Loan Prediction Model (Final).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "numeric-modem",
   "metadata": {},
   "source": [
    "# Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "outer-trustee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abstract-separation",
   "metadata": {},
   "source": [
    "# Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "anticipated-weight",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sr_no</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Education</th>\n",
       "      <th>Applicant_Income</th>\n",
       "      <th>Family_Income</th>\n",
       "      <th>Total_assets</th>\n",
       "      <th>Business_type</th>\n",
       "      <th>Credit</th>\n",
       "      <th>Loan_Amount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>...</th>\n",
       "      <th>Competition</th>\n",
       "      <th>Rent_Wages</th>\n",
       "      <th>Size_Shop</th>\n",
       "      <th>Location</th>\n",
       "      <th>Supply</th>\n",
       "      <th>Total_Workers</th>\n",
       "      <th>No_customers</th>\n",
       "      <th>Freq_Supply_pay</th>\n",
       "      <th>Nature</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sr_no  Cases     Education  Applicant_Income  Family_Income  Total_assets  \\\n",
       "0      1      1      Graduate              5849            0.0             0   \n",
       "1      2      2      Graduate              4583         1508.0             3   \n",
       "2      3      0      Graduate              3000            0.0             0   \n",
       "3      4      0  Not Graduate              2583         2358.0             2   \n",
       "4      5      2      Graduate              6000            0.0             2   \n",
       "\n",
       "  Business_type Credit  Loan_Amount  Loan_Amount_Term  ...  Competition  \\\n",
       "0           Yes    Yes          NaN             360.0  ...            3   \n",
       "1            No    Yes        128.0             360.0  ...            0   \n",
       "2            No    Yes         66.0             360.0  ...            1   \n",
       "3           Yes     No        120.0             360.0  ...            0   \n",
       "4           Yes    Yes        141.0             360.0  ...            0   \n",
       "\n",
       "   Rent_Wages  Size_Shop  Location  Supply  Total_Workers  No_customers  \\\n",
       "0           1          3         0       3              6             4   \n",
       "1           5          0         0       5              1             1   \n",
       "2           4          0         5       5              2             3   \n",
       "3           3          2         5       3              7             4   \n",
       "4           4          3         0       3              2             2   \n",
       "\n",
       "   Freq_Supply_pay  Nature  Loan_Status  \n",
       "0                0       0            Y  \n",
       "1                1       1            N  \n",
       "2                4       1            Y  \n",
       "3                5       1            Y  \n",
       "4                0       2            Y  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data_set (Final).csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "confident-fortune",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sr_no</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Applicant_Income</th>\n",
       "      <th>Family_Income</th>\n",
       "      <th>Total_assets</th>\n",
       "      <th>Loan_Amount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Rating_Reviewportal</th>\n",
       "      <th>Daily_Sales</th>\n",
       "      <th>Competition</th>\n",
       "      <th>Rent_Wages</th>\n",
       "      <th>Size_Shop</th>\n",
       "      <th>Location</th>\n",
       "      <th>Supply</th>\n",
       "      <th>Total_Workers</th>\n",
       "      <th>No_customers</th>\n",
       "      <th>Freq_Supply_pay</th>\n",
       "      <th>Nature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.00000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>307.500000</td>\n",
       "      <td>1.973941</td>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>2.488599</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>2.473941</td>\n",
       "      <td>2.47557</td>\n",
       "      <td>2.612378</td>\n",
       "      <td>2.555375</td>\n",
       "      <td>2.421824</td>\n",
       "      <td>2.620521</td>\n",
       "      <td>2.532573</td>\n",
       "      <td>3.522801</td>\n",
       "      <td>2.586319</td>\n",
       "      <td>2.521173</td>\n",
       "      <td>2.359935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>177.390811</td>\n",
       "      <td>1.388359</td>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>1.679655</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>1.642166</td>\n",
       "      <td>1.66194</td>\n",
       "      <td>1.750202</td>\n",
       "      <td>1.653321</td>\n",
       "      <td>1.748338</td>\n",
       "      <td>1.711958</td>\n",
       "      <td>1.669635</td>\n",
       "      <td>2.319919</td>\n",
       "      <td>1.625111</td>\n",
       "      <td>1.757401</td>\n",
       "      <td>1.701863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>154.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>307.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>460.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Sr_no       Cases  Applicant_Income  Family_Income  Total_assets  \\\n",
       "count  614.000000  614.000000        614.000000     614.000000    614.000000   \n",
       "mean   307.500000    1.973941       5403.459283    1621.245798      2.488599   \n",
       "std    177.390811    1.388359       6109.041673    2926.248369      1.679655   \n",
       "min      1.000000    0.000000        150.000000       0.000000      0.000000   \n",
       "25%    154.250000    1.000000       2877.500000       0.000000      1.000000   \n",
       "50%    307.500000    2.000000       3812.500000    1188.500000      3.000000   \n",
       "75%    460.750000    3.000000       5795.000000    2297.250000      4.000000   \n",
       "max    614.000000    4.000000      81000.000000   41667.000000      5.000000   \n",
       "\n",
       "       Loan_Amount  Loan_Amount_Term  Rating_Reviewportal  Daily_Sales  \\\n",
       "count   592.000000         600.00000           614.000000    614.00000   \n",
       "mean    146.412162         342.00000             2.473941      2.47557   \n",
       "std      85.587325          65.12041             1.642166      1.66194   \n",
       "min       9.000000          12.00000             0.000000      0.00000   \n",
       "25%     100.000000         360.00000             1.000000      1.00000   \n",
       "50%     128.000000         360.00000             2.500000      3.00000   \n",
       "75%     168.000000         360.00000             4.000000      4.00000   \n",
       "max     700.000000         480.00000             5.000000      5.00000   \n",
       "\n",
       "       Competition  Rent_Wages   Size_Shop    Location      Supply  \\\n",
       "count   614.000000  614.000000  614.000000  614.000000  614.000000   \n",
       "mean      2.612378    2.555375    2.421824    2.620521    2.532573   \n",
       "std       1.750202    1.653321    1.748338    1.711958    1.669635   \n",
       "min       0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%       1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "50%       3.000000    3.000000    2.000000    3.000000    2.000000   \n",
       "75%       4.000000    4.000000    4.000000    4.000000    4.000000   \n",
       "max       5.000000    5.000000    5.000000    5.000000    5.000000   \n",
       "\n",
       "       Total_Workers  No_customers  Freq_Supply_pay      Nature  \n",
       "count     614.000000    614.000000       614.000000  614.000000  \n",
       "mean        3.522801      2.586319         2.521173    2.359935  \n",
       "std         2.319919      1.625111         1.757401    1.701863  \n",
       "min         0.000000      0.000000         0.000000    0.000000  \n",
       "25%         1.000000      1.000000         1.000000    1.000000  \n",
       "50%         4.000000      3.000000         2.000000    2.000000  \n",
       "75%         6.000000      4.000000         4.000000    4.000000  \n",
       "max         7.000000      5.000000         5.000000    5.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "premium-authority",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "assured-string",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 22 columns):\n",
      "Sr_no                  614 non-null int64\n",
      "Cases                  614 non-null int64\n",
      "Education              614 non-null object\n",
      "Applicant_Income       614 non-null int64\n",
      "Family_Income          614 non-null float64\n",
      "Total_assets           614 non-null int64\n",
      "Business_type          614 non-null object\n",
      "Credit                 614 non-null object\n",
      "Loan_Amount            592 non-null float64\n",
      "Loan_Amount_Term       600 non-null float64\n",
      "Rating_Reviewportal    614 non-null int64\n",
      "Daily_Sales            614 non-null int64\n",
      "Competition            614 non-null int64\n",
      "Rent_Wages             614 non-null int64\n",
      "Size_Shop              614 non-null int64\n",
      "Location               614 non-null int64\n",
      "Supply                 614 non-null int64\n",
      "Total_Workers          614 non-null int64\n",
      "No_customers           614 non-null int64\n",
      "Freq_Supply_pay        614 non-null int64\n",
      "Nature                 614 non-null int64\n",
      "Loan_Status            614 non-null object\n",
      "dtypes: float64(3), int64(15), object(4)\n",
      "memory usage: 105.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "civil-johns",
   "metadata": {},
   "source": [
    "# Preprocessing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "tropical-steel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sr_no                   0\n",
       "Cases                   0\n",
       "Education               0\n",
       "Applicant_Income        0\n",
       "Family_Income           0\n",
       "Total_assets            0\n",
       "Business_type           0\n",
       "Credit                  0\n",
       "Loan_Amount            22\n",
       "Loan_Amount_Term       14\n",
       "Rating_Reviewportal     0\n",
       "Daily_Sales             0\n",
       "Competition             0\n",
       "Rent_Wages              0\n",
       "Size_Shop               0\n",
       "Location                0\n",
       "Supply                  0\n",
       "Total_Workers           0\n",
       "No_customers            0\n",
       "Freq_Supply_pay         0\n",
       "Nature                  0\n",
       "Loan_Status             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "tutorial-printer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill the missing values for numerical terms - mean\n",
    "df['Loan_Amount'] = df['Loan_Amount'].fillna(df['Loan_Amount'].mean())\n",
    "df['Loan_Amount_Term'] = df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "viral-killing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sr_no                  0\n",
       "Cases                  0\n",
       "Education              0\n",
       "Applicant_Income       0\n",
       "Family_Income          0\n",
       "Total_assets           0\n",
       "Business_type          0\n",
       "Credit                 0\n",
       "Loan_Amount            0\n",
       "Loan_Amount_Term       0\n",
       "Rating_Reviewportal    0\n",
       "Daily_Sales            0\n",
       "Competition            0\n",
       "Rent_Wages             0\n",
       "Size_Shop              0\n",
       "Location               0\n",
       "Supply                 0\n",
       "Total_Workers          0\n",
       "No_customers           0\n",
       "Freq_Supply_pay        0\n",
       "Nature                 0\n",
       "Loan_Status            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check whether all null values are removed\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "younger-brown",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea53f4508>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# categorical attributes visualization\n",
    "sns.countplot(df['Cases'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "permanent-cursor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7832308>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Education'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "enclosed-corruption",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea788c6c8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating\n",
    "sns.countplot(df['Total_assets'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "external-excess",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea78e2e48>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# yes / no\n",
    "sns.countplot(df['Business_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "extended-engineering",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7977788>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# yes / no\n",
    "sns.countplot(df['Credit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "secondary-manhattan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea79b7d48>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating\n",
    "sns.countplot(df['Rating_Reviewportal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "russian-sperm",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7a35b48>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating\n",
    "sns.countplot(df['Daily_Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "amended-occurrence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7ab4508>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating\n",
    "sns.countplot(df['Competition'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "radical-connection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7b21688>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating\n",
    "sns.countplot(df['Rent_Wages'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "helpful-share",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7b95688>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating\n",
    "sns.countplot(df['Size_Shop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "olympic-positive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7be87c8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating\n",
    "sns.countplot(df['Location'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "willing-canberra",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7c788c8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UhnOv029oaUvyQOBTwOuq6id951lMVXVnVT2BwQwAhydZNocAkxwNbKuqTX1nGaEjquowBrNDn9Qdzu2NpTAcp9/Yg3XH2j8FnF1Vn+47z6hU1R3ApcBRPUdZTEcAx3TH3T8BPDPJx/uNtLiq6tbu+zbgfAaHq3tjKQzH6Tf2UN2J2NOBzVX1nr7zLLYkU0n27x7fH3gW8M1+Uy2eqjqlqtZU1VoG/+++WFUv6TnWokmyb3cBBEn2Bf4E6PUqQEthCFW1Hbhr+o3NwLlLbPqN3ZLkHOC/gUcn2ZrkxL4zLaIjgJcy+Avzqu7rOX2HWkSrgEuSXM3gj5eLqmrZXba5jB0IfDnJN4Argc9V1ef7DOQlqZKkxj0FSVJjKUiSGktBktRYCpKkxlKQJDWWgrQTSd7SzTx6dXcp65MW+effnGTlYv5MaXctqdtxSktFkqcARwOHVdUvu1/ee/ccSxo59xSk+a0Cbq+qXwJU1e1Vdevcv+6TTCe5tHv89iQfS/LFJDcl+ctu/BlJLktyfpLrk/xLkrv9v0vyjrn3eUhyapLXjOuNSnNZCtL8vgAclOTGJB9K8vQhtnk8gymenwK8NcnDu/HDgTcAjwMeCTx/h+1OB9YDdIVxAnD27r8FaddZCtI8unsUPBHYAMwCn0zy8nvZ7IKq+t/uZimX8JuJza7s7sVxJ3AO8Ec7vNbNwA+SHMpg7puvV9UPFu3NSLvAcwrSTnS/xC8FLk1yDYO/5rfzmz+m9tlxk50s72x8rtOAlwMPA85YWGJp97mnIM0jyaOTrJsz9ATgO8DNDPYgAP5sh82O7e6Z/FDgGQwmqIPBPQ4O7g4NvYjBLTN3dD6DKa//kMHEi1Iv3FOQ5vdA4APdtNTbgS0MDiU9Bjg9yZv57Tu4XQl8DngE8I7uxPSjGMxA+04G5xQuY1AAd1NVv0pyCXBHt4ci9cJSkObR3enrqfM89Z/Ao3ay2Y1VNd/tFH9RVS+a5zXW3vW424t4MvCCXU8rLR4PH0k9S3IIgz2Ri6vqpr7zaLJ5PwVJUuOegiSpsRQkSY2lIElqLAVJUmMpSJKa/wfhXmLInABvtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating\n",
    "sns.countplot(df['Supply'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "convenient-excuse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7cd5348>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# value\n",
    "sns.countplot(df['Total_Workers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ruled-salon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7d2bbc8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating\n",
    "sns.countplot(df['No_customers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "announced-volume",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7a42848>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating\n",
    "sns.countplot(df['Freq_Supply_pay'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "focused-enzyme",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7e4da48>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating\n",
    "sns.countplot(df['Nature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "macro-conjunction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea7eb4a88>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# target attribute\n",
    "sns.countplot(df['Loan_Status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "recreational-digit",
   "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>Sr_no</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Education</th>\n",
       "      <th>Applicant_Income</th>\n",
       "      <th>Family_Income</th>\n",
       "      <th>Total_assets</th>\n",
       "      <th>Business_type</th>\n",
       "      <th>Credit</th>\n",
       "      <th>Loan_Amount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>...</th>\n",
       "      <th>Rent_Wages</th>\n",
       "      <th>Size_Shop</th>\n",
       "      <th>Location</th>\n",
       "      <th>Supply</th>\n",
       "      <th>Total_Workers</th>\n",
       "      <th>No_customers</th>\n",
       "      <th>Freq_Supply_pay</th>\n",
       "      <th>Nature</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>Total_Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "      <td>5849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>6091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>4941.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sr_no  Cases     Education  Applicant_Income  Family_Income  Total_assets  \\\n",
       "0      1      1      Graduate              5849            0.0             0   \n",
       "1      2      2      Graduate              4583         1508.0             3   \n",
       "2      3      0      Graduate              3000            0.0             0   \n",
       "3      4      0  Not Graduate              2583         2358.0             2   \n",
       "4      5      2      Graduate              6000            0.0             2   \n",
       "\n",
       "  Business_type Credit  Loan_Amount  Loan_Amount_Term  ...  Rent_Wages  \\\n",
       "0           Yes    Yes   146.412162             360.0  ...           1   \n",
       "1            No    Yes   128.000000             360.0  ...           5   \n",
       "2            No    Yes    66.000000             360.0  ...           4   \n",
       "3           Yes     No   120.000000             360.0  ...           3   \n",
       "4           Yes    Yes   141.000000             360.0  ...           4   \n",
       "\n",
       "   Size_Shop  Location  Supply  Total_Workers  No_customers  Freq_Supply_pay  \\\n",
       "0          3         0       3              6             4                0   \n",
       "1          0         0       5              1             1                1   \n",
       "2          0         5       5              2             3                4   \n",
       "3          2         5       3              7             4                5   \n",
       "4          3         0       3              2             2                0   \n",
       "\n",
       "   Nature  Loan_Status  Total_Income  \n",
       "0       0            Y        5849.0  \n",
       "1       1            N        6091.0  \n",
       "2       1            Y        3000.0  \n",
       "3       1            Y        4941.0  \n",
       "4       2            Y        6000.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total income\n",
    "df['Total_Income'] = df['Applicant_Income'] + df['Family_Income']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "palestinian-pierre",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea8f0fb88>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Applicant_Income_Log'] = np.log(df['Applicant_Income'])\n",
    "sns.distplot(df['Applicant_Income_Log'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "suspended-customs",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea9009a88>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Loan_Amount_Log'] = np.log(df['Loan_Amount'])\n",
    "sns.distplot(df['Loan_Amount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "becoming-reduction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea90e26c8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Loan_Amount_Term_Log'] = np.log(df['Loan_Amount_Term'])\n",
    "sns.distplot(df['Loan_Amount_Term_Log'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "unlimited-salon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea91961c8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Total_Income_Log'] = np.log(df['Total_Income'])\n",
    "sns.distplot(df['Total_Income_Log'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vital-catalyst",
   "metadata": {},
   "source": [
    "# Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "suitable-compact",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea9241688>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O+x/1Y8Kcrzkbdoa9O603XNR6sC6BMxYwZnowRYuX5NuFcwAoWbocwyfNZtTU+Xzy1VimBA7FYknKtrjSSk62sGTmWDr1G8qA0bPYvWUdZ0P/skuzbd3P5MnnycBx82nR5n8sX2CdxNuydgUA/UcG0WXACJbNm0xycjIWi4WvZ0+k26ej+XjEDIqVLMuGVcuyO7QM9u7YxtmwM4yfvZQOH/Zj+jjH5Tl93HA6dO/L+NlLORt2hn228ixRuiw9PxtKlRoZv9wpXLQYI6fOY+TUeU6fLALrOXo27AxT5n9D5x79mDxmmMN0UwKH0alHP6bM/4azYWfYs8Ma6zMvvsa4oAUEzgimdv3GLJ5rbYeefakdgTOCCZwRTLv3OlOt1v1OmyyCrDtHc+XOzctvduD1Dh9kWyy3klJ35yzl/e79bhrn9LHD6fBRX8bPWWoXZ8nSZek1MGPdXfPTcgBGzwjm02HjmDt1HMnJyVkbzE0kWyzMnjSK3oNHMXxKMNs2rCH0ZmO0oCU89oz9dfR5B2O0PHnz8dWEOSn/fP0LU7th8+wKyc6+nds4F3aGsbOW8l63vgSNd1yGM8YNp323voydtZRzYWfYt2s7AN8tmUf1+2szdtZSqt9fm+WLrZNE/gFF+GzEJEZMmc+zr77N9LGp183Zk8dQq3Z9xgQtZsTkeRQrWTrL47wZw4Dm9Ury3eoTzPvuMBXLeONd0MMuzfE/YwhefoQF3x9h16FzNKlbwu71pnVKcCrsYnZm+45s3riR06dO8cPKlXw6aBCfDxrsMN3rb73N8hU/seSbb9m3Zy+bN25MeS0+Pp4F8+dRo2bN7Mr2HUtOtrBy4SRe6TqYjgOncGjnBqLCT9ulKVyyHO9+PJYOn06iyoONWfvNTCflNnOs/dwzTA2+9TV08phhdO7Zj6nB3xAemnoNBYiKjGDf7hC7fm6tB+owLiiYsUHBdO3zCeNHOP5OxhkMAx5qWoZvVxxl9sJ9VKrgi7dXHrs0x05EM3fxfuYtOcDOveE0b1QagIrlfHB1dWHu4v3MX3qQmlUDKODp7oQoRHKm//SEEXDVNM370vz7659+oGmaU0zTnHsX8pYj/PnbUQKKFMO/cFHccuWiXpOH2Buy2S7N3pBNNG7ZGoA6jZpzZP9uTNPE3d0DV1frIrjriYkYWO+qCD9zinKVqqa8XqnafezZthFn+uPEUQKKFse/SDHccuWiftOH2L19k12aPSGbaPJQGwDqNm7OYVucHh55qFStFrly5bZLH3kunMJFS1CgoBcA1e+rw84t67MlnszasnE9jzzWFsMwqFqjJvGXLxMTHZUhXdUaNfHx9ctw/P7adfDwsHYYqlavSVTkvXtX160c3nSYuNjLzs7GHdm2eT2tWlvLsEp1x2UYEx3Flfh4qlavhWEYtGrdlq2bfrVLY5omG9etpsXDrbMz+3ck7OQJvP2L4u1XBDe3XFSv24zj+7bbpfHyDaBwiTJ2d3MBRIWfplSlGri6upLb3YPCJcrw+6Hd2Zn9v2X75g081LoNhmFQuVoN4uMuExsdbZcmNjqaK/HxVKleE8MweKh1G7ZvWg/Aiu++5vnX3iBXbmv7VMjLO7tDuKntmzbwUOvHbXW3BnFxccSmq7ux0VFciY9LE9vjbLsR27KveeG1N8l9D8Z2w+8njhBQtDgBtmtLg2atMlxbdm3bRJNWjwFQr0kLDu3blXJtqVy9VkrZpVWhSnW8vO+9+1a2b9lAy0cft6+vMenqa4y1vlauZi3Tlo8+zvbNG1JenzFhDG916JrSZwDYsHYlDZq0wN82uHZ2We/cupFmDz+GYRhUrGqN83y6OM/HRHPlSjyVqtbAMAyaPfwYO7ZY47yvdv2U/lHFKtWJiYoEwN0jtd+UmJiIMzeG+Ov3Y/gGFMM3oChubrl4oGFLDuzcapfmwK4t1Gv2CAD312/G8UN7ME2Tc6GnqFTjAQA8C3qRJ19+Tv95HEwTTJPEhKuYpsnVq1co6OX8erxz20aatWpjK8/qxMfFOSzPq2nLs1Ubdmy19l2LlypDsRKlnJH1v23Hlo20eMQaa6WqNYiPv/052uKRNoTYztG8+fKnpEu4djXDNRZg09pVNG35aNYGchtZdY565MlDlRr3OWyXnWHn1o00fzi17l65Sd1NG2fzh9uwc8ut627oqZPUuL82AAW9vMmb35M/ThzN+oAccDhG22Z/Hd29fRNNW91ijHaL8joXdoZLF85TuXqtLI3jZnZu20jTVra6WqU68fE3b38q2sqwaavH2LnVWld3bdtEM1vszVq1YadtTF2pWk3yexYAoELlasREW+vwlfh4jh7cR8vWTwDglisX+fJ7ZkusjgT45uPi5QQuxSWSnGxy4mQsZUsWskuTeD11sjKXmyuYqa+VLVmIi3EJxFy4xr3u13XreOKppzAMg5q17uPy5UtE2dqWG/LkyUPdevUA64RnlapViYhIXXE1cdxY3nznHdzd7/0v3cNPnsDLvyhefkVwdctFtdpNOb5/m12a0pVqkSu3dYKwWJnKXLoQ7eij7hkhWzbS4tE2f6uf2+LRNnb93KAJY3gzXT83T968KdfTm11bnaWwf34uXLzGxUsJJCebHP89mvJlvOzSJF63pPw/l5uLtb8HYJrkyuWCYYCbqwuWZJPERAsikj3+6xNGGRiGUdowjE2GYeyx/WtoO97cMIwNhmEsMQzjhGEYQw3DeNUwjB2GYRw0DKOcLd1AwzB6pvvMhwzDWJbm54cNw/g2k3k5ahjGdMMwDhuG8YthGHlsr5U3DGONYRj7bfksZ1iNMAzjkC1PL/7NvPsZhvGNYRg7bf8a3b2/rGPnY6Lw9vVP+dnL189hJ/ZGGldXN/Lky0fcZetdPn8cP8zHndsx4IM3eaNTT1xd3SheqgzHD+8n7tJFEhKucWD39pROrLOkj9Pb15/zMVEZ0/ilxpk3bz7iLt38bqbCRYoRHnqKqIizWCxJ7N62kVgnx5ledFRkyhdwAL7+AURH3Vkef/p+GfUaNL5bWZNMio6KxM8/dem+r78/MVHpJoyiovD1T63fvn7+Gcr50P49eHl5U6xEyZRj586G0enNl+nZ+V0O7tuTRRH8fZfOR1PAK3UCs4CXL5fOx2TqvQElyvD7wV0kJlwj/vJFTh47wKXYjJOk94roqCj8/NOco34BRKdrR6KjI/H1C7BPY6sD4WdOc3j/Pj5s/wa9u7TnxNHD2ZPxTIiJjsQ3Xd2NTjdhFB0dZR+bf0DK9SLszGkOHdjLh++9Tq8u73E8TWznzobR+a1X6NXlPQ7t35vFkdzc+egofNLk39vXj1gH1xYfX2uaG9eWy7e4ttzLYqLsy8vHzz/li9bUNJH4+tm3RzfarJAtG/Dx86NM+Yp27wk/c5q4uEv069aBD9u3Y92qFVkYxe3FRjuI08FEvU+afoWPn3+GCVGAtSt/4P66DVJ+PnH0EN3eeYmP3nuFDh/2TfnSOrtdjI3GyydN/8/Hl4vno26axtXVlTx58xF/+RLFSpXj4M4tWCwWoiPPcubPE5yPicLVzY0X3/2QL3u9S//3n+dc6F80bPlYtsblSGx0FD5prpE+vhnLKjY6yu7GmZuVZ3qR58Lp9f7rfPpRR44e3Hf3Mn2HMrS7vv4Z+uAx0ZH4+NnX3bRp5s2YxNsvtGXDmpW88lYHu/cmXLvGnp3badDUfnVvdsvKc/ReEhMdZVdW3jeN0++WadIrXbYCO7ZuwmJJIuJsOH+eOEaMk24Ki42xL6e7MUZLa+uG1dRv+pDTvqBN30/wcdBPiHU0TrWV4cXzsXj5WCfevXx8uXThfIbf8evKH7ivjrUOR54Lo0DBQkwe9Tl9Or3OlDFfcu3a1bseV2blz5uby/GJKT/HxSeSP2/GCb6alf1449nqNK5dnA0h1lUqbm4u1K5emJB9/44triIjIwgonNqnDwgoTGTEzcfdly5dYsP6X6lX31p2R48c4dy5czRr7tz2NbMuXYihQJqbQgp4+XL5ws3Ha/u2rKJ8tdrZkbU7FhMVid8d9XOtaUK2bHTYzwXYtulXOrZ7nsF9P+KDPgOyKIK/L3++3FyOS0j5+XJcIvnzZZywvK96AO+8ej9NG5Zi3ea/ADjxZyzXryfz/pu1af/6A+zaF861BOesnBfJif7rzzDKYxjGjdHVSdM0nwEigYdN07xmGEYFYCFw48pSC6gCxAJ/AjNM06xrGEY3oCtws42J1wETDcPwM00zCngLmJXJPFYAXjZN8z3DMJYAzwHzgWBgqGmaywzD8MA6ufcscJ8tn77ATsMwbiytyUzexwJjTNPcbBhGSWCV7T1ZxjQdHDTSp8mY6MYdE+UqVePLifMIP/MX0wO/pMaD9ShaojRtnn2VEZ92x90jLyXKlMfV1TULcp95jmJIH6jDNLcYXOTzLMBbnXsyYeinGC4GFarUIPLcvdWhzUzcmbH65xUcP3qEwClB/zxT8vdkolqaDhKlHxj/unoVzdOsLvL28WX+tz9RoGAhfjt2hIH9ejBt/lLypbm7+F6S2XF++WoPEn7yBEFDe5Avf0FKlKuMi5Pbn1ty1L5mKOCbp7FYkoi7fIkxU2dz4uhhvvqsHzMXL78n7ly71bXjVmlutFEWi8Ua27Q51tg+7cusJd/j5ePL3G9W2OruUQZ/3IMp85Y4pe46zn36GB2kuQfK587cvr46bo/g2rVrLJk/i8EjJmR43WKx8MfxY3w+ehIJCQn06vw2lapWd9qqDod1N32RZaLP8HXwLFxdXWn6UGrbW7FKdcYGLSL01EnGDx/M/XUbkDt39t9JnKl+kcMLEDRo8RgRYacY3u99vP0CKFOxGq4urliSkti0+nv6DJ2Kb0BRls4axy/LFtD6OedulZmZ/t3N6u2teHn7Mjl4OZ4FCvLHiWOMGNib0dMXkjdfvn+S3X/EYagZOvYO0qQJtt27nWj3bie+Dp7NimVLeeWt9imv7di6iSrVazp1OzrI2nP0nnLHfYRbf2zLx9oSevov+nR6C1//wlSqVgMXJ01eZybGvztGS2vbhrV06vnJHWXtbnDY0t7F+A7t2826VT8wePRUwHo9Pfn7Cd7q3IMKlasxe/IYli+ey4tvdLjNJ2UfR+3tgWNRHDgWRaUy3tSpVYTVm/+i/n1F2XskgutJztku8W/LzPlqk5SURN+ePXnltdcoXqIEycnJjBw2lMFfOt6++d50+37+DQe2r+Psqd94vYfjLRnvZZnpsxuGQcK1ayydP4tBI8Y7TNOgSQsaNGnBof17CA6aypDRE+92Vu+Iw/Ac1OV9hyLYdyiCyhV8qf9gMVau+4PC/vmtz+Odsxt3d1deero6p0MvcvFSgoMPFZG77b8+YXTVNM30G4LnAiYYhnEfYAHSTs/vNE3zLIBhGH8Av9iOHwRueiuGaZqmYRjzgNcMw5gFNABev1n6dE6apnljUms3UNowDE+gmGmay2yff82Wp8bAQtM0LUCEYRgbgDrApUzmvRVQNc1FqYBhGJ6madrtpWUYRnugPUDvQSN4+sXMhpKRt6+f3aqY89FRGbbBuZHG29cfiyWJq/Hx5LMtgb+haInSuHt4EHbqJGUqVKbZI21p9khbAL6eOxWvNHdNOYO3r79dnLHRkSl3a9mliYrExxbnlSvxKUv9b+aBeo15oJ511c26n5fj4uL8RYHLli5ixXLrArrKVasRmWaZe3RkBL5+Gbeeu5XdO7Yzf/YMAicHpWwNJVnr+28W8/P31kWRFatUs9sKMDoyEu902wf6+vkTHZlav6OjIu3uNrUkJbFlwzomzAxOOZY7d+6U8qxQuSpFixUn7PRpKlapmiUx/R3WFUWpd19eOh+NZyGfTL+/aduXadr2ZQC+njYMb/+idz2P/8QP3y5h1Q/fAda/fVRkmnM0KgIfn/TlG0B0VIR9Gl/flNcaNmth236oOoZhcOnCBQp62W8lkF1++GYJK3+4UXerEp2u7t7I9w1+fv72sUVGpNRdXz9/GjVtaRfbxQsXKOTllabuVqFI0eKEnTlNxcrZX3e9ff2ISZP/2OgoB9cWP2KiI/Dxy/y15V6yYtkSVv2YWl/TlldMlKP2yH4la7QtzbnwUCLOhvPBO6+kHP+w/WuMnjwbHz9/ChQshEeePHjkyUP1Wvdz8o/fsnXC6OflS1Oe61G+ooM4052X6VdlWNOklv2vv6xg9/bNDBwx0eGXDcVLlcHdw4PTJ/9MeRB9dirk48f5mDT9v5joDNvHFfK2pvHy8cNisXD1Sjz58hfAMAyeeyP1mSGjPumCX5FihP71OwB+hYsB8ED95vyyfGE2RJPRyuVfp5ZnpSrEpLlGxkTblxVYVx2lXZURE2WN+1Zy5c6dshVWuYqVCShSjLOhpymXzeW5YtlSVq+wnqPlK6drd6MznqPp75h2VL8Bmj70KEP6dbebMNr06y80afnI3Q4hU7L7HHWWn5d/zVpbnOUqVrErq9goB3U33Yqi2JuUZ1qurm681Sn1PsuPP3iPIsVL3OIdWcc73Sq42OhICmUYi/79MRrAqT9/I9lioUyFjA+tz0qrvv+atT9/D9wowzR11cFY2+cW49SCXt6cj4nGy8eX8zHRFCiU2r879efvTAv8ir6fj06ZxPXx9cfHz48KlasBUK9xC5YvmZc1gWZC3JVEPPOljh/z58tN/JXrN01//GQsLRqUZDVQ2C8fFUp70bh2cdxzu2KakGRJ5sCxe2fngEULgvl26dcAVKtRnYhzqX36iIhz+Pk7PhcHf/YZJUuV4rXX3wCszy76/bffePcN6/c60dHRdOvcibETJ1GtevUsjuLOFCjky6XzqTvTXDofTf5CGbcU/vPoXjb/vJg3egzDLVeu7MxipqxYtpRf0vRzo27Tz/VJt5PHjX7uWVs/t9s7r6Yc/7B9O0ZNnmU3Nqhe6wECwwdx6cIFChSy357RGS7HJeKZP/XGJc/8uYm7knjT9Md+i6ZV0zLAH1Sp4MvJ0xdITja5ejWJ8HOXCfDLrwkjkWzi/G+fs193IALripzaQNpvqNO2PMlpfk7m9pNrs4DXgJeBpaZpZnatZNrfabH9npuNKm412shM3l2ABmme6VQs/WQRgGma00zTrG2aZu1/MlkEUKZCZSLCQ4k6F07S9euEbFrL/fXstx27r25jNq9bCcDOLeupUvMBDMMg6lx4ysOaoyPPcS7sNL627c9uLJePiYpg17aN1G/a6h/l858qW7Ey58JCibTFuX3j2pSJnhseqNeYTWt/AmDH5vVUrfngbQeQF21xxl++xJoV39L80SeyJoC/4ZnnX2LG/CXMmL+ERk1b8MvPP2KaJkcOHiBf/vwOn1V0M78dP8booZ/zxYhAvLzvveeH/Fc9+dyLTJ6ziMlzFtGwaXPWrLSW4dFDB8jroAx9fP3ImzcvRw8dwDRN1qz8kQaNm6e8vmdXCCVKlbbb2u7C+fNYLNY9hs+GhRJ25jSFixXLlvhup2jpisREhHM+6hxJSdc5tGMDlWrVz9R7k5MtXIm7BMC5MyeJCD1JuWoPZmV2/7Ynnn2BCbMWMGHWAho0ac7alT9hmibHDh8kX/78ePum/6LElzx583Hs8EFM02Ttyp+o37gZAPWbNGP/7l0AhJ4+RVJSklMHH0889wITZy9k4uyFtthW2Orujdjs6663rx958ubj6KEbsa2gfhNrbA2aNmffnp1AamwFCxXKUHfDQ09TpKhz6m65ilU4F556bdm2YQ0P1re/tjxYvwmb1vwMQMimX6lW6/bXlnvJ48+8wLigBYwLWkD9xs1Zt2pFSn3Nmy9/hi8vvX18yZM3b0p9XbdqBfUbNaN02fLM/+4XghZ/T9Di7/H18ydw2ny8fHyp37gZhw/uxZKUxLVr1zh+5BAlsvkh3Y899Tyjps5n1NT51G3UlA2rf8Y0TU4cscaZfiLQy8eXPHnycuKINc4Nq3+mTsOmgPVB9d8tmkvfISNx90h9uHfE2dR+U2TEWcJDT+NfuEj2BZlGqXKViToXRnTkWZKSrrNn6zpq1rbflqtG7YaEbLDe37R3+wYqVrsfwzBITLhGgm2Lo6MHduHi4kqR4qUp6O3LudBTXL50AYBjB3dTuJhzVom1fup/jJw6j5FT51GnUTM2rPnJVp6HblOeh6zlueYn6jRoesvfcfFCalsUcTaMs2Gh+BfJ/hsUHn/meQJnBBM4I5j6jZrx6y/WWI8fOUi+W5yjx21199dffqJuI2us4aGpDyzfsXUjxdKch/FxcRzev5d6jZplS1zpZcc5ei94LE3drduoGetX/726u371Tylx3kzCtWtcu2o9h/fvDsHV1ZUSpcpkWUy3UrZiZbvr6PaNazNcRx+o15iNa1LHaNUyMUYD2LZhDQ2aZ/8Y9NEn/8fwyXMZPnkudRo2ZeMaW109eoi8efM5LEOPvPk4cdRahhvX/JzS/tSu35gNttg3rPmJ2g2aANax96jBfenc61OKFk/dbrqQtw8+vgGEnzkFwKF9uyiezdfTtCKi4ylUwIMC+XPj4mJQsYw3f565YJemkGfql9Vlihfkgu3L5q9/Ps6srw8y6+uD7D0Syc4DZ++pySKAl155lSXLlrFk2TJaPPQQPyxfjmmaHNi/j/yenvj5ZbxpdsLYQOLiLtO7X7+UY56enmzYuo2f16zl5zVrqVmr1j09WQTW8VpsZDjno89hSbrO4V0bqZhuvHb29B/8NH88L3b6lHwFnD854sjjzzzP2KBgxgYFU69xM35d9dPf6uf+uuon6jVqSumy5Zn33SpmLF7OjMXLbf3ceXj5+BIeeiZlJeEfJ46RlJSEZ0HnrtS94VxkHIUKelDA0x0XF4NK5X3546T91peFCqZeJ8uW8uL8ReszxS7FJVCymDUONzcXigTkJ/aC87bAlFszXAz9+4f/7jX/9RVGjhQEQk3TTDYM4w3gruwlZJpmuGEY4cAA4OF/+FmXDMMINQzjadM0vzMMw92Wz41AB8Mw5gDeQFOgF5DZ25p+AboAIwAMw7gvzeqmLOHq6sZrHbozcmAPkpOTadLqcYqVLMO3wTMoU74y99drTNOHH2fa6M/p3f4l8nkWoGOvgQCcOHqAFUOCcXVzw8UwaPf+R3jaOgIThg4g7vJFXF3deP397k592CZY43yjY3eGf/IRyckWmj3cluKlyvL1vOmUqVCZB+s3odkjbZkycggfvfsC+T0L0KX3oJT3f/jWc1y9Ek9SUhK7tm2i7+djKFayDPOmBnL6pPWO2mdefosixUreLAtOUb9RE0K2bua1557A3cODPp+kxvTuay8wY/4SAKaMH8PaVT+TcO0az7d9hMefeoY33+vIlPFjuHrlCgM/7gVAQOEifDFyrFNi+Sd6LuhF9eY1KOBbgJlnZrPws2BWz1zt7GxlSt0Gjdm5bTNvvfAU7h4e9Ph4YMprHd94iclzFgHQtefHjPziMxITEqhdvyF1GqQ+Am3Dml9o3sp+u5WD+/Ywd8ZkXN1ccXVx5YNeH1PAyVvM3ODq6kqbVzoyL3AAZrKF+xs9gn+xUqz7bi5FS1ek8n31CTt5nEWThnAtPo4T+0NY//18Og+eisViYeYw62Ps3PPk5dl3ezl9S8xbqdOgETu3b+Gdl57G3cOD7v0+S3mty1uvMGHWAgA69+jLmC8HkmAr39r1reX7yONPEfjVYDq+/gJubrn46OOB98xkRJ0Gjdm5bQtvv/gUHh4edE9Tdzu/+TITZ1tXHnTp2Y/RXwwkIeEadeo3ok6a2MZ8NYj3272AWy43evS3xnZo/x7mzZiCq6srLq4udOn5sdO2R3J1dePNjh8xdEB3ki0Wmj9ivbYsnTudshWt15bmj7Zl0ojBdH/7efJ5FqBr38Ep4V5S1wAAIABJREFU7//gjWdTri27t26k7xeBFC9VhgVBE9n66y8kJlyjy2tP0bz1E/zvtXedEmNates3YlfIFtq/+gzu7h506/NpymsfvPMK44Ks9bVT974EDh1EYmICD9ZtyIP1Gt7yc0uUKsODdRvS9Z1XMAyDRx5/ilJly2dpLLfyQL1G7Nmxlc6vP4e7uwede6VuZ9Sjw2uMmjofgPbd+jBhxGASExK4v24DHqhrjXPGhJFcv57I4D5dAes2dB0+7MvRQ/tYtmgubm5uGIYL733QmwIFnfMFiqurKy+83ZWJX/bBTLZQv/ljFClRhh+XzKJk2YrUrN2Ihi3aMHfClwz84DXy5ffkrW7Wv8PlixeY+GVvDMOFQt6+vNHF+qVXIW9fHvvf6wR+9iGubm54+/rzWqc+TokvrQfqNmRvyFa6vvE/crt70Lln6rMDenZox8ip1rvv3/ugNxNHDiExIYH76jRIea5NyOb1zJw4iksXL/DVgI8oXa4iA4aO5ejBvSyeM93aFrm40L5bb6dv1fZg/UbsCtnK+689i7u7B137pNbdD999lcAZ1pXG73fvw7ihg0lMTOCBNOfo3GkTCTtzCsPFBf+AwnTs3jfl/ds3r+e+2vXwyJMne4NyIKvOUYD3X33a2i5fv86OLRv4dNg4SpQqm80RWj1QryF7dmyly+v/w93dg069blJ3u/Vm4oghKXGmrbtBE2x1t7+17n4ybCwXL8Tyed8PMVwMvH38+KDvZw5/f3awXke7M2yAbYz2SMYxWvNH2zJ55BA+eucF63W0T+p4ptub6cZoX4yheEnr5Nf2TevoPWiks0ID4P66Ddm7cyvd3nqe3O7udOyRWoa9O77O8MlzAXi3ay8mjfyc64kJ3Fe7fsoziZ568XUCv+jPryt/wNc/gO79vwDg6+CZxF2+RNAEa3yurq58NcG64/5bnT9i/LCBJCVdx79wMTr26J+dIdsxTVi//TRPP1wRw4Ajv8cQe+Ea9e8rSkRMPCfPXKRmFX9KFilAsmlyLSGJXzafdFp+/4kmTZuxeeNG2rZ+FA8PDwZ/8WXKay888wxLli0j4tw5pk+dSpmyZXnpuecAeOnVV3j2f887K9t3zMXVldYvdWTB2AGYycnUavQI/kVLsf77eRQpVYFKteqz9psgEhOu8c0061Z7Bbz9eKmz89qb26ldvxG7Q7bS4VXrNfSDNNfQbu+8ytgg6zW0Y/c+jHVwDb2ZbRvXse6Xn3BzdSO3uzu9P/3inhmvmSas23SS556ogothcOhYJDHnr9KwTgkiouL446/z3F+jMCWLFyQ52XqOrlxr/f5r38FzPNqyPG+8VAsDOHQsiuiYK84NSCQHMRzvMf7fYBhGnGma+dMdqwB8A1wBfgW6mqaZ3zCM5kBP0zTb2tKtt/28K+1rhmEMBOJM0xxpGMZs4EfTNL+2vecl4EPTNG95q7phGH9hXd2U3/b+6rbjPYH8pmkOtOVzKtZnFV0HngdOAsOBx7Bu6vq5aZqL/0befYGJWJ9b5AZsNE3z/VvlddvxyP9uBUnDzTVnLLYr5uu8fe+z0/te/75O8Z0YF7XY2VnINtuOOudhydmtbqW/t6Xjv9W9MojJaucv55wtEzzz3HvbgGSFxH/Lsw7+obMx8c7OQrYI8M7r7Cxkm9xuOaOvm2TJEUMXx88A+w9KSLQ4OwvZIqeMRQE2rP/T2VnIFh3aPeDsLGSLrzf9Oyfg/q46lXxvn+g/4sdlR5ydhWzRo1ODnDEgzSZD/AbmjI5JFvokauA9VSf/0yuM0k8W2Y79BtRMc6if7fh6YH2adM3T/D/lNdM0B6Y5/ma6j28MTM9Evkrb/hsNVE9zfGSa//8GtHTw9l62f2k/L7N5jwZevF3+REREREREREREREQkZ/lPTxhlJ8MwdgPxQA9n50VEREREREREREREROTv0ITRXWKaZoannhuGEQK4pzvczjTNg9mTKxERERERERERERERkdvThFEWMk2znrPzICIiIiIiIiIiIiJy1+WQZxTnJDnn6YoiIiIiIiIiIiIiIiLikCaMREREREREREREREREcjhNGImIiIiIiIiIiIiIiORwmjASERERERERERERERHJ4TRhJCIiIiIiIiIiIiIiksO5OTsDIiIiIiIiIiIiIiLyL+NiODsHcpdphZGIiIiIiIiIiIiIiEgOpwkjERERERERERERERGRHE4TRiIiIiIiIiIiIiIiIjmcJoxERERERERERERERERyOE0YiYiIiIiIiIiIiIiI5HCaMBIREREREREREREREcnh3JydARERERERERERERER+ZdxMZydA7nLtMJIREREREREREREREQkh9OEkYiIiIiIiIiIiIiISA6nCSMREREREREREREREZEcThNGIiIiIiIiIiIiIiIiOZwmjERERERERERERERERHI4N2dnQERERERERERERERE/l0Mw3B2FuQu0wojERERERERERERERGRHE4TRiIiIiIiIiIiIiIiIjmcJoxERERERERERERERERyOE0YiYiIiIiIiIiIiIiI5HCaMBIREREREREREREREcnh3JydARERERERERERERER+ZdxMZydA7nLtMJIREREREREREREREQkh9OEkYiIiIiIiIiIiIiISA6nCSMREREREREREREREZEczjBN09l5kHvYsfCLOaKCXIhLcHYWsoUlOUcUJ8V88zs7C9niA78XnZ2FbDPq7AJnZyFb5JRz9PKV687OQrZwc8s5ezkb5IxYkyzJzs5CtvDMm8vZWcgWyTmjOHOU6ItXnZ2FbFHI093ZWZC76GJcorOzkG3yuOeMx2hbcsgFxtvTw9lZyBY5pTwBEq7njFhrlvTKGYOXbPJ5iS9yxhcZWWjAmf73VJ3UCiMREREREREREREREZEcThNGIiIiIiIiIiIiIiIiOVzOWA8sIiIiIiIiIiIiIiJ3j3FP7aYmd4FWGImIiIiIiIiIiIiIiORwmjASERERERERERERERHJ4TRhJCIiIiIiIiIiIiIiksNpwkhERERERERERERERCSH04SRiIiIiIiIiIiIiIhIDufm7AyIiIiIiIiIiIiIiMi/jIvh7BzIXaYVRiIiIiIiIiIiIiIiIjmcJoxERERERERERERERERyOE0YiYiIiIiIiIiIiIiI5HCaMBIREREREREREREREcnhNGEkIiIiIiIiIiIiIiKSw2nCSEREREREREREREREJIdzc3YGRERERERERERERETkX8bQepT/GpWoiIiIiIiIiIiIiIhIDqcJIxERERERERERERERkRxOE0YiIiIiIiIiIiIiIiI5nCaMREREREREREREREREcjhNGImIiIiIiIiIiIiIiORwbs7OgIiIiIiIiIiIiIiI/LsYLoazsyB3mVYYiYiIiIiIiIiIiIiI5HCaMBIREREREREREREREcnhNGEkIiIiIiIiIiIiIiKSw2nCSEREREREREREREREJIfThJGIiIiIiIiIiIiIiEgO5+bsDIiIiIiIiIiIiIiIyL+Mi+HsHMhdphVGIiIiIiIiIiIiIiIiOZwmjERERERERERERERERHI4TRiJiIiIiIiIiIiIiIjkcJowEhERERERERERERERyeHcnJ0B+e8zTZPp40exO2Qr7h4edOvzKeUqVs6Q7vfjRxk3bDAJCQk8WK8h73XtgWEYBM+cQsiWjbgYBgW9vPmgz6f4+Prx7aJ5bFyzEgCLxULo6b+Yu2wVngUKZneIABzYHULw9LEkJyfT7OG2tH3+NbvXr19PZNroL/jrj+Pk9yxAp96D8Asowh8njjB7wgjA+rd6+pW3qd2gKWdDTzNp+Gcp7488F86zr77Do0+9kK1xpXdwTwgLpo/DTE6mycOP8/j/MsY5Y8wXnPrjBPk8C9Cx10B8A4qkvB4TFcGALq/z1Etv0vqZlwFY/cNSNv7yI6Zp0vSRtjzypHNjBGtZTA4cwY5tm/Hw8KBH/0FUqFQlQ7rfjh1h5BcDSUi4Rt0Gjen4YS8Mw+CLT/oQevoUAPFxl8mX35PJcxZx7mw4773yHMVLlgKgcrUadOvdP1tju1MfBHWjdts6XIy8SNcanZ2dnX/ENE2mjRvFrpAtuLt78GG/zyh/k3ZpzFeDSExMoHa9RrT/wNYuzZrGqh+/o2ChQgC8/l5n6tRvlN1hOJRVbW7c5UuMGz6Ec+Fh5M6dm669P6FUmXJOiNBq/67tzJsWSHJyMs0feYInX2hn9/r164lMGTWEk78fx9OzIF36DsYvoAgH9+5g8awpJCVdx80tFy+/05lqtR4EYMmcqWxet5L4uMsEfbMmW+PZt3M7c6YEkmyx0PKxJ3jqxdft40lMZOKIIZz87Rj5CxSk28dD8C9sbVu/WzSXX1f+gIurK292/JBatevf8jNXLv+an5ctJuJsGNOW/ESBgtZ6HHf5ElNHf0nE2TBy5crN+z0+pkTprC1j0zSZPXkMe3dY62vHHp9QtkKlDOn+/O0Yk0YOITEhgfvrNuTNjt0xDIO4SxcJ/PIToiLO4hdQhA/7f05+zwJsWreK75fMA8DDIw/vdO1N6XIVAJg86nP2hGylQCEvRk0LztL4HNm/azvzptrq7qOO6+7kkUP46/fj5PcsSNd+1rp7+dJFxn7Znz9PHKNpq8d4s1OPDJ89alBvIs+FM2zy/OwK56ZutEW7tlvL9sO+N2+Lxg61tkW166e2RTcsWzSfWVPGMf+7Xyhga3PBev3t1elten36BY2aP5QtMTlimiYzJqS2uR/0vkmcJ6xtbqKtzX23S2qbu2PrRgzDoGAhb7r1+RRvX7+U9/127Ah9urxNz0++oGEz58UJWRdryJYNLJg1FcMwcHV15Z3OH1G1xn1OiDCjQ3tCWBg0nuTkZJq0epw2z71q9/r164kEjf2SU3+cIL9nATr0/Axff2vbfOavP5g3eSTXrl7BMAwGjJhKrtzuzgjDIdM0mTlxNHt3bCO3uztden9C2QoZy/OPE8eYOHwIiYkJ3F+3AW93/gjDMNi6YS1L5s4g7PRffDVhJuXT9JP/+vM3po0ZxpUr8bgYLgydNJPcToo9q+JMSkpi8qgvOfnbcSzJSTRr1YZnX3kju8NLcXBPCAtnpI7R2jyXcYwWFJg6Rnu/p3WMFh1xlgFd21G4aEkAylaqyusdewIwZlBPLpyPIdlioULVmrzWvjsurq7ZHlt6+3dtZ+6UQJKTLbRo/QRPvpCxzzR5VGqf6YN+Q1Kvo1/0548TR2n6cBvesl1HE65dY+yX/Yk4G4aLiysP1GvEy293ckZodg7s2s68aWNtfd22POGgvzB11Oec/N36/UJqX3cnS2ZNJikpCTc3N15K09dNun6dOZNHc+zgXgwXF55/vT11GjV3QnSpTNNk6riR7NpuHZd17zeQ8pUynqO/HT/KmC8HWsdl9RvR4YOetmvLVFb9+B0FCnkB8MZ7najToDERZ8N5v93zFLsx7q5anS49P87W2NLKqn7R+tUr+WbhXADy5MlDx+59KFO+YrbF5YhpmsyaNJo9O7bh7u5O5163aHdHWNvdB+o24K1O1nZ324a1LJlna3fHz6Scrd397dhhpo4ZeuO38Hy7d6nXuHn2BSaSg2iFkWS53SFbORt2hinzv6Fzj35MHjPMYbopgcPo1KMfU+Z/w9mwM+zZsQ2AZ158jXFBCwicEUzt+o1ZPHcGAM++1I7AGcEEzgim3XudqVbrfqdNFiVbLMydMpoeA0fy1cR5bN+4hrDTJ+3SbPxlBfnyezJi2iIefeoFlsyeAkDxkmUZOGY6Q8bNouegkcyeOAKLJYkixUsyZNwshoybxaAxM3B39+DBBk2dEV6KZIuF+VPH0P2zEXw+YS4hm9YSdvovuzSbVlvjHDp1IY88+QJL50yxe31R0HhqPFAv5efQU3+y8ZcfGTByKoPGzmT/zm1EhJ/JjnBuaee2LYSFnmbW4uV06z2A8SO/cphu3Miv6NanP7MWLycs9DS7tm8FoP+QYUyes4jJcxbRqPlDNGrWMuU9RYoVT3nt3zJZBLB29hoGtv7s9gn/BXaFbCU89DTTgr+lS8+PmTR6qMN0E0cPpUvPj5kW/C3hoafZHbI15bWnn3+Z8UELGB+04J6ZLIKsa3OXBs+mbPmKjAtawIf9BjJj/KjsCimDZIuFOZNH0XvQKIZPDnbY5q5f9SP58nsyesYSWj/9IotmTQLAs0Ahenw2jKGT5tHhowFMGTU45T0P1GvEoDHTszUWsMYzc+JI+n4+ilHTF7Dl1zWEnrKP59dVP5A/vydjZy/l8WdfZEGQNZ7QUyfZun4NI6cF0++L0QRNGEmyxXLLz6xUrQb9h47DN6Cw3e/4btFcSpWrwPAp8+jU6xNmTw7M8tj37dzGubAzjJ21lPe69SVo/HCH6WaMG077bn0ZO2sp58LOsG/Xdmuel8yj+v21GTtrKdXvr83yxdZJIv+AInw2YhIjpszn2VffZvrY1HO82SOP0++LMVkemyPJFguzJ42i9+BRDJ8SzLYNawi9Wd0NWsJjz7zIwpnWss6VOzfPt3uPV95xPGG/c8t63D3yZnkMmbU7ZCvhoWeYGnzrtmjymGF07tmPqcHfEB6a2hYBREVGsG93CH7p6qrFYmH21PHcX6d+lsaQGTfa3MnzvqHTR/2YEug4zqljhtHpo35MnpexzR07YwGB04Op06Axi+fNSHmPxWJh7rTx3Ffb+XFC1sVa84E6BE4PJnB6MF17fcLEkV9kW0y3kmyxEDwtkA8/Gc6QcXPYsXkt4Wf+skuzec0K8uXz5KvJC3j4ief5eu5UACyWJGYEfk6793sweNwceg0Zi6vrvXWv5t4d2zgbdobxc5byfvd+TBvruP2dPnY4HT7qy/g5Szkbdoa9O63lWbJ0WXoNHEqVdJN7FksS474aSPsP+xAYtJBBoyY5NfasinPbhrVcv57I6BnBDJ80h9UrlhF5LjzL43Ek2WIheOoYun86giHjrWO09HV10+oV5M3vyVdTFvLwky/w9dzUMZpf4WIMDJzJwMCZKZNFAO/3GsSgwFkMHjeHyxcvsHPr+uwJ6BaSLRZmTRxJ7yGjGDF1AVvXZ+wzrf/lB/Ll92TMzKU89rT9dfR/7d7j1Xe7ZPjcx597hVHTF/HVhNmcOHKQfTu3ZUiTnax93dH0GjSSYZPns81BX3eDrb8wasZiWj/9IotnTQbAs0BBPvpsOF9Nmkv7jwYwddSQlPcsXzyXAoW8GDF9EUMnz6dydedPzu/avoXw0DNMX7CMrr36M3G043H3pFFf0bVXf6YvWEZ46Bm7cdlTz7/ChJkLmDBzAXUaNE45XqRYsZTjzpwsgqzrFwUUKcpXY6cwfuYCXnz9HSaOcvz3y04p7e7spXT4sB/Tx92k3R03nA7d+zJ+trXdvXHelShdlp6fZWx3S5Yux7BJsxg5dR79vwxk2thhWCxJWR6PSE6kCaM0DMPwMQxjn+3fOcMwwtL8nNtBem/DMN7PxOe6GYZxIWtyfdvf3dIwDKeOMnds2UiLR9pgGAaVqtYgPv4ysTHRdmliY6K5Eh9P5Wo1MQyDFo+0IWTzBgDy5sufki7h2lW7uytu2LR2FU1bPpq1gdzCn78dJaBIMfwLF8UtVy7qNX2IPSGb7dLsCdlE44daA1CnUXOO7N+NaZq4e3ikDKKuJyY6jO/w/t34FSmKr3/hDK9lpz9/O4p/4TRxNnmIfTvs49wbspmGLa1x1m7UjKMH9mCaJgB7tm/CL6AoRUuWTkl/NvQUZStWxd3d+neoVP0+9mzflG0x3cy2zetp1bothmFQpXpN4i9fJiY6yi5NTHQUV+LjqVq9FoZh0Kp1W7Zu+tUujWmabFy3mhYPt87O7GeJw5sOExd72dnZuCtCNm+g5aOPYxgGlavVID7Ocbt09Uo8Vapb26WWjz7Odlu7dC/Lqjb3zF8nqflAHQCKlyxNZMRZLsTGZFNU9v44cZSAosXxL1IMt1y5qN/0IXanazf2hGyiyUNtAKjbuDmHbW1u6XIV8fKx3r1fvFQZricmcv16IgDlK1fHy9s3e4MBfj9+hMJFixNgi6dh81bs2mYfz65tm2j68GMA1GvSgsP7dmGaJru2baJh81bkyp0b/8JFKVy0OL8fP3LLzyxTvlLK6qS0wk6fpPp9tQEoVrI0URFnuXA+Nktj37ltI01bPYZhGFSsUp34+DjOp6uv523nYsWqNTAMg6atHmPnVmt93bVtE81aWcu5Was27Ny2EYBK1WqS37MAABUqVyMmOjLl86rWuD/ltezmsO6mK+vd2zfRtFXGuuvhkYdK1WqRK3eGLinXrl7hp2WLefpl593dnl7Ilo20eLTNbdtZu7bo0TZ27WzQhDG82aErBvZ9ox+/XULDpi0paLub2Jl2bN1I84fTtLk3i/NKapzNH25DyJaMbe61a1ftYl2xbAkNmrakoJfz44SsizVPnrwp15prN+nrO8PJ347iX6QYfrZ+b93GLTP0e/ft2ELDFtYxyIMNm3HM1u89vG8XxUuVo0SZ8gDkL1DwnliZkdbONOVZsWp1rsQ5bn+vXImnkq39bf5wG3ZusbazxUuVoViJUhk+d/+uHZQqWz5lVadnwYK4OjH2rIrTMAwSrl3FYkkiMSEBN7dc5MmbL1tiSu/PDHX1IfaGpK+rm2nYwjZGa2g/RruZG/FYLBYsSUncC6fm7yeOEJCmf9OgWasMfcBd2zbRpFVqn+mQrc/k4ZGHytUzXkfdPTxSVuC45cpF6fIViU3Tb3CGjP2FVuzenv77hc00fsgaZ8a+rrU/m76vu3H1ipSVSi4uLngWLISzbd+8gZbp+wvR6a4t0dZzNHVc1oZtm9Y7J8N3KKv6RVWqp/Z5K1WtTnSUc+suWPv3zVqltrvxN2l3r6Zpd5u1asOOrbdud9N+d5aYmMg90CTJDYahf//03z1GE0ZpmKYZY5rmfaZp3gdMAcbc+Nk0zUQHb/EGbjth5GQtAadOGMVER+LrH5Dys6+vv90XNzfS+Pj5p/zs42efZt6MSbz9Qls2rFnJK291sHtvwrVr7Nm5nQZNW2RRBLd3PiYKb9/U/Hv7+Dm8IN5I4+rqRp58+Yi7dBGAP44fpl+ndvTv+iZvdOqZ4S68kE1rqd+0VRZHcXsX0sQA4OXjx/kY+0mUC7EO4rx8kYRrV/n52wU8+dKbdumLlSzDiSP7ibt0kYSEaxzcvd3pHXSA6KhI/NLWW39/YqLSTRhFReHrn/r38PXzz9BBO7R/D15e3hQrUTLl2LmzYXR682V6dn6Xg/v2ZFEEcisx0VF27ZKPnz8x6couJspRu5RaB35ctpQub71M4NDBxF2+lPWZzqSsanPLlKvAto3WCdETRw8Tee6c0wYkGdpcX/8MbdH5mCi8/VLborx5U9vcG3ZuWU+pshXJlSvjF/DZKTYmCh+/1DLz9vUjNt0EdWx0apobbevlSxdtx+3/FrExUZn6zPRKlqnAji3rAfj92BGiIyKyvD0+H22fTx9fP2LTlWWso/K2xXLxfGzKlyJePr5cunA+w+/4deUP3FenQVZk/2+LjYnC5y7U3fSWzptOm2dfwt3d4+5n+g7FREXi53f7dtbXz/46eiNNyJaN+Pj5ZdhWJSYqku2b19P6yWezMPeZF5uuzfXx889w3sQ6aHPTppkfNIl3XmzLxjUrednW5sZERRKyeT2PPnFvxAlZFyvA9k2/0vmN5/n844/o0mtAFkaReedjo/HK0O/N2L/3StvvzWvt90aEn8EwrFt6De7xLj8vW5Ctec+MmPTXj3T9nJQ0abZIdJQmvfDQ0xiGwZA+3ej1/ut8Z1v56SxZFWf9pi1x98jDey+05f1Xn+LJ51912m4XacdfYK2rF2LTXVvSj9FsdRUgOuIsA7u/w7D+XTlxeL/d+0YP7EH3N57EI09eajdonrWBZEL6foO3g37D+ZgofHxT+0x581r7TJkRH3eZPSFbqGa7gcZZMvZ1M467rX29zPR1K5ArV27i46w3/n0zbwYDPnibcV8O4GIW3xiUGTHRUfiluTHW1y/gJmOXgHRp0o7LltD5zZcIHDqIy2nGZefOhtP1nVfo07U9h/bvzcIobi+r+kVprV7xPQ/WdX6fNzY6Cp8035P4+Po7HtukaXet/YVbt7sAvx09RPd3X6ZH+1d5r1ufe271rsh/hSaMMskwjN6GYRyy/etqOzwUqGRbgTTUMIwChmGsMwxjj2EYBwzDaJvJz3b4PsMwPA3D+NkwjP223/s/2/ERhmEcsaUdZjsWYBjGt4Zh7DIMY4dhGPUNwygHvAv0suWxoWEYL9k+a79hGL/ePFd3j6Mbl9LfKYqjNGlmWNu924mZS36kWavWrFi21C7djq2bqFK9ptM66HCTGNOH6CDRjRjLVarGV5PmMXD0NH5cOp/ExISUNEnXr7M3ZAt1GzlvQuwG00FBpb8L1GGcGHy3cCYPP/k8Hnnst8spWqI0jz37CiM/+4gxA3tSonQ5XFzugTswM1Ommfh7/Lp6Fc3TrC7y9vFl/rc/MWn2Qjp0/Yihg/oTHx93V7IsmXer8/FWaW5o89RzTF+wjHFBwXj7+DJjYtZv3ZVZWdXmPvfK68TFXebDd19lxbIllK1Q0Wl3DDsum0yUX5oYQ0/9yaJZk3i7a6+7nLs7cJvyuHUax21upj4znadebEf85cv06fgGK79fSunyFXDN4vbYYUlm5lzM5F1Yh/btZt2qH3j1Jtu4Zbs7bXtuEe9ff5wgIjyMOg2b/ePsZbXMrByx3rV/jaXzZ2W4SQhg+oTRvNG+i1NXLKTluDlKX6aO3pma5rV3OhG0+EeatmrNT99Z29ygiaN5/R6KE7IuVoD6TVowcc5S+g0ezoJZU+9Ohv8ph+drhkQZ02CQbLHw+9GDvNt9AH0NW/cBAAAgAElEQVS+nMDe7Zs4emB31uTzTmWiPcrc38CexWLh2KH9dPt4EJ8HTmPH5g0c2LPzn+T0n8miOH8/dhgXFxemLf6RSfO+5YevFxARHvZPcnrHMtMvchgjBgW9fRgxfSkDxwTx4ltdmDZ6MFevxKek+WjgKEbPWsb164kcPej8G90cR3r7digz1x+LJYkJwz6j9ZPPE1Ck2B3m8O642Zg6XaKMb0zX1108azJvde0NWLe5i42OpGLVGnw+biYVqlRnYdDEu5rvO5GZfs+txm5tnv4fMxZ+x/iZC/Dy8SVoonXbYW8fX2Yv/ZHxQQt4t0t3RgwewJV7bNx9N/pFNxzYu4vVP33PGx0ybrmY3TJVpg6/S7n9Z1eoUp0xMxYydMJMli2aa/fdmYjcPZqKzQTDMOoCrwJ1AVdgh2EYG4C+QHnbiiQMw8gFPGWa5mXDMPyBLcCPmfgVV2/yvjbAX6ZpPmb7/IKGYQTYjlczTdM0DOPGGuJxwHDTNLcbhlEa+NE0zeqGYcwAok3TDLR9RhDQ3DTNiDTvTR9ve6A9wKBhgbzw2puZ/2PZrFi2lNUrvgOgfOWqREdGpLwWHR1p9zBfyHh3RUxUJN4+9mkAmj70KEP6deeVt9qnHNv06y80afnI387j3WS9czs1/7ExURRKt63RjTTevv5YLElcjY8nX7otcYqWKI27hwdhp05SxvZQwAO7t1OqXEUKenlnfSC34eVjH+d5B3HeSJM+zj9PHGXX1g0snTOFK/FxuBgGuXLn5qHHn6Ppw21p+rB1fvWbedNStovKbt9/s5ifv18GQMUq1YhKW28jM9ZbXz9/oiNT/x7RUZF2d8lYkpLYsmEdE2amPlQ9d+7c5LZtg1ChclWKFitO2OnTVKxSNUtiklQ/LlvCqh+t7VKFSvbtUkyUg/L1D8jQLt0oXy9vn5Tjj7Z9mkH9umdl1m8rO9rcvPny063Pp4B1END+5acJKFI0K8K5LW9f+zvWY6MjU1aZ2KWJisTH1hZduRKfsl1DTHQkgZ9/zPs9PiGgSPFszbsj3r5+xESllllsdFTGePysaXz8UtvW/J4F8Pa1L8u0f4vbfWZ6efPlo2NP6539pmnS9Y3n8Ct898t41fdfs/bn7wEoV7GKXT5joqMybAvoc4vyLujlbb3D38eX8zHRKQ88Bjj15+/8n737Do+iavs4/p0kkJAGqXTpEECkJ0BoKqiPYFcUEUVBAZXeFRUQRASki1QRRBT1EX0FC72FjkgndAghpEACoQTIzvvHLiEVwiPZje7vc11esrtndu+Tmb33nDkz50wfP5KBwz916EUl6flnuuPvbHxsNu2FnI/d7Bzav4ejh/bTo8MzpKamcj7pHMMHvM3gUZPzrB45Wfzjd/xxI8+GVCMu7tZ5NiDTnbnxtjKno6M4czqaHh3bpT3f8432jJ36BYcO7GPMMOtxej4pkW2bInB1daVBk+Z5XLubliz6jj8W3+L3JCAXOTcwm5z7wMMMf6cXbTu8waHIfYz50FrPC0mJbN8UgYurKw3svLizPeqaXvWadYgZNZTzSYn4OniqJL+AIM7lot17Ln2795K13esXEETl6rXw8bXWoUbdBhw/HEnV++ratQ6Z/frT9yxf8hNwI/+my0dxsfhn+p3IfHf12RzaC5m3qXZf7bT9VzusEUcPHkib1tYe7FHPtSv+oHb9hri5uVHYz58q1e+zTSNm/4GG/6mPZjtWDcNIu9O6bMUqBBcryZnok5SteHOB+gIF3akVGs6OzeuoXst++zE7uWozBQaREH+zzXS739EbZk4YRbESpfjPU8/f9bjvVNa2bhxFsmnrWnNs1nqejY9lwvB36NxncNrgl7dvYQqmWxc5tPH9rP4jN6er7r5f/ruQ32zthcoh1YiLjUl7LT7uDAEBmfvdRTPs9/i4M2nf4/T9skdaP8XQgT0B65pVN6YfrFSlKsVLluTUyRNUCrFfv9se7SK/gECOHj7I5NEj+GDUeIf9dv720/css+XdilWqkpDuPElCfDZ5NzBj3k2Ii72j80ClypTDw8ODk0ePUKFK1b8ZvYhkpjuMcqcJ8INpmpdM07wALAIaZ1POAEYZhrET+AMobRhGbhZDyGm7ncAjtruXwk3TTALOAhZghmEYTwE3Lv9pAXxuGMYOW3x+hmEUyuaz1gNzDcPoRA773zTN6aZp1jNNs97/MlgE0Oqp5xg/cz7jZ86nQXgzVv6xBNM0ObB3F15e3ll+LPwDAink6cmBvbswTZOVfywhNNzakImOOpFWbnPEGkqmW//mYnIye/76k7Bwx15VW65SCGeio4iLieb6tWtsWrOc2qEZD5HaYY1Zt/w3wHpreNX76mAYBnEx0WkL9cXHxhBz6kSGtYo2rllGg2YP2q8yt1CuUghnTkcRd8ZWz7XLqRUanqFMrdBwIlZY67l1/WpCbPUcNHIyo2csZPSMhbR87FlaPfsSD7Z6BiBtCqGEuDNs27CGMAdNv/f4M88z9ctvmPrlNzRq2pxlv/2CaZrs270TT2/vDINBYJ06ydPTk327d2KaJst++4WG6U7kbN+6idJlymaY2i7x3DlSU1MBOH0qilMnT1CspGOvYHMWrZ9qw6RZXzNp1tc0bNKcFb8vxjRN9u/ZhWdOeamQJ/v3WPPSit8XE9bYmmvSzze9Ye0qypSrYNe6ZGaPnJucfIFr164BsHTxT1S7r1aG9SjsqXzlEGJORRFry7kb1yynTljGnFsnrDFrly8BYPO6VVS7ry6GYXAx+QJjh/SjTYfOVK52nyPCz6JClaoZ6hOxahl1G2SsT90GTViz9FcANq1dSfWa1vrUbdCYiFXLuHb1KrEx0cSciqJilWq5es/MLiZf4LptH6/49Weq3lsLT6+7vx7Dw48/yydT5/LJ1LnUb9SUNct+xTRNIvftxtPTK8uJH7+AQDw8vYjct9u6LtyyX6lvO9lRr0FjVi+z7ufVy5ZQr2ETwPp7OnbYQN7q9z4lSt1DflG+cggx0RmP3cz7pU5YY9Ysu3nsVrcduzlp0eoppnz1MxPm/MAHY6ZSvGRphwwWgTUXTZg1nwmz5hPWuBkrf19y+zzreTPPrvx9CWHhTSlbviLzFv3OzG9/Yua3PxEYFMz46fPwCwhk5jc/pT3fqNkDdOnZ366DRQCPPvkc42fMZ/wMaz1XLb2znLtq6RJCG906507/+idmLLD+17DZA3Tu0d/ug0Vgn7qePnUy7Wrkw5H7uX7ter4Y5C2b1u49zfVr19i8bgU162ds99asH07Eyt8B2BaxmpAatTEMg+q1Q4k6fpiUlCukpl4ncs9flChd1gG1yOg/TzzLmGnzGDNtHqHhN/dn5N7deHp5Z5t/CxXyJHLv7rT9Wd+2P3NSq14Yx48cIuWKte57/9pOqTLl8rJaWdijnoHBRdPWxrly+TIH9+2mxD1Z19ywh8x9tM3rcuijrbT10SJWE1LD2ke7kJSIxdY3iYuJ5szpKAKLluDK5UsknrW2d1NTr7Nr20aKlXT872mFylUz/I5uWJ19m2ntsqxtpltZ+OU0Ll26SPvOPfMs9jthbeueTNdeWEadsIz7tHZYOOuWW+tpbevWSWvrjhnSjzYdumRo6xqGQe2wcPbtsk7NtmfHNoflpdZPt2Hy7K+ZPPtrGjRpzop07QUvL2/8AzMPjgVSyNMrXb9sCQ1u9MvSrXcUsXZlWr8sKTFdvzs6iuiokxSz84CuPdpFcWdiGPneAHq9MzTbdX/s5ZF0ebd+eDNWL7uzvLt62ZK09n1Ozpy+ee4s7sxpok+eICibdVlF5O/THUa5k9vVp14GCgN1TNO8bhhGFJCbyeSz3c40zX2GYdTDekfRaMMwfjFN8yPbcy2BF4CuwEO2GEMzr7WUTcPodSAMaA38ZRjGfaZpZp3w/y6q2yCcrZsi6PLS07i7e9BtwHtpr/Xs1I7xM613X3TpNYCJHw/j6tUU6oQ2om5YIwDmTp/CqZPHMVxcCC5ajK69BqZtv3HdKmrVC8OjUHZjY/bj6upG+y69GP1BHywWC01btKJUmXL896uZlK0UQp2wxjRt2Yrpnw6n3xsv4OXty5v9hwAQuXcnv3w/Hzc3NwzD4OUuvdMWn0y5coXdO7bS4a18MGUS1nq+9EZPPh3SF4vFQuMHH6XkPeX4cf4sylasQm1bPWeMG8HAzm3x8vGhc98ht33fKaPeI/l8Eq5ubrzUuRde3j55X5nbCG3YmC0b1vFqmydw9/CgzztD0l7r+soLTP3yGwC69X2HMSM+4GpKCvUaNKJ+w5sN+dXL/qB5i0cyvO+uHduZO3Mqrm6uuLq40r3fO/jmg5MiudH3637c27wGvoG+zD45hwUfzGfp7KWODut/Uq9BOFs3ruf1F5/C3d2DngPfT3utW8cXmTTLutbAm70HMu7joVxNSaFuWCPq2fLSF1MncuRQJIZhEFysOG/3fcch9chOXuXcqONHGT9yKC4uLpQuW45uDlxjwtXVjVe69uKT93pjsaTSrGVrSpUpz/fzZlCuUgh1GzSh2UOt+XzMh/Tu1AZvH1/e7j8UgKW//MCZ6CgWLZjDogVzABgwfDyFi/ixYPYUIlYt5WrKFbq9/CTNH36MZ9p1tEt9Xn2rNx+90wuLJZX7H2pN6bLlWfjlDMpXDqFewybc/0hrpnwyjB4dnsPbx5fu7wwDoHTZ8jRs+gB93njR+j5v90lbWD279wT4ddFC/u+7+SSePcuALi9TK7QhnXsN4tSJY3w2+kNcXFwoWaYcnXsNyvO61w5txJ9bIujx6nMUdHena5+bx1X/ri/zydS5AHTq1o/Pxgzn2tUUatVrkLYm0RPPv8z4Ee+y8rf/IzC4KL3eHQHA9/Nnk3zhPLMmj7H9jV0ZOfkLACaMfJ+9O7dzISmRru0e57n2nXjgkcfzvK7WONzo0LUXowbbjt2Hsh67zR9uzdQxH9K7Yxu8fHzpNmBo2vY9OjzD5UsXuX79Ols3rGXgiHGUuse+J2Jzq16DcLZtiqBzO2su6p4uF/Xo2I4Js6y5qGuvAUzIJhf9U9QNs9azy0tP4+7hQff+6XLu6+0YP8OWc3sOYOKoYaSkpFA3fc6dMYVoW84NCs7Yzs1v8qquG9asYOUfS3B1c8Pd3Z2+74/I1TQ9ec3V1Y0XX+/J+KHWdm+4rd276OtZlK0YQq3QcJq0eJSZ40cwqOuLeHn70LnPBwB4efvQ8rE2jOjXGTCoUTeM++o5fl2J9OqENWL75gjefvlZ3N09eDPd73rfzu0ZM8269tDrPfozZfSHXE1JoXZoQ2rb1sfYtG4VsyaP5XxSIiPf7U3ZCpV5b9QEvH18eezZtgx461UMw6BOaEPqNgjPNgZ7yKt6PvLEs0wZPZxenV4E0+T+h1tTtnwlh9TR1dWNdq/3ZNzQvlhSLTRukf5YrUKt0MY0adGKGeNHMKiLrY/WZwgAB/bs4KcFs3FxdcXFxYX2Xfrg7eNLUuJZJn30DtevXcVisRBSow7NH3nCIfVLz/o72puPB/fCkppKc9vv6HdzrW2mG7+jn40eRq/XnrP+jg4clrZ991eeTvsd3RaxhoEjxlPI04tF33xJidJleLfbqwA89Ngz3G+ntkF2XF3deLlrb0a/19t6fqFlK0qVKc8P82ZSrlIIdRo0Tmvr9un0PN4+vrxlO79gbeueytDW7T98HIWL+PHCq135fMyHzJ8+EZ/CRXi9Z9639W6nfoNwtm5YT6e2T+Lu7kGvQR+kvfb2ay8yeba1X/ZW74GMGzmElJQU6oU1op4tr8z+fAJHDt7sl3Xr+y4Au3ds56vZ03C1Hdtv9Rnk0IsR8qpd9M2XM7lwPonPx40CrG3eT6fPzbuK5EKd0Eb8uSmCbq88S0F3D97qm0Pe7d6fKWOsebdW/Yx5d/YUW94dbM27gz+ewP7df7Ho27m4urrh4mLQqXs/h9+NLPJvZdxqjQZnZhjGECDZNM0xtinppgGNsE1JBzwPxAIRpmlWsG3TByhlmmYvwzBaYrtbCIjBOi1cTlPA5bSdadsuxbZ+0QvAq1gHk+IMwwgC9pqmGWQYxkJgg2ma42zvWcs0zR2GYQwACpqm+aHt+QqmaR42rL2wv4AXTdPcndPfYX90klMcIInJzjHvaarFKXYnJQMdc9eDvXUPcvx0CfYy9nT+Wyw6LzjLd/TCpWuODsEu3Nwcf8LTXrLMq/8vdT3V4ugQ7MLHs4CjQ7ALi3PsTqcSn3TZ0SHYRREfd0eHIHdRUvLV2xf6lyjk7hzXLKc6yQ+Mv09uro/+53OW/QmQcs056nrfPX7O0XmxkxEhY53jREYeend/n3x1TDrHr/XfZJrmZsMwFgA3VuucaprmLgDDMLYahrELWAx8CvyfYRhbge3AwVx+xLwctqsJfGwYhgW4CnTBeifSfw3DcMc6pVxvW9m3gKmGYbyKdb+utD33E/CdYRhP2x4PNAyjHNY7kv641WCRiIiIiIiIiIiIiIg4Bw0Y5cA0zSGZHn8CfJJNucyX+Ifl8JY53idpmmZsDttFAUuyeT40m/eIA57N5vn9QI10TznuvmoREREREREREREREcmXXBwdgIiIiIiIiIiIiIiIiDiW7jCyI8MwagFzMj19yTTNf9bqviIiIiIiIiIiIiIi8q+iASM7Mk1zB1DL0XGIiIiIiIiIiIiIiIikpwEjERERERERERERERG5I4ZhODoEucu0hpGIiIiIiIiIiIiIiIiT04CRiIiIiIiIiIiIiIiIk9OAkYiIiIiIiIiIiIiIiJPTgJGIiIiIiIiIiIiIiIiT04CRiIiIiIiIiIiIiIiIk9OAkYiIiIiIiIiIiIiIiJNzc3QAIiIiIiIiIiIiIiLyD+NiODoCuct0h5GIiIiIiIiIiIiIiIiT04CRiIiIiIiIiIiIiIiIk9OAkYiIiIiIiIiIiIiIiJPTgJGIiIiIiIiIiIiIiIiT04CRiIiIiIiIiIiIiIiIk3NzdAAiIiIiIiIiIiIiIvIPYxiOjkDuMt1hJCIiIiIiIiIiIiIi4uQ0YCQiIiIiIiIiIiIiIuLkNGAkIiIiIiIiIiIiIiLi5DRgJCIiIiIiIiIiIiIi4uQ0YCQiIiIiIiIiIiIiIuLkNGAkIiIiIiIiIiIiIiLi5NwcHYCIiIiIiIiIiIiIiPzDuBiOjsApGIbxCDABcAVmmqb5cabXxwH32x56AsGmaRaxvZYK7LK9dsI0zcdv9VkaMBIREREREREREREREclnDMNwBaYALYEoYIthGD+bprn3RhnTNHulK98NqJ3uLS6bplkrt5+nKelERERERERERERERETyn1DgkGmaR0zTvAp8Azxxi/JtgQX/64dpwEhERERERERERERERMTODMN4wzCMren+eyNTkZLAyXSPo2zPZfdeZYBywIp0T3vY3nejYRhP3i4eTUknIiIiIiIiIiIiIiJiZ6ZpTgem36JIdgtFmTmUfQH43jTN1HTP3WOaZrRhGOWBFYZh7DJN83BOH6Y7jERERERERERERERERPKfKKB0uselgOgcyr5ApunoTNOMtv3/CLCKjOsbZaE7jERERERERERERERE5M64ZHfzi9xlW4BKhmGUA05hHRR6MXMhwzCqAH7AhnTP+QGXTNNMMQwjEAgHPrnVh2nASG4pMTnF0SHYRSF35/gqLB22ytEh2EXJDrUcHYJdjD39taNDsJs+xbP8Dv4rDdo/29Eh2IVXoQKODsEurl5LvX2hfwkfT+f4HY09d9XRIdiFxcxpdoN/F4+CznHcOpPAwoUcHYJduDjJPCEF3VwdHYJdpFx1nvbCigF/ODoEu3Dxd45cdPKrZY4OwS7abH/f0SHYzdquvzg6BLu4b8Vrjg5B5I6YpnndMIy3gd8BV2C2aZp7DMMYBmw1TfNnW9G2wDemmaFDVxWYZhiGBetscx+bprn3Vp+nXpKIiIiIiIiIiIiIiEg+ZJrmEmBJpufez/R4SDbbRQA17uSznOTaJBEREREREREREREREcmJBoxEREREREREREREREScnAaMREREREREREREREREnJzWMBIRERERERERERERkTtiGIajQ5C7THcYiYiIiIiIiIiIiIiIODkNGImIiIiIiIiIiIiIiDg5DRiJiIiIiIiIiIiIiIg4OQ0YiYiIiIiIiIiIiIiIODkNGImIiIiIiIiIiIiIiDg5DRiJiIiIiIiIiIiIiIg4OTdHByAiIiIiIiIiIiIiIv8wLoajI5C7THcYiYiIiIiIiIiIiIiIODkNGImIiIiIiIiIiIiIiDg5DRiJiIiIiIiIiIiIiIg4OQ0YiYiIiIiIiIiIiIiIODkNGImIiIiIiIiIiIiIiDg5N0cHICIiIiIiIiIiIiIi/zCG4egI5C7THUYiIiIiIiIiIiIiIiJOTgNGIiIiIiIiIiIiIiIiTk4DRiIiIiIiIiIiIiIiIk5OA0YiIiIiIiIiIiIiIiJOTgNGIiIiIiIiIiIiIiIiTs7N0QGIiIiIiIiIiIiIiMg/jIvh6AjkLtMdRiIiIiIiIiIiIiIiIk5OA0YiIiIiIiIiIiIiIiJOTgNGIiIiIiIiIiIiIiIiTk4DRiIiIiIiIiIiIiIiIk5OA0YiIiIiIiIiIiIiIiJOTgNGIiIiIiIiIiIiIiIiTs7N0QGIiIiIiIiIiIiIiMg/i2EYjg5B7jLdYSQiIiIiIiIiIiIiIuLkdIeR5Lmd2zYxf8YELBYLzVq2pvVzL2V4/dq1q0z/dATHDh/A28eXN/sPJahocQ5H7mXO5NEAmKbJky++Rr2GTQG4mHyB2ZNGcer4UTAMOvUYSMWQe+1eN9M0+eKzcfy5JQJ3dw/e7Pse5StVyVLuSOR+poz5kKtXU6hdvxGvvtkLwzBIPp/EuBHvEXfmNEFFi9Nr8HC8fXz5eeFXrF3xBwCW1FSiTh5j1sIlePsW5rOxw9m+MYLCRfwYO2O+vaucQdl7i/JA2/swDINda4+x+dfIDK9XD7+HZs/VIPncZQD+XHGEXWuPUbpKIPe/cF9aOf/iPvwybTOH/jxt1/hz6+Durfy24HMsFgt1mjxCk0fbZHj9WOQufvtmGmeijvLsGwOpXq9J2mtLv59F5M4tADRr3ZZ7Q5vZNfY7ZZom0yeOZeum9bi7e9Bz0AdUrBySpdyhA/sYN3IoV6+mUC8snDe698EwDOZ/MZ3ff1lE4SJFAHj59beo3yDc3tX4W7rP6kG91vVJik2iW423HB3OHdu5bRNfz5yAJdVC04da0/rZrDl3xrgRHDt0AG9fX7r2s+bcI5F7+WKKNedimjzZ9jXq2nLuHz9/x+o//g/TNGn20GM8/ESbzB9rd9b8+ynbN2/A3d2dt/q9R/lKWY/Vw5H7mTLamn/rhDbk1Td7YxgGG1YvZ+G8mZw6cYyRk2ZToUpVAGJjounZsS0lSt0DQOWq9/JGzwF2rVt6f23dyLxp47FYLDR/+DEeb9M+w+vXrl1l6pgPrfvTpzDdBg0jqGhxLpxPYsJH73Ikcj9NW/yHDm/2AeDypYsM6/9m2vZn4+NofP9DtO/c0671yo5pmsyYNJatGyNw9/Cg58D3qZBD/pnw8TBSUlKo16ARr3frk+Gqth+/+YovPp/IV4v+wNeWiwAO7t9Lvzdfo9/7Iwhv/qBd6pSd3ds3sWDWJCwWC01atOLRZ9pleP3atavMmvARxw9H4u3jS+e+HxAYXJz42NO81+1lipWwHpvlK1ejfVfrft28bgWLv5+HabFQo24Dnnulq93rldnOrRuZN93a/mv+UGsey+bYnTZ2OEcPWdt/bw+0Hru7/tzCwi+mcv36ddzc3Hih41tUr1kXgBED3ybxbAIFC7oD0H/4OAoX8bN73dLLq1x0Q1xsDL06tqXNy514/Ll2Wd7Xnpwl75qmyczJY9m2yZqLuvfPIRdF7mPiqGFcTUmhblgjOr1tawvN/pzNEWswDIPCRfzpMeB9/AOD2LVjGyPf60twsRIANGxyP8+/3Mne1ctWXuff/MI0TaZOGM2WDetx9/CgzztDqJTpOwdwcP8+xn70ASkpKdRvGE7XHv0wDIOP3h9I1InjACQnX8Db24fP5iywdzWydbfbC+mNHdqf2JhoRk39yl7VybWytYrz4Kt1MFwMdi4/zOZF+7KUqdKwNI3a1AATYo+fY/GEDQSXLULL1+tTsFABTIvJhv/u4UDECQfUIHfKVg/m/jY1MFwMdq87zubfD2Z4vXrDe2j6THWSE68AsGPlEXattx6rvaY+Qfyp8wBcOHuJRZ9tsm/w/6OQllV5+pNnMVxd2PhlBMvHLs3wul9pP9p+/hLegd5cOnuJeR2/JCk60UHR3t6uTH20Vjn00Y6n66MFFi2e9npC3Bnefas9T7R9lf881RaAvp2ew6OQJy4uLri6uvLBpzPtWqfcKFe/JA++3QAXF4O/lkSyacHOLGVCmpUj/JVaAMQePsv/jVgNQLPX61GhQWkAIubtYP+qo/YLXMTJacBI8pQlNZW5n39K/w/H4R8QxJDer1M7LJyS95RLK7Pmj8V4efswevo3bFyzjIVzPuetAUMpdU95hoybgaurG4ln4xnc/VVqhzbC1dWN+TMmUqNOGN0GDef6tWukpFxxSP3+3LKBmFMnmfjFdxzcv4eZEz/ho0mzspSbMekTOvccSKWq9zLy3d7s2LKR2qENWfTtPGrUrseTL7zMom/msujbebzU6S0eb/MSj7exNiC2bljL4v9+i7dvYQCat2zFI48/x5RPhtm1rpkZBrRoV5Pvxq7jwrnLvPTe/RzecZqE0xcylDuwOYrlX/+V4bmTB+KZO3QFAB5eBeg48mGO7Ym1W+x3wmJJZcn8KbTv/RG+foHMGN6DKrXCCC5RJq1MYf9gnny1DxF//JBh28idmzl9/DBdPphC6vVrfPFJfyrWqIdHIS97VyPXtm6KIBiC6R8AACAASURBVDrqBNPn/5cDe3fz2acf8+nnc7KUm/Lpx7zd9x1CqtdgSP8ebNsUQT3bwNCTz7Xl6RfaZ9nmn2L5nGX8MvkXes3t7ehQ7pglNZV50z6l3zBrzh3a53Vqh2bKuUsX4+ntwye2nPvdl5/zZv+hlCxTniGf3sy57/V4lVqhjTgddYLVf/wf74+djpubG2OH9KVm/YYUK1HagTWFPzdv4PSpk0ya8x0H9+1hxsRPGDlpdpZyMyZ+QudeA6lc9V4+ercXO7ZsoHZoI0qXLU/fDz5m+viPs2xTrERJxkybZ49q3JIlNZU5n41l0Ijx+AcG817PTtRp0JhS6fbnqt9/wcvbh09nLWTD6mUsmP0Z3Qd9SIGCBXmu/eucPHaEqONH0soX8vRi5OQv0x6/2/016jVqbs9q5Wjbpgiio04ybf4PHNi7m6njRjFm6hdZyk0dN4q3+g6iSrUaDB3Qk+2bN1A3rBEAcbFn2LFtE0FFi2XYJjU1lTnTJlG7fgO71CUnltRU5k8fT+8hY/ELCGJ4/87UCg2nROmyaWXWLVuMl5cPI6d+zea1y/l+7jS69B0CQFDRknwwLmM7I/l8Et9/OZX3xszAp3ARZk34iH07t1H1vrp2rFlGltRUvpz6KQOGj8M/MJj3e1mP3fS5aLXt2B0781s2rF7Gt19M5e2Bw/DxLUzvDz7BLyCQk8eOMPr93kycuyhtu679Psh2kMJR8jIXAXw5dTy16zfM62rkijPkXbDmotOnTjJ13g9E7tvN5+NHMfqzrLlo2rhRvNnbmos+HHQzFz31/Eu0e60LAL/891u+nTeTrr0GAVCtRi0GfzTOrvXJjbzMv/nJlo3riT55ktnfLGL/nt1MHjOSCTPmZik3aexIuvcfTNXqNXivb3e2boygfsNw3hl289idPulTvLy97Rl+jvKivXDDlvWrcPfwtGd1cs1wMWjZsS4LP1zJhbOXaT/yIQ5vPUVC1Pm0MkWKeRP2VHW+HryUlIvX8PS1XnBwLSWVxZM2kBiTjJdfIV4e9TDHdpwm5dI1R1UnR4YBD7atyffj13Ph3GXaDWrOoZ0xnM3c7956ihXfZD0Zf/1qKvOGr7RXuHeF4WLw7KdtmPrYZBJPJdJ7bT92L97Fmf0xaWWe+Ogptny9mS3zN1GpWWVaD3uc+Z2yfp/zgxt9tL62PtqwPq9TK1Mfbe1S63mxUdO/YdOaZSy09dFuWDBzEjXqhGV57wEjJuDjm/8G6MH2He3RkG/7/c6FuIu8MvVxDkWcIOH4zYE9v5K+NHjxPr7qvpiU5Kt4FvEAoHxYKYpVCuCL1xfhVtCVtuMe5cjmKK7mw++oyL+R005JZxhGsoM+9ynDMEzDMBza0zUMo6dhGHne8jtycB9Fi5ckuFgJ3AoUIKzpg2zftC5Dme2b1tL4wUcAqB/enL1/bcM0Tdw9PHB1tY5pXrt6Ne3qtcuXLnJg9180e6g1AG4FCuDl7ZPXVcnW1og1NG35HwzDoHLVe7l4MZlzCfEZypxLiOfyxYtUrlYDwzBo2vI/bImwXjGxZcNamrV8FIBmLR9lS8SaLJ+xftVSwu9vmfa42n218fbxzcNa5U6x8v6ci71IUvwlLKkm+zdHUaF28dtvmEnluiU5uiuG61dT8yDKv+/U0Uj8g0vgH1QcN7cC3BvajAM7NmYo4xdYlGKly2WZtzUu+gRlqtTA1dWVgu4eFCtdjkO7t9kz/Du2ad1qHni4FYZhEFK9BheTL3A20zF9NiGey5cuUvVe691lDzzcio3rVjso4rtvz9o9JJ+9cPuC+VCWnNvkQf7MlHP/3LSWxg9kk3PdM+VcrMdz9MnjVKhSLe31KtVrsX1D1lxlb1s2rKFZi0et+bfavVxMziH/XrpIFVv+bdbiUTbb8mypMuUoWbpMdm+dbxyO3EfREqUILl4StwIFaND0QbZtWJuhzLaNa2nawvo7Etq4OXts+9PDoxBVqtekQMGCOb5/zKmTnE88R8i9NfO0Hrm1af0a7n/40dvmn0sXLxJS3Zp/7n/40Qz5Z9bkcXTo3C3t+L3hl/8upFHTBxx+N8rRg/sILl6SINt3NLTxA+zYnPE7umPzehrd/zAAdRs1Y//O7ZimmeN7xp2JpmiJ0vgUtp4sqFazLts2ODYnZz12W7BtY+b23zoaP/gfIOOxW7ZCZfwCAgHr9/Ta1atcu3bV7nXIrbzMRZvXrya4eElKly2X7ev25gx5F2BzxBqat7TWs0q1W+SiSzdzUfOWj7JpvfV75+l1cxDhypXLWfJRfpSX+Tc/2bB2NQ8+Ym3nVr23BsnJySTEx2UokxAfx6WLyVSztXMffKQVEWtXZShjmiZrVi6jeYtH7Bh9zvKqvXDl8iWW/PgtT7Z9xS71uFPFK/pzLiaZpNiLWK5b2L/+BBXrlcpQpmaLivz5WyQpF60nmS+dTwHg3OkLJMZYTwldPHeZS0lXKGQbTMpvipXzIzE2Oa3ffWBrFBVr5t+B2buhTL2yxB+JJ+FYAqnXUvnz++3UaH1fhjJFQ4oTufIAAAdXR1KjVQ1HhJorR2ztvxt9tNBs+mjbN60l3NZHqxfenH227yjA9o1rCCpWPMMA0z9B8ZBAEk+dJ+n0BSzXLexbcYRKje7JUKZmq8ps/2kfKcnWtt4l211ygWWLcGJnDKbF5NqV68QdPkv5+qWyfIaI5A2nHTByoLbAOuAFB8fRE8jzAaNzCXH4BwanPfYPCMq2Y3mjjKurG4W8vEg+nwTA4QN7GPRme97t1oFX3uyLq6sbsTHR+BQuwszxH/Fej9eYNfFjUq5czuuqZOtsQhyBQUXTHgcEBnE2IS5LmYCg4HRlgtPKJJ07m3ZSxC8gkPOJ5zJsm3LlCju2bqRB4+Z5VIP/nU8RDy6cvfl3Tz53GZ8ihbKUq1S3JK8MeZDHu4bh45f19ZDQUuzfFJWnsf4d58/F4+sXlPbY1y+Q8+cScrVt0dLlOLRrK1dTrnDxQhJH9+/k/Nm422/oQAnxcQQGpzumg4JJiMt491dCXGzGYzooOENn+5cfv+PtV9sy/uNhJF84j9hP5pzrF5jLnHvhZs595632DO5+M+eWKlOOA3v+Ivl8EikpV9i5bSMJ8Y6/I/BsfBwBwZlya6aTPmfj4wgIvPn9DQjKWiY7sTHR9OvyMu/37sq+XTvuXtB36GxCHAHpf0MDgzmX6TfmXEIc/kE396en583f0NuJWL2UBk0fzDeLlCbExRIUdPv8E5gu/wSmK7Np/RoCgoIoV7Fylm02rlvFI48/nYfR5865s/H4pf+O5tAu8kv/HfW8+R2Njz3N0N4d+eTd7kTutd69G1y8FDGnThAfe5rU1Ov8uWkdZx38Hc3S/gsMynLspm8f5XTsblm/ijLlK1GgwM0TmTPGfcS7b3dg0YI5txxIs5e8ykVXLl9m0bfzeK59x7sb8N/gDHkX4Gx8bJa2UObv1Nn4rG2h9GW+mvUZHZ9vzZplv9H21c5pzx/Yu4uenV5k2MAenDh6OA9rcWfyKv/mNwnxsQSl27dBwcHZDhil798FBRfN0u7Z/def+Pn5U7J0xhOfjpJX7YXv5s3g0adfwN3d4+4HfRd4+3tyIeFS2uMLZy/hHZCxv+lX3Af/Er68+GEL2o1oSdlaWS9wLFbRH1c3FxLPOOSa4tvyLlKIC+du9rsvnLuCd3b97jolePm9+3nsjfoZ+t1uBVxo904z2g5oSsWad36BpyMULlGYc1E3z40knjpH4eKFM5SJ3n2Kmk9apzG77/GaePgWwtM/f87kkX27KGP7LzGHPlrKlcss+eFrnnjh1Szva2Aw5v3eDOnVkVW//Zy3lfgf+AR6cT72YtrjC/EX8Q7KeBrSr1Rh/Ev50m5iK9pPbk25+iUB69R05UNL4ebuSiFfd+6pVRyf4Py5f0X+jTQlXTqGYZQBZgNBQBzwqmmaJwzDeAwYDBQEEoB2pmmeMQxjCHAPUN72//GmaU68xft7A+HA/cDPwBDb882BocAZoBbwX2AX0AMoBDxpmubhW8Q3B/jFNM3vbe+XbJqmt+19hwDxwL3ANuAloBtQAlhpGEa8aZr3/60/3C1k14/PfF4qu87+jZNXFapUZ+Rn84g+eYzp4z7ivrphWFJTOX44kvade1ChSnW+mj6BX76fzzMv2X8O8Gzrl+mquuxPZuTu5Ny2jeuoUu2+tOno8pVsTjBmrunhHTHs3xRF6nULNZuV4z8d67JwzM0rabwKexBYqjDH9pzJ42DvrtyeW61YvS7RRyOZ9XEfvLwLU7pCCC6urnkb3N90q+/jrcrc8OgTz/DCyx0xDIOvZn3OzCnj6Tnw/bsep2QvN+km233MzZz70RRrzp0x/iNq1A2jROmyPPp0O0a/3wt3D09Kl6uIaz44jrM9DjMfq1my0u2/v37+gUyd/xM+voU5HLmf0UP68+mMBXh6OaCD8r9+H3OZpDasXs6bfd/7n0Kzl9wMZhmGQcqVK3z31RcMHT0py+szJn/KK2+8nS+O2+z3aZZCWctgUNgvgE+mL8TbtzDHDh9gysh3GTbxS7y8fWjXuRfTxgzFMAwqhNxL/JnovIk/l26VZ9IVyrphuj9G1PEjfPvFVPoPvzl9V9e+H+AfGMTlS5eY+NG7rF/xW9pdSo6SV7lo4dwZtH7mBQoVyj9TQTlF3iWn39LMuTe7LW+Weanjm7zU8U2+/3oOSxZ9R9sOb1ChUhWmL/iZQoU82bpxPSPf78/UeT9k90b5wt3Iv/lNbnJTbsqsWvYbzVs8fHeD+zvyoL1w7HAkZ6JP0f6NHsSdyZ/rzGYrUzVdXA38invzzZDl+AR40nZYC77ovSRt6jmvIh606taQJZM3Zvfzmy/kplV3eOdp9m+x9rvva1qWRzrU4btx6wGYPugPLiZdoXCgJ8/1akzcqSSS4i/d5h0dLLtzDZn2z0+DfuSZT58j9KUwDq8/ROKpc1iu589ZS7I7tnJ1XgyDH7+ezUNPtMEjm/bAO6M+S7vweMz7vShe6h6q3FvrbkX992V38Gb3HS1VmAW9luAT5EW7Ca2Y9dqPHNsaTfEqQbw0qTWXE69wam8sllSLXcKW/4FL/rgAUe4eDRhlNBmYa5rml4ZhvAZMBJ7EekdQA9M0TcMwOgH9gRurQYZgHQDyAQ4YhjHVNM2cJtV8EvjNNM1IwzDOGoZRxzTN7bbXagJVgbPAEWCmaZqhhmH0wDrA0/MW8d1KbaA6EA2sB8JN05xoGEZv4H7TNOMzb2AYxhvAGwADho3myedfvs1H5Mw/MCjD1XZnE+Io4h+YbRn/wGBSU69z+eJFvDJNuVaidFncPTw4dfwofoFB+AcGUaFKdcA6pdLi7+23AOdvP3/P8iXWqzcqVKlKfNzNwY6E+Li0O4ZuCAjMeIVeQnws/rYyhf38rVcSBwRyLiEe30xT5axftZTG6aajy08unLuMj//NK5e8/QqRnJjxTq8rF29OIbNzzVGaPntvhter1C/Jwe3RWFLzaeucG3cU3bxC7/y5eHyKBOR6+6at29K0tXVRyu+nj8I/uMRdj/Hv+uXHhfz+i3V9iEpVqhEfm+6YjovFP92VwgCBwUUzHtNxsWlXE/v53/zbPNz6SYYO6pWXoUsmmXPuufg4/P5mzi1XKYRmD7VOmwb0+7nTMtwhYU+//fQ9y5b8BEDFKlVJiM0+t94QEJjxyuGEuFj8AjIez5kVKFgwbVqWCpVDKFq8JKejTmRZiN4e/AODM1zVfDY+Npvf0GDOxsUSYNufly5dzNW0pcePHMSSmko5B68Fs/jH7/jjRv4JqUZc3K3zT0BQMPHp8k+8rczp6CjOnI6mR8d2ac/3fKM9Y6d+waED+xgzbDAA55MS2bYpAldXVxo0aZ7HtcvKLyCIc+m/o9m0i26USfuOXrJ+Rw3DSLvTpmyFKgQVK8mZ6JOUrRhCrfrh1KpvXUdu9R8/4+Li2EkE/AMz3m1xNj6OIgFZj13rPs567J6Nj2XC8Hfo3GcwRYuXTLeN9Xgo5OlJw2YtORy5zyEDRvbIRQf372Hj2hV8NWMyF5OTMVxcKFCgIP958rm7WJPbc5a8u2TRd/yx+BZtoYCsuShzWyhzvgJo+sDDDH+nF207vJFhqrp6DcKZNuETzicl4lvYMWtP2CP/Zu4XOcLPPyzkt//7EYDKVasRl27fxsXG4h+YMcbAoOAM/bu42DMZ/hap16+zfvVKJs2yX//zdvKivXBo/x6OHtpPjw7PkJqayvmkcwwf8DaDR03Os3rcqeSzl/AJuHkS3cffk+SzGfujFxIuEX0wAUuqSVLsRc5Gn8evuA8xh89SsJAbzwxqxtoFOzl9MHczSDjChcTLGe4Y8vHzyKbfffMU1K61x2j6dPW0xxeTrFN8JcVf4mRkPMH3FMn3A0ZJpxLxK3Xz3EiRkn6cj8l4R9z5mCS+eHEmAAW9ClLziVpcOe+Yta1vxy/zebH4bNp/OfTRjkTuZWvEKhbOmcqli8m42NqDLVo/k5ZjfYv4UadBU44c3JevBowuxF3EN91dQT6BXiRnOvYuxF0iel+s9Tsak0zCyST8SvkScyCeDfP/YsN86x31j73bjHOnNHuJiL1oSrqMGgJf2/49D2hs+3cp4HfDMHYB/bAOwNyw2DTNFNvASyxQlJy1Bb6x/fsb2+Mbtpimedo0zRTgMPCH7fldQNnbxHcrm03TjDJN0wLsSPdeOTJNc7ppmvVM06z3dwaLAMpVCuFMdBRxMdFcv3aNTWuWUzs0Y9i1wxqzbvlvgHXqkar31cEwDOJioklNvQ5AfGwMMadOEBhcjCJ+AfgHBnM66gQAe//almGx6Lz2yOPPMvrzuYz+fC6hjZqyZumvmKZJ5L7deHp5ZekY+QUEUsjTi8h9u63zXS/9lXqNmgJQr0FjVi9dAsDqpUuo37BJ2naXLiazd9ef1GvY1G51uxMxR8/hV9SbwoGeuLgahISW4vCOjFefeRW+OX1BhVolSMi0MGdIaGn2bzppl3j/VyXKVibhTDTn4mK4fv0auzevpkrN3C2abrGkcinZ2qiJOXmUM1FHqVDdcYuQ56T1U22YNOtrJs36moZNmrPi98WYpsn+Pbvw9PLOcjLIPyCQQoU82b9nF6ZpsuL3xYQ1bgaQYb77DWtXUaZcBbvWxdllyblrl1M7LGPOrRXamHUrcplzbQtX35guMyHuDFs3rKFB0xZ2rNVNjzzxLGOmzWPMtHnUD2/G6mVLrPl37248vbyzz7+FPInca82/q5ctof5tcmpS4jlSU61XJ545fYrTp6IILu6Ygd7ylUOIiY4i1rY/N65ZTt0GGfdnnbDGrFlm/R3ZvG4V1e+rm6urwjesXkbD5o7Zj+m1euo5Jsyaz4RZ8wlr3IyVvy+5ff7xvJl/Vv6+hLDwppQtX5F5i35n5rc/MfPbnwgMCmb89Hn4BQQy85uf0p5v1OwBuvTs75DBIoCylUI4czqKuDOnuX7tGpvXraCmbaDnhpr1w4lY+TsA2yJWE1KjNoZhcCEpEYvt2IyLiSb2dBSBRa3H5o3v6MXkC6z69SeatGhtx1plVb5yCDGnTqY7dpdRJyxjPWuHhbNu+a+A9ditZstFF5MvMGZIP9p06ELlajfXK0hNvc6FJOsiydevX2fHlghKlSlvv0qlY49c9OG4aXz21SI++2oRrZ5+nqfbvmL3wSJwnrz76JPPMX7GfMbPsOaiVUut9Tywdxdet8hFB/Zac9GqpUsItbXvo219FLCuh1TynrKAdUrKG1eQR+7bg2la8HHgLAL2yL/5wePPtOGzOQv4bM4CGjZpzvLfrO3cfbt34eXtnWEKRbBOMV7I04t9u631XP7bYho2aZb2+p9bN1O6TNkMU9s5Wl60F1q0eoopX/3MhDk/8MGYqRQvWTpfDRYBnD50Fr/iPhQO9sLFzYWQ8Hs4tDXjVOcHt5zinurWfVXIpyB+xX1IPJOMi5sLT/Zrwp7Vx4jcmL/7ozHHEikS7I1vgLXfXaVeKQ7/FZOhjFe69Zcq1Cye1u929yyAq5v1tF8hr4KUrOCfpU+eH53YdpzACkH4lwnAtYArtZ+tw+7FOzOU8QrwSjuGW/R9mE1zN2b3VvlCuUohxKbro23Opo9WO7Qx6219tK3p+mjvfDyFMTO/Y8zM73josedo9Vx7WrR+hpQrl7l8yTr4knLlMrt3bKHUPY5pF+Xk9P54/EoWpnAxb1zcXKj6QHkObTiRoczB9ce5xzZVZCFfd/xL+ZJ4+gKGi4GH7bgOKu9HUHl/jm45Zfc6iDgr3WF0azdue5gEfGqa5s/ppnm7ISXdv1PJ4W9qGEYA8ABwr2EYJuAKmIZh9M/mfSzpHltyes908V3HNvhnWH8x069Ymav48oqrqxvtu/Ri9Ad9sFgsNG3RilJlyvHfr2ZStlIIdcIa07RlK6Z/Opx+b7yAl7cvb/YfAkDk3p388v183NzcMAyDl7v0TlvQ+aXOPfl87DCuX79GcNESdOr5jj2rlaZ2aCO2b46ge4fnKOjuzpt9B6e91q/Ly4z+fC4Anbr347PRw7l6NYVa9RtQu35DAJ584WXGDX+XFb/9H4HBRek9eETa9pvXr6ZmnTA8CmWcn3j8R++zd+d2LiQl0uXFx2nTvhMP/OdxO9Q2I9Nisnz+Dp7pFY6Li8GudcdJiL5A+BNViTmWyOG/TlPnwQpUqFUci8XClYvX+G321rTtfQM88fEvxMnILDe55Suurq48+mJX5o0fjGlJpXb4QwSXLMOKRXMpUbYyIbUacOroAb757EOuXEwm8q9NrPr5K94aNo3U1FRmj+oLgHshT57u1C9/TIl0C/UahLN143pef/Ep3N09Mkwn163ji0yaZR2zfrP3QMZ9PJSrKSnUDWtEvbBGAHwxdSJHDkViGAbBxYrzdl/HfDf/jr5f9+Pe5jXwDfRl9sk5LPhgPktnL3V0WLni6urGS517MWaINec2adGKkveU47/zZ1KuYgi10+Xc/m+8gJePL137DQEgct9OFn84H1c3N1wMg/ZdeuPja825kz8eTPKFJFxd3Xi5Sy+8vH0cWEurOqGN+HNTBN1eeZaC7h68lS7/9u3cnjHT5gHwevf+TBnzIVdTUqhVvyG1Q635d9O6VcyeMpbzSYmMHNybshUqM/jjCezb9SfffjkDV1dXXFxceKNHf4ed0HN1daND116MGtwbiyWVZg+1plSZ8nw/bwblKoVQt0ETmj/cmqljPqR3xzZ4+fjSbcDQtO17dHiGy5cucv36dbZuWMvAEeMoZVssd+PaFfQfOsYh9cpJvQbhbNsUQed2T+Pu7kH3ATeny+vRsR0TZs0HoGuvAUz4eBhXr6ZQJ7QRdW3555/A1dWNF1/vyfihfbFYLIQ/+Cgl7ynHoq9nWe8UCg2nSYtHmTl+BIO6voiXtw+d+3wAQOTev/hpwWxcbMfmS116p10d/s2siZw8Zl0P5bE2r1CsZGmH1RGs9Xy5a29Gv9fb2v5r2YpSZcrzw7yZlKsUQp0GjWn2UGs+H/MhfTo9j7ePL2/Z2n9Lf/mBM9GnWLRgDosWzAGg//BxuHt48Ml7vUlNTcViSaV6rXrc//BjjqukTV7lovzIGfIuQN0way7q8tLTuHt40L3/zVzU8/V2jJ9hzUVdeg5g4qhhpKSkUDddLpo7YwrRJ49juLgQFFyMrr0GAhCxegW//fwDrq6uFHT3oO/gEflmDTlnyL8AoQ0bs2XDel57/gncPTzo/c6QtNfe7NCWz+YsAKBb30GMHTGEqylXqNcgnPoNbg54r1r+e/6ajo68bS/kZ6bFZNmsrTz7bnNrf3TlERKizhP+fA1iDp/l8NZTHNtxmnI1i/HquEcxLSar5+3gSvJVqjUpS6mqwRTycefe+611/XXKRmKPJTq4VlmZFpMV3+zkmR6NcHEx2L3+OAmnL9DosRDOHE/k8M4Yaj9QgQo1i2FJNbly6Sq/z7FOZBNQzIcWL9XEtIDhApt/P8jZf8CAkSXVwg99FtLlp7dwcTXYNHcjMfti+M/gVpzYfoI9S3ZRsUklWg99HNOEw+sP8X2vhY4OO0eurm6069yLsZn6aD/On0nZTH20AbY+WhdbHy0nSYnnmPyRta+dmppKg2YtqVE3zA61yT3TYrJ00gbajHoYw9Vg168HiT+WSOMOtYmJjOdQxEmObjlFuXol6Tj7KUyLyappW7hyPgXXAq60G/8oAFcvXeOXj1ZjWvLvzDQi/zZGflgs1hFurPOT6bmfge9M05xnGEYH4AnTNJ8yDONPoJNpmtsMw/gCKGeaZnPbGkbJpmmOsW2/G2htmuaxbD6vM1DHNM3O6Z5bjXVtJFegr2marW3Pr7I93moboOprmmbrW8Q3GPAxTXOAYRhPAj9aZ8+7ua3tfScDW03TnGO7W+px0zSP3urvtDEy1ikOkELuzjF2unTYKkeHYBclO+Sf27DzUr1K+ePKTXvoU/xFR4dgF4P2z3Z0CHbhVaiAo0Owi6vX8uk86nnAx9M59mnsucu3L/Qv4F4wf1/ccLd4FHSO9p8zKeDqHBNoOHjWSbsp6OYcuSghn06jlRdWDvxnXIT1d7n4F7p9oX+Bk18tc3QIdtFmu/Osybu26y+ODsEuBqx4LX9csfEv8XHDz53i3HFeGrihS746Jp2kqZktT8MwotL91xvoDrxqGMZOoD3Qw1Z2CPCdYRhrgf/1doi2wI+ZnvsBuJOzoDnFNwNoZhjGZiAMuJiL95oO/GoYxso7+HwREREREREREREREfkXctrL6kzTzGmw7IFsyv4E/JTN80MyPb73Fp/XPJvnJqZ7uCq7sqZprrrxmu3OpeziOwOkX1RlUOZtbY/fTvfvSVin2hMRERERERERERERuTMu+ermGLkLnPkOIxEREREREREREREREcGJ7zDKK4ZhBADLs3npQdM0E+wdnwDEgAAAIABJREFUj4iIiIiIiIiIiIiIyO1owOgusw0K1XJ0HCIiIiIiIiIiIiIiIrmlKelEREREREREREREREScnAaMREREREREREREREREnJwGjERERERERERERERERJyc1jASEREREREREREREZE7YxiOjkDuMt1hJCIiIiIiIiIiIiIi4uQ0YCQiIiIiIiIiIiIiIuLkNGAkIiIiIiIiIiIiIiLi5DRgJCIiIiIiIiIiIiIi4uQ0YCQiIiIiIiIiIiIiIuLk3BwdgIiIiIiIiIiIiIiI/MO4GI6OQO4y3WEkIiIiIiIiIiIiIiLi5DRgJCIiIiIiIiIiIiIi4uQ0YCQiIiIiIiIiIiIiIuLkNGAkIiIiIiIiIiIiIiLi5DRgJCIiIiIiIiIiIiIi4uQ0YCQiIiIiIiIiIiIiIuLk3BwdgIiIiIiIiIiIiIiI/LMYhuHoEOQu0x1GIiIiIiIiIiIiIiIiTk4DRiIiIiIiIiIiIiIiIk5OA0YiIiIiIiIiIiIiIiJOTgNGIiIiIiIiIiIiIiIiTk4DRiIiIiIiIiIiIiIiIk7OzdEBiIiIiIiIiIiIiIjIP4yL4egI5C7THUYiIiIiIiIiIiIiIiJOTgNGIiIiIiIiIiIiIiIiTk4DRiIiIiIiIiIiIiIiIk5OA0YiIiIiIiIiIiIiIiJOzjBN09ExSD62JyrRKQ4Qi5N8D9zdXB0dgl24ujrHgnvXrlscHYLdnLuQ4ugQ7GJkyGuODsEuBuyb5egQ7KJEgJejQ7CbuKTLjg7BLtwLOMfvqK9nQUeHYBdRccmODsFuPAq6OToEuyji7RzH7qWU644OwS4KOknfxdPDOb6fABaLc/S7r1xNdXQIdhGdcNHRIdjF/PoDHR2C3bxzYoqjQ7CLikV9nOOkkZ2Mun+WcyT3PDRgZcd8dUw6T8tERERERERERERERETuDiNfjXXIXaAp6URERERERERERERERJycBoxEREREREREREREREScnAaMREREREREREREREREnJwGjERERERERERERERERJycBoxEREREREREREREREScnAaMREREREREREREREREnJybowMQEREREREREREREZF/GBfD0RHIXaY7jERERERERERERERE5P/Zu+/oKKq/j+PvSwIJJbQ0ikqTKii9BQFRFBEVGwoqFhCk9yrSkRp6712ayk/Rxy419CYdBakB0gBJgCQk8/yxy6aCqGQX2c/rHI5m52Zyvzu3zdy5M+LmNGEkIiIiIiIiIiIiIiLi5jRhJCIiIiIiIiIiIiIi4uY0YSQiIiIiIiIiIiIiIuLmNGEkIiIiIiIiIiIiIiLi5jxdnQEREREREREREREREfmPMa7OgNxtWmEkIiIiIiIiIiIiIiLi5jRhJCIiIiIiIiIiIiIi4uY0YSQiIiIiIiIiIiIiIuLmNGEkIiIiIiIiIiIiIiLi5jRhJCIiIiIiIiIiIiIi4uY8XZ0BERERERERERERERH5jzHG1TmQu0wrjERERERERERERERERNycJoxERERERERERERERETcnCaMRERERERERERERERE3JwmjERERERERERERERERNycJoxERERERERERERERETcnCaMRERERERERERERERE3JynqzMgIiIiIiIiIiIiIiL/LSaTcXUW5C7TCiMRERERERERERERERE3pwkjERERERERERERERERN6cJIxERERERERERERERETenCSMRERERERERERERERE3pwkjERERERERERERERERN+fp6gzI/c+yLOZMGcuurSF4eXnTvufHFCtRKk26Y0cPMWnUEOJiY6lYrSYt2nXFGMOCGRPZsXkjnp6ZCSxQkA49PyZ7Dh/i4+OZPm44x44exhhDi3ZdKVu+kgsitLEsi7lTxrJ722ayeHnRvufHFC2eXpyHmTJqCHFxsVSoWoP37XGGrPuJFQtnc/bUCYZPnsvDJUsDcOPGDaYFf8Ifvx0hIfEGdZ5qyMvN3nF2eA6WZTFzUjA7t2zCy9ubTr0H8HA6x/P3I4cYP2IQcbGxVKoeRKsO3TDGOLZ/vmwR86ZPZPHqH8iVOzf7du9kaL9uBOYrAECN2k/Q9J0PnBbX7ViWxYwJY9i+ZRNeXt507TuQh0umjfm3I4cY+8lA4mJjqVI9iNadujti/nLVMr76fAUeHp5UqRFEi7adnB3GLVmWxaxJwezcGmI7pr36p1tHfz9yiIkjBxMbG0ulajX5wH5Ml8ydztZN68lkDLny5KVjr/74+vkTfeVPJo4awvnQs2TJkoUOPT+mUJFiLojQ5tedW1k6ewKJCYnUfroRjV59K8X2+Pg4Zo0bxonfj5AjZ07a9BiEf2B+jh89yLwpo22JLIvGTd+nUo3aAHz/5UrWff8VlmVR5+nneebFJs4O6x/rOKcTlRtV4XLYZTqUa+fq7Pxt+3ZtZemsiViJiTxe/zmeS+d4zh43jJPHjpLdJydtegzELzA/ERfO8VH7t8lX8CEAipUoQ/O23YmNvc60kf0JOx9KpkyZeKxKTV5750NXhJaCZVlMmzCa7ZttbW63vgMpbu8fkvvt8CGCPxlAbGwsVWoE0aZTD4wxfNK/N2dOnQQgOvoKOXL4MHX+pxw5uJ8Jo4Y5/sZb77ciqE49p8aW2q87trBo5gQSExOp+3Qjnm/ydort8fFxzAgeyh+/HyGHT07a9x6Mf2B+9u3ezop507hx4waenp680aIdjzyWcjwwdlAvwi6EMmLqImeG5GBZFvOmjmP3dttYqG33jylavGSadMePHmbKGPsYoUpN3mvbBWMM0X9eZtywjwm/cA7/wPx06TeUHD45uRoTzcQRA4kMv0BCQgLPv9qMJ55pBMDrDYJ4qLCtzfULCKTX4NFOjzkjyu6fly8xtF9Pjh4+SP1nn6dd115OjSu1/bu28umcSSQmJvL4U8/R8JU3U2yPj49jzoRPOHnsKDl8ctK6+wD8AvITEXaOjzs0J18BW1tUtEQZ3m7TjevXrjKybwfH71+MDKd6nfq80aID95K9O7awaOZ4e319nhfSqa/Tg4fwx+9H8PHJlay+bmP5vOncuBGPp2dmmqZTX13NsixmTgxmx1bbuK9zn1uPdccNH0RcXCyVqwXRqqN9XDRvJt+tWU2u3LkBaP5BO6pUD3L8XtiF87R9pwnN3v2Al994O81+M1JGtUUAB/buYv608SQk3MAnZy4GBU8DICb6CtPHDuf0iWMYY2jT7SNKlCnn1JhnT04a53bseYtx7lHbODfOPs5t2T5pnLstZD3GGHLlzkunXv3J6+fPvj07Gf5xdwJunrs8/gSvN2/ptLjSY1kWU8eNZvvmjXh5e9O936B0292jhw8yZuhA4mKvU6VGLdp2sbW7x44eYcLoYcTFxeHh4UGH7n0oVaYsp078QfCwgfx+9DDvtm7Ha82aOz+4W8iovuZekFFl98ypE0waNZhjvx3hrffb0Pj1t9L5685zYPc2Vs6bjJWYQM0nn+OZl5ql2B4fH8eCScM5fdw2pm/RZQC+AflIuHGDxdNHc/r4byQkJlCtztM0eOlNLpw9xZxxgx2/HxF2jkavv0e95151dmi3VPbpcjQLfgvjkYkNc9fxzZg1Kbb7PuTLezNb4uPnQ0xUDLPem87Fsxcd2719vBm2dwS7vtzJks6uGdvejmVZzJg4hh326ydd+tz6+sm4Twba+tHqQbTu2N1edmfw3ZrV5MydB4B3PmhLlRq1uHAulA/ffo2CDxUCoFSZsrTv3tepscktmL9OIv8tmjCSDLdrWwjnzpxmysJVHD20n5kTRjFyytw06WaMH0WbLn0oUaYsQ/t0Yfe2zVSsVpPHKlXlrZZt8fDwZOHMyXy2dAHNW7Xnx69XAzB+9lIuXYxiaJ/OjJo6n0yZXLNwbve2zZw7e5pJC1by26EDzJwwihGT08Y5a8IoWnftTYnSZRnWtwu7t2+mYtWaPFS4KD0GjmDGuBEp0m9e9xPx8XGMnb2E2OvX6dziDWrVq+84OXG2nVtDCD1zihlLPufIwf1MGzeC4Gnz06SbOm4E7bv3pWSZcgzs1Ymd20KoXM12shwedp49O7fhH5gvxe+UKVeBASPGOSOMv2XHlk2cPXOa2Z9+wZGD+5kcPJzxMxekSTcleDgde3xEqUfK0b9HJ3ZsDaFK9SD27trBlo3rmTp/GZmzZOHSxSgXRHFrO7eGcO7saaYv/oyjh/YzbdxIxkyblybd9PEjadutDyXLlGNw787s2raZStVq8tLrb/Hm+7YL6199tpzlC2fTtmsfVi6ZT9GHS9B3yGjOnDrBjPGjGDJ2qpOjs0lMSGDRjLH0GDyOvL7+DOr2ARWqBlHwoSKONOt/+JpsOXwYNXMZW9b/yMoF02nbcxAFCxVl4NhZeHh4cikqgo87vUf5qjU5d+YU677/iv7BM/H09CR4YHceq1KDfAUedEmMf9dP839kzeQ1dFnY1dVZ+dsSExJYPGMc3QaNJa+vP4O7t6J81VoUfKiwI82GH74mew4fRsz4lK3rf2Llgum06TkIgIB8BRk0Pm37/EzjNyj9aEVuxMczun8Xft25hUcrVXdWWOnavmUToadPM3fZag4f2M/kMcOZMGthmnSTgofTsWc/Sj9Sjo+7d2THlhCq1Aii7+CkPmXmpLFkz5EDgEJFizFp9iI8PD2JjAin7btNqR5UGw9P1wwNExMSWDBtLL2GjiOvXwD9u7SkYvVaKerouu/WkD2HD8Gzl7N53Y8snzeN9r0H45MzF10HjCKPrx+nTxxndP+uTFy42vF72zetwztrVleE5bB7+2bOnz3NxHkr+e3wAWZPHMUnk+akSTdr0ihad+5N8dJlGf5RV/Zs30KFqjVYvXwR5SpUpvEbzVm9bCGrly/irZbt+PbLVTxQqAi9h4zhz0sX6dTidR6v9wyemTOTJYsXo6enLSvOklFlN0sWL5q3bMPJP45x4vgxp8WTnsSEBJbMHE/XgcHk8fVnaM/WlK8aRIEHCzvSbPzxa7Jn92H4tKVs2/ATqxbO4MPuAwHwDyzIgHEpy4F31mwpPhvc7QMqVq/tjHDumK2+BtN76HhHfa2Uqr6utdfXsbNXsHndjyybN5UOvYfgkzM33QaMJI+vP6dPHGdU/y5MWvg/F0aT1g77WHemfaw7dewIxk6fnybdlLG2sW6pR8oxsGcndm4NobJ9Yqjxa01vORk0e/JYKlWtmZEh3FJGtUUx0VeYPWk0H30yDr+AfFxONtadN3Uc5atUp1v/T7gRH09s7HVnhuwY505bZBvnTh8/ktFT045zZ4wbSduutnHukD7pj3PXfL6c5Ytm06ZLHwDKlCtPv0/unXOX7Zs3cfbMKeat+B+HD+xj4ujhTJqdTrs7ejide31E6bKP8lG3DmzfEkLVGkHMmjKBt95vTdUaQWwL2cjsKRMYM2UWPjlz0bZLT0LW/+KCqG4vo/qae0FGld0cPjlp2b47WzetdXJEaSUmJLB8zgQ6fjya3Hn9GdnnQx6tXJP8yfrRkJ+/IVsOHwZNXsKOTT/zxeIZtOw6gF2b13IjPp5+Y+cSF3udwV3epUrQkwQWfIi+Y2Y79t+39Ws8VrWWiyJMy2QyvDWhOcENRxF1Jor+IYPYs2YXoYdDHWmajGhKyOJNhCzeSKm6pXllSBNmvz/Dsf2lga9wZMMRV2T/juzYsonQM6eZtdR2/WTK2OGMm5H2+snU4OF0sF8/GZCqH33xtWa80jRtP5q/YEEmz12a4TGIuDuXPpLOGBPtor/7kjHGMsakneJ2bj46G2Oy3Wb7VmPMHmPMKWNMuP3/9xhjCjsvl//etk3rqfv0sxhjKFmmHDHRV4iKjEiRJioygmtXYyj5SDmMMdR9+lm2bloHQPnK1fHwsF3AKlGmLJERYQCcPvkHj1aoAkDuPHnJnsOHY0cPOTGylLaHrKdu/YYYYyhRpixXo6O5mCrOi5ERXL0aQ8ky9jjrN2T7pvUAPFCoCAUfLJRmv8YYYq9fIyHhBnGxsXh6ZiZrtuxOiSk9Wzato94zz2GModQjtz6eV2NiKPXIoxhjqPfMc2zZuM6xffbkcbzXugPmP3IbwpaN63iyQcOUMUekijnCFnPpsraYn2zQkC0b1gLw9epVvPbWO2TOkgWwldd7ybZN63ni6YZJdTTmr4/pE083ZKv9mGbLnnRiFXv9mmNV1ekTf/BoRVsdfeChwoRdOMelqEgnRZXS8d8OEZi/IAH5CuCZOTPVHn+S3Vs3pkize+sGatVrAECVoLoc3LsTy7Lw8vJ2tEHxcXGOcht6+iTFSpZxbC/5SHl2bV7v3MD+hQMbDhAddcXV2fhHjv92iIB8KY/nnm2pj+dGatqPZ+WgOhz6dReWZd1yn15e3pR+tCIAnpkzU6hocS5GhmdcEHdo84Z1PNnA1uaWLluO6OhoIiNS5isyIpyrMdGUcbQ/zxFib39usiyL9b/8SN2nbN+Jt3dWx+RQfFxcihWgrnDs6CECCzxAQP6CeGbOTPXaT7FzS8pjumvrRmo9+SwAVWvV5YC9jhYuVoI8vn6ArS+Nj4sjPj4OgOvXrvLt6mW8+IbrVuYC7AhZT+36trFQidJliYlJf4xwLSaGEvYxQu36z7I9xNbObt+8gTr1GwJQp35DtofY2hqD4frVq1iWxfVr18jhk5NMHh7ODe4WMqzsZs1K2ccqOPpUV/rjt0ME5C+Iv70tqlqrXpq2aM+2TdR84hkAKtWsw+G/aIuSuxB6hiuXL1K8zKN3Pe//Rtr6+iQ7t2xIkWbX1g08/qStzKatr/5A2vp6r9i68c7GuteuJo37Uo91b2XzhrXkK1CQh4oUzajs31ZGtUUbf/6eakF18Quw3QyWyz7WvRoTw6F9e6jX4HnA1r9mz+HjlFhv2pbsHO1256JXryaNc+vWb+g4F00+zr1+/do9ff4SsmEt9Rs0sre7jxITfSXddjcmJoYy5R7DGEP9Bo0cE0HGwNUY2yWamOhofP1sdTVP3ryULPOIy24quZ2M6mvuBRlVdnPnyUvxUmUc5zeudOL3w/jnK4BfoK0frRRUj707NqVI8+v2TVSvY+tHK1Svw5H99n7UGGJjr5OQkEBcnO06iXfWlJfXDu/fhV++Avj6p7xR1ZWKVilG2LEwwv8IJyE+ga0rtlD++Yop0hQoXYBDvxwA4PDaQ1RItr1QhcLkDMjFgR/3OTXff8eWjeuo98wdXD9J0Y82ZHOqeikiruOu7zBqCmwE3nBxPjoDt5wwsiyrmmVZ5YH+wHLLssrb/524k50bY1w/AgCiIsLx8w90/OzrH0BUqkFcVEQ4vv4BSWn80qYB+Pn/vqJilRoAFC5WnG0h60lIuMGFc6EcO3qYiLALGRTFX4tMFUNe/4B0B6s3B963SpNa9dr18PLOygdNGvHhmy/ywmtv4pMz193N/N8QGZ72eEaGh6VKE4Zfsu/Czz+AyHBbnFs3rcPX358iD5dIs+8jB/fRoUUzBvTsyMk/XHvXcHIR4eH4ByQNMv38A4mISBlzRERYiu/Fzz+QCHvMoadPcWDvHjq3eoee7Vtx9NAB52T8DkVGhOEXkCzvfgGOidnkaVLUUf+UaRbNnsr7TRqx7sdvafZeawCKFCvOZvsJ6NFDBwg7f56IVGXFWS5GhpPXLyn/efz8071AcjONh4cnWbNnJ/rKZQCOHTlA33Zv06/ju7zTtjseHp48UKgIRw7sJfrPy8TGXufXnVvSfG+SMS4lO1YAeXz900zuXIq69fEMv3COgZ1bMKJvB44e2Jtm/1ejr7BnewilH3X9Y5IiI8LwT1Y//QPS71uStz/+AYFpyuL+vbvJkycvBR98yPHZ4QP7aPXWa3z4zut06N7HpReCUtfRvH5pj2lUZFI/6+HhSbZs2Yn+83KKNNs3raVQ0eJkzmybTFi1aDbPvvQGWby8MziC24uKTNV3+vkTdZv4bGkCHGkuX4xyTIrl8fXjz0u2x5I0ePFVzp4+Qeumz9Ot9Vu816aLY6V1fFwcvdu9x0cdW7Jt019fyL7bMrLs3isuRkWQJ01blLZvyZO8LcqW1BZFhJ1jUNcWjPqoI0cPpm2Ltm34kSq16rl8Qje1tPU1IE19vRgZTt47qq8lHPX1XhEZEZ5iXHSrsW7acVHSd7Dmi5W0f68p40cMJvrKnwBcv3aNVUsXuvSRyxnVFp07e4ro6D8Z2L0tvdq+y7ofvgEg7PxZcubOzdQxQ+nZpjnTx37C9WvXMjTG1KJSjXNt56JhadKkPp7J0yyeM5UWrzdi/Y/f0tQ+zgXbuUvnls0Y3LsTp+6Bc5fI8DD8A5OfjySdgyWlCcc/INl5WkBS+W7TuTuzpkygWeNnmTl5HO9/2N45Gf8X7ue+JiPL7r3iUlQEeXyT9aN5/bmcqh+9FJW8H/Uga7YcxFz5k4rV6+Dl5U2fD16hX5s3eOr5JmS3PyLzpp2bfqZy0JMZH8jfkLtAHqJOJ91EefFsFHkK5kmR5vSvp6n0ku3Gy4ovViZrzqxkz5sDYwyvj2zKij7LnJrnvysyIu31k/SvLwSmSpO8H11Bu3ffYPyIQVyx96MA58+F0qFFM3p1aMX+vbszMAoR93bPTRgZYwoZY34yxvxq/+9D9s+ft6+42W2M+dEYE2j/fKAxZq4xZq0x5rgxpuNf7D8HEAS0INmEkTGmrjFmnTFmhTHmqDFmhDHmTWPMNmPMPmNMsb/I33xjzKvJ9hedbL9rjTGrjDGHjTFLjE1HoADwizHmb6/tNsY8a4zZbIzZZYxZbozJbv/8jDHmY2PMJuAlY8xGY8xYY8wGY8xBY0xlY8wXxpjfjDED/+7f/Scs0t5Fmfq8N707LVOfHK9aMo9MHh7Utt/18+Szz+PrH0CPNu8yd+pYSj1SDg9X3lV7BzGkn+b2u/398AEyZcrEzOVrmLroc75atZQLoWf/TU7/pb+O81bH/Pr166xYPI8330v7XpBiJUoyZ9mXTJqzlOdffp1h/XrcvSz/W//42NrSJCTcIPrKn4ybMZ8WbTsyfECfO7672BnSy0qauyfTS5PsO3i7ZVvmrlhDnaca8PUXKwF4pVlzoqOv0Lnlm3z9xQqKFi/hsjqa7td9J+2QPVGxko/wyZRFDAieyZpVi4mLi6XAg4Vp+PKbjO7fheAB3XmwyMOubYPcSPptTKp26BbHM1deX8bMXsnA8XN44/32zAgezLWrMY40CQk3mB48mKcaveKyR38md7ty+XfSrP3xW+o+9UyKz0o9Uo6Zi1cycdYili+eT1xs7F3I8T9zJzGk31glpTlz8jjL503jvQ49ATh57DcunDtD5Zp17mpe/4k7aWfT7xduP0jYu2MrhYoWZ8anXzF62gLmTA7maoytPE9d8gUjpsyjY59BLJg+nvOhZ/5p9v+RjCy794w7Gtfdoi3K48uomSsYMHYOTd5vx6yxQ1K0RQDbNv5M1cfvrQtdcGdlNd00qerrsnlTeb/DPTTes7uT85LbjeMavvgKs5Z+wcQ5S8jr68fsKeMBWDJvBo1fa0rWbLe8XzDDZVRblJCQwB+/HaH3kGA+Gj6ez5bMI/TMKfvnR3m60cuMmrYQL++srF7u3Edlph9O6pjT+82kNG+1aMuc5Wuo/VQDvlltG+cWK16SmZ9+yfjZS2nYuAnD+/e8e5n+h/7pePfm9/HV56v4sGM3lq7+Pz7s1I2xwwenTXuPuZ/7mowqu/eW2/cVtiTpNlyc+P0QmTJlYvjMVQyZspQfv1pJxIWkx7rdiI/n1x0hVKzh+nFgculd/0kd4oren1Ly8VIM2DqEkrVLEnUmisQbCTzx4ZP8+t1eLp65tx5xn9pfjQFuleZmX9uw8avM/nQ1k+YuJY+vH3Om2B79mdfXj/kr1zBpzlJatu/C6MH9HKsiReTuuidWoKQyGVhoWdYCY8z7wESgMbYVQdUty7KMMS2BnkA3+++UAp4AfIAjxphplmXF32L/jYFvLcs6aoyJMsZUtCxrl33bY0BpIAo4Dsy2LKuqMaYT0AHbiqBb5e92KgCPAKHAJiDIsqyJxpiuwBOWZUXc9rdTMcYEAL2BJy3LumqM+QjoBHxiTxJjWVaQPW0n4JplWY8bY7oBq4FKwGXguDFmvGVZl1LtvxXQCmDAiHG89ua7fyd7APzf6pX88I3teeQPlyxDRHjSyp/I8DDHoyhuSn3nXmREmOPuNYBfvvuaHZs3MmjMFEcn4uHhyfttuzjS9OnQkvwFnfvukP/73yp+ssdZrETpFDFEhYeRN1kMkPbuQ1ualN9Faht+/p4KVWrg6elJrjx5KfnIo/ZHgRS8i5Hc3tdfrOC7Nbb3QRQvlfZ45vVLGYNtdU3SdxFhT3M+9AwXzoXSsUUzx+edW73F2GnzUxzvytWDmDZuJJcvXXK8MNjZvvp8Bd99lRRzeNh5x7aI8Av4+qYX84WUafz8HNtq1nnC/jiBshhj+PPSJXLlSXknkTN9/cVKfrC/B+zhUmVSrM6LiEh7TNPU0VuU3dpPPsOQPl1o9l4rsmXPQade/QHbgLBV08YE5nfNBfi8fv4p7ra7GBFOnrx+6abJ6xdAQsINrsXEpLlLrcCDhfHy9ubsyT8oUrwUdZ5uRJ2nbS+YX7VwRoo7zSXj5PFNdTwjw8md6njeTJP6eBpjHHezF364JAH5C3L+7GmKFLc9pXbBlDEE5n+Ap19o4ryAUvnysxV8+9UXAJQoXYbwZPUzPCyMvH4pY/XzD0jR/oSHXUhRhxNu3GDTul+YNGdxun/vocJF8Pb25sQfxyhRqszdDOWO5fVLeUdsVEQ4uX1T19EAe59jO6ZXr8Y4XrYeFRHGhKF9ad2tH4H5bf3jb4f3c+L3I3R571USEhL48/JFhvVuz0cjJjslpm+/XMVP33wJQLGSpVP2nRHhKfo9sN3Fn3osdHMckStPXttKFV9bIaOXAAAgAElEQVQ/LkZGOF4C/Mv3X9P49bcxxpCv4IME5CtA6OkTPFzqEUcbHZi/IGUerciJ34+Sr8ADGRqzs8uuq+Xx9efiHbRFF5O3RVfTaYuKlcQ/X0EuhJ6m8MO2tuj0H7+TmJBA4WIlnRfQHUpbX8PSlOe8fgFEhYfhm059jYwIY/zQvnzY7WMC82dsmbxTa5KPdUumHBelO9YNCEwzLkp6fJev4/NnGjVmUB/b+cqRgwfYtO5n5s2YREz0FYzJROYsXjz/csb2N85oi3z9AvDJmRvvrFnxzpqV0uXKc/L4b5QuWx5ff3+Kl34EgOqPP8Hq5Rn/gvZvVq/k+69vczz/6lw0nWMOULveMwzt24Wm77ZK8bivytWDmDFhFH9evkTOXM49d/nys+V886Wt3S1Z6hHCLyQ/HwlL8XQLsK0oCg9Ldp4WlpTmh/9bQ9sutknc2vXqM274kIzO/j9yP/c1zii795Lcef25GJmsH40KJ1eyNhQgt70fzePrT0JCAteuRpM9R062b/yJMuWr4uHpiU+uPBQr9Qgnjx3BL9B2vnlgz1YeLFKCnLnvrcfBXzx7kbwPJsWYp2BeLoVeTJHm0rlLTHl9IgBe2b2o1LgK1/68RrFqD1MiqCT1Wj2JVw5vPLN4Ehsdy6p+K5waQ3rWfL6Cb+39aIk7vH4Smer6yc2+Jnk/2qDRSwzq3RmAzFmyOB5JXLxkafIXLMjZ06co7qLzF5H72T23wgioAdx8g9ki4Obb6R4AvjPG7AN6YJuAuelry7Ji7RMvYUAgt9YUuLl+c5n955u2W5Z1zrKsWOAY8L39831A4b/I3+1ssyzrjGVZicCeZPv6p2oCZYAQY8we4M1U+1yeKv2X9v/uA/ZZlnXBsqzrwAls32sKlmXNtCyrsmVZlf/JZBHAs41fY+zMxYyduZiqQbVZ+/3/YVkWRw7uI1v2HGkmUvL6+uGdLRtHDu7DsizWfv9/VA2yveB317bNfLFsIX2GjsHLO+mRMrHXrzseabBnx1Y8PDx4sLBznwf+7IuvMmbGIsbMWETVoDqs/eEbLMvi6MH9ZMueI80JWB5fP7JmzcbRg/ttcf7wDVVq3v5Fxn4Bgezfs8PxfoLfDu2nwENp33WUkZ57qQkT5yxl4pylVK9Vl5+/+xrLsjh84NbHM2u2bBw+YDueP3/3NdWD6lC46MMsXv09c5Z/yZzlX+LnH8D4mYsdJ5037zI5eugAiVYiOXO57tF7z7/chMnzljJ53lJqPF6Xn779xhFz9hw50pyI5PXzI2u27I6Yf/r2G6rXst3NVP3xOuzduQOAM6dOcuPGDXK6aCLspudeeo3xs5cwfvYSqgfV4Zfvv3HU0ey3OaY36+gv33/jqKOhZ0450m0LWU/BhwoDEB19hfh429z9D1//jzKPlk9xcu1MRYqX4kLoGcLPh3IjPp6tG36iQrWUzXf5qrXY+PO3gO0xOaUfrYgxhvDzoSQk3AAgIuw858+ewi/QtsT+5qNYIsMvsGPzeqrXfsqJUbmvIsVLceHcGcIvJB3P8lWDUqQpXzWIEPvx3LFpHaXsx/PPy5dITEgAIOx8KBdCz+BvX0n0+eJZXLsaTdOWHZwbUCovvNKEqfM/Zer8T+3tj63NPbTf1v6kvvjj6+dP1mzZObT/ZvvzNTUeT7qbcveObTxYqHCKR7acDz1Lwg1bub5w/hxnTp0kMF9+5wSYjqIlSnH+7GnC7HV0y/ofqVgt5TGtUC2IjT/9HwDbNq6ljP2YxkRfYczAHjR590NKJHvXy1PPvcSkRf9j3LxVfDx6KvkKPui0ySKABi+8yujpCxk9fSFVa9Zm/Q+2sdDRQ/vJlj17+mOEbNk5esg2Rlj/w/9R2T5GqFy9luMRT+t++IYqNR4HbGOEfbtt/culi1GEnjlJQP6CRF/5k/g423th/rx8iSMHfuWBQkUyPGZnlN17SWFHW3SOG/HxbNv4M49VSVluH6sSRMgv3wGwM2QdpcpVwBjDlWRtUfj5UMLOnXFc5ALYuuGne3J1Edysr2eS1defqJiqT61YrRYbfrKVWVt9reSor8EDe9Dk3dYp6qurNXqpCZPmLGXSHNu4747GullTjnWr2cd9yd8xsnnDWgoVKQbAqMmzmLv8S+Yu/5IXXm1Kk7fezfDJInBOW1S5Zm0O799DQsINYq9f5/fDByn4YGFy5/XF1z+Q0NMnAdi3ewcP2MeJGalh49cYP2sJ42ctoVqtpHO0Ox3nrv3hG6rWvP0492JUynMXy0p0yWPDX3jldaYvWMb0BcuoWbsuP3y7xt7u/kr27Om3u9myZePQ/l+xLIsfvl1Dzcfr2rf58evunQDs2bmNAg8692bMO3U/9zXOKLv3kkIPlyLs3Fki7P3ozk0/82jlminSPFq5JlvW2frR3VvWUbKsrR/N6xfIkf27sSyL2OvX+OPoIQILJj1ScMfGn6lSq55T47kTf+w4TuDDgfgV9sMjswfVmlRnz5qUj1bL4ZvDcaP0cz2fZ+MC2/viZr07nR7Fu9CzZDdW9P6UkCUb74nJIoBGLzdh8tylTJ67lOqP1+Xn75JdP8n+19dPfv4u6fpJ8vcdhWz4xdGPXr50kQT72Olc6BlCz5wmnxNvpBZxJ/fiCqPUbq5TnASMtSzrS2NMXWBgsjTJn6GSwC3iMsb4AvWAssYYC/AALGPMzfXjyfeTmOznxFvtM1n+bmCfgDO2lj35w7jvKH9/g8G2SurtW2yPSfVz8jhSx5jhZaBStSB2bQ2h7duv4OXtTfseHzu2dW31FmNn2u7kad2pF5NGDSYuNpaKVWtQsaptoDB70hji4+MY1NN2Aa9E6bJ82KU3ly9FMbhXJ0ymTPj6+dOxz8CMDuW2Klarya5tIbRv/ipeXt607dHPsa1767cZM8N2N90HnXoyZfQQ4mJjqVC1BhWq2t7JtHXjWuZMDubPy5cY/lFXChcrwccjJ9DgxVeZMnooXVo2A8viiWcaUbhocZfECLY76HZs3USrN1/Cy8vbsYIEoGOLZkycY5tPbdulN+NHDCIuLpZKVWtSqVrNW+0SgE3rfuabL1fh4eGJVxYvevYfds88s79KjSC2b9lEizca4+XtTZc+Axzb2r/XjMnzbDG369abcZ8MJDY2lsrVa1K5uu2i0dPPvcj44YNp07wJnp6Z6dp34D0TG0Cl6kHs2BrCh2+9jJeXNx16JdXRzi3fZPzsJQB82KUXE0cMJi4ulorJjunCmVM4e/okJlMmAgLz0aZLbwDOnPyD8cMHkSlTJh4sXIQOyeqEs3l4ePJW6y6MGdiNxMREHn/qOQo+VITPl8ymyMOlqFCtFrXrP8fMsUPp2eoNsvvkpE2PgQAcPfQrXw9ZgoenJ5mM4e0Pu+KT0zbhN3lEP6KvXMbDw5PmH3Zx+ouc/43uS3tQtm45cvrlZO7p+Xw6YAk/zP3B1dm6Ix4enrzVqjNjB3YnMTGRWk82pOBDRfhiyRwKP1zScTxnjRtG79ZNye7jQ+vuAwE4emAPq5fOJZOHB5kyZaJ5m27k8MlJVEQYa1YuIv8DDzGoa0sAnmz4MrXtK8hcpWqNWmzfvIn3X38RL29vuvYd6NjW9t2mTJ3/KQAduvcheNhA4mKvU7l6EFWqJ120XvvTd2kes7L/1z2sWDwfT09PTCZD+269yZXbdasePTw8ad6mK6M/7kpiYiK16z/HA4WK8tmi2RQpXoqK1WtR5+lGTB8zhG4tXyeHT07a9RwIwA9rPuNC6FlWfzqf1Z/OB6Dn0HEujSe1ClVtY4SO775GFi8v2nZPag97fNic0dNtj2hq2bEHU0cPJS4ulvJVqlPB/t7Gxm80Z9zQj/j526/wCwika79hALzy5ntMHT2Ubq3eBAvebNGOnLlyc+TAr8ycMJJMmTKRmJhI49ffdsqEUXIZVXYBmr/aiKsxMdy4Ec/mDWsZNnYKhYo496YhsJXbZh90ZvwgW1sUZG+LVi+dQ+GHS1G+ahCPP9WQ2eOH0adNM7Ln8KF1N9sY4ujBvfzv06S26K0PuzpW4ADsCPmFTv1GOj2mO+Hh4ck7bbow6uOuJCYmUKd+Ix4oVJRVi2ZRpHgpKlV/3FFfu7ZsQg6fnLTvOQi4WV/PpKivvYaOv6fqa+XqQezYsokPmtnGup17J411O7RoxqSbY92uvRk3YhBxsbFUqlaTyvZx0bxpEzn++1GMMQTky0/77n1dEkd6MqoteuChwpSvXJ3urd8mk8lEvWef5yH7Bb7323Vl4oiB3LgRT0C+grTt/pFTY65ULYidN8e53t507JlsnPvBm4yfZR/ndu7FxJGDiY1Nee6ycNYUQu3jXP+ApHFuyLqf+fbLz/Dw8CCLlzfd+7n+3KVqzVps27yRd1+ztbvdPxro2PbhO28wfYHtvtmOPfoyeugA4mJjqVKjJlVq2NrdLr0/Zur40SQmJJA5ixede9nKR1RkBO3ff4urMTGYTIYvli9l1tJVZHfRjWDJZWRf42oZVXYvRkXQ/cN3uXo1BmMMX322jEnzlrnkxj4PDw9eb9GRycN6kpiYSI0nnqXAg0X4atlcChUryaNVgqhZ7znmT/qEAe3fJFuOnLToYvseaj/TmEVTRzK063tYFtR4ogEPFLK1O3Gx1zn8606aterq9Jj+SmJCIos7L6Trmp5k8jBsnL+e0ENnadz/ZU7s+oM9a3ZTsnZpXh36GpYFRzccZnEn5z7K89+qUj2IHZs30bJpY7y8Ul0/eb8Zk+far5907c244fbrJ9WSrp/MnT6B478l9aMd7P3G/j27WDx3Bh72sVO7bn1c+n5vSeYeur4ld4dx5Xs0jDHRlmXlSPXZl8BKy7IWGWPeBV60LOslY8xuoKVlWTuNMfOAIpZl1bW/hyfasqwx9t/fDzSyLOtEOn+vNVDRsqzWyT5bB/TDNnnU3bKsRvbP19p/3mGfoOpuWVaj2+SvH+BjWVYvY0xj4Avb0/OSfte+38nADsuy5ttXS71gWdYff/E9vQtUtiyrvf3nQGA7UNeyrOP29xcVsCzrN2PMGaDszcfMGWM2Au0ty9pjjHnK/v+NU2+71d8+cObSvfOilQyUeA+9TyYjeXm6x/tVPDzco7OKv5Ho6iw4zcUrrnu3ijMNL/W+q7PgFL0OzXF1FpyigG92V2fBacIvO/cl5q7ildk9+tGc2bL8daL7wJlw93nuvXeW/8J9gv9e7hzuUXavxt5wdRacIoubnLtk83aP+gmQmOge593X4xJcnQWnCI1Mfa/y/WlJld6uzoLT9D01xdVZcIqHA33c46KRk4xquNA9GvcM1POb5vdUmXT1I+myGWPOJPvXFegIvGeM+RV4G9u7ecC2omilMWYD8Lfe+ZNMU+CLVJ99BjT7G/u4Vf5mAXWMMduAaqRd5ZOemcD/GWN++Rt/H8uyLgAtgOXGmL1ACFDi7+xDRERERERERERERETkJpeuMJJ7n1YY3V+0wuj+ohVG9x+tMLq/aIXR/UcrjO4vWmF0/9EKo/uLVhjdf7TC6P6iFUb3H60wkn9CK4z+Pa0wEhERERERERERERERkXvKfXkrizHGF/gpnU1PWpYV6ez83AljzFbAK9XHb1uWtc8V+REREREREREREREREfdxX04Y2SeFyrs6H3+HZVnVXJ0HEREREREREREREZE7kumeepqa3AV6JJ2IiIiIiIiIiIiIiIib04SRiIiIiIiIiIiIiIiIm9OEkYiIiIiIiIiIiIiIiJvThJGIiIiIiIiIiIiIiIib04SRiIiIiIiIiIiIiIiIm/N0dQZEREREREREREREROQ/xrg6A3K3aYWRiIiIiIiIiIiIiIiIm9OEkYiIiIiIiIiIiIiIiJvThJGIiIiIiIiIiIiIiIib04SRiIiIiIiIiIiIiIiIm9OEkYiIiIiIiIiIiIiIiJvThJGIiIiIiIiIiIiIiIib83R1BkRERERERERERERE5D/GGFfnQO4yrTASERERERERERERERFxc5owEhERERERERERERERcXOaMBIREREREREREREREXFzmjASERERERERERERERFxc5owEhERERERERERERERcXOers6AiIiIiIiIiIiIiIj8x2g5yn1Hh1RERERERERERERERMTNacJIRERERERERERERETEzWnCSERERERERERERERExM1pwkhERERERERERERERMTNacJIRERERERERERERETEzXm6OgMiIiIiIiIiIiIiIvIfY4yrcyB3mVYYiYiIiIiIiIiIiIiIuDlNGImIiIiIiIiIiIiIiLg5PZJObivRslydBafw8vRwdRacwsI9jqcx7jEXniWzB5GXr7s6G06RPWtmV2fBKXodmuPqLDjFyNItXJ0Fp/j4t/muzoLTeGV2j340+lq8q7PgFO4y/nOXvgUgIcE9junlmDhXZ8EpjJs8+uVGwg1XZ8EprsbeIJN7nL6QO7uXq7PgFDcSE12dBafI4+Mex/ODX8e4OgtO45HJPfoXEbk9NxmWiIjcf9xlskhERERE5H7lLpNFIiIi8t+goYmIiIiIiIiIiIiIiIib04SRiIiIiIiIiIiIiIiIm9M7jERERERERERERERE5G9xl3cruhOtMBIREREREREREREREXFzmjASERERERERERERERFxc5owEhERERERERERERERcXOaMBIREREREREREREREXFzmjASERERERERERERERFxc56uzoCIiIiIiIiIiIiIiPzHGFdnQO42rTASERERERERERERERFxc5owEhERERERERERERERcXOaMBIREREREREREREREXFzmjASERERERERERERERFxc5owEhERERERERERERERcXOaMBIREREREREREREREXFznq7OgIiIiIiIiIiIiIiI/MdkMq7OgdxlWmEkIiIiIiIiIiIiIiLi5jRhJCIiIiIiIiIiIiIi4uY0YSQiIiIiIiIiIiIiIuLmNGEkIiIiIiIiIiIiIiLi5jRhJCIiIiIiIiIiIiIi4uY8XZ0BERERERERERERERH5jzHG1TmQu0wrjERERERERERERERERNycJoxERERERERERERERETcnCaMRERERERERERERERE3JwmjERERERERERERERERNycJoxERERERERERERERETcnKerMyAiIiIiIiIiIiIiIv8xxtUZkLtNK4xERERERERERERERETcnCaMRERERERERERERERE3JwmjERERERERERERERERNyc3mEkGc6yLOZOGcvubZvJ4uVF+54fU7R4qTTpjh09zJRRQ4iLi6VC1Rq8364rxhhC1v3EioWzOXvqBMMnz+XhkqUBiI+PZ+b4ERw7chiTyfBe2y6ULV/J2eE5WJbFzEnB7NyyCS9vbzr1HsDDJdLG+fuRQ4wfMYi42FgqVQ+iVYduGJP0wM/Ply1i3vSJLF79A7ly52bf7p0M7deNwHwFAKhR+wmavvOB0+JKzbIsZk0KZseWELy8vencuz/FbhHnhBGDiY2NpXL1mnyQKs4vli22x/k9OXPndnz+2+GD9Gj7Pj36DyOo7pNOiSk9lmUxfcJotm+2Hc9ufQc6yl5yvx0+xNhPBhAbG0uVGkF82KmHI87/rVrGV5+twMPDg6o1a9GibScunAul1Zuv8sBDhQAo9Ug5OvTo69TYktu7YwuLZo4nMTGRuk8/zwtN3k6xPT4+junBQ/jj9yP4+OSife/B+AfmZ9/ubSyfN50bN+Lx9MxM0xbteOQxW/1bsWAGG3/+lpjoK8z57EdXhJUuy7KYN3Usu7ZtxsvLi3Y9btMWjba1RRWr1uC9tra2aPO6n1ixyN4WTZpLMXt5CDsfSucWTSnwwEMAlChdlladezk1tuT27drK0lkTsRITebz+czz36lsptsfHxzF73DBOHjtKdp+ctOkxEL/A/ERcOMdH7d8mX0FbHMVKlKF52+7Exl5n2sj+hJ0PJVOmTDxWpSavvfOhK0L7xzrO6UTlRlW4HHaZDuXauTo7f8v9Xkdt9XIcu7eH4OXlTdvuH1O0eMk06Y4fPcyUMfYxQpWavNe2C8YYov+8zLhhHxN+4Rz+gfnp0m8oOXxyAnBg7y7mTxtPQsINfHLmYlDwNMf+EhMS6N3+PfL6+dN7SLDT4oV/Xkdvigy/QL/2zXnxjXdp8FJTAL7/3wrW/7AGYwwFCxWlRcfeZM7i5ZR49mzfwoLp40lMSKDes8/z4uvNU8YTF8eU0UP447fD5MiZi059hxCQzxbP6mUL+eXbr8jk4cG7bTrzWOXqt93nt/9bxf99sZwL584yc8U35MyVO8XfOnbkIP06t6JT38FUf7xexsW8YwsLp40nMTGBJxqkH/PUMcli7jME/2Qxr/3uKzJl8uCdZDHHRF9h5vjhnDlxHIyhdZe+lChTji3rf2bV4jmEnj7BkAmzKVYi7VjEFTKibbpX7N2xhUUz7LE9k35s08YM4cTvR8jhk4sOfWyxXfnzMhM++YjjRw9T+6lnebdtNwCuXY1hcM+2jt+Pigin1hNP83brzk6NK7W9O7awcHpSOX6hSdpyPC04qRx37DMkKc5hH3Hs6CFq12/Ie/Y4Y69fZ8InH3Hh3FkyZfKgYrUgmr7fNr0/7VR3O06A5fOns+EnWz8674ufnBrPvdDmHti7izEDexFgPz+tGlSHV95631lfAZZlMS3VeVrxW5ynBSc7T2tjP0/7pH9vzpw6CUB09BVy5PBh6vxP+fPyJYb268nRwwep/+zztOvquvE83N/XUZzZj960ZtVSlsyezIzlaccPzvDrzq0snT2BxIREaj/diEbpjP9mjRtm61ty5qRNj0H4B+bn+NGDzJsy2pbIsmjc9H0q1agNwPdfrmTd919hWRZ1nn6eZ15s4uyw0uUudVTkfqYVRpLhdm/bzLmzp5m0YCUfdunDzAmj0k03a8IoWnftzaQFKzl39jS7t28G4KHCRekxcASly5VPkf7Hb/4HwNjZS+g/ciILZ0wkMTExY4O5jZ1bQwg9c4oZSz6nXbe+TBs3It10U8eNoH33vsxY8jmhZ06xc1uIY1t42Hn27NyGf2C+FL9TplwFJs5ZysQ5S106WQQ34zzNjCWf0a5bH6aNG5luumnjRtKuex9mLPmM0DOn2bVts2NbeNgF9uzcmibOhIQE5s+YRIUq1TM0hjuxfcsmQk+fZs6y1XTs0Y/JY4anm25y8HA69uzHnGWrCT19mh1bbMdz767tbNmwjqkLljFj8UpeaZp0sSF/wQeYMv9Tpsz/1KWTRYkJCSyYFkzPQcGMmraELet/5OypP1KkWfvdGrLn8GHs7BU0aPw6y+ZNBcAnZ266DRjJiKmLaN21H9ODBzt+p2K1IAaNm+XUWO6Eoy2av5LWnfswa+It2qKJo2jdpTeT5tvaoj32tujBwkXpPiBtWwSQr0BBxsxYxJgZi1w6WZSYkMDiGePoMmA0QycvZOuGnzh76kSKNBt++JrsOXwYMeNTnn6hCSsXTHdsC8hXkEHj5zJo/Fyat+3u+PyZxm/wydTFDBw3h98P7+fXnVucFdJd8dP8HxnYYICrs/G3uUMd3b19M+fPnmbivJW06tyb2beql5NG0bpzbybOW8n5s6fZs91WBlcvX0S5CpWZOH8l5SpUZvXyRYDtosHsSaPpNXgUY2ctpWu/YSn2980XKyj4UOEMjS09/7aOAiybM4lyFas5fr4YGc6Pa1bRP3gWQyYtIDExka0bfnZGOCQmJDB3yhh6Dw0meNZSNv3yI2dOpiyjv3z3FTly+DBh/kqee/l1ls6xldEzJ/8gZO2PjJm5hD7DxjJn8hgSExJuu8+Sj5TjoxET8Us1friZl6VzpvJYpWpptt3tmOdNGUOvocGMmbmUkLXpx5w9hw/j562k4Uuvs3RuUsyb1/3I6BlL6D1sLHOn2GIGWDB9PI9Vqk7w7GWMnLrQUT4fLFyUrh9/QqmyafseV8motulekJiQwPypwfQcHMyo6UvYvO5HztwqtjkrePal1/nUfnwzZ8nCa29/QLMWKW9MyJotO8MnL3D88wvIR+WadZ0VUrpuluOeQ4IZPSP9crz2e1s5Hjd3Jc82Thnnq29/wJst26fZ73OvNCN41jKGT57P0YP7HGMoV8moOCtWq8WQCbOdEkNy91KbW6rsY4yctoCR0xY4dbIIks7T5i5bTafbnKdNsp+nzU11ntZ38Aimzv+UqfM/pVadegTVeQKALFm8aN6yDR+0c+1k7k3363UUZ/ejYLvZZt+ubfgFBDotzuQSExJYNGMsXQeM4ZMpi9iaTr+5/oevyZbDh1Ezl6UY/xUsVJSBY2cxZMI8ug0cw/ypo0lIuMGZk8dZ9/1X9A+eyZCJ89i7I4TzoaddEV4a7lJHRe5nTp0wMsYkGGP2GGP2G2O+MsbcdlrfGJPbGNM22c8FjDGr7nKeBhpjztrzddAY0/Rf7GuwMeapu5m/f5CHwsaYZneYbr8z8rQ9ZD116zfEGEOJMmW5Gh3NxciIFGkuRkZw9WoMJcuUwxhD3foN2b5pPQAPFCpCwQcLpdnvmZN/UK5CZQBy5clLthw+HDt6KOMDuoUtm9ZR75nnMMZQ6pFyxERfISpVnFGREVyNiaHUI49ijKHeM8+xZeM6x/bZk8fxXusOGEzq3d8ztm5azxPPNPxbcT7xTMMUcc6ZPI5304lzzecrqFm7Hrly53FKLLezZcM6nmxgO56ly5YjOjqaqIjwFGmiIsK5GhNN6bK2OJ9s8BybN6wF4OsvVtHkrXfJkiULALnz5HV2CH/p2NFDBBZ4gID8BfHMnJnqtZ9k55YNKdLs2rqBx59sCEDVWnU5sHcnlmVRuFgJ8vj6A7Y6Gh8XR3x8HAAPlypLnrx+zg3mDmzfvJ46TyW1RTG3aIuuJWuL6jzVkG0ht2+L7iXHfztEQL6CBOQrgGfmzG5vITwAACAASURBVFR7/En2bNuYIs3urRupWa8BAJWD6nDo111YlnXLfXp5eVP60YoAeGbOTKGixbkYGX7L9PeiAxsOEB11xdXZ+NvcoY7uCFlP7frP2upl6bLExNyiXsbEUMJeL2vXf5btIbY+ZfvmDdSpb4u/Tv2GbLfX140/f0+1oLr4BdgucuVK1gZHhoexa9smnmzwgjNCTOHf1tFdWzbgH1iAAqkmuxISEoiLiyUh4QZxsdfJndfXKfH8fuQg+Qo8QKC9jNas+xQ7Nqcsozs2b6B2/WcBqPb4ExzYswPLstixeQM16z5F5ixZCMhXgHwFHuD3Iwdvu88iD5d03Cmf2rf/W0XVWk+QM4PHEL8fOUi+/En5q1Enbcw7N2+g9lNJMe9PFnONOslizm+L+WpMDIf37eGJBs8DtrY2ew4fAAo+VJgC91jfk1Ft070g3dhSH98tG6j9VNrYvL2zUvKRx8hsH/ul5/zZ0/x56SKlyj6WoXH8ld+PHiSwQMpynPoY7ti8gcfTKcfe3lkpVTZtnF7e3o7VYp6ZM1P44RJERYQ5J6BbyIg4AYqXdk0/ei+1ua60OZ3ztMhU52mR9vO0MsnO00Ls52k3WZbF+l9+pO5Ttj7XO2tWyj5W4bZ12Jnu1+sozu5HARbOmECzlu3ARddZjv92iMD8Kcd/u7emHv9toJZ9/FclqC4H7X2Ll5c3Hh62h0PFx8U5rqGEnj5JsZJlHNtLPlKeXZvXOzewW3CXOipyP3P2CqNrlmWVtyyrLBAF/NVzYXIDjgkjy7JCLct6NQPyNc6yrPLAi8AMY0zmf7ITy7L6W5blsme7GGM8gcLAX04YOVNkRDi+/gGOn/P6B6TbWfj6+d82TWqFixZnW8gGEhJucOFcKMePHiYy7MLdzfzfEBkejp9/0h0rvv4BRIaHpUoThl+y78LPP4DIcFucWzetw9ffnyIPl0iz7yMH99GhRTMG9OzIyT+OZVAEdyYyPAz/fxSnLc3WTevTjTMyPIwtG9fS4IWXMzD3dy4yIizFHUh+AQFEpCqTEREpj7lfQCCR9hPjs6dPsf/X3XT+oDk92n/AkUMHHOnOnztLu/ea0aP9B+zfuzuDI7m1i5Hh5PVLVjf9AtJMBFyMDCev/Vh6eHiSLVt2ov+8nCLN9k1rKVS0BJkz39sDt6iIcHwDkuL19QtIdxIweVvk6582TXrCzofS48Pm9O/ahkP79ty9TP9NlyIjUhzTPL7+aY7ppaikNB4enmTNnp3oK7ZjGn7hHAM7t2BE3w4cPbA3zf6vRl9hz/YQSj96bz0+6H7lDnU0KjJV3+nnT1SqGKMiU44jfP0CHGkuX4wij6/tol0eXz/+vHQRgHP/z959R0dRtXEc/94kQEioKSAI0puiovSODVGxi4qKoigoHaSD0kR6771JtyAigoBSpAmIvtKx0kmhBkif949dNtlkwRCSXWB/n3M4mt3Z2fvsnblzZ565d44dJirqPH06taRry6asX73C8flZE0fxxrutMT7uH2h/I/toTPRlvvtyPs+82tRp+bzBoTR4/lU6v9uIDk2fJyAgkPIPVMn0WOBK3STVX1BIqOt21b7MlXgunD9nf915+z4dGZ6mdaYqR0Q42zev57GnnsuIsK7pTIryBYekrsPkMfj6+hFgj/lMZOqYz0SGE3byGLly52HS8AF0a/UWU0YOJDr6cqbHkl63c9t0OjKc4AyI7Wo2r19NtTqPOE3T7AlnIlzsZy7iDA5Jth0H2LbjtLgYdYFftm3ingqVMq7Q6ZDZcbrbzdLmAhzat5su77/JwJ4dOfLPXzca2nWJjAgjNNl5Wmg+19cXkvcvQpOdp12x+7dd5M0bxJ2F78rcAqfT7Xodxd3H0R1bNhIUHEqR4qXcEJ1rKY+beUNCXSb/rnaO9ueBPfRo1YRebZvyVstO+Pr6UahIMQ7s+Y2o8+eIiYnmfzu3ptrGPcVb9lFJxsfo343+u8l4ckq6LcCdAMaYHMaYtcaYX4wxvxtjnrUvMwgoYR/9MzT5qBhjTFNjzJfGmJXGmEPGGMf4XGNMM2PMQWPMOmPMVGPMuLQUyLKsQ8AlIK99PSXs699pjNlojClrjMltjPnHGONjXybAGHPEGJPFGDPLGPOS/fWKxpj19s+uMsYUMMbkM8bstL9/vzHGMsbcZf/7T/u6ZhljJtm/76AxpqH9fX9jzEz777PLGPNQst9hiTHmG+B7+29W2/6bdbD/Zhvtv+0vxpgaN1Jp6eLizvVUJ0kul7n2ah9+oiHBIfno2vJtZk4YSZl77sXH15OP5frvOC2Xy0B0dDSLP5vJ62+nfi5IidJlmL5wGWOnz+fpF15hQK/OGVfkDJKWk15jDDHR0Sz5bCavvd0i1ftTx43great8fX1zYwiXjdXIy5SjohyPSrDtkxCQgJRF84zcsps3m3ZjoEfd8OyLPIGhzDni28ZP3M+zVt3ZHDfnly8GJUZIfyna5X/msskq++j//7FwpkTeKfNzbddpvRfscDV99FryRsUwsR5XzN00hzeer8dowd+zKWLF2+kqOnmuvz/XacGQ+6gYIZNW0KfUdN59Z3WTB7ej8uXkuJISIhn0vB+PNrwRcec9ZK5vGEfdVn862hrryYhIYG/Dx2gW//h9Bw4ii/mzbRNA7v1J3LnyUtxF8/ec4cb2UeXLpjBY880wj97gNN7F6MusGvbTwyesogRM78iJiaaLeu+z9iCX43LzS9lH+9qy1zlOJuWdaYwe9IoXmvWEh839CFcb44p69DVIuaq+2tCQgJ//3GQxxo+z6Dxs8nm788y+/SKN6Pbum1Kw3lLWvoTV7Nl/Vpq1PXohBSAy93MRdvrYpk0xJmQEM+4wb1p8Ewj8he4M50lzBiZGadH3CRtbrGSZRg390uGTJpDg2dfYnjfbtdcPqOl9zwt5TLr1qyk3qOPZ2zhMtJteh3FncfRmOholi6cTaM3PTutf1q6stfaZkuUuYdPx8+l9/ApLP/8M2JjYyhYuChPvvA6Qz/uwPDenShcrOQtfy3llttHRW5jHrm6bozxBR4BpttfigaetyzrvDEmBNhqjFkGdAPK20f/YIwpmmJVFYAHgBjggDFmLJAAfAQ8CFwAfgBS3ybtulwPAocsy7qS1p4CvG9Z1iFjTFVggmVZDxtjfgPqAj8CTwOrLMuKu3Lwto9QGgs8a1lWuDHmFWCAZVnv2BM/uYDawA5syZ2fgDDLsi7Z11HUvv4SwI/GmJLYR2NZlnWvMaYs8L0x5soQjerAfZZlnTbG1AM6WZZ1JdEUADxmWVa0MaYUsAC45q1expjmQHOAjweO4KXXm6bl53Py3defs9Y+N26J0uWcRqCcDg8jKNh5CH9wijthbMuEci2+vn683TJp7tIebd+jQKHC113WG/HtV4tZtXwpAKXK3k1EeNKdOZHhYQSFOMcQEpqfiGS/RYR9mZPHj3LqxHHaNnvN8Xr75m8wYuIsx53TAJWq1WTiyMGcO3uW3Hnc96DGb79awvfJ4gz/jziDQ/O5jPOEPc52zV53vN6+eROGT5zJHwf2MaxfLwDOnzvLzm2b8fX1pVrtepkcXZJvvljMym++AqB0ubuJSHanVURYGMEhztttaGg+pzqPCDvluMMrJDQfNes8jDGGMneXxxjDubNnyZM3r2OaulJly1GgYCGOHTlM6bJ3Z3Z4qQSF5HOaKuR0RJjT9uZYJjyM4JB8JCTEc+nSRccD5SMjwhj1SQ/e//Aj8hco5Nayp9XKrz93zNNdskw5IsOS4o2McNEWhTi3RZHhYY6pc64mS9asjmHxJUqXJX+BOzlx9DAlXDzYM7PlDQ51qtMzkeHkSTFlypVlgux1evniRQJz5sIY47jLu2jJMuQrcCcnjx2hmP3hurPHDyN/gULUf+bmeJiqN7hd99GVyz5n7YplAJQoU8752BkRnirG4BDnkazJ993ceYM4ExlB3uAQzkRGOKYjCw7JR85cefDPnh3/7Nkpd28F/v3rEH8fOsCOrRvZtX0zsbGxXL50kTGD+tC2W59MjtrmRvbRvw7uY8fm9SyZPYlLF6PwMYYsWbOSK08QIfkLOB7g/GC1OvyxfzfV69XP9HiCQkKJTFZ/p13UX1CobZng0KR4cuTMRVCKek2+ff/XOlP66+B+Rg/8GIAL587x68+2PkTlGnVvOMaUUsYcGRGeamqq4BDnmC9dK+agEIJD8hEUEkrJsvcAtul3vr6JE0a3a9sEtnJHpogt5T56rdiu5d+/DpGYkOA4rnpSmvbdkFAiI5Jtx2mMc9rowdxRsBBPPP9Khpf7emVmnJ5ws7S5AYGBjv9/oEoNpo8bxvlzZx3HocywLMV5Wniy87TwsDCCUpynhaQ4TwsPO+V0zpoQH8+m9T8ydvpnmVbm9PCG6yjuPI6eOnGM8JPH6frBm/blw+nR+m0+GT3NbdP3wpWRe8n6fy5ivrJMyv5fcgULFyWbvz/H/v2bYqXKUrd+Q+rWbwjA53MmkzfZKCZ385Z9VMRbuHuEUXZjzK9AJBAErLa/boBPjTH/A9ZgG3mUlqfRrbUs65xlWdHAXqAIUAVYb1nWacuy4oAlaVhPB2PMAWAb0Adso56AGsASe5knA1cm8F0EXOkBv2r/O7kyQHlgtf2zvYArZ0ObgZpAHeBT+39rA8knbV1sWVaifcTTX0BZoBYwF8CyrP3Av8CVhNFqy7JOXyW2LMBUY8zv9t/iP69KW5Y1xbKsSpZlVUpPsgjgiWdfcjwAvkrNuqxbvQLLsji4dzcBgTlSdULzBoeQPXsAB/fuxrIs1q1eQeUada75HTHR0URftg0z/m3nNnx9fSlcpFi6ypteTz3/MmOmz2fM9PlUq1WPH1Z9i2VZ7N/zOwGBOVJ16IKCQ8geEMD+Pb9jWRY/rPqWajXrUrR4ST5b+j3TFy1j+qJlhITmY9SUzxwXwK7cfXFw3x4SrURy5c7t5jgbMXr6PEZPn0fVWnX5cdWK64rzx1UrqFqzDkWLl2Tu0lVMW/Q10xZ9bY9zLnmDQ5i28GvH6zXqPsz77bu4NVkE8PSLLzN+1gLGz1pA9dr1WLvSVp/7dv9OYI4cqRJjQSGhZA8IZN9uW5xrV35Ltdq2i1PV69Tj11+2A3D08L/Ex8eTO08ezp45Q4L9wZwnjh3l+NHDFCjombsvi5cuy8ljRwk7eZz4uDi2bljLg1VrOS3zYNVabFxrm8rp55/Wcfd9FTHGcDHqAsP7dOblpi0offd9nih+mjRI1hZVrlmX9Wuury1av2YFlatfuy06dzapTk+dOMaJY0fJV8AzI3CKlSrLqRNHCT9lq9NtG9dSoUpNp2UqVKnJ5h9WArBj03rK3vcgxhjOnzvreGhs2MnjnDp+lFD7SKIvP5vK5UtRNH63jXsD8nK36z7a4JmXGDppDkMnzaFKjTpsWP2dbb/ct5uAwEDX+2VAIAf32fbLDau/o5K9j1CpWi3HdHPrV6+gcvXattdr1GH/7l9JSIgnJjqaP/bv5c7CRXmtWUsmzV/G+Llf0b5Hf8pXqOi2ZBHc2D7afeA4hk5dzNCpi3ns6Zd46qU3eOSpFwkKyc9fB/YSExNtO2b9bycFCrnnmTclypRz2kY3r1tDxWrO22jFarXZsPo7ALZt/JF77rdtoxWr1WLzujXExcYSdvI4J48dpWSZu9O0zpTGzvmCcXO+ZNycL6la+yHeadMpU5JFjpiPJ5Vvy/qrxLzGdcxb1ieL+bgt5jxBwQSH5uf4kX8B2L1rB4Xucm+f9nrcrm0T2GM77hxbyvp9sGotNqxJiu0ee2z/Zcv6NVSv5/nRRWC7EJ2W7Xiji+34WhbPnsylSxdp0uLmeCB5ZsXpKTdLm3v2dKTj/PSP/XuxEi1y5src89NnXnyZCbMWMOEq52nBKW9gdHGeVr120nFh146fKVykqNO0WTcDb7iO4s7j6F3FSjB50QrGzvmSsXO+JCgklE/HzXRrsgjs/b/jRwk/mdT/eyDFcbNClVr8ZO//bd+0jnL2/l/4yeMkJMQDEBF2kpPHDhOS3/aMzitTMUeGn2LHlg1Uq+O5Y4y37KMi3sLdI4wuW5ZVwRiTG1iObdTMGOB1IBSoaB+p8w/gn4b1xST7/wRs8aSndzfSsqxhxpgXgDnGmBLYkmlnr4xuSmEZMNAYEwRUxDaKKTkD7LEsq7qLz27EliAqAnwNdMU2EHx5smVSjs20uHZc15r7qANwCrgfW0zR11g2UzxYtQa//LyZ1m++RLZs/rTs3MvxXqcWTRg22XYH5XvtujB+aH9iY2J4oEp1Hqhi+/m2/bSO6eOGc/7cWQb27EjREqX5aPBozp09zSfd2mN8DEHBobTt1tvdoTmpVK0mO7Ztovnrz5Mtmz/tun7seK9ts9cYM30+AC07dGPUoL7ExsZQsUoNKla99iyBm9b/wIpln+Pr60e2rNno8vEAj57EVKpWk53bNtPi9RfIls2ftl0/crzXrtnrjJ4+D4APOnRl9KB+xMbG8GAa4rzZVK5ei+1bNvHOK8/i7+9Phx59HO+1atqY8bMWANC6U3dGDOhDTEw0lavVpHI124W/+k89y8iBfXm/ycv4ZfHjw559MMaw+7dfmDttEr6+vvj4+tC6U49MP8G6Gl9fP976oANDPupIYmICdR9rSKEixfl87lSKlSpLxWq1qVu/IZOG9afjuy+TI2cuWnfpC8Dq5V9w6vhRli6YxdIFswDo+skocufJy4IZ49m8bjWxMdG0efM56j3+NC++3swjMSb3YJUa7Nq2mTZvvUTWbP606nSVtqhtF8YPs7VFFSo7t0Uzxtvbol62tqjXoNHs+30Xi2ZPtdWpjw/N23XxaJ2+0bw9I/p0IjExkVqPPMmddxXjq3nTKVqyDA9UrUWdx55i6sgBdGvRmMCcOWnRqQ8AB/f8ytL5M/Cxx/HmBx+SI2cuTkeEsXzJXAoUuou+Hd8F4JEnX6CO/W62W0Gn+Z0pX+9ecoXkYsaRWSzoPY/VM1b/9wc9zBv20Qeq2PoIbZs2Imu2bLRMtl92fv9Nhk6aA8C7bTszYegnxMbGUKFyNR6obNsvn3v1TUZ+0pMfVn5DSL78dOw1AIBCdxWlQqVqdGrRBB/jw8NPPM1dxUq4P8AUbmQfvZoSZe6mUo169O3wLr6+vtxVvBR1H3/abfG83aojn/boQGJiAg/Vb0jhosVZPHsqxUuXpVL12jzUoCHjh/SjXdNG5MiZi7Y9+gFQuGhxqtd5mA+bv2ZbT+sPHVPKuVonwHdLF/PNknmcPX2aru+/SYUq1WnRobtbYk0ec9OWHRnY01a+evbyLZlj2y8rVa9NvQYNmTCkH+3ftsXcpntSzNXqPEynFq/h6+PH262SYm7asgPjhvQlPi6O/AUK0qJjTwC2b1rPrIkjOH/uLEM+7kTR4qXo/ukot8acUma1TTcDX18/mn7QgcG97LHVTx1bvccbMnFYfzo2e5nAnLlo07Wv4/Ptmr7I5UsXiY+PZ8eWjXQbMNKR/Nu68Qe69B3mqdCc2OLsyKBeHUhMsG3HhYrYtuPipZPinDC0Hx3eaWSLs1s/x+fbvvWCI86dmzfQbcAosgcEsnThbAoWLkLPNm8DUP/pF3mowTOeCjNT4ixUpBjzp49n84/fExsTTes3nqVeg6d56Y133RLPzdDmbt34I2uWf4WPry9Zs2Wjbfd+bj0/rZLsPC2bvz8dk52ntWzamAn287Q2nbozfEAfYmOiqZTsPA1g3dpVLqe6evOlhly6eJH4+Di2bFzHgBHjKVKseKbH5Mrteh3F3cfRm4Gvrx9vtOjAsD4fkpiYSO1Hn+LOu4rx5bxpFCtZ1tH/mzLiE7o0f5XAnLn4oHMfAA7u+x/f9p+Hr58fPsbQ5P2O5MxlG803blAvoi6cw9fXjzff70BgjpwejDKJt+yjIrcz43oO6kz6MmOiLMvKYf//B7AlTEoALYGSlmW1sT+b5wegGLYp5X6xLKuI/TNFgeWWZZU3xjQFKlmW1dr+3nJgGHAI2IRtqroLwFrg9yvLuShTHyDKsqxh9r+/BlZYljXZGLMZWzJpibH1gO6zLOs3+3JLsCVfLliW1dL+2ixsiZ9l2EY8NbEsa4t9irrSlmXtscewAdhgWdYbxpgV2EYj3W9Z1hn7OvIBDe2/wXqgpP03useyrGb2qehWYxth1DjF71ARGGFZVl373yOBo5ZlDTfGvA3MsCzLJP8tr1Vnvx85474NxIOy+d0cc71mNlfPTrgdZfGS+ow85/b8r8dky+oddXrhUqyni+AWg8t5PoHoDh8dmuXpIrhN1iyefCym+0RdjvN0EdwiwN+Tz4R0HzeeBnlcQoJ3BHuTDgzJcDfrCBhJHx/vOIQCkCcwm6eL4BZR0d7RX4iLT/R0EdwiJjbB00VwmzuCAv57odtAsdAcOpBmoKGNF3lHRzMTdV7wyk21TXqsa2JZ1i5szxZ6FZgHVDLG7MA22mi/fZlIYJMxZrcxZmga13sM21Rv27BNb7cXOHcdResHdDTG+NjL0sz+zKI9wLPJllsEvEHq6eiwLCsWeAkYbP/sr9imt8OyrH/si22w//cnbCOZziRbxQFsiaLvsD1DKRqYAPjap5ZbBDS1LCv5CKsr/gfEG2N+M8Z0sH/uLWPMVmwJJs88iV1EREREREREREREbh9G/274303GrSOM3MUYk8OyrChjjB/wFbZRNV95ulxpcWWUkmVZn3u6LKARRrcbjTC6vWiE0e1HI4xuLxphdPvRCKPby214GnRVGmF0e9EIo9uLRhjdfjTC6PaiEUa3H40wylhDX9MIoxvVeb5GGLlDH2PMr8Bu4G9gqYfLIyIiIiIiIiIiIiIictO6LW8ftCyrU8rXjDE9gUYpXl5iWdYA95QqbSzLaurpMoiIiIiIiIiIiIiIiHe5LRNGrtgTQzdVckhERERERERERERERORmcLtOSSciIiIiIiIiIiIiIiJp5DUjjEREREREREREREREJIMY4+kSSAbTCCMREREREREREREREREvp4SRiIiIiIiIiIiIiIiIl1PCSERERERERERERERExMspYSQiIiIiIiIiIiIiIuLllDASERERERERERERERHxckoYiYiIiIiIiIiIiIiIeDk/TxdARERERERERERERERuLcbHeLoIksE0wkhERERERERERERERMTLKWEkIiIiIiIiIiIiIiLi5ZQwEhERERERERERERER8XJKGImIiIiIiIiIiIiIiHg5JYxERERERERERERERES8nJ+nCyAiIiIiIiIiIiIiIrcY4+kCSEbTCCMREREREREREREREREvp4SRiIiIiIiIiIiIiIiIl1PCSERERERERERERERExMspYSQiIiIiIiIiIiIiIuLllDASERERERERERERERHxcn6eLoCIiIiIiIiIiIiIiNxijPF0CSSDaYSRiIiIiIiIiIiIiIiIl1PCSERERERERERERERExMspYSQiIiIiIiIiIiIiIuLllDASERERERERERERERHxckoYiYiIiIiIiIiIiIiIeDkljERERERERERERERERLycn6cLICIiIiIiIiIiIiIitxgf4+kSSAZTwkiuyT+Lr6eL4BaW5ekSuMfF6HhPF8EtLMs74vTz856DcmxcgqeL4BYFgwM9XQS3+OjQLE8XwS36l2rq6SK4zYDDcz1dBLfwMd7R7uYJzObpIriF8ZL6BLgc4x19I28Rl5Do6SK4RbyXxJnoHWECcPpCjKeL4Ba5ArJ4ughuERvnHRtvvrzZPV0Et7lwOc7TRRCRm4CmpBMREREREREREREREfFyShiJiIiIiIiIiIiIiIh4OSWMREREREREREREREREvJwSRiIiIiIiIiIiIiIiIl7Oz9MFEBERERERERERERGRW4zxdAEko2mEkYiIiIiIiIiIiIiIiJdTwkhERERERERERERERMTLKWEkIiIiIiIiIiIiIiLi5ZQwEhERERERERERERER8XJKGImIiIiIiIiIiIiIiHg5JYxERERERERERERERES8nJ+nCyAiIiIiIiIiIiIiIrcYYzxdAslgGmEkIiIiIiIiIiIiIiLi5ZQwEhERERERERERERER8XJKGImIiIiIiIiIiIiIiHg5JYxERERERERERERERES8nBJGIiIiIiIiIiIiIiIiXs7P0wUQEREREREREREREZFbjIaj3HZUpSIiIiIiIiIiIiIiIl5OCSMREREREREREREREREvp4SRiIiIiIiIiIiIiIiIl1PCSERERERERERERERExMspYSQiIiIiIiIiIiIiIuLl/DxdABERERERERERERERucUY4+kSSAbTCCMREREREREREREREREvp4SRiIiIiIiIiIiIiIiIl1PCSERERERERERERERExMspYSQiIiIiIiIiIiIiIuLllDASERERERERERERERHxckoYiYiIiIiIiIiIiIiIeDk/TxdARERERERERERERERuLcYYTxdBMpgSRpLpLMtiypjh7Ni2iWzZ/GnfvTclS5dNtdwfB/YxcmBfYmNjqFS1Js3bfogxhnkzp7Bq+VJy58kDwJvvtaJytZrs2r6NWVPGER8Xh1+WLLzzQVvuf7Cyu8NzsCyLKWOHs3PrJrL5+9Ou29XjHDWoL7ExMVSsVpPmbT50aly/XDiXmZPG8NnS1Y6Yf9+1k6njhhOfEE+u3HkYNHqK2+IC+HXHVuZMHEViYgIPNXiaZ1950+n9uNhYJgzrz9+H9pMjV27ade9P6B0FAFi6cA7rVn2Dj48vb33QnvsrVQNgxZcL+WHlNxgDhYuW4P0Pe5I1azYmj/iUvw7tx7IsChQqzAcf9sI/e4Bb4wX4bcdW5kxKivmZl1PHPHF4Usxtu/cnNH8BLpw/x+gBPfnz4D7qPPYkb7f80PGZRbMmsXHtSi5GXWDml0OwTwAAIABJREFUV2vdGs+v27cye9IoEhMSePgJ13U4fmiyOuzRn3zJ6vDHld/g4+tL02R1eLV1rvz6c777ahGnThxjyuIV5Mpt246jLpxn8ohPOXXiGFmyZOX9D3tQuGgJt/0Gv+3YytzJo0hMTKTe40/zzMtNnH+DuFgmDuvPP38cIEfO3LTp3i+pTj/tyV8H91Pn0Sdoaq/Ty5cu0q9LS8fnT0eEU+uh+jRp0d5tMbliWRYTRw9l+xZbW/Rhjz6UKlMu1XKH9u9j+Ke9iYmJoXL1mnzQrjPGGD79uBtHD/8LQFTUBXLkyMmEWQs4sHc3o4cMcHzHG+80p2bdh90aW3K/7djK3Cn2+qzvuj4nDe/P338cIGfO3LTuZqvP33f9zKKZk4iPj8PPLwuNm7XinvsrArB49mR++sG2j07/Yo0nwrohbae3o1LDypwLO0ebe1t5ujg3xLIsZowfwa6ft5A1WzZad/mI4qVSH1P/PLif8UP6ExsbwwNVqvNOq44YY5gzeSw7tv6En58fdxQsRKvOvQjMkdMDkaT2v53bmD9tNIkJidSp35CGL73h9H5cXCxTRw6wtUW5cvFB576E5i/geD8y/BQ9WjXhucZv88TzjYkMP8XUUQM4d+Y0xhjqPf4M9Z9p5O6wUsmstuj8ubN80qsLB/fv5bEnnqZVx67uDs2JZVlMHDWUn7f8hL+/Px/27HuVOPcybEAfYmKiqVK9Fh+0t8U54KOujjgvRl0gMEdOJs5eSHx8HCMH9uePg/tJSIjn0QYNefXNd9wdnhPLspg2bjg7t20mm78/bbt8TAlXfd2D+xgzuJ+tr1u1Bu+2tvfpZ0zi580bMMaQO08Q7bp+TFBIqONzh/bvpWvrd+j00QBq1H3EnaE5yaw4t21az/yZkzHG4OvrS7NWHbn73goeiNDGsixmThjBLz9vIVu2bLTqfI12dqitnX2wSnXebmlrZ7esX8viudM4dvgfBo6dQQn7dv/bzm3Mmz6B+Lh4/LL40eS9Ntz7QCW3xpYZ5y5t3nyB7AEB+Pj44uPry6djZwDw+dxp/LByGbly5wXglaYteKBKDTdGa5PR5y4x0dGM/rQnp04cw8fHlwer1qTxOy1dfbXbZcZ52qBeHTh7OpKEhATKlr+ft1t+iI+vr7tDc3Kj11Gu+HLhXGZMHMO8r23XF6IunGfUoP6cPH6ULFmz0q7rRxQtXtKdoTnxlm3Xsiwmjx7G9q22+uzYow8ly6Suz0MH9jHi0z7ExsRQuVpNWrTr5KjPZZ8v5JsvF+Pr60fl6jVp1rIdv2zfyqxJ44iLjyOLXxbeadmOChU9d10MMu/4cmj/HiaPHHTlW2jU5F2q1qrnvsBEvIimpJNMt2PbZo4fPcyUeV/SulMPJowY5HK58SMG0bpTD6bM+5LjRw+zc9tmx3vPNWrM2OnzGTt9PpWr1QQgV+48fDxwBONnLaRD994MH9DbLfFczU57nJPnfUmrD3swcaTrOCeMtMU5+UqcPyfFGR52kl93/kxo/jscr0VduMDEUYPp9ekIJsxaTLc+rtebWRITEpg5fhhdPxnOsCnz2bxuDUf//dtpmR9XfUNgjpyMmrmEJ59/hfkzJgBw9N+/2bJ+DUMnz6PbgBHMGD+MxIQETkeEs/LrJXw6dgZDJ88jMTGRLetsF2ebtGjH4IlzGDJpLiGh+Vm17HO3xps85i79hzN0suuY131vi3nkjCU88dwrLLDHnCVrVl5q8h6vv9s61XofrFqL/qOnuSWG5BITEpgxfhjdPhnO8Knz2fSj6zrMkSMno2ct4akXXmH+9KQ63LxuDcOmzKP7gBFMH2erw2uts8w999Jz0BhCkm3HYDsBL1KiFEMmzaVl54+YNXGUe34AbL/BrAnD6dJvOEMmzWPL+jUcPZyiTlctJzBHTkZMX8wTzzvXaaMm7/FaM+cL8NkDAhk4brbjX0i+O6hUo567Qrqq7Vs3cfzIEWYsXEq7zr0YN2ygy+XGDh9I2y69mLFwKcePHGHHVltb1KPfICbMWsCEWQuoVfdhatZ9CIAixUswdtpcJsxawCfDxzJm6KckxMe7La7kEhMSmD1xOF36DmfIxHls3bCGY1erz2mLafDcKyycaavPnLny8GHvwQyaMJcWHXsxaXg/x2cerFqTviOnujWWjLR21hr6NPDssTCj7Pp5CyeOHWHs7CW836E7U0YPcbnc1NFDaNGxG2NnL+HEsSPs2r4FgPsqVmHktHmMmDqPAoUK8+WC2e4s/lUlJiQwd/IIOvYexqfj57LNxba7YfW3BOTIyZApC6n/zMssmT3J6f3508Zy74NVHX/7+vry6jutGDjhMz4aOpm1K75MtU5PyKy2KGvWbLz57ge818qzyfkrtm/ZxLGjh5m56GvadenF2KvEOWbYQNp17cnMRV9z7OhhR5w9+w9m4uyFTJy9kJr1HnEk4jf8sIa4uFgmz13MuBnzWPH1F5w8cdxtcbmyc9tmThw7wsS5X9CyY3cmjRrscrnJIwfTsmN3Js79ghPHjvDLz7b98vlX3mD0tPmMmjqPytVrsWhuUp8oISGBOVPGUsF+cd6TMivO+x6szKip8xg1dR5tOn/E+GED3BaTK452dtYSWrTvztQxV2lnxwyhRYdujJ1la2d/tbezhYsWp1PvQZRLkfTKlTsP3foNY8TUebTu/DFjB/fN9FiSy4xzlyt6DR7HoAmzHcmiK558/lUGTZjNoAmzPZIsyqxzl6defI3hUxcycNwsDu793VH3npRZsbbt/gmDJsxhyKTPOH/uLFs3/uCWeK4lI66jhIedZNcO5+sLiz+bSfFSpRk3cwEde/RlytjhmR7L1XjTtrtj6yaOHT3CtAVf0bZLT8YNd91fGD98IG0792Tagq84dvQIO+z1+dsvO9j60wYmzFrIpLmLebGx7Wa53Lnz0HvwSCbOXkTHnn0Y/snHbovpajLr+HJX0RIMnjCTYZPn0vPTUUwZPZiEBM+cj4rc7pQwSgNjTIIx5ldjzB5jzG/GmI7GmGv+dsaYgsaYz+3/X88Yszwd31vGGLPO/t37jDHXHFZijClqjNl9vd+T2bb9tJ6HH38KYwxl77mXi1EXOB0Z4bTM6cgILl+6SLny92GM4eHHn2LrT+uvud4SpcsQbL8rsUixEsTFxhIXG5tpcfyXrZvSFuelixcpe4/rOKeNG8nbLdpgSLojaP3alVSv/RD57J28PHmD3BOQ3R8H9nJHgULkL3AnflmyUL3uo+zYstFpmZ1bNlLn0ScAqFr7IXb/ugPLstixZSPV6z5KlqxZyXdHQe4oUIg/DuwFbBcGYmNjSEiIJzYmmrzBIQAEBAYCtrtSYmNjPTK09Y+De8lf0DnmnVudY96xZSO1XcTs75+dsuXvJ0vWrKnWW6pcefIGhbglhuT+OLCXO5LFU6Ne6jrcsWUjdR5LimdPsjqsUS9ZHRa01eG11lmsZBnH6KTkjh3+m/IVbHeY3nlXUcJPneDsmdOZHL3Nnwf3kb9gIfLZy1utziPsTLkdb91InUefBKBKrXrs+W2no07L3OO6Tq84eewI58+eoWz5+zM1jrTYsnE9jzSwtUXlyt9LVFQUkRHhTstERoRz6WIUd9vb3EcaPMXmjeuclrEsiw0/rqHeow0A8PfPjq+fbWBynIf2zStc1meKffSXbRup/Ujq+ixaojR5g23HjkJFitmOHXG2Y0fJsp7ZRzPKno17iDp9wdPFyBDbN2+g3mNPYoyh9N3luRQVxZkUx9QzkRFcunSRMnffaxtZ89iTbN+0AYAKlari62vbXkuXK09keJjbY3Dlr0P7yF/gTvLdURC/LFmoWvsRdm37yWmZXds2Uuth235XuWY99tq3XYCdWzcQekcB7ryrmGP5PEEhFC1RBoDsAQEULFQ01W/lCZnWFmXPTvn7H7hmm+xOW35ax6MNGtrjvI+LFy5cJc6L3F3+fowxPNqgIZs3/ui0jGVZbPhhNQ89ZovTGEN09GUS4uOJjYnBL0sWRx/JU35Otl+Wufsafd1LSX3deo89ybZNtr5uQGAOx3LR0Zed+rvffrWY6nUeJnfevO4J5hoyK87s2QMcx87o6Msen75l+5YN1H00qZ29eJV29nKydrbuo0/y82ZbO1uoSDHuLFwk1XqLlSzjGDlWuGhx4mJj3HqOllnnLjezzDh3yebv7xiB7ZclC0VLluZ0hOePpZl1nnalfU1ISCA+Ls7j+ydkzHWUqeNG8vb7bZziOfzP346ZWQoXKUrYyROcOR3pnqBS8KZtd+tP63mkwZPO9RmRoj4jbNeLyjn6RU+y1d4v+nbp5zR64y1HvFeuC5UoXdbpulish6+LQeYdX7L5+zv697GxsXh+LxW5fSlhlDaXLcuqYFnWPcBjwJPANW/htSzruGVZL93g944BRtq/uxww9gbX5xGREeGE5Mvv+Ds4NF+qCzeR4WEEh+ZzXibZyfbyr5bQ+u3GjBrUj6gL51N9x6b1P1C8VGmPXjyIDA8nJPS/4wxJFmdIaD4iw21xbtu0nuDQUIqVLO30meNHDhMVdZ7u7VrQvnkTflj1bSZGkdqZyHCCk8cVEsqZSOcLIaeTLePr60dAYCAXzp+zfzYp3qCQfJyJDCcoJJSGLzWmdZPn+eC1ZwgIzMF9FZPulp40/BPeb9yQ40f+5XEPTKtzJsI55qCQUE6niPlMZDjBIcliDrDFfDM6HekinhQXs05HONdhdnsd2l53rsPTkeFpWmdKdxUrxc+b1gHwx/69RJw65bbO++nIcIJDUm+LyZ2JDCfIHuuVOo1KY51uXr+aanUeuSlOLiMjwghN1uaG5svn8uJl8vYqNF9+IlPUxe7fdpE3bxB3Fr7L8dr+Pb/T/I1GvP/WK7Tp1N2RQHI3Wzty4/W5fdM6ihQvTZYsN8eFZ0kSmbLtCXW9HQcnm87K1TIAP6z8hgerVM+8wl6HlNtu3pBQlyfQV5a50h5HXThHTPRlVnwxn+deffuq6w8/dYJ//zpIiTJ3Z04A1yEz26KbSUS4c5wh+ZL6dldEhocTks+5/xcRnjLOX5zirP3QI/j7Z6fxs/V544UnealxE3Llyp2Jkfy30xFhqfr0KY/jpyNS9+mTL/PZ9Ak0e6UhG9aspPHbLQBb/3jbT+t4/OkXMjmCtMmsOAG2bvyRVm814pMeHWnduVcmRvHfTkeEE5xsuwwOyee6f5isnbXFee3+XnJbN/5IsZLuPUfLjHMXsCVxB/ZoT4/Wb7N2xVKn9a1a9jld3m/CpBEDXJ6rZrbMPne5GHWBX7Zt4p4K7p1a0JXMjHVgz/a83/gpsgcEULXWQxlb8HS40eso2zatJzgklOIpri8UK1GKzRtsNy0c2LeHsFMnPXZjjTdtuxHh4YTmSxrpFRKan4gUx5aIiDCnflFIaH4i7H2K40cOs+e3X2nf/C26tG7OwX17Un3HpnVrKVGqjMdvqsnM48uhfbvp8G5jPmz+Ou+16+pIIIlIxlLC6DpZlhUGNAdaG5uixpiNxphf7P9qgOvRPsYYH2PMIWNMaLK//zDGXO125gLA0WTf/Xuydaf6zhTf5WuMGWqM2W6M+Z8xpoX99QLGmA32UUu7jTG1M+J3uZYrd8WmKN9/LnPFk8++yNT5XzFm+jyCgkOYNt55Gqt///6TWZPH0vrDHhlT4HRLQ5wul4Ho6GgWfzaT199+P9X7CQkJ/HlgP70HjaLvkLEsnDOdY0f+zbhi/weXVZOq/lwtYlzXqzFEXTjPji0bGTPrcybMW0ZM9GU2rl3pWOT9D3sxcd4yCt5VhC0b3P8cEZchk7aYb0ppKetVl3GxzWLSts4Unn2lCRcvXKDrB2+xctkSipYsha+Pm+YGT287lMY63bJ+LTXqPpquomU0l21uqu33v5dZt2Yl9R593Om1svfcy5TPljBm6lwWfTaL2JiYDCjx9XN9zLi++jz6718snDmBd9p0zuDSSYZIwz7rehnnv7+YNxNfXz9qP9IgI0uXbmnYdK+6f341fwaPP/vyVZ/rF335EuMG9eK1d9uSPcCzI1Egc9uim0oaDh2u+3/OC/24ehX1HkvaTg/s3YOPjy/zv17FnM+X88WCzzhx7GjK1bhVevuEyTfyN5q1ZPqi5dR5tAErli4BYPr4EbzZvDW+Hn5eyBWZFSdAtdoPMX72Err3G8L8mZMzpsDplJZ+z9XOXdLiyD9/MW/aeJq375ae4qVbZpy7APQZMYmB42fR9ZPhfP/Nl+z7fRcAjzZ8gdEzlzBowmzyBgXz2VT33+OZmecuCQnxjBvcmwbPNCJ/gTvTWcKMk5mxdh8wignzlhEXF8ee33ams4QZ50auo0RHR7No7kzeeCf19YVGr7/FxQvnadPsNZZ/sYgSJUt77HlN3rTtpr9va1smISGeqAvnGTl5Fs1atmVg7+5O9f/v338yY9JY2nT29HWxzD2+lCpXnpHTFjBo3Ay+WjiH2FjPnI+K3O6Uik0Hy7L+sk9Jlw8IAx6zLCvaGFMKWAC4vH3BsqxEY8xnwOvAKOBR4DfLsq42b8hI4AdjzGbge2CmZVln0/idzYBzlmVVNsZkAzYZY74HXgBWWZY1wBjjC6S66mCMaY4tKUa/IaN4tcnV72S9muVfLWbVctudV6XK3E1E2CnHe5HhYU4PuAUIyZff6a6WyPAwx90GeYOCHa8/3vA5+nbv4Pg7IuwUA3p1oWOPvhS4s9B1l/NGfZs8zrJ3ExH+H3GG5ne6ozTCvszJ40c5deI4bZu95ni9ffM3GDFxFsGh+ciVOw/+2bM7pmH5+89DLofoZoagkFAik8cVEZ5qyqZg+zLBoflISIjn0sWL5MiZi6AQ57ugTkeEkTcohN27dpAvf0Fy5bFNO1K5Zj0O7vvd6YKej68v1es8yvLP51GvfsNMjtJZyphPR4Q7psxzWiYiWcyXbDHfjNIUT6hzHV6+Vh3aP/tf60wpIDCQDzrZ7qa1LIs2b71I6B0Fbzi+tAgKyed01/rpiDDyBKWs03ycDg8jOOT66vTfvw6RmJBAMRcP8nSXZV8sZuU3XwFQutzdhCdrc8PDwggKcY7Vdnd78mVOObVXCfHxbFr/I2Onf+by++4qWgx/f3/++ftPSpd1/0iGoBDnO7mTb5dOy1ylPiMjwhj1SQ/e//Aj8hdw/7FDXPvu689Zu+JrAEqULufc9oSHEZSijlOORrYtk7Qdr/v+W3Zu3UTvoeNumoS+bTRmUlxnXBxTrywTFJLUHgfmzMVfB/eyffM6Fs2ayKWLUfgYQ5YsWXm04YvEx8czblAvqtd9jEo16ro5qiTubos8ZdkXi/hu2ZU473GKMyLMVf8vHxFhzv2/4FRx/sC4GfMcr/24+jsqVauOn18W8uQN4u777ufg/r1u7++uWLqE77+9Rp8+2DnWlHfAu+oPA9R5+HE+6dGBxk2b88fBfQzrb+sfXDh3ll+2bcbH15dqbnyQtTviTO6e+x/k5OC+nD93lly582RkKNe08uvPWWNvZ0uWKUdksu0yMsJFOxvi3M5Ghoc5pnW9lsjwMIb26UrrLh9zR0H3brOZce4COLaB3HmCqFyjDn8e2Ee5ex9wmi784QbPMqR3p8wMz6XMPHeZNnowdxQsxBPPv5Lh5U6PzD5Py5o1GxWr1mLH1o3c+2CVDC17WmTUdZSTx2zXF9okv77w3huMmDSLvMEhtO9umzDHsiyavfosdxRwzzlZSrf7tvvNl4tZ9U3S9aLwsJOO9yLCTxEc7Op60SnnZex9p5DQ/NSo+5B9utTyGGM4f/YsufPmJSLsFP17dObDnp65LgbuO75cUaiI7Xz0yN9/UaJMuRssvdwwDUe57ahK0+/KlYcswFRjzO/AEuC/rpzNAN60//87wMyrLWhZ1kygnH299YCt9uRPWr6zPvCmMeZXYBsQDJQCtgNvG2P6APdalpXqYQeWZU2xLKuSZVmV0pMsAmj4/MuMnT6fsdPnU712PX5Y9S2WZbF/z+8EBOZIdbAICg4he/YA9u/5Hcuy+GHVt1StZbvgkXye3i0b11GkWAkAoi5coE+3DrzVvBV33+uZ54Y89fzLjJk+nzHT51OtVhrjDHCOs1rNuhQtXpLPln7P9EXLmL5oGSGh+Rg15TPyBodQrVZd9vy+i4T4eKKjozmwdzeF7yrqthhLlCnHyeNHCTt5nPi4OLasX0PFarWclqlYrTYb1nwHwLaNP3LP/RUxxlCxWi22rF9DXGwsYSePc/L4UUqWuZuQfPk5tH8PMdHRWJbF7l93cGfholiWxcnjtrtnLcvil20/UdBNiTGnmEunLeaNLmK+GZUoU46Tx5Li2bzuKnW42nUdbl6XrA6P2eowLetM6WLUBeLj4gD44btllCtfwW3PYyheuqxTnW7dsDZVeR+sWosNa1YA8PNP67jnvrTV6Zb1a6hez7Oji5558WXHw+Gr167H2pW2tmjf7t8JzJHD6cIk2C6UZA8IZN9uW1u0duW3VK+ddJF5146fKVykqNM0SyePHyMh3vZQ0VMnT3D08L/kd/GsKncoXrqs0/a3dcNaHqyauj43rk2qz7vt9Xkx6gLD+3Tm5aYtKH33fZ4ovlzFE8++xLDJcxk2eS5VatZl3eoVWJbFwb27CQjMkeoCQl573+Hg3t1YlsW61SuoXKMOYHvY7tKFc+nafyjZ/P09EY5LxUqV5dTxo4Tbt91tG9fyQIptt0KVWvz0g23U7fZN6yh334MYY+gxaDzDpy1h+LQl1H+6EQ0bNeHRhi9iWRYzxg6iQKGiNHjuVU+E5eCOtuhm8MyLrzBx9kImzl5IjTr1WLNyuT3O/xFwlTgDAgLYt/t/WJbFmpXLqZ4sGfLLjm2p4gzNX4Bfd27HsiyiL19m/57fKVykqJsiTPLkc40YNXUeo6bOo2qtpP3ywN7fCbxGX/fA3t8d+2UV+355/Ohhx3I/b97Anfb+7JT5XzN1ge1f9boP06JdF7cmi8A9cZ44dsRx1/WfB/cTHxdPTjdPM9ggWTtbuWZd1q+5vnZ2/ZoVVK5e55rfcTHqAgN7deS1Zh945NmOmXHuEh19mcuXLgK250/975efKVS0OIDTtKLbN6+nsP11d8qsc5fFsydz6dJFmrRon2llv16ZEWv05UucOW2rx4SEeH7dsYWChdx/DgoZdx2laImSzPv6e2YsWsaMK9cXptquL0RduECc/Zxs1fKl3HPfA07PXnOn233bffqFlxk3cz7jZs6394tWOOozMEeOVDfSBIWEkD0g0FGfa1euoJr9uli12nX5becOAI4e/pf4+Hhy5clD1IUL9O7SnqYtWnHPfRXcHuMV7ji+nDpxnIQE2/lo+KkTHD9ymFAPnY+K3O7MtaYCExtjTJRlWTmS/V0cW+IlBNuzjHIAXbAl4KIty/IzxhQFlluWVd4YUw/oZFlWQ/vnvwOGAtOBkpZlJaSxHLuBt4Cn0/CdXwBTLMta5WI9BYGngLbAUMuy5lztOw+dPH/DG4hlWUwaNYSdP28hWzZ/2nf7mFL2O9LbNHuNsdPn275r/15GDupLbEwMFavW4P12nTHGMPyTj/nrj4MYY8h3RwFad+pBUHAIC+dMZ8m8WRQsVNjxXf2HjXO6yyvtZbzRKO1xjh7CL/Y423VNirNts9cYkyzOUYP6EhsbQ8UqNWhhjzO5Zq88w4jJc8idx3bH4ZcL57Lmu28wxlD/qWd5ttFr6SpjVHRcuj636+fNzJk8msTEBOrVb8jzjZuyZM5UipUqS6XqtYmNjWHCkH788+dBcuTMRZvu/RzDvr9aMIt13y/H18ePN99vR4XKtudILJk7ja3r1+Dj60vREqVp3t72PJS+nT7g8qWLWJZFkeKleKd15+tOKmREfe76eTNzp4wmMcEW83P2mIuXLkvFavaYh/bj3z8PEpgzF226JcXc9q0XuHzpIvHx8QQG5qDbgFEUKlKM+dPHs/nH7zlzOoK8QSHUa/A0L73xbrrL6HMdKf9dP29m9iRbHT5UvyHPv9aUxbNt8Vypw/FD+vHPH7Y6bNsjWR3On8WP3y/H19dWhw/Y69DVOgG+W7qYb5bM4+zp0+TOk5cKVarTokN3Du79nQlD++Pj48OdRYrRokP3NN/tl5Bw45X66/bNzJ08hsTEBOrWb8hzr77F53Nt2/GVOp04rH9SnXbtSz77b9Cu6YuOOg0IzEG3ASMpZH/ofPt3GtGl77AMSW4G57rxC9uWZTF+xGB2bttMNn9/Ovbo4xgF1LJpYybMWgDAwf17GT6gD7Ex0VSqVpOWHbo42qJhA3pT7p57eeq5pEfxrVn5LYs/m4Wfnx/Gx/B60/eoUSd987tHnIu+wSht9fnZFHt9PtaQZ13U56Rh/fnnL9s23bqLrT6XLpzFN4vnkj/ZHc9dPxlF7jx5WTBjPJvXrebs6QjyBIVQ7/GnefH1ZukuY/9STW84zuvRaX5nyte7l1whuTh76iwLes9j9YzVbvnuAYfnZuj6LMti2thh/Lp9K9my+dOycy9K2u8e7NSiCcMm277vjwP7GD+0P7ExMTxQpTrNWn+IMYbWb75EXFys42JsqXLladG+6w2XK+pS+o6jyf22Ywvzp40hMTGR2o8+xTMvv8mX86ZRrGRZHqhai9jYGKaM+ITDfx0iMGcuPujch3wpRmN+NX8G/tmz88TzjTm493982q0VhYoUx9gPDC81ac79ldL/3KY7glxPe3c9MqstAnjzpYZcuniR+Pg4cuTIyYAR4ylS7Pov0mbEjR62OAexY+sWsvn782GPPpQuZ4vzg7deZeLshQAc3LeXYQN6ExsTQ6VqNWjVsWtSnJ/0puw999Lw+aQ4L1+6xPBP+/Dv338BFvWffIZGr7+V7nJejolPf5B2lmUxZcxQW1/X35+2XT6ipP15We3fe51RU20jpP44sJcxg/sRE2Pr677XthPGGAZku354AAAgAElEQVT17srxI/9ifHwIzXcHH3To5vTsDYDRg/tSuVotatR95IbLm16ZFeeXC2bz4/cr8PXzI1u2bLzVoi1335u+i3txCYkZEuf0scP4dcdWsmbzp1WnXo67tJO3s38e2Mf4YbZ2tkLlpHZ220/rmDF+OOfPnSUwMAdFS5Sm16DRfDHPNk3QHQWTztE+GjSa3Ok4R4tPZ5wZfe5y6sQxRvTrDtimC6/50GM837gpAOOH9OXfvw4BhtD8BXi3bZf/HHWf0s147pI9IJA2bz5HwcJFHM96rP/0izzU4JkbL+wNyuhYc+bKzdDenYiLiyMxMZF77q9IkxZtb+jZKLkCstxwnDd6HSW5d155hpH26wv7dv+PEZ/2wdfXh8JFitGu60fpninjfAb0i26FbTdPjht/JpBlWUwYOcTRL+rQvbejX9T67dcYN9NWnwf372Xkp32IsfcXPmhv6xfFxcUxamA//vrjAH5+WWjWqj0VKlZmwexpLP5sFncWSnrW4ycj0nddDOBidMb0FzLj+LJ+9XcsXTQHX18/fHwML73RjCo10zey/r678t6cd/veooZ/sFTJhRv04cTnbqptUgmjNEieMLI/f2gesMWyrN7GmJHAUcuyhhtj3gZmWJZl/iNh9CIwFphrWdZVr14YYxoAay3LijPG3AHsAh4AuqbhO5sDTwKN7J8vDRzDluQ6ZllWvDGmPVDUsqyr3naREQmjW4G37AbpTRjdarylPq8nYXSry4iE0a0gIxJGt4KMSBjdCtydMPKkjE4Y3awyImF0K8iIhNGt4GYdGZwZMiJhJDePjEgY3QrSmzC61XjLuYs3yYiE0a0gIxJGt4KMSBjdKjIiYXQrUMIoYylhdONutoSRnmGUNtntU7tlAeKBucAI+3sTgC+MMY2AH4GLaVjfMmxT0V11Ojq7+sBoY8yVK2udLcs6aYxJy3dOA4oCvxjb2XA48By2qe06G2PigCiSpscTEREREREREREREREvpYRRGliW5XuN9w4ByR+C0N3++j9Aefv/rwPWJVvmfuA3y7L2/8f3dgQ6pvM7E4Ee9n/Jzbb/ExERERERERERERERAZQwcjtjTDfgA+B1T5dFRERERERERERERCRdvGiaZ2+hhJGbWZY1CBiU/DVjTE+gUYpFl1iWNcBtBRMREREREREREREREa+lhNFNwJ4YUnJIREREREREREREREQ8wsfTBRARERERERERERERERHPUsJIRERERERERERERETEyylhJCIiIiIiIiIiIiIi4uWUMBIREREREREREREREfFyfp4ugIiIiIiIiIiIiIiI3GKM8XQJJINphJGIiIiIiIiIiIiIiIiXU8JIRERERERERERERETEyylhJCIiIiIiIiIiIiIi4uWUMBIREREREREREREREfFyShiJiIiIiIiIiIiIiIh4OT9PF0BERERERERERERERG4xGo5y21GVioiIiIiIiIiIiIiIeDkljERERERERERERERERLycEkYiIiIiIiIiIiIiIiJeTgkjERERERERERERERERL6eEkYiIiIiIiIiIiIiIiJdTwkhERERERERERERERMTL+Xm6ACIiIiIiIiIiIiIicosxxtMlkAymEUYiIiIiIiIiIiIiIiJeTgkjERERERERERERERERL6eEkYiIiIiIiIiIiIiIiJdTwkhERERERERERERERMTLKWEkIiIiIiIiIiIiIiLi5fw8XQAREREREREREREREbnFGOPpEkgG0wgjERERERERERERERERL6eEkYiIiIiIiIiIiIiIiJdTwkhERERERERERERERMTLKWEkIiIiIiIiIiIiIiLi5fw8XQC5uZ27GOvpIrhFFj/lTm8nObNn8XQR3OJSTLyni+A2OQO843AVfu6yp4vgFtmy+Hq6CG4x4PBcTxfBbXre1cTTRXCL4Sfme7oIbvHvqQueLoJb5PCS/gJA9mzecRy9cMk7zl0CvWTbTUiwPF0Et/D19Z6Hhfv5esd594XLcZ4uglvkyZHV00Vwi8jz0Z4ugtsE+nvH8UVErs07zhzk/+zdd3hURdvH8e+QQAKEmkJVKVIFFelFQAUFxfrafVAUBaUjRRCQKiAkEDqEXkXgURQbTekJENojvUlvSUBKgJTNef/YZckmARGSDbi/z3Vx6e6Z3cy9M2dmzt7nnBURERERERERERERST+ecS6AR1GTioiIiIiIiIiIiIiIeDgljERERERERERERERERDycEkYiIiIiIiIiIiIiIiIeTgkjERERERERERERERERD6eEkYiIiIiIiIiIiIiIiIdTwkhERERERERERERERMTDeWd2BURERERERERERERE5B5jTGbXQNKZrjASERERERERERERERHxcEoYiYiIiIiIiIiIiIiIeDgljERERERERERERERERDycEkYiIiIiIiIiIiIiIiIeTgkjERERERERERERERERD+ed2RUQEREREREREREREZF7jDGZXQNJZ7rCSERERERERERERERExMMpYSQiIiIiIiIiIiIiIuLhlDASERERERERERERERHxcEoYiYiIiIiIiIiIiIiIeDgljERERERERERERERERDycd2ZXQERERERERERERERE7jG6HOVfR00qIiIiIiIiIiIiIiLi4ZQwEhERERERERERERER8XBKGImIiIiIiIiIiIiIiHg4JYxEREREREREREREREQ8nBJGIiIiIiIiIiIiIiIidyFjTCNjzB5jzH5jTLc0tjczxkQZY7Y6/n2YbNt7xph9jn/v/d3f8k7vyouIiIiIiIiIiIiIiMidMcZ4AWOAhsAxYKMx5gfLsnamKPqNZVltUrw2P9AbqAJYwCbHa8/d6O8pYSQiIiIiIiIiIiIiIv+MMZldA09QDdhvWdZBAGPMXOBFIGXCKC3PAEstyzrreO1SoBHw9Y1eoFvSiYiIiIiIiIiIiIiI3H2KAEeTPT7meC6l/zPG/M8Ys8AYc98/fK2TEkYiIiIiIiIiIiIiIiJuZoxpYYyJTPavRcoiabzMSvF4EVDMsqyHgWXA9H/wWhe6JZ2IiIiIiIiIiIiIiIibWZYVBoTdpMgx4L5kj4sCJ1K8R0yyhxOBr5K9tn6K1664WX10hZGIiIiIiIiIiIiIiMjdZyNQyhhT3BiTDXgT+CF5AWNMoWQPXwB2Of5/MfC0MSafMSYf8LTjuRvSFUaS4bZFRjBzQihJSUnUf+Z5Xni9qcv2hIR4xgX359D+PfjlykPb7v0ILFCIixfOM2JgDw7u3U3dBo1p1qqT8zXrVizl+29mYIwhn38ArTp/Qa48ed0dGpZlMXXscLZsXIePjy+tOveiRKkyqcod3LubMcH9iY+Po1LVWrzfqiPGGC5dOM/wL3sRdfokgQUK0bHnAPxy5eZy7CVGDu5DTNRpbDYbz7/6Nk8804RDB/YyceRQrlyOJUuWLLzyVjNq1W/g1pi3RkYwY1woSUk2nmj0PC++8a7L9oT4eMYG9+fPfbvxy52H9t37E1jQPmYtnDuDFYsXkSWLF+990oFHqtQA4Odv5/Lbr4swBu4rVpKPO/UgWzYft8aVkmVZhI0KYVPEWnx8fWnfrTcPli6bqtz+PbsIHdyX+Lg4KteoTYu2nTDJfvDv27kzmTp+JLMWLiVPXnsf/WPLJiaODiHRlkjuPHkZPOJmJxGkP8uymDZuOFs2rMPH15dPOt2g3+7bzdjg/sTHxVGpWi2afXK934YOvN5vO/Sw99vVvy3mh3kzAfD1zU7ztl0pVrIUAONCBrB5/Tpy581HSNhst8Z7jWVZTBwVQmSEPe4O3b6g5A3adMTgfsTFxVGlRi0+StGm382d5WjTJeTOe33c2bd7J11afUCXL76kdv2n3BJTWv4XGcHMsBH2MffpJjyfxpg7IWQAf+7fg1+u3LTpZh9z/9iykXlTx5GYmIi3tzdvNm/NQ49UdnntsL6fceb0CQaPnenOkJwyaswF2LFtM9PGhWKzJZIrdx76hoxzvl+SzUa3Nu+TPyCQbv1D3BZvWizLYsqYYWzZEE42Hx/adO1FiVKp+/GBvbsZM8TxGVSryQetP8UYw4wJo4iMWIO3tzcFCxeldZee5PTLlQmR3L52k9tTpUlVzp85T9uKrTO7OnfEsizCRoYQuX4tPj6+dOh+47lm+KC+xMfHUaV6bVq0s49Ls6eGsfjHhc755d2PWlO1Rm13h5Gm7VvWM2/KaJKSbNR56jkavfKOy/aEhHimjhzEkYN7yJkrDx99+gUBQYVYv2opS76f6yx3/PBBegwNo0Dh+5gQ3IeoU8fJksWLh6vU5JWmLd0dVir/5nVuSpZlMXnMMDavt4/Bbbr2SnMePbB3F6OG2NcPj1WvRXPH+DN9wkgiw9fg7Z2VAoWL0LZrL3L65SIhIYHxwwdxYO9ujDE0b/0pFR6tnEYN3ON/m9Yze6J9Hq3XsAlNXvuPy/aEhHjChn3JoQP2ebRV174EFijEgb07mTZ6KGD/rF56+wOq1KwLQOyli0wZ9RXHD/8JxvBh+248WLaC22NL7k7nk3UrlzNvxiSOHznEoNFTeLBMOedrDh3cR9jwr7h8OZYsJguDx07J9LU9OPbXMMf++nTa++v4kP78uX8PuXLlSbZG2sA3U8eTmJiAt3dW3kpjjXQ38KTjtKljh7F5Qzg+Pj607nKTvjvU3ncfq1aT91vZ+274yuXMm+nou6OmUNLRd/ft3sGE4YOv/RVea/oh1evUd19gZEwbxl66SFjoII4dOgjG0LLj55QuX5HZE0ezef0avBxj8sef9siUNaFlWUwYEczGCPta6NPP+/BgmdTtuW/PLoYN7EN8XBxVa9SmZfvOzmO0HxbMZdG38/Dy8qZqzdo0b9WePTu3M2roQOffeOeDFtSq+4RbY0suI47REhMSmD5uGLv/2ILJkoXX3m1B1dr1MyE6V56yXhBxJ8uyEo0xbbAneryAKZZl7TDG9AMiLcv6AWhnjHkBSATOAs0crz1rjOmPPekE0M+yrLM3+3u6wugGjDEFjTFzjTEHjDE7jTE/G2NKu7kOxYwxbyd7XMUYM9Lx//WNMbWSbfvYGPNuWu+TmZJsNqaNDaFrvxCGjJ9N+MplHDvyp0uZFYt/JKdfLoZNnkfjl9/g6yljAciaLRuvNf2It5u7fhlksyUyc0IoPQePYvDYGdxXrCRLFv3XbTElt2VjOKeOH2Xk1Pm06NCNSSOHpFlu4qghtOzQjZFT53Pq+FG2bowAYOE3M6lYqQojp82nYqUqLPzG/iXsrz8soOgDxRk6fiZ9ho5hRthIEhMSyObjS5uuXzBs4hw+HzicaeNDib100W3xJtlsTB0TzGcDQggOm8O6Fcs4dti1PX9fvIicfrkInTqfZ19+gzmO9jx2+E/CVy5j6ITZdPtyGFPGBJNks3E2Oopfv5/PwFFTGDphNklJSYSvWOa2mG5k0/p1nDh2hAmzv6V1p88Z5zx4cDV2+GDadP6cCbO/5cSxI2zasM65LerMKbZu2kBggYLO5y5dvMi40K/oOXAYY6fNo1uftN83I2119NsRU+fzUftuTB6Vdr+dNHIILdp3Y8S1fhvp6LfzZlKhUhVGTJ1PhUpV+N7Rb4MKFKL30LEMHT+LV975gIkjrsdW7+nn6P7l8IwP7ibsbXqUCbP/S+tO3Rk3/Ks0y40b/hWtO3dnwuz/cuLYUTZvCHduizpzmq2b1ru0KYDNZmPahFFUqlojQ2P4O0k2G9PHDaNL32C+GjeL8FXLOJ5izF3pGHNDJn1Do5fe4Jup9sRIrtx5+LT3EAaNnUGLT3syIaS/y+s2rl2Jb/bsboslLRk15sZeusikUUP5rN8Qhk2cw6c9v3R5v5+/m0eR+4tlaGy3asuGcE4eP8qo6fP5uGN3wkbc4DMYMYSWn3Zj1PT5nDx+lC0b7f344crVGD5pNsMmzqZQ0fv49uvpab7+brZ82jL6NOqd2dVIF5GOuSZs9re06fw5Y4elPSeMGWafa8KuzTXrr881L732FqMmz2HU5Dl3TbIoyWbj64kjaNvjK/qETmfjmt84cfSQS5m1y38mp58fA8bMoUGTV/l2pv3kiep1G9IrZDK9QibzQbse+AcW5L7i9pMPnn7hDfqNmknP4Ikc2LOd7ZvXuzs0F//2dW5Kmzes4+Sxo4yZsYCPP+12w/FnQugQPunYnTEzFnDy2FG2OObRRypXI3TyHIZPmk3hovfz3zn28WfZTwsBCJ00h95DRjFtvP0LtcyQZLMxY/wwOvUJZtCYmUSkMY+uWvITOf1yMTRsLs+8+Drzpo0HoOj9JegzfCL9R06lc99gpo0Zis2WCMDsiSOp+Fh1Bo+fzYCRUylU9AG3x5bSnc4n9xcrQZc+gylX8VGX8jZbIiMH9aFFh88Infw1fUPG4uWV+eep2tdIIXTtG8KQcbPTbFvn/jppHo1eeoO5U+37a67ceenU+ysGj51Jy097Mj6kX2aEcFOedJzm7LvT5tOyQ3cm3mg9OHIILTt2Y9Q0e9/d6ui79xUrQefeqfvu/cVK8tXYqQRPmEmPgaGEjfjKuQ+7Q0a0IcD08aE8UrkGIZPm8tXYGc41bcXHqjJkwiyGjJ9JoSL38f03M9wWa3KREWs5fuwok77+jnZdezA6ZFCa5caEDKJdlx5M+vo7jh87SqRjLbRtcyQRa1Yxdtpcxs+cx/+9ZU/EPFDiQUZMnMHoqXPoHzyKUUMHYkt0X3sml1HHaN9/M4PcefMxdOJcBo+bRdkKrn06s3jCekFSMEb/7vTfLbAs62fLskpbllXSsqwvHc994UgWYVlWd8uyHrIs6xHLsp6wLGt3stdOsSzrQce/qX/3t5QwSoOxn6bwHbDC0Qjlgc+BAm6uSjHAmTCyLCvSsqx2jof1gVrJto23LCtzZvibOLB3FwUKFyWoUBG8s2alRt2n2BS+2qXMpojV1G3wLADV6tRnx7ZNWJaFr292yjz0CFmzZXMpb1n2f1evXsWyLK5cjiWff4DbYkouct0q6jZsjDGG0uUqEBt7iXMx0S5lzsVEcyU2ltLlK2KMoW7DxmxctxKAjeGrqdfQHnu9hs+ycd0qAAyGq5cvY1kWV69cwS9XbrJ4eVG46P0UKmK/ZWV+/0Dy5M3HhfN/uS3e/Xt2UrBQUQo42rNmvQZEpmzP8NXUbdAYgOqPP8H2rZFYlkVk+Gpq1mtA1mzZCCpYmIKFirJ/z07A/mV7fHwcNlsi8XFXM609k4tYu5Inn3kOYwxlH6pI7KWLnE3RtmdjorkcG0vZhx7GGMOTzzxHxJqVzu2TRg/n/ZZtMcl+X27l8l+p+fgTBDkSDnnz5XdPQMlsDF9F3Qa30G8vJ+u3Da7328jw1dRz7LP1GjzLxnB7vy3z0MPOqzVKlX2ImOgzzvcrX7GSc1tmWb92FU888+w/atMnnnnWpU0njx5OsxRtCvDjt/OoVfdJ8uTN55ZYbiT1mNuATRFrXMpsXr+GOk/Z99HkY26xkqWd+17RB4qTEB9PQkI8AFevXObXhXN58c333BtQChk15q75bQnVa9cnIMi+X+ZJtl/GRJ1h84a1PNXoBXeE+Lc2rltF/Yb2fly6fAUuX0r7M7h8OZYyjs+gfsNn2bjWHuujVao7v7grXa4CMVFnUv2Nu92O1Tu4dNZ9J0tkpPVrbm2uuXI5lnIV0p5r7kZ/7t9NUMEiBBYsjHfWrFSp8yTbNq51KbNtw1pq1G8EwGM167H7D/tYlNyGNcupWsd+xWY2H1/KVKwEgHfWrNxfvDTnYqLcEM2N/dvXuSltWLuK+k/bx+Ay5W/eX8s85Bh/nm7M+rX2/vpolRrXx5/yFZzrhKOH/+ThSlUB+7oop18uDuzdRWY4uG8XBQoVIcjRd6vXfYrN61POo6up85S971atXZ+djjb18fV1xpcQH+888/3K5Vj2bN9GvaebAPb+ezdc2Xmn80nRB4pT5L7Uia9tkRt4oMSDzqvMc+XJg5eXV8YH9DfS3F8jXPfXzetX8/hTqfdX+xopEEi9RrpbeNJx2sbwVdRrcL3vxt6g715J1nfrNXiWDetu3neT78Px8fFp/kJ4RsqINrwcG8vuP7byRKPnAdfx5+HK19eEpcpW4Gx05sypEWtW8lSjFMdo0Snmlmj7Mdq1tdBTjZ4lYvUKAH5auIDX/vOecz69dnzt6+uLl/e19oxzuWOEu2XUMdqqpT85r1TKkiXLXXE1MnjGekHk304Jo7Q9ASRYljX+2hOWZW0F1hhjhhpjthtj/jDGvAHOq31WGmPmGWP2GmMGG2PeMcZscJQr6Sg3zRgz3hiz2lGuieN5L8f7bjTG/M8Yc+3+GoOBx40xW40xHR1/50djTDHgY6CjY9vjxpg+xpjOjvd71BgT4Xiv7xz3J8QYs8IY85WjXnuNMY9n9Ad5NiYK/4Ag5+P8AUGpDu7PxUSRP9BexsvLmxw5cnLpwvkbvqe3tzfvt+lMt1ZNafOfFzl+5BD1HQdg7nY2JoqAwOt5RP+AQM6miO9sTBT+gUHJygQ5y5w/d9Y5+efzD+DCX+cAaPTiqxw/eoiWbz1Pp5b/4f1POpIli+vuun/3DhITEihQqEiGxJaWczFR+KeIN2V7nk1WxsvLmxw5c3LxwnnHa1P3hfwBgTR59S3aNH2ZT95+gRw5/Xi4cnX3BHQTMVEp2jYwKNUXqzFRZwhIFlNAYBAxUfbPY/3alfgHBlL8QdcLE08cPcKlSxfo3r4lHVo05bfFP2VgFGk7F526HdPqt/lT7rvRN++3yf3+6yIerVozI6p/22KizhB4W21qL7N+7ao02zQm6gwRa1bQ6IVXMrD2t+Zcqna70T568zF349oVPFCiFFmz2g+8FsycROOX3ySbj28GR3BzGTXmnjxu3y/7dG7FZ62asXLpz87XTxsXyn8+bIPJcncsmWKiU4ylgUHEpDjAj4mOwj8g8KZlAH77dRGPVbu79lNPExMdRUDQ349LLn06RXv++N182rz/FqGD+3Hp4oWMr/Qt+OtsFPmS9cF8+QP5K8W++tdZ+xoA7GNR9hx+xF50HYsi1/5O1cefTPX+l2Mv8r/IdZSt+FgG1P7W/dvXuSmdjU69Nkr5BePZ6DTG4LTGn18W8ZhjnVCsZCk2rFuFzZbI6ZMnOLB3N9FnTmdQFDeXah71D0zzi+hrZby8vMme83qbHtizg+6tmtKjbTPea9UZLy9vzpw6Qa48eZkUOpBe7T9g8sjBxF294r6gbiA955PkThw7gjGG/p+1p8vH7zqv5s1sqddIt7e/2tdIpZ1rpLuFJx2nnY2Owj/o5uPM2RR9N63xKi37dm2n44dv0anFO3zU/jO3Xh2XEW145tRxcufJy/iQL+nW+j3Chg/iahrjz4olPzpvYedu0VFRBAZdv3tDQGABoqNd10LR0Wdc5p+AwAJEO467Txw9wo5tW+nQ4j26tmnB3l07nOV279jOx01fp1WzN2nTubszgeRuGXGMdu1OM/+dOYme7T5g5MCenD930ztMuY0nrBdE/u3ujm8/7j4VgE1pPP8K8CjwCNAAGJrsB6UeAdoDFYGmQGnLsqoBk4C2yd6jGFAPeA4Yb4zxBZoD5y3LqgpUBT4yxhQHugGrLct61LIs572cLMs6BIwHhju2uZ52AjOAzyzLehj4A0h+3xZvR706pHg+Y6Q4UxRIdWZHyrNJHYVu+JaJiYks/+k7Bo6eyuhZ33N/8ZJ8Py+zfk8j9XMprzxIM76/OV9pW+R6HihRiglfL2LouOlMHh3C5dhY5/ZzMdGMGtKPTzr3TJVIykhph5Iy3rSKmBu286WLF4gMX83IaQsYO/sH4q5eYfXyX9OnwnfkFvpummXsZwXPmzWVd97/ONV2m83GgT276T04lL5DRjF3xmSOHz2cftW+BWk3453tl8lt37qJ3xYv4p0Ut9m5G93KmWbGGOKuXmX+rKm8/X7q38uYOHoY77Voc1ecPZtWu6Uck26wkzr/99jhg3wzdRzvt+0KwOED+zh98hhVatVL17rejowac202G3/u20O3/iH0GBTKf2dPtd/2K2INefLmo0Qa99zONLcwr6ZdxvXxf2dPxcvLm8cdZ8lL5khzn72V8djh2Rf/j4lzvmPk5Nnk9w9g0pjQdK/jbbnN9ULyMn/u3Uk2Hx+K3F/CpYjNlsik4f154rlXCCxYOB0qewf+5evclG607nEpcwufyYLZU8ni5UXdBvbx56nGz+MfGESXT5oxZewwyj5UMdPm1FtprpvFWLLMQwwaO5M+w8L4cf4s4uPjSLLZOHxgL08++xL9R0zBxzc7Py7InN9zdJFO80lKNpuN3du30f7zvgwIDWPDmpX8b/PGm7/IDW5lffB3++uxwweZO3UsH7Ttks61u3OedJx2K+PqrYxXaSlVrgLDJ33N4NFT+G7uDOLj4263mv9YRrShzWbjz/17adjkZQaPmY6Pry8/pEjifvf1NLJ4eVHnyWfuoPZ34LbHInsZmy2RSxcvMHzCNJq3aseg3t2dn0fZhyowfuY8QsNmMG/WVOLj3NeeyWXEMZr9tpFnKF2+IgNGTqFUuQp8PXlMutb7dnnCekHk3y7zbyZ8b6kDfG1Zlg04bYxZiT3BcwHYaFnWSQBjzAFgieM1f2C/YumaeZZlJQH7jDEHgbLA08DDxphXHWXyAKWAf3yduzEmD5DXsqxr9yqZDsxPVuRbx383YU9epfUeLYAWAN0HhPDKm7f/00j5A4Jcbkt1NvoMefMHpCpzNuoM/gFB2GyJXL4ce9NbVx0+uA+AAoWKAlD98adYNN99B9K//rCA5T//AEDJMuWIjrp+RkNMdFSqy/T9A1zPFo6JPkN+R5k8+fJzLiaafP4BnIuJJrfjdla/L/mJl95oijGGgkXuI6hgYU4cPcSDZR/icmwsg3t14s1mLeVi1k4AACAASURBVChdzr0/lps/IJCYlPHmTxmvvYx/oKM9Y+3tmT/F53A2+gz58gewfUskQQUKO2OvWrs+e3f9kSlfYv703TwW/2i/L26psuVd2zbqjPNM6GvsZzZdjynaUebUiWOcPnmCds3fdj7focV/GDZuGv6BQeTOkxff7NnxzZ6dCo9U4s8D+9K8LUJ6WvzDApb/4ui3pcvdQjsGcTbFvpvvb/otwOGD+wkLHUS3AcPIlTtPRoZ0S376bj5LkrVp1N+0qX9gUJptetLRpu2bv+N8vkOLpoSMm8r+PbsI7tcTgAvn/2LT+nV4eXlR4/H6GRxdavlTtVsUef1Tj7n22FOPuWejzzBiwOe07NTTefXivt3bObR/Dx3ffxWbzcaF8+f4slsbegwe7ZaY3DHm+gcEkSv39f2yXMVHOXxwH3/u20NkxGq2bFxHfHw8Vy7HMnJwH9p165PBUbv65fsFLP/5e+Da/pusjaOux3dNyitQ7GWu9/UVS35iU8Raeg8dnam35/BUPyafa8qUdzkzMs25JqiAa5+OOuM8azpffn/n8880eYm+3TtmZNVvWV7/QOdVqQDnzkalWv/l8w/kbHQU+fztY9GVy5fI6Xd9/bdx7W/O29ElN2t8CEGFitKgyWsZF8At+jeuc1P6ZeF8ljrGnwfLpF4b5fNPPY+mHIOTj9O/L/6JyPA19A0e4xx/vLy8+aDV9b7bve2Hzlswu1v+gEDXeTQmdd+9VubaPHolNpacKdq08H3F8PH15fjhP8kXEEj+gEBKlnkIsK91f1owK+ODSUN6zydp8Q8MovzDlcjtuDVSpeq1+HPfHh5+rGp6hXFbUq+RzqRaQ9xsf42JPkPogM/5uFMv5/55N/m3H6f9+v0CljnHonLEnEl7rXeNf4Br301rvLqZog8Ux9fXl6N/HqRkmXJ3WPtbkxFt6B8QRP6AQB4sax9/qj/+hPP3ZwFWLv2ZLevX0mPwKLeuCRd9O4/Fi5Ido5055dwWHXUaf/+0jrtPu5YJCHBuq1XvCcftzypgjOHCX3+RJ9/1Y9T7ixXH1zc7h/48QOmy5TMytDRlxDGaX+48ZPPxpXLNugBUq/MEK5f86KaIUvO09YLIv52uMErbDqByGs/fbAZNfqpCUrLHSbgm5lKm0S3H+7Z1XC30qGVZxS3LWkLGuFYvGzdIGFqWFWZZVhXLsqrcSbIIoETpspw6cYwzp06QmJBAxKrlVK5Rx6XMY9XrsGqZ/fY/G9as4KGHK990sZLfP4DjRw5x4bz9VkLbt2yg8H3F7qie/0SjF15l6PgZDB0/g2q16rJq6S9YlsXeXdvJkTNnqgOPfP4BZM+Rk727tmNZFquW/kKVWvZJvUqNOs5bH61c+jNVa9rvEhgQVIA/tkQC8Ne5s5w4dpigQkVITEgguO9n1G3QmJp1U3+JktFKlinn0p7hK5elas/KNR5n1bJfAFi/+nceesTenpVr1CF85TIS4uM5c+oEp04c48Ey5QkIKsC+3TuIc9yrf/vWSIq4sT2Te+7l1xk5eQ4jJ8+hRp36/Lb4JyzLYveOP8iR0y/VgUh+/wCy58jB7h1/YFkWvy3+iRq161GsxIPMWriEyd/8wORvfiAgMIjQsFnk8w+gRp167PhjC7bERK5evcqendu57/6Mj/eZF15lyLgZDBk3g6q16rJqWbJ+myPtfuubvN8u+4WqNZP1W8c+u3LZz1Rx9NvoM6cI6deN1l2+oHDR+zM8plvx3MuvMWLybEZMnk31OvX4ffHP/6hNf1/8M9Vr16VYiQeZuXAxk775nknffO9o05nk8w9g0tzvnc/XqvckH3fominJInCMucePJhtzl/FY9douZSpVr82a5fZ9dMOaFZR/+DGMMcReukhwny683uxjSpd/2Fm+wXMvM2rm9wyfuoBeQ8dSsMh9bksWgXvG3Cq16rJ7+1ZstkTirl5l/+6dFLmvGG83b8X4OT8wZuZ3dPi8PxUerez2ZBFA4xdfJXjCTIInzKRa7XqsWGrvx3t3bidHTr+0P4PsOdi70/4ZrFj6M1Udn8GWDeEsnDuTz/oPxcc3c28x6KmavPw6oybPYdTkOdR8/Bbnmuyuc031OvYr/pLfDz589QoeKF7SrbHcSLEHy3Dm5DGiT58kMSGByDW/8UiVWi5lHq5ai4gV9jPVN4evpGyFx5zrv6SkJDatW0HV2q63o1s4ZxJXYmN5/f02bonj7/wb17kpNX7pNYaFzWJY2Cyq1a7LiiX2MXjPzhv3V98cOdiz095fVyz5hWq17ePP5g3hfDd3Bt0HBLuMP3FXr3L1iv0WSVsj1+Pl5cV9xVyvLHOX4qXKcvrEMaIcbbp+1XIqVXNt00rV67DGcZXFxrUrKOeYR6NOncBms/+gevSZU5w6foSAoILkzedP/oAgTh47AsDObZsyrU3Tcz65kUerVOfwwf3EXb2KzZbIzm2bKfpA8YwM65bY10iu++tj1VPvr6uXX99fyzv219hLFwnp04XXm7V0WSPdTf7tx2mNkvXdqrXrsXLZP+u7K5f97DyWuZHTJ6/vw1GnT3Li6BECCxa66WvSU0a0Yd78/vgHFuCE444W27dEUvR++/64NTKCRfNn0bnPELevCZ9/5XVGT53D6Kn2tdDyX68fo+X08yN/QMpkin19f20ttPzXn6nhWAvVeLwe2zbZvzs5duQwiYmJ5M6bl1MnjmNLtLfn6VMnOXbkMAUy6crkjDhGM8ZQqXptdv2xBYAdWzNvbgHPWy+I/NuZm93mwlMZ+1FcBDDJsqyJjueqAs8CtRz/zQ9EAtWxXyXU2bKsa79JtMLxONIYU//aNmPMNCAIaAIUB1YCDwLvOt7zNcuyEowxpYHjjvcdZllWPcf7Jn+vTkBuy7J6O7b1AS5ZlhVsjNkGtLEsa7Xj+TyWZXVMUa8AINKyrGI3+ywiD0TfcQfZunEdMyeMJCnJRr2nm/DSm++xYOZEipcqS+UajxMfH8e44P4cPrCXnLly0/azvgQ5zppo3+z/uHI5lsTERHLk9KPbl8Mpen9xlv30HYt/mI+XlzcBQQVp+WmPO7qaIav37eVOLcti8uhgtkWuJ5uPD60696RkafsZSF0+fpeh42cA9h85HDt0APHxcTxatQYftO6EMYaLF84zfEAPos+cJiCoAJ/2/BK/3Hk4GxPF2KEDOHc2Gix48Y2m1G3QiFXLfmVcyACKPnB9UmzdpSfFSpZOs34p2ZLufH/fsmEdMyaMICnJRv2nm/DyW82YP8PenlVq2ttz7JB+HDqwF79cuWnbvZ/zLJjvvp7GiiU/4pXFm3c/bu/8jZv5MycRsXIZWby8KFayNC06dE/1I9D/hJ9v1juO07Isxo8YwuYN4fj4+NL+sy8o5TgbqV3ztxk5eQ4A+3bvJHRwX+Lj46hcrRYt23dJ9UVQ8zdeYNiEGeTJaz/T8tu5M1n2yyKMMTz93Iu8+Nrbt1XHy3GJtx3blDHX++0nna73266fvMuQccn6bfAAEuLjeLRKDd5P1m9Dv7zebzv2sPfb8cMHsmHNCgIc96D28vJi0OipAIwY9AU7/7eZi+f/Ik++/LzW9EOebPTCLdc5u8+dX2puWRYTRgx1tmm7z3o527R983cYMdl+e5h9u3cyYnA/4uPjeKxaLVq275yqTT9840WGTZhO7ryuPywaOqgvVWvWoXb920vono+98x9Q3roxnNlhI0hKSqJuw+d48c33+O/MSRQvVZbHatQhPj6O8cH9OXxwH365ctO6ax+CChVh4dxpLJo3i4KFr58123XAcPIku4Is6vRJQvp2ZfDYOzvb3Sfr7bVnRo25AD/Mm8XvS34ii8nCk42f57lX3nT52zu2bWbRgtl06x9yy/XNkiX9z9S0LItJo4LZujECHx9fWnXpyYOOM187t2xK8AR72+zfs4sxQ/sTHxdHpWo1ad7G/hm0efdVEhLinXNmqXIVaNnhszuuV4/7m97xe9yqznO6UKF+RXIH5Oav03/xde/ZLJ2y1C1/O+TknHR9P8uyGB86hE2OcalDt+tzTdvmbzMq2VwzfHBf4uPiqFy9Fh875pqQAV9wcP9ejDEEFSxEm86fpzogvx3Ho2P/vtDf+GNTBPOmjiYpKYnaTzbm2Veb8sPXU3jgwTI8UrU2CfFxTBk5kKN/7iOnX24+7PiF8xZze7Zv4btZYXQbPM75fudiztCtxesULHI/3lnt8/wTjV+mToPb/30fv+x3vl64F9a5ANl97vzGEpZlMXHkULZsjMDH15c2XXo5x59PW/yHYWH2q2b279nFqCH9iI+L47FqNfmwrX0ebdX0/1zGn9LlKvBxx26cOXWCfp+1x2TJgn9AIK069yCowO19SXvx8p3Po9siw5k9caR9Hm3wHC+88S7fzppEsVJleay6fR4NGzaAwwftfbdV1z4EFSzM2t9+5ccFs/H29sYYw4tvNnOe+X344D6mjPqKxMQEggoU5sMOnzt/eP525EyHvnun88n6NSuYPDqEC+f/ImdOP4qVLE2vr0YAsGrZL3z79QyMMTxWrSZNW7S9YT1u5mqc7Y7jTG7rxnXMCnPsrw2b8GIa++v44P4cOmg/jmnTtW+yNdJMCiRbI302INRljXQnvLzSZ71wLxyneXvd+TnLlmUxeVQwWyMjyObjS+vOPZ1XASXvuwf27GJMsL3vPlrVte9OGePad3sOHsHKpb+w8JsZeHl5kyWL4dX/NKda7du7JXOiLem2XpcRbXjowF7CQgc7fv+4MC0/7YFfrtx0eP81EhISnGPyg2Uf4sN2Xf9RffPkvPPf8rIsi7HDh7Bp/Tp8fH3p2L238yqgNu+/zeip9rXQ3t07GT6wD3FxcVSpUYtPOnTFGENCQgKhg/pxcP8evL2z0rx1Bx6tXJXlv/7E/NnTnWPyW80+olbd+rdVx5gLV+84zow4Ros+c4rxwf25HHuJXHny8lGH7s5j8duVM52+S7nb1wsPFc2r2yyko5DPflVy4Q51+qrRXdUnlTC6AWNMYSAU+5VGV4FD2H/3pwXQGPuVQQMsy/omeSLH8doV3DhhdA6oAhQAPrUs60djTBZgAPA89quNooCXgMvAr0AAMA3Ykuy9SgMLsF/B1BZ4iusJo0ex/8ZRDuAg8L5lWecyK2F0L7jdhNG9Jj0SRveC9EgY3QtuN2F0L0qPhNG9ID0SRveC200Y3WsyImF0t3JnwigzpXfC6G6VHgmje0F6JIzuFemRMLoXpEfC6F6QHgmje0F6J4zuVumVMLoXpEfC6F5wuwmje016JIzuBemRMLpXpEfC6F6ghFH6CumuhNGd6jTo7koYecaRw22wLOsE8Hoam7o4/iUvuwJYkexx/RttA9ZaluVyg3nHbxp97viXUsrT1Fc4XrMXSH49/Opk77cVqJHyjVLUK5ob/IaRiIiIiIiIiIiIiIh4Fs84vUNERERERERERERERERuSFcYuZFlWc0yuw4iIiIiIiIiIiIiIiIp6QojERERERERERERERERD6eEkYiIiIiIiIiIiIiIiIfTLelEREREREREREREROSfMSazayDpTFcYiYiIiIiIiIiIiIiIeDgljERERERERERERERERDycEkYiIiIiIiIiIiIiIiIeTgkjERERERERERERERERD6eEkYiIiIiIiIiIiIiIiIfzzuwKiIiIiIiIiIiIiIjIPcZkdgUkvekKIxEREREREREREREREQ+nhJGIiIiIiIiIiIiIiIiHU8JIRERERERERERERETEwylhJCIiIiIiIiIiIiIi4uGUMBIREREREREREREREfFwShiJiIiIiIiIiIiIiIh4OO/MroCIiIiIiIiIiIiIiNxjspjMroGkM11hJCIiIiIiIiIiIiIi4uGUMBIREREREREREREREfFwShiJiIiIiIiIiIiIiIh4OCWMREREREREREREREREPJwSRiIiIiIiIiIiIiIiIh7OO7MrICIiIiIiIiIiIiIi9xhjMrsGks50hZGIiIiIiIiIiIiIiIiHU8JIRERERERERERERETEwylhJCIiIiIiIiIiIiIi4uGUMBIREREREREREREREfFwShiJiIiIiIiIiIiIiIh4OO/MroCIiIiIiIiIiIiIiNxjTGZXQNKbrjASERERERERERERERHxcEoYiYiIiIiIiIiIiIiIeDjdkk5u6tKVhMyugluUKJwns6vgFidjYjO7Cm4Rn5iU2VVwi0SbZ8QJcOZcfGZXwS1y58yW2VVwC0+ZW7IYz7k2P+TknMyuglt0KvR2ZlfBLQYdnZXZVXALn6xemV0Ftzkf6xnzaA7frJldBbeIT/CMNWBAHt/MroJbXPSQdRFAfIIts6vgFrYkK7Or4BZnzl3J7Cq4hU82z1kvxCd6xj4qIjenK4xEREREREREREREREQ8nBJGIiIiIiIiIiIiIiIiHk4JIxEREREREREREREREQ+n3zASEREREREREREREZF/Jovn/Havp9AVRiIiIiIiIiIiIiIiIh5OCSMREREREREREREREREPp4SRiIiIiIiIiIiIiIiIh1PCSERERERERERERERExMMpYSQiIiIiIiIiIiIiIuLhvDO7AiIiIiIiIiIiIiIico8xJrNrIOlMVxiJiIiIiIiIiIiIiIh4OCWMREREREREREREREREPJwSRiIiIiIiIiIiIiIiIh5OCSMREREREREREREREREPp4SRiIiIiIiIiIiIiIiIh/PO7AqIiIiIiIiIiIiIiMg9xmR2BSS96QojERERERERERERERERD6eEkYiIiIiIiIiIiIiIiIdTwkhERERERERERERERMTDKWEkIiIiIiIiIiIiIiLi4ZQwEhERERERERERERER8XBKGImIiIiIiIiIiIiIiHg478yugIiIiIiIiIiIiIiI3GOymMyugaQzXWEkIiIiIiIiIiIiIiLi4ZQwEhERERERERERERER8XBKGImIiIiIiIiIiIiIiHg4JYxEREREREREREREREQ8nBJGIiIiIiIiIiIiIiIiHs47sysgIiIiIiIiIiIiIiL3GGMyuwaSznSFkYiIiIiIiIiIiIiIiIdTwkhERERERERERERERMTD6ZZ0kuG2b1nPvCmjSUqyUeep52j0yjsu2xMS4pk6chBHDu4hZ648fPTpFwQEFWL9qqUs+X6us9zxwwfpMTSM+4qXcj43ZtDnRJ8+Qe/Qae4K54Ysy2Ls8CFsWLcWH19fuvTqS6ky5VKV27t7J0P79yY+Lo5qtWrTqmNXjDEc2LeHEUO+5MrlKxQsVJhufb8kZ04/Nm2IYPLYkSQkJJA1a1Y+atOBSlWqZUKEdv/bFMGssBEkJSVR7+kmPP9aU5ftCQnxTBg2gEP79+CXKzetP+tHYIFCbN+ykXnTxpGYmIi3tzdvftCa8o9UBiB85VIWzZuJMYa8+f35uNMX5MqTNzPCc7IsiyljhrF5wzqy+fjStmsvSpQqm6rcgb27GD2kP/HxcTxWrRYftP4UYwzTJ4wkMmIN3t5ZKVi4CG269CKnXy727d7B+OGDnH/jjXc/onqd+m6OztW2yAhmTgglKSmJ+s88zwuvp27TccH9HW2ah7bd7W168cJ5RgzswcG9u6nboDHNWnVK9d4hfbty5tQJvho3y13h3ND2zev5evIokpKSeLzBczz7f6nHoskjBnL4wF78cuWmZefeBAQVIvrMSXq1fZeChe8HoETp8jT9xB7rhjW/8dOCmVhJSVSsXIPX3vvE7XGBvS9NHTucLRvX4ePjS6vOvShRqkyqcgf37mZMsL2/Vqpai/dbdcQYw6UL5xn+ZS+iTp8ksEAhOvYcgF+u3FyOvcTIwX2IiTqNzWbj+Vff5olnmgDwRqPa3F+sJAABQQX4rN9Qt8b8x+b1zJk4EispiccbPsdzr/7HZXtCQjyThn/J4QN7yZkrN5906UNAgULO7TFRp+nZ5l1efLMZjV5+C4Al389j1dIfMcZQ5IESNG/XjazZfNwaV0r/27SeOZNGkGRLou7TTWiSRpwTh39p3z9z5+aTLn0JTBHn562b8tJb79P45beIiTrNxNAvOX/uLMYY6j/zAk+/8Jq7w/pblmURNjKEyPVr8fHxpUP33jxYOvUYvH/PLoYP6kt8fBxVqtemRbtOGGOYPTWMxT8uJE9e+1zy7ketqVqjtrvDuCPtJrenSpOqnD9znrYVW2d2df4xy7KYPGYYm9fbx6U2XXtRMo02PLB3F6OG9Cc+Lo7HqteiefJ5NNw+jxYoXIS2Xe3zaGJiImODv+Tg/j3YbDbqN2zM/73dzP0BOliWxYSRwURG2Ptqx+59eLBM6jj37dnF8IF97H21Rm1atuts76tTJrD4x4XkzpsPgPc+akXVmnVISEhgdPBA9u3eSZYsWWjRrhMPV6ri7vBcbIuMYMb4UJKSbDzR6HleeP1dl+0J8fGMC+nPn/t245c7D+2697++XviyBwf27qJuw2d5P9l6YXDPjvx1NgabzUbZCo/wfqtOZPHycndoLuxz6jA2bwjHx8eH1l1utAbczZih19aANXm/lb3vhq9czryZkzh+5BCDRk2hpON44MypE3Ro/haFi9rXE6XLVaBFh8/cGltyntKeyVmWxbgRQ9kYbj9e6/R5nzSP1/bt3kXIwN7ExcVRtWZtPmnfBWMMA7/oxrEjhwG4dOkifn65GDvta3eHcUMZ1XeviTpzio7N3+L1dz/khdfeSfW+7pIRxy6JCQlMGzeMXf/bgslieP3dFlSr84S7Q7uh2z0GP7BnJ1NHDwHs/ePltz+gSq16mRHCDd3uWvfg3p1MHeM4/rAsXnrrAyrXrAvA4u+/YeUS+5q+6AMlaN6+O9kyYU2/NTKCGeOuj7MvvpF6nB0bfH2cbd+9P4EF7ev4hXNnsGLxIrJk8eK9TzrwSJUaAMReukhY6CCOHToIxtCy4+eULl8RgF+/n8+SH/5LFi8vKlWrxTsfumf9uHVjBNPHh5Jks/Fk47TjHDM0WZyf9ycoWZy//7qILF5eNHPEGR8fR99OrUhISCDJZqP640/w2rsfAtD700+4euUyABf+OkfJMuXo3Ocrt8Qp4kl0hZFkqCSbja8njqBtj6/oEzqdjWt+48TRQy5l1i7/mZx+fgwYM4cGTV7l25lhAFSv25BeIZPpFTKZD9r1wD+woEuyaHPEKnx8s7sznJvaEL6G40ePMG3+93To1pORQwamWW7kkIF07NaTafO/5/jRI2yMWAvAsEH9aP5JOybOnk/tek8wf9Z0APLkyUu/oaFMnD2fLr368VXfnm6LKaUkm40Z44bRuW8wg8fOImLlMo4f+dOlzMolP5IzZy6CJ35Doxff4Jtp4wDwy52Hjl8MYeCYGbTo2JMJIf0BsNkSmRU2gu4DR/Ll6OncV+xBlv74X7fHltLmDes4efwoo6cv4JOO3QgbMSTNcmEjhvDxp90ZPX0BJ48fZcvGcAAeqVyN0ElzGD5xNoWL3s+3X9vb8/5iJRkydhohE2bRa9AIxocOxmZLdFtcKSXZbEwbG0LXfiEMGT+b8JXLOJaiTVcs/pGcfrkYNnkejV9+g6+njAUga7ZsvNb0I95unvZCdOPaFfj45sjwGG5Fks3G7LBQOvQaQv+R09mwZnmqsWjNsp/ImTMXg8bNoeHzr7FgxgTntsACReg9fDK9h092JosuXTjPgunj6Nx3OP1GTufCX+fY9b9N7gzLacvGcE4dP8rIqfNp0aEbk0am3V8njhpCyw7dGDl1PqeOH2XrxggAFn4zk4qVqjBy2nwqVqrCwm9mAvDrDwso+kBxho6fSZ+hY5gRNpLEhAQAsmXzYej4GQwdP8PtyaIkm41ZE4bTsfdQBoyewfrVyzl+5JBLmdVLfyKnXy4GT/iap194nfnTx7tsnzt5FBUfq+58fC4mimU/LuCLkIn0HzWdpKQk1q/+zR3h3FCSzcbMCcP4tHcwA8fMZP2q1GPuqqU/kcMvF0PC5qYZ55xJrnF6eXnx5getGTR2Fr2GTmD5z9+mes+7QeT6dZw4doSw2d/SpvPnjB02OM1yY4YNpk3nzwmb/S0njh1h0/p1zm0vvfYWoybPYdTkOfdcsghg+bRl9GnUO7Orcds2b1jHyWNHGTNjAR9/euN5dELoED7p2J0xMxZw8thRtmxINo9OnsPwSfZ59L9z7PPoupXLSUiIJ3TSHILHTWfJjws5c+qE2+JKKTJiLSeOHWXinO9o26UHY4YNSrPc2JBBtO3Sg4lzvuPEsaMuffXF195m9JQ5jJ4yh6o16wCweNF39tdN/4YBw8YwaYz9y9HMkmSzMXVMMF37hzB0whzWrVjGscMp1gtLFpHTLxfDp8yn8Uuu64VXm37EOx+2SfW+7boPYPDYGQwZP4sL5/8iIpPHXYAtG8I5efwoo6bNp2WH7ky80Zw6cggtO3Zj1LT5nDx+lK2ONeB9xUrQufdgylV8NNVrChYuQvCEmQRPmJmpySJPas/kNkas5cTRo0yZu5D2XXoyOjjt/XVUyCDade3JlLkLOXH0KJER9v31836DGTvta8ZO+5o69Z6kdr27J6EAGdt3AaaPC6VS1ZoZVv9bkVHHLgu/mU7uPPkImTSXIeNnU65iJbfEcyvu5Bi86AMl6Bs6iQGjptGlXwhTxwzN1GPPlO5krVvkgRL0GTaR/iOm0qlPMNPG2mM7FxPF0kX/pc+wSXw5eoZjTb88U2KbOiaYzwaEEByW9jj7+2L7OBs6dT7PvvwGcxx99djhPwlfuYyhE2bT7cthTBkTTJLNBsD08aE8UrkGIZPm8tXYGRS5vxgAO7ZtYlP4ar4aN4PgsNk0efUtt8U5ZUww3QaEEDJxDmt/TztOP79cjJg2n+deeYM5k6/HuW7FMoLDZtP9y2FMHm2PM2vWbPQaMooh42cweNx0tkZGsG/XdgD6DhvH2UXFZQAAIABJREFUV+Om89W46ZQqV4Fqteu7JU4RT6OEUQrGGJsxZqsxZrsxZpEx5rYvczDGNDPGFL7J9heNMQuTPe5ujNmf7PHzxpgfbvfv3w3+3L+boIJFCCxYGO+sWalS50m2bVzrUmbbhrXUqN8IgMdq1mP3H5uwLMulzIY1y6la5ynn46tXLrNs0TyefdX1zJrMFL5qJQ0aN8EYQ/kKD3Pp0kVioqNcysRER3E5NpbyFR/BGEODxk1Yt3IFAMcOH+bhSvYrbh6rVoPVK+yLmgfLlCUgMAiAYiVKEh8fT3x8vPsCS+bA3l0EFSpKUMEieGfNSo26DdgcscalzOaINdR5qjEAVevUZ+c2e3sWK1mafP4BABR5oDjxCfEkJMRjWYAFcXFXsSyLK5djneUy08Z1q6jXsDHGGEqXr0jspYuci4l2KXMuJprLl2MpU74ixhjqNWzMhrUrAXi0Sg28vOwXcZYuV4GYqDMA+Pj6Op+Pj48ns38a8MDeXRQoXJSgQtfa9Ck2ha92KbMpYjV1GzwLQLU69dnhaFNf3+yUeegRsmbLlup9r165zM/ffcNLb73nljj+zp/7dhFU6PpYVK3Ok2zd4Np3t25YS60nngGgcq167P7f5lRjUXJRp09QoPB9zqvhyj9SmU3hKzMuiJuIXLeKutf6a7kKxMZeSrO/XomNpbSjv9Zt2JiN6+z13Ri+mnoN7W1cr+GzbFy3CgCD4erly1iWxdUrV/DLlfuuOFP44L5dBBUsQpCjPas//lSq9tyyfg21nrTPLVVq12NXsvbcHLGawAKFKew4wLrGZrMRHx+HzZZIfNxV8ub3d0s8N3Jw3y4KFHKNc8v6lHGupo4jzqq1r4+5AJsiVhFYsBBF7i/uLJ83fwDFStqvPsueIweFixZL1VfuBuvXrOTJZ57DGEPZh+xj8NkU9TwbE82Vy7GUq/AwxhiefOY5ItZkzj6YEXas3sGlsxczuxq3bcPaVdR/2j4ulSl/8zYs85B9XKr/dGPWpzWPlq9ATLR9HjUG4q5edeyncXh7e5M9R073BpdMxJqVPPnMs659NTpFnNH29cL1vvos4atX3PR9jxz6k0cqVwUgb778+PnlYt/unRkVxt/av3cnBQoXpYBjvVCzXgM2RbiuFyLDV/N4A/sasPrjT7B9a6RzvVC2QtrrhRw57W1ns9lITEjA3AU/mrwxfBX1GjzrWANWIPbSDebU5GvABs+ywTF3Fn2gOEXueyAzqn7LPKk9kwtfvZKnGtnnlnIVKnLp0qUbHK9dorxjf32q0XOsS7G/WpbFqt+XUb9BIzfW/u9lZN/dsHYlQYWKcF+x4mlud5eMOnZZueQnXnjD/t1ClixZMv1uF8ndyTF48mPPhPj4u26fvJO1ro9PitiSHVknJbmu6fPld//3C/v37KRgIddxNjJlXw1fTd00xtnI8NXUrNeArNmyEVSwMAULFWX/np1cjo1l9x9beaLR8wB4Z81KTr9cACz98TteeL2ps3/nyZvffXEmm09q1U8dZ2T4auo2vB7njmRx1qqfLM7C9jiNMfhmt594aktMtCc5U/TdK5dj2bFtE1Vq1XVLnCKeRgmj1K5YlvWoZVkVgLPAnVzD2Qy4YcIIWAckP0WnJnDBGBPkeFwLWJvqVfeQv85GkS8g0Pk4X/5A/oqJSlUmv6OMl5c32XP4EXvxvEuZyLW/U/XxJ52Pf5g7hYYvvEE2n8y9VVBy0VFnCCpQ0Pk4ILAA0Y4kQfIyAUFBzseBQdfLFCtR0vnlwarflhJ15nSqv7H692U8WLoM2dJY5LrDuZgo/AOv1z9/QCDnUrRn8jJeXt7kyJGTSxdc23Pj2hU8UKIUWbNmw9vbm/dadeLz1u/S7t2XOHH0EPUaNsn4YP7G2egoAgILOB/7BwaleUDpHxDkUuZsijIAy39dRKVq13f1vbu20775m3z60du07NDNudDNDGdjXGPIHxCUZpvm/5s2TWn+zIk8+8qb+Pj4pn+lb8O5s9HkSxZnPv/ANA+gr5Wxj0U5ueQYi6LPnKTvp80Z0qMde3duAyCoUFFOHT9C9JmT2GyJbFm/hrPRrvu8u5yNSdFfAwI5m6Idz6bYf/0Dgpxlzp8760zU5vMP4MJf5wBo9OKrHD96iJZvPU+nlv/h/U86kiWLfemQEB9Pt9bv06Pdh85Eqbv8FRNN/lTtmXJuuV7Gy8ub7Dnt7Rl39Qq/fDuHF95s5lI+n38gjV5+ky4fvkbHZi+TI8f/s3ff4VEVfRvHv4cEEkhoaVSl96L0XlRUEGyvguWxYAURRKQXpUsLvfeOUpQiKtJ7DUWkht4hhd5SNuf94yybbLIUSUjA3J/req5HktndmczszO+cOTPjRckyKbf9J9i/e3HL6ee63d6tnH/8Mos33v3kru8feuEcJ44GU6BI8UdTgEQIDwvFLyBeHxxvTA0PDXFu0/H66cXz59L8k/cY0rcH169dffSZFieuxtH4Y+TFMBf9kotxdOWfv1HW/lR7lZov4OHpyWcN6/Pl+6/xeqP/kTFT5kdUivsLDwvFP8A5/guPNxaEh4XgG+dvYaWJ21bn8HXjdxnStzvX7G01X8FCbF6/Blt0NOfPnuFw8H7CXMSGyeVSWKhTGXxcjDOXwkPx9bPS3IkXrt0nXgDo0/lbmr5Xn/QZMlDpMdgC6mJYKL4B926XF8NC8Y1zjXO3GDC+kPNnadv0I3747iv2/7Mr6TL9L6Wm+owrPCwE/zhji3+A6/g+bt/lH5DwO73n751kzepDrqeefrQZ/pceVdu9fesWC2ZPp+GHnyVthh/Co7h2uXHdejhj3rTxdG7xCUN/7MKVSxcfQe4fTmKvwY8c3EvHZh/QqfnHNG7WJkWvPeNLTKwLVtk6ff0hXb5pzMf2smX19afuG+/S+rO3+fbjN0jv5Z0iMb1VJ87XZ/Hr7WKcNG5u7mTwsvrZhHVutfOQ82fIlDkLYwb2psPXHzNucB9u374FwPkzpziw92+6tPyc7m2bceRg8jxkcjHcxXjiMt6LLWd6eznjx4E+ca5PY2w22n/1MV++U59SZSpQqGgJp/fctmEtJZ4t53hQQUSSliaM7m0TkOvOPwzDaGsYxjbDMHYbhtHd/rO8hmHsNwxjvGEYew3DWGoYRnrDMN4GygMz7SuWEuydZppmKHDFMIyC9h/lAn7BmijC/v8b7Z8z2jCMIPtndI+Tp1cMwzhgGMZ6wzCGGYax2P5zL8MwJtnzu9MwjNftPy9hGMZWe552G4YRu8fbo+Dq4fx4Twa4fIA/TppjwftI5+FBrqfzA3Dq2CFCzp2hTKUaSZjRxDNdFDbBEzwuynonTevO3Vj4yxyaNX6fWzdv4u6e1ind8aNHmDBqGN+2T7kt6e5SgH+V5vSJo8yZMppPmrcDIDo6mpV/LKDnsMkMm7aAp/IW4Le505Mwzw/H1cqShEW9/99j3szJuLm5UfOF2CcQCxcrydCJP9Nv5GR+/WkqkZERSZHlh+OynPG/ow9S77GOHwnmwtkzVHic9sd+kPp09R3GIHNWX/qPm0PXQRNp9OnXjB/Uk1s3b+DlnZH/NWnF2MDu9OvUAt+A7Lil0Oobl1XEA9Tjfda4/R20hTz5CzH2p98YMHoqE0cM5OaNGwCMmjmfviMn803H7kwdM4TzZ08/bPb/tQfpb11+hzFY8NMkXnytoeOptTtuXL/Gzi3r6TduNoMmzyci4jabVi9N2oz/Sw9SZXcr5/xZk3j59UYJynnH7Vs3GdG3C+9//k2Krs64G9d98IO0acsrr7/F+FnzGTZxJj6+fkwYOSTJ8yj35vp7Gi/NA9TzvJmTSePmRk37k/yHDuwljZsbE+b8zugZ81k0dxbnz55Juoz/Sw8yRt6rnK+88TYTflrA8EmzyOrrx8SRgwF46ZXX8PMPoOWXHzFu+ECKlSidois8XXdH94/pH+Rp9o69hzBq5iKioqLY+3fKbO0a1wPV6QO07/iy+vgxeuZCBoyZxsdNWzK0zw+OMTW5pab6jOtuY+a/TbN6+RJq13k5aTOXBB5V250zbTwN3nqX9HeJKZLVI7h2ibHZuBgWQuHipeg9fDKFipZk5oQRic5q0kncNXiBIiXoM2oG3QaPZ/HcGSl77RlPYmJdsMr248jpdB04jsXzrLLdiekHjJ/N4CkLiLh9i42r/noEub+3+93nulsawzDu2oZtNhvHDgfzYoM36TtyKh6eniyybyVus0Vz49pVeg4Zz/8+b87QH7+/Z6ycZB5krLhrmrvXbRo3N/qNnsqomQs4cnA/p44fcUq3YfUyqtV+8WFzLUnN0P8S/b/HzOPzaMFjxjAMN+AFYKL93y8BhYCKWFW5yDCMmsBJ+8/fM03zC8Mw5gBvmaY5wzCM5kAb0zSD7vFRG4Gq9s87BGwGXrZP/JQGttnTdTZN86I93QrDMEoDwcBYoKZpmscMw4h74mZnYKVpmp/at9XbahjGcqApMNQ0zZmGYaQDElx5GobxJfAlwHc/9OfVhh/ET/LAsvj6cynO0wWXLoaSJd5y4Ky+1hMIWX0DsNmiuXXzOl7emRy/37ZhpdN2dEcP7uPk0WA6NX0Hm83GtauXGfhDS1r3GPrQ+XxYC+fN5o9FvwJQpFgJQi6cd/wuLPSC09NbAH4BAYSFxD6hFhoSm+bpvPnoN9Taa/j0yRNs2bDOKV23Dt/R7vue5Mz91CMrz/1k9XV+wvtiWGiC5d130vj4WfV58+YNvDNmsqcPYWjvTnz5XRey5bDmYk8ePQTg+HfFGs+zeO6M5ChOAn8unMvyPxYCULBwccJCY5/kDQ8NwcfXuT6tp9lD4qWJ/XusWvo72zevp9uAkS4vsHPnyYeHpycnjx2loIsDd5ODj59zGS6GhST4jvr4BXAxNARfF3XqyuEDezl2+AAtG7+FzWbj6pVL9GrfnC79Uu7CK6uvP5filPNSuOu+6FJYbNu9dfMGXhkzYRgGadNaq/ryFiiCf/ZcXDh7irwFi/JshWo8W8E6G2XN0kWO1TfJYcmieaz4w9q1tECRYs7tNSw0wdaOvn7O39/wsNj2mjmrj7XCytePS+FhjsPXVy39nTfe+RDDMMie6ykCsufk7KnjFCxawvF9yJYjF8VLl+X44WCy58z9SMt8hzVu3L8+L8atzxtWfR4N3k/QxjXMnTqGmzeuk8YwSJsuHZmy+OCXLQeZ7FuQlK1ck8MH9lCl9kvJUiZXrCf04pTTRZ97J03Ccu5j28bVzJ4yOracadNRp8FbREdHM6JvF6rUevGxOvh48fw5/LXY2qm3UJHiTqsprHEl/piazblNh4Y4xtSscbYTfLnBG3Tv2OpRZl3s/lwwl2V3xtEiCcfRrK7G0Xj9Uty+a9VfvxO0aT3dA2PH0XUr/qJMhcq4u7uTJasPRUuW5kjwfrLnzEVyWfzrHJbY22rhosUJDYkX/8Urp59/NsLj/C3CQi84+t+4bbVugzfp3uFbANzc3fmyReyB7K2/+jRFVzP4+Pk7leGii3HGx8+f8LAL+Po/WLwQV7p0HpSrVJ2gzesoVTb5nwRfsnBebAxYpBjhIa7Hyzt8/ZxXprhq3/GlTZfOsVVQgcJFyZYjF+dOn6RACsSA//X6jGvRL3NYYj8TrHCx4k67OISGhODj51xuP/8Ap74rNOSC0/hji45mw5pVDJ+YMtcr8SVH2z10YC+b161kxvgR3Lh+HSNNGtKmTUe9NxomYUkezKO4dvHOlBkPD09HTFSpxnOsXvrboynAQ0jsNfgduZ7Ki4enJ6dPHCN/oaLJkvf7SUysG1dOe9nOnDhG6IVz9pjeup4pX6UWhw/scWw9nlzi97PhLsrma0/j6GdvWPXm4xe/zkPI6uOHr18APn7+FLSvtqlU4zkW2ieMfPwCqFitNoZhULBIcYw0BteuXHZc1yVXOV2OJ/7O5bx1r3LGe62Xd0aKP1OGXdu28FTeAgBcu3qFIwf30bqr63PoRCTxtMIoofSGYewCwgEfYJn95y/Z/7cT2AEUxZooAjhmmuadPQW2A3n/xedtwFpJVBVrRdNWoBJQBjhomuZte7pGhmHssH9+CaC4PQ9HTdO8c6Jc3Amjl4AO9rKsBjyBp+2f0ckwjPZAHtM0b8XPkGma40zTLG+aZvnETBYB5C1YhJBzpwm7cI7oqCiC1q/kmfJVndKUrlCVzauXALBj0xqKlizruCkQExPD9o2rqVAtdju6WnVfp/+EX/hxzGza9h5Othy5U2SyCOD1t99h7LTZjJ02m2o1n2P5n4sxTZN9e3bj5eWdYMLI18+f9F4Z2LdnN6ZpsvzPxVSpaQWmly5ay95jYmKYOXk8Dd58G4Dr167RpXULPvuqBSWfcX0AaXLJX7goF86eIvT8WaKjoti8djllKjkfJF62UjXWr/gTgG3rV1O8tFWfN65fY2C3tjT6uCmFi5d2pM/q68+ZU8e5esXaAmvPzm3kTKF93+u93pCBY2cwcOwMKlaryZplf2KaJsH7/iGDl3eC4CWrrx/p02cgeN8/mKbJmmV/UsG+h+7OrZtY8PM0OvQMxMMzdlu2C+fOOg4aDblwjrOnTxKQPUfyFTKe/IWLcv7saUIcdbqCcpWrO6UpW6k6a5f/AcDW9aspUbrcPZ8wrVP/TUbOWMTQKb/QNXA0OXI9laKTRQB5CxXlwrnThNr7oq3rV/JMBee2+0yFao6nz7ZvXEPRUmUwDCvQvnPIaOj5s4ScO41fNmu30Ttbt924fo3Vfy6kRp3k206x7mtvM2DMNAaMmUbFqjVZe6e97t9DBi8v1+01gxfB+/dYe+8v+9Ox53P5ytVZs8yq4zXL/qBCFWsFp19ANv7ZaT3zcPnSRc6ePkFAjlxcv3aVKPtZalevXObg3t3kzpMvuYpOPkd9Wu12y7oVPFvRuT6frViNjSutsSVowxqK2vuijn1GMGD8HAaMn8OLr75N/bc/4IX6b+Hjl42jB/c5zlPbv3s7OXKn7BkU+QoV5cLZ044+d8u6FZSp5Pz9fLZiddbby7ltw2qK2cvZqe9IBk6Yy8AJc3np1YY0aPghdRq8hWmaTBrelxy581L3jXdTolh31eDNRgyfOIvhE2dRpUZtVv71O6ZpcmCv1QfHv+nlY++DD+y1+uCVf/1OperWmBr3rJxN61aTJ1+BZC1LalXvjYYMGjeDQeOscXT1UqtfOrjv7nXomSEDB+3j6Oqlf1KxmtUv7di6ifk/T6NjL+dx1C8gO//sDHKcrRa8b0+ynxfT4P8aMWLSLEZMmkXlGrVZ+dcfjrbq5eWd4Aa0j5/V/8a21T+ofKetxjnvaOO6VY62evv2bW7fssL0nds24+bmxtN58ydTCRMqULiYU7ywac3yBPFCuco1WLfcigG3rFtFiWfuHS/cvnWTSxet8tts0ewK2kTOFOp3677+NoFjpxM4djoVqtVizfI/7DHgnvvEgNaYumb5H1Socu9zFK5cvoTNHk9cOHeGc2dOE5DjXruXPzr/9fqM67W3GjFqyk+MmvITVWrUZsUSa2zZv+cfvLzvcr2WwYv9e6zv64olv1OlRuzDFTuDtvJUnrxOW9ulpORouz0Hj2XUjAWMmrGA+v/3Dv/33scpMlkEj+baxTAMylSqxv7dOwHYsyvI6fzHlJaYa/DQ87HXnmEh5zl35qTTNqopLTGxbvyynT9zEr9s2fH1D+DIwb2OmH7f39vJkQL3FwoUebB+dq2LfrZc5epsWrOcqMhIQs6f5fzZ0xQsUpwsPr74+mfj7KkTAOzZGURue1stX7WmY1XnudMniY6KTpazuAoUKcb5M7Hl3Lj6LuVc5rqcG1fHKecZq5xXL19ybBUZGRHBPzuCnO4RbV67krKVqpEu3eNzRIXIf42RLEsUnyCGYVw3TdPbMIzMwGJgrmmawwzDGAgEm6Y5Nl76vMBi+5lHGIbRBvA2TbObYRiruc8KI8MwSmBN9GwBxpumudUwjC3APCC7aZqtDcPIhzVxVcE0zUuGYUzBmgT6GxhimmYt+3u9BnxpmmYDwzC2A++bpnnQxWcWAOoD3wKfm6a58m75W73nXKIbyD/bNzNn8ghiYmKo9nw9Xnn7Qxb9NIk8BYvwTIVqREVGMGnYj5w6dggv70x83uoH/LNbF08H9+xk/oxxdOg72uV7h4WcY+SPHek6ZEqi8pg/Z+L3vjdNk+GBfQnashEPD0/adOlGkWLWkx9NPrImlgAO7t9LYK+uREREUKFyNZq3bo9hGPw6exaLfrHSVK/9PJ999Q2GYTBz8nh+njaJnHGeKu07ZDRZff79IYbnwhO/7cXf2zYxY/xQzJgYar5Yn9fe+ZhfZkwgX6GilK1UncjICMYO7MmJo4fw9s5Es/bdCMiei4U/T+G3uTOcViC06zmYTFmysvKPBfy1aC7u7u74+mfji1adE3UegZdn2vsnug/TNJkwfAA7t23Gw8OTr9t+71gF1LrJBwwcaz1VePjgfkYM6EFkRARlKlbh8+ZtMAyDrz96i6ioSEc5ChcrSZNvO7B62R/M/3ka7u7uGEYaGn74GZWqPdwT/rcjoxNdToBd2zYyfewwYmJs1HqpAW+8+zHzpo8nX6GilKtcg8jICEYH9uTEkWC8MmaiRfvuBNhXhLVs/Ba3bt4gOjqaDF7edOg92BG0gnVGSmC3tvQbnbinMG9FJL6su7dvZvbE4VZf9MIrNGj4IQtmTbRWClW0+qIJQ3pz8thhvLwz0qR1V/yz52T7pjUs/GkSadzcSJMmDa+9+4ljVdG4gd0dy+NfbfQxFWu8cK8s3Fcmr4c7n8w0TSaOCOTvoC2k8/CgWZsuFChstde2TT9iwJhpgHVo7qgBvYiMjODZCpX59OvW1qTY1SsM7tWZsJAL+AVk47suvfHOlJmL4aGMGtDLugFkwuvvfEjNOnU5uHc344b2I02aNMTExFD/zXd4vt5rD5zf67eiHqqcce0O2sRP9vqs/sIrvNroI+bPnEjegkUoU6k6UZERjB/cm5NHD+GVMSNN2nQjILvzjbkFP03C0zM9dd98z/r3rElsXb/SujGbvxCNm7dzrC57GGmS4GDhv4M2MWvCMGJiYqhRpz6vNfqIX2dOIF/BopSx97njBvWylzMTX7VNWM75sybhmT499d58j+B9u/mxw9fkzpMfw74i7u0Pv+SZ8lVcffwD88uctOeVmabJmCH92b51Ex4ennzb4QcKFbXOWmrx2fsMnzgLgEMH9jG4b3ciIyIoV6kqTVu2xTAMBvb6gaOHgzEMg4DsOWjeplOCyYqH0TrH+4l+jwfVZlZbStYuRSa/TFy+cJmfus5k2aRl939hEuhzKvFPzpumyfhh9nHU05PmccbR7778gEHjYsfR4f2tcbRsxSp83sIaR5t9mHAcbdqqA7du3WRE/56cPnEM0zR5vm4D3rAfVP5veaRN/BZvpmkyenB/tm+14r9WHbs62mrzT99nxKQ4bbVPNyIiIihfqSpNv22HYRgE9vqeo4di22qLNp3x8fPjwrmzfN+mOYaRBl//AL5t/32iHjC5ciMy0WXduXUj08cNJcZmo/ZLDXjjvcbMnTae/IVj44VRA3rExgsdejhWkH/z8f854gUvL2869B5CxkyZGdC1DVFRUcTExFDimXJ82OSbRJ2vkdY98c9DmqbJxOGB7AraTDoPT75u08WxCqhNkw8JHGs9zX3k4H5GBvYkMiKCZytU4bPm1pi6Zf1qJo0cyNUrl/Hy8iZvgcJ06TuUzetWMnvqeNzs8USjj76gfJWH22I7Kjom0eV8EurTJ2PS3gw0TZORg/qxfctGPDw9+a5TNwrbv6/NGr/HqCnW85fBB/YxsHc3IiNuU75yNZq1aueYcAjs3ZViJUpR/423kyxf15IgLoJH13bjmjNtPJ7pM/Baw/89VB4jo2yJKySP5tol9MJ5Rgf24OaN62TKnIUvW3XCLxETK7aYpL3P9rDX4BtWLmHxvBm4ubljpEnDG+82ptx9Jgj/jZgkKOfDxrobVi3h93kzcXN3J41h8Nq7jSlX2Srb/FkT2bIuNqb/tEX7RMX0HukeLl7YuXUj08YOJSbG6mfftPez+QoVpXwVez/bvwfHjwTjnTETLTrG9rPzf5rC6qWLcUvjzkdNW/Ks/SzH40eCGTekL9FRUWTLkZMm33XGO2MmoqOiGDOoNyeOHsLdPS3/+6I5JZ8t/6/z/DCXLzu3bmTqGKucz73UgDffb8ycqdZ4cqecI/v34Phhq5zfdIpTzllTWLV0MW5uVjnLVKjCiaOHGR3Yk5iYGGJiYqhS8wXe+uBTx+d1b/s1rzf6kGcrVP73mbUrk9f3MdwE7Mk1sM8qTS4kUuuOzz1WbVITRvHcmTCy/3cZYCFQAHgO6Am8YJrmdcMwcgFRQAbuPmH0GzDINM1V9/g8AwgFwoBSpmlGGYYxFqgDtDNN8xfDMJ4BpmGtOvIHdgPtgdlY29LVME3zuGEYM4HM9gmjH4FMQAvTNE3DMMqYprnTMIz8WCuiTMMwhgDHTdO868b+STFh9CRIigmjJ0FSTBg9CZJiwuhJkFQTRk+CpJgwehI87ITRkyYpJoyeBEkxYfSkSOoJo8dVck4YpaSkmDB6EiTFhNGTIikmjJ4ESTFh9CRIigmjJ0FSTxg9rpJqwuhJkBQTRk+CpJ4welwlxYTRk+BhJ4yeRKnl8kUTRklLE0aJ97hNGKWOiPohmaa5E2sVz7umaS4FZgGbDMP4B2sFUMb7vMUUYIxhGLsMw0h/l88wsVYXhZmmeSdS3ATkxzrfCNM0/8baim4vMAlrGzvs28k1A5YYhrEeuABcsb9HTyAtsNswjD32fwO8A+yxb1VXFGsiSkREREREREREREREUrGHXx/+H3VndVGcf78a57+HAq4OyykZJ01gnP/+BfjlAT6zfrx/T8GabIr7s8Z3efkq0zSL2lcqjQSzryTxAAAgAElEQVSC7OlvAU1cfFYfQCfDiYiIiIiIiIiIiMjDS/NYLY6RJKAVRk++L+yrhfYCmYGx90kvIiIiIiIiIiIiIiLiRCuMkolhGPOBfPF+3N40zb8S876maQ4GBifmPUREREREREREREREJHXThFEyMU3zzZTOg4iIiIiIiIiIiIiIiCvakk5ERERERERERERERCSV04SRiIiIiIiIiIiIiIhIKqct6URERERERERERERE5N8xjJTOgSQxrTASERERERERERERERFJ5TRhJCIiIiIiIiIiIiIiksppwkhERERERERERERERCSV04SRiIiIiIiIiIiIiIhIKqcJIxERERERERERERERkVROE0YiIiIiIiIiIiIiIiKpnHtKZ0BERERERERERERERJ4wWo7yn6MqFRERERERERERERERSeU0YSQiIiIiIiIiIiIiIpLKacJIREREREREREREREQkldOEkYiIiIiIiIiIiIiISCqnCSMREREREREREREREZFUzj2lMyAiIiIiIiIiIiIiIk8Yw0jpHEgS0wojERERERERERERERGRVE4TRiIiIiIiIiIiIiIiIqmcJoxERERERERERERERERSOU0YiYiIiIiIiIiIiIiIpHKaMBIREREREREREREREUnl3FM6AyIiIiIiIiIiIiIi8oQxjJTOgSQxrTASERERERERERERERFJ5TRhJCIiIiIiIiIiIiIiksppwkhERERERERERERERCSV04SRiIiIiIiIiIiIiIhIKqcJIxERERERERERERERkVROE0YiIiIiIiIiIiIiIiKpnHtKZ0Aeb+nSuqV0FpLFodOXUzoLySJThnQpnYVkcS78RkpnIVk8nc07pbOQbGJMM6WzkCxSy3c0tdRnFi+PlM5Csjlx4VpKZyFZ9Dk1I6WzkCw6PvVBSmchWXQ/NjWls5Bsom0xKZ2FZHHx6u2UzkKy8MnkmdJZSBbhqaQ+PdKljmtugJu3o1M6C8kitXxHI6NtKZ2FZOGbSuoT4ExY6riXIklMy1H+c1SlIiIiIiIiIiIiIiIiqZwmjERERERERERERERERFI5TRiJiIiIiIiIiIiIiIikcpowEhERERERERERERERSeU0YSQiIiIiIiIiIiIiIpLKuad0BkRERERERERERERE5AljGCmdA0liWmEkIiIiIiIiIiIiIiKSymnCSEREREREREREREREJJXThJGIiIiIiIiIiIiIiEgqpwkjERERERERERERERGRVE4TRiIiIiIiIiIiIiIiIqmcJoxERERERERERERERERSOfeUzoCIiIiIiIiIiIiIiDxhDCOlcyBJTCuMREREREREREREREREUjlNGImIiIiIiIiIiIiIiKRymjASERERERERERERERFJ5TRhJCIiIiIiIiIiIiIiksppwkhERERERERERERERCSVc0/pDIiIiIiIiIiIiIiIyBNGy1H+c1SlIiIiIiIiIiIiIiIiqZwmjERERERERERERERERFI5TRiJiIiIiIiIiIiIiIikcpowEhERERERERERERERSeU0YSQiIiIiIiIiIiIiIpLKuad0BkRERERERERERERE5AljGCmdA0liWmEkIiIiIiIiIiIiIiKSymnCSEREREREREREREREJJXThJGIiIiIiIiIiIiIiEgqpwkjERERERERERERERGRVE4TRiIiIiIiIiIiIiIiIqmcJoxERERERERERERERERSOfeUzoCIiIiIiIiIiIiIiDxhDCOlcyBJTBNG8sj9s30LsyYMJcYWQ82XGlD/7Q+cfh8VFcn4wb05cfgg3pky8VXb7vhly+H4fXjoBTp//SGvv/cJ9d58D4A2nzfEM30G0qRJg5ubG10HTUjWMrmyd+dW5k4egRljo+oL9Xn5zfedfh8VFcnU4X04dTQYr4yZ+KxVV3wDshMdFcWscYM4eeQgRhqDhp+0oHCJZwFYOGsCW9Yu5db1awye8WdKFOue/g7azPRxQ4iJiaH2S6/yWqMPnX4fFRXJmIE9OXb4IBkzZqZ5hx74Z8vBPzu3MnvyGKKjo3B3T8t7n31NiWfKpVApXNu3ayvzpowgJiaGqs+/wktvJKzP6SP7ctJen5+2/MGqz+gofho3iJNHg0ljGLzVuLmjPoM2rOCv+bMwDIPMWX35uHknvDNlToniOTFNk/HDBxK0eSMenp582+EHChQumiDd4YP7Gdq3BxEREZSvXJUvWrTGiBMYzP95BpPHDGPGgqVkypLF8fNDB/bRttmntP2hN9Vqv5AsZXJld9Bmpo8bam+vDXjVRXsdO7AXxw4fxDtjpjjtdRtzJo8mOjoad3d33o3TXnt3aM7li+GkS+cBQLteg8mcJWuyly0u0zQZPXQA2zZtwMPTk9adulGoSLEE6Q4d2M/AH7sSERFBhSrV+KplWwzD4McfOnD65AkArl+/hrd3RkZN+YmrVy7Tq0s7gg/s48V6r/L1d+2TpTy7tm1m6pghxNhsPF/vVV5/5yOn30dFRjJyQE+OHTqAd6bMtOzUk4Ds1hiy4OdprFryG2nc3Gj81bc8U77yPd9zycJ5/Dl/NhfOnWHcnD/IlDmL02cdObiPLt9+SctOPahc4/lkKP1/rz7vZc/OLcyZNIKYGBvVX6hP3f/7n9Pvo6IimTysDyePHsQrY2a++O4H/AJysGXtMpYu/NmR7syJo3QeMI5sOZ9ibGA3Qs+fIU0aN0qXr8L/fdgkuYuVgGmaTBw5iB1bNuLh4Unzdt+77HOPBO9neP+eREZEULZSVT77+jsMw2Dq2GEEbVqPu3tasuXMRYt23+PlnZHo6GhGBfbm6OGD2Gw2ar9Yj7feb5z8BXwI30xsSfkGFbgScoUWpb5O6ey4ZJomU0cPZufWTXh4evJV6y7kK1QkQbqjhw4wOrAXkRERlKlYhY+/aoVhGFy/epWhP35P6IVz+GfLQcvOPfHOmOme7ztzwkh2bt0IwP+9/wlVa9cBYMygHzkafAAwyZ7rKZq16YJn+gzJ9rcA2L19MzPsY2qtlxrwakMXY+qgXhy3j6lft7fG1CMH9zF5RH/A+pu++f6nlK9aK1nzfj//5Rjw76DNTBszhJgYG8/VfZXXGiUcU0cPjB1Tv+nYE3/7ddnC2dNY/ddvpEnjxkdffcsz5awx9c8Fs1m1ZBGmCc/XfY16b74DwJxp49i+aR1p0qQhU+YsNG3dhay+/slbYOzXK2Pt1ysvu75eGR3Y095WM9Oio9VWr129wtAfO3M0+AA169SjcbPWjtf0at+cyxfDSOthxX8deg15LOK/SSMHsXPrJtJ5eNC83ffkL+RqbDnAyP49iYy0+qhP7WPLxjUrmDNtAmdOHqfPiEkUtMcaUVFRjBvSlyMHD2CkMfikWStKPpuy1217dmzh50nDiYmJoUad+tRzES9MGvojJ44G450xE1+27opfgNWOTx8/wvQxgdy6dZM0hkHn/mNJm86DIT3acuVSOLYYG4WKleZ/X3xLGje3lCiew5063bF1I+k8PGlx1zrdzwh7nZatWNVRp1PHDiNosxUvZM+Zi+ZtrXjh0IG9jBncx/EZ73z0BZWq107Wsu0K2sy00bF9kav4flRgnPi+Y0/848T3d/qij+3xfWRkBD3aNCMqKgqbzUalGs/R8MPPAdizK4iZ40cQHR1FvkJFadKqI25uyX8r1DRNRg0ewLZN6/Hw9KRNl+4uY/rgA/sI7NWNyIjbVKhSnWatrJj+SPBBhg7oTWRkJG5ubrRo05GixUty8vgxBvbuxuHgAzRu8jUN3/8o4Ycns6S+B3ju9ElGD+jq+H3o+bO8+f5nvPR6o2Qrk0hqoy3p5JGKsdmYPnYQrboG0nvkdLasXc6Zk8ec0qxb9jte3hnpN+5nXnqtEXOmjnH6/U8ThlOqbKUE792+91B6DJ38WEwWxdhszJ44lOad+/L94CkEbVjBuVPHndJsXPkHGbwz0n3ETJ5v0JD5M8YCsGHFYgC6DJrEN98H8svUUcTExABQunxV2vcZnaxleVAxNhtTRw+kXfeB9B89k80u6nb1X4vx8s7IoAlzqPvGO/w8eRQAGTNloXXXfvQdNZ0m33VhzMAeKVGEu4qJsTFn0lCadexLl0GT2b5hJedOH3dKs2nln6T3yki3YTN47pW3WThrHAAbVvwOQOfAiTTvMoD500cTExODzWZj3pSRtPxhEJ0GTCDX0/lZ89f85C6aS9u3bOTs6VOMnfkLX7fuyOjB/VymGz24H1+36cjYmb9w9vQpdmzd5PhdaMgFdm3fgn+27E6vsdlsTBk7nDIVKj/SMtyP1V4H0bZ7IP1Gz2CTi/a6xt5eB06YTd033mH2ZOu7lzFTZr7r2p8+o6bx5XddGDuwp9Prvmrbld4jptB7xJQUv1kAsG3zBs6eOsWknxfQsm0XRgT2cZlu+MA+fNOuC5N+XsDZU6cI2mzdlOzUoy+jpvzEqCk/Ub3W81Sr9RwA6dJ58NHnX/HF198mW1libDYmjQykQ6+BDBw/iw2rlnP6hHO9rfrrN7y9MzJ0ylzq/987zJpo9TOnTxxj4+rlBI6bScfeg5g4IpAYm+2e71mkRCk69x2GX7x2fCcvsyaO4plyCcejR+m/VJ/3EmOz8dP4obTo3I9uQ6aybf1KzsYbRzes+AMvb296jZxFnQZv8+t0q9+tVPNFvh84ke8HTuTTbzrj65+dp/IVAuCl196hx/DpdAkcz5GDe9izY0tyFy2BHVs3cu70KUZOm0fT7zowbmh/l+nGDunPV606MnLaPM6dPsVOe5/7TLmKDJk4i8ETZpIz99P8MmsqABvXrCAqKpIhE2YROHoqSxcvIOT82WQrV2KsmLKcbnW73j9hCtq1bRPnzpxmyOQ5fNGyPROGD3CZbuKwAXzRsj1DJs/h3JnT7AraDMDCOdMpWaYcQybPoWSZciycPf2e77tjywaOHw6m3+ip9Bo2gcXzZnHzxg0APmrSkv5jptF/zHT8ArLx16J5yfAXiBVjszFt9CDadA+k76gZbF7jYkxduhgvr4wEjp9N3dffYfYUa0zNnSc/3YdMoNfwKbTtMZDJIwdgs0Una/7v5b8cA8bYbEweGUi7ngMZMHYWG1cnHFNXL/0NL++MDJ40l3pvvMNPk2LH1E1rltN/zEza9xrEZPuYeur4EVYtWUTPIRPpO2oqO7Zu4NyZUwA0eOt/9Bs9nT4jp1KmUjV+nTU5Rco8ZdRA2vUYSP8xM9m0Zjmn73a9MnEO9d6MLXPadOlo+OEXvP+Z60nsZm270mfEVPqMmPpYxH87t27i3JlTDJ86l6atOt51bBk/tD9NvuvA8KlzOXfmFDu3WWPL03nz07ZbX4qVetYp/fI/FgIwaMJMfug3jGljhzmuVVNCjM3GrPFDaNmlPz2GTmXruhUJ4oX1y38ng3dGfhw1izqvNuSXadZ1t80WzYShvfigSWt6DJ1Km55DHRMHTdp0o+vgSXQfMoXrVy8TtGl1MpcsoR1bN3LuzClGTJ3HV63uHi+MG9qfpt91ZMTUeU51+ky5igyZMIvB46144defrHjh6bwF6D9qCgPHzuD7PkMZM6RvsvbDd/qi9r0GEjjOdV+06i+rLxoyeS6vvPkOs+L1RQPGzqRD70FMGmn1RWnTpqNLv+H0Gz2NvqOm8nfQZg7t30NMTAyjA3vxTcceDBg7E/+A7KxdljIP4m7btIEzp08yec5Cvm3fhWED7hLTD+jDt+07M3nOQs6cPsk2e0w/fuRQPvi0CWOm/szHn3/FhJFDAetatVmrdrz93ocu3y+5PYp7gDlyP02PoZPpMXQy3QZNIJ2HJ2Wr1EyW8oikVpowSiTDMDobhrHXMIzdhmHsMgyjkmEYEwzDKJ5E71/ZMIwt9vfebxhGN/vPuxmG0SYpPuNROnpoPwE5chGQPSfuadNSscYL7Nyy3inNji3rqPZ8XQDKV6vN/r+3Y5qm9bvNa/HPnoNcT+dL9rz/G8cPH8A/e078slnlLFftef4O2uCUZve2DVSu9TIAZSrX4uCeHZimybnTJyhSqiwAGTNnJYOXNyePHAQgX+HiZM7qm7yFeUBHgveTLWduAnLkwj1tWirXfIHtm9c5pdmxZR01XngFgIrVa7PXXrd5CxR2PGGYO08+oiIjiYqKTPYy3M3xwwfwy5bLqk/3tJSt+jy7t210SrM7aAOVar0EONfn+Xj1md7Lm5NHD4JpgmkSGXEL0zS5desmmbP6JXvZXNmyYS3PvfwKhmFQtEQpbly/xsXwMKc0F8PDuHnjBkVLlMYwDJ57+RU2r1/j+P3EEYNp3KQFBs5LkRf/OoeqNZ9P8QvphO21Dts3x++L1lP9hXqAq/Zq1dXj2F7j27RuDS/UrY9hGBQrWYrr168THhbqlCY8LJSbN65TvKRVny/Urc/Gdaud0pimydpVy6ldx+qfPdOnp+QzZUibLl1yFYXDB/eRPWdustnrrWrtOgRtcu5ngjato+aLVr1VqvEce3cFYZomQZvWUbV2HdKmS0dA9pxkz5mbwwf33fM98xUs4lidFN+ShfOoWP05MiVzW/4v1ee9HDt8gIDsufC3xwvlqz/P39ucx9G/t26gcm0r/2Wr1OLAP7Hxwh1b16+gQnVrJWM6D0+KlCoDgHvatDydrzCXwp3/dilh64a11H6pHoZhUKT43fvcWzdvUKREKQzDoPZL9diywepzny1f2XGDq3DxkoSHhQDWThARt29js0UTGRGBu7s76TN4JW/hHtLedXu5fvFaSmfjnoI2raNmnboYhkGhYiW5eeM6l+LV2yV7vRUubtVbzTp1Cdq4Ns7rrZioZp1XHP3O3d73zMnjFCv9LG5u7nh6pufp/AX52z75lMHLqlfTNImMiASSdxuQI8H7CciRm4DssWPqjvhj6ubYMbVC9drss4+pHp6ejvYbFRnptFL5cfBfjgEPB+8jW5zxr0qtOgli96BN66hRJ3ZM3WMfU7dvXkeVWrFjaracuTkcvI8zp05QsGhJR70WK1WGoI1WX3WnnYLVNyV3O4W7XK/EiyO2b479bsaN/zw901OkxDOPzTh5P9s2rqX2i1Y8X7h4SW5ed91H3bx5gyL2Pqr2i6+wbYPVR+XOk49cT+VJ8L6nTxyjVJnyAGTO6kMG74wcCd7/6At0F8cO78c/R2y8UKH68+za6tz/7Nq2garPWdfd5arU4sA/1nd0364gcucpwFP5CgLgnTGzYxXRnfHSZrMRHR2V4JomJWzbuJZaL9az16kVL9yvTmu9WI+truKFYiUJD7Xihbj9cGRkZLKX9PDBfWTP4dwXxY/vt29aR00XfVHQJue+KHsOK743DMOx0tYWHY0tOtq+uvcKadOmJUfupwEoVbYCWzesTtby3rFx3WperNvAHtOX5sb1ay5j+hs3blC81DMYhsGLdRuwce0qwIrzbt64DsCN69fx9bPup2T18aFI8RK4uT8eG0g96nuA+3ZvJyB7TvwCEj7gJyJJRxNGiWAYRhWgAVDWNM3SQB3glGman5umuS+JPmYq8KVpms8CJYE5SfS+yeJSeCg+fgGOf/v4+ScIci6HhznSuLm5k97Li+vXrhBx+xZ//DKL19/9JMH7GhgE/vAd3Vp9xuolix5tIR7A5YthZPWNLWdWH3+uxC/nxTCyOsrpRvoM3ty4dpXceQqwe9sGbDYbYRfOcfJoMJfCQ5I1/w8jYd0GJLgRdyk8FB//2LrNkMGL61evOKXZtmE1efIXJm3ax+di7Er8+vT148ql0LumserTixvXrpIrTwH+uVOfIec4dTSYS+GhuLm7887n3/Jj28/p3LQh508fp+rz9ZK1XHcTHhqCv382x799/QMcFxRx0/j5x/5N/OKk2bJhLb7+/uQrWDjBazavX03d1/7vEeb+wbjui5zr9GJ4KL4P1F4LObXX8YN/pHPzxiz4aUqCm9cpITwsBP+A2Pr0DwhweTHi5x83TTbHTec79vy9k6xZfcj11NOPNsP3YNVJbD59/Py5GK8sF8Ni09wZQ65dvWL/uXMfdTE89IHeM0E+wkLZtnENL9Z/IymK9a/8l+rzXi5fDCWrX+xWRVl9/Lkc7zt6+WIoPvY0bm7u9nHU+TsatGEVFVxsF3jzxjV2B22kqP1mbkq6GK++fP0D7tKuY9uvr1/CNAAr//yNshWqAFCl5gt4eHryWcP6fPn+a7ze6H9kfAy2Pf2viNvXgL3vcDGOxI+N7tTblUsXHQ8fZPX14+rlS/d836fzF2TXts1E3L7N1SuX2ff3DsJDLzjSjQ7sRdN3G3D21Anqvt4w6Qt8D5fC4/evCcfUS/cYU48c3EvHZh/QqfnHNG7WJkW2Brqb/3IMeOkB2vCl8FB8/WLH1AwZ7GNqeMI+6VJYKE/lyc+BPbu4dvUKEbdvs2vbRqcYcvaUMTT/8A02rPrLsT1UcroYHopvElyvuDJ28I90bP4x82dNfkziv3jfS3/X8YJvnLHWVZr48uYvxNaN67DZorlw7ixHgw8QHnLhnq95lC6Hh+Hj9B315/LFhPcXYr+j1sMT169d4cLZUxgGDO7Rhp6tP2fJ/FlOrxvcow2tP3kdz/QZKFcl5bfKdBUvuK7TAKc0ruKFFUt+o0zFKo5/B+/fQ8vP3uW7L96nybcdkrUfvhQvFve963VZnL7IHt8nHH9iv9MxNhsdmn1Mk3frU6psBQoWLUHGzFmw2aIdk5xb1q1yGkuTU3hoCP7ZYsttXU/Hq8/QUPwD4lxzB8Rec3/1bRvGjxzK+2/UY9yIwXzatHnyZPxfelT3AO/YsnYFlWrWeTSZFxGHxyc6fzLlAMJM04wAME0zDMAwjNVAGyAncGevrfRAOtM08xmGUQ4YBHgDYUBj0zTP3eUzAoBz9ve3AXEnoorbP+tpYIhpmsPsn/8d8Kk9zQTTNIcYhpEXWAJsAcoAwcBHpmneTET5789F7Bz/QUJXAbaBwfxZk3jp9UYu92Tv1G+U42I78IdW5Mj9NEVKPpsgXfJ5oIK6SANVnn+F82dO0q99E3z8s5G/SMkU3y/5Qbi+MDLunybO3+X0iaP8PHkU7XsNTuLcJc4Dlc1lnUOV5+px4cwJ+ndsio9/NvIVLoFbGjds0dGsW7aI9n3H4pctJ3MnD2Pp/FnUfevxWDoe34M88WsYBhG3bzN3xmS6Dxie4PfjRwzi4y+b4/YYtOe79TPxEiV8Ybz2OnvyaNrFaa9ftemKj58/t27eZNiPndmwconjieqU8iBlfZA0q5cvoXadl5M2c/+WyyqJX293S+O6jC7Lfp/2PnXMEN7/rFmK9M3/qfq8F5fdbvxy3jvNseB9pPPwINfT+Z2S2GzRTBjck+fq/x/+2XMmQWYTx9X48UCxUbxE82ZOJo2bGzXtq8YOHdhLGjc3Jsz5nevXrtLl2yaULluR7DlzJV3mU7UH6Dseon+52/s+U64SRw/u54dWTciUOQuFijnHh1+16WJt6zNqEJvWLKf2yw0eqBRJ4wHi3nukKVCkBH1GzeDMqeOMH9Sb0uUrO84CTGn/5RjQdcnu388ahnHXtp3r6by82vAD+nRqiWf69OTJX8gp7nuncVPeadyUhbOnsfS3X3g7uSeNHuA7eb/rFVeatb0T/91gSO/OrF+5hBopHP89UP/jMs293/b5eg04ffI47Zt9gl9AdoqUKEWaFJzkdfn9e4A0hmFgs9k4tP8fOvcfSzoPTwZ1bUWeAkUoVto6k6nVD4FERUYwYUgvDvyzg+LPVkjy/P8brmOBBIkSvtBFvODm5kbNF+o6fla4WEmGTvyZ0yeOMbx/D8pUrJJs/fD94rm7pTEM13H8ndemcXOj76ip3Lh+jUE9OnLq+BGeyluAFh16MH3sMKKjIilVtmKK3Wt5gOHlnuX77dd5NP2mNTWee4E1K5YyqE8P+g0bkzB9SntE9wABoqOi2LV1A29/lPJnkor812nCKHGWAj8YhhEMLAdmm6bp2KPJNM1FwCIAwzDmAGsMw0gLDAdeN00z1DCMd4DexE7wxDcYOGifGFoCTDVN87b9d0WB54CM9jSjgdLAJ0AlrOFni2EYa4BLQBHgM9M0NxiGMQloBgTG/0DDML4EvgRo131AggMI/42sfv5cjPOU88WwULL4+LlM4+MXgM0Wza0bN/DKmImjwfsI2riaOVNGc/PGddIYBmnTpqNOg7ccT2hmypKVspVrcvTQ/hSdMMri4++0KujSxVAy+zhvJZfF159LYSFk9fXHZrNx6+Z1vLwzYRgGbzeO3Rt7QOfmBGTPnWx5f1jWU7Nx6zbEUS9OaUJD8LXX7c2bN/DOmAmwnpof0qsTTVt/T7Ycj1d5s/jGq8/wsARbh9yp89j6vOGoz7c+jq3Pgd83xz9HLk4fPwyAf3brxl3ZyrVZuvCnZCiNa7/Pn8vSxQsAKFS0OKFxnrQKDw1xPMV/h69/AGFxnhgNs6c5d/Y0F86dpeVn/3P8/NsvP2Tg6MkcPrifwB5dALh65TLbt2zEzc2NyjVqP+LSJZSwvYaSxUV7tcqesL1eDAthaK9ONGndhWw5csV5jfV3Sp8hA1VqvciR4P0pMmG06Jc5LPnNOg+hcLHihMZ58jM0JAQfP+ey+vkHEBYaN80Fpzq3RUezYc0qhk+c8Yhzfm8+fv5OTwFeDAtN2M/4W2l8/WPHEO+MmRz1Gfva2D7qfu8Z39HgAwzt8wMA165cYddWqy1XeEQHtf9X6/NerDEy9inLSxddxAu+/vb6ste1fRy9Y9uGlY7t6OKaMWYgATlyU6dB8q7CiOvPBXNZZj8HomCR4k71FR4akuAg+PgrPcPjjbGr/vqdoE3r6R440nFDcN2KvyhToTLu7u5kyepD0ZKlORK8XxNGifDXol9Y+ae1kr1A4aIJ+w4fF3HPXWKjzFl9uBQeRlZfPy6Fhzm2t7T6KlR/ov8AACAASURBVNfv++b7jXnz/cYADOvTlRy5nnL6vDRublSpVYff5s5M1gmjrL7x+9eEf4s7aVyNqXfkeiovHp6enD5xzOVB7inhvxwDPtCY6udPeFjsmHqn3uKPqeFhIY446rmXX+W5l18F4OcpY5xWsNxRtfaLDOjaJtknjHz8ApxW3F4MC0kwttzreuXu73sn/vOiau0XOXJwX4pMGP25cB4r7GNLgcLFnL+XoSH4xKvf+CtUrDQJ6ysuNzd3PmkWe95hp2++IEfup+7xikcrq68/F52+o67jhUvhce4v2L+jWf38KVziWTJmygJAqbKVOXk02DFhBJA2nQfPVKjGrm0bUmTC6M+Fcx3nRhUsnDBeiF9fVp2GxEsTJ15Y+jvbN6+n24CRLh9gyJ0nHx6enpw8dpSCRYoldXFcit8XhbsYQ3z9nOP7m/eK7+O91ss7I8VKl+HvoC08lbcAhYuXottA6xy93du3OM5ZSw6LfpnNH4usmL5I0RKEXogtd1hoSIL+0i8ggNCQONfcIbFplv25mGat2gJQ8/kXGdzH+Vzdx8WjugcIsHv7ZvIUKEzmrD7JWSR5ENq/7D9HVZoIpmleB8phTa6EArMNw2gcP51hGO2AW6ZpjsSatCkJLDMMYxfQBbjr3XLTNHsA5bEmp97HmjS643fTNCPsK5tCgGxAdWC+aZo37Pn7FahhT3/KNM07BwLMsKd19ZnjTNMsb5pm+cRMFgHkK1SUkLOnCT1/luioKLauW0GZSs4fW6ZidTastIoVtGE1xUqXxTAMOvUdSeCEuQROmMtLrzakfsMPqdPgLSJu3+LWTWthVMTtW+zZtY3c8Z4mTm55ChYl5NwZwi6cIzoqiu0bVlK6fFWnNKXLV2Xzmr8A2Ll5DUVKlsEwDCIjbhNx+xYA+/8Ows3NjRxP5U3uIvxr+QsX5fyZ04TY63bz2hWUjVe3ZStVZ92KPwDYun41xUuXwzAMbly/xsBubWnUuAmFi5dOiezfU54CRQk9f4awkHNER0exY+NKSpev4pSmVPmqbFmzFLDqs3AJF/W5O4g0adzIkTsvmX38OH/6BNeuXgbgwD/byZ4r4T7hyaX+mw0ZOnEmQyfOpFL1Wqz66w9M0+TA3n/I4OWd4ALTx9eP9BkycGDvP5imyaq//qBStZrkzV+Q6Qv+YsLshUyYvRA//wCGjJtOVl8/Jvy80PHzqrWep+m37VJksgjutNdTcdrrcspWquaUpkylaqxfYR2CarXXso72GtitLY0aN3VqrzZbNNeuWPUZHR3Nrm0byZ0nZfqi195qxKgpPzFqyk9UqVGbFUt+xzRN9u/5By9v7wQXI75+/qTP4MX+PVZ9rljyO1VqxE5+7AzaylN58jpthZYSChQp5tTPbFy9nHKVnfuZcpVrOA6v3bJuFSWesfqZcpWrs3H1cqIiIwk5f5bzZ05TsEjxB3rP+IZP+4UR035lxLRfqVTjOT5t0eaRTRbBf7c+7yVvwSKEnDvtGEeD1q/kmfjjaIWqbF5txQs7Nq2haMmyjpsfMTExbN+4mgrVnLejWzBrArdu3KDRJym7bUe9NxoyaNwMBo2bQcVqNVm99E9M0+Tgvrv3uZ4ZMnBwn1Wnq5f+ScVq1uG+O7ZuYv7P0+jYKxAPT0/Ha/wCsvPPTmuP/9u3bhG8b4/L8yjkwb382lv0Gz2VfqOnUr5qTdYuX4Jpmhzav4cMGbwS3GzPaq+3Q/v3WOeGLV9C+SpWGF6ucnXWLrdiorXL/4j384TvG2Ozcc2+LdaJo4c5eewwpctVtM7KOXMawH62zHpyJnM95y9clAtnTzni+81rl1Mm3phaNs6Yui3OmBp6/qzjcPWwkPOcO3MS/8foHIL/cgxYoHAxzp+NHf82rXE9pq5b7npM3bQmzph69jQFC1tH9l65fBGw6nPbhtVUqfUigNNN2R2b15Mzd/KXOX/hok5l3rx2RYIyl60U+93cun41JezXK3cTP/7buTXl4r96r79N4NjpBI6dTsVqtVi9zIrng/ftIYOXt8s+Kn36DATvs/qo1cv+oELVex8cH3H7NrdvWe367+1bcHNz46k8KXe+cN6CRQk5d5pQe7ywbf1Knqng3P88W6EaG1dZ193bN62hSCnrO1ri2YqcOX6EiAjrvL/gfX+TI3debt+6yeWL4YBVv/9s30z2XCmzhW+91xsycOwMBo614oU1y/601+k/96lTK15Ys+xPR53u3LqJBT9Po0NP53jhwrnYfjjkwjnOnj551zM8H4UCRR6sL1r7oH1RkeJcvXyJG9etsxAjIyLYszPIMTbe6aOiIiNZNHcGdZJxi+nX3nqHMVN/ZszUn6laszbLliy2x/S78fJyHdNnyJCB/Xt2Y5omy5Yspqr9+tnXz4/dO7cDsGv7VnI+lXITt/fyKO4B3rFl3XIq1Uz4cJiIJD3jcdhv97/CMIy3gY+xVvy0MU0zyDCMF4C+QE3TNG8ZhlEKGGeaZpV7vddd3t8da2KqINACuG6aZqD9d3uwzlN6A/AxTfMH+8972l+zCFhjmmYe+8+fB1qYpvnmvT5z48GQRDeQv4M28dOEYcTExFCjTn1ebfQR82dOIG/BopSpVJ2oyAjGDerFyaOH8MqYiaZtuxEQb8uYBbMm4ZE+PfXefI+Q82cZ8WMnwDqUsnKtF3m1UeImtm5FRCfq9QB7dmxm3pSRxMTEUOW5etR76wN++3kSeQoUoXSFakRFRjJl+I+cPnaIDN6Z+KzV9/hly0l4yHmG92qHkcYgi48fH3zVFl9/68L51+ljCFq/giuXwsmc1ZeqL9SnQaPGD53HTBmS9pygXds2MmPcMGJibNR6sQGvv/sx86aPJ1+hopSrXIPIyAjGBPbk+NFgvDNmonm77gTkyMWCn6fw25zpZMsZO1favtcQMifRYfKXr0ck+j327tzMvKmjMGNsVK5dj7r/9wGL50zm6fyFKV3eqs9pI37k1PHDeHln5JOWsfU58sd2GEYasvj48b+mbfCx1+e6ZYtY/cevuLm74+MXwAfN2uOd8eHPl3g6m3eiywnWDaexQwewY+smPDw8+ab99xQqat0EaPnZ/xg6cSYAhw7sY2jfHkRGRlC2YlWatGyT4IL683deZ9DYqWTKksXp50P6dKdClepUq/1wAd6VG5EP9bq4dm3bxMxxQ4mJiaHmi/V5/d2P+WX6BPIVKkrZytUd7fXE0UN4Z8zE1+26xWmvM8gep7226zUYD09Perf7GpvNRkyMjRLPlud/n7dI1DYH/pnTJ7qcpmkyclA/tm/ZiIenJ9916kZhe302a/weo6ZYTzUHH9jHwN7diIy4TfnK1WjW6v/Zu+/wKKq3jeP3JIEEklCT0CwISFEUkF4EVFRERLFhAxVUeu8C0qUlEGoSIJRAUEAFe0OlSULoP5Wu0ksKPUDKZt4/dkkPUpJd8u73c11cymZ2ch7mzJln5pkzMyh1e/qPH6lqDz6kZ194OcO6O7zcWpfj45WcnCQvL2+Nnzpb99538xdJzsXf+D66I2qTFgdPV0qKRY891Vpt33hHKxbPU4XKVVWnoXWcmT15jA4dtI4zvT4ckzoLbNWyRfrtp2/k6uqmDl16q5btXS/ZrVOSvl+9Ql+vDNe5M2dUtFhx1azXUJ37Ds3Qnjn+4/RI/UZqkM17cjIr5nn7j/bID9tTkg6fvngbUVr9sS1SKxbOUkpKiho//oxavdxeX32yQPdWqqIadRsrKTFBC2Z8rKP/HpCnVxG91/ej1EfM7ftzh1YtnashE4NS13c2LlpDPnhVpcvdI7cCBSRJjz3TVk1a3PpMDN9iubOPzpsxRTu2RMrdw0M9Bo5Ivau33wdvaepc60ywg/v2aObkMUpMSNAj9RrqvZ7WMbdb+5eUlJSY+n6iytWqq0vfIbpy5bJmTR6rY4f/lWmaerxla73Q7tYeeTX07rduO86bMWDZQFVv/pCK+BTRudPn9MnIcP284Oc8/72j/118w8uapqmFswO0c2uk3N091KX/MFWsbN1ug7u+rUlB1nX9vX+PgvzHKTExQTXrNNS73fvJMAxdvHBegeOHKy76tEr6lVLfYePlVaRIjutNTEzQ0O7WZ/gXKuyp93oNVPmKlZWSkqJR/bvqyuV4maapeyvcr049B6qwp+d125+YlHKL/0rZ27UlQkvnTZdpO6a2afe2Pl9qO6bWtx5TQwJsx1SvIuo2eJT8SpfT77/+oG8+WypXVzcZLi564bV3VLvh9S9Y34wLuZAv5IccsEQRj/9eKBs7ojZpydzpSrFY1Pyp1nrh9Xe0Msx6TL2Wu8+ZMkaH/94vT+8i6jkk7Zi6+pNFWms7prbv3Fs1bcfU0QO66tKF83J1c9Nb7/dS9Vp1JEnTxn2ok8cOyzBc5ONXWp16Dsoyg/2/5Ma1ip1bNmlJiO185anWeiGb85Ug/7FpMQ+2nq9IUu93XtKVy/FKTk5WYU8vDRk/TT5+pTV2UHdZkpOVkmJR9Zp19db7t5f/uRe8/Udkmaap+TP9tXOLdSzpNnB46rFlQOf28g9ZIsl6bJk9ZawSExJUq15DderRX4ZhaPPGtQqdFaAL58/J09NL5StW1ohJ0xV96oTGDekjw8VQiZK+6jZgmHxL3Xpx4eyF2z9P+2NbpD5dMFNmSooaP9FKz77cXl9+Eqp7K1ZVzXrWfCF0+ngd+de6j37Qb2RqvhC57id990W4DBl6qHZ9vdyhqy6cO6MZ44coOTlJKSkpqlq9ltp17HFb7/W51X00Pes2teUL7h7qni5f6N/5LQWEpOULs6a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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "plt.figure(figsize = (30,30))\n",
    "sns.heatmap(corr, annot = True, cmap = \"BuPu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "verbal-tobacco",
   "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>Sr_no</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Education</th>\n",
       "      <th>Applicant_Income</th>\n",
       "      <th>Family_Income</th>\n",
       "      <th>Total_assets</th>\n",
       "      <th>Business_type</th>\n",
       "      <th>Credit</th>\n",
       "      <th>Loan_Amount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>...</th>\n",
       "      <th>Total_Workers</th>\n",
       "      <th>No_customers</th>\n",
       "      <th>Freq_Supply_pay</th>\n",
       "      <th>Nature</th>\n",
       "      <th>Loan_Status</th>\n",
       "      <th>Total_Income</th>\n",
       "      <th>Applicant_Income_Log</th>\n",
       "      <th>Loan_Amount_Log</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "      <th>Total_Income_Log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "      <td>5849.0</td>\n",
       "      <td>8.674026</td>\n",
       "      <td>4.986426</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.674026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>6091.0</td>\n",
       "      <td>8.430109</td>\n",
       "      <td>4.852030</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.714568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.006368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>4941.0</td>\n",
       "      <td>7.856707</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.505323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.699515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sr_no  Cases     Education  Applicant_Income  Family_Income  Total_assets  \\\n",
       "0      1      1      Graduate              5849            0.0             0   \n",
       "1      2      2      Graduate              4583         1508.0             3   \n",
       "2      3      0      Graduate              3000            0.0             0   \n",
       "3      4      0  Not Graduate              2583         2358.0             2   \n",
       "4      5      2      Graduate              6000            0.0             2   \n",
       "\n",
       "  Business_type Credit  Loan_Amount  Loan_Amount_Term  ...  Total_Workers  \\\n",
       "0           Yes    Yes   146.412162             360.0  ...              6   \n",
       "1            No    Yes   128.000000             360.0  ...              1   \n",
       "2            No    Yes    66.000000             360.0  ...              2   \n",
       "3           Yes     No   120.000000             360.0  ...              7   \n",
       "4           Yes    Yes   141.000000             360.0  ...              2   \n",
       "\n",
       "   No_customers  Freq_Supply_pay  Nature  Loan_Status  Total_Income  \\\n",
       "0             4                0       0            Y        5849.0   \n",
       "1             1                1       1            N        6091.0   \n",
       "2             3                4       1            Y        3000.0   \n",
       "3             4                5       1            Y        4941.0   \n",
       "4             2                0       2            Y        6000.0   \n",
       "\n",
       "   Applicant_Income_Log  Loan_Amount_Log  Loan_Amount_Term_Log  \\\n",
       "0              8.674026         4.986426              5.886104   \n",
       "1              8.430109         4.852030              5.886104   \n",
       "2              8.006368         4.189655              5.886104   \n",
       "3              7.856707         4.787492              5.886104   \n",
       "4              8.699515         4.948760              5.886104   \n",
       "\n",
       "   Total_Income_Log  \n",
       "0          8.674026  \n",
       "1          8.714568  \n",
       "2          8.006368  \n",
       "3          8.505323  \n",
       "4          8.699515  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "juvenile-hughes",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cases</th>\n",
       "      <th>Education</th>\n",
       "      <th>Total_assets</th>\n",
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       "      <th>Size_Shop</th>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.006368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
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       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>5</td>\n",
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       "      <td>5.886104</td>\n",
       "      <td>8.505323</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.699515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cases     Education  Total_assets Business_type Credit  \\\n",
       "0      1      Graduate             0           Yes    Yes   \n",
       "1      2      Graduate             3            No    Yes   \n",
       "2      0      Graduate             0            No    Yes   \n",
       "3      0  Not Graduate             2           Yes     No   \n",
       "4      2      Graduate             2           Yes    Yes   \n",
       "\n",
       "   Rating_Reviewportal  Daily_Sales  Competition  Rent_Wages  Size_Shop  ...  \\\n",
       "0                    5            0            3           1          3  ...   \n",
       "1                    2            1            0           5          0  ...   \n",
       "2                    2            5            1           4          0  ...   \n",
       "3                    3            1            0           3          2  ...   \n",
       "4                    5            1            0           4          3  ...   \n",
       "\n",
       "   Supply  Total_Workers  No_customers  Freq_Supply_pay  Nature  Loan_Status  \\\n",
       "0       3              6             4                0       0            Y   \n",
       "1       5              1             1                1       1            N   \n",
       "2       5              2             3                4       1            Y   \n",
       "3       3              7             4                5       1            Y   \n",
       "4       3              2             2                0       2            Y   \n",
       "\n",
       "  Applicant_Income_Log  Loan_Amount_Log  Loan_Amount_Term_Log  \\\n",
       "0             8.674026         4.986426              5.886104   \n",
       "1             8.430109         4.852030              5.886104   \n",
       "2             8.006368         4.189655              5.886104   \n",
       "3             7.856707         4.787492              5.886104   \n",
       "4             8.699515         4.948760              5.886104   \n",
       "\n",
       "   Total_Income_Log  \n",
       "0          8.674026  \n",
       "1          8.714568  \n",
       "2          8.006368  \n",
       "3          8.505323  \n",
       "4          8.699515  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop unnecessary columns\n",
    "cols = ['Applicant_Income', 'Family_Income', 'Loan_Amount', 'Loan_Amount_Term', 'Total_Income', 'Sr_no']\n",
    "df = df.drop(columns = cols, axis =  1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prepared-tyler",
   "metadata": {},
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "contemporary-surgeon",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Cases</th>\n",
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       "      <th>Rent_Wages</th>\n",
       "      <th>Size_Shop</th>\n",
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       "      <th>Loan_Status</th>\n",
       "      <th>Applicant_Income_Log</th>\n",
       "      <th>Loan_Amount_Log</th>\n",
       "      <th>Loan_Amount_Term_Log</th>\n",
       "      <th>Total_Income_Log</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.006368</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.006368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.856707</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.505323</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.886104</td>\n",
       "      <td>8.699515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cases  Education  Total_assets  Business_type  Credit  Rating_Reviewportal  \\\n",
       "0      1          0             0              1       1                    5   \n",
       "1      2          0             3              0       1                    2   \n",
       "2      0          0             0              0       1                    2   \n",
       "3      0          1             2              1       0                    3   \n",
       "4      2          0             2              1       1                    5   \n",
       "\n",
       "   Daily_Sales  Competition  Rent_Wages  Size_Shop  ...  Supply  \\\n",
       "0            0            3           1          3  ...       3   \n",
       "1            1            0           5          0  ...       5   \n",
       "2            5            1           4          0  ...       5   \n",
       "3            1            0           3          2  ...       3   \n",
       "4            1            0           4          3  ...       3   \n",
       "\n",
       "   Total_Workers  No_customers  Freq_Supply_pay  Nature  Loan_Status  \\\n",
       "0              6             4                0       0            1   \n",
       "1              1             1                1       1            0   \n",
       "2              2             3                4       1            1   \n",
       "3              7             4                5       1            1   \n",
       "4              2             2                0       2            1   \n",
       "\n",
       "   Applicant_Income_Log  Loan_Amount_Log  Loan_Amount_Term_Log  \\\n",
       "0              8.674026         4.986426              5.886104   \n",
       "1              8.430109         4.852030              5.886104   \n",
       "2              8.006368         4.189655              5.886104   \n",
       "3              7.856707         4.787492              5.886104   \n",
       "4              8.699515         4.948760              5.886104   \n",
       "\n",
       "   Total_Income_Log  \n",
       "0          8.674026  \n",
       "1          8.714568  \n",
       "2          8.006368  \n",
       "3          8.505323  \n",
       "4          8.699515  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "cols = ['Education', 'Loan_Status', 'Business_type', 'Credit']\n",
    "le = LabelEncoder()\n",
    "for col in cols:\n",
    "    df[col] = le.fit_transform(df[col])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "brown-contract",
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify input and output attribute\n",
    "x = df.drop(columns = ['Loan_Status'], axis = 1)\n",
    "y = df['Loan_Status']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interpreted-longitude",
   "metadata": {},
   "source": [
    "# Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "seeing-marine",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "certain-remedy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    109\n",
       "0     45\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "multiple-stock",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    313\n",
       "0    147\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interpreted-collapse",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "after-sunglasses",
   "metadata": {},
   "outputs": [],
   "source": [
    "# classify function\n",
    "from sklearn.model_selection import cross_val_score\n",
    "def classify(model, x,y):\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 21)\n",
    "    model.fit(x_train, y_train)\n",
    "    print(\"Accuracy is \", model.score(x_test, y_test)*100)\n",
    "    # cross validation - it is used for better validation of the model\n",
    "    # cv-5, train-4, test-1\n",
    "    score = cross_val_score(model, x, y, cv=5)\n",
    "    print(\"Cross validation is \", np.mean(score)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "advised-stress",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  67.53246753246754\n",
      "Cross validation is  66.61554732945532\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "classify(model, x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "complicated-nashville",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  55.1948051948052\n",
      "Cross validation is  55.692755156002114\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()\n",
    "classify(model, x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "handy-silicon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  58.44155844155844\n",
      "Cross validation is  63.51136964569011\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "model = RandomForestClassifier()\n",
    "classify(model, x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "different-thriller",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=3,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=8, num_parallel_tree=1,\n",
       "             objective='reg:squarederror', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = xgb.XGBRegressor(n_estimators=100, reg_lambda=1, gamma=0, max_depth=3)\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "coordinated-tunnel",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dedicated-drunk",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2697947239521267"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "clear-beads",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6, 39],\n",
       "       [25, 84]], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = model.predict(x_test)\n",
    "cm = confusion_matrix(y_test, y_pred.round())\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "undefined-albany",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "raw",
   "id": "established-newsletter",
   "metadata": {},
   "source": [
    "A confusion matrix is a summary of prediction results on a classification problem. The number of correct and incorrect predictions are summarized with count values and brokem down by each class. It gives us insight not only into the errors being made by a classifier but more importantly the types of errors that are being made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "domestic-kingdom",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bea95de888>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "incorporate-apache",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    313\n",
       "0    147\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "organic-foster",
   "metadata": {},
   "source": [
    "# Saving the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "faced-communication",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8598707 , 0.5877515 , 0.8515346 , 0.64950633, 0.78366673,\n",
       "       0.7227138 , 0.16600668, 0.48282394, 0.8982531 , 0.7856406 ,\n",
       "       0.60976386, 0.28638154, 0.70214   , 0.80018085, 0.37347215,\n",
       "       0.64889497, 0.5621005 , 0.25687397, 0.8698096 , 0.22254682,\n",
       "       0.37954682, 0.68742853, 0.09800673, 0.9684885 , 0.6074346 ,\n",
       "       0.5715346 , 0.72193736, 0.537879  , 1.0196551 , 0.80284804,\n",
       "       0.47441888, 0.3392228 , 0.75864536, 0.5430118 , 0.4612117 ,\n",
       "       0.08506689, 0.96690387, 1.0623672 , 0.61061436, 0.4254362 ,\n",
       "       0.7548808 , 0.77128047, 0.7772034 , 0.7324804 , 0.9674844 ,\n",
       "       0.90781796, 0.9716415 , 1.1614248 , 0.73526394, 0.65155226,\n",
       "       0.82869834, 0.6637775 , 0.6053545 , 0.62613195, 0.7678702 ,\n",
       "       0.9453128 , 0.60697466, 0.41619155, 0.58832216, 0.24295771,\n",
       "       0.84954864, 0.67341644, 1.0086402 , 1.0554198 , 0.7328819 ,\n",
       "       0.80003077, 0.62048787, 0.18023923, 0.5517147 , 0.48698103,\n",
       "       0.6726972 , 0.8441176 , 0.5783569 , 0.8636269 , 0.28463897,\n",
       "       0.9584033 , 0.58232   , 0.7431956 , 0.9786263 , 0.9776532 ,\n",
       "       0.30585793, 0.8394724 , 0.87807965, 0.8775702 , 0.8180783 ,\n",
       "       0.6643992 , 0.6488593 , 1.0640334 , 0.9980965 , 0.59764695,\n",
       "       0.49471268, 0.6457936 , 0.61626756, 0.61137676, 0.3064344 ,\n",
       "       1.0005718 , 0.17967185, 0.38136306, 0.5950809 , 1.0028641 ,\n",
       "       0.6048152 , 0.71241415, 0.69602925, 0.6889668 , 0.5895622 ,\n",
       "       0.37162337, 0.7579067 , 0.9342189 , 0.63054514, 0.6709311 ,\n",
       "       0.8112158 , 0.64778763, 0.7394369 , 1.1046326 , 0.46978825,\n",
       "       0.83203506, 0.47530642, 0.61621374, 0.533811  , 0.9557926 ,\n",
       "       1.0237864 , 0.51943713, 0.81965023, 0.3724205 , 0.4370825 ,\n",
       "       1.0171605 , 0.79381615, 0.81659776, 0.81858164, 0.98759556,\n",
       "       0.26970422, 0.9194672 , 0.75284463, 0.64192086, 0.7362103 ,\n",
       "       0.3414169 , 0.9967661 , 0.8307996 , 0.85531807, 0.4185864 ,\n",
       "       0.55854076, 0.7702686 , 0.87604195, 0.786967  , 0.72602445,\n",
       "       0.7335087 , 0.5816505 , 0.9733563 , 0.7321384 , 0.62379783,\n",
       "       0.92334276, 1.1445959 , 1.2904335 , 0.8639593 ], dtype=float32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "saved_model = pickle.dumps(model)\n",
    "saved_model_from_pickle = pickle.loads(saved_model)\n",
    "saved_model_from_pickle.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "mysterious-harvest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.pkl']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(model, 'model.pkl') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "prescription-idaho",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cases</th>\n",
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       "      <td>8.699515</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cases  Education  Total_assets  Business_type  Credit  \\\n",
       "131      2          0             4              0       1   \n",
       "159      0          0             3              1       0   \n",
       "327      0          0             1              0       1   \n",
       "235      2          0             3              1       1   \n",
       "65       1          0             1              0       1   \n",
       "..     ...        ...           ...            ...     ...   \n",
       "490      3          1             5              0       0   \n",
       "542      0          0             3              1       0   \n",
       "3        0          1             2              1       0   \n",
       "521      4          0             2              1       0   \n",
       "465      2          0             0              0       0   \n",
       "\n",
       "     Rating_Reviewportal  Daily_Sales  Competition  Rent_Wages  Size_Shop  \\\n",
       "131                    2            4            5           5          5   \n",
       "159                    3            1            0           4          0   \n",
       "327                    2            4            3           0          1   \n",
       "235                    3            0            1           0          5   \n",
       "65                     4            3            3           0          1   \n",
       "..                   ...          ...          ...         ...        ...   \n",
       "490                    0            4            5           5          4   \n",
       "542                    5            4            1           3          5   \n",
       "3                      3            1            0           3          2   \n",
       "521                    0            3            3           0          4   \n",
       "465                    3            1            2           1          5   \n",
       "\n",
       "     Location  Supply  Total_Workers  No_customers  Freq_Supply_pay  Nature  \\\n",
       "131         0       4              1             5                3       0   \n",
       "159         0       4              5             1                5       5   \n",
       "327         1       3              0             0                5       4   \n",
       "235         5       1              6             2                5       0   \n",
       "65          5       2              7             1                0       2   \n",
       "..        ...     ...            ...           ...              ...     ...   \n",
       "490         1       0              7             2                5       0   \n",
       "542         5       5              1             1                4       2   \n",
       "3           5       3              7             4                5       1   \n",
       "521         2       4              6             5                2       0   \n",
       "465         0       5              1             3                5       3   \n",
       "\n",
       "     Applicant_Income_Log  Loan_Amount_Log  Loan_Amount_Term_Log  \\\n",
       "131              7.607878         4.304065              5.886104   \n",
       "159              8.430109         5.541264              5.886104   \n",
       "327              8.668712         5.214936              5.886104   \n",
       "235              8.612503         5.135798              5.886104   \n",
       "65               8.652772         5.552960              5.886104   \n",
       "..                    ...              ...                   ...   \n",
       "490              7.900637         4.564348              5.886104   \n",
       "542              8.203030         4.553877              5.886104   \n",
       "3                7.856707         4.787492              5.886104   \n",
       "521              7.824046         4.007333              5.886104   \n",
       "465              8.699515         4.941642              5.886104   \n",
       "\n",
       "     Total_Income_Log  \n",
       "131          8.279697  \n",
       "159          9.230927  \n",
       "327          8.984443  \n",
       "235          8.818778  \n",
       "65           9.241936  \n",
       "..                ...  \n",
       "490          8.609590  \n",
       "542          8.203030  \n",
       "3            8.505323  \n",
       "521          7.824046  \n",
       "465          8.699515  \n",
       "\n",
       "[154 rows x 20 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "genuine-confidence",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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