Dimensionality Reduction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for Mathematical Operations\n",
    "import numpy as np\n",
    "\n",
    "# For DataFrame Operations\n",
    "import pandas as pd\n",
    "\n",
    "# for Data Visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 35)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets Import the Dataset\n",
    "data = pd.read_csv('Employee_Data.csv')\n",
    "\n",
    "# lets check the shape of the dataset\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>Over18</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>4</td>\n",
       "      <td>Single</td>\n",
       "      <td>5993</td>\n",
       "      <td>19479</td>\n",
       "      <td>8</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>5130</td>\n",
       "      <td>24907</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>2090</td>\n",
       "      <td>2396</td>\n",
       "      <td>6</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>2909</td>\n",
       "      <td>23159</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>3468</td>\n",
       "      <td>16632</td>\n",
       "      <td>9</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   EnvironmentSatisfaction  Gender  HourlyRate  JobInvolvement  JobLevel  \\\n",
       "0                        2  Female          94               3         2   \n",
       "1                        3    Male          61               2         2   \n",
       "2                        4    Male          92               2         1   \n",
       "3                        4  Female          56               3         1   \n",
       "4                        1    Male          40               3         1   \n",
       "\n",
       "                 JobRole  JobSatisfaction MaritalStatus  MonthlyIncome  \\\n",
       "0        Sales Executive                4        Single           5993   \n",
       "1     Research Scientist                2       Married           5130   \n",
       "2  Laboratory Technician                3        Single           2090   \n",
       "3     Research Scientist                3       Married           2909   \n",
       "4  Laboratory Technician                2       Married           3468   \n",
       "\n",
       "   MonthlyRate  NumCompaniesWorked Over18 OverTime  PercentSalaryHike  \\\n",
       "0        19479                   8      Y      Yes                 11   \n",
       "1        24907                   1      Y       No                 23   \n",
       "2         2396                   6      Y      Yes                 15   \n",
       "3        23159                   1      Y      Yes                 11   \n",
       "4        16632                   9      Y       No                 12   \n",
       "\n",
       "   PerformanceRating  RelationshipSatisfaction  StandardHours  \\\n",
       "0                  3                         1             80   \n",
       "1                  4                         4             80   \n",
       "2                  3                         2             80   \n",
       "3                  3                         3             80   \n",
       "4                  3                         4             80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                 0                  8                      0   \n",
       "1                 1                 10                      3   \n",
       "2                 0                  7                      3   \n",
       "3                 0                  8                      3   \n",
       "4                 1                  6                      3   \n",
       "\n",
       "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                1               6                   4   \n",
       "1                3              10                   7   \n",
       "2                3               0                   0   \n",
       "3                3               8                   7   \n",
       "4                3               2                   2   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                        0                     5  \n",
       "1                        1                     7  \n",
       "2                        0                     0  \n",
       "3                        3                     0  \n",
       "4                        2                     2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check the Top 5 Rows of the Dataset\n",
    "\n",
    "pd.set_option('max_columns', 35)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Records in the Dataset : 1470\n",
      "Number of Unique Values in Employee Count Column : 1\n",
      "Number of Unique Values in Employee Number Column : 1470\n",
      "Number of Unique Values in Over 18 Column : 1\n",
      "Number of Unique Values in Standard Hours Column : 1\n"
     ]
    }
   ],
   "source": [
    "# Lets find out all the Useless Columns\n",
    "\n",
    "print(\"Number of Records in the Dataset :\", data.shape[0])\n",
    "print(\"Number of Unique Values in Employee Count Column :\", data['EmployeeCount'].nunique())\n",
    "print(\"Number of Unique Values in Employee Number Column :\", data['EmployeeNumber'].nunique())\n",
    "print(\"Number of Unique Values in Over 18 Column :\", data['Over18'].nunique())\n",
    "print(\"Number of Unique Values in Standard Hours Column :\", data['StandardHours'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can see that either these columns has all the values unique such as the Employee Number Column or these Columns has a Unique Value.\n",
    "* These Kinds of Columns are useless for Predictive analysis as they do not have any Trends or Patterns Associated with them.\n",
    "* Lets Drop these Columns from the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Columns Left in the Dataset after Removal of 4 Columns : 31\n"
     ]
    }
   ],
   "source": [
    "# Lets remove these Columns from the Dataset\n",
    "\n",
    "data = data.drop(['EmployeeCount', 'EmployeeNumber',\n",
    "                 'Over18', 'StandardHours'], axis = 1)\n",
    "\n",
    "# lets check the Number of Columns after Removal of 4 columns from 35 Columns\n",
    "print(\"Number of Columns Left in the Dataset after Removal of 4 Columns :\", data.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets check Missing Values\n",
    "data.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>OverTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Female</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>Single</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>Male</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>Married</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>Single</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Attrition     BusinessTravel              Department EducationField  Gender  \\\n",
       "0       Yes      Travel_Rarely                   Sales  Life Sciences  Female   \n",
       "1        No  Travel_Frequently  Research & Development  Life Sciences    Male   \n",
       "2       Yes      Travel_Rarely  Research & Development          Other    Male   \n",
       "\n",
       "                 JobRole MaritalStatus OverTime  \n",
       "0        Sales Executive        Single      Yes  \n",
       "1     Research Scientist       Married       No  \n",
       "2  Laboratory Technician        Single      Yes  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets check the Columns with Object Data Types\n",
    "data.select_dtypes('object').head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Attrition'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets Encode these Object Data Types as Numerical Data Types\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "data['BusinessTravel'] = le.fit_transform(data['BusinessTravel'])\n",
    "data['Department'] = le.fit_transform(data['Department'])\n",
    "data['EducationField'] = le.fit_transform(data['EducationField'])\n",
    "data['Gender'] = le.fit_transform(data['Gender'])\n",
    "data['JobRole'] = le.fit_transform(data['JobRole'])\n",
    "data['MaritalStatus'] = le.fit_transform(data['MaritalStatus'])\n",
    "data['OverTime'] = le.fit_transform(data['OverTime'])\n",
    "\n",
    "# lets again check whether there any Object Data Type Columns are Left\n",
    "data.select_dtypes('object').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x:  (1470, 30)\n",
      "Shape of y:  (1470,)\n"
     ]
    }
   ],
   "source": [
    "# lets split the Target Column Attrition from the Dataset\n",
    "\n",
    "y = data['Attrition']\n",
    "x = data.drop(['Attrition'], axis = 1)\n",
    "\n",
    "# lets check the shape of the dataset\n",
    "print(\"Shape of x: \", x.shape)\n",
    "print(\"Shape of y: \", y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     1233\n",
       "Yes     237\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Lets Check the Attrition Column\n",
    "data['Attrition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Yes    838\n",
       "No     640\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "# defining the Model\n",
    "sn = SMOTEENN(random_state = 0)\n",
    "\n",
    "# Training the Model\n",
    "sn.fit(x, y)\n",
    "\n",
    "# Making the Samples\n",
    "x, y = sn.fit_sample(x, y)\n",
    "\n",
    "# Class Distribution\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of xtrain:  (1182, 30)\n",
      "Shape of xtest:  (296, 30)\n",
      "Shape of ytrain : (1182,)\n",
      "Shape of ytest : (296,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)\n",
    "\n",
    "print(\"Shape of xtrain: \", x_train.shape)\n",
    "print(\"Shape of xtest: \", x_test.shape)\n",
    "print(\"Shape of ytrain :\", y_train.shape)\n",
    "print(\"Shape of ytest :\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.80      0.87      0.84       127\n",
      "         Yes       0.90      0.84      0.87       169\n",
      "\n",
      "    accuracy                           0.85       296\n",
      "   macro avg       0.85      0.86      0.85       296\n",
      "weighted avg       0.86      0.85      0.86       296\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Lets create a Predictive Model\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train, y_train)\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "cr = classification_report(y_pred, y_test)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Filtering\n",
    "\n",
    "* Remove Highly Correlated Features from the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (15, 10)\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "corr = data.corr()\n",
    "mask = np.triu(np.ones_like(corr, dtype = bool))\n",
    "sns.heatmap(corr, mask = mask, cmap = 'Wistia', linewidths = 0.2)\n",
    "plt.xticks(fontsize = 10)\n",
    "plt.yticks(fontsize = 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Age is Highly Correlated with Job Level, Monthly Income, and TotalWorking Years \n",
    "* Job Role and Department are Highly Correlated\n",
    "* Job Level is Highly Correlated with TotalWorkingyears and MonthlyIncome\n",
    "* Performance Rating and PercentSalaryHike are Highly Correlated\n",
    "* YearsInCurrentRole is Highly Correlated with YearsAtCompany and YearsWithCurrManager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 25)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets Remove the Highly Correlated Columns from the Datasets\n",
    "\n",
    "data_cf = data.drop(['JobRole', 'JobLevel', 'PercentSalaryHike', 'TotalWorkingYears',\n",
    "                     'YearsInCurrentRole', 'YearsWithCurrManager'], axis = 1)\n",
    "\n",
    "# lets check the shape of the dataset after removal of 5 Columns from 35 Columns\n",
    "data_cf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Attrition'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets Encode these Object Data Types as Numerical Data Types\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "data_cf['BusinessTravel'] = le.fit_transform(data_cf['BusinessTravel'])\n",
    "data_cf['Department'] = le.fit_transform(data_cf['Department'])\n",
    "data_cf['EducationField'] = le.fit_transform(data_cf['EducationField'])\n",
    "data_cf['Gender'] = le.fit_transform(data_cf['Gender'])\n",
    "data_cf['MaritalStatus'] = le.fit_transform(data_cf['MaritalStatus'])\n",
    "data_cf['OverTime'] = le.fit_transform(data_cf['OverTime'])\n",
    "\n",
    "# lets again check whether there any Object Data Type Columns are Left\n",
    "data_cf.select_dtypes('object').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, Split the Dataset\n",
    "\n",
    "y_cf = data_cf['Attrition']\n",
    "x_cf = data_cf.drop(['Attrition'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    838\n",
       "No     641\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "# defining the Model\n",
    "sn = SMOTEENN(random_state = 0)\n",
    "\n",
    "# Training the Model\n",
    "sn.fit(x_cf, y_cf)\n",
    "\n",
    "# Making the Samples\n",
    "x_cf, y_cf = sn.fit_sample(x_cf, y_cf)\n",
    "\n",
    "# Class Distribution\n",
    "y_cf.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of xtrain:  (1183, 24)\n",
      "Shape of xtest:  (296, 24)\n",
      "Shape of ytrain : (1183,)\n",
      "Shape of ytest : (296,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train_cf, x_test_cf, y_train_cf, y_test_cf = train_test_split(x_cf, y_cf, test_size = 0.2, random_state = 0)\n",
    "\n",
    "print(\"Shape of xtrain: \", x_train_cf.shape)\n",
    "print(\"Shape of xtest: \", x_test_cf.shape)\n",
    "print(\"Shape of ytrain :\", y_train_cf.shape)\n",
    "print(\"Shape of ytest :\", y_test_cf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.74      0.87      0.80       115\n",
      "         Yes       0.91      0.80      0.85       181\n",
      "\n",
      "    accuracy                           0.83       296\n",
      "   macro avg       0.82      0.84      0.82       296\n",
      "weighted avg       0.84      0.83      0.83       296\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Lets create a Predictive Model\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train_cf, y_train_cf)\n",
    "y_pred_cf = model.predict(x_test_cf)\n",
    "\n",
    "cr = classification_report(y_pred_cf, y_test_cf)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variance Inflation Filtering\n",
    "\n",
    "* Remove all the Features having High Variance\n",
    "* VIF above 5 indicates a high multicollinearity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "        }</style><table id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183e\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >feature</th>        <th class=\"col_heading level0 col1\" >VIF</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow0_col0\" class=\"data row0 col0\" >Age</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow0_col1\" class=\"data row0 col1\" >35.471068</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow1_col0\" class=\"data row1 col0\" >BusinessTravel</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow1_col1\" class=\"data row1 col1\" >6.936126</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow2_col0\" class=\"data row2 col0\" >DailyRate</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow2_col1\" class=\"data row2 col1\" >5.499722</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow3_col0\" class=\"data row3 col0\" >Department</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow3_col1\" class=\"data row3 col1\" >11.165526</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow4_col0\" class=\"data row4 col0\" >DistanceFromHome</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow4_col1\" class=\"data row4 col1\" >2.711344</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow5_col0\" class=\"data row5 col0\" >Education</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow5_col1\" class=\"data row5 col1\" >9.876365</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow6_col0\" class=\"data row6 col0\" >EducationField</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow6_col1\" class=\"data row6 col1\" >3.981095</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow7_col0\" class=\"data row7 col0\" >EnvironmentSatisfaction</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow7_col1\" class=\"data row7 col1\" >7.125041</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow8_col0\" class=\"data row8 col0\" >Gender</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow8_col1\" class=\"data row8 col1\" >2.173840</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow9_col0\" class=\"data row9 col0\" >HourlyRate</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow9_col1\" class=\"data row9 col1\" >13.550311</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow10_col0\" class=\"data row10 col0\" >JobInvolvement</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow10_col1\" class=\"data row10 col1\" >14.368878</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow11_col0\" class=\"data row11 col0\" >JobLevel</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow11_col1\" class=\"data row11 col1\" >50.962093</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow12_col0\" class=\"data row12 col0\" >JobRole</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow12_col1\" class=\"data row12 col1\" >7.728222</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow13_col0\" class=\"data row13 col0\" >JobSatisfaction</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow13_col1\" class=\"data row13 col1\" >6.641658</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow14_col0\" class=\"data row14 col0\" >MaritalStatus</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow14_col1\" class=\"data row14 col1\" >5.636039</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow15_col0\" class=\"data row15 col0\" >MonthlyIncome</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow15_col1\" class=\"data row15 col1\" >31.802467</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow16_col0\" class=\"data row16 col0\" >MonthlyRate</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow16_col1\" class=\"data row16 col1\" >5.626539</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow17_col0\" class=\"data row17 col0\" >NumCompaniesWorked</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow17_col1\" class=\"data row17 col1\" >3.043946</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow18_col0\" class=\"data row18 col0\" >OverTime</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow18_col1\" class=\"data row18 col1\" >1.538416</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow19_col0\" class=\"data row19 col0\" >PercentSalaryHike</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow19_col1\" class=\"data row19 col1\" >33.846284</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow20_col0\" class=\"data row20 col0\" >PerformanceRating</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow20_col1\" class=\"data row20 col1\" >107.306920</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow21_col0\" class=\"data row21 col0\" >RelationshipSatisfaction</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow21_col1\" class=\"data row21 col1\" >7.378057</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow22_col0\" class=\"data row22 col0\" >StockOptionLevel</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow22_col1\" class=\"data row22 col1\" >2.335274</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow23_col0\" class=\"data row23 col0\" >TotalWorkingYears</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow23_col1\" class=\"data row23 col1\" >13.948849</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow24_col0\" class=\"data row24 col0\" >TrainingTimesLastYear</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow24_col1\" class=\"data row24 col1\" >6.054863</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow25_col0\" class=\"data row25 col0\" >WorkLifeBalance</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow25_col1\" class=\"data row25 col1\" >14.349174</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow26_col0\" class=\"data row26 col0\" >YearsAtCompany</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow26_col1\" class=\"data row26 col1\" >10.226762</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow27_col0\" class=\"data row27 col0\" >YearsInCurrentRole</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow27_col1\" class=\"data row27 col1\" >6.797882</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow28_col0\" class=\"data row28 col0\" >YearsSinceLastPromotion</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow28_col1\" class=\"data row28 col1\" >2.578519</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183elevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow29_col0\" class=\"data row29 col0\" >YearsWithCurrManager</td>\n",
       "                        <td id=\"T_4727ab98_5cb8_11eb_bea3_b46d83e4183erow29_col1\" class=\"data row29 col1\" >6.769876</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x209a0ac5208>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor \n",
    "\n",
    "# VIF dataframe \n",
    "vif_data = pd.DataFrame() \n",
    "vif_data[\"feature\"] = x.columns \n",
    "  \n",
    "# calculating VIF for each feature \n",
    "vif_data[\"VIF\"] = [variance_inflation_factor(x.values, i) \n",
    "                          for i in range(len(x.columns))] \n",
    "  \n",
    "vif_data.style.background_gradient(cmap = 'Wistia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets create the Dependent and Non Dependent Sets\n",
    "x_vif = data[['DistanceFromHome', 'EducationField', 'Gender', 'NumCompaniesWorked','OverTime',\n",
    "           'StockOptionLevel','YearsSinceLastPromotion']]\n",
    "y_vif = data['Attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    884\n",
       "No     543\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "# defining the Model\n",
    "sn = SMOTEENN(random_state = 0)\n",
    "\n",
    "# Training the Model\n",
    "sn.fit(x_vif, y_vif)\n",
    "\n",
    "# Making the Samples\n",
    "x_vif, y_vif = sn.fit_sample(x_vif, y_vif)\n",
    "\n",
    "# Class Distribution\n",
    "y_vif.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of xtrain:  (1141, 7)\n",
      "Shape of xtest:  (286, 7)\n",
      "Shape of ytrain : (1141,)\n",
      "Shape of ytest : (286,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train_vif, x_test_vif, y_train_vif, y_test_vif = train_test_split(x_vif, y_vif, test_size = 0.2, random_state = 0)\n",
    "\n",
    "print(\"Shape of xtrain: \", x_train_vif.shape)\n",
    "print(\"Shape of xtest: \", x_test_vif.shape)\n",
    "print(\"Shape of ytrain :\", y_train_vif.shape)\n",
    "print(\"Shape of ytest :\", y_test_vif.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.81      0.91      0.86       103\n",
      "         Yes       0.95      0.88      0.91       183\n",
      "\n",
      "    accuracy                           0.89       286\n",
      "   macro avg       0.88      0.90      0.89       286\n",
      "weighted avg       0.90      0.89      0.89       286\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Lets create a Predictive Model\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train_vif, y_train_vif)\n",
    "y_pred_vif = model.predict(x_test_vif)\n",
    "\n",
    "cr = classification_report(y_pred_vif, y_test_vif)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection with RFECV\n",
    "\n",
    "* Recursive Elimination of Features from the Dataset to Reduce the Complexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features : 20\n",
      "Best features : Index(['Age', 'DailyRate', 'DistanceFromHome', 'EnvironmentSatisfaction',\n",
      "       'Gender', 'HourlyRate', 'JobInvolvement', 'JobLevel', 'JobRole',\n",
      "       'JobSatisfaction', 'MonthlyIncome', 'MonthlyRate', 'PerformanceRating',\n",
      "       'RelationshipSatisfaction', 'StockOptionLevel', 'TotalWorkingYears',\n",
      "       'TrainingTimesLastYear', 'WorkLifeBalance', 'YearsAtCompany',\n",
      "       'YearsInCurrentRole'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFECV\n",
    "\n",
    "# The \"accuracy\" scoring is proportional to the number of correct classifications\n",
    "model = DecisionTreeClassifier() \n",
    "rfecv = RFECV(estimator = model, step = 1, cv = 5, scoring = 'accuracy')\n",
    "rfecv = rfecv.fit(x_train, y_train)\n",
    "\n",
    "print('Optimal number of features :', rfecv.n_features_)\n",
    "print('Best features :', x_train.columns[rfecv.support_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets Create the Dependent and Independent Sets for RFECV\n",
    "\n",
    "x_rfecv = data[['JobLevel','MonthlyIncome','MonthlyRate']]\n",
    "y_rfecv = data['Attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    646\n",
       "No     603\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "# defining the Model\n",
    "sn = SMOTEENN(random_state = 0)\n",
    "\n",
    "# Training the Model\n",
    "sn.fit(x_rfecv, y_rfecv)\n",
    "\n",
    "# Making the Samples\n",
    "x_rfecv, y_rfecv = sn.fit_sample(x_rfecv, y_rfecv)\n",
    "\n",
    "# Class Distribution\n",
    "y_rfecv.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of xtrain:  (999, 3)\n",
      "Shape of xtest:  (250, 3)\n",
      "Shape of ytrain : (999,)\n",
      "Shape of ytest : (250,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train_rfecv, x_test_rfecv, y_train_rfecv, y_test_rfecv = train_test_split(x_rfecv, y_rfecv,\n",
    "                                                                            test_size = 0.2, random_state = 0)\n",
    "\n",
    "print(\"Shape of xtrain: \", x_train_rfecv.shape)\n",
    "print(\"Shape of xtest: \", x_test_rfecv.shape)\n",
    "print(\"Shape of ytrain :\", y_train_rfecv.shape)\n",
    "print(\"Shape of ytest :\", y_test_rfecv.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.91      0.89      0.90       119\n",
      "         Yes       0.90      0.92      0.91       131\n",
      "\n",
      "    accuracy                           0.91       250\n",
      "   macro avg       0.91      0.91      0.91       250\n",
      "weighted avg       0.91      0.91      0.91       250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Lets create a Predictive Model\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train_rfecv, y_train_rfecv)\n",
    "y_pred_rfecv = model.predict(x_test_rfecv)\n",
    "\n",
    "cr = classification_report(y_pred_rfecv, y_test_rfecv)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection using the Boruta Algorithm\n",
    "\n",
    "* In Boruta, features do not compete among themselves. Instead — and this is the first brilliant idea — they compete with a randomized version of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features to Keep: ['Age', 'BusinessTravel', 'DailyRate', 'DistanceFromHome', 'Education', 'EnvironmentSatisfaction', 'HourlyRate', 'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction', 'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike', 'RelationshipSatisfaction', 'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion', 'YearsWithCurrManager']\n"
     ]
    }
   ],
   "source": [
    "# Implementing the Boruta Algorithm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from boruta import BorutaPy\n",
    "\n",
    "# defining the Model\n",
    "model= RandomForestClassifier(max_depth = 5)\n",
    "\n",
    "# define Boruta feature selection method\n",
    "boruta = BorutaPy(estimator = model, n_estimators = 'auto',\n",
    "                  max_iter = 100, random_state = 0)\n",
    "\n",
    "# find all relevant features\n",
    "boruta.fit(np.array(x), np.array(y))\n",
    "\n",
    "### print results\n",
    "best_features = x.columns[boruta.support_].to_list()\n",
    "print('features to Keep:', best_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets Create the Dependent and Independent Sets for RFECV\n",
    "\n",
    "x_bor = data[['Age', 'DailyRate', 'DistanceFromHome', 'Education', 'EnvironmentSatisfaction',\n",
    "                'HourlyRate', 'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
    "                'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'RelationshipSatisfaction',\n",
    "                'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
    "                'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion', 'YearsWithCurrManager']]\n",
    "y_bor = data['Attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    838\n",
       "No     640\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.combine import SMOTEENN\n",
    "\n",
    "# defining the Model\n",
    "sn = SMOTEENN(random_state = 0)\n",
    "\n",
    "# Training the Model\n",
    "sn.fit(x_bor, y_bor)\n",
    "\n",
    "# Making the Samples\n",
    "x_bor, y_bor = sn.fit_sample(x_bor, y_bor)\n",
    "\n",
    "# Class Distribution\n",
    "y_bor.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of xtrain:  (1182, 22)\n",
      "Shape of xtest:  (296, 22)\n",
      "Shape of ytrain : (1182,)\n",
      "Shape of ytest : (296,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train_bor, x_test_bor, y_train_bor, y_test_bor = train_test_split(x_bor, y_bor,\n",
    "                                                                            test_size = 0.2, random_state = 0)\n",
    "\n",
    "print(\"Shape of xtrain: \", x_train_bor.shape)\n",
    "print(\"Shape of xtest: \", x_test_bor.shape)\n",
    "print(\"Shape of ytrain :\", y_train_bor.shape)\n",
    "print(\"Shape of ytest :\", y_test_bor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.80      0.86      0.83       128\n",
      "         Yes       0.89      0.83      0.86       168\n",
      "\n",
      "    accuracy                           0.84       296\n",
      "   macro avg       0.84      0.85      0.84       296\n",
      "weighted avg       0.85      0.84      0.85       296\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Lets create a Predictive Model\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train_bor, y_train_bor)\n",
    "y_pred_bor = model.predict(x_test_bor)\n",
    "\n",
    "cr = classification_report(y_pred_bor, y_test_bor)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Principal Component Analysis\n",
    "* The principal components of a collection of points in a real p-space that are a sequence of p direction vectors, where the ith vector is the direction of a line that best fits the data while being orthogonal to the first i-1 vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.7284166 , 0.99802283, 0.99999208, 0.99999692, 0.99999791,\n",
       "       0.99999875, 0.99999927, 0.99999944, 0.99999958, 0.99999966,\n",
       "       0.99999972, 0.99999978, 0.99999982, 0.99999986, 0.99999988,\n",
       "       0.9999999 , 0.99999992, 0.99999993, 0.99999995, 0.99999996,\n",
       "       0.99999997, 0.99999998, 0.99999998, 0.99999999, 0.99999999,\n",
       "       0.99999999, 1.        , 1.        , 1.        , 1.        ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets determine the Value of N-Components\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Lets create the PCA Model\n",
    "pca = PCA(n_components = None)\n",
    "x_train_pca = pca.fit_transform(x_train)\n",
    "x_test_pca = pca.transform(x_test)\n",
    "\n",
    "# This result the variance explained by the number of components taken into account\n",
    "pca.explained_variance_ratio_.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Lets create the PCA Model\n",
    "pca = PCA(n_components = 2)\n",
    "x_train_pca = pca.fit_transform(x_train)\n",
    "x_test_pca = pca.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.86      0.89      0.87       132\n",
      "         Yes       0.91      0.88      0.89       164\n",
      "\n",
      "    accuracy                           0.89       296\n",
      "   macro avg       0.88      0.89      0.88       296\n",
      "weighted avg       0.89      0.89      0.89       296\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Lets create a Predictive Model\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train_pca, y_train)\n",
    "y_pred_pca = model.predict(x_test_pca)\n",
    "\n",
    "cr = classification_report(y_pred_pca, y_test)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>x_train_pca0</th>\n",
       "      <th>x_train_pca1</th>\n",
       "      <th>Attrition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>8699.529505</td>\n",
       "      <td>-3202.976502</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1470</th>\n",
       "      <td>-1477.377907</td>\n",
       "      <td>-2781.027754</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>-8311.474393</td>\n",
       "      <td>13756.033216</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>5964.857436</td>\n",
       "      <td>-2913.159831</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>6418.357134</td>\n",
       "      <td>5675.848243</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      x_train_pca0  x_train_pca1 Attrition\n",
       "1444   8699.529505  -3202.976502       Yes\n",
       "1470  -1477.377907  -2781.027754       Yes\n",
       "56    -8311.474393  13756.033216        No\n",
       "1005   5964.857436  -2913.159831       Yes\n",
       "467    6418.357134   5675.848243        No"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check How these Components will look\n",
    "\n",
    "df = pd.DataFrame({'x_train_pca0':x_train_pca[:,0],\n",
    "                 'x_train_pca1':x_train_pca[:,1],\n",
    "                 'Attrition':y_train})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bNhYsWMCqVatYt24dKSkp2Gw2KlasSN++fbn77ru56qqrgutUrFiRzz77jOeff55PPvmELVu2EBUVRfPmzRkyZMhFS6Lo0KEDffv2DfbDYrHQqVMnnnzySSpXrhzS9mL1eerUqdx4443Mnz+fTZs2ceLECYoXL05iYiL33nsv3bp1y/c+5syZw5gxY/jqq69Ys2YNpmkyatQomjdvzvjx41m/fj0//fQT69atQ9d1ypcvz6hRoxgwYECea2pKJJGMkpKSIgq7ExKJRBIu6tSpw4EDB+Qcy2Fg8uTJTJkyhVmzZoUM3UskkssLGbMokUgkEolEIjknUixKJBKJRCKRSM5JxIjFL7/8kp49e3LVVVcRGxvLwoULQ5YPGjSI2NjYkH/XX399SBuPx8OIESOoXLkyiYmJ9OzZM1sh3JSUFPr3709SUhJJSUn0798/23DVgQMH6NGjB4mJiVSuXJmRI0fmWAZCIpFIJBKJ5FInYsRieno6NWvW5Nlnn80212cmrVu3ZufOncF/S5YsCVn++OOPs3LlSubPn8+qVatITU2lR48eIVX577vvPrZv386SJUtYunQp27dvZ8CAAcHlhmHQo0cP0tLSWLVqFfPnz2fFihWMGTOmYA5cIpGElZ9++knGK4aJxx9/nJSUFBmvKJFc5kRkgku5cuWYOnVqyANq0KBB/PvvvzlOQA+BqcKqVq3KrFmzgjW8Dh48SJ06dVi6dCnJycns3LmTxo0b88knn9CkSRMANm/eTPv27fn666+pVq0aa9asoXv37vz000/B8g7vvfceQ4YMYdeuXRdltgSJRCKRSCSSSCFiPIu5YfPmzVStWpVrrrmGIUOGBKeagsBUWj6fj7Zt2wZt5cuXp0aNGsFZBrZt20Z0dDSNGzcOtmnSpAlRUVEhbWrUqBFSByw5ORmPx8MPP/xQ0IcokUgkEolEElEUmTqL119/PbfeeisVK1Zk//79TJw4kY4dO/LZZ59hs9k4evQomqZRsmTJkPVKly4dnKf06NGjlCxZMqTGmqIolCpVKqRN6dKlQ7ZRsmRJNE0753ynEolEIpFIJJcqRcaz2LVrV26++WZq1apF+/btWbp0Kbt27co2BdXZCCGyicMLafNfdkne2LVrV2F3QZID8rpELvLaRC7y2kQm8rqElyIjFs+mbNmyJCYmsmfPHgDi4+MxDCPbxPL//PNP0FMYHx/PP//8gxBZYZpCCI4fPx7S5mwP4vHjxzEMI5vHUSKRSCQSieRSp8iKxePHj3P48GESEhIAqF+/PhaLhQ0bNgTbHDp0KJjUAtCoUSPS0tLYtm1bsM22bdtIT08PabNz586QkjsbNmzAZrNRv379i3FoEolEIpFIJBFDxMQspqWlBb2Epmly8OBBtm/fTlxcHHFxcTz77LN07NiRhIQE9u/fz9NPP03p0qXp0KEDAMWLF+euu+5i3LhxlC5dmri4OMaMGUOtWrVo3bo1ADVq1OD6669n6NChTJ8+HSEEQ4cOpV27dlSrVg2Atm3bctVVVzFw4EAmTpzIiRMnGDduHH369JGZ0BKJRCKRSC47Isaz+P3339OyZUtatmyJy+Vi8uTJtGzZkkmTJqFpGr/++iu9evXi2muvZdCgQVStWpVPP/2UmJiY4DYmTZpEhw4duOeee7jpppuIiori3XffRdO0YJvXXnuN2rVrc9ttt9G1a1dq167N3Llzg8s1TeO9997D6XRy0003cc8999ChQwcmTpx4Uc+HRCKRSCQSSSQQkXUWJZc2u3btCnpyLwSH34vu84JpYNrsZFjsIXGokgsjv9dFUnDIaxO5yGsTID09Hb/fX9jdCHLq1Ck5GngWUVFR6PqFDShHzDC0RJIbogwvyrypiM1rAVAqX0nUmBmk6dZC7plEIpFcnng8HiAQDhYp2Gw27HZ7YXcjYhBCkJKSQkxMzAUJxogZhpZIzoeqqij7/4DTQhGAPb/BumXoqryVJRcfK2D3e7HI+09yGeN2u3E6nYXdDcl/oCgKsbGxpKenX9D60rMoyTWKomAxDVTDh9/qwG+aBbafnIaVVVWFPTuzr/DHr6imAcg6mJKLg6qqON3p8P7rsHsHeuPWWJM7ka5aCrtrEkmhIOsQRz75uUZSLEpyhaYoONNSEO/MgiN/oSd3wmzUmgwtfC9HXQG71w1HDqEUL4HPGY37jO0bhgENmsPb00NXbNkeQ9PBMMLWF4nkv3B4XDBhMOzfHTDs+hnl6GGsvR7EqxSel1FVVYQQMoZXkm8sCGw+D3g9CKsNt9WBcRnfVwYKCqByeZ4DKRYlucLhdSNG9YHUkwHDrp9RfR701h3D4mFUVRXH8cOIx+4BdwYC0Nt3x97tPtxaIB5RCIGvWAkswybDghngdkGHOzBrNggIyVxiESY2vwe8XoTVhsvmxCwgL6nk0kTxurOEYiYbVmLt3h+vxXbR+6OpKg5PRiAsw+5AlKskE7/ChKqq2LxuNAQGCh6r/ZJ/Xlgwse3cDi+OAVc6SqkEnGNfJr1EwiV/7GdjouA5/XoxhcCiKlhUcdmNY8lAG8k5UU/HYSmKAn8fyBKKmaz+AIv7wuIfzsbqdSNenQLujCzjx4vR3a6Qdh5Nx311M8xJbyCmLcLTvkfQu2kTJlE+N06fJ6RcUsh+TAPb95vggS7wQCeUJ+4jKi0lIodQNFUlyucm2ufG4fdGZB8vVxTdAmdfj2KxiPN4HVRVxenzEO1JJ8rrRgvTJXW60mD4HTDpERg3AOXJQUT5POHZ+GWMqqpEpaWgPT8ShtyO9uY0nGkpwWfjpYrN54EXHgPX6ef7P0cQ08di87j+e8VLEEPAwTQfv53w8OcpH25DYOZSKk6ePJmmTZuet11sbCzLly/Pb1cLlEv7jpdcEA7DR/SJozh//poYdzq64YfoHEoQxJZAaOFxTquGAf8czr4gNbuQ85uCDKuddKsD3+l3c7ThxfLWiyiDu6BOeBDHob3oOby4rX4vzHwKvO6A4e+DiDnPYPN7w3Ic4UIHHIf2ojxxH9x3E9q0x4l2p0vBWABYhYnD68Iqcu8x8Vms0PHOLIOiQP/H8NjOHeSfKTzUCQ9C/1tQRt2F88DufA/vWFQV8dEiOHkiy3hwD/z67Tk/ms7EJgyifG4cPs8lL4Lyit2TgXh2OEqj1qiPT0Opcy1q+imcl7hoUjxu8LhDjXt3ol2CQ7A//vgjJUqUoF27diH2hQsXUq5cOQ6k+UjzBZ4NXlPQqH4dZs6YkattP/TQQ3z00UfBvwcNGkSPHj2ytdu5cyc33XRTPo6i4JFPBkkIdsOHtnQePNIdpgxHDOqIff8uRLE4aNgqq6HVhnLP8HwNuWmaFnyZ+ewOaHlzaIOoGCiZcN6hNCsClsyDDSsDQ9P7/oBx/bHn5FlJTQHzrCHr3TvQjP+uD1bQIk3XdSy6FnxZ231uePpB+PtgoMEv30akqC3KKIpCtM+NdcF0tHEDsC6YTrTfnatr7VZ1jE53wUvvwbDJMPdDqFITe/opor0uLDm8VG1eF+KlJwL3J8C/xxCTHg5c6/wchzBR/jmSfcHxo+c9lmi/F8sb01AG34Y26WGijh5Evwy+RzRNw6qAfh4xrfr9KA2aw6kUzCfuR7wyEXPEXSi7fkJXFSyqQvkSsejapfUqFTYHxJxVBqfWNfgvwY+Jt99+m3vvvZcdO3awc2f2BMo0b+hHpADM82hm0zQxDIPo6GhKlChx3j4kJCRgs1388JW8cOldeUm+sPi9sOq9LINpIF59FiFMzIFj4Ln/g1EvwKxluEqVuaD4FYfDQbTfg+O7jTi2rifa58ZUFMTNPaH7/VCmAtRvgjL5DVzW89fJ0r1u2PZZqNHjhn/+zv6yLBYHZ2+zXhP856jTaDf9xLjSiDq0h2ifO0dvZX5QFIVovwf7mvexvzYF5587sRs+FFc6ZKSFNv7pa7SzhW6EY1ECpWUisbSRPXOobd1yOLw/8P8Lo3P+yMgBl2YlvUQZMq5uDooKk4fBwA4wuAu2reuwnfUBogkBO7eHbiQ9DfLppfIrKuKWnqFGXYfGbf6zSLJVmPDOLPj8o0D4x57fEGP7B5LMLmGchg/Hj1uwzp6A/dP3iTbOHeIhdB2lSVvE8gVnGAXi1WdxutOxrVhA6Tefw75hBVGG7yIdQcHjttpRxs2CskkBw1X1UR56Co9uwyJMon0uYtJSiPK50YrwaIfL5WLJkiXcfffddOzYkQULAtd548aNPPjgg6Snp9OySjxtqsbz5vSpPNKrM0cOHeDpJ8cSGxtLbGwskOWF/PTTT2natCmlS5dm586dIcPQkydPZtGiRaxevTq47saNG4Hsw9C//PILnTp1okyZMlSqVIlBgwZx8mRWGFimh3L27NlcddVVVKxYkQceeICMjDPCuMKMTHCRhOLN4UWZ8g+KEKRbbCgJFVDKJOUryLlqyVgYeRccPxowRBfDOW0RqbYoLB3uxHLDbZiajtdiy9V+TE1HrVAla3unUWJLZfNKui02HONeRswYC0cPQ/2mcM8wvJoOZ7W1mX709csRC2YGDFY79qfn4CpfJU8JNf+F0+eG8YPgwJ6ADP3sQ/QhT0PdRmCxgu8MT2LlKzELMdM2LyiKQpTXBcsXwK6f0Ru1RrTuQHoYs+fzi2b4YMcPocZfvwvYc1nkXQiBxTTg/16G3b8GjB43zH4G6+wmeBzRwbaGoqBVqw27fs7agCMKbI58HYdpmvjLJqGPmwUfvAF2B8odg3A7Yv5zPYvPA9s+DzVmpAWGs0uVzVefIhWLAuqn78O7cwKGzesQW9bhGPUCGTlcc4/VgdMZlX004uQJOPFPYEQDYPs2lP17sPUejCdCf6MWBSweF0JR8dqd//kM8wvIKJOEbcKrqEJgqBpuiw2L6cf67SaY8wzC50WJK43zqdmcUIrmpAjLly+nQoUK1K5dmx49enDPPffw5JNP0rhxYyZPnsyECRPY9t0P7D3lRbM5ub3v/fS/tS133dmbe++9N2Rbbreb559/nhdffJFSpUqRkJAQsvyhhx7i999/58SJE8EphuPi4rL1KSMjg9tvv52rr76adevWceLECR5++GEGDx4cFLMAmzdvJiEhgWXLlnHo0CH69u1L1apVGTZsWAGcKelZlJyF6YiChHKhxrad8J0ebhZC5EsoapqG+s3GUGGXdgrx8WIsmoZPCDKsDtyaJdf78ehWlPtHQVzpgEFV4c6H8NmyeyX9KGRUrI6Y9AbM+xj/kKdJtzpyHOq2+L2wcFaWweuGl5/GFqakHgDl1Ak4sCfUuHR+IIB66DNgPx0DF18W5aHxeHLhaY0EHF43PPMwrFwIv/0Ib09HWTQ7T3GBBY6iBcTamURFg3r+OL8z0Xye7B5DIRBHDoZ4rDxWB8ojEyCxYsBQPA7l8Wm4w5A97VZ1XNXr4h82Gd+DT5IeXx7feTw+hqpBhStCjaqKcvbw4yWE1euGlf8XYlN+/wn1HN5UvxAYzmioGDqdn3JdO8Q3m0Ibb1gReGZEIFGGD9uHC1FH9UGb/AiOA39gOc9v0TBNMix20qwOXLoVIQRWnxdmT8j6iD1xDDHr6UDMeRHk7bffpmfPgFe+RYsWOBwOVq1ahdVqpVixYiiKQvky8TSrXoGGSSVpVqUMuqYSExNDQkJCiCA0DIOpU6fSpEkTqlatSkxM6MdadHQ0drsdm80WXNdqzS6ylyxZQnp6OnPnzqVWrVq0aNGCl156iZUrV7JnT9a7IiYmhmnTplGjRg3atm1L586d+fzzz7NtL1xIz6IkBJfVQdSEVxHvzgnEVjVNRiR3whumQgGKokB6avYFaacueJtCCDJi4nA8twDFnQE2B17dgkfN+fY2TJN0i/3MDeTcV48HcbZgPXoovF9YOQ3P6jomAn+ta7C9/EGwzllRKvGj+jyw96z4nw0rsfboj9cSGYLXY7Vh6z8KZjwZuAcUBe5/DE8e+2dY7Wh1GgYqBmSiqihlK4R8hJimSXqxktiffhXV8CE0HbfVgT9M5W0Mw8DI9Nzm4j7xWu1YBo5BjL0/4ClTNegzBK8l916iTG9VXkYCCh2rPTD8fwbKf4RJuKwOnGNnwHuvwR8/wzUtUdp3w3y4W2hDR1REphA9y3AAACAASURBVH/oqoqy4WNY/FrA8O8xGNsf+yvL8f1HQlaOuNLh7NCGP39Hicgj/2/27NnD1q1bmT9/PhB4N3Xv3p0FCxbQqVOnkLYa4ryVC3Rdp06dOvnu186dO6lVq1aI2GzcuDGqqvLbb79RuXJlAGrUqBEybV+ZMmX45ptv8r3/cyHFoiQE0zRJs0dj6TMUze/Fb3PiC+MLwO/3Y2l+Ayydl/V1qqoot/TMV71GQwjSLHYIoxAx7Q6UkvGhXtCm1+PTwzeUKqJjoXodlN9/yjL2egCvPQrDMPCdeUwX6UXs9HtR008FkoVKxJNhc+RdBOiWgBA+c72Y4gX2StE1FYv7tGjR9FzVF/ShoNZvhnXOSsThAyhlK+C12skp8kxRFBw+N6rfB6qGx2rHd/oDyouCtecAOPYX/LAFYmJRBo3Bk4PH0DRNMiw2yFxWiHUQTdMkI640jhcWBWJk7c7//Mg6myjDh/LhQti0GjW+HJb7R5ERW5pI9jF5bE7svQfDy+OzjE2vP2fMMpx+JlocWO98CM3nwW91oBo+LG07woozvJR9HsFrdRTqNc0Ji8cFGz8JNfp9iN07UGs3zNtv2xkd8L6fKbavboYognGLb7/9NoZhULt27aAt87lx8ODBPG/PZrPlqvrA+fivZ9eZIxUWiyXbsoKsqyrFoiQbQojALBQWe4EIlIPpbipNW4RYOh/8PpTb7sGTQ3xhYeO2OXFOeA0xbyrs/yOQDX77fXiU/D8QMsmw2Ih67AX46evAPpq3wxdXKmwxkXklyu9FmT0BvgkEXlMynqhJb5Bqj/rvFc/Cr1vQb+0diFkEUBSUex/FbXOcP5UwjzgNH+qmtbB+BVp8Ita7HiIjJg4jF/vxqDoeezRK5ZrnvP+C8ZfPDoM/fgW7A9t9I1GvuS4orNItdmwPPY1u+hGKittqxx9Zt3OOGOaFfWRZFFBWLoT/vRkwHD2MGH0PjulLA9uLUPymib9Bc/QXFsG3G6FKTUSl6rhyEaPqRQmcJyFA1VE79UFr0Q7jj1/Ral+DPyYWX4Q9wwAM3YJWrmJWTG0mCYl5fua6rXYc4+cgZoyDQ3/C1c3hvpEYGUWrpqff72fRokU8+eST2UrmDBgwgIULF1K5cuUcn8NWq/WCn8+5WffKK69k4cKFpKamBr2LW7duxTRNatSocUH7DQdSLEouOv+mplGqbHUs944EIfApasQJRQgMV6dFx2IbPB7V8OO32sM2HJ+JEII0zYp2TUuUa1thGEahnQur1Yry94EsoQhw/Cjigzew9hpMXqKx3KqOo+NdaK1uhn1/oNSoi9cRhT/MQtGiqqgbVsGbLwYMu3cgfv4Gx4vv5Um0/Nc5t5gGLJgZEIoQ8LjOehrrnJV47NHB9d26FTgtOiLvdg4rOXqr0tPg+BGUspUi8veciVuzoJRORLulF6ZpXvDQuUu3opRJ4pChkVi8VJh7GT58qoat1wOIn76BE8cCxja3YsaWzPN18gvIKFsR25OvoCoKfkXFo1uhiInF1atXc/z4ce6+++5spW26du3K/PnzmTdvHm63mw0bNlC3bl0cDgdOp5OkpCQ2b95M9+7dsdlslCxZMtf7TUpKYu3atezatYsSJUpQrFixbB7Cbt26MXnyZAYOHMjo0aNJSUlh6NCh3HrrrcEh6MJAJrhICgUhBF4R+FqP5BeLEAK3ZiHD6gi7UDwTwzDw+/2Fei4sFktWXccz+Wsf6gWUBXHpVtJLJZJxbStSo4rnengzL+juDFjzv1Bj6snA/OJhGhrTfR7Y+WOoUQjE3+HbR1HD1LTsiXAAxeIi+veciRACv9+f7xhLIQTp6eFLeCsIhBCkRxUPlD2btghmr8C4awgZ2oVlMBuGEUh80W24NUuRuN5ns2DBAq677rocayB27tyZAwcOkJ6eTr9+/bj33nupUqUK06dPB2D06NEcPHiQq6++mipVquRpv3fffTfVq1enTZs2VKlShS1btmRr43Q6ef/990lNTSU5OZlevXrRsGFDXn755Qs72DChpKSkFL0rLSnS7Nq1i2rVqp2/oeSi8scff3B1uTKIBzvDmTUCH56Aq2HrQhsa/y/shg992uPw81mB3dOXkhYbHm+PVZhY/28GrF2WZVRVlDkrSbXlbXj+QinM34ymaVjdGShC4HU48RtmYNrC43/D6H5Z9UBv7YW/yz24I6g80sVAPs/g5MmTFC8eWVn0brcbuz1yQyIKiwu9VnIYWiKRAKe9qHYn9gmvIt54AVJPwc09MOs3iUihCOC12LD0G4547J6sKRybJmPmND3lhe5DUbHeMQiO/gXbt0FMcZSBOSewXGrYTAPL7l9gwQzwerB37oNxzXW4sOAukYB9xlL4528oFoff7rzshKJEcrkgxaJEUoSwmQYWTBTDQFisuBQNI0zDQBaLBZ+iYiZVw/rYNBRT4LOHNxs+3JimiatkGeyz/hco1VMyHjOu1AUPsZ2LdIsd28MTi1wCS35QFAVrWgpiwuCsDN9ZT6ONexm1Wl38pkmaxY6SeEWRHIqUSCS5R4pFiaSIYDP9WP/5O1DE+/uvUMpfQVRyR9Lt0fny/DkMH1raSa4y0tF8btyKHZd+2msWwUIxEz8KaVYH6lUNEELkW7ioqopFUzEEwenyLrcEFggMP4ttn2cvBbNmGXrV2sGEJykUJZJLHykWJZIiglUI+HYj4t25QZv45gsco1644HIlDsOHtmAmbFiBBmB34Hj2TdJLli0aBZbPIBz9jfa5UY8eQqxdDuUqQutbSLcWnWLo4cQ0TShXKfuCpMoIVQXj8jsnEsnlisyGlkiKCIrXhfh4cahx1y+onpynKssNekYqbFiRZXC7EPOew+qLzFIYBZl97MBE+fkbzNH3ItavQCyYiXiif2D+7ssQ0zQR1WsjajbIMpapADfchk8KRUkeUYRANQ1U00CJpGk/JblCehYlBYZFVVENH379wouYSs5A1SGHqdjEf0xVdjaapmHxujFVDb+mI06lZG/0zxFUww8RlKxgwcTm9cCRg1AyAb/NEfZkCt2dgVgROm8wh/ejHDsc8bUDC4oM3YZj+LMoaSkofj9mbElcVkeRCE+QRA6qMOHff+DUCQAURxRKQiKmIv1VRQUpFiVhR1EUonweWPMB/P4TlqbJiGuvI12NHPFRFPE4orH2HIQ4c6qya1rgt+VuCNpu+NB3/wzLFkB0DLY7BiHKlIeomND5utt2wGd3hn2mlQtFU1Xsf+5CPPUA+AP1HvU7H8KW3Cm8tRtNE3KarusivNBUVQ1LvGW4EUKQoVshNj7LKIXiRUVRFHRdxzTNovvR7fMGhSIQmGM69RQUiy28PknyhBSLkrDj8LlhyqOQOd/xd1+idOyNtet9BVrY+lLHY5qo11yH/vxC2LoBqtTErF4bVy4yf1VVRf9zNzw9OGgT32xCmfUBPPsm4s1pcOQwtL4FkdwRX4QIRQCbJwMx6+mgUATgnVewtLoZjzV8jzDTEYXa+W7E86OykjoqVkOUjC8wEaepCg6PK5DJHV0MEV+ODIst4kSjpHCwGz70vw/A5rVQvQ6i1jWkhznT/6LgduVgS0cpVhwh3wlFAikWJWFH9XqyhGImnyzF2vEuvHlMxFBVFYvhQzFNvBbbRUs0UBQFq+FH83sxLFa8qh4RL3CXqqHEl0fr3DdPU5VZDB+sXBhq9LoRP2zB3exG9AfHk3HyJLZSpSNKKMLpwOp/DocaTQPF6wGrI2z7cWkWoqrXQZ38BuKrtVCuIsq1LUnNnA84zCiKgjPtJGLEnZB2KmCsXgfnqOdJ1y/9Go4Xk0jz3FpUJTBlIuCxR+XoMbSoCvqmtfDalKBNuboZjsHjczWXdUThyKF4fVQxKRSLEFIsXkaop2PbClpwKZqGUJTQF6zDmedqIxZM7P/8jXhnNnhc6Lfdgy+pGh6tYG9bRVGI8mQE5hv+7QfUmg2w9HmYdJszIl42mVOV5WkdVYPiOcxhGlsS0zRxaxZ2Hz9BtRKRN8etT7dgaXYDfPFxljE+ETOXw++5xTRN0mxOLImV0Lvdh1+z4PX5CkQoAliEiVj8WpZQBJTff4KDe1Er17wsM7DDjaYoOLwu2PMbOKIQiUlkWOyF+jt2Gj7Uzz+Ble+AzYHj7ofxVamV7blm9bhg8auhK3//FbrPC0VMLApdRylVBv49Gvg9FYsF58WZ/UgSHmR06WWAqqpE+z04f/4a53cbifa50Qowq9SnW+H6LqHGPg/jseXNC2R3pQe8Ll9/Hpg5Y/wgLH/vD4regsLhdcOU4fDlp3D8KGz8BF54DHuEZgjnBh8Kyu39wBmdZaxYDSpfFfGixKNo0HcodLgjMB9x4zbw9BxcNmfY95U5Z3mGSUAoFiCKYQRenmfz77HLds7pcON0pcHQnjDpERh7P8rTg4kqxOx2TdNQd/4Ir78Axw7DwT0w6RGs6adyXiFHL3/hf7DmFaGoiJjiUKEKJFVFxJUu1OSW/v3706JFC7xeb4j9888/p1SpUmzdurWQeha5SM/iZYDT64LH7wlMVwYQUxznC4tILYCXLYBbs+C8YyBqmw6wewfUbYg/Jg5/HoY3NU1DbP0MzvKgKR8tQh8wBm/Oq4UF1fDBH7+GGn/7Ec3wFbkv+kyEEGREFcMxYyns/DEwBFShChkWW4F5zsJJmmbF2q0/eqc+GLoFr2ZBRLjIPR9+qw1L+x7w4xkvJqsNpdY1RTeRIYKwqApi2VuQekbG/75dgXJTta4tlI8kzfDD+pWhRiEQ321CS+4Sct29Vge2rn3hzZeymta+FrOITjMpyFvlhoJk6tSpNG/enClTpjB27FgATp06xYMPPsiQIUNo3LhxIfcw8oiMKycpMDRNg+++zBKKAKknEavexaIV3OXP0Kykl6+Cq01H0uMSLqzMSVz2IVFRIh4NQbTPTXT6SaJ87vB7GlUt1AMHEFM8MJRbhDEEpFnsZNRtSkaVWqTr1ogYVs8tXhQyrA48BRA/qqoqVgRWpWBrOZ6JYRgY1evA8GehRj1o2BJl6tu48uiBL2ooioIVE7vfg16A4kExz+W5PVponluhagGP/tlUqJLtnvYJgdHyFhg/G9p2hAfGogybhKuIisULQUGgmiaqaaKE8TcfGxvLrFmzmD59Ot9++y0Ao0ePJjY2lr59+9KvXz8qVqxIxYoV6d69O7t37w6ue/DgQe644w4qVapE2bJladiwIe+//37Y+hapSM/iJY6iKKElCzI5eaLAQ4uFEBfsITEMA2o1gPJXwMG9AWOxOJROd6JtWQevTQWfFyU+EedTc0iPKh42AeGxOrANGgMvPgGmERCPg8biucBZUiKNSB92vtjYTAPLkf3wv7fA7sTWtR+umFj8FyH43qVZ0K5ujqVmA4Sq4tOtl/T10VQVZ/pJxKLZ8PdB9DYdMBu1IaMAanr6VR3bLXcERigy0S0oDVoUmufWZ5rY2ncLZDcf+jNgbNAMUbFajtfdpVlQq9RCq143q3ROEfrAywv7Un1M/C6VwxkGZZ0aY6+OppJxCk7+C4ASWwKleImwDV+3bt2afv36MXDgQJ544gmWLFnC2rVr6dKlC40aNeKjjz7CarUyc+ZMOnXqxLZt23A6nQwfPhyPx8PKlSuJiYnhjz/+CEt/Ih0lJSXl0rzzJEFiXKmIwbdllR5RFJTnF5JWulyheJZ27dpFtWo5fF0DdtOPxedFGH5Mqw1V1QJDRx4XVK2FYhqIAR1Ca73Vb4J/yISwFmm2mn5sPi/i+BGUUgl4dBveIupZVFUVTdPOW6ftv65LJKJpGqqq4vf7L/g+VhSF6CMHECPvynoJW+0oM98n1R45AfhF7dqci2i/B4b3gpTjWca7H8Zz/W0FkoVvN/3of/wCy94GhxOl94O4SyTgC+OHQF6vjaqqOLwulNSTKBYLhiOKjKJYDucMTp48SfHixS94/X2pPjqvPs7e1Kzn0xXRKstrpVLJccZ9kZiEmUvPu9vtxm7/7w98l8tFy5Yt2b17N+PHjycuLo4XX3yRb7/9Nuh9NgyDqlWrMm3aNLp06UKzZs3o2LEjjz32WN4PNAK40GslPYuXAW57FLYX3oHFr4HXA1374S2REHFDkE6/F3Xpa4hPloIQqJWvRB39EiK+LMLqwKXbcBw9mL0o8N6dgVigMIpFr6rjteko5SpH3HnKC07Dh7rzF9j2GdRthKjdkPQImpnlQggWfd/8Oezbha11B/yly17Qx4JFITBry5nX2OtGbNuA1qZTxMYO6nrg0Z3XrPhcbVtVApm4gNfuxB+mqf0URYF/joQKRYDVH2Bp0R6fNfyee7eqo13VAEuVmghFxadbQjx4FlVFMQ38qnbRPLqmaQZKI8XFn7/xZcLE71JDhCLA3jSTiXutzKt5RmJheiqKPXxVKRwOBw899BCjRo1i8ODBjBw5kn379lG+fPmQdhkZGezdGxjhGjhwIMOGDWPdunW0atWKDh06UL9+/bD0J5KRYvEywKeo+OPisfR/HEUIfJoecUNdiqKgHv8bPl6SZdzzG2L524FK/999ifPJWYjSZcBqB+8ZGY31m+IvoDieoiwUrcJEXbkQ/vdmwLB2GUqLdtj7PZonYZXpmTQMIyLuG6fPAxMfChSyBvh4MZZHp+Cv3wx/HsWdQIHoYtkXROVgiwB0BA5XGuLzVYEh1ZbtybA5McJ0n9oNH/rXX8KS1wJ/d++P/+pm2e4XVVWxeV1opompqLhtjvPeG0IIFGcUwmZHadQ6UE7r+81QvESBJj4YhoGR2f/TfVRVFacnAz5ZAgf3Yr2hC8YVV+Iq4h9SRZXDGTn/bg97z/IAO6LC/kzOHKFQVRXTNKlTpw6vv/56tnZxcXEA9OnTh+TkZNasWcNnn33GjTfeyNChQ3n88cfD2q9IQya4XCYIIfCi4FHUiHjhn42qqrD392x28ecuSEiEUycQ00Yj/H6UcS8HSqgoClzbEvo8XGSHiAsSq98LH74TavzyU3R/7kvCOA0vzl++wfb2izh/+w6nUbDlZHKDknoiSyieRrw7F6s7Pc/b8pkmSqe7QhOa4sui1G0U1oD6cOHISEUM6QaLZsOCGYhhPXF4MsKybVVV0Y8chJfHw5FDgX8zn0Q/eigkIURVVaJS/0Wb9Ajc3x51wmCiThxFO4fgswkjkJDmc2MWi0Wd8hbElgSfD/WBsSiDx+EpAK/if+HwumDMvbB0PmxZDxMeQvt2I3pO0z2eB1nmKP+UdeZ83ss6z/BnxRQHe8Emf9WrV489e/ZQokQJKleuHPIvUywClCtXjr59+/Lmm28yevRo3nrrrQLtVyQgPYuSiMAwDJRaDbJVEFPqNUHs+D7wx6E/UYCMitWxPTMfFfCrGp4iltV7UcnHi8xm+lHfexVWLw0YPlmK2uEObLffF6h9WFiE+VJnOGNwTF+C8uMWsFhREpMwX34Ke5O2GI1aR4y3yaJriI/eDfWqp56EzevQkzvne0ha0zT4/KPsC774GP3OIfhO1520e1yIZ4fD/tMZovt2ISY9gn3iPNLPSgKLMnwo786B9cvB5kCbugDzyYFwMpB0Jz77EOWZ+RBbOl99zwuKoqD8eywghs9k+QIs9Zvhz6VwtRl+rH4PdYo70HxuXFY7hnwMXRBPNIjhm2Pe0JjFGI0xTUtAdBkAhKIgCrg2Y7du3Zg5cya9evVi9OjRlC9fnkOHDrFq1Sr69etHlSpVGDVqFDfccANVq1bl1KlTrF27lho1ahRovyIB6VmURAxeZzEYNjlQMsdqQ2l3O8oVNeD7rwINajbAUDUM0yTDYifNYsetWaRQPAdeiw063hlqbNkev5478WPx+2DN/0KNnyzB4i+YKpeaqhLlcxPt82AT5x5OFsXi4IrqITalR3+8F5iQYghItzmhdkPEioWYI+6CHzbDnGfQ9uwo8CLwuUYQOj92JkZ44hZN04TKV2ZfUPnKkNEI1fRnCcVM/j6IclbfdE1D+fpz+PT9QL3UUmUQP38dFIpAoMbg0vkFdk/lhBACLDkklNgcuf64shk+LGv/hxjQAe3R3jC0B84TxyLnXiliVIyxsKxdSbpVdnBdGSvdKjtY1q4kFYtZMVUNU9UKXCgCOJ1OVq1aRaVKlejbty+NGjVi0KBBpKSkEBsbCwR+JyNHjqRx48Z06dKF+Ph4Zs+eXeB9K2ykZ1ESMXg0HaN+M2zPXY2CAn4v5rTHAnFGta9FGfIUbt16yZaOCDdeFPSbe6DWvga+/gLqNERUr5O/rHF7FAqBDHv8fkyHMyzTp1lMA9ueX2D2RPj3GJbr2mO56yHScsgSzbDYiHri5cDMPn/+Dm1uxVe6bJ7jFc9E0zTEl6th91nF2NetQK9et0CLwOcWv2li79ALsXZZlmh0RKE0vzEsiS6GYcC1LRHV6wSmHQREjbooDZqHJPoIVUMpXTYwA0kmxUsgzvoIUf2+QGJVcAd+yOlDxWJFuchhJGZMLErNBii/fhcwqCpKnyF47U7IxX1k9XkRi17JehalnkTMeQbro1MDzyhJnqkYY+G1ViUu+n579+5N7969g3/Hx8fzyiuvnLP9c889dzG6FXFIsSiJKPxC4D89lKXaHNhHTjs93Kzg0W3Si3gWqqqiKMo5s3YzNCtqldpo1evlOUHFr1vQkzvBmg+y9jduJuarU4IiQKlUnaixM0nT85dgZPe5EROGBOpaAmxYCXGlsHS+G99Zl1wIQZpuRWtxE2rLm/NVOicT0zShQuXsC5KqBJIvwpQRnB+EELiji2OfvgTx8WKwWlHa3U6GPSpsH1Dpug3HqOdRM9JBAdMRTYYeOsuP2+bEOWIKYuLDgRqu0cVQRkzFbXWEtDN1HWpdA99uChgO7kVJKI9IKJc1BKzrqLf2xuJ147mIc6+7LDacw58NfBwc+hOlYSs8UcVynf0u0k5mP+d/7UMzIzN7XiLJL1IsSgCC2WCGYUSMIDNNMzAdXSYR0q9IQFEUnH4Pyq6dcGgvyjUt8EYVw6Nm/0mbpnlBSU1uVSfqjkEoDZrD919C83aItFOh3qI/f4d1y9Hb98R/gYlTiqIgDuzJEoqZfP05evse5yypYhhG2ErbmKaJqFoLal2D8ktgRgcSK8KNXfBFgFDMxKeo+GPi0O94IPC33x/W34UQIiAOi537d2eYJhllKuKYtihQistqw2Wx4z+rnd8wofUt8MMW2L4VVBXz1+9Rn5mP2LQaUk+iNGyJ+Hgx4vhRbEMnXTSvnBCCdN2KelUD1FrX5t0zW7wEREVDelqWrUnbAin/I5FEAlIsSnAaXtT9f8Iv30G9xhgJ5SMmqD8ndFVFNf0YmiVi6+AVNA6fB+WlJ+CnbQCIN1/EMm4W/mp1wnpO0jULWp1GqPWaAGD7+L3sjXbvQDUNuMBCx0KIgMfp7AWVr8TIKbasgMjQbTiGTUbNSEX4fYhicbisjux1PQsZIURAJBYihhCkWexwnlmN0nUb9ocnoPm9oKj4LFas6acQ2z4LlEFZ9R640kHX0cXFP88X/CFldeCYOA8x+xk4fACatIUeA/BehFl/JJLCQIrFyxyb6Ud9/3X46N2A4d05aL0ewNquW8Q9+ALFmN2wbjns+gVatMOs07BApgqLdNT0U0GhCAS8P29PxzpmOq4wT0uY6cFTFAXbtdfBghmhDVrejKHpuYr1Ohc+ZzR6jwGwZF7Aw5iYhNL7QXyqdtE8ygGvmhWKlcwyRphQLGoIIXDpVjjDY2ixO2H3DvCckdVdrTbGRUhgCBd+IUgvlYht1PN4MzLQihXHK/NFJZcwUixe5lj8vtBC2ABLX8fatiPeCJsL2eHzwKShWUkIX3+O2v1+rB16443wEWpFUcI7vO/LIeXC7UIpwPMghMBXLBbro1MQC2aA2wW39sa8qn6+vZluzYLtpm5Yru8MPi/CaiPD5kRIsVag2BSB1eMGBH6LDddFKInk0a3YRr0AL44OlP4pVwnloacCtRaL0PU2TROXbmPXkf1UKxZ3/hUkkiKMFIuXO8LMHisWplIc4Ub1urNnq360CMuNXSNO2GaiI7D7PHD8CEpsSbxWGx41/55QEVsSpUwF+PtAlrFj70A2ZwG+cD2qBaNeU6xX1kNB4LU68YVJBHtUHY9VB+vpwrtFSDgURaL8XtR1yxBLXwe/F71FO2L6DiW1gOcp9ikqomot7C++B34fpm7N1QwwkryROR98pMSgS4o2Uixe5vh1K3rjNrB1Q5YxuSO+XNbiu5goqpY9ri2Xk8oXBpqmYT+4G54cCB43QlGw3P0ItLolx0SUvOCyOnBOfC0wXdmB3dC2E2a12hecZJIX/KYZzFiXSUdFE0VR0I4fwXwnq0SI+OJjlOq1sbbtjNdXsDP1+AWhMY9SKIYNm+nHmp6K+PU7qFgNs1SZQHiFRJIPpFi8zHFrFqIGjEa5uhn8uBUatkTUb1q4M3ScA7/FinZdO9i4OsvY52E8NgeYkSdabO50ePnprNgsIeDtGVia3xjwoOUD0zRJs9ixdLobxTQwNP2yTfaR5B2LxYLYvi2bXfywBb15O7z5/JiRFA66pmL95XvE1BFBm3rDbdjvGJS/+qqSy56Iicj98ssv6dmzJ1dddRWxsbEsXLgwZLkQgsmTJ3PllVdSpkwZbrnlFnbs2BHSxuPxMGLECCpXrkxiYiI9e/bk0KHQKZ1SUlLo378/SUlJJCUl0b9/f1JSUkLaHDhwgB49epCYmEjlypUZOXIkXm8klOUtGNI1C+4WN+Ed9ASuRm1Ij9CHikuzYN4zHJ6aA3c/Ai8txl+vCf4IFIoAKkroMDGAaaCcOV1bPvGZJl7OXWdRIskJwzBQatbPZleuqo9pyV/NTEnhYXNnIF5/PtS45gMsOcU4SyR5IGLEYnp6OjVr1uTZZ5/F4cg+tDh9+nRmzZrFlClTWL9+PaVLl6ZLly6kpqYG2zz++OOsyD3ymQAAIABJREFUXLmS+fPns2rVKlJTU+nRo0fIi/S+++5j+/btLFmyhKVLl7J9+3YGDBgQXG4YBj169CAtLY1Vq1Yxf/58VqxYwZgxYwr2BBQyfsPAK4h40ZGhWcmoXBPXDV1JL5GAO4I9ID5dh6bJocZSCZgRPHQuuTwwDCNQHLvjXaCf/g01bIXSugMeOSRctElLzW7LIQ5d0zQ5PaEk10TMnXLjjTcybtw4OnXqlO0GFkIwe/ZsHnnkETp16kTNmjWZPXs2aWlpLF26FICTJ0+yYMECnn76adq0aUP9+vWZO3cuv/zyC5999hkAO3fuZO3atbz00ks0btyYRo0a8eKLL7J69Wp27doFwPr169mxYwdz587l/9k77/AqqvSPf86U29JJQifU0BREaYqK2MEugogstlWUxYYrYkXFgqIo2HDXXlblJ4plxbIodgQboKKIQOgQEki9bcr5/TFwwyWhBFJuYD7Pw6M5c2buuXdmznznPW/p0aMHxx9/PHfffTcvv/wyJSUldfqbuFSNbdsJlTx8V0QVDS4ZC6cMhkbZ0OMoxN3/IuwN1PfQXFwo0/1wzkiUJ2ahPPUu4spbKPf4E/q+UhQFTdMQe1nD+WDD0L1w6nnxjW06YnsrAgA1AcnREP75nxH47QeSzchB+3uOHj2a9PT0SiX8vvrqK9LT0yksLKynkSUeCSMWd8eqVavYtGkTJ5xwQqzN7/fTr18/5s+fD8DChQsxDCOuT8uWLenUqVOsz4IFC0hOTqZv376xPkceeSRJSUlxfTp16kTLli1jfU488UQikQgLFy6s1e/pcmDhlKXzEh1xDfaDL2NeO5GylAws13LjkgBIKSlTPZQGUilNTqdU9yXstSmEIMmMElj8Hb4PXiO5aDNeOzGzNtQnUQTynIvgyluhW28492K44zGC2wKJhBD4y4rhmvPg8TvhgRvgjlEkGZF6Hnn94fP5eOyxxygoKKjvoSQ0ibuGtwObNm0CIDs7O649OzubDRucYvb5+fmoqkpmZmalPvn5+bE+mZmZcW9RQgiysrLi+uz8OZmZmaiqGutTFdstky57h/t7JSbueUlcDuZz07FpY3jsDvhzMQDy9emot05lQ0pjysrK9rB37ZNo5ya1fXdSOx1O2JZsWbUulpaoSUYGgQ9eRoSDFZ3Xr8L6fSFrM5oTCoX26fN8Ph9eb+L5uobDu/cPtyyLfv36sWHDBiZNmsR9990HEItRCIfDhMNh5s2bx8SJE1myZAkpKSkMHjyY22+/HY+n4UWZl5SUVKllcnNzd7tfgxCL29nZVC6l3KP5fOc+VfXfmz67a4c9/9AuFSxbtsz9vRIQ97wkLgf7uUnamh8TigBIifL6dNrdOpVQs2b1NzAS+9z4gYzsxrG/PdiIssruVGqojNY9Wu+zz3pxcTE+3/7luhX56/G8/TxiawEyI4vo4MuQjZvv8/HC4fAex6SqKrquc/fddzNixAiuvvpq2rZtGxOBPp+PLVu2MGLECIYNG8bTTz/NypUrufbaa9F1PSYuGxKpqam0atWq2vs1iGXoJk2aAFRSwwUFBTErYOPGjbEsq5KPwc59CgoK4nxypJQUFhbG9dn5cwoLC7Esq5LF0cXFxcWl9hFmFXkfo1EqJ1512R2mosHZf4tv9PkRPY6s1+BGkb8e/0M3os+bg/bHQvR5c/A/dCMif32dfP4pp5xC3759ueeeeypte+6552jSpAlTpkyhU6dODBw4kDvvvJNnnnmGYDBYxdEOTBqEWGzdujVNmjRh7tyKxNHbTcPb/Q979OiBrutxfdatW8fSpUtjffr06UNZWRkLFlTkF1uwYAHl5eVxfZYuXRqXcmfu3Ll4vV569KicasKlbhFCEDCjJAdLSAmWEDCjB61ztovLwYKdkQXNcuIbz70Yw+cGi1UH27YxmreB+56F3sfB8WciprxOyFO/v6Pn7edRdhKGyjZLY10xceJE3nnnHX7++ee49qVLl9K7d++4wNujjjqKaDTKihUr6mx89U3CLEOXlZXFfnjbtlm7di2LFy8mIyODVq1aMXr0aKZMmUJubi4dOnTg4YcfJikpiSFDhgCQlpbGyJEjmTBhAtnZ2WRkZHDbbbdxyCGHMGDAAAA6derESSedxNixY5k2bRpSSsaOHcupp54aW0Y44YQT6NKlC1dddRX33nsvW7duZcKECVx00UWkpqbWy2/jUkHAjDg1ZX/9AQmIroeTdOODlNVyiTIXF5f6w6lY9G+Y8y5izQrkSedg5XSok4pFBxoRRcPI6Yg+5k6komBQw3Xr9wGxtergElFUd9HIRxxxBGeddRZ33nkn48ZVJDXfnbvbwWSoSBix+PPPP3PmmWfG/p40aRKTJk1i+PDhTJ8+neuuu45QKMS4ceMoKiqiZ8+evP3226SkpMT2uf/++1FVlUsvvZRwOEz//v15+umnUdWKaiTPPPMM48ePZ/DgwQAMGjSIyZMnx7arqsqMGTO48cYbGThwID6fjyFDhnDvvffWwa/gsjtUVUX89B38+kOsTSz5GX78BvWokxI+R6TLwYcQAk2AkDIhHsoNFdu2KdN8aKdfiCJtTIRbS3o/sG2biFC2LePX/zUpM7Kqbk/PrLK9tpgwYQJ9+/bl008/jbV17tyZWbNmYdt2zLo4b948PB4Pbdu2rdPx1SeiqKio/q8Ul4OKfXUI13Ud76uPw+w34jecMpjIJf/EqOV6tgc6ieyon4hoSHxGxCnn6PUR8vixdhCDmgB/eQnynZchWI44ZySRRk2IKtUvpemem8TFPTdOgEtaWto+77/dZ3HHpWi7cXNC4x7e5yCXvQlwGT16NFu2bGHGjBmxtnHjxvHKK68QDodZvnw5kUiEXr16MWzYMK666iry8vK45pprGDp0aIMMcNnXc9UgfBZdXGBbdZljTq28of8g16roskt8tklyNERyNISnhqwoGuDPWwrXDoEx58ANwwlsXI26g1+TPxxEjr0APp4JX32IvHEE3sKNB9XSlYvL3iC3CUPjqJMwuxyOcdRJ+yUU94ebbroJTatYdG3evDlvvvkmixcv5thjj+Xqq6/mvPPOY8KECXU+tvqkRi2LS5YsYdGiRQwfPrymDulyALI/b+I+y0D7/nOY8W9n9WTo37GOPJFQgtazbkgciBaSJCuKeO4h+HYOeLww7ErMAWcQ3s/rJdkIw9hhUFpc0di4OfL+5ynXfaiqiv+r2fCvSfE7HjuQ6BU3E63mrHsgnpsDBffc7L9lsTbYG8viwci+nqsa9VmcPXs2999/vysWXWqNsKqj9zsVzxHHAhD1+TFs15PCpTKaqiDmfgjf/M9piITh5WloPY5EZDXfP/9B04gXigD56xE7+tFVFanrD4AQ4Pouuri4NCDcZWiXBodh25R7fJR7fK5QdNklqhGFn7+tvOGPRZXqz1cXqenQeKdk0O06Y28LprMsC7r3gaymFdt9fsQ5F2O4l6yLi0sDY4+WxaFDh+71wfLy8vZnLC4uLi41hqV50Lv3gUXz4zd07LbfkbRhb4DA7Y8jH7kV8v6Ejt1g7H2EPX7Yduygx0/gwZeczw+VI3ofR8gXcCOiDzJ8loFuGkjLxPZ4Cek+9xpwaXDsUSzOmTOHlJQU0tPT93iwkpLKZYRcXFxc6gPTtuH4M+GPRfD9l6B74LxLsRpl7/fD2rJtyjMa47v9cRQklhBEPP44Eeqke/Gi9jne2ceyEiFLiUsdErCiKP/3DPLjmU6JwvZdSbr1Ucq0xKijLIRAty0ntZOmu+mIXHbJHsViTk4OHTp04K233trjwR566CEmTZq0x34uLi4udUG55sU7+g60KwwQgqjuJSpqxvvGtm2CunfHhir7uZH6BydCCJTNG+CjNysaly+B2TPQz7m43l1oNCT+rZuRM/4N4SCewZcQbd6WiJow6ZddEog9XhU9e/aMK6G3O9yUEC4uLomElNKJfHaj5V3qGEVRYOWflTcs+w3FMKCeRZk/XI68cQQYUQDkT9+gP/AiRot2+2Rh3F2lE5fEYH9WVPb4in3MMceQkpLC2rVr93iwQw89lAsuuGCfB+Pi4uKyK9wHkUtDwrIsxCFHVN5w1AlYev2WJ1VVFbng85hQjPHf19Fk9YWiz+cjGAzWzOBcagUpJUVFRSQlJe3T/nt8tbnsssu47LLL9upgAwcOZODAgfs0EBcXF5eq0G0LrxmFjWsR2c2Ien1EFHepzKVm0IXAEw0BEPXWbCquaFIq+tj74IVHIFgGJw9G9j0hMWpapzWqsk0KBaopGL1eL6ZpUlxcvOfOdURJSQmpqam73K4gUWwbbAsUFUvVDvjAo5SUlLiE49XBnXFd9hlFUfBHgohoGHQvUd1DVFS/lJmLy67QFIF32RKYNBYsEykE+hU3I/udXGO+hy4HLwHLQPn8vzDrJVBVvMOvQu09YL+Ttm8noupYRxyL95CeCCCqeerluo29cG3ZDI2yiGpeOLQXNM+B9audTsmpiLNGYOyjkN1Xi1VtkZ+fT6tWrarc5pEWnp++gafvg2gEspqi3PUURkoaIfcZViWuWHTZJ4QQBMqK4K7RsGkdaDqey25AHHWya/VxqTG84RA8OREs02mQEl58BE/v/kQ9/vod3Da2L48f6FaJAw1FUVD+WgovT6tonH4fWttOiKata+x8mraNqddfJRFNCLx//QoP/hNME1QN7/X3EjnsKDz3PAsr/4BQELr0IOj1w0GQu9ZjROPnlYKN2E/di3b2SJJyD6Fc9SCEwG+EUSJhhKpi6N4ae4loiFT7qb5582ZeeeUVFi5cSHFxcSVHWCEE7733Xo0N0CUx8ZpReHayIxTBqWjxzGQ8PfsT8bli0aVmEEgoKohvjEac662exaKiKASiIccy4/Egs5oSrOEceoqioKoqKSkpNXZMFwdVEYivPqyczWjep6hDrsA0zfoYVo3jjYbg8bscoQiOhX76vXimzaTM40Pp1ANwovsPBqEIQHlphVDcTt6fCEUg/vwF5ZDezr1933Ww4g8koB07kMAlNxDU6tfftL6o1lP9jz/+4PTTT6e8vJz27dvz+++/07lzZ4qKitiwYQNt27alRYsWtTVWlwRCsUxYsTS+UUpkUQE0TazlCJeGi6lqaD2PgR++qmhs1Q7pqf88dYFIEG6+GAo2ASDadiTp9ico0xyrhBCiyqhSRVFQFGWPYsRvGairV8IXH9KuQ1eU7EZENR0tEsZWNSKax7Vm7gcSgezYDT7/IH5D7qEHVL5BYdtQsjW+MVgGlgH4Dqjvutckp0KPo6Co0EmqD9CtN3L571BajOewo+CDWbDij4p9vvoI5ZTBKG06H5S/WbWcJ+666y50Xee7777jvffeQ0rJpEmTWLJkCc888wxFRUXcc889tTVWlwTC9HjhiH7xjR4vZDapk89XFAW/GSEQDaEnYJCsG7lbM0Q0D+Kq2xAnnQ2Nm8ORJ6Bcdw9iP8v17S+6qjj587YJRcBJk/L7zwSwSSrcQODHL0kuL8ZrO6JQCEGSGSXwy3x8H80gpXQLHrvqHIy6IlDnzYE7RsGcWShP3wcPj8e76i+Umy5Ce+wOkku3oioKGpJkI0RyWRHJRhj1ILr0hBD4zSjJ0RBJRhitGt/dNE048gRk18MrGnseg+x82AElBixVg86HxTe27YR90FrIJMIyEb37I84YjnLvMzDgDJTBlyA//D/oMwBpROGv3yrvvGLpQTu3V8uyOG/ePP7xj3/Qpk0btm513lS2v9kOGTKE7777jjvuuIP333+/5kfqklBEUfBcOAZKi+GHLyG7GeLquwjrtW/x0ZD4N61BPjMZCjfiHXAGntMuoDwB/El8ZhStvBTy1yNy2hP2+DB2cmj3WQaaZYIQRHQvBgfn5LO3yC35CF8AccGVsH419j3XQP9B6ENHYdSTZU1IidiwpvIS5qa1KOEgPHH3to4Czz8nYXU/Cq8RgQdvgD9/BUD+50m8dz2N1a5LpcTdnkgIZj4Xf+wlPyN03bHeb92MnDAK/4Mvw6q/4KGbIByEtAwCdzxBsGnOQZEMPCkagml3wC/fI1LS8Y++jUjXI/Y60K5c8+L/5wOIaAQhFEyPh5BauyJKCIGqqti2XSeiNKx7SbrxASci+7efoFN3lMtuBNsiORrC0DxElNoL6tj+fS3LqndLuBDCSUS+Y37JVu1Qxk3Gvvc6OHUIdqv2mIqKfsyp8SsaAD2OPKBeJKpDtcSiYRg0a9YMcPIqAXGh8t26deONN96oweG5JDLluhfvVbeh2TdjAxFvAKsObiSfEUHe9neIhJ2Gmc8hdA/6aRfUa1UEn2WgvfsyvPcqAFLT8N01HbtN59iDO9mKwjMPwnefgdeH98IxqMecelA7Tu8ORVFg1V/I/74evyHvTzTLwKinYCoTgWfQUPjmk4pGRUXpezz2zZdUtEmJfPYhvFNeQykqRG4TigDYNvLVx/GMf5jQzuXfJFClBWOHtsJ8lEgIuWoZnDUC+dXHsGE18pFb8E78N8F6DKqoCzzY8Pp0+OV7p6G0CPnweLxPv0/Uu3euMFJKgpoX6qj8ns8y0LfkI+fPhXadkZ26U17L4lRKSbkngOfym9HMKIoQ2I/e6ghHTUO/8B+IAWfu9xykqiqKEFg7iGC/FUXdvBF+/QG69MBu0rJeff48toV8/en4/JJrViALN8EDLxJVNaIIsCzs7n1Rhl4Os98AXwAuuh4jJaPeBW99Ua21nJYtW7J6tRNm7/f7adq0KQsWLIhtX7JkScKFz7vUHturY5TpPoK6r06EohAC1q6sEIrb+epjtHD9JoXVzWhMKAJgmsh/TcIbLgdAUxT47D2Y96kT1RsOwfMPo5VuPWiXNvaEk9i4ZyXhJHodi1qPFSNs28Zs3gbGPwy5h8AhPeHeZxxfyrKS+M5lxQhAGkblA0UjzrWwc7PPD8NGxTce2hO5flVFf1UDVUUunIdc8QfKlbcgjjsN1q9GOQgeaFo04oiQHbFt5KZ1CXk/aYpAWzTPsWq9+Sw8eCPi8TsJmNE977yfSCmJKE5Er3zlcUcoghP08vJj+zV3CiFItqL4v/0Y37MPElj2CwHLwINE/fRdGH8RvPIY3HoZyvuv4t2F60VdIGwLQmWV2mWonKDudYTiNoKqTuTMEchpM5EPvkz4iKMP6lKI1frmxx57LLNnz+b2228HYOjQoTz11FOUlJRg2zYzZsxg5MiRtTJQFxfY5vbQqHHlDc1aIbV6ts7tLGABtmyOTT+aEYGF8yr3+fNXlKOaHRTLhvtCxJ+M99ZpyBcehtISxAlnIpq2Qj4+gaQxdxLVPRhCrfM3/rCqo3briye3G1IRRHUfvkgIpV3neMf4AWdgqDp6VlPH7zJ/fcW2cy/G8CXBTufesCVq7wFoOR3g64+x23dFO+QI7Fv/7nQQAvG3a5AfvgmLnRd2e+E8lHufRRbmY9XismKiYOlelM6HVWRkAOd3adw8Ia0/nkgIXpse3/jzPJRoGOrI2qYaUac+9c5sXIPokI6UEl0ItGgIS/diCGWPv6XfCMPUO+CXBY5bxtz3UUZei+e4QfDmTq4U77+GfvpwIvWUycDw+NDOuRgWza9oTE6Fjt2rXF42pMDYbqE/WCLFd0G1xOL1119P//79CYfD+Hw+brvtNkpKSpg1axaqqjJs2DA3wMWl1rGSU1BPHQIfz3QaUjMQl1xPRNWrtNLUFbY/CZHdDDZvqGgccDrGNj9OS/egHdobfv0xfscOXQ9aP5i9IaqoeFq1RZw/CuFPQv74FfZD40DVUNavwvP5B3gu/AflHn+diwTLsght99O1bUIeH0m3ToV3X4Y/f4M+/ZEnnEVEKBheP4H7n4eP30Ksz0Oech5mTnvMXbwkhFUdpWV71BHXsHr1alr5kvA9+BIU5iPSM+H3n5EvT63YwTSRC79D3DCJiO6FA/yaiioq+t+ugbV5jgDy+eHy8UQTdPldgJPyaef2OjxPpteLeng/Z3VmO4qKaNkWKaXjJjN7BvzwFWr7LnguuIqgL2m385NiROGXBfGNb7+A0n8g9s7f1zLrdY62LAuzdS7avc/A+69BRhbinIsOmvyS+0O1xGKrVq3iMqJ7vV6mTp3K1KlTd7OXi0vNElI9+IZdiXb2SCdfVkYWwXoQCpXG5Q2QdM+/ka8+AWuWw5EnIk8dElvaMGyJ95TB8Ocv8NM3oHvg/Csw0zLrfewJj6Yjn7oHGY1UtHU5HLniD2dpf+1KfFX5/tUxUkrKdB/60FGoRgTT44+VdrNt29l29kiEbWMKZY8vCduDIMrKyjARlOk+aJqDrih4Vy6t/OBt1oqQ1499gOQI3B1SSsq9Afy3PIpiGaCoRDy+hA0Yi3r9eM+9GJ6fUtHYoSuWr+6sbIYUzhi2bHbcYdIbwejbCes+Z3n4panwxWync96fsOxXfBOeIribwEXhZEONR1GRigoDToPPdgh47Xs8Zj2vAIVVHaV1J7R/TEAKBROQrlDcIwfvArxLgyas6pCU5vyDen1b3Y5t25QFUvFcfhOKYWD6KteZLde8eMfcGYuGjupet2zdXhDRPHjHP+wsd5UWQU57lBFXYz9ys9Phz19QTbPOAhX2hCHB0Kq27hnWtrZq1t+NO4Zt4zt9OPLz/0Lxthx6LdogD+19wCST3huklI6QqYMsDPuLYUv0YwaitGiD+OIDZIdD4JhTnRecOpy/yjUv3stvQrv0BiROYKJp2yQbYfj6k/jOq5ejGJHd/r6m7kHt3R++/7KicdgowroXz9+uRXQ6DH78Grr3QfY7KSGC+Wzbdl7iE+C50VColli85ZZb+Pjjj/npp5+q3N6zZ09OO+00dyna5aBFSklEqOBRq1zWkFIS1jx15qN0oGAIBZnbDd+jb6CYBnL5Euwn7oL8bUv+vgDUc+7FuiboS8L/yBvw1xLw+qBVO8r1uhUeLtUjqOooHQ9D63QYNsJxQajj8yWlJKxosD2TwLYXGgmItAzH6rgdRYU9WAJDmoek0bcjBiyEpYvhqBOxmrR0XphUHe3oU1GOPBFbUWNWdpeGR7Vm108++YTBgwfvcvu5557LRx99tN+DcnFJVHTFqRfqkXZCRlweyJgSynQfoUAK0rJhXZ6zQQj4+zginsT0VastLCkp07wEu/Yk2P4QyjSv687QALBtm6hl79JXtb6IeANw5a3xL13nX4GxF8vG5aqH0GFHER52FeUt2hHawXpoWhZRhCsUGzjVsiyuW7eOnJycXW7Pyclh3bp1u9zu4tKQSbIMxCfvwrf/Q23VDu+IMZQHUt3glDrGkKB0643n6f8i169CNMsh6k1cX7Xaxr3+XGoC07aJduyGd/p7yLxliOatMZJSiOxlLlM3m8OBTbXEYkpKCnl5ebvcvnLlyliybheXAwkPEvHOi/Def5yGlUuRvy/EP+kFyhM0+vJAJqJoRHwaov0hrjXNxaWGiCoaUV8yStee7kuISxzVWobu378/zz//fJWCMS8vjxdeeIH+/fvX1NhcXBIG3YjER/UBbN6AEiyvnwG5ALhC0cWlFnCFosvOVMuyeOutt/K///2Po48+mgsvvJCuXbsihOC3337j9ddfR1VVbrvtttoaq4tLvSEBkZFVuTqHa0l3cXFxcdkHtvu9N4SX3mqJxfbt2/Pxxx9z44038uyzz8ZtO/roo5k8eTK5ubk1OkAXl0Qg4ksiMOoW5F2jncSyAKcNw3Cjml1cXFxcqoEQAr8ZQdlaAMFyaJ5DyOOvk5K5+0q18yx26dKFDz74gMLCQvLy8pBS0q5dOxo1alQb43NxSQgsyyLcqj2+6e8iV/4JTVtipaQlRM4wFxeXgxMhBDoSpI2paO7ycR2jaRpCCEzTrJZ1MGBE4JFb4Ldt1bzSGhGY/DJl/pSEtTLuc1LuzMxMMjMza3IsLi4JjSEUDG+S6/zt4uJS72hI/CVbkDOfg1AQz+BLMZq0QLEs1O0VbXTvQZsloDZRFIVANARfvA+b1uE96VyM9My9ihxXFAWxbmWFUAQo3oKc+Tz6364lWovj3h+qLRaLiop44okn+OSTT1izZg3glAE85ZRTGDNmDBkZGTU+SBeXRMIVii4uLvWNP1yOvGE4bC+BuXg+3mlvYj8xERbPB18A76VjUXoPIOKugNQogWgIbr0MNm1LFTh7Bp6J/8Zo02mPzwchREUxgR3JX4ewTFATs7BetaKh//rrL/r168eUKVMwTZNjjjmGo48+GtM0mTJlCv369WPZsmW1NVYXFxcXF5cGj7Kf1YZUVUXO/7xCKALi2IHId15yhCJAOAjT70MPuxkbahIhhFMQYNMOOaWlRM54Go8R3uP+lmUhuvUCbSdReMp5mJ7ELVtZLQk7btw4ysrKePfddyulyPniiy8YOXIk48eP5+23367RQbq4uLi4uDR0vJaBJ1SOXLUM0aYjUV8SkX21JCWnxv/dqj3ys3cr91v1F8qhfdwVkQQi7Avgm/Qi8qWpUF4Cpw/H6nJ4Qic2r9ZVOn/+fK6++uoqcyked9xxXHnllTz55JM1NjgXF5eGgRAiYR2zXVwSAR2JPv8z5L8mAU46Lv2q27CPOqnafoWWZcFhfaFxc8hf7zRuWos4tJcTgLcjrTu4QrEGkVJCizbQpEWFdVEIxLCriOq+WK3t3WGgYDXNwXPDJIRtYXgDCV8OsVpiMS0tjfT09F1uT09P3+12FxeXA4uQ8LLFgOKIRU6KRipR98Hk4lIF3mgYXpoa3/jio3h7HoPh8Vf7eEGPn8CkF+CPRRAOIbv3RagKrFoe81nkkrEYvuQa+gYu2wl6/ATufx6++cQRjCedSzQ9s1pzn23bTjYNVd8rgVnfVEssjhw5kldffZWRI0eSkpISt624uDi2zcXF5cAnKLyM+SKfWcvLAGgcUJl3fhuylIhrZTwI0XUniMIwjHoeSYIiJYRD8W3hoNNykcgNAAAgAElEQVS+D9i2TZnmRel+JEIIxxdOCHzX3eNEQwuFiOfgrZlem2z/7bUTz92n1DkNkWqJxdzcXIQQ9OrVi+HDh9OuXTsAli9fzhtvvEF2dja5ubnMmjUrbr9zzz235kbs4uJS7wghWFNuxYQiQH7Q4q7vNjP16Eao0hUMBwtSUdlq6fz7pyIsW3Jl9wwaKQaKTFz/q/rA0jTU7n1g8YKKxh5HYu5n9OuO1iwpJSHNA26xgDrBNM36HkKdUa2rdNSoUbH/nzZtWqXt+fn5jBo1Kk5hCyFcsejicoAhhCCvpLIg/KvYIGJD6rZIv4NpMj1YKbJ1uv9nJWWGI1oeX7SVX//WjizFFYs7Eta8JF1/L7zzMvz6A3TrDWeNJKJ798q6qCgK/kgQJRQErw9D9xDei7x+Li41QbWutPfff7+2xuHi4tKAsG2bvk39eFRB1Kp40F3cJQ1LUbnv5yIyfArDO6aRJiKuH+MBiqZpvPJLcUwoAoQtyfRftnJ3z5QD4mXBa1voloEEIh4/5j4uN0opKVM96EMuRzvrb9heP5aiwl5EwAohSCovRt5+BbJgIwiBNvhSfKcPd6tI1SBCCEyhEbYhRZXYVt1dv5qioEdCWLoHQygJt6xdLbF4zDHHVOvg4XCYWbNmkZ+fT+PGjau1r4uLS2KTIgy+HJLDDV/mkx+yuPLQdAa2Sab9C38R3aYdpvy0hR+HtyWZ0O4P5tJgUURlnziFbfnoGjhJloF45TH48kNEcir+K24i2rXXvqe7AQwJHkVB/fV79L+WwJEnYGY22a3o81iGk2alYKPTICW89Tz6CWcSTnYLYdQEiqKwxfYwcX4BvxZGGZ6bwgWdUvBbe86duL8kWQZizvvw7RzU1u3xXnAV5f6UhHrJrlUbdklJCWPGjGHWrFmuWHRxOcBQpUXXZIWZg5piSUjRBVd/mR8TigCbghafrSnn3Bw9oXOIuewbpmnyt86pTP6xkJJtJz6gCa7qnoGxFwmKExldEYhP3oG521bUircgp9yC/uQ7RJLS9vm4fiuKeHIi/Pi10/DW82hj7kQ78sRdpk9RTANW/VWpXW7eiEhplHBWqIZIifRw3FurWbXNvWbBxhD5YYtx3ZNR7NqzMHqQiHdehPf+4zQsX4L87Sf89z1Hue6rtc+tLrXu8OBexC4uBy62bRPYVs3UtnXMKp51dh1NAUIItG2VMQxXmNYZ6YrB4hFteeWPYgxbcmnXdNIVAxLHKLJPqNEwfP9lfKOU8NcSlMOPrmT1EUKgqSoAxm6W37VoBPnj19DpMJRzRkJKOrKsGK8R3WWwi+n1ox55PLz94g4H0qFFG/cZW0MURe2YUNzOs78W8Y9D00ih9sSiHo3Ap+/FN25ahxIqh4NJLLq4uNQfqqpi23adPFBU2+TmXpm8uayE7W6MWX6VE1sFsGp5KcdrmXi25iNnz4BG2fhOOY9yX1JCLeM0dBRFwRYqCo4vlxCCcnQitoIm4LruqSi2hWmGG7xQBLC9fjjuNFi93Elxs52cdpXuJw0bf1kp8oM3QNPwDTqfkH8XEsO2oesRKOdfgf3URKdOcIs2qOMfRsloXOU1a0jwnHEhorwUvvwIspogrrqNsJ645eEaGgGtcgnGJgENQe3OnVKASM+E8tL4Dd7EEYrgikUXlwaD4wAdRKoaUc2zWyHktU2nrNhvP0KrdtjZzQnWcjoNKSUtfDa//K0d03/ZSiOvymWHpJEqovuaSm6vUBQFfd1q5K2XVYzlf7MITHmNMs19mO4LhtAIorGx3KR5koYmYFFhlOd+20L3LA+XdU1HAhd8uJ6v1wfxa4IHj27MsPZ+GvovLoQgyYjArwsRpoG451/I2f+H/GI2nHsxZkpGnFhUVRV/eSnyuqFgOFZ2+fFM/NNmUupLqnR8y+tDvXA09iO3wpbNTuO6POzJN+G7azrBXQjActWD58Ix6EOvcIJtfEmua0cNoCgKpuohFcH1PTKYunArAJoCjx3XhDTV2psYpH0m4g0QGHUz8u4xYG/7oEHnYyRY+qMGIxYnTZrEgw8+GNfWuHFj/vzTKW0kpeSBBx7gpZdeoqioiJ49e/Lwww/TpUuXWP9IJMLtt9/OW2+9RTgcpn///kyZMoUWLVrE+hQVFXHTTTfx0UcfATBw4EAmT57sVqZxqXNUIfBFQwjLRGg68qdv4MP/g9QM9IuvJ9SoMWYVCXdVRUFf+ity0thYSg5lwBn4Lrqu1iMndWnSQrd4oE8aUkpMM1yrQhFAN6Pw5rPxjUWF8NdvKF17udbFamIpGh+vNbj4k1VYEs7rkMKJrZL4x1wnuOKtv8CwoTBs8fV6x+IWMiXXfrGJgW3a0biygaZBETDCMPFqWLUMCUhVQ5n0AnLkNRhCIbJTupq0tFTHor1NKAJORZUvZ6OdNrxSRHhI95GS2Ri5XShuZ+0KFHv3qiSK4pSUg72KonbZPULTKDB1nl64laKoxZjujbiqewaLNofp09RPsjCxajki2rJtwjm5+Ka/h1y+BJrnYKVkJFyUe4O6rXNzc1m6dGns37fffhvbNm3aNJ588kkefPBBPvvsM7Kzszn33HMpLa0w7d5yyy28//77PPfcc8yePZvS0lKGDRsW93Z2+eWXs3jxYt58801mzpzJ4sWLufLKK+v0e7rUL6qqEjDCJEeCBIwIilL3t4kqBIHN6xC3XgpPToQFnzv/XfEHLJyHHH8xvmjVEcbeSBCenxKfu+3z/6IbkToZu5QSwzDqLm2KEKBV8d6bYJNtQyEoNa76bGPMleDMdik88vMWDsv28tE5OXx6Xmsu6JRG9ywf+k63xoJNESJq9UvXJQpCCMTGtbBqWUWjZWK/9iQmopJQBJwiz9t8FePYhf+hlBJL90J6ZvyG5q2xlSqOc5CgKooz7xph/GY0LppeVdVamYeLbZ3er69k8o+F/PuXIo54bQWFYZvTcnxkEEaXdTOHGYpKqS+JYLe+lDdq6iRWTzAalFjUNI0mTZrE/mVlZQHOzTd9+nSuv/56zj77bLp27cr06dMpKytj5syZgFOO8JVXXmHixIkcf/zx9OjRg3/961/89ttvfP755wAsXbqUOXPmMHXqVPr27UufPn149NFH+fjjj1m2bNmuhuVyAOGItPUoE0bBqNNQ7h5NUlFBnQtGXzTkWAbzNyC690V+9VF8h2gY/vy1ynEJKaG8pPJBD9AybIaqIy64Kv7h3KQFtO14wFkVFUUhJDyU4UGtSqDUAFGbuLyJhi35R/cMZp3RCp8mEMC1n29k8eYwj/RvGrdvm1Sd2+cVYCgNU6gLISBShX9tJLzLxNnFJSWIQcPAt4NITkpBHDtwly9MYY8PbnkEMrKdhsbNEDc9RNjbcIX2/qAKQWDjapQ7RsHlA1EnjyM5VIYXSXJ5Mf4vPyCw/DeSzOieD7aXaJrGnNXlFIYrjEWWhEd/LqTMrJ+0T3XlX74vNCixmJeXR5cuXejevTuXXXYZeXl5AKxatYpNmzZxwgknxPr6/X769evH/PnzAVi4cCGGYcT1admyJZ06dYr1WbBgAcnJyfTt2zfW58gjjyQpKSnWx+XAxhcNIe+/HtavdhrWrEA++E98keDud6xhFCMKBZucP0qLENsfKjvSKDtuYlEUhSQj4uS4O+W8+L4t22L7AlV+lhACvxnl0OZNCBhh1HqwpO4Ptm0TzmyKeGwmDLsSRt8O9z9P0HNgPXhNofF7mcoFn2zijP9u4O3VBmGl5i0QXkXStVGF31xp1ManCjq+9BcDZq7inPfXcFPPTL7ZEOKIxj5SPQppXoVpxzXh/RWlvPJHMRHZMC1ktm1D6w6VrX7nXoKxCyFnWRahQDJi2kwYMQYuug4x9f8I7uJ+A0eUhJq1Rj70Cjz9PvL+Fwg2anLAvdzsLf5oCDnxatiwbd5dugj52AQ8JYVw9bnw7wfgrtGIx+4gUEOCUUqJrlQWhZoQqA0/RWiNU6s+i6mpqTz55JNxfoP7Sq9evXjqqafIzc2loKCAhx56iFNOOYXvvvuOTZuch2p2dvwDNTs7mw0bNgBOKUJVVcnMzKzUJz8/P9YnMzMzzvwthCArKyvWZ1ckguVRCEEgEMA0TSKRully3FcS4feqim5NMtE2b4hvXLsSKxJmWd4aVFWtE6fyw1s1h6wmULAJ+eWHKOMfRi6cVxEx160PwZRGMZ9dXdfpkpmOuGcMsnATyk0PwRXjkd99hmydi3XGhfy5YVOl68Lj8dA5Kx112gTUpYugaSu819/DSumhpLy81r9nTaKqKsm9TsS2bUpXrq7v4dQYQgjSM7MoslTmrClldYnByhKDiz/ZwNunNaOTuYFgsOZeZnw+H++e3pYbvi5g/qYwJ7RKovfrK2JpkYoiNuO+3sQ1hzWiMGwxZ3BrtkYsXlpSxGtLSzi9bTLCjJK3Jg8jwa3ZHo8Hy7Li7umA30+HSS+ifDgDsXkj1qlDKErLYvUff8S2t0pNQhcCE0hNSWHFqtUYhkFKj+MAKFm9bpcWIiEEaakpJKsKmyybopLSWrUmBZKS8WW3IGgLkjRBpLiAssIKf0lFUdA0jWi05qx21aV7djpqaVF8428/IoLlyB0F9KL5yJKtLN9atlfCetmyZWiaRkrTHKKqFynBK6OUbXTOV//cQ2mWpLGh3LEA6wrc0juLgjUrKG9g89/+kpubu9vt+yQWly5dSl5eHlu3bq3yIh8+fDjgTDoXXnjhvnxEJU4++eS4v3v16kWPHj147bXX6N27N1C5YoCUco9VBHbuU1X/vTnOnn7o2sZWNEpsjblrgzRL0ejWxkOyjCSkSXvZsmV06tSJUltDCgWPsPFKo8qxbl9m3XliUBQFtvn3SMusse+pGmHHqlBUWNHYpAW6z8/hbVpBwUZERhOiHm/V/ks1hLQN1Gvuwn78bijYiP3ef1AeeR25fjUyJQ07Ixtb85Cb4iQH9tkm6tP3wqZ1ANiTboDDjkRcczcRfxKGLcnJSa70OT7LQH3kZli6yGnYuAblvuto/+gMypo3r7XvV9s0bdp0z50aCCHFx1O/FPHGF2tpm6bz4inNuWPeZr5cF+SZJaW8fEIbdFmzokwIi2ePzyYiFcotJ6BlR37fEqV1qk5uuodXfy/i9nkFBDTBrDNaoiqCp/8IcXrbDrRJUvDJ+hMhu0IT4IuGYfVfkJ6JnZYZly0gBOhDRyGkjSkUvLZNbqMsx3q/NR95/3WwYTV642Z0uHEyUrehWQtCHh+WZLdFKJItAz7/ABZ+S3a33nDiOZRrnlqZqxVFYXVE46i3V5MftNAVeOL4pgzumIUuTULCw1ZDsKokStdML8kY6EISlCoC8AurTqydqhF2lvHDO/hht+2EzN9Qua9p0L59+z0ec9myZeTm5hJUfJz/4Xq+Xu8cu29TP7NO70zADqMoBvMvaMPMZSVsCdtc1CWNTN1CbcBzX21RrafdqlWruPLKK1mwYMFu35q2i8XaJDk5mc6dO7NixQrOOOMMwLEMtmzZMtanoKAgZm1s3LgxlmVRWFgY83Xc3qdfv36xPgUFBXHiUEpJYWFhJatlIiGEYG1Yoc8bKwhv80rv2djHe2e2IEkmXhWFpJRU/gyqXPa/9fyxNcrZ7ZJ5tH9jUqgQt4qiUIKH+RvDRCzJsS38pBLFloIy4WHJlihpXiiO2OQke2jssVBrIMt+2OPDf/MUmHQDFG+BRtlwxxOIDaudZZJoBCkE+uXjkEefSlTUznJbRPXg070oo8ZDIAXKirHXryLa/lAMKie7V0wDVi6taJDSCYTJX4+Zs+sXGU3a8OuP8Y2lxYhIKKESwh6MKIpCUHh4bFERk753Xl6WFUX5fmOId85sxXEzV9E2VUdTJNSwsVtKiRcDL6BoPhoHVPKDFR9yWptkmgc0kjG4vEsywzqmErXhvgUFvLbU8Ze9Z0EBjx3XhJEdfIharICxJ7YLQxEKIv1+oh4/3q2bkeMvjuVPVPoeT2DULXGC0dgukmSFWPJFgsgH/lmxXJq/AfngP1GuuhX7nqsJTH6Vsp1S6+yIzzbh2Qfh2zlOw+IFsHQx3n9MqJXo1zKp8fc5G2PnzrBhzNyNDLqkPZZQmPxTEY9uSxXjUQU/DW9LYdhk4vzNKALuPjKLjqkamhXFVHRC29wLkoRZo1VNIpoH7z8fgEdvg2AZZDZGXH8vcvVOK1DZzZw5eS9RVZX/rQ7GhCLA/I0h3ltZzoh2jlU5lRCjOjkuBqYZrvF76UChWmJx7NixLF68mPvuu4+jjz66XtPJhMNhli1bxrHHHkvr1q1p0qQJc+fO5YgjjohtnzdvHhMnTgSgR48e6LrO3LlzGTp0KADr1q1j6dKlMR/FPn36UFZWxoIFC2JtCxYsoLy8PM6PMdGICI1bv90cE4oAP+aHWV5scFiqSDjroi+rOUfOWB0rD/bmslI8imDq0Y3QcSwkJXg5+s1VsYz6mT6V3/7WllJLUhAy8WsKa0pNvt0QpH2qhxNbBWiuW/v9XS0JoeZt8U15DWFEkfq2h8fjd0F02xKulPDCo3j6HE+0lvziTCmJNG+D14jA1s3QvitR3UN0F9/P9Pjw9DzWSa2zHU2HJi12+5tYgNqusxNlvR2vD3mQOtonCoqisC6qs2RrhP/8ER+stDViUxSx6Ziuc1PPRohaTnieIqJ8OjiHy+ds5LfCCKe1SWJK/yakiyiWZaEBPgWKFF9MKG5nwnebGdy+Lcm1WAFjd6iqin/DKuRdo5HlpaDpeO9/Hvn69PhE2/PnolxwFTRqstvjKbYF61fFNxbmgz8JysuQrz+FfvnNREXVfr+aacC8T+Mbf/jKaa8FsWih8FthvOuJaUNx1CZZV2JCESDLp5IfsjjxrdWxNNSfrinnh+FtaRbwM+n7Qp7+tYhuWV6eOr4pjf1+NGGTKkxkFSl/FEVBURQsa8/zsiEUZMfu+Ka9CUYU6fES8vjxpqSjjJsMn70HLdrAWSMcX+S9tHYqisKP+ZXvj+83hRnZwXkZFkLEjVEIgVQ0FCGx6yqjQwOgWmJx3rx5XHvttYwePbq2xrNLbr/9dgYOHEjLli1jPovBYJDhw4cjhGD06NFMmTKF3NxcOnTowMMPP0xSUhJDhgwBIC0tjZEjRzJhwgSys7PJyMjgtttu45BDDmHAgAEAdOrUiZNOOomxY8cybdo0pJSMHTuWU089td6XmXeHJWFrpPLNujViOxd+gonFoqiMCcXtfJBXxqR+Weg4E/wHK8riSi8Vhi0iNlz/+SY+yCsD4LBsL9NPaIYCPLVoC/f3Tccy9385zpLSqcm5zbKWbERgZz9G03DyqtWgWFQUBX8kiBIJge7F0D2UefzQJGeP+0YReIZeDmXF8O2nkN0UMWYCYc/uUyRHPH6Srr8XOXGME1ATSIax9xJJwNQNO6KqKlLK2BKZEIIwGhEUfAr4MRt0wuJSqTHsw3Vc3DWdnBSd1aXx13WHdJ255+WQQnRvn5v7jLRtWntN3j6tKTYCr7DxyjDWTnUcqyrrGLVkLde/2D3ecBA57Y4KX9/tFvgtVfigl2xBZDbd7XxpKypK85yKADhwIppD24RnwSYUy4Td3T8eb3zEte5x0j/VAl5hMahNEm8uq0ghl+5VyPAoFO00B5+Uk8TrS4vjzpct4ZXfixnYJonHFm2lQ7qHxwc05Yo5G1hcEKFlssaMQS04JFVFU5xnjaIoFNsaP2+OYEuLzo28bA1bNE3SUBDowsYvDeydBKaJcOa77XOqlARVHfWwo9C69sRWNQwp91ooglO7fHjHVB7bQRQD/L1rGsWWymdrI/y+Jcqwjqk09TrfPC8I037eQpOAxrU9MshQovF+kwcp1RKLaWlplQJE6or169dz+eWXx5aRe/Xqxf/+9z9ycpwH6XXXXUcoFGLcuHGxpNxvv/02KSkpsWPcf//9qKrKpZdeGkvK/fTTT8eloHjmmWcYP348gwcPBmDQoEFMnjy5br9sNUlWbMYe3ohv1q+LtaV7FY7I9mLbibcMne5VUAXsYAjlkEwvmpAgnQd/QTh+Ivlb51R+KYzEhCLAos0R3l9RxvBOKaT51Fory2SqKlqfAfDdZxWNTVog9yDEqoMQgqRgKfLOK5Eb14IQaOdejO+MEXu9PFWmevBedhP6xWOxFRWpKGiGgfAF0MPliGgUPB7Cui9m57Ftm2B6Nr4HXsIKlqP5A4Q9viqTfdcXiqJgoKIJiS0lJVLni9VBsvwqh2X5SBUGGwyNG77M5/ctEc5sm8LowzJI1zS8diR2jKDUMCSkqjaylhPtVvUd9tbKAo5FaHFBhGd/3cpjA5pyzvtrKDec/UZ3SydTl/jsSJ1V1bNtm6RtNcBxbtNK+BSbE1oF+GxNhcXuusMbERBW1TvUAYoA1uXFtcmfv0WceA7y+YcrGgPJ0KLtHs9NxBsgcNPDTsaE/PWQ2RhlzATst15wOpw6xIma3sWLStTjxTP8KnhxakXjkL8TraWyfV5p8mj/Jpi280LeOcPDCyc3IwkDPDotkjXWlTn3QlHE4ojGlV1PWiRrzNu2jHvDEY244ctNLC5w7qu1ZSYv/17EDUdk8e6KEs7PTaM4ZDHp+3xyUnX8msJZ761FAsm6wqwzWuLXFaSETikanr3IY2hZFpai7jJ10e6QUtI2WeGFk5tx1/wCpITbemeSk6pz5vtrY1bH+74v5IOzW9IySaPPG3mx/V/8vZhFI9qQQuI9R+saUVRUtNdnYOLEiXz33XfMnj27Nsfksg+EFQ8/bDZ4fNFWWiZr3NI7i2w1Md+INmwp4bOSANd9sQnThsYBlbmDc2jlNWOWogLbS9dXVxLdpiiXjGzHrOWl3PZtfNWDc9unMPW4JlhS0qiGb2hVVVGljSUcix+vT4efv4U2neDycQST02vM+dsjbTzPPQRffRjXLh5/i9LUvX9B0zQNVdroBRvhlWngDaAMucwJeNm8AZKS4YZJRDocirGTINzuEF5fmEKjHI2CoEnjJD32bFhfbjDphy0cke3l/I5p9Hp9RUw4dcvy8sHZrejzeh4bg86Dp3WqztMnNKN9mo5fhRTFYlVIMPbLfNaVGVx2SBoXdUrFXwcvUoqqERQ6fxUZzFldzlntkmnuB98eAlLKhIdB763nl4II1/bI4PrDM1m6NULzJJ1GPoU0IgmXZkUIQbnwMnN5GV+vDzG0QwrHNPPit+svM4PPMtAemwAL51U0NmqC8tDLyO8+Rc55B7KaIS66lmBaZtwL7K7YXllJMU0EEvujN+GXH2DQUOwjjiG4h5c7n2WgFW+BPxZBx0OxMrIJ1WIC+e1W9ygqCjYpwplnFUUh39S5+ZvNLNkSZVjHFC47JJ2jZuSxdpuAbJuq8/aZrfjvilLumLeZz4e0ZsDMimX41qk6z5/cnIGzVvH+Wa3YFLTIzfDQb0Ye313Qln4zVsZZnA/L9nJtj0Z0beQl26eQpdTetbHjfCZUjRLLMQqlqhbLywVHvJ4X1//wbB939M1i8H/XxrXPGNScQc3VhLvf6ppqWRZPPvlk5s6dy5lnnsmll15Ky5Ytq0wM27NnzxoboMve4bOjHNdYpc9J2WiAKiPIqtaFEoCywk2c37ELZ7ZtT7kpSdEVMlQLY4eQywzV5IcL2nDvggLSfSprS00GtUmuJBaHd0pFU8AjoSbNLEmWgfh+LuLnechexyB79MMYeS3asCuxNZ2IqteoEFfNKOzszA3IjetQ0rP3OFFpgD9chvz0XZRjB2LfdBFEw4gxE7CfnFixjF5eBg+Nx/vE2xgJFMBiKxqfbzS5fM4a/nt2Dtd8von3VpTSPElj8rFNaORV2RqxuXv+ZqR06raaNvy+JUJhyIoJxeZJGm8MasE/v9zEtxtCtEzWePXUFjz9SyGfrXFSYYz/ejO6EFzeyY9dSxZGIQRlwsfzvxXz/aYQp7ZOpmumjz4z8nj2xGYMah0gakuSFMtZttyJVGEyY1ALzp+9jhGd0znyjZWoiqA4YtHIpzJvWOt6tXYIVaXU1gCBV1jotpPNICDDXNrBy0Ud/KjYWFb9pvCKaB60MROcPH2LvoPWuShX34k9ewZIibj6LuyMLMo1716761S4qTgW4/L+Z9HojBEYvgDmXrg+hFUdkdkU5djmdZKEuSJYyXlB2f5YsG2bbDXK9P6ZhKUgRbFRiDB/WBvmbQwTtiQtkjWGz17La4Na8PX6IH9ujdKzsS9mkRuam8qTi7Zg2JCTqnPX/AKmHNsEAYRNu5Jrwl9FUZol6SwvNthQBic1VepEhEnLJGXbeoq0wJSVxblhS/QqEiz6tYaVd7a2qJZYPO2002L//80331Tavj2KeMuWLfs/MpdqY1kWnm2hXIkpEyvQpYkQUC5VHvihkBRdYVS3dDKUKNg2qrRo7bWZ3j8TVVMpMgSWDa8PasEt3+QTNG2u79GIXk38tHthOcsubsf+eg/6bBPNNBDShuW/I599CBkOwjefIE46BzHi6opEzztM8JqmoVsmGFEMjw9zHyY/0+NH7XM8rPqrolHVEK077NVk6g+XIa8dAk1bIhs1diq8AKJZK+Ty3+M7h4O1Gu2sqioWCorc+7Qb5VLj8jlrGN4pjZd/L+Kd5Y6P1doyk4s+XsfX57fl501BLuiYyiVd00nRFX7YFOKmr/Px6woC55q/ukcj7p5fwLcbQrH9T39vNR+f25o3/qzw23rx92LO75BE5WRCNUMZHs7aYZnrvRVl3NQrk0u6pnPbvM008jfjoo/XMaZ7Bld3T69k5bRtm5Yeg8/Pa8XUhUXkhypESLDMZNbyMi7L9dVdScUdMITGVxtNxsxdz+aQyYWdUnnw6Mb4Lec3d3xFrYQIKpVSUu4N4P3HHaiWiWLb2A/+E7bdE/LtF/6fvfMOk6JI//inqsOkzSxLzlGCIIg5JzBgQMEseoo/9fRQxJyzImfGM516mBPmU8+cAx9Xes0AACAASURBVKKYUFQySGbzhA5Vvz96I7uEhVl2xf08zz0n1T2zPTPdVW+94fsiLrwVue1Om5TjqpRi8ZpCIq3yG9SrWWvdLHJqtdZYuFgAKthvx0iwR7swv5W4PD27hBt2aU37qOSRfQtQwmC39lFGvrKQOcUumZbAq7AIBRAzJT+uTrFPpxiOr2kTNVheo4p+ZPdMPl4SZ3iXGLkhgd5C7fTWpkPUoHeuza+F1bJOlwxrRb9cC9sQVRGtHtkWQwuaZzrXlqZBxuKUKVMa6zpa+IshhGC5Ixn0ZLXY770/FPLD8d3IqvCYaK3xgbcWJBn/4TIKUz437Nya/x3RGVdpIqbgHx8sZdd2gRdjc4j6DvK5B+GtaWitYKd9kBdPRl19VnDCe69iHf1/pOzqR0YIQVS5GKtXoZ95AL1qGeZ+h+MP3X2Doai1cbXGPnAMomQNfPhfyGuN+L9Lg7ZgG8A0TfRbLwRJ82UliJy8qs2Cnv8b9BsCs76pfkFmdqNUO1d6056aXcJXy5Oc0CeLYQUbF4b0lKYopRhaEOam6avWOgbzih12bh/lwJcWVQnoHt83m/v2aUfMFFy0fStu/no1g1uHuf7L2t7nclfj+Bopqr0qXbIsbLGO5LvNRAhB3KdOFeYDPxTy+IgOvDSnFK0Dcesbpq+mR7bN6K5WHeNBKUXY8FgZr7ug1je2pShRBke+tqDqq5v6cwnds2zOHRiDLZwLujForUlKE2naRL//ospQrOL1p7H6DMJv5kVdWxLbTzIwUzJ4x2x830dVVtxryLIlHx7ZiZSCqClZWObx6twyvlqe5LId8jnmjcU8c1BH5pc4vHJoZy76ZDmz1qQ4sGsGp/XP5cU5JSQ9RUGW2WTFl5nC4b1RnXnsl2J+Wu0wbkA2fbIMLDx+PrE7034voSBqsl+nGJlsudzg5kyDjMV0CWy30IKWJpO/WVNlKEKgmfjK3DLG9gxVLZxFvsFxb86vOueiT1dS5GjaRiUju2cxqmcW/VuFkUJs8sIvpUTO/R3eeK568PN30dsMhgFDAx3CetItIm4Ko2gV6uozobQ4+Fyzv8cYdxHmHgdV77iFwEIjlMI1zHV628oNC/u4v2ONHocmSKb3G+qlXF1R5Vlx3fqVx5ETb0Y9OAl++xHadECcfxMJO5x2Q6kMm8NeW8zXy4OF5dlfS5i0W0GgYbYBTTZbBjlDc4pdtisI/r8m2+SFmPT1qipDEeCJX4o5f0gemTicu23gcXSVZvs2ET5aUl1kETIEeWGjylDMCUkm7VpACCetX4EjTMq1yW+FDt2z64auYpYk6WnO3DaXaXOqJWae+rWEgzu3JlSPL873PP4+KJcHfyqqun5Lwol9s/G8LR/ilVLy7dJUne/t1XlljOuXSWyLX9HGo7WG7Ly6B3JaoYz0CexLKQk5SYTyccNR3GaaDrQhlFL1zlVKKTJIkSEAH7pHTb47vhsP/lDE9oPCfHhUV16ZW8rO7SL0yDJ5ZkQ74qpSlUNw7uA8QniEmlCsPfgMCc7eJoIWMVA+SgXX00r4nLlNtML7m2gxFCto1HZ/LbSwLgRg1teXs8YaK6XkqxpiqpXMLXYY2T2PflPnVGlLHtEjgyl7FhDVDV9ApZTw4/S6B377CdGhG/rHGXDYiThrVT8bvotetqjKUKxEv/U81g5749lhDAHR8mL0tIehaA3WIcfhdepRp8LZNINH0fE8nMrw8EYaip7nIQ4YhX79KUglUfdcgzz7KjhpPKqsBJXfDnXxbUjlo4UgsSlG6EZQ7lFlKFYy+ZvVHNurS1W+0LrIFC4vHtKBq75YyQVD8/lxVYpfCh0sCTftWoAlg64ha7OoxKFnJOgS0tYAwzb4937tOOjlRfxW5JBlSx7dvx1twoIfT+jG6oRPjxyLDNy05vRqafL2EpcT3gw8bnfv1ZbRvTJrSZZctWNr2kYN4jk2N3xV7T0dWhAiLDW6nqik1pp2IcVnR3Xi5m+KsCRcvkM+eWbTVBgrpeibV9cDN6xNmLDU680b1sKgHIvVKUVOSBLBx05z55n1obVGt+sMfbZFzP4+GIzEEMeeibuJ1bZrY6KJLFuI/vetULiK0H6HY+17eIMjDZUpMQCOFVqnbmNzwNIeXUKCG3fMRhCknpzTPxZUMbtxTKDWzNmMbOfKlIm1aYr0jubOeo3FW265BSEEEydORErJLbfcssE3FEJw4YUXpu0CW9hKUR7nD8lj6s/FpCoMvvyIwcFdM/Ercp+01gzMryspcXzfbC79dEUtEfIX55Rx7U6tiW5CJEkpBYN3gUr5i0qG7oZOJeH6h1DtO+Ow1oStNSKWVXfuy2mFrvBERpwkeuJxQWEJwPSPMK+agtFrW3zfx9KKcLwE/c7LQaXyHgcFxlwDF65EJJPIXc8H1Z1CoLv1IRHNRCHqegcaKaFc1mP8hwwRSMht4OMopcgVKSbtnIcvJG8f0YmUr7ENQUR4hA3NsX0y+XZltTFqG4LBBeFa+US+79PG1Lw/qiNJPzgnQ7hIlaSjBZ1sgW6E/KMybfL39xdVfcyLP13OY8M7MG5ALjNXJBjRNYOCiEQC9/1QWOUlHNAqxNmD8tB+3U1RJZb2iK5ZyEN79Qg2WetojbmpCCGwlYfhOnh2qO59vhatbLh8h1bcNH01vg4q0i8blo+h1v0ZpJT8npDs88I8ilIKKeC2PQo4rkd0ixqMcStE9MLJsGwhFBdC74Ek7Ejavs+wk0Rf+rdAgxXgiSnISBRzr5F4/sY9dzHfRTxyG3z6Fpg29phxyH0ObZQOL+lCa11L47Yp+0y30DisVzonNzcXIQTLli3Dtm1yc3M3/IYtBS4tbIBKSQMlDdb4FlN/LiLLNji6VyZZorbcT1La3P19KTd/vQpfB20Mnzu4Iwe9tJBfCmtPSB8e1ZnBWZtWXRj1XeSbz8JLU0H5sN8R6DGnkwpF1hmOiXoOxoJf0a8+GUjqANghxM2PUt66Q3DOd5/D5Itqv3DQjnjn3kDKsMgoWomecAxU7mSzc+GfT1G2icUnlR7KTdkZN0Q6RwiBkiZKg1XhTYgLm3HvraylhTn1gHYc1tlOi65hQoa554ciHvmpmLYxk7v3bEPfTI1Rn0tuC1NMmC6PzKkzvujUnuRKt5a+YlLYJLTE9TUxEzJwNnjPNpaskRCCWCoOj98dSLkM2B6OPZPyDRhQjghavzlKEzEgS7jrLWYqFzYjX1vKNxV5nGFD0DfP5rVDO5LRBMUDQojgHk7Dxqnyt5FSEp01A246r/YJPfrhX3I7iY3QUjSlJPzRa/DgWtq+k5+gvKBjs2uw0JxpaimwrY31ehYLCwvX++8WWtgcpPLJFz4XbhvUpHpesk4kKKwczt02k3EDc/AUhA3IMhWnD8hhwsfVXRjywgY9sq1N9hrFDQv74OOwDxiF1BotTRzDXK/RlbBCRLv1QY49F3H4SejSYugTeCoqdcyIZdZ9YSwTJSQWGj3tkWpDEQJvx3dfYOy47yZVSzZm+ERKSYm2cLQALXj8xyJ+WJ3iwqGt6BqVRLXDg/u2YfqKbGasSHF4haag9uv3HDV0wY6oJBO3zeCM/tlINJnS2yKyGxuDJRR7dozy4eLqXMmd20UwtF/nNwlrhzCAwToFrrcUETcJN58Pc2YFA8uXwIo/CJ97A4n1FHzY2sXGBQno+ru31EQhmV0YpIiMG5DDmdvm8tPqFH+UK9rFImRoB7EFjX6tddoNL6015Lete6BdR5S5cRlfhu/CN5/VPTDrG2TboAFFpbh7c7n3W/hr0JKz2EKTsyEDx1IOORAkOipQDhzbO4OIJXn4pyK6ZVlcv3NrMvXmFSwI5SNm/4B6eDKUlWAPPxLr8JMpX0f4R2tNubQw8tshC4IezDU/i1IK3bkHokvPakkcO4w45gxcaWCh68+TaobeAyklcxMmR7y2iHklLt2yLB7avz2vzy9nh6fn8/WxXekZEURVkr0LJPu2jeH7Xr0LcqV485xil+UJnx3bRMgQDqKe/rJ1XltDL605rZUxXB4f3p5rv1jF+0vi7N4+wrU75ROjbjFIUyGEIOQ5mL4LQpKywkjPrTYUK/lhOsZG/BYNISR8DuueyZLyQC912FPzqgSwT9omm0uH5dPO0s2yicDGorVG5bRC7j4CPn4zGMzOhePPxpHmRj3XvmlhDRwGMz6pfaDvICJuCqZ/BL9+D3sciN+xe6OKef+ZqLy3pe/hWSG0HSInJ6epL2urokEdXFpoIR2kKzxgGAYlvoEtNbau3zBpCJmlheizj6g9+H+XkNztwE3SToSKMJ+Xgt9/guI1iEE7kwhF8Ai8arHCFTDh2GrJkcwcuP3pTQ5Dbw7r+13KRIjdnl/E/Br9urtmWfxzjzYc+dpiTuibxT275cE6vIgQfBdSSuKYnPXBSqb9XlrVBuyrY7rSwdpwOLa540uLuBJEpMZKwz1ZSTqemQzPgXuuDlImohnwt4mIwTujzz4ckjXyDTNz0Lc/HQhPbySVv+26vOGVskqzCh3+/v6yWvp2ANOP7UbbsCCTphXx3hTW/m0ivoMZL0eXl0J+GxJWuEE5yBm+C/ddD199CKYFR4yF4Uehb7sUUVMC69QLSe118J+22jpdSCmJlhUFwutaI0+ZgP7mE1R5WZDrGcuq062qhYbTYM/iL7/8wn333cfMmTMpLi6u4woXQjBz5sy0XWALLawL3/eJ4YPa/FCeYRjomhNxJZ+9g7njPnibuIPXWlNm2MhthiKEqLWYaq1JZbcifOdz6Leeh4xMxD6HBcLfzcxocpSoZSgCzC9xyQsHhTw5tkF9dSxCBJIZCRFiYbnP58uS7NLe4OJh+UwY0oqrvljJOwvLufzzldy/ZyusLVDsUHlNjYGhXDIhLfdkOrGEhpf+U51bGy+De65G3PMi3Pgw+oITgw2LlIgzLwv0PTfyA0R8B6NoNcz7FdFnIE4sk5Ss/bxorYnpBH1ywvVqRJa7ChXeOgJdCcOGTBuRlRfcZw2818oMi/AZl2OedhEIgWOFsBNltQ1FgBf+jb3zPs2qE1NTEEkl4JqzYMUfyJseQV35f8HGHNAvTSV8+9P4Oa2RUjYbMfQ/Iw16Or/88ksOP/xwMjIyGDJkCN999x177LEHqVSKr776ir59+zJ48ODGutYWWmg0lFLQpR7PTc9+gVjvZhoX68ovcoXEy8rDPPas6jB2MzMUAWyp6ZJlsaCGwdg506Iw6ZNlS84bkof2q/NFE8Km2BOsjHt0z7a5+7s13Px1deHb1Tu1ZnXC4/Id8vm9yGFZuYerId1BtaSwKFcGK+IeXbIsPAU/rU7SKdOmlQ2RJtR625KYTgp+qCsPpWd9g07GEfe/il66CNGmIyk7hLeRt2BIeRivPQXTAiUBDVj/uBa1/Z649dzHGdLj1AE5TJ5RfS/0yLbwNZiifgtbCIEjTBSSDEPhuluuenpz2JwNSdKwoMYGNSRE3a9GiGYzV1hoQk4SlMI3TZJWuMGf3xBBNblwUmCHSFrhDQhuBQgnCcsWQ99B6J9nQnGNAlvPRb/4H2JHnx4oTnTqjh4wlHgD2ju2ENAg8abrr7+e9u3bM336dO69914AJkyYwJtvvskbb7zBkiVLOOqooxrlQltooTHRWqNat4UDRlUPdu0NhxxX76KX7r/tum6z1vaq1EHsnBksYJ0zLZ49qAMSzU8ndCNmaIoIU0iYMhFm0rdF9J06l92fX8j8Uo/J39RWSLh1xioO65HFpK9Xc3K/HM4ZlEumTG++WkpY3PdTGT0encNZ7y9n5qoUvf4zhwNfXsyAx+dx3fRCkuKv0bXDt8LQf0idcdGxKzzzAFpDeZc+lIaiOKKuAP26sDw3UBCoySO3YTv1y+hI32PidrnctkcBO7eLcEq/HJ47uCO9si0yRfX9H9I+GU6CkJtisWNx+gerGf3mMl5d5FImIwjx1wor+nYItt2x9uCY03HC0aa5oBqElEfoy/fgnFHwfwdjTLqQ2Dp+/3VhSEl0xRLExOPhrMPg3DFEFv6KuTE/s2UH/5MSdN05RPg+vPwYPPcg3HYJ3DKRqNvSvq+hNMiz+O2333LBBReQk5NTVRld6THZcccdGTt2LDfccAP77LNP+q+0hU3GEmBX9ApOhSJVnUVaqE3csAkfcybWUaeiPQ8VCpOwws1m996UaKXoEYHPRnfCUUHHlRzDp3+mQamGiZ+u4vFfgq4kh3bP4JzBedz+bTBHaKjVqQcg6WksCYvLXK7tlk/HqMT305uvltAG13wZiF+PG5jDFZ+trNL0BLj3hyLOH9qK8F/A7nAAe9QpMO/XoPVjKIwYczr6uy8hUQ5sYnWw7wVSUzWJl603QyzsJzm9T5SjemZiSQHKJyZSVWtJ2Hcx33sFnn2AVbe9xLCX5hOvcHV+tCTOY8PbM7xzFNtrmEHyZ0VKCa5Gnn0lurwM/e1nsM1gvDYdm0W+opVKwL3XVQ/M/g6euR/7hH/gbGSuYCgVR0++CIpWBwNlJehJFxLeiPxtxwphj7sQHpyEOPk8dGZ2daMEw0QcMAp12yVV54vZ30NZCeS0btDn/KvTIGNRCEF2djYA0Wiwo6mpqdizZ08eeuihNF5eC5tL1HeRb0+D156EUITwiefgDtyBVEsVXb0kDSsIAVVKorUYilXoijZflcmJnhcsZF+vSFUZigCvzC1jr46xKimZGcsDUeo3a+gvju6dxfuL45zaP4e+mQLPTX9hg6vgf6M6Y0lBfsTg2i9W1TmnOOXT6i+S8lVuhYlNuBFZUgjxMvRHb6D/Nw12H4G3iX2RlWUjew+EX3+oHtzjQFxz/fOL57lk4VY1z6hp81iJMvTjd0OPbfhwjawyFCt5dFYxQwrCdLQaL/e0uSClJFa0Ej3pAtSiudCxG+KCScRbtW0WuXdCCFiyoO6Bn2diOEmwN64HvVQqCCXXpLQoEDffkLEoJGLYXtiDd0Yl4sh/Pol65yUoK0UeOBr93ivVbVArSVO1vyEEYScRhM4tm5Qd3mqLaRoUhu7cuTNz584FIBQK0aVLF95///2q45999hl5efX03myhSTAMAzlrBjz1r2CntWoZ3H4ZVknhXy6M00LjIKWs1Ye5km9WJOmZHRgg13y5ism7F3DLbgUM7xLjll0LOHtQHq3DkqN7xvDc9OcNamEwv9RlzOuL2ev5BfxnVhEn9s2qdU7bqElBZONDrn92tNaUWWH8nHzU4vno4jXwf5eiTzmfpNy04pKEFYaLJsOok6HfdnDSePSJ/yDVgFB2TaSU6KWLgn+Ul9EmWvd9CqIGnr91G4mVhFNx9E0TYFGw7rJ4HvrGcwkny5v2wirQWiM6dA3yJ2vSf3t8e+N3YUoa0K1P7cH8tmhr4zYxKWlSGopRltOaklCM5GFjWbDfaOK5BeiO3Wuf3LE7OmvDDUY2hJSSaPEqxCWnBKHz8aMJzf4OayvtJt0gY3Hvvffm5ZdfrtrNjR07lieeeIJDDz2UkSNH8swzzzB69OhGudAWGo7hezDzc+jcA2SNSXfGJxjGX2eRbKHxUEpxSLeMOuNH9MjElILu2RZnbZtDq5DgzL4RHt+vgHH9MugaE5zYM0xYNY5UShkWo19fTGEqmLjvmlnIwd0zuXbnfPq3CjGqZyYfj+5MlvhrFLjUpNywSO42Auesq0jufuA6dUQ3Bq01ZWaI1OGn4J1/C8n9j9ys91NKITp1D/LPli1ioC5kWJtqoyPLlvxjcB4FUbler6IvLcqwUcafu8Ja+h78sZbnbvkShOdhGAYRN0XUSQTh/CbCCYXh3OurGxBsuyOMOW2jQ9AAyVAEccEk6LFNMNCpO+Lyu0hupGdybTzPY82aNXi+j7fdLnDtA4g9D4KTxsM1/yKehgrykJNA33EFrFwaDMTL4J8XE2qEKElzoEE6i0VFRcyfP5/+/ftjWRZaa26//XZefPFFDMPgwAMPZMKECVhWS4izqanU95OL5qKXLUJ07Y169sFA7PXqe4n3GNBkHQBa2jA1Tzb1d0nIEP+eVcrNX6/G15p/DM7lvEG5oBWuhkxDr1d/sTFYoUL0/s/cWmOWhN/G9kQITUT4hNKog9jY/JWeGVv52L9+D/fdCEKw/Oan+TlpsbjMY1ibMDkhSaZO1Tt/CSEoESGu/3I1H/+RYN9OUS7ePo8MnWq037oxf5uYmww8V5UGCUBea5g0FbFiKfrfk6G0EIaPRu09kngTpRdZgqpqaM8wSVkNrzaWUhJyEhhKoYQkWdFqdVOp+btIKTGFQAFemsL3GV4KThtRJ1VJ3PMipZmb77lsbrSIcm+lxDwHbrsE8dOMYCAcQV5+N+rjt9BjxlG+iTlKm4spJaUrl5PVuqBZJGe3UM3mLHrKMClXJgKISh+5hY3DtSkTIYY+vYDl8eqFYfs2YV4+pD2xRvJmNiZ/JWMRwJSCUCqBADzDwrVslDQQWqHWoxoQFzZHvrGMz5dWF78c0DnGf/YraDSZpMb8bQwhiC5fhL7xXChcBdl5cOntQVvB0w+uFvMHOOsKkrsckDZj6M9OYz8zYc/BvP1S+PHr6sGMLLjj2SZpqtDYbHQYOpFIkJeXx+TJkxvzelpIA0IIxKpl1YYiQDKBevkx9NFNZyjGlEv4nWm0fmQSodefIsN3WnIntxKk72GiKPc0s4oVZSJcO/VhC5MhXP57WCd65gT3+natwzw1oj2ZNA+Nvsq+2C3Uj6c05VaYMitMUhr4vo92nfUaigApLfl8aQJTwsXDWvHx6K5cuVNrXIwt+n2HtCLmJIh4qaCaeRPxtSbepiP61sfhX6+g//kkqfbdAs1Mf63v4oPXMVN/jQrx5kDKCiHOuQb6DAoG2nZCXHlPIGi/FbLRCR2RSITWrVuTlZW14ZNbaFKEEFBSWPdA8RqU0tAEa3hYeYiHb4OP3wgGfpgOs74lPP46EvUYr43ZZaOF9JMQNtdNL+TeH4qAILfs0zFd6BLSTZPuoBS9ovDBqI74CCwUGcJtstSLSoQQOEaYNU4gVZMXkoRV/SHVFhqOKSBqCu7Ysy0/rk6x9wvz8RQc2DXGv/dtS1Q3vr5ehu/AY3fBlx9gdOhC7KwriLdq16CWfzXxdVDJXlkVLLQm1LZj3RM7dEFtoAq9hfShtaYskkH4wlsxlI8SglQoir+VPssN2vIcccQRvPjiiy0TWzNHKRVUlkXXKjw4cEyTibiangufvlV78PsvMbzanh4TTYabIDbnJzKKVhHx/noFCH9GSjxRZSgClDiK8R+uIK6brsBAKUWGTpGtk0S10yzmrVIR5slfSzngxYUc+NIinvmtlOIqnab0E1IemU6CzJLVZLhJjK3cmxkRHvft0442UZO7Zq6p0vd8Y345j88uQTZywYutFTx2N7z/WlDw8NtP6MvHEWmgSPX6CBoItIed960ebFWAOOo0XLHpXswWGo7WmoRpU2ZHiFvhrdZQhAbqLB588MF89NFHjBgxgpNOOomuXbsSidStVho6dGjaLrCFjWNtT1zCjhC99TH0E1NgzUo4cAxq4A5Np80lBIQiFQLAFZhmUPVYgZSSyIrF6Ev+BhUi4sa+hxM57qx6vY9bisowUnMwNpojQghWJuqGB+cVOzgats6gTMMxTZOfVzuM/3B51dg5Hyxnm7wQO7Wy8Lz0hshDysX64DX0Y3eDUpCdR/T6BynPab3V3sum8jikS5hHZ9eVlnlvcYJT+sTS3lKyJpabgukf1h4sLw2ky/LS9yTETZvIuIswjz8bknF0Tj7loQh6K/1dNwYpJSXapszTRExBVkGHpr6krYoGGYuHHnpo1X9Pnz69Tg6I1hohRC2h7hYaF0srwm4SvXAOtG6Pn5FFwrDwtaYsMw/79EsQvo8bijSpiGvKDhM64Wx48JbqwSNOwa2ho2U7SfSDt1QZigC8+xLmqJMhY8sbi1JKok4C5v4ChgldehK3N69Cb2tEa03XTIssW1LiVH83x/XJIsvQ4AffZbk28ZDY+IRpXpXIlXNZY16TaZo8O3t1nfFpv5eyc0EepNlYtB0HPfWu6mrN4jXo+2/EnnATSWPrbXNo+A57dqjrxDika4yQUI2qgqekgdGhK8z+vnpQSkSlrEwaSRg2ZNpQWXn7F56XhBCs8m32nraQBSWBLPb52+Vy/nZ2oxQ2GYaB6Toow2z0drDNhQYZi/fcc09LUnYzwjAMwgvmoK86o2qhMUYeT/iIk0kagbRRShhgGtDEFXKuBmPn/TAHDMX/6VuMPgPxc/NrifdK5ddV2gd0eQlk5GzJywUgmorDhScGnlmANh2I3vgwZWbjhQ3/rGQIl0/HdGH8hyuYV+xwfN8s/r5tDvhJpJQscy1OfXcZ36xIsFfHGP/auw3ZovHkTDYWQwgiThKWzEPEMvFzW5PYBNmPjcHzFUPahOGn2uPbFYTT1lGiJrqksG4HokXzMHy/SfKWtxRaazpEBP/cvYArv1hJ0tMc3zeLI3tkoPzGzVlM2WGiZ14OV4wLvIlSwknjcTZSXHp9tEQ4qhFCkBImKSXIkAoXwWWfr2RBSbAOamDyt4X8bUAukTRnHkR9F/nDF/Deq9CxG6GRxxMPx7b636VBX+Pxxx/fWNfRQgMwDAMpJVYqgX7gptoeiVefwBp5XNCyrhHYnMKTpGEhctuwpNtAOrTugK08MipyeRw7jBuKEtr7EHj2weoXZeZAXkE6Lr1BmKYJb75UbSgCLF8CX32IsfuBVV7aLeGR+lOgfLqENE/uX4CjqfAoBgtzibYY8fIi5hYH9+nr88o43lE8O7wNEZouJ1UIQTRegr7gRCgtQgNiwPZEz7uxURQDijzBbu2j7Ng2wpfLgvt+l3YRRnTJwHXrdsHZbHLzIRKrnfqxw164W2m1Zk3C2uGU3hHG9OqOBsL42Krxi1uUUiTyCojc/iyUlyCiMRzTJtWADjlrz7FVEY55vwabih7bm9yhDgAAIABJREFU/KUjHFJKVvk2F36ygu9Xpzi0WwbnDM7l+1V1JbHmFTt0yE9fsaQpJfKj/8G/bw0GvvkUvniPyE2PUL6VOxEaZCyOHDmSiRMnsueee9Z7/KOPPuLWW2/l1VdfTcvFtVCXmO8gvv0KfvwasdO+cNSp6Nsvr+2ZSCYhFEvr3w0pDysVh0XzoVNXXDtKahOSxbXWlJeXE/UcxLMPwNsvgmFgHz4Wf8RR6BFjEFYoqJpu2wkx9lzidmSL92gW6Hq9nKxejhACQ8ogaX3JfAhH0a0KiFvhJjcahWnhYIBS2NrdotejlCKME+Qo1rwdfaoMxUo+XhLHRRKBqm5CWzpNwtIK/eyDQQ/aCsSPX8PShYjOvRrhuxMc+fpC7tizLW2jJlIIlsc9/EbwKgIkrTCR6x5AT7kWli6CnfeBY89oUGeNLYWtFbabAt9FmaHNFmQGkMojk/VL7TQGvoYyKwQ5rRv0OhNF2EnB4rmI/La4sSyShhUYipecAiv+CE7MbU301qmUWRvf3UQIgWEYKKX+9EZmibbYZ9pC5ld4EScXrqFHjs2oHpn8tLraYLQkDMgPodNYAW8ny+H1p2sPrvgDWbQa8tun7e80Rxq02n/yySecdNJJ6zy+atUqPv30082+qBbqJ+y7iPtvgi+Dftz6nZcQR/4NMXwU+o3ngpPy26DWroLeTCw01tcfw5RrqsfOvhq1w564uuELT0ZGBuKHr+DNimv2PXj2AYxtdyDepTfGiNGYex2CMk2cinD6lsbzFeERo9Fvv1htqHboijhgFIbrYHsO+vzjoLgiP7fvIKIXTGrS3WWZjPD9ihTvLy5ljw5RBuaHyZWpJk96DxmCDEtS5lZfR+dMC4mmXEb4aHGcLlkWvXOjRPBxnS0jmi19r3ZnjEpWLUN06Z3W+04IQZahmLRLAcvjPnOLXKb+XERWyGDqfgVB3CzNeEB5m06ELr8LqTWeaZNqhtWyId/D+uJteOR2cB1kl17ELruTsnCsyTdfWwrDMAgvXhCEr10HDZgjjyMy6m/BfF9pKAIUroR3X8E85Hi8DehOQiBbZhatDgpvuvVB99iG8j9xzmq5R5WhWMlFn6xg1ok9WJ30eeyXYjrETO7ZszWxdG8WTBtx9Onge+ivPw5+G63B3rq9itBA6ZwNsWTJEmKx9Hq0WqjGdJ0qQ7ES/dqTiOFHBf2fd90fcf1DJEKb1k9zXYScZLXbvZKHJxPaxEU9ErIR0z+qe2Dm50gpcZUmYYdJSbPJFgutNU5Oa8T1D8HgnRAnT0COvw7971uxplyNWLoQsUu1dIX45TvEgt83S4B3c3CMMLd9s4aDXl7ErTNWM/KVRdzx7RrKG1GWZWOJCZeH92+HbQQbiwxL8vjwdtiG5PDXFjGsbZSZK1P8/f3lPDs3SZkRJSUaXy/OtcNwwKjag5aN6LddWr0vSWEzP2UxLw7zyjxu+GoVU38p5qZd2zD1gLbEGlEoXClFwgxRboWbpaEIYDkJeOAWcCtSEhb8hn78bmy15b2CWxohBKZpEkol4P4bqr8DgFefxHRSsKaeCMeaFQjANCQRN0nYc+qde0xDYv4wHc47Gp68F24Yj7j3OiJ+85Eka2gdRMQUrN0KuyBqIrTPdcOy+fmEbrxzeEc6JJdi6vTdQ5ZWGMsWot98Hv3SVESvAYjx18KQXfCj6S9gam5s0LP4+uuv89///rfq348++igffPBBnfOKior48MMPW2RztjRCojKy8a+YgrIsHGmm35OkFSTXyqmKl21y9V3ScdGDdoJP1tJdHDC0WYVIUoaJ27kX9rk3YJUWocaPqQr3qxmfIK+5D/3Zu9XexZVLEb23bZJrTSjB3d/VViG49/tC/jE4j6ZR1qxGKp+925r8PrY7JY4i25ZkGj5frEwyZe92XPzJcp7/vRSAZ34tYdyAHMYNyKVjLES0EVvz+b6P6r898uyrgtBSZg5i7HiSofR9Y540eeCnUt5bHGdUzyzOqyGbM/ylBcw+qQe+DCGFJrMZiIZvaYQQQS7w2vz6I9J1wE7vxrc5EfI9rOLV8PGbyD0PRK2qxyhMlCP2OgQ97T/VqUZCIEaMxnSSGF9/BK8+AdEMzLHnkmrXpZbWop2Mw+N3137Prz8OjNBI03oXXWGSwGRhqUfHDJOo8LH1hjdOYeFz5Q75XP3lKiAINz+wT9uq5yejYvO1tKwsrdcbTsXRl4+r6pyjH7sLMf469PgbiDfD1I50s0Fj8eeff+aFF14Aggd7+vTpzJgxo9Y5Qgii0Sg77bQTN998c+NcaQt4loW56/7w6dvVg6NOxrHC1WUCjeCJ8w0Lo/9QqNk+cMD2+OamlZmVlpaih+6G2HV/+OwdEBKGj0J17tnsFkulFL4dxnzvldp5oUqhP30bMXgn9If/BdNEDNqpyeSJNEGuVE08pZvNFGZpDwuPLAsEgjIVZlFpil65YV6oMBQreWRWERdtn8+sNSl2yJONek/EDQtzx32xBu+ClhLHCqX178W1yfXTV3PpsHxemlP7c3oKXplXxpO/FJPwNE8f1IHO9l/LYNRaI9p1Rkuj9vM1eCf8rTi0ZxgG1uyZcOO5AOhkHHYbDm88U31Sbmt0RhYp0yZ8y3/QT98XbNBHn4bbqh3mT1/BlGurz798HOEpL+JGqr1cAqC+CFBT94+WBl+u9DnitQW4CqSAh/Ztx2FdbIwNeJRt5XJG/0yO7ZPFglKXvrk2UTxUI+X+QvB76XpaLOr3X8MbtHMgrbaVs8FPOHHiRCZOnAhAbm4uU6ZMYfTo0Y1+YS3UJWnYxE69ALHL/oHhtuPeqE49Gr2eNGmFiE24EZ59CGbNgP5DYfRpJM3QJhun5YZF+NQLMceeB4Bn2SQbUDG4NoaUhFJxpNb40iBlh9O26GqtIadV3QO5+Wg7BAOHIU4aTzLcdCkYMak4tncWT8wuqRo7pncWttQ0qrDcJlAmQuz34kJ+XuPw+8k9kaK2oWsKgUbzydIEO+U3viSFpxSeVWGYpPlv+RocX7Ms7tE1q25ovVOGxfK4z8JSl8NeWcT7R3Yhi79Wf9+kFSJ00WT41/WBl37o7jD6NJz0Zkk1C2ytsD0HUgqemFI1rt95GXnZHeisbPSn70CnbnDSeBIV7eP8tp2xzr4GgcYxbSzPhf+9WPvNfQ/93ZcYuw4P3lNrHDtC6LAT4T93VJ/XrQ9qHZ28pJRorRs1/UcIQTkmN3+9lEzbYE3SR2k4+4Nl7H9S940qSgprh7ABbXNFWgtY1oVSCtrU02KxfWe0aTZKznFzo0Grc2FhPf2G10NJSQmXXHIJ48ePp3fv3g16bQv1U27YGIN2Rm63axBGS/PiZhkSoRSeqPboaK0pM0PYx52F4aTw7VBQUbmZE0rSsCANEj8GEF22EP3PS2DZIoxeA4hNvJmySGZaJj3P82DXA+CVx6srpHNbw76H4QNaGmn3SG0sQkhKsPlueYprdylgr04x3pxfxoiuGRzUNUbMb16GhxCC34tdfl4TbHF+KUxx2oAc7v+hiAxL8MgB7RlaEEEAp/TLxve3TLFLYxESir07Rnl6djFvHtGFtxaUsaQsWAyHdwk2FwtLg7DZnGKXlNKYlrlRhQsNxUIH+ce+h7JCKMPE9BwUkArHGt0rbkqJ7SYRgGtYVVXZrjTQ2wwhNPkJBOBJg5Rpb3EFhMYm5ruIp++DD15HnH0luub37SRR152DuOt5/P1GVRf3VcwpSilSskIcUykwLUTbjnVsFNGzH9HSQvR3XwaGTIduqD0PRrbvDB/+F3r0g70OJm7V3uhbWhF2kug5sxAF7fFz8omnWT5KCEG5sEkpAQj+vX97HF8zfXmCM95bRtzTOErTkHDIlspp11qj23dBDN4JZn4RDOa1Row6GWfruk3XSaP6TpPJJE899RRjxoxpMRbTiO/7aZ/YpZSBCPVrL8Diedj7HYbfrS+JGsacg4AtqNFmVoS5N7RwRtwk+rpzoKRiM/Pbj+g7Lic8cVLa2gTGQ1Gikx6D334MJtneAymvqXXWJIaiYLWy2f7p+axJBvfDaQNy+NfebYhoB7+ZGYqVODXciEf/dzGfH92NQ7tn0jfX5vnfSznzvWUAXDC0FSf0ySKmm+fn2BhiuDwxoj33fl/Ifd8X8ubhnSn3FDFLMmt1ir+9XV3l2i5msjzukZttYKa5itNWHvYPX8G/bgDAvPZ+9KtPoL94D9mmA5G/X0WyXWe8RkpcCKEJlRWi5/0ayLgUtMdq1YbyClF+T2s8q8bcspUZipYQiLeeg/8FKV36g9cRBx+Nvv+m6pPadkKFwiQqFRXW8x1YbhKx72HoL9+v1oIdMQbiZYFuaEVIVgzZBf33q0n0H4axzRCUNPB8v9Z7SykJ/bEQfdmp4LloQO4+gsjJE9LaZrWMEJ8vS7Imqbj40xWUOD7H9cnmH9vlcfOuBTwyq4iQpNl66cpNm+g51yLLiiCRgNZtKbcjf5nOOY0eaP+rSB/82Yk4Cbh8HCxbFAx8/g7G36/E3Hk/PL/xHwYTTdhNBZ67VgUIrdHvvgGJOGKfQ0lEM9e9kKWS1YZiJT/PxNDpu26lFGVmCNl/WNW/m3qSUNLg0Z+KeGiHCP2zDeaW+Vz8Y4IvlyXZs6C5ZCvWRmvNNrkhOmSYLCnzKHc12z4+l9/Hduf3YpeLPqlO8r/okxVs1zrMLvmN42nbEmitifoJJg7MIKEEUcMH5SMNk6IMi1BFhXh+xOCBfdtxz8w1TNo1n6w0X0fIc9F3XAHKR4w8Dv3mc+iP3ggOLpoLV/0f4SkvUWZteDMYVh5mRSMAPxTGqMiJq5U7vfbfd5Koq86AFUsDW6BjN4yL/4mZnY/3J1hsI56D6Tpo5QcGnRVukPKBpX2EYaJ79oPfZwVizoN2RF5zP+rD16BzT9h9BHErvEFDWUoJ389ATXsUOeFGKCuBcATdqg36jrU0d7/5DCNeim/agfRpPU6GkJOAhybVbu7w8ZuYx5yxSW1WhRBYFRafSyCILaVkdpFD92ybo16fV3Xu1J+L6ZFtc1yfLEb1yCBLOKhmbDLETRtyChC5FULfjXjvmobESsTRRtNFrmpdT5P+9RbWi6wQfpblZWDbeHa4lqevIdiA9FJ4dqTeyVkWrkJXGoqVvDQVe/AueI3sTTQMg8gf89FXng6ui7zpYdT146sMQP3SVCK3P0NZVl79m49QGMLR2hXbXXs3SqpeUz+wNdFCMr59kuy7L4XfZ9Gjcw+GnTcZM5IgtjpYQFw73GjdfDaVLOHw+ZguPPBjEfNLPM4cmENBxODWb4Jq7iN6ZHLekFYorckJGRRriYtJVHjYaZTC2KIojwigK9Zq33PpnhXh8REdCBmClK+ZPGM1XTMtYmnOMxVCoFcvrzYieg1EP3pb7ZNSSVi9HNp2We97xXwXMfVO+OgNxJmXYRatRk97FLTCPuwkzOFHEV/rfrNtG/3G07Cihqbl4nnobz7D2uMgvGYq6VNJzHMQT/0LuvZCdu2N9FwyuvZCf/0ZSMnAvoNxZP2FWEKI4PVfvAeL5gYyZ4dH0bdfjn7kNvS9r5A69UI0ItgQbYRzRQgBjgPzZqMuHwfZeeA5iPNvgfK6FcA6tf6cPqEUFNftWa7jZfW2WZVSEknFEZ6LNi1SdgSv4rotrQgXr0FPewSEIHTEySSz8lDCoMzx+aWwbpTg/cXlHNsni3zTw/Oaz/y6PtLtBBNCYEoJAlzPD1oKfvou/G8a5ORhnXweidw2TSAxX02LsdiMiSXL0VecXmXEGXseTPSk8Q3KJRFCEHMSMO0RmPMz5g57ovc+lPK1JnRt1WNQhMJoKbFMA7TGUxtOfDYMA611g4yqUDKOvvc6SCag/1D0j1/X9hS6DvrlqVgnnVtvfkjSChG+YBLcdnEwWea3RUy4MZBAaUbG3aYgpcRUHghZtUuvJCNZirzjIpg3OxgoLiTPLUNd/3/oitxKc/9RRI45I63hpM1FKUW2SHHWwBwSPkgpWJlQDGsTYV6Jy/F9sznwpQWUu5qoKXh8RAce/qmI0b0yOaTThqslNwVLCuxUAg04oegW8XbZysVVirFvLWN53OPArhlcvVM+hkpv2F1rDa3aBBWbvhcIPHfuEYQvC9oHHqXCVYj6irhqYEqJ+ORt+OA1aNcZEctE1azGfeZ+ZK/+yD6Daz//StUvfl64Em3Z0Iy9xlJKxMLfkfsfjnrsLvRDkyC/DfKsKwID/IkpmLn5mLc+Xq9XNuym4K4r0d9V5Ll9/CbisBMRw49EhyN44TCu17CUIt/3YdCOEMuE8tKgKEgIdJv2cMixgZewkoJ2QdvH9eCFo9j7HRHoMFaSmw95dV8npSRWtBJ9w7mwbBEiO4/IBbeQ6NQLXwjCZSXoCcdU/ab64zcJT3mJhJXD4NZhMkN1f+shrcNk2jJt0YPKbjWZmXW1D4UQWFohPRcvFAlC8o2IYRgIIdb72SwU4ZJi9GtPgmkROfgY1OJ58EC1soy+aCzhe16krAmlpFqMxWaKDcHurKa378PXkQcfg2jbeZ1GmxACS/kIrXFNi7CTgOvOgQW/BSfM/h6x/A/s4/6OU2NHrzJyEP2GIGZ9U/lGcPolmL6L8fwTUFpE+OBjSeXk49RTtWwrj1C8FD3jE+jQFd21D3ErtF7j0tKakJdCphJw8rmop+4P/m4kAzp2h8Vzq09eSxbBkJJwKo7wfZRh4PQeiH3n8+Ck0HaIxFbQOzXsu1gL56FffRJyWhEadTLxaGZV5bD0/WpD0TARZ10OpoWceAt63mz0U/fB29MwDj4acts03QephxJCHPrKEr5dmcSScPGwfE4fkEOvHJsx/11MuRt8yLinOf3dpTw2vAOjX1/M3id2IyvN++uI72J8+g48/zBCGoSPOQNvyK6N7pG1tMfu+QbTj+mMRhBCEdbJRknZSlk2oQtvhSnXov/3AvKyOyFRhl6xFGHZ0K4zqQ3oSxqeAzOCDl2ie99ASmQtxGdvB7lxNZ491/cJDT8S3n25xokCsfdIUmlYrBuzP7uUEgGoFx6BHyukw1YtR908EXnrY6gnpkDhKvjsbcx9j6hjFBiuA5WGYgX6zeeQ90zDNTddASIRihC97Wn0f5+G0mLEyONIRbMwd9kfmZMH774CHbshDj1hvXl1hhCEyksQ2+6ANk305+8GG4HjziJu191sh50E+rZLq9el4jXomyYQvvM5UtFM9NvTqo3/aAbyH9eg5/9KZMVS2m+3C8TaMGFIHnd+uwZfw87tIvxjuzxs0mO02conVFaM/vB1uuUVYOTnVvXRNqQkWl6MfuYBWL4Yc5/DUEN2reMJTwdCCGJuCr6fASuWInbah2QsE7ciR7fmeeGSYvS5o6u+N/X2NOSND6PsMDgVXuFUEub+guzXdFrELcZiM0V6Diz4ve6BJfMR7brUOzFaWhEuXBk8DK6DdeTfEDmt0JWGYiXvvYI95nQcq1rHLGGFiE68OSjgWDwfscNeEImizx0T5MQA+r1XCd0yFa9t51o3rGEYhOb/gr7mrOrJZcD2RM67cZ1eUEsrQrNmwJ1XoFJJyGuNPP9msCx0WQni8BMRHbuh/j0Z5v2COHwsFfYDhhBEVy5B3zIRli9Btu9M6OLbKM8tQFXu7v/kubJSSsy5vwXfaQX60/8RufO5Kg+GlhLRtiMsW4z4v0tg1reo1y4MvEeDd0Je8k/UFf+HWP4HslW7ZmM8+9Li2i9W8+3KYCJ0FVz35SoO7Z5Bp8xASqYmqxI+UVNQ5io8RYOqJTeElBJj/pygg0gl91yNOWkqom39z1k60cons8ZC2Vh/zRUVFcf/fBKhAaFR15wFa1YGf7NLT6wrp5BaT7tK37KxBgyDbz5FL1uMGLRDnXN0/6Gotb4zrTVOfnvsK+5GP/tQoEl67Jmk1pVWspEERXmJwHAxDHRB+7T3Z/d9P6g6/qm2tjBORZ60aQWe2dLiel8vpKj7m5omvpCbJRXmK01pOIY1elxQXV4R9XEwMAbvitl/e5Rp4ar159VFnCT6orHo0iIYujtitxHQvTeJjGz8enLVpdbVG9RKykshlURHMqBGq1nxt/NRb78IMz6pGBC0u/ROxvQcyhkDc/E0LI97/G9BGUd3C232vS+lxF46H33p30ApDIDXniB63YOUmSEiTgJ90djqJgqzvkWeegHmXiPTHkmIukm49uwqJ41+Ygqhmx7Gb9+t1jxsGkZg8NfcZKSS6K8+gG2HwdcfV4/ntW7SGpDmnSzyF8azw7D7iNqDUsI2g9a56IcTZeiJx8Pn7wY32aV/C3bdaydiZ+WgqTuhlxs28X7bkxwxhkReAfr7L6sMRSAQop72COZaXj47GUdPvbPWpCR+/BpZVv8EChDyHLjj8mDHBLBmJerhyejlS9DXno2+5xrUjechz74KcdfzJDNzqx6UsJNA33hudeeHPxaibz6fcCq+jr/258P2HHjpP7UHy0th9ndVifXJUBRx/s3QtTcitxX65ceqRWNnfoH+9nPYcS9E117NqtAsoQRfLK8bap25MoXWMKh1bYOlX16IJeUeQwrChGR6J3VDSsSHr9c98On/MAyj7vifGE9ryu0IqWgM9eZz1VW0EGxMZ32z3s/s+Qr2OhiG7gZzZiHyCmCn6paXDN0Nvd0u9So1pAyTeJ/BeBfeijfhZsq79N7s9oPRVBwuPRkuPw0uOQVx9ZmBNyeNaK0DcfAe29Q+YJpBP2DPDYzfvUfWG2p0TbvuPH70GTjrCScqaVIqwpSKEMKwiAubNTpMyopSLCIs9cMUiQhJGcL1FY6vaj3fvu+TkmZgKK4HIQSsWBLkO574D+QRYxE9+sLP32Elyut9jS8E9OxXezAzB0JhPM9D7D0SsnJBGoj2XaoNRQCtEY/eRk6qiP6PzaHf1Dl8vqiEse08YolSwr7b4NZ/NbHdFDz5r9rG8R8LYcl8QoZEJMoRI4+Htp2qj7/5HNba3ck2EyEEYvmS6mgeBJGxx+8JrnFtrHocKuFo7fGhu6PyCv48xqLjbFj+eenSenJTWmgwnlLoHfeG0adBdi507AZX3IMTql/42TRN9Cdv1d6haI2e8zMcekL1mBAw7qJ1hpyUUnieFxikVj1ehnq6KgitIFH3gRPuuu8XkUzU7Sww71dEdl71v0sKUe+9TCorr1YLK+G5sGp57df+sQDZiAr+G4sQgrAhyHCTZDgJwpuYX6eErLVLr6LGmK8U8TYd4Zp/oZcurnOq/n0WYtQp6JmfYzWj4uioVIzoUvs+FkD/ViEe/bmQFw7uxF4do9iGYPcOUR7evx1fL4/z4iEdqlp5pQsN6J796x7o0a9ZGdjpRGiNWP5H3QMrlm5wsS43bby/XwUPvI7q2hs97iLEA68j7n8N/6wrKTfWnRvr+z5J0yZpWpvt5TZNE957tXbRzKK58O1naTfy41YYcebl0K5zMBCOIM65Bj13Nux5EOrWJ0jGaufHmaaJYVqkTBt9ygS4/C446lS4+T/4uw3HXce9lZAh7vqpnP6Pz2PIUwt4cm6StxenOPP9ZRSmFHu/sIBtps6hxyO/868fS0jITe9yo7WGaAx5ye3oX2aiLjsVde3Z4LnIdRjyKTuCmHAjdOkZDOS3hcvuJFlRBBkPReG2p+CsK+pdK0jG6ZZhMufkHqwZ24EL3e8wzjsaTj8I8/4biXmbYexrDfXMt8L3sN55EX3dOegfvgraew6oaEuclYNuQFX7hhBCEPI9RCwDsesBtTu7OKk6ES/X8xAHjgmMw0oycxCDd0IediLiqimI255GnXVF2nUvG0qD/OB77LEH9913H4MHD673+NNPP80ll1zCvHlBaXx+fj7fffcdbdo0r3ypPwvlhoU18gTsA45EC0GqQs2/PrTWQVVcTVoVoJMJ9BFjEXseFEymvQbghmMbdLsrpaDfdpDfptows2zEUacGhluNm94JR4kcejz6vhur36CgPcIwyPBSlNcTGtLhCGRm1w7fbDssMG5rsnplnTCKNi1EXuvanpG2HYOWYU1IxHcwV68I8kK79UH/+j3m/N+JjT23TkHRhnClQeiYM4Ic0EqjulN3dOdeKKWQUlKqLTwEGaYme9thdd5DbLdz0IowXgq7DKe5CJhJ5XHe4DzmFru8OKeUVmGDW3Zrw7JyjzMH5pKhUzw9vC2ulv/P3lmHWVXt//+11o4TcyaZGRi6W1JFxAIsFAEDsbuxMTCuhcFVlGuCXuPqtQNbwRZFRQy6kW4GmDyx917r98eeOswAM4R4f1/fz8PDM2uf3Wuv9VmfeL+RQoPW3NItHZv4HjfgXNeFg/r5vHcLZ/mNnQ9Ad95/n0k37m24COwBpyZrs0sD0bvfTosMtNbVyfT3wSQm0LBuRfUN61fvlneqJmitKYlkEBr1b4QTR1s2W6wIMWkjDhmMV7CR9DK/i5CSrdrm6elb2RT1GN41k4YBg0C7bsgOPfA8b7t92DAMflwX566fNlW0Xf7VOj4e3IRRvXO5dcoG/igooyzScPfUTZzRPp3Qbtg6OiMHPfUbmPqN35CIo9/8N7JXX0ROo2rXqpSiJDWL4D+eRHou2jCIVZmXPK0ptoIYB/UnFC+FRs1h9bLKAww8AzcYJE3FiMSLYOytlXPJ1K8ReU1IPeEsXKhzUV7CDmINvRg9u0rKQHZ9RFYu+u7h/t/rV6OWzEPeOhZ1x2WI80eQsIN7pBDSkIJwwWb0i/9Cb1iNOKg/cuTDqH+O8J04J52PEwhVoy+KhlMJPfa2/z26CUS3g1Hj74dIGurKuyjdjXSFPQmxdevWWo++3bt3Z/Xq1YwYMYIbbrihYgWXn5/Ptddey0cffUTfvn2ZMGHCXrvgv7F9RNw43HI+bFyLuOgmP9dmyTx0h26oRi2I1YGryTAMLCeOBejff4DNGxCHHEM0lIK7jUPa1orA+pU+HcZPXyPqN0Qvqs9wAAAgAElEQVQMOBX91YfomVNRIx+htEou1KJFi2jfti3hDat8XrBVS2G/A5BX3oX6x8V+tWYZ5P3Po3PycKVB1LQxhSDoxJCug/5uIvrt5yE1HXHrWEqzG27XmN7bCCgP6+NX4e3nKtrEBTfAprXo+o2JHXZ8nfNiTDShWKn//DOyoE1nSqwg0rL4o1hw6Vdrmb85zqAWqYzuk03ud+/DS49CIo449BhE3xNQ912DePhVijN2nu+yaNEi2rRps0v3X1cIIYhhkvAziwhIja29Gj0Df8a1hJw4RqwUhMALhIjupDjrz8ZuvxtpUqwEEVMglYvtxDEXz4F3XoBAAM4cTrx+42oJ+HsCJpqQE4ctGyEtEycQJrabIWgpJeE1S+HmcysbhUA8+lat+vquQAjBVoLc/dNGPltRQtecIHf0ymFtiUPvXIuASlAkg3R/dRmbor5BIAX8NKw57VNqobZlWFw6eTNvLCxMah7Rox6X7pfBwA9WsnBLcrRm4pCm9M5Su3y/AeVi/es2mDE1ecPFNxM9fOAuLZiq8k+G46Uw8S1YthCOGIjq2INSw/Lf35xfYPT1yTs3b4s4cgh6xk/oy2/foad6u/dTWoRYPBetFKLz/ugn7qp2f/L+59A5ecTsEM4eSoKOODG4bliSA0ScejGkZqBbtsPNa7bDormAZWE5cfhjHqRlojKy/1LjUJ1M1ilTpnD77bczevRoJk2axPjx41mwYAHXX3890WiUMWPGcOGFF+6ta/0bO0GpHSL8wAuI/A0w62fUqKsqtskBQ7FPvbRWSdUhz8GYMw0mvY3ObYQ45QLikQwS23gd/Ek2hul5Pt9Xs9aIzvvDlk2o2y5CXnsv+p3nkU4Ctkmc97SmtEFTAneNQwKulCjDIjDyYb+KNx5FHDvUHzTeeAbjgMOIXHYbYt509LMPoooL4PDjkU++i6c1UTu0zwxF8HMM9bvJOYb6tXHIMS+jn38Yo8/RuHWciF0ERcEUjEOOraAjMqRks2vS950/KlRbXppfgELz+GEnkHrg4Uit0CuWoH7/ETH2DWKR9L/MgFMOrTUBHALlYeV96MTTWvshnqoExH+x57UjSCkp1BYKgYEmVThJhknUCPLD2ijdc8NMy0+QFTCpHw6Q0Wl/zFYdQYi9RvprGAah1X+g77zMp8YSAuu865CHDaBU7noVqlIKN7cR5u2PwZv/9vMGzxhOvEpu855GiQyyscTj+JapXNA5k5/WRrn8yzVc16Me+Y6gsSX5bnW0wlAEUBrun5bPs0fUw9wJeaaB4uC8UDVjcVBLP/XkmKYpScaiJaF9lo3eDYUjz7SxuvWuZkyJ9tvPja8JSprEhYWjYcbGGDFX0zsvhA6AOfhchOfimVaF8amUQjRuUaNcIWuWw7TJiIN/wjigb60MVkNAMBHzq88TCdQfC9ANGiEMg5oq4nRmDkX2jqv/6wIhBGxaV63QSU/+FO59ltJAeKfPM+44xJGIVp0pFgHiSqCU9tOaSFRjBPmzUSdjMRwO88gjjzBw4ECuuuoqDjnkEFzXpVevXowbN47mzZvvpcv8G7WB1hqFwMysh3rrueSNk97BPOkCsHf8yk0pMX6ZAo/fVXncn7/Gfvj1pOppgBQnCg9cDxfcAE4CFs9FL57rbxQCbNv/fzvhYc/zfMWCKnBzGhG48i7MlUvQLzwCS8qON2sasqQINebmyh9/+T4qrwmJY07dp4YigFAe2tvGKxaP+fferTeeacEuKuFUHSy3KotNMbfCUCzHh0uLuffgXETZ8zTbd0e07467g7DX3/jfh5SS5XGT0z5dzZz8OF1zArwxoBENLd9gdKXFY9O3cH6nTA58fWmFITOgeQrP9M8jtTzUt5e+n2C8FP3E3b6hCH4e9UuPYfbuD4HdoyyJSROjfXfsm8agpSBhBfdaxb9hGKwr9Tji7eUUO/45Lu+SybB26TRNtZi8qpSzWwexaijAsmXtCvg9z2No61TeWlTI92v853Veh3QygwbHv7eCz09uxuaYx1uLi2gSMRnfLw9TgIuJuYtk9a5ScPjxsHAm/PS1X1Qx9CKczNp7Zz1h8ONGj6yQ4OyJq1lSFirPDhn8clpzMoiBkNXCr044gnnedfDKk/780aYT4qgTUeUh49m/Inv136mxaAgIr1uBfmAEastGyM3zo1QvPQpzpyOG/wN9zalQXrQz8Aw/HLwHobVGRNKqJ/rUq+87QurQL0tkkKu+Wc+ExUUYAs7vlMFtB2RTT8b3KaPFLsUCpJQIIXAcB601jRs3JiOjOtP7/zqeffZZunTpQv369Tn88MP54Ycf9vUl7RApTgz5oE8ng7tNcYlSUAv5OysehY9eS24s2AJrliXlAtngh7AWz0UvX1yZMFyO/Q9DL5gJxw3DNWs/KSil8AwDPnu70lAEyGuCXjCj+g7TJmPE9512cPkzUdKALr2Stx18JHgO9Dl6j0kmelqQbhvIbWafthm2T2tRBtd1cVx3nxiKhmFgWVad5NB263zCLyhKLdpCJBH1w9n/R1CoLU74YBVz8v281hkb45z00WoKtUVM2BQqk+Fds7jzp41JHq9Pl5WwvMjZ6xXfUitYW8bJ16g5YuhFiBPPhURsj5zb8zyiVoCYYe/VibRYmVz1zboKQxFg3MwtHNYojKuga45fDdw7L0jjSOWC3JJw+4HZtTbmUnSMN4/NY8l5rVh6fivGHprNz2ujLC5wOPbdFZzfKYOF57biy5Ob0SHLZnWJy9JSsVvPstiwcC++BZ75GJ56j8TRJ9eJY7QUi+fnFFDqKIZ3zWJwq1Sk8Cmvnpy5BWnW7KCIGRaJIwbCuPeRT76LGHAqasxIKC1Toem9c0MRfI+ifuB6P80BYMNa1Lj7EEPOQU+ZhIjHEE9MgPufRzz5Hu5J5+8VDlU3FPHZAsoRCCIuvIFELSQ0hRAkpEXcCPHR0hImLC4C/NzUZ2dv5dcNMWJi3+Yu1mk0j0aj3HjjjZx00knk5OQwZcoU7rnnHj788EP69OnDl19+ubeu80/HhAkTGDlyJCNGjGDy5MkceOCBDB06lJUrV+58530ASwj46FVYMBP92w8+hUFV9DwErxYfiJYSIjUo025TmWs4cT9xuVNP9KS3kWddhTj+NGi7H2LoRcjzrkPv1wt18oV1/jBdDfrQAcmNm9YjWnao/uM2nfHsPz/B3lIeqYlSUpbMIVJSgBcIIS+/DXHyBdClF+L0yxEDT0eHIpTswQIAS2qKHY+7e+dUGIwZAcm4fnnUs/a9kZTiOYR++oLgs6MJL5xB2Nuz1cvbQkpJuGAT3HgW+soT4bITsH/4gsA+yH3cUyhXoKhNoUZcwbLC5Gc8d3OchJbc/GM+LV9YTEJplhdWfw8ri9y9btC7pu3TNw0YirxgBPqP+T7va2kphrt3+8aehKNhaUH163UULC+K0zxFkhItJtct5qdhzXniiPrc2SubuWe3pGGw9jmFWmvCOk62jGNLmJ7vkBqQTBnWnJywyda4x5aY4oLP19Lp5T8YNXUTWSGTYrV7hkTMsCi2wxRbIRK1NAssKbC1Qgq4q3cO/569lVcXFHBA/SDvDGyMKWFNsYvS2+/HCWFQbIVwQhH05o1+GDcYgmGXoFp2qNUCQDgJnxi9KtauQGRk+9XIhVt9r2ZOHtoO+FGeWkIIQdBziDgxwk4MYwffS9S08c65Bh59C+54Ep58j2i9Bju8h/KF9VYCXDk5n7HTt/DFyuq0RT+tjfre2X2IOhW49OzZkxUrVnDddddx0003+fQFwPz587nsssuYOXMm5557LmPHjt1rF/xnoX///nTq1InHHnusoq1Hjx4MHjyYO++8cx9eWc0IugnMMTfBvOlgB5A3Pohet8onk+28Pxx8FCWmvdNBS0pJyvqV6JHnVQjL6449YMToJKMn7LmY61eiZ/6MaNkOvXULLJgBwy7FM02E55EIhnFq8KjVJlk/7DnIrz+E917yiW/PuAx6HAIfv+a3KQUt28Ot/6pRZqsqbO1hOwlfstC0dmlVKYQg7MSQ0RI/tOx56NsurJQlPPsq9BEnIKLFiBV/oAMBaNGOEnPPJihLKSkgwJa4ImAItsQ8GqSYpBoaw92xBuzOUP5eyg2IunpqQm4C46m7K1Q+ADj9chIDhtUo07gnEHQTmA/fDHN/r2yUBuLpjyjah9JYu4qg52AW5MPC2dBuP9y0LGKGtd1vpkgE6Pn6cjZUITJvlmbx2ZCmtHtpCQArLmjN24uLuH5yJd1UwBDMO6cVWdTNK1+uYat3ImFWobAkBEJKRP4G1E1nV4a7peEXo6TX+59IkzAMk3dXOoyfuYUf1vrPLDMgmXFWSwLxUuqNPN3PWWvaCm4ZS7zsvnZVwq5UBjnug9VMLyOuzwhIJp3YjDRbMvjD5EKXQxuFee3YhkT2sEzk9uCToZfCJ6/Dyj/Q/QbzRWo7jvuioCIMe9uB2fxRkODabpm0T6ldKowlNIEy5oeEFSBRy8KTFCeGuPkcKJM4BXz1mlMv8aNTWiMOOcbvfympcOGNON0OJm7s2MCukModdx/89r3Pz3j13cQaNkdJA8Mw/EhYDd5PIcQO79k0TaLC5u1FhXTMDnLTdxuYui7KIQ3DnNo2jau/WZf0+4lDmtI7W6L38uJ7R6iTsdirVy/Gjx9P9+7dq23zPI+HHnqIsWPHsn79+hr2/t9BIpEgLy+P5557jiFDhlS033DDDcydO5dPPvlkH15dzTClJDjpTXj5icrGzj0R14wiGozUqRLXQhGMlaJn/QK5eehGLZKk+yyhCXzzMTw/pmIfMfAM9JFDEIB+/mF/4Ow3CN2vug51bSs7LSGwE/4AGC/T6w0oF8tJgOei7ADRnSg2hLwExsS3fW1s14EDD4fLbqe4jgZjxI3DvVdXqhd07YUcdFZlEZFhwvgPKQmEMU1zu4PInoBhGBRqC4TAEBDBqdWkFBUBijw/tJFmSdJEIuka161ZTeucejD3N19ysWW7GmmPtoeIl0B8+iZsWOPLhiXiPn/YExN2atDvKlISUcR1w5LJ4wHGvk5xVoO9cs5dhQUEElF/0WKYxLepdLS1wv5iQuU3bFpw+T9IHHAocxYurvmbkQYzC2DIR6vZHPPIDRt8NKgJS7bGOX2iz0E4/5xWLClIMGNTnJfnFZAbNvhHrxzaZ1ik1GBgmIaBFStBGyYJszK8G1Audv469CdvQU59OOqkGhP3LeURWLYAxo2CRAL54H/R772I/uSN5BOdeC7xoZfgOH9tD2Oqm0CsXor6+VuizTuwqvX+3D7b4Z7eObSWpRhXDKrMhwPo0A3vxocq8uJq+jaDysV0HdAKz7CIVekLUkomb9QM/CCZO/Wizhlc36MeHcsWAVWx/ILWpO9GoUtdkOLEEbdfCOsqr6/ovBu5xOjDW0v859AizeLjwU3JtjxsvXffb4Wi14M3+ilYDZshr78foqXotSvQE15AXnAD6v5rK3ca9wHF4eQImhCCgJvw34sQeJaN8eyDyRRToTDi8Qno1cvgu4nQriu6Zx9Ka+kUkFJShM3Xq6MsLXDo3zSFsCno9srSit98OKgJ7y4p4qV5W7Gk4Pru9bi8S8afthjYHurku/72228JBmse9A3DYOTIkRx33HF75ML2JfLz8/E8j5ycnKT2nJwcNmzYUOM+ixYtqrH9z4IQgg6HHIu94g/E95MgJYI6/Hg2FRWzevnqXTpeuFFbXNclvnR50rZOjfMQ8VK4+m5YNAf99YfoiW8hBp+NvupkKGfEf/lxtNZs6nYYWwqSq8T+jOdlWRYdbODNZyobf/4W1b4rGzv3YWth4Xb3rYqM9DTCM75HVpW5mjEVfeix0LytTwvhuXixKAuXVyfH/iugXrM2XPH9eiYu9wfz/bIDvH9CE5yCfAo3rMYwDDrlZME1p0BJWc5Qo+ZYtz/BnNU7JtoXQtC2QS5i7i/oRXMQLdoh//ki6oHroLiIeCLOomV++oaUEtu2SSQSSUaGZVk0TI8QCQSIJRKsKY4Sje58cGyYlUn9Hn1g8qeVjaEUnGDKXuljtm3TJD2VkCFRQrC2JMqWwqKdX2dmOjlL58DzD0NpMWaXXugr7mDumnUVz6Fzw/rwht9XxREDEQOGopcuILB+Na1ys1m8eHGNE1J2ahrTTskjoSU2isSW1bTPyKvY/vSsLVzaJZM5+XFGHlCPoCFom2FTsm4ZCQGphiSqNBsLi2iWnUl4wXTkp29BahrizCtZqQzinqJNvBB99+WVJ/7sXawHXmD2qsr+IYSgc16uv7DyXF/y7esPENuQVgOotExWrVpFYS2/w32BvPr1SZ03DfXUKABCQOuOPXjluvtZsv4PCNvJhiJAVg5mrBT58WvoYAjrkGNZsqWAklK/P7eun4Pxyasw6W1flq5HH6xLbmFO2XNMSUlhdSK72rWsKXYJm4JUW1KUqPx2GkVMPCfBoqWV/d0wDNLS0iCUBlYAL15KbPOGWglr7AhSSjpHAhjrkse51ImvcdHlfXirzI5tkGKSamqWL5yb9DvLsmiQFiHFNCh1PdYWFlcsFoQQNMhMJyvoG14bonE2bd2+ClhVpITDNL79CWwpMS0bNf93+PgNSEtHjngA9eSopN97fyxgRVp94vF4xX11bFgfc/x9MP1HCIYx738ePf3HpP3EQf39sealR/2GL95D7HcA3sW3smTDNqHwGpDdoh1Hf7iSRVv993Dvz5v4cVgL9qtnMyvfbxv68SpuOaAei89rjdYKGSth3cJZe90DvzMHTp2Mxe0ZilXRpUuXuhzyL41tc4a01tvNI9pb/HQmmqATByeBtmxidggFFS7wqhOuA8jzR2CdczUacOwQYa1pUy93j12PlJJgSQFq4RxY8T6i60HIO55AjbvPpzzYRjpJfvsJTY44nuzcymv4s/j8DMPA+PqD6vcw6xca9x1MTi3J4m0pMN6bX73SbfVyn7R82UJ/NRsK06aNT4xuGRKhwdF6n4fZpJT8mA+fraic1GZtivPcnK2c3Dqddu0imFojX36s0lAEWL0Me+k82u3Xa4chaRuw338RPeEFAPT0H9HTf0Dc+i9fcjCSTps26QQ8FytWAksXIJq1wQlHiBmWH+6Jl8CTo2DmVOxmbUi/ZhSlTZrg1SJtQp5zDSoRh18mQ6PmyCv+ASmptGlT3UDZHQghiMRK0A/eAIvnQiSN5sPvIK9D953yE0aihfDE3ZUNM6diffhfOpx6aUW4zYqX+lWhbfdD9O6HuvXCCvnG1MMHst+51xDdrkdcU8E/VD+XmJC8dHQeN32/kX/P3srBeSHOaJdGzNOEDEEqcfLqZfh5zr9OIaNtZ/LOHI6Y8yv6ibsRhx2L6DcI6SRokZOHY1rIR/5V+SyOOQXRdyCBWCndW7dAuy5ocAIhzCVzKq5btO6EvuNSxP3Po794v7IIIbs+8tBjqW8F/9KiDZFEFP32s0ltYu5v2MUFNG3aFCNWAqEUaNUeOeRcP7c7sx7qqXsRs6b5b/aDV2g79nWKraDf1zeuRu5/KBx6DJgW+pfvsad9S4e+g3DLPP3HECRobCLmVfb/q7tlkiIV4/o14PzP1uAoCJmCl45uSD0bssrG1JiwKPQkjhZc8dVaflq3iT55YZ7p345sI7HbhUBmYX71sTAYJqYgzZbEPc3dB+VgGSJpnJdSkhItQj/zT5j3O6F2Xci+9BZKwmkopUjxHMQ7z8Gkd8AO0OT0K8jrc1St04YUUJ6IY3ftTaJZO8Jp6X7q0rKFSb+1mrWiXShS4dG1BFgTnvcNRfDnsVk/+86A2b9U7Cf6HIV69B/JJ541jXTb3OmcVu4xLjcUAeKe5t6fNzKuXx5HvLMcV/k63x2zAqQLB6FdCJqkt25dq2ewN/HXoAb/i6FevXoYhlHNi7hp06Zq3sa9CRNNcMlcePhmKClGZDcg/I/H/Q/1xy+gzX7oFm2TiEvj0iBeTlWzh42UoOdgxxOoOy6tIM7Wn70DpcWIE89FpGdVH0Ry89A7yQ3ZW1BKITp2r35N3Q/Gs2pPZeMisI84AaZ8ntQuDjwcPe93OOQYxNlXUxoIYyhFOFqE/vBVKCkiMOgs4pnZJGrBbymEICFMYkoQlBpb7341c9BzsEpK6b15HUsHN+K2WXFeXuIPqQu2xPl+jUGjliFSdQJRsLn6AQq37LTQwnJiPvFuVSxdiBASr/8QHO2HYK3fvqswmDRgXjAC+7Djka7rUzXN/Lls3wXou68g+NDLlOwkfB1IRFFPjkK02w9x3KnoTetRrzyFdfXdxPdw6Nv2HPTzY3xDEfzQ95iRBMZ/iLOD/EgpJaxcWn3DrGkYQ86DMqk01zQxex6C6NUX9fr4Sp1vQHz7EcZpl0C4dhNnUDsMbmrT77SmgCAsPKSKEhGAAlu58NwYmPKZv8PyRYiW7dE/fIEYfDbkNUU9dLOvR97tYKyr7kSHfIlGMWAoNGyOem0c4uyrEUvmwQtjobgA+9ihyP6DUUL444/ngmmixo1C3vQgeuUfICWiW2+KA+G9Rtmzp6CFQNSksWz4dCgxK0DgrvEYpUWox+7wCy2ycpBX3YVyEjB/BhRthV+/w+hzrL+4MS3U0/f7UoVSIk44E9G0JdJzKSfZSZMuP5/WnDt+2sjmmGJEjyy6Z5lYXpxjGwVYfF5rNsc8skMGYZ1Al3PwSZNJK+L0bBDitE9WMbusSv6LlSWc9PFqPjqhERF2LbdZS4NibREzI2R3PQhm/FR2Tok691raNmvAp/X9HOof15RgZSUXZATjUT9UXP79/DYFPfp6grc9TjwYRvz6LXz8ur/NdeC5B7E6dCWe23i746AQAtOQaJJzaBMIFm3cTJuMekQGnOobfPNngB1EnHox+vvPMH79nsiN/6Q4GMFMxGDWL0nH1p++ibxlLOreq2HjWp9WKCdvO4UmtcuvLHGq9/dSV7O21GXBua1ZX+LQKGISxkX8xYr09m15zV8Utm3TrVs3vv7666T2r7/+ml69em1nrz2PoBOHh26q9PZsWof+122IRbPh9adh1JWIFx4h+CckvVpozB+/8HMRqyisAOifvkR07IFOxHzOrnKkRBDnXUd8H2laaq1xMnPg7Kv8CVlK6HM0+tBj6kRlo5RCteoAF4yArBxo0BgxYjRu/cZ4I0bjXHQTxaEISilCiSj6utP81ew3H6FvOIPA5g07NbjK1SGu+X4zvd9ayTXfb6aAwG5JlwU8F3Pim+grBhO+51Ia3XgSjzTZSq8GvmFzfItUVhU5GAJcaaCOPz35AHYQ0b1PLXIvBYRr0Cx3HZTnEnQThBKliNJiqOrlfvkJbDeBqVWloViOLZsQ8Z1PaobW8PsU9OvjUXdchn7sTpg51ee4LP+NlEScOJGCTaTGS3b5ezFcxy8gqwrPrfSWbQdKKV/bfVt07IFXRT83bgZg+B2I5m1h65bqv4/VLWdJey7p0iXbLSKoEkl9yfRcf8FZFauXIZq2QRx1Enr8fb6hCDD9B/R7LyLOucYvTDl8IPrHL5EnnYfwXPR918CqP2BrPrw+3lccumYUSAM9+VPE0Itg6ULULeejP3rVl4izKnMhhRB/Gs1SXREPhBFDtxGaaNMJHU71C1gQOGkZqCfuqqzI3bwR9dS9yBOrqMuU3auRiPsLgXJNa6XQ7/8X0bgFXpVFtaE9mgUc/n1ENm8ek0u/+pKA9j1SpkqQrqO0CCRIVVGMKlRRxdrg+7WlOIoKQ7Ec0zfGKHA0zi5QsAghWBM36PDfpbR/bzPTzridgpGPw4U3oh+fwDOJpnR4aQl93lzGce+t4PDGKRgq+TuTyq00FMuxdCHSczA81+d53AZ6xtTt0gIFlEukIJ/AB/8l+NHLRJxYBa2fYRgEAv63VWIF8W58CPn0R8g7n4SNa9Gvj4dFs9FPjybgJnDtQHX6t3WrUIEQ+oEX4Kn34Kn3cTJyYOjFyb/rfjCutfM5TimfdD0rmHw/V3XNZL+sAGnCoWOKS7qOYe0ib+bexN+exe1g+PDhXHrppfTs2ZNevXrx/PPPs27dOs4///w/7yLiser5MEsXIho0qfSWfTcR6+yr9gpvVFUEnDj89zG4a7xfDVyVTT6nIXrhbJg5FX3utT59TOFmaNCEUrv2BRJ7AzHDwj7yJOzD/Fxa17RqpWJTFVJKosLGPGIQdu8j0UKQKCu4qdDI1dof1H77PvmdKYV+90WsS25he9lCUkq26ACnfrKaqet8g+DVBYUsK3R4e0ADwtvdc8ew3QT6rSohNCdB1vP3c9/FY/mxRQ6ZAcl5HdKxtIPSmvxIJrmjnkG/9xKEUhDDLiEa3LnKQSIYJnjONehHbq1oE4cfhw4EsRJx9MuPo6Z+Ax27I296CPXwLf6CIxEHz0MZBrJxS9/gKEcg6MvQ7QQKgWzRFpZWCTOFI0jbxhACBYSLtqBvuwi2bEQLgXnS+QSPP73O34xnWsgO3eCnryobDRMydx5tcAIhrMtvgxce8Y2+jt3hlAuTKj611hQbNsG8pphHDoG3/l15gOz6vpZ6HRD2HOSvk+HL96FBYwKnXkxpOA1PKTQg0jJ9A6/8/D98jnzoFfS2hjvAzJ9Rg85GPvY2xEqQJ5yB+uQNRIfu1bXbv5uIvvY+xNMfoZ04wg4iDjwCvWQutOyAG0mvoGeJCpsNcZiTH+eA+kHSDVWtIEIIgStMYgpSDY3alvx+L8JVCveAIzAbNoNvP4FWHeHAw/2CrbJxTXhuciUu+J6olLICikga7H8YnudhO3FYvjj5t0KgE3FfEctNYJQU+rr3jZtjWEFcxLZ81juAoFeDMFvjHvWCBvlVyPtzwwZrSlxSMyws6vYM48Lk9h83ck33LI5rHsHVMNlIpfd+BzJ5dZQrf6zMi1+wJcHL8wq4qlM4ydunpUQ0aIzo2B3sIPqX7/xwrzRQhgmdesDP3ySfuO1+NYbNLSkJRO7VzNMAACAASURBVEvRv32PyG6AyGuCeugmUm58EF1SBL9NoV2TVkgvQYlhEzVtjKKtcNs2hv/c3zA8l5hhERh0lh+unjHVp+855xrcQNCfL6pEKUJ9jsJo2Q5++BzadUF33p9oLWUJ00jw6+nNGfv7ZtaXelzbLZPW6Qa2l9jn6Uo7w9/G4nZw0kknsXnzZh566CHWr19Phw4dePPNN2natOmfdxGBIKRm+GGMcrTvil6RPNjoPyOUoxXEouipXyFOuQD9ZtlEZlrIc69BffAKnHEFUSuASq+HyMj2O/+f3P+llEgp8aoolyQQJGoIE0opMQElREWuUFXYyiMQj6IXz4FGzfHSMikpP872nrldQ+gzGPKVbGoYDIQQFBBgTYlbYSiW44e1URJassuiVPFY9XNuWMuh9S36xEswF/+M6NCNuBnGEZJVm7eS0q491pV3o6XEEbJWA5jjeQQ69kSOfhE9exqiXRe0kOh7r4bchshhl6I2rIXpP6JKi/3+89S90LE7nmmRCIQIX3cv+q4r/L5uBxFX30PM3HEYWQiBNk3krf+CNcv9PrhwFuKyW1GvPEn4zCtxTMuvzi/3/mkN7zyP2X8QpNRNSCBhWNgX3IDOXw+L5vjG2/A7iNfgVZBSYrkJtDRwhCRuWKiDjiS4/2G+2g+CuB2qsU/ElCZlwKmItAw/mb5JC7xTLiJeh7CtKSXyu8/h2Qf9hjm/on/9ntCYVym2AsTtEMFLb4EHb6y8huNORzsJX1N+2wO274ZjB0nYIVJDYUQiDkvmIfoNqv7bZq1JWDaOBsq/FyuI7Hl40sSfEBaPzixg9C+bOaxRmICZ5edqWZIUQ7PVNUgojYFg1sYYD/+2mSEtI5zeNpWQ2j2aqLogaljI5u0xWnWqZDmo8t4cBFZuw+SIS15TME0Ydgmi/xBKA2G/Cj4QwjioL0z4j/+7pq2QV/wDvWY5oaULEO27oZ4f4xssgSCh+1+gJLdRrfMMQ8Kjftjgq5XFPN63ARd+voaoqwmbgv8c3ZAnp2/m7t45pNUx2OMowalt0/lhbZTD3lqGp+HABiHeHdiYn9dXfxez8uMIHSYlEUUJSTwQwrHDBP/xuJ+iE4sir7oLHUwhaof88ffQAT7t1syp/nh59MmoRs2q3bsQgmBhPmrEmRU58rpRc8QNo2He7/DwLQC+6nyvfoQuGUnUtH2j3bQqKOEAaNu5wqNbYgUJXj0Kw3MRUoLrYG5cQyQrl5gdxC175VHDQjZtg9GifZ1ZL7RWZBJj1P7pfu2B9lCu+2dPk7uEOlHn/I0/F4YQhNevQD98i09T0KYTcvid6M/fhXZdIBFDL1uEOvFcSs2avTA743vaHmw0ZiKGMi3ihuVTCjx5N/w2BXHaZYiuvdAFWxBNWqAnvo1u0Q63a29itchP3FsFLmEvgVyxGBbORvTog5OVu13vUdhzkIvnIL6biG7bGfocncSJaEhJaOEMuO+aygn6hDNwT9wx+3/EjcPN58CmMvooO4gY+zrFNWjWSinZ6Nlsjiu2xj1O+XgVBfHKgTEzIJl5ZgtS9a5NjDXxj4kjh0DbLuin7vEbDBNx93hKm7dj/vz5u/xehBCE3YSfd/Xx6+j3quhkp2chR9yPuuMyAL9S+vtJMOTcCmoeAwg5MUQsig6GiFs2zk6yZCIo5Ma1viEYSkGnZfr8l68+BdO+hfOuQx8xEDHyPFi3DZn+Ay8QbdK6zvRGQgiCTgzDddFSErdD1Xw0Qc/BXDofPnwV0rMQwy6hNJJB0E0g3hjve/oCQTjzStyD+m+3P5mGxIxHUabNgqXLaNGihlD2dhBKRDHuutwnzq+K0f+hpGELtNb+YigRRS9bhGjU3JdfcxOIad9CPIZ+4xl/wdG+K/KG0SghSRgWShqESgrQ91+LGHqxT5NUHtJu0ARGPUNxLTguC0SQFi8sYUDzCJful8mVX69jRZHDIQ1DvHJsY07+eCW/lBkiZ7ZPZ0DzCGdNXM3NPbO4uXtqWY5f3SGEQAixQwMs6DmYngOanXKzrlm9mnZhy8/HW7PcV6q5eQzxnIa+5OI21DkRLwGvPQXffYa899+o0ddXjhehFORdT6HuudJPA+jYHXfEP4nVIZUnYQZZWaJ4YvpmLuycScLTZAYla0tchn68igVntyRSxzHFNE1mFgoOfmNZUvutB2RzfIsIfd5Mbv/8pKb0nTfJj0blNkRcdjtk1kNffUpl5EVKxEMvU5zTqKJ4NOjE/ZC0ZRM3bDYRwFFgSkiRCsuLYwuw//MwfPVh0jnFI6+hR4+oliYlxn9IUSgVSysCC6bDB6+QaNEBS3vIE86gOJyWNDYHPQdz0lsVzASEUhD3Plsno/3/R/ztWfwLw9Oa0gbNCNz7LGYi7hNsWzZk56JfetTnfDrnWrxtwqqmlNjxKIZW6GgJwkmgMupRWkvOvIiXgLeehV+/QzZri3nBCKKRDMzhd/iGwC/foZ0EHH8artJwwlk4wXCduBz3NIKeg3zxUfj2YwD0q09hXTIS4+CjKJVm0n1bAuRX78PLT/gruu8moqd8TujGhygtG5QD8VJ4ZnSyJ+ej17BOOHOHE0epHSL8z5fg9x/8oqTe/YgGwsSFhTIMYh6EDU1IO2xRNn3eXMZ7g5rw7OytPHhIfS7/ai1K+3qn4/vnERHuLntno4EwkVH/Rr3wMKxciuhxMGLIOairh1b+yHPRz4/BvvVf2z9QLaC1psSwSFEe4rN3kjcWbPaTwqXhTxhZOcSHXoyjK72tHvihvVoWpdiALMj3udM2l3kNu/ZCXn4b+vcyUnDXwbUDWAf1g3LjNRhCXHmnH/L75VtEh27EAr5ntbb3GTUDsJ3FmWEYmItm+tQx5fv8/A2hce/7IbZJZc/GLYZnRmN26I6o16DG79L1FCqYgtZ1z+nT0qg5bB1KqfS4S4NEMILs2NNXDnHiiPWrfe9Ll17I/Q70q3W1Rj0wAlYsxu53Avq0y4imZRG+eQzq8Tt9mp+TzgPLRqWmU2qHauUBdZRGabixZz0GfbCSwjI6mIYRi6dmbq4wFAFemV/Aia1SaZZm8eycAq7smkFqHUOpPrF+HLF+FRQVQqv2RAMhvG0KWMJeAvnKk/DVB6A15sFHkXLRTdX4YstRUlpKSZN2BO95Gqk8lDSIBcKo7dDUFBs29plXY515FXra5EpDESBagv7yA8RB/dBfvg+b1iFV8oJmpxERN0abFIt7euewpMAhaAqmrovx2O/5fDakKWHqnrOrtWb+5ur3M3l1KX0ahvjvMQ0Z81s+cVdz0/71sKQAy/KLwIoL0a8+4dPObJui895LWBePJFF2ji1WhHVKMHVlKX0aWQz9eAXzNifIDRs8d1RDDsgJEnBL/fe3DQSga5J+9Twsy8IzbAq7HMKG1gdxz8/57FfP5pxIBpFtvNSWm6iMnIH/TsbfR2Dkw/63/38UfxuLf3F4ShG1Q6SsXwVfvAdao198tGK7vv8a7MffIZ7qU7YEPQfzt5/gvRfRlo088Tz03N8R834n5fbHKN5JbkVAeX5e1XcT/YYNa2H5QoL3v0CxFcQafC7GgGF4dhBH68oSqX284rLcBLrMUCyHfm0cRvM2pNRvTHGVj9xOxHwVmCoQ82cgEjEoMxYF+EZO0gG1H8LYwXihlKLYDGAcdKSfa+W6RGWAVaWar1YW8tnyErpkB7i+Rz3yYw49coPM2BAlK2gwY2OMKac2Z1WxS9fsAFmWQni7zoumlCKWmklg4Bl+DpUd8BVntqE3oqgAsYfyZRQCo0Fj+GN+ZaOUEAiBbSOuvY/SYEq1CXpb7MwjbnmOH87aXKW4ZMZUWLsKWneCZQsRhw4g7rhYg8/y7/m7iYhrRqG/+Qj9g+8J04ZJ8O7xqObt9giJupWIwQevJDdGSxAb1qK3zccCmDUN2W9wtXMLKdmqbJ78bQvrSj0u79SChDBqRXBsaYWFRpx9NerOS6Hcs9WzD6oGA1IpRYqbgDE3ocsLeOwg4t5nIBBCDx9cGXad9A6ifmM46mSK07MJ3jQGqRSeEL6BpFStx4KQhK45/sdUWIU3sGW6xfSN8Wq/n5Mfp1mqxcaou0v9NezE4Z/X++o4ACmphMe8QnFKehIhtlw4z/f+luOHzxEHHIpxQN/t9hGlFKVVFzo7eAZSSmQigTZN9LY56eD31XJeyr6DcMpC2OBHRIxVK2DS2+i8pnDUiTUSoyvXIRWHHumSUm3SJCXMwKYh0qSzS94xz/M4OK+6t/jY5ik8O3sr+TGPy7tkkRWQ1A+bbCh1YUUV8vDSEt943BbBEGVmHlqavPtHlMu/XseTfRtw8RdrmVdmoG4o9Tjj09X8fkYLUkwLe/DZ6GnfVjlOGJ2RDQPPgFeerGxv3pbNGQ0odASP/ryJ/JjHpftlcO/BORzz7kpeW1jEp4MakUJlf9MlRdXTQ9avRu7CcxNCEPAcTNdBi3Jxif/NYO5fswTtbyRBa41u0NRfbVb9QPyN6N+mYBiGv+JcsRge/YefRL14LmrMzYiDj/SZ7WdN87kHDYOQEyeciPqa0lVgeU71KskNa5Fl1aWO1sSsgG8o/oVQY95mIg6OAx++gpV0m9r30G4DUcV741gB6Dso+QeNmqNqKSHneR6u6/pVzgnBi3O3cvP3G/hyZQljf9/Mh0uLUAi6ZAewDMkVXTI5pXUqszbF6ZkbINNwMXfDUCyH4cQgtyE6p6F/z8VFkL2Nssmxp+DUopClNogHQogrbi+bBHyI0y5Fp2bAE+8Sbdp6h4ZiwHNJjRaTsvoPIk4MaztuVQ3o9Wuqt29cC4cei/jXm0SDfoV2sbRInH4FPPY2Iq8J/FClf5d7Vrc1oPG9hLYUmNupxgR/4i+v1jQMw8+TqsEg06bpF7Vsizadapy8iwiw/+vLePDXzbw0r4Deb69kXqHaqYfRMAyCa5ahLzne12y/7znEZbci7n8eNfxOSmtYLAohEBvXIKpWeidiqLeeRRdtrT5xTv0aMxH1DSQzQLEdotQK1tkIiZDgoxMakxkwCJmVH+i0dTEGNKusrpcC/tkjhZtaeLzZ3WPqsWlkmnU7l5QSsXIJotxQBCgpQr/1LJZWSb9jzq/V9hczpiJ3g5mgHJZWhFctwXx4JPL+a5E9+/gpCRUnEoijT0avWgZnDkcfc0rFWJsiNMacX9C3XYie/Cm88TTccj5hN4FpmjUyJyilCOoEqTpGhPhuhVEzTcVLR+eRHTIwJVzQKZ3zO2YwqGWEsCVZuCVOk1SLJ2ZsoXdeKHlBvmg2omuvZDYEO4gYcm6Fn7NEm9zyg58y0yEr4OshV0FRQlUsKhINm8O9z8IBh0PfExCPvErUtFD9h8ANo+HAI1DDLoF7xlPqCfZ/9Q8em76ZV+YXcPhby1mwxeE/R+cxc1OcapLfqRn+v6ro3R+npnz0nSDFjWM+fT9cchzi+jMIzfjRd8hsBxYQcaJE4iWE3PhusWHsafztWfwfQakVINJvkL+q79UXVi31wxRFBdCkFVprTK0Qn09Inl619qks2u4Hq5Zide+DtWIR+rmHoGALgaNPxjrmZEqlv+pTgMxpCGtXVB5DGmh7z7vfhWlR7ElSDMCt7kmoC5RlIzr1RFQZ6MVxw9D1chGmiZmI41gBgspFahD3PI3+8FX05xP8yfDAI3Cr5AUlEFinXoTIrg8/fQkt2sHQi4kGahdiK4eUEldrXphbWaQ0qGUEraHnq5Xce8c2j3BGuzQObBAgW+4+cS741BLGey+hP3gZccsj6Jef8L3NNz2InvQOet0qxBHH4/U8FGcPrXaVUkRzGhF84l3I34BIz8KxA7WqPA4oF2vyx+j/lGnL2wGCdz6FatoGb5vnkQiECPUfhJ45tbLRMBFdDiSWmonUHpbjgBXA9TwSCAKeCxuTNVeBMs9q8vHDnoOc/xti8qfoNp3hsAFJOa0VIc0Vi2HtCkT3g6FwK/rd/yBPPA/1y+RK+p4WbdHpWYh+g3zv56xpvrf1uNNQuY1qzGX9aV2UjdHkSeX+afm81D+HADsgSI+V+lyQnoue/KnvQW3aCnHjP2s0FMvvhaIqShnSgJ59EPsd6FNFbYvWHdF2EKTl6+6qXeMD1VqTSoxgyOb1AY0457M1FMQVSwsTDGyZyuKtCZ6bs5XHDkjh7D8mYvzrabKVgnq5iFH/xo1k1Pq8QohKapuq2LwBoTwoS+VRSkHPQ6pFHvSBfVF7YIEcLC3yK/PLDAb1wiPIMS/7xVnRUsSJ5+Jm5qCvvgc3GKr4Lk1DIlcuQX/wcuXBLBtx9lWIFYsJLpkPXXvhZmbvNWaMLK+UU9dN5cg+zdGhMClGgoBTxLCWIY5plkJC+TyCDx9Wn+xEIWTU86vthYCTL8SxAhgP/hd+m+Jz8x7Un2gwXPEONZU8hHM3x+mdF6rQ4QZItSUZAQOlHOKGidOsLdbwO/1iPPxIhGtYGN36YOx3IGs2bqJpOJWvFhSxpUouuAbGzdzMg4fUJ8USmNusv6KBEOH7nkU/M9rPQe3dH06+oNZa1eWwBH6UoZw9oXAL+uFbsMd9SDwUAfzFXXkEKqA8rBk/+ulPpcUYHbqRMmL0XpNKrSv+Nhb/h6BdB/H7D+hZ0xBtOiFvfwz12Tvopq1RSqENA92ohiT43Dw/h+6QY7Gixeg7L68k/H19PDI9A/OQ43CVIh4IE77yTvRdl/tqEgBnDcfZg1yJhmFQYoR47PctfL6ihIMahBh5QDYZxGo0ksp5s7ZVrKmKqBUkZcQDiO8moedPR3TthUjPQt14DuKau9GBIClOAvGfR9DfT0LbAcSwSxB3jUPHoug2narRH5RIC+voUzAPPx7Psv3BohZGXEWituuAYRAyM8kIGJQ4/jM/s306N3yXrJ8+cVkxjx/RgKw9ZCgCWK7jK3QAQmt0cQEUbEHdPdxX6OjaC921F6V7eHJxEf4A16BuzAGW68B/H69sSMTRT9xNYNQzySE+fL1d1aUX8uKb0B+/AZE0xHnX4YRT/eKLCS+gVy3FPOw41AGHEbOC6F+n+J7F7AY+X2g5jjnZ96yqcu3zbXJap3yO/uELQjePqSgkC7lxGHtrhbqDlgby7vHQrDXqzWeQo/6NXrUUnVEP3awNpZa/X/Da+zDcBEiJa9rbpXEKGtUnpqAhkH7Ebseoavi5DvwxH11STGlKLlsdsA1BitSEtL9AU0r5i6GUCEgTOfJh9Oxf0HN+QUTSkGNeQd12oW/8Nm9L4bCrKNImczbGaZhikRUIkiYTyXRadYDlJTg012T2mS2Ie5qgITCE5squmYzoWY9GJRsQD4yr3CF/A/r5hwkMv6PWNFie5yE69/QXvYkqC9PjTsO1ApRz0yilUI1aIIddAu++5LNADBiKat91t9MUTNP0UxGqPqdfvkOlZ+Gddx2epsLowbAq+iOAGY/B2pVJEREx7FL4/UfU15WFHualt2L1OdqvRN+DMKVETPkS85nR5AJk1KN4wBlsOWoolge/b4xz6ierKHE0GQHJJ4ObcMC/3kAUbEGEwjimTQwJZgCj91EVBlJVhITHBR0zeHr2Vsb+ls9LxzTi0i/XMjs/ToOwyetHZpNuanRZWbJSiriQZd9D5Q17noeHZMvWApoBoW2twbI2Q0DfxmEiRtLueEpTkpmLff39SM/DtYN1NhQBTCfuz7tVoTUsX4TZaX+CsRKY8i0kooiDjwIN+tF/VHry502H18Zhn3NNBdXUvsTfxuL/CMJODB67Az3D96TodSvRa1YgbnmY4jJDzvE8Akef5Bd5rC/jvWrTGdGkFfrM4biZ9TCn/5SkDAHAt59iHtgX1wzgKUWsUQsC496H9WugXi6OHaxUhdkDyGzUjKu+Wc+ExT7p728bYvy6IcZbxzcijeTQQ1C5mFs3wdRvoGU7dKtOlNRguJZz1EUOG4Ao2or+5mP0/Bn+tqfuxXzsbZgyqVJDOBZFv/go4pHXibbqtN2JwFHKD0nXASluHB693Sea7tKLvKtH8fChuZz2qR82taQg7lUfzZVSGGL38+YqD+hVGLf6l+8RfU/wORRLitCfvgV5TdH9B++58+0mRCKO3rZvblhT4zBpCIHcvAG9fLFfXKE1ZGZjxUpRt19cqTA0+1fkGcMxjhsGWdmo/4xF3vjPMs/qSsTBR6H7HJXkWbUTMXj/v8nXtnCWTxdTZizKwq1JMmAoD/Xy44gLbkCsX4265Xy47j5i7br6k2LZBBCzAshAaIeGh1KK7jlBWqVbLCmLkVkS7joo21f12cEzLAymETzyZEKvPFbZWC8XlZlD//dWVeSAndgqwpNH5BJWvuEUtYOEH3oF1q5AvfGMT18C6KnfoI8/HfHU++hEnKJIFvOK4KgJSygtm7TP65DOvQfnEKFupOFVIZVLKi6pkrIcAwgZIBCI/PXVd1ixGOk6YPtTWLAsLwwhcEyLeA1GZNQOEXzwv35OW3EBnHAmXpvq336pYWEfdxr2kSciDAOUQioPyzT9oqxdhFIK6jeqviE9ExeJoxXbWwko00Lnb0QOOgs1f4aff3xQP8TGNdC9N3rSO374/JUnsA84DGcPe6PMRLxSwaphM1bf9CQjZjr8/O463hnYmDMmrqakzELdGlecPnENU05pQiSj+nvYXt83lcNdverRLTfIe0uKmL1mK58OyMUr3IJVtJWc397DPWZonVhn4/E4hzQK0zzNYlmh/y0FDMG13bPQaJ7p14BwDTRMSilihl3Gv7NrkNJAtO6IXp6sUS8aNSMYK4UbzqzgOdWfTUBcPLJ6ysec3zAS8UoKqn2Iv43F/wEYhoFREkPNmJq8YclctOf5iQ5lKA2ECd//POSvR1g2pGbgCEkiEEIphdmgcfUTNG6BqmKAOULiWCFEk9a7RRRqGAZ2rAShNYlgSmW1dCDMu4uTw4FT10WJuzopi9Y0JOaMn6Eq2XOPgwlfeVe1kJqUZZyAiRj67eeSL6RwC8JNoH+dUu0a9dzfILeGAXwXYUoJn70PM39GXHYbaE3gwREcM+RCFpzRg28W59Mh6HB91wxG/lhJiNwjN0jEZI/yUnqmjezcEzH7V/Q3HyJvfAjq1UdP/QqatYEh5xKtZeXqnwEVCCJy8yrVLQB698OtwfMZTER9moyNa9GfTfh/7J13YBVV/vY/50y5LT0kIRBqKIo0EQVEEAQbYsGO2OuKimV11V17x7V3V13LuupaFl17r4gIIoqCiEiHACGk3Toz57x/THKTS0JH1/29Pv9l7mTu3CnnPOdbngcAcdzZ0GePlg5Dbz2Ptc/B0L03aI26/jzE8AMRg0ai9xxNvWwlsrrhgqRLT6RhkBWr9aM7rdUORuth+c++A8pHr/s2d82ubdhNIdeuhCU/IXbZjVQkuwWpkVJiuylCXh2fHtWJtxZHqYi5HN41TJuARm+iXMC0Atwyq4Z9e+1H/zNyyf38DZzSjogjT+eRZTJNFAGmLKznnH757JHv29Z5Guqz8sgqM9JEMY13XkKPO4n6cA41yuKyqSvTRBHgiXk1XDywkNyAsUOahJpDaw1t2/vkqHlEcNAI3IYJNOKlEA/e6MslSQNrzDEYh5/cYoxwEUQLSrAnXolQCmcTpD2FxLQDGEsW+M/T7C8IdO6Buc/BWyzAvCGUUugefaFbL0Sjk0lhMRx4DM5m3sEUgsCIMej3piBveBQiWej/PI36+HUoKEaecB66pB3607e3IQa2eXiWjdm1J3z/FZUnXcqYTxN83+AQE3VUhuQXwJJaB28biHVYJzmhW5DjSlxCT92OnP5BU4PWFfdsZf+7n4HIClh8elRn3l5Sz+qYyxHdcsgNSCKq9UzWDoPyEPsfgV68ABbOBdPyNWZNy39Wmwnis2YloqANekM93l0yXZ7+m/idLP4PwE7GoGqNX3TbeyDy4OP8Cd400Rs0aiiliNkhZPuuGcLU6TRLYQly2AFN3c5t2iKOOsOXDtmAGG4PUQwoF2vhd/Dk3ZBMEDz0BLxBI4kbFmjIDxpUNXMXsA2BIf3BxbIs3+0gHoOn78s88KzPMZIJCDf8bmlQpy2+WJWgTchgcDiA1aUH5OQjDz/Fd9gwTZQVgL67NxnFN2IjDgHbCsN1YO7X0LOff38avJCzn/wrOSdMovzVZ6BqDaedeAm7jOjGUysEA4oCnLBzLlk6sc1c0ZQSKxEDIUgFw3ieR8IKELnoZr+Lfu4s1LrVMHwM7p774VmWn6rait9uWRZVromHL/9joKl1BZ7ShA2IsH0uBPFAmPC1D8Ojf4UlC/zasWPOJGmYLZ5N4bp+h3dzJJKtNi6RnYcWgpgZIHzVfYiVS9GxeujWy5fC2ODYyUCY4IRz4L5r/A1lXZBnX+F7olcsh+xcxEU3ofc7HBqIKoDY9zD0Fx8ijjkTOvfIeLbCaIz538DXn6M/fRudjGNfciten0HpRZSJJrSuwpfFqqmi+ICjOWHQCJJGmPnz59O2vHyT1y+pNN9WJpk8s57RHQZw2JgB/JwQjEnl8Obilr7f31UmGVTQFIHSWqOlbCkg3+yaJjzN8rqWHdnrE4pOv9CclrCCBK99CB643iduQ/eDcSeToiE9+tl7/uQLfjT9tWeQg/dBdOze4nnUWvsEXcKmbFFswFy5GPXsg1BXixg5FtqUYKxYguzcM2PMKCvMJyvlR1VTVoDUJmSYYlaA8OV3wbrVPvkt7bDFUkPRYITQvkcgtQfvTEE3SlStWoq6/XLkTY+hs/JIbYH13NbC0ZrAoSfC7C+IFZXx/bS69Gfrkx5dciwW1TY9F7uXBLGEwsKPSrp2YLOp8bCbQq5e7tcA77IbHHAkev63fjnUuBNRXXfeprFaOEkEAQ7oFCZoSJTWBH9pogh40kC8MwUxdjyiIaKsZ3ziv0+pDaKZrote9CNi0nXoRyZDrB527g/jz/5NpKDhd1Hu/wmEUgmM/zwF/YcgkwnfsD4Rh0AQcdkddLIoFAAAIABJREFUxMt7pdXlw24KuXIJfD8T+g3GK2nfYiUc8lI+4UrEITuPWEPUcUegsVszUl2JnnRE5qTzl7uJ7bQrVdW1fFAb5tR3myb7G/cs4pReudQ7mq/WJOhdGKAgICi58kTfgqkZxH1TqMvORwhBhWsz8LnF1DV0yY0rz+b5ITbGisWo2y7zHUFCEbj0NujcHe69Br76zHdXOOwkvDHH+gR2B8GUkuAnr/up8LmzM6I08rqHUQ/e2NQ81G0X6i68FSe/iKC37YNXyHMwZn/up04DIcSJk0i270qqoXTAkhLppvBMu1UtTE+axLRJynEJ2SaGhJQSeApChiaMS422mLE6wfVf+k0CF+5aQLc8m9cW1XPjl5UMKQ3x4ph2RLbTWUMIQaBB3HtTtUIRJ4H48ymZUcj+gxFnXIp+6h6Y3uAzKw3k1fcTbVZqsClRZkMIgqk40vMQddWomZ8gBo9C33dNpq9tMIy890X0lCfRq1cghowCfLkmefPjeIZFzPIbYiKeg/zcr3sUHboiRh2CuuMvfhPKzY8TbUgZZqXicM64zInkghuID9x7iwTTDcPgtRUe499ssl4TwM+nlDNtVZzj3lqZsX3uCV0pNf3oUEA52I7jk61/P56O1gKI0y4msfdYHA0JI8Sds9czeWZTVKQgaPDNhK5kq5Yd5TsKhmEQSEQRgGPaaUJmC419/7V+mUpzTDgX9jvcj9hk5+LYwa1q/MhJRlETD22KaoH/bAHJkYfgOD4xingOPH0P4qM3fH3KI07B23fcZqOPjV2u27K4CqUSGDec28I2UFx6G6rXrkS3J3e6CUgpCSXjrDaz6f70YuINk86A4iD3jGjLBR9X8NXqBMPLwjw+upSO1MN/nvbLcXoPhIOPJ2oFWv3NYTeFfOjGJtIvDcR1D6PadwbPIWWHtroOc0PzB8MwfM3QXymTIoQgkozB/dfC7C+gXUc4/wYSbTsSSsZ8gfJGFQZpIO58jkR+EYFUHJTCM0y/1vo3ojzyO1n8H4ApJcFZnyLbdUTdcH6m/l9OPvqOZ4laQb+b6t+P+c4RjTjmLFJjjt2mAt2tgZSSWmxmr00SNCW7WHGKrjmlqXYSYPA+pM6+ku8X/ESHnfpQ7cDHy2P0yLdxPc2auMvxb61MR9euG1LEGeUWRaePbDpGl57oK+4latp40uK8z6p4Zn6mQGv1+DZkXXlKZjoyOxfu/BeuNDDdFAiJY9kkxY4fWCOeg/x2uh9F+viNpg9KO/ruDD99j4jkQNsyUuFsUrTUFBRCYKGQjoMbCLUqvgv+dQ//+A1cf17zjYj7/k3dJuzskjJAQktcBa8vruOyz9YQczX7doxw+/ASRry4mLyAwS1Di+ldGKDO0Qx6blHGMd4a15FsS3Loq8uojHvcNqyYs3oGd3gqsjUYQhBet8pPRa9eAW3LEJfejpPfBitej1i1zCdx/QaTCmdjeA6yeh1aSnROQZrINYeUkkjVar+5a30llJQhJl0LbcvQp+3f4hzkzY+ji0th5mfoD18DJ4U863JUYbFPMoRA2QHklCfhlWbdtWVdkRMmou69Bn3Pi0StoH8f50z37feaY+f+uBffypxFS7bIXSchgzz+Qx33zK4i25bculcxQ4stXAR/n1vHPd9UkW0b3LZXEUOKLQLa8ev93nvZdxXJK0Te9He/zuqn7xG9BqDzCknkF+NIAy0NarTN37+v5sUFdXTNtfjrsBLa2+6v6tncCMMwCE17F+6/LmO7vOUJ1P3XwbIGv/HDT8Ede9wWEUbLsgh+8R76nqszP+i9G+Kki4iVdsTzPP+7P3/Hj3g2x+QniZZ23mGTvCctYtofp0LCI+wlsf/+16b66waIe1+iLqdwm79HCJG2St0UXGny0uIkf3i/Ao3vrvLeuI70yDV9nVUU+SqGec/VfudzI/oNwpt0vW+9h9/wI7XG1ZpITSX63MMzv6i8F97ldxLfyprxRvxSTmFbg8bFr6k8FJAMRvA8D1NAKFaHfu1ZSMYRB08gkVOwxeYA/w38nob+H4CrFG6fPbDj0ZZC0bXr/ZSc5QtT8/q/Mj+f8gT26MNa9UbekVivbfZ4bjGrY/5A0zPf5qOL76boT0c1RRc7lKMNg4K2HUhpwfqES3muxZ+nruHBfUoZ/9aKjDTsjV9WMmGncrjqPsT7r6DLd4a9D/I7S7XG1aS/rzmk8lrUrVFXA06KRDjH7zTcDgghMA2JpmVHH0DUsAjsuieBnn3QX33muxgA5OajTRvZoRz16K1QsQxrr/0xx07IcIeQUhKJ1/kuAiuXYo4cixo4LC1v1Bym8jJSoYDvjDDjE4xR41od+BMywCerkpz3YQXPH1TGpI+aGgjeXRrlke+qOaRrNk/MrWH8myv44aRynvimZRrzlYV1jOwQZmBJiLcW1/P5qjin9Qwj+OXJoqc1sTbtCN74GMJz0YZJPBDGU4pUVj5m91xEz36kPEXEScDVf0Av98muKO9F5C93tRCoD6Ti6HuuapJYWb0cfe1ExAMv+ymh5jqEkWy/0/i9V6BDV+Rxf0CvrfClZu69FmZ/DkJgTn4K9e4G92f5z5Bb4HdhWwFM4TsGyU7d0Rff4t/3RkHjNiWoLbDQbERQJTh7pxDH9+iIEJArXTwvhQVM3CXMiTtlIwTkSBflNTTPpJLoZ3whYzH8QPSzD6J//M4nye+/CjVVBB96FScQRiiPApnk3D65nNIrl5ABIZVEtdKw9UvAQhNwU74kGIK4DKF3G4bYd5xv/2aacNTp6HVrmogiwL8fx9rvcBKhzb/7ZjKGyM5rURIiituj27RNv1OG58JXn7Y8wLczMMrKWx0bthZxGeC+b2u4fVYVCpjYJ5/LB+bRZsK56B/n+GURQsBhJ+I0CnlvA2LCZmlU8c3aJMPahymwFMGNCMCbymVcpyD7nVLOsjqHTjkWYVws3VRTaqZSmUQR4Jvpvu+yFfDfyY9fRfw0F3vEWN9Le8Pyh7rqFpJW2wLDMFivLDQCS2giOvmrReu01v4CpXF8b3h2XI1vQXjs2YDGVfo3E0HcGH4ni/8jSBgWRjCE6NwjMy3bqRvKbHgQtfalHppjBwxYm4NhmDw4uzqDuM1fn+K92lzG9+gL87+Bdh1xxhxLpQrwZVSR7SSpdxQ3Tq/k4dGlhE3Bug105ZKexvE08R79MLr3QQnpR9ga3Qyk4rx++by/rMkFIWwKDNuC8l5+UXEjiktb1HduCwKei1W50idopR188tqKg0ISiZuVR/juF+DnH3zHCa1h7UrUDZOaCOS/n0AohX34qaQaSwmScfRlJze5k8ydhTzpfKzRh7fQQ9TSgPadWp5oaYdWBx8hBDElOf29VXTMtvi2sqW+5bRVMY7pkQvU4GlYUJ1ip/yWq/sO2RYFAYNlDTVs43vkYKJ+Barow1PKT+E2zv8N90Ap5XdMKg/TNOH9N2F5s6jowrnw7XSMgSMyyLShgUWZJQ+Nwu7i/Ot979+ff4A2bf0I4stPIfoPRvfs6xPFgcPRn7zhE0XwCc2aFZCV7dcgNUdOHvrg49FAcOFcuOPPqLpqP+p47lWou670J8vxZ+NsrRJBQ2cxOrMsT3hN25urt+jmC9DOPXxtxrWrMvy09ZqV6RpApRQ2CWwAb+t7sgzDIKoklgBTb7lGo61cArVVftS4Yjmy1wAieYXUByPYE87FOuYsf0cp0Zcc3+L/dW01hLaAUCnlp5T7D/bThwB5hYijTydqWk3PmWFC30EtU+C9dt0h0XUpJd+vd7hxRlPK/67ZVQxrH+KA9nnYNzyGTMbBtn1pmi2UEdoQSWFx08xq7vt2fXrbP/Yv5eAyG1Tr84etHWwcCnIEWiewBBhuCtcO+qUuUkIwnOkWFQiClIScBNx0ASyc5z87n7wJp12MOOAo9JvPN+0/5pgMSattgmExpxZOeHspC2scRpaFeXy/duSLxG+CnDm/QhZmR+F3svg/hLgdInLZ7X7TxA/fQM++cO7VJBqEol3Twhw8KtOBZZ+Dd6hGYmvwEGnC0BzLPBtx8c3oulp0fht+ckMMfmZhWmJhv04RLtu9DZNnruOy3Qo5rDybKQubCqcHlgQJm6JBN8v/pozv9TyGlNh8cHhHXltcz9q4y+UD2+AGPIKXTEY/MhkxZBSiU3fIycPZighNazAMA2sD31/efonwzY9n2Ammz09pHCExp76Lnj0NnCTyghubiGIjPnsbc8x4UoGG6O/6tZk2dgBvvYi91wEEhIBoHSIcIWXaJDEJjDkWPn073fChd+oH3Vp3BhFCUN/ghLCkzqFvm5bnPaQ0zPfrmurmQqbkkK7ZPDRnfbqjtldBgAM6RciyJUvrHK7Yow1DS4N43vbVLO5oCDQsW9jyg6U/I3YfmbHJlRKz36BMbbTsXLRlEwuEiVx+J6Jyla9V+fJTfuTx7CuoNwOIdp2x0NjfZbp/6PdeQZx0Ifq2S5uiJvsejg6GEVoRdJLoyX/064cBlv+MeupuxOW3oyM5xAKhTXZA7xAUFjdN7CsWQfddMpuHpEQUle6QyTUhA3yyIkGNoxjWPoItTd8rXW0+2hNwHV9YfsYnQIO25eV3EOzZl7gwSFo+qbbR2HvuC682s12MZEFBcWuHbYFUIIwBiJEHI8ad7BP9/DZ4wcxFoet56CGjEXNm+MLLhgljx6M2slDbWhiGwRuLWnogv/xzPQe0z2dtIJcZNQHmr0pxSNcIBcLD1lsfHEhgcH8zoghw0SdrGDm+E1lb0H+c5TRYXC6Yg7n7CBh5EEJrxK1PoX/4Bv3sQ/6YdsIkUlaAQH01euG8jGPoKU8ib3nCv24rFiNGjEXvOhiPLREW3ThqlMkBL/+cdn75cHmMiR9U8OioYsJ6+4wg/n/D72TxfwSNBfmJrFzsi25GSImrPJJmIG11lzAsImdciug/CGZPh92HofvvuUM1EltD0EtyXr88/tmsdtCUcGS3bOqMJBSGSQiLSz5ag9awV7swlXGXd5ZEuWhAIctqUyyrd7hnRFvK8yzeWRJlUNsQVwxqQ4F0NhocFUJQkKpn75XfMXz9Cth9OCkjTsqTRCO5RCZeib7zL+jvZoIQGPscQnjCORt1stgc7GQMmhvMg68DuGopomOP1iN5ngc/zvFlEkyzVSs4ituhmlnKiVC45fCYnYt0UuhLT4S6GrSUWCdfCMMO9OWSbnnCdyexbcgt9MWjWzkfpRTZtqRTjsWSWocPl8e4Za9irp++lqij2a9jhON3ymXEi4sRwKT+BeTakskzK/nnAe3T+pClEZNv1ibIDxi8dkgHTOlHejdhLvJfgac0jD7MT1E2x7ADWkR/lGUhzr4CPnrNTwWXdkCcf33ayi4ajBAubg/VVXDIBOi2S7qTVWuNZxgweGRTcw343fenX4K4/2VfLL6sKzgp9AVHg2EgrvZF4TMwfw46kku9FdiiqIqUEiHENkezElaA0A1/Qz9wA3rqO8hL70BVLPejqMEwnHEpqR0g32EYBu8sTZBlS5ZXpej79M+kPM3Yzlk8NLqUHL3pJi+RiKWJIuBrWz7zAObld0Kgya4yhcAad5K/UPjsXWjbHnHW5cTtYKvvxIbwPA+nc3fsdWt8aZqyLtCjj9+0ssH5RQ2L1LHnUHDqH/06aNPeYeOt53mMLAtzx9eZJSD7dwwT0wbHvLWST1f4z86ln63l7XEdGFK49fJFjtItxhufXG2+zj3kJODmi9JZHLHH3r7U0guPoV0Heu3qd2krDyeU5UsAtVaXZ1q40sA45iz45gtfeuqN54icdw2xghK2tcqhtplFYCM+WBYlpSU7xuD0/x/8ThZ/47C0IuAkfVJS0h719n988jFkFMauQ1sQlKhhYQ49ADlkXzy543XPNkTYSyH/9Qg9C9vx7r77cuM8h4AhuGFIEQVmU47K1YIBxUGuGlzER8tjlGWZtAmZ1CY9Di3PoVdBgI+X1zOhZw7n9i0g2wLDieNugnyEnSTcciF6wff+hifvwr7hEdwO3ZBCoD99q0k4WWt4/xXk8AORXXttW0eckNCaT7DrYApa7dZzgyGMkWN9IWDXRf/wjZ9ueesFf4dQBHHGZaTsYHoi8sJZyEHNSIdhwsSrEPW1iEtvB89F/edpeOJO7CGjSEozHd1KPw+bmBSzpMfLB3fgjPdWct30tdy0ZxHfnVBOytO4Cgyh+fq4rtiGoDaluHLaGl79uZ6dCgK+gPrqOPPWp1AantivHQurk5zWOx/hbbv0z9ZCiJZNQa1BKYXbtiPmRTfBi4/59++4iTi5hRnWfREnAf95Gj1vNgwYinz4NVx8GZ3Gd0gpRb0ZQPbeI/13c/LgeR663xDEQePh7Rd9iYxjz8IJZ5MwLKzBownM+gxuu7TpBBPRlum6nfvhbqEnrGeFWJvSeApybBtDuQQ2Umu2MbgIoiUdCfz5biSapB3E+Mvdvui1NEhaQZwd4FFb6xl8W1nH0T3yOPy15entry2u57Hv1nNOv3wsJ77x+5pqJRIUrfW16TbcbNjYR52BecgJKGmQsjftXb3h85SUFqniMszx5/g2cq67UXmbJZVV2JtppBBCEMfC0YKAVAS2IP2ulGJgcYDTdsnj8bnVaA3H9MhmVFmYVTGVJorgD7OXTV3LqweVEtmCQhApZfp6hKSfxZm5uikrcHrvPIJi8zUG0kk1lftEshDddkFdeWbTDnO/Rr35AskjT0uPj14ghOw3yLe+bMRxE8EO+E1FM5sWBPrqPxC6/dlttrzLtiW2IUg1Y5v9i4PIX22k+r+D38nibxiGlAQX/+gX2Z97Neqpu2H+t/6HMz9FHHoi9uGntOh0TnfO/sJEUUqJ/Gk+vP0C2cDI8ncYsNdYZJ/dsbJ1RoF3nm2wX6cshr+wOE0Ah5SG+OcB7SmNWJz9wSqO6pFLliX9epLN6CQIIRBVa6CRKIJfiPX0vdh/nOzXcTZvSGjEj98hu/XeJrLoBsNYR52Buv7cJjJW1tWXUXGdtL9sczhKY486zJfK+PA19LdfIs68DHHI8X6tWHE74lYg43xihk34zMuR407yC9j77I5cX4m6saETPjsXee7VvshvtB5sf428pakvy0vRPWLxwpgyPGBxTYqDXl7K3KoUV+1RyB/7ZRNVJge/upyv1vgTyO4lQcZ0zuLCj1enh9ksS9KnMMCgtiFypYPeziYHy7JY6xhY0m/M2LBBQAiBYwaocaCi3qN9lklYKmxv0+mkhGFhDhiGtfMAEJBq6EhsRMhJwq1/8mtrAX6cg1r+M+q0P6X3s4TASsbQUpK0M6WmgsrFcpLoaB1k5+EefRbmuJMASNrB9CQpUkn46LWMc1OvPI285FbUfdf4jTVdeiLOvYak1XpkuDnqZYifqxyW1TusjXuEDMG+nbIIG2qrF4lKqaauU43vVNNKacX2wBSas/oW8Pqi+haffboyzkm98siRFtJr3aND5RZAUWlGilwccDSJYKRVIpfSoqmxbyPve8A0sRNR9IrFkFeIl5WTlr3RWqclcrYHUkoqXIuzP1jNrDVxRneMcMewYnLE5lPvIZXkpkF5XLWH3+UcEIqgSpDwWjbqRB3lPzOb4PVxYVOVgkW1Dn3aBIngkkWKV8aW8cC365lWkWBceRZHlmdhbokMlmH49YlKQZtS9NJWSj7mzcJIHZ92wopbAcLnX4+Y9w0s+gExZBROfpHvErNhU0zNekQiDttIFoNC8cjoUs5+fxUxV9Mh2+Rvo0opMNxfo5z//xR+J4u/YQSSMfQDN4DrItp28AVKm+PN57EPnkBqG6UFthdSSl9DqxEL55G7cB5ixEEkzrg8Y98aD66atjYjUjhtVZw1cZdHv1vP0T1yOKZHFgEvueU1WhsKmwIk4git8QwThozOTAkCst8ghJuCLRA6FULgCRMF2Hg4jkOwrDPyuofRX34MhcWIHn1QtdW4ZeUbJedRw8La/yjMvQ9CGyapBnIosvL8yaKVnxszbUS7Loj2XQklY6i//qmpE76uBnX/9cg/3ozKytns72gNruMQFIJbZ1Vz1+z1aKBPmwBn9M5DuwkiwuXVg9tRldQIIci3fN/av40u5ekfaigKGUzqX8i0VTGO7x7e7kaqqBHilQX1PDRnPQVBg8l7FVOeZWM2EIe4CLCoXvFFRT27Fgd5b2mUB76p4q1xnegSCWCrTRNGt8HjFWhxn6STaiKKjfj8PcyTLwJpEvEcxLsvwfv/gd67Ez7sBJK5hTgIX3bmnZfQ/3rYn6izczFveJRYQYlPKJvdW2WY0KMPzGzWQTv9Q/TY8b7UixAowyRhh9KlJRuDEAJXw9tLo7y4oJayLIu/7NGGxbUpcgoszF+tzWjLETE06xzNsHYtE4DD24dJuoqYZZC1kf+PBcJEbnoM/e8nYPkixIixeAP23Kz7SWswhCDsJBBrlqGu+gPE/SY5Y+8xhE68IC3vsr3Qhkm1Mtl/ytK0aPULC+qoTSmeHFVMcAvM6wLaIUADaW1co4ZlCyHsP+0coFjFiW6kNjshLG6b7XdWg28h+c64jgzMl0S8OJf0yyLRJ5uwVKgtrD12TBvrkAnw8j9g9QpElx4th7Nd9/RdSNJJD03UsJF9ByH7D0mbR4SVh+xYntnAadnowLZbFwZVkv3Lgsw9sZyoo8i2JNnS2SGd6lsDIQQBz0G4Lk4ojOv9xup1tgC/XVGf34FEQGWDLV5raaBQiJbVJr8elFIwYGiL7XrgcNQGK2ZP06J2BHznh3HdsjiqWxa2E9/sJJn+Dq2hbQe/OL85xp2UdjBRffdAHHoC2EHIykGcOAk9dxbBmqq0KO5Gjy8NVrs2F36+npM+qGRmtSApLGoDOSQNG22a6EU/4C2aj+ree7ORHEdp4naIhGGlo1Kbiyo0dp4KpTL1KgFq10ObtsS3wwM2pFNcNiCXRaeUs+CkrkzZt4AckunvDqskZVaK9maSsE4SwCMgBQd2yqJLjs2tMys5rDwb3K1xa20J27Z5d2mMiR9W8G1lko+Wxxj+whJqPT/lnxAW182oYs/nF3PRJ6sZ+eIS2kVMRpRFuHzqGqJqO1OkhuHXkzZHJNsPsBkSMfVtmPou8qKbEF17ImZ8QigZwxRgOUl47qGmKGBdDTx8k+8vvQFcpRCjDvMJYwPE8AMhFsUzLOoDEWJmYIui3qYd5Km5Ndz4ZSXz16d4f1mUQ19dRn7QRP0GhnUh/PelDhstTRLCYkFM8vnKOEprbt2rmLApkAKO6JbNCTvncvzbK3h+QR1iI2RHKUWdHSY1fiLuhTcRG7RPq3JSW4JQKg6fvY16+r40UQTg4zcwoi2bSrYW0jCpESEWxSRJ5QuXN8c7S6I423Gf2rh1fDY6whX9shhXns0b+2RzaO13vld06ydEXJvcMaup/tFRMPHDCmoa3jM8l6B2UFsRlU5KE+/gE+D2Z+HMy9CFJYhzr/alpaSEIaPggKNaLdFRSuG6Ten4RCDs65pmN+jDmhacfYUfZd8OBFWSPB2nzEqRo+OIX1kP1BSQVb0W876rMW6cRPCj1wirTUespZQE3RQhN4nRWunTfwG/RxZ/w3BME2uv/fz05dxZiBEH+YW/jThhkt9B+wt3SwohsD0X03NQCJINji9KKVSHrsjDT/aFwLXyOz17DcggT9Iw+XRZjPN3LeCUd5oGs7Zhk10KA+SKFHobumhjdojwLU/CG//yXVH2PxK3c/f0d6cCIUI790fuNhQcF/3JG75I9ppVmBPOxdnI6lIIQY22GfDsz9Q7/sT9+qJ63j+8I//+qY7pq7P5Y69x5FoQC+Uw1IoQ8uKtHmtHQBkGckPJpKJSvEBou+NH6aiFhAXLFtJmE7VXQZ3iwA4WcR3CVZqghEgzi8JGlw0JeEKS3EydWCOqHMETc6sztiU9zYfLohzTxSbhGjw0J/Pza6ev5dHR7Zj0UQVJD7bHtMKxbKyjzoBnH2zaeMofSdlBrEQcPn4TOfFK3xGoYfGmX3iU0J3PoZsTjUasXIqhWr8z9VaArEv/iojWg+OgqytRHbszT2eRSkBpyCCLzacnq1OKZ3+sydhWl1Isq3PokSXY/uTptkNIyWrX4qJP1jC3KsmFuxawX6cspq2K8t7SKMuiLkd2y+bYnjlEHU3IFMyoiHH+rm24fOpqjujWCYlBSCrMVibVlMbXrdvGMhvDMGDmF76zU+XqljvUVCHyira9o1kazKkVHPn6YlZFXbrmWjy+bzsmfVzBN2v9xVjnHMtvwGmArRW2m0IDqWalCxuDa1iUvPUYV9bX4RSUEPxwGqxYDPe/3GJfIQQVjklF3GkRWlgZdRv8iJv23drfHTdtRFE7RHF7lFJYe4wk0H8IoHENi6RhbVFjkVKKWGEpoTuf85uZgiFSZmCHCVVv7e+SUhJIxhHa9xFvfk9MKfzPEKQse5OmF8FUAv2nE5vqkh+9FSkE5t4HtRphtJVHYOkCX/vU8wgdeRpu997bLI20o/A7WfwNIykMrBMm+S4tH76OOO1i5OjDUAt/gH6DcHPyW+ju/RKIOEl48k748iOM9p2JTLyKeFEprvbr6+xDT8QecwwgQEi052FZdvrcXAQvLazjmB45TBlbxqPfV9M52+Ki3QppY7qkUtu20lNKUW+HsA4/FaE8XGm0ICd69Qr043dk/mN27ibjsZ4ZZEmVw9WDi5jyUy2fr/KJ4O2zquhX5BeCj2+YYwqCCaYe3ZmIJbepDnJLkLBDRP50K/q2y/wO1Q5dERdP3uLuzh0JS7lYuDQqWqSJooDw6mXo2y+HlUswevYlcuGN1IdzNjtIBw1J55yWEaJOOX4U1m2tWzOpCJuSU3fJo23YwHC3vZkrKQzEvodjDt4HFi9AdOtFKpSFo0GYFsZue6Hnz2mK8gMkYuhX/4kYfzZc6Msh6Q9f9S0BB++DY7ce8dVaU2cGMQsimKkE8eKO7PPqKr5e42vp9S8K8vq6UtDhAAAgAElEQVQh7YnoTS+eDBQds620lFEjOudYOK2VZ/yKqNM2e7+4hFVRt+GcbP42Z306/fnvn+p4f2mUB/ZpiyFg0oer+c+iep45sD19CgNUJhSegpSCjtlh8kSKqJJ4SECTI5ztf9eEhDlfIPYcjX7h0abt4Swo7bhVxMIwDLQ0cREE8IgKiyNeX0xF1H8ef65x+MMHq7hijyLO/7iCkWVh/rx7of87tO/4JF54BN6dgggECZwwCWOPEZt0m0khsI88DePJuzDeeBbatofrHibRLArnmQGSDS5Ny+qSeIi0CkIjTtgpl7BQ2J5HwEmi16xAFLUjZQd8D+0thNZNotKOBqd5xmMrrqUHfjPLdmRMdgRMNKFVS9CP3ALr1hDY5xDsg44lKi0CysX+/mv0o7dCfR32vuOwjjg1w1ihEUIIPz0/9lh/bpz2vq/5+t4UrEEjcTf4nUIIAnXr0Ved2XTdbroAc/KTiB3oCrQtMC677LJr/mvf/js2i5Q00L12RQzdD7egmER+MW7XnUkFI7jbuOISQhAVQeq0iSMtLEP65vStIKAVxj/ugo/f8OvS1lfC1Lcx9zucVEO6yMP32jV+ngd3XYl4+wVMy8bs3A3PMJFoCiIBDn9tBT/XpDikazag6V0YILwDYiBKa7xWLPOUUtjtO/kahI2rupx8xNl/IbERmz9l2Kx3JXWOYqd8m306RCjPtXl/WZQhpUEMIZhe0RRFDBiCs/vmExa/XGpDa40bDCP3HI08eAJqxMHEs/NapPq3F1VVVRQWbrldmBB+zV7Ac7BdB/2X05sI1brV8NNcjMH74G5CSiQugzwwp5rjdsrj5YV1xBr8ZkeUhTmjdx628huHPl4ZZ2W06Rqf3juf4e1DtI2Y3D27impX0ikvhL1BrLXRB3pzcKWBE8rCbdfJT3tZVkPNqiDQtQf89D3MmZF57BPPh6+noac8CSsXI48+E/ruAfuOI76ZiVZpDXaAh+bF+McPTWnPiphL3zZBdsk3M57nDe+NLWFI+2ye/qE2LWd0/E45jOsSxtzIu7wxCCGwLAtHmsSVJGhsfXSpOVY7klu/akp3XjqwDRM/XIXTjN8trXM4r38BB/9nGV9U+OS2bcTkij3a8NJPdZzw9kr+/n01T86r4bAe+Xy2MsGY/yznlUX1jOycS74ttnmhpLXGLm4LzzyA3P8I3z2kZj2U74y4ZDKJSO4WKUBJKUnKADK3iNeXxqlKgTYs6hzNab3z6JlnM7sywcSdwhzdXrJfl1yO6JHHmriHq6B9bpCIAea09/xSBqXAScHMT5EjxuKENy0gnpIGot9gjDFHo/Y+iERBCZ7WGIZBvQyyNqm5/stKbv2qCk/D/p0iHNwli+qkQgo4s08+Fw8o8EtM5s2CP58K778CbzyH0bEc3a4j6he2iW28joZhblNUc1PY2vGsOcKpBPxxvC9HFo/B3K8RWbno7rsQqK9F/+U0f7vnwoLvEEWlqK49fZF9z7fQNJSHNEyseL0vdF+1FnnU6ZCV4wvjDxrZYg63LAvzrRdh7qzME1IKvdteqF8hOLQx/B5Z/B+AowXOZrr6tga1IshRb6zgi1VxgobgpqHFHNc9QrCVJgHTSUJzbTOAaL1fm1XQtCqyo7Xomy7wB/DCYiq7D2DOOqhyPfYsDTGwSPPgyLbcNmsdj8+tZvLQYgpMtdnBwTQkdjKGVBo3ECLZCincFGKBMOHbnobvvwJPQZ+BxOyQHxhrxRt4eVIw8sVFrI37crB/GljIUd1zeGFBgD/vXsTV09Zk/M8Fu/oyP9r9ZV/ijG5Vf8Mv+n2bgxCCSDIGD98M336JuO7hTCcQgB++wWjmKCSEIIZFUksMAVmm5onva7l2+jreWhJjytgOrIg6dMu1KQkb5DSkuCMN3ZoPzlnPFxUJxpVnc1h5Nm8squOM931y+iDVDG8f5rn92xImiRCCsJNErF7upyvbdSS2QRfzhtBao4WkVgR5f0mMbFsyuG2IBUmDfsMORLz4GCQbonZ994C1Fei/35b+fzX5YsT9U1rYCG70+xD8sD7F6b3zOLJ7DkrDv36sYf76FLJLYJORUqUU7QMec0/owk/VDkVhgwJLtPoObwpJYbHeFcSTgltmVLKo1uGUXrmM6xLe6mM1ItuSGVLKa+MuQUMSdfzfc0qvPMb3zCFsCibslMsds9YRdTQHdM4iaEqun16Z/t+qhMfEDyq4dGAhrx/agbH/Wcb+U5Yxe3xn8m0DwzCIuwpDuVsVWY7ZIcKTn0J9/i6irDPiirvx7CAJa8tqRj3DZnlCcNnU1Sypczhxp1xGdwyS9DQ3zahk1poE+3eKMOPoThT+626sebPQuw1j7dAjuGd2DTVJRb+iAO8dWoZUYA4ZhZjxiW8hCTBnBnKfQzf7m5LSaNJ19Dw8w6YekxkVca7/0j8PgO/XJVlcm2JM52y65Frs0yHMYeVZBN04AScBD9zQZOujFPztFgL37t4072wGhmFQ4xlIAdnS2+h5e9IiqgRBqQlol6QRYEVUMbfKL70YVRZOi2UbhuHrlzYcSwjRYtsvAdM00evrEAOHUz36SNwuvXC0pnDB17415g+tqGxMex9z6P6EnCT6get8a8POPbAmXYd6/A6Y9zUAatZU5HUPQ0EJWkgiqQRKirTCgummIK8lwRWlHdC/AnHfFH4ni/+fQZlBrptWyRcNqdWEp7nok9WM6VxO21YCQEpKjPZdMrtFpURkNa16DcNAz5qaXumv/eOd7D8rwLcNUaYcW/LV+M5M6BZgbOcyYtEo7XIEnrfpqGLYczC++Bj9+rOQlYN9zFlYpR2JBSN4W0gYG7XxjAHDAN//N1S91o80FpQQa6i/TBhBEkqwsDpBeZ7N2ngcDUyeuY5jeuTy4tgOGMC9e5ewX6cspq6Mc2SPbPoWBgj+ivqC/y0ElOd7j0frICsHJQ2492q/Gz6SjY5EEKMORX/xvr+YAN+KstkAVy+CnPzuKmaujnNk9xz+OKCAd5f4NX/TK+IMe2Ex7bNM/rZPW3qE3XTkVGtNtkhyXr98Tt9FEzQFnlJcM70y4xw/WREj6kFY0uAHfbbvwwxQVOo77WwivSWEYK1nMeCZRelmrPJci2fHlPHQcovjJj9L9pfvYYQjsPtw9P3XZR7Ac9HffYUxeNQWTWZaeVy+exue/7GWv870idrJvfI4ukcOlS6Y0iDuamxDkN+2PdoM+E03qiENqzyy8Ng1V6D11vvuGYbBzLUuWZbBEa8vp7LBbvPLijixYcWc3jOE3oZmgKDwuGxgITfP9FPr/5hbzfVDipj4YQW37lWMo+CI15cTdxXH9sjl5bEd+PfCOtoEDZbUtqyrm78+ScLTvDS3mjN65zG3KkVMCe6bVYerNBN2ysVTkjYhmyxDY6lURlq0NSilqLeCmKPGATR1x24BURRCUKcMhr7wMzVJf/9LPluDq2FtzOGpeX4t6XfrkiytTfFwQTusRc8hFv1I+eqVXDHoD1wys55Te+Xx1TqHxxnMLoN357SjJ1Fy63l+/XX31h2YNnlOIsjstUk65wgKQ2aaKDbilYX1XDu4mNcX1XHiTjkElV/CIJSCusyaYBKxTE/ITSApA3ywIsktM1cTMCT3jyihR44JbipDy7ReBLlxxjreXFzP0NIQtwwr4d6vq3hlYR29CgNcvFshN8xYx1W75VFYs8rPCJV2QPQfjNIaGatHT33XjwT3GUjUDOzQSKSfcQswbWWCglA7up19I9UpxbWfr6XOUZzTbwAD7ABtu+zU8p+774IhQT91d5NF5KL5qOvOQU66FnXtOf42rf3fMOFcrCduh2kfYJR2IDLxSuJF7RDJBKLbzuiO5U3+8MXtEHuP+dU7uDfE72Tx/zBaC+vXuvBFRctmjO/WJWhf2rLuK2mHiEy8An3lmX4HrjTg5AtJNZOW0FpDh3IAolc9RKxdN54v1dQkPa75Yi1vLo5y44x13DEkn2xSVKxcTElk440UUkoC2sOY+xX6wRvS29X15yJv+juhArnVIq2e5xFxU4j7r4FZDXZubUow7nyRejPMDdMr+ef8GsqyLG7as5hnf6zhuQZHmvUJl4e+reXNJfVMO6oTR3e2GF8e9G0IvfgvShQb3TmUUhkDryMtospn9wIIS5cAvutCjTIwpf8/WcLd7vouW3tYX34AD9/iTyDhLMxrH0LF6mHQCOS4U9BvvQiRLOTVD6Cef8T3w77wprQVpZImt8+qImAIXj+sI0/Nq+GOWVXcslcJt89ax79+9K/1inqXsmwLpZpq8YQQVCmbg15exvfrkgQNwe3DSzi7Tz5XTMu0RdQ0NDB8OdV/XnvtCquW+dp8H7yKedD4jQ66Wpr8dca6jK79hTUOc9clqfUEq7Pb8k6/o/h8ZYwj4mF6H3MueXNOykyHlnZo9XpLKfGEgUQ3EbAGMjikNES7LJMuORYz1yRZXOtQEjY55Z2VzF6b4KIB+Zy0cz5//qiSlNJcNrCQLlkmgYZrtK0TZp0yuOvrSv7QtyBNFBvx8HfVHN0twhY4KbdAUDuc3zeHU3bJZWW9S3muhRQwuLQLcVcz9PnF6X2f/qGG3UuC/Gm3Avr/cxFvjutIbkCmSRjA2C7ZTF0Z45WFdTy1f3uO6ZFL/3822Ybe/00VU4/uzH5TlrJrUZC7hpcQtgWGAJ1KbJK4b8sEbFkW365KZpwjwFPzqrlg18yo0EsL67nrwD3Jwa+bNqa+w9jDzuHDzr5A0AFTlqb3/ccim08vu4+CL9/FK9k6y8AoNuPfWsGnK+J8PaEL4JfIJJtpn7YJGbSx4YlRRUjPSUuUeYaJ0Xs3aG5VWd7L16rdDKRpMmedy/g3V3DaLnlctnsh01bFeWZ+gqO6Z9M1SxLUKRKYTPp4NS/95Nu5juuWzdXT1vLY9z5J/WF9ipmrEzw0qpS4q+H8o9LvlR52AMY+h6CuO6fpXevSk/AV9xLdgVa2MSzu+7aaEWURLvt0DXePLGHEC0tINFzDt5dEefWQDgwp6kDegUfDWy+A1jhjJ1A57myWRz2KTr0G76QryalYRNFLD/i6yFm50L2336DopNAFRfDZW74UF/ge2VecQfC+f5OyA8gvPkCefKFfkuB5fo29ae3wNP3W4ney+H8AQgiCThLDc0BIlGlhpBLoiuWIth0yipWDpmB4+zBzKjNTTH3btO7rq5Qill9E6I5nEfEoOhjGMa2M4melFLpjN6pvfY53VFvOefZnqpOKkWVhHhndjhu+XEtFzMMFNvdqW1IQrFsP879FbyBgjOui534N/YcgCkJb9eIIIRArlzQRxYIiuPoB9E9zebCuPQ9860cDapJJjnpjOR8f2YkXfqwlbEm65No8/UMNMVcTdTVZxsbTLJuCKSV2KoFhGuhUCs8wSW4i7RUTAVbHNWtiLr0Kg2g0GoECKmpdJry9hEU1Dvt1ivC3UaVMq0ySH5Ssi6d4d2mUk3vlETRMygKKhJa4SCRbTyBtNwWPTG6KNMTqUfdejThuIiKvDeqqM9PuGvrtl5B3P+/X5KxcRjiSTcIOUa0E0yri3Ll3W0a+uNifEIAn5tXw0ZGdeHdplKqExx/65FEUICNKlhIml3y6hu/X+d+R8DSTZ1by4ZGdGFYWJupoHvq2ivVJjyyj4V4XtUVcdBN6wXeIw3ui11agf5qX0YG6IRR+2nNDrKh36ZZnc+lU38kG4N5v1nPr0BLOPfQk7Jef8HfcbS9025aTfEpYLKrX3DW7irZhk0n98ykQ/gS6oj5JxJJUJxWOgt2Kg6yqd1hUk2L22gTZtuSM3gX0fXphuubvlYV1fHVcF7qFtr2pSkmTOlcQczX5gZYphZKwibkdtrxBnSIooSRXoHUCNPTICvC3H1p2j7+xuJ6qhEdNSvHW4nrePKwjF3y8mgXrkxxSns0pu+Rx0CtL6ZlvEzAEr/5cmyaKADFX8+S8Goa3D3NOv3yqUh6Pz6ujJGwxoixM0ABTKCz8xhkbb7sWUEopikMtr1lZltXi+SkKGYhYk989uQXUpDSHd8vm9lnrMvZdUJ1ihc4lq3MPVtW7FOSHMDYjsSIMExdJVYK0o8vHy2Pk2JK/7NGGqxoWUwK4bVgJ1toV5KxdgrNTv3R3bcIKELngRvjnfX65Tu89EMefQ9IObbbjPI7FI9+t4eahxezTIcKZ763iw+V+ffidX1fxt1GlHNvVxlEGUxY2XYdRHSIc+2amHNjSOoeIKRDx+owFmOjRG/Xsg5mLskXzEVVrEFtJqjeFhJIcVp7D6H8voW+bIB8ui6WJYiMe+76aXUeUoMafTeKYiQg0K70gX1ck+akmxTVf+FrCXXICvHP+nYSEYl6Ny/IJtzGyUFP0xlOERh6MuvzkDS5kFNZXkipqj33wBNTfJsOCOVBU6pszfPwGkeEHUr+DhfK3Br+Txf8DiDhJuPtK+HY62AGMo87wdeOevAstBNYFN6D6D8UB6pKKiwcU8nONw1uL68kLSCbvVULQEBudGDy9+Q61qGlTW9KF4x//KX2YD5fHuOHLtVw6sA1La1ME8TZZOB7xUsgFP/irsaH7IebOanlKbUoQ9qbTD1JKopgIJJ4GpRW5Jn6kaY8RsGopcsI5qLuuoG7MSby0IjMakPI0P1WnOLBzFlcOKuL2r9YRczWmhJC58eu0KYQ9B+PLTxFFpegfvvH13PILiZx6MfGi9nhCYCkPqTwcO0hUGSyJKqZXxHGUJuHBHz5Yxaqoy74dI9w0tIjjeuYwpDTM6pjH/d+uJ2gIrpteydE9chhRFub4t1YweVgJ+UUBVsU9alMuOQFJvm1TZKS2eMIUiQR6wwjMiiWI8p1JfT2ddVc+hheOEFoyn/xn7kS9MwVWLoEvPoBgiNBfn8bJb8upvXKZ8lNtmig2Xutn59cy+7jOuMr3lw7qzA7fhBLMaGZFVhQy+NeYMk54ayWfr4rTJmTw4D6lDG0bIKKTSFfD0oWov98OgM7ORVx+J3LXwQTrajBtm0QrKSxTe/xxQAEvLGia1EKmYFTHCFrrNFFsxA0zqjj+6JNp23sAFLfDjeQQ2yDSIaXkhxrF8BebIkhPzqvhm+M6Y2r4sTrFBR83ybdcslshE/vmpyf5E3bK5bn5NRnNIRq4/5v1TB5aDIbGlmBql1TKv24JYZPQfuF8ULS8ngD12uScDyo4o3c+S+ocDu6alf59AUNwx7BiIsLdLlWuxvqydEQcxR5tWwpy71YcYs66JL0LA5y0cx5nfbCSyUOLCVuS53+s5aBX/Gv34D6ldMsxmboy1uIYAjirTx71jmbEs4vS4v89823+vm87yrJMnv6pniGlYXJtiyxLkidTICSGlHjulndYu65Lu6wQ48qz0wQoYglu2LM449ykgPtHtKXwhWsaNkjcMy9jatRmYFubgNGyQdF2UwRvv5S2BxyDO+5U/7c1WAW6QmJoRaShma5GB7h/dhVzq1KcuHMej4wu5cz3VnH51DV8M6ErdSnFIV2zWVSTomuezaPfVTO8zKBg8h+x73wOo7CEGNIXybZDWCddRLUMEVeSpXUOnU2LgAlZKrnRa5PyNL0KAuxaHMTTOk0UG3Ht9LXs36kzntaM7himPqWZsTpOZdyjLMvM6OYXQG7AIDJ/XuaXGGZTLWdz7EC9RNcIIJRACF8PuM7xWmhjgq+XaUjBlzW+zqxf8x+iV2Egg/wuqnX402drOKtPHiFT0rVNFt85iq5HnEe5lcQoKcuUbRICkePrS0atIFln/glRsRxdvQ716jM+iV++CPvE80n9l3RUfyeL/+OwhIA3nvOJIkAqif7nfcjrHkJHsv0as7/dTOCuF3DsEK6Giz+u4PbhJdy1dwlSwKIaB6k3PVBKwyCqJEEpEK3YcQkhWFCVyuBRuQFJ3NW4SjOgjYXahC1b2E0hHrgW/VWD3dMb/0Le+hR65qd+BzbAwL2IlvelPpKPQpPbSiG1YZqs8ywW1qTIsaEy7nLF52s4vFsOx/YezhOpnuxVKBiVFYOF8witWcou+T2Zu0FvRv+iIIPbhvh/7J13eBVV/sY/Z9rt96YnJIEUqoAgRXpRARVQsSGCvbvrqmsvq651XXvZta3uWtaOvWBvWBAUEKQaaggBQki7vcyc3x8TLgkBRUVcf+Z9Hv5g7mTuzNyZc97zLe97/Dvrmb0xhgBuGZ7HT1E2VFUV5ZvPkV/NhG69kc8+aH+woRJ55am47nvFlmJ55gEINqIdMo1Ej315cmkT9y+s54sppYx+YW3a3/Tz6giqEKwPpTj2rfV08uncOiKPgKFw/exanv+uiXP7ZrGyMUGJV2NVU4oJr1YSTFgYquCf+xUwsdSNh12TWJFOF2TmbPsdAPYdRdxw826nEZz2yRbyXCan9RrIcXe+Qs6cd2D9anu/WBT59P04z7qSIzr7uG9hQ5vju1Vh22/J1A6JuFuRHNTJQ4ZT5dByHz5DYV0wyYpG+zmsjZpMfauKVSd3tjtdk1Hk09s0E5VrH8B6/G5ks9uQ2m8onnOva9OIYlkW5R6VzyaXcNu8OjIMhdP3zuTG2Zu5clBO2/sCsGk91l1/QbnzOWIt/L23IobK379uXVtZFzP5rDrK0EI3L1U08eakjnh1BV0VvFhhp+M7eOyhuXNAx6ltmxx6ZTu4ZnAO3TIdRExJOGHyj4X17JPj5JAyL6qwU3qPLa7j2e+aOLqrj9uG5+KyYiiqStRScKmwYGOc99eFGV7kZmiBk4v6Z3Nx/2xqoin65jhxarZWo9q8vNMUBUc8goBmXbm2XZxISco0SaJSZ6q8sSJE5wydgXlOvM1NC0nT4vx9MvnngnpMCWM7eji7TyZJ08KhSDxKkgcP6MDTyxoZVezhj30zmdLNR6FHxUsSEglO7pnBHfPq0hqoHl0wpXsAtya4elZNK5eo5fUJ1gaTpCxJt0wH41+ppCFucc3gHA4r93HfgjqynSpn9ckkoIOR2rV3IkCce/bL54p9c9gcTdEjy8E5H21kZKGbT44uoaIhQa9sB/kuFfWk80nufxiRjl1YYbo4MS9A5jczublvDya8v63eeUi+g7z6KohFcM14BuuQY4kaThpxcMe8Oo7qGkBFku9xoCuCZ5c38s8FDYSSFq+tCnHbyDwO6+zj1ZVBvmtIcPUXNQQcKnUxk2X1CQKGwuXZNsFKfPkJnww4kl4FPnxKAmlZNKluXlwZ4ryPbUtPXYGXD+3I4DwHurVzHdkp3fw8uayR8aVtvXdsCST7Ck/tmUllMMmdowt4bnkDd48u4NDX1qXHtYsHZJHjVMgwWjdyyGULEIefhLyzhStYfhHkdmiz4NvaBPNjIsdS1ZizOcnxb69nxhElKAIWbI7TLdNgrywjTWgDDoWL+mcTSpiMfH4NEpjU2ccbq0MUedum7L/ZHCPLqbLfC2vTVoPTJxQTdDrJPOsK5FVnNJd2KTDtHBLNUUMpJbKu1laXaIm5n6Ede/Y2C8s9jHay+BuHmoy1rjVphly7AvIKYfVyu+GgmQwGdKiPW/R8YiWZDoVwSvLEgR3wqzuvZ44qTuZvTlDgUVjTlKBnlpNMXeJo0S0ppaRLhpHugrxhaC6TOvtYVhcnmLTIMQSKohOxBP7s3LbXEY9sI4oA4SDWE/ei3PgwbKxCZuRQk1vKxZ/X8uKK1ZQFdB4e04GefhWlxYnXmTqjX1ib1hIbUejm+qF5THx1HRJYEITbFoXYOF7DC3jffZ5b/jqRWZsSVIVSCOwO50yHgpsEL0wooiqUIt+t4VFMdLnztFBLmRYpJYpipwn1eBTeeREx9ADkxzNa/1EiDquWIp99yP6tAL79itgjH/PI4gYyHAobI6n0gApwYs8MHlnUwKNL7NT54i1xjnqjivnHlafvf1PCxKkpuHWFyTMqGVXkZnCBi9WNSf7yxSYOLCnf6XVsj6jhwn39Q8j7b4DKFdBvOJxyIbWKi6PfXc8tI/IZXOCiMWEyb0uSvkPG4+09GI/TDbM+hKYGhGmip6Kc3DPAPd/UpdN1PkPh7D4ZpL5nklatJFcNzuHu+XXs94LtLT662M1Lh3TkgBdtEp2yYH04hd/dfAO2ag0O2g+5allrW8r5sxAL56AOHN1msWHIJH38Cv/eLxsUlfu+beSN1SEmlnk5qMTDO2u3pVEv6+0m8OlTiOHjkBULce09iPB2JEoF3HrbLkaPrqAJ+NuIfKa9tZ7KYBJNgVtG5KMKuLB/FiOLnOyb5yZuSf7+tYYiBP8a04FT36tmeX0Cv6HwyNgOXNw/i38uqOeFiiYeHFPAjNVBOvo1PjiqhENereTILj5GFrqYXhHkhRUhRhY6mdTZtom87svNdPLpjC52M62bnzyXxqGvVlIdMTllrwBX7JtFIBnGWP6NXYrQWIcxZhL6MWcSUXUUJO5oGPnqCxCP4ZxwDBVaJr2eXcvWR3ZIgYuXJnbAk0pQ4DbwGSqfHlNKhkNFAZKWhUux8GD7i+eIFBfu7Wnueo2S7QJapGMzRIJvjy/nsSUNSCmZ1iODiz7dyO0j8oml2pKEaMpiS9Tk3gV1NMQtumcaDCpwse8zq9NE7dEljXx4VAl5DgPXDiKx20NaFn6iZHpV8pMNIPKpDiX5yxc1eHRBgVujIW7xzbRSQr5MtL65aIpGLyRaPAgfvsqIQWFWTB3NgiYg1MgwR5jsW660v0DYkba40Lnis82c3SeLp5Y3cECxlxPfraY6nGJqdz+vHdaR8a9UEjclDy9q4L79C4gkLXxWnDuGZXPYjI2EkhYeXfDMSB/ZL94CQGNhZy6Z08i6YC3zppWRSYyoJbjk05r0PUlacMb7G/jk6BI66DYB0y0TNZUk6XCRsizcJFkfFwwrdLGmKcmwDq60Li3ApQOzEAgmvlrJ8nr7vro1wdfTyskxBCtOKmdJXYISv06mDk4rSqpLL7SbH4N3X0AUdET0G4pc/R3KZbcjZ32ALLoprooAACAASURBVCqFMZOIGK5WizOnmUSvr0XO+xS69KZ7QdEuSSAFLZ1jZtgGDK+sbOKvg3O5fvZmTnm3mscOLCSYtGiImYwocuNSJNfM2tatv3+xm/8ubeThsR0wVNFqnD6kzMsTSxvTcmDrgikumLmJ58YX4mgu7SIcRLg8JDSDeAvnIhHIbBZLb0GGS7valqG/EtrJ4m8cpuFE22dwG29bUdYduak5gtWzP2azYKgiJI8dWEg4aRFJWXg0QY4hsXbWmazqvL0mjN+pMfjZVel02DWDczinlxdHC/IU0C3+NbYD8ZTFyCIP/13aSLZLpTxgUJ8U3DWvns82RNi/yM1fcuxoQ3pluKPamBWLketWUV/UjSdrnXy7qo6nmptOltYlGPtSJRUnlRNQJEYyDorGTQvrWonOflYdYXM0g55ZDl5eEeS4HgFeWxWi0enH27knrFxCx3suZM5pV1Of0xG320nStGx7QqnjViV7B7DJzPek5KLCIGgqSGBhbYzOAQNDEXh0BbemkTr7ekzdiauoO5n3Xg4N2+qVREY2cjsnCdFYj1MTNCUsSnw6R3T2saQuzvL6BIMLXFz/ZevGjpgpWVIXJ9etogqBU1M4vLMPVQju2a8D82qivLs2TK9sB68d1ulHaTSaUhLOyMVx8a0olklKN0iqOmuakuxX5ObgEi+PLW3gxYomSvwG1wzOoUsgi5fHXcQBR55DfnAjSYcLaZpkiDgLppXy4oogKQmTu/oIiCTfN6oLIdgUSaVFncGuy3plZZAju/h4dnkTqoAij443GUaEgzDiIOTMtxCDRsOyhW0PumwByuAxqEgS27koWJaFhgVWkjN7+RlS4GLm+gj37V/AZ9URZlZFmdLFTX+lCWfgAOJ5HXmpKkl0jclBZW78IpGexDSZ4ppBOby2KpSeSDoHdAbkOZBCcP3szVQG7ec1ZcHFMzcxsdTL6qYkjy1p4q01Ec7qk8nrh3UkaUou/3xzetJtSlgc/041X08t47geAc54fwObIian9c5kS8xkYW2MSwbk8NG6CKGE5I8f2c/Yh+vC9Ml1MaWbn+e+a6IymOSDyjBXD8qhxxMr06nnexfUU+TV+HNn4OYL7InL6UZ03xtlUxWeeAylsATr4Vvga1teS74zndxbniXbpVITsd/pLzdG2RyTqE4Hbl1wwl4BdEVwyrvVfLLeTlse2MnDY+MKcDeLkX9f44kqJJ0iNVy95FnYvIFw9d48MOYYpKpw2b45fFS1LeWf51bpkemgg2db+vqILj4e+ra+1eu8JWbyaXWEw8u9P6rMxDRNYuEQnTMCzJjUkaPerGL2xhhbYibPHFyERyRBbr2eFCnAUnWCf7ieN1c28eTnDfTJMrhw70xy7roh7f8eO+xEXlovGV6iMqSDi9vn1nL5vjmMnL4mHTn9z+JGMgyVY7v7eXxJI15dIdNQeKbjBjL+9QCJrHyWH3U2wUA+vnAdmc/fizZ3Jubeg6ns0IOF8+3F5kPfNnB1fx+xmGxTo7cxnEIRAkVRcIeb7KxI5UrUkQchR00grEI3r0rY6+Dmr7bwt+F5fLkxytK6OFO7+Sn26iyojaWfWbBrTG+YvZk7RubhlzGGZgmkjKfHgJiqoxSXo552Gaq00MNBZCATWdIVq9cAEopGyjRbEUVdEWhzZiLvuyG9zTX8QMzTLvlecXOAcMpKR6lv+WoLFw/I5vMpZXg0gSkl7661I+QqkAiFOWefbBRV5aGF9dRETIp9OvcvrOeNwzpy9ocbqAwmObKLn4sH5tDziRWtvmt+Tcx2uLIkId2Jmu2hSeokLIFLkbixy4MSugP91IvhsbvsdHtOPuKMy4lrxh43YtiKdrL4G0fSkjgOOhrWVMCcj+3B/IRzbc07f4btzTn1D8R0B1FhcMUXW5ixNsSdo/Lpk+Mkw6mAhCAKGapFwgKnriCkJJFIEDQV+uS68OoKi07ojGVJXloZ5Na5Wzi9V8Y2g3tAT8WZ0sXD8kaTAc+sSg9qTx1cyD8W1KflepbWJahoTPLE2Dxc2IOI5fYiWsoFAOKQaVh79WPS2/X8oa+Pd9a2zhXHTEllMMUQ6pAP3kS0zzCWOw5oc4/s7lKVbJdKMGGiCFiDl+ifbidn0ef41lcgUkk+Xd3A4C65fFIV4bElDdwzuoCVjUmEsFeQfsVE2UEKPiF0Xl8bJcelccyMqvR1n7m3LYOyqDbO1bOCBBONHFCcw5PXPkre5cfa8hTDxiE9/jbSFRkr5vHXQaPIcGpUh+0Gi2O6+ZHY3dk9sx2sbmpN8Iu9OhNKPFwyMBfTsrhpWC6GgBmrg/xzQT1gy8t8uj7CjEkdv3dSFEKgGwamJbGa67miLevxLIuOPgcX9s/miaUN3Nosk7KyMcnXm6LMnVZGvlfnokVh7hjZC39z85SUFj5inN7dTqX8EAkHu+5vexkQgLmboowscuM3FB4c04EMMwwXTkXGYyhX3gkdy+2BddBoeP/l1n88aD9uX9BEsUdlYumONUYBXFacodkKw3O9WFaCozrpHNdRQTz0d6heS835t3L0p3GO6uKn1KPzyOJGTusZIMDW65UUOUyWnlDOKyuD5LpUxnT04CNOveVo02gmsYW5D399HQ3N3bbPftfE11PLiKUkcza1TgcmTMmmSIpsp8qU5ufj7TUhnlzWyB/6ZHFsdw8rGpJc9lnrxchZ71cz+9hyrtg3h1WNCTp6NT5ZH2lTozh9RZCTc1Wyt9Yd/uEvyE/ftstDsN9b5ap7sNZW2B3nqRS+t5/m6qFnM782zifro7g0BZ+h8NyKEJd9XsPgAheHlfvSRBHg3cowH62Pclix9oPNY45E1HYKqlgEgCcaxterL7i9ODKLmTm5hAcX1pPn1ji5ZwYOFaJJk4BDpbZZEFtX2kZ7DUX8aEcsXYA/ryMPLg+xuDHEvSNzyddM9FScTI89nm4PS6g8uCLG1bPsGtH3K8O8vibM2xf/g4zl80gimO0s5pSPt1Dsa+LNwzry5uoQy+oTbB84fXttmHP6ZvLfpY1cPSiH2vom9nn+PqhYhAHkz3qP/LxClKv/AcecSuiYs/isSePkj4KM6+RBVwSWlKSEiksTzDm2lH8vruehb20iOa7Eg0MFIxJCXnW6/RsDrFyCaKzHOOJkEpaJjyjXDMwgKlVK/DqbIykeW9LARQOyaIy3/T2bElY68ryj+nPLsrCAJBBzeVF67WunliU7DCwY8Sg8+c9W28Tn76KfeP4PkkWfLsh0KNTHLSRw29wtfLEhwrMHF5GRCnFm7wwUReG096p5rzKMSxNcMTCHvw3P474FdTw3oZhDXq1kaIGT5ycUE0pafFIVJpI0UUXrLrGxnTzozaVMiqqy2TS4eOYmvtwYZXihi9tH5pNrmJiWhhxxMMaQAxCJGJbDRdThRv6K+rrtZPH/AcKaA8dZf0E7/VIQgrjhBGmh3vAwpuEggYJQVFY2WcysjvDp5FJumL2ZW7/eQr9cJ9cOzcWpKsyvT7G0PkmeS6VPjgNddaEJQWMiwQfrwgzMd7G2KYkqBB8fVUrClAQ1Fz4S6Rx2MCm5vrkjbCs6+Yw0UdyK9yrDJKSCV1VB1YlKgefyO+G9l5HVaxFDDoC+g6kVTkwEa5rsGqCqUOuIQ6FbQV54OtTX4oqEOePEMby2atvnirA77x5f0sBD++fjW7eMkyflUSuTHD8ritcYSGHJED6fF2VDOMzSngX8+ZNNzJxcyqnvVbOiwSaHfkPhs2NKKXI50czW3r1RqaIrClfPan3d/13ayAX9shn74rZIx4dVEW7OzuCW25/BKVOkPD6ElIjOe8HK5sJulwdH7wEc7vXx8KJGTn3PrnkbW+Tg0SFuCnwpTi10cromeaoijCLgyn1zKHCr3DoiDzdJTNNEWglCwsmjzb7Le2UZ3DA0jzy3hgmEFReeFr/dVkRUF6uaUny6PsR+xW46eJy4RKpVFBns5omygMG5n7QmIk0JizVNSa77cjM3DsvnlZUhTunaWmT6x0iWWJbF0A5tGyMOLvVyZBcfp/bKIEtL4X7qn+nIjHXtOTBgBGLa2eAJIKb+AfnGM3Yh+WHHES0s54bna0lZcNeoPE7r5sL6noL5RlMhKTVUAQFFwT16PLz2JJ81qvxtmC3hs3hLnINKPARTEr/bhUjYz7wqTbKFyVk9XOnUqgU4VMmEUm9aPgTshhq/oabTpef3y6JbhoNNkRTRlMXoIjdvrA612t+tKSRMSalf56uNURQhsCRc8fkmZh9bxl5ZBpu3k8apjVkYKsSiFmd+sAFLwrPji9pcd98cBx538zThy0D4AmmiCNid8S8/gdj/UOTz/4LcDmiHTOWUee9wdtVKtgw/hM3FPdgSs/jzzE1YEjr59DYkGWD2xhiHd/L/IFlUpUwTRcq6oxxxMtaN50M4SI6q4bn5Ce4d3cWulZP2olLVFF49tCMnvrOe6RVN/PegQl5fFUxnSkr8On1zHRhC/qjIYpM0GPtmdbpT/4FvG3h5/wCHvnAd7H8I5qAD2lxPk6VyX/PibSsqGhKsDZoszO7N08sb001Wa5uSuHWFEYVuynZgidknx0HXDIMlJ5Tj18AQOvQbtu3+ANRUIxWVlCdArWlw0+xaXp9UwttrQnh1hcndfCxrSOLSVebWxDikzM8lA3K5Z/4WLhmYg5uk3dG9lShuxQevok+Ykq6hc8oETkBRVHSXytUDM/GIJAd09OAzFIItmPMf+2TiUPnejEJL7FINYrztMyV3QSvSQ5I3J3Vi6tvrWduUZECek8fHdqDDC/9AGXIA+tKFXJ9/MO9V2iUo0ZTkmi83M29qKXv5Fcp8Gt8cV87qpiSZToVcl0rnXpmoAt4+ohMnvmPPIwd09HD//vk4iSOBBstg8ptVzG1eCE+vCLI+lOKZ8UVUNJh0zfASMEzYjYYcPwftZPH/AaSUxFTN7hqD5sFO2faQYXdIzqpu5N79Cvh6U4yz9s6iIW7iMxTumruF0R09FLo13Jrg+YomygPZGJadRt0YTtI5w+CKz2vSVndXflHDu0d04oY5tTw8pgP5qq0DqApaGa4DpKTErYl07QZAhkMBIfi6XuDSTDobFu4bz4XSboi8QuSMZxGVq8ibNI3pBxfybEUjfxuex+It66gKpVAF3LRvBhnhLdsaL1YuYWjtYh4Z3ps7lsbw6go3D8sjW5d8PjGL3FvORny3EDSdor/ezzMH92LEC5V8XBXBoQruHp3P2qYkA/OdLKyNpYki2ATovgX1HN3Vh4pCj0wNd3M0KiUhw6mwMdyabBR59fQk0hIfVUWpHVCEv7noXwiB+8q7EevXQrAeeuzDFmcmhgXza6IIYEShk6d7hMi66kxorEP3Z/L4lXfxt2HdQFVxCRPDikIKWp+FJMup4tPh3+MKOfndar6rT2CogmsH5zK5q498TWJZlt11qbq4fW4dd87fFsW9fmgux3cP4FJbS444rTh+w02JT291rwDyXBoLauNo9s/886DaJO22EXnc+FUtoYTFsd39TO0eIJGyyCSGljRbT2aWCV99gtxnKIlR43GMOwJl4CikImhwZTHxo6Y0sX/w2waO7rxjTcGtGo9Hz1jP15ti5LpUHj+wkOHd++E5twvdRQbjXq5kU3PK9ZnlTaQsybVDcsl1ODFayFFtT5BdpLhi3xyaEiYvrwxS5je4b/8C3loTpH+ek7tHF3Dxp5uYVxNldJGH20flcfvIfLbETGZtiFLo0bh3vwKeWd7IOX2zKLQkV35ew38PLuK8j2MoAiwJ2WqSO0flc8yMbd2aZ/TOQMci26Wyb56Tt9aGWdWU5IzeGTy8yCavXTIMrh6Ug24G4dDj4OtPkQ2t5V4A29qx72AAlLOuwLrzCpxr7fRbzgevEH14JhVNZjpqOadZQmnrImYrDi337lL3tYlALetuy6dMPBbr8bvsRj4AM4XryhPx3PRvrGv/CP94kQaHj4QF/TIFnxzVEROBISRLTujM08ub8DsUDi7xkuUQP1pgf0PEbPOO//XbGEP3O4qcz99A6z8CcztrUUuK5lrk1sdyaoJrv6xjYpk3TRYFoCA5pVeAZXUJLuifxT3z67AklAd0rh6cQ6FTILZG6CVw8NGwbiXM/ghcHjj1IhIuL3FVI8uh8tT4IgY+s5raqMnLhxSzJWpiIhj0zOp0Grp/npNXD+1IgJg9Njicbe9LRpZdV7cdLNMWi0faz1+WluLrqWXc/FUtdTGTc/pm0SvLwGnGd5Ur/iCSuhPj0GnQwudbdu+LNH5YakZYJr19kllHFZOMxXBs2UDODafA6mVYX80kdNm9fDS7bQf+knV1TM2NEpUaKSnp5xfIrU16zTert1fh4yOKkUKgYeFmW6AhYZEmilvxxYYodXGLfLfGwa+s5cWJnQg43WQo1q/u+d5OFn8HUBSF2RujDOngptCjctWszTzTXPuX61J5+dCOZDvtMHyp3+DGYXm8ujLI9IomygM6lwzIIZwwW3kiWxKumrWZKd38XPvlZu4dmYNmxvEpJlcMzOadNaH04PLqyiZbRPlD29FFAP/cr4Bw0uLfixt4rzJE3xwH9136AKX/uBi+eB+w7dDo1oui8h5csPBZ5Eofc446jiap4lHBv/hLfFoXuOnfEItiPfsAGf+4ghMPO55DDpiMUl9L9rzPEL36Y11xyjaphVQS7ruO8rOuZP6IDBqzirE0nYcWNbBPjhOPrrQR3AXYEkvx5uoQd8+vs2usxubjlnF0BZZsiTOtR4B/fLONZClAz6y2g9WIQjc+lTSrk1IS1hyI0u4oikIjDv61sJ6vN8UY28nLn/bJIjveQNbtl6UjZzTVwy0XU3jbk4QV506jIX6R4u5R+czfHOfWr7fwXXPtUMKU/OWLGsaVePCoGm4S+DOz2RyzuHdB63T/37+q5bgeASJSxbnd8O5VLW4flc/o6WvSYtan9srg65ooHl3BtCSTyr071PDcVTSaKqe9X02XDIPnJxTjVAUfVkWIJi2+qYkyvkglJTSM8cfYpRhboWmIAcNJWJKEauDNyWdd2KLb85taSdHkfY+mYAyNcz7exNfN0j2boyZHvlHFvOPKyHZnoyVkmihuxWurQvyxbxYeXf9eXVFppvCqGjcNy+OKfXNYVpfgiSWNnNsvk945Tk58Zz1rmksNPlgX5o8fbuS8vpk8N6GYpoTJhnCKb2piXNg/m5pIkg+rIjw9vpizPtiABCZ39WMoYKZS7Fegs+zEcj5ZH6FPtoNSr4rTiuNSVR4e24ENEZOaSIoJJTn8ZVAOsZSFVxP4RYIoOs4jTkY/9DiEaO6Oj20bC8TYw5EZ2XD8nyArD9a2rtPSQw0UerPRFbtpojKYpMSnccPQXO79pg5FwAX9s6kMJukRcP2gEHjc4cJz4c3Im85D5BQg161uvUMqZf9rft81K4kuBI1JHRAYWLhlEpciuaSvl6QUCCuFlUr9eFWsHaSz0/ypa28sVW8TEVKFvZiY+tb69Pcd3tnHgs02wW+Zlb2gfxZOYeI0kwzI0umakcV5+2QRSVp4dAWPYtlEsQVCqoHzjMvRTr0YKQQJw5lewKvS4vVVwbQIe+eAwcZIinu/qWtVrzivJsayersEw7IskroDbdyR8N5LzQfS7Bo6p+cHtRitVJIOmsntI3KJmeBRJUoqtlu97ROANn4KSklX+Owd6N6H1NBxJHTnLtX4WZZFXrwezprY+oMNlfgUi7HFLmZtlx0bUJpNVI+TauE0taPjeomnx5aWe2iKnbFqaQKQ7VRxa4KaSIoPjyzh1rl1LKmLc0QXP4eWefGYbUnrnkI7WfwdQEpJtkvj9VVBju0eSBNFsCe/2+du4e/D8xj23BqmdPOzV5aDa5sbKGZtiPJ+ZZhPJ5e2OW44aeFUBRvCKaKWwAckk0m6BpzMmVrGAwvrKHDrnN47A5cC404qZ3lDgm5+DUuoXPZ5Da8065S9WxnhgPoEs0+6nLxrTgJA9OiDKCzBuuBYiIQQQN5z/yL/jqchkAn1G7HOu9zuKM7JR7nwb1iP3IbWfxg5d12MmHQ88tO3EN17t9Xk2rwRoRvkXTmNPCGIHziZy8dNwfJlcWrPAN2zHNw4R7TSBDxprwwumLmp+XzDbIlL3Ab4FItJ5T5ipiTbqfD6qlCzxEku82qi3D4yn2u/3EwoaTG62M1fBuUgUjt+6cPCwZurQnxcFWHm+ggz1oQ4a+9MrurthWb7xDTqaxGp5PfqX1qWych8nb65Tsa8VNnqMwmsaUpQ6LIJreX0EUlabeqi4qbtSqqIthqTMhmng8vJ/OPKWRdM4tIUvtoU5eavapk+sZhuGTpeK/6zXG5MKVgXTDJrQ5T/NlupARxc4mFQBxeW1dwUUdIN7dLb4JUnwOVGnHAeMacnvX8UBY9b5+BObl5fY99/hyq443s0BRMofLadrl/MlFQFU7ywIcJJPTPQFFrds26ZBqsbk/h0hWxPW1eklnBaMZy6jkdTyS50MarIiVuBTIeaJopbMWuD/SxJKSl223qZg3J1DJmiZNlHlA0cx9Wza5FILuqfxbl9M/EqJlh2l3eBCtPKDCzLtCMg2Gk6L1G6uQTd3QJrq0SKSjoyBHbTQUzV0QQ4b3kCHr8bttTAuCMwh4wh4XBB36E4N1e3ucaMWTNYPnwaTxxUxEUzN1EXM6mPWyyrT/Dw2EIkkmeXN7FoS5yDigt3eq+2wrIswhk5uG58BCkt2HcUzPpg2w6BLHtBOP4Yu7FK6NSlFE59fwOzNkQZ0sHFv8cUkKcl09qUP/X5LHCp9M52sKhFdPG63k5yZs2FY84guYPUoUCytC7OohPKmbE6RFnAwJSS09+v5s3DOtElQ2fvbIPSgE6WzjadTDOJv7lGPEtvdvPYyaMVU3XYWqvX4uKEECRbkEILSZ5bTdfHtsSGcIpElgOl+XiuY89GnXAMbKxCdN6LmOHaZXMCy7LQrChe+0t/EderqOHC7L8fYp9ROFXJosWL6dp1505h20MKFZGdZz/XW+HPRAs3clqvDsyvTfDG6hAeXXDzsDwyDUlK/vS0iVcxuW//Ak5+txpT2uTx3v0KuGZWDXNr4vx9eB6NCYt31oZ5Z22Yyn1zuLhfACX560QY28ni7wBSSsr9KutCyTbpQoBVjQkW18XJc6tMKPVyw5zWunCbIiYbIyn65BgsrN3292f0zuSVlUFO3MsuIt8aKXNbMUrcBrcOz8NqNn13WQlcCuRmC9atW41R2IXXVwVbfc+6YIqwr1nPrtcAxNgjkXNnQqSFGLJlIl9+HHHKhchH77AjCAC1m2ypnUtvw3r8bsS+o2xrwlXL7BRVQTFsrNp2nKEHIP0Z4M+Epnoc8z8jf+IxhElwaEeDKCpfTy3nb19tpilhcWbvTD6rjrS6f+GUhXAIhJXErzt4enkjA/Kc9MlxkuNU8GkwvIOLYYWCyV19WBJ0IfHJthaBqqaxOaVz2+xavqtPcEw3P2f0zuCkd6t5bEkDl/cvheIyqGoRRSkoRu6CJZchk2SqcHAnDw8t2pb60xXonunAIezRW6QSrI1pHFLmbVUXN6273+6wJtUmbSSlbNbwM+iZZRA3JeNLvRxW7iVLs0glfr4dYkCXTOse4Na521KgOS6VDh7N1t5LExoNtc8QjG59kIpC0nDaE2rzhG0CDsXiX2MKWBs0WRtMMrjAhU/sXJDZQDKkwNVKMsdQBV5D4dEljZzSM8CdI/PT9Xg+Q+G2kfncOHszjx9UlCZl3wsziWdro5gETdFQhd4m6lDq18l0qmRpJmYigQeQCUgqCrovg+zzD+PvZ15NsGcPXI01ZAejhPzZre7/zib3H/JS3oqUhEhWPsafrkVYZrOEiky/h9IXQDSrDGyFs6wLfbI0kkLlk8klGIpAEbA+lOTw19el93v9sGICqvlDgSqgmTDqToQQeE6/FFQV5n4OnTqjnHIRVixC/VFnM68uRZYTTnuvioXNdZKfVEWY9MZ63p1UhIeda7/uCjwkmHFoB2asjbKoLsHJPfx0diSxik8nqjl2GNXyiyQn7hXgrvl1nLV3JrM3RlnZmODLY8rIM0x0M8qIHIGUiZ2yqp/qWpJKpTiyi4+/zq4lmLB4oSLIqb0CnLtPZqtFUaZDoX+eE6XFGx/VDER2B0RO4c+2EN3dSKhO6hLw+NJ6ltcnOKFHgH4lu04UAWJOF+6Lb0HeeJ49Z7i98OcbWeHuwMRXqrhrVD7/3K8ARUi8IoXyA+46PwTNTDCu2MGKU7qwrC5BR5/G3fPreO47e16cMqOKz44p5b9LG0ha8K9F9Zy5dwYZP+tbfzpEQ0PDr9OH3Y49CiEETcJJ0oKeT6xslXL4+/A8Div3siaYpEemg5poipQFL1Y0cff8OiSw+ITOeHTB/QvqqWhIMK2HH1NCfcxkbCcP+T/CEaSiooK8bn1s6ZsW5EtXYNVJ5RRVL0WuXYmc+ymi7xDkI7e2PsC4IxCTz0CeOaH1dqcL5d7pSEWFFUuQt11qp0h6DUD50zVYj90Dq5fBgBEw+XRimgNHPIpIJZG6TszhxmxxDUII4ooDE4UP1oU5/p1tUZMir8bsKSV4m6NaiqLQJHWaknbTgWsnzhk7Q5PiYuhza1jfooHnpmF5zKuJ8lFVhG+OK6OoqRp566WwtgI6liMuuZVIZl6rc/4+hBUX53y8kddWhSjyatw7uoDB+U7czdGktWvX4u7YjQ0Rk4/WRfhqU5QxHT1M7ubHQxKxM3mlPYCw6uZf39bzfEUTnQMGt43Mp9AJcierbJeZRG2otcl1194knB4Syrbasa2amD/0zAoh2GI5mPRGFd/Wxsl0KNw9uoAP1oVZ05TkuYPy0ITtiLKmMYmmCB5dXM/UHgF6Z+m4fkL6XQhBEw6+qolz3NvriaYkAYfCjEmd6J6hou3gmt1mEmXmW/DK46DpMO2PpPYZ+oOdoLsbQgg8h9X6vgAAIABJREFUqTh8MwvWrkCMGk8iM6+VhtzW/cLCwZxNMRbXJZhU7iXPkBjfo2P6fd9pmCm0VAKpqlgONwlF47smE48mUFWFZXUJ3l4T4t+LG9J+yatOLidL/DyyCPZ41qNHDxRFIZVK7RKRE0IQQyMuBX5doEnbYGCPeP8qKlssnXvm1xE3JRf1z8alCebVxHnw23ry3SoXD8gm26Hi+hXTnrsKoWqsiioc/3Z1q/rRO0flc1o3Z9qLXVVVhBDfe5817AV9JCmJJ01uXhThnkX2wrlvroM3D+lgL1B3I+JCZ3nQ4vxPNjG/pvXz+PDYDtz69RYqGhJ0Duh8cFQJGXLnAum/JNrJ4u8MQjP4LgTnfbyR6nCKk/cKcEqvDEZNX81zE2ydqAWb7Tq8ywbm4NIU5my0Nea8VhRFM4hIlZSEuphFvltFtxIo1q53t1ZUVLDXXnsxvwEOfLmSaEqiCLhjVD5Ty13kRLbA/C+gQyeUjuVYl5wA9c26goYDcddzdt3UuUe1jjoO3g/OuoKQ6sBhpjBSCWQkBF4/MacbNZlATSVJGU4S/Lj0QVRx8El1jH8vbqR7psEl/bPIVHadIH8fhBAsDmkMe35Nq+1dMwyuHJRDMGEypasPlxXHGY+iSBNLKMQc7h/1/UII4kInJlUk4FdNRGoboV2/fj0lpWU0miox00JXVTyqREvFflUD+62QupOQKdAVgVsxkcnEDs/LaaXQXvoPvP60vUEIuPxOYj0HkGpxv1RVxRELIyQkHC6SO7lGRVFokAabYxa1UZMHF9bzbmWIzyeXUu6yfbZVVaVRGiQtW5cyoIFhxX96BEhohKSGhSCasvAbKm7VRE3unNzoAoxEFCkh4fS0utY9DVVVd4k8KYqCoii7jSglhUZlVHDHvDrGdvJQ6NE5/5ON1ERSTOsRYGKZj0NfrSTTqTJ/aik++fNTehUVFT8q3fm/ACEEEdXJ7XPreHFFkKcPLuK8TzZyYCcv9XGT575r4sEDCphQpP0o5YJfA0EMVocsRk1f22p7R5/GF5M74SdBWDj4YF2Eb2rjHNPNT6FHw0EKw0oSFQ4ilsBQJEJReWRRPV9ujHFkZx/75DoZOX0NcVPyl32zubivF7EbbQa3Iqq6uOzzGp5Y2tRq+wdHlXDE6+sIJiyen1jMhGKdZGLXgxC7E+1k8XcIoRtURQRL621Lo3M+2ki/XBcNcZMnWtSEASw6oZwsh4LbbLuaEUL8pAF+6+BqKSphDKrDKfJcGk4F3NImJlt9ZQHc8Qh8ZaejxYgDiTo9gMC5sdL2xN5QCf2GIf54NSHD9YsRG7XZLk0XEvEjyPGuoDLpoM+Tq1ptG1Tg4smDCvFqdmp/d0MIQQSduFRQFcHXGyO8sDLMqEIXh5Z58MifTnZ+CSSFxvqYwp3z6/Dpggv6Z5OtJBGydd7SF48gz5rYOgWY2wF586OEm+s7DcvEWL0M/nO7nXI6eDLWuCOJfE8kLioM6pK2UHG3DAOvSCK2k+b4qe/EjiCEQCoaAli14jvKysp2y3F/ynmkhEZK2t7dLVPZiqLscgr7l4YQghVRjYHPrMFQBR8dXcLI59fQUmf6r0NyWV4X4+SeGQzJFju3rfoR+C2SRdiWDZFC4aUVQc7fTgJrYpmXR8fkt+ro/19ETOisjQgGP9u60albpsGHhxehCMHktzbweYsGlfsPKCCcMJnWI4M/frCBV1eHWH1qF6a+tb6VzNv5+2SR71ZZUhfn1uG5v8g4DM2qC9LJ2JfW2tq+2E5iJ+wV4JOqCAeXesnU5U71YPcE2msWf4eQyQRFDhV/vt0lt7IhyZRuAR5cWN9m34r6BOOKDFI7GFN/7gShNIu52vrMyVaFzy0npJDuRB053p60Wqxyo0VlOK5/CAVIKSpxzfhFJy3TNDGaJSF2J4QQ+A2llZ2cbf2WR7aj2dLxF0BYcXLqexsY28mDU1M4/+ONmBKeWtbIKyUe/jNmm2j6noSiKOl00VYIIVgfU+j/zOp008XjS5tYfEIZge2q/GUq2bZWrLEe0SKabERDcOO52zpVn3kAJTsfdciYndb1uWSCIg2KMwRS7lhMfHc+f3YDQxLJj9Ol/FlQNGJS4FakrTupqNSZGhujFroiyHEZBJMWAonfobJsSxy3rlDu13/1xYWlaNwxzy6b6ZJhML8mxnaGJMxYHWT6hGJ8oq2+6O8NWzt1NVWjPNC2b79rhoFDkcj/8dvkIkWuy8nYTh7er9xWW3zzsFwCqklVXGtFFAHumLuFa4fk8siiBhy6wgEd3URTso0e8EPf1lNxchccJDF+IaII9ruep6d48ZCONCUsdMXWIj7mzSoO7+InoAvb3eUXO4MfhvLDu7Tj/yUsE5+MkaGmmNbdz7yaGPt39LTaRQAdvBoNqZ8rlPfzYZpmmwnTNE0iupOQ7iSm6v8T0Y2fAiklupDcNjKf94/sxAMHFLD4hM7oQqLt5ghmGqrOKyubuHZoHm5NIZaSfD6ljIH5duTtrbVhYnLPDg+KohBSnMyoNnm9KkVQcaEo9jnIZiLQsmM5lLR4dWUIVW2tYycNp+3e0hJjDiOh2xOiqqqweG5bkduZM9C/J82bPv5v9Dn7PgghCCsublkQYvI7NTywNEJUcRJBpzpiMWVGFeGkxVFvVLH3k6vo/eRqjnyjCoemMHJ6JYe/sZ6Q+GFNu18SCrYUCdjR384ZbQlQnxwnftVE/V9nQHsQqVSKfrkO9i/eJnxf4te5oF8Wchfeh18bUkqylASPjuvAy4cUc9OwXL49vpzeRgjTNHdIsExpqzvMWBOif56TYq+dUdh+pvMaduTc+JnNLLsC0zTZHEkyavoahj63hmtmbWZFY5JoyuKub+rYbDq2aSn/CmiPLP7OoZgpLuqXyQPfNjC+zEd1KMnLzbZkNw/P579LGzmvbyZe9YeP1Y6fDi8JIqrOW2uCCARCSiaVe5DmLzNYx6VgWKGX/V9YQ32zbEauS+WNSZ0YOX1N2sd4T6IJB4OeXUN1s7h5rktl7tRS/MQQgEdvu2jZ0baow4X7r/fDi/+xu+EH74/c/5B0naplWVC0g5RuSRdMVftldD3+xxFG55i31vN5tR1Z+XR9hEVb4vxlUA7Xz95M54DB7I3RViLCX2+KMXtjlNHFbj6uirC4LsGQLOXX65S1UlzUP4v/LmtkS8yksinJqT0z+E+z+Hd5QOfqQdmoP1PK6f8jfDLGEwcVUheziKYsirwamUqS//FyxW1ozlKNLVAZ18GNaSb4bs0G8jO6kqFDv1wn8zdve3bP3SeL6RVNDC1wsaIhwQsrmrh+aC4n9gzw+JJtpVh/G5ZHpmaxJ3r7LMuiV5aTAzt5eLc5Qlrq1/lzv2w2hpMs2hKnX65BQOyhRqjt0E4W24HLinFBHy9Nlsr5/bK4sH82W2ImDy+q5921YS4bkPW7nED3JKRlkacmuLxfgIQl8Kkm1i9AFIWiEMRgU8TipZWNaaIItubmm6uDtnexbnd079bfXdEIShUhwa+msFqkezVNY/rypjRR3Ho+/17cyCV9PJimTQSeWNqYdgIq9GgcVOLF3K6e1rIsQroT49g/oiTjpByuVs0eUkqs/CKU4ePg8/fsjR06waQT2rgP/V4Qs0SaKAL4dYXje2Tg1BSuGpTLysYEc7a3HMGW3SpqjspUh1KI7D3bfd0SUkpytBRLTijntVUhXBpcPzSHawbnEDUtfBr4RQLrR/o//x4gpS3p5TMAA7CSbfRWfwvYUQmJlwRvHFbESytDzK2JMbE5KFIVSvKP/fL5YF2IGatDvLzC9tee3NXPNzUxDir10sGtYib3XPex24rx+Nh8auJQGzMp9mqc+X41H1VFGNrBxeMHFuLRdVRzz5cHtZPFdgAgzBRZqmSTqjD2pUqaEhaaAv8a0wE3v5Xl5W8bKaETkSpRUyIVFaFIYqZEU0Sznd/PX97WWQb7PruGg0q86ZRdS8RNyXWDsyn2ajh3Q42OJTTCaMQteGF5kDvnbcGpKtw0LIdxRQ6cLaRStvcvbrmtJRF4eWUQr64wvtSLT+7cni0BoDt26Kka0Qxcp16CdsJ5yEQc6fERNVy/uv/qrwVNEa3ExWcdW8p9C+p5+LV6FCE4u08mf+6fzf0LW1v0Te0e4IY5m3Gqgv2K3T/LqWd3QFgmAUxO7mKnxLcuJDK2Exlvx+8HUko8MsYpXZ2c0M1L2BTETIOjO3vwyhhHdHIwpqgTCAhoFv5cg33zHHhFiuQeJIpb4ZJxMh1OwilBv6dWpRfHszZEOf+TTTx8QJ4tbr6H0V6z2I40TNOkq8diyfFlLDq+jFUnd+bQjgbarggLt+NnIaU6+C4kGfLcGvo9vYqqcIoj3qym82OrKH90JXcuaCIqvs88bheg6twxr47aqMnba0JM7R5AazECGKrg9F4Z9PBau6XrT1VVloXg2Lermbk+yuWf1VATMakMJjnhnQ1siNlah2DXTZ20V+vzUQT8sU9GOlogLJMMYpzR3cWxZQZeK/qz0jFRzSDo8hEK5BDWHP9zQsN7Ek5MLh2QDcBRXXx8Wxvn/oX1JC17AXHP/Doq6hM8dXAhXTIMumQY/HP/AjZHTY4o9zH72FL8yv/OOGGa5i67i7Tj94FUKgXJGB4rSraIpxuypJnCRxyfjGMlExhmDEcqSjL56+nKuknhVJU0UdyKmevDtg3ur4D2yGI7WkFYtgm8V6c99bwH0WQKTnm3mg3hFCf1DPDUsibmNHtxmxL+/nUdU7r5KXf+dGkWU9q+vEC6zOC9I0v4z6IGdAUu2CeDLHXnbiY/Fo2WxqnvVTGmk4e31oTafP7qyiAX9/GkG5eyNZO5U8u4aU4tKQuuHJRNvmG1ud7/dd233yIMmeTcvf1M7upDVwS3zatrs8/ba8IUeTWuGWy7LD33XROD8p2ct7cf1dp9z0072vF7hyZT5Lp0HKpIi8gDDMx3of1KPdG/mcjixIkTycjIaPXv1FNPbbVPQ0MDZ555Jp06daJTp06ceeaZNDS0TpusW7eOKVOmUFhYSHl5OZdeemnaH3QrFi9ezIQJEygoKGCvvfbilltu+X/ZAdmO/w0oikLKgmX19nPYtVn2Y3ss2BxPR+J+CnRMzu2bmf7/f5c2csq71fx9eC53DsskVV2xm7tEBauakqwLJumR1TYq2j/P2YpgaDJFqSPJA6OyeXi/bLq5TfT2qPYeg0smKHemKPfAuO2UEQDGdnLzbW2ME9+p5sR3qllWF+e0XhmIVLydKLajHbsZLpnk2fGF6XKhbpkGj4wpwL2bHWR2Fb+pyOJxxx3HNddck/6/0+ls9fnpp59OVVUV06dPRwjBeeedx1lnncVzzz0H2KmJKVOmkJmZyYwZM6ivr+cPf/gDUkpuu+02AJqamjjiiCMYNmwYH374IRUVFZxzzjm43W7OPffcPXex7fjdwLIsDEUwuMDF7I1R5tXE2K/Y3cqrFWBIBxfWzxBltSyLPpkGrxxSzG3ztuDVFf42LBcnCdiBNNHPhSFMjij38uLKIJcNzOHN1SHmNZPgiaVeBuY5sLZLd0spUZvrGNvpx56HlJJEIsH+xW6O6+7n6eVNCAEn7xVgYL6TffMMLh6QTcK0KPZq+Ii3/07taMcvAE2mGJ2vsuj4MhKWxKnYvuK/VoPWb4osut1u8vPzd/jZ8uXLef/993n77bcZPHgwAHfddRfjx49PK+x/+OGHLF26lG+//Zbi4mIArrvuOs477zyuvvpq/H4/06dPJxqN8sADD+ByuejZsyffffcd999/P3/6059+VmSnHe3YGXxKiscOLOTkd6t5bVWQC/plsy6Y5KnljQQMlTtH2RIOP7c0wCETjClQGXxwPgrgEuYvFhVyyBR3jsrHZyj86aON3DIij44+HUMBl7J76iLb8cvAY0W5Y0QONw7LQwhwComzuVmkzNHsLmOl2oliO9rxC0KxTPyYtgDkr9yg9ZtJQwO8+OKLlJeXM2TIEK666iqCwWD6szlz5uD1etNEEWDIkCF4PB5mz56d3qd79+5poggwZswY4vE433zzTXqfoUOH4nK5Wu2zYcMG1q5t7T3ZjnbsLqhWkiJHiukTCll5chfKfCp3DM9m9cmd+WZaKZM6GTjk7kk/mKaJSyZxyF+2zkxKiceKcsuQTF49pJA+mSqFWpxsEcMtfx1/03bsOpxWnEyiZMhom8749rKcdrTj94XfTGRx8uTJdOzYkYKCApYtW8Z1113HokWLeOWVVwCoqakhOzu7VeRPCEFOTg41NTXpfXJzc1sdNzs7G1VVW+1TWFjYap+tf1NTU0NpaekOz6+iomK3XOfvBe336//au/eoqsr0geNfRQSs4CBXpRDxxqWRi3oyyJhgvCC6zFKBaWq8jFSGLcCjkqJZMXJ1RIO0vMSqyVKRRmmcWbqW5A0MZxRzFBwcxJAlGBiWyFE88Pujxf55QuCcIhB4PmuxFuz9nHe/ez/u7XPefTNOZSctR/Ly4JLcPLgkNw8myYvh2nu/eZcWi/Hx8aSmprYZk5OTw4QJE5g7d64yzdPTExcXF4KCgigsLMTb2xvgvqeIm5qaWhSQ99NWTPO36LZOQXfHF8l3lebLAsSDRfLy4JLcPLgkNw8myUvH6tJi8dVXX2XOnDltxtx7yvhePj4+mJiYUFpaire3N/b29lRXV+sVh01NTdTU1Cgjg/b29sop6WY1NTXodDq9mOZRxmbV1dUALUYlhRBCCCF6ui4tFm1sbLCxsflZnz137hw6nU654UWtVnPz5k0KCgqU6xYLCgqoq6tT/lar1aSmplJRUYGTkxMAubm5mJmZKaOTarWaNWvWoNVqlbutc3NzGTRoEEOGDPlF6yuEEEII0d10ixtcLl26RFJSEqdPn+by5cscOHCABQsWMHr0aMaPHw/AqFGj+N3vfkd0dDQnT56koKCA6OhoJk+erAxFBwYG4u7uziuvvMKZM2f48ssvWb16NS+99BKWlpYAzJo1CwsLCxYtWsT58+fZt28faWlpLFq0SO6EFkIIIUSv0y1ucDE1NeXw4cNs3ryZuro6nJycmDRpErGxsZiYmChxW7ZsYfny5Tz33HMABAcHk5ycrMw3MTFh586daDQapkyZgrm5ObNmzSI+Pl6JsbKy4vPPP0ej0fDMM8+gUql47bXXiIyM7LwVFkIIIYR4QPSpra2VZyAIIYQQQoj76hanoYUQQgghRNeQYlEIIYQQQrRKikUhhBBCCNEqKRaFEEIIIUSrpFgUQgghhBCtkmJRGC0zM5Np06bh7OyMSqXi8uXLLWJqa2uJiIjA2dkZZ2dnIiIiqK2t1YspLy8nNDSUwYMH4+rqyrJly7hz545ezLlz55g6dSqOjo64u7uTlJSkvH6x2bFjxwgICMDBwQEvLy+2b9/e8SvdTYWEhKBSqfR+5s+frxfTmbkSxtm6dSujR4/GwcGBgIAA8vLyurpLPUpCQkKL/WPkyJHK/KamJhISEnBzc8PR0ZGQkBCKior02rh9+zZLly7F1dWVwYMHExYWRkVFhV6MIftYb3f8+HHCwsJwd3dHpVLxySef6M3vzFwYcrzrbaRYFEa7desWgYGBxMbGthrzpz/9ia+//prdu3eTlZXF119/zcsvv6zM1+l0hIaGcvPmTfbv38+2bdvYt28fK1euVGK+//57Zs6cib29PYcOHSIxMZF3332X9PR0JaasrIw5c+agVqs5cuQIMTExLFu2jL179/46K98NvfDCC1y4cEH5Wb9+vd78zsqVME52djaxsbEsWbKEI0eOoFarmT17NuXl5V3dtR5lxIgRevvHvQX5hg0byMjIICkpiUOHDmFnZ8fMmTP54YcflJg33niDnJwctm3bxv79+/nhhx8IDQ1Fp9MpMe3tYwLq6urw8PAgMTERCwuLFvM7KxeGHO96I3nOovjZTp8+zTPPPMOZM2f0XoV44cIFnnjiCf75z38qb9jJz88nODiYkydPMmLECA4ePMicOXM4e/as8v7vnTt38vrrr1NSUoKlpSXbtm1jzZo1/Pe//1UOHikpKWzfvp3z58/Tp08f3nzzTXJycjh16pSy/MWLF1NcXMzBgwc7cWs8mEJCQvDw8CAlJeW+8zszV8I4QUFBeHp6snHjRmWar68vM2bM4M033+zCnvUcCQkJ7Nu3j/z8/BbzmpqacHNzY+HChWg0GgDq6+sZMWIE77zzDvPmzePGjRsMHz6cjIwM5syZA8CVK1f4zW9+Q1ZWFkFBQQbtY0Kfk5MTycnJvPDCC0Dn5sKQ411vJCOLosMVFBTw8MMPK+/kBhg/fjwPPfQQX331lRIzatQoZWeEH/9zvH37NoWFhUrMk08+qfctMygoiKtXryqnvgsKCggMDNRbflBQEKdPn6ahoeFXW8fuZM+ePbi6ujJ+/Hji4uL0vol3Zq6E4e7cuUNhYWGLf9uBgYFKXkTHKCsrw93dndGjRzN//nzKysoAuHz5MlVVVXo5sLCwwM/PT8lBYWEhDQ0NejGPPvooo0aN0tt/2tvHRNs6MxeGHO96IykWRYe7du0aNjY2eqNJffr0wdbWlmvXrikxdnZ2ep+zsbHBxMSkzZjmv9uLuXv3LjU1NR27Yt3Q7Nmz2bJlCzk5OSxdupR9+/bx4osvKvM7M1fCcDU1Neh0uvtuU9meHWfs2LG899577N69m40bN1JVVcWkSZO4fv06VVVVAG3m4Nq1a5iYmGBjY9NmTHv7mGhbZ+bCkONdb9Qt3g0tfn3x8fGkpqa2GZOTk8OECRMMau9+px2bmppa7KTtffanMc03TBgb05MYk6u5c+cq0zw9PXFxcSEoKIjCwkK8vb2Bzs2VMM79tqlsz44zceJEvb/Hjh2Lt7c3O3bsYNy4ccDPy4Eh+4/k0nidlQtDjne9jRSLAoBXX31Vuc6jNfcOy7fF3t6e6upqvR2wqamJmpoa5Rubvb19i1MwPx1Nsbe3b/FNrrq6GqDdmH79+jFw4ECD+tvd/JJc+fj4YGJiQmlpKd7e3p2aK2G41kYyqqurZXv+ih5++GHc3NwoLS1l2rRpwI8jTffuT/fmwN7eHp1OR01NDba2tnoxfn5+Skx7+5hom4ODA9A5uTDkeNcbyWloAfz4n9PIkSPb/BkwYIBBbanVam7evElBQYEyraCggLq6OuVaEbVazYULF/Qea5Cbm4uZmZky4qVWq8nPz0er1erFDBo0SLmhRq1W8+WXX+otPzc3Fx8fH0xNTX/WtnjQ/ZJcnTt3Dp1Opxx8OzNXwnD9+/fH29ub3Nxcvem5ubl611uJjqXVaikpKcHBwYEhQ4bg4OCglwOtVkt+fr6SA29vb0xNTfViKioqlBspwLB9TLStM3NhyPGuNzKJjY1d09WdEN1LVVUVpaWllJSUkJOTQ2BgIHV1dfTv3x8LCwtsbW3517/+RVZWFqNHj6aiooLo6Gh8fX2VRxS4uLiQk5PDoUOH8PT0pLi4GI1Gw+zZs5k+fToAw4YN48MPP+Ts2bOMGDGC/Px8Vq9eTVRUlLJjDx06lLS0NL799lsee+wx9u/fz7p164iPj8fNza3LttGD4NKlS3zwwQc89NBD3Llzh4KCAqKionByciIuLo6+fft2aq6EcR555BESEhJwdHTE3NyclJQU8vLySE9Px8rKqqu71yPExcXRv39/GhsbuXjxIkuXLqW0tJT169ejUqnQ6XSsX7+e4cOHo9PpWLlyJVVVVaSlpWFmZoa5uTmVlZVs2bKFxx9/nBs3bhAdHY2lpSVvvfWWwfuYgJs3b1JcXExVVRUff/wxHh4eWFpacufOHaysrDotF4Yc73ojeXSOMFpCQgJJSUktpmdkZCiPOvjuu+9Yvnw5//jHPwAIDg4mOTkZlUqlxJeXl6PRaDhy5Ajm5ubMmjWL+Ph4zMzMlJhz586h0Wg4deoUKpWKefPmsXz5cr1rR44dO8aKFSsoLi7G0dGRqKioFg+e7o2uXLlCREQERUVF1NXV4eTkxKRJk4iNjcXa2lqJ68xcCeNs3bqVDRs2UFVVhbu7O2vXrsXf37+ru9VjzJ8/n7y8POXU5dixY1m5cqXyRbOpqYnExEQyMzOpra1lzJgxpKam4uHhobSh1WpZtWoVWVlZaLVann76adatW6d3utSQfay3O3r06H2LsfDwcDZt2tSpuTDkeNfbSLEohBBCCCFaJdcsCiGEEEKIVkmxKIQQQgghWiXFohBCCCGEaJUUi0IIIYQQolVSLAohhBBCiFZJsSiEEEIIIVolxaIQQnQRlUpFQkJCV3dDCCHaJMWiEEK0IiUlhS+++KKru9Et7dq1Cz8/PxwcHHj88cdZu3YtDQ0NXd0tIcTPIA/lFkKIVjg4OPDcc8+xadOmX6V9rVZLv3796Nev36/SflfZsWMHixYtYvLkyUydOpVz586xdetWwsPDycjI6OruCSGM1LOOUEII0UVu3brFgAEDjPqMubn5r9SbrnP79m1Wr15NQEAAO3fuVKZbW1uTlJREREQEXl5eXdhDIYSx5DS0EKJHqa+vR61W4+vrS11dnTK9rq4OHx8f1Go1Wq223XZUKhW3b9/m008/RaVSoVKpCAkJAeCTTz5BpVJx5MgRYmNjGTlyJIMHDwZ+fPdsXFwcfn5+PProozg5OTFt2jROnDhx32Xce81ic7t5eXm8/fbbjBo1CkdHR2bOnElZWZlR2+Ho0aOoVCp27drF2rVrcXNzY9CgQTz77LOUlJS0iL948SILFixg+PDhODg44OvrS2xsrDL/m2++YcmSJYwbN45Bgwbh7OxMaGgoRUVFLZZbXV3NwoUL9aYvXLiQPn368Le//c2o9RBCdD0ZWRRC9CgWFhZs3ryZSZMmsXr1atatWwfAqlWrKC8v58CBAwaN6L3//vtERkYyduxY5s6dC4C9vb1ezPLly7GysiImJobvv/8egLJZNb28AAAGh0lEQVSyMvbu3cuMGTNwdXXlxo0bfPTRR8yYMYPc3Fw8PDzaXfaKFSuwsLAgOjqampoa0tPTiYiI4MCBA0ZuDUhLS6OxsZHIyEhqa2t5//33mT59Onl5eQwcOBCAoqIiJk+eDMC8efMYOnQo33zzDdnZ2SQmJgJw+vRpjh8/zvTp03F2dubq1at8+OGHTJ06lRMnTuDg4ADAmTNnABgzZoxeP2xtbXFxcVHmCyG6DykWhRA9jq+vL9HR0aSkpCijgdu3b2fZsmX4+voa1EZoaCivv/46Li4uhIaG3jdmwIABfPHFF3rXHHp4eFBYWIiJiYkybe7cuYwbN47NmzezcePGdpfd3G7fvj+e/LG2tmbFihUUFRXh7u5uUP+bffvtt5w8eRKVSgXAhAkTmDFjBunp6axevRoAjUZDQ0MDx44dY9iwYcpn4+LilN8nTpzIjBkz9NoODQ3lySef5OOPP0aj0QBQWVkJoBSP93J0dFTmCyG6DzkNLYTokZYtW4aXlxeLFy8mMjISLy8vli5d2qHL+OMf/9ji5hQzMzOlUNRqtVy/fp3GxkbGjBlDYWGhQe3OmzdPKRQB/P39AYw+FQ0QFhamFIoAAQEBuLu7K6OU1dXVHD9+nN///vd6hSKg14d7r8e8desW169fx8rKimHDhumtV319PaampnqfbWZubk59fb3R6yCE6FoysiiE6JFMTU1577338Pf3p1+/fmRnZ2Nqatqhy3BxcWkxrbGxkQ0bNpCZmcnly5f15g0ZMsSgdh977DG9v5uLve+++87oPv60AGyedvToUeD/C9D2To9rtVrWrl3Lrl27WowO2tjYKL9bWFjQ0NBAU1MTffr0adGGhYWF0esghOhaUiwKIXqsQ4cOAXD37l0uXLiAm5tbh7Z/v8InLS2Nt99+m/DwcOLi4hg4cCAmJib85S9/4dKlSwa1e+8p7Hs1NRn/pLOfFmw/baf59/vF3Ss2NpaPPvqIiIgIxo8fj6WlJX379uWNN96gsbFRiWs+/VxZWcmgQYP02qisrGTo0KFGr4MQomtJsSiE6JGKi4uJj4/n+eefp6KigiVLluDn54ednZ3BbbRXQN1PdnY2Tz31VItnM3bVm1ouXrzYYlppaakyeunq6grA+fPn22wnOzubsLAw5YaXZrW1tcqNMgDe3t4A/Pvf/2batGnK9JqaGsrKynj22Wd/3ooIIbqMXLMohOhx7t69yyuvvMLAgQNJTU1l06ZN1NfXExUVZVQ7AwYMoLa21qjPmJiYtBgB/OqrrygoKDCqnY7y2Wef6a3D4cOHKSoqYuLEicCPp5D9/f3ZsWNHi5HPe9fjfuuVlZXF1atX9aY99dRT2NjYsHXrVr3pH3zwAU1NTS1ukhFCPPhkZFEI0eOkpqZSWFjIrl27sLa2xtramrfeeguNRsOnn35KeHi4Qe34+Phw+PBh3n33XQYPHoytrS0BAQFtfiY4OJjExERefvll/Pz8+N///kdmZiZubm7cvHmzI1bPKHZ2dkyZMoU//OEP3Lhxg82bN2Nvb09kZKQSk5ycTHBwML/97W+VR+eUl5eTnZ3NqVOnlPX67LPPeOSRR/Dw8ODs2bNkZ2e3uG7T3NycNWvWsHjxYkJDQwkJCeE///kPW7duJSwsTBl5FEJ0H1IsCiF6lDNnzrBu3TpeeuklJk2apExfsGABf//734mNjeXpp5/Gycmp3bYSExOJiYkhMTGRuro6/P392y0WY2JiqK+vZ/fu3ezduxd3d3e2b9/Onj17OHbs2C9eP2NFRUVRUlJCeno6tbW1PPHEEyQnJ+vdlOLp6cnBgwf585//TGZmJlqtFicnJ6ZMmaLEJCYmYmpqyueff85f//pXvL292bNnD6tWrWqxzBdffBFTU1M2btyIRqPBzs6OmJgYli1b1inrLIToWPJuaCGE6IGOHj3K9OnT2bZtG88//3xXd0cI0Y3JNYtCCCGEEKJVchpaCNGr6HQ6qqur24wxNzfHysqqk3pknPr6euXVgq2xtLTspN4IIXoDKRaFEL3KlStX8PLyajMmPDy8xaNvHhTZ2dm89tprbcZkZGTg7OzcST0SQvR0cs2iEKJX0Wq1nDhxos0YR0fHDn+Ad0eprKykuLi4zRg3NzccHR07qUdCiJ5OikUhhBBCCNEqucFFCCGEEEK0SopFIYQQQgjRKikWhRBCCCFEq6RYFEIIIYQQrZJiUQghhBBCtOr/AL6+R3hztL5XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets plot the Principal Components\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (9, 4)\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "sns.scatterplot(df['x_train_pca0'], df['x_train_pca1'],\n",
    "                hue = df['Attrition'])\n",
    "plt.title('Principal Components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### t-SNE\n",
    "* t-distributed stochastic neighbor embedding is a machine learning algorithm for visualization based on Stochastic Neighbor Embedding originally developed by Sam Roweis and Geoffrey Hinton, where Laurens van der Maaten proposed the t-distributed variant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "tsne = TSNE(n_components = 2)\n",
    "x_train_tsne = pca.fit_transform(x_train)\n",
    "x_test_tsne = pca.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.86      0.89      0.87       132\n",
      "         Yes       0.91      0.88      0.89       164\n",
      "\n",
      "    accuracy                           0.89       296\n",
      "   macro avg       0.88      0.89      0.88       296\n",
      "weighted avg       0.89      0.89      0.89       296\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Lets create a Predictive Model\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train_tsne, y_train)\n",
    "y_pred_tsne = model.predict(x_test_tsne)\n",
    "\n",
    "cr = classification_report(y_pred_tsne, y_test)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Discriminant Analysis\n",
    "* a technique that is used to reduce a large number of variables into fewer numbers of factors. The values of observed data are expressed as functions of a number of possible causes in order to find which are the most important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implementing LDA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "\n",
    "# lets define the Model\n",
    "lda = LDA(n_components = None)\n",
    "x_lda = lda.fit_transform(x_train, y_train)\n",
    "\n",
    "# Create array of explained variance ratios\n",
    "lda_var_ratios = lda.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a function\n",
    "def select_n_components(var_ratio, goal_var: float) -> int:\n",
    "    # Set initial variance explained so far\n",
    "    total_variance = 0.0\n",
    "    \n",
    "    # Set initial number of features\n",
    "    n_components = 0\n",
    "    \n",
    "    # For the explained variance of each feature:\n",
    "    for explained_variance in var_ratio:\n",
    "        \n",
    "        # Add the explained variance to the total\n",
    "        total_variance += explained_variance\n",
    "        \n",
    "        # Add one to the number of components\n",
    "        n_components += 1\n",
    "        \n",
    "        # If we reach our goal level of explained variance\n",
    "        if total_variance >= goal_var:\n",
    "            # End the loop\n",
    "            break\n",
    "            \n",
    "    # Return the number of components\n",
    "    return n_components\n",
    "\n",
    "# lets run the function\n",
    "select_n_components(lda_var_ratios, 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets define the Model\n",
    "lda = LDA(n_components = 1)\n",
    "x_train_lda = lda.fit_transform(x_train, y_train)\n",
    "x_test_lda = lda.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.74      0.76      0.75       135\n",
      "         Yes       0.79      0.78      0.78       161\n",
      "\n",
      "    accuracy                           0.77       296\n",
      "   macro avg       0.77      0.77      0.77       296\n",
      "weighted avg       0.77      0.77      0.77       296\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Lets create a Predictive Model\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_train_lda, y_train)\n",
    "y_pred_lda = model.predict(x_test_lda)\n",
    "\n",
    "cr = classification_report(y_pred_lda, y_test)\n",
    "print(cr)"
   ]
  }
 ],
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