Decision Trees.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "existing-forty",
   "metadata": {},
   "source": [
    "# ***Decision Trees: Drug dataset***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "touched-million",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import seaborn as sns\n",
    "import wget\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "precious-surge",
   "metadata": {},
   "source": [
    "## Download the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "blond-scott",
   "metadata": {},
   "outputs": [],
   "source": [
    "# url = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/drug200.csv\"\n",
    "# wget.download(url, \"Drugs.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "normal-tennis",
   "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>Sex</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Na_to_K</th>\n",
       "      <th>Drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>25.355</td>\n",
       "      <td>drugY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>13.093</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>10.114</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>7.798</td>\n",
       "      <td>drugX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>F</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>18.043</td>\n",
       "      <td>drugY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex      BP Cholesterol  Na_to_K   Drug\n",
       "0   23   F    HIGH        HIGH   25.355  drugY\n",
       "1   47   M     LOW        HIGH   13.093  drugC\n",
       "2   47   M     LOW        HIGH   10.114  drugC\n",
       "3   28   F  NORMAL        HIGH    7.798  drugX\n",
       "4   61   F     LOW        HIGH   18.043  drugY"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drug_data = pd.read_csv(\"Drugs.csv\")\n",
    "drug_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incredible-cooling",
   "metadata": {},
   "source": [
    "## Data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "improving-tablet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K', 'Drug'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drug_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "interpreted-pharmacy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drugY    91\n",
       "drugX    54\n",
       "drugA    23\n",
       "drugB    16\n",
       "drugC    16\n",
       "Name: Drug, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drug_data.Drug.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "about-failing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=drug_data.Age, color=\"orange\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "found-paradise",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of the dataset:  (200, 6)\n"
     ]
    }
   ],
   "source": [
    "print(\"Size of the dataset: \",drug_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "protected-jesus",
   "metadata": {},
   "source": [
    "## Feature matrix & data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "heard-surfing",
   "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>Sex</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Na_to_K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>25.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>13.093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>10.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>7.798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>F</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>18.043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex      BP Cholesterol  Na_to_K\n",
       "0   23   F    HIGH        HIGH   25.355\n",
       "1   47   M     LOW        HIGH   13.093\n",
       "2   47   M     LOW        HIGH   10.114\n",
       "3   28   F  NORMAL        HIGH    7.798\n",
       "4   61   F     LOW        HIGH   18.043"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = drug_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "protected-horror",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    drugY\n",
       "1    drugC\n",
       "2    drugC\n",
       "3    drugX\n",
       "4    drugY\n",
       "Name: Drug, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = drug_data.Drug\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selected-repository",
   "metadata": {},
   "source": [
    "### **Scikit Learn's decision trees** are unable to handle the categorical values and thus we have to convert them to numerical values. \n",
    "### ***pandas.get_dummies()*** function will help us to generate dummy indicators/values for the categorical attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "heavy-sleep",
   "metadata": {},
   "source": [
    "## Always execute the full code and don't execute it in parts(it will give an error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "divine-parcel",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\envs\\MachineLearning\\lib\\site-packages\\pandas\\core\\generic.py:5494: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    },
    {
     "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>Sex</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Na_to_K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7.798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  BP  Cholesterol  Na_to_K\n",
       "0   23    0   0            0   25.355\n",
       "1   47    1   1            0   13.093\n",
       "2   47    1   1            0   10.114\n",
       "3   28    0   2            0    7.798\n",
       "4   61    0   1            0   18.043"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transforming the data into numerics\n",
    "\n",
    "le_sex = preprocessing.LabelEncoder()\n",
    "le_sex.fit(['F','M'])\n",
    "x.Sex = le_sex.transform(x.Sex) \n",
    "\n",
    "\n",
    "le_BP = preprocessing.LabelEncoder()\n",
    "le_BP.fit(['LOW', 'NORMAL', 'HIGH'])\n",
    "x.BP = le_BP.transform(x.BP)\n",
    "\n",
    "\n",
    "le_Chol = preprocessing.LabelEncoder()\n",
    "le_Chol.fit(['NORMAL', 'HIGH'])\n",
    "x.Cholesterol = le_Chol.transform(x.Cholesterol) \n",
    "\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "finnish-conservation",
   "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>Sex</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Na_to_K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7.798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  BP  Cholesterol  Na_to_K\n",
       "0   23    0   0            0   25.355\n",
       "1   47    1   1            0   13.093\n",
       "2   47    1   1            0   10.114\n",
       "3   28    0   2            0    7.798\n",
       "4   61    0   1            0   18.043"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "figured-papua",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "convinced-stock",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    drugY\n",
       "1    drugC\n",
       "2    drugC\n",
       "3    drugX\n",
       "4    drugY\n",
       "Name: Drug, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "amended-recovery",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "accepted-location",
   "metadata": {},
   "source": [
    "## **Decision Tree model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "convinced-azerbaijan",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(x,y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "silent-frequency",
   "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>Sex</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Na_to_K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9.709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33.486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>22.456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Sex  BP  Cholesterol  Na_to_K\n",
       "79    32    0   1            1   10.840\n",
       "197   52    1   2            0    9.894\n",
       "38    39    0   2            1    9.709\n",
       "24    33    0   1            0   33.486\n",
       "122   34    1   2            0   22.456"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "outstanding-orange",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "electoral-position",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree's accuracy:  100.0\n"
     ]
    }
   ],
   "source": [
    "# Create the model\n",
    "\n",
    "dectree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=4)\n",
    "dectree.fit(train_x, train_y)\n",
    "\n",
    "# Make predictions\n",
    "preds = dectree.predict(test_x)\n",
    "\n",
    "# Evaluation\n",
    "print(\"Decision Tree's accuracy: \", accuracy_score(test_y, preds)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "future-study",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "advised-modem",
   "metadata": {},
   "source": [
    "## ***Decision Tree Visualization***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "oriented-filter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAM9CAYAAAB5Rim2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdd3yN5/sH8M8dkZyTRBBEYiUhJMSsrTSI3VKhdq1S1Sq+qL1rtVYraO0Vqkq1Ru3aqb1qRoxIkaCokYRIzvX7I3J+juz5ZHzer9f1evU8537u53qOxnHlvp/7ViICIiIiIiIiSh4zrRMgIiIiIiLKilhMERERERERpQCLKSIiIiIiohRgMUVERERERJQCLKaIiIiIiIhSgMUUERERERFRCrCYIiIiIiIiSgEWU0RERERERCnAYoqIiIiIiCgFWEwRERERERGlAIspIiIiIiKiFGAxRURERERElAIspoiIiIiIiFKAxRQREREREVEKsJgiIiIiIiJKARZTREREREREKcBiioiIiIiIKAVYTBEREREREaUAiykiIiIiIqIUYDFFRERERESUAiymiIiIiIiIUoDFFBERERERUQqwmCIiIiIiIkoBFlNEREREREQpwGKKiIiIiIgoBVhMERERERERpQCLKSIiIiIiohRgMUVERERERJQCLKaIiIiIiIhSgMUUERERERFRCrCYIiIiIiIiSgEWU0RERERERCnAYoqIiIiIiCgFWEwRERERERGlAIspIiIiIiKiFGAxRURERERElAIspoiIiIiIiFKAxRQREREREVEKsJgiIiIiIiJKARZTREREREREKcBiioiIiIiIKAVYTBEREREREaWAudYJEBERxUev14e8ePGisNZ5EGVlOp3uXnh4uIPWeRBlR0pEtM6BiIgoTkop4fcUUeoopSAiSus8iLIjTvMjIiIiIiJKARZTREREREREKcBiioiIiIiIKAVYTBEREREREaUAiykiIiIiIqIUYDFFRESURD169IBSCsOGDTM5HhISAqUU9u/fnybXOXr0KJRSCAwMTJP+YtSvXx+9e/c2Ofbbb79Br9dj1KhRaXqt1Dh48CBatmyJIkWKQCmF1atXJ9h+3759yJUrF1xdXRPt++HDh+jVqxeKFi0KvV4Pd3d3fP/998b3IyMjMXLkSFSuXBk2Njawt7dH69atcenSJZN+QkNDMWLECJQsWRI6nQ4VKlTAhg0bUnS/RJR1sZgiIiJKBp1OBx8fnzQvdLSwcOFCdOjQATNmzMDUqVNT3M8///yDyMjINMsrNDQUlSpVwrx58xJt++DBA/To0QONGzdOUt+9evXCsWPH8PPPP+Py5csYOnQohg0bBl9fXwDAy5cvcfz4cQwfPhwnTpzArl27EBkZCS8vLzx+/NjYz2effYb169dj0aJFuHjxIvr06YOOHTti586dKbtpIsqaRITBYDAYjEwZ0V9TmUf37t2lYcOGUqNGDenYsaPxeHBwsACQffv2GY+NGjVK3N3dRa/XS9GiReXTTz+Vx48fJ3qNmzdvCgCT8PT0FBERg8EgU6dOFScnJ8mdO7eUKVNGli1bluT8PT09pVevXiIiMnHiRLG0tJR169Yl+fw3PX36VJYvXy4NGjQQpZT8999/KeonMQDE19c3zvcMBoM0b95cJk2aJOPHj5dSpUol2l+BAgXEx8fH5Fjt2rXlyy+/jPecx48fi1JKNm/eLCIi4eHhYm5uLmvXrjVp16pVK3nvvfcSzSGjvf450vznmcHIjsGRKSIiomRQSmHmzJlYt24djh8/Hm87vV6PRYsW4dKlS1i1ahUOHz6M/v37J9p/8eLF8ccffwAAjh8/juDgYGzcuBEAMHfuXEyaNAnjxo3DhQsX8Omnn6J3797Ytm1bkvM3GAz44osvMGvWLGzfvh3t27dP8rmRkZHYvn07OnfujMKFC2PChAmoWbMmzp8/j7x58xrbeXh4wMbGJsFICzNnzkRYWFiypijWrVsX69evR0hICEQEBw4cwIULF9C8efN4z3n69ClEBHZ2dgCAV69eITIyEjqdzqSdXq/H0aNH8erVq5TdEBFlPVpXcwwGg8FgxBfIhCNTXl5eIiLSunVrqVevnojEPTL1ts2bN4uFhYVERUUlep0jR44IALl586bJ8aJFi8rw4cNNjnXs2FHq1q2bpPw9PT3FwsJClFLi5+eXpHNERE6fPi3/+9//pHDhwmJnZyd9+vSRgwcPisFgiLN9YGCgBAQEJBhJhXhGpo4ePSr29vYSFBQkIpLkkalnz55JmzZtBICYm5tL7ty5ZdGiRfG2j4qKkpYtW0qVKlUkMjLSeLxu3bpSvXp1uXnzpkRFRcm2bdtEp9MJALl7926S7y8jgCNTDEa6hbl2ZRwREVHW9e2338LDwwO//fYbateuHev9TZs2Yfbs2QgICMDTp09hMBgQERGBkJAQFClSJNnXe/r0Ke7cuYN69eqZHPf09MTo0aOT3E+tWrVw9epVjB49Glu2bEnSKJG3tzeCgoLwv//9D9OmTYOlpWWC7Z2cnJKcT0o8e/YMHTt2xA8//IDixYsn69yvv/4a169fx/bt21GkSBHs378f//vf/1CkSBG8//77Jm1FBJ9//jlOnz6NgwcPIleuXMb3Vq9ejV69eqFkyZIwMzODq6srevXqhfnz55u0I6LsjdP8iIiIUqBMmTL47LPPMHz48FjTuo4fP442bdqgXr16+O2333D69GksXrwYABAREZGq6yqlTF6LSLLOL126NA4ePIjr16+jadOmePr0aaLn/Pzzz+jTpw9WrFiB0qVLY/jw4fj777/jbZ/e0/yuX7+OwMBAdOjQAebm5jA3NzcWSebm5vjll1/iPO/GjRuYMWMGFi5ciGbNmqFixYoYMGAAOnbsiGnTppm0NRgM6NWrF7Zt24b9+/ejZMmSJu87OTlhz549eP78OW7duoUrV67A2toatra2KFiwYKruj4iyDo5MERERpdD48ePh6+uLBQsWmBw/dOgQChYsiMmTJxuP/fbbb0nu18LCAgAQFRVlPGZra4uiRYvCz88PLVq0MB4/fPgwPDw8kpV3TEHVsGFDeHl5YdeuXcifP3+87WvVqoVatWrBx8cHW7duha+vL6pXr47SpUujS5cu6Ny5s8lo1LZt29L1uSF3d3ecP3/e5NgPP/yArVu3Ytu2bfGOVoWFhQEAzMxMf5dsbm5uUpRGRkaia9euOHLkCA4ePAgXF5d4c7GysoKVlRUiIiLw66+/onXr1rH6J6Lsi8UUERFRChUqVAgjRozApEmTTI67ubnhwYMHWLp0KRo0aIDDhw9j7ty5Se63RIkSMDMzw7Zt29ChQwdYWloib968GD58OEaOHAk3NzfUrl0bW7Zswbp167B58+Zk5+7s7GwsqBo0aIDdu3ejUKFCCZ5jYWGBNm3aoE2bNnj06BF+/vln+Pr6YvTo0fjvv/9ga2sLIPXT/J4/f45r164ZXwcFBeHs2bOwsbGBq6srdDodypcvb3KOvb09LCwsTI4fP34c3bp1w6pVq1CjRg24u7vDzc0NX375JWbPno0iRYpg3759WLlyJaZMmQIgupBq164d/vrrL2zevBl6vR4hISEAYDKqtnv3bkRERKBs2bL4559/MGHCBISGhqZqiXkiyoK0fmiLwWAwGIz4Apl4AYoY4eHhUrx48VgLUIwZM0bs7e3FyspKmjVrJj///HOci0rEZ+bMmVKkSBExMzOLtTR6iRIlUr00eozg4GDx8PCQsmXLpnjhhICAAImIiEjRuXHZt29frOXh8cYS8XGJawGKmH7e/HO5fv26dOjQQRwcHESv14u7u7tMnz7duDBIXEvTx8T48eON/WzYsEFcXV3FwsJC7OzspFOnThIYGJhmn0FaAhegYDDSLZRI8uZaExERZRSllPB7iih1lFIQEZV4SyJKLk7qJSIiIiIiSgEWU0RERBmsefPm8a5y17dv3xT1uWbNmnTfJJeIiExxmh8REWVa2XWa3507dxAeHh7ne7a2trC3t092n8+ePcO9e/fifd/V1TXZfVL2wGl+ROmHxRQREWVa2bWYIspILKaI0g+n+REREREREaUAiykiIiIiIqIUYDFFRESUybm7u2PChAlap5HuLl68iLZt28LZ2RlKKUyePDlJ5926dQudOnWCg4MDrKys4OXlhXPnzhnfj4yMxMiRI1G5cmXY2NjA3t4erVu3xqVLl0z6WbRoESpXroy8efPC2toalStXxvLly9P0Hokoe2ExRURElE28fPlS6xRSJSwsDCVLlsQ333wDBweHJJ/TuHFjPH78GNu3b8epU6fg7OyMhg0bGhfkePnyJY4fP47hw4fjxIkT2LVrFyIjI+Hl5YXHjx8b+ypSpAgmTZqE48eP4++//0aPHj3w6aef4vfff0+P2yWi7EDrXYMZDAaDwYgvor+msi6DwSCzZ88WV1dXsbS0FHd3d/Hx8RGDwWBsA0DmzZsnnTt3FmtraylWrJh8++23xvc9PT0FgEncvHlT9u3bJwBk8+bNUqtWLbGwsJBly5aJwWCQqVOnipOTk+TOnVvKlCkjy5YtM8kLgHz33XfSunVrsbKyEkdHR5k9e7bx/W7dukmTJk1i3U/16tVlwIAB6fBJxebk5CSTJk1KtN3u3bsFgAQHBxuPRUZGip2dnYwbNy7e8x4/fixKKdm8eXOC/VepUkX+97//JT3xTOj1z5HmP88MRnYMzRNgMBgMBiO+yOrF1NixY8XFxUU2b94sN27ckI0bN0rBggVl/vz5xjYAxN7eXhYtWiQBAQEye/ZsASD79+8XEZGHDx+Kq6urDBkyRIKDgyU4OFgiIyONxZSbm5ts2bJFbty4IXfu3JE5c+aIXq+XpUuXir+/v8yYMUPMzMzkjz/+MLlm/vz5xcfHR/z9/cXHx0fMzc1l48aNIiLi5+cnZmZmEhgYaDzn77//FgDy999/x3u/zZo1E2tr6wTj1q1bSfrsklpMbdmyRQDI48ePTY47ODhI/fr14z3v1q1bAkAOHz4c5/tRUVGya9cu0ev18vvvvycp58yKxRSDkX6heQIMBoPBYMQXWbmYCg0NFb1eLzt37jQ5Pn36dClXrpzxNQDp37+/SZtSpUrJmDFjjK/d3Nxk/PjxJm1iiilfX1+T40WLFpXhw4ebHOvYsaPUrVvX5JpdunQxadO1a1d59913ja/Lly9vMrLTv39/qVmzZkK3LLdv35aAgIAE49WrVwn2ESOpxdSDBw8kX7588sknn8iTJ0/kxYsXMmnSJAEgZcqUifOcqKgoadmypVSpUkUiIyNN3rt165ZYW1uLubm56HQ6Wbx4cZLyzcxYTDEY6RfmGTOZkIiIKGe5ePEiwsPD4e3tDaX+f4ufyMhIGAwGk7ZVqlQxeV20aNEEN+B9U40aNYz//fTpU9y5cwf16tUzaePp6YnRo0ebHKtTp47J63fffRdbt241vv7ss88wffp0jB8/Hq9evcKaNWswffr0BHMpWrRoknJOSwULFsTGjRvRt29f5MuXD2ZmZmjcuDGaN2+OwMDAWO1FBJ9//jlOnz6NgwcPIleuXCbvFylSBGfPnsXz58+xZ88eDBkyBEWLFkXz5s0z6I6IKCthMUVERJQOYgqm9evXo0yZMgm2zZ07t8lrpVSsgis+VlZWsY69WbwB0QVEYkTE5LyuXbti+PDh2LlzJx4/foxXr16hQ4cOCfbRvHlzHDp0KME2ly5dQokSJRLNJzkaNGgAf39/PH78GFFRUShYsCBq1qyJUqVKmbQzGAzo3bs3du/ejf3796NkyZKx+jI3N4erqysAoHLlyrh58yYmTpzIYoqI4sRiioiIKB14eHhAp9Ph5s2baNGiRar6srCwQFRUVKLtbG1tUbRoUfj5+Zlc8/Dhw/Dw8DBpe/ToUXzxxRfG10eOHEHZsmWNr/PmzYv27dtjyZIlePz4MTp27AgbG5sEr79kyRKEh4cn2KZIkSKJ3kdK5c+fHwAQEBCAkydPYsmSJcb3IiMj0bVrVxw5cgQHDx6Ei4tLkvo0GAx48eJFuuRLRFkfiykiIqJ0YGNjgxEjRmDUqFHIlSsXvLy88OLFC5w+fRp3797FyJEjk9yXi4sL/Pz8EBQUBCsrK9jZ2cXbdvjw4Rg5ciTc3NxQu3ZtbNmyBevWrcPmzZtN2m3duhXz5s1D06ZNsXPnTvz000/45ZdfTNp89tlnqFevHqKiovDtt98mmmdqp/lFREQY936KiIhASEgIzp49CwsLC5QrVw4AcPz4cXTr1g2rVq0yTnFcsWIFSpcuDUdHR5w9exaDBg3Ce++9h27dugGILqTatWuHv/76C5s3b4Zer0dISAiA6D+nmCJx1KhRaNKkCZycnPDixQts374dS5cuxTfffJOq+yKibEzrh7YYDAaDwYgvkIUXoIixYMECKV++vFhYWIidnZ3UrVtX1q5da3wfcSwi4enpKb169TK+Pn36tFStWlV0Ol2spdH/+ecfk3NjlkYvUaJEgkujz549W1q2bCl6vV4cHBxk1qxZceZfoUIFqVSpUio/haS5efNmrGXgAYiTk5OxTcx979u3z3hs9OjR4ujoKLlz55YSJUrIsGHDJDQ0NNF+AZgs7NGnTx9xcXERS0tLsbOzk1q1asmqVasy4M7TF7gABYORbqFEEp9HTUREpAWllPB7Ku0ppeDr64uPP/44wXavXr1C8eLFMXbsWPTr1y+DsqO0ppSCiKjEWxJRcnGaHxEREZkwGAy4d+8efvjhB0RERKB79+5ap0RElCmxmCIiIiITQUFBcHFxgaOjI5YvX57owhNERDkVp/kREVGmxWl+RKnHaX5E6cdM6wSIiIiIiIiyIhZTREREhAkTJhg3qyUioqRhMUVERERZwosXL9CjRw9UqFAB5ubmaNSoUbxt161bh6pVq0Kv1yN//vxo0qSJyfsREREYNGgQ7O3tYWVlhSZNmuDq1avpfQtElM2wmCIiIqIsISoqCnq9HgMGDEiwkPrhhx/Qp08f9OrVC2fPnsVff/2FHj16mLQZMmQI1qxZg5UrV+LIkSOwtLRE48aNERYWls53QUTZCYspIiKiDLJv3z7UrFkT1tbWyJs3L6pVq4aTJ08CAEQEn376KUqVKgW9Xg8XFxeMGDECL168MJ4fMxXvl19+gaurK6ysrNC6dWs8ffoU69evR5kyZZAnTx60bdsWT548MZ7Xo0cPNGrUCDNnzkSRIkVgZWWFjz76CA8fPkww3x07dqBmzZrQ6/UoUaIE+vfvj2fPniXpftKDtbU1fvzxR3z66adwcHCIs81///2HYcOGYcaMGfjiiy/g5uaGsmXLonPnzsY2T58+xaJFizBt2jQ0b94clSpVwurVq3Hv3j2sW7cu3fInouyHxRQREVEGiIyMhLe3N+rUqYOzZ8/ixIkTGDp0KHLnzg0gupiyt7fHTz/9hMuXL8PHxwe+vr6YMmWKST/BwcFYuXIlNm7ciB07duCvv/7CRx99hKVLl+KXX37Bjh074Ofnh8mTJ5ucd/z4cRw8eBA7d+7E9u3b8ffff+OTTz6JN989e/agbdu26NmzJ86fP4+ff/4ZR48eRa9evZJ0P3GZOnUqbGxsEow1a9ak9CMGAOzevRuhoaGwsrJCtWrV4ODggCZNmuDMmTPGNidPnkRERITJ1L+8efOiZs2a8PPzS9X1iShn4T5TREREGeDp06d48uQJ2rRpg9KlSwMAypQpY3zfzMzMpHBydnZGYGAgfHx8MGnSJOPxly9fYuXKlShYsCAAoFOnTpg3bx6Cg4Nhb28PAOjSpQv27Nljcn2DwYBVq1YhX758AIAFCxbAy8sLAQEBxnzeNGnSJAwcOBB9+/YFALi6uuKHH35AjRo18ODBA+TKlSvB+4lL37590b59+wTbFC5cOMH3E3P9+nUAwOjRozF79mw4Ozvj+++/R/369XHlyhU4OjoiJCQkzmsVLlwYwcHBqbo+EeUsLKaIiIgygJ2dHXr06IEmTZrAy8sLXl5e8Pb2hrOzs7HNsmXLsHDhQgQGBiI0NBSRkZEwGAwm/RQtWtRYSAGAg4MDHBwcjIVUzLF79+6ZnFeuXDljIQUAderUgVIKly9fjrOYOnnyJI4dOwYfHx/jsZg9vwICAlCnTp1E7yeuz8DOzi7Bzym1Yj6v0aNHo23btgCiP1cnJyf4+vpi2LBh8Z4rIlCK2zERUdJxmh8REVEGWb58OY4dO4YGDRpgy5YtcHNzw6ZNmwAAGzZsQN++fdGhQwds27YNZ86cwcSJE/Hq1SuTPt6eRqeUivPY20VYXBIqHgwGA4YPH46zZ88a49y5cwgICECVKlUSvZ+4ZMQ0vyJFigCILh5j5M6dG6VLl8atW7cAwPi81dsF5/379+N9FouIKC4cmSIiIspAFStWRMWKFTFkyBC0bt0ay5Ytw4cffogDBw6gSpUqGDx4sLFtYGBgml338uXLePr0KWxtbQEAR44cAQCULVs2zvZVq1bFpUuXEt17Kr77iUtGTPN79913AQD+/v6oW7cugOjnu65fv44PPvgAAFCtWjVYWFhg9+7dxufGnj59imPHjsVa9Y+IKCEspoiIiDLAzZs3sXDhQrRq1QrFihXDzZs3cfr0aXTq1AkA4ObmhqVLl2LTpk0oX748tm7divXr16fZ9ZVS6NatGyZPnoxHjx7h888/R6tWreItliZOnIimTZti2LBh+Pjjj6HX63HlyhVs2bIFixYtSvR+4pIW0/wuXbqEiIgIPHr0CM+fP8fZs2cBRI9EWVhYoHTp0mjbti3Gjx+P4sWLw8nJCbNnz0ZoaCi6du0KALC1tcWnn36KkSNHokiRIihSpAjGjBmDwoULo0OHDqnKj4hyFhZTREREGcDKygpXr15Fu3bt8O+//8Le3h5t27bFxIkTAQCfffYZzp8/j549eyIyMhItWrTA119/jX79+qXJ9WvUqIE6deqgcePGePLkCZo3b45FixbF297Lywt79uzBhAkTMH/+fCilULJkSXh7eyfpftJLixYtjNP1ABinHN68edP4vNaKFSswdOhQdO7cGa9evUK1atWwb98+kyl8s2fPRu7cudG1a1c8f/4cdevWxe7du2FlZZWu+RNR9qJiHiYlIiLKbJRSwu+p1OvRowdu374da4U/yhmUUhARrqxBlA64AAUREREREVEKsJgiIiIiIiJKAU7zIyKiTIvT/IhSj9P8iNIPR6aIiIiIiIhSgMUUERHlWCtWrIC5edZY2LZHjx5QSkEphQULFmidDmUCHTt2NP4/sXr1aq3TIcqRWEwRERFlEfXq1UNwcDC6d+9uPLZo0SLUr18ftra2UErh9u3bsc6L+Qf32/H+++8n6/oHDx5Ey5YtUaRIkXj/Ab9p0ybUrFkTdnZ20Ov1cHd3x4wZM5Dc6ZrBwcHo3Lkz3NzcYGZmht69e8dq8+LFC/To0QMVKlSAubk5GjVqlKxrxCU8PBzly5eHUgqHDx82ea9NmzZwdnaGTqeDvb09PvzwQ1y8eDFZ/QcGBsb75/H2MvjXr19H+/btjZ9l+fLlTXJauHAhgoODU36zRJRqLKaIiIiyCAsLCzg4OECv1xuPhYWFoVmzZhg1alS85wUHB5vEkSNHAESPbCRHaGgoKlWqhHnz5sXbJn/+/Bg6dCgOHTqES5cuYfTo0ZgwYQLmzJmTrGu9fPkShQoVwujRo1GpUqU420RFRUGv12PAgAFpUkgBwIABA+Di4hLne++99x7WrFmDK1euYPv27TAYDPDy8sKLFy+S3H/x4sVj/XnEbM785p/HrVu3UKtWLeh0OuzYsQOXLl3C999/j4IFCxrb5M2b12TvLCLSgIgwGAwGg5EpI/prKraFCxdK/vz55cWLFybHR4wYIR4eHiIiYjAYpHfv3lKyZEnR6XTi7Owsw4cPl/DwcGP75cuXS65cueJ9LSJy8+ZNASCHDh0yHvP395dWrVqJra2t2NnZScuWLeXatWtx5ppWunfvLl5eXvG+v2/fPgEg//zzT6J9jRo1Suzs7Ew+i+QCIL6+vklq6+3tLa1bt07xtTw9PaVXr14Jtkns80mKtWvXSvny5eXy5cux/szjcu7cOQEgZ8+eTdV1O3fuLOXKlYt1rE6dOkk6P7E/i9c/R5r/PDMY2TE4MkVERFlO+/btERYWhj/++MN4TETw008/oWvXrsbX9vb2+Omnn3D58mX4+PjA19cXU6ZMSdW1Q0JCULduXTg7O8PPzw+HDh1Cvnz50KhRI4SFhcV7XvPmzWFjY5NgBAUFpSq3pHj16hWWLVuG7t27Q6fTpeu1RAQnT56En58fGjRokK7XSq3r169j4MCBWLt2bZI+l+fPn2Pp0qUoWrQoSpcuneLr/vvvv/j111/x2WefGY8ZDAZs2bIFtWrVQufOnWFvb4/KlStj3rx5EOHqlkSZSdZ46paIiOgN+fLlQ8uWLeHr64s2bdoAAA4cOIDbt2+jS5cuAAAzMzOTwsnZ2RmBgYHw8fHBpEmTUnztH3/8Ea6uribT1pYtW4aCBQvijz/+QLt27eI8b8mSJQgPD0+w7yJFiqQ4r6TatGkTQkJC0KdPn3S9jo2NDSIiIhAVFYXx48djwIAB6Xq91IiIiEDHjh0xbtw4lC9fHoGBgfG2nTp1KqZOnYrQ0FC4u7tj//79sLKySvG1V6xYATMzM+MvAQDg/v37ePbsGebPn4/Bgwdj586dOHnyJAYOHBjns1VEpB0WU0RElCV169YNH330ER49egQ7Ozv4+vqiQYMGKFasmLHNsmXLsHDhQgQGBiI0NBSRkZEwGAypuu7Jkydx4sQJ2NjYmBwPCwtDQEBAvOcVLVo0VddNKwsXLoSnpyfc3d3T9Tpnz55FeHg4jhw5ghEjRqBIkSJxLiKRGYwaNQqOjo5JKlL69u2Ldu3a4e7du5g9eza8vb3x119/IU+ePMm+rohg0aJFaN++PfLnz288HvP/aJUqVTB16lTjf/v7+2P+/PkspogyERZTRESUJTVr1gx58+bFunXr0LNnT2zYsAFz5841vr9hwwb07dsX33zzDTw9PWFra4uNGzdixIgR8fZpZhZ79vurV69MXscsOhDXIgx2dnbx9t28eXMcOnQowXu6dOkSSpQokWCb1Lh27Rr+/PNP/PTTT+l2jRiurq4AgAoVKuDJkycYM2ZMpi2m9uzZg/Pnz8daJr9+/fpo2rSpyXRSOzs72NnZoXTp0qhTpw4KFSqENWvWoG/fvsm+7t69exEQEIBVq1aZHC9UqBDMzc3h4eFhctzDwwM//vhjsq9DROmHxRQREWVJuXPnRseOHeHr6ws7OztERUUZp/wB0dP+qlSpgsGDBxuPJTR9CwDs7e0RFRWFe/fuoXDhwgCAM2fOmLSpWrUqVq1ahWLFiiXrmaPMMM1v0aJFKFCggMnnlBEMBkOyVrzLaL/++qvJn83du3fRtGlTLF++HO+9916C54pIiu9t4cKFqFixImrVqmVyPHfu3KhRowb8/f1Njvv7+8PZ2TlF1yKi9MFiioiIsqyuXbti7ty5CAsLg7e3t8nUOzc3NyxduhSbNm1C+fLlsXXrVuMS1PGpUaMG8uTJgxEjRmD06NEICAiI9XxV//79sXTpUrRp0wZjxoyBo6MjgoKCsGnTJnz++efxLkaQXtP8QkJCEBISgmvXrgGIHt36999/4eDgYLJsdkREBFasWIGePXvCwsIiRdd6/vy58ToAEBQUhLNnz8LGxsY4EjVt2jS88847cHV1RVRUFA4fPoxp06ahW7duyb7e2bNnjdd99OiR8XXlypWNbS5duoSIiAg8evQIz58/N7YpV65cku+zVKlSJq9j/j9ycXGBk5MTgOjpnYcPH4anpycKFCiA27dvY/r06TAYDPD29k72vd2/fx+///47vv/++zjfHzFiBFq1aoWpU6eiffv2OHnyJObPn48ZM2Yk+1pElI60Xk6QwWAwGIz4AvEsjf6msmXLCgDZuXOnyfGIiAjp06eP5M+fX/LkySMdOnSQ+fPny5t9xrUU+tatW8Xd3V10Op28++67sm3btljLZN+4cUM6dOggdnZ2YmlpKSVLlpRevXrJgwcPEs03peJb+nv8+PECIFaMHz/epN3atWtFKSUBAQFx9r98+XIBIDdv3ow3h5jl198OT09PY5tx48ZJmTJlRK/Xi62trVSpUkV8fHzk1atXxjYxy80vX748wXuO61pv/z/h5OQUZ5s378PJyUm6d++e4LXeFNdy+BcuXBAvLy8pWLCgWFhYSPHixaVjx45y/vx5k3O7d+8uTk5OiV5j2rRpYm1tLU+ePIm3zerVq8Xd3V0sLS3F3d1d5s+fLwaDIVY7cGl0BkOzUCJcYpOIiDInpZTweypajx49cPv2bezZsydd+h83bhx+/fVXnDt3LtazQ2lt7969eP/993Hx4kWULFkyXa8VFhaGAgUKYNmyZejUqVO6XguI3ti3bNmyWLhwYbpfK4ZSCr6+vvj444/jfV9EVIYlRJSDcJ8pIiKiLGL//v2wsbHB0qVL07zvrVu3Yt68eeleSMVca/jw4eleSAHRi0vUrFkzQwqpx48fw9/f37gCX3rr0aNHrFUliShjcWSKiIgyLY5M/b/79+/j6dOnAKIXyrC1tdU4I9LavXv38OzZMwCAg4NDvIUVR6aI0g+LKSIiyrRYTBGlHospovTDaX5EREREREQpwGKKiIgoE6tfv36m3ew2NXQ6HVasWKF1GkREqcJiioiIiLKtwMBA5M2bF/PmzYv1Xr9+/eDi4mJ8Fo2IKLlYTBEREVGm8/LlyzTpx9nZGT4+Phg2bBiuXLliPL57924sXLgQq1at4mIeRJRiLKaIiIg0JCLw8fFB2bJlYWlpCQcHB/To0SPe9rt370b9+vVhZ2eHvHnz4r333sPRo0dN2vzwww8oU6YMdDodChYsiMaNGyM0NBQAcOvWLbRq1Qp2dnawsrKCm5tbuk+3O3XqFGrUqAGdTody5cph586dJu8HBgYa90pq2rQprKysMHXqVOzfvx9KKdy+fdukvVIKq1evNr4+efIkqlevbux/165dJtMIu3fvjvfffx9du3ZFZGQk/vvvP3zyyScYNmwY6tWrl673TkTZW/pvJkFERETxGjt2LObMmYMZM2bAy8sLT548wd69e+Nt//z5c3zxxReoVKkSIiMjMWfOHDRv3hwBAQEoWLAgTp48if79+2P58uXw9PTEkydPcODAAcSsitivXz+Eh4dj7969yJs3LwICAhIcBTp06BCaN2+e4D18/PHHWLBgQZzvhYaGokWLFqhZsyaWLl2KR48e4X//+x9evXoVq+3IkSPx7bffYsGCBVBKITAwMMHrxvT//vvvo06dOli2bBkeP36MwYMHx+p/4cKFqFChAiZOnIgbN27A3t4eEydOTLR/IqKEsJgiIiLSyPPnzzFr1ixMmTIFffv2NR6vVq1avOd4e3ubvF64cCE2btyInTt3okuXLggKCoKNjQ3atGlj3HeoYsWKxvZBQUFo164dKleuDABwcXFJMMdq1arh7NmzCbZJaJrcmjVrEBoailWrViFfvnwAgFmzZsHLyytW2759+6JLly7G10kpptasWYOwsDCsXLnSmMfMmTPRoEEDk3Z2dnZYsWIFmjdvDgsLC5w6dQq5c+dOtH8iooSwmCIiItLIpUuX8OLFCzRu3DjJ59y6dQvjxo2Dn58f7t+/D4PBgLCwMNy6dQsA0LhxYzg7O6NkyZJo0qQJGjVqBG9vb+TNmxcAMHDgQHz++efYsWMHGjZsiJYtW6JGjRrxXk+v18PV1TXF93j58mWUK1fOWEgBQJ06daBU7G2PEsojof7Lli1rUtDVqlUrzv4bN26MmjVronTp0ihbtmyyr0VE9DY+M0VERJSFvP/++wgMDMT8+fNx9OhRnD17Fo6OjoiIiAAA5MmTB6dOncK6devg4uKC6dOnw83NzTjK06tXL9y8eROffPIJrl27hnr16mH48OHxXu/QoUOwsbFJMN4cVUuquDZjtrKyMnltZmYWq21c0wOT2j8A5M6dG+bm/F0yEaUNFlNEREQaKVeuHCwtLfHnn38mqf3Dhw9x8eJFjBgxAk2bNkW5cuVgZWWFkJAQk3bm5uZo0KABJk2ahHPnzsFgMOC3334zvl+0aFH06tULa9euNT6jFJ+YaX4Jxddff53gPV6+fNlk+fEjR44k6X7t7e0BAHfv3jUeO3PmjEmbsmXLxur/2LFjSeqfiCi1+KsZIiIijdjY2GDw4MEYN24crKys0LBhQzx79gx79+7FkCFDYrXPnz8/ChUqhMWLF6NUqVJ4+PAhhg0bBr1eb2yzdetWXL9+He+99x4KFCiAgwcP4vHjx3B3dwcQPc2vefPmKFOmDJ4/f45t27YZ34tLaqf5de7cGWPGjEG3bt0wefJkPHr0CF999ZVx1Ckhrq6ucHJywoQJE/Ddd9/hwYMHGDVqlEmbLl26YOzYsejZsycmTpyI//77D0OHDoWZmVmcU/2IiNISR6aIiIg0NGXKFIwfPx4zZsxAuXLl0KJFC1y8eDHOtmZmZli/fj2uX7+OihUrokePHujfvz+KFStmbJMvXz78/vvvaNSoEdzc3DBlyhT4+PgYV+QzGAzo378/PDw80LBhQ+TJkwfr1q1Lt/uztrbGH3/8gTt37qBq1aro06cPJk+enKTFH8zNzbFu3Trcv38fVapUQf/+/fHNN9/E6n/r1q0ICgoy9h+zSp9Op0uXeyIiiqHim1NMRESkNaWU8HuKkuvEiROoUaMGTp06hXfeeUfrdDSnlIKIcJiOKB2wmCIiokyLxRQlxapVq1CiRAk4OzsjICAAgwYNgoWFBU6fPq11apkCiymi9MNnpoiIiChL+/fffzFhwgTcvXsXBQsWRMOGDTFz5kyt0yKiHIAjU0RElGlxZIoo9TgyRZR+uAAFERERERFRCrCYIiIiIiIiSgEWU0RERERERCnAYoqIiIiIiCgFuJofERFlWjqd7p5SqrDWeRBlZTqd7p7WORBlV1zNj4iIKItTSnkA2A+gsojc0TiddKeU6gPgEwDvikiU1vkQUc7FaX5ERERZmFIqF4ClAMbmhELqtSUAXgLop3UiRJSzcWSKiIgoC1NK9QfQDkB9ETFonU9GUUq5AfADUFVEbmmdDxHlTCymiIiIsiilVAkApxE93c1f63wymlJqFIC6AN7n7s5EpAVO8yMiIsqClFIKwAIA3+XEQuq1GQCKAuikdSJElDNxZIqIiCgLUkp1BjACQDURidA6H60opaoD2AKgvIj8q3U+RJSzsJgiIiLKYpRSBQFcANBKRI5rnY/WlFKzANiLSFetcyGinIXFFBERURajlFoF4F8RGax1LpmBUsoawHkAX4jIDq3zIaKcg5v2EhERZSFKqWYA6gEor3UumYWIhCqlPgOwWClVXkSea50TEeUMHJkiIiLKIpRSNoie3veZiOzUOp/MRim1AsB/IvI/jVMhohyCxRQREVEWoZT6DkABEemmdS6ZkVKqAKKLzdYickzrfIgo+2MxRURElAUopWoC2ATAQ0Qeap1PZqWU6ghgNKI3882xqxwSUcbgPlNERESZnFLKAsBiAINZSCVqHYBbAIZpnQgRZX8cmSIiIsrklFJjANQG8IHwiztRSqniAE4DqCciV7TOh4iyLxZTREREmZhSyh3AYQDviEiQ1vlkFUqpfgA6AvAUEYPW+RBR9sRpfkRERJmUUsoM0dP7JrKQSrYfAeQC8JnWiRBR9sWRKSIiokxKKdUXQDdET1eL0jqfrEYpVQ7AAQBVROS21vkQUfbDYoqIiCgTUkoVA3AGQH0Ruah1PlmVUmocgGoAPuTzZkSU1jjNj4iIKJNRSikAPwCYz0Iq1b4BUBJAO60TIaLsh8UUERFR5vMRAFcA07ROJKt7vdfUpwDmKKXstM6HiLIXTvMjIiLKRF7/g/8CgLYickTrfLILpZQPABsR+UTrXIgo+2AxRURElIkopZYBCBWR/lrnkp0opfIgukjtJSJ7tM6HiLIHc60TICIiomhKqUYAGgHw0DqX7EZEnr1eHXGhUqqCiIRpnRMRZX0cmSIiIsoElFJWAM4DGCAif2idT3allFoD4K6IDNU6FyLK+lhMERERZQJKqekAiolIZ61zyc6UUoUQPd2vhYic0jofIsraWEwRERFpTCn1DoDtACqIyH2t88nulFJdAQwGUENEXmmdDxFlXVwanYiISENKqdwAlgIYykIqw6wGcA/AEK0TIaKsjSNTREREGlJKDQfgBaCp8Es5wyilnAGcBFBbRAI0ToeIsigWU0RERBpRSpUGcARAdRG5qXU+OY1S6n8AWgNoKCIGbbMhoqyI0/yIiIg0oJRSABYBmMJCSjNzAegBcCNfIkoRjkwRERFpQCnVG0AfRE8zi9I6n5xKKVUBwJ8AKovIXa3zIaKshcUUERFRBlNKOQL4G0AjETmndT45nVJqEoByItJW61yIKGvhND8iIqKMNxfAIhZSmcYUAB5KqTZaJ0JEWQtHpoiIiDKQUsobwDcAKonIC63zoWhKqXoAfgbgISL/aZwOEWURLKaIiIgyiFIqH4ALADqLyEGN06G3KKV+BJBLRPponQsRZQ0spoiIiDKIUmohABGRvlrnQrEppfIiutjtKiL7NU6HiLIAc60TICIiygmUUp4A3gfgoXUuFDcReaKU6gdgkVKqkoiEa50TEWVuXICCiIgonSmldAAWA+gnIk+0zofiJyKbAZwFME7jVIgoC+A0PyIionSmlJoCoIyItNM6F0qcUsoB0UvXNxGRsxqnQ0SZGIspIiKidKSUqgRgN4CKIhKidT6UNEqpngD6AaglIpFa50NEmROn+REREaUTpZQ5gCUARrKQynJWAHgCYKDGeRBRJsaRKSIionSilBoM4AMAXsIv3CxHKVUKwDEANUXkutb5EFHmw2KKiIgoHSilSgI4juhpYte0zodSRik1FEBTAI1ZEBPR2zjNj4iIKI0ppRSAhQCms5DK8r4DkB9Ad60TIaLMhyNTREREaUwp1R3Rz9rU4OIFWZ9SqgqAnQAqiMg9rfMhosyDxRQREVEaUkoVRvSy2s1F5LTW+VDaUEp9A8BFRDponQsRZR4spoiIiNKQUmotgCARGa51LpR2lFJ6RBfJQ15v7EtExGKKiIgorSilPgDwPaL3lArTOB1KY0qpBgBWAfAQkada50NE2mMxRURElAaUUrYALgLoLiJ7tc6H0odSajGACBHpp3UuRKQ9FlNERERpQCk1D4BeRHppnQulH6VUfgAXAHQQkcNa50NE2mIxRURElEpKqXcBrEf09K/HWudD6Usp1RbAZABVROSF1vkQkXa4zxQREVEqKKUsASwGMICFVI6xEcAVAKO0ToSItMWRKSIiolRQSk0EUAmAt/BLNcdQShUBcA5AQxE5r3U+RKQNFlNEREQppJQqD2AfgMoickfrfChjKaX6APgEwLsiEqV1PkSU8TjNj4iIKAWUUrkALAEwloVUjrUEwEsAX2qdCBFpgyNTREREKaCU6g+gHYD6ImLQOh/ShlLKDYAfgGoiEqhxOkSUwVhMERERJZNSygnAKURP7/LXOh/SllJqFID3ADTnc3NEOQun+RERESWDUkoB+BHAdyyk6LUZABwBdNE6ESLKWByZIiIiSgalVGcAIxA9rStC63woc1BKVQewFUB5EXmgdT5ElDFYTBERESWRUqoggAsAWonIca3zocxFKTULQGER+VjrXIgoY7CYIiIiSiKllC+AByIyWOtcKPNRSlkDOA+gn4hs1zofIkp/5lonQERElFkppVTMggJKqWYA6gIor21WlFmJSKhS6jMAi5VSFUTkmdY5EVH64gIUREREcVBKlQGw5/V/2wBYAKCviIRqmhhlaiKyG8B+AJM1ToWIMgCLKSIioriVRvSGrAAwCcBBEdmpYT6UdQwB0F4pVUvrRIgofXGaHxERUdyKAbijlKoJoBMAD43zoSxCRB4qpQYBWKKUeoerPhJlXxyZIiIiiltRAMEAlgAYBMBWKfWbUuorbdOiLGIdgEBEL6NPRNkUiykiIqK4FQNQAcBtAG4ATryOuVomRVnD64VLPgcwQClVTut8iCh9cJofERFR3MoAqArgAYDnAN4RkSBtU6KsRET+UUqNR/TqfvVExKB1TkSUtrjPFBERURyUUk8AhAP4WET2aJ0PZU1KKTMAhwGsFpEftM6HiNIWiykiIqI4KKUaAvATkZeJNiZKwOtpfgcBVBGRf7TOh4jSDospIiIionSmlBoHoDqAViIiSqlDABqxWCfK2rgABREREVH6+waAC4D2r1+XAFBEu3SIKC1wAQoiojfo9fqQFy9eFNY6D6LMSqfT3QsPD3fQOo+sRkQilFKfAtiolNoD4A6il9+/qW1mRJQaLKaIiN7w4sWLwpz+TBQ/pRR/2ZBMSqnpiF4RcgaA9QBmIXrJ/aJa5kVEqcdpfkRERETpax6AigAuAvAD0ABALkTvZUZEWRhHpoiIiIjS0ev9yT5SSjVG9KbP9wA0RvRUPyLKwjgyRURERJQBRGQ3okeo1gPQA2iobUZElFpcGp2I6A1KKeHfi0TxU0pBRJTWeWR1Sil3AOVEZKPWuRBRyrGYIiJ6A4spooSxmCIi+n+c5kdERCZ69OgBpZQx7Ozs0LBhQxw9etSknbOzs7GNTqeDu7s7pk+fDoPBoFHmcbt06RIaNmwIvV4PBwcHDB06FJGRkQme8/ZnoJRCsWJcK4CIiExxAQoiIoqlXr16+OWXXwAADx48wOTJk9GsWTPcvn0bNjY2xnbDhw/H//73P4SHh2Pz5s0YNGgQlFIYOnRoiq777NkzvHz5EgULFkyT+3j27BkaN26M6tWr49ixY/jnn3/QvXt3AMCMGTMSPPfNzwAAcuXKlSY5Udy4xxtlRdx3jTgyRUREsVhYWMDBwQEODg6oUKECxowZgydPniAgIMCknY2NDRwcHODi4oKBAwfCy8sLGzcm7xGQyMhIbN++HZ07d0bhwoXh5+eXZvexZs0aPH78GL6+vqhYsSLef/99TJ48GfPnz0doaGiC5775GTg4OKBQoUJplhfFFrPHG4ORlYK/ACAWU0RElKDw8HD4+vqiYMGCKF26dIJt9Xo9Xr16laR+T58+jUGDBqFYsWLo1KkTdDodtm7dipYtWxrb9O3bFzY2NgnGoUOH4r3GX3/9hdq1ayNPnjzGY02bNkV4eDjOnDmTYH7Hjx9H4cKF4erqiq5duyIoKChJ90VERDkHp/kREVEs+/fvN07nCwsLg52dHTZu3Ggyxe9NBoMB27dvx86dOzFo0KB4+719+zZWr14NX19fXLt2DS1atMC8efPwwQcfQKfTxWr/9ddf46uvvkow16JFi8b7XkhICAoXNv3Fcczr4ODgeM9r1qwZ2rZtCxcXF9y+fRuTJ09G7dq1ceHCBeTPnz/BfIiIKOdgMUVERLHUrFkTK1euBAA8ffoUa9euxYcffogDBw6gYsWKxnaTJk3CN998g4iICCil0K1bN0yYMCHefseMGYOVK1eicePG2L17N4oUKZJgHvb29rC3t0/VvSgV98Jz8R0HgI4dOxr/u3z58qhTpw5cXFywYsWKBItFIiLKWTjNj4iIYtHr9XB1dYWrqyveeecdzJgxA4ULF8bMmTNN2vXr1w9nz57FjRs3EB4ejsWLF8c5whRj/PjxGDduHK5fv46yZcuiR48e2L17N6KiouJsn9ppfg4ODggJCTE5du/ePeN7SWVra4ty5crh6tWrST6HiIiyP45MERFRkpiZmcVatMHOzg6urq5J7sPFxQUTJ07ExIkT4efnB19fX3To0AGWlpbo0KEDunTpgurVqxvbp3aaX506dTB48GA8f/7cOEVx165d0Ov1qFKlSpLzDg0Nhb+/Pxo3bpzkc4iIKPvjyBQREcUSERGBkJAQhISE4OrVq5g4cSIuX76M1q1bp9k13n33XSxYsAAhISGYP38+bt26hbp162LLli3GNvb29sYRsvhCr9fHe40uXbogX7586NatG86fP49t27ZhzJgx+OKLL2BtbQ0AuHPnDtzd3fHbb78BAJ4/f46vvvoKf/31FwIDA+Hn54fWrVvj1atX6NmzZ5rdPxERZX0spoiIKJZDhw7B0dERjo6OqFq1Kn777TcsXrwYXbt2TfNrWVhYoE2bNvjtt98QHByMmjVrplnfefLkwZ49e/Dff/+hevXq6NGjB3r06IFvvvnG2ObVq1fw9/fHkydPAETvJ3X+/Hl4e3ujTJky6NixIwoUKIBjx46hePHiaZYb5Tzu7u4JPlOYnfj5+aFGjRrQ6XQoUaIEpk+fnug5IoIpU6agWLFi0Ol0qF27No4dO5YB2RKlnBIRrXMgIso0lFLCvxeJ4qeUgojEv3pHyvvN9j977u7u6NixY6IF1cuXL2FpaZkxSaWDW7duwcPDA126dMHAgQNx5swZ9O7dGzNnzkS/fv3iPW/WrFkYN24cli5digoVKmD27NnYsGEDLl++nOhiNVpJr58Hyjo4MkVERESUABHBd999h9KlS0On06Fs2bKYO3cu3iz+lFKYP38+unTpAhsbGxQvXtxkNKZ+/frw9/fHxIkToZSCUgqBgYHYv38/lFLYsmULateuDUtLS/z0008QEUybNg3Ozs6wsLCAm5sbli9fbpKXUgrff/89vL29YW1tjSJFiuC7774zvt+9e3c0bdo01v3UqFEDAwcOTIdPKtqPP/4IOzs7LFiwAOXKlTMWVQmNTokIZs2ahcGDB6Njx47w8PDA4sWLYWNjg4ULF6ZbrkSppvXO0QwGg5GZIvqvRSKKz+ufkRz1szd27FhxcXGRzZs3y40bN2Tjxo1SsGBBmT9/vrENALG3t5dFixZJQECAzJ49WwDI/v37RUTk4cOH4urqKkOGDJHg4GAJDg6WyMhI2bdvnwAQNzc32bJli9y4cUPu3Lkjc+bMEb1eL0uXLhV/f3+ZMWOGmJmZyR9//GFyzfz584uPj4/4+/uLj4+PmJuby8aNG0VExM/PT8zMzCQwMNB4zt9//y0A5O+//473fps1aybW1tYJxq1bt+I9v169evLJJ5+YHNu7d68AkH/++SfOc65fvy4A5ODBgybHu3XrJl5eXvFeS2vp9fPAyDrB1fyIiIiI4hEWFoaZM2fi999/R5MmTQBEr0p57do1zJ8/H1988YWxbYcOHfDpp58CAAYNGoT58+djz5498PT0hJ2dHXLlygUbG5s4l+UfM2YMPvjgA+Pr6dOnY8CAAfjkk08AAF999RVOnTqFadOmoUWLFsZ2LVq0QP/+/QEAZcqUwYkTJzBr1ix4e3ujTp06KFeuHJYtW4aJEycCABYvXoyaNWuiQoUK8d7zkiVLEB4enuDnktC0u5CQENStW9fk2JubZRcrVizOc95s9+Z5J0+eTDAXIi2xmCIiIiKKx8WLFxEeHg5vb2+TjZ4jIyNhMBhM2r693H7RokWN+5olpkaNGsb/fvr0Ke7cuYN69eqZtPH09MTo0aNNjtWpU8fk9bvvvoutW7caX3/22WeYPn06xo8fj1evXmHNmjWJLgaR0HYDSfX2ptgiEufxpJyX2DlEWmIxRURERBSPmIJp/fr1KFOmTIJtc+fObfJaKRWr4IqPlZVVrGPxFSQJebv46Nq1K4YPH46dO3fi8ePHePXqFTp06JBgH82bN09wM2wAuHTpEkqUKBHne3Ftln3//n3je/GdA0SPUJUuXdrkvORssE2U0VhMEREREcXDw8MDOp0ON2/eNJlelxIWFhaIiopKtJ2trS2KFi0KPz8/k2sePnwYHh4eJm2PHj1qMtXwyJEjKFu2rPF13rx50b59eyxZsgSPHz9Gx44djRtYxye10/zq1KmDtWvXmhR2u3btQokSJeKc4gdET510dHTE7t27jSNyBoMBf/75J3r16pVgLkSa0vqhLQaDwchMgUz8EHx6cXNzk/Hjx2udRrq7cOGCtGnTRpycnASATJo0KdFzbt68KQDijC+++MKk7bVr16Rdu3aSP39+0el04uHhIYcOHTK+/+2330qNGjXE1tZW8ufPL15eXvLXX3+l+X2mN+TABSgmTJggtra28uOPP8rVq1fl77//lhUrVsjUqVONbQCIr6+vyXmenp7Sq1cv4+tWrVpJgwYN5NatW/LgwQOJiooyLkDx9sIMPj4+Ym1tLStXrpSrV6/KrFmzJFeuXHEuQDF37ly5evWqzJ0712QBihhHjhwRc3NzUUrJ8ePH0/KjiVNgYKBYW1tL37595dKlS/LTTz+JXq+XuXPnGtscO3ZM3Nzc5NixY8ZjM2bMEGtra/n555/l4sWL0rt3b7G1tZU7d+6ke84plV4/D4ysExyZIiKiJMnqe9+EhYWhZMmSaNeuHQYNGpSkc4oXL47g4GCTY4cPH0a7du3QsWNH47Fbt26hVq1aaN68OXbs2IFChQrh+vXrKFiwoLHN/v370adPH1SvXh25c+fGjBkz0LhxY5w9exaurq5pc5OULsaPHw8HBwfMmzcPAwcOhI2NDcqVK5fgnklxmTBhAj799FO4ubnhxYsXuHnzZrxtv/zySzx//hxjx45FcHAwXFxcsHjx4lijY2PHjsWuXbswbNgw5M2bF99++y28vb1N2tSqVQtly5aFmZkZqlevnqycU8LJyQk7duzAoEGDULlyZdjb22P8+PH48ssvjW3CwsLg7++PsLAw47EhQ4bgxYsXGDx4MB4+fIjKlStj165dmXaPKSIAHJliMBiMNwOZ+LfjbzMYDDJ79mxxdXUVS0tLcXd3Fx8fHzEYDMY2AGTevHnSuXNnsba2lmLFism3335rfN/T0zPWiMvNmzeNvy3fvHmz1KpVSywsLGTZsmViMBhk6tSp4uTkJLlz55YyZcrIsmXLTPICIN999520bt1arKysxNHRUWbPnm18v1u3btKkSZNY91O9enUZMGBAOnxSsTk5OSVpZCounTt3lnLlysU6VqdOnWT1ExUVJQUKFBAfH58U5aEV5MCRqcwKcYyGxSUiIkIKFy4s8+bNy4Cscpb0+nlgZJ3QPAEGg8HITJGV/kHHvW+St/fNm1JaTD148EAsLS1lzpw5xmNRUVGSJ08eGTx4sHTq1EkKFSoklSpVkrlz55oUtm8LDQ0VGxsbWb16dbLz0BKLqcwjsWIqKipK7t69K2PGjJH8+fPLs2fPMjC7nIHFFEPzBBgMBiMzRVb5B11oaKjo9XrZuXOnyfHp06ebjJoAkP79+5u0KVWqlIwZM8b4Oq5npmKKqbf/oVa0aFEZPny4ybGOHTtK3bp1Ta7ZpUsXkzZdu3aVd9991/i6fPnyMm7cOOPr/v37S82aNRO6Zbl9+7YEBAQkGK9evUqwjxgpLaZmzJgher1eHj16ZDwWHBwsAMTS0lJGjhwpp0+flkWLFoler09wJOCLL76QYsWKydOnT5Odh5ZYTGUeiRVTMc/8OTo6yu+//56BmeUcLKYYfGaKiCgL4t43GU9EsGjRIrRv3x758+c3Ho/5vKtUqYKpU6ca/9vf3x/z58+P87maqVOnYvXq1dizZw/y5MmTMTdA2Y5IwkulOzs7J9qGiFKHxRQRURbEvW/iltDeN6m1d+9eBAQEYNWqVSbHCxUqBHNz81hLVnt4eODHH3+M1c+ECRPw/fffY/fu3RmyGAAREaUfFlNERFkQ976JW3qu+rVw4UJUrFgRtWrVMjmeO3du1KhRA/7+/ibH/f394ezsbHJs6NChWLp0Kfbs2YNq1aqlW66Uc0yYMAGrV6/GtWvXtE6FKEdiMUVElAXZ2NhgxIgRGDVqFHLlygUvLy+8ePECp0+fxt27dzFy5Mgk9+Xi4gI/Pz8EBQXBysoKdnZ28bYdPnw4Ro4cCTc3N9SuXRtbtmzBunXrsHnzZpN2W7duxbx589C0aVPs3LkTP/30E3755ReTNp999hnq1auHqKgofPvtt4nmmdppfhEREbh06ZLxv0NCQnD27FlYWFigXLlyAIDjx4+jW7duWLVqlckUx/v37+P333/H999/H2ffI0aMQKtWrTB16lS0b98eJ0+exPz58zFjxgxjmy+//BLLly/Hhg0bUKxYMYSEhAAA9Ho98ubNm6p7I8rMJkyYgIkTJ8b53vHjx01GaNetW4fp06fj0qVL0Ol0qF69Onbt2pVRqRIln9YPbTEYDEZmCmSxh+AXLFgg5cuXFwsLC7Gzs5O6devK2rVrje8jCRuJnj59WqpWrSo6nS7W0uhvbyQaszR6iRIlElwaffbs2dKyZUvR6/Xi4OAgs2bNijP/ChUqSKVKlVL5KSRNfBvwOjk5GdvE3Pe+fftMzp02bZpYW1vLkydP4u1/9erV4u7ublymfv78+bGWqY8runfvnsZ3mr7ABSgylfHjx0upUqW0TiNBz549M64WGhMfffSRuLq6mvyMzJ8/X2xtbWX+/Ply5coVuXTpkqxZs0bDzBOXXj8PjKwTmifAYDAYmSn4D7rUi6uAiwv3vsmacmIxtXfvXqlRo4ZYWVmJra2tVK1aVU6cOCEi0b9g6N27t5QsWVJ0Op04OzvL8OHDJTw83Hh+TMGzbt06KVWqlOj1evnwww/lyZMn8ssvv0jp0qXFxsZG2rRpI//995/xvO7du4uXl5fMmDFDHB0dRa/XS9u2beXff/+N1febtm/fLjVq1BCdTifFixeXL7/80mTVyITuJyP8999/YmVlJdOnTzcee/z4sVhbW8vChQszLI+0wGKKYZYhw19ERESvGQwGBAcH4+uvv0ZERAS6d++udUpE8YqMjIS3tzfq1KmDs2fP4sSJExg6dKhxYRcRgb29PX766SdcvnwZPj4+8PX1xZQpU0z6CQ4OxsqVK7Fx40bs2LEDf/31Fz766CMsXboUv/zyC3bs2AE/Pz9MnjzZ5Lzjx4/j4MGD2LlzJ7Zv346///4bn3zySbz57tmzB23btkXPnj1x/vx5/Pzzzzh69Ch69eqVpPuJy9SpU2FjY5NgrFmzJsmf6apVqxAZGYkePXoYj+3evRuhoaGwsrJCtWrV4ODggCZNmuDMmTNJ7pdIC3xmioiIMlRQUBBcXFzg6OiI5cuXJ7rwBJGWnj59iidPnqBNmzYoXbo0AJisoGlmZmZSODk7OyMwMBA+Pj6YNGmS8fjLly+xcuVKFCxYEADQqVMnzJs3D8HBwbC3twcAdOnSBXv27DG5vsFgwKpVq5AvXz4AwIIFC+Dl5YWAgABjPm+aNGkSBg4ciL59+wIAXF1d8cMPP6BGjRp48OABcuXKleD9xKVv375o3759gm0KFy6c4PtvWrRoEdq0aYNChQoZj12/fh0AMHr0aMyePRvOzs74/vvvUb9+fVy5cgWOjo5J7p8oI7GYIiKiNCXCvW8o+7Czs0OPHj3QpEkTeHl5wcvLC97e3iYrNS5btgwLFy5EYGAgQkND49zvrWjRosZCCgAcHBzg4OBgLKRijr29B1y5cuWMhRQQvYebUgqXL1+Os5g6efIkjh07Bh8fH+OxmJ+3gIAA1KlTJ9H7ieszSGhhmuTw8/PDhQsXMHfuXJPjMZ/X6NGj0bZtWwDRn6uTkxN8fX0xbNiwNLk+UVrjND8iIiKiBCxfvhzHjh1DgwYNsGXLFri5uWHTpk0AgA0bNqBv377o0KEDtm3bhjNnzmDixIl49eqVSR9x7feW0j3gRCTWfm8xDAYDhg8fjrNnzxrj3LlzCAgIMG7gndD9xCUtp/ktWLAAbm5uqF+/vsnxmG0NYlbWBKI/s9KlS+PWrVtJ6ptICxyZIiIiTXB/HMpKKlasiIoVK2LIkCFo3bo1li1bhg8//BAHDhxAlSpVMHjwYGPbwMDANLvu5cuX8fTpU9ja2gKI3rMNgMm+bW+qWrUqLl26BFdX1wT7je9+4pJW0/wePXqEDRs2YOrUqbHee/fddwFE789Wt25dANHPd12/fh0ffPBBon0TaYUjU0RERAnYv38/6tWrh7x58yJ//vxo1KgRjh8/Hmfb8PBwlC9fHkopHD58OIMzpfRw8+ZNjBgxAn/99ReCgoJw4MABnD59Gu7u7gAANzc3nD9/Hps2bcL169cxZ84crF+/Ps2ur5RCt27dcOHCBRw8eBCff/45WrVqFW+xNHHiRPz2228YNmwY/v77bwQEBGDLli3o06dPku4nLnZ2dnB1dU0w8uTJk+i9rFy5EgDiXHSmdOnSaNu2LcaPH49du3bB398f/fr1Q2hoKLp27ZqUj4pIEyymiIiI4nHnzh28//77qFixIk6cOIHDhw+jQIECaNq0KZ49exar/YABA+Di4qJBppRerKyscPXqVbRr1w6lS5fGxx9/jDZt2hg3of3ss8/QtWtX9OzZE1WqVMGRI0fw9ddfp9n1a9SogTp16qBx48Zo1qwZPDw8sGzZsnjbe3l5Yc+ePTh+/Dhq166NKlWqYPTo0cYFHBK7n/S0aNEifPTRR/E+f7VixQq0bNkSnTt3Ro0aNXDt2jXs27cPDg4O6Z4bUYppvTY7g8FgZKZAJt7rJiW4P07qbNmyRQCY5ODv7y8A5OTJkyZt165dK+XLl5fLly8LADl06FC65aUl5MB9prQS83NEmVd6/Twwsk5wZIqIKJvi/jipf3D+nXfegZWVFRYsWIBXr14hPDwcixYtgouLi8mD8tevX8fAgQOxdu1a6HS6+P9QiIgoW+ECFERE2RT3x0n9g/NFihTB3r170a5dO4wYMQIiglKlSmHXrl3Q6/UAgIiICHTs2BHjxo1D+fLl03TxASIiytxYTBERZVPcHyf1++M8ePAAvXr1QqtWrdCzZ09ERERg+vTp+OCDD3DixAnkyZMHo0aNgqOjI/r165fi6xDFZcWKFVqnQESJ4DQ/IqJsjPvjpG6a39y5cxEVFYV58+ahatWqqF27Nn7++WcEBQVh3bp1AKKnJ/7xxx8wNzeHubm5cZW1+vXr4/3330/0MyEioqyLI1NERNkc98dJ+TS/sLAwmJmZ/t7RzMwMZmZmxlGzX3/9FeHh4cb37969i6ZNm2L58uV47733Erw2ZZwVK1agd+/eiIyM1DqVRPXo0cO4jPiPP/5onPpKGePKlSvGv6dKlSrFvfAoQRyZIiLKprg/Tur3x2nZsiUuX76MESNGwN/fH+fPn0ePHj2glEKTJk0ARP9jq3z58saIeY7LxcUFTk5OKf34KIerV68egoODTfZkEhFMmTIFxYoVg06nQ+3atXHs2LEU9b948WK4urrC0tISFStWxLZt21LUz6ZNm1C+fHlYWlqidOnSKZ6a6Ofnhxo1akCn06FEiRKYPn16svuI+dl8O8zMzHD//n2TtvPnz4eHhwcsLS1RqFAhk8VxSpcujeDgYAwZMiRF90I5C4spIqJsivvjpJ6npyfWr1+PvXv3onr16vD09ERISAh27NjBQonSlYWFBRwcHIwLnQDA7NmzMXXqVMycOROnTp1CuXLl0KRJE9y9ezdZff/666/4/PPPMWjQIJw7dw4tW7ZE69atcebMmWT1c+zYMbRt2xZt2rTBuXPn0L9/f/Tq1Qtbt25NVj+3bt1C06ZNUaVKFZw+fRrTpk3D+PHjMX/+/GT1M2fOHAQHB5tEzZo10aBBA5NnPIcNG4bJkydj1KhRuHjxIvbu3YsPPvjA+H6uXLng4OAAGxubZF2fciit12ZnMBiMzBTgXjdpgvvjZF/I4H2mFi5cKPnz55cXL16YHB8xYoR4eHiISNL2TFu+fLnkypUr3tciIjdv3oy1R5i/v7+0atVKbG1txc7OTlq2bCnXrl1LxieWfHH9/BgMBnF0dJQxY8YYj0VFRUmRIkVk3Lhxyeq/Zs2a8vHHH8c61q1bt2T106FDB6lbt26sY++9916y+hk+fLgUL15cDAaDybESJUokq5+3xewJ98svv5gcU0rJzp07Ez0/rr3w3pZePw+MrBMcmSIiIqJMq3379ggLC8Mff/xhPCYi+Omnn9C1a1fj66TsmZZcISEhqFu3LpydneHn54dDhw4hX758aNSoEcLCwuI9r3nz5okufBIUFJSsXG7evIng4GDj9FIg+vm9Ro0awc/PL8n9RERE4NSpUyb9AEDTpk2T1Q8A/PXXX3H2c+zYMURFRSWrn8aNG5ssTtO0aVMEBQXh9u3bycrpTQsXLoS9vT1at25tPLZp0yZYWFjg4cOH8PDwQNGiReHt7Y0bN26k+DqUs3EBCiIiIsq08uXLh5YtW8LX1xdt2rQBABw4cAC3b99Gly5dACR9z7Tk+vHHH+Hq6oo5c+YYjy1btgwFCxbEH3/8gXbt2sV53pIlS0wWJYlLkSJFkpVLSEgIgNgLphQuXBgnT55Mcj///vsvIiMj4+wnODg42TnF1c/Lly/x6NEjFCpUKMn91K1bN1Y/QPSm4cWKFUtWXsD/74/36aefmqw+ev36dRgMBkycOBFz5sxBvnz5MG7cODRo0ACXL1+GlZVVsq9FORuLKSIiSnPcH4fSUrdu3fDRRx/h0aNHsLOzg6+vLxo0aGDyj+yk7JmWXCdPnsSJEydiPTsTFhaGgICAeM8rWrRoqq6bkLe3FhCJf7sBrfqJ63hG9RNjw4YNePToET799FOT4waDAa9evYKPj49xVG3t2rVwcHDA1q1bE139k+htLKaIiIgoU2vWrBny5s2LdevWoWfPntiwYQPmzp1rfD9mz7RvvvkGnp6esLW1xcaNGzFixIh4+3x7yXsAsfZYMxgM8PLywrx582K1TWgz6ObNm+PQoUMJ3tOlS5dQokSJBNu8ycHBAUD0KM6bm17fv3/f+F5SFCxYEObm5saRrpT2E5NTXP1YWFggf/78qe4n5r2UWLBgAZo0aYKSJUuaHI8ZESxXrpzxmJ2dHRwdHXHr1q0UXYtyNj4zRUSUgVasWAFz86zxe6w3lxlesGCB1ulQOqtVq5bxz/vw4cNap2Mid+7c6NixI3x9fbFp0yZERUUZp/wBMNkzrWrVqihdunSie6bZ29sjKioK9+7dMx57ezW7mL3PihUrFmtJ/YSKqSVLlphsPh1XJHean4uLCxwdHbF7927jMYPBgD///BPvvvtukvuxsLBA1apVTfoBgF27diWrHwCoU6dOnP3UqlULuXLlSlY/e/bsMY5GxfRTokSJFE3xu3TpEg4fPozPPvss1nsx9+jv72889uTJE4SEhMDZ2TnZ1yLSfAUMBoPByEyBdF7NL64VxDKr7t27S7169SQ4OFjCwsKMxxcuXCienp6SJ08eASD//PNPrHMBxBktWrRIVg4HDhyQDz74QBwdHQWA+Pr6xmrz+++/S40aNSR//vyi0+nEzc1Npk+fbrIyWFLcvXtXOnXqJGXKlBGllPTq1StWm/DwcOnevbuUL19ecuXKlSYrFoaFhYmHh0esVeRERLy9vcXJyUksLS2lUKFC0qpVK7lw4UKyr2EwGGTy5MlStGhRsbS0lFq1asnRo0dN2jx8+FCOHz8eZx5vQgav5hcjJrdKlSrFWolu7ty5otfr5ffff5dr167J999/LwUKFJA3+3z7Z+/hw4eSJ08e6dGjhwQEBMi2bdukfPnyJvcfEhIiDg4O0rx5c/Hz85MbN27I/v37ZdCgQXL16tUE802N+FbDnDFjhlhbW8vPP/8sFy9elN69e4utra3cuXMnWf2vX79ecuXKJT/88INcvnxZxo4dK7lz55bTp08nq5+jR49Krly5ZMKECXLlyhWZO3eu5MqVS7Zs2ZKsfgIDA8Xa2lr69u0rly5dkp9++kn0er3MnTs3Wf3EGDBggDg6OsqrV69ivRcVFSXVq1cXDw8P8fPzkwsXLsiHH34oLi4uEhoaatKWq/kxkhIcmSIionjFtddNWFgYmjVrhlGjRsV73tt7vRw5cgQA0LFjx2RdPzQ0FJUqVYpzmlWM/PnzY+jQoTh06BAuXbqE0aNHY8KECSaLBiTFy5cvUahQIYwePRqVKlWKs01UVBT0ej0GDBiARo0aJav/+AwYMAAuLi5xvvfee+9hzZo1uHLlCrZv326cdvbixYtkXSMp+xPZ2dklecEALVSvXh1ly5bFuXPnjKv4xUjJnml2dnZYu3Ytjh49igoVKmDKlCmxNootXLgw/vrrL9ja2qJly5YoW7YsPvnkEzx9+jRZ09jSypAhQzBixAgMHjwY77zzDs6fP49du3aZjHJNmDAh0eeMPvroI/zwww+YOXMmKlWqhN9++w2//fYbqlSpYmyzYsUKKKUSHOGrWbMmNmzYgF9++QUVK1bE999/jyVLlpjs2bR//34opbB///54+3FycsKOHTtw8uRJVK5cGcOGDcP48ePx5ZdfGtsEBgZCKZXo85jh4eFYtWoVevXqFecsADMzM2zZsgUVKlRA8+bN8d577wEA/vzzTy4+QSmjdTXHYDAYmSnAvW6MEtsrat++ffGOTL1t1KhRYmdnZ/JZJBfiGZmKi7e3t7Ru3TrF1/L09IxzZOpNabGX1tq1a6V8+fJy+fLlREeERETOnTsnAOTs2bNJvkZy9ieK6/+9t0GjkamcJDX/b3Xt2lUaN26c6hzGjh0r5cqVi3N0JzmWLl0q9vb28vjx41T18+eff4pOp5Pr16+nqp/k4MgUIynBkSkioiTgXjcp9+rVKyxbtgzdu3eHTqdL12uJCE6ePAk/Pz80aNAgXa+VWtevX8fAgQOxdu3aJH0uz58/x9KlS1G0aFGTBQgSk1b7E1HG2r9/P2xsbLB06dIknxPzDFVCI7lJtXXrVsybNy/Vz3hu3boV3377LfLly5fqfoYPHx5rQYn0cPXqVdjY2GDq1Knpfi3K+rLGU9BERBrjXjcpt2nTJoSEhKBPnz7peh0bGxtEREQgKioK48ePx4ABA9L1eqkRERGBjh07Yty4cShfvnyCU6mmTp2KqVOnIjQ0FO7u7ti/f3+ypiOl1f5ElHGmT5+OMWPGAIheKCOpzMzMcOfOnTTJ4fTp02nSz8aNG9Okn9mzZ6dJP0nh4uKCs2fPAoie6kyUEBZTRERJxL1uUmbhwoXw9PSEu7t7ul7n7NmzCA8Px5EjRzBixAgUKVIEvXv3TtdrptSoUaPg6OiIfv36Jdq2b9++aNeuHe7evYvZs2fD29sbf/31F/LkyZOsa6bVvkKU/uzt7ZNVRFHayp07N1xdXbVOg7IIFlNEREnEvW6S79q1a/jzzz/x008/pds1YsT846dChQp48uQJxowZk2mLqT179uD8+fOxplDVr18fTZs2NZlOamdnBzs7O5QuXRp16tRBoUKFsGbNGvTt2zdJ10qr/YmIiCg2FlNEREn05l43dnZ2Ce51EyM5e93ETMOKa6+bVatWoVixYsl65igzTPNbtGgRChQoYPI5ZQSDwZDsFe8y0q+//mryZ3P37l00bdoUy5cvN64uFh8RSda9vbk/Ub169QD8/7M1vXr1StkNkIn69evD1dUVS5Ys0TqVNKXT6bBgwQL06NFD61SIMi0WU0REydC1a1fMnTsXYWFh8Pb2Npl65+bmhqVLl2LTpk0oX748tm7divXr1yfYX40aNZAnTx6MGDECo0ePRkBAQKznq/r374+lS5eiTZs2GDNmDBwdHREUFIRNmzbh888/j3cxgvSa5hcSEoKQkBBcu3YNQPTo1r///gsHBweTkY6IiAisWLECPXv2TPFzB8+fPzdeBwCCgoJw9uxZ2NjYGEeipk2bhnfeeQeurq6IiorC4cOHMW3aNHTr1i3Z14t5TuL58+d49OiR8XXlypWNbS5duoSIiAg8evQIz58/N7YpV65cku+zVKlSJq9j/j9ycXGBk5MTgOjpnYcPH4anpycKFCiA27dvY/r06TAYDPD29k7yPSmlMHjwYEyYMAEeHh6oUKECvvvuOzx79izOTU2JMlpgYCAqVaqEKVOmmCyHDgD9+vXDtm3bcO7cOdja2mqUIVECtF5OkMFgMDJTIAnLM5ctW1YAyM6dO02OR0RESJ8+fSR//vySJ08e6dChg8yfPz/BjUNFRLZu3Sru7u6i0+nk3XfflW3btsVanvrGjRvSoUMHsbOzE0tLSylZsqT06tVLHjx4kGi+KRXf8szjx4+Pc0Pe8ePHm7Rbu3atKKUkICAgzv6XL18uAOTmzZvx5hCz/Prb4enpaWwzbtw4KVOmjOj1erG1tZUqVaqIj4+PyZLOMUt+L1++PMF7jutab/8/4eTkFGebN+/DyclJunfvnuC13hTXkuQXLlwQLy8vKViwoFhYWEjx4sWlY8eOcv78eZNzu3fvLk5OTgn2bzAYZNKkSVKkSBGxtLSUmjVrxtq0N7483gYujR5LUpbSz4osLS0T/Jl5e6uI1FixYoXo9Xq5fPmy8diuXbskV65ccvDgwTS7TlpLr58HRtYJzRNgMBiMzBRZ+R90aS0t9lFKSFrtY5MUGblHTWhoqOh0Ovnpp5/S/VoiIvXq1ZM+ffqkSV8spuJmMBhkzpw54u7uLhYWFlK4cGGTYvntYmrXrl3i6ekp+fPnF1tbW6lXr54cOXLEpM/58+dL6dKlxdLSUgoUKCCNGjWS58+fi4hIYGCgtGzZUvLnzy96vV7KlCmT6C8CUuvkyZNSvXp1sbS0lLJly8qOHTtMiqmY/zdWrVolTZo0Eb1eL+PGjYt3vzm8tS/ciRMnpFq1asb+d+7cGatY++ijj6RatWry6tUrefz4sRQrVkxGjhyZrvedWiymGJzmR0RE8YrZ62bOnDlp/nxNWu1jk9RrZdQeNXv27EHNmjXRqVOndL/W48eP4e/vj99++y3VfTVs2BDHjh1Lg6yyn7Fjx2LOnDmYMWMGvLy88OTJE+zduzfe9s+fP8cXX3yBSpUqITIyEnPmzEHz5s0REBCAggUL4uTJk+jfvz+WL18OT09PPHnyBAcOHICIAIie2hYeHo69e/cib968CAgIwMuXL+O93qFDh9C8efME7+Hjjz/GggUL4nwvNDQULVq0QM2aNbF06VI8evQI//vf/2IthgMAI0eOxLfffosFCxZAKZXoc6Ex/b///vuoU6cOli1bhsePH2Pw4MGx+l+4cCEqVKiAiRMn4saNG7C3t8fEiRMT7Z9IU1pXcwwGg5GZApn4t+MZ7d69exIQECABAQHy5MkTrdOhdPbPP/8Y/7zDw8PjbYccNjL17Nkz0el0MmvWrHjbJDbNz2AwSIECBWT16tUiIvLrr7+Kra2tPHv2LM72FSpUkK+//jrJOYaFhRn/7OKLe/fuxXv+woULxdraWh4/fmw89ueff5pMjY0ZmZo0aZLJuUkZmVq4cKHY2NiY/D0Sc97bI24xU/v0er1cunQpyZ+BVtLr54GRdYIjU0REFCfudZOzvLlfGv2/S5cu4cWLF2jcuHGSz7l16xbGjRsHPz8/3L9/HwaDAWFhYbh16xYAoHHjxnB2dkbJkiXRpEkTNGrUCN7e3sibNy8AYODAgfj888+xY8cONGzYEC1btkSNGjXivZ5er0/VvkiXL19GuXLlkC9fPuOxOnXqxLkPWUJ5JNR/2bJlTRaQqFWrVpz9N27cGDVr1kTp0qVRtmzZZF+LKKPF3uCEiIiIiFLs/fffR2BgIObPn4+jR4/i7NmzcHR0REREBAAgT548OHXqFNatWwcXFxdMnz4dbm5uxilzvXr1ws2bN/HJJ5/g2rVrqFevHoYPHx7v9Q4dOgQbG5sEI6n7kr1JRGIds7KyMnkds1fem23jmh6Y1P6B6G0oMmL6L1FaYDFFRJSD1a9fP9NubJsaOp0OK1as0DoNygbKlSsHS0tL/Pnnn0lq//DhQ1y8eBEjRoxA06ZNUa5cOVhZWSEkJMSknbm5ORo0aIBJkybh3LlzMBgMJs++FS1aFL169cLatWuNzyjFp1q1ajh79myC8fXXXyd4j5cvX8bTp0+Nx44cOZKk+40Zvb57967x2Nt75ZUtWzZW/3w+j7ILFlNERESpcOXKFVhZWcUq3i5dugS9Xo9ly5ZpkxilCRsbGwwePBjjxo3DokWLcO3aNZw5cwazZs2Ks33+/PlRqFAhLF68GFevXsWRI0fQoUMH6PV6Y5utW7dizpw5OHPmDIKCgrBu3To8fvwY7u7uAKKn+e3YsQM3btzA33//jW3bthnfi0vMNL+EIqEpu507d4aVlRW6deuGCxcu4ODBg/jqq6+Mo04JcXV1hZOTEyZMmIArV67g0KFDGDJkiEmbLl26wMrKCj179sSFCxdw+PBhDB06FGZmZnFO9SPKSlhMERFRjpPQymjJ5e7ujm+++QYDBgzAzZs3AURPc/r444/RtGlTfPLJJ2l2LdLGlClTMH78eMyYMQPlypVDixYtcPHixTjbmpmZYf369bh+/ToqVqyIHj16oH///ibPpOXLlw+///47GjVqBDc3N0yZMgU+Pj7GFfkMBgP69+8PDw8PNGzYEHny5MG6devS7f6sra3xxx9/4M6dO6hatSr69OmDyZMnI3fu3Imea25ujnXr1uH+/fuoUqUK+vfvj2+++SZW/1u3bkVQUJCx/5hV+nQ6XbrcE1GG0XoFDAaDwchMgUy6olhKcX+cjNkfx2AwSOPGjaVu3boSFRUlI0aMkMKFC8v9+/fT9d61gBy2mh+lj+PHjwsAOXXqlNappEp6/Twwsk7w6T4iomyM++P8v/TcH0cpheXLl6NChQr4+OOP8csvv2DTpk0oVKhQotcgyglWrVqFEiVKwNnZGQEBARg0aBCqVKmCd955R+vUiFKFxRQRUTb1/PlzzJo1C1OmTDFZyatatWrxnuPt7W3yeuHChdi4cSN27tyJLl26ICgoCDY2NmjTpg1sbGwAABUrVjS2DwoKQrt27VC5cmUAgIuLS4I5xjw4n5A3l1N+25o1axAaGopVq1YZl3WeNWsWvLy8YrXt27cvunTpYnydlGJqzZo1CAsLw8qVK415zJw5Ew0aNIjVtmjRopg4cSIGDBiA7t274/3330+0f6Kc4t9//8WECRNw9+5dFCxYEA0bNsTMmTO1Toso1VhMERFlU9wfx1R674/z6tUrrFixAlZWVjh+/DhevHjB50GIXhs8eDAGDx6sdRpEaY4LUBARkRH3x0n5/jgTJkzAnTt3cPr0afz3338YNWpUclImIqIsiMUUEVE2xf1xEpaW++McOXIE3377LRYvXgw3NzcsXboUc+bMwYEDB5KUCxERZU2c5kdElE29uT+OlZUVGjZsiGfPnmHv3r2x9oEBTPfHKVWqFB4+fIhhw4bF2h/n+vXreO+991CgQAEcPHgw1v44zZs3R5kyZfD8+fMk74+TUp07d8aYMWPQrVs3TJ48GY8ePUrR/jjfffcdHjx4EGs0qUuXLhg7dix69uyJiRMn4r///ou1P87z58/RtWtXfPLJJ2jZsiUAoHnz5ujduzd69OiBv//+G3ny5EnxPRIRUebFkSkiomyM++PEL632xxk0aBAAYPbs2Sbnzp49G7ly5TK+T0RE2Y+Ka943EVFOpZQS/r1ICTlx4gRq1KiBU6dO5chlnZVSEJHYK3Ckkl6vD3nx4kXhtO6XKD3pdLp74eHhDlrnQdphMUVE9AYWU/S2uPbHsbCwwOnTp7VOTRPpVUwREWVFfGaKiIgoAdwfh4iI4sORKSKiN3BkiihhHJkiIvp/XICCiIiIiIgoBVhMERERERERpQCLKSIiIiIiohRgMUVERERERJQCXM2PiOgNOp3unlKKe90QxUOn093TOgciosyCq/kREWURSql8AFYBKACgvYjc0TYjSitKKTMAIwH0A9BJRA5onBIRESUBp/kREWUBSqmKAE4AuAmgAQup7EVEDCIyBUAPAOuUUkOUUlx+nIgok+PIFBFRJqeU+hjAdwAGiMharfOh9KWUcgKwAUAggE9E5Jm2GRERUXw4MkVElEkppSyUUvMBjEP0aBQLqRxARG4BqAfgEYDjSqmyGqdERETxYDFFRJQJKaWKATgAoAiA6iJyQeOUKAOJyAsR+QzAdAAHlVLttc6JiIhiYzFFRJTJKKUaIvr5qN8BtBGRJ9pmRFoRkeUAmgL4Rik1WymVW+uciIjo//GZKSKiTOL1ggNDAQwC0EVE9mqcEmUSSik7AKsB2CB6JccQjVMiIiJwZIqIKFNQSuUF8CuANoie1sdCioxE5BGADwD8CeCkUqquxikRERFYTBERaU4pVR7R0/qCAXiKyG2NU6JM6PXy6RMBfArgV6XUQC6fTkSkLU7zIyLSkFKqEwAfAINFxFfrfChrUEq5IHr59AAAvUXkucYpERHlSByZIiLSwOtlz+cAmATAi4UUJYeI3ATwLoBQAMeUUm4ap0RElCOxmCIiymBKqSIA9gFwAVBNRP7WOCXKgl4vn94L0Rs6H1JKtdU6JyKinIbFFBFRBlJKeSL6+ag/ALQWkf+0zYiyOhFZAqAFgFlKqelKKXOtcyIiyin4zBQRUQZ4vVDAYABfAegmIrs1TomyGaVUQQBrAFgA6Cgi9zROiYgo2+PIFBFROlNK5QHwC4COAGqykKL0ICL/InqE6hCil0+vrXFKRETZHospIqJ0pJQqC+A4gIcA6opIkMYpUTYmIlEiMg7A5wB+V0p9yeXTiYjSD6f5ERGlE6VUewDzAQwVkRUap0M5jFKqFKI3gr4A4DMRCdU4JSKibIcjU0REaUwplVspNRvANwCasJAiLYjIdQB1AEQCOKqUKq1xSkRE2Q6LKSKiNKSUcgDwJwA3AFVF5IzGKVEOJiJhAHoCmAfATyn1ocYpERFlKyymiIjSiFKqLoCTAPYAaCkijzVOiQgSbSGAlgB8lFLTuHw6EVHa4DNTRESp9PoB/wEARgHoLiI7NE6JKE5KqUIA1r5+2UlEHmiZDxFRVseRKSKiVFBK2SD6H6fdANRiIUWZ2eviqSmiV5g8pZSqqXFKRERZGospIqIUUkq5ATgG4DmAd0XkpsYpESXq9fLpowD0B7BFKdWXy6cTEaUMp/kREaWAUqotgB8BjBKRJVrnQ5QSr1f4+xXAGQCfv16wgoiIkogjU0REyaCUMldKTQcwC0ALFlKUlYlIAIDaAHIB+Ov13lRERJRELKaIiJJIKVUYwG4AFRG97PlJjVMiSrXXm/l2BbAYwBGl1Acap0RElGWwmCIiSgKlVG1EL3t+CMD7IvJQ45SI0szr5dPnA/gQwI9KqUlKqVxa50VElNnxmSkiogS8fjC/H4CxAHqJyFaNUyJKV69HYH8GEAGgM39xQEQUP45MERHFQyllDcAXQG8AtVlIUU4gIvcANAZwDtHLp1fTOCUiokyLxRQRURxer3J2FEAkgDoickPjlIgyjIhEisgwAEMAbFdK9dY6JyKizIjT/IiI3qKU+hDRD+OPBbBI+Bcl5WCv91PbiOhfLnwpIuEap0RElGlwZIqI6LXXy55PA+AD4AMRWchCinI6EfEHUBOANQA/pZSLxikREWUaLKaIiAAopQoB2AGgGoBqInJc45SIMg0ReQ6gE4CVAI4qpZprnBIRUabAYoqIcjylVE0ApwAcA9BMRB5onBJRpvN6+fQ5ANoCWKyUGq+U4r8jiChH4zNTRJRjvV72/DMAXwP4VEQ2aZwSUZaglHIEsA7AcwAfi8gjjVMiItIEf6NERDmSUsoKwAoAXwB4l4UUUdKJSDAALwCXAZxUSlXROCUiIk2wmCKiHEcpVQrAEQC5EL1/VIDGKRFlOSLySkSGABgBYJdSqqfWORERZTRO8yOiHEUp9QGApYie2vcDV+sjSj2lVFlEL59+CMAAEXmhcUpERBmCI1NElCMopXIppSYB+BFAaxGZz0KKKG2IyGUANQDkB3BIKeWkcUpERBmCxRQRZXtKqYIAtgGoi+hlz49onBJRtiMizwC0B7AWwDGlVBONUyIiSncspogoW1NKVQNwEsBZAI1F5J62GRFlX6+XT58NoAOA5UqpMVw+nYiyMz4zRUTZ0utlz3sDmAKgr4hs1DglohxFKVUEwHoAjwB0FZH/tM2IiCjt8bdFRJTtKKX0AJYA+B+AeiykiDKeiNwF0ADADUQvn15J45SIiNIciykiylaUUi4A/ABYAagpIv4ap0SUY4lIhIgMBDAWwB6lVDetcyIiSkuc5kdE2YZSqjmiN+KdAmAuV+sjyjyUUuUB/ArgTwCDROSlxikREaUaiykiyvJeP+A+DtHPSHUUkcMap0REcVBK2SL6Fx5FAXwkIv9omxERUepwmh8RZWlKKTsAWxH9bEY1FlJEmZeIPAXQFtEjVMeVUl4ap0RElCospogoy1JKvYPoZc8vA2gkIiEap0REiXi9fPp0AF0ArFZKjXi9+iYRUZbDaX5ElCUppXoCmA7gCxFZr3U+RJR8SqliiF4+/R6A7iLyROOUiIiShSNTRJSlKKV0SqlFAIYBeI+FFFHWJSK3AXgCuA3ghFKqgsYpERElC4spIsoylFJOAA4DyA+ghohc1jglIkql18unfwngawB7lVJdtM6JiCipWEwRUZaglGoC4BiAnwC0F5FnGqdERGlIRFYD8AIwUSk1VylloXVORESJ4TNTRJSpvV72fBSALwB0EpEDGqdEROlIKZUPwEoAhQC0E5E72mZERBQ/jkwRUaallMoPYBOA5ohe9pyFFFE2JyL/AfAGsAXRz1HV1zIfIqKEsJgiokxJKVUJ0cue3wDQQETuapwSEWUQETGIyDQA3QCsVUoN5fLpRJQZcZofEWU6SqluAGYBGCAia7XOh4i0o5QqAWADgH8A9Hy98S8RUabAkSkiyjSUUpZKqR8AjEb0aBQLKaIcTkSCANQD8ADAcaWUh8YpEREZsZgiokxBKVUcwEEADgCqi8gFjVMiokxCRF6KSF8A3wDYr5TqoHVOREQAiykiygSUUl4AjgP4FUBbTuMhoriIyAoAjQFMVUp9p5TKrXFKRJTD8ZkpItLM62XPhwMYAKCLiOzVOCUiygJer/S5GoAtovedC9Y4JSLKoTgyRUSaUErlBbARQCtET+tjIUVESSIijwG0BLALwEmlVD2NUyKiHIrFFBFlOKVUBUQve34bgKeI3NY4JSLKYl4vnz4JQC8AG5RSg7h8OhFlNE7zI6IMpZTqAuB7AINEZLXG6RBRNqCUckb0M5fXAPQWkWfaZkREOQVHpogoXSilKiilPn7jtYVSai6AiQC8WEgRUVoRkUAA7wJ4BuCYUso95j2lVDGlVH+tciOi7I3FFBGll28A2ACAUqoogP0AnABUE5G/NcyLiLIhEXkhIr0BzAZwSCn10eu3QgFMeD16RUSUplhMEVGaU0pVBlAZwAqlVAMAJwBsBdBaRP7TLjMiyu5EZAmAZgBmKKVmInq0ajGArzRNjIiyJT4zRURpTim1DtH7RpkBGAKgq4js1jYrIspJlFIFAKwBoAPQD8AhAOVEJETTxIgoW+HIFBGlKaVUGQBeAOoBaAegBoA/Xz+3wJW2iCjdvS6kXgJ4H8ABADsA/AngfxqmRUTZEIspIkpr0wDkRvTzUkcQvbHmfwD2AsinWVZElJOMBHAPwHkAJQFsA9AEwMDXG/4SEaUJTvMjojSjlMoF4BWAh4hecOIUoveTOi0ijzRMjYhyGKVUbgAeAKoCqAagDoAKAOaIyCAtcyOi7IPFFBGlKaWUhYhEaJ0HEdHbXhdYUSJi0DoXIsoeWEwRERERERGlgLnWCVDG0+v1IS9evCisdR5EaU2n090LDw930DoPIkocv4soO+L3UM7DkakcSCkl/HOn7EgpBRHhioFEWQC/iyg74vdQzsPV/IiIiIiIiFKAxRQREREREVEKsJgiIiIiIiJKARZTREREREREKcBiioiIiIiIKAVYTFGWExgYiNy5c8PV1RXZeSWowMBAKKVixZIlS0zaPXnyBD179kT+/PmRJ08efPTRRwgJCUmw7xUrVsTZ97Vr19LzloiIcoQePXqY/N1qZ2eHhg0b4ujRoybtnJ2djW10Oh3c3d0xffp0GAyZZ0/h4OBgdO7cGW5ubjAzM0Pv3r2TdN7bn4FSCsWKFUvnbIkyHospynIWL16Mzp07IyoqCn/++afW6Ri9ePECd+/eTfN+N23ahODgYGN06dLF5P2PP/4Yfn5+2Lx5M/bu3Yt//vkHH374YaKFZq5cuUz6DQ4OhouLS5rnT0SUE9WrV8/4d+uBAwdQqFAhNGvWDM+fPzdpN3z4cAQHB+Py5cv4/PPPMWLECMyaNSvF13327Bn+/fff1KZv9PLlSxQqVAijR49GpUqVknXum59BcHAwzpw5k2Z5EWUWLKYoS4mMjMTy5cvRvXt3dO3aFYsXL47V5vr16/Dy8oJOp0PJkiWxevVquLu7Y8KECcY2z549Q79+/eDo6AgbGxvUrFkzRYWZiODgwYP49NNP4eDggPXr16fm9uJkZ2cHBwcHY+j1euN7/v7+2Lp1K3788UfUq1cP1atXx8qVK3H8+HHs378/0b7f7NfBwQG5cuVK8/yJiHIiCwsL49+tFSpUwJgxY/DkyRMEBASYtLOxsYGDgwNcXFwwcOBAeHl5YePGjcm6VmRkJLZv347OnTujcOHC8PPzS7P7cHZ2xpw5c9CtWzfkzZs3Wee++Rk4ODigUKFCaZYXUWbBYoqylK1bt8Lc3Bz169dH9+7dsWnTJjx48MD4vsFggLe3N168eIHDhw9j/fr1WLRoEW7fvm1sIyL44IMP4O/vj40bN+LcuXPo0KEDmjdvjr///jtJefj7+2Ps2LEoWbIkmjVrhqdPn2LVqlX44osvjG2mTp0KGxubBGPNmjWJXqtTp04oVKgQatasieXLl5uMOPn5+cHCwgL169c3HnN3d0eJEiUS/TKNioqCi4sLihYtiiZNmqTply8REf2/8PBw+Pr6omDBgihdunSCbfV6PV69epWkfk+fPo1BgwahWLFi6NSpE3Q6HbZu3YqWLVsa2/Tt2zfR76JDhw6l6v7ic/z4cRQuXBiurq7o2rUrgoKC0uU6RFoy1zoBouRYtGgRPv74Y5iZmaFUqVKoVq0aVq5cia+++goAsGfPHpw/fx7Xrl1DqVKlAEQ/H+Tq6mrsY//+/Th27BgePHiAPHnyAAAGDx6MP//8EwsXLsT8+fPjvPbDhw+xdu1a+Pr64tSpU2jQoAHGjx+Ptm3bGvt5U9++fdG+ffsE76dw4cLxvmdjY4NZs2ahbt26MDc3x+7du/HFF1/gwYMHGDZsGAAgJCQEBQoUiDWiVLhwYQQHB8fbt5ubG1atWoWKFSsiNDQUS5cuhaenJw4dOoTatWsnmDMRESVu//79sLGxAQCEhYXBzs4OGzduNB57m8FgwPbt27Fz504MGjQo3n5v376N1atXw9fXF9euXUOLFi0wb948fPDBB9DpdLHaf/3118bvyPgULVo0GXeWNM2aNUPbtm3h4uKC27dvY/LkyahduzYuXLiA/Pnzp/n1iDQjIowcFtF/7FlPUFCQmJmZyeXLl43HFi1aJGXKlDG+/v7776VQoUKxznVwcJDx48eLiMj06dNFKSXW1tYmYW5uLo0bN473+uPHjxcAUrVqVbly5Ura3VgyTJ48WfLmzWt8PWXKFClSpEisdtWqVZN+/folq+9GjRpJq1atUpuipl7/v635zxiDwUg8sup3UVJ0795d6tSpIwEBARIQECCnTp2Sr776SvLlyyfnzp0ztnNychILCwuxtraW3Llzi4WFhfTu3VvCw8MT7BuANG7cWO7cuZMRt2Pk6ekpvXr1StG5T548ETs7O5k9e3YaZ5W58Hso5wVHpijLWLJkCQwGA8qXL29yPCoqCvv37zeZ6pYQg8GAvHnz4sSJE7Hee/N5pLd9/vnnsLa2hq+vLypXrowPPvgAXbp0QYsWLWBhYRGr/dSpUzF16tQEc1m4cGGsBSUSUqtWLTx58gT37t1D4cKF4eDggH///RdRUVEmo1P379+Hg4NDkvuN6XvDhg3JOoeIiOKm1+tNZkW888472LJlC2bOnIlVq1YZj/fr1w9ffPEFdDodihQpAjOzhJ/AGD9+PJycnLB69WqULVsW3t7e6NKlCxo2bBjnc699+/bF6tWrE+xz+/btqFevXjLvMHlsbW1Rrlw5XL16NV2vQ5TRWExRlhAVFYVly5Zh7NixsabOjR49GosXL0b9+vVRtmxZPHjwADdu3EDJkiUBADdv3sS9e/eM7atWrYr//vsPL1++hIeHR5JzKFy4MIYOHYqhQ4fi3Llz8PX1xRdffIHw8HB89NFH6NKlC9577z3jF2Fqp/nF5fTp09DpdMYpEnXq1EFERAQOHDiAhg0bAgCuXr2KoKAgvPvuu8nu29HRMVnnEBFR0pmZmSE0NNTkmJ2dnUnRlRgXFxdMnDgREydOhJ+fH3x9fdGhQwdYWlqiQ4cO6NKlC6pXr25sr9U0v7eFhobC398fjRs3TvdrEWUorYfGGBkfyIJTK7Zs2SJKKbl9+3as9zZs2CCWlpby77//SlRUlJQvX17q1q0rp06dkpMnT4qnp6dYW1vLhAkTRETEYDBIgwYNxM3NTTZt2iQ3btyQ48ePy/Tp0+XXX39NVl6RkZGyY8cO6dKli1hZWYmPj0+a3K+IyIoVK2T16tVy8eJFuXr1qsybN0+srKxk8ODBJu3ef/99cXNzk0OHDsmJEyekZs2aUr16dYmKijK2adiwoYwYMcL4esKECbJ9+3a5du2anDt3TgYMGCBKKdm0aVOa5a8FcHoFg5FlIit+FyVV9+7dpV69ehIcHCzBwcHi7+8vEyZMEACyatUqYzsnJyeZNGlSqq/38uVL+fXXX6V169ZiYWEhmzdvTnWfbzpz5oycOXNGqlatKt7e3sbXMW7fvi1ubm6yceNGERF59uyZDBkyRPz8/OTmzZty+PBhadSokeTLl0+CgoLSNLfMht9DOS80T4ChwR96FvwCa9WqldStWzfO98LCwsTa2to4DzsgIEAaNGggFhYW4uzsLKtXr5YSJUrIN998YzwnNDRUvvrqKylRooTkzp1bHB0d5cMPP5STJ0+mOMdnz56l6ZfEypUrxcPDQ6ysrMTGxkYqVaokP/zwg0RGRpq0e/z4sXTv3l3y5s0r1tbW0qZNGwkODjZp4+TkJN27dze+HjRokDg5OYmlpaUUKFBA6tevL7t3706z3LXCLzEGI+tEVvwuSqqY55piIubv8MWLF5u0S6ti6k0PHz6Ue/fupWmfb97LmxHj5s2bAkCWL18uItHfy02aNBF7e3vJnTu3FCtWTDp06CD+/v5pmldmxO+hnBcq+s+dchKllOSkP/eQkBAULVoU69evR5s2bbROh9KRUgoiorTOg4gSl9O+iyhn4PdQzsNiKgfK7l9gv//+OywtLeHu7o67d+9i5MiRuHHjBq5evQorKyut06N0xC8xoqwju38XUc7E76Gch5v2UrYTGhqKQYMGoVy5cmjTpg3s7Oxw8OBBFlJERERElKY4MpUD8beBlF3xN4JEWQe/iyg74vdQzsORKSIiIiIiohRgMUVERERERJQCLKaIiIiIiIhSgMUUZXnu7u6YMGGC1mlkCD8/P9SoUQM6nQ4lSpTA9OnTEz3n1q1b6NSpExwcHGBlZQUvLy+cO3fOpM2LFy8wZMgQODk5Qa/Xo2TJkhg7diwiIyONbZydnaGUihUeHh5pfp9ERFkJv4fiFxgYGOd3h1IK/fr1AwBERkZi5MiRqFy5MmxsbGBvb4/WrVvj0qVLJn2FhoZixIgRKFmyJHQ6HSpUqIANGzak270SJQWLKcoxXr58qXUKqXLr1i00bdoUVapUwenTpzFt2jSMHz8e8+fPj/ecsLAwNG7cGI8fP8b27dtx6tQpODs7o2HDhrh3756x3YgRI/Dzzz9j0aJFuHLlCmbOnAkfHx9MmzbN2ObEiRMIDg42xrVr16DX69GxY8d0vW8iouwiJ34PFS9e3OS7Izg4GOvXrwcA4/fHy5cvcfz4cQwfPhwnTpzArl27EBkZCS8vLzx+/NjY12effYb169dj0aJFuHjxIvr06YOOHTti586d6XvjRAnRetdgRsYHMsmu8waDQWbPni2urq5iaWkp7u7u4uPjIwaDwdgGgMybN086d+4s1tbWUqxYMfn222+N73t6esbakf3mzZuyb98+ASCbN2+WWrVqiYWFhSxbtkwMBoNMnTpVnJycJHfu3FKmTBlZtmyZSV4A5LvvvpPWrVuLlZWVODo6yuzZs43vd+vWTZo0aRLrfqpXry4DBgxIh08q2vDhw6V48eImn8/w4cOlRIkS8Z6ze/duASDBwcHGY5GRkWJnZyfjxo0zHqtataoMHjzY5NxOnf6PvTsPj+n6/wD+PlkniyCURIgtxK5qica+N1V7W1q7aKtUW5SgJVSp/VexFEVspbpotah9i9i3UrEEsceudrJ9fn+MzNfIOskkdyZ5v57nPO3cOffczx05c+bce+4578lbb72VYtlz584VOzs7uXLlSkZOJ0uAK88zMVlNsoS2iO2QaTLSDiXn/ffflwoVKqSa5+7du6KUkj///FNERJ48eSJ2dnayfPlyo3ytW7eW+vXrm3T8rMR2KPclzQNg0uAf3QIaMBGRESNGSMmSJeXPP/+Uc+fOycqVK6VgwYIyc+ZMQx4AUqhQIZk7d65ERkbK1KlTBYBs27ZNRERu374tPj4+MmjQIImOjpbo6GiJi4szNGK+vr7y119/yblz5+TKlSsybdo0cXJykvnz58upU6dk0qRJYmNjI2vWrDE6Zv78+SUkJEROnTolISEhYmdnJytXrhQRkfDwcLGxsZHz588b9jl69KgAkKNHj6Z4vm+88Ya4uLikmi5cuJDi/vXq1ZNevXoZbduyZYsAkEuXLiW7z19//SUA5O7du0bbPTw8pGHDhobXn332mZQrV07OnDkjIiL//POPeHp6yvfff59iPNWrV5d27dql+L4W2IgxMVlPsoS2iO1Q1rdDL7t586Y4OjrKtGnTUs134cIFASA7d+4UEZH79+8LAPn999+N8nXs2FEcHBwkJiYmXcfPamyHcl/SPAAmDf7RLaABe/TokTg5Ocn69euNtk+cONHoahUA6d+/v1Ge0qVLy1dffWV47evrK8HBwUZ5EhuxJUuWGG338vKSoKAgo22dOnWSunXrGh2zc+fORnm6du0qderUMbyuVKmS0Z2d/v37i5+fX2qnLJcvX5bIyMhUU2xsbIr7lylTRoYNG2a07fjx4wJA9u3bl+w+N2/elHz58kmvXr3k3r178vTpUxkzZowAkLJlyxryxcTESN++fQWA2NnZiVLK6Pxetn//fgEg69atS/WcsxsbMSYm60lat0Vsh7KnHXrZpEmTxMnJSe7cuZNinvj4eGnVqpVUq1ZN4uLiDNvr1q0rNWvWlKioKImPj5e1a9eKTqcTAHL16tV0HT+rsR3KfcnOPIMFiUxz/PhxPHnyBO3atYNS/1vbLi4uDgkJCUZ5q1WrZvTay8vL6Hmf1NSqVcvw//fv38eVK1dQr149ozwNGjTAl19+abTN39/f6HWdOnWwevVqw+uPPvoIEydORHBwMGJjY/Hjjz+m+RCul5dXumJOzYufFYDEHyRJticqWLAgVq5ciT59+iBfvnywsbFBs2bNEBAQgPPnzxvyzZ49G+vWrcNvv/2GMmXK4ODBgxgwYACKFCmCjz76KEm5c+bMQcmSJdG8efNMnxMRkRbYDmWMqe3Qy3nnzp2Ld999F/nz508xz8cff4xDhw5hx44dsLW1Nby3dOlSBAYGolSpUrCxsYGPjw8CAwMxc+ZMo3xE2YmdKdJEYkP1yy+/oGzZsqnmtbe3N3qtlErS0KXE2dk5ybaUGoLUiIjRfl27dkVQUBDWr1+Pu3fvIjY2Fh07dky1jICAAISFhaWaJyIiAt7e3sm+5+HhgWvXrhltu3HjhuG9lDRq1AinTp3C3bt3ER8fj4IFC8LPzw+lS5cGoJ/J74svvsDixYvRvn17AEDlypVx6dIljBkzJkln6v79+1i+fDm++uqrdDWeRESWiO1Q8rKiHUq0ZcsWREZGYvHixcm+n5CQgN69e2Pjxo3Ytm0bSpUqZfR+8eLFsWnTJjx+/Bh3796Fl5cXgoKC4ObmhoIFC6Z5fKKswM4UaaJixYrQ6XSIiorCm2++mamyHBwcEB8fn2Y+Nzc3eHl5ITw83OiYO3fuTDK99549e9C3b1/D6927d6N8+fKG13nz5sW7776LefPm4e7du+jUqRNcXV1TPf68efPw5MmTVPMUKVIkxff8/f2xfPlyowZ1w4YN8Pb2RtGiRVMtF4DhKmBkZCQOHDiAefPmAQBiY2MRGxsLGxvjyT3t7OySbeCXLl2KmJgY9OzZM81jEhFZKrZDycvKdmjOnDmoUqUKateuneS9uLg4dO3aFbt378aOHTtQsmTJFMtxdnaGs7MzYmJi8Ntvv6Ft27ZJ2jCibKP1OEOm7E+wgGemRERGjRolbm5u8v3338vp06fl6NGjsnDhQhk3bpwhD5IZb96gQQMJDAw0vG7durU0atRILly4IDdv3pT4+HjDWPWXH4gNCQkRFxcXWbRokZw+fVqmTJkitra2yT74O336dDl9+rRMnz7d6MHfRLt37zY8X5TeseKZcf78eXFxcZE+ffpIRESELFu2TJycnGT69OmGPHv37hVfX1/Zu3evYVtoaKjs3LlTzp49K7/99pt4e3tLw4YNjcahN27cWMqUKSMbNmyQqKgo+fXXX8Xd3V0++eSTJHFUqVJF3nnnnaw92QwCx6ozMVlNsoS2iO2QaTLaDomIXL9+Xezt7Y0m90gUGxsrbdu2lUKFCsmePXsME3lER0fLgwcPDPk2bNggq1evlrNnz8q2bdukYcOG4uHhIZcvX866kzYR26HclzQPgEmDf3QLaMASzZ49WypVqiQODg7i7u4udevWNZr2ND2N2KFDh6R69eqGh1BfnJL25UYscUpab2/vVKeknTp1qrRq1UqcnJzEw8NDpkyZkmz8lStXlqpVq2byU0i/sLAwqVGjhjg4OEjRokVl/PjxRu8nnvfWrVsN27788kvx9PQUe3t78fb2liFDhsijR4+M9rtx44Z88MEHUrRoUXF0dJRSpUrJsGHD5PHjx0b5du/eLQBk06ZNWXaOmcFGjInJepKltEVsh0yTkXZIROTbb78VFxcXuXfvXpIyo6Kikkwvn5henNjj119/FR8fH8O/1XvvvWc0o6ElYDuU+5LS/7tTbqKUEv67p0wphSVLlqBLly6p5ouNjUWxYsUwYsQIwyrupC2lFESED3IRWQG2RSljO2S92A7lPnxmishECQkJuH79OmbNmoWYmBh0795d65CIiCgXYTtEZDnYmSIy0cWLF1GyZEl4enoiNDQ0zQd+iYiIzIntEJHl4DC/XIhDKyin4vAKIuvBtohyIrZDuQ/nkSQiIiIiIsoAdqaIMmjUqFHw8fHROgwiIsrF2BYRaYudKaIc7MyZM2jSpAk8PDzg6OiIYsWKoW/fvrh7965Rvnv37qFnz57Inz8/8uTJg7fffjvJKvdEREQZcf78eSilkqTExeNf9uTJE1SqVAlKKezcuTOboyUyDSegIMrB7Ozs8N5776F69eooUKAATp8+jX79+uHq1av4448/DPm6dOmCU6dO4c8//4ROp8Mnn3yCNm3aYM+ePYZV7omIiDJj1apVqFWrluF13rx5k8336aefomTJkjh+/Hh2hUaUYbwzRRZt69at8PPzg4uLC/LmzYsaNWrgwIEDAPQLTn/wwQcoXbo0nJycULJkSQwdOhRPnz417J84/OHnn3+Gj48PnJ2d0bZtW9y/fx+//PILypYtizx58qBDhw64d++eYb8ePXqgadOmmDx5MooUKQJnZ2e8/fbbuH37dqrxrlu3Dn5+fnBycoK3tzf69++PBw8epOt8skKJEiXQu3dvVKtWDd7e3mjatCn69euH7du3G/KcOnUKq1evxvfff4969eqhZs2aWLRoEfbt24dt27ZlWWxERNaCbZF5uLu7w8PDw5CcnJyS5Pnpp5+wZ88eTJo0KcvjITIHdqbIYsXFxaFdu3bw9/fHkSNHsH//fgwePBj29vYA9A1YoUKFsGzZMpw4cQIhISFYsmQJxo4da1ROdHQ0Fi1ahJUrV2LdunXYtWsX3n77bcyfPx8///wz1q1bh/DwcHzzzTdG++3btw87duzA+vXr8ffff+Po0aPo1atXivFu2rQJHTp0QM+ePXHs2DFDgxAYGJiu80nOuHHj4Orqmmr68ccf0/2ZXrlyBb/++isaNWpk2BYeHg4HBwc0bNjQsK1cuXLw9vZGeHh4ussmIsqJ2BaZry1677338Morr8DPzw+hoaF4eTbHs2fP4rPPPsPy5cuh0+nSLI/IIogIUy5L+n92y3f79m0BIDt27Ej3PiEhIeLj42N4HRwcLLa2tnLz5k3Dtk8//VRsbGzk+vXrhm0DBw6UV1991fC6e/fu4uLiInfv3jVs27x5swCQ06dPG8ouXbq04f369evLsGHDjOLZt2+fAJAbN25k6Hxu374tkZGRqab79++nWc4bb7whOp1OAEirVq3k8ePHhvfGjh0rnp6eSfapWbOm9O3bN92xWoLnf9ua1zEmJqa0E9ui3NMW3bx5U6ZMmSJ79+6VgwcPyvjx40Wn08mECRMMeZ49eyY1atSQGTNmiIhIVFSUAJCwsLB0x2kJ2A7lvsRnpshiubu7o0ePHmjevDmaNGmCJk2aoF27dihRooQhz4IFCzBnzhycP38ejx49QlxcHBISEozK8fLyQsGCBQ2vE4cXFCpUyGjb9evXjfarUKEC8uXLZ3jt7+8PpRROnDiBMmXKJIn3wIED2Lt3L0JCQgzb9N+rQGRkJPz9/dM8n+Q+A3d391Q/p/SYN28eHjx4gFOnTmH48OHo06cPFi1aZHg/ueeiRITPSxFRrse2KPNtUcGCBTFw4EDD69deew1xcXEYN24chgwZAgAYPnw4PD090a9fvwwfh0gLHOZHFi00NBR79+5Fo0aN8Ndff8HX1xerVq0CAPz666/o06cPOnbsiLVr1+Lw4cMYPXo0YmNjjcp4eeiCUirZbS83fMlJrYORkJCAoKAgHDlyxJD++ecfREZGolq1ammeT3LMNbTCy8sL5cqVQ5s2bbBw4UIsXrwYJ0+eBKBvvG/duoX4+HijfW7cuAEPD480yyYiyunYFpl3yDkA1K5dG/fu3TN0Hjdt2oQ1a9bAzs4OdnZ2huneGzZsiJYtW5pUNlF24p0psnhVqlRBlSpVMGjQILRt2xYLFixAmzZtsH37dlSrVs3oatf58+fNdtwTJ07g/v37cHNzAwDs3r0bAFC+fPlk81evXh0RERFprveR0vkkp0+fPnj33XdTLa9w4cJpnYqRxIY68eFof39/xMTEYPv27WjcuDEA4PTp07h48SLq1KljUtlERDkV2yLztkWHDh2CTqdD/vz5AQC//fYbnjx5Ynj/6tWraNGiBUJDQ1G/fn2TyibKTuxMkcWKiorCnDlz0Lp1axQtWhRRUVE4dOgQ3nvvPQCAr68v5s+fj1WrVqFSpUpYvXo1fvnlF7MdXymFbt264ZtvvsGdO3fw8ccfo3Xr1ik2UKNHj0aLFi0wZMgQdOnSBU5OTjh58iT++usvzJ07N83zSU5mh1YsX74cAFC1alU4OTkhIiICQUFBqFq1KqpUqQJAP9lEy5Yt0bdvX8ybN88wNXrNmjXRoEGDDB+biCgnYFuU+bZo0aJFsLOzQ7Vq1WBvb48NGzZg1KhR6Nu3LxwcHAAApUuXNtrH1dUVAFCyZEkUL148w8cmymrsTJHFcnZ2xunTp/HOO+/g1q1bKFSoEDp06IDRo0cDAD766CMcO3YMPXv2RFxcHN588018/fXXZhtvXatWLfj7+6NZs2a4d+8eAgICMHfu3BTzN2nSBJs2bcKoUaMwc+ZMKKVQqlQptGvXLl3nkxXs7e0xYcIEnD59GrGxsShatChat26NoUOHwsbmf6N8ly5dis8//xxvvfUW4uLi0KJFC8ycOdMoDxFRbsS2KPOUUvj2228RFRUFGxsblC5dGpMnT8aHH36YZcckyi4q8aFEyj2UUsJ/99T16NEDly9fxqZNm7QOhUyglIKIcNYMIivAtihtbIusD9uh3IeXnYmIiIiIiDKAnSkiIiIiIqIM4DC/XIhDKyin4vAKIuvBtohyIrZDuQ/vTBEREREREWUAO1NkdgsXLoSdnXVMFNmjRw8opaCUwuzZs7UOh5Lx9OlTw7+RtfxdEZG22A6RObEdotSwM0W5Xr169RAdHY3u3bsbts2dOxcNGzaEm5sblFK4fPlykv0Sv1hfTqau1L5jxw60atUKRYoUgVIKS5cuTZJn1apV8PPzg7u7O5ycnFCuXDlMmjQJGRkiExERgcaNG8PJyQkeHh4YPHgw4uLiTC7nypUraNeuHVxdXeHu7o7evXvjwYMHJpdz79499OzZE/nz50eePHnw9ttv49q1a4b3dTodoqOj8d1335lcNhGRNUiuHRIRjB07FkWLFoVOp8Prr7+OvXv3Zqj8H374AT4+PnB0dESVKlWwdu1ak8tIT1t1/PhxdOjQASVKlIBSCt98802G4n3RiRMn4OLikqQTc+bMGTRp0gQeHh5wdHREsWLF0LdvX9y9e9fkY7AdosxgZ4pyPQcHB3h4eMDJycmw7fHjx3jjjTcwfPjwFPeLjo42Somr0nfq1Mmk4z969AhVq1bFjBkzUsyTP39+DB48GGFhYYiIiMCXX36JUaNGYdq0aSYd68GDB2jWrBnc3Nywd+9ezJ8/H6GhoRg2bJhJ5cTHx6Nly5a4fv06tm3bht9//x1bt25Fr169TCoHALp06YLw8HD8+eef2LJlCy5duoQ2bdoYdRQ9PDyQN29ek8smIrIGybVDU6dOxbhx4zB58mQcPHgQFSpUQPPmzXH16lWTyv7tt9/w8ccfY8CAAfjnn3/QqlUrtG3bFocPHzapnPS0VY8fP0apUqUwfvx4eHh4mFR+cp4+fYqOHTuiUaNGSd6zs7PDe++9h7///huRkZEIDQ3F5s2b0bNnT5OPw3aIMkVEmHJZ0v+zJzVnzhzJnz+/PH361Gj70KFDpWLFiiIikpCQIL1795ZSpUqJTqeTEiVKSFBQkDx58sSQPzQ0VGxtbVN8LSISFRUlACQsLMyw7dSpU9K6dWtxc3MTd3d3adWqlZw5cybZWM2le/fu0qRJkxTf37p1qwCQS5cupVnW8OHDxd3d3eizMBUAWbJkSbrytmvXTtq2bWtS+d9//704OTnJ/fv3k2x7+PBhusv5+++/BYBERkYm2Xb27Nl0l3Py5EkBIJs2bTJsO3HihACQLVu2GOVN7u/oZc//tjWvY0xMTGmn5NoitkN6CQkJ4unpKV999ZVhW3x8vBQpUkRGjhxpUvl+fn7SpUuXJNu6deuW4ZjT01YVL15cxowZk+FjiIh89NFHEhgYmK7vfxGRadOmSb58+Uw6Btshpswm3pkig3fffRePHz/GmjVrDNtEBMuWLUPXrl0NrwsVKoRly5bhxIkTCAkJwZIlSzB27NhMHfvatWuoW7cuSpQogfDwcISFhSFfvnxo2rQpHj9+nOJ+AQEBcHV1TTVdvHgxU7GlR2xsLBYsWIDu3btDp9Nl6bFEBAcOHEB4eHiyV+tSs2vXLrz++uvIkyePYVuLFi3w5MkTk65S7tq1C6VKlYKPj49hW9OmTWFra4tdu3alu5zw8HA4ODigYcOGhm3lypWDt7c3wsPD010OEeUMbIf0oqKiEB0djebNmxu22djYoGnTpiZ9N8bExODgwYNG5QD6731L/4795ZdfsG3btnSPwLhy5Qp+/fVXk9tFtkOUWXyKjgzy5cuHVq1aYcmSJWjfvj0AYPv27bh8+TI6d+4MQP9l/mKDVaJECZw/fx4hISEYM2ZMho/9/fffw8fHx+hLc8GCBShYsCDWrFmDd955J9n95s2bhydPnqRadpEiRTIcV3qtWrUK165dw4cffpilx3F1dUVMTAzi4+MRHByMTz/91KT9r127hsKFCxttS3wdHR2dqXLs7Ozg7u5ucjkFChSAra1tkphMKYeIcga2Q3qJz+sk93194MCBdJdz69YtxMXFJVuOJX/HRkVFoV+/fli/fj1cXFxSzRsQEIBt27bh6dOnaNWqFX788UeTjsV2iDKLnSky0q1bN7z99tu4c+cO3N3dsWTJEjRq1AhFixY15FmwYAHmzJmD8+fP49GjR4iLi0NCQkKmjnvgwAHs378frq6uRtsfP36MyMjIFPfz8vLK1HHNZc6cOWjQoAHKlSuXpcc5cuQInjx5gt27d2Po0KEoUqQIevfubVIZSiW//EVK200pR0Q0K4eIcga2Q//z8vdgRr8bzVVOdoiPj0enTp0wdOhQVKtWLc388+bNw4MHD3Dq1CkMHz4cffr0waJFi0w6Jtshygx2psjIG2+8gbx582LFihXo2bMnfv31V0yfPt3w/q+//oo+ffpg/PjxaNCgAdzc3LBy5UoMHTo0xTJtbJKOJo2NjTV6nZCQgCZNmiT7YKu7u3uKZQcEBCAsLCzVc4qIiIC3t3eqeTLjzJkz2Lx5M5YtW5Zlx0iUOKyucuXKuHfvHr766iuTOlMeHh64cuWK0bbr168b3jOlnM2bNxtti4uLw927d00u59atW4iPjze6Knjjxg2zPLxMRNaH7dD/vo+vXbuGMmXKGLab+t1YsGBB2NnZGc1Ml5FystODBw+wb98+HDx4EEOGDAGg79gkJCTAzs4O48aNM2wH/teZLVeuHIoUKYJatWph2LBh6b64yXaIMoudKTJib2+PTp06YcmSJXB3d0d8fLxhqAWgH25RrVo1DBw40LDt/PnzqZZZqFAhxMfH4/r164ahBi8/n1O9enUsXrzYMAVselnCML+5c+eiQIECRp9TdkhISMDTp09N2sff3x8DBw7Ew4cPDVdfN2zYACcnp3RdAXyxnDFjxuDs2bMoXbo0AGDz5s2Ij4+Hv7+/SeXExMRg+/btaNy4MQDg9OnTuHjxIurUqWPCmRFRTsF2CChZsiQ8PT2xceNG1KtXD4D+O3/z5s0IDAxMdzkODg6oXr06Nm7caHjmDNB/71vqd6ybmxuOHTtmtG3VqlUIDg7GkSNHUu3gJN6dNKVtZDtEmab1DBhM2Z+Qwmx+ifbt2ycApGrVqklmAJo+fbo4OTnJH3/8IWfOnJHvvvtOChQoIC+W+fJsN7dv35Y8efJIjx49JDIyUtauXSuVKlUymkXp2rVr4uHhIQEBARIeHi7nzp2Tbdu2yYABA+T06dOpxpsZKc3mFx0dLYcPH5YffvhBAMj69evl8OHDEh0dbZTv2bNn8sorr8jgwYMzHMODBw/k8OHDcvjwYQEgY8eOlcOHDxvNlDdu3DhZt26dnDlzRk6dOiXz58+XvHnzSv/+/U061v3798XT01PatWsnR48elTVr1kjBggVl0KBBJpUTFxcnVatWlTp16siBAwdkx44d4uPjIx06dDCpHBGRli1biq+vr4SFhcn+/fvFz89PatasKfHx8Ub5OIsSE1POSqm1RWyHRCZNmiQuLi7y008/yfHjx6V3797i5uYmV65cMan8X375RWxtbWXWrFly4sQJGTFihNjb28uhQ4dMKic9bdWzZ88MeTw9PaVfv35y+PBhOX78uEnHelly3//Lli2TZcuWyfHjx+XcuXOyevVqqVixolStWjVJ+5EWtkNMmUmaB8CkwT96Gp0pEZHy5csbOhEviomJkQ8//FDy588vefLkkY4dO8rMmTNTbcRERFavXi3lypUTnU4nderUkbVr1yaZkvbcuXPSsWNHcXd3F0dHRylVqpQEBgbKzZs304w3o1JqxIKDgwVAkhQcHGyUb/ny5aKUMmpMXhQaGioAJCoqKsUYEqdffzk1aNDAkGfkyJFStmxZcXJyEjc3N6lWrZqEhIRIbGysIU/iNL+hoaGpnvPx48elUaNG4ujoKK+88op88cUXRuWISLLn+rJLly5JmzZtxNnZWfLlyye9evUymnJdRD81bvfu3VMt5+7du9K9e3fJmzevuLi4SPv27ZN0WkXYiDEx5bSUVluU29uhhIQEGTNmjBQpUkQcHR3Fz89P9uzZY5Qnsa1Ky5w5c6RUqVLi4OAglSpVktWrVxu9b662KrEdejkVL148SZ602qqX43v53/OXX36RGjVqiJubmzg5OUmZMmVk0KBBSf6t2A4xZXXSPAAmDf7R0/HFm1uktc5UZo0YMUIqVKiQpLOSFTZv3iw6nc6kdZ6Sc/bsWVFKyc6dOzNVzqNHj0Sn08myZcsyVU4iNmJMTDkrsS3Sy0w71LVrV2nWrFmmY7DGtio92A4xZUfiM1OU623btg2urq6YNm2aSWPR02P16tWYMWMG7OyyvqqtXr0aQUFBKFWqVKbL6datW6bHim/atAl+fn547733MlXOs2fPUKBAAcTFxWWqHCIiS5WRdijxGaqtW7dm+vjW2FalB9shyg5K34mm3EQpJfx317tx4wbu378PQP+Aspubm8YR0ctEBGfPngWgn742ccKL5CilICKcy5bICrAt0mM7ZPnYDlFq2JnKhdiAUU7FRozIerAtopyI7VDuk3ThBSIiIiIiIkoTO1Nk1Ro2bGjSorXWQqfTYeHChVqHQUREaWA7RJS7sTNFRAajR4+Gra0tvv76a61DISKiXOLkyZNwdnZO0nmLiIiAk5MTFixYoE1gROnAzhSRFXr27JnZy0xISMCCBQswYsQIzJ8/37CSPBER0cvM2Q6VK1cO48ePx6effoqoqCgAQGxsLLp06YIWLVqgV69eZjsWkbmxM0UWTUQQEhKC8uXLw9HRER4eHujRo0eK+Tdu3IiGDRvC3d0defPmRf369bFnzx6jPLNmzULZsmWh0+lQsGBBNGvWDI8ePQIAXLhwAa1bt4a7uzucnZ3h6+ub5cMcDh48iFq1akGn06FChQpYv3690fvnz5+HUgpLlixBixYt4OzsjHHjxmHbtm1QSuHy5ctG+ZVSWLp0qeH1gQMHULNmTUP5GzZsSHb4xrp166CUwogRI6CUShIHEVFuxHYoe9qh/v37o3bt2ujWrRsSEhIwcuRIXL16FT/88EOWnjtRZnGdKbJoI0aMwLRp0zBp0iQ0adIE9+7dw5YtW1LM//DhQ/Tt2xdVq1ZFXFwcpk2bhoCAAERGRqJgwYI4cOAA+vfvj9DQUDRo0AD37t3D9u3bkTijVL9+/fDkyRNs2bIFefPmRWRkZKpX38LCwhAQEJDqOXTp0gWzZ89O9r1Hjx7hzTffhJ+fH+bPn487d+7g888/R2xsbJK8w4YNw4QJEzB79mwopXD+/PlUj5tYfsuWLeHv748FCxbg7t27GDhwYLLlz507F126dIGtrS26du2KuXPnpnluREQ5Hduh/8nKdkgphdDQUFSuXBldunTBzz//jFWrVuGVV15J8xhEmtJ61WCm7E+wklXnHzx4IDqdTqZMmZJingYNGkhgYGCK7yckJEiBAgVk6dKlIiLy22+/iZubmzx48CDZ/JUrV5avv/463TE+fvxYIiMjU03Xr19Pcf85c+aIi4uL3L1717Bt8+bNAkBCQ0NFRCQqKkoAyJgxY4z23bp1qwCQS5cuGW0HIEuWLDGU7+rqKvfu3UuyX2L5IiJXr14VOzs7OXXqlIiIREZGir29vVy9ejXdn4UlAFeeZ2KymmQNbRHboVARyZ52KFFISIgAkO7du6f7M7AkbIdyX+KdKbJYERERePr0KZo1a5bufS5cuICRI0ciPDwcN27cQEJCAh4/fowLFy4AAJo1a4YSJUqgVKlSaN68OZo2bYp27dohb968AIDPPvsMH3/8MdatW4fGjRujVatWqFWrVorHc3Jygo+PT4bP8cSJE6hQoQLy5ctn2Obv7w+lki5RkVocqZVfvnx5o0Uga9eunaT8BQsWoHr16ihbtiwAwMfHBzVr1kRoaCiGDx9u8nGJiHICtkPGsrIdAvTPSS1cuBDOzs7Yt28fnj59Cp1OZ/IxibITn5miHKVly5Y4f/48Zs6ciT179uDIkSPw9PRETEwMACBPnjw4ePAgVqxYgZIlS2LixInw9fU1DFUIDAxEVFQUevXqhTNnzqBevXoICgpK8XhhYWFwdXVNNfXp08fk89Bf3DLm7Oxs9NrGxiZJ3uSGZaRVvohg/vz52LdvH+zs7Axp9+7dmDdvXrKxEBFR8tgOmd4OJRo1ahSuXLmCQ4cO4b///uPFPLIKvDNFFqtChQpwdHTE5s2bUbly5TTz3759G8ePH8fatWvRokULAMDVq1dx7do1o3x2dnZo1KgRGjVqhJEjR8LLywu///47BgwYAADw8vJCYGAgAgMD8d133yE4OBgTJkxI9pg1atTAkSNHUo3rxatxyZ3jvHnzcP/+fUO+3bt3p3muAFCoUCHDORYrVgwAcPjwYaM85cuXT1L+3r17jfJs3LgRly5dwu7du+Hi4mLY/uzZM/j7+2PTpk0mXZUlIsop2A6lzlztUOIxJ0yYgN9//x2+vr6YP38+3nrrLbRp0wYNGjRIVzxEWmBniiyWq6srBg4ciJEjR8LZ2RmNGzfGgwcPsGXLFgwaNChJ/vz58+OVV17BDz/8gNKlS+P27dsYMmQInJycDHlWr16Ns2fPon79+ihQoAB27NiBu3fvoly5cgD0wysCAgJQtmxZPHz4EGvXrjW8l5zMDq94//338dVXX6Fbt2745ptvcOfOHXzxxReGq32p8fHxQfHixTFq1Cj83//9H27evJnkKl7nzp0xYsQI9OzZE6NHj8Z///2HwYMHw8bGxjDEYu7cuWjcuDH8/PySHKN58+aYO3cuO1NElCuxHUqdudqhhw8fomvXrujVqxdatWoFAAgICEDv3r3Ro0cPHD16FHny5MnwORJlKa0f2mLK/gQreOg3UUJCgkyePFl8fHzE3t5ePDw8pGfPnob3X37wd9u2bVKlShVxdHSUsmXLyooVK8TX11eCg4NFRCQsLEwaNmwo7u7uotPppFy5cjJr1izD/p988on4+PiITqeTAgUKSPv27SUqKipLz3H//v1So0YNcXBwEF9fX1m7dq04OjomefA3LCwsyb579uyR1157TXQ6nVStWlV27txp9OCviMi+ffsM5ZcvX17Wrl0rNjY28tNPP8n169fF3t5e5s2bl2xsixYtEnt7+1QfXrYk4IO/TExWk6ylLWI7lLXtkIhI7969pXTp0kkm5Xj48KGULl061Qk+LA3bodyXlP7fnXITpZTw3z332r9/P2rVqoWDBw/itdde0zocs1JKQUSSPtVMRBaHbVHuxXaIchJ2pnIhNmC5y+LFi+Ht7Y0SJUogMjISAwYMgIODAw4dOqR1aGbHRozIerAtyj3YDlFOxmemiHK4W7duYdSoUbh69SoKFiyIxo0bY/LkyVqHRUREuQTbIcrJeGcqF+LVQMqpeEWQyHqwLaKciO1Q7sN1poiIiIiIiDKAnSkiIiIiIqIMYGeKiIiIiIgoA9iZIiIiIiIiygDO5pcL6XS660qpwlrHQWRuOp3uutYxEFH6sC2inIjtUO7D2fwox1NKuQI4BqCPiKzXOp6sppTqCaAfgNoiEqd1PEREBCil+gLoDKCeiCRoHU9WUkrpAPwDIEhE/tA4HKIsxc4U5XhKqakACopIN61jyQ5KKQVgI4C/RWSK1vEQEeV2SqliAA4BaCAiEVrHkx2UUvUBLANQUUTuaR0PUVZhZ4pyNKVULQB/AqgkIre0jie7KKVKA9gLwE9EzmodDxFRbvX8AtefAPaLyNdax5OdlFKzAUBE+mgdC1FW4QQUlGMppRwAzAMwMDd1pADgeQdqPIA5zxtyIiLSxrsASkL/nZzbBAF46/ldKqIciZ0pyskGA7gMYLnWgWjkOwD5AHTXNgwiotxJKVUA+u/i3iISo3E42e758L5PAPzw/DkqohyHw/woR1JKlQOwE8BrInJR63i0opR6FcB6AFVEhDMMERFlI6VUKID7IvKZ1rFoSSn1C4BTIvKV1rEQmRs7U5TjKKVsAGwH8LOITNc6Hq0ppb4FUEpEOmodCxFRbqGUagb9UPNKIvJA63i0pJTyAHAUQFMROap1PETmxGF+lBN9CP0aarO0DsRCfA3gNaVUa60DISLKDZRSLgDmQL8kR67uSAGAiFwDMAzAfKWUrdbxEJkT70xRjqKU8gJwBEBDETmucTgWQynVCMBi6Keova91PEREOZlSajIADxHponUsluL5ZEibAfwlIv+ndTxE5sLOFOUYz7+o/wBwRESCNQ7H4iilfgAQIyL9tI6FiCinUkrVALAaQGURual1PJZEKeUDYA+AmiISpXU8RObAYX6Uk3QAUAbAOK0DsVCDAbRVStXROhAiopxIKWUPYD6AL9iRSkpEzgCYCGA2l+2gnIKdKcoRlFLuAEKgn372mdbxWCIR+Q9AfwDzOEUtEVGW+AJANIAftQ7Egk0F8AqArloHQmQOHOZHOYJSaj6AJyLyidaxWDql1EoA/4rISK1jISLKKZRSZQHsAlBDRM5rHI5FU0q9BuBv6IdC3tA6HqLMYGeKrJ5SqgmAUOinn+XkCmlQShWBfpKOJiJyTONwiIis3vMlObYA+F1EpmkdjzVQSk0EUFRE3tc6FqLM4DA/smpKKWfop5/ty45U+ojIVQBfQr8iPaeoJSLKvN4AnADM0DoQKzIKQC2lVEutAyHKDN6ZIqv2/MpWMRF5T+tYrAmvohIRmcfzu/3/AGjMu/2mUUo1xv9GluT69bjIOrEzRVaLY64zh+P7iYgy7/lzqMdFZITWsVgjPvNM1o6dKbJKz6ef3QfgOxFZpHU81kopNQxAAwABwi8DIiKTKKXaAxgL4FXOJJsxSqn8AI4DeFtEdmkdD5Gp+MwUWasBAG4CWKx1IFZuMgAPAHwAmIjIBM87AdMBfMCOVMaJyF0An0K/bIej1vEQmYp3psjqKKXKANgNrqBuFkqpGgBWQz9ckotMEhGlg1LqBwCxItJX61is3fMFfH8HcERERmkcDpFJ2Jkiq/L8C3cLgL9EZKrW8eQUSqnJADxEpIvWsRARWTqlVCPoR0ZU5Eyy5qGU8oJ+2Y6GInJc43CI0o3D/Mja9ALgCiBE60BymGAA/kqpAK0DISKyZEopJwBzAfRjR8p8ROQKgBHQD/fjsh1kNXhniqyGUsoT+ulnm4nIP1rHk9MopZoCmAf9FLUPtY6HiMgSKaXGAygpIh21jiWneb5sxzYAP4sI1+wiq8DOFFkNpdSvAE6JyJdax5JTKaVCAdwXkc+0joWIyNIopaoBWA/9M6bXtY4nJ1JK+QLYCaC6iFzUOh6itLAzRVZBKdUOwHgAVUXkqdbx5FRKqQIA/gXQTkT2aB0PEZGlUErZAdgLYIaIhGodT06mlPoSgD+At7hsB1k6PjNFFk8plQ//m36WHaksJCK3AXwO/Zh1B43DISKyJJ8DuAtgobZh5AqTABQD0EnrQIjSwjtTZPGUUrMBQET6aB1LbvB8xsQ/AewTkTFax0NEpDWlVGno70r5ichZrePJDZRStQCsgn5I5S2t4yFKCTtTZNGUUvUBLIN++tl7WseTWyiligI4DKC+iJzQOh4iIq08v8C0CcDfIjJZ63hyE6XUVAAFRaSb1rEQpYTD/MhiKaV0AH4A8Ak7UtlLRC5DP136vOezKxER5VY9AOQD8J2mUeROIwDUU0q10DoQopTwRxJZshEAjonIH1oHkkvNfv5fDq8kolxJKeUBYAKAQBGJ0zqe3EZEHkHfBs1WSrlqHQ9RcjjMjyySUqoqgI3Qz94XrXU8uZVSqgKA7QBeE5FLWsdDRJSdlFI/AzgrIsO0jiU3U0otBnBLRAZqHQvRy9iZIovzfOXzPQBmi8h8rePJ7ZRSIwHUBNCaU9QSUW6hlGoDYDKAKiLyROt4cjOlVEHol+1oLSL7tI6H6EUc5keW6DMADwAs0DoQAqBf36skgHe1DoSIKDsopfICmAHgQ3aktPd8Nr8B4LIdZIF4Z4osilKqFIB9AF4XkUit4yE9pVRtAL8DqPR8LSoiohxLKTULgL2IfKB1LKT3fFbF1QB2ichYreMhSsTOFFmM51+U6wFsFpEJWsdDxpRS0wC4iUhPrWMhIsoqSql6AH6CfkmO/zQOh16glPIGcAhAXRE5qXU8RACH+ZFl6QqgIIApWgdCyfoSQCOlVDOtAyEiygovLMnRnx0pyyMiFwGMBvADl+0gS8E/RLIISqlCACYB6M3pZy2TiDwE8DGAOUopF63jISLKAl8CiBCRlVoHQimaBcAWwIdaB0IEcJgfWQil1HIAl0RkiNaxUOqUUksBXBORL7SOhYjIXJRSlQFsgX5Jjqtax0MpU0pVBLANwKsickXjcCiXY2eKNKeUegv6leWriMhjjcOhNCilXgFwDMBbInJA63iIiDLr+ZIcuwDMF5G5WsdDaVNKjQJQDUBbLttBWuIwP9KUUioP9LfsP2RHyjqIyE0AXwCYr5Sy1zoeIiIz6A/gCYB5WgdC6fYtgDIA3tY6EMrdeGeKNKWUmgHASUQCtY6F0u/5zItrAewQkW+1joeIKKOUUiUAHADgLyKnNQ6HTKCUeh3Ab9Av23FH63god2JnijSjlKoD4BfwS9AqKaWKAzgI/gAhIiv1/MLQ3wC288KQdVJKTQfgzIuypBUO8yNNKKUcoZ9+9jN2pKyTiFwAMAbAXE5RS0RWqjMADwCTtQ6EMmw4gGZKqSZaB0K5E38AkVaGATgN4FetA6FMmQHACUBvrQMhIjLF88l0pkC/JEes1vFQxojIAwB9oV+2w1nreCj34TA/ynac0jRn4XTCRGSNlFI/AojmMg85A5dYIa2wM0XZ6vn0szsBLBKR2VrHQ+ahlPoa+mff2msdCxFRWpRSb0J/Z72yiDzSOh7KPKVUIeiX7QgQkUNax0O5B4f5UXbrCyAWANfxyFnGAiivlGJniogs2vMlOb6HfkkOdqRyCBG5AWAwuGwHZTPemaJso5TyBnAIQB0ROaV1PGReSqm6AFZAf4fqrtbxEBElRykVAiCPiPTUOhYyr+ezM64HsFlEJmgdD+UO7ExRtnj+BbcawC4RGat1PJQ1lFKzANiLyAdax0JE9LLn6xKthP6iz22t4yHzU0qVBLAfwOsiEql1PJTzcZgfZZf3ABQDMEnrQChLDQXwhlKqkdaBEBG9SCnlAGAegM/Zkcq5RCQK+qHnc59fyCXKUuxMUZZTShUEMBX66WdjtI6Hso6I3AfQD/pGzEnreIiIXjAUwDkAP2sdCGW5EAAuAHppHQjlfBzmR1lOKbUYwC0RGah1LJQ9lFIrAESJyFCtYyEiUkpVALAdwGsicknreCjrKaWqAtgI/bId0VrHQzkXO1OUpZRSLQDMhn58OmdNyiWUUoWhn6K2hYgc1joeIsq9lFI20C/JsVREZmkdD2UfpdRYAL4i8rbWsVDOxWF+lGWUUq7Qd6T6sCOVu4jIdQBBAOYppey0joeIcrWPAQj07RHlLmMAVFZKtdM6EMq5eGeKsoxS6v8AFBCRblrHQtnv+YO/GwGsE5HJWsdDRLmPUqoYgMMA6otIhNbxUPZTStUHsAz6ETL/aRwO5UDsTFGWUEr5AVgFoCJnTcq9lFKlAewF4CciZ7WOh4hyj+cXdP4CsE9EvtY6HtKOUmo29L95P9I6Fsp5OMyPzO759LM/ABjAjlTu9rwDNR6copaIsl9HACWg/w6i3C0IQEulVAOtA6Gch50pygpDAFwC8JPWgZBF+A5AXgA9tA2DiHILpVQBAP8HLslBAETkHoBPAPyglNJpHQ/lLBzmR2allCoH/axJr4nIRa3jIcuglHoVwAYAVUTkmsbhEFEOp5RaCOCeiHymdSxkOZRSvwCIFJHhWsdCOQc7U2Q2z6ef3Q5ghYjM0DoesixKqW8BlBaRd7WOhYhyLqVUcwBzoZ9w4KHW8ZDlUEp5ADgKoJmI/KN1PJQzcJgfmdOHAGwBfK91IGSRvgZQTSnVRutAiChnUkq5AJgD4GN2pOhlz0dGDAOX7SAz4p0pMgulVFHop59tKCLHtY6HLJNSqiGAJdBfMb6nbTRElNMopaYAKCwiXbSOhSzT88mQNgNYLSJTtY6HrB87U5Rpz7+YVgE4JCKjNA6HLJxSai6AeBH5WOtYiMi6KaX6AbgsIquUUjUBrIb+Ys1NjUMjC6aU8gGwB0AtETmndTxk3TjMj8zhbQA+AL7VOhCyCkMAtFZK1dM6ECKyenUBuCql7AHMAzCIHSlKi4icATARwBwu20GZxc4UZYpSyh3ANACBIvJM63jI8j1fgb4/OEUtEWWeF4DLAAYDiAbwo7bhkBWZCqAAgG5aB0LWjZ0pyqxJAH4Tkd1aB0LWQ0RWAogA8KXWsRCRVSsKwAHAQAAfAaiolPrt+XO8RCkSkTgAvQFMUkoV1joesl58ZooyTCnVBEAogIoi8kDreMi6KKWKAPgHQBMROap1PERkXZ4Pz3oCYD/0z0p5AngfwCgAs0UkQbvoyFoopSYC8BaRTlrHQtaJd6YoQ5RSztCv4/ExO1KUESJyFcBw6KeotdU6HiKyOgUBxAMoAuAzAM7QX9ybxY4UmWAUgBpKqVZaB0LWiZ0pyqhRAPaKyBqtAyGrNh/AY+ifoSIiMkV56DtQjwG0FZEPOfkEmUpEHkO/TuYspZSb1vGQ9eEwPzKZUuo1AH8DqCwiN7SOh6ybUqosgF0AaojIeY3DISIroZQqAKAPgG95J4oySyk1H8ATEflE61jIuvDOFKVJKVVaKTXy+f/bQ383YTA7UmQOInIawBS8MEWtUmrK8x9KRETJEpHbIjKWHSkyky8AtFdK1QH0F46VUp9qHBNZAXamKD1qAKj8/P8HArgBYIl24VAONBlAYQBdnr+uDf0QHiIioiwnIncBfAr9c7yO0E+b3lrbqMga2GkdAFmFogCuKKXKQL+WR03h+FAyIxGJVUr1BrBGKbUewBXo/+6IMsTJyena06dPOd0x5Tg6ne76kydPPLSOI4f6DfqLesMBrADbIUoHdqYoPRIXRZwLYCyAq0qpwQAiOAEFZZZSqgsAVwA/QH/H8/+g/3vz0jIusm5Pnz4tzGs+lBNxTSTzU0q5QD/cfDKAfgCOAFgLtkOUDhzmR+nhBaAsABcApwAcA1AP+jWCiDJrP/Rrw+wDsAbA6wDcwEaMiIiyx2MAZwDsAfAxgK8BTAMAzvBHaeFsfpQmpdQ+AL4ADgAoDuAz3pEic3o+8cR7ACZB31mvBWCbiLTXNDCyWkopjkamHEkpBRFRWseREymlvKBvh+oAeAT9c1ONRCRC08DIovHOFKVHRQBOALYCqMSOFJmb6C2DftKJYwDyQj8JBRERUbYQkSsi8j6A7tA/ClMIQFVtoyJLxztTlCal1A8AJj2fwpooyyml/AB0EpEBWsdC1ol3piin4p2p7PF8KZjJAEJF5IjG4ZAFY2eKiIhyHHamKKdiZ4rIsnCYHxERURYpUaIEvvnmm0yXo5TC0qVLzRCRdnLCORARvSxbpkbneh9kjSxpLQ/WIbJGllSHssKtW7cwfvx4/Pnnn7h48SLy5cuHChUq4MMPP8S7774LGxvLvV75xhtvwMPDAwsXLtQ6FE3FxMQgKCgIP/74Ix4+fIi6detixowZKFu2bIr7bNu2DY0aNUqyfePGjWjatGlWhqs5tkVkbbKjHcqWzhTX+yBrZElrebAOkTWypDpkbpcvX0adOnWQL18+fPvtt6hevTpiYmIQFhaGUaNGoXbt2ihRooTWYWa5hIQExMfHw97ePsuPJSK4cOGCWT/XQYMGYcWKFVi0aBGKFCmCr776Cs2aNcOJEyfg7Oyc6r6HDh2Cp6en4bW7u7vZ4rJUbIvI2mRHO2S5l82IiIgs1Mcff4xnz54hLCwMHTp0QIkSJVC2bFkEBgbiyJEj8PD434XQmJgY9O/fH/nz50fhwoXxxRdfID4+3uj9L774Ap6ennB0dMSrr76K1atXp3r8Bw8eoF+/fvD09ISrqyv8/PywefNmozI/+eQTFClSBI6OjihatCg+/fRTAECPHj2wfv16LFq0CEopKKWwbds2AEB0dDS6du2KAgUKIG/evGjcuDEOHTpkKHfhwoWws7PDxo0bUbVqVTg6OmLnzp0ZOof0OnXqFEaMGIFSpUphwADzzUlz//59zJ07F99++y0CAgJQtWpVLF26FNevX8eKFSvS3P+VV16Bh4eHITk4OJgtNiKyHuxMERERmeDOnTtYu3Yt+vfvDze3pOt56nQ66HQ6w+uQkBAULVoU+/btw//93//hu+++w48//mh4f+jQoVi4cCFmzZqFo0ePonnz5mjbti2OHTuW7PFFBG+99RZOnTqFlStX4p9//kHHjh0REBCAo0ePGo75xx9/4Mcff0RkZCR+/fVXVKpUCQAwbdo0NGrUCO+++y6io6MRHR0Nf39/PHnyBI0aNUJsbCw2btyI/fv3o0aNGmjcuDGio6MNx09ISMDw4cMxbdo0nDx5ElWqVDH5HNJy69YtzJgxA35+fqhQoQLCwsIwfPhwhIaGGvKEhYXB1dU11dSnT58Uj3HgwAHExMSgefPmhm158+aFn58fwsPD04yxbt26KFy4MOrXr48///wzQ+dJRDmAiGR5gmEZGSLr8fzvNlvqSFqJdYiskZZ1KCvrzN69ewWArFy5Ms28xYsXl1atWhlta9KkiXTp0kVERB4+fCgODg7y/fffG+V5/fXXDXlE9J/lkiVLRERky5Yt4ujoKPfv3zfa580335S+ffuKiEj//v2lSZMmKcbVokUL6d69u9G2BQsWSLFixSQuLs5oe4UKFWTixIkiIhIaGioAJCwszPB+Rs4hOc+ePZOff/5ZWrVqJfb29lK5cmWZMGGCXLp0Kdn8jx8/lsjIyFTT9evXUzzejz/+KADk2bNnRtvfeecdefPNN1Pc7+TJkzJ79mw5cOCA7Nu3T4KCgkQpJT/99FOK+5hTTq1XRFkhO+pLtjwzRURElFPo2+f0q1atmtFrLy8vw52es2fPIiYmBvXq1TPKU79+fWzcuDHZ8hLvqLz4vA4APHv2zDAxQo8ePdC8eXP4+vqiWbNmaNGiBd58803Y2tqmGOeBAwdw9epV5M2b12j706dPERkZabStZs2ahv/PyDkkZ9euXXj33XdRqFAh/P7772jZsmWq+Z2cnODj45Pu8tNLRKBUyjOP+/r6wtfX1/C6Zs2auH37NsaNG4eOHTuaPR4ismzsTBEREZmgTJkysLGxQUREBNq1a5dm/pcnZ1BKISEhIcm2F6XWYUtISEDevHmxf//+JO85OTkBAF577TVERUVhw4YN2Lp1KwIDA1G2bFls27YNdnbJN/0JCQkoX748fv/99yTvvTic0dbWFo6OjknymHIOyfHz88OiRYuwZMkStG3bFq+//jo6d+6Md955J9nJHcLCwhAQEJBqmV26dMHs2bOTfS/xubbr16+jWLFihu03btxAmTJlTIq9du3anPadKJdiZ4qIiMgE7u7uCAgIwIwZM5J9burp06cAYPTcVEp8fHzg4OCA8PBwVKhQwbB9586dqFixYrL7VK9eHf/99x+ePXuWYh4AyJMnDzp06IAOHTqgd+/eqFatGo4dO4Zq1arBwcHBaBKMxHIXL14MNzc3FCpUKM3YM3MOyXFyckK3bt3QrVs3XLlyBcuWLcPMmTPRv39/vPHGG+jcuTNat25t6DDWqFEDR44cSbXM5J5pS1SjRg04ODhg48aN6NWrFwD9pBR79+5Fjx490h03kHRmPyLKPTgBhRmUK1cOo0aN0jqMbBEeHo5atWpBp9PB29sbEydOTHMfEcHYsWNRtGhR6HQ6vP7669i7d282REvWhPUodaxHlmXWrFmws7NDjRo1sHz5cpw4cQKRkZFYuHAhqlWrhmvXrqWrHGdnZ3zyySf46quv8Oeff+LUqVMYOnQo9u7di8GDBye7T5MmTdCoUSN06NABf/75J6KiorB//35MmjQJK1euBABMnTrVENfZs2exdOlSODk5oXjx4gCAkiVL4uDBgzh79ixu3bqF2NhYdO7cGcWLF0fr1q2xefNmnD9/Hrt370ZwcDDCwsLMeg5p8fLywuDBg3H06FHs378fZcuWxYABA9ClSxdDnsRhfqml1DqFbm5u+OCDDzBs2DCsW7cOR48eRZcuXVC4cGGj4XqJHbxE3333HX7//XecPn0aJ06cwDfffIPZs2ebdaZB0gbbodSxHUpBVj+UJbnggUVfX18JDg5OM9/Tp0+zPpgsdP78eXFxcZEPP/xQjh8/LkuXLhWdTiczZsxIdb/JkyeLs7OzLF++XP7991/p1auXuLm5yZUrV7Ip8owBJ6DIVqxHOa8eaVmHsqPO3LhxQwYMGCA+Pj7i4OAghQoVkkaNGsmKFSskPj5eRPQTUIwZM8Zov+7duxtNDvHs2TMZNGiQFC5cWOzt7aVq1ary119/Ge2DlyZvePTokXzxxRfi7e0t9vb24unpKW3atJEDBw6IiMjcuXOlWrVq4urqKq6uruLv7y9btmwx7B8VFSX169cXFxcXASBbt24VEZGbN2/KBx98IB4eHuLg4CDFihWTTp06yZkzZ0REPwGFra1tks8iI+dgqri4ODl16lSG90/Os2fP5PPPP5eCBQuKTqeTpk2bJjlGgwYNpEGDBobXEyZMkDJlyohOp5O8efOKn59ftk0+IZLz65WW2A6xHcpIytWVLyEhQaZOnSo+Pj7i6Ogo5cqVk5CQEElISDDkASAzZsyQ999/X1xcXKRo0aIyYcIEw/sNGjQQAEYpKipKtm7dKgDkzz//lNq1a4uDg4MsWLBAEhISZNy4cVK8eHGxt7eXsmXLyoIFC4ziAiD/93//J23bthVnZ2fx9PSUqVOnGt7v1q2bNG/ePMn51KxZUz799NMs+KT0goKCpFixYkafT1BQkHh7e6e4T0JCgnh6espXX31l2BYfHy9FihSRkSNHZlms5sDOVPqwHpkmN9Uj/ugjMj/Wq6TYDpmG7ZCZ60VWH0AsuPKNGDFCSpYsKX/++aecO3dOVq5cKQULFpSZM2ca8gCQQoUKydy5cyUyMlKmTp0qAGTbtm0iInL79m3x8fGRQYMGSXR0tERHR0tcXJyh8vn6+spff/0l586dkytXrsi0adPEyclJ5s+fL6dOnZJJkyaJjY2NrFmzxuiY+fPnl5CQEDl16pSEhISInZ2dYRre8PBwsbGxkfPnzxv2OXr0qACQo0ePpni+b7zxhri4uKSaLly4kOL+9erVk169ehlt27JliwBIceras2fPCgDZsWOH0fZu3bqlOm2vJWBnKn1Yj1iPUsIffUTmx3qVFNshtkMpYWcqCz169EicnJxk/fr1RtsnTpwoFSpUMLwGIP379zfKU7p0aaOeeXK3hRMr38tDGry8vCQoKMhoW6dOnaRu3bpGx+zcubNRnq5du0qdOnUMrytVqmR0JaB///7i5+eX2inL5cuX01yTIzY2NsX9y5QpI8OGDTPadvz4cQEg+/btS3af8PBwAZBk2MTgwYONPmdLxM5U2liPWI9Swx99RObHemWM7RDbodRkR33JtbP5HT9+HE+ePEG7du2MpnONi4tLMmVtcmuEXL9+PV3HqVWrluH/79+/jytXriRZi6NBgwb48ssvjbb5+/sbva5Tpw5Wr15teP3RRx9h4sSJCA4ORmxsLH788cc0Hx708vJKV8ypSWnq29TW5Ehpv7T2IcvHepQxrEdERObBdihj2A6ZT67tTCVWsF9++QVly5ZNNW961ghJibOzc5JtGVmL4+U/1q5duyIoKAjr16/H3bt3ERsbm+ZigQEBAanOyAQAERER8Pb2TvY9Dw+PJDNU3bhxw/BeSvsAwLVr14zW7bhx40aK+5D1YD1KHusREVH2YDuUPLZD2SfXdqYqVqwInU6HqKgovPnmm5kqK7n1OpLj5uYGLy8vhIeHGx0zubU49uzZg759+xpe7969G+XLlze8zps3L959913MmzcPd+/eRadOneDq6prq8efNm4cnT56kmqdIkSIpvufv74/ly5cbfRFs2LAB3t7eKFq0aLL7lCxZEp6enti4caPhCk5CQgI2b96MwMDAVGMhy8d6lDzWIyKi7MF2KHlsh7JRVo8jFAsdYysiMmrUKHFzc5Pvv/9eTp8+LUePHpWFCxfKuHHjDHmQzDjZBg0aSGBgoOF169atpVGjRnLhwgW5efOmxMfHG8bYvvwgX0hIiLi4uMiiRYvk9OnTMmXKFLG1tU32gcXp06fL6dOnZfr06UYPLCbavXu32NnZiVIqxTGu5pQ4lWafPn0kIiJCli1bJk5OTjJ9+nRDnr1794qvr6/s3bvXsG3SpEni4uIiP/30kxw/flx69+5t8VNpimg7Lv3lZKl1SIT1yFS5qR5pWYcsuc5kRHqnbM4Jdu7cKTVr1hRHR0cpVqyY0YxrKTl//rx06tRJChcuLE5OTtK4cWM5cuSIUZ6bN2/Khx9+KEWLFhWdTic1a9Y0TAmf6OHDhxIUFCQlS5YUR0dHqVSpkvzyyy/mPL1MY71Kiu2QadgOmbleZPUBxIIrn4jI7NmzpVKlSuLg4CDu7u5St25dWb58ueH99FS+Q4cOSfXq1UWn0yWZSvPlypc4lWbi2iApTaU5depUadWqlTg5OYmHh4dMmTIl2fgrV64sVatWzeSnkH5hYWFSo0YNcXBwkKJFi8r48eON3k887xcbqISEBBkzZowUKVJEHB0dxc/PT/bs2ZNtMWcUO1Ppx3pkmtxSj/ijz3y4/k3K6988evRIypQpIy1atJBDhw5JRESE9OrVS9zd3eXatWsioq8/devWlerVq0t4eLicPn1avvzyS3F0dJRjx44ZyurcubOUKlVKNm7cKGfOnJGQkBCxtbWVdevWZfm5pxfrVfLYDpmG7ZAZ60VWH0AsvPJZouQqfHJiYmKkcOHCaS6yRhnDzpR1Yz3SHn/0cf0bU2Vk/ZuNGzcKAImOjjZsi4uLE3d3d8MsaZGRkQJAdu/ebbRvlSpVpFu3biIi8uTJE7GzszP6AS6iv1tRv379TJ+bubBeWQ+2Q9rLjvpik/EBgqSVhIQEREdH4+uvv0ZMTAy6d++udUhEVof1iLJDcHAwpk+fjqlTp+LEiRMYN24cvv76a3z//fdG+b7++ms0bNgQR44cwcCBAxEUFITt27cDAFauXAkfHx8MGjQI0dHRiI6ORrFixQz7Dh48GF9++SVOnjyJFi1aYPr06RgzZgxGjhyJf//9Fx988AF69+6NtWvXJjlm48aNcfjwYQwbNgxDhgzB77//DkA/w9imTZtw4cIFQ/5jx45h//796N27d4rnGxAQAFdX11TTxYsXU9x/165daNasmdED+i1atMDFixdx+fLlZPd5+vQpAECn0xm22drawsHBATt27EgxDwA4OTkZ8sTGxiIuLi7ZPHv27EFsbGyKcRNlBNuhHCKre2vCKxkmQxpXMqKiogSAeHp6yh9//JGNkeUu4J0pq8Z6pD0t65Al1Bmuf5M969/cvHlT8uXLJ7169ZJ79+7J06dPZcyYMQJAypYtKyL6K/8lS5aUN998U65fvy6xsbESGhoqSilxcHAwlFW3bl2pWbOmREVFSXx8vKxdu9Yw5Ovq1aupnnt2ye31ypqwHdJedtSXXDubnyXT/9unrESJEmnmIcrtWI9Ia1z/JmNMXf+mYMGCWLlyJfr06YN8+fLBxsYGzZo1Q0BAAM6fPw9APyX277//jt69e6Nw4cKwtbVF9erV0blzZ/z222+GspYuXYrAwECUKlUKNjY28PHxQWBgIGbOnAlbW9tMnxvlLmyHcgd2poiIiLIA179JnrnXvwGARo0a4dSpU7h79y7i4+NRsGBB+Pn5oXTp0oY8VatWxf79+/HgwQM8evQIHh4e6Nixo1Ge4sWLY9OmTXj8+DHu3r0LLy8vBAUFwc3NDQULFkz1vIgod2JnyoKNGjUKS5cuxZkzZ7QOhcgqsQ6Rlrj+TfLMvf7Ni/Lnzw8AiIyMxIEDBzBv3rwkefLkyYM8efLgzp07WL9+PT777LMkeZydneHs7IyYmBj89ttvaNu2LWxs+Jg5mY7tUM7HbwbKlKdPn6JHjx6oXLky7Ozs0LRp0yR5Fi5cCKVUsumXX37RIGoiy5GeOvTff//hzTffRNGiReHo6IgiRYqgS5cuuHTpkgYRU3q5urpi6NChGD58OGbPno3IyEgcO3YMixYtwrfffmtSWSVLlkR4eDguXryIW7dupXrXKigoCNOmTcPixYsRGRmJqVOnYsWKFRg6dKhRvtWrV2PGjBmIjIzEjBkzsGzZMgwaNMgoz0cffYQ///wT27ZtwwcffJBmnF5eXvDx8Uk12dmlfB33448/xu3bt9G3b1+cOHECy5cvx7Rp0zB48GBDnn379qFcuXLYt2+fYdvChQsRHh6Oc+fOYeXKlWjatCnq16+Pbt26GfL89ttv2Lx5M6KiorB+/Xo0bNgQRYoUMSp748aNWLNmDc6dO4ft27ejRYsWePToEcaNG5fmuRNZqzNnzqBJkybw8PCAo6MjihUrhr59++Lu3btG+T777DNUr14djo6O8PHx0Shay8M7U5Qp8fHxcHJywqefforffvsNcXFxSfJ07NgRb7zxhtG2iRMnYv78+Zm+Wktk7dJTh5RSaNmyJUaNGgUPDw9cunQJX3zxBVq1aoUjR45kf9CUbsHBwfDw8MCMGTPw2WefwdXVFRUqVEC/fv1MKmfUqFH44IMP4Ovri6dPnyIqKirFvJ988gkePnyIESNGIDo6GiVLlsQPP/yQ5Pt2xIgR2LBhA4YMGYK8efNiwoQJaNeunVGe2rVro3z58rCxsUHNmjVNijkjihcvjnXr1mHAgAF49dVXUahQIQQHB+OTTz4x5Hn8+DFOnTqFx48fG7adOXMGw4cPx61bt+Dp6YlOnTohODjY6Dmna9euYeDAgYiOjkaBAgXQtm1bjBkzxuhu2/379zF06FBcvHgRrq6uaNGiBRYuXGiWZ8GILJWdnR3ee+89VK9eHQUKFMDp06fRr18/XL16FX/88Ychn4igV69e2Lt3L3bt2qVdwJYmq2e4EAuf/WXLli1Sq1YtcXZ2Fjc3N6levbrs379fRPTrg/Tu3VtKlSolOp1OSpQoIUFBQfLkyRPD/sHBwVK6dGlZsWKFlC5dWpycnKRNmzZy7949+fnnn6VMmTLi6uoq7du3l//++8+wX/fu3aVJkyYyadIk8fT0FCcnJ+nQoYPcunUrSdkv+vvvv6VWrVqi0+mkWLFi8sknn8j9+/fTdT5ZLfGc0hIfHy/FihWTvn37ZkNUGQfO5pcurEPmk946JCKyatUqAWD0mVgaLeuQJdcZSwCuf2O1WK+SYjtkftOmTZN8+fIl+15y52SpsqO+5OphfnFxcWjXrh38/f1x5MgR7N+/H4MHDzY8CCwiKFSoEJYtW4YTJ04gJCQES5YswdixY43KiY6OxqJFi7By5UqsW7cOu3btwttvv4358+fj559/xrp16xAeHo5vvvnGaL99+/Zhx44dWL9+Pf7++28cPXoUvXr1SjHeTZs2oUOHDujZsyeOHTuGn376CXv27EFgYGC6zic548aNS3NNkB9//DGjH3Gy1qxZg0uXLuGjjz4ya7mU/ViHtKlDt2/fxtKlS1GtWjXkzZvXrGUTAVz/hqwH2yHzt0NXrlzBr7/+ikaNGqV7n1wtq3trYsFXMm7fvi0AZMeOHeneJyQkRHx8fAyvg4ODxdbWVm7evGnY9umnn4qNjY1cv37dsG3gwIHy6quvGl53795dXFxc5O7du4ZtmzdvFgBy+vRpQ9kv9vzr16+fZP2Nffv2CQC5ceNGhs7n9u3baa4J8uLVktSk96p6y5YtpXbt2umOUSvgnak0sQ5lbx366KOPxNnZWQDI66+/Ljdu3Eh3nFrQsg5Zap2xFOD6N1aL9coY2yHztUNvvPGGYV21Vq1ayePHj5PNxztTxilXPzPl7u6OHj16oHnz5mjSpAmaNGmCdu3aoUSJEoY8CxYswJw5c3D+/Hk8evQo2fVBvLy8jKZM9fDwgIeHBwoVKmS07eU1QypUqIB8+fIZXvv7+0MphRMnTqBMmTJJ4j1w4AD27t2LkJAQwzb934l+5iJ/f/80zye5z8Dd3T3Vz8mcLl68iL///hvz58/PtmNS1mEdyt469PXXX2PgwIE4f/48vv76a3Tq1AkbNmzg+jdkssS/+5Rw/RuyFmyHzNcOzZs3Dw8ePMCpU6cwfPhw9OnTB4sWLcp0uTldrh7mBwChoaHYu3cvGjVqhL/++gu+vr5YtWoVAODXX39Fnz590LFjR6xduxaHDx/G6NGjERsba1RGcuuDZHTNEBFJcWHChIQEBAUF4ciRI4b0zz//IDIy0rDgY2rnk5zsHqL0ww8/wM3NLc21Ssh6sA5lXx0qVKgQypYti+bNm2PlypXYsmULNm7caJayiYisFdsh87RDXl5eKFeuHNq0aYOFCxdi8eLFOHnyZJr75Xa5+s5UoipVqqBKlSoYNGgQ2rZtiwULFqBNmzbYvn07qlWrhoEDBxryJq6mbg4nTpzA/fv34ebmBkC/xgcAo3U+XlS9enVERESkOR1lSueTnD59+uDdd99NtbzChQundSrpEhcXhwULFqBbt25wcnIyS5lkGViHsqcOvSjxSubTp0/NXjZRIq6RQ9aC7ZB526HETiPbmLTl6s5UVFQU5syZg9atW6No0aKIiorCoUOH8N577wEAfH19MX/+fKxatQqVKlXC6tWrzbouklIK3bp1wzfffIM7d+7g448/RuvWrVOsYKNHj0aLFi0wZMgQdOnSBU5OTjh58iT++usvzJ07N83zSY45bg1HREQgJiYGd+7cwcOHDw1TNVeoUAEODg6GfH/99ReuXr2KDz/8MFPHI8vBOpQ9dWj9+vW4dOkSatWqhbx58+Ls2bMYNWoUihQpgiZNmmTq2ETWbseOHfjuu++wZ88e/PfffyhevDg++OADDBgwwHB34L///sP777+Po0eP4ubNmyhQoAAaN26Mb7/9FsWKFdP4DCgz2A5lvh1avnw5AKBq1apwcnJCREQEgoKCULVqVVSpUsWQ78yZM3j48CGuXbuGmJgYQ1vl4+OT5oLeOVmu7kw5Ozvj9OnTeOedd3Dr1i0UKlQIHTp0wOjRowHoFys8duwYevbsibi4OLz55pv4+uuvTV4fJCW1atWCv78/mjVrhnv37iEgIABz585NMX+TJk2wadMmjBo1CjNnzoRSCqVKlTKsC5LW+WSVN998ExcuXDC8TrxNHRUVZTTGd86cOahbty4qVqyYpfFQ9mEdMo+06pBOp8PChQsxZMgQPH78GEWKFEGzZs2wZMkS5MmTJ0tjI7J0u3fvRtmyZTFw4EB4eXlh586d6NOnD2JiYgwLFXOttpyL7VDm2dvbY8KECTh9+jRiY2NRtGhRtG7dGkOHDoWNzf+eCOrduze2b99ueJ3YVm3duhUNGzbMsvgsXlbPcCEWOvuL1kxZT4a0Ac7mZ9FYhyyflnXImuoM18gxvwEDBshrr72Wah5rWKstOaxXloPtkOXLjvqS6yegICIi0grXyMmaSVz++++/VIc9ca02IjKXXD3Mj4iISEv379/HvXv30L59e8M0ymXLljW8b2NjY9RxKlGiBM6fP4+QkBCMGTPGsP3Zs2dYtGiRYWrn9957DzNmzEB0dLRhaufOnTtj06ZNRsdPSEjA4sWLDVM7z549G02aNEFkZGSy0zqPGTMGn332Gfr06QNA/6zErFmzUKtWLdy8eRO2trapnk9yzP3w/K5du7BkyRL8/PPPyR5ryZIlePz4MV5//XWsX78+3eUSESWHnSmNLFy4UOsQiKwa6xDlBFwjx7xrtR09ehStW7fG4MGDDc+gvIhrtZE5sR0igOtMERERaYpr5JhnmN/BgwfRqFEjfPDBBxg3blyyebhWGxGZW47oTC1cuBB2dtZxk61Hjx5QSkEphdmzZ2sdTq5z8uRJw+ef1hoPuQnrEKUX61DWSFxPZsuWLQgICMCCBQsAwGiNnOrVq6NMmTJZskZOIlPWyHk5vbh+YErnk5w+ffoYdc6SS61bt071PHbt2oUmTZqgX79++Pbbb9N17ol31LiOjuVgW0TpZUltUY7oTFmbevXqITo6Gt27dzdsmzt3Lho2bAg3NzcopXD58uUk+yX+0bycWrZsaXIMiestODo6okyZMhm+VR0eHo5atWpBp9PB29sbEydOzFA5ERERaNy4MZycnODh4YHBgwcjLi7O5HKuXLmCdu3awdXVFe7u7ujduzcePHhgeL9MmTKIjo7GoEGDMhQnWYbk6pCIYOzYsShatCh0Oh1ef/117N271+SyV65ciRYtWqBgwYJQSmHnzp1J8sydOxevvvoq8ubNCxcXF7z66qsIDQ3N0LmwDuVuUVFRGDp0KHbt2oWLFy9i+/btOHToEMqVKwdAv0bOsWPHsGrVKpw9exbTpk3LkjVy/v33X+zYsSNda+T8/vvvGDJkCI4ePYrIyEj89ddfhvUD0zqf5Li7uyfbOXsxpbYEwPbt29G8eXN07twZffv2xbVr1wwp0fr16zFv3jwcPXoUFy5cwJYtW/DOO+9wrTbKlKxsiwDghx9+gI+PDxwdHVGlShWsXbs2Q+WY4zffqlWr4OfnB3d3dzg5OaFcuXKYNGmS4aIEoJ8I5/3334evry9sbGzQu3fvDMULWFdbxM6UBhwcHODh4WF0Fe/x48d44403MHz48BT3i46ONkqJVxA7depk0vH37t2LDh06oH379vjnn3/Qv39/BAYGYvXq1SaVc+HCBbRo0QLVqlXDoUOH8O233yI4OBgzZ840qZwHDx6gWbNmcHNzw969ezF//nyEhoZi2LBhJpUTHx+Pli1b4vr169i2bRt+//13bN261WhmKltbW3h4eOTqxeVyguTq0NSpUzFu3DhMnjwZBw8eRIUKFdC8eXNcvXrVpLIfPXqEOnXqYPz48SnmKVKkCMaMGYN9+/bh6NGj6NGjBz744AP88ccfJh2LdYheXFOmTJky6NKlC9q3b2+0Rk7Xrl3Rs2dPVKtWDbt378bXX39ttuO/uEbOG2+8gYoVK6Z6FylxjZx9+/bh9ddfR7Vq1fDll1/C09MzXeeTFUJDQ/Ho0SPMmjULnp6eRilR4lptDRs2hK+vL3r37o3y5ctj165dXKuNMiwr26LffvsNH3/8MQYMGIB//vkHrVq1Qtu2bXH48GGTyjHXb778+fNj8ODBCAsLQ0REBL788kuMGjUK06ZNM+R59uwZXnnlFXz55ZeoWrWqSeW/yOraoqyee11SWZdgzpw5kj9/fnn69KnR9qFDh0rFihVFJH1rbISGhoqtrW2Kr0VEoqKiBICEhYUZtp06dUpat24tbm5u4u7uLq1atZIzZ84kG6u5pLUmwdatWwWAXLp0Kc2yhg8fLu7u7kafRXp07NhR6tatm2Rb/fr1TSonKChIihUrJgkJCUbbvL29TSrn+++/FycnJ6N1ShK3PXz4MN3l/P333wJAIiMjk2w7e/asUd7k1k55GaxgnSnWIb2EhATx9PSUr776yrAtPj5eihQpIiNHjszQcZI739RUq1ZNPv/8c5OOwTrE9XC0xDVyrJMl1iu2RXrmbIv8/PykS5cuSbZ169bNpHLM9ZsvOe3atZO2bdsm+16DBg0kMDAwQ+Wasy3Kjvqi6Z2pd999F48fP8aaNWsM20QEy5YtQ9euXQ2v07PGhqmuXbuGunXrokSJEggPD0dYWBjy5cuHpk2b4vHjxynuFxAQkOaDshcvXsxUbOkRGxuLBQsWoHv37tDpdCbtu2vXLjRv3txoW4sWLbB3717Ex8ebVE6zZs2MHlRu0aIFLl68mOwwxdTKef31142uDrZo0QJPnjwx6QrMrl27UKpUKaPhKU2bNoWtrS127dqV7nKsCeuQXlRUFKKjo43+rm1sbNC0aVOEh4dn+BzTIyEhARs3bsTJkydNXgGedYiIcgK2RXrmaotiYmJw8ODBZH+rmdqmmes334tEBAcOHEB4eDgaNWqUoTJSY21tkaZP+eXLlw+tWrXCkiVL0L59ewD6sc+XL19G586dAaR/jQ1Tff/99/Dx8TG6PblgwQIULFgQa9aswTvvvJPsfvPmzcOTJ09SLbtIkSIZjiu9Vq1ahWvXrhnGqZvi2rVrSdbsKFy4MJ49e4Y7d+7glVdeSXc5devWTVIOoB+SWLRo0UzFk1hOeiVXjp2dHdzd3U0qx5qwDuklPhuR3N/RgQMHTCorvS5evIgKFSrg2bNnsLOzw/Tp09GmTRuTymAdIqKcgG2Rnrnaolu3biEuLi7Zckz9LjbXb75Erq6uiImJQXx8PIKDg/Hpp5+atH96WFtbpPmUKd26dcPbb7+NO3fuwN3dHUuWLEGjRo2MfkSkZ40NUx04cAD79+9PMtby8ePHiIyMTHE/Ly+vTB3XXObMmYMGDRqk+lBval6e9lZ/JzTpdq3KSWu7KeWIpDzNb07AOvQ/yf09ZtW/fZEiRXDkyBE8fPgQmzZtwqBBg+Dl5YWAgACTymEdIq1wjRwyJ7ZF/2Outigry0lue3ocOXIET548we7duzF06FAUKVIkUxNNpMSa2iLNO1NvvPEG8ubNixUrVqBnz5749ddfMX36dMP7iWtsjB8/Hg0aNICbmxtWrlyJoUOHplimjU3S0Ysvr8mRkJCAJk2aYMaMGUnyprZ4YEBAAMLCwlI9p4iICHh7e6eaJzPOnDmDzZs3Y9myZRna38PDw2iWIwC4ceMGHBwckD9//kyXk/ieKeVcuXLFaFviwpKmlrN582ajbXFxcbh7965J5Vgb1qH//Z1cu3bNaKHRGzduZNm/vZ2dnWEIwquvvoqoqCiMHj3apM4U6xAR5RRsi8zXFhUsWBB2dnbJtg+mfheb6zdfosR2r3Llyrh37x6++uors3emrK0t0rwzZW9vj06dOmHJkiVwd3dHfHy84RYxYLzGRqK01tgoVKgQ4uPjcf36dcNtwpefG6hevToWL15smLoyvSxhmN/cuXNRoEABo8/JFP7+/ti4cSNGjBhh2LZhwwbUrl3bpFXg/f39sXz5cqMrBRs2bIC3t3e6hyclljNw4EA8fPjQcGVpw4YNcHJyMiwCmd5yxowZg7Nnz6J06dIAgM2bNyM+Ph7+/v7pLsfasA4BJUuWhKenJzZu3Ih69eoB0DewmzdvRmBgoEllZVRCQoLJ69WwDlmvhQsXonfv3hmafj679ejRA4sWLQKgHxLVp08fjSOilz19+tQwI5ytra1V/F29jG2R+doiBwcHVK9eHRs3bjQ8cwbov9fr1KljUkzm+s2XnIy0e+lhdW1RVs9wIemYVWnfvn0CQKpWrZpk5pLp06eLk5OT/PHHH3LmzBn57rvvpECBAvJimS/P9nL79m3JkyeP9OjRQyIjI2Xt2rVSqVIlo9lfrl27Jh4eHhIQECDh4eFy7tw52bZtmwwYMEBOnz6daryZkdLsSdHR0XL48GH54YcfBICsX79eDh8+LNHR0Ub5nj17Jq+88ooMHjw4wzHs2bNHbG1tZdSoUXLy5EmZPn262Nrayl9//WVSOefPnxcXFxfp06ePREREyLJly8TJyUmmT59uUjn3798XT09PadeunRw9elTWrFkjBQsWlEGDBplUTlxcnFStWlXq1KkjBw4ckB07doiPj4906NAhSV5Ln4ns5cQ69D8p1aFJkyaJi4uL/PTTT3L8+HHp3bu3uLm5yZUrV0wq//bt23L48GFZs2aNAJDQ0FA5fPiwXLhwwZBn2LBhsnXrVjl37pxERETIlClTxN7eXqZMmWLSsViHtKszmZXcLGOWqnv37lKvXj2Jjo6Wx48fG7YnJCTIN998I15eXuLo6Ci1a9eWPXv2ZOgYc+fOldKlS4uDg4NUrlxZ1qxZY3IZ27dvl7feeks8PT0FgCxZsiRJnn///Vfat28vxYsXFwAyZsyYDMV79epVee+996Rs2bKilEp21rHt27dLu3btxNPTU5ycnKRcuXIyZcoUo9k30+u///6THj16SL58+cTV1VU6dOiQpH2Pjo6W7777Ls2/K0uuV2yLzNcW/fLLL2JrayuzZs2SEydOyIgRI8Te3l4OHTpkUjnm+s03btw4WbdunZw5c0ZOnTol8+fPl7x580r//v2N8h0+fFgOHz4s1atXl3bt2hlem8KcbVF21BeLqHwiIuXLlzd0Il4UExMjH374oeTPn1/y5MkjHTt2lJkzZ6Za+UREVq9eLeXKlROdTid16tSRtWvXJplK89y5c9KxY0dxd3cXR0dHKVWqlAQGBsrNmzfTjDejUqp8wcHBAiBJCg4ONsq3fPlyUUoZTRf5otDQUAEgUVFRqcbx+++/S4UKFcTBwUFKly4toaGhRu8nTtG+devWVMsJCwuTGjVqiIODgxQtWlTGjx9v9H7iFKYvl/+y48ePS6NGjcTR0VFeeeUV+eKLLyQ2NtYoT3Kfx8suXbokbdq0EWdnZ8mXL5/06tXLaLroRJb+Q/DlxDr0PynVoYSEBBkzZowUKVJEHB0dxc/PL8kPw8R6lprEOvRy6t69uyHPhx9+KCVLlhRHR0dxd3eX2rVry+LFi43KYR2y7B99mWVtnank6szkyZPF2dlZli9fLv/++6/06tUrQz/6fv31V7G1tZUZM2bIiRMnZPjw4Rn60bd27Vr58ssv5bfffkuxM7Vv3z754osvZPny5eLh4ZHhzlRUVJR8+umnsmjRInn11VeT7UyNHz9egoKCJCwsTM6dOyeLFy8WZ2dn+fbbb00+3ltvvSVlypSRHTt2yL59+6RWrVpSq1atJB2z9PxdWXq9YltknrZIRD/lfKlSpcTBwUEqVaokq1evNno/O3/zjRw5UsqWLStOTk7i5uYm1apVk5CQkGTbmeTSy3myqy3KVZ2p3CKr1/UYMWKEVKhQIckft6nmz58vhQoVkrt372aqnM2bN4tOp0uyLoCpzp49K0op2blzZ6bKSWTpPwRfTqxD/5OZOtS1a1dp1qyZmSNKHuuQZf7o43o4epa4Hs6LUupMvah48eIZ7ky9yJT1cAYMGCCvvfaaSeWfPHlSAMimTZsM206cOCEAZMuWLUZ5c0JnKrewhLbI0n7zpUd2t0XZUV80XWcqt9q2bRtcXV0xf/58s5e9evVqzJgxA3Z2mXscbvXq1ZgwYQLy5cuX6XKCgoJQqlSpTJfTrVs3k8cKv+z06dNwdXXFuHHjMlUOaSsjdShx3HpyDylnBdYhy8T1cPQscT0ca/Dff/+lOqlBcsLDw+Hg4GC0Dl25cuXg7e2dIz+j3ETrtsjSfvOl91g5rS3SfAKK3GbixIn46quvAOgfrDS3Q4cOmaWclStXmqWcqVOnmqUcc61jULJkSRw5cgSA/gFPsj4ZrUM2NjZJZrzLSqxDlonr4ehZ4no4lm7Xrl1YsmQJfv75Z5P2u3btGgoUKJDkYf+c+BnlJpbQFlnab770yIltETtT2axQoUJZ0omi9LG3tzdaUZusD+uQtnJCHeJ6OP9jaevhWKqjR4+idevWGDx4MNq1a2fy/ta0Zg6lD9sibVlSW8RhfkRElKu8uB7O06dP8euvv6Jbt26G9xPXw+nYsSPWrl2Lw4cPY/To0UnWt3mRKevhHDlyxCidPn061enKs2KY34vr4bxIy/VwLNXBgwfRqFEjfPDBBxkaUuTh4YFbt24hPj7eaHtO+oyIcjN2plLRsGHDLFnVWWs6nQ4LFy7UOgzKBViHyBK9uB7OqlWrUl0Pp3r16ihTpoxJ6+EkSm49nIiICBQtWhQ+Pj5GKbXncObNm5ekA/Zyysx6OIkSn+Uw5VmGF9fDeVFG1sOxRLt27UKTJk3Qr18/fPvttxkqw9/fHzExMdi+fbth2+nTp3Hx4sUc8RlZA7ZFlJXYmaJsM3r0aNja2uLrr7/WOhQiq6KUMiQ7OzsUK1YMffv2xYMHD7QOzWp17doVu3fvxrfffot27doZDb3z9fXFsWPHsGrVKpw9exbTpk3DL7/8kmp5tWrVQp48eTB06FCcOXMGf//9d5Lnq/r374/Y2Fi0b98eu3btQlRUFLZv346BAwemOczv5c7Xy8nUB9CVUhg4cCCmTp2KFStWICIiAh999BEePHiAjz76yKSyvvjiCyxbtgzff/89Tp48iZEjR+LgwYP4/PPPTSrn4cOHhs4hAFy8eBFHjhzBmTNnDHliYmIMeWJiYnDt2jUcOXIEERERJh0LgKGchw8f4s6dO0bHBvSd6ubNm6Nz587o27cvrl27ZkimKFeuHFq2bIm+ffti586dOHDgALp164aaNWuiQYMGJsdNpIWTJ0/C2dk5SectIiICTk5OWLBggTaBWYKsni5QrHgqTVOmS7Umjo6Oqa5Z8/KUweYQHx8v3t7eEhwcLN7e3hIfH2/2Y5gbODV6prEOmQcAmTFjhkRHR8ulS5dk/fr14uXlZfGfrZZ1KD11huvhWNZ6OIlr3bycGjRoYMiTON38y6l48eJJ8qS1Nlty5bx4rt27d0/XmjnFixc3WocuOXfv3pXu3btL3rx5xcXFRdq3b59k0V4RTo2eVdgWmce0adMkT548cu7cORHRf1dWq1ZN2rRpY9bjmFN21JdcXfkSEhJk2rRpUq5cOXFwcJDChQsbfSG+XPk2bNggDRo0kPz584ubm5vUq1dPdu/ebVTmzJkzpUyZMuLo6CgFChSQpk2bysOHD0VE5Pz589KqVSvJnz+/ODk5SdmyZdP8ss+sAwcOSM2aNcXR0VHKly8v69atM6p8iY3O4sWLpXnz5uLk5CQjR440NGqXLl0yKg8vrf2xf/9+qVGjhqH89evXJ1u516xZI8WLF5e4uDgpXry4rF27NkvP2xzYmUob61D21KGX9xERGThwoFSpUiXLztsc+KPPMuSk9XDSw1xrs6XHo0ePRKfTybJly8xSHjtTGcO2KHvaooSEBGnWrJnUrVtX4uPjZejQoVK4cGG5ceNGlp57ZmRHfcnVs/mNGDEC06ZNw6RJk9CkSRPcu3cPW7ZsSTH/w4cP0bdvX1StWhVxcXGYNm0aAgICEBkZiYIFC+LAgQPo378/QkND0aBBA9y7dw/bt29P/AJCv3798OTJE2zZsgV58+ZFZGQknj17luLxwsLCEBAQkOo5dOnSBbNnz072vUePHuHNN9+En58f5s+fjzt37uDzzz9P9iHqYcOGYcKECZg9ezaUUmk+H5BYfsuWLeHv748FCxbg7t27GDhwYLLlz507F126dIGtrS26du2KuXPnpnluZPlYh/4nq+vQi86fP48NGzagbt26aR6DCPjfejjTpk1DYGBguvZJfIZq69atmT6+udbDSe+xzLE2W3ps2rQJfn5+eO+99zJVzrNnz1CgQAHExcWZKbLchW3R/2RlW6SUQmhoKCpXrowuXbrg559/xqpVq/DKK6+keYwcLat7a2KhVzIePHggOp1OpkyZkmKetG4LJyQkSIECBWTp0qUiIvLbb7+Jm5ubPHjwINn8lStXlq+//jrdMT5+/FgiIyNTTdevX09x/zlz5oiLi4vRitabN282Gv6QeCXj5VXk03MlY86cOeLq6ir37t1Lst+LVzKuXr0qdnZ2curUKRERiYyMFHt7e7l69Wq6PwstgHemUsU6FCoi2VOHAIijo6O4uLiITqcTANKwYUPDVVJLpWUdssQ6o5Xr168b/t5f/Fsjy5GQkGD4Nzpz5kyqeVmvjLEtChWR7GmLEoWEhAiANIe3WoLsqC+59s5UREQEnj59imbNmqV7nwsXLmDkyJEIDw/HjRs3kJCQgMePH+PChQsAgGbNmqFEiRIoVaoUmjdvjqZNm6Jdu3bImzcvAOCzzz7Dxx9/jHXr1qFx48Zo1aoVatWqleLxnJycMjWH/okTJ1ChQgWjFa39/f2TXdcitThSK798+fJwc3MzbKtdu3aS8hcsWIDq1aujbNmyAAAfHx/UrFkToaGhGD58uMnHJcvAOmQsK+sQAIwdOxZt2rRBQkICLl68iOHDh6NNmzbYsGFDstNyEyXiejiWTyllMWvmWBu2Rcayui2KjY3FwoUL4ezsjH379uHp06fQ6XQmHzMnYQtsgpYtW+L8+fOYOXMm9uzZgyNHjsDT0xMxMTEAgDx58uDgwYNYsWIFSpYsiYkTJ8LX19dwizUwMBBRUVHo1asXzpw5g3r16iEoKCjF44WFhaW5tkhqa5OkRN9RN+bs7Gz0OvHH2Yt50xp6lFz5IoL58+dj3759sLOzM6Tdu3dj3rx5ycZCORfrkOl1KFHhwoXh4+ODsmXLomnTpggJCcHmzZuxbds2EyInIiK2RRlvi0aNGoUrV67g0KFD+O+//3hRHLm4M1WhQgU4Ojpi8+bN6cp/+/ZtHD9+HEOHDkWLFi1QoUIFODs7J5ki1c7ODo0aNcKYMWPwzz//ICEhAb///rvhfS8vLwQGBmL58uWGMa0pqVGjRppri6Q2zXiFChVw4sQJ3L9/37Bt9+7d6TrfxKuYV69eNWx7ec2U8uXLJyl/7969Rnk2btyIS5cuYffu3UZx79+/H1euXMGmTZvSFQ9ZHtah1JmrDqUksYF89OhRuvKT9eIaOUQpY1uUOnO2Rbt378aECRPwww8/wNfXF/Pnz8e0adOM1lDLjXLtMD9XV1cMHDgQI0eOhLOzMxo3bowHDx5gy5YtGDRoUJL8+fPnxyuvvIIffvgBpUuXxu3btzFkyBA4OTkZ8qxevRpnz55F/fr1UaBAAezYsQN3795FuXLlAOhvCwcEBKBs2bJ4+PAh1q5da3gvOZm9Lfz+++/jq6++Qrdu3fDNN9/gzp07+OKLL9I1JMjHxwfFixfHqFGj8H//93+4efNmkqsPnTt3xogRI9CzZ0+MHj0a//33HwYPHgwbGxvDreG5c+eicePG8PPzS3KM5s2bY+7cuSbdmifLwTqUOnPVoUT37t3DtWvXICK4cOEChgwZgldeeQX+/v4ZPj+i3OLkyZN47bXXMGvWLPTo0cOwPSIiAtWrV8fMmTPRq1cv7QKkDGNblDpztUUPHz5E165d0atXL7Rq1QoAEBAQgN69e6NHjx44evQo8uTJk+FztGpZ/VCWWOgDiyL6Bw4nT54sPj4+Ym9vLx4eHtKzZ0/D+y8/sLht2zapUqWKODo6StmyZWXFihXi6+srwcHBIiISFhYmDRs2FHd3d9HpdFKuXDmZNWuWYf9PPvlEfHx8RKfTSYECBaR9+/aprrlhDolTXTo4OIivr6+sXbs22ak0X1wHJdGePXvktddeE51OJ1WrVpWdO3cmmUpz3759hvLLly8va9euFRsbG/npp5/k+vXrYm9vL/PmzUs2tkWLFom9vX2qD11qCZyAIk2sQ1lbhxLhhfVtlFJSqFAhadWqlRw5ciRLzz2ztKxDllpnMoJr5JiHNa6RkxzWq6TYFmV9W9S7d28pXbp0kkk5Hj58KKVLl7bY76jsqC+5uvKR+e3bt08AyMGDB7UOJdPYmSItsA7lvjrDNXK4Ro4pWK8oO+SUtig76kuuHeZH5rF48WJ4e3ujRIkSiIyMxIABA1CtWjW89tprWodGZBVYh4hr5PwP18gh0gbbooxjZ4oy5datWxg1ahSuXr2KggULonHjxpg8ebLWYRFZDdah3O3hw4eYMmUKxo4dazSbV40aNVLcp127dkav58yZg5UrV2L9+vXo3LkzLl68CFdXV7Rv3x6urq4AgCpVqhjyX7x4Ee+88w5effVVAEDJkiVTjTHx4fnUvDil8st+/PFHPHr0CIsXLzZM7TxlyhQ0adIkSd4+ffqgc+fOhtfp6Uz9+OOPePz4MRYtWmSIY/LkyWjUqFGSvF5eXhg9ejQ+/fRTdO/eHS1btkyzfKLcgG1RxrEzRZkycOBADBw4UOswiKwW61DuxjVyjHGNHCJtsC3KuFw7NToREZE14ho5XCOHiCwHO1NEREQa4Ro5qeMaOURk6TjMj4iISCNcIyd1XCOHiCwd70wRERFpaOzYsQgODsakSZNQoUIFvPnmmzh+/HiyeW1sbPDLL7/g7NmzqFKlCnr06IH+/fujaNGihjz58uXDH3/8gaZNm8LX1xdjx45FSEiIYUa+hIQE9O/fHxUrVkTjxo2RJ08erFixIsvOz8XFBWvWrMGVK1dQvXp1fPjhh/jmm29gb2+f5r52dnZYsWIFbty4gWrVqqF///4YP358kvJXr16NixcvGsofPXo0ABiehxowYAAAYOrUqUb7Tp06Fba2tob3iYhMpZIbU2xuTk5O154+fVo4yw9EZEY6ne76kydPPLSOA2AdIuukZR1SSkl2tG9kmfbv349atWrh4MGDOW5qZ6UURCTp7BrZgG0RWZvsaIeypTNFRESUndiZyl2SWyPHwcEBhw4d0jo0s9OyM0VESfGZKSIiIrJqXCOHiLTCO1NERJTj8M4U5VS8M0VkWTgBBRERERERUQawM0VERERERJQB7EwRERERERFlADtTREREREREGcDZ/IiIKMfR6XTXlVJcD4dyHJ1Od13rGIjofzibHxERkZVTShUE8C+ANiKyV+t4sppSaiqAgiLSTetYiCh3Y2eKiIjIyimlFgG4IyIDtI4lOyilXAAcA/CxiKzXOh4iyr04zI+IiMiKKaVaAGgAoJLWsWQXEXmklOoDYI5SqrKIPNQ6JiLKnXhnioiIyEoppVzxvzs067SOJ7vltjtyRGR52JkiIiKyUs+fHXpFRLpqHYsWlFIFoH9WrG1ueFaMiCwPO1NERERWSClVC8CfACqJyC2t49GKUqoTgOEAaohIjNbxEFHuwnWmiIiIrIxSygHAPAADc3NH6rkVAC4CGKJ1IESU+/DOFBERkZVRSn0JoA6AlsKGHEopbwAHAdQTkZNax0NEuQc7U0RERFZEKVUOwE4A1UXkgtbxWAql1CcAOgJoICIJWsdDRLkDh/kRERFZCaWUDYC5AEazI5XELAC2AD7UOhAiyj14Z4qIiMhKPF9bqTuAuiISr3U8lkYpVQHAdgDVROSy1vEQUc7HzhQREZEVUEp5ATgCoKGIHNc4HIullAoGUB1AGz5PRkRZjcP8iIiILJxSSkE/jG0WO1JpGg+gNIC3tQ6EiHI+3pkiIiKycEqptwF8Df3wtWdax2PplFKvA/gN+jW47mgdDxHlXOxMERERWTCllDuAfwG8LSK7tI7HWiilQgC4ikgvrWMhopyLnSkiIiILppSaD+CJiHyidSzWRCmVB/pOaKCIbNI6HiLKmey0DoCIiIiSp5RqDKAZgEpax2JtROSBUupjAHOUUpVF5LHWMRFRzsM7U0RERBZIKeUM4CiAz0VktdbxWCul1I8ArojIEK1jIaKch50pIiIiC6SUmgDAW0Te0zoWa6aUegX64X5vishBreMhopyFnSkiIiILo5R6DcDfACqLyA2t47F2SqmuAAYCqCUisVrHQ0Q5B9eZIiIisiBKKXsA8wEMYUfKbJYCuAF9h4qIyGx4Z4qIiMiCKKWGAGgKoIWwkTYbpVQJAAcAvC4ikRqHQ0Q5BDtTREREFkIp5QNgD4CaIhKldTw5jVLqcwBtADRmR5WIzIHD/IiIiCyAUkoBmAtgHDtSWWY6ABcAgVoHQkQ5A+9MERERWQClVCCAPtAPQ4vTOp6cSilVBcAmAFVFJFrreIjIurEzRUREpDGllCeAfwA0E5F/tI4np1NKfQOgnIi8rXUsRGTdOMyPiIhIeyEAfmBHKtt8A6CSUqqd1oEQkXXjnSkiIiINKaXaApgA/bCzpxqHk2sopeoBWA6gkoj8p3E4RGSl2JkiIiLSiFIqH4B/AXQWke0ah5PrKKW+B2AjIh9pHQsRWSd2poiIiDSilJoNfVvMH/MaUErlBXAc7MwSUQbZaR0AERFRbqSUqg/gLQAVtY4ltxKRe0qpfgB+UEpV4TBLIjIVJ6AgIiLKZkopHYAfAHwiIve0jic3E5FVAI4AGKlxKERkhTjMj4iIKJsppcYC8OXU3JZBKeUB4Cg4NT0RmYidKSIiomyklKoKYCO4aKxFUUr1AvAxuGgyEZmAw/yIiIiyiVLKFsA8AMPYkbI4oQDuA/hU60CIyHrwzhQREVE2UUoNANAKQBNhA2xxlFKlAewFUEtEzmkdDxFZPnamiIiIsoFSqiSA/dAPI4vUOh5KnlJqMIDmAJqzw0tEaeEwPyIioiymlFIA5gCYxI6Uxfs/AAUAdNM6ECKyfLwzRURElMWUUt0AfA798DFObmDhlFKvAfgbQBURua51PERkudiZIiIiykJKqUIAjgEIEJFDWsdD6aOUmgCguIh00joWIrJc7EwRERFlIaXUMgCXRWSI1rFQ+imlnKBfe2qgiPyldTxEZJnYmSIiIsoiSqmWAKZBP1zssdbxkGmUUo0ALAJQSUTuax0PEVkedqaIiIiygFIqD4DjAHqIyBat46GMUUrNA/BURD7ROhYisjzsTBEREWUBpdR0AM4iEqh1LJRxSqn8AP4F8K6IhGsdDxFZFnamiIiIzEwp5Q/gV+iHh93ROh7KHKVUBwDfAHhVRJ5pHQ8RWQ6uM0VERGRGSilHAPMAfMaOVI6xEsBJAMO1DoSILAvvTBEREZmRUmoUgFcBtBM2sjmGUsoLwBEAjUTkX43DISILwc4UERGRmSilKgLYBv1wsCsah0NmppT6CEBPAHVEJF7reIhIexzmR0REZAZKKVvoh/eNYEcqx/oBwDMA/bQOhIgsA+9MERERmYFS6hMA7wJoKCIJWsdDWUMp5QsgHEB1EbmgdTxEpC12poiIiDJJKeUN4CCAuiJySut4KGsppYYDqAugJZ+LI8rdOMyPiIgoE5RSCsD3AL5jRyrXmASgKID3tQ6EiLTFO1NERESZoJR6D8AwADVEJEbreCh7KKVqAvgLQGURual1PESkDXamiIiIMkgpVRDAvwBai8g+reOh7KWUmgKgkIh01ToWItIGO1NEREQZpJRaDOC2iAzQOhbKfkopF+g70x+LyDqt4yGi7GendQBERETWSCnVAkA9AJW0joW0ISKPnq89NVcpVUlEHmodExFlL96ZIiIiMpFSyhXAMQB9RGS91vGQtpRSiwDcFZHPtY6FiLIXO1NEREQmUkpNBVBQRLppHQtpTylVAPrhfm1FZK/W8RBR9mFnioiIyARKqVoA/gRQSURuaR0PWQalVCcAX0K/mC9ndSTKJbjOFBERUToppRwAzAMwgB0peskKABcADNE6ECLKPrwzRURElE5KqS8B+AN4S9iA0kuUUt4ADgGoJyIntI6HiLIeO1NERETpoJQqB2AngNdE5KLW8ZBlUkp9AqATgPoikqB1PESUtTjMj4iIKA1KKRsAcwGMZkeK0jAL+t9XH2kdCBFlPd6ZIiIiSoNSqg+AbtAPW8ag7QAAJiNJREFU34rXOh6ybEqpCgC2A6gmIpe1joeIsg47U0RERKlQSnkBOAKgoYgc1zgcshJKqWAA1QG04fN1RDkXh/kRERGlQCmloB+2NZMdKTLReAClAbyjdSBElHV4Z4qIiCgFSqm3AXwN/XCtZ1rHQ9ZFKfU6gJUAKorIHa3jISLzY2eKiIgoGUopdwD/AuggIru1joesk1IqBICriPTSOhYiMj92poiIiJKhlJoP4LGI9Nc6FrJeSqk80HfKA0Vkk9bxEJF52WkdABERkaVRSjUB0AxARa1jIesmIg+UUn0BzFFKVRaRx1rHRETmwwkoiIgo11NKvaaUev/5/zsDmAPgYxF5oG1klBOIyBoAewGMTtymlJqglLLVLioiMgd2poiIiIAmAGo8//9gAPue/wAmMpfPAHRTSlV//ro7gMIaxkNEZsDOFBEREeAF4LJS6jUAPQB8rmk0lOOIyE0AXwCYr5SyB3AZ+r87IrJi7EwREREBRQFcAzAfwGAA/ymlPlVKvappVJQjKKUClVKNACwFcB3AIABXoP+7IyIrxs4UERGR/g5BHQA3oO9UHQPQ4vn/E2XWRQChAJZDP4z0CwAPwDtTRFaPnSkiIiKgBIDOABIAfA9gkIi0FBF2pijTRGQjgAoATgNYDWA3gLrgnSkiq8fOFBER5WrPZ1QrDMARwC4AFUVktbZRUU4jIo9FZCQAPwACwBtAA22jIqLM4jpTRESU2wmAzQA+EpFzWgdDOZuInAXQWin1IYByWsdDRJmjRETrGIiIiIiIiKwOh/kRERERERFlAIf5ERGZgZOT07WnT59yAU6yGjqd7vqTJ088tI4jEesQWSNLq0eU/TjMj4jIDJRSwu9TsiZKKYiI0jqORKxDZI0srR5R9uMwPyIiIiIiogxgZ4qIiIiIiCgD2JkiIiIiIiLKAHamiIiIiIiIMoCdKSIiIiIiogxgZ4qIiIiIiCgD2JkiIqI0lStXDqNGjdI6jGwRHh6OWrVqQafTwdvbGxMnTkxzHxHB2LFjUbRoUeh0Orz++uvYu3dvNkRL1oT1KHWsR2SN2JkiIiKzefbsmdYhZMqFCxfQokULVKtWDYcOHcK3336L4OBgzJw5M9X9pk6dinHjxmHy5Mk4ePAgKlSogObNm+Pq1avZFDnlJKxHrEdkRUSEiYmJiSmTSf91ankSEhJk6tSp4uPjI46OjlKuXDkJCQmRhIQEQx4AMmPGDHn//ffFxcVFihYtKhMmTDC836BBAwFglKKiomTr1q0CQP7880+pXbu2ODg4yIIFCyQhIUHGjRsnxYsXF3t7eylbtqwsWLDAKC4A8n//93/Stm1bcXZ2Fk9PT5k6darh/W7duknz5s2TnE/NmjXl008/zYJPSi8oKEiKFStm9PkEBQWJt7d3ivskJCSIp6enfPXVV4Zt8fHxUqRIERk5cmSWxZpZz/9mNa87iclS65AI65GpWI+YclPSPAAmJiamnJAs9YfgiBEjpGTJkvLnn3/KuXPnZOXKlVKwYEGZOXOmIQ8AKVSokMydO1ciIyNl6tSpAkC2bdsmIiK3b98WHx8fGTRokERHR0t0dLTExcUZfgT6+vrKX3/9JefOnZMrV67ItGnTxMnJSebPny+nTp2SSZMmiY2NjaxZs8bomPnz55eQkBA5deqUhISEiJ2dnaxcuVJERMLDw8XGxkbOnz9v2Ofo0aMCQI4ePZri+b7xxhvi4uKSarpw4UKK+9erV0969epltG3Lli0CQC5dupTsPmfPnhUAsmPHDqPt3bp1kyZNmqR4LK1Z2o9AS61DIqxHrEcps7R6xKTBd5fWATAxMTHlhGSJPwQfPXokTk5Osn79eqPtEydOlAoVKhheA5D+/fsb5SldurTRFWJfX18JDg42ypP4I3DJkiVG2728vCQoKMhoW6dOnaRu3bpGx+zcubNRnq5du0qdOnUMrytVqmR0Rbp///7i5+eX2inL5cuXJTIyMtUUGxub4v5lypSRYcOGGW07fvy4AJB9+/Ylu094eLgAkFOnThltHzx4sNHnbGks7UegJdYhEdYj1qPUWVo9Ysr+ZGe+AYNERGRJjh8/jidPnqBdu3ZQShm2x8XFISEhwShvtWrVjF57eXnh+vXr6TpOrVq1DP9///59XLlyBfXq1TPK06BBA3z55ZdG2/z9/Y1e16lTB6tXrza8/uijjzBx4kQEBwcjNjYWP/74Y5oPsXt5eaUr5tS8+FkB+ouOyW1Pz35p7UOWj/UoY1iPKLdgZ4qIKIdK/KH3yy+/oGzZsqnmtbe3N3qtlEryQzElzs7OSbal9EMqNS//aOratSuCgoKwfv163L17F7GxsejYsWOqZQQEBCAsLCzVPBEREfD29k72PQ8PD1y7ds1o240bNwzvpbQPAFy7dg1lypQx2i+lfch6sB4lj/WISI+dKSKiHKpixYrQ6XSIiorCm2++mamyHBwcEB8fn2Y+Nzc3eHl5ITw83OiYO3fuRMWKFY3y7tmzB3379jW83r17N8qXL294nTdvXrz77ruYN28e7t69i06dOsHV1TXV48+bNw9PnjxJNU+RIkVSfM/f3x/Lly83+kG6YcMGeHt7o2jRosnuU7JkSXh6emLjxo2GOwkJCQnYvHkzAgMDU42FLB/rUfJYj4ie03qcIRMTE1NOSLDQ5z1GjRolbm5u8v3338vp06fl6NGjsnDhQhk3bpwhD5J5XqNBgwYSGBhoeN26dWtp1KiRXLhwQW7evCnx8fGGZz1efqA8JCREXFxcZNGiRXL69GmZMmWK2NraJvvg/PTp0+X06dMyffp0owfnE+3evVvs7OxEKZXisxbmdP78eXFxcZE+ffpIRESELFu2TJycnGT69OmGPHv37hVfX1/Zu3evYdukSZPExcVFfvrpJzl+/Lj07t1b3Nzc5MqVK1kec0bBwp71sNQ6JMJ6ZCrWI6bclDQPgImJiSknJEv+ITh79mypVKmSODg4iLu7u9StW1eWL19ueD89PwIPHTok1atXF51Ol2RK55d/BCZO6ezt7Z3qlM5Tp06VVq1aiZOTk3h4eMiUKVOSjb9y5cpStWrVTH4K6RcWFiY1atQQBwcHKVq0qIwfP97o/cTz3rp1q2FbQkKCjBkzRooUKSKOjo7i5+cne/bsybaYM8LSfgRach0SYT0yFesRU25JSiTt8bdERJQ6pZTw+zT9lFJYsmQJunTpkmq+2NhYFCtWDCNGjEC/fv2yKbrcQSkFEbGYJ/tZh0zHeqQ9S6tHlP34zBQREVmchIQEXL9+HbNmzULM/7d3p2FRnVccwP8DCDOAoIiIS5UAitJEa0xcMOICStBHFI2xTQx7Gq1Vq8YtjwuujY1pKmrUWMCKJkZjrJEQ465UI4qCWsSIEYwb4A64spx+oEwysshclhng/3ue82HuvPfc96Jnnjkz773z9CkCAwMNPSWiOod1RFTz2EwREZHR+fnnn7UXpEdHRz/3gnkiKo11RFTzuMyPiKgacIkS1TXGtjyJNUR1kbHVEdU+E0NPgIiIiIiIqC5iM0VERAYRHh4OV1dXQ0+DqE5jHREZFpspIiKicjx+/BhBQUF46aWXYGZmBm9v7zLHTZo0Cd26dYOFhQXf2BI9ozJ1dPbsWYwZMwbt2rWDRqOBi4sL5syZg8ePHxtgxkSVxxtQEBERlaOwsBAajQYTJ07Etm3bUFBQUOY4EUFISAgSEhJw9OjRWp4lkXGrTB2dOnUKtra2iI6OhpOTE86ePYv33nsPN2/exJo1awwwa6LK4TdTRET12IEDB9CjRw9YWVnB1tYWr7zyChITEwEUNwDvvvsuXFxcoNFo8MILL2DmzJk6nwSXLCHasmULXF1dYWlpieHDhyMnJwdbt25Fhw4d0LhxY4wcORL379/X7hcUFARvb28sW7YMrVq1gqWlJd544w3cvn27wvnu2rULPXr0gEajQdu2bTFhwgTk5uZW6nxqgpWVFVavXo13330Xjo6O5Y6LiIjA+PHj4ezsXGNzIcNhHVVNZeooMDAQq1atwoABA+Ds7Ixhw4bhgw8+wNatW2tsXkTVgc0UEVE9VVBQAH9/f3h4eCA5ORknTpzAtGnT0KhRIwDFbwIdHBzw+eefIzU1FREREYiJicHixYt18ty4cQP/+te/8PXXX2PXrl04evQo3njjDURGRmLLli3YtWsXjhw5gkWLFunsd/z4cRw+fBjff/89vvvuO5w5cwYhISHlznfv3r0YOXIkgoODcfbsWWzevBnHjh1DaGhopc6nLEuWLIG1tXWFsWnTJqV/YmoAWEeGq6N79+7Bzs6u2vMSVSsRYTAYDEYVo/jl1Ljcvn1bAMjhw4crvU9ERIS4urpqH8+bN09MTU3l5s2b2m0TJ04UExMTycrK0m6bMmWK/O53v9M+DgwMFCsrK7l796522759+wSAXLhwQZvbxcVF+7ynp6fMmjVLZz7Hjx8XAJKdna3ofG7fvi1paWkVRk5OTqVyBQYGipeXV4Vjnj0nY/b//7MGr52SMMYaEmEdidR+HYmIpKWlia2trXzyySeVnqchGFsdMWo/eM0UEVE9ZWdnh6CgIAwaNAheXl7w8vKCv78/nJyctGOioqKwdu1aZGRk4MGDBygoKEBRUZFOntatW8Pe3l772NHREY6OjnBwcNDZlpWVpbOfu7s7mjRpon3s4eEBlUqF1NRUtG/fvtR8ExMTkZCQgIiICO02keLfHUpLS4OHh8dzz6esvwE/2aaqYB3Vfh1duXIFPj4+GDJkCCZNmlRrxyVSgsv8iIjqsejoaCQkJKB///7YuXMn3NzcsGPHDgDAV199hbFjx2L06NGIi4tDUlIS5s+fj/z8fJ0czy7/UalUZW579s1jWUQEKlXZv29ZVFSEGTNmIDk5WRunT59GWloaunbt+tzzKQuX+VF1YB3VXh2lp6fD09MTvXr1woYNG8o9TyJjwW+miIjquc6dO6Nz586YOnUqhg8fjqioKAwbNgyHDh1C165dMWXKFO3YjIyMajtuamoqcnJyYGNjAwD44YcfAACdOnUqc3y3bt1w7ty5595avLzzKcvYsWPx5ptvVpivRYsWzzsVItZRLdTRjz/+CC8vL3h7eyMqKgomJvzMn4wfmykionoqPT0da9euhZ+fH9q0aYP09HScOnUKf/jDHwAAbm5uiIyMxI4dO/Diiy8iNja2Wu+cpVKpEBAQgEWLFuHOnTsYN24c/Pz8yn2TN3/+fPj4+GD69OkYM2YMNBoNzp8/j507d+Kzzz577vmUpTqWJ507dw5Pnz7FnTt3kJeXh+TkZADFy6/Mzc0BABcvXkReXh4yMzPx9OlT7RhXV1dYW1tX6fhkWKyj2qmjlJQUeHl5oWfPnliyZAmys7O1+zZv3hympqZVOj5RTWEzRURUT1laWuLChQsYNWoUbt26BQcHB4wcORLz588HALz33ns4e/YsgoODUVBQgMGDB2PBggUYP358tRy/e/fu8PDwwMCBA3H//n34+vris88+K3e8l5cX9u7di/DwcKxatQoqlQrOzs7w9/ev1PnUlMGDB+Py5cvaxyVLpdLT07XXmYSFheHQoUOlxhw4cAD9+vWr0flRzWIdVY/n1dHWrVuRlZWFHTt2lFpy+OtaIzI2qpKLEomISDmVSiV8Pf1FUFAQrl69ir179xp6KlQOlUoFETGaC1JYQ6WxjoyfsdUR1T4uRiUiIiIiIlKAzRQREREREZECXOZHRFQNuESJ6hpjW57EGqK6yNjqiGofv5kiIiIiIiJSgM0UEVEdsH79epiZ1Y0bsAYFBUGlUkGlUmHNmjWGnk6Dc/78ee3f/3m/NdSQsIaoslhDpA82U0REVO369OmDGzduIDAwULtNRLB48WK0adMGarUavXr1QkJCgqL869atg6urKywsLNC5c2fExcXpnePw4cMYOnQoWrVqBZVKhY0bN5Yak5KSgpEjR8LJyQkqlQqLFi1SNF+g+Hd2BgwYAI1GA0dHR0ybNg0FBQV657l27Rr8/f1hbW0NOzs7hIWFITc3V/t8+/btcePGDUydOlXxXMnw6kINAdD+vpaFhQXat2+P9evX652jMnW2Y8cOvP7663BwcICVlRW6du2KDRs2KJoza4iqE5spIiKqdubm5nB0dIRGo9Fu+/vf/44lS5Zg2bJlOHnyJNzd3TFo0CBcv35dr9zbtm3DuHHjMHnyZJw+fRpDhw7F8OHDkZSUpFeeBw8eoEuXLli5cmW5Yx4+fAhnZ2d8+OGHcHR01Cv/r+Xm5mLgwIGwsbFBQkICIiMjER0djVmzZumVp7CwEEOGDEFWVhYOHjyI7du348CBAwgJCdGOMTU1haOjI38suI6rCzWUkJCAkSNHYsSIETh9+jQmTJiA0NBQxMbG6pWnMnV26NAheHp64ptvvsGZM2cQFBSE4OBgfPHFF3odizVE1U5EGAwGg1HFKH45LW3t2rXStGlTefz4sc72mTNnym9/+1sRESkqKpKwsDBxdnYWtVotTk5OMmPGDHn06JF2fHR0tJiampb7WEQkPT1dAEh8fLx2248//ih+fn5iY2MjdnZ2MnToULl48WKZc60ugYGB4uXlpbOtqKhIWrZsKbNnz9ZuKywslFatWsncuXP1yt+jRw8ZM2ZMqW0BAQGK5wxAYmJiKhzTrl07WbhwoaL8q1evFo1GIzk5OaW25eXlVTrPd999JwAkLS2t1LaffvpJZ+y8efPExcWl3Fz//z9r8NopCdbQL+pKDY0ePVpee+21Uts8PT31yvNr+tSZv7+/jBgxQq/81VlDIsZXR4zaD34zRURUg9588008fPgQ3377rXabiODzzz/HO++8o33s4OCAzz//HKmpqYiIiEBMTAwWL15cpWNnZmbitddeg5OTE44cOYL4+Hg0adIE3t7eePjwYbn7+fr6wtrausL4+eef9ZpLeno6bty4gUGDBmm3mZiYwNvbG0eOHKl0nqdPn+LkyZM6eQDAx8dHrzy17ejRo+jVqxcaN26s3ebj44NHjx7p9W3A0aNH4ezsrHMdh7e3N0xNTXH06NFqnbOxYA0VM8YaOnr0aJl5EhISUFhYqFcuJe7duwc7Ozu99mmINUQ1q25ciUlEVEc1adIEQ4cORUxMDEaMGAGgeLnK1atX8fbbbwMofkP06zd9Tk5OyMjIQEREBBYuXKj42KtXr4arqyuWL1+u3RYVFQV7e3t8++23GDVqVJn7/fOf/8SjR48qzN2qVSu95pKZmQkAaNGihc72Fi1aIDExsdJ5bt26hYKCgjLz3LhxQ6851abMzMwy5wxAr3mXlcfMzAx2dnZGff5VwRoqZow1VN7/6ydPnuDOnTto3ry5Xvn0sXXrVsTHx2Pp0qV67dcQa4hqFpspIqIaFhAQgDfeeAN37tyBnZ0dYmJi0L9/f7Rp00Y7JioqCmvXrkVGRgYePHiAgoICFBUVVem4iYmJOHHiRKl1/w8fPkRaWlq5+7Vu3bpKx62ISqX7cywiUmpbbeapTeXNT995lzW+Lpx/VbCGfmFsNVRWnrK2V6e9e/ciKCgIK1aswKuvvqr3/g2xhqjmcJkfEVENe/3112Fra4svv/wSjx8/xldffYWAgADt81999RXGjh2L0aNHIy4uDklJSZg/fz7y8/PLzWliUvrl+9nxRUVF8PLyQnJysk5cuHABY8eOLTd3TSxRKrmovOTT9RLZ2dl63djB3t4eZmZmVc5T2xwdHUvNOSsrS/tcVfIUFBTg7t27Rn3+VcUaMs4aKuv/Y3Z2NszNzdG0aVO9clXWd999Bz8/P3z88ccV/huUp6HWENUcfjNFRFTDGjVqhN///veIiYmBnZ0dCgsLtcuVgOIlS127dsWUKVO02zIyMirM6eDggMLCQmRlZWmXrDx77U23bt2wYcMG7W2UK6smlii98MILaNmyJfbs2YM+ffoAKH6jum/fPoSGhlY6j7m5Obp164Y9e/Zor5cBgN27d6N37956zak2eXh4YMqUKcjLy9N+y7F7925oNBp07dpVrzwLFy7ETz/9BBcXFwDAvn37UFhYCA8PjxqZuzFgDRlnDXl4eGDPnj2YM2eOTp6ePXvC1NRUr1yVsX37drz11ltYsWIFwsLCFOVoqDVENcjQd8BgMBiM+hAo505kJY4fPy4ApEuXLqXuorVixQrRaDTy73//Wy5evCj/+Mc/pFmzZvLrnM/eeez27dvSuHFjCQoKkrS0NImLi5MXX3xR505kmZmZ4ujoKL6+vnLkyBG5dOmSHDx4UCZPniwXLlyocL5VUdadyEREPvroI7GyspLNmzdLSkqKhIWFiY2NjVy7dk2v/Fu3bhVTU1P59NNPJTU1VebMmSONGjWSU6dO6ZUnNzdXkpKSJCkpSQDI4sWLJSkpSecuX0+ePNGOadmypYwfP16SkpIkJSVFr2Pl5ORIy5Ytxd/fX86cOSPffvut2Nvby9SpU/XKU1BQIF26dJHevXtLYmKiHD58WFxdXWXkyJGlxtaXu/mVYA0ZXw0dO3ZMTE1NJTw8XM6fPy8rVqwQU1NT2blzp155KlNnmzdvFjMzM/nrX/8qN27c0EZ2drZex6rOGhIxvjpiGOC1y9ATYDAYjPoQz3sjKCLSqVMnASDff/+9zvanT5/KH//4R2natKk0btxYRo8eLatWrarwjaCISGxsrHTs2FHUarX07t1b4uLiSt3W+dKlSzJ69Gixs7MTCwsLcXZ2ltDQULl58+Zz56tUeW8Ei4qKZOHChdKqVSuxsLCQHj16yLFjx3TGzJs3Tyrzt1y7dq04OzuLubm5vPjiixIbG6vzfHR0tACQ9PT0cnMcOHBAAJSKvn37aseU3Cr72WjXrl2pMdHR0RXOOSUlRfr37y8WFhbSvHlzef/99yU/P19nDACZN29ehXmuXLkiw4YNE0tLS2nSpImEhITo3HK9RH1rpkRYQ8ZWQyIi27dvF3d3dzE3NxcXF5dSdVBSZwcOHCg3R2XqrG/fvs8dI1K7NVRyPDGC+mEYLlQiUq3fdBERNUQqlUr4elosKCgIV69exd69e/XeNyAgAJmZmdi9e3eV5jB37lxs27YNp0+fhplZza5o379/P4YMGYKUlBQ4OzsrznPp0iW4uroiPj6+WpYshoeHY+PGjbh48WKZz6tUKoiI0Vxxzxr6RX2qoaioKMyaNQs//vgjmjRpUqU5PU9t1xBgfHVEtY83oCAiomp38OBBWFtbIzIystL7lFz/sXLlyiofPzY2FitXrqzxRqrkWDNmzKhSI1WSJyAgoMpvAi9cuABra2ssWbKkSnnIsOpLDcXGxmLp0qU13kiVHIs1RLWN30wREVUDfqr+i+zsbOTk5AAovsjfxsbGwDNqWPLz83H58mUAxTcbaNu2bZnjjO0TddbQL1hDhlXZGgKMr46o9rGZIiKqBnwjSHWNsb0JZA1RXWRsdUS1j8v8iIiIiIiIFGAzRUTUgPXr10/x77UYM7VajfXr1xt6GtQAsIaIGjY2U0RERNXgyJEjGD58OBwcHKBWq9G+fXsEBwfjv//9r6GnRmTUzp8/D0tLy1LN27lz56DRaBAVFWWYiRFVApspIiJqcJ48eVKt+aKjo9G3b180b94c33zzDdLS0rBp0yY4ODhg+vTp1XosImNQnTXUsWNHfPjhh5g4cSLS09MBFN8EYsyYMfDx8UFISEi1HYuourGZIiKqx0QEERER6NSpEywsLODo6IigoKByx+/Zswf9+vWDnZ0dbG1t4enpiWPHjumM+fTTT9GhQweo1WrY29tj4MCBePDgAQDg8uXL8PPzg52dHSwtLeHm5lbjS4VOnjyJ7t27Q61Ww93dHd9//73O8xkZGVCpVIiJiYGPjw8sLS2xZMkSHDx4ECqVClevXtUZr1KpsHHjRu3jxMREvPrqq9r8u3fv1lkCdf36dYwbNw7vvvsu1q1bh549e+I3v/kNunfvjqVLl2LTpk01ev5Us1hDNV9DADBhwgT07NkTAQEBKCoqwty5c3H9+nWsW7euRs+dqKpq/gc4iIjIYObMmYPly5fjo48+gpeXF+7fv4/9+/eXOz4vLw9/+tOf0KVLFxQUFGD58uXw9fVFWloa7O3tkZiYiAkTJmi/ibl//z4OHTqEkruwjR8/Ho8ePcL+/ftha2uLtLS0Cj/Bjo+Ph6+vb4XnMGbMGKxZs6bM5x48eIDBgwejR48eiIyMxJ07d/CXv/wF+fn5pcbOmjULS5cuxZo1a6BSqZCRkVHhcUvyDxkyBB4eHoiKisLdu3cxZcoUnfxbtmzBkydPMHv27DJzNG3a9LnHIePFGvpFTdUQUNyARUdH46WXXsKYMWOwZcsW7NixA82bN3/uMYgMSkQYDAaDUcUofjk1Lrm5uaJWq+Xjjz8ud0zfvn0lNDS03OeLioqkWbNmsnHjRhER2bZtm9jY2Ehubm6Z41966SVZsGBBpef48OFDSUtLqzCysrLK3X/t2rViZWUld+/e1W7bt2+fAJDo6GgREUlPTxcAsnDhQp19Dxw4IADkypUrOtsBSExMjDa/tbW13L9/v9R+JfnHjRsnNjY2lT5nY/H//7MGr52SYA0Va4g19GsRERECQAIDAyv9NzAkY6sjRu0Hv5kiIqqnzp07h8ePH2PgwIGV3ufy5cuYO3cujhw5guzsbBQVFeHhw4faH7AcOHAgnJyc4OzsjEGDBsHb2xv+/v6wtbUFAEyaNAnjxo3Drl27MGDAAAwdOhTdu3cv93gajQaurq6KzzE1NRXu7u5o0qSJdpuHhwdUqtI/+1LRPCrK36lTJ50fTe3Zs6dOfhH+NlJ9xRrSVVM1VCI/Px/r16+HpaUljh8/jsePH0OtVut9TKLaxGumiIhIa8iQIcjIyMCqVatw7NgxJCcno2XLlnj69CkAoHHjxjh58iS+/PJLvPDCC/jb3/4GNzc37XKf0NBQpKenIyQkBBcvXkSfPn0wY8aMco8XHx8Pa2vrCmPs2LF6n0dZDY6lpaXOYxMTk1Jjy1ra9Lz8bm5uyMnJwbVr1/SeJ9U/rCH9a6hEeHg4rl27hlOnTuHevXv44IMP9JkykUGwmSIiqqfc3d1hYWGBffv2VWr87du3kZKSgpkzZ8LHxwfu7u6wtLREZmamzjgzMzP0798fCxcuxOnTp1FUVITt27drn2/dujVCQ0PxxRdfaK+vKM8rr7yC5OTkCmPBggUVnmNqaipycnK023744YdKna+DgwOA4htIlEhKStIZ06lTp1L5ExISdMaMGjUKFhYWWLJkSZnHuXv3bqXmQ8aHNVSx6qqhkmMuXboU69atg5ubGyIjI7F8+XIcOnSoUnMhMhQu8yMiqqesra0xZcoUzJ07F5aWlhgwYAByc3Oxf/9+TJ06tdT4pk2bonnz5li3bh1cXFxw+/ZtTJ8+HRqNRjsmNjYWP/30Ezw9PdGsWTMcPnwYd+/eRceOHQEUL1Hy9fVFhw4dkJeXh7i4OO1zZanqEqW33noLs2fPRkBAABYtWoQ7d+7g/fff135iXhFXV1e0a9cO4eHh+OSTT3Dz5s1Sn4S//fbbmDNnDoKDgzF//nzcu3cP06ZNg4mJiXaZUuvWrbFy5Uq89957uHfvHkJDQ+Hs7Ixbt27h66+/RnJyMuLi4hSfIxkOa6hi1VVDeXl5eOeddxASEoKhQ4cCAHx9fREWFoagoCCcOXMGjRs3VnyORDXK0BdtMRgMRn0IGOHF8yLFF78vW7ZMXF1dpVGjRuLo6CjBwcHa55+9eP7gwYPSuXNnsbCwkA4dOsiXX34pbm5uMm/ePBERiY+Pl379+omdnZ2o1Wrp2LGjfPrpp9r9//znP4urq6uo1Wpp1qyZjBgxQtLT02v0HE+cOCGvvPKKmJubi5ubm8TFxYmFhUWpi+fj4+NL7Xvs2DF5+eWXRa1WS5cuXeQ///mPzsXzIiLHjx/X5u/UqZPExcWJiYmJbN68WSfX4cOHxc/PT+zt7cXc3FxcXFwkODhYUlJSavT8lYKRXTjPGirWEGsoLCxMXFxcSt2UIy8vT1xcXCq8wYehGVsdMWo/VCK8cJaIqKpUKpXw9bRhOHHiBLp3746TJ0/i5ZdfNvR0FFOpVBCR0ncBMBDWUMNRX2oIML46otrHZoqIqBrwjWD9tWHDBrRt2xZOTk5IS0vD5MmTYW5ujlOnThl6alVibG8CWUP1V32tIcD46ohqH6+ZIiIiqsCtW7cQHh6O69evw97eHgMGDMCyZcsMPS2iOoM1RPUZv5kiIqoG/FSd6hpj+0SdNUR1kbHVEdU+3hqdiIiIiIhIATZTRERERERECrCZIiIiIiIiUoDNFBERERERkQK8mx8RUTVQq9VZKpWqhaHnQVRZarU6y9Bz+DXWENVFxlZHVPt4Nz8iIiIiIiIFuMyPiIiIiIhIATZTRERERERECrCZIiIiIiIiUoDNFBERERERkQJspoiIiIiIiBRgM0VERERERKQAmykiIiIiIiIF2EwREREREREpwGaKiIiIiIhIATZTRERERERECrCZIiIiIiIiUoDNFBERERERkQJspoiIiIiIiBRgM0VERERERKQAmykiIiIiIiIF2EwREREREREpwGaKiIiIiIhIATZTRERERERECrCZIiIiIiIiUoDNFBERERERkQJspoiIiIiIiBRgM0VERERERKQAmykiIiIiIiIF2EwREREREREpwGaKiIiIiIhIATZTRERERERECrCZIiIiIiIiUoDNFBERERERkQJspoiIiIiIiBRgM0VERERERKQAmykiIiIiIiIF2EwREREREREpwGaKiIiIiIhIATZTRERERERECrCZIiIiIiIiUoDNFBERERERkQJspoiIiIiIiBRgM0VERERERKQAmykiIiIiIiIF2EwREREREREpwGaKiIiIiIhIATZTRERERERECrCZIiIiIiIiUoDNFBERERERkQJspoiIiIiIiBRgM0VERERERKQAmykiIiIiIiIF2EwREREREREpwGaKiIiIiIhIATZTRERERERECvwPvxp3K0sBZzcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = ['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']\n",
    "class_names = ['drugA','drugB','drugC','drugX','drugY']\n",
    "\n",
    "plt.figure(figsize=(15,15))\n",
    "sklearn.tree.plot_tree(dectree, feature_names=feature_names, class_names=class_names);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "marine-affect",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "experienced-great",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x22c7581f6a0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(dectree, test_x, test_y, cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "median-italic",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}