{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Telco customer churn \n",
    "\n",
    "https://www.kaggle.com/datasets/blastchar/telco-customer-churn\n",
    "\n",
    "- **customerID**: Customer ID\n",
    "- **gender**: Whether the customer is a male or a femal\n",
    "- **SeniorCitizen**: Whether the customer is a senior citizen or not (1, 0)\n",
    "- **Partner**: Whether the customer has a partner or not (Yes, No)\n",
    "- **Dependents**: Whether the customer has dependents or not (Yes, No)\n",
    "- **tenure**: Number of months the customer has stayed with the company\n",
    "- **PhoneService**: Whether the customer has a phone service or not (Yes, No)\n",
    "- **MultipleLines**: Whether the customer has multiple lines or not (Yes, No, No phone service)\n",
    "- **InternetService**: Customer’s internet service provider (DSL, Fiber optic, No)\n",
    "- **OnlineSecurity**: Whether the customer has online security or not (Yes, No, No internet service)\n",
    "- **OnlineBackup**: Whether the customer has online backup or not (Yes, No, No internet service)\n",
    "- **DeviceProtection**: Whether the customer has device protection or not (Yes, No, No internet service)\n",
    "- **TechSupport**: Whether the customer has tech support or not (Yes, No, No internet service)\n",
    "- **StreamingTV**: Whether the customer has streaming TV or not (Yes, No, No internet service)\n",
    "- **StreamingMovies**: Whether the customer has streaming movies or not (Yes, No, No internet service)\n",
    "- **Contract**: The contract term of the customer (Month-to-month, One year, Two year)\n",
    "- **PaperlessBilling**: Whether the customer has paperless billing or not (Yes, No)\n",
    "- **PaymentMethod**: The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))\n",
    "- **MonthlyCharges**: The amount charged to the customer monthly\n",
    "- **TotalCharges**: The total amount charged to the customer\n",
    "- **Churn**: Whether the customer churned or not (Yes or No)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_data = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\classification\\telco-customer-churn.csv\"\n",
    "churn_data = pd.read_csv(path_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "churn_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_data.drop(columns=[\"gender\",\"customerID\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_data[\"MultipleLines\"] = churn_data[\"MultipleLines\"].apply(lambda x: 0 if x in [\"No\",\"No phone service\"] else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in [\"OnlineSecurity\", \"OnlineBackup\", \"DeviceProtection\", \"TechSupport\", \"StreamingTV\", \"StreamingMovies\"]:\n",
    "    churn_data[col] = churn_data[col].apply(lambda x: 0 if x in [\"No internet service\", \"No\"] else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_columns = [\"Partner\", \"Dependents\", \"PhoneService\", \"PaperlessBilling\", \"Churn\"]\n",
    "for col in binary_columns:\n",
    "    churn_data[col] = churn_data[col].map({\"No\":0, \"Yes\":1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_data[\"TotalCharges\"] = churn_data[\"TotalCharges\"].apply(lambda x: np.nan if x==' ' else x).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TotalCharges', ylabel='Density'>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=churn_data, x=\"TotalCharges\", hue=\"Churn\", fill=\"Churn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1600 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploratory Data Analysis on categorical variables\n",
    "\n",
    "cat_columns_plot = [col for col in churn_data.select_dtypes(include=[\"object\",\"int\"]).columns if col not in [\"Churn\",\"tenure\"]]\n",
    "\n",
    "plt.figure(figsize=(16,16))\n",
    "for i,col in enumerate(cat_columns_plot):\n",
    "    plt.subplot(5,3,i+1)\n",
    "\n",
    "    df_plot = pd.crosstab(churn_data[col], churn_data[\"Churn\"].map({0:\"No Churn\", 1:\"Churn\"}), normalize=\"index\").apply(lambda x: np.round(100*x)).reset_index(drop=False)\n",
    "    df_plot.rename_axis(None, axis=1, inplace=True)\n",
    "    df_plot = df_plot.melt(id_vars=[col])\n",
    "\n",
    "    sns.barplot(data = df_plot, x=col, y=\"value\", hue=\"variable\")\n",
    "    plt.xlabel(\"\")\n",
    "    plt.ylabel(\"\")\n",
    "    plt.title(str(col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = churn_data.drop(columns=[\"Churn\"])\n",
    "y = churn_data[\"Churn\"]\n",
    "\n",
    "# Split train/test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, OneHotEncoder\n",
    "\n",
    "num_columns = list(churn_data.select_dtypes(include=[\"float\"]).columns)\n",
    "num_columns.append(\"tenure\")\n",
    "\n",
    "cat_columns = churn_data.select_dtypes(include=[\"object\"]).columns\n",
    "\n",
    "# Build data processing pipeline\n",
    "ct = ColumnTransformer(\n",
    "    [(\"numerical\", MinMaxScaler(), num_columns), \n",
    "     (\"categorical\", OneHotEncoder(sparse_output=False), cat_columns)], \n",
    "      remainder='passthrough')\n",
    "\n",
    "X_train_pp = ct.fit_transform(X_train)\n",
    "X_test_pp = ct.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy 0.8759318423855165\n",
      "Test Accuracy 0.7757274662881476\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "\n",
    "rf = RandomForestClassifier(max_depth=12, class_weight=\"balanced_subsample\")\n",
    "rf.fit(X_train_pp, y_train)\n",
    "\n",
    "y_pred_train = rf.predict(X_train_pp)\n",
    "y_pred_test = rf.predict(X_test_pp)\n",
    "\n",
    "print(\"Train Accuracy\", accuracy_score(y_train, y_pred_train))\n",
    "print(\"Test Accuracy\", accuracy_score(y_test, y_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x255137ceb20>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred_test, normalize=\"true\")*100\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=rf.classes_)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] feature_fraction is set=0.9, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9\n",
      "[LightGBM] [Info] Number of positive: 1495, number of negative: 4139\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000387 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 627\n",
      "[LightGBM] [Info] Number of data points in the train set: 5634, number of used features: 25\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265353 -> initscore=-1.018328\n",
      "[LightGBM] [Info] Start training from score -1.018328\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(feature_fraction=0.9, learning_rate=0.05, num_leaves=30)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LGBMClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(feature_fraction=0.9, learning_rate=0.05, num_leaves=30)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier(feature_fraction=0.9, learning_rate=0.05, num_leaves=30)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "params = {\n",
    "'boosting_type': 'gbdt',\n",
    "'num_leaves': 30,\n",
    "'learning_rate': 0.05,\n",
    "'feature_fraction': 0.9\n",
    "}\n",
    "\n",
    "clf = lgb.LGBMClassifier(**params)\n",
    "clf.fit(X_train_pp, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] feature_fraction is set=0.9, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9\n",
      "[LightGBM] [Warning] feature_fraction is set=0.9, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9\n",
      "Train Accuracy 0.8473553425630103\n",
      "Test Accuracy 0.7913413768630234\n"
     ]
    }
   ],
   "source": [
    "y_pred_lgb_train = clf.predict(X_train_pp)\n",
    "y_pred_lgb_test = clf.predict(X_test_pp)\n",
    "\n",
    "print(\"Train Accuracy\", accuracy_score(y_train, y_pred_lgb_train))\n",
    "print(\"Test Accuracy\", accuracy_score(y_test, y_pred_lgb_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='importance', ylabel='variable'>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZWVlkZGTcc7apQoUKeueKonDno+IMbdvS0rJcr6loxumzzz6jRYsWennGxsYAeHt7c+rUKXbs2MHu3bvp378/nTp1YtOmTeVqSwghhBDPHplpEuIZZGNjQ/Xq1YmPj9dLj4+Pp169egCYmpoCUFBQUKzMuHHj6NatG/Xr10en03Hx4sVS2+rbty+mpqbMmzevxPyrV68aHHd52y5Sp04dDh48qJd253m1atWoXr06J0+exM3NTe8o2hADbvfbgAED+Oyzz/jqq6/YvHkzly9fNjh+IYQQQjybZKZJiGfUpEmTmD59OrVr16Zx48ZERESQkJDAunXrAKhatSrm5ubs3LmT5557DjMzM2xtbXF3d2ft2rX4+PiQnZ3NpEmTMDc3L7UdJycnFi5cyJgxY8jOziYgIAAXFxf++OMP1qxZg5WVlcHbjpe37SJjx45lxIgR+Pj40KpVK7766iuOHTuGq6urViYsLIxx48Zha2tLly5dyM3N5dChQ1y5coUJEybw4Ycf4ujoSJMmTTAyMuLrr7/GwcFBHsgrhBBCCBk0CfGsGjduHFlZWUycOJHMzEzq1avH1q1bcXd3B8DExITFixczY8YMpk2bxosvvkhsbCyff/45I0eOxNvbGycnJ+bMmXPP5z2NGjUKDw8P5s+fT69evfj7779xcXGhR48eepsl3Mv9tA3g7+/PyZMnCQkJ4ebNm/Tv35+goCBth0CA4cOHY2FhwQcffMCkSZOwtLSkYcOGvPnmmwBYW1szb948jh8/jrGxMc2aNWP79u0YGZVvQv6HWa/K/U1CCCHEM0ZR77yhQAghnhGdO3fGwcGBtWvXPpb2srOzsbW1JSsrSwZNQgghxFPC0L/fMtMkhHjq3bhxg2XLluHr64uxsTEbNmxg9+7d7Nq160mHJoQQQohngAyahBBPPUVR2L59O7Nnz+bmzZvUqVOHzZs306lTp8ceS5t3N2Csu/d9WEII8bQ4/EHAkw5BiCdOBk1CiKeeubk5u3fvftJhCCGEEOIZJVuOCyEeu3bt2mkbMAghhBBC/NPJoEkI8a+lqiq3bt160mEIIYQQ4h9OBk1CiMcqKCiIuLg4Fi1ahKIoKIpCeno6v/zyC127dsXKyopq1aoxZMgQvQfbtmvXjnHjxjF58mQqVqyIg4MDoaGhWn56ejqKopCQkKClXb16FUVRiI2NBSA2NhZFUdixYwdNmzZFp9Oxb98+CgsLCQ8Pp1atWpibm+Pl5cWmTZseU48IIYQQ4p9OBk1CiMdq0aJFtGzZkhEjRpCRkUFGRgbW1tZ06NCBJk2acOjQIXbu3Mlff/1F//799a5dvXo1lpaW7N+/n3nz5jFjxoz72iFvypQpzJ07l5SUFBo1akR4eDhr1qxh2bJl/Prrr7z11lsMHjyYuLi4UuvIzc0lOztb7xBCCCHEs0k2ghBCPFa2traYmppiYWGBg4MDALNmzaJJkybMmTNHK7dq1SqcnJz4/fff8fDwAKBRo0ZMnz4dAHd3dz755BNiYmLo3LlzuWKYMWOGdk1ubi5z5sxh9+7dtGzZEgBXV1f27dvH8uXLadu2bYl1hIeHExYWVr4XL4QQQoinkgyahBBPXGJiInv27MHKyqpYXlpamt6g6U6Ojo5kZmaWuz0fHx/t5xMnTnDjxo1iA6+8vDyaNGlSah1Tp05lwoQJ2nl2djZOTk7ljkUIIYQQ/3wyaBJCPHE5OTn07NmT999/v1ieo6Oj9nOFChX08hRFobCwEAAjo9urjVVV1fLz8/NLbM/S0lKvbYBt27ZRo0YNvXI6na7UmHU6XZn5QgghhHh2yKBJCPHYmZqaUlBQoJ17e3uzefNmXFxcMDG5v3+WqlSpAkBGRoY2Q3TnphClqVevHjqdjjNnzpS6FE8IIYQQ/24yaBJCPHYuLi7s37+f9PR0rKysGD16NJ999hmvvvqqtjveiRMn+PLLL1m5ciXGxsb3rNPc3Jznn3+euXPnUqtWLTIzM3n33XfveZ21tTUhISG89dZbFBYW8sILL5CVlUV8fDw2NjYEBgY+jJcshBBCiKeY7J4nhHjsQkJCMDY2pl69elSpUoW8vDzi4+MpKCjgpZdeomHDhrz55pvY2dlpy+4MsWrVKm7dukXTpk158803mTVrlkHXzZw5k/fee4/w8HA8PT3p0qUL27Zto1atWvf7EoUQQgjxDFHUO28AEEIIcV+ys7OxtbUlKysLGxubJx2OEEIIIQxg6N9vmWkSQgghhBBCiDLIoEkIIYQQQgghyiAbQQghxEPU5t0NGOvMy33d4Q8CHkE0QgghhHgYZKZJCPHQhIaG0rhx4ycdhhBCCCHEQyWDJvGvFRQUxCuvvGJweUVRiIqKemTxlFdsbCyKonD16lW99AsXLvDGG29Qs2ZNdDodDg4O+Pr6Eh8f/8hjCgkJISYm5pG3AxAZGYmiKCiKgrGxMfb29rRo0YIZM2aQlZWlV9aQPnFxceGjjz56LLELIYQQ4ukiy/OEeMzy8/OpUKHCI6u/T58+5OXlsXr1alxdXfnrr7+IiYnh0qVL911nXl4epqam9yxnZWWFlZXVfbdTXjY2NqSmpqKqKlevXuXHH38kPDyciIgI4uPjqV69OvBo+kQIIYQQ/x4y0yQE0K5dO8aNG6c9WNXBwYHQ0FAt38XFBYBevXqhKIp2DvDtt9/i7e2NmZkZrq6uhIWFcevWLS1fURSWLl3Kyy+/jKWlJbNnz9aWsa1duxYXFxdsbW0ZOHAg165d064rLCwkPDycWrVqYW5ujpeXF5s2bQIgPT2d9u3bA2Bvb4+iKAQFBXH16lX27t3L+++/T/v27XF2dqZ58+ZMnTqVl19+Wav76tWrDB8+nCpVqmBjY0OHDh1ITEzU8oviW7lyJbVq1cLMzIwVK1ZQvXp1CgsL9frOz8+P4OBgvevutGrVKurXr49Op8PR0ZExY8YYHMe9KIqCg4MDjo6OeHp6MmzYMH788UdycnKYPHmy1oYhfSKEEEIIURoZNAnx/61evRpLS0v279/PvHnzmDFjBrt27QLg4MGDAERERJCRkaGd7927l4CAAMaPH09ycjLLly8nMjKS2bNn69UdGhpKr169SEpK0gYYaWlpREVFER0dTXR0NHFxccydO1e7Jjw8nDVr1rBs2TJ+/fVX3nrrLQYPHkxcXBxOTk5s3rwZgNTUVDIyMli0aJE20xMVFUVubm6pr7Vfv35kZmayY8cODh8+jLe3Nx07duTy5ctamRMnTrB582a2bNlCQkIC/fr149KlS+zZs0crc/nyZXbu3Im/v3+J7SxdupTRo0czcuRIkpKS2Lp1K25ubuWKo7yqVq2Kv78/W7dupaCgwOA+Ka/c3Fyys7P1DiGEEEI8m2TQJMT/16hRI6ZPn467uzsBAQH4+Pho9+dUqVIFADs7OxwcHLTzsLAwpkyZQmBgIK6urnTu3JmZM2eyfPlyvboHDRrE0KFDcXV1pWbNmsDtmaTIyEgaNGjAiy++yJAhQ7T2cnNzmTNnDqtWrcLX1xdXV1eCgoIYPHgwy5cvx9jYmIoVKwK3BwkODg7Y2tpiYmJCZGQkq1evxs7OjtatW/Of//yHY8eOabHs27ePAwcO8PXXX+Pj44O7uzvz58/Hzs5Om8mC20vy1qxZQ5MmTWjUqBH29vZ07dqV9evXa2U2bdpE5cqVtVmvu82aNYuJEycyfvx4PDw8aNasGW+++Wa54rgfdevW5dq1a1y6dMmgPrkf4eHh2NraaoeTk9MD1SeEEEKIfy4ZNAnx/zVq1Ejv3NHRkczMzDKvSUxMZMaMGdpshpWVFSNGjCAjI4MbN25o5Xx8fIpd6+LigrW1dYntnThxghs3btC5c2e9utesWUNaWlqZMfXp04dz586xdetWunTpQmxsLN7e3kRGRmox5+TkUKlSJb26T506pVe3s7OzNjgs4u/vz+bNm7UZm3Xr1jFw4ECMjIr/U5KZmcm5c+fo2LFjqX1nSBz3Q1VV4PbyPUP65H5MnTqVrKws7Th79uwDxSyEEEKIfy7ZCEKI/+/uzRkURSl2/87dcnJyCAsLo3fv3sXyzMzMtJ8tLS3L1V5OTg4A27Zto0aNGnrldDpdmTEVtd25c2c6d+7Me++9x/Dhw5k+fTpBQUHk5OTg6OhIbGxssevs7OzKjLlnz56oqsq2bdto1qwZe/fuZeHChSXGYG5e9rOKDI3jfqSkpGBjY0OlSpW0tLL65H7odDqD3gshhBBCPP1k0CSEgSpUqEBBQYFemre3N6mpqXr36TwM9erVQ6fTcebMGdq2bVtimaLd7O6OqbT6irZL9/b25vz585iYmOhtaGEIMzMzevfuzbp16zhx4gR16tTB29u7xLLW1ta4uLgQExNT4vK9B4mjLJmZmaxfv55XXnmlxBmwInf2iRBCCCFEWWTQJISBigYArVu3RqfTYW9vz7Rp0+jRowc1a9akb9++GBkZkZiYyC+//MKsWbPuuy1ra2tCQkJ46623KCws5IUXXiArK4v4+HhsbGwIDAzE2dkZRVGIjo6mW7dumJubk5ubS79+/QgODqZRo0ZYW1tz6NAh5s2bh5+fHwCdOnWiZcuWvPLKK8ybNw8PDw/OnTvHtm3b6NWrV4lLCe/k7+9Pjx49+PXXXxk8eHCZZUNDQ3n99depWrUqXbt25dq1a8THxzN27NgHjgNuL8M7f/68tuX4Tz/9xJw5c7C1tdU21bh06dI9+6TIn3/+SUJCgl6as7Mz9vb294xFCCGEEM8uGTQJYaAFCxYwYcIEPvvsM2rUqEF6ejq+vr5ER0czY8YM3n//fSpUqEDdunUZPnz4A7c3c+ZMqlSpQnh4OCdPnsTOzg5vb2/+85//AFCjRg1tI4qhQ4cSEBDA8uXLadGiBQsXLiQtLY38/HycnJwYMWKEdp2iKGzfvp133nmHoUOHcuHCBRwcHGjTpg3VqlW7Z1wdOnSgYsWKpKamMmjQoDLLBgYGcvPmTRYuXEhISAiVK1emb9++DyUOgOzsbBwdHVEUBRsbG+rUqUNgYCDjx4/HxsYGuP3sqHv1SZH58+czf/58vbS1a9fec3AohBBCiGebohbdMS2EEOK+ZWdnY2trS1ZWljZgE0IIIcQ/m6F/v2X3PCGEEEIIIYQogwyahBD/SPXr19fbivzOY926dU86PCGEEEL8i8g9TUKIf6Tt27eTn59fYp6h9zw9CW3e3YCxruzt1h+mwx8EPLa2hBBCiH8rmWkS4h8qNDSUxo0bP+kwisURFBTEK6+88sjbdXZ2xs3NrcTjzocC36/Y2FgUReHq1asPHqwQQgghnmkyaBIP7H6+RCuK8o96Rk5pX6AvXLjAG2+8Qc2aNdHpdDg4OODr60t8fPwjjykkJISYmJhH3g5AZGQkiqIUO1auXPlY43hU2rVrx5tvvqmX1qpVKzIyMrC1tX0yQQkhhBDiqSHL88RTLT8/nwoVKjyy+vv06UNeXh6rV6/G1dWVv/76i5iYGC5dunTfdebl5WkPpi1L0f07j4uNjQ2pqal6aba2tpibmz/yOFRVpaCgABOTx/dPkqmpKQ4ODo+tPSGEEEI8vWSmSTx07dq1Y9y4cUyePJmKFSvi4OBAaGiolu/i4gJAr169UBRFOwf49ttv8fb2xszMDFdXV8LCwrh165aWrygKS5cu5eWXX8bS0pLZs2dry8fWrl2Li4sLtra2DBw4kGvXrmnXFRYWEh4eTq1atTA3N8fLy4tNmzYBkJ6eTvv27QGwt7dHURSCgoK4evUqe/fu5f3336d9+/Y4OzvTvHlzpk6dyssvv6zVffXqVYYPH06VKlWwsbGhQ4cOJCYmavlF8a1cuZJatWphZmbGihUrqF69OoWFhXp95+fnR3BwsN51d1q1ahX169dHp9Ph6OjImDFjDI7jXhRFwcHBQe8wNzcvdZlgWFiY1tbrr79OXl6eQf0N/zezt2PHDpo2bYpOp2Pfvn0lxpWUlESHDh0wNzenUqVKjBw5kpycHC2/aKaztHiCgoKIi4tj0aJF2gxaenp6ibOL8fHxtGvXDgsLC+zt7fH19eXKlSsG96EQQgghnk0yaBKPxOrVq7G0tGT//v3MmzePGTNmsGvXLgAOHjwIQEREBBkZGdr53r17CQgIYPz48SQnJ7N8+XIiIyOZPXu2Xt2hoaH06tWLpKQkbYCRlpZGVFQU0dHRREdHExcXx9y5c7VrwsPDWbNmDcuWLePXX3/lrbfeYvDgwcTFxeHk5MTmzZsBSE1NJSMjg0WLFmkzPVFRUeTm5pb6Wvv160dmZiY7duzg8OHDeHt707FjRy5fvqyVOXHiBJs3b2bLli0kJCTQr18/Ll26xJ49e7Qyly9fZufOnfj7+5fYztKlSxk9ejQjR44kKSmJrVu34ubmVq44HpaYmBhSUlKIjY1lw4YNbNmyhbCwMC2/rP6+05QpU5g7dy4pKSk0atSoWDvXr1/H19cXe3t7Dh48yNdff83u3bv1Bov3imfRokW0bNmSESNGkJGRQUZGBk5OTsXaSkhIoGPHjtSrV4+ffvqJffv20bNnTwoKCkrsg9zcXLKzs/UOIYQQQjybZHmeeCQaNWrE9OnTAXB3d+eTTz4hJiaGzp07U6VKFQDs7Oz0lkeFhYUxZcoUAgMDAXB1dWXmzJlMnjxZqwtg0KBBDB06VK+9wsJCIiMjtQ0ChgwZQkxMDLNnzyY3N5c5c+awe/duWrZsqdW9b98+li9fTtu2balYsSIAVatWxc7OTqs3MjKSESNGsGzZMry9vWnbti0DBw7UvuDv27ePAwcOkJmZiU6nA2D+/PlERUWxadMmRo4cCdxekrdmzRrttQN07dqV9evX07FjRwA2bdpE5cqVtVmvu82aNYuJEycyfvx4La1Zs2bliqMsWVlZesvwrKysOH/+fIllTU1NWbVqFRYWFtSvX58ZM2YwadIkZs6cSX5+/j37u8iMGTPo3LlzqTGtX7+emzdvsmbNGiwtLQH45JNP6NmzJ++//762i15Z8dja2mJqaoqFhUWZy/HmzZuHj48Pn376qZZWv379UsuHh4frDRSFEEII8eySQZN4JO6eNXB0dCQzM7PMaxITE4mPj9ebWSooKODmzZvcuHEDCwsLAHx8fIpd6+Liorej2p3tnThxghs3bhT7cp6Xl0eTJk3KjKlPnz50796dvXv38vPPP7Njxw7mzZvHypUrCQoKIjExkZycHCpVqqR33d9//01aWpp27uzsrDdgAvD392fEiBF8+umn6HQ61q1bx8CBAzEyKj4BnJmZyblz57QB1t0MjaMs1tbWHDlyRDsvKY4iXl5e2vsB0LJlS3Jycjh79iw5OTkG93dJ7+WdUlJS8PLy0gZMAK1bt6awsJDU1FRt0FRWPM7OzmW2UaRoBtBQU6dOZcKECdp5dnZ2iTNYQgghhHj6yaBJPBJ3b86gKEqx+3fulpOTQ1hYGL179y6WZ2Zmpv185xdoQ9oruv9l27Zt1KhRQ69c0axMWczMzOjcuTOdO3fmvffeY/jw4UyfPp2goCBycnJwdHQkNja22HV3zliVFHPPnj1RVZVt27bRrFkz9u7dy8KFC0uMwdy87Of+GBpHWYyMjPSW+92v8vR3Sf3ypNyrj++m0+kM+vwIIYQQ4ukngybxRFSoUKHYvSLe3t6kpqY+lC/ud6pXrx46nY4zZ87oLQ27U9FudqXdv3J3fUXbpXt7e3P+/HlMTEz0NrQwhJmZGb1792bdunWcOHGCOnXq4O3tXWJZa2trXFxciImJKXH53oPEcT8SExP5+++/tYHGzz//jJWVFU5OTlSsWPGe/W0oT09PIiMjuX79ujbAio+Px8jIiDp16hgUD9x+f+/13jZq1IiYmBhZcieEEEKIYmTQJJ6IogFA69at0el02NvbM23aNHr06EHNmjXp27cvRkZGJCYm8ssvvzBr1qz7bsva2pqQkBDeeustCgsLeeGFF8jKyiI+Ph4bGxsCAwNxdnZGURSio6Pp1q0b5ubm5Obm0q9fP4KDg2nUqBHW1tYcOnSIefPm4efnB0CnTp1o2bIlr7zyCvPmzcPDw4Nz586xbds2evXqdc/lZ/7+/vTo0YNff/2VwYMHl1k2NDSU119/napVq9K1a1euXbtGfHw8Y8eOfeA4yisvL49hw4bx7rvvkp6ezvTp0xkzZgxGRkYG9beh/P39mT59OoGBgYSGhnLhwgXGjh3LkCFDtKV594oHbn/e9u/fT3p6OlZWVto9bHeaOnUqDRs2ZNSoUbz++uuYmpqyZ88e+vXrR+XKlR+804QQQgjx1JLd88QTsWDBAnbt2oWTk5N2n4uvry/R0dF8//33NGvWjOeff56FCxcafE9KWWbOnMl7771HeHg4np6edOnShW3btlGrVi0AatSooW1EUa1aNcaMGYOVlRUtWrRg4cKFtGnThgYNGvDee+8xYsQIPvnkE+D2MsDt27fTpk0bhg4dioeHBwMHDuT06dN6X+pL06FDBypWrEhqaiqDBg0qs2xgYCAfffQRn376KfXr16dHjx4cP378ocRRXh07dsTd3Z02bdowYMAAXn75Zb1t5e/V34aysLDgu+++4/LlyzRr1oy+ffvSsWNHrf8NjSckJARjY2Pq1atHlSpVOHPmTLG2PDw8+P7770lMTKR58+a0bNmSb7/99rE+O0oIIYQQ/0yKqqrqkw5CCCHuV9EztYqWTD4p2dnZ2NrakpWVhY2NzRONRQghhBCGMfTvt8w0CSGEEEIIIUQZZNAkxL9A/fr1tYf13n2sW7fuSYcnhBBCCPGPJsvzhPgXOH36NPn5+SXmVatWTe8ZV+L+FE3ve41dhrGu7O3LD38Q8JiiEkIIIURZDF2eJ3c4C/Ev8DA203gYQkNDiYqKIiEh4UmHIoQQQghhMFmeJ0QpgoKCeOWVVwwuryjKE9+M4E6xsbEoisLVq1f10i9cuMAbb7xBzZo10el0ODg44OvrS3x8/COPKSQkhJiYmEfeDkBkZCSKotClSxe99KtXr6IoSokPAhZCCCGEKInMNAnxD5Ofn0+FChUeWf19+vQhLy+P1atX4+rqyl9//UVMTAyXLl267zrz8vK0BwSXpeg+qsfFxMSE3bt3s2fPnhIfCiyEEEIIYQiZaRLCAO3atWPcuHFMnjyZihUr4uDgoPccIBcXFwB69eqFoijaOcC3336Lt7c3ZmZmuLq6EhYWxq1bt7R8RVFYunQpL7/8MpaWlsyePZvQ0FAaN27M2rVrcXFxwdbWloEDB3Lt2jXtusLCQsLDw6lVqxbm5uZ4eXmxadMmANLT07VBgr29PYqiaFtz7927l/fff5/27dvj7OxM8+bNmTp1Ki+//LJW99WrVxk+fDhVqlTBxsaGDh06kJiYqOUXxbdy5Upq1aqFmZkZK1asoHr16hQWFur1nZ+fH8HBwXrX3WnVqlXUr18fnU6Ho6MjY8aMMTiOe7G0tCQ4OJgpU6aUWS4pKYkOHTpgbm5OpUqVGDlyJDk5OWVek5ubS3Z2tt4hhBBCiGeTDJqEMNDq1auxtLRk//79zJs3jxkzZrBr1y4ADh48CEBERAQZGRna+d69ewkICGD8+PEkJyezfPlyIiMjmT17tl7doaGh9OrVi6SkJG2AkZaWRlRUFNHR0URHRxMXF8fcuXO1a8LDw1mzZg3Lli3j119/5a233mLw4MHExcXh5OTE5s2bAUhNTSUjI4NFixZpMz1RUVHk5uaW+lr79etHZmYmO3bs4PDhw3h7e9OxY0cuX76slTlx4gSbN29my5YtJCQk0K9fPy5dusSePXu0MpcvX2bnzp34+/uX2M7SpUsZPXo0I0eOJCkpia1bt+Lm5lauOO4lNDSUpKQkbUB5t+vXr+Pr64u9vT0HDx7k66+/Zvfu3XqDt5KEh4dja2urHU5OTgbHJIQQQoinjCqEKFFgYKDq5+enqqqqtm3bVn3hhRf08ps1a6a+/fbb2jmgfvPNN3plOnbsqM6ZM0cvbe3ataqjo6PedW+++aZemenTp6sWFhZqdna2ljZp0iS1RYsWqqqq6s2bN1ULCwv1xx9/1Ltu2LBh6quvvqqqqqru2bNHBdQrV67oldm0aZNqb2+vmpmZqa1atVKnTp2qJiYmavl79+5VbWxs1Js3b+pdV7t2bXX58uVafBUqVFAzMzP1yvj5+anBwcHa+fLly9Xq1aurBQUF2nVeXl5afvXq1dV33nlHLYkhcZQlIiJCtbW1VVVVVadMmaJ6eHio+fn56pUrV1RA3bNnj6qqqrpixQrV3t5ezcnJ0a7dtm2bamRkpJ4/f77U+m/evKlmZWVpx9mzZ1VA9Rq7TPUOWV3mIYQQQoh/hqysLBVQs7KyyiwnM01CGKhRo0Z6546OjmRmZpZ5TWJiIjNmzNB7LtKIESPIyMjgxo0bWjkfH59i17q4uOhtBX5neydOnODGjRt07txZr+41a9aQlpZWZkx9+vTh3LlzbN26lS5duhAbG4u3tzeRkZFazDk5OVSqVEmv7lOnTunV7ezsTJUqVfTq9vf3Z/Pmzdos1rp16xg4cCBGRsX/qcnMzOTcuXN07Nix1L4zJA5DvP3221y4cIFVq1YVy0tJScHLywtLS0strXXr1hQWFpKamlpqnTqdDhsbG71DCCGEEM8m2QhCCAPdvTmDoijF7t+5W05ODmFhYfTu3btYnpmZmfbznV/YDWmv6H6bbdu2UaNGDb1yOp2uzJiK2u7cuTOdO3fmvffeY/jw4UyfPp2goCBycnJwdHQscXc5Ozu7MmPu2bMnqqqybds2mjVrxt69e1m4cGGJMZibl/0sI0PjMISdnR1Tp04lLCyMHj16lOtaIYQQQggZNAnxkFSoUIGCggK9NG9vb1JTU/Xu03kY6tWrh06n48yZM7Rt27bEMkW72d0dU2n1FW2X7u3tzfnz5zExMdHb0MIQZmZm9O7dm3Xr1nHixAnq1KmDt7d3iWWtra1xcXEhJiamxJ3tHiSOkowdO5bFixezaNEivXRPT08iIyO5fv26NhCMj4/HyMiIOnXqPHC7QgghhHj6yaBJiIekaADQunVrdDod9vb2TJs2jR49elCzZk369u2LkZERiYmJ/PLLL8yaNeu+27K2tiYkJIS33nqLwsJCXnjhBbKysoiPj8fGxobAwECcnZ1RFIXo6Gi6deuGubk5ubm59OvXj+DgYBo1aoS1tTWHDh1i3rx5+Pn5AdCpUydatmzJK6+8wrx58/Dw8ODcuXNs27aNXr16lbiU8E7+/v706NGDX3/9lcGDB5dZNjQ0lNdff52qVavStWtXrl27Rnx8PGPHjn3gOO5mZmZGWFgYo0ePLhbv9OnTCQwMJDQ0lAsXLjB27FiGDBlCtWrVytWGEEIIIZ5NMmgS4iFZsGABEyZM4LPPPqNGjRqkp6fj6+tLdHQ0M2bM4P3336dChQrUrVuX4cOHP3B7M2fOpEqVKoSHh3Py5Ens7Ozw9vbmP//5DwA1atQgLCyMKVOmMHToUAICAli+fDktWrRg4cKFpKWlkZ+fj5OTEyNGjNCuUxSF7du388477zB06FAuXLiAg4MDbdq0MWgQ0aFDBypWrEhqaiqDBg0qs2xgYCA3b95k4cKFhISEULlyZfr27ftQ4iitvQULFpCcnKylWVhY8N133zF+/HiaNWuGhYUFffr04cMPP7yvNn6Y9arc3ySEEEI8YxRVVdUnHYQQQjztsrOzsbW1JSsrSwZNQgghxFPC0L/fsnueEEIIIYQQQpRBlucJIZ5K9evX5/Tp0yXmLV++vNQH6j5qbd7dgLGu7J0BH7fDHwQ86RCEEEKIp5oMmoQQT6Xt27eTn59fYp5s4CCEEEKIh0kGTUL8yyiKwjfffMMrr7zypEN5IM7Ozg+trmelT4QQQgjxaMg9TeKRO3/+PGPHjsXV1RWdToeTkxM9e/YkJibmobXRrl073nzzzYdWX1liY2NRFIWrV68afE1QUBCKovD6668Xyxs9ejSKohAUFPTwguT2dt6NGzd+KHVFRkaW+4Gy/0QPs0+EEEII8e8hgybxSKWnp9O0aVP+97//8cEHH5CUlMTOnTtp3759seflPGqqqnLr1q3H2uadnJyc+PLLL/n777+1tJs3b7J+/Xpq1qz5xOISQgghhBBlk0GTeKRGjRqFoigcOHCAPn364OHhQf369ZkwYQI///wzAGfOnMHPzw8rKytsbGzo378/f/31l1ZH0ezA2rVrcXFxwdbWloEDB3Lt2jXg9ixOXFwcixYtQlEUFEUhPT1dmxHasWMHTZs2RafTsW/fPtLS0vDz86NatWpYWVnRrFkzdu/erRd3bm4ub7/9Nk5OTuh0Otzc3Pj8889JT0+nffv2ANjb25drhsjb2xsnJye2bNmipW3ZsoWaNWvSpEmTYu2PGzeOqlWrYmZmxgsvvMDBgwe1/KLXFhMTg4+PDxYWFrRq1YrU1FTg9sxQWFgYiYmJWp9ERkZq11+8eJFevXphYWGBu7s7W7duLTXu2NhYhg4dSlZWllZXaGgoAFeuXCEgIAB7e3ssLCzo2rUrx48fL7MfimL/7rvvaNKkCebm5nTo0IHMzEx27NiBp6cnNjY2DBo0iBs3bvwj+6QonuzsbL1DCCGEEM8mGTSJR+by5cvs3LmT0aNHY2lpWSzfzs6OwsJC/Pz8uHz5MnFxcezatYuTJ08yYMAAvbJpaWlERUURHR1NdHQ0cXFxzJ07F4BFixbRsmVLRowYQUZGBhkZGTg5OWnXTpkyhblz55KSkkKjRo3IycmhW7duxMTEcPToUbp06ULPnj05c+aMdk1AQAAbNmxg8eLFpKSksHz5cqysrHBycmLz5s0ApKamkpGRwaJFiwzuk+DgYCIiIrTzVatWMXTo0GLlJk+ezObNm1m9ejVHjhzBzc0NX19fLl++rFfunXfeYcGCBRw6dAgTExOCg4MBGDBgABMnTqR+/fpan9zZp2FhYfTv359jx47RrVs3/P39i9VdpFWrVnz00UfY2NhodYWEhAC3B6yHDh1i69at/PTTT6iqSrdu3UrdoOFOoaGhfPLJJ/z444+cPXuW/v3789FHH7F+/Xq2bdvG999/z8cff/yP7BOA8PBwbG1ttePOz5wQQgghni0yaBKPzIkTJ1BVlbp165ZaJiYmhqSkJNavX0/Tpk1p0aIFa9asIS4uTm8WobCwkMjISBo0aMCLL77IkCFDtHuibG1tMTU1xcLCAgcHBxwcHDA2NtaunTFjBp07d6Z27dpUrFgRLy8vXnvtNRo0aIC7uzszZ86kdu3a2szC77//zsaNG1m1ahW9evXC1dWVjh07MmDAAIyNjalYsSIAVatWxcHBAVtbW4P7ZPDgwezbt4/Tp09z+vRp4uPjGTx4sF6Z69evs3TpUj744AO6du1KvXr1+OyzzzA3N+fzzz/XKzt79mzatm1LvXr1mDJlCj/++CM3b97E3NwcKysrTExMtD4xN/+/bbCDgoJ49dVXcXNzY86cOeTk5HDgwIESYzY1NcXW1hZFUbS6rKysOH78OFu3bmXlypW8+OKLeHl5sW7dOv7880+ioqLu2RezZs2idevWNGnShGHDhhEXF8fSpUtp0qQJL774In379mXPnj3/yD4BmDp1KllZWdpx9uzZe75mIYQQQjydZNAkHhlVVe9ZJiUlBScnJ73/pa9Xrx52dnakpKRoaS4uLlhbW2vnjo6OZGZmGhSHj4+P3nlOTg4hISF4enpiZ2eHlZUVKSkp2kxTQkICxsbGtG3b1qD6y6NKlSp0796dyMhIIiIi6N69O5UrV9Yrk5aWRn5+Pq1bt9bSKlSoQPPmzfX6BKBRo0baz46OjgAG9cud11laWmJjY6NdV79+faysrLCysqJr166l1pGSkoKJiQktWrTQ0ipVqkSdOnW0OLt27arVVb9+/VJjqFatGhYWFri6uuqlFcX0pPukJDqdDhsbG71DCCGEEM8m2XJcPDLu7u4oisJvv/32wHVVqFBB71xRFAoLCw269u6lgSEhIezatYv58+fj5uaGubk5ffv2JS8vD0Bv9uFRCA4OZsyYMQAsWbLkgeq6s18URQEwqF/K6s87n3/0oH2xcuVKbeOLu9u8O/YHeY/LqhcevE+EEEII8e8mM03ikalYsSK+vr4sWbKE69evF8u/evUqnp6enD17Vm9pU3JyMlevXqVevXoGt2VqakpBQYFBZePj4wkKCqJXr140bNgQBwcH0tPTtfyGDRtSWFhIXFxcqW0BBrd3ty5dupCXl0d+fj6+vr7F8mvXro2pqSnx8fFaWn5+PgcPHnxkfXInZ2dn3NzccHNzo0aNGqXW5enpya1bt9i/f7+WdunSJVJTU7U4a9SoodX1IM9VetJ9IoQQQoh/Nxk0iUdqyZIlFBQU0Lx5czZv3szx48dJSUlh8eLFtGzZkk6dOtGwYUP8/f05cuQIBw4cICAggLZt2xZbVlcWFxcX9u/fT3p6OhcvXixzhsDd3Z0tW7aQkJBAYmIigwYN0ivv4uJCYGAgwcHBREVFcerUKWJjY9m4cSNwe1ChKArR0dFcuHCBnJyccvWJsbExKSkpJCcn6917VcTS0pI33niDSZMmsXPnTpKTkxkxYgQ3btxg2LBhBrfj4uLCqVOnSEhI4OLFi+Tm5pYrzrvrysnJISYmhosXL3Ljxg3c3d3x8/NjxIgR7Nu3j8TERAYPHkyNGjXw8/O777ZK8k/sEyGEEEL8e8jyPPFIubq6cuTIEWbPns3EiRPJyMigSpUqNG3alKVLl6IoCt9++y1jx46lTZs2GBkZ0aVLF71d0wwREhJCYGAg9erV4++//+bUqVOllv3www8JDg6mVatWVK5cmbfffrvYdtFLly7lP//5D6NGjeLSpUvUrFmT//znP8Dt2ZOwsDCmTJnC0KFDCQgI0Nu62hD3uv9l7ty5FBYWMmTIEK5du4aPjw/fffcd9vb2BrfRp08ftmzZQvv27bl69SoRERH3/QDdVq1a8frrrzNgwAAuXbrE9OnTCQ0NJSIigvHjx9OjRw/y8vJo06YN27dvL7bU7WH4p/VJaX6Y9arc3ySEEEI8YxTVkLv1hRBClCk7OxtbW1uysrJk0CSEEEI8JQz9+y3L84QQQgghhBCiDLI8T4gHdObMmTI3I0hOTqZmzZqPMSLxJLV5dwPGuke7A+O9HP4g4Im2L4QQQjxrZNAkxAOqXr06CQkJZeYLIYQQQoinlyzPE+Iezp8/z9ixY3F1dUWn0+Hk5ETPnj2JiYkBwMTERNtWu6TDxMSw/5to164db7755iN8Jf8nNjYWRVG4evVqua47e/YswcHBVK9eHVNTU5ydnRk/fjyXLl16NIEKIYQQQvwDyKBJiDKkp6fTtGlT/ve///HBBx+QlJTEzp07ad++PaNHj37s8aiqyq1btx57uwAnT57Ex8eH48ePs2HDBk6cOMGyZcuIiYmhZcuWXL58+YnE9agVPfRYCCGEEP9eMmgSogyjRo1CURQOHDhAnz598PDwoH79+kyYMIGff/5ZK3fmzBn8/PywsrLCxsaG/v3789dff2n5oaGhNG7cmLVr1+Li4oKtrS0DBw7k2rVrAAQFBREXF8eiRYtQFAVFUUhPT9dmhHbs2EHTpk3R6XTs27ePtLQ0/Pz8qFatGlZWVjRr1ozdu3frxZ6bm8vbb7+Nk5MTOp0ONzc3Pv/8c9LT02nfvj0A9vb2KIpi0Lbbo0ePxtTUlO+//562bdtSs2ZNunbtyu7du/nzzz955513tLIuLi7MmTOH4OBgrK2tqVmzJitWrNCr7+zZs/Tv3x87OzsqVqyIn5+f3kOG76SqKm5ubsyfP18vPSEhAUVROHHiBHD7gcnDhw+nSpUq2NjY0KFDBxITE7XyhvSbi4sLM2fOJCAgABsbG0aOHHnPvhFCCCHEs00GTUKU4vLly+zcuZPRo0djaWlZLN/Ozg6AwsJC/Pz8uHz5MnFxcezatYuTJ08yYMAAvfJpaWlERUURHR1NdHQ0cXFxzJ07F4BFixbRsmVLRowYQUZGBhkZGTg5OWnXTpkyhblz55KSkkKjRo3IycmhW7duxMTEcPToUbp06ULPnj05c+aMdk1AQAAbNmxg8eLFpKSksHz5cqysrHBycmLz5s0ApKamkpGRwaJFi+7ZF9999x2jRo3C3Fx/kwMHBwf8/f356quvuPMJBgsWLMDHx4ejR48yatQo3njjDVJTUwHIz8/H19cXa2tr9u7dS3x8PFZWVnTp0qXEmR1FUQgODiYiIkIvPSIigjZt2uDm5gZAv379yMzMZMeOHRw+fBhvb286duyozYIZ0m8A8+fPx8vLi6NHj/Lee++V2Ce5ublkZ2frHUIIIYR4NslGEEKU4sSJE6iqSt26dcssFxMTQ1JSEqdOndIGOmvWrKF+/focPHiQZs2aAbcHV5GRkVhbWwMwZMgQYmJimD17Nra2tpiammJhYYGDg0OxNmbMmEHnzp2184oVK+Ll5aWdz5w5k2+++YatW7cyZswYfv/9dzZu3MiuXbvo1KkTcPtBw3deD1C1alVt8FeW48ePo6oqnp6eJeZ7enpy5coVLly4QNWqVQHo1q0bo0aNAuDtt99m4cKF7Nmzhzp16vDVV19RWFjIypUrURQFuD0AsrOzIzY2lpdeeqlYG0FBQUybNo0DBw7QvHlz8vPzWb9+vTb7tG/fPg4cOEBmZiY6nQ64PfiJiopi06ZNjBw5Ei8vrzL7rUiHDh2YOHFimX0SHh5OWFjYPftOCCGEEE8/mWkSohSGPvc5JSUFJycnvZmhevXqYWdnR0pKipbm4uKiDZgAHB0dyczMNKgNHx8fvfOcnBxCQkLw9PTEzs4OKysrUlJStBmThIQEjI2Nadu2rUH1G6o8z8Ju1KiR9rOiKDg4OGivNzExkRMnTmBtbY2VlRVWVlZUrFiRmzdvkpaWVmJ91atXp3v37qxatQqA//73v+Tm5tKvXz+tzpycHCpVqqTVaWVlxalTp7Q679VvRe7u75JMnTqVrKws7Th79qzBfSOEEEKIp4vMNAlRCnd3dxRF4bfffnso9VWoUEHvXFEUCgsLDbr27uWBISEh7Nq1i/nz5+Pm5oa5uTl9+/bVlrbdvYTuQbm5uaEoCikpKfTq1atYfkpKCvb29lSpUkVLK+v15uTk0LRpU9atW1esrjvruNvw4cMZMmQICxcuJCIiggEDBmBhYaHV6ejoSGxsbLHrimbT7tVvRUpajnk3nU6nzWgJIYQQ4tkmgyYhSlGxYkV8fX1ZsmQJ48aNK/ZF+urVq9jZ2eHp6cnZs2c5e/asNtuUnJzM1atXy3zo7d1MTU0pKCgwqGx8fDxBQUHaACYnJ0dvE4WGDRtSWFhIXFyctjzv7rYAg9urVKkSnTt35tNPP+Wtt97SG5SdP3+edevWERAQoC21uxdvb2+++uorqlatio2NjUHXwO0lf5aWlixdupSdO3fyww8/6NV5/vx5TExMcHFxKfH6e/WbEEIIIURJZHmeEGVYsmQJBQUFNG/enM2bN3P8+HFSUlJYvHgxLVu2BKBTp040bNgQf39/jhw5woEDBwgICKBt27YGLfMq4uLiwv79+0lPT+fixYtlzkK5u7uzZcsWEhISSExMZNCgQXrlXVxcCAwMJDg4mKioKE6dOkVsbCwbN24EwNnZGUVRiI6O5sKFC+Tk5Nwzvk8++YTc3Fx8fX354YcfOHv2LDt37qRz587UqFGD2bNnG/xa/f39qVy5Mn5+fuzdu1eLb9y4cfzxxx+lXmdsbExQUBBTp07F3d1dew/g9vvQsmVLXnnlFb7//nvS09P58ccfeeeddzh06JBB/SaEEEIIURIZNAlRBldXV44cOUL79u2ZOHEiDRo0oHPnzsTExLB06VLg9rKzb7/9Fnt7e9q0aUOnTp1wdXXlq6++KldbISEhGBsbU69ePapUqVLsPps7ffjhh9jb29OqVSt69uyJr68v3t7eemWWLl1K3759GTVqFHXr1mXEiBFcv34dgBo1ahAWFsaUKVOoVq2a3iYIpXF3d+fQoUO4urrSv39/ateuzciRI2nfvj0//fSTtrmEISwsLPjhhx+oWbMmvXv3xtPTk2HDhnHz5s17zjwNGzaMvLw8hg4dqpeuKArbt2+nTZs2DB06FA8PDwYOHMjp06epVq2awf0mhBBCCHE3RS3Pnd1CCPGE7d27l44dO3L27FltMPRPkJ2dja2tLVlZWeVaciiEEEKIJ8fQv99yT5MQ4qmQm5vLhQsXCA0NpV+/fv+oAZMQQgghnm2yPE8IwZkzZ/S26b77KGup4OOyYcMGnJ2duXr1KvPmzXvS4QghhBDiX0SW5wkhuHXrVpm7yLm4uGBiIhPTZSma3vcauwxj3cPd8r0khz8IeORtCCGEEM86WZ4nhDCYiYkJbm5uTzoMIYQQQoh/JFmeJ4SBzp8/z9ixY3F1dUWn0+Hk5ETPnj2JiYl5qO20a9eON99886HWWZrY2FgUReHq1asGlVcUpcwjNDT0kcYrhBBCCPEkyEyTEAZIT0+ndevW2NnZ8cEHH9CwYUPy8/P57rvvGD16NL/99ttjjUdVVQoKCh77krmMjAzt56+++opp06aRmpqqpVlZWT3WeB6HvLw87WHAQgghhPh3kpkmIQwwatQoFEXhwIED9OnTBw8PD+rXr8+ECRP4+eeftXJnzpzBz88PKysrbGxs6N+/P3/99ZeWHxoaSuPGjVm7di0uLi7Y2toycOBArl27BkBQUBBxcXEsWrRIm71JT0/XZoR27NhB06ZN0el07Nu3j7S0NPz8/KhWrRpWVlY0a9aM3bt368Wem5vL22+/jZOTEzqdDjc3Nz7//HPS09Np3749APb29iiKQlBQUJn94ODgoB22trYoioKDgwPm5ubUqFFDGzwWFhZSsWJFnn/+ee3aL774AicnJ+08KSmJDh06YG5uTqVKlRg5cmSpD9lVVRU3Nzfmz5+vl56QkICiKJw4cQKAq1evMnz4cKpUqYKNjQ0dOnQgMTFRK29If7m4uDBz5kwCAgKwsbFh5MiRZfaJEEIIIZ59MmgS4h4uX77Mzp07GT16NJaWlsXy7ezsgNsDBT8/Py5fvkxcXBy7du3i5MmTDBgwQK98WloaUVFRREdHEx0dTVxcHHPnzgVg0aJFtGzZkhEjRpCRkUFGRobeQGPKlCnMnTuXlJQUGjVqRE5ODt26dSMmJoajR4/SpUsXevbsqbfbXUBAABs2bGDx4sWkpKSwfPlyrKyscHJyYvPmzQCkpqaSkZHBokWL7quPbG1tady4MbGxscDtAZGiKBw9elQbCMXFxdG2bVsArl+/jq+vL/b29hw8eJCvv/6a3bt3l/qQXUVRCA4OJiIiQi89IiKCNm3aaPdj9evXj8zMTHbs2MHhw4fx9vamY8eOXL58GcCg/gKYP38+Xl5eHD16lPfee6/EmHJzc8nOztY7hBBCCPFskkGTEPdw4sQJVFWlbt26ZZaLiYkhKSmJ9evX07RpU1q0aMGaNWuIi4vj4MGDWrnCwkIiIyNp0KABL774IkOGDNHui7K1tcXU1BQLCwttRsfY2Fi7dsaMGXTu3JnatWtTsWJFvLy8eO2112jQoAHu7u7MnDmT2rVrs3XrVgB+//13Nm7cyKpVq+jVqxeurq507NiRAQMGYGxsTMWKFQGoWrWqNnt0v9q1a6cNmmJjY+ncuTOenp7s27dPSysaNK1fv56bN2+yZs0aGjRoQIcOHfjkk09Yu3at3szcnYKCgkhNTeXAgQMA5Ofns379eoKDgwHYt28fBw4c4Ouvv8bHxwd3d3fmz5+PnZ0dmzZtArhnfxXp0KEDEydOpHbt2tSuXbvEeMLDw7G1tdWOOwe3QgghhHi2yKBJiHswdFf+lJQUnJyc9L4816tXDzs7O1JSUrQ0FxcXrK2ttXNHR0cyMzMNasPHx0fvPCcnh5CQEDw9PbGzs8PKyoqUlBRt5iQhIQFjY2NtsPIotW3bln379lFQUEBcXBzt2rXTBlLnzp3jxIkTtGvXDrjdV15eXnozd61bt6awsFDvHqk7Va9ene7du7Nq1SoA/vvf/5Kbm0u/fv0ASExMJCcnh0qVKuk9Y+rUqVOkpaUB9+6vInf3c0mmTp1KVlaWdpw9e7bcfSaEEEKIp4NsBCHEPbi7u6MoykPb7KFChQp654qiUFhYaNC1dy8PDAkJYdeuXcyfPx83NzfMzc3p27cveXl5AJibP/rnBRVp06YN165d48iRI/zwww/MmTMHBwcH5s6di5eXF9WrV8fd3f2B2hg+fDhDhgxh4cKFREREMGDAACwsLIDbAyJHR0dttutORUso79VfRUpahnk3nU6HTqd7oNcjhBBCiKeDDJqEuIeKFSvi6+vLkiVLGDduXLEv1FevXsXOzg5PT0/Onj3L2bNntdmm5ORkrl69Sr169Qxuz9TUlIKCAoPKxsfHExQURK9evYDbA4c7H1LbsGFDCgsLiYuLo1OnTiW2BRjcXlns7Oxo1KgRn3zyCRUqVKBu3bpUrVqVAQMGEB0drTfb5enpSWRkJNevX9f6Mz4+HiMjI+rUqVNqG926dcPS0pKlS5eyc+dOfvjhBy3P29ub8+fPY2JigouLS4nX36u/hBBCCCFKIsvzhDDAkiVLKCgooHnz5mzevJnjx4+TkpLC4sWLadmyJQCdOnWiYcOG+Pv7c+TIEQ4cOEBAQABt27Y1aLlXERcXF/bv3096ejoXL14scxbK3d2dLVu2kJCQQGJiIoMGDdIr7+LiQmBgIMHBwURFRXHq1CliY2PZuHEjAM7OziiKQnR0NBcuXCh19zpDtWvXjnXr1mkDpIoVK+Lp6clXX32lN2jy9/fHzMyMwMBAfvnlF/bs2cPYsWMZMmQI1apVK7V+Y2NjgoKCmDp1Ku7u7lrfw+3+b9myJa+88grff/896enp/Pjjj7zzzjscOnTIoP4SQgghhCiJDJqEMICrqytHjhyhffv2TJw4kQYNGtC5c2diYmJYunQpcHuZ3bfffou9vT1t2rShU6dOuLq68tVXX5WrrZCQEIyNjalXrx5VqlQpdr/NnT788EPs7e1p1aoVPXv2xNfXF29vb70yS5cupW/fvowaNYq6desyYsQIrl+/DkCNGjUICwtjypQpVKtWrdTd6wzVtm1bCgoKtHuX4PZA6u40CwsLvvvuOy5fvkyzZs3o27cvHTt25JNPPrlnG8OGDSMvL4+hQ4fqpSuKwvbt22nTpg1Dhw7Fw8ODgQMHcvr0aW0gZkh/CSGEEELcTVENvctdCCH+Afbu3UvHjh05e/ZsmbNSj1t2dja2trZkZWVhY2PzpMMRQgghhAEM/fst9zQJIZ4Kubm5XLhwgdDQUPr16/ePGjAJIYQQ4tkmy/OEEJozZ87obdd991HWUsFHbcOGDTg7O3P16lXmzZv3xOIQQgghxL+PLM8TQmhu3bpV5m5yLi4umJjIBHVJiqb3vcYuw1j36Ld6P/xBwCNvQwghhHjWGbo8T2aanlEuLi589NFHD73eoKAgXnnllYde74Nq164db7755pMO44GsWLECJycnjIyMHsl7l5qaioODA9euXSu1jImJCW5ubqUeT+uAKTIyUntWkyGef/55Nm/e/OgCEkIIIcRT5YkOmoKCglAUBUVRMDU1xc3NjRkzZnDr1q0nGdZDUdqXtHbt2qEoCnPnzi2W1717dxRFITQ09IHb+Se48/1VFIVKlSrRpUsXjh079qRDIzY2FkVRuHr16pMOBbj9vxxjxozh7bff5s8//2TkyJEPvY2pU6cyduxYrK2tH2q9iqIQFRX1UOt8ECX9h8GAAQP4/fffDa7j3XffZcqUKbIduRBCCCGAf8BMU5cuXcjIyOD48eNMnDiR0NBQPvjggycd1iPl5OREZGSkXtqff/5JTEwMjo6OTyaoR6To/c3IyCAmJgYTExN69OjxpMMyWF5e3mNp58yZM+Tn59O9e3ccHR2xsLC4r3ry8/NLrT86OpqgoKAHiPLpZW5uTtWqVQ0u37VrV65du8aOHTseYVRCCCGEeFo88UGTTqfDwcEBZ2dn3njjDTp16sTWrVuB289UadiwIZaWljg5OTFq1Cjt4ZvXr1/HxsaGTZs26dUXFRWFpaUl165dIz09HUVR2LhxIy+++CLm5uY0a9aM33//nYMHD+Lj44OVlRVdu3blwoULevWsXLkST09PzMzMqFu3Lp9++qmWV1Tvli1baN++PRYWFnh5efHTTz8Bt2cxhg4dSlZWljbLcufsUY8ePbh48SLx8fFa2urVq3nppZeKfbHLzc0lJCSEGjVqYGlpSYsWLYiNjTWonRs3bhAcHIy1tTU1a9ZkxYoVenUnJSXRoUMHzM3NqVSpEiNHjtR7uGlBQQETJkzAzs6OSpUqMXnyZMp7C1zR++vg4EDjxo2ZMmUKZ8+e1evvt99+Gw8PDywsLHB1deW9997T+/IfGhpK48aNWbt2LS4uLtja2jJw4MAyl5lt27YNW1tb1q1bVywvPT2d9u3bA2Bvb4+iKNpgol27dowZM4Y333yTypUr4+vrC5T9WYT/m/H77rvv8PT0xMrKShswFomNjaV58+ZYWlpiZ2dH69atOX36NJGRkTRs2BC4/TwoRVG0+4q+/fZbvL29MTMzw9XVlbCwML2ZWEVRWLp0KS+//DKWlpbMnj27xP7YuHEjXl5e1KhRQ0u7dOkSr776KjVq1MDCwoKGDRuyYcMGvetKmrVp3Lix9jlzcXEBoFevXiiKop3D7edD1a5dG1NTU+rUqcPatWv16lEUheXLl9OjRw8sLCzw9PTkp59+4sSJE7Rr1w5LS0tatWpFWlqadk1aWhp+fn5Uq1YNKysrmjVrxu7du7X8du3acfr0ad566y3td+LO9+dO//3vf2nWrBlmZmZUrlyZXr16aXnGxsZ069aNL7/8ssT+FEIIIcS/yxMfNN3N3Nxc+999IyMjFi9ezK+//srq1av53//+x+TJkwGwtLRk4MCBRERE6F0fERFB37599ZYgTZ8+nXfffZcjR45gYmLCoEGDmDx5MosWLWLv3r2cOHGCadOmaeXXrVvHtGnTmD17NikpKcyZM4f33nuP1atX67X1zjvvEBISQkJCAh4eHrz66qvcunWLVq1a8dFHH2FjY6PNsoSEhGjXmZqa4u/vrxd7ZGQkwcHBxfpjzJgx/PTTT3z55ZccO3aMfv360aVLF44fP37PdhYsWICPjw9Hjx5l1KhRvPHGG6SmpgK3B52+vr7Y29tz8OBBvv76a3bv3q33cNMFCxYQGRnJqlWr2LdvH5cvX+abb74x/M28S05ODl988QVubm5UqlRJS7e2tiYyMpLk5GQWLVrEZ599xsKFC/WuTUtLIyoqiujoaKKjo4mLiytxiSPA+vXrefXVV1m3bh3+/v7F8p2cnLT7VVJTU8nIyGDRokVa/urVqzE1NSU+Pp5ly5YBZX8Wi9y4cYP58+ezdu1afvjhB86cOaO9H7du3eKVV16hbdu2HDt2jJ9++omRI0eiKAoDBgzQvvgfOHCAjIwMnJyc2Lt3LwEBAYwfP57k5GSWL19OZGRksYFRaGgovXr1IikpqcTPENx+tpGPj49e2s2bN2natCnbtm3jl19+YeTIkQwZMoQDBw6UWEdJDh48CNz+vcvIyNDOv/nmG8aPH8/EiRP55ZdfeO211xg6dCh79uzRu37mzJkEBASQkJBA3bp1GTRoEK+99hpTp07l0KFDqKqq95nMycmhW7duxMTEcPToUbp06ULPnj21Xf22bNnCc889x4wZM7TfiZJs27aNXr160a1bN44ePUpMTAzNmzfXK9O8eXP27t1b6mvPzc0lOztb7xBCCCHEM0p9ggIDA1U/Pz9VVVW1sLBQ3bVrl6rT6dSQkJASy3/99ddqpUqVtPP9+/erxsbG6rlz51RVVdW//vpLNTExUWNjY1VVVdVTp06pgLpy5Urtmg0bNqiAGhMTo6WFh4erderU0c5r166trl+/Xq/tmTNnqi1btiy13l9//VUF1JSUFFVVVTUiIkK1tbUt9hratm2rjh8/Xk1ISFCtra3VnJwcNS4uTq1ataqan5+venl5qdOnT1dVVVVPnz6tGhsbq3/++adeHR07dlSnTp1aZjvOzs7q4MGDtfPCwkK1atWq6tKlS1VVVdUVK1ao9vb2ak5OjlZm27ZtqpGRkXr+/HlVVVXV0dFRnTdvnpafn5+vPvfcc9p7di+BgYGqsbGxamlpqVpaWqqA6ujoqB4+fLjM6z744AO1adOm2vn06dNVCwsLNTs7W0ubNGmS2qJFC+28qF8/+eQT1dbWVvsMlGbPnj0qoF65ckUvvW3btmqTJk3u+dru/ixGRESogHrixAktbcmSJWq1atVUVVXVS5cuqUCpcR09elQF1FOnTmlpHTt2VOfMmaNXbu3ataqjo6N2DqhvvvnmPeP18vJSZ8yYcc9y3bt3VydOnKidOzs7qwsXLixWV9FntCiGb775Rq9Mq1at1BEjRuil9evXT+3WrZvede+++652/tNPP6mA+vnnn2tpGzZsUM3MzMqMuX79+urHH39cZsx3/560bNlS9ff3L7Peb7/9VjUyMlILCgpKzJ8+fboKFDu8xi5TvUNWP/JDCCGEEA8uKytLBdSsrKwyyz3xrbCio6OxsrIiPz+fwsJCBg0apC392b17N+Hh4fz2229kZ2dz69Ytbt68yY0bN7CwsKB58+bUr1+f1atXM2XKFL744gucnZ1p06aNXhuNGjXSfi56IGbRcqiitMzMTOD2DExaWhrDhg1jxIgRWplbt25ha2tbar1F9yJlZmZSt27de75uLy8v3N3d2bRpE3v27GHIkCHFdiZLSkqioKAADw8PvfTc3Fy9mZrS3Bmfoig4ODhorzMlJQUvLy8sLS21Mq1bt6awsJDU1FTMzMzIyMigRYsWWr6JiQk+Pj7lWqLXvn17li5dCsCVK1f49NNP6dq1KwcOHMDZ2RmAr776isWLF5OWlkZOTg63bt0qtuWji4uL3uyho6Oj9lqKbNq0iczMTOLj42nWrJnBMd6tadOmxdLu9VkEsLCwoHbt2iXGWLFiRYKCgvD19aVz58506tSJ/v37l3kPW2JiIvHx8XozSwUFBcXavXsGqSR///03ZmZmemkFBQXMmTOHjRs38ueff5KXl0dubu593091p5SUlGKbWbRu3VpvRg8M+928efMm2dnZ2NjYkJOTQ2hoKNu2bSMjI4Nbt27x999/l/v5UQkJCXq/3yUxNzensLCQ3NxczM2LbyE+depUJkyYoJ1nZ2fj5ORUrjiEEEII8XR44oOmoi/VpqamVK9eXRs4pKen06NHD9544w1mz55NxYoV2bdvH8OGDSMvL0/7Yjd8+HCWLFnClClTiIiIYOjQodp9DEUqVKig/VyUd3da0S5ZRfepfPbZZ3oDBrh9n8O96i3PblvBwcEsWbKE5OTkEpdE5eTkYGxszOHDh4u1bWVldc/674yvKMbHvRuYpaUlbm5u2vnKlSuxtbXls88+Y9asWfz000/4+/sTFhaGr68vtra2fPnllyxYsECvHkNeS5MmTThy5AirVq3Cx8en2OegPDHfydDPYkkx3jnAjIiIYNy4cezcuZOvvvqKd999l127dvH888+XGEdOTg5hYWH07t27WN6dA6C74y1J5cqVuXLlil7aBx98wKJFi/joo4+0+7XefPNNvc0vjIyMig2SS9ts4n4Y8rsJ//d7FRISwq5du5g/fz5ubm6Ym5vTt2/fcm/YUdIg6G6XL1/G0tKy1LI6nQ6dTleudoUQQgjxdHri9zQVfamuWbOm3kzL4cOHKSwsZMGCBTz//PN4eHhw7ty5YtcPHjyY06dPs3jxYpKTkwkMDHygeKpVq0b16tU5efJksWfU1KpVy+B6TE1NKSgoKLPMoEGDSEpKokGDBtSrV69YfpMmTSgoKCAzM7NYLA4ODga3UxJPT08SExO5fv26lhYfH4+RkRF16tTB1tYWR0dH9u/fr+XfunWLw4cPl7utOymKgpGREX///TcAP/74I87Ozrzzzjv4+Pjg7u7O6dOn76vu2rVrs2fPHr799lvGjh1bZllTU1MAg/rO0M+iIZo0acLUqVP58ccfadCgAevXry+1rLe3N6mpqSU+L8nIqHy/uk2aNCE5OVkvLT4+Hj8/PwYPHoyXlxeurq7FtuWuUqWK3n1B2dnZnDp1Sq9MhQoVivWjp6en3kYnRe2V9Dkvj/j4eIKCgujVqxcNGzbEwcGh2MN4DfmdaNSoETExMWWW+eWXX2jSpMkDxSuEEEKIZ8MTn2kqjZubG/n5+Xz88cf07NlT76b8O9nb29O7d28mTZrESy+9xHPPPffAbYeFhTFu3DhsbW3p0qULubm5HDp0iCtXrugtxymLi4sLOTk5xMTE4OXlhYWFRbFlT/b29mRkZBSboSji4eGBv78/AQEBLFiwgCZNmnDhwgViYmJo1KgR3bt3N6idkvj7+zN9+nQCAwMJDQ3lwoULjB07liFDhmjLpMaPH8/cuXNxd3enbt26fPjhh+V+rlFubi7nz58Hbi/P++STT8jJyaFnz54AuLu7c+bMGb788kuaNWvGtm3bHmizCQ8PD/bs2UO7du0wMTEp9SGxzs7OKIpCdHQ03bp1w9zcvNTZO0M/i2U5deoUK1as4OWXX6Z69eqkpqZy/PhxAgICSr1m2rRp9OjRg5o1a9K3b1+MjIxITEzkl19+YdasWeVq39fXl+HDh1NQUKDNWhYtD/3xxx+xt7fnww8/5K+//tIb2HTo0IHIyEh69uyJnZ0d06ZNKzbr6eLiQkxMDK1bt0an02Fvb8+kSZPo378/TZo0oVOnTvz3v/9ly5Ytejvd3Q93d3e2bNlCz549URSF9957r9iMo4uLCz/88AMDBw5Ep9NRuXLlYvVMnz6djh07Urt2bQYOHMitW7fYvn07b7/9tlZm7969vPTSSw8UrxBCCCGeEY/jBqvS3LkRREk+/PBD1dHRUTU3N1d9fX3VNWvWlHjzfkxMjAqoGzdu1Esv2rDh6NGjWlpJGwCUtJnCunXr1MaNG6umpqaqvb292qZNG3XLli2l1nvlyhUVUPfs2aOlvf7662qlSpVUQLtxvmjDgtLcfZN9Xl6eOm3aNNXFxUWtUKGC6ujoqPbq1Us9duxYme0YcgP/sWPH1Pbt26tmZmZqxYoV1REjRqjXrl3T8vPz89Xx48erNjY2qp2dnTphwgQ1ICCgXBtBcMcN8tbW1mqzZs3UTZs26ZWbNGmSWqlSJdXKykodMGCAunDhQr33Y/r06aqXl5feNQsXLlSdnZ2187v7NTk5Wa1atao6YcKEUuObMWOG6uDgoCqKogYGBpZYT5F7fRZL+gx98803atGv2Pnz59VXXnlFdXR0VE1NTVVnZ2d12rRp2iYDJW0EoaqqunPnTrVVq1aqubm5amNjozZv3lxdsWKFlk8JmzCUJD8/X61evbq6c+dOLe3SpUuqn5+famVlpVatWlV99913i72/WVlZ6oABA1QbGxvVyclJjYyMLPY52rp1q+rm5qaamJjovSeffvqp6urqqlaoUEH18PBQ16xZoxfT3bEb8vt66tQptX379qq5ubnq5OSkfvLJJ8Xes59++klt1KiRqtPptP4v6f3ZvHmz9jteuXJltXfv3lreH3/8oVaoUEE9e/bsPfv2zr7CgBtJhRBCCPHPYejfb0VVy/ngnX+gtWvX8tZbb3Hu3Dlt2ZUQQt+SJUvYunUr33333ZMO5R/v7bff5sqVK8WebVaW7OxsbG1tycrKKraRiRBCCCH+mQz9+/2PXZ5niBs3bpCRkcHcuXN57bXXZMAkRBlee+01rl69yrVr1/R2IhTFVa1a1eCluEIIIYR49j3VM02hoaHMnj2bNm3a8O233xq0o5x4OM6cOVPmTf3JycnUrFnzMUYkxJNV9D9VXmOXYay79+58dzr8Qen3tgkhhBDi0TF0pumpHjSJJ+fWrVvFdi27k4uLS7HnTgnxLJNBkxBCCPH0MXTQ9MS3HBf/XC4uLqXuPmdiYlLiVthFR1kDpqCgIF555ZVHE/RDoCgKUVFRwO1nNCmKQkJCwhONqSyG9ueQIUOYM2fOow/oH6isz/Lddu7cSePGjR/7M82EEEII8c/11A+agoKCUBQFRVEwNTXFzc2NGTNmcOvWrScd2gOLjIzEzs6uWHq7du1QFIW5c+cWy+vevTuKohAaGvrA7fxTnD9/nrFjx+Lq6opOp8PJyYmePXve8zk7D4OTkxMZGRk0aNAAgNjYWBRFKffW609aYmIi27dvZ9y4cQ+13tDQUBo3bvxQ63wQpX2WDx48yMiRIw2qo0uXLlSoUIF169Y95OiEEEII8bR66gdNcPtLTkZGBsePH2fixImEhobywQcfPOmwHiknJyciIyP10v78809iYmJwdHR8MkE9Aunp6TRt2pT//e9/fPDBByQlJbFz507at2/P6NGjS70uPz//obRvbGyMg4PDP2Kp4YO8po8//ph+/fr9a+/7q1KlikHPLysSFBTE4sWLH2FEQgghhHiaPBODJp1Oh4ODA87Ozrzxxht06tSJrVu3AvDhhx/SsGFDLC0tcXJyYtSoUeTk5ABw/fp1bGxs2LRpk159UVFRWFpacu3aNW151saNG3nxxRcxNzenWbNm/P777xw8eBAfHx+srKzo2rUrFy5c0Ktn5cqVeHp6YmZmRt26dfn000+1vKJ6t2zZQvv27bGwsMDLy4uffvoJuD2jMXToULKysrSZtDtnj3r06MHFixeJj4/X0lavXs1LL71E1apV9eLIzc0lJCSEGjVqYGlpSYsWLYiNjTWonRs3bhAcHIy1tTU1a9YstgVzUlISHTp0wNzcnEqVKjFy5EitfwEKCgqYMGECdnZ2VKpUicmTJ1Oe2+hGjRqFoigcOHCAPn364OHhQf369ZkwYQI///yzVk5RFJYuXcrLL7+MpaUls2fPBuDbb7/F29sbMzMzXF1dCQsL05uFPH78OG3atMHMzIx69eqxa9cuvfbvXJ6Xnp5O+/btgdsPJlYUhaCgoFJjj4+Pp127dlhYWGBvb4+vry9XrlwBbi8Be+GFF7R+6dGjB2lpacXa/eqrr2jbti1mZmasW7fuvvqzoKCATZs2aQ8ULrJ27Vp8fHywtrbGwcGBQYMGkZmZqeWXNGsTFRWFoihaflhYGImJidpnp2ggf+bMGfz8/LCyssLGxob+/fvz119/afUUzVCtWrWKmjVrYmVlxahRoygoKGDevHk4ODhQtWpV7X0sUtbvc1mf5buX5129epXXXnuNatWqYWZmRoMGDYiOjtbye/bsyaFDh/Tek7vl5uaSnZ2tdwghhBDi2fRMDJruZm5uTl5eHgBGRkYsXryYX3/9ldWrV/O///2PyZMnA2BpacnAgQOJiIjQuz4iIoK+ffvqbcs8ffp03n33XY4cOYKJiQmDBg1i8uTJLFq0iL1793LixAmmTZumlV+3bh3Tpk1j9uzZpKSkMGfOHN577z1Wr16t19Y777xDSEgICQkJeHh48Oqrr3Lr1i1atWrFRx99hI2NDRkZGWRkZBASEqJdZ2pqir+/v17skZGRBAcHF+uPMWPG8NNPP/Hll19y7Ngx+vXrR5cuXTh+/Pg921mwYAE+Pj4cPXqUUaNG8cYbb5CamgrcHnT6+vpib2/PwYMH+frrr9m9ezdjxozRuz4yMpJVq1axb98+Ll++zDfffGPQ+3j58mV27tzJ6NGjsbS0LJZ/9xf60NBQevXqRVJSEsHBwezdu5eAgADGjx9PcnIyy5cvJzIyUvsiXlhYSO/evTE1NWX//v0sW7aMt99+u9R4nJyc2Lx5MwCpqalkZGSwaNGiEssmJCTQsWNH6tWrx08//cS+ffvo2bMnBQUFWt9NmDCBQ4cOERMTg5GREb169Sp2H82UKVMYP348KSkp+Pr63ld/Hjt2jKysLHx8fPTS8/PzmTlzJomJiURFRZGenl7mIPBuAwYMYOLEidSvX1/77AwYMIDCwkL8/Py4fPkycXFx7Nq1i5MnTzJgwAC969PS0tixYwc7d+5kw4YNfP7553Tv3p0//viDuLg43n//fd59913279+vXVPW7/O9PstFCgsL6dq1K/Hx8XzxxRckJyczd+5cjI2NtTI1a9akWrVq7N27t9TXHx4ejq2trXY4OTkZ3HdCCCGEeMo84ofsPnKBgYGqn5+fqqqqWlhYqO7atUvV6XRqSEhIieW//vprtVKlStr5/v37VWNjY/XcuXOqqqrqX3/9pZqYmKixsbGqqqrqqVOnVEBduXKlds2GDRtUQI2JidHSwsPD1Tp16mjntWvXVtevX6/X9syZM9WWLVuWWu+vv/6qAmpKSoqqqqoaERGh2traFnsNbdu2VcePH68mJCSo1tbWak5OjhoXF6dWrVpVzc/PV728vNTp06erqqqqp0+fVo2NjdU///xTr46OHTuqU6dOLbMdZ2dndfDgwdp5YWGhWrVqVXXp0qWqqqrqihUrVHt7ezUnJ0crs23bNtXIyEg9f/68qqqq6ujoqM6bN0/Lz8/PV5977jntPSvL/v37VUDdsmXLPcsC6ptvvlnsNc6ZM0cvbe3ataqjo6Oqqqr63XffqSYmJnp9s2PHDhVQv/nmG1VV/+99Onr0qKqqqrpnzx4VUK9cuVJmPK+++qraunXre8Zd5MKFCyqgJiUl6bX70Ucf6ZW7n/785ptvVGNjY7WwsLDMGA4ePKgC6rVr11RVLflz8c0336h3/rMxffp01cvLS6/M999/rxobG6tnzpzR0oo+2wcOHNCus7CwULOzs7Uyvr6+qouLi1pQUKCl1alTRw0PDy815rt/n8v6LC9cuFBV1dvvu5GRkZqamlpqvaqqqk2aNFFDQ0NLzb9586aalZWlHWfPnlUB1WvsMtU7ZHW5DiGEEEI8GVlZWSqgZmVllVnuyd+o8RBER0djZWVFfn4+hYWFDBo0SFuWs3v3bsLDw/ntt9/Izs7m1q1b3Lx5kxs3bmBhYUHz5s2pX78+q1evZsqUKXzxxRc4OzvTpk0bvTYaNWqk/VytWjUAGjZsqJdWtLTp+vXrpKWlMWzYMEaMGKGVuXXrFra2tqXWW3QvUmZmJnXr1r3n6/by8sLd3Z1NmzaxZ88ehgwZUuzem6SkJAoKCvDw8NBLz83NpVKlSvds4874FEXBwcFBe50pKSl4eXnpzQK1bt2awsJCUlNTMTMzIyMjgxYtWmj5JiYm+Pj4GLREz5Ayd7p7JiUxMZH4+Hi9JV4FBQXa+5+SkoKTkxPVq1fX8lu2bFmuNkuTkJBAv379Ss0/fvw406ZNY//+/Vy8eFGbYTpz5oy26cTdrykrK+u++vPvv/9Gp9Npy+qKHD58mNDQUBITE7ly5YpeDGU9g+teivr1zpmXevXqYWdnR0pKCs2aNQNuL5m7cza3WrVqGBsbY2RkpJd255LBe/0+GyIhIYHnnnuu2O/E3czNzblx40ap+TqdDp1OZ1CbQgghhHi6PRODpvbt27N06VJMTU2pXr26NnBIT0+nR48evPHGG8yePZuKFSuyb98+hg0bRl5envYla/jw4SxZsoQpU6YQERHB0KFDi33BrFChgvZzUd7daUVfOovusfjss8/0vuACekuASqu3PFsdBwcHs2TJEpKTkzlw4ECx/JycHIyNjTl8+HCxtg3ZFODO+IpifFxbMbu7u6MoCr/99ptB5e9ewpeTk0NYWBi9e/cuVtbMzOyhxFgac/Oyn9PTs2dPnJ2d+eyzz6hevTqFhYU0aNBAW1ZapKRlieVVuXJlbty4QV5eHqampsD/La309fVl3bp1VKlShTNnzuDr66u3tPXuwdjD2mADSv5slfV5M/T3+V7u9d4UuXz5MlWqVDGorBBCCCGebc/EPU2Wlpa4ublRs2ZNvZmWw4cPU1hYyIIFC3j++efx8PDg3Llzxa4fPHgwp0+fZvHixSQnJxMYGPhA8VSrVo3q1atz8uTJYs8vqlWrlsH1mJqaavfAlGbQoEEkJSXRoEGDEmcHmjRpQkFBAZmZmcVicXBwMLidknh6epKYmMj169e1tPj4eIyMjKhTpw62trY4Ojrq3ZNy69YtDh8+bFD9FStWxNfXlyVLlui1UeRe2357e3uTmppa4nOkjIyM8PT05OzZs2RkZGjX3Lm5REmKBh336q9GjRqVuiX6pUuXSE1N5d1336Vjx454enpqG0SU5X77s2hL8OTkZC3tt99+49KlS8ydO5cXX3yRunXr6s3owO0d565du6bX93c/r6qkz05Rv549e1ZLS05O5urVqw80g2XI77Mhn+VGjRrxxx9/8Pvvv5da5ubNm6SlpdGkSZP7jlcIIYQQz477GjSlpaXx7rvv8uqrr2pftHbs2MGvv/76UIN7UG5ubuTn5/Pxxx9z8uRJ1q5dy7Jly4qVs7e3p3fv3kyaNImXXnqJ55577oHbDgsLIzw8nMWLF/P777+TlJREREQEH374ocF1uLi4kJOTQ0xMDBcvXixxqZC9vT0ZGRmlfkH38PDA39+fgIAAtmzZwqlTpzhw4ADh4eFs27bN4HZK4u/vj5mZGYGBgfzyyy/s2bOHsWPHMmTIEG0J4/jx45k7dy5RUVH89ttvjBo1qlzPOFqyZAkFBQU0b96czZs3c/z4cVJSUli8ePE9l9JNmzaNNWvWEBYWxq+//kpKSgpffvkl7777LgCdOnXCw8ODwMBAEhMT2bt3L++8806ZdTo7O6MoCtHR0Vy4cEFvp8A7TZ06lYMHDzJq1CiOHTvGb7/9xtKlS7l48SL29vZUqlSJFStWcOLECf73v/8xYcIEg/rjfvqzSpUqeHt7s2/fPi2tZs2amJqaar8bW7duZebMmXrXtWjRAgsLC/7zn/+QlpbG+vXri21z7+LiwqlTp0hISODixYvk5ubSqVMnGjZsiL+/P0eOHOHAgQMEBATQtm3bYksoy8OQ32dDPstt27alTZs29OnTh127dnHq1CltQ4oiP//8Mzqd7qEt1xRCCCHEU668N0vFxsaq5ubmaqdOnVRTU1M1LS1NVdXbGyH06dOn/HdfPaA7N4IoyYcffqg6Ojqq5ubmqq+vr7pmzZoSb+SPiYlRAXXjxo166XdvBKCqJW8GUNIN6OvWrVMbN26smpqaqvb29mqbNm20TQ1KqvfKlSsqoO7Zs0dLe/3119VKlSqpgLa5Q9FGEKW5cyMIVVXVvLw8ddq0aaqLi4taoUIF1dHRUe3Vq5d67NixMtu58+b50uo+duyY2r59e9XMzEytWLGiOmLECG0jAVW9vVHB+PHjVRsbG9XOzk6dMGGCGhAQYNBGEEXOnTunjh49WnV2dlZNTU3VGjVqqC+//LJeP3HH5g132rlzp9qqVSvV3NxctbGxUZs3b66uWLFCy09NTVVfeOEF1dTUVPXw8FB37txZ5kYQqqqqM2bMUB0cHFRFUdTAwMBS446NjVVbtWql6nQ61c7OTvX19dU+M7t27VI9PT1VnU6nNmrUSI2Njb1nuw/Sn59++qn6/PPP66WtX79edXFxUXU6ndqyZUt169atxdr85ptvVDc3N9Xc3Fzt0aOHumLFCr2NIG7evKn26dNHtbOzUwE1IiJCVdXbG5C8/PLLqqWlpWptba3269dP2xxEVUveQKKk3+W7P+uG/D4b8lm+dOmSOnToULVSpUqqmZmZ2qBBAzU6OlrLHzlypPraa6+V2ad3M/RGUiGEEEL8cxj691tR1fLdbd+yZUv69evHhAkTsLa2JjExEVdXVw4cOEDv3r35448/HsZY7rFbu3Ytb731FufOndOWYAnxrPj777+pU6cOX331lcye3MPFixepU6cOhw4dKtdy2uzsbGxtbcnKysLGxuYRRiiEEEKIh8XQv9/l3ggiKSmJ9evXF0uvWrUqFy9eLG91T9yNGzfIyMhg7ty5vPbaazJgEs8kc3Nz1qxZ81T+jj5u6enpfPrpp+UaMAkhhBDi2VbuQZOdnR0ZGRnFvlAcPXqUGjVqPLTAHpd58+Yxe/Zs2rRpw9SpU590OP8q99raOjk5mZo1az7GiJ5t7dq1e9IhPBV8fHwe6N6rNu9uwFhn2A59T4vDHwQ86RCEEEKIJ6rcg6aBAwfy9ttv8/XXX2vbAcfHxxMSEkJAwNP3hzU0NFR7ppN4vKpXr15sN7a784UQQgghhHjSyr173pw5c6hbty5OTk7k5ORQr1492rRpQ6tWrbRdyYQwhImJSYnbgRcddz+o9364uLjw0UcfPXiwdwkKCuKVV155aPUpikJUVNRDq+9xa9euHW+++eYjqTs2NhZFUcq162J5RUZGYmdn98jqF0IIIcTTrdyDJlNTUz777DPS0tKIjo7miy++4LfffmPt2rXFHp4qDBMUFISiKCiKgqmpKW5ubsyYMYNbt2496dAeWGlfRtu1a4eiKMydO7dYXvfu3VEUpVwzgP/kL713vr93Hl26dHlkbT7uQdiWLVuKbVkuhBBCCPGsuO//yq9Zs6bcb/IQdenShYiICHJzc9m+fTujR4+mQoUKz/R9Vk5OTkRGRjJlyhQt7c8//yQmJgZHR8cnGNnDV/T+3kmn0z2haG7Ly8t7aBufVKxY8aHUI4QQQgjxT2TQTNOECRMMPsT90el0ODg44OzszBtvvEGnTp3YunUrAB9++CENGzbE0tISJycnRo0apT1U9fr169jY2LBp0ya9+qKiorC0tOTatWukp6ejKAobN27kxRdfxNzcnGbNmvH7779z8OBBfHx8sLKyomvXrly4cEGvnpUrV+Lp6YmZmRl169bl008/1fKK6t2yZQvt27fHwsICLy8vfvrpJ+D2sqqhQ4eSlZWlza7cOXvUo0cPLl68SHx8vJa2evVqXnrpJapWraoXR25uLiEhIdSoUQNLS0tatGhBbGysQe3cuHGD4OBgrK2tqVmzJitWrNCrOykpiQ4dOmBubk6lSpUYOXKk3kNrCwoKmDBhAnZ2dlSqVInJkydTzp36tff3zsPe3r7U8mfPnqV///7Y2dlRsWJF/Pz8SE9P1yuzatUq6tevj06nw9HRkTFjxgC3lyQC9OrVC0VRtPPQ0FAaN27MypUrqVWrFmZmZsDtDTn8/PywsrLCxsaG/v3789dff2ntFF23du1aXFxcsLW1ZeDAgVy7dk0rc/fyvNzcXN5++22cnJzQ6XS4ubnx+eefl/p6DSl/+PBhfHx8sLCwoFWrVqSmpurlf/vtt3h7e2NmZoarqythYWF6s7VXr17ltddeo1q1apiZmdGgQQOio6NLjOfChQv4+PjQq1cvcnNzS405Oztb7xBCCCHEs8mgQdPRo0cNOsq6qV+Uj7m5OXl5eQAYGRmxePFifv31V1avXs3//vc/Jk+eDIClpSUDBw4sNosRERFB3759sba21tKmT5/Ou+++y5EjRzAxMWHQoEFMnjyZRYsWsXfvXk6cOMG0adO08uvWrWPatGnMnj2blJQU5syZw3vvvcfq1av12nrnnXcICQkhISEBDw8PXn31VW7dukWrVq346KOPsLGxISMjg4yMDEJCQrTrTE1N8ff314s9MjKS4ODgYv0xZswYfvrpJ7788kuOHTtGv3796NKlC8ePH79nOwsWLMDHx4ejR48yatQo3njjDe0L9/Xr1/H19cXe3p6DBw/y9ddfs3v3bm0AUnR9ZGQkq1atYt++fVy+fJlvvvnG8DeznPLz8/H19cXa2pq9e/cSHx+PlZUVXbp00T4TS5cuZfTo0YwcOZKkpCS2bt2Km5sbAAcPHgRufwYyMjK0c4ATJ06wefNmtmzZQkJCAoWFhfj5+XH58mXi4uLYtWsXJ0+eZMCAAXoxpaWlERUVRXR0NNHR0cTFxZW4tLJIQEAAGzZsYPHixaSkpLB8+XKsrKweqPw777zDggULOHToECYmJnqfk7179xIQEMD48eNJTk5m+fLlREZGMnv2bAAKCwvp2rUr8fHxfPHFFyQnJzN37twSlxSfPXuWF198kQYNGrBp06ZSZwTDw8OxtbXVDicnp1JfnxBCCCGebgYtz9uzZ8+jjkP8f6qqEhMTw3fffcfYsWMB9P4H38XFhVmzZvH6669rsz7Dhw+nVatWZGRk4OjoSGZmJtu3b2f37t16dYeEhODr6wvA+PHjefXVV4mJiaF169YADBs2jMjISK389OnTWbBgAb179wagVq1a2hfSwMBAvXq7d+8OQFhYGPXr1+fEiRPUrVsXW1tbFEXBwcGhxNcbHBzMiy++yKJFizh8+DBZWVn06NFDb6bozJkzREREcObMGW1HvZCQEHbu3ElERARz5swps51u3boxatQoAN5++20WLlzInj17qFOnDuvXr+fmzZusWbMGS0tLAD755BN69uzJ+++/T7Vq1fjoo4+YOnWq1g/Lli3ju+++K+0tLFF0dHSxQcB//vMf/vOf/xQr+9VXX1FYWMjKlStRFAW4PQCys7MjNjaWl156iVmzZjFx4kTGjx+vXdesWTMAqlSpAtx+PMDd/ZGXl8eaNWu0Mrt27SIpKYlTp05pX/rXrFlD/fr1OXjwoFZnYWEhkZGR2iB8yJAhxMTEaIOSO/3+++9s3LiRXbt20alTJwBcXV1L7RtDy8+ePZu2bdsCMGXKFLp3787NmzcxMzMjLCyMKVOmaJ9LV1dXZs6cyeTJk5k+fTq7d+/mwIEDpKSk4OHhUWobqampdO7cmV69evHRRx9p/V+SqVOn6s2uZ2dny8BJCCGEeEY90PZkZ8+eBZAvCg9B0Zfq/Px8CgsLGTRokDZw2L17N+Hh4fz2229kZ2dz69Ytbt68yY0bN7CwsKB58+bUr1+f1atXM2XKFL744gucnZ1p06aNXhuNGjXSfq5WrRoADRs21EvLzMwEbs/ApKWlMWzYMEaMGKGVuXXrFra2tqXWW3QvUmZmJnXr1r3n6/by8sLd3Z1NmzaxZ88ehgwZUmzXvKSkJAoKCrQvu0Vyc3OpVKnSPdu4M76igVXR60xJScHLy0sbMAG0bt2awsJCUlNTMTMzIyMjgxYtWmj5JiYm+Pj4lGuJXvv27Vm6dKleWmn3ASUmJnLixAm9WUKAmzdvkpaWRmZmJufOnaNjx44Gt1/E2dlZGzDB7dfv5OSk9ztcr1497OzsSElJ0QZNLi4uevEUDc5LkpCQgLGxsTbAuRdDy5f2OatZsyaJiYnEx8frDeIKCgq035OEhASee+65Yp+hO/3999+8+OKLDBo0yKAdF3U63RO/L00IIYQQj0e5B023bt0iLCyMxYsXa/d9WFlZMXbsWKZPn06FChUeepD/BkVfqk1NTalevbo2cEhPT6dHjx688cYbzJ49m4oVK7Jv3z6GDRtGXl4eFhYWwO3ZpiVLljBlyhQiIiIYOnRosf8lv/O9Kcq7O62wsBBAe28/++wzvQEDUGxJU0n1FtVjiODgYJYsWUJycjIHDhwolp+Tk4OxsTGHDx8u1nZZS75Kiq8oxvLE9zBYWlpqy+fuJScnh6ZNm7Ju3bpieVWqVMHIqNybXurFcT/K04fm5uV7sKuh5cv6nOXk5BAWFqbNBt7JzMzMoDZ0Oh2dOnUiOjqaSZMmPZUP6xZCCCHEo1Hub19jx45lxYoVzJs3T7uXad68eXz++eeMGzfuUcT4r1D0pbpmzZp6My2HDx+msLCQBQsW8Pzzz+Ph4cG5c+eKXT948GBOnz7N4sWLSU5O1ls+dz+qVatG9erVOXnyZLHnJ9WqVcvgekxNTSkoKCizzKBBg0hKSqJBgwbUq1evWH6TJk0oKCggMzOzWCxFy88Maacknp6eJCYmcv36dS0tPj4eIyMj6tSpg62tLY6Ojuzfv1/Lv3XrFocPHy53W4by9vbm+PHjVK1atdjrtbW1xdraGhcXF2JiYkqto0KFCgb1h6enJ2fPntVmjQGSk5O5evVqie+FIRo2bEhhYSFxcXGPpHxJvL29SU1NLfF5X0ZGRjRq1Ig//viD33//vdQ6jIyMWLt2LU2bNqV9+/Yl/p4JIYQQ4t+p3IOm9evXExkZyWuvvUajRo1o1KgRr732Gp9//jnr169/FDH+q7m5uZGfn8/HH3/MyZMnWbt2LcuWLStWzt7ent69ezNp0iReeuklnnvuuQduOywsjPDwcBYvXszvv/9OUlISERERfPjhhwbX4eLiQk5ODjExMVy8eJEbN26UGHtGRkapgwAPDw/8/f0JCAhgy5YtnDp1igMHDhAeHs62bdsMbqck/v7+mJmZERgYyC+//MKePXsYO3YsQ4YM0ZYwjh8/nrlz5xIVFcVvv/3GqFGjyv2g1dzcXM6fP693XLx4sdSYKleujJ+fH3v37uXUqVPExsYybtw4/vjjD+D2jnYLFixg8eLFHD9+nCNHjvDxxx9rdRQNqs6fP8+VK1dKjatTp040bNgQf39/jhw5woEDBwgICKBt27b4+PiU6zXe2XZgYCDBwcFERUVp8W/cuPGhlC/JtGnTWLNmDWFhYfz666+kpKTw5Zdfag/cbtu2LW3atKFPnz7s2rWLU6dOsWPHDnbu3KlXj7GxMevWrcPLy4sOHTpw/vz5++oDIYQQQjxj1HKqUqWKmpycXCw9OTlZrVy5cnmrE6qqBgYGqn5+fqXmf/jhh6qjo6Nqbm6u+vr6qmvWrFEB9cqVK3rlYmJiVEDduHGjXvqpU6dUQD169KiWtmfPnmJ1REREqLa2tnrXrlu3Tm3cuLFqamqq2tvbq23atFG3bNlSar1XrlxRAXXPnj1a2uuvv65WqlRJBdTp06erqqqqbdu2VcePH1/qa/by8tLKqqqq5uXlqdOmTVNdXFzUChUqqI6OjmqvXr3UY8eOldmOs7OzunDhwjLrPnbsmNq+fXvVzMxMrVixojpixAj12rVrWn5+fr46fvx41cbGRrWzs1MnTJigBgQElPme3SkwMFAFih116tTRygDqN998o51nZGSoAQEBauXKlVWdTqe6urqqI0aMULOysrQyy5YtU+vUqaP1x9ixY7W8rVu3qm5ubqqJiYnq7OysqqqqTp8+XfXy8ioW3+nTp9WXX35ZtbS0VK2trdV+/fqp58+f1/JLum7hwoVavapa/P38+++/1bfeekt1dHRUTU1NVTc3N3XVqlWl9lFZ5Uv6rB49elQF1FOnTmlpO3fuVFu1aqWam5urNjY2avPmzdUVK1Zo+ZcuXVKHDh2qVqpUSTUzM1MbNGigRkdHq6pa/LOfn5+v9u7dW/X09FT/+uuvUuO+U1ZWlgrovUdCCCGE+Gcz9O+3oqrle+DMjBkz+O2334iIiNBugs7NzWXYsGG4u7szffr0hzCUE/dj7dq1vPXWW5w7d+6hPbRUCGGY7OxsbG1tycrKwsbG5kmHI4QQQggDGPr326CNIO6+uXr37t0899xzeHl5Abd3+8rLy7uv3bzEg7tx4wYZGRnMnTuX1157TQZMQgghhBBCPEQGDZru3mK6T58+euey5fiTNW/ePGbPnk2bNm2YOnXqkw7nX+XMmTNlbpiQnJxMzZo1H2NE4klr8+4GjHXl20GwLIc/CHhodQkhhBDi/pR7eZ4Q4jYXFxfGjh2Ln59fmWXufu7UvQQFBXH16lWioqIeMMLyS09Pp1atWhw9epTGjRsTGxtL+/btuXLlCnZ2dvddr4uLC2+++abeg5rLQ1EUvvnmG1555ZX7jqEsd7/u+1E0ve81dpkMmoQQQoinhKHL8+7/gS/iqRAUFISiKCiKgqmpKW5ubsyYMYNbt2496dAeWGTk/2vv3uN6vP/Hjz/enc9F6GAdUBQqNXOcMyunOR9DCTOHYWbMDDGEhc3MYUw1Y7MZrY8No8khZ1NMLUktm8hQySGp6/eHn+u7tw4rQw7P++123W7v9/V6Xa/X83pp8tzrdb2u8GL/Id+6dWs0Gg3z588vUta5c2c0Go364uD/0g/c222tuG2u7x/lTZjK6/6f75tvvlmkbMyYMWg0GgIDA8vcnoODAxkZGdSvX/8RRimEEEII8Wx7qKRp06ZN9O3blyZNmuDj46N1iKePn58fGRkZJCcn88477xAcHMxHH31U0WE9Vg4ODoSHh2ud++uvv4iOjsbOzq5ignpMHBwc+Oabb7h165Z67vbt22zYsKHcSwN1dXWxtbV97MmeEEIIIcSzpNxJ09KlSxk6dCg2NjacOHGCRo0aYW1tzblz5+jYsePjiFH8R4aGhtja2uLk5MSoUaNo3749UVFRACxevBgPDw9MTU1xcHBg9OjR5ObmAnDjxg0sLCzYtGmTVnuRkZGYmppy/fp10tLS0Gg0fPvtt7Ro0QJjY2NeeeUVzpw5w9GjR2nYsCFmZmZ07NiRy5cva7WzZs0a3N3dMTIyws3NjeXLl6tl99vdvHkzbdq0wcTEBC8vLw4ePAhATEwMQ4cOJTs7W51J++fsUZcuXfj777+JjY1Vz0VERPDaa69RrVo1rTjy8vKYNGkS1atXx9TUlMaNGxMTE1Omfm7evElQUBDm5uY4Ojry+eefa7V96tQp2rZti7GxMdbW1rzxxhvq+AIUFBQwceJErKyssLa2ZvLkyZR3xayPjw8ODg5s3rxZPbd582YcHR3x9vbWqrt9+3ZeffVVtb8uXbqQkpJSZNzj4uJK7G///v3qn7WDgwPjxo3TejlwZmYmXbt2xdjYmBo1arB+/foy3cfatWupV68ehoaG2NnZMXbsWK3yv//+mx49emBiYoKrq6v6M3zfb7/9RseOHTEzM8PGxobBgwdrvQursLCQhQsX4uLigqGhIY6OjsydO7fYWAoKCggKCsLNzY309PQyxS+EEEKI51e5k6bly5fz+eef8+mnn2JgYMDkyZPZuXMn48aNIzs7+3HEKB4xY2Nj7ty5A4COjg5Lly7l9OnTRERE8MsvvzB58mQATE1N6d+/P2FhYVrXh4WF0bt3b8zNzdVzM2fO5IMPPuDXX39FT0+PgQMHMnnyZD755BP27dvH2bNnmTFjhlp//fr1zJgxg7lz55KYmMi8efOYPn06ERERWn1NmzaNSZMmERcXR+3atRkwYAB3796lWbNmfPzxx1hYWJCRkUFGRgaTJk1SrzMwMMDf318r9vDwcIKCgoqMx9ixYzl48CDffPMNJ0+epE+fPvj5+ZGcnPyv/SxatIiGDRty4sQJRo8ezahRo0hKSgLuJZ2+vr5UqlSJo0eP8t1337Fr1y6tZGDRokWEh4ezdu1a9u/fz9WrV9myZUvZ/zD/v6CgIK17Xbt2LUOHDi1S78aNG0ycOJFjx44RHR2Njo4OPXr0oLCwsEz9pKSk4OfnR69evTh58iQbN25k//79WvcUGBjI+fPn2b17N5s2bWL58uVkZmaW2u6KFSsYM2YMb7zxBqdOnSIqKgoXFxetOrNmzaJv376cPHmSTp064e/vz9WrVwHIysqibdu2eHt7c+zYMbZv386lS5fo27evev3UqVOZP38+06dPJyEhgQ0bNqgvMP6nvLw8+vTpQ1xcHPv27Stxti4vL4+cnBytQwghhBDPqfK+AMrY2FhJS0tTFOXei27j4uIURVGUM2fOKJUrVy5vc+Ix++eLcwsLC5WdO3cqhoaGyqRJk4qt/9133ynW1tbq98OHDyu6urrKhQsXFEVRlEuXLil6enpKTEyMoij/94LbNWvWqNd8/fXXCqBER0er50JCQrRe5lqrVi1lw4YNWn1/+OGHStOmTUts9/Tp0wqgJCYmKopS/Mt4FeX/XrQaFxenmJubK7m5ucqePXuUatWqKfn5+Vovt/3jjz8UXV1d5a+//tJqo127dsrUqVNL7cfJyUkZNGiQ+r2wsFCpVq2asmLFCkVRFOXzzz9XKlWqpOTm5qp1fvzxR0VHR0d9eaydnZ2ycOFCtTw/P1956aWXyvXi3G7duimZmZmKoaGhkpaWpqSlpSlGRkbK5cuXlW7duikBAQElXn/58mUFUE6dOqUoStEXFj/4Ytlhw4Ypb7zxhlYb+/btU3R0dJRbt24pSUlJCqAcOXJELU9MTFSAIi8Z/id7e3tl2rRpJZYDygcffKB+z83NVQBl27ZtiqLc+9l57bXXtK45f/68AihJSUlKTk6OYmhoqKxevbrY9u/f9759+5R27dopr776qpKVlVViPIpy76W/FPPSYq+3Vio+kyIe2SGEEEKIx6esL7ct94MLtra2XL16FScnJxwdHTl06BBeXl6kpqaWe1mReDK2bt2KmZkZ+fn5FBYWMnDgQHWJ2a5duwgJCeH3338nJyeHu3fvcvv2bW7evImJiQmNGjWiXr16RERE8N577/HVV1/h5OREy5Yttfrw9PRUP9//v/ceHh5a5+7PNty4cYOUlBSGDRvGiBEj1Dp3794tsr39P9u9/yxSZmYmbm5u/3rfXl5euLq6smnTJnbv3s3gwYOLPKtz6tQpCgoKqF27ttb5vLw8rK2t/7WPf8an0WiwtbVV7zMxMREvLy9MTU3VOs2bN6ewsJCkpCSMjIzIyMigcePGarmenh4NGzYs939LVatWpXPnzoSHh6MoCp07d6ZKlSpF6iUnJzNjxgwOHz7M33//rc4wpaenl2nzh/j4eE6ePKm15E5RFAoLC0lNTeXMmTPo6enx8ssvq+Vubm6l7ryXmZnJhQsX/vU9b/8ca1NTUywsLNSxjo+PZ/fu3ZiZmRW5LiUlhaysLPLy8v61jwEDBvDSSy/xyy+/YGxc+g54U6dOZeLEier3nJwcef2CEEII8Zwqd9LUtm1boqKi8Pb2ZujQobz99tts2rSJY8eOFXkJrng6tGnThhUrVmBgYIC9vb2aOKSlpdGlSxdGjRrF3LlzqVy5Mvv372fYsGHcuXMHExMTAIYPH85nn33Ge++9R1hYGEOHDkWj0Wj1oa+vr36+X/bgufv/QL//TM/q1au1Ega4txHBv7Vb1qVkcG/Z2meffUZCQgJHjhwpUp6bm4uuri7Hjx8v0ndx/wB/0D/jux9jeeJ7lIKCgtRlcp999lmxdbp27YqTkxOrV6/G3t6ewsJC6tevry7X/De5ubmMHDmScePGFSlzdHTkzJkz5Y7735KT+0ob69zcXLp27cqCBQuKXGdnZ8e5c+fK1EenTp346quvOHjwIG3bti21rqGhIYaGhmVqVwghhBDPtnInTZ9//rn6D5UxY8ZgbW3NgQMHeP311xk5cuQjD1D8d6ampkWeDwE4fvw4hYWFLFq0CB2de4+3ffvtt0XqDRo0iMmTJ7N06VISEhIICAj4T/HY2Nhgb2/PuXPn8Pf3f+h2DAwMKCgoKLXOwIEDmTRpEl5eXsW+hNbb25uCggIyMzNp0aLFQ/dTHHd3d8LDw7lx44Y62xQbG4uOjg516tTB0tISOzs7Dh8+rM7c3b17l+PHjz/UTpR+fn7cuXMHjUaDr69vkfIrV66QlJTE6tWr1Xvdv39/ufrw8fEhISGh2J8nuDerdP8eXnnlFQCSkpLIysoqsU1zc3OcnZ2Jjo6mTZs25Yrnn3F9//33Jb4Xy9XVFWNjY6Kjoxk+fHiJ7YwaNYr69evz+uuv8+OPP9KqVauHikcIIYQQz5dyJ006OjrqP7AB+vfvT//+/R9pUOLJcHFxIT8/n08//ZSuXbsSGxvLypUri9SrVKkSPXv25N133+W1117jpZde+s99z5o1i3HjxmFpaYmfnx95eXkcO3aMa9euaS15Ko2zszO5ublER0fj5eWFiYmJOjv2z9gzMjKKzFLcV7t2bfz9/RkyZAiLFi3C29uby5cvEx0djaenJ507dy5TP8Xx9/dn5syZBAQEEBwczOXLl3nrrbcYPHiwuoRx/PjxzJ8/H1dXV9zc3Fi8eHGpCUZpdHV1SUxMVD8/qFKlSlhbW/P5559jZ2dHeno67733Xrn6mDJlCk2aNGHs2LEMHz4cU1NTEhIS2LlzJ8uWLaNOnTr4+fkxcuRIVqxYgZ6eHhMmTPjX2aTg4GDefPNNqlWrRseOHbl+/TqxsbG89dZbZYprzJgxrF69mgEDBjB58mQqV67M2bNn+eabb1izZg1GRkZMmTKFyZMnY2BgQPPmzbl8+TKnT59m2LBhWm299dZbFBQU0KVLF7Zt28arr75arjESQgghxPOnTLvnnTx5Up1dOnnyZKmHeHZ4eXmxePFiFixYQP369Vm/fj0hISHF1r2/ZK+43ecexvDhw1mzZg1hYWF4eHjQqlUrwsPDqVGjRpnbaNasGW+++Sb9+vWjatWqLFy4sNh6VlZWWs8VPSgsLIwhQ4bwzjvvUKdOHbp3787Ro0fVXdPK2s+DTExM2LFjB1evXuWVV16hd+/etGvXjmXLlql13nnnHQYPHkxAQABNmzbF3NycHj16lHkMHmRhYVHi26x1dHT45ptvOH78OPXr1+ftt98u9/u6PD092bNnD2fOnKFFixZ4e3szY8YM7O3t1TphYWHY29vTqlUrevbsyRtvvFFkm/cHBQQE8PHHH7N8+XLq1atHly5dSE5OLnNc9vb2xMbGUlBQwGuvvYaHhwcTJkzAyspK/Z8806dP55133mHGjBm4u7vTr1+/Enf1mzBhArNmzaJTp04cOHCgzHEIIYQQ4vmkUcrwxLmOjg4XL16kWrVq6OjooNFoin1QXaPRPNQyJvH0W7duHW+//TYXLlzAwMCgosMR4qmTk5ODpaUl2dnZJSauQgghhHi6lPX3d5mW56WmplK1alX1s3hx3Lx5k4yMDObPn8/IkSMlYRJCCCGEEC+cMi3Pc3JyQqPRkJ+fz6xZsygsLMTJyanYQzxfFi5ciJubG7a2tkydOrWiw3mhpKenY2ZmVuKRnp5e0SEKIYQQQrwQyrQ8758sLS2Ji4sr17MnQojyu3v3LmlpaSWWl7RTnKgY96f3vd5aia5h2bZRryjHPxpS0SEIIYQQT4WyLs8r00zTP3Xv3p3IyMj/EpsQogz09PRwcXEp8XhSCVPr1q2ZMGHCE+kL7iWDH3/8cal1goODadCgwROJRwghhBCi3P/qcnV1Zfbs2cTGxvLyyy8X2ZWsuJdeCiEej8uXLzNjxgx+/PFHLl26RKVKlfDy8mLGjBk0b978kfSxefPmErdsL6+cnBwWLFjA999/T1paGlZWVtSvX5/Ro0fTo0cPNBoNR48e1fp7RaPRsGXLFrp3766emzRpUpm3IxdCCCGE+K/KnTR98cUXWFlZcfz4cY4fP65VptFoJGkS4gnq1asXd+7cISIigpo1a3Lp0iWio6O5cuXKI+ujcuXK/+n6goICNBoNOTk5vPrqq2RnZzNnzhxeeeUV9PT02LNnD5MnT6Zt27ZYWVmpm86U5v5zXUIIIYQQT0K5l+elpqaWeJw7d+5xxCiEKEZWVhb79u1jwYIFtGnTBicnJxo1asTUqVN5/fXX1TrDhw+natWqWFhY0LZtW+Lj49U27i9zW7duHc7OzlhaWtK/f3+uX7+u1nlwed61a9cYMmQIlSpVwsTEhI4dO2q9Uyk8PBwrKyuioqKoW7cuhoaGpKen8/7775OWlsbhw4cJCAigbt261K5dmxEjRhAXF6cmQf9cnufs7AygzkLd//7g8jyNRlPkuF8X4LfffqNjx46YmZlhY2PD4MGD+fvvv7Xucdy4ceqLcW1tbQkODv4PfzpCCCGEeJ6UO2kSQjwd7s+2REZGkpeXV2ydPn36kJmZybZt2zh+/Dg+Pj60a9eOq1evqnVSUlKIjIxk69atbN26lT179jB//vwS+w0MDOTYsWNERUVx8OBBFEWhU6dO5Ofnq3Vu3rzJggULWLNmDadPn6ZatWp88803+Pv7a70I95/3UtwzWkePHgXuvTA3IyND/f6gjIwM9Th79iwuLi60bNkSuJc4tm3bFm9vb44dO8b27du5dOkSffv21WojIiICU1NTDh8+zMKFC5k9ezY7d+4scRzy8vLIycnROoQQQgjxfHqoJ8n//PNPoqKiSE9P586dO1plixcvfiSBCSFKp6enR3h4OCNGjGDlypX4+PjQqlUr+vfvj6enJ/v37+fIkSNkZmZiaGgIQGhoKJGRkWzatIk33ngDgMLCQsLDwzE3Nwdg8ODBREdHM3fu3CJ9JicnExUVRWxsLM2aNQNg/fr1ODg4EBkZSZ8+fQDIz89n+fLleHl5AZCZmcm1a9dwc3Mr1z3eX6pnZWWFra1tifXulymKQq9evbC0tGTVqlUALFu2DG9vb+bNm6fWX7t2LQ4ODpw5c4batWsD4OnpycyZM4F7z24uW7aM6OhoOnToUGyfISEhzJo1q1z3I4QQQohnU7mTpujoaF5//XVq1qzJ77//Tv369UlLS0NRFHx8fB5HjEKIEvTq1YvOnTuzb98+Dh06xLZt21i4cCFr1qzhxo0b5ObmYm1trXXNrVu3SElJUb87OzurCROAnZ0dmZmZxfaXmJiInp4ejRs3Vs9ZW1tTp04dEhMT1XMGBgZ4enqq38v5ZoOH9v7773Pw4EGOHTuGsfG9bb/j4+PZvXt3sc9ApaSkaCVN/1TaOABMnTqViRMnqt9zcnJwcHB4FLchhBBCiKdMuZOmqVOnMmnSJGbNmoW5uTnff/891apVw9/fHz8/v8cRoxCiFEZGRnTo0IEOHTowffp0hg8fzsyZMxk9ejR2dnbExMQUucbKykr9/ODOeBqNhsLCwv8Uk7GxMRqNRv1etWpVrKys+P333/9Tu6X56quvWLJkCTExMVSvXl09n5ubS9euXVmwYEGRa+zs7NTP5R0HQ0NDdQZPCCGEEM+3cj/TlJiYyJAh916MqKenx61btzAzM2P27NnF/qNECPFk1a1blxs3buDj48PFixeLfd9TlSpVHqptd3d37t69y+HDh9VzV65cISkpibp165Z4nY6ODv3792f9+vVcuHChSHlubi53794t9lp9fX0KCgpKjevgwYMMHz6cVatW0aRJE60yHx8fTp8+jbOzc5FxePCVCUIIIYQQxSl30mRqaqo+x2RnZ6e1zOefu1EJIR6vK1eu0LZtW7766itOnjxJamoq3333HQsXLqRbt260b9+epk2b0r17d37++WfS0tI4cOAA06ZN49ixYw/Vp6urK926dWPEiBHs37+f+Ph4Bg0aRPXq1enWrVup186dOxcHBwcaN27Ml19+SUJCAsnJyaxduxZvb29yc3OLvc7Z2Zno6GguXrzItWvXipRfvHiRHj160L9/f3x9fbl48SIXL17k8uXLAIwZM4arV68yYMAAjh49SkpKCjt27GDo0KH/mowJIYQQQsBDJE1NmjRh//79AHTq1Il33nmHuXPnEhQUVOT/8AohHh8zMzMaN27MkiVLaNmyJfXr12f69OmMGDGCZcuWodFo+Omnn2jZsiVDhw6ldu3a9O/fnz/++AMbG5uH7jcsLIyXX36ZLl260LRpUxRF4aeffvrXF+BWrlyZQ4cOMWjQIObMmYO3tzctWrTg66+/5qOPPsLS0rLY6xYtWsTOnTtxcHDA29u7SPnvv//OpUuXiIiIwM7OTj1eeeUVAOzt7YmNjaWgoIDXXnsNDw8PJkyYgJWVFTo6soGoEEIIIf6dRinnE9rnzp0jNzcXT09Pbty4wTvvvMOBAwdwdXVl8eLFODk5Pa5YhRDiqZWTk4OlpSXZ2dlYWFhUdDhCCCGEKIOy/v4u90YQ8+bNY9CgQcC9pXorV658+CiFEEIIIYQQ4ilX7rUply9fxs/PDwcHB959913i4+MfR1xCCCGEEEII8VQo9/I8gGvXrvHdd9+xYcMG9u3bh5ubG/7+/gwcOBBnZ+fHEKYQQjzd7k/ve721El1D48fa1/GPhjzW9oUQQogXRVmX5z3UU9CVKlXijTfeICYmhj/++IPAwEDWrVuHi4vLQwcshBBCCCGEEE+j/7R1VH5+PseOHePw4cOkpaX9px25hBDPp8DAQDQaDRqNBgMDA1xcXJg9e3aJ72Uqa5vdu3d/dEEKIYQQQpTioZKm3bt3M2LECGxsbAgMDMTCwoKtW7fy559/Pur4hBDPAT8/PzIyMkhOTuadd94hODiYjz76qNztFBQUUFhY+BgiLNn999IJIYQQ4sVV7qSpevXqdOrUib///pvPP/+cS5cusXbtWtq1a4dGo3kcMQohnnGGhobY2tri5OTEqFGjaN++PVFRUSxevBgPDw9MTU1xcHBg9OjRWi+5DQ8Px8rKiqioKOrWrYuhoSFBQUFERETwww8/qDNYMTExpKWlodFo2Lx5M23atMHExAQvLy8OHjyoFcv+/ftp0aIFxsbGODg4MG7cOG7cuKGWOzs78+GHHzJkyBAsLCx44403ir2nvLw8cnJytA4hhBBCPJ/KnTQFBweTkZHBli1b6N27N4aGho8jLiHEc8zY2Jg7d+6go6PD0qVLOX36NBEREfzyyy9MnjxZq+7NmzdZsGABa9as4fTp0yxdupS+ffuqs1cZGRk0a9ZMrT9t2jQmTZpEXFwctWvXZsCAAepSwJSUFPz8/OjVqxcnT55k48aN7N+/n7Fjx2r1GRoaipeXFydOnGD69OnF3kNISAiWlpbq4eDg8IhHSQghhBBPi4faPU8IIcoqMDCQrKwsIiMjURSF6OhounTpwltvvVVkid6mTZt48803+fvvv4F7M01Dhw4lLi4OLy+vYtu8Ly0tjRo1arBmzRqGDRsGQEJCAvXq1SMxMRE3NzeGDx+Orq4uq1atUq/bv38/rVq14saNGxgZGeHs7Iy3tzdbtmwp9b7y8vLIy8tTv+fk5ODg4CC75wkhhBDPkMf2clshhCivrVu3YmZmRn5+PoWFhQwcOJDg4GB27dpFSEgIv//+Ozk5Ody9e5fbt29z8+ZNTExMADAwMMDT07PMff2zrp2dHQCZmZm4ubkRHx/PyZMnWb9+vVpHURQKCwtJTU3F3d0dgIYNG/5rP4aGhjLTLoQQQrwgJGkSQjx2bdq0YcWKFRgYGGBvb4+enh5paWl06dKFUaNGMXfuXCpXrsz+/fsZNmwYd+7cUZMmY2Pjcj0vqa+vr36+f939zSNyc3MZOXIk48aNK3Kdo6Oj+tnU1PSh7lMIIYQQzydJmoQQj52pqWmR97gdP36cwsJCFi1ahI7Ovccrv/322zK1Z2BgQEFBQbnj8PHxISEhQd4pJ4QQQohy+U/vaRJCiIfl4uJCfn4+n376KefOnWPdunWsXLmyTNc6Oztz8uRJkpKS+Pvvv8nPzy/TdVOmTOHAgQOMHTuWuLg4kpOT+eGHH4psBCGEEEII8U8y0ySEqBBeXl4sXryYBQsWMHXqVFq2bElISAhDhvz7JgcjRowgJiaGhg0bkpuby+7du3F2dv7X6zw9PdmzZw/Tpk2jRYsWKIpCrVq16Nev3yO4o3v2zhlQ6oOkQgghhHj2yO55QgjxCJR19x0hhBBCPD3K+vtblucJIYQQQgghRClkeZ4QQjxCLT/4+rG/p6kk8v4mIYQQ4vGQmSYhhBBCCCGEKIUkTUKIp1Z4eDhWVlYVHYYQQgghXnCSNAnxnAoMDESj0aDRaNDX18fGxoYOHTqwdu1a9WWvL6KYmBg0Gg1ZWVkVHYoQQgghnhGSNAnxHPPz8yMjI4O0tDS2bdtGmzZtGD9+PF26dOHu3bsVHZ4QQgghxDNBkiYhnmOGhobY2tpSvXp1fHx8eP/99/nhhx/Ytm0b4eHhAGRlZTF8+HCqVq2KhYUFbdu2JT4+Xm0jODiYBg0asGrVKhwcHDAxMaFv375kZ2dr9bVmzRrc3d0xMjLCzc2N5cuXq2VpaWloNBo2b95MmzZtMDExwcvLi4MHD2q1ER4ejqOjIyYmJvTo0YMrV64UuacffvgBHx8fjIyMqFmzJrNmzdJKADUaDWvWrKFHjx6YmJjg6upKVFSUGkebNm0AqFSpEhqNhsDAQAA2bdqEh4cHxsbGWFtb0759e27cuFHi2Obl5ZGTk6N1CCGEEOL5JEmTEC+Ytm3b4uXlxebNmwHo06cPmZmZbNu2jePHj+Pj40O7du24evWqes3Zs2f59ttv+d///sf27ds5ceIEo0ePVsvXr1/PjBkzmDt3LomJicybN4/p06cTERGh1fe0adOYNGkScXFx1K5dmwEDBqgJz+HDhxk2bBhjx44lLi6ONm3aMGfOHK3r9+3bx5AhQxg/fjwJCQmsWrWK8PBw5s6dq1Vv1qxZ9O3bl5MnT9KpUyf8/f25evUqDg4OfP/99wAkJSWRkZHBJ598QkZGBgMGDCAoKIjExERiYmLo2bMnpb3GLiQkBEtLS/VwcHB4iD8NIYQQQjwL5OW2QjynAgMDycrKIjIyskhZ//79OXnyJJ9//jmdO3cmMzMTQ0NDtdzFxYXJkyfzxhtvEBwczJw5c/jjjz+oXr06ANu3b6dz58789ddf2Nra4uLiwocffsiAAQPUNubMmcNPP/3EgQMHSEtLo0aNGqxZs4Zhw4YBkJCQQL169UhMTMTNzY2BAweSnZ3Njz/+qBXn9u3b1eeP2rdvT7t27Zg6dapa56uvvmLy5MlcuHABuDfT9MEHH/Dhhx8CcOPGDczMzNi2bRt+fn7ExMTQpk0brl27pm4y8euvv/Lyyy+TlpaGk5NTmcY3Ly+PvLw89XtOTg4ODg54vbVSthwXQgghnhFlfbmtvKdJiBeQoihoNBri4+PJzc3F2tpaq/zWrVukpKSo3x0dHdWECaBp06YUFhaSlJSEubk5KSkpDBs2jBEjRqh17t69i6WlpVa7np6e6mc7OzsAMjMzcXNzIzExkR49emjVb9q0Kdu3b1e/x8fHExsbqzWzVFBQwO3bt7l58yYmJiZF+jE1NcXCwoLMzMwSx8PLy4t27drh4eGBr68vr732Gr1796ZSpUolXmNoaKiVaAohhBDi+SVJkxAvoMTERGrUqEFubi52dnbExMQUqVPWrb5zc3MBWL16NY0bN9Yq09XV1fqur6+vftZoNADl2skvNzeXWbNm0bNnzyJlRkZGxfZzv6/S+tHV1WXnzp0cOHCAn3/+mU8//ZRp06Zx+PBhatSoUeb4hBBCCPF8kqRJiBfML7/8wqlTp3j77bd56aWXuHjxInp6ejg7O5d4TXp6OhcuXMDe3h6AQ4cOoaOjQ506dbCxscHe3p5z587h7+//0HG5u7tz+PBhrXOHDh3S+u7j40NSUhIuLi4P3Y+BgQFwb4bqnzQaDc2bN6d58+bMmDEDJycntmzZwsSJEx+6LyGEEEI8HyRpEuI5lpeXx8WLFykoKODSpUts376dkJAQunTpwpAhQ9DR0aFp06Z0796dhQsXUrt2bS5cuMCPP/5Ijx49aNiwIXBvFicgIIDQ0FBycnIYN24cffv2xdbWFri38cK4ceOwtLTEz8+PvLw8jh07xrVr18qcdIwbN47mzZsTGhpKt27d2LFjh9bSPIAZM2bQpUsXHB0d6d27Nzo6OsTHx/Pbb78V2TSiJE5OTmg0GrZu3UqnTp0wNjbm9OnTREdH89prr1GtWjUOHz7M5cuXcXd3L8doCyGEEOK5pQghnksBAQEKoACKnp6eUrVqVaV9+/bK2rVrlYKCArVeTk6O8tZbbyn29vaKvr6+4uDgoPj7+yvp6emKoijKzJkzFS8vL2X58uWKvb29YmRkpPTu3Vu5evWqVn/r169XGjRooBgYGCiVKlVSWrZsqWzevFlRFEVJTU1VAOXEiRNq/WvXrimAsnv3bvXcF198obz00kuKsbGx0rVrVyU0NFSxtLTU6mf79u1Ks2bNFGNjY8XCwkJp1KiR8vnnn6vlgLJlyxataywtLZWwsDD1++zZsxVbW1tFo9EoAQEBSkJCguLr66tUrVpVMTQ0VGrXrq18+umn5Rrv7OxsBVCys7PLdZ0QQgghKk5Zf3/L7nlCiFIFBwcTGRlJXFxcRYfyVCvr7jtCCCGEeHqU9fe3vKdJCCGEEEIIIUohzzQJIcQj1PKDrx/Le5rkHUxCCCFExZGZJiGeI+Hh4WXeKrysgoODn7mleYGBgXTv3r2iwxBCCCHEc0KSJiGeMYGBgWg0GjQaDQYGBri4uDB79mzu3r1b0aFpSU1NZeDAgdjb22NkZMRLL71Et27d+P333x9735988gnh4eGPvR8hhBBCvBhkeZ4QzyA/Pz/CwsLIy8vjp59+YsyYMejr62NnZ1fRoQGQn59Phw4dqFOnDps3b8bOzo4///yTbdu2kZWV9dDt3rlzR33PUmksLS0fug8hhBBCiAfJTJMQzyBDQ0NsbW1xcnJi1KhRtG/fnqioKLV8x44duLu7Y2Zmhp+fHxkZGWpZYWEhs2fP5qWXXsLQ0JAGDRpovQ8pLS0NjUbD5s2badOmDSYmJnh5eXHw4EGtGPbv30+LFi0wNjbGwcGBcePGcePGDQBOnz5NSkoKy5cvp0mTJjg5OdG8eXPmzJlDkyZN1DbOnz9P3759sbKyonLlynTr1o20tDS1/P4yu7lz52Jvb0+dOnV4//33ady4cZEx8fLyYvbs2VrX/fOeFy5ciIuLC4aGhjg6OjJ37twyxyGEEEKIF5skTUI8B4yNjblz5w4AN2/eJDQ0lHXr1rF3717S09OZNGmSWveTTz5h0aJFhIaGcvLkSXx9fXn99ddJTk7WanPatGlMmjSJuLg4ateuzYABA9QlgCkpKfj5+dGrVy9OnjzJxo0b2b9/P2PHjgWgatWq6OjosGnTJgoKCoqNOT8/H19fX8zNzdm3bx+xsbFqknf/XgCio6NJSkpi586dbN26FX9/f44cOUJKSopa5/Tp05w8eZKBAwcW29fUqVOZP38+06dPJyEhgQ0bNmBjY1OuOB6Ul5dHTk6O1iGEEEKI55MkTUI8wxRFYdeuXezYsYO2bdsC95KAlStX0rBhQ3x8fBg7dizR0dHqNaGhoUyZMoX+/ftTp04dFixYQIMGDfj444+12p40aRKdO3emdu3azJo1iz/++IOzZ88CEBISgr+/PxMmTMDV1ZVmzZqxdOlSvvzyS27fvk316tVZunQpM2bMoFKlSrRt25YPP/yQc+fOqe1v3LiRwsJC1qxZg4eHB+7u7oSFhZGenk5MTIxaz9TUlDVr1lCvXj318PLyYsOGDWqd9evX07hxY1xcXIqM0fXr1/nkk09YuHAhAQEB1KpVi1dffZXhw4eXK44HhYSEYGlpqR4ODg5l/nMTQgghxLNFkiYhnkFbt27FzMwMIyMjOnbsSL9+/QgODgbAxMSEWrVqqXXt7OzIzMwE7r3A7cKFCzRv3lyrvebNm5OYmKh1ztPTU6sNQG0nPj6e8PBwzMzM1MPX15fCwkJSU1MBGDNmDBcvXmT9+vU0bdqU7777jnr16rFz5061jbNnz2Jubq62UblyZW7fvq01i+Th4VHkOSZ/f381aVIUha+//hp/f/9ixyoxMZG8vDzatWtXbHlZ43jQ1KlTyc7OVo/z58+XWFcIIYQQzzbZCEKIZ1CbNm1YsWIFBgYG2Nvbo6f3f/8p6+vra9XVaDQoilLuPv7ZjkajAe49GwSQm5vLyJEjGTduXJHrHB0d1c/m5uZ07dqVrl27MmfOHHx9fZkzZw4dOnQgNzeXl19+mfXr1xdpo2rVqupnU1PTIuUDBgxgypQp/Prrr9y6dYvz58/Tr1+/Yu/D2Lj0dyaVNY4HGRoaYmhoWGrbQgghhHg+SNIkxDPI1NS02KVo/8bCwgJ7e3tiY2Np1aqVej42NpZGjRqVuR0fHx8SEhLKFYNGo8HNzY0DBw6obWzcuJFq1aphYWFR9psAXnrpJVq1asX69eu5desWHTp0oFq1asXWdXV1xdjYmOjoaHVJ3oP38rBxCCGEEOLFIMvzhHjBvPvuuyxYsICNGzeSlJTEe++9R1xcHOPHjy9zG1OmTOHAgQOMHTuWuLg4kpOT+eGHH9SNIOLi4ujWrRubNm0iISGBs2fP8sUXX7B27Vq6desG3FtiV6VKFbp168a+fftITU0lJiaGcePG8eeff/5rDP7+/nzzzTd89913JS7NAzAyMmLKlClMnjyZL7/8kpSUFA4dOsQXX3zxSOIQQgghxPNPZpqEeMGMGzeO7Oxs3nnnHTIzM6lbty5RUVG4urqWuQ1PT0/27NnDtGnTaNGiBYqiUKtWLXWJ3EsvvYSzszOzZs1StzC///3tt98G7j17tXfvXqZMmULPnj25fv061atXp127dmWa8enduzdjx45FV1dXa3vx4kyfPh09PT1mzJjBhQsXsLOz480333wkcQghhBDi+adRHuZhByGEEFpycnKwtLQkOztbki0hhBDiGVHW39+yPE8IIYQQQgghSiFJkxBCCCGEEEKUQp5pEkKIR6jlB1+ja1j6NudPi+MfDanoEIQQQohngsw0CSFUGo2GyMjIUusEBgb+68YLD3J2dubjjz9+6Lge1Lp1ayZMmPDI2hNCCCGEKI0kTUI8wwIDA9FoNOpOcP80ZswYNBoNgYGBD9X2/V3v4uLitM5/8sknhIeHP1SbZRUeHo6VlVWJ5Zs3b+bDDz98rDEIIYQQQtwnSZMQzzgHBwe++eYbbt26pZ67ffs2GzZswNHR8ZH3Z2lpWWpC8yRUrlwZc3PzCo1BCCGEEC8OSZqEeMb5+Pjg4ODA5s2b1XObN2/G0dERb29v9VxxS+QaNGhAcHBwse3WqFEDAG9vbzQaDa1btwaKLs9r3bo1Y8eOZezYsVhaWlKlShWmT59OaW8zyMrKYvjw4VStWhULCwvatm1LfHx8me/5weV5zs7OzJs3j6CgIMzNzXF0dOTzzz/Xuub8+fP07dsXKysrKleuTLdu3UhLS1PLY2JiaNSoEaamplhZWdG8eXP++OOPMsckhBBCiOeXJE1CPAeCgoIICwtTv69du5ahQ4f+pzaPHDkCwK5du8jIyNBKyh4UERGBnp4eR44c4ZNPPmHx4sWsWbOmxPp9+vQhMzOTbdu2cfz4cXx8fGjXrh1Xr1596HgXLVpEw4YNOXHiBKNHj2bUqFEkJSUBkJ+fj6+vL+bm5uzbt4/Y2FjMzMzw8/Pjzp073L17l+7du9OqVStOnjzJwYMHeeONN9BoNCX2l5eXR05OjtYhhBBCiOeTJE1CPAcGDRrE/v37+eOPP/jjjz+IjY1l0KBB/6nNqlWrAmBtbY2trS2VK1cusa6DgwNLliyhTp06+Pv789Zbb7FkyZJi6+7fv58jR47w3Xff0bBhQ1xdXQkNDcXKyopNmzY9dLydOnVi9OjRuLi4MGXKFKpUqcLu3bsB2LhxI4WFhaxZswYPDw/c3d0JCwsjPT2dmJgYcnJyyM7OpkuXLtSqVQt3d3cCAgJKXd4YEhKCpaWlejg4ODx07EIIIYR4uknSJMRzoGrVqnTu3Jnw8HDCwsLo3LkzVapUeWL9N2nSRGtWpmnTpiQnJ1NQUFCkbnx8PLm5uVhbW2NmZqYeqamppKSkPHQMnp6e6meNRoOtrS2ZmZlqn2fPnsXc3Fztr3Llyty+fZuUlBQqV65MYGAgvr6+dO3alU8++YSMjIxS+5s6dSrZ2dnqcf78+YeOXQghhBBPN3lPkxDPiaCgIMaOHQvAZ599VqRcR0enyHNG+fn5TyS2f8rNzcXOzo6YmJgiZf9lgwl9fX2t7xqNhsLCQrXPl19+mfXr1xe57v6MWlhYGOPGjWP79u1s3LiRDz74gJ07d9KkSZNi+zM0NMTQ0PCh4xVCCCHEs0OSJiGeE/efz9FoNPj6+hYpr1q1qtbsSU5ODqmpqSW2Z2BgAFDsbNGDDh8+rPX90KFDuLq6oqurW6Suj48PFy9eRE9PD2dn539t+1Hw8fFh48aNVKtWDQsLixLreXt74+3tzdSpU2natCkbNmwoMWkSQgghxItDlucJ8ZzQ1dUlMTGRhISEYpOVtm3bsm7dOvbt28epU6cICAgott591apVw9jYmO3bt3Pp0iWys7NLrJuens7EiRNJSkri66+/5tNPP2X8+PHF1m3fvj1Nmzale/fu/Pzzz6SlpXHgwAGmTZvGsWPH1HoFBQXExcVpHYmJieUYkf/j7+9PlSpV6NatG/v27SM1NZWYmBjGjRvHn3/+SWpqKlOnTuXgwYP88ccf/PzzzyQnJ+Pu7v5Q/QkhhBDi+SIzTUI8R0qbRZk6dSqpqal06dIFS0tLPvzww1JnmvT09Fi6dCmzZ89mxowZtGjRotgldQBDhgzh1q1bNGrUCF1dXcaPH88bb7xRbF2NRsNPP/3EtGnTGDp0KJcvX8bW1paWLVtiY2Oj1svNzdXaMh2gVq1anD17tpQRKJ6JiQl79+5lypQp9OzZk+vXr1O9enXatWuHhYUFt27d4vfffyciIoIrV65gZ2fHmDFjGDlyZLn7EkIIIcTzR6OU9jIVIYT4F61bt6ZBgwZF3gH1osnJycHS0pLs7OxSk1chhBBCPD3K+vtblucJIYQQQgghRCkkaRJCCCGEEEKIUsgzTUKI/6Sk55xeVC0/+BpdQ+OKDuOpcPyjIRUdghBCCPFIyEyTEM+xtLQ0NBoNcXFxwL0ER6PRkJWVVaFxPUmtW7dmwoQJFR2GEEIIIZ5hkjQJ8ZQ6f/48QUFB2NvbY2BggJOTE+PHj+fKlSsP3WazZs3IyMjA0tLyEUYKe/bsoW3btlSuXBkTExNcXV0JCAjgzp07j7Sfh7F582Y+/PBD9buzs/MLv2mFEEIIIcpHkiYhnkLnzp2jYcOGJCcn8/XXX3P27FlWrlxJdHQ0TZs25erVqw/VroGBAba2tmg0mkcWa0JCAn5+fjRs2JC9e/dy6tQpPv30UwwMDMr0YtzH5X7CVrlyZczNzSssDiGEEEI8+yRpEuIpNGbMGAwMDPj5559p1aoVjo6OdOzYkV27dvHXX38xbdo04N6sybx58wgKCsLc3BxHR0c+//zzEtt9cHleeHg4VlZW7NixA3d3d8zMzPDz8yMjI0PrujVr1uDu7o6RkRFubm4sX75cLfv555+xtbVl4cKF1K9fn1q1auHn58fq1asxNv6/Z3v2799PixYtMDY2xsHBgXHjxnHjxg21PC8vjylTpuDg4IChoSEuLi588cUXWnH+U2RkpFbyFxwcTIMGDVizZg01atTAyMgI0F6e17p1a/744w/efvttNBoNGo2GGzduYGFhwaZNm4q0b2pqyvXr10v7oxJCCCHEC0CSJiGeMlevXmXHjh2MHj1aK+kAsLW1xd/fn40bN3L/FWuLFi2iYcOGnDhxgtGjRzNq1CiSkpLK3N/NmzcJDQ1l3bp17N27l/T0dCZNmqSWr1+/nhkzZjB37lwSExOZN28e06dPJyIiQo0pIyODvXv3lthHSkoKfn5+9OrVi5MnT7Jx40b279/P2LFj1TpDhgzh66+/ZunSpSQmJrJq1SrMzMzKfB8AZ8+e5fvvv2fz5s3qc1z/tHnzZl566SVmz55NRkYGGRkZmJqa0r9/f8LCwrTqhoWF0bt37xJnqfLy8sjJydE6hBBCCPF8kt3zhHjKJCcnoygK7u7uxZa7u7tz7do1Ll++DECnTp0YPXo0AFOmTGHJkiXs3r2bOnXqlKm//Px8Vq5cSa1atQAYO3Yss2fPVstnzpzJokWL6NmzJwA1atQgISGBVatWERAQQJ8+fdixYwetWrXC1taWJk2a0K5dO4YMGaK+JC4kJAR/f391xsfV1ZWlS5fSqlUrVqxYQXp6Ot9++y07d+6kffv2ANSsWbOcI3dvSd6XX35J1apViy2vXLkyurq6mJubY2trq54fPny4+ryXnZ0dmZmZ/PTTT+zatavEvkJCQpg1a1a5YxRCCCHEs0dmmoR4St2fSfo3np6e6meNRoOtrS2ZmZll7sfExERNmAA1aQC4ceMGKSkpDBs2DDMzM/WYM2cOKSkpAOjq6hIWFsaff/7JwoULqV69OvPmzaNevXrqMr/4+HjCw8O12vD19aWwsJDU1FTi4uLQ1dWlVatWZY67OE5OTiUmTKVp1KgR9erVU2fPvvrqK5ycnGjZsmWJ10ydOpXs7Gz1OH/+/EPHLYQQQoinmyRNQjxlXFxc0Gg0JCYmFluemJhIpUqV1ORAX19fq1yj0VBYWFjm/oq7/n7ClpubC8Dq1auJi4tTj99++41Dhw5pXVe9enUGDx7MsmXLOH36NLdv32blypVqOyNHjtRqIz4+nuTkZGrVqlVkGeKDdHR0iiSR+fn5ReqZmpqW+b4fNHz4cMLDw4F7S/OGDh1a6oYZhoaGWFhYaB1CCCGEeD5J0iTEU8ba2poOHTqwfPlybt26pVV28eJF1q9fT79+/R7pDnglsbGxwd7ennPnzuHi4qJ11KhRo8TrKlWqhJ2dnbrRg4+PDwkJCUXacHFxwcDAAA8PDwoLC9mzZ0+x7VWtWpXr169rbRxR3DNLZVHSrn6DBg3ijz/+YOnSpSQkJBAQEPBQ7QshhBDi+SNJkxBPoWXLlpGXl4evry979+7l/PnzbN++nQ4dOlC9enXmzp37xGKZNWsWISEhLF26lDNnznDq1CnCwsJYvHgxAKtWrWLUqFH8/PPPpKSkcPr0aaZMmcLp06fp2rUrcO9ZqwMHDjB27Fji4uJITk7mhx9+UDeCcHZ2JiAggKCgICIjI0lNTSUmJoZvv/0WgMaNG2NiYsL7779PSkoKGzZsUGeFysvZ2Zm9e/fy119/8ffff6vnK1WqRM+ePXn33Xd57bXXeOmll/7DqAkhhBDieSJJkxBPIVdXV44dO0bNmjXp27cvtWrV4o033qBNmzYcPHiQypUrP7FYhg8fzpo1awgLC8PDw4NWrVoRHh6uzjQ1atSI3Nxc3nzzTerVq0erVq04dOgQkZGR6jNKnp6e7NmzhzNnztCiRQu8vb2ZMWMG9vb2aj8rVqygd+/ejB49Gjc3N0aMGKHOLFWuXJmvvvqKn376CQ8PD77++muCg4Mf6n5mz55NWloatWrVKvL807Bhw7hz5w5BQUEP1bYQQgghnk8apaxPmwshxHNu3bp1vP3221y4cAEDA4NyXZuTk4OlpSXZ2dnyfJMQQgjxjCjr72/ZclwI8cK7efMmGRkZzJ8/n5EjR5Y7YRJCCCHE802SJiHEC2/hwoXMnTuXli1bMnXq1P/UVssPvkbXsPTdAJ82xz8aUtEhCCGEEE81eaZJCPHCCw4OJj8/n+joaMzMzCo6HCGEEEI8ZSRpEuIFlJaWhkajUbftjomJQaPRkJWVVaFxPYxnOXYhhBBCPBskaRLiGXP+/HmCgoKwt7fHwMAAJycnxo8fz5UrVx66zWbNmpGRkYGlpeUjjPTei3LvH3p6ejg6OjJx4kTy8vIeaT9CCCGEEI+TJE1CPEPOnTtHw4YNSU5O5uuvv+bs2bOsXLmS6OhomjZtytWrVx+qXQMDA2xtbR/LC3PDwsLIyMggNTWV5cuXs27dOubMmfPI+xFCCCGEeFwkaRLiGTJmzBgMDAz4+eefadWqFY6OjnTs2JFdu3bx119/MW3aNODeC1znzZtHUFAQ5ubmODo68vnnn5fY7oNL3MLDw7GysmLHjh24u7tjZmaGn58fGRkZWtetWbMGd3d3jIyMcHNzY/ny5UXatrKywtbWFgcHB7p06UK3bt349ddf1fKUlBS6deuGjY0NZmZmvPLKK+zatUurjby8PKZMmYKDgwOGhoa4uLjwxRdfFHsvN2/epGPHjjRv3pysrCwCAwPp3r27Vp0JEybQunVr9Xvr1q0ZO3YsY8eOxdLSkipVqjB9+nRKeyNDXl4eOTk5WocQQgghnk+SNAnxjLh69So7duxg9OjRGBtr785ma2uLv78/GzduVP+hv2jRIho2bMiJEycYPXo0o0aNIikpqcz93bx5k9DQUNatW8fevXtJT09n0qRJavn69euZMWMGc+fOJTExkXnz5jF9+nQiIiJKbPPMmTP88ssvNG7cWD2Xm5tLp06diI6O5sSJE/j5+dG1a1fS09PVOkOGDOHrr79m6dKlJCYmsmrVqmI3bMjKyqJDhw4UFhayc+dOrKysyny/ERER6OnpceTIET755BMWL17MmjVrSqwfEhKCpaWlejg4OJS5LyGEEEI8W2TLcSGeEcnJySiKgru7e7Hl7u7uXLt2jcuXLwPQqVMnRo8eDcCUKVNYsmQJu3fvpk6dOmXqLz8/n5UrV1KrVi0Axo4dy+zZs9XymTNnsmjRInr27AlAjRo1SEhIYNWqVQQEBKj1BgwYgK6uLnfv3iUvL48uXbpobevt5eWFl5eX+v3DDz9ky5YtREVFMXbsWM6cOcO3337Lzp07ad++PQA1a9YsEu/Fixfp168frq6ubNiwodzvWnJwcGDJkiVoNBrq1KnDqVOnWLJkCSNGjCi2/tSpU5k4caL6PScnRxInIYQQ4jklM01CPGNKWzL2T56enupnjUaDra0tmZmZZe7HxMRETZgA7Ozs1Otv3LhBSkoKw4YNw8zMTD3mzJlDSkqKVjtLliwhLi6O+Ph4tm7dypkzZxg8eLBanpuby6RJk3B3d8fKygozMzMSExPVmaa4uDh0dXVp1apVqfF26NABFxcXNm7c+FAvp23SpInWM11NmzYlOTmZgoKCYusbGhpiYWGhdQghhBDi+SQzTUI8I1xcXNBoNCQmJtKjR48i5YmJiVSqVImqVasCoK+vr1Wu0WgoLCwsc3/FXX8/YcvNzQVg9erVWkvtAHR1dbW+29ra4uLiAkCdOnW4fv06AwYMYM6cObi4uDBp0iR27txJaGgoLi4uGBsb07t3b+7cuQNQZCliSTp37sz3339PQkICHh4e6nkdHZ0iiWZ+fn6Z2hRCCCGEAJlpEuKZYW1tTYcOHVi+fDm3bt3SKrt48SLr16+nX79+j2UHvAfZ2Nhgb2/PuXPncHFx0Tpq1KhR6rX3k6r79xAbG0tgYCA9evTAw8MDW1tb0tLS1PoeHh4UFhayZ8+eUtudP38+AQEBtGvXjoSEBPV81apVi2xgcf/9VP90+PBhre+HDh3C1dW1SBIohBBCiBePJE1CPEOWLVtGXl4evr6+7N27l/Pnz7N9+3Y6dOhA9erVmTt37hOLZdasWYSEhLB06VLOnDnDqVOnCAsLY/HixVr1srKyuHjxIhcuXGDPnj3Mnj2b2rVrq89mubq6snnzZnUJ38CBA7VmxJydnQkICCAoKIjIyEhSU1OJiYnh22+/LRJTaGgo/v7+tG3blt9//x2Atm3bcuzYMb788kuSk5OZOXMmv/32W5Fr09PTmThxIklJSXz99dd8+umnjB8//lEOmRBCCCGeUbI8T4hniKurK8eOHWPmzJn07duXq1evYmtrS/fu3Zk5cyaVK1d+YrEMHz4cExMTPvroI959911MTU3x8PBgwoQJWvWGDh0K/N9zVS1btmTevHno6d3762fx4sUEBQXRrFkzqlSpwpQpU4ps371ixQref/99Ro8ezZUrV3B0dOT9998vNq4lS5ZQUFBA27ZtiYmJwdfXl+nTpzN58mRu375NUFAQQ4YM4dSpU1rXDRkyhFu3btGoUSN0dXUZP348b7zxRrnHZe+cAfJ8kxBCCPGc0ShlfapcCCGeU61bt6ZBgwZ8/PHHD91GTk4OlpaWZGdnS9IkhBBCPCPK+vtblucJIYQQQgghRClkeZ4QQjxCLT/4Gl3Dsu34V1bHPxrySNsTQgghRPlI0iSEeOHFxMRUdAhCCCGEeIrJ8jwhnlExMTFoNBqysrIqOpSnikajITIysqLDEEIIIcRzRJImIR6xwMBANBoNGo0GfX19bGxs6NChA2vXri3Xy2X/TbNmzcjIyMDS0vKRtJeWlqbGrdFosLa25rXXXuPEiROPpN3i3o30XwQHB9OgQYMi5zMyMujYseMj7UsIIYQQLzZJmoR4DPz8/MjIyCAtLY1t27bRpk0bxo8fT5cuXbh79+4j6cPAwABbW9tH/jLbXbt2kZGRwY4dO8jNzaVjx44lzmbl5+c/0r4fBVtbWwwNDSs6DCGEEEI8RyRpEuIxMDQ0xNbWlurVq+Pj48P777/PDz/8wLZt2wgPDwfuvfR1+PDhVK1aFQsLC9q2bUt8fDwAZ86cQaPRqC9ovW/JkiXUqlULKH55XmxsLK1bt8bExIRKlSrh6+vLtWvXACgsLCQkJIQaNWpgbGyMl5cXmzZtKhK7tbU1tra2NGzYkNDQUC5dusThw4fVGaONGzfSqlUrjIyMWL9+PYWFhcyePZuXXnoJQ0NDGjRowPbt29X2atSoAYC3tzcajYbWrVurZWvWrMHd3R0jIyPc3NxYvny5Vix//vknAwYMoHLlypiamtKwYUMOHz5MeHg4s2bNIj4+Xp0Zuz+uDy7PO3XqFG3btsXY2Bhra2veeOMNcnNz1fLAwEC6d+9OaGgodnZ2WFtbM2bMmH9NCPPy8sjJydE6hBBCCPF8kqRJiCekbdu2eHl5sXnzZgD69OlDZmYm27Zt4/jx4/j4+NCuXTuuXr1K7dq1adiwIevXr9dqY/369QwcOLDY9uPi4mjXrh1169bl4MGD7N+/n65du1JQUABASEgIX375JStXruT06dO8/fbbDBo0iD179pQYs7HxvV3g7ty5o5577733GD9+PImJifj6+vLJJ5+waNEiQkNDOXnyJL6+vrz++uskJycDcOTIEeD/ZrDu3//69euZMWMGc+fOJTExkXnz5jF9+nQiIiIAyM3NpVWrVvz1119ERUURHx/P5MmTKSwspF+/frzzzjvUq1ePjIwMMjIy6NevX5H4b9y4ga+vL5UqVeLo0aN899137Nq1i7Fjx2rV2717NykpKezevZuIiAjCw8PVJKwkISEhWFpaqoeDg0Op9YUQQgjx7JLd84R4gtzc3Dh58iT79+/nyJEjZGZmqkvJQkNDiYyMZNOmTbzxxhv4+/uzbNkyPvzwQ+De7NPx48f56quvim174cKFNGzYUGu2pl69esC9WZF58+axa9cumjZtCkDNmjXZv38/q1atolWrVkXay8rK4sMPP8TMzIxGjRpx69YtACZMmEDPnj3VeqGhoUyZMoX+/fsDsGDBAnbv3s3HH3/MZ599RtWqVYH/m8G6b+bMmSxatEhtq0aNGiQkJLBq1SoCAgLYsGEDly9f5ujRo1SuXBkAFxcX9XozMzP09PS02nzQhg0buH37Nl9++SWmpqYALFu2jK5du7JgwQJsbGwAqFSpEsuWLUNXVxc3Nzc6d+5MdHQ0I0aMKLHtqVOnMnHiRPV7Tk6OJE5CCCHEc0qSJiGeIEVR0Gg0xMfHk5ubi7W1tVb5rVu3SElJAaB///5MmjSJQ4cO0aRJE9avX4+Pjw9ubm7Fth0XF0efPn2KLTt79iw3b96kQ4cOWufv3LmDt7e31rlmzZqho6PDjRs3qFmzJhs3bsTGxoa0tDQAGjZsqNbNycnhwoULNG/eXKuN5s2bq0sNi3Pjxg1SUlIYNmyYVmJy9+5ddWOLuLg4vL291YTpYSQmJuLl5aUmTPdjKywsJCkpSU2a6tWrh66urlrHzs6OU6dOldq2oaGhPDslhBBCvCAkaRLiCUpMTKRGjRrk5uZiZ2dX7PuBrKysgHsbGrRt25YNGzbQpEkTNmzYwKhRo0ps+/5SuuLcf4bnxx9/pHr16lplD/7Df+PGjdStWxdra2s1ln/6ZwLysO7Hs3r1aho3bqxVdj95Ke1+HjV9fX2t7xqN5pHudCiEEEKIZ5s80yTEE/LLL79w6tQpevXqhY+PDxcvXkRPTw8XFxeto0qVKuo1/v7+bNy4kYMHD3Lu3Dl1CVxxPD09iY6OLrasbt26GBoakp6eXqS/B5eUOTg4UKtWrWITpgdZWFhgb29PbGys1vnY2Fjq1q0L3NvlD1CfrQKwsbHB3t6ec+fOFYnn/sYRnp6exMXFcfXq1WL7NjAw0GqzOO7u7sTHx3Pjxg2t2HR0dKhTp86/3p8QQgghBEjSJMRjkZeXx8WLF/nrr7/49ddfmTdvHt26daNLly4MGTKE9u3b07RpU7p3787PP/9MWloaBw4cYNq0aRw7dkxtp2fPnly/fp1Ro0bRpk0b7O3tS+xz6tSpHD16lNGjR3Py5El+//13VqxYwd9//425uTmTJk3i7bffJiIigpSUFH799Vc+/fRTdeOFh/Xuu++yYMECNm7cSFJSEu+99x5xcXGMHz8egGrVqmFsbMz27du5dOkS2dnZAMyaNYuQkBCWLl3KmTNnOHXqFGFhYSxevBiAAQMGYGtrS/fu3YmNjeXcuXN8//33HDx4EABnZ2dSU1OJi4vj77//Ji8vr0hs/v7+GBkZERAQwG+//cbu3bt56623GDx4sLo0TwghhBDiXylCiEcqICBAARRA0dPTU6pWraq0b99eWbt2rVJQUKDWy8nJUd566y3F3t5e0dfXVxwcHBR/f38lPT1dq72+ffsqgLJ27Vqt87t371YA5dq1a+q5mJgYpVmzZoqhoaFiZWWl+Pr6quWFhYXKxx9/rNSpU0fR19dXqlatqvj6+ip79uxRFEVRUlNTFUA5ceJEsfdVUnlBQYESHBysVK9eXdHX11e8vLyUbdu2adVZvXq14uDgoOjo6CitWrVSz69fv15p0KCBYmBgoFSqVElp2bKlsnnzZrU8LS1N6dWrl2JhYaGYmJgoDRs2VA4fPqwoiqLcvn1b6dWrl2JlZaUASlhYmKIoigIoW7ZsUds4efKk0qZNG8XIyEipXLmyMmLECOX69etaf17dunXTinf8+PFacZZFdna2AijZ2dnluk4IIYQQFaesv781iqIoFZWwCSHE8yInJwdLS0uys7OxsLCo6HCEEEIIUQZl/f0ty/OEEEIIIYQQohSye54QQjxCLT/4Gl3DJ7fzX3kd/2hIRYcghBBCPHNkpkkIUSbBwcE0aNCgosMQQgghhHjiJGkS4jmi0WhKPYKDgx9r/5cvX2bUqFE4OjpiaGiIra0tvr6+RbYkf5rFxMSg0WjIysqq6FCEEEII8ZSQ5XlCPEcyMjLUzxs3bmTGjBkkJSWp58zMzB5r/7169eLOnTtERERQs2ZNLl26RHR0NFeuXHms/T4q+fn5FR2CEEIIIZ5CMtMkxHPE1tZWPSwtLdFoNFrnvvnmG9zd3TEyMsLNzY3ly5drXf/nn38yYMAAKleujKmpKQ0bNuTw4cNaddatW4ezszOWlpb079+f69evA5CVlcW+fftYsGABbdq0wcnJiUaNGjF16lRef/11ANLS0tBoNMTFxantZWVlodFoiImJAf5vpufHH3/E09MTIyMjmjRpwm+//aZeEx4ejpWVFZGRkbi6umJkZISvry/nz5/XinXFihXUqlULAwMD6tSpw7p167TKNRoNK1as4PXXX8fU1JQRI0bQpk0bACpVqoRGoyEwMPCh/zyEEEII8XyQpEmIF8T69euZMWMGc+fOJTExkXnz5jF9+nT15ba5ubm0atWKv/76i6ioKOLj45k8eTKFhYVqGykpKURGRrJ161a2bt3Knj17mD9/PnBvFsvMzIzIyMhiXzRbXu+++y6LFi3i6NGjVK1ala5du2rNBN28eZO5c+fy5ZdfEhsbS1ZWFv3791fLt2zZwvjx43nnnXf47bffGDlyJEOHDmX37t1a/QQHB9OjRw9OnTrFrFmz+P777wFISkoiIyODTz75pNj48vLyyMnJ0TqEEEII8XyS5XlCvCBmzpzJokWL6NmzJwA1atQgISGBVatWERAQwIYNG7h8+TJHjx6lcuXKALi4uGi1UVhYSHh4OObm5gAMHjyY6Oho5s6di56eHuHh4YwYMYKVK1fi4+NDq1at6N+/P56eng8Vb4cOHQCIiIjgpZdeYsuWLfTt2xe4t5Ru2bJlNG7cWK3j7u7OkSNHaNSoEaGhoQQGBjJ69GgAJk6cyKFDhwgNDVVnkwAGDhzI0KFD1e+pqakAVKtWDSsrqxLjCwkJYdasWeW+LyGEEEI8e2SmSYgXwI0bN0hJSWHYsGHqjJCZmRlz5swhJSUFgLi4OLy9vdWEqTjOzs5qwgRgZ2dHZmam+r1Xr15cuHCBqKgo/Pz8iImJwcfHh/Dw8HLH3LRpU/Vz5cqVqVOnDomJieo5PT09XnnlFfW7m5sbVlZWap3ExESaN2+u1Wbz5s212gBo2LBhuWMDmDp1KtnZ2erx4NJAIYQQQjw/ZKZJiBdAbm4uAKtXr1ZnZu7T1dUFwNj4398tpK+vr/Vdo9FoLd8DMDIyokOHDnTo0IHp06czfPhwZs6cSWBgIDo69/4/jaIoav2K3nzB1NT0oa4zNDTE0NDwEUcjhBBCiKeRzDQJ8QKwsbHB3t6ec+fO4eLionXUqFEDAE9PT+Li4rh69eoj7btu3brcuHEDgKpVqwLau/z9c1OIfzp06JD6+dq1a5w5cwZ3d3f13N27dzl27Jj6PSkpiaysLLWOu7t7ka3OY2NjqVu3bqnxGhgYAFBQUPBvtyaEEEKIF4TMNAnxgpg1axbjxo3D0tISPz8/8vLyOHbsGNeuXWPixIkMGDCAefPm0b17d0JCQrCzs+PEiRPY29trLZUryZUrV+jTpw9BQUF4enpibm7OsWPHWLhwId26dQPuzWY1adKE+fPnU6NGDTIzM/nggw+KbW/27NlYW1tjY2PDtGnTqFKlCt27d1fL9fX1eeutt1i6dCl6enqMHTuWJk2a0KhRI+DeRhJ9+/bF29ub9u3b87///Y/Nmzeza9euUu/DyckJjUbD1q1b6dSpE8bGxo99q3YhhBBCPN1kpkmIF8Tw4cNZs2YNYWFheHh40KpVK8LDw9WZJgMDA37++WeqVatGp06d8PDwYP78+eryvX9jZmZG48aNWbJkCS1btqR+/fpMnz6dESNGsGzZMrXe2rVruXv3Li+//DITJkxgzpw5xbY3f/58xo8fz8svv8zFixf53//+p84CAZiYmDBlyhQGDhxI8+bNMTMzY+PGjWp59+7d+eSTTwgNDaVevXqsWrWKsLAwWrduXep9VK9enVmzZvHee+9hY2PD2LFjy3T/QgghhHh+aZR/PlwghBAVLCYmhjZt2nDt2rUSd68LDw9nwoQJZGVlPdHYSpOTk4OlpSXZ2dlYWFhUdDhCCCGEKIOy/v6WmSYhhBBCCCGEKIUkTUIIIYQQQghRClmeJ4QQj8D96X2vt1aia/jv27c/jY5/NKSiQxBCCCGeKFmeJ4R4poSHh5f4DJMQQgghREWSpEmI58Dly5cZNWoUjo6OGBoaYmtri6+vr/qeIo1GQ2RkZMUG+S/69evHmTNnHll7aWlpaDSaUo9Fixahq6vLX3/9VWwbrq6uTJw48ZHFJIQQQohnk7ynSYjnQK9evbhz5w4RERHUrFmTS5cuER0dzZUrV8rcxp07d7S29H7SjI2NMTZ+dMvaHBwctF6iGxoayvbt27Xe02RqasqCBQuIiIjg/fff17p+7969nD17lmHDhj2ymIQQQgjxbJKZJiGecVlZWezbt48FCxbQpk0bnJycaNSoEVOnTuX111/H2dkZgB49eqDRaNTvwcHBNGjQgDVr1lCjRg2MjIzU9oYPH07VqlWxsLCgbdu2xMfHq/2lpKTQrVs3bGxsMDMz45VXXinywlhnZ2fmzJnDkCFDMDMzw8nJiaioKC5fvky3bt0wMzPD09OTY8eOqdc8uDzvfnzr1q3D2dkZS0tL+vfvz/Xr19U6169fx9/fH1NTU+zs7FiyZAmtW7dmwoQJ6OrqYmtrqx5mZmbo6elpnTM3N2fw4MGEh4cXGde1a9fSuHFj6tWr9x//hIQQQgjxrJOkSYhnnJmZGWZmZkRGRpKXl1ek/OjRowCEhYWRkZGhfgc4e/Ys33//PZs3byYuLg6APn36kJmZybZt2zh+/Dg+Pj60a9eOq1evApCbm0unTp2Ijo7mxIkT+Pn50bVrV9LT07X6XbJkCc2bN+fEiRN07tyZwYMHM2TIEAYNGsSvv/5KrVq1GDJkCKXtRZOSkkJkZCRbt25l69at7Nmzh/nz56vlEydOJDY2lqioKHbu3Mm+ffv49ddfyzV+w4YNIzk5mb1796rncnNz2bRpU6mzTHl5eeTk5GgdQgghhHg+SdIkxDNOT0+P8PBwIiIisLKyonnz5rz//vucPHkSgKpVqwJgZWWFra2t+h3uLcn78ssv8fb2xtPTk/3793PkyBG+++47GjZsiKurK6GhoVhZWbFp0yYAvLy8GDlyJPXr18fV1ZUPP/yQWrVqERUVpRVXp06dGDlyJK6ursyYMYOcnBxeeeUV+vTpQ+3atZkyZQqJiYlcunSpxHsrLCwkPDyc+vXr06JFCwYPHkx0dDRwb5YpIiKC0NBQ2rVrR/369QkLC6OgoKBc41e3bl2aNGnC2rVr1XPffvstiqLQv3//Eq8LCQnB0tJSPRwcHMrVrxBCCCGeHZI0CfEc6NWrFxcuXCAqKgo/Pz9iYmLw8fEpdtnZPzk5OWklUfHx8eTm5mJtba3OYJmZmZGamkpKSgpwbxZm0qRJuLu7Y2VlhZmZGYmJiUVmmjw9PdXPNjY2AHh4eBQ5l5mZWWJ8zs7OmJubq9/t7OzU+ufOnSM/P59GjRqp5ZaWltSpU6fUey5OUFAQmzZtUpf+rV27lj59+mj1/aCpU6eSnZ2tHufPny93v0IIIYR4NshGEEI8J4yMjOjQoQMdOnRg+vTpDB8+nJkzZxIYGFjiNaamplrfc3NzsbOzIyYmpkjd+88bTZo0iZ07dxIaGoqLiwvGxsb07t2bO3fuaNXX19dXP2s0mhLPFRYWlhjfP+vfv6a0+g+rf//+vP3223z77be0bNmS2NhYQkJCSr3G0NAQQ0PDRx6LEEIIIZ4+kjQJ8ZyqW7euus24vr5+mZat+fj4cPHiRfT09NQNIx4UGxtLYGAgPXr0AO4lWmlpaY8o6rKrWbMm+vr6HD16FEdHRwCys7M5c+YMLVu2LFdb5ubm9OnTh7Vr15KSkkLt2rVp0aLF4whbCCGEEM8gWZ4nxDPuypUrtG3blq+++oqTJ0+SmprKd999x8KFC+nWrRtwb5lbdHQ0Fy9e5Nq1ayW21b59e5o2bUr37t35+eefSUtL48CBA0ybNk3d6c7V1VXdOCI+Pp6BAwc+ltmff2Nubk5AQADvvvsuu3fv5vTp0wwbNgwdHR11Fqs8hg0bxoEDB1i5ciVBQUGPIWIhhBBCPKskaRLiGWdmZkbjxo1ZsmQJLVu2pH79+kyfPp0RI0awbNkyABYtWsTOnTtxcHDA29u7xLY0Gg0//fQTLVu2ZOjQodSuXZv+/fvzxx9/qM8gLV68mEqVKtGsWTO6du2Kr68vPj4+T+ReH7R48WKaNm1Kly5daN++Pc2bN8fd3V3dPr08Xn31VerUqUNOTg5Dhgx5DNEKIYQQ4lmlUUrb71cIIZ4hN27coHr16ixatOiJv5Q2JycHS0tLsrOzsbCweKJ9CyGEEOLhlPX3tzzTJIR4Zp04cYLff/+dRo0akZ2dzezZswHUZYlCCCGEEI+CJE1CiGdaaGgoSUlJGBgY8PLLL7Nv3z6qVKlS0WEJIYQQ4jkiy/OEEOIRuD+97/XWSnQNjSs6HC3HP5JntIQQQojilHV5nmwEIYTQEh4err6T6VmWlpaGRqMhLi6uokMRQgghxDNOkiYhHqHLly8zatQoHB0dMTQ0xNbWFl9fX2JjY4F7u9Pdf3fS06pfv36cOXPmkbfbunVrNBoN8+fPL1LWuXNnNBoNwcHBj6w/BwcHMjIyqF+//iNrUwghhBAvJkmahHiEevXqxYkTJ4iIiODMmTNERUXRunVrrly5UuY27ty58xgj/HfGxsZUq1btsbTt4OBAeHi41rm//vqL6Oho7OzsHmlfurq62Nraoqcnj24KIYQQ4r+RpEmIRyQrK4t9+/axYMEC2rRpg5OTE40aNWLq1Km8/vrrODs7A9CjRw80Go36PTg4mAYNGrBmzRpq1KihvmMoKyuL4cOHU7VqVSwsLGjbti3x8fFqfykpKXTr1g0bGxvMzMx45ZVX2LVrl1ZMzs7OzJkzhyFDhmBmZoaTkxNRUVFcvnyZbt26YWZmhqenp/riWii6PO9+fOvWrcPZ2RlLS0v69+/P9evX1TrXr1/H398fU1NT7OzsWLJkCa1bt2bChAla8XTp0oW///5bnXkDiIiI4LXXXiuSqF27do0hQ4ZQqVIlTExM6NixI8nJycC99cfGxsZs27ZN65otW7Zgbm7OzZs3i12e99tvv9GxY0fMzMywsbFh8ODB/P3332r5pk2b8PDwwNjYGGtra9q3b8+NGzeK++MWQgghxAtEkiYhHhEzMzPMzMyIjIwkLy+vSPnRo0cBCAsLIyMjQ/0OcPbsWb7//ns2b96s/iO/T58+ZGZmsm3bNo4fP46Pjw/t2rXj6tWrAOTm5tKpUyeio6M5ceIEfn5+dO3alfT0dK1+lyxZQvPmzTlx4gSdO3dm8ODBDBkyhEGDBvHrr79Sq1YthgwZQml7wqSkpBAZGcnWrVvZunUre/bs0VpmN3HiRGJjY4mKimLnzp3s27ePX3/9tUg7BgYG+Pv7ExYWpp4LDw8nKCioSN3AwECOHTtGVFQUBw8eRFEUOnXqRH5+PhYWFnTp0oUNGzZoXbN+/Xq6d++OiYlJkfaysrJo27Yt3t7eHDt2jO3bt3Pp0iX69u0LQEZGBgMGDCAoKIjExERiYmLo2bNnieOSl5dHTk6O1iGEEEKI55MkTUI8Inp6eoSHhxMREYGVlRXNmzfn/fff5+TJkwBUrVoVACsrK2xtbdXvcG9J3pdffom3tzeenp7s37+fI0eO8N1339GwYUNcXV0JDQ3FysqKTZs2AeDl5cXIkSOpX78+rq6ufPjhh9SqVYuoqCituDp16sTIkSNxdXVlxowZ5OTk8Morr9CnTx9q167NlClTSExM5NKlSyXeW2FhIeHh4dSvX58WLVowePBgoqOjgXuzTBEREYSGhtKuXTvq169PWFgYBQUFxbYVFBTEt99+y40bN9i7dy/Z2dl06dJFq05ycjJRUVGsWbOGFi1a4OXlxfr16/nrr7/UZ8L8/f2JjIzk5s2bwL3Zpx9//BF/f/9i+122bBne3t7MmzcPNzc3vL29Wbt2Lbt37+bMmTNkZGRw9+5devbsibOzMx4eHowePRozM7Ni2wsJCcHS0lI9HBwcShw/IYQQQjzbJGkS4hHq1asXFy5cICoqCj8/P2JiYvDx8SnyHM+DnJyctJKo+Ph4cnNzsba2VmewzMzMSE1NJSUlBbg30zRp0iTc3d2xsrLCzMyMxMTEIjNNnp6e6mcbGxsAPDw8ipzLzMwsMT5nZ2fMzc3V73Z2dmr9c+fOkZ+fT6NGjdRyS0tL6tSpU2xbXl5euLq6smnTJtauXcvgwYOLPHeUmJiInp4ejRs3Vs9ZW1tTp04dEhMTgXvJoL6+vpokfv/991hYWNC+ffti+42Pj2f37t1a4+nm5gbcm0nz8vKiXbt2eHh40KdPH1avXs21a9dKHJOpU6eSnZ2tHufPny+xrhBCCCGebfKEtBCPmJGRER06dKBDhw5Mnz6d4cOHM3PmTAIDA0u8xtTUVOt7bm4udnZ2xMTEFKl7/3mjSZMmsXPnTkJDQ3FxccHY2JjevXsX2UhCX19f/azRaEo8V1hYWGJ8/6x//5rS6v+boKAgPvvsMxISEjhy5MhDtWFgYEDv3r3ZsGED/fv3Z8OGDfTr16/EjR9yc3Pp2rUrCxYsKFJmZ2eHrq4uO3fu5MCBA/z88898+umnTJs2jcOHD1OjRo0i1xgaGmJoaPhQsQshhBDi2SIzTUI8ZnXr1lU3E9DX1y9x2do/+fj4cPHiRfT09HBxcdE6qlSpAkBsbCyBgYH06NEDDw8PbG1tSUtLe5y3UqyaNWuir6+v9YxWdnZ2qduWDxw4kFOnTlG/fn3q1q1bpNzd3Z27d+9y+PBh9dyVK1dISkrSqu/v78/27ds5ffo0v/zyS4lL8+DemJ4+fRpnZ+ciY3o/adVoNDRv3pxZs2Zx4sQJDAwM2LJlS7nGQwghhBDPH0mahHhErly5Qtu2bfnqq684efIkqampfPfddyxcuJBu3boB95a5RUdHc/HixVKXfrVv356mTZvSvXt3fv75Z9LS0jhw4ADTpk1Td7pzdXVVN46Ij49n4MCB/2n252GZm5sTEBDAu+++y+7duzl9+jTDhg1DR0dHncV6UKVKlcjIyFCfi3qQq6sr3bp1Y8SIEezfv5/4+HgGDRpE9erV1bEEaNmyJba2tvj7+1OjRg2t5XwPGjNmDFevXmXAgAEcPXqUlJQUduzYwdChQykoKODw4cPMmzePY8eOkZ6ezubNm7l8+TLu7u7/bYCEEEII8cyT5XlCPCJmZmY0btyYJUuWkJKSQn5+Pg4ODowYMYL3338fgEWLFjFx4kRWr15N9erVS5wZ0mg0/PTTT0ybNo2hQ4dy+fJlbG1tadmypfoM0uLFiwkKCqJZs2ZUqVKFKVOmVNgObosXL+bNN9+kS5cuWFhYMHnyZM6fP69un16cf25rXpywsDDGjx9Ply5duHPnDi1btuSnn34qsrRwwIABLFy4kBkzZpTanr29PbGxsUyZMoXXXnuNvLw8nJyc8PPzQ0dHBwsLC/bu3cvHH39MTk4OTk5OLFq0iI4dO5ZrLPbOGYCFhUW5rhFCCCHE002jlLbPsBBCPIQbN25QvXp1Fi1axLBhwyo6nCciJycHS0tLsrOzJWkSQgghnhFl/f0tM01CiP/sxIkT/P777zRq1Ijs7Gxmz54NoLWUTgghhBDiWSVJkxDikQgNDSUpKQkDAwNefvll9u3bp25a8SJp+cHX6BoaV3QYT9zxj4ZUdAhCCCHEYyNJkxDiP/P29ub48eMVHYYQQgghxGMhu+cJIVQxMTFoNBqysrIqOhSVRqMhMjISgLS0NDQaDXFxcUDReMPDw/91gwkhhBBCiPKSpEmIChAYGIhGo0Gj0WBgYICLiwuzZ8/m7t27FR3aExMcHKyOgUajwdLSkhYtWrBnzx6tehkZGWXewa5fv36lvh9KCCGEEOJhSNIkRAXx8/MjIyOD5ORk3nnnHYKDg/noo48qLJ78/Pwn3me9evXIyMggIyODgwcP4urqSpcuXcjOzlbr2NraYmhoWKb2jI2NqVat2uMKVwghhBAvKEmahKgghoaG2Nra4uTkxKhRo2jfvj1RUVEsXrwYDw8PTE1NcXBwYPTo0eTm5qrX3V+CFhkZiaurK0ZGRvj6+nL+/Hmt9n/44Qd8fHwwMjKiZs2azJo1S2smS6PRsGLFCl5//XVMTU2ZO3dusXHu37+fFi1aYGxsjIODA+PGjePGjRtq+fLly9U4bGxs6N27t1q2adMmPDw8MDY2xtramvbt22tdq6enh62tLba2ttStW5fZs2eTm5urNVv0z+V5/+bB5XnBwcE0aNCAdevW4ezsjKWlJf379+f69etqnevXr+Pv74+pqSl2dnYsWbKE1q1bM2HChFL7ysvLIycnR+sQQgghxPNJkiYhnhLGxsbcuXMHHR0dli5dyunTp4mIiOCXX35h8uTJWnVv3rzJ3Llz+fLLL4mNjSUrK4v+/fur5fv27WPIkCGMHz+ehIQEVq1aRXh4eJHEKDg4mB49enDq1CmCgoKKxJSSkoKfnx+9evXi5MmTbNy4kf379zN27FgAjh07xrhx45g9ezZJSUls376dli1bAveW1Q0YMICgoCASExOJiYmhZ8+elPRquLy8PMLCwrCysqJOnTr/aSwfvIfIyEi2bt3K1q1b2bNnD/Pnz1fLJ06cSGxsLFFRUezcuZN9+/bx66+//mu7ISEhWFpaqoeDg8Mji1kIIYQQTxfZPU+ICqYoCtHR0ezYsYO33npLa4bD2dmZOXPm8Oabb7J8+XL1fH5+PsuWLaNx48YARERE4O7uzpEjR2jUqBGzZs3ivffeIyAgAICaNWvy4YcfMnnyZGbOnKm2M3DgQIYOHap+P3funFZsISEh+Pv7qzG5urqydOlSWrVqxYoVK0hPT8fU1JQuXbpgbm6Ok5MT3t7ewL2k6e7du/Ts2RMnJycAPDw8tNo/deoUZmZmwL1E0NzcnI0bNz7Sl8MWFhYSHh6Oubk5AIMHDyY6Opq5c+dy/fp1IiIi2LBhA+3atQMgLCwMe3v7f2136tSpTJw4Uf2ek5MjiZMQQgjxnJKkSYgKsnXrVszMzMjPz6ewsJCBAwcSHBzMrl27CAkJ4ffffycnJ4e7d+9y+/Ztbt68iYmJCXBvWdsrr7yituXm5oaVlRWJiYk0atSI+Ph4YmNjtWaWCgoKirTTsGHDUmOMj4/n5MmTrF+/Xj2nKAqFhYWkpqbSoUMHnJycqFmzJn5+fvj5+dGjRw9MTEzw8vKiXbt2eHh44Ovry2uvvUbv3r2pVKmS2ladOnWIiooC7i2T27hxI3369GH37t3/GltZOTs7qwkTgJ2dHZmZmcC9JDE/P59GjRqp5ZaWlmWa6TI0NCzzs1ZCCCGEeLbJ8jwhKkibNm2Ii4sjOTmZW7duERERweXLl+nSpQuenp58//33HD9+nM8++wyAO3fulLnt3NxcZs2aRVxcnHqcOnWK5ORkjIyM1Hqmpqb/2s7IkSO12omPjyc5OZlatWphbm7Or7/+ytdff42dnR0zZszAy8uLrKwsdHV12blzJ9u2baNu3bp8+umn1KlTh9TUVLX9+zsHuri44O3tzfz586levToff/xx+QazFPr6+lrfNRoNhYWFj6x9IYQQQjz/JGkSooKYmpri4uKCo6Mjenr3Jn2PHz9OYWEhixYtokmTJtSuXZsLFy4Uufbu3bscO3ZM/Z6UlERWVhbu7u4A+Pj4kJSUpCYk/zx0dMr+n72Pjw8JCQnFtmNgYADcm/Vq3749Cxcu5OTJk6SlpfHLL78A9xKU5s2bM2vWLE6cOIGBgQFbtmwptU9dXV1u3bpV5hj/i5o1a6Kvr8/Ro0fVc9nZ2bJtuRBCCCG0yPI8IZ4iLi4u5Ofn8+mnn9K1a1diY2NZuXJlkXr6+vq89dZbLF26FD09PcaOHUuTJk3UZWYzZsygS5cuODo60rt3b3R0dIiPj+e3335jzpw5ZY5nypQpNGnShLFjxzJ8+HBMTU1JSEhg586dLFu2jK1bt3Lu3DlatmxJpUqV+OmnnygsLKROnTocPnyY6OhoXnvtNapVq8bhw4e5fPmymtjBveTv4sWLwP8tz0tISGDKlCn/cSTLxtzcnICAAN59910qV65MtWrVmDlzJjo6Omg0micSgxBCCCGefpI0CfEU8fLyYvHixSxYsICpU6fSsmVLQkJCGDJkiFY9ExMTpkyZwsCBA/nrr79o0aIFX3zxhVru6+vL1q1bmT17NgsWLEBfXx83NzeGDx9erng8PT3Zs2cP06ZNo0WLFiiKQq1atejXrx8AVlZWbN68meDgYG7fvo2rqytff/019erVIzExkb179/Lxxx+Tk5ODk5MTixYt0npR7enTp7Gzs1PvqVatWqxYsaLI/T5Oixcv5s0336RLly5YWFgwefJkzp8/r7WMsTz2zhnwSDeyEEIIIUTF0ygl7f8rhHgqhYeHM2HCBLKysio6lOfSjRs3qF69OosWLWLYsGFlvi47OxsrKyvOnz8vSZMQQgjxjLi/+21WVhaWlpYl1pOZJiHEC+3EiRP8/vvvNGrUiOzsbGbPng1At27dytXOlStXAGTbcSGEEOIZdP36dUmahBCiNKGhoSQlJWFgYMDLL7/Mvn37qFKlSrnaqFy5MgDp6eml/qX7orj/f+5k5u0eGQ9tMh7aZDy0yXhok/HQ9qjHQ1EUrl+//q/vaJTleUII8Qjk5ORgaWlJdna2/FJDxuNBMh7aZDy0yXhok/HQJuOhraLGQ7YcF0IIIYQQQohSSNIkhBBCCCGEEKWQpEkIIR4BQ0NDZs6ciaGhYUWH8lSQ8dAm46FNxkObjIc2GQ9tMh7aKmo85JkmIYQQQgghhCiFzDQJIYQQQgghRCkkaRJCCCGEEEKIUkjSJIQQQgghhBClkKRJCCGEEEIIIUohSZMQQvxHn332Gc7OzhgZGdG4cWOOHDlS0SE9Fnv37qVr167Y29uj0WiIjIzUKlcUhRkzZmBnZ4exsTHt27cnOTlZq87Vq1fx9/fHwsICKysrhg0bRm5u7hO8i0cnJCSEV155BXNzc6pVq0b37t1JSkrSqnP79m3GjBmDtbU1ZmZm9OrVi0uXLmnVSU9Pp3PnzpiYmFCtWjXeffdd7t69+yRv5ZFYsWIFnp6eWFhYYGFhQdOmTdm2bZta/iKNxYPmz5+PRqNhwoQJ6rkXbTyCg4PRaDRah5ubm1r+oo0HwF9//cWgQYOwtrbG2NgYDw8Pjh07ppa/SH+nOjs7F/n50Gg0jBkzBnhKfj4UIYQQD+2bb75RDAwMlLVr1yqnT59WRowYoVhZWSmXLl2q6NAeuZ9++kmZNm2asnnzZgVQtmzZolU+f/58xdLSUomMjFTi4+OV119/XalRo4Zy69YttY6fn5/i5eWlHDp0SNm3b5/i4uKiDBgw4AnfyaPh6+urhIWFKb/99psSFxendOrUSXF0dFRyc3PVOm+++abi4OCgREdHK8eOHVOaNGmiNGvWTC2/e/euUr9+faV9+/bKiRMnlJ9++kmpUqWKMnXq1Iq4pf8kKipK+fHHH5UzZ84oSUlJyvvvv6/o6+srv/32m6IoL9ZY/NORI0cUZ2dnxdPTUxk/frx6/kUbj5kzZyr16tVTMjIy1OPy5ctq+Ys2HlevXlWcnJyUwMBA5fDhw8q5c+eUHTt2KGfPnlXrvEh/p2ZmZmr9bOzcuVMBlN27dyuK8nT8fEjSJIQQ/0GjRo2UMWPGqN8LCgoUe3t7JSQkpAKjevweTJoKCwsVW1tb5aOPPlLPZWVlKYaGhsrXX3+tKIqiJCQkKIBy9OhRtc62bdsUjUaj/PXXX08s9sclMzNTAZQ9e/YoinLv/vX19ZXvvvtOrZOYmKgAysGDBxVFuZeI6ujoKBcvXlTrrFixQrGwsFDy8vKe7A08BpUqVVLWrFnzwo7F9evXFVdXV2Xnzp1Kq1at1KTpRRyPmTNnKl5eXsWWvYjjMWXKFOXVV18tsfxF/zt1/PjxSq1atZTCwsKn5udDlucJIcRDunPnDsePH6d9+/bqOR0dHdq3b8/BgwcrMLInLzU1lYsXL2qNhaWlJY0bN1bH4uDBg1hZWdGwYUO1Tvv27dHR0eHw4cNPPOZHLTs7G4DKlSsDcPz4cfLz87XGxM3NDUdHR60x8fDwwMbGRq3j6+tLTk4Op0+ffoLRP1oFBQV888033Lhxg6ZNm76wYzFmzBg6d+6sdd/w4v5sJCcnY29vT82aNfH39yc9PR14MccjKiqKhg0b0qdPH6pVq4a3tzerV69Wy1/kv1Pv3LnDV199RVBQEBqN5qn5+ZCkSQghHtLff/9NQUGB1l/SADY2Nly8eLGCoqoY9++3tLG4ePEi1apV0yrX09OjcuXKz/x4FRYWMmHCBJo3b079+vWBe/drYGCAlZWVVt0Hx6S4Mbtf9qw5deoUZmZmGBoa8uabb7Jlyxbq1q37Qo7FN998w6+//kpISEiRshdxPBo3bkx4eDjbt29nxYoVpKam0qJFC65fv/5Cjse5c+dYsWIFrq6u7Nixg1GjRjFu3DgiIiKAF/vv1MjISLKysggMDASenv9e9B5JK0IIIcQLbMyYMfz222/s37+/okOpUHXq1CEuLo7s7Gw2bdpEQEAAe/bsqeiwnrjz588zfvx4du7ciZGRUUWH81To2LGj+tnT05PGjRvj5OTEt99+i7GxcQVGVjEKCwtp2LAh8+bNA8Db25vffvuNlStXEhAQUMHRVawvvviCjh07Ym9vX9GhaJGZJiGEeEhVqlRBV1e3yA4+ly5dwtbWtoKiqhj377e0sbC1tSUzM1Or/O7du1y9evWZHq+xY8eydetWdu/ezUsvvaSet7W15c6dO2RlZWnVf3BMihuz+2XPGgMDA1xcXHj55ZcJCQnBy8uLTz755IUbi+PHj5OZmYmPjw96enro6emxZ88eli5dip6eHjY2Ni/UeBTHysqK2rVrc/bs2Rfu5wPAzs6OunXrap1zd3dXlyy+qH+n/vHHH+zatYvhw4er556Wnw9JmoQQ4iEZGBjw8ssvEx0drZ4rLCwkOjqapk2bVmBkT16NGjWwtbXVGoucnBwOHz6sjkXTpk3Jysri+PHjap1ffvmFwsJCGjdu/MRj/q8URWHs2LFs2bKFX375hRo1amiVv/zyy+jr62uNSVJSEunp6VpjcurUKa1/+OzcuRMLC4si/6B6FhUWFpKXl/fCjUW7du04deoUcXFx6tGwYUP8/f3Vzy/SeBQnNzeXlJQU7OzsXrifD4DmzZsXeUXBmTNncHJyAl7Mv1MBwsLCqFatGp07d1bPPTU/H49kOwkhhHhBffPNN4qhoaESHh6uJCQkKG+88YZiZWWltYPP8+L69evKiRMnlBMnTiiAsnjxYuXEiRPKH3/8oSjKve1xrayslB9++EE5efKk0q1bt2K3x/X29lYOHz6s7N+/X3F1dX0mt8dVFEUZNWqUYmlpqcTExGhtlXvz5k21zptvvqk4Ojoqv/zyi3Ls2DGladOmStOmTdXy+9vkvvbaa0pcXJyyfft2pWrVqs/kNsrvvfeesmfPHiU1NVU5efKk8t577ykajUb5+eefFUV5scaiOP/cPU9RXrzxeOedd5SYmBglNTVViY2NVdq3b69UqVJFyczMVBTlxRuPI0eOKHp6esrcuXOV5ORkZf369YqJiYny1VdfqXVetL9TCwoKFEdHR2XKlClFyp6Gnw9JmoQQ4j/69NNPFUdHR8XAwEBp1KiRcujQoYoO6bHYvXu3AhQ5AgICFEW5t0Xu9OnTFRsbG8XQ0FBp166dkpSUpNXGlStXlAEDBihmZmaKhYWFMnToUOX69esVcDf/XXFjAShhYWFqnVu3bimjR49WKlWqpJiYmCg9evRQMjIytNpJS0tTOnbsqBgbGytVqlRR3nnnHSU/P/8J381/FxQUpDg5OSkGBgZK1apVlXbt2qkJk6K8WGNRnAeTphdtPPr166fY2dkpBgYGSvXq1ZV+/fppvZPoRRsPRVGU//3vf0r9+vUVQ0NDxc3NTfn888+1yl+0v1N37NihAEXuUVGejp8PjaIoyqOZsxJCCCGEEEKI54880ySEEEIIIYQQpZCkSQghhBBCCCFKIUmTEEIIIYQQQpRCkiYhhBBCCCGEKIUkTUIIIYQQQghRCkmahBBCCCGEEKIUkjQJIYQQQgghRCkkaRJCCCGEEEKIUkjSJIQQQoinQuvWrZkwYUJFhyGEEEVoFEVRKjoIIYQQQoirV6+ir6+Publ5RYdSRExMDG3atOHatWtYWVlVdDhCiCdMr6IDEEIIIYQAqFy5ckWHUKz8/PyKDkEIUcFkeZ4QQgghngr/XJ7n7OzMnDlzGDJkCGZmZjg5OREVFcXly5fp1q0bZmZmeHp6cuzYMfX68PBwrKysiIyMxNXVFSMjI3x9fTl//rxWPytWrKBWrVoYGBhQp04d1q1bp1Wu0WhYsWIFr7/+OqampowYMYI2bdoAUKlSJTQaDYGBgQBs376dV199FSsrK6ytrenSpQspKSlqW2lpaWg0GjZv3kybNm0wMTHBy8uLgwcPavUZGxtL69atMTExoVKlSvj6+nLt2jUACgsLCQkJoUaNGhgbG+Pl5cWmTZseyZgLIcpGkiYhhBBCPJWWLFlC8+bNOXHiBJ07d2bw4MEMGTKEQYMG8euvv1KrVi2GDBnCP580uHnzJnPnzuXLL78kNjaWrKws+vfvr5Zv2bKF8ePH88477/Dbb78xcuRIhg4dyu7du7X6Dg4OpkePHpw6dYpZs2bx/fffA5CUlERGRgaffPIJADdu3GDixIkcO3aM6OhodHR06NGjB4WFhVrtTZs2jUmTJhEXF0ft2rUZMGAAd+/eBSAuLo527dpRt25dDh48yP79++natSsFBQUAhISE8OWXX7Jy5UpOnz7N22+/zaBBg9izZ8+jH3QhRPEUIYQQQoinQKtWrZTx48criqIoTk5OyqBBg9SyjIwMBVCmT5+unjt48KACKBkZGYqiKEpYWJgCKIcOHVLrJCYmKoBy+PBhRVEUpVmzZsqIESO0+u3Tp4/SqVMn9TugTJgwQavO7t27FUC5du1aqfdw+fJlBVBOnTqlKIqipKamKoCyZs0atc7p06cVQElMTFQURVEGDBigNG/evNj2bt++rZiYmCgHDhzQOj9s2DBlwIABpcYihHh0ZKZJCCGEEE8lT09P9bONjQ0AHh4eRc5lZmaq5/T09HjllVfU725ublhZWZGYmAhAYmIizZs31+qnefPmavl9DRs2LFOMycnJDBgwgJo1a2JhYYGzszMA6enpJd6LnZ2dVtz3Z5qKc/bsWW7evEmHDh0wMzNTjy+//FJrGaAQ4vGSjSCEEEII8VTS19dXP2s0mhLPPbgU7lEwNTUtU72uXbvi5OTE6tWrsbe3p7CwkPr163Pnzh2teqXFbWxsXGL7ubm5APz4449Ur15dq8zQ0LBMMQoh/juZaRJCCCHEc+Pu3btam0MkJSWRlZWFu7s7AO7u7sTGxmpdExsbS926dUtt18DAAEB9zgjgypUrJCUl8cEHH9CuXTvc3d3VzRvKw9PTk+jo6GLL6tati6GhIenp6bi4uGgdDg4O5e5LCPFwZKZJCCGEEM8NfX193nrrLZYuXYqenh5jx46lSZMmNGrUCIB3332Xvn374u3tTfv27fnf//7H5s2b2bVrV6ntOjk5odFo2Lp1K506dcLY2JhKlSphbW3N559/jp2dHenp6bz33nvljnnq1Kl4eHgwevRo3nzzTQwMDNi9ezd9+vShSpUqTJo0ibfffpvCwkJeffVVsrOziY2NxcLCgoCAgIcaJyFE+chMkxBCCCGeGyYmJkyZMoWBAwfSvHlzzMzM2Lhxo1revXt3PvnkE0JDQ6lXrx6rVq0iLCyM1q1bl9pu9erVmTVrFu+99x42NjaMHTsWHR0dvvnmG44fP079+vV5++23+eijj8odc+3atfn555+Jj4+nUaNGNG3alB9++AE9vXv/b/vDDz9k+vTphISE4O7ujp+fHz/++CM1atQod19CiIejUZR/7NMphBBCCPGMCg8PZ8KECWRlZVV0KEKI54zMNAkhhBBCCCFEKSRpEkIIIYQQQohSyPI8IYQQQgghhCiFzDQJIYQQQgghRCkkaRJCCCGEEEKIUkjSJIQQQgghhBClkKRJCCGEEEIIIUohSZMQQgghhBBClEKSJiGEEEIIIYQohSRNQgghhBBCCFEKSZqEEEIIIYQQohT/D+aoV/cFUhOvAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns_transform = [col.split(\"__\")[1] for col in ct.get_feature_names_out()]\n",
    "\n",
    "df_features_imp = pd.DataFrame({\"variable\":columns_transform,\n",
    "                               \"importance\":clf.feature_importances_})\n",
    "\n",
    "sns.barplot(data=df_features_imp, x=\"importance\", y=\"variable\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = churn_data.drop(columns=[\"Churn\"])\n",
    "y = churn_data[\"Churn\"]\n",
    "\n",
    "# Split train/test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)\n",
    "\n",
    "# Split train/validation set\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.048569\n",
      "0:\tlearn: 0.6627881\ttest: 0.6623821\tbest: 0.6623821 (0)\ttotal: 32.5ms\tremaining: 32.5s\n",
      "1:\tlearn: 0.6372805\ttest: 0.6359148\tbest: 0.6359148 (1)\ttotal: 62.2ms\tremaining: 31.1s\n",
      "2:\tlearn: 0.6118743\ttest: 0.6094715\tbest: 0.6094715 (2)\ttotal: 97.7ms\tremaining: 32.5s\n",
      "3:\tlearn: 0.5898587\ttest: 0.5870121\tbest: 0.5870121 (3)\ttotal: 123ms\tremaining: 30.6s\n",
      "4:\tlearn: 0.5729321\ttest: 0.5694306\tbest: 0.5694306 (4)\ttotal: 141ms\tremaining: 28.1s\n",
      "5:\tlearn: 0.5563865\ttest: 0.5527201\tbest: 0.5527201 (5)\ttotal: 166ms\tremaining: 27.5s\n",
      "6:\tlearn: 0.5406930\ttest: 0.5370096\tbest: 0.5370096 (6)\ttotal: 194ms\tremaining: 27.5s\n",
      "7:\tlearn: 0.5268732\ttest: 0.5229710\tbest: 0.5229710 (7)\ttotal: 223ms\tremaining: 27.7s\n",
      "8:\tlearn: 0.5149236\ttest: 0.5106099\tbest: 0.5106099 (8)\ttotal: 255ms\tremaining: 28.1s\n",
      "9:\tlearn: 0.5067475\ttest: 0.5020982\tbest: 0.5020982 (9)\ttotal: 271ms\tremaining: 26.9s\n",
      "10:\tlearn: 0.4972163\ttest: 0.4926949\tbest: 0.4926949 (10)\ttotal: 301ms\tremaining: 27s\n",
      "11:\tlearn: 0.4896058\ttest: 0.4852133\tbest: 0.4852133 (11)\ttotal: 327ms\tremaining: 26.9s\n",
      "12:\tlearn: 0.4825590\ttest: 0.4780927\tbest: 0.4780927 (12)\ttotal: 357ms\tremaining: 27.1s\n",
      "13:\tlearn: 0.4756378\ttest: 0.4711545\tbest: 0.4711545 (13)\ttotal: 383ms\tremaining: 27s\n",
      "14:\tlearn: 0.4698411\ttest: 0.4662418\tbest: 0.4662418 (14)\ttotal: 410ms\tremaining: 26.9s\n",
      "15:\tlearn: 0.4652496\ttest: 0.4619031\tbest: 0.4619031 (15)\ttotal: 438ms\tremaining: 27s\n",
      "16:\tlearn: 0.4611658\ttest: 0.4577089\tbest: 0.4577089 (16)\ttotal: 465ms\tremaining: 26.9s\n",
      "17:\tlearn: 0.4574404\ttest: 0.4541869\tbest: 0.4541869 (17)\ttotal: 500ms\tremaining: 27.3s\n",
      "18:\tlearn: 0.4528654\ttest: 0.4497378\tbest: 0.4497378 (18)\ttotal: 531ms\tremaining: 27.4s\n",
      "19:\tlearn: 0.4482357\ttest: 0.4459038\tbest: 0.4459038 (19)\ttotal: 564ms\tremaining: 27.6s\n",
      "20:\tlearn: 0.4447377\ttest: 0.4426946\tbest: 0.4426946 (20)\ttotal: 597ms\tremaining: 27.8s\n",
      "21:\tlearn: 0.4414096\ttest: 0.4397617\tbest: 0.4397617 (21)\ttotal: 630ms\tremaining: 28s\n",
      "22:\tlearn: 0.4383806\ttest: 0.4370010\tbest: 0.4370010 (22)\ttotal: 666ms\tremaining: 28.3s\n",
      "23:\tlearn: 0.4357141\ttest: 0.4346098\tbest: 0.4346098 (23)\ttotal: 710ms\tremaining: 28.9s\n",
      "24:\tlearn: 0.4340841\ttest: 0.4333834\tbest: 0.4333834 (24)\ttotal: 739ms\tremaining: 28.8s\n",
      "25:\tlearn: 0.4316206\ttest: 0.4315814\tbest: 0.4315814 (25)\ttotal: 775ms\tremaining: 29s\n",
      "26:\tlearn: 0.4291486\ttest: 0.4294672\tbest: 0.4294672 (26)\ttotal: 809ms\tremaining: 29.2s\n",
      "27:\tlearn: 0.4272860\ttest: 0.4279721\tbest: 0.4279721 (27)\ttotal: 845ms\tremaining: 29.3s\n",
      "28:\tlearn: 0.4255243\ttest: 0.4265458\tbest: 0.4265458 (28)\ttotal: 879ms\tremaining: 29.4s\n",
      "29:\tlearn: 0.4237533\ttest: 0.4252464\tbest: 0.4252464 (29)\ttotal: 919ms\tremaining: 29.7s\n",
      "30:\tlearn: 0.4220748\ttest: 0.4242429\tbest: 0.4242429 (30)\ttotal: 954ms\tremaining: 29.8s\n",
      "31:\tlearn: 0.4208790\ttest: 0.4230531\tbest: 0.4230531 (31)\ttotal: 989ms\tremaining: 29.9s\n",
      "32:\tlearn: 0.4196280\ttest: 0.4219187\tbest: 0.4219187 (32)\ttotal: 1.02s\tremaining: 30s\n",
      "33:\tlearn: 0.4188299\ttest: 0.4213500\tbest: 0.4213500 (33)\ttotal: 1.06s\tremaining: 30.1s\n",
      "34:\tlearn: 0.4175882\ttest: 0.4202557\tbest: 0.4202557 (34)\ttotal: 1.09s\tremaining: 30s\n",
      "35:\tlearn: 0.4163712\ttest: 0.4194352\tbest: 0.4194352 (35)\ttotal: 1.12s\tremaining: 30s\n",
      "36:\tlearn: 0.4152490\ttest: 0.4189529\tbest: 0.4189529 (36)\ttotal: 1.15s\tremaining: 30s\n",
      "37:\tlearn: 0.4143886\ttest: 0.4184102\tbest: 0.4184102 (37)\ttotal: 1.19s\tremaining: 30.1s\n",
      "38:\tlearn: 0.4134848\ttest: 0.4177953\tbest: 0.4177953 (38)\ttotal: 1.23s\tremaining: 30.2s\n",
      "39:\tlearn: 0.4123643\ttest: 0.4172443\tbest: 0.4172443 (39)\ttotal: 1.26s\tremaining: 30.2s\n",
      "40:\tlearn: 0.4112168\ttest: 0.4163683\tbest: 0.4163683 (40)\ttotal: 1.29s\tremaining: 30.2s\n",
      "41:\tlearn: 0.4105189\ttest: 0.4158805\tbest: 0.4158805 (41)\ttotal: 1.32s\tremaining: 30.2s\n",
      "42:\tlearn: 0.4093483\ttest: 0.4151292\tbest: 0.4151292 (42)\ttotal: 1.35s\tremaining: 30.2s\n",
      "43:\tlearn: 0.4084758\ttest: 0.4149052\tbest: 0.4149052 (43)\ttotal: 1.39s\tremaining: 30.1s\n",
      "44:\tlearn: 0.4078956\ttest: 0.4143354\tbest: 0.4143354 (44)\ttotal: 1.42s\tremaining: 30.1s\n",
      "45:\tlearn: 0.4073410\ttest: 0.4139428\tbest: 0.4139428 (45)\ttotal: 1.45s\tremaining: 30s\n",
      "46:\tlearn: 0.4065905\ttest: 0.4139550\tbest: 0.4139428 (45)\ttotal: 1.48s\tremaining: 30s\n",
      "47:\tlearn: 0.4060895\ttest: 0.4136256\tbest: 0.4136256 (47)\ttotal: 1.51s\tremaining: 30s\n",
      "48:\tlearn: 0.4059262\ttest: 0.4135441\tbest: 0.4135441 (48)\ttotal: 1.53s\tremaining: 29.8s\n",
      "49:\tlearn: 0.4052365\ttest: 0.4129055\tbest: 0.4129055 (49)\ttotal: 1.57s\tremaining: 29.8s\n",
      "50:\tlearn: 0.4045429\ttest: 0.4128889\tbest: 0.4128889 (50)\ttotal: 1.6s\tremaining: 29.8s\n",
      "51:\tlearn: 0.4040049\ttest: 0.4129957\tbest: 0.4128889 (50)\ttotal: 1.65s\tremaining: 30.1s\n",
      "52:\tlearn: 0.4036238\ttest: 0.4127998\tbest: 0.4127998 (52)\ttotal: 1.7s\tremaining: 30.3s\n",
      "53:\tlearn: 0.4032770\ttest: 0.4127099\tbest: 0.4127099 (53)\ttotal: 1.73s\tremaining: 30.4s\n",
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      "955:\tlearn: 0.2622457\ttest: 0.4317170\tbest: 0.4098372 (105)\ttotal: 39.2s\tremaining: 1.8s\n",
      "956:\tlearn: 0.2620939\ttest: 0.4317736\tbest: 0.4098372 (105)\ttotal: 39.2s\tremaining: 1.76s\n",
      "957:\tlearn: 0.2619733\ttest: 0.4317704\tbest: 0.4098372 (105)\ttotal: 39.3s\tremaining: 1.72s\n",
      "958:\tlearn: 0.2619089\ttest: 0.4318042\tbest: 0.4098372 (105)\ttotal: 39.3s\tremaining: 1.68s\n",
      "959:\tlearn: 0.2618360\ttest: 0.4318529\tbest: 0.4098372 (105)\ttotal: 39.4s\tremaining: 1.64s\n",
      "960:\tlearn: 0.2617942\ttest: 0.4318758\tbest: 0.4098372 (105)\ttotal: 39.4s\tremaining: 1.6s\n",
      "961:\tlearn: 0.2616908\ttest: 0.4317335\tbest: 0.4098372 (105)\ttotal: 39.5s\tremaining: 1.56s\n",
      "962:\tlearn: 0.2615510\ttest: 0.4317445\tbest: 0.4098372 (105)\ttotal: 39.6s\tremaining: 1.52s\n",
      "963:\tlearn: 0.2614934\ttest: 0.4318071\tbest: 0.4098372 (105)\ttotal: 39.6s\tremaining: 1.48s\n",
      "964:\tlearn: 0.2614533\ttest: 0.4317726\tbest: 0.4098372 (105)\ttotal: 39.7s\tremaining: 1.44s\n",
      "965:\tlearn: 0.2613535\ttest: 0.4317367\tbest: 0.4098372 (105)\ttotal: 39.7s\tremaining: 1.4s\n",
      "966:\tlearn: 0.2612109\ttest: 0.4318428\tbest: 0.4098372 (105)\ttotal: 39.8s\tremaining: 1.36s\n",
      "967:\tlearn: 0.2609667\ttest: 0.4319677\tbest: 0.4098372 (105)\ttotal: 39.8s\tremaining: 1.32s\n",
      "968:\tlearn: 0.2608695\ttest: 0.4319832\tbest: 0.4098372 (105)\ttotal: 39.9s\tremaining: 1.27s\n",
      "969:\tlearn: 0.2607816\ttest: 0.4320860\tbest: 0.4098372 (105)\ttotal: 39.9s\tremaining: 1.23s\n",
      "970:\tlearn: 0.2606318\ttest: 0.4321960\tbest: 0.4098372 (105)\ttotal: 40s\tremaining: 1.19s\n",
      "971:\tlearn: 0.2604781\ttest: 0.4321198\tbest: 0.4098372 (105)\ttotal: 40s\tremaining: 1.15s\n",
      "972:\tlearn: 0.2603706\ttest: 0.4322072\tbest: 0.4098372 (105)\ttotal: 40.1s\tremaining: 1.11s\n",
      "973:\tlearn: 0.2602050\ttest: 0.4324677\tbest: 0.4098372 (105)\ttotal: 40.1s\tremaining: 1.07s\n",
      "974:\tlearn: 0.2600767\ttest: 0.4324600\tbest: 0.4098372 (105)\ttotal: 40.2s\tremaining: 1.03s\n",
      "975:\tlearn: 0.2600121\ttest: 0.4325333\tbest: 0.4098372 (105)\ttotal: 40.3s\tremaining: 990ms\n",
      "976:\tlearn: 0.2598170\ttest: 0.4324558\tbest: 0.4098372 (105)\ttotal: 40.3s\tremaining: 949ms\n",
      "977:\tlearn: 0.2596001\ttest: 0.4322601\tbest: 0.4098372 (105)\ttotal: 40.4s\tremaining: 908ms\n",
      "978:\tlearn: 0.2594574\ttest: 0.4322242\tbest: 0.4098372 (105)\ttotal: 40.4s\tremaining: 867ms\n",
      "979:\tlearn: 0.2594378\ttest: 0.4322351\tbest: 0.4098372 (105)\ttotal: 40.5s\tremaining: 826ms\n",
      "980:\tlearn: 0.2592908\ttest: 0.4321100\tbest: 0.4098372 (105)\ttotal: 40.5s\tremaining: 785ms\n",
      "981:\tlearn: 0.2592523\ttest: 0.4320976\tbest: 0.4098372 (105)\ttotal: 40.6s\tremaining: 744ms\n",
      "982:\tlearn: 0.2591417\ttest: 0.4321435\tbest: 0.4098372 (105)\ttotal: 40.7s\tremaining: 703ms\n",
      "983:\tlearn: 0.2589016\ttest: 0.4324111\tbest: 0.4098372 (105)\ttotal: 40.7s\tremaining: 662ms\n",
      "984:\tlearn: 0.2588772\ttest: 0.4324367\tbest: 0.4098372 (105)\ttotal: 40.8s\tremaining: 621ms\n",
      "985:\tlearn: 0.2586841\ttest: 0.4325090\tbest: 0.4098372 (105)\ttotal: 40.8s\tremaining: 580ms\n",
      "986:\tlearn: 0.2585862\ttest: 0.4324917\tbest: 0.4098372 (105)\ttotal: 40.9s\tremaining: 539ms\n",
      "987:\tlearn: 0.2585499\ttest: 0.4324673\tbest: 0.4098372 (105)\ttotal: 41s\tremaining: 498ms\n",
      "988:\tlearn: 0.2585340\ttest: 0.4324465\tbest: 0.4098372 (105)\ttotal: 41s\tremaining: 456ms\n",
      "989:\tlearn: 0.2584440\ttest: 0.4323728\tbest: 0.4098372 (105)\ttotal: 41.1s\tremaining: 415ms\n",
      "990:\tlearn: 0.2583457\ttest: 0.4324994\tbest: 0.4098372 (105)\ttotal: 41.1s\tremaining: 374ms\n",
      "991:\tlearn: 0.2581422\ttest: 0.4326306\tbest: 0.4098372 (105)\ttotal: 41.2s\tremaining: 332ms\n",
      "992:\tlearn: 0.2580069\ttest: 0.4326181\tbest: 0.4098372 (105)\ttotal: 41.2s\tremaining: 291ms\n",
      "993:\tlearn: 0.2579531\ttest: 0.4326442\tbest: 0.4098372 (105)\ttotal: 41.3s\tremaining: 249ms\n",
      "994:\tlearn: 0.2577599\ttest: 0.4324447\tbest: 0.4098372 (105)\ttotal: 41.3s\tremaining: 208ms\n",
      "995:\tlearn: 0.2576217\ttest: 0.4324315\tbest: 0.4098372 (105)\ttotal: 41.3s\tremaining: 166ms\n",
      "996:\tlearn: 0.2575789\ttest: 0.4324588\tbest: 0.4098372 (105)\ttotal: 41.4s\tremaining: 124ms\n",
      "997:\tlearn: 0.2574557\ttest: 0.4326492\tbest: 0.4098372 (105)\ttotal: 41.4s\tremaining: 82.9ms\n",
      "998:\tlearn: 0.2573330\ttest: 0.4326792\tbest: 0.4098372 (105)\ttotal: 41.4s\tremaining: 41.5ms\n",
      "999:\tlearn: 0.2571166\ttest: 0.4327456\tbest: 0.4098372 (105)\ttotal: 41.5s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.4098372131\n",
      "bestIteration = 105\n",
      "\n",
      "Shrink model to first 106 iterations.\n",
      "CatBoost model parameters:\n",
      "{}\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "\n",
    "cat_features = [col for col in X_train.select_dtypes(include=[\"object\",\"int\"]) if col != \"tenure\"]\n",
    "\n",
    "clf = CatBoostClassifier()\n",
    "\n",
    "\n",
    "clf.fit(X_train, y_train, \n",
    "        cat_features=cat_features, \n",
    "        eval_set=(X_val, y_val), \n",
    "        verbose=True\n",
    ")\n",
    "\n",
    "print('CatBoost model parameters:')\n",
    "print(clf.get_params())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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