Liver_Disease_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>Liver Disease Detection</center>\n",
    "\n",
    "## Overview:\n",
    "\n",
    "Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, intake of contaminated food, pickles and drugs. This dataset was used to evaluate prediction algorithms in an effort to reduce burden on doctors.\n",
    "\n",
    "\n",
    "## Dataset Information:\n",
    "\n",
    "This data set contains 416 liver patient records and 167 non liver patient records collected from North East of Andhra Pradesh, India. The \"Dataset\" column is a class label used to divide groups into liver patient (liver disease) or not (no disease). This data set contains 441 male patient records and 142 female patient records. Any patient whose age exceeded 89 is listed as being of age \"90\".\n",
    "\n",
    "Indian Liver Patient Records - [Kaggle - Liver Disease Detection](https://www.kaggle.com/datasets/uciml/indian-liver-patient-records)\n",
    "\n",
    "### Independent Feature:\n",
    "* Dataset: (Patient with liver disease, or no disease)\n",
    "### Dependent Features:\n",
    "* Age of the patient\n",
    "* Gender of the patient\n",
    "* Total Bilirubin\n",
    "* Direct Bilirubin\n",
    "* Alkaline Phosphotase\n",
    "* Alamine Aminotransferase\n",
    "* Aspartate Aminotransferase\n",
    "* Total Protiens\n",
    "* Albumin\n",
    "* Albumin and Globulin Ratio\n",
    "\n",
    "### Training : Testing ::  564 : 394 :: 80 : 20\n",
    "\n",
    "### Evaluation Metrics Considered:\n",
    "* Accuracy, Precision, Recision, F1 Score, ROC AUC Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Importing Required Libraries\n",
    "* NumPy: For numerical computations and array manipulation.\n",
    "* Pandas: For data manipulation and analysis.\n",
    "* Seaborn: For statistical data visualization.\n",
    "* Matplotlib: For creating plots and visualizations.\n",
    "* Scikit-learn: For machine learning algorithms and evaluation metrics.\n",
    "* XGBoost: For Extreme gradient boosting machine learning.\n",
    "* Catboost: For CatBoosting machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, roc_auc_score, confusion_matrix\n",
    "\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "\n",
    "\n",
    "from evidently import ColumnMapping\n",
    "from evidently.report import Report\n",
    "from evidently.metric_preset import DataDriftPreset, TargetDriftPreset, ClassificationPreset, DataQualityPreset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Ingestion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('Data/Liver.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>60</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>98</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>52</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>245</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>184</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>216</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>583 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0     65  Female              0.7               0.1                   187   \n",
       "1     62    Male             10.9               5.5                   699   \n",
       "2     62    Male              7.3               4.1                   490   \n",
       "3     58    Male              1.0               0.4                   182   \n",
       "4     72    Male              3.9               2.0                   195   \n",
       "..   ...     ...              ...               ...                   ...   \n",
       "578   60    Male              0.5               0.1                   500   \n",
       "579   40    Male              0.6               0.1                    98   \n",
       "580   52    Male              0.8               0.2                   245   \n",
       "581   31    Male              1.3               0.5                   184   \n",
       "582   38    Male              1.0               0.3                   216   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                          16                          18             6.8   \n",
       "1                          64                         100             7.5   \n",
       "2                          60                          68             7.0   \n",
       "3                          14                          20             6.8   \n",
       "4                          27                          59             7.3   \n",
       "..                        ...                         ...             ...   \n",
       "578                        20                          34             5.9   \n",
       "579                        35                          31             6.0   \n",
       "580                        48                          49             6.4   \n",
       "581                        29                          32             6.8   \n",
       "582                        21                          24             7.3   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0        3.3                        0.90        1  \n",
       "1        3.2                        0.74        1  \n",
       "2        3.3                        0.89        1  \n",
       "3        3.4                        1.00        1  \n",
       "4        2.4                        0.40        1  \n",
       "..       ...                         ...      ...  \n",
       "578      1.6                        0.37        2  \n",
       "579      3.2                        1.10        1  \n",
       "580      3.2                        1.00        1  \n",
       "581      3.4                        1.00        1  \n",
       "582      4.4                        1.50        2  \n",
       "\n",
       "[583 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65  Female              0.7               0.1                   187   \n",
       "1   62    Male             10.9               5.5                   699   \n",
       "2   62    Male              7.3               4.1                   490   \n",
       "3   58    Male              1.0               0.4                   182   \n",
       "4   72    Male              3.9               2.0                   195   \n",
       "\n",
       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                        16                          18             6.8   \n",
       "1                        64                         100             7.5   \n",
       "2                        60                          68             7.0   \n",
       "3                        14                          20             6.8   \n",
       "4                        27                          59             7.3   \n",
       "\n",
       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0      3.3                        0.90        1  \n",
       "1      3.2                        0.74        1  \n",
       "2      3.3                        0.89        1  \n",
       "3      3.4                        1.00        1  \n",
       "4      2.4                        0.40        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(583, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                           0\n",
       "Gender                        0\n",
       "Total_Bilirubin               0\n",
       "Direct_Bilirubin              0\n",
       "Alkaline_Phosphotase          0\n",
       "Alamine_Aminotransferase      0\n",
       "Aspartate_Aminotransferase    0\n",
       "Total_Protiens                0\n",
       "Albumin                       0\n",
       "Albumin_and_Globulin_Ratio    4\n",
       "Dataset                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 583 entries, 0 to 582\n",
      "Data columns (total 11 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   Age                         583 non-null    int64  \n",
      " 1   Gender                      583 non-null    object \n",
      " 2   Total_Bilirubin             583 non-null    float64\n",
      " 3   Direct_Bilirubin            583 non-null    float64\n",
      " 4   Alkaline_Phosphotase        583 non-null    int64  \n",
      " 5   Alamine_Aminotransferase    583 non-null    int64  \n",
      " 6   Aspartate_Aminotransferase  583 non-null    int64  \n",
      " 7   Total_Protiens              583 non-null    float64\n",
      " 8   Albumin                     583 non-null    float64\n",
      " 9   Albumin_and_Globulin_Ratio  579 non-null    float64\n",
      " 10  Dataset                     583 non-null    int64  \n",
      "dtypes: float64(5), int64(5), object(1)\n",
      "memory usage: 50.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>583.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44.746141</td>\n",
       "      <td>3.298799</td>\n",
       "      <td>1.486106</td>\n",
       "      <td>290.576329</td>\n",
       "      <td>80.713551</td>\n",
       "      <td>109.910806</td>\n",
       "      <td>6.483190</td>\n",
       "      <td>3.141852</td>\n",
       "      <td>0.947064</td>\n",
       "      <td>1.286449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.189833</td>\n",
       "      <td>6.209522</td>\n",
       "      <td>2.808498</td>\n",
       "      <td>242.937989</td>\n",
       "      <td>182.620356</td>\n",
       "      <td>288.918529</td>\n",
       "      <td>1.085451</td>\n",
       "      <td>0.795519</td>\n",
       "      <td>0.319592</td>\n",
       "      <td>0.452490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>175.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>2110.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>4929.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "count  583.000000       583.000000        583.000000            583.000000   \n",
       "mean    44.746141         3.298799          1.486106            290.576329   \n",
       "std     16.189833         6.209522          2.808498            242.937989   \n",
       "min      4.000000         0.400000          0.100000             63.000000   \n",
       "25%     33.000000         0.800000          0.200000            175.500000   \n",
       "50%     45.000000         1.000000          0.300000            208.000000   \n",
       "75%     58.000000         2.600000          1.300000            298.000000   \n",
       "max     90.000000        75.000000         19.700000           2110.000000   \n",
       "\n",
       "       Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "count                583.000000                  583.000000      583.000000   \n",
       "mean                  80.713551                  109.910806        6.483190   \n",
       "std                  182.620356                  288.918529        1.085451   \n",
       "min                   10.000000                   10.000000        2.700000   \n",
       "25%                   23.000000                   25.000000        5.800000   \n",
       "50%                   35.000000                   42.000000        6.600000   \n",
       "75%                   60.500000                   87.000000        7.200000   \n",
       "max                 2000.000000                 4929.000000        9.600000   \n",
       "\n",
       "          Albumin  Albumin_and_Globulin_Ratio     Dataset  \n",
       "count  583.000000                  579.000000  583.000000  \n",
       "mean     3.141852                    0.947064    1.286449  \n",
       "std      0.795519                    0.319592    0.452490  \n",
       "min      0.900000                    0.300000    1.000000  \n",
       "25%      2.600000                    0.700000    1.000000  \n",
       "50%      3.100000                    0.930000    1.000000  \n",
       "75%      3.800000                    1.100000    2.000000  \n",
       "max      5.500000                    2.800000    2.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset\n",
       "1    416\n",
       "2    167\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Dataset\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Checking Data Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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bzebRc4YNG+bW1osuukjjx4/Xiy++2KO2XKisrEw2m00nT55s9VhaWprS0tK8ejwAgG/84he/kMlkUmpqapuPXzgWbdy4USaTSXv37u2lFnbNsGHDNG/ePH83A0AI4fOzb5k3b56GDRvWref+7//+r2655RYlJSUpMjJSMTExGj16tB555BF9+umnbnV7cq5jMpl0//33d+u57WmrPd05j+yqltdxy09ERIQSExN1xx136OOPP+72fts7f961a1eXz8FDXYS/G4Dg9O6777ptP/bYY9q5c6feeecdt/Krrrqq033l5ubqtttu06233urNJnbJddddp5///OeSpGPHjunnP/+55s6dqzNnzui//uu/vHKMsrIy5eTkaN68ebr44ovdHnv22We9cgwAgO/95je/kSQdPHhQf/rTnzR+/Hg/t6h7tmzZotjYWH83A0AI4fMz8DU3N+vee+/Viy++qGnTpikvL0/Dhg1TQ0ODysvLtWHDBv3mN7/R0aNH/d1Uj7z77ru69NJLfXqMDRs26Morr9SXX36p//u//9MTTzyhnTt36qOPPtKAAQM83l9758/f+MY39O6773bpHDzUkSiDT3zzm9902/7KV76isLCwVuV93cUXX+zW5ilTpmjo0KFavXp1u4mypqYmnT17VhaLpcfH50MMAALD3r179de//lXTp0/Xm2++qfXr1wfsid7o0aP93QQAIYTPz+CwatUqvfjii8rLy9Py5cvdHrvpppu0YsUK/epXv/JT67qvN85fU1NTNXbsWEnnZrU1NTXpkUce0WuvvaZ7773Xa8eJjY0NuPNxf+Grl/CbL774QgsXLtQll1yiyMhIXXbZZVq5cqUcDoerjslk0pkzZ1RYWOiaktoyHfZf//qXFi5cqKuuukoXXXSRBg8erBtvvFF//OMffdbmiy++WCNGjNCRI0ckSYcPH5bJZFJ+fr4ef/xxpaSkyGKxaOfOnZKkrVu3asKECYqOjlZMTIzS09PdZtvZbDb9+Mc/liSlpKS0+kpqW9N/Gxsb9fjjj+vKK6+UxWLRV77yFd17773617/+5VZv2LBhyszM1Pbt2/WNb3xDUVFRuvLKK11X7FrU19dr6dKlSklJUb9+/RQfH6+xY8fqpZde8mbXAUBQW79+vSTpySef1MSJE1VcXKz6+vouPbempkb33nuv4uPj1b9/f82YMUP/+Mc/3Oq093WeC8eJlq9VFBUV6cEHH1RiYqIuuugizZgxQ59//rnq6ur0//1//58GDRqkQYMG6d5779Xp06c7PFbLPl966SWtXLlSSUlJio2N1ZQpU3To0KGudRAAtIPPz67p6rlPy/nJz3/+c61evVopKSm66KKLNGHCBL333nut9rtx40aNGDFCFotFX/va17q1zExjY6Py8/OVmpraKknWIiIiQosWLep0X105Rzzfr371K11xxRWyWCy66qqrVFxc7Pa4zWaTyWRq9byWrz0ePny4w/a097XfnTt36r/+6780aNAgDRw4ULNmzdJnn33WaXxd0ZI0+/zzz11lX375pZYsWaKvf/3riouLU3x8vCZMmKDXX3+9VXvbO39u76uXnZ2zhiISZfCLL7/8UjfccINefPFFZWdn680339Tdd9+t/Px8zZo1y1Xv3XffVVRUlG6++Wa9++67evfdd11fR/ziiy8kSY888ojefPNNbdiwQZdddpnS0tJ89r1rp9OpI0eO6Ctf+Ypb+S9+8Qu98847+vnPf6633npLV155pYqKinTLLbcoNjZWL730ktavX6+amhqlpaVpz549kqT77rtPixcvliS9+uqrrhi/8Y1vtHn85uZm3XLLLXryySc1Z84cvfnmm3ryySdlt9uVlpamhoYGt/p//etftWTJEv33f/+3Xn/9dV199dWaP3++du/e7aqTnZ2tdevW6Uc/+pG2b9+uTZs26bvf/a5OnDjhza4DgKDV0NCgl156Sddee61SU1P1/e9/X3V1dfqf//mfLj1//vz5CgsLU1FRkQoKCvTnP/9ZaWlpba5d2VUPPfSQqqurtXHjRj311FPatWuX7rzzTn3nO99RXFycXnrpJS1btkybNm3SQw891OV9HjlyRL/+9a/1/PPP6+OPP9aMGTPU1NTU7XYCCG18fnb989PTc59f/vKXstvtKigo0G9/+1udOXNGN998s06dOuWqs3HjRt1777362te+pldeeUU/+clP9Nhjj7VaLqcze/fu1cmTJzVjxgyPnnehrp4jtti6dat+8Ytf6NFHH9Xvf/97DR06VHfeead+//vf96gdXXHffffJbDarqKhI+fn52rVrl+6++26v7LuyslKSdMUVV7jKHA6HvvjiCy1dulSvvfaaXnrpJV1//fWaNWuWW3Kzo/PntnTlnDUkGUAvmDt3rtG/f3/X9nPPPWdIMn73u9+51Vu1apUhySgpKXGV9e/f35g7d26nxzh79qzhdDqNyZMnG9/+9rfdHpNkPPLIIx61eejQocbNN99sOJ1Ow+l0GpWVlcbcuXMNScaPf/xjwzAMo7Ky0pBkXH755UZjY6PruU1NTUZSUpIxatQoo6mpyVVeV1dnDB482Jg4caKr7Gc/+5khyaisrGzVhkmTJhmTJk1ybb/00kuGJOOVV15xq1deXm5IMp599lm39vfr1884cuSIq6yhocGIj483FixY4CpLTU01br31Vo/6BgDwHy+++KIhyXjuuecMwzj3WX/RRRcZ/+///T+3eheORRs2bDAktRqz/u///s+QZDz++OOusqFDh7Y5Fl44TuzcudOQZMyYMcOtXlZWliHJ+NGPfuRWfuuttxrx8fFuZRceq2WfN998s1u93/3ud4Yk4913323VLgDoCj4/u//52d65T8v5yahRo4yzZ8+6yv/85z8bkoyXXnrJMIz/nK984xvfMJqbm131Dh8+bJjNZmPo0KFdbktxcbHb7/F8LedSLT/nu/B34Mk5oiQjKirKqKqqcuuTK6+80vjqV7/qKnvkkUeMttIeLa+h88/BLmxPy3Haeu0tXLjQrV5+fr4hyTh+/HirY7WnZV/vvfee4XQ6jbq6OmP79u2G1Wo1vvWtb7Xqr/O1/P7nz59vjB492u2x9s6fW16PO3fuNAzDs3PWUMOMMvjFO++8o/79++u2225zK2+ZqvyHP/yhS/t57rnn9I1vfEP9+vVTRESEzGaz/vCHP+hvf/ubV9q5bds2mc1mmc1mpaSk6He/+50WL16sxx9/3K3ezJkzZTabXduHDh3SZ599pnvuuUdhYf95m1100UX6zne+o/fee6/LU8rP97//+7+6+OKLNWPGDJ09e9b18/Wvf11Wq7XV1aSvf/3rGjJkiGu7X79+uuKKK1xfHZWkcePG6a233tLy5cu1a9euVrPSAAAdW79+vaKionTHHXdIOvdZ/93vfld//OMfu3TXqrvuustte+LEiRo6dKjra/zdkZmZ6bb9ta99TZI0ffr0VuVffPFFq68PtWXmzJlu21dffbUkuY0pAOAJPj89+/z05Nxn+vTpCg8Pb/eYLecrc+bMcftq4tChQzVx4kSP2tWekydPus6lWn46ulOpp+eIkydPVkJCgms7PDxct99+uz755BMdO3bMKzG0x5tj4je/+U2ZzWbFxMTopptu0oABA/T6668rIsJ9Sfn/+Z//0XXXXaeLLrrI9ftfv359t899fXXOGgxIlMEvTpw4IavV2ur74oMHD1ZERESXvvbXsqD++PHj9corr+i9995TeXm5brrpJq8le66//nqVl5dr7969+vDDD3Xy5En94he/UGRkpFu9xMREt+2W9l9YLklJSUlqbm5WTU2Nx+35/PPPdfLkSUVGRrYadKqqqvTvf//brf7AgQNb7cNisbj1zy9+8Qs9+OCDeu2113TDDTcoPj5et956a49uSQwAoeKTTz7R7t27NX36dBmGoZMnT+rkyZOuP/IvXBeyLVartc2ynnwFPj4+3m27Zdxqr/zLL7/sdJ8XjiktN63hAguA7uDz07PPT0/PfTo7ZksftdeHnmi5MH9hkigmJkbl5eUqLy/XI4880ul+PD1H7Kjtvl5Gxptj4osvvqjy8nK98847WrBggf72t7/pzjvvdKvz6quvavbs2brkkku0efNmvfvuuyovL9f3v//9Lr0G2+Krc9ZgwF0v4RcDBw7Un/70JxmG4fZBWF1drbNnz2rQoEGd7mPz5s1KS0vTunXr3Mrr6uq81s64uDjXYoodufDDvOWD8/jx463qfvbZZwoLC+vWrX5bFovcvn17m4/HxMR4vM/+/fsrJydHOTk5+vzzz12zy2bMmKGPPvrI4/0BQCj5zW9+I8Mw9Pvf/77NNVEKCwv1+OOPu13Vv1BVVVWbZV/96ldd2/369WtzIeN///vfXRozAaCv4fPTM94+92k5X2mvDz0xZswYDRgwQG+88YZyc3Nd5eHh4a5zqQMHDnSpTZ6cI3bU9pb4+vXrJ+ncGl8tySxJrSYY+NPXvvY1Vz/dcMMNampq0q9//Wv9/ve/dyWON2/erJSUFL388stufdPeTQ66wlfnrMGAGWXwi8mTJ+v06dN67bXX3MpbFiKcPHmyq+zCGVAtTCaT24edJH3wwQd94g4dI0aM0CWXXKKioiIZhuEqP3PmjF555RXXXUUkz64+ZGZm6sSJE2pqatLYsWNb/YwYMaJH7U5ISNC8efN055136tChQyE71RYAuqKpqUmFhYW6/PLLtXPnzlY/S5Ys0fHjx/XWW291uJ/f/va3bttlZWU6cuSI293Yhg0bpg8++MCt3t///nfuOgkgIPH56Tlvn/uMGDFCiYmJeumll9zOV44cOaKysjKP9hUZGakf//jHOnDggFatWtWt9kienSNK576Kef6dIZuamvTyyy/r8ssv16WXXirp3O9fUqvXwBtvvNHtdvpafn6+BgwYoJ/+9Kdqbm6WdO73HxkZ6ZYkq6qqanXXS6n98+cLeXLOGmqYUQa/+N7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",
      "text/plain": [
       "<Figure size 1500x1500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(15,15), bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Feature Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Dataset'] = df['Dataset'].map(lambda x: 0 if x == 2 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Gender'] = df['Gender'].map(lambda x: 0 if x == 'Male' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>500</td>\n",
       "      <td>20</td>\n",
       "      <td>34</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>98</td>\n",
       "      <td>35</td>\n",
       "      <td>31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>245</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>184</td>\n",
       "      <td>29</td>\n",
       "      <td>32</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>216</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>583 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0     65       1              0.7               0.1                   187   \n",
       "1     62       0             10.9               5.5                   699   \n",
       "2     62       0              7.3               4.1                   490   \n",
       "3     58       0              1.0               0.4                   182   \n",
       "4     72       0              3.9               2.0                   195   \n",
       "..   ...     ...              ...               ...                   ...   \n",
       "578   60       0              0.5               0.1                   500   \n",
       "579   40       0              0.6               0.1                    98   \n",
       "580   52       0              0.8               0.2                   245   \n",
       "581   31       0              1.3               0.5                   184   \n",
       "582   38       0              1.0               0.3                   216   \n",
       "\n",
       "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                          16                          18             6.8   \n",
       "1                          64                         100             7.5   \n",
       "2                          60                          68             7.0   \n",
       "3                          14                          20             6.8   \n",
       "4                          27                          59             7.3   \n",
       "..                        ...                         ...             ...   \n",
       "578                        20                          34             5.9   \n",
       "579                        35                          31             6.0   \n",
       "580                        48                          49             6.4   \n",
       "581                        29                          32             6.8   \n",
       "582                        21                          24             7.3   \n",
       "\n",
       "     Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0        3.3                        0.90        1  \n",
       "1        3.2                        0.74        1  \n",
       "2        3.3                        0.89        1  \n",
       "3        3.4                        1.00        1  \n",
       "4        2.4                        0.40        1  \n",
       "..       ...                         ...      ...  \n",
       "578      1.6                        0.37        0  \n",
       "579      3.2                        1.10        1  \n",
       "580      3.2                        1.00        1  \n",
       "581      3.4                        1.00        1  \n",
       "582      4.4                        1.50        0  \n",
       "\n",
       "[583 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Checking Feature Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Removing Data Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Removing Duplicates (583, 11)\n",
      "After Removing Duplicates (570, 11)\n"
     ]
    }
   ],
   "source": [
    "print(\"Before Removing Duplicates\",df.shape)\n",
    "\n",
    "df = df.drop_duplicates()\n",
    "print(\"After Removing Duplicates\",df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df=df.dropna(how='any')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Training and Testing Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = df.drop('Dataset', axis=1)\n",
    "\n",
    "y = df[\"Dataset\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=20,stratify=df.Dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(566, 10)\n",
      "(452, 10)\n",
      "(114, 10)\n",
      "(566,)\n",
      "(452,)\n",
      "(114,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "\n",
    "print(y.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    'Logistic Regression': LogisticRegression(),\n",
    "    'Decision Tree': DecisionTreeClassifier(),\n",
    "    'Random Forest': RandomForestClassifier(),\n",
    "    'Support Vector Machine': SVC(probability=True),\n",
    "    'Naive Bayes': GaussianNB(),\n",
    "    'Gradient Boosting': GradientBoostingClassifier(),\n",
    "    'XGB': XGBClassifier(),\n",
    "    'KNN':KNeighborsClassifier()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: Logistic Regression\n",
      "Training Accuracy: 0.745575221238938\n",
      "Testing Accuracy: 0.6754385964912281\n",
      "Precision: 0.7115384615384616\n",
      "Recall: 0.9135802469135802\n",
      "F1 Score: 0.8\n",
      "Specificity: 0.09090909090909091\n",
      "ROC AUC Score: 0.5022446689113356\n",
      "==================================================\n",
      "Model: Decision Tree\n",
      "Training Accuracy: 1.0\n",
      "Testing Accuracy: 0.5964912280701754\n",
      "Precision: 0.7333333333333333\n",
      "Recall: 0.6790123456790124\n",
      "F1 Score: 0.7051282051282052\n",
      "Specificity: 0.3939393939393939\n",
      "ROC AUC Score: 0.5364758698092031\n",
      "==================================================\n",
      "Model: Random Forest\n",
      "Training Accuracy: 1.0\n",
      "Testing Accuracy: 0.6929824561403509\n",
      "Precision: 0.7555555555555555\n",
      "Recall: 0.8395061728395061\n",
      "F1 Score: 0.7953216374269005\n",
      "Specificity: 0.3333333333333333\n",
      "ROC AUC Score: 0.5864197530864197\n",
      "==================================================\n",
      "Model: Support Vector Machine\n",
      "Training Accuracy: 0.7367256637168141\n",
      "Testing Accuracy: 0.7017543859649122\n",
      "Precision: 0.7117117117117117\n",
      "Recall: 0.9753086419753086\n",
      "F1 Score: 0.8229166666666666\n",
      "Specificity: 0.030303030303030304\n",
      "ROC AUC Score: 0.5028058361391694\n",
      "==================================================\n",
      "Model: Naive Bayes\n",
      "Training Accuracy: 0.5420353982300885\n",
      "Testing Accuracy: 0.5964912280701754\n",
      "Precision: 0.972972972972973\n",
      "Recall: 0.4444444444444444\n",
      "F1 Score: 0.6101694915254238\n",
      "Specificity: 0.9696969696969697\n",
      "ROC AUC Score: 0.7070707070707071\n",
      "==================================================\n",
      "Model: Gradient Boosting\n",
      "Training Accuracy: 0.9380530973451328\n",
      "Testing Accuracy: 0.6666666666666666\n",
      "Precision: 0.7415730337078652\n",
      "Recall: 0.8148148148148148\n",
      "F1 Score: 0.7764705882352941\n",
      "Specificity: 0.30303030303030304\n",
      "ROC AUC Score: 0.5589225589225588\n",
      "==================================================\n",
      "Model: XGB\n",
      "Training Accuracy: 1.0\n",
      "Testing Accuracy: 0.7280701754385965\n",
      "Precision: 0.7906976744186046\n",
      "Recall: 0.8395061728395061\n",
      "F1 Score: 0.8143712574850299\n",
      "Specificity: 0.45454545454545453\n",
      "ROC AUC Score: 0.6470258136924804\n",
      "==================================================\n",
      "Model: KNN\n",
      "Training Accuracy: 0.8030973451327433\n",
      "Testing Accuracy: 0.6491228070175439\n",
      "Precision: 0.7733333333333333\n",
      "Recall: 0.7160493827160493\n",
      "F1 Score: 0.7435897435897436\n",
      "Specificity: 0.48484848484848486\n",
      "ROC AUC Score: 0.6004489337822672\n",
      "==================================================\n",
      "\n",
      "The best model based on accuracy is XGB with Testing Accuracy: 0.7280701754385965\n"
     ]
    }
   ],
   "source": [
    "model_list = []\n",
    "train_accuracy_list = []\n",
    "test_accuracy_list = []\n",
    "precision_list = []\n",
    "recall_list = []\n",
    "f1_list = []\n",
    "specificity_list = []\n",
    "roc_auc_list = []\n",
    "\n",
    "for model_name, model in models.items():\n",
    "\n",
    "    model.fit(x_train, y_train)\n",
    "    y_pred_train = model.predict(x_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "\n",
    "    train_accuracy = accuracy_score(y_train, y_pred_train)\n",
    "    test_accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "    tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "    specificity = tn / (tn + fp)\n",
    "    roc_auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "    print(f\"Model: {model_name}\")\n",
    "    print(f\"Training Accuracy: {train_accuracy}\")\n",
    "    print(f\"Testing Accuracy: {test_accuracy}\")\n",
    "    print(f\"Precision: {precision}\")\n",
    "    print(f\"Recall: {recall}\")\n",
    "    print(f\"F1 Score: {f1}\")\n",
    "    print(f\"Specificity: {specificity}\")\n",
    "    print(f\"ROC AUC Score: {roc_auc}\")\n",
    "\n",
    "    model_list.append(model_name)\n",
    "    train_accuracy_list.append(train_accuracy)\n",
    "    test_accuracy_list.append(test_accuracy)\n",
    "    precision_list.append(precision)\n",
    "    recall_list.append(recall)\n",
    "    f1_list.append(f1)\n",
    "    specificity_list.append(specificity)\n",
    "    roc_auc_list.append(roc_auc)\n",
    "\n",
    "    print(\"=\" * 50)\n",
    "\n",
    "max_accuracy_index = test_accuracy_list.index(max(test_accuracy_list))\n",
    "print(f\"\\nThe best model based on accuracy is {model_list[max_accuracy_index]} with Testing Accuracy: {test_accuracy_list[max_accuracy_index]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall (Sensitivity)</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>ROC AUC Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XGB</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.728070</td>\n",
       "      <td>0.790698</td>\n",
       "      <td>0.839506</td>\n",
       "      <td>0.814371</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.647026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.736726</td>\n",
       "      <td>0.701754</td>\n",
       "      <td>0.711712</td>\n",
       "      <td>0.975309</td>\n",
       "      <td>0.822917</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.502806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692982</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.839506</td>\n",
       "      <td>0.795322</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.586420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.745575</td>\n",
       "      <td>0.675439</td>\n",
       "      <td>0.711538</td>\n",
       "      <td>0.913580</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.502245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.938053</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.741573</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.776471</td>\n",
       "      <td>0.303030</td>\n",
       "      <td>0.558923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.803097</td>\n",
       "      <td>0.649123</td>\n",
       "      <td>0.773333</td>\n",
       "      <td>0.716049</td>\n",
       "      <td>0.743590</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.600449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.542035</td>\n",
       "      <td>0.596491</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.610169</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.707071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.596491</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.679012</td>\n",
       "      <td>0.705128</td>\n",
       "      <td>0.393939</td>\n",
       "      <td>0.536476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Train Accuracy  Test Accuracy  Precision  \\\n",
       "6                     XGB        1.000000       0.728070   0.790698   \n",
       "3  Support Vector Machine        0.736726       0.701754   0.711712   \n",
       "2           Random Forest        1.000000       0.692982   0.755556   \n",
       "0     Logistic Regression        0.745575       0.675439   0.711538   \n",
       "5       Gradient Boosting        0.938053       0.666667   0.741573   \n",
       "7                     KNN        0.803097       0.649123   0.773333   \n",
       "4             Naive Bayes        0.542035       0.596491   0.972973   \n",
       "1           Decision Tree        1.000000       0.596491   0.733333   \n",
       "\n",
       "   Recall (Sensitivity)  F1 Score  Specificity  ROC AUC Score  \n",
       "6              0.839506  0.814371     0.454545       0.647026  \n",
       "3              0.975309  0.822917     0.030303       0.502806  \n",
       "2              0.839506  0.795322     0.333333       0.586420  \n",
       "0              0.913580  0.800000     0.090909       0.502245  \n",
       "5              0.814815  0.776471     0.303030       0.558923  \n",
       "7              0.716049  0.743590     0.484848       0.600449  \n",
       "4              0.444444  0.610169     0.969697       0.707071  \n",
       "1              0.679012  0.705128     0.393939       0.536476  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = {\n",
    "    'Model': model_list,\n",
    "    'Train Accuracy': train_accuracy_list,\n",
    "    'Test Accuracy': test_accuracy_list,\n",
    "    'Precision': precision_list,\n",
    "    'Recall (Sensitivity)': recall_list,\n",
    "    'F1 Score': f1_list,\n",
    "    'Specificity': specificity_list,\n",
    "    'ROC AUC Score': roc_auc_list\n",
    "}\n",
    "\n",
    "results = pd.DataFrame(results).sort_values(by=['Test Accuracy', 'Precision', 'Recall (Sensitivity)'], ascending=False)\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Hyperparamter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'Logistic Regression': {\n",
    "        'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "        'penalty': ['l1', 'l2', 'elasticnet', 'none'],\n",
    "        'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n",
    "    },\n",
    "    'Decision Tree': {\n",
    "        'criterion': ['gini', 'entropy'],\n",
    "        'max_depth': [None, 5, 10, 15, 20],\n",
    "        'min_samples_split': [2, 5, 10],\n",
    "        'min_samples_leaf': [1, 2, 4]\n",
    "    },\n",
    "    'Random Forest': {\n",
    "        'n_estimators': [100, 200, 300],\n",
    "        'max_depth': [10, 15, 20],\n",
    "    },\n",
    "    'Support Vector Machine': {\n",
    "        'C': [0.1, 1, 10],\n",
    "        'kernel': ['linear', 'rbf'],\n",
    "        'gamma': ['scale', 'auto']\n",
    "    },\n",
    "    'Naive Bayes': {},\n",
    "    'Gradient Boosting': {\n",
    "        'n_estimators': [50, 100, 150],\n",
    "        'learning_rate': [0.01, 0.1, 0.5],\n",
    "    },\n",
    "    'XGB': {\n",
    "        'n_estimators': [50, 100, 150],\n",
    "        'learning_rate': [0.01, 0.1, 0.5],\n",
    "\n",
    "    },\n",
    "    'KNN': {\n",
    "        'n_neighbors': [3, 5, 7],\n",
    "        'weights': ['uniform', 'distance'],\n",
    "        'metric': ['euclidean', 'manhattan', 'minkowski']\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best parameters for Logistic Regression: {'C': 100, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "Train Accuracy for Logistic Regression: 0.7433628318584071\n",
      "Test Accuracy for Logistic Regression: 0.7017543859649122\n",
      "Precision for Logistic Regression: 0.7238095238095238\n",
      "Recall for Logistic Regression: 0.9382716049382716\n",
      "F1 Score for Logistic Regression: 0.8172043010752689\n",
      "Specificity for Logistic Regression: 0.12121212121212122\n",
      "ROC AUC Score for Logistic Regression: 0.5297418630751963\n",
      "==================================================\n",
      "\n",
      "Best parameters for Decision Tree: {'criterion': 'entropy', 'max_depth': 5, 'min_samples_leaf': 4, 'min_samples_split': 10}\n",
      "Train Accuracy for Decision Tree: 0.754424778761062\n",
      "Test Accuracy for Decision Tree: 0.6666666666666666\n",
      "Precision for Decision Tree: 0.7047619047619048\n",
      "Recall for Decision Tree: 0.9135802469135802\n",
      "F1 Score for Decision Tree: 0.7956989247311828\n",
      "Specificity for Decision Tree: 0.06060606060606061\n",
      "ROC AUC Score for Decision Tree: 0.4870931537598204\n",
      "==================================================\n",
      "\n",
      "Best parameters for Random Forest: {'max_depth': 15, 'n_estimators': 200}\n",
      "Train Accuracy for Random Forest: 1.0\n",
      "Test Accuracy for Random Forest: 0.6929824561403509\n",
      "Precision for Random Forest: 0.75\n",
      "Recall for Random Forest: 0.8518518518518519\n",
      "F1 Score for Random Forest: 0.7976878612716763\n",
      "Specificity for Random Forest: 0.30303030303030304\n",
      "ROC AUC Score for Random Forest: 0.5774410774410774\n",
      "==================================================\n",
      "\n",
      "Best parameters for Support Vector Machine: {'C': 0.1, 'gamma': 'scale', 'kernel': 'linear'}\n",
      "Train Accuracy for Support Vector Machine: 0.7146017699115044\n",
      "Test Accuracy for Support Vector Machine: 0.7105263157894737\n",
      "Precision for Support Vector Machine: 0.7105263157894737\n",
      "Recall for Support Vector Machine: 1.0\n",
      "F1 Score for Support Vector Machine: 0.8307692307692308\n",
      "Specificity for Support Vector Machine: 0.0\n",
      "ROC AUC Score for Support Vector Machine: 0.5\n",
      "==================================================\n",
      "\n",
      "Best parameters for Naive Bayes: {}\n",
      "Train Accuracy for Naive Bayes: 0.5420353982300885\n",
      "Test Accuracy for Naive Bayes: 0.5964912280701754\n",
      "Precision for Naive Bayes: 0.972972972972973\n",
      "Recall for Naive Bayes: 0.4444444444444444\n",
      "F1 Score for Naive Bayes: 0.6101694915254238\n",
      "Specificity for Naive Bayes: 0.9696969696969697\n",
      "ROC AUC Score for Naive Bayes: 0.7070707070707071\n",
      "==================================================\n",
      "\n",
      "Best parameters for Gradient Boosting: {'learning_rate': 0.01, 'n_estimators': 100}\n",
      "Train Accuracy for Gradient Boosting: 0.745575221238938\n",
      "Test Accuracy for Gradient Boosting: 0.7105263157894737\n",
      "Precision for Gradient Boosting: 0.7105263157894737\n",
      "Recall for Gradient Boosting: 1.0\n",
      "F1 Score for Gradient Boosting: 0.8307692307692308\n",
      "Specificity for Gradient Boosting: 0.0\n",
      "ROC AUC Score for Gradient Boosting: 0.5\n",
      "==================================================\n",
      "\n",
      "Best parameters for XGB: {'learning_rate': 0.01, 'n_estimators': 50}\n",
      "Train Accuracy for XGB: 0.7146017699115044\n",
      "Test Accuracy for XGB: 0.7105263157894737\n",
      "Precision for XGB: 0.7105263157894737\n",
      "Recall for XGB: 1.0\n",
      "F1 Score for XGB: 0.8307692307692308\n",
      "Specificity for XGB: 0.0\n",
      "ROC AUC Score for XGB: 0.5\n",
      "==================================================\n",
      "\n",
      "Best parameters for KNN: {'metric': 'manhattan', 'n_neighbors': 7, 'weights': 'distance'}\n",
      "Train Accuracy for KNN: 1.0\n",
      "Test Accuracy for KNN: 0.6228070175438597\n",
      "Precision for KNN: 0.7317073170731707\n",
      "Recall for KNN: 0.7407407407407407\n",
      "F1 Score for KNN: 0.7361963190184049\n",
      "Specificity for KNN: 0.3333333333333333\n",
      "ROC AUC Score for KNN: 0.537037037037037\n",
      "==================================================\n",
      "We got Support Vector Machine as the model with the highest test accuracy of 0.7105263157894737\n"
     ]
    }
   ],
   "source": [
    "modelss = []\n",
    "train_accuracies = []\n",
    "test_accuracies = []\n",
    "precisions = []\n",
    "recalls = []\n",
    "f1_scores = []\n",
    "specificities = []\n",
    "roc_auc_scores = []\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    if model_name in param_grid:\n",
    "        grid_search = GridSearchCV(model, param_grid[model_name], cv=5, n_jobs=-1)\n",
    "\n",
    "        grid_search.fit(x_train, y_train)\n",
    "        best_model_cv = grid_search.best_estimator_\n",
    "        best_params = grid_search.best_params_\n",
    "        print(f\"\\nBest parameters for {model_name}: {best_params}\")\n",
    "\n",
    "        y_train_pred = best_model_cv.predict(x_train)\n",
    "        y_pred = best_model_cv.predict(x_test)\n",
    "\n",
    "        train_accuracy = accuracy_score(y_train, y_train_pred)\n",
    "        test_accuracy = accuracy_score(y_test, y_pred)\n",
    "        precision = precision_score(y_test, y_pred)\n",
    "        recall = recall_score(y_test, y_pred)\n",
    "        f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "        tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "        specificity = tn / (tn + fp)\n",
    "        roc_auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "        modelss.append(model_name)\n",
    "        train_accuracies.append(train_accuracy)\n",
    "        test_accuracies.append(test_accuracy)\n",
    "        precisions.append(precision)\n",
    "        recalls.append(recall)\n",
    "        f1_scores.append(f1)\n",
    "        specificities.append(specificity)\n",
    "        roc_auc_scores.append(roc_auc)\n",
    "\n",
    "        print(f\"Train Accuracy for {model_name}: {train_accuracy}\")\n",
    "        print(f\"Test Accuracy for {model_name}: {test_accuracy}\")\n",
    "        print(f\"Precision for {model_name}: {precision}\")\n",
    "        print(f\"Recall for {model_name}: {recall}\")\n",
    "        print(f\"F1 Score for {model_name}: {f1}\")\n",
    "        print(f\"Specificity for {model_name}: {specificity}\")\n",
    "        print(f\"ROC AUC Score for {model_name}: {roc_auc}\")\n",
    "        print(\"=\" * 50)\n",
    "\n",
    "best_test_accuracy_index = test_accuracies.index(max(test_accuracies))\n",
    "print(f\"We got {modelss[best_test_accuracy_index]} as the model with the highest test accuracy of {test_accuracies[best_test_accuracy_index]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall (Sensitivity)</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC AUC Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.714602</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830769</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.745575</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830769</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XGB</td>\n",
       "      <td>0.714602</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>0.710526</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830769</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.743363</td>\n",
       "      <td>0.701754</td>\n",
       "      <td>0.723810</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.817204</td>\n",
       "      <td>0.529742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.692982</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.851852</td>\n",
       "      <td>0.797688</td>\n",
       "      <td>0.577441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.754425</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.704762</td>\n",
       "      <td>0.913580</td>\n",
       "      <td>0.795699</td>\n",
       "      <td>0.487093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KNN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.622807</td>\n",
       "      <td>0.731707</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.736196</td>\n",
       "      <td>0.537037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.542035</td>\n",
       "      <td>0.596491</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.610169</td>\n",
       "      <td>0.707071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Train Accuracy  Test Accuracy  Precision  \\\n",
       "3  Support Vector Machine        0.714602       0.710526   0.710526   \n",
       "5       Gradient Boosting        0.745575       0.710526   0.710526   \n",
       "6                     XGB        0.714602       0.710526   0.710526   \n",
       "0     Logistic Regression        0.743363       0.701754   0.723810   \n",
       "2           Random Forest        1.000000       0.692982   0.750000   \n",
       "1           Decision Tree        0.754425       0.666667   0.704762   \n",
       "7                     KNN        1.000000       0.622807   0.731707   \n",
       "4             Naive Bayes        0.542035       0.596491   0.972973   \n",
       "\n",
       "   Recall (Sensitivity)  F1 Score  ROC AUC Score  \n",
       "3              1.000000  0.830769       0.500000  \n",
       "5              1.000000  0.830769       0.500000  \n",
       "6              1.000000  0.830769       0.500000  \n",
       "0              0.938272  0.817204       0.529742  \n",
       "2              0.851852  0.797688       0.577441  \n",
       "1              0.913580  0.795699       0.487093  \n",
       "7              0.740741  0.736196       0.537037  \n",
       "4              0.444444  0.610169       0.707071  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results1 = {\n",
    "    'Model': modelss,\n",
    "    'Train Accuracy': train_accuracies,\n",
    "    'Test Accuracy': test_accuracies,\n",
    "    'Precision': precisions,\n",
    "    'Recall (Sensitivity)': recalls,\n",
    "    'F1 Score': f1_scores,\n",
    "    'ROC AUC Score': roc_auc_scores\n",
    "}\n",
    "results1 = pd.DataFrame(results1).sort_values(by=['Test Accuracy', 'Precision', 'Recall (Sensitivity)'], ascending=False)\n",
    "results1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "results1.to_csv(\"Liver_Evaluation_Metrics.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC AUC Curve Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yZDZu3HhScRYXF5OYmOi11ON4HM/76FSEhIQ0uVRi5syZGAwGzxrwzMxMfv31V6+CkQ0/70aMGOHz3H/44YdH/fkkhBBthazpFkKIFpaamuopnjZx4kScTif/+c9/+OSTT5gxYwZxcXGA+xf2pgpYASQnJwPuddcNo7ZHEhcXR3BwMG+++eYR24/muuuu45lnnvGsKf/qq6+488470Wq1XvcYOHAgTz75ZJP3SExM9Pr+eNeVnnXWWcydO5cvvvjCZyS3QcO+12eddZbX8YKCAp9zG441JI3NEfeiRYvo1asXH374oVf74cXO/CU2NpYNGzb4HG/q8Tfl7LPP5l//+hfr169v1grqJ/q8NPXcfvDBBwQFBfHNN994ffhw+F7n8fHxgLug3OEfvhyPuLg4YmNjj1gBPzw8/JixNkWn03H33Xdz9913U1FRwY8//sjcuXM5++yz2bdv3wlX+I6Pj2f16tW4XK4TSryHDx9OTEwMX375JQsWLDhm/A3P9eH/VkdKgI90v+joaC644ALeffddnnjiCd566y2MRqPXh4wNP38++eQTkpKSjvsxCSFEWyIj3UIIEWBPP/000dHRPPzww7hcLpKTk+nXrx+bN29m+PDhTX41JAHTpk1j+fLlXtM3D3fuueeyc+dOYmNjm7zX4XsDHy41NZXTTjuNt956i/feew+r1eqztVnD/sd9+vRpso/Dk9fjNXz4cKZMmcLChQtZs2aNT/vq1at58803mTp1qlcRNYBly5Z5VXB3Op18+OGH9OnTxzMi2hxxK4qCXq/3SjwKCgqarNJ9+GhwczjjjDOorq5myZIlXsebqtTelLvuuovQ0FBuueUWKisrfdpVVT2pLcNO5Hk52j10Op3XBzz19fX897//9TpvypQpaLVaXnnllaPe70jP/7nnnktpaSlOp7PJ10HDh1ynIioqihkzZnDrrbdSVlbmmaHRsOXc8bwupk2bhsViOWZF+sMFBQXx97//nW3btvH44483eU5RUZHnPdbwM2HLli1e53z11Vcn1C+4P7Q7cOAA3377LYsWLeLCCy8kKirK03722Wej0+nYuXPnEX/eCSFEWycj3UIIEWDR0dHMmTOHv/3tb7z33ntcddVVvPbaa0ybNo2zzz6ba6+9lq5du1JWVkZWVhabNm3i448/BmDevHksWbKE8ePHM3fuXAYMGEBFRQXfffcdd999NykpKdx55518+umnjB8/nrvuuouBAwficrnYu3cvP/zwA/fccw+nnXbaUWO8/vrrmT17NgcOHGD06NE+Sci8efNYunQpo0eP5o477iA5ORmLxcKePXv49ttvefXVV0966u+7777LmWeeyZQpU7jjjjuYPHky4F7r++KLL5KSktJkEhIXF8ekSZN46KGHPNXLt23b5pWMNkfcDdtH3XLLLcyYMYN9+/bx+OOP06VLF3bs2OF17oABA1ixYgVff/01Xbp0ITw8/JQTulmzZvH8889z1VVX8cQTT9C3b1+WLFnC999/D3DMEdFevXp5ZjEMHjyY2267zbPfdGZmJm+++SaqqnLhhReeUFwn8rwcyTnnnMNzzz3HlVdeyZ///GdKS0t59tlnffZG79mzJ3PnzuXxxx+nvr6eK664gsjISDIzMykpKfFs6TVgwAA+++wzXnnlFYYNG4ZGo2H48OFcfvnlLF68mOnTp/PXv/6VkSNHEhQURF5eHsuXL+eCCy444ccP7v2vTSYTw4cPJz4+ntzcXF544QWSkpI8FfsHDBgAwIsvvsisWbMICgoiOTnZZ3Qd3Ov033rrLW6++Ways7OZOHEiLpeLX375hdTUVC6//PIjxnLfffeRlZXFI488woYNG7jyyivp3r07lZWVrFy5ktdff53HHnuMMWPGkJCQwJlnnsmCBQuIjo4mKSmJZcuW8dlnn53wczBlyhS6devGLbfcQkFBgc8Hdj179mTevHk88MAD7Nq1y1PnorCwkA0bNhAaGur59xNCiDYrsHXchBCi42ioXn54FXFVVdX6+nq1R48ear9+/VSHw6Gqqqpu3rxZvfTSS9VOnTqpQUFBakJCgjpp0iSfCsT79u1Tr7/+ejUhIUENCgpSExMT1UsvvVQtLCz0nFNTU6M++OCDanJysqrX69XIyEh1wIAB6l133eVV4fvw6ssNKisr1eDg4KNWTi4uLlbvuOMOtVevXmpQUJAaExOjDhs2TH3ggQfUmpoaVVUbqx8/88wzJ/Tc1dTUqPPnz1cHDx6shoSEqCEhIerAgQPVJ554wnPvQwHqrbfeqr788stqnz591KCgIDUlJUVdvHixX+J+6qmn1J49e6oGg0FNTU1V33jjDfWRRx5RD//f7B9//KGOGTNGDQkJUQFPZeojVS8PDQ316aup++7du1e96KKL1LCwMDU8PFy9+OKL1W+//VYF1C+//PKoz22DnTt3qrfccovat29f1WAwqMHBwWpaWpp69913e1XWPuOMM9T09HSf62fNmuVTGfx4n5eGf6+mvPnmm2pycrJqMBjU3r17qwsWLFAXLlzYZMXvd999Vx0xYoRqNBrVsLAwdciQIV6VtsvKytQZM2aoUVFRqqIoXnHY7Xb12WefVQcNGuS5PiUlRZ09e7a6Y8cOz3lJSUnqOeec02Ssh79//vnPf6qjR49W4+LiVL1er/bo0UO94YYb1D179nhdN2fOHDUxMVHVaDRer4PDq5erqvtnxcMPP6z269dP1ev1amxsrDpp0iR17dq1TcZ0uC+//FI955xz1Pj4eFWn06nR0dHqxIkT1VdffVW1Wq2e8/Lz89UZM2aoMTExamRkpHrVVVepGzdubLJ6eVOv00PNnTtXBdTu3bt7dl443BdffKFOnDhRjYiIUA0Gg5qUlKTOmDFD/fHHH4/rcQkhRGumqOrBsq9CCCFEO6EoCrfeeiv//ve/Ax1KwDTsfbx3796TnmUghBBCiFMn08uFEEKINq7hw4WUlBTsdjs//fQTL730EldddZUk3EIIIUSASdIthBBCtHEhISE8//zz7NmzB6vVSo8ePfj73//Ogw8+GOjQhBBCiA5PppcLIYQQQgghhBB+IluGCSGEEEIIIYQQfiJJtxBCCCGEEEII4SeSdAshhBBCCCGEEH7S4QqpuVwuDhw4QHh4OIqiBDocIYQQQgghhBCthKqqVFdXk5iYiEbTPGPUHS7pPnDgAN27dw90GEIIIYQQQgghWql9+/Y127abHS7pDg8PB9xPYkRERICjEUIIIYQQQgjRWlRVVdG9e3dP3tgcOlzS3TClPCIiQpJuIYQQQgghhBA+mnMpshRSE0IIIYQQQggh/ESSbiGEEEIIIYQQwk8k6RZCCCGEEEIIIfxEkm4hhBBCCCGEEMJPJOkWQgghhBBCCCH8RJJuIYQQQgghhBDCTyTpFkIIIYQQQggh/ESSbiGEEEIIIYQQwk8k6RZCCCGEEEIIIfxEkm4hhBBCCCGEEMJPJOkWQgghhBBCCCH8RJJuIYQQQgghhBDCTyTpFkIIIYQQQggh/ESSbiGEEEIIIYQQwk8k6RZCCCGEEEIIIfxEkm4hhBBCCCGEEMJPJOkWQgghhBBCCCH8JKBJ98qVKznvvPNITExEURS++OKLY17z888/M2zYMIxGI7179+bVV1/1f6BCCCGEEEIIIcRJCGjSXVtby6BBg/j3v/99XOfv3r2b6dOnM27cOH7//Xfmzp3LHXfcwaeffurnSIUQQgghhBBCiBOnC2Tn06ZNY9q0acd9/quvvkqPHj144YUXAEhNTWXjxo08++yzXHzxxX6KUgghhBBCCCFEh+ByNfstA5p0n6h169YxZcoUr2Nnn302CxcuxG63ExQU5HON1WrFarV6vq+qqvJ7nEIIIYQQQgjRUrLXrWbtR4uwWeoDHUqzq3PUUWuvRVVVv9xfVTSgD8GlD0NrCMOli2r2PtpU0l1QUEDnzp29jnXu3BmHw0FJSQldunTxuWbBggU89thjLRWiEEIIIYQQQrSotR8touxAXqDD8JvgZloVrQKq3oAzOAxncChOYyhOQzDbd2hYsQIuvhjC9dZj3eaEtamkG0BRFK/vGz7xOPx4gzlz5nD33Xd7vq+qqqJ79+7+C1AIIYQQQgghWlDDCLeiaAiNjg5wNM2rqK4IFRUFBa2iPaFrVY0W1RCKyxCGaghF1YeCtjEFzs2FH3+EvDx3LvnTTyoXnd90Xnkq2lTSnZCQQEFBgdexoqIidDodsbGxTV5jMBgwGAwtEZ4QQgghhBBCBExodDSzX3kn0GE0q5GLR1LvqKdvVF8+v+DzI57ncDgoLCxk//795OXlkZeXR1lZWZPnFhTAsmWQk+OdYOurrEyvHMWTzfoI2ljSffrpp/P11197Hfvhhx8YPnx4k+u5hRBCCCGEEEK0L6qqUllZ6Umu9+/fT35+Pg6H46jXWcrqWLncxTpzuNfx1D7BzH/6XMaWrWPfluZPkQOadNfU1JCTk+P5fvfu3fzxxx/ExMTQo0cP5syZw/79+3n33XcBuPnmm/n3v//N3XffzU033cS6detYuHAh77//fqAeghBCCCGEEEIIP9K5dIRWh7Jq1SpPkl1TU3PUa7QahS66KrrZdhBtK+KNpUm8sykZh6txinr3bmHMe3wyV189EK1WQ+nC9f6J3y93PU4bN25k4sSJnu8b1l7PmjWLt99+m/z8fPbu3etp79WrF99++y133XUX//d//0diYiIvvfSSbBcmhBBCCCGEaFVasqJ4bXm53/toUFRXxK3LbmV7+Xb/dKBCuD2cGEsMMdYYppaPRqONAEXDspxlR7wsrLqG2LJSYkpLiS0tIaqiAu3B7b+cqsKK3V09CXe01sLsuCwuC92J4ek32f70wZu4XDDl2WZ/SAFNuidMmHDU0u9vv/22z7EzzjiDTZs2+TEqIYQQQgghhDg1gagorjcG+72PH3N/ZFvZtma7n8Fp8CTYMdYYoq3RBKmHLB1uImMNstmIKSsjtqSU2NJSYsrKMB6yTbRdVdAqKuBes60F7ozbwt/zR3FtTDbXRm8jTHtwKnrzb8vto02t6RZCCCGEEEKItqClK4rrjcGMuewqv/djc9o8f+8e3p1IfeRxX6u4FAx1BoJrgjHWGjHWGtHb9Me4xkVkZSWx5eUkGAzE19UTYbOh2GqhtgRsZRDqglBwqAqfFPXlX3kDeXXQJgZ00YM+DIDpKoy27STaoACpR+zPQgevXi6EEEIIIYQQbUl7rCje4K5hd3FW0llNtqmqSllZmVc18YKCAlyuow8th4eH061bN7p160bXrl2pueRStNXVGPr1o/eHb8OWj2DTO1CU6bnGpcKnmf14cNUktheGAvB/0dfy/Zcn/iHEpr8uOeFrjkWSbiGEEEIIIYQQp6S+vt6TYDf8WV9/9PXsOp2Orl270rVrV0+SHRnpPXK+zelABajaD/9MAWfjNHJVhR/3pTBn1RR+y/FObYODddTX2wkODvwuV5J0CyGEEEIIIYQ4boqqEGGLIH9bPp9v+pz9+/dTUlJyzOvi4uI8yXW3bt3o1KkTWq226ZNriuCP98B2MHG3VHol3Bvsk5mzdBQ//eqd2I8fn8RTT03m9NO7n/Tja26KerRKZu1QVVUVkZGRVFZWEhEREehwhBBCCCGEEC2gbksxVUtzUa3OFumvpqIc1eVC0WgIi/Lvmu56u5MaqwN/ZXb1WCjTVFGmVFGuqaBCqcapHL0zvRpEtCuCGDWCGFckUWo4eo496qzHhlG1YsCGAlitRhw6I6CAAtVWJ/cvyeaHHcVe16XEh3H3+N6M7xWDopz8umyHrZb0F6c3a74oI91CCCGEEEKIdq9qaS6OYv9v39UgWBMKGvffnVW2o598ivRATDMVALPjpESpokhTRbGmkiJNFXWK9ajXaFSFWDWceFcEnVyRdFIjCFeDUU4qJgNg8BQV1xm8k1aDUUNhjcXzfY8oI/eN6835qZ3RnEKy3aC6Ge5xOEm6hRBCCCGEEO2eZ4RbAW340StmN4eWHOkuqrbiPDjMrT2BpFFFpVqpo1yppExTRblSRZVSi3qMUewQVU+0Gkq0K5pYVzSRahhaGqeJ24Eyxd3D0Xo3YMeoWtBj90nPnWiwKAaqa3WEGfUcere7xvXhvv9lcsvpPZkxsAt6rQaLCs0x1F/vav7pApJ0CyGEEEIIIToMbbieLnNP83s/r/1lFjVlpYTFxDL7af9WL79w/jIKqiwkRBhZP3fyEc+rra31qia+f/9+rNajj2Lr9XrPGuxu3bqxuno1L2S8AMBzE55j4hGqlx9R2S7Y9K57vXZNoXebooG+Z8LQWdQkTuSFf/3G/H/+zOypJtI7a7jxjQsA6A9cZjkHo7H509mqqip4qXnvKUm3EEIIIYQQQrQzDoeDgoICr2ri5eXlR71GURQ6derklWTHxcWh0Wg856wzrzuJYKyQ9bV7q6/dK33bI7vDkKthyExswV14/fXfePzxVygqqgXgqw17SDu3l9cl/ki4/aXtRCqEEEIIIYQQwpeqEqZYSXBWsWTJEvbv309+fj5O59GLxoWFhXntiZ2YmIjBYDhmd0EOFaMNNJU1OI6WyBfvgC0fQMZn7urjgGehu6J1j2oPvBx6jcWpKnzwwXYeW/Axu3OrPLfQKNAlJgS7s+3W/5akWwghhBBCCAFA9rrVrP1oETZLyxUcaymTwq8gWBNKTUU5r/1llt/7qz3GqPKpsFgsHDhwwDNNfKJ9NwaDHZzwyy9NX6PT6ejSpYtXkh0ZGXnMSt9Op4Pd5jXsW7+M2j/+oOu2XN4tcKJVgRfnsuO4Ig4++HW4zajqZn6uTeSF4oFst0V5tU4N38uYMycT3SkBvbP6uHpqjSTpFkIIIYQQQgCw9qNFlB3IC3QYfqGGukADqstFTVlpi/WrNzaVbB4/l8tFUVGR1zTx4mLv7bKaGpuOjY31mibeuXPnI++JfYiygly2r/mG0t/Wo83cSdyeCkItKgmn9CiaVuPUcfP+M9hUH+91fExIPnfGbyHdWM6ayOlYAUVz7NhbK0m6hRBCCCGEEACeEW5F0RAa7d+K2y1NObguWdFoCIuJbZE+9cZgxlx21QldU11d7VXobP/+/djt9qNeY0NHkTOUen0kj14+jq5duxISEnLMvmz1dWzf+AMHNqzAtiWDiJwC4ksdRAKRR7jGBRQlGAhK7EKP0K4o1flQmQfWKt+T9WHu9doRiaD13aM7VAXDNxFwcGLF4E42/j6yhjFdFWAQAMrBjxQ04eHHfDytlSTdQgghhBBCCC+h0dHMfsW/FbdbWv78X3BW2QiLivZ7NfHjZbfbyc/P9yTZeXl57urZR6HRaOjcubPXNPFzX/uDAouVhGAj/fr1a/I6l8vF/pzf2b32B6p+34ghO5dO+2rRO6H7UfqrDNNQ1jsGxZRC/LDR9B81lfTave6iaBmfg73O+4KgUDBdBENnQbfhcMj09by8Krp2Dfea0v7c+jyuv/5LnnxyEn/6U4rPdHft/Wugwoqia7upa9uNXAghhBBCCCHaCFVVKS0t9ZomXlhYiMvlOup1kZGRXtPEu3TpQlDQYaPGTazLrq4oInvN/yjeuBY1Yxsxu8qIrHERD8T7nO1m00JR91CsyUlEDBlOr9FTSO47xF29vK4MNr8PH/wJirf5Xpw4xJ1omy4GY4RXU2FhDY8/vpLXX/+NL7+8nGnTGj8YGDWqG2bzLWg0x7+/eFsjSbcQQgghhBBCNLP6+nqvaeJ5eXlYLJajXhMUFORJsBv+DD+OadWq6qRX/VaGlm/jm5v+SeiOA3QqsBIKhB7luuJYHVX9EtAPMJE48gxSh09hUPAh09JdLtizyj2qnfU1OG3eNzBEwsBLYeg10GWgz/0rKy08++xann9+PbW17inyc+Ys4+yz+3ol2e054QZJuoUQQgghhBCiWdTX17Nt2zYyMjLYuXMnqnr0ba7i4+M9I9jdunUjPj7ea0/sIympL2FL8Ra2FG9ha8lWZm7dwFl/OI56Ta1RobhXFGpaX2KGnka/0dNJ7dKr6ZOrC+CPxbDpv1C+27e9x2h3op12Aeh9147X19t5+eVfmT9/NWVljZXwQ0ODOP/8ZOx2JwZDx0lFO84jFUIIIYQQQohmZrPZyM7Oxmw2k5OTc8S9sUNDQ71GsBMTEzEajce8v8VhYVvZNjYXb2ZryVa2FG8hvzbf65xbsrwTbqcChYlG6pO7EzpoMD1OP4v+6aej1R4l/XM5IedH2PQuZC8B9bDHERILg65wTyGP79/kLRwOF++88wePPvozeXmNa9ODgjTcfPNwHnhgHJ07hx3zMbc3knQLIYQQQgghxAmw2+3k5ORgNpvJzs7G4fAdZY6MjCQlJcUzih0VFXXMPbFVVWVv9V6vUezssmwc6tFHsTUHl4WXhRoJeeo++o+aiik85vgeTMVe+H2R+6tqv2977wnuRDvlHNA1tTmZW1WVlZEj3yA7u3E7NkWBq64ayGOPTaBXr/ZVDf9ESNIthBBCCCGEEMfgcDjYtWsXZrOZbdu2YbPZfM4JCwsjPT0dk8lEt27djplkV1orMZeY3Ul2iTvJrrRWHvWaYF0w6bHpDIgfwMC4gQyIG8CBZ6YANuqCohlz1pXHfjBOO2R/6x7VzlkGHDYNPrwLDJ4JQ6+G6J7Hvh8QEWGgf/9YT9J9/vnJPPHERAYM6Hxc17dnknQLIYQQQgghRBNcLhd79uzBbDaTlZVFfX29zzkhISGkpaVhMpno0aPHEddk2112dpTv8Ixgbynewp6qPceMoXdkbwbGu5PrQfGD6BPVB53GO407cLwPqHSnuyjaH+9BbbF3m6KBfme712r3mwJHm4oObN5cwIABnb2KoD355CSqqqw8+eQkxozpcbxRtXuSdAshhBBCCCG8OFwOdpTvCHQYzcrocqBw7MemqirF+cXkbs9l7469WOp8K44H6YPo3rc7Sf2TSOiWgEarwY6dnZU7Pee4VBe5VbmeJDuzNBOL8+jVy6MN0Z4Ee2D8QExxJsL1x65eflR2C2R9Bb+9A7mrfdujesCQa2DITIhIPObtsrNLePDB5XzySSYffjiDSy9N97QNGNCZFSuuPbV42yFJuoUQQgghhBAAVNmq0AClllIu+uqiQIfTrP5reZI4oim1lHL1Vzd7N6oQZYuie013utV2I8TpW5HboTg4EHKAvNA8CkMKcdW7YDPur5MQpAkiNSa1cZp4/AC6hR17SnpTSmIHkddjKragEN6+f437oMsOtjqw1x2cPT7r4BeAAkFGCAoFix6WKLBkN9BEpfKDyqosfLlmF6u35uM6WJX9jpu/perXMnTaY1dcP1l1lVa/3bulSNIthBBCCCGEANyVskPQBjqMlqFChD3Ck2iHOXyrajsVJwXBBewL20dBcAFOTdOVyY9Ht7BuDIh3TxEfEDeAlJgU9Fr9qTwCj31J06gPca+ddlQcmqQaD341wXrwC9+16YeqsdhZ+vs+fs44gMPZuPY7PDiIM9K7UFdhRevHpLuB3th2X5eSdAshhBBCCCG8aNBwcb+LAx1GswreHQwOsOvgYs3FUADUNnGiAsQCCaDtpKWrritd6XpSfcYYYxgUPwhTnInY4NhTiP4oVBWn9mBVcdVFqLbMu11RQBfs3k9be+Tq44ez2Bws3biPJb/sod7W+GFDsF7L9FE9OWtYDwz6lkmE9UYtI8/r3SJ9+YMk3UIIIYQQQggvGkXDo6MfDXQYzaa8vJz1y74jR3+AUqphp3e7oij06tWL9PR0UlNTCQnxnV7e6tRXwNaP4bd3ULgLAIO9kmu73ORu72xyb/U18BIIPrHtuiorLaSm/h/5+TWeY0ajjttvH8nf/z6G2Ng28Py0IpJ0CyGEEEIIIdqdqqoqMjMzMZvN5OXluQ8eNgu6R48emEwm0tLSCAvznV7e6qgq7F3nLoqW+QU4mijMNnQWDJsFiUPdo9wnITLSyIQJPXn/fTMajcL11w/mkUcm0K1bxKnF30FJ0i2EEEIIIYRoF2pra8nKysJsNrNnz54mz4lXIhly1mmkp6cTGRnpt1gc5eXYc3Ob52aWStjxA2QvgYq9hzQEeZ3mRAvnv3RCt1ZVlWXLdjNhQk90usZPJebNm4jTqfLYYxNISYk7heCFJN1CCCGEEEJ0QA6Xg5yKHK99o4ehHvvCZlS3pZiqpbmo1pMvUGZV7exxFZLjyme/WoraxGOIUcLpbe9Eb2cnosOj6DL6tFMJ+5gs2dn8cvOT7Oo2Fafu+NdRH93Qg1++rPqT+/BgzZq9zJmzjFWr9rJw4flcf/0QT1vfvjF8+OGMk7qv8CZJtxBCCCGEEB1AYW2hO7ku2cKW4i1klmZS76j3OmfYwYJhWk3LFMiqWpqLo7j+2Ccexo6DvZoSdmoLydOU4lJ8E+1IVwi9XZ3dibbaOHVcMfj/sdWuXsOublOpC03we1+HcnF8H15s3VrIAw/8xNdfb/cce/TRFVx55QCMRkkRm5s8o0IIIYQQQrQz9Y56Mksz2VrcmGQX1hUe9RqdoiNIEwSoROhbZu2uZ4RbAW340bfPcqhO9qrF7HTls9dVhAOXzzlhBNNX04U+mi7EKuE+e14rBi0RU5KaLf4jUxtHuFUXwTrHMc/H5XTvre1qKnFWQKsDTRAovttz1dkcOFQnm2OD+MtRetm9u5xHHlnBokVbUA/5nCI5OZb58ydjaIEPJDoiSbqFEEIIIYRow1yqiz1Ve9wJ9sGp4tvLt+NUjz7qmRiayMD4gQyIG8DA+IGkxqby9ro/U1NXiqaJxM6ftOF6usz1nfLtcDjYtWsXZrOZbdu2YbP77ikdFhaGyWQiPT2dbt26+STagRYSrHDdC1ObbizfA5v+C38shur8wxoV6DPJXRSt/zTQHflDiVHzl1FQZSEhouk9uQsLa3jyyVW8+upG7PbGDyu6dYvgsccmcM01g7zWc4vmJUm3EEIIIYQQbUi5pdyzBntryVa2lmyl2lZ91GtCdCGY4kxeSXZccOssjuVyudizZw9ms5nMzEwsFt8K3SEhIaSlpWEymejRowcaTRtKGB02yP6fuwL5rhVw+Br0iK4w5CoYPBOiT31UvrLSQnLyv6mstHqOxcQEM3fuWG69daRMJ28B8gwLIYQQQghxApwuJ1tKtmBparsmP1BVld1Vuz2J9r7qfUc9X0GhT1QfBsUP8iTYvSN7t9g67ZOhqiq5ubmeRLu2ttbnHIPBQGpqKiaTiV69eqHVtt7H06Ti7bDpHdj8PtSVercpWug/1T2q3fdMaMZ/q8hII5dems4bb2wiJCSIu+8exb33jiYysulRcdH8JOkWQgghhBDiBNy94m5+2vdToMPwiDXGMiB+gCfJTo9NJ0zf+vecVlWVIlcFObr97LIXUfuW74cYQUFBpKSkYDKZ6NOnDzrdyacvOb8VseHrXdgsJ18p/Xi5artj1btH3+vq6/j9sdMZomb6nJdHZ77Snsm3momU7I6B3QArTri/omr3c6c6VRYt2sIll6RhMDQ+V488cgbBwTrmzh1H586t/7XR3kjSLYQQQgghxAlYe2BtwPrWa/SkxqYyIO5gkh0/gMTQxFa3jvlIVFWlsLCQjIwMzGYz5Y5yn4xEq9XSv39/TCYT/fr1Q68/eoG147Xh612UF9Q1y72OTQcH/0kiqPBKuK2qjh9cw3nfOYl1rjRUGqbGn/zMCVVVqd9egXltPlcX1VFWVs8ddzSuke/aNYIXX5x20vcXp0aSbiGEEEIIIU5CjDGGi/td3CJ9xQXHMTB+IMnRyQRpg1qkz+ZUUlKC2WzGbDZTUlLi065BoU+/vphMJpKTkzEam3/qc8MIt6JASGRz7Z19CNUF9nqw1+GyOnDZFbQOK4MNHwGwm258qT2LJZozqFTc1eE7N0O3lTsr2Pf9Hmr313iOPfHESm66aSjBwW3vtdIeSdIthBBCCCHESYgLjuOOoXcEOoxWq7y83DOiXVBQ4NOuKAqJxNDb1oneIYn0mjmuReIKiTRw7VNjmudmqgr7f4Pf3gbzZ2B3r0UvzQqlaHMkAAXjEuH67+nV/TTuVBTubJ6e+e23A8yZs4xflu7yOj5mTHeeeupMSbhbEUm6hRBCCCGEEM2iqqqKzMxMzGYzeXl5TZ7To0cPTCYTaWlpVL+UgbPehlZpninkLaauDLZ8BJvehaIM3/bwLoB7KvsnmulM7DGq2brOzi7hoYeW8/HH3mvEBwzoxIIFk5k+vV+bWW7QUUjSLYQQQgghmp3daefXgl+xOFumwndLcqiOQIfQqtTW1noS7dzc3CbP6dq1K+np6aSnpxMZGek5fvSNzloZVYXcNe6tvjK/BKfVu10fDgNmuCuQL9kEq59t9hAqKy0MG/Y6tbV2z7FevaJ4/PGJXHHFADQaSbZbI0m6hRBCCCECpG5LMVVLc1Gt/q+m3NLKrRWEOK2EBDoQP3iLxwDQaXTkm38JcDTNa1L4FaihLhSNhvz5R35sVtXOblchO1357FdLUQ/faxqIUcLpq+lCH00XIopDYAXUrdjGoaXMnNW25n8QR2Cxu99nRdUWRs1fdtzXxagVTHct53znj/Qg36d9i5LMl5ozWaaOwbLVCFvLmGrO4bJmi7xRZKSR2bOH8dxz6+nUKZSHHx7PTTcNQ69vY9undTCSdAshhBBCBEjV0lwcxfWBDsMvIgiBdplye3PaWi5pbAnBmlAaimk7q7wfmx0HuZoSdmkLydOU4lJ8E+1IVwi9XZ3p7exMtBrqOe7k6M+TYvB/0lhjdRAMuFxQUHX0GRgaXIzVbOVy7XLO0vxGkOL9wVi5GsZnznF84JzIDrXbIS3u+1ZbG0eiDUEaTkZtrY1XXtnIX/4ynNDQxun3c+aMIzY2hDvuOI2wsDY2Lb+DkqRbCCGEECJAPCPcCmjD29cvz4V1RYCKRtEQomt/ybeiaDDqjGhoX9N5ayrKUV3uke6wqGgcqpO9ajE7XfnsdRXhwOVzTTjB9Dk4oh2rhJ/wemLFoCViSlJzPYQjcqkqDft4JUQ0XR09Xi3hPNdPnOdcRheKfdo3Kia+1JzFz5rTsB1ch57QxH3CDY1FzM4fnHhCcdpsTv7zn03Mm/czhYW12GxO5s5tLDIXFxfi9b1o/STpFkIIIYQIMG24ni5zTzv2iW3IeYuGY3Va6R/dn0/P/zTQ4Yjj9NpfZlFdVkZQQle6p0xh27Zt2Oy+o9Th4eGkp6djMpno2rVrmyrcpdHA+rmTGw847bD9e9j0DuT86N7661ChnWDITBhyNcNj+zD8OPooXbiHok3uvw/tEX1ccblcKh98YOahh5aza1e55/g//7mOu+4aJdXI2zBJuoUQQgghhOjgnE4ne/bsoSIijvq47qDVUb5li9c5ISEhpKWlYTKZ6NGjBxrNyU2bbjXKdsGm/8Ifi6Gm8LBGBfqdBUOvgf5TwY97o6uqynff5TBnzjI2b/aO4+KLU3niiUmScLdxknQLIYQQQgjRAblcLvbt24fZbCYzM5Pa2loIDvc6x2g0kpqaSnp6Or169UKrbfsFu4yqDd45H3b/7NsY0Q2GXg1DroLIbr7tzWzt2n3MmbOMlSu9q75PmtSLp56azIgRXf0eg/A/SbqFEEIIIQ7RkhXFW7Jys2i7/rcln+eWZlPbHK9JVSVCraGLq5gurmKMTRU4czlRamvYGH06Jc5ovsjQQEYu0PR2YG1BT3Uf49QYIIwIqr0Tbo0OkqfB0FnQZxJoWuaDhcpKC2efvYiamsZ/g2HDuvDUU2dy5pm9WyQG0TIk6RZCCCGEOEQgKoq3ROVm0XY9tzSbncW1p3AHlWilnl7aMnpqyojQWH3OcKgKea4oehZuJayqiFpNMFuDQuEYVcdbMyNWztWu53LtcoZrtvM2b1BLWOMJMb3d08cHXQnhnVs8vshII3ffPYp581bSv38sTz45iYsvTm1T6+PF8ZGkWwghhBDiEC1dUbylKjeLtqthhFujQKfwpqtuNyVUrSPBVUwXZzFh+H6Q5EKhRIkmXxNPkSYGp6KjT906FNWFRqMcscJ3a5fs2sn5rh8527WKMK9dwd2c2mCY9TX0HActlOAWFdXyzDNrmDt3HNHRwZ7j99wzmh49Ipk1azA6XRtfIy+OSJJuIYQQQogmtMeK4qJt6xRu9K663YTy8nIyMjIwm80UFBT4tCuKQq9evTCZTKSkpBAS4r2d22t/eZeasho6hRuO2VerYqmCrR+7K5Dnb/Ztj0+FqkioA21oDPQa0yJh1Th1zH93H/++6EVqa+0EBWmZP7/xeY2IMHDDDUNbJBYROJJ0CyGEEEII0YZVVVV5Eu39+/c3eU6PHj0wmUykpaURFhbW5DltjqrCvg2w6V3I+Azsh41qB4WA6SIYei10Gw5z1kKd79R6f7DYVd4p689rZelU5OR5jr/xxiYeemi8VCPvYCTpFkIIIYQQzareUY/r8L2ORbOqra0lMzMTs9lMbm7TBc66du3qSbQjIyNbOEI/qiuDzR+4k+3iLN/2xCHuomimi8EY0aKhORwu/vvfzTz0QAH7yxpHsHU6DX/+81AefFAS7o5Ikm4hhBBCCHHSXKqLPVV72Fq8la0lW9lSvIXt5dtxqv6v/t7R6FQ7mzZtIiMjg127dqGqqs85nTt3xmQykZ6eTkxMTACiPDE5vxWx4etd2CzHer2o4LCBvRbsFiAOuLuxWdFAUDAEhcKuINgFfLLV6w51lf4b5VZVlS++2MYDD/xEVlaJV9slE+NY8MYV9OnT+v89hH9I0i2EEEIIIY5bhaWCLSVbPAn21pKtVNuqj3h+akxqC0bX/litVro4i0gLKqSbvZKvvvJNtGNjYzGZTJhMJuLj4wMQ5cnb8PUuygt8i50dWejBryY4AYsLOHpyrTc2/24B1dU2brzxa8rKGgvWjQ89wF/jtnDm/Y8RIQl3hyZJtxBCCCGEaJLdaSe7PJstxVvciXbxVvZW7z3qNQoKfaL6MCh+EIM7DeacXue0ULTth91uZ8eOHZjNZrZv384gpwMOyxOjoqI8iXbnzp3b7DZTDSPcigIhkYaDR1VwWMFWBw6L+/tDKRr3em19CGhObKq23qhl5HnNvwd2RISBOXPGct99Sxk9ujv3j7XQ98sPmr0f0TZJ0i2EEEIIIVBVlQO1B9havJXNxZvZWrKVrNIsbK6j79Mca4xlYPxABsYPZEDcANJj0wnTt5NCXS3I4XCwc+dOMjIy2LZtGzab7/NuQc+EUUMxmUx07dq1zSbaTQmJNHDt/T3g9//C74ugqomCcL0nuNdqp5wDOoNvewvZvr2Uxx9fyTPPnEVCQuNr/dZbR5CaGsf06f0oe/NNigIWoWhtJOkWQgghhOiAau21mEvMXqPYpZbSo16j1+hJjU11J9lx7kS7S2iXdpX8tSSn08mePXswm81kZWVhsVh8zgkJCSHLEklmfSRKWBxPTT0zAJH608FR7LpSeOEcfEa1wxJgyEwYcjXE9Grx6A61f38V8+b9zMKFv+N0qkRFGfjXv6Z72oODgzjnnP4BjFC0VpJ0CyGEEEJ0ABaHhaW5S9lYuJEtxVvYWbET9fAE5zA9wnt4RrAHxQ+if3R/grRSeflUuFwu9u3bh9lsJiMjg7o63/XMRqOR1NRUTCYTPXv2ZMw/VlCoWkhoTx9ulO5076ldPQiI9p5Grmig3xT3qHa/KaBtnpTFUVpK1fffo1pOrKBaRa2Ll5ZU8fqP1Vjsje+ZzxZv4u/p+QTrNT7X1G349ZTjFe2HJN1CCCGEEO1YQW0BH2z7gE92fEKltfKI54XrwxkYN5AB8QMYEOf+ijZGt2Ck7Zeqquzfv9+TaFdX+xae0+v1JCcnYzKZ6NOnDzpdO/w13W6BrK/dyfaeVe5j6huN7ZE9YOg1MPhKiOza7N3n3XIr9Zs3H/f59S4ti8r7s7AslSqX3nM8VGPn+uhtXBOTTfULDo5cRlAIt3b4bhZCCCGE6NhUVWVz8WYWZS3ix9wffbbv0ipa+kf394xiD4wfSFJEEhrFd8ROnBxVVSksLMRsNmM2m6moqPA5R6fT0b9/f9LT0+nXrx96vd73Ru1BYaY70d78AVgqDms8OHofEgt/3Qwa/70GLdu3H9d5dlXh08revFJiotgZ7DmuV5xcEbWDP8dkEq07eq0DD52O4EGDTiZc0Y5I0i2EEEII0U7YnXa+2/Mdi7MWk1Ga4dWm0+iY2nMqF/W7CFOciWBd8BHuIk5FcXExGRkZmM1mSkpKfNo1Gg19+/bFZDKRnJyMwRC4gmB+Za2BjM9g07uQ18RU69i+7unjX3WGSgfojH5NuA+l69KFzn//2xHb6yxOXrt+M8VOO+AO68oz47l/Zle6dxp9Qn0FDx5MUOfOpxSvaPsk6RZCCCGEaONK6kv4OPtjPtr+ESX13olejDGGS5Mv5dL+lxIf0rb2cG4rysvLPSPahYWFPu2KotCrVy9MJhOpqakEB7fTDzxUFQ787h7V3vopHL5/u84IaRe4k+2k0e59wr5eAzhaNExteDgRU6cesT0CeCS/E7fc8i0XXZTKE09MJDVV3jvi5EnSLYQQQgjRRmWWZrI4azFLdi/B7rJ7taXGpHJV2lVM7TkVvbadTlsOoKqqKs+I9v79TWxvBSQlJXkS7bCwdryNWn0FbP3YnWwXbPVt72xyJ9oDL4Hg1lUnYN26fTzyyAreeOM8kpKiPMdvvHEoI0Z0ZfjwxMAFJ9oNSbqFEEIIIdoQh8vBsr3LeC/rPTYVbfJq0ygaJveYzFWpVzGk0xDZyquZ1dTUkJmZSUZGBrm5uU2e07VrV0wmE2lpaURGRrZwhC1IVWHveneinfEFOOq92/VhYLrYnWx3Heoe1T78Fi4XqtUCKLhqaij+v//zb8j2xg+mMjKKeOCBn/jyy2wAHn30Z9566wJPe1CQVhJu0Wwk6RZCCCGEaAMqrZV8sv0TPsj+gILaAq+2CH0EF/e/mCuSr6BLWJcARdg+1dfXk5WVhdlsZvfu3aiq7zZrOruVYEstRmstrsLdbNm0mi3NGMM51VZcLhWNRuG1v7zbjHf2VVtefowTSmDz++612iVNFCbrOsydaJsuAkP4UW9Vs3IlzooKMETjqq2h5F//PvnAj9N+ewiv/pHE5wNfxeVq/Ldcvz6P+no7wcGyJZ5ofpJ0CyGEEEK0YjnlOSzetphvdn6DxWnxausT2YeZaTM5p9c5hASFBCjC9sdqtZKdnY3ZbCYnJweXy+VzjsZaj66qDF1VOVqb+9/F4nNW8/D8yzqhpqzGT7140xsPWXfucsHun92j2lnfwGFLGTBGwsDL3dt9JZiOuw/bzp1AVLPEeyxlDgOvl6XxfkVf7KqWhj3BExPDefTRM7j22sEEBWlbJBbR8UjSLYQQQgjRyrhUF6vyVrEoaxHr89d7tSkojO82npmpMxnVZZRMIW8mdrud7du3k5GRwfbt23E4fIt7RUVFYTKZ2Pblh1iK8tEoGkKjo4FQv8ZWdMhId6dw/1c71xuDGXPZVVCVD38sgk3/hYomptMnjXGPaqedD0GnVhxOExpK99dfO6V7NKXe6uSF9/fy0gf7qKlv3DovKsrInDljue22kYSEyOi28C9JuoUQQgghWokaWw1f5HzBe9veY1/1Pq+20KBQ/tT3T1yZciU9InoEKML2xeFwsHPnTsxmM9nZ2dhsvnsvh4eHk56ejslkomvXriiKws5P/4sVCI2OZvYr7/g9zlHzl1FQZSEhwsj6uZP925nTATlLYdP/wfffw2F7vBMSB4OvdCfbcX2brVtFF0TY+PHNdr8GepuT9/78b0/CHRys4847R3HffaOJjm6nVeRFqyNJtxBCCCFEgO2t2st7297ji5wvqLXXerV1D+/OzNSZXNDnAsL07bgCdgtxOp3s2bMHs9lMVlYWFovvpPCQkBBPot29e3c0LbR/dECV58Lv/4XfF0P1gcMaFegz0Z1oJ08HXeuthq+qqtfsD71ey7x5E7j++q+46aahPPTQeLp0OfpacyGamyTdQgghhBABoKoq6/LX8V7We6zMW4mKd4GuUV1GcVXqVYzrNg6N0gGSPj9yuVzs3bsXs9lMZmYmdXV1PucYjUZSU1MxmUz07NkTrbYDrO912CD7f/DbO7BrBRz2GiQ8EYZc5f6KTgpEhMdNVVW++iqbhx9ewYcfziAlJc7TduWVAxgzpge9e7eu7cpExyFJtxBCCCFOWN2WYqqW5qJancc+uY1xVvtOMW5O9Y56vt75Ne9lvcfOyp1ebUatkfP6nMeVKVfSN7r5pu52RKqqsn//fsxmMxkZGVRXV/uco9frSUlJIT09nT59+qDTHd+vxha7+3VfVG1l1PxlzRp3U4qqm7lEW8kOd1G0P96HuhKvphzLWDbYbsCmiQSLAX5Q4Ic8IK95YwBctd2x6k/9A6Wff97D/fcvY/16d4wPPvgTn3xyqaddq9VIwi0CSpJuIYQQQpywqqW5OIrrj31iG6YYmnekM78mn/ez3+fT7Z9SZavyaksITeCKlCu4uN/FRBra8d7OfqaqKoWFhZjNZsxmMxUVFT7n6HQ6+vfvj8lkol+/fgQFnXgRrWqLg2DA5VIpqPJXzXJfoafymrTXQ+aX7lHtvWt926OSYOg1bPhxFOUVDR88+fcDKNDBwZngupOoZfb77/nMnfsT332X43U8P79Gtv8SrYok3UIIIYQ4YZ4RbgW04a13fefJUgxaIqac+nRaVVXZVLSJxVmL+WnvTzgPK0o1tNNQZqbOZFKPSeg08mvZySouLvYk2qWlpT7tGo2Gvn37YjKZSE5OxmA4tQrgh07CTogwntK9jleoQcs9U5JP/MKCre49tbd8CJZK7zatHlLOhWGzoOd40GiwfbsGAEWBkEj/Vkp31dbiqqlG67Ay+LT4474uJ6eMhx5azgcfmL2Op6fHM3/+ZM47r79U9Retivx0F0IIIcRJ04br6TL3tECH0erYnDaW7F7C4qzFZJVlebUFaYKY1msaM1NnkhabFqAI276ysjIyMjIwm80UFhb6tCuKQu/evUlPTyc1NZXg4OavVK3RKP6vJn4yrNVg/tQ9qn1gk297XH93UbRBV0BobJO3CIk0cO1TY/waZunChRQ98ywAXa9+8Zjn19TYuO++H/jPf37H4WjcOz0pKZJ58yYyc+YAtFqpfyBaH0m6hRBCCCGaSUl9CR9mf8hH2R9RZinzaosLjuPS5Eu5pP8lxAXHHeEO4mgqKyvJzMzEbDazf//+Js9JSkrCZDKRmppKWFgHqvauqrD/N/da7a2fwmFV8NEFQ/qF7lHt7qe5h7LbmOBgHStX7vUk3PHxITz44Hhmzx6GwSBpjWi95NUphBBCCHGKMkoyWJS1iO/2fIfD5fBqS49NZ2bqTKb2nEqQVtaYnqiamhpPor13794mz+natSsmk4n09HQiIiJaOMIAqy+HLR+5R7WLMnzbEwa4R7UHXALBUce8nepwv36dVVXkP/ZYMwfrzZqZddR2u91JUFDjOnatVsOTT07imms+5957R3PXXaMID/fvFHghmoMk3UIIIUQ70NLVxP1d4bstsLvsLMtdxqKsRWwu3uzVplW0nJV0FjNTZzIoflCHXF/6vy35PLc0m9qTeE3qVDudXaV0cRUTq1bQ1LNXpYSSr4mnQBNPfbERltfB8l9PPfDjMM2lHvskf1JVyF3jTrQzvwSn1btdHw4DZrhHtROHnNCtnZUVoA1Hra+j4v0Pmi/mY1AO2Qvdbnfy5pu/8/jjK/nqqysYOrSLp+2CC5LZvfuvxMaGtFhsQpwqSbqFEEKIdiBQ1cSbu8J3W1BuKefTHZ/ywbYPKKzzXkscZYhiRv8ZXJZ8GQmhCQGKsHV4bmk2O4trj33iQTqc9NBU0EtbSqKmCq3im9hWuIzsdsawxxVDpXroGu2WqyB+qBb/KKWmGDa/5y6MVprj295tJAy9xj2N3HCSU+udTmjht7U2OpqQ4cNxuVQ+/jiDBx9cTk6Oe3nG3LnL+O67qzznKooiCbdocyTpFkIIIdqBQFQTb64K323F9vLtLM5azP92/Q/rYSOLfaP6clXqVZzT+xyMupapZt3aNYxwaxToFN70c6JRncSrZXRxFhOvlqPF5XNOHUbyNfHka+KpUUJAUQgGmr8s2vHTaBRwQrixBX6Vdrlg10/uUe3sb+Gw5QsER8PAy93JdudmLMyn1dHrs0+b735HEdSrFz+u3M+cOR/y++8FXm0hIUFYLA6MLfFcC+En8uoVQggh2hGpJt68nC4nP+f9zOKsxWwo2ODVpqAwofsErkq9ihEJIzrkFPLj0Snc6FXh2+FwsHPnTsxmM9u2bcPusPtcEx4ejslkwmQykZiY2Oqe29f+8i41ZTUYg/w4JFy5H/5YDJv+C5VNrGXvOQ6GXeve8ivIDx/0KGBM8391/fXr85gz/SNWrNjjdXzChJ4sWDCZUaO6+T0GIfxNkm4hhBBCiMNU26r5fMfnvL/tffJq8rzawoLCuLDfhVyRcgXdw7sHKMK2xel0snv3bk+ibbH4TgcPDQ0lLS0Nk8lE9+7d0Wg64NZPTgfs+N49qp2zFNTDRv5DO8HgK92j2rF9AhNjM6mqsjJr1hd88cU2r+NDhiTw1FNnctZZvVvdhy1CnCxJuoUQQgghDrI5bby06SU+3v4xdY46r7akiCSuTLmSC/peQGhQaIAibENUlc5KFWmOPP75z9+oq6vzOcVoNJKamorJZKJnz55otR2vRgAAZbvd67T/eA9qCg5rVKDvme6iaP2nQjupgB8ermffvkrP9337xvDEExO55JJ09/R9IdoRSbqFEEIIIQ76367/8U7mO17HtJYUdDXjKd6fwktZGl5ifYCiawNUlUi1mi6uYiY68jEYVHDBofm24nJhtNZhtNRgsNVTkJtFwXefBS7mk1BbXn7qN3FYYds37lHt3T/7tkd0gyFXwZCryNlpYMOHu7BZNvie18ysQf7Zcq2qykpEROP2Xoqi8NRTZ3LNNZ/zyCNncP31Q7y2BxOiPZGkWwghhBDioDJLmefv9ioTtuIpuGydDh6RbdKaphKj1NNLW0pPTRnhmoPP06Gzw10udDUV6KrK0NVUoqgqDsDR1O3aEL3xJMq5FWe7E+3N70N9mXebRucezR52LfSZBBp3Errh6/WUF/jOFPALxf0Pp3P5rrU/GdXVVp5/fj3PPruW//3vSsaNayy+eOaZvdm1669SJE20e/IKF0IIIYRogrNqCJ2MPUCKkTcpVK2ji7OYBFcxYTSxXZ2quhPt6nIidRo0qgpBWoiOaflg/UBvDGbMZVcd+0QAWx1kfuFOtvc1MVMiprd7nfagKyG8s+/lFncleEWBkEiDT3tzchQVorVb6Gf9DZhx0vexWh289tpvPPHESoqL3R8YzJmzjFWrrvNaqy0Jt+gIAv4qf/nll3nmmWfIz88nPT2dF154gXHjxh3x/MWLF/P000+zY8cOIiMjmTp1Ks8++yyxsbEtGLUQQggh2rvI4CDW3z352Cd2IGVlZWRkZGA2myksLPRpVxSF3r17YzKZWP/GS9SVFhMWE8vsl99u+WBbg/zN7kR768dgrfJu0+oh9Xz3Wu2ksXAcheNCIg1c+9QYPwXrtm3IUNT6egz9+5/U9U6ni8WLt/Lww8vJzW1cs63VKgwY0Amr1SmJtuhwAvqK//DDD7nzzjt5+eWXGTNmDK+99hrTpk0jMzOTHj16+Jy/evVqrrnmGp5//nnOO+889u/fz80338yNN97I559/HoBHIIQQQgjRvlVWVnoS7QMHDjR5TlJSEiaTibS0NEJD3UXmNhxeebujsFSB+RN3sp3/h297fKo70R54GYS0j1F/AFVV+eqrbB544CcyMoq92i6/3MS8eRPo108GyUTHFNCk+7nnnuOGG27gxhtvBOCFF17g+++/55VXXmHBggU+569fv56ePXtyxx13ANCrVy9mz57N008/3aJxCyGEEEK0ZzU1NWRmZmI2m9m7t4k9ooFu3bp5Eu2ICP8U32ozVBXyfnUn2hmfgf2w9ddBIZB+kTvZ7jbCPU+8HamstDBt2mLWrfPeXm/q1L7Mnz+JIUO6BCgyIVqHgCXdNpuN3377jfvvv9/r+JQpU1i7dm2T14wePZoHHniAb7/9lmnTplFUVMQnn3zCOeec0xIhCyGEEEK0adnrVrP2o0XYLL5rsF2KBoshhHpjGDa9scnEUGe3EmypxWipxVm4m82/rWLzEfpqlgrfrVTOb0Vs+HoXtnq7O8G21YHLDpxx8Osgrd6dcAcFw3oNrLcDTf+eezR1ldbmCt0vIiIMGAyNacWoUd1YsGAyEyb0DFxQQrQiAUu6S0pKcDqddO7sXSyic+fOFBQcvj+h2+jRo1m8eDGXXXYZFosFh8PB+eefz7/+9a8j9mO1WrFaG39QVVVVHfFcIYQQQoj2bO1Hiyg70DgaqWo0OMKisEfE4AyL8FSuPpTGWo+uqoygqjI0NvfvVJYT6POkKny3Zi4XGz41U+4pPB4ERB7hXMBOw39Omd7YOrbU2revkm7dIjwF0RRFYcGCydx441c8+eQkzj8/2atYmhAdXcCrGBz+hlRV9Yhv0szMTO644w4efvhhzj77bPLz87nvvvu4+eabWbhwYZPXLFiwgMcee6zZ4xZCCCGEaGtslnpURYMzPAo1tjMWQ3CTibbWYcdoqSXYWoPOYUcBCAsDwk6ovxOq8N3aVRfCH4th07vYKuYCcSg4CdEcHNHXGkAfArpgv0wf1xu1jDyvd7Pf90Tk51fz+OMreeONTXz99RVMndrX0zZqVDe2bPkLGo0k20IcLmBJd1xcHFqt1mdUu6ioyGf0u8GCBQsYM2YM9913HwADBw4kNDSUcePG8cQTT9Cli+96kTlz5nD33Xd7vq+qqqJ79+7N+EiEEEIIIVo3h8NBTk4O5RHxWOJ7ePZ/PlRERATp6emYTCYSExNlpBLA5YScZbDpHdj+Hbi8dxYP0VZy7fmb3dt9xScHKEj/q6iw8PTTa3jhhfXU17ufgzlzljFlSh+vJFsSbiGaFrCkW6/XM2zYMJYuXcqFF17oOb506VIuuOCCJq+pq6tDp/MOWat1/09DVdUmrzEYDBgM/t3PUAghhBCitXE6nezevRuz2UxWVpZ7uV2w90h1aGgoaWlpmEwmunfvjuY4tq3qECr2we+L3F9Veb7tOgPYgPAEOPvJFg+vpdTV2fn3vzfw1FOrKS9vXFQQFqbnT39Kxm53eq3lFkI0LaDvkrvvvpurr76a4cOHc/rpp/P666+zd+9ebr75ZsA9Sr1//37effddAM477zxuuukmXnnlFc/08jvvvJORI0eSmJgYyIcihBBCCBFwLpeLvXv3YjabyczMpK6uzvckp4Ngaz2X3HwrSUlJngGMDs9ph+wlsOldyPkROGxAJywBhsyEIVfD0wfAZgXa58iuQ1X4LDeWl/v9iwMHqj3H9Xott9wynLlzxxEfHxrACIVoWwKadF922WWUlpYyb9488vPzMZlMfPvttyQlJQGQn5/vtU3FtddeS3V1Nf/+97+55557iIqKYtKkSfzjH/8I1EMQQgghjuh/W/J5bmk2tVan3/t6rTqIWBSKqq1cOH+Z3/trr2xhOUesidVaqapKXl6eJ9Gurq72OUev15OSksK+n3/AXrCf8JgYevcO7Prg5uapKG45wfeby9FYgVx1Apce/AJQ3KPa+lCwGGCpAksPtPpq4qei2qnj0t3T2GOPANyvJY1G4ZprBvHoo2eQlBQV0PiEaIsCPh/klltu4ZZbbmmy7e233/Y5dvvtt3P77bf7OSohhBDi1D23NJudxbUt0pcTHaDgVFUKqk6ktrQ4lD7IQcOiNGNQ651qraoqBQUFmM1mMjIyqKio8DlHp9PRv39/TCYT/fr1IygoiNeWfonj8BHcdmLD17soL2hiZP+4GA5+NcF28Mv9Hy+tpZp4cwrXOuiprz6YdMMFFyTz5JOTSE/vFODIhGi7Ap50CyGEEO1Vwwi3RoFO4Ua/9qWtVkAFraKQ4Oe+2jObQefZ3OmCwV0DGktTioqKyMjIwGw2U1pa6tOu0Wjo168fJpOJ/v37d6i6Ng0j3IoCIZFHeNwuu3tE214HquuwRgWCjO59tXUGjjV1vDVUE28Of/xRwKBBnb0K590Zv4V6YwTPf/1XTj9dChALcaok6RZCCCH8rFO4kfVzJ/u1j/z5v+CsstEp3MD6ueP92ld7tnDrHl7Y5P770KTowAZzUFlZGWazGbPZTFFRkU+7oij07t0bk8lESkoKwcHtbF/sExQSaeDap8Y0HrDVgvkzdwXyvF8h5LALYvu6q48PuhLC4ls01kDKyirmwQeX89lnWXz88SXMmJHmaetvqOS9ATvoLQm3EM1Ckm4hhBBCiFamsrLSM6J94MCBJs9JSkrCZDKRlpZGaKgUtfKiqnDgd3eivfVTsB22zl1rgPQ/uZPtpDF+2Ve7tdq7t5LHHlvB229vxuVyLzV44IGf+NOfUtDpWu+SCiHaMkm6hRBCCCFagZqaGjIzMzGbzV6FZA/VrVs3T6IdERHRwhG2EbZaeG0cFGz1beuUDsNmwcBLIbh1zGRoKSUldSxYsIr/+79fsR5S3DEhIYy77hoVwMiEaP8k6RZCCCH85HS7wsWEEl6tIX/+L37ty1ntW+RJtH5bfl7Oiv99RY1Wj01vbHLEVWe3EmypxWipxVm4m82/rWLzSfZXU14GQF2ljbfvX3MKkbcyTit11SqggKXCO+EOCoUBF8PQa6Hr0A41qg1QU2Pj+efX8cwza6k+5OdEZKSB++8fy+23jyQ0VB/ACIVo/yTpFkIIIfzkMquObrgLnDmrWiYpVgztr5pye2OxWMjOzsZsNrNj+3YI8d2jTGOtR1dVRlBVGRqbe3uq5qxJr6pB1Fa0t22v3Mm0Xjn4TCUOdY9qmy4GQ3gA4wqcykoLKSn/R0FBjeeY0ajjjjtG8ve/jyUmpmOv/xeipUjSLYQQQvhJ8MGdmZyo6CP8X0VaMWiJmJLk937EibPZbGzfvp2MjAy2b9+O03lweu8ho65ahx2jpZZgaw06h92dQoaFAWHNFkddpQ1VDSIoZDShUW2xsrkKDhvYa8FucX9/CL3WxshBxXDuakgYEJgQW5HISCMTJvTkgw/MaLUKN9wwhIcfPoOuXWVpghAtSZJuIYQQws8qFBg097RAhyFamMPhICcnB7PZTHZ2Nna73eccjdOBtqKECJzc9tLrXts2+cPb96+htsJKaNRhFb5bu6p8+GMx/P5fKN/j2540BobOgrSLIahjjt6qqsrSpbuYPLkXWm1jQbTHH5+IqqrMmzeR/v1jAxihEB2XJN1CCCGEEM3E6XSye/duzGYzWVlZWK2+U7hDQ0NJT08nPT2dJU89TG1ZKfqYWL8n3G2O0wE5P7orkG//HlSnd3tIHAy+wp1sx/ULTIytxKpVucyZs4w1a/bx5pvnc911QzxtffvG8MEHMwIYnRBCkm4hhBBCiFPgcrnIzc0lIyODzMxM6urqfM4xGo2kpaVhMplISkpCq3WvvZc0uwnlufD7IvdX9eHbpSnQZ6I70U6eDrr2XwDMWV1NwbzHsW7f7tOWVRnMs1ldWFHUWBfggVs/YdSn8zBoVZ/zj4dqac7qAUIIkKRbCCFOyv+25PPc0mxqrc5jnyw6rJdVHZJWnbrsdatZ+9EibJZ6v/dVa6/lEntXADLWvEGO9t0mz1MBe5CBekMoFmMoLq3vr1SKy4XR6q46brDVk5+bRf6ST73Oaelq4nWVrbR4msMG2d+6R7V3LufwtdqEd4EhV8GQqyG6Y9UtqFqyhKqvv/Y6ttcWxr9KTHxbnYR6yM+Y3vpK7ozegrpjP9ZT/NGjGI2ndgMhhIck3UIIcRKeW5rNzuLaQIchWj13ASyZNXxq1n60iLIDeS3WX+jBX4/slhrsNFZ9VgGXIRhHRAz2iBhUfROFyFxOdDWV6KrK0NVUoqgqDsBxjD5bupq43thKqtyX7HAn2n+8D3Ul3m2KFvqf7R7V7nsmNPHBRkfgqqry/L2YMF4tSePj0p44aFy33SWojtsStnF+9D50igqcWsKsjYgg9vrrT+keQohGHfOnlxBCnKKGEW6NAp3CZTRANE1b7d4uLMwg/7s9FQ0j3IqiITQ62q991dprqbG7E+0oQxQGrQG7NgiLMZR6YxhOXZDvRaqKwVpHsLUWg7UOjapCkBaiY47ZXyCqieuNWkae17tF+mqSvR4yv3In27lNjO5HJcHQq2HwVRDRpeXja6WqnUGcd+ACquoaZ1jFxgbzwAPj+MtfRmA0ys8ZIVoreXcKIcQp6BRuZP3cyYEOQ7RS+fN/wVllIziolYwqtnGh0dHMfuUdv/axcOtC3tr0AqH2UGZ3voz6ffWUFBX5nKcoCr1798ZkMpGSkkJw8MlVzG6z1cRPRoHZnWhv+RAsld5tmiBIPdc9qt3rDNBomr5HBxautfOnM2J5d0kRoaFB3HPP6dxzz2giWmA7QiHEqZGkWwghhBACqKyspGp7FRP3TyTGFkNuXq7POT179sRkMpGamkpoaGgAomxjrNVg/hQ2vQv7f/Ntj+sPQ6+BQVdAaFzLx9dKORwu3ntvK5ddlu51/P6ruhOT3Is5c8bRqZO8/oRoKyTpFkIIIUSHVV1dTWZmJmazmX379gEQg/e08G7dumEymUhLSyMiIiIQYbY91mpY+Qz8uhBsNd5tumBI/5N7VLvHqDZZ9KDyyy8p/c9/cFmadx2+qsJ3RbE8vzOJ3XXB5Dz5HDOjsj3tXeMNPP/8Wc3apxDC/yTpFkIIIdqBnN+K2PD1LmyW9ldRv67S5vmzOSp8O7FRpy2iVlOIRVPWZIF5xRFMmCuBCLUrupxgtuU42fbF1lPu+3Cttpr4yVJVyPgMvn8AqvO92xIGuBPtAZdAcFRAwmsuhc88i7Ok5NgnnoC1tZ15oWQgZkus59jLOxO5qPdGDAdn22tCQpq1TyFEy5CkWwghhGgHNny9i/IC3/2h2wOXS/X8ebIVvl2KA5uhFKuxGJuhHBTfPYy1jhAM9fEYLPHonO7kxgbY8H9i3GqqiZ+Kom2w5D7YvbLxmNYAg69wJ9uJQ9rkqHZTXDUHR+81GrSRkUc/+Ri21ETw3N7+rKuK9To+PLyMe3rsICTcff+QESMIHTnylPoSQgSGJN1CCCFEO9Awwq0oEBLZvgor2aoUXE7QaJQTqvDtwkm9pphabSF1mhJQXD7nKE49WlsUWlskitOIQRtMSGgwQRp9cz6Eowp4NfFTZa2Gn5+G9S+D65DN0fqdDdOegpg2/NiOwdCvH72//OKkrt22rYQHH/yJTz/N8jo+aFBnFiyYzNSpfVHayYcUQnR0knQLIYQQ7UhIZPurgv3aX16npgxCIvXHfGwOh4OcnBzMZjPZ2dnY7Xafc+q0deSF5bEvdB8V+gqijFFc0v8SLk2+lITQBH89jPbnSFPJo3rAtKcheVrgYmvlKistDBv2OnV1ja/P3r2jeeKJiVx2mQmNRpJtIdoTSbqFEEII0aY5nU527dpFRkYGWVlZWK2+08EtWgt5oXnkheZRaigFBfpF9+Ou1LuY3ms6Rp0xAJG3YcXZ8O29vlPJx94JY++CoJPbQq2jiIw0Mnv2MJ5/fj0JCWE8/PB4brhhKHp9O1hmIITwIUm3EEK0Ae25SFZL6oRKH1Ra6tdaA+4aXbVVzVMA7GjaXUGuY3C5XOTm5mI2m8nMzKS+vt7nHKfOyd7gvewL3UexsRgUUFCY2H0iV6VexYiEETJ990RZa+DnfzQxlXwKTPtHu55KfrJqamy88sqv3HLLCEJDG5ctzJkzlk6dQrn99pFex4UQ7Y8k3UII0Qa05yJZLalXuI5QbcsnWXanSm11yyTF7aIg1xGowL59+zCbzWRkZFBTU+NzjqJTKAorYrthO4XBhagHC6aFBYVxYb8LuSLlCrqHd2/hyNsBVYWMzw9OJT/QeDyqB0z9h3squXyA4cVmc/LGG7/x+OMrKSysxW53MXfuOE97fHwo998/NoARCiFaiiTdQgjRBrTnIlktKQh3IS0VWqAetZsT2K3VnFABsJPV5gtyNUFVVew6PZZO3aiLimXhwoU+52h1WlxxLjYqG9mr34tL01gwrWdET65MvZIL+lxASJBst3RSZCr5CXG5VN5/fysPPbSc3bsrPMefe24dd999Okaj/PotREcj73ohhGhD2mORrJaUP/8XnFU2dBF6us89rcX6TW6xntqPoqIizGYzZrOZstiuPu1arZaYbjHsMO5ghWUFTo330osxiWOYmTqTMV3HoFE0LRV2+2KtgZVPw7r/k6nkx0FVVb79dgdz5/7Eli2FXm2XXJLG449PlIRbiA5K3vlCCCGEaBVKS0vJyMjAbDZTVFTke4Kq0qtPbyzxFpbULmFn7U73RtoHc+pgXTDn9zmfK1OvpHekJIQnTaaSn7DVq/cyZ84yVq/e63X8rLN6M3/+ZIYPTwxQZEKI1kCSbiGEEEIETEVFhSfRzs/Pb/Icva0epbQQnauW15KXUl1U7dXeNawrV6RcwYX9LiRCH9ESYbdfxdnw7X2w++fGYzKV/KgqKixMnbqI2trG7b9GjEhkwYLJTJ4sH/4IISTpFkL40f+25PPc0mxqre2v4nZUqY3p9QaM1fi9KjV0vMrU7UX2utWs/WgRNotvZe2OzKHRYDGEUG8Mw6FvOonTWGvR1Jah1Jahr7GhoFBrdFBtb0y4h3cezlWpVzGh+wS0muYpIGcvKiLv9tux7shplvu1GaoKThu4GhLHg/uVKzrQ6eHzT4FPAxVdq6NaLJ6/R0UZufvu03n88ZWkpMTx5JOTuPDCFKmML4TwkKRbCOE3zy3NZmdxbaDD8Ivp9QZiXe45rbUVLZcQt+fK1O3R2o8WUXYgL9BhtAqqVos9PBpHRAzOkPAmpydr6msJqipDV1WOxmE7pMV9rl2rotfomd57OjNTZ5ISk9LscVZ/9z2WzVua/b5tx+Hr311gtzR5ZkdU4jCwsCyVm2MziNTa0YaHA3DvvaPp3Tuaq64aiE4nNQSEEN4k6RZC+E3DCLdGgU7hxgBH07yMDYNtCoS2UDXx9liZur1rGOFWFA2h0dEBjuboXKoLl6qi4sKlulBV1X2MQ/6uulDx/vvRqIoWQqNRQ2MhOBKaKmhmq0OpLYWaUnBYsAN2HZ7fUBQUFEUDQRoSzjydpRfcQYwxptkffwOXtTHB1HXpgjYszG99BZzTBtX5YDv0w1EFQuMgJLbpf68Oqtqu4Y1dCby1pxN1Ti0hsZH8bXgN8X+9A4CICAPXXjs4sEEKIVotSbqFEH7XKdzI+rmTAx1Gs3r7/jXUVlgJlWri4jiERkcz+5V3/N6PS3VRbaum0lpJhbXC82eVrcr7e2tV4zm2Sqpt1ce++XHSurR0qetCt9puJNQnoFV9Z2fYDXYcnRwYuhmIjO1MpKE/kYZIogxRROojiTQ0fgXrAreGOOGBuYSfeWbA+vcbaw2sfOZgVfLGdcj0PctdlTy2T+Bia2Xq6+28/PKvzJ+/mrKyxmUin1b15bn/3CnVyIUQx0V+UgghhBCtjKqq1NhrmkyQG441JNGV1kqv48cafT5VWkVLpCGSCH2EO0k2RBIZFEloZSjaQi2WfAuq0zeGiIgITCYTJpOJLl26yHrXQFBVyPzCXZW8an/j8cgeMO0pSJ4uVckPcjhcvPPOHzz66M/k5VV5jgcFabj55uE88MA4SbiFEMdNfloIIYQQfuZUnWSVZnkS50pLY6LsSZwPGZWutFbiVP1bgFBBIcIQ4TO6HGWI8jrekFg3HAsLCkNRFJxOJ7t27cJsNrNt2zasVt/aBqGhoaSnp2MymejWrRsajUxXDpji7bDkPti1ovGYVg9j7nRXJdeHBCqyVkVVVT77LIsHHviJ7OxSz3FFgauuGshjj02gV6/WvVRECNH6SNIthGg3cn4rYsPXu7BZ/F8tXaqJN5+6LcVULc1FbYEq944qGwpQU1HOa3+Z5de+nKqTuooKFKCkvoRLv7nUb32FB4U3nTQ3fK/3/j7SEEm4PhzNSazZtVgs/Prrr6xfv57aWt9CicHBwaSmpmIymejZs2ezJNolr71OycsvozaR2ItjkKnkJ6SqyspNN31NeXnj2v7zzuvPk09OYsCAzgGMTAjRlknSLYRoNzZ8vYvygroW7VOqiZ+6qqW5OIpbZkuthomzdoeFmrLSo57brP1pj2/Kd2hQqM+os2d9sz6SKKPvmucIfQQ6jf//d15bW8svv/zCL7/84jOqbTAYSElJwWQy0bt3b7Ta5n1flP7nPwFJuLVRUS3eZ7ORqeQnJTLSyP33j+Xvf/+RsWN78NRTkxkzpkegwxJCtHGSdAsh2o2GEW5FgZAWqCgu1cSbh2eEWwFtuN6vfdVUlGN3WDBXrCYsJtavfdXaa6mx12DXqlSMiGZG/9FNTuU+NKkO0gb5NaaTUV1dzdq1a9m4cSN2e+NIqaIopKSkMHDgQPr27UtQkP9ib0i4FaMRY3Ky3/o5VMjIEQQPHdoifTU7mUp+XLKzS3j88ZX8859T6Ny5sUr97bePZMCATkyd2ldqDwghmoUk3UKIdidEKoq3SdpwPV3mnubXPl77yyxqykoJi4n1ezXx/2z9D29tehGAFyf+nUk9Jvm1v+ZWXl7OmjVr+P3333E6G6f+azQaBg0axNixY4mN9e8HF4fT9+xJzw8/aNE+2xSZSn5c8vKqmDfvZ95883ecTpWYmGBeemmapz04OIhp0/oFMEIhRHsjSbcQQgghPEpKSli1ahVbt27F5XJ5jut0OoYOHcro0aOJasvTrtsjmUp+XMrK6nnqqdX8618bsFgcnuNffLGNp58+S6qRCyH8Rn66CCGE6HBUVaXO7t/1/3an/dgntSIFBQWsWrWKjIwMr+N6vZ7hw4dz+umnEx4eHqDoxBEdcSr5X2Hs3TKVHKittfHii7/w9NNrqDykCGZEhIG//W00f/3rKEm4hRB+JT9hhAiwlqy43dIuqgaXy4imGt6+f43f+5OK4m2T3WZDQ8tUFK8pdxdPK6ov4rT3/DuVva3Yt28fq1atYvv27V7HjUYjp512GqeddhohIZK4tTpHnEp+Jkx7WqaSA3a7kzfe2MS8eT9TWNhYad9g0HL77SO5//6xxMbKa1sI4X+SdAsRYIGouN1SQhtqNzuhtqLlEmKpKN622OrrMCohqC5Xi1QUh+OvJt5cYowxLdrfsaiqyu7du1m1ahW7d+/2agsNDeX0009nxIgRGAz+L0goTpCqQuaX8P3cw6aSd4epT0HKOTKV/CCbzemVcGs0CtdfP5iHHz6D7t0jAxydEKIjkaRbiABr6YrbLamo2oLLBRoNdAo3tkifUlG87VFV1bO3lr8rihfVFWHTushMrWNUl1F+7avBiIQRDIof1CJ9HYuqquzYsYOVK1eSl5fn1RYREcGYMWMYOnSoXyuRi1NQsgO+vQ92LW88JlPJjyg0VM/DD5/Brbd+y8UXp/LEE5NISYkLdFhCiA5Ikm4hWon2WHF71PxlFFRZSIgwsn5u+3psovkpGo3fK4oP/e9Q7C47qTGpvDHlDb/21Zq4XC6ysrJYtWoVBQUFXm0xMTGMHTuWgQMHotPJrwWtkq3WPZV87b9lKvkRrFmzl0cf/ZmFC8+nR4/GUeybbhrKaad1ZdiwxABGJ4To6OT/rkIIIUQ75XQ62bp1K6tWraK01HvqfqdOnRg3bhxpaWlotbIko1XyTCV/AKoOmZkgU8k9tm4t5IEHfuLrr901CR59dAVvvnmBpz0oSCsJtxAi4CTpFkIIIdoZu93OH3/8wZo1a6ioqPBqS0xMZPz48fTv3x+NRhOYAMWxHWkq+eg7YNw9HX4q+e7d5TzyyAoWLdqCekiJhl9+2Y/F4pBq5EKIVkV+IgkhhPBSt6WYqqW5qNaWqahvVIJP+Bq7006lrZIKS4X7T2sFVdYqKqwVVFoPfm877HtrFXZX29rG60TZbDY2btzI2rVrqamp8WpLSkpi3Lhx9OnTB6WDj462akeaSt5nMkx/psNPJS8srOHJJ1fx6qsbsdsb95Hv2jWcxx6bwKxZg9Hp5MMkIUTrIkm3EEIIL1VLc3EU17dYf4ri/gXZodpYsW+FJ0mutFa6v5pIquscp1bxPzbYvwXbWlp9fT0bNmxg/fr11Nd7/9v17duXcePGkZSUFKDoxHFRVcj6Cr6b28RU8gWQcm6HnkpeV2fnqadW89xz66itbfwwIiYmmLlzx3LLLSMIDpYCgEKI1kmSbiGEEF48I9wKaMP1x3cNoKouXKi4VJfn76rqwqW6cKkH/4777y7VhYoLVVUJtmpxOu1srF3BMz8ta/bHE6QJIsoQRaQhkkhDJF1Cu3DjgBubvZ9AqK2tZf369WzYsAGr1XtbvpSUFMaPH09ioqxnbfVkKvkxabUK//3vFk/CHRISxN13j+Lee0cTGdkyu2MIIcTJkqRbCCGEh+tgsgzgClXIneVoctTZ8/3BUelqWzUqJ7f39SU/dSXUoqPW6DjqeTpFR4QhgkhDpDuJ1kd6EulDk+rD24N1we1uOnVVVRVr167lt99+w25vHPVTFAWTycS4cePo1KlTACMUx8VWCyufhbX/kqnkh1FV1et9azDomDdvAtdf/xWzZw/jwQfHk5AQFsAIhRDi+EnSLYQQ7ZCqqtQ56nzWNB9pzXPDV5Wtirfr5hFHNGWWMm7+8YFmj02jaIjQRxBliCLCEIFBWwM4CdGFcOvgW4+YQIcGhba75PlElZeXs3r1av744w+czsY19xqNhsGDBzN27FhiYmICGKE4LjKV/IhUVeXzz7fxyCMr+OSTS0hObtxX+8orBzBuXBI9e0YFLkAhhDgJknQLIUQbUG4pp6iuyDdpPkIhsUpbJQ7X0UeOT5WCQrg+3JMYRxgiPAmy1/eHjjwbIwkLCkOjNBY6eu3rWdTUlhKuD2f2oJv9GnNbVVxczKpVq9i6dSvqIaWadTodw4YNY/To0URGRh7lDs3PZbHgKCpqkb4OfcxtXkkOLLkPdv7UeEymkgPw00+7mTNnGRs27AfgwQeX8/HHl3jatVqNJNxCiDZJkm4hmpDzWxEbvt6FzeL/6s21le51mEXVFkbNb/71rIFUVG0JdAhtks1pI6ssi63FW9lSsoUtxVvYX7Pfr32GBYV5Rpb1u/TggGBdCLMHzvYZeW5IqsP14Wg1sr+zP+Xn57Ny5UqysrK8juv1ekaOHMmoUaMIC2v5KbbWnTvZc+VMXJWVLd53m3W0qeTTnoa4voGLLcB+++0Ac+f+xA8/7PQ6XlBQI9t/CSHaBfkpJkQTNny9i/KCU6uOfKIsqkpBlfXYJ7ZBoQZJzI5EVVXyavLYUryFrSVb2VK8hW1l2056a6sQXUiTI88+07UPSaAjDBEEaRqr/uZv+QWn1UaEPpzbhtzWXA9VnIC9e/eycuVKcnJyvI4HBwczatQoRo4cSXDwiW+11lxqVqwISMId1KVLi/d5ylQVsr6G7+bIVPLDbN9eykMPLeejjzK8jptMnViwYDLnnNOvwy8pEUK0D5J0C9GEhhFuRYGQSINf+yqqtmBRVdYE20mIaH8VWEMNWu6ZkhzoMFqNals1W0u2ekaxtxZvpdxaftRrjFojabFp9Irs5TVd22s6tzGKCH0Eeu3xVRsXrY+qquzatYtVq1axZ88er7awsDBGjx7NsGHDMBj8+zPpeKjOxv2RQ4YPR9cCybA2IoKYa672ez/N6ohTyW8/OJU8NHCxBVB1tZV77/2BhQt/x+lsXDrQs2cUjz8+kSuuMKHVyl7bQoj2Q5JuIY4iJNLAtU+N8Wsfo+Yvo6DKSkKEkfVzJ/u1L9GyHC4HORU5XqPYuyt3H7PKd8+IngyMH8jAuIEMiB9Av+h+XiPRon1RVZXs7GxWrVrF/v3eywgiIyMZM2YMQ4YMISiodb4GYq67lvDJ8rPLi60WVv0T1rx02FTySTDtmQ49lRwgODiIVav2ehLuTp1Ceeih8fz5z8PQ62VmlBCi/ZGkWwghmklhbaE7uT64DjuzNJN6R/1Rr4k0RHqS64FxAzHFmYg0tGxBLBEYLpeLjIwMVq1aRdFhBcliY2MZO3YsAwcORKuVJKTNONpU8rPnQ+p5HXIqud3uJCio8XWs02l48slJzJr1BX/72xjuvHMUYWEyS0cI0X5J0i2EaDeqbdUcqDnQYv1V2arIKMnwJNmFdYVHPV+n6EiOSWZg/EAGxA1gUPwguod3lzWL7Zizuhr7YaPXTpeLjD17WJ+VRVl1tVdbfFQUo9PSSO7WDY1Gg33HDk5udb9/OYqLAx1C61OSA0v+BjsPKYipCYIxd3TYqeR2u5P//GcTTzyxim++uYIhQxqXIfzpTyns2XMnMTGBq00ghBAtRZJu0Sb8b0s+zy3Nptbq/2riABdVQyhKi1QUlwrfzSO7LJurl1x9zJHllpQYmuhJsAfGDyQ1NhWDNvDrcY/FbrOhAWoqynntL7P83l9t+dHXtLdVluzt7Ln8ctR692vSodWyu1cvtqWmUBfqnYDFlJSSlplJ4oEDKEBuAOIVJ6lhKvnaf4HT1ni8A08ld7lUPvzQzEMPLWfnTvf7e+7cn1iyZKbnHEVRJOEWQnQYknSLNuG5pdnsLK5tsf5cLuPBP6GgqmWSYqnwfWpW7V8V0IQ7RBeCKc7klWTHBccFLJ5TYauvw6iEoLpc1JSVtli/emP7+gW8dtVK1Pp67DodO/v0ITslGcthFcc7FRaSlplJp8Ii2up8h6Bu3QIdQmAcaSp5RDd3VfIOOJVcVVW+/34nc+Ys448/CrzaQkKCZPsvIUSHJT/5RJvQMMKtUaBTuP8rfGuqASdoNLRIRXGp8H3qVLWxONmYxDEkhiX6vc8gTRD9o/szMH4gvSN7t5s9q1VVpSEDDIuJbZE+9cZgxlx2VYv01VLqHU4y0tPY3r8/tsMqjvdQYQiQ0KkzdOocmACbQcjIERiTO+DPLplK7mPdun3MmbOMn3/2nqcxcWJPnnrqTEaO7BqgyIQQIvAk6RZtSqfwlqnw/fb9a6itsB7sz7/Vy0XzuzzlciZ0nxDoMNo8RaNh9ivvBDqMNqempoZ169axobAA+4ABXm1paWmMGzeOLm1xv2khU8mbUFlpYdasL/jyy2yv48OGdWHBgsmceWZvqVshhOjwJOkWQgghmkFlZSVr1qxh06ZNOBwOz3HF5SI1IYGJl1xCfHx8ACMUJ61hKvn3c6FyX+PxDjyVvEF4uIG8vCrP9/36xfDkk5O4+OI0NJqO+ZwIIcThJOkWQgjRYaguF5asLFSrtdnuWV5Twy85OZj37cN1yDIHjQq9cnJI2baNlKf/Qbgk3G1T6U749j7fqeSjb4fx93a4qeSVlRYiIxuXXWk0CgsWTObaa7/k0UfP4NprB3ttDyaEEEKSbiFEG+d0OVmxbwXf7fku0KGINmD/nXdR/cMPzXKvyogIMtPS2NejO6pG4zmudTjok7OT5OxsQupbTzV9cYJsdQenkr/kPZW890SY/gzE9QtcbAFQVWXluefW8c9/rmPJkpmMHdvD03bmmb3ZufMOKZImhBBHID8dhRBtUpWtis93fM77295nf433PsidQjoFKCrR2tWsWHHK9yiLjiYzPY39h1XtDrLZ6LdjB/2278B42Ei6PinplPsVLURVYds37qrkPlPJ50Pq+R1qKrnF4uDVVzfy5JOrKCmpA+D++39k1arrPGu1FUWRhFsIIY5CfkIKIdqU3ZW7WZy1mK92fuWzRVjPiJ78eeCfSYtNC1B0otU7OP1bGxND5AUXnNClB5xONjns7HO5vI4bgYG6IEzGYAzDR8DwEV7tISNGYOjb8QpstUmlO91VyXN+bDzWQaeSO50u/vvfLTzyyAr27q30HNfpNAwa1Bmr1SmJthBCHKeT+mnpcDhYsWIFO3fu5MorryQ8PJwDBw4QERFBWFhYc8cohOjgXKqLtQfWsihrEWv2r/FpH5M4hpmpMxnTdQwaRdPEHYTwpkvoTOe//+2Y56mqys6dO1m5ciV79+71agsPD2f06NEMGzYMvV7vr1BFS5Cp5B6qqvLll9k88MBPZGYWe7VdeeUA5s2bQJ8+MQGKTggh2qYTTrpzc3OZOnUqe/fuxWq1ctZZZxEeHs7TTz+NxWLh1Vdf9UecQogOqM5ex5c7v+S9rPfYU7XHqy1YF8z5fc7nytQr6R3ZOzABinbL5XKRnZ3NqlWrOHDggFdbVFQUY8eOZfDgweh0MtLXph1xKnnXg1XJO9ZU8ooKC1OnLuKXX7yX7Eyf3o8nn5zE4MEJAYpMCCHathP+beGvf/0rw4cPZ/PmzcTGxnqOX3jhhdx4443NGpwQomPKq87j/W3v8/mOz6m2V3u1dQ3ryhUpV3BhvwuJ0EcEKELRXjmdTjIyMli1ahXFxd6jfHFxcYwdO5YBAwag1Up15jbviFPJb4Px93WoqeQNIiMNXlPGR4/uzoIFkxk/XmoSCCHEqTjhpHv16tWsWbPGZypdUlIS+/fvP8JVQghxdKqqsrFwI4syF7EibwUu1Xvd7IiEEcxMmcmE7hPQajpewpO9bjVrP1qEzeL/atgTQy+DDjZL3+FwsHnzZlavXk15eblXW0JCAuPGjSM1NRWNpoM9Me2RTCX32Lu3ku7dI7wKoi1YMJk///kb5s+fxLnn9ve0CSGEOHknnHS7XC6cTqfP8by8PMLDw5slKCFEx2FxWPh297cszlrM9vLtXm16jZ5zep/DzNSZJMckByjC1mHtR4soO5DXMp0dHODrCL9s22w2Nm3axNq1a6mqqvJq69atG+PHj6dfv34d4rlo91QVtv3v4FTyQ9bnR3SFs+dD2gUdZir5gQPVzJv3M//5zya++eZKpk5tLPR3+und2bz5ZjSajvFcCCFESzjhpPuss87ihRde4PXXXwfcv5TV1NTwyCOPMH369GYPUAjRPhXWFvJh9od8vP1jKqwVXm2dgjtxWcplzOg/gxijFOwBPCPciqIhNDrar30pB0dz9cEhfu0nkGwaDatXr2bdunXU1tZ6tfXq1Yvx48fTs2dPSbbbC5lKDkB5eT3/+McaXnrpF+rrHQDMmbOMKVP6eCXZknALIUTzOuGk+/nnn2fixImkpaVhsVi48sor2bFjB3Fxcbz//vv+iFEI0Y5sLt7M4szFLM1dikN1eLUNjBvIzNSZnNXzLII0QQGKsHULjY5m9ivv+LWP/Pm/4KyyEdQOK3Jb9Xq2J/dnR0oKth9/9Grr378/48ePp9th+2+LNsxWB6ufgzUvduip5HV1dl566Rf+8Y81VFRYPMfDw/VceGEKDocLvb7jLdsRQoiWcsJJd2JiIn/88QcffPABv/32Gy6XixtuuIGZM2cSHBzsjxiFEG2c3Wnn+9zveS/rPbaWbPVq0yk6pvScwszUmQyMHxigCEV7V11dzbp169gwbSqOwyqOp6enM27cOBISpDJzuyFTyQGw250sXPg78+b9TH5+jee4Xq/l1ltHMHfuOOLi2u+MFiGEaC1OOOleuXIlo0eP5rrrruO6667zHHc4HKxcuZLx48c3a4BCiOa1q3IXuyp2tVh/ORU5fJT9EcX13pWgow3RXJJ8CZclX0ankE4tFo/oWCoqKlizZg2bNm1y1yM5mHArqsqgIUMYO3YscXFxAY5SNKvSnbDk75CztPFYw1TycfeCISxwsbWgigoLI0a8QU5OmeeYRqMwa9YgHn10Aj16RAYwOiGE6FhOOOmeOHEi+fn5dOrk/UtyZWUlEydObLLImmif/rcln+eWZlNr9f+/eVSpjen1BozV8Pb9a/zeX12l1e99BMLGgo1c9/11xz7Rj5Kjk5mZOpPpvadj0Bqa9d51W4qpWpqL2gKvyZY2KfwK1FAXikZD/vxf/NqXs9p27JNauZKSElavXs2WLVtwuRor4WucTnrv2sVAh4OBjz0WwAhFszviVPIJMO0ZiO8fsNACISrKSP/+sZ6k+8ILU3jiiUmkpcUHODIhhOh4TjjpVlW1ycIypaWlhIZ2jEIkwu25pdnsLK499onNYHq9gViXu7hTbUXLJcR6Y/ta47apaFNA+tUoGiZ2n8jM1JkM7zzcb8Wpqpbm4ij2/5ZagRCsCfVs4+WsapmkWDG0vdd/YWEhq1atIiMjA1VVPceDgoIYMWIE8Q88iLG6GkNaagCjFM1KVSH7W1hyf4eeSv777/kMHpzg9fN1wYLJWCwO5s+fxGmnSa0CIYQIlONOui+66CLAXa382muvxWBoHKFyOp1s2bKF0aNHN3+EotVqGOHWKNAp3OjXvozVB/+iQGhk846OHoneqGXkeb1bpK9AuKDPBfSJ6uP3fgxaA2d0P4OuYV393pdnhFsBbXj7KgJWU1GO6nKPdIdF+bd6ObgT7ogpSX7vp7nk5eWxatUqsrOzvY4bjUZGjhzJqFGjCAkJYds996Ie4R6iDTrSVPLTb3VXJe8AU8kzMop48MHlfPHFNj7++BJmzEjztA0c2Jlly64JYHRCCCHgBJLuyEj32h9VVQkPD/cqmqbX6xk1ahQ33XRT80coWr1O4UbWz53s1z7evn8NtRVWQiMNXPvUGL/21VGcmXQmE7pPCHQYfqEN19Nl7mmBDqNZvfaXWdSUlRIWE8vsp/1bvbytUFWV3NxcVq5cya5d3nUKQkJCOP300xkxYgRGo38/FBQBYKuD1c/Dmhc67FTy3NwKHn30Z959dzMul/ujpAcf/Ik//SkFnU4T4OiEEEIc6riT7rfeeguAnj17cu+998pUciGEEAGhqio5OTmsXLmSffv2ebWFh4czZswYhg4dir4dbnnW4R11KvmTkPandj+VvLi4lvnzV/Hyyxux2RrrVyQmhnP33acHMDIhhBBHcsJruh955BF/xCGEEKKDqs/IwLpjxzHPU1WVnMpKfi0spKjee+1+pF7P8M6dSY2ORldQQN2331LX1D0OKaom2pjSnfDd/bDjh8ZjGh2cfluHmEpeXW3luefW8eyz66ipaRzdj4oyMmfOWG67bSQhIUEBjFAIIcSRnHDSDfDJJ5/w0UcfsXfvXmw274I+mzYFplCTEKJpdfY6Vu5fyY+5P7Iyb2WgwxHCS+369ey99ugV9V2Kwt6kHmSlplIV6b3NUURlJamZWfTYuxeNqlJ8hHuINkymklNRYSE5+d8UFTUWLw0O1nHnnaO4777RREcHH+VqIYQQgXbCSfdLL73EAw88wKxZs/jyyy+57rrr2LlzJ7/++iu33nqrP2IUQpygKlsVP+/7maW5S1l7YC1Wp3fFd42ioWdEz8AEJ8Qh6jdvOWKbU6NhT6+eZKWmUhvmPYoZXVZGWkYmXffv52QmExvT0o59kgisI00lD0+EqfM7xFTyBlFRRiZO7MmHH2ag02m46aahPPTQeLp0CQ90aEIIIY7DCSfdL7/8Mq+//jpXXHEF77zzDn/729/o3bs3Dz/8MGVlZf6IUQhxHMot5Szft5wfcn/gl/xfcLgcPudEGiKZ2H0iM/rPoGdkz5YPUoijiLrsMowpydhdLswVFWwqL6fG4f06TgwOZkRsLEn9+6OcfnLrV7WRkYRNmtQcIQt/6cBTyVVV5fvvd3LWWb3RahsLoj3++ES0Wg2PPTaBvn1jAhegEEKIE3bCSffevXs9W4MFBwdTXe3ey+nqq69m1KhR/Pvf/27eCIUQR1RcV8yyvcv4MfdHNhZuxKk6fc6JNcYyucdkzkw6k+EJwwnSyJo/0ToFjRnNVqOR9evXU1fnvSK7T58+jBs3jp49ewYmONEyjjSVvNcZMP0ZiE8OWGgt4eef93D//ctYvz6Pt966gGuvHexp69cvlsWLLwpccEIIIU7aCSfdCQkJlJaWkpSURFJSEuvXr2fQoEHs3r0bVZXdT4Xwt/yafH7c+yNLc5fyR9EfqE3sOtw5pDNnJZ3FmUlnMjh+MFqNNgCRCnF8rHo925P7s/OXX7A6vT84Sk5OZvz48XTt6v993kUAqSpkL4Hv/g4Vh00lP/tJSL+wXU8l//33fObO/YnvvsvxHHv44eVccYUJg+Gkyu8IIYRoRU74J/mkSZP4+uuvGTp0KDfccAN33XUXn3zyCRs3buSii078E9iXX36ZZ555hvz8fNLT03nhhRcYN27cEc+3Wq3MmzePRYsWUVBQQLdu3XjggQe4/vrrT7hvIdqKvVV7WZq7lB9zf8Rcam7ynG5h3TyJtinOhEaRfVrFyXGUl1Pz00+4rNZjn3wKahwONuzMIev883DqdHAw4VYUhfT0dMaNG0fnzp39GoNoBcp2wZK/NzGV/FYY/7d2PZU8J6eMhx5azgcfeP9cT0+PZ/78yej18oGpEEK0ByecdL/++uu4Dm65cvPNNxMTE8Pq1as577zzuPnmm0/oXh9++CF33nknL7/8MmPGjOG1115j2rRpZGZm0qNHjyavufTSSyksLGThwoX07duXoqIiHA7ftavC/3rUwfQqA8ZqePv+NX7tq67Sv7/8t0Y7K3byQ+4P/Jj7I9vLtzd5Tu/I3pyZdCZnJZ1FcnTy/7N333FVV/8Dx1/3svcUQUVwpLgVceBWUBylttTS0lyVmqm5zT0zNa2+mZmrpZamPysXTnCPHJgkDhQXTgTZcO/n9wd58SogKJfLeD8fDx7F+Yzzvhfh3vc973MOqmI8ElTYnDu4jwO//kRqctKzT84HCTExBdIPwLUPPiTp1CmD3T/Bxppwn2pEVqyA1tpa165WqahTty7NmjXDxcXFYP2LQiItKaOUfN9CeHyxxwotoOO8Yl1KfuPGQ6ZP38v3358gPT1zGzsvLwemTWtNz5619OZzCyGEKNrynHSr1WrU6swXgm7dutGtWzcArl+/nqcSwAULFtCvXz/69+8PwMKFC9m2bRuLFy9m9uzZT52/detW9u7dy6VLl3B2zlhEROb3GU/dB+Cgzfi3kPCgYJJic8vi+6m/oij8e//fjBHtqB1ExkZmeV5Vp6q6RLuSY6UCjlI8cuDXn7h/41qB92tuafitgZLPnjXIfePs7AivXo0rXl4oj72OmKSnUyHyMu2nTsG1cmWD9C0KkRJeSh4Tk4SPz9c8fJg5Z71UKWsmTGjOBx/4STm5EEIUQ/nylz06OpqZM2fy/fffk5SUu1Gf1NRUjh8/ztixY/Xa27Vrx4EDB7K8ZtOmTfj5+TF37lx+/PFHbGxs6Ny5M9OnT8fKKus3oikpKaQ8ViIZFxeXy0clnsX0v6nEWhTsHC0N3p+5pQkNX6lo8H4KklbRcubuGV3p+LX4rJO4Wq61CPQKJLB8IOXts64CEQXr0Qi3SqXGxsmpQPo0t7SiafdeBdIXgGkZD9w+/viF73M7MZEjt25x/sEDvXYztZo6rq74urnh+v77mHt6vnBfopC7fyljC7Dz2zLbSkgp+SNOTlZ061aDZctOYGtrzsiR/owY4Y+dnYWxQxNCCGEguU66Hzx4wODBg9m+fTtmZmaMHTuWIUOGMGXKFObNm0eNGjVYvnx5rju+e/cuGo3mqfl6pUuXJjo6OstrLl26xL59+7C0tGTDhg3cvXuXQYMGcf/+/Wz7nj17NlOnTs11XCLvkkzgozlNjR1GkaHRajhx+wQ7onaw48oObiXeeuocFSrqudXTzdF2t3E3QqQiN2ycnHh/8Spjh2EQJo6OOHTp8tzXX716lZCQEM6fP6/XbmlpSePGjWnYsCHWj5WXi2KshJaSp6Vp+OWXMN56q5be/OwpU1phb2/BuHHNKFXKxogRCiGEKAi5TrrHjx9PSEgIvXv3ZuvWrQwfPpytW7eSnJzMli1baNmy5XMF8OQcVEVRsp2XqtVqUalU/Pzzzzg4OAAZJepvvPEG//vf/7Ic7R43bhwjRozQfR8XF4enjKaIApamTeNo9FF2XNnBzqid3E9+ek97E5UJfu5+tC3flgCvAFytXI0QqRAvRlEUIiMjCQkJ4fLly3rHbGxs8Pf3p0GDBlhYyKheiXFuC2wZ/UQpuQcEzSq2peRarcK6dWf59NNdnD9/n4cPUxkypKHueLly9ixYEGTECIUQQhSkXCfdf/31FytWrCAwMJBBgwZRuXJlqlSpwsKFC5+rY1dXV0xMTJ4a1b59+3a2q9V6eHhQtmxZXcINUK1aNRRF4dq1a7z00ktPXWNhYSFv7oRRpGpSOXTzEMFXgtl9dTexKbFPnWOqNqWxR2PaebWjlWcrnCwLpkxZiPymKAoRERGEhoZy7Zr+NAl7e3uaNm2Kr68vZmayT3yJkV0peeNB0HI0WNgZLzYDURSF4OBLjBu3k7//vqlrnz49hP79fbG0lPnaQghREuX6r/+NGzeoXr06ABUrVsTS0lK3ANrzMDc3p379+gQHB/Pqq6/q2oODg+mSTTlj06ZN+e2334iPj8fWNmPeV0REBGq1mnLlyj13LMXFheO3OfLHJVKTNc8+OR9YFUw3RUpSehL7r+8n+EowIddCiE+Lf+ocCxMLmpZpSqBXIC09W2Jvbm+ESIXIH1qtlrNnzxIaGsqtW/pTJZydnWnWrBm1a9fG1FSSjRKjhJaSHz58jXHjdrJ792W99pYtvZgzJ1ASbiGEKMFy/Qqg1Wr1RihMTEywsXmxeUgjRozgnXfewc/PD39/f7777juioqJ0W4+NGzeO69ev88MPPwDw9ttvM336dN577z2mTp3K3bt3GTVqFH379s12IbWS5Mgfl4iJTiyw/tRklASmF7/KwDxJSEsg5FoIwVeC2Xd9H0npTy8maG1qTYtyLQj0CqR52eZYm8k8VlG0aTQawsLCCA0N5d69e3rH3NzcaN68OTVq1NDb7UKUAOe2ZOy5/eBKZpudx3+rkr9WLEvJw8PvMGHCLjZs+FevvV49d2bPDqBdu0qynaMQQpRwuU66FUWhT58+ulLt5ORkPvjgg6cS799//z3XnXfv3p179+4xbdo0bt68Sc2aNdm8eTNeXl4A3Lx5k6iozDlgtra2BAcH89FHH+Hn54eLiwvdunVjxowZue6zOHs0wq1SgbWD4Uvqbz9MJllROONY8t5MxKbEsufqHnZc2cGBGwdI1aY+dY6dmR2tPFsR6BVIkzJNsDQ1/ArvQhhaWloaJ0+eZP/+/Tx4YjXyMmXK0KJFC6pUqSLJdklTAkvJAR48SKZ+/e9ISkrXtVWu7MyMGa15880aqNUl7/VRCCHE03KddPfu3Vvv+1698mfbmkGDBjFo0KAsj61cufKpNh8fH4KDg/Ol7+LK2sGCPgWwmnjjWTuJjkvB3bpkJJP3ku6x6+oudlzZwZGbR0hX0p86x8nCiTbl2xDoFUgj90aYmcj8VVE8pKamcuzYMQ4cOEB8vP60CS8vL1q0aEHFihVlRK+kSUvKKCPf94V+Kbl384xScjcfo4VWEBwdLXn//fosXHgYd3dbJk9uSb9+9TAzM3n2xUIIIUqMXCfdK1asMGQcQhRKtxJusTNqJzuidnD81nG0ivapc0pZlSKgfABtvdriW9oXU7XM2xPFR1JSEkeOHOHQoUMkJelPnahcuTLNmzfXVSeJEqaElZLHx6fyzTdHGTy4ATY25rr28eOb4+5uy0cfNcLaWj5oFUII8TTJDoR4glbRsi5iHZsubuLUnVNZnuNh40GgVyBtvdpSp1Qd1CoppRXFS7KFBf+4u3N+4UJSUlL0jlWrVo3mzZtTpkwZI0UnjOp+JGwdCxFbM9uKcSl5Sko63313nOnTQ7hzJxGtVmHs2Ga646VK2TBmTLMc7iCEEKKkk6RbiCdsidzC9EPTn2ovb1eetl5taevVluou1aWMthBKS01FDcQ/iGHJh72fef6LSIiJMej9jSUuLo6/a9fiYoUKaExN4b+EW6VSUbNmTZo3b46bm5uRoxRGUcJKyTUaLb/8EsakSXu4fPmBrn3+/IMMG9ZYViMXQgiRa/KKIcQTrsdf1/2/t7037Su0J7B8IFWcqkiiXcilJiViqbJG0WqJv3/v2RfkA3PL4rFzwv3799m/fz8nT55E89JLuna1Wk3dunVp1qwZzs7ORoxQGNW5rbBldIkoJVcUhT/+iGDChF2cOXNb71j37jWYPr21JNxCCCHyRF41hMjBSL+RtPRsaewwRC4pisJ/O9lh6+xi8P7MLa1o2j1/FpU0ltu3b7Nv3z7CwsIynr//mKSnUyUmhvazZuHg4GDECIVRZVtK/iG0HFPsSslDQq4wduwODh68ptceFFSJWbMC8PX1MFJkQgghijJJuoUQxY5Kreb9xauMHUahduPGDUJDQwkPD9drNzc3p9LpMKqEh+NQqaIk3CVVWhLsXwShC0pEKTlkbP/VsePPJCSk6doaNSrL7NkBtG5dwYiRCSGEKOqeK+n+8ccf+fbbb4mMjOTgwYN4eXmxcOFCKlSoQJcuXfI7RiGEEPkkKiqKkJAQLly4oNduZWVF48aNadiwIVcaNkJJS8vmDqLYy66UvN0MqPl6sSolf5yjoyXDhzdmxoxQqlVzZdasALp0qSrTioQQQrywPCfdixcvZtKkSQwbNoyZM2ei0WgAcHR0ZOHChZJ0C4M4decUu6N2k6Y1fCJw+s5pg/chREFSFIVLly4REhLClStX9I7Z2trSpEkT6tevj4WFhZEiFIXC/UjYOg4itmS2FdNS8ps3H/L55weYNKkljo6WuvaRI5tQubIzvXrVxsREdqUQQgiRP/KcdH/11VcsXbqUrl27MmfOHF27n58fI0eOzNfgioO/Tt9kQfA5ElI0Bu/rtYdgg4rbD5NpPGunwfu7/TDZ4H3Ep8az8O+FrD231uB9ZUVGOF7MhXWhpB+JxaSAZrJYqIrHomb5RavVEhERQWhoKNevX9c75uDgQLNmzahbty5mZrK3cIlWgkrJHzxI5vPP97Nw4WESE9OwsjJl5swA3XEHB0t6965rvACFEEIUS3l+JxwZGUm9evWearewsCAhISFfgipOFgSf4+KdgnletFrL//4L0XGGT4gfsbEwMch9Q6+FMu3QNKITog1y/2exN7enrltdo/RdXKQficVWXfBzgjWkF3ifhYlWq+Wff/4hNDSU27f1V192cXGhWbNm1K5dGxMTw/zuiiIkq1JyW/eMVcmLUSl5UlIaX399hNmz9xETk/n6uGzZCSZObCmrkQshhDCoPL/KVKhQgZMnT+Ll5aXXvmXLFqpXr55vgRUXj0a41Spws7N8xtkvRv0Q0IBaDe72hu3rERsLEz5pVzVf7/kg+QFzj87lj0t/6NqsTK0YXHcwdUrVyde+clLFqQrWZtYF1l9x9GiEW6toSVGSCqRPDemYNSqZi3+lp6dz+vRp9u3bx/379/WOlS5dmubNm1O9enXUaimbLfFiLsOWsU+Xkjf6AFqNLTal5OnpWlasOMHUqXu5fv2hrt3c3IQPP/Rj/PjmknALIYQwuDy/0owaNYrBgweTnJyMoigcOXKE1atXM3v2bL7//ntDxFgsuNlZcmh8wLNPfAErx+4n4UHKf301NWhfhqAoCtuubGP24dncT85MGBp7NGay/2TK2ZUzYnTiRaQoSbw0t72xwyi20tLSOHHiBPv37yc2NlbvWNmyZWnRogVVqsg+84LMUvJ9X0D6YxVR3s2h4+fgVs14seUjRVFYt+4sn366m4iIe7p2lQrefbcOU6a0wtvb0XgBCiGEKFHynHS/9957pKenM3r0aBITE3n77bcpW7YsixYtokePHoaIUZQAtxNvM+PQDHZf3a1rszO3Y5TfKLpW7irJghBZSElJ4dixYxw4cOCp6T3e3t60aNGCChUqyO+PyBCxLaOUPOZyZlsxLCUHiI1NYeDAP3nwIPODhS5dqjJjRhtq1nQzYmRCCCFKoueqqRowYAADBgzg7t27aLVa3NzkBUw8H0VR2HBhA/OOzuNhWmbpX0D5ACY0mkAp61JGjE6IwkFJTSVmzVpS/tvmKxk4A5xRQcoTiVJ5RaGeAu6XIuFSJM+zIoKSXrLnxBc7WZWSq0wyVyW3tDdaaIbi6GjJ2LFNGTt2Jy1aeDFnTgD+/p7GDksIIUQJleeke+rUqfTq1YtKlSrh6upqiJhECXH14VWmHpjK4ejDujZnS2cmNJpAW6+2MjonxH/itgdza9Yski0sOOdTlQuVK5P++IrjikK5q9eofvYsTg8eAPAgH/pVqWTud5GWlvxfKfmCYl1K/u+/d5k+PYQvvgjCzc1G1/7RR42oW9eddu0qyeuJEEIIo8pz0r1+/XqmTZtGgwYN6NWrF927d6dUKRmNFLmn0Wr45d9f+OrEVySlZy6u1blSZ0b5jcLR0tF4wQlRCN2PiuJv33pcqlgRjWnmn22VVovXlStUCw/HPu5hDnd4Pnbtg/L9nqKAlIBS8qtXY5k6dS8rVpxEq1VwdbVi0aIOuuPW1mYEBVU2YoRCCCFEhjwn3adPn+aff/7h559/ZsGCBYwYMYLAwEB69epF165dsbaW1Z5F9i7EXGDygcmcvnta1+Zh48Ek/0k0K9vMiJEJUfjcu3ePffv2cer2LbRVqujaTdRqalesSKNq1XC0tTVI3yZ2dpiVKWOQewsDirkMW8fBuc2ZbcWslPzevURmz97H118fIeW/HUIANm48x2eftZXVyIUQQhQ6z/XKVKNGDWbNmsWsWbPYv38/v/zyC8OGDeODDz4gLi4uv2MUxUCaJo1lZ5ax5PQS0rWZ80Xf8nmLj30/xsbMJoerhShZbt26xb59+zhz5gyKoujaTdLTqevlRcvu3bG3L/rJk8hH2ZWSezXLKCUvXfS39IyPT2XhwkN8/vkB4uJSdO0ODhaMGdOUoUMbScIthBCiUHrhVycbGxusrKwwNzfn4cP8L28URd+Zu2eYdGAS52PO69q87b2Z2mQqvqV9jRiZEIXL9evXCQ0N5d9//9VrN1epqHzmDFXORVBp4RfYScItHlfMS8lTUzV8991xpk8P4fbtzFX6LS1NGTq0IWPGNMPZ2cqIEQohhBA5e66kOzIykl9++YWff/6ZiIgIWrRowZQpU3jzzTfzOz5RhCWlJ/HNyW/44ewPaBUtACYqE96r+R4f1PkACxMLI0coROFw+fJlQkNDuXjxol67tbU1jRs3ptI/Z4kLW2Ok6EShVQJKyQHS0jTMmJGZcJuYqOjf35eJE1tQtmzxeIxCCCGKtzwn3f7+/hw5coRatWrx3nvv6fbpFuJxR6OPMuXAFKIeRunafJx9mNZkGtVciseKuUK8CEVRuHjxIiEhIURFRekds7Ozo0mTJtSvXx9zc3Puhv+bzV1EiVQCSskfZ2NjzqRJLRk8eDPdu9dg2rTWVKniYuywhBBCiFzLc9LdunVrvv/+e2rUqGGIeEQR9zD1IV8c/4LfIn7TtZmrzfmw7of0rtEbM7VZDlcLUfxptVrOnTtHSEgIN2/e1Dvm6OhIs2bNqFu3LqamMjdVZCFi+3+l5JGZbbbu0G4G1HqjyJeSh4ZeYcqUvaxY0YXy5R107f37++LvX4569TyMGJ0QQgjxfPL8rm7WrFmGiEMUAyHXQph6cCq3E2/r2uq51WNKkylUdKhoxMiEMD6NRsM///xDaGgod+7c0Tvm6upK8+bNqVmzJiYmJkaKUBRqMVf+KyX/K7OtGJWSnzoVzfjxu9i8OWPtjylT9rB8eRfdcXNzE0m4hRBCFFm5SrpHjBjB9OnTsbGxYcSIETmeu2DBgnwJTBQd95Pv89mRz9gcmTmv0MrUimG+w+jh0wO1Sm3E6IQwrvT0dE6dOsW+ffuIiYnRO+bu7k7z5s2pVq0aarX8nogspCXDgS8hdH6xLCW/dCmGiRN3s3p1GI8t1M/RozdITk6X1ciFEEIUC7l6NTtx4gRpaWm6/xcCMuakboncwpwjc4hJyUwmmpZpyiT/SZSxlT1+RcmVmprK33//zYEDB57aStHT05PmzZvz0ksvoSri5cDCgLIsJS8N7WYW+VLy6Oh4ZswIYcmS46Sna3Xtnp72TJ3ainfeqYOpqXwQJYQQonjIVdK9e/fuLP9flFyJaYmMCR3Dnqt7dG325vaMaTiGVyq+IomEKPYSjhzhwZo1aFNS9dpTVSrCLS05Y21F8hOj12VSU6mbmIT7nbuo/j7BtVz2lXr5cv4ELYqGYlxKnpiYxqxZoXzxxSESE9N07S4uVkyY0JwPP2wgo9tCCCGKnTy/svXt25dFixZhZ2en156QkMBHH33E8uXL8y04Q2ozbw+mljYG7+f2w+Rnn1QEbbiwQS/hbuvVlvGNxuNq5Wq0mIqqcwf3ceDXn0hNTjJ2KPmutU13KKaDVTfGjiX9RuZCaCnm5pyv8hIRVaqQZm6ud26Za9epfvYsLvfvA5DA81PJnO/iK9tS8qbQcV6RLyWHjO2+fvrptC7htrEx45NP/PnkkybY28s2kkIIIYqnPCfdq1atYs6cOU8l3UlJSfzwww9FJum+/TAFdWrBvXm1sSheb5TjU+N1//9J/U/oU7OP8YIp4g78+hP3b+R2zLOI+e9zreJY+ZB+5y4ASZaWnKtalYuVK5Fu9tjq/IqCZ9RVqp89i2NsbL70aV65EtYNG+bLvUQhcz4YNo8qdqXkiqLo/f5bWJgybVpr+vffxIcf+jFhQgvc3Az/AbgQQghhTLlOuuPi4lAUBUVRePjwIZaWlrpjGo2GzZs34+bmZpAgDUGtAnd7y2efmA9sLEz4pF3VAunLGCo6ysrkL+LRCLdKpcbGycnI0eQv1X/l1eZW1kaOJP8lWFvxb+VaXKpUCe1jZeRqtZpaVavSxNcXl3z+eZq4uBTLDzBKtOxKyRt9AK3GFtlSckVR+P33cCZP3sP69d2oWjWzCqpnz1q0bOmFl5ej8QIUQgghClCuk25HR0dUKhUqlYoqVao8dVylUjF16tR8Dc6QXG0tODQ+wNhhCKFj4+TE+4tXGTuMfHVz1mE0camYPVFuXZTdvXuXffv2cSooCOWxZNvExARfX1+aNm2Ko6Oj8QIURUNaMhz4CkLnFbtS8p07LzF27E6OHbsBwMSJu/n11zd1x01M1JJwCyGEKFFynXTv3r0bRVFo06YN69evx9nZWXfM3NwcLy8vypSR1aqFEMVTdHQ0oaGhnD17FkVR4L+E21SjoWHz5vj7+z817UaILGVbSj4Dar1ZZEvJjx69zvjxu9ix45Je+61bCaSkpGNhIQukCSGEKJly/QrYsmVLACIjIylfvryUOAohSoRr164REhJCRESEXrtZaipVIiKoqVJTbfp0I0UnipSYK7BtPPz7Z2ZbMSglP3fuLp9+upt1687qtdeuXZrZswPo0KGyvGcQQghRouUq6T59+jQ1a9ZErVYTGxtLWFhYtufWrl0734IToiRwN63AS2VfxczUkpuzDhs7nHyleZj67JMKIUVRuHz5MqGhoVy6pD9qZ21tjb+/Pw5DP8YsKQnL6kW3DFgUkBxLyT+H0jWMF9sLiItL4ZNPtrFixUk0GkXXXrGiE9Ont6ZHj5qo1ZJsCyGEELlKuuvWrUt0dDRubm7UrVsXlUqVUV75BJVKhUajyfcghSjOqlrWx9bEEQBNXNFMUp9FVURW71cUhfPnzxMaGsrVq1f1jtnb29OkSRN8fX0xNzcnPD3dSFGKIuV8MGwZDfcf+/CmGJSSA1hbm7Fv31Vdwl26tA2TJrWkf39fzM2Lxu+8EEIIURBylXRHRkZSqlQp3f8LIfKPiSpjmylF0WLqUDAr6hcklYUJ9u28jB1GjrRaLeHh4YSGhhIdHa13zMnJiWbNmlGnTh1MTWVOqsilbEvJ3/+vlNzBeLE9p9RUjV4ybWqqZubMNrz33v8xZkxTPv64ETY2xWfRRCGEECK/5OodpJeXV5b/L4TIP8lKEi+Nb2nsMEoUjUbDmTNnCA0N5e7du3rHSpUqRfPmzalRowYmJjJqJ3JJV0o+H9KTMtvLN4FO84pkKXlqqoalS48zc2Yomzf3pG5dd92xV1/1oXVrb5ycrIwYoRBCCFG45XnYZtWqVbi6utKpUycARo8ezXfffUf16tVZvXq1JOVCiEIvPT2dkydPsm/fPh48eKB3zMPDgxYtWlC1alXUj20JJsQznd8BW0bpl5LbuGWUktfuVuRKybVahdWrw5g4cTeRkQ8AGD9+J5s399Sdo1KpJOEWQgghniHPSfesWbNYvHgxAAcPHuTrr79m4cKF/PnnnwwfPpzff/8934MUQoj8kJqayvHjxzlw4AAPHz7UO1a+fHlatGhBpUqVZKVlkTfFrJRcURQ2bz7P+PG7OH36lt4xGxtz2f5LCCGEyKM8v2pevXqVypUrA7Bx40beeOMNBg4cSNOmTWnVqlV+xyeEEC8sOTmZI0eOcOjQIRITE/WOVapUiebNm+Pt7W2c4ETRlZ4CB76EkOJTSr5vXxTjxu1k374ovfa2bSsya1YAfn5ljBSZEEIIUXTlOem2tbXl3r17lC9fnu3btzN8+HAALC0tSUpKesbVQghRcBISEjh06BBHjhwhJSVF75iPjw/NmzenbNmyRopOFGnFrJT8wYNk3nlnA3/+qb8ffYMGZZg9O4CAgIpGikwIIYQo+vKcdLdt25b+/ftTr149IiIidHO7//nnHxkpEkIUCnFxcRw8eJBjx46Rlpama1epVNSsWZNmzZpRunRpI0YoiqwHUbB1XLEpJX/E3t6Cmzczp1xUrerCrFkBvPqqj0y3EEIIIV5QnpPu//3vf3z66adcvXqV9evX4+LiAsDx48d566238j1AIYTIrZiYGPbv38+JEyfQaDS6drVaTZ06dWjWrJnub5YQeZJtKbk/dJwH7jWNF9tzePAgGUfHzC0K1WoVs2cH0LfvJqZObcW779bB1FQWEhRCCCHyQ56TbkdHR77++uun2qdOnZovAQkhRF7duXOHffv2cfr0aRRF0bWbmpri6+tL06ZNcXAomiOQohA4vwO2jIb7FzPbimgpeWxsMvPmHeCLLw6xbVsvmjYtrzsWGFiRCxc+kkXShBBCiHz2XK+sDx48YNmyZYSHh6NSqahWrRr9+vWTN7VCiAJ18+ZNQkNDOXv2rF67ubk5DRo0wN/fH1tbWyNFJ4q87ErJGw6E1uOKVCl5cnI6//vfEWbN2sf9+xkj9WPH7iQkpI+ufFylUknCLYQQQhhAnl9djx07RlBQEFZWVjRs2BBFUfjiiy+YNWsW27dvx9fX1xBxCiGEztWrVwkJCeH8+fN67ZaWljRu3JiGDRtibW1tpOhEkZeeAge+gpB5Rb6UPD1dy6pVJ5kyZS/XrsXp2s3M1NStW5rUVI0k2kIIIYSB5fmVdvjw4XTu3JmlS5diappxeXp6Ov3792fYsGGEhITke5BCCKEoCpGRkYSEhHD58mW9YzY2NjRp0gQ/Pz8sLCyME6AoHi7sgM1ZlZJPh9rdi0wpuaIobNjwLxMm7OLff+/q2lUq6NmzNlOntqJiRSfjBSiEEEKUIM810v14wg0Z8yZHjx6Nn59fvgYnhBCKohAREUFISAjXr1/XO2Zvb0/Tpk3x9fXFzMzMSBGKYuHBVdg2DsL/yGxTqaHh+0WulDwmJomgoJ84evSGXvvLL1dh5sw21K4tK/cLIYQQBSnPSbe9vT1RUVH4+PjotV+9ehU7O7t8C0wIUbJptVrOnj1LaGgot27d0jvm7OxMs2bNqF27tt4HgELkWTEqJX/E0dESK6vMD6GaNvVkzpxAmjUrn8NVQgghhDCUPL9b7d69O/369WPevHk0adIElUrFvn37GDVqlGwZJoR4YRqNhtOnT7Nv3z7u3bund8zNzY3mzZtTo0YN1GrZzki8oGJSSn7lygPKl3fQWxBt9uwAPvzwL2bNakPHji/JXttCCCGEEeU56Z43bx4qlYp3332X9PR0AMzMzPjwww+ZM2dOvgcoCg+NVsOl2EucvnOafdf3GTscUQQ8WL+ee98vQ0lJeea5GpWKi+7u/ONZjgRLS71jLnEPqRkVRbn7oajWreeSoQLOi7Q0Y0cgnlcxKSW/di2OadP2snz5CTZv7km7dpV0x5o08eTkyfcl2RZCCCEKgTwn3ebm5ixatIjZs2dz8eJFFEWhcuXKslJwMXQ36S5hd8I4ffc0YXfCOHPvDAlpCU+dZ2limcXV+Svx9B3igq+gpGgM3hdAWmoqqUmJens+G4qFysrgfRjL7bmfo4mNzfGcNFNTLlaqxDmfqiRb6T8XpW7fpvo/Zyl96xYqIN2AsT4vtfztKzpyLCX/HNxrGS+2PLh/P4k5c/bx1VdHSE7O+K0YN24ngYEVUaszk2xJuIUQQojCIddJd2JiIqNGjWLjxo2kpaURGBjIl19+iaurqyHjEwUkRZNC+L1wTt85TdjdMMLuhnE9/nqO16hValqVa0U9t3oGjy8u+Arpd5KefWI+UQOWKmsowPesmkKZUr4YTWJixv+YmGDipL9ScqqpKec8yxHh6UmKubneMY+7d6kZeZlSjxL2Qvp3xsTBAZf3Bxo7DJEbWZaSl4J2M4pMKXlCQiqLFh1m7tz9xMZmVo/Y21vw6qs+pKdrMTc3MWKEQgghhMhKrpPuyZMns3LlSnr27ImlpSWrV6/mww8/5LfffjNkfMIAFEXh6sOrnL57OiPJvhPGvzH/kq7NOelzs3ajtmttapeqTS3XWlR3qY61WcGM8ulGuFVgYmee88n5IP5BDIpWm9FlAcwd1pCOWaOiUdL6PCyrVqXC7+sBSEhI4ODBgxw9epSUJ8rOq1WrRvPmzSlTpowxwhTF0YOrsG08hG/KbFOpoeFAaDUOrByNFlpupaVp+P77v5k2LYTo6Hhdu4WFCUOGNGTs2Ga4ukrFhRBCCFFY5Trp/v3331m2bBk9evQAoFevXjRt2hSNRoOJiXyyXpjFpcZx5s4ZTt09RdidjFHsBykPcrzG0sSS6i7VqVOqDrVK1aKWay3cbdwLJuAcmNiZ4zG+kcH7WfJhb+Lv38PW2YX3F68yeH8lQWxsLAcOHOD48eO69SAgowS2Vq1aNGvWDDc3NyNGKIqV9BQ4+HVGKXlaYma7Z2PoNK/IlJLHxCTRoMFSLl6M0bWp1Sree68ukye3xNOz+H5YJ4QQQhQXuU66r169SvPmzXXfN2zYEFNTU27cuIGnp6dBghPPJ12bxtp/12bMxb4bRmRs5DOvqeBQQTeKXbtUbSo7VsZULVsxiRcXb2vD32XLcnHRIrT/VQ8AqNVq6tWrR9OmTXF2djZihKLYubATNo96upS87XSo06NIlJI/4uRkRdWqrrqk+/XXqzF9emuqVStl5MiEEEIIkVu5zqo0Gg3mT8y7NDU11RuxEk+7cPw2R/64RGqy4RcAS4hNBlTcS77PosMzsj3P0cJRVyJeu1RtarrWxN7cPs/9XVgXSvqRWEzyvh5fnlmqrFCp1MQ/iGHJh70N3l9CTMyzTxI5io+P56hvPSK9vFDUavgv4TY1NaV+/fo0adIEBwcZpRP5qBiUkv/9903q1XPXWwRt1qw2pKZqmDmzDQ0bljVidEIIIYR4HrnOlhRFoU+fPlhYWOjakpOT+eCDD7CxsdG1/f777/kbYRF35I9LxEQnPvvEfJHxJi3NJFnXYqo2pZpzNV2CXdu1NuXsyuXLqrbpR2KxVRds0pSWnkz8/XvPPjGfmFsW35XFDUWj0XD06FF2795NSoUKunYLCwsaNGhA48aNsbW1NWKEotgpBqXkZ87cZsKEXWzadI51697k9der647VqeNOcPA7RoxOCCGEEC8i10l3795Pjy726tUrX4Mpjh6NcKtUYO1g8YyzX8ytxFukmSQTXjGE0Q1GU7tUbXycfbAwMUy/j0a4tYqWFMXwK4trlDTOp53A1tnF4H1BRsLdtLv8G8+LS5cusWXLFu7cuaNrM0tNpcb9GIK+WICVlXyIIfLZhZ2wZTTcu5DZVoRKyS9ffsDkyXv48cdTPNqhcMKEXXTp4oOpqeEXcRRCCCGE4eU66V6xYoUh4yj2rB0s6DOnqUH7qPtDXTSKhhouNXinesGNiqQoSbw0t32B9OXDKwXSj8ibmJgYtm/fTnh4uF57hcjL1D55EsdKlSThFvkru1LyBgOg9fhCX0p++3YCM2eGsHjxMdLSMtc6KFvWjpEjmxgxMiGEEELkN1kpSwjx3NLS0ti/fz/79u3TW9+hbNmydOjQgYcdOkJamhEjFMVOTqXkHT8Hj9rGiy0X4uJSmD//APPnHyQhIfN3w8nJkvHjmzN4cAOsrMyMGKEQQggh8psk3UKIPFMUhfDwcLZt20ZsbKyu3cbGhsDAQOrUqYNarSY8h3sIkWfZlpJPg9o9QF24y7FjYpKoWvVr7tzJ/LDA2tqM4cMbM3JkExwdLY0YnRBCCCEMRZJuIUSe3L59my1bthAZmbkVnVqtplGjRrRs2RJLS0kcRD4r4qXkjzg5WdG6dQV+/fUfTE3VvP9+fT79tAXu7rKwoBBCCFGcSdIthMiV5ORk9uzZw+HDh1EerfgEVKxYkQ4dOlCqlOwbLPJZeup/peSfP1FK3gg6zivUpeSKorBlywWCgiphYpI5Aj99emvMzU2YOrUVFSs6GS9AIYQQQhQYSbqFEDnSarWcPHmSnTt3kpCQoGt3dHQkKCgIHx+ffNmCTgg9F3fB5tFw73xmm7UrtJte6EvJd++OZOzYnRw5cp2VK7vQu3dd3bEqVVz48cdXjRecEEIIIQrccyXdP/74I99++y2RkZEcPHgQLy8vFi5cSIUKFejSpUt+xyiEMJJr166xefNmbty4oWszNTWlefPmNGnSBDMzWfBJ5LPYaxml5Gf/L7OtiJSS//33TcaN28n27Rd1bZMm7aFHj5pYWMhn3EIIIURJled3AYsXL2bSpEkMGzaMmTNnotFk7EPt6OjIwoULJekWohiIj49nx44dnDx5Uq+9evXqtGvXDkdHR6PEJYqxIlxKfv78PSZO3M3atf/otdes6casWW0wNzcxUmRCCCGEKAzynHR/9dVXLF26lK5duzJnzhxdu5+fHyNHjszX4IQQBUuj0XD48GH27t1LSkqKrr1UqVJ06NCBihUrGjE6UWwV0VLyGzceMm3aXr7//m80msx1Dry9HZk+vTVvvVVTbz63EEIIIUqmPCfdkZGR1KtX76l2CwsLvfmeouB1udKQN2I7YK6y4HzIVoP3Z6GyMngfouBcvHiRLVu2cPfuXV2bpaUlrVu3xs/PDxMTGa0T+SzbUvL+0HpCoS4lf7T9V3x8qq6tVClrJk5swcCB9aWcXAghhBA6eX5XUKFCBU6ePImXl5de+5YtW6hevXq+BSby7o3YDjiZuhZ4vxrSC7xPkX9iYmLYtm0b//77r167r68vAQEB2NjYGCkyUWylp8Kh/8HeuUWulPwRJycrunWrzvLlJ7GzM2fUqCYMG9YYOzsLY4cmhBBCiEImz0n3qFGjGDx4MMnJySiKwpEjR1i9ejWzZ8/m+++/N0SMIpfMVeYAaBUtKUpSgfSpIR2zRg4F0pfIX6mpqezbt4/9+/fr1mYAKFeuHB06dKBs2bJGjE4UWxd3w+ZRT5eSt50Gdd4qlKXkaWkafvrpND171tabnz1lSiucnKwYO7YZrq7WRoxQCCGEEIVZnpPu9957j/T0dEaPHk1iYiJvv/02ZcuWZdGiRfTo0cMQMYo8StImUPXzjsYOQxRSiqJw9uxZtm3bRlxcnK7dxsaGtm3bUrt2bdSFMPERRVzsNdg2Ac5uzGzTlZKPB6vCt2e1Vqvw66//8Omnu7h4MYbExDQGD26oO+7p6cC8ee2MGKEQQgghioLnmnQ2YMAABgwYwN27d9Fqtbi5ueV3XEIIA7h16xZbtmzh8uXLuja1Wk3jxo1p0aIFlpaWxgtOFE/ZlZKXawid5hfKUnJFUdi27SLjxu3k5MloXfv06SH07+8r87WFEEIIkScv9M7B1bXg5w8LIfIuKSmJ3bt3c/ToURQlc5XlSpUq0b59e0qVKmXE6ESxVQRLyQ8evMq4cTvZu/eKXnvr1t7Mnh0gCbcQQggh8uy5FlJTqVTZHr906dILBSSEyD9arZYTJ06wc+dOEhMzRxmdnJwICgqiatWqOf4+C/FcYq//tyr5xsy2Ql5KfubMbSZM2MWmTef02n19PZgzJ4DAwIryuyKEEEKI55LnpHvYsGF636elpXHixAm2bt3KqFGj8isuIcQLunr1Kps3b+bmzZu6NjMzM5o3b46/vz9mZmZGjE4US+mpcOib/0rJH9tCslxD6DQPPOoYL7Yc3L+fRIMGS0lOztyJ4aWXnJk5sw2vv14dtVqSbSGEEEI8vzwn3R9//HGW7f/73/84duzYCwckhHgxDx8+ZMeOHZw6dUqvvUaNGrRr1w4HB1ltXhjAxd2wZTTcjchsK+Sl5I84O1vx/vv1WbToMGXK2DFlSkv69KmLmZnsTS+EEEKIF5dvk9M6dOjAuHHjWLFiRX7dUgiRB+np6Rw+fJi9e/eSmpqqa3dzc6Njx454e3sbLzhRfMVeh+0T4J8NmW0qNfj1gzYTCl0peVxcCv/73xGGDm2EjY25rn38+OaUK2fP4MENsLKSKhAhhBBC5J98S7rXrVuHs7Nzft1OCJEH58+fZ+vWrdy7d0/XZmlpSZs2bahfvz4mJjJiJ/JZESslT05O59tvjzFzZih37yaiUqkYO7aZ7ribmw0jRzYxYoRCCCGEKK7ynHTXq1dPbzEZRVGIjo7mzp07fPPNN/kanBAiZ/fv32fbtm2cO6e/+FP9+vVp06YNNjY2RopMFGuX9mSsSq5XSu7yXyn524WqlFyj0fLjj6eZPHkPUVGxuvYFCw4yfHhjWY1cCCGEEAaX53cbXbt21fterVZTqlQpWrVqhY+PT37FVWykJISTEhtCalwaSz78zqB9tbbpZtD7i8IjNTWV0NBQDhw4gEaj0bV7enrSsWNHPDw8jBidKLaKUCm5oij83/+dY8KEXZw9e0fv2Ntv12LatFaScAshhBCiQOTpHUd6ejre3t4EBQXh7u5uqJiKlaSYUBTtfRQg/r6BO7ORFXaLO0VROHPmDMHBwcTFxenabW1tadeuHbVq1ZJtjUT+y7aUvAF0nAdl6hottKzs2XOZsWN3cPjwdb32jh1fYubMNtStK69fQgghhCg4eUq6TU1N+fDDDwkPDzdUPMWOon20oJUKWwPPeVdUCgAa82ecKIqk6OhotmzZwpUrV3RtarUaf39/WrRogYWFhRGjE8VWESolh4ztvzp1+oXExDRdm79/OebMCaRFCy8jRiaEEEKIkirPtXWNGjXixIkTeHnJm5e8UJvY8v7iVQbt4+Snm7BJB62JjHQWJ4mJiezevZtjx46hKIquvXLlyrRv3x5XV1cjRieKrWxLyftCm08LVSn545ydrRg+vDEzZ4ZSo0YpZs0K4JVXqkgFiBBCCCGMJs9J96BBg/jkk0+4du0a9evXf2qhptq1a+dbcEKUZFqtlr///pudO3eSlJSka3d2dqZ9+/ZUqVLFiNGJYqsIlZLfuPGQuXP3M2VKKxwdLXXto0Y1oWpVF95+uxYmJoVrJF4IIYQQJU+uk+6+ffuycOFCunfvDsDQoUN1x1QqFYqioFKp9BZ1EkI8n6ioKDZv3kx0dLSuzczMjBYtWuDv74+pqSwAJQwgu1LywKlQt2ehKSWPiUli7tz9LFp0mKSkdGxtzZkxo43uuIODJe+8U7i2LBNCCCFEyZXrd+6rVq1izpw5REZG5msA33zzDZ9//jk3b96kRo0aLFy4kObNmz/zuv3799OyZUtq1qzJyZMn8zWm/FTOqhI1HPwwVVtwc9Zhg/bllO5g0PsLw4uLiyM4OJiwsDC99lq1ahEYGIiDg/yMhQHE3YBtE+Cf3x9rVEGDftB6Algbdj2K3EpMTOOrrw4zZ85+HjxI1rUvW3aCiRNbyGrkQgghhCiUcv0O5dFc0vycy7127VqGDRvGN998Q9OmTVmyZAkdOnTg7NmzlC9fPtvrYmNjeffddwkICODWrVv5Fo8h1HBsiL1ZxtxHTVzqM85+MSZkjEKlmBi2H5H/0tPTOXToEHv37iUtLXMBqNKlS9OxY0dZQ0EYRnoqHF4Mez4r1KXkaWkali8/wdSpe7l5M17Xbm5uwuDBDRg3rpkk3EIIIYQotPL0LiW/F6JZsGAB/fr1o3///gAsXLiQbdu2sXjxYmbPnp3tde+//z5vv/02JiYmbNy4MV9jym+mKjMAFEWLqYPlM85+MbcSb5GoTmGP10nq08WgfYn8ExERwdatW7l/P3NPOSsrK9q0aUP9+vVRF5KSXlHMXNr7Xyn5ucy2QlZKrtUq/PbbP3z66W4uXMj8/VCrVfTuXYfJk1vi5eVovACFEEIIIXIhT0l3lSrPXgH28cQhJ6mpqRw/fpyxY8fqtbdr144DBw5ke92KFSu4ePEiP/30EzNmzMhVX4VBkiaRKuNbGrSPDj/URaNoqOFSw6D9iPxx7949tm7dyvnz53VtKpUKPz8/WrdujbW1tRGjE8VWESklB4iLS+GDD/7SKyXv2tWHmTPbUL16KSNGJoQQQgiRe3lKuqdOnZpvc0rv3r2LRqOhdOnSeu2lS5fWWzzqcefPn2fs2LGEhobmeiGplJQUUlJSdN/HxcU9f9BC5IOUlBRCQ0M5ePCg3sKD5cuXp0OHDnh4eBgxOlFsZVdKXtYPOs2DMvWMF1s2HB0tGTOmKePG7aRlSy/mzAmkceNyxg5LCCGEECJP8pR09+jRAzc3t3wN4MmR80eroD9Jo9Hw9ttvM3Xq1DxtlTR79mymTp36wnEK8aIURSEsLIzg4GAePnyoa7ezs6N17dr4eHigun+fpFxWixQJWq2xIxBQJErJz569w/TpISxa1B43t8ytKIcObYSvrwdt21aUvbaFEEIIUSTlOunO7zc7rq6umJiYPDWqffv27adGvwEePnzIsWPHOHHiBEOGDAEy9jFWFAVTU1O2b99OmzZtnrpu3LhxjBgxQvd9XFwcnp6e+fpYhHiWmzdvsmXLFqKionRtJiYm+Pv7U3nzFpKWfs8VI8YniqnsSsn9+kKbTwtFKfmVKw+YMmUvP/xwCq1WoXRpGxYubK87bm1tRrt2lYwYoRBCCCHEi8nz6uX5xdzcnPr16xMcHMyrr76qaw8ODqZLl6cXAbO3t39qG6VvvvmGXbt2sW7dOipUqJBlPxYWFlhYWORr7ELkVmJiIrt27eL48eN6v0NVqlQhKCgIFxcXwoePyOEOxYOZfNBVsDRpcGgx7P0MUjNX+y5MpeR37iQwa1Yo33xzjNTUzGkW//d/5/jss0BZjVwIIYQQxUau39VoDVAmOmLECN555x38/Pzw9/fnu+++Iyoqig8++ADIGKW+fv06P/zwA2q1mpo1a+pd7+bmhqWl5VPtQhibVqvl2LFj7Nq1i+TkzEWgnJ2dad++vf4Uif+ScRMnJ+w7tH/yVkWe2sEBp+7djR1GyZFVKbmVM7SdCnV7Gb2U/OHDFL744hDz5h3g4cPM7Q0dHS0ZO7YpH33USBJuIYQQQhQrRn1n0717d+7du8e0adO4efMmNWvWZPPmzbo9iW/evKlXjitEUXD58mW2bNmit4e8ubk5LVq0oHHjxtkuAmhWpgzukyYVVJiiuIm7Ads/hTPrH2ssPKXkqakavv32GDNmhHDnTqKu3crKlI8/bsTo0U1xcrIyYoRCCCGEEIZh9OGEQYMGMWjQoCyPrVy5Msdrp0yZwpQpU/I/KCGeQ2xsLMHBwZw5c0avvXbt2gQGBmJvb2+kyESxVgRKySEj6Z41K1SXcJuYqBgwwJeJE1tSpoydkaMTQgghhDAcoyfdQhR1aWlpHDx4kNDQUNLS0nTt7u7udOzYkfLlyxsxOlGsRYZklJLf+TezrRCVkj/O1taciRNbMGTIFnr0qMm0aa146SUXY4clhBBCCGFwknQL8ZwURSEiIoKtW7cSExOja7eysiIgIABfX1/UhSjpEcVIIS8lDwm5wpQpe1i1qiueng669gED6tO0aXnq1nU3YnRCCCGEEAVLkm4hnsPdu3fZunUrFy5c0LWpVCoaNGhA69atsbKSuanCADRpcPhb2DPniVLy+tBxHpT1NV5swMmT0Ywfv5MtWzJ+L6ZO3cv333fWHTc3N5GEWwghhBAljiTdQuRBSkoKISEhHDx4UG9Ffy8vLzp06IC7uyQUwkAKcSn5hQv3mTRpN6tX669ncPToDVJS0mU1ciGEEEKUaPJOSIhcUBSF06dPExwcTHx85gijvb097dq1o0aNGqhUKiNGKIqtuJv/lZKve6xRBX7vQZuJRi0lv3nzIdOnh7B06d+kp2d+CFW+vAPTprWiV6/amJjIFAshhBBClGySdAvxDDdu3GDLli1cvXpV12ZiYkLTpk1p1qwZ5ubmRoxOFFuFuJQ8ISGVmTNDWbjwEElJ6bp2V1drPv20OR984Cej20IIIYQQ/5F3RUJkIyEhgV27dnH8+HG99qpVqxIUFISzs3EXqxLFWHal5IFToN47Rl+V3NRUzc8/h+kSbltbcz75xJ8RI/yxt7cwamxCCCGEEIWNJN1CPEGj0XDs2DF2795NcnKyrt3FxYUOHTpQuXJlI0YnirVCWkquKIre9AkLC1OmTWvFwIF/8uGHfowf3xw3NxujxCaEMCyNRqO3HaYQQhR1ZmZmmJiYFGifknQL8ZjIyEi2bNnC7du3dW3m5ua0atWKhg0bYmoqvzLCALIrJS/jC53mG62UXKtVWL/+LFOm7GXDhu5UqZK5r3avXrVp3boC5cs75HAHIURRpSgK0dHRPHjwwNihCCFEvnN0dMTd3b3A1mSSDEIIIDY2lu3bt/PPP//otdepU4fAwEDs7OyMFJko9iJDYfPIQlVKrigKO3ZcYty4nRw/fhOAiRN3s3btG7pzTEzUknALUYw9Srjd3NywtraWxUKFEMWCoigkJibqBtg8PDwKpF9JukWJlpaWxoEDBwgNDSU9PXNBKA8PDzp27Iinp6cRoxPFWnal5PX7QMAko5WSHzlynXHjdrJrV6Re++3bCbL9lxAlhEaj0SXcLi4uz75ACCGKECsrKwBu376Nm5tbgZSay7snUSIpisK5c+fYunWrXumctbU1gYGB1K1bF7WRF6sSxZQmDQ4vgT2zC1UpeXj4HT79dDe//x6u1163rjuzZwcQFFRJRrqEKCEezeG2trY2ciRCCGEYj/6+paWlSdIthCHcuXOHrVu3cvHiRV2bSqWiYcOGtGrVSvfplxD5LjL0v1XJH0tsrZwhcDLUe9copeRxcSkMH76VlStPodUquvZKlZyYMaMN3brVQK2WZFuIkkg+aBNCFFcF/fdNkm5RYiQnJ7N3714OHz6MVqvVtXt7e9OhQwdKly5txOhEsRZ3E4InQthvjzUav5QcwNrajP37r+oSbnd3WyZPbkm/fvUwMyvYlT2FEEIIIYojqZ8VxZ5Wq+XkyZN89dVXHDx4UJdwOzg48Oabb9K7d29JuIVhaNLgwNfwtZ9+wl3GFwbsglcWFnjCnZqq0fve1FTNzJltcHS0ZPbsAC5eHMoHH/hJwi2EEFnw9vZm4cKFz339ypUrcXR0zLd4ipNWrVoxbNiwAulr4sSJDBw4sED6KklSUlIoX748x48fN3YohY4k3aJYu379OsuXL2fjxo0kJCQAYGpqSsuWLRk8eDA1atSQ8jlhGJf3wbfNYfuEzLnbVk7wyiLov7PA526npKTz1VeHKV/+C06ejNY79tpr1YiM/JixY5thbW1WoHEJIUR+6dOnD127djVoH0ePHs11spZVgt69e3ciIiKeu/+VK1eiUql0X6VLl+aVV155aveVouj3339n+vTpBu/n1q1bLFq0iPHjxxu8L2NJSUnho48+wtXVFRsbGzp37sy1a9dyvMbb21vv39ajr8GDB+vOiY+PZ8iQIZQrVw4rKyuqVavG4sWLdcctLCwYOXIkY8aMMdhjK6qkvFwUS/Hx8ezcuZMTJ07otVerVo127drh5ORkpMhEsVfISsk1Gi2//BLGpEl7uHz5AQATJuzir7/ezoxOpcLR0bJA4xJCiKKoVKlSL3S9lZXVC68dY29vz7lz51AUhevXrzN69Gg6depEREQE5ubmL3TvnKSlpWFmZrgPZp2dC+b1cdmyZfj7++Pt7f1C9zH08/Eihg0bxh9//MGaNWtwcXHhk08+4eWXX+b48ePZLhp29OhRNJrMargzZ87Qtm1b3nzzTV3b8OHD2b17Nz/99BPe3t5s376dQYMGUaZMGbp06QJAz549GTVqFOHh4VSrVs2wD7QIkZFuUaxoNBoOHTrEV199pZdwu7q68s4779C9e3dJuIVh6ErJGzxRSl4PBuws8FJyRVH4449z1K27hHff3ahLuAHs7MxJSUnP/mIhhCiG9u7dS8OGDbGwsMDDw4OxY8fqbRf68OFDevbsiY2NDR4eHnzxxRdPlTw/OXo9ZcoUypcvj4WFBWXKlGHo0KFARqn0lStXGD58uG7EELIuL9+0aRN+fn5YWlri6urKa6+9luPjUKlUuLu74+HhgZ+fH8OHD+fKlSucO3dOd86BAwdo0aIFVlZWeHp6MnToUF3FH8DNmzfp1KkTVlZWVKhQgV9++eWpx6ZSqfj222/p0qULNjY2zJgxA4A//viD+vXrY2lpScWKFZk6dare85jdcwLwzTff8NJLL2FpaUnp0qV54403dMeefK5jYmJ49913cXJywtramg4dOnD+/Hnd8UfP5bZt26hWrRq2tra0b9+emzdv5vj8rVmzhs6dO+u1bd26lWbNmuHo6IiLiwsvv/yy3oK7ly9fRqVS8euvv9KqVSssLS356aefAFixYgXVqlXD0tISHx8fvvnmG717jxkzhipVqmBtbU3FihWZOHGibocAQ4iNjWXZsmXMnz+fwMBA6tWrx08//URYWBg7duzI9rpSpUrh7u6u+/rzzz+pVKkSLVu21J1z8OBBevfuTatWrfD29mbgwIHUqVOHY8eO6c5xcXGhSZMmrF692mCPsSiSpFsUG5cuXeLbb79l69atpKSkABllLkFBQXz44YdUqlTJyBGKYkuvlPxhRpuVE7y88L9S8voFGk5o6BWaNVtB585rOHPmtq49KKgSx48PZM2aN2S/bSFEiXL9+nU6duxIgwYNOHXqFIsXL2bZsmW6RBJgxIgR7N+/n02bNhEcHExoaCh///13tvdct24dX3zxBUuWLOH8+fNs3LiRWrVqARml0uXKlWPatGncvHkz20Twr7/+4rXXXqNTp06cOHGCnTt34ufnl+vH9eDBA3755RcA3ahrWFgYQUFBvPbaa5w+fZq1a9eyb98+hgwZorvu3Xff5caNG+zZs4f169fz3Xffcfv27afuP3nyZLp06UJYWBh9+/Zl27Zt9OrVi6FDh3L27FmWLFnCypUrmTlz5jOfk2PHjjF06FCmTZum27a1RYsW2T62Pn36cOzYMTZt2sTBgwdRFIWOHTvqJayJiYnMmzePH3/8kZCQEKKiohg5cmS294yJieHMmTNPPccJCQmMGDGCo0ePsnPnTtRqNa+++qrewruQkUAPHTqU8PBwgoKCWLp0KRMmTGDmzJmEh4cza9YsJk6cyKpVq3TX2NnZsXLlSs6ePcuiRYtYunQpX3zxRbYxAtSoUQNbW9tsv2rUqJHttcePHyctLY127drp2sqUKUPNmjU5cOBAjv0+kpqayk8//UTfvn31pmE2a9aMTZs2cf36dRRFYffu3URERBAUFKR3fcOGDQkNDc1VXyWFvOsSRd6DBw/Ytm0b4eFP7i9cl8DAQGxtbY0UmSj2HkbD9k+zKCXvDQGTC7yU/MGDZHr2/J3Nm8/rtTdqVJbZswNo3bpCgcYjhCg+XvlqH3cephR4v6XsLPjjo2YvfJ9vvvkGT09Pvv76a1QqFT4+Pty4cYMxY8YwadIkEhISWLVqFb/88gsBAQFAxghmmTJlsr1nVFQU7u7uBAYGYmZmRvny5WnYsCGQUSptYmKCnZ0d7u7u2d5j5syZ9OjRg6lTp+ra6tSpk+NjiY2NxdbWFkVRSExMBKBz5874+PgA8Pnnn/P222/rRo1feuklvvzyS1q2bMnixYu5fPkyO3bs4OjRo7rk8/vvv+ell156qq+3336bvn376r5/5513GDt2LL179wagYsWKTJ8+ndGjRzN58uQcn5OoqChsbGx4+eWXsbOzw8vLi3r16mX5GM+fP8+mTZvYv38/TZo0AeDnn3/G09OTjRs36kqe09LS+Pbbb3UDK0OGDGHatGnZPndXrlxBUZSnfq6vv/663vfLli3Dzc2Ns2fPUrNmTV37sGHD9CoRpk+fzvz583VtFSpU0H0Y8eg5+vTTT3Xne3t788knn7B27VpGjx6dbZybN2/OcTQ8p7L26OhozM3Nn6rsLF26NNHR0dlcpW/jxo08ePCAPn366LV/+eWXDBgwgHLlymFqaopareb777+nWTP939GyZcty+fLlXPVVUkjSLYqstLQ09u/fz759+/TKmsqWLUuHDh0oV66cEaMTxZomDY58B7tnZ45sQ0Ypeaf5BT6y/Yi9vQXR0fG676tVc2XWrAC6dKkqCwYKIV7InYcpRMclGzuM5xYeHo6/v7/e38KmTZsSHx/PtWvXiImJIS0tTZcgQsYuJ1WrVs32nm+++SYLFy6kYsWKtG/fno4dO/LKK69gapr7t9cnT55kwIABeXosdnZ2/P3336Snp7N3714+//xzvv32W93x48ePc+HCBX7++Wddm6IoaLVaIiMjiYiIwNTUFF/fzAU9K1eunOX0uydHhI8fP87Ro0d1I9uQMbUvOTmZxMTEHJ+Ttm3b4uXlpTvWvn17Xn31VaytrZ/qNzw8HFNTUxo1aqRrc3FxoWrVqnqDLNbW1nqVjB4eHlmO2D+SlJQEgKWl/jomFy9eZOLEiRw6dIi7d+/qRrijoqL0ku7Hn487d+5w9epV+vXrp/czTE9Px8HBQff9unXrWLhwIRcuXCA+Pp709HTs7e2zjRHAy8srx+PPQ1GUXL8XWLZsGR06dHjqw4kvv/ySQ4cOsWnTJry8vAgJCWHQoEF4eHgQGBioO8/Kykr3gZDIIEm3KHIURSE8PJxt27YRGxura7exsSEwMJA6deqgVsvMCWEgl/fBXyPhzmOVFVZOGSPbvu+CuuC22nrwIFlvATS1WsWsWW0YMOAPpk1rzTvv1MbERH4XhBAvrpSdRZHuN6uEQ1EUIGPu8uP/n9U5WfH09OTcuXMEBwezY8cOBg0axOeff87evXtzvcDW8yyqplarqVy5MgA+Pj5ER0fTvXt3QkJCgIytUt9//329udSPlC9fXm/u9+Oyeqw2NjZ632u1WqZOnZrlvHNLS8scn5NHHxbs2bOH7du3M2nSJKZMmcLRo0efmuee3fP+5M/xyef58Z9lVlxdXYGMMvPHF8V75ZVX8PT0ZOnSpZQpUwatVkvNmjVJTU3N9vl4lJgvXbpU78MBQLdY2aFDh3SVDEFBQTg4OLBmzRrmz5+fbYyQUV5+5cqVbI97eXllu2K9u7s7qampxMTE6H2Qcvv2bV3VQE6uXLnCjh07+P333/Xak5KSGD9+PBs2bKBTp04A1K5dm5MnTzJv3jy9pPv+/fsvvOhgcSNJtyhSbt++zdatW7l06ZKuTa1W07BhQ93CFkIYxMNo2D4Rwn59rNE4peSxscnMnbufhQsPs317L5o2La871q5dJc6f/0jmbAsh8lV+lHgbU/Xq1Vm/fr1e0nbgwAHs7OwoW7Ysjo6OmJmZceTIETw9PQGIi4vj/PnzegtJPcnKyorOnTvTuXNnBg8ejI+PD2FhYfj6+mJubq63GnRWateuzc6dO3nvvfee+7ENHz6cBQsWsGHDBl599VV8fX35559/dIn5k3x8fEhPT+fEiRPUr59RmXXhwgUePHjwzL58fX05d+5ctveGnJ8TU1NTAgMDCQwMZPLkyTg6OrJr166nkvjq1auTnp7O4cOHdYnivXv3iIiIeKEVsStVqoS9vT1nz56lSpUquvuGh4ezZMkSmjdvDsC+ffueea/SpUtTtmxZLl26RM+ePbM8Z//+/Xh5eTFhwgRdW07J9CMvUl5ev359zMzMCA4Oplu3bkDGwnlnzpxh7ty5z+x7xYoVuLm56RLrR9LS0khLS3tqYMvExOSpue9nzpzJdupASSXvykSRkJyczJ49ezh8+LDeJ5iPSpTc3NyMGJ0o1nIqJe84H8oVXCl5UlIa//vfUWbP3sf9+xklcmPH7iQkpI/uTaRKpZKEWwhRYsXGxnLy5Em9NmdnZwYNGsTChQv56KOPGDJkCOfOnWPy5MmMGDECtVqNnZ0dvXv3ZtSoUTg7O+Pm5sbkyZNRq9XZluSuXLkSjUZDo0aNsLa25scff8TKykpXGuzt7U1ISAg9evTAwsJCN8r6uMmTJxMQEEClSpXo0aMH6enpbNmyJcf5vk+yt7enf//+TJ48ma5duzJmzBgaN27M4MGDGTBgADY2NoSHhxMcHMxXX32Fj48PgYGBDBw4kMWLF2NmZsYnn3yClZXVM8uPJ02axMsvv4ynpydvvvkmarWa06dPExYWxowZM3J8Tv78808uXbpEixYtcHJyYvPmzWi12ixL+F966SW6dOnCgAEDWLJkCXZ2dowdO5ayZcvqtqZ6Hmq1msDAQPbt26fb093JyQkXFxe+++47PDw8iIqKYuzYsbm635QpUxg6dCj29vZ06NCBlJQUjh07RkxMDCNGjKBy5cpERUWxZs0aGjRowF9//cWGDRueed8XKS93cHCgX79+fPLJJ7i4uODs7MzIkSOpVauW3mh0QEAAr776qt4Ce1qtlhUrVtC7d++npknY29vTsmVLRo0apfuZ7t27lx9++IEFCxbonRsaGloge64XJfLOTBRqWq2WkydPsjM4mIT/5uEA2Jua0tzFlYqoUG3bzn0jxvjCnvEpuDCiy/th80i4fTazzQil5OnpWlauPMmUKXu4fj0z8TczU+Pr605qqkYSbSGEAPbs2fPUCFvv3r1ZuXIlmzdvZtSoUdSpUwdnZ2f69eunt8jVggUL+OCDD3j55Zext7dn9OjRXL16NdsqOkdHR+bMmcOIESPQaDTUqlWLP/74AxcXFwCmTZvG+++/T6VKlUhJScmy7LlVq1b89ttvTJ8+nTlz5mBvb5/jit7Z+fjjj/nyyy/57bff6NatG3v37mXChAk0b94cRVGoVKkS3bt3153/ww8/0K9fP1q0aIG7uzuzZ8/mn3/+eWbFYFBQEH/++SfTpk1j7ty5mJmZ4ePjQ//+/Z/5nDg6OvL7778zZcoUkpOTeemll1i9enW2K3GvWLGCjz/+mJdffpnU1FRatGjB5s2bX3hv7IEDB9KvXz/mzp2LWq1GrVazZs0ahg4dSs2aNalatSpffvklrVq1eua9+vfvj7W1NZ9//jmjR4/GxsaGWrVq6Rax69KlC8OHD2fIkCGkpKTQqVMnJk6cyJQpU17oMTzLF198gampKd26dSMpKYmAgABWrlypt0f3xYsXuXv3rt51O3bsICoqSm/xvMetWbOGcePG0bNnT+7fv4+XlxczZ87kgw8+0J1z8OBBYmNj9baDE6BScpr4UAzFxcXh4OBA/Yn/x7FpnZ99wQuKGLkFa1NbEtPjqTKvg0H7qvtDXTSKhhouNVjz8hqD9lUQrl27xubNm7lx44auzSQ9nWrh4VT99xymxSxZtaxRgwrr1xk7DAGFppRcURTWrw/n0093ce7cvcxIVPDOO3WYOrUV3t6OBRKLEKLkSE5OJjIykgoVKpToaVsJCQmULVuW+fPn069fP2OHY1DXrl3D09OTHTt26FZvL64URaFx48YMGzaMt956y9jhFDtvvvkm9erVY/z48cYOJUc5/Z17lC/GxsY+c9G73JKhEVHoxMfHs2PHjqfKw8pFXaXuyZPYFNPVEK3q1jV2CEKT/l8p+Syjl5Lfv59EUNBPHDt2Q6+9c+eqzJzZhpo1ZUqFEELkpxMnTvDvv//SsGFDYmNjdVtPvUg5c2G1a9cu4uPjqVWrFjdv3mT06NF4e3s/1yh7UaNSqfjuu+84ffq0sUMpdlJSUqhTpw7Dhw83diiFjiTdotDQaDQcOXKEPXv2kJKSuRdoqVKlqLFuPaVv3sSsbFlKPbaXZXFh4uiAjb+/scMo2QpJKfkjTk6WWFll/olu3rw8c+YE0qSJZ4HGIYQQJcm8efM4d+4c5ubm1K9fn9DQ0CznYhd1aWlpjB8/nkuXLmFnZ0eTJk34+eefX7h0u6ioU6fOM/dDF3lnYWGhN2VDZJKkWxQKFy9eZMuWLXpzSywsLGjdujUNGjQgYnHG/pMmjo44vPKyscIUxdHDaAieBKfXPtaoyki0AyaDjUuBhHHlygPKl3fQWxBt9uwABg/ezOzZAbRvX1n22hZCCAOqV68ex48fN3YYBSIoKIigoCBjhyFEiSFJtzCqmJgYtm3bxr///qvX7uvrS5s2bbC1tTVSZKLYKySl5FevxjJ16l5WrDjJ1q09adu2ku5Y06blOXHifUm2hRBCCCGKMEm6hVGkpqayf/9+9u/fT3p6uq69XLlydOjQgbJlyxoxOlHsZVVKbukIgZPBt3eBlJLfu5fI7Nn7+PrrI6SkZCwKOG7cTgICKqJWZybZknALIYQQQhRtknSLAqUoCmfPnmX79u3Exsbq2m1sbGjbti21a9dGrVYbMUJRrBWCUvL4+FQWLjzE558fIC4uc+0CBwcLXnutGhqNFnUBzx8XQgghhBCGI0m3KDC3bt1i69atREZG6trUajWNGzemRYsWJXpbEmFg2ZWSe9SFTvOhnJ/BQ0hN1fDdd8eZPj2E27cTdO2WlqYMHdqQMWOa4exsZfA4hBBCCCFEwZKkWxhcUlISe/bs4ciRIzy+LXylSpVo3749pUqVMmJ0oti7cgD+Ggm3/8lsK+BS8vv3k/Dz+47IyAe6NhMTFX371mPSpJaUK5c/e0AKIYQQQojCR5JuYTBarZYTJ06wc+dOEh/bW9vJyYmgoCCqVq0q81WF4Ty8BcETnygl579S8ikFtio5gLOzFT4+rrqku1u3Gkyf3poqVQouBiGEEEIIYRwyeVYYxNWrV1m6dCl//PGHLuE2NTWlTZs2DBo0CB8fH0m4hWFo0uHQYvjaTz/h9qgL/XdC568MnnAfO3ZDr6oDYNasANq1q8SxYwNYu/YNSbiFEKII8/b2ZuHChfl+bnFw7tw53N3defjw4bNPFnkycuRIhg4dauwwxHOQpLsYUBSFqLgoFJRnn2xgDx8+ZMOGDSxbtoybN2/q2mvUqMFHH31EixYtMDMzM2KEoli7cgCWtICtYyElLqPN0hE6LYABuww+d/v06Vu8/PIvNGiwlA0b9LfBq1vXnW3belG/fhmDxiCEECVVnz59UKlUqFQqzMzMKF26NG3btmX58uVotdp87evo0aMMHDgw3899Ho8/7uy+CtKECRMYPHgwdnZ2BdpvQVq/fj3Vq1fHwsKC6tWrs2HDhhzPv3z5cpY/l61bt2Z5/v79+zE1NaVu3bp67aNHj2bFihV66yOJoqHElpd3vgkrx+43eD9NDHDP2JRYztw9w+m7pwm7E0bY3TAepDwwQE+5l56ezuHDh9m7dy+pqam6djc3Nzp27Ii3t7fxghPF38Nb/61Kvka/vYBKyS9dimHSpN388ksYjwa4J0zYRefOVTE1lc82hRCioLRv354VK1ag0Wh0C7h+/PHHrFu3jk2bNmFqmj9vffOyHo2h165ZtGgRc+bM0X3v4eHBihUraN++fZbnp6amYm5ubpBYrl27xqZNm154ZN+QMb6ogwcP0r17d6ZPn86rr77Khg0b6NatG/v27aNRo0Y5Xrtjxw5q1Kih+97Z2fmpc2JjY3n33XcJCAjg1q1besfc3Nxo164d3377LZ999ln+PCBRIErsu0FrjYqEBykG/3pR6dp0wu+Fs/bftUzYN4FXNrxCszXN+GDHB3xz8htCr4c+lXA38sj5Fz6/XbhwgcWLFxMcHKxLuC0tLenQoQPvv/++JNzCcPRKyR9LuAuolPzWrXiGDNmMj8/X/PxzZsJdrpw9o0Y1QWZQCCFEwbKwsMDd3Z2yZcvi6+vL+PHj+b//+z+2bNnCypUrdefFxsYycOBA3NzcsLe3p02bNpw6dUrvXps2bcLPzw9LS0tcXV157bXXdMeeLBmfMmUK5cuXx8LCgjJlyuiVAD95blRUFF26dMHW1hZ7e3u6deuml1xNmTKFunXr8uOPP+Lt7Y2DgwM9evTItlzbwcEBd3d33ReAo6Oj7vsePXowZMgQRowYgaurK23btgXg7NmzdOzYEVtbW0qXLs0777zD3bt3dfdVFIW5c+dSsWJFrKysqFOnDuvWrcvx+f/111+pU6cO5cqV07Xdu3ePt956i3LlymFtbU2tWrVYvXq13nWtWrV6rhi3bt1Ks2bNcHR0xMXFhZdffpmLFy/mGOOLWrhwIW3btmXcuHH4+Pgwbtw4AgICcvVBg4uLi97PKqsPFt5//33efvtt/P39s7xH586dn3r+ROFXYke6tSjYOFoYvqP0jMQ7t2++oxOiOX3nNGF3wzh95zRn750lWZOc4zWOFo7Ucq1F7VK18XXzpYF7gxeNOlfu37/Ptm3bOHfunF57/fr1adOmDTY2NgUShyihsluVPGAS1O9j0FXJY2OTmTfvAF98cYiEhDRdu4uLFePHN2fQoAZYWpbYP69CCFGotGnThjp16vD777/Tv39/FEWhU6dOODs7s3nzZhwcHFiyZAkBAQFERETg7OzMX3/9xWuvvcaECRP48ccfSU1N5a+//sry/uvWreOLL75gzZo11KhRg+jo6KcS+EcURaFr167Y2Niwd+9e0tPTGTRoEN27d2fPnj268y5evMjGjRv5888/iYmJoVu3bsyZM4eZM2c+13OwatUqPvzwQ/bv34+iKNy8eZOWLVsyYMAAFixYQFJSEmPGjKFbt27s2rULgE8//ZTff/+dxYsX89JLLxESEkKvXr0oVaoULVu2zLKfkJAQ/Pz0p3IlJydTv359xowZg729PX/99RfvvPMOFStW1BsZfp4YExISGDFiBLVq1SIhIYFJkybx6quvcvLkSdTqrMcWZ82axaxZs3J8vrZs2ULz5s2zPHbw4EGGDx+u1xYUFJSrpLtz584kJyfz0ksvMXz4cN544w294ytWrODixYv89NNPzJgxI8t7NGzYkKtXr3LlyhW8vLye2acoHErsu8JkE+gzp6nB+zk/+r+5Gllk3YlpiZy9d1ZXJn76zmluJ93O8X6malOqOVejlmstapWqRR3XOpSzK1eg83VSU1MJDQ3lwIEDaDQaXbunpycdOnSgTBmZsyoMKLtS8nrvQOAUsHE1aPf37ydRpcpX3LuXpGuzsTFjxAh/PvnEHwcH2W9eCFEMLWkJ8Tm/RzEIWzd4f+8L38bHx4fTp08DsHv3bsLCwrh9+zYWFhkDMPPmzWPjxo2sW7eOgQMHMnPmTHr06MHUqVN196hTp06W946KisLd3Z3AwEDMzMwoX748DRs2zPLcHTt2cPr0aSIjI/H09ATgxx9/pEaNGhw9epQGDTIGTrRaLStXrtTNi37nnXfYuXPncyfdlStXZu7cubrvJ02ahK+vr17yuXz5cjw9PYmIiKBs2bIsWLCAXbt26UZcK1asyL59+1iyZEm2Sffly5epX7++XlvZsmUZOXKk7vuPPvqIrVu38ttvv+kl3XmNsUqVKrz++ut6fS1btgw3NzfOnj1LzZo1s4zxgw8+oFu3btk+V49izk50dDSlS5fWaytdujTR0dHZXmNra8uCBQto2rQparWaTZs20b17d1atWkWvXr0AOH/+PGPHjiU0NDTHaRCPYrt8+bIk3UVIiU26jeHSg0ucunOKsLsZ87DPx5xHo2hyvKasbVlqu9amVqmMkWwfZx8sTApghD4LiqLwzz//sH37duLi4nTttra2tG3bltq1a8uK5MJwNOlwdCnsnpW5SBqARx3oOB88C6bCw9nZijZtKvDbb2cxM1PzwQd+TJjQnNKlbQukfyGEMIr42/DwhrGjeG6Koujeoxw/fpz4+HhcXPSnHyUlJelKk0+ePMmAAQNyde8333yThQsXUrFiRdq3b0/Hjh155ZVXskycwsPD8fT01CXcANWrV8fR0ZHw8HBd0u3t7a23EJmHhwe3bz//hx5Pjj4fP36c3bt3Y2v79GvXxYsXiY2NJTk5WVfm/Uhqair16tXLtp+kpCQsLfU/fNZoNMyZM4e1a9dy/fp1UlJSSElJeaoiMq8xVqlShYsXLzJx4kQOHTrE3bt3dQvmRUVFZZt0Ozs7ZzmXOi+efL/7+L+vrLi6uuqNjvv5+RETE8PcuXPp1asXGo2Gt99+m6lTp1KlSpUc+7aysgLQ245XFH6SdBtQmjZNt22QVtHw2v91yfF8GzMbarrWpLZrbWqXqk1N15q4Whl21C63oqOj2bJlC1euXNG1qYHaajW+KSmY//UXt7Ipu8oXmpw/nBDF3JWDsHkk3DqT2WbpCAETof57BislVxSFLVsuEBRUCROTzDK1GTPaYGlpytSprahQwckgfQshRKFi61ak+w0PD6dChQpAxiiyh4eHXjn3I46OjkBmYpMbnp6enDt3juDgYHbs2MGgQYP4/PPP2bt371M7tmSXnD3Z/uR1KpXqhVZgfzLB1Wq1vPLKK1kuxuXh4cGZMxmvt3/99ddTo76PqgOy4urqSkxMjF7b/Pnz+eKLL1i4cCG1atXCxsaGYcOG6S28+zwxArzyyit4enqydOlSypQpg1arpWbNmk/d+3EvWl7u7u7+1Kj27du3nxr9fpbGjRvz/fffAxm7/xw7dowTJ04wZMgQIOPxK4qCqakp27dvp02bNkDG9E4w/AJ9In9J0m1AWyK3UJWskwG1Sk1lx8rUcq1FnVJ1qOVaiwoOFTAx4DzU55GYmMiePXs4evSo3r7D3g4OVP/lF+wexpMAJBRUQDKSXrLE384oJT/1xIIhBVBKvmtXJGPH7uDo0RusWtWVd9/NLCusUsWFH3541WB9CyFEoZMPJd7GsmvXLsLCwnQjjb6+vkRHR2NqaprtYq+1a9dm586dvPfee7nqw8rKis6dO9O5c2cGDx6Mj48PYWFh+Pr66p1XvXp1oqKiuHr1qm60++zZs8TGxlKtWrXnf5B55Ovry/r16/H29s5yRP7RdlhRUVHZlpJnpV69epw9e1avLTQ0lC5duujKqLVaLefPn3/m431WjPfu3SM8PJwlS5boEuR9+/Y9M8YXLS/39/cnODhYb+R6+/btNGmStz2LTpw4ofvwwN7enrCwML3j33zzDbt27WLdunW6D4wAzpw5g5mZmd4q6KLwk6TbgG7G36Qqmas3tvFsQ+1SGaPYNVxqYG1mbcTocqbVavn777/ZuXMnSUmZc1ednJxo3749zjt3cvdhfIHHZRfQpsD7FEagSYej38PumQVeSn7s2A3Gj99JcPAlXdukSbvp0aMm5uaF60MxIYQQ+lJSUoiOjtbbMmz27Nm8/PLLvPvuuwAEBgbi7+9P165d+eyzz6hatSo3btxg8+bNdO3aFT8/PyZPnkxAQACVKlWiR48epKens2XLFkaPHv1UnytXrkSj0dCoUSOsra358ccfsbKyynK+bWBgILVr16Znz54sXLhQt5Bay5YtnyqvNqTBgwezdOlS3nrrLUaNGoWrqysXLlxgzZo1LF26FDs7O0aOHMnw4cPRarU0a9aMuLg4Dhw4gK2tLb17987yvkFBQfTv3x+NRoOJScZrZuXKlVm/fj0HDhzAycmJBQsWEB0d/cyk+1kxOjk54eLiwnfffYeHhwdRUVGMHTv2mY/9RcvLP/74Y1q0aMFnn31Gly5d+L//+z927Nihl/B//fXXbNiwgZ07dwIZi8SZmZlRr1491Go1f/zxB19++aVuFF+tVj9VDu/m5oalpeVT7aGhoTRv3jxP1RjC+CTpLiBqlZpFbRYZO4xciYqKYvPmzXqlM2ZmZrRo0QJ/f39MTU25+9+qkQBuY8Zg7Zv9/J78YuLggLlsP1b8ZVlK7vDfquSGKyU/d+4uEyfu5rff9D+hr127NLNnB2BmVmJ3WBRCiCJj69ateHh4YGpqipOTE3Xq1OHLL7+kd+/eutWsVSoVmzdvZsKECfTt25c7d+7g7u5OixYtdCXCrVq14rfffmP69OnMmTMHe3t7WrRokWWfjo6OzJkzhxEjRqDRaKhVqxZ//PHHU3PGH/W9ceNGPvroI1q0aIFaraZ9+/Z89dVXhntSslCmTBn279/PmDFjCAoKIiUlBS8vL9q3b697nqZPn46bmxuzZ8/m0qVLODo66rZhy07Hjh0xMzNjx44dBAUFATBx4kQiIyMJCgrC2tqagQMH0rVrV2JjY18oRpVKxZo1axg6dCg1a9akatWqfPnll7Rq1SrfnqesNGnShDVr1vDpp58yceJEKlWqxNq1a/UWhbt79+5TW5fNmDGDK1euYGJiQpUqVVi+fLlu9D8vVq9erbfAnygaVMrjNcMlQFxcHA4ODkzt/39MWtrZoH0tObWEVj+Vw8bEniRtPC/N7WDQ/l5UXFycblXNx9WsWZO2bdvi4OCga7u7eDF3Fn0JQLlvF2Nn4D9wogQwUin5tWtxTJu2l+XLT6DRZP45rFDBkenTW/PWW7VQq2VagxCi5EhOTiYyMpIKFSo8tSiWEM/yzTff8H//939s27bN2KEUO3/99RejRo3i9OnTOa5wLp4tp79zj/LF2NhY7O3t86U/+WkJ0tPTOXToEHv37iUtLXPP4dKlS9OxY0fZjkAYVnal5O61odMCg5aS37+fhI/P13p7bZcubcPEiS0YMKC+lJMLIYQQeTRw4EBiYmJ4+PCh3grs4sUlJCSwYsUKSbiLIPmJlXARERFs3bpVtxIiZCwG0qZNG+rXr68rMRLCIIxUSv6Is7MV3bvXYPnyk9jbWzB6dBM+/rgxtrbmBu1XCCGEKK5MTU2ZMGGCscMolp61AJwovCTpLqHu3bvHtm3biIiI0LWpVCrq169PmzZtsLYuvIu8iWLACKXkqakafvrpNL161dYbwZ48uRXOzlaMHdsMFxf5dy+EEEIIIfKXJN0lTEpKCqGhoRw8eBDNY3tfly9fng4dOui2LhDCIDTpcGwZ7JqRRSn5fPBsmO9darUKa9acYeLE3Vy6FENycjqDBmWWrJcv78Dnn7fL936FEEIIIYQASbpLDEVRCAsLIzg4mIcPH+ra7ezsaNeuHTVr1kQle2ALQ8qulLzNRPDrm++l5IqisGXLBcaN28np07d07dOnh9CvXz0sLOTPnxBCCCGEMDx511kC3Lx5ky1bthAVFaVrMzExwd/fn+bNm2NhYWHE6ESxF38bgifDqV/02+v1gsCpBikl378/inHjdhIaGqXX3rZtRWbNCpCEWwghhBBCFBh551mMJSYmsmvXLo4fP87jO8NVqVKFoKCgLPePFCLf6ErJZ0LKY3txGrCUPCzsFhMm7OKPPyL02v38yjBnTgABARXzvU8hhBBCCCFyIkl3MZL49988+PU3NKkp/GtmxjELC1IeW33cXqPBPyWF8seOkXzsGNdfoK+U8xdePGBRfEUdgr9Gwq2wzDYDlpJDxvZfDRt+T3Jyuq6talUXZs5sw2uvVZPpE0IIIYQQwigk6S5GbowcxfW0NE741uPBYxu5m6alUf2fs1SJiMBEqyUuh3s8D5WJ7GUs/pNTKXnAFLAtZbCunZ2tGDjQly+/PELZsnZMndqK3r3rYmoq294JIYQQQgjjkXejxURsbCwh3l7sDmjDAycnXbvX5ct0/Gsz1f79FxOtNt/7NfMqj3X9+vl+X1HEaNLh8BL4yk8/4XavBf2Cocv/8jXhjo1NZtasUBIT0/TaJ0xowbx5bTl//iP69fOVhFsIIUSB8fb2ZuHChcYOo8hJTU2lcuXK7N+/39ihFDt//vkn9erVQ2uAHEDkjYx0F3Hp6ekcPHiQkJAQ0sqX17WXdnEhqEkTPN3dDdq/qYcHKrUkNiVaAZaSJyen8803R5k1K5R795IwMVExZkwz3XE3Nxs++aRJvvUnhBCi6OjTpw+rVq0CMhaMLVOmDJ06dWLWrFk4PTYgUdxMmTKFqVOnPtUeHBxMYGCgESLKiGnjxo2cPHnymed+9913eHl50bRpU8MHZiRhYWEMGTKEI0eO4OzszPvvv8/EiROznfq2Z88eWrduneWxI0eO0KBBA722e/fuUadOHa5fv05MTAyOjo4AvPzyy0yaNIlffvmFXr165etjEnkjSXcRpSgKERERbN26lZiYGF27eUoK9e/eo+2kSaglGRaGlF0ped1eEDglX0e209O1/PDDKaZM2cPVq5kTJBYsOMSwYY1lNXIhhBAAtG/fnhUrVpCens7Zs2fp27cvDx48YPXq1cYOzaBq1KjBjh079NqcnZ2f616pqamYm5vnR1i58tVXXzFlypQXukdaWhpmZmb5E1A+i4uLo23btrRu3ZqjR48SERFBnz59sLGx4ZNPPsnymiZNmnDz5k29tokTJ7Jjxw78/PyeOr9fv37Url2b69efXrHpvffe46uvvpKk28gkKyuC7t69y88//8zq1at1CbdKpaLyhQt0/GszVWJiJOEWhqNJh8PfZV9K3jX/SskVReH338OpVWsx/fpt0iXcKhX07FmLgwf7ScIthBBCx8LCAnd3d8qVK0e7du3o3r0727dv1x3XaDT069ePChUqYGVlRdWqVVm0aJHePfr06UPXrl2ZN28eHh4euLi4MHjwYNLSMqc03b59m1deeQUrKysqVKjAzz///FQsUVFRdOnSBVtbW+zt7enWrRu3bt3SHZ8yZQp169Zl+fLllC9fHltbWz788EM0Gg1z587F3d0dNzc3Zs6c+czHbWpqiru7u97Xo8Q5LCyMNm3aYGVlhYuLCwMHDiQ+Pv6pxzt79mzKlClDlSpVALh+/Trdu3fHyckJFxcXunTpwuXLl3XX7dmzh4YNG2JjY4OjoyNNmzblypUrrFy5kqlTp3Lq1ClUKhUqlYqVK1dmGffff//NhQsX6NSpk177mDFjqFKlCtbW1lSsWJGJEyfqPf+PP3cVK1bEwsICRVGIjY1l4MCBuLm5YW9vT5s2bTh16pTuuosXL9KlSxdKly6Nra0tDRo0eOrDivz2888/k5yczMqVK6lZsyavvfYa48ePZ8GCBXq7Cz3O3Nxc72fp4uLCpk2b6Nu371Oj44sXL+bBgweMHDkyy3t17tyZI0eOcOnSpXx/bCL35N1qEZKSkkJISAgHDx7Um5vh5eVFhw4diAkIBI3GiBGKYq8AS8l3745k7NidHDmi/6ltp04vMXNmG+rUMezUCSGEEEXbpUuX2Lp1q94IqFarpVy5cvz666+4urpy4MABBg4ciIeHB926ddOdt3v3bjw8PNi9ezcXLlyge/fu1K1blwEDBgAZierVq1fZtWsX5ubmDB06lNu3b+uuVxSFrl27YmNjw969e0lPT2fQoEF0796dPXv26M67ePEiW7ZsYevWrVy8eJE33niDyMhIqlSpwt69ezlw4AB9+/YlICCAxo0b5/k5SExMpH379jRu3JijR49y+/Zt+vfvz5AhQ/QS4Z07d2Jvb09wcDCKopCYmEjr1q1p3rw5ISEhmJqaMmPGDNq3b8/p06dRq9V07dqVAQMGsHr1alJTUzly5AgqlYru3btz5swZtm7dqktoHRwcsowvJCSEKlWqYP/YAsAAdnZ2rFy5kjJlyhAWFsaAAQOws7Nj9OjRunMuXLjAr7/+yvr16zH5b1HfTp064ezszObNm3FwcGDJkiUEBAQQERGBs7Mz8fHxdOzYkRkzZmBpacmqVat45ZVXOHfuHOUfm6b5uNDQUDp06JDj8zx+/HjGjx+f5bGDBw/SsmVLLCwsdG1BQUGMGzeOy5cvU6FChRzvDbBp0ybu3r1Lnz599NrPnj3LtGnTOHz4cLZJtZeXF25uboSGhlKxomydaiySdBcBiqJw+vRpgoOD9T6ZtLe3p127dtSoUQOVSkVMDvcQ4oXE34Edk+HkE5/kG6CUHDK2/3r55dV6C6U1berJ7NkBNG/ula99CSGEeLbuf3bnbtLdAu/X1cqVtS+vzfX5f/75J7a2tmg0GpKTkwFYsGCB7riZmZne/OcKFSpw4MABfv31V72k28nJia+//hoTExN8fHzo1KkTO3fuZMCAAURERLBlyxYOHTpEo0aNAFi2bBnVqlXTXb9jxw5Onz5NZGQknp6eAPz444/UqFGDo0eP6ubkarVali9fjp2dHdWrV6d169acO3eOzZs3o1arqVq1Kp999hl79uzJMekOCwvD1tZW93316tU5cuQIP//8M0lJSfzwww/Y2NgA8PXXX/PKK6/w2WefUbp0aQBsbGz4/vvvdaPjy5cvR61W8/333+tGVlesWIGjoyN79uzBz8+P2NhYXn75ZSpVqgSg9/htbW11o+85uXz5MmXKlHmq/dNPP9X9v7e3N5988glr167VS7pTU1P58ccfKVUq4z3Irl27CAsL4/bt27oEd968eWzcuJF169YxcOBA6tSpQ506dXT3mDFjBhs2bGDTpk0MGTIkyxj9/PyeOTc9p1L+6OhovL299doePe/R0dG5SrqXLVtGUFCQ7t8SZAzGvfXWW3z++eeUL18+x5HssmXL6lUpiIInSXchd+PGDbZs2cLVq1d1bSYmJjRp0oTmzZsX6JwbUQJp0uHYctg1A1JiM9vda0GnBeDZ0CDdOjtbMXx4Y2bODKVmTTdmzw6gU6eXZK9tIYQwkrtJd7mdePvZJxpZ69atWbx4MYmJiXz//fdERETw0Ucf6Z3z7bff8v3333PlyhWSkpJITU2lbt26eufUqFFDN3oK4OHhQVhYRpVXeHg4pqamenNrfXx8dItXPTrH09NTL0mqXr06jo6OhIeH65Jub29v7OzsdOeULl0aExMTvWmCpUuX1htFz0rVqlXZtGmT7vtHSWd4eDh16tTRJdwATZs2RavVcu7cOV3yV6tWLb33lMePH+fChQt6sQEkJydz8eJF2rVrR58+fQgKCqJt27YEBgbSrVs3PDw8cozzSUlJSVhaWj7Vvm7dOhYuXMiFCxeIj48nPT39qdFwLy8vXcL9KOb4+HhcXFye6uPixYsAJCQkMHXqVP78809u3LhBeno6SUlJREVFZRujlZUVlStXztPjetKT718elZXn5n3NtWvX2LZtG7/++qte+7hx46hWrVqu5mpbWVmRmJiYh4hFfpOku5BKSEhg165dHD9+XK+9atWqBAUFPffiGELkWtRh2PwJRD9WSm7hAAH5W0p+/Xocc+fuZ9q01jg4ZL7wjhrVBB8fV956qyYmJrJGgRBCGJOrlWuR6NfGxkaXIH355Ze0bt2aqVOnMn36dAB+/fVXhg8fzvz58/H398fOzo7PP/+cw4cP693nyUW5VCqVbmpfbhImRVGyPP5ke1b95NR3dszNzbNMDLOL48n4H0/KIWMEvn79+lnOVX+U6K5YsYKhQ4eydetW1q5dy6effkpwcHCeyuBdXV11H2Y8cujQIXr06MHUqVMJCgrCwcGBNWvWMH/+fL3zsorZw8NDr3z/kUcfiIwaNYpt27Yxb948KleujJWVFW+88QapqanZxvii5eXu7u5ER0frtT36EOXRhx45WbFiBS4uLnTu3Fmv/dHI/rp164DMf5eurq5MmDBBr6Lj/v37eh9QiIInSXcho9FoOHbsGLt379aVRQG4uLjQvn17XnrpJSNGJ0qEbEvJe0Lg1HwrJb9/P4nPPtvHl18eITk5HXt7C6ZPb6M77uBgSa9etfOlLyGEEC8mLyXehcnkyZPp0KEDH374IWXKlCE0NJQmTZowaNAg3TmPRkFzq1q1aqSnp3Ps2DEaNsyo+Dp37hwPHjzQnVO9enWioqK4evWqbrT77NmzxMbG6pVhG1r16tVZtWoVCQkJuiR1//79qNVq3YJpWfH19WXt2rW6BcmyU69ePerVq8e4cePw9/fnl19+oXHjxpibm6PJxTpD9erVY/HixXofDuzfvx8vLy8mTJigO+/KlSvPvJevry/R0dGYmpo+Vc79SGhoKH369OHVV18FID4+/pll1y9aXu7v78/48eP1VoXfvn07ZcqUyTbORxRFYcWKFbz77rtPfRizfv16kpKSdN8fPXqUvn37Ehoaqiv5h8zqhHr16uXYlzAsGT4qRCIjI1myZAlbtmzRJdzm5ua0bduWDz/8UBJuYVhaDRxZCl/X10+43WtB3+3Q9Zt8SbgTElKZPTuUihUXMXfuAZKT0wFYtuwEKSnpL3x/IYQQ4pFWrVpRo0YNZs2aBUDlypU5duwY27ZtIyIigokTJ3L06NE83bNq1aq0b9+eAQMGcPjwYY4fP07//v2xsrLSnRMYGEjt2rXp2bMnf//9N0eOHOHdd9+lZcuWWW75ZCg9e/bE0tKS3r17c+bMGXbv3s1HH33EO++8k+Moa8+ePXF1daVLly6EhoYSGRnJ3r17+fjjj7l27RqRkZGMGzeOgwcPcuXKFbZv305ERITuAwVvb28iIyM5efIkd+/eJSUlJct+WrduTUJCAv/884+urXLlykRFRbFmzRouXrzIl19+yYYNG575WAMDA/H396dr165s27aNy5cvc+DAAT799FOOHTumu/fvv//OyZMnOXXqFG+//fYzqwgelZfn9JVT0v32229jYWFBnz59OHPmDBs2bGDWrFmMGDFC90HDkSNH8PHxeWrLr127dhEZGUm/fv2eum+lSpWoWbOm7uvR3PBq1arh5uamO+/QoUNYWFjg7+//zOdQGI4k3YVAbGwsv/32G6tWrdKbs1OnTh0++ugjmjZtiqmpFCUIA4o6DN+1hM0jIfm/udsWDtDhcxiwB8o3euEu0tI0LF58lMqVv2L8+F3Exma8AFtYmPDJJ/6cPv2hbP8lhBAi340YMYKlS5dy9epVPvjgA1577TW6d+9Oo0aNuHfvnt6od26tWLECT09PWrZsyWuvvabbpuoRlUrFxo0bcXJyokWLFgQGBlKxYkXWri3YigFra2u2bdvG/fv3adCgAW+88QYBAQF8/fXXz7wuJCSE8uXL89prr1GtWjX69u1LUlIS9vb2WFtb8++///L6669TpUoVBg4cyJAhQ3j//fcBeP3112nfvj2tW7emVKlS2e6T7uLiwmuvvaZXxt6lSxeGDx/OkCFDqFu3LgcOHGDixInPfKwqlYrNmzfTokUL+vbtS5UqVejRoweXL1/WfcDwxRdf4OTkRJMmTXjllVcICgrC19c3t0/nc3FwcCA4OJhr167h5+fHoEGDGDFiBCNGjNCdk5iYyLlz5/S2RYOMBdSaNGnyQtURq1evpmfPnlhbWz/3PcSLUynZbRBXTMXFxeHg4MDU/v/HpKWdn33BC1hyagmtfiqHjYk9Sdp4XpqrPx8kLS2NAwcOEBoaSnp65gifh4cHHTt21Ft8IzfCa9QEjQbLmjWpsO63fHkMopiLvwM7psDJn/Tb87GUXKtVWLv2DBMn7ubixcw19tVqFe+9V5fJk1vi6Zn1ViJCCCEKXnJyMpGRkVSoUCHLRa6EyE9hYWEEBgZmuXCbeDF37tzBx8eHY8eO5WqV9JIkp79zj/LF2NjYHKdX5IUMKxmBoiicO3eOrVu36s3/sba2JiAggHr16umtWilEvtNq/luVfHrmyDZklJJ3nJ8vI9uPxMYmM2jQZh48yFyj4PXXqzF9emuqVZNFPYQQQoiSrFatWsydO5fLly9Tq1YtY4dTrERGRvLNN99Iwl0ISNJdwO7cucPWrVv1Fu1QqVQ0bNiQVq1a6c0HEsIgrh6Bvz6B6NOZbRYO0ObTjFXJTfL3z4KTkxVjxjRl3LidtGlTgdmzA2jYsGy+9iGEEEKIoqt3797GDqFYatiwoW6xP2FcknQXkDTS2bZtG4cPH9ZbsMHb25sOHTrkassAIV5IjqXkU8DWLaur8uTMmdtMnx7CV191wM0tcyuPoUMb4edXhsDAii/chxBCCCGEEEWJJN0GpqAQob7JEYvzJB/MXBzBwcGBdu3aUb169Rz3eRTihWVXSl66FnSaB+Vzv59mdi5ffsDkyXv48cdTKAp4eNiycGF73XFrazNJuIUQQgghRIkkSbcBadO17LT8lzsm8bo2ExMTmjVrRtOmTXV79QlhMAYuJb99O4GZM0NYvPgYaWmZFRybNp3js88CZTVyIYQQQghR4sk7YgNKuJBA/GMJt4+PD0FBQTg5ORkxKlEiGLiUPC4uhfnzD7BgwSHi41N17U5Olowf35zBgxtIwi2EEEIIIQSSdBtU6p3MZKRVchVa9ehhxGhEiWDgUvKUlHQWLz7GzJmh3L2bqGu3tjZj2LBGjBrVFEdH2V5GCCGEEEKIRyTpNpD09HRS72ck3XZaS8pqZXRbGFi2RR3wgwAAcg9JREFUpeQTwK9fvqxKnpamZfbsfbqE29RUzcCBvnz6aQs8PGRvTSGEEEIIIZ4km0EbyPXr1+G/Ka7uiiTcwoAS7sL/DYZlbfUT7jpvw0fHoNH7+bYNmK2tORMntgDg7bdr8e+/g/nf/zpJwi2EEEKIQm3KlCnUrVs3x3NatWrFsGHDCiSeiRMnMnDgwALpqyRJSUmhfPnyHD9+3Nih6JGk20CuXLmi+393raPxAhHFl1YDR5bCV75w4rG526VrQd9t8OriF5q7vWfPZVq1WsnVq7F67QMH1ufkyff5+efXqFTJ+bnvL4QQQuSn27dv8/7771O+fHksLCxwd3cnKCiIgwcPGju0XNuzZw8qlYoHDx5ke8769esxMTEhKioqy+M+Pj4MHTr0hWPx9vZm4cKFL3yf7PTp0weVSsUHH3zw1LFBgwahUqno06ePwfrPyu+//8706dMN3s+tW7dYtGgR48ePN3hfxpKSksJHH32Eq6srNjY2dO7cmWvXrj3zuuvXr9OrVy9cXFywtrambt26ugQ6LS2NMWPGUKtWLWxsbChTpgzvvvsuN27c0F1vYWHByJEjGTNmjMEe2/OQpNtAHk+6PSTpFvnt6lFY2ho2j8ycu21hDx3mwsA9LzR3+++/b9K+/U+0br2KvXuvMG3aXr3j5uYm1Knj/gLBCyGEEPnv9ddf59SpU6xatYqIiAg2bdpEq1atuH//vrFDy5W0tLRnnwR07twZFxcXVq1a9dSx/fv3c+7cOfr165ff4T231NTUbI95enqyZs0akpKSdG3JycmsXr2a8uXLF0R4epydnbGzM3z13rJly/D398fb2/uF7pPbfzPGMGzYMDZs2MCaNWvYt28f8fHxvPzyy2g0mmyviYmJoWnTppiZmbFlyxbOnj3L/PnzcXR0BCAxMZG///6biRMn8vfff/P7778TERFB586d9e7Ts2dPQkNDCQ8PN+RDzBNJug1Ao9Fw9epVAKy0ZtgpVkaOSBQbulLyQLh5KrO9ztvw0fEXKiU/f/4ePXqso37979i27aKu/dixm6SmZv8HUgghhDC2Bw8esG/fPj777DNat26Nl5cXDRs2ZNy4cXTq1AmAy5cvo1KpOHnypN51KpWKPXv2AJkjzX/99Rd16tTB0tKSRo0aERYWprtm5cqVODo6snHjRqpUqYKlpSVt27bVvfd7ZPHixVSqVAlzc3OqVq3Kjz/+qHdcpVLx7bff0qVLF2xsbOjfvz+tW7cGwMnJKduRXjMzM9555x1WrlyJoih6x5YvX079+vWpU6cOsbGxDBw4EDc3N+zt7WnTpg2nTp3SO3/Tpk34+flhaWmJq6srr732GpBRZn3lyhWGDx+OSqVCpVLprlm/fj01atTAwsICb29v5s+fr3dPb29vZsyYQZ8+fXBwcGDAgAHZ/djw9fWlfPny/P7777q233//HU9PT+rVq6d37tatW2nWrBmOjo64uLjw8ssvc/HiRb1zrl27Ro8ePXB2dsbGxgY/Pz8OHz6sd86PP/6It7c3Dg4O9OjRg4cPH+qOPVle7u3tzaxZs+jbty92dnaUL1+e7777Tu9+169fp3v37jg5OeHi4kKXLl24fPlyto8ZYM2aNU8lis96fI/+/f7666+0atUKS0tLfvopo9JxxYoVVKtWDUtLS3x8fPjmm2/07j1mzBiqVKmCtbU1FStWZOLEiQZN2GNjY1m2bBnz588nMDCQevXq8dNPPxEWFsaOHTuyve6zzz7D09OTFStW0LBhQ7y9vQkICKBSpUoAODg4EBwcTLdu3ahatSqNGzfmq6++4vjx43qVHy4uLjRp0oTVq1cb7DHmlSTdBhAdHa37VK+U1g4VqmdcIcQzZFtKXhPe2/pCpeQ3bjzkgw/+pFq1/7F27T+6dm9vR374oSvHjg3A3NzkRR+BEEIIYTC2trbY2tqyceNGUlJSXvh+o0aNYt68eRw9ehQ3Nzc6d+6sl6QkJiYyc+ZMVq1axf79+4mLi6PHY7vUbNiwgY8//phPPvmEM2fO8P777/Pee++xe/duvX4mT55Mly5dCAsLY9q0aaxfvx6Ac+fOcfPmTRYtWpRlfP369ePSpUvs3ZtZjZaQkMCvv/5Kv379UBSFTp06ER0dzebNmzl+/Di+vr4EBAToRv7/+usvXnvtNTp16sSJEyfYuXMnfn5+QEbiW65cOaZNm8bNmze5efMmAMePH6dbt2706NGDsLAwpkyZwsSJE1m5cqVefJ9//jk1a9bk+PHjTJw4Mcfn+r333mPFihW675cvX07fvn2fOi8hIYERI0Zw9OhRdu7ciVqt5tVXX0WrzVhEKT4+npYtW3Ljxg02bdrEqVOnGD16tO44wMWLF9m4cSN//vknf/75J3v37mXOnDk5xjd//nz8/Pw4ceIEgwYN4sMPP+Tff/8FMv4dtG7dGltbW0JCQti3bx+2tra0b98+2xH+mJgYzpw5o3uuc/v4HhkzZgxDhw4lPDycoKAgli5dyoQJE5g5cybh4eHMmjWLiRMn6lVC2NnZsXLlSs6ePcuiRYtYunQpX3zxRY6Pu0aNGrrfq6y+atSoke21x48fJy0tjXbt2unaypQpQ82aNTlw4EC21z36EOjNN9/Ezc2NevXqsXTp0hzjjI2NRaVS6UbDH2nYsCGhoaE5XluglBImNjZWAZSp/f/PYH3s379fmTx5sjJ58mTlj7HLlatjQpSIUZsN1t8jZ6vXUM5W9VEuvf6GwfsSBSjqiKJ821xRJttnfs0qpyiHvlWU9LTnvu3DhynKmDHBipXVDAWm6L5KlZqrfPnlISU5+fnvLYQQouhKSkpSzp49qyQlJRk7lDxZt26d4uTkpFhaWipNmjRRxo0bp5w6dUp3PDIyUgGUEydO6NpiYmIUQNm9e7eiKIqye/duBVDWrFmjO+fevXuKlZWVsnbtWkVRFGXFihUKoBw6dEh3Tnh4uAIohw8fVhRFUZo0aaIMGDBAL74333xT6dixo+57QBk2bJjeOY/6j4mJeebjbdSokfLuu+/qvl++fLliZWWlxMTEKDt37lTs7e2V5ORkvWsqVaqkLFmyRFEURfH391d69uyZ7f29vLyUL774Qq/t7bffVtq2bavXNmrUKKV69ep613Xt2vWZ8ffu3Vvp0qWLcufOHcXCwkKJjIxULl++rFhaWip37txRunTpovTu3Tvb62/fvq0ASlhYmKIoirJkyRLFzs5OuXfvXpbnT548WbG2tlbi4uL0Ym/UqJHu+5YtWyoff/yx3mPp1auX7nutVqu4ubkpixcvVhRFUZYtW6ZUrVpV0Wq1unNSUlIUKysrZdu2bVnGceLECQVQoqKisn9ysnh8j/79Lly4UO88T09P5ZdfftFrmz59uuLv75/tvefOnavUr18/x/4vX76snD9/Ptuvy5cvZ3vtzz//rJibmz/V3rZtW2XgwIHZXmdhYaFYWFgo48aNU/7++2/l22+/VSwtLZVVq1ZleX5SUpJSv379LP8dL1q0SPH29s62r5z+zj3KF2NjY7O9Pq9kyzADeHw+t5vGTuoJxPNJuAs7JuuPbAPUeQvaTnuhRdIgY7uv1avPkJSUDoCdnTmjRjVh2LDG2NlZvNC9hRBCFC+Rr79B+t27Bd6vqasrFdavy9W5r7/+Op06dSI0NJSDBw+ydetW5s6dy/fff5/nBbn8/f11/+/s7EzVqlX15oeamprqjVT6+Pjg6OhIeHg4DRs2JDw8/KmVqZs2bfrUyPWTo5150a9fP4YNG8bXX3+NnZ0dy5cv57XXXsPR0ZHjx48THx+Pi4uL3jVJSUm6kuWTJ0/mWPqdlfDwcLp06aLX1rRpUxYuXIhGo8HExCTPj8vV1ZVOnTqxatUq3Qi9q6vrU+ddvHiRiRMncujQIe7evasbAY6KiqJmzZqcPHmSevXq4eyc/SKv3t7eenO2PTw8uH37do7x1a5dW/f/KpUKd3d33TXHjx/nwoULT80DT05Ofqr0/ZFH89ctLS3z9Pgeefy5vXPnDlevXqVfv356P8v09HQcHBx0369bt46FCxdy4cIF4uPjSU9Px97ePsfH7eXllePx56Eoit5UhSdptVr8/PyYNWsWAPXq1eOff/5h8eLFvPvuu3rnpqWl0aNHD7Ra7VPl9ABWVlYkJibm7wN4AZJ05zOtVqtLutXmauwTLJ9xhRBP0Grg+ArYOS1zkTTIKCXvOA+8/LO/NgdP/qGztDRl6tRWvP/+nwwe3IDx45vj6mr9gsELIYQojtLv3iX91i1jh/FMj+ZXt23blkmTJtG/f38mT55Mnz59UKszRkGUx+ZB52Ve65PJQlbJw+NtTx7PKuGwsbHJdf9P6tGjB8OHD2ft2rW0atWKffv2MW3aNCDj/aiHh4durvrjHpXhWlnlfc2hrB6D8sS8csj74+rbty9DhgwB4H//+1+W57zyyit4enqydOlSypQpg/b/27vvsCiutoHDv6UXBRSVqmDDBgqCPXYillhji8Ze4mfsvcRu7KhRo1GjEN+o2BOjxl5iL6ixxo4VVBAx0mH3+4MwutKRovLc17XX6545M3Nmmey7z5znnKNW4+zsrKRxp+d69PX1td6rVKok6dsZ2UetVuPu7s7atWuT7Fe4cOFkj5f4QCE0NFSrTlrXl+jtzzaxHStXrqRatWpa9RIfgJw6dYqOHTsyZcoUvLy8MDc3x8/PL8lY/HdVqFBBqyPxXQ4ODly9ejXZbdbW1sTExBAaGkqBAm+WTn727Bk1a9ZM8Zg2NjaUL19eq6xcuXLKsItEsbGxtG/fnnv37nHw4MFkHyC8ePEixb9BbpCgO4s9e/aMqKgoAPQt9VGFynhukQEPz8Ku4dqTpBmaQf3xUKV3piZJU6s1bNp0lSlTjvD77x0pXfrNU+8uXSrSsGFxihY1T+UIQggh8jq9ZHoeP4bzli9fnt9++w14EwQFBgYqk3S9Pana206dOqXMnh0aGsrNmzcpW7assj0uLo5z585RtWpVIGEM9suXL5U65cqV49ixY1q9cydOnKBcuXKpttfAwAAg1RmeE+XPn5927drh4+PD3bt3KVGiBPXq1QMSJigLCgpCT08vxRmyK1asyIEDB+jRo0eKbXm3HeXLl+fYsWNaZSdOnMDJyUkJ8jLj7THQXl5eSbaHhIRw/fp1li9fTu3atQGStKNixYr8/PPPvHjxItXe7qxUuXJlNmzYoExWlx4lS5bEzMyMa9eu4eTkBKTv+pJjZWWFnZ0dd+/epXPnzsnWOX78OA4ODowfP14pSy2YTrRr165UH0q9+zDibe7u7ujr6yuTnkHCf3dXrlxhzpw5Ke5Xq1Ytbty4oVV28+ZNrV73xID71q1bHDp0KEk2R6IrV64kmYwvN0nQncXevokNCxnC7VxsjPh4hAfD/slwQXtmUyp9BZ5TIL9Vhg+p0WjYu/cOY8ce4MKFIAAmTDiEn19bpY6uro4E3EIIIdKU3hTv3BISEkK7du3o2bMnFStWJH/+/Jw7d445c+Yo6dDGxsZUr16dWbNm4ejoSHBwMN99912yx5s6dSqWlpZYWVkxfvx4ChUqRKtWrZTt+vr6DBw4kEWLFqGvr8+AAQOoXr26EoSPHDmS9u3bK5OX/fHHH2zdujXVmZshofdQpVKxY8cOmjZtirGxMfny5Uuxfq9evahduzbXrl1jxIgRSi+0p6cnNWrUoFWrVsyePZsyZcrw5MkTdu3aRatWrfDw8GDSpEnKzNAdO3YkLi6OP//8k1GjRgEJqdh//fUXHTt2xNDQkEKFCjF8+HCqVKnCtGnT6NChAydPnmTJkiXJpvdmhK6urpK+n1zwnjgz+IoVK7CxseHBgweMGTNGq85XX33FjBkzaNWqFTNnzsTGxoYLFy5ga2urNVwgK3Xu3Jm5c+fSsmVLpk6dir29PQ8ePGDr1q2MHDkSe3v7JPvo6Ojg6enJsWPHlHsqPdeXksmTJzNo0CDMzMxo0qQJ0dHRnDt3jtDQUIYNG0apUqV48OABfn5+VKlShZ07d7Jt27Y0j/s+6eXm5ub06tWL4cOHY2lpScGCBRkxYgQuLi54enoq9Ro2bEjr1q2VLIehQ4dSs2ZNZsyYQfv27Tlz5gwrVqxQZoyPi4ujbdu2nD9/nh07dhAfH09QUMJv3IIFCyoPrQCOHj2aI2uup1eujzZeunQpxYsXx8jICHd391Rnmdu6dSuff/45hQsXxszMjBo1arBnz54cbG3a3g66DQoZpFJTCBJSyc/+DIvdtQPuIhWgx5/Q+qdMBdynTj2iQYM1NG68Vgm4AZ4/j5Dlv4QQQnxy8uXLR7Vq1ViwYAF16tTB2dmZCRMm0KdPH5YsWaLUW716NbGxsXh4eDB48GCmT5+e7PFmzZrF4MGDcXd3JzAwkO3bt2v9oDcxMWH06NF06tSJGjVqYGxsjJ+fn7K9VatW/PDDD8ydO5cKFSqwfPlyfHx8lJ7olNjZ2TFlyhTGjBmDlZWVEoyk5LPPPqNMmTK8evWKbt26KeUqlYpdu3ZRp04devbsiZOTEx07diQgIAArq4TfFfXq1WPTpk1s374dV1dXGjRooLW81tSpUwkICKBkyZJKlkDlypXZuHEjfn5+ODs7M3HiRKZOnZrhMfPJMTMzS7G3WEdHBz8/P/z9/XF2dmbo0KHMnTtXq46BgQF79+6lSJEiNG3aFBcXF2bNmvVePfBpMTEx4a+//qJYsWK0adOGcuXK0bNnTyIjI1Pt+e7bty9+fn5Kenh6ri8lvXv35ueff8bX1xcXFxfq1q2Lr68vxYsXB6Bly5YMHTqUAQMG4OrqyokTJ9KcUT4rLFiwgFatWtG+fXtq1aqFiYkJf/zxh9bf486dOwS/NVdElSpV2LZtG+vXr8fZ2Zlp06axcOFCpRf/0aNHbN++nUePHuHq6oqNjY3yentW9JMnTxIWFkbbtm86mnKbSpPcQIwcsmHDBrp06cLSpUupVasWy5cv5+eff+batWtKSs/bhgwZgq2tLfXr18fCwgIfHx/mzZvH6dOn050+8OrVK8zNzZnS+3cmrmyR9g4ZoNFomDdvHuHh4RgaGlKgSQHqry2Kqa4ZkerXlJ7TJEvP967rFZwhPh4jZ2eKb96UrecSWeDROdg5LEtTya9efcb48Qf5/Xft1JzKlW2YObMhn39eItUJLIQQQoioqCju3bundIrkJYcPH6Z+/fqEhoYmWYIoka+vL0OGDOHly5c52jbx6dBoNFSvXp0hQ4bw1Vdf5XZzPjnt2rXDzc2NcePGpVgnte+5xHgxLCws3cMG0pKr6eXz58+nV69e9O7dG4CFCxeyZ88eli1bxsyZM5PUX7hwodb7GTNm8Pvvv/PHH398EDn7ISEhhIeHA1CsWDFeq17ncovEBymlVPKKHRNmJc9Ez3ZYWBSDB+9mzZq/efsxWunSBZk+vQFt25ZHR0eCbSGEEEKI3KZSqVixYgWXLl3K7aZ8cqKjo6lUqRJDhw7N7aZoybWgOyYmBn9//yTjFRo1apTqoulvU6vV/Pvvv6lOlhAdHU10dLTy/tWrV5lrcDq8nVru4ODAVZKf0U/kUep48Pf9b1byl2/Ki1SAZvPAIeXZHNNiamrAqVOPlIDb1jY/kybVpUcPV/T1sy+tSgghhBBCZFylSpWoVKlSbjfjk2NoaJjiXA25KdeC7uDgYOLj45VxJYmsrKyUAfFp8fb2Jjw8XJkVLzkzZ85kypQp79XW9Ho36L51+zSqxF7HeDU3qlXP3gakY6ZLkUtSTCUfB1X6ZDiVPDo6DkPDN/vo6enw/fcN6N37D8aO/YwBA6piYpLyrJJCCCGESKpevXrJLoH1tu7du2fJGGYhRN6R67OXp2cNw+SsX7+eyZMn8/vvv1OkSJEU640dO5Zhw4Yp71+9ekXRokUz3+BUJAbd+vr62NjYUHjNTVSaN8tLqMPCUto1S+nkT3mWS5HDwkPgwGQ4v0a7PJOp5NHRcfz00zlmzDjG3r1fU6mStbKtTZtyNGxYAguLvDX+TgghhBBCiA9ZrgXdhQoVQldXN0mv9rNnz5L0fr9rw4YN9OrVi02bNmlNO58cQ0NDDA0N37u9aXn58iVh/wXV9vb26OnpoRutvbadQQrrJGYlXXNzCvfvn+3nEWnI4lTy+Hg1v/56iYkTD/PgQcJ9Nn78QXbs6KTUUalUEnALIYQQQgjxgcm1oNvAwAB3d3f27dtH69atlfJ9+/Yp6ykmZ/369fTs2ZP169fTrFmznGhqugQEBCj/TnZdOx0VJXf/mXMNErnn0TnYORwCL74py2QquUajYfv2G4wbd5Br155rbTMzMyQmJh4DAxmzLYQQQgghxIcqV9PLhw0bRpcuXfDw8KBGjRqsWLGCBw8e0K9fPyAhNfzx48esWZOQmrt+/Xq6du3KDz/8QPXq1ZVecmNjY8zNzXPtOiDpeG6RB2VxKvmRIwGMGXOAU6ceaZU3aVKKGTMa4upqncKeQgghhBBCiA9FrgbdHTp0ICQkhKlTpxIYGIizszO7du1SgtbAwEAePHig1F++fDlxcXF8++23fPvtt0p5t27d8PX1zenma0kMunV1dbG3t8/VtogclloqedO54FgrQ4cLDY2kU6et7N59W6u8Rg17Zs5sSN26ju/dZCGEEEIIIUTOUGnSmqLxE5O42Pm0L9tQuHDWTDgWr6PLs8LFANCPiaJQaCAAUa9CaWLzDSZ6ZkTG/0vpuU2z5HziA/LI/79ZyS++KTPIDw3GZ2pWcgC1WoOHxwouXEjI5KhQoTAzZjSkeXOndE0yKIQQQryPqKgo7t27R/HixTEykrlChBCfntS+5xLjxbCwMMzMzLLkfLk+e3luUalf8/pFdNoV0yE2f4E3x30VyusXIW+f6Z3/FZ+E8BA4MOW/VPK3nltV7PBfKnn6U79DQyMpUMBYea+jo2LmzIb07buDqVPr8fXXFdHV1cnCxgshhBAiNfXq1cPV1ZWFCxfmdlM+WBMmTODp06esWLEit5vySYmOjqZ06dJs27YNd3f33G6OyCJ59pe8BhX5ClpmyUunYGHluPl03xxXT08H1X+JBLr6efb5xqdFHQ/nVsMSdzj/C0rAXaQ8dN8FbVakO+B++TKKceMOYG+/gJMnH2pta9SoJLduDaRbN1cJuIUQQoh06N69OyqVilmzZmmV//bbbxnOFNu6dSvTpk3LyuYlkdjexJelpSWNGzfm0qVL2XrerPD06VN++OEHxo0bl9tNyTbR0dEMHDiQQoUKYWpqSosWLXj06FGq+zg6Omr9TRNfbw+LfffvrlKpqF69urLd0NCQESNGMHr06Gy7NpHz8m4kqGPKN8t+yZJDLV26lIhnz1CpVHwz5wdlibI/J/bEMCwe9EFHAqeP3yN/2DUcnlx4U2aQP2FW8qp9QFc/XYeJiIhlyZIzzJp1jNDQKADGjDnA4cPdlB8FKpVKZiUXQgghMsjIyIjZs2fzzTffUKBAgbR3SEHBggWzsFUpa9y4MT4+PgAEBQXx3Xff8cUXX2jNafQhWrVqFTVq1MDxPZfDjY2NRV8/fb+fctqQIUP4448/8PPzw9LSkuHDh/PFF1/g7++Prm7yv9HOnj1LfHy88v7KlSt8/vnntGvXTqve2393SFjV6W2dO3dm5MiRXL9+nXLlymXhVYncIpHge4qIiODZs2cA2NjY5Mia4CKHhYfA9kHwc0PtgLtiBxh4Dmr0T1fAHRsbz4oV/pQuvZjRo/crAbeBgS6VK1sTG6vOrisQQggh8gRPT0+sra2ZOXNminVCQkL46quvsLe3x8TEBBcXF9avX69Vp169egwZMgRIWE3n7Z7IRBUrVmTSpEnKex8fH8qVK4eRkRFly5Zl6dKlabbX0NAQa2trrK2tcXV1ZfTo0Tx8+JDnz98sEzp69GicnJwwMTGhRIkSTJgwgdjYWCBhyVodHR3OnTunddzFixfj4OBA4tRN165do2nTpuTLlw8rKyu6dOlCcHCwUn/z5s24uLhgbGyMpaUlnp6ehIeHp9huPz8/WrRooVW2e/duPvvsMywsLLC0tOSLL77gzp07yvaAgABUKhUbN26kXr16GBkZ8euvv6brs0vtM8gOYWFhrFq1Cm9vbzw9PXFzc+PXX3/l8uXL7N+/P8X9ChcurPw9ra2t2bFjByVLlqRu3bpa9d7+u1tbWyd5yGNpaUnNmjWT3Jfi4yVB93uSpcI+YVmUSq5Wa9i48SoVKizlm2928OTJv0DC2O3u3V25eXMACxY0lp5tIYQQ4j3p6uoyY8YMFi9enGIqcFRUFO7u7uzYsYMrV67Qt29funTpwunTp5Ot37lzZ06fPq0VQF69epXLly/TuXNnAFauXMn48eP5/vvvuX79OjNmzGDChAn88kv6sypfv37N2rVrKVWqFJaWlkp5/vz58fX15dq1a/zwww+sXLmSBQsWAAnpzJ6enlq9ppAQxCamMQcGBlK3bl1cXV05d+4cu3fv5unTp7Rv3x5IWC3oq6++omfPnly/fp3Dhw/Tpk0bUpprOTQ0lCtXruDh4aFVHh4ezrBhwzh79iwHDhxAR0eH1q1bo1ZrdyqMHj2aQYMGcf36dby8vNL12aX2GaSkQoUK5MuXL8VXhQoVUtzX39+f2NhYGjVqpJTZ2tri7OzMiRMnUj1vopiYGH799Vd69uyZZHjD4cOHKVKkCE5OTvTp00fpwHtb1apVOXr0aLrOJT58eTe9PIu8G3Q/DX/K5eDLXHp+CVXoDSrIY42PUxalkoeERNCo0a+cPx+oVd6qVVmmT69PhQpFsrLVQgghRJ7XunVrXF1dmTRpEqtWrUqy3c7OjhEjRijvBw4cyO7du9m0aRPVqlVLUt/Z2ZmKFSuybt06JkyYAMDatWupUqUKTk5OAEybNg1vb2/atGkDQPHixbl27RrLly+nW7duKbZ1x44d5MuXsJpOeHg4NjY27NixAx2dNz8gv/vuO+Xfjo6ODB8+nA0bNjBq1CgAevfuTb9+/Zg/fz6Ghob8/fffXLx4ka1btwKwbNkyKleuzIwZM5TjrF69mqJFi3Lz5k1ev35NXFwcbdq0UTqQXFxcUmzz/fv30Wg02NraapV/+eWXWu9XrVpFkSJFuHbtGs7Ozkr5kCFDlM8pvZ9dWp9Bcnbt2pVqb3hqae1BQUEYGBgkGaJgZWVFUFBQivu97bfffuPly5d0795dq7xJkya0a9cOBwcH7t27x4QJE2jQoAH+/v5aGbN2dnYEBASk61ziwydB93uIjIvk+p3ryvshF4bw+PRj5X2baDX8Nym1jiz19HHIwlnJAQoWNMbE5M2Xet26Dsya5Un16rKWuxBCiI/HxhlniXgVk+PnNTEzoP24Khneb/bs2TRo0IDhw4cn2RYfH8+sWbPYsGEDjx8/Jjo6mujoaExNTVM8XufOnVm9ejUTJkxAo9Gwfv16Jf38+fPnPHz4kF69etGnTx9ln7i4OMzNzVNtZ/369Vm2bBkAL168YOnSpTRp0oQzZ84oAfDmzZtZuHAht2/fVgLkt5cxatWqFQMGDGDbtm107NiR1atXU79+fWW8tb+/P4cOHVKC+7fduXOHRo0a0bBhQ1xcXPDy8qJRo0a0bds2xTHxkZGRAEmWWbpz5w4TJkzg1KlTBAcHKz3cDx480Aq63+4hT+9nl9ZnkJzsyEDVaDTpnpRv1apVNGnSJMnDiQ4dOij/dnZ2xsPDAwcHB3bu3Kn1MMLY2JiIiIisabjIdRJ0p5NaoybgVQCXnl/i8vPLXA6+zN2QuzR73gwVKsL0w3gc8zjF/XVV8lF/0NTxCSnkB6ZCZOib8iLloek8cKyVrsPcuxeKo6OF1oRos2Y1ZODAP5k5syGNGpWUtbaFEEJ8dCJexRD+MmuWWs0JderUwcvLi3HjxiXpafT29mbBggUsXLgQFxcXTE1NGTJkCDExKT9U6NSpE2PGjOH8+fNERkby8OFDOnbsCKAElytXrkzSU57ShFuJTE1NKVWqlPLe3d0dc3NzVq5cyfTp0zl16hQdO3ZkypQpeHl5YW5ujp+fH97e3so+BgYGdOnSBR8fH9q0acO6deu0ljpTq9U0b96c2bNnJzm/jY0Nurq67Nu3jxMnTrB3714WL17M+PHjOX36NMWLF0+yT6FChYCENPPChd+s4NO8eXOKFi3KypUrsbW1Ra1W4+zsnORzffvhRno+u/R8BsmpUKGCVkbquxwcHLh69Wqy26ytrYmJiSE0NFTr4cOzZ8+oWbNmqueFhGyA/fv3K9kGqbGxscHBwYFbt25plb948ULr8xUfN4kEUxAaFaqkiV8Ovszl55f5N/ZfrTpWkVao/lt/+7nxc0z0THAp5IJLYRcqFqpIibDzcCe5o4sPymN/2Pl+qeQPHoQxadJh1qz5m927O/P55yWVbbVqFcPfv68E20IIIT5aJmYGaVf6wM47a9YsXF1dlRTwREePHqVly5Z8/fXXQELgd+vWrVRniba3t6dOnTqsXbuWyMhIPD09sbKyAhJSju3s7Lh7964yxjuzVCoVOjo6Sm/y8ePHcXBwYPz48Uqd5ALJ3r174+zszNKlS4mNjdXqMa1cuTJbtmzB0dERPb3kf/qrVCpq1apFrVq1mDhxIg4ODmzbto1hw4YlqVuyZEnMzMy4du2a8tmGhIRw/fp1li9fTu3atQE4duxYmtebns8uvZ/Bu94nvdzd3R19fX327dunNfb9ypUrzJkzJ81z+/j4UKRIEZo1a5Zm3ZCQEB4+fIiNjY1W+ZUrV3Bzc0tzf/FxkKAbiI2P5Z8X/3Ap+JISaD/892Gq+6hQUZrSyvvun3Wnvkd9dHXePNEM1v8HSQr5gKWUSu7SHhpNS1cq+fPn4cyceYwffzxLTEzCEhFjxx6gYcMS6Oi8CbIl4BZCCPExy0yKd25zcXGhc+fOLF68WKu8VKlSbNmyhRMnTlCgQAHmz59PUFBQmkszde7cmcmTJxMTE5NkEq/JkyczaNAgzMzMaNKkCdHR0Zw7d47Q0NBkA9dE0dHRyhjh0NBQlixZwuvXr2nevLnS1gcPHuDn50eVKlXYuXMn27ZtS3KccuXKUb16dUaPHk3Pnj0xNjZWtn377besXLmSr776ipEjR1KoUCFu376Nn58fK1eu5Ny5cxw4cIBGjRpRpEgRTp8+zfPnz1P8PHR0dPD09OTYsWO0atUKgAIFCmBpacmKFSuwsbHhwYMHjBkzJtXPM72fXXo/g3e9T3q5ubk5vXr1Yvjw4VhaWlKwYEFGjBiBi4sLnp6eSr2GDRvSunVrBgwYoJSp1Wp8fHzo1q1bkoccr1+/ZvLkyXz55ZfY2NgQEBDAuHHjKFSoEK1bt9aqe/To0WxfK17knDwcdGuYfWY2l4Iv8U/IP8SoUx+nZGlkScXCFRNehSpSoVAF/Nb48ZCE4LxauWpaAbf4gKnjEwLtA1O0U8kLl4Nm88DxszQP8e+/0SxYcIp5807w779v7h0LCyPatStPfLwaHbkfhBBCiFw1bdo0Nm7cqFU2YcIE7t27h5eXFyYmJvTt25dWrVoRFhaW6rHatWvHwIED0dXVVYLNRL1798bExIS5c+cyatQoTE1NcXFxUcZ9p2T37t1KD2f+/PkpW7YsmzZtol69egC0bNmSoUOHMmDAAKKjo2nWrBkTJkxg8uTJSY7Vq1cvTpw4Qc+ePbXKbW1tOX78OKNHj8bLy4vo6GgcHBxo3LgxOjo6mJmZ8ddff7Fw4UJevXqFg4MD3t7eNGnSJMV29+3bl169ejFnzhx0dHTQ0dHBz8+PQYMG4ezsTJkyZVi0aJFyHalJ67PLyGeQlRYsWICenh7t27cnMjKShg0b4uvrqzVk4M6dO1pLrwHs37+fBw8eJPk7QELK/OXLl1mzZg0vX77ExsaG+vXrs2HDBvLnz6/UO3nyJGFhYbRt2zb7LlDkKJUmpfUAPlGvXr3C3NycaV+2YkPz28nWMdAxoLxl+YQ08f+CbBtTG63eypiYGGbNmoVarcbS0pKBAwcmOU7wsmVE/GOHjnEBVIYa7KbUybbrEun02B92joAn59+UGeSH+mOhat80U8mjo+NYvtyf6dP/4vnzN3kMxsZ6DB5cjVGjalGggHEqRxBCCCE+bFFRUdy7d4/ixYsnmSxLfLi+//57/Pz8uHz5crafS6PRUL16dYYMGcJXX32V7efLa9q1a4ebmxvjxo3L7aZ8slL7nkuMF8PCwtKcsC+98nBP9xvF8hejYuGKuBRyoVLhSjgVcEI/jeDr8ePHyuQPsj73RyDiRULPtv9b621DhlLJQ0IicHdfwf37b56E6+qq6NOnMhMm1MXWNn8qewshhBBCZL3Xr19z/fp1Fi9enGPpyCqVihUrVnDp0qUcOV9eEh0dTaVKlRg6dGhuN0VkoTwddC9tuBSXQi5YGFlkeN+3182ToPsDplb/Nyt55lPJE1lamlC+fGEl6O7Y0ZmpU+tRurRlVrdaCCGEECJdBgwYwPr162nVqlWyKc3ZpVKlSlSqVCnHzpdXGBoaaq1LLj4NeTjoVlHbvnam93571kQJuj9Q75lKfvbsYzw8bLWGFcyY0RC1WsOMGQ2pXNkmlb2FEEIIIbKfr68vvr6+ud0MIUQq8nDQnXlxcXE8evQISJjd0MLCIncbJLSlmEreDj6fBmapB8t//x3E2LEH+PPP22zd2p7Wrd/M3unqas3u3V9nU8OFEEIIIYQQnxoJujPhyZMnxMXFAeDo6Ji7jRFvqNVwYQ3sn5ypVPI7d14wceJh1q17MwHJ+PEHad68DHp6OtnUaCGEEEIIIcSnTILuTJDU8g9QSqnk9cZAtW9STSUPCnrNtGlHWLHiPHFxaqW8aFEzRo2qhSyxLYQQQgghhMgsCbozQYLuD0jECzgwFfx9yWgq+cuXUcyde5yFC08TERGrlBcqZML48bXp188DIyP5T0QIIYQQQgiReRJRZFB8fDwPHjwAIF++fBQsWDCXW5RHpZZK3nQuFE99kryQkAicnJbw4kWkUpYvnwHDh9dg2LAamJkZZlPDhRBCCCGEEHmJBN0Z9PTpU2JiYoCEXm6V5B7nvMfnYdeIhJTyRAb5oN7YNFPJE1lamtCgQXE2b76GgYEu//d/HowbV5siRUyzseFCCCGEEEKIvEZmh8ogWZ87F0W8gD+GwMoG2gG3SzsYcA5qDkg24NZoNOzYcZP4eLVW+fTp9enWrRI3bgxg4cLGEnALIYQQIsO6d+9Oq1atlPf16tVjyJAhudaeD9XkyZNxdXXNkXPFxMRQqlQpjh8/niPny0t27NiBm5sbarU67cpCIUF3Bsl47lygVics/7XYHfx9UMZuFy4L3XbAlz+nOHZ7//67VKmykubN12vNSg5QpkwhfH1b4ehokb3tF0IIIUSOCAoKYvDgwZQqVQojIyOsrKz47LPP+Omnn4iIiMiRNmzdupVp06Zl6THfDexTq6dSqZSXpaUljRs35tKlS1nanrSoVCp+++03rbIRI0Zw4MCBHDn/ihUrcHBwoFatWjlyvtxw+fJl6tati7GxMXZ2dkydOhWNRpNi/cOHD2vdG2+/zp49q9QbPHgw7u7uGBoaJvuQ5IsvvkClUrFu3brsuKxPlgTdGaBWq5Xx3MbGxhQuXDiXW5QHPD4Pqzzhj0EQ+SKhzCAfNPoe+h1Lcez2mTOPadhwDZ9//j/8/QMBmDDhEDEx8TnVciGEEELkoLt37+Lm5sbevXuZMWMGFy5cYP/+/QwdOpQ//viD/fv3p7hvbGxsitsyqmDBguTPnz/LjpdRjRs3JjAwkMDAQA4cOICenh5ffPFFrrUnUb58+bC0tMyRcy1evJjevXu/1zGy8p7Iaq9eveLzzz/H1taWs2fPsnjxYubNm8f8+fNT3KdmzZrKfZH46t27N46Ojnh4eCj1NBoNPXv2pEOHDikeq0ePHixevDhLr+lTJ0F3Bjx//pzIyISJt4oVK4aOjnx82SbiBewYmjSV3Lltqqnk168/58svN1Kt2s8cPHhPKa9UyYply5qhry9/MyGEEOJT1L9/f/T09Dh37hzt27enXLlyuLi48OWXX7Jz506aN2+u1FWpVPz000+0bNkSU1NTpk+fTnx8PL169aJ48eIYGxtTpkwZfvjhB61zxMfHM2zYMCwsLLC0tGTUqFFJehffTS+PiYlh1KhR2NnZYWpqSrVq1Th8+LCy3dfXFwsLC/bs2UO5cuXIly+fEjhDQlr2L7/8wu+//670TL69/7sMDQ2xtrbG2toaV1dXRo8ezcOHD3n+/LlS5/LlyzRo0ABjY2MsLS3p27cvr1+/Vrar1WqmTp2Kvb290uO5e/durWsaMGAANjY2GBkZ4ejoyMyZMwFwdHQEoHXr1qhUKuX9u+nlib338+bNw8bGBktLS7799lutYDcwMJBmzZphbGxM8eLFWbduHY6OjixcuDDF6z9//jy3b9+mWbNmWuWjR4/GyckJExMTSpQowYQJE7TOldi+1atXU6JECQwNDdFoNISFhdG3b1+KFCmCmZkZDRo04O+//1b2u3PnDi1btsTKyop8+fJRpUqVVB/wZIW1a9cSFRWFr68vzs7OtGnThnHjxjF//vwUe7sNDAyU+8La2hpLS0u2b99Oz549teaoWrRoEd9++y0lSpRI8fwtWrTgzJkz3L17N8uv7VMlEUgGvJ1anvgFIrLY26nk51aTJJW87apkU8kfPgyjV6/fcXZextat15XyEiUKsG5dG86f/4YmTUrLxHdCCCHEJygkJIS9e/fy7bffYmqa/Bwt7/4GmDRpEi1btuTy5cv07NkTtVqNvb09Gzdu5Nq1a0ycOJFx48axceNGZR9vb29Wr17NqlWrOHbsGC9evGDbtm2ptq1Hjx4cP34cPz8/Ll26RLt27WjcuDG3bt1S6kRERDBv3jz+97//8ddff/HgwQNGjBgBJKRlt2/fXqsHu2bNmun6XF6/fs3atWspVaqU0sscERFB48aNKVCgAGfPnmXTpk3s37+fAQMGKPv98MMPeHt7M2/ePC5duoSXlxctWrRQ2rxo0SK2b9/Oxo0buXHjBr/++qvy2zgxVdnHx4fAwECt1OV3HTp0iDt37nDo0CF++eUXfH198fX1VbZ37dqVJ0+ecPjwYbZs2cKKFSt49uxZqtf8119/4eTkhJmZmVZ5/vz58fX15dq1a/zwww+sXLmSBQsWaNW5ffs2GzduZMuWLVy8eBGAZs2aERQUxK5du/D396dy5co0bNiQFy9eKJ9x06ZN2b9/PxcuXMDLy4vmzZsr2bHJOXr0KPny5Uv1NWPGjBT3P3nyJHXr1sXQ8M1qO15eXjx58kRr/qnUbN++neDgYLp3756u+m9zcHCgSJEiHD16NMP75lUye3kGyHjubPbkAuwcnsys5GOgWr8UZyUPCYmgbNkftdbatrbOx8SJdejVqzIGBrrZ3XIhhBDik/br2CGEvwxNu2IWM7UowNczF6ZZ7/bt22g0GsqUKaNVXqhQIaKiogD49ttvmT17trKtU6dO9OzZU6v+lClTlH8XL16cEydOsHHjRtq3bw/AwoULGTt2LF9++SUAP/30E3v27EmxXXfu3GH9+vU8evQIW1tbICGI3r17Nz4+PkpgFRsby08//UTJkiUBGDBgAFOnTgUS0rKNjY2Jjo7G2to6zc9ix44d5MuXD4Dw8HBsbGzYsWOHkqG5du1aIiMjWbNmjfKAYsmSJTRv3pzZs2djZWXFvHnzGD16NB07dgRg9uzZHDp0iIULF/Ljjz/y4MEDSpcuzWeffYZKpdL6XZw4/NLCwiLN9hYoUIAlS5agq6tL2bJladasGQcOHKBPnz78888/7N+/n7Nnzyrpzz///DOlS5dO9ZgBAQHKZ/227777Tvm3o6Mjw4cPZ8OGDYwaNUopj4mJ4X//+59yDQcPHuTy5cs8e/ZMCXDnzZvHb7/9xubNm+nbty+VKlWiUqVKyjGmT5/Otm3b2L59u9aDjLd5eHgoQX1KUluWOCgoKEkHoJWVlbKtePHiqR4bYNWqVXh5eVG0aNE06ybHzs4u3QG+kKA73TQajRJ0GxgYKDe2yAIRL+DgNDj31iRpkJBK3mh6ipOkJbK0NKFDhwr4+FzE3NyQ0aNrMWhQNUxNDbK33UIIIUQeEf4ylNcvQnK7GWl6tzf7zJkzqNVqOnfuTHR0tNa2t8exJvrpp5/4+eefuX//PpGRkcTExCgp0WFhYQQGBlKjRg2lvp6eHh4eHimm9J4/fx6NRoOTk5NWeXR0tNb4ZhMTEyXgBrCxsUmzRzcl9evXZ9myZQC8ePGCpUuX0qRJE86cOYODgwPXr1+nUqVKWhkBtWrVQq1Wc+PGDYyNjXny5EmSSchq1aqlpFV3796dzz//nDJlytC4cWO++OILGjVqlOG2VqhQAV3dN50jNjY2XL6cMPHtjRs30NPTo3Llysr2UqVKUaBAgVSPGRkZiZGRUZLyzZs3s3DhQm7fvs3r16+Ji4tL0hvu4OCgNWeTv78/r1+/TjIWPTIykjt37gAJDzamTJnCjh07ePLkCXFxcURGRqba021sbEypUqVSvY60vHuvJ96D6cnqfPToEXv27NHK4sgoY2PjHJuc8FMgQXc6vXjxQhnrUqxYMa0vCJFJajVc+B/sn/xmkjRISCVvOheK10myS0xMPGvW/E3XrpW0erAnT65HkSKmjBpVi4IFjXOg8UIIIUTeYWqReqCT2+ctVaoUKpWKf/75R6s8cVyqsXHS3wbvpqFv3LiRoUOH4u3tTY0aNcifPz9z587l9OnTmWx9wthoXV1d/P39k/x2TOyNBtDX187mU6lUqc5EnRpTU1OtgM7d3R1zc3NWrlzJ9OnT0Wg0KQZmb5cnF9QlllWuXJl79+7x559/sn//ftq3b4+npyebN2/OUFuTu+7EpahSuv60PpdChQopgXuiU6dO0bFjR6ZMmYKXlxfm5ub4+fnh7e2tVe/de0KtVmNjY5PsGHoLCwsARo4cyZ49e5g3bx6lSpXC2NiYtm3bEhMTk2Ibjx49SpMmTVK9jnHjxjFu3Lhkt1lbWxMUFKRVlviQJj0dgz4+PlhaWtKiRYs066bkxYsXMql0BkjQnU6SWp7FnlyAnSPg8bk3ZamkksfHq1m//goTJhwiIOAlMTHx9O9fRdlerJg5s2Z55lTrhRBCiDwlPSneucnS0pLPP/+cJUuWMHDgwBTHdafm6NGj1KxZk/79+ytlib2ZAObm5tjY2HDq1Cnq1EnoGIiLi1PG+SbHzc2N+Ph4nj17Ru3aya+4kh4GBgbEx2duBRaVSoWOjo4yGXD58uX55ZdfCA8PVz6n48ePo6Ojo4yFtrW15dixY8p1Apw4cYKqVasq783MzOjQoQMdOnSgbdu2NG7cmBcvXlCwYEH09fUz3d5EZcuWJS4ujgsXLuDu7g4kDCN4+fJlqvu5ubmxbNkyrYcEx48fx8HBgfHjxyv13v5tn5LKlSsTFBSEnp5eivM5HT16lO7du9O6dWsgYYx3WmnX75teXqNGDcaNG0dMTAwGBgmZnXv37sXW1jbNeac0Gg0+Pj507do1yUOP9IqKiuLOnTu4ubllav+8SCZSS6e3/+ORoPs9JM5KvqK+dsDt3BYGnIWaA7UCbo1Gw44dN3FzW06XLtsICHgJwLRpf8nyX0IIIYRQLF26lLi4ODw8PNiwYQPXr19XJvn6559/0sxSLFWqFOfOnWPPnj3cvHmTCRMmJJkEbPDgwcyaNYtt27bxzz//0L9//1SDQCcnJzp37kzXrl3ZunUr9+7d4+zZs8yePZtdu3al+9ocHR25dOkSN27cIDg4ONXlrKKjowkKCiIoKIjr168zcOBAXr9+rcze3rlzZ4yMjOjWrRtXrlzh0KFDDBw4kC5duii9pCNHjmT27Nls2LCBGzduMGbMGC5evMjgwYMBWLBgAX5+fvzzzz/cvHmTTZs2YW1trfT+Ojo6cuDAAYKCgggNzdxcAGXLlsXT05O+ffty5swZLly4QN++fTE2Nk41hbp+/fqEh4dz9epVpaxUqVI8ePAAPz8/7ty5w6JFi9KcAA/A09OTGjVq0KpVK/bs2UNAQAAnTpzgu+++49y5c8qxt27dysWLF/n777/p1KmT0lufksT08tReqQXdnTp1wtDQkO7du3PlyhW2bdvGjBkzGDZsmPLZnDlzhrJly/L48WOtfQ8ePMi9e/fo1atXsse+ffs2Fy9eJCgoiMjISC5evMjFixe1eu5PnTqFoaGh1lALkToJutMp8WmYnp5espMziDSkNCt5oTLQ7Y//ZiXX/lyPHr1P7do+NG++nsuX34xratSoJDt2fCUTpAkhhBBCUbJkSS5cuICnpydjx46lUqVKeHh4sHjxYkaMGMG0adNS3b9fv360adOGDh06UK1aNUJCQrR6vQGGDx9O165d6d69u5KCntjDmZLEXsXhw4dTpkwZWrRowenTpzM0gVWfPn0oU6YMHh4eFC5cmOPHj6dYd/fu3djY2GBjY0O1atWUGcrr1asHJIwf37NnDy9evKBKlSq0bduWhg0bsmTJEuUYgwYNYvjw4QwfPhwXFxd2797N9u3blUnM8uXLx+zZs/Hw8KBKlSoEBASwa9cuZbI2b29v9u3bR9GiRd+rN3TNmjVYWVlRp04dWrduTZ8+fcifP3+yY7YTWVpa0qZNG9auXauUtWzZkqFDhzJgwABcXV05ceIEEyZMSPP8KpWKXbt2UadOHXr27ImTkxMdO3YkICBAeUCxYMECChQoQM2aNWnevDleXl4pZj5kFXNzc/bt28ejR4/w8PCgf//+DBs2jGHDhil1IiIiuHHjRpIHNKtWraJmzZqUK1cu2WP37t0bNzc3li9fzs2bN3Fzc8PNzY0nT54oddavX0/nzp0xMTHJngv8BKk0mR0w8pF69eoV5ubmTPuyNd9t3pqufV6+fKmsB+jo6JjuqfWDly0j4h87dIwLoDLUYDcl6RjlPCGDqeSXLj1l7NgD7Np1S6u8alU7Zs5sSIMGac/IKIQQQojMiYqK4t69exQvXjzV4EaInPbo0SOKFi3K/v37adiwYYr1Ll++jKenJ7dv3yZ//vw52MJP3/Pnzylbtiznzp1L1yzpH6rUvucS48WwsLAkk+1llozpTgcZz51JES/g4HTtnm0A5y//m5U8acZASEgEVauuJDr6Tep42bKFmDGjAa1alZV1toUQQggh8oiDBw/y+vVrXFxcCAwMZNSoUTg6OmqNNU+Oi4sLc+bMISAgABcXlxxqbd5w7949li5d+lEH3LlBgu50eDvoTmtyAkFCKvnFXxNmJY94a3mRQmWg2bxkZyVPZGlpQt++7ixefIaiRc2YMqUeXbpUQk9PRkIIIYQQQuQlsbGxjBs3jrt375I/f35q1qzJ2rVr0zUBWLdu3XKghXlP1apVtSbUE+mTZ4PupjadCZyRviUg7sTcAEAHFbrrAglUpW/dxPjwMqiM8ti44ycXYedw7VRyfdM3qeR6b9bODguLYsmSMwwdWgMTkzdfnuPH16Z4cQv+7/+qYGSUZ29RIYQQQog8zcvLCy8vr9xuhhDvLc9GNMZ6psS/Snn9vEQRRBNmFA5AIbUZOv/GE096Z83WJzEbWvWpf9IZSCWPjIzlxx/PMnPmMV68iERfX5dRo2op262s8jF0qMyGKIQQQgghhPj4feqhYIrUGjW6ZgZp1numDoG4hH/b6lmia5T2Poniw8NRv36NJi4KE9cimW3qhy21VPKmc6FEXaUoLk7NL79cZPLkIzx69Eopnz//JEOGVJfZyIUQQgghhBCfnDwbdEfHR2Izrlqa9c7v3An/LdFY4asa2Py3VEJ6BC9bxvMfFgFQsN2yTLXzg5bOVHKNRsPWrdcZP/4gN268CcxVKvj664pMmVJPAm4hhBBCCCHEJynPBt3plTiJmkqlytB6ip+0lFLJK7RJSCU3t1OKDhy4y5gxBzh37onWIZo3d+L77xvg4mKVQ40WQgghhBBCiJwnQXcqIiIiePYsYdI0a2trWatSrYaLa2H/pDRTySFh+a/mzdcTGRmnlH32WTFmzWpIrVrFcqrVQgghhBBCCJFrJOhOxYMHD5R/5/n1uVNMJR8N1f5Pa1byRJaWJgwZUp2ZM49RsaIVM2c2pEmTUrLWthBCCCGEECLPkKA7FbI+NxAZmpBKfnYVqaWSP3r0ijlzjjNtWn3Mzd9kBIwaVQtn5yJ07OiMjo4E20IIIYQQQoi8RSe3G/AhezvoLlYsj6VDq9Vw/n+w2B3O/owScBcqA11/h3Y+YG5HSEgEI0fupVSpRSxefAZv75Nah7GwMKJTJxcJuIUQQgiRp9SpU4d169bldjM+OZcvX8be3p7w8PDcbooQ6SZBdwqio6MJDAwEoEiRIpiYmORyi3LQk4uwuhFsH/Bm7La+KXw+FfodgxL1eP06hu+//4sSJRYxb95JoqMT1i5fvfoCMTHpXcdcCCGEEOL9xcfHU7NmTb788kut8rCwMIoWLcp3332nVb5lyxYaNGhAgQIFMDExoUyZMvTs2ZMLFy4odXx9fVGpVMorX758uLu7s3Xr1jTbs2PHDoKCgujYsWPWXOAH6MGDBzRv3hxTU1MKFSrEoEGDiImJSbF+QECA1uf59mvTpk1J6kdHR+Pq6opKpeLixYtKuYuLC1WrVmXBggXZcVlCZAsJulPw8OFDNJqE3t08M547MjRh3PbK+vDo7JvyCm1gwFmoNZgYtS4//niGUqUW8d13h3j1KhoAIyM9Ro2qyaVL/yfLfwkhhBAiR+nq6vLLL7+we/du1q5dq5QPHDiQggULMnHiRKVs9OjRdOjQAVdXV7Zv387Vq1dZsWIFJUuWZNy4cVrHNTMzIzAwkMDAQC5cuICXlxft27fnxo0bqbZn0aJF9OjRAx2dzP/Ujo+PR61WZ3r/7BQfH0+zZs0IDw/n2LFj+Pn5sWXLFoYPH57iPkWLFlU+y8TXlClTMDU1pUmTJknqjxo1Cltb22SP1aNHD5YtW0Z8vHT0iI+DBN0peDu1/JMPutVquPDrm1RyzX9f8IWclFRydX5b1q69RNmySxgw4E+ePk1I6dHVVdGnT2Vu3RrI7NmfU7CgcS5eiBBCCCHyqtKlSzNz5kwGDhzIkydP+P333/Hz8+OXX37BwCBhwtdTp04xZ84c5s+fz/z586lduzbFixenbt26jB8/nl27dmkdU6VSYW1tjbW1NaVLl2b69Ono6Ohw6dKlFNsRHBzM/v37adGihVb5/PnzcXFxwdTUlKJFi9K/f39ev36tbPf19cXCwoIdO3ZQvnx5DA0NuX//PjExMYwaNQo7OztMTU2pVq0ahw8fVvYLCQnhq6++wt7eHhMTE1xcXFi/fn0WfKIp27t3L9euXePXX3/Fzc0NT09PvL29WblyJa9evUp2H11dXeWzTHxt27aNDh06kC9fPq26f/75J3v37mXevHnJHsvLy4uQkBCOHDmS5dcmRHaQidRSEBAQoPz7kw66n1yEXSO0e7aTmZU8LCyKb7/dRVhYtFKtXbvyTJtWnzJlCuVwo4UQQgiRk54uvoD635RTh7OLTn4DrAa6pbv+wIED2bZtG127duXy5ctMnDgRV1dXZfv69evJly8f/fv3T3b/1FZYiY+PZ82aNQBUrlw5xXrHjh3DxMSEcuXKaV+Ljg6LFi3C0dGRe/fu0b9/f0aNGsXSpUuVOhEREcycOZOff/4ZS0tLihQpQo8ePQgICMDPzw9bW1u2bdtG48aNuXz5MqVLlyYqKgp3d3dGjx6NmZkZO3fupEuXLpQoUYJq1aol28YHDx5Qvnz5FK8B4Ouvv+ann35KdtvJkydxdnbW6on28vIiOjoaf39/6tevn+qxAfz9/bl48SI//vijVvnTp0/p06cPv/32W4rDOw0MDKhUqRJHjx6lQYMGaZ5LiNwmQXcyYmNjefz4MQAFCxYkf/78udyibBAZCge/h3Or3vRsA1RoDY2+V2YlT1SggDGjR9di3LiDfP55CWbMaIiHR/IpP0IIIYT4tKj/jSH+Vc4H3RmlUqlYtmwZ5cqVw8XFhTFjxmhtv3nzJiVKlEBP781P4Pnz52ulnz9+/Bhzc3MgYUx4Yi9sZGQk+vr6Sip6SgICArCyskqSWj5kyBDl38WLF2fatGn83//9n1bQHRsby9KlS6lUqRIAd+7cYf369Tx69EgJcEeMGMHu3bvx8fFhxowZ2NnZMWLECOUYAwcOZPfu3WzatCnFoNvW1lZrnHRyzMzMUtwWFBSElZWVVlmBAgUwMDAgKCgo1eMmWrVqFeXKlaNmzZpKmUajoXv37vTr1w8PDw+tTrB32dnZpbpdiA+JBN3JePTokTKG5pPr5Var4e91sG8SRAS/KS/kBE3nQol6XLr0lOnTN/Hjj00pXNhUqTJoUDWqVbOnQYPiudBwIYQQQuQWnfwGH815V69ejYmJCffu3ePRo0dJln19tze7Z8+etGjRgtOnT/P1118rc/oA5M+fn/PnzwMJvdD79+/nm2++wdLSkubNmyd7/sjISIyMjJKUHzp0iBkzZnDt2jVevXpFXFwcUVFRhIeHY2qa8HvLwMCAihUrKvucP38ejUaDk5OT1rGio6OxtLQEEnrgZ82axYYNG3j8+DHR0dFER0crx0yOnp4epUqVSnF7eiSXFaDRaFLNFkgUGRnJunXrmDBhglb54sWLefXqFWPHjk3zGMbGxkRERKS/wULkIgm6k/HJjucO/Bt2joBHZ96U6ZtC3VFQvT/3HoYzscs21q69hEYD9vZmzJ/vpVQ1NTWQgFsIIYTIgzKS4p2bTp48yYIFC/jzzz+ZM2cOvXr1Yv/+/UogWLp0aY4dO0ZsbCz6+voAWFhYYGFhwaNHj5IcT0dHRys4rVixInv37mX27NkpBt2FChUiNDRUq+z+/fs0bdqUfv36MW3aNAoWLMixY8fo1asXsbGxSj1jY2OtoFWtVqOrq4u/vz+6utoT1Sb2wHt7e7NgwQIWLlyojBkfMmRIqjOJv296ubW1NadPn9YqCw0NJTY2NkkPeHI2b95MREQEXbt21So/ePAgp06dwtDQUKvcw8ODzp0788svvyhlL168SDXjQIgPiQTdyXg76H736ehHKY1U8qdR5kwfup/ly/2JjX2zffv2G8ya5SmzkQshhBDigxcZGUm3bt345ptv8PT0xMnJCWdnZ5YvX06/fv0A+Oqrr1i8eDFLly5l8ODBmTqPrq4ukZGRKW53c3MjKCiI0NBQChQoAMC5c+eIi4vD29tbSTvfuHFjmudyc3MjPj6eZ8+eUbt27WTrHD16lJYtW/L1118DCYH6rVu3kowpf9v7ppfXqFGD77//nsDAQGxsbICEydUMDQ1xd3dP46oSUstbtGhB4cKFtcoXLVrE9OnTlfdPnjzBy8uLDRs2JEmVv3LlCm3btk3zXEJ8CCTofkdcXBwPHz4EwNzcHAsLi9xt0PtILZW8yRzCCtVg3rwTLFhwivDwN09ZCxY0Zty4z/j226oScAshhBDiozBmzBjUajWzZ88GoFixYnh7ezNs2DAaN26Mo6MjNWrUYPjw4QwfPpz79+/Tpk0bZSmrVatWoVKptMZiazQaZYxyZGQk+/btY8+ePVpjwN/l5uZG4cKFOX78OF988QUAJUuWJC4ujsWLF9O8eXOOHz+eYi/y25ycnOjcuTNdu3bF29sbNzc3goODOXjwIC4uLjRt2pRSpUqxZcsWTpw4QYECBZg/fz5BQUGpBt3vm17eqFEjypcvT5cuXZg7dy4vXrxgxIgR9OnTRwnWHz9+TMOGDVmzZg1Vq1ZV9r19+zZ//fVXkpniIeFv9rbE3vySJUtib2+vlAcEBPD48WM8PT0zfQ1C5CRZMuwdgYGBxMXFAR95anng37DaC37/9k3ArW8KnlOI7nGE+b8bUbLkIqZPP6oE3CYm+nz3XW3u3h3E8OE1MTKSZzJCCCGE+PAdOXKEH3/8EV9fX62xzH369KFmzZr06tVLGas9b9481q1bx4ULF/jiiy8oXbo07dq1Q61Wc/LkSa0e3levXmFjY4ONjQ3lypXD29ubqVOnMn78+BTboqurS8+ePbXWC3d1dWX+/PnMnj0bZ2dn1q5dy8yZM9N1bT4+PnTt2pXhw4dTpkwZZfx50aJFAZgwYQKVK1fGy8uLevXqYW1tTatWrTLy8WWYrq4uO3fuxMjIiFq1atG+fXtatWqltcRXbGwsN27cSDLuevXq1djZ2dGoUaNMn3/9+vU0atTo4/6tLvIUlebt2SLygFevXmFubo7/gM1UXvxlku3Hjh1j//79ADRv3jxdKTIpCV62jOc/LALA/qdl5K9XL9PHSrd0zEr++nUMJUr8wPPnCV+C+vo6fPONO999Vwcrq3wpHFgIIYQQeUFUVBT37t2jePHiyU4IJtL29OlTKlSogL+/vwSGWSw6OprSpUuzfv16atWqldvNER+p1L7nEuPFsLCwVIdZZIT0dL/jo12fW62GC2thsQecXfkm4LYsDV1+g3a+yjJg+fIZMGFCHVQq+PrrivzzzwAWL24qAbcQQgghRBawsrJi1apVPHjwILeb8sm5f/8+48ePl4BbfFQkf/gtarVa+XI0NTVVlmL44CU7K7kJ1B3FwchmTOl5grVrX2Fv/+ZJTd++7tSr54iLS9ozTAohhBBCiIxp2bJlbjfhk+Tk5JRkCTUhPnTS0/2WoKAgZXkFBweHdK0zmKsiX8KukbCinnbAXb4V52rtotHUQjRstJ6//rrP1KlHtHY1NNSTgFsIIYQQQgghspn0dL/lo1mfW62Gv9fDvonas5JbluZG2SlMWBnBpk3btXY5d+4JMTHxMhu5EEIIIYQQQuQgCbrf8lEE3YGXYNcIeHj6TZm+CY/KjWDqrlKsHupPfPybufGKF7dg2rT6dOzojK6uJDYIIYQQQgghRE6SoPs/arVaCbqNjIwoUqRILrfoHZEv4dD3cPZnrVnJX5dozVT/Fiyefp2oqItKeZEipkycWIc+fdyld1sIIYQQQgghcokE3f8JDg4mMjISSOjl1tH5QHqFU0klp+kc9OzqsGHCEqKiEtYWNzMzZNSomgweXJ18+QxyqdFCCCGEEEIIIUCCbsUHmVqeTCq5Rs8EVb1RUP1b0DPACJgypR79+u1g4MCqjBnzGZaWJrnVYiGEEEIIIYQQb5Gg+z8fVNCdTCq5WgN+oZ2ZutuJPzp0obTem17sLl0q4ulZQmtJMCGEEEIIIYQQue8DyaHOXRqNhoCAAAAMDAywtrbOnYao1XBxHSzxgDMrQKNGo4FdQTWpvGkCnRdbceNWGBMnHtbaTVdXRwJuIYQQQogPSJ06dVi3bl1uN+OTc/nyZezt7QkPD8/tpgiRbhJ0Ay9evOD169cAFC1aFF3dXJh4LOgy+DSB3/4Pwp8DcCKwOHV/G0mz5dX5+/qbL5bg4AhiYuJzvo1CCCGEEB+o7t2706pVK62yzZs3Y2RkxJw5cwCYPHkyKpWKfv36adW7ePEiKpVK6YQJCAhApVJRpEgR/v33X626rq6uTJ48OdW27Nixg6CgIDp27Phe1/Qhe/DgAc2bN8fU1JRChQoxaNAgYmJi0tzv5MmTNGjQAFNTUywsLKhXr54yr1KinTt3Uq1aNYyNjSlUqBBt2rRRtrm4uFC1alUWLFiQ5dckRHaRoJtcTi2PfAm7RsHyOvDwFABXnlnScuc31FrRmqOX3gTXHh627NvXhX37usiM5EIIIYQQqfj555/p3LkzS5YsYdSoUUq5kZERq1at4ubNm2ke499//2XevHkZPveiRYvo0aPHe03MGx8fj1qtTrtiLoiPj6dZs2aEh4dz7Ngx/Pz82LJlC8OHD091v5MnT9K4cWMaNWrEmTNnOHv2LAMGDND6nLZs2UKXLl3o0aMHf//9N8ePH6dTp05ax+nRowfLli0jPl46ocTHQYJucino1kolXw4aNS+jDOn2Zzsq/tSN7edMlaplyliyeXM7zpzpjadniZxpnxBCCCHER2rOnDkMGDCAdevW0bt3b61tZcqUoX79+nz33XdpHmfgwIHMnz+fZ8+epfvcwcHB7N+/nxYtWmiVz58/HxcXF0xNTSlatCj9+/dXMi0BfH19sbCwYMeOHZQvXx5DQ0Pu379PTEwMo0aNws7ODlNTU6pVq8bhw4eV/UJCQvjqq6+wt7fHxMQEFxcX1q9fn+72ZsbevXu5du0av/76K25ubnh6euLt7c3KlSt59epVivsNHTqUQYMGMWbMGCpUqEDp0qVp27YthoaGAMTFxTF48GDmzp1Lv379cHJyokyZMrRt21brOF5eXoSEhHDkyJFsvU4hsopMpMaboFtPTw87O7vsP2HQZdg5QunZBkDfhHx1R3BqmwEazQsA7OzyM2VKPbp1c0VPT56PCCGEECJ3LF++XCtAzCn58uXjm2++ydA+Y8aM4ccff2THjh14enomW2fWrFlUqVKFs2fPUqVKlRSP9dVXX7Fv3z6mTp3KkiVL0nX+Y8eOYWJiQrly5bTKdXR0WLRoEY6Ojty7d4/+/fszatQoli5dqtSJiIhg5syZ/Pzzz1haWlKkSBF69OhBQEAAfn5+2Nrasm3bNho3bszly5cpXbo0UVFRuLu7M3r0aMzMzNi5cyddunShRIkSVKtWLdk2PnjwgPLly6d6HV9//TU//fRTsttOnjyJs7Mztra2SpmXlxfR0dH4+/tTv379JPs8e/aM06dP07lzZ2rWrMmdO3coW7Ys33//PZ999hkA58+f5/Hjx+jo6ODm5kZQUBCurq7MmzePChUqKMcyMDCgUqVKHD16lAYNGqR6HUJ8CPJ80P3y5UtevnwJgL29PXp62fiRRL6EQzPg7ErQqImO08VQLx7Kt4RG36NnUZTp06/yzTc7GDeuNt9+WwVjY/3sa48QQgghRDq8fv06ydjmD9Gff/7J77//zoEDB1INxipXrkz79u0ZM2YMBw4cSLGeSqVi1qxZNG/enKFDh1KyZMk02xAQEICVlVWS1PIhQ4Yo/y5evDjTpk3j//7v/7SC7tjYWJYuXUqlSpUAuHPnDuvXr+fRo0dKgDtixAh2796Nj48PM2bMwM7OjhEjRijHGDhwILt372bTpk0pBt22trZcvHgx1eswM0t5kt6goCCsrKy0ygoUKICBgQFBQUHJ7nP37l0gYVz9vHnzcHV1Zc2aNTRs2JArV65QunRprTrz58/H0dERb29v6taty82bNylYsKByPDs7O2UMvhAfujwfdD948ED5d7amlt89Amd7Q/hzouJ0WXa2MrNO1mDfuupU/OJN+tGXX5anUaOSmJsbZV9bhBBCCCEyIF++fB/FeStWrEhwcDATJ06kSpUq5M+fP8W606dPp1y5cuzdu5ciRYqkWM/Ly4vPPvuMCRMmpGs28sjISIyMkv6OO3ToEDNmzODatWu8evWKuLg4oqKiCA8Px9Q0YVihgYEBFStWVPY5f/48Go0GJycnrWNFR0djaWkJJIyvnjVrFhs2bODx48dER0cTHR2tHDM5enp6lCpVKs1rSY1KpUpSptFoki0HlPHp33zzDT169ADAzc2NAwcOsHr1ambOnKnUGT9+PF9++SUAPj4+2Nvbs2nTJq2sB2NjYyIiIt7rGoTIKXk+6M6x8dynfiTOOob//V2BSUdq8jAs4f8Exi8P548v3lTT0VFJwC2EEEKID0pGU7xzi52dHVu2bKF+/fo0btyY3bt3pxh4lyxZkj59+jBmzBhWrVqV6nFnzZpFjRo1GDlyZJptKFSoEKGhoVpl9+/fp2nTpvTr149p06ZRsGBBjh07Rq9evYiNjVXqGRsbawWtarUaXV1d/P39k6yuk/hAwtvbmwULFrBw4UJlzPiQIUNSnUn8fdPLra2tOX36tFZZaGgosbGxSXrAE9nY2AAkOW+5cuWUTrDk6hgaGlKiRAmtjjJIWH0oPZkHQnwI8nzQnZiWoqOjg729fdYdOPIl3NwLgEYDf9wrzvRt1bgebKlUUanAzMyQmJh4mY1cCCGEECILFCtWjCNHjlC/fn0aNWrEnj17UkyVnjhxIiVLlsTPzy/VY1atWpU2bdowZsyYNM+fOBY5NDSUAgUKAHDu3Dni4uLw9vZW0s43btyYrmPFx8fz7NkzateunWydo0eP0rJlS77++msgIVC/detWkjHlb3vf9PIaNWrw/fffExgYqATKe/fuxdDQEHd392T3cXR0xNbWlhs3bmiV37x5kyZNmgDg7u6OoaEhN27cUMZ5x8bGEhAQkKRz7MqVK0kmWBPiQ5Wng+7Xr18TEhICJHz5GBgYvP9BNRr42w/2TYBHkZyJKMX85xW5dLOQVrWmTUszY0YDKlWyfv9zCiGEEEIIhb29PYcPH9YKvM3NzZPUs7KyYtiwYcydOzfNY37//fdUqFAhzfl/3NzcKFy4MMePH+eLLxLSGUuWLElcXByLFy+mefPmHD9+PMVe5Lc5OTnRuXNnunbtire3N25ubgQHB3Pw4EFcXFxo2rQppUqVYsuWLZw4cYICBQowf/58goKCUg263ze9vFGjRpQvX54uXbowd+5cXrx4wYgRI+jTp48SrD9+/JiGDRuyZs0aqlatikqlYuTIkUyaNIlKlSrh6urKL7/8wj///MPmzZuBhEC/X79+TJo0iaJFi+Lg4KD8bdq1a6ecPyAggMePH6c4UZ4QH5o8PSV2lqeWB10GnybwWz9eBP9L+6NN6P6wAZei3gTcNWsW5ciR7uzc2UkCbiGEEEKIbGJnZ8eRI0d4+fIln3/+uTJx7rtGjhyZrrHjTk5O9OzZk6ioqFTr6erq0rNnT9auXauUubq6Mn/+fGbPno2zszNr165l5syZ6boOHx8funbtyvDhwylTpgwtWrTg9OnTFC1aFIAJEyZQuXJlvLy8qFevHtbW1rRq1Spdx84sXV1ddu7ciZGREbVq1aJ9+/a0atVKa03z2NhYbty4oTXuesiQIYwdO5ahQ4dSqVIlDhw4wL59+7TSxOfOnUvHjh3p0qULVapU4f79+xw8eFDJGgBYv349jRo1yrmlfoV4TyqNRqPJ7UbkpFevXmFubo7/gM0ENTHmzJkzAHTq1CnJJBXpFhWWMCv5mRWgSZgAQq2BSkv7ciU44Uu8fHFTZv3QnC++cEpxggkhhBBCiNwWFRXFvXv3KF68eLITgom0PX36lAoVKuDv7y+BYRaLjo6mdOnSrF+/nlq1auV2c8RHKrXvucR4MSwsLNVhFhkhPd0kzL5YrFixjB9Ao4GL62GxB6GHfZSAG8tS6HTZyvgOdtjpv2aW9UmOr3SnefMyEnALIYQQQnzirKysWLVqVZLJv8T7u3//PuPHj5eAW3xU8uyY7hjiePr0KZAwA2OGn+QGXYFdIwi9cZ7Zx6uw6LQbB3r8QY0uXaHGANAzpGH5G+wsvgsDlRpdXQm2hRBCCCHyipYtW+Z2Ez5JTk5Omc9OFSKX5Nmg+7nuv8q/M5T2818qefgxHxadqsjs470Ii04I2Mdc7c/hz/oqvdkqlQoDlTpL2y2EEEIIIYQQ4uORZ4PuYN3Xyr/TFXRrNHBpA7F/TuTno1ZM/asbQa/fTLphYKCLezVHYmPVsvyXEEIIIYQQQgggDwfdb/d0pzmeO+gK6h0j2PDnCyYcasadUAtlk46Oiu7dKzFpUj2KFUu6FIUQQgghxMcoj821K4TIQ3L6+y3PBt2hOhEYYkjhwoUxNTVNvlJUGByaSfChNXiuac3fT2tobW7TphzTp9enXLnCOdBiIYQQQojsp6+vD0BERATGxsa53BohhMh6iUvZJX7fZbc8G3Tz37xmhYOCeDJ23DsbNfAiAJ6ch7goNBp9DKLfPA2pWUzN2HpxVLa9CGsu8iSFU0Td+CcbGi6EEEIIkX10dXWxsLDg2bNnAJiYmMjqK0KIT4JGoyEiIoJnz55hYWGBrm7ODAvOu0H3f8xPnybswUOtsocxphQ1CCdhRTUTAAblv8ysSH2GFv6bGkZPUZ2GsAycR6WTp1dnE0IIIcRHxNraGkAJvIUQ4lNiYWGhfM/lhDwfdBd6Hqz8+3GsCT8Gu/D7K0dW2h+mpulTZZu7STAbHfaSmQe9ejY2mLi7Z0VzhRBCCCGynUqlwsbGhiJFihAbG5vbzRFCiCyjr6+fYz3ciXI96F66dClz584lMDCQChUqsHDhQmrXrp1i/SNHjjBs2DCuXr2Kra0to0aNol+/fpk6t0X+/Dhv3sSz0zuZs/AEK847EROf8AdYGu9K5x/dURWvlaljv82gaFFUOTReQAghhBAiq+jq6ub4j1MhhPjU5GrQvWHDBoYMGcLSpUupVasWy5cvp0mTJly7di3ZGcXv3btH06ZN6dOnD7/++ivHjx+nf//+FC5cmC+//DLD57cqUICZw2bhvbsIr2PKKeUF8kPHwW3QrVMLfX35PxohhBBCCCGEEJmj0uTiehDVqlWjcuXKLFu2TCkrV64crVq1YubMmUnqjx49mu3bt3P9+nWlrF+/fvz999+cPHkyXed89eoV5ubmNGw4Bv+TOryMMFC2GRuoGTrAlZETmmBhYfQeVyaEEEIIIYQQ4mOTGC+GhYVhZmaWJcfMtdm9YmJi8Pf3p1GjRlrljRo14sSJE8nuc/LkyST1vby8OHfuXIbHGx04gBJw6+mo6d/JmjsBI/jeu7UE3EIIIYQQQgghskSupZcHBwcTHx+PlZWVVrmVlRVBQUHJ7hMUFJRs/bi4OIKDg7GxsUmyT3R0NNHR0cr7sLDEOccTytrW1WW8dzdKlLYCNLx69SrzFyWEEEIIIYQQ4qOVGA9mZUJ4rk+k9u66jxqNJtW1IJOrn1x5opkzZzJlypRktiwAYPMR2OzxfQZaLIQQQgghhBDiUxYSEoK5uXmWHCvXgu5ChQqhq6ubpFf72bNnSXqzE1lbWydbX09PD0tLy2T3GTt2LMOGDVPev3z5EgcHBx48eJBlH6IQue3Vq1cULVqUhw8fZtnYEyFyk9zT4lMk97X41Mg9LT5FYWFhFCtWjIIFC2bZMXMt6DYwMMDd3Z19+/bRunVrpXzfvn20bNky2X1q1KjBH3/8oVW2d+9ePDw80E9hSS5DQ0MMDQ2TlJubm8uXg/jkmJmZyX0tPilyT4tPkdzX4lMj97T4FOnoZN30Z7k2kRrAsGHD+Pnnn1m9ejXXr19n6NChPHjwQFl3e+zYsXTt2lWp369fP+7fv8+wYcO4fv06q1evZtWqVYwYMSK3LkEIIYQQQgghhEhRro7p7tChAyEhIUydOpXAwECcnZ3ZtWsXDg4OAAQGBvLgwQOlfvHixdm1axdDhw7lxx9/xNbWlkWLFmVqjW4hhBBCCCGEECK75fpEav3796d///7JbvP19U1SVrduXc6fP5/p8xkaGjJp0qRkU86F+FjJfS0+NXJPi0+R3NfiUyP3tPgUZcd9rdJk5VzoQgghhBBCCCGEUOTqmG4hhBBCCCGEEOJTJkG3EEIIIYQQQgiRTSToFkIIIYQQQgghssknGXQvXbqU4sWLY2RkhLu7O0ePHk21/pEjR3B3d8fIyIgSJUrw008/5VBLhUi/jNzXW7du5fPPP6dw4cKYmZlRo0YN9uzZk4OtFSJtGf2uTnT8+HH09PRwdXXN3gYKkQkZva+jo6MZP348Dg4OGBoaUrJkSVavXp1DrRUibRm9p9euXUulSpUwMTHBxsaGHj16EBISkkOtFSJ1f/31F82bN8fW1haVSsVvv/2W5j5ZESt+ckH3hg0bGDJkCOPHj+fChQvUrl2bJk2aaC099rZ79+7RtGlTateuzYULFxg3bhyDBg1iy5YtOdxyIVKW0fv6r7/+4vPPP2fXrl34+/tTv359mjdvzoULF3K45UIkL6P3dKKwsDC6du1Kw4YNc6ilQqRfZu7r9u3bc+DAAVatWsWNGzdYv349ZcuWzcFWC5GyjN7Tx44do2vXrvTq1YurV6+yadMmzp49S+/evXO45UIkLzw8nEqVKrFkyZJ01c+yWFHzialataqmX79+WmVly5bVjBkzJtn6o0aN0pQtW1ar7JtvvtFUr14929ooREZl9L5OTvny5TVTpkzJ6qYJkSmZvac7dOig+e677zSTJk3SVKpUKRtbKETGZfS+/vPPPzXm5uaakJCQnGieEBmW0Xt67ty5mhIlSmiVLVq0SGNvb59tbRQiswDNtm3bUq2TVbHiJ9XTHRMTg7+/P40aNdIqb9SoESdOnEh2n5MnTyap7+Xlxblz54iNjc22tgqRXpm5r9+lVqv5999/KViwYHY0UYgMyew97ePjw507d5g0aVJ2N1GIDMvMfb19+3Y8PDyYM2cOdnZ2ODk5MWLECCIjI3OiyUKkKjP3dM2aNXn06BG7du1Co9Hw9OlTNm/eTLNmzXKiyUJkuayKFfWyumG5KTg4mPj4eKysrLTKraysCAoKSnafoKCgZOvHxcURHByMjY1NtrVXiPTIzH39Lm9vb8LDw2nfvn12NFGIDMnMPX3r1i3GjBnD0aNH0dP7pP6vS3wiMnNf3717l2PHjmFkZMS2bdsIDg6mf//+vHjxQsZ1i1yXmXu6Zs2arF27lg4dOhAVFUVcXBwtWrRg8eLFOdFkIbJcVsWKn1RPdyKVSqX1XqPRJClLq35y5ULkpoze14nWr1/P5MmT2bBhA0WKFMmu5gmRYem9p+Pj4+nUqRNTpkzByckpp5onRKZk5LtarVajUqlYu3YtVatWpWnTpsyfPx9fX1/p7RYfjIzc09euXWPQoEFMnDgRf39/du/ezb179+jXr19ONFWIbJEVseIn1V1QqFAhdHV1kzx9e/bsWZInFImsra2Tra+np4elpWW2tVWI9MrMfZ1ow4YN9OrVi02bNuHp6ZmdzRQi3TJ6T//777+cO3eOCxcuMGDAACAhWNFoNOjp6bF3714aNGiQI20XIiWZ+a62sbHBzs4Oc3NzpaxcuXJoNBoePXpE6dKls7XNQqQmM/f0zJkzqVWrFiNHjgSgYsWKmJqaUrt2baZPny4ZpOKjk1Wx4ifV021gYIC7uzv79u3TKt+3bx81a9ZMdp8aNWokqb937148PDzQ19fPtrYKkV6Zua8hoYe7e/furFu3TsZSiQ9KRu9pMzMzLl++zMWLF5VXv379KFOmDBcvXqRatWo51XQhUpSZ7+patWrx5MkTXr9+rZTdvHkTHR0d7O3ts7W9QqQlM/d0REQEOjra4YWuri7wpndQiI9JlsWKGZp27SPg5+en0dfX16xatUpz7do1zZAhQzSmpqaagIAAjUaj0YwZM0bTpUsXpf7du3c1JiYmmqFDh2quXbumWbVqlUZfX1+zefPm3LoEIZLI6H29bt06jZ6enubHH3/UBAYGKq+XL1/m1iUIoSWj9/S7ZPZy8SHK6H3977//auzt7TVt27bVXL16VXPkyBFN6dKlNb17986tSxBCS0bvaR8fH42enp5m6dKlmjt37miOHTum8fDw0FStWjW3LkEILf/++6/mwoULmgsXLmgAzfz58zUXLlzQ3L9/X6PRZF+s+MkF3RqNRvPjjz9qHBwcNAYGBprKlStrjhw5omzr1q2bpm7dulr1Dx8+rHFzc9MYGBhoHB0dNcuWLcvhFguRtozc13Xr1tUASV7dunXL+YYLkYKMfle/TYJu8aHK6H19/fp1jaenp8bY2Fhjb2+vGTZsmCYiIiKHWy1EyjJ6Ty9atEhTvnx5jbGxscbGxkbTuXNnzaNHj3K41UIk79ChQ6n+Rs6uWFGl0UiuhxBCCCGEEEIIkR0+qTHdQgghhBBCCCHEh0SCbiGEEEIIIYQQIptI0C2EEEIIIYQQQmQTCbqFEEIIIYQQQohsIkG3EEIIIYQQQgiRTSToFkIIIYQQQgghsokE3UIIIYQQQgghRDaRoFsIIYQQQgghhMgmEnQLIYQQ78HX1xcLC4vcbkamOTo6snDhwlTrTJ48GVdX1xxpjxBCCPGpkaBbCCFEnte9e3dUKlWS1+3bt3O7afj6+mq1ycbGhvbt23Pv3r0sOf7Zs2fp27ev8l6lUvHbb79p1RkxYgQHDhzIkvOl5N3rtLKyonnz5ly9ejXDx/mYH4IIIYT49EjQLYQQQgCNGzcmMDBQ61W8ePHcbhYAZmZmBAYG8uTJE9atW8fFixdp0aIF8fHx733swoULY2JikmqdfPnyYWlp+d7nSsvb17lz507Cw8Np1qwZMTEx2X5uIYQQIrtI0C2EEEIAhoaGWFtba710dXWZP38+Li4umJqaUrRoUfr378/r169TPM7ff/9N/fr1yZ8/P2ZmZri7u3Pu3Dll+4kTJ6hTpw7GxsYULVqUQYMGER4enmrbVCoV1tbW2NjYUL9+fSZNmsSVK1eUnvhly5ZRsmRJDAwMKFOmDP/73/+09p88eTLFihXD0NAQW1tbBg0apGx7O73c0dERgNatW6NSqZT3b6eX79mzByMjI16+fKl1jkGDBlG3bt0su04PDw+GDh3K/fv3uXHjhlIntb/H4cOH6dGjB2FhYUqP+eTJkwGIiYlh1KhR2NnZYWpqSrVq1Th8+HCq7RFCCCGyggTdQgghRCp0dHRYtGgRV65c4ZdffuHgwYOMGjUqxfqdO3fG3t6es2fP4u/vz5gxY9DX1wfg8uXLeHl50aZNGy5dusSGDRs4duwYAwYMyFCbjI2NAYiNjWXbtm0MHjyY4cOHc+XKFb755ht69OjBoUOHANi8eTMLFixg+fLl3Lp1i99++w0XF5dkj3v27FkAfHx8CAwMVN6/zdPTEwsLC7Zs2aKUxcfHs3HjRjp37pxl1/ny5UvWrVsHoHx+kPrfo2bNmixcuFDpMQ8MDGTEiBEA9OjRg+PHj+Pn58elS5do164djRs35tatW+lukxBCCJEpGiGEECKP69atm0ZXV1djamqqvNq2bZts3Y0bN2osLS2V9z4+Phpzc3Plff78+TW+vr7J7tulSxdN3759tcqOHj2q0dHR0URGRia7z7vHf/jwoaZ69eoae3t7TXR0tKZmzZqaPn36aO3Trl07TdOmTTUajUbj7e2tcXJy0sTExCR7fAcHB82CBQuU94Bm27ZtWnUmTZqkqVSpkvJ+0KBBmgYNGijv9+zZozEwMNC8ePHiva4T0JiammpMTEw0gAbQtGjRItn6idL6e2g0Gs3t27c1KpVK8/jxY63yhg0basaOHZvq8YUQQoj3pZe7Ib8QQgjxYahfvz7Lli1T3puamgJw6NAhZsyYwbVr13j16hVxcXFERUURHh6u1HnbsGHD6N27N//73//w9PSkXbt2lCxZEgB/f39u377N2rVrlfoajQa1Ws29e/coV65csm0LCwsjX758aDQaIiIiqFy5Mlu3bsXAwIDr169rTYQGUKtWLX744QcA2rVrx8KFCylRogSNGzemadOmNG/eHD29zP8E6Ny5MzVq1ODJkyfY2tqydu1amjZtSoECBd7rOvPnz8/58+eJi4vjyJEjzJ07l59++kmrTkb/HgDnz59Ho9Hg5OSkVR4dHZ0jY9WFEELkbRJ0CyGEECQE2aVKldIqu3//Pk2bNqVfv35MmzaNggULcuzYMXr16kVsbGyyx5k8eTKdOnVi586d/Pnnn0yaNAk/Pz9at26NWq3mm2++0RpTnahYsWIpti0xGNXR0cHKyipJcKlSqbTeazQapaxo0aLcuHGDffv2sX//fvr378/cuXM5cuSIVtp2RlStWpWSJUvi5+fH//3f/7Ft2zZ8fHyU7Zm9Th0dHeVvULZsWYKCgujQoQN//fUXkLm/R2J7dHV18ff3R1dXV2tbvnz5MnTtQgghREZJ0C2EEEKk4Ny5c8TFxeHt7Y2OTsI0KBs3bkxzPycnJ5ycnBg6dChfffUVPj4+tG7dmsqVK3P16tUkwX1a3g5G31WuXDmOHTtG165dlbITJ05o9SYbGxvTokULWrRowbfffkvZsmW5fPkylStXTnI8fX39dM2K3qlTJ9auXYu9vT06Ojo0a9ZM2ZbZ63zX0KFDmT9/Ptu2baN169bp+nsYGBgkab+bmxvx8fE8e/aM2rVrv1ebhBBCiIySidSEEEKIFJQsWZK4uDgWL17M3bt3+d///pck3fltkZGRDBgwgMOHD3P//n2OHz/O2bNnlQB49OjRnDx5km+//ZaLFy9y69Yttm/fzsCBAzPdxpEjR+Lr68tPP/3ErVu3mD9/Plu3blUmEPP19WXVqlVcuXJFuQZjY2McHBySPZ6joyMHDhwgKCiI0NDQFM/buXNnzp8/z/fff0/btm0xMjJStmXVdZqZmdG7d28mTZqERqNJ19/D0dGR169fc+DAAYKDg4mIiMDJyYnOnTvTtWtXtm7dyr179zh79iyzZ89m165dGWqTEEIIkVESdAshhBApcHV1Zf78+cyePRtnZ2fWrl3LzJkzU6yvq6tLSEgIXbt2xcnJifbt29OkSROmTJkCQMWKFTly5Ai3bt2idu3auLm5MWHCBGxsbDLdxlatWvHDDz8wd+5cKlSowPLly/Hx8aFevXoAWFhYsHLlSmrVqkXFihU5cOAAf/zxR4pjmb29vdm3bx9FixbFzc0txfOWLl2aKlWqcOnSJWXW8kRZeZ2DBw/m+vXrbNq0KV1/j5o1a9KvXz86dOhA4cKFmTNnDpAwI3vXrl0ZPnw4ZcqUoUWLFpw+fZqiRYtmuE1CCCFERqg0Go0mtxshhBBCCCGEEEJ8iqSnWwghhBBCCCGEyCYSdAshhBBCCCGEENlEgm4hhBBCCCGEECKbSNAthBBCCCGEEEJkEwm6hRBCCCGEEEKIbCJBtxBCCCGEEEIIkU0k6BZCCCGEEEIIIbKJBN1CCCGEEEIIIUQ2kaBbCCGEEEIIIYTIJhJ0CyGEEEIIIYQQ2USCbiGEEEIIIYQQIptI0C2EEEIIIYQQQmST/wdhh1f7+nGykAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc_curve(ax, y_true, y_score, model_name):\n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_score)\n",
    "    roc_auc = roc_auc_score(y_true, y_score)\n",
    "    ax.plot(fpr, tpr, lw=2, label='%s (area = %0.2f)' % (model_name, roc_auc))\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "for model_name, model in models.items():\n",
    "    model.fit(x_train, y_train)\n",
    "    y_score = model.predict_proba(x_test)[:, 1]\n",
    "    plot_roc_curve(ax, y_test, y_score, model_name)\n",
    "\n",
    "\n",
    "ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "ax.set_xlim([0.0, 1.0])\n",
    "ax.set_ylim([0.0, 1.05])\n",
    "ax.set_xlabel('False Positive Rate')\n",
    "ax.set_ylabel('True Positive Rate')\n",
    "ax.set_title('Receiver Operating Characteristic Curve')\n",
    "ax.legend(loc=\"lower right\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = models['Logistic Regression']\n",
    "pickle.dump(best_model, open('Liver_Model.pkl', 'wb'))\n",
    "pickle.dump(scaler, open('Liver_Preprocessor.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}