCCESR/KTatIAJYQQQoiESX4SQgghhEgcyU8qySTkQgghhBBCCCGEECJFSQ8oIYQQQiRIupALIYQQQiSO5CfVpAFKCCGEEAmSACWEEEIIkTiSn1STBighhBBCJEjykxBCCCFE4kh+Uk0aoIQQQgiRILmDJ4QQQgiROJKfVJNJyIUQQgghhBBCCCFEipIeUEIIIYRIkNzAE0IIIYRIHMlPqkkDlBBCCCESJF3IhRBCCCESR/KTatIAJYQQQogESX4SQgghhEgcyU+qSQOUEEIIIRKkri4JSgghhBAiMSQ/qSaTkGcikydPplSpUmldDSGEEJmImlrSFiHSO8lPQgghkpvkJ9WybA8oHx8fpk+fzoEDB/Dy8iJXrlyUKlWKwYMHU6dOnTSpk7W1NS9evABAV1eX3LlzU6FCBXr37k3t2rW/u//w4cMZMGBASlcz1d27dZ3d2zfwxP0B7976MW66K5Udv1yPLWuXcfbEEfx8fdDU1MLWvigde/THvmgJRZnH7g9Yv2I+Hg/dUFfXoEqNOnTvNxw9fX2l9zp26G/27tiM16sX6OsbUK1mPfoMHZtq55oY2fR1mNS3Kf+rXZKcptm47f6K4bN2cf2+Z7yyC8e1ocev1RgxexeLt55SrP9zfi9K2uUhp5kh7wNDOHnZnfEL/8bb74OiTKt6pRnRrQGF8uXCPyCY5dtPM2/j8dQ4xWSxbs1KTh4/yvNnT9HR0cWhVGkGDB6GtbUNAK+9vPhf47oq950xex516zdUvN7/9x62bFqP54vnGBhko279BowaOzFVziO5rVudwHWxsYlXNjY2lkF9e3Hh/FnmzF9EzdpfrteVSxdZvmQhjz0eoaenT5P/NafvgMFoamaer5dG9Wvj/dor3vrf2rRj7PhJ+Pv7MW/OLC5dvMDHkI9YW9vQvWdv6tZrkAa1FSJlSX7KONxuX2fP9o08efSA92/9GT11LpUcaym2L3CZxMkj+5X2KV2+MpNmL1G8/nPTaq5dOsezx4/Q1NRk64EzKt/r+KF97PtzM69feqJvYECVmnXpNXhMypxYEn0vPzWvXZLuv1ajdJF8ZDcxoOLvLtx5pPwd0PWXqvzeqBylCufFKJse5o4j+BAcqlTm4YEp5LfMrrRuwsK/mbPuaMqeYAq6fu0q69eu4cH9e/j5+TFv4RJq11HOUE+fPGG+62yuX7tKVHQ0BQsUZO78RVhYWqZRrZPX7/+rj4/363jrW/zahiGjxitex8bGMnJQH65cPMe02QtwrPnl8/GNjzeuM5y4ee0qevr6NGzyP3r0y1zZCWDZkkWsWLZYaZ21jQ179x8GYNefOzh04B8ePnDj48ePnLlwFSMjo7SoqkhFmeun/Ac9f/6cqlWrYmJiwuzZsylRogSRkZEcOXKEfv368fDhw586bnR0NGpqaqir/3zHMicnJ3r06EFERATPnz9n8+bN1K1bl6lTpzJu3DiV+8TGxhIdHU22bNnIli3bT7/3j4iIiEBbWztF3+NrYWGhFChoR73GLXAePzTe9jxW+ek9eDTmlnkJDw/j751bmDCsD6u27cPYxIy3/r6MH9oLx9oN6D14DCEfg1m1aDbzXCYyduocxXH27NjEnh0b6dpnCPZFSxAWFoqvii+Y9GLZxHYUtbWk6/gNePt9oG3jChxYPoAyrabx+j8NSP+r5UCFEta89g2Id4wzVx8xe80RfPw/YJnLBJchLdk6uxu1OrsCUL9qUdZN78zQWX9y7OIDCtuYs3RiO0LDI1m+Q3UITW9uXLtK69/bUbRYcaKjo1myaB79e3fjz7/+QU9fn9zm5hw+rnwue3btZNOGtVSp5qhYt3njerZsXMegoSMoXsKB0NBQXqtolMgobly7Sus2/7kuCz9dlz3/xGuY3bp5A6i4E/PI/SGD+vWia49eTJk+A1/fN7hMnUJMdAyDh49MpTNJeVu27yImJlrx+rGHB717dKHep8bJ8WNGERQUyPzFyzA1MeXQwf2MHDaYrTt2U7hI0bSqdrKRSTTFZ5Kffl7a5KcwbAraUbdxc2ZMGK6yTJkKVRgwarLitdZXdYyKiqRqzbrYF3Pg2IG9Ko/x987N/L1zE516D8auSHHCw0Lx9fFOrtNIdt/LT/p62ly49YTdR2+wbGJ7lcfQ19Xi6IX7HL1wn6kDmyf4XlOW/sO6v84rXgd9DE/280lNoaEh2Nvb0+KXVgwd1D/e9peennTu0I6Wv7SiT/+BZDPIxpPHHmjr6KRBbVPGig3biY6OUbx+9sSDYf17ULNufaVyf27bpPL7Mzo6mlGD+2KWPTtL1mzmrb8fzpPHoqGpSc9+g1O6+qmuoG0hVqxep3itoaGh+HdYWChVqzlStZojC+fPTYvqpSjJT6plySF4ffv2RU1NjStXrtCqVSvs7OwoVqwYQ4cO5dKlS4pyrq6ulChRAgMDA6ysrOjbty/BwcGK7evXr8fExIR9+/ZRtGhRdHR08PSM3/skMQwNDTE3NydfvnxUr16dlStXMmHCBCZOnIi7uzsAp06dQk1NjUOHDlG2bFl0dHQ4d+6cUhfyf//9F11dXQICApSOP2jQIKW7gefOncPR0RE9PT2srKwYOHAgHz9+VGy3trZm6tSpdOzYESMjI3r27Jmk8/sZ5SpVo0OP/lSprvouZs16jSlVrhLmlnnJb2NL9/7DCPkYzLMnHgBcvXAGTU1N+gwZQ9581tgVKU6/YeO5cPoYr1/F/f8KDgpk8+olDB03jZr1GmORxwqbgnZUrFYztU4zUXR1tGhRpxTj5u/l/I0nPH3pz/QVB3ny0o8erb80mljmNMZ1VGu6jF1PZFR0vOMs2nKSK3ef4+n9nku3nzFn3VEqlLBGUzPuo6FdkwrsP3Wb1bvO8dzrLYfPuTF77b8M61wv1c41qRYtW0Wz5i0paFsIO/vCTHZywcfbmwcP3IC4L8IcOXIqLSdPHKdu/Ybo6xsAEBj4gWVLFjBl+gwaNm5KXqt8FLKzp0bN799ZT68WLf/qukz9dF3uuymVc3/4gC0b1jPRaXq8Yxw9fIhCdvb06N0Pq3z5KVuuAgOHDOfPHVuVPkcyOjMzM6WfjzOnT2JllY9y5SsAcPvWTdq2+4MSJRzIa2VFj159MTQ04r6b23eOnDFIF3LxmeSnjJWfylasSvvu/ajkmPB3laaWNqbZcyiWbIbKvQ/adunD/1r/QX4bW5X7BwcFsmXNUgaNcaJG3UZY5LHCuqAdFarWSNZzSS4/kp+2HbiKy8rDnLjknuBxFm89xZx1R7l85/k33y/4Yxhv3gYplpCwiOQ8nVRXzbEG/QcNoU5d1Tlw0cJ5VKtenSHDR1KkSFGs8uWjZu06ZM+eXWX5jMjE1IzsOXIolovnTpMnrxWlypRXlPFwf8jOLRsYNWFqvP2vXrrAi2dPGO80g0L2halU1ZFuvfuz98/tREZGpuappIqvc7apqZli2x8dOtO1e09KOJRMwxqmHMlPqmW5Bqh3795x+PBh+vXrh4GBQbztJiYmin+rq6uzcOFC3Nzc2LBhAydOnGDkSOW7+iEhIcycOZPVq1fj5uZGrly5kr3OgwYNIjY2lr///ltp/ejRo5kxYwYPHjzAwcFBaVudOnUwMTFh9+7dinXR0dHs2LGD9u3j7uY8efKEhg0b0qpVK+7cucOOHTs4d+4c/fsr39GYM2cOJUuW5ObNm0yYMCHZzy85RUZGcnjfbgyyZcOmoJ1inaamltKd1c93Yu7fvQnAzasXiYmN4a2fL73/aEmnVvWZMWkEfm98Uv8kfoCmhjqamhqERSh/UYWFR1KldEEgrtV9zbSOzNtwnAdPv38epkb6tGlUjku3nxEVFXdnR0dbk7DwKKVyoeER5DU3JZ+FmarDpHvBwUEAGBkZq9z+4L4bj9wf0Lzlr4p1ly9eIDYmBl/fN/zaogmN69Vk9Igh+KTjO7yJpbguxl+uS1hoKONHj2DkuAnkyJEz3j4RkRFoayvf1dTR1SE8PDxeQ1ZmERkZwcF/9tG8ZSvFna2SpUpz5PAhPnwIICYmhsMHDxAeEU65ChXSuLbJQ01NLUmLyBwkP2XO/HTv1jU6tahD3w4tWe7qTOCHgETtf+vaJWJjYnjn70f/jr/Q7deGzJo8Cj/fjJufktOwLvV5dXImF7eNYkjHOmhoZN4/vWJiYjh7+hT581vTu0c3ajpWpn2b1pw4fiytq5ZiIiMjOXroHxr9r6Xi+y4sLJSpE0YyeOQ4sufIEW8ft7u3KVCwEGbZv2yrUKkqHz8G8+zp41Sre2rx9HxBvVrVaNKwDmNGDcM7HY8uSW6Sn1TLvJ+CCXj8+DGxsbEULlz4u2UHDx5MrVq1sLa2pnbt2kybNo2dO3cqlYmMjGTp0qVUqVIFe3t79L8aupIczMzMyJUrF8+fP1da7+TkRL169ShYsCBmZsqNARoaGrRp04atW7cq1h0/fpyAgABatWoFgIuLC+3bt2fw4MEUKlSIKlWqsHDhQjZu3EhYWJhiv9q1azNs2DAKFixIwYLxv5zDw8MJDAxUWiLCU7eL8ZULZ/i1QWV+qVuBvX9uZurc5RibmALgUKY879+9Zfe29URGRhIcFMj6FQsBePfWHwCf117ExsTw5+Y19Bg4gjFOcwgKDGTCsN7p8m5EcEg4l24/ZUyPRljkNEZdXY02jctT0cEG8xxxdy+HdalHVHQMS7ad+uaxpg1sjv+Fubw+PQsrCzNaD1mp2Hb0wgOa1ylJzQp2qKmpYZsvF4P+iBvDbpFTdQNOehYTE8PcWS6ULFUG20J2Ksv8vWcXNgUKUrJUacU6r1eviImJZd3qlQwbMYaZc+cT+CGAfr26ERmZse9mwn+uS2nl6zJ39gwcSpaiZi3V87pUrlKNO7dvcvjgAaKjo/F984bVy5cC4O/nlyp1T20njh8jKCiI/7VoqVg3a+58oqKiqFG1IhXKlGCa00Rc5y8mX778aVjT5CMBSoDkp8yYn8pUqMLgsVOZ4rqcjj0Hcu/2daaOGkB0dPwe0wl589qL2NgYdm1eS7f+wxk5ZRbBgR+YPKxvhs1PyWXpttN0HL2Ohj0XsGb3eUZ0a4Dz4BbJ+h7pybu3bwkJCWHtmlVUrebI8pVrqV2nHkMH9efa1StpXb0UcfbUcYKDg2jUtIVi3WLXWRR3KEW1Gqp7Hr5764/pVz3CPr9+5++fYnVNCyUcHHCa5sKS5asZN2EyXq+86NqxPR8/Bn9/50xA8pNqWa4BKjY29ofLHjt2jDp16pAnTx4MDQ3p0KEDbz99uH6mra0d7+7Z14oVK6aYX6BRo0Y/Xe+vfxDLlSv3zX3at2/PqVOneP06rqV5y5YtNGnSRHGX8vbt26xfv15Rt2zZstGgQQNiYmJ49uzZD7+Pi4sLxsbGSsvyhbN/4ix/nkPp8ixcs4PZSzdQtkJVZk4aScD7dwDkt7FlyFgn9uzYRKv6lfijRR3MLSwxMcuOulrcr0BsbAxRUVH0HDiSshWqULiYAyMnufD6lSd3bl5N1XP5UV3Hb0RNDZ7+O50Pl+fTr20Ndh6+RkxMLKWLWNGvbU16Ttr83ePM23iMSm1m0qT3YqKjY1g9tYNi29q/zrN8+xn+WtCbwCvzOb1xGH8euQ7ENVpkNDOdnXjyxAPnWarHmYeFhXH40AGat2iltD4mNoaoqEhGjBpH5arVKOFQiukz5vLS8wXXrmT8UDVzuhNPHnvgPPPLdTl98gTXrlxi2KiEJ5GtVKUqA4eOwGXaZKqUK8kvzRpR1TFu2EVmffTs3r92U7VadXLlyq1Yt3TxAoKCAlmxej1btu/mj45dGDl8MB6PEh6+IURGI/kpdfLTykVzvrlPcnKs04AKVWtgXaAQlRxrMd5lAR4P3bh369oPHyPmU37qPnAEpStUwb6YA8MmuuDt5cm9DJifktPCzSc4e92Dex6vWb3rHKNd/6LP7zXQ1sqcU/DGxMblwlq16tChU2cKFylCtx49qV6jJn/u2J7GtUsZB/f9RYXK1ciRM64H5/nTJ7lx7TL9h45O45qlD9Uca1C/QSPs7AtTpaoji5etJCgokH8PH0rrqok0lDk/Ab+hUKFCqKmpfXeizOfPn9O0aVP69OnD9OnTMTMz49y5c3Tr1o2IiAjFnTo9Pb3vtlAePHhQcRdIT08v0XV++/Ytfn5+2Hz1dCpVXeD/q3z58hQsWJDt27fTp08f9uzZw/r16xXbg4OD6dWrFwMHDoy3b758+X74fcaMGcPQocqTg78MSN3GCV09PSzz5sMybz4KF3OgR9tm/HtgD7/90Q2ImyeqZr3GvH/3Fl3duP9ne3duxtwyDwCmn7rB5rP+cofS2MQMI2MT/N6kz2FWz175U7/7AvR1tTHKpouPfyCbZnThmZc/VUsXJJdZNh4ddFKU19TUYMbQX+jfvhaFm0xSrH8b8JG3AR957OmL+zMfHh+ZRkUHGy7fiQvR4xf+zcTF+zDPboTf+2BqVbSPe3+vt6l7wkk003kq586cZuXaTeTOba6yzPGjRwgLDaNJM+UJRT8PP7P5zx1sUzMzTExM8fHJ2F2JFddl3SZym3+5LteuXOLVy5fUqlpRqfzIoYMoVaYsK9duBOCPjp1p36ET/n5+GBoZ4f3ai8ULXMmT1ypVzyM1vH7txeVLF5g7f5Fi3UtPT7Zv3cyuvf9ga1sIAPvChbl54xo7tm1h/CSnhA6XYWTim3AiESQ/rVdsT8n89OxdVAKlU565ZV6MjE3w8XpJybIVv78DKIYRWeUvoFhnbGKKobFJuh2G9638lJKu3n2OlpYG+S3N8Hjhm6LvlRZMTUzR1NSkwFe9/WwKFOTWjetpVKuU4+P9mutXLjF11nzFuhvXLvP61Uua1q6sVHbiqCE4lCrDghXrMcueg4dud5W2v38bl6nNVAzZy0yMjIzIl9+al0mc8y+jkPykWpZrgDIzM6NBgwYsWbKEgQMHxgsHAQEBmJiYcP369bhhKXPnKuYO+rr7+I/Knz9pwzAWLFiAuro6LVq0SPS+7du3Z8uWLeTNmxd1dXWaNGmi2FamTBnu37+Pra3qiSV/lI6ODjpfPd1COzQ0gdKpIzY2lsiI+EOjTM3iurj+e2AvWtralCpXCYCiJeKGW716+Zwcn3o2BAV+IPBDALlyW6RSrX9OSFgEIWERmBjqUbdKEcbN/5u9x29x4rJy74v9S/ux9cAVNv59KYEjfem18vXduZiYWMWT9X5rWJZLt5/i/z5jdJ+NjY1llss0Tp04xoo1G8iTN2+CZf/eu5vqNWth+tWQjM/D8V48f6ZovPrwIYCAgPdYWORJucqnoO9dl07detD8l1+V1rVp1ZyhI0bjWKOW0no1NTVyfpq/5cihA+Q2t8gUT3/72t97/sLMLDuO1Wsq1oWFxX3Wfe5N+Zm6ugYxiegxkp5l5m7g4sdJfkql/JSGD3Dw931DUOAHTLPHn/MvIYWLlwLA66v8FPQhgJwZMD+lpJL2eYmOjsHvXVCKvk9a0dLWpljxEjx//kxp/YsXz7GwzJhZ6VsO7d+DiakZlapWV6xr16k7TZor96Lv0rYl/YaMpKpjTQCKlSjJ5nUref/ureLvkquXL2JgkA1rm+Sfhyw9CQn5yKuXL8nR7Mc/YzIyyU+qZbkGKIAlS5ZQtWpVKlSogJOTEw4ODkRFRXH06FGWLVvGgwcPsLW1JTIykkWLFtGsWTPOnz/P8uXLU7xuQUFB+Pj4EBkZybNnz9i8eTOrV6/GxcXlp4JO+/btmTx5MtOnT+fXX39VCjqjRo2iUqVK9O/fn+7du2NgYMD9+/c5evQoixcvTs7TSpLQkBC8vb60lL/x9uKpx0OyGRljZGTCjk2rqFi1JmbZcxD4IYB/9uzgrb8v1Wp9eULH/t3bKVK8JHr6+ty8epF1y+bTqddAxdNe8ljlp1K1mqxcOIsBwyegZ5CNDSsXkjefNQ7/eapFelK3chHU1ODRc18KWuXEeUgLHj17w8Z9F4mKiuHdB+UQGxkVzRv/QMVdt/LF81O2WH4u3HxCQFAINnlzMqlvE554+il6P2U3MaBl3dKcueaBrrYmHZtX4pe6panffUGqn+/PmunsxOFDB5g7fzH6Bgb4+8fNTZQtmyG6urqKci89X3Dz+jUWLFkR7xj5rW2oUasOc2Y6M26iEwYGBixZOA9raxvFk9AympnTP12XBaqvy+enlXzN3MJCqbFq47o1VKnqiJq6GiePH2X9mtXMmOOq9JjdzCAmJoZ9e/+iWfMWaGp++eq0timAVb78THOayJDhozAxNuHkiWNcuniehSp+ljIiyU/iM8lPcTJWfnqpeO3r48VTD3cMjYzIZmjMjg0rqFy9DiZmOfB5/ZINKxZgkceK0uW/9N7we+NNUGAg/r4+xMTE8NQj7uaWRR4r9PT1yWOVnwpVa7Jm0Rz6Dh+Pnr4Bm1YtIk8+a0qU/vYQxLTyrfwEcQ9lsTI3xSJX3FyXdtZxDWtv3gby5m1c41Hu7Ibkzm5EwXxxPVaKF7Ik6GMYL33e8z4whIoONpQvnp/T1zwI+hhGJQcbZg5vxbaDVwkIStubtEkR8vGj0hMrvV694uGDBxgbG2NhaUmnLt0YOWwIZcuWp3yFipw/d5Yzp06yet3GNKx18ouJieHQ/r00bNJcKRN8fjLe13KbW2CRJy47la9Uhfw2BZk+aQy9Bwzl3du3rFm+iBat26CtrZ1q55AaXGfPpHrNWlhYWuLn68uyJYvQ0FCnYeOmAPj7++Hv76/oEfXY4xH6BgZYWFhgbGyShjVPHpKfVMuSDVAFChTgxo0bTJ8+nWHDhuHt7U3OnDkpW7Ysy5YtA6BkyZK4uroyc+ZMxowZQ/Xq1XFxcaFjx44pWreJEycyceJEtLW1MTc3p1KlShw/fpxatWp9f2cVbG1tqVChAleuXGH+/PlK2xwcHDh9+jTjxo3D0dGR2NhYChYsyO+//54MZ5J8PNzdGDuoh+L16sVx89TUadiMfsPG8+rFc44fHkbghwCMjEwoVLgYMxetVXpk8KOH99i6bhmhoSHkzWdDv+Hjqd2gqdL7DB03jVWL5jB51ADU1dUpXrIsU2YvRVNTK3VONJGMs+niNOB/5MltwrsPIfx9/BaTluxXPMHue0LCImleuyTjezfBQE8bH/8P/HvhATNXrSUi8ssQgD+aVcRlSEvU1ODynWc06LGAa24vUuq0kt2unXHzDvTq1klp/SQnZ5o1/zKR9L69f5ErtzmVKldVeZwp02bgOtuFwf17o66uRpmy5Vm4bBWaWunz5+N7FNel61fXZarydfmeC+fOsnb1CiIjIihkZ8/cBYup6lj9+ztmMJcuXsDb+zUtWirf2dTS0mLxspUsnDeXQf16ExIaQj6rfEydPgPH6unzMeSJJXfwxGeSn+JklPz02P0+E4b0VLxeu8QVgFoNmtF76BieP/Xg5JF/+BgchGn2nJQqX4n2Xfui9Z8/greuXc7JI/sVr4f2aAvA1HkrFQ1Mg8c6sWbJXKaOHoi6ujrFSpZh4qzFGTY/NalRglVOX+bD3DSzKwDTlh9k+oqDAHT/1ZHxvRsryhxbOwSAHhM3sXn/ZcIjImndoCzjejdGR0uT56/fsmjLSRZuOpFap5ki3Nzu0b3Ll9/lObNcAPhf85ZMdZ5Bnbr1GD9pMmtXrWSmyzSsrW2YO38hZcqmz8bIn3X9ykXe+HjT+H8/npc+09DQYMa8JbjOmErfrn+gq6dHwyb/o2uv/t/fOYN588aHMSOHEhAQgKmZGaVLl2Xjlp2Khz/8uWM7K5Z9abTv2inuSaNTprnQvMUvaVLn5CT5STW12MTMKinED/J4k3Hv7qQkh4Yj0roK6ZLvxYVpXYX0Sb63VNJUz3LPz/ghein0t17ZqSeTtP/1CT/XACBEVvTAO+2G4KVnZRqPSusqpEvvr6afHnfpSUBI+nsCY3pgnFJBIYOT/JS6smQPKCGEEEL8GLmBJ4QQQgiROJKfVJMGKCGEEEIkSLqQCyGEEEIkjuQn1aQBSgghhBAJkvwkhBBCCJE4kp9UkwYoIYQQQiRI7uAJIYQQQiSO5CfVZCZXIYQQQiRITS1pixBCCCFEVpPa+cnLy4s//viD7Nmzo6enR4kSJbh27Zpie2xsLBMnTsTCwgI9PT3q1q2Lh4eH0jHevXtH+/btMTIywsTEhG7duhEcHKxU5s6dOzg6OqKrq4uVlRWzZs1KVD2lAUoIIYQQQgghhBAiA3r//j1Vq1ZFS0uLQ4cOcf/+febOnYupqamizKxZs1i4cCHLly/n8uXLGBgY0KBBA8LCwhRl2rdvj5ubG0ePHuWff/7hzJkz9OzZU7E9MDCQ+vXrkz9/fq5fv87s2bOZPHkyK1eu/OG6yhA8IYQQQiRIupALIYQQQiROauanmTNnYmVlxbp16xTrbGxsFP+OjY1l/vz5jB8/nubNmwOwceNGcufOzd69e2nTpg0PHjzg8OHDXL16lXLlygGwaNEiGjduzJw5c7C0tGTLli1ERESwdu1atLW1KVasGLdu3cLV1VWpoepbpAeUEEIIIRIkQ/CEEEIIIRInqfkpPDycwMBApSU8PFzle+3bt49y5crRunVrcuXKRenSpVm1apVi+7Nnz/Dx8aFu3bqKdcbGxlSsWJGLFy8CcPHiRUxMTBSNTwB169ZFXV2dy5cvK8pUr14dbW1tRZkGDRrg7u7O+/fvf+i6SAOUEEIIIRKkpqaWpEUIIYQQIqtJan5ycXHB2NhYaXFxcVH5Xk+fPmXZsmUUKlSII0eO0KdPHwYOHMiGDRsA8PHxASB37txK++XOnVuxzcfHh1y5cilt19TUxMzMTKmMqmP89z2+R4bgCSGEECJB0oYkhBBCCJE4Sc1PY8aMYejQoUrrdHR0VJaNiYmhXLlyODs7A1C6dGnu3bvH8uXL6dSpU9IqksykB5QQQgghhBBCCCFEOqGjo4ORkZHSklADlIWFBUWLFlVaV6RIETw9PQEwNzcH4M2bN0pl3rx5o9hmbm6Or6+v0vaoqCjevXunVEbVMf77Ht8jDVBCCCGESFBqD8HLKI8RFkIIIYRISGrmp6pVq+Lu7q607tGjR+TPnx+Im5Dc3Nyc48ePK7YHBgZy+fJlKleuDEDlypUJCAjg+vXrijInTpwgJiaGihUrKsqcOXOGyMhIRZmjR49ib2+v9MS9b5EGKCGEEEIkKDUDVEZ6jLAQQgghREJSMz8NGTKES5cu4ezszOPHj9m6dSsrV66kX79+iroMHjyYadOmsW/fPu7evUvHjh2xtLSkRYsWQFyPqYYNG9KjRw+uXLnC+fPn6d+/P23atMHS0hKAdu3aoa2tTbdu3XBzc2PHjh0sWLAg3lDBb0n0HFAvX75ETU2NvHnzAnDlyhW2bt1K0aJFf/jRe0IIIYTIGFJzDqiM9BjhxJL8JIQQQmQdqZmfypcvz549exgzZgxOTk7Y2Ngwf/582rdvrygzcuRIPn78SM+ePQkICKBatWocPnwYXV1dRZktW7bQv39/6tSpg7q6Oq1atWLhwoWK7cbGxvz777/069ePsmXLkiNHDiZOnJioHJPoHlDt2rXj5MmTQNxM5/Xq1ePKlSuMGzcOJyenxB5OCCGEEOlYUu/gZdbHCCeW5CchhBAi60jtKQyaNm3K3bt3CQsL48GDB/To0SNefZycnPDx8SEsLIxjx45hZ2enVMbMzIytW7cSFBTEhw8fWLt2LdmyZVMq4+DgwNmzZwkLC+PVq1eMGjUqUfVMdAPUvXv3qFChAgA7d+6kePHiXLhwgS1btrB+/frEHk4IIYQQmVhmfYxwYkl+EkIIIURWl+gheJGRkYrZ148dO8b//vc/AAoXLoy3t3fy1k4IIYQQaUoeI5w8JD8JIYQQWUdqDsHLSBLdA6pYsWIsX76cs2fPcvToURo2bAjA69evyZ49e7JXUAghhBBpJ6ldyDPrY4QTS/KTEEIIkXWk9hC8jCLRDVAzZ85kxYoV1KxZk7Zt21KyZEkgbt6Gz13LhRBCCJE5qKklbUmMjPQY4cSS/CSEEEJkHamZnzKSRA/Bq1mzJv7+/gQGBiqFtJ49e6Kvr5+slRNCCCFE2lJPxRQ0ZMgQqlSpgrOzM7/99htXrlxh5cqVrFy5ElB+jHChQoWwsbFhwoQJCT5GePny5URGRqp8jPCUKVPo1q0bo0aN4t69eyxYsIB58+al2LlJfhJCCCGyjtTMTxlJohugIO4xyNevX+fJkye0a9cOQ0NDtLW1JUAJIYQQmYw8Rjj5SH4SQgghsgZpf1It0Q1QL168oGHDhnh6ehIeHk69evUwNDRk5syZhIeHs3z58pSopxBCCCGygKZNm9K0adMEt39+jLCTk1OCZT4/RvhbPj9GOLVIfhJCCCFEVpfoOaAGDRpEuXLleP/+PXp6eor1LVu2VJqTQQghhBAZn0yimTwkPwkhhBBZh+Qn1RLdA+rs2bNcuHABbW1tpfXW1tZ4eXklW8WEEEIIkfbUM28GSlWSn4QQQoisQ/KTaolugIqJiSE6Ojre+levXmFoaJgslRJCCCFE+pCZ78KlJslPQgghRNYh+Um1RA/Bq1+/PvPnz1e8VlNTIzg4mEmTJtG4cePkrJsQQggh0pg8Rjh5SH4SQgghsg7JT6qpxcbGxiZmh1evXtGgQQNiY2Px8PCgXLlyeHh4kCNHDs6cOUOuXLlSqq4iA/ENikzrKqRLQaFRaV2FdCkmUZ9CWUceM93vFxLiE32tlEkrTVZcSdL+B3pVSKaaZGySn8SPeOIXmtZVSJciImPSugrpkrmJ5ARVNGTsk0paGonue5Il6GmlzHElP6mW6CF4efPm5fbt22zfvp07d+4QHBxMt27daN++vdKkmkIIIYTI+NSQIJ8cJD8JIYQQWYfkJ9US3QAFoKmpyR9//JHcdRFCCCFEOiM3kpOP5CchhBAia5D8pFqiG6A2btz4ze0dO3b86coIIYQQIn2RSTSTh+QnIYQQIuuQ/KRaohugBg0apPQ6MjKSkJAQtLW10dfXlwAlhBBCZCKSn5KH5CchhBAi65D8pFqiG6Dev38fb52Hhwd9+vRhxIgRyVIpIYQQQqQP6pKgkoXkJyGEECLrkPykWrJMhV+oUCFmzJgR7+6eEEIIIYRQTfKTEEIIIbKSn5qEXOWBNDV5/fp1ch1OCCGEEOmA3MBLWZKfhBBCiMxH8pNqiW6A2rdvn9Lr2NhYvL29Wbx4MVWrVk22igkhhBAi7ckkmslD8pMQQgiRdUh+Ui3RDVAtWrRQeq2mpkbOnDmpXbs2c+fOTa56CSGEECIdkPyUPCQ/CSGEEFmH5CfVEt0AFRMTkxL1EEIIIUQ6JJNoJg/JT0IIIUTWIflJtWSZhFwIIYQQQgghhBBCiIT8UA+ooUOH/vABXV1df7oyQgghhEhf5P7dz5P8JIQQQmRNkp9U+6EGqJs3b/7QwWSiLSGEECJzke/2nyf5SQghhMia5LtdtR9qgDp58mRK10MIIYQQ6ZC65KefJvlJCCGEyJokP6mW6EnIhRBCCJF1yB08IYQQQojEkfyk2k81QF27do2dO3fi6elJRESE0ra//vorWSomhBBCCJGZSH4SQgghRFaW6Kfgbd++nSpVqvDgwQP27NlDZGQkbm5unDhxAmNj45SooxBCCCHSiJpa0hYRR/KTEEIIkXVIflIt0Q1Qzs7OzJs3j/3796Otrc2CBQt4+PAhv/32G/ny5UuJOgohhBAijaipqSVpEXEkPwkhhBBZh+Qn1RLdAPXkyROaNGkCgLa2Nh8/fkRNTY0hQ4awcuXKZK+gEEIIIdKOulrSFhFH8pMQQgiRdUh+Ui3RDVCmpqYEBQUBkCdPHu7duwdAQEAAISEhyVs7IYQQQqQpuYOXPCQ/CSGEEFmH5CfVEj0JefXq1Tl69CglSpSgdevWDBo0iBMnTnD06FHq1KmTEnUUQgghRBrJvBEodUl+EkIIIbIOyU+q/XAD1L179yhevDiLFy8mLCwMgHHjxqGlpcWFCxdo1aoV48ePT7GKCiGEEEJkNJKfhBBCCCHi/HADlIODA+XLl6d79+60adMGAHV1dUaPHp1ilRNCCCFE2lLPxN3AU4PkJyGEECLrkfyk2g/PAXX69GmKFSvGsGHDsLCwoFOnTpw9ezYl6yaEEEKINCaPEU4ayU9CCCFE1iP5SbUfboBydHRk7dq1eHt7s2jRIp4/f06NGjWws7Nj5syZ+Pj4pGQ9hRBCCJEGZBLNpJH8JIQQQmQ9kp9UU4uNjY392Z0fP37MunXr2LRpEz4+PjRs2JB9+/b91LF8fHyYPn06Bw4cwMvLi1y5clGqVCkGDx6cZpNzWltb8+LFCwD09fWxt7dnzJgxtG7dOk3qk5H4BkWm6PH9fN+wbJErly+cIywsjLx58zFm0lQKFy0OgGO54ir36zNwKO06dgXA/eF9li905eF9N9Q11KlRux79h4xEX18/xeodFBqVbMe6e+s6u7dt4LH7A9699WP8dFeqVK+t2L557TLOHD+Cn68PWppa2NoXpWOP/hQuVuJLfQI/sGz+DC6fP4O6uhpVa9Sl18CR6P3nGsTGxvLX9o0c2rcb3zfeGBub0KTlb7Tp2CPZziXmpz+F4rt36zq7t2/gyafrMm66K5Udv1yXLWuXcfZE3HXR/M91sS8ad13u3LzK2EGqz811xWbsihQnIjycJXOn8dj9AS89n1GhsiPjnecn30l8ksdMN9mPmZDo6GiWL13MwX/28dbfn5w5c9GsRUt69Oqj+BI8fvRfdu3czoP7bnz48IHtu/ZgX7hIqtUxrXzv2kRGRrJ00QLOnT3Nq1evyJYtGxUrVWHgkKHkypU71eqpr5UyYaXXLrck7b/i12LJVJPMQ/KTSMgTv9BkO9bdW9fZvfU/OcH5S06Iiopk48olXL10Dp/XrzAwMKRUuYp06TOQ7DlyKY6xfcMqrl48y1OPR2hqafLn4XMq3+vowb/Zs2MzXi9foK9vQLVa9eg3bGyynUtEZEyyHcvt9nX27NjIk0cPeP/Wn9FT51KpWi3F9gUzJnHyyH6lfUqXr8ykWUsUr3u0aYLfG2+lMh16DKBVuy6K17Gxsfy9cxP//vMXvm+8MTI2oVHz1rT+o3uynYu5ScrlhN07t/PXru14v/YCoEABW7r27EOVatUBePXSk0XzZnP75g0iIiOoXKUaQ0eNI3v2HIpjeL54zqJ5s7lz+yaRkZHYFrKnV98BlC1fMcXqDaCRgs+wX7dmJSePH+XFs6fo6OjiUKo0/QcPw9raRlHm1UtPFsydxa1bN4iMiKByVUeGj1a+Nv9rVAfv16+Vjt1v4FA6d0u+fP01LY0f7nuSLJYtWcSKZYuV1lnb2LB3/2GldbGxsfTv04Pz587iumAJtevUTc1qoqeVMseV/KRaop+C91+2traMHTuW/PnzM2bMGA4cOPBTx3n+/DlVq1bFxMSE2bNnU6JECSIjIzly5Aj9+vXj4cOHP3Xc6Oho1NTUUFf/+V82JycnevToQWBgIHPnzuX3338nT548VKlS5aePmZFFRESgra2dpnUICvxA324dKF2uArMXLMfE1JRXL19gaGSkKLP38CmlfS5dOMvMqROpWbseAP5+vgzp253a9RoyZOQ4Pn4MZuHcmThPHse0WfNS83R+WlhYKDa2dtRv0oJp44bG257HKj99hozG3DIvEeFh7NmxhfHD+rBm2z6MTc0AmOU0lvdv/Zjuupzo6CjmuUxk4WwnRk2aoTjOigWzuHH1It37DcW6QCGCgj4QFPgh1c4zscLCQilQ0I56jVvgPF71dek9OO66hIeH8ffOLUwY1odV2/ZhbGJGkeKl2LTnmNI+m9Ys4fb1KxQqHPdFEBMTg7aOLs1+bcuF08dT5bxS2vo1q9i1YxtO02dQ0NYWN7d7TB4/lmzZstHuj44AhIaGUqpMWeo1aMTUyRPSuMap53vXJiwsjAf379OjV1/s7O0JDAxk9gxnBvfvy9adu9O6+iIdkvyU9aRFfgoLTTgnhIeF8fjRA9p26kGBQvYEBwayfMEspowazMI1WxXloqIiqVarHoWLleTfA3tUvs9f2zexZ/tGuvYdQuFiJQgLDeWNz2uVZdODsLAwbAraUbdRc2ZMHK6yTJkKVRgwarLitZZW/P93bbv0oX7TlorXenoGSttXL5rNrWuX6Nx7CPkL2BIU+IHgoMDkOYlUkCt3bvoNGELefPkBOLB/LyOH9Gfj9t1YWOZhUN8e2NrZs3jlOgBWLl3IiEH9WL1xm+KzY9jAPljly8/iFevQ0dFhx9ZNDBvYl937D5M9R840O7ekuHHtKq1/b0fRYsWJjo5m6aJ5DOjdjZ1//YOevj6hISH0792dQnb2LFu1HoDlSxYydEBf1m3ervS52qvvAFq0+tIwb6Bv8PXbZXgFbQuxYvU6xWsNDY14ZTZv2pC5x5wJJT+dLM6cOUPnzp0xNzdnxIgR/PLLL5w/f/6njtW3b1/U1NS4cuUKrVq1ws7OjmLFijF06FAuXbqkKOfq6kqJEiUwMDDAysqKvn37EhwcrNi+fv16TExM2LdvH0WLFkVHRwdPT8+fPUUADA0NMTc3x87OjiVLlqCnp8f+/fuJjo6mW7du2NjYoKenh729PQsWLFDa99SpU1SoUAEDAwNMTEyoWrWq4o7g7du3qVWrFoaGhhgZGVG2bFmuXbum2PfcuXM4Ojqip6eHlZUVAwcO5OPHj4rt1tbWODs707VrVwwNDcmXLx8rV65Uev8LFy5QqlQpdHV1KVeuHHv37kVNTY1bt24pyty7d49GjRqRLVs2cufOTYcOHfD391dsr1mzJv3792fw4MHkyJGDBg0aJOl6JoctG9aSK7c5YydNo2jxEljmyUuFSlXJkzefokz2HDmUlnOnT1K6XAUs81oBcOHsaTQ1NRk6ajz5rG0oUqwEw8dO5PSJo7x6mbSfmdRSvlI1OvXor9Tr6b9q1WtM6XKVsLDMS34bW3oOGEbIx2CePfEAwPP5U65fPs/AUZMoXKwExRxK03vwaM4cP8Jbf19FmQN7/2Siy3wqVauJuWUeCtkXpUz5yql2nolVrlI1OnzjutSs15hS5Sph/um6dO+vfF20tLQwzZ5DsRgaG3P53CnqNm6u6Amkq6dHv2HjaNisFaZm2VPt3FLS7Vs3qVGrDo41amKZJy/16jekUpWquN29qyjT9H/N6dWnH5Uqp9///ynhe9fG0NCQ5avXUr9hI6xtCuBQshSjx07gwX03vL3T7x9hP0pdTS1Ji1Am+UnyU2opX7kanXr2p0qN+N+HBtkMcZ6/gup1GpA3nzWFizvQd+hoHrvfx9fnS8+eP7r1peXvHbAuaKvyPYICA9m0agnDxk+jVv3GWOSxwsbWjkrVaqbUaSVZ2YpVad+tH5UcVecEAE0tbUzNciiWbIZG8cro6esrldHV01Nse/niKYf37WLsNFcqVK1Bbos82NoXpVS5SilyTinBsUYtqjjWIF9+a/Llt6ZP/8Ho6+tz784d7ty6ifdrLyZOcca2kB22heyY6OTCg/v3uHYl7rMn4P17Xnq+oGOXuMaYfPmt6TtwKGFhoTx57JHGZ/fzFi1bRbPmLSloWwg7+8JMcnLBx9ubBw/iervc/nRtJk11UVybyVPjrs3VK5eUjqVvYECOHDkVi14KjsJIKxoaGkrnaPrpJvhnDx8+YNOGtUyZ6pxGNUw5kp9US1QD1OvXr3F2dsbOzo6aNWvy+PFjFi5cyOvXr1m1ahWVKiX+Q/Xdu3ccPnyYfv36YWAQv9XXxMTkS2XV1Vm4cCFubm5s2LCBEydOMHLkSKXyISEhzJw5k9WrV+Pm5kauXLlILpqammhpaREREUFMTAx58+blzz//5P79+0ycOJGxY8eyc+dOAKKiomjRogU1atTgzp07XLx4kZ49eyr+gG3fvj158+bl6tWrXL9+ndGjR6OlFdf/78mTJzRs2JBWrVpx584dduzYwblz5+jfv79SfebOnUu5cuW4efMmffv2pU+fPri7uwMQGBhIs2bNKFGiBDdu3GDq1KmMGjVKaf+AgABq165N6dKluXbtGocPH+bNmzf89ttvSuU2bNiAtrY258+fZ/ny5cl2PX/WuTMnsS9SjAmjhtKsXnW6tvuVfXt2JVj+3Vt/Lp47Q9PmvyjWRUREoKWlpXQXQkcnrhvznVs3Uq7yaSQyMpJD+3ZjkC0bNrZ2ADx0u0O2bIbYFf7SvbN02Yqoqavjfv8eAJfPn8bcMg9XLpyhy2+N6dy6EfNnTEnXPaASIzIyksOfr0tBO5VlLp87TVDgB+o1ap7KtUtdJUuV5srli7x4/gwA94cPuXXjBlUdq6dxzdLez1yboOAg1NTUMFTxR0tGI5NoJp3kJ8lP6SE/fc/H4GDU1NTIZmj4w/vcvHqRmNgY3vr50qt9Szq0rI/zhBH4vcnYc5vdu3WNTi3r0LdjS5bPcybwQ0C8Mn9tXU+H5rUY0qMte7ZvIDr6yzQLVy+cIbdlHq5eOkvPtk3p0aYJi2c7Zdj8FB0dzdHDBwkNDaWEQ0kiIiJQU1ND6z+9+rR1dFBXV+f2pxxtbGJCfmsbDv6zj9DQEKKioti7ewemZtkpXDTzDC0KDg4CwMjIGEBxbbRVXZubyn9jbFi7mrrVK9H+t1/YtH4NUVHJN1VHeuHp+YJ6tarRpGEdxowapnRjLjQ0lLEjhzFm3ERyZNAecd8i+Um1Hx6C16hRI44dO0aOHDno2LEjXbt2xd7ePskVePz4MbGxsRQuXPi7ZQcPHqz4t7W1NdOmTaN3794sXbpUsT4yMpKlS5dSsmTJJNftvyIiIpg7dy4fPnygdu3aaGlpMWXKFMV2GxsbLl68yM6dO/ntt98IDAzkw4cPNG3alIIFCwJQpMiXuVI8PT0ZMWKE4rwLFSqk2Obi4kL79u0V51uoUCEWLlxIjRo1WLZsGbq6cQ0ljRs3pm/fvgCMGjWKefPmcfLkSezt7dm6dStqamqsWrUKXV1dihYtipeXFz16fBlXvHjxYkqXLo2z85cW57Vr12JlZcWjR4+ws7NTvP+sWbMSvDbh4eGEh4crr4tQR0dH58cvcCJ4e73i7907+K19Rzp06cHD+/dYMMcFLS0tGjWN30hw6J996BvoU73Wl/HEZctXZPG82WzduJbWbTsQFhrC8kVxQ+/e+vulSL3TwuXzZ5g5ZRThYWGYZc/BdNflGJuYAvD+rb9iKN5nGpqaGBoa8f5t3F1cH28vfN94c/bkUYaNm0ZMTDQrF81h+oThzFiwKtXPJ7lcuXCGWZ+ui2n2HEyd++W6fO3fA3soXb4yOVJxLp+00KV7T4I/fqRls8ZoaGgQHR1Nv4GDady0WVpXLc0l9tqEh4ezcN4cGjZuQrZs2VK5tskvM0+EmRokP0l+SojK/BQek2L56VsiwsNZt2wBNeo2RN/gxz+3fF57ERsTw45Na+g1aCQGBtnYuGoJ44b0ZsmGPxWNgxlJmQpVqOxYm1wWlvi8fsXm1YuZOnoAMxavVwwfavpLWwrYFcbQ0IiHbnfYtGoR79/607XfMADeeHvh5+PNhVNHGTTGiZiYGNYumcusySOY6rryW2+frjz2eESPTm2JiIhAT0+fmXMXYlPQFhNTM3T19FiyYC59+g8mlliWLHAlOjpakaPV1NRYtHwNI4cMoHbV8qirq2Nqasb8JSsUjTUZXUxMDK6zXChZqgy2heJ+70s4lERXT49F8+fQb8AQYmNjWfzp2vj7ffkb4/e2HShcpChGxsbcuXWTJQvn4e/nx5ARo9PqdJJdCQcHnKa5YG1tg7+/H8uXLqFrx/bs2rsfA4NszJnlQslSpalVO3XnfEotkp9U++EGKC0tLXbt2kXTpk1Vjt38WYmZA/3YsWO4uLjw8OFDAgMDiYqKIiwsjJCQEMXE0dra2jg4OHzzOMWKFVN05XZ0dOTQoUMJlh01ahTjx48nLCyMbNmyMWPGDJo0aQLAkiVLWLt2LZ6enoSGhhIREUGpUqUAMDMzo3PnzjRo0IB69epRt25dfvvtNywsLAAYOnQo3bt3Z9OmTdStW5fWrVsrgtbt27e5c+cOW7ZsUbpOMTExPHv2TBHE/nueampqmJub4+sbN3TK3d0dBwcHRdgCqFChgtK53b59m5MnT6r8A+nJkyeKAFW2bNlvXk8XFxelMAkwfPR4Royd+M39flZMTAyFixajV7/BANgVLsLTJx78vXunygaog/v2UK9hU6VAZ1PQlnFTprN43ixWLlmAuro6rdq0xyx7dtTVUneCvpRUskx5Fq/dQeCHAA7v/wuXSSOZt2IzJl81PCUkNiaGyIgIho2bppgDYPCoyQzs3pZXns/Jm886BWufchxKl2fhmrjrcmT/X8ycNJK5Kq6Lv+8bbl69yKjJCf8BkVn8e/gQh/7Zj/PMORS0tcX94UPmzHQmZ65c/K95y+8fIBNLzLWJjIxk5LDBxMbC2AmT06bCySzzfCKmDclPkp8Soio/DRg+lkEjx39zv+QWFRWJy8SRxBJL/+HjErVvbGwMUVFR9B48kjIV4ub3GjXZhfbN63LnxlXKVsx4c3451v4yXNK6QCGsCxSid/v/ce/WNUqWjZs8u/lvf3wpU9AOTU1Nlrk606HHALS0tYmJiSEyMoJBY6aSxyouP/UfMZFhvdrj5fmcPBkkP+W3tmbj9r/4GBzMiWNHcJo4lmWrN2BT0BbnWfOY5ezEzm2bUVdXp17DxtgXKYrapxwdGxvLbJepmJqZsXztJnR0dNm3ZxfDB/Vj3ead5MiZ8Xu8zHJ24skTD1at//KZY2pmxozZ85kxfQo7tsZdm/oNG1O4SFHU/zM5evuOnRX/LmRnj5aWFs7TJtNv0NA0n283uVRzrKH4t519YYqXKEnj+rX49/AhTM3MuHL5Ejt2qZ5bLjOQ/KTaDzdA/ezTWb6nUKFCqKmpfXeizOfPn9O0aVP69OnD9OnTMTMz49y5c3Tr1o2IiAhFgNLT0/tua+PBgweJjIxUlP+WESNG0LlzZ8UY/8/H3r59O8OHD2fu3LlUrlwZQ0NDZs+ezeXLlxX7rlu3joEDB3L48GF27NjB+PHjOXr0KJUqVWLy5Mm0a9eOAwcOcOjQISZNmsT27dtp2bIlwcHB9OrVi4EDB8arT758X+Y5+vqukpqaGjExP/6UkODgYJo1a8bMmTPjbfsc9ACVXfv/a8yYMQwdqjy55YeIlPuVy54jJ/ltCiqty29TgNMnjsUre/vmdTxfPGOKy+x42+o1bEK9hk1499YfXT191NRg55aNWObNm2J1T226enpY5s2HZd58FC7mQPe2zTjyzx5+79AN0+w5+PD+nVL56KgogoICMf30lA6z7DnQ0NBUND4BWH16yofvG+8M2wD19XXp0bYZ/x7Yw29/dFMqd/TQ3xgaGVOxWo0EjpR5zJ87my7de9CwcdwfiIXs7PH2fs261SuzfAPUj16byMhIRg0bgvfr16xcuz5T9H4SSSf5SfJTQlTlp1eByfe0tx8RFRWJy4SR+Pp447JwZaJ6PwGKvJDP+ksuMzY1w8jYJN5T4jIqc8u8GBmb4OP1UtEA9TW7IiWIjo7C1+c1efJZK/LT58YngLz54/KTn69PhmmA0tLSxupTBixctBj33e6xY9smRo+fQsXKVdm9/wgB79+joamBoaERjes6kqdBIwCuXbnE+bOnOXr6Egafvg8LF5nIlUsXOLh/Lx27ptzT3lLDLOepnD1zmpVrN5E7t7nStkpVqrL3wL9x10ZDA0MjIxrUdqT+p7loVSlWwoHoqChev/ZSeqJeZmJkZES+/Na89PTEw+MRr1564li5vFKZ4UMGULpMOdas35RGtRQpLUlPwUsOZmZmNGjQgCVLljBw4MB4X9YBAQGYmJhw/fp1YmJimDt3rmLens/zBSRW/vz5v1/okxw5cmBrG3/ixfPnz1OlShVFF26Iu+v1tdKlS1O6dGnGjBlD5cqV2bp1q2KuBzs7O+zs7BgyZAht27Zl3bp1tGzZkjJlynD//n2V7/uj7O3t2bx5M+Hh4YqeP1evXlUqU6ZMGXbv3o21tTWamj//o6CjoxOvu3hYUORPH+97SpQszcsXz5XWvXzxAvP/hL7P/vn7L+yLFMXWLuEhCmafwtOBv/9CW1uHchUz7wTLMTGxREZGAFC4mAPBwUF4uN+nkH1RAG7fuEJsTAz2RYsDULREKaKjo/D2eolFnrgvTa+XcXe/c+W2TIMzSBmxsbFERkTEW3fs4N/UbtAMTc2MN4QgscLCQhV3LT9TV1dP1B9lmdWPXJvPjU+eni9YuXYDJgkM6cyIpAt5+iT5KXPmJ53w0J8+XmJ9bnx6/cqTGQtXYWRskuhjFC1RGoBXns8VQ9WDAj8Q+CGAXObxc1lG5O/3hqDAD5hmT7jHzrPH7qirqyumNihcPH5+ev3pITc5c2fc6xIbG0tEhHLGNzGN+767duUS79+9w/HTpPdhYWEAqKkrf4eoq6sTE5txs0Vcz65pnDpxjOVrNpDnGzeuP1+bq5cv8f7dWxxrJjzx/SP3h6irq2Nm9mOjFDKikJCPvHr5khzNclK/YSN++c8TAAF+bdmM4SPHUKNmrTSqYfKS/KRauugZtmTJEqKjo6lQoQK7d+/Gw8ODBw8esHDhQip/etqSra0tkZGRLFq0iKdPn7Jp06Y0ndCxUKFCXLt2jSNHjvDo0SMmTJigFFCePXvGmDFjuHjxIi9evODff//Fw8ODIkWKEBoaSv/+/Tl16hQvXrzg/PnzXL16VdE1fNSoUVy4cIH+/ftz69YtPDw8+Pvvv+NNovkt7dq1IyYmhp49e/LgwQOOHDnCnDlzgC+/DP369ePdu3e0bduWq1ev8uTJE44cOUKXLl2Ijo5OxquVvH5r1wG3u3fYuHYlr156cvTwAfbv2UXL1m2Vyn0MDubUsX9p2ryVyuPs3rEV94f38XzxnL92bmPeLGd69R+UYSYNDg0J4YnHQ554xN39fuPtxROPh/i+8SYsNJT1Kxby0O0Ob3xe4+F+n3kuk3jr74tjrXoA5LMuQNmKVVk40wn3+3dxu3OTpfNmUL1OA7LniJt8tlS5StjaFWGey2SePHqIh/t9Fs2eRunylZR6RaUnoSEhPPV4yNP/XJen/7kuG1bGXRdfn9c8dr/P/Blx16Xap+vy2e0bV3jj7aX0iOX/8nz+hKceDwkKDOTjx2Cl98yIqtesxZpVyzl7+hSvvV5x4thRNm9cT+06X67Lhw8BuD98oPhj8fmzZ7g/fIB/Jpo3TZXvXZvIyEhGDB3Efbd7TJ8xm5iYaPz9/fD391M0+GZk6mpJW0TKkfwk+elbEswJPt5ERUXiPH4EHu73GTHRmeiYGN699efdW39FLzcAXx9vnng8xO+NDzHRMYrjhYaEAJA3X34qOdZkxYJZ3L97i+dPHzN32gTy5rPGoUx5lfVKa6GhITx97M7Tx3ETz/t6e/H0sTt+b7wJDQ1h/fJ5uN+Py0+3r1/GedwQLPJYUfrTE4Afut1m364tPHv8CJ/Xrzh99CBrl86lRt3GiqfllSxbkQKFCrN41hSeejzksft9lrlOo2S5Skq9otKzpQtduXn9Gq9fe/HY4xFLF7py49oVGjRuCsTd5L135zavXnpy6MA+xo4cQpv2Hcn/qfdOCYdSGBoZ4TRhLB7uD/F88ZxF82bz2usVVTNwz/KZzk4cOrifqTNmo29goPi+/9zgBrBv71/cvXOLVy89OfjPPsaMGEzbPzopejbduX2TrZs38Mj9Ia9eveTQgf3Mmz2DRk2aZZr5sQBcZ8/k2tUreHm94tbNGwwZ2B8NDXUaNm5Kjhw5FU8J/LwAmFtYkucbPcUyEslPqqV5DyiAAgUKcOPGDaZPn86wYcPw9vYmZ86clC1blmXLlgFQsmRJXF1dmTlzJmPGjKF69eq4uLjQsWPHNKlzr169uHnzJr///jtqamq0bduWvn37KuZD0NfX5+HDh2zYsIG3b99iYWFBv3796NWrF1FRUbx9+5aOHTvy5s0bcuTIwS+//KKYB8DBwYHTp08zbtw4HB0diY2NpWDBgvz+++8/XD8jIyP2799Pnz59KFWqFCVKlGDixIm0a9dOMa+BpaUl58+fZ9SoUdSvX5/w8HDy589Pw4YNlZ4Ol94UKVaC6XPms3LxAjasXo6FZR4GDBtF/UZNlcod//cQsbGx1G3YWOVxHrjdZe3KJYSGhJDP2obhYyfSsMn/UuMUkoWHuxujB37pvrxq8VwA6jZsRv/h43nl+Zzp44fx4UMARkYm2BUpxuzFa8lv8+XO8MiJziyd58LYwb1QU1enao069B705Wk/6urqTJq5gGXzZzKyf1d09fQoW7EqPfoPS70TTSQPdzfGDvpyXVZ/ui51Gjaj37DxvHrxnOOHhxH46boUKlyMmYuUrwvA0QN7KFK8JFb5VXeDnjyyv9Kjqgd2awPAP2duJfMZpY5RY8ezdNFCnKc58f7dW3LmzMWvrX+nZ58vvRROnzzBpPFjFa9Hj4gbOtKrTz969xuQ6nVOLd+7Nn6+bzh98gQAbX5tobTvqrUbKFdB9ZCNjCIzh6CMTvKT5Kdv8Xj4VU5Y9CknNGpG+669uXTuFAD9uyhfnxkLVykajzavWcqxQ/sV2wZ0aROvzPDx01i5cA6TRwxATV2dEqXKMnXu0nTbe/ix+30mDOmpeL12qSsAtRo0o/eQMTx/4sHJI//wMTgI0+w5KVWuEu279lU88U1LS5tzJ46wff0KoiIjyWVhSbNf29O89Zd5odTV1RnvvICVC2cydlB3dHX1KFOxCl36KA+5TM/ev3vHlAmjeevvR7ZshhQsZMf8pauoWCluXq8Xz5+zdNE8Aj98wMIyD5279aLtH50U+5uYmjJ/8UqWL1lAv15diIqKokABW2bNW0wh++8/PCG92r1zOwC9u3VSWj/RyZlmn4blv3j+jCUL466NpaUlXbr3pl2HL+W1tbU5evggq5YvITIiAss8eWnboRPtO3ROtfNIDW/e+DBm5FACAgIwNTOjdOmybNyyM1P38vovyU+qqcUmZhZLkaFt2bKFLl268OHDh+/O3ZBUvik4BC8jCwrNfI9XTQ4x8imkUh4z3e8XEuITfa2USTrD9rsnaf+5zZL+xDch0lJq5qcnfqk3BC8jiYjMuEO2UpK5ieQEVTTkL3+VtDTSTwN5eqKXQm3lkp9U+6EeUImZQPN//8s4PUgyu40bN1KgQAHy5MnD7du3GTVqFL/99luKhychhBCZh+T4nyf5KWOS/CSEECKpJD+p9kMNUC1atPihg6mpqaWrse9ZnY+PDxMnTsTHxwcLCwtat27N9OnT07paQgghRJYg+SljkvwkhBBCpAwZgidShAzBU02G4KkmQ/BUkyF4IjFSagjeyANJ60I+q0nm7EIuREqQIXiqyRA81WQInmoyBE81GYKnWkoNwZP8pFq6mIRcCCGEEOmTujxGWAghhBAiUSQ/qfZTDVAfP37k9OnTeHp6EhGh/IjpgQMHJkvFhBBCCJH25H5p8pH8JIQQQmQNkp9US3QD1M2bN2ncuDEhISF8/PgRMzMz/P390dfXJ1euXBKghBBCiExEbuAlD8lPQgghRNYh+Um1RDfMDRkyhGbNmvH+/Xv09PS4dOkSL168oGzZssyZMycl6iiEEEIIkaFJfhJCCCFEVpfoBqhbt24xbNgw1NXV0dDQIDw8HCsrK2bNmsXYsWNToo5CCCGESCPqampJWkQcyU9CCCFE1iH5SbVEN0BpaWmhrh63W65cufD09ATA2NiYly9fJm/thBBCCJGm1NSStog4kp+EEEKIrEPyk2qJngOqdOnSXL16lUKFClGjRg0mTpyIv78/mzZtonjx4ilRRyGEEEKkEXmadfKQ/CSEEEJkHZKfVEt0DyhnZ2csLCwAmD59OqampvTp0wc/Pz9WrlyZ7BUUQgghRNqRLuTJQ/KTEEIIkXWkVX6aMWMGampqDB48WLEuLCyMfv36kT17drJly0arVq148+aN0n6enp40adJE8XCUESNGEBUVpVTm1KlTlClTBh0dHWxtbVm/fn2i65foHlDlypVT/DtXrlwcPnw40W8qhBBCCJGVSH4SQgghREq6evUqK1aswMHBQWn9kCFDOHDgAH/++SfGxsb079+fX375hfPnzwMQHR1NkyZNMDc358KFC3h7e9OxY0e0tLRwdnYG4NmzZzRp0oTevXuzZcsWjh8/Tvfu3bGwsKBBgwY/XMdE94ASQgghRNYhcxgIIYQQQiROauen4OBg2rdvz6pVqzA1NVWs//DhA2vWrMHV1ZXatWtTtmxZ1q1bx4ULF7h06RIA//77L/fv32fz5s2UKlWKRo0aMXXqVJYsWUJERAQAy5cvx8bGhrlz51KkSBH69+/Pr7/+yrx58xJVz0T3gLKxsUHtG1fk6dOniT2kEEIIIdIpmcMgeUh+EkIIIbKOpOan8PBwwsPDldbp6Oigo6Ojsny/fv1o0qQJdevWZdq0aYr1169fJzIykrp16yrWFS5cmHz58nHx4kUqVarExYsXKVGiBLlz51aUadCgAX369MHNzY3SpUtz8eJFpWN8LvPfoX4/ItENUF+/QWRkJDdv3uTw4cOMGDEisYcTQgghRDqmhrRAJQfJT0IIIUTWkdT85OLiwpQpU5TWTZo0icmTJ8cru337dm7cuMHVq1fjbfPx8UFbWxsTExOl9blz58bHx0dR5r+NT5+3f972rTKBgYGEhoaip6f3Q+eV6AaoQYMGqVy/ZMkSrl27ltjDCSGEECIdS8seUDNmzGDMmDEMGjSI+fPnA3ETaQ4bNozt27cTHh5OgwYNWLp0qVIo8vT0pE+fPpw8eZJs2bLRqVMnXFxc0NT8EntOnTrF0KFDcXNzw8rKivHjx9O5c+cUOxfJT0IIIUTWkdT8NGbMGIYOHaq0TlXvp5cvXzJo0CCOHj2Krq5u0t40FSTbHFCNGjVi9+7dyXU4IYQQQqQD6mpJW37WtybS3L9/P3/++SenT5/m9evX/PLLL4rtnyfSjIiI4MKFC2zYsIH169czceJERZnPE2nWqlWLW7duMXjwYLp3786RI0d+vsI/SfKTEEIIkfkkNT/p6OhgZGSktKhqgLp+/Tq+vr6UKVMGTU1NNDU1OX36NAsXLkRTU5PcuXMTERFBQECA0n5v3rzB3NwcAHNz83hPxfv8+ntljIyMfrj3EyRjA9SuXbswMzNLrsMJIYQQIhMIDw8nMDBQafl6ToOvZZSJNJOD5CchhBBC/Kw6depw9+5dbt26pVjKlStH+/btFf/W0tLi+PHjin3c3d3x9PSkcuXKAFSuXJm7d+/i6+urKHP06FGMjIwoWrSoosx/j/G5zOdj/KhED8ErXbq00iSasbGx+Pj44Ofnx9KlSxN7OCGEEEKkY9+aOPtHJGYOg88yykSaiSH5SQghhMg6kpqffpShoSHFixdXWmdgYED27NkV67t168bQoUMxMzPDyMiIAQMGULlyZSpVqgRA/fr1KVq0KB06dGDWrFn4+Pgwfvx4+vXrp+h11bt3bxYvXszIkSPp2rUrJ06cYOfOnRw4cCBR9U10A1Tz5s2VLqa6ujo5c+akZs2aFC5cOLGHE0IIIUQ6llpzGHyWkSbSTAzJT0IIIUTWkZ6eIjxv3jzU1dVp1aqV0vyZn2loaPDPP//Qp08fKleujIGBAZ06dcLJyUlRxsbGhgMHDjBkyBAWLFhA3rx5Wb16NQ0aNEhUXRLdAPWtO5ZCCCGEyFySegPvW48M/lpGm0gzMSQ/CSGEEFlHKnWAUunUqVNKr3V1dVmyZAlLlixJcJ/8+fNz8ODBbx63Zs2a3Lx5M0l1S/QcUBoaGkpjAz97+/YtGhoaSaqMEEIIIdIXdTW1JC2JkdEm0kwMyU9CCCFE1pGa+SkjSXQDVGxsrMr14eHhaGtrJ7lCQgghhMiaMtpEmokh+UkIIYQQWd0PD8FbuHAhEDeZ1urVq8mWLZtiW3R0NGfOnJE5DIQQQohMJjXnMMhoE2n+CMlPQgghRNaTnuaASk9+uAHq86OJY2NjWb58uVJ3cW1tbaytrVm+fHny11AIIYQQaSa99QJPTxNp/mh9QfKTEEIIkZWkt/yUXqjFJtQnPAG1atXir7/+wtTUNKXqJDIB36DItK5CuhQUGpXWVUiXYhL1KZR15DHLXJMwi5Slr5UySWfJ+edJ2r9fVetkqUdGJ/lJ/IgnfqFpXYV0KSIyJq2rkC6Zm0hOUEVDup6opKWR6Nl3sgQ9rZQ5ruQn1RL9FLyTJ0+mRD1EJhMuQUGl3MYSFFTR1JCgoErzlZfTugrp0t89K6Z1FbIUuYOXPCQ/iR/x4aPcwFPFKrt+WlchXdLTlgcYqDL6wMO0rkK6NKOJDPdOTZKfVEt0M2irVq2YOXNmvPWzZs2idevWyVIpIYQQQojMRPKTEEIIIbK6RDdAnTlzhsaNG8db36hRI86cOZMslRJCCCFE+qCulrRFxJH8JIQQQmQdkp9US/QQvODgYJWPC9bS0iIwMDBZKiWEEEKI9EFd+pAnC8lPQgghRNYh+Um1RPeAKlGiBDt27Ii3fvv27RQtWjRZKiWEEEKI9EFNLWmLiCP5SQghhMg6JD+plugeUBMmTOCXX37hyZMn1K5dG4Djx4+zbds2/vzzz2SvoBBCCCHSjtzBSx6Sn4QQQoisQ/KTaolugGrWrBl79+7F2dmZXbt2oaenh4ODA8eOHaNGjRopUUchhBBCpBHJT8lD8pMQQgiRdUh+Ui3RDVAATZo0oUmTJvHW37t3j+LFiye5UkIIIYQQmY3kJyGEEEJkZYmeA+prQUFBrFy5kgoVKlCyZMnkqJMQQggh0gn1JC5CNclPQgghROYl+Um1nz63M2fO0LFjRywsLJgzZw61a9fm0qVLyVk3IYQQQqQxNTW1JC1CmeQnIYQQIvOT/KRaoobg+fj4sH79etasWUNgYCC//fYb4eHh7N27V57gIoQQQmRCmTcCpR7JT0IIIUTWIvlJtR/uAdWsWTPs7e25c+cO8+fP5/Xr1yxatCgl6yaEEEKINKauppakJauT/CSEEEJkPZKfVPvhHlCHDh1i4MCB9OnTh0KFCqVknYQQQgghMgXJT0IIIYQQcX64B9S5c+cICgqibNmyVKxYkcWLF+Pv75+SdRNCCCFEGlNL4pLVSX4SQgghsh7JT6r9cANUpUqVWLVqFd7e3vTq1Yvt27djaWlJTEwMR48eJSgoKCXrKYQQQog0oKaWtCWrk/wkhBBCZD2Sn1RL9FPwDAwM6Nq1K+fOnePu3bsMGzaMGTNmkCtXLv73v/+lRB2FEEIIkUbkKS7JQ/KTEEIIkXVIflIt0Q1Q/2Vvb8+sWbN49eoV27ZtS646CSGEECKdUE/iIuKT/CSEEEJkbpKfVEuWc9PQ0KBFixbs27cvOQ4nhBBCCJHpSX4SQgghRFbyw0/BE0IIIUTWk5m7gQshhBBCpATJT6pJA5QQQgghEiTxSQghhBAicSQ/qSYNUEIIIYRIkNzBE0IIIYRIHMlPqkkDlBBCCCESlJknwhRCCCGESAmSn1STBighhBBCJEju4AkhhBBCJI7kJ9WkYU4IIYQQQgghhBBCpCjpASWEEEKIBMn9OyGEEEKIxJH8pJo0QAkhhBAiQdKDXAghhBAicSQ/qSYNUBlc586d2bBhQ7z1Hh4e2NraJunYp06dolatWrx//x4TE5MkHSsp7ty8xs4t6/Fwf8Bbfz+mzJhP1Rq1FdtjY2PZsGopB/ftJjgoiGIOpRg0cjx5rfIrykwYMYDHHu4EvH+HoaERZcpXonvfweTImQuAly+eMX/WNF48e8LHj8Fkz5GT2vUb07FbbzQ1tVL9nH/WjWtX2bh+DQ8euOHv58ec+YupVbuuYvvbt/4snDeHSxfPExQURJky5Rg5Zjz58lvHO1ZsbCwD+/bkwvmz8Y6T0e3cvpWdO7bx2ssLgIK2hejVpy/VHGsA4O/nh+vcWVy6cIGPIR+xtrahR8/e1K3fIC2rnSR/lM9Dh/J5lda9fB9K9213yG2ozcYOpVXuN+2IB2efvAPALpcBXStZUSinAbGx4O4bzJqLL3n6NgQALQ01BtawoVBOA/KZ6nH5+XumHPZI2RNLBd/7eQG4fesmixbM4+7dO2ioq2NfuAjLVq5BV1c3raqdbNTlHp7IhLJCfnpw9wb//LmJpx4PCXjnz9BJsylfpaZie8D7t2xbs4g71y8T8jGIwsVL07nfCCzy5APAz+c1Azs1V3nsQeNcqFRdORcEBQYwuk973vn7snr3CQyyGabYuSU3P983LFvkyqULZwkLCyNv3nyMnTSNwkWLxys723kKf/+1k4FDR/Fbu46K9YEfApg325nzZ0+hrqZOjdr1GDR8NPr6Bql4JqlrzaoVHD/6L8+ePUVHV5dSpUozeOhwrG0KpHXVUkSdQmY0K5qL00/eseeeb7ztvSrlpUjubKy5/Iq7PsGK9fObF45XdsM1L256BSleV7MxwdHGFFN9LQJCIzn66C1XXwamzImkkUb1avP6tVe89b+3acfYCZPSoEYpS/KTatIAlQk0bNiQdevWKa3LmTNnGtUm+YWFhVKgkD0Nm7Zk8pgh8bbv2LyOPX9uZeSEaVhY5mHdysWMHtybtVv3oq2jA0DJMhVo26k72bPnxN/PlxWL5uI0dhgLV20CQENTi3qNmlHIvgjZshny5LE7ri5TiI2JoVufQal6vkkRGhqKnX1h/teyFSOGDFDaFhsby7BB/dDU1MJ1wVIMDAzYsmk9fXp2Zdeef9DT11cqv3Xzhkw7eV6u3OYMGjKcfPnzExsby/6/9zKofz927N6DrW0hxo0dRVBgIAsWL8PU1JSDB/YzYthgtu7cTZEiRdO6+j/t+dsQRu97qHgdHRsLgF9wBG3W3VAq27hYLn4tZcHVFwEA6GqqM72pPZeeBbD4zHM01NToUCEv05vZ88fGW0THxKKupkZEVAx/3/GhWkGzVDuvlPa9n5fbt27St1d3unbvxehxE9DU0MDd/SHq6pljmsVM+jEgRKbPT+FhoeQrYEfNBv/D1Wmk0rbY2Fhcp4xAQ0OT4ZPnoKdvwMG/tuI8uh+zV+1EV1eP7Dlzs2zbIaX9jh/cwz+7NlOqfJV477fSdRr5bGx55x//D/P0LDDwA326/UGZchWYs2A5JqZmvHr5AkMjo3hlT588htu924obmP81ZcIo3vr7MW/JaqKiInGZMp5Z0yczefrs1DiNNHHt6hV+b9ueYiVKEB0VzaIFrvTu0Y2/9h1A/6tcmdFZmehSJb8JXh/CVG6vUcCU2G/sv/WGNw98vzRKhUbGKP5d1dqEpkVysuOWD54BYeQz1eX3UuaERMTg9iZY1eEypC07dhETHa14/fixB726d6Feg4ZpWKuUI/lJtcyRjrM4HR0dzM3NlRYNDQ1Onz5NhQoV0NHRwcLCgtGjRxMVFaXYLzw8nIEDB5IrVy50dXWpVq0aV69eBeD58+fUqlULAFNTU9TU1OjcuXNanB4VKjvStdcAqtWsE29bbGwsf+3YTPvOPahavRYFbO0YNXE6b/39OH/mhKLcr207ULR4SXJbWFLMoRRtOnblgdsdoqIiAbDMk5eGTVtQsJA9uS0sqeJYizoNmnD39o1475meVXWsTt8Bg6ldp168bZ4vnnP3zm3GjJ9EseIlsLYpwJjxkwkPC+PwoQNKZd0fPmDzhnVMdJqeWlVPVTVr1caxeg3y57fG2tqGAYOGoK+vz53btwC4ffMmbdv/QQkHB/JaWdGzd18MDY144OaWthVPoujYWN6HRiqWwLC4z4OYWJTWvw+NpIqNKWeevCUsKi4gWZnqYaSrxcarr3gVEMaL96FsvvoKM31tcmfTBiA8KoZFZ55z6IEf70Ii0+w8k9v3fl5mz3ShbfsOdOvRE1vbQljbFKBBw8Zoa2unbcWFEN+U2fNTqfJV+b1zH8pXrRVvm4+XJx4P7tJ1wCgK2hfD0sqargNGExEezoWTRwBQ19DAxCyH0nL1wikqVa+Lrp5y48LR/bv4+DGIJr/+kSrnlpy2bFhDrtzmjJ00naLFHbDMk5cKlaqSJ28+pXJ+vm+YP9uZiVNnoampfA//+bMnXL5wjtHjnShW3IGSpcoyeMRYjv97CH+/jNUglxjLVq6hectfsLUthH3hwjhNn4G392se3M/Yeelr2hpqdChryY7bPkoNR5/lMdKhlq0Z2256J3iM0MhogsK/LFExX5qrylkZceF5ADdfB/E2JJKbXkFcfP6BOoUyz808ADMzM3LkzKlYzpw6iZVVPsqVr5DWVROpSBqgMikvLy8aN25M+fLluX37NsuWLWPNmjVMmzZNUWbkyJHs3r2bDRs2cOPGDWxtbWnQoAHv3r3DysqK3bt3A+Du7o63tzcLFixIq9NJkPdrL9699adM+UqKddmyGVKkaAnu37utcp/ADx84fuQgRUuUSnB4nddLT65eOo9D6XIpUu+0EBERAaDoFQagrq6OtrY2t25eV6wLDQ1l3OjhjBo3kRw5Ms+d4IRER0dz6OABQkNDKFkybhhaydKlOXL4EB8CAoiJieHQwQOER4Rn+C/IPMa6bO1UmvXtSzKqbkFyZlPdQGKbUx/bnAYceeCnWPcqIJQPoZE0KJITTXU1tDXUaFgkJy/eheITFJ5ap5Dmvv55efv2LXfv3MYse3Y6tm9DrepV6NrpD25cv5bWVU02akn8T4iMJKvkp8jIuJsE2trKmUBTSwt3t1sq93nq8YAXTx5Rq8H/lNa/evGUv7aupu+IKairZbw/Lc6fOUnhIsUYP2oITes50qVdK/bt+VOpTExMDFMnjqZthy4UKBh/iOa9O7fJZmikNGSvXIXKqKur43bvToqfQ3oRHBQ3pMzI2DiNa5K8fnUw5/6bYB75hcTbpqWhRodyluy684ag8GgVe8dp5ZCbaQ1t/9/efcc1dfVhAH/C3lMRQaaggIrixoG77lHbqm0dqHXvrW3dA9w4qLhR37rqXnWiVlGsioATURFQcaJsBELeP6iRSEBQwg3wfN9P3k9z78nNL5GEh3PPORfjPGzQwFr2/VFTUUFGluz4qQxxFqyNtaFSSn+NZqSn4+iRQ+jW/btSO+OC+Uk+TsErBY4cOQI9PT3p/fbt26NKlSqwsrLC6tWrIRKJ4OTkhGfPnmHKlCmYMWMGUlNTsWbNGvj7+6N9+/YAgPXr1+PUqVPYuHEjJk2aBBOT7F53MzOzfNcweP/+Pd6/f//Jtuwzi4r29s1rAICxianMdiMTU8S9eSOzbb3vchzcswNpaWlwru6KeUtW5zre6EF9EHH/LjLS09Gx6/fwHDRCccUXM1s7e5hXtMDqFcvw24zZ0NbWxp/btuDFi+d4/fpjR8OyxV5wremG5i1yjzgrTSLuh6PPT72Qnv4eOjo6WL7SF5X/W/dj8VIfTJ4wDh6NG0BNTQ1aWlpYvmI1rG1sPnNU5XXvRRKWBDzCk3epMNHRQO96llj6rQuG7AzLdTavnbMZouJScee57FDxSQfvYlb7KvipjiUA4Fl8Gn49fA9Z+Y05LyXy+nn5MArKz3c1xk+ajKpOzjhy8AAGD/TE3oNHYCNnfbWSppTmQiKlzE/p79/LnChSFAsrW5QzM8eOTb74Zcw0aGlp49i+7Yh7/RLv4t7IfczZ4wdhaW2HKtVqSrdlpKdjldfv+OmX0ShnZo6XsbnXd1F2z54+wYG9u9Dz537o238w7t65CZ8lXlBXV0f7Tt0AZI+SUlVVww+95I/winvzGsbGsqNV1NTUoG9giLj/smppl5WVhUULF6CWW204OlYRupwi42apj0pGmlh2Pkru/m+rmyEyLhW3nuc9Ve7Y3VeIeJ2CdHEWnMrr4nvXCtBUU8E/j94CAO69TEJDGyPcjE3Ek/j3sDLSQkMbI6ipiKCnoYqEfDq2SqqAgNNITExEl27fCl2KwjA/yVfyTlNQLi1atEBISIj0tnLlSty9exfu7u4yPcqNGzdGUlISnjx5gocPHyIjIwONGzeW7ldXV0f9+vVx9+7dQj2/l5cXDA0NZW6+PouK7PUVlR4/e8Jvy24sXLEWKiqqWDjnN0gksn85/z5vMfz8d+HX2d64cukf/LXdX5hiFUBdXR1Llq9EdNRjtGjSAI3ru+Hav1fQuImH9Izl+bMBuPrvFUycMk3gahXP1tYOu/cewP927MYPPX/E9F+n4OGDBwAA31UrkJiYgHUb/bF911706dcfkyeMRcT9cIGr/nLXouNx4WEcIt+k4npMPH4/Eg49DVV4OMh23mqoitDC0RQn7r7MtX18C3vcjk3E2H23MX7/HTyOS8XcjlWhoVr6f8Pm9fOSlZXdefd9j57o9u13cHZ2waSpv8LWzg4H9u0VuOqioQLRV92IlJUy5qfNa5YV2evLj5qaGsbNWITnT6Mw6PtW6NelKW6HXkOteo3kjkZIf5+GS2dPoPkno592bvaFpbUtmrbqUCx1K0JWVhaqOLlgyIixqOLkjK7de6BLt+9xYO9uAMC9u7fx185t+G3W/FI7UqMoLJg3Gw8jIrBoyXKhSykyRlpq6F69ArZdj5WZMvdBNXM9OJbTxf6bL/I9zsn7bxAZl4qn8e9x5kEcAh7EoYXDxw7Lk+FvcO9FEsZ52GJp56oYWN8SV2PiAQC5J/yVDvv37kXjJh4wM6sgdCkKw/wkH0dAlQK6urpffcWWrzFt2jSMHz9eZtvL5OJ5bmPTcgCAt3FvYJpjuti7uDeoXKWqTFtDI2MYGhmjkrUtrG3t8GPXb3D3Vhhcanw8k2dWwRwAYGNXGVlZWVjuPQff/9gPqqqqxfBqFM/ZpTp2/HUAiYmJyMzIgLGJCfr+1AMu1bKHjF/9NwhPYqLRvLHsVLPJ40fDrXYdrNu0TYiyFUJdQ0M6osmlWnXcvnUTf/5vK/oP+AU7t/8Pew8egYODIwCgqpMTgq9fw84df2L6zDlCll1kktPFeBKfBgtD2au0Na1sCk01FZwOlz1j26JKOVTQ18TYvbeli2x6n3qAvQPrwN3OGOcfxBVT5cLI6+dlwC+DAAD2lSvLtLezr4znsc+KvU5F4N9bVFopY366E1t8U5rtHZ3hvWY7UpKTkJmRAQMjY/w+2hP2VZxztb1yIQDv36fBo3VHme23Q64i+vFDXGmfvRSC5L/fEIN/aINuP/bHD32HKP6FfCXTcuVhayf7HW5jZ49zAacAAGE3ruNtXBy+6/Txqn9isRirfRZj945t2HP4FExMy+HtW9nfg5mZmUhMiIfJf1m1NFswbw7+OX8Om7b8DxXMzYUup8hYGWlBX0sNE5vZSrepqohgb6qNJnbGCHz8Dqa66vDqIDviq399Szx6k4rVgdFyjxv1NhVtq5aDqooI4iwJMrIk2BHyHLtCn0NfUw0JaZloZGuEtAwxkkvh6Kdnz57iStAlLFuxSuhSFIr5ST52QJVSzs7O2Lt3LyQSifRsTWBgIPT19VGpUiWYmppCQ0MDgYGBsPnvj6qMjAxcvXoVY8eOBQDpArpicf5ffJqamrmm28VnFk+AqmhhCRPTcrhx7QocqmRf4jQ5OQl379xE5+498nxc1n9nMdIz0vNpk4XMzExIJFkASkcH1Af6+tmXRo6Oeoy7d25h2MjRAADPgYPQrfv3Mm17ftcF4ydNhUezlsVeZ3HKyspCRno60tJSASDXOhYqKqqQlKK5ZlpqKrAw0MKZZNmOprbO5RH0+B3i0zJltmuqqSBLIpG5wsuH+ypl8Dfsh58XS8tKKG9mhseRkTL7ox4/RpOmHgJVR0RfSuj8pBFX/Jdd19HNnoYY+zQajyLuoke/obnanD1xEHUaesDAyFhm+7jpi5Ce/vGqYA/D72DtsrmYuXQdKlhUUmzhRaRGTTdER8l+h8dEPYZ5RQsAQNsOXVC3vrvM/vGjBqNth87o2Dl7+lB115pISkzAvbu34eRcDQAQfO0KsrKyUK26azG8CmFIJBJ4zZ+LgDOnsNF/GypVshK6pCJ1/3UKvAMeyWz7ya0iXiSl40zEGySni3Hp8VuZ/VNb2uPArZf5TsmzNNRCcroY4k9yZZYE0vzlZmmA2y+S8r2yXkl1cP8+mJiYoqlHc6FLIQGwA6qUGj58OHx8fDBq1CiMHDkS4eHhmDlzJsaPHw8VFRXo6upi2LBh0rUKrK2tsWjRIqSkpGDgwIEAABsbG4hEIhw5cgQdOnSAtra2zFoJxSU1JQVPn3w8gxD77Cke3L8HfQNDVDCviO49e+NP/3WwtLKGeUVL+K/3hWm58mjskd1hcvd2GMLv3Eb1mm7Q1zfAs6cx8F/nCwtLK7hUzx79dObEUaiqqsHOwRHq6hq4f/c2Nq5Zieat2+a5ULkySklJRkz0x/fq2dMnCL93FwaGhqhY0QKnTh6HsbExzCta4EHEfSxZOB/NW7SCe6MmAIBy5crLXXjcvKIFLCuVjCBZECuWL0WTph4wr1gRKcnJOHb0CK5d/Rdr1m2ErZ09rK1tMHf2DIyfOAVGRkYICDiNoMuBWPXHWqFL/2KDGlkj6PFbvEx8D1NdDfSpVwliiQTnIj6u9WFhoIkaFvqYfiT3VMMbMfEY5G6NkR62OHjzOVQgQo/aFhBnSRD69OMfTNbG2lBTEUFfUw06GqqwN82+UtKjN7kX7iwp8vt5EYlE8Ow/EGt8V6FqVSdUdXLGoYP78TjyEZYuXyl06UWiDPYvUhlWmvJTWmoKnj+Lkd5/9fwZHj8Mh56+IcqZmSPon9MwMDSGqVkFxEQ+xBa/pajn3gyudRrKHOf50xjcu3kDk+f65HqOTzuZEuOzpw1ZWttBV0+/6F+UAvT8qS+GDuiNrZvWoWWbtrhz+yYO7d+Dyb/NAgAYGhnB8JO1vNTU1GBqWg7WtnYAAFu7ymjQqAkWzZuJidNmIDMzE8sWzUerb9qjXHmzYn5FxWfB3Nn4+9gR+Kz6A7o6unj9KntNUT19fWhpaX3m0crvfWYWnifKnqxOF0uQki6Wbpe38PjblAzp1YCrVdCDvqYqHr9NRWaWBFXL66K1oynO5hg5Xl5XHdbG2oh6mwoddVU0r2yCigaa2B6c91X1SqqsrCwc3L8Pnbt2y3U1ydKG+Um+0v2vXoZZWlri2LFjmDRpEmrWrAkTExMMHDgQv//+u7SNt7c3srKy0KdPHyQmJqJu3bo4ceIEjI2NpceYPXs2pk6div79+6Nv377w9/cv9tcSfu82Jo4YKL3vt3IxAOCbDl0wefo89OzdH2mpqVjuPQdJSYmo7uoG7+VrpIt4ampq4eL509iy4Q+kpaXC1LQc6jZsjOmei6VnKVVUVbHrf5vwJCYKEokEFcwt0PX7Xvi+V59if71f487tWxgysJ/0/rLF3gCATl26YfY8b7x+9RLLF3vjzZs3KFe+PDp27opBQ4YJVa5g4uLe4PdpU/Dq1Uvo6eujSpWqWLNuI9wbZa/psdpvHVYsW4rRI4ciJSUF1lbWmLvAG009mglc+Zcrp6uBaW0coK+lhvjUzOy1nPbelhnp1Na5PF4npeP6f+sO5BTzLg0zj4Xj53qV4NO9GiQS4MHrZPx2JFwasgBgbseqMDf4eEZ/Tc8a2cf+44oCX51ife7npXdfT7x/n47Fi7wQHx+PqlWd4Ld+E6ysrT9z5JKhNF+JhehTpSk/Pbp/F3MnfxzNtG1t9to8Hm06YtjEWXgX9xrb1i5H/Ls4GJuUQ9PWHdD9p19yHefciUMwKWeWq2OqtHCuVgMLlqzA2tU+8N+wBhUtKmH0hCn4pn2nQh1n5tyFWLZoPsYMHwgVkQqatWyDsZNK95qau3ftAAAM9JTNy3PmeaHrt92FKEnpiCUSNLEzRrcaZhBBhNfJ6Th46yUuR72TtlERidCisgnM9DQglkjw4HUKVlyIQlxqRt4HLqGCLl9CbOwzdOv+ndClKBzzk3wiyaerMBMVgZi4snNZ9sIw1pV/2fuyTq0MLGL9JbquK7mdNop0cHADoUtQSloKOqV05t7XXcGplVPpX/+EqKgEPy7+KXglgdV/o2lJlr42xxLIM/XoPaFLUEreHZ2ELkEpMT8VL35rERERUZ54Bo+IiIiocJif5GMHFBEREeWJaxgQERERFQ7zk3wqn29CRERERERERET05TgCioiIiPLEIeREREREhcP8JB87oIiIiChPKsxPRERERIXC/CQfO6CIiIgoTzyDR0RERFQ4zE/ysQOKiIiI8sRFNImIiIgKh/lJPi5CTkRERERERERECsURUERERJQnnsAjIiIiKhzmJ/nYAUVERER5UuEYciIiIqJCYX6Sjx1QRERElCfGJyIiIqLCYX6Sjx1QRERElDcmKCIiIqLCYX6Si4uQExERERERERGRQnEEFBEREeVJxFN4RERERIXC/CQfO6CIiIgoT1xDk4iIiKhwmJ/kYwcUERER5Yn5iYiIiKhwmJ/k4xpQRERElDfRV96IiIiIyppizE9eXl6oV68e9PX1YWZmhm7duiE8PFymTVpaGkaMGAFTU1Po6enhu+++w4sXL2TaREdHo2PHjtDR0YGZmRkmTZqEzMxMmTbnzp1D7dq1oampCQcHB/j7+xeqVnZAERERUZ5EX/k/IiIiorKmOPPT+fPnMWLECAQFBeHUqVPIyMjAN998g+TkZGmbcePG4fDhw/jrr79w/vx5PHv2DN27d5fuF4vF6NixI9LT03Hp0iVs2bIF/v7+mDFjhrRNZGQkOnbsiBYtWiAkJARjx47FL7/8ghMnThT8fZFIJJJCvTqiAoiJey90CUrJWFdD6BKUkpoq/0iVp+u6K0KXoJQODm4gdAlKSUtBk+qvRSZ81ePr2hkUUSVEpV/w46/7vJVWVqY6QpeglPS1uZqKPFOP3hO6BKXk3dFJ6BKUUmnMT69evYKZmRnOnz8PDw8PxMfHo3z58ti+fTu+//57AMC9e/fg7OyMy5cvo2HDhvj777/RqVMnPHv2DBUqVAAA+Pn5YcqUKXj16hU0NDQwZcoUHD16FLdu3ZI+V69evfDu3TscP368QLVxBBQRERHlSST6ulthlKQh5ERERER5+dr89P79eyQkJMjc3r8v2CCP+Ph4AICJiQkA4Pr168jIyEDr1q2lbZycnGBtbY3Lly8DAC5fvowaNWpIO58AoG3btkhISMDt27elbXIe40ObD8coCHZAERERUZ6KcwmokjSEnIiIiCgvX5ufvLy8YGhoKHPz8vL67PNmZWVh7NixaNy4MapXrw4AeP78OTQ0NGBkZCTTtkKFCnj+/Lm0Tc7Opw/7P+zLr01CQgJSU1ML9L5w3CYRERHl7StnyL5//z7XGTtNTU1oamrmavvp8G1/f3+YmZnh+vXr0iHkGzduxPbt29GyZUsAwObNm+Hs7IygoCA0bNgQJ0+exJ07d3D69GlUqFABtWrVwty5czFlyhTMmjULGhoa8PPzg52dHZYuXQoAcHZ2xsWLF7F8+XK0bdv2614wERER0Vfmp2nTpmH8+PEy2+Rlp0+NGDECt27dwsWLF7+uAAXhCCgiIiLK09cuovmlZ/AA5R5CTkRERJSXr81PmpqaMDAwkLl9rgNq5MiROHLkCM6ePYtKlSpJt5ubmyM9PR3v3r2Taf/ixQuYm5tL23y6pMGH+59rY2BgAG1t7QK9L+yAIiIiIoWZNm0a4uPjZW7Tpk377OOUfQg5ERERkTKQSCQYOXIk9u/fj4CAANjZ2cnsr1OnDtTV1XHmzBnptvDwcERHR8Pd3R0A4O7ujps3b+Lly5fSNqdOnYKBgQFcXFykbXIe40ObD8coCE7BIyIiojwVdiHxT+U13e5zlH0IOREREVFevjY/FcaIESOwfft2HDx4EPr6+tITboaGhtDW1oahoSEGDhyI8ePHw8TEBAYGBhg1ahTc3d3RsGFDAMA333wDFxcX9OnTB4sWLcLz58/x+++/Y8SIEdIcN3ToUKxevRqTJ0/GgAEDEBAQgN27d+Po0aMFrpUjoIiIiChPxbkI+QclYQg5ERERUV6KMz+tWbMG8fHxaN68OSpWrCi97dq1S9pm+fLl6NSpE7777jt4eHjA3Nwc+/btk+5XVVXFkSNHoKqqCnd3d/Tu3Rt9+/bFnDlzpG3s7Oxw9OhRnDp1CjVr1sTSpUuxYcOGQq2fKZJIJJJCvj6iz0rNELoC5STO4sdNHhV2hculUpynTkqQ208ShC5BKdWxNVDIcUNjEr/q8TWt9AvcViKRYNSoUdi/fz/OnTsHR0dHmf3x8fEoX748duzYge+++w5A9hByJycnXL58GQ0bNsTff/+NTp06ITY2FmZmZgCAdevWYdKkSXj58iU0NTUxZcoUHDt2DDdv3pQe+6effkJcXFyuhdCJilPi+yyhS1BKGZnMT/LoaKoKXYJS4l+38j18mSR0CUqpuqWeQo5bnPmpJOEUPCIiIsqT6Gsv41IIJWkIOREREVFeijM/lSQcd0BERERKoSQNISciIiKiwuEUPFIITsGTj1Pw5OMUPPk4BU8+TsGTT1FT8G4++boh+zUqKWZoO1FpxCl48nEKnnycgicf/7qVj1Pw5FPUFDzmJ/k4BY+IiIjyxG5QIiIiosJhfpKPHVBERESUNyYoIiIiosJhfpKLHVBERESUJy6iSURERFQ4zE/ysQOKiIiI8sSlyIiIiIgKh/lJPi79S0RERERERERECsURUERERJQnnsAjIiIiKhzmJ/nYAUVERER5Y4IiIiIiKhzmJ7nYAUVERER54iKaRERERIXD/CQfO6CIiIgoT1xEk4iIiKhwmJ/k4yLkRERERERERESkUBwBRURERHniCTwiIiKiwmF+ko8dUERERJQ3JigiIiKiwmF+kosdUERERJQnLqJJREREVDjMT/KxA4qIiIjyxEU0iYiIiAqH+Uk+LkJOREREREREREQKxRFQRERElCeewCMiIiIqHOYn+dgBRURERHljgiIiIiIqHOYnudgBRURERHniIppEREREhcP8JB87oIiIiChPXESTiIiIqHCYn+RjBxQRERHlifmJiIiIqHCYn+TjVfCIiIiIiIiIiEihSnwHlK2tLXx8fPJtIxKJcODAAQDA48ePIRKJEBISovDaiIiISjzRV95IKTE/ERERKRDzk1yCTsHz9PTEli1bAADq6uqwtrZG37598euvv0JNTTGlWVlZITY2FuXKlSv0Y2fNmoXZs2fn20YikXxpaUrJ398fY8eOxbt374QuJV8vXrzAimWLEXjxAtLSUmFlbYPZcxegWvUaAICUlGSsWL4UZwNOI/7dO1haVsKPP/fBDz1/FLjyorNpw1qcPXMKjyMfQVNTC6613DB67ATY2tlL2+zbswvHjx3Bvbt3kJycjHMX/4W+gYHMcaIeR2LFssUICQlGZkYGHKpUxbARo1GvfsPifkkKk5ychD9WrUTAmdN4G/cGVZ2cMXnqb6hWI/vnxa26k9zHjR0/Cf0GDCzOUgX34sUL+CxbjMALHz9bc+Z9/GyVdAd3bsbVwLN4FhMFDQ1NOLq44seBI2FhZSttc+bYPlw6ewKPH4QjNSUZ6/cGQFdPX+Y4SQnx8P9jMW5cuQiRSIT6TVqi77AJ0NLWyfWcz5/G4NcRvaGiooIN+84q+iV+NS6iqXyYn5SfMuanzRvW5coJo8ZOgK2dnbTN4AF9EXztqszjuv/QE79OnyW9/zz2Gbzmzca1q/9CR1sHnbp0w4gx4xT2s1fctm5ejzWrlqPHj30wbtI0AMCTmGis8lmMsBvBSM9IR8NGTTBh8m8wMc3+PMQ+e4pN69fg+tUrePPmNcqXN0Pb9p3g+csQqKtrCPlyiszG9Wtx5tRJREY+gqaWFmrVcsPY8RNlcubrV6+wbOkiBF26hOSUZNja2mHQ4KFo/U1bAStXvDW+q7B2zWqZbbZ2djhw+DgAYO7sGbhy+RJevXoJHR0d1KzlhjHjJsLOvrIQ5RaJ26HBOLhrKx5F3MXbN68xec4SNGjSQqbNk6hIbFu3EnfCrkMsFqOSjT0mzVqE8hUq4uXzZxj2U2e5x54wwxuNmrcBALx6EYt1Pl64FXINWto6aP5NJ/QeNBKqqsr/fcP8JJ/g/3Lt2rXD5s2b8f79exw7dgwjRoyAuro6pk2bppDnU1VVhbm5+Rc9duLEiRg6dKj0fr169TB48GAMGjRIbvv09HRoaJTcXzoZGRlCl1AgCfHx8OzzI+rVb4DVfuthYmyMqKgoGBgYStssWeSNq1eCMN9rMSwsLXH5UiC85s1GeTMzNG/RSsDqi07wtav4oddPqFatBsRiMVavXI4RQ3/Bnv1HoK2T/UdwWmoa3Bs3hXvjpli9Ypnc44wdNRRW1rZYu2ELNDU1sf1/WzF25DAcPHYS5cqVL86XpDBzZkzHgwcRmOe1EOXNzHDs8CEMHdQfew8ehVmFCjh17oJM+8AL/2D2jN/Rqs03AlUsjIT4eHj2/hF16zeAr996GJsYI/qTz1ZJdzcsGG06/4DKVVwgFouxy/8PeP86CovW74aWljYAID0tDTXruqNmXXfs3OQr9zi+C6fjXdxrTPNajczMTKxdOgcbfBZg5LR5Mu0yMzOx2vs3VK1eCxF3whT++ooCF9FUTsxPyktZ89OHnOBSrTrEYjF8Vy7HyKED8VeOnAAA3373A4aMGCW9/+G7EADEYjHGjBgK03LlsGnrdrx+9Qozf58KNTU1jBgzrlhfjyLcuX0TB/buhoNjVem21NQUjB0xCA6OVbFq7WYAwPo1KzFx7Ahs2LIDKioqeBz5CJKsLEz5bRYqWVnj0cMIeM2didS0VIweN1mol1Okrl39Fz1//BnVatSAOFOMVSuWYeiggdh36Ch0/vv5+e3XKUhMSMCK1WtgbGyMY0cPY9KEsdi+ey+cnV0EfgWKVdnBEWs3bJbeV1VVlf63s0s1dOjYGeYVKyIhPh5+f6zCsMEDcfTEGZl2Jcn7tFTYVq6CVu27YNHMSbn2P38ag9/GDESr9l3R03MIdHR0EfP4ETQ0NAEApuUrYMOeEzKPOXVkHw7u2ga3Bo0BZH/fLPh1DIxMymHBqs14++Y1VnnPgJqaGn7+ZaTiX+RXYn6ST/ApeJqamjA3N4eNjQ2GDRuG1q1b49ChQwCA5s2bY+zYsTLtu3XrBk9PT5ltiYmJ+PHHH6GrqwtLS0v4+sr/AwGQP4T89u3b6NSpEwwMDKCvr4+mTZvi4cOHuR6rp6cHc3Nz6U1VVRX6+vrS+7169cLIkSMxduxYlCtXDm3bZvf2L1u2DDVq1ICuri6srKwwfPhwJCUlSY/r7+8PIyMjnDhxAs7OztDT00O7du0QGxsrbXPu3DnUr18furq6MDIyQuPGjREVFQUg+8xirVq1sHbtWlhZWUFHRwc9evRAfHy89PFZWVmYM2cOKlWqBE1NTdSqVQvHjx/P9b7s2rULzZo1g5aWFv7880/0798f8fHxEIlEEIlEmDVrVp7vrVA2b1oPc3NzzJnnhRo1XGFZyQqNGjeBlbW1tE1oyA107toN9eo3gKVlJXz/Q09UqeqEWzdLxh+ABbHabwO6dO2Oyg6OqFLVCbPneuF57DPcvXNb2uanPv3Qf+Bg1HCtKfcYb9++RXRUFPoPGATHKlVhbWOLUWPHIy0tFQ8fRBTXS1GotLQ0nDl9EmPHT0SduvVgbW2DoSNGwcraGn/t2gEAKFeuvMzt3NkA1KvfAJWsrASuvnht2rgeFczNMXe+F2q4uqKSnM9WSTd1wSo0+6YzKtlWhk3lKhg6YSZev3yOyIi70jbtu/+ELj094eAkf9TX0+hIhF67jEHjfoeDU3U4Va8Fz+ETcfn8Sbx980qm7V/+a2BhZYuGHq0V+rqKEkeQKyfmJ+anwlrltx6du34rzQmz5nrheWysTE4AAC0tLZnfgXp6etJ9QZcCEfnoIeZ6LUJVJ2c0buqBoSNGY/eu7cjISC/ul1SkUlKSMeu3yZg6fbbM6PCwkBuIffYU02cvgINjFTg4VsH02V64d+cWrl0NAgC4N26K32cvQAP3xrCsZIWmzVripz6eOB9wWqiXU+TWrNuIrt92h4ODI6o6OWHOfG/EfpIzQ2/cwI8/987ODFZWGDx0OPT1DXD39u18jlw6qKqqynxujI1NpPu+/6En6tStB0vLSnB2qYYRo8bi+fNYPHv6VMCKv07tBo3x08DhaNC0pdz92zf9gdr1G6PvkDGwd3SCuaUV6jVuBsP/3hdVVVUYm5STuf178RwaNW8D7f9Gj4deC8KTqEiMmTYXdg5VUbtBY/TqPwzHD+5W2o7+nJif5BO8A+pT2traSE8v3C+wxYsXo2bNmrhx4wamTp2KMWPG4NSpUwV67NOnT+Hh4QFNTU0EBATg+vXrGDBgADIzM7+kfGzZsgUaGhoIDAyEn58fAEBFRQUrV67E7du3sWXLFgQEBGDyZNmzISkpKViyZAm2bduGf/75B9HR0Zg4cSKA7DPm3bp1Q7NmzRAWFobLly9j8ODBEOXoVn3w4AF2796Nw4cP4/jx47hx4waGDx8u3b9ixQosXboUS5YsQVhYGNq2bYsuXbogIkK2U+HD+3f37l20aNECPj4+MDAwQGxsLGJjY6U1KZPzZwPgUq06Jo4fjRYe7uj5fTfs3bNbpk3NWm44dzYAL168gEQiwdV/gxD1OBLujZoIVLXiJSUlAgAMDAs+WsXIyAg2tnY4cvggUlNSkJmZib1/7YKJiSmcXaopqtRiJRZnQiwWQ0NTU2a7pqYWbgRfz9X+zevXuPjPeXTr/l1xlag0zp8NQLVq1TFx3Gg0b+qOHt91w96/dn/+gSVYSnL2H7d6+gafaflRxN2b0NHTh32Vj2d3q9euD5FIBQ/u3ZJuux1yFUEXTsNzROk4G07KhfmJ+amw8soJfx87glYe7ujxbWesXrEMaamp0n03w0Lg4FgFpqYfp2K6N2qC5KQkPHzwoHgKV5Al3vPQqEkz1G/QSGZ7eno6RCIR1HOMytPQ1ISKigrCbgTnebzkpKRSNWL4U0mJuX9+arq54cTxvxH/7h2ysrLw97GjeJ/+HnXr1ReqzGITHR2FNi2aoGO7Vpg2ZQJiY5/JbZeakoKDB/bBslIlmFf8slGlyi4rKwvXgy7CwsoacyaPQP/urTF1eF9cuZj3sgMP799F5INwtGrfVbot/E4YrO0cYGRiKt1Wq547UpKTEfM498kOKhkEn4L3gUQiwZkzZ3DixAmMGjXq8w/IoXHjxpg6dSoAoEqVKggMDMTy5cvRpk2bzz7W19cXhoaG2LlzJ9TV1aXH+FKOjo5YtGiRzLacZyFtbW0xb948DB06FH/88Yd0e0ZGBvz8/FC5cvZc4JEjR2LOnDkAgISEBMTHx6NTp07S/c7OzjLPkZaWhq1bt8LS0hIAsGrVKnTs2BFLly6Fubk5lixZgilTpqBXr14AgIULF+Ls2bPw8fGROeM5duxYdO/eXXrf0NAQIpEo32H379+/x/v372W2ZaloQvOTP/AV5cmTGPy1awd69+2PXwYNxa1bN7HIax7U1dXRpeu3AICpv07HnFnT0baVB9TU1CASiTBj1jzUqVuvWGosbllZWViyaAFqutWGg2PBf55FIhHWrNuMCWNHoKl7HaioqMDYxASr1qwvNSFKV1cPrjVrYb3fH7Czt4epaTkcP3YUYaEhckf2HD50ADo6umjZumxNvwOyP1u7d+1An379MXDwUNy+eRMLP3y2un0rdHlFLisrC9v8lqFKtZqwsnUo8OPexb2BoZGxzDZVVTXo6RvgXdwbAEBiwjv4LZmN4VPmQEdXT95hlBaHkCs35qfSlZ/SoV4s+SkrKwtLF3nlygntOnRCxYoWKF/eDBER4Vi1fCmiHkdi8fJVALJPypiYmsocy/S/+29ev1Z43Ypy6sQxhN+7g03bcp9kqe5aE1ra2vBdsRTDRo6FBBL8sXIZxGIxXr9+JedoQEx0FP7a9SdGjc09Nak0yMrKwqKFC1DLrTYcc/z8LF7qg8kTxsGjcQOoqalBS0sLy1eshrWNjYDVKl4NV1fMmecFW1s7vH79Cn5/+GJA35+x58Bh6P73O3/Xzj/hs3QJUlNTYGtnB791m0vN+mCfin8Xh7TUFOzf4Y8f+w9Hn8GjcePfS1g8cxJmL1uLajXr5HrMmWMHUMnGDk7VP87SeBf3Rjpi6gOj/+5/yFfKjPlJPsFHQB05cgR6enrQ0tJC+/bt0bNnz0IPU3Z3d891/+7du3m0lhUSEoKmTZtKw9PXqlMn9wfq9OnTaNWqFSwtLaGvr48+ffrgzZs3SElJkbbR0dGRhiMAqFixIl6+fAkAMDExgaenJ9q2bYvOnTtjxYoVMsPLAcDa2loanoDs9yArKwvh4eFISEjAs2fP0LhxY5nHNG7cONf7VLdu3UK/Zi8vLxgaGsrcFi/0KvRxvlRWlgROztUweux4ODm74PsfeqL7dz2wZ/dOaZsdf27DzbAQrFi9Btt37cWESVPhNX82gi5fKrY6i5P3/Dl4+CACXgvlr/OUF4lEgoUL5sDExBQb/P/Elj93o3mL1hg3ahhevXqpoGqL3zyvRZBAgrYtm6FBbVfs+HMb2rXvCBVR7q/Eg/v3on2nTsXWoapMsrIkcHbJ/mw5O7vg+x490f37Hvgrx2erNNm8ehFioh5i1LT5RX7sDT7z0ahFWzjXqF3kx1Y8DiJXRsxP2Upbflq6yLvQx/kSC//LCQsWLpXZ3v37HnBv3AQOVaqgfcfOmD3fG2fPnMaTmOhiqUsIL57HYvliL8yet0ju73pjYxPMX7gcgRfOoWWTumjj0QBJiYmo6uQCFZXcueHlyxcYN3IwWrZui67dfyiGV1D8FsybjYcREVi0ZLnMdt9VK5CYmIB1G/2xfdde9OnXH5MnjEXE/XCBKi0eTZo2wzdt26NKVSc0atwUq9esQ2JiAk4e/1vapkPHLti5Zz82+v8PNja2mDxxbK4O6NJCkpV9UYl6jZqh8w8/w86hKrr/1B91GjbFiUN7c7V//z4NF84clxn9VDowP8kjeAdUixYtEBISgoiICKSmpmLLli3Q1dUFkD30+tOrohT1fE9tbe3PNyqED7V/8PjxY3Tq1Amurq7Yu3cvrl+/Lj1jlnOo/KcBTiQSybz2zZs34/Lly2jUqBF27dqFKlWqICgoqEhrl1d/QUybNg3x8fEyt0lTFLMIqjzly5eXCZ8AYGdvLx36mpaWhlUrlmPCpGlo1rwlqlR1Qq+feqNtuw7Y6r+x2OosLgsXzMHFf85h7YatqFDIBWOvXgnChX/OYcGiZajlVhvOLtUw7feZ0NTSwpFDBxRTsACsrK2x0f9/uPRvMP4+fRb/2/kXMjMzYVlJdo2n4OvX8DgyEt+W0gD5OeXLl4f9J58t+xyfrdJk8+pFuHHlAn5ftAam5SsU6rFGJqaIf/dWZptYnImkxATpsPHbIddwdM+f6N2+IXq3b4h1y+chJTkJvds3xLkTh4rsdSiCSPR1N1IM5qdspS0/TZg8tchr+9TCBXNx8Z/z8Nuw5bM5oXoNVwBATHR2B5RpuXKIeyM78uDNf/dNv+AKicrg3t3beBv3Bp4/f48m9WqgSb0auHH9Kv7a+T80qZd9YZcG7o2x59AJHDt9EX8HBGLmvIV49eoFLCwryRzr1auXGDnYEzVqumHq7/lf+bGkWjBvDv45fw7rN8v+/MRER2Pn9v9h9rwFaNDQHVWdnDB0+Ei4VKuOnTv+FLDi4mdgYABrG1vp5wYA9PX1YWNjizp162HJ8pWIjHyEgDMFm/Jc0ugbGkFVVRVWNvYy2yvZ2OH1y+e52l8+fwbp79PQ7JtOMtuNTEwR/zZOZtu7/+7nnJanrJif5BN8Cp6uri4cHORPdShfvrzMmSqxWIxbt26hRQvZSzx+GiSCgoJyDbHOi6urK7Zs2YKMjIwiO4uX0/Xr17OHOS9dKj1Lsnv3l62h4ubmBjc3N0ybNg3u7u7Yvn07GjZsCACIjo7Gs2fPYGFhASD7PVBRUUHVqlVhYGAACwsLBAYGolmzZtLjBQYGon79/Odka2hoQCwW59tGUzP3dLvUYlwXrqZbbTx+HCmzLSrqMSpWzD6jmZmZiczMDKioyH6SVVRVkZVVei77LJFIsMhrLs4GnMa6jVthWanS5x/0ibS07HUecr1XIhEkWVlFUqcy0dbRgbaODhLi43Hp0kWMHS+7RseBfXvg7FINVZ2cBKpQWLXcauNx5CefrcePYWFhmccjSh6JRAJ/38W4dukcfl/sBzPzwr82R+caSElKxKOIu7B3zP7dczvkGiSSLDg4VQcAzPbZhKysj9+l1y/9g8N/bcWs5RtgbGpWNC9GQUpxBirRmJ8KriTlp8T3ivtdm50T5uFcwGms3bilQDkhPPweAKBc+eyr4NZwrYVN69ci7s0b6VS8K0GXoKunB/vKBZ+6rEzq1nfH/3YflNk2f9ZvsLG1Q2/PX2SuUmZknD3d+tq/QXgbF4emzT4uwPzy5QuMHOwJJ+dq+H3WfLmjo0oyiUQCr/lzEXDmFDb6b0OlT07aSTPkJ6PJVVRUpSNiyoqUlGQ8iYlBuc7yrx4tkWT/X2HX7Ssp1NXV4VC1Gp7GRMlsfxYThfIVcnd6B/x9EHUbNcu1nEFVF1fs+3MT4t/GSafihV6/Ah1d3VydW8qI+Uk+pf5mbNmyJY4ePYqjR4/i3r17GDZsGN69e5erXWBgIBYtWoT79+/D19cXf/31F8aMGVOg5xg5ciQSEhLQq1cvXLt2DREREdi2bRvCw4tmqKiDgwMyMjKwatUqPHr0CNu2bZMurllQkZGRmDZtGi5fvoyoqCicPHkSERERMiFRS0sL/fr1Q2hoKC5cuIDRo0ejR48e0rUHJk2ahIULF2LXrl0IDw/H1KlTERIS8tn3ydbWFklJSThz5gxev34tM+xdWfTu0w83w0KxYZ0foqOjcOzoYezdsxs9f/wJQPbVd+rUrY/lSxfj6r9X8PRJDA4e2Icjhw6gZauScyWqz/GePwfHjh7GfO8l0NHVxevXr/D69SukpaVJ27x+/Qrh9+5Kz8g8iLiP8Ht3ER//DgBQo6Yb9A0MMPO3qbgffg9RjyPhs3QRnj59iiYezQV4VYpxKfACAi9ewNMnTxB0KRCDBvSDnZ09unT7uH5HUlISTp08gW+/K5ujnwCgd98cn62oKBw7chh7cny2SoPNqxciMOBvjJw6F9raOngX9xrv4l4j/f3Hz827uNd4/DAcL57FAABiIh/g8cNwJCVkXynL0toONeu6Y4PPfDy4dxvht0Ph77sY7s2+gbFpeWkbK1sH6c24XHmIRCJY2ToUasFzooJgfsrG/CRr4fw5+PvoYczzXiw3JzyJicaGtX/g7p3bePb0Kc6fDcDM36aidp26cKxSFQDQsFFj2NlXxozfpuB++D1cDryINatWoEfPn6ChUTLXs9HV1UVlB0eZm5a2NgwMjVDZwREAcOTgPtwKC8WTmGgcP3oIv00Zh14/94WNrR2A7M6nEYP6oYJ5RYwcNwnv3sbhzetXeJPHGlEl0YK5s3HsyCF4L1oKXR1dvH71Cq9fffz5sbWzh7W1DebOnoGbYWGIiY7GFv9NCLociBalKG/Ls2zxQly7+i+ePn2CkBvBGDd6JFRVVdCuQyc8iYnBxvVrcef2LcTGPkPIjWBMGj8amppaaNq02ecPrqRSU1MQ+SAckQ+yv/Nfxj5D5INwvHqRffKja88+uHTuJE4d2YfYpzE4tn8Xrl2+gHZdZXN17NMY3AkLRusO3XI9R826DVHJxg4rvKbj8cP7uHH1EnZs+gPtuvaQuSgAlSyCj4DKz4ABAxAaGoq+fftCTU0N48aNy3X2DgAmTJiAa9euYfbs2TAwMMCyZcukl/D9HFNTUwQEBGDSpElo1qwZVFVVUatWrVzz/b9UzZo1sWzZMixcuBDTpk2Dh4cHvLy80Ldv3wIfQ0dHB/fu3cOWLVvw5s0bVKxYESNGjMCQIUOkbRwcHNC9e3d06NABcXFx6NSpk8winaNHj84e2j1hAl6+fAkXFxccOnQIjo6O+T53o0aNMHToUPTs2RNv3rzBzJkzleJSwjlVr+GKZT6rsXLFMqzz84WlZSVMmvIrOnbqIm2zcMkyrPRZhl+nTkRCfDwqWlhg5Ohx+KHnjwJWXrT27N4BABg8QPZna+bcBejSNbtjZe/unVjn93HR1F/695ZpY2xsjNVr1sN3lQ+G/tIPmZmZsK/sgGUrfFGlaukZBZSUmIRVPsvw4sVzGBoaoVWbNhgxepzMWfwTfx8FJBK069BRwEqFVb2GK5atWI2VPsuwdo0vLCtVwuRPPlsl3ekj2WsRzJ00VGb7kAkz0Oybztltju7Dvv+tl+6bM3FwrjYjpsyFv+9iLJg6HCKRCPWbtES/4cp31asvUZqHgZdWzE/ZmJ9kfVgbc8iAfjLbZ85dgM5dv4Waujr+DbqMHf/bitTUVFQwN0fL1m0wcPAwaVtVVVX4rF4Dr3mz0b/Pj9DW1kanzt0wZEThFsAvaaKjHmPN6uX/ZUhLeA4cgl4/f3wfrwZdwpOYaDyJiUbXdrKftcvBd4q7XIXYvSs7Zw707COzfc48L3T9tjvU1dWx2m8dVixbitEjhyIlJQXWVtaYu8AbTT1KbkdLQbx48RzTJo/Hu3fvYGxiAje3Otj6526YmJggMzMDwcHX8Oe2LUhISICpqSlq162LLf/bkWtB/5LkYfgdzBz/8bvUf032urPN23bCqCmz0aBpSwwe9yv2bd+MTauXwMLKBpNmL4JzDTeZ4wT8fRCm5c1Qs27DXM+hqqqKafNXYJ2PF6aN9ISWljaaf9MJvfoPzdVWGTE/ySeSfLpIAJU4s2bNwoEDBxASEiJ0KVLFOQWvJBGXsSHIBVXKRqkXGRX+5pLr9pMEoUtQSnVsFTOS6nn8132hmxsW/fQsoqKgjPlJkVPwSrKMTOYneXQ0VT/fqAziX7fyPXyZJHQJSqm6pWKuTsz8JJ9Sj4AiIiIigbEflIiIiKhwmJ/kYgcUERER5Yn5iYiIiKhwmJ/k4xQ8UghOwZOPU/Dk4xQ8+TgFTz5OwZNPUVPwXiZ+3Re6mX7pHEJOpAicgicfp+DJxyl48vGvW/k4BU8+RU3BY36Sj3/2ERERERERERGRQnEKHhEREeVJxEHkRERERIXC/CQfO6CIiIgob8xPRERERIXD/CQXO6CIiIgoT8xPRERERIXD/CQfO6CIiIgoT1wLn4iIiKhwmJ/k4yLkRERERERERESkUBwBRURERHniIppEREREhcP8JB87oIiIiChPHEJOREREVDjMT/JxCh4RERERERERESkUR0ARERFRnngGj4iIiKhwmJ/k4wgoIiIiIiIiIiJSKI6AIiIiojxxEU0iIiKiwmF+ko8dUERERJQnDiEnIiIiKhzmJ/nYAUVERER5Yn4iIiIiKhzmJ/nYAUVERER5Y4IiIiIiKhzmJ7nYAUVERER54hoGRERERIXD/CQfr4JHREREREREREQKxRFQRERElCcuoklERERUOMxP8rEDioiIiPLE/ERERERUOMxP8rEDioiIiPLGBEVERERUOMxPcrEDioiIiPLERTSJiIiICof5ST4uQk5ERERERERERArFEVBERESUJy6iSURERFQ4zE/yiSQSiUToIogU5f379/Dy8sK0adOgqakpdDlKg++LfHxf5OP7Ih/fFyIqrfj9Jh/fF/n4vsjH90U+vi9lGzugqFRLSEiAoaEh4uPjYWBgIHQ5SoPvi3x8X+Tj+yIf3xciKq34/SYf3xf5+L7Ix/dFPr4vZRvXgCIiIiIiIiIiIoViBxQRERERERERESkUO6CIiIiIiIiIiEih2AFFpZqmpiZmzpzJBe4+wfdFPr4v8vF9kY/vCxGVVvx+k4/vi3x8X+Tj+yIf35eyjYuQExERERERERGRQnEEFBERERERERERKRQ7oIiIiIiIiIiISKHYAUVERERERERERArFDigiIiIiIiIiIlIodkARlXIZGRkYMGAAIiMjhS5FqWRkZKBVq1aIiIgQuhQqIebMmYOUlJRc21NTUzFnzhwBKiIiIkVhfpKP+YkKi/mJcuJV8IjKAENDQ4SEhMDOzk7oUpRK+fLlcenSJTg6OgpdCpUAqqqqiI2NhZmZmcz2N2/ewMzMDGKxWKDKiIhIEZif5GN+osJgfqKcOAKKqAzo1q0bDhw4IHQZSqd3797YuHGj0GVQCSGRSCASiXJtDw0NhYmJiQAVERGRIjE/ycf8RIXB/EQ5qQldAJGiPHz4EJs3b8bDhw+xYsUKmJmZ4e+//4a1tTWqVasmdHnFytHREXPmzEFgYCDq1KkDXV1dmf2jR48WqDJhZWZmYtOmTTh9+rTc92XZsmUCVaYcBgwYgBUrVkBfX19me3JyMkaNGoVNmzYJVFnxMjY2hkgkgkgkQpUqVWRClFgsRlJSEoYOHSpghURERYf56SPmJ/mYn/LH/JSN+Ynk4RQ8KpXOnz+P9u3bo3Hjxvjnn39w9+5d2Nvbw9vbG9euXcOePXuELrFY5Td0XCQS4dGjR8VYjfJo0aJFnvtEIhECAgKKsRrlk9eQ6devX8Pc3ByZmZkCVVa8tmzZAolEggEDBsDHxweGhobSfRoaGrC1tYW7u7uAFRIRFQ3mJ1nMT/IxP+WP+Skb8xPJwxFQVCpNnToV8+bNw/jx42XOPrRs2RKrV68WsDJhcAFN+c6ePSt0CUopISEBEokEEokEiYmJ0NLSku4Ti8U4duxYrlBVmvXr1w+ZmZkQiURo2bIlrKyshC6JiEghmJ9kMT/Jx/wkH/OTLOYnkocdUFQq3bx5E9u3b8+13czMDK9fvxagIuWQnp6OyMhIVK5cGWpq/Ph/8ODBAzx8+BAeHh7Q1tbOc656WWFkZCQzZPpTIpEIs2fPFqAy4aipqWHYsGG4e/eu0KUQESkM85N8zE/yMT/JYn7KjfmJPsVvUCqVjIyMEBsbm2vo9I0bN2BpaSlQVcJJSUnBqFGjsGXLFgDA/fv3YW9vj1GjRsHS0hJTp04VuEJhvHnzBj169MDZs2chEokQEREBe3t7DBw4EMbGxli6dKnQJQri7NmzkEgkaNmyJfbu3SuzQKSGhgZsbGxgYWEhYIXCqF+/Pm7cuAEbGxuhSyEiUgjmJ1nMT/IxP8nH/CQf8xPlxA4oKpV69eqFKVOm4K+//oJIJEJWVhYCAwMxceJE9O3bV+jyit20adMQGhqKc+fOoV27dtLtrVu3xqxZs8psgBo3bhzU1dURHR0NZ2dn6faePXti/PjxZTZANWvWDED21AMrKyuoqPCCqQAwfPhwTJgwAU+ePJG76Kqrq6tAlRERFQ3mJ1nMT/IxP8nH/CQf8xPlxEXIqVRKT0/HiBEj4O/vD7FYDDU1NYjFYvz000/w9/eHqqqq0CUWKxsbG+zatQsNGzaEvr4+QkNDYW9vjwcPHqB27dpISEgQukRBmJub48SJE6hZs6bM+/Lo0SO4uroiKSlJ6BIF9+7dO2zcuFE6dLpatWoYMGCAzEKSZYW8ICkSiaRTDsRisQBVEREVHeYnWcxP8jE/fR7z00fMT5QTR0BRqaShoYH169dj+vTpuHXrFpKSkuDm5gZHR0ehSxPEq1ev5C56mJycXKbn6icnJ0NHRyfX9ri4OGhqagpQkXK5du0a2rZtC21tbdSvXx9A9qWV58+fj5MnT6J27doCV1i8uBgtEZV2zE+ymJ/kY37KH/OTLOYnyokjoIjKAA8PD/zwww8YNWoU9PX1ERYWBjs7O4waNQoRERE4fvy40CUKokOHDqhTpw7mzp0rfV9sbGzQq1cvZGVllbnLTX+qadOmcHBwwPr166WLrmZmZuKXX37Bo0eP8M8//whcIRERkeIwP8nH/JQ/5ieivLEDikqN8ePHF7jtsmXLFFiJ8rl48SLat2+P3r17w9/fH0OGDMGdO3dw6dIlnD9/HnXq1BG6REHcunULrVq1Qu3atREQEIAuXbrg9u3biIuLQ2BgICpXrix0iYLS1tbGjRs34OTkJLP9zp07qFu3LlJSUgSqTDgPHz6Ej4+PdEi9i4sLxowZU+Z/Voio5GJ+yhvzk3zMT/ljfsqN+Yk+4BQ8KjVu3LhRoHZlcch0kyZNEBISAm9vb9SoUUM6/Pfy5cuoUaOG0OUJpnr16rh//z5Wr14NfX19JCUloXv37hgxYgQqVqwodHmCMzAwQHR0dK4AFRMTA319fYGqEs6JEyfQpUsX1KpVC40bNwYABAYGolq1ajh8+DDatGkjcIVERIXH/JQ35if5mJ/yx/wki/mJcuIIKCIikmv06NHYv38/lixZgkaNGgHIDgyTJk3Cd999Bx8fH2ELLGZubm5o27YtvL29ZbZPnToVJ0+eRHBwsECVERERkbJgfpLF/EQ5sQOKSqX4+HiIxWKYmJjIbI+Li4OamhoMDAwEqkwYrVu3Ru/evdG9e/cy99rzExYWJne7SCSClpYWrK2ty/Rimunp6Zg0aRL8/PyQmZkJAFBXV8ewYcPg7e1d5t4bLS0t3Lx5M9divPfv34erqyvS0tIEqoyIqGgwP8lifpKP+Sl/zE+ymJ8op9zXRCQqBXr16oWdO3fm2r5792706tVLgIqEVa1aNUybNg3m5ub44YcfcPDgQWRkZAhdluBq1aoFNzc3uLm5oVatWtL7tWrVgpOTEwwNDdGvX78y+4tRQ0MDK1aswNu3bxESEoKQkBDExcVh+fLlZS48AUD58uUREhKSa3tISIjcqyQREZU0zE+ymJ/kY37KH/OTLOYnyokdUFQqXblyBS1atMi1vXnz5rhy5YoAFQlrxYoVePr0KQ4cOABdXV307dsXFSpUwODBg3H+/HmhyxPM/v374ejoiHXr1iE0NBShoaFYt24dqlatiu3bt2Pjxo0ICAjA77//LnSpgtLR0YGxsTGMjY3lXna5rBg0aBAGDx6MhQsX4sKFC7hw4QK8vb0xZMgQDBo0SOjyiIi+GvOTLOYn+ZifCob5KRvzE8mQEJVCOjo6krCwsFzbw8LCJNra2gJUpFxSU1Mlu3fvltSsWVOioqIidDmCqVevnuT48eO5th8/flxSr149iUQikezfv19ib29f3KUpBbFYLJk9e7bEwMBAoqKiIlFRUZEYGhpK5syZIxGLxUKXV+yysrIky5Ytk1haWkpEIpFEJBJJLC0tJT4+PpKsrCyhyyMi+mrMT/ljfsrG/JQ/5idZzE+UE9eAolKpRYsWqF69OlatWiWzfcSIEQgLC8OFCxcEqkx4z58/x86dO/G///0PwcHBqF+/PoKCgoQuSxB5XSb33r17cHNzQ2pqKh4/fgwXF5cyecncadOmYePGjZg9e7b0qiUXL17ErFmzMGjQIMyfP1/gCoWTmJgIAGXyajZEVHoxP+WN+ekj5qf8MT/ljfmJ2AFFpVJgYCBat26NevXqoVWrVgCAM2fO4OrVqzh58iSaNm0qcIXFKyEhAXv37sX27dtx7tw52Nvb4+eff8bPP/+MypUrC12eYNzc3FCzZk2sW7cOGhoaAICMjAwMGjQIoaGhuHHjBgIDA9G7d29ERkYKXG3xs7CwgJ+fH7p06SKz/eDBgxg+fDiePn0qUGXCevnyJcLDwwEATk5OKF++vMAVEREVDeYnWcxP8jE/5Y/5ST7mJwIANaELIFKExo0b4/Lly1i8eDF2794NbW1tuLq6YuPGjbmuwFAWVKhQAcbGxujZsye8vLxQt25doUtSCr6+vujSpQsqVaoEV1dXAMDNmzchFotx5MgRAMCjR48wfPhwIcsUTFxcXK6zm0B2aIiLixOgImElJiZi+PDh2LFjB7KysgAAqqqq6NmzJ3x9fWFoaChwhUREX4f5SRbzk3zMT/ljfpLF/EQ5cQQUURlw6tQptGrVCioqvO7ApxITE/Hnn3/i/v37AICqVavip59+4tBgAA0aNECDBg2wcuVKme2jRo3C1atXy9zUg549e+LGjRtYtWoV3N3dAQCXL1/GmDFjUKtWLblXjiIiopKL+SlvzE95Y36SxfxEObEDikqlZs2aYeDAgfjhhx+gra0tdDlEJdL58+fRsWNHWFtbywSGmJgYHDt2rMxNxdDV1cWJEyfQpEkTme0XLlxAu3btkJycLFBlRERFg/mJ6OsxP8lifqKcOAWPSiU3NzdMnDgRo0aNQo8ePTBw4EA0bNhQ6LKKVe3atXHmzBkYGxvDzc0NIpEoz7bBwcHFWJlyiYiIwNmzZ/Hy5UvpsOAPZsyYIVBVwnr06BHs7OzQrFkz3L9/H3/88Qfu3r0LAOjevTuGDx8OCwsLgassfqampnKHiRsaGsLY2FiAioiIihbzE/NTQTE/5cb8JB/zE+XEDigqlXx8fLBkyRIcOnQIW7ZsgYeHBxwcHDBgwAD06dMHFSpUELpEhevatSs0NTUBAN26dRO2GCW1fv16DBs2DOXKlYO5ublMyBSJRGU2QDk6OiI2NhZmZmawsLBAREQE/vjjjzLxucnP77//jvHjx2Pbtm0wNzcHkH1VpEmTJmH69OkCV0dE9PWYn5ifCoL5ST7mJ/mYnygnTsGjMuHly5dYt24d5s+fD7FYjA4dOmD06NFo2bKl0KUpnFgsRmBgIFxdXWFkZCR0OUrFxsYGw4cPx5QpU4QuRamoqKjg+fPnMDMzAwAYGBggJCQE9vb2AlcmLDc3Nzx48ADv37+HtbU1ACA6Ohqampq5Fucty2fFiaj0YH5ifpKH+Uk+5if5mJ8oJ46AolLv33//xebNm7Fz506YmZnB09MTT58+RadOnTB8+HAsWbJE6BIVSlVVFd988w3u3r3LAPWJt2/f4ocffhC6DKXH8xTZeCaciMoS5ifmp7wwPxUM81M25ifKiR1QVCq9fPkS27Ztw+bNmxEREYHOnTtjx44daNu2rXSYsKenJ9q1a1fqAxQAVK9eXTovnT764YcfcPLkSQwdOlToUpSKSCTKteZFfmtglBUzZ84UugQiIoVifpLF/CQf85N8zE/yMT9RTuyAolKpUqVKqFy5MgYMGABPT0+UL18+VxtXV1fUq1dPgOqK37x58zBx4kTMnTsXderUga6ursx+AwMDgSoTloODA6ZPn46goCDUqFED6urqMvtHjx4tUGXCkkgk8PT0lK6BkZaWhqFDh+b6udm3b58Q5QkmJiYGIpEIlSpVApA9OmD79u1wcXHB4MGDBa6OiOjrMT/JYn6Sj/lJPuYn+ZifKCeuAUWl0oULF8rcJU7zo6KiIv3vnGdiJBIJRCIRxGKxEGUJLr8zmiKRCI8ePSrGapRH//79C9Ru8+bNCq5EuTRt2hSDBw9Gnz598Pz5c1SpUgXVq1dHREQERo0aVWYXXSWi0oP5SRbzk3zMT/IxP8nH/EQ5sQOKSqXIyEhkZmbmWtguIiIC6urqsLW1FaYwgZw/fz7f/c2aNSumSohKLmNjYwQFBaFq1apYuXIldu3ahcDAQOk0hLIauImo9GB+ksX8RPT1mJ8oJ07Bo1LJ09MTAwYMyBWgrly5gg0bNuDcuXPCFCYQBqT8paenIzIyEpUrV4aaGr8WSb6MjAzpsPrTp0+jS5cuAAAnJyfExsYKWRoRUZFgfpLF/JQ/5icqCOYnyknl802ISp4bN26gcePGubY3bNgQISEhxV+QwCIiIrBkyRKMHDkSo0aNwvLly3m2AUBKSgoGDhwIHR0dVKtWDdHR0QCAUaNGwdvbW+DqSNlUq1YNfn5+uHDhAk6dOoV27doBAJ49ewZTU1OBqyMi+nrMT7KYn+RjfqLCYH6inNgBRaWSSCRCYmJiru3x8fFlbr6+l5cXXFxcMGXKFOzduxd//fUXJk6cCCcnpzJxBZv8TJs2DaGhoTh37hy0tLSk21u3bo1du3YJWBkpo4ULF2Lt2rVo3rw5fvzxR9SsWRMAcOjQIdSvX1/g6oiIvh7z00fMT3ljfqLCYH6inLgGFJVKnTt3hra2Nnbs2AFVVVUAgFgsRs+ePZGcnIy///5b4AqLx9mzZ9G6dWtMnz4dY8aMgbGxMQAgLi4OPj4+WLBgAQICAuDh4SFwpcKwsbHBrl270LBhQ+jr6yM0NBT29vZ48OABateujYSEBKFLJCUjFouRkJAg/SwBwOPHj6GjowMzMzMBKyMi+nrMT9mYn/LH/ESFxfxEH7ADikqlO3fuwMPDA0ZGRtKruVy4cAEJCQkICAhA9erVBa6wePTs2RNGRkZYu3at3P2DBw9GYmIiduzYUcyVKQcdHR3cunUL9vb2MgEqNDQUHh4eiI+PF7pEIiKiYsP8lI35KX/MT0T0pTgFj0olFxcXhIWFoUePHnj58iUSExPRt29f3Lt3r8yEJwD4999/0adPnzz39+nTB0FBQcVYkXKpW7cujh49Kr3/4RLLGzZsgLu7u1BlkZJ68eIF+vTpAwsLC6ipqUFVVVXmRkRU0jE/ZWN+yh/zExUG8xPlxMsVUKllYWGBBQsWCF2GoF68eJHvJZPt7Ozw/Pnz4itIySxYsADt27fHnTt3kJmZiRUrVuDOnTu4dOnSZy+9TGWPp6cnoqOjMX36dFSsWFEauImIShPmJ+anz2F+osJgfqKcOAWPSo2wsDBUr14dKioqCAsLy7etq6trMVUlLBUVFTx//jzPudUvXryAhYVFmVtYNKeHDx/C29sboaGhSEpKQu3atTFlyhTUqFFD6NJIyejr6+PChQuoVauW0KUQERUZ5qfcmJ8+j/mJCor5iXLiCCgqNWrVqiUNC7Vq1YJIJIK8/lWRSFSmAsOGDRugp6cnd5+8K92UNZUrV8b69euFLoNKACsrK7nfKUREJRnzk3zMT/ljfqKCYn6inDgCikqNqKgoWFtbQyQSISoqKt+2NjY2xVSVsGxtbQs0zDUyMrIYqlE+wcHBUFdXl56tO3jwIDZv3gwXFxfMmjULGhoaAldIyuTkyZNYunQp1q5dm+/UDCKikoT5KTfmp/wxP1FhMD9RTuyAolLpzZs3MDU1BQDExMRg/fr1SE1NRZcuXaRXdSGqV68epk6diu+++w6PHj2Ci4sLunfvjqtXr6Jjx47w8fERukRSIsbGxkhJSUFmZiZ0dHSgrq4usz8uLk6gyoiIigbzExUE8xMVBvMT5cQOKCpVbt68ic6dOyMmJgaOjo7YuXMn2rVrh+TkZKioqCA5ORl79uxBt27dhC6VlIChoSGCg4NRuXJlLFy4EAEBAThx4gQCAwPRq1cvxMTECF0iKZEtW7bku79fv37FVAkRUdFifqLCYH6iwmB+opzYAUWlSvv27aGmpoapU6di27ZtOHLkCNq2bSudoz5q1Chcv369TF86lz4yMDDA9evX4ejoiDZt2qBTp04YM2YMoqOjUbVqVaSmpgpdIhERkcIxP1FhMD8R0ZdiBxSVKuXKlUNAQABcXV2RlJQEAwMDXL16FXXq1AEA3Lt3Dw0bNsS7d++ELZSUQsuWLWFlZYXWrVtj4MCBuHPnDhwcHHD+/Hn069cPjx8/FrpEUlJpaWlIT0+X2WZgYCBQNUREX4f5iQqD+Ym+FPMTqQhdAFFRiouLg7m5OQBAT08Purq6MDY2lu43NjbmlUtIysfHB8HBwRg5ciR+++03ODg4AAD27NmDRo0aCVwdKZvk5GSMHDkSZmZm0u+WnDciopKK+YkKg/mJCoP5iXJSE7oAoqL26VVLCnIVEyqbXF1dcfPmzVzbFy9eDFVVVQEqImU2efJknD17FmvWrEGfPn3g6+uLp0+fYu3atfD29ha6PCKir8L8RAXF/ESFwfxEOXEKHpUqKioqaN++PTQ1NQEAhw8fRsuWLaGrqwsAeP/+PY4fPw6xWCxkmYLq2LEjNmzYgIoVKwpdiuBiYmIgEolQqVIlAMC///6L7du3w8XFBYMHDxa4OlI21tbW2Lp1K5o3bw4DAwMEBwfDwcEB27Ztw44dO3Ds2DGhSyQi+iLMT5/H/PQR8xMVBvMT5cQpeFSq9OvXD2ZmZjA0NIShoSF69+4NCwsL6X0zMzP07dtX6DIF9c8//3BxyP/89NNPOHv2LADg+fPnaNOmDf7991/89ttvmDNnjsDVkbKJi4uDvb09gOz1Cj5cNrhJkyb4559/hCyNiOirMD99HvPTR8xPVBjMT5QTp+BRqbJ582ahS6AS5NatW6hfvz4AYPfu3ahevToCAwNx8uRJDB06FDNmzBC4QlIm9vb2iIyMhLW1NZycnLB7927Ur18fhw8fhpGRkdDlERF9MeYnKgzmJyoM5ifKiSOgiMoYGxsbqKurC12GUsjIyJBONzh9+jS6dOkCAHByckJsbKyQpZES6t+/P0JDQwEAU6dOha+vL7S0tDBu3DhMmjRJ4OqIiEiRmJ8+Yn6iwmB+opy4BhQRlVkNGjRAixYt0LFjR3zzzTcICgpCzZo1ERQUhO+//x5PnjwRukRSYlFRUbh+/TocHBzg6uoqdDlERETFgvmJvgbzU9nGDigiKrPOnTuHb7/9FgkJCejXrx82bdoEAPj1119x79497Nu3T+AKSVlkZGSgXbt28PPzg6Ojo9DlEBERCYb5iQqK+Yk+xQ4oIirTxGIxEhISYGxsLN32+PFj6OjowMzMTMDKSNmUL18ely5dYoAiIqIyj/mJCor5iXJiBxQREVEBjBs3DpqamvD29ha6FCIiIqISgfmJcuJV8IhKOYlEgpiYGJiZmUFLS0vocpTOnj17sHv3bkRHRyM9PV1mX3BwsEBVkTLKzMzEpk2bcPr0adSpUwe6uroy+5ctWyZQZUREVNSYn/LH/EQFxfxEOfEqeESlnEQigYODA2JiYoQuRemsXLkS/fv3R4UKFXDjxg3Ur18fpqamePToEdq3by90eaQkVFVV8fLlS9y6dQu1a9eGvr4+7t+/jxs3bkhvISEhQpdJRERFiPkpb8xPVBDMTyQPp+ARlQHVqlXDxo0b0bBhQ6FLUSpOTk6YOXMmfvzxR+jr6yM0NBT29vaYMWMG4uLisHr1aqFLJCWgoqKC58+fc00LIqIyhvlJPuYnKgjmJ5KHI6CIygBvb29MmjQJt27dEroUpRIdHY1GjRoBALS1tZGYmAgA6NOnD3bs2CFkaURERCQw5if5mJ+I6EtxDSiiMqBv375ISUlBzZo1oaGhAW1tbZn9cXFxAlUmLHNzc8TFxcHGxgbW1tYICgpCzZo1ERkZCQ4OpZw2bNgAPT29fNuMHj26mKohIqLiwPwkH/MTFRTzE32KHVBEZYCPj4/QJSilli1b4tChQ3Bzc0P//v0xbtw47NmzB9euXUP37t2FLo+UiJ+fH1RVVfPcLxKJGKCIiEoZ5if5mJ+ooJif6FNcA4qIyqysrCxkZWVBTS27L37nzp24dOkSHB0dMWTIEGhoaAhcISkDrmFARET0EfMTFQTzE8nDDiiiMiYtLS3X5XINDAwEqkY4QUFBOHz4MNLT09GqVSu0a9dO6JJISamqqiI2NpYBioioDGN+ysb8RAXF/ETysAOKqAxITk7GlClTsHv3brx58ybXfrFYLEBVwtmzZw969uwJbW1tqKurIyEhAQsXLsTEiROFLo2UEM/gERGVTcxPspifqDCYn0geXgWPqAyYPHkyAgICsGbNGmhqamLDhg2YPXs2LCwssHXrVqHLK3ZeXl4YNGgQ4uPj8fbtW8ybNw8LFiwQuixSUjNnzvzsAppERFT6MD/JYn6iwmB+Ink4AoqoDLC2tsbWrVvRvHlzGBgYIDg4GA4ODti2bRt27NiBY8eOCV1isdLT00NISAgcHBwAAOnp6dDV1cXTp095loaIiIgAMD99ivmJiL4WR0ARlQFxcXGwt7cHkL1ewYfLBjdp0gT//POPkKUJIiUlRWbdBg0NDWhpaSEpKUnAqoiIiEiZMD/JYn4ioq+lJnQBRKR49vb2iIyMhLW1NZycnLB7927Ur18fhw8fhpGRkdDlCWLDhg0yw4IzMzPh7++PcuXKSbfxsrBERERlF/NTbsxPRPQ1OAWPqAxYvnw5VFVVMXr0aJw+fRqdO3eGRCJBRkYGli1bhjFjxghdYrGytbWFSCTKt41IJMKjR4+KqSIiIiJSNsxPspifiOhrsQOKqAyKiorC9evX4eDgAFdXV6HLISoRIiMjkZmZCUdHR5ntERERUFdXh62trTCFERFRsWB+Iio85ifKiWtAEZVBNjY26N69O8MTUSF4enri0qVLubZfuXIFnp6exV8QEREVK+YnosJjfqKc2AFFVIoFBATAxcUFCQkJufbFx8ejWrVquHDhggCVEZU8N27cQOPGjXNtb9iwIUJCQoq/ICIiUgjmJ6Kiw/xEObEDiqgU8/HxwaBBg2SuWPKBoaEhhgwZgmXLlglQGVHJIxKJkJiYmGt7fHw8xGKxABUREZEiMD8RFR3mJ8qJHVBEpVhoaCjatWuX5/5vvvkG169fL8aKiEouDw8PeHl5yYQlsVgMLy8vNGnSRMDKiIioKDE/ERUd5ifKSU3oAohIcV68eAF1dfU896upqeHVq1fFWBFRybVw4UJ4eHigatWqaNq0KQDgwoULSEhIQEBAgMDVERFRUWF+Iio6zE+UE0dAEZVilpaWuHXrVp77w8LCULFixWKsSHl17NgRsbGxQpdBSszFxQVhYWHo0aMHXr58icTERPTt2xf37t1D9erVhS6PiIiKCPNTwTE/0ecwP1FOIolEIhG6CCJSjFGjRuHcuXO4evUqtLS0ZPalpqaifv36aNGiBVauXClQhcpDX18foaGhsLe3F7oUIiIiEhDzU8ExPxFRYbADiqgUe/HiBWrXrg1VVVWMHDkSVatWBQDcu3cPvr6+EIvFCA4ORoUKFQSuVHgMUCRPWFgYqlevDhUVFYSFheXblpflJiIqHZifCo75ieRhfqK8sAOKqJSLiorCsGHDcOLECXz4uItEIrRt2xa+vr6ws7MTuELlUL16dfz999+wsrISuhRSIioqKnj+/DnMzMygoqICkUgEeb82RSIRr+RCRFSKMD8VDPMTycP8RHlhBxRRGfH27Vs8ePAAEokEjo6OMDY2FrokIqUXFRUFa2triEQiREVF5dvWxsammKoiIqLiwvxEVHjMT5QXdkAREREREREREZFCqQldABERkbI6dOhQgdt26dJFgZUQERERlQzMT5QXjoAiIiLKg4qKSoHacQ0DIiIiomzMT5QXdkARUZkkkUgQExMDMzOzXJdYJiIiIqLcmJ+I6GsUrGuSiKiUkUgkcHBwQExMjNClEBEREZUIzE9E9DXYAUVEZZKKigocHR3x5s0boUuhEuTMmTPo1KkTKleujMqVK6NTp044ffq00GUREREVC+Yn+hLMT/QBO6CIqMzy9vbGpEmTcOvWLaFLoRLgjz/+QLt27aCvr48xY8ZgzJgxMDAwQIcOHeDr6yt0eURERMWC+YkKg/mJcuIaUERUZhkbGyMlJQWZmZnQ0NCAtra2zP64uDiBKiNlVKlSJUydOhUjR46U2e7r64sFCxbg6dOnAlVGRERUfJifqDCYnygndkARUZm1ZcuWfPf369evmCqhkkBPTw8hISFwcHCQ2R4REQE3NzckJSUJVBkREVHxYX6iwmB+opzYAUVERFQAP/30E9zc3DBp0iSZ7UuWLMG1a9ewc+dOgSojIiIiUk7MT5STmtAFEBEpg7S0NKSnp8tsMzAwEKgaUhYrV66U/reLiwvmz5+Pc+fOwd3dHQAQFBSEwMBATJgwQagSiYiIBMP8RPIwP1FeOAKKiMqs5ORkTJkyBbt375Z7NRexWCxAVaRM7OzsCtROJBLh0aNHCq6GiIhIeMxP9DnMT5QXjoAiojJr8uTJOHv2LNasWYM+ffrA19cXT58+xdq1a+Ht7S10eaQEIiMjhS6BiIhIqTA/0ecwP1FeOAKKiMosa2trbN26Fc2bN4eBgQGCg4Ph4OCAbdu2YceOHTh27JjQJRIREREpFeYnIvpSHAFFRGVWXFwc7O3tAWSvV/DhssFNmjTBsGHDhCyNlNCAAQPy3b9p06ZiqoSIiEg4zE9UGMxPlBM7oIiozLK3t0dkZCSsra3h5OSE3bt3o379+jh8+DCMjIyELo+UzNu3b2XuZ2Rk4NatW3j37h1atmwpUFVERETFi/mJCoP5iXJiBxQRlVn9+/dHaGgomjVrhqlTp6Jz585YvXo1MjIysGzZMqHLIyWzf//+XNuysrIwbNgwVK5cWYCKiIiIih/zExUG8xPlxDWgiIj+ExUVhevXr8PBwQGurq5Cl0MlRHh4OJo3b47Y2FihSyEiIip2zE/0JZifyiaOgCIi+o+NjQ1sbGyELoNKmIcPHyIzM1PoMoiIiATB/ERfgvmpbGIHFBGVOQEBARg5ciSCgoJgYGAgsy8+Ph6NGjWCn58fmjZtKlCFpIzGjx8vc18ikSA2NhZHjx5Fv379BKqKiIioeDA/0ZdgfqKcOAWPiMqcLl26oEWLFhg3bpzc/StXrsTZs2flzlmnsqtFixYy91VUVFC+fHm0bNkSAwYMgJoaz+kQEVHpxfxEX4L5iXJiBxQRlTk2NjY4fvw4nJ2d5e6/d+8evvnmG0RHRxdzZURERETKifmJiL6WitAFEBEVtxcvXkBdXT3P/Wpqanj16lUxVkTKLCsrCwsXLkTjxo1Rr149TJ06FampqUKXRUREVKyYn6gwmJ9IHnZAEVGZY2lpiVu3buW5PywsDBUrVizGikiZzZ8/H7/++iv09PRgaWmJFStWYMSIEUKXRUREVKyYn6gwmJ9IHk7BI6IyZ9SoUTh37hyuXr0KLS0tmX2pqamoX78+WrRogZUrVwpUISkTR0dHTJw4EUOGDAEAnD59Gh07dkRqaipUVHgeh4iIygbmJyoM5ieShx1QRFTmvHjxArVr14aqqipGjhyJqlWrAsheu8DX1xdisRjBwcGoUKGCwJWSMtDU1MSDBw9gZWUl3aalpYUHDx6gUqVKAlZGRERUfJifqDCYn0geLjlPRGVOhQoVcOnSJQwbNgzTpk3Dh354kUiEtm3bwtfXl+GJpDIzM3Od6VVXV0dGRoZAFRERERU/5icqDOYnkocjoIioTHv79i0ePHgAiUQCR0dHGBsbC10SKRkVFRW0b98empqa0m2HDx9Gy5YtoaurK922b98+IcojIiIqdsxP9DnMTyQPO6CIiIjy0b9//wK127x5s4IrISIiIioZmJ9IHnZAERERERERERGRQnH5eSIiIiIiIiIiUih2QBERERERERERkUKxA4qIiIiIiIiIiBSKHVBERERERERERKRQ7IAiIiIiIiIiIiKFYgcUESktT09PdOvWTXq/efPmGDt2bLHXce7cOYhEIrx7906hzyMSiXDgwAGFPgcRERGVbsxPRKSs2AFFRIXi6ekJkUgEkUgEDQ0NODg4YM6cOcjMzFT4c+/btw9z584tUNviCj3p6ekoV64cvL295e6fO3cuKlSogIyMDIXWQURERMqL+UkW8xNR2cQOKCIqtHbt2iE2NhYRERGYMGECZs2ahcWLF8ttm56eXmTPa2JiAn19/SI7XlHQ0NBA7969sXnz5lz7JBIJ/P390bdvX6irqwtQHRERESkL5qePmJ+IyiZ2QBFRoWlqasLc3Bw2NjYYNmwYWrdujUOHDgH4OOx7/vz5sLCwQNWqVQEAMTEx6NGjB4yMjGBiYoKuXbvi8ePH0mOKxWKMHz8eRkZGMDU1xeTJkyGRSGSe99Mh5O/fv8eUKVNgZWUFTU1NODg4YOPGjXj8+DFatGgBADA2NoZIJIKnpycAICsrC15eXrCzs4O2tjZq1qyJPXv2yDzPsWPHUKVKFWhra6NFixYydcozcOBA3L9/HxcvXpTZfv78eTx69AgDBw7E1atX0aZNG5QrVw6GhoZo1qwZgoOD8zymvDOQISEhEIlEMvVcvHgRTZs2hba2NqysrDB69GgkJydL9//xxx9wdHSElpYWKlSogO+//z7f10JERESKwfwki/mJqOxhBxQRfTVtbW2ZM3VnzpxBeHg4Tp06hSNHjiAjIwNt27aFvr4+Lly4gMDAQOjp6aFdu3bSxy1duhT+/v7YtGkTLl68iLi4OOzfvz/f5+3bty927NiBlStX4u7du1i7di309PRgZWWFvXv3AgDCw8MRGxuLFStWAAC8vLywdetW+Pn54fbt2xg3bhx69+6N8+fPA8gOet27d0fnzp0REhKCX375BVOnTs23jho1aqBevXrYtGmTzPbNmzejUaNGcHJyQmJiIvr164eLFy8iKCgIjo6O6NChAxITEwv3Zufw8OFDtGvXDt999x3CwsKwa9cuXLx4ESNHjgQAXLt2DaNHj8acOXMQHh6O48ePw8PD44ufj4iIiIoO8xPzE1GZIyEiKoR+/fpJunbtKpFIJJKsrCzJqVOnJJqampKJEydK91eoUEHy/v176WO2bdsmqVq1qiQrK0u67f379xJtbW3JiRMnJBKJRFKxYkXJokWLpPszMjIklSpVkj6XRCKRNGvWTDJmzBiJRCKRhIeHSwBITp06JbfOs2fPSgBI3r59K92WlpYm0dHRkVy6dEmm7cCBAyU//vijRCKRSKZNmyZxcXGR2T9lypRcx/qUn5+fRE9PT5KYmCiRSCSShIQEiY6OjmTDhg1y24vFYom+vr7k8OHD0m0AJPv378+z/hs3bkgASCIjI6V1Dx48WOa4Fy5ckKioqEhSU1Mle/fulRgYGEgSEhLyrJuIiIgUj/lJPuYnorKFI6CIqNCOHDkCPT09aGlpoX379ujZsydmzZol3V+jRg1oaGhI74eGhuLBgwfQ19eHnp4e9PT0YGJigrS0NDx8+BDx8fGIjY1FgwYNpI9RU1ND3bp186whJCQEqqqqaNasWYHrfvDgAVJSUtCmTRtpHXp6eti6dSsePnwIALh7965MHQDg7u7+2WP/+OOPEIvF2L17NwBg165dUFFRQc+ePQEAL168wKBBg+Do6AhDQ0MYGBggKSkJ0dHRBa7/U6GhofD395d5LW3btkVWVhYiIyPRpk0b2NjYwN7eHn369MGff/6JlJSUL34+IiIi+nLMT7kxPxGVLWpCF0BEJU+LFi2wZs0aaGhowMLCAmpqsl8lurq6MveTkpJQp04d/Pnnn7mOVb58+S+qQVtbu9CPSUpKAgAcPXoUlpaWMvs0NTW/qI4PDAwM8P3332Pz5s0YMGAANm/ejB49ekBPTw8A0K9fP7x58wYrVqyAjY0NNDU14e7unucioyoq2ecHJDnWcfj0SjBJSUkYMmQIRo8enevx1tbW0NDQQHBwMM6dO4eTJ09ixowZmDVrFq5evQojI6Over1ERERUOMxPuTE/EZUt7IAiokLT1dWFg4NDgdvXrl0bu3btgpmZGQwMDOS2qVixIq5cuSKdY5+ZmYnr16+jdu3actvXqFEDWVlZOH/+PFq3bp1r/4cziGKxWLrNxcUFmpqaiI6OzvPMn7Ozs3RB0A+CgoI+/yKRvZhm8+bNceTIEVy6dEnmyjaBgYH4448/0KFDBwDZayW8fv06z2N9CJaxsbEwNjYGkH3WMqfatWvjzp07+f5bqKmpoXXr1mjdujVmzpwJIyMjBAQEoHv37gV6TURERFQ0mJ/kY34iKjs4BY+IFO7nn39GuXLl0LVrV1y4cAGRkZE4d+4cRo8ejSdPngAAxowZA29vbxw4cAD37t3D8OHDZa5g8ilbW1v069cPAwYMwIEDB6TH/DCE28bGBiKRCEeOHMGrV6+QlJQEfX19TJw4EePGjcOWLVvw8OFDBAcHY9WqVdiyZQsAYOjQoYiIiMCkSZMQHh6O7du3w9/fv0Cv08PDAw4ODujbty+cnJzQqFEj6T5HR0ds27YNd+/exZUrV/Dzzz/nexbSwcEBVlZWmDVrFiIiInD06FEsXbpUps2UKVNw6dIljBw5EiEhIYiIiMDBgweli2geOXIEK1euREhICKKiorB161ZkZWVJr6xDREREyov5ifmJqLRhBxQRKZyOjg7++ecfWFtbo3v37nB2dsbAgQORlpYmPaM3YcIE9OnTB/369YO7uzv09fXx7bff5nvcNWvW4Pvvv8fw4cPh5OSEQYMGSS+ha2lpidmzZ2Pq1KmoUKGCNFTMnTsX06dPh5eXF5ydndGuXTscPXoUdnZ2ALKHXu/duxcHDhxAzZo14efnhwULFhTodYpEIgwYMABv377FgAEDZPZt3LgRb9++Re3atdGnTx+MHj0aZmZmeR5LXV0dO3bswL179+Dq6oqFCxdi3rx5Mm1cXV1x/vx53L9/H02bNoWbmxtmzJgBCwsLAICRkRH27duHli1bwtnZGX5+ftixYweqVatWoNdDREREwmF+Yn4iKm1EkpwTZImIiIiIiIiIiIoYR0AREREREREREZFCsQOKiIiIiIiIiIgUih1QRERERERERESkUOyAIiIiIiIiIiIihWIHFBERERERERERKRQ7oIiIiIiIiIiISKHYAUVERERERERERArFDigiIiIiIiIiIlIodkAREREREREREZFCsQOKiIiIiIiIiIgUih1QRERERERERESkUP8H/D4jzvv0b0oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mapping = {'Bicycle':0, 'Car - Driver':1, 'Car - Passenger':2, 'Foot':3,\n",
    "       'Public Transport':4}\n",
    "\n",
    "def conf_matrix(y_true, y_pred, title, ax):\n",
    "    cm = pd.DataFrame(confusion_matrix(y_true, y_pred), index=mapping.keys(), columns=mapping.keys())\n",
    "    sns.heatmap(cm, annot=True, fmt='g', cmap=\"Blues\", ax=ax)\n",
    "    ax.set_title(title)\n",
    "    ax.set_ylabel('Actual Values')\n",
    "    ax.set_xlabel('Predicted Values')\n",
    "    \n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
    "\n",
    "conf_matrix(y_test, y_pred_xgb_baseline, title=\"XGBoost\", ax=axes[0, 0])\n",
    "conf_matrix(y_test, y_pred_rf_baseline, title=\"Random Forest\", ax=axes[0, 1])\n",
    "conf_matrix(y_test, y_pred_tabnet_baseline, title=\" TabNet\", ax=axes[1, 0])\n",
    "conf_matrix(y_test, y_pred_knn_baseline, title=\"KNN\", ax=axes[1, 1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"confusion_matrices_baseline.pdf\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1b6a7e-5fff-44e6-8fe0-f40c4edd6285",
   "metadata": {},
   "source": [
    "<a id='section13'></a>\n",
    "# Feature_Importance_RF+XGB_TabNet\n",
    "[Back to Table of Contents](#Table-of-Contents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0cdc5680-3fec-451d-9bba-5ed7db364400",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0  | loss: 1.21051 |  0:00:30s\n",
      "epoch 1  | loss: 0.76109 |  0:00:55s\n",
      "epoch 2  | loss: 0.63905 |  0:01:20s\n",
      "epoch 3  | loss: 0.60596 |  0:01:46s\n",
      "epoch 4  | loss: 0.5884  |  0:02:11s\n",
      "epoch 5  | loss: 0.57477 |  0:02:36s\n",
      "epoch 6  | loss: 0.57428 |  0:03:01s\n",
      "epoch 7  | loss: 0.56227 |  0:03:28s\n",
      "epoch 8  | loss: 0.55571 |  0:03:54s\n",
      "epoch 9  | loss: 0.55056 |  0:04:21s\n",
      "epoch 10 | loss: 0.54676 |  0:04:48s\n",
      "epoch 11 | loss: 0.54563 |  0:05:14s\n",
      "epoch 12 | loss: 0.53902 |  0:05:41s\n",
      "epoch 13 | loss: 0.54108 |  0:06:08s\n",
      "epoch 14 | loss: 0.5391  |  0:06:35s\n",
      "epoch 15 | loss: 0.53353 |  0:07:01s\n",
      "epoch 16 | loss: 0.53333 |  0:07:28s\n",
      "epoch 17 | loss: 0.5284  |  0:07:55s\n",
      "epoch 18 | loss: 0.52842 |  0:08:22s\n",
      "epoch 19 | loss: 0.52788 |  0:08:48s\n",
      "epoch 20 | loss: 0.52402 |  0:09:16s\n",
      "epoch 21 | loss: 0.52404 |  0:09:44s\n",
      "epoch 22 | loss: 0.51976 |  0:10:12s\n",
      "epoch 23 | loss: 0.52224 |  0:10:40s\n",
      "epoch 24 | loss: 0.52007 |  0:11:08s\n",
      "epoch 25 | loss: 0.5186  |  0:11:36s\n",
      "epoch 26 | loss: 0.51628 |  0:12:04s\n",
      "epoch 27 | loss: 0.51352 |  0:12:32s\n",
      "epoch 28 | loss: 0.51318 |  0:13:00s\n",
      "epoch 29 | loss: 0.51174 |  0:13:28s\n",
      "epoch 30 | loss: 0.5155  |  0:13:56s\n",
      "epoch 31 | loss: 0.51166 |  0:14:24s\n",
      "epoch 32 | loss: 0.50698 |  0:14:53s\n",
      "epoch 33 | loss: 0.50728 |  0:15:21s\n",
      "epoch 34 | loss: 0.50786 |  0:15:49s\n",
      "epoch 35 | loss: 0.50475 |  0:16:17s\n",
      "epoch 36 | loss: 0.50367 |  0:16:45s\n",
      "epoch 37 | loss: 0.50322 |  0:17:13s\n",
      "epoch 38 | loss: 0.50473 |  0:17:42s\n",
      "epoch 39 | loss: 0.50388 |  0:18:08s\n",
      "epoch 40 | loss: 0.50116 |  0:18:34s\n",
      "epoch 41 | loss: 0.50235 |  0:19:00s\n",
      "epoch 42 | loss: 0.49791 |  0:19:27s\n",
      "epoch 43 | loss: 0.49468 |  0:19:52s\n",
      "epoch 44 | loss: 0.49963 |  0:20:18s\n",
      "epoch 45 | loss: 0.4936  |  0:20:44s\n",
      "epoch 46 | loss: 0.49916 |  0:21:10s\n",
      "epoch 47 | loss: 0.49463 |  0:21:35s\n",
      "epoch 48 | loss: 0.49105 |  0:22:01s\n",
      "epoch 49 | loss: 0.49384 |  0:22:27s\n",
      "epoch 50 | loss: 0.4945  |  0:22:53s\n",
      "epoch 51 | loss: 0.49126 |  0:23:19s\n",
      "epoch 52 | loss: 0.49027 |  0:23:44s\n",
      "epoch 53 | loss: 0.48618 |  0:24:10s\n",
      "epoch 54 | loss: 0.49042 |  0:24:35s\n",
      "epoch 55 | loss: 0.48258 |  0:25:01s\n",
      "epoch 56 | loss: 0.48742 |  0:25:26s\n",
      "epoch 57 | loss: 0.48551 |  0:25:51s\n",
      "epoch 58 | loss: 0.48584 |  0:26:16s\n",
      "epoch 59 | loss: 0.48031 |  0:26:41s\n",
      "epoch 60 | loss: 0.48603 |  0:27:06s\n",
      "epoch 61 | loss: 0.486   |  0:27:31s\n",
      "epoch 62 | loss: 0.48575 |  0:27:59s\n",
      "epoch 63 | loss: 0.48583 |  0:28:25s\n",
      "epoch 64 | loss: 0.48558 |  0:28:50s\n",
      "epoch 65 | loss: 0.48318 |  0:29:15s\n",
      "epoch 66 | loss: 0.48391 |  0:29:40s\n",
      "epoch 67 | loss: 0.47932 |  0:30:05s\n",
      "epoch 68 | loss: 0.47879 |  0:30:30s\n",
      "epoch 69 | loss: 0.47937 |  0:30:55s\n",
      "epoch 70 | loss: 0.48132 |  0:31:21s\n",
      "epoch 71 | loss: 0.4852  |  0:31:47s\n",
      "epoch 72 | loss: 0.47888 |  0:32:14s\n",
      "epoch 73 | loss: 0.47929 |  0:32:39s\n",
      "epoch 74 | loss: 0.47231 |  0:33:04s\n",
      "epoch 75 | loss: 0.47313 |  0:33:30s\n",
      "epoch 76 | loss: 0.47285 |  0:33:55s\n",
      "epoch 77 | loss: 0.47526 |  0:34:20s\n",
      "epoch 78 | loss: 0.47334 |  0:34:45s\n",
      "epoch 79 | loss: 0.47101 |  0:35:11s\n",
      "epoch 80 | loss: 0.47412 |  0:35:36s\n",
      "epoch 81 | loss: 0.47242 |  0:36:02s\n",
      "epoch 82 | loss: 0.47404 |  0:36:28s\n",
      "epoch 83 | loss: 0.46926 |  0:36:53s\n",
      "epoch 84 | loss: 0.46819 |  0:37:18s\n",
      "epoch 85 | loss: 0.46743 |  0:37:43s\n",
      "epoch 86 | loss: 0.46841 |  0:38:08s\n",
      "epoch 87 | loss: 0.46864 |  0:38:34s\n",
      "epoch 88 | loss: 0.46794 |  0:38:59s\n",
      "epoch 89 | loss: 0.47329 |  0:39:24s\n",
      "epoch 90 | loss: 0.47395 |  0:39:49s\n",
      "epoch 91 | loss: 0.46923 |  0:40:14s\n",
      "epoch 92 | loss: 0.46496 |  0:40:39s\n",
      "epoch 93 | loss: 0.47007 |  0:41:05s\n",
      "epoch 94 | loss: 0.47017 |  0:41:30s\n",
      "epoch 95 | loss: 0.48513 |  0:41:55s\n",
      "epoch 96 | loss: 0.474   |  0:42:20s\n",
      "epoch 97 | loss: 0.46726 |  0:42:45s\n",
      "epoch 98 | loss: 0.46602 |  0:43:10s\n",
      "epoch 99 | loss: 0.46474 |  0:43:35s\n",
      "                                                   Importance\n",
      "TripLegDistance                                  3.142836e-01\n",
      "TotalTravelDuration                              1.044127e-01\n",
      "NumberOfTripLegs                                 6.562620e-02\n",
      "PCA_dr_wl_cl_duration_1                          5.810506e-02\n",
      "FrequencyOfUsecarAsAPassenger                    5.504386e-02\n",
      "License                                          4.511378e-02\n",
      "PCA_dr_wl_cl_duration_2                          4.335747e-02\n",
      "FrequencyOfUseOfNonEelectricBicycle              4.159069e-02\n",
      "TripPurpose_Other motive                         4.112090e-02\n",
      "NofBusinessEstablishments                        2.987252e-02\n",
      "Gender                                           2.476827e-02\n",
      "Weekday                                          2.430044e-02\n",
      "TripPurpose_Maintenance Activities               2.301377e-02\n",
      "AvgDistToTrainStation                            1.895199e-02\n",
      "TripPurpose_Subsistence Activities               1.811797e-02\n",
      "TripPurpose_Leisure Activities                   1.605794e-02\n",
      "FrequencyOfWalkingOutdoors                       1.273234e-02\n",
      "NumberOfCarsInHousehold                          1.205349e-02\n",
      "ElectriBicycleIHousehold                         9.885940e-03\n",
      "WorkCommuteReimbursement_Reimbursement provided  8.645604e-03\n",
      "EducationLevel                                   7.711046e-03\n",
      "WorkCommuteReimbursement_No reimbursement        6.550788e-03\n",
      "LatitudeLiving                                   6.103524e-03\n",
      "SurroundingAddressDensity                        5.083217e-03\n",
      "LongitudeLiving                                  3.697825e-03\n",
      "WorkCommuteReimbursement_Not applicable          3.241481e-03\n",
      "DisposableIncome                                 5.575534e-04\n",
      "HouseholdSize                                    5.743982e-08\n",
      "DepartureTime                                    0.000000e+00\n",
      "AgeClass                                         0.000000e+00\n",
      "AvgDistToSupermarket                             0.000000e+00\n",
      "N.OFHouseholdMemberUnd6                          0.000000e+00\n",
      "RoadNetworkDensity                               0.000000e+00\n",
      "NumBusStops                                      0.000000e+00\n",
      "PCA_dr_wl_cl_duration_3                          0.000000e+00\n"
     ]
    }
   ],
   "source": [
    "# part of this code is from: https://www.kaggle.com/code/optimo/tabnet-with-loop-feature-engineering-explained\n",
    "from pytorch_tabnet.tab_model import TabNetClassifier\n",
    "tabnet_parm =joblib.load('study_tabnet.pkl')\n",
    "tabnet_params = { 'mask_type': 'entmax','n_d': 10,  'gamma': 1.0, \"n_steps\":3,\n",
    "    'n_shared': 4, 'lambda_sparse': 0.0003665999408830711, 'device_name': 'cuda'}\n",
    "tabnet_classifier = TabNetClassifier(**tabnet_params)\n",
    "                 \n",
    "tabnet_classifier.fit(X_train_top_mi, y_train_split)\n",
    "feature_importance_tabnet = tabnet_classifier.feature_importances_\n",
    "\n",
    "feature_importance_tabnet_df = pd.DataFrame(feature_importance_tabnet, index=selected_feature_rfecv, columns=['Importance'])\n",
    "feature_importance_tabnet_df =feature_importance_tabnet_df.sort_values(\"Importance\", ascending=False)\n",
    "feature_importance_tabnet_df\n",
    "\n",
    "\n",
    "feature_importance_tabnet_df.to_csv(\"feature_importance_tabnet.csv\")\n",
    "print(feature_importance_tabnet_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "48f86cf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>License</th>\n",
       "      <td>0.235216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTripLegs</th>\n",
       "      <td>0.207944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCA_dr_wl_cl_duration_1</th>\n",
       "      <td>0.087852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TripPurpose_Subsistence Activities</th>\n",
       "      <td>0.053125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TripLegDistance</th>\n",
       "      <td>0.047833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FrequencyOfUseOfNonEelectricBicycle</th>\n",
       "      <td>0.044630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalTravelDuration</th>\n",
       "      <td>0.039904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FrequencyOfUsecarAsAPassenger</th>\n",
       "      <td>0.033891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfCarsInHousehold</th>\n",
       "      <td>0.029601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCA_dr_wl_cl_duration_2</th>\n",
       "      <td>0.023533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElectriBicycleIHousehold</th>\n",
       "      <td>0.022346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TripPurpose_Leisure Activities</th>\n",
       "      <td>0.021343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TripPurpose_Other motive</th>\n",
       "      <td>0.016664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeClass</th>\n",
       "      <td>0.015357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>0.012672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekday</th>\n",
       "      <td>0.011757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FrequencyOfWalkingOutdoors</th>\n",
       "      <td>0.011754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCA_dr_wl_cl_duration_3</th>\n",
       "      <td>0.010449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TripPurpose_Maintenance Activities</th>\n",
       "      <td>0.007224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkCommuteReimbursement_Not applicable</th>\n",
       "      <td>0.005712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducationLevel</th>\n",
       "      <td>0.005109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SurroundingAddressDensity</th>\n",
       "      <td>0.004883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseholdSize</th>\n",
       "      <td>0.004830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkCommuteReimbursement_Reimbursement provided</th>\n",
       "      <td>0.004798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkCommuteReimbursement_No reimbursement</th>\n",
       "      <td>0.004774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N.OFHouseholdMemberUnd6</th>\n",
       "      <td>0.004500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepartureTime</th>\n",
       "      <td>0.004028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoadNetworkDensity</th>\n",
       "      <td>0.003891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongitudeLiving</th>\n",
       "      <td>0.003766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DisposableIncome</th>\n",
       "      <td>0.003725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LatitudeLiving</th>\n",
       "      <td>0.003629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NofBusinessEstablishments</th>\n",
       "      <td>0.003418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBusStops</th>\n",
       "      <td>0.003323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AvgDistToTrainStation</th>\n",
       "      <td>0.003269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AvgDistToSupermarket</th>\n",
       "      <td>0.003254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        0\n",
       "License                                          0.235216\n",
       "NumberOfTripLegs                                 0.207944\n",
       "PCA_dr_wl_cl_duration_1                          0.087852\n",
       "TripPurpose_Subsistence Activities               0.053125\n",
       "TripLegDistance                                  0.047833\n",
       "FrequencyOfUseOfNonEelectricBicycle              0.044630\n",
       "TotalTravelDuration                              0.039904\n",
       "FrequencyOfUsecarAsAPassenger                    0.033891\n",
       "NumberOfCarsInHousehold                          0.029601\n",
       "PCA_dr_wl_cl_duration_2                          0.023533\n",
       "ElectriBicycleIHousehold                         0.022346\n",
       "TripPurpose_Leisure Activities                   0.021343\n",
       "TripPurpose_Other motive                         0.016664\n",
       "AgeClass                                         0.015357\n",
       "Gender                                           0.012672\n",
       "Weekday                                          0.011757\n",
       "FrequencyOfWalkingOutdoors                       0.011754\n",
       "PCA_dr_wl_cl_duration_3                          0.010449\n",
       "TripPurpose_Maintenance Activities               0.007224\n",
       "WorkCommuteReimbursement_Not applicable          0.005712\n",
       "EducationLevel                                   0.005109\n",
       "SurroundingAddressDensity                        0.004883\n",
       "HouseholdSize                                    0.004830\n",
       "WorkCommuteReimbursement_Reimbursement provided  0.004798\n",
       "WorkCommuteReimbursement_No reimbursement        0.004774\n",
       "N.OFHouseholdMemberUnd6                          0.004500\n",
       "DepartureTime                                    0.004028\n",
       "RoadNetworkDensity                               0.003891\n",
       "LongitudeLiving                                  0.003766\n",
       "DisposableIncome                                 0.003725\n",
       "LatitudeLiving                                   0.003629\n",
       "NofBusinessEstablishments                        0.003418\n",
       "NumBusStops                                      0.003323\n",
       "AvgDistToTrainStation                            0.003269\n",
       "AvgDistToSupermarket                             0.003254"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb_params = joblib.load('study_xgb.pkl')\n",
    "xgb_classifier = XGBClassifier(**xgb_params.best_params)\n",
    "xgb_classifier.fit(X_train_top_mi, y_train_split)\n",
    "y_pred_xgb_baseline = xgb_classifier.predict(X_test_top_mi)\n",
    "\n",
    "\n",
    "feature_importance = xgb_classifier.feature_importances_\n",
    "\n",
    "feature_importance_xgb = pd.DataFrame(feature_importance, index=selected_feature_rfecv).sort_values(0, ascending=False)\n",
    "feature_importance_xgb.to_csv(\"feature_importance_xgb.csv\")\n",
    "feature_importance_xgb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a190152-541d-44fc-a98c-3f14f7ec647d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PCA_dr_wl_cl_duration_1</th>\n",
       "      <td>0.223147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TripLegDistance</th>\n",
       "      <td>0.148665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTripLegs</th>\n",
       "      <td>0.148381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalTravelDuration</th>\n",
       "      <td>0.082092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>License</th>\n",
       "      <td>0.071188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FrequencyOfUseOfNonEelectricBicycle</th>\n",
       "      <td>0.050865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FrequencyOfUsecarAsAPassenger</th>\n",
       "      <td>0.022976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LatitudeLiving</th>\n",
       "      <td>0.018053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SurroundingAddressDensity</th>\n",
       "      <td>0.017939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongitudeLiving</th>\n",
       "      <td>0.017446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoadNetworkDensity</th>\n",
       "      <td>0.016679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AvgDistToTrainStation</th>\n",
       "      <td>0.016039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NofBusinessEstablishments</th>\n",
       "      <td>0.015949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeClass</th>\n",
       "      <td>0.015801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepartureTime</th>\n",
       "      <td>0.014093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TripPurpose_Subsistence Activities</th>\n",
       "      <td>0.013415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCA_dr_wl_cl_duration_3</th>\n",
       "      <td>0.010328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBusStops</th>\n",
       "      <td>0.010262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AvgDistToSupermarket</th>\n",
       "      <td>0.009875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DisposableIncome</th>\n",
       "      <td>0.009834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCA_dr_wl_cl_duration_2</th>\n",
       "      <td>0.009665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElectriBicycleIHousehold</th>\n",
       "      <td>0.009281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>0.006668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfCarsInHousehold</th>\n",
       "      <td>0.006585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FrequencyOfWalkingOutdoors</th>\n",
       "      <td>0.006176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TripPurpose_Leisure Activities</th>\n",
       "      <td>0.005797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseholdSize</th>\n",
       "      <td>0.005434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EducationLevel</th>\n",
       "      <td>0.004023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekday</th>\n",
       "      <td>0.003654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkCommuteReimbursement_Not applicable</th>\n",
       "      <td>0.002224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TripPurpose_Maintenance Activities</th>\n",
       "      <td>0.001934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkCommuteReimbursement_Reimbursement provided</th>\n",
       "      <td>0.001775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TripPurpose_Other motive</th>\n",
       "      <td>0.001750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkCommuteReimbursement_No reimbursement</th>\n",
       "      <td>0.001114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N.OFHouseholdMemberUnd6</th>\n",
       "      <td>0.000893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        0\n",
       "PCA_dr_wl_cl_duration_1                          0.223147\n",
       "TripLegDistance                                  0.148665\n",
       "NumberOfTripLegs                                 0.148381\n",
       "TotalTravelDuration                              0.082092\n",
       "License                                          0.071188\n",
       "FrequencyOfUseOfNonEelectricBicycle              0.050865\n",
       "FrequencyOfUsecarAsAPassenger                    0.022976\n",
       "LatitudeLiving                                   0.018053\n",
       "SurroundingAddressDensity                        0.017939\n",
       "LongitudeLiving                                  0.017446\n",
       "RoadNetworkDensity                               0.016679\n",
       "AvgDistToTrainStation                            0.016039\n",
       "NofBusinessEstablishments                        0.015949\n",
       "AgeClass                                         0.015801\n",
       "DepartureTime                                    0.014093\n",
       "TripPurpose_Subsistence Activities               0.013415\n",
       "PCA_dr_wl_cl_duration_3                          0.010328\n",
       "NumBusStops                                      0.010262\n",
       "AvgDistToSupermarket                             0.009875\n",
       "DisposableIncome                                 0.009834\n",
       "PCA_dr_wl_cl_duration_2                          0.009665\n",
       "ElectriBicycleIHousehold                         0.009281\n",
       "Gender                                           0.006668\n",
       "NumberOfCarsInHousehold                          0.006585\n",
       "FrequencyOfWalkingOutdoors                       0.006176\n",
       "TripPurpose_Leisure Activities                   0.005797\n",
       "HouseholdSize                                    0.005434\n",
       "EducationLevel                                   0.004023\n",
       "Weekday                                          0.003654\n",
       "WorkCommuteReimbursement_Not applicable          0.002224\n",
       "TripPurpose_Maintenance Activities               0.001934\n",
       "WorkCommuteReimbursement_Reimbursement provided  0.001775\n",
       "TripPurpose_Other motive                         0.001750\n",
       "WorkCommuteReimbursement_No reimbursement        0.001114\n",
       "N.OFHouseholdMemberUnd6                          0.000893"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_parm =joblib.load('study_rf.pkl')\n",
    "\n",
    "rf_classifier = RandomForestClassifier(**rf_parm.best_params)\n",
    "rf_classifier.fit(X_train_top_mi, y_train_split)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "y_pred_prob_xgb_baseline = rf_classifier.predict_proba(X_test_top_mi)\n",
    "y_pred_xgb_baseline = rf_classifier.predict(X_test_top_mi)\n",
    "\n",
    "feature_importance = rf_classifier.feature_importances_\n",
    "\n",
    "\n",
    "\n",
    "feature_importance_rf = pd.DataFrame(feature_importance, index=selected_feature_rfecv).sort_values(0, ascending=False)\n",
    "feature_importance_rf.to_csv(\"feature_importance_rf.csv\")\n",
    "feature_importance_rf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5fe457e1-7846-4130-9947-726a5d8aa96b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 14))\n",
    "\n",
    "plt.subplot(311) \n",
    "sns.barplot(y=feature_importance_xgb.index[0:10], x=feature_importance_xgb.iloc[0:10, 0],  palette=\"viridis\")\n",
    "plt.title('XGBoost Feature Importance (Top-10)', fontsize=18)\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.tick_params(axis='y', labelsize=10)\n",
    "\n",
    "\n",
    "plt.subplot(312) \n",
    "sns.barplot(y=feature_importance_rf.index[0:10], x=feature_importance_rf.iloc[0:10, 0],  palette=\"viridis\")\n",
    "plt.title('RF Feature Importance (Top-10)', fontsize=18)\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.tick_params(axis='y', labelsize=10)\n",
    "\n",
    "plt.subplot(313) \n",
    "sns.barplot(y=feature_importance_tabnet_df.index[0:10], x=feature_importance_tabnet_df.iloc[0:10, 0], palette=\"viridis\")\n",
    "plt.title('TabNet Feature Importance (Top-10)', fontsize=18)\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.tick_params(axis='y', labelsize=10)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('xgb_rf_tabnet-feature', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c95911e4-0e03-4eaa-af9f-7602ab43502d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PCA_dr_wl_cl_duration_1', 'TripLegDistance', 'NumberOfTripLegs',\n",
       "       'TotalTravelDuration', 'License', 'FrequencyOfUseOfNonEelectricBicycle',\n",
       "       'FrequencyOfUsecarAsAPassenger', 'LatitudeLiving',\n",
       "       'SurroundingAddressDensity', 'LongitudeLiving'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://shengyg.github.io/repository/machine%20learning/2017/02/25/Complete-Guide-to-Parameter-Tuning-xgboost.html tuning xgboost why\n",
    "feature_importance_rf.iloc[0:10].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f5bdddaa-fd46-4249-be9b-6a1b7410f453",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jaccard Similarity RF & TabNet: 0.5384615384615384\n",
      "Jaccard Similarity RF & XGBoost: 0.5384615384615384\n",
      "Jaccard Similarity XGBoost & TabNet: 0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "# this function is  from:  https://www.geeksforgeeks.org/how-to-calculate-jaccard-similarity-in-python/\n",
    "def jaccard_similarity(set1, set2):\n",
    "    # intersection of two sets\n",
    "    intersection = len(set1.intersection(set2))\n",
    "    # Unions of two sets\n",
    "    union = len(set1.union(set2))\n",
    "     \n",
    "    return intersection / union\n",
    " \n",
    "\n",
    "# top 10 featues\n",
    "print(\"Jaccard Similarity RF & TabNet:\", jaccard_similarity(feature_importance_rf.iloc[0:10].index, feature_importance_tabnet_df.iloc[0:10].index))\n",
    "print(\"Jaccard Similarity RF & XGBoost:\", jaccard_similarity(feature_importance_rf.iloc[0:10].index, feature_importance_xgb.iloc[0:10].index))\n",
    "print(\"Jaccard Similarity XGBoost & TabNet:\", jaccard_similarity(feature_importance_xgb.iloc[0:10].index, feature_importance_tabnet_df.iloc[0:10].index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a329b3a2-31f6-4a12-babf-15093bd38a5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jaccard Similarity RF & TabNet: 0.3333333333333333\n",
      "Jaccard Similarity RF & XGBoost: 0.36363636363636365\n",
      "Jaccard Similarity XGBoost & TabNet: 0.7391304347826086\n"
     ]
    }
   ],
   "source": [
    "# this function is  from:  https://www.geeksforgeeks.org/how-to-calculate-jaccard-similarity-in-python/\n",
    "def jaccard_similarity(set1, set2):\n",
    "    # intersection of two sets\n",
    "    intersection = len(set1.intersection(set2))\n",
    "    # Unions of two sets\n",
    "    union = len(set1.union(set2))\n",
    "     \n",
    "    return intersection / union\n",
    " \n",
    "# top 20 features\n",
    "print(\"Jaccard Similarity RF & TabNet:\", jaccard_similarity(feature_importance_rf.iloc[0:20].index, feature_importance_tabnet_df.iloc[0:20].index))\n",
    "print(\"Jaccard Similarity RF & XGBoost:\", jaccard_similarity(feature_importance_rf.iloc[0:20].index, feature_importance_xgb.iloc[0:10].index))\n",
    "print(\"Jaccard Similarity XGBoost & TabNet:\", jaccard_similarity(feature_importance_xgb.iloc[0:20].index, feature_importance_tabnet_df.iloc[0:20].index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fc58cad-8be5-4485-85d8-33f4bd3cb264",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "901e205d-4a7d-4436-8510-e20724cfbb44",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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