diff --git "a/Lab03_Credit_Card_Fraud_Modeling.ipynb" "b/Lab03_Credit_Card_Fraud_Modeling.ipynb"
deleted file mode 100644--- "a/Lab03_Credit_Card_Fraud_Modeling.ipynb"
+++ /dev/null
@@ -1,2927 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "3343b1d8",
-   "metadata": {},
-   "source": [
-    "# **Lab03: Credit Card Fraud Modeling**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9e4bec96",
-   "metadata": {},
-   "source": [
-    "## Initial Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0ad10ad5",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 284807 entries, 0 to 284806\n",
-      "Data columns (total 31 columns):\n",
-      " #   Column  Non-Null Count   Dtype  \n",
-      "---  ------  --------------   -----  \n",
-      " 0   Time    284807 non-null  float64\n",
-      " 1   V1      284807 non-null  float64\n",
-      " 2   V2      284807 non-null  float64\n",
-      " 3   V3      284807 non-null  float64\n",
-      " 4   V4      284807 non-null  float64\n",
-      " 5   V5      284807 non-null  float64\n",
-      " 6   V6      284807 non-null  float64\n",
-      " 7   V7      284807 non-null  float64\n",
-      " 8   V8      284807 non-null  float64\n",
-      " 9   V9      284807 non-null  float64\n",
-      " 10  V10     284807 non-null  float64\n",
-      " 11  V11     284807 non-null  float64\n",
-      " 12  V12     284807 non-null  float64\n",
-      " 13  V13     284807 non-null  float64\n",
-      " 14  V14     284807 non-null  float64\n",
-      " 15  V15     284807 non-null  float64\n",
-      " 16  V16     284807 non-null  float64\n",
-      " 17  V17     284807 non-null  float64\n",
-      " 18  V18     284807 non-null  float64\n",
-      " 19  V19     284807 non-null  float64\n",
-      " 20  V20     284807 non-null  float64\n",
-      " 21  V21     284807 non-null  float64\n",
-      " 22  V22     284807 non-null  float64\n",
-      " 23  V23     284807 non-null  float64\n",
-      " 24  V24     284807 non-null  float64\n",
-      " 25  V25     284807 non-null  float64\n",
-      " 26  V26     284807 non-null  float64\n",
-      " 27  V27     284807 non-null  float64\n",
-      " 28  V28     284807 non-null  float64\n",
-      " 29  Amount  284807 non-null  float64\n",
-      " 30  Class   284807 non-null  int64  \n",
-      "dtypes: float64(30), int64(1)\n",
-      "memory usage: 67.4 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.metrics import confusion_matrix, f1_score, classification_report, roc_auc_score, roc_curve\n",
-    "df = pd.read_csv('creditcard.csv')\n",
-    "df.head()\n",
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "726028ed",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 284807 entries, 0 to 284806\n",
-      "Data columns (total 31 columns):\n",
-      " #   Column         Non-Null Count   Dtype  \n",
-      "---  ------         --------------   -----  \n",
-      " 0   V1             284807 non-null  float64\n",
-      " 1   V2             284807 non-null  float64\n",
-      " 2   V3             284807 non-null  float64\n",
-      " 3   V4             284807 non-null  float64\n",
-      " 4   V5             284807 non-null  float64\n",
-      " 5   V6             284807 non-null  float64\n",
-      " 6   V7             284807 non-null  float64\n",
-      " 7   V8             284807 non-null  float64\n",
-      " 8   V9             284807 non-null  float64\n",
-      " 9   V10            284807 non-null  float64\n",
-      " 10  V11            284807 non-null  float64\n",
-      " 11  V12            284807 non-null  float64\n",
-      " 12  V13            284807 non-null  float64\n",
-      " 13  V14            284807 non-null  float64\n",
-      " 14  V15            284807 non-null  float64\n",
-      " 15  V16            284807 non-null  float64\n",
-      " 16  V17            284807 non-null  float64\n",
-      " 17  V18            284807 non-null  float64\n",
-      " 18  V19            284807 non-null  float64\n",
-      " 19  V20            284807 non-null  float64\n",
-      " 20  V21            284807 non-null  float64\n",
-      " 21  V22            284807 non-null  float64\n",
-      " 22  V23            284807 non-null  float64\n",
-      " 23  V24            284807 non-null  float64\n",
-      " 24  V25            284807 non-null  float64\n",
-      " 25  V26            284807 non-null  float64\n",
-      " 26  V27            284807 non-null  float64\n",
-      " 27  V28            284807 non-null  float64\n",
-      " 28  Class          284807 non-null  int64  \n",
-      " 29  Amount_scaled  284807 non-null  float64\n",
-      " 30  Time_scaled    284807 non-null  float64\n",
-      "dtypes: float64(30), int64(1)\n",
-      "memory usage: 67.4 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "## Used some inspiration from the Kaggle site and decided to normalize the Amount and Time variables\n",
-    "ss = StandardScaler() # object of the class StandardScaler ()\n",
-    "df['Amount_scaled'] = ss.fit_transform(df['Amount'].values.reshape(-1,1))\n",
-    "df['Time_scaled'] = ss.fit_transform(df['Time'].values.reshape(-1,1))\n",
-    "df2= df.drop([\"Amount\",\"Time\"],axis=1)\n",
-    "df2.head()\n",
-    "df2.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "bc0c942d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_31727_row44_col1 {\n",
-       "  background-color: lightgreen;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_31727\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_31727_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
-       "      <th id=\"T_31727_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_31727_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
-       "      <td id=\"T_31727_row0_col1\" class=\"data row0 col1\" >419</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_31727_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
-       "      <td id=\"T_31727_row1_col1\" class=\"data row1 col1\" >Class</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_31727_row2_col0\" class=\"data row2 col0\" >Target Type</td>\n",
-       "      <td id=\"T_31727_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_31727_row3_col0\" class=\"data row3 col0\" >Label Encoded</td>\n",
-       "      <td id=\"T_31727_row3_col1\" class=\"data row3 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_31727_row4_col0\" class=\"data row4 col0\" >Original Data</td>\n",
-       "      <td id=\"T_31727_row4_col1\" class=\"data row4 col1\" >(284807, 31)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_31727_row5_col0\" class=\"data row5 col0\" >Missing Values</td>\n",
-       "      <td id=\"T_31727_row5_col1\" class=\"data row5 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_31727_row6_col0\" class=\"data row6 col0\" >Numeric Features</td>\n",
-       "      <td id=\"T_31727_row6_col1\" class=\"data row6 col1\" >30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_31727_row7_col0\" class=\"data row7 col0\" >Categorical Features</td>\n",
-       "      <td id=\"T_31727_row7_col1\" class=\"data row7 col1\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_31727_row8_col0\" class=\"data row8 col0\" >Ordinal Features</td>\n",
-       "      <td id=\"T_31727_row8_col1\" class=\"data row8 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_31727_row9_col0\" class=\"data row9 col0\" >High Cardinality Features</td>\n",
-       "      <td id=\"T_31727_row9_col1\" class=\"data row9 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
-       "      <td id=\"T_31727_row10_col0\" class=\"data row10 col0\" >High Cardinality Method</td>\n",
-       "      <td id=\"T_31727_row10_col1\" class=\"data row10 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
-       "      <td id=\"T_31727_row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
-       "      <td id=\"T_31727_row11_col1\" class=\"data row11 col1\" >(227845, 30)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
-       "      <td id=\"T_31727_row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
-       "      <td id=\"T_31727_row12_col1\" class=\"data row12 col1\" >(56962, 30)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
-       "      <td id=\"T_31727_row13_col0\" class=\"data row13 col0\" >Shuffle Train-Test</td>\n",
-       "      <td id=\"T_31727_row13_col1\" class=\"data row13 col1\" >True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
-       "      <td id=\"T_31727_row14_col0\" class=\"data row14 col0\" >Stratify Train-Test</td>\n",
-       "      <td id=\"T_31727_row14_col1\" class=\"data row14 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
-       "      <td id=\"T_31727_row15_col0\" class=\"data row15 col0\" >Fold Generator</td>\n",
-       "      <td id=\"T_31727_row15_col1\" class=\"data row15 col1\" >StratifiedKFold</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
-       "      <td id=\"T_31727_row16_col0\" class=\"data row16 col0\" >Fold Number</td>\n",
-       "      <td id=\"T_31727_row16_col1\" class=\"data row16 col1\" >10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
-       "      <td id=\"T_31727_row17_col0\" class=\"data row17 col0\" >CPU Jobs</td>\n",
-       "      <td id=\"T_31727_row17_col1\" class=\"data row17 col1\" >-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
-       "      <td id=\"T_31727_row18_col0\" class=\"data row18 col0\" >Use GPU</td>\n",
-       "      <td id=\"T_31727_row18_col1\" class=\"data row18 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
-       "      <td id=\"T_31727_row19_col0\" class=\"data row19 col0\" >Log Experiment</td>\n",
-       "      <td id=\"T_31727_row19_col1\" class=\"data row19 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
-       "      <td id=\"T_31727_row20_col0\" class=\"data row20 col0\" >Experiment Name</td>\n",
-       "      <td id=\"T_31727_row20_col1\" class=\"data row20 col1\" >clf-default-name</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
-       "      <td id=\"T_31727_row21_col0\" class=\"data row21 col0\" >USI</td>\n",
-       "      <td id=\"T_31727_row21_col1\" class=\"data row21 col1\" >065b</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
-       "      <td id=\"T_31727_row22_col0\" class=\"data row22 col0\" >Imputation Type</td>\n",
-       "      <td id=\"T_31727_row22_col1\" class=\"data row22 col1\" >simple</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
-       "      <td id=\"T_31727_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Iteration</td>\n",
-       "      <td id=\"T_31727_row23_col1\" class=\"data row23 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
-       "      <td id=\"T_31727_row24_col0\" class=\"data row24 col0\" >Numeric Imputer</td>\n",
-       "      <td id=\"T_31727_row24_col1\" class=\"data row24 col1\" >mean</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
-       "      <td id=\"T_31727_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Numeric Model</td>\n",
-       "      <td id=\"T_31727_row25_col1\" class=\"data row25 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
-       "      <td id=\"T_31727_row26_col0\" class=\"data row26 col0\" >Categorical Imputer</td>\n",
-       "      <td id=\"T_31727_row26_col1\" class=\"data row26 col1\" >constant</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
-       "      <td id=\"T_31727_row27_col0\" class=\"data row27 col0\" >Iterative Imputation Categorical Model</td>\n",
-       "      <td id=\"T_31727_row27_col1\" class=\"data row27 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
-       "      <td id=\"T_31727_row28_col0\" class=\"data row28 col0\" >Unknown Categoricals Handling</td>\n",
-       "      <td id=\"T_31727_row28_col1\" class=\"data row28 col1\" >least_frequent</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
-       "      <td id=\"T_31727_row29_col0\" class=\"data row29 col0\" >Normalize</td>\n",
-       "      <td id=\"T_31727_row29_col1\" class=\"data row29 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
-       "      <td id=\"T_31727_row30_col0\" class=\"data row30 col0\" >Normalize Method</td>\n",
-       "      <td id=\"T_31727_row30_col1\" class=\"data row30 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
-       "      <td id=\"T_31727_row31_col0\" class=\"data row31 col0\" >Transformation</td>\n",
-       "      <td id=\"T_31727_row31_col1\" class=\"data row31 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
-       "      <td id=\"T_31727_row32_col0\" class=\"data row32 col0\" >Transformation Method</td>\n",
-       "      <td id=\"T_31727_row32_col1\" class=\"data row32 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
-       "      <td id=\"T_31727_row33_col0\" class=\"data row33 col0\" >PCA</td>\n",
-       "      <td id=\"T_31727_row33_col1\" class=\"data row33 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
-       "      <td id=\"T_31727_row34_col0\" class=\"data row34 col0\" >PCA Method</td>\n",
-       "      <td id=\"T_31727_row34_col1\" class=\"data row34 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
-       "      <td id=\"T_31727_row35_col0\" class=\"data row35 col0\" >PCA Components</td>\n",
-       "      <td id=\"T_31727_row35_col1\" class=\"data row35 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
-       "      <td id=\"T_31727_row36_col0\" class=\"data row36 col0\" >Ignore Low Variance</td>\n",
-       "      <td id=\"T_31727_row36_col1\" class=\"data row36 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
-       "      <td id=\"T_31727_row37_col0\" class=\"data row37 col0\" >Combine Rare Levels</td>\n",
-       "      <td id=\"T_31727_row37_col1\" class=\"data row37 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
-       "      <td id=\"T_31727_row38_col0\" class=\"data row38 col0\" >Rare Level Threshold</td>\n",
-       "      <td id=\"T_31727_row38_col1\" class=\"data row38 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
-       "      <td id=\"T_31727_row39_col0\" class=\"data row39 col0\" >Numeric Binning</td>\n",
-       "      <td id=\"T_31727_row39_col1\" class=\"data row39 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
-       "      <td id=\"T_31727_row40_col0\" class=\"data row40 col0\" >Remove Outliers</td>\n",
-       "      <td id=\"T_31727_row40_col1\" class=\"data row40 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
-       "      <td id=\"T_31727_row41_col0\" class=\"data row41 col0\" >Outliers Threshold</td>\n",
-       "      <td id=\"T_31727_row41_col1\" class=\"data row41 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
-       "      <td id=\"T_31727_row42_col0\" class=\"data row42 col0\" >Remove Multicollinearity</td>\n",
-       "      <td id=\"T_31727_row42_col1\" class=\"data row42 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
-       "      <td id=\"T_31727_row43_col0\" class=\"data row43 col0\" >Multicollinearity Threshold</td>\n",
-       "      <td id=\"T_31727_row43_col1\" class=\"data row43 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
-       "      <td id=\"T_31727_row44_col0\" class=\"data row44 col0\" >Remove Perfect Collinearity</td>\n",
-       "      <td id=\"T_31727_row44_col1\" class=\"data row44 col1\" >True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
-       "      <td id=\"T_31727_row45_col0\" class=\"data row45 col0\" >Clustering</td>\n",
-       "      <td id=\"T_31727_row45_col1\" class=\"data row45 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
-       "      <td id=\"T_31727_row46_col0\" class=\"data row46 col0\" >Clustering Iteration</td>\n",
-       "      <td id=\"T_31727_row46_col1\" class=\"data row46 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
-       "      <td id=\"T_31727_row47_col0\" class=\"data row47 col0\" >Polynomial Features</td>\n",
-       "      <td id=\"T_31727_row47_col1\" class=\"data row47 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
-       "      <td id=\"T_31727_row48_col0\" class=\"data row48 col0\" >Polynomial Degree</td>\n",
-       "      <td id=\"T_31727_row48_col1\" class=\"data row48 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
-       "      <td id=\"T_31727_row49_col0\" class=\"data row49 col0\" >Trignometry Features</td>\n",
-       "      <td id=\"T_31727_row49_col1\" class=\"data row49 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
-       "      <td id=\"T_31727_row50_col0\" class=\"data row50 col0\" >Polynomial Threshold</td>\n",
-       "      <td id=\"T_31727_row50_col1\" class=\"data row50 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
-       "      <td id=\"T_31727_row51_col0\" class=\"data row51 col0\" >Group Features</td>\n",
-       "      <td id=\"T_31727_row51_col1\" class=\"data row51 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
-       "      <td id=\"T_31727_row52_col0\" class=\"data row52 col0\" >Feature Selection</td>\n",
-       "      <td id=\"T_31727_row52_col1\" class=\"data row52 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
-       "      <td id=\"T_31727_row53_col0\" class=\"data row53 col0\" >Feature Selection Method</td>\n",
-       "      <td id=\"T_31727_row53_col1\" class=\"data row53 col1\" >classic</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
-       "      <td id=\"T_31727_row54_col0\" class=\"data row54 col0\" >Features Selection Threshold</td>\n",
-       "      <td id=\"T_31727_row54_col1\" class=\"data row54 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
-       "      <td id=\"T_31727_row55_col0\" class=\"data row55 col0\" >Feature Interaction</td>\n",
-       "      <td id=\"T_31727_row55_col1\" class=\"data row55 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
-       "      <td id=\"T_31727_row56_col0\" class=\"data row56 col0\" >Feature Ratio</td>\n",
-       "      <td id=\"T_31727_row56_col1\" class=\"data row56 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
-       "      <td id=\"T_31727_row57_col0\" class=\"data row57 col0\" >Interaction Threshold</td>\n",
-       "      <td id=\"T_31727_row57_col1\" class=\"data row57 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
-       "      <td id=\"T_31727_row58_col0\" class=\"data row58 col0\" >Fix Imbalance</td>\n",
-       "      <td id=\"T_31727_row58_col1\" class=\"data row58 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_31727_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
-       "      <td id=\"T_31727_row59_col0\" class=\"data row59 col0\" >Fix Imbalance Method</td>\n",
-       "      <td id=\"T_31727_row59_col1\" class=\"data row59 col1\" >SMOTE</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x22d8d3e2f10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from pycaret.classification import *\n",
-    "df2_setup = setup(data = df2, train_size = 0.8, target = 'Class', session_id = 419)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "e701d47e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_917ab th {\n",
-       "  text-align: left;\n",
-       "}\n",
-       "#T_917ab_row0_col0, #T_917ab_row0_col2, #T_917ab_row0_col3, #T_917ab_row1_col0, #T_917ab_row1_col1, #T_917ab_row1_col2, #T_917ab_row1_col3, #T_917ab_row1_col4, #T_917ab_row1_col5, #T_917ab_row1_col6, #T_917ab_row1_col7, #T_917ab_row2_col0, #T_917ab_row2_col1, #T_917ab_row2_col2, #T_917ab_row2_col3, #T_917ab_row2_col4, #T_917ab_row2_col5, #T_917ab_row2_col6, #T_917ab_row2_col7, #T_917ab_row3_col0, #T_917ab_row3_col1, #T_917ab_row3_col2, #T_917ab_row3_col3, #T_917ab_row3_col4, #T_917ab_row3_col5, #T_917ab_row3_col6, #T_917ab_row3_col7, #T_917ab_row4_col0, #T_917ab_row4_col1, #T_917ab_row4_col2, #T_917ab_row4_col3, #T_917ab_row4_col4, #T_917ab_row4_col5, #T_917ab_row4_col6, #T_917ab_row4_col7, #T_917ab_row5_col0, #T_917ab_row5_col1, #T_917ab_row5_col2, #T_917ab_row5_col3, #T_917ab_row5_col4, #T_917ab_row5_col5, #T_917ab_row5_col6, #T_917ab_row5_col7, #T_917ab_row6_col0, #T_917ab_row6_col1, #T_917ab_row6_col3, #T_917ab_row6_col4, #T_917ab_row6_col5, #T_917ab_row6_col6, #T_917ab_row6_col7, #T_917ab_row7_col0, #T_917ab_row7_col1, #T_917ab_row7_col2, #T_917ab_row7_col3, #T_917ab_row7_col4, #T_917ab_row7_col5, #T_917ab_row7_col6, #T_917ab_row7_col7, #T_917ab_row8_col0, #T_917ab_row8_col1, #T_917ab_row8_col2, #T_917ab_row8_col3, #T_917ab_row8_col4, #T_917ab_row8_col5, #T_917ab_row8_col6, #T_917ab_row8_col7, #T_917ab_row9_col0, #T_917ab_row9_col1, #T_917ab_row9_col2, #T_917ab_row9_col3, #T_917ab_row9_col4, #T_917ab_row9_col5, #T_917ab_row9_col6, #T_917ab_row9_col7, #T_917ab_row10_col0, #T_917ab_row10_col1, #T_917ab_row10_col2, #T_917ab_row10_col3, #T_917ab_row10_col4, #T_917ab_row10_col5, #T_917ab_row10_col6, #T_917ab_row10_col7, #T_917ab_row11_col0, #T_917ab_row11_col1, #T_917ab_row11_col2, #T_917ab_row11_col3, #T_917ab_row11_col4, #T_917ab_row11_col5, #T_917ab_row11_col6, #T_917ab_row11_col7, #T_917ab_row12_col0, #T_917ab_row12_col1, #T_917ab_row12_col2, #T_917ab_row12_col3, #T_917ab_row12_col4, #T_917ab_row12_col5, #T_917ab_row12_col6, #T_917ab_row12_col7, #T_917ab_row13_col0, #T_917ab_row13_col1, #T_917ab_row13_col2, #T_917ab_row13_col4, #T_917ab_row13_col5, #T_917ab_row13_col6, #T_917ab_row13_col7 {\n",
-       "  text-align: left;\n",
-       "}\n",
-       "#T_917ab_row0_col1, #T_917ab_row0_col4, #T_917ab_row0_col5, #T_917ab_row0_col6, #T_917ab_row0_col7, #T_917ab_row6_col2, #T_917ab_row13_col3 {\n",
-       "  text-align: left;\n",
-       "  background-color: yellow;\n",
-       "}\n",
-       "#T_917ab_row0_col8, #T_917ab_row1_col8, #T_917ab_row2_col8, #T_917ab_row3_col8, #T_917ab_row4_col8, #T_917ab_row5_col8, #T_917ab_row6_col8, #T_917ab_row7_col8, #T_917ab_row8_col8, #T_917ab_row9_col8, #T_917ab_row10_col8, #T_917ab_row11_col8, #T_917ab_row12_col8 {\n",
-       "  text-align: left;\n",
-       "  background-color: lightgrey;\n",
-       "}\n",
-       "#T_917ab_row13_col8 {\n",
-       "  text-align: left;\n",
-       "  background-color: yellow;\n",
-       "  background-color: lightgrey;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_917ab\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_917ab_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
-       "      <th id=\"T_917ab_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
-       "      <th id=\"T_917ab_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
-       "      <th id=\"T_917ab_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
-       "      <th id=\"T_917ab_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
-       "      <th id=\"T_917ab_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
-       "      <th id=\"T_917ab_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
-       "      <th id=\"T_917ab_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
-       "      <th id=\"T_917ab_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row0\" class=\"row_heading level0 row0\" >et</th>\n",
-       "      <td id=\"T_917ab_row0_col0\" class=\"data row0 col0\" >Extra Trees Classifier</td>\n",
-       "      <td id=\"T_917ab_row0_col1\" class=\"data row0 col1\" >0.9996</td>\n",
-       "      <td id=\"T_917ab_row0_col2\" class=\"data row0 col2\" >0.9466</td>\n",
-       "      <td id=\"T_917ab_row0_col3\" class=\"data row0 col3\" >0.7933</td>\n",
-       "      <td id=\"T_917ab_row0_col4\" class=\"data row0 col4\" >0.9571</td>\n",
-       "      <td id=\"T_917ab_row0_col5\" class=\"data row0 col5\" >0.8660</td>\n",
-       "      <td id=\"T_917ab_row0_col6\" class=\"data row0 col6\" >0.8658</td>\n",
-       "      <td id=\"T_917ab_row0_col7\" class=\"data row0 col7\" >0.8704</td>\n",
-       "      <td id=\"T_917ab_row0_col8\" class=\"data row0 col8\" >2.0550</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row1\" class=\"row_heading level0 row1\" >knn</th>\n",
-       "      <td id=\"T_917ab_row1_col0\" class=\"data row1 col0\" >K Neighbors Classifier</td>\n",
-       "      <td id=\"T_917ab_row1_col1\" class=\"data row1 col1\" >0.9995</td>\n",
-       "      <td id=\"T_917ab_row1_col2\" class=\"data row1 col2\" >0.9185</td>\n",
-       "      <td id=\"T_917ab_row1_col3\" class=\"data row1 col3\" >0.7802</td>\n",
-       "      <td id=\"T_917ab_row1_col4\" class=\"data row1 col4\" >0.9183</td>\n",
-       "      <td id=\"T_917ab_row1_col5\" class=\"data row1 col5\" >0.8420</td>\n",
-       "      <td id=\"T_917ab_row1_col6\" class=\"data row1 col6\" >0.8418</td>\n",
-       "      <td id=\"T_917ab_row1_col7\" class=\"data row1 col7\" >0.8454</td>\n",
-       "      <td id=\"T_917ab_row1_col8\" class=\"data row1 col8\" >11.2820</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row2\" class=\"row_heading level0 row2\" >rf</th>\n",
-       "      <td id=\"T_917ab_row2_col0\" class=\"data row2 col0\" >Random Forest Classifier</td>\n",
-       "      <td id=\"T_917ab_row2_col1\" class=\"data row2 col1\" >0.9995</td>\n",
-       "      <td id=\"T_917ab_row2_col2\" class=\"data row2 col2\" >0.9505</td>\n",
-       "      <td id=\"T_917ab_row2_col3\" class=\"data row2 col3\" >0.7724</td>\n",
-       "      <td id=\"T_917ab_row2_col4\" class=\"data row2 col4\" >0.9414</td>\n",
-       "      <td id=\"T_917ab_row2_col5\" class=\"data row2 col5\" >0.8470</td>\n",
-       "      <td id=\"T_917ab_row2_col6\" class=\"data row2 col6\" >0.8467</td>\n",
-       "      <td id=\"T_917ab_row2_col7\" class=\"data row2 col7\" >0.8517</td>\n",
-       "      <td id=\"T_917ab_row2_col8\" class=\"data row2 col8\" >7.6840</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row3\" class=\"row_heading level0 row3\" >lda</th>\n",
-       "      <td id=\"T_917ab_row3_col0\" class=\"data row3 col0\" >Linear Discriminant Analysis</td>\n",
-       "      <td id=\"T_917ab_row3_col1\" class=\"data row3 col1\" >0.9994</td>\n",
-       "      <td id=\"T_917ab_row3_col2\" class=\"data row3 col2\" >0.9003</td>\n",
-       "      <td id=\"T_917ab_row3_col3\" class=\"data row3 col3\" >0.7750</td>\n",
-       "      <td id=\"T_917ab_row3_col4\" class=\"data row3 col4\" >0.8570</td>\n",
-       "      <td id=\"T_917ab_row3_col5\" class=\"data row3 col5\" >0.8129</td>\n",
-       "      <td id=\"T_917ab_row3_col6\" class=\"data row3 col6\" >0.8126</td>\n",
-       "      <td id=\"T_917ab_row3_col7\" class=\"data row3 col7\" >0.8141</td>\n",
-       "      <td id=\"T_917ab_row3_col8\" class=\"data row3 col8\" >0.3730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row4\" class=\"row_heading level0 row4\" >lr</th>\n",
-       "      <td id=\"T_917ab_row4_col0\" class=\"data row4 col0\" >Logistic Regression</td>\n",
-       "      <td id=\"T_917ab_row4_col1\" class=\"data row4 col1\" >0.9992</td>\n",
-       "      <td id=\"T_917ab_row4_col2\" class=\"data row4 col2\" >0.9747</td>\n",
-       "      <td id=\"T_917ab_row4_col3\" class=\"data row4 col3\" >0.6093</td>\n",
-       "      <td id=\"T_917ab_row4_col4\" class=\"data row4 col4\" >0.8628</td>\n",
-       "      <td id=\"T_917ab_row4_col5\" class=\"data row4 col5\" >0.7128</td>\n",
-       "      <td id=\"T_917ab_row4_col6\" class=\"data row4 col6\" >0.7124</td>\n",
-       "      <td id=\"T_917ab_row4_col7\" class=\"data row4 col7\" >0.7240</td>\n",
-       "      <td id=\"T_917ab_row4_col8\" class=\"data row4 col8\" >0.9520</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row5\" class=\"row_heading level0 row5\" >dt</th>\n",
-       "      <td id=\"T_917ab_row5_col0\" class=\"data row5 col0\" >Decision Tree Classifier</td>\n",
-       "      <td id=\"T_917ab_row5_col1\" class=\"data row5 col1\" >0.9992</td>\n",
-       "      <td id=\"T_917ab_row5_col2\" class=\"data row5 col2\" >0.8731</td>\n",
-       "      <td id=\"T_917ab_row5_col3\" class=\"data row5 col3\" >0.7466</td>\n",
-       "      <td id=\"T_917ab_row5_col4\" class=\"data row5 col4\" >0.7702</td>\n",
-       "      <td id=\"T_917ab_row5_col5\" class=\"data row5 col5\" >0.7539</td>\n",
-       "      <td id=\"T_917ab_row5_col6\" class=\"data row5 col6\" >0.7535</td>\n",
-       "      <td id=\"T_917ab_row5_col7\" class=\"data row5 col7\" >0.7557</td>\n",
-       "      <td id=\"T_917ab_row5_col8\" class=\"data row5 col8\" >2.5440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row6\" class=\"row_heading level0 row6\" >ada</th>\n",
-       "      <td id=\"T_917ab_row6_col0\" class=\"data row6 col0\" >Ada Boost Classifier</td>\n",
-       "      <td id=\"T_917ab_row6_col1\" class=\"data row6 col1\" >0.9992</td>\n",
-       "      <td id=\"T_917ab_row6_col2\" class=\"data row6 col2\" >0.9761</td>\n",
-       "      <td id=\"T_917ab_row6_col3\" class=\"data row6 col3\" >0.6946</td>\n",
-       "      <td id=\"T_917ab_row6_col4\" class=\"data row6 col4\" >0.8110</td>\n",
-       "      <td id=\"T_917ab_row6_col5\" class=\"data row6 col5\" >0.7467</td>\n",
-       "      <td id=\"T_917ab_row6_col6\" class=\"data row6 col6\" >0.7463</td>\n",
-       "      <td id=\"T_917ab_row6_col7\" class=\"data row6 col7\" >0.7493</td>\n",
-       "      <td id=\"T_917ab_row6_col8\" class=\"data row6 col8\" >6.0260</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row7\" class=\"row_heading level0 row7\" >svm</th>\n",
-       "      <td id=\"T_917ab_row7_col0\" class=\"data row7 col0\" >SVM - Linear Kernel</td>\n",
-       "      <td id=\"T_917ab_row7_col1\" class=\"data row7 col1\" >0.9991</td>\n",
-       "      <td id=\"T_917ab_row7_col2\" class=\"data row7 col2\" >0.0000</td>\n",
-       "      <td id=\"T_917ab_row7_col3\" class=\"data row7 col3\" >0.5416</td>\n",
-       "      <td id=\"T_917ab_row7_col4\" class=\"data row7 col4\" >0.8472</td>\n",
-       "      <td id=\"T_917ab_row7_col5\" class=\"data row7 col5\" >0.6591</td>\n",
-       "      <td id=\"T_917ab_row7_col6\" class=\"data row7 col6\" >0.6587</td>\n",
-       "      <td id=\"T_917ab_row7_col7\" class=\"data row7 col7\" >0.6761</td>\n",
-       "      <td id=\"T_917ab_row7_col8\" class=\"data row7 col8\" >0.4310</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row8\" class=\"row_heading level0 row8\" >gbc</th>\n",
-       "      <td id=\"T_917ab_row8_col0\" class=\"data row8 col0\" >Gradient Boosting Classifier</td>\n",
-       "      <td id=\"T_917ab_row8_col1\" class=\"data row8 col1\" >0.9990</td>\n",
-       "      <td id=\"T_917ab_row8_col2\" class=\"data row8 col2\" >0.6659</td>\n",
-       "      <td id=\"T_917ab_row8_col3\" class=\"data row8 col3\" >0.4951</td>\n",
-       "      <td id=\"T_917ab_row8_col4\" class=\"data row8 col4\" >0.8111</td>\n",
-       "      <td id=\"T_917ab_row8_col5\" class=\"data row8 col5\" >0.5996</td>\n",
-       "      <td id=\"T_917ab_row8_col6\" class=\"data row8 col6\" >0.5992</td>\n",
-       "      <td id=\"T_917ab_row8_col7\" class=\"data row8 col7\" >0.6239</td>\n",
-       "      <td id=\"T_917ab_row8_col8\" class=\"data row8 col8\" >30.2140</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row9\" class=\"row_heading level0 row9\" >ridge</th>\n",
-       "      <td id=\"T_917ab_row9_col0\" class=\"data row9 col0\" >Ridge Classifier</td>\n",
-       "      <td id=\"T_917ab_row9_col1\" class=\"data row9 col1\" >0.9989</td>\n",
-       "      <td id=\"T_917ab_row9_col2\" class=\"data row9 col2\" >0.0000</td>\n",
-       "      <td id=\"T_917ab_row9_col3\" class=\"data row9 col3\" >0.4174</td>\n",
-       "      <td id=\"T_917ab_row9_col4\" class=\"data row9 col4\" >0.8200</td>\n",
-       "      <td id=\"T_917ab_row9_col5\" class=\"data row9 col5\" >0.5522</td>\n",
-       "      <td id=\"T_917ab_row9_col6\" class=\"data row9 col6\" >0.5517</td>\n",
-       "      <td id=\"T_917ab_row9_col7\" class=\"data row9 col7\" >0.5840</td>\n",
-       "      <td id=\"T_917ab_row9_col8\" class=\"data row9 col8\" >0.3540</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row10\" class=\"row_heading level0 row10\" >dummy</th>\n",
-       "      <td id=\"T_917ab_row10_col0\" class=\"data row10 col0\" >Dummy Classifier</td>\n",
-       "      <td id=\"T_917ab_row10_col1\" class=\"data row10 col1\" >0.9983</td>\n",
-       "      <td id=\"T_917ab_row10_col2\" class=\"data row10 col2\" >0.5000</td>\n",
-       "      <td id=\"T_917ab_row10_col3\" class=\"data row10 col3\" >0.0000</td>\n",
-       "      <td id=\"T_917ab_row10_col4\" class=\"data row10 col4\" >0.0000</td>\n",
-       "      <td id=\"T_917ab_row10_col5\" class=\"data row10 col5\" >0.0000</td>\n",
-       "      <td id=\"T_917ab_row10_col6\" class=\"data row10 col6\" >0.0000</td>\n",
-       "      <td id=\"T_917ab_row10_col7\" class=\"data row10 col7\" >0.0000</td>\n",
-       "      <td id=\"T_917ab_row10_col8\" class=\"data row10 col8\" >0.3600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row11\" class=\"row_heading level0 row11\" >lightgbm</th>\n",
-       "      <td id=\"T_917ab_row11_col0\" class=\"data row11 col0\" >Light Gradient Boosting Machine</td>\n",
-       "      <td id=\"T_917ab_row11_col1\" class=\"data row11 col1\" >0.9954</td>\n",
-       "      <td id=\"T_917ab_row11_col2\" class=\"data row11 col2\" >0.6117</td>\n",
-       "      <td id=\"T_917ab_row11_col3\" class=\"data row11 col3\" >0.4225</td>\n",
-       "      <td id=\"T_917ab_row11_col4\" class=\"data row11 col4\" >0.1862</td>\n",
-       "      <td id=\"T_917ab_row11_col5\" class=\"data row11 col5\" >0.2535</td>\n",
-       "      <td id=\"T_917ab_row11_col6\" class=\"data row11 col6\" >0.2517</td>\n",
-       "      <td id=\"T_917ab_row11_col7\" class=\"data row11 col7\" >0.2754</td>\n",
-       "      <td id=\"T_917ab_row11_col8\" class=\"data row11 col8\" >0.9910</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row12\" class=\"row_heading level0 row12\" >nb</th>\n",
-       "      <td id=\"T_917ab_row12_col0\" class=\"data row12 col0\" >Naive Bayes</td>\n",
-       "      <td id=\"T_917ab_row12_col1\" class=\"data row12 col1\" >0.9785</td>\n",
-       "      <td id=\"T_917ab_row12_col2\" class=\"data row12 col2\" >0.9600</td>\n",
-       "      <td id=\"T_917ab_row12_col3\" class=\"data row12 col3\" >0.8268</td>\n",
-       "      <td id=\"T_917ab_row12_col4\" class=\"data row12 col4\" >0.0620</td>\n",
-       "      <td id=\"T_917ab_row12_col5\" class=\"data row12 col5\" >0.1154</td>\n",
-       "      <td id=\"T_917ab_row12_col6\" class=\"data row12 col6\" >0.1126</td>\n",
-       "      <td id=\"T_917ab_row12_col7\" class=\"data row12 col7\" >0.2230</td>\n",
-       "      <td id=\"T_917ab_row12_col8\" class=\"data row12 col8\" >0.3580</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_917ab_level0_row13\" class=\"row_heading level0 row13\" >qda</th>\n",
-       "      <td id=\"T_917ab_row13_col0\" class=\"data row13 col0\" >Quadratic Discriminant Analysis</td>\n",
-       "      <td id=\"T_917ab_row13_col1\" class=\"data row13 col1\" >0.9763</td>\n",
-       "      <td id=\"T_917ab_row13_col2\" class=\"data row13 col2\" >0.9708</td>\n",
-       "      <td id=\"T_917ab_row13_col3\" class=\"data row13 col3\" >0.8605</td>\n",
-       "      <td id=\"T_917ab_row13_col4\" class=\"data row13 col4\" >0.0585</td>\n",
-       "      <td id=\"T_917ab_row13_col5\" class=\"data row13 col5\" >0.1096</td>\n",
-       "      <td id=\"T_917ab_row13_col6\" class=\"data row13 col6\" >0.1068</td>\n",
-       "      <td id=\"T_917ab_row13_col7\" class=\"data row13 col7\" >0.2208</td>\n",
-       "      <td id=\"T_917ab_row13_col8\" class=\"data row13 col8\" >0.2270</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x22da2382370>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "best_model = compare_models()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "71e58d79",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_89c3a_row10_col0, #T_89c3a_row10_col1, #T_89c3a_row10_col2, #T_89c3a_row10_col3, #T_89c3a_row10_col4, #T_89c3a_row10_col5, #T_89c3a_row10_col6 {\n",
-       "  background: yellow;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_89c3a\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_89c3a_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
-       "      <th id=\"T_89c3a_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
-       "      <th id=\"T_89c3a_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
-       "      <th id=\"T_89c3a_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
-       "      <th id=\"T_89c3a_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
-       "      <th id=\"T_89c3a_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
-       "      <th id=\"T_89c3a_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >Fold</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_89c3a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_89c3a_row0_col0\" class=\"data row0 col0\" >0.9996</td>\n",
-       "      <td id=\"T_89c3a_row0_col1\" class=\"data row0 col1\" >0.9995</td>\n",
-       "      <td id=\"T_89c3a_row0_col2\" class=\"data row0 col2\" >0.8205</td>\n",
-       "      <td id=\"T_89c3a_row0_col3\" class=\"data row0 col3\" >0.9412</td>\n",
-       "      <td id=\"T_89c3a_row0_col4\" class=\"data row0 col4\" >0.8767</td>\n",
-       "      <td id=\"T_89c3a_row0_col5\" class=\"data row0 col5\" >0.8765</td>\n",
-       "      <td id=\"T_89c3a_row0_col6\" class=\"data row0 col6\" >0.8786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_89c3a_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_89c3a_row1_col0\" class=\"data row1 col0\" >0.9995</td>\n",
-       "      <td id=\"T_89c3a_row1_col1\" class=\"data row1 col1\" >0.9468</td>\n",
-       "      <td id=\"T_89c3a_row1_col2\" class=\"data row1 col2\" >0.7179</td>\n",
-       "      <td id=\"T_89c3a_row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
-       "      <td id=\"T_89c3a_row1_col4\" class=\"data row1 col4\" >0.8358</td>\n",
-       "      <td id=\"T_89c3a_row1_col5\" class=\"data row1 col5\" >0.8356</td>\n",
-       "      <td id=\"T_89c3a_row1_col6\" class=\"data row1 col6\" >0.8471</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_89c3a_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_89c3a_row2_col0\" class=\"data row2 col0\" >0.9996</td>\n",
-       "      <td id=\"T_89c3a_row2_col1\" class=\"data row2 col1\" >0.9596</td>\n",
-       "      <td id=\"T_89c3a_row2_col2\" class=\"data row2 col2\" >0.8205</td>\n",
-       "      <td id=\"T_89c3a_row2_col3\" class=\"data row2 col3\" >0.9697</td>\n",
-       "      <td id=\"T_89c3a_row2_col4\" class=\"data row2 col4\" >0.8889</td>\n",
-       "      <td id=\"T_89c3a_row2_col5\" class=\"data row2 col5\" >0.8887</td>\n",
-       "      <td id=\"T_89c3a_row2_col6\" class=\"data row2 col6\" >0.8918</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_89c3a_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_89c3a_row3_col0\" class=\"data row3 col0\" >0.9995</td>\n",
-       "      <td id=\"T_89c3a_row3_col1\" class=\"data row3 col1\" >0.8948</td>\n",
-       "      <td id=\"T_89c3a_row3_col2\" class=\"data row3 col2\" >0.7436</td>\n",
-       "      <td id=\"T_89c3a_row3_col3\" class=\"data row3 col3\" >0.9667</td>\n",
-       "      <td id=\"T_89c3a_row3_col4\" class=\"data row3 col4\" >0.8406</td>\n",
-       "      <td id=\"T_89c3a_row3_col5\" class=\"data row3 col5\" >0.8403</td>\n",
-       "      <td id=\"T_89c3a_row3_col6\" class=\"data row3 col6\" >0.8476</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_89c3a_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_89c3a_row4_col0\" class=\"data row4 col0\" >0.9993</td>\n",
-       "      <td id=\"T_89c3a_row4_col1\" class=\"data row4 col1\" >0.9077</td>\n",
-       "      <td id=\"T_89c3a_row4_col2\" class=\"data row4 col2\" >0.6667</td>\n",
-       "      <td id=\"T_89c3a_row4_col3\" class=\"data row4 col3\" >0.9286</td>\n",
-       "      <td id=\"T_89c3a_row4_col4\" class=\"data row4 col4\" >0.7761</td>\n",
-       "      <td id=\"T_89c3a_row4_col5\" class=\"data row4 col5\" >0.7758</td>\n",
-       "      <td id=\"T_89c3a_row4_col6\" class=\"data row4 col6\" >0.7865</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_89c3a_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_89c3a_row5_col0\" class=\"data row5 col0\" >0.9998</td>\n",
-       "      <td id=\"T_89c3a_row5_col1\" class=\"data row5 col1\" >0.9460</td>\n",
-       "      <td id=\"T_89c3a_row5_col2\" class=\"data row5 col2\" >0.8947</td>\n",
-       "      <td id=\"T_89c3a_row5_col3\" class=\"data row5 col3\" >1.0000</td>\n",
-       "      <td id=\"T_89c3a_row5_col4\" class=\"data row5 col4\" >0.9444</td>\n",
-       "      <td id=\"T_89c3a_row5_col5\" class=\"data row5 col5\" >0.9444</td>\n",
-       "      <td id=\"T_89c3a_row5_col6\" class=\"data row5 col6\" >0.9458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_89c3a_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_89c3a_row6_col0\" class=\"data row6 col0\" >0.9996</td>\n",
-       "      <td id=\"T_89c3a_row6_col1\" class=\"data row6 col1\" >0.9594</td>\n",
-       "      <td id=\"T_89c3a_row6_col2\" class=\"data row6 col2\" >0.8421</td>\n",
-       "      <td id=\"T_89c3a_row6_col3\" class=\"data row6 col3\" >0.9412</td>\n",
-       "      <td id=\"T_89c3a_row6_col4\" class=\"data row6 col4\" >0.8889</td>\n",
-       "      <td id=\"T_89c3a_row6_col5\" class=\"data row6 col5\" >0.8887</td>\n",
-       "      <td id=\"T_89c3a_row6_col6\" class=\"data row6 col6\" >0.8901</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_89c3a_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_89c3a_row7_col0\" class=\"data row7 col0\" >0.9996</td>\n",
-       "      <td id=\"T_89c3a_row7_col1\" class=\"data row7 col1\" >0.9459</td>\n",
-       "      <td id=\"T_89c3a_row7_col2\" class=\"data row7 col2\" >0.8158</td>\n",
-       "      <td id=\"T_89c3a_row7_col3\" class=\"data row7 col3\" >0.9118</td>\n",
-       "      <td id=\"T_89c3a_row7_col4\" class=\"data row7 col4\" >0.8611</td>\n",
-       "      <td id=\"T_89c3a_row7_col5\" class=\"data row7 col5\" >0.8609</td>\n",
-       "      <td id=\"T_89c3a_row7_col6\" class=\"data row7 col6\" >0.8622</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_89c3a_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_89c3a_row8_col0\" class=\"data row8 col0\" >0.9997</td>\n",
-       "      <td id=\"T_89c3a_row8_col1\" class=\"data row8 col1\" >0.9852</td>\n",
-       "      <td id=\"T_89c3a_row8_col2\" class=\"data row8 col2\" >0.8158</td>\n",
-       "      <td id=\"T_89c3a_row8_col3\" class=\"data row8 col3\" >1.0000</td>\n",
-       "      <td id=\"T_89c3a_row8_col4\" class=\"data row8 col4\" >0.8986</td>\n",
-       "      <td id=\"T_89c3a_row8_col5\" class=\"data row8 col5\" >0.8984</td>\n",
-       "      <td id=\"T_89c3a_row8_col6\" class=\"data row8 col6\" >0.9031</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_89c3a_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_89c3a_row9_col0\" class=\"data row9 col0\" >0.9995</td>\n",
-       "      <td id=\"T_89c3a_row9_col1\" class=\"data row9 col1\" >0.9212</td>\n",
-       "      <td id=\"T_89c3a_row9_col2\" class=\"data row9 col2\" >0.7949</td>\n",
-       "      <td id=\"T_89c3a_row9_col3\" class=\"data row9 col3\" >0.9118</td>\n",
-       "      <td id=\"T_89c3a_row9_col4\" class=\"data row9 col4\" >0.8493</td>\n",
-       "      <td id=\"T_89c3a_row9_col5\" class=\"data row9 col5\" >0.8491</td>\n",
-       "      <td id=\"T_89c3a_row9_col6\" class=\"data row9 col6\" >0.8511</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_89c3a_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
-       "      <td id=\"T_89c3a_row10_col0\" class=\"data row10 col0\" >0.9996</td>\n",
-       "      <td id=\"T_89c3a_row10_col1\" class=\"data row10 col1\" >0.9466</td>\n",
-       "      <td id=\"T_89c3a_row10_col2\" class=\"data row10 col2\" >0.7933</td>\n",
-       "      <td id=\"T_89c3a_row10_col3\" class=\"data row10 col3\" >0.9571</td>\n",
-       "      <td id=\"T_89c3a_row10_col4\" class=\"data row10 col4\" >0.8660</td>\n",
-       "      <td id=\"T_89c3a_row10_col5\" class=\"data row10 col5\" >0.8658</td>\n",
-       "      <td id=\"T_89c3a_row10_col6\" class=\"data row10 col6\" >0.8704</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_89c3a_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
-       "      <td id=\"T_89c3a_row11_col0\" class=\"data row11 col0\" >0.0001</td>\n",
-       "      <td id=\"T_89c3a_row11_col1\" class=\"data row11 col1\" >0.0308</td>\n",
-       "      <td id=\"T_89c3a_row11_col2\" class=\"data row11 col2\" >0.0627</td>\n",
-       "      <td id=\"T_89c3a_row11_col3\" class=\"data row11 col3\" >0.0335</td>\n",
-       "      <td id=\"T_89c3a_row11_col4\" class=\"data row11 col4\" >0.0428</td>\n",
-       "      <td id=\"T_89c3a_row11_col5\" class=\"data row11 col5\" >0.0429</td>\n",
-       "      <td id=\"T_89c3a_row11_col6\" class=\"data row11 col6\" >0.0403</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x22d8da94970>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "et = create_model('et')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "24b58ef5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 800x550 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_model(et, plot = 'auc')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "3245c6e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_6df17_row10_col0, #T_6df17_row10_col1, #T_6df17_row10_col2, #T_6df17_row10_col3, #T_6df17_row10_col4, #T_6df17_row10_col5, #T_6df17_row10_col6 {\n",
-       "  background: yellow;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_6df17\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_6df17_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
-       "      <th id=\"T_6df17_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
-       "      <th id=\"T_6df17_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
-       "      <th id=\"T_6df17_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
-       "      <th id=\"T_6df17_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
-       "      <th id=\"T_6df17_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
-       "      <th id=\"T_6df17_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >Fold</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6df17_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_6df17_row0_col0\" class=\"data row0 col0\" >0.9989</td>\n",
-       "      <td id=\"T_6df17_row0_col1\" class=\"data row0 col1\" >0.9970</td>\n",
-       "      <td id=\"T_6df17_row0_col2\" class=\"data row0 col2\" >0.8462</td>\n",
-       "      <td id=\"T_6df17_row0_col3\" class=\"data row0 col3\" >0.6471</td>\n",
-       "      <td id=\"T_6df17_row0_col4\" class=\"data row0 col4\" >0.7333</td>\n",
-       "      <td id=\"T_6df17_row0_col5\" class=\"data row0 col5\" >0.7328</td>\n",
-       "      <td id=\"T_6df17_row0_col6\" class=\"data row0 col6\" >0.7394</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6df17_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_6df17_row1_col0\" class=\"data row1 col0\" >0.9991</td>\n",
-       "      <td id=\"T_6df17_row1_col1\" class=\"data row1 col1\" >0.9760</td>\n",
-       "      <td id=\"T_6df17_row1_col2\" class=\"data row1 col2\" >0.7692</td>\n",
-       "      <td id=\"T_6df17_row1_col3\" class=\"data row1 col3\" >0.7143</td>\n",
-       "      <td id=\"T_6df17_row1_col4\" class=\"data row1 col4\" >0.7407</td>\n",
-       "      <td id=\"T_6df17_row1_col5\" class=\"data row1 col5\" >0.7403</td>\n",
-       "      <td id=\"T_6df17_row1_col6\" class=\"data row1 col6\" >0.7408</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6df17_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_6df17_row2_col0\" class=\"data row2 col0\" >0.9991</td>\n",
-       "      <td id=\"T_6df17_row2_col1\" class=\"data row2 col1\" >0.9823</td>\n",
-       "      <td id=\"T_6df17_row2_col2\" class=\"data row2 col2\" >0.8462</td>\n",
-       "      <td id=\"T_6df17_row2_col3\" class=\"data row2 col3\" >0.6875</td>\n",
-       "      <td id=\"T_6df17_row2_col4\" class=\"data row2 col4\" >0.7586</td>\n",
-       "      <td id=\"T_6df17_row2_col5\" class=\"data row2 col5\" >0.7582</td>\n",
-       "      <td id=\"T_6df17_row2_col6\" class=\"data row2 col6\" >0.7623</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6df17_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_6df17_row3_col0\" class=\"data row3 col0\" >0.9986</td>\n",
-       "      <td id=\"T_6df17_row3_col1\" class=\"data row3 col1\" >0.9515</td>\n",
-       "      <td id=\"T_6df17_row3_col2\" class=\"data row3 col2\" >0.7692</td>\n",
-       "      <td id=\"T_6df17_row3_col3\" class=\"data row3 col3\" >0.5660</td>\n",
-       "      <td id=\"T_6df17_row3_col4\" class=\"data row3 col4\" >0.6522</td>\n",
-       "      <td id=\"T_6df17_row3_col5\" class=\"data row3 col5\" >0.6515</td>\n",
-       "      <td id=\"T_6df17_row3_col6\" class=\"data row3 col6\" >0.6592</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6df17_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_6df17_row4_col0\" class=\"data row4 col0\" >0.9986</td>\n",
-       "      <td id=\"T_6df17_row4_col1\" class=\"data row4 col1\" >0.9515</td>\n",
-       "      <td id=\"T_6df17_row4_col2\" class=\"data row4 col2\" >0.6923</td>\n",
-       "      <td id=\"T_6df17_row4_col3\" class=\"data row4 col3\" >0.5870</td>\n",
-       "      <td id=\"T_6df17_row4_col4\" class=\"data row4 col4\" >0.6353</td>\n",
-       "      <td id=\"T_6df17_row4_col5\" class=\"data row4 col5\" >0.6346</td>\n",
-       "      <td id=\"T_6df17_row4_col6\" class=\"data row4 col6\" >0.6368</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6df17_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_6df17_row5_col0\" class=\"data row5 col0\" >0.9993</td>\n",
-       "      <td id=\"T_6df17_row5_col1\" class=\"data row5 col1\" >0.9895</td>\n",
-       "      <td id=\"T_6df17_row5_col2\" class=\"data row5 col2\" >0.8947</td>\n",
-       "      <td id=\"T_6df17_row5_col3\" class=\"data row5 col3\" >0.7556</td>\n",
-       "      <td id=\"T_6df17_row5_col4\" class=\"data row5 col4\" >0.8193</td>\n",
-       "      <td id=\"T_6df17_row5_col5\" class=\"data row5 col5\" >0.8189</td>\n",
-       "      <td id=\"T_6df17_row5_col6\" class=\"data row5 col6\" >0.8219</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6df17_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_6df17_row6_col0\" class=\"data row6 col0\" >0.9989</td>\n",
-       "      <td id=\"T_6df17_row6_col1\" class=\"data row6 col1\" >0.9878</td>\n",
-       "      <td id=\"T_6df17_row6_col2\" class=\"data row6 col2\" >0.8421</td>\n",
-       "      <td id=\"T_6df17_row6_col3\" class=\"data row6 col3\" >0.6400</td>\n",
-       "      <td id=\"T_6df17_row6_col4\" class=\"data row6 col4\" >0.7273</td>\n",
-       "      <td id=\"T_6df17_row6_col5\" class=\"data row6 col5\" >0.7268</td>\n",
-       "      <td id=\"T_6df17_row6_col6\" class=\"data row6 col6\" >0.7336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6df17_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_6df17_row7_col0\" class=\"data row7 col0\" >0.9989</td>\n",
-       "      <td id=\"T_6df17_row7_col1\" class=\"data row7 col1\" >0.9823</td>\n",
-       "      <td id=\"T_6df17_row7_col2\" class=\"data row7 col2\" >0.8684</td>\n",
-       "      <td id=\"T_6df17_row7_col3\" class=\"data row7 col3\" >0.6111</td>\n",
-       "      <td id=\"T_6df17_row7_col4\" class=\"data row7 col4\" >0.7174</td>\n",
-       "      <td id=\"T_6df17_row7_col5\" class=\"data row7 col5\" >0.7168</td>\n",
-       "      <td id=\"T_6df17_row7_col6\" class=\"data row7 col6\" >0.7280</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6df17_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_6df17_row8_col0\" class=\"data row8 col0\" >0.9991</td>\n",
-       "      <td id=\"T_6df17_row8_col1\" class=\"data row8 col1\" >0.9900</td>\n",
-       "      <td id=\"T_6df17_row8_col2\" class=\"data row8 col2\" >0.7895</td>\n",
-       "      <td id=\"T_6df17_row8_col3\" class=\"data row8 col3\" >0.7143</td>\n",
-       "      <td id=\"T_6df17_row8_col4\" class=\"data row8 col4\" >0.7500</td>\n",
-       "      <td id=\"T_6df17_row8_col5\" class=\"data row8 col5\" >0.7496</td>\n",
-       "      <td id=\"T_6df17_row8_col6\" class=\"data row8 col6\" >0.7505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6df17_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_6df17_row9_col0\" class=\"data row9 col0\" >0.9989</td>\n",
-       "      <td id=\"T_6df17_row9_col1\" class=\"data row9 col1\" >0.9356</td>\n",
-       "      <td id=\"T_6df17_row9_col2\" class=\"data row9 col2\" >0.7436</td>\n",
-       "      <td id=\"T_6df17_row9_col3\" class=\"data row9 col3\" >0.6591</td>\n",
-       "      <td id=\"T_6df17_row9_col4\" class=\"data row9 col4\" >0.6988</td>\n",
-       "      <td id=\"T_6df17_row9_col5\" class=\"data row9 col5\" >0.6982</td>\n",
-       "      <td id=\"T_6df17_row9_col6\" class=\"data row9 col6\" >0.6995</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6df17_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
-       "      <td id=\"T_6df17_row10_col0\" class=\"data row10 col0\" >0.9990</td>\n",
-       "      <td id=\"T_6df17_row10_col1\" class=\"data row10 col1\" >0.9744</td>\n",
-       "      <td id=\"T_6df17_row10_col2\" class=\"data row10 col2\" >0.8061</td>\n",
-       "      <td id=\"T_6df17_row10_col3\" class=\"data row10 col3\" >0.6582</td>\n",
-       "      <td id=\"T_6df17_row10_col4\" class=\"data row10 col4\" >0.7233</td>\n",
-       "      <td id=\"T_6df17_row10_col5\" class=\"data row10 col5\" >0.7228</td>\n",
-       "      <td id=\"T_6df17_row10_col6\" class=\"data row10 col6\" >0.7272</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6df17_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
-       "      <td id=\"T_6df17_row11_col0\" class=\"data row11 col0\" >0.0002</td>\n",
-       "      <td id=\"T_6df17_row11_col1\" class=\"data row11 col1\" >0.0196</td>\n",
-       "      <td id=\"T_6df17_row11_col2\" class=\"data row11 col2\" >0.0601</td>\n",
-       "      <td id=\"T_6df17_row11_col3\" class=\"data row11 col3\" >0.0573</td>\n",
-       "      <td id=\"T_6df17_row11_col4\" class=\"data row11 col4\" >0.0500</td>\n",
-       "      <td id=\"T_6df17_row11_col5\" class=\"data row11 col5\" >0.0501</td>\n",
-       "      <td id=\"T_6df17_row11_col6\" class=\"data row11 col6\" >0.0497</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x22d83cc0fa0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tuned_et = tune_model(et)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "a5385642",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAroAAAH7CAYAAAAjETxNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAADNHElEQVR4nOzdd1QUVxsG8Gd32aX3qmCPICI2FBs2LDH2HrsmlthNNH7GEls0dhNrLIk19mhMbDFqEo1GjRqVWLCiolhQQDrL7s73B2FgBWFXgQH2+Z3jkbkzd+bdmS3v3r1zr0wQBAFERERERMWMXOoAiIiIiIjyAxNdIiIiIiqWmOgSERERUbHERJeIiIiIiiUmukRERERULDHRJSIiIqJiiYkuERERERVLTHSJiIiIqFhioktEVMxxXiAiMlVmUgdARLnr27cv/v77b70ymUwGKysrlC1bFv3790eHDh2y1Pvtt9+wdetW/Pvvv0hKSkKJEiXQpEkTDBgwACVKlMj2WIcPH8bOnTtx/fp1JCYmwsvLC61bt0a/fv1gY2OTa6yxsbHYtGkTDh8+jIcPH8LCwgLe3t7o378/goOD3+wEFCJnzpzBtGnT8OjRI9StWxfffvttnu4/u2udWbVq1bBz506D9vXkyRNMnToVU6dOhZeXV77GBQCdOnXC3Llz3+o4ecmQ5/LZs2fRr18/bNq0CXXq1Cmw2Pr27QsA2Lx5MwDgxo0bmDBhAm7fvo3SpUtj0KBBmDhxIo4dO/bW147IlDHRJSoiKleujGnTponLWq0WT548wYYNG/C///0PDg4OaNy4sbh+xowZ2Lp1K9q0aYMvvvgCdnZ2uH37NjZv3owff/wRS5cuRd26dcXtdTodxo8fj19++QVdunRBz549YW1tjUuXLuG7777D0aNHsWHDBtjZ2b02xjt37mDw4MHQ6XTo168fKlWqhMTEROzbtw/Dhg3DmDFjMHz48Pw5QQVk/vz50Ol0WLNmDZydnfPlGK9e68ysra0N3s9ff/2F48eP50lM06ZNQ3x8vLg8Y8YMsTydk5NTnhzrbeXFczm/vXp9V6xYgYiICKxYsQJOTk7w9PTEjh074ObmJlGERMUDE12iIsLGxgbVq1fPUt6oUSPUq1cPe/bsERPdLVu2YOvWrZg7dy46deokblu3bl107NgRgwcPxscff4z9+/fDxcUFAPDtt99i//79WL58OVq0aCHWqVevHgIDA9G7d2+sWLECEydOzDa+1NRUfPzxx1Aqldi6dateEti8eXN8/vnnWLJkCYKDg1GpUqW8OCWSiImJQe3atVG/fv18O8brrrWU3nnnHb3l9BbRwhYn8PbP5YLw6vmMjo6Gt7e33pfVwvLFgagoYx9doiLO3NwcKpUKMpkMQFpL7zfffIOgoCC9JDedjY0NZs2ahejoaGzZsgVAWpK6bt06NGrUSC8xSBcQEIDRo0dn+XDO7Pjx47h58ybGjBmTbUvn6NGj0adPH2g0GgDAZ599lqUrw8OHD+Hj44M9e/YASPtZ2cfHB9u3b0fTpk1Rs2ZN/Pjjj/Dx8cHNmzf16h49ehQ+Pj64du0agLSEdOrUqahfvz78/f3RvXt3nD59Wq/OqVOn0L17d9SoUQO1a9fGsGHDcOfOnWwfX3psjx49wt69e+Hj44OzZ88CAP79918MHDgQderUQc2aNTF06FDcunVLrJvd4zh16tRrz6Uhjh07Bh8fHyxbtkwsu3PnDqpWrYpJkyZhz549YiLXrFkzfPbZZwCA4OBgfPnll+jfvz+qVq2KyZMnAwBCQ0MxcuRI1K1bF35+fmjYsCFmzZqF5ORko+JatmwZWrRogeXLlyMwMBBBQUF4+fIlAGDXrl1o06YNqlSpgiZNmmDZsmXQarV69c+fP48+ffqgWrVqCAwMxIQJExAVFSWu1+l0+OqrrxAcHIwqVaogODgYixYtQmpqKoC8eS4fPXoUvXr1Qo0aNVClShW0atVKfK2k27hxI1q1agV/f380bNgQ06dP12vxzu251bdvX7H7go+PD/7++2+cO3dOfP7v2bMHPj4+ePjwocHnZs+ePahcuTJ27dqFBg0aIDAwELdv3379xSIyAUx0iYoIQRCg0WjEfykpKbh79y4mTpyIhIQEsY/u9evXERkZmWN/2AoVKqBSpUo4duwYAODq1auIjo5G06ZNX1tn+PDh6Nat22vXnzhxAgqFQq9FKjNXV1d8/vnnqFKliiEPV8/y5csxYcIETJ06Fe+++y6srKxw4MABvW3279+PihUronLlykhJSUH//v1x7NgxfPLJJ1i+fDk8PDwwaNAgMdkNDw/H8OHDUaVKFXzzzTeYPXs2wsLCMGTIEOh0uiwxuLm5YceOHXB1dUXjxo2xY8cO+Pn54cyZM+jZsycA4Msvv8SsWbPw+PFj9OjRI0vSnPlx1KhR47WP99Vrnflf+o1lzZo1Q/v27bF69WrcuXMHGo0G//vf/+Du7o7JkyejSZMmGDZsmHjczF1GtmzZAn9/f6xcuRJdu3bFs2fP0Lt3byQlJWHu3LlYu3Yt2rRpg82bN2PTpk1GX6+IiAgcP34cX331FSZOnAh7e3usXr0an3/+OerVq4dVq1ahd+/eWLt2LT7//HOx3rlz5zBgwABYWFjg66+/xqRJk/D333+jX79+YsK9du1abNu2DSNGjMC6devQs2dPfPfdd/jmm28AvP1z+Y8//sCIESPg5+eHlStXYtmyZShVqhRmzpyJy5cvA0h7ri1YsAC9e/fGd999hxEjRuCnn37CF198AcD459aOHTtQuXJlVK5cGTt27ECTJk2ybGPIuQHSvuiuW7cOs2fPxsSJE1GhQoVcrhZR8cauC0RFxLlz5+Dn56dXJpPJ4O3tjSVLlogf7OktQLndwFKmTBmxVfHx48cG1cnJkydP4OjoaFQfUkP16tULrVq1EpffffddHDx4EJ988gkAICEhAb///jtGjBgBAPjpp58QGhqKnTt3olq1agDSunj07dsXCxcuxO7duxESEoLk5GR89NFHcHd3BwB4eHjg2LFjSExMzHLjnUqlQvXq1aFSqeDk5CT+ZL9o0SKUKVMGa9asgUKhAAAEBQWhRYsWWLp0KZYsWfLax/E62V3rdEuWLBH3MWXKFJw5cwYzZ85E3bp1cf36dWzduhXW1tawtrZG6dKlAQC+vr5617ZkyZL49NNPxeWTJ0/C19cXS5YsER93/fr1cerUKZw9exZDhgzJNebMNBoNJkyYgFq1agEA4uLisHLlSrz//vuYMmUKgLRz5ODggClTpuCDDz5AxYoVsWjRIpQrVw6rV68Wz2W1atXQpk0b7N69G71798bff/+NKlWqoEuXLgCAwMBAWFpawtbWFsDbP5dv376NTp06iS3dAFCjRg3UqVMHZ8+eRbVq1fD333/Dy8sLvXv3hlwuR2BgIKysrMSWa2OfW9WrV8+1K4gh5ybd0KFDs02WiUwRE12iIsLPz0+8AejZs2f4+uuvkZqaiq+//hrly5cXt0tv8TMzy/nlrVAosmybXWuToRQKRZafofOKr6+v3nKHDh3w448/IiQkBFWrVsWxY8egVqvRvn17AMDp06fh6uoKPz8/sasEADRt2hTz58/Hy5cvUa1aNZibm6Nr165o1aoVGjVqhDp16qBq1aoGx5WYmIh///0XI0eOFJMPALCzs0PTpk2z3Aj26uN4nczX+lXpySsA2Nvb44svvsBHH32Ev//+G8OHDzeoz+yrcQQFBSEoKAipqam4ffs27t+/j5s3byIqKgoODg4GxZzTMS5evIjk5GQEBwfrXY/0Xx1OnToFLy8vXL58GQMHDhRbtAGgVKlSqFChAk6dOoXevXujTp06WLRoEXr16oXg4GA0adIEffr0Eff5ts/lQYMGAUj78hQWFoYHDx7g33//BQCo1WoAaX3dd+zYgc6dO6N58+Zo3Lgx2rVrJ3YfyovnVmZJSUkGnZt0hj7PiEwBE12iIsLa2hr+/v7icrVq1dC+fXt8+OGH2LNnj3jjiqenJwDg0aNHOe4vPDxc3LZkyZK51omKioKNjQ1UKlW26z09PfHHH38gISHhta26T548gYeHR45xZcfKykpvuU6dOnB3d8eBAwdQtWpVHDhwAIGBgeK+Y2JiEBkZ+dpW0cjISLzzzjv4/vvvsWbNGvzwww/YtGkT7Ozs0KtXL3z88cdi0pKTuLg4CIIg3tCXmYuLC+Li4nJ8HK/z6rXOSf369eHm5oZnz57l+HN9TnHodDosXrwYW7ZsQWJiIkqUKIGqVavC3NzcoP1lJ/NzICYmBgBe2zL87NkzxMbGQqfTYe3atVi7dm2WbdJjGTRoEKytrbF7924sXLgQCxYsQMWKFTFlyhTUrVv3rZ/LUVFRmDZtGo4ePQqZTIYyZcqILdPpXwxbt24NnU6HrVu3it0bPD098emnn6J169bw8vJ66+dWZoaem3SGPs+ITAETXaIiysXFBVOnTsWYMWMwe/ZsLFq0CABQpUoVuLm54ZdffkH37t2zrRseHo5r165h8ODBANJagFxcXHDixAm9lqHMpkyZgkuXLuGPP/7INkEICgrC5s2b8eeff2b783xUVBSaNWuGXr16YfLkyZDJZFlagBMTEw167HK5HO3atcP+/fsxdOhQnDp1CjNnzhTX29raomzZsli4cGG29dN/1q5atSqWL18OtVqNCxcuYMeOHVi1ahUqVaqE9957L9c4bG1tIZPJ8Pz58yzrIiMj37g11BjLly9HTEwMypcvjylTpmDXrl1QKpVG7WPNmjXYsGEDZsyYgZYtW4rdALp27ZonMaYP47Vw4UKULVs2y3oXFxdYW1tDJpNhwIABaNOmTZZtLC0tAaRd+969e6N379548eIFjh8/jlWrVmHUqFE4deqU0c/lV3366ae4e/cuNmzYgBo1akClUiEpKSnL2MVt27ZF27ZtERcXh5MnT2Lt2rUYP348AgIC4O7u/tbPrcwMPTdElBVvRiMqwlq1aoWGDRti//794mD+crkcI0eOxKlTp7Bt27YsdZKTkzFp0iTY2tqiV69eYp0BAwbgjz/+wG+//ZalzpkzZ3D8+HG0atXqtS26QUFB8Pb2xldffYXo6Ogs6xctWgSNRoN27doBSPvwjo6ORkpKirjNhQsXDH7sHTp0wJMnT7BixQooFAq0bNlSXBcYGIjHjx/D2dkZ/v7+4r9Tp07h22+/hUKhwIYNG9C0aVOo1WqoVCrUq1dPvJkoIiLCoBisrKxQpUoVHDp0SC9pj4uLwx9//IGAgACDH8+bCAkJwbfffothw4ZhwYIFuHnzpnhTFpB2XQ1x4cIFvPPOO+jSpYuY5D59+hQ3b958q+4s6apVqwalUomnT5/qXQ8zMzMsXrwYDx8+hI2NDSpXroy7d+/qbVOxYkUsW7ZMHOGiR48emDVrFgDA2dkZnTt3Ru/evREbG4v4+Pi3fi5fuHABLVu2RJ06dcT1J06cAJDRHeLjjz8W+4Pb2trivffew/Dhw6HRaPDs2bM8eW5lZui5IaKs2KJLVMRNmjQJ7du3x6xZs/Djjz9CoVDg/fffx507dzB9+nScO3cOrVu3hr29Pe7evYuNGzciMjISX3/9tXijDAAMGDAA586dw6hRo9C9e3c0btwYcrkc586dw+bNm+Hr64tx48a9Ng4zMzPMnz8fH374Ibp06SJOGBEVFYU9e/bgzz//xLhx48R+ik2bNsXmzZsxefJkdO3aFTdv3sT69ev1+rrmxNvbG76+vti6dSvee+89vRt8OnfujO+//x4ffPABhg4dihIlSuCvv/7C2rVr0adPHyiVStStWxcLFy7EiBEj0KdPHygUCmzfvh0qlcrgLgAAMG7cOAwcOBBDhgxBr169kJqaijVr1kCtVovJkLHi4+Nx6dKl16739/eHVqvFZ599hgoVKmDw4MFQKpXo06cPVq9ejebNm6Ny5cpiS+qRI0fQqFGj196BX7VqVaxcuRJr1qxB9erVcf/+faxevRpqtRpJSUlv9Bgyc3R0xKBBg7BkyRLEx8ejTp06ePr0KZYsWQKZTCaOqzx27FgMGTIE48aNQ/v27cURBC5fviyOGlG7dm2sW7cOLi4uqFGjBp4+fYr169cjMDBQ7L7zNs/lqlWrYt++ffDz84OHhwf++ecfrFmzBjKZTDwXdevWxbRp0zBv3jw0atQIsbGxWL58OcqWLYtKlSpBqVTmyXMrM0PODRFlxUSXqIgrX748+vbti3Xr1mHbtm3ijTmTJk1Cw4YNsWXLFkyfPh2xsbHiFMD9+/cX+zKmUyqVWLlyJXbs2IGffvoJBw8ehFqtRqlSpTB8+HD06dMn175/vr6++OGHH7B+/Xps27YNT58+hZWVFXx8fPDtt9+iYcOG4rYNGjTAhAkTsHnzZhw+fBh+fn5Yvnw5evToYfBj79ChA+bOnSvehJbOysoKW7ZswaJFi7BgwQLExcXB09MT48aNw4cffggAqFSpElatWoUVK1Zg7Nix0Gq1qFKlCtatW6d3c19u6tWrh/Xr12Pp0qUYO3YsVCoVatWqhXnz5qFixYoG7yeza9eu4f3333/t+nPnzmHVqlUICwvDtm3bxK4KH3/8MY4cOYIJEyZg9+7dqFOnDurXr49Fixbh9OnTWLNmTbb7++ijjxAdHY1NmzZhxYoVKFGiBDp06ACZTIbVq1cjNjb2rWcR+/jjj+Hq6oqtW7fi22+/hb29PerVq4exY8eKrchBQUH47rvvsHz5cowePRpKpRJ+fn5Yv369eJPdmDFjoFKpsHv3bqxYsQK2trYIDg7WS1zf5rk8d+5cfPHFF2ILbNmyZTFjxgz8/PPPOH/+PIC0VuXU1FRs374dW7duhYWFBerVq4fx48dDqVTm2XMrM0PODRFlJRPSe9cTERERERUj7KNLRERERMUSE10iIiIiKpYKRaKrVqvRtm3bHO8cvXbtGrp164Zq1aqhS5cuuHLlSgFGSERERERFjeSJbkpKCsaOHYtbt269dpvExEQMGTIEtWrVwp49e1CjRg189NFHBo+5SURERESmR9JE9/bt2+jevTsePHiQ43YHDx6Eubk5/ve//6FChQqYPHkyrK2t8csvvxRQpERERERU1Eia6P7999+oU6cOduzYkeN2ly9fRkBAgDhtokwmQ82aNXMcZ5KIiIiITJuk4+imz8qUm/R56TNzdnbOsbvDqy5evAhBEIyeGpOIiIiICkZqaipkMhlq1KiRJ/srEhNGJCUlZZmqUaVSQa1WG7wPQRAgCALUajVShSQIgjbH7c1klpDLMmZoUusSAeQ8FaZSZgWZLKORXK2LzzUupcxabKkWBB1Shdz6HcugkluLSzpBC42Q88xFMiiglGfMha4TNNAIyTnWkcvMYCazEJe1ghpaIefzLZcpYSYzF5c1Qgp0QmqOdeLUCiRrMs6znbkG5oqcz/PLFDOotRnn2cEiFUp5zsNBRycrodHJxGVnq1TIkXOd54lKCPjvVwQALlY5P34dZHiRmPFFSiET4GSZ8+PX6GSITs6oo5Tr4GChybGOWivHy5SMl66FmQ62qpzrJGnkiFdn1LFSamGtzPk1kJCqQGJqxrWxVWlgYZbztYlNMUNKpmtjb6GBSp5znZhkM6TqMuo4WaZCIcv52rxIUkInZFxPFys1ZDlsDwCRiRnvIXKZAOdcro1WkCEqKePamMkFOFrkXCdVJ0NMputprtDBzjzna5OskSMu07WxNNPCRpXztUlMVSAh07WxUWlhaZZznTi1GZI1Gef5TV5rjhapMMvltRaVpIRWMPa1phK3KKjXmkqhg30u1yZFK0fsW77WrJVaWBXAa83BQgNlMXmtKeUCHHJ9rckRk2xm0P6p8NNqdYh/mQwLKxVsLO0glxs2Q6YhikSia25uniWpVavVsLCweE2NrJRKJdRqNcqWLYt7Ly8gJumpXiL7Kl/3BrCzdBWXLz88ghTNK0moTP/l7lOyGSyUGUno+fv7IbzmDV7231uFf+nWkP+XHKu1yQh5dCybbTM9DoU5fD1ricvxKdG48fR0ppCyvgVZqxzg415TXI5OfIx7Ly5nG1U6Oyt3lHP2FZefxYUh4uXN10QFCALgbFMGJe29xbJVp3/Hzcjbetu9TNZ/Q7ry1B53o9POmQABtUpGo4q7ABlk4umVydLOVvryzRfuiE2xgkwmg1wGVHaNgLUyRdwuKVULtVYHO6UMSqUZ5DI57kSXQorWQozax/kuFP99KGR+JJnP353o8hCEtJeIXKbDO07pj0WWUSdTZUGQ435sRXELlSIFnrb3snwoyGRAvFoDOWQwN7PCk4Ty4mMzV8TD1eqh3o5frZ+itcaLpFJivFZmL+Fg8QQ5SdQ4IDYlYyY0W1UUbFQvst02/XjxqW5ISHURl+1UkbBUvsxmy4ylWHVJpGgdxDX25uFQyhOzfV6mi1WXgUZn/d8+ZHBIuQe5TP3f48u+zsuUihCQ8WFql3IDKclJsLCwgFyuyLbeyxQ/3HoRD28XW8iQCtvUO6+NCQAEwQxxqT7iskKWCGt1zvcTaHWWSNBkzHyllL+EZcrDHOtodPZI0pYSl1Xy57BIfppjnRSdM1K0HuKyhfoxVPKoHOskaz2g1jmLy5apD6CUxeVYJ0lTGhohYwYx69RHkMty/pKcoKkAnZDx3myjuQdZLo0Eaec5/f1YC1vN3YyV2VxLrVbA/SeucHR0hFKphBzJsNLkfJ51gjkSNRlTICtk8bBU51xHq7NBkra0uKyUR8M85XGOiV6qzhEpuhLisir1GazNbsFCEYH0xpL41EoQkJ7YCbBVXtXrSPhCIwB6+bQMcan+mZbUsFHe0K+j1v+s0UGFhNRKeo/XyixMv06Kfh2tYKN3jszk0bBUPNB7Ib5I1q+TqnNEcqZzpJI/hbki8/NXQOQrTxm1zg3qzOdI/ggq+Qu9Ok9eqfMy0RVQuEP+3xThlor7MJO/BCDgecp/j1lQQKuzhk6wgJApvZHLEiGDDgLMoBMyGn1kMjXkSPnvsVsi4zkIKGQJSPtUUkEnmGfaVzJkSP2vjhUyTqjwXx1AB3MIQuZkPwmy/y6oVvhvqnJZ2pYKWeJ/sVvqx4wEyGQCABm0QkZeAWig+O81qBOsIGS6oApZWuOaICigQ6bHiVTIZemP0xoZL6qMmAXBDAIsMtVRQy5TQwCgEzKmV9ePWQkB5pnqJEMu00AQ5NAh88yDmWM2z/TcB+RIgiBosHj0ejx7+AJWtlbYvnM77M3tkVeKRKLr7u6O58+f65U9f/4cbm5uRu1HK6ghM9MhoHxLo2Oo593B6DqNfLsbtb0VrNCoUjfj6lhZwc2xq5F1KsDTJfs571+nrJUfyrr7Zbvu6+PXMPHARai1VwC8Ouybc3ZVAAB/jW6FOmVcX7v+bSQmJuL69evw9fV9zVSfzd5gr8FvG5YJCnyDOrVy3+QViYm+uVzv7NQx+jhA0BvUKQhv8hNfYa6T83Mg+9d3vTc4TsE4ePkunsVlJPsfNugE6/8+yAVBh42nJuVYXwZgdHBGV7+45CjsPj8/xzr25jYY2DCjzpOXd/HLv9lPAZ3O094V71XtKS7feXYRf958AOTQIu/rURYNfTKmqw4J/x3/3D+c43Fql/VDzTLvist/3d6Dm0+e51AD8HMojQb+bcTrfeTqOjyKjtHbpmaZYFQv/Sbv7SS1SsuD0L17d4wdMxa2FrZ5uu8ikehWq1YNa9euhSAIkMlkEAQB//zzD4YOHWrUfrSCOmurLL21DefuQK19fYtNKQcrdK5aWq+siocjAku75Hdoxdref75CYsrLHLfpUPMT8QMVALacnp7j9jIAveplbJOQEoO9/3yVYx1rcwd0rPmJuPws9j6OXF2XYx03uzJo4fehuBwWeRl/3d6TY52yLv5oUDHjS92VhydwKfxo2oIA6HQ6XL/0k14LYBXPRqheurm4fPbuPtx6ci7H4wSWbwdvj9ri8u/XN+NRdM73AzT17QNPx4xfM/ZdWo6XiTm3yratPhIOVu7i8vazs5CqTcmxTo86U6BUpLWgpGiSsPPv2TlurzKzxPuBk8Xl6IQn2H95eY51HK1KoG31EeLyw6hQ/Hb9+xzrlHLyQVPfvuJy6OMz+Pvuvhzr+JSoizrl24nLF+79gquP/syxTs0y76KKVyNx+UzYjwiPuZpjnUbePVDOtaq4fChkFZ7F5dwy/57/ULjZZbxn7To3F4kpsTnW6Vp7gl7yuulUxnl/9Ze9Xefm5LivVwkQsOHkZ0bViU+JMrrO09gwo+vcibyIO5EXjaoTEv47QsJ/N6rOo9QL2PnPhRy3sbVwMmqfJI30HC4gIEAsa9SoES5dugQPDw+EhITk6fEKbaIbGRkJW1tbWFhYoFWrVli0aBFmz56NHj16YPv27UhKSsJ7771n9H5z6q5AOTtyIwLDd5/FyyT97gcvEtM+nOuWcUHPGuX01pVytEbbyp5QyCUfsrlIi016gRRNIiyU1uKbeao2GWptzj8jvyo11+2zdknJLfl6db0gCLnW0Wj1uyLpBF2udbQ6/b6ROkGbdT+vfN/S6bSvLGug0eXc/1MQ9HeiNajOKz8B61Kh0eXcb/CVKtDqUqHNpc6rXj0nua0XIOReR8haRyfkXEf3yjkTBAG6XO6DePU8G1Ln1ZZFAbos+8m1jiC8QR0dhFy6X2TdQ859Xynv2Vg4Sh0C5eL58+f45JNPcODAAezZswdNmjQR13l4eLy+4lsotIluUFAQ5syZg86dO8PGxgarV6/GtGnTsHPnTvj4+GDNmjVG/ESZQS5jwmWIq09i8DhW/ya3Xt//iajE13/gt/AuiZENK712Pb2ZJHUc9lxYAACwNrdHt9oTAQCVSwblmhymt/6ly9y6aQiVmUWudVQK/b7y1uYOqFG6RY51rM0d9JadrEvkWsfBWv9N0N2+HGqUSeuGlKpORWRkJFxdXaFUZfT/crMto1enlFNlWOXS98vF1ktvuYJbTbjZlc2xjp2l/q8TlUs2QHJqQo51LFXWesvVSzeHVpdzopf5i7pCboaaZVrluL1Crv8Wb6m0RUDZnOtYKG30lu0tXRFQNudGhVdb0tzsSqNWLnWcbErqLXs5+ejd45Add3v9L9JlnPzhbl/mNVuncbQuobdcqWQ9lE7JvhtWOhtz/YSpWulmWb5UvUr/dSBD7XJtxKV/w48jWZPWfzKwXNsc90NZqVNT8fTpU7i7u0P1mpGT7Kxcs7zeqfARBAFnzpwBAHz11Vd6iW5+kQmvNkUUU//++y/ik6Pg7e0LZ3vj+vYWV4Ig4LdbT3D7hf4NKbsv38exWznf3DS5ub/esou1OfrXrgB7S9VrahSs3PvoFh23np7HqVs/AADc7cqhlf8QiSMqfIrT9abcFbXrvfefxXiZFAlvjzqo/04nqcMpcora9aac7du3D3/++SemTZsGa+usX25DQkIgk8ng7++fTW3jFdoWXcp/f959hparjxpdb/q71fB5y6q5b0g5Cgn/HdciTkIn6NC22gi9VsEdf8+GVpt+Z29GK9/T2DBsPDWxwGMtKq4Y11WQijheb6LC7bfffsPNmzf17qlq164d2rVrl0OtvGVyiW5u4/8VR1qdDv22nsLZ+/p3tYZF5TzObzs/L3zZWv+OaWuVGco42bymBhnj34fHxT6zr/6wotYkG91fk4gKN1v2ISUTsnbtWkyYMAEKhQKBgYGoWbNm7pXygckluqbo0qNobL9477XrVQo5ns7sBnOzjP5/MgAqM+lu3NMJOpy+vQcv4h+l3dKRKRHMfJOHj0cgfEs2EJcv3j+CsOeXodPpoFarEXb12H9juKbVEQSgZtl3Ud61mljnz5s78eTlnUyHEDKO819ZI58eKOGQMSTbwZBv8DIxMtuY0rWuOgwOVhndZHadm4PUTP380pNcT0cfmCv1f46rWqqp3g0zj6JvITLuPoC0UQFIn1qtzujDpyoc3Wco/xTF621hZo3SLjn3DSYqTlq2bIkvvvgCFhYWiImJkSwOE0x0Ta9NN/PQX92rl4GHbeYBs4G2lb1gZ1G4PixiEp7gWex9JKrjchwpICk1/pXlOMQmZbRcq7O5V+vV/SWnxiMhl2G6Xr0TXa1JMmCoOv3kN0WTlO0NLZVK1M1yE061Uvpj9qZqkhEZdx/W5vaonCmxpzSJiYnQvrgObzf24TMFvN5EhU9KSkraBC7/jbJUpkwZbNq0CVWqVIGLi3TDiZpUoiuXqaCQZ3/HpqkY27gyaheS8WuT1PE4F3YACSkxANKmaBYgoIyzH6p4NUbHmmNxLeIkohPSbozTn10r7W9nG/075D3sykMGOTQaDaKjo+Hk5AQzM7P/aqTVyTx2KQCUca4CB0u3/2b/yZjyF5CJ34tszfXvKvf2qIPk1IQc5i8DzM30k9eqXk2zDO1ka+EET0cf5CQhJQZXI07muA0REZFUQkJCMHToUPTt2xfDhg0TywtiVIXcmFSiayZTQakoXC2XpuzOs39wN5uBxp3+Gw5IJpPBz7OhUfss71Yd5d2qp92lm3AdvqVyb/Hx9jB+9q43aVWtWqqp0XUAICImYxplS2XezhhDRET0NgRBwLhx4xAaGoovvvgCrVu3RpkyhWeoN5NKdE3BhH0XsOvyfb0fzVM0uQ3CXvDikqNw/t5BAGljg5Zy8oVMJoMM8iyttKYu841qjXx6SBgJERGRPplMhiVLlqBdu3aYPHkySpcunXulAsREtxiJTVZj4R/XctzGzcYix/UF5XrEKfFvWwtnNPXtI2E00nl1RqnsZSS6liq26BIRkXR0Oh0OHTqE1q1bi10KK1eujMuXL8PGpvCNymRSia5GSPlvJqniefOCRpeREL1bqSQqu+vPAFWvrGuhGRrMza4sklMTcTfyIupWaC91OJI4H3YQVx/9yalCiYioSHj69CkGDx6MkydPYsWKFejZs6e4rjAmuYCJJbo6IdWAudSLh141y6FPQPkCP25EzG08ibkDALgTeRFqTVKWRK6V/xCUdfFHWRd/NPJ5v8BjLCxuPT1vVJJrqbTNMqUrERFRQbG1tcXjx48BANu2bUOPHj1euVG88OGnJhksVavG3WcXswyr5WhdAqWcKgEAnr4MQ8jD33PekWnMOp2r9CTX09EHXo6VctxWJgNKOlSEXCbd2MZERGTarKyssGLFChw5cgTjx48v9EkuwESXDHTzyd/46/aebNdVdK8lJrpKhQoWyoyfL3Q6Dcq7VYelyk4ss1LZZ9lHdtKGGzOkD2tWOkEHQdBBJ+gKbyv+fwm/i40XfEvWkzgYIiIifXv27MGFCxcwe/ZssSwwMBCBgcaPViQVJrqUq7jkqCxJrpk8Y5g2uSzjaVTFqzGqeDV+62NqtGocuLwS0YlP3mo/V7KOXkZERES5+O677zB+/HgAQFBQEN577z2JI3ozJpjoFv5m9oKUqI5DfHK0XlmKJhG3n55HnfLtYWVuh6j4CHGdlcoOjXx6wMM+f/v/Po9/+NZJblFha+GU+0ZEREQFqEuXLli8eDHMzMzg4OAgdThvzAQTXdP0OOY2zJXW4mQMAHDpwVFcenD0tXWqlW4GK3M7eDp6o0214YhOeIp33AMgl8nzPd7MY8dW9WoKK3O7HLbOSq1W48mTJ/Dw8IBKVXgnCbFS2cHLKef+uURERPktLi4OZmZmsLS0BAA4ODhgx44dKFOmDGxti+7Qlkx0TUBs0nMcvvId6r/TCY5WHmLn8WuZxrLNzo3HZ1DvnU4wU6jgalsarrZ5Pwi0IOj0ktrM5enKuVaDo7WHUftNTExE6vPreMc195nRiIiITNnJkycxcuRItGnTRq8/bpUqVSSMKm+YXKJrih0XIuPCAQi48uhPeDn5wuq/SQfcbMvgUfQN2Fu5oUHFrnp1FDKF0cmlsaLiI/Dr1XVITo3P1+MQERHR661YsQIPHjzAqlWr0KdPH/j6+kodUp4xqURXITOHQq6UOowCIQg6HLu2EY9jbouzbyWp42CZaUSE5n4DJIouTXh0aK5Jrlym4GxgRERE+Wjx4sVo164dJk2aVKySXMDkEl2l6Qy4r3uG8KjrekWutqUL15h3/3VZkMsUCCzfLttN3GxLw0JpXZBRERERFVtqtRqbN29G//79YWaWlhOVKFECZ8+ehUJR/MZqN5Gsz/Ro1T8DAGSQo0aZFlDIzVDWparEUelLnzBBLlegUom6EkdDRERUvD19+hTvv/8+QkJCEB0djU8//VRcVxyTXICJbjFmCSAJbnZlULVUU6mDySIi+laOIz4QERFR3nJxcYGFhQUA4K+//oJOp4Ncnv8jKUnJpBLdVCEJqVo1gOJ/F75cWQMBXo4o6+IvdSjZuv/iivi3rYWzhJEQEREVX4IgiN0WFQoFVq5ciV9//RVDhgwp9kkuABT/R5iJIGjfeErZokZuVgV+ng1hbe4gdSjZyjygWIvKH0gWBxERUXEkCALWrVuHvn37QqfLyH3Kly+PoUOHmkSSC5hYi25xp9NpMKFhGAQAOq0XgLybvUwQBGgFTR7uTwsAsLd0M3oyCCIiIsrZ5s2bxT6469atw6BBgySOSBoml+jKivFIugIEeLsk/reQmGf71WhTceDyCpOZkpeIiKioe//997FmzRrodDrUrl1b6nAkY3KJbnGmP8NY3iX0L+If5VuSa2vhlC/7JSIiMiWRkZFQKBRwckr7XDU3N8f27dv1bkAzRUx0i5WsU+nm9X59POrAyaZknuxVITNDKafiNTA1ERFRQTtw4AA++eQT1K9fH+vXrxdvPvPy8pI4Mukx0S3GBEGAVvf2/Woz98319giEs43nW++TiIiI8sbRo0fx/Plz/Pzzz7h48SJq1qwpdUiFBhPdYkTI1PIqCDL88u8aPI0NkzAiIiIiym8zZ87ErVu3MHbsWCa5rzCNsSX0FN+b0aDXRzc5z5NcuUwBa3P7PN0nERERGS4hIQGzZ89GXFycWGZra4v9+/cjODhYwsgKJ5Nq0TWTWUAhL74P+XU9dCuVqAcn6xJvvX8X21KwUNq89X6IiIjIeFFRUWjZsiXu3r2L58+f46uvvpI6pEKv+GZ92ZDLzCCX5V0jtkaX+korqrQ0OnWmpYzBoT0dfVDKqVLBB0RERER5xtHREf7+/rh79y5evHgBjUYDMzOTSuWMxrPzhs7fO4QrD08g/0Y6eDuC5pzUIRAREdFbypzMymQyLFy4EK1bt0bXrl3F0RXo9Uywj27euPvsIgprkvsqjlVLRERUtGg0GixcuBAtWrRASkqKWO7s7Ixu3boxyTWQSbXoqnWJeTLcFpAxwoGnozfKOFfJk30a4+StvZDLdHplP113RXSSEgDwYZ13ULeMCxytPeBg5Vbg8REREdGb++mnn/Dll18CABYsWIApU6ZIHFHRZFKJLqDTG4IrN4IgvNLvVX8dADhal4C3R2CeRGcojS4Vf93eAwA4/cAeN19YAQAuRNghQZ12See0rw1vD9cCjYuIiIjyRqdOnfD9998jKSkJPXv2lDqcIsvEEl3DaXUaHLi8AlEJj6UOJYuU1ETxb5miDHr9N4d1r//KvF3tULeMiwSRERER0ZsIDw+HTqdDmTJlAAByuRzr16+Hra0tFAqFxNEVXUx0XyM68YlBSa6dhXMBRKMvJTVB/LuMkzMG161Y4DEQERFR3ti2bRsmTJgAPz8/7N+/X0xsHRwcpA2sGDC5RFdm6IQRmXo4VHSvBVfbMlk2sVTZwtOx4JPMFE1Gi65OUBX48YmIiCjvREREID4+HufOncPff/+NevXqSR1SsWFyia4hdIJWr29uRffacLPLmugWhISUlwh9fBqp2mQAaZM/ONt4YeVZb2iFJLSrwgkciIiIirIxY8bg5s2bGDx4MGrVqiV1OMUKE91XRCc8xS//rkGKJiH3jQvApQdHcetpxpi4Xk6+cLByx70YG7xIVKJtFaWE0REREZExoqOjMX36dHz22WcoUSJt1lIzMzOsXr1a4siKJya6r4iIuamX5Mogh425o2TxJKnT5rJWyJWwNreHmZyJLRERUVEUFxeHoKAgPH78GBEREdi5cyfHw81nnDDiFenDhskgQ4N3uqBdjVGwMreTLp7/Ogu725VF54BP4WFfXrJYiIiI6M3Z2tqiU6dOAAAPDw+o1dkPYUp5x6RadM1kVpDLch6iQyv8N6GETIaKHrULIKqcCULapBD8xkdERFT0xMXFwdbWVlyeMmUKgoODERwcLGFUpsOkWnTlMnmOCeOtJ+dw8f6vBRhR7jJamE3qUhERERVpSUlJmDx5MurVq4eYmBix3MLCgkluAWL2lMm9F1fEv+0sCseECzpBCwCQyXipiIiIioq//voL33zzDSIiIjBjxgypwzFZJtV1IXcZg+e+6z84346SoklEslp/VIdkTQIeRd2ADmldFcq5VIWzjScsVbawVNpDozPHvah4cXutYPhUxkRERFSwmjVrhp49eyIyMhL/+9//pA7HZJlUoqvWJYgtpJkJgoBUbQp0urQk09PRG1Yq2yzb5YXnceE4GLIq2zgyc7Ryh7ONJ/566I/PD2kBqAH8mC8xERER0du5fv06UlJSUL16dbFs8eLFUKlUvM9GQiaV6Ka12Oo/2QRBh4MhqxAZ96BAIrA2d8S7VQbh34d/4GH0jWy3sVTZQvHfMGKHrj/KcX9+Hg55HSIREREZ4dtvv8WUKVPg5eWF48ePw9raGgBgbm4ucWRkYoluVvEp0VmS3Lzon6sTdLj04AhiEp5mFMpkCPbtC5WZBXxLNoBvyQZ6dZQKc7jals72m1+Dsq74tKmfXllJeysEeDm9daxERET05qytraFWq/Ho0SNcuHABjRo1kjok+o/JJ7qZu7qWdfFHOZdq8HT0eev9Pnl5FyHhv79SmpbAKuRm8HT0Nmp/Je2t0L5KqbeOi4iIiPJWjx49cPv2bXTv3h0+Pm+fQ1DeMdlb+QVBQIomCanaZLGsglsAyrhUgZni7WcfS9Vk7NfVtjQ87MvDw77cW++XiIiIpBMREYHevXvj2rVrYplMJsPnn3/OJLcQMskWXa1Og/2XliM68UmBHK9Jpd6wNrcvkGMRERFR/khKSkLz5s3x5MkTPHr0CL/++itUKpXUYVEOTLJFNybxWbZJrp2lswTREBERUVFgaWmJUaNGQSaToXHjxuKkTlR4mVyLrgwyZB4v19+rCewtXeFo7QF7S1fpAiMiIqJC59GjR/D09BSXP/roI9SvXx/VqlWTMCoylEm26GYew9bT0RvvuAfA2cYzhxrGc7T2QGD5dggs3w4qhUWe7puIiIjyV2xsLEaMGIF69erh/v37YrlcLmeSW4SYVKKrlFkjJvEpDlxeme/HsrN0QeWSDVC5ZAMozTiOHhERUVFy9+5d7Ny5E/Hx8Zg5c6bU4dAbMqmuCzKZDM/iwzKWIYOtBfvlEhERkb7q1atj7NixuH//PubOnSt1OPSGTCrRBQAhU//c9jXGcDQEIiIiwt9//42XL1+iRYsWYtmECRMgl5vUj9/FjkkluoKgg06XCgCQyxRwtPbIt2M9i72PKw+PAwDqV+wCC6V1vh2LiIiI3tyKFSswbdo0ODg44K+//oKbmxsAMMktBkzqCqYKibj6+HiBHCtRHYsHUdfwIOoatDqNQXU0Wh1SNFq9fzoOXUJERJSvqlSpAp1Oh9TUVFy/fl3qcCgPmVSLbmZ2li6SHTv06Us8fJmoV/ZL6CN8dZwvLiIiovwmCAJkMpm43LhxYyxatAgtWrSAl5eXhJFRXjO5RNfZ2hN+Xo1Q0qGCJMc/HBqB1muPGV2vgottPkRDRERkWm7duoURI0Zg8uTJaNy4sVj+wQcfSBgV5ReTS3StVA4o7yrd+HeXHkXluN5aZYb57QL0yhwslehQpVR+hkVERFTspaamokuXLnj48CFGjhyJU6dOwc7OTuqwKB+ZXKJbWJibyXH+kzZ6ZRZKBco7s+WWiIgoPyiVSsyaNQuDBw/GwIEDYW3NG8WLO5NLdBNSoqUOAQAgl8lQ2cNB6jCIiIiKLUEQcOXKFfj7+4tl7du3R82aNdkX10SYXKLr6eBb4Mc8cO0h5PIYAMDukPs5b0xERERv7dmzZ/j4449x5MgRHD58GDVr1hTXMck1HSaX6MplBTOi2qOYjFEVhv1wFjHJygI5LhEREQFJSUk4efIktFotFi1ahC1btkgdEknA5BLdzMOJ5JWnsfcQGftAXPawL4cnCXZYfibtBrIEtSJLnY68uYyIiCjflClTBrNnz8bVq1cxdepUqcMhiZhcogvkbaKbkBKDw/+uhU7QimW1yraGXF4WFx+n3cl5Zsx78LS3Etcr5DK421rmaRxERESm7MiRI3j69Cn69OkjlvXt21fCiKgwMLlEV5bHie6Tl2Fikpu276z797C1RMlMiS4RERHlnaVLl2L69OmwsLBAYGAgvL29pQ6JCgmTmgI4PzyPCwcAmJtZo1+DL9E/6EtU8WokcVRERESmo3nz5lCpVLCzs8OzZ8+kDocKEZNr0UUe9tE9f+8Qbj09DwBwtfXKl/6/REREpC8lJQUqlUr83K1cuTLWr1+PwMBAODs7SxwdFSaStuimpKRg0qRJqFWrFoKCgrBu3brXbnvkyBG89957qFGjBnr27ImrV6++0TE12pQ3DTeLJHUcNDo1AMDdvlye7ZeIiIiyd/HiRTRu3Bjbt2/XK3/vvfeY5FIWkrbozp8/H1euXMHGjRsRERGBCRMmoGTJkmjVqpXedrdu3cK4ceMwc+ZM1KxZExs2bMBHH32EI0eOwNLSuJu67C3dDN72wYtrePzyjricnBqP6IQnCCzfDiUd3kEJ+wowk6tgqbJBpRL1jIqDiIiIjKPT6TB8+HDcvHkTn332GYKDg+Hu7i51WFSISZboJiYmYteuXVi7di38/Pzg5+eHW7duYcuWLVkS3VOnTuGdd95Bx44dAQBjx47Fli1bcPv2bb3ZTgxh6M1oyanx+P369xCgy7IuvVX4HfcAvOMeYNTxiYiI6M3I5XIsWbIE3bt3x/Tp0+HmZnjjFZkmybouhIaGQqPRoEaNGmJZQEAALl++DJ1OP7l0cHDA7du3ceHCBeh0OuzZswc2NjYoXbp0vsQWm/Qcf97cJSa5lipb2Jg7wcHKDeVda8DGwilfjktEREQZtFotfv/9d72ywMBAhISEYMCAAbw3hnIlWYtuZGQkHB0doVKpxDIXFxekpKQgJiYGTk4ZyWTr1q3x22+/oVevXlAoFJDL5Vi9ejXs7e2NPq5anYrExMQct/n34Z96E0AEle8BR6sSetvkto+UlIy+wEnJSUhM5IuxICUlJen9T8Ubr7dp4fU2DRERERg1ahTOnj2LmTNnomzZsuI6MzOzXD+HqWgSBCFPv8BIlugmJSXpJbkAxGW1Wq1XHh0djcjISEydOhXVqlXDtm3bMHHiRPz4449Gdzx/+vQpEp/nfAItUBplzBzwQHcalnJHPL4XjSeyGKOO8/BhrPj37Vu3EWfNKYClcO/ePalDoALE621aeL2Lt9jYWNy6dQsAcODAAdSrx3thTMWr+eHbkCzRNTc3z5LQpi9bWFjolS9cuBDe3t7o3bs3AOCLL77Ae++9h927d2PIkCFGHdfD3QOlXX0N2rYG6hi178xuCY8APAQAvFPxHXhxwogClZSUhHv37qFs2bJG37BIRQ+vt2nh9TYdS5Yswblz5/Dee+/xepuI9C83eUWyRNfd3R3R0dHQaDQwM0sLIzIyEhYWFrCzs9Pb9urVq3rT+MnlclSqVAkRERFGH9fc3BxWVvmfdJqbm4t/W1pYFsgxKStLS557U8LrbVp4vYsPQRCwZ88e3L59GxMmTBDL27Rpg6ZNm+L69eu83iYir/tdS3Yzmq+vL8zMzHDp0iWx7MKFC/D394dcrh+Wm5sb7ty5o1cWFhYGLy8v4w9swAl8+vIewqNCERVvfCJNRERExlm2bBkGDx6M+fPn49SpU1KHQ8WIZImupaUlOnbsiOnTpyMkJARHjx7FunXr0K9fPwBprbvJyckAgO7du2Pnzp3Yu3cv7t+/j4ULFyIiIgKdOnUy+riGfE/4++4+HLu2AVcjThq9fyIiIjJOt27d4ODggNKlS4u/8hLlBUmfTRMnTsT06dPRv39/2NjYYNSoUWjZsiUAICgoCHPmzEHnzp3RunVrJCQkYPXq1Xjy5Al8fX2xcePGN5wBJfdUV4DwBvslIiIiQ8TGxsLKykpMakuUKIGdO3fCx8cHtra2EkdHxYmkia6lpSXmzZuHefPmZVl348YNveVu3bqhW7dub31MwyaMYKJLRESUH06cOIGRI0eif//+GDdunFheq1YtCaOi4kqyrgtSMaaTs6GzqBEREVHuBEHAnDlz8PDhQ8ybNw8PHjzIvRLRWzC5RFepyH1oErbnEhER5T2ZTIbly5ejVKlSWLt2bb7NcEqUzuQSXYPaaAWmukRERG9LrVZj8+bNEDJ9rlaoUAHnz59Hhw4dJIyMTIXp3dpoTNcFzqFNRET0Rh4+fIhevXrhypUrUKvVGDhwoLhOqeRsoVQwTK5Fl4iIiPKfq6srdDodAODYsWN6rbpEBcXkWnQNucHM3b4crM0d4GhdogAiIiIiKn7Mzc3xzTff4PTp0xg0aBB/JSVJmFyia0gv3boV2G+IiIjIUDqdDt999x0uXbqE5cuXi0mtv78//P39JY6OTJnJJbr8QklERJS3Vq1ahSlTpgAAgoOD0aVLF4kjIkpjgn10mekSERHlpb59+6JMmTLw8/ODj4+P1OEQiUyuRdcQdyMvQ61JgoOVGzzsy0sdDhERUaHy7NkzWFlZwcbGBgBga2uL3bt3w9PTE+bm5hJHR5TB5Fp0DbkZ7fKDYzhzZy/uRl7K/4CIiIiKkJ9//hkNGjTA1KlT9crLly/PJJcKHZNLdA2TPgQKuzkQERFltmfPHrx48QIbN27EjRs3pA6HKEcml+gaMrwJR/ojIiLK3qJFi1CzZk3s2bOH/XGp0DPBRFdh+Lb5GAcREVFhFx8fjwULFiAlJUUsc3Z2xpEjR9C4cWMJIyMyjMndjGZY8squC0REZNoeP36MNm3a4N69e0hKStLrk8vJH6ioMLkWXYOS1//yXL6OiYjIVLm7u6NMmTIAgHv37onT+RIVJSbXoktERETZ02q1UCjSuvjJ5XIsW7YMZ86cQZcuXdiKS0WSCbbo5k5g1wUiIjIhqampmD9/Pjp16gStViuWe3l5oWvXrkxyqcgyuUTXkBdredfq8PGoC3e7svkfEBERkcS2bNmCuXPn4uTJk1i5cqXU4RDlGXZdyEaNMi2kDoGIiKjA9O7dG5s3b4a5uTnatm0rdThEecYEE928/flFEAQM330Wv916old++3lcnh6HiIgor9y/fx/m5ubw8PAAACiVSmzfvh1OTk5iH12i4sDkui7ktdvP47Dm9C3cfh6n9y+dSiGHnYVSwgiJiIgybN68GQ0bNsTo0aMhCBlTJLm6ujLJpWLH5Fp0DWnPvfLwOFI0SXCzK4NSTr45bqvWZgy30t7PC6UcrPXWt6xUEvaWqjcJlYiIKM+FhoYiPj4ev//+Oy5fvozq1atLHRJRvjG5RNeQVDf08RnEp0TDt2SDXBPdzEYEVUJz7xJvExwREVG+mjJlCh48eIBPPvmESS4Ve6bXdcGILrocTIWIiIqyFy9e4NNPP0VMTIxYZmlpic2bN6NmzZrSBUZUQEyuRVdmQPqaMY4uERFR0fT8+XM0bNgQT58+RVxcHFavXi11SEQFzvRadI3CNl0iIiqaXFxcEBwcDCCtFVej0UgcEVHBM7kWXcOktegmp2rxPD5ZLP3pajiO3HgMeaZJJ649jSno4IiIiLKVmJgIKysrcXnOnDno3LkzmjVrJmFURNIxwUQ391bamCQ1lHLgm79uYueVWIP3bKXksCxERFTwEhMTMXPmTPz+++/4/fffxWTXzs6OSS6ZNJNLdA3pjJCUqoXSPOdt6pZx0Vuu7umEOq+UERERFYTDhw9jzZo1AID58+dj+vTp0gZEVEiYXKJrmLSuC6UdbbC5d5DemvLONqhT2gUyGfvvEhFR4dCxY0f88MMP0Ol0GDZsmNThEBUappfoGpCg/vXAE/EpiaheqjR61SxXAEEREREZ7tq1a1AoFPDx8QEAyGQyrF27FpaWlmyIIcqEoy5k48Jjdxy65Yr4VDepQyEiItLzzTffIDg4GEOHDkVqaqpYbmVlxSSX6BUml+gaMo4uERFRYSUIAtRqNW7cuIGLFy9KHQ5RoWZ6XReIiIiKsKFDh+LBgwf48MMP4e3tLXU4RIWaybXoGqLVO2EYXOshHFThUodCREQm7OHDh+jXrx8ePHgglsnlcsydO5dJLpEBTLBFN/euCz4u0bBRpeJp4ssCiIeIiCir2NhYNGnSBFFRUYiJicHevXshl7N9isgYJveKYT99IiIqCuzs7PDhhx9CLpcjICAAWq1W6pCIihy26GZLyPcoiIiIXvXs2TO4uWWM+PPpp5+iVatWqFmzpoRRERVdJteia4j0VJjpLhERFYSXL19i6NChaNCgAZ49eyaWq1QqJrlEb4GJbo7Yz4GIiPLf5cuXsXPnTrx48QJffvml1OEQFRsm13WB4+gSEVFh06hRIwwaNAjx8fGYMWOG1OEQFRsml+gagjesERFRfjpz5gwAoG7dumLZnDlzoFAopAqJqFgyua4LnB6RiIiktHjxYrRp0wZDhgxBbGysWM4klyjvmVyia4iDt8ph9TkvxKR4SR0KEREVM+XLl4cgCIiNjcW1a9ekDoeoWGPXhWzceO6Euy/iUaecvdShEBFREScIgt6viR07dkR4eDg6d+4MT09PCSMjKv7YoktERJRPbt68iffeew8XL17UKx81ahSTXKICwESXiIgoHyQlJaFt27b4+++/MWzYMCQlJUkdEpHJYaKbjR5VQvFZozA4md+TOhQiIiqiLC0tMXnyZKhUKvTq1QsqlUrqkIhMDvvoZsPTLh4WZlo8TuC3byIiMowgCLhx4wYqVaoklvXr1w+NGjVCuXLlJIyMyHSZWIsuhxYjIqK89+TJE/Ts2RPBwcG4efOmWC6TyZjkEknIxBJdw8ggSB0CEREVIVFRUfjjjz+QnJyMhQsXSh0OEf3HxLouMIElIqK8V7lyZUyaNAlPnjzB559/LnU4RPQfE0t0jcWuDkRElNWvv/6KpKQkdOjQQSwbPXq0hBERUXaY6GaDswQTEdHrzJs3D/PmzYOdnR0CAgLg5cVZNIkKK/bRzQE7OhAR0auaNGkCuVwOS0tLPHr0SOpwiCgHbNElIiLKQWpqKpRKpbhcp04drF69Gk2bNoWTk5OEkRFRbtiim40tIZUw53g5RKeUljoUIiKS0D///IOgoCD88ssveuVdunRhkktUBDDRzUZEnC1uR1khVWcldShERCQRtVqN/v3749atW/j444/x8uVLqUMiIiO9caKrVqtx9+5daDQapKam5mVMREREklOpVFi8eDFsbGwwadIk2NnZSR0SERnJ6ERXEAQsXLgQtWvXRtu2bfH48WNMmDABkydPZsJLRERFllarxZ9//qlX1qJFC1y+fBn9+vWDjEPyEBU5Rie6mzdvxk8//YRp06ZBpVIBAJo3b46jR49i+fLleR6gFIbVvoT5796Es8VdqUMhIqICEB4ejrZt26Jjx444ffq03jpHR0eJoiKit2V0ortjxw5MnToVnTt3Fr/dtm7dGrNmzcK+ffvyPMC8Zdi3cXtzNZytUiGXsYWaiMgUmJmZ4caNGxAEAWvXrpU6HCLKI0YPL/bw4UP4+vpmKa9UqRIiIyPzJKjCgz9TERGZghIlSmDBggW4e/cuPvnkE6nDIaI8YnSi6+npiX///TfLTDAnTpxAqVKl8iwwIiKi/CAIAnbu3ImoqCgMGzZMLO/SpYuEURFRfjA60R04cCBmzJiByMhICIKA06dPY8eOHdi8eTM+++yz/IgxDxk215lMxjnRiIiKq7lz52LBggVQKpUICgqCv7+/1CERUT4xOtHt0qULNBoNvvnmGyQnJ2Pq1KlwcnLCxx9/jJ49e+ZHjNJhvktEVOx0794dK1asgLu7O0cLIirmjE50IyIi0K1bN7z//vuIioqCIAhwdnaGRqNBSEgIqlatmh9xEhERvZHY2FjY2tqKN1BXqFABO3bsQLVq1WBjYyNxdESUn4wedaFZs2aIiYkBADg5OcHZ2RlA2k1qffv2zdPgpCLL5i8iIip6fv/9d9SvXx/r16/XK2/QoAGTXCITYFCL7pYtW7Bu3ToAaZ34u3TpArlcP0eOjY1FyZIl8z5CIiKiN6DVavH5558jIiICU6dORdu2beHm5iZ1WERUgAxKdDt37ozo6GgIgoAVK1agVatWsLa21tvG2toaLVu2zJcgC9qys9XxICYR/WqXlToUIiJ6QwqFAitXrkTPnj0xb948JrlEJsigRNfS0hIjR44EAMhkMgwcOBCWlpb5GpiU4tTmiE5KhU5QSh0KEREZKCUlBT///DO6desmllWtWhX//PMPzM3NJYyMiKRidB/dkSNHQqlU4unTp4iIiEBERAQePXqEsLAw/Pzzz0btKyUlBZMmTUKtWrUQFBQkdo/Izo0bN9CzZ09UrVoV7dq1w5kzZ4wNnYiIiql79+6hWbNm+Oijj7B79269dUxyiUyX0aMunDx5EhMmTEBUVFSWdRYWFmjfvr3B+5o/fz6uXLmCjRs3IiIiAhMmTEDJkiXRqlUrve3i4uLw4YcfIjg4GHPnzsVPP/2EkSNH4vDhw+LNcHlFrUlGXa8I3I/iTQpEREWFs7Mz4uPjAQAHDhzg5A9EBOANWnQXL16MypUrY/Xq1bCwsMDy5csxadIk2NjYYMGCBQbvJzExEbt27cLkyZPh5+eHFi1aYNCgQdiyZUuWbX/88UdYWVlh+vTpKFOmDEaPHo0yZcrgypUrxoafq7DIy2hWPhxrOl6Hk/m9PN8/ERHlPVtbW6xcuRLz58/Ht99+K3U4RFRIGN2ie/v2bXz55ZeoVKkSfH19YWVlhb59+8LKygrfffcdmjdvbtB+QkNDodFoUKNGDbEsICAAq1atgk6n0xvV4e+//0azZs2gUCjEsld/mjJMzsOFxSY9x+k7P4rLMWrPNzgGERHlJ51Ohz179kCj0WDevHlief369VG/fn0JIyOiwsboFl2FQgFbW1sAQJkyZXDz5k0AQN26dXHnzh2D9xMZGQlHR0eoVCqxzMXFBSkpKeI4venCw8Ph5OSEzz//HA0aNED37t1x4cIFY0PP1bmwA+LfMclmvBmNiKgQWrJkCb755husXbsWx44dkzocIirEjG7RrVixIn777Tf07dsX5cuXx4ULF9C/f388efLEqP0kJSXpJbkAxGW1Wq1XnpiYiDVr1qBfv35Yu3YtDhw4gIEDB+LQoUMoUaKEEUcVkJSUlKX03osQPIy5hscvb4tlWy+XQGDpVCQmJhqxfyos0q9zdtebih9eb9PSrVs3fPfddyhRogQcHBz4Pl3M8fVtWgRBEGcxzAtGJ7pDhgzB6NGjoVQq0bZtWyxbtgxDhgzBjRs3ULduXYP3Y25uniWhTV+2sLDQK1coFPD19cXo0aMBAJUrV8apU6fw008/YejQoUbFf+/ePb1lnaDFteSfIUAnli0/7YOLT8zwjk00rl+/btT+qXB59XpT8cbrXTxFRUXBxsZGr3FkwYIF8PRM617G92nTwNe36Xi1IfRtGJ3oNm/eHLt27YJCoUCJEiXw7bffYv369WjWrJmYiBrC3d0d0dHR0Gg0MDNLCyMyMhIWFhaws7PT29bV1RXly5fXKytbtiweP35sbPgoW7as3hjAqVo1Uh8H4t6Ly9AJGviVaIKYlJcAEuDk5AhfX1+jj0HSS0pKwr1797JcbyqeeL2Lr3379mHixIno06cPPvvsMwAZLXu83qaBr2/TcuvWrTzdn9GJLgD4+fmJfwcGBiIwMBAAcPXqVTg4OBi0D19fX5iZmeHSpUuoVasWAODChQvw9/fPMr1w9erVce7cOb2yu3fvom3btkbHbmlpCSsrq0wlVqhv2xH10VEskcnTbkgzUypf2ZaKmqzXm4ozXu/iRRAEbN++HTExMVi1ahWGDBkCLy8vcT2vt2nh9TYNedltATDiZrSQkBDMmzcPixYtQmhoqN66lJQUzJs3D++//77BB7a0tETHjh0xffp0hISE4OjRo1i3bh369esHIK11Nzk5GQDQo0cP3LhxA8uWLcP9+/exZMkShIeHo0OHDgYfLzspqYlIUsdDrUl+q/0QEVHek8lkWLZsGfz8/LB79269JJeIyBAGJboHDx5Ejx49sG3bNmzbtg1du3YVW1gvXryItm3bYv369UZNFgEAEydOhJ+fH/r3748ZM2Zg1KhRaNmyJQAgKCgIBw8eBAB4enri22+/xe+//462bdvi999/x5o1a+Du7m7U8V71R+hW7Ph7Fv68ueOt9kNERG8vLi4OS5YsgVarFcu8vLxw4sQJNGzYUMLIiKioMqjrwtq1a9G8eXMsXLgQcrkcc+fOxddff40PP/wQY8aMgYeHB9avX4969eoZdXBLS0vMmzdPbxzEdDdu3NBbDggIwJ49e4zaf24ybkDL22ZyIiIyzr1799CxY0c8ePAAMplM756PvP4pk4hMh0Etuvfu3cOwYcOgUqlgZmaG0aNH4/Lly5gyZQrat2+Pn3/+2egktzAQBAEA30SJiKTm6ekpTul+/fp18f2ZiOhtGNSim5SUBFdXV3HZzs4OZmZmaNOmDaZMmZJvweU3Af8lusbPm0FERG8p8yyYSqUSK1euxJUrV9ClSxeJIyOi4sLgURdebfWUyWRG3XxWKLFFl4iowKWmpmLBggW4du0aNm/eLL4H+/j4wMfHR+LoiKg4eaumzFcndij89BNa3X99dGXso0tEVGBWrVqFhQsX4uDBg9iyZYvU4RBRMWZwi+7Fixdhb28vLguCgJCQkCxT/9auXTvvostvbNElIipwgwYNwvfffw9XV1eOpkBE+crgRHfUqFFZbg4YN26c3rJMJitSUzFm9NFloktElF/CwsLg6OgoTihkaWmJvXv3wt3dPcsEQUREecmgRPfYsWP5HYckzM2sYKG0gdKsqHXBICIq/ARBwIYNGzB16lS0adMGq1atEteVKFFCwsiIyFQYlOh6enrmdxwFRL9FumWVgRLFQURU/MlkMpw/fx4JCQnYs2cPxo0bh4oVK0odFhGZEP5mRERE+WbOnDlo1qwZjhw5wiSXiAocE10iIsoTz58/x8SJE5GYmCiW2dnZYdeuXahWrZqEkRGRqTL4ZrTiKCrhMXQ6LSyUNrCxcJA6HCKiIuvhw4do1qwZIiMjIQgC5s6dK3VIRESm3aL7x/Ut2H95Oa48Oi51KERERZqnpycCAgIAABqNhlP4ElGh8EYtus+ePcPOnTtx9+5dTJ48GefOnYO3tzfKly+f1/HlK4ETRhARvbHk5GRx4iCZTIavvvoKAwcORLNmzSSOjIgojdEtuvfv30e7du3w448/4tdff0ViYiIOHjyILl264PLly/kRY74ROGEEEZHREhMTMWHCBLRu3Rqpqaliubu7O5NcIipUjE50586di+bNm+Po0aNQKpUAgMWLFyM4OBgLFy7M8wDzE1t0iYiMt3PnTqxduxaXLl3CsmXLpA6HiOi1jE50//nnH3zwwQd6raBmZmYYPnw4rl27lqfB5TexD5nMpLsqExEZpV+/fqhfvz5at26NPn36SB0OEdFrGd1HV6fTQafTZSlPSEiAQqHIk6Dyj37LLbsuEBHl7sqVK7C3t0epUqUAAHK5HNu2bYONjQ3fP4moUDO6KTMoKAirV6/WS3ZjYmKwYMEC1K1bN0+Dy2/CfzOlsesCEVH2li5dimbNmmHEiBF67/u2trZMcomo0DM60f3ss89w5coVBAUFISUlBcOGDUPTpk3x8OFDTJgwIT9izDeC8F8fXb5ZExFlKy4uDqmpqbhw4UKR655GRGR01wV3d3fs3bsX+/fvx/Xr16HT6dCzZ0906NABNjY2+RFjvnGyKYlUTTKsVPZSh0JEVCiNHz8ez549w+jRo1GhQgWpwyEiMorRie6SJUvQuXNndOvWLT/iyWf6A5i/W2WQRHEQERU+Dx8+xOeff4758+fD1dUVAKBSqbBkyRKJIyMiejNGJ7r79u3DqlWrULNmTXTu3BmtWrWCtbV1fsRGREQFJDIyEkFBQYiNjYVGo8GmTZvYrYuIijyj++gePXoUW7Zsgbe3NxYuXIigoCD873//w+nTp/MjPiIiKgCurq7o1q0b5HI5fHx8sh1dh4ioqHmjKYBr1qyJmjVrYvLkyTh58iQOHDiAESNGwMHBAb/99ltex5hv7j+/AgBwsHaHvaWrxNEQERWsqKgoODk5icvTp09Hjx49EBAQIGFURER5561mSoiKikJYWBjCw8ORkpKCMmXK5FVcBeL4jW34PfR7hL/gncREZDpiYmIwZMgQNG3aFLGxsWK5tbU1k1wiKlaMbtGNj4/H4cOHsW/fPpw7dw4lS5ZEp06d8NVXX6FEiRL5EWOeEwQBFx8cgU7Q/lfCfmhEZDr+/PNP/PDDDwCAhQsXYubMmRJHRESUP4xOdOvXrw+lUomWLVti48aNqFWrVn7Ela+evLyLkPCMLhYqMwsJoyEiKljt2rVDly5dYG5ujk8//VTqcIiI8o3Rie6MGTPQqlUrWFpa5kc8+U6r0+DwlbXickX32ijrUlXCiIiI8tfp06dhbW2NqlUz3uu++eYbmJm90W0aRERFhkHvcufOnUONGjVgZmYGLy8vXLly5bXb1q5dO8+Cyw8RL2+Kf5dx9kODil0kjIaIKH/NmTMHCxcuhLe3N37//XdYWKT9gsUkl4hMgUHvdH379sWpU6fg7OyMvn37QiaTQRCELNvJZDJcv349z4PMOzIkqGMggxwCdAgo21rqgIiI8lWJEiUgCAKePHmC0NBQVK9eXeqQiIgKjEGJ7rFjx+Do6Cj+XZSVd66JqMRwxCZHwc7SWepwiIjyVf/+/fH06VP07dsXJUuWlDocIqICZdDwYp6enpDL0zZdvnw57O3t4enpqffP2toas2fPztdg84LKzAJuduVQt0IHqUMhIspToaGhaNOmDW7duiWWyWQyTJgwgUkuEZkkg1p0L1y4gPDwcADA3r174efnBxsbG71t7ty5UwRmRxPwPD4cVUs1kToQIqI89fLlS7z77ruIi4vD8OHDcejQIfbDJSKTZ9C7oEwmw2effSb+PWvWrCzbWFlZYeDAgXkbXT54Fn8Ppd18pA6DiChP2dvb45NPPsG8efPQoUMHyGQcH5yIyKBEt2bNmggNDQUAVKpUCSdPnoSLi0u+BpZflHJzqUMgInprgiAgLCwM5cuXF8tGjRqFtm3b4p133pEwMiKiwsPoKYBDQ0OLbJILAEoFJ4cgoqLt8ePHeP/99xEcHIxHjx6J5QqFgkkuEVEmBrXo9uvXD8uXL4ednR369euX47abNm3Kk8Dyi1KhkjoEIqK3cv/+fRw7dgyCIGDx4sVYtGiR1CERERVKBiW6mUddKFmyZJHu+yWXKaQOgYjordStWxejR4+GRqPB5MmTpQ6HiKjQMijRnTNnjvj33Llz8y2YgsBEl4iKml9++QVKpRLNmjUTy6ZOnVqkGx2IiAqC0X10AeCff/5BVFQUgLThxj766COsXr0629nSChuZ7I0eMhGRJGbMmIFevXph5MiR4vsuACa5REQGMDrr2759O3r37o0bN24gNDQUEydORGpqKjZs2IAVK1bkR4x5ii26RFSU1K9fH0BaYnv//n2JoyEiKlqMTnQ3btyIKVOmoF69ejh48CAqVqyIdevWYf78+dizZ09+xJinrFT2UodARPRaWq1Wb7lFixZYunQpTp06hRo1akgUFRFR0WR0ovvw4UMEBwcDAE6dOoVGjRoBACpUqIDnz5/nbXR5TgYrlZ3UQRARZevcuXOoX78+zpw5o1fep08fODo6ShQVEVHRZXSi6+zsjGfPniEyMhLXr19HgwYNABT98XWJiKSUkJCAXr164datWxg+fDgSExOlDomIqMgzOtFt06YNPv30UwwcOBAeHh4IDAzEwYMHMXnyZLRp0yY/YiQiKvasra3x5ZdfwsbGBuPGjYOlpaXUIRERFXkGDS+W2bhx4+Dh4YHw8HD07t0bCoUCL168QI8ePTBq1Kj8iDEPCUhOTYAVrKQOhIhMnFarxYULFxAYGCiWde3aFU2aNIGrq6uEkRERFR9GJ7pyuRx9+/bVK3t1uTDT6NRSh0BEJi4sLAxDhw7FpUuXcOzYMVSpUgVA2sgKTHKJiPLOGw0qe+zYMXTv3h3Vq1dHrVq10KNHDxw5ciSvY8sXMnDsSSKSlk6nw5UrV5CamopVq1ZJHQ4RUbFldIvur7/+ijFjxqBZs2Zo06YNBEHAuXPnMGbMGCxbtkxv5p5CiYOsE5HEKlSogFmzZiE6OhqjR4+WOhwiomLL6ER35cqVGDFiBEaOHCmWDRgwAMuXL8eqVasKfaLLFl0iKkiCIGD79u0QBAG9evUSyz/44AMJoyIiMg1Gd124e/cu2rVrl6W8bdu2uHnzZp4ElZ84bSYRFaSpU6dixIgRmDBhAsLCwqQOh4jIpBid6Lq5uWU7DeX9+/dha2ubJ0HlJ7boElFB6tq1K8zMzODu7o64uDipwyEiMilGJ7pt27bF9OnTcfz4ccTHxyM+Ph7Hjx/HjBkz0Lp16/yIMY8x0SWi/JOQkKC3XK1aNWzduhXHjx9H1apVJYqKiMg0Gd1Hd9iwYbh58yY++ugjsRuAIAho0qQJxo4dm+cB5jV2XSCi/HLs2DGMHj0as2bNQqdOncTy5s2bSxgVEZHpMjrRNTc3x8qVK3Hnzh3cvHkTgiDAx8cHFSpUyI/48pyZXCV1CERUDKnVaowbNw6PHz/Gp59+imbNmsHOzk7qsIiITJrBie6TJ09w5MgRqFQqNG7cGBUqVCgyyW0GGRRyo3N7IqJcqVQqLF++HIMHD8aiRYuY5BIRFQIGZX3nz5/HoEGDkJycDACwsrLC0qVLERQUlK/BEREVVsnJyThy5IjeKDRBQUH4559/YGlpKWFkRESUzqCb0ZYsWYJ69erhxIkTOHXqFBo2bIi5c+fmd2xERIXSrVu3EBwcjP79++PYsWN665jkEhEVHgYluteuXcO4cePg5uYGZ2dnTJo0CXfu3EF8fHx+x5fHBGh1WqmDIKIiztHREc+fPwcA7NmzR+JoiIjodQzqupCYmAgHBwdx2d3dHUqlEi9fvoSNjU1+xZZPBKkDIKIizsXFBUuXLsXjx48xYMAAqcMhIqLXMCjRFQQhy7BcCoUCOp0uX4IiIiosdDodvvnmGyQkJOB///ufWN6qVSsJoyIiIkNwCAIiohzMnDkTS5cuhVwuR9OmTVG7dm2pQyIiIgMZnOiuW7dO7yYLjUaDTZs2wd7eXm+7kSNH5l10+YITRhCR4QYNGoR169ahQoUKRWKacyIiymBQoluyZEkcOnRIr8zV1TXL3cYymawIJLpERK/37NkzODs7Q6FQAAC8vLywb98+VK5cGUqlUuLoiIjIGAYlur/99lt+x0FEJLndu3dj/Pjx+OSTTzBq1CixvFq1ahJGRUREb8qg4cWKExl7LhBRNrRaLVauXImYmBjMnTsXkZGRUodERERvyeQSXSKi7CgUCqxYsQLe3t7YuXMnXF1dpQ6JiIjekoklujLITO0hE1G2YmNjsWbNGghCxtjalSpVwl9//YUGDRpIGBkREeUVkxte7NXxgInI9Ny4cQPdu3dHeHg4rKys0KdPH3GdXM4vw0RExcVbvaOr1eq8ioOIqMCUKlUK5ubmAIALFy5IHA0REeWXN2rR3bZtG9auXYsnT57g8OHD+Pbbb+Hu7o7hw4fndXxERHki8wyPVlZW+Oabb/DgwQN06tRJ4siIiCi/GN2iu2/fPixatAidOnUSx5SsUKECVq1ahXXr1uV5gHlLgE7gtMVEpkStVmP27NlZxvgOCAhgkktEVMwZneiuW7cOkydPxqhRo8S+bP369cPUqVOxY8eOPA+QiOhtLFy4EIsWLcK2bduwf/9+qcMhIqICZHSiGxYWhlq1amUpr1OnDh4/fmzUvlJSUjBp0iTUqlULQUFBBrUIP3z4EDVq1MDZs2eNOhYRmaYRI0agZMmSCAoKQtWqVaUOh4iICpDRfXRdXFwQFhaGUqVK6ZVfvHgRbm5uRu1r/vz5uHLlCjZu3IiIiAhMmDABJUuWRKtWrV5bZ/r06UhMTDQ2bCIyEXfv3oWHhwesrKwAAPb29jh06BA8PT05ogIRkYkx+l3//fffx8yZM3Hs2DEAaR8q27Ztw+zZs9G5c2eD95OYmIhdu3Zh8uTJ8PPzQ4sWLTBo0CBs2bLltXV+/vlnJCQkGBuyHhk4vBhRcSQIAjZs2IBGjRph5syZeutKlSrFJJeIyAQZ/c4/ePBgtG7dGmPHjkVSUhI++ugjzJ49G+3atcPQoUMN3k9oaCg0Gg1q1KghlgUEBODy5cvQ6bLeMBYdHY0FCxZk+QAjIkr3xx9/IDExERs2bMDDhw+lDoeIiCT2RsOLjR07FsOGDcPt27chCALKly8PGxsbo/YRGRkJR0dHqFQqsczFxQUpKSmIiYmBk5OT3vZz585Fp06dULFixTcJWZSUlJTrpBGCLm2mJE1qKrtJFFFJSUl6/1Pxlv66njlzJhISEjB9+nQ4OTnx9VtM8fVtWni9TUvmoSDzgtGJbkREhPi3s7MzgLSpNGNjYwEAJUuWNGg/SUlJekkuAHH51Yko/vrrL1y4cCFP7pi+d+9eridQnZp2/KioaFy/fv2tj0nSuXfvntQhUD6Jjo7GDz/8gA8++ABmZmlvZQkJCeKvPnztFn98fZsWXm/T8Wp++DaMTnSDg4NzTBQN/XAxNzfPktCmL1tYWIhlycnJmDp1KqZNm6ZX/qbKli0r3qTyOqpD9wGkwsnJEb6+vm99TCp4SUlJuHfvHsqWLQtLS0upw6E8dvfuXQwbNgwvXryAl5cXhg0bxuttQvj6Ni283qbl1q1bebo/oxPdTZs26S1rtVqEhYVhw4YN+Oyzzwzej7u7O6Kjo6HRaMTWmMjISFhYWMDOzk7cLiQkBOHh4Rg9erRe/cGDB6Njx45G9tmVwdLSMtdEVyZPS+TNlMpct6XCzZDrTUWPr68vvL29cfr0aSQkJIgffrzepoXX27TwepuGvOy2ALxBohsYGJilrF69eihVqhSWLVuG4OBgg/bj6+sLMzMzXLp0SRyX98KFC/D399e7O7pq1ar49ddf9eq2bNkSs2bNQoMGDYwNP89PIBEVDLVaLf6cpVAosGLFCoSFhaFp06bsi0tERNl6o5vRslO2bFmEhoYavL2lpSU6duyI6dOn48svv8SzZ8+wbt06zJkzB0Ba666trS0sLCxQpkyZLPXd3d3FPsJEVHzFx8dj+vTpuH37Nvbs2SN+ES5btizKli0rbXBERFSovdXNaOni4+OxevVqeHl5GbWviRMnYvr06ejfvz9sbGwwatQotGzZEgAQFBSEOXPmGDU2LxEVP+vWrRNnTVy/fj0GDhwocURERFRU5MnNaIIgwMrKCgsWLDBqX5aWlpg3bx7mzZuXZd2NGzdeWy+ndTkT3rAeEUll6NCh+OGHH1C2bFm0b99e6nCIiKgIeeub0QBAqVTC29sb1tbWeRIUEZmukJAQlChRAq6urgDShpnZt28f7Ozs2MeeiIiMYvTMaJs2bYKzszMCAwPFfzVq1GCSS0RvRRAELFy4EM2bN8e4ceMgCBm/wNjb2zPJJSIioxmd6J45cwbm5ub5EQsRmTCZTIanT59Co9Hgt99+Q1hYmNQhERFREWd0otupUycsXLgQt27dyjLhAxHR25g+fTq6deuGEydOoHz58lKHQ0RERZzRfXSPHz+OBw8e4PDhw9mu57SbRGSIBw8eYPbs2ViwYIE4SYy1tTVWr14tcWRERFRcGJ3oDhs2LD/iICITEh4ejqCgIMTHx0OlUmHZsmVSh0RERMWQQYmur68vTp48CWdnZ3Tq1Cm/YyKiYq5UqVJo2bIlfvrpJ5QoUQKCIPBmMyIiynMGJbqZ734mInoTL1++hL29vbi8YMECDBs2DAEBARJGRURExZnRN6MVbWwxIipoUVFR+PDDD9GqVSskJyeL5Y6OjkxyiYgoXxncR/fQoUOwsbHJdbuOHTu+TTxEVMwcOHAAe/fuBQAsW7YM48ePlzYgIiIyGQYnurNmzcp1G5lMxkSXiPT06dMHP/30Ezw9PTF06FCpwyEiIhNicKJ76tQpODs752csRFQMnDx5Eh4eHnjnnXcApH0B3rp1K1QqlcSRERGRqTGoj27xuRuaN9UR5acZM2agffv2GDZsGDQajVjOJJeIiKRgUKLLUReIyBDpEz/cvXsXt27dkjgaIiIydQZ1XejUqRPMzc3zOxYiKuJGjRqFly9fYujQofDw8JA6HCIiMnEGtejOmTPHoBEXiMh0XLt2DR07dsSjR4/EMjMzM0yfPp1JLhERFQomNo4uEeWFZ8+eoUWLFjhx4gTGjBnD7k1ERFQoMdElIqO5ublh0KBBsLCwQLNmzZjoEhFRoWTw8GJEZLoEQcDDhw9RqlQpsWzixIno06cPKlasKGFkREREr8cWXSLKUUREBLp164aWLVsiKipKLLewsGCSS0REhRoTXSLK0dWrV/Hbb7/h6dOnWLJkidThEBERGczEui4Ul4kviApOixYtMGDAANjZ2WHixIlSh0NERGQwE0t09ak1Why5+RhxKal65XdfxEsUEZH0Dh48CGdnZ9SpU0csW7RoUTGaIZGIiEyFSSe6nx34B0tOhEodBlGh8dlnn2HNmjUoW7YsTpw4IY6fzSSXiIiKIpPuoxv6LPa16yyVCrT29SzAaIikV7NmTQBASkoKHjx4IHE0REREb8fEWnSzH+uzSQV3bOvbUK/MWmUGa3NlQQRFJBmdTge5POP7brdu3fDy5Ut0794d9vb2EkZGRET09ky6RTedykwBN1tLvX9Mcqm4O3v2LIKCgnD16lWxTCaTYfDgwUxyiYioWGCiS2SCYmJi0K1bN4SGhmLo0KFITU3NvRIREVERw0SXyAQ5ODhgypQpsLW1xYgRI2BmZmK9mIiIyCQw0SUyARqNBpcvX9YrGzRoEM6ePYsePXpwVAUiIiqWmOgSFXO3b9/Ge++9h7Zt2yIsLEwsl8vl8PDwkDAyIiKi/MVEl6iYi4+Px+XLl5GQkIDVq1dLHQ4REVGBYcc8omKuevXqmDhxIuRyOUaOHCl1OERERAWGiS5RMSIIArZs2QIHBwe0bdtWLP/kk08kjIqIiEgaJpbo8oYbKt7Gjx+PdevWwcnJCbVr14a7u7vUIREREUmGfXSJipEOHToAAJycnPDixQuJoyEiIpKWibXoEhUvSUlJsLS0FJcbNmyITZs2ITg4GFZWVhJGRkREJD226BIVUUeOHEGtWrXw22+/6ZW3bduWSS4RERFMLtEVpA6AKE8kJCRg5MiRePz4MUaPHo2kpCSpQyIiIip0TCzRJSoerK2tsXjxYri5uWHhwoV63ReIiIgoDRNdoiIgKSkJR44c0Str06YNzp8/j1atWkkUFRERUeHGRJeokLt27RqaNm2Knj174vz583rrbGxsJIqKiIio8GOiS1TI2draIiIiAjqdDjt37pQ6HCIioiKDw4sRFXKlSpXCwoULkZSUhH79+kkdDhERUZHBRJeoENFqtfjmm2+gUqkwZMgQsbx79+4SRkVERFQ0MdElKkQ+++wzfPfddzA3N0fDhg3h6+srdUhERERFFvvoEhUiAwcOhLm5OXx9fWFmxu+hREREb8PEPkllUgdApOf58+dwdnaGTJb23KxUqRJ++ukn1KhRA0qlUuLoiIiIija26BJJQBAE7Nq1C7Vr18bWrVv11gUGBjLJJSIiygNMdIkkoFarsWDBArx8+RJTpkxBbGys1CEREREVO0x0iSRgbm6OFStWoEKFCti6dSvs7OykDomIiKjYMbFEV5A6ADJRsbGx2Lx5s15Z7dq1cfr0adSrV0+iqIiIiIo3E7sZjajgXb16FT169MCjR4/g7OyM1q1bi+s4sgIREVH+MbEWXaKC5+XlBZ1OBwD4888/JY6GiIjIdLA5iSgfCIIgDhlmb2+PlStXIiYmBh06dJA4MiIiItPBRJcoD6WkpGD+/PlQq9X44osvxPLGjRtLGBUREZFpYqJLlIdmzJiBVatWAQBatmyJhg0bShwRERGR6WIfXaI8NHr0aDg6OqJRo0YoV66c1OEQERGZNLboEr2FsLAweHl5iTOZeXh44Ndff0W5cuUgl/N7JBERkZT4SUz0BnQ6HdasWYOgoCB89dVXeusqVKjAJJeIiKgQMLFPY5nUAVAxIQgCfvrpJyQlJWHp0qV48eKF1CERERHRK0ws0SXKGwqFAsuXL0edOnVw+PBhODs7Sx0SERERvYKJLpEBnj59irlz54oTPwBAuXLlcPDgQfj5+UkYGREREb2Oid2MJkgdABVBV69eRYcOHRAVFQUnJycMGTJEXJc+KQQREREVPmzRJcrFO++8gxIlSgAAIiIiJI6GiIiIDGViLbpEhtFoNDAzS3t5mJubY/Xq1YiMjOQMZ0REREUIE12iTOLj4/H5558jKioKGzZsELsmVK5cWeLIiIiIyFhMdIkyWbJkCTZu3AgA2LNnD7p06SJxRERvRxAEpKamQqvVSh1KnklJSRH/Zz/54o/Xu/hQKBRQKpUFeh3ZR5cokzFjxqBs2bJo3749mjRpInU4RG9Fo9Hg+fPnUKvVUoeSp1QqFcqVKweVSiV1KFQAeL2LD7VajefPn0Oj0RTYMdmiSybt8uXLKFeuHOzs7AAANjY2OHLkCJycnNhyQEWaIAiIjo6Gi4tLsXsup7dOW1hYQKFQSBwN5Tde7+LF2toaz58/L7D3JrbokknS6XSYN28eWrRogSlTpuitc3Z2LnaJAZme1NRUWFpa8rlMRIWKTCaDpaUlUlNTC+R4THTJJMnlcty8eRMajQY//vgjhw2jYker1bL1i4gKJYVCUWD3DZhY1wW2bFCGBQsWQKPRYPr06ShZsqTU4RAREVEek7RFNyUlBZMmTUKtWrUQFBSEdevWvXbbP/74Ax06dECNGjXQrl07HDt2rAAjpaLu3r17GD16NJKTk8UyJycnbNy4EeXKlZMwMiIiIsovkia68+fPx5UrV7Bx40ZMmzYNy5cvxy+//JJlu9DQUIwcORJdunTB3r170aNHD4wZMwahoaESRE1FzY0bN9CwYUN8//33mDNnjtThEFEugoOD4ePjI/6rVKkSAgMDMWzYMDx+/Fhv27i4OMybNw9NmzaFv78/WrRoga+//hqJiYlZ9vv48WNMmTIFjRo1QvXq1dGxY0fs3bv3jeMUBAGff/45qlevjmbNmhlVt2/fvli2bNkbH/tNREdHY9SoUahRowaCg4Px008/Gb2Pvn37onr16oiPj8+yzsfHB2fPns1SvmzZMvTt21evLK+vRXbCw8MxYMAAVK9eHa1bt8bJkydfu60gCPjuu+8QHByMWrVqYeLEiUhISAAAnD17Vu/5mPkfu70VfpJ1XUhMTMSuXbuwdu1a+Pn5wc/PD7du3cKWLVvQqlUrvW3379+PunXrol+/fgCAMmXK4LfffsOhQ4dQqVIlKcKnIsTb2xuBgYE4ceIErK2tpQ6HiAwwadIktG7dGkDazaO3b9/GtGnTMGHCBKxfvx4AkJCQgN69e0OpVGLmzJkoV64cbt++jcWLF+PEiRPYvHmz+Jq/d+8eevXqhZo1a2LJkiVwdnbG6dOnMW3aNERFReHDDz80OsbQ0FDs3LkTa9asgY+PT949+HwyceJEJCcnY8eOHbh8+TKmTJmCcuXKoWrVqgbVf/r0KS5evAg3NzccPnz4jccZz49r8SpBEDBixAh4e3tj9+7dOHr0KEaOHImDBw9m21Vtx44dWL58Ob744gv4+Phgzpw5GDduHFatWoUaNWpkSZI//vhjODg4sNtbESBZohsaGgqNRoMaNWqIZQEBAVi1ahV0Oh3k8ozG5k6dOmV7d15cXJyRRxXeNFwqQgRB0GttkMlkWLp0KZ49e6b3fCOiwsvW1haurq7isru7O0aPHo3x48cjLi4OSqUSS5cuhVqtxo4dO2BlZQUA8PLyQkBAANq1a4fly5djwoQJAIAZM2agUqVKWLZsmTgSRenSpaFWq7F48WJ07dpVHGbQUOmfQY0aNSr0o1s8ePAAv//+O44dOwYvLy94e3vj0qVL2Lp1q8GJ7sGDB+Ht7Y2aNWti7969b5zo5se1eNWZM2cQHh6O7du3w8rKChUqVMDp06exe/dujBo1Ksv233//PT744AO0bdsWADB37lw0atQId+/eRfny5fWei/v378fNmzdx+PDht4qRCoZkiW5kZCQcHR31BoB2cXFBSkoKYmJi4OTkJJZXqFBBr+6tW7dw+vRp9OjRw+jjJiUliX/r/rvjT6fVZvszFxU9UVFRmDhxIiIiIjBnzhzxejs6OsLR0ZHXuZhKv86ZX9+mLiUlBSqVqkjOiCYIAnQ6XZbYzczSPrJkMhm0Wi1+/PFHfPLJJzA3N9fb1srKCn369MHatWvxySefIDIyEqdPnxYbUjLr1KkTfHx8suwj3Z07dzBv3jxcvHgR1tbW6N69O4YOHYrz589jwIABAIBKlSph+PDhGDlyZJb6GzZswPfff4/o6GjUrFkT06ZNg5eXl95jVKvV+Oqrr3Do0CFERUXBzc0NQ4YMQffu3QGkJW3z5s1DWFgY3NzcMHDgQLz//vsAgEOHDmHZsmWIiIiAl5cXPv74YzRv3jxLHBcvXoSHhwdKlCghPs4aNWpg7dq1Bj9H9u/fj1q1aoldwR48eABPT0+9bbK7bjqdDoIgQKvV4smTJ0ZfC0EQ8PPPP2P69OnZxnXkyJEscVy8eBG+vr56+6pRowYuXryY7eMNDw9HlSpVxHXOzs5wcnLCP//8gzJlyojbpaam4uuvv8aQIUNgb29fJF9fhYFOp4NarYYgZG2AFAQhT784SpboJiUlZZnlJH05p1l8oqKiMGrUKNSsWdPoPlFA2k8m6dJb/eIT4nH9+nWj90WFzw8//IADBw4AAPbt24eOHTtKGxAVqMyvb0K2N1q+TE7FzUhjfw17O96utrC3UBq8ffq0xZlvHg0PD8fq1atRv359mJmZ4e7du4iPj4e3t7fedumqVKmCmJgY3L59G+Hh4RAEARUrVsyyrUwmg5+fHzQaTZbZmqKjo9G3b180atQIGzduxIMHDzBz5kyoVCq8//77WLBgAcaPH49ff/0VVlZWWfb9ww8/YOXKlZg8eTJ8fX2xfPlyjBkzBlu2bIFOp4NGo0FycjJWr16NP/74A/Pnz4eTkxP27duHWbNmoUGDBnBwcMAnn3yC3r17o3Xr1rh06RKmTp0Kf39/ODg4YMKECZgyZQpq1aqFo0ePYvz48fjll19gb2+vF8vjx4/h4uKiF6OdnR2ePHmS7fl7VXh4OK5cuYJRo0bB398f1tbW2L17N4YMGaK3nVqtzrI/jUYDnU6H5ORkXLly5Y2uRcuWLVG/fv1sY3NwcMiyrydPnsDZ2Vmv3N7eHo8fP8728To5OeHRo0fiuqSkJLx8+RKRkZF62x88eBCxsbHo3LmzQeeNspeSkoKwsLDXrs/LWfAkS3TNzc2zJLTpyxYWFtnWef78OT744AMIgoClS5fqdW8wVNmyZWFpaQkAsDn3AkACbKxt4Ovra/S+qPCZOHEizp07h4oVK6Jly5Z615uKr6SkJNy7d4/XO5P0Ft3M76cvk9Tw/+oXxCQVzEDt6Rwslbj1WQfYWxr24SWTyfDll19i3rx5ANLGBFYqlQgODsbEiRNhbm6O2NhYAGm/BGb3mZH+U3NSUpKYkLi4uIitwoY4evQoLCwsMGvWLJiZmaFy5cqIiYnBypUrMWjQIPEYXl5e2db/8ccf0a9fP3To0AEAMHXqVLF/sVwuh5mZGSwsLODn54egoCAEBAQAAEaMGIG1a9fi8ePHsLa2xsuXL+Hh4YHy5cujfPny8PT0hKenJx49egSNRgMvLy+UL18egwcPhp+fH+zs7LKcE41GAwsLC71ya2trpKamvvYzN7Njx47B3t4e9evXh0KhQJMmTXDgwAGMHj1ab7tXn3NAWku8XC6HhYXFG12L9FY/e3t7g1v60idMyRyLlZXVax9v69atsWHDBtSpUwdeXl74+uuvxWNn3n7v3r3o2rUrHBwcDIqDXq98+fIwNzfPUn7r1q08PY5kia67uzuio6Oh0WjEJ3tkZCQsLCyy7Zvz9OlT8Wa0TZs26XVtMIalpaXYl0v+32DqcoVCLKOi5c8//xTf+NPt378fOp0O169f17veVPzxemdITwgyTxqR9rcUfUllUCgUBk9gIZPJMHr0aLRs2RIJCQlYtmwZHj16hE8//RTOzs7QarXi50RUVBTKly+fZR/Pnz8HkNZSl95lKSEhwajPjrCwMFSpUkXvwzggIADPnz9HQkKC2Njyusd17949+Pv7i+vd3d3x2WefiY9RLpdDoVDg3XffxalTp7BgwQLcvXsX165dE/fh7OyMnj17YurUqVi1ahWaNm2KLl26wMnJCY6OjmjSpAkGDRqEcuXKoVmzZujWrRtsbGyyxGJhYYHU1FS9WNOTX0Ouy8GDB9G0aVOxpe3dd9/F/v37cfHiRdSqVQtARteS7PanVCqhUCjE82/MtdBqtTh48CBmz56dbaJ74MCBLDeFWVhYICYmJsvjtbS0zDa+ESNG4OHDh2jfvj3MzMzQo0cPVKpUCXZ2duL2L168wIULFzB16lROxvKW0r/4ZNcwkdf93SVLdH19fWFmZoZLly6JL5ILFy7A398/S0ttYmIiBg0aBLlcjk2bNul1CifTJAgCJk+ejFWrViE4OBi7du0SXxwWFhbsi0uUDXtLFe5O7oTQZy8L9LiV3OwNbs1N5+zsLPaNXLJkCbp27Yrhw4djx44dkMvlKF26NOzt7XH16lXxMySzK1euwMHBAaVKlRJbAq9cuYJGjRrpbZeYmIgRI0ZgwoQJWUbxya61Kb1fqSF9Mw1tsfzqq6+wa9cudO7cGR07dsS0adMQHBwsrp8+fTp69+6No0eP4ujRo9ixYwdWrlyJxo0bY/Xq1QgJCcGxY8dw5MgRbN26FVu3bs3yK6W7u7uY/Kd7/vy5QZ+noaGhuH37Nu7evYt9+/bprdu7d694/m1tbbMddiwuLg62trYAAD8/vze6Fo0bN0ZAQEC2Caabm1uWMnd3d9y+fTvL481uWyCttXfJkiWIi4uDTCaDjY0N6tWrp9eI8ueff8LLy6tIjLBBGSRLdC0tLdGxY0dMnz4dX375JZ49e4Z169aJ45xGRkbC1tYWFhYWWL16NR48eIDNmzeL64C0hCb9xWMIAcC6c3fFb6SHQzn+XVElk8nED5FLly7h/v37KFu2rLRBERUB9pYq1ClTtBoLVCoVZs2ahffffx8bNmzAhx9+CDMzM3Tu3Bnfffcdunbtqjd0YHx8PNavX4/OnTvDzMwMTk5OaNCgATZu3IiGDRvqtRjt3r0b58+fR4kSJbIct1y5cvj111+RmpoKpTKtj/HFixfh5ORk0E/XZcqUQWhoqJi0RkdH47333sMPP/ygt9327dsxffp0vPfeewAgJmiCICAyMhIrV67ExIkTMWzYMAwbNgwDBw7Eb7/9Bi8vL/zwww+YMGECqlatio8//hht2rTBn3/+mSXRrV69Oh49eoQnT57Aw8MDQFrjUvXq1XN9HAcPHoSdnR02b96s1xC1atUqHDp0CFOmTIGFhQV8fHxw8eJFtGjRQq/+5cuXxXtq3vRaWFtbw9nZ2eCW1GrVqmHNmjVITk4Wux5cuHBB7B7yqvnz56NixYro1KkTACAkJARxcXF6I/WEhISgZs2aBh2fCg9JJ4yYOHEi/Pz80L9/f8yYMQOjRo1Cy5YtAQBBQUE4ePAgAODw4cNITk5Gt27dEBQUJP6bPXu20ccc8/NFDPvhLIb9kDGodSEfFYZeY9KkSRgyZAhOnTrFJJeomKtatSq6du2KlStX4tmzZwCA4cOHw8XFBX379sWpU6cQERGBU6dOoV+/fnB1ddUbRmrixIkICQnBmDFjEBISgrCwMKxbtw4LFizAuHHjsty8BQDt2rWDWq3G1KlTcefOHRw9ehTLli1Dz549Dfp5tW/fvti4cSOOHj2KsLAwccSFV/v0Ojg44Pfff0d4eDjOnz+P//3vfwDS7luxt7fHkSNH8OWXX+LBgwc4d+4cQkNDUblyZdjZ2WHbtm1YuXIlwsPD8ccff+DRo0eoXLlyllhKlSqFoKAgjB8/HqGhodi1axf279+P3r17A0hroY6MjMz2ZvADBw6gXbt2qFSpEry9vcV/AwYMQHx8PI4ePSo+3u+//x7btm1DeHg4rl69ipkzZ+LevXt6Q5G9ybUwVmBgIEqUKIGJEyfi1q1bWLNmDUJCQtC1a1fx3EZGRoot825ubli+fDlCQkJw5coVjB8/Hj179tT7QnPr1i288847bx0bFTDBRISEhAin/v5DkI/dpPfP6n9bhI3nbksdHuXiypUrQteuXYWoqCiDtk9ISBDOnz8vJCQk5HNkVBjwemeVmJgoJCYmSh3GG2natKmwe/fuLOUvXrwQateuLYwdO1aIj48XNBqNEB8fLyxevFho3ry54O/vLzRv3lz46quvsn0u3Lx5Uxg1apRQv359oVq1akKnTp2En3/+OcdYrl69KvTq1UuoUqWK0KhRI2HlypWCVqsVBEEQzpw5I3h7e7+2rk6nE1atWiUEBQUJ1atXF4YMGSI8evRIEARB6NOnj7B06VJBEATh/PnzQtu2bcX4V69eLXTt2lVYtWqVIAiCcPnyZeH9998XqlWrJtSvX19YvHixGMOJEyeE9u3bC/7+/kKTJk2EDRs2vDae58+fCx999JHg7+8vBAcHC/v27RPXhYeHC97e3sKZM2f06ly8eFHw9vYWrl69mu0+O3XqJHz44Yfi8v79+4VOnToJ1apVE2rVqiUMHjxYCA0NzVLPmGuRfp01Gs1rH1t27t27J/Tu3VuoUqWK0KZNG+HUqVPiuvRrFx4eLh5j1qxZQmBgoFC3bl1hzpw5Qmpqqt7+WrVqJWzbts2oGCh7Ob0/Xb58WQgJCcmzY8kEIZtBzIqhf//9F3HJ0fDxrgErq4zOz2ZyOZQKSRu2KRcPHjxAYGAg1Go1OnfujG+//TbXOomJibh+/Tp8fX15c5IJ4PXOKn1M4eI4CoVWqxV/kuZNQXln6dKl4pS8hQmvd/GT0/tTSEgIZDIZ/P398+RYJpXhCQJgqVTAUmkm/mOSW/iVLl0a3bt3h6WlJQIDA7MdYJqIiN5cfHw8Tp8+nW23B6KijFkeFTo6nQ6PHz/WK5s1axb++OMPDBkypNBPtUlEVNTY2Nhg06ZNeTpQP1FhwESXCpWHDx+iS5cuaNu2rd4wNXZ2dqhYsaKEkRERFW/pI0sQFScmlejKZPzJu7D766+/cPz4cYSFheGbb76ROhwiIiIqwiQbR5coO926dcOhQ4dQvnz5LFNLEhERERmDiS5J6ueff8Y777wj3gAhk8nw3XffZZkdj4iIiMhYzCZIMh9//DEGDBiAoUOH6g1SziSXiIiI8gIzCpJM+lzm0dHRePDggcTREBERUXHDrgtUYARB0BsabMiQIVCr1RgwYADs7OwkjIyIiIiKI7boUoE4c+YMGjVqhLCwMLFMLpdj9OjRTHKJSE9wcDB8fHzEf5UqVUJgYCCGDRuWZYztuLg4zJs3D02bNoW/vz9atGiBr7/+GomJiVn2+/jxY0yZMkWc/atjx47Yu3fvG8cpCAI+//xzVK9eHc2aNTOqbt++fbFs2bI3PvbbiImJQf369fHw4UOj6/bt2xfVq1fXG/4xnY+PD86ePZulfNmyZejbt69eWV5fi+yEh4djwIABqF69Olq3bo2TJ0++dltBEPDdd98hODgYtWrVwsSJE5GQkAAAOHv2rN7zMfO/iIiIPI2Z8h4TXcp3T548QceOHXH16lWMGDECOp1O6pCIqJCbNGkSTp48iZMnT+L48eP46quvcOvWLUyYMEHcJiEhAb169cLZs2cxc+ZMHDp0CJMnT8Zvv/2GPn36iIkKANy7dw9dunRBTEwMlixZgp9//hk9e/bEtGnTsG7dujeKMTQ0FDt37sSSJUuwZcuWt37MBeHly5cYOnQoXrx4YXTdp0+f4uLFi3BycsLhw4ffOIb8uBavEgQBI0aMgIuLC3bv3o0OHTpg5MiRr01Md+zYgeXLl2Ps2LHYtm0bnj59inHjxgEAatSoIT4X0//VqlULzZs3R8mSJfMkXso/7LpA+c7DwwOffPIJVq1ahQ8++IAzmxFRrmxtbeHq6iouu7u7Y/To0Rg/fjzi4uKgVCqxdOlSqNVq7NixA1ZWVgAALy8vBAQEoF27dli+fLmYGM+YMQOVKlXCsmXLxPeg0qVLQ61WY/HixejatavRvy7FxcUBABo1alQk3tfOnz+PCRMmwNra+o3qHzx4EN7e3qhZsyb27t2LLl26vNF+8uNavOrMmTMIDw/H9u3bYWVlhQoVKuD06dPYvXs3Ro0alWX777//Hh988AHatm0LAJg7dy4aNWqEu3fvonz58nrPxf379+PmzZtvlexTwTGpFl0Bhf+NqDhITU3FtWvX9MrGjh2Lv/76C926dSsSHwhExZVak4zIuAcF+k+tSc6T2NOnp5XL5dBqtfjxxx/Rr18/MclNZ2tri379+mHPnj3QarV48uQJTp8+jQEDBmR5/+natSvWrl2bZR/p7ty5g4EDB6JmzZpo2LAhli9fDp1Oh7Nnz4o/x6cnbdlZv349goODUaNGDQwcOBDh4eFZtlGr1ZgzZw4aNmwIPz8/BAcHY8eOHeL606dPo0OHDvD390ezZs2wfft2cd3Bgwfx7rvvwt/fH61bt8bRo0dfe/5OnjyJLl26vHGXif3796N27dpo2rQpzp0790ZdH970Wvz888+oXLlytt0Hsovj8uXLqFy5st6+AgICcOnSpWzjCg8PR7Vq1cRlNzc3ODk5Zdk+NTUVX3/9NYYOHQonJycjHjlJxbRadDkxWr67ceMGhg8fjvv37+PUqVNwd3cHkDa1ZIkSJSSOjsi0qTXJ+OHcXKi1eZN4GkqlsEDX2p9BZWbxxvt48OAB1qxZg4YNG8La2hrXrl1DfHw8/P39s90+ICAAMTExePDgAR48eABBELLd1tLSErVq1cp2H1FRUejVqxeCg4Oxa9cuhIWFYcqUKbCxsUGvXr2wbNkyjBo1CidPnsw2Odu+fTuWL1+OL774ApUrV8bixYsxZswY7NmzR2+7NWvW4I8//sCyZcvg7OyMH3/8EV988QWaNWsGR0dHcSjGdu3a4Z9//sGECRNQq1YtODo64n//+x9mzpyJOnXq4JdffsHYsWNx4sQJODg4ZInn448/BoA3SlAfPHiAK1euYPz48QgICICNjQ327t2LkSNHGrWfGzduvNG1aNmyJZo2bQqFQpFlXXYJZ2RkJNzc3PTKnJ2d8eTJk2z37+zsjKdPn4rLiYmJePnyJaKjo/W2O3ToEOLi4tC7d+9s90OFj2klupTvnj17hosXLwJIe/P+/PPPJY6IiIqiadOm4YsvvgAAaDQaKJVKNGvWDJMmTQIAxMbGAgDs7e2zrZ/+03dMTIy4ra2trVEx7N+/H5aWlvjiiy9gZmaGChUqIDIyEitWrMCAAQPEY2f+WTuzHTt2YMCAAWjdujUAYOrUqfjuu++QnKz/RaNSpUqoW7cuqlevDgAYOnQoVqxYgXv37sHMzAwxMTFwcXGBl5cXvLy84ObmBldXVzx69Aipqanw8PCAp6cnPvzwQ/j4+MDc3Nyox2nouXBwcEDt2rWhUCjQpEkT/PTTT0Ynum96LSwsLODg4JBtopudpKQk8ReAdCqVSm/M9sxat26N1atXIyAgAF5eXpg7dy6AtBbczHbu3ImuXbvCwuLNv7RRwWKiS3mqYcOGGD16NJydnTF8+HCpwyGiTFRmaS2rL5OeFehx7S3djG7NHT16NFq2bImEhAQsW7YMjx49wrhx4+Do6AitVismspGRkShTpkyW+s+epT1GBwcHcYSA2NhYo35uvnPnDvz8/GBmlvFRWaNGDURGRooJW07CwsLg5+cnLru4uOjdTJeuefPmOHXqFObOnYu7d++KXb+0Wi0cHBzQs2dPTJkyBStXrkTTpk3RpUsX2Nvbw87ODk2aNMEHH3yAcuXKoVmzZujWrRssLS0NfoyGOnDgAJo0aSImmi1btsS+fftw/vx5sRXWzMws25uNdTqdeA7TW5qNvRYHDx7E7Nmzs+36duDAgSw3hZmbmyMmJkavTK1WvzZBHT58OMLDw9GmTRuYmZmhR48eqFSpEmxsbMRtXrx4gfPnz7MBp4gxqURXJmPfhbwkCAI2bdqEsmXLonHjxmL59OnTpQuKiHKkMrOAq21pqcPIlbOzs5jALlmyBF27dsXw4cOxY8cOyOVylC5dGvb29rh69Wq2P3dfuXIFDg4OKFWqFOzt7SGTyXDlyhU0atRIb7vExESMGDECEyZMECexSZddy2h6IqfVanN9DJkT5Jx89dVX2LVrFzp37oyOHTti2rRpCA4OFtdPnz4dvXv3xtGjR3H06FHs2LEDK1euROPGjbF69WqEhITg2LFjOHLkCLZu3YqtW7fC19fXoGMbIjQ0FLdv38bdu3exb98+vXV79+4Vz7+trW22w47FxcWJLbh+fn5vdC0aN26MgICAbFt0X+2iAKTdvHj79m29sufPn2e7LQBYWVlhyZIliIuLg0wmg42NDerVqwdPT09xmz///BNeXl7w8fHJdh9UOJnUzWiUt0aOHIlPPvkEI0aMwMuX/2/vzuNiXP//gb9a5UgidKzJUkO7SkIHydJGRecUypJd9k72PUIO5xSOPWWXFNlPnHwce6iptCgtY89e2pvr90e/7m9jJhUp6v18PObxaO657vt+33PV9J7rft/X/b62wyGE1FHy8vLw8vJCfHw89u3bB6AkiXRwcMCePXtEphEDgOzsbPj7+8PBwQGysrJo1qwZevfujYCAADAmOuARHByMyMhIidcQqKurIy4uTuT0den0WpJqYD+lpqaGhIQE7vnbt2/Rs2dPsRrZI0eOYOnSpfDw8ICVlRVyc3MBlAwmZGZmYuXKlVBTU8PUqVMRHByMnj174vLly0hJScH69euhq6uLOXPm4MyZM2jVqhWuXr1aYWxVcfbsWSgpKSEkJAShoaHcw9raGufOneNKMTQ1NbnStbJKLwwD8MV90ahRI6ipqUl8SPpCoaenh7i4OJEykbt374pccFbWhg0bEBISgsaNG0NRURF8Ph9ZWVkwMDDg2vD5fHTv3r0S7xj5nlCiS76YpaUlgJIPoNLThIQQ8i3o6upixIgR2LZtG/d5M23aNDRv3hwuLi64du0anj59imvXrsHV1RUtWrQQmUZq4cKF4PP5mDVrFvh8PlJTU7F37174+Phg3rx5Emt9bW1tUVBQgGXLliElJQXh4eHw8/ODs7NzpWaPcXFxQUBAAMLDw5Gamorly5dzdbZlKSsr499//4VAIEBkZCQ8PT0BlJxqb9KkCf755x+sXbsWGRkZuHPnDhISEtCtWzcoKSnh8OHD2LZtGwQCASIiIvDkyRMuqayK4uJiZGZmSqxhPXPmDGxtbcHj8aChocE9xo4di+zsbG6mBxcXFxw4cACHDx+GQCBAXFwcVq1axc2bW+pL+qKqevTogVatWmHhwoV4+PAhdu7cCT6fjxEjRgAoeW8zMzO5kfmWLVtiy5Yt4PP53EV3zs7OIl9oHj58iM6dO391bKSGsXqCz+eza7cj2MePH2s7lB9WXl6e2LKgoCCWk5NTC9F83sePH1lkZCT1dz1B/S0uJyfnu/zbrIz+/fuz4OBgseWvX79mxsbGbO7cuSw7O5sVFRWx7OxstmnTJmZhYcF0dHSYhYUF27x5s8TfhaSkJDZjxgzWq1cvpqenx+zt7dmpU6c+G0tcXBwbOXIk09bWZr/88gvbtm0bKy4uZowxdvPmTaahoVHuukKhkG3fvp316dOH6evrs0mTJrEnT54wxhgbPXo08/X1ZYwxFhkZyWxsbLj4d+zYwUaMGMG2b9/OGGMsOjqa/fbbb0xPT4/16tWLbdq0iYvhf//7Hxs6dCjT0dFh/fr1Y/v27avw/RUIBExDQ4MJBAKxZTdv3hRpe//+faahocHi4uIkbsve3p6NHz+ee3769Glmb2/P9PT0mJGREZs4cSJLSEgQW68qfVHaz0VFRRUeW1lpaWls1KhRTFtbm1lbW7Nr165xr5X2Xel7UFRUxLy8vFiPHj1Yz549mbe3NyssLBTZ3pAhQ9jhw4erFAOR7HOfT9HR0YzP51fbvqQYY/WicDUmJgZZeW+gr2Vc7nyJpHwXLlzA3LlzERgYCENDw9oOp0I5OTmIj49H165dqb/rAepvcaWnv7/FhUm1rbi4GHl5eVBQUKj0VfikYr6+vtwteb8n1N91z+c+n/h8PqSkpMqdOrCqqHSBVOjt27eYOHEinj17hmnTpolNt0IIIeTHlp2djRs3bnxR2QMh3zNKdEmFmjZtijVr1uDnn3+Gl5cX5OTkajskQggh1UhRURGBgYFic88S8qOjRJeIyc3NxX///SeybPTo0bh58yYGDhxYS1ERQgj5lmgQg9RFlOgSETExMejXrx8cHR2RmJjILZeSkuImaCeEEEII+RHUq0SXsYqng6nv5OTkkJGRgfz8fBw+fLi2wyGEEEII+WL16s5opGI8Hg9eXl5o0KABRo0aVdvhEEIIIYR8sXqV6NaLedSqoLi4GFu3boWqqip+++03brmbm1stRkUIIYQQUj3qVaJLRM2YMQNHjhxB48aN0atXL7Rr1662QyKEEEIIqTb1qkZXWorGdMsaN24cpKWl0aVLF5oblxDyQ1qwYAEWLFhQ22H80B4/fgxNTU2Rh5aWFvr06YPVq1eL3RY4LS0Nc+fOhYmJCfT19eHg4IDjx49L3Pa9e/cwefJkmJiYwNjYGOPGjcP9+/dr4rBqhIuLC/T19ZGdnS32mqamJm7duiW23M/PDy4uLiLLnj17hiVLlnA37LCzs0NoaOg3iTk9PR26uroVtjt9+jQsLCygp6eH6dOn482bN9xrjDFs3LgRPXv2RI8ePbBhwwYIhcJvEu/XqleJbn337t07kefGxsYIDQ3F+fPn0bFjx9oJihBCvsLixYuxePHi2g6jTggKCsJ///2H//77j7sb5rFjx7Bz506uTXx8PBwdHQEAu3btwqlTp+Ds7AwfHx8sW7ZMZHsXLlzAmDFjwOPxEBgYiCNHjkBDQwOurq64e/dujR7bt/DixQvcv38fzZo1w4ULF754O2lpaRg+fDjevXuHv/76i3tPly9fjr1791ZjxCUJ9eTJk5Gfn//Zdnw+H4sXL4a7uzuOHj2KDx8+YOHChdzr/v7+OH36NLZs2QJfX1+EhYXB39+/WmOtLpTo1gOMMRw5cgT6+vo4e/asyGt9+vSBrCxVsBBCfkyNGzdG48aNazuMOqFZs2Zo0aIFWrRogbZt28LBwQHDhg1DeHg412bBggXo27cvNm3aBF1dXbRv3x6Ojo7YvXs3goKCEBERAaDkTmvLli3D1KlTMWfOHGhqaqJTp05YuHAh+vXrBx8fn1o6yupz9uxZaGhowNzc/KtGX1euXAkejwc/Pz8YGBigffv2+O233+Dh4QE/Pz98+PChWuINDw+Hg4NDpW4KcuDAAVhaWsLOzg48Hg8bNmzAlStXIBAIAACBgYGYOXMmjIyM0LNnT3h4eODgwYPVEmd1o0S3HsjOzsbq1avx4cMHeHh4IC8vr7ZDIoQQiUpPo0dERMDc3BwGBgbw8vJCUlISHBwcoK+vj6lTp+Ljx48AxEsXTp48iSFDhkBPTw9OTk548OCBSLuhQ4fC1NQUaWlpeP/+PZYuXYpevXrB0NAQv//+O96/f19ubAUFBfD29oaZmRm0tLRgbm6Oo0ePAgAOHz4Mc3NzkfZHjx7FoEGDuHW9vLxgYmICExMTeHh4cGfZSo9569atMDY2xqpVq8AYw/bt22Fubg5tbW306dMHW7Zs4bYtFAqxceNGbnvbtm3DwIEDuVPlHz58wO+//47u3btzJQhf8tkvLy8PGRkZACWjfAkJCZgyZYpYOx0dHfTt2xfHjh0DAFy+fBnZ2dlwdXUVazt//nx4eXmVu8/y+nDRokViZSplywPMzc3h4+ODPn36wM7ODo6OjvD19RVp7+TkhG3btgEAkpKS4OLiAl1dXQwePLjKidrp06dhbGyM/v37486dO3j8+HGV1geA58+f48aNGxg7diykpESnQB0xYgR27dqFn376SWy9EydOiJWblD7KiyMiIgKzZs2q1BmQ6OhoGBkZcc9btWqF1q1bIzo6Gi9evMCzZ89gbGzMvW5oaIgnT57g5cuXlT30GkOJbj3QuHFj+Pr6olOnTvD394eCgkJth0QIqUWZWRkVPsoqFhZV2P519hORdQqL8iVuq7J27tyJbdu2YfXq1di/fz/c3d0xb9487NmzB1FRUQgJCRFb5+rVq1i8eDHGjBmDU6dOQVtbG5MnT+ZqTE+ePInZs2djx44d6NChA9zd3REfH4/t27fD398fKSkpn6333blzJyIiIuDn54fz58/Dzs4Oq1evxqtXrzB48GC8ePECsbGxXPuLFy/C0tISALBp0ybExsZi165dCAwMRHZ2NmbNmiWy/Xv37iE4OBiurq4IDQ1FQEAA1qxZg/Pnz2P69Onw8/NDXFwcAGDHjh0IDQ3FH3/8AX9/f0RERHCjbUBJSUdWVhYOHz6Mbdu2ISYmBqtWrar0+88Yw61btxAWFobBgwcDAGJjY/HTTz+hU6dOEtfp3r07+Hw+ACAhIQEdO3aEoqKiWLu2bduic+fOErchqQ+nTZtW6etIwsLCsGfPHqxbtw7W1tb4559/uNdevHiBqKgoWFtbIy8vDxMnToShoSFOnTqF+fPnY9u2bZUemc3IyEBsbCz69++PHj16QFFR8YtGdRMTE8EYg46OjthrDRs2hJGRkcSzrlZWVlyZyaePVq1aSdyXl5cXnJycKhXXy5cv0bJlS5FlKioqeP78OTIzMwFA5PXmzZsDKEncvzd0zroOev/+PcLDwzF8+HBu2YABA3D9+nW6xSMhBGeit1XQQgpj+3hzz3ILsitcp1EDZTga/1+S+ObjM5yL2Q4AGNtnXZVjnDZtGng8Hng8HtauXQtra2v07t0bALgR2U8dPXoUNjY2cHZ2BgB4enpCTk6OG6XV0dHhRl0TEhJw+/ZtnD9/Hurq6gAAHx8fWFlZ4dGjRxKvW+DxeOjZsyf09fUBAFOmTMHWrVuRlpbGncK9ePEitLW18f79e9y6dQuenp7Izc3FgQMHEBwcDE1NTQDAhg0bYGJigsTERDRq1AgAMGbMGLRv3x5AScLg7e0NU1NTAICzszO2bt2Khw8fQktLC4cOHcLs2bPRp08fAMC6deu4pDojIwPh4eG4ffs2V9axevVq2NnZYeHCheWWetjY2HCjigUFBWjWrBlcXV25KSffv3+Pxo0bi408lmrSpAk3Sp2VlSUxya2IpD6UkZH57Eh7WUOHDuXe46ZNm2L9+vVIS0tDhw4dcPHiRXTr1g1qamoICgqCiooKZs+eDQDo0KEDnjx5gsDAQNjZ2VW4n9OnT0NZWRnGxsaQkZFBv379cPLkSbi7u1fpeEvLEqpafqOgoPBNB63y8vLEShzk5eVRUFDAnRko+3rpz59euPg9oES3jrl37x5cXV3x9OlTqKqqch+CAN3HnBDy4yg73aGCggLatGkj8lzSCF9qaqrIiJW8vDzmz5/PPS+7jUePHkFJSYlLcgGgU6dOaNKkCR49eoR9+/YhLCyMe+3MmTOwsLDAtWvXsG7dOjx69Ig7pV5cXAwAsLa2xs6dOzF37lxcunQJampq0NTURFJSEgoLC8VG04RCIdLS0qClpSUWX8+ePREdHY0//vgDKSkpiI+PR2ZmJoRCId68eYOXL1+KjAJ27NgRTZo0AQCkpKRAKBTil19+Edtfeno6tLW1xd9wlIxYq6qq4unTp1i1ahV4PB6mTJnClS40adIEb968gVAohLS0+Anhly9fQllZGQCgrKz8RbWlkvrQ09Oz0mUXZd9DVVVVGBkZ4eLFi5g0aRIuXrwIKysrACX9n5CQAAMDA659cXExd6wVOXPmDPr168e1HzRoEMLCwhAZGcmd8peVlZU4E4FQKORGaUvfrw8fPqBZs2aV2jcAnDp1CsuXLy83ttatW1d6W5I0aNBALGktKChAw4YNRZLaBg0acD8DJaPQ3xtKdOuY1q1bIzc3F0BJjVTZRJcQQgDAWm9aldo3lFescB1pKdEEoVmjVlXeT1mfJhySEqtPVXRhbek/ZQDlXpBTXFyM4uJizJo1S+TmOS1btsTmzZsRFBQEBwcH2NnZYfny5SJ1uQMHDsTy5cvx8OFDkbKF0kT40KFDYvWWKioq3Cho2fiCgoKwdu1aODo6YtCgQZg/fz5X71p6nIyJTplZ+ry4uBiNGzdGcHCw2PGpqqqW+/60bt0abdu2hZqaGnbs2IFhw4Zh/fr1WLJkCQBAT08PhYWFSEpKAo/HE1s/NjaWS761tLSwd+9eZGdni43sRkZGYt++ffDx8RFLjKpycXRRUZHYsrLvIVByiv/48eMYPnw47t27h3Xr1nHrmpqais0UURkJCQlITk7Go0ePRL4MAUBoaCiX6DZu3FjitGNZWVncCK6WlhakpKQQGxsr9sUkJycH06dPx/z588Xeb3Nzc+jp6UmM79OSgy+hqqqKV69eiSx79eoVWrRowf0OZWZmom3bttzPANCiRYuv3nd1oxrdOubnn3+Gr68v9u/f/0V/wISQuq9F4/YVPsqSkZatsL2KYhuRdeRkG0jc1rekpqaGhIQE7nlxcTHMzc0lTmWlrq6ODx8+4NGjR9yy5ORkZGdnQ11dHSoqKlBTU+MesrKyOHLkCJYuXQoPDw9YWVlxgwqlCWbjxo1hZmaGc+fO4fr167C2tgZQMjotIyODd+/ecdtTVFSEt7c3Xr9+LfFYDh8+jOnTp2PRokWws7ND06ZN8fr1azDGoKSkhJYtW3L1ugAgEAi4EVR1dXVkZWVBSkqK219eXh42bNhQ6VPL7du3x4wZM3DgwAFER0cDKEnKtLW1xS7wAkouVLty5Qo39ZiZmRkaN26MAwcOiLUNCAjA8+fPJY7+SerDgQMHIioqCnJyctxFiKXHXJHBgwcjMTERQUFB0NHR4UZ81dXVkZqayiX2ampqiIqKwv79+yvc5tmzZ6GkpISQkBCEhoZyD2tra5w7d44bfdbU1JQ4Z3B0dDS6desGoGSmi969eyMgIEDsi0twcDAiIyMl1twqKiqK/H5++rv6tfT09ET+bp49e4Znz55BT08PqqqqaN26tcjrd+/eRevWraslya5u9SrRZUxyXdGPKj8/HytWrMCmTZtElltbW3MfsIQQUl+4uLjg1KlTCAkJQXp6Ory9vcEY40oDyurUqRN++eUXzJ8/H3w+H3w+H/Pnz4exsTE0NDQkbl9ZWRn//vsvBAIBIiMj4enpCUC0LtHa2hr+/v7o2LEjVxahqKgIR0dHrFixArdu3UJycjI8PT2Rnp7OjYh9qmnTprhx4wZSU1MRGxuLOXPmoLCwkNuXi4sLfH19cePGDSQkJHBznEpJSaFTp04wMzODh4cH+Hw+4uLisHDhQuTk5EBJSanS76erqys6deqEVatWcafgvb29ce/ePcybNw8xMTF4/PgxQkJCMGXKFDg6OnIj3I0aNcKiRYvg5+eHP//8kyu/WLp0KSIiIrhR4k9J6kOhUAgejwcdHR1cu3YNN27cQFJSElatWlVhSV6zZs1gYmKCHTt2cCPsQEktb15eHpYtW4aUlBRcuXIFa9asgYqKCoCSBDszM1PiF4MzZ87A1tYWPB4PGhoa3GPs2LHIzs7mpmNzcXHBgQMHcPjwYQgEAsTFxWHVqlXcvLmlFi5cCD6fj1mzZoHP5yM1NRV79+6Fj48P5s2bx5WkfEsFBQXIzMzkzj44Ozvj5MmTCAoKQkJCAjw9PdGvXz+upMjZ2RkbN27ErVu3cOvWLfzxxx8SZ9j4HtSr0oW6dl+00nnrZGVlMWDAgHJPYxBCSH1gbGyM5cuXY+vWrcjMzIS2tja2b99e7kU769evh5eXF8aOHQsZGRkMGDBAZFL8T61duxYrVqyAtbU1VFVV4ejoCBkZGcTHx3Onnfv37w/GGFcLWmrBggVYv349Zs6cicLCQhgbG2Pnzp3l1oQuWrQIixYtwrBhw6CiogJLS0s0bNgQ8fHxAIDx48fj5cuXmDFjBmRkZDBp0iRERkZyid+GDRu4Y5OVlYWZmVm5yWV5ZGVlsWTJEowdOxbBwcFwdHSEhoYGgoKCsGXLFkydOhXZ2dno2LEj5syZw43mlho6dCiUlJSwa9cuHDx4EFJSUtDR0cHBgwfLvTOXpD78+++/oaCggKFDhyIqKgrTpk1D48aNMWvWLKSnp1d4HNbW1rh+/bpIoquoqIhdu3Zh7dq1sLOzg7KyMkaNGoXJkycDKBnBHDBgAAIDA2FiYsKtFxUVhcePH2PEiBFi+9HV1YWWlhZCQkJgY2MDCwsLeHt7Y8+ePVi/fj3k5ORgYGCAAwcOiJzi79y5Mw4dOgQ/Pz9u6ryOHTtizZo1sLW1rfD4qsP9+/fh6uqKS5cuoW3btjAwMMCqVavg6+uL9+/fo3fv3li9ejXX3s3NDa9fv4a7uztkZGQwYsQIjB07tkZirSop9ulYeR0VExOD97lv0V3bSOKcdD+iR48e4ZdffoGJiQl8fX1FivDru5ycHMTHx6Nr1651pr9J+ai/xZWeVv8eLw75WsXFxcjLy4OCgkKlLx6qa/73v/9BW1ubu4DpzZs3MDU15RKVuqS2+tvX15e7JS+pXp/7fOLz+dyXoupQr0oXfnTp6ekiV3B27NgR4eHhOH78OCW5hBBSjxw9ehSLFi1CcnIyUlJSsGLFCujo6NS5JLe2ZGdn48aNG1wtLflx1atEV1rqxxy8FgqF2L59O0xNTbFnzx6R13g8XrlzGhJCCKmbli1bBmlpaTg5OeHXX3+FUCjE1q1bazusOkNRURGBgYGVul0u+b7VqxrdH1VRUREOHjyIvLw8eHt7w8nJie7tTggh9Ziqqip3K1vybdDc83VDvRrR/VHJy8tj+/btMDQ0xOnTpynJJYQQQgipBEp0v0PPnz+Hr6+vyJx6Wlpa3O0LCSGEEEJIxah04Ttz7949ODo64u3bt2jdurXIFCZUi0sIIYQQUnk0ovud0dDQ4CaHfvjwYS1HQwghhBDy46IR3e+AUCjk7uOuqKiI7du3o6CgAH369KnlyAghhBBCflyU6NairKwsLFmyBNLS0ti8eTO3vEePHrUYFSGEEEJI3UClC7VozZo12L9/PwICArh7YxNCCCG1zc/PD5qamiIPPT092Nra4uLFi2Ltz5w5A0dHR+jp6cHU1BQzZsxAQkKCWDuhUIiAgAAMHToUenp66N+/P7y8vPDu3bsaOKpvTyAQQFNTE7///rvYaydOnIC5ubnE9czNzXHixAmRZREREXBxcYGhoSF69uyJ6dOnIzk5uVrjPX36NCwsLKCnp4fp06fjzZs35bZ9/fo1Zs6cCSMjIwwcOFAk3gULFoj9vmhqasLV1bVa4/0S9SrRFbLv62IuT09PqKqqws7ODt27d6/tcAghhBCOgYEB/vvvP+4RFBQEHo+HuXPnIj09nWvn5+eHJUuWwMbGBmFhYdizZw+aNm0KJycn3LhxQ2Sbs2bNQkBAAKZMmYLTp09j3bp1uHfvHiZMmID8/PyaPsRqd/bsWbRv3x7h4eH4+PHjF28nICAAs2fPRv/+/XHs2DHs27cPCgoKGDVqFFJTU6slVj6fj8WLF8Pd3R1Hjx7Fhw8fsHDhQoltGWOYPn06nj9/jsDAQCxatAjr1q3jvvQsXrxY5Hfl6NGjkJeX/y4SXbB6gs/ns6u3rrCPHz/WWgzR0dEsLy9PZNmzZ89qKZq67ePHjywyMrJW+5vUHOpvcTk5OSwnJ6e2w/gmioqKWHZ2NisqKqrtUOosX19fNnr0aLHl+fn5TE9Pj+3bt48xxlhsbCzj8Xjs2rVrYm1Xr17N+vbty/3fO3nyJNPS0mLp6eki7TIzM5menh47evSoxFh+pP62sbFh+/btY8bGxiw4OFjkteDgYNa/f3+J6/Xv359rn5GRwbS0tFhoaKhIm+LiYjZ8+HA2d+7caon1999/Z/Pnz+eeP336lGlqarKMjAyxtnw+n2loaIi8tmPHDvbrr79K3Pb48eOZh4dHufv+3OdTdHQ04/P5lT2MCtWrEd3aUlRUhLVr12LAgAHw9vYWee3nn3+upagIIeT78/jxY2hqaiIiIgLm5uYwMDCAl5cXkpKS4ODgAH19fUydOpUbLSsoKIC3tzfMzMygpaUFc3NzHD16lNteTk4Oli1bBhMTE5iYmGDp0qXcyKGmpib++usvmJiYYMqUKQCA+/fvw9nZGfr6+jA3N8fhw4c/G29ycjLc3NxgYGAAHR0djBw5EikpKQCAX3/9Fb6+viLtnZycuDuaJSUlwcXFBbq6uhg8eDAOHjzItfPz88O0adMwatQo9OjRA7dv38aLFy8wc+ZMGBsbQ1tbG/b29rh79y63jkAgwNixY7kSgz179oicKo+MjISDgwN0dXVha2uLCxcuVLl/ZGRkICsrC1nZkkt8jh8/Di0tLfTq1Uus7bRp0/DixQtcvXoVABASEoKBAweiffv2Iu2aN2+OgIAADBo0SOI+c3Jy4OXlBVNTU4l9eOvWLa5t2fKAW7duwdzcHMuXL4ehoSG2bNkCTU1NCAQCrn1aWhp4PB6ePXsGADhy5Aj3e+fi4oLExMRKvzfJyclISkqCiYkJzMzMEBISUul1yzp9+jSUlZVha2srslxaWhrr16/H7NmzJa7n4uIisXzAxcVFYvvo6GgYGRlxz1u1aoXWrVsjOjparK1AIECzZs3Qrl07bpmmpiZiY2NRWFgo0vbGjRu4c+cO5s6dW9lD/qbqVaJbW4ULMjIyiIyMRHFxMfbt2/fZGhhCCPnWMrMyKnyUVSwsqrD96+wnIusUFuVL3FZl7dy5E9u2bcPq1auxf/9+uLu7Y968edizZw+ioqK4JGLnzp2IiIiAn58fzp8/Dzs7O6xevRqvXr0CACxZsgR3797Ftm3bsHfvXty9exd//vknt59///0Xhw8fhoeHB1JSUjBmzBgYGxvjxIkTmDFjBtavX49//vlHYoxCoRBTpkxBmzZtcPLkSRw5cgTFxcXw8fEBAFhZWYms++LFC0RFRcHa2hp5eXmYOHEiDA0NcerUKcyfPx/btm1DaGgo1/7SpUuwsbFBQEAAdHV14eHhgeLiYhw5cgShoaFQVVXFihUrAJQMqEyePBlKSkoIDg7GpEmTsGXLFm5bmZmZmDx5MhwcHBAWFoYJEyZgwYIFiIyMrHSf5OTkwNfXFwUFBejbty8AIDY2Fjo6OhLbN2vWDB06dACfzwcAJCQklNtWT08PysrKEl9btmwZoqKisHXrVol9+DlPnjxBQUEBTpw4AQcHB/B4PJE+uXDhAgwMDNCqVStcvnwZW7ZswdKlSxESEgJDQ0O4urri/fv3ldrX6dOn0aZNG/B4PAwYMAB37tzBkydPKl7xEwkJCdDW1uZmYyqrU6dOIslmWX5+fiLlA6UPPz8/ie1fvnyJli1biixTUVHB8+fPxdo2b94cWVlZyM3N5ZY9f/4cRUVFyMrKEmm7c+dO2Nvbo1WrVhUea02oV7MuSEmxiht9k/1Kwc/PD56enli7di2aNWtWK3EQQggAnIneVkELKYzt839nn3ILsitcp1EDZTgaL+Cev/n4DOditgMAxvZZV+UYp02bBh6PBx6Ph7Vr18La2hq9e/cGAJiamiItLQ0AwOPx0LNnT+jr6wMApkyZgq1btyItLQ1ycnI4f/48/P39YWhoCABYtWoV4uPjuf389ttv6NixIwDA29sb3bp140aiOnbsiJSUFOzevRsDBw4UizEvLw9OTk4YOXIkfvrpJwCAvb09du/eDQCwtLTE+vXrkZaWhg4dOnB3t1RTU0NQUBBUVFS40bkOHTrgyZMnCAwMhJ2dHYCS5MLZ2RlASY2khYUFBg8ezJ0JHDVqFCZNmgQAuHnzJp49e4Zjx45BUVERnTt3RlJSEs6cOQMAOHjwIHr16oXRo0cDANTU1BAfH4+AgACRUb2yIiMjYWBgwO0/Pz8f3bp1w65du9C2bVsAwPv376GkpFRuPzZp0oS70CwrK6vKt7B///49Lly4gL///hvdu3eHjIyMWB9WZMKECVBTUwMAWFtb4+LFixg/fjyAkkTX3t4eALB7925MnjwZ/fv3BwDMnj0b//vf/3Dq1KlyR0XLOnv2LDea3LdvX8jLyyM0NBTTp0+v0jFnZWV9UZ5Q3heF8uTl5UFeXl5kmby8PAoKCsTa6unpoWXLlli9ejWWLFmCzMxM+Pv7A4DIiK5AIMDNmzexePHiKsf/rdSrRLemPHr0CDt37sSaNWsgIyMDAGjTpo3IaSlCCCHlKztqpaCggDZt2og8L/3namFhgWvXrmHdunV49OgRHjx4AAAoLi5Geno6iouLoaWlxa1rZGQkktiV3W5KSgp0dXVF4jAwMMCRI0e4n0sZGhpi9+7dcHZ2RmhoKGJjY7n9N2/eHACgqqoKIyMjXLx4EZMmTcLFixdhZWUFoOT/REJCgsg2i4uLuf8Zn8YmJSUFZ2dnnD17Fvfu3UNqaipiY2MhFAoBAImJiVBXV4eioiK3jr6+PpfoPnr0CP/++6/I/goLC6Guri7x/QcAbW1tbNy4EUKhEFevXoWvry/GjRsHExMTrk2TJk240XNJXr58yU2ZqaysXOnR0VKlfdi1a1du2ad9WJHSpBwoGWXfvHkzXrx4gcLCQiQkJGDIkCEASvrfx8cHmzZt4trn5+dzX6o+h8/nIz09HRYWFgCARo0aoVevXjh58iSX6MrKynL99SmhUMiVgygrK+PDhw+VPr5SEyZMECllKVX6u/qpBg0aiCW1BQUFaNiwocS2f/75J2bPng1DQ0OoqKhgwoQJ8Pb2Fvmdu3DhArp27YrOnTtXOf5vhRLdahYVFQUbGxvk5OSgVatWmDVrVm2HRAghIqz1plWpfUN5xQrXkZaSEXnerFGrKu+nrLIJHwCJp3EBYPPmzQgKCoKDgwPs7OywfPlyblRNTk6uwv00aNBA4s+lhEIhiouLAUCkrEBBQQEfP37EiBEj0LRpU5ibm8PGxgaPHj3C3r17uXZWVlY4fvw4hg8fjnv37mHdupLR7aKiIpiammLZsmWVik0oFGL8+PH48OEDrKysYG5ujsLCQri7uwMoeb8YEz1rWfZ5UVERbG1tuVrkUqXJlSQKCgrcSKi6ujry8vIwf/58tGvXDnp6egBKRvokJVdASbnE8+fPuXIFLS0txMXFSWy7adMmqKioYMyYMSLLK9OHZZX2VVll38e2bdtCR0cH4eHhyM/Ph5GREVq0aMGtu2jRIpiamoqsXzaRK0/pF4rSkWKgpM8YY7h79y4MDQ2hpKQkdpq/VFZWFjcyrqWlBX9/fzDGICUlWnR59uxZXL16Vex6H6BkytK8vDyx5QoKChL3qaqqKvYl5dWrV9z78SldXV1cvnwZmZmZaNq0Ka5du4amTZuiUaNGXJurV69iwIABEtevLfWqRrcmaGtrg8fjQVZWVuxDhxBCvgctGrev8FGWjLRshe1VFNuIrCMn20DitqrbkSNHsHTpUnh4eMDKyoqrIWSMoV27dpCRkRGZzzU8PJw7Vf0pdXV1sQtx7t+/z416qqmpcQ9VVVXcvn0bL1++RGBgICZMmIBevXrh6dOnIp/9gwcPRmJiIoKCgqCjo8ON0qqrqyM1NRVt27blthkVFYX9+/dLjC05ORl37tzBvn37MGXKFPTr1w8vX77kjrVLly5IS0tDdnY2t07ZpFJdXR3p6ekix3Dp0iWEhYVV+r12c3NDly5dsGTJEi6hHDFiBBITEyXOBf/333+jefPm+OWXXwAAQ4cORXh4uMjFYEBJ7fLBgwclJt2lfZiUlMQtK9uHcnJyItN4fbptSaysrBAREYHw8HBYW1tzy9XV1fH8+XOR92j79u2Iior67PaEQiHOnTuHYcOGITQ0lHuEhIRAUVGR+4KkqamJ7OxssblwU1JSkJ2dzY1aDxkyBO/evcPp06dF2hUXF8Pf3x85OTkS41BVVRWJvezvqiSffkl59uwZnj17xn2JKevdu3dwdnbG27dv0aJFC8jKyiIiIkLkBleMMcTExHx306VSovuVGGMiv3SysrLYvn07/vnnn3KvjCSEEFI9lJWV8e+//0IgECAyMhKenp4ASk7BKioqws7ODmvWrAGfz0dMTAw2b96Mnj17StzWyJEjER8fj02bNiE1NRUhISE4dOgQRo0aVe6+c3JyEB4ejsePHyMoKAgHDx4UOR3crFkzmJiYYMeOHbC0tOSWDx06FHl5eVi2bBlSUlJw5coVrFmzBioqKhL3paSkBGlpaZw5cwZPnjzB+fPnuYuMCgoKYGpqilatWmHp0qVISUnB+fPnERgYKHJssbGx2Lx5M9LS0hAWFoZNmzahdevWlX6vZWRksHTpUiQlJeHQoUMASmqkZ82ahd9//x379++HQCBAYmIivLy8EBwcjHXr1nEjqlZWVujRowfGjBmDc+fOQSAQ4MqVK3Bzc0OnTp0wYsQIsX0qKipi2LBh8PHxkdiHOjo6OHDgANLS0nDp0iWxmy5IYmlpicjISMTGxorM9DBu3DgEBAQgNDQUGRkZ8PHxwblz59CpUycAwMePHyVeTB4ZGYkXL17AxcUFGhoa3KNr164YOnQozp07h/z8fLRq1QoDBgyAh4cHbt26hcePH+O///7D3LlzYWVlxSWkbdq0gbu7OxYvXox9+/YhLS0NfD4fM2bMQEZGBubNm1fpPvscZ2dnnDx5EkFBQUhISICnpyf69evHlQ1lZWVx9dWlv+s+Pj4QCAQICgpCcHAwJkyYwG3vyZMn+Pjx43dVtgCgfs2je+12RLXOs5mZmclcXFzYiBEjmFAorLbtkq9H86rWL9Tf4n7UeXQFAgHT0NBgAoGAW1Z2jlHGGPP09GTz5s1jRUVFLDIyktnY2DAdHR1mYWHBduzYwUaMGMG2b9/OGGMsKyuLLViwgHXv3p2ZmJiwlStXsvz8fMYYYxoaGuzmzZsi+79+/Tqzs7NjWlpabODAgezw4cOfjdfPz4/17NmTGRgYMCcnJ3b8+HGmqanJnj9/zrUJDg5mPB6PvXjxQmTd2NhYNnLkSKatrc369OnD/vzzT1ZcXMwYkzyP7ZEjR5iZmRnT19dn9vb2LCwsjHXr1o3du3ePMcZYcnIyc3Z2ZlpaWszW1pZ5eXmxQYMGcetfu3aN2dvbMy0tLWZubs72799f7nGVN48uY4x5eHgwIyMj9vr1a27ZxYsXmZOTEzMwMGAmJiZsxowZLD4+XmzdvLw85uvrywYNGsR0dHRY//792Zo1a9i7d+/KjeX9+/ds3rx5EvswLi6OOyYnJyd29OhRbq7amzdvMg0NDYnbHD16NHNzcxNbHhAQwPr37890dHSYvb09u379ush7Imke3KVLlzJbW1uJ+0lMTGQaGhrs9OnTjLGSz6rVq1ezX375hWlpabG+ffuydevWic2xzxhjISEhzMHBgRkYGDBTU1Pm7u7OUlJSyn2fvkRwcDDr27cv09fXZ9OnT2dv3rzhXps/f77I70BKSgobPXo009PTY9bW1uzy5csi24qKimIaGhpc33xOTc6jK8VY/Ti/HhMTg6y8N9DXMuaujv1aa9euxcaNGwEA27Ztg5OTU7Vsl3y9nJwcxMfHo2vXrtXW3+T7Rf0trvQUvqQLS350xcXFyMvLg4KCglgtb331+vVrPHjwAGZmZtyy3bt348qVK+WWQ/wovqf+Hj9+vEgNNvkyn/t84vP5kJKSKncquqqi0oWvMHfuXGhqamLcuHGwsbGp7XAIIYTUY1OnTsWhQ4fw5MkTXL9+HQEBAdyMAuTrnTp16rurPyUVo1kXquDKlSvQ1dVF06ZNAZRcyRgeHi5yxSEhhBBS01RUVPDnn3/ir7/+gre3N5o3b47Ro0dj5MiRtR1anWFpaVnlWSBI7atXia6Qfdm90YRCIRYsWIDdu3dj+PDh2LVrF/caJbmEEEK+BxYWFtw8rqT6UZL7Y6LShUqQlpbmZlaIiIiQeHs8QgghhBDyfalXI7pfY+3atZCTk8PChQvF7g1NCCGEEEK+P/VqRLeyhQsxMTFwcXERmYRaSUkJmzdvpiSXEPJDkJGRkXiXKEIIqW2f3u76W6pfia5UxTOpPXjwABYWFjhz5gxWrFjx7YMihJBvQE5ODrm5uXSHRkLId4Uxhtzc3BqreabShU907doVgwYNwuXLl6GpqVnb4RBCyBeRkpJC06ZN8erVKzRs2LDW5x+tTkKhEPn5+QBKrqEgdRv1d91RXFyM3NxcNG3aFFJSXzZBQFXV+0RXKBTizZs3aN68OYCSfw6bNm3Chw8fuNv+EULIj0hWVhbNmzdHYWFhnSpjKCgoQGpqKjp27AgFBYXaDod8Y9TfdYe8vDwaNWpUY0kuUMuJbn5+PlauXImLFy9CQUEB48ePx/jx4yW2ffDgAZYvX46kpCR07twZK1euhLa29lftXyAQwN3dHe/evcM///wDeXl5AECLFi3QokWLr9o2IYR8D6SkpLjPtrqitByjQYMGdfLOb0QU9Tf5GrV6DmDDhg2IjY1FQEAAli9fji1btuD8+fNi7XJycjBp0iQYGRnhxIkTMDAwwOTJk7kpv77UuXPncPXqVcTExMDf3/+rtkUIIYQQQr4vtZbo5uTkICgoCIsXL4aWlhYGDhyICRMm4ODBg2Jtz549iwYNGsDT0xOdOnXC4sWL0ahRI4lJcVVMmDABffv2xbx58zBu3Liv2hYhhBBCCPm+1Fqim5CQgKKiIhgYGHDLDA0NER0dDaFQKNI2OjoahoaGXE2HlJQUunfvjqioqCrtsyCvCBkZGdxzaWlpHD9+HIsXL65zp/YIIYQQQuq7Wkt0MzMz0bRpU5EEs3nz5sjPz8e7d+/E2n46f62KikqV71CW/T4Xc+bMEUmk69KVyIQQQggh5P/U2sVoubm5YqOopc8LCgoq1fbTdp9TWFiIFi1aYObMmYiKioKsbL2fcKJOK7144eHDhzV6dSepHdTf9Qv1d/1C/V2/FBYWVms/11q216BBA7FEtfT5p9OHlNe2KtOMSElJQU5ODm3btv3CiMmPpC5eaU7KR/1dv1B/1y/U3/WLlJRU3Uh0VVVV8fbtWxQVFXGjq5mZmVBQUICSkpJY21evXokse/XqVZVux1u2FpgQQgghhNR9tVaj27VrV8jKyopcUHb37l3o6OiI3flET08P9+/f505fMMZw79496Onp1WTIhBBCCCHkB1JriW7Dhg1hZ2eHFStWgM/nIzw8HHv37oWrqyuAktHdvLw8AMCQIUPw4cMHrFmzBsnJyVizZg1yc3NhaWlZW+ETQgghhJDvnBQrHSatBbm5uVixYgUuXrwIRUVFuLm5YezYsQAATU1NeHt7w8HBAQDA5/OxfPlypKSkQFNTEytXrkS3bt1qK3RCCCGEEPKdq9VElxBCCCGEkG+lVm8BTAghhBBCyLdCiS4hhBBCCKmTKNElhBBCCCF1EiW6hBBCCCGkTqpTiW5+fj4WLVoEIyMj9OnTB3v37i237YMHD+Do6Ag9PT0MHz4csbGxNRgpqQ5V6e+IiAgMGzYMBgYGsLW1xaVLl2owUlIdqtLfpR4/fgwDAwPcunWrBiIk1akq/Z2YmAhnZ2fo6urC1tYWN2/erMFISXWoSn//888/sLS0hIGBAZydnREXF1eDkZLqVFBQABsbm89+Rn9tvlanEt0NGzYgNjYWAQEBWL58ObZs2YLz58+LtcvJycGkSZNgZGSEEydOwMDAAJMnT0ZOTk4tRE2+VGX7OyEhAe7u7hg+fDhCQ0Ph5OSEWbNmISEhoRaiJl+qsv1d1ooVK+jv+gdV2f7OysrC+PHj0blzZ4SFhWHgwIFwd3fH69evayFq8qUq298PHz7EvHnzMHnyZJw8eRJdu3bF5MmTkZubWwtRk6+Rn5+PuXPn4uHDh+W2qZZ8jdURHz9+ZDo6OuzmzZvcsq1bt7LRo0eLtQ0KCmLm5uZMKBQyxhgTCoVs4MCBLDg4uMbiJV+nKv3t4+PD3NzcRJaNHz+ebdq06ZvHSapHVfq71MmTJ5mTkxPT0NAQWY98/6rS3wEBAczCwoIVFRVxyxwcHFhERESNxEq+XlX629/fn9nb23PPs7KymIaGBuPz+TUSK6keDx8+ZEOHDmW2traf/YyujnytzozoJiQkoKioCAYGBtwyQ0NDREdHQygUirSNjo6GoaEhpKSkAABSUlLo3r27yO2IyfetKv1tb28PDw8PsW1kZWV98zhJ9ahKfwPA27dv4ePjg1WrVtVkmKSaVKW/b9++jQEDBkBGRoZbFhwcjL59+9ZYvOTrVKW/lZWVkZycjLt370IoFOLEiRNQVFRE+/btazps8hVu374NExMTHD169LPtqiNfk/2aQL8nmZmZaNq0KeTl5bllzZs3R35+Pt69e4dmzZqJtO3cubPI+ioqKp8dPiffl6r0d6dOnUTWffjwIW7cuAEnJ6cai5d8nar0NwCsW7cO9vb26NKlS02HSqpBVfpbIBBAV1cXS5cuxeXLl9GmTRvMnz8fhoaGtRE6+QJV6W8rKytcvnwZI0eOhIyMDKSlpbFjxw40adKkNkInX2jkyJGValcd+VqdGdHNzc0V+SMBwD0vKCioVNtP25HvV1X6u6w3b95gxowZ6N69OwYMGPBNYyTVpyr9ff36ddy9exfTpk2rsfhI9apKf+fk5GDnzp1o0aIFdu3aBWNjY7i5ueHZs2c1Fi/5OlXp77dv3yIzMxPLli3DsWPHMGzYMCxcuJBqsuuo6sjX6kyi26BBA7EDL32uoKBQqbaftiPfr6r0d6lXr15hzJgxYIzB19cX0tJ15te/zqtsf+fl5WHZsmVYvnw5/T3/wKry9y0jI4OuXbti5syZ6NatG37//Xd06NABJ0+erLF4ydepSn9v3LgRGhoaGDVqFLS1tbF69Wo0bNgQwcHBNRYvqTnVka/Vmf/0qqqqePv2LYqKirhlmZmZUFBQgJKSkljbV69eiSx79eoVWrZsWSOxkq9Xlf4GgBcvXmDUqFEoKChAYGCg2Klu8n2rbH/z+XwIBALMnDkTBgYGXM3fxIkTsWzZshqPm3yZqvx9t2jRAh07dhRZ1qFDBxrR/YFUpb/j4uLA4/G459LS0uDxeHj69GmNxUtqTnXka3Um0e3atStkZWVFCpTv3r0LHR0dsZE7PT093L9/H4wxAABjDPfu3YOenl5Nhky+QlX6OycnBxMmTIC0tDQOHDgAVVXVGo6WfK3K9reuri4uXryI0NBQ7gEAXl5emDVrVg1HTb5UVf6+9fX1kZiYKLLs0aNHaNOmTU2ESqpBVfq7ZcuWSElJEVmWmpqKtm3b1kSopIZVR75WZxLdhg0bws7ODitWrACfz0d4eDj27t0LV1dXACXfDvPy8gAAQ4YMwYcPH7BmzRokJydjzZo1yM3NhaWlZW0eAqmCqvT3jh07kJGRgfXr13OvZWZm0qwLP5DK9reCggLU1NREHkDJqICKikptHgKpgqr8fTs5OSExMRF+fn5IT0/HX3/9BYFAgGHDhtXmIZAqqEp///rrrzh27BhCQ0ORnp6OjRs34unTp7C3t6/NQyDVqNrzta+dC+17kpOTwzw9PZm+vj7r06cP8/f3517T0NAQmXctOjqa2dnZMR0dHTZixAgWFxdXCxGTr1HZ/h48eDDT0NAQe8yfP7+WIidfoip/32XRPLo/pqr0d2RkJLO3t2fa2tps2LBh7Pbt27UQMfkaVenvY8eOsSFDhjB9fX3m7OzMYmNjayFiUl0+/Yyu7nxNirH/Px5MCCGEEEJIHVJnShcIIYQQQggpixJdQgghhBBSJ1GiSwghhBBC6iRKdAkhhBBCSJ1EiS4hhBBCCKmTKNElhBBCCCF1EiW6hBBCCCGkTqJElxDyw3JxcYGmpqbER+md8Cpy69YtaGpq4vHjx98kxsePH4vF1q1bN5iammL27Nl4+vRpte3L3Nwcfn5+AEpulRkSEoLXr18DAE6cOAFNTc1q29enSrdf9tG1a1cYGxtj3LhxePDgQZW29/TpU5w5c+YbRUsIqS9kazsAQgj5GpaWlli8eLHY8oYNG9ZCNOXz8/ODgYEBAEAoFEIgEGDx4sWYPHkyTp06BSkpqa/ex/Hjx9GgQQMAwJ07d7BgwQJcunQJAGBlZQUzM7Ov3kdF/vvvP+7n4uJipKamYu3atXBzc0N4eDgaNWpUqe3Mnz8fbdq0gbW19bcKlRBSD1CiSwj5oSkoKKBFixa1HUaFmjRpIhKnqqoq3N3d4eHhgcTERPB4vK/eR7NmzbifP73ppYKCAhQUFL56HxX5tC9+/vlnLFu2DKNHj8bNmzcxYMCAbx4DIYSUotIFQkid9v79eyxZsgRmZmbQ0tKCqakplixZgtzcXInt09LS4ObmBkNDQxgYGMDNzQ2JiYnc61lZWVi6dCl69uwJQ0NDuLq6IiYm5otik5GRAQDIyckBAJ49ewYPDw/07t0b+vr6cHNzQ0JCAtf+9evXmDlzJkxMTKCrqwsnJyfcvn2be720dOHWrVtwdXUFAAwYMAAnTpwQKV1YsGABHB0dRWJ58uQJeDwerl+/DgC4d+8eRo0aBV1dXfTr1w8rV65Ednb2Fx1n6SizrGzJ2IpQKMSOHTswePBgaGtro3v37pgwYQIyMjIAlJSk3L59GyEhITA3NwcAFBQUwMfHB2ZmZjAwMMCvv/4qMnpMCCGSUKJLCKnTFixYgAcPHmDLli24cOECFi5ciNDQUBw9elRi+7lz50JVVRXBwcEICgqCtLQ03N3dAZSMkk6cOBECgQA7duzAsWPHoK+vD2dn5yrVoAqFQsTHx+Pvv/8Gj8eDuro6srOz4ezsjBcvXuDvv//GkSNHoKCggNGjR+PJkycAgBUrViA/Px8HDhxAWFgY1NXVMW3aNOTk5Ihs38DAgKvVDQoKgpWVlcjrDg4O4PP5XGIJAGFhYfj555/Rs2dPJCQkYNy4cTAzM8OpU6ewceNGxMXFYfz48WIjxRURCATw8fFB69atYWxsDAAIDAzEnj17sGDBAly4cAFbt25FWloa1q1bB+D/yjwsLS1x/PhxAMDChQtx7do1bNy4ESEhIbC0tMSUKVMQERFRpXgIIfULlS4QQn5oYWFhuHDhgsgyQ0ND7N69GwDQu3dvGBsbc6OZbdu2xYEDB5CUlCRxexkZGejVqxfatGkDOTk5rF27Fo8ePYJQKMStW7cQFRWFmzdvQllZGUBJYnzv3j0EBgZyiZokEydO5EZwCwoKwBiDkZERVq9eDWlpaZw6dQpv377FiRMnuBKEP/74AxYWFjh48CA8PT2RkZEBDQ0NtGvXDgoKCli8eDFsbW257ZaSl5dHkyZNAJSUM3xasmBsbIx27drh1KlTXBIfFhaGYcOGQVpaGnv27EHv3r0xZcoUAECHDh24WG7fvg0TE5Nyj7O0DhkACgsLIScnhz59+sDb2xs//fQTAKB9+/ZYv349+vfvDwBo06YNhgwZgvPnzwMAlJWVIScnBwUFBTRr1gzp6ek4ffo0QkND0bVrVwDAuHHjkJCQgD179qBfv37lxkMIqd8o0SWE/NDMzc3h4eEhsqxsYjdy5EhcvnwZISEhSEtLQ3JyMh4/foyOHTtK3N6cOXOwdu1aHDp0CD169ICZmRlsbGwgLS2NuLg4MMa4BK1UQUEB8vPzPxunl5cX9PT0AJScwldRURGJMykpCR06dBCps1VQUICuri6XlLu7u+P333/HhQsXYGhoiD59+sDGxoYrDagsKSkp2NnZISwsDO7u7njw4AGSk5Oxbds2AMCDBw+Qnp4ukrSWSklJ+WyiGxoaCqCkzOLPP//E69evMXv2bLRt25ZrY25ujujoaPz1119ITU1FamoqkpOToaqqKnGbpaPlI0eOFFleWFgIJSWlKh07IaR+oUSXEPJDa9SoEdTU1CS+JhQKMXnyZDx8+BA2NjawsrKClpYWli5dWu72Ro0ahSFDhuDKlSu4ceMGfH198ffffyM0NBRCoRCKioo4ceKE2Hry8vKfjVNVVbXcOAHxi8fKHkNpbevAgQNx9epVXL16FdevX4e/vz+2bNmCY8eOoUuXLp/d/6fs7e2xZcsWxMTE4OzZs+jevTsXn1AohK2tLTeiW1bZRFyS0m2oqalhx44dcHR0hJubG0JCQtC0aVMAwM6dO7F161bY29vD1NQUY8eOxaVLl8qdTqz0vTl48KDYrA3S0lSBRwgpH31CEELqrPj4ePzvf//DX3/9BQ8PDwwdOhTt27dHRkaGxMTy9evXWLVqFQoLC+Hg4AAfHx+cOnUKmZmZuH37NjQ0NJCdnY3CwkKoqalxj127dnHTeH0pTU1NpKWlcfPeAkB+fj5iY2PRuXNnFBQUwNvbGwKBAFZWVvDy8kJ4eDikpaUl1qlWNF1ZmzZtYGJiggsXLuDcuXNwcHDgXuvSpQuSk5NFjrGoqAje3t549uxZpY+pYcOG2LhxI169eoVVq1Zxy7dv347p06djxYoV+O2336Cvr4+0tLRyk/3SJD4zM1MkptKL7AghpDyU6BJC6qzmzZtDVlYW586dg0AgQExMDGbPno3MzEwUFBSItW/SpAkiIiKwZMkSxMfHQyAQ4MiRI5CTk4O2tjbMzMzQtWtXzJkzBzdv3kR6ejq8vb1x4sQJdOrU6atitbW1hbKyMmbPng0+n4+EhAR4eHggJycHv/32G+Tl5RETE4OlS5ciKioKjx8/xokTJ5CTkyOxxKC0HjYhIQEfP36UuE97e3scOnQI7969g6WlJbd8/PjxePDgAVauXImUlBTcv38f8+bNQ1paGjp06FCl4+LxeJgwYQLOnj2Ly5cvAwBatWqFa9euITk5GY8ePcLmzZtx8eJFkT5p1KgRnjx5gufPn6NLly7o378/li9fjsuXL0MgEGDXrl3YsWMH2rdvX6V4CCH1CyW6hJA6S1VVFevWrcPly5dhZWWFWbNmQVVVFWPHjkVsbKxYe1lZWezatQvS0tIYO3YsrK2tcf36dezcuRPt27eHjIwM9u7dC21tbcyePRtDhw7FnTt3sGXLFpiamn5VrI0bN8aBAwegpKSEsWPHYuTIkcjLy8Phw4fRrl07AMDmzZvRrl07TJ06FUOGDMGRI0ewceNGGBkZiW1PQ0MDffv2xezZs8udYWLw4MEAAAsLCygqKnLL9fX1sXv3bsTHx8Pe3h5Tp06Furo69u3bV2GJhiTTpk1Dx44duSnKNmzYgLy8PAwfPhyjR49GUlISVq5cidevX3N3inNyckJSUhKGDh2K4uJibN68GYMGDcKyZctgZWWF0NBQrFmzBvb29lWOhxBSf0ixqs4VQwghhBBCyA+ARnQJIYQQQkidRIkuIYQQQgipkyjRJYQQQgghdRIluoQQQgghpE6iRJcQQgghhNRJlOgSQgghhJA6iRJdQgghhBBSJ1GiSwghhBBC6iRKdAkhhBBCSJ1EiS4hhBBCCKmTKNElhBBCCCF1EiW6hBBCCCGkTvp/i3wi9iVOFlgAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 800x550 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_model(tuned_et, plot = 'auc')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d2ce9ddc",
-   "metadata": {},
-   "source": [
-    "## Splitting Data into Training (80%) & Test (20%)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "ea16c01e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    227458\n",
-       "1       387\n",
-       "Name: Class, dtype: int64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "y = df2['Class']\n",
-    "X = df2.drop(['Class'], axis=1)\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2)\n",
-    "\n",
-    "y_train.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "77ce6836",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Found documentation on how to utilize the SMOTE package from the imblearn class\n",
-    "from imblearn.over_sampling import SMOTE\n",
-    "smote = SMOTE(sampling_strategy=\"minority\")\n",
-    "X_train_sm, y_train_sm = smote.fit_resample(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "d1bbb659",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    227458\n",
-       "1    227458\n",
-       "Name: Class, dtype: int64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y_train_sm.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "6ae77385",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RandomForestClassifier()"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "smote_random_forest = RandomForestClassifier()\n",
-    "smote_random_forest.fit(X_train_sm,y_train_sm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "0610155c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "smote_rand_pred = smote_random_forest.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "020423f2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[56848     9]\n",
-      " [   23    82]]\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       1.00      1.00      1.00     56857\n",
-      "           1       0.90      0.78      0.84       105\n",
-      "\n",
-      "    accuracy                           1.00     56962\n",
-      "   macro avg       0.95      0.89      0.92     56962\n",
-      "weighted avg       1.00      1.00      1.00     56962\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(confusion_matrix(y_test,smote_rand_pred))\n",
-    "print(classification_report(y_test,smote_rand_pred))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "444a0fc5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 800x550 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_model(smote_random_forest, plot = 'auc')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "3f3b3a2d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_11d4e_row10_col0, #T_11d4e_row10_col1, #T_11d4e_row10_col2, #T_11d4e_row10_col3, #T_11d4e_row10_col4, #T_11d4e_row10_col5, #T_11d4e_row10_col6 {\n",
-       "  background: yellow;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_11d4e\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_11d4e_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
-       "      <th id=\"T_11d4e_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
-       "      <th id=\"T_11d4e_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
-       "      <th id=\"T_11d4e_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
-       "      <th id=\"T_11d4e_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
-       "      <th id=\"T_11d4e_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
-       "      <th id=\"T_11d4e_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >Fold</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_11d4e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_11d4e_row0_col0\" class=\"data row0 col0\" >0.9991</td>\n",
-       "      <td id=\"T_11d4e_row0_col1\" class=\"data row0 col1\" >0.9865</td>\n",
-       "      <td id=\"T_11d4e_row0_col2\" class=\"data row0 col2\" >0.5897</td>\n",
-       "      <td id=\"T_11d4e_row0_col3\" class=\"data row0 col3\" >0.8214</td>\n",
-       "      <td id=\"T_11d4e_row0_col4\" class=\"data row0 col4\" >0.6866</td>\n",
-       "      <td id=\"T_11d4e_row0_col5\" class=\"data row0 col5\" >0.6861</td>\n",
-       "      <td id=\"T_11d4e_row0_col6\" class=\"data row0 col6\" >0.6956</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_11d4e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_11d4e_row1_col0\" class=\"data row1 col0\" >0.9993</td>\n",
-       "      <td id=\"T_11d4e_row1_col1\" class=\"data row1 col1\" >0.9741</td>\n",
-       "      <td id=\"T_11d4e_row1_col2\" class=\"data row1 col2\" >0.6154</td>\n",
-       "      <td id=\"T_11d4e_row1_col3\" class=\"data row1 col3\" >0.9231</td>\n",
-       "      <td id=\"T_11d4e_row1_col4\" class=\"data row1 col4\" >0.7385</td>\n",
-       "      <td id=\"T_11d4e_row1_col5\" class=\"data row1 col5\" >0.7381</td>\n",
-       "      <td id=\"T_11d4e_row1_col6\" class=\"data row1 col6\" >0.7534</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_11d4e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_11d4e_row2_col0\" class=\"data row2 col0\" >0.9992</td>\n",
-       "      <td id=\"T_11d4e_row2_col1\" class=\"data row2 col1\" >0.9568</td>\n",
-       "      <td id=\"T_11d4e_row2_col2\" class=\"data row2 col2\" >0.6410</td>\n",
-       "      <td id=\"T_11d4e_row2_col3\" class=\"data row2 col3\" >0.8333</td>\n",
-       "      <td id=\"T_11d4e_row2_col4\" class=\"data row2 col4\" >0.7246</td>\n",
-       "      <td id=\"T_11d4e_row2_col5\" class=\"data row2 col5\" >0.7242</td>\n",
-       "      <td id=\"T_11d4e_row2_col6\" class=\"data row2 col6\" >0.7305</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_11d4e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_11d4e_row3_col0\" class=\"data row3 col0\" >0.9991</td>\n",
-       "      <td id=\"T_11d4e_row3_col1\" class=\"data row3 col1\" >0.9359</td>\n",
-       "      <td id=\"T_11d4e_row3_col2\" class=\"data row3 col2\" >0.5897</td>\n",
-       "      <td id=\"T_11d4e_row3_col3\" class=\"data row3 col3\" >0.8214</td>\n",
-       "      <td id=\"T_11d4e_row3_col4\" class=\"data row3 col4\" >0.6866</td>\n",
-       "      <td id=\"T_11d4e_row3_col5\" class=\"data row3 col5\" >0.6861</td>\n",
-       "      <td id=\"T_11d4e_row3_col6\" class=\"data row3 col6\" >0.6956</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_11d4e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_11d4e_row4_col0\" class=\"data row4 col0\" >0.9988</td>\n",
-       "      <td id=\"T_11d4e_row4_col1\" class=\"data row4 col1\" >0.9144</td>\n",
-       "      <td id=\"T_11d4e_row4_col2\" class=\"data row4 col2\" >0.4872</td>\n",
-       "      <td id=\"T_11d4e_row4_col3\" class=\"data row4 col3\" >0.7308</td>\n",
-       "      <td id=\"T_11d4e_row4_col4\" class=\"data row4 col4\" >0.5846</td>\n",
-       "      <td id=\"T_11d4e_row4_col5\" class=\"data row4 col5\" >0.5840</td>\n",
-       "      <td id=\"T_11d4e_row4_col6\" class=\"data row4 col6\" >0.5961</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_11d4e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_11d4e_row5_col0\" class=\"data row5 col0\" >0.9993</td>\n",
-       "      <td id=\"T_11d4e_row5_col1\" class=\"data row5 col1\" >0.9448</td>\n",
-       "      <td id=\"T_11d4e_row5_col2\" class=\"data row5 col2\" >0.6579</td>\n",
-       "      <td id=\"T_11d4e_row5_col3\" class=\"data row5 col3\" >0.9259</td>\n",
-       "      <td id=\"T_11d4e_row5_col4\" class=\"data row5 col4\" >0.7692</td>\n",
-       "      <td id=\"T_11d4e_row5_col5\" class=\"data row5 col5\" >0.7689</td>\n",
-       "      <td id=\"T_11d4e_row5_col6\" class=\"data row5 col6\" >0.7802</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_11d4e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_11d4e_row6_col0\" class=\"data row6 col0\" >0.9993</td>\n",
-       "      <td id=\"T_11d4e_row6_col1\" class=\"data row6 col1\" >0.9720</td>\n",
-       "      <td id=\"T_11d4e_row6_col2\" class=\"data row6 col2\" >0.6316</td>\n",
-       "      <td id=\"T_11d4e_row6_col3\" class=\"data row6 col3\" >0.8889</td>\n",
-       "      <td id=\"T_11d4e_row6_col4\" class=\"data row6 col4\" >0.7385</td>\n",
-       "      <td id=\"T_11d4e_row6_col5\" class=\"data row6 col5\" >0.7381</td>\n",
-       "      <td id=\"T_11d4e_row6_col6\" class=\"data row6 col6\" >0.7489</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_11d4e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_11d4e_row7_col0\" class=\"data row7 col0\" >0.9990</td>\n",
-       "      <td id=\"T_11d4e_row7_col1\" class=\"data row7 col1\" >0.9645</td>\n",
-       "      <td id=\"T_11d4e_row7_col2\" class=\"data row7 col2\" >0.5789</td>\n",
-       "      <td id=\"T_11d4e_row7_col3\" class=\"data row7 col3\" >0.7857</td>\n",
-       "      <td id=\"T_11d4e_row7_col4\" class=\"data row7 col4\" >0.6667</td>\n",
-       "      <td id=\"T_11d4e_row7_col5\" class=\"data row7 col5\" >0.6662</td>\n",
-       "      <td id=\"T_11d4e_row7_col6\" class=\"data row7 col6\" >0.6740</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_11d4e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_11d4e_row8_col0\" class=\"data row8 col0\" >0.9993</td>\n",
-       "      <td id=\"T_11d4e_row8_col1\" class=\"data row8 col1\" >0.9880</td>\n",
-       "      <td id=\"T_11d4e_row8_col2\" class=\"data row8 col2\" >0.6053</td>\n",
-       "      <td id=\"T_11d4e_row8_col3\" class=\"data row8 col3\" >0.9200</td>\n",
-       "      <td id=\"T_11d4e_row8_col4\" class=\"data row8 col4\" >0.7302</td>\n",
-       "      <td id=\"T_11d4e_row8_col5\" class=\"data row8 col5\" >0.7298</td>\n",
-       "      <td id=\"T_11d4e_row8_col6\" class=\"data row8 col6\" >0.7459</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_11d4e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_11d4e_row9_col0\" class=\"data row9 col0\" >0.9991</td>\n",
-       "      <td id=\"T_11d4e_row9_col1\" class=\"data row9 col1\" >0.9407</td>\n",
-       "      <td id=\"T_11d4e_row9_col2\" class=\"data row9 col2\" >0.5641</td>\n",
-       "      <td id=\"T_11d4e_row9_col3\" class=\"data row9 col3\" >0.8800</td>\n",
-       "      <td id=\"T_11d4e_row9_col4\" class=\"data row9 col4\" >0.6875</td>\n",
-       "      <td id=\"T_11d4e_row9_col5\" class=\"data row9 col5\" >0.6871</td>\n",
-       "      <td id=\"T_11d4e_row9_col6\" class=\"data row9 col6\" >0.7042</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_11d4e_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
-       "      <td id=\"T_11d4e_row10_col0\" class=\"data row10 col0\" >0.9991</td>\n",
-       "      <td id=\"T_11d4e_row10_col1\" class=\"data row10 col1\" >0.9578</td>\n",
-       "      <td id=\"T_11d4e_row10_col2\" class=\"data row10 col2\" >0.5961</td>\n",
-       "      <td id=\"T_11d4e_row10_col3\" class=\"data row10 col3\" >0.8531</td>\n",
-       "      <td id=\"T_11d4e_row10_col4\" class=\"data row10 col4\" >0.7013</td>\n",
-       "      <td id=\"T_11d4e_row10_col5\" class=\"data row10 col5\" >0.7009</td>\n",
-       "      <td id=\"T_11d4e_row10_col6\" class=\"data row10 col6\" >0.7124</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_11d4e_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
-       "      <td id=\"T_11d4e_row11_col0\" class=\"data row11 col0\" >0.0001</td>\n",
-       "      <td id=\"T_11d4e_row11_col1\" class=\"data row11 col1\" >0.0225</td>\n",
-       "      <td id=\"T_11d4e_row11_col2\" class=\"data row11 col2\" >0.0456</td>\n",
-       "      <td id=\"T_11d4e_row11_col3\" class=\"data row11 col3\" >0.0621</td>\n",
-       "      <td id=\"T_11d4e_row11_col4\" class=\"data row11 col4\" >0.0492</td>\n",
-       "      <td id=\"T_11d4e_row11_col5\" class=\"data row11 col5\" >0.0493</td>\n",
-       "      <td id=\"T_11d4e_row11_col6\" class=\"data row11 col6\" >0.0497</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x22d8dab8250>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tuned_smote_rf = tune_model(smote_random_forest)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "d7350d72",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 800x550 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_model(tuned_smote_rf, plot = 'auc')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "302fcabb",
-   "metadata": {},
-   "source": [
-    "## Cross Validation with RandomForestClassifier (From Kaggle to Compare Results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "9000c9f3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    227452\n",
-       "1       393\n",
-       "Name: Class, dtype: int64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "y_cr = df2['Class']\n",
-    "X_cr = df2.drop(['Class'], axis=1)\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X_cr,y_cr, test_size = 0.2)\n",
-    "\n",
-    "y_train.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "7df699fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clf = RandomForestClassifier()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "e086c61a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import cross_val_score, cross_val_predict, cross_validate, train_test_split \n",
-    "cvs = cross_validate(clf,X=X_train, y=y_train, scoring=['accuracy','average_precision','balanced_accuracy','f1','precision','recall','roc_auc'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "a5f60150",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "fit_time               [143.05808663 136.16519642 144.83082938 135.07303357 145.75911236]   Avg:    140.98\n",
-      "score_time             [1.17401242 1.13700676 1.17800713 1.14001083 1.19800782]   Avg:    1.17\n",
-      "accuracy               [0.99958305 0.99945138 0.99956111 0.99940749 0.99964888]   Avg:    1.00\n",
-      "average_precision      [0.82939825 0.80192686 0.8774015  0.80247249 0.91927439]   Avg:    0.85\n",
-      "balanced_accuracy      [0.89740292 0.85254212 0.90500833 0.86070454 0.94296103]   Avg:    0.89\n",
-      "f1                     [0.86713287 0.81481481 0.86486486 0.80851064 0.8974359 ]   Avg:    0.85\n",
-      "precision              [0.95384615 0.96491228 0.92753623 0.91935484 0.90909091]   Avg:    0.93\n",
-      "recall                 [0.79487179 0.70512821 0.81012658 0.72151899 0.88607595]   Avg:    0.78\n",
-      "roc_auc                [0.95314246 0.94033097 0.96720158 0.95374502 0.98040966]   Avg:    0.96\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "for k, v in cvs.items():\n",
-    "    print(f'{k.replace(\"test_\",\"\"):23}{v}   Avg:{\"\":4}{np.mean(v):.2f}' )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "60000b39",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAroAAAH7CAYAAAAjETxNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAADmXUlEQVR4nOzdd3xT5dsG8Cs76d5ltGw62HtZUabIHoKIAiqIgICDVwFRQH8iW2XKUFAQEVTEAQ7EjYiKCDLKLFBooS3dbZp53j/SnDbdKUnTNtf381Gak3NO7vY07d3n3M/9SARBEEBEREREVMtIXR0AEREREZEzMNElIiIiolqJiS4RERER1UpMdImIiIioVmKiS0RERES1EhNdIiIiIqqVmOgSERERUa3ERJeIiIiIaiUmukRE5La4ZhJR7cZEl6iKjB8/HpGRkTb/RUVFoUOHDhg5ciQ+//zzEo/74YcfMHnyZHTt2hVt2rTBfffdhyVLliAxMbHU1/r2228xadIk9OjRA+3atcPgwYOxYcMGZGdnVyjWzMxMrFu3DkOGDEH79u3RvXt3TJw4ET/88EOlPvfq5o8//sB9992HVq1aYfLkyQ4/f+/evYtd6+joaHTu3Bnjxo3DL7/84vDXLM3169cRGRmJvXv3VsnrlfR9Xvi///77r0riKM/NmzcxZcoU3Lhxo9hz//33H55//nnce++9aNOmDfr27YuXX34Z8fHxNvtFRkZi7dq1VRUyAGDt2rWIjIwUH2dnZ2Pq1Klo27YtOnfujCtXrlTp9Saq7uSuDoDInbRo0QILFy4UH5tMJty8eRPvvfceXnjhBfj5+eGee+4Rn3/llVfw4YcfYtCgQfjf//4HHx8fXLx4ETt27MBnn32GNWvWoFu3buL+ZrMZzz//PL755huMGjUKDz30EDw9PfHvv//i3Xffxffff4/33nsPPj4+pcZ46dIlPPHEEzCbzZgwYQKioqKQm5uLL7/8EtOmTcPTTz+N6dOnO+cLVEWWL18Os9mMzZs3IzAw0Cmvcc8999h8nYxGI65du4bNmzdj+vTp+OSTTxAVFeWU13a1ot/nhTVt2rSKoynZ77//jp9//rnY9p07d+L1119H165dMXv2bISEhODq1at499138d133+H999936XUbPXo07r77bvHxvn378OOPP2LBggVo3rw56tWrh927d6NBgwYui5GoOmGiS1SFvLy80K5du2Lbe/bsie7du2Pv3r1iortz5058+OGHWLp0KUaMGCHu261bNwwfPhxPPPEEnnnmGXz11VcICgoCALzzzjv46quvsG7dOvTr1088pnv37ujSpQsefvhhrF+/HvPmzSsxPoPBgGeeeQYKhQIffvihTRJoHdVavXo1evfuXaOTtPT0dHTu3Bk9evRw2msEBAQUu9adOnVC27ZtMXDgQHzxxRc1+mtYltK+z6u7Y8eOYfHixXj44Ycxf/58cXvXrl3Rt29fDB8+HC+++KJLR0vr1KmDOnXqiI/T09MBAOPGjYNEIgGAGvm1J3IWli4QVQMqlQpKpVL8RWUymfD2228jJibGJsm18vLywmuvvYa0tDTs3LkTgCVJ3bp1K3r27GmT5Fp17NgRs2bNQrNmzUqN4+eff8b58+fx9NNPlzjSOWvWLDzyyCMwGo0AgLlz56J37942+xS9VX706FFERkbio48+Qq9evdChQwd89tlniIyMxPnz522O/f777xEZGYkzZ84AsPwSX7BgAXr06IHWrVtjzJgxOHLkiM0xhw8fxpgxY9C+fXt07twZ06ZNw6VLl0r8/Kyx3bhxA/v27UNkZCSOHj0KwHK7etKkSejatSs6dOiAqVOn4sKFC+KxJX0ehw8fLvVrWRrraLr1WlvjeuGFFxATE4OWLVuie/fueOGFF5CWlibu07t3b6xZswbLli1Djx490KZNG0yaNAlXrlyxOf93332HoUOHok2bNhgxYgRiY2OLxZCUlIR58+bhnnvuQZs2bfDAAw/g0KFDNvtERkZi165dmDt3Ljp27IguXbrgtddeQ15eHpYtW4Zu3bqha9eumD9/PnQ6nd1fB5PJhJ07d2LIkCFo06YN7r33XqxcudLmXHPnzsXEiROxcOFCdOjQAQMHDoTJZBJH4/v164dWrVrhvvvuw44dO2zOf+3aNUydOhVdu3ZF27Zt8eCDD4ojuHv37hX/2OvTpw/mzp0LAHj33Xfh7e2N5557rli8AQEBmDt3Lvr06YPc3NwSP6fY2FjMmDED3bp1Q8uWLXH33XeLXzOr8r5fy4obsC1dGD9+vFg6ERUVhblz55ZYqpKQkIDnnnsOXbp0Qdu2bTFx4kTxPQYUvC+2bduGAQMGoG3btvj000/LunxENQYTXaIqJAgCjEaj+J9Op8Ply5cxb9485OTkYNiwYQCAs2fPIjk5uVgSWVjTpk0RFRUlJiinT59GWloaevXqVeox06dPx+jRo0t9/pdffoFMJrMpnygsODgYL7/8Mlq1alWRT9fGunXrMGfOHCxYsAD33XcfPDw8sH//fpt9vvrqKzRv3hwtWrSATqfDxIkTcejQITz77LNYt24d6tSpg8mTJ4vJbnx8PKZPn45WrVrh7bffxuLFixEXF4cpU6bAbDYXiyEkJAS7d+9GcHAw7rnnHuzevRstW7bEH3/8gYceeggA8Prrr+O1115DYmIixo4dWyxpLvx5tG/fvtTPt+i11mq1iI2NxZw5c6BQKDB48GAAgFarxYQJE3Dp0iUsXLgQ7777LiZMmID9+/fjzTfftDnn9u3bcfnyZSxZsgSvvfYaTp06hTlz5ojP//DDD5g1axYiIyOxfv163H///Xj++edtzpGSkoIHHngAf//9N5599lmsXbsW9evXx1NPPYUvvvjCZt8VK1ZAqVRi3bp1GD58OHbs2IHhw4cjMTERK1euxPjx4/HJJ58USzKLfu7W/wpP/FqwYAGWLFmCvn374u2338bDDz+MDz74ANOnT7fZ7++//0ZiYiLWr1+P2bNnQyaTYdGiRVizZg2GDh2KjRs3YsCAAXj99dexfv16AJYSnieffBJarRbLly/Hhg0b4Ofnh2nTpuHq1au49957MW3aNPF6Wl/zt99+Q/fu3aHRaEq8pgMHDsRTTz0FDw+PYs8lJSXh4YcfhlarxdKlS7FlyxYMGjQIO3bswPbt2wGU//1aXtxFLVy4EA888AAAYPfu3SWWFKWmpmLs2LE4ffo0Xn75ZaxatQpmsxkPP/xwse/ttWvX4oknnsDy5ctx1113lfg1IKppWLpAVIX++usvtGzZ0mabRCJBREQEVq9eLSap169fBwCEhYWVeb6GDRuKo4rWyWnlHVOWmzdvwt/fH56enpU+R2nGjRuHAQMGiI/vu+8+HDhwAM8++ywAICcnBz/++COeeuopAMDnn3+O2NhY7NmzB23btgVgKfEYP348Vq5ciU8//RQnT55EXl4ennzySYSGhgKw3No9dOgQcnNz4eXlZRODUqlEu3btoFQqbUoLVq1ahYYNG2Lz5s2QyWQAgJiYGPTr1w9r1qzB6tWrS/08SrNv3z7s27fPZptcLkerVq3w7rvvIjo6GgBw5coV1KlTB8uWLUN4eDgAS3nKiRMn8Oeff9oc7+Pjgw0bNogxXrt2DWvXrkVaWhr8/f2xfv16tGnTBitWrAAAsZZz1apV4jm2bduG1NRUfPvtt6hfvz4ASz3xo48+iuXLl2Pw4MGQSi1jIM2aNcOrr74KAOjSpQs+/vhjGAwGrFy5EnK5HDExMfj222/xzz//2MRZ0vc5ALzxxhsYNGgQLl68iE8++QSzZ8/GlClTAAB33XUXQkJC8MILL+CXX34R/9gyGo149dVXxdv1cXFx2LNnD5577jnx2JiYGEgkEmzatAnjxo2D0WjE5cuXMX36dPE8bdq0wbp166DX6xEQECDWsEZHRyMsLAypqanQ6XSVfv+cP38e0dHRWL16tfh916NHDxw+fBhHjx7FlClTyv1+1Wq1ZcZdVLNmzcSvi/V72fqzw+r9999Heno6du3aJV7vnj17YuDAgVi9ejXWrFkj7nv//fdj1KhRlfr8iaorJrpEVahly5Z45ZVXAFhGgN566y0YDAa89dZbaNKkibifdURLLi/7LSqTyYrtW9JIZkXJZDKYTKZKH18Wa2JnNWzYMHz22Wc4efIk2rRpg0OHDkGv12Po0KEAgCNHjiA4OBgtW7YUSyUAoFevXli+fDkyMjLQtm1bqFQqPPDAAxgwYAB69uwpdqeoqNzcXPz333+YMWOGmEAClqSyV69exSYsFf08StOrVy8xab927RpWrFiB0NBQrFu3DsHBwTbn+/DDD2E2m3HlyhVcvXoVFy9exOXLl20+bwBo3bq1TYzWJEer1UKj0eD06dN4+umnbY65//77bRLdP//8E+3btxeTHquhQ4di3rx5uHz5sljeUnjEWiaTwd/fHy1btrT5vvTz80NWVpbNuQp/nxdmTS6tCfygQYNsnh80aBDmzZuHo0ePiomen5+fTU3qH3/8AUEQ0Lt3b5uvT+/evfH222/j2LFj6NOnD5o1a4aXX34Zv/32G2JiYtCzZ89Sa9Otnx+ASn//x8TEICYmBgaDARcvXsTVq1dx/vx5pKamws/PDwDK/X719PS0O+7yHDlyBNHR0QgNDRW/XlKpFD179iw2gl/R722imoSJLlEV8vT0ROvWrcXHbdu2xdChQ/H4449j7969CAgIAAAxCSmp9VFh8fHx4r716tUr95jU1FR4eXlBqVSW+Hz9+vXx008/IScnp9RR3Zs3b9okHhVV9HZv165dERoaiv3796NNmzbYv38/unTpIp47PT0dycnJJY4MAkBycjKaNWuGDz74AJs3b8Ynn3yC7du3w8fHB+PGjcMzzzxjUwdbmqysLAiCIE7oKywoKKhYElfSbeuS+Pn5ide6devWiIyMxKhRo/DEE09gz549Ntdg27Zt2LhxI9LT0xEUFIRWrVpBo9EUe+2it9StI69msxkZGRkQBAH+/v42+4SEhNg8zsjIEEeOi36ugKW1nFXREXGgYp9/0e/zojIyMgDAJuEHLH+s+fv723zeRb8PrZOviibJVrdu3YJEIsHWrVvx9ttv4+DBg9i3bx8UCgX69u2LV155Bb6+vsWO8/X1haenJxISEkqNOzc3FwaDocTjzWYz3njjDezcuRO5ubmoW7cu2rRpA5VKJe4TFhZW7vervXGXJz09HVevXi31faTVasWPK/q9TVSTsEaXyIWCgoKwYMECJCYmYvHixeL2Vq1aISQkBN98802px8bHx+PMmTNiHW90dDSCgoLK7NH60ksv4d577y3xNihgGZUym8349ddfS3w+NTUVffr0EWOVSCTFRsBKm6hTlFQqxZAhQ/DNN98gLS0Nhw8fFmuUAcDb2xuNGjXCJ598UuJ/1lvM1lu7R48exXvvvYe77roLGzduLPNrV5i3tzckEglSUlKKPZecnCyOxt2pZs2aYdasWTh79izWrVsnbv/yyy+xdOlSPPHEEzhy5AgOHz6MTZs2oVGjRnad38/PD1KptNjnYU0MrXx9fZGcnFzseOu2oomyM1gTtqJxGAwGsQyjNNbJfO+//36J3xfWspLQ0FAsWrQIv/32G/bt24dJkybhu+++w1tvvVXquWNiYnD06NFSJ9ft2bMH3bp1w+nTp4s9t3nzZrz33nt46aWX8Pfff+Onn37CmjVrxD9ercr7fq1M3GXx9vZGly5dSn0flfZHL1FtwUSXyMUGDBiAu+++G1999ZV4S1cqlWLGjBk4fPgwdu3aVeyYvLw8vPjii/D29sa4cePEYx599FH89NNPJS7s8Mcff+Dnn3/GgAEDSv3lFhMTg4iICLz55ps2M/6tVq1aBaPRiCFDhgCwjLalpaXZJAbHjh2r8Oc+bNgw3Lx5E+vXr4dMJkP//v3F57p06YLExEQEBgaidevW4n+HDx/GO++8A5lMhvfeew+9evWCXq+HUqlE9+7d8b///Q8AyhyZK8zDwwOtWrXC119/bZO0Z2Vl4aeffkLHjh0r/PmUZ+LEiYiIiMDWrVvFbgnHjh2Dj48PJk+eLCZFOTk5OHbsmF1lKCqVCu3bt8d3331nM5mr6PdC586dcfz48WIj/1988QWCg4PRsGHDSn52FdelSxcAKDYZcf/+/TCZTGV+zTt16gQASEtLs/m+SE1NxerVq5Geno7jx4+jR48eOHnyJCQSCaKjo/Hss88iIiJC/L6wjoYX9vjjjyM9Pb3EpDI5ORlbt25Fs2bNShwdPXbsGJo1a4ZRo0bB29sbgGV0+fz58+J1LO/7tSJx26tLly6Ii4tD48aNbb5en3/+OT755BObUhii2oilC0TVwIsvvoihQ4fitddew2effQaZTIYHH3wQly5dwqJFi/DXX39h4MCB8PX1xeXLl/H+++8jOTkZb731ljipBQAeffRR/PXXX5g5cybGjBmDe+65B1KpFH/99Rd27NiB6OhozJ49u9Q45HI5li9fjscffxyjRo0SF4xITU3F3r178euvv2L27NliTWGvXr2wY8cOzJ8/Hw888ADOnz+Pbdu2VfiXZ0REhFijev/999vcKh85ciQ++OADPPbYY5g6dSrq1q2L33//HVu2bMEjjzwChUKBbt26YeXKlXjqqafwyCOPQCaT4aOPPoJSqSyz+0RRs2fPxqRJkzBlyhSMGzcOBoMBmzdvhl6vF+tsHUEul+PFF1/Eo48+itdffx2bN29GmzZtsGvXLixduhS9evVCUlIS3n33XaSkpNh9q/q5557DxIkTMWPGDDz44IOIi4vDxo0bbfZ57LHH8MUXX+DRRx/FjBkz4Ofnh3379uGPP/7A66+/XmIC6GjNmjXDiBEjsGbNGmi1WnTu3Fkc6e7atavNgghFRUZGYujQoXj55Zdx48YNtGrVCnFxcXjzzTcRFhaGRo0awWg0Qq1W44UXXsDMmTMRFBSE33//HWfPnsWECRMAFIwMHzx4ED179kTTpk3Rrl07PP3003jrrbdw6dIlDB8+HP7+/rhw4QLeffdd6HS6UkdW27Rpgw0bNmDz5s1o164drl69ik2bNkGv14vlAeV9v9avX7/cuO316KOP4vPPP8ejjz6Kxx9/HP7+/jhw4AD27NlzR7W/RDUFE12iaqBJkyYYP348tm7dil27duGRRx4BYEmA7777buzcuROLFi1CZmYm6tati3vvvRcTJ04U63KtFAoFNmzYgN27d+Pzzz/HgQMHoNfrER4ejunTp+ORRx4ptw4vOjoan3zyCbZt24Zdu3bh1q1b8PDwQGRkJN555x2bJOSuu+7CnDlzsGPHDnz77bdo2bIl1q1bh7Fjx1b4cx82bBiWLl0qTkKz8vDwwM6dO7Fq1SqsWLECWVlZqF+/PmbPno3HH38cgKV36MaNG7F+/Xo899xzMJlMaNWqFbZu3Wozua883bt3x7Zt27BmzRo899xzUCqV6NSpE5YtW4bmzZtX+DwVfa377rsP3377LX788UeMGDEC169fx6effooPP/wQoaGhuOeeezBu3Di8/PLLuHTpUoVXE+vUqRO2bNmCN954AzNmzEBYWBhef/11TJ06VdwnODgYu3btwqpVq/Daa6/BYDAgKioKGzZsQJ8+fRz6uZZl8eLFaNiwIT799FNs2bIFISEhmDBhAqZPn15usr1kyRJs2rQJH330EW7evInAwEAMHDgQzzzzDGQyGWQyGbZu3YpVq1Zh8eLFyMzMRKNGjfDqq69i5MiRACw14j169MCqVatw5MgRbN68GQAwbdo0tGjRQlwhLSMjQ3zPWf/gKsmTTz6JtLQ0bN++HevXr0fdunUxbNgwsRtEZmZmhb5fy4vbXqGhofjoo4+watUqLFq0CDqdDo0aNcLixYvF1mREtZlEKHyPi4iIiIiolmCNLhERERHVSkx0iYiIiKhWqhaJrl6vx+DBg8U150ty5swZjB49Gm3btsWoUaNw6tSpKoyQiIiIiGoalye6Op0Ozz33HC5cuFDqPrm5uZgyZQo6deqEvXv3on379njyyScr3K+TiIiIiNyPSxPdixcvYsyYMbh27VqZ+x04cAAqlQovvPACmjZtivnz58PT07PCDeGJiIiIyP24NNH9888/0bVrV+zevbvM/U6cOIGOHTuKy3lKJBJ06NAB//77bxVESUREREQ1kUv76FpXdCqPdU37wgIDA8ssdyjq+PHjEAQBCoXCrhiJiIiIqGoYDAZIJBK0b9/eIeerEQtGaLXaYkuWKpVK6PX6Cp9DEAQIggCtLgcmoeR1zK2kEgXkEpX42CjoYRbKfi2pRAm5pCBGo5AHs2As8xiZRAWZpCDxNghaCIKpjCMAuUQDqaRg1Sm9ORdA2cuEKiQekEgKBu/15hwAZbdPVkg8xRF0QTDDIJRdD20WJEjVKsSzyqUC/NWGMo8xmCVIzyv4/FUyM3xUZX/NdCYpMnUF37YauQleyrK/ZrkGGXIMBV8zL6UJGnnZx2TrZdAaC47xURmhkpX9dc7QyaE3FXyd/dQGKKRlf53T8hQwmiXi40CNAVJJ2cek5CogoOCYYI+yvzcFACm5Bd+bMomAAE3Z18YoSJCmLbg2CqkZfuqyr43eJEVGoWujlpvhrSz7GK1Rimx9wTEeChM8FWVfmxyDDLmFrqe30gi1vOxrk6mTQ1fo2viqjVBKyz4mPU8Og7ngmACNAbJyrs1trQJmoeDaBHnoC12pkiUXujZSiYDAcq6NKf+9ZuWs91qeSYqsKnivZellyLvD95q/2gB5Oe+1VK0CpkLXJtDDAGk5PwdTcpXiHhJYrmdZzJDgdm7B17lC7zWzBGmFro1SaoYv32vu9V4zSpGld/57TWeSwmiWQGuQQUDF3msCACH/+1oqEeCrNkJezrWxHgeg3GtiZTKZkZ2RB7WHEl4aH0iljluaukYkuiqVqlhSq9froVarK3wOhUIBvV6PwDpeuJV7EwAgsV4Ca0lE/v/9PeohzD9aPPZW5mUkZV0p8/whPo0R6t1YfHw97SzStIllHlPfLwz+HgWr7FxOOY4cfVaZxzQMbAYvlb/4+NytP6A3acs8pmlIK6jkGvHxqYSfIJTzAz6qbkdI85Njg0mHszd/x5W0HJxNykRCpha3c22vh84oxTcXgsTH/moDejZOAwA09vdAmK/talxpWj18NX7IMVhWfJJIJJBLM+CpL7te22j2hdbUQHyslKZApbtZbD+z2QStNg8ajRpGhEBnKvg6qwyJUEpvi48z8vTw0yht3pB6U10YhILPR2W6Crkks8zY8kwNYRI8xcca0w1IJWVfG62xGcwouDYe5iuQoJxfPsYoFLx1zbhmvlTm/gIkyDW2FB9LoIOH6XKZx5gFFbSmghXBZJIsqI3xZR5jEryQZ2okPpYbUqHSJ5R5jMHsD725vvhYYbwFpS65zGP05hAYzCHiY5XxOiRCKrRaLTQaDWSy4hVZOlMYjIKf+FhtioNMUvYfb1pTI5iFgmujMcVDKin7l1yusTkEFPwy9TBfLvcHfY6xBQp++hjgYYorc3+zIIfWFCk+liIXauP1co7xQJ6p4OeTTJIBlaHsY0xmX+jMYeJjhSEFCn1SmccYzIEwmAuWpFYaEyGXppV5jN5UB0YhQHysMsZDJi3752COrg6SUowICPCHQqGA2pQIqSSvzGPyjE1gRsHvDA/TNUBSTjJhjARg/YVrgof5Spn7Q5Ait9C1kUAHjelq2YcISmhNBSvfySTZUBnLft+YzJ7QmQt+DiqM6VDqy/59YxT8oTcX/BxUGJOg0N0u4wjAYA6GsdDPQaXpBmTl/BzUm+vBJBQs5a0yXYW0nPeaztQAZhT8jlCbL0KCgveayWRCrlYLD41GXF48z9TU5r12LbvsrzMAaE1RKPxeU5vK/n0jQIE8U8HdZClyoTKW9XUWYBaU+T/T5BCggMyYCaXhJqTIhUSihwRmAAIkEjMsg1RmCFDALGigMzWGUQiA3HgbCn0yVNJ4y9dOYoYEAix/SlmPEyCRAWm6/jAKgVCYbkIuSYeH/D9IJWUn1qmG+wCJDErzdcgkWdDIy/6ZIwiAdXwtpeLji+WeE5BAgARmswQrZ+5G0vVUeHh7Ytee3fBTVTy/K0+NSHRDQ0ORkpJisy0lJQUhISGlHFEyo6BHqH8kmoa1suu4xh6t0LiOfcdEeHS0a38AaNXgLruPad+4t93HdGk2sML7JmTkYtuf8Xj3qA5X03QAVPn/WShkUrSr548OYYFY0FgNP40Svmol/DQK+GmUaBrojXB/z1LP7yy5ubk4e/YsoqOjS1jytjK3Q9pV42M6VOKYrpU45u7yd3GJduVc75KPqczrVM0xnStxTI9KHFMV2jnlmOLX2zmvU5z9P9cr916rrtq65JiKvb+Lv44gmGE0G2EyG2AyG2A0GSCVyuCttv5h1QmZ2hQkZV6FyWyE0bpf/r+WY84j1Lcxmod2yj+mO84mHEZs4lEYzXqY8s9vNBsgCNbR0b/QoeEAtAm/V4zll3Mf4XLyuTI/6/tatEfTkILP4+uTm3ArM6WMI4Chrf3grfaHzqiC3hiMPy+fg85YdqIboP62zOeLkpTx17oEUijlaijlGqjkHlDLPaBWekIl98z/18Pyn8JD/Fip0EAutS0jjVh3H8aMGYPnnn4WPmrH5gw1ItFt27YttmzZAkEQIJFIIAgC/vnnH5v12yvCUn7AFY/LYzKb8e25RGw5ch77z96AyVzwNQvyVKFP87ro1jAIXRoGoV29AKgVjrvFQEREtZMgCDALpoKE0lSQUJrMJoT6NhL31Rm1uJz0D4xmI/J0ubhluAlt/FVIpIKYWKoVXujRbIR4zK2MOPx8bheMJj2MZmOJ5YP1/Jqjf6tJ4uObGXH4/eKnZcYtkUgKJbqW2DK0Zd/dMJlt7/7IpMXnB0kkUsilCsikckglcuToMnAj7Tx0xlzoDLmQSCTwUQfCDAGCYIbZbLIk1oIBJrPlc/v1/J4y46goCaRQKTRQ5ierSrkGKkX+x4WSVEvCqoFK7gmVQgO5VCmWOVaUIAg4duwYOnYs+MOxZ8+e+Pfff1GnTh2cPHnSIZ+TVbVNdJOTk+Ht7Q21Wo0BAwZg1apVWLx4McaOHYuPPvoIWq0W999/fyXObN8FcSfX03Ow7c9LePfoBcSn295q6tO8DiZ3a45hrcKhkjOxJSKqDcyC2WbEs9iopsmAEJ9GUCkKRlJP3/gNOkNOsX0tiasRRrMefVpMhFphGZkzm0348I9XYDIbSi2bk0ikmHjX6+JjvTEXRy9/abNPSpGqJi9VgM1jAQJy9WWXVhiLJKBFRxYtiaflP2sS6qH0sdkn0CsMEXW6iPvIpQpIpXJIJTJLQiqYoVZ4Ii75pCVpNeZCCgkaBLaE0aSH3pgHvVELvUkLnUELg8kyb+ifq45omSqBUq4uITG1HVUVP1ZYklaFTGkzl8dZUlJS8Oyzz2L//v3Yu3cv7r33XvG5OnXqOOU1q22iGxMTgyVLlmDkyJHw8vLCpk2bsHDhQuzZsweRkZHYvHlzBW9R2pIw0bVhMpvxTWwCNh+5gANnb8AsFPwQCvFS49HOTTGpWzM0C/Ip4yxERORI1oTIenu86Cio0WxAgGdd+HsWJAeXk/5FUtbV/MTVKI5sFk5GOzYagPCAgjko3/y3CUmZZde33t96qs1o65mE35CjSy/zGINJJya6EokUJrOxzLkhlgTRJE62to6ASiUyyKRyCCYJVEo1FHJVfoIph4fS1+YcXip/tKrfsyBRlSnExNX6r1ppe1u8QVBLPBSwwHKMRA6JRAKj2QC9QSsmqTpDLs7f/DP/Yy10xhzoDLnQGW33MZczmdxeCpnKJjFVyjVQKzygzN9W+GNLwmrZR1oFCWtlCYKAP/74AwDw5ptv2iS6zlJtEt1z586V+bhNmzb47LPPqjKkWi0+LQfb/ryIrX9eLHH0dkr3CAxtGQYlR2+JiERGkx7ZuvRiSWfhfzUKbzQMKpjXcSsjDudv/WVzq95YpBa0UVAbdG48SDzm5PWfcDL+hzJjad+gn02im5B+AReTjpV5TJ4+2+ZxSbfUiyp6G14t94TRZIBcJodMqoRcKrdJJuUyS9JoJZFI0KHRfZBKpPlJqBxy63EyBeRSJWRSuc1AlEbhjQl3vQ6pRFrhGnwvtT86NS6Yg2I2m4olo2k5N3Ez/XL+Ni10hlzojbnIM1r+1Rlyi4363imZVJGfsGryR1I9xcS0pHIA63ZHdh6oLoKDg/HGG2/g119/xcKFC6vkNatNoltV7K0lqU2MJjO+jr2BLX9cwNdnE4qN3j7WpSkmdW2OpkHeLoySiKhs1lpPodDPMEEwIznruk0iKf5rMog1m+0a9BWPydVn4cjFz0qYgFTw2EPpixEdnxOPSc66hm9PvVNmfHV8m9gkutm6NFxK+qfMY7RFEtCit9RLUjQh0yi94a0OKJ50FnrsowmyOaZFvbvQOKiNmGzKZAW3463HaxS2d/SGtJ9ZbmxFtQ67x679JRKJmPiaBTOMgh5ZebeRbUyxJKT5SWnhhFVXZLu1JMBRpBJZqeUASnGENX9SlsJT/Fguc9/+/T/88APOnz9vM6dqyJAhGDJkSJXF4HaJrjsSBAFv/nwWq385i+sZtqO3/SLq4onuzTGkBUdviajyzGaTOEqpkKmgkBW0fkrOuoZcfZZY82m5na7PTyYtt+Wj6/aAjyZQPOan2A+RrUvL399QcDs+/1Y+ADQ2NIAnCm5FHzi5odw424b3sekRHp96psz9DSbbtmUyqbKUPQtY47NSK7wQ4FmvWPJZeGQz0DvM5pgGgS3gowksUi9aOAGVQy5T2RzTsdEAdGw0oNz4CitcxuBsgiDAYNIVSVLLLgfQ5z8GgLNlX6oKk0BiueVfUv2qTR2r7ahrZSZeubMtW7Zgzpw5kMlk6NKlCzp0qEyHoDvHRNcNHL+Riue/LLidFeqtxmNdmmFS12ZoEsjRW6LaqKTWSnKZEp6qgrrGtJybuJ19o0giaVsLGh7YAg0DC/ow/3P1O1xN+a9QkqqHyWSEUGjhmpjmo9EstGBG9bEr3+BmRtm9mxsERNskurezbyArr+w+r4VvqUskUsik8mJJppUs//a6STBCnr9Qj0KmQqhPY8hlhW+/W0Y25TLLvyq57a1yP48Q9Gv5uO2kJfEWvnUGve2gQX3/CNT3jyjzcynKzyMUfh6h5e/oAoIgWOpYjbnIy09YrR/rjVroDDnFElZrQiuUs8CRvZQyNVQKzxITU2s5gFKhgVqsc/WAUqaqkolX7q5///743//+B7VajfT0dJfFwUTXDZxMSBc/3vlIDEa1aQhFCU31ich5bFormWxvlQNAkHe4uG+OLgPXbp8qmEhUQi2onyYEHRrdJx5zJeU//Hn5yzJbKzUL6YiYiNHi42u3T+P4tYNlxu2h8rVJdPMM2cjQlr2oR9Fks6Tb8AWtlSyjlEUTjzD/SGgN2Zbb6EVuv5tNApJv3S6WhN7f+klIJbL8JNU6Ymr5t6TERilX4/42T5b5uZR0jL1Ja3VmMhtLvO1fWjmANWEtb+VPeylkKstkq/zEtHDCKjHLcTspDY0aNIWPp3+hcoHqPfHK3eh0OigUCkillmvSsGFDbN++Ha1atUJQUFA5RzuPWyW6Ukn5t5xqo3NJGQCAAA8lHmzXiLdeiPJVpLVSXb/mYo2dIAg4Gf9Dic3lra2VTIIR97d+Unyf5ejS8dmxVTCajSitj7enyg+jO88VH2fnpRVrrVRUno9tTadZMNvdWqnoRKTit8jl4sx5q1CfxgAkJU9Ayv84yMv2Nnz3ZiNhFkz5tZ9KyGTFRz2L6tp0aKnP5ebmwnT7LJSFVnwEbP9YcDdmwZRfApA/wmqwTLCy3v4vPNmqcMJqNDtoqat8lolXZZQDiBOyCkZYVXINZNLS05Hc3FycTT2Ler4Rleq2RM538uRJTJ06FePHj8e0adPE7VXRVaE8bpXoyt000Y3NT3Qjg32Z5FKNYDDqoDfllZBIGsRb5sHe4TYTa24bL+Gf+CuQSC23VYvOcDeZDbir+WgEF0qG9v69Etm61DJjGdXpeXjLLLfUJRIJ/o0/VGgFpJKZBZM461wqkZU7i7tYc3lZwbFyqUKcIFQ4sSw82x4A/D1CxdZKRUdArf8WLlsAgMi63dC8Tieb1krlaRrSHk1D7FtdsOjrUukEwQy9Ma/ISGrZ5QA6Y45TJl5Z21nZTrYquX7V+rE7T7xyV4IgYPbs2YiNjcX//vc/DBw4EA0bNnR1WCK3SnTd1bkkyyhPZAh74dKd0RvzkKvPyL/9bp0YZLC5xe6jCbS5tRt/+wyuiDWdhdoqFWouH13vLptZ2X/GfYkLt/4uM5YezUbZJLqZphtISL5VTvxam8dyWfk/Ao2mohOLPGE2mwslk/L8us6CBLNwNwCFXI32DfsXn0wklUMmU+bfmrf9IzzQs57YWqmi/D3r2LRWqgjLhDH3HABwNpuJVzYJa65l4lXR1lbiaKwWjlzBs2DiVUn1qx759a2aIqOuHpDLOPGKKkYikWD16tUYMmQI5s+fjwYNGrg6JBtMdGs5g8mMS7ezAABRIRxVqQ0EwQyT2WiTHJnMRtzOvlEk6bRtNC+TytGy/t3iMem5STh25WubCUXGQjPhTWYDgrzCcF/rJ8Rj4lPP4tfzu8uMr0lwO5tENy03CZeSj5d5jM5g2w2kYr09bW+5KiQaeKn88ycWWScUFU4sldAobSdftg3vA4NJX2QE1FrXqYRcVrwp/YNd5pcbW2FyqQJtw3vbdYxEIuXSNtWEIFiWnC25U0BJ9asFnQPKG/m3l2Xile0Iq7VWVRxtLdI1QClTc+IVOZTZbMbXX3+NgQMHin8MtWjRAidOnICXl5eLoyvOrRJdo+DYWzs1weXbWTCaLaMDHNF1jqLrtyvlGpvJNzczLkNnyC25rjN/ZLNtgz42tZDf/rcFBpO+2L5Gk0GcBFJ4xE9nyMWBk2+XGadG6W2T6BpMOsSnni3zmKK3QyvS27PoRCRPlS8CPetDJpMXu51uTTBDfRvbHNMstCPq+DYpGPm0HlcocVUUaa0UpuxcbkP5ohoHt63wvlTzmczGIrf9i7ezKvyv3qhFniHX4ROv5DJlibf9SysHsNSyqsutayZytlu3buGJJ57Ab7/9hvXr1+Ohhx4Sn6uOSS7gZomuWXDsaic1QWxSweQUd0p0Lcmn5ZeaOU+fn4CqbUb0krPikZGbVOIEJOvt9eahnVDHt4l4zO8XP8PNjMs2NaNF12/v32oS6vk1L3TMXmRqU8qMN6pud5tENznrWgXqOo2Q5o/qysq5BW9NFAtTyT1Qx7eJ7S31QiObcqkCnio/m2NCfBrmt1ayTCgqfjteUWw1n8rUdAZ5hRWb0ERkZVlAQIfMvBRkGwXoDDn5k62KdgrQ2pQJOH7ilbzsdlZyDygVHlDLPWz6tpY18YqoOvP29kZiYiIAYNeuXRg7dmy1L3Hhu62Ws3ZckEslLu+ZaxZM+bfHDYVqOw2QSeQ2E2sytSm4kXa+1HpOk9mAYO+GaBXWUzwmNvEITsb/WKy10umTBa/fJqyXTTumC7f+xvmbR8uMOdi7gU2im6vLQGZ5rZVMZc9sB6zrtxeMahZdA75hYCuYYS6WSBZev71w/aZCpsagttNLnIBkaa1U/AeRjyYQA1pPKfNzKUqj9EZ9JXsvk2MUn3hVTjmAuOKVZREHhy0gIJGWMJKqKaV+VSP2beXEK3I3Hh4eWL9+PQ4ePIjnn3++2ie5ABPdWs86Ea1ZkLfTeucazQZk56UiKy8NJrMejYLaiM+lZMXj21PvwmQ2wCyYSjw+2LsBBrWdLj6+nZ2Ao5e/KPM1i9acmcxGu1srFR7hlEBik0hab5kr5WqbY+r7R8JT5Wtbz1mkZ2dgkZHIPtETLK9XqP1SeZOM7o58sMzni5JKpAj2rl4TAMh9FJ14pTdoC7W2sk62Kt6fVW/UFvsj705YJl6VlJiWVg5g6dvKiVdEJdu7dy+OHTuGxYsXi9u6dOmCLl26uDAq+zDRreUKOi7c+UQ0o0mPxPSLyNCmIDPvNrK0KcjMS0GOLhPWWcJqhZdNoiuRyIotoVlU0dZKcmlBa6WS12yXw1cTbHNMkFc4WtW/R5yAZDYCSbeSEVa/ATzUnpDJFPBWB9gc075BP7Rt0Cd/dFRWoV900fW6l7tPUV5qf7uPIXIVo0lfqJ1V+QsHWD929MQrhUxdajsriSBHyq00NG7QFD5eAWIyy4lXRI7z7rvv4vnnnwcAxMTE4P7773dxRJXDRLcWEwRB7KEbZWd9riAIyMpLhbc6QEwADSYdDp3dXuZxeYZsGE16sSOAh9IH7Rr0tan7tK0DVRZr+l4/IAoT7lps18SLUN9GCPVtJD7Ozc2F8fZZNAwofXKSQq4qcTtRbVAw8cq2TtU6wip+bF22NT9hLW0J3cqSS5XFRlLLKgdQKTT5K16V/v63LhhR17c5FxAgcpJRo0bhjTfegFwuh5+fn6vDqTQmurVYcnYe0rSWyRcRwWWP6JoFE9JybuJWRhxuZV5FUuYVaA1ZGN7hOfh5hACw1Gf6aIKQpb0NT5U/fDSB8NEEwVsdCB9NILzVAWJ7JyuN0gvtGvS1K24u6UhUwCyY8idZ5SejJS4cUHzU1dETr6QSeQXaWRVMyLKOwHLiFVHNkJWVBblcDo3GMvjk5+eH3bt3o2HDhvD2rrlzM/gTqBY7l1xQsxoVWnxENzkrHglp53Er8wqSsq7CaCr+izEp84qY6ALAfa2egFrhyV9eRHYSBDP0prxSE9Oi5QDW0dbySn/sZTvxqvR2VkWXcS26qAUR1R6//fYbZsyYgUGDBtnU47Zq1cqFUTkGs5VarHBrsSb+CmTnpdnUi56/ebTE1af8PEIR4tMIoT6NUNevmc1zXMqT3J0gCDCa9Mgz5pTQzqr0/qyOnngFSIoko5oS21mp5R5QFkpoFTIVJ14RkY3169fj2rVr2LhxIx555BFER0e7OiSHcatEVyZxr5pMa2uxpoEyHDqzBmEBUbg7Yoz4fIhPI1xKOo4grzBLYuvbCCHeDaFSsOaN3IPRZCinHKDQ9kKdAkrrIFJZCpm69PrVUhYUUMo58YqIHOONN97AkCFD8OKLL9aqJBdwu0TXvXoeWkd0ezex/KKOSz6JHs1GimUHjYPaoHFQW/aCpBrPZDbBIOQhQ5uELINg084qL7/dVeEJWc6deGVNRj2LlQZY2llZRl1V+Uu4KmWaYotsEBE5i16vx44dOzBx4kTI5ZZ8oG7dujh69Chkstr3s8itEl13Yx3RjQq2/Ft0RR7W3FF1Y5l4lVehdlaFt1vry2PLXtG4wqwTr5RF6lRLq1+1/svadSKqzm7duoUHH3wQJ0+eRFpaGv7v//5PfK42JrkAE91aK89gwpXUHGjkJvgqLcvPNq/T2cVRkbsomHhVtE7VMsHKWgKQl79NfN5pE6/KLwdQWkdY5R6QSxWsYyWiWicoKAhqtWUhpN9//x1msxlSae0ugXKrRNcgaF0dQpW5mJIJsyCgVWg2JBJLI/cGAS1cHBXVNNaJV2KyWmzCVUkdBLTQG3OdMvHKuupV4clWKrkGEkGBlJupaNywGXy8/DnxiogonyAI4s9BmUyGDRs24LvvvsOUKVNqfZILuFmiKzh4Akl1Zq3PbVc3C4Bl4YZAr3quDIlcTJx4VU47q8IfO2filarE+tWCcgDPYiOwCrm6zP7Kubm5MKacRR2fplxAgIgIlgR327Zt+OGHH7B9+3YxqW3SpAmmTp3q4uiqjlsluu7kXFIGZBIBbUItiW54QDRnaNcSlhWvLKOm5Y+uFmwvutTynZJLFfkTrCwjrIU/VhZOVIusgMWJV0REzrdjxw6xBnfr1q2YPHmyiyNyDSa6tdS55ExEBOXAQ2kpWwhn2UK1YxbMBUuvVjhhzS1xYY87IZXIyl3dqmjCqlRoIJeyWwcRUXX14IMPYvPmzTCbzejc2X3n6DDRraXOJWXCW2WCzqiEpwqo69fU1SHVWpaJV7qCyVZltLMq3J/V4ROvIC21nZVK4Wm7bGuhfTjxioio5ktOToZMJkNAQAAAQKVS4aOPPrKZgOaOmOjWQoIgIDYpA9k6X/SP7oHnuoaz7VEFCIIAo1lfamJadOEAaxJrWfHK7MBIJFDK1SWMpJZSDqDgxCsiIne2f/9+PPvss+jRowe2bdsm/i4ICwtzcWSux+ynFkrI1CJbZ2mEHxHsB19NsIsjqnpGswEGQYv03FvI0JtLbGdl29rKksQ6Z+JV2e2sbFtbWToLlDXxioiIqLDvv/8eKSkp+OKLL3D8+HF06NDB1SFVG0x0a6HYWxnix1Ehvi6M5M6ZzMZCdaz55QDWUdYS211p81e8sky8io11TBxyqcKmnZWlfrWEhQOKdBDgxCsiInK2V199FRcuXMBzzz3HJLcIJrq10PnkTAyPvoX4DA2aBKpcHQ6AkiZe2ZYD2LS2MmiRZ8yB3qiFwaRzaBxSiaxC5QCWelZPTrwiIqJqJScnB2+99RZmzZoFb29vAIC3tze++uorF0dWPblVoiuXuEcx9qWURAyJsqyGlpB2HP4edzvs3IIgQG/KK9TaqvTuAIVXxdIb8wAHLiBgnXhVWjsriVmO5JupaCIuIGDpICCXKlnHSkRENVJqair69++Py5cvIyUlBW+++aarQ6r23CrRlUrc49PV6uLEjxsEltxWrGDilbU7gG1iWrgcQG/UIq/Qx86deFVyOyuxHCC/a4BSpiqzL3Bubi4MKWcR6tOECwgQEVGt4O/vj9atW+Py5cu4ffs2jEYj5HL3yG0qi1+dWsBoNhQkpIYchHlfAwAYTCqcSzxaan9Wp028KtzayqadlSZ/1avCE7I48YqIiKg0hZNZiUSClStXYuDAgXjggQd4h7ICmOhWI2azqUgyWlIf1uL9WYuueFXXUrIDhUyHUzd+sTsOmVRRgXZWtgsKKOUatjAjIiJyEKPRiLfeegv79+/HN998A5XKMucmMDAQo0ePdnF0NYdbZSZ6c26VvI7txCvbyVZF21kVTmIdPfEKkECj8CqxHKBwO6uiiaxcxolXRERErvT555/j9ddfBwCsWLECL730kosjqpncKtFFJWpLDUYd8ozZpdavlrRcq+MnXkmKtLYq2jWgoBzg4JnvIJjjkZ4nx8gOc9AgwNthcRAREVHVGDFiBD744ANotVo89NBDrg6nxnKzRLfiBEHAX3Ff4WzC7xAcmLQqZer8xFRTajmAUqHJ7ySQX+tazsQrK7PZBKPpJmQS4PQtHzzt7+WwuImIiMh54uPjYTab0bBhQwCAVCrFtm3b4O3tDZmMPdkri4luKU5e/xFnEg6X+rxcpoRK7gF1fmJa0mIBRetbnT3xKjnrGmQSS71umq4ui9SJiIhqgF27dmHOnDlo2bIlvvrqKzGx9fPzc21gtQAT3RLEJZ/A8avfAQD8PeqgfcN+hToJWBLZ6jjxKsSnET442QFq2XUEeIW7OhwiIiKqgISEBGRnZ+Ovv/7Cn3/+ie7du7s6pFqj+mVrLpaUeRW/nv8YAKBReqNvy0fhqfJzbVAVJAjAkatG5BmD8cqAAFeHQ0RERBXw9NNP4/z583jiiSfQqVMnV4dTq7CBaSFZebdx6Mx2mAUj5FIF+rSYWGOSXAC4lp6DPKOlN25EsI+LoyEiIqKi0tLS8PTTTyMxMVHcJpfLsWnTJia5TsAR3Xw6Yy6+P/0edMYcABL0jByLIK8wV4dVYanZCTh9M0d8HBXi68JoiIiIqKisrCzExMQgMTERCQkJ2LNnD+fTOBlHdAGYzEb8dHYnMrTJAIDOjQeiQWBLF0dln8MX9yLp9rsY1yYREgnQPJhtxYiIiKoTb29vjBgxAgBQp04d6PV6F0dU+7nViK5C4lFsmyAI+OPSPiRmXAIARNbpihb1Yqo6tDuSo8vA7ezrkEiAlFwFGvl7QaNwq0tLRERULWVlZcHbu2Dw6aWXXkLv3r3Ru3dvF0blPtxqRLekXrSnbvyMC7f+BgDU84tA16ZDa9xthPjUs+LH/yZ6IzKE9blERESupNVqMX/+fHTv3h3p6enidrVazSS3CrlVolvUlZT/cOzKNwAAP49Q3Bs1DlJJzWvKHJ96BgBwK1uNpBwVE10iIiIX+/333/H2228jISEBr7zyiqvDcVtue387Oesafj2/GwCgVnihb4tHoZSrXRyV/QxGHRLTLWUXf9+wrIQWyYloRERELtWnTx889NBDSE5OxgsvvODqcNyWWyW6erOlK0FWXioOndkOk9kImVSBvi0mwkvt7+LoKudG+nmYBUtLseOJlhogdlwgIiKqWmfPnoVOp0O7du3EbW+88QaUSmWNK4msTdyudMEsmPHDme3IM2QDkKBnxIMI8q65q4hdu30m/yMNrqRpAABRLF0gIiKqMu+88w569eqFyZMnIyenoNWnSqVikutibpfoXk87g7TcmwCAjo3uQ8OgVi6OqPLMggnX02IBABn6uhAgga9agRCvmleCQUREVFN5enpCr9fjxo0bOHbsmKvDoULcqnQBAGJv/Q4A8FYHoGX9ni6O5s6YBTM6NLwP126fwR9nvAHkISrEl389EhERVaGxY8fi4sWLGDNmDCIjI10dDhXiZiO6AtK1ltHclvV7QlpCu7GaRC5VIKpuN/Rv9TgOX1EBADsuEBEROVFCQgIefvhhnDlzRtwmkUjw8ssvM8mthtxuRBcA1ApPNAvp6OowHMZoMuNCShYATkQjIiJyFq1Wi759++LmzZu4ceMGvvvuOyiVSleHRWWo2UOaldQstBPkMoWrw7gjZsEsfhyXmg2DyfI4giO6RERETqHRaDBz5kxIJBLcc889EATB1SFROdxyRNffo46rQ7hj/13/CXHJ/6JBYCtcz4oSt3NEl4iIyHFu3LiB+vXri4+ffPJJ9OjRA23btnVhVFRRbjmi660OcHUIdyz+9hmk5ybhVsZlnE+2lC3IpBI0DfRycWREREQ1X2ZmJp566il0794dV69eFbdLpVImuTWImya6ga4O4Y7k6jKRkn0dABAe0ALnkjIBAE0DvaGU17wljImIiKqby5cvY8+ePcjOzsarr77q6nCoktyudMFL5Q+1wtPVYdyR+NSz4scNAlvgXNLfANhxgYiIyFHatWuH5557DlevXsXSpUtdHQ5Vktslur2aP1rj+8zGp1pamvhqQuCjCUJs/ohuZDATXSIiosr4888/kZGRgX79+onb5syZA6nULW9+1xpudvUk0Ci9XR3EHTGYdEhIvwQACA+MRkp2Hm7n6gAAkZyIRkREZLf169dj4MCBmDZtGpKSksTtTHJrPje7gjW/DciNtPMwC0YAQIOAFjiXnCk+F8XSBSIiIru1atUKZrMZBoMBZ8+eLf8AqjHcLNGt+az1uWqFF4K9wxGblCE+xxFdIiKi8hXtf3vPPfdg1apVOHz4MO655x4XRUXOwES3hpFLFVDIVAgPiIJEIhU7LgR5qhDoqXJxdERERNXbhQsXcN999+Hnn3+22f7YY48hLCzMRVGRs7jdZLSarnuzEejSZAgMJktdrnVElwtFEBERlc1gMGDUqFG4fv06ZsyYgcOHD8PHh2V/tRlHdGsgmVQutkizjuiytRgREVHZFAoFXnvtNSgUCkyaNAmenjW73SiVjyO6NZjOaEJcajYAjugSEREVJQgCTp06hdatW4vbhg4dig4dOrBMwU1wRLeGyMhNxpGL+3Aj7TxMZkvXhUspWTCZLQX1HNElIiIqkJSUhIcffhi9e/fGP//8Y/Mck1z3wUS3hrh6+xTO3fwD35/eBoMpDwDEhSIAJrpERESFabVa/PbbbzCZTFi1apWrwyEXYelCDXEtfzW0YJ+GUCu8AADn8ieiKWVSNPL3cllsRERE1U3Dhg2xePFinD59GgsWLHB1OOQibpbo1sylf3P1mUjJigcANAiIFrdbF4toHuwNuYyD80RE5L4OHjyIW7du4ZFHHhG3jR8/3oURUXXgZoluzXQ9NVb8ODywhfixdUSXC0UQEZE7W7NmDRYtWgS1Wo0uXbogIiLC1SFRNcFhwBrg2m1L2YKvJhi+mmAAlpmk1hrdyGDW5xIRkfvq27cvlEolfHx8kJSU5OpwqBrhiG41ZzDpkJB+EQAQHlAwmnszS4vMPAMAjugSEZF70el0UCqVkEgsJYktWrTAtm3b0KVLFwQGBro4OqpOXDqiq9Pp8OKLL6JTp06IiYnB1q1bS9334MGDuP/++9G+fXs89NBDOH36dCVeUSh/l2omIe0CzIKlnVgDm7KFgo4LUey4QEREbuL48eO455578NFHH9lsv//++5nkUjEuTXSXL1+OU6dO4f3338fChQuxbt06fPPNN8X2u3DhAmbPno0nn3wSn3/+OaKjo/Hkk09Cq9W6IOqqlZhxCQCgVnghyDtc3M7WYkRE5G7MZjOmT5+O8+fPY+7cubh165arQ6JqzmWJbm5uLj7++GPMnz8fLVu2RL9+/TB58mTs3Lmz2L6HDx9Gs2bNMHz4cDRo0ADPPfcckpOTcfHiRRdEXrW6NhmCQW2no1vTYZBKCi6XdSJaPR8NfNRKV4VHRERUZaRSKVavXg0fHx+88sorCAkJcXVIVM25LNGNjY2F0WhE+/btxW0dO3bEiRMnYDabbfb18/PDxYsXcezYMZjNZuzduxdeXl5o0KBBVYdd5SQSKYK9G6BRUGub7eJENI7mEhFRLWUymfDjjz/abOvSpQtOnjyJRx99VKzRJSqNyyajJScnw9/fH0plwWhkUFAQdDod0tPTERAQIG4fOHAgfvjhB4wbNw4ymQxSqRSbNm2Cr6/9k7BqS7lD7K10AEDTAE/k5ua6NphqyHqda8v1prLxersXXm/3kJCQgJkzZ+Lo0aN49dVX0ahRI/E5uVzO3321lCAIDv0DxmWJrlartUlyAYiP9Xq9zfa0tDQkJydjwYIFaNu2LXbt2oV58+bhs88+s7vw/MqVK3cUd1URBAFZ5kR4SoMhkyhsnsszmhGfbnmD+5pycPbsWVeEWCPUlOtNjsHr7V54vWu3zMxMXLhwAQCwf/9+dO/e3cURUVUpmh/eCZcluiqVqlhCa32sVqtttq9cuRIRERF4+OGHAQD/+9//cP/99+PTTz/FlClT7HrdRo0aQaPR3EHkVSNTm4xvzn4CqUSKu5o8iLq+zcXn/ktMhwDLIhJ3t2qO6Gahrgqz2tJqtbhy5UqNud50Z3i93Quvt/tYvXo1/vrrL9x///283m7C+seNo7gs0Q0NDUVaWhqMRiPkcksYycnJUKvV8PGxrTs9ffq0zTJ+UqkUUVFRSEhIsPt1NRoNPDw87iz4KnDxdhwAwCwIqBfYBBplQcxXMguaYbcJC64Rn4+r1JTrTY7B6+1eeL1rD0EQsHfvXly8eBFz5swRtw8aNAi9evXC2bNneb3dhKPrrl02GS06OhpyuRz//vuvuO3YsWNo3bo1pFLbsEJCQnDp0iWbbXFxcQgLC6uKUF0iPtWyGlqIdwNolN42z1k7LmgUMoT7eVZ5bERERI60du1aPPHEE1i+fDkOHz7s6nCoFnFZoqvRaDB8+HAsWrQIJ0+exPfff4+tW7diwoQJACyju3l5eQCAMWPGYM+ePdi3bx+uXr2KlStXIiEhASNGjLDzVWvG7MxcfRaSs+IBAOGFFomwOpdcsPSvVFozPiciIqLSjB49Gn5+fmjQoIF4l5fIEVz63TRv3jwsWrQIEydOhJeXF2bOnIn+/fsDAGJiYrBkyRKMHDkSAwcORE5ODjZt2oSbN28iOjoa77//fq1dAeV66llYV3ELD4gu9vw5sbUYl/4lIqKaJzMzEx4eHmJSW7duXezZsweRkZHw9vYu52iiinNpoqvRaLBs2TIsW7as2HPnzp2zeTx69GiMHj26qkJzqfhUSxcFH00Q/Dxsm2GbzQLOJVtKF9hDl4iIappffvkFM2bMwMSJEzF79mxxe6dOnVwYFdVWLl0CuOoJrg6gXAaTHgnplhmHDQKKly1cz8hFrt4EgIkuERHVLIIgYMmSJbh+/TqWLVuGa9euuTokquXcLNGt/hLSL8BkNgIouT43Nn8iGgBEsXSBiIhqEIlEgnXr1iE8PBxbtmxxixVOybWY6FYzQV710anR/Qjzj0Kwd/EfAOfz63MBICKYI7pERFR96fV67NixA4JQcEe1adOm+PvvvzFs2DAXRkbuglMbqxlPlR9ahd2DVmH3lPi8dUS3ob8nPJS8fEREVD1dv34d48aNw6lTp6DX6zFp0iTxOYVCUcaRRI7DEd0axtpxgaO5RERUnQUHB8NsNgMADh06ZDOqS1RVOCRYjRhNBshlZf+Vax3RjQplfS4REVVfKpUKb7/9No4cOYLJkyc7fMUroopgoltNCIKAL/5dA6VcjVb1e6JRUOti+2TlGZCQqQXAjgtERFR9mM1mvPvuu/j333+xbt06Malt3bo1Wrcu/vuMqKow0a0mMrTJyNQmAwD0Rm2J+1hXRAPYcYGIiKqPjRs34qWXXgIA9O7dG6NGjXJxREQWrNGtJuJTz+R/JClxNTTAtrVYJGt0iYiomhg/fjwaNmyIli1bIjIy0tXhEIncbES3+tYHxd+2rIYW7B0OjbLk5Q/P5Se63ioF6vpoqiw2IiKiwpKSkuDh4QEvLy8AgLe3Nz799FPUr18fKpXKxdERFXCzEd3qmehq9VlIyrKsDhNewmpoVrH5HReiQnxY1E9ERC7xxRdf4K677sKCBQtstjdp0oRJLlU7bpboVk/xqbGwLk/coITV0Kysi0VEsj6XiIhcZO/evbh9+zbef/99nDt3ztXhEJWJiW41YK3P9VYHwlcTXOI+JrMZ55MLRnSJiIhcYdWqVejQoQP27t3Lelyq9tws0a1+zaqNJj0S0i8CsIzmllaScCU1B3qTpfF2BBNdIiKqAtnZ2VixYgV0Op24LTAwEAcPHsQ995S8gidRdeJmk9GqX6KbZ8hFXd8mSEi/hAZl1ucWdFxgazEiInK2xMREDBo0CFeuXIFWq7WpyeU8Eaop3CzRrX681H7o2/IxGEw6yKSlr4pmLVuQSiRoFlRyVwYiIiJHCQ0NRcOGDXHlyhVcuXIFZrMZUqmb3QimGo+JbjWhkJU9U9U6otsk0AsquawqQiIiIjdjMpkgk1l+x0ilUqxduxZ//PEHRo0axVFcqpH4p1kNcS6/40IEF4ogIiIHMxgMWL58OUaMGAGTySRuDwsLwwMPPMAkl2osJroudObGb/g77msk5/fQLYt1RJf1uURE5Gg7d+7E0qVL8dtvv2HDhg2uDofIYZjoulDszaM4deNnnLj2Q5n7pebqkJxtmfEayY4LRETkYA8//DDat2+Pbt26YfDgwa4Oh8hhWKPrIhm5ycjUJgMoe5EIoKBsAeCILhER3bmrV69CpVKhTp06AACFQoGPPvoIAQEBYo0uUW3AEV0XsS4SAUgQFhBd5r6FW4txRJeIiO7Ejh07cPfdd2PWrFkQhIK2m8HBwUxyqdZxs0S3+hTTX8tPdIO9w+GhLLtdmHVEN8BDiSBPriNORESVFxsbi+zsbPz44484ceKEq8Mhcio3K12oHomuVp+NpEzLBLTwckZzAduJaJz5SkREd+Kll17CtWvX8Oyzz6Jdu3auDofIqdxsRLd6uJ56FtZV2sqrzwWA8/kjuixbICIie9y+fRv/93//h/T0dHGbRqPBjh070KFDB9cFRlRF3GxEt3q4lnoWAOCtDoSvJqTMfQ0mMy7dzgLAiWhERFRxKSkpuPvuu3Hr1i1kZWVh06ZNrg6JqMq52YiuUP4uTmYWzLidfR0A0CAgutxShEspWTCaLXFzsQgiIqqooKAg9O7dG4BlFNdoNLo4IqKq52Yjuq5PdKUSKR7oNAc3M+PgqSp/hLZwx4WoUI7oEhFR6XJzc+Hh4SE+XrJkCUaOHIk+ffq4MCoi13GzEd3qQSqVoZ5fM/hqgsvd19pxQSGTonGAl7NDIyKiGig3Nxdz585Fr169kJubK2738fFhkktujYluNXcu2ZLoNgvyhkLGy0VERMV9++232Lx5My5cuIDly5e7OhyiasPNShdcK1ObgkztbdTxawK5VFGhY87lly6wPpeIiEozfPhwfPLJJzCbzZg2bZqrwyGqNpjoVqHzt/7Cqes/QyX3wJguL0ImLfvLLwgCYvNLF6LYWoyIiPKdOXMGMpkMkZGRAACJRIItW7ZAo9Gw3zpRIbwXXoXib1vaigV7h5eb5AJAUnYe0rV6AEAkW4sRERGAt99+G71798bUqVNhMBjE7R4eHkxyiYpgoltFMrUpyNAmAQDCA8pfJAIomIgGcESXiIgsBEGAXq/HuXPncPz4cVeHQ1StsXShily7fUb8uCLL/gK2rcU4oktERAAwdepUXLt2DY8//jgiIiJcHQ5RteZmI7quu6UTn2pJdIO8wuGhqtjorHVEN9RbDT+N0mmxERFR9XT9+nVMmDAB165dE7dJpVIsXbqUSS5RBbjZiK5rEt08Qw6SMq8CAMIDKzaaCxSM6HLpXyIi95OZmYl7770XqampSE9Px759+yCVutn4FNEd4jumCsSnnoWQvypbgwrW5wLA+fweupGszyUicjs+Pj54/PHHIZVK0bFjR5hMJleHRFTjuNmIrmvEp1q6LXirA+DnEVqhY/IMJsSlZgPgiC4RkbtISkpCSEiI+Pj//u//MGDAAHTo0MGFURHVXG42oiu45FXbhvdG2/DeiKrbvcKtXy6kZELID5eLRRAR1W4ZGRmYOnUq7rrrLiQlJYnblUolk1yiO8BEtwoEetVH+4b90bL+3RU+JpatxYiI3MaJEyewZ88e3L59G6+//rqrwyGqNVi6UE1Zl/5Vy2Vo4O/p4miIiMiZevbsicmTJyM7OxuvvPKKq8MhqjWY6DqRIJihN+mgkmvsPtbaWiwi2AcyzrIlIqpV/vjjDwBAt27dxG1LliyBTCZzVUhEtRIzKCdKzrqOj47+D9+eegeZ2tt2HWsd0Y1g2QIRUa3yxhtvYNCgQZgyZQoyMwvK1JjkEjkeE10nik89A0Ew41ZGHDQKrwofJwiCWKPL+lwiotqlSZMmEAQBmZmZOHPmTPkHEFGlsXTBiayrodX1awaFXFXh425k5CJHbwTApX+JiGo6QRBsOu4MHz4c8fHxGDlyJOrXr+/CyIhqP47oOkmmNgXpuZYWMeEBFV8NDSiozwU4oktEVJOdP38e999/P44fP26zfebMmUxyiaoAE10nuXa74HaUPauhAbaJLnvoEhHVTFqtFoMHD8aff/6JadOmQavVujokIrfjZoluxRZrcATramhBXmHwUNmXrMbmT0QL8/WAl0rh8NiIiMj5NBoN5s+fD6VSiXHjxkGpVLo6JCK342Y1ulWT6OYZcpCUeQWA/WULQEGiG8myBSKiGkMQBJw7dw5RUVHitgkTJqBnz55o3LixCyMjcl9uNqJbNa6nxkLIX4WtQWBLu48/J3Zc4EQ0IqKa4ObNm3jooYfQu3dvnD9/XtwukUiY5BK5EBNdJ1DJNQj1aQRvdSD8PELtOjZbZ8D1jFwATHSJiGqK1NRU/PTTT8jLy8PKlStdHQ4R5XOz0gWhSl4lPLAFwgNbwGQ22rSUqYjzyYUmorF0gYioRmjRogVefPFF3Lx5Ey+//LKrwyGifEx0nUgmtf/LG8vWYkRE1d53330HrVaLYcOGidtmzZrlwoiIqCRuluhWf9alfz2VctT39XBxNEREVNSyZcuwbNky+Pj4oGPHjggLC3N1SERUCtboOpAgmHHw9FacuHYIWXmplTrHuUJL/9pb9kBERM537733QiqVQqPR4MaNG64Oh4jKwBFdB0rJvoEbaedxI+08fD2C4a0OsPsc1kSXS/8SEVUPBoMBCkVBT/OuXbti06ZN6NWrFwIC7P85T0RVhyO6DhSfvxqaVCJDfb9Iu483mc3iZDT20CUicr1//vkHMTEx+Oabb2y2jxo1ikkuUQ3ARNeBrqVaEt26fk2hkKvsPz4tB3lGEwCO6BIRuZper8fEiRNx4cIFPPPMM8jIyHB1SERkp0onunq9HpcvX4bRaITBYHBkTDVSpvY20nNvAQDCA1pU6hzsuEBEVH0olUq88cYb8PLywosvvggfH/5cJqpp7E50BUHAypUr0blzZwwePBiJiYmYM2cO5s+f79YJb3z+aC5QuWV/gYIeuhIJ0DyIP1CJiKqSyWTCr7/+arOtX79+OHHiBCZMmMAJwkQ1kN2J7o4dO/D5559j4cKFUCqVAIC+ffvi+++/x7p16xweoGM574fUtfz63ECvMHiqKld2EJvfWqyRvxfUCpnDYiMiorLFx8dj8ODBGD58OI4cOWLznL+/v4uiIqI7ZXeiu3v3bixYsAAjR44U/7odOHAgXnvtNXz55ZcOD9CxnJPo5hlykJR5BQDQoJKjuUDhjgsczSUiqkpyuRznzp2DIAjYsmWLq8MhIgexu73Y9evXER1dPJmLiopCcnKyQ4KqaVJzEiGVymAyGxEeWLn6XKBgRDeKE9GIiKpU3bp1sWLFCly+fBnPPvusq8MhIgexO9GtX78+/vvvv2Irwfzyyy8IDw93WGA1ST2/ZhjbdQFuZlyGv0edSp0jXavHraw8ABzRJSJyJkEQsGfPHqSmpmLatGni9lGjRrkwKiJyBrsT3UmTJuGVV15BcnIyBEHAkSNHsHv3buzYsQNz5851RowOZHbamRUyJcIDoip9vHXpX4CtxYiInGnp0qVYsWIFFAoFYmJi0Lp1a1eHREROYneiO2rUKBiNRrz99tvIy8vDggULEBAQgGeeeQYPPfSQM2J0C2wtRkRUNcaMGYP169cjNDTUrbsFEbkDuxPdhIQEjB49Gg8++CBSU1MhCAICAwNhNBpx8uRJtGnTxhlxVltXUv6DRuGFYJ+GkEoqv/6GdUTXT6NEiJfaUeEREbm9zMxMeHt7ixOomzZtit27d6Nt27bw8vJycXRE5Ex2Z2Z9+vRBeno6ACAgIACBgYEALJPUxo8f79DgqjtBMOPPy1/i6/824cjFvXd0LuuIblSID3s1EhE5yI8//ogePXpg27ZtNtvvuusuJrlEbqBCI7o7d+7E1q1bAViK+EeNGgWp1DZHzszMRL169RwfYTV2O/sGcvWWBLWeX/M7Opd1sQjW5xIROYbJZMLLL7+MhIQELFiwAIMHD0ZISIirwyKiKlShRHfkyJFIS0uDIAhYv349BgwYAE9PT5t9PD090b9/f6cEWV1dy18NTSqRob5/ZKXPYzCZcTElCwAQGcz6XCIiR5DJZNiwYQMeeughLFu2jEkukRuqUKKr0WgwY8YMAIBEIsGkSZOg0WicGlhNEH/7LACgjm8TKOWVr6uNS82GwWTpCMHWYkRElaPT6fDFF19g9OjR4rY2bdrgn3/+gUqlcmFkROQqdtfozpgxAwqFArdu3UJCQgISEhJw48YNxMXF4YsvvrDrXDqdDi+++CI6deqEmJgYsTyiJOfOncNDDz2ENm3aYMiQIfjjjz/sDd2hsvJSkZZ7EwAQHlD5RSIAIPZWQWsxLhZBRGS/K1euoE+fPnjyySfx6aef2jzHJJfIfdnddeG3337DnDlzkJqaWuw5tVqNoUOHVvhcy5cvx6lTp/D+++8jISEBc+bMQb169TBgwACb/bKysvD444+jd+/eWLp0KT7//HPMmDED3377rTgZrqpdu31G/LhBYOWX/QUK6nPlUgmaBnnf0bmIiNxRYGAgsrOzAQD79+/n4g9EBKASI7pvvPEGWrRogU2bNkGtVmPdunV48cUX4eXlhRUrVlT4PLm5ufj4448xf/58tGzZEv369cPkyZOxc+fOYvt+9tln8PDwwKJFi9CwYUPMmjULDRs2xKlTp+yM3nHdDOLz63MDPevDU+V3R+eyLv3bJNAbClnlW5QREbkrb29vbNiwAcuXL8c777zj6nCIqJqwe0T34sWLeP311xEVFYXo6Gh4eHhg/Pjx8PDwwLvvvou+fftW6DyxsbEwGo1o3769uK1jx47YuHEjzGazTVeHP//8E3369IFMJhO3Fb01VTGOSXR1hlzcyrgCAAi/w9FcADiXZO24wPpcIqLymM1m7N27F0ajEcuWLRO39+jRAz169HBhZERU3dg9fCiTyeDtbbm93rBhQ5w/fx4A0K1bN1y6dKnC50lOToa/vz+USqW4LSgoCDqdTuzTaxUfH4+AgAC8/PLLuOuuuzBmzBgcO3bM3tAdRiFXoX+rSWhR7y40DLyzpSMFQcDZ/Bpd1ucSEZVv9erVePvtt7FlyxYcOnTI1eEQUTVm94hu8+bN8cMPP2D8+PFo0qQJjh07hokTJ+LmzZt2nUer1dokuQDEx3q93mZ7bm4uNm/ejAkTJmDLli3Yv38/Jk2ahK+//hp169a141UFaLVau+Isja+yLnzr1BXjq6zkHB3StJbPt7Gf+o7ORQWs19lR15uqN15v9zJ69Gi8++67qFu3Lvz8/Phzs5bj+9u9CILg0IWz7E50p0yZglmzZkGhUGDw4MFYu3YtpkyZgnPnzqFbt24VPo9KpSqW0Fofq9W2rbpkMhmio6Mxa9YsAECLFi1w+PBhfP7555g6daod0Qu4cuWKHfs7379JBT+gldm3cfYs38iOVN2uNzkXr3ftlJqaCi8vL5vBkRUrVqB+/foAgLNnz7oqNKpCfH+7j6IDoXfC7kS3b9+++PjjjyGTyVC3bl2888472LZtG/r06SMmohURGhqKtLQ0GI1GyOWWMJKTk6FWq+HjY1urGhwcjCZNmthsa9SoERITE+0NH40aNbqjHsB5hhzIZUrIpYpKn6OwozlxAK4AAPp1ao0AD8ddXHem1Wpx5cqVO77eVDPwetdeX375JebNm4dHHnkEc+fOBVAwssfr7R74/nYvFy5ccOj57E50AaBly5bix126dEGXLl0AAKdPn4afn1+FzhEdHQ25XI5///0XnTp1AgAcO3YMrVu3Lra8cLt27fDXX3/ZbLt8+TIGDx5sd+wajQYeHh52H2d14uK3uJT0DxoFtUFMxOjyDyhHXHoeACDYS4WwIL87Ph/ZutPrTTULr3ftIggCPvroI6Snp2Pjxo2YMmUKwsLCxOd5vd0Lr7d7cGTZAmDHZLSTJ09i2bJlWLVqFWJjY22e0+l0WLZsGR588MEKv7BGo8Hw4cOxaNEinDx5Et9//z22bt2KCRMmALCM7ublWZLAsWPH4ty5c1i7di2uXr2K1atXIz4+HsOGDavw6zmCIAiITz0Lo9kAk2B0yDmtrcU4EY2IyJZEIsHatWvRsmVLfPrppzZJLhFRRVQo0T1w4ADGjh2LXbt2YdeuXXjggQfEEdbjx49j8ODB2LZtm12LRQDAvHnz0LJlS0ycOBGvvPIKZs6cif79+wMAYmJicODAAQBA/fr18c477+DHH3/E4MGD8eOPP2Lz5s0IDQ216/Xu1O3sG8jVW1qB3elqaFbWxSLYWoyI3F1WVhZWr14Nk8kkbgsLC8Mvv/yCu+++24WREVFNVaHShS1btqBv375YuXIlpFIpli5dirfeeguPP/44nn76adSpUwfbtm1D9+7d7XpxjUaDZcuW2fRBtDp37pzN444dO2Lv3r12nd/RruUvEiGVyBDmH3nH59MZTbh827KST2QwR3SJyH1duXIFw4cPx7Vr1yCRSGzmfDj6ViYRuY8KjeheuXIF06ZNg1KphFwux6xZs3DixAm89NJLGDp0KL744gu7k9yaKD5/2d86vk2glKvL2bt8F1OyYBYEABzRJSL3Vr9+fXFJ97Nnz0LI/9lIRHQnKjSiq9VqERwcLD728fGBXC7HoEGD8NJLLzktuOokKy8VabmWXsHhAXe+GhpQUJ8LsEaXiNxP4VUwFQoFNmzYgFOnTmHUqFEujoyIaosKd10oeutIIpHYNfmsprOO5gKOq8+1Lv2rlEnRKMDTIeckIqruDAYDVqxYgTNnzmDHjh3i75fIyEhERt55WRgRkZXdSwAXVnRhh+qv8p9ufKqlIXmAZz14qf0cEo010W0e7A2Z9I4uBRFRjbFx40asXLkSBw4cwM6dO10dDhHVYhUe0T1+/Dh8fQturwuCgJMnTxZb+rdz586Oi64aqePXFDqjFg0CHTOaCwDn8ksXIlm2QERuZPLkyfjggw8QHBzMbgpE5FQVTnRnzpxZbHLA7NmzbR5LJJJauxRj2/DeaBveG4Jgdsj5BEFAbP6IbhQnohFRLRYXFwd/f39xQSGNRoN9+/YhNDS02AJBRESOVKFE99ChQ86Oo4rc+SxeicQxP5QTM7XI0hkAcESXiGonQRDw3nvvYcGCBRg0aBA2btwoPle3bl0XRkZE7qJCiW79+vWdHUcVqT7tas7lLxQBsOMCEdVOEokEf//9N3JycrB3717Mnj0bzZs3d3VYRORGeM+oHAnpF/HNf5tx+sZvMJj0Djtv4dZiEcHeDjsvEVF1smTJEvTp0wcHDx5kkktEVY6Jbjmu3T6FmxmX8e+1g5A6qGwBKOi4UM9HAx+10mHnJSJylZSUFMybNw+5ubniNh8fH3z88cdo27atCyMjIndV4clo7kgQBFy7bZlcV98/EjKp475csbcsI7osWyCi2uD69evo06cPkpOTIQgCli5d6uqQiIg4oluW1JwE5OotCWkDBy0SYXU+v0aXS/8SUW1Qv359dOzYEQBgNBq5hC8RVQuVGqJMSkrCnj17cPnyZcyfPx9//fUXIiIi0KRJE0fH51LX8ldDk0ikqB/guNV6cvVGXE3LAcBEl4hqrry8PHHhIIlEgjfffBOTJk1Cnz59XBwZEZGF3SO6V69exZAhQ/DZZ5/hu+++Q25uLg4cOIBRo0bhxIkTzojRZayrodXxaQKVXOOw854v1HGBrcWIqKbJzc3FnDlzMHDgQBgMBnF7aGgok1wiqlbsTnSXLl2Kvn374vvvv4dCoQAAvPHGG+jduzdWrlzp8ABdJc+Qg9ScBABAff8Ih567cMcF1ugSUU2zZ88ebNmyBf/++y/Wrl3r6nCIiEpld6L7zz//4LHHHoNEIhG3yeVyTJ8+HWfOnHFocI4nKX+XfDczLokf1/Nr5tAorB0XPJQyhPl6OPTcRETONmHCBPTo0QMDBw7EI4884upwiIhKZXeNrtlshtlcfBncnJwcyGQyhwTlPBVPdDO1tyGBBEq5B/w96zg0CmuiGxnsC6m04jEREbnCqVOn4Ovri/DwcACAVCrFrl274OXlZTPoQURU3dg9ohsTE4NNmzbZJLvp6elYsWIFunXr5tDgXKlNeC+M7bYA/Vo+6rBlf63O5ZcuRARzIhoRVW9r1qxBnz598NRTT9n83Pf29maSS0TVnt0Z3Ny5c3Hq1CnExMRAp9Nh2rRp6NWrF65fv445c+Y4I0aXUck1CPIOd+g5zWZBXP43ih0XiKiay8rKgsFgwLFjx2pAeRoRkS27SxdCQ0Oxb98+fPXVVzh79izMZjMeeughDBs2DF5eXs6I0YFc39cxPj0HWoMJADsuEFH19/zzzyMpKQmzZs1C06ZNXR0OEZFd7E50V69ejZEjR2L06NHOiMfJKpboxqeehYfSBwGedR1ftlCotVhUKEd0iaj6uH79Ol5++WUsX74cwcHBAAClUonVq1e7ODIiosqxO9H98ssvsXHjRnTo0AEjR47EgAED4Onp6YzYXEIQBBy+8CnyDNmIqtsd3ZoOc+j5zxVqLdY8iIkuEVUPycnJiImJQWZmJoxGI7Zv384aXCKq8ewervz++++xc+dOREREYOXKlYiJicELL7yAI0eOOCO+Kpeeewt5hmwAQIhPI4efPza/40JDf094KCu1MB0RkcMFBwdj9OjRkEqliIyMLLG7DhFRTVOpTKtDhw7o0KED5s+fj99++w379+/HU089BT8/P/zwww+OjrFKJaZfFD+u6+v4ejTriC7rc4nI1VJTUxEQECA+XrRoEcaOHYuOHTu6MCoiIse5owLU1NRUxMXFIT4+HjqdDg0bNnRUXC6TkJ/o+nvUgUbp+Ml11hFddlwgIldJT0/HlClT0KtXL2RmFswb8PT0ZJJLRLWK3SO62dnZ+Pbbb/Hll1/ir7/+Qr169TBixAi8+eabqFu3rjNirDJmwYRbmXEAgLoOXg0NADLz9EjM1AIAIpjoEpGL/Prrr/jkk08AACtXrsSrr77q4oiIiJzD7kS3R48eUCgU6N+/P95//3106tTJGXG5RErWdRhMOgDOSXStK6IBQBRLF4jIRYYMGYJRo0ZBpVLh//7v/1wdDhGR09id6L7yyisYMGAANBqNM+JxKWt9rkQiRR2fxg4/f6xNossRXSKqGkeOHIGnpyfatGkjbnv77bchl3NCLBHVbhX6KffXX3+hffv2kMvlCAsLw6lTp0rdt3Pnzg4LzvHKbpVjrc8N9g6HQq5y+KtbJ6L5qBWo4137/lAgoupnyZIlWLlyJSIiIvDjjz9CrVYDAJNcInILFfpJN378eBw+fBiBgYEYP348JBIJBKH44gsSiQRnz551eJCOU3ai27L+3fD3DIWfRx2nvHrhpX/Zn5KIqkLdunUhCAJu3ryJ2NhYtGvXztUhERFVmQoluocOHYK/v7/4cW3VILAFGgS2cNr5rSO6EcGszyWiqjFx4kTcunUL48ePR7169VwdDhFRlapQe7H69etDKrXsum7dOvj6+qJ+/fo2/3l6emLx4sVODbYmM5rMuJCcBYD1uUTkHLGxsRg0aBAuXLggbpNIJJgzZw6TXCJySxUa0T127Bji4+MBAPv27UPLli3h5WXbY/bSpUs1YHW04uUW4jOC4NRygitp2dCbLCsNcbEIInK0jIwM3HfffcjKysL06dPx9ddfsw6XiNxehX4KSiQSzJ07V/z4tddeK7aPh4cHJk2a5NjoHK7kRDfPkIN9/7yJOr6N0SasFwK8HD/ywY4LRORMvr6+ePbZZ7Fs2TIMGzaM8wCIiFDBRLdDhw6IjY0FAERFReG3335DUFCQUwOrSjczLiPPkI0rKf+hdVgvp7zG+fxEVyqRoGmQt1Neg4jchyAIiIuLQ5MmTcRtM2fOxODBg9GsmeP7gBMR1UR2LwEcGxtbq5JcoKB/rkruiQBP53RciM2fiNYk0Asqucwpr0FE7iExMREPPvggevfujRs3bojbZTIZk1wiokIqNKI7YcIErFu3Dj4+PpgwYUKZ+27fvt0hgVUla//cun5NIJHYnftXiHVVtEiWLRDRHbp69SoOHToEQRDwxhtvYNWqVa4OiYioWqpQolu460K9evVqVe1Xdl46svJuAwDq+jpvJMQ6osulf4noTnXr1g2zZs2C0WjE/PnzXR0OEVG1VaFEd8mSJeLHS5cudVowrpCYcVH8uK6fcxLd2zk6pOToAHBEl4js980330ChUKBPnz7itgULFtSqQQciImeo1H36f/75B6mpqQAs7caefPJJbNq0qcTV0qo7a32up8oP3uoAp7yGdaEIAIjkYhFEZIdXXnkF48aNw4wZM8SfuwCY5BIRVYDdie5HH32Ehx9+GOfOnUNsbCzmzZsHg8GA9957D+vXr3dGjE4jCAIS0y8BAOr5NXPaLw62FiOiyurRowcAS2J79epVF0dDRFSz2J3ovv/++3jppZfQvXt3HDhwAM2bN8fWrVuxfPly7N271xkxOpBtIptnyIFCrgLgvLIFoGBEN9BDhSAvtdNeh4hqPpPJZPO4X79+WLNmDQ4fPoz27du7KCoioprJ7kT3+vXr6N27NwDg8OHD6NmzJwCgadOmSElJcWx0Dmeb6GqUXhjZ8f8wuvNchPtHO+1VrSO6HM0lorL89ddf6NGjB/744w+b7Y888gj8/f1dFBURUc1ld6IbGBiIpKQkJCcn4+zZs7jrrrsA1Oz+up4qP3Fk1xnOJ1sS3QgmukRUipycHIwbNw4XLlzA9OnTkZub6+qQiIhqPLsT3UGDBuH//u//MGnSJNSpUwddunTBgQMHMH/+fAwaNMgZMdZoeqMJl25nAWBrMSIqnaenJ15//XV4eXlh9uzZ0Gg0rg6JiKjGq1B7scJmz56NOnXqID4+Hg8//DBkMhlu376NsWPHYubMmc6I0YEKukJk5CbjVmYc6vo1hbc60GmveOl2Nkxmy+uytRgRWZlMJhw7dgxdunQRtz3wwAO49957ERwc7MLIiIhqD7sTXalUivHjx9tsK/q4+ipIdK/c/g/Hr34HCaQY122h00oXYgu1FuOILhEBQFxcHKZOnYp///0Xhw4dQqtWrQBYOiswySUicpxK9dE9dOgQxowZg3bt2qFTp04YO3YsDh486OjYnMraPzfYO9y59bn5E9EUMikaB3g57XWIqOYwm804deoUDAYDNm7c6OpwiIhqLbtHdL/77js8/fTT6NOnDwYNGgRBEPDXX3/h6aefxtq1a21W7qmujCY9kjIt/Sid2VYMKBjRbRbkDbmsUn9XEFEt07RpU7z22mtIS0vDrFmzXB0OEVGtZXeiu2HDBjz11FOYMWOGuO3RRx/FunXrsHHjxhqR6CZlXoVZsPSqrOvX1KmvdS5/RJf1uUTuSRAEfPTRRxAEAePGjRO3P/bYYy6MiojIPdg9xHj58mUMGTKk2PbBgwfj/PnzDgnK2RLyyxbkUgWCvRs47XUEQRBHdFmfS+SeFixYgKeeegpz5sxBXFycq8MhInIrdie6ISEhJS5DefXqVXh7ezskKGdLzLAkuqG+jSGT2j2oXWG3svKQkWcAwBFdInf1wAMPQC6XIzQ0FFlZWa4Oh4jIrdid6A4ePBiLFi3Czz//jOzsbGRnZ+Pnn3/GK6+8goEDBzojRofSG7W4nZ0AAKjr69z63HP5C0UAQGQwE10id5CTk2PzuG3btvjwww/x888/o02bNi6KiojIPdk9nDlt2jScP38eTz75JCQSy5K6giDg3nvvxXPPPefwAB3tds51WNuM1fFt7NTXKtxaLJKlC0S13qFDhzBr1iy89tprGDFihLi9b9++LoyKiMh92Z3oqlQqbNiwAZcuXcL58+chCAIiIyPRtKlzJ3U5hgR+HnXRo9koJGddQ4BnPae+2rn8RLeOtwZ+GqVTX4uIXEuv12P27NlITEzE//3f/6FPnz7w8eGdHCIiV6pwonvz5k0cPHgQSqUS99xzD5o2bVpDktvCJNAovBDh2xkRdTo7/dVi8zsuRLE+l6jWUyqVWLduHZ544gmsWrWKSS4RUTVQoUT377//xuTJk5GXlwcA8PDwwJo1axATE+PU4Go662IREUx0iWqdvLw8HDx40KYLTUxMDP755x9oNBoXRkZERFYVmoy2evVqdO/eHb/88gsOHz6Mu+++G0uXLnV2bE4glL+Lg2gNRlxJywbA1mJEtc2FCxfQu3dvTJw4EYcOHbJ5jkkuEVH1UaFE98yZM5g9ezZCQkIQGBiIF198EZcuXUJ2draz43O4A6fX4fCFTyAIZqe+zoXkLAj5eTVbixHVLv7+/khJSQEA7N2718XREBFRaSpUupCbmws/Pz/xcWhoKBQKBTIyMuDl5eWs2JxAQLYuFSnZKkgkzl2Ot3DHBY7oEtUuQUFBWLNmDRITE/Hoo4+6OhwiIipFhRJdQRDEVmJWMpkMZrNzR0WdJdSnodNfw7r0r1ouQwM/T6e/HhE5h9lsxttvv42cnBy88MIL4vYBAwa4MCoiIqoI5y0LVo35edRx+mtYW4tFBPtAKpWUszcRVVevvvoq1qxZA6lUil69eqFzZ+d3bCEiIseocKK7detWm0kWRqMR27dvh6+v7W35GTNmOC46J/HRBDr9NayrorE+l6hmmzx5MrZu3YqmTZvWmGXOiYjIokKJbr169fD111/bbAsODi4221gikdSMRFcd5NTzm82CWKPL+lyimiUpKQmBgYGQyWQAgLCwMHz55Zdo0aIFFAqFi6MjIiJ7VCjR/eGHH5wdR5WRSRXwUDk3+byRkYtcvQkAR3SJapJPP/0Uzz//PJ599lnMnDlT3N62bVsXRkVERJXl3NYD1VAd7yaQOrnjgrVsAWCiS1RTmEwmbNiwAenp6Vi6dCmSk5NdHRIREd0ht0t06/tFOf01zhVqLRYRzESXqCaQyWRYv349IiIisGfPHgQHB7s6JCIiukNuluhKqiTRjc1vLRbu5wEvFWv6iKqjzMxMbN68GYJQsGJiVFQUfv/9d9x1110ujIyIiBzFzdqLSaCQqZz+KtYR3UhORCOqls6dO4cxY8YgPj4eHh4eeOSRR8TnpFI3+/ufiKgWu6Of6Hq93lFx1CrWxSIiWbZAVC2Fh4dDpbL80Xvs2DEXR0NERM5SqRHdXbt2YcuWLbh58ya+/fZbvPPOOwgNDcX06dMdHZ+DCeXvcoey8gy4npELgK3FiKqTwis8enh44O2338a1a9cwYsQIF0dGRETOYveI7pdffolVq1ZhxIgRYk/Jpk2bYuPGjdi6davDA3Qs5ye659lxgaha0ev1WLx4cbEe3x07dmSSS0RUy9md6G7duhXz58/HzJkzxVq2CRMmYMGCBdi9e7fDA6xpYgt1XIgK5YgukautXLkSq1atwq5du/DVV1+5OhwiIqpCdie6cXFx6NSpU7HtXbt2RWJiol3n0ul0ePHFF9GpUyfExMRUaET4+vXraN++PY4ePWrXa1UVa32ul0qOej6acvYmImd76qmnUK9ePcTExKBNmzauDoeIiKqQ3TW6QUFBiIuLQ3h4uM3248ePIyQkxK5zLV++HKdOncL777+PhIQEzJkzB/Xq1cOAAQNKPWbRokXIzc21N+wqY10sIjLYR6wHJKKqc/nyZdSpUwceHh4AAF9fX3z99deoX78+OyoQEbkZu3/qP/jgg3j11Vdx6NAhAJZfKrt27cLixYsxcuTICp8nNzcXH3/8MebPn4+WLVuiX79+mDx5Mnbu3FnqMV988QVycnLsDblKsbUYkWsIgoD33nsPPXv2xKuvvmrzXHh4OJNcIiI3ZPdP/ieeeAIDBw7Ec889B61WiyeffBKLFy/GkCFDMHXq1AqfJzY2FkajEe3btxe3dezYESdOnIDZbC62f1paGlasWFHsF1h1YjKbxcloUZyIRlTlfvrpJ+Tm5uK9997D9evXXR0OERG5WKXaiz333HOYNm0aLl68CEEQ0KRJE3h5edl1juTkZPj7+0OpVIrbgoKCoNPpkJ6ejoCAAJv9ly5dihEjRqB58+aVCVmk1Wrv6PiyxKVmQ2e0JOmNfdXVusSitrNeZ2deb6o+tFotJBIJXn31VeTk5GDRokUICAjge7CW4vvbvfB6u5fCrSAdwe5ENyEhQfw4MDAQgGUpzcxMy0hmvXr1KnQerVZrk+QCEB8XXYji999/x7FjxxwyY/rKlSt3fI7S/J6QJX4sy0rG2bNZZexNVcGZ15tcKy0tDZ988gkee+wxyOWWH2U5OTniXZ+zZ8+6MjyqAnx/uxdeb/dRND+8E3Ynur179y4z067oLxeVSlUsobU+VqvV4ra8vDwsWLAACxcutNleWY0aNYJG45xuCIfSLgCIh0QC9OvUBmqFzCmvQ+XTarW4cuWKU683uc7ly5cxbdo03L59G2FhYZg2bRqvtxvh+9u98Hq7lwsXLjj0fHYnutu3b7d5bDKZEBcXh/feew9z586t8HlCQ0ORlpYGo9EojsYkJydDrVbDx6egvvXkyZOIj4/HrFmzbI5/4oknMHz4cDtrdiXQaDTibGxHu5xmua3SOMALAb7eTnkNso8zrze5TnR0NCIiInDkyBHk5OSIv/x4vd0Lr7d74fV2D47uWGV3otulS5di27p3747w8HCsXbsWvXv3rtB5oqOjIZfL8e+//4p9eY8dO4bWrVvbzI5u06YNvvvuO5tj+/fvj9deew133XWXndE7t90XOy4QOY9erxdvZ8lkMqxfvx5xcXHo1asXa3GJiKhElZqMVpJGjRohNja2wvtrNBoMHz4cixYtwuuvv46kpCRs3boVS5YsAWAZ3fX29oZarUbDhg2LHR8aGirWCFcXsUnsuEDkaNnZ2Vi0aBEuXryIvXv3in8IN2rUCI0aNXJtcEREVK3d0WQ0q+zsbGzatAlhYWF2nWvevHlYtGgRJk6cCC8vL8ycORP9+/cHAMTExGDJkiV29eYtn+DAc9lKy9UhKTsPABARzESXyFG2bt0qrpq4bds2TJo0ycURERFRTeGQyWiCIMDDwwMrVqyw61wajQbLli3DsmXLij137ty5Uo8r67myOS/Rta6IBgBRLF0gcpipU6fik08+QaNGjTB06FBXh0NERDXIHU9GAwCFQoGIiAh4eno6JKiaKPZW4USXI7pElXXy5EnUrVsXwcHBACxtZr788kv4+HBZbSIiso/dK6Nt374dgYGB6NKli/hf+/bt3TrJBQomovlrlAj2uvM2aETuRhAErFy5En379sXs2bMhCAV3YHx9fZnkEhGR3exOdP/44w+oVCpnxFKjWUsXIkM46kRUGRKJBLdu3YLRaMQPP/yAuLg4V4dEREQ1nN2J7ogRI7By5UpcuHCh2IIP7oytxYju3KJFizB69Gj88ssvaNKkiavDISKiGs7uGt2ff/4Z165dw7ffflvi8+647KbBZMbFFMtyv6zPJaqYa9euYfHixVixYoW4SIynpyc2bdrk4siIiKi2sDvRnTZtmjPiqNEu386C0WypJ+SILlH54uPjERMTg+zsbCiVSqxdu9bVIRERUS1UoUQ3Ojoav/32GwIDAzFixAhnx1TjWBeKAIBI9tAlKld4eDj69++Pzz//HHXr1oUgCKxtJyIih6tQolt49jMVdz4/0ZVLJWga5O3iaIiqp4yMDPj6FtzxWLFiBaZNm4aOHTu6MCoiIqrN7J6MVrM5Z8QoNn8iWtNAbyhkbvYlJSpHamoqHn/8cQwYMAB5eXnidn9/fya5RETkVBWu0f3666/h5eVV7n7Dhw+/k3iczDmJ7rmkgtZiRGRr//792LdvHwBg7dq1eP75510bEBERuY0KJ7qvvfZauftIJJJqnug6niAI4ogul/4lKu6RRx7B559/jvr162Pq1KmuDoeIiNxIhRPdw4cPIzAw0JmxVAHH1xqn5OiQprX0E47giC4RfvvtN9SpUwfNmjUDYPkD+MMPP4RSqXRxZERE5G4qVFBae2ZDOz7RtY7mAhzRJXrllVcwdOhQTJs2DUajUdzOJJeIiFyhQokuuy6Uzqa1GEd0yc1ZF364fPkyLly44OJoiIjI3VWodGHEiBFQqVTOjqVGsi79G+KlRoAHv0bk3mbOnImMjAxMnToVderUcXU4RETk5io0ortkyZIKdVxwR7HsuEBu6syZMxg+fDhu3LghbpPL5Vi0aBGTXCIiqhbY9PUOnWeiS24oKSkJ/fr1wy+//IKnn36a5U1ERFQtMdG9A3kGE+JSswFwIhq5l5CQEEyePBlqtRp9+vRhoktERNVShduLUXEXUzJhzv8FH8lEl2oxQRBw/fp1hIeHi9vmzZuHRx55BM2bN3dhZERERKXjiO4dKNxxIYqlC1RLJSQkYPTo0ejfvz9SU1PF7Wq1mkkuERFVa0x078D5ZEuiq5JL0dDf08XREDnH6dOn8cMPP+DWrVtYvXq1q8MhIiKqMDcrXXDswhfWxSKaB/lAJuXfDFQ79evXD48++ih8fHwwb948V4dDRERUYUx078A5dlygWujAgQMIDAxE165dxW2rVq2qRSskEhGRu3CzRNdxBEEQR3TZcYFqi7lz52Lz5s1o1KgRfvnlF7F/NpNcIiKqidzsfrvjWiAlZGqRrTMCACI4oku1RIcOHQAAOp0O165dc3E0REREd8bNRnQdl+hal/4FOKJLNZfZbIa0UH356NGjkZGRgTFjxsDXl9/XRERUs7nZiK7jnCvUWiwymCO6VPMcPXoUMTExOH36tLhNIpHgiSeeYJJLRES1AhPdSrLW59b39YC3WuHiaIjsk56ejtGjRyM2NhZTp06FwWBwdUhEREQOx0S3kqyLRXChCKqJ/Pz88NJLL8Hb2xtPPfUU5HI3q2IiIiK3wES3kqyLRUSwbIFqAKPRiBMnTthsmzx5Mo4ePYqxY8eyqwIREdVKTHQrIUdnwLW0HACciEbV38WLF3H//fdj8ODBiIuLE7dLpVLUqVPHhZERERE5FxPdSjifnCV+zMUiqLrLzs7GiRMnkJOTg02bNrk6HCIioirDwrxKiGVrMapB2rVrh3nz5kEqlWLGjBmuDoeIiKjKMNGtBGtrMQ+lDPV9PVwcDVEBQRCwc+dO+Pn5YfDgweL2Z5991oVRERERuYabJbqOqdQ4l2wZ0Y0M9oVUykk8VH08//zz2Lp1KwICAtC5c2eEhoa6OiQiIiKXYY1uJVhHdFmfS9XNsGHDAAABAQG4ffu2i6MhIiJyLTcb0b1zZrMgJrqszyVX02q10Gg04uO7774b27dvR+/eveHhwbIaIiJyb242oivc8Rmupecgz2gCwB665FoHDx5Ep06d8MMPP9hsHzx4MJNcIiIiMNG1m3U0FwCiQpnokmvk5ORgxowZSExMxKxZs6DVal0dEhERUbXjZonunTuX31pMIgGaBzHRJdfw9PTEG2+8gZCQEKxcudKmfIGIiIgsmOjaKTZ/RLehvyc8lCxxpqqh1Wpx8OBBm22DBg3C33//jQEDBrgoKiIiouqNia6drCO6kZyIRlXkzJkz6NWrFx566CH8/fffNs95eXm5KCoiIqLqj4munc4l57cW40Q0qiLe3t5ISEiA2WzGnj17XB0OERFRjcF773bI0OqRmGmZ9MMRXaoq4eHhWLlyJbRaLSZMmODqcIiIiGoMJrp2sI7mAkAUF4sgJzCZTHj77behVCoxZcoUcfuYMWNcGBUREVHNxETXDrH59bkAF4sg55g7dy7effddqFQq3H333YiOjnZ1SERERDUWa3TtYO2h66NWINRb7eJoqDaaNGkSVCoVoqOjIZfz71AiIqI74Wa/Se8sry9Y+tcHEonEEQGRm0tJSUFgYKD4/RQVFYXPP/8c7du3h0KhcHF0RERENRtHdO3A1mLkKIIg4OOPP0bnzp3x4Ycf2jzXpUsXJrlEREQOwES3gowmMy6kZAHgRDS6c3q9HitWrEBGRgZeeuklZGZmln8QERER2cXNEl2h0kfGpWbDYDID4Igu3TmVSoX169ejadOm+PDDD+Hjwz+eiIiIHI2JbgUVbi3GxSLIXpmZmdixY4fNts6dO+PIkSPo3r27i6IiIiKq3dxsMlrlnbtlqc+VSSVoGuTt4mioJjl9+jTGjh2LGzduIDAwEAMHDhSfY2cFIiIi53GzEd3Ki83vuNAkwAsquczF0VBNEhYWBrPZUvby66+/ujgaIiIi98HhpApixwWyhyAIYsswX19fbNiwAenp6Rg2bJiLIyMiInIfTHQryDqiG8mOC1QGnU6H5cuXQ6/X43//+5+4/Z577nFhVERERO6JiW4FpGTn4XauDgATXSrbK6+8go0bNwIA+vfvj7vvvtvFEREREbkv1uhWQOGOC1EsXaAyzJo1C/7+/ujZsycaN27s6nCIiIjcGkd0KyA2vz4XYKJLtuLi4hAWFiauZFanTh189913aNy4MaRS/h1JRETkSm72m1hSqaPO5dfnBnqoEOipcmRAVEOZzWZs3rwZMTExePPNN22ea9q0KZNcIiKiasDNfhvfWaLLpX/JShAEfP7559BqtVizZg1u377t6pCIiIioCDdLdCuHrcWoKJlMhnXr1qFr16749ttvERgY6OqQiIiIqAgmuuXQGU24nJoNgCO67uzWrVtYunSpuPADADRu3BgHDhxAy5YtXRgZERERlcbNJqOZy9+liEspWTCZBQBAZChHdN3R6dOnMWzYMKSmpiIgIABTpkwRn7MuCkFERETVD0d0y2FdKAIAIoM5ouuOmjVrhrp16wIAEhISXBwNERERVZSbjeja73yypT5XIZOicYCXi6OhqmI0GiGXW94eKpUKmzZtQnJyMlc4IyIiqkGY6JbDOqLbPMgbchkHwGu77OxsvPzyy0hNTcV7770nlia0aNHCxZERERGRvZjoloMdF9zL6tWr8f777wMA9u7di1GjRrk4IqI7IwgCDAYDTCaTq0NxGJ1OJ/7LOvnaj9e79pDJZFAoFFV6HTlEWQZBEMQR3Uh2XHALTz/9NBo1aoShQ4fi3nvvdXU4RHfEaDQiJSUFer3e1aE4lFKpROPGjaFUKl0dClUBXu/aQ6/XIyUlBUajscpekyO6ZbiVlYfMPAMAJrq11YkTJ9C4cWP4+Fiur5eXFw4ePIiAgACOHFCNJggC0tLSEBQUVOu+l62j02q1GjKZzMXRkLPxetcunp6eSElJqbKfTRzRLUNsftkCAESxdKFWMZvNWLZsGfr164eXXnrJ5rnAwMBalxiQ+zEYDNBoNPxeJqJqRSKRQKPRwGAwVMnrMdEtA1uL1V5SqRTnz5+H0WjEZ599xrZhVOuYTCaOfhFRtSSTyaps3oCblS7YN7JhnYhW10cDXw1rg2qbFStWwGg0YtGiRahXr56rwyEiIiIHc+mIrk6nw4svvohOnTohJiYGW7duLXXfn376CcOGDUP79u0xZMgQHDp0qBKvaF+iK05E42hujXflyhXMmjULeXl54raAgAC8//77aNy4sQsjIyIiImdxaaK7fPlynDp1Cu+//z4WLlyIdevW4Ztvvim2X2xsLGbMmIFRo0Zh3759GDt2LJ5++mnExsY6NT7rYhFsLVaznTt3DnfffTc++OADLFmyxNXhEFE5evfujcjISPG/qKgodOnSBdOmTUNiYqLNvllZWVi2bBl69eqF1q1bo1+/fnjrrbeQm5tb7LyJiYl46aWX0LNnT7Rr1w7Dhw/Hvn37Kh2nIAh4+eWX0a5dO/Tp08euY8ePH4+1a9dW+rUrIy0tDTNnzkT79u3Ru3dvfP7553afY/z48WjXrh2ys7OLPRcZGYmjR48W27527VqMHz/eZpujr0VJ4uPj8eijj6Jdu3YYOHAgfvvtt1L3FQQB7777Lnr37o1OnTph3rx5yMnJEZ8/c+aMzfdkZGQkRo4c6dB4yTlcVrqQm5uLjz/+GFu2bEHLli3RsmVLXLhwATt37sSAAQNs9v3qq6/QrVs3TJgwAQDQsGFD/PDDD/j6668RFRXlnPj0RlxNs3yTR7HjQo0WERGBLl264JdffoGnp6erwyGiCnjxxRcxcOBAAJbJoxcvXsTChQsxZ84cbNu2DQCQk5ODhx9+GAqFAq+++ioaN26Mixcv4o033sAvv/yCHTt2iO/5K1euYNy4cejQoQNWr16NwMBAHDlyBAsXLkRqaioef/xxu2OMjY3Fnj17sHnzZkRGRjruk3eSefPmIS8vD7t378aJEyfw0ksvoXHjxmjTpk2Fjr916xaOHz+OkJAQfPvtt5XuM+6Ma1GUIAh46qmnEBERgU8//RTff/89ZsyYgQMHDpRYqrZ7926sW7cO//vf/xAZGYklS5Zg9uzZ2LhxIwDg4sWLiI6OxpYtW8RjrKtnUvXmsqsUGxsLo9GI9u3bi9s6duyIjRs3wmw2QyotGGweMWJEibPzsrKy7HxVc4X3vJCSCUGwfMwR3ZpFEASb0QaJRII1a9YgKSnJ5vuNiKovb29vBAcHi49DQ0Mxa9YsPP/888jKyoJCocCaNWug1+uxe/dueHh4AADCwsLQsWNHDBkyBOvWrcOcOXMAAK+88gqioqKwdu1asRNFgwYNoNfr8cYbb+CBBx4Q2wxWlPV3UM+ePat9d4tr167hxx9/xKFDhxAWFoaIiAj8+++/+PDDDyuc6B44cAARERHo0KED9u3bV+lE1xnXoqg//vgD8fHx+Oijj+Dh4YGmTZviyJEj+PTTTzFz5sxi+3/wwQd47LHHMHjwYADA0qVL0bNnT1y+fBlNmjTBpUuX0LRpU5vvSaoZXJboJicnw9/f36YBdFBQEHQ6HdLT0xEQECBub9q0qc2xFy5cwJEjRzB27Fi7X1er1VZov5PxyeLHDbwVJd4Go+onNTUV8+bNQ0JCApYsWSJeb39/f/j7+/M61lLW61zR97c70Ol0UCqVNXJFNEEQYDabi8VuHUGTSCQwmUz47LPP8Oyzz0KlUtns6+HhgUceeQRbtmzBs88+i+TkZBw5ckQcSClsxIgRiIyMLHYOq0uXLmHZsmU4fvw4PD09MWbMGEydOhV///03Hn30UQBAVFQUpk+fjhkzZhQ7/r333sMHH3yAtLQ0dOjQAQsXLkRYWJjN56jX6/Hmm2/i66+/RmpqKkJCQjBlyhSMGTMGgCVpW7ZsGeLi4hASEoJJkybhwQcfBAB8/fXXWLt2LRISEhAWFoZnnnkGffv2LRbH8ePHUadOHdStW1f8PNu3b48tW7ZU+Hvkq6++QqdOncRSsGvXrqF+/fo2+5R03cxmMwRBgMlkws2bN+2+FoIg4IsvvsCiRYtKjOvgwYPF4jh+/Diio6NtztW+fXscP368xM83Pj4erVq1Ep8LDAxEQEAA/vnnHzRs2BAXL15EREREjXw/VUdmsxl6vR6CdUSxEEEQHPqHo8sSXa1WW2yVE+vjslbxSU1NxcyZM9GhQwe7a6IAyy2Tivj9rCXRVckkyE68irM3q/df62TxySefYP/+/QCAL7/8EsOHD3dtQFSlKvr+dhclTbTMyDPgfLK9d8PuTESwN3zVigrvb122uPDk0fj4eGzatAk9evSAXC7H5cuXkZ2djYiICJv9rFq1aoX09HRcvHgR8fHxEAQBzZs3L7avRCJBy5YtYTQai63WlJaWhvHjx6Nnz554//33ce3aNbz66qtQKpV48MEHsWLFCjz//PP47rvv4OHhUezcn3zyCTZs2ID58+cjOjoa69atw9NPP42dO3fCbDbDaDQiLy8PmzZtwk8//YTly5cjICAAX375JV577TXcdddd8PPzw7PPPouHH34YAwcOxL///osFCxagdevW8PPzw5w5c/DSSy+hU6dO+P777/H888/jm2++ga+v7Z3IxMREBAUF2cTo4+ODmzdvlvj1Kyo+Ph6nTp3CzJkz0bp1a3h6euLTTz/FlClTbPbT6/XFzmc0GmE2m5GXl4dTp05V6lr0798fPXr0KDE2Pz+/Yue6efMmAgMDbbb7+voiMTGxxM83ICAAN27cEJ/TarXIyMhAcnIy8vLycPHiRRiNRgwdOhTZ2dno0aMHnnnmGXh5eZXzlaOS6HQ6xMXFlfq8I1fBc1miq1KpiiW01sdqtbrEY1JSUvDYY49BEASsWbPGpryhoho1agSNRlPufmn/WX4RRAT7oGWLFna/DrnGvHnz8Ndff6F58+bo379/ha831WxarRZXrlzh9S7EOqJb+OdphlaP1m9+g3Rt1TRqt/LTKHBh7rAKt2mUSCR4/fXXsWzZMgCWnsAKhQK9e/fGvHnzoFKpkJlp6YoTFBRU4u8M6y1mrVYrJi9BQUF21VV+//33UKvVeO211yCXy9GiRQukp6djw4YNmDx5svgaYWFhJR7/2WefYcKECRg2bBgAYMGCBWJ9sVQqhVwuh1qtRsuWLRETE4OOHTsCAJ566ils2bIFiYmJ8PT0REZGBurUqYMmTZqgSZMmqF+/PurXr48bN27AaDQiLCwMTZo0wRNPPIGWLVvCx8en2NfEaDRCrVbbbPf09ITBYCj1d25hhw4dgq+vL3r06AGZTIZ7770X+/fvx6xZs2z2K/o9B1hG4qVSKdRqdaWuhXXUz9fXt8IjfdYFUwrH4uHhUernO3DgQLz33nvo2rUrwsLC8NZbb4mvLZPJcP36dYSHh+P1119HZmYmli5dioULF2L9+vUVioeKa9KkCVQqVbHtFy5ccOjruCzRDQ0NRVpaGoxGo/jNnpycDLVaXWJtzq1bt8TJaNu3b7cpbbCHRqMRa7nKcjHVMhEtuo5fhfYn1/j111/FH/xWX331FcxmM86ePVvh6021A693AWtCUHjRCMvHrrg7JYFMJqvwAhYSiQSzZs1C//79kZOTg7Vr1+LGjRv4v//7PwQGBsJkMom/J1JTU9GkSZNi50hJSQFgGamzlizl5OTY9bsjLi4OrVq1svll3LFjR6SkpCAnJ0ccbCnt87py5Qpat24tPh8aGoq5c+eKn6NUKoVMJsN9992Hw4cPY8WKFbh8+TLOnDkjniMwMBAPPfQQFixYgI0bN6JXr14YNWoUAgIC4O/vj3vvvReTJ09G48aN0adPH4wePbrEUUa1Wg2DwWATqzX5rch1OXDgAHr16iWOtN1333346quvcPz4cXTq1AlAQWlJSedTKBSQyWTi19+ea2EymXDgwAEsXry4xER3//79xSaYqdVqpKenF/t8NRpNifE99dRTuH79OoYOHQq5XI6xY8ciKipK/KPh6NGjUKlUUCgsdyb8/f0xatQopKSkIDQ0tEKfBxWw/uFT0sCEo+vdXZboRkdHQy6X499//xXfJMeOHUPr1q2LjdTm5uZi8uTJkEql2L59u9OLwc1mAeesrcWCORGtOhIEAfPnz8fGjRvRu3dvfPzxx+KbQ61WsxaXqAS+GiUuzx9hs7x5VYgK8bV70Z3AwEA0bNgQALB69Wo88MADmD59Onbv3g2pVIoGDRrA19cXp0+fFn+HFHbq1Cn4+fkhPDxcHAk8deoUevbsabNfbm4unnrqKcyZM6dYF5+SRpusdaUVqdWs6Ijlm2++iY8//hgjR47E8OHDsXDhQvTu3Vt8ftGiRXj44Yfx/fff4/vvv8fu3buxYcMG3HPPPdi0aRNOnjyJQ4cO4eDBg/jwww/x4YcfIjo62uY1QkNDxeTfKiUlpUK/T2NjY3Hx4kVcvnwZX375pc1z+/btE7/+3t7eJbYdy8rKwv+3d+dxNeX/A8dfbWSECI01y0xFpVIJY802SlTqO0Imy9i3se9rZPsyhLGMdayTyL5MY5ixy5aQlNK1Zy9pvef3R9/Or+teFBH1eT4e9/Fwz/2cc97nfm7X+37OZylRogQAFhYW71UXTZs2xc7OTmOSWr58ebVtxsbGREVFqV2vprKQ2dq7cOFCEhIS0NLSwsDAgAYNGsiNKK//eMgaO/TgwQOR6H7m8i3RLVasGG5ubkyZMoWZM2fy8OFDVq9eLc9zGh8fT4kSJdDX12f58uXExcXx+++/y69BZkKT9ceTl24/TyIpNfNLzExMLfZZ0tLSkv8TuXjxIrdu3aJatWr5G5QgfAFKFSuCo8mXNXK8SJEi+Pn58cMPP7B27Vp69OiBrq4uHh4erFq1Ck9PT5WpAxMTE1mzZg0eHh7o6upSpkwZvvvuO9atW0fjxo1VWoyCgoIIDQ2lQoUKauetXr06hw4dIi0tTW7Ju3DhAmXKlMHQ0PCdcZuYmBARESEnrU+fPqVt27Zs27ZNpdyWLVuYMmUKbdu2BZATNEmSiI+PZ+nSpYwdO5Z+/frRr18/evbsyeHDh6lcuTLbtm1j9OjR1KlTh6FDh+Li4sK///6rluja2Nhw584d7t+/z9dffw1kNi7Z2Ni88zr27dtHyZIl+f3331UaopYtW8b+/fuZMGEC+vr6mJmZceHCBVq1aqWy/6VLl+QxNe9bF8WLF8fIyCjHdwWsra1ZsWIFycnJcleFc+fOyd1DXjdnzhy+/fZb3N3dAQgLCyMhIQFbW1uioqLw8vJi165dVKlSBYBr166hq6sr/xgTPl/5umDE2LFjsbCw4Mcff2Tq1KkMGjSI1q1bA9CoUSP27dsHwMGDB0lOTsbLy4tGjRrJjxkzZnyUuK5na+0wF1OLfbbGjRtH7969OX78uEhyBaGAq1OnDp6enixdupSHDx8C0L9/f8qWLYuPjw/Hjx/n7t27HD9+nG7dulGuXDmVaaTGjh1LWFgYQ4YMISwsjJiYGFavXs3cuXMZPny42uAtAFdXV1JTU5k0aRLR0dGEhIQQEBCAt7d3jm6v+vj4sG7dOkJCQoiJiZFnXHi9T6+hoSF///03CoWC0NBQRo0aBWSOWylVqhR//vknM2fOJC4ujrNnzxIREUHt2rUpWbIkmzdvZunSpSgUCo4cOcKdO3eorWFcSZUqVWjUqBEjR44kIiKCwMBA9uzZQ5cuXYDMFur4+HiNg8H37t2Lq6sr5ubmmJqayg9fX18SExMJCQmRr3fDhg1s3rwZhULBlStXmDZtGrGxsSpTkb1PXeRWvXr1qFChAmPHjuXGjRusWLGCsLAwPD095fc2Pj5ebpkvX748ixcvJiwsjPDwcEaOHIm3tzeGhobUqFEDExMTJk6cSGRkJKGhoUycOBEvL688iVX4yKRCIiwsTDpx5oj08uXLd5YN+OeapD1svaQ9bL2UmJz6CaIT3iU8PFzy9PSUnjx5kqPyL1++lEJDQ3NU38KXT9S3uqSkJCkpKSm/w3gvzZs3l4KCgtS2P378WHJwcJCGDRsmJSYmSunp6VJiYqI0f/58qWXLlpKVlZXUsmVLacGCBRo/C5GRkdKgQYOkhg0bStbW1pK7u7u0a9eut8Zy5coVqXPnzpKlpaXUpEkTaenSpVJGRoYkSZJ06tQpydTU9I37KpVKadmyZVKjRo0kGxsbqXfv3tKdO3ckSZKkrl27SosWLZIkSZJCQ0Oldu3ayfEvX75c8vT0lJYtWyZJkiRdunRJ+uGHHyRra2upYcOG0vz58+UY/vnnH6l9+/aSlZWV1KxZM2nt2rVvjOfRo0dSnz59JCsrK8nJyUnavXu3/JpCoZBMTU2lU6dOqexz4cIFydTUVLpy5YrGY7q7u0s9evSQn+/Zs0dyd3eXrK2tJXt7e+mnn36SIiIi1PbLTV1k1XN6evobr02T2NhYqUuXLpKlpaXk4uIiHT9+XH4tq+4UCoV8Dj8/P6levXpS/fr1JX9/fyktLU0uf/fuXWnAgAGSvb29VK9ePWn69OlSSkpKruIR/t/bvp8uXbokhYWF5dm5tCRJwyRmBdDly5dJTH6KtYX9OwerDAw6za8nIqlaujgxE8QSf/ktLi6OevXqkZqaioeHB7/99ts790lKSuLatWvUqlVLDE4qBER9q8uaU7ggzkKRkZEh35LO6a1s4d0WLVokL8n7ORH1XfC87fspLCwMLS0trKys8uRc+dp14dPL2Ui+6w8zp60xLSf6534Oqlatyn/+8x+KFStGvXr1NE4wLQiCILy/xMRETp48qbHbgyB8yQpZopsz1+MzE11zMRAtXyiVSu7du6eyzc/PjyNHjtC7d+/PfqlNQRCEL42BgQHr16/P04n6BeFzIBLd1yQkp3HneebUVGIg2qd3+/ZtOnbsSLt27VSmqSlZsiTffvttPkYmCIJQsGXNLCEIBUkhS3SV7yyR1ZoLYmqx/HDixAmOHj1KTEwMv/76a36HIwiCIAjCFyzf5tH9XGWfSN1MtOh+cl5eXuzfv58aNWqoLS0pCIIgCIKQGyLRfU3k/waiGRTVpWLJgjda+XOza9cuvvnmG3kAhJaWFqtWrVJbHU8QBEEQBCG3RDbxmqwWXfPypcSgp49s6NCh+Pr60rdvX5VJykWSKwiCIAhCXhAZxWuyphYT/XM/vqy1zJ8+fUpcXFw+RyMIgiAIQkEjui5kk6FUEilPLSb65+Y1SZJUWsl79+5Namoqvr6+lCwpflgIgiAIgpC3RItuNrFPXpKakTkzg1gsIm+dOnWKJk2aEBMTI2/T1tZm8ODBIskVBEGFk5MTZmZm8sPc3Jx69erRr18/tTm2ExISmD17Ns2bN8fKyopWrVrxyy+/kJSUpHbce/fuMWHCBHn1Lzc3N4KDg987TkmSmDhxIjY2NrRo0SJX+/r4+BAQEPDe5/4Qz549o2HDhty+fTvX+/r4+GBjY6My/WMWMzMzTp8+rbY9ICAAHx8flW15XReaKBQKfH19sbGxwdnZmWPHjr2xrCRJrFq1CicnJ+zt7Rk7diwvX76UX7969arKZ9LMzAwPD7Fy6pdAJLrZZJ9aTCwWkXfu37+Pm5sbV65cYcCAASiV757mTRCEwm3cuHEcO3aMY8eOcfToURYsWMCNGzcYPXq0XObly5d07tyZ06dPM23aNPbv38/48eM5fPgwXbt2VUlUYmNj6dixI8+ePWPhwoXs2rULb29vJk+ezOrVq98rxoiICP744w8WLlzIxo0bP/iaP4Xnz5/Tt29fHj9+nOt9Hzx4wIULFyhTpgwHDx587xg+Rl28TpIkBgwYQNmyZQkKCqJDhw4MHDiQu3fvaiy/detWFi9ezLBhw9i8eTMPHjxg+PDh8utRUVHUqlVL/kweO3aMVatW5Umswsclui5kc/1/A9G0tbT4pqxIdPPK119/zc8//8yyZcvo3r27GOQnCMI7lShRgnLlysnPjY2NGTx4MCNHjiQhIQE9PT0WLVpEamoqW7du5auvvgKgcuXK2NnZ4erqyuLFi+XEeOrUqZibmxMQECB/B1WtWpXU1FTmz5+Pp6dnru8uJSQkANCkSZMv4nstNDSU0aNHU7x48ffaf9++fZiamlK3bl2Cg4Pp2LHjex3nY9TF606dOoVCoWDLli189dVX1KxZk5MnTxIUFMSgQYPUym/YsIHu3bvTrl07AGbNmkWTJk24efMmNWrUIDo6mpo1a6p8JoUvQyFr0X37F1HWjAvVyxigr6fzKQIqkNLS0rh69arKtmHDhnHixAm8vLy+iP8QBKGgSk1PJj4h7pM+UtOT8yT2rOVptbW1ycjIYMeOHXTr1k1OcrOUKFGCbt26sX37djIyMrh//z4nT57E19dX7fvH09OTlStXqh0jS3R0ND179qRu3bo0btyYxYsXo1QqOX36tHw7Pitp02TNmjU4OTlha2tLz549USgUamVSU1Px9/encePGWFhY4OTkxNatW+XXT548SYcOHbCysqJFixZs2bJFfm3fvn20adMGKysrnJ2dCQkJeeP7d+zYMTp27PjeXSb27NmDg4MDzZs35+zZs+/V9eF962LXrl3Url1brfuAmZmZxjguXbpE7dq1VY5lZ2fHxYsXNcalUCiwtraWn5cvX54yZcrI5aOjo6lWrVqur1fIf4WsRfftCVbWjAumotvCe7t+/Tr9+/fn1q1bHD9+HGNjYyBzackKFSrkc3SCULilpiez7ewsUjPyJvHMqSI6+ng6jKGIrv57HyMuLo4VK1bQuHFjihcvztWrV0lMTMTKykpjeTs7O549e0ZcXBxxcXFIkqSxbLFixbC3t9d4jCdPntC5c2ecnJwIDAwkJiaGCRMmYGBgQOfOnQkICGDQoEEcO3ZMY3K2ZcsWFi9ezPTp06lduzbz589nyJAhbN++XaXcihUrOHLkCAEBARgZGbFjxw6mT59OixYtKF26tDwVo6urK+fPn2f06NHY29tTunRpRo0axbRp03B0dOTAgQMMGzaMf/75B0NDQ7V4hg4dCvBeCWpcXBzh4eGMHDkSOzs7DAwMCA4OZuDAgbk6zvXr19+rLlq3bk3z5s3R0VFvhCpTpozatvj4eMqXL6+yzcjIiPv372s8vpGREQ8ePJCfJyUl8fz5c54+fQpkJrpKpRJXV1cSEhJo0qQJo0aNwsDA4M0XK3wWClmi+3ZZia7on/v+Hj58yIULF4DML++JEyfmc0SCIHyJJk+ezPTp0wFIT09HT0+PFi1aMG7cOABevMj8vi5VSvMMOVm3vp89eyaXLVGiRK5i2LNnD8WKFWP69Ono6upSs2ZN4uPjWbJkCb6+vvK533Q7e+vWrfj6+uLs7AzApEmTWLVqFcnJqj80zM3NqV+/PjY2NgD07duXJUuWEBsbi66uLs+ePaNs2bJUrlyZypUrU758ecqVK8edO3dIS0vj66+/plKlSvTo0QMzMzOKFi2aq+vM6XthaGiIg4MDOjo6NGvWjJ07d+Y60X3futDX18fQ0FBjoqvJq1ev5DsAWYoUKaIyZ3t2zs7OLF++HDs7OypXrsysWbOAzDuUaWlpKBQKKleuzMyZM3nx4gX+/v6MHDlSLFX/BShkia70xleeJKXwMDHzy0cs/fv+GjduzODBgzEyMqJ///75HY4gCNkU0c1sWX3+6uEnPW+pYuVz3Zo7ePBgWrduzcuXLwkICODOnTsMHz6c0qVLk5GRISey8fHxmJiYqO3/8GHmNRoaGsozBLx48UJj69+bREdHY2Fhga7u//9XaWtrS3x8vJywvU1MTAwWFhby87Jly6oMpsvSsmVLjh8/zqxZs7h586bc9SsjIwNDQ0O8vb2ZMGECS5cupXnz5nTs2JFSpUpRsmRJmjVrRvfu3alevTotWrTAy8uLYsXyflXPvXv30qxZMznRbN26Nbt37yY0NFRuhdXV1dU42FipVMrvYVZLc27rYt++fcyYMUNj17e9e/dSsWJFlW1Fixbl2bNnKttSU1PR19f8Oezfvz8KhQIXFxd0dXXp1KkT5ubmGBgYoKenx6lTpyhatCh6enpAZh/ejh078uDBA/nOpfB5Eonu/2S15oJo0c0pSZJYv3491apVo2nTpvL2KVOm5F9QgiC8VRFdfcqVqJrfYbyTkZGRnMAuXLgQT09P+vfvz9atW9HW1qZq1aqUKlWKK1euaLzdHR4ejqGhIVWqVKFUqcyVLsPDw2nSpIlKuaSkJAYMGMDo0aPlRWyyaGoZzUrkMjIy3nkN2RPkt1mwYAGBgYF4eHjg5ubG5MmTcXJykl+fMmUKXbp0ISQkhJCQELZu3crSpUtp2rQpy5cvJywsjL/++os///yTTZs2sWnTJmrVqpWjc+dEREQEUVFR3Lx5k927d6u8FhwcLL//JUqU0DjtWEJCgtyCa2Fh8V510bRpU+zs7DS26L7eRQEyBy9GRUWpbHv06JHGsgBfffUVCxcuJCEhAS0tLQwMDGjQoAGVKlUCUOuiULNmTQCR6H4BCtlgtDfLGogGYCbm0M2RgQMH8vPPPzNgwACeP3/+7h0EQRDeQ5EiRfDz8+PatWusXbsWyEwiPTw8WLVqlco0YgCJiYmsWbMGDw8PdHV1KVOmDN999x3r1q1DklQbPIKCgggNDdU4hqB69epcuXKFtLQ0eVvW9Fqa+sC+zsTEhIiICPn506dPqV+/vlof2S1btjBx4kRGjBiBs7Mzr169AjIbE+Lj45k6dSomJib069ePoKAg6tevz+HDh4mOjmb27NnUqVOHn3/+mb1791KhQgX+/fffd8aWG/v27aNkyZLs2LGD4OBg+eHi4sL+/fvlrhhmZmZy17XssgaGAe9dF8WLF8fExETjQ9MPCmtra65cuaLSTeTcuXMqA86ymzNnDjt27KBEiRIYGBgQFhZGQkICtra2REVFYWtrqzKQ8Nq1a+jq6mq8myB8XkSi+z9ZLbqlixWhnMH7D5goTNq2bQtkfgFl3SYUBEH4GOrUqYOnpydLly6Vv2/69+9P2bJl8fHx4fjx49y9e5fjx4/TrVs3ypUrpzKN1NixYwkLC2PIkCGEhYURExPD6tWrmTt3LsOHD9fY19fV1ZXU1FQmTZpEdHQ0ISEhBAQE4O3tnaPZY3x8fFi3bh0hISHExMQwefJkuZ9tdoaGhvz9998oFApCQ0MZNWoUkHmrvVSpUvz555/MnDmTuLg4zp49S0REBLVr16ZkyZJs3ryZpUuXolAoOHLkCHfu3JGTytzIyMggPj5eYx/WvXv34urqirm5OaampvLD19eXxMREeaYHHx8fNmzYwObNm1EoFFy5coVp06bJ8+ZmeZ+6yK169epRoUIFxo4dy40bN1ixYgVhYWF4enoCme9tfHy83DJfvnx5Fi9eTFhYmDzoztvbG0NDQ2rUqIGJiQkTJ04kMjKS0NBQJk6ciJeXV57EKnxkUiERFhYmnThzRHr58qXG191WHZa0h62Xvlu4/xNH9uVITk5W2xYYGCglJSXlQzRv9/LlSyk0NPSN9S0ULKK+1SUlJX2Wf5s50bx5cykoKEht++PHjyUHBwdp2LBhUmJiopSeni4lJiZK8+fPl1q2bClZWVlJLVu2lBYsWKDxsxAZGSkNGjRIatiwoWRtbS25u7tLu3btemssV65ckTp37ixZWlpKTZo0kZYuXSplZGRIkiRJp06dkkxNTd+4r1KplJYtWyY1atRIsrGxkXr37i3duXNHkiRJ6tq1q7Ro0SJJkiQpNDRUateunRz/8uXLJU9PT2nZsmWSJEnSpUuXpB9++EGytraWGjZsKM2fP1+O4Z9//pHat28vWVlZSc2aNZPWrl37zvdXoVBIpqamkkKhUNt26tQplbIXLlyQTE1NpStXrmg8lru7u9SjRw/5+Z49eyR3d3fJ2tpasre3l3766ScpIiJCbb/c1EVWPaenp7/z2rKLjY2VunTpIllaWkouLi7S8ePH5dey6i7rPUhPT5f8/PykevXqSfXr15f8/f2ltLQ0ufzdu3elAQMGSPb29lK9evWk6dOnSykpKbmKR/h/b/t+unTpkhQWFpZn59KSJOnNHVcLkMuXL5OY/ARrCweN08DUnrWT6/Ev8HWoyapODfMhws/bwYMHGTZsGOvXr8fOzi6/w3mnpKQkrl27Rq1atd44P6ZQcIj6Vpd1+/tjDEzKbxkZGSQnJ6Ovr5/jUfjCuy1atEhekvdzIuq74Hnb91NYWBhaWlpvnDowt0TXBSAtQ0n048wVbszFjAtqnj59yk8//cS9e/fo37+/Sn81QRAE4cuXmJjIyZMn36vbgyB8zkSiC0Q/SiBdmdmwLRaLUFe6dGlmzJjB119/jZ+fnzy9iiAIglAwGBgYsH79erW5ZwXhSycSXeB6vJhaLLtXr15x7NgxlW1du3bl1KlTtGrVKp+iEgRBED4m0YghFEQi0QWu/29qMV1tLWoY5W61loLm8uXLNGvWDC8vL65fvy5v19LSkidoFwRBEARB+BIUskRX83QwEf+bWuybsiXQ0ylkb8lr9PT0iIuLIyUlhc2bN+d3OIIgCIIgCO+tkK2MpjnRzWrRNRULRWBubo6fnx9FixalS5cu+R2OIAiCIAjCeytkia46SZLkFt3CNuNCRkYGS5YswdjYmB9++EHe3rNnz3yMShAEQRAEIW8U+kQ3PjGZZ68yV4IxK2SJ7qBBg9iyZQslSpSgYcOGVKlSJb9DEgRBEARByDOFrEOq+toYWa25AObGhavrQvfu3dHW1ubbb78Vc+MKgvBFGjNmDGPGjMnvML5ot2/fxszMTOVhYWFBo0aNmD59utqywLGxsQwbNgxHR0dsbGzw8PBg27ZtGo99/vx5+vTpg6OjIw4ODnTv3p0LFy58isv6JHx8fLCxsSExMVHtNTMzM06fPq22PSAgAB8fH5Vt9+7dY8KECfKCHW5ubgQHB3+UmG/dukWdOnXeWW7Pnj20bNkSa2trBgwYwJMnT+TXJEli3rx51K9fn3r16jFnzhyUSuVHifdDiUT3f/1zAcwKeB/dZ8+eqTx3cHAgODiYAwcOUKNGjfwJShAE4QOMHz+e8ePH53cYBUJgYCDHjh3j2LFj8mqYf/zxBytWrJDLXLt2DS8vLwBWrlzJrl278Pb2Zu7cuUyaNEnleAcPHuTHH3/E3Nyc9evXs2XLFkxNTenWrRvnzp37pNf2MTx48IALFy5QpkwZDh48+N7HiY2NpWPHjjx79oyFCxfK7+nkyZNZvXp1HkacmVD36dOHlJSUt5YLCwtj/PjxDBw4kK1bt/LixQvGjh0rv75mzRr27NnD4sWLWbRoEbt372bNmjV5GmteKWSJrrrr/2vRLW+gT+mviuZzNB+HJEls2bIFGxsb9u3bp/Jao0aN0NUt9D1YBEH4QpUoUYISJQr3tJB5pUyZMpQrV45y5cpRuXJlPDw86NChAyEhIXKZMWPG0LRpU+bPn0+dOnWoWrUqXl5e/PbbbwQGBnLkyBEgc6W1SZMm0a9fP37++WfMzMyoWbMmY8eOpVmzZsydOzefrjLv7Nu3D1NTU5ycnD6o9XXq1KmYm5sTEBCAra0tVatW5YcffmDEiBEEBATw4sWLdx8kB0JCQvDw8MjRoiAbNmygbdu2uLm5YW5uzpw5czh69CgKhQKA9evXM3jwYOzt7alfvz4jRoxg48aNeRJnXhOJbnzWQLSC25qbmJjI9OnTefHiBSNGjCA5OTm/QxIEQdAo6zb6kSNHcHJywtbWFj8/PyIjI/Hw8MDGxoZ+/frx8uVLQL3rws6dO/n++++xtramU6dOXL16VaVc+/btadCgAbGxsTx//pyJEyfSsGFD7OzsGDlyJM+fP9cYF0Bqair+/v40btwYCwsLnJyc2Lp1KwCbN2/GyclJpfzWrVtp3bq1vK+fnx+Ojo44OjoyYsQI+S5b1jUvWbIEBwcHpk2bhiRJLFu2DCcnJywtLWnUqBGLFy+Wj61UKpk3b558vKVLl9KqVSv5VvmLFy8YOXIkdevWlbsgvM93f5EiRdDR0QEyW/kiIiLo27evWjkrKyuaNm3KH3/8AcDhw4dJTEykW7duamVHjx6Nn5/fG8/5pjocN26cWjeV7N0DnJycmDt3Lo0aNcLNzQ0vLy8WLVqkUr5Tp04sXboUgMjISHx8fKhTpw5t2rTJdaK2Z88eHBwcaN68OWfPnuX27du52h/g/v37nDx5El9fX7S0VGeG8vT0ZOXKlXz11Vdq+23fvl2tu0nW401xHDlyhCFDhuToDsilS5ewt7eXn1eoUIGKFSty6dIlHjx4wL1793BwcJBft7Oz486dOzx8+DCnl/7JiET3f10XCvJAtBIlSrBo0SJq1qzJmjVr0NfXz++QBEHIR/EJce98ZJehTH9n+ceJd1T2SUtP0XisnFqxYgVLly5l+vTp/P777wwcOJDhw4ezatUqLl68yI4dO9T2+ffffxk/fjw//vgju3btwtLSkj59+sh9THfu3MnQoUNZvnw51apVY+DAgVy7do1ly5axZs0aoqOj39rfd8WKFRw5coSAgAAOHDiAm5sb06dP59GjR7Rp04YHDx4QHh4ulz906BBt27YFYP78+YSHh7Ny5UrWr19PYmIiQ4YMUTn++fPnCQoKolu3bgQHB7Nu3TpmzJjBgQMHGDBgAAEBAVy5cgWA5cuXExwczH//+1/WrFnDkSNH5NY2yOzSkZCQwObNm1m6dCmXL19m2rRpOX7/JUni9OnT7N69mzZt2gAQHh7OV199Rc2aNTXuU7duXcLCwgCIiIigRo0aGBgYqJWrXLky33zzjcZjaKrD/v3753gcye7du1m1ahWzZs3CxcWFP//8U37twYMHXLx4ERcXF5KTk/npp5+ws7Nj165djB49mqVLl+a4ZTYuLo7w8HCaN29OvXr1MDAweK9W3evXryNJElZWVmqvFStWDHt7e413XZ2dneVuJq8/KlSooPFcfn5+dOrUKUdxPXz4kPLly6tsMzIy4v79+8THxwOovF62bFkgM3H/3BTqe9bJaRnEPMnsQF6QWnSfP39OSEgIHTt2lLe1aNGCEydOiCUeBUFg76Wl7yihhW8jf/nZq9TEd+5TvKghXg7/nyQ+eXmP/ZeXAeDbaFauY+zfvz/m5uaYm5szc+ZMXFxc+O677wDkFtnXbd26lXbt2uHt7Q3AqFGj0NPTk1tprays5FbXiIgIzpw5w4EDB6hevToAc+fOxdnZmZs3b2oct2Bubk79+vWxsbEBoG/fvixZsoTY2Fj5Fu6hQ4ewtLTk+fPnnD59mlGjRvHq1Ss2bNhAUFAQZmZmAMyZMwdHR0euX79O8eLFAfjxxx+pWrUqkJkw+Pv706BBAwC8vb1ZsmQJN27cwMLCgk2bNjF06FAaNWoEwKxZs+SkOi4ujpCQEM6cOSN365g+fTpubm6MHTv2jV092rVrJ7cqpqamUqZMGbp16yZPOfn8+XNKlCih1vKYpVSpUnIrdUJCgsYk91001aGOjs5bW9qza9++vfwely5dmtmzZxMbG0u1atU4dOgQtWvXxsTEhMDAQIyMjBg6dCgA1apV486dO6xfvx43N7d3nmfPnj0YGhri4OCAjo4OzZo1Y+fOnQwcODBX15vVLSG33W/09fU/aqNVcnKyWheHIkWKkJqaKt8ZyP561r9fH7j4OSjUie6NRy+Q/jc+zbSAtOieP3+ebt26cffuXYyNjeUvQRDrmAuC8OXIPt2hvr4+lSpVUnmuqYUvJiZGpcWqSJEijB49Wn6e/Rg3b96kZMmScpILULNmTUqVKsXNmzdZu3Ytu3fvll/bu3cvLVu25Pjx48yaNYubN2/Kt9QzMjIAcHFxYcWKFQwbNoy//voLExMTzMzMiIyMJC0tTa01TalUEhsbi4WFhVp89evX59KlS/z3v/8lOjqaa9euER8fj1Kp5MmTJzx8+FClFbBGjRqUKpX5/1h0dDRKpZImTZqone/WrVtYWlqqv+FktlgbGxtz9+5dpk2bhrm5OX379pW7LpQqVYonT56gVCrR1la/Ifzw4UMMDQ0BMDQ0fK++pZrqcNSoUTnudpH9PTQ2Nsbe3p5Dhw7Ru3dvDh06hLOzM5BZ/xEREdja2srlMzIy5Gt9l71799KsWTO5fOvWrdm9ezehoaHyLX9dXV2NMxEolUq5lTbr/Xrx4gVlypTJ0bkBdu3axeTJk98YW8WKFXN8LE2KFi2qlrSmpqZSrFgxlaS2aNGi8r8hsxX6c1OoE12VqcUKSItuxYoVefXqFZDZRyp7oisIggDgYt0/V+WLFTF45z7aWqoJQpniFXJ9nuxeTzg0JVave9fA2qz/lIE3DsjJyMggIyODIUOGqCyeU758eRYsWEBgYCAeHh64ubkxefJklX65rVq1YvLkydy4cUOl20JWIrxp0ya1/pZGRkZyK2j2+AIDA5k5cyZeXl60bt2a0aNHy/1ds65TklRnEsp6npGRQYkSJQgKClK7PmNj4ze+PxUrVqRy5cqYmJiwfPlyOnTowOzZs5kwYQIA1tbWpKWlERkZibm5udr+4eHhcvJtYWHB6tWrSUxMVGvZDQ0NZe3atcydO1ctMcrN4Oj09HS1bdnfQ8i8xb9t2zY6duzI+fPnmTVrlrxvgwYN1GaKyImIiAiioqK4efOmyo8hgODgYDnRLVGihMZpxxISEuQWXAsLC7S0tAgPD1f7YZKUlMSAAQMYPXq02vvt5OSEtbW1xvhe73LwPoyNjXn06JHKtkePHlGuXDn5MxQfH0/lypXlfwOUK1fug8+d1wp1H93I//XPLaqrjUnp4vkcTd74+uuvWbRoEb///vt7/QELglDwlStR9Z2P7HS0dd9Z3sigkso+erpFNR7rYzIxMSEiIkJ+npGRgZOTk8aprKpXr86LFy+4efOmvC0qKorExESqV6+OkZERJiYm8kNXV5ctW7YwceJERowYgbOzs9yokJVglihRgsaNG7N//35OnDiBi4sLkNk6raOjw7Nnz+TjGRgY4O/vz+PHjzVey+bNmxkwYADjxo3Dzc2N0qVL8/jxYyRJomTJkpQvX17urwugUCjkFtTq1auTkJCAlpaWfL7k5GTmzJmT41vLVatWZdCgQWzYsIFLly4BmUmZpaWl2gAvyByodvToUXnqscaNG1OiRAk2bNigVnbdunXcv39fY+ufpjps1aoVFy9eRE9PTx6EmHXN79KmTRuuX79OYGAgVlZWcotv9erViYmJkRN7ExMTLl68yO+///7OY+7bt4+SJUuyY8cOgoOD5YeLiwv79++XW5/NzMw0zhl86dIlateuDWTOdPHdd9+xbt06tR8uQUFBhIaGauxza2BgoPL5fP2z+qGsra1V/m7u3bvHvXv3sLa2xtjYmIoVK6q8fu7cOSpWrJgnSXZeK2SJrurlZrXompYriU4OWgs+NykpKUyZMoX58+erbHdxcZG/YAVBEAoLHx8fdu3axY4dO7h16xb+/v5IkiR3DciuZs2aNGnShNGjRxMWFkZYWBijR4/GwcEBU1NTjcc3NDTk77//RqFQEBoayqhRowDVfokuLi6sWbOGGjVqyN0iDAwM8PLyYsqUKZw+fZqoqChGjRrFrVu35Bax15UuXZqTJ08SExNDeHg4P//8M2lpafK5fHx8WLRoESdPniQiIkKe41RLS4uaNWvSuHFjRowYQVhYGFeuXGHs2LEkJSVRsmTO715269aNmjVrMm3aNPkWvL+/P+fPn2f48OFcvnyZ27dvs2PHDvr27YuXl5fcwl28eHHGjRtHQEAAv/zyi9z9YuLEiRw5ckRuJX6dpjpUKpWYm5tjZWXF8ePHOXnyJJGRkUybNu2dXfLKlCmDo6Mjy5cvl1vYIbMvb3JyMpMmTSI6OpqjR48yY8YMjIyMgMwEOz4+XuMPg7179+Lq6oq5uTmmpqbyw9fXl8TERHk6Nh8fHzZs2MDmzZtRKBRcuXKFadOmyfPmZhk7dixhYWEMGTKEsLAwYmJiWL16NXPnzmX48OFyl5SPKTU1lfj4ePnug7e3Nzt37iQwMJCIiAhGjRpFs2bN5C5F3t7ezJs3j9OnT3P69Gn++9//apxh43NQqLsuZM24YPqFLhSRNW+drq4uLVq0eONtDEEQhMLAwcGByZMns2TJEuLj47G0tGTZsmVvHLQze/Zs/Pz88PX1RUdHhxYtWqhMiv+6mTNnMmXKFFxcXDA2NsbLywsdHR2uXbsm33Zu3rw5kiTJfUGzjBkzhtmzZzN48GDS0tJwcHBgxYoVb+wTOm7cOMaNG0eHDh0wMjKibdu2FCtWjGvXrgHQo0cPHj58yKBBg9DR0aF3796EhobKid+cOXPka9PV1aVx48ZvTC7fRFdXlwkTJuDr60tQUBBeXl6YmpoSGBjI4sWL6devH4mJidSoUYOff/5Zbs3N0r59e0qWLMnKlSvZuHEjWlpaWFlZsXHjxjeuzKWpDn/99Vf09fVp3749Fy9epH///pQoUYIhQ4Zw69atd16Hi4sLJ06cUEl0DQwMWLlyJTNnzsTNzQ1DQ0O6dOlCnz59gMwWzBYtWrB+/XocHR3l/S5evMjt27fx9PRUO0+dOnWwsLBgx44dtGvXjpYtW+Lv78+qVauYPXs2enp62NrasmHDBpVb/N988w2bNm0iICBAnjqvRo0azJgxA1dX13deX164cOEC3bp146+//qJy5crY2toybdo0Fi1axPPnz/nuu++YPn26XL5nz548fvyYgQMHoqOjg6enJ76+vp8k1tzSkl5vKy+gLl++TGLyM6wt7Pjqq6+QJAnD8VtITElnfEsrprW1ye8Qc+3mzZs0adIER0dHFi1apNIJv7BLSkri2rVr1KpVS+MchELBIupbXdZt9c9xcMiHysjIIDk5GX19/RwPHipo/vnnHywtLeUBTE+ePKFBgwZyolKQ5Fd9L1q0SF6SV8hbb/t+CgsLk38U5YUv7359Hrn74hWJKZkd2c2+kIFot27dUhnBWaNGDUJCQti2bZtIcgVBEAqRrVu3Mm7cOKKiooiOjmbKlClYWVkVuCQ3vyQmJnLy5Em5L63w5Spkie7/J4kRD/5/Tj7zz3xqMaVSybJly2jQoAGrVq1Sec3c3PyNcxoKgiAIBdOkSZPQ1tamU6dO/Oc//0GpVLJkyZL8DqvAMDAwYP369TlaLlf4vBXaPrrXs00t9rm36Kanp7Nx40aSk5Px9/enU6dOYm13QRCEQszY2Fheylb4OMTc8wVDIWvR/X8R/xuIVqnUVxgU/bw/zEWKFGHZsmXY2dmxZ88ekeQKgiAIgiDkQKFPdD/HhSLu37/PokWLVObUs7CwkJcvFARBEARBEN6t0HZdiIzP7Lpg9pn1zz1//jxeXl48ffqUihUrqkxhIvriCoIgCIIg5FyhbNFNTElD8SwJ+PxadE1NTeXJoW/cuJHP0QiCIAiCIHy5CmWLblZrLnwei0UolUp5HXcDAwOWLVtGamoqjRo1yufIBEEQBEEQvlyFMtGNyDbjQn5OLZaQkMCECRPQ1tZmwYIF8vZ69erlW0yCIAiCIAgFRaHsuhD5v0S3eBFdKpXKv1WUZsyYwe+//866devktbEFQRAEIb8FBARgZmam8rC2tsbV1ZVDhw6pld+7dy9eXl5YW1vToEEDBg0aREREhFo5pVLJunXraN++PdbW1jRv3hw/Pz+ePXv2Ca7q41MoFJiZmTFy5Ei117Zv346Tk5PG/ZycnNi+fbvKtiNHjuDj44OdnR3169dnwIABREVF5Wm8e/bsoWXLllhbWzNgwACePHnyxrKPHz9m8ODB2Nvb06pVK7V4/fz81D4zGzZsyNN430chS3QzLzdrxgWz8iXR1s6/AV6jRo3C2NgYNzc36tatm29xCIIgCMLrbG1tOXbsmPwIDAzE3NycYcOGcevWLblcQEAAEyZMoF27duzevZtVq1ZRunRpOnXqxMmTJ1WOOWTIENatW0ffvn3Zs2cPs2bN4vz58/Tq1YuUlJRPfYl5bt++fVStWpWQkBBevnz53sdZt24dQ4cOpXnz5vzxxx+sXbsWfX19unTpQkxMTJ7EGhYWxvjx4xk4cCBbt27lxYsXjB07VmNZSZIYMGAA9+/fZ/369YwbN45Zs2ap/OiJjo5m+PDhKp+Zjh075kmsH6KQJbqZshaLMPvE/XPDwsJU/pDLlCnD33//zerVq+X1ygVBEAThc6Cnp0e5cuXkh6mpKTNmzEBXV5cjR44AcOXKFZYuXcqSJUv48ccfqVq1KrVr12batGl4enoyduxY+f+9Xbt28ffff7N27VqcnZ2pUqUKjo6OrFixgqioKHbu3JmPV5s39uzZQ9euXdHT0+PgwYPvdQyFQsHcuXOZOnUqPXr0oGbNmpibmzN37lyqVKnC4sWL8yTWDRs20LZtW9zc3DA3N2fOnDkcPXoUhUKhVjY8PJwLFy7w3//+l9q1a9O8eXN69eqlslprdHQ0tWvXVvnMFCtWLE9i/RCFLtHNUEqffGqx9PR0Zs6cSYsWLfD391d57euvv/4kMQiCIHwJbt++jZmZGUeOHMHJyQlbW1v8/PyIjIzEw8MDGxsb+vXrJ7eWpaam4u/vT+PGjbGwsMDJyYmtW7fKx0tKSmLSpEk4Ojri6OjIxIkT5cTLzMyMhQsX4ujoSN++fQG4cOEC3t7e2NjY4OTkxObNm98ab1RUFD179sTW1hYrKys6d+5MdHQ0AP/5z39YtGiRSvlOnTrJK5pFRkbi4+NDnTp1aNOmDRs3bpTLBQQE0L9/f7p06UK9evU4c+YMDx48YPDgwTg4OGBpaYm7uzvnzp2T91EoFPj6+spdDFatWqVyqzw0NBQPDw/q1KmDq6vreyViOjo66OrqoqubOcRn27ZtWFhY0LBhQ7Wy/fv358GDB/z7778A7Nixg1atWlG1alWVcmXLlmXdunW0bt1a4zmTkpLw8/OjQYMGGuvw9OnTctns3QNOnz6Nk5MTkydPxs7OjsWLF2NmZqaSyMXGxmJubs69e/cA2LJli/y58/Hx4fr16zl+b6KiooiMjMTR0ZHGjRuzY8eOHO+b3Z49ezA0NMTV1VVlu7a2NrNnz2bo0KEa9/Px8VHrOmBmZoaPj4/G8pcuXcLe3l5+XqFCBSpWrMilS5fUyioUCsqUKUOVKlXkbWZmZoSHh5OWlkZiYiIPHjygWrVqub/gj6yQJboSiudJJKdnAJ9u6V8dHR1CQ0PJyMhg7dq1b+0DIwiC8LHFJ8S985FdhjL9neUfJ95R2SctPUXjsXJqxYoVLF26lOnTp/P7778zcOBAhg8fzqpVq7h48aKcRKxYsYIjR44QEBDAgQMHcHNzY/r06Tx69AiACRMmcO7cOZYuXcrq1as5d+4cv/zyi3yev//+m82bNzNixAiio6P58ccfcXBwYPv27QwaNIjZs2fz559/aoxRqVTSt29fKlWqxM6dO9myZQsZGRnMnTsXAGdnZ5V9Hzx4wMWLF3FxcSE5OZmffvoJOzs7du3axejRo1m6dCnBwcFy+b/++ot27dqxbt066tSpw4gRI8jIyGDLli0EBwdjbGzMlClTgMwGlT59+lCyZEmCgoLo3bu3SstffHw8ffr0wcPDg927d9OrVy/GjBlDaGhojuskKSmJRYsWkZqaStOmTYHMlj4rKyuN5cuUKUO1atUICwsDICIi4o1lra2tMTQ01PjapEmTuHjxIkuWLNFYh29z584dUlNT2b59Ox4eHpibm6vUycGDB7G1taVChQocPnyYxYsXM3HiRHbs2IGdnR3dunXj+fPnOTrXnj17qFSpEubm5rRo0YKzZ89y586dd+/4moiICCwtLeXZmLKrWbOmSrKZXUBAgEq3gaxHQECAxvIPHz6kfPnyKtuMjIy4f/++WtmyZcuSkJDAq1ev5G33798nPT2dhIQEoqOj0dLSYtmyZTRp0oT27du/d6Kf1wrZrAsSNx4lyM8+1YwLWlpaBAQEMGrUKGbOnCm6KQiCkK/2Xlr6jhJa+Db6/7tPr1IT37lP8aKGeDmMkZ8/eXmP/ZeXAeDbaFauY+zfvz/m5uaYm5szc+ZMXFxc+O677wBo0KABsbGxAJibm1O/fn1sbGwA6Nu3L0uWLCE2NhY9PT0OHDjAmjVrsLOzA2DatGlcu3ZNPs8PP/xAjRo1APD396d27doMGzYMgBo1ahAdHc1vv/1Gq1at1GJMTk6mU6dOdO7cma++yhzY7O7uzm+//QZA27ZtmT17NrGxsVSrVk1e3dLExITAwECMjIzk1rlq1apx584d1q9fj5ubG5CZXHh7ewOZfSRbtmxJmzZt5DuBXbp0oXfv3gCcOnWKe/fu8ccff2BgYMA333xDZGQke/fuBWDjxo00bNiQrl27AmBiYsK1a9dYt26dSqtedqGhodja2srnT0lJoXbt2qxcuZLKlSsD8Pz5c0qWfHOjUalSpeSBZgkJCblewv758+ccPHiQX3/9lbp166Kjo6NWh+/Sq1cvTExMAHBxceHQoUP06NEDyEx03d3dAfjtt9/o06cPzZs3B2Do0KH8888/7Nq1642totnt27dPbk1u2rQpRYoUITg4mAEDBuTqmhMSEt4rT3jTD4U3SU5OpkiRIirbihQpQmpqqlpZa2trypcvz/Tp05kwYQLx8fGsWbMGgLS0NG7evImWlhY1atSga9eunD17lokTJ2JgYKDxb+dTKmSJLkTGZya6Wlrwbbnc/cHl1M2bN1mxYgUzZsxAR0cHgEqVKqnclhIEQRDeLHurlb6+PpUqVVJ5npaWBkDLli05fvw4s2bN4ubNm1y9ehWAjIwMbt26RUZGBhYWFvK+9vb2Kold9uNGR0dTp04dlThsbW3ZsmWL/O8sdnZ2/Pbbb3h7exMcHEx4eLh8/rJlywJgbGyMvb09hw4donfv3hw6dAhnZ2cg8/+JiIgIlWNmZGTI/2e8HpuWlhbe3t7s27eP8+fPExMTQ3h4OEqlEoDr169TvXp1DAwM5H1sbGzkRPfmzZv8/fffKudLS0ujevXqGt9/AEtLS+bNm4dSqeTff/9l0aJFdO/eHUdHR7lMqVKl5NZzTR4+fChPmWloaJjj1tEsWXVYq1YtedvrdfguWUk5ZLayL1iwgAcPHpCWlkZERATff/89kFn/c+fOZf78+XL5lJQU+UfV24SFhXHr1i1atmwJQPHixWnYsCE7d+6UE11dXV25vl6nVCrl7iCGhoa8ePFCY7m36dWrl0pXlixZn9XXFS1aVC2pTU1N1divtmjRovzyyy8MHToUOzs7jIyM6NWrF/7+/hgYGODm5kbz5s3lZNvc3JzY2Fg2b94sEt1PLfJ/LbompYtTTC/vL//ixYu0a9eOpKQkKlSowJAhQ/L8HIIgCB/Cxbp/rsoXK2Lwzn20tXRUnpcpXiHX58kue8IHaLyNC7BgwQICAwPx8PDAzc2NyZMny61qenp67zxP0aJFNf47i1KpJCMjs7tb9m4F+vr6vHz5Ek9PT0qXLo2TkxPt2rXj5s2brF69Wi7n7OzMtm3b6NixI+fPn2fWrMzW7fT0dBo0aMCkSZNyFJtSqaRHjx68ePECZ2dnnJycSEtLY+DAgUDm+yVJksr+2Z+np6fj6uoq90XOkpVcaaKvry+3hFavXp3k5GRGjx5NlSpVsLa2BjJb+jQlV5DZXeL+/ftydwULCwuuXLmisez8+fMxMjLixx9/VNmekzrMLquussv+PlauXBkrKytCQkJISUnB3t6ecuXKyfuOGzeOBg0aqOyf/cfDm2T9oMhqKYbMOpMkiXPnzmFnZ0fJkiVJSEjQuH9CQoLcMm5hYcGaNWuQJAktLdWZofbt28e///6rNt4HMqcsTU5OVtuur6+v8ZzGxsZqP1IePXokvx+vq1OnDocPHyY+Pp7SpUtz/PhxSpcuTfHixQH1FuUaNWpw6tQpjcf6lApZH93/b9H9WAPRLC0tMTc3R1dXV+1LRxAE4XNQrkTVdz6y09HWfWd5I4NKKvvo6RbVeKy8tmXLFiZOnMiIESNwdnaW+xBKkkSVKlXQ0dFRmc81JCREvlX9uurVq6sNxLlw4YLc6mliYiI/jI2NOXPmDA8fPmT9+vX06tWLhg0bcvfuXZXv/jZt2nD9+nUCAwOxsrKSW2mrV69OTEwMlStXlo958eJFfv/9d42xRUVFcfbsWdauXUvfvn1p1qwZDx8+lK/122+/JTY2lsTERHmf7Ell9erVuXXrlso1/PXXX+zevTvH73XPnj359ttvmTBhgpxQenp6cv36dY1zwf/666+ULVuWJk2aANC+fXtCQkLURvU/ePCAjRs3aky6s+owMjJS3pa9DvX09FSm8dI0Y8DrnJ2dOXLkCCEhIbi4uMjbq1evzv3791Xeo2XLlnHx4sW3Hk+pVLJ//346dOhAcHCw/NixYwcGBgbyDyQzMzMSExPV5sKNjo4mMTFRbrX+/vvvefbsGXv27FEpl5GRwZo1a0hKStIYh7GxsUrs2T+rmrz+I+XevXvcu3dP/hGT3bNnz/D29ubp06eUK1dOnnkjq7V+4cKF+Pr6quwTEREhdwvKT4Uu0b3xKPNLwDyPBqJJkqTyodPV1WXZsmX8+eefbxwZKQiCIOQNQ0ND/v77bxQKBaGhoYwaNQrIvAWbdUt1xowZhIWFcfnyZRYsWED9+vU1Hqtz585cu3aN+fPnExMTw44dO9i0aRNdunR547mTkpIICQnh9u3bBAYGsnHjRpXbwWXKlMHR0ZHly5fTtm1beXv79u1JTk5m0qRJREdHc/ToUWbMmIGRkZHGc5UsWRJtbW327t3LnTt3OHDggDzIKDU1lQYNGlChQgUmTpxIdHQ0Bw4cYP369SrXFh4ezoIFC4iNjWX37t3Mnz+fihUr5vi91tHRYeLEiURGRrJp0yYg8xb1kCFDGDlyJL///jsKhYLr16/j5+dHUFAQs2bNkltUnZ2dqVevHj/++CP79+9HoVBw9OhRevbsSc2aNfH09FQ7p4GBAR06dGDu3Lka69DKyooNGzYQGxvLX3/9pbaIgSZt27YlNDSU8PBwlZkeunfvzrp16wgODiYuLo65c+eyf/9+atasCcDLly81DiYPDQ3lwYMH+Pj4YGpqKj9q1apF+/bt2b9/PykpKVSoUIEWLVowYsQITp8+ze3btzl27BjDhg3D2dlZTkgrVarEwIEDGT9+PGvXriU2NpawsDAGDRpEXFwcw4cPz3GdvY23tzc7d+4kMDCQiIgIRo0aRbNmzeRuQwkJCXL/6qzP+ty5c1EoFAQGBhIUFESvXr0AaN68OWfPnmXVqlXExcWxadMmgoODVVq4841USISFhUknzhyRtIetl7SHrZeWnbj+wceMj4+XfHx8JE9PT0mpVOZBlEJeefnypRQaGiq9fPkyv0MRPgFR3+qSkpKkpKSk/A4j1xQKhWRqaiopFAp5W/PmzaWgoCD5+ahRo6Thw4dL6enpUmhoqNSuXTvJyspKatmypbR8+XLJ09NTWrZsmSRJkpSQkCCNGTNGqlu3ruTo6ChNnTpVSklJkSRJkkxNTaVTp06pnP/EiROSm5ubZGFhIbVq1UravHnzW+MNCAiQ6tevL9na2kqdOnWStm3bJpmZmUn379+XywQFBUnm5ubSgwcPVPYNDw+XOnfuLFlaWkqNGjWSfvnlFykjI0OSJElatGiR1LVrV5XyW7ZskRo3bizZ2NhI7u7u0u7du6XatWtL58+flyRJkqKioiRvb2/JwsJCcnV1lfz8/KTWrVvL+x8/flxyd3eXLCwsJCcnJ+n3339/43VpOn+WESNGSPb29tLjx4/lbYcOHZI6deok2draSo6OjtKgQYOka9euqe2bnJwsLVq0SGrdurVkZWUlNW/eXJoxY4b07NmzN8by/Plzafjw4Rrr8MqVK/I1derUSdq6davUvHlzSZIk6dSpU5KpqanGY3bt2lXq2bOn2vZ169ZJzZs3l6ysrCR3d3fpxIkTKu9J1rGzmzhxouTq6qrxPNevX5dMTU2lPXv2SJKU+V01ffp0qUmTJpKFhYXUtGlTadasWVJycrLavjt27JA8PDwkW1tbqUGDBtLAgQOl6OjoN75P7yMoKEhq2rSpZGNjIw0YMEB68uSJ/Nro0aNVPgPR0dFS165dJWtra8nFxUU6fPiwyrH+/PNPydXVVbKyspK+//576eDBg28879u+ny5duiSFhYV94JX9Py1JKhz31y9fvkxi8hMabcmc6uavfq1o9s2HzWE7c+ZM5s2bB8DSpUvp1KnTB8cp5I2kpCSuXbtGrVq15NHQQsEl6ltd1i38z2HC9ryWkZFBcnIy+vr6an15C6vHjx9z9epVGjduLG/77bffOHr06Bu7Q3wpPqf67tGjh0ofbOH9vO37KSwsDC0trTdORZdbharrQvaMPi+mFhs2bBhmZmZ0796ddu3affDxBEEQBOF99evXj02bNnHnzh1OnDjBunXr5BkFhA+3a9cu6tatm99hCLlUyGZdyBy9WEpfD+MSmkchvs3Ro0epU6cOpUuXBjJHMoaEhMgjDgVBEAQhPxgZGfHLL7+wcOFC/P39KVu2LF27dqVz5875HVqB0bZt21zPAiHkv0KV6GZ10jAvX0ptyo63USqVjBkzht9++42OHTuycuVK+TWR5AqCIAifg5YtW8rzuAp5TyS5X6ZC2XUht0v/amtryzMrHDlyROPyeIIgCIIgCMLnpVC16GbJbaILmQPP9PT0GDt2rNra0IIgCIIgCMLnp1C16GZ512IRly9fxsfHR2US6pIlS7JgwQKR5AqC8EXQ0dHRuEqUIAhCfnt9ueuPqVAlutpamZ0X3jbjwtWrV2nZsiV79+5lypQpnygyQRCEvKWnp8erV6/ECo2CIHxWJEni1atXn6zPc6HruqCjrUVNozevW12rVi1at27N4cOHMTMz+4SRCYIg5B0tLS1Kly7No0ePKFasWL7PP5qXlEolKSkpQOYYCqFgE/VdcGRkZPDq1StKly6dq0kBPkShS3SrlS5OEd3//8JXKpU8efKEsmXLApn/OcyfP58XL17Iy/4JgiB8iXR1dSlbtixpaWkFqhtDamoqMTEx1KhRA3393E8VKXxZRH0XHEWKFKF48eKfLMmFfE50U1JSmDp1KocOHUJfX58ePXq8cV3kq1evMnnyZCIjI/nmm2+YOnUqlpaWuT6nadkS8r8VCgUDBw7k2bNn/PnnnxQpUgSAcuXKUa5cufe7KEEQhM+IlpaW/N1WUGR1xyhatGiBXPlNUCXqW/gQ+XoPYM6cOYSHh7Nu3TomT57M4sWLOXDggFq5pKQkevfujb29Pdu3b8fW1pY+ffrIU37lhmm5/0909+/fz7///svly5dZs2bNB12LIAiCIAiC8HnJt0Q3KSmJwMBAxo8fj4WFBa1ataJXr15s3LhRrey+ffsoWrQoo0aNombNmowfP57ixYtrTIrfJXuLbq9evWjatCnDhw+ne/fuH3Q9giAIgiAIwucl3xLdiIgI0tPTsbW1lbfZ2dlx6dIllEqlStlLly5hZ2cn9+nQ0tKibt26XLx4MVfnTE1Op1Tac/m5trY227ZtY/z48QXu1p4gCIIgCEJhl2+Jbnx8PKVLl1ZJMMuWLUtKSgrPnj1TK/v6/LVGRka5XqEs8fkrVs+eqpJIF6SRyIIgCIIgCML/y7fBaK9evVJrRc16npqamqOyr5d7m7S0NMqVK8ewoUO4ePEiurqFbsKJQiVr8MKNGzc+6ehOIX+I+i5cRH0XLqK+C5e0tLQ8red8y/aKFi2qlqhmPX99+pA3lc3NNCNaWlro6elRuXLl94xY+JIUxJHmwpuJ+i5cRH0XLqK+CxctLa2CkegaGxvz9OlT0tPT5dbV+Ph49PX1KVmypFrZR48eqWx79OhRrpbjzd4XWBAEQRAEQSj48q2Pbq1atdDV1VUZUHbu3DmsrKzUVj6xtrbmwoUL8u0LSZI4f/481tbWnzJkQRAEQRAE4QuSb4lusWLFcHNzY8qUKYSFhRESEsLq1avp1q0bkNm6m5ycDMD333/PixcvmDFjBlFRUcyYMYNXr17Rtm3b/ApfEARBEARB+MxpSVnNpPng1atXTJkyhUOHDmFgYEDPnj3x9fUFwMzMDH9/fzw8PAAICwtj8uTJREdHY2ZmxtSpU6ldu3Z+hS4IgiAIgiB85vI10RUEQRAEQRCEjyVflwAWBEEQBEEQhI9FJLqCIAiCIAhCgSQSXUEQBEEQBKFAEomuIAiCIAiCUCAVqEQ3JSWFcePGYW9vT6NGjVi9evUby169ehUvLy+sra3p2LEj4eHhnzBSIS/kpr6PHDlChw4dsLW1xdXVlb/++usTRirkhdzUd5bbt29ja2vL6dOnP0GEQl7KTX1fv34db29v6tSpg6urK6dOnfqEkQp5ITf1/eeff9K2bVtsbW3x9vbmypUrnzBSIS+lpqbSrl27t35Hf2i+VqAS3Tlz5hAeHs66deuYPHkyixcv5sCBA2rlkpKS6N27N/b29mzfvh1bW1v69OlDUlJSPkQtvK+c1ndERAQDBw6kY8eOBAcH06lTJ4YMGUJEREQ+RC28r5zWd3ZTpkwRf9dfqJzWd0JCAj169OCbb75h9+7dtGrVioEDB/L48eN8iFp4Xzmt7xs3bjB8+HD69OnDzp07qVWrFn369OHVq1f5ELXwIVJSUhg2bBg3btx4Y5k8ydekAuLly5eSlZWVdOrUKXnbkiVLpK5du6qVDQwMlJycnCSlUilJkiQplUqpVatWUlBQ0CeLV/gwuanvuXPnSj179lTZ1qNHD2n+/PkfPU4hb+SmvrPs3LlT6tSpk2Rqaqqyn/D5y019r1u3TmrZsqWUnp4ub/Pw8JCOHDnySWIVPlxu6nvNmjWSu7u7/DwhIUEyNTWVwsLCPkmsQt64ceOG1L59e8nV1fWt39F5ka8VmBbdiIgI0tPTsbW1lbfZ2dlx6dIllEqlStlLly5hZ2eHlpYWAFpaWtStW1dlOWLh85ab+nZ3d2fEiBFqx0hISPjocQp5Izf1DfD06VPmzp3LtGnTPmWYQh7JTX2fOXOGFi1aoKOjI28LCgqiadOmnyxe4cPkpr4NDQ2Jiori3LlzKJVKtm/fjoGBAVWrVv3UYQsf4MyZMzg6OrJ169a3lsuLfE33QwL9nMTHx1O6dGmKFCkibytbtiwpKSk8e/aMMmXKqJT95ptvVPY3MjJ6a/O58HnJTX3XrFlTZd8bN25w8uRJOnXq9MniFT5MbuobYNasWbi7u/Ptt99+6lCFPJCb+lYoFNSpU4eJEydy+PBhKlWqxOjRo7Gzs8uP0IX3kJv6dnZ25vDhw3Tu3BkdHR20tbVZvnw5pUqVyo/QhffUuXPnHJXLi3ytwLTovnr1SuWPBJCfp6am5qjs6+WEz1du6ju7J0+eMGjQIOrWrUuLFi0+aoxC3slNfZ84cYJz587Rv3//TxafkLdyU99JSUmsWLGCcuXKsXLlShwcHOjZsyf37t37ZPEKHyY39f306VPi4+OZNGkSf/zxBx06dGDs2LGiT3YBlRf5WoFJdIsWLap24VnP9fX1c1T29XLC5ys39Z3l0aNH/Pjjj0iSxKJFi9DWLjAf/wIvp/WdnJzMpEmTmDx5svh7/oLl5u9bR0eHWrVqMXjwYGrXrs3IkSOpVq0aO3fu/GTxCh8mN/U9b948TE1N6dKlC5aWlkyfPp1ixYoRFBT0yeIVPp28yNcKzP/0xsbGPH36lPT0dHlbfHw8+vr6lCxZUq3so0ePVLY9evSI8uXLf5JYhQ+Xm/oGePDgAV26dCE1NZX169er3eoWPm85re+wsDAUCgWDBw/G1tZW7vP3008/MWnSpE8et/B+cvP3Xa5cOWrUqKGyrVq1aqJF9wuSm/q+cuUK5ubm8nNtbW3Mzc25e/fuJ4tX+HTyIl8rMIlurVq10NXVVemgfO7cOaysrNRa7qytrblw4QKSJAEgSRLnz5/H2tr6U4YsfIDc1HdSUhK9evVCW1ubDRs2YGxs/ImjFT5UTuu7Tp06HDp0iODgYPkB4Ofnx5AhQz5x1ML7ys3ft42NDdevX1fZdvPmTSpVqvQpQhXyQG7qu3z58kRHR6tsi4mJoXLlyp8iVOETy4t8rcAkusWKFcPNzY0pU6YQFhZGSEgIq1evplu3bkDmr8Pk5GQAvv/+e168eMGMGTOIiopixowZvHr1irZt2+bnJQi5kJv6Xr58OXFxccyePVt+LT4+Xsy68AXJaX3r6+tjYmKi8oDMVgEjI6P8vAQhF3Lz992pUyeuX79OQEAAt27dYuHChSgUCjp06JCflyDkQm7q+z//+Q9//PEHwcHB3Lp1i3nz5nH37l3c3d3z8xKEPJTn+dqHzoX2OUlKSpJGjRol2djYSI0aNZLWrFkjv2Zqaqoy79qlS5ckNzc3ycrKSvL09JSuXLmSDxELHyKn9d2mTRvJ1NRU7TF69Oh8ilx4H7n5+85OzKP7ZcpNfYeGhkru7u6SpaWl1KFDB+nMmTP5ELHwIXJT33/88Yf0/fffSzY2NpK3t7cUHh6eDxELeeX17+i8zte0JOl/7cGCIAiCIAiCUIAUmK4LgiAIgiAIgpCdSHQFQRAEQRCEAkkkuoIgCIIgCEKBJBJdQRAEQRAEoUASia4gCIIgCIJQIIlEVxAEQRAEQSiQRKIrCIIgCIIgFEgi0RUE4Yvl4+ODmZmZxkfWSnjvcvr0aczMzLh9+/ZHifH27dtqsdWuXZsGDRowdOhQ7t69m2fncnJyIiAgAMhcKnPHjh08fvwYgO3bt2NmZpZn53pd1vGzP2rVqoWDgwPdu3fn6tWruTre3bt32bt370eKVhCEwkI3vwMQBEH4EG3btmX8+PFq24sVK5YP0bxZQEAAtra2ACiVShQKBePHj6dPnz7s2rULLS2tDz7Htm3bKFq0KABnz55lzJgx/PXXXwA4OzvTuHHjDz7Huxw7dkz+d0ZGBjExMcycOZOePXsSEhJC8eLFc3Sc0aNHU6lSJVxcXD5WqIIgFAIi0RUE4Yumr69PuXLl8juMdypVqpRKnMbGxgwcOJARI0Zw/fp1zM3NP/gcZcqUkf/9+qKX+vr66Ovrf/A53uX1uvj666+ZNGkSXbt25dSpU7Ro0eKjxyAIgpBFdF0QBKFAe/78ORMmTKBx48ZYWFjQoEEDJkyYwKtXrzSWj42NpWfPntjZ2WFra0vPnj25fv26/HpCQgITJ06kfv362NnZ0a1bNy5fvvxeseno6ACgp6cHwL179xgxYgTfffcdNjY29OzZk4iICLn848ePGTx4MI6OjtSpU4dOnTpx5swZ+fWsrgunT5+mW7duALRo0YLt27erdF0YM2YMXl5eKrHcuXMHc3NzTpw4AcD58+fp0qULderUoVmzZkydOpXExMT3us6sVmZd3cy2FaVSyfLly2nTpg2WlpbUrVuXXr16ERcXB2R2STlz5gw7duzAyckJgNTUVObOnUvjxo2xtbXlP//5j0rrsSAIgiYi0RUEoUAbM2YMV69eZfHixRw8eJCxY8cSHBzM1q1bNZYfNmwYxsbGBAUFERgYiLa2NgMHDgQyW0l/+uknFAoFy5cv548//sDGxgZvb+9c9UFVKpVcu3aNX3/9FXNzc6pXr05iYiLe3t48ePCAX3/9lS1btqCvr0/Xrl25c+cOAFOmTCElJYUNGzawe/duqlevTv/+/UlKSlI5vq2trdxXNzAwEGdnZ5XXPTw8CAsLkxNLgN27d/P1119Tv359IiIi6N69O40bN2bXrl3MmzePK1eu0KNHD7WW4ndRKBTMnTuXihUr4uDgAMD69etZtWoVY8aM4eDBgyxZsoTY2FhmzZoF/H83j7Zt27Jt2zYAxo4dy/Hjx5k3bx47duygbdu29O3blyNHjuQqHkEQChfRdUEQhC/a7t27OXjwoMo2Ozs7fvvtNwC+++47HBwc5NbMypUrs2HDBiIjIzUeLy4ujoYNG1KpUiX09PSYOXMmN2/eRKlUcvr0aS5evMipU6cwNDQEMhPj8+fPs379ejlR0+Snn36SW3BTU1ORJAl7e3umT5+OtrY2u3bt4unTp2zfvl3ugvDf//6Xli1bsnHjRkaNGkVcXBympqZUqVIFfX19xo8fj6urq3zcLEWKFKFUqVJAZneG17ssODg4UKVKFXbt2iUn8bt376ZDhw5oa2uzatUqvvvuO/r27QtAtWrV5FjOnDmDo6PjG68zqx8yQFpaGnp6ejRq1Ah/f3+++uorAKpWrcrs2bNp3rw5AJUqVeL777/nwIEDABgaGqKnp4e+vj5lypTh1q1b7Nmzh+DgYGrVqgVA9+7diYiIYNWqVTRr1uyN8QiCULiJRFcQhC+ak5MTI0aMUNmWPbHr3Lkzhw8fZseOHcTGxhIVFcXt27epUaOGxuP9/PPPzJw5k02bNlGvXj0aN25Mu3bt0NbW5sqVK0iSJCdoWVJTU0lJSXlrnH5+flhbWwOZt/CNjIxU4oyMjKRatWoq/Wz19fWpU6eOnJQPHDiQkSNHcvDgQezs7GjUqBHt2rWTuwbklJaWFm5ubuzevZuBAwdy9epVoqKiWLp0KQBXr17l1q1bKklrlujo6LcmusHBwUBmN4tffvmFx48fM3ToUCpXriyXcXJy4tKlSyxcuJCYmBhiYmKIiorC2NhY4zGzWss7d+6ssj0tLY2SJUvm6toFQShcRKIrCMIXrXjx4piYmGh8TalU0qdPH27cuEG7du1wdnbGwsKCiRMnvvF4Xbp04fvvv+fo0aOcPHmSRYsW8euvvxIcHIxSqcTAwIDt27er7VekSJG3xmlsbPzGOEF98Fj2a8jq29qqVSv+/fdf/v33X06cOMGaNWtYvHgxf/zxB99+++1bz/86d3d3Fi9ezOXLl9m3bx9169aV41Mqlbi6usotutllT8Q1yTqGiYkJy5cvx8vLi549e7Jjxw5Kly4NwIoVK1iyZAnu7u40aNAAX19f/vrrrzdOJ5b13mzcuFFt1gZtbdEDTxCENxPfEIIgFFjXrl3jn3/+YeHChYwYMYL27dtTtWpV4uLiNCaWjx8/Ztq0aaSlpeHh4cHcuXPZtWsX8fHxnDlzBlNTUxITE0lLS8PExER+rFy5Up7G632ZmZkRGxsrz3sLkJKSQnh4ON988w2pqan4+/ujUChwdnbGz8+PkJAQtLW1NfZTfdd0ZZUqVcLR0ZGDBw+yf/9+PDw85Ne+/fZboqKiVK4xPT0df39/7t27l+NrKlasGPPmzePRo0dMmzZN3r5s2TIGDBjAlClT+OGHH7CxsSE2NvaNyX5WEh8fH68SU9YgO0EQhDcRia4gCAVW2bJl0dXVZf/+/SgUCi5fvszQoUOJj48nNTVVrXypUqU4cuQIEyZM4Nq1aygUCrZs2YKenh6WlpY0btyYWrVq8fPPP3Pq1Clu3bqFv78/27dvp2bNmh8Uq6urK4aGhgwdOpSwsDAiIiIYMWIESUlJ/PDDDxQpUoTLly8zceJELl68yO3bt9m+fTtJSUkauxhk9YeNiIjg5cuXGs/p7u7Opk2bePbsGW3btpW39+jRg6tXrzJ16lSio6O5cOECw4cPJzY2lmrVquXquszNzenVqxf79u3j8OHDAFSoUIHjx48TFRXFzZs3WbBgAYcOHVKpk+LFi3Pnzh3u37/Pt99+S/PmzZk8eTKHDx9GoVCwcuVKli9fTtWqVXMVjyAIhYtIdAVBKLCMjY2ZNWsWhw8fxtnZmSFDhmBsbIyvry/h4eFq5XV1dVm5ciXa2tr4+vri4uLCiRMnWLFiBVWrVkVHR4fVq1djaWnJ0KFDad++PWfPnmXx4sU0aNDgg2ItUaIEGzZsoGTJkvj6+tK5c2eSk5PZvHkzVapUAWDBggVUqVKFfv368f3337NlyxbmzZuHvb292vFMTU1p2rQpQ4cOfeMME23atAGgZcuWGBgYyNttbGz47bffuHbtGu7u7vTr14/q1auzdu3ad3bR0KR///7UqFFDnqJszpw5JCcn07FjR7p27UpkZCRTp07l8ePH8kpxnTp1IjIykvbt25ORkcGCBQto3bo1kyZNwtnZmeDgYGbMmIG7u3uu4xEEofDQknI7V4wgCIIgCIIgfAFEi64gCIIgCIJQIIlEVxAEQRAEQSiQRKIrCIIgCIIgFEgi0RUEQRAEQRAKJJHoCoIgCIIgCAWSSHQFQRAEQRCEAkkkuoIgCIIgCEKBJBJdQRAEQRAEoUASia4gCIIgCIJQIIlEVxAEQRAEQSiQRKIrCIIgCIIgFEgi0RUEQRAEQRAKpP8D014F+sRFdwkAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 800x550 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_model(clf, plot = 'auc')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47f19608",
-   "metadata": {},
-   "source": [
-    "### Groupby While Using the Sample of the Maximum Number of Fraud Flags (492)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "b00bf467",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>V1</th>\n",
-       "      <th>V2</th>\n",
-       "      <th>V3</th>\n",
-       "      <th>V4</th>\n",
-       "      <th>V5</th>\n",
-       "      <th>V6</th>\n",
-       "      <th>V7</th>\n",
-       "      <th>V8</th>\n",
-       "      <th>V9</th>\n",
-       "      <th>V10</th>\n",
-       "      <th>...</th>\n",
-       "      <th>V22</th>\n",
-       "      <th>V23</th>\n",
-       "      <th>V24</th>\n",
-       "      <th>V25</th>\n",
-       "      <th>V26</th>\n",
-       "      <th>V27</th>\n",
-       "      <th>V28</th>\n",
-       "      <th>Class</th>\n",
-       "      <th>Amount_scaled</th>\n",
-       "      <th>Time_scaled</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>65503</th>\n",
-       "      <td>-1.812196</td>\n",
-       "      <td>0.602968</td>\n",
-       "      <td>1.812659</td>\n",
-       "      <td>0.450942</td>\n",
-       "      <td>0.069070</td>\n",
-       "      <td>0.081691</td>\n",
-       "      <td>-0.198589</td>\n",
-       "      <td>0.738268</td>\n",
-       "      <td>0.017042</td>\n",
-       "      <td>-0.688166</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.026336</td>\n",
-       "      <td>-0.577950</td>\n",
-       "      <td>0.046298</td>\n",
-       "      <td>0.734182</td>\n",
-       "      <td>-0.355035</td>\n",
-       "      <td>0.141399</td>\n",
-       "      <td>-0.081070</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.325643</td>\n",
-       "      <td>-0.909615</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>238374</th>\n",
-       "      <td>2.286639</td>\n",
-       "      <td>-1.446495</td>\n",
-       "      <td>-1.242360</td>\n",
-       "      <td>-1.612324</td>\n",
-       "      <td>-1.031333</td>\n",
-       "      <td>-0.581510</td>\n",
-       "      <td>-0.990543</td>\n",
-       "      <td>-0.232085</td>\n",
-       "      <td>-1.143929</td>\n",
-       "      <td>1.569486</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.109465</td>\n",
-       "      <td>0.119493</td>\n",
-       "      <td>-0.798501</td>\n",
-       "      <td>-0.102570</td>\n",
-       "      <td>-0.112793</td>\n",
-       "      <td>0.003544</td>\n",
-       "      <td>-0.054988</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.173316</td>\n",
-       "      <td>1.154609</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53397</th>\n",
-       "      <td>1.443768</td>\n",
-       "      <td>-0.598984</td>\n",
-       "      <td>-0.068101</td>\n",
-       "      <td>-1.023305</td>\n",
-       "      <td>-0.511125</td>\n",
-       "      <td>-0.107239</td>\n",
-       "      <td>-0.676385</td>\n",
-       "      <td>0.031882</td>\n",
-       "      <td>-0.848702</td>\n",
-       "      <td>0.826015</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.180736</td>\n",
-       "      <td>-0.195817</td>\n",
-       "      <td>-0.839429</td>\n",
-       "      <td>0.594834</td>\n",
-       "      <td>-0.150356</td>\n",
-       "      <td>-0.007018</td>\n",
-       "      <td>-0.007289</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.293258</td>\n",
-       "      <td>-1.028845</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>123478</th>\n",
-       "      <td>1.196765</td>\n",
-       "      <td>0.629697</td>\n",
-       "      <td>-0.262377</td>\n",
-       "      <td>1.260867</td>\n",
-       "      <td>0.225960</td>\n",
-       "      <td>-0.724639</td>\n",
-       "      <td>0.228037</td>\n",
-       "      <td>-0.113160</td>\n",
-       "      <td>-0.104847</td>\n",
-       "      <td>-0.613676</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.046326</td>\n",
-       "      <td>-0.091243</td>\n",
-       "      <td>-0.030958</td>\n",
-       "      <td>0.622944</td>\n",
-       "      <td>-0.298302</td>\n",
-       "      <td>0.049224</td>\n",
-       "      <td>0.048121</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.349231</td>\n",
-       "      <td>-0.376323</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>233754</th>\n",
-       "      <td>2.063453</td>\n",
-       "      <td>0.003071</td>\n",
-       "      <td>-1.621522</td>\n",
-       "      <td>0.014389</td>\n",
-       "      <td>0.640003</td>\n",
-       "      <td>-0.249409</td>\n",
-       "      <td>0.155028</td>\n",
-       "      <td>-0.175328</td>\n",
-       "      <td>0.175997</td>\n",
-       "      <td>0.133856</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.966468</td>\n",
-       "      <td>-0.041594</td>\n",
-       "      <td>0.288921</td>\n",
-       "      <td>0.342333</td>\n",
-       "      <td>-0.132884</td>\n",
-       "      <td>-0.018340</td>\n",
-       "      <td>-0.064248</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-0.342475</td>\n",
-       "      <td>1.113504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>191074</th>\n",
-       "      <td>-1.836940</td>\n",
-       "      <td>-1.646764</td>\n",
-       "      <td>-3.381168</td>\n",
-       "      <td>0.473354</td>\n",
-       "      <td>0.074243</td>\n",
-       "      <td>-0.446751</td>\n",
-       "      <td>3.791907</td>\n",
-       "      <td>-1.351045</td>\n",
-       "      <td>0.095186</td>\n",
-       "      <td>-0.084500</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.786681</td>\n",
-       "      <td>-0.151178</td>\n",
-       "      <td>-0.582098</td>\n",
-       "      <td>-0.956062</td>\n",
-       "      <td>-0.334369</td>\n",
-       "      <td>0.715600</td>\n",
-       "      <td>0.370450</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2.528591</td>\n",
-       "      <td>0.721890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>77348</th>\n",
-       "      <td>-1.271244</td>\n",
-       "      <td>2.462675</td>\n",
-       "      <td>-2.851395</td>\n",
-       "      <td>2.324480</td>\n",
-       "      <td>-1.372245</td>\n",
-       "      <td>-0.948196</td>\n",
-       "      <td>-3.065234</td>\n",
-       "      <td>1.166927</td>\n",
-       "      <td>-2.268771</td>\n",
-       "      <td>-4.881143</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.081931</td>\n",
-       "      <td>-0.221348</td>\n",
-       "      <td>-0.523582</td>\n",
-       "      <td>0.224228</td>\n",
-       "      <td>0.756335</td>\n",
-       "      <td>0.632800</td>\n",
-       "      <td>0.250187</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.353189</td>\n",
-       "      <td>-0.796134</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12108</th>\n",
-       "      <td>-16.917468</td>\n",
-       "      <td>9.669900</td>\n",
-       "      <td>-23.736443</td>\n",
-       "      <td>11.824990</td>\n",
-       "      <td>-9.830548</td>\n",
-       "      <td>-2.514829</td>\n",
-       "      <td>-17.290657</td>\n",
-       "      <td>1.820408</td>\n",
-       "      <td>-6.264903</td>\n",
-       "      <td>-12.916636</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.972755</td>\n",
-       "      <td>1.241866</td>\n",
-       "      <td>-1.051086</td>\n",
-       "      <td>0.038009</td>\n",
-       "      <td>0.672317</td>\n",
-       "      <td>2.108471</td>\n",
-       "      <td>-1.421243</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.349231</td>\n",
-       "      <td>-1.553398</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>143188</th>\n",
-       "      <td>-3.003459</td>\n",
-       "      <td>2.096150</td>\n",
-       "      <td>-0.487030</td>\n",
-       "      <td>3.069453</td>\n",
-       "      <td>-1.774329</td>\n",
-       "      <td>0.251804</td>\n",
-       "      <td>-4.328776</td>\n",
-       "      <td>-2.425478</td>\n",
-       "      <td>-0.985222</td>\n",
-       "      <td>-3.995211</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.245648</td>\n",
-       "      <td>-0.269241</td>\n",
-       "      <td>0.537102</td>\n",
-       "      <td>-0.220757</td>\n",
-       "      <td>-0.059555</td>\n",
-       "      <td>0.460710</td>\n",
-       "      <td>-0.033551</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.345233</td>\n",
-       "      <td>-0.202848</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76555</th>\n",
-       "      <td>-7.901421</td>\n",
-       "      <td>2.720472</td>\n",
-       "      <td>-7.885936</td>\n",
-       "      <td>6.348334</td>\n",
-       "      <td>-5.480119</td>\n",
-       "      <td>-0.333059</td>\n",
-       "      <td>-8.682376</td>\n",
-       "      <td>1.164431</td>\n",
-       "      <td>-4.542447</td>\n",
-       "      <td>-7.748480</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.092437</td>\n",
-       "      <td>0.320133</td>\n",
-       "      <td>-0.434643</td>\n",
-       "      <td>-0.380687</td>\n",
-       "      <td>0.213630</td>\n",
-       "      <td>0.423620</td>\n",
-       "      <td>-0.105169</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.260317</td>\n",
-       "      <td>-0.804199</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>984 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               V1        V2         V3         V4        V5        V6  \\\n",
-       "65503   -1.812196  0.602968   1.812659   0.450942  0.069070  0.081691   \n",
-       "238374   2.286639 -1.446495  -1.242360  -1.612324 -1.031333 -0.581510   \n",
-       "53397    1.443768 -0.598984  -0.068101  -1.023305 -0.511125 -0.107239   \n",
-       "123478   1.196765  0.629697  -0.262377   1.260867  0.225960 -0.724639   \n",
-       "233754   2.063453  0.003071  -1.621522   0.014389  0.640003 -0.249409   \n",
-       "...           ...       ...        ...        ...       ...       ...   \n",
-       "191074  -1.836940 -1.646764  -3.381168   0.473354  0.074243 -0.446751   \n",
-       "77348   -1.271244  2.462675  -2.851395   2.324480 -1.372245 -0.948196   \n",
-       "12108  -16.917468  9.669900 -23.736443  11.824990 -9.830548 -2.514829   \n",
-       "143188  -3.003459  2.096150  -0.487030   3.069453 -1.774329  0.251804   \n",
-       "76555   -7.901421  2.720472  -7.885936   6.348334 -5.480119 -0.333059   \n",
-       "\n",
-       "               V7        V8        V9        V10  ...       V22       V23  \\\n",
-       "65503   -0.198589  0.738268  0.017042  -0.688166  ...  0.026336 -0.577950   \n",
-       "238374  -0.990543 -0.232085 -1.143929   1.569486  ... -0.109465  0.119493   \n",
-       "53397   -0.676385  0.031882 -0.848702   0.826015  ...  0.180736 -0.195817   \n",
-       "123478   0.228037 -0.113160 -0.104847  -0.613676  ...  0.046326 -0.091243   \n",
-       "233754   0.155028 -0.175328  0.175997   0.133856  ...  0.966468 -0.041594   \n",
-       "...           ...       ...       ...        ...  ...       ...       ...   \n",
-       "191074   3.791907 -1.351045  0.095186  -0.084500  ...  1.786681 -0.151178   \n",
-       "77348   -3.065234  1.166927 -2.268771  -4.881143  ...  0.081931 -0.221348   \n",
-       "12108  -17.290657  1.820408 -6.264903 -12.916636  ...  0.972755  1.241866   \n",
-       "143188  -4.328776 -2.425478 -0.985222  -3.995211  ...  1.245648 -0.269241   \n",
-       "76555   -8.682376  1.164431 -4.542447  -7.748480  ...  1.092437  0.320133   \n",
-       "\n",
-       "             V24       V25       V26       V27       V28  Class  \\\n",
-       "65503   0.046298  0.734182 -0.355035  0.141399 -0.081070      0   \n",
-       "238374 -0.798501 -0.102570 -0.112793  0.003544 -0.054988      0   \n",
-       "53397  -0.839429  0.594834 -0.150356 -0.007018 -0.007289      0   \n",
-       "123478 -0.030958  0.622944 -0.298302  0.049224  0.048121      0   \n",
-       "233754  0.288921  0.342333 -0.132884 -0.018340 -0.064248      0   \n",
-       "...          ...       ...       ...       ...       ...    ...   \n",
-       "191074 -0.582098 -0.956062 -0.334369  0.715600  0.370450      1   \n",
-       "77348  -0.523582  0.224228  0.756335  0.632800  0.250187      1   \n",
-       "12108  -1.051086  0.038009  0.672317  2.108471 -1.421243      1   \n",
-       "143188  0.537102 -0.220757 -0.059555  0.460710 -0.033551      1   \n",
-       "76555  -0.434643 -0.380687  0.213630  0.423620 -0.105169      1   \n",
-       "\n",
-       "        Amount_scaled  Time_scaled  \n",
-       "65503       -0.325643    -0.909615  \n",
-       "238374      -0.173316     1.154609  \n",
-       "53397       -0.293258    -1.028845  \n",
-       "123478      -0.349231    -0.376323  \n",
-       "233754      -0.342475     1.113504  \n",
-       "...               ...          ...  \n",
-       "191074       2.528591     0.721890  \n",
-       "77348       -0.353189    -0.796134  \n",
-       "12108       -0.349231    -1.553398  \n",
-       "143188      -0.345233    -0.202848  \n",
-       "76555        0.260317    -0.804199  \n",
-       "\n",
-       "[984 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2_sample = df2.groupby(\"Class\").sample(n=492, random_state=2022) # pull 4000 obs per each value in Label col\n",
-    "df2_sample"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "4acd7a8a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_86e08_row44_col1 {\n",
-       "  background-color: lightgreen;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_86e08\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_86e08_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
-       "      <th id=\"T_86e08_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_86e08_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
-       "      <td id=\"T_86e08_row0_col1\" class=\"data row0 col1\" >100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_86e08_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
-       "      <td id=\"T_86e08_row1_col1\" class=\"data row1 col1\" >Class</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_86e08_row2_col0\" class=\"data row2 col0\" >Target Type</td>\n",
-       "      <td id=\"T_86e08_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_86e08_row3_col0\" class=\"data row3 col0\" >Label Encoded</td>\n",
-       "      <td id=\"T_86e08_row3_col1\" class=\"data row3 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_86e08_row4_col0\" class=\"data row4 col0\" >Original Data</td>\n",
-       "      <td id=\"T_86e08_row4_col1\" class=\"data row4 col1\" >(984, 31)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_86e08_row5_col0\" class=\"data row5 col0\" >Missing Values</td>\n",
-       "      <td id=\"T_86e08_row5_col1\" class=\"data row5 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_86e08_row6_col0\" class=\"data row6 col0\" >Numeric Features</td>\n",
-       "      <td id=\"T_86e08_row6_col1\" class=\"data row6 col1\" >30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_86e08_row7_col0\" class=\"data row7 col0\" >Categorical Features</td>\n",
-       "      <td id=\"T_86e08_row7_col1\" class=\"data row7 col1\" >0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_86e08_row8_col0\" class=\"data row8 col0\" >Ordinal Features</td>\n",
-       "      <td id=\"T_86e08_row8_col1\" class=\"data row8 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_86e08_row9_col0\" class=\"data row9 col0\" >High Cardinality Features</td>\n",
-       "      <td id=\"T_86e08_row9_col1\" class=\"data row9 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
-       "      <td id=\"T_86e08_row10_col0\" class=\"data row10 col0\" >High Cardinality Method</td>\n",
-       "      <td id=\"T_86e08_row10_col1\" class=\"data row10 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
-       "      <td id=\"T_86e08_row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
-       "      <td id=\"T_86e08_row11_col1\" class=\"data row11 col1\" >(787, 30)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
-       "      <td id=\"T_86e08_row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
-       "      <td id=\"T_86e08_row12_col1\" class=\"data row12 col1\" >(197, 30)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
-       "      <td id=\"T_86e08_row13_col0\" class=\"data row13 col0\" >Shuffle Train-Test</td>\n",
-       "      <td id=\"T_86e08_row13_col1\" class=\"data row13 col1\" >True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
-       "      <td id=\"T_86e08_row14_col0\" class=\"data row14 col0\" >Stratify Train-Test</td>\n",
-       "      <td id=\"T_86e08_row14_col1\" class=\"data row14 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
-       "      <td id=\"T_86e08_row15_col0\" class=\"data row15 col0\" >Fold Generator</td>\n",
-       "      <td id=\"T_86e08_row15_col1\" class=\"data row15 col1\" >StratifiedKFold</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
-       "      <td id=\"T_86e08_row16_col0\" class=\"data row16 col0\" >Fold Number</td>\n",
-       "      <td id=\"T_86e08_row16_col1\" class=\"data row16 col1\" >10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
-       "      <td id=\"T_86e08_row17_col0\" class=\"data row17 col0\" >CPU Jobs</td>\n",
-       "      <td id=\"T_86e08_row17_col1\" class=\"data row17 col1\" >-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
-       "      <td id=\"T_86e08_row18_col0\" class=\"data row18 col0\" >Use GPU</td>\n",
-       "      <td id=\"T_86e08_row18_col1\" class=\"data row18 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
-       "      <td id=\"T_86e08_row19_col0\" class=\"data row19 col0\" >Log Experiment</td>\n",
-       "      <td id=\"T_86e08_row19_col1\" class=\"data row19 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
-       "      <td id=\"T_86e08_row20_col0\" class=\"data row20 col0\" >Experiment Name</td>\n",
-       "      <td id=\"T_86e08_row20_col1\" class=\"data row20 col1\" >clf-default-name</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
-       "      <td id=\"T_86e08_row21_col0\" class=\"data row21 col0\" >USI</td>\n",
-       "      <td id=\"T_86e08_row21_col1\" class=\"data row21 col1\" >5338</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
-       "      <td id=\"T_86e08_row22_col0\" class=\"data row22 col0\" >Imputation Type</td>\n",
-       "      <td id=\"T_86e08_row22_col1\" class=\"data row22 col1\" >simple</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
-       "      <td id=\"T_86e08_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Iteration</td>\n",
-       "      <td id=\"T_86e08_row23_col1\" class=\"data row23 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
-       "      <td id=\"T_86e08_row24_col0\" class=\"data row24 col0\" >Numeric Imputer</td>\n",
-       "      <td id=\"T_86e08_row24_col1\" class=\"data row24 col1\" >mean</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
-       "      <td id=\"T_86e08_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Numeric Model</td>\n",
-       "      <td id=\"T_86e08_row25_col1\" class=\"data row25 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
-       "      <td id=\"T_86e08_row26_col0\" class=\"data row26 col0\" >Categorical Imputer</td>\n",
-       "      <td id=\"T_86e08_row26_col1\" class=\"data row26 col1\" >constant</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
-       "      <td id=\"T_86e08_row27_col0\" class=\"data row27 col0\" >Iterative Imputation Categorical Model</td>\n",
-       "      <td id=\"T_86e08_row27_col1\" class=\"data row27 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
-       "      <td id=\"T_86e08_row28_col0\" class=\"data row28 col0\" >Unknown Categoricals Handling</td>\n",
-       "      <td id=\"T_86e08_row28_col1\" class=\"data row28 col1\" >least_frequent</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
-       "      <td id=\"T_86e08_row29_col0\" class=\"data row29 col0\" >Normalize</td>\n",
-       "      <td id=\"T_86e08_row29_col1\" class=\"data row29 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
-       "      <td id=\"T_86e08_row30_col0\" class=\"data row30 col0\" >Normalize Method</td>\n",
-       "      <td id=\"T_86e08_row30_col1\" class=\"data row30 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
-       "      <td id=\"T_86e08_row31_col0\" class=\"data row31 col0\" >Transformation</td>\n",
-       "      <td id=\"T_86e08_row31_col1\" class=\"data row31 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
-       "      <td id=\"T_86e08_row32_col0\" class=\"data row32 col0\" >Transformation Method</td>\n",
-       "      <td id=\"T_86e08_row32_col1\" class=\"data row32 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
-       "      <td id=\"T_86e08_row33_col0\" class=\"data row33 col0\" >PCA</td>\n",
-       "      <td id=\"T_86e08_row33_col1\" class=\"data row33 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
-       "      <td id=\"T_86e08_row34_col0\" class=\"data row34 col0\" >PCA Method</td>\n",
-       "      <td id=\"T_86e08_row34_col1\" class=\"data row34 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
-       "      <td id=\"T_86e08_row35_col0\" class=\"data row35 col0\" >PCA Components</td>\n",
-       "      <td id=\"T_86e08_row35_col1\" class=\"data row35 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
-       "      <td id=\"T_86e08_row36_col0\" class=\"data row36 col0\" >Ignore Low Variance</td>\n",
-       "      <td id=\"T_86e08_row36_col1\" class=\"data row36 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
-       "      <td id=\"T_86e08_row37_col0\" class=\"data row37 col0\" >Combine Rare Levels</td>\n",
-       "      <td id=\"T_86e08_row37_col1\" class=\"data row37 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
-       "      <td id=\"T_86e08_row38_col0\" class=\"data row38 col0\" >Rare Level Threshold</td>\n",
-       "      <td id=\"T_86e08_row38_col1\" class=\"data row38 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
-       "      <td id=\"T_86e08_row39_col0\" class=\"data row39 col0\" >Numeric Binning</td>\n",
-       "      <td id=\"T_86e08_row39_col1\" class=\"data row39 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
-       "      <td id=\"T_86e08_row40_col0\" class=\"data row40 col0\" >Remove Outliers</td>\n",
-       "      <td id=\"T_86e08_row40_col1\" class=\"data row40 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
-       "      <td id=\"T_86e08_row41_col0\" class=\"data row41 col0\" >Outliers Threshold</td>\n",
-       "      <td id=\"T_86e08_row41_col1\" class=\"data row41 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
-       "      <td id=\"T_86e08_row42_col0\" class=\"data row42 col0\" >Remove Multicollinearity</td>\n",
-       "      <td id=\"T_86e08_row42_col1\" class=\"data row42 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
-       "      <td id=\"T_86e08_row43_col0\" class=\"data row43 col0\" >Multicollinearity Threshold</td>\n",
-       "      <td id=\"T_86e08_row43_col1\" class=\"data row43 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
-       "      <td id=\"T_86e08_row44_col0\" class=\"data row44 col0\" >Remove Perfect Collinearity</td>\n",
-       "      <td id=\"T_86e08_row44_col1\" class=\"data row44 col1\" >True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
-       "      <td id=\"T_86e08_row45_col0\" class=\"data row45 col0\" >Clustering</td>\n",
-       "      <td id=\"T_86e08_row45_col1\" class=\"data row45 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
-       "      <td id=\"T_86e08_row46_col0\" class=\"data row46 col0\" >Clustering Iteration</td>\n",
-       "      <td id=\"T_86e08_row46_col1\" class=\"data row46 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
-       "      <td id=\"T_86e08_row47_col0\" class=\"data row47 col0\" >Polynomial Features</td>\n",
-       "      <td id=\"T_86e08_row47_col1\" class=\"data row47 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
-       "      <td id=\"T_86e08_row48_col0\" class=\"data row48 col0\" >Polynomial Degree</td>\n",
-       "      <td id=\"T_86e08_row48_col1\" class=\"data row48 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
-       "      <td id=\"T_86e08_row49_col0\" class=\"data row49 col0\" >Trignometry Features</td>\n",
-       "      <td id=\"T_86e08_row49_col1\" class=\"data row49 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
-       "      <td id=\"T_86e08_row50_col0\" class=\"data row50 col0\" >Polynomial Threshold</td>\n",
-       "      <td id=\"T_86e08_row50_col1\" class=\"data row50 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
-       "      <td id=\"T_86e08_row51_col0\" class=\"data row51 col0\" >Group Features</td>\n",
-       "      <td id=\"T_86e08_row51_col1\" class=\"data row51 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
-       "      <td id=\"T_86e08_row52_col0\" class=\"data row52 col0\" >Feature Selection</td>\n",
-       "      <td id=\"T_86e08_row52_col1\" class=\"data row52 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
-       "      <td id=\"T_86e08_row53_col0\" class=\"data row53 col0\" >Feature Selection Method</td>\n",
-       "      <td id=\"T_86e08_row53_col1\" class=\"data row53 col1\" >classic</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
-       "      <td id=\"T_86e08_row54_col0\" class=\"data row54 col0\" >Features Selection Threshold</td>\n",
-       "      <td id=\"T_86e08_row54_col1\" class=\"data row54 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
-       "      <td id=\"T_86e08_row55_col0\" class=\"data row55 col0\" >Feature Interaction</td>\n",
-       "      <td id=\"T_86e08_row55_col1\" class=\"data row55 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
-       "      <td id=\"T_86e08_row56_col0\" class=\"data row56 col0\" >Feature Ratio</td>\n",
-       "      <td id=\"T_86e08_row56_col1\" class=\"data row56 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
-       "      <td id=\"T_86e08_row57_col0\" class=\"data row57 col0\" >Interaction Threshold</td>\n",
-       "      <td id=\"T_86e08_row57_col1\" class=\"data row57 col1\" >None</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
-       "      <td id=\"T_86e08_row58_col0\" class=\"data row58 col0\" >Fix Imbalance</td>\n",
-       "      <td id=\"T_86e08_row58_col1\" class=\"data row58 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_86e08_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
-       "      <td id=\"T_86e08_row59_col0\" class=\"data row59 col0\" >Fix Imbalance Method</td>\n",
-       "      <td id=\"T_86e08_row59_col1\" class=\"data row59 col1\" >SMOTE</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x22d84cf9760>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from pycaret.classification import *\n",
-    "df2_setup_sample = setup(data = df2_sample, train_size = 0.8, target = 'Class', session_id=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "c8cefb14",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_c393c th {\n",
-       "  text-align: left;\n",
-       "}\n",
-       "#T_c393c_row0_col0, #T_c393c_row0_col2, #T_c393c_row0_col3, #T_c393c_row0_col4, #T_c393c_row1_col0, #T_c393c_row1_col1, #T_c393c_row1_col3, #T_c393c_row1_col4, #T_c393c_row1_col5, #T_c393c_row1_col6, #T_c393c_row1_col7, #T_c393c_row2_col0, #T_c393c_row2_col1, #T_c393c_row2_col2, #T_c393c_row2_col3, #T_c393c_row2_col5, #T_c393c_row2_col6, #T_c393c_row2_col7, #T_c393c_row3_col0, #T_c393c_row3_col1, #T_c393c_row3_col2, #T_c393c_row3_col3, #T_c393c_row3_col4, #T_c393c_row3_col5, #T_c393c_row3_col6, #T_c393c_row3_col7, #T_c393c_row4_col0, #T_c393c_row4_col1, #T_c393c_row4_col2, #T_c393c_row4_col3, #T_c393c_row4_col4, #T_c393c_row4_col5, #T_c393c_row4_col6, #T_c393c_row4_col7, #T_c393c_row5_col0, #T_c393c_row5_col1, #T_c393c_row5_col2, #T_c393c_row5_col3, #T_c393c_row5_col4, #T_c393c_row5_col5, #T_c393c_row5_col6, #T_c393c_row5_col7, #T_c393c_row6_col0, #T_c393c_row6_col1, #T_c393c_row6_col2, #T_c393c_row6_col3, #T_c393c_row6_col4, #T_c393c_row6_col5, #T_c393c_row6_col6, #T_c393c_row6_col7, #T_c393c_row7_col0, #T_c393c_row7_col1, #T_c393c_row7_col2, #T_c393c_row7_col3, #T_c393c_row7_col4, #T_c393c_row7_col5, #T_c393c_row7_col6, #T_c393c_row7_col7, #T_c393c_row8_col0, #T_c393c_row8_col1, #T_c393c_row8_col2, #T_c393c_row8_col3, #T_c393c_row8_col4, #T_c393c_row8_col5, #T_c393c_row8_col6, #T_c393c_row8_col7, #T_c393c_row9_col0, #T_c393c_row9_col1, #T_c393c_row9_col2, #T_c393c_row9_col3, #T_c393c_row9_col4, #T_c393c_row9_col5, #T_c393c_row9_col6, #T_c393c_row9_col7, #T_c393c_row10_col0, #T_c393c_row10_col1, #T_c393c_row10_col2, #T_c393c_row10_col3, #T_c393c_row10_col4, #T_c393c_row10_col5, #T_c393c_row10_col6, #T_c393c_row10_col7, #T_c393c_row11_col0, #T_c393c_row11_col1, #T_c393c_row11_col2, #T_c393c_row11_col3, #T_c393c_row11_col4, #T_c393c_row11_col5, #T_c393c_row11_col6, #T_c393c_row11_col7, #T_c393c_row12_col0, #T_c393c_row12_col1, #T_c393c_row12_col2, #T_c393c_row12_col3, #T_c393c_row12_col4, #T_c393c_row12_col5, #T_c393c_row12_col6, #T_c393c_row12_col7, #T_c393c_row13_col0, #T_c393c_row13_col1, #T_c393c_row13_col2, #T_c393c_row13_col4, #T_c393c_row13_col5, #T_c393c_row13_col6, #T_c393c_row13_col7 {\n",
-       "  text-align: left;\n",
-       "}\n",
-       "#T_c393c_row0_col1, #T_c393c_row0_col5, #T_c393c_row0_col6, #T_c393c_row0_col7, #T_c393c_row1_col2, #T_c393c_row2_col4, #T_c393c_row13_col3 {\n",
-       "  text-align: left;\n",
-       "  background-color: yellow;\n",
-       "}\n",
-       "#T_c393c_row0_col8, #T_c393c_row1_col8, #T_c393c_row2_col8, #T_c393c_row3_col8, #T_c393c_row4_col8, #T_c393c_row5_col8, #T_c393c_row6_col8, #T_c393c_row7_col8, #T_c393c_row9_col8, #T_c393c_row10_col8, #T_c393c_row12_col8 {\n",
-       "  text-align: left;\n",
-       "  background-color: lightgrey;\n",
-       "}\n",
-       "#T_c393c_row8_col8, #T_c393c_row11_col8, #T_c393c_row13_col8 {\n",
-       "  text-align: left;\n",
-       "  background-color: yellow;\n",
-       "  background-color: lightgrey;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_c393c\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_c393c_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
-       "      <th id=\"T_c393c_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
-       "      <th id=\"T_c393c_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
-       "      <th id=\"T_c393c_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
-       "      <th id=\"T_c393c_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
-       "      <th id=\"T_c393c_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
-       "      <th id=\"T_c393c_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
-       "      <th id=\"T_c393c_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
-       "      <th id=\"T_c393c_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row0\" class=\"row_heading level0 row0\" >gbc</th>\n",
-       "      <td id=\"T_c393c_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Classifier</td>\n",
-       "      <td id=\"T_c393c_row0_col1\" class=\"data row0 col1\" >0.9428</td>\n",
-       "      <td id=\"T_c393c_row0_col2\" class=\"data row0 col2\" >0.9790</td>\n",
-       "      <td id=\"T_c393c_row0_col3\" class=\"data row0 col3\" >0.9128</td>\n",
-       "      <td id=\"T_c393c_row0_col4\" class=\"data row0 col4\" >0.9741</td>\n",
-       "      <td id=\"T_c393c_row0_col5\" class=\"data row0 col5\" >0.9418</td>\n",
-       "      <td id=\"T_c393c_row0_col6\" class=\"data row0 col6\" >0.8857</td>\n",
-       "      <td id=\"T_c393c_row0_col7\" class=\"data row0 col7\" >0.8886</td>\n",
-       "      <td id=\"T_c393c_row0_col8\" class=\"data row0 col8\" >0.0650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row1\" class=\"row_heading level0 row1\" >lightgbm</th>\n",
-       "      <td id=\"T_c393c_row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
-       "      <td id=\"T_c393c_row1_col1\" class=\"data row1 col1\" >0.9415</td>\n",
-       "      <td id=\"T_c393c_row1_col2\" class=\"data row1 col2\" >0.9809</td>\n",
-       "      <td id=\"T_c393c_row1_col3\" class=\"data row1 col3\" >0.9104</td>\n",
-       "      <td id=\"T_c393c_row1_col4\" class=\"data row1 col4\" >0.9743</td>\n",
-       "      <td id=\"T_c393c_row1_col5\" class=\"data row1 col5\" >0.9402</td>\n",
-       "      <td id=\"T_c393c_row1_col6\" class=\"data row1 col6\" >0.8832</td>\n",
-       "      <td id=\"T_c393c_row1_col7\" class=\"data row1 col7\" >0.8869</td>\n",
-       "      <td id=\"T_c393c_row1_col8\" class=\"data row1 col8\" >0.0400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row2\" class=\"row_heading level0 row2\" >et</th>\n",
-       "      <td id=\"T_c393c_row2_col0\" class=\"data row2 col0\" >Extra Trees Classifier</td>\n",
-       "      <td id=\"T_c393c_row2_col1\" class=\"data row2 col1\" >0.9389</td>\n",
-       "      <td id=\"T_c393c_row2_col2\" class=\"data row2 col2\" >0.9771</td>\n",
-       "      <td id=\"T_c393c_row2_col3\" class=\"data row2 col3\" >0.8880</td>\n",
-       "      <td id=\"T_c393c_row2_col4\" class=\"data row2 col4\" >0.9915</td>\n",
-       "      <td id=\"T_c393c_row2_col5\" class=\"data row2 col5\" >0.9359</td>\n",
-       "      <td id=\"T_c393c_row2_col6\" class=\"data row2 col6\" >0.8782</td>\n",
-       "      <td id=\"T_c393c_row2_col7\" class=\"data row2 col7\" >0.8844</td>\n",
-       "      <td id=\"T_c393c_row2_col8\" class=\"data row2 col8\" >0.0470</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row3\" class=\"row_heading level0 row3\" >rf</th>\n",
-       "      <td id=\"T_c393c_row3_col0\" class=\"data row3 col0\" >Random Forest Classifier</td>\n",
-       "      <td id=\"T_c393c_row3_col1\" class=\"data row3 col1\" >0.9352</td>\n",
-       "      <td id=\"T_c393c_row3_col2\" class=\"data row3 col2\" >0.9756</td>\n",
-       "      <td id=\"T_c393c_row3_col3\" class=\"data row3 col3\" >0.8930</td>\n",
-       "      <td id=\"T_c393c_row3_col4\" class=\"data row3 col4\" >0.9789</td>\n",
-       "      <td id=\"T_c393c_row3_col5\" class=\"data row3 col5\" >0.9328</td>\n",
-       "      <td id=\"T_c393c_row3_col6\" class=\"data row3 col6\" >0.8706</td>\n",
-       "      <td id=\"T_c393c_row3_col7\" class=\"data row3 col7\" >0.8757</td>\n",
-       "      <td id=\"T_c393c_row3_col8\" class=\"data row3 col8\" >0.0530</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row4\" class=\"row_heading level0 row4\" >lr</th>\n",
-       "      <td id=\"T_c393c_row4_col0\" class=\"data row4 col0\" >Logistic Regression</td>\n",
-       "      <td id=\"T_c393c_row4_col1\" class=\"data row4 col1\" >0.9326</td>\n",
-       "      <td id=\"T_c393c_row4_col2\" class=\"data row4 col2\" >0.9781</td>\n",
-       "      <td id=\"T_c393c_row4_col3\" class=\"data row4 col3\" >0.9029</td>\n",
-       "      <td id=\"T_c393c_row4_col4\" class=\"data row4 col4\" >0.9634</td>\n",
-       "      <td id=\"T_c393c_row4_col5\" class=\"data row4 col5\" >0.9316</td>\n",
-       "      <td id=\"T_c393c_row4_col6\" class=\"data row4 col6\" >0.8653</td>\n",
-       "      <td id=\"T_c393c_row4_col7\" class=\"data row4 col7\" >0.8680</td>\n",
-       "      <td id=\"T_c393c_row4_col8\" class=\"data row4 col8\" >0.0060</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row5\" class=\"row_heading level0 row5\" >knn</th>\n",
-       "      <td id=\"T_c393c_row5_col0\" class=\"data row5 col0\" >K Neighbors Classifier</td>\n",
-       "      <td id=\"T_c393c_row5_col1\" class=\"data row5 col1\" >0.9314</td>\n",
-       "      <td id=\"T_c393c_row5_col2\" class=\"data row5 col2\" >0.9676</td>\n",
-       "      <td id=\"T_c393c_row5_col3\" class=\"data row5 col3\" >0.8879</td>\n",
-       "      <td id=\"T_c393c_row5_col4\" class=\"data row5 col4\" >0.9757</td>\n",
-       "      <td id=\"T_c393c_row5_col5\" class=\"data row5 col5\" >0.9291</td>\n",
-       "      <td id=\"T_c393c_row5_col6\" class=\"data row5 col6\" >0.8630</td>\n",
-       "      <td id=\"T_c393c_row5_col7\" class=\"data row5 col7\" >0.8676</td>\n",
-       "      <td id=\"T_c393c_row5_col8\" class=\"data row5 col8\" >0.0120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row6\" class=\"row_heading level0 row6\" >ada</th>\n",
-       "      <td id=\"T_c393c_row6_col0\" class=\"data row6 col0\" >Ada Boost Classifier</td>\n",
-       "      <td id=\"T_c393c_row6_col1\" class=\"data row6 col1\" >0.9237</td>\n",
-       "      <td id=\"T_c393c_row6_col2\" class=\"data row6 col2\" >0.9726</td>\n",
-       "      <td id=\"T_c393c_row6_col3\" class=\"data row6 col3\" >0.9103</td>\n",
-       "      <td id=\"T_c393c_row6_col4\" class=\"data row6 col4\" >0.9395</td>\n",
-       "      <td id=\"T_c393c_row6_col5\" class=\"data row6 col5\" >0.9236</td>\n",
-       "      <td id=\"T_c393c_row6_col6\" class=\"data row6 col6\" >0.8475</td>\n",
-       "      <td id=\"T_c393c_row6_col7\" class=\"data row6 col7\" >0.8496</td>\n",
-       "      <td id=\"T_c393c_row6_col8\" class=\"data row6 col8\" >0.0290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row7\" class=\"row_heading level0 row7\" >svm</th>\n",
-       "      <td id=\"T_c393c_row7_col0\" class=\"data row7 col0\" >SVM - Linear Kernel</td>\n",
-       "      <td id=\"T_c393c_row7_col1\" class=\"data row7 col1\" >0.9199</td>\n",
-       "      <td id=\"T_c393c_row7_col2\" class=\"data row7 col2\" >0.0000</td>\n",
-       "      <td id=\"T_c393c_row7_col3\" class=\"data row7 col3\" >0.9279</td>\n",
-       "      <td id=\"T_c393c_row7_col4\" class=\"data row7 col4\" >0.9173</td>\n",
-       "      <td id=\"T_c393c_row7_col5\" class=\"data row7 col5\" >0.9212</td>\n",
-       "      <td id=\"T_c393c_row7_col6\" class=\"data row7 col6\" >0.8399</td>\n",
-       "      <td id=\"T_c393c_row7_col7\" class=\"data row7 col7\" >0.8423</td>\n",
-       "      <td id=\"T_c393c_row7_col8\" class=\"data row7 col8\" >0.0040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row8\" class=\"row_heading level0 row8\" >nb</th>\n",
-       "      <td id=\"T_c393c_row8_col0\" class=\"data row8 col0\" >Naive Bayes</td>\n",
-       "      <td id=\"T_c393c_row8_col1\" class=\"data row8 col1\" >0.9173</td>\n",
-       "      <td id=\"T_c393c_row8_col2\" class=\"data row8 col2\" >0.9552</td>\n",
-       "      <td id=\"T_c393c_row8_col3\" class=\"data row8 col3\" >0.8629</td>\n",
-       "      <td id=\"T_c393c_row8_col4\" class=\"data row8 col4\" >0.9726</td>\n",
-       "      <td id=\"T_c393c_row8_col5\" class=\"data row8 col5\" >0.9138</td>\n",
-       "      <td id=\"T_c393c_row8_col6\" class=\"data row8 col6\" >0.8350</td>\n",
-       "      <td id=\"T_c393c_row8_col7\" class=\"data row8 col7\" >0.8415</td>\n",
-       "      <td id=\"T_c393c_row8_col8\" class=\"data row8 col8\" >0.0030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row9\" class=\"row_heading level0 row9\" >qda</th>\n",
-       "      <td id=\"T_c393c_row9_col0\" class=\"data row9 col0\" >Quadratic Discriminant Analysis</td>\n",
-       "      <td id=\"T_c393c_row9_col1\" class=\"data row9 col1\" >0.9161</td>\n",
-       "      <td id=\"T_c393c_row9_col2\" class=\"data row9 col2\" >0.9587</td>\n",
-       "      <td id=\"T_c393c_row9_col3\" class=\"data row9 col3\" >0.8829</td>\n",
-       "      <td id=\"T_c393c_row9_col4\" class=\"data row9 col4\" >0.9496</td>\n",
-       "      <td id=\"T_c393c_row9_col5\" class=\"data row9 col5\" >0.9144</td>\n",
-       "      <td id=\"T_c393c_row9_col6\" class=\"data row9 col6\" >0.8324</td>\n",
-       "      <td id=\"T_c393c_row9_col7\" class=\"data row9 col7\" >0.8354</td>\n",
-       "      <td id=\"T_c393c_row9_col8\" class=\"data row9 col8\" >0.0050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row10\" class=\"row_heading level0 row10\" >dt</th>\n",
-       "      <td id=\"T_c393c_row10_col0\" class=\"data row10 col0\" >Decision Tree Classifier</td>\n",
-       "      <td id=\"T_c393c_row10_col1\" class=\"data row10 col1\" >0.8983</td>\n",
-       "      <td id=\"T_c393c_row10_col2\" class=\"data row10 col2\" >0.8984</td>\n",
-       "      <td id=\"T_c393c_row10_col3\" class=\"data row10 col3\" >0.9054</td>\n",
-       "      <td id=\"T_c393c_row10_col4\" class=\"data row10 col4\" >0.8974</td>\n",
-       "      <td id=\"T_c393c_row10_col5\" class=\"data row10 col5\" >0.9003</td>\n",
-       "      <td id=\"T_c393c_row10_col6\" class=\"data row10 col6\" >0.7966</td>\n",
-       "      <td id=\"T_c393c_row10_col7\" class=\"data row10 col7\" >0.7988</td>\n",
-       "      <td id=\"T_c393c_row10_col8\" class=\"data row10 col8\" >0.0050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row11\" class=\"row_heading level0 row11\" >ridge</th>\n",
-       "      <td id=\"T_c393c_row11_col0\" class=\"data row11 col0\" >Ridge Classifier</td>\n",
-       "      <td id=\"T_c393c_row11_col1\" class=\"data row11 col1\" >0.8982</td>\n",
-       "      <td id=\"T_c393c_row11_col2\" class=\"data row11 col2\" >0.0000</td>\n",
-       "      <td id=\"T_c393c_row11_col3\" class=\"data row11 col3\" >0.8256</td>\n",
-       "      <td id=\"T_c393c_row11_col4\" class=\"data row11 col4\" >0.9708</td>\n",
-       "      <td id=\"T_c393c_row11_col5\" class=\"data row11 col5\" >0.8913</td>\n",
-       "      <td id=\"T_c393c_row11_col6\" class=\"data row11 col6\" >0.7971</td>\n",
-       "      <td id=\"T_c393c_row11_col7\" class=\"data row11 col7\" >0.8077</td>\n",
-       "      <td id=\"T_c393c_row11_col8\" class=\"data row11 col8\" >0.0030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row12\" class=\"row_heading level0 row12\" >lda</th>\n",
-       "      <td id=\"T_c393c_row12_col0\" class=\"data row12 col0\" >Linear Discriminant Analysis</td>\n",
-       "      <td id=\"T_c393c_row12_col1\" class=\"data row12 col1\" >0.8982</td>\n",
-       "      <td id=\"T_c393c_row12_col2\" class=\"data row12 col2\" >0.9622</td>\n",
-       "      <td id=\"T_c393c_row12_col3\" class=\"data row12 col3\" >0.8256</td>\n",
-       "      <td id=\"T_c393c_row12_col4\" class=\"data row12 col4\" >0.9708</td>\n",
-       "      <td id=\"T_c393c_row12_col5\" class=\"data row12 col5\" >0.8913</td>\n",
-       "      <td id=\"T_c393c_row12_col6\" class=\"data row12 col6\" >0.7971</td>\n",
-       "      <td id=\"T_c393c_row12_col7\" class=\"data row12 col7\" >0.8077</td>\n",
-       "      <td id=\"T_c393c_row12_col8\" class=\"data row12 col8\" >0.0050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c393c_level0_row13\" class=\"row_heading level0 row13\" >dummy</th>\n",
-       "      <td id=\"T_c393c_row13_col0\" class=\"data row13 col0\" >Dummy Classifier</td>\n",
-       "      <td id=\"T_c393c_row13_col1\" class=\"data row13 col1\" >0.5095</td>\n",
-       "      <td id=\"T_c393c_row13_col2\" class=\"data row13 col2\" >0.5000</td>\n",
-       "      <td id=\"T_c393c_row13_col3\" class=\"data row13 col3\" >1.0000</td>\n",
-       "      <td id=\"T_c393c_row13_col4\" class=\"data row13 col4\" >0.5095</td>\n",
-       "      <td id=\"T_c393c_row13_col5\" class=\"data row13 col5\" >0.6751</td>\n",
-       "      <td id=\"T_c393c_row13_col6\" class=\"data row13 col6\" >0.0000</td>\n",
-       "      <td id=\"T_c393c_row13_col7\" class=\"data row13 col7\" >0.0000</td>\n",
-       "      <td id=\"T_c393c_row13_col8\" class=\"data row13 col8\" >0.0030</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x22d8d68fd30>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "best_model_sample = compare_models()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "246f3163",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_b257e_row10_col0, #T_b257e_row10_col1, #T_b257e_row10_col2, #T_b257e_row10_col3, #T_b257e_row10_col4, #T_b257e_row10_col5, #T_b257e_row10_col6 {\n",
-       "  background: yellow;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_b257e\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_b257e_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
-       "      <th id=\"T_b257e_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
-       "      <th id=\"T_b257e_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
-       "      <th id=\"T_b257e_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
-       "      <th id=\"T_b257e_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
-       "      <th id=\"T_b257e_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
-       "      <th id=\"T_b257e_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >Fold</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b257e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_b257e_row0_col0\" class=\"data row0 col0\" >0.9367</td>\n",
-       "      <td id=\"T_b257e_row0_col1\" class=\"data row0 col1\" >0.9692</td>\n",
-       "      <td id=\"T_b257e_row0_col2\" class=\"data row0 col2\" >0.8750</td>\n",
-       "      <td id=\"T_b257e_row0_col3\" class=\"data row0 col3\" >1.0000</td>\n",
-       "      <td id=\"T_b257e_row0_col4\" class=\"data row0 col4\" >0.9333</td>\n",
-       "      <td id=\"T_b257e_row0_col5\" class=\"data row0 col5\" >0.8736</td>\n",
-       "      <td id=\"T_b257e_row0_col6\" class=\"data row0 col6\" >0.8807</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b257e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_b257e_row1_col0\" class=\"data row1 col0\" >0.9873</td>\n",
-       "      <td id=\"T_b257e_row1_col1\" class=\"data row1 col1\" >0.9897</td>\n",
-       "      <td id=\"T_b257e_row1_col2\" class=\"data row1 col2\" >0.9750</td>\n",
-       "      <td id=\"T_b257e_row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
-       "      <td id=\"T_b257e_row1_col4\" class=\"data row1 col4\" >0.9873</td>\n",
-       "      <td id=\"T_b257e_row1_col5\" class=\"data row1 col5\" >0.9747</td>\n",
-       "      <td id=\"T_b257e_row1_col6\" class=\"data row1 col6\" >0.9750</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b257e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_b257e_row2_col0\" class=\"data row2 col0\" >0.9494</td>\n",
-       "      <td id=\"T_b257e_row2_col1\" class=\"data row2 col1\" >0.9833</td>\n",
-       "      <td id=\"T_b257e_row2_col2\" class=\"data row2 col2\" >0.9250</td>\n",
-       "      <td id=\"T_b257e_row2_col3\" class=\"data row2 col3\" >0.9737</td>\n",
-       "      <td id=\"T_b257e_row2_col4\" class=\"data row2 col4\" >0.9487</td>\n",
-       "      <td id=\"T_b257e_row2_col5\" class=\"data row2 col5\" >0.8988</td>\n",
-       "      <td id=\"T_b257e_row2_col6\" class=\"data row2 col6\" >0.8999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b257e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_b257e_row3_col0\" class=\"data row3 col0\" >0.9494</td>\n",
-       "      <td id=\"T_b257e_row3_col1\" class=\"data row3 col1\" >0.9769</td>\n",
-       "      <td id=\"T_b257e_row3_col2\" class=\"data row3 col2\" >0.9500</td>\n",
-       "      <td id=\"T_b257e_row3_col3\" class=\"data row3 col3\" >0.9500</td>\n",
-       "      <td id=\"T_b257e_row3_col4\" class=\"data row3 col4\" >0.9500</td>\n",
-       "      <td id=\"T_b257e_row3_col5\" class=\"data row3 col5\" >0.8987</td>\n",
-       "      <td id=\"T_b257e_row3_col6\" class=\"data row3 col6\" >0.8987</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b257e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_b257e_row4_col0\" class=\"data row4 col0\" >0.9114</td>\n",
-       "      <td id=\"T_b257e_row4_col1\" class=\"data row4 col1\" >0.9603</td>\n",
-       "      <td id=\"T_b257e_row4_col2\" class=\"data row4 col2\" >0.8750</td>\n",
-       "      <td id=\"T_b257e_row4_col3\" class=\"data row4 col3\" >0.9459</td>\n",
-       "      <td id=\"T_b257e_row4_col4\" class=\"data row4 col4\" >0.9091</td>\n",
-       "      <td id=\"T_b257e_row4_col5\" class=\"data row4 col5\" >0.8229</td>\n",
-       "      <td id=\"T_b257e_row4_col6\" class=\"data row4 col6\" >0.8253</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b257e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_b257e_row5_col0\" class=\"data row5 col0\" >0.9747</td>\n",
-       "      <td id=\"T_b257e_row5_col1\" class=\"data row5 col1\" >0.9994</td>\n",
-       "      <td id=\"T_b257e_row5_col2\" class=\"data row5 col2\" >0.9750</td>\n",
-       "      <td id=\"T_b257e_row5_col3\" class=\"data row5 col3\" >0.9750</td>\n",
-       "      <td id=\"T_b257e_row5_col4\" class=\"data row5 col4\" >0.9750</td>\n",
-       "      <td id=\"T_b257e_row5_col5\" class=\"data row5 col5\" >0.9494</td>\n",
-       "      <td id=\"T_b257e_row5_col6\" class=\"data row5 col6\" >0.9494</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b257e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_b257e_row6_col0\" class=\"data row6 col0\" >0.9367</td>\n",
-       "      <td id=\"T_b257e_row6_col1\" class=\"data row6 col1\" >0.9891</td>\n",
-       "      <td id=\"T_b257e_row6_col2\" class=\"data row6 col2\" >0.8780</td>\n",
-       "      <td id=\"T_b257e_row6_col3\" class=\"data row6 col3\" >1.0000</td>\n",
-       "      <td id=\"T_b257e_row6_col4\" class=\"data row6 col4\" >0.9351</td>\n",
-       "      <td id=\"T_b257e_row6_col5\" class=\"data row6 col5\" >0.8738</td>\n",
-       "      <td id=\"T_b257e_row6_col6\" class=\"data row6 col6\" >0.8809</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b257e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_b257e_row7_col0\" class=\"data row7 col0\" >0.9231</td>\n",
-       "      <td id=\"T_b257e_row7_col1\" class=\"data row7 col1\" >0.9822</td>\n",
-       "      <td id=\"T_b257e_row7_col2\" class=\"data row7 col2\" >0.9250</td>\n",
-       "      <td id=\"T_b257e_row7_col3\" class=\"data row7 col3\" >0.9250</td>\n",
-       "      <td id=\"T_b257e_row7_col4\" class=\"data row7 col4\" >0.9250</td>\n",
-       "      <td id=\"T_b257e_row7_col5\" class=\"data row7 col5\" >0.8461</td>\n",
-       "      <td id=\"T_b257e_row7_col6\" class=\"data row7 col6\" >0.8461</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b257e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_b257e_row8_col0\" class=\"data row8 col0\" >0.9103</td>\n",
-       "      <td id=\"T_b257e_row8_col1\" class=\"data row8 col1\" >0.9618</td>\n",
-       "      <td id=\"T_b257e_row8_col2\" class=\"data row8 col2\" >0.8500</td>\n",
-       "      <td id=\"T_b257e_row8_col3\" class=\"data row8 col3\" >0.9714</td>\n",
-       "      <td id=\"T_b257e_row8_col4\" class=\"data row8 col4\" >0.9067</td>\n",
-       "      <td id=\"T_b257e_row8_col5\" class=\"data row8 col5\" >0.8210</td>\n",
-       "      <td id=\"T_b257e_row8_col6\" class=\"data row8 col6\" >0.8278</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b257e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_b257e_row9_col0\" class=\"data row9 col0\" >0.9487</td>\n",
-       "      <td id=\"T_b257e_row9_col1\" class=\"data row9 col1\" >0.9783</td>\n",
-       "      <td id=\"T_b257e_row9_col2\" class=\"data row9 col2\" >0.9000</td>\n",
-       "      <td id=\"T_b257e_row9_col3\" class=\"data row9 col3\" >1.0000</td>\n",
-       "      <td id=\"T_b257e_row9_col4\" class=\"data row9 col4\" >0.9474</td>\n",
-       "      <td id=\"T_b257e_row9_col5\" class=\"data row9 col5\" >0.8976</td>\n",
-       "      <td id=\"T_b257e_row9_col6\" class=\"data row9 col6\" >0.9024</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b257e_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
-       "      <td id=\"T_b257e_row10_col0\" class=\"data row10 col0\" >0.9428</td>\n",
-       "      <td id=\"T_b257e_row10_col1\" class=\"data row10 col1\" >0.9790</td>\n",
-       "      <td id=\"T_b257e_row10_col2\" class=\"data row10 col2\" >0.9128</td>\n",
-       "      <td id=\"T_b257e_row10_col3\" class=\"data row10 col3\" >0.9741</td>\n",
-       "      <td id=\"T_b257e_row10_col4\" class=\"data row10 col4\" >0.9418</td>\n",
-       "      <td id=\"T_b257e_row10_col5\" class=\"data row10 col5\" >0.8857</td>\n",
-       "      <td id=\"T_b257e_row10_col6\" class=\"data row10 col6\" >0.8886</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b257e_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
-       "      <td id=\"T_b257e_row11_col0\" class=\"data row11 col0\" >0.0237</td>\n",
-       "      <td id=\"T_b257e_row11_col1\" class=\"data row11 col1\" >0.0119</td>\n",
-       "      <td id=\"T_b257e_row11_col2\" class=\"data row11 col2\" >0.0419</td>\n",
-       "      <td id=\"T_b257e_row11_col3\" class=\"data row11 col3\" >0.0254</td>\n",
-       "      <td id=\"T_b257e_row11_col4\" class=\"data row11 col4\" >0.0245</td>\n",
-       "      <td id=\"T_b257e_row11_col5\" class=\"data row11 col5\" >0.0474</td>\n",
-       "      <td id=\"T_b257e_row11_col6\" class=\"data row11 col6\" >0.0461</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x22d84dc0f10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gbc = create_model('gbc')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "d15fdf03",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 800x550 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_model(gbc, plot = 'auc')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "9fd7c2b3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_50d83_row10_col0, #T_50d83_row10_col1, #T_50d83_row10_col2, #T_50d83_row10_col3, #T_50d83_row10_col4, #T_50d83_row10_col5, #T_50d83_row10_col6 {\n",
-       "  background: yellow;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_50d83\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_50d83_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
-       "      <th id=\"T_50d83_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
-       "      <th id=\"T_50d83_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
-       "      <th id=\"T_50d83_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
-       "      <th id=\"T_50d83_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
-       "      <th id=\"T_50d83_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
-       "      <th id=\"T_50d83_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >Fold</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_50d83_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_50d83_row0_col0\" class=\"data row0 col0\" >0.9241</td>\n",
-       "      <td id=\"T_50d83_row0_col1\" class=\"data row0 col1\" >0.9647</td>\n",
-       "      <td id=\"T_50d83_row0_col2\" class=\"data row0 col2\" >0.8500</td>\n",
-       "      <td id=\"T_50d83_row0_col3\" class=\"data row0 col3\" >1.0000</td>\n",
-       "      <td id=\"T_50d83_row0_col4\" class=\"data row0 col4\" >0.9189</td>\n",
-       "      <td id=\"T_50d83_row0_col5\" class=\"data row0 col5\" >0.8484</td>\n",
-       "      <td id=\"T_50d83_row0_col6\" class=\"data row0 col6\" >0.8583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_50d83_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_50d83_row1_col0\" class=\"data row1 col0\" >0.9747</td>\n",
-       "      <td id=\"T_50d83_row1_col1\" class=\"data row1 col1\" >0.9891</td>\n",
-       "      <td id=\"T_50d83_row1_col2\" class=\"data row1 col2\" >0.9500</td>\n",
-       "      <td id=\"T_50d83_row1_col3\" class=\"data row1 col3\" >1.0000</td>\n",
-       "      <td id=\"T_50d83_row1_col4\" class=\"data row1 col4\" >0.9744</td>\n",
-       "      <td id=\"T_50d83_row1_col5\" class=\"data row1 col5\" >0.9494</td>\n",
-       "      <td id=\"T_50d83_row1_col6\" class=\"data row1 col6\" >0.9506</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_50d83_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_50d83_row2_col0\" class=\"data row2 col0\" >0.9620</td>\n",
-       "      <td id=\"T_50d83_row2_col1\" class=\"data row2 col1\" >0.9923</td>\n",
-       "      <td id=\"T_50d83_row2_col2\" class=\"data row2 col2\" >0.9250</td>\n",
-       "      <td id=\"T_50d83_row2_col3\" class=\"data row2 col3\" >1.0000</td>\n",
-       "      <td id=\"T_50d83_row2_col4\" class=\"data row2 col4\" >0.9610</td>\n",
-       "      <td id=\"T_50d83_row2_col5\" class=\"data row2 col5\" >0.9241</td>\n",
-       "      <td id=\"T_50d83_row2_col6\" class=\"data row2 col6\" >0.9268</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_50d83_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_50d83_row3_col0\" class=\"data row3 col0\" >0.9747</td>\n",
-       "      <td id=\"T_50d83_row3_col1\" class=\"data row3 col1\" >0.9808</td>\n",
-       "      <td id=\"T_50d83_row3_col2\" class=\"data row3 col2\" >0.9750</td>\n",
-       "      <td id=\"T_50d83_row3_col3\" class=\"data row3 col3\" >0.9750</td>\n",
-       "      <td id=\"T_50d83_row3_col4\" class=\"data row3 col4\" >0.9750</td>\n",
-       "      <td id=\"T_50d83_row3_col5\" class=\"data row3 col5\" >0.9494</td>\n",
-       "      <td id=\"T_50d83_row3_col6\" class=\"data row3 col6\" >0.9494</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_50d83_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_50d83_row4_col0\" class=\"data row4 col0\" >0.9241</td>\n",
-       "      <td id=\"T_50d83_row4_col1\" class=\"data row4 col1\" >0.9558</td>\n",
-       "      <td id=\"T_50d83_row4_col2\" class=\"data row4 col2\" >0.8750</td>\n",
-       "      <td id=\"T_50d83_row4_col3\" class=\"data row4 col3\" >0.9722</td>\n",
-       "      <td id=\"T_50d83_row4_col4\" class=\"data row4 col4\" >0.9211</td>\n",
-       "      <td id=\"T_50d83_row4_col5\" class=\"data row4 col5\" >0.8483</td>\n",
-       "      <td id=\"T_50d83_row4_col6\" class=\"data row4 col6\" >0.8526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_50d83_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_50d83_row5_col0\" class=\"data row5 col0\" >0.9620</td>\n",
-       "      <td id=\"T_50d83_row5_col1\" class=\"data row5 col1\" >0.9968</td>\n",
-       "      <td id=\"T_50d83_row5_col2\" class=\"data row5 col2\" >0.9500</td>\n",
-       "      <td id=\"T_50d83_row5_col3\" class=\"data row5 col3\" >0.9744</td>\n",
-       "      <td id=\"T_50d83_row5_col4\" class=\"data row5 col4\" >0.9620</td>\n",
-       "      <td id=\"T_50d83_row5_col5\" class=\"data row5 col5\" >0.9241</td>\n",
-       "      <td id=\"T_50d83_row5_col6\" class=\"data row5 col6\" >0.9244</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_50d83_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_50d83_row6_col0\" class=\"data row6 col0\" >0.9367</td>\n",
-       "      <td id=\"T_50d83_row6_col1\" class=\"data row6 col1\" >0.9840</td>\n",
-       "      <td id=\"T_50d83_row6_col2\" class=\"data row6 col2\" >0.8780</td>\n",
-       "      <td id=\"T_50d83_row6_col3\" class=\"data row6 col3\" >1.0000</td>\n",
-       "      <td id=\"T_50d83_row6_col4\" class=\"data row6 col4\" >0.9351</td>\n",
-       "      <td id=\"T_50d83_row6_col5\" class=\"data row6 col5\" >0.8738</td>\n",
-       "      <td id=\"T_50d83_row6_col6\" class=\"data row6 col6\" >0.8809</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_50d83_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_50d83_row7_col0\" class=\"data row7 col0\" >0.9359</td>\n",
-       "      <td id=\"T_50d83_row7_col1\" class=\"data row7 col1\" >0.9875</td>\n",
-       "      <td id=\"T_50d83_row7_col2\" class=\"data row7 col2\" >0.9250</td>\n",
-       "      <td id=\"T_50d83_row7_col3\" class=\"data row7 col3\" >0.9487</td>\n",
-       "      <td id=\"T_50d83_row7_col4\" class=\"data row7 col4\" >0.9367</td>\n",
-       "      <td id=\"T_50d83_row7_col5\" class=\"data row7 col5\" >0.8718</td>\n",
-       "      <td id=\"T_50d83_row7_col6\" class=\"data row7 col6\" >0.8721</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_50d83_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_50d83_row8_col0\" class=\"data row8 col0\" >0.9103</td>\n",
-       "      <td id=\"T_50d83_row8_col1\" class=\"data row8 col1\" >0.9658</td>\n",
-       "      <td id=\"T_50d83_row8_col2\" class=\"data row8 col2\" >0.8500</td>\n",
-       "      <td id=\"T_50d83_row8_col3\" class=\"data row8 col3\" >0.9714</td>\n",
-       "      <td id=\"T_50d83_row8_col4\" class=\"data row8 col4\" >0.9067</td>\n",
-       "      <td id=\"T_50d83_row8_col5\" class=\"data row8 col5\" >0.8210</td>\n",
-       "      <td id=\"T_50d83_row8_col6\" class=\"data row8 col6\" >0.8278</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_50d83_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_50d83_row9_col0\" class=\"data row9 col0\" >0.9487</td>\n",
-       "      <td id=\"T_50d83_row9_col1\" class=\"data row9 col1\" >0.9783</td>\n",
-       "      <td id=\"T_50d83_row9_col2\" class=\"data row9 col2\" >0.9000</td>\n",
-       "      <td id=\"T_50d83_row9_col3\" class=\"data row9 col3\" >1.0000</td>\n",
-       "      <td id=\"T_50d83_row9_col4\" class=\"data row9 col4\" >0.9474</td>\n",
-       "      <td id=\"T_50d83_row9_col5\" class=\"data row9 col5\" >0.8976</td>\n",
-       "      <td id=\"T_50d83_row9_col6\" class=\"data row9 col6\" >0.9024</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_50d83_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
-       "      <td id=\"T_50d83_row10_col0\" class=\"data row10 col0\" >0.9453</td>\n",
-       "      <td id=\"T_50d83_row10_col1\" class=\"data row10 col1\" >0.9795</td>\n",
-       "      <td id=\"T_50d83_row10_col2\" class=\"data row10 col2\" >0.9078</td>\n",
-       "      <td id=\"T_50d83_row10_col3\" class=\"data row10 col3\" >0.9842</td>\n",
-       "      <td id=\"T_50d83_row10_col4\" class=\"data row10 col4\" >0.9438</td>\n",
-       "      <td id=\"T_50d83_row10_col5\" class=\"data row10 col5\" >0.8908</td>\n",
-       "      <td id=\"T_50d83_row10_col6\" class=\"data row10 col6\" >0.8945</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_50d83_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
-       "      <td id=\"T_50d83_row11_col0\" class=\"data row11 col0\" >0.0214</td>\n",
-       "      <td id=\"T_50d83_row11_col1\" class=\"data row11 col1\" >0.0127</td>\n",
-       "      <td id=\"T_50d83_row11_col2\" class=\"data row11 col2\" >0.0417</td>\n",
-       "      <td id=\"T_50d83_row11_col3\" class=\"data row11 col3\" >0.0173</td>\n",
-       "      <td id=\"T_50d83_row11_col4\" class=\"data row11 col4\" >0.0228</td>\n",
-       "      <td id=\"T_50d83_row11_col5\" class=\"data row11 col5\" >0.0427</td>\n",
-       "      <td id=\"T_50d83_row11_col6\" class=\"data row11 col6\" >0.0404</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x22da23824c0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tuned_gbc = tune_model(gbc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "1f7fa604",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 800x550 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_model(tuned_gbc, plot = 'auc')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}