mushroom.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This data set includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family. Each species is classified as definitely edible, definitely poisonous .We need build a model to correctly predcit the ediblity  of the sample by evaluating  other factors inthe data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the required liabraries and Loading DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "#from sklearn.tree import export_graphviz\n",
    "#import graphviz\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
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       "      <td>n</td>\n",
       "      <td>...</td>\n",
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       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
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       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0            c         n          k  ...                        s   \n",
       "1            c         b          k  ...                        s   \n",
       "2            c         b          n  ...                        s   \n",
       "3            c         n          n  ...                        s   \n",
       "4            w         b          k  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the dataset \n",
    "df =pd.read_csv('mushrooms.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Class column is the label and rest are featutres "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8124, 23)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # it has total 8124  records and 23 columns \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### it has total 8124  records and 23 columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8124 entries, 0 to 8123\n",
      "Data columns (total 23 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   class                     8124 non-null   object\n",
      " 1   cap-shape                 8124 non-null   object\n",
      " 2   cap-surface               8124 non-null   object\n",
      " 3   cap-color                 8124 non-null   object\n",
      " 4   bruises                   8124 non-null   object\n",
      " 5   odor                      8124 non-null   object\n",
      " 6   gill-attachment           8124 non-null   object\n",
      " 7   gill-spacing              8124 non-null   object\n",
      " 8   gill-size                 8124 non-null   object\n",
      " 9   gill-color                8124 non-null   object\n",
      " 10  stalk-shape               8124 non-null   object\n",
      " 11  stalk-root                8124 non-null   object\n",
      " 12  stalk-surface-above-ring  8124 non-null   object\n",
      " 13  stalk-surface-below-ring  8124 non-null   object\n",
      " 14  stalk-color-above-ring    8124 non-null   object\n",
      " 15  stalk-color-below-ring    8124 non-null   object\n",
      " 16  veil-type                 8124 non-null   object\n",
      " 17  veil-color                8124 non-null   object\n",
      " 18  ring-number               8124 non-null   object\n",
      " 19  ring-type                 8124 non-null   object\n",
      " 20  spore-print-color         8124 non-null   object\n",
      " 21  population                8124 non-null   object\n",
      " 22  habitat                   8124 non-null   object\n",
      "dtypes: object(23)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() \n",
    "# All are not null comumns of  object type\n",
    "# Will need encoding to apprroach for Heatmaps etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All are not null comumns of  object type\n",
    "### Will need encoding to apprroach for Heatmaps etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class                       0\n",
       "cap-shape                   0\n",
       "cap-surface                 0\n",
       "cap-color                   0\n",
       "bruises                     0\n",
       "odor                        0\n",
       "gill-attachment             0\n",
       "gill-spacing                0\n",
       "gill-size                   0\n",
       "gill-color                  0\n",
       "stalk-shape                 0\n",
       "stalk-root                  0\n",
       "stalk-surface-above-ring    0\n",
       "stalk-surface-below-ring    0\n",
       "stalk-color-above-ring      0\n",
       "stalk-color-below-ring      0\n",
       "veil-type                   0\n",
       "veil-color                  0\n",
       "ring-number                 0\n",
       "ring-type                   0\n",
       "spore-print-color           0\n",
       "population                  0\n",
       "habitat                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking data quaity for Null records :though all columns are not null\n",
    "df.isnull().sum()\n",
    "# No missing values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No missing values found\n",
    "### Checking for class Imbalance in counts and Visualize ofr better understanding "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e    4208\n",
       "p    3916\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for class Imbalance in counts\n",
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "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['class'].hist(grid = False)\n",
    "plt.title('Poisonus vs Edible')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data set is looking balanced and so sampling will be required"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Manipulation.\n",
    "\n",
    "### As all the columns are categorical  and OBJ type we need to entire table ENCODING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LabelEncoder\n",
    "\n",
    "lab_enc =LabelEncoder()\n",
    "#Each and Every data in the features are categorical . \n",
    "# Categorical data will not have any outliers \n",
    "# so All the Care\n",
    "for i in df.columns:\n",
    "    df[i]  = lab_enc.fit_transform(df[i])\n",
    "    #pd.Series(i)\n",
    "    #df[i] = i\n",
    " # Data cinvertion completed   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data description post ENCODING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "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>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-above-ring</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   class  cap-shape  cap-surface  cap-color  bruises  odor  gill-attachment  \\\n",
       "0      1          5            2          4        1     6                1   \n",
       "1      0          5            2          9        1     0                1   \n",
       "2      0          0            2          8        1     3                1   \n",
       "3      1          5            3          8        1     6                1   \n",
       "4      0          5            2          3        0     5                1   \n",
       "\n",
       "   gill-spacing  gill-size  gill-color  ...  stalk-surface-above-ring  \\\n",
       "0             0          1           4  ...                         2   \n",
       "1             0          0           4  ...                         2   \n",
       "2             0          0           5  ...                         2   \n",
       "3             0          1           5  ...                         2   \n",
       "4             1          0           4  ...                         2   \n",
       "\n",
       "   stalk-surface-below-ring  stalk-color-above-ring  stalk-color-below-ring  \\\n",
       "0                         2                       7                       7   \n",
       "1                         2                       7                       7   \n",
       "2                         2                       7                       7   \n",
       "3                         2                       7                       7   \n",
       "4                         2                       7                       7   \n",
       "\n",
       "   veil-color  ring-number  ring-type  spore-print-color  population  habitat  \n",
       "0           2            1          4                  2           3        5  \n",
       "1           2            1          4                  3           2        1  \n",
       "2           2            1          4                  3           2        3  \n",
       "3           2            1          4                  2           3        5  \n",
       "4           2            1          0                  3           0        1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data describe \n",
    "df.head()\n",
    "# from the describe we can see Veil type is equal ,0 thus will not impact the classification results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
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       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
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       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
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       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
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       "      <td>8124.000000</td>\n",
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       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.482029</td>\n",
       "      <td>3.348104</td>\n",
       "      <td>1.827671</td>\n",
       "      <td>4.504677</td>\n",
       "      <td>0.415559</td>\n",
       "      <td>4.144756</td>\n",
       "      <td>0.974151</td>\n",
       "      <td>0.161497</td>\n",
       "      <td>0.309207</td>\n",
       "      <td>4.810684</td>\n",
       "      <td>...</td>\n",
       "      <td>1.575086</td>\n",
       "      <td>1.603644</td>\n",
       "      <td>5.816347</td>\n",
       "      <td>5.794682</td>\n",
       "      <td>1.965534</td>\n",
       "      <td>1.069424</td>\n",
       "      <td>2.291974</td>\n",
       "      <td>3.596750</td>\n",
       "      <td>3.644018</td>\n",
       "      <td>1.508616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.499708</td>\n",
       "      <td>1.604329</td>\n",
       "      <td>1.229873</td>\n",
       "      <td>2.545821</td>\n",
       "      <td>0.492848</td>\n",
       "      <td>2.103729</td>\n",
       "      <td>0.158695</td>\n",
       "      <td>0.368011</td>\n",
       "      <td>0.462195</td>\n",
       "      <td>3.540359</td>\n",
       "      <td>...</td>\n",
       "      <td>0.621459</td>\n",
       "      <td>0.675974</td>\n",
       "      <td>1.901747</td>\n",
       "      <td>1.907291</td>\n",
       "      <td>0.242669</td>\n",
       "      <td>0.271064</td>\n",
       "      <td>1.801672</td>\n",
       "      <td>2.382663</td>\n",
       "      <td>1.252082</td>\n",
       "      <td>1.719975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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",
       "      <td>0.000000</td>\n",
       "      <td>...</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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
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       "  </tbody>\n",
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       "<p>8 rows × 22 columns</p>\n",
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      ],
      "text/plain": [
       "             class    cap-shape  cap-surface    cap-color      bruises  \\\n",
       "count  8124.000000  8124.000000  8124.000000  8124.000000  8124.000000   \n",
       "mean      0.482029     3.348104     1.827671     4.504677     0.415559   \n",
       "std       0.499708     1.604329     1.229873     2.545821     0.492848   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     2.000000     0.000000     3.000000     0.000000   \n",
       "50%       0.000000     3.000000     2.000000     4.000000     0.000000   \n",
       "75%       1.000000     5.000000     3.000000     8.000000     1.000000   \n",
       "max       1.000000     5.000000     3.000000     9.000000     1.000000   \n",
       "\n",
       "              odor  gill-attachment  gill-spacing    gill-size   gill-color  \\\n",
       "count  8124.000000      8124.000000   8124.000000  8124.000000  8124.000000   \n",
       "mean      4.144756         0.974151      0.161497     0.309207     4.810684   \n",
       "std       2.103729         0.158695      0.368011     0.462195     3.540359   \n",
       "min       0.000000         0.000000      0.000000     0.000000     0.000000   \n",
       "25%       2.000000         1.000000      0.000000     0.000000     2.000000   \n",
       "50%       5.000000         1.000000      0.000000     0.000000     5.000000   \n",
       "75%       5.000000         1.000000      0.000000     1.000000     7.000000   \n",
       "max       8.000000         1.000000      1.000000     1.000000    11.000000   \n",
       "\n",
       "       ...  stalk-surface-above-ring  stalk-surface-below-ring  \\\n",
       "count  ...               8124.000000               8124.000000   \n",
       "mean   ...                  1.575086                  1.603644   \n",
       "std    ...                  0.621459                  0.675974   \n",
       "min    ...                  0.000000                  0.000000   \n",
       "25%    ...                  1.000000                  1.000000   \n",
       "50%    ...                  2.000000                  2.000000   \n",
       "75%    ...                  2.000000                  2.000000   \n",
       "max    ...                  3.000000                  3.000000   \n",
       "\n",
       "       stalk-color-above-ring  stalk-color-below-ring   veil-color  \\\n",
       "count             8124.000000             8124.000000  8124.000000   \n",
       "mean                 5.816347                5.794682     1.965534   \n",
       "std                  1.901747                1.907291     0.242669   \n",
       "min                  0.000000                0.000000     0.000000   \n",
       "25%                  6.000000                6.000000     2.000000   \n",
       "50%                  7.000000                7.000000     2.000000   \n",
       "75%                  7.000000                7.000000     2.000000   \n",
       "max                  8.000000                8.000000     3.000000   \n",
       "\n",
       "       ring-number    ring-type  spore-print-color   population      habitat  \n",
       "count  8124.000000  8124.000000        8124.000000  8124.000000  8124.000000  \n",
       "mean      1.069424     2.291974           3.596750     3.644018     1.508616  \n",
       "std       0.271064     1.801672           2.382663     1.252082     1.719975  \n",
       "min       0.000000     0.000000           0.000000     0.000000     0.000000  \n",
       "25%       1.000000     0.000000           2.000000     3.000000     0.000000  \n",
       "50%       1.000000     2.000000           3.000000     4.000000     1.000000  \n",
       "75%       1.000000     4.000000           7.000000     4.000000     2.000000  \n",
       "max       2.000000     4.000000           8.000000     5.000000     6.000000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data describe \n",
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WE can observe that veiltype is having uniform values , hence will be droped \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop Columns with uniform values\n",
    "df=df.drop([\"veil-type\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8124 entries, 0 to 8123\n",
      "Data columns (total 22 columns):\n",
      " #   Column                    Non-Null Count  Dtype\n",
      "---  ------                    --------------  -----\n",
      " 0   class                     8124 non-null   int32\n",
      " 1   cap-shape                 8124 non-null   int32\n",
      " 2   cap-surface               8124 non-null   int32\n",
      " 3   cap-color                 8124 non-null   int32\n",
      " 4   bruises                   8124 non-null   int32\n",
      " 5   odor                      8124 non-null   int32\n",
      " 6   gill-attachment           8124 non-null   int32\n",
      " 7   gill-spacing              8124 non-null   int32\n",
      " 8   gill-size                 8124 non-null   int32\n",
      " 9   gill-color                8124 non-null   int32\n",
      " 10  stalk-shape               8124 non-null   int32\n",
      " 11  stalk-root                8124 non-null   int32\n",
      " 12  stalk-surface-above-ring  8124 non-null   int32\n",
      " 13  stalk-surface-below-ring  8124 non-null   int32\n",
      " 14  stalk-color-above-ring    8124 non-null   int32\n",
      " 15  stalk-color-below-ring    8124 non-null   int32\n",
      " 16  veil-color                8124 non-null   int32\n",
      " 17  ring-number               8124 non-null   int32\n",
      " 18  ring-type                 8124 non-null   int32\n",
      " 19  spore-print-color         8124 non-null   int32\n",
      " 20  population                8124 non-null   int32\n",
      " 21  habitat                   8124 non-null   int32\n",
      "dtypes: int32(22)\n",
      "memory usage: 698.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "# All are INT data ; Veil type delted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows',None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
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       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   class  cap-shape  cap-surface  cap-color  bruises  odor  gill-attachment  \\\n",
       "0      1          5            2          4        1     6                1   \n",
       "1      0          5            2          9        1     0                1   \n",
       "2      0          0            2          8        1     3                1   \n",
       "3      1          5            3          8        1     6                1   \n",
       "4      0          5            2          3        0     5                1   \n",
       "\n",
       "   gill-spacing  gill-size  gill-color  ...  stalk-surface-above-ring  \\\n",
       "0             0          1           4  ...                         2   \n",
       "1             0          0           4  ...                         2   \n",
       "2             0          0           5  ...                         2   \n",
       "3             0          1           5  ...                         2   \n",
       "4             1          0           4  ...                         2   \n",
       "\n",
       "   stalk-surface-below-ring  stalk-color-above-ring  stalk-color-below-ring  \\\n",
       "0                         2                       7                       7   \n",
       "1                         2                       7                       7   \n",
       "2                         2                       7                       7   \n",
       "3                         2                       7                       7   \n",
       "4                         2                       7                       7   \n",
       "\n",
       "   veil-color  ring-number  ring-type  spore-print-color  population  habitat  \n",
       "0           2            1          4                  2           3        5  \n",
       "1           2            1          4                  3           2        1  \n",
       "2           2            1          4                  3           2        3  \n",
       "3           2            1          4                  2           3        5  \n",
       "4           2            1          0                  3           0        1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()# data is same scale  no scaling required\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x1800 with 22 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(20,25))\n",
    "plt_no=1\n",
    "for i in df:\n",
    "    if plt_no<24:\n",
    "        ax=plt.subplot(5,5,plt_no)\n",
    "        sns.stripplot(df[i])\n",
    "        plt.xlabel(i)\n",
    "    plt_no +=1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Correaltion and HEAT MAP Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CULduTX77bSF167Z+KbHVUsv8VLXMvzFtAN8aKfO3T1/j0p4zfLpm/Es5b1at2jajYuUKBLzSDc8GdZgwcxQ9Ohh+9fmML78lPi4egFGTv+Cdd99kybxfeOfdN7h5/TYf9R6CQ6mSbDvyJ5vXbSM1NS1f8TRv04QKlcvTpdlb1K1fm9FfDaNvx4EG6b6Z8H1GuR4y8VN6vNud5fN/5cTB0+zfcQiAqjU9+GrxZLq3fCdfsRjTtM0rlHMvS48WvaldvybDZnzBwE6G39D2/cQfSVDj+2zCR3Tv/xq//rD6pcRQz6c+zu6ujPL5lMreVek9bSBTu442SHfj9FWC9pxi1JrJBvuun7zC3PdmvJR4AOr6eFPG3ZUxPp9R2bsqvaYNZLqRmG6evsb5PacZvmaSYbwnrzLvZcSkEdT+qj/H35xOUkgkLXZMI3zHaeKy9P2Xxv6CS4eGRl/iWLeppEa93AnA+r4NcKvkxsetPqCad3U+mPYRI7sMM0h3YMM+vhv0DQBD5g2j3Vv+7Ph1G3HRsfw0YTGvBDR5KfH4tmuBe+WKtG70Kt4N6zF19ji6+huvK3W9amFnb5tpW/STGCaM/oqAwDYvJR7dmO1dzr45jeSQSBrtmMHjHaeIz3Ldro1djlOW65ZwK5QTbUdmvE6Lcwt5tPXEy4krIz4NZb78hIfvjSE1/DEVf/+e+L3HSLl173kcx4K4u+cYAGbV3HH7dgzBHQ375YLE4D79fa68NYmU0EjqbJ3Jkx0nSbzx/EOY5PvhXO7+Jdqn8ZT09abyzA+5+OoolLR07k7+hYQLt9FYW1B3+2yeHjiX6Vjpf5O8fUMqVA296mJvZ/vihAXg5OVBTHA4sfcekZ6q5dbGY1T0b5ApTUX/Btz4UzdAjDhzCzM7ayzLlMzxdVPjEjN+NrU0R/e1vHlnV78KCXfCSLobgZKqJWLDEZzaZ558SH0cQ2zQLZRUrcHxwkSDxsIMYaLBxMqMlLAn+Yqjml8Dzq87CMDDszexsLPCxkgeVGpWm8tqJ3pu3QGq++s63dSE5Iw0JazMgef5ce/EVRKjM0/qFMSrr/rx22+6T7hPnDiLvb0dLi5lXtrrZ6eOf0NOrj8AwN2zN7G0tcLOqaRBuirNanNu63FdfOsOUFfNI5eqZbl+WPcJeMStEBzLOWFT2h4AexdHarWpz7E1e/IVm4N3FeLuhJNwT1eOHmw4iktA5nKe8jiG6KDbKGmG5UirXj9NCRM0piaQz/KclW9ASzb/vh2AC2cuYWtnQ+kypQzSPXvjBmBuaYbCyzm/Pk2FqqQ/DkWJCgdtGmlnD2Ja55VcHy/sS2FSsyFpx3a91Lg6dwrg11W6Cc7jJ85gX9I+T+U5Pv553llbWeW7LTImt2X+4aVgoh48emnnzapth9Zs/F03GXPu9EXs7G1xMlqO4jN+NrcwzyhHigLWNtYAWFlb8TQ6hjQj9SC3fNq3ZMsfeSzXFs/7icSE5/2HpZXFS6tvz7QIaMb2P3Xl9NKZK9ja21CqjKNBuoRs4nsZvP0bcWS9boL89tkbWNlaY2+k7Ny7dIfIQiw7+rz8G3F0/T69mKyMxnT/X4ippNr3J6p9f8iGozhnmTBOeRzD06DbpBvp+wtLY/8m7F2n64eun72GtZ01DmUcDNKd2Xs64+cbQTco7VoagKeRT7l5/gZpafmb8MvKr4Mv69bqVjWdPXUeO3tbyjiXNkin0WgYO3EIMyZ+m2l75OMozp+9lO8JyKzs6lch8U54xpgtfMMRSudhzPaMY8u6JAaHk/Tg8UuJ6xmLetVJvRdK6oMwSE0jZut+rNs0zZRG0VsNpbGyeKn1HsDGuwpJwaEk3wtHSU0jcuMhHAIaZ0oTd+oa2qe69jr2zHXMXHXtZ2rEExIu3AYgPT6JxJsPMvZJ/9vkpMR/hBCijxDivBDinBBiZZZ97wshTqr71gkhrNTtbwghLqrbD6jbagshTgghgtTXq1oUf8/LZO3qQFxoVMbv8WFRWLtm7mCtXRyIC4l8niY0CmsXNY2iEPjbKLpunUKNd3wzHddwxBv0PDGXKq814/Ts/C13N3dxJFnv3MkhkZi7GA4ejUkJe8K9BZtpdmYBzc8vJi0mgaj9+VsCbOviSIxeHDFhUdg6Z84nSwcbkmLiUbS65WWxoVHYujxPUz2gIR/tnkXPZcPZNHxxvuLIDTc3Fx48eL7S5eHDMNzcnI2mnThxGCdObGfmzC8xMzMr0HntnR2J1suj6LAo7LNcK2sHWxJjEkhX8+hpaBT2zro0D6/co157XcdcwdMDh7KlKake/9r4vmyesSrfgwNLVwcS9WJLCo3C0jV35QgAjcD3n+l0uLiQiAMXeHLWcLlufpRxdSI8JDzj9/DQR5RxdTKadvJ3Y9lzYQvuVSqyeunzZdr1GtTh992/8MNv3+BR3T3fsQj7UijRzweASvRjhL3hYMekUnUsh83F4v0JaJzLZ2w37zqAlC3LX/r3iLu5ufDgvl55fhBKWTcXo2knTx7JmdO7mD1rYqby3KVLey5c2M/Gjb8w8P2hLy22klnK/FMjZf7f4OziRKheOQoLicDZ1fjEzfS54zl0aTuVq1Ti1590t96sWvo7HlUrceDCNjbtX830sd8UaCBexqU04SERGb9HhEbg5Gr4Rglg4rej2XV+E5WqVGTtz8+X+vt2aMW6g6uYu3IWkwa/vNUAAE4upYnIFN8jnFyMxzdmzgg2B/1JxSrl+fPngi+1f6aksyNRIc/r25OwSBxc8vbmokr96kza9g2Dl4/FrWr5Fx/wwphKEaVXnp+ERVEyjzF51K/GhG2zGbR8LG5Vy+U7FguXLG12SCQWLoZv/rOn8Mra0bTYOY3yvV/SKgCglEspIkOfX7fIsEgcc8gjE1MTWnfz5cz+09mmKQgX1zKEPAzL+D0sJNxo3e87oCe7tu8jIvzlvsnPysLFkSSDMVterpuO82vNCP/r8MsMDQDTMqVIC3s+oZYW/pgSzobXz6ZdMyr9vYSyCyYTPu5bg/0FYeZSihS9PEoJjcQsh/FImZ7tiN571mC7eTknrOu4E3fG8Na4/7fS04v+XzElJyX+A4QQtYGxQBtFUTyBQVmSrFcUpZG67wrwnrp9PBCgbu+sbvsQmKsoihfQEDBYLyWEGCiEOCWEOPXTipezzLNwCcNNWceiwlgaXaJNr03mrw7j2N57FrX6tsPlleoZSU7N/IPVjQdx868j1Orvl8/wDM+d20+JTe2tcWrfiKONPuGw5weYWFng3L3lywrDYNAujCZ6/uO1HadY0HY4v7//LT5D38hXHLlhLA5jbzDGj5+Jp2cbWrTojINDSYYOLdjtI7nJI6PFTU2ze8FGLO2tGbb1K1r2bc/DS8Gka7XUalOf2MinPLhoeG9zAYPL/fHpCnvbjWGH96c4eHtgWyP/g+0XxZXdm8HxX0yjnWdnbt+4S0CXdgBcOX+N9g278Wbbvqxe+iffLvvqpcaSNY+0D24RP2UAibMHkXpoCxbv6u7TNqnVECXuKekPXs5kTeawcpdHY8fNoE6dVjRp2hFHx5IMH/5xxr6NG7dTt25rur/+HhMnvoRbXTKCM9z0sj9Vy10cuS9HYwZNplXdQG7dCCawiz8ALXybcOXidVrV7cBrbd7hyxnDM1ZOvKx4smu2Jw6eQYBXV+7cuIt/57YZ2/duO0D3lu8w9N3RfDTiJS6dJvdlCmD6kJl0qf8mwTfu0bazr9E0hR2DMXcv3mZY8w+Z0GEou5dv4/PFI19CTEY25jGmkc0/YlKHYexZvpVPChKT0WBy78irEznkN4YTb39Npf7+ODapUaDXy0lO1+2DaR9x+cRFrpwo2POaspObclTGxYmOXfxYvuRfGJMW8LoBiBImlPZvQMTmYy8hoKwvnrt6F/fPEYI7vk/IZ5Mo9XmflxyDkW3ZFCG7ZnUo07Mt96atyLRdY2VB1Z9GEDz+Z7R6K5Ol/11yUuK/oQ3wp6IojwEURYnKsr+OEOKgEOIC8A5QW91+GFguhHgfMFG3HQXGCCFGAhUVRTFoCRRFWawoSkNFURoO6NOzMP6elyo+NAobvRlaaxdH4rPc4hAfGoWN2/OZZGtXR+LVh1Y+e3hlUmQMwdtP4+TlYXCOWxuO4J7leQ+5lRwaibneuc3dSuX6FgyHVnVJvBdBamQsSpqWR38fx75RtVyfu2EfP97fOp33t04nNjwaO7047FwciYuIzpQ+ISoWCztrhImuabB1dSQ23DDWeyeu4lCxDJYONrmO5UU++KAPx45t5dixrYSGhlOunFvGvrJlXQgNjTA4JixMty0lJYUVK/6gYUOvPJ+3eW9/hm39imFbv+Jp+BNK6uVRSRdHYrL8/fFRsVjaWaFR88je1ZGYCF2a5LhE1gxfyOzAUawa8gM2peyIvP8I94bVqNOuAV8emkefeZ9TtVlt3vnW8P7vnCSGRGGpF5uFqyOJ+biVJzUmgcdHruDs65nnY5/p0b8ba/9Zztp/lvMo7DHOeqtYnF2deBSW/SdZ6enp7Nj4D+06+gC65e/Plrof2n0U0xKmlHS0z1dcSvRjRMnnnxaLkqVRYrI0l8mJkKJb2qq9chpMTMDaFhP3WpjUbozVuCWY9x6OSdV6mL8zJF9xAHz0YV9OndzJqZM7CQ0No1x5vfJczpWQ0HCDY/TL8/Jf1tKoobdBmkOHjlO5ckVKlcr7J3fPtOjtz/CtXzHcSJm3N1LmC8vb777BX3tW8deeVUSEPcZVrxy5uJUhIiz75fXp6els27AL/1d1b7Jf69mJXX/vBeDenQc8uBdC5aoV8xTPm/26sXrXMlbvWsaj8Mc4uz3/tLaMa5kXluudm3bTtqPhsz7OHDtHuUpu+S7Xz3Tr24XlOxezfOdiHodFUiZTfE48Do/M9tj09HR2b9qLT8f8TWo/06Z3eyZtnc2krbOJDo/C0e15fXNwKUV0eNbhSfaS4hJJVpeZn993BpMSJtg45P12T9/e7Rm/dRbjt84iOvwJjnrl2cHFMd8xXdh3Nt8xgbqaTb/NditFUh7a7GS1HqY8jiFs60lKehuOTXKrQ59A5myby5xtc3kSEUUpvVU/pVxK8SSbPHrzi7ewc7Rn2eSl+T63MX3e68HWfb+zdd/vhIc9wq3s85VjLm7OBnW/Tt0aVHSvwP5TWzh0dhuWVhbsP7nlpcb0TFJoJBZZxmzJeexrS7X1JvbCHVIePX3Z4ZEW/hhTl+erEU2dS5MWkX0ZTzx1EbPyrmhK2r20GFJCIzHTyyMz11KkhBnGYFWzIpVnf8y1/jNI03tumzA1odpPw3m8/gBPth1/aXH9JyjpRf+vmJKTEv8NgmznIAFYDnyqKEpdYBJgAaAoyofAOKA8ECSEKKUoym/oVk0kAjuEEC9vTWAReXTuNnbuLtiWd0JTwgSPLk24t+tMpjR3d56h6ustAChT34OU2AQSI6IxtTSnhLUFoHtuRLlWdXhyTbd4xM79+QC5on99om+F5iu+2LO3sKrsikUFJ0QJE8p0bcbjHadydWzyw8fY1a+KxlK3jNuhZV0Sbjx8wVHPnVqxiyWBY1gSOIZrO09RT11lUda7CkmxiQaTEgDBRy9TK1B3C4Jn91ZcUx9u6VBR7w1DnUqYlDAl8cnLe47EokUraNIkkCZNAtm8eSdvv90dgMaNvYmJic14w6ZP/778zp39uXzZ8Gn9L3J45U5mB45iduAoLu48RaNurQCo6F2FxNgEYh5FGxxz8+hlPAN1zypo3L0VF3fqrqeFnRUmJXTzf03easOt41dIjkvk75lrmNT0E6a0+IwVn33PjSOXWDX4hzzFGR10C5vKLlip5ahc16aE7czdclqzUraUsNM9uFFjUQKnlnWIvRnygqOyt3bZenq060ePdv3Yu/0And5sD0Dd+rWJi43ncYThm6Pylcpm/NzavwV3bt4FoJTT8wnFOt410QhBdFT+BnLp92+gcXJDODqDiSmm3i3RXsw84BG2JTN+1lSoCkID8bGk/L2ChMnvkjD1fZJXzkJ74zzJq+bkKw6ABQt/oWEjfxo28mfjph30eud1AF5pXJ+YpzEvLM9dOrfn0uWrAHh4VMrY7u1VBzOzEkRG5n/i4NDKncwKHMWswFFcyFLmk7Ip84Xht5//4LU27/Bam3fYvW0fXd7sCIBngzrExsTxyEg5quD+fIWPb0BLbqvlKPRhGE1b6SaOSzk54l6lIvfv5r6tBPh9+Xp6+vWnp19/9m07yKtv6JfruBeW61Z+zblz857B9hp1q1GiRIl8l+tn1v+ykX7+A+nnP5ADOw7R/nXd6r3a9WsSFxNPpJE3JmUrPZ8Ma+7XjLs37xukyYs9K7czIXAYEwKHcWbnCZp1003CVPauSmJsAk/zUHb0n13i7lkFIUTGt2Dkxd6V25kcOJzJgcM5u/METbv5FHlMAE/P3sK6sguWapvt1rUp4Tty12abWJljoo5NTKzMcfKpR+zV/D8IcNuKrQzpMIghHQZxfMcxfLvrhn7VvKuTEJvAkwjD9qTdW/54t6rPnE9nvfTVUyuWriXQ500Cfd5k59Y9dO+he/Cvd8N6xMbEGtyisWfXQRrVakML7w608O5AYkISrRu9+lJjekY3ZnPJGLM552HM9ozLa80J/+tIocSXdOEaJSq6YVrWGUqYYhfYmvi9mVdklKjgmvGzea0qiBKmpEfHvLQY4oJuYuHuinn5MogSppTq0oInOzN/I5RZ2dJU+2kENz+fS9LtzOPnyt98QuKNh4QtLtg3JEn/v8hv3/hv2A38JYT4VlGUSCFE1hu3bIFQIUQJdCslHgIIITwURTkOHBdCdALKCyHsgduKonwvhKgM1APy9+S9XBg+4StOnj1PdHQMbbv24uP3etO9U8BLPYeiTefIl7/QYdUIhEbDtbX7eXL9ITV76TrdK7/u4f6eIMq38aTHoW9IS0ph/xDd8xAsnezw++kLADQmJtzccIQH+3TPbGg8ugf2lV1RFIW4B4/z980banzXR/+M15qxuq+XWr2X+GsPcOujG1CGrNiFmZM9DXd+hamtJUq6QvmBgRxvOYSYMzd5tOUYjXZ9jaLVEnchmIcr/8lXHDf3BFHF14tPDswhTf1K0GfeWj6cLSOWEBcRze4Zq+k2/zN8hr1B2KW7BK3dB0DNDo2o170l2lQtackprP9kXsbxr33/CRWb1sTKwZZBx+ax/9s/CcrHN4Q8s337HgICfLl06QAJCYl88MHzJ4P/9ddyPv54BKGhESxbNpfSpR0RQnD+/GU++2xMvs8JcHnvWWr6ejF2/1xSEpNZM/z519i+v2wka0cuJibiCVu++o3e8z6nw9AePLwUzLHfdZ/QOlcpyzvffEx6ejrhNx6yZsSi7E6VZ4o2nfNjltNs9SjdV4Ku3kfstYdU6qNbLh68YjfmTvb47JiKqa0lpCt4vN+e3a1GYFGmJPW//whhokFoBA83HSN8l+H9nflx8J8jtGjblC3H/iApMYnxX0zL2Dd/1WwmDfmKxxGRTPn+S2xsrRFCcO3SDaaNnAWAXydf3uz7GmlpWpKTkhn5YQG+3SE9neT1i7AcOBE0GlJP/EN6+H1Mm+reXKYd3Y6pZ3NMm3WAdC2kppC0clZB/vxc2bZtNx3at+HqlcMkJiYyYMDzFRibNq7ggw+HExoazopf5uPk5AhCcP7cJT7+RPf1uq+9FkivXq+TlppGYmIS77zz0UuL7fLes9Ty9eJLtcz/plfmP1g2ktVqmW/Vrz1tP+iErVNJRm7/mst7g1gz6uU9V2b/P4dp1a45O0/8RVJCEmMGPf+mhkW/fceXg6fyKCKSr+ZNxMbGGoTg2uUbTByuu91nwTdLmTFvApv2rQYhmD1lfoEmAQ7tPkqLtk3ZeHQtSYlJTBw8PWPf97/OYvLQr4iMiGLS3LFYq+X6+uWbzBip+6apNh19ePWN9qSlppGclMyoDw2/6aggju4+TtM2r/D74V9JSkxi+pDnXzE8e8UMvho+m8iIKMZ9NwprGyuEENy8fItZo797aTGc33uGer71+Xr/D6QkJrN0+POJ1sHLxrJs5I9ERzyhXb9AOnzQFXunkkzePocLe8+wbNQCGnVoim+vALRaLalJKSz8rOD3vV/Ye4a6vvWZvn8+KYnJLBv+Y8a+QcvGsHzkAp5GPKFtv0ACPuiCvVNJJm7/hgt7z/DLqIU06NAEn14BpGu1pCSlsPiz7/Idi6JN5+Lo5TReMxphouHB6n3EXXtAhT66W9furfgHcyd7mu+cltFmVxrYgQMth1OilC0Nl+naCWFiQshfh3m091yB8uaZ03tO0cC3IQsOLtZ9JeiwuRn7xi2fwA8j5/EkPIoPp3/Mo4cRfLVB10Ye236U3+euoaRTSWZt+RYrGyuU9HRefa8zn7f9mMR8LsHfs+sgvn4tOXDqb91Xgn72Zca+5Wt+YMQXE3NcNeVUphSbd6/Bxtaa9PR03v2wF+2adSUuNj7bY3KiaNO5NvpnvNeMARMNoav3EX/tAWXV6/ZwxT+YOdnTaOeMTGO2Yy2Hoo1LRGNphmOrulwZVkjP3dKm82jqj5T7aRpoNMSs30nKzbvY9wgE4Onardj4t8CuSzuU1DSU5BRChrzcZ9qgTSd47E/U+G08wkRDxJrdJF6/T5neutvpIlbupNzgNzF1sMV9hu5bi5Q0LRc7jMC2cQ2c3vAh/nIwdXfpvt3l/oxVRO85k+3ppP8NokjuHZXyTAjRFxgOaIGzQDDqV4IKIT4CRgB3gQuAraIo/YQQ64Gq6FZa7Aa+AEYBvYBUIAx428jtIBlSH98uVgVkuVfhfB1dQXikphR1CJkctihR1CEYmB5xqKhDyORD56YvTvQva51UvBauTSC4qEMwcPht4w/yKyoO8wvnwW8F8bFbwZbnv2w7El7+MzoKysrEvKhDMGCpKdhDel+2aiXyf3tQYTExeiN70Xktsfj1tT+Zv7zViy/Dmfi7RR2CgZ9NaxZ1CJmUd3z5t3gUVNTTl/eV2C9Lk5D1xasByKeUu2eK/H2VWcX6xTIv5UqJ/whFUX4Bfslm3wJggZHt3Ywkn6H+kyRJkiRJkiRJkqQiJSclJEmSJEmSJEmSJKkwFeMHTRa14rVeWJIkSZIkSZIkSZKk/xlyUkKSJEmSJEmSJEmSpCIhb9+QJEmSJEmSJEmSpMKULm/fyI5cKSFJkiRJkiRJkiRJUpGQKyUkSZIkSZIkSZIkqRAp8kGX2ZIrJSRJkiRJkiRJkiRJKhJyUkKSJEmSJEmSJEmSpCIhb9+QJEmSJEmSJEmSpMIkH3SZLaEoSlHHIBVjS8r1KlYFpF/Q5KIOwcCUhl8WdQjFnmmxKkVgUtQBGFE+TRR1CJlEFsNMsilmffmTYphHTmlFHUFm8cVwPeYTTTFrkKT/pOLWZgOcKZFa1CFk4qwUv88+Y0Tx6khqpBa/juRucRu0ARPvrip+FS4fkm8cKfLMNa/arFjmZTEcLkiSJEmSJEmSJEmS9L+g+E1hSpIkSZIkSZIkSdL/J/LbN7IlV0pIkiRJkiRJkiRJklQk5EoJSZIkSZIkSZIkSSpM6dqijqDYkislJEmSJEmSJEmSJEkqEnJSQpIkSZIkSZIkSZKkIiFv35AkSZIkSZIkSZKkwiQfdJktuVJCkiRJkiRJkiRJkqQiIVdKSJIkSZIkSZIkSVJhSpcrJbIjV0pIkiRJkiRJkiRJklQk5EoJKV/K+dSj6aTeCBMN11bv49wPmw3SNJ3cm/JtvEhLTGb/4MVEXgwG4K2j35Ian4SiTSc9TcuGjuMBaDDsdSoG1Id0hcTHMewfsoiE8OiXHvu46XM4cPgEjg4l2fDrwpf++tkJnNCHqr6epCam8NewRYReCjZIU7KcE2/O/xRLextCLgWzfvCPaFO1VGpSk7cXD+HJg0cAXNl+kn3f//WvxvCMW73KDPxrEr9/Oo/L204A0PS99jTo4YuiKIRfu8+G4YtJS07NVUwBE5/HtHHYIsIuGompvBPd532KRUkbwi4G89fgH0lP1VLNrwG+Q19HSVdI12rZMWkl909dB+DzQ9+R/KycabX81OnLXMXjP7EPHmo8W7KJx768E6/N+xRLNZ6Najy1uzaj6YedAEhNSGLb2GVEXLmHrasjnb/9CBsne5R0hbO/7eHksh25isfNpx6NJvdGaDTcXL2Pi0bqWqPJvSnbxgttYjKHBy8m6mIwdh6utFrwaUYamwplODf7T678tAOHWhVo8lV/TK0siHvwiEOfLiA1LjFX8VRsXQ+fib3RmGi4uGYfJ380jMdnUm/cfb1ITUxm59DFRKh56DfrfSq39SIhMoaVfqMz0jcd+joe/vVR0hUSI2PYMXQR8Xmo+4XRHj1T94NAmnz5Nivqfkjyk7hcx9R2Ym8qq3mwbdhiwrMpR53mfYJlSRvCLwazZfAC0tW6lt3x5nZWtP96AKWrlQMUtg1fQsiZmy+Mp6xPPV5Ry9H11fu4YCSPXpncm3JqHh3SyyMzOyuazx5AyerlQFE4NHQJj04/P2edDwJpNP5tfquTtzzSV8GnHi0n6q7h5dX7OGOkXLWc1JuKany7hyzm0cVgTMxL0O3PcZiYmSJMTLi19QQn5qzPVwxZBUzsQxW1LdiUQ9vUTa9t2qDXNvnotU079dqm4hCHiXkJ+v7+JaZmpmhMTbiy9QT7v11XZPHYuTrSRa+NPPPbHk7kso38N+LLi7I+9Wis1rUb2dS1xlnq2rM22ydLmx00+08u/7QD7+GvU96/Pii68dGhwYtILMD4qPuEftTy9SYlMZlVwxbw4NIdgzQt+wTg824gTpVcGO09gPgnsQCU8XDjnVkfUb62O1tmr2HPki35iqEw+lqATw59R4pe3/9zLvt+gM4T+lLd14vUxBR+H7aAECNjJIdyTrw9/3Os7K15eCmYtYN/QJuqpdXAV/Hu2hwAjYkJZaqUZXL9gaQmpfDh2vGYmJfAxMSEC9uOs+vbP18YS2G02d7DX6eCf30URSHpcQwH81GOOuiN2TYMW0RoNvXrdfW6hapjtqzjyAEbJvHnp/O4vPUEdq6OvKZX/0//tofj+az/0n+XXClRzAkh+gkh5hd1HPqERtB8al+2957Jn74j8OjShJJV3TKlKd/GE3t3F35vMZRDI5fSYka/TPu3vDGN9QFjM70BOL/wb9b7jWF9wFju7T5L/S9eK5T4uwb6sXDO1EJ57exU9fGklLsLc32GsmnMUjpN6280nf+otziydBtzfYeS9DSe+j18MvbdPXmNBYFjWBA4Jl8TEi8jBqER+I96i5sHzmdss3V2oEm/ABZ2GscPAaPQaDTU6dQ0VzFV8dXFNL/1ULaMXkrHqcZjajvqLY4t3cYPPkNJfBqPtxrTncMXWdR+NIsDx7Bp+GI6ff1+puNWvDWVxYFjcj0h4eHriaO7CwtaD2Xr6KW0zyaeNqPe4sTSbSzw0eWRlxpP9P1H/PrmFH5qP5pD328gcMZ7ACjadHZPXcWitiNY3nUCDfr4Ubpq2RfGIzSCV6b1ZXevmWzyHUGlrk2wz1LXyrbxxM7dhQ0thnJ05FJeUetazK1QtviPZYv/WP5uPw5tYjL3tp0CoOmsAZyZvpbN7UZzf9span/UMVf5IzSCNlP7sqHvTH5pO4LqnZvgmCWeSr6elKzkwrJWQ/ln1FLaTOuXse/yHwf4q88sg9c9vehvfg0Yw6oOY7m9+yxNBuW+7hdWewRg7epIuZZ1iH3wONfxAFT29cTB3YUlrYeyY/RS/Kb2M5qu9ai3OLV0O0t8hpH0NJ56ajnK6fi2E3pzZ/95lrYdwbL2Y4i8GfLCeIRG0GRaX3b2mslfviOobKQclVPL0boWQzkycilN9fLolcm9ebD3PH+1HsFGvzE8vfH8nNZujri1qkNcHvMoa3ytp/Zlc5+Z/NZmBNW6NMEhS3wVfT0p6e7Cry2HsnfkUlpP18WnTU5lQ4/prAkYy9r2Y6ngUw9nb498x/JMFbUt+KH1UP4evZTAHNqm40u38aPaFui3TYvbj2ZJ4Bg2D1/Mq1napqKOQ5ucysqe01jcYQyLO4zBo3U9ynpXKbJ40rXp7Jq6igVtR/Bz1wk0zGUb+W/Fl1vP2uxdvWaywXcE7jm02evVNrupXpu9yX8sm/zHsllts++qbfbFBX+zyW8Mm/zH8uCfs3gNzv/4qJaPF07uLkzxGcTaMUt4c9p7RtPdOX2NH3pNJfJBRKbtCdFxrJu4nN1LDN8k51Zh9bXP/PrWVH4KHJOnCYnqPl6Udndhls9g1o9ZwmvZ5EvgqLc5tHQrs3yHkPg0nkY9fAE4sHgLcwNHMzdwNNtnruH28SskPo0nLTmVxW9PZW6HUXwXOIpqrT2p8IK6Vlht9sUFf7NRLUf381GOqqrX7fvWQ9mcw5jNTx2zzctSv579bX6j3+KW3jgyXZvOzqmr+KHtCH7qOoHGffxwykf9/09Q0ov+XzElJyWkPHPy8iAmOJzYe49IT9Vya+MxKvo3yJSmon8Dbvx5CICIM7cws7PGskzJHF9X/5NaU0tzFEV56bEDNPSqi72dbaG8dnZq+DcgaP1BAB6cvYmFrRU2TiUN0rk3q83lrbrVB0HrDlDTv2GxiqFJvwAubztJfGRMpmM0JiaUsDBDY6KhhKU5seFPchVTdb8GnFuni+nh2ZuY21lhY6Sc6Md0ft0BaqgxpSYkZ6QxszJHoWBlpppfA86r8YScvYlFNvFUalabK3rxVFPjeXj6BkkxCbqfz9zAztURgLiI6IxPgVLik4i8GYKts8ML4ynl7UFscDhxal0L3niM8gGZ61r5gAbcUuva4zO3MLM3rGsuLWoTezeC+IeRANh5uBJ+7Kru7zx4kQqBjV4YC4CLlwfRweE8VeO5tvkYHlnqvod/A66s08UTdvYW5nbWWKvxPDxxjaRow0/SU/TqfgmrvNX9wmqPAJpM7MXxaWsgj21RFb8GXFLzIPTsLSz08kBfhWa1uKaWo4vrDlJVjTu7481sLCn3SnXOr9kHQHqqlmS1vOWkdJZydHvjMSpkKUcVAhpwU82jR3rlqISNJc6vVOfG6ufnTNE7Z+OJvTg5bU2B2mtnLw+eBocTo8Z3Y9MxKme5hu7+Dbiq5km4Wq6s1Dx91g5oTE3QmJpSwGYAyNwWPHxBW/CsbTq37gDVjbRNJazMyW9QhRlHpnwrYZKra1hY8WRtIx/nso38t+LLrax17U42de2Wkbqmz7VFbWL02uxM46M8tpFZ1fVvxIn1BwAIPnsDS1tr7IyMBR5cCiZKXZ2pLy4yhnvnb5GepjXYl1uF1dcWRG3/BpxWx0j3zt7E0tYKWyP54tGsNhe2Hgfg9LoD1DYyTvPs3Ixzm45k/J6ilisTUxNMTF9c1wqrzc5ajvLat+mP2R7kcN2yjiNr6OXRK8/GkY+fjyPjIqIzVlykxCfxKJ/1X/pvk7dvFIAQog8wDF2vdR74HRgHmAGRwDuKooQLISYCHkBZoDwwU1GUJUZe7w1gAqAFniqK0krd5SaE2K6+xl+KooxQ0y8AGgGWwJ+KokxQtwcDawFf9fi3FUW5KYRwAhYCFdTtXyiKcjivf7e1qwNxoVEZv8eHRVEmyydT1i4OxIVEPk8TGoW1iwOJEdGgKAT+NgpFUbi6ag9XV+3NSNdwxBtUfb0FKTEJ/P3m9LyGVmzZOTvyVC8/YsKisHNxIO5RdMY2KwcbkmLiSdfqZjGfhkZlapTL16/Cx9umExsezfZpq3h04+G/GoOtswM1AxqyrOc0ynoOzDgmNvwJh5f8zZAj35OWlMLNgxe4dfBCrmKydXEkRi+m2DDd+eIinsdkqcakqDHFhEZh6/I8X6oHNKTtiB5Yl7Zjdf/nn8IrKPT6dRSKAmdW7ebM6uflLLfxxOQjnmc83/Lh1r5zBtvty5XGuXZFHgbdemE8Vi4OxIc8r2sJoVGUzlLXrFwcSNCLOSE0CqtndU3l3qUpdzYczfg9+tp9yvvX5/7OM1R89RWs3XI3oLNxcSBWL5640ChcvDwM04Q+jycuLAobFwfi9eIxptnwN6jVvQXJsQn82SP3db+w2qMKfvVJCHtClLokOC9sXRyMluv4LOUoOSYhoxzFhuryKafj07VaEiNj6TB7IGVqVSD8QjC7J64kNfH5GyljjJUjJyPlKD5LHlm5OKBotSRFxtLi24E41qpA5Plgjo9fSVpiMuX96pMQ+oQnl/OeR/qsjZSrrKsdbLJcwzg1vxIiohEawZtbp2JfyZkLv+wiPBd160Xy0xbEGmmb2hhpm4pLHEIjGLBlGo6VnDm1Yhchuci3fyNf7MuVxiWXbWRRxJeTrHUtPg91Lac2G8B75BtUUcdH29/I//jI3tmBaL3zR4dFYu/iSIzeWKCwFW5fq/C22vefXbWbs7no+8FwjPQ0LAo7F0diM42RbEnMNEaKxM45c/9ZwsKM6q092Th+WcY2oRF8vmU6pSq6cHTlTu6/oGwXVpsNUF+vHG3LYzmyM3Ld7LJct6zjyJhQ3VgTdOPIGgEN+aXnNMrOHIgxJcuVxjWf9f8/QT7oMltypUQ+CSFqA2OBNoqieAKDgENAE0VRvIE1wAi9Q+oBHYGmwHghhBuGxgMB6ut11tvuBfQA6gI9hBDl1e1jFUVpqL52ayFEPb1jYhRFaQzMB75Tt80FvlUUpRHQHfgpm79toBDilBDi1IH4G8ZSGG7KOtkqjKXRJdr02mT+6jCO7b1nUatvO1xeqZ6R5NTMP1jdeBA3/zpCrf5+xsL7TzKeHUouEun+C70YzJzmg/ixwxiOLd/B24uH/OsxdBjfm51frUFJz3yMhZ0VNfwa8G3LL5j1yqeYWZlTT72vMj8xZZ25F0YS6Se5tuMUP7Ydztr3v8Vn6BsZ25d1m8SSjuP4re9MGvbxo0LjGvmKJ2seGYsna/mv2LQWXj182DNjTabtJazM6b7wC3ZNXplpdUD28eSvrunHrClhQjn/+tzdcjxj25EhS6jez4+O26ZQwtqC9NS0F8aS/bkMEuUYT3aOzPqDn5oM4uqGI3j1y0vdf/ntkYmFGd6fd+bU7Bff92s8pFzkQU7XNpvjNSYmONepRNCvu/klcBwpCcm88nGnXISTi+uWTR4JExNK1a3E1RW72RQwjrSEZOp+2gkTCzM8P+/Mmfzm0QvP/eI0z/JUSVdY234syxt/jrOXB47VyxVOSHlsC67tOMWCtsP5PUvbVFziUNIVlgSO4bsmn+Hm5YFTtRfnW2HnSwkrc95Y+AU7c9lG/tvx5S+AXAWZ8aOmhAnl/esTrNdmA5z9+g/+aDSI238doWYBxkfG//7CWZmafQzGQng5fe0v3SaxtOM41vSdSYM+fpTPRd+fQ1B5jrtmu/oEn7pG4tP452nSFeYGjmZ6008o7+mB8wvqWmG02c+c+foPfm80iFv5KUf5HEc+S9J+Qm/+MTKOfMbMypw3F37B9skrSc5H/Zf+2+RKifxrg251wmMARVGihBB1gbVCCFd0qyX0nxy0UVGURCBRCLEXaAxsyPKah4HlQojfAf0nde1WFOUpgBDiMlARuA+8KYQYiO46ugK10K3YAFit9/+36s/tgFp6jZ2dEMJWUZRY/SAURVkMLAZYUq6XQcsRHxqFjd5SOWsXR+LDnhimcStF+LM0ro4ZD6579vDKpMgYgrefxsnLg7Dj1zIdf2vDEQJ+GcaZb17OA8uKQuPefjToqVus8vDcbezdSmXss3NxJDbLw4USomKxsLNGY6IhXZuOvasjsRG6fNVvnG/sO4dmqglWDjYkvOChci8zhrL13Hljnu4hXFYOtlT18SRdq8XE1IQn9x+REKUrRpe3n6RCg6qc32B8EU7DPn7Uf0sXU8j529jpxWTr4khslk/Un8UkTDQo2nTsXB2JM3J7yL0TV3GoWAZLBxsSn8RlzNwnRMZwbccpynpV5t6JqwbHNejjh3c28di5OGb6BCC7ePRvVylTozwdvx7Amr4zSdS7VUFjakL3hV9wccNhrm0/ZTRvsooPjcq0isHK1ZGELH97QmgUVnoxW7k6ZnpwVVlfT6IuBJOkt1Qy5lYo/7z9NQC2lV0o19YrV/HEhUZhqxePjasj8RGZ44kLi8LW9Xk8Ni6OeXpo5dUNR+i6fBhHc/mwwsJoj5KfxmNb3onuO6dnpO+2fSobXp1A4qOnRuPw7tOOemo5ClPL0bO1TLZGylFiVCzmdlYZ5chWr1zHhkYZP15RiA2NIlT9BOn61hO5mpTIbTmy1itH1q6OurxRFOJDo3h8VnfO4L9PUPfTTthVKoNNBSe67HqeR513TGVLx+zzKKf4DMpVlvji1GuYOU10pjQpMQk8PHqFij71iLr2IE8xgK5tKkhbYJulLXgma9tU3OJIjkng7tErePjU49F1w3z7t+LRmJrwxsIvuLDhMFdz2Ub+m/HlRkKWumadl7qmKuvrSWSWNlvf7b+O0G7FMILyMD5q2dufpj3bAnDv3C1K6p2/pEspnubylsuC+Lf62qx9v5tXZe4b6fsBmvb2o3HPNgA8yDJGsndxJCZLvsRHxWKZaYxUKmOM9Ixnp8y3buhLikng9rErVG/tSbiRupZxnkJos7O6/dcR/HJRjhr18aOBet0eGrlu2Y3ZnuWR/nVzq+fO68/GkY62VPX1JD1Ny9Wdp9GYmvCmWv+v5KH+S/9/yJUS+ScwnP+eB8xXFKUu8AFgobcva1pFCDFNCBEkhAgCUBTlQ3S3f5QHgoQQz2q+/tpcLWAqhHBHd+tIW0VR6gF/53C+Zz9rgKaKonip/8pmnZDIjUfnbmPn7oJteSc0JUzw6NKEe7vOZEpzd+cZqr7eAoAy9T1IiU0gMSIaU0tzSljrwjS1NKdcqzo8UQePdu7OGcdX9K9P9K3QvIZWrJxYuSvjwZRXd57Cq1tLAMp5VyEpNjHTbRPP3Dl6mVqBjQHw6t6KKztPA2DjZJ+RpqxnZYQQL5yQeNkxfNtyMN+2+IJvW3zB5W0n2PLlcq7uPM3TkEjKe1ehhIUZAJWb1+ZRDg/fO7ViF4sDx7A4cAzXdp7Cs7suprLeVUiOTTQYmAAE68VUr3srru3SxeRQ8XmZcalTCZMSpiQ+iaOEpTlmajkrYWlO5VZ1icjmTcrpFbv4KXAMPwWO4frOU9RT43HLIZ67Ry9TUy+eG2o8dm6l6L7oCzYOXkDUnbBMx3Sc+T6RNx9y4qdt2eZNVpFBt7F1d8FGrWuVujTh/s7Mde3+zjN4qHWtdH0PUmMSMi0DrtTVcBmwRSk73Q9CUG9QF66v3J2reMLO3cbB3QU7NZ7qnZpwO0vdv73rDDW76+Jx8dbV/RfdulGy0vPr6OFXnyd5qPuF0R49ufqAX70+YU3TwaxpOpj40CjWtx+X45vtsyv+4ZfAsfwSOJYbO09TW80DV28PkrPJg3tHL1NdLUd1urfkhhr3zX/OGD0+/tFTYkKjcKzsCkDF5rWJzMVtXI+DdHn0rBxVNlKO7u08QxU1j5zqe5CilqPER0+JD4nCzkN3TtcWtYm+/pAnVx+wxvMT/mwymD+b6PJoU0DOeZSd8HO3sa/0/BpW7dyEO1mu4Z1dZ6ih5omzWq4SIqKxcLTFzM4KABOLEpRvWYcnuXj4pzGnVuxiSeAYlqhtUz29tikpF22TZy7apuISh5WjLeZqvpmal8C9RW0ibxqvd/9WvnSa+T6Pbz7keB7ayH8zvtzIWtfcX9Bm69e1ZyobabNt9cZH5f3r8zSP46ODK3cyM3AkMwNHcn7nSRp3090dXMm7KkmxCf/KrRv/Rl9rrO9/lMME5dGVuzIeTnlp5ykaqGOkCt5VSIpNyHTrxjO3jl6ibuArADTo3opL6hgJwMLWksqv1OTSrufbrB1tsdCra1Wa1yHiVs5tVGG02ZB5nF0hl+Xo5IpdLAwcw0J1HPlszFYuh+uWdRz5rH7NbTGY71p8wXctvuDy1hP8rY4jAbqo9f9oHuv/f42iaIv8X3ElV0rk327gLyHEt4qiRAohHAF7yPhwq2+W9F2EEDMAa8AHGKUoykZ0t4AAIITwUBTlOHBcCNEJ3eREduyAeOCpEMIZ6ADs09vfA/hK/f9Z77YT+BSYpZ7PS1GUoLz80aD7JoEjX/5Ch1UjEBoN19bu58n1h9TspZttvvLrHu7vCaJ8G096HPqGtKQU9g9ZDIClkx1+P30B6B6OeHPDER7s0y3uaDy6B/aVXVEUhbgHjzk0epnR8xfU8AlfcfLseaKjY2jbtRcfv9eb7p0CCuVcz1zfG0RVXy++2D9H93Wcwxdl7Ou1bDgbRy4hNiKaXV+t5o15n9F26BuEXrrLmd/3AVCrQ2Ma92pHulZLalIqf3yW9y9kKWgM2XkQdItL207w4d/TSE/TEnrpLqdW78lVTDf2BFHF14tPD8zJ+Pq2Z3ouH87mEUuIi4jmnxmr6T7/M3yHvUHYpbucXauLqWaHRtTr3pL0VC1pySms+2QeANal7Xhz8WBAt0Lh4sYj3Np/3uD8Wd3cE4SHrxcfq/Fs0Yunx/Lh/K3Gs2fGal6b/xmth71B+KW7BKnxtBz0GpYOtnSYonsi9bOvIyvXsBr1urck/Mo9BmzVfbK8d9Zabu01fOaEPkWbzolxv9DuN11du7l2P0+vP6Rab11du75yDw93B1G2jSevHf6GtMQUjqh1DcDEwgy3VnU4NvLnTK9bqWtTavRrB8C9rae4ufbAC/PmWTx7vvyFbitHIEw0XFq7n8jrD6mn1v3zv+7hzp4gKvl60v+gLp6dw57H02HeJ5RvWhMLBxsGHP+eo3PWcWntflqM6oGDhytKukLsw8f8k4e6X1jtUUHc3hNEZV9P3j+gy4NtennQffkwdoz4ibiIaPbPWEPn+Z/SctgbhF8K5oJajnI6fveEX3h17kdoSpjy9F4EW/X25ZRHx8b9gr9ajm6s3U/09YdUV8vRtZV7eLA7iHJtPOl++Bu0iSkc1CtHx7/8hdbzdOeMvRfBoSEvPmdeKNp0Dnz5C11+1ZWry2v3E3X9IbXVa3jp1z3c3RNExTae9D6ky5PdQ3UxWJcpSbtvP0CYaBAawc3NxwneHVTgmG6qbdMnB+aQlqVtemv5cLaobcHuGavpNv8zfNS2KShL26RV26b1attUXOKwKVOSLnM+RGh0+XZ5y3Fu7DlbZPGU12sj39drI2++oI38t+LLrWd1zU+vzTZW18q28aSbWtcOZWmzXVvV4UiWNrvB6B7Yq21k/MPHHB2V//HR5b1nqe3rzfj9c0lJTGHV8AUZ+z5YNorVIxcRE/GEVv3a0+6Dztg6lWTU9plc3hvE6lGLsHWyZ/imGVjYWJKuKPi8G8h0v6Ek5WG5fWH1tdal7Xhdr++/tPEIt3PR9wNc3XuW6r5ejNj/HSmJyfyhN0bqv2wEf45cQmzEE7Z9tZq3532G/9A3CbkUzMnfnz+zonZAI24cPJ/pOT+2ZRx485uP0Kh17fzfx7j6grpWWG22fjmKy0c5urFHN478XL1uG/Wu2zvLh7NphG4c+c+M1bw+/zPaDFPHkep1y06FhtXwVOv/h2r93z1rLTfyWP+l/zZRWN9w8L9ACNEXGI5u9cJZ4C90t0o8BI4BjRRF8VEfdOmG7kGVFcj+QZfrgaroVmHsBr5AN7nRUFGUT9U0W4DZiqLsE0IsB14BbqNbTbFJUZTl6oMulwGB6FZH9FQfdFka+AGoiW5C6oC6OiNbxm7fKEr9giYXdQgGpjTM/VdO/a8yLValCEyKOgAjyqcZe8BG0YkshplkU8yeD/WkGOaRUy4fD/JviS+G6zGfaIpZgyT9JxW3NhvgTInUog4hE2el+H32GSOKV0dSI7X4dSR3i9ugDZh4d1Xxq3D5kBS0pcgz18Lr1WKZl8WvtfgPURTlF+CXLJs3ZpP8uqIoxh81+/z1uhnZvFz99yzNq3o/98vh5X5QFGVSltd/jG7lhCRJkiRJkiRJkiQVuWL4GYYkSZIkSZIkSZIkSf8L5EqJf4GiKBP/5fNV+jfPJ0mSJEmSJEmSJOUgvXjdPlScyJUSkiRJkiRJkiRJkiQVCblSQpIkSZIkSZIkSZIKkyJXSmRHrpSQJEmSJEmSJEmSJKlIyEkJSZIkSZIkSZIkSZKKhLx9Q5IkSZIkSZIkSZIKU7q2qCMotuRKCUmSJEmSJEmSJEmSioRcKSHlyCM1pahDyGRKwy+LOgQDX56aUtQhZDK7wfiiDsGAa1pRR5BZUjGcjg02VYo6hEz2aMOLOgQD07X2RR1CJouKYQ9a09SyqEPI5K4oXn0IQIsUs6IOwYCZUrzqf5xGFHUIxV5oMaz/zkrxCipcFLPOH+hdzMa1xzTWRR2CgWZJ8tN86d9XvFovSZIkSZIkSZIkSfr/Rn77RraK4eeFkiRJkiRJkiRJkiT9L5ArJSRJkiRJkiRJkiSpMKXLlRLZkSslJEmSJEmSJEmSJEkqEnJSQpIkSZIkSZIkSZKkIiFv35AkSZIkSZIkSZKkwiQfdJktuVJCkiRJkiRJkiRJkqQiIVdKSJIkSZIkSZIkSVJhkg+6zJZcKSFJkiRJkiRJkiRJUpGQkxKSJEmSJEmSJEmSJBUJefuGVGCOvp5UndofYaIhdNVu7s7bmGm/VRU3as79GNu67tyasYb7CzZn7Cv/QUdc324DKMRfuc+VQT+Snpxa4JgCJ/Shqq8nqYkp/DVsEaGXgg3SlCznxJvzP8XS3oaQS8GsH/wj2lQtlZrU5O3FQ3jy4BEAV7afZN/3fxU4puyMmz6HA4dP4OhQkg2/Liy08wD4TeyNh68XqYnJbBm2mPCLwQZp7Ms70XXeJ1iUtCHsYjCbBy8gPVWLo4crr84eiHPtSuyf/QcnFm/NOKZh/wC8evqAEJxbvZeTP+/Ic2xlferReHJvhEbDjdX7uPDDZoM0jSf3plwbL9ISkzk0eDFRF4Ox83DFZ8GnGWlsKpQhaPafXP4p7zGU96lH84m9ESYarqzeR9CPhjE0n9SbCmoMe4cs5rFeHgqNoPvfU4gPe8K2/t8A0HBwN2q+7UNiZCwAJ77+nXt7z+UproCJz8vzxmGLCDNy3UqWd6L7vE8zrttfg38kPVVLNb8G+A59HSVdIV2rZcekldw/dT1TzAO2TCU27Alr3p2dp7ie+WzyJzRp05ikxGS+GjyTGxdvGqQZO2801etVQ5uaxpWga3wz6lu0aVqsba0Z+/0oypQtg4mJCWsX/cH23/N+7TLywdcL98nvgomGiN9283B+5rpbultLyn7yGgDa+ERuj1pMwuW7AHjM+RhHv4akPn5KkO/gfMdgzDsT3sXTtz4piSksGTaPu5fuGKRp16cD/u92xLmSK5949yPuSWzGvhpNavP2+P6YmpoS+ySGGT3G5zmGdnr1/+8c6n8Xtf6HZ6n/HdX6fyBL/QddOeq3ZQqxYU/4891v8hwbwJsT+lPb15uUxGRWDPuR+0byqHWfANq825EylVwY5v0e8Woe1fNrSKchPVAUhfQ0LX9MXs6tU9fydH5Xn3o0nKJrg26u3sfl+Yb1v8GU3pRV6//RwYt5ciEYgBrvt8fjbR9QFKKvPuDo4MWkJ6dSslYFGn/VnxLWFsQ9eMThTxaQFpeY57wBcPath/dkXft0+7d9XMsSn20VVxp9+wEl61bi4le/c32hXhs9531c/bxJfhzDTt9R+To/6NrpV9R2+no27fQrWdrpSLWcmdlZ0Xz2AEpWLweKwqGhS3h0WtdW1OzvR83+/qSnaXmwO4hT09YUaUwOtSrQ7Kv+lLCyIPbBIw58uoDUPFy3wuhrHSu70nX+876uZIUyHJzzZ677W/+JffBQ+5Et2fQj9uWdeG3ep1iqMW1U+5HaXZvR9MNOAKQmJLFt7DIirtwDwNzOio5fv49TtXKAwpbhi3l4xrAPyEn3Cf2opdb9VcMW8MBI3W/ZJwCfdwNxquTCaO8BGXW/jIcb78z6iPK13dkyew17lmzJ07mNsfPxpsLk9xAaDY9W/0PYD+sz7Xd8rRWuH+v6kfSEJIJHLyLxcjDCvAQ11k1DY26KMDEh6u+jhHyT+7Ksr4JPPVqp45HLq/dx2sh4pNWk3lRUy/U/Qxbz6GIwNq6O+H33IVZO9ijpCpd+28s5tYw0H9sT93beaFPTeHo3gn+GLiYlJiFf8ZXy9aTG1L4IEw0PVu0heN6mTPutqrhRZ+6H2NV158aMtdxd8Py6mNpZUXvOB9jUKIeiwKXBC3l66ka+4vjPkbdvZEuulPiPEUK0FEJcEkIECSEsizoeNILqX73Hubenc7zlYMq81hyramUzJUmNjuP62GXcW5C5QTVzcaDcgA6cChjFidbDQKOhTNdmBQ6pqo8npdxdmOszlE1jltJpWn+j6fxHvcWRpduY6zuUpKfx1O/hk7Hv7slrLAgcw4LAMYU6IQHQNdCPhXOmFuo5ADx8PXFwd2Fh66FsG72U9lP7GU3nO+otTizdziKfYSQ9jcdTzZek6Hh2TVjJ8SWZ34yUrlYOr54+LO88gaXtx+DR1huHSs55ik1oBK9M68uuXjPZ4DsC965NsK/qlilN2Tae2Lm7sL7FUI6OXErTGbr4Y26Fssl/LJv8x7K5/Ti0icnc3XYqT+d/FkOLqX35u89M1rYZQZUuTXDIEkMFX0/s3V1Y3XIo+0cupeX0fpn2132vPU9uhhi89vmftvNn+7H82X5snickqvjqyvP81kPZMnopHacaL89tR73FsaXb+MFnKIlP4/FWr9udwxdZ1H40iwPHsGn4Yjp9/X6m4155tz2PjcScW6+0aUw597K806Iv34z8lsEzBhlN989fu+nTuj/9272PuYUZHXsGAtC1b2eCb9xlgP8HfPHGUD4e/wGmJfI5X67RUHn6+1x+ZxpBrb+gdNcWWFYrlylJ8r0ILnb7knNth/Dguz/xmPVhxr5Hv+/j8ttT8nfuHNTzqY+LuysjfD5l2ZgF9J020Gi666evMrPXJB49iMi03crOij5T3ue7AV8xxv8L5n+c98mjymr9X9R6KNtHLyUgm/rvM+otTi7dzuJs6v+JLPX/mYYFLEe1fbwp4+7CBJ/P+W3MYnpOG2A03a3T15jbawqRWfLo2uELTOswnOmBI1g5YgG9vv7Q6PHZERpBo+l92fvOTLb4jKBSlybYZan/bmobtKn5UI6PWEpjtQ2ydHGg+nv+bO/wJX+3GY3QaKjUpQkATWYPIGj6Wv5uO5r7205R66OOeYorg0ZQf3o/Dr4zk+2tR1Cha1Nss/S1KU/iOTtuBdcX/m1wePDvBzn49sz8nVslNIIm0/qys9dM/vIdQWUj7XQ5NY/WtRjKEb12GnQTAw/2nuev1iPY6DeGpzd05cWlWU0qBDRgQ7vRbGgziosLjZexfzOm5rMGcGr6Wja0G829baeok4frVlh9bdTtUH4OHMvPgWNZ9uo4UhOTubYjd32dh68nju4uLGg9lK2jl9I+m36kzai3OLF0Gwt8dOMiLzWm6PuP+PXNKfzUfjSHvt9A4Iz3Mo7xn9Cb2/vPsajtcJa0H53ndqCWjxdO7i5M8RnE2jFLeHPae0bT3Tl9jR96TTWo+wnRcaybuJzdSwzftOeLRkPFaQO50WsKF30/p1TXFlhUzdyPpNwP5+rr47jkN5iQ7/6g0tcfAaAkp3LtzfFc8hvCJf8h2Pt4Y12/Wp5DEBqBz9S+bOozk1VtRlDNyHikoq8nJd1dWNlyKHtGLsVHHY+ka9M5NOU3VrUZyR9dJlK3b7uMY+8dvMCqdqNY7T+G6NuhNPykUz4yCNAIan71Lmfe/orDLYfi+lpzrLO0R2nRcVwdu5zgBYaTRDWm9uXx3iAOtxjK0TYjiL/+MH9xSP+vyEmJ/xAhhAnwDjBbURQvRVHy93HLS2RXvwoJd8JIuhuBkqolYsMRnNo3ypQm9XEMsUG3UFK1BscLEw0aCzOEiQYTKzNSwp4UOKYa/g0IWn8QgAdnb2Jha4WNU0mDdO7NanN56wkAgtYdoKZ/wwKfOz8aetXF3s620M9T1a8BF9cdAiDk7C3M7ayxLlPSIF3FZrW4qubLxXUHqebfAICEyBhCz98mPct1LF3FjYdnb5GWlIKiTef+8atUC8hbXpb29iA2OJy4e49IT9VyZ+MxKgQ0yJSmQkADbv2pi//RmVuY2VtjmSV+1xa1ibkbQfzDyDydH6CMlwcxweHEqjHc2nSMSv6ZY6jk34Drah5GqHlopcZg7eJIhTZeXFm9L8/nzkl1vwacW6crzw/P3sTczgobI9dNvzyfX3eAGmp5Tk1IzkhjZmWOgpLxu62LI1XbeHF2zd58x9fcvxk7/twFwOUzV7Cxs8GxjKNBuuN7TmT8fCXoGk6upQFQFLCytgLA0tqS2OhYtGmGbUVu2HhXITE4jOR74SipaTzeeAjHgMztUeypa2ifxut+Pn0dM9dSGftijl0m7Ulcvs6dk/r+jTi8fj8At87ewMrWGnsjbdK9S3d4rK7Q0tekc0tObz9OVMhjXdyRMXmOIT/1/8K6g1TVq/9hRuo/6MqRRxsvzq/Zl+e4nvH0b8ix9QcAuKPmkZ2RPHpwKZgoI3mUnLWcK4pBmpyUytIG3d14jPJZ2qByAQ24rbZBkWobZKHmoTA1wUTty0wtzUgI1/Vldh6uRBy7CkDYgYtU6Ji5POaWo7cHccHhxN97hJKq5f7GY5TNEl9yZAxPzhm/Ro+PXSWlgGU7azt9O5t2+qaRdrqEjSXOr1Tnhto+pqdqMz6hrdGnHed/2Ex6ShoASXko34UVk72HK+HqdQs5eJFKgbm/boXV1+qr1Lw20fciiMllX1fNrwHn1X4k5OxNLLLpRyo1q80VvX6kmtqPPDx9gyQ1bx6euYGdq66NN7OxpMIrNQhS6356qpbkPH7yXte/ESfUuh989gaWeaz7cZEx3Dt/i/R89htZWXtXJTk4NKMfidp4CIeAxpnPqdePxJ25lqkfSU9IAnRtgihhouvk8sjZy4Po4HBi1HJ9fdMxKmcZj1T2b8AVtZyF641HEiKieaSugkmNT+LJzRBsXHTX6/6Biyha3Sf1YWdvYeNq2Ffnhr069k9Ux/5hG45Qpn3mcV/K4xhigm4bjP1NbCxxaFqTh6t04w4lVUtaPldr/BcpirbI/xVXclLiBYQQfYQQ54UQ54QQK4UQnYQQx4UQZ4UQ/wghnNV0E9X9e4QQN4QQ72fzem8IIS6qr3dA3dZPCDFfL80WIYSP+nOcEGKyEOI4MBp4ExgvhFglhLARQuwWQpwRQlwQQnTJLm51m5MQYp0Q4qT6r3lB88fcxZHkkOedYnJIJOYuuWvkUsKecG/BZpqdWUDz84tJi0kgav/5goaEnbMjT/ViigmLws7FIVMaKwcbkmLiSVcb56ehUdg6P09Tvn4VPt42nd7LR+BUNfPs73+VrYsDMXr5EhuW+W8GsHSwITkmIaPTigmNwjZL3mX16PoDKjSujmVJG0wtzPDw9cTOrVSOx2Rl5eJAfEhUxu/xoVFYZb1mLg7E68VvLI17l6bc2XA0T+d+xtrFgTi9GOJCo7DO8vq6NJFG0zSb2Itj01dDuuEApE5fP97YOR2f2e9jZm+Vp7hsXRxzdd2SYuKzvW7VAxry8e5Z9Fw2nM3DF2dsD5jQm3+mr0YxEnNuObmU5lHI84Hio9BHOLmUzja9iakJ/t3bcWLfSQD+Wr6BilUrsO70Wpb9s4R543/M8xvKZ8xdHEl5+Djj95TQKMxcsi+Lzj3bEr3nbL7OlRcOzo5EhjyPKyosEocc4srKpbIbVvbWjFoziUmbZ9K8W+s8x2Dr4kBsHut/bC7qP0DbCb3YW8ByVNLZkSd6efQkLJKSuexLnvEMaMSE3d/yyc+jWTliQZ6OtXRxIEGv/ieERmHpatgGJejlYUKIrg1KDHvClQVb6XpyLt2C5pMSm0DY/osARF+7T7mA+gBUePUVrNzy9ybA0sWRBL03oAmhUVjm4tq8TFnb6QQjbWR27bRtRSeSImNp8e1AOu+YSvNZAzC1NAfArrILzo2r8+rmiXT4cyylPSsXeUzR1+5TwV933Sq9+grWebhuhdXX6qvZuSmXN+W+r8vaj8Tkox95xvMtH27t0634c6hQhoTIWF6d/QHvbZ1Gx68HUELNw9yyd3YgWi+26LBI7PNY918mMxdHUkL0+5FISuTQXju91Y6ne88836DRUHvnHLzOLyfmwDniz+b9tgRj4xGbXIxHsqaxLVcap9oVCTt7y+Actd5sxd29+RtzW7g4kqR37qSQqFyP/a0qliElMobacz+iyT8zqDVnICZWeSsz0v9PclIiB0KI2sBYoI2iKJ7AIOAQ0ERRFG9gDTBC75B6QEegKbqJAzcMjQcC1NfrnIswrIGLiqK8oijKVGATMFxRlHeAJOA1RVHqA77AN0LHWNwAc4FvFUVpBHQHfsp1ZmRHCINN+p/E5sTU3hqn9o042ugTDnt+gImVBc7dWxZGSIZvcowm0v0XejGYOc0H8WOHMRxbvoO3Fw8pcEzFgvGMeWGaF70/jLwZwtGFW3hr1Sh6rBhB+OV7ef/EIofrkXOa54k0JUwo71+f4C3H83buHF7f4G83mkahQlsvkiJjeKzeX67v0sp/+K3FEP4IGEtCRDTNvnynoGEZBCZeEPu1Haf4se1w1r7/LT5D3wCgahtv4iOfEmrkvuKCBpjTpMLg6YM4f/w8F07o3rQ19mnIzUu36N6gBwMCPmDQ1E+xssnbxE1OsWRXgO2a1aHM2225O21l/s6VF3mIyxgTExMq1fVgTv/pzOozhc6fvYGzu2uBY8hNu/iiMD3aeJEQGWP0nvk8KWAeAZzbcZJJbQezcOAsOg/pkcfT578NMrO3olxAfTa+Mpj13p9hamVOpW66Of9jQ5ZQrZ8f7bdPoYSNRcZqgLzKVb9WyF7UzqiJDA9UFISJCaXqVuLqit1sChhHWkIydT/VLR3XmGgwt7dmS6eJnJy6Gp+Fnxq+xr8c06EhS6jRz49O26ZQwtoCbWoerlsh9bXPaEqYULVdfa78nfu+LjflJzd1oGLTWnj18GHPDN1zEjQmGlzqVOLMr/+wNHAsKQnJNPs4b7cEGD/vv1u2M8lDPLbN6lC6ZzvuT9frR9LTueQ/hHMNB2DtXRXL6hXyEcKLy4fxNM8TlbAyJ3DRIA5O/NXgeSgNP+tMujada38dznNsupMb25i7ayZMTbCt686DX3ZxrN1otAnJVPqsy4sPlP7fkw+6zFkb4E9FUR4DKIoSJYSoC6wVQrgCZoD+03g2qrdUJAoh9gKNgQ1ZXvMwsFwI8TuwnhfTAuuy2SeA6UKIVkA6UBZwNha3mr4dUEuvIbMTQtgqihKb6UWFGAgMBBhs24BXLbP/1CI5NBJzvU/Fzd1K5foWDIdWdUm8F0Gq+gDAR38fx75RNcLVJYZ50bi3Hw16+gLw8Nxt7PVisnNxJDY8OlP6hKhYLOys0ZhoSNemY+/qSGyELu5kvcb7xr5zaKaaYOVgQ0IhLOsubPX7tMPrLV2+hJ6/nWkFg62LI7ER0ZnSJ0bFYm5nhTDRoGjTsXN1JC78xdfz/Nr9nF+rW57eevibxIZFveCIzBJCozJ9EmXt6pix/DlzmlJZ0jyPv6yvJ5EXgkl6nPel7aD79MxGLwYbIzHo0pTKkiaayoGNqehXnwq+npiYl6CErSVt5n7EnkELSNSL58pve+mwfOgLY2nYx4/66nULycV1e1aeX3Td7p24ikPFMlg62FC+YTWqt2tAVR8vTM1LYG5rSdfvPmLDFy/+hLlr3868+rbumRBXz13Hyc0pY5+TqxOPw40vKe47uDclHe35cuS3Gdvav9me335YDcDD4BBC74dRoUp5rgbl7SGFoGuPzMo+X6Vh5upISrhhWbSqWZEq33zE5XemFsrtGgBte7endc92ANw5d5NSbqV59nmZo0spnhiJKztRYZHEPokhJTGZlMRkrp24TIWalQi/E5rjcfX7tMNTr/7bZilHcS+o/7a5qP/lGlajSrv6ePjoyr65rSWvfvcRW3JRjlr3DqB5z7YA3D13Cwe30oDuuju4lCI6F22PMTdPXKF0RResHWwzHob3IgmhUZlWMVi5OpIYZtgGWenloZWbrv67tKxD3P1HJEfpznV/6ymcGlYleP1hYm6Gsqfn1wDYVnbBra1Xvv6mhNAorMrqndvVkaQs/Vphi8/STlvlpZ1WFOJDo3isfmIb/PeJjAmA+NAnGc8Behx0GyVdwdzRNiM/iyKmp7dC2fm27rrZVXah3Auu27/V1wJ4+HgSfjGYhBf0dQ36+OGdTT9iZ6T+G+tHYvViKlOjPB2/HsCavjNJjNa1mzFhUcSERhESpMvDq1tP5GpSomVvf5qqdf/euVuU1IutpEspnuaz7r8MKaGRmLnp9yOlSDXSXlvWrEilWZ9wvfcUtEbaGW1MArFHLmLv403itXt5iiHOyHgkPkuexBkZj8SrbYLG1IQOiwdxbcMRbm3P/NyRGq+3pFJbbza8NSNPMelLCo3CQu/cFm6OJOdy7J8UEklySBRP1Yehhm8+jvtnufmM9v8J+aDLbMmVEjkTGE79zQPmK4pSF/gAsNDblzWtIoSYpj6UMghAUZQPgXFAeSBICFEKSCPztdB/zSQl+xuA3gGcgAaKongB4eqxxuJGPUdT9XkUXoqilM06IaHGuFhRlIaKojTMaUICIPbsLawqu2JRwQlRwoQyXZvxOJcPXkp++Bi7+lXRWJoB4NCyLgk38vewmxMrd2U8mPLqzlN4ddOtuCjnXYWk2ETiHkUbHHPn6GVqBeruE/Tq3oorO08DYONkn5GmrGdlhBD/yQkJgDMr/sl4MNb1naep070FAG7eHiTHJhCfZVACcPfoZWqo+VKne0tu7DpjkCYrq1J2ANi5laJ6+4Zc3ngkT3E+DrqNnbsLNuWd0JQwwb1LE+7vzHze+zvP4PG6Ln6n+h6kxCSQqBd/5a75v3UDIOLcbewruWCrxuDRuQnBWf724F1nqKbmYRlvD1JiE0iIiObE17/za+PPWdVsMP988gMhhy+zZ5DuTZmV3n277u0bEnXtwQtjObViF4sDx7A4cAzXdp7CU11BVNa7CsmxiQaDSYBgvfJcr3srru3SlWeHis8fOupSpxImJUxJfBLHnplr+a7JZ3zf4gvWfTafO0cu52pCAmDDL5sYEPAhAwI+5ND2wwS87gdArfo1iY+NJyrCcADXsWcHGrVuyORPp2X6NCfiYQQNWuiWSTuULkl5j/KE3s35zXZ24oJuYunuinn5MogSppTu0oKoLO2RWdnSVF86nBuffU/S7fydJzd2r9zO+MBhjA8cxpmdJzJuufDwrkpibAJPjbRJ2Tmz8wTVGtVEY6LBzMIMD6+qhNx8cTk6s+IflgWOZVngWG7ksv7f06v/dXNR//fP/J0fm3zOghaD2fTZD9w9cjlXExIA+1fuYHrgCKYHjuDczhM06dYKAHc1j2LykEdOeuW8fG13TEuY5npCAiAy6Da27i5Yq/W/YpcmPMjSBj3YeYbKahtUSm2DkiKiiX8YSen6VTBR+zKXFrV5elPXl5mrbSNCUGdQF26s3J3rmPQ9CbqNjbsLVuV1fW35Lk0I2XE6X6+VX1nb6cpG2ul7O89QxUg7nfjoKfEhUdh56Fb4uLaoTbT6cLt7O07h2rwWoJsAMDEzzdWERGHGZKF33TwHdeHaC67bv9XXAtTq3JRLubh14/SKXfwUOIafAsdwfecp6qn9iFsO/cjdo5epqdeP3FD7ETu3UnRf9AUbBy8g6k5YRvr4R0+JCY3EsbIuDys1r82jXIzjDq7cyczAkcwMHMn5nSdprNb9St5VScpj3X/Z4oNuYO7uipnajzh2acGTnSczpTFzK02VJSO5M+g7km8/f7CnqaMdJna6lX7Cwgy7lp4k3sr7uDb83G1KVnLBTi3X1To34U6W8nFn1xlqquXMWW88AtB21gCe3AghaMm2TMdU8KlHg49eZcu7c0hLSslzXM/EnL2FVWUXLNWxv0vXZkTksj1KefSUpJBIrNR6V6plHfmgSwkA8W8v//svUW+D+AvdG/lIIYQjsBsYoCjKaSHEMsBdURQfIcREoCvQBN0tF2fR3eYRkuU1PRRFuaX+fBboD9gAM4EW6FY7XAI6K4qyTwgRpyiKjd7xy4EtiqL8KYQYBFRRFOUzIYQvsAdwV8+fKW51lcdvwFlFUWapr+WlKEpQTnmwx/nNFxaQUm29qTpF97VAIav3cve7v3Dro3uTErJiF2ZO9jTc+RWmtpYo6Qra+CSOtxyCNi4R9+FvUKZLMxStlrgLwVwZshAlh+Wt+yxLvCgcADpO7kfV1vV0Xwk6fBEhF3QLWnotG87GkUuIjYjGobwTb8z7DMuS1oReusu6wT+iTUmjcR8/GvdqR7pWS2pSKtun/Mr9M9nfE/jlqYI9rX/4hK84efY80dExlHIsycfv9aZ7p4B8v97sBtl/VaD/lL5UVvPl72GLCVPz5c3lw9g64ifiIqIpWd6JLvPVrwS7FMzmLxagTUnD2smefpunYG5jiZKeTkpCMkvajSQlLpFef3yJpYMN2tQ0dk/9jbuHL2U6r2suVr6WbeNJ40m9dF/Ht3Y/57/fRPXebQC4tnIPAK9M60tZn3poE1M4NGQxked18ZtYmPHGqbmsazqE1NgXP/81KZvp2Aq+njSb2AthouHa2v2cmbeJWr10MVz+VRdDi6l9Ke9Tj7TEFPYNXcyj85m/usytSU08PwjM+ErQNt99SKnaFUFRiH3wmAOjfs4YOOiL0GRf1TpM6YeHet02DVtEqHrdei4fzuYRSzKuW/f5uvIcdukuf32hK8/NPnyVet1bkp6qJS05hV3Tfsv0laAAFZvUpOnAjpm+EnSPNjznTNQzaOpnNPZpRHJSMl8PmcW187rX/2rFNGYNn0NkeCS7g3cQ9iCcxHjdA60ObDvEiu9+pZRzKUbNGU4p51II4Lcf17BrvfE3ANO19ka36yvZpj7uk3VfURy+Zg8P567DuY8/AOErduIx+yNKdWxCsvrANEWr5Xz7kQBU/XEw9s1qY+poS+qjp9yfvZaI1dm/GVmUh9tge08eQL3W3iQnJvPT8B8IvqD7ZHHIsrH8PPJHoiOe4NcvkMAPumLvVJKYyKec33uGn0fp3uB3GNiFlm/4oqQr7F/7Dzt/NvyGBYCaSvZfyuSnV/+36tX/N5YPY5ta/+316n94lvrfN0v9/0mt/89UaFKTxgMDM30l6F2R+4HvW5Pfo1ZrT1ISU1gx/EfuXbgNwCfLRvHryEU8jXiCb78O+H3QGTunksRGPuXS3rP8OmoR/h924ZVurdCmaUlNSmH99JXZfiVoixQzo9vd2njSYJKu/t9as59L32+iqtoG3VDboEbT++KqtkFHBy8mSq3/dYd1o2LnJihpWp5cvMuxYT+RnpJG9fcCqNZPt2Lm/rZTBE1fa/TcZrkYi7m08cRL/UrQO2v2c3XuRir30X3afHvFbsyd7Gm3fSolbHXXKC0+mR2tR5AWl8grP36CU7OamDvakvQohkuz/yR49f5szxWnMbo+m3J67fSNbNrpJnrt9EG9dtqxdgWazxqApoQpsfciODRkMSlPE9CUMKHFNwNxrF2B9FQtJ6f8Rujhyy/Mj8KMqdZ7AdRQr9vdrac4PcPwuoXlsNa4sPpaUwszPj02lwUth5BspK9Lz2EJfYBeP7JFrx/psXw4f+v1I6/N/wyLktaEX7rLRrUf6fj1AKp3aEzMA92zFtK1Wn7u9CUAzrUq0vFrXR5G34tgy7BFGQ/FDBe5u+3ljcnvUlOt+6uGL+C+Wvc/WDaK1SMXERPxhFb92tPug87YOpUkLvIpl/cGsXrUImyd7Bm+aQYWNpakKwop8UlM9xtKUjZf4do79cVtkn2b+lSY9B5oNDxeu5vQ7//EqbduXPZo5Q4qzfoYh8CmpDxU+5E0LZcDh2NZsyLu332O0GhAo+HJ5sOEfPd7juc6prE2ur2iryctJ/ZCY6Lh8tr9nJq3iTrqeOSiOh5pPbUvFX1013T30MVEnL+Da6NqvL5+PI+v3Mt4zs/Rr3/n7t5z9D74DSZmpiSpH7SFnbnJvjHLDM5dPRe3mZVu60V1dez/cPVe7ny3gXJ9dHXmwYp/MHOyp8nO6ZnG/odbDkMbl4ht7YrUmjMQjZkpiXcjuDhoIWnqg0Oz4x++xnij9B+TuPenIn/jbek7oFjmpZyUeAEhRF9gOLrbKM6ie7P/LfAQOAY00puUcAM8gArATEVRlhh5vfVAVXSrGXYDX6i7fgW8gIvobsGYmItJidLAZqAEEAQ0BzooihKcNW5FUfqp6X8AaqK7deeAunIjW7mZlPg35XZS4t9U0EmJly2nSYmikptJiX9TdpMSRSmnSYmikJdJiX9LbiYl/k15mZT4t+Q0KVEU8jIp8W/JblKiKOVmUuLflN2khPRcTpMSRSWnSYmikNtJiX9TbiYl/k3ZTUoUpdxMSvzb5KTEy1NcJyWKYZNavCiK8gvwS5bNG7NJfl1RFONfQP/89bpls8vo0+/0JyTU3/vp/fwY3UM1jR1nELeaPm9P/5IkSZIkSZIkSZKkQiInJSRJkiRJkiRJkiSpMMkHXWZLTkq8JIqiTCzqGCRJkiRJkiRJkiTpv6QY3lktSZIkSZIkSZIkSdK/SQjRXghxTQhxUwgxysh+eyHEZiHEOSHEJSFE/5dxXrlSQpIkSZIkSZIkSZIKk1K8b98QQpig+1IEP+ABcFIIsUlRFP2vRPoEuKwoSichhBNwTQixSlGUAj1FVq6UkCRJkiRJkiRJkqT/bY2Bm4qi3FYnGdYAXbKkUQBbIYQAbIAooMBf2SJXSkiSJEmSJEmSJElSYSoGD7oUQgwE9L8tcrGiKIvVn8sC9/X2PQBeyfIS84FNQAhgC/RQlIIvAZGTEpIkSZIkSZIkSZL0/5w6AbE4m93C2CFZfg8AgoA2gAewSwhxUFGUmILEJW/fkCRJkiRJkiRJkqT/bQ+A8nq/l0O3IkJff2C9onMTuAPUKOiJ5UoJSZIkSZIkSZIkSSpMxfxBl8BJoKoQwh14CLwFvJ0lzT2gLXBQCOEMVAduF/TEclJCytFhixJFHUKxN7vB+KIOIZNhpycXdQgG5tYvXnm0KPFKUYdgYKhFzaIOIZPhqU5FHYKBBRbJRR1CJgEpVkUdgoHB8SeLOoRMjleoWNQhGFgaW7qoQzBgYnTFbNGxK4bj5uKVQ2BfDPMoTlO8cunVxOIVD8AQ0wKtMH/pqpuYFHUIBkzMLIo6BAP+RR3A/whFUdKEEJ8COwAT4GdFUS4JIT5U9y8EpgDLhRAX0DXNIxVFeVzQc8tJCUmSJEmSJEmSJEkqTMXgQZcvoijKVmBrlm0L9X4OoRDmieQzJSRJkiRJkiRJkiRJKhJyUkKSJEmSJEmSJEmSpCIhb9+QJEmSJEmSJEmSpML0H7h9o6jIlRKSJEmSJEmSJEmSJBUJuVJCkiRJkiRJkiRJkgpT8f9K0CIjV0pIkiRJkiRJkiRJklQk5KSEJEmSJEmSJEmSJElFQt6+IUmSJEmSJEmSJEmFST7oMltypYQkSZIkSZIkSZIkSUVCrpSQ8i1gYh+q+HqSmpjCpmGLCLsYbJCmZHknus37FIuSNoRdDGbD4B9JT9VSza8BPkNfR0lXSNdq2TlpJfdPXQeg06z3qdrGm/jIGBb5j8p1PIET+lBVjeevYYsIvWQknnJOvDn/UyztbQi5FMz6wT+iTdVm7HerV5mBf03i90/ncXnbCQCavteeBj18URSF8Gv32TB8MWnJqbmKyW9ibzx8vUhNTGbLsMWEG8kj+/JOdJ33SUYebR68gPRULY4errw6eyDOtSuxf/YfnFi8NeOYhv0D8OrpA0JwbvVeTv68I9f5lBvjps/hwOETODqUZMOvCwv8em0m9cbd14u0xGS2DV1MRDb58Op8XT6EXwxm6xe6fMjp+Eqt69FmYm+EiYYLa/Zx4sfNALz6w6c4VnYFwNzOiuSYBFZ0GAtA6Rrl+X3aCGxsrUlPV+jm35uU5JRsY/9y+nBat2tOYkISIz+fyOXzVw3STP/uS+p41kIIQfDtu4z8bCIJ8YnY2dsyY+4EKlQqR3JyMqMHTebG1Vt5yrvyPvVorv6NV1bvI0j9G/U1n9SbCm10+bN3yGIe6+Wv0Ai6/z2F+LAnbOv/DQCVOzam4eBuOFR1Y32nCTw6fydPMelz8vWk1tQ+CBMN91ft5da8TZn2W1dxw3PuB9jVdef6jLXcXvB35hfQCFrsnE5SWBSnes3Kdxz6ek98D0/f+iQnJrN42HzuXrxtkKZd3w60f/dVnCu58pFXX+KexAIQ+EEXmnVpBYCJqQluVcrysXd/4p/G5fr8rj71aDSlN0Kj4ebqfVyab3jNGk7pTVn1mh0dvJioC8EA1Hi/PVXe9gFFIfrqA44MXkx6cir1hnajyts+JEXp4gya8Tshe87lMWeem/71WNr5tyYhIYnPPx7F+XOXs007Y+Y4er7TjUpl62dsa9aiMdNmjMG0hClRkU/o0rF3vmMBsGzekFIjP0KYaIhZv52nS9dm2m/l2xSHT/tCugJaLY+/XkDy2UuYODtRZvpwTEo7Qno6MX9uJWbVhnzH0XFCH6r7epGamMK6YQsJMdKPOJRzosf8z9R+5A5/6vUj7k1q0nF8bzSmpiQ8ieWnHlMA6DZzINXVfu37gJF5iqmo+7bs2ll9eW2jW4/pSeV23qSnphF9N4LtwxaTHJOQ8Xq2bqXov/trjny7nlN6fd+z1/RVX/NiNvH46sWzPUs8xo51qlWBdtPfxdS8BOlaLbvHLifs3G0sStrQaeHnuHhW5tIfB9gzfkUOV0qnvE89WkzsjcZEw+XV+zhrJL4Wk3pTUa3/u9U228S8BF3/HIeJmSkaExNubT3ByTnrM46p28+POv38UdK03N0TxNHpa3KMo+3E3lRWxx/bchh/dJr3CZZqv7tl8PN+N7vjPzj0LSnxSaRr01G0WlZ0Gg9AmVoV8J/2LibmJVC0WnaOWw7Hr78wv0r5elJjal+EiYYHq/YQnKUPsariRp25H2JX150bM9Zyd8GWjH2mdlbUnvMBNjXKoShwafBCnp668cJz5sagyZ/QpM0rJCcmM33wTK5fNHzdL+eNpoZnddJS07gSdJVZI79Fm6bFxt6G0d8Mp2xFN5KTU/hq6CzuXAsuUDw9J7xLXV9vUhJT+HnYfO5dMuy3ffu0x+/djpSp5MoX3v0z+rVnKtXzYMxf01n06bec3nYszzEUVtkG8PogkGbj3ubneh+S9CT3/e1/jnzQZbbkSon/h4QQhV6bq/h64ujuwg+th/L36KUETu1vNF3bUW9xfOk2fvQZStLTeLx7+ABw5/BFFrcfzZLAMWwevphXv34/45hzfxzkt74z8xRPVR9PSrm7MNdnKJvGLKXTNOPx+I96iyNLtzHXVxdPfTUe0L158x/1FjcPnM/YZuvsQJN+ASzsNI4fAkah0Wio06lprmLy8PXEwd2Fha2Hsm30UtpP7Wc0ne+otzixdDuLfIaR9DQeTzWmpOh4dk1YyfElmQdkpauVw6unD8s7T2Bp+zF4tPXGoZJzrmLKra6BfiycM/WlvJYoYYlDJReWthrKzlFL8ZvWz2i6VqPf4tRP21naWpcPddV8cPf1NHq80AjaTe3Lur4zWdZ2BDU6N6FUVTcAtnwynxUdxrKiw1hubDvJje0ndceYaOg49yPGD59OYMs36dV1IGmpadnG3rpdcypWLk+7xl35cuhUJs8cbTTd9HFz6Ozbk04+bxHyIIxe7/UA4KMv3uXKxWt08nmLEZ9MYNy0YXnLO42gxdS+/N1nJmvbjKBKlyY4qH/jMxV8PbF3d2F1y6HsH7mUltP7Zdpf9732PLkZkmlb1LUH7Bg4l9Dj1/IUjwGNoPZX/Tnx9tfsbzkMt9eaYVOtbKYkqdFxXBr7C3f0BpL63N/vQNyNhwWLQ4+nb32c3V0Z1voTfh69kP5TBxpNd+PUVb56ZyKP7kdk2r510UbGBQ5lXOBQfv/6V64ev5ynCQmhETSe3pc978xks88IKnVpgn2Wa+bWxhNbdxc2Nh/K8RFLaTyjHwCWLg7UeM+fbR2+ZEub0QiNhkpdmmQcd2XJdrb6jWWr39gCTUi082tFZY9KNPb2Z+igL5k5Z2K2aT2962Bnb5dpm529LTO/mUCvnh/RssmrvNd3UL5jAUCjofTYTwn7eCz3u7yPTQcfSlSukClJ4rGzPOz+IQ/f+IhH47/BadIQ3Q6tlsjZi3nQZQAP3xmE3VudDY7NrWo+XpR2d2GOzxA2jPmJztPeNZouYFRPDi/dxre+Q0h6Gk+DHr4AWNhZ0XlKf1YO+Ibv/Uew+uO5Gcec+fMAv/T9Os8xFXXfllM7+0x+2ujggxdY7jeKXwLG8OROKK980inTa/qOf4c7+wzLuNAI2k7ty/q+M1nedgTVOzfBMZt4fm41lF2jltJOL57sjm01pidHv1vPyg5jOfLNOlqN6QlAWnIqR775k/3TfjOa78bia6W22avbjKBqDm32qpZD2TdyKa3VNlubnMrGHtP5PWAsv7cfSwWfejh7ewDg1rQmlfwbsNZ/NGvajSJo0dasp86ksjr+WNJ6KDtGL8Uvm/FH61FvcWrpdpao4496arl50fFr3prGL4FjMyYkAFqP7snhuev5JXAsh+asw2d0zxdnmEZQ86t3OfP2VxxuORTX15pjnaUPSYuO4+rY5QQb6UNqTO3L471BHG4xlKNtRhB//eX0JU3aNKacezl6tujDzJFzGDrDeBu366/dvNOqH33bDsDcwpxObwcC0Oezt7lx6Sb9/N5n2qCvGDT5kwLFU9fHmzLurozx+YwVYxbSa5rxfu3m6Wt802syjx9EGOwTGg3dR/Xi0oH89R2FVbYBbFwdKdeyDrEPHucrNun/Bzkp8T9OCGGSn+Oq+TXg/LqDADw8exMLOytsypQ0SFepWW0ub9V9KnNu3QGq+zcEIDUhOSNNCStzQMn4/d6JqyRG521epYZ/A4LW6+J5cPYmFrZW2DgZxuOuF0/QugPUVOMBaNIvgMvbThIfGZPpGI2JCSUszNCYaChhaU5s+JNcxVTVrwEX1x0CIOTsLcztrLE2kkcVm9XiqhrTxXUHqebfAICEyBhCz9/O+NTimdJV3Hh49hZpSSko2nTuH79KtYCGBq9bEA296mJvZ/tSXkuYWXNJzYfQHPKhfLNaXFfz4dKfB6kSoMuHKv4NjB7v4uXBk+Bwnt57RHqqlqubj+Gh5p2+aq++wpWNRwGo1Kouj67c5+ol3Sce0U+ekp7D/X3t2rdmw1rdJ/tBpy9ia2+Dk3Npg3RxcfEZP1tYWICiK89Vqlfm6EHdhMjtm8GUK+9GKSfHHHIrszJeHsQEhxOr/o23Nh2jUpa/sZJ/A66r+ROh5o+Vmr/WLo5UaOPFldX7Mh0TfTOEp7dDcx1HdkrWr0LCnTAS70agpGoJ2XAU5/aZy2LK4xieBhmWYwALV0fK+Hlzf9XeAsfyTH2/xhxatw+AW2evY2VnjX0ZB4N0dy/d4fGDRzm+VpMuLTi68WCezl/K24PY4HDi1GsWvPEY5QIyX7PyAQ2486fumj0+cwsze2ss1WsmTE0wsTBDmGgwsTQjMZftTV6079iWtas3AHD61Dns7e1wdnYySKfRaJg4eQSTx2dewdL9jU78vXkXDx/oytDjx1EFise8bnVS74WQ9iAM0tKI37Yfa99mmdIoiUkZPwvL53VM+ziKlCs3dWkSEkm9cw9TI3U0N2r6N+Cs2o/cV/sRWyP9SOVmtbm09TgAZ9YdpJbaj3h2bsal7Sd5GhIJkKkvCT5xlYQ8TG49U9R9W27a2fy00XcPXkTR6trekDO3sHFxzPR6T+89ItLIG0wXLw+i9V7z2uZjVMkSj4d/Ay5nE0+2xyoK5raWAJjbWhGn5kVaYjIPT15Hm5S71ZFlvDx4GhxOjHqOm5uO4Z4lPnf/BlxT4ws/ewszvTY7TR0baUxN0JiaPivm1OndjrM/biY9RTeJnpjlWmZVxS/zNbHIpt+t0KwW1/TGH1XVWHN7fCaKgrmNXh5GvLjtss/Sh4RtOEIZI31ITNBtlCx9iImNJQ5Na/JQ7T+UVC1peqttCqJFQHO2/7kTgMtnrmBjb0OpMoZ997E9JzJ+vhJ0FSdXXTtaqVpFTh86C8C9W/dxKeeCQ2nDfii3vPwbcXT9PgBun72Bla0V9kbagfuX7hCZTb/Wtl8Hzmw7Tkzk03zFUFhlG6D5hF4cnbYGRX+j9D9HTkoUAiFEHyHEeSHEOSHESiFEJyHEcSHEWSHEP0IIZzXdRHX/HiHEDSHE+9m8nrMQ4i/19c4JIZqp24cIIS6q/74wcpwQQsxS918QQvRQt/sIIfYKIX4DLuTnb7R1cSRGHXgBxIRFYeucucG1dLAhKSY+Y+ARGxqFrcvzNNUDGvLR7ln0XDacTcMX5yeMDHbOjhkDwWfx2LlkjsdKjSddjedp6POYbZ0dqBnQkJOr/sl0TGz4Ew4v+ZshR75n+IkfSIpN4NbB3GWZrYtDpjyKzSaPkmMSMvIoJkseGfPo+gMqNK6OZUkbTC3M8PD1xM6tVK5iKgrCxITY0Mz5YOOScz7E6eWDjYuD0eNtXRyIDXn+Zigu1DB/yzWuTsLjp0QHhwPgUNkFBYWff5/Pht2reP/TPjnG7uxahtCQ8Izfw0IicHYxfPMG8NX3Ezh6aSeVq1ZixU+6pedXLl3Hv6PuU9R63rVxK++Ci2uZHM+pz9rFgbgsf6N1lrzTpYk0mqbZxF4cm75at+S9EFi4OJCod+6kkEgsXlB+9dWa0ocrk39DeYkPfnJwcSQq5PmnLVFhkTg6534i6BkzCzPqtfbmZB6XuFq5OJCgd80SQqOwcs1S3l0ciNfLt/iQKCxdHEgMe8LlBVt57eRcugfNJzU2gdD9FzPSVe/vR8d/ptNkzvuY2Vvl+W96xtXVmZCHYRm/h4SE4eJmuNpqwMBebN+2m/DwzINcD49KlCxpx4YtK/hn/zrefKtLvmMBMC1TmrSw5+dIC3+EibNhm2bVpjnlNi3F5YcpPBr/jeHruDljXqMKSUZuscoNO2cHnupdO+P9iG2mfiQmNBI7td0pVdkVS3tr3lszjo83T8OrW8t8xZE5pqLt23LTzhakjQao26MVd/bpVnGUsDSn8UevcuS79QbpMs6l95qxoVHY5DKenI7dO+lXWo3pycBjc2k1ricHv858+1Bu5afNjtdLIzSCN7dPo3/Qj9w/eIGIIN3tfiUru+DauDrdN02kyx9jKeNZOcc48jP+iA193jfndLyCwpu/jqLPlil49vTNSLN78q/4jOnJh0fn4jO2JwdykYcWLo4kZepDojB3yV17bVWxDCmRMdSe+xFN/plBrTkDMbEyz9WxL+LkUpqIkOdt0qPQR5R2yX6y08TUhIDufhzfq/sQ4ubl27QO1NX/ml7VcS7njJNr/iZLAUo6lyJKL5+ehEVR0iX3476Szo54BzRm36qd+Y6hsMp2Jb/6xIc9IfLKvXzH9p+Snl70/4opOSnxkgkhagNjgTaKongCg4BDQBNFUbyBNcAIvUPqAR2BpsB4IYQbhr4H9quvVx+4JIRoAPQHXgGaAO8LIbyzHNcN8AI8gXbALCGEq7qvMTBWUZRaRv6GgUKIU0KIU6fibmbzdxpuyzrDKYwmev7jtR2nWNB2OL+//y0+Q98wep7cyk08xhPp/uswvjc7v1qDkuXNm4WdFTX8GvBtyy+Y9cqnmFmZU69r84IE9cI0L5oojrwZwtGFW3hr1Sh6rBhB+OV7pKcZfgpdfBQsH0R2x+ci72p0acpVdZUE6D4ZLNewGkM/HMdbr76HX6AvTVs2yj5yo+cwfoFGfT6J5nXbc+v6HTp29QNg8dzl2Je0Y9Pe3+g9oAeXL1xDq83DtcpN+cgmxgptvUiKjOGx+qyCQmGsjOdSGT9v3SdgBXiehTF5uWY58W7XiBunrubp1g01ACPnz5rEeJk2s7eifEB9NrwymHXen2FqZY57N117c/2Xf9jYdAh/+40lMTya+hPeyVtcLzh/1jxydilD567t+WnRrwZpTU1NqOdVm7ff/IA3XxvA0BEfU9mjUr7jMVbFjTWECXsO86Dze4QPmqR7voT+S1ha4PzteB5/vQAlPn+flhrPlxfH+izvTExMcKvrzor+s1je5yt8P3uNUu4u+YrleUzZny/nRLr/Cty35aY8F6CNfuXTzqSnpXPlr8MANBvSjdNLt2daTZk5nJzHFdnFoyhKjsd69m7LvsmrWNxkEPsmryJgltHPiF4oN2Uou/oPoKQr/N5+LL80/hxnLw8cq5fTHWOqwdzemnWdJ3J02mr8f/z0RYEYOUXuy01Ox//WbTK/dBzHn31n4d2nHeUaVwfAu1db9kxZxcKmg9gzeRXtZ+YiD412Iblrr4WpCbZ13Xnwyy6OtRuNNiGZSp8VbII047VzU+/0DJ0+iKDj5zl/Qjex9+v81dja2/DzzkV0f/c1bly88X/s3XdYFMf/wPH33oH0qiigqCi2WCj2DnYxsSaxRI1ppqixdxONPaaYRKOJiYk1amKMLRbsxl6xN1BUpClF2tHu9vfHnXDHHcqBBvL9zet5fIS72dsPszOzc7Ozs+ad+wsRzzM7i3r6ffoWf85fU6wLAC+ibFtYl6HhiO6c+mpjkeMS/neIhS6fv3bARlmWHwHIspwgSVJ9YINuQKAMoN8L3yLLsgpQSZJ0AO1gwWYTnzlY93lq4LEkSa2Av2RZTgOQJGkT0Bo4r7ddK2CdbptYSZIOAY2BZOCULMsmvw3IsrwMWAYwq8obuU1Oo8Ed8e+nHRWPunjb4Oq8o7srqXFJBp+TnpCCtaMdklKBrNbg4OFqcnrovVPXcalSHhsXe1RmLG7TZFBHGupG6R9cuI1TvnhSYk3Ho1Aq0Kg1OHm4kqKbXlixgTevLdKe5G1dHKgR6ItGrUZpoSTx/kPSdYvLXd11msoNa3Bx81GTMQUM7oCfLo+i8+WRg7srKfnySJWQgpWjbW4eOXq45k4bfZqLGw5xccMhANqOf52UmOJNn37eFNaOKKy1t3/IOZk4eBjmQ2q+Y5M/H+z18iElJsHk9gpLCxw8866o2Hu4GkwXlZQKanRpzOpun+S+lhKdwP2T10lM0O7/0N6j1G1QO/cWC4A33n6NvoN6AXDx/FU89K4gu3uWJy624HseNRoNO7aE8O6wwfy5bhupqWlM+viz3PcPnN1G5N2oArfPLy06Aft8f2N6vvKhTVM2X5okqgU3oUrHACoH+aK0ssTSwYZ2337I/pFLC73/Z8mITsBGb9/WnmXJiCnc7QYuTWpRvnMAQe39UFhbYmlvg9/3wwgd9r3ZcXQY3IXAftqBoNsXw3D1zLsi5epelsRCTCPOr9krrTi+9YjZ26VHJ2Crd8xsPVxR5cuT9OgE7DzL8uQ6nJ2nK6rYJNxb1yP1/kMyde3NvR1nKNeoBnc2HSXjUd507bC1BwhaNdasuN5+dwCD3nwdgPPnL+FZMe/LsqenO7HRhvcgN2hQB+9qlTl1XntlzcbWhlPnQ2ji34moqBji4xNJT1eRnq7i+LEz1Ktfm9vhEWbF9ERO7CMs9GYgWVRwQx1XcJuWcfYSlpU8UTg7oklKBgslFRZ+Surf+0nfZ7ptLkjTQR1prDuPRF64jZPesdOeR/Idu3znEUePsrnt+uOYeNISU8hWZZKtyiTi1DU86lQh/k4M5ihN57aU6ISntrNQ9Da67qutqd7en9/7z8t9zcPfh5rBTWgzuR9WjrbIsow6M5vzK/eYjMehkPGkxSahzBeP/rZ1+7TmwPTVANzcfpJOn79LUaQWos1Ozddm23lo49OXlZzOg+PXqBzYgIQbkaRFJ3J75xkA4kJvI8sy1q4OuQvfAtR7swO1dOUmRtf/eHIDjIOJPlr+866D/nk3OqHA7Z/8nx6fzK3dZ/Hwq07kqRvU69OafTO0eXjj75N0+fxdnrXMZUZ0AtYG5xBXMgt5DsmIiiczKoHH57QXz2K3ncR7RPdCbWtKrzd78Mob2jUhrofeoLxnXpvk5uFGfGy8ye2GjB6Ec1lnvnh3eu5r6anpzBuTd9vb7yfWEn3PvHYgaFAXWvdvD0DEhXBc9fLJxd2VpNjC9/uqNKjG0EWjAbB3caB+YABqtZrQkNPP2DLPiyjb9w5dxMHLjdd3z839zNd2zmbjK9NRPSzabSalnljoskBipsTzJ2E8zLsIWCzLcn3gfcBa7738aWVJkuZIkhQqSVLoM/ZTmFgKkvaU90w6s2oPPwVP4afgKdwIOUODPtqpaRX9fchIURmd8AAijl/lpeAmAPj2acONPWcBcKmi90WvXlWUlhZmDUgAnFq9h6XBU1gaPIXrIWdyp8pWehLPQ+N47ujF49enDddCtPEsbD2aha1GsbDVKK7uPMX2T1ZwPeQsj6Pi8fL3wdK6DADVWtblYVjBXyrPrdrLL8FT+SV4KjdDzlKvTysAPP2rk5mSTpqJPLp7/Cq1dTHV69OaW3vOPfNvty2rXXjO0bMstbo04uqWY8/c5t+kyUgmJ+kBOUkP0GSmUVeXDx5PyYf7x69SU5cPdV9tTXiINh/C95wzuX3Mhdu4eLvj5OWGwlJJ7VeaEa6Xd1Va1SMhPIpUvQGbiMMXcatdGWsba5RKJY1bBBB203Bsbu0vf9A9aADdgwawd+dBevbtBoBfw3qkJKfy0MSgRGXvSrk/B3VqQ/itCAAcHO2xtNSO/b4+sBenj58zWH/iWeIu3MapqjsOur+xevdmROQrHxF7zlFTlz/l/auTlZJOelwSpz7/nTVNPmZti9HsHfY9UUevPtcBCYDH58Oxq+aOTWU3JEslnj2bE7v7bKG2vTFnPfv9h3Og8cecf/87Hh29UqQBCYC9q3blLk55NuQUrfoEAlDdvybpKek8NnNQwsbBltrNXuJcyKlnJ84nPvQ2Dt7u2OmOWdUezYgMMTxmkSHn8H5Ve8zKBVQnKzkdVVwSaQ/iKRfgg9JG2964t6pLcpj2a4GN3v3cXl0bkXQj0qy4fvn5N4Ja9ySodU92bt9L3/49AWjYyJfk5BSjWzT2hByibs1WNGzQnoYN2qNKV9HEvxMAO//eR7MWjVAqldjYWBPQsAE3b5j3VBl9mZdvYFmlIhYV3cHCAruubUk7eNwgjYVX3iTCMnV8kCwttAMSgNtnY8i+fY/Hq/40e98nV+9hcfAUFgdP4VrIGfx15xEvfx8yU1SkmDiP3D5+lbrBTQEI6NOaayHaL4vXQs5StXEt7RoN1mXw8vMhLsz8hfdK07ntWe0sFK2Nrtq2AU0+fJm/3vmanIy8px+tf3UWP7UczU8tR3Pul92cXLw1d0DiSTzO3u446j6zVgHxvFRAPAVtmxqbSKVmdQCo3LIuSRHmfYF8In+b7dO9GXdMtNm1dPFV0GuzrV0dKOOovS1LaW1Jpdb1chcpvrP7DBVbaie2Onm7o7S0MBiQALi8ci8rg6eyMngqt0LOFuq8e+/4VWqZ6H+E7TV9TC1trChjp+3CWtpYUbVNPR7p2qLUuES89PIwsRB5mHw+HFu9c4h7zxbEFfIckvXwMRlR8dhW104ALtu6XrEWuvxr5Rbe7vQ+b3d6n392H6XLq9r27qWAOqQmpxFvYqD05f7BNAlszIxhsw1mUtg72mGhO/e/MiCYCycvkp5q3gyuA6t3MTN4PDODx3M+5BTNewcCUM2/BqqUdB6baAcKMrn1MCa1+ohJrT7i7M4TrP3kJ7MGJODFlO2E65Gs8B/GmhajWdNiNKnRCfzRddr/7oCE8FRipsTztw/4S5KkhbIsx0uS5Ao4Qe6A85v50veQJGkeYAcEApNkWd6C9hYQ/c/8EPhGtzClHXAYWCFJ0ny0gw+9gPzPZDsMvC9J0krAFWgDjAdqF/ePDNsfik+QH8MOf02O7pGgT/RbMZ7tE34iNS6JffPW0XvxCALHvUbMlbuEbjgIQJ2ujWnQpzXqbDU5mVlsGrYod/te3w2jSvM62Lo4MPLEIg4t3EioblZAQW4eCKVGkB+jDn2tfWza+Lx4Bv46ni0TfyIlLok989fx2qIRtB/7GtFX7nLu94NP/dzI0HCu7DzFB3/PQZOjJvrKXc6s21+oPArfH0r1IF8+OPwV2aos/h6Xt27G6yvGsWPCz6TGJXFw3np6LB5O23GvEXMlggu6PLJzc2LItllY2dsgazQ0frsLP3WYSFaqit4/jMTGxR51dg67P11JxnNa3OmJ8dPnc/r8RZKSkmnfcyAfvTOIPq90LtJnydkqku7F8e4/2nzYpZcPvVeMY/fEn0mLTeLwvPW8vHg4rca/RtyVCC7p8uH2/lC8g3yNtpfVGvZ9spI+qyegUCq4tOGQwcJotbs34/pWwy82mY/TOfPzTjaFrEKWZQ7tPcrBPQVfDT+45whtO7Rk36ktqFQZTPp4Ru57P637lqmjZvEwLp4Fiz/D3t4eSYLrV24xfbz2yl/1mt588f1M1GoN4TduM3nUTPPyTq3hyCcr6bZmApJSwY0Nh0i8+YCXBrYD4Oqa/dzbH0rldr70P/IVOaosDo599vosVbs0otXMwdi4OtB1xTjir97l74HmPfHmSXyXJ6+gyfrJ2se5rTtI6o1IKg/uAMC9VXuxcnOiZcgcLBxsQCNTdWhXDrceT06qyuz9FcaF/WfxCwrgy8NLyFJl8tO4xbnvjVsxlZ8nLCEpLpFOQ4Lp9kEvnNycmbt7IRcOnGP5xCUANOrclMuHL5CpMj2F/GlktYbTU1fS/jftMQtff4jHNx9QY5D2mN1avZ8H+0LxbO9Lj2PaY3Z8tPaYxZ8P597fpwjePRs5R03C5bvcWqNdxM1/Wj9c6lYBWSYt8hEnJ/xS5DzaE3KIDp3acip0D6p0FR8Pm5L73ro/ljFqxDRiY4xXb3/i1s3b7N/7D4eObUWj0bB21UauXyvGY/jUGh7NXYz7D3ORlApS/tpNdvhdHF7TDgim/PE3dh1b4fBKB+QcNXJmJrHj5wBg5V8Xh+4dybx5m4p/aAfdEr77BdU/5nW4AW4cCKVmkB9jDi0kW5XJJr3zyOBfJ/DXxGWkxCWxe/46+i0aQcexrxF15S5ndOeRh+FR3Dx0kRG75iNrZM5sOEDcTe0Xtte/G061Ztrz2oTji9i38E/OPuP8AyV/biuonfXVtUEX1uwvUhvdftabKMtY8Npa7WO/o86HsXfKr8/MD1mtYb/eZ17WfWYDXTwX1+znzv5QqgX58o4unt168ZjaFmDPpOW5jwpVZ2YTMml57j7fPbqQMg42KC0t8OnciI0D55Nwy/TFCVmt4Z9PVvKKrs2+rmuz6+riu7JmP3d1bfYbujZ7v67NtivvTLuF76NQKkAhEb7tJHf3hQJwbcMh2n05lL5756HJUrNv9I8m9//EbV0evHdYu4+deufdPivGsVvX/zg0bz3dFw+n9bjXiM133jW1vW05R3otGwVoFyy8uuUYdw5p1wPZNXE57XWPi8zJzGb3pOV48HSyWsP1yb8SsH4KklLBg3UHSLsRSSXdOSRy1V7KuDnRLGQuFg42yBqZKkO7crT1ONSpKq5P+ZX6S4ajKGOB6m4cl0cW/xHmAMf3naRZu6asP7qaDFWGwayHBavm8vn4r4iPjWfs/FHERsbyw1ZtH/bwjiOs+GY1VWpUYeq3E9GoNUTcvMv8cV8WK55LB85RPyiAuYcWk6XK5NfxS3LfG/nrFFZMXMrjuETaDwmm8/s9cHJzZsaur7h04BwrJz2fPHlRZVsQnpDESqfPnyRJb6L98q9GezvFX8BCtAMTJ4DGsiwHSpI0A/AEqgOVgQWyLP9k4vMqoL2dopruMz+UZfm4JEljgCfPLPtZluVvdOlTZVm2l7Q3dy0AuqKdkTFbluUNkiQFAuNkWX75WX+L/u0bpUF20W9hf2Fs5NIV1Liz5n3x/Td8G/DpsxP9i35UXSvpEIyMta5T0iEY8MoqfeuUrLc2f5DgReqcXfTFJl+U0Wnmfxl/kU5WrlLSIRhZnlL0BedeFGWhJj/+exw1pSseKNz00H+TTanqHWmllrL5z34ZBT9yu6TMsnj6U5f+bbUsi/5UjhclQG397ET/so/uryltTUCRqDbOLvGWw+bVaaUyL8VMiRdAluWVwMp8L28pIPlNWZZNP3A47/NiAaPVe2RZ/hr42sTr9rr/ZbSDI+PzvX8QOPi0fQqCIAiCIAiCIAjCi1bKxlQFQRAEQRAEQRAEQfj/QsyUKEGyLM8o6RgEQRAEQRAEQRCEF6wYj2X9XydmSgiCIAiCIAiCIAiCUCLETAlBEARBEARBEARBeJHEAyYKJGZKCIIgCIIgCIIgCIJQIsSghCAIgiAIgiAIgiAIJULcviEIgiAIgiAIgiAIL5JY6LJAYqaEIAiCIAiCIAiCIAglQsyUEARBEARBEARBEIQXScyUKJAYlBCeam7ckZIOwcDk8q1KOgQjHjklHYGhbwM+LekQjIw8N7OkQzCQ2GhaSYdgpGKGuqRDMDDXIrakQzCSoEot6RAMLOlXvqRDMKLc2KikQzDw+HHpOmYA41tGl3QIxkpZP3X7PxVLOgQjUkkHkI9NKfxyEW9RuiZAt55bqaRDMCJ9+rCkQzAQlGVd0iEYuVCmdPVHhP8fSlfrJQiCIAiCIAiCIAjC/xtipoQgCIIgCIIgCIIgvEhy6ZthVVqImRKCIAiCIAiCIAiCIJQIMVNCEARBEARBEARBEF6kUrgWTWkhZkoIgiAIgiAIgiAIglAixKCEIAiCIAiCIAiCIAglQty+IQiCIAiCIAiCIAgvkiyXdASllpgpIQiCIAiCIAiCIAhCiRAzJQRBEARBEARBEAThRRILXRZIzJQQBEEQBEEQBEEQBKFEiJkSwnP31Vcz6Nw5iPR0FUOHjiM09LJRmmXLvqR162Y8fpwMwNCh47h48Wqx9tt5xmBqBPmSrcpiy7gfibkcYZTG2cuNPouGY+1sT8zlCP4avQRNtpqaHRsSNPZVZI2MRq1m92eruX/mJgAfH/mGzLQMZLUGjVrNz698YnZsFQMb0GTmICSFglvrDnLp+21GaZrMHESldn7kqDI5MnoZCZcjcKzuQeDS4blp7CuXJ/TLjVz9efdT99fus0F4B2k/a+fYZcSZyAsnLzdeXjwMa2d7Yi9HsGPUUjTZ6qduX7VtA9rNGISkVHBp/UFOLdH+HS9/PxzXah4AWDnakpmczqquUwEoV9sLpZMnkqQdA81JegCYd0/dtLlfc/joKVxdnNm85geztjVXt+mDqRXkR7Yqiz/H/UDUlQijNC6V3Oi7eAQ2TvZEXbnDxtFLUOvyzrtZHbp9OgiFhQXpiSn83HcWTh6uvPr1h9i7OSNrZE6v28/xX3eZHZtbkC/1Zg1GUiq4t/YAYYu3Grxv7+OJ7zfv41Tfm+vzN3B76d+GH6CQaLN7LhkxCZwa9IXZ+y+MUTOH07xdUzJUGcwZvYCbl28ZpZn05Thq+9ZCAu7fiWTOqM9RpWc8tximzBlLmw4tyFBlMGXETK5eumGUZvbCadT1q4MkQUT4PaZ8PJP0NBVvDxvIy326AGChVFKtZlVa1unM46TkIsejrBOAde+hoFCQfTyErL0bDd/3qY/Ne9PQxMcCkHPxGFm71oOFJbYjPwcLS1AoyAk9StbO34oUg0dgAwJmadug8HUHubbYuA0KmDUYz3a+qFVZnBj9I4mXIgCo+U5nqr8RhCRJhK89wI2ftWXX+aXKNJ7/NhZ21qRFPuTYsCXkpKqKFJ9DW38qzXgPSakgfv0eYpf8afC+S8+2VPiwNwDqtAwipy5FdS0iL4FCQa3tX5EdG8/tt2YXKQZ9Fr6NsRk8HBRKsg78TebWdSbTKavVwn7W96R/O5PsU4cBkGztsBk6HmUlb0Am/ccFqG8V7/yWG9MQXUz7/yZzSwExVa+F/ezvSf9mJtkn9WJ6fzxKL11MS4sWk0dgAxrrylHYuoNcMVGOGs0aREXduez46GUk6MpR7fe64DMgEGSZpOuRHBu9DE1mdu52dT4IpuGnA/ij3gdkJqSaFVMjvZiumoipYb6YnpTtWu90xueNQJAkwtYe4Ibu3Npg/KtU6hyALMtkPkrm+KgfUcUmFTqmJyoENaCBrs2OWHuAm/lis/fxpOE37+NcvypX5//OLV2bbePpSqNFH2Ll5gyyzJ3V+wn/2fxzxhOVAhvQ/DPtufvGuoNcMNEHaT5zEF66PDo0ehnxuvN+v+MLyX7SB8pRs7nbpwC0WzIc5+ra834ZR1uyktPZ1Hmq2bEdvR3Hgn2X0cgyvRpU5u1mNYzSnL73iC/2XyFHrcHFpgzLB7QEYO2Z22y6eA9ZluntW4WBjaqZvf+CfDxzGM3aNSVTlcm8As5lnyyaTC3fWuRk53At9DpfTlyIOkeNvZM9k74aT8UqnmRlZjF/7BfcuRFh1v6LU65rv9eF6np17biurrnUrUyT+W+jsLZEzlFzevIK4kNvmxVX9+lvUlvXR/p93FIeFNBHemPxx9g62fHgSgTrR3+POltN26Ev499Te+wUSiXlfSryWcBQVI/TmHTkOzJTVcgaDZocDd91N78sCf9tYlCihEiSVBXYLstyvSJu3wgYLMvyx881sGLq3DmI6tW9qVevLU2a+PPdd7Np06anybRTpszlr792PJf9+gT5UtbbncVtx1LR34dus99iec/pRunaT+rHieU7ubLtBMFz3sa/byBn1+zjztHL3NxzFoDytb149fuPWdJ+fO52q/rNRpVY+E6SPkkh0XTOm4T0n096dAIv75jJvZCzPL4VlZumYjtfHL3d2dRqLG4B1Wk+bwh/vzKD5PBotnaamvs5r59dxN2dZ566P+8gX1yqurO8zVg8/KvTcc4Q1vaYYZSuzeR+nPl5Fze2naDD3Leo3zeQC2v2Fbi9pJDoMPtN/nhjPinRCQzcNpPwPWeJvxXF9mGLcz83cNoAMlPStTErFXT79kPUqY9AnQWSAnMHJAB6BndkQJ/uTJn1pdnbmqNmoB/lvN35OnAMXv4+dJ/zNj/0/NQoXedJ/Tm6fCeXth2nx5y3adg3iFNr9mLtaEv3WW+x4s3PeRwVj11ZRwA0ORp2zl5L1JUIythZM2zbHML+ucTDsAeFD04hUX/eW5x4fS6q6Hha75pDTMhZUm/mfUZWUiqXp63Eo0sjkx9R7b2upNx6gKWDjXkZU0jN2zWlkndF+rYaRN2AOoybN4qhrwwzSvfdjCWkp2rLyIjpH9LnrV6s+d70FyxztWnfgirVvOjStA++Devx6YKJ9Ov6tlG6eZ8sJC01DYCJM0cx4O3X+HnRKn75fg2/fL8GgMBOrXjz/QHFGpBAUmD92oekfz8NOSke23ELybl8Ek3MfYNk6vArqJbNNNw2J5v0RVMgKwMUSmxHLSDn2lk0EcaDLE8PQaLh3CEc6DcPVXQCnXbM4sHucyTfyis7Hu18cfB2Z3vLsZQN8KHRvLfY8/J0nGpVovobQYR0+xRNVg6Bv03kwb7zpN6JpcmX73J+5m88PHGdav3aUufDblz6YuNTIimAQoHX7PcJe2M62dHx1Nr2JY/3nCLjVl4eZd2P5dbrU1A/TsMxMACv+cO42SOvjXZ7+2Uywu6jdLA1f//5SQps3hpJ2tzxaOIf4jDnB7LPHkPz4K5ROusBQ8m5cNrgZZs3R5Bz4RTp38wApQWSldXzientkaTN0cU07weyz5gR0xBdTAuLHpOkkGgy90329dOey7rumEnkbsNzmaeuHG1pOZZyAdVpMm8Iu16egY27C7Xf6cS2wImoM7Jp/cMIqvZoxu3f/wHA1tMVjzb1SI18ZHZMjee+yX5dTF10MSXni8nR252tLcdSVhfT7pdn4FSrEj5vBLKr23Q0WTkE/TaBqH2hpNyJ5erSv7moK8u13ulE/dG9ODXpV/MyTCHhO+8tjrw+D1V0PEG7ZhMdco4UvTY7OymViybabDlHw6UZa0m6FIGFnTVBIXOIO3zJYFtz8qjl7DfZMWA+adEJ9Px7JndDzpKkl0de7Xxx8nbn91ZjKR9QnVbzhrDllRm5729/bQ6Z+fpA+z/KO+83/WQAWbrzvjnUGpl5ey/xw+vNqOBgwxur/qGtjzvVyznkpknOyGbenkt8/1pTPBxtSUjLBCDsYTKbLt5jzaBWWCoVDPvjJK2rlaeKq73ZceTXrF0TKnlXYkCrwbwUUIcx80bywSvDjdLt+Wsfs0bMA+DT76fy8oBgtqzaxqARAwi7Esa0d6dTuboXo+d+zOi+4422L0hxyrWNuwu13unEdl1da6VX1/yn9efS15uIOnARz3a++E/rz95X5xQ6rtq6PtKCwNFU9veh15x3WNzT+EJd8KQB/LN8Bxe2Haf3nHdo3DeIE2v2cmjZdg4t2w5AnfYBtH4nGNXjtNztfuw/m/TElELH858kbt8okLh9oxSTJElZ0HuyLJ8pbQMSAC+/3JHfftNe4Tp16jxOTo64u5d/4fut1bEhF/7Udm4enA/DytEW+/LORum8W9Tl6o5TAFz88zC1O2k7AtnpmblpythaIRfhi3NByvlXJyUiltR7D9Fkq7mz5QSVOzc0SFO5c0PCNx4B4OG5cMo42WGTL36PVnVJvhtH2oP4p+7Pp1NDrvyp/azo8+FYOdphZyIvvFq8xE1dXlzZ+A8+upgK2t7drzqJEbE81v0d17edoHqnhkafW/PlplzbchyAqm3q8/Dafe2ABIBctMa4kV99nBwdnp2wmOp0asj5TdpydP98GNYOtji4ORulq9aiLld2nATg3J//8JKuHPl2b8GVXad5HKU9Rmnx2i+zKQ+TcmdcZKVl8DD8AY7uLmbF5uLvQ9qdGNLvxSFnq4nafBz3zoYd2axHyTwOvY0mR220vbWHK+U7+HNv7QGz9muOVp1bsGvjHgCunLuGg5M9Zcu7GqV7MiABYGVthfwcV6Nu17UNW37XDnZeOHsZRycH3MqXNUr3ZEACwNra9Be0br06s+Ovp89KehZFlZpoHkYjx8eCOoecc4exqN+s8B+QpZtBorQApbJIK3e7+lcnNSKWNF3dvbflBJXytUGVOjckYqO27MefC6OMky3W5Z1xrOFJ/Lkw1KosZLWGuOPX8OraGADH6p48PHEdgJjDl/Dq1sTs2ABs/WqQGRFD1r1Y5OwcErf9g1Mnw89KO3sdta7zmnb+BpYeecfU0r0sTu0bEb9+T5H2n5/SpzaamCg0cdGgziHr+H4sG7U0SmfVpRfZJ/9BTk7Ke9HGFmXtBmQd0A24q3OQ09OMti1STLF6MR3bj2VjEzF11cX0OF9MdRqQtb94MZXNdy6LMFGOvDo35I7uXPYo37lMslCitC6DpFSgtCmDKjYxd7uGMwZybvZ6s8t3/pjubjmBl4myfVsXU7wuJuvyzjjV8OTRuXC9sn0dr67aNlV/xo+FTdHaKFd/H9LuxOa22ZGbj+ORL7bMR8kkht5GztdmZ8QlkaS76p2TlkHKrQfYmHnOeMLNrzrJEbGk6PIofMsJquQ7d1fp1JBbujyKOxdOGUfjPsjTVHulKeG68745Lkcn4uVsRyVnOyyVCjrX8eRgWIxBmp3XHtCupgcejtoBR1c7bXt9Oz6VBh4u2FhaYKFQ0NCrLPtvxRjtoyhadW7J7o0hAFw9dw37As5lJ/afyv35Wuh13DzcAKhaswpnj5wH4F74fdwrueNSrvDHrzjlGgzrmoVNGdJ1dU2W5dyLEpaOtgZ1sDBe6tSQc7o+0r3zYdgU0EfyaVGXS7o+0pk/D1O3k/GFEr/uLQjdesys/Qv/28SgRMmykCRppSRJFyVJ2ihJkq0kSRGSJH0qSdIR4DVJkg7qZkUgSVI5SZIidD8HSpK0XfdzW0mSQnX/zkuS5KB7fbwkSad1n/+Z7jU7SZL+liTpgiRJlyVJ6vs8/yBPT3ciI/NGch88iMHTs4LJtDNmjOPUqV0sWPAJZcqUKdZ+HdxdSY7K+7KeEpOAQwXDE4CNiz0ZyWnIau0X4+ToBBz0TvK1Ojfio31f0P/X8Wwbvyz3dRmZgWsm8e722QT0DzI7Nlt3F9KiEnJ/T4tOwDZf50KbJv6pabx7NOfO5mef9O3dXUiJNswLe3fjvMhMTs/Ni1S9vChoewd3F1L0/o7UaOM8rtSkFumPHpMUoZ2G7lLNHRkZpaM7Fs4VUdg4PTP+kuRYwYXHen9jckyC0eCBrYsDGclpaHLLUTyOunwoW80DGyc73lk/jY+2zcGvd2ujfThXKofHS1WJDA03KzZrDxdUemUkIzoea4/Cd3LqzhrMtVm/FXlgqDDc3MsRFxWX+3tc9EPc3MuZTDvl6wlsC91IFR8vNv7y13OLoYJ7eWKiYnN/j4mKo7yH6YHROd9+wj9XduLtU4U1P28weM/axopW7ZoRsr14gzgK57Jokh7m/q5JeoTkZDxIovSuje3ERdh8MAOFe+W8NyQFthO+w37uGnJuhKK5e9PsGGzdXUnXKzvp0QnY5Cs7Nu6uBm1QepS2DXp8PRK3prUp42KP0qYMnu38sPXUds6Tbtynoq6j7PVy09zXzVXGvSxZUXlXyLOi47GsYJxHT5Tt25HkA+dyf684410ezF0JmuczuKVwKYcmPq8ca+IfonAxLMeSSzksG7cma6/hLVTK8h7IyUnYfjAR+3nLsHlvHFhZFz8mVzNi2lNATB9OxH7+MmzeL1pMtu4upOu1j+nRCdgalaN857KoBGzcXVDFJHJ16Q56nf6WPqGLyU5JJ/qQ9tbOSp0CUMUkknT1ntkx2ZiIKX/Z1sZtXLaTrkdSvmktvbLti61nXrnznfgaPc98S9XeLbj4heHtRIWRv81WRSdg42F+HbH1KodzvaoknDPvnPGEnYcLqdF6fZCYBOzy5ZGduwup+fogdk/OfbJM8G+T6LljFrXfMO4DuTetherhY5LvxBq99yxxqRm4683cq+BgTVyK4a18dxNSSc7I5p11x+i/8jDbLmtnUPm4OXA2Mp4kVRaq7ByO3I4jNqVot4/lV869HHFRee32w+iHlCvgXAagtFDSuU9HTh3QzlAKu3qbNsHa838dv1pUqFQBN4+Ct8+vOOVaFZPItaU76Hn6W3qHLiYrJZ0YXV07++ka/D/pT88z3xLwSX9C5xqe957FqYIrSXr7TIpJwMndsEzbujig0usjPY6Ox6mCYRpL6zLUauvLpZ0n816UZd5bPZmPt82haf92ZsX1nyJrSv5fKSUGJUpWLWCZLMsNgGTgI93rGbIst5JleX0hP2ccMEyWZT+gNaCSJKkTUANoAvgBDSVJagN0AaJkWfbV3TpidJOiJElDJUk6I0nSmZwc825ZkCTJ6DVTVxg+/XQBvr7taNWqOy4uzowd+4FZ+zHer4kX8+3XdGx5P9/YfYYl7cez4b2FBI59Lff1X3t/xk/dpvHbmwtoNLgjlZvULn5w+bPEZJq8RApLJV6dAojYftI4Xf6P4umfVdD+niQpcPtn5B9A7R7Nua53tUShVFKpUU3UKXHkJEUhlbFDsix+B/1FeVYZ0SYy3u5JGVcqlXjW92bVW1+wYvB8gkb0oqy3e266MrZWDFg6mr9nribT3HvvC1OOClC+o792FsXFO+bt00yFrf8Ac8csoEfA60Tcukf77uYP9hUcg/FrBcUwdeQs2tbvxu1bEXTt0dHgvaBOrTl/6mLxbt0oSL541JFhpE5/m/TPR5B1eDs2707TS6shfcHHpH46BGWVmig8qpi/v0K0jwWV6+SwKK4t2UbQ+kkErp1I4tV7aHK0nZqTY5ZRY0hHOu+ajaW9DZqsHPNjK2x8OvbN61O2bwei5q0EwLF9I3IeJaG6VLQvbKbjMRmQwW82g4eh+u1H4w6eUonSuyaZe7aSOnkoZGZg3b3/vxPTkELENGkoZGRg3aMIMRWifTTVBiDLlHGyxatzAJubjuZP/xFY2Frh3bslSpsy1Pu4OxeKcttPgft7dtzoyvbVJdtpv34S7dZOIOnqPYNZZhc+/4PNjUYSsekYNd/uaPwZRYrNvIEzpa0VTX8ezcVPVxd5vRbTlTt/koJj3dprJn91ncauQV/w0psdcG9ayyBZ9R7NizRLQm8XTw1FrZG5FpPE4j5NWPJaU5Ydu8XdhFSqlXXgraY+fLDhOMP+OElNN0eUJuuJ+cw5jwCMmTuSCycvcvHUJQDWLl6Hg5M9y0N+pPfbvbh1+RZqtfEMxoL3X/RjVsbJlkqdA9jSdDSbdHWtam/trKoab7bn7PS1bG40krMz1tL06/cKHVNB+8yfL4XJu5c6BBBx5obBrRtL+szg25ensHzI5zQf3Alvc/vawn+eWFOiZN2XZfmo7uc1wJPbMcwbuoSjwNeSJK0FNsmyHKkblOgEnNelsUc7SPEP8KUkSZ+jXdPin/wfJsvyMmAZgI1NlWeeQd9/fzBvvdUPgLNnL1KpkmfuexUruhMdHWe0TUyM9rWsrCxWrfqDUaOGFv6v1Wk0uCMB/bRfZqIu3sZR7wqHg7srKXFJBunTE1KwdrRDUiqQ1RocPVxJNTF17d6p67hUKY+Niz2qxFRSdZ+THp/Mjd1nqOhXjXunrhc6zvToBOz0riDaebjmTqUzTFM2X5q8+CsG+RJ/KYKMR6a/INV+swM1dVcwIi/exsHDMC9S8y3QpUpIwcrRNjcv7PXyIiUmweT2CksLHPT+DnsPV1Lj8v4OSamgRpfGrO6Wd39hSnQC909ep1ZQJQDkrHQkCyvk7Oe3qGFxNR3Ukca6GTCRF27jpPc3Orq7kpL/WOnKkUKpQKPW4OhRNresPY6JJy0xhWxVJtmqTCJOXcOjThXi78SgsFAy4IfRXNh8lKu7De/3LoyMqARs9MqItUdZMmIKN/XStXEtKnQKoHx7PxRWllja2+C/eBjnh39vdhz59X6zB93f6AbAtdAblPfMm5VQ3sONR7EF326k0WjYt/UAAz7sy47fi76I24C3X+XVgT0BuHz+Ku56s7PcPcvzMOZhAVtqY9i5eQ9vDxvEX+u3574e3KsTf/8VUuSYcj8/KR5LZ7fc3xXO5ZCTEwwTZeR92VBfPQOvfYhk54icplffVWmob11CWScATXS+dQSeIT06weAKsK2HK6qYJIM0Kl0b9GS+gq2na+7CfrfXHeL2ukMANJj0Oum6q64pYdEc7D8fAIdq7ni29zMrrieyouMp45l3FbGMR1my4xKM0lnXrkLlBcMIHzwTdZL2vmO7RnVw6tgEx6CGKKzKoHSwpco3o7k7amGRYgHQJDxEUTavHCvKuqFJNCzHymq1sPtYu96M5OCEhV9T0KjJuXUVOeEh6vBr2r/t5CGsewwociy5McWbGZOjExb+TUGtiyn+Ieqw4sWkLUd57aO2HJk+lz2pcXa6cuTeuh6p9x+SmaA9bvd2nKFcoxokXr2LfWU3uu2dm/uZwbtnszN4OhkPHz+3mAzKv2fe+TV83SHCdWXbV69s64v46xiBq8dx6ctNz4xHnypfm21jIrankSyUNFs+mvubjhK1w/xzxhNp0QnY683QsHN3JS1fHGnRCdh7luXJXAc7D1fSdHn0JK8y4pOJ2HUWN7/qxJzUrmsjKRVU7dqYzcHmLwAO2pkRMXqzG2JTMnCztzZK42xTHpsyFtiUsaChlys34pKp4mpPrwaV6dVAO7Psu8PXqFCM9ZJ6vdmDl98IBuB66A3Ke+a1224ebsQXcC4bMnoQzmWdmfZu3jpm6anpzB+Tt5j0hhNrib5X+FtLilOu89e1+zvO4NaoBhGbjlLttdac/WQ1APe2naTZl+8+M5bmgzrmzly4f+E2znr7dHZ3JTlfHyktIQUbvT6Sk0dZkuMM0/i+YnzrxpM0afHJXNl9Gi/f6twxo68t/PeJmRIlK/8X/ie/69/smUPecTJ5iVmW5fnAu4ANcEKSpNpoh8bnybLsp/vnI8vyclmWbwINgUvAPEmSjFfxM9OPP66iWbNgmjULZtu2EAYM6ANAkyb+JCen5A5A6NNfZ6J7905cvWrewm0AZ1btYVnwFJYFT+FGyBl8+2inylX09yEzRZU7mKAv4vhVXgrW3qvcoE8bbugWt3Spovclpl5VlJYWqBJTsbSxooydNtstbayo1qY+cTcizYrzUehtHL3dsfdyQ2GpxLtHM+6HnDNIcz/kHNVfbQWAW0B1spLTUenFX63n02/duL5yL1s7TWVrp6mE7T5L3T7az/Lwr05mSjppJvLi/vGr1NTlRd1XWxOuiyl8zzmT28dcuI2LtztOur+j9ivNCN+T93dUaVWPhPAoUmPyOnURhy/iVrsyT67USJbWyDlZhcy5f8fJ1XtYHDyFxcFTuBZyBn/dLRdeunKU8jDJaJvbx69SN7gpAAF9WnMtRLv46LWQs1RtXAuFUoGldRm8/HyI0y1m2fvzocSFPeDo8qIt7poUGo5dNXdsKrshWSrx7NmcmJCzhdr2+tz17A0Yzr7GH3Pug+94dPTKcxmQANi0cgtDOg1lSKehHN59hC6vaq8o1g2oQ2pyGvEmvlxWrJo3cNmyYwvuht03SmOO337ZSO92A+ndbiD7dh6ix+vajqVvw3qkJKfyMM64M1nZu1Luz4GdW3M7LCL3d3sHOxo192f/rkPFigtAc+8mCjdPJNcKoLTAIqANOZcMZzxJDs65Pysq1wRJQk5LRrJ3BBs77RuWZVDW8kMTa177A5AQehsHb3fsdHW3co9mROYrOw9CzlH1VW3ZLxvgQ3ayigxdu2GlW7DVtmJZvIIbc3fzMYPXkSTqjuxJ2Op9ZscGkH7hFlbeHpTxKo9kaYHLK615vOeUQRpLz3JUWzaZu6O+IfNO3i2C0Z+v5krTd7jacigRw78k5djFYg1IAKjDr6Nwr4jCzR2UFpRp3o7ss4Yd6JSRA0j+uD/JH/cn++QhVL98Q/aZo8iPE9HEx6Hw8NLGXS8AdWREseIxGVOLdmSfyRfTiAEkj+hP8oj+ZJ84hGr5840pPl85qtqjGZH5zmWRIefw1p3Lyumdy9IexFMuwAeljfZWTfdWdUkOe0DS9Ug2NhjG5qaj2dx0NOnRCezoPK1QAxKmYqpSQEzVdDGV1cVkumw3yi3bDt55fYKKnQNIDos2M7cgMTQc+2ru2Ora7Eo9mxNdyDYbIGDhUFJuPSDsx+ItCP7wgrYP4qDLo+o9mnFvj2Ee3Q05Rw1dHpUPqE5Wiva4WdhYYanrA1nYWFGpTT0S9fpAFVvX43F4FGkmBnMKo66HM/cS03iQlE62WsPua1G09XE3SBNYw53zkQnkaDSosnO4FJ1EtbLaxSyfLHoZnZzO/pvRdK3jabSPwvpr5Rbe6fQ+73R6n392H6Xzq50AeCmgDmkFnMu69Q+mSWBjPhs222A2gL2jHRaW2uu+Lw8I5sLJiwZrKT1Lccq1qbr2WNcPUcUmUr55HQAqtKpL8p1nD5QcX72Hb4In803wZK6EnCFA10eq7O+DKiXdZB8p/PgV6uv6SI36tOGqXrm3drChWtM6XNmT95qljRVWen3tGq0bEHPT/HPdf4GskUv8X2klZkqUrMqSJDWXZfk40B84AvjnSxOBdhDhFPCqqQ+RJKm6LMuXgEuSJDUHagO7gVmSJK2VZTlVkqSKQDbaY54gy/IaSZJSgSHP8w/atWs/nTsHceXKYdLTVbz//rjc9/76awUffTSB6Og4fv31W8qVc0WSJC5evMqIEVOKtd9b+0PxCfJj+OGvyVZlsXXcj7nv9V8xnm0TfiI1Lom989bRZ/EIgsa9RsyVu5zfcBCAOl0b06BPazTZanIys/hz2CIA7Mo58vqy0QAoLJRc3nKM8EMXzYpNVms4MW0lHX+boH2004ZDJN18QK1B2pHnG6v3E7kvlIrtfOl99CvUqiyOjMlb00JpXQaPNvU4NvGXQu3v9v5QvIN8efefr8hWZbFrXN5n9V4xjt0TfyYtNonD89bz8uLhtBr/GnFXIriky4uCtpfVGvZ9spI+qyegUCq4tOEQ8Xorgdfu3ozrWw0HTjIfp3Pm550Ef/UOAJqsdORs86egjp8+n9PnL5KUlEz7ngP56J1B9Hmls9mf8yw3DoRSM8iPMYcWkq3KZNP4vHI0+NcJ/DVxGSlxSeyev45+i0bQcexrRF25y5nfDwLwMDyKm4cuMmLXfGSNzJkNB4i7GUmVRrXw79OamGv3GL5De0UwZMHv3DwYWujYZLWGy1NW0GzdZCSlgvvrDpJ6I5IqgzsAcHfVXqzcnGi9ew4WDjagkan2XlcOthlfjGm/5jm+7yTN2zXl96NryFBlMHfMgtz3vlw1j/njvyQ+LoFp30zCzt4WSZIIuxrOF5O/eW4xHNp7lDYdWrD71CYy0jOYMnJW7ns//raQaaPn8CgunnmLpmNvb4ckSVy/eovPxn+em65DcCDHDp58Po8p1WjI2PgDth/N1D4S9MQeNDH3sGzZFYDsozux8GuFZauu2lW5szNRrdTmm+Tois3A0dqn1kgKckL/QX3F/CumslrDmakrCPxtIpJSwe31h0i++QCfQe0BCFu9j6h9oXi09+PlY1+jVmVxcnRe2W/180isXBzQZOdwZsoKsh9rO9dVejanxhDtIFTkztPcXl/EQRy1hshPllF99QztI0E37CPj5n3KDtQ+mjV+zS7cR/ZD6eJApdnv525z4+WxRdvfs2g0qFZ8h93kBaBQkHVwJ5rICMp0eAWArL3Gj+bTp1rxHbbDpyJZWKCJjSb9x8+fmr7QMf3yHXZTihjTr99hO0IXU1w06UvNj0lWazg9dSXtf5uApFQQvv4Qj28+oIbuXHZr9X4e7AvFs70vPY59RY4qi+OjteeP+PPh3Pv7FMG7ZyPnqEm4fJdba4q/6K62bK+k3VNiitoXSsX2vnQ/pj2/PokJoM3PI7FysUeTncPpKSvJ0pVtvyl9cazugayRSXvwiFMTzXzyhi620CkraLluEpJSwd11B0m58QDvwdp6d2fVPqzcnGi3ezYWDjbIGhmf97qwp80EnF6qTJXXWvP46j3a6WaRXJn3O7H7QosUx7FPVtJ1rbYPcmPDIRJvPqDOQG0eXVuzn/v7Q/Fq50vfI1+Rk5HFIV0fxMbNkY4/jwK0t2OGbT5G5MG8PlD17s0IL8RaVwWxUCiY1KEeH/5xAo0s06O+Fz7lHPjjfAQAr/lXpVpZB1p4u/H6r4eQJIleDSrj46YdTBq75QyPVVlYKBRM7lgfR+virU/2xAnduWzd0dVkqjKYpzfrYcGquXw+/iviY+MZO38UsZGxLN2q7TMe3nGEld+spkqNKkz9diJqtYa7N+8yf5x5Tw4rTrl+Ute66upa4uW7hOnq2snxy2k4cxAKpQJ1Zjanxi83K67rB85TO8iPiYe+IUuVyR96faS3f53Axok/kRyXyI756xiwaASdx75O1JUITv2eV9frdm7MzX8ukq3KW2DeoZwTg5eNAbTlLHTLUW4eumBWbMJ/n/Q8Vz0XCk/3SNAdwGGgBXALGARcBRrJsvxIl6428DuQCuwHBsqyXFWSpEBgnCzLL0uStAgIAtS67YfIspwpSdJItDMo0G0/EPABvgA0aAcpPpRlucBnTBbm9o1/0+TyrUo6BCOVc57PPYzPy6MCn9lSckaem/nsRP+iGY2mPTvRv6xZRqmqasxXmr9o2YuWkF20x/K+KKf6vfgnC5lr+8bStZhsbYvSdcwAvFu+gHVCiquUrT22/Z+KJR2CkdJ1pgWbUvhov3iL0jUBeuBn7s9O9C/r/On5Zyf6F70ve5R0CEYulCn8+hf/lgUR60pbE1Ak6ctGl3hnz3bowlKZl2KmRAmRZTkCeMnEW1XzpbsONNB7aZru9YPAQd3PIwrYx7fAt/leDkc7i0IQBEEQBEEQBEH4N5TCwczSonQNqQqCIAiCIAiCIAiC8P+GmCkhCIIgCIIgCIIgCC9S/kc2C7nETAlBEARBEARBEARBEEqEGJQQBEEQBEEQBEEQBKFEiNs3BEEQBEEQBEEQBOFF0pT4wzdKLTFTQhAEQRAEQRAEQRCEEiFmSgiCIAiCIAiCIAjCiyQeCVogMVNCEARBEARBEARBEIQSIWZKCE/1QYXmJR2CAWUpvBUro5QN7f2oulbSIRhJbDStpEMwMOPM7JIOwciMUpZH72Z7lnQIRtqUf1jSIRgYt9WmpEMw8nnbqJIOwcC7xxxKOgQjAw54lHQIRhItSteJpKo6u6RDMFLari8etFGWdAhG/DNLVyep6/TQkg7ByNbA0pVH3xwpXfEAOMmlqz0S/n8QgxKCIAiCIAiCIAiC8CKJ2zcKJIbCBEEQBEEQBEEQBEEoEWKmhCAIgiAIgiAIgiC8SHLpu12ntBAzJQRBEARBEARBEARBKBFiUEIQBEEQBEEQBEEQhBIhbt8QBEEQBEEQBEEQhBdJLHRZIDFTQhAEQRAEQRAEQRCEEiFmSgiCIAiCIAiCIAjCi6QRC10WRMyUEARBEARBEARBEAShRIhBCUEQBEEQBEEQBEEQSoS4fUMosl7T36ROkD/ZqkzWjVtK5JUIozSuldwYvHgktk52RF6JYO3oxaiz1dg42tHvi/cpV7kC2ZnZrJ/wAzE3I3O3kxQSY7bN5XFMIj+/s6BQ8XSaMZjqQb5kq7LYPu5HYi4bx+Pk5UavRcOxcbYn5nIEW0YvQZOtpm7PFjT/4BUAstMz2Dn1V+Ku3cPBw5XuCz/E3s0JWSNz/rf9nP51d6Hi8QpsQMsZg5CUCq6tO0jokm1GaVp+NojK7fzIUWVyYMwyHunFLCkk+vw9i7SYRHa+9RUAjUb3ps6AQFTxKQCc+vx37h24UKh4TPlk7njadmiJKj2DiR/P4OrF60Zp5n7zCfV8X0KSJCJu32XiiBmkp6lwdHJg3rfTqVy1EpmZmUweOZNb18PNjqHb9MHUCvIjW5XFn+N+IMpEOXKp5EbfxSOwcbIn6sodNo5egjpbDYB3szp0+3QQCgsL0hNT+LnvLJw8XHn16w+xd3NG1sicXref47/uMju2p5k292sOHz2Fq4szm9f88Fw/+2leRH6Zo2JgA5rOHISkUHBz3UEufW9crpvOHEQlXbk+MnoZ8bpyXcbRlpZfvotzrUogyxwZ+xMPz4blblfv/WAafzqA3+p9QGZiqllxPWHTshHlJn2ApFSS/OdOkpb/bvC+bVBzXEcMBo2MrFYTP/8HMs5fAcBt1hjs2jRFnZDE/V7vF2n/Bek7/S3qBQWQpcpkxbjvuX/ljlGawMFdaP92N8pXdWeM/9ukJWrruW/HRnQf0w9ZltHkqNkwcwXhZ4zramFZ+DbGZvBwUCjJOvA3mVvXmUynrFYL+1nfk/7tTLJPHQZAsrXDZuh4lJW8AZn0HxegvnW1yLHoe3vGe/gHNSJLlcnicd9w5/JtozQjvx1Dtfo+qHPUhF24xY+Tv0edo8azekWGfTmSanWrs+7L1WxdtrlYsVQIakCDWYORlAoi1h7g5mLDcm7v40nDb97HuX5Vrs7/nVtL/wbAxtOVRos+xMrNGWSZO6v3E/5z0dueioENaPbZIBRKBTfWHeSiifrWbOYgvHT17XC++tbqi3dx0dW3f8b+RNy5MBpP60/lDv5osnNIvhvHP2OWkZWcbnZsrkG+1Jw9BEmpIGrtfu4u2mLwvq2PJy99+yEO9b0Jn7eee0u3a1+v7kG9ZaNy09lUKc/tBX9wf9kOs2PIr2yQL7V0MT1Yu58IEzHV/fZDHOt7EzZvPXf1YmqQL6bwBX9wr4gxFaed9m5Wh4HLxpIYGQfAlV2nOfDdXwC0eKcrjfoGgSwTc+M+m8b/SE5mtlmxVQhqgN9Mbd/kzm8HuZGvbDv4eNBoobZsX5n/Ozd/yMuDhl+/h0dHfzIfJbMnaJKZuVKwETOH0axdEzJUmcwfvYBbl8OM0kxdNJlaDWqizs7hWugNvpq0EHWOGjsHO6Z+N4nyFcujVCrZ8OMf7Pq9cP20glg0aIzNoOGgUJB1cAeZ257SRn62mPRFs8g+dRiFhxd2Iz7JfV9R3oOMjSvI3PVnkeLoOmMwNXT92s3jfiTaRL/W2cuNV3X92ujLEfyld74H8GxQjXc3f8bG4Yu4uuNU7uuSQmLo9tmkxCTy29tfFjqmF9HXdq3mQe/FI/L+psrlOfT1Rk7/8nz7baWCLBa6LIiYKfE/RpKkGZIkjXvR+6kT6IebtwdzA0fx+5SfeHXOuybTvTJpAIeW/83coNGoHqfStG87ADoM60nU1bt80XUiv41dQq/pQwy2a/NWV2LDogodT/UgX1y93Vnadiw7Ji+ny+y3TKZrN6kfp5bvZGngWDIep+HXNxCApPsPWfP6LH7uMpkj320meN47AMhqDftmr+XH9hNY0XM6DQd3pFyNis+MR1JItJr9Jn8PXsCGdhPw6dEMlxqeBmkqB/ni5O3OutZjOTRxOa3nGuZB/Xe6kGgiDy7+vIuNXaayscvUYg1ItO3QkirVvOjQpCefjJ3NzAWTTaabO+1rugf155XAfkRFxjDwnb4AfDjqba5dvsErgf2YMGw60+aYX+xqBvpRztudrwPHsHnKz3Sf87bJdJ0n9efo8p0sDBpDxuM0GvYNAsDa0Zbus95i9btf8V2nCaz76FsANDkads5ey7cdxvNDr09pNqgjbj7PPm7m6BnckR++nv1cP/NZXlR+FZakkGg2501CBi7gr6AJVOvZDKd85bpSO18cvd35s9VYjk1cTvN5Q3LfazpzEJEHLvJX2wls6TiFx7fyyredpyuebeqRGvnIrJgMKBS4TRtG9IfTuNf9PeyDg7CsVtkgierEeSJ7f0jkqx/x8JOvcftsdO57KZtDiPpgatH3X4B6gf6U9/bgk8ARrJnyI2/Mec9kuvCz1/lm4Ewe6b6MPHH96GVmdR3H7ODxrJywhMGff1D0YCQFNm+NJO3zSaSMG0KZFu1RVKxiMp31gKHkXDht8LLNmyPIuXCKlHFvkjLxXTQP7hY9Fj3+QQ3x8PZkRNv3+WHy9wyd/aHJdIc3H2Jku48Y02kEZazK0L5fJwBSk1L5Zfoytv70V/GDUUj4znuLowMWsKfNeCr1aoFDTcP2IzsplYvTVuYORjwh52i4NGMte9uM52Dwp1R7q6PRtoUlKSRazH6TkEEL+DNoAtV6NMO5gPr2R6uxHJm4nBZ69a3ZZ4OIPHiRPwMn8FenKSTpzidRhy+xqf0k/uo4heTb0fgOf8X84BQStea/TeiAeZxoPYYKvVpiZyKPbkxdwd2lhl9608OjOdV+ovZfx0moVVk81PuyVGQKidrz3+b8gHkcaz0G96fEFGEiphPtJ2r/6WKKK2JMxW2nASJOX2dx8BQWB0/JHZBwrOBC8yGdWfLKVL7rPBGFQkH9V5qbF5xCwn/uEI68sYDdbSfg1bO5UfnMSkwjdNoqbv7wt9Hmd3//hyMDCnehqLCatmtCJe+KvNHqTb6auJDR80aaTLf3r30MbvsWb3V4DyvrMnTrHwxAzze7E3HrLu92ep9Rr43lo0/fx8KyGNdcJQU2Q0aStmASKRPeokzzdgW3kf2GknPxTO5Lmuj7pEwZqv039QPkzEyyzhwpUhg1dP3a79qOZdvk5XQroF/bcVI/TizfySJdv9Zf16/VhijRcXI/wg9fNNqu2dtdeGRGPxteXF874XY0PwdP4efgKSx/eSrZqkxu7D5j8rOF/11iUOL/OUmSitRy1+vUiNObtFfO7p4Pw8bBFkc3Z6N0Pi3qcmHHSQBO/XmY+p0aAeBeoyI3j14GIC48CtdKbtiXcwLAyd2Vl9oFcGL9/kLHU7NjQy7++Q8AUefDsHa0xb68cTxVW9Tlmq6jcfHPw9TUxfPg7C0ydFeKHpy7haOHKwCpcUm5o8BZaRnEh0XhUMHlmfGU96tOckQsKfceoslWE771BFU7NTSMpVNDbv6pPVnFnQ/HytEOW13Mdu6uVG7nx7V1BwudB+bq0KUtmzdoOx2hZy/j4GSPW4VyRulSU9Nyf7a2tgZZu0iPT61qHP9H+4XldlgElbw8KevmalYMdTo15Pwm7XG7fz4MawdbHEyUo2ot6nJFV47O/fkPL+mOm2/3FlzZdZrHUfEApMUnA5DyMCn3ylRWWgYPwx/g6P7s42aORn71cXJ0eK6f+SwvKr8Kq5x/dVIiYknVlevbW05QubNhua7cuSFhG7Xl+uG5cMo42WFT3hlLexsqNK3FLV2Z1mSrDa7ONpkxkNNz1iPLRV8Eyqp+LbLvRZETGQM5OaTuPIhdO8NOu6zKyP1ZsrEG8vaXcfYymscpRd5/QXw7NebEpkMA3Dl/CxsHO5Pt5f0rEcRHPjR6PTM9L2YrW+ti5ZHSpzaamCg0cdGgziHr+H4sG7U0SmfVpRfZJ/9BTk7Ke9HGFmXtBmQd0F05Vecgp6cZbVsUjTs25eCfBwC4df4Gto52OJc3rrPnD5zN/Tnswk3KepQFIDn+MeEXwwyuEBaVq78PaXdiSb8Xh5ytJnLzcTzylfPMR8kkht5GzjHcX0ZcEkmXIgDIScsg5dYDbIrY9rjlO4/c3nKCyvnOI1U65atvjnn1zb1pLW6aqG8PDl9GVmuv1sWdC8fWw7x2G8AxwAfVnVgy7mrzKHbzMcp1aWyQJvtRMimh4chPOSaureujiogloziDkTpOAT6k34lFpYspZvMx3EzElPyCYypuO/00CqUSS+syKJQKLG3KkBKbaFZsrv7VSY2IJe3eQ+RsNfe3nMAzf9mOTybxwm2TefToxHWyijiLrSAtO7Vg98Y9AFw9dw17R3tcyxuXyZP78waJroXewM1D21+RZbC1swXAxs6GlKQU1DlFbweU1WujiX2A5qGujTyxH8uGLYzSWXXuRfbpw8jJpo+BRb0ANHFRyI9iixRHrY4NuaDr10Y+pV/r3aJu7gyI0D8PU1uvHDUd0pmrO0+T9sjwXO/o7kqNdn6cW3/ArJheVF/bYNuW9Ui8F0fyg+K3CcJ/ixiU+I+RJGmMJEmXdf9G6V6bKknSDUmS9gK19NL6SZJ0QpKki5Ik/SVJkovu9YOSJM2VJOkQYHpI+hmcKriSpPtiA5AUk4CTu2HjYufigCo5HY2u8/M4OgGnCto0D67do0GXJgBU9q2OS8VyOOu27/Xpm2ybt9asjreDuyvJevEkxyQYDR7YuNiTkZyW2xlLjk7AwURn0bdfIOEHjWcgOFUqR4W6VXgQ+uxbFOzcXUiNSsj9PTU6Abt8+9KmiTeZpsWMgZyYu87kKr313uzIayFzCfzyPco42T4zloJU8ChPdFTeyTImKo4K7m4m087/bjrHr4RQrUZVVv28AYBrV27SqZv2yk4D/7p4ernj7lHerBgcK7jwWC+fkmMSjAYPbF0cyEhOyy1HydHxOOqObdlqHtg42fHO+ml8tG0Ofr1bG+3DuVI5PF6qSmQhjltp92/k19PYuruQprf/dBPlWpsmr1ynRSdg6+6CQxU3MuJTaLVwKN13z6blF+9iYWMFgFfHANKjE0m8es+sePKzKF+WnJi8L/U5sY+wKG880GbXvgVeW3/GY8ks4j75ulj7LAznCq4kGLSX8bi4m/dF0K9zEz7b9w3Df5nMqglLixyLwqUcmvi8mRia+IcoXAzzSHIph2Xj1mTt3WrwurK8B3JyErYfTMR+3jJs3hsHVtZFjkVfWfeyxEflHbuEmHjKVihbYHqlhZI2vYMIPXjuuexfn7WHCyq946WKTsCmCF/cbb3K4VyvKgnnitb22Hq4kBatV99iErDzeHp9e1InHSq7kZGQQuuvh9Jz12xa6dU3fTX7tiHygPFV1GexdnclQ2+/mVHxWBVh8KVCrxbE/nXU7O1MsXJ3JfM5xOTeqwUxxYipuO00QOWAGgzfOY83V0ygvG52ZnJsIkd++pvxxxYx6dQSMlJUhP1zyazYbNxdUT3IV7af84C9udzcy/FQr+4/jH6Im7txu/2E0kJJpz4dOHVQe1HkrxWbqVKjMn+e3cCve39i0adLijVwq3DN10YmPELhYtg3klzKYdmoFVl7jW+nesKyWRBZxwp/cS0/RxP9WscK+cuRfb5ylFfWHCq4ULtzI86s2Wv02V2mD2LP3HXIZj4J4t/oa9ft3oyrW4+ZFdd/ikYu+X+llBiU+A+RJKkh8BbQFGgGvKd7rR/gD/QG9C8LrAImyrLcALgETNd7z1mW5bayLH9VtFiMXzM6CTwlzb6lW7BxsmPcjvm0frMLD65EoFGrealdACnxj4m8bHy/dXHjkUwmMvy1SvOX8OsbyP556w1et7S1os8Po9gzczVZqaoiBWR0jjSZRqZyez8y4pN5pLvapu/K6r381moMf3SeSnpcEi0+eePZsRQYoun9mzLp489oWb8L4Tfv0K1nRwCWfbsCJ2dHth74jUHv9uXqpRuo1eZdnTAdQ/5Exts9iVOpVOJZ35tVb33BisHzCRrRi7Le7rnpythaMWDpaP6euZrMwhy3Uu5F59fz2b/JAJCUSsrWr8r1VfvY2nkaOemZ1B/+CkrrMvh+3J1zX24sdBxPCdDkvvNL23eM+93fJebjGbgOf7P4+31mWIXIt2cI3X2K6e1HsXToArqP6VucYEy8aBiMzeBhqH770fjeV6USpXdNMvdsJXXyUMjMwLp7/6LHYhCXiaiekknvzf6AqyevcO3081nPwiCUQpajp1HaWtH059Fc/HQ1OUVue55dbgpqxxUWSsrWq8r11fvY3EVb3xoMM7xNw3dEdzRqDeGbivAF3GQ5MvMjLJWU69SQuG0niv1Z2g98PjG5dWpIbDFiKm47HXU5gi9afszirpM5viKEN5aNBcDa0Y46HRvyZeuRzG86jDK2Vvj2NJ7l9PTgTLxWjC/wz4UZfRGA0XNHcvHkRS6d0s62bRLYiLAr4fRp2Jd3O7/PyNnDsbUv+gWbAg6Owa82g4ahWr+s4PUBlBZYNmxB9slDzzkMowbARBrt/12mD2Lv/PVGAw812/mTFv/Y5PoUzwzpBfe1FZZKanRoyLW/T5odm/DfJxa6/G9pBfwly3IagCRJm4BuutfSda9t1f3vhHbg4UmLuBL4Q++zNhS0E0mShgJDAdq7NqK+Q3UAWg7qRPP+2jUh7l0Ix9kz7yqWs7sryfmmEaYlpGDjaItCqUCj1uDk4UpynDZNZqqK9ePzFgf85Mgi4u8/xP+VFtTr0JCXgvyxsLLE2t6GNxYOY+3o743ibDi4I/79tFfqoy7exlEvHkd3V1LjkgzSpyekYO1oh6RUIKs1OHq4Gkx9LF/bi26fv8v6NxegSsqbnqiwUNLnh1Fc3nyUG7sKd49bWnQC9p55V9bsPVxJz58/0QnY68WsTZNEteAmVOkYQOUgX5RWllg62NDu2w/ZP3IpKr0peNd+O0DXFWMLFc8Tb7z9Gn0H9QLg4vmreHhWyH3P3bM8cbEFT5fTaDTs2BLCu8MG8+e6baSmpjHp489y3z9wdhuRd599f2LTQR1p3F973CIv3MZJL58c3V2NpqM+OW5PypGjR1lSdMf2cUw8aYkpZKsyyVZlEnHqGh51qhB/JwaFhZIBP4zmwuajXN1teF/8f8m/lV+FkRadgJ3e/m1NlOv06ATs9Mq1na5cI8ukRSfw6Lz2qnHE36eoP/wVHKuWx76yGz32zM1N3333bLZ3m47q4ePCZZJOTuwjLPRm+1hUKEfOw/gC02ecvYyllwcKZ0c0SebdyvIsgYM606p/BwAiLoTh6lmWJ9fLnd3LkhSbUPDGT3Hr1DXcqrhj5+KQuxCmOTQJD1GUzZvRpCjrhibRMI+U1Wph9/GnAEgOTlj4NQWNmpxbV5ETHqIOvwZA1slDWPcYUKS/A6DL4ODcNSHCL96irKcboP1sV/eyJMSZzqPXRvbD0dWJHyfPK/K+n0YVlYCNXhm28XBFFVP4afKShZJmy0dzf9NRonYUve1Jj07ATm+Ghq27K+kxxucR/fpmm6++PdTVtzt/n8JXb1DC59XWVO7gz46+RcvDjOh4rPX2a+VZlkwz8gigbHt/Ui7dIcvMel6QzOh4rIoZU7kixvQ822n9AfSbB0PpPvstbF0cqNb8JRLvx5GeoK33V3adpkrDmlzYXPhBJVV0AjYV85Xt2CSz/tbnoeeb3Xl5gHZNiOsXbuLmmdduu3m48SjWdLv95uhBOLs68cnEhbmvdXm9C799r12I8kFEFNH3Y6js48X10BtFis2ojXQthybJsG+k9K6J3XDtgpaSgxMWvk1BrSb7rPZYWPg1QR1xq8BbOwrSeHBHGur6tQ9M9GtTCujX5pWjvLLm2cCbVxcNB8DW1YEaQb5octRU9POhVoeG1Aj0w8LKEisHG3p/8yGbRpmegfdv9bUBfAL9iLkcYXS7yf8SWSMWuiyImCnx31LQZYCiDHMXeCOwLMvLZFluJMtyoycDEgBHV4fwZfAkvgyexOWQMzTu3QaAKv4+qFLSSX6YZPRZYcev4hvcFIAmfdpwOUT7pd7a0RalpRKAZv3aEX7yGpmpKv5esJ7Pmg9jVqsRrBrxHbeOXTE5IAFwdtWe3IVxboacoUEf7VR0T38fMlNURg0lwN3jV6kTrL1tpEGfNtzao70/2dGzLH1+HMWW0UtJyPcFrduC94gPe8Cpn3cWlGVG4i7cxqmqOw5ebigslVTv3oyIPYbTjCP2nKNmn1YAlPevTlZKOulxSZz6/HfWNPmYtS1Gs3fY90Qdvcr+kdqTha3evXveXRqRcCMSc6z95Q+6Bw2ge9AA9u48SM++3QDwa1iPlORUHpoYlKjsXSn356BObQi/FQGAg6M9lrrFpF4f2IvTx88ZrD9RkJOr9+Qu4HUt5Az+ulsIvHTHLcVEObp9/Cp1deUooE9rrunK0bWQs1RtXEt7f611Gbz8fIgLewBA78+HEhf2gKPLi7+ie0n6t/KrMB6F3sbR2x17Xbmu1qMZ90MMy/W9kHP4vKot124B1clKTkcVl4Tq4WPSohJwrO4BgEeruiTdfEDi9UjW+w5jY7PRbGw2mrToBLZ2nmb2gARA5uUbWFauiEXFCmBhgX3XQNIOGF7xtPDKWyiwTB0fJEuL5z4gAXBw9W5mB49ndvB4QkNO06x3WwC8/WsU2F4WxK1K3mwWr7reKC0tijQgAaAOv47CvSIKN3dQWlCmeTuyzxpOlU0ZOYDkj/uT/HF/sk8eQvXLN2SfOYr8OBFNfBwKDy8ALOsFoI6MKFIcALtW7WB88CjGB4/iVMhJAvtoO741/GuRnpJOUpxxh759v474tfXnmxFfFmuK9tMkhoZjX80d28puSJZKKvVsTnTI2WdvqBOwcCgptx4Q9mPx2p6HF4zr2709T69v2SmG9c2pmra+ebaqS+ItbV2vGNiABh+9zJ63vkadkVWk2FLOh2NbzR1rXR5V6NmCR2YuTOfeqyWxfz2/adrJ+WJy79mCh0WIKaYIMT3PdtrezSk3TSXf6kiSRHpiCklRj/Dyr4GldRkAqresa1b7DZAYeht7b3dsvbR55NWjGdG7C1+2n5fNK7fybucPeLfzBxzZdZTOr2pnYL4UUIe0lDSTA5Ld+nelcdtGzBw+x6Duxz2Io2GrAABcyjnjVd2L6LvRRY5NfTtfG9msHdlnjxukSRn9BsmjBpA8agDZpw6hWvFt7oAEoG1Xi3DrxulVe/gheAo/BE/hesgZfHX92kpP6dfeOX6Vl3T9Wr8+bbih69d+22o037QaxTetRnF1xyn+/mQF10POsm/BBr5uNoJvWo1i44jF3Dl2tcABCfj3+toAL3VvzpX/5Vs3hKcSMyX+Ww4DKyRJmo92gKIX8Cbwq+41C+AV4EdZlh9LkpQoSVJrWZb/AQYBxZhHZujqgfPUCfJj6qFvyVJlGsx6eO/XiWyYuIzkuES2z/+NQYs+puvYvjy4EsGJ37WL6lTwqcgbX32ERqMh9tYD1k/4sVjxhO0PpXqQHx8d/jr3MUVP9F0xnr8n/ERqXBL7562j1+IRtB33GrFX7hK64SAArUf2wsbFga6ztCsJa9RqfnnlEyo1qkmDPq2JvXaPd3dor+Qe+GID4c946oWs1nDkk5V0WzMBSangxoZDJN58wEsDtTNNrq7Zz739oVRu50v/I1+Ro8ri4Nhlz/w7m03pR9m6VUCWSYl8xOFJvxQluwA4uOcIbTu0ZN+pLahUGUz6eEbuez+t+5apo2bxMC6eBYs/w97eHkmC61duMX289spa9ZrefPH9TNRqDeE3bjN51EyzY7hxIJSaQX6MObSQbFUmm8bnHbfBv07gr4nLSIlLYvf8dfRbNIKOY18j6spdzvx+EICH4VHcPHSREbvmI2tkzmw4QNzNSKo0qoV/n9bEXLvHcN1xC1nwOzcPhhY5v/IbP30+p89fJCkpmfY9B/LRO4Po80rn5/b5pryo/CosWa3hxLSVdPptApJCwa0Nh0i6+YBag7Tl+sbq/UTuC6VSO1/6HP0KtSqLf8bkleuTn6yk7aIPUVhakHIvjiNjnl3mzaLW8Gju93j8OBdJqSD5rxCyw+/i+Lp28C3597+x79gKh+4dkHNykDMyiR03N3fz8gsmYdO4AUpnJ6rsXUPCktWkbCreo+UALh84R/0gf2YfWkSWKouV4/MGWof/OpnVE3/gcVwiQUO60vn9Hji6OfPpri+5fOA8qyf9QEDXpjTr3RZ1jprsjCx+Gr7wKXt7Bo0G1YrvsJu8QPe4u51oIiMo00F7Ff1p90gDqFZ8h+3wqUgWFmhio0n/8fOix6Ln3P4zBAQ1ZPHhH8lUZbJk3He5701Z8SlLJywmMS6BoXM+4uGDOOb8pX0CwMldx9n43Qac3Zz5fNvX2NjbIms0dHu7O6M6DENVhFsnZLWG0CkraLluEpJSwd11B0m58QDvwe0BuLNqH1ZuTrTbPRsLBxtkjYzPe13Y02YCTi9VpsprrXl89R7t9mrL1pV5vxO7L7RIcRz/ZCVd1mrr201dfautO49cX7Of+/u19e21I1+Rk2FY347r6puyjAUpd+M4rDvHtJj9JooyFnRZp32kY9y5MI5N/tXs2G5M/gX/9VNAqSB63UHSbkRScbB2dtCDVXsp4+ZE45B5uXnkNTSYE63Hok5VobApg2ub+lwb9/zagCcxBayfon1MqS6mSrqYInUxNdWLqfLQYI4955iK207X69qUJgM7oFFr6/uGEYu08YeGc2XnSYb9PRdNjpqoKxGcXmfeF98nZbv1uonax92uP0TyzQdU05Xt27qy3X7XbCwdbJA1Gnze60pI2wnkpKposmQYbi3qYOXqQPDZRVz9ciMR64rXtTyx/yRN2zVh7ZFVZGZk8vmYL3Lfm79qDl+M/5r42HjGzBtFTGQsS7Zo24bDO4+w6ps1rPp2DZO+Hs8ve39CApbN/YnHicUYaNZoUK1YhN3Ez7WPTT60E82DCMq017WR+57eRlLGCot6DUlfXox2Gri1P5QaQX58rOvXbtHr176xYjxbJ/xESlwSe+et49XFI2g37jWir9zlnK5f+yK8qL42gIV1Gbxb12PnlOUvLH6hdJNe1JUG4cWQJGkM8OT5Uj/LsvyNJElTgcHAXSASuCrL8peSJPkBPwC2wG3gLVmWEyVJOgiMk2X5mZcQRlftV6oKSDlZWdIhGCmrKf59rM/TVxnXSjoEI6/b1Xp2on/RjDP/7qM8C2NGo2klHYIBn+zSN5GujYvxkylK0oJUx5IOwcjnzUvXiuXvHvt3n1BTGAMy7Us6BCOJFqWrvlXNzi7pEIyUtknPB21KX3/EP7N09UcWK4v25IkXaXPb0lWSvjniUdIhGLEs6QBMmHp3bekq3EWUNmdwiX+vspu66ql5KUlSF+BbQIn2u+Z8E2kCgW/QFpdHsiy3LW5cYqbEf4wsy18DX+d7bQ4wx0TaULQLYuZ/PfAFhScIgiAIgiAIgiD8x0iSpAS+BzqivdB9WpKkrbIsX9VL4wwsAbrIsnxPkiTzHr1XADEoIQiCIAiCIAiCIAgvUkFPbCk9mgBhsizfBpAkaT3QA9B/1NUAYJMsy/cAZFmOM/qUIihd8wUFQRAEQRAEQRAEQXjuJEkaKknSGb1/Q/Xergjc1/s9UveavpqAiyRJByVJOitJ0uDnEZeYKSEIgiAIgiAIgiAI/+NkWV4GFLSir6n1JvKvg2EBNATaAzbAcUmSTsiyfLM4cYlBCUEQBEEQBEEQBEF4kTQlvs7ls0QCXnq/VwKiTKR5JMtyGpAmSdJhwBco1qCEuH1DEARBEARBEARBEP5/Ow3UkCTJW5KkMkA/YGu+NFuA1pIkWUiSZAs0BYr96D8xU0IQBEEQBEEQBEEQXiRN6V7oUpblHEmShgO70T4S9BdZlq9IkvSB7v0fZFm+JknSLuAi2qc1/yzL8uXi7lsMSgiCIAiCIAiCIAjC/3OyLO8AduR77Yd8v38BfPE89ytu3xAEQRAEQRAEQRAEoUSImRKCIAiCIAiCIAiC8CKV/oUuS4wYlBCeqm1G6ZpMk6w09aSakhVhUboamLHWdUo6BCMVM9QlHYKBGY2mlXQIRmacmV3SIRhY6/tpSYdg5Fx8uZIOwYDCKrukQzASur905dFbpbCbsc+m9N3Tm0XpaiNtNaXvuJU2n24dXNIhGNnYZXVJh2DAx8K5pEMwsuiIVUmHYKBlRk5Jh2DkhLWo/8K/T5Q6QRAEQRAEQRAEQXiR5NI3KF5alK7L4IIgCIIgCIIgCIIg/L8hBiUEQRAEQRAEQRAEQSgR4vYNQRAEQRAEQRAEQXiRxEKXBRIzJQRBEARBEARBEARBKBFipoQgCIIgCIIgCIIgvECyRix0WRAxU0IQBEEQBEEQBEEQhBIhBiUEQRAEQRAEQRAEQSgR4vYNQRAEQRAEQRAEQXiRxEKXBRIzJQRBEARBEARBEARBKBFipoRQbOWDGlB/1mAkpYK7aw9wa/E2g/ftfTwJ+OZ9nOpX5dr83wlb+jcACitLWm/+FEUZCyQLJVHbT3L9iz+LFINnYAMazxyEpFAQtu4gl7/fZpSm8cxBVGznh1qVydHRy0i4HIFjdQ/aLB2eF2vl8lz4ciPXft6Ny0uVaTb/LSxsrUmNfMiR4UvJTlWZFVfnGYOpEeRLtiqLLeN+JOZyhFEaZy83+iwajrWzPTGXI/hr9BI02WpqdmxI0NhXkTUyGrWa3Z+t5v6Zm7nbSQqJd7fPJiUmkfVvf/nMWLwCG9ByxiAkpYJr6w4SusQ4j1p+NojK7fzIUWVyYMwyHunFKykk+vw9i7SYRHa+9RUA1bo1odHo3rjU8GTTK9N5ePGOWfmjzy3Il3q6cnRv7QHCFm81eN/exxPfb97Hqb431+dv4LauHOVSSLTZPZeMmARODfqiyHHk1236YGoF+ZGtyuLPcT8QdSXCKI1LJTf6Lh6BjZM9UVfusHH0EtTZagC8m9Wh26eDUFhYkJ6Yws99Zz232PKbNvdrDh89hauLM5vX/PBC9lExsAFNdHXt1rqDXDJR15rMHEQlXTk6olfXAvPVtdAvN3L1591UebkJfmN641zDk+3dphNfjHJUIagB/jO15fz2bwe5ka89cvDxoPHC93GuX5XL83/n5g87ct9r9PV7eHT0J/NRMiFBk4ocgymvT3+LukH+ZKkyWTVuCfevGP+NbQd3pt3b3Shf1Z1x/u+QlpgCQIOOjXhlTF9kWUaTo+aPmSsIP3OjyLG4BvnhM/stJKWC6LX7uLdos8H7tj6e1Pp2GA71vbkzbx33l+blYcX3gvEc2B6QiF67l8hlO3ge3IJ8eWm2tv7fX3uA8EWG9d/OxxPfb9/Hsb43N+eZrv+tQrT1/8zAotf/XtPfpI7uOK0bt5QHJuq7ayU3Bi0eia2THZFXIvht9GLU2WqsHWx4Y+FwXCqWQ6FUcOCn7Zz+4xAArd/qSrN+7ZAkOLF+P4d/2Vmk+F6dPiS3HK0et5RIE+WozeDOBL0djFtVdyb6v5tbjhr1aEXHD7oDkJmewYZpy3lw7a5Z+/cIbEDALG39D193kGuLjet/wKzBeLbzRa3K4sToH0m8FAFAzXc6U/2NICRJInztAW78vAsA55cq03j+21jYWZMW+ZBjw5aQY8a59kXE1OKHEThW9wDA0tGW7OR0dnWcYk5WGTly/iqf/7oJjUZD7/bNeadXR4P3k1PT+XTJb9yPfYSVpQWffTSAGpU9i7XPJzwCG9BoVl4f6aqJPGo4S9tHylFlcnz0stw8qvVOZ3zeCARJImztAW78vBuABuNfpVLnAGRZJvNRMsdH/YgqNqlI8fWf/jb1g/zJUmXxy7jF3DNRroMGd6Hj290oX9WDUf5vkaor17Wa1WXYsgk8iowD4Nyuk2z/bmOR4tDXecZgfHT9t61P6b/11uu/bdbrvwXq9d9C8vXfzFU2yJdas4cgKRU8WLufiEVbDN639fGk7rcf4ljfm7B567m7dLv29eoeNFg2KjedTZXyhC/4g3vPqd0uTXkk/Pf9v5opIUnSTEmSOuh+PihJUiPdzxGSJJUz43P8JEkK1vs9UJKkFsWIK7Wo2xZxf8WK14BCwnfeWxwfsIB9bcZTqVcLHGpWNEiSlZTKxWkrcwcjntBkZnOkz2wOtJ/MgfaTKR/ki0uAj9khSAqJpnPeZN/ABWwNmkDVns1wqmF4Iq/YzhdHb3c2txrL8YnLaTpvCADJ4dFs7zSV7Z2m8neXaahVmdzbeQaA5l+8y7m5G9jWYTL3d56h7ofdzIrLJ8iXst7uLG47lu2Tl9Nt9lsm07Wf1I8Ty3fyfeBYVI/T8O8bCMCdo5f5sctklgVPYev4Zbzy+XsG2zV9uwuPwqIKFYukkGg1+03+HryADe0m4NOjGS758qhykC9O3u6saz2WQxOX03ruEIP367/ThcR8+0u4Ecnuod8SfbLoX5AAUEjUn/cWJwd8zoE24/Ds1QJ7E+Xo8rSV3NadbPOr9l5XUm49KF4c+dQM9KOctztfB45h85Sf6T7nbZPpOk/qz9HlO1kYNIaMx2k07BsEgLWjLd1nvcXqd7/iu04TWPfRt881vvx6Bnfkh69nv7DPf1LX9gxcwOagCXg/pa5t0tW15np1bWunqWztNJVturp2V1fXkq5HcuC9b4k9UfxyFDB3CP+8sYBdbSdQuWdz4/YoMY3z01Zx84e/jTaP+P0f/hmwoHgxmFA30J/y3u5MD/yY36Yso/+cd02mCz97g28HziJe17l+4sbRS8zpOp65wRNYPWEpAz//oOjBKBTUmP8OFwfM4VTr0ZTv1RLbmpUMkmQnpRI29ReDwQgAu9peeA5sz9kukznTbhxlOzbExtu96LHkxiRRd/5bnBrwOYdam67/2UmpXJm6kjsF1H/v97qSWsz6XyfQj3LeHswNHMUfU37i1QKO08uTBnBo+d/MCxqN6nEqTfu2A6DloM7Ehj3gy64T+b7fTHpMHYTSUol7zUo069eOb3pM5cuuE3mpXQDlqpqfby8F+uHm7c5ngSNZN+Un+s15x2S622dvsGjgbKNyFH8/jm/6fsa8rhPYtWgT/ee9Z3L7gkgKiYZzh3DwjQXsCJxAlR7NcaxheJw82vni4O3O9pZjOTVhOY3mac97TrUqUf2NIEK6fcrODpPx7OiPvXcFAJp8+S6hc9ezs/0kIneeoY4Z59oXFdOxDxaxq+MUdnWcQuTfp7m/47RZeZWfWq1h7vI/WDr1AzYvnMLOo2cJvx9tkOanTSHU8q7In19NYs6IQXz+66Zi7fMJSSHReO6bHHhjAdsDJ1C1RzMc87Xbnrp2e2vLsZycsJwmunbbqVYlfN4IZFe36ezoMIWKHf1x0OXR1aV/s6PDFHZ2nMqDveepP7pXkeKrH+hPeW8PpgSOYNWUHxg4Z6jJdGFnb/DVwJm5gw/6bp2+zszg8cwMHv9cBiR8gnxx9Xbn+7Zj+XvycoKf0n87uXwnSwLHkpGv/7asy2R+Cp7CtvHLePlz8+qaAYVE7flvc37API61HoN7r5bYmWgfb0xdQUS+Njs9PJoT7Sdq/3WchFqVRdyOU0WPRU+pyqP/Eo1c8v9Kqf9XgxKyLH8qy/Le5/BRfkCw3u+BwPP5kv/vCOQ5xevi70PqnVjS78UhZ6uJ3Hwc984NDdJkPUomKfQ2co7aaHt1eiYACkslCgslyOZXlrL+1UmJiCX13kM02WoitpzAK18MXp0bEr7xCACPzoVTxskOm/LOBmncW9Ul5W4caQ/iAXCs7kHsiesARP1zmcrBjc2Kq1bHhlz48x8AHpwPw8rRFvt8+wTwblGXq7qTxMU/D1O7UyMAsnV5A1DG1gqZvLxxcHelRjs/zq8/UKhYyvtVJzkilhRdHoVvPUHVToZ5VLVTQ27+qc2juPPhWDnaYauL187dlcrt/Li27qDBNklhUTy+bdixKgoXfx/S7sTklqOozcdx79zIIE3Wo2Qeh95GY6IcWXu4Ur6DP/fWFi4/CqtOp4ac36Q9hvfPh2HtYIuDm7NRumot6nJlx0kAzv35Dy/pjqFv9xZc2XWax1HaMpUWn/xc48uvkV99nBwdXtjnl8tX1+5sOUHlfHWtsl5de1hAXfNoVZdkvbr2OCyK5PDilyNX/+qkRsSSdu8hcraa+1tOUDFffJnxySReuI0m27gcPTpxnazE5z9G7NupESc2HQbgzvlb2DrY4WiiHEVeiSAh8qHR65n524IitJNPOAb4oLoTQ8bdOOTsHOI2H6VcF8O6lv0omZTQcOTsHIPXbWtUJPnsLTSqLGS1hqRjVykX3KTIsTzhHOBD+p0YVHfz6n+FLgXUfxPHzdrDlfId/blfzPpfr1MjzuiO093zYdgUUN99WtTloq6+n/7zMPU6PYlVxsrOGgArW2vSk1LR5Gio4FORu+dvkZ2RhUatIfzkNep3Nu98AtCgU2NO6eKLOH8LGzPL0Z1zN1Elp+l+voWze1mz9q9fvzTZau5tOUGlfPWrUueGRGzUtpnx58Io42SLdXlnHGt4En8uDLWu7MQdv4ZXV20eOFb35KHuXBtz+BJe3Qpfpl5UTPq8ujfl7uZjhc8oEy6H3aWyuxuVKpTD0tKCLi0DOHDmkkGa25ExNK1XEwDvihWIehhPfFLxzxn5+0h3TfSRKnVuyG1dux2va7etyzvjVMOTR+fC9fLoOl5dteVdfzaLhU3R2yW/To05vukgALfP38LWwRYnE+X6/pU7xJso1y9CzY4NuajXf7MuoP9WVa//duHPw9Qy0X+ztLUCit5mOwX4kH4nNrd9jNl8DLcuhuU0+1EyyaHhyCbaxydcW9dHFRFLRuSjIseirzTlkfC/4X92UEKSpE8kSbouSdIeSZLWSZI0TpKkFZIkvWrGZzSRJOmYJEnndf/XkiSpDDAT6CtJUqgkSROBD4DRut9bS5L0iiRJJ3Xb7ZUkqYLu8+wlSfpVkqRLkiRdlCSpj96+5kiSdEGSpBN66VdIkrRUkqQDkiTdliSprSRJv0iSdE2SpBV623aSJOm4JEnnJEn6Q5Ike93rEZIkfaZ7/ZIkSbUlSaqaP97i5LONhwsq3RcugIzoBGw8XAv/AQqJoL1z6Xr5B+IOXyLxfLjZMdi6u5AWlZD7e3p0ArbuLkZp0vXiNJXGu0dz7mw+nvt70o37eHUKAKDKy02x8zTj70I7cJCst8+UmAQcKhju08bFnozkNGS19rnFydEJOOjFVatzIz7a9wX9fx3PtvHLcl/vPH0Qe+euQy7kiKeduwupenmUGp2AXb6/X5sm3mSaFjMGcmLuuhc2wmptVI7isfZwecoWhurOGsy1Wb+B/Hyf/+xYwYXHevmWHJOAY/6y5eJARnIamtxjGI+j7jiXreaBjZMd76yfxkfb5uDXu1jVrcTlr2tpBdS1NL1jaSpN/rr2vNi4u5L+wLCe27gXvhy9KM4VXEmMyusIJsbE4+xuXnvi27kx0/ctZNgvk1k9YWmRY7FydyVT7/hkRiVgVcgvp2nX7+PUrA4WLvYobMrg2iEAq4qFnmRYIGv3fPU/Kh5rM47bS7MGc23mb8V+/rtjBVeS9OJIiknAKd9xsnNxICM5Pbe+P45OwKmCNs2Rlbup4FORGaeWMn73F/z12UpkWSb6xn2qNamDrbM9ltZlqBPkh7OHeQMCAM4VXEg0iM/8cvREi75BXD0YatY2tu6uRudRm3zttI27q0H9T4/S1v/H1yNxa1qbMi72KG3K4NnOD1vdOTXpxv3cwUOvl5vmvl6SMT3h1rQ2GQ8fk3onttAxmRKbkESFss65v1dwdSYu/rFBmppVK7Lv5AUALt26S/TDRGLjk4q1XwAbdxfS8/WR8ueRUR9Jl0dJ1yMp37SWXh75YuuZV3Z9J75GzzPfUrV3Cy4W8fZb5wplSdDbd2JMgtkDZtUDajJ955eMXDEVzxqVnr3BM+TvvyUXov+WYqL/9qGu/7ZVr/9mLuM2Ox6rIpzX3Hu1IOavo0WOI7/SlEf/KbKm5P+VUv+TgxK62zL6AP5Ab6DR07co0HWgjSzL/sCnwFxZlrN0P2+QZdlPluXPgR+Ahbrf/wGOAM10260HJug+7xPgsSzL9WVZbgDs171uB5yQZdkXOAzoz2FyAdoBo4FtwEKgLlBfdxtJOWAa0EGW5QDgDDBGb/tHuteXAuNkWY4wEa8BSZKGSpJ0RpKkMyHpYU/PIUkyfs2c0XKNzIEOU9jtPxwX/+o41Db/ZCKZjMEokXESvTgVlkoqdQrg7vaTua8dG/MTtYZ0pNvOWVjaWaPJd9Xw2XGZeDFf3piKXT/Jjd1nWNJ+PBveW0jg2NcAqNHOn7T4x0SbuHfPnGCMDlMBeVS5vR8Z8ck8umTG/sxVmGNYgPId/bVXUYuxDkFBnnV8tImMt3tStpRKJZ71vVn11hesGDyfoBG9KPs8pruXlCLWNfLVNa9OAUTo1bXnxfSuS8HVl+K2k8CF3af5rP1ofhj6Bd3H9C1GLCZCKWRlS7/1gHuLt+D7+yc0WDeVtCsRJmfAmR+TqcaycJ7U/+TnUP8L02abyr8naWq18eXB1bvMaPIhXwVPpPfMt7CytyEuPIoDP2zlgzVTGbpyMlHX7uYOahQ3wKKU7xrN69K8bzu2zF9r5v5NvFaI/JFlmeSwKK4t2UbQ+kkErp1I4tV7aHK0eXByzDJqDOlI512zsbS3QZNlxrn2BcX0RJWezbn3AgZQwfj88k7PDiSnqXht3Oes23mI2t6VUCqVz30/QKHb7eSwKK4u2U779ZNot3YCSVfvGcxWvPD5H2xuNJKITceo+XZH488oVHwmXjSjXN+9fJuJLT/ks67j2L9iB8OWTSxSHM+KKX9de1a+3th9hqXtx/O7Xv/tuQVj7kdYKnHr1JDYbSeK/Vm5n1ma8kj4n/C/utBlK2CLLMsqAEmSjFf0KRwnYKUkSTXQViPLQm5XCdggSZIHUAZ40lvqAPR7kkiW5UTdj1nAkxtlzwL6Lfs2WZZlSZIuAbGyLF8CkCTpClBVt6+XgKO6yl8G0D+DPrkp8SzaAZpnkmV5GbAMYLP7gKeeGVRRCdjojZpbe7iiikl8yhamZSen8+jYNSoE+ZJyPdKsbdOiEwxmMdh6uJIeaxhDenSCwei+rYerwYJMFYN8SbgUQcajvKmSyeHR7B3wOQAO1dyp1N7vmbE0GtyRgH7a9QSiLt7GUW+fDu6upMQlGaRPT0jB2tEOSalAVmtw9HAlNdY4/+6duo5LlfLYuNjj1agmtTo0pEagHxZWllg52NDzmw/ZPKrgq6dp0QnY6+WRvYk80qYpmy9NEtWCm1ClYwCVg3xRWlli6WBDu28/ZP/Iol+tzS/DqByVJaOQ5ci1cS0qdAqgfHs/FFaWWNrb4L94GOeHf1+kWJoO6kjj/tpjGHnhNk56+ebo7kpK/rKlO4YKpQKNWoOjR9nc4/w4Jp60xBSyVZlkqzKJOHUNjzpViL8TU6TYSlp6vrpmV0Bds9M7lna6cvRExSBf4vPVtecZn21Fw3qeUcSF14qr7aDOtOzfHoC7F8Jx8SwHaNfMcHEvS5KJel4YYaeuUa6KO3YuDrkLGJojMzoBK73jY+XpSlZMwlO2MBTz235iftOOp3tP6W9wBa+oMqLz1X/Pwtd/lya1KN85gKD2fiistfXf7/thhA4rXP1vOagTzfpr14S4fyEcZ704nN1deZy/nUxIwdrRNre+O3m48jhOm6bJa23Zt1S7QOeju7Ek3I+jQnVP7l0I5+TvBzj5u/b2kuDx/UiKLly+tRnUiRYG5Ug/vrJG8T2LZ+3KDJg/lKVD5pOWZN6tSibPozFJBmlUuvr/ZF6QrWfeufb2ukPcXqdd+LPBpNdJj9aWu5SwaA72nw9oz7WehTjXvuiYACSlAq/gxuzqMq3Q8RSkgquzwayH2IQk3FwdDdLY29owa9gbgPbLXddhn1GxfNFmwujT5pFhHyl/P80oHz3z2u3wdYcI1+WRb748eiLir2MErh7HpS8Ltw5G0KAutNaV64gL4bjq7dvF3ZWk2MK3SRl6t5FcOnieN2YrsXdxyF0Is7AaDe6IfwH9N0d3V1Kf0X9z8DDuH4Bh/01VhNsDM6Pj87XZZck0s59drr0/KZfukPXw8bMTP0VpzSPhf8P/5EwJTI+dP3sjSRqmu6UhVJIkT2AWcECW5XrAK4B1IT9qEbBYluX6wPt620mYvv6bLecNL6oxHCx6ctOVRu/nJ79b6D5zj27Wg58syy/JsvyOie3zf+5zkRQajn01d2wruyFZKqnUszkxIWcLtW2Zsg5YOtoCoLC2xK11PVIKuXCjvvjQ2zh4u2Pv5YbCUknVHs24H3LOIM39kHNUf7UVAOUCqpOdnI5Kr/Gs2tN4Orl1WV2HQZJoMLIHN1fve2YsZ1btYVnwFJYFT+FGyBl8+2in61f09yEzRWXUYANEHL/KS7r7shv0acONPdr8c6lSITeNe72qKC0tUCWmsn/BBr5pNoLvWo3izxGLuXPs6lMHJADiLtzGqao7Dro8qt69GRF7DPMoYs85avbR5lF5/+pkpaSTHpfEqc9/Z02Tj1nbYjR7h31P1NGrz3VAArTlyK6aOza6cuRpRjm6Pnc9ewOGs6/xx5z74DseHb1S5AEJgJOr97A4eAqLg6dwLeQM/rpbLrx0xzDlYZLRNrePX6VucFMAAvq05lqIdgHHayFnqdq4FgqlAkvrMnj5+RAX9nwX4/w3PQq9jaNeXfN+Rl1zC6hOVr66Vs1EXXteEkNvY+/tjq2Xthx59WhG1O7ClaPn7dDq3cwNnsDc4AlcCDlFs95tAPD2r4EqJZ1kE+WoIG56bYFXXW8sLC2KNCABkHI+DJtqHlhXLo9kaUH5ni15tPtMobe3LKdtF60qlsMtuClxz2E68OPzxvU/tpDH7cac9ez3H86Bxh9z/n1t/S/sgATA0dUhfBU8ia+CJ3Ep5AyNdMepir8PGSnpJut72PGrNNDV98Z92nBZV98To+Kp2bIeAPblnChfzZP4e9pF+ex15xNnz7LU79KY81sLt0bB4dUhzA+eyPzgiVwMOU0TXXxVi1COXDzL8t4PY1k1+nvi7pi/hkuC7lxrp6v/lXs0IzJfO/0g5BxVX9W2mWUDfMhOVpGhq/9WujywrVgWr+DGues0WOmda+uO7ElYIc61LzomAPfW9UgOi0Jl4ku4uer6VOZu9EMiY+PJzs5h19FzBDaqb5AmOS2dbN2MzD/3HSegTnXsbW2Kve/4fHlUpUczIvO125Eh56ima7fL6tpt03nUKDePnix4CVCxcwDJYYUvUwdW78pdmPJ8yCma9w4EoJquXD82o1zrr6vi7euDJElmD0iAtv/2U/AUftL13xro9d8yCtF/8y1E/60oks+HY1vNHWtd++jeswUPzWizAdx7tSTmr+KtiwKlN4/+U0p6kctSvNDl/+pMiSPAj5IkzUP7N3YDfnrWRrIsfw/k9mYkSXICnnyDGKKXNAVwyPe7/pC3/nZv6r0eAgwHRuk+30VvtkRRnQC+lyTJR5blMEmSbIFKsiw/7bk6+eMtMlmt4eKUFbRYN0n7SNB1B0m58YCqg3Uj4Kv2YeXmRODu2Vg42IBGpvp7XdjXZgLW5Z0J+O5DJKUCSSHxYOsJYvecL1IMp6atpMNvE7SPu9pwiMc3H1BzkPbq183V+3mwL5SK7XzpdfQrclRZHBuTd++a0roMnm3qcWLiLwafW7Vnc2oP6QDAvR1nCNtw2Ky4bu0PxSfIj+GHv859XNIT/VeMZ9uEn0iNS2LvvHX0WTyCoHGvEXPlLuc3HASgTtfGNOjTGk22mpzMLP4ctsjsvHlCVms48slKuq2ZgKRUcGPDIRJvPuClgdo8urpmP/f2h1K5nS/9j2jz6ODYZ9/fV7VLI1rNHIyNqwNdV4wj/upd/h5o/tMLZLWGy1NW0GzdZO0jAdcdJPVGJFUGa/P/7qq9WLk50Xr3nNxyVO29rhxsM96sR8eZ68aBUGoG+THm0EKyVZlsGp93DAf/OoG/Ji4jJS6J3fPX0W/RCDqOfY2obbxpMAABAABJREFUK3c58/tBAB6GR3Hz0EVG7JqPrJE5s+EAcTfNmwlkjvHT53P6/EWSkpJp33MgH70ziD6vdH5uny+rNZyYtpKOenUt6eYDaunq2o3V+4nU1bXeR79CrcriSL665tGmHsfy1bXKXRrRdPZgrF0d6LBqHAlX7rLnjaKVo/NTVtBm3UQkpYI76w+RfPMB1XTt0W1de9Rh12wsHWyQNRpqvNeV3W0nkJOqoumSYbi1qIOVqwPdzi7iypcbidBdISyOywfOUy8ogJmHviNLlcWq8Uty3xv26yTWTPyRx3GJBA3pSsf3u+Po5sy0XV9w5cB51kz6Ef+uzWjauw3qHDXZGVn8PHxhkWOR1RpuTV5Og/VTtY8EXXeA9BuReA7WTtCLWrWHMm7ONAyZj1JX1yoN7cap1qNRp6qou3wcli4OyDk53Jz8MzmP04qdP7Jaw+XJK2iyXlv/I3X1v7Ku/t/T1f+WIXn1v+rQrhxu/Xzr/7UD56kT5MeUQ9+Srcpk3fi8x+q+9+tENkxcRnJcItvn/8bgRR8TPLYvkVcicmdA7PluE/2//JDxuxaAJLF9/m+5g0dDlo7B1sUeTY6aTZ/8mrvgpDmuHDhP3SB/ph/6lmxVFmvG5w0Of/jrJH7TlaO2Q7rQQVeOpuxawJUDofw26Ue6fvwqdi729J2tvW6hyVGzoHvhH3MpqzWcmbqCwN+09eu2rn75DNLWr7DV+4jaF4pHez9ePvY1alUWJ0fntZmtfh6JlYsDmuwczkxZQfbjdEB7i0SNIdryF7nzNLfXF77OvaiYACr3aM7d5zSAaqFUMuWdV/lwzhLUGg09g5rh4+XB7yHaxSVf79SKO5GxTF28BoVConoldz77cMBz2bc2j1bS7jft+T98vbaPVEPXbt9avZ+ofaFUbO9L92Padvv46Lx2u83PI7FysUeTncPpKSvJ0uWR35S+OFb3QNbIpD14xKmJvxYpvksHzlE/KIC5hxaTpcrkV732ceSvU1gxcSmP4xJpPySYzu/3wMnNmRm7vuLSgXOsnPQDDbs2I3BgZzRqNVkZWSwb8U3RM0snTNd/G3b4a3Ly9d/6rRjPdl3/bd+8dfRePIJAXf8tNF//Ta3rv20qZv/txuRfCFg/BUmpIGrdQdJuRFJJ1z5GrtpLGTcnmobMw8LBBlkjU3loMMdaj0WdqtKu/9OmPtfGPd81G0pTHgn/G6RScb/tCyBJ0gygP3AXeAgcBFoC22VZ3ihJ0kG0ayyckSQpAmgky/KjfJ/RHFip234/MEiW5aqSJLkCu9HezjEPOA9sRDt7YQTginbthwdoBw0ay7IcqFuA8nugIdqZC5/JsrxJkqRUWZafLE75KvCyLMtDdItZPom3qu7nerp0+u+1Az4HrHShT5Nleav+36VbZ+NLXRw19eM1ta7EE8+6fePflqwsfZN7IixKVRZRXlP8+w+ft4pPWRG6JJywLn15NOPMi3uUZ1Gs9f20pEMwYlfKRvj3W2WXdAhG+qpKVx6llcJrH/tsSlceAWSVspXnW2aVvuNW2vTeNbikQzCyscvqkg7BwIEyGSUdgpEqstWzE/2LmmWYt17Zv+GEdemr/5/cXVv6Om5FkDLqlRJv7B2+2VYq87L0lbrn50tZlmfoZg4cBr6SZTl3toQsy4F6P1c19QGyLB8Hauq99Inu9QQg/3OjGuT7fYuJz0vFcObEk9ft9X7eiHbAAFmWh+i9HgHU0/td/739JuIx+LtkWT6D9lGg6GZR5I9XEARBEARBEARBEP5V/8uDEsskSXoJ7XoOK2VZPvesDQRBEARBEARBEARB+Pf8zw5KyLL8fG7GEwRBEARBEARBEITiKGW3oZYmpe8GfUEQBEEQBEEQBEEQ/l/4n50pIQiCIAiCIAiCIAilgkZT0hGUWmKmhCAIgiAIgiAIgiAIJUIMSgiCIAiCIAiCIAiCUCLE7RuCIAiCIAiCIAiC8CKJhS4LJGZKCIIgCIIgCIIgCIJQIsRMCUEQBEEQBEEQBEF4kcRMiQKJQQnhqaYTUdIhGBiirFbSIRjZr44t6RAMjM92K+kQjMy1KF159G62Z0mHYGSt76clHYKBNy7MLOkQjIxtNLmkQzAQmGlZ0iEYuWchlXQIBoI8o0s6BCPL4+xKOgQjsly6Oqq+NqVvhfisHGVJh2BgXdfVJR2CkQbKlJIOwcDm0lWsASivti7pEAwEJx4p6RCMDPRsVtIhCP8Pids3BEEQBEEQBEEQBEEoEWKmhCAIgiAIgiAIgiC8QKVtVlxpImZKCIIgCIIgCIIgCIJQIsRMCUEQBEEQBEEQBEF4kcRClwUSMyUEQRAEQRAEQRAEQSgRYlBCEARBEARBEARBEIQSIW7fEARBEARBEARBEIQXSdy+USAxU0IQBEEQBEEQBEEQhBIhBiUEQRAEQRAEQRAEQSgR4vYNQRAEQRAEQRAEQXiBZHH7RoHEoITwXEycPZpW7ZuTocrgk5GzuX7pplGaGV9P5iXf2kiSxN3b9/nk49mo0lU0auHPNys+58G9KAD27zjEj1//atb+q7RtQOCMQSiUCi6vP8jpJduM0gR+NgjvID+yVZmEjF1G3OUIADp+8R7V2vuRHp/M6o6Tc9M3H/sq1TsFIGtkVPHJ7B77I2mxSWbFpW/EzGE0a9eEDFUm80cv4NblMKM0UxdNplaDmqizc7gWeoOvJi1EnaPGzsGOqd9NonzF8iiVSjb8+Ae7ft9d5Fjcgnx5afZgJKWC+2sPEL5oq8H7dj6e+H77Po71vbk5bwO3l/5t+AEKiVYhc8mISeDMwC+KHMfTjJo5nObtmpKhymDO6AXcvHzLKM2kL8dR27cWEnD/TiRzRn2OKj2jyPusGNiApjMHISkU3Fx3kEvfG5ejpjMHUamdHzmqTI6MXka8rhyVcbSl5Zfv4lyrEsgyR8b+xMOzece43vvBNP50AL/V+4DMxFSzYmqii+lWATE1yRdTwuUIHKt7ELh0eG4a+8rlCf1yI1d/3k2Vl5vgN6Y3zjU82d5tOvEX75iRS4U3be7XHD56ClcXZzav+eGF7MOUPtOH8FKQP1mqTNaOW0rkFeO/r/XgzgS+HYxbVXcm+79LWmIKAOWre/LGFx/iVdeb7V+uZ/9P259rbO5BDfCfOQhJqeD2bwe5vtjweDr4eNBk4fu41K/Kpfm/c+OHHc9lv56BDWisK0dh6w5y2UQ5ajxzEBXb+aFWZXJUV44A6rzXhRr9A5FlmaTrkRwdswxNZjZVXm6C75jeONXwZEcxy5F188Y4jx0GCgVpW3aQsnK94fttWuD0wVsgayBHTeLXS8i6cBkAjy1r0aSng0b7XuybHxU5Dn3vzHjv/9g77/AoqrYP37Ob3gshm9ASQu8JvSdAQlGKoAJKk65Ikw5KbyKCKEpRXwELoKIiSu+C0nvoJUBII71t2u58f+yS7GY3kIbhfb9zX1eu7M48M+e3p89zyhAQ1IRMdSarJ3/C3St3TWwmrHoPv/rV0ORouHXxFmtnfI4mR0MFvwq8u3w8Vev68cPyb9m+/rfS0TRvJI2DGpOpzuSzSau4e+WOGU2TqNZAr+nCTdbkaqrI2OXjqVrPj+8/+pbt638tkRb7do1RfTASSakgYete4tb9ZHTeqUcg5Ua9CoA2PYPIDz4n8/o9rHwrUPHT6bl2lpVUPP7kO+I3bC+RHgDH9gFUmDMcSakkbsteYtZsMzrv2qs95Uf30WtS83DWGjKuhQFQ59iXaNLUoNEiazTc7D6pWBqeR53tP+VVKoUEgCyjjk3m2MR1qIvZH3EK9Kfi3BGgVBC3eR/RX5jGkeqd3gBo0jJ4OHMNan0cAaBQUOvPj8mOiuPOWwuLpeFZvDV3BAH6fP755FXcM1P2xunLXk5ODrcv3mL9jC/Q5GiKHWalwAa0mqerm69vPswFM+nWav5AKuvT7fDE9cTq60gASSHRe+cC0qIS2D3kYwA6ffEuzn5eAFg72ZGZnM62zrOKpW/livl06dIBtVrNsGETOX/hionN11+tpG3bFiQn69qzYcMncvFiKN27hzBv7hS0WpmcnBwmTZrD8b9PF0uHIW/MGUqDoACy1Fl8Pfkz7ptpazsO6krw0Jfw9PFirP8QUvVtbc0WdRm3fhqx4TEAnN19kt8//cnkesH/H8TyjWIiSdJ8SZI66T8fliSpif5zmCRJ5f5lLU0kSfr03wzTkDYdW1K5akW6t3yd+ZM/5P0Pp5i1+2j2Kl7vOJjXOgwiKjya/kNfzT13/uRF+nYaQt9OQ4rskJAUEh0WDua3wcvY2HEqNXu0wK26t5GNT1BDXHxUfNNuEvunf02HRUNyz1396Si/DjJ9sD677k++6zyT77vO4u6B87QY/0qRdBnSvEMzKvpW4M02g/l42komLhlv1m7/rwcY1P4t3uo0AmsbK17q3w2AXoN7EHbrPsNDRjHhtUm8M3sUFpbF9CkqJOoufYtTb3zIkbaT8X6lFQ41KhiZZCemEjprI/fWmH8g8x3RldRbj4oXfiFo2aE5FX0r0LfNQJZNW8HkJRPM2n069wuGBI9gcPAIoh/F0Oet4qeRpJBosWgwewcs49egqVTt1QLnfPmoYoeGOPmq2NZmEn9P+5qWS4bknms+fyDhhy7xa/upbA+eSdKtiNxz9t5ueLerR2p4bJE1NV80mH0DlvFb0FR8zWiqoNf0S5tJ/GOgKflOJL+HzOL3kFns6PI+GnUm93edASDxejiHRqwi+sSNIukpKr26BbN2xfPptBZEncBGePiqWBA4nq0zv+T1RcPM2t07e4PPBywkTt8hekJ6Yirb5m7gwJemHdKSIikkGi8ewtE3l7G7/VSq9GqJU76yl5WQxvn3N3Fj7Z8F3KV44TZfNJgDA5bxe9BUfJ6Sj37T56Pm+nxkq3Kl1tAQ/uz2ATs6zkBSKvDt2QLQ5aPDpZGPFApcp47j8fgZRL0+FLuQDlj4VjEyyTx9jug3RhD95ijiFyzH7X3jB8bHoycR/eaoUnNIBAQ1xsvXmzHtR7F2xueMXPi2Wbujvx1hbId3mBAyFitrKzr1CwEgNTGVr+esZ/uXJXvwz6/J28ebd9qNYs30zxm1qCBNh3k36G3GB7+LlY2hphS+mrO+xM4IABQKvOa+zYOhc7jd+W2cu7fDqlolI5Ps8GjC+k/n7kvvErt6M96LxgKQde8Rd7uP1f31HI+ckUnK3r9LRVPFBaO4O3ge1zuNwbVHO6yrG2vKfBjN7ddncKPLOKI+3UqlJWOMzt/uN4sb3SYU2yHxvOrsK2v+5PfgmfweMovw/edpNLGYbZ1CQaWFo7g9aB7XOryLa8+22OSLo6yH0dx8bSbXQsYTtWorlT80jqPyw14m4/bD4oVfCPyDGuPl68XY9qNZN+NzRhRQ9v767QjjO7zDpJBxWFlb0bFfcLHDlBQSrRcOZufAZfwYNJVqPVvgki/dKnVoiLOvii1tJnF02te0MWj/AeoN60LC7QijY/vfWc22zrPY1nkWd3ee5t6u4jkCunTpQLVqvtSu04a3357G6tVLCrSdPmMhTZqG0KRpCBcvhgJw8OAxAhoH06RpCCNGTmLtuuXF0mFIg8AAPH29mB74LhtmrmHgopFm7W6dvc5HA+blOh8MuXn6GnO6TWZOt8n/fxwSWrns/15QhFOimMiyPFuW5f1lrQNAluUzsiyPK6vwgzq3ZcePuwG4fC4URycHypV3N7FLS03P/Wxta4VM6RQMVSM/EsOiSXrwGG22hhs7TuAX0tjIxi+kMde2HQMg6vwdrJ3ssS/vAsCjUzfISDQduc5KVed+trSzRpaLr7d1SCv2/LwPgKvnruHg5IBbeTcTu5MHT+V+vnbhBh5eOv+WLIOdvR0Atva2pCSmFHtEwCWgGun3olDfj0HO1hDx2z94dmliZJMVm0zShbtos03DsPFyo3ywPw+/P1Ss8AtDm86t2K2Pr9Bz13B0dsDdTHylG+Ypm5KlUTl/P1LCoknV56O7209QubNxPqrcuTG3f9blo8fn7mDlbI9teRcsHWzxbF6TW5sPA6DN1pCVnKet2dwBnF60pcj68mu6V4CmO2Y0GeLVpi7J92NIexQHQNLtCJLvRBZJS3Fo0qg+zk6Ozz0cQ+qHNOXUL0cBCDt/C1tHe5w8XEzswkPDiA9/bHI8NS6ZB5fuoC3BiFtBuOnTM02fng+2n6BCvvTMjEsm/qL5sldc3PPlo7DtJ6iUL9xKBvkoNl8+UlgoUdpYISkVWNhakR6VAJRePrKqW4vsh4/QPIqEnBzS9x3Ctn0rIxtZnTcDSrK10VWKz5Fmwc05vE1Xx908fwN7J3tcy7ua2J07dDb3862LN3H30rV9SXFJ3L50G00ppmOzkBYc2nawaJou3KKcvh3RabpFTk5OibXYNqxB1v0Ish9GQXYOSX8cxbFTCyMb9blraJN1bWv6+RtYqEz7BfatGpL1IJLsCNOyWFTsGlUnMyySrIfRyNk5JOz4C+fg5kY26Wevo0lO030+dwNLr9IdQ3pedXa2QX/EogT9EftG1ckMiyLrgT6Ofv8L55BmRjZpZ6+jSdLFUdr5G1h65aWbpcodpw5NiN28r1jhF4amwc04oi97t87fxN7JHhcz+fy8QT6/ffEW7iVIy/KN/EgOiyZFn263t5/AJ18/0iekMTf16RZzTtePtNOnm72XG1U6NuL6D4cLDMOve3Nub/+nWPp6dO/Md9//DMDJU+dwdnFGpSpf6OvT0vL6I/Z2diXqKz3BP6Qpf/9yBIC7529h52iPs5m29kHoPeLMtLUCQX6EU6IQSJL0gSRJ1yVJ2idJ0mZJkiZLkrRBkqRXn3117j3aS5J0Qf93XpIkR0mSAiVJOipJ0q+SJF2VJGmtJEkKvf0aSZLOSJIUKknSPIP7NJUk6W9Jki5KknTK4D5/6M/PlSTpP/rZG3clSRpncK3J7yiN+Cnv5UF0RHTu9+jIx5T38jBrO/+TWRy8/Ae+1aqw+es8r2iDxvX48cBGPv/hY/xq+hYpfAeVKykR8bnfUyPjcfB0NbWJjMuziYrHQWXayOWn1ZTXGH5iFbV6teKfj7c9074gPFTleGzQ6Xoc+RgPVcENqNJCSUifTpw6rPOq/7rhN6pUr8y2s1v5Zv+XfDb7i2I3KjYqV9QReXGRERGHTSHi4gl1Fgzi2vwfkLXaYoVfGDxU5YiJyPOqxzwlvmaumMqOCz9TpVolfv5P8UcA7VSupBnko/TIeOzzxYvOJi/u0iLjsVO54ljFg4y4FNqsHEmPPQtp/dFwLGytAagUHEB6ZAIJVx+UWNOT8AqjyRDfni2591vxOkP/bTh7upJoEB+JUXE4q0wdWmWBrcoN9aM8bemR8dgWoewVF3N521w+So+IM7FRRyUQunYnfU6t4rXzq8lKTifyqOm04ZKg9CiHJjqvftREP0bpYVrebQNbo/rpG8qtXET8AoORPlnGY/UyPDetwf6Vl0pFk5vKnViDOjsuKg43T9OH6tzfYKEksHcQ5w+fK5XwzeGucicuMm+2VVxUHG5mHvQNNbXvHcS5I2cLtCkuFp7uZBtoyYmKxfIp8eP6egipZnQ4vdyOpB1HSkWTpcpYU3ZkLJZPiR+3fsGkHM7TJAN+382nxh8rcO/fuVganmed7T/tNV47vYqqr7Ti/EfF649YqtzJijCMo7inxpF7v2CSD+Xl6Ypzh/No8cbnOtrqpnInLsIwn8c+s+y16x1YorJn5+VKaqRBukXFY+9lnCb2T0m3VnMHcGLR5gL7ZV7Na6J+nETyvWiz55+Ft7eK8Id5szAehUdSwVtl1nb+/GmcO7uP5R/NxcrKKvd4z55duHz5CNu3b2TkiOLNBDLExdONeIN0SoiKw/Upeckc1QJqMm/Xx0zcMAvvfDN2BP//EE6JZ6BfltEH8Ad6A02efkWBTAbGyLLcCGgLPHF7NwMmAfUBP30YALNkWW4CNADaS5LUQJIkK2ArMF6W5YZAJ4P7GFIL6Ky/9xxJkiyL8jskSRqpd4iciUsvRAUqSSaHCqqYZ09YRKeGPbh76z6de3YC4NqlG3Rp0pvXOw5m89c/s/Kbpc8O85nhmxgVWqMhf3/0E1+1GM/13/6m0ZDiTw0sShwBTFw8nksnL3H5lK7z3yywCbdD79CncV+Gdx7F+IXvYudgV2paCkv5YH+yYpNJfk57EDxBKkJ8LX5vGT0DXifs1gM69ggq5TBNjEwvlGUkpRL3+j5c33SA3zu/T056JvXf7Y7SxoqG43pwbvnPxRVlJrzCaXqCwlJJpZAAwv44WTwN/2WYS8fnPapeaMwVvX9Bm/k4MTEyNZFlrJztqNQ5gF9aTOSngLFY2Fnj27t1KQs0c8xMvKgPHyfqtbeImzIb59FDco9HDx9P9MDRPB4/A4dXe2LtX7/kksxmo4LTauTC0Vw9Gcq101dLHHZReJqmUYve5uqpK1w79Rw0FaEdsWvRAJfXQohZlm9ppqUFjh2bk7zzWGmJMj1UQPw4tKyPe99gIpZszD12q/c0br40kbuD51FuUDfsm9UthoTnV2ef//Anfmo6nru//k3tt4rZHylCHeTQsj7l+nbSOSEAp45NyIlLRH3ZdB+T0qSodfjwhaO5djKU6yUoe5LZvGMizKyuyh0boY5NJvZyWIH39+vZstizJHRBF65PNOv9JdSr144WLV/Czc2FKVPylrNt376b+vXb0+fVYcyda36Z9fPQVBD3r9xlcuvRzOk6iQMbdjFu/bQSa/qvQPsC/L2gCKfEs2kDbJdlWS3LcgpQ3IXGx4EV+pkLLrIsP5k/eUqW5buyLGuAzfrwAF6XJOkccB6oC9QBagKRsiyfBpBlOdngPob8KctypizLsUAM4FmU3yHL8npZlpvIstzE3c7TrE3ft3qzdf8Gtu7fwOOoWDy98+w8vTx4HFXw2nmtVsue7fvp9FIgoFvWoU7X+VaOHfgHC0sLXNycC7w+P6mR8Th6542EOni5kRaTYGwTFY+jwRREB5VbkTatvP7b31Tr2rTQ9qDbB+KrPWv5as9a4qLj8PDOmz3i4eVBbHSc2esGTxyIi5szn8/L2xiwy+tdOLrrLwAehUUQ+TCKytWK51XOiIzH1jsvLmy83cmISnjKFXm4NqtJ+c4BBJ3+FP914yjXui6NPh/z7AsLQe/BPdmwdz0b9q4nNiqO8t55UxPLPyW+QJenDvx+iMCX2hY7/LTIeOwN8pGdlxvp0cbxkh4Zj71B3Nl7uZEenUh6ZDxpkfHEntd11sL+PIV7fR+cfMrjUNmDnvsW8+qJldh7udFjz0JsPQqXv9PzabIvgqYnVAhqSNzlMDJikwsV5n8jbQeGMHXnh0zd+SFJ0Qm4GMSHi8qdpOjC5e/njToyHtsKedrsvNyKvVldUShs3rbzNtXm1bYeqQ8ekxmfgpyj4cGuM5RvUr1U9WliYlF65tWPSk8PNLEFl/fM85exqOCNwtkJAK3eVpuQiPrwMazq1iqWji6DuvHxzk/4eOcnxEfHU86gznZXuZMQE2/2utfH98PJzZlvFnxdrHCfRtdB3VixaxUrdq0iISbeaIq6u8qdhOgCNE3Qa5pf+ppAPzPCQIuFqhzZZupo65o+eC8ex8NR89Ekphidc2jfhIzQO2jiEktFU3Y+TZZe5cg2Ez82tXyo9OG73Bu+yEhTjj59c+KSSNpzArtGRc/n/0adfffXv6nSrWj9kSdkR8Zh5W0YR+5m48i2VhWqfDSGO8MW58aRQ5PaOAc3o+7f6/H9fDKOrRvgs2pisXTkp/Ogbny0cyUf7VxJfHQ87t6G+bwc8QWUvVfH98XJzYmNC/5TovDTIuNx8DJIN5Ubafn6RWkFpJuqaQ2qhATwxj8r6fT5GLxb16HDp3n7YEhKBb5dm3JnR9EGBt4ePZgzp/dy5vReIiOjqFgpb4+LChW9iIg0HTSMitLNMM3KymLDxq00beJvYnPs2EmqVq2Cu3vRZ+l1GNiFeTuXM2/nchKj43EzSCdXlTuJBdRH5shIVZOp35j80uFzKC2VOLj+u8s9BS8WwinxbIo1rCxJ0hiD5RresiwvBYYDtsAJSZKe9JryuxVlSZJ80c2s6CjLcgPgT8BGr6UwbshMg88adG9ZKf7wuBm2fvNL7saUh3YfpfvrXQCoH1CX1JQ0YmNMOyeVfPI2dGsf0oZ7t+8D4O6R1xDU86+NQpJIjE8qtJaoi3dx9VXhVMkDhaWSmt1bcHef8TS+u/vOUbuPzt+j8vcjKyWdtJjEp97XxSfP0eIXHEBCEddO/7bxd4Z3Hs3wzqM5tvs4nV/VjWzUCahNWkqa2Ub2pf5dadq+CfPfXWTkcY55FEPjNgEAuJZzoZJfJSLvF28td9L5O9hXVWFb2QPJUol3r5ZE7ync9N4bi7Zw0P9dDjUdx/lRnxJ7PJQLYz4vlo78/LJxO0NCRjIkZCRH9xyjiz6+6gbUJjU5jTgz8VXBJ6+Rbh3civsl2Hwr9sJdnHxVOOjzUdWeLXi41zgfPdh7jmqv6vKRR4AfWcnpqGMSUT9OIi0iHif9LttebeqSePMRCdfD2dJwDD+3mMjPLSaSFhnP753fR/24cPk7vyZfM5oe7j2HnxlNT6ja639/6cZf3+5lWbdpLOs2jUt7T9OsdzsAfPyrk5GSTvLjxLIVqCf+wl0cfVXY69Ozcs8WPCpk2SsJcfpwn+Qjn2fko3IBfmTr81Haozg8AqqhtNFNA/ZqU5ekUt7kNuvqdSwrV0DprQILC+yCg1AfNd740KJiXlm3rFkdLC3RJiUj2dgg2dkCINnYYNOiCdl3woqlY/emnUzqNoFJ3SZwau9JAvvoZl7V8K9Jeko6CTGmzq1O/YJp1N6flWOXl8o67fzs2rST97qO572u4zm55wRBfToUQlMI/u0CWPHuR89FE4D60k2sfCpgWdETLC1wfrkdqQeMH7osvDyotGYWjyZ/TFZYhMk9nLuX3tINgPSLt7D29caqkieSpQWu3duSvM9Yk6V3OXzXzeD+xJVk3svTpLC1RmFvm/vZsV0jMm4Ufcnd86qzHX3z+iOVQgJIKuZeLmkXb2Ht44VVpfK6OOrRlqR9p4xsLL3L4fvlDMLGf2IURxEffsuVZsMIbTWSe2OWk3L8EmHjVxZLR372bNrJlG4TmdJtIqf3nqC9vuxV969BekoaiWbyeYd+wTRqH8CqsR+XOJ/HXLyLs68KR326VevZgvv5+pH3956jhj7dygfo+pHpMYmcWvoj3zcdxw8tJ7J/zOdEHL/KwXFrcq+r2LYeiXciSIss/AM7wJq1G3M3rNz++x4GvKlbMd68WQDJScm5DghDDPeZ6NmjC6FXrwPg5+eTe9y/UT2srCyJiyu6s/7gt7tzN6Y8t/cUrXq3B6Cqf3XUKekkFaGtNdzrybdhNSRJyn0zx/8yslYu878XFfFK0GdzDFgnSdISdPH1EvDlsy6SZflzIPdpTZIkP1mWLwOXJUlqiW6JRSLQTO+EuA/0BdYDTkAakCRJkifQFTgMXAe8JUlqKsvyaUmSHDG/fKPUfkdh+Gv/37Tp2JI/TvxEhjqD2RMW5Z5b/f1y5r23lNiYOBZ8+gEOjvZIksSN0FssmqZ740Vw9yBeH/wKOTkaMjMymTZ6dpHClzVaDn6wkd7fTkVSKgjdeoS4m49oMEDXebv03UHuHbyAT1BD3vrrY3LUWeydvD73+q6fjaFSy9rYuDow/OSn/LNiG6Fbj9Bmel9c/byQtTIpj2LZP6NobwUx5MTBkzTv0Izvj20iMyOTD9/Le9vH0k2L+GjKCuKi43hvyQSiwqP5YrvuZSpHdx1j0yffsWnVd0xfMYX/7P8SCVi/+EuSEoo38i1rtFyZsYFmW3Q76YdvPkzqjXAqD9Itp3mwaT/WHs603rsIC0db0Mr4jOzK0bZTyEktbHYrGf8cOEnLDs358fh3ZKgzWPzestxzyzctYemU5cTFxPP+J9Oxd7BDkiRuX73DRzM+KXaYskbLifc3EvLDVN2r3LYeIfHmI2oO1OWjG98eJPzABSp2aEif4x+jUWfx13t5+ejkBxtp/9nbKCwtSHkQwzGDcyXVFKzXdLsATRU6NKS3XpNhuEobK7za1ePvacajSJW7NKH5wkHYuDnSadNk4kPvs+/NZZQ2U+Ys5fT5SyQmJtOx1wDeGTaQPt2Lt1a7sFw9dJ66Qf7MPrKKLHUW30/J6xyO+mY6m6etIzkmgXZDutBpVA8cPVyYvnsZVw9dYPP0dTh6ODPl9yXYONiilWUCh3ZjcfAkMkoh78saLedmbqD95mm6V4JuOULyzUf4DeoIwJ1NB7DxcCZ490IsHW2RtVpqjOjKrvZTS1T2ZI2WU+9vpJNBPkq6+Yga+nx089uDPNLno1eO6+rIv/X5KPb8He7/eYqX9yxEm6MhPvQ+N/Wb3Fbq0oRm+nzUYdNkEkLvs784+UijJWHZZ3h8+iGSUkHq77vIuXsf+94vA5D2yx/YdmiH/UvByDk5yBlZxM1cAIDC3ZVyy3TbLkkWStJ2HyDjn5K/6u7swTMEBDXmi6Pr9K8EzXvB1awNs/li6moSYuIZtegdHj+KYcmvut99Yvc//PTpVlw8XPhoxwpsHeyQtVpeHtqDcZ3GoC5BOp49eIbGQU1Y89d63StBJ6/KPff+hjl8Pu0zEqLjGb1Yp2npbx/lavpx1Radpj9WYvdE07AejOv4TvE0abREzVtD5Q0LkBQKEn/eR+atB7j27wpAwuZdeIztj9LFCa95uinkskbDvV4TAJBsrLFv7U/krNXFjg9zmsJnr6PqprlISgXxP+4n49ZD3N/UDZrEfb8b1fh+KF0dqbRgdK6mm90nYVHOBd/1M3X3sVCSuP0IKUeKvkfB86qzG8/oi7O+P5L2KJZ/phezP6LR8vCD9VT7ThdHcVsPkHHzIeUG6OIo9rvdeE3oh4WLI5UWjcr9TTdeKvkeBIXl3MGz+Ac14bOja8lSZ/L55M9yz83Y8AFrp35OQkw8Ixe9zeNHMSz69UMATu4+wc+fbi1WmLJGy7EPNtLte1263dh6hISbj6it70de++4gDw5eoHKHhvQ79jE5GVkcLmQb79ejBbdLODCwa9cBunbpwPVrx1Gr1Qwf/l7uud+3b2LU6ClERkazaeNqPDzcQJK4dDGUd8boXr37yivdGDDgVXKyc1CrM3jzTfNvNCkKlw6do0FQAB8e+ZwsdSZfT8kboJr4zSy+mfYFiTEJdBrSja6jeuHs4cL83Su4fOgc30xfQ9OuLQka0BmNRkN2RhZrx5aOg0vw34v0vLzo/0tIkjQX6I/OcfAYnYOgNfCHLMs/S5J0GJgsy/IZSZLCgCb6pROG9/gMCEI3c+EqMARoCczW37M+cBR4R5ZlrSRJG4DmwF10Mx9+l2V5gyRJTYHP0M24UKPbV6KJPvyX9VpTZVlerg/3CvCyLMth5n6HLMtPdUw0VLV6oTLIEKuqZS3BhO3aqLKWYMSUbPObjJYliy2Kt7nT82I43s82+pcp1alMpcCbF+eXtQQTJjWZUdYSjGid+eL59TMVL1ZOCvJ+/m95KSrjY+zLWoIJL1pfbJHli7fwOCtHWdYSjLiQ41TWEkxooHixRpqXvIDzsTtpXqwlAu/GPL83mRWXAd4tnm30L/NN2LYXq3ErJolvdijzyt7l+4MvZFy+eD2qF5PlsizPlSTJDp3j4GPDh3lZlgMNPvuYu4Esy2PzH9NvEpMuy3JfM/ZDCrjPaSB/bXFY/4csy3Pz2dd72u8wF4ZAIBAIBAKBQCAQCEqRF3j5RFkjnBKFY70kSXXQ7euwUZbl5/fOr+fL/8rvEAgEAoFAIBAIBALB/wDCKVEIZFl+4znd9zD6GQ7/Bs/rdwgEAoFAIBAIBAKB4Cm8eCvjXhhewNVeAoFAIBAIBAKBQCAQCP4/IJwSAoFAIBAIBAKBQCAQCMoEsXxDIBAIBAKBQCAQCASC54gsNrosEDFTQiAQCAQCgUAgEAgEAkGZIGZKCAQCgUAgEAgEAoFA8DwRG10WiJgpIRAIBAKBQCAQCAQCgaBMEDMlBE/l+BvlylqCEZt/LGsFpizWOJe1BCPW2GSWtQQT4tWpZS3BiHblH5e1BBPOxb1YZW1SkxllLcGEj88sKWsJRjhUbF/WEkw44ta0rCUY0eNhRllLMOEdC6+ylmBC6gs2RKS0jCtrCSZYlbWAfNhmvXhrw1dYKMtaghGHEq6UtQQTNn7QpqwlGFFpeduylmCChbqsFQj+PyKcEgKBQCAQCAQCgUAgEDxHxEaXBfOC+eYFAoFAIBAIBAKBQCAQ/H9BOCUEAoFAIBAIBAKBQCAQlAli+YZAIBAIBAKBQCAQCATPE/H2jQIRMyUEAoFAIBAIBAKBQCAQlAlipoRAIBAIBAKBQCAQCATPEVnMlCgQMVNCIBAIBAKBQCAQCAQCQZkgnBICgUAgEAgEAoFAIBAIygSxfEMgEAgEAoFAIBAIBILniVi+USBipoRAIBAIBAKBQCAQCASCMkHMlBCUGGWtAKx7DQeFkuwTe8k+uM34vF89bIbOQhsfDUDO5X/I3rs1z0BSYDtxBXJSHBlfLyiWhoqBDWg5byCSUsGNzYe5+PkOE5uW8wdSqUMjctSZHJm4nrgrYQD0+2cl2WkZyBot2hwNv7002+i6+qO60eKDN9hUfzSZCanF0ucS1Ajf+UNBqSDmhwM8Wv2r0flyvdtSYcwrAGjS1Nydvp70q/cB8FvxDm7BTciOTeJC0MRihV8QA+cOo2FQAJnqTNZPXs39K3dNbDoN7kqXoS/j6ePF240Gk5qQAkC3UT1p1bMdAEoLJd7VKvCO/1ukJRUvjgBmLppEu06tyFBnMHPsfK5evmFis3Dl+9RtVBtJgrA7D5g5bj7paWqGjhnAy326AGChVFK1hg+ta3cmKTG52HpsWzeh3PTRSEolydt2kfj1j0bn7YJa4jZ2EGhlZI2GuKVryTgfCoDHgvewb9ccTXwiD18ZVWwNhngGNcB/vi6f3/3hMDdWG+dzx2peNF05Cpf6PlxZ+iM31+7MPddkxQi8gv3JjE1mb9D0UtHzhD5zhlAnyJ8sdSbfT15DeOg9E5u2gzoTOLQbHj4qZvgPJ02fj8r7efPmR29Tqa4vfyzfwsEv/yhVbfl5f/EKjh4/hZurC799t/a5hmXIio/n0aVLB9LT1Qwf8R4XLlwxsfnyyxW0a9ucpCRd3Awf8R6XLl2lX79eTJ70DgCpqWmMHTeTy5evFVuLc6A/PguGIikUxGzeT0S++sj9lXZ4j+kFgDY9g3vT15N+NQyAqivG4NpJVx9d6jCh2BrMMXXhBFp3bEmGOoM54xdx/fJNE5s5K6ZTp2EtkCQe3H3I7HGLUKerc8/XaVSLTX+uZ/qo2ez/43CRwn9e7Ujdt4KpMyQEOUfDg4MXOLVoS6E1VWnfgMC5A1EoFVzZcpjTX5hqCpw3EN+gRmSrM9k7aT0xek3BH42gasdGpMcl823wjFz76i81o+XE3rhV82ZzjzlEXzItr0XFrk1jPGeNBoWCpJ93E//lT0bnHTq0oNz4QchaLWg0xCxej/pcaInDzY99u8aoPhiJpFSQsHUvceuMdTj1CKTcqFcBXd6O/OBzMq/rfr/bW71weT0EkMm8cZ+IqSuRs7JLTZsqX/193Uz93WzlKFzr+3B56Y/cMKi/S5uBc4fRyKDtDzPT9gcbtP2jDdr+lwzafoWFkgrVKvB2Cdv+xcvep1NIe9Tpasa+PZ1LF68WaLvkow/o/2ZvfLz9c4+1btOMhUtnYWlpQXxcAj26DSi2FgCFTz2sOr4BkkTOpb/IOWWaFopKNbHq0B8USmR1KplbPkRydMWq23Ake2eQZXIuHiHn3P4SaQEoF9SQOgsHIykVPPz+IHc/+93ovH01bxqsGo1TfV9uLtnKvTV57Wjg6c/QpKmRNVrkHA3HO88qsR4A96CG1NJrCv/+IGH5NNlV86aeXtOtJVu5b6DJwsmOuitG4VCrIrIMoRPXknTmVqnoetERG10WjHBKvCBIkjQfOCrL8n5Jkg4Dk2VZPiNJUhjQRJbl2ELcwxv4VJblV5+vWsNAFVj3HoV67WzkpDhsJ35MTugp5OiHRmaau1cLdDhYtuuONuYhkrVdMSVItF44mJ1vLCUtMp5ef87n/t6zJN6KyLWp1KEhzr4qfmwzifIBfrRZMoTt3efmnv/jtUVmHQ72Xm5UbFuPlPBnRn/BKBRUXTyC0L7zyYqMo8GuD4nfexr1zfBck8wHMVzp/QGapDRcOvjj99FoLr+k60Q+/vEwUd/sovqn44qvwQwNgwLw9PVicvsx+PnX4K2FI5nby/Rh9daZ61w4cIaZW4zTb+e67exctx0A/45N6DK8e4k6Je06tqJK1Up0ad6Hho3rMXvZNPp1HWpit+SDlaSlpgEwbf4E3hj6Gl99ton/fP4d//n8OwACQ9oweNQbJXJIoFDg8f4YIkbMICcqlopbPyPt0Amy7z7INVGfOE/4oX8AsKrhi+fyWTzsMRyAlN/2kvTD73gunlJ8DUZ6JAIWD+Fo3yWkR8bTadcCIvaeI+Xmo1yTrIQ0zr+/iQpdG5tcHvbjX9z+Zh/NPh1dOnr01AlshIevigWB4/Hxr87ri4axotf7Jnb3zt4g9OA5xm4xdvqlJ6aybe4G6oc0KVVdBdGrWzBv9OnBzAXL/5XwALp0DqJaNV/q1G1Ls2b+fPbpYtq262HWdvqMRfz6q3EHOCzsIZ2CXyMxMYnOIYF88fmHBV7/TBQKfBeP4Fq/eWRFxlFv5zIS9pxGfcugPnoYzdU++vooyJ+qy0Zz5WVd3fB46yGivtlFtVWlWx+16diSylUr0rNlX+oH1GXmh5MZ1G2kid3y2Z+SlpoOwKS5Y+k3tA/frP5O/9MUjH//Hf45fKrI4T+vdsSrVW2qhDRmW/AMtFk52Lg7FUlTh4WD+eXNpaRExvPGjvnc2XeWeANNPkENcfFR8U27Saj8/eiwaAhbeuo0Xf3pKBc37qPzSmOnaNyNcHaMXEXHJab1a7FQKPCcPYbwoTPJjo6lyk+rSD14kqw7eXVl2okLpB48AYB1DR+8PplJmJn0LakOr7lvc3/w+2RHxVL115WkHDhB1u28/kh2eDRh/aejTU7FoX1jvBeN5V6f97DwdMdtcHfudH4bOTOLCp9Ox6l7e5K2lfyBEnRp2XjxEA73XYI6Mp5gff2dXMj6uzRpGBSAyteLSfq2f0gBbf/NM9c5f+AMs/K1/X+u286fpdj2dwppT1U/H5o1CqZx04Z8tHIenTu8Zta2kX89nJ2Ny5CTsyPLVszl9d7DeBQeSblybsXWAoAkYRU8gMwfP0ZOicdm4Gw0dy4gx+WVO6xtseo0kMyfVyCnxIOdIwCyVkvWoa3IMQ/A0gabQbPR3L9qfG1RUUjUXTqUU68vIiMijtZ7FhOz5yypBnknOzGVq7M24Nm1qdlbnOi9gOz4lOJrMKOp9tKhnNVrarFnMY/3nCXNQFNOYirXZ22gvBlNtRYOJvbQBS4OX4lkqURpa1162gT/tYjlGy8IsizPlmW5RK2fLMsR/6pDAlBUro42NhI5Pho0OeSc/wuLes0Lfb3k7I6ydhNyTuwrtgaPRn4kh0WT8uAx2mwNd7afoEqIcaNeJaQxt34+BkDMuTtYOdljW97lmfduMXcAJxdtAVkutj4H/2qow6LIfBCNnJ1D7PZjuHU2rqRTztxAk6R70E45exMrL/fcc8knrpJTzBkaTyMguBnHth0G4M75m9g52eNc3tXE7n7oPWLDHz/1Xi16tuGf7X+VSE+Hru3Y/qPuYezi2Ss4OTviUd7dxO6JQwLAxsZ8Q/bSK53Z+eueEumxrl+T7AcR5IRHQU4OqbsOY9+hpZGNrM7I/SzZ2gB5+STj7BW0SaXXCXDz9yM1LJq0B4+RszU83H6CCp2N83lmXDIJF++izdaYXB974jpZzyEf1Q9pyqlfjgIQdv4Wto72OHm4mNiFh4YRbyYfpcYl8+DSHbQ5ppqfB00a1cfZyfFfCesJ3buH8N33uhlkp06dx8XFCZWqfKGvP3HiLImJSQCcPHWeChW8iq3Fwb8aGWGRufVR3PZjuHZuZmSTalgfnTOuj1JOXkWTUIqdWz3tO7fhjx93A3D5XCiOTo6UM1v+03M/W9taIxuUuX7DXuXAn4eJj00ocvjPqx2pM7ATFz7fgTYrB4CMuMI7SlWN/EgMiyZJr+nGjhP45dPkF9KYa9t0mqLO38HayR57vaZHp26QkWha5uNvR5BwN7LQOp6FTYMaZD+IIDs8CrJzSNl5BIeOLYxs5HSDutLOpkRtakHYNqxB1v0Ish/qdCT9cRTHTsY61OeuoU3WxUn6+RtYqPLymGShRLKxAqUCha01OdFxpabNzd+PFH39rc3W8KCA+ju+gPq7NGmcr+23d7LHpZhtf8tSaPu7duvIj5t1s7XOnr6Is7Mjnp4eJnYKhYK5C6Yy74NlRsf7vNadP3bs5VG4Lk/HxsaXSI/CqypyQgxy0mPQasi5fhJltUZGNha1W6C5dVbnkABI19eJaUk6hwRAdgbauEgkB5cS6XEJqEb6vSjU92OQszVE/vY3nl2MnfhZsckkXbiL/JzzzhOc82mK+u1vypvRlGxGk9LBFteWtXn0/SEA5GwNOcnpCATCKVEGSJL0gSRJ1yVJ2idJ0mZJkiZLkrRBkqRCOxQkSWovSdIF/d95SZIcJUnykSTpiv78VwbnH0uSNEd/fIokSaclSbokSdK8Ev8WZ3fkxLxZBHJiLJKzaUdS6VMT28mrsBkxB4Vnpdzj1r2Gk/XHhhLNZ7L3ciU1Mq8RSouKx97LuIG1V7mSGpHXwUiLjMdepbeRZbr9MJ1eOxdQ682gXJvKwQGkRyUQf+0BJcFa5UbWo7w4yoqMx0plGkdP8OzfkcSD50sUZmFwVbkRH5GnKz4qDjfPoo8wWNlY0aC9P6d3nSiRHk9VeaIionO/R0XEUN7L/IPbolUf8FfoLnyrVeG7r7YanbOxtaZNhxbs/eNQifRYlHcnJyqvQ5YTHYtF+XImdvYdW1Hp96/w+mIBMR+sKFGYT8NW5Ub6o7w8nB4Zj63KtCP5b+Ps6UqiQdlKjIrDWVXCkar/Mby9VYSH542UPXoUibe3yqzt/HlTOXN6Lx8tm4OVlZXJ+beG9GPP3uLnbSuVO1kG6ZUVGYeVV8HpVb5/JxIPPf/6qLyXB1ERMbnfoyNjKO9l+mACMPeTmey/vAOfalXY8vXPAHioytGhWzt+3vhbscJ/Xu2Ic1UVquY16bljLi//PItyDasWWpODypWUiDxNqZHxOHi6mtpE5mlKjYrH4V+uFyw8y5EdaVBXRsVi4Wnaxjl0aoXPzvVUXDufqFkrn4MOd7Ij89q0nKhYLM3oeILr6yGkHjmrs42OI+6rX6jx1wZq/PMd2pQ00o6VXr63VbmhfkHqb1eVG3H52n7XMmz7vbw9eRQelfs94lE0Xt6eJnbDRw1g966DREcbO0r8qvng4uLM9j+/5cCRX3i9f68S6ZEcXPKcDYCckoDkYJxWkqsKbOyx7jsVm4GzUdZtZXofJ3cUnpXRRpoujSkKNio3MgzqHXVEPNZFamNlmm2dSeu9i6k0sGOJtBSkKaMImuyqlCcrLpm6q96mxf4l1FkxEqXd/6OZEtoX4O8FRTgl/mUkSWoC9AH8gd5AcecsTwbGyLLcCGgLqA1PyrI8XH+uJxAHbJAkKQSoDjQDGgGNJUlqV8zwdUiS6bF8IyCa8DukLRiOevl4so/9gc1Q3Xo2ZZ0myKlJaMPvlEgCmNNQeJ2/vzKfX7u+z+6BH1FncCdUzWuitLHCf1wPziz/uYTanh52fpxa1aP8Gx25v+jbkof7DCQzuuRijF75d2rKrTPXSzR9U6fH9FhBemaNX0D7+i9x91YYXXsGG50LCmnL+VOXSrZ0o2BBJofSDvzNwx7DiRo3F7d3B5cszCLLKf3RxqJiLh89j1HQ/2YKW9Y++GAp9RsE0qr1y7i6OTN58ttG59u3b8mQIX2ZNWtxCcSYOVZAcjm1qkf5/h15sGhT8cMrJEWpj+ZOWExIw57cuxVGSE9dJ3vKgvGsWrAGrba4Pa7Sb0cAJKUCa2d7tnefy8mFm+m05t0iSDIXJ8/W/SLUC+byVOr+vwnrNpJH786n3LhBpR+mufQpALsWDXB5LYSYZd8AoHBywLFTC24FDuVmq4FIdjY49wx6xl2Kos3MsTJKp9Js+2+WStv/bD0qVXl69OrKl2tN+0YWFhY0bFSX/q+N5LVXhjF56jv4VfMpiSIzx/LFj0KBwrMKmb98QsbPK7Bs2R3J1cCRYmmNdc8xZB/cDFkZlAiz2brw6fXPy3M4HjyD028spcpbIbi2qFUyPSXUJFkocazvS/jGfZzoNANNeiY+Y3uWXJOg1JAkqYskSTckSbotSVKBm5BJktRUkiRNUQbVn4bYU+Lfpw2wXZZlNYAkSaa7VhWO48AKSZK+B36RZTk8f8UuSZIN8BPwrizL9yVJGguEAE/c/w7onBRH8103EhgJsKpjA4Y2qFKgCDkxFsklb/RYcimHnJxv6lxmnr9Ec+0s9BkN9o4ofeugrNsMu9qNwcIKycYO6zffI/P7oo02p0XG42Aw0mevciMtKsHUxtudJ+Pw9l5upEUnApCu/58Rl0zY7rN4NPIjMykNx0oe9Nm7ONe+9+6F/PbyHNSPk4qkLzMyDqsKeXFk5eVGVrTp9EK72lWo9vHbXH1z4XNZrgHQaVAXAvvpHuLvXrqNm3eeLjeVOwkxRZ/23KJ7G/75/Vix9Lwx9FVeHdALgCvnr6IyGB1ReZfncVTBU0e1Wi27ftvH0DED+XVL3gZK3V4J4c9f9xZLjyE50bFYqPJGai08y5HzuODpvBlnr2BZyQuFixPakjpEzJAeGY9dhbxRPzsvNzL0efffpu3AEFr21z0MPrh4BxfvPF0uKneSoouej/7XGD1qMEOH9gfgzNmLVKzonXuuQgUvIiOjTa6JitLNFMjKymLTph+ZOCFvL4B69Wqxds1H9OgxkPj4xGLryoqMw8ogvay83MmKMl8fVV3+DtcHLHhu9dHrb/Wm95u6vTFCL1xD5Z03M8rTqzyPowrey0er1bJ3+wEGvfMGv2/ZSZ2GtVi6Tjf5z8XNmTYdW5KTo+Hw7sJNLX8e7UjUyRukRSUQtusMAI8v3EXWyti4OZJRiPXdqZHxOHrnaXLwciMtXx2dGhWPo8HyGgdVnqZ/i5zoWCwNZrVYqMqRE1NwXak+cwXLyl4oXZzQlGJdmRMVi6VXXptmoSpHtpklGNY1ffBePI4HQ2ejSdSlg33rRmQ9jEYTr9OTsudvbANqk7S9ZDPunqCOjMc2X/2t/hfTqdOgLgQZtP3u+dr+xGK0/S1L0PYPHfEmAwe/DsCFc5epUDFv5ph3BU+iImOM7Os3rINv1cqcvqBb6mtnZ8upC/to1iiYiEdRxMclkJ6uJj1dzd/HT1O3Xi3u3A4rljY5NQHJMa/cSY6uyKmJxjYpCWjVqZCdBdlZaB/eROFRCU1CNCiUWPccQ861E2hunSuWBkMyIuOxMaizbb3dyIwqfHpl6tvjrNhkoneexsW/GgknrpeqJpsiaMqIiCMzIp6kc7cBiN5xEt+xxdwj6b+QF32jS0mSlMDnQDAQDpyWJOl3WZavmrH7ECjZWmkDxEyJf5/Cu/INL5KkMQbLMbxlWV4KDAdsgROSJJlzfa5F57B4sleFBCyRZbmR/q+aLMtf579IluX1siw3kWW5ydMcEgDah7dQeHgjuXmC0gIL/7Zorpw01u7okvtZUbk6SApISyHrz02kzx9K+sIRZH77EZpbl4rskAB4fPEuTr4qHCt5oLBU4tezBQ/2GTcE9/eeo/qrbQAoH+BHVko66phELGytsbS3AcDC1pqK7eqRcCOchOvhfNdoDFtaTmRLy4mkRcbzS5f3i+yQAEi9cBtbXy+sK5VHsrSgXM82xO85Y2RjVaEcNb+ewq2xn5JRimt987N/027e7zaJ97tN4uzeU7TpEwiAn38N0lPSSSpix8TW0Y5aLepwbm/RN5YD+OE/P9O7wwB6dxjAgV1H6Pl6NwAaNq5HSnIqj810bCv7Vsz9HNi5LXcNOh4OjvY0aenPwd1HiqXHkMwrN7CsXAGLCp5gYYFD10DSDhlPU7WolPegaVW7GpKlxXNxSAAkXLiLg68Ku0oeSJZKKvVsQcSes88lrGfx17d7WdZtGsu6TePS3tM0662bcOXjX52MlHSSHyeWia4XibXrNtKseReaNe/Cjt/3MODNPgA0a+ZPUlJKrgPCEMN9Jnp070xoqO7tM5UqefPj1i95a+h4bt0u2ZsSUi/cxsagPnLv2YaEvaeNbKwqlKPGV1O5PW7Vc62PfvzmF/p1GkK/TkM4tPsoL7+ue3tO/YC6pKakEmum/FfyqZD7uV1Ia8Ju695S9HKz13ip6au81PRV9v9xmCXTlxfaIQHPpx0BuL/7DN6t6wDg7KtCYWVRKIcEQNTFu7j6qnDSa6rZvQV382m6u+8ctfvoNKn8dZrSYhIL/btLg4zLN7Gs4o1lBU+wtMCxW/vcTS2fYFk5bx8U6zp+SJYWpeqQAFBfuomVTwUsK+p0OL/cjtQDxv0RCy8PKq2ZxaPJH5MVlrekKifiMbaNaiLp9ymyb9WQzDvGG3aXhPgLd3H0VWGvT8vKPVvw6F+sv/dv2s2sbpOYVUDbX1SnREnb/v98+T1BbXoS1KYnO//cz+v9dW8fa9y0IcnJqSZLNPbtOUzd6q0JqN+BgPq6txg1a6Rzsuz68wAtWjZBqVRia2tD4yYNuXmj+DNwtZH3kFw9kZzLgUKJRa3maG5fMLLR3D6PoqK+P2thhcLLF228rq606vIW2rhIcs6UfHAEIOn8HeyrqrCtrGv7vXq1IrqQeUdpZ41SXzcp7awpF9iAlOslz9fJ5+9gZ6BJ1asVMYXUlPU4iYyIOOz8dHWCe9t6RhtkCsqcZsBtWZbvyrKcBWxBN+s+P2OBbYBpZ6aYiJkS/z7HgHWSJC1BF/8vAV8+6yJZlj9H57kCQJIkP1mWLwOXJUlqCdQCLhicHwM46p0XT9gDLJAk6XtZllMlSaoAZMuyXPwMpdWS+cs6bEfOBYWC7FP70UY/xKKlrnOZ889uLBq2xqJVV9BqIDuLjG8/KnZw5pA1Wv7+YCNdv5+KpFBwY+sREm4+ovaADgBc++4gDw9eoFKHhvQ99jE5GVkceW89ALYeTgR/NQEAhVLJ7d/+JvzwpVLVh0bL3ZlfUWfzB0hKBdFbDqK++RDPQSEARG/aS6WJr2Hp6kjVJSP0v0nDpS7TAKj+xUScW9XFws2RxmfX83D5VmI2HyixrIsHz9IoKIDlR78gS53Jl5NX556bvGEWX039gsSYBEKGdOOl0a/g7OHC4j0ruXjoHF9P+wKAJp2bc+XoRTLVmSXWc2T/cdp1asWeU7+QkZ7BzPF5O36v+2El709cRGxMHEs+m4ODgz2SJHH96i3mTfkw165Tt0D+PnwSdXoJp0sCaLTELv4cr3WLkZQKkn/dS/ad+zi9/hIAyT/+iUNwGxx7dELOyUHOyCR6ct60+vLLpmPbtAFKF2eq7P+O+C++JeWX4juUZY2W8zM30G7zNCSlgntbjpB88xFVB+lmLNzddABrD2c67V6IpaMtslZL9RFd2dN+Kjmpapp/MQaPVrWxdnPkpbOfEbr8Z8I2l9x5c/XQeeoG+TP7yCqy1Fl8P2VN7rlR30xn87R1JMck0G5IFzqN6oGjhwvTdy/j6qELbJ6+DkcPZ6b8vgQbB1u0skzg0G4sDp5ERqr6KaEWnylzlnL6/CUSE5Pp2GsA7wwbSJ/unZ9LWE/YtfsgXbp04NrVY6SnqxkxclLuue2/bWT021OJjIxmw4ZP8SjnjiRJXLwUyrvv6t7AM3PmBNzcXPh01SIAcnI0tGr9UvHEaLSEzfqKWj/MRlIqiNlyAPXNh5QfqKuPYr7dS8WJr2Ph6ojvEt3bEeQcDVe6TgWg2hcTcWpZDws3R/zPfEn4x1t4XAr10bH9/9CmY0t+P/EjGeoM5k7IK0uffb+c+e8tJTYmjvmfvo+9o6783wy9zeJppdOePK925MbWI7T7eCR99i9Bm63hyIR1RdJ08ION9P52KpJSQejWI8TdfEQDvaZL3x3k3sEL+AQ15K2/PiZHncXeyetzr+/62RgqtayNjasDw09+yj8rthG69Qh+nZsQNH8Qtm6O9PxmMo+v3ufXgcsKkvFsNFpiFqyh4tcLQaEkadtesm4/wLmvzsmctHUnjiFtcOrZUVdXZmYROXHpM25aPB1R89ZQecMCJIWCxJ/3kXnrAa79uwKQsHkXHmP7o3Rxwmue7hW7skbDvV4TUF+8Qcru41T9fRWyRkNG6F0St+wqNWmyRsu5mRtor6+/7+rrbz99/X1n0wFsPJwJNqi/a4zoyi59/V2aXDh4loZBAXysb/vXP6Xtf1nf9i/Rt/1fGbT9l0up7d+35zCdQtpz+uJ+1Olqxr2T9/razT9/ycR3Z5l14j7h1s07HNx/lKP/7ECr1fLdpp+4fq0Er5eUtWTt/w7rV98DhYKcy8eQ4yKwaBgIQM7Fw8jxkWjuXcFmyHyQteRc/gs59hGKCtWxqNsK7eOHKAfPBSDr6Da09y4XX45GS+iMb2i2ZSYoFYRvPkTqjXAqD+oEwINN+7HycKb13sVYONqCVsZnZFf+ajsZS3dHGn+ja28kpYKIX48Te+hi8ePGQNP1Gd8QsGUmklLBo82HSLsRTkW9pnC9phZ6TbJWpsrIrhxvOxlNqprrM7+h/hfvorCyQH0/hivj/73XcwuMZ8TrWS/L8pPGowJg6LkKB4zeYKB/fnwF6ACYf+VLcXS9EGsP/58hSdJcoD9wH3gMHAZaA3/IsvxzYV4JKknSZ0AQoAGuAkMAL/096kmSdA/IBp5sabtWluW1kiSNRzfDAiAVGCDLcoEu5dT3erxQGWTzj4V/ndq/RV3N83l4Ki5rrF6oJAPgjDr82Ub/In+Y2Wm8rDkXZ7qJZlnyl3VOWUsw4eMzS8paghEOFduXtQQTjriVWv+gVHhba7pMpax5x8K3rCWYkPqCzVvtZld6b54oLTTZL1YkXU53KWsJJuywerH6I7sTrpS1BBMefNCmrCUYcXj5i/fmiRdxxDokekuxZpq/aMR0bF/mnfTyB44UGJeSJL0GdJZlebj++0CgmSzLYw1sfgI+lmX5hCRJG9A/v5ZU14uY7/4/sFyW5bmSJNmh28/hY1mWc2dLyLIcaPDZx9wNDDOHAWFAPf15s70uWZZXAauKK1wgEAgEAoFAIBAIBP9zhAOVDL5XBCLy2TQBtuj3MiwHdJMkKUeW5d9KErBwSpQN6yVJqgPYABtlWS75TjgCgUAgEAgEAoFAIHghedE3ugROA9UlSfIFHgH9gDcMDQwHvg1mSvxW0oCFU6IMkGX5jWdbCQQCgUAgEAgEAoFA8PyRZTlHkqR30e1DqAT+I8tyqCRJo/Xnn9sGIMIpIRAIBAKBQCAQCAQCwf9zZFneCezMd8ysM0KW5SGlFa5wSggEAoFAIBAIBAKBQPA8kf8n9ut8LrxYWxkLBAKBQCAQCAQCgUAg+H+DmCkhEAgEAoFAIBAIBALBc+S/YKPLMkPMlBAIBAKBQCAQCAQCgUBQJginhEAgEAgEAoFAIBAIBIIyQSzfEAgEAoFAIBAIBAKB4Dkia8VGlwUhnBKCp+K6+mxZSzBikVdQWUswYd0LVoo6Z9mVtQQTvuhXvqwlGDH5d9uylmCCwjq7rCUYEZhpWdYSTHCo2L6sJRiRGn6krCWY8FbjyWUtwYifHR3KWoIJsfHpZS3BBFuLF6v8b8pwLWsJJmQjl7UEI7KtNWUtwYSXsl6stm397HZlLcGE2ktOlbUEI4Y51C9rCSZcIqWsJZgQUtYCBM8dsXxDIBAIBAKBQCAQCAQCQZnwgo3xCgQCgUAgEAgEAoFA8L+FePtGwYiZEgKBQCAQCAQCgUAgEAjKBDFTQiAQCAQCgUAgEAgEgueILIuNLgtCzJQQCAQCgUAgEAgEAoFAUCYIp4RAIBAIBAKBQCAQCASCMkEs3xAIBAKBQCAQCAQCgeA5Ija6LBgxU0IgEAgEAoFAIBAIBAJBmSBmSggEAoFAIBAIBAKBQPAckbVio8uCEDMlBAKBQCAQCAQCgUAgEJQJYqaEoFRYuWI+Xbp0QK1WM2zYRM5fuGJi8/VXK2nbtgXJySkADBs+kYsXQ+nePYR5c6eg1crk5OQwadIcjv99usgaOs4dSNWgRmSrM9k1eT3RV8JMbJwredD9szHYujgQfSWMPyauQZuteer11k52dPlwOOVqVARkdk35kohzt4us7805Q2kYFECWOosvJ3/G/dB7JjadBnUlZOhLePp4McZ/CKkJKbnnarWoyxuz38LCwoKUhGSW9J1dpPC9AhvQdMFAJIWC25sPE7p6h4lNkwUDqdChETnqTP6ZuJ74y2G6sEd0odobgSDLJF4P5++J69FmZtNgUm+qvRFIRrxO54UlPxJx8GKRdD1BWTsAm94jQaEg+5+9ZO3/2fh8tfrYjngfbVw0ADmX/iZr9xawsMRu/IdgYQkKBTkXjpO164diaTBH3zlvUS8ogCx1Jhsmf85DM+kWOKgLHYe+RHkfFe/5DyVNn24Ng5vQ471+yLKMNkfD1vkbuHPmeon0vD7nLeoG+ZOlzmTT5C/M6mk/qDMd9Hom+w/L1dMguAnd3+ubq+en+Ru4c+ZGifQYogpqgP/8gUhKBXd/OMz1fHnMsZoXzVaOwrW+D5eX/siNtTtLLWxDVnw8jy5dOpCermb4iPe4YKY++vLLFbRr25ykJF3cDB/xHpcuXaVfv15MnvQOAKmpaYwdN5PLl689F50A7y9ewdHjp3BzdeG379Y+t3DyM3DuMBoFBZCpzmT95NWEXblrYhM8uCtdhr6Mp48XoxsNzq2PbB3tePuT8bh7e6C0ULBz/e8c/elgifTYtm5CuemjkZRKkrftIvHrH43O2wW1xG3sINDKyBoNcUvXknE+FKXKA8/FU1CWcwWtTPLPO0n67rcSaQFwDvSnyoKhSAoFMZv3E7n6V6Pz7q+0w3tMLwA06RmETV9P+tUwrLzd8Vs1DsvyrshaLTHf7SP66z9LrAfAoX0AFWaPAKWC+K37eLzGuI506dkej9F9ANCmZ/Do/S/IuBYGgMLJnopLx2JTswrIMuFTV5F+rnhl/6U5g6gZ1IhsdRbbJq8lIjTMxMa1ogd9V4/F1tmBiNB7/DzxCzTZGnxb1GbA+kkkhMcAELr7NIc+1cVtq2FdadI3CGSZqBsP+WXKOnIyswulqcecwdTSa/px8hoeFaDpzdXjsHO251FoGFsmfo4mW0P7kS/j36u1Lp6USspXq8C8gJGok9KYfuxTMlPVyFot2hwtn/aYVSg9r8wZTO0gf7LVmWyevIZwM3rcKnowaPV47JztCQ8N4/uJq9Fka7B1sqffR6MoV9mT7MxstkxdS9TNcADaD+tGi75ByDJE3njA5ilrCxVHXoENCNC3/3c2H+aamfY/YMEgvDs0RKPO4sTEdSTo2/8awzrj92YQkiRx5/tD3Phqd+411YeGUOOtYOQcLREHLnBh4eZCxY8hCp+6WHXoD5KCnMt/kXNql6lNpZpYBfUFhRJZnUrm1o9AaYF1v2lISgtQKNDcPEv2378XOfyCmLtkGkGd2qJWZzD53Q+4cqngdmDe0um81r8Xdaq0AKDXq90YPW4oAOlp6cyavJBroTeLrKHz3EFUD2pItjqL7ZPXEWWmX+tSyYM+n72LjYsDUVfC+HXiF2izNdQIbkzQpFeRtTJajYY9877l4Zk8DZJCYvgfC0mJSmDL0OVF1pafoXNH4B/UhCx1Jqsnf8I9M23K+FXvUbV+NTQ5Gm5fvMW6GZ+jydGUOGzB/wbCKVEGSJI0Hzgqy/J+SZIOA5NlWT4jSVIY0ESW5dgS3r9U7lNYunTpQLVqvtSu04bmzQJYvXoJrdt0N2s7fcZCfvnFuIN28OAxduzYC0D9+rX54Ye11K/fvkgaqgY1xNVXxZftJ+Hl70fwwiF812uuiV376f048/Vuru84Qciit2jQN5AL3x146vUd5wzk3pFLbH/7UxSWSixtrYukDaBBYAAqXy+mBr6Ln391Bi8ayfxeM0zsbp69zoWDZ5i+Zb7RcTsnOwYtGMHywQuJj4jF0d2pSOFLColmiwdzoN9S0iPj6bpzPuF7zpJ0KyLXxrtDQxx9VWxvPYlyAX40WzKE3S/PxVblSq1hIewInIYmI5u2a8fi07MFd3/8C4BrX+7mWkkfLiUFNq+9Tfrn7yMnxmE3eSU5V06ijXpoZKa5E4p6vXHckJNN+mczISsDFErsJiwj59pZtGElf9iuF+hPeV8vPggci69/dd5cNIKlvWaa2N05e53LB8/y3pa5RsevH7/CxX2TAahQqzIjP3+POR0nFFtP3UB/yvuqmBM4Dl//6vRfNJxlvUw7yXfO3uDywXO8t2WO0fEbxy9zad+ZXD3DP5/IvI4Ti63HEEkh0XjxEA73XYI6Mp7gXQuI2HuO5JuPcm2yEtI4//4mKnRtXCphmqNL5yCqVfOlTt22NGvmz2efLqZtux5mbafPWMSvvxrn3bCwh3QKfo3ExCQ6hwTyxecfFnh9adCrWzBv9OnBzAUl7xQWloZBuvpoUvsx+PnXYMjCkcztNd3E7uaZ65w/cIZZWxYYHQ8e1JVHt8JZMWwJjm5OfHToM47/dhRNdk7xBCkUeLw/hogRM8iJiqXi1s9IO3SC7LsPck3UJ84TfugfAKxq+OK5fBYPewyHHA2xH60n69ptJDtbKv64mvS/zxldWxw9PotHcL3fPLIi46i7cxmJe06jvhWea5L5MJqrfT5Ak5SGc5A/vstGE/rydOQcLffnbyT98l0U9jbU272c5KMXja4trqYK80dzb8AHZEfFUe33FSTvO0nm7bw6MuthNHf7zkCTnIZjYGMqLnmX27109Y/3nBGkHjnHg3eWIllaIBWjHQOoEdiIcr4qVgS+RyX/avRYNJS1vUwd5J2n9+f417u4vOMfei4aSuO+QZz6bj8AYaev8+0w4/zu5OlKyyGdWdVpCjmZ2fRbPY763Vty/uejz9RUS69pWeBEKvtX45VFw1jd6wMTu27T3+Cvr3dyccc/9F40jKZ9gzjx3X6OrP+DI+v/AKB2xwDaDuuGOikt97p1/ReSbjBA8CxqBzbCw9eLxYETqOJfjVcXDeeTXu+b2HWf/gZHvv6T8zv+4bVFw2jetwN/f7ePTmN6EXH1Pt+MWkF5P2/6zB/KmjcX4uzpStshXfiw0ySyM7MZvHo8/t1bcfrnI0/V86RuPtRPVzeH7FzAoz3nSL6VVzd76dv/P1pPwj2gGk2WvMW+l+fgXLMifm8Gsfel2Wizcgj8YRqPDpwn9V405VvVoWLnxuzqOANtVg7WReyX6MRJWHV6k8yfViCnJGAz4H00dy4gx0Xm2Vjb6mx+/gQ5JR7sHHXHNTlk/rgcsjNBocS6/zQU966gjTR9GC4qQZ3a4Fu1Cu2bvox/kwYsXP4+vULeNGtbv1EdnJwdjY49vP+I17u/RXJSCoEd27Bk5ZwCry+IakENcfdVsbr9JCr4V+OlhW/xda85JnYdp/fjxNe7CN1xgm6LhuLfN5Cz3x3g3vEr3Nx3FoDytSrx6ufj+KLjlNzrmg/tQuztCKwdbIukyxz+QY3x8vVmbPtRVPevyciFbzOj1xQTu6O/HWHV+BUATPh0Mh37hbD3O1Mn1P8yslzWCl5cxPKNMkCW5dmyLO8vax0Ako4S5YMe3Tvz3fe6EZuTp87h7OKMSlW+0NenpaXnfra3s0MuRomtFtyY0G3HAIg8fwcbJ3vsy7uY2FVuVYcbO08BcGXbX1QPafzU660cbKnYvCaXthwGQJutITM53eS+zyIgpCnHf9F1HO6cv4Wdoz3OHqb6HoTeIzb8scnxFj3acnb3SeIjdH6mlLjkIoXv7u9HSlg0qQ8eo83WELb9BBU7Gz8YVurcmHs/6+Ig9twdrJztsdXHoWShRGljhaRUoLS1Qh2dUKTwn4WiSg20jyOR46JBk0POuaNY1G9R+BtkZej+Ky1AqSy1Wr9hSFNO6NPt3vlb2Dra42Qm3R6GhhFnJt0y0zNyP1vb2RQrbxvracKJX47m6rErQE94aBjxZvVk5n62srMusR5D3PR5LE2fxx5sP0GFfHksMy6Z+It3c2cnPQ+6dw/hu++3AXDq1HlcXJyKVB+dOHGWxMQkAE6eOk+FCl7PRecTmjSqj7OT47MNS5HGwc04tu0wAHfO38TeyR6X8q4mdvcLqI9kWcZW35G1sbchLTEVbQlGu6zr1yT7QQQ54VGQk0PqrsPYd2hpHKY6ryxJtjaALu9qYuPJuqabuSanq8m++xALz3LF1gLg4F+NjLBIMh9EI2fnEL/9GK6dmxnZpJ65gUb/4Jp67iZWXu4AZMckkH5Z91CkTcsg43Y4lvpzJcGuUXWy7keS9VCnKXHHUZxCmhvZpJ+7jiY5LfezpUoXDwoHWxya1SN+q875L2fnoE1OozjUDmnM+V90DumH529j42iHo5k6qGqruoTuPAnAuW1/USekyTPvrVAqsbSxQqFUYGlrRUoh25k6IY05p9f04PxtbAvQVK1VXS7rNZ3ZdpS6ZjQ16tGKC7//XahwC6JeSBNO6+vp+3o95urpaq3qclGv59S2o9TX61FVr8DN47rZXTF3InCr6IFDOWcgfxxZk1yIOHLz9yM1X92cv/2v2LkxYT/r4jDu3G2snO2wKe+CU3Vv4s7dRqPOQtZoifnnGpW6NgWg+qCOXF39O9osnTMys4j9EgCFyhc5IQY5KRa0GnKun0Lp18jIxqJ2czQ3z+kcEgDpBg6ibH2bplAiKUqv7Q/uGsS2rbrZJOfPXMLJ2ZHyZuoVhULBrLnvsWTuSqPjZ09fJFk/C+/cmYt4eRe+DXpCzeDGXNymS5NH529j7WSHg5l+rW+rulzV92svbTtKLX0+ys7f3pMXN44qN6p3aMT5LYeKrMscTYObc3ib7l63zt/AroA25fyhs7mfb1+8iXsp1I2C/x2EU+I5I0nSB5IkXZckaZ8kSZslSZosSdIGSZJeLcI9HCRJ+kaSpMuSJF2SJKmP/nh//bErkiR9WMC17+nPX5EkaYL+mI8kSdckSfoCOAdUKslv9PZWEf4wb8T9UXgkFbxVZm3nz5/GubP7WP7RXKysrHKP9+zZhcuXj7B9+0ZGjphUZA2OKleSI+Jyv6dExePoaVwh2ro6kJmcjqzRvY8nJTIeB5XrU693qeyBOi6FrstHMnjnQrp8OLxYMyVcPd2Ii8ibuBIfFYerqvCVsaqqN3bO9kzfMo95O5bRunfRZpLYqVxJj4jP/Z4eGY+dV774UbmSZhAHaRHx2KpcUUclcHXNTl45vYo+F1aTnZJO5JG86fA13wrmpf2LabFiBFbOdkXS9QSFizvaxLyHH21iLJKzafwofWthN+0zbEfPRaGqnHdCUmA39VMcFn9Hzo0LaO8XfZqkOVw83Yg3iJPEqDhcVW5Fukejzs2Yd+AT3v3PDDZNXVNiPQkG+SghKg6XIupp2Lkpcw6sZMx/ZvBtCfUYYqtyQ/0oL67SI3X559/G21tFeLhBffQoEu+C6qN5Uzlzei8fLZtjVB894a0h/dizt3Q6bS8Srioz9ZFn4fPRvo078a5WgdWnv2bJnpV8O+8/JXJwWZR3Jycqr/znRMdiUd70AcC+Yysq/f4VXl8sIOaDFab38fbEqrYfGZdKtkTKSuVOlkG5z4qMw9Kr4Pjx6N+JxEPnTe9T0QO7er6knSt5fWTp6U62QZplR8Zh6VlwG+LaN4SUw7oHAKvKKnLikqi4fALV//yEikvHFnumhJOnK0kGbUlyVDxO+cq5nasjGclpaPVtbXJkHE4G7XHlgOq8u2sJgzdMpXz1Cjqb6ASOffknU/7+jOmnviAjRc3tvy4XSpOzpxuJRvV0PM756kU7V0fUBpqSIuNwzpfnLW2sqNm+IZd3ncw7KMuM+HYG43Ysonn/DqWmx97VEXVyuoGe+Fw9j649oEEXnROsckM/XCuUw0XlRlJ0Aoe//IPZf3/OvFNryUhJ58Zfl56px07lRnpEvrrZpP13M2r/0yPisVO5knQ9HI/mtbBydUBpa4V3h0bYeet0Ovp54dG8FsF/zKPjtvdxa1i1UPFjiOToipyS51iRUxOQHI21Sa6eYGOHdd8p2Az4AGUdA4elJGEzaDa276xAc/8q2ijT5YzFQeVVnohHUbnfoyKi8fQydSwMHt6ffbsPExNd8MTkfgN6c3j/8SJrcFS5Fapfm5GcltuvTY6Mx9GgPNbs3IR3DnxE/2+msGPK+tzjnecMZP/izcja0nHiuKvciYvIq8Pjo+Jwf0r9pLRQ0q53EBcOnyuV8P+bkLVSmf+9qAinxHNEkqQmQB/AH+gNPHuowDwfAEmyLNeXZbkBcFCSJG/gQ6AD0AhoKklSr3zhNwbeApoDLYARkiT560/XBDbJsuwvy/L9fNeNlCTpjCRJZ7TaZ4+mSJJpBjfXQZ31/hLq1WtHi5Yv4ebmwpQp7+Se2759N/Xrt6fPq8OYO9d0ylchRDxbgxmbXMdxAdcrlEo86/lw4bsDbOz2PlnpmTR/x/zSlKLqK4pHX6lU4lPfjxVvLeajQQvoMfY1PH2LMIJr9vflNzGv0crZjkqdA/it+US2+Y/Fws4a39669bc3N+5ne8v3+DN4FuroRALmFG164lPJJ1ATfpvUOUNJ/3AsWUf/wHa4wXRYWUv6snGkzh6CskoNFF5VSkWC+bxdtHtc2HOKOR0nsGbkMnq817ekgkyPFVHQxT2nmddxImtHflRyPYaYa+fKYJ5iYeujDz5YSv0GgbRq/TKubs5Mnvy20fn27VsyZEhfZs1a/Ny0lhWFjaOCqN/en/uhYbzbdBizuk5i0PzhuTMniinI9JgZPWkH/uZhj+FEjZuL27uDjW9ha4Nq5QfEfbgWOa3os9mMb2bmWAHR49SqHuX7d+Thok1GxxV2NtT4air3Z/8HTaq6ZHqgSGXfvmV93PoGE7l0g+5SpRLben7EfbeTWy9NQKvOoPzbhR4XySejEHWiWak6o4grYXzUehyru87gnw17eXO9bhDCxsme2sGNWd52PEubj8HKzpqG+n0eCiGqwPCeYmJiU6dTAGFnbhgt3fiiz1xWvTyTr4d8SMtBIfg2q1UcOWb6IwXbHFizHVtneybvXErbwV14FBqGVqPba6JecGMWtB3LnOZvY2VnTeNebZ6pp1B1cwF6km9HcO2LHQRtmU7g99NIuPoAbY7uAVhSKrBytmffy3M4v+AHWq8b+2wthSG/NoUShWcVMn9ZRca2lVi2fFnnqNDbZmyaj3rdFBQqX6Ry3qUioTB1ZHmVBy/1DGbDlwXvo9GyTVP6DniFJfNWFmhTsAYzB03y9dPL4409Z/ii4xS2jlhJ4KTXAKjewZ+0uCQizexPUWwKk+cNGLFwNFdPhnLt9NXS0yD4r0fsKfF8aQNsl2VZDSBJkunOQoWjE9DvyRdZlhMkSWoHHJZl+bH+3t8D7YDf8oX/qyzLaXqbX4C2wO/AfVmWT5gLTJbl9cB6AEurCmZrlbdHD2bYMN0D6JkzF6hYKa8hqFDRi4jIaJNroqJ0G1tlZWWxYeNW3ps42sTm2LGTVK1aBXd3V+Linj4t0X9QJxr0C9Ld+9JdnLzdebJC0lHlRmpMopG9Oj4Fayc7JKUCWaPF0cuNVP3Ux5TIePPXyzIpkfFEXrgDwM2dpwrtlOg4sAvt+3cC4N7F27h7l+OW/pybyp2E6PiCL85HfFQcKQnJZKkzyVJncuPUVSrX9iH6XuSzL0Y/M8I7b6TGzssNdVSCiY29tztPfN323m6ooxNRta1H6sPHZOo3s3yw8wzlmlTn3i/HyYjNm655+/tDBG0q+iwXAG1iHJYuHrnfFS7lkJPzxU9GXsdec/UMvPY2kr0TcprBlFF1Gppbl1HWDkAbeZ/iEDiwM2306RZ28TZu3u7c0Z9zUbmTWIR0M+TWqWt4VFFh7+qYu/FkYWg/sDOt+3cE4P7FO7h6lwN0+2W4qtxJLOZSmtunrlGuGHoKQh0Zj22FvJEROy9d/vk3GD1qMEOH9gfgzNmLVKxoUB9V8CLyGfXRpk0/MnHCqNxz9erVYu2aj+jRYyDx8YnPV/y/RKdBXQjqFwzA3Uu6+ugJbip3EmMKn4/av9aBHV/8AkD0/SgeP4zBy68Cdy8WfQNg0M+MUOWVfwvPcuQ8jivQPuPsFSwreaFwcUKbmAwWSlSffEDKnwdJK8aIZH6yIuOw8s7Ly1Ze7mRHmZZ729pV8F3+DjcGLCAnITX3uGShpPpXU4j95SgJhqPuJSA7KhZLgzSz9HInO8ZUk00tHyouHcu9IXPRJKbkXpsdFYv6gm7GRuLO40VySjQfGEzT/rq2NvziXZwN2hInlZvJMov0+BRsnOxRKBVoNVqcvNxJ0bfHmQYOmpuHL9Bj4VvYuTpStWUdEh7GkK5vZ0J3n6ZK4xpc/M18erYcGJw7c+Hhxbu4GKSXi8rNZFlDWnwKtgaanL3cSc6X5xt2N1268cQmLS6Z0D2nqdTQj3unTGfitB4YQku9ngcX7xRSj52BHrfcsDJT1WyZkrfp7QfHPiPu4WNqtWtA3MPHpOnj6NLuU/g0rsHZ346ZjaMn6Nr/fHVzVKKRjVrf/j8Z77fzzqu/724+wt3NumWMDaa/TnpkfO414Tt1m5LHX7iLrJWxdnPM7SsUBjnFeGaE5OCKnJpoYqNVp0J2FmRnoQ2/icKjIpoEg3o9U43m4Q2UPvXIiY2gOAwa1pd+A3UbxV46H4p3hbwZdipvT2KijJey1atfiyq+lTlyRrcfia2dDUdO/0H7pi8DUKtOdT78ZC6D+75DYkJSoTQ0GRRMgL5fG6Hv1z7BUeWWW46e8KSsPenXOhn0aw15cOo6rlXKY+vqQKUmNajZqTHVAxthYW2JtaMtvT55m98mFG3mZJdB3ejYLwSAO5du4e7tAeg2A3VTuRNvpn4CeG18P5zcnFk3Y0mRwhP87yNmSjxfijVHRpKkMZIkXdD/eevvU4ixiCKFX7wFpXrWrN1Ik6YhNGkawvbf9zDgTV0Hp3mzAJKTknM7/IYYruvu2aMLoVd1Dbufn0/ucf9G9bCysnymQwLg/Kb9bOw2i43dZnFr71nq9tGNGHj5+5GZkk5avsob4ME/V6nZTTctsl6fttzap5s6dnv/ObPXpz1OIjkyHrequlkJVVrXJc5gc6inceDb3czuNpnZ3SZzbu+p3CUXfv7VUaekk/TYVF9BnNt7ihpNa6NQKrCyscKvUXUibhd+47S4C3dx9FVhX8kDhaUSn54tCN9rPG0ufO85fF/VxUG5AD+yktNRxySS9iiOcgHVUNrqprer2tQl+bYuDmwN1jdW6tqExBvF28xN++AmCg9vJDdPUFpgEdCOnMvGnXnJMS8sReUaIEnIaclIDk5ga687YWmFsmYjtNHF31Tu8Ld7WNhtCgu7TeHC3tO00Kebrz7dkouQbh5V8jo1ler6orS0KLID4Mi3e1jcbSqLu03l4t5TtOjdrgR6PI30WBRDT0HE58tjlXu24NGes8++sBRYu24jzZp3oVnzLuz4fQ8D3tR1LJs18ycpKeWZ9VGP7p0JDdU5eipV8ubHrV/y1tDx3LpdOlOBXwT2b9rNrG6TmNVtEmf3nqJNn0AA/PxrkJ6SXiSnROyjx9Rt3QAAp3LOeFX1JuaBqeOnsGReuYFl5QpYVPAECwscugaSdsjYZ25h4Pi2ql0NydJC55AAys9/j6y7D0na9EuxNRiSeuE2Nr5eWFcqj2RpgVvPNiTsNX4jlFWFctT4aip3xq0i466xc9j34zGobz0ian1xxyFMSb94CysfbywreiJZWuDSvR3J+04Z2Vh6e1Bl7QweTlxB1r28B7Ocx4lkR8RiXVW3VMKxdUMybxlvIvw0Tn67j9XdZrK620yu7T2Df++2AFTyr0ZmipoUM3XQ3X+uUrebbs+LgD5tubZXt8Gug4dzrk3Fhn5IkkR6QgqJEbFU8q+OpY2unfFrXZeY2wW3tf98u49Pus3gk24zCN17hgC9psr+1VCnpJvVdOefUOrrNTXp046re/PqJxtHW6o2r03ovrxjlrbWWNvb5H6u3rZB7lsw8nP8270s7zad5d2mc2XvGZrq6+kqej3m6unb/1yloV5Psz7tuKKPIxsnO5SWSgBa9OvAnZPXyExVkxARh49/tdw4qtG63lPj6Anm6ubwvcZ186O95/B5VReH7gHVyE5Wk6HvQz3ZwNKugjuVujXl/m86x0347rN4tqkDgGNVFQoriyI5JAC0UWFIrp5IzuVAocSiVjM0d4zf4KW5fQFFheogKcDCCoVXVbTxkWDrANb6GVoWliir1EYbH2UmlMKx6eutdAt8nW6Br7N350H69NUNQPk3aUBKcorJEo2D+/6iaZ0OtPHvShv/rqjTM3IdEt4VVKzbuJKJb8/k3p3CD5Cc2bSP9d1msr7bTG7sPUPDPro0qaAva/kH2wDC/rlKHX2/tkGfdtzQ52FXg/ZeVc8HpaUF6oRUDi7byictxvJpmwlsG7uae39fLbJDAmD3pp1M6TaBKd0mcGrvSQL76Jwp1f1rFtimdOwXTKP2/nwydnmp7mn130RZL914kZdviJkSz5djwDpJkpagi+uXgC+fdZEsy58Dnz/5LknSXuBdYIL+uytwElglSVI5IAHoD3yW71ZHgQ2SJC1F56B4BRhYsp9kyq5dB+japQPXrx1HrVYzfPh7ued+376JUaOnEBkZzaaNq/HwcANJ4tLFUN4Zo9vt/ZVXujFgwKvkZOegVmfw5ptvFxRUgdw9eIGqQQ0ZcfRjctRZ7Jqct3auz4bJ7Jn6FakxiRxZsoUeq9+l7eTXiA4N4/LWw8+8/sCcjby86m0UlhYkPYhhp8G5wnLx0DkaBAXw0ZHPyVRn8tWU3OTlvW9m8Z9pX5AYk0DwkG50G9ULZw8XFu5ewaVD5/jP9DVE3nnE5SMXWLh7BbJW5sjW/Ty6WfhOpazRcnrWRjr+MBVJqeDOliMk3XxE9YG6kZ1b3x7k0YELeHdsSM+/dXHwz0Td74w7f4cHf56i256FyDka4q/c59Z3unX2/u/3w7Wu7hVzaeGxnJz6nyLHDQBaLRk/r8Xunfm6V4Ke2Ic26gGWrbsCkH18FxaN2mDZpitotZCdiXrjMgAkJzdsB0zUdVgkBTkX/kITWvRXyprjyqFz1A/yZ+GRz8hSZ7HRIN3e/WYG305bS1JMAkFDutJ5VE+cPFyYvXs5Vw6d59vpawno2pwWvdujydGQnZHFl+8WfQqnsZ7z1AsKYP6RT8lSZ7Fpyhe558Z8M53vpq3L1RM8qgdOHi68v/sjQg+d57vp6/Dv2oLmvdvl6vmqhHoMkTVazs3cQPvN03SvBN1yhOSbj/AbpJvlcWfTAWw8nAnevRBLR1tkrZYaI7qyq/1UckpjerueXbsP0qVLB65dPUZ6upoRI/Nm72z/bSOj355KZGQ0GzZ8ikc5dyRJ4uKlUN59V/c2nJkzJ+Dm5sKnqxYBkJOjoVXrl0pNX36mzFnK6fOXSExMpmOvAbwzbCB9und+buEBXDh4loZBAXx89Auy9K8EfcLkDbP4aqquPgoZ0o2XR7+Cs4cLS/as5OKhc3w17Qt++/QnRn08liV7VoIksXXpt0avLy4yGi2xiz/Ha91iJKWC5F/3kn3nPk6v6+I9+cc/cQhug2OPTsg5OcgZmURP1i2rsfGvi2OPTmTevEvFn3XlIX7VN6T/VYI6QKMlbNZX1PxhNpJSweMtB1DffEj5gbpRwZhv91Jh4utYuDris2QkAHKOhtCuU3FoVguP1wJJvxpGvX0fA/BwyfckHSzh2mmNlojZa6m6aR4oFST8uJ/MWw9we7OL7jd/vxvPcf1QujpRYeHbuZpu99C1x4/mrqPSJ5OQLC3IehhN+ORPiiXjxqEL1AhqxHtHVpKtzuSXKetyzw36Ziq/TltPSkwie5Zupt9nYwme9BoRofc58+NhAOp1bU6zAZ3QanR10Naxum5L+IU7hO46yZg/F6PN0RARGsbpzYV7zez1Q+epFdSIaUc+IUudyU8GmoZ+M5Wfp31JckwCO5du5o3PxtJ50utEhIZx6se8/WLqdm7Kzb8uka3O2xzQsZwzg9br4k+hVHJh+3FuHnn2K6+vHjpP7aBGzDqyiix1ptGshxHfTGPrtPUkxyTwx9IfGPjZOLpO6suj0DBO6PV4VqvAmx+/g1arJfrWI7ZM1f2eBxduc3HXSSb9uQRtjpZHoWH8vfnAM/XIGi1nZm0g8AfjurnaQF3dfPvbA0QcuIBXx0a8/PcKNOosTk7Mi8M2X43H2tURbXYOZ2ZuIDtJtzzq7pbDNF8xkq4Hl6LNzuHk+GK80ljWknXgB6z7TNC90vvyceS4CCwa6gYDci4eQY6PRBN2BZshc0GWybn0F3JsBFK5ilh3HQoKBUgSOTdOo7377D02CsPBfX8RFNyWo2f+1L0SdGze21w2bPmcqRPmmsycMGT8lNG4urmw4CPd27E0Gg3dO/YvkoZbBy9QLagR7x5dQbY6i98n56VJ/w1T2DH1S1JjEtm/ZDN9Vo8laPJrRIXe57y+X1u7a1Ma9GmLNltDTmYW28bkf0QoPc4dPENAUGNWH11HpjqTLyZ/mntu5obZrJm6moSYeEYueofHj2JY9Kuu/3Zy9z/8/OnW56ZL8N+F9P/VU/VvIUnSXHQOg/vAY+Aw0Br4Q5blnwvzSlBJkhzQOSkaAxpgnizLv0iS9AYwA53DYacsy1P19rn3kSTpPWCo/lZfybL8iSRJPvrw6z1Lf0HLN8qKRV5BZS3BhFCp9B6oSoPgrJK/3qm06flqYllLMGLy7y9eHCmKN7HquRGYaVnWEkwYlPDs1wP+m6SGP/1VfGXBW40nl7UEI+Y5lmhS3nMhNt6+rCWYYGuVXdYSjNjKv/tGmMKQXdDGHmXEi6YHoFmWsqwlGNHzvRevra295NSzjf5FhjnUL2sJJlyidGZQliY/3//9xeokFZN7DYPLvOLwvbjvhYxLMVPi+bNcluW5kiTZoZu58LEsy7mzJWRZDjT47GPuBrIspwKDzRz/AfjBzHEfg88rgBX5zocBz3RICAQCgUAgEAgEAoFA8DwRTonnz3pJkuoANsBGWZb//73/RiAQCAQCgUAgEAgEAjMIp8RzRpblN8pag0AgEAgEAoFAIBAIyo4XeaPJska8fUMgEAgEAoFAIBAIBAJBmSBmSggEAoFAIBAIBAKBQPAckWUxU6IgxEwJgUAgEAgEAoFAIBAIBGWCcEoIBAKBQCAQCAQCgUAgKBPE8g2BQCAQCAQCgUAgEAieI7K2rBW8uIiZEgKBQCAQCAQCgUAgEAjKBEmW5bLWIHiBGe/T74XLII2yX6wJPtEvlhwAPk48XdYSjPjUvklZSzCha/uIspZgxIWD5cpaggkPLKzLWoIRNeT0spZgwmrLslZgzDdnl5e1BBOGN5lS1hKMaKS1LWsJJqRIL1ZT2zlbXdYSTFAqXqwhxvhsm7KWYMICi8dlLeGF53NLu7KWYMKlLOeylmDEN4qYspZgwoHwvf8TO0TertO5zCv7alf3vJBx+QI+TgkEBfOiOSReRF40h8SLyIvmkHgRedEcEi8iL5pD4kXkRXNICAQCwYvEi+aQEDxftOLtGwUilm8IBAKBQCAQCAQCgUAgKBPEsLNAIBAIBAKBQCAQCATPEVnMlCgQMVNCIBAIBAKBQCAQCAQCQZkgnBICgUAgEAgEAoFAIBAIygSxfEMgEAgEAoFAIBAIBILniKwVyzcKQsyUEAgEAoFAIBAIBAKBQFAmiJkSAoFAIBAIBAKBQCAQPEdkuawVvLiImRICgUAgEAgEAoFAIBAIygThlBAIBAKBQCAQCAQCgUBQJojlGwKBQCAQCAQCgUAgEDxHxEaXBSOcEoJSp/ecwdQJ8idbncn3k9cQHhpmYtN2UGfaD+2Kh4+Kmf4jSEtIKVGYFQIb0Hz+QCSFgpubD3P58x0mNs3nD6Rih0bkqDM5NnE9cVd0uqyc7Gi9fDguNSuCLHNs0pc8Pns797p6o7rRdPYb/FBvNJkJqUXS1WnuQPyCGpGtzuTPyeuJ1odpiHMlD3p+NgYbFweir4SxY+IatNka3Py8eGn5SDzr+nB0+U+cWr/T6DpJITHkjwWkRCXw89CPi6TrCYs/nEWnkPakp2cw7p3pXLp4tUDbJcvep/+bvfGpEJB7rFWbZixaMhMLSwvi4xLo+dLAIoXvFdiAgAW6dLuz+TDXVpumW8CCQXh3aIhGncWJietIuBwGQI1hnfF7MwhJkrjz/SFufLUbAJc6lWm6dCgW9jakhT/m7zFfkJOqLpKuJ1g0bIrtoHdBoSTr0J9k/r7ZrJ2yak0cFnxO+qr5ZJ86CoBkZ4/tyCkoK/oCMunrlqG5VXD8Fga3oEZUW/gWklJB5PcHePDZb0bn7ap5U3PVGBzr+3JvyWYersmLzwojuuE9oCMgEfn9fsLz5afC4h3YgKb6snZ782GumClrTecPpEKHRmjUmRyfuJ54fb6vPaIL1fsHIssyidfDOf7eerSZ2VR5uRkN3+uNc3Vvdr40h7hL94qlDcA50B+fBUORFApiNu8nYvWvRufdX2mH95heAGjTM7g3fT3pV3X6qq4Yg2unJmTHJnGpw4RiazDHwLnDaBQUQKY6k/WTVxN25a6JTfDgrnQZ+jKePl6MbjSYVH29aOtox9ufjMfd2wOlhYKd63/n6E8HS1WfIe8vXsHR46dwc3Xht+/WPrdw8vPmnKE0DAogS53Fl5M/436oaT7oNKgrIUNfwtPHizH+Q3LjCKBWi7q8MfstLCwsSElIZknf2UUKv0r7BgTOHYhCqeDKlsOc/sI0bwfOG4ivvk7fO2k9Mfq8HfzRCKp2bER6XDLfBs/ItW8xsTf1+weSHqfTeXzZj4QdulgkXZ3nDqJ6UEOy1Vlsn7yOKDPtiEslD/p89i42Lg5EXQnj14lfoM3WUCO4MUGTXkXWymg1GvbM+5aHZ27mXicpJIb/sZCUqAS2DF1eJF3Ogf5UMShrkU8pa5r0DML0Zc3K2x2/VeOwLO+KrNUS890+or/+s0hhF4RToD+V5w0HpYLYzfuI+vwXo/Nur7RD9U5vALRpGdyfsRb1tbA8A4WCOjuXkxUVx+0hi0qsxz2oIbUWDkZSKgj//iBhn/1udN6umjf1Vo3Gqb4vt5Zs5f6aP3LPWTjZUXfFKBxqVUSWIXTiWpLO3CqxJoDx88fQokNzMtWZLJ64jJtXTO/7wWczqNWwJjnZOVy7cJ2Ppq1Ek6PBwdmBGR9PoUIVbzIzs1g66SPu3Qj7n9Lj0D6ACrNHgFJB/NZ9PF7zs9F5l57t8RjdB9C1I4/e/4IMfT5SONlTcelYbGpWAVkmfOoq0s/dKLIGr8AGNF2Q19aGmukfNVmga2tz1Jn8M3E98fr+Ua0RXaj2RiDo29q/J+ra2jZr38XJzwvQ9X2zktPZGTyryNqeMGb+OzTv0JRMdSbLJi7n1pXbJjYzPptOzQbVycnWcP3CdVZOX4UmR8Pro1+j4ysdAFAqlVSuXok+DV8nJbFkzwOC/17E8o1iIEnSBEmS7Aphd1iSpCb6z0V7ms27xxBJklYX59qyoE5gIzx8vVgYOIEtM7/ktUXDzdrdPXuDLwYsIi78cYnDlBQSLRYNZu+AZfwaNJWqvVrgXN3byKZih4Y4+arY1mYSf0/7mpZLhuSeaz5/IOGHLvFr+6lsD55J0q2I3HP23m54t6tHanhskXVVDWqIq6+Kde0nsXvG13ReOMSsXeD0fpz+ejfrAyeTkZRGw76BAGQkprFvzrec+tL8w2OToV2IvR1h9lxh6BTcjqp+PjTzD2HS+A9YtmJugbYN/evh5OxkdMzJ2ZFlH89hQP+3adviZYYNHl+k8CWFROPFQzj85jJ2Bk6lSs+WOFWvYGTj1aEhjr4q/mg9iVNTv6bJkrcAcK5ZEb83g9j70mx2dZqBd7A/Dr6eADRbPpwLi7ewq+N0wnedofbbLxVJl4FAbN8aT9qH00mZPASrVh1RVKhi1s7mjZHkXDxtdNh28FhyLp4iZfJgUqYNR/vofvF0PEGhoPrSYVx6YxGn2k6k/CutsatR0cgkOzGV27P+Y+SMALCvVQnvAR0522UGZzpMxj24Mba+qiJLkBQSzRcN5sCAZfweNBUfM2Wtgr6s/dZmEv9M+5rm+rJmq3Kl1tAQ/uz2ATs6zkBSKvDt2QKAxOvhHB6xiugTRe+4GaFQ4Lt4BNffXMjFwPG492yLbXXjOMp8GM3VPh9wudN7PFr5E1WXjc4993jrIa69uaBkGszQMCgAla8Xk9qP4esZaxmycKRZu5tnrrPkzbk8fhhjdDx4UFce3QpnVtf3WNR3Nm+8Pxil5fMbU+jVLZi1KxY+t/ubo0GgLo6mBr7LNzPXMHhRAXF09jrLBszjcbhxHNk52TFowQg+Gb6UmSETWP1O0R6wJYVEh4WD+W3wMjZ2nErNHi1wy5e3fYIa4uKj4pt2k9g//Ws6LBqSe+7qT0f5ddBHZu997qvdfN91Ft93nVVkh0S1oIa4+6pY3X4Sf8z4mpcWvmXWruP0fpz4ehefB05CnZSGv74duXf8Cuu6zGB9t5n8PmU93T8cYXRd8+K2IwoFPotHcOPNhVwqZFnz1Zc1OUfL/fkbudR+HKEvT8dzSFeTa4uFQkHlhaO4OXA+oUFjcevZFpv8mh5Ec+PVWVwNnkDEqh+psuwdo/Oew15GfTu85FoAFBK1lw7l3BtLOd52El6vtMa+hnEbl5OYyvVZGwgzcEY8odbCwcQeusDxNpP4p8NU0m4+KhVZLTo0o6JvRfq3GcSyaSuYtMR8273v1wO82W4IgzsOx9rGmu5vdANg0Ng3uBV6myHBI1g0finj54/5n9KDQkGF+aO5N2QuN4PH4NKjHdbVKhmZZD2M5m7fGdzqOo6Yz7ZSccm7uee854wg9cg5bnZ8m1tdx5FRjPwkKSSaLR7MwTeXsSNwKj49Tdtab33/aHvrSZyc+jXNDNvaYSHs6voBf3SYgaRQ4KNva4+NXs3O4FnsDJ7Fgz9P83Dn6fxBF5pmHZpS0bcCg9q8xYppnzB+yTizdgd+PcCQ9sMY3mkk1jbWdOvfFYAf1/7EqM5vM6rz23y99D9cOnH5/4VDQitLZf73oiKcEsVjAvBMp8T/R+qFNOH0L7qR4vvnb2PraIeTh4uJ3aPQMOJLwSEBUM7fj5SwaFIfPEabreHu9hNU7tzYyKZy58bc/vkYAI/P3cHK2R7b8i5YOtji2bwmtzYfBkCbrSErOT33umZzB3B60RbkYmyXWz24MVe26cKMOH8Hayd77Mu7mNhVaVWH6ztPAXB5219UD9FpT49LJurSXbTZGpNrHFVu+HVoxKUth4us6wldXurI1s2/AXD2zEWcnZ3w9PQwsVMoFMydP5X5s4073H1e686fO/bxKDwSgNjY+CKF7+bvR2pYNGn6dHuw/QQV86Vbxc6NCfv5LwDizt3GytkOm/IuOFX3Ju7cbTTqLGSNlph/rlGpa1MAnPy8eXziOgBRRy9T6aVmRdL1BGW1WmijItDGRIImh6x/DmLZpLWJnXWXV8g++RdycmLeQVs7lLUakHVI71DS5CCnpxVLxxOcAqqhvhdFxv0Y5OwcYn47TrkuTYxssmOTSblwBzk7x+i4XfUKJJ+9hVYfX4l/X6Vct6LHi3u+sha2/QSV8qVZpc6NuaMva7EGZQ1AYaFEaWOFpFRgYWtFelQCAEm3I0i+E1lkPflx8K9GRlgkmQ+ikbNziNt+DNfOxr8z9cwNNEm6tEg5dxMrL/fccyknr6Ip4awtczQObsaxbYcBuHP+JvZO9riUdzWxux96j1gz9aIsy9g62AJgY29DWmIq2hzTeqG0aNKoPs5Ojs/t/uYICGnK8V+OAHDn/C3sHO1xNtN2PCggjlr0aMvZ3SeJj9A5kFPikosUvqqRH4lh0STp8/aNHSfwCzHO234hjbmmr9Oj8tXpj07dICOxWGMPT6VmcGMubtPVgY/O38bayQ4HM+2Ib6u6XNW3I5e2HaVWiK5uyE7PzLWxsrNGJq8tc1S5Ub1DI85vOVRkXfnLWvwzylqqQVnLjkkg/bJuppA2LYOM2+FYGpTD4mLfqDqZYZFkGWhyCWluZJN2Nk9T2rkbRuXf0ssd545NiP1hX4m1ADgHVCP9XhTq+zHI2Rqifvub8vnq7KzYZJIv3EXO184rHWxxbVmbR9/r0kbO1pBj0DcpCW06t2b3z3sBuHruGg7ODriXdzOxO3HwVO7naxeu4+Gl6x/41KjC2WPnAXhw5yGqiipcy5nWZ/+teuwaVSfrfiRZD3X5KHHHUZzy5aP0c9fRJKflfrZUlQNA4WCLQ7N6xG/V/R45OwdtctHbfnNtbf7+UaXOjblXQFsrGbS1Slsr1NEJJmFU6dGcsN/+KbK2J7QOacXen3Vl5dq56zg42eNmJt1OHcxzfFy/cAMPr3ImNkG9Ajm4vej1kOB/C+GUeAaSJNlLkvSnJEkXJUm6IknSHMAbOCRJ0iG9zRpJks5IkhQqSdK8Z9yvnCRJ/0iSZDJ8K0nSa/owLkqSdNTglLckSbslSbolSdIyA3uz4UqSFCZJ0oeSJJ3S/1XTH/eQJGmbJEmn9X+mT1glxMXTjcSIuNzvSVHxOKtMK6nSxE7lSlpE3gNxemQ89ipXMzZ5utIi47FTueJYxYOMuBTarBxJjz0Laf3RcCxsrQGoFBxAemQCCVcfFEuXo8qVFIMwU6LicfQ01mXr6kBmcjqyRquziYzHUfXsxrTjnAEcWrwZWVv8dwt5eXkS8Sgq93tERBQqb08Tu+EjB7B71wGio40fBPz8fHBxceK3Pzax/8g2Xu/Xs0jh26ncSDeIn/TIeGy98sWPys0o3dIjdOmWdD0cj+a1sHJ1QGlrhXeHRth56/JZ4o2HVNA33pVebp57vKgoXMuhjcsbjdXGPUbhatyYSq7lsGzalqz9xlNyleW9kJMTsRs9DYcl67EdMRmsbYql4wnWKjcyDeIiMyIea1XhOvJp1x/i3KI2Fq4OKGytcOsUgHUF047BszBX1uzMlLX86WqnckUdlUDo2p30ObWK186vJis5ncijV4qs4WlYqdzJMgg7KzIOK6+C0798/04kHjpfqhrM4apyIy4ib7ZVfFQcrp6Fz5f7Nu7Eu1oFVp/+miV7VvLtvP8Uy1H6IuPqaSaOCpm/AVRVvbFztmf6lnnM27GM1r3bFyl8B5UrKQZ5OzUyHod89bWDypWUyLz8lRoVj0Mh6uuGg4MZsGcxwR+NwNq5aOMZjio3kgvRjmQkp+W2I8n52pGanZvwzoGP6P/NFHZMWZ97vPOcgewvZjtirqxZPqWseRRQ1qwqemBXz5e0czfNXFVETV5uZEXm5aGsqKeX/3L9OpF06Fzu90pzhxG+aGOpvbPPRuVGhkEcZUTEY13I/pBdlfJkxSVTd9XbtNi/hDorRqK0sy4VXR6qcsRE5LXnjyMfU05VcHugtFDSuU8wJw/pHi5vX71L+25tAajdqCaeFT3NPmj+t+qx9HQn26Auyo6Mw9Kz4LrItW8IKYfPAmBVWUVOXBIVl0+g+p+fUHHpWCTboqebrh3N19aa9I/y9Wsj4rHVt7VX1+zkldOr6HNhNdkp6UQeMW5ryzevScbjJFLuRRdZ2xPKqdx5bJRusZR7Sp2ttFAS3Kcjpw+fMTpubWNN08Am/LXzWLG1CP43EE6JZ9MFiJBluaEsy/WAT4AIIEiW5SC9zSxZlpsADYD2kiQ1MHcjSZI8gT+B2bIsm1tAORvoLMtyQ6CHwfFGQF+gPtBXkqQn88ieFm6yLMvNgNV6zQCrgJWyLDcF+gBfFaBzpN7ZceZKyh2zkVIgZmYFPe/OsySZBmoSpBkbZBlJqcS9vg/XNx3g987vk5OeSf13u6O0saLhuB6cW/6z6XWFF2YmSLkQNk+/rV8H3bplc/tTFAXz8WYcuKeqPD16deGrdd+Z2FpYKGnQqC5vvD6K118ZzqSp71DVz6cIAswcM4kfcyYyybcjuPbFDoK2TCfw+2kkXH2ANkfXIT/53nqqDwmm8+6FWDrYos3KMb1JofSZFWj0zXbQGNQ/rANZa2ymVKL0rUHmvt9JnTESMjOw6dG/eDpy9ZhTU7iylX7rEQ9Wb6fhjx/QYPMs0kLDkIsx0m4uz5hIKCBfWTnbUalzAL+0mMhPAWOxsLPGt3cp+0WfnWS5OLWqR/n+HXmwaFPpajBDYcra06jf3p/7oWG823QYs7pOYtD84bkzJ/5nKKCOLixKpRKf+n6seGsxHw1aQI+xr+Hp61Wi8E2DL3o6Xvp2P9+0fY/vuswiLSaRdu+/WXhN5mWZCHtWG3hjzxm+6DiFrSNWEjjpNQCqd/AnLS6JyOK2I8Uoaw/zlTWFnQ01vprK/dn/QVPMfX+eJaqg5HFsVY9y/ToRrtfk3LEJObFJpF8uYp+naHIoMJLyX2qhxLG+L+Eb93Gi0ww06Zn4jC2a47/AexexnzZp8XgunLzEpVOXAfhu9WYcnR34z9519Bn6Creu3EKjKf7MrRdNT1HqIvuW9XHrG0zk0g26S5VKbOv5EffdTm69NAGtOoPyb79aKhpMu4/mdT5pa39rPpFt/ubbWp9eLUs0S6KwGg0Zv3gsl05e5vIpYwdJy+AWhJ6++v9i6QaALEtl/veiIja6fDaXgeWSJH0I/CHL8l9mKoLXJUkaiS4+vYA6wKV8NpbAAWCMLMtHCgjrOLBBkqQfAcPdmQ7IspwEIEnSVaAK8PAZ4W42+L9S/7kTUMdAv5MkSY6yLBvVBLIsrwfWA4z36ffMFrTNwBBa9tdtVvPg4h1cvPM8pc4qN5LNTBsrTdIi47E3GA2383IjPV+Y6ZHx2BvosvdyIz06EWSZtMh4Ys/rOiJhf56i/rvdcfIpj0NlD3ruW5xr32PPQv54aQ7qx0kFagkY1ImG/XS+qshLd3E0CNNR5UZqTKKRvTo+BWsnOySlAlmjxdHLjdRnxFfFJjWo1ikAv8CGKK0tsXa05eVP3uaPCWueeh3A0OFvMHDw6wCcP38Z7wp5+wp4e6uIjjRep92gQW18q1bm1HndVERbO1tOnd9LM/8QIiKiiItLID1dTXq6mn/+PkO9+rW4eyfsmTpA7/k3iB87LzfUUYlGNmp9uj0Zs7DzdkMdrbO5u/kIdzfrilKD6a+THqkbVUi5Hcnh/ksBcKyqwrtjo0LpyY82/jEK9/K53xXuHmgT4oxslFVrYj9Ot5me5OiMRaPmoNWQc+sqcvxjNHeuAZB18gg2Pd8olo4nZEbGY20QX9bebmRFFX7JTNQPB4n6Qbc5ou/M/kazLgpLYcuaSbpGJ+LVth6pDx6TGa+rbh7sOkP5JtW598vxIusoiKzIOKwMwrbycjcbR3a1q1B1+TtcH7CAnCJuXltYOg3qQlC/YADuXrqNu3feyJ2byp3EmMLXi+1f68COL3RNQvT9KB4/jMHLrwJ3L5puKvbfRMeBXWjfvxMA9y7q4ujJ9nZuKncSogufv+Oj4khJSCZLnUmWOpMbp65SubYP0fcKtywoNTIeR4O87eDlRlq+NEqNisfRYLq/g8qNNH19VBDpsXnLSK5sPkTPbyY9U0uTQcEE6NuRiEt3ccrXjqTka0fS41OwcbLPbUecCmhHHpy6jmuV8ti6OlCpSQ1qdmpM9cBGWOjbkV6fvM1vhWhHwHxZyzZT1mxrV8F3+TvcyFfWJAsl1b+aQuwvR0nYdbJQYRZKk8EIuZWqYE1Vlr3LrYHz0egfhBya1sIlpCnOHRqjsLZE4WiH76cTuDfuk2LryYiMx8Ygjmy83ciMKly5z4iIIzMinqRzujIeveMkvmN7POOqgnllcE+6v6nbg+H6hRuU985bqunh5UFctPn2YMjEgfwfe/cdH0XRP3D8M3fpjSQQSEINHUEg9A6hExUQLFgoFmyAgnRQQKVJEwFBsdEUUEFE6R1EpIfeIZSQAum93M3vjz2SS+4CaTzJ73nm/XrxIrmb3f1mdnd2dnZm1r20O7PfnJz5WVJCEjM+zBrO+cu/PxF6K8za4v9v4jGXHnYfW7Py2tanNOkRlseRQ+0qVJg5jBuDpmQeR+lh90kPu09ykNbzJ2bzwQI1SmjX0ezX2uQw6/XaB30VnE31I++29Ui4bXat3XyMMmbXWqHXUTGwKVu6f5zvuHoNfIZA01wel05dwivbfiuT637rP+JV3D3dmTzWsjN5QC81dEPRqJ4SjyClvAw0RmucmCGEyDadtxDCDxgFdJJS1kfrCWGtn3YGcBzoZrbsNCFEkBAiyLStd4CPgIpAkBDiwdUs1Ww9BsAmD9uVVn7WAS2llA1N/8rnbJAoiL9Xbmd24DhmB47jzPZjNO3TDoDK/tVJiU8i7l5MYTfxUPeDruPm541LRS90tnqq9mrB7e0nsqW5tf0E1Z9rA4BXo2qkxSWRHBFD8r1YEu9GZc5G7NOmLjGXQ4i+eIc1DYbwW4sR/NZiBImhUWzs9tFDGyQATqzYyY+BE/kxcCJXth+nXl9tm77+1UiNTyIxR2US4Nah89Q2je9/sm9bruw4YZHG3L5Zv7C4xfssaTOCjcO+4uY/5/PUIAHww3c/E9C2NwFte7Plr528+FJvABo3aUBcXLzFEI0d2/dRt2YbGtfvROP6nUhOSqaZf1cAtmzaRYtWTdDr9Tg6OtCocX0uX8r7U6aooOu4+nnjbNpvlXq14M7249nShGw/QZXntG6ZpRtVJz0umRRTHtqX1ibedCpfmoqBTbm54Z9snyMEdT/ozdWVu/IckznDtYvovMuj8/IGvQ12LTuSfvyfbGniP3iZuPdfIu79l0g/vI/kH+aTfuwgMjYaY2QEOh+tU5NtvUYY7gQXKI7MbZ28imNVHxwqlUXY2lC2d2vubzv26AVNbMto+WJfvgxegc2J+D3/jQGRpn324FyrYuVcu739BNVM51qZRtVIN51riSGReDWqjt7BDtDOtdgrRTNx2wMJQVdx8PPBvqKWR6V7tSF6e/aJvOzKl6Hmd2O4+v6XpFwv/DwWudm5YisTA0cyMXAkx7cfoU3fDgBU869JUnxSvhol7ofco25rrSOcW5lS+FT1JeJWwbvdlhS7Vm5lUuAoJgWO4sT2I5lDLqr51yA5PonYfFw7Tmw/Qs2mddDpddg52FGtYQ3u5mOCubBT1/Hw88bNdGzXeqYF13OUxdd3nKCOqUz39q9GWi5lujnzeYSqdWtC5KVHx3RsxQ6WBk5gaeAELm0/RoO+WhlY3r86qfHJFo3bAMGHzvOE6TpSv287Lu3QylKPyllD8rzrVUFva0NydAK7Z61lfothLGgznHXDFnHjn/N5bpAAy3PN8yHn2jUr55rf3CEkXwkhbKnlGwUKKvHUFRz8fLAziylmx5Fsaex8y1Dt23Hc+OALUm9kTfAZMnMVp5u+yZmWb3F9yFziD54uVIMEQNzJazhV9caxkhfCVo9371ZEbDv+6AWBtHuxpNyNxMlUNyndtl6hJrr8ffkfvN71bV7v+jYHth2k+3PadfyJRnVIiEsk0spN99MvBdKsQ1OmDJmareeCi5szNqaJdp95OZBTh0+TlJC/+S5KWjzmkk5dwa6KL7YVyiFsbXB/ph1xOY4jW18vKn89ntsj5pFmdhxl3Ish/e597KtqE5q6tm5A6pXb+Y4hMkf9qEqvFtzJca29s/0EfmbX2jSza22ZRtXRO2rXWu82dYm7mnXseLetR9zVu5kPcvLjj+V/Zk5OeXDrP3R9Tmt4r9OoNonxiURZ2W+BL3WnafvGTB063aIHjLOrE/VbPMk/2wrZa+P/ESmL/19JpXpKPIIQwheIklKuMr1BYxAQD7gC9wE3IBGINQ3P6AHstbIqCbwO/CqEGCelnCmlnAhkvotHCFFNSnkYOCyEeAatcSI3j9rui8BM0/8PzvbtwFBgtml7DaWUQXnLibw5v+ckTwQ05ON9X5KWnMrPo7NeJ/f2j2NZPXYpcRHRtBvUnU5vP4Orlztjt37O+T1BrBm39CFrzp00GPn3o+V0/XkMQqfjytp9xFwOoVZ/rffGpZW7ubMriAodG9D34FwMyWkc+DBrW4c/Xk77he+is7Uh/lYEf39YsDhyurY7iKoBDXh7/1zSk9PYPCprvc8vG8WWMd+REBHDnhlr6LVoKO1GPU/4uWBOr90LgLNXKQb++Rn2Lo5Io5Emr3fnu85jSSuSbq5ag0Pnru05ErSD5KRk3h8yIfO71b8uZfiwjwgPi8h1+SuXr7N75wH2/bMRo9HITyt+4+KFvL+uTBqMHJu4jA4/j0XodVxfs4+4yyFU798JgKsrd3F3VxA+nRry9D/zMCSncXjEN5nLt/nuA+w9XDGmZ3BswjLSY7VKSOXeLakxSLtQ3tlylOtrcuuY9AhGI8nLFuA8fhbodKTt3YLxTjB2nZ8BIG3nwyvTycsW4DR0IsLGBmN4KEnffF6wOEykwciV8d9Tf81E7ZWgq/eQdOkOvgO0v/Xuih3YebnTePtM9K6OYJRUeOspjrQdgSEhmbrfj8LWwxWZkcHl8d+REZv/ybekwciRj5bT2XSuXV27j9jLIdQ0nWuXV+4mZFcQ5Ts24NmDc8lITuMf0/l0/+Q1bm46wtPbpmLMMBB17iaXTZO4VezehGZTB+Dg6UrHFaOIPneTna/MyjWOXBmMBE/8jto/T0LodUSs2UXy5duU7a9VeCNWbqfCiBew8XDFb4b2dgeZYeBsjzEAVF88AreW9bDxdMX/2LfcmbuGe6sL1qhlLmj3cRoENGLu/sWkmV4J+sCoZRP5bsxiYiKi6TookKffeZZSXu7M2PYFp/ac4Luxi9mw4FfenjuMGdu+ACFYO3NltldhFrXRk2dy9ORpYmLi6NT7Vd57oz99n+n26AUL4dSeE9QPaMTsfV+RmpzKd6O/yvzuwx8n8sNYLY+6DAok8O3elPJyZ+rWeZzec4Ifxi0h9FoIZ/YFMXXrPKRRsm/tTkIu5/1mQBqM7P54OX1WjkHodZxbu4/IyyHUf1U7tk+v2s2N3UFUCWjAawe0Y3u7WZneY+EQKrasg4OHC28eXsChees4t3YfbSf0w+uJytqwszv32TX+h3zly5XdQVQPaMjQ/fNIT05j46isMvClZaP5c8y3JETEsHPGavouGkbAqOcJO3eTk6brSJ0eTanfty3GdAMZqWmsG7IwX9vPlelcq2U61+5ZOdfKm861Kmbn2rkeY3BpVhuv5zuQdD6Yeju011nfnvETsbsf3iCfl5huffwtNX+aDDo9kWt3knL5Nl6vasfuvVXb8BnxIjburlSe/uBNIAYuPDWqcNvNhTQYuTj+RxqtmYDQ6whZvYfES3eoMEDrHXRnxU7svErRYvt0bFwdkUZJ5bd6cLDtKAwJyVyc8CNPLh6Kzs6G5JsRnP2gaF7Pe2jXYVp0bM6agytJSU7J1stg1orpfD56LpHhkYycOZzwO+F8vVE7ZvZv/ptl81dSuUZlJn45FqPBSPDlm8wclb833ZT0eDAYuTvpa6qu+AT0OqJ/2UnqlVt4vtIdgKiftlLu/X7oPdwoP/VdQDuOrvb8EICQKd9Qcf5IhK0NabfDuTNqfr5DkAYjRycup9PPWnl0bY12ra1hutZeMV1rfTs1oNc/Wnl0aIRWHkWevMatTUcI3DYVmWEg6uxNrqzK6olQpVeLwg/dAA7vPkLzjs1Y+fcyUlJSmf1hVr5PXzGVuaPnERkexfAZHxB+J5yFf3wJwN9b/mbl/J8AaNO9Ncf3nSAlOaXQ8Sj//4n/tsmyipoQohvaTbwRSAfeBVoCQ4BQKWWAEGIZ0By4jtarYaOUcpkQYi8wSkp5TAiRIKV0EULYAX8Cf0gpF+fY1nqgBtpIxF1ob/kYCDSRUg41pfkLmCOl3PuQ7QYDPwKBaL0jXpJSXhVClAG+AuqgNUjtN/XOyFVehm/8JzVML3ntaOElLKS5MQV/xdPjssC5yaMT/Qf1aF/wV6k+LkG7Cz4x1+Nwy6ZoJlUrSjVl0cw+X1QW2RZ3BJZ+PF7ICnkRe7PJ6OIOwUJDY8mbjyNelKhLLd3Si6bxuyjpdcZHJ/oPikov3OTFj8NnNkXzVrP/Zl/ZlqyX551OK1XcIVj4UZf7A6nisuvO9pI7GUI+nK7yTLEX9vWD/yyReVnCbqdKHinlNmBbjo+PAQvN0gzKZdkOZj+7mP5Pw2wIR470fax8vMz070Gapx+1XZOvpJTZBm9JKe+j9ZxQFEVRFEVRFEVR/kOMJXiiyeKm5pRQFEVRFEVRFEVRFKVYqJ4S/4WklFWKOwZFURRFURRFURRFU5JfyVncVE8JRVEURVEURVEURVGKhWqUUBRFURRFURRFURSlWKjhG4qiKIqiKIqiKIryGKmXXuZO9ZRQFEVRFEVRFEVRFKVYqEYJRVEURVEURVEURVGKhRq+oSiKoiiKoiiKoiiPkVG9fSNXqlFCeahtSdeKO4RsatjXKu4QLNwUacUdQjaHK1Uu7hAsxMYmFHcI2bz5j2txh2DhtRJWHAf4hhZ3CBZ63k4p7hCy+c3VpbhDsPBmk9HFHUI23x2bXdwhWJjZ+OPiDsFCtfSSVVGNNtgVdwgWdIbijiA7Y3EHYIWLrmTtt48znIo7BAuN7v5b3CFk09SrZnGHYOFZffniDkH5H1SyasGKoiiKoiiKoiiK8l9Gqp4SuVJzSiiKoiiKoiiKoiiKUixUo4SiKIqiKIqiKIqiKMVCDd9QFEVRFEVRFEVRlMdITXSZO9VTQlEURVEURVEURVGUYqF6SiiKoiiKoiiKoijKYySLO4ASTPWUUBRFURRFURRFURSlWKhGCUVRFEVRFEVRFEVRioUavqEoiqIoiqIoiqIoj5Ga6DJ3qqeEoiiKoiiKoiiKoijFQvWUUIrExGkjade5NSnJKYwf9gnnz1yySDP1i4+o17AOQgiCr91i/PufkJSYjIurM7MXf4ZPhXLo9Tb8uHgV69f8WeBYKnWoT9sp/RF6HedX7+XEYst1tf2kP5U7NiQjOZVdHy7l3tlg9Pa29PntI/R2Ngi9nmubj3Bk3voCx5HTC5Nfo26AP2nJqawYtZjb525YpGk/oBsdX3+KslW8GeX/BonR8QDU79KEZz58ESklxgwDv366jGvHLPM4rxxbN6H02HcReh1x67cS+/3abN87BbTEY+hAMEowGLj/+RJST55DX86LstNHoy/jCUYjcb9tJu6nDQWOw5xre38qTBmM0OuIXLOD8MXrsn3v0bs95d7tA4AhMYU7E5eQfCE4K4FOR62/5pIeHsn116YWSUwAr08ZjH9AE9KSU1k0aj43zl63SPPBlx9S9cnqGDIMXD11hW/Gf4Uhw4BvtfIMmfMBVetWY/WclWxcuqFQsXgFNOCJqQMQeh23f9rDtYUbs33vXN2XBl++jduTflyesZbrSzZlX4FO0Gb7dFLCojj26uxCxfKAQ8umuI8cAjodiX9sJn75muzft2tFqXdeA2mEDAPR8xaTduosAD5//IQxKQmM2nfhA98rkpjGTB1O604tSUlOYfIH07h45rJFmsnzxvFEg9ogBLeu32bS+9NITkrO/P6JhrVZsWkp496exM6/9hYqHsfWTSgz7h2EXk/cui3EfP9Ltu+dAlriOWwAGCXSYCBy5teknDyH3tuLctNHoy/jAUZJ3G+biV21oVCxPPDK5NdpENCItOQ0vh21kJtWyqPOA3rQ9fWnKFfFhyH+g0gwlUcAtVvU5eVJr2FjY0N8dBwzXpxUJHFZ89H0eew/eARPD3c2rPr6sW0HoNuUAVQPaEB6chobR31D2NlgizTuFb3os3AoDu4uhJ0NZsOIxRjTDdTr3YpW7zwDQFpSClsm/kj4hVuUrupDn0XDMpf3qFSWvfN+48gPWx8Zj0+H+jT5rD9Cp+Pq6r2cX2R5PWv8WX/Km65nh0YsJfqMFnPtwd2p9nIHkJKYi3c4NGIpxtR02nw9FNdqPgDYuTmRFpfEli4T851XZQIaUGfqQNDruPPTbm5YKY+e/PKdzPIoeMlfmd/ZuDlRb97buNSuABLOjviamGNX8h1DTqUDGlB76kCEKabgHDE5VfelnimmKzPWcjNHTHVNMUkJ50Z8TWwhYyqJeQTwzifv0LRjU1KTU5n74Vyunb1mkWbMgjHUqF+DjIwMLgddZsG4BRgyDDzZ4kkmfz+ZsNthAPyz5R9+/vLnAsfiHtAQv09fB72OiJ93EbLo92zfl+nTlvJDngXAkJjM9XFLSTp/E4Bq897Ds0sT0u/HEhQwosAxWPPFvE/p0b0jScnJvPHGCE4GnbVI8/13X9CubQti47Sy8Y03R3Dq1LnM75s0bsDBv//kpVfeZf36TRbL58eIT4fRqmNzUpJT+GzE51w+a3ksTJgzmtoNaiGAWzfuMHX4TJKTUqhcrSITvxhLrXo1+Obz7/n5m18sN/AQnab0p2pAQ9KTU9kyainhVsrFUhW9eGbhEBzdXQg/G8xfI5ZgTDfkurxnVR+eWTQ0c3n3SmX5e95vHP9hGz0XDcWjqlZGObg5kRKXlK94SzL5/6CnhBCiO/AloAe+k1LOzPH9K8BY068JwLtSylOF3a5qlHjMhBDDgaVSyoeeUUKIvcAoKeUxIUSClNLlMcc1CNgupbxb2HW169SKylUr0a15Hxo0rsfkWeN4scdrFulmfPwFiQmJAIz7dDivvP4C3y5cziuvP8/Vy9d5t/+HeJR2Z8s/v/Hnui2kp2fkOxahE7SfOpA/Xp5JQmgUL/z1KTd2HCf6StafWTmgAe5+3qxqO5Jy/tVoP30Qv/WcgiE1nQ0vTic9KRWdjZ4+6z/m5p5ThJ+0vFjnV90O/pT182Zyh/fx86/BS9PeZFZvy0rgteOXOLP7BB+umZzt80sHz3B6xzEAyteuxJtfjeCTTgW8AOt0lJk4lNC3xpERdp/yaxaStOcQ6ddvZSZJ/vckSXsOAWBX04+ycz7iTs83wGAgcs5S0i5cRTg5Un7tVyQfOpFt2YLGVHHq21x9ZTLpoZHU+nMOsTuOkHLldmaStNvhXHlhAobYRNw6NKLizCFc7jU683uv158m5ept9K5OhYvFjH9AY3z8fBnW/m1q+NfiranvMr73aIt0+zfs48sP5gEwfMEoOvXryvZVW0iISeCHyUtp1q1F4YPRCerOfI3DL0wn5W4kbbZNI3zbcRIuh2QmSY9J4NzE5Xj3aGJ1FX6De5BwJQQbV8fCxwOg0+Ex5n0iho7BEH6PcssXk7z/EBk3bmYmST16gvD9/wBgW70qpWd8TNjzWeXDvXdGYoyNK5p4gDadWlKpagV6tXyRJxvVZcLnoxgQ+JZFujmTFpCYoBXLI6cMo9/rfflx0SrTn6Xjg4/e49DeI4UPSKfD66Mh3B08noyw+1RYu5DEPf9anG93zM63cnMmcrvnm5Bh4P7srPOtwi+LSPqn8Odb/Q6N8PbzYUyHoVTzr8HAaW/xae/xFukuH79I0O5jjFvzabbPndycGPDZYOYMnErU3fu4lnYrVDyP0juwCy/37cmEz+Y81u1UD2iAp583X7UfSXn/6gROfY0fek+2SNdpXD8Of7+Fc3/+S+C01/F/sQPHV+0i5vY9VrzwGSlxSVTr0ICnZrzBD70nE3k9lG8DJwDaNWr44UVc2nbskfEInaDp9IHs7jeTpNAoum/+lDvbjhNndj3z7dgANz9vNrYeSelG1Wg2YxDbnp6Co7cHtd7oyl8dxmJISafN18Oo0qsF1385wN/vLMpcvtGkl0mLL0CFXyd4YubrHH1hGil3I2m5bToR246TmKM8Oj9xGeV6NLVYvM7UgdzfE0TQm18gbPXoHe3zH4OVmOrMfJ3jpphabJvOvRwxZcQkcHHiMspaiam2KaZTRRVTScwjoGlAU3z9fHmj7RvU9q/N0OlDGdHTsj6x5/c9zHp/FgBjF42l+0vd2bRSu7E+e+QsU16bUvhgdDqqTh/MuRc/JS00kvpbPidq+1GSL9/JTJJ6K4KzfT7GEJuIe0d/qs1+hzNPaeXVvV/2EvbjFmoseL/wsZjp0b0jNar7UfuJNjRv1oivFs2gVZtnrKYdO36q1QYHnU7HjOkT2b59b6HjadmxORX9yvN8m1ep26gOY2aM4M1nLBvx50/5iiTTde39ye/x3GvPsvKr1cTFxPPFxwtp171NvrddNaABHn7efNt+JD7+1egydRCrek+xSNd+XD+Ofb+Vi3/+S9dpr1H/xQ4ErdqV6/JR10NZHqjVg4VO8O7hhVwxlYsbh2aVUQEfvUxqXBLl6lbOd+xK/gkh9MBXQBfgDnBUCLFRSnneLNkNoL2UMloI0QNYCjQv7LbV8I3HbzhQdHdJVgghCtK4NAjwLYrtd+rRnj9+0QrkU8fP4lbKFa+ypS3SPWiQALB3sEeaXowjJTi7OAPg5OxEbEwcGRmGAsVSrmE1YoPDibt1D2O6gSsb/6Vq18bZ0vh1bczFdX8DEH7yGvZuzjiVdQcgPSkVAJ2NHp2NTZG9u6dB1yb8u34/ADdOXsHJ1Rk3L3eLdHfOBRN1557F56mmuADsnOyRsuCB2T9Zi/Rbd8m4EwYZGSRu2YdzQKtsaWRySubPwtFB20mA4X4UaReuammSkkm/cQubcmUKHMsDTg1rkBocRtqtcGR6BtF/HqBU12bZ0iQev4ghVjuGEk9ewtYn6xiz9S5NqU5NiFyzo9CxmGvapTl71+0B4MrJSzi5OeNe1sMi3ck9xzN/vnrqMqVNscVFxnLt9FUM6QU7ns25N6pO0o0wkm9GINMN3N1wiHLdszc+pN2PIzboeubTCXMOPp6U7eLP7Z/2FDqWB+zq1ib9dgiGkFDIyCBpxx4c2+ftWHpc2ndrw1+/aE+gz5w4h6ubK2WslkdZN2L2jlnlEUC/N55j16a9RN2PLnQ8Oc+3hC17ce7YMlsaizwil/Pt+u0iOd8adW3KwfX7ALhmKo9KWSmPbp27wX0r5VGLnm05vvUwUXfvAxAfWXSNStY0afgkpdxcH+s2AGp2aczpdQcACDl5FQc3J1xM1wZzVVrV5fxmrcHq1Lr91OqqnYd3jl/JfKIXcuIKrj6eFsv6ta5H9K0IYkPuPzKe0v7ViA8OJ8F0Pbv5x79U7Jb9elahW2Ou/6ZdzyJPXMOulDMOppiFjR69gx1Cr8PG0Y6kcMvjuVLP5tzccOiRseSUszwK2/CP1fIoLug6Mkd5pHdxxKNlHe6YyiKZbiCjCJ6ElrISU9l8xhRShDGVxDwCaNG1BbvW7QLg4smLuLi54GHlunZ0z9HMny8FXaKMT+HLnpxc/KuTHBxGqunaf/+Pv/Hslr2BJv7Ypcxrf/zxy9iZXfvj/j1PRnRCkcf1zDPdWPnTbwAcPnKCUu6l8PYum691DB3yOut/30TEvchCx9OuW2u2/LYdgHMnLuBSypnSZS3LlyTz65qDXWZdMToyhgunLpFRgId91bs05pypzhx68hoObs44WykXK7V6gkumcvHsugPUMNW987J85dZ1ibkVQVyIZV7Veqo5Fzbmv4xSCqwZcFVKeV1KmQasAXqZJ5BS/iOlfHBB+ReoUBQbVo0SRUgI4SyE2CSEOCWEOCuEmIx2479HCLHHlGaJEOKYEOKcEOKTR6yvjBDikBDiKSvfLRNCzDOt93MhREMhxL9CiNNCiN+FEB6mdBafCyGeA5oAPwkhgoQQhXpsWs7bi9C74Zm/h92NoJyP9cJ7+peT+PvcVqpWr8Kq77QhAz99/wvValRh/5ktbNy3mukT5xb4ptvZ24P4u1GZvyeERuHsnf1i6+LtQcLdyGxpXExphE7w4tZpvB60mNsHzhAeVPheEgDu5TyJvptVCY0Oi8Td2/KC8jANujVl8q4vGPLDeFaOWVLgWGzKliEjLOtGIyP8HvpyljdtTh1bU2Hj93h/9Rn3Js21XI9vOexrVyfl9MUCx/KAnXdp0szyJy00ElsrMT1Q+sUuxO05kfl7+SlvEjJ9uTbcpAiV9i5N5N2svIoKi6T0Q+LS2+hp1yeAoL0nck1TUA7eHiSbHbcpdyNx8LasSObmic8GcOHTn5FGY5HFpPcqgyE8K38M4ffQe1lWXB07tMb71x8p88U0osyfdkuJ16JZlFuxBOdnLYq5Ainr40XY3YjM38NDIyjr42U17ZT5E9h55k+qVK/Mmu+1CqiXdxk6Brbjt+UbiiQem7Klc5xv97Epa5lHzp1aUXHjd/gs/oyIj+dZrse3HHZ1qhXJ+eZRzpNIs/MtKiwSD+/cj+ucvKv64lTKmXFrPuGTP2fRuk/7QsdUErh6exJndo7FhUXhWi77Oebo4UJKXCLSoJ1H8aFRuFo5Dxv268C1vZa9Wev2bMHZjf/kKR5Hbw+SzK5nSaFROPpk35aTtwdJZjEn3Y3CyduD5LBoLizZTO+jX9InaBFp8UmE7cve/bxs81qk3Isl/kY4+WXv7ZmjPIrCPo/XNKfKZUmLjOPJL9+l1c4Z1J33FnqnwvcCcPD2JKWQMdX98l1a7JzBE0UQU0nMI9Cua/fNzv/7ofcp4517g4PeRk+nPp04tjerd0+dxnX4attXfLriUyrVrFTgWOy9PUkLMb/2R2H3kLKo3EudiNl9ssDby6vyvt7cuZ3VIynkTijlfb2tpv3s07GcOL6DubOnYGdnB4Cvrze9e3Xnm6UriyQeL+8yhJtd1+6F3scrl302cd4YNgWto3L1Svz6w+9W0+SHq7dHtnIxPpdyMTUuKVu5+KBenZfla/dsabXhoUKzWiTdjyU6OP9lVEllLAH/hBBvme5FH/wz705aHrht9vsd02e5eQPYUoCssKAaJYpWd+CulLKBlLIeMB+4CwRIKQNMaSZKKZsA9YH2Qoj61lYkhCgHbAImSSlzG4hWE+gspRwJrADGSinrA2eAB31OLT6XUv4GHANekVI2lFImm6/U/GCNSbZ8SmYlWIuPcmtUmPDBp7R7MpBrV4IJ7NUVgDYBLbhw9jLtnuzBsx1f4eMZozN7TuSblVgsejs8JF5plKztPpFlzd6nXMNqeNYqksa/XOLK3w30qW1H+aTTCL5+azY9P3yxELFY+cxKLEm7D3Kn5xuEf/CJNr+E+SocHSj3xSTuf74EmVgET2/yGBOAS8snKf1iZ+7OWA6AW6cmZNyPIflM0TQgPSquhzWYDZ76DucPn+PC0fO5pil4LAUfh1i2i7/2RO605bwBhZLH/Za89yBhz79G5OhJlHpnUObn4W9+QHj/d7j3wXhcnuuFvf+ThQ8pH+XRlOHT6dqgFzeuBNO1VycARn/2AV9+tgRjUTXe5PHcT9z1D7d7vknY+1PwtHK+eX/xMZGff11E51vhyiO9Xk+VJ6sx77XpzB7wGT2HPU85P5/Cx1XMrGeLzJHm0deYyi2fwP/FDuyakX1+FZ2tnpqdG3Nh0+E8xlOw6xlSYlfKiQrdGvFH8xGs9x+GjZM9Vfq0zh5n75YEF6CXhLZdax/m7RgSNnrcnvTj1vId/NN5PIakVPyG9Xr0go85Jtcn/bizfAf/mmKqUtiYSmIekb8yEmDItCGcPXyWc0e0uRKunb3GwBYDGdJtCH/++CeTvivEfDL5KIvcWtWj7MuduDmtaG70HyaveTTxoxnUrdeOFi2fwsPTnTGjtSEV8+Z+wvgJ0x/rdSS3XTbtw1k80+h5gq/conPPAOuJCr1t+cg0mYf6I5bX2eqp3rkRl6yUi3VyaaxQCkdKuVRK2cTs31Kzr63W7KytRwgRgNYoMdba9/ml5pQoWmeAOUKIz4G/pJQHrBRsL5hapGwAH+AJ4HSONLbALmCIlHLfQ7b3q5TSIIQoBbibpV0O/Jrb54/6I0wH51KA2mWbWj0QX379eZ5/tTcAZ06ex8e3XOZ33r5liQjLvTHDaDSyZcMO3hjyKuvX/MmzLz3Dtwu0G8xbN+5w59ZdqtaozJmT+b+xSwyNwtU360mEi48niTm6rCaERuHiWzpHmphsadLikgg5dIHKHeoTdekOBdG+fzdav6Td6Nw8dQ0P3zKANjmlh3dpYqx0pc2Lq0cuUKayN84erpkTYeZHRvh9bLyznhzblPPCEBGVa/qU42ewreCLzt0NY0wc2Ogp98UkEjbtJmnXwQL9DTmlhUZi55vV6m/nU5p0KzE51K5MpVlDuDbgUwwx2t/u3KQOpbo0wy2gMTp7O/SuTlSeP4Kbw78oUCzdBwTSqZ/WYHbt9BVK+3oBFwDw9C5NVC559fwH/XDzLMU342cUaLuPkhIahaPZcevgW5qUsLwdQx7NalG2WyMCOjVE52CLrYsjDb8aQtCQrwoVkyHiPvpyWceSvpwXhvu5d1VNPXkGm/K+6Eq5YYyNw2hKa4yOIXnv39jVrU3qyTP5juOF1/rQ55WeAJwLuoC3b1ZPrXI+ZbkXlntXeaPRyPY/djHgvZfZuGYzTzSozcxvtE5s7p6laNOpJRkZBvZuPZDvuMDa+VaGjId05005fhbbij7Zzjfv+R8Tv2k3iTsLfr516t+d9i91BuDGqauU9i3Dg2nSPL1LEx2eexmQU1RYJPHRcaQlp5KWnMqlI+epVKcK4TdCCxxfcWkyoAv+/bSK+93T13EzO8fcvD1JiIjJlj4pKh4HN2eEXoc0GHH18STerCwvW7siT3/+JqsHziI5Jnu38uodGhJ6NpjE+3kb7pIUGoWT2fXMyceT5BznvJYmK2YnX0+SwmPwbluPhNv3SI3Sysnbm4/h1aQGweu1Y0jodVQMbMqW7h/nKZacUi3KI09S81gepdyNJPVuFLEntKFJ4X8exm9YzwLFkW29oVE4lKCYSlIePT3wabq/1B2Ay6cuU8bselvGpwyR4dbLpJeHv0yp0qWYOi5r8mjzIQJH9xxlyLQhuHm4ERed/2FcqaGR2JU3v/Z7kmalLHKqU5nqc9/l/CtTH8twDYB33xnIG2+8AsCxY0FUqJg1wrl8BR/uhlo+rQ8L03ovpKWlsXz5Wj4c8Q4AjRvV56dViwEoU8aTHt07kpGRwcaN2/IcT9+Bven5itaD8ELQRcqZXde8fMpwP/zh17VdG/fwyrsvsumXR0+om5P/gM7UN5WLYaZy8cFMKK5WysXkqHjs3ZyylYsJpnIxPjTqoctX7dCA8LPBJOUoF4VeR83uTVn+dMHKqJJKWm+tLEnuABXNfq+A9oA9G9ND9e+AHlLKwo9RQvWUKFJSystAY7TGiRlCiGzNx0IIP2AU0MnUc2ET4GBlVRnAcaCb2bLTTEMtgszSJeZc8D/l5x9+5dmOr/Bsx1fYtWUvvV7QCs4GjesRH5fAvQjL47OSX1avg4Bubbl+VZsMLzQkjJbttDGEpb088atemds3QyyWz4vwU9cpVcUb14pe6Gz11OjZghs7snelv7HjBLX7apP9lPOvRlp8EkkRMTh4umLnpk3/oXewpWLbekRfLfg8oPtWbmN64BimB47h1PYjtOjTDgA//xokxycRdy8mz+vyqpzV6FOxrh82tjYFapAASD17CdvK5bEp7w02Njj3aE/i3uwt0TZmF2O7OtURtjbaDRLg9cmHpF+/ReyK7G/HKIykU1ew9/PBrmJZhK0NHs+0JXZH9kkGbX3LUHXpeG4On0/qjaz9Evr5Ss41f4Pzrd8ieOgc4v85XeAGCYCtKzYzOnA4owOHc2T7YTr01S7MNfxrkRSfREyEZaWyU78uNGzvz/xhcwo138fDxJ68hnNVbxwreSFs9fj2bkn4tuOPXhC4NG0Nu/2Hsqfp+5x8ewH3D54rdIMEQNr5i9hWKo/eVzuWnLoEkLw/e9d0mwpZx5JtrRpga4sxNg7h4IBw0kaOCQcHHFo0If1acIHi+OXH9fTrPIh+nQexZ+t+nn5Bq3w/2aguCfEJ3LdSHlWsktUbsV3X1gSbyqOnmz3PU02f46mmz7Hzr73MGDenwA0SYDrfKpXHpnw5sLHBpUcHEvf8my3Nw863sp9+SNr128SuKNybgHat3MqkwFFMChzFie1HModcVDOVR7H5KI9ObD9CzaZ10Ol12DnYUa1hDe5eLVjjbXE7tmIH3wZO4NvACVzafoz6fdsCUN6/OinxyRaVb4DgQ+d5IlCb86ZB33Zc2qGdh26+pXn+m+H8MWIJUTfCLJar17Ml5/I4dAMgMug6rn7eOJuuZ5V7teDO9uzXszvbT1D1Oe16VrpRNdLikkiJiCExJJIyjaqjd9S6k3u3qUvs1azrqnfbesRdvUtyaN4bo8zFnryGk1l55N27FRF5LI/S7sWSfDcSZ9MbQEq3rZdt8seCiitkTCl3I3EqwphKUh79tfwvhnYfytDuQzm07RCd+moPTGr71yYxPpFoK9e1bv260bh9Yz4f+nm265qHV1bX+5oNayJ0okANEgAJQVdx9PPB3nTtL9OrDVE5JoG1K1+GWt+P5sqwBaRcf3wNn0u+Xk6Tpl1p0rQrGzduo/8rzwHQvFkj4mLjMhsgzJnPM9GzZ3fOndeG19Wo1ZLqNVtQvWYL1q3fxND3J+SrQQJg3fINDOw6mIFdB7N/20F6PKc9LKnbqA6JcYlEWnlAUqFK1rWkTZeW3LxasEmRT67YyfLAiSwPnMiV7cepa6oz+/hXIzU+iUQr5eKtQ+epZSoX6/VtyxVT3fvqzhMPXT633hBV2tQj6tpdEsIKVkYpBXYUqCGE8BNC2AH9gGyvDRJCVALWA/1N975FQvWUKEJCCF8gSkq5SgiRgDaZZDzgCtwH3NAaEmJNwzN6AHutrEoCr6P1dhgnpZwppZwIWH1nl5QyVggRLYRoK6U8APQH9uX2uWmxB3EV2r6dB2nXuTXbj/xOSlIKEz7Imqn9m5/n8/GIqdyLiGTmwim4uDiDEFw6f4Upo7U3zCyZ+z0zFk5m497VIARzPltETFRsgWKRBiP7P15Or1VjtFeCrt1H1OUQ6r7aEYBzq3Zzc3cQlTs2oP/fc8lITmPXSK3XknNZdzp/8TZCr0PoBFf/PEzwrqDCZY7J2T0nqRfQiE/3LSAtOY0Voxdnfjfkx3GsGvsNsRHRBAzqQZe3e+Lm5c5HW2dzbs9JVo37Bv8eLWjepx2GDAPpKWl8N7TgN90YjNyfvgjvr6cj9Drif99G+rWbuD6vNSzF/7oJ5y5tcH2mMzLDgExNJXz0NADs/evi2rMLqZevU/5XbV6LqAU/kHzgaK6by2tMdz5eSrWVU7RXgq7dRcrl25R+Vbu5jFy1Fe8P+qH3cKXC1Lczl7n09MjCbfcRTuw+RqOAxiza/w2pyaksHrUg87sJyyaxZMwioiOieGvae9wLiWDa79pM5Ye3HuK3BWtx93Ln8z/n4ejihDQaeer1ngzvPITkhOTcNpkraTBydvwymq0Zr73ubvVeEi7dodIA7en3rRU7sfcqRevt07S3axglVd7qwf62o8kowPbyxGAketZCvBZ8jtDrSNi4hYzrN3Hu8zQAiev/wrFjO5yf6oLMyECmpBE54TMAdKU9KDNL65EgbPQkbt1FyqFCHkfA3zsP0aZTSzb++wspySlMGT4987uFP83h0w9ncj8ikk8XfISzqzNCCC6fu8r0sUXzilQLBiP3p3+Fzzfa+Rb3+3bSr93EzdSQG/fLJly6tMG1Z2dTHqUSPkqL2cG/Lq49O5N6+ToVftPKjKgvfySpkOfbqT0nqB/QiNn7viI1OZXvRmc1UH3440R+GLuYmIhougwKJPDt3pTycmfq1nmc3nOCH8YtIfRaCGf2BTF16zykUbJv7U5CLt9+yBYLZ/TkmRw9eZqYmDg69X6V997oT99nuj16wXy6ujuI6gENGbJ/HhmmV4I+0G/ZaP4a8y0JETHsmrGaPouG0WHU84Sdu0nQ2r0AtPvgWRw9XOnxmfZ2GaPBwPfPaE/5bBzs8Gtbj00Tvs9zPNJg5NjE5XT8WbueXVuzj9jLIdTor13Prqzczd1dQZTv1ICe/8zFkJzGoRHa9Szy5DVubTpCj21TkRkGos/e5OqqrEluK/dqUaAJLs1jOz/+R5qsmWAqj/aQcOkOFU3l0e0VO7HzKkWr7dOxcXVEmsqjA21HYUhI5sKEH6m/eCg6OxuSbkZw5oPCv+pVGoxcHP8jjUwxhazeQ+KlO1QwxXTHFFMLs5gqv9WDg6aYLk74kSdNMSXfjOBsIWMqiXkEcHT3UZp2bMoPf/9ASnIKX4zMqk98uvxT5o+ZT1R4FMNmDCMiJIJ5G7Q5bh68+rNNYBue6v8UBoOBtJQ0Zg6ZmdumHs1g5PqE73hi9ccIvY7wNbtJvnybcgO0G/DwFdupOOJ5bD1cqTpjMADSYOB0d623eI3FIyjVqi42nq40Pr6U23PWErF6V8HjMdm8ZRfdu3fk0oWDJCUn8+abH2Z+9+cfK3jrndGEhoazcvkiynh5IoTg1KlzvDdkXKG3bc0/u/6lVcfm/HpwFanJqUz98PPM7+aumMGM0XOIjIji4/njcXZxAiG4ev4as8Zr+9bTy4Mft3yDs4sTRqPkxcHP8VKHQdl6veTm+u4gqgY0YPB+rc68ZVRWT/++y0axbcx3JETEsG/GGnouGkrbUc8Tfi6YM6Zy8WHL2zjYUaVtPbZN+MFiu7WfaaGGbhQDKWWGEGIosA3tlaA/SCnPCSHeMX3/NTAJKA0sNo0IyDBNTVAo4nE91ftfJIToBsxGm0ckHXgXaAkMAUKllAFCiGVor025DqQCG6WUy6y9EtTUQvUn8IeUcnGObS1DGyLym+n3hsDXaG/6uA68ZnpVS26f9wWmA8lAy5zzSjyQ2/CN4jLUvlZxh2DhnD6tuEPIZrRrTHGHYCE2toheQVlEpokSdVgD8FpKAedReUzqV7J8KlTcet5OeXSi/6DfyjzWNzcXyKcJJSum7449psaeQpjZuOR1B66aXrK69JY25H+W/setpHXtLbopg4vOl3YFe6DzuHyc8VhfPlcg7SL/fXSi/6CmXjWLOwQLz9o8bF7D4jHm5qqSVUgW0N5yzxd7BbRD+K8lMi9VT4kiJKXchtayZO4YsNAszaBclu1g9rOL6f80zIZw5Eg/KMfvQUALK+ly+3wdUHT97xVFURRFURRFURQln0paw7OiKIqiKIqiKIqiKP8jVE8JRVEURVEURVEURXmMjCX/7RvFRvWUUBRFURRFURRFURSlWKieEoqiKIqiKIqiKIryGEnVUyJXqqeEoiiKoiiKoiiKoijFQjVKKIqiKIqiKIqiKIpSLNTwDUVRFEVRFEVRFEV5jIzFHUAJpnpKKIqiKIqiKIqiKIpSLFRPCUVRFEVRFEVRFEV5jNREl7lTjRLKQznp7Ys7hGyidbK4Q7DQJs2uuEPI5vv4MsUdgoXRrUOLO4RsXt7jU9whWNjlWLI69X0f4VzcIVh4z6Zk7bf7UUnFHYKFhjaOxR1CNjMbf1zcIVgYd/yz4g7BQvzg14o7hGwOHCpf3CFYsJEl6/q/y7FkxQPweqpncYeQTbXad4o7BAs/XexQ3CFkk1oCb1IviZJVH1H+N6jhG4qiKIqiKIqiKIqiFAvVU0JRFEVRFEVRFEVRHiPVByV3qqeEoiiKoiiKoiiKoijFQvWUUBRFURRFURRFUZTHSPWUyJ3qKaEoiqIoiqIoiqIoSrFQjRKKoiiKoiiKoiiKohQLNXxDURRFURRFURRFUR4jWQJfAVtSqJ4SiqIoiqIoiqIoiqIUC9VTQlEURVEURVEURVEeI6PqKJEr1VNCURRFURRFURRFUZRioXpKKEVi9Gcf0KZTS1KSU5g8fDoXz1y2SDNp7jieaFAbIeDm9dtM/mA6yUnJtO/WhvfGvInRKDEYDMyZtICgI6cLFU+3KQOoHtCA9OQ0No76hrCzwRZp3Ct60WfhUBzcXQg7G8yGEYsxphuo2aUxHUY+hzRKjAYD2z9Zye1jln/Po/h0qE+Tz/ojdDqurt7L+UV/WqRp/Fl/yndsSEZyKodGLCX6jBZn7cHdqfZyB5CSmIt3ODRiKcbUdNyfqESzma9h6+xAwp17HByyhIyE5DzH9NTkAdQKaEh6chrrRn3N3XOW+eJRwYsXFw3DsZQLd8/d4LcRizGkGwDwa1GHpyb1R2djQ1J0PN+9+BkAfWa9Ra2O/iRGxrGg29h85xWATYOmOA4YCjo9aXs2kbpxtdV0+qq1cPnsK5K+/JT0I/sBEE7OOL41Gn0FP0CS9M0sDFfOFygOc+UC6lP/swEIvY7gn/ZwOcc+dKnuS+P5b+P+ZBXOz/yFK0s2AeDo60mThe9i7+UOUnJj5W6ufbe1wHE8O3kgdQL8SUtOZfWoJYRY2W+eFbzov+gDnEo5c+dcMD+PWIQh3YCDqyOvfDEUj/Jl0Ol17Pn2L47+ug+Atq/1oEW/jggB/67Zzf4fthQovjemDKZRQBNSk1NZNGo+189et0gz/MsPqfZkdQwZBq6cusLX47/CkGGgfLXyDJ3zAVXrVuPnOSv5Y+mGfG+/Qof6tPykP0Kv49LqvZz6yvJca/lpfyqazrV9I5YSaSoT+h36gvTEFKTBiDHDwIanJmUuU/e1LjwxqCsyw8Ct3UEcmbYm37EBlOrgT+XPXkfodESs3knoot+zfV/62Xb4DukNgCEpheBxS0k6H4ydb2mqffk+tmU9kEYjEat2EP79pgLFULl9fTpM6Y9Or+Psmr0cXWyZRx0+6Y9fQEPSk1PZPnIpEaY86jJ7MFU7NSQpMo6VXcZnpm8xog9PvtSBpMh4AA7O+oXgPafyFVdhyup6vVvR6p1nAEhLSmHLxB8Jv3CL0lV96LNoWObyHpXKsnfebxz5oeDnYE4fTZ/H/oNH8PRwZ8Oqr4tsvQ9j698MpzeGgU5H6s5NpKz/2Wo6ffXauM1cTMLcT0g/tC/rC50Ot9lLMUbdI2HaeKvL5kfZgPo8+dkA0Ou49dMerlgpH/3nv02pJ6twYeYvXDOVjzp7W9psmITOzgZho+fuX4e5NHtdoeMB8ApowBNTtTL79k97uLZwY7bvnav70uDLt3F70o/LM9ZyfUmO80knaLN9OilhURx7dXaB4yhMme3o5ky/2W9TulI5MlLTWTPma8Iu3wHgxVlv80THRiRExjG72+gCxVYuoD7+n2rl5fWf93Ipx35zre5D0y+069rZmb9w+evNmd81mTcYny7+pN6PY3vAuAJtPye7ps1wHToM9DqSN20iaXX249q+dWucX3sDpBEMBuIXLSL97BkAHPv2xempp0EIkv/6i6R1vxVJTOa8c+TXRSv51eyLt/F4sgpnZv7CJbP8Kiq+HerT9NOs+uRZK9e4pp9q9UlDcioHRywlylSW1hncnRovdUCa6pMHP9TqkwVVmHqkX4s6vLp0JNF3IgA4t/UoexZo18NWb/SgyYsBICVhl26zfvQ3ZBQiTuX/nwL1lBBCDBdCOOUh3V4hRBPTzwkF2VY+42orhDgnhAgSQjg+7u3l2Hbm31ochBCfCiE6F8e2W3dsQaWqFenVqh9TR89m/MxRVtPNnbyAfp0H8WKnQYSFhPPi630BOHLgOC92GsRLXV7jkxEz+HhuwW5qH6ge0ABPP2++aj+STeO/J3Dqa1bTdRrXj8Pfb2Fxh5GkxCbi/2IHAG4cPMvS7uP5NnACf45eytOfD853DEInaDp9IHtemcVfHcZQpVcL3Gr4Zkvj27EBbn7ebGw9ksNjvqfZjEEAOHp7UOuNrmzt8TGbOo5H6HRU6dUCgBZz3iRo+lo2dRrP7S3HeOLdp/IcU80ODSnj5828Dh+yYcJ39Jz2utV03ca9xMHvt/BFwIekxCbS+MUAABzcnOj52WusfHMuC7qOYfV7X2Yuc+K3/Swf+Hl+sig7ocPxtQ9I/Hwc8aMGYdeqE7ryla2mc3j5LTJOHc32sePAYWScOkL8qIHEj30TY8jNgsfygE7QYMZrHHx5FjvajabCs61wrVk+W5L0mAROf7Q8szHiAZlh5MyUn9jZbjR7AydR9bUuFsvmVZ0ODSnj58P0DsP5dcK3PDftTavpnh73Mvu+38SMgBEkxybQ/MWOALTu343wqyHM6TGWr/p9Sq+J/dHb6vGuWYEW/Toyv9dE5vQYyxMdG1Gmine+42sU0BgfP1+GtH+br8d/xVtT37Wabv+GfQzr+B7Duw7Dzt6Ozv26ApAQk8D3k5fyx7e/W13uUYRO0HrqQLb2n8VvAWOo1qsF7jnOtYodG1DKz5tf2ozk77Hf08Z0rj3w1/PTWN9tYrYGCZ9WdajctTHruoznt07jOF3QiqZOR5Xpg7n0ylROd/iA0r3a4lijQrYkqbfDOd/3Y850/pCQL37Fb9Y7gHYc3fx0Oafbv8+5p8dRblAPi2XzQugEHacOZMPAWSzvNIZaPVvgmSOPqgQ0wL2KNz+2G8nOcd/TcdqgzO/O/7qf3wdYv0E78d1WfuoxkZ96TMx3g0Rhy+qY2/dY8cJnLO0+ngMLNvDUjDcAiLweyreBE/g2cALfPT2R9ORULm07lq/YHqV3YBe+nje1SNf5UDodTm8NJ/6zMcS+PxC7Np3QVbBSRup0OA14m/SgoxZfOTz9HIY7RVA2AugE9We8xqGXZ7G73WjKWykf02ISOPPR8szGiAeMqekc7DuVvZ3Gs7fTeMoFNMCjUfUiianuzNc48vLn7Gs7Ct9nW+Fipcw+N3E5N5b8ZXUVfoN7kHAlpFBhFLbM7jykNyHnbzKnx1h+HrmY3pMHZS5z9Ld9LB04o+DB6QSNpg/iwCuz2Np+DJV6t7Tcb9GJnPxoBZe/tmwADf7lAAdenlXw7VvEo8P1g+HEjBtD5KCBOHTqhL5y9uM67fgJot58najBbxI363PcRmuNMfoqfjg99TSR775D5BtvYNeyJfryBbvO5kboBI2nD2K/Kb8q926JWy75dclKfhVVDM2nDWTXq7PYGDCGKr1bUCpH+V3eVJ/c0GYkh8Z+T3Oz+mTt17uyKfBj/uw0HqHX4WeqTxZEYeuRAMFHL7IocAKLAidkNki4lfOg5aBuLH5mIgu6jUWn0/HkMy0LHGdJZkQU+7+SqqDDN4YDj2yU+E8SQuiBV4A5UsqGUsq8Pz7+f04IoZdSTpJS7iyO7Xfo3pa/ftWeQJ05cQ5XNxfKlC1tkS4xISnzZ3sHe6SUACQnZe0qRycHMH1eUDW7NOb0ugMAhJy8ioObEy5l3S3SVWlVl/ObjwBwat1+anXV2pTSk1Iz09g62QP5j6e0fzXig8NJuHUPY7qBm3/8S8VujbOlqdCtMdd/+xuAyBPXsCvljIMpTmGjR+9gh9DrsHG0Iyk8GgC3aj5E/HsRgLD9Z6n0VNM8x1Sna2NOrtfy5fbJqzi4OuHq5W6RrmqrupzbfBiAE+sO8IQpXxr0bMW5rUeJvRsJQGJkXOYywUcukhRb8HZHffXaGMPuYowIBUMGaYd2Y9uktUU6++7Pkn74ADIuJutDRyf0teuTtsd002jIQCYlFjiWBzz9q5N4I5ykWxHIdAN3NhzCJ8c+TL0fR3TQdWSGIdvnKRExxJh6vWQkphB/JQRHb48CxVGvaxOOrdd6hNw8eRXHXPZb9VZ1OW3ab0fX7ade1wdtpBJ7ZwcA7J0cSIpJwJhhpFz18tw8eYX0lDSMBiPXDl/gyW55P54eaNalOXvX7QHg8slLOLs541HW8m89sed45s9XTl2mtI9WRsRGxnL19NXM3jj55dWwGnHB4cSbzrVrf/xL5a7Z91Plro25YjrXIk5cw87NGUcrZYK5J/p3JuirPzGmZQCQYna854eLf3VSgkNJvRWOTM8g6o+/8ejWLFuahGOXMMRqx2zCicvYmfImPSKapDNarxNjYgopV+9g62NZtj6Kd8NqxASHE2vKo0t//ku1HHlUrWtjLqzT8ijs5DXs3ZxxNuVRyJFLpMQU/XOFwpbVd45fISVOu66EnLiCq4+nxbJ+resRfSuC2JD7RRp7k4ZPUsrNtUjX+TA2NepgDA3BGB4KGRmk/b0bu2ZtLNLZB/Yh7dA+ZGx0ts9FaS9sG7cgdaf1m/H88shRPoZsOIR3jvIx7X4cMUHXMWZYntsG03VWZ6tH2OgLfd0HcG9UnaQbYSTf1GK6u+EQ5bpnf1aUdj+O2KDrGK2UNw4+npTt4s/tn/YUKo7CltnlapTnysGzAERcu4tnBS9cypQC4PqRiyTFFvz65ulfjYTgcBJv3UOmG7j9x7+Uz3ldi4wj+pT1PLr/70XSoouuLLCtXQfD3RAModpxnbJ7N/atsx/XMiWrjigcHDOrZDaVK5N+/jykpoLRQPqpU9i3bVdksYGWX/Gm/DKmG7iVS35F5ZJfRSFnfTLYSn2yYrfGXDNd4+6b6pMPrnG6nPXJsOicm8izwtYjH0an12PrYIdOr8PW0Y748ILHqfz/9MhGCSGEsxBikxDilBDirBBiMuAL7BFC7DGlWSKEOGbqpfDJI9ZXRghxSAhh8YhXCPG8aRunhBD7TZ8NEkIsMkvzlxCig+nnBFMPgcPAeOAFYJIQ4ichhIsQYpcQ4oQQ4owQopfZOgYIIU6btrPS9JmXEGKdEOKo6Z/lHZGWbpLp+7NCiKVCCPMmp1eFEP+YvmtmSu8phNhg2t6/Qoj6QgidECJYCOFutt6rQohy+Ygj2BTL38DzQohlQojnzL77xOxvr232N+4wff6NEOKmEKLMw/ZXXpT1LkP43YjM3yNCI/Dysb7aKV+MZ8fpjVSpXpm1P2R1swvo0Y51B37iy5Wz+WREIZ4CAK7ensSZbpwB4sKicC2X/SbJ0cOFlLhEpMEIQHxoFK5mN421ujXh3V2zeenH0WwcvTTfMTh6e5B0Nyrz96TQKBx9ssfg5O1BklmcSXejcPL2IDksmgtLNtP76Jf0CVpEWnwSYfu0CkrMpdtU6NYIgEpPN8fJ17ISnhu3ch7EmsUUFxaFW44bZScPV1LiEjGa8iUuNBI3U96VruqDYyln3ljzEe/9OY2GfdrmeduPovMogzEy6xgyRt5D55H9GBIeZbBt2pa0ndm74+rL+iDjYnB6ZywuM5biOHgU2DsUOiYHHw+SzfZPcmgUjlZueh7FqWIZ3OtVIerEtQLF4VbOkxizOGLCoijlnT0OZw9XUuKSMvdbbGgUpcppaf5evo1y1csz5cgSRm+bze+fLEdKSeil21RtVgcndxdsHeyoE9AQ9wLc8Hp6l+b+3XuZv0eGReJZLvf16G30dOgTwMm9J/K9LWucfTxICM06rhPDonDOca45e3uQYJaHiaFROD849qUk8Odx9N78GbVfyXqaU6qqN97Na9Hrzyk8/dtEyjSoWqD47LxLk2a27bTQSGwfchx5vdSZmD0nLddTwQunen4knsj/UDIXbw/izc79hNAoXHKUiS7eHsSHZsWZEBaFSx4a0hoM7MKr26bTZfZg7Evl71lFUZTVDzTs14Frey17atTt2YKzG//JV1wlkfAsg+F+jjKydBmLNHYt2pK6bWPOxXF+fShJy78GY+Fv/sF6+eiQn/JRJ+iwczrdz37Nvf1niD5ZsPIxW0ze2WNKuRuJQz4ag5/4bAAXPv0ZaTQWKo7Cltl3L9ziye5aw2WlBtXwKF8Gd+/8X3uscfT2JCnErN4RGlXgBvOioCtTBmOE2XF97x76Mpb1R/s2bSm9fAXuM2YSN0vrlZlx4wa29Rsg3NzA3h675i3Qe5Ut0vgcvT1JLub8cvL2IDFHfdIpZ90tZ30yNKs+ee7rzfQ98iXPn1xEWlwSofvPFjiWwtYjASo1qsHQLTMYuGwMZWtovU7iwqP5+9tNjP5nIeOOLCYlPpmrB84UOM6STJaAfyVVXnpKdAfuSikbSCnrAfOBu0CAlPJBDW6ilLIJUB9oL4Sob21FQohywCZgkpTSWj+nSUA3KWUDoGceYnMGzkopm0sppwIbgdFSyleAFOBZKWUjIACYKzR1gYlAR9N2PjCt60vgCyllU6Av8F0u21wkpWxqygtH4GnzeKSUrYD3gB9Mn30CnJRS1gcmACuklEbgD+BZU740B4KllOH5iAMgRUrZRkppbaDzfdPfvgR4MJ5iMrDb9PnvQCVrKxVCvGVqZDp2PynsIZvPXMDys1yO+ikjZtCtYW9uXLlJ156dMj/fs2U/fdu+wsjXx/PumPwPl3hkODmewohHxHxp2zGWdBrNL4O/oMPI5wsQQx7yxHqg2JVyokK3RvzRfATr/Ydh42RPlT5a29S/H35LzUFd6L71M2xdHDKf4hY0JouHUw/JO71ej++Tfqx4bTbLBswkYNizlPbLf3f/XIKz8mH24BwHDCH552+0caXm9Hr0fjVJ3bGRhPFvQWoKDj1fKoKQrO+f/NA72dP8uxGcnrQyX3N/ZI/Dyoc543hImlrtGhBy/iZTmr3L3MCx9Pn0NexdHIm4dpc9X2/knVUTeWv5eO5euJlZiShsfDnPN3NvTX2H84fPceFo4ef8MEVgJYCcSXLflxuf/ZTfe3zE1v6zeWJgZ7yb19IW0euwL+XMH89M4fDU1XReMrTIwsutfHRrVY+yL3Xi9rQV2T7XOTlQ87sx3Jz0A4aCHEd5OfetBPqw/QhweuVOfmz7Iau6TyQxIoZ2H71S2LDyXVYDVG75BP4vdmDXjOyXQp2tnpqdG3Nh0+F8xVUi5SEfnN8YRtKKbyDHTbVtk5YYY2MwXM9/g1bu4RSyfDRK9naewDb/obj7V8O1dv6HJVkJqsCLlu3iT9r9OOJO33g8YeSjzN615A+cSjkzcvNM2gzsTsi5YIyGonkKn9/y+rHLY/0x9e8DRA4cQMzHE3F+XRsyYLh1k8Q1P+Mxey4en88m49pVpCHvdaK8xWfls/9wfhW0PilN9cmK3RqxvsUIfm2k1Sf9+lh91lngWPJTj7x7NpjZrd9nUY/xHFq2nVeWjgTAwc2ZOl0aM6ftB8xsPgQ7J3sa9C54nMr/T3mZ6PIMMEcI8Tnwl5TygJWD8gUhxFum9fkATwA5Zyq0BXYBQ6SU+7DuILBMCPELsD4PsRmA3GZHEsB0IUQ7wAiUB8oBHYHfpJT3AaSUD5r8OgNPmP1tbkIIVyllfI71BgghxqANX/EEzgEPZpxZbVrnfiGEm6knRBu0xgWklLuFEKWFEKWAtWiNMD8C/Uy/5ycOzJax5kH+HQf6mH5ug6khREq5VQhhtW+UlHIpsBSgkU8bq6XvC4P68Owr2gRj505doJxvVut0WZ+y3AvLvaus0Whk+8ZdDHz3JTauzT5O+8S/p6hQxRd3z1LERMU+5M/LrsmALvj309rI7p6+jptv1pNaN29PEiJisqVPiorHwc0ZodchDUZcfTytdhW7deQiHpXL4ujhQnI+uiwmhUZl68Xg5ONJco4uc1qarDidfD1JCo/Bu209Em7fIzVK2+W3Nx/Dq0kNgtcfJO5qKLtf0p4SuFb1xrdTw4fG0bx/F5q+pOXLnVPXKWUWk5u35d/8IF90eh1GgxE3n9LEm/IuNiySxOh40pNTSU9OJfjIBXzqVCbyRh4arh7BGHUPXemsY0hX2gtjdGS2NPqqtXB+XxvzL1xLYdOwORgNZFw5j4y6h+HaBQDSDu/DodfLhY4p+W4Ujmb7x9HKPnwYYaOnxfcjuL3+IHc3W47vfpjW/bvS4iVtfPHtU9dwN4vD3duT2Bz7LTEqHgc3p8z9VsrHk9gILU2z59uza4n25PT+zXCibkdQrpovt05d4/Avezj8i9ZVOXB0P2JCs+d5broPCKSLaU6Iq6evUMbXC9Dyv7R3aaIjoqwu98IH/XDzLMWs8YXrDWUuMTQKF7MntM7eniTm2E+JoVG4+JYm/EEaH08Sw2MASDL9nxIZR/DW43g1rEbY4UskhkUTvEWbh+Be0HWkUeLg6UpKlLWiOHdpoZHYme0/O5/SpIdZ5o9jncr4zXmPS69+RoZZWSNs9NT4bjT31+8nekvBbq4TQqNwNTv3XXw8SYzInkcJYVG4mvWUcfHOyqPcJN3PGtJydvUeev048pGxFHVZXbZ2RZ7+/E1WD5xFco4hJtU7NCT0bDCJZnH+fyUj76Evk6OMjMp+ndVXq4XLSK2M1LmWwrZxCxINBmxq1sGuaStsGzdH2NohnJxxHj6RxPnTChyPtfIxpQDdwjPikoj85wJlAxoQf/FOgeMBSAnNHpODb+k8x+TRrBZluzUioFNDdA622Lo40vCrIQQN+SpPyxdlmZ2akMya0VmTp37090Iib9+jKCSFRuFU3qze4eNJyiPO88fJeO8eurJmx7WXF4bI3OuP6adPY+NbHuFWChkXS8rmzaRs1uqRLm8OxnCvaPLpgeTQKBxz5Ffyfzi/EkOjcM5Rn0zKWXfLWZ80xenTth4Jt7Lqk7e2HKNskxrcWH8wz9svynpkqlmj+uW9QfSc+hpOHq5UbfkE0bcjSDLFeW7rUSo3rsmpDXmPU/n/75E9JaSUl4HGaI0TM4QQk8y/F0L4oT2J72TqDbAJsNZ3OgPtBrmb2bLThDYpZZBpW+8AHwEVgSAhRGnTcuZxmq87RUqZW/PxK4AX0FhK2RAINy0rsP6cSge0NM1H0VBKWV5KGS+E2GaK8TshhAOwGHhOSvkk8G2OeHKuV5L7c7JDQHUhhBfQm6xGhEfGYbaehw0sfDAxgoGsxqcim93kl2XreanLa7zU5TX2bjnA0893B+DJRnVJiE/gfoTlzU3FKlmTA7Xr0pobV29ZfF77yZrY2trmq0EC4NiKHZkTm13afoz6fbWhBeX9q5MSn2xR0QUIPnSeJwK1LpIN+rbj0g5tzLtH5XKZabzrVUFva5OvBgmAyKDruPp541zRC52tnsq9WnBne/bu6ne2n6Dqc9rYydKNqpEWl0RKRAyJIZGUaVQdvaOdFkObusRe1Sbesi/tpi0sBPU+6MWVlbseGsfhlTsyJxS6sP0Y/qYhFxX9q5Man0z8Pct8uX7oPHUDmwPQqG9bLmzXbswubD9Olaa1tPF+DnZUbFidiKuFmxDsAcO1i+i8y6Pz8ga9DXYtO5J+PHuX6/gPXibu/ZeIe/8l0g/vI/mH+aQfO4iMjcYYGYHOpyIAtvUaYbgTXOiYooOu4VLVG6dKXghbPRV6tyR0+/FHL2jS6Iu3iL8SwtVv8j9B4sGV25kbOI65geM4s/0YTfpo42Qr+1cnJT7J6n67eug89U37rWnfdpw17bfou5HUbF0PAJcypShb1ZfIW1p3WRfT8eTuW5onuzflZB67uW9dsZmRgcMZGTicI9sP06GvVmGp6V+LpPgkoiMsbwQ69+tCw/b+fDFsTpE+mbt36jpuft64ms61ar1acGtH9nPt5vYT1DCda2UbVSMtPonkiBhsHO2xNc23YeNoT4V29Yi+pN0Y3dx6DN/WTwBQys8bnZ1NvhskABKCruLg54N9xbIIWxs8e7Uhenv2Riq78mWo+d0Yrr3/JSnXQ7N95zd3CMlXQghbajnbel6FnbqOh583bqY8qvVMC67nyKPrO05Qp6+WR97+Wh4lWik3zTmbzf9QrVsTIi89+qayKMtqN9/SPP/NcP4YsYQoK42j9Xq25Nx/wdANgIwrF9H5VEBX1htsbLBr05H0o9kr7LHv9CP2be1f2qF9JH7zBelH/iZ51bfEDH6e2Lf7kTD3U9LPnChUgwRATNA1nM3Kx/K9WxKWx/LRrrQrNm7aUB+dgy1ebeuRcPVuoeIBiD2pxeRoism3d0vCt+UtpkvT1rDbfyh7mr7PybcXcP/guTw3SEDRltkObk7obfUAtOjXkWuHL2S7mSuM6KDruPh541RRy6OKvVpwN4959DikX7yIvnwFdN7ace3QsSOp/2Q/rvW+WXVEmxo1wMYGGafVEYW7OwC6smWxb9uWlF1FO7VaVI66XKVeLQj5D+fXg/qkiymGKr1acDtHffL29hNUM13jyjSqRnqcdo1LDInEq1F19A5afdKnTV1i8zmRa1HWI128SmWmqdCgGkIIkqLjibl7n4r+NbA1xVmtdd0iq1+WNMYS8K+kemRPCSGELxAlpVwltDdoDALiAVfgPuCGdnMcaxqe0QPYa2VVEngd+FUIMU5KOVNKORFtKMWDbVWTUh4GDgshnkFrnAgG3hNC6NB6OzSzXLVVpYAIKWW6ECIAeDCd7y7gdyHEF1LKSCGEp6m3xHZgKDDbFEtDKWWQlNK8EcXd9ON9IYQL8Bxg/v6hF9Hm2mgDxEopY4U2N8YrwGdCmwvjvpQyzrS+34F5wAUp5YO7+EfGUQh/o8278bkQoitQJAPj/t51iDadWvLHobWkJKcwZcT0zO8WrJrNpyNnEhkRxSdfTsTZ1RkhBJfPX2XG2DkAdHyqA08/352M9AxSU1IZ987kQsVzdXcQ1QMaMmT/PDJMr5l7oN+y0fw15lsSImLYNWM1fRYNo8Oo5wk7d5OgtXsBqNOjKfX7tsWQbiAjNY31QxbmOwZpMHJs4nI6/jwGoddxbc0+Yi+HUKO/9iTlysrd3N0VRPlODej5z1wMyWkcGqHNXRF58hq3Nh2hx7apyAwD0WdvcnWV9jS7Su+W1BykvWTl9pZjXF+zP88xXdoTRM2Ahny47wvSk1NZPzorXwb8OIbfxy4lPiKGbTNX02/hMLqMfJ67525y7BctX+5du8vlfacZtnUm0ig5tnYPEabXlL2wYChVW9TBycOVMYcWsuuLdRw3LZcnRiPJyxbgPH4W6HSk7d2C8U4wdp1Nr/vb+fAbsuRlC3AaOhFhY4MxPJSkbwrxJhATaTASNGEZrVePQ+h13Fy9l/hLIfgN0IYd3VixC3uvUnTcNhUbV0ekUVJ9cHd2tBtDqScqUfn5tsSev0XHndr5cG7GL4TvCsp3HBf2nKROQEMm7PuS9ORUVps9QRv841jWjl1KXEQ0f838mQEL3ydw5IvcORec2QNix4L1vDTnXUZvnQVC8NfMn0mM1m6uBy35ECcPF4wZBtZ//CPJcfmfQO347mM0CmjM4v3fmF4JuiDzu4nLJrF4zCKiI6J4e9p73AuJYMbv2szt/249xK8L1uLu5c7sP+fh6OKENBp5+vWevN95CMl5rIRLg5F/Pl5Oj5/GIHQ6Lq3dR/TlEOq8qp1rF1bt5vbuICp2bMCLf88lIyWNfR9q55qjlxtdvhsOaJNsXd3wD3f2ap38Lq3dR7u5b9F35wyM6Qb2Df/G6vYfyWAkeOJ31Pp5EkKv496aXSRfvk3Z/lpPk4iV2yk/4gVsPFypMuMt7W/KMHCuxxhcmtXG6/kOJJ0Ppt6OuQDcnvETsbvzNx+HNBjZ/fFy+qzUyqNza/cReTmE+qY8Or1qNzd2B1EloAGvHZhLRnIa20dlzaXTY+EQKrasg4OHC28eXsChees4t3YfbSf0w+uJykgpibtzn13jf8gtBKsKW1a3++BZHD1c6fGZ9tYOo8HA9898DICNgx1+beuxacL3+Yopr0ZPnsnRk6eJiYmjU+9Xee+N/vR9pigu07kwGkj6dj6uk+dorwTdtRnD7WDsu2kjXa3NI/E4SYOR0xOW0dJUPt4ylY9VTOVjsKl8bG8qHzFKqg3uzu52Y3Ao647/gncReh1CJwjZ+C/hOyznUSlITGfHL6PZGu0tA3dW7yXh0h0qDdCumbdW7MTeqxStt0/LjKnKWz3Y33Z0gYfXWVPYMrtc9fK8PPc9jEYj4VdCWDsm67x4dcEwqrd4AmcPVyYd+optX/yWuVxeSIORkxOW0W71WIRex401+4i7HEJV0367btpvnbdOxdbVEWk0UmNwD7a1H0NGQjLNFw/Bq1Ud7D1deer4Qs7N+Y3g1bl1fs4Do4H4BfPxmKUd1ylbNmMIDsbxGe24Tv5zI/bt2uHYrRsyIwOZmkbsp1lT17l/8hk6NzekIYP4L+cjE4p2Ql5pMHJiwjLam/Lruim/qpny69qKXTh4laKLWX7VHNyDLab8KqoYjny0nM4/a9e4q2u1+mRNU33y8srdhOwKonzHBjx7UCu//zFd4+6fvMbNTUd4ettUjBkGos7d5HIhJnItbD2yXo/mNHu1M0aDgfSUNNYO0+rXd4KucW7LYYZsmo4xw8Ddc8EcXb27wHEq/z+JRz2xEkJ0Q7tBNgLpwLtAS2AIECqlDBBCLAOaA9fRntBvlFIuE0LsBUZJKY8JIRKklC5CCDu04Q5/SCkX59jWeqAG2hP9XWhv+QBYBTQEzqINwZgipdz7YJ1myy9DG2Lym2kCxz/Rho0EAa2BHlLKYCHEQGA0Wi+Ck1LKQab0XwF10Bpr9pt6buTMj6lowy2CgdvATSnlFNPfeghoj9ZQ87qU8ogQwhNtiIYfkAS8JaU8bVpXE+AoMEhKudz0WV7jCAaaPBiGkuNvz/zOtI05UsoOQoiyaENMPIB9aI0oflLK1JzrfyC34RvF5Vm7yo9O9B9WNb1kvV7nvF3Jawcd3TL00Yn+g3bv8SnuECz87VCy9luwTHp0ov+wHga34g4hm/rGkpdH/9iUqBdjkSBK1CUEgHHHPyvuECzED7b+OtTicuBQ0b5asSjYFOfcB1bscixZ8QC0TNUXdwjZtKtduCE5j8Pei0Uwf0kRStWVrDokwCXbklUfAZgW/HPJy6gCWO/9crEXHH3CSmZePrKnhJRyG7Atx8fHgIVmaQblsmwHs59dTP+nYTaEI0f6PtY+R+tpYC29S47fB5n9fB+t8cTacsuB5Tk+u492k/5QUsqP0IaY5Py8Qy7po4BeuXx3jBxDKvIRR5Ucvw+y9p1pGw9ii0WbSDRDCNESbbLSXBskFEVRFEVRFEVRlMIzFmJC3v92eZnoUvnvUQn4xTQUJg0o3GsuFEVRFEVRFEVRFKUQVKPE/xAp5RXAv7jjUBRFURRFURRF+V9S7GM3SrBHvn1DURRFURRFURRFURTlcVCNEoqiKIqiKIqiKIqiFAs1fENRFEVRFEVRFEVRHqOS916TkkP1lFAURVEURVEURVEUpVionhKKoiiKoiiKoiiK8hgZ1RtBc6V6SiiKoiiKoiiKoiiKUixUTwnloRx1dsUdQolnJ0vWC370lMBm2BI2iC7apuS1x6ZhKO4QspEl7LgGSChhu83RJr24Q7AQXwL3W7X0klUmxQ9+rbhDsOD67Y/FHUI2aU9+XNwhWEgTJes4silpFzYg0qZk5ZEoYWU2gEMJKyNjSthxDWArS15Myn8/1SihKIqiKMpjUdIaJBRFURSluBhL4oPDEqIEtmEqiqIoiqIoiqIoivK/QPWUUBRFURRFURRFUZTHqGQNHipZVE8JRVEURVEURVEURVGKhWqUUBRFURRFURRFURSlWKjhG4qiKIqiKIqiKIryGBnVPJe5Uj0lFEVRFEVRFEVRFEUpFqqnhKIoiqIoiqIoiqI8RsbiDqAEUz0lFEVRFEVRFEVRFEUpFqpRQlEURVEURVEURVGUYqGGbyhFbvinQ2nZsTkpySlMGzGLy2evWKQZN2cUtRvUQgC3b9xh2vDPSU5KKdR2u00ZQPWABqQnp7Fx1DeEnQ22SONe0Ys+C4fi4O5C2NlgNoxYjDHdQM0ujekw8jmkUWI0GNj+yUpuH7uM3t6Wgb98jI2dDTobPRc2H2HfF+vyHVu5gPr4f9ofoddx/ee9XFr0Z7bvXav70PSLt3F/sgpnZ/7C5a83Z37XZN5gfLr4k3o/ju0B4/K9bXOBkwdQw5RHv4/6htBzwRZp3Ct48cKioTiWcuHuuWDWj1iMId2Q+b1v/aq89fsn/DJ0Iee3HAGg5RvdafxiAFJKwi/dZsPopWSkpucrNpsGTXEcNBR0etJ2byL1j9VW0+mr1cJl6lckzf+U9MP7ARBOzji+PRp9RT9AkrRkFoYr5/O1fYDyHerT4pP+6PQ6Lq3ey+mv/rRI0+LT/lTs2JCM5FT2j1hKpOk4s3Nzos3sN/GoVQGk5MDIb4k4cZWmH71Epc7+GNMziLsZwYEPl5IWl5Tv2B54bvIg6gb4k5acyspRS7hz7oZFmnYDuhHweiBeVbwZ6/8midHxADTp1YYu7/QEIDUphbUffU/IhZsFjuWBNz55i8YBjUlNTmXhyC+5fvaaRZrhX46kev3qGDIMXAm6zJLxX2HIMFC+WgWGzfmAqvWq8dPslfyx9Pd8b79y+/p0mKLtt7Nr9nJ0seV+6/BJf/wCGpKenMr2kUuJMO23LrMHU7VTQ5Ii41jZZXxm+hpPNaPliD54Vvdldc/JhJ+2zOe8cmnfiPKTBoNeR9TaHdxb8lu27917tcfrnb4AGJNSCPloMSkXtPh0bs5UmDkMh1qVQUrujPmSpBOXChRHtylZ5/8fDykj+5qVkb+blZEBZmXkNlMZ+YDQCd78ayrxYdGseX3OI2Px6VCfJp/1R+h0XF29l/OLLPdZ48/6U950rh0asZToM1q8tQd3p9rLHUBKYi7e4dCIpRhT02nz9VBcq/kA2vmYFpfEli4TC5RXtv7NcHpjGOh0pO7cRMr6n62m01evjdvMxSTM/YT0Q/uyvtDpcJu9FGPUPRKmjbe6bFH6aPo89h88gqeHOxtWff3Ytwcl57pWEmPqOXkgtQMakp6cxi+jlhBi5VrrUcGLVxa9j1MpZ0LOBbNmxFcY0g20f+tp/Hu3BkCn11O2enk+afQWLqXdeGXR+5nLe1Ysy/YvfuPvH7bkK7aKHerTeoqWRxdW7yXISnnZ+pP+VDKde3s+XMp9s7JC6AR9N31GYlg0W16bm69tW2PXtBkuQ7RzLWXzJpLWZD/X7Fq1xuW1N5BGIxgMJCxeRPrZMwA49umLY+DTIATJm/4ief1v1jaRb2UD6vPkZwNAr+PWT3u4kuM4cqnui//8tyn1ZBUuzPyFa0s2AaCzt6XNhkno7GwQNnru/nWYS7PzX2cEbT+1+kTbTxdX7yXISn2k1adZ+2nvCMv91Geztp+2DtL2U+fFQyllKiPt3ZxIjUtiXbeClZEAPcyuKRtGfUNoLteU5xYOxdHdhVDTNSVnnfLNDZ/w29CFnN98pMCx/H8hizuAEqzAPSWEEMOFEE55SLdXCNHE9HNCQbeXj7jaCiHOCSGChBCOj2kbwUKIMvlIP0gIsehxxGK2jU+FEJ0f5zbyomXH5lTwK8+Lbfoza+w8Rs0YbjXdgimLGdRlMAO7DCY8JIK+rz1bqO1WD2iAp583X7Ufyabx3xM49TWr6TqN68fh77ewuMNIUmIT8X+xAwA3Dp5laffxfBs4gT9HL+XpzwcDYEhNZ+VL01jaYwJLe0ygWvv6lPevnr/gdIJG0wdx4JVZbG0/hkq9W+Jas3y2JGnRiZz8aAWXv95ksXjwLwc48PKs/G3TihodGlDaz5svO4xk44TveWaa9TzqOq4f/3y/hS8DtDxqZMoj0C5yXcf14+r+05mfuZbzoMWgbnz9zEd81W0cOp2Oes+0zF9wQofj6x+QOGMc8R8Owq51J3TlK1tN5/DyW2ScOprtY8dBw8g4dYT4DwcSP/pNjCH5v9EWOkGrqQPZ3n8W6wLGULVXC9xr+GZLU6FjA9z8vPm1zUj+Hvs9rWYMyvyuxSf9ubP3NOs6jOH3rhOIuXoXgLv7z7C+0zh+7zKBuOuhNBj6TL5je+CJDg3x8vPmkw4fsHrCt/Sb9obVdNePX2Lhq1OJvBOR7fPI2xHMf/ETZvQYw9aF63lpxuACx/JAo4DG+Fbx5b12b7Nk3Fe8Pe1dq+n2b9jL0IB3+aDLUOwc7OjcrysACTHxfDd5aYEaI0Dbbx2nDmTDwFks7zSGWj1b4Jljv1UJaIB7FW9+bDeSneO+p+O0QZnfnf91P78PmG2x3shLd/jzrS+5c7hgDQCZdDrKf/oONwZN4XKXIbj3bId99YrZkqTdDuf6i+O50uN9IhaupcKMoZnf+U4eTMK+E1zu9C5XerxPytU7BQqjeoB2/i9qP5K/xn/PUw8pI//9fgtfdRhJco4y8pvu41kaOIGNo5fyzOfZj53mr3fnvumYfxShEzSdPpA9r8zirw5jqNKrBW459pmv6Vzb2Hokh8d8TzPTuebo7UGtN7qytcfHbOo4HqHTUaVXCwD+fmcRW7pMZEuXidzedJTbm4/m3HTe6HQ4vTWc+M/GEPv+QOzadEJXwUp5pNPhNOBt0oMst+Pw9HMY7hS+wS+vegd24et5U/9j2ysp17WSGFPtDg0p4+fNrA4jWDfhW57NpZwOHPcyB77fzKyAD0mOTaTpiwEA7Fv6F/MDxzM/cDxbZq3h+uELJMcmcu96aObnXz49gfSUNM5uy98xLnSCNlMHsmnALNZ2HEP1Xi3wyHHuVQpoQCk/b1a3Hcm+sd/TdvqgbN8/+UZ3ovN4rj+STofr+8OJGT+GqNcHYt+xE/rK2c+19BMniBr8OtFvv0ncnM9xHTkaAH0VPxwDnyZqyDtEDX4D+xYt0Zcvb20r+YxJUH/Gaxx6eRa7242m/LOtLI+jmATOfLQ8szHiAWNqOgf7TmVvp/Hs7TSecgEN8GiUzzoj2n5qPXUgm/vP4pcAbT/lrI9U7KjtpzVtRrJ/7Pe0MauPANSzsp92vreIdd0msq7bRK5vPsqNLQUsI4Eapnr3gvYj+fMh15QupmvKwhz17gd/Z5fx/bhmVqdU/ncVZvjGcOCRjRL/SUIIPfAKMEdK2VBKmVzcMf0nCCH0UspJUsqdxR1Lm26t2PrbDgDOnbiAaykXSpf1tEiXlJD1pNjewR4pC9d2WLNLY06vOwBAyMmrOLg54VLW3SJdlVZ1M1tiT63bT62uTQBIT0rNTGPrZI95W+aD73Q2enS2+nzH6ulfjYTgcBJv3UOmG7j9x7+U79Y4W5rUyDiiT13HaNZ6/MD9fy+SFl349rzaXRsTtF7Lozsnr+Lg6oSLl7tFOj+zPApat586pjwCaDGoG+e3HCUxMi7bMjq9HlsHO3R6HbaO9sSHR+crNn312hjD72KMCAVDBmn/7Ma2aWuLdPY9niX98AFkbEzWh45O6OvUJ2236YmXIQOZlJiv7QN4NaxGXHA48bfuYUw3cP2Pf6nUNft+qty1MVd/+xuAeyeuYefmjGNZd2xdHPFuXovLq/cCYEw3ZPaGCNl/FmnQpjaKOHENJx/L8yGv6ndtypH1Wu+Q4JNXcHR1xs3KPrxzLpioO/csPr9x4jLJcYmmn6/g7l26wLE80KxrC/as2w3A5ZOXcHZzxqOsh0W6E3uOZ/58JegKZXy0dt3YyFiunr5CRkZGgbbv3bAaMcHhxJr226U//6Vajv1WrWtjLqzT9lvYyWvYuznjbCofQo5cIiXG8vyKunqX6OuhBYrJnFPDGqTdDCXtdjgyPYOYP/fj1rV5tjRJJy5iMO2XpBMXsfXW8kbn4ohLs3pErd0OgEzPwBiX/2MboFaXxpwyKyPtcykjzc//0+v2U9tKGWnnZI80KyNdvT2p0bEhJ9fsyVMspf2rER8cToJpn938418q5igTK3RrzHXTuRZ54hp2pZxxMMUrbPToHewQeh02jnYkWSlvKvVszs0Nh/IUT042NepgDA3BGB4KGRmk/b0bu2ZtLNLZB/Yh7dA+ZGz27YvSXtg2bkHqzr8KtP2CaNLwSUq5uf7HtldSrmslMaYnujbmhOlae+vkVRxdnXC1Uk5Xb1WXM5sPA3Bs3X7qml1rH2jYsxVBG/+xXLZ1PSJvhhMTcj9fsZXNcZ27tvFfquQoL6t0bcxlU3kZYSovnUznnrO3J5U6NuSC6VpXWDa165AREoIxVDvXUvfsxr5V9nNNpmRV5YWDY2b1zKZSZdIvnIfUVDAaSDt9Cvs27Qodk4d/dRJvhJN0KwKZbiBkwyG8cxxHaffjiAm6jjHD8jgyPKgz2uoRNnooQP025366+kcu+8lURkacyLGffDyp3KkhF3/em+s2qj3TnKt/FKyMhOzXlDsPqXfnrFPWNjvOmz+oU96Ps1juv5VRFP+/kipPjRJCCGchxCYhxCkhxFkhxGTAF9gjhNhjSrNECHHM1Evhk0esr4wQ4pAQ4ikr3z1v2sYpIcR+02fZehoIIf4SQnQw/Zxg6iVwGBgPvABMEkL8JIRwEULsEkKcEEKcEUL0MlvHACHEadN2Vpo+8xJCrBNCHDX9s7wryjJaCHHE9K96XpcXQlQ2xXTa9H8lIYReCHFdaNyFEEYhRDtT+gMP1p9jPcFCiElCiL+B54UQy4QQz5l994nZ313bLL4dps+/EULczE+Pj7zw8i5DxN2sp7MRoffw8ra+iQnzxvBn0G9Url6R334o2FPSB1y9PYm7G5n5e1xYFK7lst8YOXq4kBKXmHmDGB8ahat3Vppa3Zrw7q7ZvPTjaDaOXpr5udAJBm+ezsgTS7hx4Cx3gyy7pj+Mo7cnSSFZsSWFRuHo7fGQJR4Pt3KexObII7cccTiZ8shoyqPY0Kx8dC3nQZ1uTTj6U/a2r/jwaA5+u4kP/1nA6CNfkRKfxLUDZ/IVm86zDMbIrOPGGHkPnUf240Z4lMG2aVvSdmzM9rm+rA8yLgand8fiMnMpjm+PAnuHfG0fwMnHg8TQqMzfk8KicPbJkT/eHiTezb4vnb09cK3kRUpUPG3nvUXvrVNpM/tNbBztLbZR88V23NlT8CcC7uU8iDbbfkxYJO7eBWvkaPViAOf3BhU4lgdKe5cmMjSrYhwZFonnQxo79DZ62vcJ4MS+47mmyQ8Xbw/i72btt4TQKFxynPsu3h7Eh2blW0JYFC7/oXPQtlxp0u9m5U96aCS25XLPH48XuxK/V8sbu0reZETGUmHOcGpsmk+FmcMQVo6rvMhZRsbnoYyMs1JGvmcqI/80KyO7Te7Pzumrkca8Vb4dvT1IMttnSaFROFo515LMz7W7UTh5e5AcFs2FJZvpffRL+gQtIi0+ibB9Z7MtW7Z5LVLuxRJ/IzxP8eQkPMtguJ+jPCpdxiKNXYu2pG7bmHNxnF8fStLyryGP+fH/UUm5rpkrKTGVKudJTLZyOopSOcppJw9XkrNdayMpVS57GlsHO2q1b8CZLYctttHwGeuNFY/i7O1BQo7y0jlHHmlpIq2maTXlVf6dvrrIjm19mTIY75mda/fuoStjWWe0a90Wzx9X4D5tJnFzPgcgI/gGtvUbINzcwN4e++Yt0HmVLXRMDj4eJJv9/cmhUTjk52GCTtBh53S6n/2ae/vPEH0yf3VG0OojCWb1kUQr9RHnHPWRxFCtjATTfpq2OteHaD7Na5F8L5a4ApaRAG5W6t1u5R5ep4wLzap3upbzoHa3JhxbVezPU5USIq89JboDd6WUDaSU9YD5wF0gQEoZYEozUUrZBKgPtBdC1Le2IiFEOWATMElKadl/DiYB3aSUDYCeeYjNGTgrpWwupZwKbARGSylfAVKAZ6WUjYAAYK7pxr8uMBHoaNrOB6Z1fQl8IaVsCvQFvnvIduOklM2ARab8yOvyi4AVUsr6wE/AAimlAbgMPAG0AY4DbYUQ9kAFKeXVXGJIkVK2kVKusfLdfdPfvQQYZfpsMrDb9PnvQKWH/H0FIoRlE1xuheL0D2fRq9ELBF+5RaeeAVbT5H27lp/l3K612MwHd13adowlnUbzy+Av6DDy+awkRsm3gROY32IYvg2r4VWzQpHH9p+Qpzgekkc9JvVn+8w1FjceDm5O1O7SmC/aDmd286HYOdlTv/fD2vPyGFyOkXeOg4aQ/PM3IHO8UEmvR+9Xk9QdG0kY9xakpODQ66X8bV8LwjICi+yxfnzrbPSUrleFiyt3saH7R2QkpVJ/SPZhGg2G9cRoMHJt/cECxJYZgNXt51eNlnVp+WJH/pj5U8FjeYiHxfT2tHc5f+QsF47kf84Pq6zmiUUiK2n+Q+eg9RPPalLnlk/i+WIXQmcu0xbV63GsV43IVZu58tRwjMkplH33uSILI2cc1o/vrJ8vbTvG4k6jWWtWRtbo6E9iZKzVscS5x/LwsjjXgKXErpQTFbo14o/mI1jvPwwbJ3uq9Mle3lTu3ZLgAvaSyH3b2X91fmMYSSu+AWP28si2SUuMsTEYrl/mv1lJua6ZKzEx5aGczkusT3RuRPCxSyTHZu8dpbfV80TnxpzebNlYUbDY8pJGUqlTQ1Ii47hvmtulaOShLADSDh4g6rUBxE6aiMug1wEw3LpJ0pqfcZ81F/eZs8m4dhUMBetxly2ifJTZVhkleztPYJv/UNz9q+FaO391RgCRl3zJJc5KnRqSfP/h+6lar5aF6iWhbd/a5h9dp3yQpPvk/uy0UqdU/nfldaLLM8AcIcTnwF9SygNWTtoXhBBvmdbpg3aDnfORoC2wCxgipdyHdQeBZUKIX4D1eYjNAOQ2i4wAppt6HRiB8kA5oCPwm5TyPoCU8kFzZGfgCbO/zU0I4SqljLey7tVm/3/xsOVzLNcS6GP6eSXwYADjAaAd4AfMAAYD+4CHDfha+5DvHuTdcbPttQGeBZBSbhVCWO1jb9qPbwFULVULb2dfa8ky9RnYi56vaJ1eLgRdoqxvVkt1WR8v7odH5rYoRqORXRv38PK7L7L5l60P3U5OTQZ0wb+f1phx9/R13Hyznj66eXuSEBGTLX1SVDwObs4IvQ5pMOLq42l1mMGtIxfxqFwWRw8Xks26cqbGJXHz0AWqdajPvct5H9edFBqFU/ms2Jx8PEkJj8l9gSLUrH8XGr+k5VHIqeuUypFH8TnieJBHOr0Oo8FIKR9P4iO0PCpf34/nF2pj3Z08XKnRoQFGgwG9jZ7o2/dIitJOk/Nbj1KpcQ1Ob8j7zbf2JDLruNGV9sIYnf240VethfP7kwAQbqWw8W8OBgMZV84jI+9huHoBgLTD+3Do9XKet535t4dG4Wz2NMTJ25OksOzHR2JoFM6+2fdlUngMSEliaBT3TE9Ebmw6QgOzRonqz7WlUmd/Nr84I99xtevflVYvdQLg5qlreJht3927NLH5HCrjW7sSL898iyWDZpJoZdhCXvQYEEiXl7oBcPX0FUr7ZD3ZKu1dmujwKKvLvTC8H26epVgy7qsCbdeahNAoXH2z9puLjyeJEdnzJCEsClefrHxz8fYk8T90DqaH3cfWNyt/bH1Kkx5hmT8OtatQYeYwbgyagiEmPnPZ9LD7JAdpN7gxmw/mq1GiyYAuNMqljHT19iT+EWWkm48nCY8oIys2qUmtzo2p0aEhNva22Ls60nv+u2wYviTXuJJCo3Ay22dOPp4k5zjXtDRm55qvdq55t61Hwp8vfjgAAQAASURBVO17pJrKm9ubj+HVpAbBpsY+oddRMbApW7p/nMdcsiQj76Evk6M8isreTV5frRYuI7XySOdaCtvGLUg0GLCpWQe7pq2wbdwcYWuHcHLGefhEEudPK3A8JVFxXtdyU5wxtezfheYvdQTg9qnruGcrpz2Jy3EeJUbF45jtWluauBzlVoNcekPU6tCQkLM3SLgfm+84E0OjcMlRXuYc/qSlKZ0jTQxVA5tRuUsjKgU0QG9vi62rIx2/fJfdH+R+rj+K4f69bL0bdF5eGCNzH5KSfuY0et/yCLdSyLhYUrZsJmWLNnTT+Y3BGO9ZDlvMr+S7UTia/f2OPp6khOXvOguQEZdE5D8XKBvQgPiL+ZsLKDE0Chez+oiztyeJj6iPOD/YT081o3LXRlTqaLafFrzL7ve1/ST0Ovx6NGV9YP7LyKYDutDYdE0JsVLvzu2a8uA4dzOrd/vW9+O5B3VKT1dqBDTAmGHg4vai6UVZUhkfneR/Vp56SkgpLwON0RonZgghJpl/L4TwQ3sa38nUA2ATYK3/dAbaTXI3s2WnmSalDDJt6x3gI6AiECSEKG1azjxW83WnmHoaWPMK4AU0llI2BMJNywqsT4CqA1qa5qNoKKUsL6WMF0JsM8Vo3vNBWvnZ6vK5xJZz2QNAW6AZsBlwBzoAD4awWIvhYYOLHwwANpDV+JSnkURSyqVSyiZSyiaPapAAWL/8DwZ1fYtBXd9i/7a/6f5cFwDqNqpDQlwikVYq4OWrZK23dZdW3Lx6Oy+hZXNsxQ6+DZzAt4ETuLT9GPX7ttXW7V+dlPhki0YJgOBD53kisBkADfq249IOrfDzqFwuM413vSrobW1Ijk7AydMVezdt6hQbe1v82tQl8mr+xplHB13Hxc8bp4peCFs9FXu14O62/0yhe2TlDpYETmBJ4AQubj9Gwz5aHlV4kEf3YiyWuWGWRw37tuOC6QLxRdsRfNFmOF+0Gc75LUf46+NlXNx+nNi7kVT0r46tgx0AVVvX5V4+J8EyXLuIzrs8Oi9v0Ntg16oj6ceyV8bih71M3LCXiBv2Eun/7iP5+/mkHzuIjI3GGBmBzkebPNC2XiMMd4LztX2Ae6eu4+bnjUtFL3S2eqr2asGtHSeypbm1/QTVn9PGu3o1qkZ6fBLJETEk34sl8W4Upapqs1r7tqlL9JUQQHujR/33nmbHa/MwpKTlO679K7czM3AsMwPHcnr7UZr10cbMVvGvQXJ8EnFW9mFuPHxLM/jrkawY8RURNwo+X8KWFZv5sMcHfNjjAw5v+5eAvlplvKZ/LZLik4iOsKzAde7XFf92jZg3dHaRPr0MO3UdDz9v3Ez7rdYzLbieY79d33GCOn21/ebtX420+CQSrZQPj0PSqSvYVfHFtkI5hK0N7s+0I25H9hnGbX29qPz1eG6PmEfajaxzJ+NeDOl372NfVZtkzbV1A1Kv5L2sPLZiB0sDJ7DUVEY2MCsjU/NQRtbPQxm5e9Za5rcYxoI2w1k3bBE3/jn/0AYJgMig67j6eeNs2meVe7Xgzvbs++zO9hNUNZ1rpRtVIy0uiZSIGBJDIinTqDp6R6288W5Tl9irIVmxta1H3NW7JIdabxjLi4wrF9H5VEBX1htsbLBr05H0o9kbWWPf6Ufs29q/tEP7SPzmC9KP/E3yqm+JGfw8sW/3I2Hup6SfOfFf1yABxXtdK4kxHVq5I3MSynPbj9HIdK2t5F+d5Pgk4q2U09cOnePJQG1+mSZ923He7GbMwdWRqs3rcG6HZfwNe7Yi6M/8D90AiDh1nVJVvHE1nXvVerYgOEd5GbzjBDVN5WVZU3mZFBHDkc9/YVWz9/mp1Qh2DvmKuwfPF6pBAiDj4kVsyldA562da/YBHUn9J/u5pvfNmmTSpkYNsLVBxmkNMsLdHQBd2bLYt2lLyu7CDwWICbqGc1VvnCppx1H53i0Jy+ONsl1pV2xMdUadgy1ebeuRUIBJQSNOXaeUX9Z+qt6rBTdz7Keb209Q01RGlm1ktp9m/sJPTd/n55Zm++n9rP1UoW09Yq7dzTZcNa+OrtjB14ET+NpUp3xwTanwkGtKzjrlg2vKl21GML/NcOa3Gc75zUfYZKpTKv+78tRTQgjhC0RJKVcJ7Q0ag4B4wBW4D7ih3SDHmoZn9AD2WlmVBF4HfhVCjJNSzpRSTkQbSvFgW9WklIeBw0KIZ9AaJ4KB94QQOrTeDs3y+PeVAiKklOlCiADgwZS+u4DfhRBfSCkjhRCept4S24GhwGxTLA2llEFSym5W1v0iMNP0/4M+UFaXz7HcP0A/tF4SrwB/mz4/DKwArkspU0yNNG8DTwPkEkN+/Y0258bnQoiuQJEPtjy06zAtOzbnl4OrSElOYfqHWTNZz1kxg5mj5xAZEcVH88fh7OKEEIKr568xe/z8Qm336u4gqgc0ZMj+eWSYXgn6QL9lo/lrzLckRMSwa8Zq+iwaRodRzxN27iZBa/cCUKdHU+r3bYsh3UBGahrrhywEwKWsO73mvYPQ6RA6wfm/DnNl98l8xSYNRk5OWEa71WMReh031uwj7nIIVQdoT7+vr9iFvVcpOm+diq2rI9JopMbgHmxrP4aMhGSaLx6CV6s62Hu68tTxhZyb8xvBq3PraJS7y3uCqBHQkOH75mmvBB2dlUev/jiaP8Z+S3xEDDtmrub5hcPoNPJ5Qs/d5MQvex+63jtB1zi35QjvbJqGMcNA6LmbHFu9O3/BGY0k/7AA5wmzQKcjbe8WjHeCseus9TZI22n5KixzyT8uwGnYRISNDcaIUJKWfJ6/7aPtp0MfL6f7T2MQOh2X1+4j5nIItV/VbrgvrtrN7d1BVOjYgOf/nktGShoHPswaV3/o4+W0X/guejsb4m9GsH+k9l2rqQPR2dnQfbX2irmIE1f5Z/yP+Y4P4Nyek9QN8Gfyvi9JT05j1eisisa7P47j57HfEBsRTftB3en8dk/cvNyZsHUW5/YE8fO4b+jx/nM4e7jw4lRtNnhjhoFZPScUKJYHju8+RuOAJiw5sFR7JeioLzO/+2jZZL4au5Do8Cjemf4e90IimLlBe9PFv1sP8cuXa3D3cmf2X1/g5OKENBp5+o2evN/pPZIT8jZPsTQY2f3xcvqsHIPQ6zi3dh+Rl0Oob9pvp1ft5sbuIKoENOC1A3PJSE5j+6is/dZj4RAqtqyDg4cLbx5ewKF56zi3dh/VujUh4NMBOHq60uvHUdw7f5Pf+xdgZn6DkbuTvqbqik9AryP6l52kXrmF5yvdAYj6aSvl3u+H3sON8lO1N5fIDANXe34IQMiUb6g4fyTC1oa02+HcGTU//zEAV0xl5ND98zJfm/zAS8tG86epjNw5YzV9Fw0jwFRGnsxRRhpNZeQ6UxlZENJg5NjE5XT8Wdtn19bsI/ZyCDX6a/vsysrd3N0VRPlODej5z1wMyWkcGqHts8iT17i16Qg9tk1FZhiIPnuTq6uyJtis3KtFgSe4zGQ0kPTtfFwnz9FeCbprM4bbwdh3M71O18o8EsVt9OSZHD15mpiYODr1fpX33uhP32eKotpgXUm5rpXEmC7uOUntgIaM3TeftORUfjW71r7+4xh+G/stcRHRbJ65mpcXDqPbyBe4ey6YI79kHcd1uzXl8oHTpCenZlu3rYMdNdo8yfoJDxtd/PA8+vvj5Ty1Sjv3Lq3dR/TlEJ4wlZfnV+3m1u4gKnVswEt/a+Xl3pFLH7HWQjAaiF84H/fP5yB0OpK3bMZwMxiHp7VzLeWvjdi3a4dDl27IjAxISyPus6xp60pN+QydmxsyI4P4BfORCYWfqFQajJyesIyWq8ch9Dpurd5L/KUQqpiOo2DTcdR+21RsXB3BKKk2uDu7243Boaw7/gveRei1OmPIxn8J35G/OuODGP7+eDmBpvrIg/1Ux7SfLpjtp36m+sjeD/O2n6r1bMHVwpaRaNeUGgENed90TfnD7JryyrLRbByj1Sl3zljNc4uG0XGUqU5puqYoSk4iL0+shBDd0G60jUA68C7aMIQhQKiUMkAIsQxoDlxHe0q/UUq5TAixFxglpTwmhEiQUroIIeyAP4E/pJSLc2xrPVAD7an+LrS3fACsAhoCZ9GGYEyRUu59sE6z5ZehDTH5zTSJ459ow0aCgNZADyllsBBiIDAarSfBSSnlIFP6r4A6aA02+009N3LmRzDwIxCI1jviJSnl1dyWF0IMAppIKYcKIaoAPwBlgHvAa1LKW6b1HgAOSCknCCFeBhYDnlLmHESfGUOTB0NQcvzdmd8J7XWsc6SUHYQQZdGGm3igDQ15EfCTUqbmXP8Drct3LFGDvbrb+BR3CBZq5//h92N1pmDz4D1WHzYvoteHFZF1B4vgtWFF7KRNenGHkE2IMenRif7D2lGquEPIppMu/92nH7cN0q24Q8imWnrJm+q7R/P898x73Fy/LVhD5eOy4cmCD3/5X3HUvuR1xK5q0Bd3CNn0qVnyzrVD50rW9T/MpjAvQnw8QvUlquoPwJSbP5W8i0kBfFPh1WLP3LfvrCqReZmnnhJSym3AthwfHwMWmqUZlMuyHcx+djH9n4bZEI4c6ftY+xytV4G19C45fh9k9vN9tMYTa8stB5bn+Ow+2o36Q0kpq5h+/CTH51aXl1IuA5aZfg5Gm9PC2nrbmv38M/BzHmJ48Psga99JKY+hDQMBiEWbRDRDCNESbaLSXBskFEVRFEVRFEVRFOVxyutEl8p/h0rAL6ZhMGlok2kqiqIoiqIoiqIoj5EskX0USgbVKPE/REp5BfAv7jgURVEURVEURVEUBfL49g1FURRFURRFURRFUZSipnpKKIqiKIqiKIqiKMpjVPKmxy05VE8JRVEURVEURVEURVGKheopoSiKoiiKoiiKoiiPkeopkTvVU0JRFEVRFEVRFEVRlGKhGiUURVEURVEURVEURSkWaviGoiiKoiiKoiiKojxGsrgDKMFUo4TyUDVtPYo7hOxK4NmcoBPFHUI2biVwwNpfB8oXdwjZVDGkF3cIFpyMJas4buBY8g4kvW1kcYeQzYqUElY+Aj3Tk4s7hGyiDXbFHYKFA4dKVnkEkPbkx8UdQja9z3xW3CFYSB77dnGHkE3C1nLFHYIF2xJWR1p0rUJxh2DhBafY4g4hm28ySt61tqfRs7hDUP4HlaxasKIoiqIoiqIoiqL8lzGWrOeYJYqaU0JRFEVRFEVRFEVRlGKhGiUURVEURVEURVEURSkWaviGoiiKoiiKoiiKojxGJW8GkZJD9ZRQFEVRFEVRFEVRFKVYqJ4SiqIoiqIoiqIoivIYqZ4SuVM9JRRFURRFURRFURTlf5wQorsQ4pIQ4qoQYpyV74UQYoHp+9NCiEZFsV3VKKEoiqIoiqIoiqIo/8OEEHrgK6AH8ATwkhDiiRzJegA1TP/eApYUxbZVo4SiKIqiKIqiKIqiPEayBPx7hGbAVSnldSllGrAG6JUjTS9ghdT8C7gLIXzymxc5qUYJRVEURVEURVEURfkvJ4R4SwhxzOzfW2Zflwdum/1+x/QZ+UyTb2qiS6VIvDz5deoHNCItOY3vRy3k5rkbFmk6DehBl9efolwVH4b5DyIhOh6AWi3q8v7Ssdy/EwHA8a2H2bjg13zH0G3KAKoHNCA9OY2No74h7GywRRr3il70WTgUB3cXws4Gs2HEYozpBmp2aUyHkc8hjRKjwcD2T1Zy+9hl3Hw86fXFu7h4lUIaJSd+3s2RH7flKZ7yHerT/NP+CJ2Oy6v3cuarPy3SNP+0PxU6NiQjOZW/Rywl0hSznZvT/7F33uFRFe/fvmc3vScQ2FATeg8JvSeUICiCYgGVqmIBVKSDhQ42FEFBbKAgWFAUpEpHOiT0loTQ0kjvbfe8f+wh2U02IQlg8v29c18XF5tz5pz57DPt2efMzKHLxy/h1rgWKAoHJ37NnZOhADQd1Yemo4Iw5Om5tSuEE/PXF6vBu0cres4ahtBqOLt+L8e+LKqh5+xh+AQaNWyduJJYVUNx1/aYMZR6vf0w5OaRdD2WbZNWkp2SkX8/5xpVGLXrAw59+jsnVm4p0UZeAa1oN9doo9B1ezm/rKi+tnOHUVO10eEJK0k4a9TX5OVHaPBcACgKSZducWjCSgzZufnXNX21P23ee45fW7xKdkJaiTqKwyPQl0bzRiK0GiLX7ub60j/Nzjs0qEGzJa/h3NKHsIXrubF8s/F4fS9arHwrP5193WqEf/grN+9hD0t4BbTCX7VR2Lq9XLRgI/+5w6nR0xd9Zg5HJnxFomqjRi/2pf7zgQghCFu7h8vfbAPArVkd2i0ajZWjHem37nBo7JfkpWWWWRuAY/c26N4dg9BqSPx5B/Ffmbddl8cDqPrKUwAYMrKIevcLsi9dw8anJrU+L1iqaF1bx53P1pCwytzG94ND1zZUn/kqaDQk/7aNhK/NtTn17EjVN4ejGAyg1xO7YCWZp84/kLwffX84jQNbk5uZw4ZJK4g8H1EkjXstT55dNh57Vyciz1/jtwlfos/V49OxKS+snEii2iee33acPZ//AUDnF/vR9tlAUBSiL9/k98lfkWdS70uDa4AfdeeORmg0xK77h6hlf5idr/JEd2qMHQSAPiOLiGkrybgQgU2NKtRf8gbW1dxRDAZi1+wk5tu/y24cC1QN9KXpvBGg1XBr7W6uLf3L7Lxjgxq0XPIqLi19uLLwZyLUtgZg5eJAi8Wv4NSkFihwbsIKkk5cvS891QJb0XLucNBquLF2D1cLtTunBjXw++wVXFt6c3HRL4QtN9pBY2tN143vobGxQlhpidx8lMsfbbgvLXepHtgKvznGPjn8p71cLqTJuYEX7T59BbeW3pxb9AtXVhT0N20Xv4xXHz+y41LYEVhkifBD4Z0Fi9n/7zE83N3YuGbFf5KntkVb7Ia+jhAacg5sJWfrzxbTabwb4TjzczJXzCfv5AEAbHo/gXX3foAgd/8Wcv75w+K19+JhjP0By8fhUt8rP01OSgZ/Bc0staaHMda2mvgkDZ4LICvB6MuFLPyFyN2nS62p//vDaaj6bH9M+oooC32kWy1Pnlk2Tu0jI/hd7SPvUqNVPcb8MZtfxi3lwtZjAHQc1Zc2Q4zj3sn1ezj83bZSa7qLU3d/vN4bAxoNib/sIG7Fb2bnXQcG4PnKYAAM6VlEvvslWeq4Vnvp1Px0NrV1xH62hvjvzfuz8vDK7FdoF9iO7MxsFk9cTNi5sCJpJi+ZTMNWDcnLy+NKyBWWTl+KPq/AXg1bNWTxn4tZNHYR/275t8waage0oovqF15ct5cQCz5ll9nDqKPWoz1vryTOxBcXGsHgv+eSHp3I1lGfANBu0lN4B/mjGBQy41PY8/ZXZMQklVmbpPQoirISWFnMaWHpknKkKTP/WVBCCPEWsFJRlIx7pNsLTFIU5YQQIk1RFKcHrOOB3/Nh3rcM+W8BnlMUJem/zrtVgD/VfbyYFjCOen4NGTZ/DPMGTS+S7urJS4TsPsG09XOKnLty/CJLXlxYbg0NAn3x8NHxRY+J1PRrQP95o/hu0PtF0vWaNoSj327l/KYj9J8/Gr9nAzi5ZhfX/j3HlZ0nAajWpDaDv3iD5b0mY9Ab2DlvLdHnIrBxtOOlzfMIP3iOuKu3S9QjNIKO80ewfegiMqISGLBlDjd2nCT5amR+mlo9fXHx0bGh60Q8/evTaeFINg+YBRgdllt7zrBnzOdorLVY2dsCoOvclDp927Cx93QMOXnYVXEpUUPveSP49flFpEYl8MKmOYTtPEm8iQafQF/cvXV8230iXn716TN/JGsHzirx2ogDZ9n/wc8oegPdpz9Lh7ED2L+wwPELfO95ru29t2MiNIL2C0awa4jRRv22zOHWdnMb1ejpi7OPjj+7TKSqf33aLxzJtsdmYa9zp8mLQWwKmIo+K5duK8bjPbAj4b8YnUuHGh54dW9B2q24e+ooFo2g8aLRBD8zn+zIeNptX0jc9hOkXyko+9ykNC7PXIVnv7Zml2aERXGs19T8+3Q9vYI7W46VWYLQCNosGMmeIQvJjEogaMtcbm8/RYpJ/fNSbbS5y0Sq+Deg7cJR7HzsfVwb16L+84HsePQ9DDl5BPw0ldu7gkm7FkP7j18ieM5P3DlyiXpDetD0tUc5+9FvJSgpzkYavGa9xvUR75AbHUe9Pz4lddcRckILAui5t2KIGDoNQ0oaTj3aUGP+eK4Nfpuca7cJHzA+/z6NDv1A6o5DZddQgrbq743l1ugZ5MbEUffXJaTtPkpO2I38JOlHQkjbfQQA20beeH02g4j+Y4q7Y6lpFNCaqj46Fge8TW2/Bjw+fzQrBr1XJF3faUP599utnN10mIHzR9Pm2UCOrfkHgIjjl/jxxY/N0rtUd6fTyL4s6T2ZvOxchix7g5YDOhH82/7Si9No8F7wMpeGzCYnKp7mWz4kaftxMq/eyk+SfTOGC4PfRZ+cjmugHz4fvsr5x6ah5Bm4Pmc1GWfD0Tja0WLbx6TsP212bbnQCJotGs3xZ+aTFRlPp+0LiN1+skhbuzBzFdX7tStyedN5I4jbE0LIS58irLVo1f7yfvS0WjiKQ88sJDMqnh7b5hG94xSpJnpyktI4+85qvB4xb/uG7Fz+HTwPfUY2wkpLt7/eJ3bXaRJPhd63Jv8FI9n/7EIyohLovXUukYU1JaYT/M4P1OzXpsjlEb8cIPT7nbT//NX701EGBvXvw3ODH2fG3I/vnfhBIDTYPz+e9E+moiTG4fjuMvJCDmOIulEknd1TL5F37mT+IU1Nb6y79yN93njIy8VhwkLyzhzDEFvyWF9UwsMZ+/e+tiz/+nbvPUdOSomudBFND2usvfj1Ni6uKHuwvWGAL1V8dCwJmEgtvwYMmD+KlRZ8tqBpQzj07VbObTrCgPmj8X82gONrduV/r6BpQwjdfyY/fbVGtWgzJJCVA99Dn5vHsNVTubw7mISImNKL02ioMfs1rg1/h7zoeOpt/JTUf46SbTKu5dyMJnzINAwp6cZxbcE4wp+cSM6124Q99kb+fRofXk3K9sNltk9h2ga2paZ3TV7q/hKN/Rozbv44JgycUCTdno17+OjNjwCYsnQKfYf0ZcuaLaocDaOnj+bUvlPl0iA0gq7zRrD5uUWkRyXw5OY5XN95kkSTelQn0BdXHx3ruk2kml99ui0YyR+Pz8o/3/LFR0gMjcTGyT7/WMiKvzn+sdH/aDEqiDZvPsGBGd+XS+P/AgZLP+crF7eA2iZ/1wIiy5GmzPyXyzfeAhz+w/zuG3Wzj0qNugOqRlGU/hURkADwC2rHod/3ARAefBUHZ0dcPd2KpLtx/hrxt+48FA2N+rThzAbjIHk7OBQ7FwecqhXV4N25ORfUH4enN+yncZDRoczNyM5PY+1gy92AX1psUv6Mi5z0LOJCI3Gu7n5PPVX96pMaEUPajTsYcvWE/3mEOn3NHcU6fdsQ+ttBAO6cCsPG1RH7am5YO9lTvUNjrq7bC4AhV5/vgDQZ3pszX2zCkJMHQFZ8SrEadK3rkxgRQ7Kq4dKmI9QPMtfQIKgN5zcYNUQFh2Hr4ohjNbcSr71+4ByK3vhSo8hTYTjpPMzul3zjDvFX7u3IVSlko4g/j1CrkI1q923DNdVGcSY2AhBWWrR2NgitBq29DZkxifnXtZn1AqfmrQel/IFbF/8GZF6LIet6LEqunpiNh6j6iPkPoty4FFJDwlBMntwUxqNbSzIjYsgqR4DEw68+aRExpKs2umHBRrX6tiHiN2Pdjz8Vio2rA3bV3HBpWIP4U6HoM3NQ9AZiD1+ktvqDzqV+De4cuQRA9P6z1H60fZm1Adj7NiLneiS5N6MhN4/kzftx7t3RLE3mqYsYUowzVTKCL2Olq1LkPo6dfcm5EUVu5IPrH+xaNSL3RiS5t4zaUrfsw6mXuTYlIyv/s3Cwu6/6YkrToDYE/24sk5vBodg5O+BsoU+s17k557ccBeDUhgM0C2pbJE1hNFot1nY2aLQarO1tSDWp96XBya8BWRFRZN+IQcnNI+HPg7j3NS//tBOX0SenGz+fuoKNl7HMcmMTyTgbDhifDmaF3sLaq2h5lhU3/wZkXIsmU21r0RsPUb3Qj/2cuBRSQsKLtDWtkz3unZpya+0eAJRcPXll+MFmCXe/BqRfiyHjhlHP7Y2H0RVqdzlxKSSFhGPIK9r29ep4orHWIqy0D6RemfYFSq6em38eoWYhTdnxKSSeDsdgoT+KO3KJnMTyzRgrL21bt8TVxfk/y09brzGG2EiUuGjQ55F7bC9Wfp2LpLPpNZDckwdRUpPyj2m86qAPuwQ52WAwkHf5DFb+Xcqs4WGN/ab4DOjAtT9L/0P3YY615aVJUBtC1D7yltpHOlnoI31MfLaQDftpatJHdhzZlwtbj5Nu4gd5NqjBreBQcrNyMOgNRBy9SLO+RQOZJWHv24js61Hk3jT2kcmb9+Pcp/C4dglDirGPzAi+hLWuapH7OHX2Jef6gxnXOgZ1ZNcGYzDmcvBlHF0cca9W1Bc9sedE/ucrIVeo6lWga8CoAfy79V+S4pPKpaFa6/qkRMSQqtajsL+O4F3Ip/QOasMV1aeMVX1KB7UeOeo8qNOzNRfV+n2XXJNZmqb+t6TCOA40FEL4CCFsgCFA4ak+fwHD1d+gHYFkRVGi7jfjhxKUEEI4CiH+FkKcFkKcE0K8D9QA9ggh9qhplqvrWM4LIWbf435VhRCHhRCPWjhXXQjxh5rXaSFEZ/X422re59RZGoWvE0KIj9TzZ4UQz6rHA4QQe4QQPwFnLVy3UQhxUtU9ptC5T4QQp4QQu4QQnuqx1kKII+orU/4QQrgLIZoKIY6ZXOcthDijfm4jhNin5rHd0sYhavqLQogvgVNAbSFEhGqnu+e+VjXuEELYq9e1U3UcvvvdS7J7aXGr7kFCZMEPrsToeNwt/PAoiQb+jZm99RMmrJpJjYa1731BIZx1HqRExuf/nRKdUCR4YO/uRFZKev4P6tSoBJx1BWka923La7s+Yuj3k/lrctFZTa61qqJrXpfbIUWnzBXGQedOemRC/t8ZUQk46twtpCnQnB6VgIPOHee6nmTFp9L10zE8vn0eXT56Kf9piUs9HdXbN+axTbPo99tMqvrWK8Em7qSaaEiLKmoTJ507qVEFGlKjE3DSuZfqWoCWz3bn2l7jUwpre1vav/YYhz77vUTbmH7/jEI2cvAqVGaFbRSZgL3OnczoRC4s38ITx5cwOGQZuakZRO0zVudaQf5kRieSdKHQ07EyYqfzIMsk7+zIeGx19w5IFab6E52J+aPs0yQBHHQeZJhoyIhKwL6IjTzMbJQRaaxHyZdu4dmhCTbuTmjtbajRszUONYwBpKTLN/N/0NR+rEP+8bJiVb0KuVEFbT8vOg7r6sW3ffdngkjbd7LIcZfHupO8aV+5NBSvrSq5UQXOYF50HFYWtDn17oz3lpXUWjGH6JmfPpC8Xaq7k2xSt1OiE3Ap3P7dnclKSceg9kcpUfG4mLSxOv4NGbd1ISNWTaFaQ+NSzZSYRA5+/TeTDy1l2rEvyUrNJPRAkWGqRGx0VcgxqS85UfFYexVf/p5De5O0J7jofWp54tDCh/RTV8qUvyVsdR5kmmjKikzAVle6OulQtxo58Sm0XPIanf9ZSPPFY9A63N9MCTsvdzM9mVEJ2JVgoyJoBAH/LOCRcyu4s/8sicH3HjPuhb3Og4zbhfqCcvRH/5cRblUxJBS0eSUxDo1b1UJpqmDl35XcvZvNjhtuR6Bt1BLh6Aw2tli1ao/Gw7PMGh7W2H+X6h0ak3knmZRrpX/y/7DGWoDGo/rw6D8L6Lj4ZWxcS//c0aW6B8mFfLaifaSTWR+ZbOKHOFd3p2nfthxf+4/ZNTGXb1G3fRPs3ZywtrOhUWBrXMrSdgFrXRXzsSPq3uNa6r4TRY67DuhO8qYyzGIrgaq6qtwx0RQXHUdVC4GQu2ittPR8sicn1fG2SvUqdO7bOX/WRHlw1LmTVsgvLFy3jWniLabpPOsFjixYB4aiQYf2U57mhaNLaPhEZ45//GCWu1VWDJXgX0koipIHjAO2AxeBXxRFOS+EeFUIcXeq3RYgHAgFvgZeL689THlYMyUeASIVRfFVFKUF8BnGaR2BiqIEqmlmKorSFmgF9BBCtLJ0IyFEdeBv4D1FUSwtXv0c2Kcoii/gD5wXQrQBRgEdgI7Ay0IIv0LXPQm0BnyB3sBHJgGA9qq+wq9AARitKEoboC3whhDibk/lCJxSFMUf2AfcnYf2AzBVUZRWGIMc7yuKchGwEULc/UX5LPCLEMIaWAo8pebxHTDfkl2Axhh3PvVTFOV6oXMNgS8URWkOJAGD1ePfA68qitIJKPbRrukGKJdTi+4NYSF9kWNKGZ4MXT8XzqQur/J+v4nsWrWVN1ZOvfdFRTQUPVZYgyWdpgHZy9tPsLzXZH55+VMCJj5tlszawZanV7zFjjk/klOKtfeWbVIq0Qitliotvbn0wy7+6vsOeRnZtBw3AACNVoOtqyObB8zi+Lx1BKwYV5KIe2oQlpaFKUqpru0w7nEMeQYuqj+4O7/9JCe/3WY266RESqOvGBvZuDpQu68/GztMYIPfeKwcbPF5sgtaextavPE4p8uzFKEU+sp8C2stVYPaELvpSDlvYOFY0UK0kEQhJTSSi19uInD9NALWTiXxwg0Mecbh6OjbK2k4sg99t83D2sk+f+ZN2fWV3kYOHVvh9nQQsR8WmpZpbYVzrw6kbDlYPg1lwUK3lPbPISL6j+H2uDlUfWP4A8mmdO3fgjw1UeS5CD7q8gbL+k3n8KodPL9yIgB2Lo407dOGj7u9yaIOY7FxsMV3UBmf5pZhJahL5xZUG9qLm/N/MDuucbCj0TdTuP7ed+jLuRfJPTWV8mmZsNLi0tKHG6t3cqj3dPQZ2fiML7xReBnlFNPvlBqDwt7eM9juNw43v/o4N6l1X3qMmixJkk8UzbDYH5nbyG7o62T/9g0o5q65IeoGOVt/xmHiBzhMWIDhZjiKvvgZcMVLeDhj/13qDepEeBlmSRSX3/2OtQBXVv/Dn53e5u8+M8mMScL//efvR1LR+lyCz9bvvWHsWLQepdAP3LiwSA6u2MSINdMYtnoq0Rdv5Ac17oti2ppjx5a4PxNEzAerzKVbW+Hcqz3JWx/euFZS+x87fyznjp3j/DHjHkljZo3hu4XfYTDchy3KWbcVRaFOr9ZkxacQp+5TUphjH/7Kmg5vcvWPQ7QY2af8GiUPBEVRtiiK0khRlPqKosxXj61QFGWF+llRFGWser6loihFo3Ll4GHtKXEW+FgI8QGwWVGUAxY6vGfUmQZWgBfGd6GeKZTGGtgFjFUUpbjHaD2B4QCKouiBZCFEV+APRVHSAYQQvwPdANPHPV2Bdeo1MUKIfUA7IAU4pihKcb/G3xBCPKF+ro0xABCPMfh0d2H9GuB3IYQr4GaifTVwd6e1X4BngEUYgxLPYgw0tAB2qvbSAsVNh7muvobFEtcURQlRP58EvIUQboCzoih3F23/BDxm6WLTDVBGeQ+22Ov1HPYIPYb2NmZ2OhSPGgURW3ddFZJiEixdZpEsE6f2zN5TDJv3Mk7uzvkbYRZH2+F98BtijHFFngnHpUZBJNtF50FabJJZ+oyEVOxcHBFaDYregLOXh8WpzzeOXcK9bjXs3Z3ITExDY6Xl6RVvcXbjv1zaVrp2lx6VgKPJ02cHLw8yCuWVEZWAo4lmRy8P4+Y+ikJ6VAJx6tO1iL+P5Tsm6VGJXN9q1BAXEo5iULD1cCY7oaitUqMScDbR4OTlQVqsuYbU6AScTaZfO+s8SItJQmNtVeK1zZ/qRv1efvwytGAfEC+/BjTq357u04dg6+KAoijos3MJXr3Too0yohLMntA7eHmQGW3ZRnefDzjW8CAzJgldtxak3byT/71vbDlB1bYNSbxwHac6njz6z4L8e/bfPo+t/d8n606yRR3FkRUVj51J+djWqEJ2dNH6UhJVevmRevYaOWXM+y5GGxVoMNooySxNpmqju/MVHFQbAYSv20f4OmP302raM2REGdtlamgUe4cuAsC5no4avVqXS19edBzWJtNDrXRVyY2JL5LOtrE3NRa8wY3R76FPMq+rTj3aknU+DH05p5QWqy0mDmuvgiedVrqq5MUW1XaXzBPnsK7jhdbNBX1S8cuiiqPDsD60G2rsj26dDsfVpG676Ir2NXf7I41Wg0FvwMWrCqlqn5Vt0ide2RvC4/NG4eDuTL1OzUi8GUuGWu/PbztO3TaNOL2x9DNxcqLisTGpUzZeVciNLtpf2zeti8/Hr3P5hbnkmUz7F1ZaGn4zmbjf95O49Wip8y2J7KgE7E002dXwKHVby4qMJzsygWR1z4aYTUfxGf/4fenJjDTXY+/lQVYZ2z5AXkoG8YcuUi3Ql9RL97fvRkZUAg41zfuCLLkZnBlK4h2z2Q3CvSqGJPM2r63bEPtXZhjPO7li1bIdWQY9ecGHyD24jdyDxk0RbZ8cjSGx7NPuH9bYDyC0Gur2a8df/d4tk6aHMdZe+/1fsuIK+snQtXsI/GFiiTraD+tDG7WPvH06HNdCPltqofpcuI909fIgVfVDarby4emlxocyDu7ONAzwxaDXc2nHSU79so9TvxjHvd6TnyE5qvT+KEBudLz52OFVldzYovewbeJNzYVvEDH6fQvjWhvjuBaXVOS60vLY8MfoO7QvAFfPXMXTRFNVXVXiLYy1AM+99RyuHq4snbY0/1jDlg2Ztsy4wa2LhwvtAtthyDNweEfpA1zpUQk4FfILC9dtY5oqhdIkUa9/e+r28adOoC9aW2usne3pueQ1dr+53Oz6qxsP0X/1JE4sLt2MW8n/LR7KTAlFUa4AbTAGJxYKIcx2+BJC+ACTgF7qDIK/ATsLt8rD+KO6r8m184UQIUKIkBIklObxXUlp7gYzat/NS522EoBxVkUndWZGcDG64d6PeX7GGJhphDHodFXVdF5RlNbqv5aKogQV1mGqsRhMH1XrMQZ+HujWKrt/3Mb7/Sfxfv9JnNpxjM5P9gCgnl9DMlMzSL6TVOp7uZisI/TxbYAQ4p4BCYATP+zk6/4z+Lr/DC7vOEGrwd0AqOnXgKzUzCJBCYCIwxdo1t+4ftp3cHcuq5tbutetnp9G18IbrbUVmaojPuDDl4kLvc3Rb7aW+jvFhYTj4qPDqbYnGmst9QZ25OYO882Fbuw4RYOnugLg6V+fnJQMMmOTyLyTTHpkQv5O215dm5Ok7tFwY/sJvLoYJ/C41NOhtbGyGJAAiD4djruPDldVQ5MBHQnbaa4hbOcpmg82avDyq092agbpsUklXuvdoxXtX3uMP15cTF5WTv691j81l6+7TODrLhM49d12ji77q9iABEB8SDjOPjoc1Ty8B3bkViEb3dpxCh/VRlVNbJR+O56q/g3Q2tsAoOvanJTQ2yRdusVvrcayscMENnaYQEZUAlv6vlPmgARAanAYDvV02NXxRFhrqT6oM3HbyxYM1j3RhZg/yr95Y0IhG9UZ2JFbO8yXP9zecQrvp4x1v4p/A3JTMslS676tuhGqQ80q1O7fjusbD5kdRwiavzmI0B93lUtf5pkr2HjXxLpWdbC2wvWx7qTtMv+hauXlSe3lM7k96RNyIorug2Sc4vpgl24AZJ29gnXdGljXNGpz7t8jf1PLu1jXKVgdZ9usPsLaqlwBCYCjP+5kWf8ZLOs/g4s7TuD3pLFMavs1IDs1k1QLfWL44Qs0798BAP/B3bi4w1i/nDxd89PU8q2PEIKMxFSSIuOo7dcQaztjva/fpTmxoWXbiC8tJBQ7Hy9sa1dDWFvhMbAriTuOm6WxqVmVRt9MIeyNJWSFm8fFfT4ZS+bV20SvLLrrenlJVtuavdrWdIM6E7u96DIfS+TcSSYzMh5Htb+s0q2F2QaZ5SEpJAzHejocVD01B3Uiekfp9NhUccbKxTiNXWNnjWe3FqSF3vf+XySGhOPko8OhtlFT7YEdiSyljf5/QX/tMprqNRFVdaC1wrp9AHkh5j+60qYNJ23qMNKmDiP35AGy1iwlL9jYLwpnN+P/Hp5Y+Xch9+ieMmt4WGM/QI1uLUgOjcwPLpeWhzHWAvl7TgDU7teWpMslB96O/biT5f1nsLz/DC7tOEFrtY+sdddns9BHXjPx2VoP7s5FtR1+2m0Cn3Z9i0+7vsWFrcfY/O4qLqnnHNXxzbVGFZo+0o6zf5VtDM48cwVb7xpY16qOUMe11H/MxzXrGp7U+XIGNyd+Qs41S+NaD5Luc+nG5h82M77feMb3G8/h7YfpNbgXAI39GpOemk5ibNFAad8hffHv7s8H4z4wm0kxuutoRnUZxaguozi45SBfvPNFmQISALGnw3H11uGs1qP6j3ckopBPGbHzFI1Un7KaX31yUjPIiE3i2Ae/sKb9G6ztPIF/xn5B5L8X8gMSrt4F/rd3H38SQ+97a4JKjVIJ/lVWHspMCSFEDSBBUZQ1Qog0YCSQCjgDcYALxh/VyeryjH7AXgu3UoDRwK9CiGmKoixSFGUmYPoepF3Aa8BnwrgxpSOwH1glhFiE8cf4E8CwQvfeD7wihFgNeADdgclAk/zMFeUmxiUed7/XQCBRUZQMIUQTjEtD7qIBngLWA88BBxVFSRZCJAohuimKckDVsE+9d5gQQg+8S8EMi8uApxCik6Ioh9XlHI0URTlfSIe3BVuViKIoiUKIVCFER3WGxZCy3qM4zuw5RatAfz7Y9wU5mdl8O/mL/HMTvp/J91O/JCk2kd4j+9PvlUG4eroxZ9tizu45xffTltOuXycCX+iLXq8nNyuHFePLvq47dHcIDQJbM3b/YvLUV4LeZciqyWye8jVpsUnsWriOJ5eNJ2DS00Sfv07Iz3sBaNqvHa0Gd0OfqycvO4ffxxojzLXbNqLV4G7EXLzBy1uMT9/3fPQzoXtKfruEojdw5J3VBP00BaHRcPXnfSRduU3jYT0BuPzjbm7tCqFWT18G//sJ+swcDrxdsI/F0XdX02Ppa2isrUi9EctB9dzV9fvo+skYBu1aiCFXz4G3vrKY/10Nu95dzeAfp6DRajj78z7ir9zG9wWjhtNrdhO+OwSfQF9eOvAJuZk5bJu0ssRrAXrNHYHWxoqn1xqj7pHBofxTjp2SFb2B4zNX0+unKQithrD1+0i+cpuGqo2u/rib27tCqNHLl4GHPiEvM4fDE4z64oPDuPH3Mfpvn4eSpyfh3HWurim7A3kvfZenf4ff+hmg1RC1bi/pl29Rc7hxhtDtH/7BxtOVdjsWYuVsj2JQqD2mP0e6TUSflonG3gaP7i25OKm4ty6VTsOJmasI+Gmq8TWA6/eRcuU2DYYZnZPQH3cRuSsEr16teezQYvSZORydUFAnun7zJrbuzhhy8zgxYxW5ycZN0+oO6kRDdYrkra3HCV9fzqCA3kD07OXUWTUXodGQ9NtOsq/ewH1oPwAS123Fc/xQtG4ueM1+Xf1Oeq4NegsAYWeLYxc/omYuKy6H8qM3EDt3ObW+nQcaLckbdpATegPXZ/sDkPzzFpyDuuIysBdKXh5Kdg5RExY9kKwv7wmhUWBr3t73KbmZ2fw+uaBMhn8/hT+mriQ1Nonti9YxZOl4+kx8msjz1znxy14AWvTrQPsXemNQ+8Sfxxv7o1shYZzfepSxfy/AkKcn8nwEx9ftLrNdImZ+Q+Of3kNoNdxZv4vMKzepNiwIgNgfd1BzwjNYuTvjvdC4bZKSp+d8vyk4tW+C59MBZFyIoMVO4+vcbi5cS/Lu8u3mfhdFb+DC9O9pu34GQqvh1ro9pF2+RW21rd1U21rnHQvy25r3mH4c6DYJfVomF2d8T6svx6GxsSLjeixn37y/108qegNnZqyi07ppCK2GG+v2knr5Nt7Dje0u4odd2Hq60mP7PKyc7cGgUP/lR9jdfQp21dzw+/w1hFaD0Ahu/3WEmJ1F9+Qoj6bgGavovs7YF1xT+4J6qqZwVVPvbfOwdrZHMRho+HI/tveYQl5aJh2+HItn56bYejjz6MmlnP/4NyLWPfhgoCmT31/E8eAzJCWl0GvQC7z+4jAGD+h77wvLi8FA1tplOExYiNBoyDm4HUPkdax7GCeF5u7bXOLl9q+/h3ByAX0eWWuXQUbZNwZ9WGM/gM/AjmVfusHDG2v93hmCe/O6xhket+I4OuW7Umu6sieEhoGteWvfYuMrQU36yBe+n8yfU78mNTaJnYvW8fTS8fSa+DRR569zSu0jS2LI8jexd3fGkJfH3++uIqusG9/qDUTOWoH36jkIjYbEX9Vx7Tl1XPtpK57jh2Dl7kKNOepSer2eMPVtGMLOFqeurYl858GNa8d3H6ddYDu+PfAt2ZnZfDqpwE+evWo2S6YuISEmgXELxhF7O5ZPNhr750PbDrFuyboHokHRGzj47moeXWOsR5d/3kfilds0U33KC2t2c2N3CHV6+jL0oLEe7Z14b/+nw/RncavvhWJQSL0V93/6zRuSkhEPY02iEKIv8BHGJQ25GIMGnYCxQJSiKIFCiFUY93wIx/hk/y9FUVYJC68EFcbdPzcBfyqK8mWhvKpjXGpQD+OsgNfUH/RvYwxoAHyjKMpnavq79xTAhxgDIgowT1GUn9XZEJMURSmytEEIYQtsBGqiBhCAWYqi7FWDL58C/YFk4FlFUe4IIVoDKzC+eSQcGKUoSqJ6v0mqnXwURYlQj7XGuE+GK8ag0WeKonxdSIc3xmUxLUyORWDc58LJ9Jyah5OiKLOEEB0wbkiSjjEI1F1RlBIXJBe3fKOiqKcUNzGl4qiVV7ne7xNfCd8ZoyvnlgUPCy99bkVLKEKs9j97Q3Op8LVPqmgJRdBaP4C1wQ+QH7Iq30aDj+dk3TvRf0ii3qaiJRQhW/yXLx4rHTkPYA+bB8mgs3MrWkIRMqe+UtESzNiwrfq9E/3HWFcqjw2u2FQyQcAzmvItp3xYTMmrXOMawONK+Ta/fpi8enNN5eoky8n8us9XeKOYeX1tpbTlQ/GCFUXZjnHXTlNOYNzE8W6akcVcG2Dy2Un9PweTJRyF0scARXa1UhRlMbDYwvG791QwzoyYXOj8XizP2kBRlGyMQQxL55zUj+8WOh6C+YwK03MfAx9bSN/dUnqTNBEY954wPeatfowzPafmcZfz6nIZhBDTMJaJRCKRSCQSiUQikUgkFULlejQnedg8KoSYjrHcr2NcViORSCQSiUQikUgkEkmFIIMS/x+hKMrPFOxfIZFIJBKJRCKRSCSS/4DKt1in8lD5FlZKJBKJRCKRSCQSiUQi+f8COVNCIpFIJBKJRCKRSCSSh0iF73JZiZEzJSQSiUQikUgkEolEIpFUCDIoIZFIJBKJRCKRSCQSiaRCkMs3JBKJRCKRSCQSiUQieYjIjS6LR86UkEgkEolEIpFIJBKJRFIhyJkSEolEIpFIJBKJRCKRPEQMoqIVVF5kUEJSIlpk6/lfozKWWGXTJKfP3ZucPG1FSyiCTUULKERuJdxHW6upXLVbo69oBUWxUipfueWIytVLZk59paIlFMH+g68qWoI5296raAVFqFy1CKwqX1MjT1/ZJolXrj5bIqkoKlvLlEgkEolEIpFIJBKJRPL/CXKmhEQikUgkEolEIpFIJA8RQyWcYVlZkDMlJBKJRCKRSCQSiUQikVQIcqaERCKRSCQSiUQikUgkDxE5T6J45EwJiUQikUgkEolEIpFIJBWCDEpIJBKJRCKRSCQSiUQiqRDk8g2JRCKRSCQSiUQikUgeIvIFsMUjZ0pIJBKJRCKRSCQSiUQiqRBkUEIikUgkEolEIpFIJBJJhSCXb0gkEolEIpFIJBKJRPIQMcj3bxSLDEpIHghD3x9Ny0A/cjJz+G7SMm6cv1YkTeDwR+gz+lGqeXvxlt8o0hJTAWjcsTljV04h7lYsAKe2HWXz57/dl56+s4bTINCX3Mwc/pr0FdHnIoqkcavtyZNLx2Hn5kT0uQg2TvgSQ66eRn3aEDDxKRSDgkGvZ8fsH7l54kqZNdQMaEWHOcMQGg1X1u3l7BebiqTpMGcYtXq2Ji8zm4MTVhKv6rRxcaDLxy/h1rgWKAoHJ37NnZOhuDerQ+dFo7B2sCP11h32j1tOblpmsRq8e7QicNYwhFbDufV7OfZlUQ2Bs4fhE2jUsG3iSmJVDcVd69msDr0XjMbK1hqDXs+umauIPh2OnZsTA1a8gc63Hud/3c/u9364p428AlrRdq7RRqHr9nJhWVF9beYOo6Zqo8MTVpJ41qiv8Yt9afB8AAhB6No9XP5mOwCtJj9Frb7+KIpCdlwKh9/6isyYpHtqsUSVQF8azxuJ0Gq4vXY3EUv/NDvv0KAGzZe8hktLH0IXruf68s3G4/W9aLXyrfx09nWrEfbhr9xYuaXMGrwCWuGv2ihs3V4uWrCR/9zh1Ojpiz4zhyMTvsq3UaMX+1L/+UCEEISt3cPlb7YB0HnFeFzqewFg7eJAbkoG2/rMKLM2AOce/tR8/yWEVkv8+h3ELt9gdt59UA+qvToYAENGJjdnLifrolFfs4Nfo0/PBL0BRa/nyoCJ5dJgimP3NujeHYPQakj8eQfxX/1qdt7l8QCqvvKUqieLqHe/IPuSsb/yGDUIt2eCAIXsy9eJnPIpSk5uuXQ8/v4ImgS2Jjczh18mLef2+YgiadxrefL8sjdwcHXk9vkI1k/4An2unh5jHsNvUBcANFot1RrUZLb/GDKT05l28HOy0zJRDAYMeQY+f3xmmbW5BPhRZ/ZLoNUQt24n0V/8bnbe44nu6F5/EgBDehbXp68g86KJfo2GZls+Jic6ntCR88ucvyWqBPrSZN4IhFbDrbW7iVj6l9l5hwY1aLHkVVxa+nB14c/5bQ3AysWB5otfwalJLRQFzk9YQfKJq/elxzPQl2bzhiO0Gm6u3UNYIT2ODWrgu+QVXFr6cGXhz4Qv/9v8BhpB1x0LyIpO4MQLH92XlrtUD2yF3xxjnxz+014uF+oLnBt40e7TV3Br6c25Rb9wZUVBf9N28ct49fEjOy6FHYHTHogebYu22A19HSE05BzYSs7Wny2m03g3wnHm52SumE/eyQMA2PR+Auvu/QBB7v4t5PzzxwPRVBLvLFjM/n+P4eHuxsY1Kx5aPg9j7AdoOqoPTUcFYcjTc2tXCCfmry+1pvsZa5u8/Aj1nwsARSHp0i0OT1iJITsX9+Z1aL9oNBo7a5Q8PcenryI+JLzUmvrOGk5D1U/7swQ/bbCJn/aHiZ8WaOKnbS/kpwmN4KXN80iNTmT96I9LrekulW1cA3hl9iu0C2xHdmY2iycuJuxcWJE0k5dMpmGrhuTl5XEl5ApLpy9Fn6fPP9+wVUMW/7mYRWMX8e+Wf8usoXZAK7qofuHFdXsJseBTdpk9jDpqPdrz9kriTMpVaASD/55LenQiW0d9AkDHmUOp29sPQ24eKddj2TNxJTkpGWXWJvnf5z8NSggh3gJWKopSYm0TQuwFJimKckIIkaYoitMD1lHuewohvIHNiqK0KMM1q9Rr7u+Xdsl5bAGeUxQl6WHlURwtA/yo5uPFjIDx1PNryAvzx7Bg0PQi6UJPXubM7pNMXj+7yLmrxy+x9MWFD0RPg0BfPHx0fNFjIjX9GtB/3ii+G/R+kXS9pg3h6LdbOb/pCP3nj8bv2QBOrtnFtX/PcWXnSQCqNanN4C/eYHmvyWXSIDSCjvNHsH3oIjKiEhiwZQ43dpwk+WpkfppaPX1x8dGxoetEPP3r02nhSDYPmAUYHZZbe86wZ8znaKy1WNnbAtDlo5c4PvcnYo5couGz3Wnx2qMEf2S5WgmNoNe8Efz2/CJSoxJ4ftMcQneeJMFEg0+gL+7eOr7rPhEvv/r0nj+SnwbOKvHa7jOGcviz34nYewafQF+6zxjKL8/OJy87l0Of/EaVxrWo2qhWqWzUbsEIdg8x2uiRLXO4tf0kKSb6aqg2+qvLRKr416f9wpFsf2wWro1r0eD5ALY9+j6GnDwCf5pC5K4QUq/FcGH535xRbdL4xSBaTniCY9O+L1P5AaARNFk0mlPPzCcrMp4O2xdyZ/sJ0q/czk+Sm5TG5Zmr8OzX1uzSjLAojvSamn+f7qdXELvlWJklCI2gzYKR7BmykMyoBIK2zOX29lOkXC3Q4NXTF2cfHZu7TKSKfwPaLhzFzsfex7VxLeo/H8iOR9/DkJNHwE9Tub0rmLRrMRx6dWn+9X7vPU9OajkdAI2GWnNfIez598iNjqfRX5+Q/M8xsq/ezE+SfTOG0Gemo09JxznAn9oLx3J1UEF7Ch0yE70aoLxvNBq8Zr3G9RHvkBsdR70/PiV11xFyQgv05N6KIWLoNAwpaTj1aEON+eO5NvhtrKpXwWPEAML6voaSnUPNz6fhMqAHyRv+KbOMJgGtqeqj48OACdTxa8AT819k2aB3i6TrP+05Dny7hdObDvPk/Bdp92wgR9b8w76Vm9m30viju2kvf7q92J/M5PT8674aOo+M8tpMo6HOvFe48tz75EbF0/Tvj0jacYysq7fyk2TfiOHyUzPRJ6fjEuhP3Q9f59KAKfnnq7/4GJmht9A62ZdPQxFNgqaLRnNSbWsdty/gzvaTZm0tLymNSzNXUa1fuyKXN5k3grg9IZx+6VOEtRat2l/ej57mi0Zx9JkFZEXG03X7fGK2nyStUNs/P3M1ukJt/y4+L/cj7eptrJwfnI38F4xk/7MLyYhKoPfWuUTuOEWqiaacxHSC3/mBmv3aFLk84pcDhH6/k/afv/pg9AgN9s+PJ/2TqSiJcTi+u4y8kMMYom4USWf31EvknTtZ8FVqemPdvR/p88ZDXi4OExaSd+YYhtjbPEwG9e/Dc4MfZ8bcsv9ILS0Pa+zXdW5Knb5t2Nh7OoacPOyquJRJU3nHWnudO41fDGJzwFT0Wbl0XTEe74EdCf/lAH7vDOXs4t+J3HOGGj198XtnKP88VbogZYNAX6r46Fim+mmPzhvFt8X4aUdK4ac99cUbfGnip3UY/QhxoZHYlqePqmzjGtA2sC01vWvyUveXaOzXmHHzxzFh4IQi6fZs3MNHbxqDoFOWTqHvkL5sWbNF/VoaRk8fzal9p8qlQWgEXeeNYPNzi0iPSuDJzXO4vvMkiSb1qE6gL64+OtZ1m0g1v/p0WzCSPx6flX++5YuPkBgaiY1Judw6cJaji35G0RvoMP1Z/MYO4OhCywHO/wvIeRLF81/vKfEW4PAf53lfCCG0Fa2hJIQRjaIo/SsiIAHQOqgdh3/fC0B48FUcnB1w9XQrku7m+WvE37rz0PU06tOGMxuMT2NuB4di5+KAU7Wierw7N+eC+kPx9Ib9NA4yOpe5Gdn5aawdbClPF1LVrz6pETGk3biDIVdP+J9HqNPX3FGs07cNob8dBODOqTBsXB2xr+aGtZM91Ts05uq6vQAYcvX5UWPX+l7EHLkEQOSBc3j3L+qg30XXuj5JETEkqxoubzpCgyBzDfWD2nBhg1FDVHAYti6OOFZzK/laRcFWdbRtnR1Ii0kEIC8zm9vHr6DPKt2T5SqFbHT9zyPULmSjWn3bEK7aKF61kV01N1wb1iDuVBj6zBwUvYHYw5eorf44yDOZOWJlb4uilG8IcPVvQMa1GDKvx6Lk6oneeAjPR8ztnRuXQkpIGEquvpi7gEe3lmRGxJB1K67MGjz86pMWEUO6aqMbfx6hlgUbRfxmrO/xp0KxcXXArpobLg1rEH8q1MRGF6lt4Qdd7cc7cH3joTJrA3Bo3ZDsiChybsag5OaRuOkArn06mKXJOHkJfYrxB3XGqctYe1UtV16lwd63ETnXI8m9GQ25eSRv3o9z745maTJPXcSQkmbUE3wZK12V/HPCSouwswGtBo29LXkx8eXS0SyoDad+N5bJjeBQ7J0dcLbQJzbo3JyzW44CcGLDfpoHFf2B2/rxzoT8Vb7ysYTj3TK7YSyzhD8P4hZkXmbpJy+jV4Mg6acuY+NVYCNrryq49mpL3E87H5gmY1uLNmtr1R4xt0VOXAopIeFF2prWyR73Tk25vXYPAEqunrz7fMrmVkhP5MbDVLegJzkkHIOFtm/n5UG1Pn7cVDU9CEz7AiVXz80/j1CzUF+QHZ9C4mnLmuKOXCInMe2B6dHWa4whNhIlLhr0eeQe24uVX+ci6Wx6DST35EGU1KT8YxqvOujDLkFONhgM5F0+g5V/lwemrTjatm6Jq4vzQ83jYY39TYb35swXmzDk5AGQFZ9Sak33M9aCsV/U2tkgtBqs7G3IUMd8RVGwVn0BaxcHMtXjpaFxnzacNvHTbIvx03xM/LQzG/bTxIKfZuNgi2LipznrPGjYszXB68vX/irbuAbQMagjuzbsAuBy8GUcXRxxr+ZeJN2JPSfyP18JuUJVE10DRg3g363/khSfVC4N1VrXJyUihlS1HoX9dQTvQj6ld1Abrqg+ZazqUzqo5eqo86BOz9ZcVOv3XW7tP4eiN76TIiY4DCcvj3Lpk/zv89CCEkIIRyHE30KI00KIc0KI94EawB4hxB41zXIhxAkhxHkhRNHH5+b3qyqEOCyEeNTCuepCiD/UvE4LITqrx99W8z6nztIofJ0QQnyknj8rhHhWPR4ghNgjhPgJOGtBjpUQYrUQ4owQ4jchhIN6XRshxD4hxEkhxHYhhJeFPHsJIYLV/L4TQtgKIdoLIX5Xzw8UQmQKIWyEEHZCiCJz4YQQ3kKIi0KIL4FTQG0hRIRqo7vnvlbtukMIYa9e107VfPju9y7J5qXFrXoVEiILnPfE6ATcTBz90lDfvxHvb/2YN1fNpEbDez9lLwlnnQcpJnpSohNwrm7eedu7O5GVkp7fEaZGJeCsK0jTuG9bXtv1EUO/n8xfk1eWWYODzp30yIT8vzOiEnDUuVtIU6AzPSoBB507znU9yYpPpeunY3h8+zy6fPRS/tOSpMs3qRPkD4D3Yx1wrFF85+2kcyfVRENqVAJOhezgpHMnNapAQ2p0Ak469xKv3TN7Dd1nDGXMkSV0f2coBz4oX0TbXudORiEb2XsVtVGGiY0yIo02Srp0i2odGmPj7oTW3oYaPX1xqFFQ53ynPs2gE0vwfrIzZz4yn3ZZWmx1HmSb5J0dGY+trqgTcC90T3Qm+o+yT5MEcNB5mH9/Czay13mY1aO7Nkq+dAvPDk1MbNQah0L1xbNDE7LuJJN2LaZc+qx1VciNKgi25EbFYV1C2/cY0ofUvQVPTBWg/po5NNq8mCpD+5ZLgylW1c315EXHYV29eD3uzwSRts+oJy8mnvhvfqfRgVU0OrwGQ2o66QeDy6XDtboHSSZlkhSdgKvO3PYO7s5kpqRjUPug5Kh4XKubp7G2s6FxD1/Obj1acFBRePnH6byxaT4dhvYsszYbLw9yTGyUEx2PTQlOYNUhvUneU/BkrfasF7k1fzWUM9hnCTudB1km9sqKTMBWVzrH1KFuNXLiU2i+5DU6/rOQZovHoHW4v5kSdjp3Ms30xGNXhrbfbO5wLs75CcXw4F7+Zq/zION2ob6gHP3Rg0K4VcWQUPCQQUmMQ+NWtVCaKlj5dyV372az44bbEWgbtUQ4OoONLVat2qPx8PxPdD9sHtbY71JPR/X2jXls0yz6/TaTqr71Sq3pfsbazOhELi7fwqDjS3gyZBk5qRlE7zO6jiffW4Pfu0MZdGIJ/u8OJWRB6X2Bwn5aain8tBQLftrrqp+2ycRP6/v+MP5ZsA7FUL4+qrKNawBVdVW5E1XQ3uKi46iqKz4QorXS0vPJnpxUx7cq1avQuW/n/FkT5cFR506aST1Ks1C3jWniLabpPOsFjixYByWUS5NnunNjz5lya5T8b/MwZ0o8AkQqiuKrLnX4DIgEAhVFCVTTzFQUpS3QCughhGhl6UZCiOrA38B7iqL8bSHJ58A+RVF8AX/gvBCiDTAK6AB0BF4WQvgVuu5JoDXgC/QGPjIJJLRX9TWzkF9jjMtQWgEpwOtCCGtgKfCUoihtgO8As3lsQgg7YBXwrKIoLTEun3kNY2DhrrZuwDmgnar9KJZpDPygKIqfoijXC51rCHyhKEpzIAkYrB7/HnhVUZROQLGPdoUQY9Rg0YlLqfdeHyiEhYNlcFivnwtnapfXmN1vErtXbWHsyqmlvra0ego/LRcWExV8vLz9BMt7TeaXlz8lYOLT5dBQ9P5FTGJZKEKrpUpLby79sIu/+r5DXkY2LccNAODg21/TZGQfBmydi7WjHfrcvDJpKDzpQ2BJp1Litb7DerF3zlpWdnyTvXPW0vejl4vVUBKl0VecjVJCI7nw5WZ6rZ9Gz7VTSLpwA4PJusnTH/zKxrZvEvH7IRqN7lMufZYrdhlvYa3FM6gNMZuOlPMGFo4VrkjF1PeU0EgufrmJwPXTCFg7lcQLNzDkmf9IqjuoEzc2Hi6ftuIzt5jSqVNLqjzbh8iFq/OPXX1yKlcenUD4iNlUHd4fx/bN70MLZSozh46tcHs6iNgPjUt7NC5OOPfuyNWA0VzpPAzhYIfrwMB73KX0Oor2QUUvK5ymWW9/Ik5cNlu68eXgWSx5bAbfjvyATsOD8GnfpKziLORrOaVz5xZUHdKbW/ON+8O49mpLXlwyGWeLrmW+LywWW+nGEGGlxbmlD7dW7+RI7+noM7LxHj/wPvWUv+1X6+NnnNVxpui+SvdDaerLf4rlgd/sL7uhr5P92zegmPc7hqgb5Gz9GYeJH+AwYQGGm+Eo+uJnm/0v8bDGfo1Wg62rI5sHzOL4vHUErBh3X5pKO9bauDpQq68/f3aYwO9+47FysMX7SeOsloYjenHy/bVsbPsmJ2etpcPi0vsCpfEb72XLy9tP8GWvyfxs4qc17OlHenwyURb2pyg9lWxcK4aS2v/Y+WM5d+wc54+dB2DMrDF8t/A7DPcTKC1n3VYUhTq9WpMVn0Kcuk+JJfzHP46iN3C1nA9x/lcwVIJ/lZWHuafEWeBjIcQHGPdTOGChg3lGCDFG1eEFNAMKh8isgV3AWEVR9hWTV09gOICiKHogWQjRFfhDUZR0AHUmQjfA9NFXV2Cdek2MEGIfxmBACnBMUZTivIqbiqLcbTVrgDeAbUALYKf6PbVAVKHrGgPXFEW5uxvPavV7fSaECBVCNMUYDFkMdFfvcaAYDdcVRSnul841RVFC1M8nAW8hhBvgrCjK3XnAPwGPWbpYUZSVwEqAl7yfstjrBQ57hG5DewEQcToMD5On1O46D5JiEixdZpEsk+n2Z/cG8/w8LU7uzvkbYZaGtsP74DfE+AMi8kw4LiZ6XHQepMUmmaXPSEjFzsURodWg6A04e3mQamHq4Y1jl3CvWw17dycyyzD1NT0qwWwWg4OXR/6Ux3wNUQk4muh09PIgIyYJFIX0qATigo2Of8Tfx/Idk+SwKHY894Hxe9XTUatX62I1pEYl4GyiwdnLg7RYcw2p0Qk4m0zNdtZ5kB6ThNbaqthrmw/uxp73fwTgyuajBH3w0j3tYYmMqASzJ/cOXh5kRhe1kekMCIcaqo2AsHX7CFtn7BJ8pz1DRlTROhfxxyECfpzE2Y9/L3LuXmRHxWNrkrdtjSpkRxetIyVRtZcfqWevkXMnucz5g4Xv7+VBZnSSWZpMtR7dfa7jUMMjf2PP8HX7CFdt1KqQjYRWQ+3+7dj2yDvl0gaQGx1nNm3V2qsquRbavl0Tb2p/MI7wEbPRJxW067xYY9q8+GSStx/BoXVD0lUnqjzkFdJjpatKroUlGLaNvamx4A1ujH4vX49jl9bk3IxBn2CcFp26/RD2/k1J/rN0U4A7DeuTP3Ph5ulw3EzKzU3nQUqh9p+ekIq9iyMarQaD3oCrVxVSCrVP3wFFl27cTZMen8L57cep7Vufa8culUojQE5UPDYmNrLRVSE3umiZ2TetS90Px3F12Jx8Gzm1a4JbUDtce7ZBY2uNxtkBn8/f4tobn5U6f0tkRSVgZ2IvuxoepW5rWZHxZEcmkHzKuBlgzKaj+Ix//L712JvpqUJWKfW4t29Mtb7+BPZqjcbOGmsne1p/MZaQsV/cl6aMqAQcapr3BVnl3MD3QaAk3jGb3SDcq2JIMm9r2roNsX/FuIGucHLFqmU7sgx68oIPkXtwG7kHjRvv2j45GkPiw1/a+V/wsMb+9KhErm81Ts2PCwlHMSjYejiTnXBvP+l+xlpdtxak3byTn8/NLSfwbNuQiN//pd7T3Tj5rtEXuLHpKB0/LtkXaDu8D/7F+GnOOg9S7+GnuXh55C8XNcXUT6vdthGNe7ehYUBrrGytsXW2Z9Bnr7HxreX3tNNdKsu49tjwx+irzrS4euYqnl4F7a2qrirxxSwvfO6t53D1cGXptIK9oxq2bMi0ZcYNbl08XGgX2A5DnoHDO0r/UCI9KgEnk3rkZKFuG9NUKZQmiXr921O3jz91An3R2lpj7WxPzyWvsftNY7k0eqobdXr5sXnIg9lbTvK/yUObKaH+8G6DMTixUAjxnul5IYQPMAnopc44+Buws3CrPIw/rPuaXDtfCBEihAgpQUJpHnWUlOZuMKP23byEEHd3iCr8Q11R73VeUZTW6r+WiqIElSG/A0A/IBf4B2PApCuwvxgN6cXcByDb5LMeY9Dn/h/7mrDnx23M6T+ZOf0nE7zjGJ2eDACgnl9DMlMzSL6TVOp7uZistfbxbYAQokwBCYATP+zk6/4z+Lr/DC7vOEGrwd0AqOnXgKzUzCJBCYCIwxdo1r89AL6Du3NZ3TTJvW71/DS6Ft5ora3KFJAAo9Pg4qPDqbYnGmst9QZ25OYO882Fbuw4RYOnugLg6V+fnJQMMmOTyLyTTHpkQv7bEby6NidJ3cwsf3MrIfB9cyCXf9xVrIbo0+G4+ehwUTU0HtCRsJ3mGsJ2nqLZYKMGL7/6ZKdmkB6bVOK1aTGJ1OrYFIA6XZqTFBFdJtvcJT4kHGcfHY5qHnUHduRWIRvd2nGKeqqNqqg2ylLL0la1hUPNKtTu3zZ/XwRnn4Lyq9nXn5TQwrHB0pESHIZDPR12dTwR1lp0gzpzZ/uJe19ogu6JLkT/Uf79ABIK2ajOwI7c2nHSLM3tHafwfspY36v4NyA3JbMYG7Uz2ztC160FKaGRZFoI5pSWjNNXsfWpgU3t6ghrK9wHdCNlp/nkLusaVfH5ajrXJ3xK9rWCDbE09rZoHO3zPzt3b03W5UKb5JWRzDNXsPGuiXWt6mBthetj3UnbZa7HysuT2stncnvSJ+REFOjJi7yDfevGCDvjdGnHzr5kh92ktBz+cSef9Z/OZ/2nc37HCfyfNJZJHb8GZKZmkGqhTww7fJ6W/Y1rldsO7s4Fk7K1c7anXoemnN9ZcMza3hZbR7v8zw27tSL6yi3KQvrpq9j5eGFTuxrC2gqPgV1J2mm+CatNjarU/3oa1940L7Pbi9Zwpt1LnO00hvCxn5D675n7DkhAQVuzN2lrsdtP3vtCIOdOMlmR8Tio/WWVbi3MNsgsD8nBYTia6KkxqBMxpdRzef56dvuNY0+7Nwh+5XPi/j1/3wEJgMSQcJx8dDjUNmqqPbAjkaXU9DDQX7uMpnpNRFUdaK2wbh9AXoj5D5y0acNJmzqMtKnDyD15gKw1S8kLNvZBwtnN+L+HJ1b+Xcg9+uD236hIHtbYf2P7Cby6GCfuutTTobWxKlVAAu5vrE2/HU9V/wZo7W0A0HVtTnKoUVNmTCLVOhl9gepdm5NyrWRf4MQPO1nZfwYrVT/N18RPyy6Fn9aqFH7a7g9/5rOO4/m861tsGL+Ma4culCkgAZVnXNv8w2bG9xvP+H7jObz9ML0GGx8ENvZrTHpqOomxRQM0fYf0xb+7Px+M+8BsJsXorqMZ1WUUo7qM4uCWg3zxzhdlCkgAxJ4Ox9Vbh7Naj+o/3pGIQj5lxM5TNFJ9ymp+9clJzSAjNoljH/zCmvZvsLbzBP4Z+wWR/17ID0jUDmhF69ceY9voxeRl5ZRJ0/8iBpQK/1dZeWgzJYQQNYAERVHWCCHSgJFAKuAMxAEuGH9YJ6vLM/oBey3cSgFGA78KIaYpirJIUZSZgOl70HZhXAbxmTBuTOkI7AdWCSEWYfxB/gQwrNC99wOvCCFWAx4YZydMBvLnwyqKchPjEo+738sbqCOE6KQoymFgKHAQuAx43j2uLudopCiKaXj0EsZZCw0URQlV9dyd/bEf+AHjkow7QogqgA5joEOxoKFMKIqSKIRIFUJ0VGdYDCnrPYrj7J5TtAz0Z8G+ZeRkZvP95C/zz735/QxWTV1OcmwivUb2p+8rA3H1dGPWtk84u+cUq6etoE2/jgS80BeDXk9OVg4rx392X3pCd4fQILA1Y/cvJk99JehdhqyazOYpX5MWm8Suhet4ctl4AiY9TfT564T8vBeApv3a0WpwN/S5evKyc/h97NJicioeRW/gyDurCfppCkKj4erP+0i6cpvGw4xPUi//uJtbu0Ko1dOXwf9+gj4zhwNvF6yJPPruanosfQ2NtRWpN2I5qJ6rN6gTTUb2BuD6lhNc/Xl/iRp2v7uawT9OQaPVcO7nfcRfuU2rF4wazqzZzbXdIdQL9OXFA5+Qm5nD9kkrS7wWYOe0b/NfFarPzmXHtG/z83zp30+xcbZHa21Fg75t+e2FRWZv+yis78TM1fT8aQpCqyFs/T6Sr9ymoWqjqz/uJnJXCDV7+fL4IaONDk8osFH3b97E1t0JQ24ex2esJifZuCFY6xnP4lLfC8WgkH47jmNTy/HmDVXf5enf4b9+BkKrIXLdXtIv36LWcKP9b/3wDzaernTYsRArZ3sUg0KdMf051G0i+rRMNPY2eHRvycVJZd+TxNxGqwj4aarxNYDr95Fy5TYNhhmdk9AfdxG5KwSvXq157NBi9Jk5HJ1QUN+7fvMmtu7OGHLzODFjFbnJBRsA1hnYiev3tXQD0Bu49d5X1PthFkKrIeGXf8i6epMqzz8CQPzabejeHILW3Znac19Vv5PxFWlWVd3wWam+htRKS9Kf+0gt567gpnqiZy+nzqq5CI2GpN92kn31Bu5D+wGQuG4rnuOHonVzwWv26/l6rg16i8zTl0nd9i/1/lqCoteTdT6cpPVbyyXj0p5gmgS2Zuq+z8jJzObXyQVlMvr7Kfw29WtSYhPZsmgdzy0dT9+JzxB5PoJjvxT8MGvetx1XDpwhN7Mgxuxc1ZXhK98GjK8KDfnzX67sO11mG91492sarX0fNFrif/6HrCs38XzBGPe/s2Y7XhOexcrNmboL1DLL03Px0UnlskVpUPQGLk3/Pr+t3V63x2Jb67hjQX5bqzumH/92m4Q+LZNLM76n5Zfj0NhYkXk9lnNv3t/rHhW9gXPTV9F+/XTjK0rX7SXt8i3qqHpu/PAPtp6udNkx3/h2DYOC95h+7O822Wyj3QeJojcQPGMV3dcZ+4Jral9Qb7ixLwj/YRe2nq703jYPa2d7FIOBhi/3Y3uPKeSlZdLhy7F4dm6KrYczj55cyvmPfyNiXXGTT0uBwUDW2mU4TFiI0GjIObgdQ+R1rHsYJ2Dm7ttc4uX2r7+HcHIBfR5Za5dBxoPbhLM4Jr+/iOPBZ0hKSqHXoBd4/cVhDB7wYNb83+Vhjf1X1++j6ydjGLRrIYZcPQfe+spi/sVpKu9YGx8cxo2/j9Fv+zyUPD2J564TusbYTx2d/C1t5gxDo/oCxyZ/W6yGwlxV/bRx+xfnv7r9LkNXTWaT6qf9s3Adg5eNJ1D104IL+WkG1U/bUA4/rVgq27gGHN99nHaB7fj2wLdkZ2bz6aRP88/NXjWbJVOXkBCTwLgF44i9HcsnG42v2zy07RDrlqy77/zBWI8OvruaR9cY69Hln/eReOU2zVSf8sKa3dzYHUKdnr4MPfgJeZk57J14b/+n69wRaG2seOwn40yOmFOhHJhRPr9N8r+NeFhrEoUQfYGPMC5fycUYNOgEjAWiFEUJFMZXZXYAwjE+3f9LUZRVwsIrQYUQNsAm4E9FUb4slFd1jMsN6mGcGfCaGhh4G2NAA+AbRVE+U9PfvacAPsQYEFGAeYqi/CyECFDzL7K8QQ0IbMEYROgMXAWGKYqSIYRojXF/C1eMAZ/PFEX5Wpi8ElQI0Qv4WD1/XNWarW5GmQQMUBRlhxBiJaBTFKXIPFRh4bWkQogIoC3gZHpOCDEJcFIUZZYQogPwNcZg0F6gu6IoJW55XdzyjYqirnKfr3p7CNTKe6CTUO6bhEr4vhhd8VtfVAjV9KV7Q8h/SZz2P31D8z1pav3gXmf2oLCxqlzrzlfpS/9avv+KZ/Iezo/i8pKQa2kCZMWS+2AnDj4QMjT/9cvQSqZv/9iKllAE+w9K/0P8v2CN73v3TvQfY1OpPDYIs65kgoDHqVxj2wylkjlIwONK5XsDxqs311S+jrscvO09pMIbxeKI9ZXSlg/NC1YUZTuwvdDhExg3g7ybZmQx1waYfHZS/8/BZAlHofQxQJGdrRRFWYxxf4bCx+/eU8E4M2JyofN7sTxrA0VRIjDufWHpXAjG2RaFj480+byLgk0tTdNkArYmf4+xlIeJhhaFjnmrH+NMzymKYvpC7vPqUhmEENMwlodEIpFIJBKJRCKRSB4iFR6RqMRUrkdzkofNo0KI6RjL/TrGJTUSiUQikUgkEolEIpFUCDIo8f8RiqL8DJT+RdISiUQikUgkEolEIrlvKvMrOSuayrWIUSKRSCQSiUQikUgkEsn/N8ighEQikUgkEolEIpFIJJIKQS7fkEgkEolEIpFIJBKJ5CGiyK0ui0XOlJBIJBKJRCKRSCQSiURSIciZEhKJRCKRSCQSiUQikTxE5EaXxSNnSkgkEolEIpFIJBKJRCKpEORMCUmJPJFpXdESzLhjJSpaQhGiKlkrcq2EYVh7Q+UStddeW9ESivDeX8MrWoIZ6/r9WNESimCfU7nWYuba6itaQhEScu0qWoIZlavlG9llX7nqEYBVJbNU2rbqFS2hKNveq2gFZrxwek5FSyjC136Vy0Y3RHZFSyjCDsWloiWYMSancrV9gBDbytdHSv7vU8l+TkkkEolEIpFIJBKJRPJ/C4Pc6LJY5PINiUQikUgkEolEIpFIJBWCnCkhkUgkEolEIpFIJBLJQ0TOkygeOVNCIpFIJBKJRCKRSCQSSYUggxISiUQikUgkEolEIpFIKgS5fEMikUgkEolEIpFIJJKHiNzosnjkTAmJRCKRSCQSiUQikUgkFYIMSkgkEolEIpFIJBKJRCKpEOTyDYlEIpFIJBKJRCKRSB4ihooWUImRMyUkEolEIpFIJBKJRCKRVAhypoTkvvEM9KXZvOEIrYaba/cQtvQvs/OODWrgu+QVXFr6cGXhz4Qv/zv/XODxz8lLz0TRG1DyDPzbd2a5NNQMaEX7OcMQGg1X1+3l7BebiqRpP2cYtXq2Ji8zm4MTVpJwLgKX+l4ELB+Xn8apTjVCPv6NC99sx2/yU9QO8gdFITMuhYMTviIzJqlMuvrMGkb9wNbkZmazedJKYs5FFEnjWtuTQUvHYufmRPS5CDZNWI4hV49HfS8e+3gM1Zt7s+/jXzm2cgsAHvW8GLSsQLNbnWocWPwbx7/bXqKW2gGt6DprGBqthgvr9hL8ZVEbdZ09jLqqjXa9vZK4cxFoba0Z9Ns7aG2s0Gi1hG05xvHFv+df03JkH1qMDELJ03N9dwiHF6wvk43uUj2wFa3mGutRxNo9XFlmrs+pQQ3afPYKbi29ubDoF66q9ci+hgdtl76GracbKArXftxN2DfbyqUB4NH3h9M4sDW5mTlsmLSCyPMRRdK41/Lk2WXjsXd1IvL8NX6b8CX6XD0+HZvywsqJJN6KBeD8tuPs+fwPADq/2I+2zwaCohB9+Sa/T/6KvOzccus8GHyBD77/HYPBwJO9OvHiE33MzqekZfDelz9xMyYOW2srZr/+HA3r1Ch3fneprG3NErrAVvjNGYbQagj/aS+XCtUp5wZetP/0FdxbenN20S9cXrHlvvO8yxPvj6BpoB+5mdmsm7ScWxbqkUctT4YvexMHV0dunY9g7YRl6HP12Ls4MuSjV6hapzq52bmsn7KC6Cu3AOjxYn86PhuIokDU5Rusm7yizPWoSqAvTeaNQGg13Fq7m4hCfbZDgxq0WPIqLi19uLrwZ64v35x/zsrFgeaLX8GpSS0UBc5PWEHyiatlN1Ahqgb60nTeCFA1XbMwjrRUNV1Z+DMRhTS1UDWhwLkJK0gqp6a75ZajltvtYsptmEm5/VSo3KrUqU5eoXJ79sNXaNbTn7T4FD7qO7lMmh5/fwRN1D7pl2I0udfy5Pllb+Dg6sjt8xGsn/AF+lw9PcY8ht+gLgBotFqqNajJbP8xOFVx4fllbxR8p9rV2PHpbxz8bmuJWmoGtKKD2v6vFNP+OxRq//HquGfj4kCXj1/CrXEtUBQOTvyaOydDCVg+Dpf6XvlpclIy+Cuo9L7Aw9AE0HRUH5qOCsKQp+fWrhBOzC/f2FYS7yxYzP5/j+Hh7sbGNSse+P3vUtnH/6Hvj6ZloB85mTl8N2kZN85fK5ImcPgj9Bn9KNW8vXjLbxRpialm571b1WfGHwv4atynnNx6pNR595o1jHqqn7a1BD9twNKx2Ls5EXMugs2qn1bc9Vpba577RbWblZbLW47x76cFdvMf2Qf/4UEY9HoSdwZzfu66EjVWC2xFS9U/ur52D1ct+Ef+n72Ca0tvLi76hVAT/8h/6WvYebqhKAoRP+4m/D78I4B+s4bTMNCX3MwcNk76iigL9nKr7clTS8dh7+ZE1LkI/lB9pLvUaFWPlzbO5rdxS7mw5RgAAz96mUY9/UiPT+HLoGn3pbEyo8iNLotFBiUqGCHEIUVROgshvIHNiqK0uM/7PZD7lBqNoPmiURx9ZgFZkfF03T6fmO0nSbtyOz9JblIa52euRtevrcVbHHlyHrkJqRbPlQahEXSYP4IdQxeREZXAY1vmcGPHSZKvRuanqdnTFxcfHb93nYinf306LRzJ3wNmkRIWle/8CI3gmZNLub71BADnlv9N8Ee/AdB0dBCtJzzB4Wnfl1pX/UBf3H10rOgxkRp+9Xlk3khWD5pVJF3gtCEc+3YbFzcdoe/8Ufg+G0Dwml1kJaWz8/0fadi3jVn6hPAovutfoHnc0aVc3n7injbqPm8Em55bRFpUAk9tnkPEzpMkmtioTqAvrj461nabSHW/+vRYMJINj89Cn53Ln88uIC8jG42Vlid+f5cbe04TExxGjU5N8Q5qw89B0zHk5GFfxaXU9jFDI/BdOIqDzywkMyqewG3ziNpxitRC9ejMO6vxesS8Hil5Bs7OWkvS2QisHO0I3DGf2P1nza4tLY0CWlPVR8figLep7deAx+ePZsWg94qk6zttKP9+u5Wzmw4zcP5o2jwbyLE1/wAQcfwSP774sVl6l+rudBrZlyW9J5OXncuQZW/QckAngn/bX2aNAHq9gQXf/srKd8dS3cONodM/JqBtC+rX9spP8/XvO2jsU5PPprzEtdsxzP/mV755f1wJd703lbWtFae1zYKR7H12IZlRCfTZOpfIHadIMakXOYnpBL/zAzX7tSnhTmWnaUBrPH28WBDwFnX9GvDU/Jf4bNA7RdINmPYc+779m+BNh3l6/ot0eLYnh9bspPfYQUReuM73ryymWv0aDJ4zmuXPz8O1ujvdRj7CB70nkpudy4hlb+I3oDPHf9tXenEaQdNFozn5zHyyIuPpuH0Bd7afJN3ELnlJaVyauYpq/doVubzJvBHE7Qnh9EufIqy1aO1ty2WjwpqaLRrNcVVTp+0LiC2kKTcpjQszV1HdgqamqqaQ+9TUNKA1VQuV2xIL5faYWm4hmw7zVKFyu21Sbk/OGc2K5+cBcPy3fRxcvZ3nFo8tk6Ymap/0YcAE6vg14In5L7Js0LtF0vWf9hwHvt3C6U2HeXL+i7R7NpAja/5h38rN7FtpDOA07eVPtxf7k5mcTmZyOp/1nw4Y28o7R7/k3PbjJWoRGkHH+SPYrrb/ARbafy21/W8waf+bB8wCjIGBW3vOsGfM52istVip5bT3tWX517d77zlyUjJKbZ+HpUnXuSl1+rZhY2/j2GZX3rHtHgzq34fnBj/OjLkf3ztxOans43/LAD+q+XgxI2A89fwa8sL8MSwYNL1IutCTlzmz+yST18+28B01DJ72Auf3ny5T3vVUP+3rHhPx8qtPn3kjWWPBT+sxbQgnvt3GpU1HCJo/ilbPBhCyZlex1+uzc1k/dAG5qt2e++1dwveeJio4jDqdmtKgTxu+f2Q6+pw8Wjg7lSxS9Y/+Vf2jgG3ziC7kH+UU4x8Z8gycm7WWZNU/Ctgxnzvl9I8AGgb64uGj4/MeE6nl14BH543im0HvF0nXZ9oQjny7lXObjvDY/NH4PRvAiTW7AGN97DN9CGH7z5hdE/LrAY6t3skTi18tlzbJ/z5y+UYFoyhK54rMXwhxX4EpN/8GZFyLJvN6LEqunsiNh6leqFPMiUshOSQ8P6r8oKnqV5/UiBjSbtzBkKvn2p9HqFPoh3ydvm0I++0gAHdOhWHj6oh9NTezNF5dm5NyPZb02/EA5KZl5p+zcrBFUcoW3WzYpw3nNhjzjAwOw9bFEcdCeQLU7dyMS2qk+NyGAzQKMmrPiE8h6kzJdvPu0pykG7GkqJqLo1rr+iRHxJCi2ij0ryP4BJnbyCeoDZdVvTHBYdi4OOKg6s3LyAZAY6VFY2XFXVO0GNab4C83YcjJAyAzPqVEHcXh4deA9GsxZNww1qNbGw/jVagMs+NSSAwJR8kzt0dWbBJJZyOMOtOzSL16G3ude7l0NA1qQ/DvBwC4GRyKnbMDzp5uRdLV69yc81uOAnBqwwGaBVkOuJmi0WqxtrNBo9VgbW9DakxiuTQCnAu9Th2dJ7WqV8Xa2opHuviz58RZszTht6Lp0KIRAD41qxN5J574pPKVz10qa1uzhIeqNV3VeuPPI9QsXKfiU0g4/eD7phZBbTn+uzHgdD04FHtnB1ws1KMGnZtzWq1Hxzbsp6Vaj3QNa3Ll33MAxIZF4lHLE6eqrkDhemRLShnrkWuhPjt64yGqWeizU0LCUQrZRetkj3unptxeuwcAJVdPXhl+QBZH4XEkeuMhi+NISZpuPQBNLYLacqJQuVlq/w06N+eMWm7HN+ynhVpu1RvW5Gox5RZ+7BIZyell1tQsqA2n1D7pxj00nVU1ndiwn+YW+qTWj3cm5K9DRa/t0oL46zEk3Y4rUUvh9h9eTPsPtdD+rZ3sqd6hMVfX7QXAkKu3GHzwGdCBa38eLlHHf6GpyfDenPmiYGzLKufYdi/atm6Jq4vzQ7n3XSr7+N86qB2Hf98LQHjwVRycHXC1UMdvnr9G/K07Fu/Ra2Q/Tm09Skp8cpnybtCnDefV7x0VHIZdMX5anc7NuGzipzVU7VfS9bkmdtNaW3H3AXnrF3pz9MtN6FW75cSVbDd3vwakFfKPdIXqeE5cCkkW/KPs2CSSC/lHduX0jwAa92nD6Q3G/uhWcCh2Lg44WbCXT+fm+TMgQjbsp4lJf9RhZF8ubD1OeqHvff3YJTKT0sqtTfK/jwxKPGCEEB8IIV43+XuWEGKiEGKyEOK4EOKMEGK2yfl7tkAhRAMhxD9CiNNCiFNCiPrCyEdCiHNCiLNCiGctXGcnhPhePR8shAhUj48UQvwqhNgE7Lif72uncyczsuAHcVZkfBk7PIUOP0+n64751B7Ws1waHHTupEcm5P+dHpWAQyENxjTxJabxGdiJaxvNnSG/qU/z9PEl1HuiM8EfbSiTLmedOykmeaZGJ+Bc3TxPe3cnslMyUPTGrW9SohJwLoP9mj7eiQt/3duBc9S5k2Zio7SoBBwL5WNMY26ju2mERvDMtvmMCvmSmwfOEhsSBoBbPR1e7Rsz+K9ZDPx1JtV865Vauyl2Xub1KDMqAXsvjzLfx6F2VdxaeJNwKqxcOlyqu5NsYqeU6ARcCtcld2eyUtIx5JdZPC4m5VrHvyHjti5kxKopVGtY05gmJpGDX//N5ENLmXbsS7JSMwk9YB5EKAsxCUlUr+KW/3d1DzdiCzljjbxrsuuo8anR2avXibqTSEx8UrnzhMrb1ixhr/Mg0yRYlxGVUO5gVVlxre5BkokNkqITcNWZ12dHd2cyUzLy61FyVAKu1Y1pbl+8QatH2gNQx7c+7jWr4qbzIDkmkb1fb+a9Q18w+9gKslIzuHzA/GnTvbDTeZBl1mcnYKsrXVtzqFuNnPgUmi95jY7/LKTZ4jFoHe5/poStzqPQOFJ2TS2XvEbnfxbS/D40uZSy3LKKKbfIizdoaaHc7ofS1CUHd2cyTfqk5Kj4fE13sbazoXEPX85uPVokj9YDLAcrClO4/WdYGEeKa//OdT3Jik+l66djeHz7PLp89FL+rIS7VO/QmMw7yaRci7mnloetyaWejurtG/PYpln0+20mVcs5tlUGKvv471a9CgkmeSdGJ+Cmq1KG6z3w69uevWvL7s6Wx09LjUrASbVNSdcLjWDElvmMO/UlEQfOEqXazd1HR632jXlh4yyG/jwTt9Yl282+kH+UdR/+kWsLbxLL6R8BuOg8zL5vSnSCmf8D4ODuVMhHKvCjnKu706RvW06oM0v/f8RQCf5VVmRQ4sGzHjANEDwD3AEaAu2B1kAbIUT3MtxzLfCFoii+QGcgCnhSvZcv0Bv4SAjhVei6sQCKorQEhgKrhRB26rlOwAhFUcoXCbiLEPd1+aHHZnGwzwyOPfcB3qOC8OjY5MFoKPyg1WKagkQaay21g/yJ2GzusAV/8Cu/tnuT8D8O0XRUn8J3KIcu5Z5pSvuQWGOtpWFvfy7+XdTJLCrl3vlYSnM3kWJQ+OWRmaxu/wbVW9fHo3Et4zVWGmxdHdnw+CwOz19H0JflWx5QUt6lRetgS4dvJnDmvR/JM3nyfr86isiwKNWYKPJcBB91eYNl/aZzeNUOnl85EQA7F0ea9mnDx93eZFGHsdg42OKrrvN+UBTW/uKg3qSkZ/L0pA9Yt3UfTXxqodVq7zeToscqQ1uzqNXCsQcwA6NUWZdQRwoSFZ9m1/I/sXd1ZNKWRXQb8Qi3z0dg0Bv3LGjRpw1zu43n/Q6vYeNgS5tBXcsoztLB0tlFWGlxbunDrdU7OdJ7OvqMbLzHDyxb/g9Bk0tLH26s3skhVZNPOTVZHM5KUW6YlJuDqyMTtyyiq0m53RcW+yTlXkmKpGnW25+IE5fJLDRbQ2utpVnvNvkzP0qWUpr+0XL7F1otVVp6c+mHXfzV9x3yMrJpOW6AWbJ6gzoRXoZZEg9Tk0ZrHNs2D5jF8XnrCFhxf0vfKpLKP/5bOFiGvnrIe6PYsGgNiqEcP7dK0b5KHPdKuF4xKKzuP5PlHd/Aq3V9qjYy2k1jpcHO1ZE1g2axZ8E62q18o8g97p1/2f2j9t9M4Ox9+EdGLZaklN6vfeT9YfyzaD2KQe6rICmK3FPiAaMoSrAQopoQogbgCSQCrYAgIFhN5oQxSHHPBeVCCGegpqIof6j3z1KPdwXWKYqiB2KEEPuAdoDpY7OuwFL1uktCiOtAI/XcTkVRErCAEGIMMAZgnHNbHrFvUKy+rKgE7GsURLTtalQhK7r004mz1anHOXEpRG85jptffRKOXCr19aA+GalREDV29PIgo9CUZmOaKoXSJOX/XTPQl/izEWQVM40u/I9D9P5hEiGf/G7x/F38h/em9ZBAAKLOhONikqezzoPU2CSz9JkJqdi6OCC0GhS9ARcvD9JKOR27foAvMeciyLjH1D8wPhlxMrGRkwUbGdOY2yi90GaDOSkZ3D58kToBrUi4fIv0qETC1X0BYkPCURQFOw9nssq4R0hmpHk9svfyILMM9UhYaen47QRu/v4vkVtKXhddmA7D+tBuqLHMbp0Ox9XETi46jyLLLDISUrFzcUSj1WDQG3DxqpJfrtkmg/2VvSE8Pm8UDu7O1OvUjMSbsWSodjm/7Th12zTi9MZ/y6T1LtU93MxmPcQkJOHpYb6e18nBnrljnweMTkO/sbOpWe3+ntpWprZ2LzKjErCvWaDDwcvjgWyeWRxdhgXRaagxxnvjdBhuJjZw03kUWWaRnpCKvYtDfj1y9fIgJdaYJjstk/WTCza9e/fgUuJv3qFJ91bE37xDulqPzmw7hnebRpzceLDUOrOiErAz67M9yC5lW8uKjCc7MoHkU8aNAGM2HcVn/OOlzrs4souMI/+dpi7DguiolttNC+WWbKHc7AqVW3Ix5faOWm5lpdOwPnTI1xReyrrkaKKpSn5duotvMbMhGge05va5a6TF3Xvae3qh9u9QlvavKKRHJRAXbHxKG/H3MbOghNBqqNuvHX/1K7pfRkVoSo9KzN/zJi4kHMWgYOvhTPZ97H9VUVTG8T9w2CN0G9oLgIjTYXiY5O2u8yApxqJ7apG6reoxZukE43dzd6ZlgD96vZ6QHZZ9Ab/hvWml+mnRqp92d4cFZ50Haffw05xN/LTUqIR7Xp+dksGNwxfxCWhF3JVbpEYlcmWb0W7Rp8PBoGBTxZmceMt2K+wf2ZXDP2qv+kdRZfSPANoN70Mb1V63C/m1Lhb82qI+UoEfVaOVD08tNQavHDycaRjoiyFPz6UdJ8us638VudFl8ciZEg+H34CnMM6YWI8xtrhQUZTW6r8GiqJ8W9zF6pKLECHEFop5hlTC8dKmKXZxq6IoKxVFaasoStuSAhIAycFhONbTYV/HE2GtpcagTsRsL13nonWwRetol//ZM6AVqZdulepaU+JCwnHx0eFU2xONtRafgR25ueOUWZqbO05R/ynjE0VP//rkpGSQadKR1htUdDq5s0/1/M+1g/xJDou6p5ZTP/zDd/1n8l3/mVzZcZIWg4151vCrT3ZqBumFOm+A64cv0KS/ccpvi8HduLrzVJE0lmj2eCfOl2LpBkDs6XBcvXU4qzZq8HhHrhXKJ2LnKRqreqv71ScnNYOM2CTsPJyxcXEAQGtnTa1uLUgMNW6QdW37CWp2aQaAq48OrbVVmQMSAIkhYTjV0+Gg1qNagzoRVYZByv/TMaRevU3oV2V/e8LRH3eyrP8MlvWfwcUdJ/B7shsAtf0akJ2aSeqdpCLXhB++QPP+HYx5D+7GxR1GB8PJ0zU/TS3f+gghyEhMJSkyjtp+DbG2swGgfpfmxIaWb6MpgOYN6nA96g63YuLJzc1j27+nCGjb0ixNSnoGubnGNasbdh3Gv2l9nBzsy50nVK62di8SQsJx9tHhqGqtM7Ajt0vZN5WHf3/cwcf9p/Fx/2mc23GCdk8aJ8PV9WtAZmoGKRbqUejhC/iq9aj94O6cU+uRnYsDWmvjrJaOQ3oSdvQi2WmZJEbG4+3XIL8eNerSosz1KCU4DAeTPls3qDOxpbRLzp1ksiLjcVDflFClWwuzzSjLS/J9asqMjMexnJr+/XEHn/Sfxif9p3F2xwnampRbVmqGxfYfevgCrdRya1eKcisrh3/cyWf9p/NZ/+mc33ECf7VPqqPWJUuawg6fp6Wqqe3g7lww6T/tnO2p16Ep53cWtWnrxzsTsuneSzegaPuvZ6H939hxigYW2n/mnWTSIxPy37Lh1bU5SSblVKNbC5JDI8mIKv2P0Yep6cb2E3ipY5tLPR1aG6v/yYAEVM7xf8+P25jTfzJz+k8meMcxOj0ZAEA9v4ZkpmaQbKGOF8f0bmOZ1vV1pnV9nZNbj7D23a+LDUgABP/wD6v7z2R1/5lc3XGS5ur39irBT7tx+AKNLfhpof+csni9vYcztqrdrGytqdu1BQmq3UJ3nKBuZ6Pd3H10CGurYgMSAEkW/KPoMvhHfp+OIe3qbcLK4R8BHP9hJyv6z2BF/xlc2nEC38HG/qiW6iMVDsIAXDt8gWaqvVoP7s5lte9Z0nUCn3V9i8+6vsWFLcf4+91V/18FJCQlIx7EhmISc4QQzYGvgapAD6AlMBfopShKmhCiJpCrKEqsECJNURSnkt6aIYQ4AixSFGWjEMIW0AKPAK8A/QEP4ATQAbC7ex8hxNtAc0VRXhRCNAJ2YpwpMRRoqyjKPefa/V196D0riGev1jRTX1V0a91eQj/bSJ3hvQG48cM/2Hq60mXHfKyc7cGgkJeexf5uk7Gu4kzb7982fketlsg//iX0s40l5nXHyvL085o9fWk/+wWERkPoz/s48/lfNFb3qLj8424AOswfQc2AVugzczj49krizxhfOaW1s+HpE0vY0OltclMLHMiAlW/gWt8LxaCQfjuOw9O+J8NCdDqqhPlGQXNHUK9HK3Izc/h70kqizxrzfGbVJLZM+Ya02CTcansycJnx1UnR5yPY9NZy9Dl5OHq6MnLTXGyd7FEMBnIysvm691Ry0jKxsrNh3JElLO/2Ntmp5k6vazEzGOsE+tJ11gsIrYZLP+/j5NK/aP6C0Ubn1xht1G3eCOoEtCIvM4fdE1dy58w1qjSpTc9PX0Gj1YBGELbpKCeWbASMU/F7fjyGKs3rYMjRc2jeT9w+dKFI3rrce0+rrN6rNa3U1zdeX7eXy0v+xGe48UnKtR92YevpSs/t87BytkcxKOjTs9jZfQquzerQ46/3Sb5wI3/65vmFvxCzK6TYvE7aFXuKAXNG0rCHL7mZ2fw++Stuq2U2/Psp/DF1JamxSbjXrsaQpeOxd3Mk8vx1fp3wBfqcPDoOD6L9C70x6PXkZuWwde4abpwyvpqw14TBtHysE4Y8PZHnI/hj2tf5G10BvPfXiHvayJQDp87z4arf0RsMDArsyJjBffllh/GJ+TNBXTl9+Rozl61BoxHUr6Vj9mvP4eLkUOr7r+v3o8XjFdnW7Ms45dOrp2/BK0HX7+Pikj+pr9apsB92YefpSp9t87B2NraxvPRstvaYUurprUdsi5+aP3jOKJr0aE1OZjbrJ6/g5tlwAF7+fio/T11JSmwiVWpXY9jSN3Bwc+L2+QjWTFiGPiePuv4Nef6T1zEYDMRcvc36KV+RmWKMIz8y4SlaP9YJQ57B+OrHaV+Z1aN+pZBetVdrGs81vhL09ro9XPtsI7XUPvvWD/9g4+lKxx0LzNrav90moU/LxLl5XZotHoPGxorM67Gce3MFeSVs4FjaCdVVe7Wmqarp1ro9hH+2kdqqppuqps6FNB0w0dRC1ZRxPZaz99C0o4TY3JNqueVmZrNu8gpuWSg3j9rVGK6WW/6rXNVye86k3H42KbcXPh9Pg47NcHR3JjUume2f/sbRX/bk52tVwjOEQXNG0biHLzmZ2fw6+at8TaO/n8JvU7/O1/Tc0vE4uDkReT6CdWqfBNDmqe407uHLT+OXmt3X2s6GGYeX8UH3N8kqNI40zbU81tYyaf9Xi2n/HU3a/wGT9u/RvA5dPnoJjbUVqTdiOfj2SnKSjRtLdv10DHdOhebfoyw8DE0aay1dPxmDR/M6GHL1HJ/7E1H/mo9tL5yeU2athZn8/iKOB58hKSmFKh5uvP7iMAYP6Fvu+33tV/RtUVBx4/8pbVapdD835yVaqP3l95O/5PpZ4+yVN7+fwaqpy0mOTaTXyP70fWUgrp5upMYnc3bPKVZPM3+N6qiPx3Jm18kSXwnaSDF3AHrPHYFPD+P33mripw1eNYntqp/mWtuTx5eNw87NiZjzEfyt+mnFXe/ZpDb9F7+C0GgQGsHlzUc59HmB3fp9NIZqzYx16+r7a4j7t6jfZEr1Xq1paeIfXVnyJ97qWBah+kcBqn9018/e1X0KLs3q0F31j1D9owv38I8AQmyL74/6zx1JA9Wv/XPSV0Sq9np+1WT+mvK16iN58tQyo48Udf46v7/1pdk4BTDo41e4sjs4f0PMwZ+PxbtTUxzcnUmPS2HPp78R/HPBm6VmXV97f+vFKwmjvAdX+A/v7yM2VEpbyqDEQ0IIcRaIUxTl7uaSbwIvqafTgBcURQkrZVCiIfAVxiBHLvA0cA34EOiHcXXbPEVRfja9j7p/xAqgDZAHvK0oyh4hxEgeYFDiv6S4oERFUlJQoiIoLihRkZQmKPFfUlJQoqIoa1DiYVNcUKIiKWtQ4mFTUlCioihNUOK/pHK1fCMlBSUqipKCEhVBcUEJSQEPIijxoCkuKFFRlDYo8V9SOChR0TTKrny9ZElBiYri/0pQYkQlCEqsrqRBiUr2c+r/DurmkqZ/LwGWWEjnpP4fARQJSKjnrgKWNqScrP4zTZt/H3X/iZEW7rcKWHWPryCRSCQSiUQikUgkEslDRQYlJBKJRCKRSCQSiUQieYgY5AqFYpEbXUokEolEIpFIJBKJRCKpEGRQQiKRSCQSiUQikUgkEkmFIJdvSCQSiUQikUgkEolE8hCRizeKR86UkEgkEolEIpFIJBKJRFIhyJkSEolEIpFIJBKJRCKRPEQMcq5EsciZEhKJRCKRSCQSiUQikUgqBBmUkEgkEolEIpFIJBKJRFIhyOUbEolEIpFIJBKJRCKRPEQUuXyjWGRQQlIi39imVbQEM2prHCpaQhGqK5WrGaVpREVLKEK8VeWalOWXXfkGhd8e+bGiJZjRSpta0RKKsNhKW9ESzHg0x76iJRRhrlV0RUsww0ljU9ESijA626OiJRQh3qpy9dvWla+LpHJZCL72e6+iJRTh5eA5FS3BjB9aVz4b9a5WufrI1+IqXx85R1/5NEn+71O5filIJBKJRCKRSCQSiUQi+f8GGZSQSCQSiUQikUgkEonkIWKoBP/uByGEhxBipxDiqvq/u4U0tYUQe4QQF4UQ54UQb5bm3jIoIZFIJBKJRCKRSCQSiaQkpgG7FEVpCOxS/y5MHjBRUZSmQEdgrBCi2b1uXLkWw0skEolEIpFIJBKJRPJ/DMP//kaXA4EA9fNqYC8w1TSBoihRQJT6OVUIcRGoCVwo6cZypoREIpFIJBKJRCKRSCSSkqiuBh3uBh+qlZRYCOEN+AFH73VjOVNCIpFIJBKJRCKRSCSS/+MIIcYAY0wOrVQUZaXJ+X8AnYVLZ5YxHydgA/CWoigp90ovgxISiUQikUgkEolEIpE8RJRKsHxDDUCsLOF87+LOCSFihBBeiqJECSG8gNhi0lljDEisVRTl99Lokss3JBKJRCKRSCQSiUQikZTEX8AI9fMI4M/CCYQQAvgWuKgoyuLS3lgGJSQSiUQikUgkEolEInmIVPTrQO/3laDAIqCPEOIq0Ef9GyFEDSHEFjVNF2AY0FMIEaL+63+vG8vlGxKJRCKRSCQSiUQikUiKRVGUeKCXheORQH/180FAlPXeMigheSC8OHsMbQLbkJ2ZzdKJSwg/F1YkzVtLJtKgVQP0eXquhlxh+fQv0OfpqVm/FuM/fpN6Leqz9qMf+XPlH/etZ/D7I2kW6EdOZjZrJy3n1vlrRdJ0G96XgNH98fTWMd3vJdITUwGoVr8Gz3/0GrWb+7D54/Xs/npzuXUEzRpO/UBfcjNz2DzpK6LPRRRJ41rbkyeWjsPezYnocxH8OeFLDLl6mg/qTKdXBwCQm5HF1pnfE3vxBgC2Lg48+sHLeDaqBShsnryS26dCLWroNWsY9QJbk5uZzdZJK4kpRsOApWOxd3Mi5lwEmycsx5CrL/H6Vw5+Sk56Fga9AUWv54cB7xnt16wOQfNHo7W1RtHr2fHOKrJPhVvUViugFZ1mD0NoNVxet5fTX2wqkqbTnGHU7tmavMxs9k1YSbya/5DDn5KbnoWiN2DI07PxUWP+Pb8ch1t9LwBsXBzIScng975l2psnn+qBrWg9x6jv2k97ubzMXJ9zAy/afvoKbi29Ob/oF66s2JJ/rs3il/Hq40d2XAo7Ay29xrl0eAW0ou3cYQiNhtB1e7mwrKiN2swdRk3VRocnrCTxbAQAjV/sS4PnA0AIQtfu4fI32wFoNfkpavX1R1EUsuNSOPzWV2TGJJVLn0uAH7VmvQxaDfHrdhLz5Qaz8+6DeqB7/UkA9OlZ3JyxnMyLEQUJNBqa/P0JudHxhI2aVy4NhRk260VaB/qTnZnNyknLiDhXtP71GdGPR0Y/RnVvL15tPYI0tf0/+spAOg/sbpRmpaVmg5q85jeK9OS0UufvFdAKf7XMwtbt5aKFMvOfO5waPX3RZ+ZwZMJX+WXW6MW+1H8+ECEEYWv3cPmbbfnXNBwdRKNRfVDyDETuCiFk3rqymMWMN+eMpWPPDmRnZrNgwodcOXe1SJp3l06niW9j8nLzuBhyiY+mfoo+T4+TqxPTP5lMzbo1yM7OYdHEj7h2OaLcWu7y6uxXadezHdmZ2Xzy9ieEWRhHpnw+hYatGpKXl8eVkCt8Pu1z9Hl6WnZsyfvfvk/0zWgADm09xE9Lfiq3luqBrfBT2354MW2/ndr2zxVq+21N2v6O+2j7hakd0Ious4yaLq7bS8iXRetVl9nDqKP2BXveXkmcSX8vNILBf88lPTqRraM+KZcGr4BWtDPpj85bqNttC/VHCWrdbvLyIzR4LgAUhaRLtzg0YSWG7FxaTXySBs8FkJVgbIMhC38hcvfpMmkqbx/Z5OVHqG+i6bCqyb15HdovGo3GzholT8/x6auID7E8jhWmdkArus4ahkar4cK6vQRbKKeus4dRV9WzSy0nra01g357B62NFRqtlrAtxzi+uGApdsuRfWgxMgglT8/13SEcXrC+1DYqLe8sWMz+f4/h4e7GxjUrHvj971IzoBUdZxttdHndXs5YGPs7moz9+03G/mcKjf1/qWO/R7M6dFlk9D0MeXoOzVxFXCnLrDB2ndvhMel10GpI+2MrKavMbW3fozNur48Eg9H/Sfx4Odkh5woSaDR4rfmSvDtx3HnznXJpKMwrs1+hXaCxf1w8cbHF/nHykslm/ePS6UvR5+nzzzds1ZDFfy5m0dhF/Lvl3/vS4xLgR53ZL4FWQ9y6nUR/Yb5tgMcT3fPHfkN6Ftenrygy9jfb8jE50fGEjpx/X1ok/zeQyzfugRBiixDCraJ1lIQQYqQQYllF5e8f2IYa3jV4vfsrLJ/2Ba/Mf81iuv0b9zIu8DXe7DMOGzsbeg8JAiAtKZVv3l/5QIIRAM0CWuPpo2NuwJv8PONrnpn/osV0105e5osX5hF/y3yPloykNDbMWsWur4sOkmWhfqAvHj46lveYyJbp3/LIvFEW0/WcNoRj325lecBEspLTaf1sAABJN++w5pm5fPPIdA5+vpH+Cwu+R9D7wwjfd5qvek3m60emExcaafHe9QJ9cffR8XWPiWyf/i195o20mK7HtCGc+HYbXwdMIis5nVaqhntdv37IfFb3n5kfkADoMX0o/y75ndX9Z3Jw8QYCpg+1mKfQCLrMG8G2YR/yW+AU6g/siFvDGmZpavf0xdVHxy9dJ3Jw6rd0XWie/+an5/N735n5AQmA3a8v4/e+M/m970yubTnOta3HLeZ/TzQCvwUjOfj8h2zvMYXagzrh3KimWZKcxHRC3vmBKyv+LnL59V8OcPC5D8uXt4rQCNotGMGe5z9kc8AUvAd2xKWQjWr09MXFR8dfXSZydMq3tFdt5Nq4Fg2eD2Dbo++zpfcMavbxw9mnOgAXlv/Nlt4z2NpnJrf/CablhCfKJ1Cjofa8VwgdPpuLPcfhPrAbdg1rmyXJuRnDladncDHoTaKX/EydD8aana/24mNkhd4sX/4W8A30R+fjxcQeY/l2+gpGzhtjMd2VE5dY+Pws7tw0b/9/f/UnM/tPZGb/ifzywRouHr1QpoCE0AjaLBjJ3uc/ZEvAFOoO7IRLQ/N649XTF2cfHZu7TOTYlG9pu9DYN7g2rkX95wPZ8eh7bO09nRp9/HBSy6xa52bU6tuGrb2msyVwKheXF61zpaVjz/bU8qnF0K7D+XDqYiYufNNiup1/7OL57iMZ0eslbO1sGfCccfbl8PHPcfV8KCP7vMz8Nxfx5pyxFq8vC+0C21HDpwYvdnuRz6d+zrgF4yym2/PHHl4OeJnXer+GjZ0Njwx9JP/cuWPnGPfIOMY9Mu6+AhJoBP4LRnLg+Q/Z1mMKdYpp+8HFtP2IXw5w4D7bfmGERtB13gj+Hv4hP/ecQoOBHXEv1BfUCTT2l+u6TWTf1G/ptmCk2fmWLz5CYjFjRWk1tF8wgt3Pf8gmtT9ytdAfOfvo+LNQf2Svc6fJi0Fs7fcum3tOR2g0eA/smH/dxa+3saXPTLb0mVmmgMT99JH2OncavxjEtn7v8nchTX7vDOXs4t/Z2mcmZz7agN87lscxS3q6q+W0rucUGpZQTmu7TWTv1G/poZaTPjuXP59dwC99Z/LLIzOpE9CK6n71jd+hU1O8g9rwc9B01veeRshXWwpn/UAY1L8PKxY/mOBwcQiNoPO8EewY9iEbAqdQz8LYX0sts1/Vsb9zobF/y9Pz2dh3Zn5AAqD9zKEEf/o7G/vO5NQnG2g/s3RlVgSNBo+p44kdP4PIwS/i+Egg1j51zJJkHTtF1LNjiBr6KvGzP6bKu2+bnXce+gS5126UL38LtA1sS03vmrzU/SU+n/Y54+YX0z9u3MOYwDG83ud1bOxs6Dukr8nX0jB6+mhO7Tt1/4I0GurMe4Urw+ZwPnA8HgO7YdewllmS7BsxXH5qJhf6vEXkkl+o++HrZuerv/gYmaG37l/L/xiKolT4v8qKDEqUgLpRx2OKoiRVtJaHiRBCez/Xtw/qyJ4NuwG4EnwZRxdH3Ku5F0l3as/J/M9XQ65S1asqAMnxyYSeuUpeXt79yMinZVA7jv2+H4CI4KvYOzvi4ulWJN2t8xEk3LpT5HhafAo3zoRhMIkul4dGfdpwZsMBACKDQ7FzccCpWlEd3p2bc3HLMQDObNhPo6C2ANw+eZWslAzj51NXcfHyAMDGyZ46HZoQsn4vAIZcPdlqusI06NOG8xsOAhAVHIadiyOOFjTU6dyMy6qGcxsO0DCoTZmuN0NRsHWyB8DW2YG02ESLyTxb1yclIobUG3cw5OoJ+/MIddV871I3qA1XfzPmH3sqDBsXR+zvlb8J9QZ0IOzPw6VOb4qHX33SImJIv3EHJVfPzT+PUKOvub7s+BQST4ej5BatK3FHLpGTWPofs5ao4lef1IgY0lQbXf/zCLULaajVtw3hqo3iT4Vh4+qIXTU3XBvWIO5UGPrMHBS9gdjDl6jdz1i38tIy86+3srct9yDl2Loh2RHR5NyIQcnNI/GvA7gGtTdLk37yEvrkdOPn4MtYe1XJP2etq4JLz7bErdtZrvwt0aZPew5u2AtAWPAVHF0ccbPQH10/f404C+3flE4Du3L4zwNlyt+03hhy9dz48wi1LJRZxG/G+8afCsXG1QG7am64NKxB/KlQkzK7SO1+7QBoOLwXF5b9hSHH2E9mx9/z7VrF0rVvF7b9tgOAC6cu4uTqRJVqHkXSHdl9LP/zxZBLeHp5AuDdqC4nDwYDcCPsJrpaOtyrFrVxWegY1JFdG3YBcCn4Ek4uThbHkeN7CoKMl0Mu548jDxJLbb9mMW3f8JDafmGqFe4v/zqCd6H+0juoDVfU/jo2OAxbF0cc1P7SUedBnZ6tubhub7k1FO6PIizU7dp923BN7Y/i1P7obp8trLRo7WwQWg1aexsyYyyPDfejqSx9ZGFNVvY2ZKiaFEXB2tk4jlm7OJRaa7XW9UmOiCFF1RP61xF8CpWTT1AbLqvlFBNsHNfullNeRjZgnKWlsbLibtfcYlhvgr/clN/+M++j/ZdE29YtcXVxfij3vkvhsT/8zyPUsTD2h6pldqeUY7+iKFirvoeNs0N+WZYVmxaNybsVSd7tKMjLI337XuwDupjnlZmV/1nY24HJGxW01api360DaRsfXODItH+8XIKffWLPifzPV0KumPWPA0YN4N+t/5IUn3Tfeoxjf1T+2J/w50HcgjqYpUk/eblg7D91GRvTsd+rCq692hL304Mb+yX/+8igRCGEEN5CiItCiC+BU4BeCFHV5PjXQojzQogdQgh79Zp2QogzQojDQoiPhBDnirn3XiHEB0KIY0KIK0KIbupxs5kOQojNQogA9XOaes1JIcQ/Qoj26n3ChRCPm9y+thBimxDishDifZN7vaDmFyKE+OpuAEK97xwhxFGg0/3YrIquCvFRcfl/x0fH46GrUmx6rZWWHk8GcmrfyWLT3A+u1d1JiozP/zspOh5XXVGH+2HjrPMgxURHSnQCztXNBxF7dyeyUtJR9MatZ1KiEnDWFR1ofIcEELbX+PTIvU41MuJTeezjV3hxy3we/eAlrO1ti9HgbqYhtRgN2SkZ+RpSoxJwUjWUdL2CwjNrpjF881x8hwbmp9k1Zw0BM4by6uElBMwcyv4PfraozdHLnbSohPy/06MTcPQy1+aocyfNJP/0qAQc79pHUej/0zQGbZlLk+cDKYyuQ2My7ySTci3GYv73wl7nQebtgrwzoxKwt1A2DxN7nTsZkQU2yohKwL6QjRx07mSY2CgjMgEHnTtJl25RrUNjbNyd0NrbUKOnLw41Ctql79SnGXRiCd5PdubMR+ZLLkqLta4KOZEFbT83Kh7rEtp+lSF9SNlT8JSm1qyXuL1gNRgeXOTeXedBvImmhOh43KuXvf3b2NnQqocfx7ceKdN1DjoP8/KwUGb2Og/SLZRZ8qVbeHZoYlJmrXGoYdTuXN8Lzw5N6LN5Nr02vIOHb70yf6e7eOqqEhtZEJC5E3WHqrrif9xrrbT0HdyHo2pAIPRCOD36dwOgaevGVK9VHc/7DA5U0VUhzqTc4qLi7qmp15O9OLG3wAlv2qYpX2z/gjk/zKFOozrFXnsv7HUeZNwuVIb/cdsvjLEvLOgL0kz7QrM08RbTdJ71AkcWrLuvtuZgoT9yKFK33c3qdnqk0XaZ0YlcWL6FJ44vYXDIMnJTM4jaV+AqNR7Vh0f/WUDHxS9j4+pQak3300dmRidycfkWBh1fwpMhy8hJzSBa1XTyvTX4vTuUQSeW4P/uUEIWWB7HClOecjId14RG8My2+YwK+ZKbB84SG2Kcou9WT4dX+8YM/msWA3+dSbX7aP8VjYOXO+kmY3+GhbHfoVA9yig09j/y0zQGbplLY5Ox/8isNbR/ZyjPHltC+3eHcmJh6cqsMFaeVcmLLphBp4+9g7Za0XHNPrALNTZ8R7Ul84mb/XH+cfdJr5O05GuUBziuVdVV5U5UQZ8dF33v/rHnkz05qfrZVapXoXPfzmxZ82ACJTZeHuSY+P050fHYeBU/zlYd0ptkk7G/9qwXuTV/NVTip/YPCwNKhf+rrMighGUaAz8oiuIHXDc53hD4QlGU5kASMFg9/j3wqqIonYB7PV63UhSlPfDW/2PvvMOjKto+fM+m9x6SUENHWkLvJIFQIgiICihNVJQmIh1EEGkKiAqKYAMEAV9RQKR36TX0DqGmQAppm7Z7vj92SXazG0jjTXy/ua8rV3bPec6Z387MmZnznGfmAFOfYQvgAOxVFKUhkATMQLfaaQ9guoFdE+ANIAB4VQjRSAhRC+gFtFQUJUCv7Q2D855XFKWpfkGSbIQQg4UQJ4QQJyKSDX9+/nnak9d3Zw7h4rHzXDp2sVDnfha6ABcTQc8lrafrMCdDyWVjzsj4a8XmLxDQK4jds3VzGlUWKnzqVOLUyp38GDaZjNR0Wgztmm8RJmXzNA1POf7Xl6ez/MWP+H3AXAL7t6dckxoABPZtx+5PV/Fd85Hsnr6KTp+/Y16buTVwchfTU8pyY4/p/Nn5I7b2m8sLA9rj07SGkVmVbs0LHSWRl7z/dj3KT/3IK48Srz/g4rebaLdmAiGrxpFw8Y5R9M+Zz/7D+kYjifjjENUHhRZSoJlteeSRY/O6ePZqr3NCAM7tGpEVm4D6nOm82KJgLs8KEwkS2L4xV09cLtDUDZ0AM9tMrjlzJroyu/TtXwSvmUDQqvHEX7yDNkvnLBQWKqxdHNjRZSqnP/2VlktGFEyXYfL5aJsMGT1rJOFHz3L22DkAVi5ajZOLIz9tX0LPQT24dv4aGk3RIssKWm7DZg7j/NHzXDh2AYAb528woNkAhnUcxl8//8XHP3yc57HP1mK6rcRDXs3mT35sFCq0CyAtNpFH+nUUnqeGvPpfaxd7yndswPqmo1gXOAJLexv8X9Y9fb66fCcbmn/I36GTUUcn0GDqG6bnyFNS4dtIaxd7ynVswIamo/hDr6mSXlO1Ae04OXUV6xuN5OS0VTT9Iq9+7Nl68ptHAIpW4bdOk1ne5H3KBFTBvYYuJF5YqrBxcWDdS9M4PHM1Hb41H77/76BwefTkGtzUYzobOn/Etn5zqWXQ99fq346jn6xibZORHJ22ilbz8ldmpvLyUacA9Z6DPOg5iIejp+I6RDcFz651U7RxCWRcMl2jp7h5Zvt4LKd9HDxtMD/N/gmtthjevQDkpwyf4NSiDp6923Nv5goAXNo1IuvRY1KLue+X/PuRC12a57aiKOYej91SFCVc//kkUEm/3oSToiiH9Nt/Bbo85dxPVoI5CVTKh5YM4MlKZ+eAdEVRMoUQ53Idv0O/IipCiD+AVkAW0BA4rm/g7YAn7l8NYPbxqKIoS4GlAD0qdDXbzHTuH0ZoH91ctetnr+Fh8JTMw8eD+Og4c4fx2ge9cXZ3YfGEb576owtK634daN5HtxjsnTM3cDV4Iuzq48HjYggTzQ8N+4cS2FvnuX9w9ibOBjqcfdxJjkkwsk+NS8LW2QFhoULRaHH2dSfJQKt3zfK8+NnbrBnwOeoE3c1RYlQciZFxPNA/Qbm8+ZiRUyKwf3vq6TVE6TXc1+9zMqNBHZeEjbN9tgYnX3eS9RqSIuPyPP7J/9TYRK5tO4lvQBXuHbtCnZ6t2TXtFwCu/H2UTp+9bTavUiLjcDTwrDv4uJMSFW9q4+fBk1gHB193UvQLMqbq/6fFJhKx9SReAVWIOnoF0N3AVercmPVhU8ymnR/UkXHYlc0pPztf90IvBllYUiPjsp+UA9j7uqPOlUc6mxyd9n7u2XlzY/U+bqzeB0D9Ca+RGml6XUb8eYigX8Zwbt4fJvueRWZkLNZ+Ode+la8HmWaufbuaFak4dxjX+01Hk6BbzM6xUS1cQpvgHNwQlY01Fk72VPpqFBEjFxRYR/v+nQjurXOs3Dx7HQ8DTe4+HiTkMYXoaTTv2orDGw882zAXJuXh6446KsHIRh0Zh4OfB0+eM9n75dStm6v3cVNfZvUMykwdGce9zbpIhbjwmyhaBRt3J9L1iwM+ix4DutH1Dd2aEJfDr+Dt55W9z8vXi9joWLPHDRzVD1cPV+a+neNDT01OZfaHc7O//3ZkFZF3ovKlw5AuA7pkrwlx9cxVPA3KzdPXM09Nr3/wOi4eLsyYkDP3PTU5Zwrb8T3HGTZzGM5uziTGFzzMPTUyDvuyxmWY9l++9nOjawtz2gJHX3eT8PQn7aWxTQKVw5pQMbQBFYLrY2FjhZWTHSFfDWH3yMUF0pDf9sjBz4Mnz3Qd9HXbp3Udku8+zK6vdzafwLNRNW79cZC0RzlldH3VHoJXjC52TebayNya7m4+gVejakT8cZDKr7bm5BRdP3bnr6M0m2e+H8tNcj7KKTlXORn2a0/ISEzl/uFLVAiqR9yVe6RExnNziy4qKCb8JoqiYOvulL046L+J1Mg4HAz6fnsfd1LN9P0OudrRVDN9/+2tJ/HU9/3VXmnNkY91ZXZr01Fazc1fmeUmK+Yhlj7e2d8tvL3QPDTfFgGknzqHVTlfVK7O2NSvg13b5pRt1QRhbY1wsMdjxgRiP5pTYB1d+neho36cfe3stezpc6CLnHhq++juwsIJC7O3VatbjQmLdIvuOrs70zi4MdosLYe3F+7BTUZkLNYG435rHw8yo8z0/bUqUvHz4Vwz7Psb18S1Q2NcQhqisrFC5WSP/9cfcOv9LwulRfK/g4yUME9KHtvTDT5r0Dl18nzliRDiZ/20CcN4qSfneHI86JwHhmVha/A5U8lxh2qfHK8oihZjp1Ju54Gi17ZcUZQA/V8NRVGm6fenKYpS6EdbW1Zs5sPOI/mw80iObjtCcM8QAKoH1iA1KZV4MzcB7Xt3ILBNA74YPrfYnzr988t2Pg8bz+dh4zm7/ThNXtatnl8psBppSakkPkwo1vTy4uSKHfwQNokfwiZxdfsJ6vXUhTj7BVYlPUlt4hAAuH34IrXCdPPw6/Vsw7UdunA7Zz8Pei75gA2jFhN3K2fAn/LwMYmRsbhX1r1holLL2jy8dj97/+kVO1keNpnlYZO5tv0ktXu2AsA3sArpSamkmNFw5/BFaug11OnZmms7dGF213eeMnu8lZ0N1g66amplZ0OlNnV4dEW3YFFyTDzlm9UCoELL2sRHmL9ZeXjmJs7+PjiV90JlZUGVbs24s8N4Aabb209R7RVd+t4NqpCRlIo6JgFLOxus9Olb2tlQrk0d4q/kLJhUtnUdHt94YBQiWlDiw2/i6O+DfXkvhJUF5bs1I3Lb85lylBex4Tdx8vfBQZ9HFbs149524zy6t/0UlfV55NGgChmJqaTpy9jGwxkA+7IelA9rxO31Ot/pkwUvAcp2bEDi9chC6Us5cw2bSr5Yl/dGWFni9lJrHu84ZmRj5eeJ//cTiRj5Jem3chbZe/DZL5xv8hYXWgzm1rB5JB08WyiHBMDOFVuzF6c8uf0YrXoGAVAlsDqpSakFdkrYOdlTs9kLnNp+7NnGuYjLVWYVujXj3nbjenN/+ykqvaJrGzwaVCUzUZ1HmTXOLrN7W09SptULADhV9kFlbZlvhwTAn8s3MKjDuwzq8C7/bDtIp1d0Cw2/0KAWyYkpxMaYXitd+oTRJKgx04bNMGqzHZ0dsLTSdT9dXw/jzNGzRk6B/LJp+abshSkPbztMu546x3LNwJqkJKWY7Uc69u5Iw7YN+Wz4Z0aa3Lxywr+rB1RHqEShHBJg/tp/8F++9nMTc+YmLpUM2suXmhGRq72M2HGK6vr22jtQ116mxiRw7LPfWNnkfVa1GMXOYd/w4ODFAjskwLQ9qpRHe+Svb4889e2ROiaBlPuxeDaoioWdNQA+rWqTeF3XbxmuFVC+cyMSDNrygmoqSBtpTtNjvSZ1dDzezXX9WJlWtUm8lT+nW+5yqvpSM26ZKaca+nIqY1BOtu5OWDvrpq5Y2FpRrnWd7IVJb207QdmWuuvfxd8HCyvLf6VDAnL6fkd9HlU20/ff2X6Kqvoy82pQhcw8+v6yBn1/anQ8Pvoy822Z/zLLTcaFK1iWL4ulnw9YWuLQMQj1vkNGNpblcxbmtK5ZFays0CYkkrDoR+537sP9Ln15OHEmaSfCC+WQANi0YhMjOo9gROcRRu1jjcAaT20fG7RpYNI+Dmo1iDdbvsmbLd/kwOYDfPPRN4V2SICu77f1z+n73bu1IiFX32/t50mV7ydwa+QCo77//pyVnG38NueaD+bmsPkkHTz7/8ohoS0Ff6UVGSlRRBRFiRdCJAkhmumjK3ob7DP/ugVTIoChQggVUBbdVIyCEiqEcAfUQHdgEJAKbBBCLFAUJUa/30lRlMLNyciDk7tP0DC4EYv/Wap7JeiYr7L3fbRsKt+MX0h8dBzvzRrKw/sxzFmve8J2ZOthfvtqDa5erszdtAB7R3sUrZYub73E++2GojZYjK8gXNxzmtrBgXy87ysy1BmsGpsz+Hr35wmsHr+ExJh42gzsRPt3X8LJy5UJWz/n4p5wVk9YgpOXC2M3zsbW0Q6tohA0KIxZoaNJK6Ce67vDqRIcwND9X2S/EvQJvZaN5e9x35Mck8Du2avpsWgEbce8SvSF24Sv3QtA65E9sHNzovOnumqk1Wj4qavuyf/2qSvo/tVQVFaWJNyJMTq3ITd3h1M5uD7v7J9PljqDLWOWZu/ruWwM28b9QHJMAvtmr+GlRcNpPeZVoi9EcE6vIa/j7T2d6bH0A0C3INfFDYe4te8sAFvH/0g7/evQstIz2TbhR7PaFI2WQ1OW03nVOIRKxZW1+4i/ep9afXUOrksrd3N3dzjlQ+rT68B8stIy2PehLn07L2dCf9Cnb2HB9fWHuLf3bPa5q7zUjBvrizB1Q68vfNIyWq8ej7BQEbFmH4lX71O5v25gcHPFLmy8XGi3dQZWTnYoWi1V3+nM9rbjyEpW0+TbYXi1qIWNuxNhJxdycd7vROifgBdEw4nJywn5dRzCQsWNNft4fPU+1frp8ujaL7t5sCucsu3q89Kh+WjUGRwelVPGbX4YiY2bI9rMLI5PWk7GY92NY8CkXjhX8UXRKqTcf8Sx8T8XLpM0Wu5OWUrVldMQFipi1+4i7epdPPvqnn4/WrkV3w96Y+nqRPmZ72b/pisv5v9JaEEJ332S+sENmL//WzL0rwR9wphlk/lh3LckxMTTYWAYXd7rgYuXK7O3LeDMnlP8MP5bABp1bMq5/WdIV6fnlUye6MpsGUG/6urNTX29qdpPV2+u/7KLB7vC8W0XQJdDX6BRZ3B0VM712+qHkdi4OaHNzOLEpGVk6svs5pq9NP1iMJ13z0GbmcXRkYV/Xd/hXUdpFtKUNQd/IU2dZhT18PmKWXw2dj6x0bGMnvMB0fei+W6j7onb/s0HWPblL1SsVpHJX41Hq9EScfU2c8bMyyupfHN893EahzTmpwM/kaZOY8HoHAfV9OXT+XLcl8RFxzFi9ghi7sfwxfovgJxXf7YKa8WL/V5Eo9GQkZbBnGGFuxEAXRmenrSMNvpr/1Ye1357g2u/2jud2aa/9psaXPsvnlzIhUJc++Y0HZiynBdX6tqCJ+3lC/r28uLK3dzZHU6FkPr0OaBrr/eOXvqMsxZcw/HJy2n3lPbo/q5w/NrVp9shnYYn7VHs6Rvc+fsYYdtmoGRpiDt/m2sr9wAQ+FFv3GpXBEUh5d4jjo77qUCaCttGPtHUWa8p/vxtrus1HR37Iw2n6/oxTXomx8aa78fM6flnynK66svpsr6cauvL6cLK3dzWl9Mb+nLarS8nB29XQha8i8pCBSrBjb+OcntXOACX1u4jZN5geu2cjTZDw65R5vv8ojJ26hyOnz5LQkIi7br3Zehb/ejZteOzDywAikbL4SnL6aTv+6+u3UfC1fvU1OfRZX3fXy6kPq/q+/5/DPr+dgZ9/431h7iv7/sPjPuRZp/0Q1jqyuzA+PyVmQkaLXGfLcT7mzmgUpG8cSuZN2/j2FMXBJ28bhP2Ia1x6BIKWVko6Rk8Mojaeh4c332cxsGN+fGfH0lXp7NgTE77+MmyT/hq/FfERccxfNZwYu7HMH+97pW/h7YeYvVXhX91dJ5otNyZ8j3VV00FlQWxa3eSdvUuXn11deXhym34juqFpasTFWe9B4CSpeHSi2OKX4vkfwZR4vMkSxlCiErAJkVR6ui/RwCNAMdc28cAjoqiTBNCNAW+RxdhsRdooyhKSzPn3guMURTlhBDCEzihKEol/Vs+VqJbD+I8UAaYpijKXiFEsqIojvrjpwHJiqLM039PVhTFUQgxEAhDt05EVeBXRVE+0dv0Aiaii8TIBIYpinLE8LxPI6/pGyVFeVX+F8D6b1FGKV2+Pau8g3dKDLeiTTcvdtw0papaA5Bubh5rCVLLovQ9hftCVaQXBRU7L2balbQEE74VhXs6+LxwVFmXtAQTBmX99xc+fhaxlqXr+ncsZW02PCUstYR4XLqaIwDeOT392Ub/RVYEFH5tl+dFe+/S1UYOeVT62sjp2tKnqdG99aWtCSgUXSq8WOID0E13/i6VeVm67qZKAYqiRAB1DL5X0n98lGu74aOhC4qi1AMQQkwATmAGRVGCDD4/Qr8mhH56htmVnQwdBwZTL4z2KYqyDFiWx/FrAZMliPPjkJBIJBKJRCKRSCQSieR5Ip0SxcOLQoiJ6PLzNjCwZOVIJBKJRCKRSCQSiURS+pFOiWIgr2gEiUQikUgkEolEIpFItObeLysB5Ns3JBKJRCKRSCQSiUQikZQQMlJCIpFIJBKJRCKRSCSS54h8wUTeyEgJiUQikUgkEolEIpFIJCWCdEpIJBKJRCKRSCQSiUQiKRHk9A2JRCKRSCQSiUQikUieI9qSFlCKkZESEolEIpFIJBKJRCKRSEoE6ZSQSCQSiUQikUgkEolEUiLI6RuSp3Iq5XZJSzCikWPdkpZgQrTIKmkJRnRRi5KWYELrWeVKWoIRnaeGl7QEE6paupa0BCPWl8IFovfEny9pCUYs/bhNSUsw4dsFJa3AmClZ9iUtwYQqNe+VtAQTRCl7RLToRulqswEsS1mbdEekl7QEE1YEfFzSEozoHz69pCWY4FGxfUlLMGKrc2BJSzBhlnVmSUsw4Y+SFlBMKJSyhqwUUcq6QYlEIpFIJBKJRCKRSCT/X5CREhKJRCKRSCQSiUQikTxHtDJSIk9kpIREIpFIJBKJRCKRSCSSEkE6JSQSiUQikUgkEolEIpGUCHL6hkQikUgkEolEIpFIJM8RRZHTN/JCRkpIJBKJRCKRSCQSiUQiKRFkpIREIpFIJBKJRCKRSCTPEbnQZd7ISAmJRCKRSCQSiUQikUgkJYJ0SkgkEolEIpFIJBKJRCIpEeT0DYlEIpFIJBKJRCKRSJ4jipy+kSfSKSEpFqbNHk9w+9ao1WmMGT6F82cv5Wn7yZwJvNqnOy9UbAZAlWqVmLfwU2rXq8W8mQtZ+s3yQmnoMK0/VYLrk6nOYNOYJUSdjzCxcSnvRY+Fw7FzdSTqfAQbRn2LNlND7e4taP5eVwAyU9PYMvlnYi7dAWDYgS/JSElD0WjRajT81HVKofT1nDqQF4IDyVCns2rMYu5duGVi07p/R4IGheFVyYeJgW+TEp8EgHcVP96YO4Tytf3ZNG8Nu7/fVCgNT/AIrk/NGQMQFirurdpNxMKNRvvtq/pR56v3cK7rz7XZa7m9OCc9S2d7an/xLo41y6EocGHUdzw+ca1IegAO3ozh813n0SoKPepVYFCzaiY2x+88Yu7uC2RptLjZWfPj6y0BWHXiJn+cvYOiKLxcvyJ9G1Uusp4njJg+jGYhTUhTpzNn1OdcO3/dxGbywonUqFcdTWYWl8KvMH/CAjRZGhycHJj89QS8y3pjYWHB2iX/Yetv24qkp8/UQdQNDiRDncFPYxZxx0w9Cu7fidBBL+JdyZcPAt8kWV+PajSrzbCl43h0LwaAU1uPsunr34ukJzdvTnuHBsENSVen882Yr7h1/qaJzftffUiVulXJysri+plrLJ34LZosTbFpmPX5R7Tv0BZ1qpoRQyZw9szFPG1nz51CnzdeppJfYPa2lq2aMGPOZKysLImLjeelsL5F0qOqVBvrkD4gVGSd+4esY1tMbcrXwDq4F6gsUNTJpK+dCxaW2PQej7CwBJUKzdWTZB7aaCaFgjNy+jCahTQlXZ3OrFGfc/W86TU8ZeFEatavQVZmFpfCLzN3vK5eO7o4MnH+WMpW9CM9PYM5o+dy60pEkfS4BgfgP30QWKiI+XUX9xf9abTf8+XWlB3WAwBNipqbE5aSevE2AFW+GIp7aCMyHz0mPHhUkXQ8wbpxE5yGjwALFeq//yZ19a9G+21atsThzbdA0YJGQ9KiRWSePweAXc+e2L/YBYRAvWkTqeuK5xqzbtwEx2EjQKUibfPfpK4x1mTdoiWOb76FotVpSv7WQNPLPbEL02v6exPqPwqvKWxqf6rp+9o/xywh8kKEiY1rOS9eWzQcOxdHHlyI4I9R36LJzLnG/epVZvCfn/Db8IVc3HIMgGZvdqRh72CEEJxcs4fDP23Nt6aO03I0bcij/3ct70XPhcOx1ff/f+r7/+qhDQke/QqKVkGr0bDtk1+4e+Jq9nFCJXh70wySouJZM2he/jNKT1Ha7CdUqleFSX/OYsnwBZzccqTAGsoG1aPZJ/1QWai4snovZ7/5y8Sm2fR+lA8JIEudzv5RS4nV5+FrhxeQ+WQMlKVh44sfA+D+QgVazhmEhY0V2iwNhyYv41G4aXtfVD6a9QX7Dx7D3c2V9Su/K/bz58Xncz+mQ8cgUtVpDHl3LGfCL+RpO3feVN7o9wp+ZeoC4OrqzDeLP8O/ckXS09IZOmQ8ly5ezfP4Z1Ha2scnvDXtHRoENyJdnc6iMV9y00x//4G+v9dkabh25hrfTfwGTZaGslXKMnzeSCrXrsKv835hw9L1xapN8u9DTt/4LyCE2CyEcC2mcw0UQvgVx7mKi+D2rfCvXJG2jbsw8cPpzJj3UZ62dQNewNnFyWhbQnwiUyfO4ftCOiMAqgTXx93fh8VtR7N54o90mvGmWbuQCb059uMWFgeNJu1xCgG9gnQa7j5k5Wuf8kOniRz4ej1hs98yOm5l7xn8EDap0A6JF4IC8PL34dOgkayd9D2vzXzLrN2tk1f4pu8MYvU3jU9ITUhm3bRl7PredCBRYFSCWnMGcer1ORxsPRrfHi1xqF7WyCQrIZnLk5cRsdjU+VFzxgAe7QnnYKvRHA4ZR8rV+0WWpNEqzN55jm9ebcofbwWz9dIDbjwyHpQlpmUye8c5vnq5MX+8Fczcbo0AuP4wkT/O3mFlv1b89mZb/rkRze245CJrAmga0oRy/mV5o9UA5o9fwKjZI83a7fxzF/3bvsmb7d/BxtaaF/uEAdB9wEtEXLvN2x3e5YNXRzP043extCq8L7huUCDe/r5MChrBiknf0XfmYLN2109eYX7f6dnOB0OuHb/M9LCxTA8bW+wOicDghvj6+zKi7XssmfgN78wYYtbun/X7GBkylNEd3sfaxpp2vUOLTUP7Dm2pXKUSTQJC+XDkFOYu+CRP24DAOri4OBttc3Zx4vMvptG393u0avoig/q/XzRBQmDd/g3S131J2s9TsKzZBOHha2xjY6ez+XMRacumkv6XfuCtySL9t3mkrfiEtBXTUfnXQeVbdIdbs5AmlPMvR59W/fl8/BeMzqNe7/hzF2+0GciAdm9jY2tD19d19br/iNe5duE6A0PfYebIOYycPqxoglQqKs96h4tvzCS87Qd4dm+FXfVyRibpd2I4//IUzrT7kHtf/k6Vue9l73v4214uvv5p0TTk0uM08gMSJowjduAAbNu1w6JiRSOTjJOniHt7EHHvvE3i55/hPHYsABaV/LF/sQuxQ94j9q23sG7eHIuyZc2lUnBN739AwsRxxA0agE2IqabMU6eIe2cQ8e++TeK8z3AanaPJLqwLccPeI+6dt7BpVnhN1YLq4+Hvw1dBo9k46Ue6zjTf13aY0JtDP27hq2BdX9tA39eC7ia/w4TeXN9/Nnubd/VyNOwdzNJuH/Nt54lUDwnEvVKZfGmqGqzTtKjtaDZN/JEX8+j/203ozZEft/BN0GjUj1MI1Gu6dfA8SzpNZGnYJDaOXUrXz94xOq7poE48uv4gX1pyUxxttlCp6DmhLxf2nymUBqEStJgxgO39Pmdd8Dgqd2uGazXjYWS5kPo4+/vwn1ajOTD+R1rMHmi0f/OrM1nfcXK2QwKgyeQ+nF7wB+s7TubU/HU0mdynUPqeRfewUL77YsZzOXdedOgYRJWqlQioF8LI4ZNY8GXe7UtgYF1cXI37kdFjh3Lu7CVaNA1j8Duj+Wzux3kcnQ9KW/uop0FwQ3z9/RjW9l2+m/gNg/Po7/ev38eIkKF80GEE1jbWtO/dAYDkhGR+nLqUDd//afa4/1W0ilLif6UV6ZR4zgghBNBFUZSEYjrlQKBUOSVCOwezbq3uZvn0ibM4uzjhXcbTxE6lUjF52ofMnrbAaHvsozjOnr5AZmZWoTVUD23I2XX/APDg9HVsne1x9HY1savUojaXNuueypxdt5/qHXQ3tvdPXiMtMVX3+dQ1nH3dC63FHHU7NObYH/sBiDh9DTsnB5y9TPXduxBB3L2HJtuTYxO5c/YG2mJ4muzSoCqpt6JQ345BydQQtf4Q3p0aGdlkPEokMfwmSqZxehaOdrg1r8X9VXsAUDI1ZOnzrSicj4ynvKsD5VwdsLJQ0bGWH3uvRxnZbLl0n5Dqvvg62wPg7mADwM3YZOr5umFnZYmlSkXD8h7svhZlkkZhaNmhBdt+3wHAxVOXcHR2xN3btG4c3X0s+/Ol8Ct4+erqv6KAvYNOr52DHUkJSUWKCAjo0JjDf+wF4Obpa9g72eNiph7dvXCLWDP16HnTOLQJ+9bp6sa101dxcHbA1dvNxO70npPZn6+fuYaHr2l7UVg6h7Xjt9W6Qc7J42dwcXGiTBkvEzuVSsW0T8fxyZTPjbb3fLUrm/7azv17kQA8ehRXJD0qH3+U+BiUx49AqyHr8jEsqgQY2VjWaorm6imUJH1aqQYOucx0/YksECoLXaUqIq06tmTr79sBfb12ccTDTL0+YlSvL+Plq8vHStUrcvLAaQDu3LiLTzkf3DxNyzm/OAZWRR0RRfqdaJTMLB5tOIB7x8ZGNkknrqB5nKL7fPIq1r4e2fsSj1wkK754HJEAVjVroXlwH01kJGRlkbZ7NzYtWxnZKGnq7M/C1o4nEbmWFSuSefEipKeDVkPmmTPYtG5TZE2WNWuRdf8+Wr2m9D27sWmRT00VKpJ5KUdTxtkz2LQqnKaaHRoS/oeur713+jq2TvY4mmmD/FvU5qK+rw1ft59aHXL6mGYDO3Jxy3FSYhOzt3lV9ePe6etkpmWg1WiJOHqJF3LVgbyoEdqQM/r+//7p69jk0f8bajq7bj819ZoyU9OzbaztbYzCq5183KkWEsDpNXvypSU3xdFmtxvYmVNbjpIY+7hQGrwCqpAYEU3SnYdoMzXc3HCECh0aGtlU7NCQ678fAODhqRtYOztgZyYPDVEUBStHOwCsnexJjY4vlL5n0SigLi7OTs82LEbCXmzP6l91/cjx4+G4uDhTxsd8P/LpzAlM+WiO0faaNauxd+8hAK5dvUnFCmXx8i5cP1fa2scnNAltyl59f3/19BUcnB1wM9PfnzLo76+duYqHXtvj2MdcP3vdKIJK8v8b6ZR4DgghKgkhLgkhvgVOARohhKfB9u+FEBeEENuFEHb6YxoLIc4KIQ4LIeYKIc6bOe8rQCNglRAiXAjxohDiT4P9oUKIP/Sfk4UQ84UQp4QQu4QQXvrtVYQQW4UQJ4UQ/wghahb19/r4evPgfs5NYNSDaMr4epvYDXi7Dzu27iUm+lFRkzTBycedxAex2d8To+JwKmPcONq5OZKWmIKi0epsIuNw8jFtQOv3DuLGXsMnEgqvr5zAoE0zCOwTXCh9LmXcSDDQlxAVi4tP8To+8outjztpBlrSHsRhk08t9hW9yYhNpPZXQ2i2czYvfDEYC3ubImuKSU7Dx8ku+3sZJ1tiktKMbG7HJZOYlslbqw/RZ/l+/jp/F4CqXk6cvBdLgjoDdWYWB27GEJ2kpjjw8vHk4YOcgeLDyId4+eQ9sLCwtKBDz/Yc23scgD+XraditQqsO7mWn3d+z8KPv0Upwk2laxkP4gzKLj4qDlcfj6ccYUqVBtWZumUeI5dNxq9auWcfUADcfTyIfZBzfcdGPcK9TN76LCwtaPNyEKf3nio2Db5+Zbh/L6c9enA/Gl8/0yeub7/bl61bdhMdbXwjUKVqJVxdXdjw9y/s2vcHr/XpXiQ9wskNJSlnsK4kxyOcjNsd4VYGbO2x6TUW275TsHihucFOgW3/j7Eb+gWa2xfRRpmGfhcULx9PYnLVa89n1OuOPUM5ukdXr69fvEnbsNYA1AqoQZlyZbIdcYXBxsedjPs59SYjMg7rp9TrMn3akbD7dKHTexYqT0+0MTlPrLUPH2Lhafr7bFq1xmP5ClxnzyHx888AyLp1C6t69RHOzmBjg3XTZlh4mfaHBcXC0xPtQ2NNKjOarFu2xv3nFbjOnEPiPL2mCGNNNk2boSqkJucy7jzO1dc65+pH7fV9rVbf1z6OzOmPncq4UatjI46v2ml0TPSVe1RsUhM7V0esbK2pHhyQ74cDufv/pEL0/zU6NmLorrn0+Xksf41dmr2949R+7Jy1GkVbuHa7qG22axl3Ajs2Ye+q7YVKH8De142UyBznampUHA6+ucrMx40UA52pkXE4PMkfRaHTrxPotvlTaryRMwY6Mm0lTT7qQ69jX9FkSh9OzF5baI2lDT8/H+7pHdMA9x9E4efrY2L37nv92bJ5F9FRxv3IuXOXeKlbRwAaNqxH+QplKetnenx+KG3t4xPcfTx4ZNCPxEbFPrO/D3o5uFj7e8n/FnJNiedHDeBNRVGGCiEiDLZXA/ooivKOEOI3oCewEvgZGKwoyiEhxBzT04GiKL8LIYYDYxRFOaGPwpgvhPBSFOUh8Kb+PAAOwClFUUYLIT4GpgLDgaXAe4qiXBNCNAW+BUIM0xFCDAYGA7jbl8XR9ukDA50ME61G3719vHixWyi9XjI/baGomJFgosGcztzrzVRs/gIBvYJY0XN69rblL39CckwC9h7OvL5yAo9uRHL32OUC6jMrsEDnKDbMSDHJiLwOtbTAqa4/lyct4/Gp69SYMYBKI7px47PfiiTJXFbkzjKNVuFSVAJLezUnLUtD/5UHqefnRmUPJ95sWpX31h7G3tqS6l7OWJjL78KQj7ptyKhZIzl79Cznjul8ik2CGnH9wg1GvTaGspX8mPfrZ5ztcI7U5MJFl5j9WQWoR7fP32R8yyGkp6ZRNyiQYUvHMzl4RKG0mNdXsHr+9oz3uHT0ApeP573mQ3FoyF1mPj7evNS9M93MrBVhaWlJ/YDavNx1ALa2tmzdtZaTx8O5cT2i2DSa5InKAlWZiqT/Zz5YWmP7+kS0kTdR4qNBUUhbMR1s7LDpNgzh6YfyqHCh5E/IT3tpyOhZIwk/epazx3TrE6xctJqR04fx0/Yl3Lx8i2vnr6HRFOFpVwHqjXOLOni/3o7z3SYXPr1C6THdlH7gH9IP/INVvXo4DBpEwpjRaO7cJmXNr7jNnY+iVpN14zqKpvBRgAai8qUp4+A/xB38B6u69XAcOIiEcTpNqWt+xfXzHE0UUlO+6s5T8q/zx/3YPmeNyU3+oxsPOPDdXwxYOYGMlHSiLt3JdmoURlPu+mO+Xcj5fGXbCa5sO0GFJjUJGv0qK9+YTbWQQFJiHxN5PoKKzWrlS0thtD2N3h+/ybo5K3XrhBSap/92eHq7uanHdFKjE7D1cKbT6vE8vv6AqKNXqNW/HUc/WUXE5uP4d2lKq3nvsLWP2eHrv4789iPde4QR1sl02sqC+d/x2dyPOXB4ExcvXOHsmYtkFbYdKG3to56C9iODZ7zHxaMXuFSM/f2/kdI7eaLkkU6J58dtRVHMrUZ0S1GUcP3nk0Al/XoTToqiHNJv/xXo8qwEFEVRhBC/AH2FED8DzYH++t1a4InbeiXwhxDCEWgB/MegwTV5zK0oylJ0zgsqetQze/30f6sXvfv1BODs6Qv4lc3xAPv4lSEml9e4Tt2aVPSvwL4TujUK7Oxt2Xd8E20bP/Nn5knD/qEE9tZ57R+cvYmzX46H1tnHneSYBCP71LgkbJ0dEBYqFI0WZ193kgzCDb1rlufFz95mzYDPUSfkhLo9OU9qbCJXtp3AL6ByvpwSrft1oHmfdgDcOXMDVwN9rj4ePH5OoY7PIi0yDlsDLbZ+7qRH5U9L2oNY0h/E8fiUbrHH6L+O4j/ipSJrKuNkS5RBdEN0UhpejrYmNq523thZW2JnbUnD8u5ciUmkorsjPepVoEe9CgB8vf8SZQyiLgpK9wEv0UU/d/7ymat4+eWEbHr5evEoOtbscQNG9cPV3YUp43OmJ3V6rRO/frMagPsRD4i8G0WFquW5HH4l33qC+3Witb4eRZy5gbtB2bn5uJMQnf/pBWnJOXl8bu9p3phhgaObk8miagWhY/8w2uvXhLh+9joefjlPbz18PImLMa/vlZG9cHZ3Zt7Ebwud9hMGvfMG/Qa8BkD4qXOULZfTHvmVLUNUpPE87br1X8C/cgWOh+um5tjb23EsfAdNAkJ5cD+KuNh4UlPVpKaqOXTwOLXr1Cy0U0JJMo6MEI5uKMkJJjZadTJkZkBmBtp7V1F5lUMTH51jlK5Gc/cKFpXqkFUIp0SPAd3o+oa+XodfwTtXvY7No14PHNUPVw9X5r49NXtbanIqsz+cm/39tyOriLxT+ClT6ZGxWJfNqTfWvu5kmKnX9rUqUnX+EC6+MeO5hCM/QfvwISrvnEgClZcXmti8I/wyz57F0q8swtkFJfExaZs3k7Z5MwCOb7+D5mHRp1JpHj00im5QeXmhfZqmc2exMNS0ZTNpW3SaHN56B20BNDXpF0pDfZTg/TM3ccnV1yZFJxjZP+lrVRYqtBotLr7uJMXo+piy9fx5deFwAOzdnKgWVB+tRsPl7Sc59ds+Tv22D4D2Y1/jcWTebVuj/qE0yKP/d/JxJykf/X+ymT74zrHLuFX0xs7NkfKNqlOjfUOqBQVgaWOFjZMd3b8cwvoPFj81v4qzza5YrzKDF+oWJ3R0c6JuUAM0Gg3h24/n+xypkXE4GESd2Pu4k5qrz0+JjMPBQKe9rzup+nJ98j8tNpHbW0/iGVCFqKNXqPZKa458/AsAtzYdpdXct/OtqTTyzuB+DHizFwCnTp6lXLmctX/K+vkQGRVtZF+/fm0qV6lI+DndFAZ7ezvCz+4moF4ISUnJDH1vXLbtuYv7uR1xr1C6SlP72Kl/GKH6NSGun72Gp58XoFvY3sPHg/g8+vvXRvbG2d2FzyfOfi66JP8byOkbz4+UPLanG3zWoHMM5flYVwjxs36qxuY8TH4G+gJ9gP8oipKXK1ZBV94JiqIEGPwVyv2/4se1hAW9RljQa2zfvJuevXRvrghsVI+kxCSTKRq7d/xD4xdCaBXYmVaBnVGnphXJIQFwcsUOfgibxA9hk7i6/QT1eurCif0Cq5KepDZxSgDcPnyRWmFNAKjXsw3Xdujmujn7edBzyQdsGLWYuFs5g2srOxusHWyzP1duU5eHV/LXsfzzy3Y+DxvP52HjObv9OE1e1s3hrRRYjbSkVBIfmur7b5B4+gb2lX2wq+CFsLLAp3sLYradfPaBQMbDx6Q9iMW+iq6z9mhdp1gWuqzt68qd+BTuJ6SSqdGy7dID2lY1DnUMqubD6XtxZGm1qDOzOBeZQGUPRwDiUnSXVWRiKruvRtK5VuGXXVm/fCNvd3yPtzu+x4GtB+n4iu6G+4UGtUhJSjF7k/1in840btuI6cNnGj0piLkfQ8NWDQBw83SlfJXyRN6ONDn+aez5ZWv2wpSntx+j+ctBAFQOrIY6KZXHBahHhuuY+NevihCiSA4JgG0rNjM2bBRjw0ZxfPsR2vbU3ShUC6xOalIKCTGmA/+Q3qEEtG3AVyPmF2k6yxN++n4Vwa26EdyqG5v/3slrfXSrkDdsXJ/ExGSTKRo7tu2ldrWWNKgbQoO6IaSmqmkSoCvnLX/volnzRlhYWGBnZ0vDRvW5euVGobVpoyIQbmUQLp6gssCyZhM0N4wXrNNcD0dVthoIFVhao/KtjDYuEuwcwUbvYLO0wqJiLbRxhbv5/3P5BgZ1eJdBHd7ln20H6fSKbmD5QoNaJCemEGumXnfpE0aToMZMGzbDqJwcnR2yF2zt+noYZ46eLXT0D0By+HXs/H2xKe+NsLLEs1sr4radMLKxLutJjR/Hcm3E16TdLNg1VFAyL1/Gomw5VD4+YGmJbUgI6YcOGtlY+OUsFGlZrRpYWqIk6ub8C1dXAFTe3ti0bk3aLuOpCoUh6/JlLA002QTnQ5NVHppatSZtd/41HftlB4vDJrE4bBKXt58g4GVdX1susCppSWqSzbRBtw5f5AV9XxvQsw2Xtuv6mAWtR7Gg1QcsaPUBF7ccY9OUZVzW73Pw0C0W6OLnQa1OjTm38ZDJeZ9wYsUOloZNYmnYJK5sP0F9ff9f9in9f4SBpno923BF3/+7VcyZ3uVTpxIWVpao45PZ/flavmw2gq9bfcC6EYu4dejiMx0SULxt9sTWw5jQaigTWg3l5JYjrJryfYEcEgAPz9zE2d8Hx/JeqKwsqNytGXd2GIfQ39l+iqqv6NYo8WpQhcykVNQxCVja2WClHwNZ2tlQtk0d4vVjoNToeHya64aQvi1rk3ireNZyKim+X/oLrZp3oVXzLvz91w76vK7rRxo3DiAxMclkisa2bXuoVrkpdV9oQ90X2pCaqiagni7w2MXFCSsrKwAGDOzFoYPHSEoqnKOgNLWPW1dsZnTYB4wO+4Bj248SpO/vqwfWIDUplXgz/X373qEEtA1kwYh5xdLf/9vRopT4X2lFRkqUAhRFiRdCJAkhmumjK3ob7Mu9jHQS4GSw/4EQ4gHwEWC4hL0KeAVYA7wOHFAUJVEIcUsI8aqiKP/RT/+opyhK4ZZ01rN7xz8Eh7Zm/4m/da8EHZHzhopla75h3AfTTCInDPHy9uCvXWtwdHJAq9Uy6L2+tG/RneSkvPw6plzfHU6V4ACG7v8i+5WgT+i1bCx/j/ue5JgEds9eTY9FI2g75lWiL9wmfO1eAFqP7IGdmxOdP9Vl95NXfzp4OvPKUt1TCpWlBRc2HOLmvrMm6T+Li3tOUzs4kI/3fUWGOoNVY3MGNu/+PIHV45eQGBNPm4GdaP/uSzh5uTJh6+dc3BPO6glLcPJyYezG2dg62qFVFIIGhTErdLTRk+/8omi0XJ74Mw3WTEJYqLi/eg8pV+5Rrn97AO6t2Im1lwvNts/C0skORatQcXBnDrYegyZZzeVJP1P32+GorC1R347h/Miiv6LLUqViQvs6DPnPEbSKQre65anq6cR/TkcA8GpgJSp7ONHC34vXft6HEIIe9SpQ1Us3iB294QSP1RlYqlRMDK2Ls611kTUBHNl9lKYhTVh1YAXpael8ZvB0eM6Kmcwd+wWx0bF8OPsDou5F8+2GrwHYv+UAK75cyYqvVjLhi7H8tPN7BLB01vc8jk/MI7Vnc27PKeoGN2DWvkVkqNP5eWxOlMHInyexbPxiHsfE025gGB3f7YaLlyvTts7n3J5TLJ/wHQ07NyOob0e0Gg0ZaRksHfFlobWY49TukwQGN2Lh/u/IUKfzzZiF2fsmLpvCd+O+IT4mjsEzh/Dwfgwz/9TNeT+69Qi/f10885F3bNtL+w5tOX5mJ+pUNe8PnZi9b/Xv3zNq+GSiokxXuH/Ctas32L1zP/sP/4VWq2Xliv9w+VIRXnmraMnY9Ss2PT8AlYqscwdRYh9gWb8tAFln9qHERaKJOI/twGmgKGSd/Qfl0QOEZzlsOg8ClQqEIOvKcbQ3C97+5ObwrqM0C2nKmoO/kKZOM4p6+HzFLD4bO5/Y6FhGz/mA6HvRfLdRV477Nx9g2Ze/ULFaRSZ/NV63IOHV28wZU/DXJBqh0XJz0g+8sHoKwkJF9JrdqK/epUx/neMkesV2yo96FSs3JyrP1r0ZQdFoONtpPADVvh2FS4vaWLo70fDkUu7OW0vM6l2F16PVkPT1l7h9Pk/3+s0tm9FERGDXVRcVpv5rIzZt2mDXsSNKVhZKegaPp+e85cX1k09ROTujaLJI+upLlORieGqp1ZC08EtcP5uHUKlQb9mM5nYEtl10mtI26TTZhuo0kZFB4qc5mlym6TVlZZH0deE1Xd0TTrXgAD7Yp+tr/xyb09f2/XksG8Z/T1JMAjvmrObVhSNoN/pVIi/c5tRve5957t6LR2Ln5oQ2K4u/pyzLXnz6WVzbHU7V4ACG6/v/jQb9f59lY/lL3//vnL2anotGEDzmVaIu3Oa0vv+v1bkx9Xq2RpupISs9g3XDFuaRUsEpaptdHCgaLYenLKfTqnEIlYqra/eRcPU+NfvqbqAvr9zN3d3hlAupz6sH5pOVlsE/H+rW1bDzcqbdDx8AoLKw4Mb6Q9zfq2uDDoz7kWaf9ENYqtCkZ3Jg/I/Fojc3Y6fO4fjpsyQkJNKue1+GvtWPnl07Ppe0nrBt2x46dAzizLk9pKrTGPpuTtTD73/8xPChE57aj9SoUZUl389Ho9Fw+fJ1hg8dX3gxpa191HNy9wkaBDfk2/1L9K8E/Tp73+RlH/PtuEXEx8Tx7syhPLwfw+w/dYtKH9l6mP98vRZXL1fm/vUFdo72KFotXQa9xPvth6EuxLhW8r+BkF6r4kcIUQnYpChKHf33CHQLVDrm2j4GcFQUZZp+fYfv0UVY7AXaKIrS0sy5ewKzADXQXFEUtRCiN/CBoijNDOySgQVAGPAY6KUoykMhhD+wGPAFrIA1iqJMz53OE/KavlFSDHasW9ISTIgWxTFfuPjooi6m9RSKkdazindBxaLSeWp4SUswoaqla0lLMOKxklnSEkzYE3+ppCUYcefjor9VobjpuKDoC2EWJ59pXUtagglVahb/YstFRZSyuNVFN0pXmw1gWapGI3BHpD/b6L9M8yzbZxv9F+kfnufwssTwqNi+pCUYsdU5sKQlmDDPuvT1/3/c3lj6BreFoGXZkBJvyQ7e310q81JGSjwHFEWJAOoYfK+k//go13bDx0sXFEWpByCEmAAYx2blHLMOWJdrcyt0Do3ctlOAKbm23QI65e+XSCQSiUQikUgkEomkqJTm6RMljXRKlB5eFEJMRFcmt4GB+TlICHESXXTF6OcnTSKRSCQSiUQikUgkkuJHOiVKCYqirCXnbRkFOa5hHtsdiyxKIpFIJBKJRCKRSCRFRi6bkDelbBajRCKRSCQSiUQikUgkkv8vSKeERCKRSCQSiUQikUgkkhJBTt+QSCQSiUQikUgkEonkOSIXuswbGSkhkUgkEolEIpFIJBKJpESQTgmJRCKRSCQSiUQikUgkJYKcviGRSCQSiUQikUgkEslzRJHTN/JEOiUkT+Uny1olLcGI7UJb0hJM6JeZUdISjPjQMrGkJZggPn5Y0hKM2BhU+jqFhQdsSlqCEd4a25KWYMLyKa1KWoIRtWYfK2kJJvzlVLGkJRjR4MGRkpZgwqrLQSUtwQTbUvaauNfsH5e0BBOyNKUruHe74lzSEkxo7x1V0hKM8KjYvqQlmBB7e2dJSzDi/UYTSlqCCe+rS9e1Jvn/gXRKSCQSiUQikUgkEolE8hxRSpkDujQhXWESiUQikUgkEolEIpFISgTplJBIJBKJRCKRSCQSiURSIsjpGxKJRCKRSCQSiUQikTxHtHKhyzyRkRISiUQikUgkEolEIpFISgQZKSGRSCQSiUQikUgkEslzRC50mTcyUkIikUgkEolEIpFIJBJJiSCdEhKJRCKRSCQSiUQikUhKBDl9QyKRSCQSiUQikUgkkueIXOgyb2SkhEQikUgkEolEIpFIJJISQUZKSIqMe3B9qs8YiLBQ8WDVbm4v3GC0376qHy98NQSnuv7cmL2GO4s36bZX8aXO0g+y7ewqenPz8/9wd+nmQul4aeoAagQHkKnO4Lcxi3lwIcLExq2cF68veh97FwfuX4hg7ahv0GRqaDO4C4HdWwKgsrDAu2pZpjcYTGZaBu+t/RgLGyssLCw4t+UoOxb8XmBtzkGBVJj+FkKl4uHqnUR984fRfvcebfAd2gMAbWoaEROXoL4YgbCxoua6mahsLBEWFsT9fZgH89cUPHPyYOT0YTQLaUq6Op1Zoz7n6vlrJjZTFk6kZv0aZGVmcSn8MnPHL0CTpcHRxZGJ88dStqIf6ekZzBk9l1tXIoqk530DPbOfoqeGgZ55Bnom6PVkFJMey3qNses3HFQqMvZuJv2v1WbtLCrXwPGTRaQu/JTMY/tR+ZbHYcSU7P0qb1/Sfl9G+tZ1RdID0HFaf6oG1ydTncHGMUuIOh9hYuNa3ouXFw7H1tWRqPMRrB/1LdpMDdVDGxI0+hUUrYJWo2H7J79w98TVAqVfPqgeLT7ph7BQcXn1XsK/+cvEpsX0flQICSBLnc7eUUt5ZKBRqAQvb/6UlKh4tg6cD0D7b4fjUsUXABtne9ITU1nXcXKBdD1BVakO1u1eByHIOvsPWcdM2xNV+RpYh/QBlQWKOpn0NZ8hnNywDnsb4eACikLWmX1kndpZKA3mmDZ7PMHtW6NWpzFm+BTOn72Up+0ncybwap/uvFCxGQDdXwnjvfcHAZCaksrkMTO4dKFg5WaIY9sGlP34HbBQEbd2Bw8XG7dprt3a4vVeT0DXHt3/6FvSLkUAoHJ2oNycEdjWqAiKwr1xX5F66kqhtRiy4IvpdO4UQqpazVtvjeJ0+HkTmx9/WECb1s14nJgEwFtvj+LMmQvZ+xs1rM/BA3/R540h/PHH38Wiyye4HoHTdXX+5q97ubzIuM47VfWlyYJ3catbiXNzfuPKd4Xrw56Fd3A96n7aHyxU3Fm1h2u5dDhW9SPwy3dxqVuJS3N+48Zi3e9X2VjRav3HqKwtEZYWPNh0lCtzi94WObZpgO/Hg0GlIv637Tz6zrgeuXQLwutdfT1KSePBlG9Ju3wLa/+ylF84PtvOurwPMV+uJPbnjUXW5NS2AWWnvo2wsCB2zXZiFhv/TrfubfHOrttq7k5enF23XzjwPZoUNWi0KBoNV7uOLlDa7ab1o3JwAJnqdLaMWUq0mbbZpbwXXRcOw87VkejzEWwatRhtpibP4y1srHj9t4+wsLZEZWnBlc3HOLggZ/zQYGAoDfp3QKvRcGN3OCz98pk6bVs0xn3MULBQkfznFhKXGY8p7Nq2wHXoQNDq8iF+3mLSDa9FlQrfld+S9fARD0d+VKA8yovP535Mh45BpKrTGPLuWM6EX8jTdu68qbzR7xX8ytQFwNXVmW8Wf4Z/5Yqkp6UzdMh4Ll0sfPv4LD6a9QX7Dx7D3c2V9Su/e27p5Oa1qW9SOziQDHU6K8Z8y90Lt0xs2vbvSMigF/Gu5MOYwLdIide1k/VCG9H1w14oioI2S8N/pi/jxomitdvuwQFUnfEmwkJF5Kpd3Fm43mi/fVU/anw1DKe6/tyavZq7i3Paq7LvhOHXtx0giFy1k3uFHPf/G1FkpESeSKfEc0YI8R6QqijKiqfYBAB+iqIU+aoUQgQBYxRF6VLUc+ULlaDGnEGcfm0m6Q9iabxtNo+2nSDl6v1sk8yEZK5MXoZX50ZGh6beiORYu/HZ52l15jsebj5WKBk1ggLw9PdhbtAoKgRWpcfMt/im+xQTu7AJr3Pgx82c+eswPWa+ReNewRxZuZP9Szexf6nOWVKrXQNavRWG+nEKAEtfn0FGajoqSwuG/D6NK3vDuXP6ev7FqVRUnDmYq32mkREZywubPydh+zHSrt3LNsm4G83lVz5C8zgFl+AGVPpsCJe6jkdJz+TKax+jTU1DWFpQ889ZPN5zipRTRe9wm4U0oZx/Ofq06s8LDWoxevZI3u063MRux5+7+HTEbACmfjOZrq+HsX7FX/Qf8TrXLlxn8ttTqVClPB/Oep8Peo0tsp7X9Xo+nD2S956h5+NvJtPl9TA2rPiLfiNe5/qF63yk1zNq1vuMKoIehAq7gSNJmT0WbdxDnD5dTOapQ2jv3zaxs+09mKyzJ7I3aSPvkjRpcPZ+50W/kXHiQOG16KkaXB93fx++aTuasoFVCZvxJj91n2pi125Cb47+uIULfx0hbOYgAnsFcXLlLm4dPM/VHScB8K5Znp7fvM/idvnPI6EStJwxgL9fn0NKZBwv/z2diO0nSbj2INumfEh9XPx9WNNqNN4NqtBq9kDWd52Wvb/OW52Iv/4Aa0e77G07hy7K/txsyutkJKUWJFsMBAqsQ/uS/tt8lKQ4bPt9jOZGOEpsjj5s7LBu34/0379ASYoDeycAFK2WjD1rUWLugJUttv0/RnP7ovGxhSS4fSv8K1ekbeMuBDaqx4x5H9G9wxtmbesGvICzi5PRtru37/Na1zdJfJxEULtWzF4wNc/jn4lKRdnp73Gr7xQyo2KpuvELEnccJf363WyTjLvR3Ow1EU1iCk5BDSk3ezjXu48BwG/qOyTvO8WdoXMQVpYIO5vC6chF504hVKvqT80XWtG0SQO+WTSbFq26mrUdP3GGWYeDSqVi9qzJbN++t1g0ga7ON5w1kL29ZqOOjCN0y6c82H6KRIM+LiM+hdMfraBs54bFlq4JKkG92W9y6LXZqCNjabt1BlHbT5FkqCMhmXMfLce3k3Ffq03P5GDPGWhS0xGWFrTeOJWYXWeIP1WAfsxEjwq/T4Zwq/9HZEXFUnn9ApJ25q5HUdzsPQFtYgqObRviN2s4N18eTcat+9zo8n72eWocXk7itsOF12Kgqdyn73LjjY/JjIql+sb5PN55jPRrOZrS70Zz/bUndbsB5WcP41r3nDbweu/JaPQ3cgWhcnB93Px9+L7taHwDqxA6YyAru08zsWs7oTcnftzK5b+O0GHmm9TrFUT4yl15Hq9Jz2RNn1lk6scgr/8+hZt7zxB5+gYVmteiamhDfu40EU1GFvYezpi/YozzyH38CGKGjicr+iG+K79Bve8QmbfuZJukHTtF5L5DAFhV88drzhQe9ByUvd+pTw8yb91BONoXOJ/M0aFjEFWqViKgXgiNGwew4MtPCQl62axtYGBdXFydjbaNHjuUc2cv8UafIVSrXpn5C6bz0ot9i0WbObqHhfJ6z5eY9Om855ZGbmoHBeLt78PUoPfxD6xGn5lv83l3U8f9jZNXOLf7FB+uMR4XXDl4jrM7dGOUsjUr8PY3o/ik3ajCC1KpqDbnLc689inpD+JoqB/7p17NGddmJiRzffJPeHZuYnSoQ83y+PVtx8lOE1Eysqi3ZjKxO06hvhVVeD2S/wnk9A0DhBDF6qQRQlgqivLd0xwSegKAsOJMO78U9Tc7N6iK+lY0abdjUDI1RK8/hGenxkY2mY8SSQq/gaJ/GmAO99Z1UUdEk3bvUaF01O7QkJN//APAndPXsXOyx8nL1cSuSovanNt8FICT6/ZTu0MjE5v6L7XgzMZD2d8zUtMBsLC0wMLSosCv83EIrEZ6RCTpd6JRMrOI23AAt47GjXTyiSto9E6Q5FNXsPb1yN6nTU0DQFhaIKwsoJheJ9SqY0u2/r4dgIunLuHo4oiHt7uJ3ZHdOY6iS+GX8fL1AqBS9YqcPHAagDs37uJTzgc3T7ci6dlWivRYVKmJNvo+2oeRoMki48hurBq2MLGz6diDzOP7URLjzZ7Hsk4DtDEPUB5FF1rLE6qHNuTsOl09v3/6OrbO9jh6u5rYVWpRm4t6B9+Zdfupoa/nmfq6DGBlbwMF9Nh7B1QhMSKapDsP0WZquL7hCJU6GN+IVerQkKu/6xwwMaduYOPsgL1eo4OvOxXbBXD51715plGla1OubyjcDYrKtzJKfAzK44eg1ZB1+SgWVQOMbCxrNUNz7aTOIQGQqr8BSXmsc0gAZKahjY1EOLoWSkduQjsHs26t7inR6RNncXZxwruMp6l+lYrJ0z5k9rQFRttPHj9D4mOdzlMnzuDr511oLfYB1ci4HUnGXV17lPDXfpw7NDWyST11GU1iSvZnKx+dVpWjHY5N6hC3VnedKplZaPV2RaVr1478skr3pP3osVO4uLrg41Ow3zl82CD++PNvYh7GFosmAPfAKiRFRJOir/N3NhyhbEfjOp8em0jcmZvZT7yfB26BVUm5FU3qHV1fe3/9YXxy6ch4lEhC+E20WaY6NPprX2VlgbAsej9iV7866bcjydTXo8eb9uMU2szIRn3qcnb9SD2dU48McWxRn4zbkWQ+eFgkPaCr2+kROXU7/q9/cAnNVbdPGtbtK1j5mmoqDFVDG3Jhna7dizx9A1tnBxzMtM0VWrzAFX3bfH7dP1TTt59PO/5Ju62ytMDCyjK72Q7o256j3/6FJiNL93tiE5+p07pODbLuPSDrfiRkZZGybS92QS2NbBR1WvZnYWeLYT9h4e2JXeumJK8vvifbYS+2Z/WvfwJw/Hg4Li7OlPHxMrFTqVR8OnMCUz6aY7S9Zs1q7N2rG7Ndu3qTihXK4uVdPOVqjkYBdXFxdnq2YTFSv0MjjvyxH4Bbp69h7+SAs5kx7r0LEcTdM72W0g36fmt7myK/llI39o/Sj/2ziFl/EM9cztCcsX+W0Xb7amVJPHkNrToDRaMl4dBFPMOMx8SS/5/8q50SQggHIcTfQogzQojzQoheQogIIcRnQohj+r+qetuKQohdQoiz+v8V9NuXCSG+EELsAT4TQlQRQmwVQpwUQvwjhKiZR9rJQoj5QohT+vN56bfvFULMEkLsA0YKIaYJIcYY7Hui7aoQorUQwhqYDvQSQoQLIXqZSauxEOKQ/nceE0I4CSFshRA/CyHOCSFOCyGCzRznLoRYr//NR4QQ9fTbpwkhlgohtgPPcpg8FVsfd9Ie5AwA0x/EYuNT8BvBMj1aEP3nwULrcC7jzmMDHY+j4nD2Mb6htXdzQp2Yglaj1dlExuJcxtjGytaaGm3rc27L0extQiUYuXk2U04u4dqBc9wNv1EgbdY+7mQ8yHG2ZETGYuXjkae9V+/2PN5zKmeDSkXt7V8QcHYZifvPkHLadEpDYfDy8STGYCD4MPIhnmYGjU+wsLSgY89Qju45DsD1izdpG9YagFoBNShTrgxeRRjgeRZSzzEDPW2KUY/K3RNtbEz2d23cI1RuxgMl4eaJVaNWZOw0ncLwBKtmwWQc2l1oHYY4+biTaFDPE6PicCpjfL3ZuTmSlpiCoq/nSZFxOBlckzU6NmLIrrn0+XksG8cuLVD69r5uJEfGZX9PiYrDwdc4fQcfN1IMNKZExmGvT7/FtL4cmbk6zwGRb9MaqB8+JvFW4Rw4wtE1x9kAKEnxCEdjfcLNB2wdsOk1Dtt+H2NR29TRJJw9UJWpgDbyZqF05MbH15sH93OeAkU9iKaMr+kN94C3+7Bj615iovN2zvbu+zJ7dxa+rbQq40GmQXuUGRmLVZm82yO3Xh1I2quLrrGu4ENW7GPKzfuAan9/Sbk5I4otUqKsnw/37uZEpdy/F0lZPx+ztp9OH8+pkzuYP3ca1tbWAPj5+dC9WyeWLP2lWPQ8wc7HHfX9nPqcGhmHXSH6uKJi6+uG2uC6UkfGYetr6rTNE5UgaOcsOp3/jof7zxF/umD9WG6sfDzIjMxpr7MiHz29Hr3WgaR9J0y2u3Rtw+O/9hdJi7Emw7r96Kl9rXvv0Oy6Dbpb7yorp1N90xd49OlYoLSdfNyM2uakPNrm9MRUo7bZUV+Xnna8UAkGbJ7J8FPfEvHPOSL1YxA3fx/KNalB3/XT6LN2Mj71Kj9Tp6WXJ1lROf2aJuYhFt6meWQX3BK/dT/h/dVMHn2SExHgNmYoCV99j6ItvhB0Pz8f7t2LzP5+/0EUfr6m1/677/Vny+ZdREcZ33SfO3eJl7rpyqthw3qUr1A2z7bj34prGXfiDdrt+KhYXH0KcP0D9Ts2ZuquBQz7aSK/jFtcJD02Pu6kG43947B5yrVmSMrlu7g0q4WlmyMqO2vc2zfApuzzcyKVNrSKUuJ/pZV/tVMC6AQ8UBSlvqIodYCt+u2JiqI0ARYBX+q3LQJWKIpSD1gFfG1wnupAe0VRRgNLgRGKojQExgDf5pG2A3BKUZQGwD7AMFbKVVGUtoqizDdznKVe2wfAVEVRMoCPgbWKogQoirLW0FjvtFgLjFQUpT7QHlADwwAURakL9AGWCyFsc6X1CXBa/5snYeyAaAh0UxTl9Tx+X/4QokiHAwgrCzw7NCTmryPFqyPXhWfexNimVvsGRJy4kj11A0DRKnwVNpFZzYdRvn4VylQvV+zanuDUog6efdpzd5bBwFqr5UKHDznT6G0cAqthV6NCwdIvkKy8G6vRs0YSfvQsZ4+dA2DlotU4uTjy0/Yl9BzUg2vnr6HRFP5JYUH1fDhrJGcM9KzS6/lx+xJeLgY98Oxys+s3DPWapaBozZ/CwhKrhi3IPLqvCDoMFOUjj4RZo5yPV7adYHG7sfz2zgKCRr9asPTN5km+RFKhXQDqR4k8OheR5/mrdGte6CiJJwqfKVClQlWmIul/fEna719g1bwrwq1Mzn4rG2y6DSNz92rISKM4MFcmucvN28eLF7uFsux78+uWADRv1ZhefXsw+5MFedrkQ4zptjyuM4fmdXHvFUrknGW6Qy0ssKtThdiVm7n24gdo1Wl4D3ml8FqMZD07jwAmfzSb2nXa0Kz5i7i5uzJu7FAAvpj/CRMnzUKrzeNaLLQwM9tKYFBn/rougA6twt72k9gWOBzXwCo41SxgP5Yf8qpHzeri9loHoj9bZrRdWFni1K4Jj7cUfWqb/oz51uTYvC4evUJ5MHt59rZrL4/n6oujuDngEzz7h+HQpHYBks5H/X1a2/yU4xWtwvKwySxu9j6+AVXw1I9BVJYqbF0cWNl9Gntmrealb02nO+ZHp7mAOfWegzzoOYiHo6fiOuRNAOxaN0Ubl0DGpeJ5MJIj6dl55+PjTfceYXy3eLmJ7YL53+Hq6sKBw5t4d8gAzp65SJYmy8TuX01Rr3/gzLbjfNJuFN8NnstLH5o8/yygHjNy8hl5mXrtPncWbaD+b1Oot3oyKRciUMxEd0n+//FvX1PiHDBPCPEZsElRlH/0jduTUd1q4MnorTnwZJLaL8DnBuf5j6IoGiGEI9AC+I9BI5nXYyAtOmcBwErAcOXCtabm2TyxOwlUeordE2oAkYqiHAdQFCURQAjRClio33ZZCHEbnXPFkFZAT73NbiGEhxDCRb9vo6IoanMJCiEGA4MBPnBqSBe7KnmKS4uMxdYvxztq4+dBepT5MPa88GgXSNK5W2Q8fFyg45r3C6VJnxAA7p25iYuBDhcfdxKjjXWkxCVh5+yAykKFVqPFxdeDpBhjm/pdjaduGJKWmMrNI5eo0bY+0Qbz5p5FRmQs1n45XmBrXw8yo+NM7OxqVaTS3GFc7fep2TmtmsRUkg6dxyUoEPWVOyb780OPAd3o+oZuptDl8Ct4++U8+ffy9SI22nzY88BR/XD1cGXu2zm+t9TkVGZ/ODf7+29HVhF5p2BzAnsM6EaXIuj5KJeeOQZ61hZCjyHauIeoPHKeZqvcPdEmGD/BtvCvjsNw3dolwskFy/pNQaMh86TuSbZlQBM0EdfynNqRHxr1DyWwty4Q6sHZmzgb1HNnH3eSYxKM7FPjkrB1dkBYqFA0Wpx83UmKNk3/zrHLuFX0xs7NEXV8cr60pETG4WjwdNbBx52UXNd7SmQcDgYaHXzdSY1OoPKLTajYoQEVQupjYWOFlZMdIV8PYff7uic2wkKFf+fG/BFmuhZMflGS4xFOOfqEkxtKcoKxTVI8WnUyZGZAZgbau1dReZVHEx8NKgtsug0j69IRNNdOURT6v9WL3v10C+qdPX0Bv7I5T+58/MoQk+tpX526NanoX4F9J3Rr29jZ27Lv+CbaNtYtD1TzhWp89uU0BvQaSkJ8wdpKQzKjHmFl0B5Z+XqQGWPaHtnWrES5OSO4NXAamoSk7GMzox6hDtetaZOw+WCRnBJD3hvAW2/p1sY4cSKccuX9sveVLefLg0jTiJko/VPejIwMli9fy4ej3gOgYYN6rFqpe4bg6elO504hZGVlsXHjtkLrA11Egl3ZnPps7+uOOjqhSOcslI4HcdgZXFd2vu6kFbCvBchKTCX20CW8g+uTdDn//VhuMqNisfLNaa8tfT3N1iObmpUoO/t9IgZNza5HT3Bs25C0CzfQPEootA5jTY+MpmNY+Xqa7Wtta1ai/GfDuTngEyNNWXr9WbGPebztCPYB1Ug5lveCi4H921NP3zZH6dvmJyt8OJlpm9VxSdg42xu1zcn6tjkpMu6Zx6cnpnLn8CX8g+rx6Oo9kiLjubpVF30SdeYmilZB5eqCNiHv9iEr5iGWBtOiLLy90DxlulP6qXNYlfNF5eqMTf062LVtTtlWTRDW1ggHezxmTCA213SK/PDO4H4MeFN3Y3zq5FnKlfPN3lfWz4fIKONrv3792lSuUpHwc3sAsLe3I/zsbgLqhZCUlMzQ98Zl2567uJ/bEYWv26WFtv060rJPOwBun7mBm58noFuc0s3HgwQz/Xp+uH7sEp4VfXBwc8peCLOgpEfGYWM09ncnI8r0WsuLqF93E/WrLoLUf1Ifo6iL/3XkQpd586+OlFAU5Sq6J/7ngNlCiI+f7DI0y+twg89PHourgAR9xMKTv1pCCAv91IpwIcT0ApzPHE8mdmnIwykkhNimT+sHdP5Ic78hPyEKT3tsmKdGRVGWKorSSFGURk9zSAAknb6BfWUfbCt4IawsKNO9BY+2mYZoPg2fHi2J/tO8I+BpHP5lB1+FTeSrsIlc2H6Chi/rQvcrBFYlLSmVpIcJJsfcOHyBumG6OaYNe7bhwvac0E1bJzsqN63FhR052xzcnbB11i3mZGljRdWWdYi5UbCF71LCr2Hj74t1eW+ElSXu3VoRv/24kY21nydVvx/PrZFfkn4z5/yW7s5Y6NMXttY4t66P+sZ9CsufyzcwqMO7DOrwLv9sO0inVzoA8EKDWiQnphBrZlDZpU8YTYIaM23YDKOnF47ODlha6apw19fDOHP0LKnJBVug8M/lG3irw7u8pdfT0UBPSh56XtTr+eQperoUUo8hmpuXUfmUReXlAxaWWDcLIfOk8VP8pFFvkPjB6yR+8DqZx/ahXvZVtkMCwLp5CJlFnLpxYsUOvg+bxPdhk7iy/QT1eurqednAqqQlqU0GrgARhy/ygn6OZv2ebbiir9NuFXMiAnzqVMLCyjLfDgmAmDM3cfH3wam8FyorC6p2a8btHcY377e3n6L6K60A8G5QhYykVFJjEjg25zdWNX6fX5uPYuewb3hw8GK2QwKgXOs6JNx4QEpk/gc2udFG3kK4lUG4eILKAsuaTdFcDzey0Vw/japcNRAqsLRG5euPNk4XOmzd6U20sZFkndheaA1PWPHjWsKCXiMs6DW2b95Nz166JegCG9UjKTHJZIrG7h3/0PiFEFoFdqZVYGfUqWnZDgm/sj4sWb6AUUMmcevGbZO0CkLqmWtYV/LDqlwZhJUlrl3bkLjDeIFhKz8vKn43kbujviDjVk57lPUwgcwHj7CpXBYAp5b1jRYRLCiLv1tOo8YdaNS4Axs3bqPfGzoHR9MmDUh8nJjtgDDEcJ2Jl17qxIWLlwGoVqM5Vas3o2r1Zqz742+Gvz+pyA4JgLjwmzj5++Cgr/MVujXj/raTzz6wmEkIv4FDZR/s9X1t2e7NidqePx3WHk5Y6vsRla0VXq3rkHy9aAu4qs9excagHrl0aUPSzqNGNlZ+XlT4dhJ3R883qkdPcOnaloRimroBurpt4++HdXmdJreurUnckVuTJ/5LJnJ71ALSDTSp7GxQOdhlf3ZqE0DaM5z/p1fsZHnYZJaHTeba9pPU7qlr93wDq5CelEqKmbb5zuGL1NC3zXV6tuaavv28vvOU2ePt3J2wMRiDVGxVhzh92V3ffoKKLV4AdFM5LKwsn+qQAMi4cAXL8mWx9PMBS0scOgah3mc8/rI0cA5a16wKVlZoExJJWPQj9zv34X6XvjycOJO0E+GFckgAfL/0F1o170Kr5l34+68d9Hld9/axxo0DSExMMpmisW3bHqpVbkrdF9pQ94U2pKaqCaineyjl4uKElZUVAAMG9uLQwWMkJeW/Xyut7PtlG7PCxjErbBxnth+j2cttAPAPrIY6KZVEM2PcvPAy6PvL1/bH0sqy0A4JgKTT17Gr7IttBd241rt7ywKN/a08dYuV2pT1xCusKTFFmL4t+d/hXx0pIYTwA+IURVkphEgGBup39QLm6P8/uYs4BPRGFyXxBmASL6goSqIQ4pYQ4lVFUf4jdOES9RRFOYNuMUpDVMArwBrgdXPnKwBJQPaqOYqiZE9m1E/f8BNCNFYU5bgQwgnd9I39+t+xWwhRHaiAzoXa3OC8T2w+1b+V45H+NxZBqjGKRsuViT8RuGYSWKiIXL2XlCv3KNu/PQD3V+zE2suFxttnY+lkh6JVKD84jCOtR6NJVuvmk7Wpy6UxBZvbnpvLe05TIziAcfu+JEOdzn/GLsne9+bP4/h9/PckxcSzZc5qXl84gg6jX+PBhQiO/7Yn2652x8Zc++csmeqcBYGcvN14bf4QVCoVQiU4+/cRLu8+XTBxGi13PvqeGr9OBZWKR2t3kXb1Ll79dMX88Jdt+I16DUs3JyrOehcAJUvDxbCxWJVxw//L9xEqle61a38d5PHOgjl98uLwrqM0C2nKmoO/kKZOM4p6+HzFLD4bO5/Y6FhGz/mA6HvRfLdxIQD7Nx9g2Ze/ULFaRSZ/NR6tRkvE1dvMGVO0laiP7DpK85CmrD74C+n50LPYQM9yAz0ajZbbxaAHrRb1soU4jP8MVBZk7NuC9n4E1u10N5cZu/JeRwIAaxss6zQk9ccihNrn4vrucKoGBzBs/xdk6V8J+oTey8ayadz3JMcksGv2al5eNIKgMa8SdeE24Wv3AlCrc2Pq9WyNJlNDVnoGfwxbWKD0FY2WA1OWE7ZqHEKl4srafcRfvU+tvrrB4aWVu7mzO5wKIfXpfWA+WWkZ7P0wf9d2lZeacX19EVfgV7Rk7FyJzSsfgkpF1rkDKLEPsKwfBEDWmb0ocZFobp3HduB0ULRknfsH5dF9VGWrYVm7BdqHd7EYMA2AjP3r0N46VzRN6BwOwaGt2X/ib90rQQ1eF7tszTeM+2CaSeSEISPHvoebuyufztWttq7RaOjark/hxGi0PPj4Oyqv+AQsVMT/tpP0a3dwf6MTAHGrtlLm/d5YuDlTdsYQQNceXX/pQwDuT1tC+S9HI6wsybgbzb0xXxZORy42b9lFp04hXLl0kFS1mrff/jB7318bVjD4vbFERkbzy/JFeHq5I4TgzJkLDB02oVjSzwtFo+XUpGW0XT1e90rQNftIvHqfKv11TzBvrNiFrZcLoVtnYOVkh6LVUv2dzmxpO46sZLPBiIXWcXbSMpqvnoCwUHFn9V6Srtynkl5HxIpd2Hi50HbbDCyd7ECrUOWdTuxuMw5bb1cCvx6CsND1Y/c3HiF6RwH7sdxotDyY9h2Vlk9HqFTE/2cH6dfu4PZ6ZwDif92C14jeWLo54zd9qP4YDTe66Vb8F7Y2OLYK4MFHi/JKoVCa7n28hMorpiEsVMT9tpO0a3fx0Nft2FVb8RnZGws3J8p/qouwefLqT0tPV/yXTtKdx9KChA37SNqX/2ipm7vDqRxcn3f2zydLncEWgzFNz2Vj2DbuB5JjEtg3ew0vLRpO6zGvEn0hgnP6tjmv4x29XQn74l2EfgxyZdNR3as/gbO/7aPz3MG8uX022kwNm0cvMRoA5pVHcZ8txPubOaBSkbxxK5k3b+PYU+cATV63CfuQ1jh0CYWsLJT0DB5NmJHvfCgM27btoUPHIM6c20OqOo2h7+ZEPfz+x08MHzrBrIPyCTVqVGXJ9/PRaDRcvnyd4UPH52lbHIydOofjp8+SkJBIu+59GfpWP3p2LdgaJAXl/J7T1AluwPR9X5OhzmDF2JyZ5cN+nsDK8Ut4HBNP8MDOhL77Es5erny0dS4X9pxm5YQlBHZuRtOX26DJ0pCZlsEPw4s2LlE0Wq5N/JF6aybrXgm6eg+pV+7h1z8UgAcrdmDt5UrD7XOw0LdH5Qa/yLHWo9Akq6n94xis3JxQsrK4OvEHsh4Xz4LJkn83oqgrsJYkQoiOwFx0UykygSHA78DP6N5moQL6KIpyXQhRCfgJ8AQeAm8qinJHCLEM3dSP3/Xn9AcWA76AFbBGURST6Ai9E2SBPp3HQC9FUR4KIfaieyXnCb3dNCBZUZR5hvuEEJ7ACUVRKgkh3IFt+vRmm1lXojG6qRp26BwS7YEs4Dt0kSJZwIeKouwxfCWo/rw/A/5AKjBYUZSzhpqelce7yvQqVRVku13pC+55JbN45p0XFx/y7BW4/9sUnxuseNgYVKqqNQALD/g+2+i/iLemtJUa9BtpXdISjKg1p3gchMXJX04VS1qCEQ0eFG0azPNglUdQSUswwbaUjcWq2Je+fiRLU7r6/+04P9vov0wvj9L1WsW6V4pnseDiJPb2zpKWYMT7jZ6vY7Uw9FKXrvYIICj6P6VvUFIIank3KfHMvRRzrFTm5b86UkJRlG3obuaz0UcBfKMoyie5bCOAEDPnGJjr+y10C2jmJ/0pwJRc24JyfZ9mbp+iKI/QrymhKEocYPweTeNzHAeamdk1MPcGRVH2AnsNztvNjM203NskEolEIpFIJBKJRCL5b/OvdkpIJBKJRCKRSCQSiURS2pELXebN/5xTQlGUSv+ldBz/G+lIJBKJRCKRSCQSiUTyv0rpmqAnkUgkEolEIpFIJBKJ5P8N/3OREhKJRCKRSCQSiUQikZQmtKVsUePShIyUkEgkEolEIpFIJBKJRFIiSKeERCKRSCQSiUQikUgkkhJBTt+QSCQSiUQikUgkEonkOSLfvpE3MlJCIpFIJBKJRCKRSCQSSZ4IIdyFEDuEENf0/92eYmshhDgthNiUn3NLp4REIpFIJBKJRCKRSCTPEa2ilPhfEZkA7FIUpRqwS/89L0YCl/J7Yjl9Q/JUyrs/LmkJRtRM9CxpCSYcUTmUtAQjalhYlLQEE4IzbEtaghFfHih94XMt07JKWoIRYfEHSlqCCeXntS5pCUa85Vi3pCWYcDZDlLQEIxp7VS9pCSakU7ryCCBBlC5NS7K0JS3BDKVL0+CM0qUHYMgj65KWYMRW58CSlmDC+42edg/13+frE3NKWoIJbzYcU9ISTAgqaQGSJ3QjpziWA3uB8bmNhBDlgBeBmcCH+TmxjJSQSCQSiUQikUgkEolE8jTKKIoSCaD/752H3ZfAOArgTZaREhKJRCKRSCQSiUQikTxHSsNCl0KIwcBgg01LFUVZarB/J+Bj5tDJ+Tx/FyBGUZSTQoig/OqSTgmJRCKRSCQSiUQikUj+x9E7IJY+ZX/7vPYJIaKFEL6KokQKIXyBGDNmLYGXhBBhgC3gLIRYqShK36fpktM3JBKJRCKRSCQSiUQieY4oirbE/4rIRmCA/vMAYIPpb1QmKopSTlGUSkBvYPezHBIgnRISiUQikUgkEolEIpFIns4cIFQIcQ0I1X9HCOEnhNhclBPL6RsSiUQikUgkEolEIpFI8kRRlFignZntD4AwM9v3ontDxzORTgmJRCKRSCQSiUQikUieI9pSsNBlaUVO35BIJBKJRCKRSCQSiURSIshICYlEIpFIJBKJRCKRSJ4jiiIjJfJCRkpIJBKJRCKRSCQSiUQiKRFkpISkWLFv1RDvSUNApeLx71uJ/+E3o/0OIc3wfH8AilYLGg0xs5eQdupCkdMtG1SPptP7IVQqrq7ey7lv/jKxaTq9H+VCAshSp3Ng1FJiz0cAYO1sT8t5b+NaoxwoCgdGf8/Dk9cJHPsKFTo0QFEU0h4l8s+oJaijE/KtqUJQPdpM64ewUHFx9V5Ofmuqqc0n/aio17Tzw6U8PB+Bo687oV++h72XC4pW4cKvezjz0zYAWk7ug3/7QDSZWTy+HcPO0UvJSEwtVJ4B9Jk6iLrBgWSoM/hpzCLuXLhlYhPcvxOhg17Eu5IvHwS+SXJ8ktH+SvWqMOnPWSwZvoCTW44UKH3foHo0+lRXbtdX7+XiItM8avhpP8rq8+jwqKXEn4sAoOY7najyehAoCgmX73F41FK06Zm41a5AkzmDUNlaoWRpOD5xGbHhN/OtqfO0/lQLrk+mOoP1Y5YQqa8nhriW9+KVhcOxc3Uk8nwEf476Fk2mJnu/X73KvL3+E34fvpCLm49lbxcqweBNM0iKiufXQfPyrekJHsH1qTFjIMJCxf1Vu4lYaPwmJvuqftT+agjOdf25PnsNtxdv0m2v4ku9pR9k29lV9ObG5//hztIiLZSczYIvptOpUwhqtZq33hrF6fDzJjY//rCA1q2bkZioqz9vvT2KM2cu0LVrBz6ZNhatViErK4vRo6dy8NDxQmvxDK7PCzMGICxU3F21m5sLNxrtd6jqR72v3sO5rj9XZ6/llj6PAIKOL0STokbRaFGyNBzsOLnQOjoa1KMNY5YQlUc96rlwOLaujkTp65E2U0P10IYEj34FRaug1WjY9skv3D1xNfs4oRK8ra9Ha/JZj3yD6tHY4Fq7YOZaa5TrWoszuNaqGlxrh/TXWqvvhuNcxRfQtaMZialsDi18no2aPoIWIU1JU6fx6ajPuHr+monNpHljqVm/BgK4c+seMz6Ygzo1jYpVyjN5wXhq1KnGks9+5Nclv5kmUAD8gurReHpOfp0306c0nq7LL406nYOjlhKnL+Na73SiWp8gFH1+HfxQl1+FoXxQPVp8outHLq/eS7gZHS2m96OCvtz2jlrKI4O6JlSClzd/SkpUPFsHzgeg/bfDcdGXm42zPemJqawrZF1/95N3aRzcmHR1Ol+M/oIb52+Y2Iz9aizV6lUjKyuLq+FXWThxIZqsnPayWr1qfLHhC+YMm8PBzQcLpaO0avIOrkfdT/sjLFTcXrWHa7muO8eqfjT48l1c6lbi0pzfuL74bwDs/NxpsHAItl6uKIpCxC+7ufnD1kLryE1pyiPX4AD8pw8CCxUxv+7i/qI/jfZ7vtyassN6AKBJUXNzwlJSL94GoMoXQ3EPbUTmo8eEB48qtAZzvDb1TWoHB5KhTmfFmG+5a2Z81LZ/R0IGvYh3JR/GBL5Fin58VC+0EV0/7IWiKGizNPxn+jJunLhSrPoM+WjWF+w/eAx3N1fWr/zuuaWTm37T3iIguAHp6nSWjllExHnTsVbogM50GtSFMpV8eS9gQPYY8sV3u9GiWxsAVJYWlK1aliGBb5LyOPm/pl9SupCREqUQIUQlIYTpqN7U5nWD742EEF8/f3VPQaXCe8ow7g/+iIiug3F+MQjrKhWMTFKPhHO7+xDuvDyMqMkL8Pn0gyInK1SCZjMHsL3v5/wZPI7K3ZvhUs3PyKZcSH2c/X1Y12o0h8b/SPPZA7P3NZ3ej3t7zvJn23FsCJ3E42sPADi/+G82hE5iY4fJ3N15moBRPQqkKWjGADb2/5xVIeOo3q0Zbrk0VQyuj6u/D7+0Hs3u8T8SNEunSavRcuDTX1kVMp7/dJtG3QHts4+98885VrWfwOoOk0i4GUmjYV0LkWM66gYF4u3vy6SgEayY9B19Zw42a3f95BXm953Oo3sxZn6nip4T+nJh/5kCpy9UgsazBrDnjc/ZFDSOSt2a4Zwrj/z05bax5WiOjvuRJvpys/Nxo8ZbHdjaeQp/h0xEqFRU6tYMgMCP+nDuiz/YEjqZs3PXEfhRn3xrqhZcH3d/H75uO5q/Jv7IizPeNGsXOqE3R37cwsKg0aQ9TiGwV5DR7wqd2Jsb+8+aHNdsUCceXX+Qbz1GqAQ15wzi9OuzOdT6Q3x6tMShelkjk8yEZK5MXkbEYuOBb+qNSI60G6/7C52ARp1BjIGzpCh06hRC1ar+1HqhFUOGjGfRotl52k6YOINGjTvQqHEHzpzROSN37z5Ag4ahNGrcgXcGj+a7JQV31mSjEtSeM4jjr89hf+vR+PVoiaOZPLo4eZmRM8KQIy9/yoF2E4rkkKgaXB8Pfx8WtR3NpqfUo3b6evRN0GjUBvXo1sHzLOk0kaWY6BIDAAB7KUlEQVRhk9g4dildP3vH6LimBaxHQiVoMmsAu9/4nL/011ruNtIvpD5O/j5sMHOt1XyrA1s6T2FTrmvtwHuL2Bw6mc2hk7nz93Hubi68M6l5SFPK+5fl1VZ9mTN+PuNmm7/B+HLaN/QPfZt+oW8TfT+GV97UtcuJCUksmLKwyM4I0OVX05kD2NX3czYGj6OSmT6lrL5tWt9qNIfH/0hTw/wa1IG/w6bwV7uJCAsV/vr8KoyOljMGsLnf5/wWPI6q3ZrhmktH+ZD6uPj7sKbVaPaP/5FWBn0bQJ23OhGfq67sHLqIdR0ns67jZG5uPs6tLYUrt0bBjShbqSxvt3mbryd8zfCZw83a7Vm/h8HBgxkaOhRrW2s69u6YvU+lUjFo4iBO7TtVKA2lWpNKUH/2mxx+/XN2tRlLuR4tcMrVHmUkJHP2o+XZzognaLO0nJ+2il1txrI/7GMqvxlqcmxhKV15pKLyrHe4+MZMwtt+gGf3VthVL2dkkn4nhvMvT+FMuw+59+XvVJn7Xva+h7/t5eLrnxZNgxlqBwXi7e/D1KD3+XXSUvrMfNus3Y2TV/iq76fE5hofXTl4jpmdxzIrbBy/jFtM38/eM3t8cdE9LJTvvpjxXNPITf3gBvj4+zK67TB+nPgdA2eYH0NePXGZ2W9M4+Fd4zz6e8kGJoeNZnLYaH77bCWXjl78f+GQ0KKU+F9pRTol/r1UArKdEoqinFAU5f2SkwO29WqQeSeSzHtRkJlF4uZ9OIQ0N7JRUtOyP6vsbYtlbpVnYBWSIqJJvvMQbaaGmxuOUKFjQyObCh0bcv33AwA8PHUDaxcH7LxdsXK0o0zTGlxbvRcAbaYmO/IgM1mdfbylvQ0UQGuZgCokRESTqNd0deMRKncw1lS5Q0MurdNpij59AxtnB+y9XUmNSeCh/klXZkoa8dcf4OjjDsDd/edRNFoAok7fwNHXPd+achPQoTGH/9gLwM3T17B3ssfFy9XE7u6FW8Tee2j2HO0GdubUlqMkxj4ucPoeucrt9oYjlM9VbuU6NuSmvtxi9eVm663TKCwtsLC1RliosLSzJjU6HtDN17NysgPAytketX57fqgR2pAz6/4B4N7p69g62+OoT88Q/xa1syMgwtftp2aHRtn7mg7syMUtx0l5lGh0jLOPO9VCAji1Zk++9Rji0qAqqbeiUd+OQcnUELX+EF6dGhvZZD5KJDH8BopB1EZu3FvXRR0RTdq9R4XSkZuXunZk5arfATh67BQuri74+Hjn+/iUlJxIHwd7+yK1Ca4NqpJ6Kyo7jyLXH6JMp0ZGNhmPEnkcfvOpeVRUDOvR/dPXsclHPTprUI8yU9OzbaztbVAMBhFO+np0ugD1KPe1FrHhCOVyXWvlOzbklv5ae2TQRoLxtWZhZ232mqr4UlMi1h/Ot6bctOnYki2/bwfgwqlLOLo44OFt2r6lJufUFxtb6+z6Eh+bwKUzV8jKzCq0hieYy6/cbVP5jg25kUd+qXK3TVH5b4MM8Q6oQmJENEl6Hdc3HKFSrn6kUoeGXNXriDmV048AOPi6U7FdAJd/3ZtnGlW6NuX6hsKVW7MOzdi1bhcAV05fwcHZATdvNxO7E3tOZH++Gn4VT1/P7O9d3+zKwS0HSYhNKJSG0qzJLbAqybeiSb2ja4/urT+MT656lPEokYTwmyhZxu1RekwCj/WRSlkpaSRdu4+tj+nvKAylKY8cA6uijogi/U40SmYWjzYcwL2jcb+WdOIKmscpus8nr2Lt65G9L/HIRbLii/9Gtn6HRhz5Yz8At05fw97JAWcz46N7FyKIMzM+Ss/dhj/ndQQaBdTFxdnpuaaRm4ahTTiwbi8AN05fxcHZAVcz9ej2hVs8ymMM+YTm3VpxeMM/z0Om5F+EdEoUAn2UwmUhxHIhxFkhxO9CCHshRDshxGkhxDkhxE9CCBu9fYQQ4jMhxDH9X1X99mVCiFcMzmvSsurT+kcIcUr/10K/aw7QWggRLoQYJYQIEkJs0h/jLoRYr9d2RAhRT799ml7XXiHETSFEsToxLL09yIrKaXiyoh9hVcbDxM6xfQsq/f09ZRdPJ/qjBUVO197HjZQHcdnfUyPjcMjVeetsYrO/p0TGYe/jhlNFL9Jik2i1YDAvbZtBy7lvY2lnk23XYPyrvHb8K6r0aMGpuevyrcnBx41kA03JkXE45tKks4l9qo1TOU+8alck6rRpaOULr7Xh9h7Tp/H5xbWMB3EG6cdHxeHqY1peeR/vTmDHJuxdtb1Q6dv5uJGaq9zsfE3LLdVAY+oDXbmpo+K5tHgz3Y9/xcvhi8hISiVqny646OTHKwmc0ofuJ76iwZQ+hM9am29Nzj7uJBqklxgVh3OZXJrcHElLTEGrdw4lRsbhrC83pzJu1OzYiBMrd5qcu9PUfuyYtRpFW7jBiY2PO+kG2tIfxGJTiEGqT48WRP1Z9BDpJ/j5+XDvbs6T2Pv3Iinr52PWdvr08Zw6uYN5c6dhbW2dvb1bt06cO7ePDRuWM/id0YXWYuvjTppBHqkfxGHjUxDHnUKTtZNouX0W5fuZvIY73zjlqkdJUXE45apHdvp6pBjUIyeD8qzRsRFDd82lz89j+Wvs0uztHaf2Y2cB65G9mWvNPte1Zpe7jXwQh53+Wru4eDM9jn9Fz/BFZCalErnPOJDPu2kN0h4+JulWdL415cbLx5PoBzlP0h5GPsLLx9Os7eQvxvF3+DoqVq3Af37606xNUTDXp9ib6VOM2qbInLbpwneb6XnsK149vYiMxFQi9z818DFvHb5uJEfm6EiJisPB17QfMde3AbSY1pcjM1fneUPk27QG6oePSSxkuXn6ePIwMqfPfxT1CM88ygzAwtKCkJdDOLnvJAAeZTxo0bEFm1cWzzSy0qbJztcNtUHZpEXGYVeIBwn25T1xqVOJ+FOm44DCUJryyMbHnYz7OQ7yjMg4rJ8yDinTpx0Ju08XOd1n4VrGnfgHObrio2JxLVBfAvU7NmbqrgUM+2kiv4xbXNwSSxw3H3diDfIoLioWtzIFr9/WttbUaxvI8QJO//23oihKif+VVqRTovDUAJYqilIPSAQ+BJYBvRRFqYtuvY4hBvaJiqI0ARYBXxYgnRggVFGUBkAv4MkUjQnAP4qiBCiKkvvO/hPgtF7bJGCFwb6aQEegCTBVCGGVO0EhxGAhxAkhxIm1CXfzr1QIk03mKn/yzkNEvPgOD0Z8gsf7/fN//jyTNZfus7WhKAgLCzzqVuLyil1s7PgRWanp1B2eMyXi1Gf/4bfGI7nx5yFqvRlarJrM2+QYWdnbELZkJP9MW2kUtQHQaMRLaDVarhTh5tJclhQkGqT3x2+ybs5K3foghUrfXJmYGJmxUbB2sadcxwZsaDqKPwJHYGlvQ6WXWwJQbUA7Tk5dxfpGIzk5bRVNv3jH9Bx5ijKXnEnBmZME6BwPO+esMblhrB4SSErsY7PrU+Rfm7kCK+AprCzw6tCQ6L+Kr/N/Vj1+wuSPZlOnThuaNX8Rd3dXxo4dmr1vw4at1K3blp6vvMW0aWOLIMbcxvzX6cNdpnIwdCLHX59DxTc74NasZuFk5OPaelYbcWXbCb5tN5a17ywgaPSrAFQrbD0qZHv05For37EB65uOYp3+WvPXX2tPqNS9eZGiJPKr8QkzP/ycrg1eJeLaHdq/FFy0dM1KKVzbpBjk1x/NRvGfBubzK986zDZIz9aBolChXQDqR4k80j9tN0eVbs0LHSWRF08b8A6bOYzzx85z4Zhu6tbgaYP5afZPaAvZh5R6TXmUTUGwsLehyQ+jOPfxL2TlGgcUJ/+GPHJuUQfv19txe+YvRU/3WRRD2Z3ZdpxP2o3iu8FzeenDXsUkrPSQ377/WQS2b8zVE5f/X0zdkDwdudBl4bmrKMqTO8KVwBTglqIoT1YjWw4MI8cBsdrgf0HCA6yARUKIAEADVM/HMa2AngCKouwWQngIIVz0+/5WFCUdSBdCxABlgHuGByuKshRYCnC1Vqd8tzBZ0Y+w9PHK/m5ZxpOsmLg87dUnzmNd3heVqzPahMQ87Z5FSmQcDn453ll7X/fsUP4npEbG4eCX43138HUnNToBFIWUyDge6SMRIv4+ZuSUeMLNPw8RumIM4fP/yJem5Mg4HA00Ofq6k5JLk87GI5dNAqAL/+28dCRX1h/ixtYTRsfVfKU1ldoFsr533nP38yK4Xyda99E9AY44cwN3g/TdfNxJiM67vHJTsV5lBi/Uzft2dHOiblADNBoN4dvzNz85NTIO+1zlpo4yLTd7A432frpy82ldh+S7D0mP0y2YdHfzCbwaVSPij4NUfrU1J6foBi13/jpKs3nm54I+oXH/UBr21t3Y3D97E2eD9Jx93EmKSTDWFJeErbMDKgsVWo0WZ193kvRl61fPn1cW6ubn2rs7US24PtosDWUDqlKjfUOqBQVgaWOFjZMdL385hD8+yP/Tk/TIWGwMtNn4eZBewLBwz3aBJJ27RcbDgk+3MWTIewN46603ADhxIpxy5XPmuZct58uDSNMnr1FRuqfgGRkZLFu+lg9Hmc6xPXDgKJUrV8TDw43Y2IKHvKdFxmFrkEd2fu4FyqN0fTlmPEokevNxXAOrEn/kcr6ObdQ/lAb6evQgVz1yeko9EhYqFH09SjYzLeLOscu4VfTGzs2R8o2qm9Sj7l8OYf0z6lF+rzUHPw+ePD918HNHbeZau7P5BJ6NqnHrD133JyxUlA9rzJZOU/KVT4b0HNCdl954EYBL4Zcp45cz7cfL15NH0XlPMdJqtezauIc3hvTi79+KbwFAyH+fYtQ2+eryy7d1HZLvGOTXlhN4G+RXQXUYTtFz8HEnJVe5peTRt1V+sQkVOzSgQkh9LGyssHKyI+TrIex+X1dXhIUK/86N+SOsYOXWpX8XOvbRrS1w7ew1vHxz+nxPH09io2PNHvf6B6/j4u7CwgkLs7dVq1uNCYsmAODs7kzj4MZos7Qc3l4wR0lp1AS6SC07g7KxNXPdPQ1haUGTH0dx94+DRBZhvRYovXmUHhmLddmcKA1rX3cyzIxD7GtVpOr8IVx8Y8Zzma4B0LZfR1rqx0e3z9zAzc8T0C1O6ebjQUIBpoIacv3YJTwr+uDg5pS9EOa/lfb9OxHcW/eA7ubZ63j45ZSdu48HCTEFz6PmXVtxeOOBYtMo+fcinRKFp6DuQMXM5yz00SpC53K0zn0QMAqIBurrbdPM2OTG7HM6/f90g20airEOpJ27glVFPyzLliErJhbnsLZEjv3MyMaqgi+ZdyIBsHmhKsLKskgOCYBH4Tdx9vfBsbwXqVFxVO7WjH3DvjWyubP9FLUGhnJrw2G8GlQhIzEVtf4mIeVBHM5VfEm8EYlvq9okXL0PgLN/meyw1godGvD4RmS+NUWfuYlrJR+cy3uRHBVH9ZeasW2EsaZbO05Rb2Ao1zYcpkxgFTKSUknVa2o3923irz0g/PstRsdUCKpHwyFdWPfqDLLSMgqSTQDs+WUre37RDeDrBjcgZEBnjm08SOXAaqiTUnn8MCHf55rYelj25zfnDePsrpP5dkgAxIbfxMnfB4fyXqij4qjYrRkHc5Xbve2nqPFmKLfXH8ZDX25pMQmk3I/Fs0FVLOys0agz8GlVm9izulWf1dHxeDevRczhS5RpVZvEW1FP1XF8xQ6Or9gBQLWQAJoM6MD5jYcpF1iV9CQ1ybluJgFuHb7IC2FNOP/XEQJ6tuHKDl1Y61etchbn6z7vXa7uPs3l7Se5vP0kuz7XTSOp1KwWLQa/WCCHBEDi6RvYV/bBtoIX6ZFx+HRvwbkhBVvb1qdHS6L+PFSgY8yx+LvlLP5uOQCdO7dj6JCBrF27gaZNGpD4ODHbAWGUto939vZuL3XiwkXdzX6VKpW4cSMCgMCAOlhbWxXKIQHw+PQNHCr7YFfBi7TIOHy7tyB8yMJnH4juiSRCoElJw8LeBs+gelybn/8pWydW7OCEQT1qPKADFzYepuxT6lGEvh5d+OsI9QzqkVvFMsTf1rU9PnUqYWFliTo+md2fr2W3vh5VbFaL5oNffKZDAkyvtUrdmnHAzLVW/c1QItYfxtOgjTR3rcWdzVlh3ad1HRKvPyA1Mv8OzSesW76edcvXA9CiXTNeGdidHRt2U7tBLVISU4g149QuV8mPexG66UKtQptz+/qdAqf7LJ7k15M+pVK3ZvyTK7/ubj9FzYGhRGzQ5VemQX55NaiKha01mrQMfFvVJvZM/t/+Y0jMmZu4+PvgVN6LlKg4qnZrxq7hxjpubz9F7TdDubHhMN4NcvqRY3N+49gc3aKfvs1rUf/dsGyHBEC51nVIuPGAlAKW26YVm9i0QrdIbOOQxnQd0JV9G/dRI7AGKUkpxJu5KenYuyMN2jRgUp9JRk9SB7UalP151PxRHNt1rFA3tqVRE0BC+A0cK/tgX8ELdWQc5bo358TQRfk+PnDBYJKv3efGkqJPkyiteZQcfh07f19synuTERWHZ7dWXB36pZGNdVlPavw4lmsjvibtZv7HYQVl3y/b2PeL7k1ndYIDCRrQiRMbD+KvHx8lFmB85FWxDA/1bXj52v5YWln+6x0SADtXbGXnCt0YMiCkIaEDOnN44wGqBFYnNSm1wE4JOyd7ajZ7gcUffPkc1JZOtKV4+kRJI50ShaeCEKK5oiiHgT7ATuBdIURVRVGuA/2AfQb2vdCtA9ELeNJ6RwANgd+AbuiiInLjAtxTFEUrhBgAWOi3JwF5rWqzH3gD+FQIEQQ8UhQl0WxIanGi0fJwxreU+2EmqFQk/rGdjOu3cekVBsDjtZtx7NAK527tUTKzUNIzePBhwZ/250bRaDny0XI6/DoOoVJxbe0+Eq7ep0a/EACu/LKbe7vCKRdSn54H56NRZ/DPhzlztI9OWU7bhUNQWVmSdCeGA/p9DSf2wqWKL4pWIfn+Iw5P+LlAmvZNWc5LK8ehslBxce0+4q7ep05fnabzK3cTsTuciiH16X9gPpnqDHaN1qXr27g6NV9pzaNLd+i9dSYAhz/7jdt7ztD20wFYWFvS/VfdU4qoU9fZOyn/ugw5t+cUdYMbMGvfIjLU6fw8NmewO/LnSSwbv5jHMfG0GxhGx3e74eLlyrSt8zm35xTLJxT9lVOKRsuJycsJ+XUcwkLFjTX7eHz1PtX05Xbtl9082BVO2Xb1eemQrtwOj9LlUezpG9z5+xidt81AydIQf/4211fqFv47OvZHGk7vh8pChSY9k2Njf8y3pmu7w6kWHMD7+7/IfpXjE95YNpaN474nKSaBnbNX88qiEYSMeZXIC7c5tXZvkfPjWSgaLVcm/kSDNZMQFioerN5LypV7lOvfHoB7K3Zi7eVC0+2zsXSyQ9EqVBgcxqHWo9Ekq1HZWePepi6Xxix9RkoFY8uWXXTuFMLlSwdRq9W8/faH2fs2bljBu++NJTIymhXLF+Hl5Q5CcPbMBYYO09XhHj3C6Nv3FbIys1Cr03jjjSF5JfVMFI2WCxN/psmaSWCh4t7qPSRfuUcFfR7d0edRy+2zsHSyA61CpcGd+af1GKw8nGj4s249C2Gh4sGfB3m0p+BvlQFdPaoaHMBwfT3aaFCP+iwby1/jvidZX496LhpB8JhXibpwm9P6elSrc2Pq9WyNNlNDVnoG64blz7HytHw5Pnk57Z5yrd3fFY5fu/p0OzSfLDPXWpj+Wos7f5trK3MW2azUrVnRp24Ah3YdoUVIU/5zcCXp6nRmfJjj0J6/Yjazx84jNiaOKV9OxMHRHoTg+sUbfD5RF3jo7uXGz1uW4OBoj1ar0OudV+gTNNBoYcz8omi0HPtoOe31fcr1tbr8qq7Pr6v6/CobUp8eB3X5dUjfbzw6fYPbfx+jy7YZaLM0xF24zdVVhVvcVtFoOTBlOWGrdDqurN1H/NX71NL3I5dW7ubO7nAqhNSn94H5ZKVlsPfD/F3fVV5qxvUiltvx3cdpHNyYH//5kXR1OgvG5ASBfrLsE74a/xVx0XEMnzWcmPsxzF+veyXpoa2HWP3V6rxO+z+jSdFoOTtpGS1WT9C9EnT1XpKu3KdSf3204opd2Hi5ELRtRnZ7VOWdTuxqMw7nFypQ4dXWPL54h+CdswC4OPs3oneFF1lXacojNFpuTvqBF1ZPQVioiF6zG/XVu5Tp3wGA6BXbKT/qVazcnKg8WzcVU9FoONtpPADVvh2FS4vaWLo70fDkUu7OW0vM6l1FlnV+z2nqBDdg+r6vyVBnsMJgfDTs5wmsHL+ExzHxBA/sTOi7L+Hs5cpHW+dyYc9pVk5YQmDnZjR9uQ2aLA2ZaRn8MLzo66c9jbFT53D89FkSEhJp170vQ9/qR8+uHZ99YBEI332S+sENmL//WzL0rwR9wphlk/lh3LckxMTTYWAYXd7rgYuXK7O3LeDMnlP8MF6Xn406NuXc/jOkq9PzSkby/whRmhe8KK0IISoBm9Hd/LcArqFzQjQH5qFz9hwHhiiKki6EiAB+BsLQRTv0URTluhCiDLBBv20XMEJRFEf9+TcpilJHCFENWAekAnsMbKyArYAnurUsTgNjFEXpIoRw16fnrz9usKIoZ4UQ04BkRVHm6X/HeaCLoigRef3Wgkzf+G9wMDHvxZhKiuRStjLLGYv8BNP8dwnOsC1pCUZcsypV1RqAlmlFf2tAcRIWX/rCKTe4tS5pCUacsLV4ttF/mcqZz9n5XEC+EfdLWoIJQyieVysWJ+rSVWxsUBU88uX/G4MzXEtagglLrRNKWoIRH2XalbQEE36xKV3t9tcn5pS0BBPebDimpCWYsPL2H6WslSwcvq4vlPgANDLhYqnMSxkpUXi0iqLknhS9CwjMw/4bRVE+MdygKEo0YPgC84n67RFAHf3na0A9MzaZQO7l4ffq98Whi7wwQlGUabm+18lDq0QikUgkEolEIpFIigmlwLP///9Qyp7xSiQSiUQikUgkEolEIvn/goyUKASGkQz5tK/03MRIJBKJRCKRSCQSiaRUI5dNyBsZKSGRSCQSiUQikUgkEomkRJBOCYlEIpFI/q+9+w6Pqtr6OP5dobfQpCodFAXpiKIoIKigYAHba8EuVlRARa699wIqoojYsCtYwEIVRXpHEERUEER6Cy1Z7x/7TDJJJg3N7BNYn/vwwJyZOL+bmTnnzDp7r22MMcYYY7yw6RvGGGOMMcYYY0w+SrFGl1mykRLGGGOMMcYYY4zxwkZKGGOMMcYYY4wx+cgaXWbNRkoYY4wxxhhjjDHGCytKGGOMMcYYY4wxxguxYSQmOz9VPydUb5CxRUr4jpBJ213JviOks7xo+GZlrSwUrt9RWQ1fPTbFd4AMVsgu3xEyuTCpiO8I6QwtvtN3hEw2JYfrdTu1UGXfETLZImH7tEERFd8R0qmaEq48YbQ2IVSnRwCckbzDd4R0HikUvs/azbuK+Y6QzmvFw3V+BDB81lO+I2RS5JC6B8ROqUKZBt53HBu3LQvl7zJ8Z+bGGGOMMcYYY4w5KITvkqoxxhhjjDHGGHMAsRkKWbOREsYYY4wxxhhjjPHCihLGGGOMMcYYY4zxwqZvGGOMMcYYY4wx+SgFm76RFRspYYwxxhhjjDHGGC9spIQxxhhjjDHGGJOPrNFl1mykhDHGGGOMMcYYY7ywooQxxhhjjDHGGGO8sOkbxhhjjDHGGGNMPkqx6RtZsqKE+dfKtm9O7QevQBISWDfyO/4a/Gm6+yuefSLVbzgLgJSdu/jtzqHsXLySotUrUu/5mylauTyaksK6t79l7bAv9ztHl/supUGHpuxN2sNn/V5hzcKVmR5TrkYleg66kRLlSrNm4Uo+vfUlkvcmp95fvUldrvrsfj66cRCLv5pOYrUKnP3sdZSuVBZNUWa9O55pw7/Oc7aKHZrS8KFeSKEEVr0znpWDRqe7v2T96jR+vjeJR9dh2aPv8/vLX6TeVzixJI2euZbSDQ9DFRbdOoQtM5flOUON9k044b5LSCiUwOKRE5nz0ueZHnPC/ZdQq2Mz9iXtZtxtQ1m/cCWFihXhrI/+R6GihUkoVIhfv5rOjGc+Sfdzza7tStv//R+vN+nNrk3b85Sr+729aNihGXuT9vBBv5dZvWhlpseUP6wSFw2+mZJlS7F60Ureu/VFkvcmc9I1Z9D8rOMBSChUiMr1D+X+FteQtGUHd055gd3bk9CUFFL2pfBC94G5ynPKfZdSL3gffdHvFdbGeB+VrVGJs4P30dqFKxl160uk7E2m0VltOa53NwD27tzFmIHDWffzH1SoW41zBt+U+vPlalZm0jMfMeP1sXn6XQGcet+l1A/yjc4iX7kalThn0I0UD/J9FuQ7vHNL2vftiaYoKcnJfHP/W/w585c8Z8jo/+69giYdWrAnaQ/D+g3i90W/ZXrMyZd2ofMVp1OldjVuan4Z2zdtA+CIYxtx89A7WL9qHQCzxk5j9Asf7neWMHzWcnLFfVfTvEMr9iTtZnC/5/ht4YpMj+nz/G3UPbo+yfuSWT5vGa8MeJHkfckx/mv754YHrqdNx9bsTtrNE7c+xbKFyzM9ZsCgOzmiSQP27U1mydwlPHvn8yTvS+a83udy8tkdAShUqBA1G9SgR9Pz2LZ5W47Pe/J9l1C3QzP2Ju1mTL+h/J3F56vboBsoUa40fy9cyRe3vkxKsJ+O9fMV6laj2+AbU3++XM3KTHnmI2a9/jXdB99I+brVACieWJJdW3fywukDssx3+r2XckSwP/q43xD+ymJ/dP7gmyhRtjR/LfqNj4LjSJ1jj+TioX3ZFLyXF42dwYQX3PGw7ZVdaHV+B1Bl7dI/+aT/K+zbvTfH31dG+XGcy4sa7Ztw/H2XIIUS+HnkRObGOI4cf/8l1AyOIxOC40iEJAg9vnyQHWs3MebypwFo3a8ntU9pgaYoSRu2MuG2V9j592avmY4deCG1OjUnZe8+tv6+jgl9h7Jn685cZ8qv1+nMJ6/m8I7N2bFhKy+dcmeu80RLbN+cmvdfBYUSWD/yW9a+mP54XuHsE6l6/TkApOzYxe8DhpD0c1T+hASO+uop9qzdwPLLHt6vDBlded/VtOjQit3BPnFFjH3iLc/fRr1gn7hs3jKGBPvEQ+sdyo1P9aFuo3q8+9RbjBr62b/OU6FDM+o/dDlSKIE174zjj0Hp/5sl61fniOdvoMzRdfjt0ZH8+XLae+7Qq7tS/eKTAWHNO9+xauhX/zoPwCX3XUmzDi3YnbSbof0GszLG76hzry6cdsUZVKldjd7NeqUeZ0+/9kzannkiAAmFC3Fo/UO5rvnl7NiSt/O13PrfI88w+YfpVChfjs/eHpIvz2EOPDZ9wyMRqS0iC/Pw+DdEpGeM7a1E5IXg3+1FpG0u/lu5elyOEhKo88jVLLnoIea170PFM9tRosFh6R6y+8+/WdzjbhZ0uo3Vz35I3Sd6A6D7Uvj9gRHMO+lmFp5xJ1Uu65LpZ3OrQYemVKhTlRdO6svnA4Zx+kOXx3xc5zsv4KdhYxjUvi+7tuyg+fntU++TBKHzgAv4dfL81G0pySl889A7vHjy7bx21r0cc2lnKjU4NG/hEoQjH7uC2f/3GD+060u1s4+n1OHp/xv7Nm9nycA3WBn1BSmi4UO9WD9hLj+c0JepHW9nxy+r8/b8wf+3Ex/qxZeXPsHIjrfT4MxjKd+gerrH1OzQlLJ1qvJOu75MvGMYJz1yGQDJu/cy6vxH+ODUgXxw2kBqtm9Cleb1Un+udLUKHNauMdtWrc9zrobtm3FInao80f5WPr7rVc5++MqYj+t65//x/bCveKLDbSRt2UHr8zsAMGnoFzzXdQDPdR3AmCfeY8W0n0nasiP151658CGe6zog1wWJesH76OWT+vLVgGGclsX7qOOdFzB92BheDt5HzYL30eY//+Ht8x7ktdMGMOWFz+j6qPv/s3HFGl7rehevdb2LYWcMZG/SbpZ+PTO3v6ZU9YN8L57Uly8HDKNrFvlOvvMCpg0bw0sZ3ue//bCQoacN4NWud/F5/6Gc8fjVec6QUZP2LahSpxp3tr+RN+56mUsevibm45bNWsKTF9+fWnyI9suMn7m3az/u7drvXxUkwvBZy0nzDi2pVqc6N510LUMGvMg1D10X83GTP5tEn47Xc9spN1G0WFFOvuCU/yzDMR1bc1idQ7n0hMt55o7n6PPozTEfN+7TcVx20pVc1ekaihUvRtcLuwDwwZAPufbU67j21OsY9tjrzP9pQa4KEnU7NKV8naq8elJfvh4wjM4PXRbzcSfdeQEzh43l1fb92LVlB02C929WP79xxRpGdB3IiK4DefOM/7E3aTfLgs/X6BsHp973y9gZLBs7I8t8hwf7o2fa38Znd71G94eviPm4U++8kB+GjeHZDrexa8sOWgb7I4CVM5YwuOtdDO56V2pBIrFKeY677FRe6jaQF069g4SEBI7udlyOv6+M8us4l1uSIJwQHEfe73g79bM5joxs15dJdwyjXXAciTj6ytPYtPyvdNvmDvmSD0+5i49OG8jv382hZZ+zvWda9f0CPuh0Jx+echebV6yh+Q3dcp0pP1+nuR9+z9u9nsh1lkwSEqj50LX8cskDLOpwExXObEfxjOdsf/zN0p4DWdz5Fv56/gNqPXF9uvurXHkGSctX7X+GDFoE+8QbcrFPvKnj9dwS7BM7BfvE7Zu3M+zeoYx69dOYP5dnCQk0eOxK5v/fw0xvdyuVzz6ekoen/x3t3byd5QNfT1eMACjVsAbVLz6ZWacNYGbHflTs3JISdar+60hNO7Sgap1q9D3pBoYNGMJlD8U+zv4ycwmPXnQf//yZ/jj75SujGNi1LwO79uWDx9/m52mL860gAXBW184MeeahfPvvF2Qagv+FlRUlDgCqOlNVI2eV7YHcFBty+7hslW5en10r17D7j7/RvfvYMGoK5U89Jt1jts9cSnLwRXHb7F8oWq0iAHvXbWLnAlfpTdmxi6Tlq1Lvy6sjOrdk3sffA7BqznKKJ5akdOVymR5Xp22j1CsOcz+eTMNTWqXe1+ayU1k8ZgY71m9Ny75uc+oVjj07dvHP8r8oU6V8nrKVbVGfnb+tJen3dejeZNZ+9iOVT2uV7jF71m9l69wV6N70V0ILlS5B+eOOZPU7EwDQvcnsy8PVmojKzeqxZeXfbP3jH1L2JrN89E/UOaVlusfUOaUlSz+eAsDfc36laGIpSga/w307dwOuwp5QuDDRo8+Ov/dipj783n51FD7qlJbM/sS9bn/MWU6JMiUpU6lcpsfVb9uIBV9NA2Dmx5NpdEqrTI9p1r0tc0f/mOcM0Q7v3JL5wfvor2zeR7XbNuLn4H00/+PJHB7kWT1rGbuC12f17GUkVquQ+WePb8ymP9axdXXeizjR+VbnkC/yPp/38WSOCPLtDV5HgCIli8F/cHBqfkprfvxkEgAr5iyjZJlSlI3xGv6x6Dc2rPrnXz9fdsLwWctJ685tmPixe45lc5ZSMrEU5Spn3qfMmTAr9d/L5/1Cxf3cN8Zy/Clt+eajbwH4efYSSieWokLlzO/V6ePTvsAvmbuUStUOyfSYDme1Z/yoCbl63vqdW7Io2MesmfMrxRNLUSrG+7dm26NYGrx/F378PQ2CfVVufr7W8Y3Y/Mc6tq7ekOm/e8Tpbfh59NQs8x15SkvmBPujP+csp3gW+6O6bRuxKNgfzf74e46KsT/KKKFQIYoUL0pCoQSKlCjKtr835fgzmfLn03Eutyo3q8fWlX+zLTiO/Dr6J2pnOI7UPqUlvwSv0bo5v1Is6jhSqmoFanZsxs8jJ6b7mb3bk1L/ndf9Un5lWjV5IZqcArjjYekY+/Ks5Ofr9Pv0JSRt3v8vk6WaNWD3yjXsCc7ZNo6aQrlT2qR7zI5ZaedsO2YvTXdeVqRaRcqe3Ir173673xkyOiZqn/jLnKWUSixF+Rj7xNlR+8RlUfvELRu2sHz+8nSjTP6NxBb1SfptLbt+X4fu3ce6z37gkAzHkb3rt7Jt7q/o3n3ptpdscChbZy0jJWkPmpzC5h8Xc0jX9OfE+6Nl52OY8vFEAH6d8wulsjhu/L7oN9bncJw97swTmDrq+3+dKTutmh1N2cQy+foc5sBjRQn/ConIqyKySES+EZESInK1iMwQkXki8rGIlIx6fCcR+V5EfhGRMyB11MMXIlIb6A3cKiJzRaSdiHQTkWkiMkdEvhORKrEet7/hi1atyJ6/0k7+9qzZQNFsDt6VL+zE5glzMm0vdlglSjWuw/bZ+zeUPLFqBbZG5di6diOJGYoHJcuXZtfWHaQEJxpb12wksap7TJkq5Wl4aitmvv1dls9R7rBDqNaoFqvn/pqnbMWrVmBXVLZdf22kWNXcneCUrFWZPRu20uj56zj2u0c56plrKFSyWJ6eH6BU1fJs/2tj6u3tazZSqmr5GI9Jy7kj6jGSIJw39mEun/sSf36/gHXB76B25xbsWLuJDT//kedMAGWrVGBz1HNuXruRshl+NyXLlyEp6nXbsmYDZaukf0yR4kU54qSmLBgzLW2jKle/NYCbP3+YNhd2zFWeMjHeRxmLUCWC95FGvY/KVM18ctD0gvb8OnFepu2Nuh/L4v0snuxPvm0Z8h1xaiuuG/ckFw7vz+j+Q/crR7RyVSqw8a+0AsumtRsoXzVvX6DrtziC+8c8za1vDKR6gxr7nSUMn7WcVKxakQ1/pZ00bly7gYpVsv59FSpciBPP6cDcibP/swyHVK3IP1EZ/lmznkOyec0KFS5E5x4nM2Ni+tE9xYoXo3X7Vnz/1ZRcPW+ZquXTvX+3ZfH+3b11Z7r3b+nIfjoXP9+w+3ExCw+HHXMEO9dvYdPKv7PMl1ilPFui9pNb16YdIyJKli+T4TiyId2xpmaLBtw45lF6vXE7lYNRdVv/3sSUV7+k/4+DuHP6S+zalsTy7xdkmSPLfHE4zmVnf44j0Y9pe9/F/PTISEjJXHQ45vZzuXja8zQ4uy0znvo4FJkiGp53In9MyP3IEt+vU3aKVqvAnjVp++s9a7M/Zzvkgk5smZC276lx35WsengE/Ifz4itUrcj6qP3RhrUbqJDDPrH9OR2Y8x/uE6MVq1qB3VGv3+6/NlIsl8e0HUv+pOyxR1K4fGkSShSlQqcWFDs0czE3r8pXrcCGqOPsxrUbKF8l94WyiKLFi9LkpObMGPPTv85kzH/NihL+NQBeVNVGwGagB/CJqrZW1abAz0D0mPbawEnA6cAQESkeuUNVVwJDgGdVtZmqfg9MAY5V1ebAe8DtWTwulYhcIyIzRWTmZzszzw1PR2Jsy+JYldi2MZUvPJk/Hn4z3faEksVp8NrtrLzndZKjrpjkSYwcma7cS+YHRR5y2r2X8N1j76FZnJgULVmM84bcwtgH3mJ3XjPG+h3l8kqQFC5EmaPrsGrEt/zUaQDJO3dT+6Yz8/b8gGTz/z27x0QepCnKB6cNZMQxN1OlWT0qHHEYhYsXpeVN3Zn+9Ed5zhP1pDGeUnN6SKbHHNWpBStnLk03deOlHvfx/Bl3Meyyxznu0lOoc0zD/YkTI0+sB6W/Weu4o2h2fnvGP/peuu0JRQrRoFNLfv5yGvvjv8i39OuZvHxyfz64+lna9z13v3Lk9Hx5GTXz+8IV9Du+N/d26cu4N8Zw89A7/kWYWBvj+1nL+Ykyb8ru93X1Q71ZPG0RP89Y/B9myHl/EK3PIzcxf9oCFkxPP9vwuM7HsmjG4lxN3cj6eXPeT6e+hDn8fEKRQtTv1IKlMT5fR2ZRrEj/1Ln4vWTz+v21cCVPHn8zg7sMYOob33DR0L4AFE8sxZGdW/JUuz481uYGipYsRtOgF06e5PNxLufnz83vJ/ZrVPPkZuzasJX1C1bG/E9Pf+JD3m7Th2Wf/kjjyzqHIhNAi5u6o8kpLPv0hzxkyrwprq9THsNl9dkv07Yxh1zQiVXBOVvZk1uxb/0Wdi7I24WZHBPlcZ94TX7sE9MFipEnl8eRnctW88fgUTT94G6ajBzIjkUr0f+gF9C/Pc5GNO/Uml9mLsnXqRsmeymq3v+ElTW69O83VZ0b/HsWrujQWEQeAsoBpYHozoofqGoKsExEVgA5fdM6DHhfRKoBRYEcqgygqkOBoQA/VT8n23fvnjUbKFo9rYJctFpF9qzdmOlxJY+sRd2nrmfJxQ+yL6oRohQuxOGv9Wf9J5PZNCZvX9RaX9qZlhe4ubyr568gMSpHYtUKbFu3Od3jd27cRvHEUiQUSiAlOYXEahVSh9BWb1KHnoNco7SSFcrQoENTUvYls+SbWSQULsR5Q25hwWc/8PPYvPcB2LVmI8WjshWvXoHda3M3dHfXXxvY/ddGtsx2Tej+/nwadW7qnucM29dspHT1tKp66WoV2Jlh+LB7TFrOUtUqsCNDs7E9W3eyeurP1GzfhD8mzadMjUqc9/Ujqf/Nc8c8xEfd7iXpny1ZZjnuks6pIxf+nLeCclHPWa5qBbZmyLVj4zZKRL1uZatVZOu69I9p2i3z1I3IY3Zs2Mqir2dQo2k9fpu+JFOelpd2pnnwPvorxvtoexbvIymUgGZ4HwFUbliD0x+/ivd6PZFpmG399s1Yu3BlnoZOt/qX+cpkyBfxx/QllK9VmRLlS5OUx+akHS85jZMu7ATAb/OWU6F62pWg8lUrsvnvzPuArOyKKvLNnzibSx66mtLly6Q26MqLMHzWYjnt0q6pPSF+nb+MitUr4erN7irhxnWxf1/n9rmAxApleWXAo/86w5m9utH1/7oCsHTeUipVr5R6X6Vqh7Dh78zTHQAuufViylUox7133J/pvg5n5jx1o/mlnWgSvH/XBu/fSKeOMjHev0kbt1EssWS69+/24P27bc3GbH++bvum/L1wJTszfL6kUAKHn9aaEWfcnSlfm0s60/pCl2/VvBWUjdpPJlbN/NnJfBypmHqsiS5Y/zJxLt0fupyS5ctQ97ij2PTnOnZudO/pRWNnUKvl4cz7LOcvuvE6zuXGjlwcR3ZkOI64x2ymbtdjqNW5BTU7NKVQsSIUKVOCjs9fx/g+L6f7+WWf/UjXEf2YmaGZso9Mh/dsR82Tm/PFBTl//sL0OmXHjWZN218XrVqRvTHO2UocWYtaT9zIskseIDkoOpZu3ZByp7SmbMeWJBQrQkKZktR54RZ+u/m5POc47dKudA72icvnL+OQqH1ixaoV2ZTFPvG8YJ/4xH+wT8zK7jUbKRb1+hWrXiHmeW1W1r47nrXvjgegzl0Xpht1kRedLj2NDhe4At2K+cupGHWcrVC1IpvX5e7YFu24bicwdXTuRrYZE29WlPBvd9S/k4ESwBvAWao6T0Quw/V/iMhYJMip5DUIeEZVR4tIe+C+/Y+a2fa5yylepxrFalRmz9qNVDzzBJbf8Gy6xxQ99BAOf+12lt/8PLtWrEl3X92nbyBp2WrWDs3cLTsnM978lhlvunmNDTo245hep7Bw9FQOa16f3duSMp3sAvw2dTFHdT2GhZ//RLMeJ7L0W3eQf/6EW1Mfc9ZT1/LL+DmpJwBnPnE165evZuprY/KcEWDrnF8pWbcqJWpWYteajVQ9qy3zrxuUq5/d888Wdv21gZL1qrHz1zVUbNd4v5rvrZu3grK1q1KmRiV2rN1I/e7H8u1NL6V7zMpvZ9P4ss4sHzWVKs3rsWfbTnau20zxCmVI2ZfMnq07KVS8CIe1a8yclz5n45JVvNH8htSfv/jHZ/no9LtzXH1j6lvfMvUt97o17NCctr1OYe7oH6nZvD5J23ay7Z/NmX7m16mLOLprG+Z9PpVWPU5kcdTJWfEyJajb5khG3vJi6rYiJYqRkCDs3rGLIiWK0aBdE757IfZJ7qw3v2VW8D6q37EZrXqdwuLRU6mezfvo96mLObLrMSz+/Cea9DiRZcH7KLF6RXq8cgujbn2Zjb+tzfRzR3U/jkV5nLox881vmRmVr3WvU1g0eiqHNq/PrizyrQze54s+/4mmUe/z8rWqsOl3N3y9auPaFCpSOM8FCYDxb41l/Ftu5ZAmHVpwcq8uTBs9hbrNG5C0bSdbYryGWUmsVI6twePrNK2PiOxXQQLC8VmLZeybXzH2TdeBvUXHVnTpdTo/jJ5Mg+ZHsHPbzpgnlydf0JlmJzXn/gvv3q8rYhmNGvE5o0a4/Wybjsdw1uVnMmHURI5s0ZAd23bELIx0vfA0Wp/Ukn4X3JEpQ6kyJWly7NE8etPj2T7vnDe/Y86bbhh63Y7NaNGrMz+Pnkq15vXYvW0nO2K8f/+Yupgjuh7Dks9/onGPdiz71g3TXv7d7Gx/PqvRELVPaMzGX/9ie4wvFtPe+pZpwf7oiA7NOLbXKcwfPZUawec/1v5oxdTFNOrahgWfT6VFj3b8/I0rVpeuVJbtQUH2sKb1EBF2btrG5r/WU6N5A4oUL8reXXuod3wjVs/P3Dk/lngd53Ij43GkXvdjGZfNcaRy1HFk+uMfMP3xDwCofuyRNL22a+qX/7K1q7AlmFZTu3MLNi1Pf57gI1ON9k1odt0ZjD73Ifbt2pNjjjC9TtnZMW8ZxetUo2iNyuxdu5EKZ57AihufSfeYotUPod6rd/Jbn2fZ/VtaA9DVj73N6sfeBqDMcY2pcu2Z+1WQgPT7xJbBPnHK6MkcHuwTN8XYJ3YK9on3/Uf7xKxsm7OcEnWrUbxmZXav2Ujls45n8XXP5/rnixySyN71Wyl26CFU6tqG2afnrtF2Rt+9OZbv3nTH2WYdW9K5Vxemjp5CveaHZ3ncyE6JMiVpeOxRvHzLc/uVx5j8ZkWJcCoDrBGRIsBFQPSZ8bkiMgKoA9QFlgLHRt2/DUiMul026ud7ZfO4/ZOcwsqBr9Hw3XuQQgmse28cSb/8SeVLXAV83VvfcNit51G4fBnqPOq6Beu+ZBZ2uZ0yxzSk0rnt2bF4JUd/65bh+vPRd9g8Pu/zBJeNn0uDDs24efIz7E3aw6h+r6Ted9Eb/Rl9+6tsW7eZ7x4dSc/BN9Gx37msWfQ7s9+fmO1/t2arw2naox1///wHvb9yIwLGPfk+yyZk7hWQFU1OYcmA4bR47y6kUAKrR05gx9JVHHapu9K86s3vKFqpLMd+8wiFy5RAU5Ra13Thh3b9SN6exJK7hnP0SzeSULQwSb+vY2GfvC+vpMkpfH/3CLq9fTtSKIEl709i0y+raXSxG7Gw6O3x/D5+LjU7NuWiKU+zL2kP4/u6fgOlKpej47PXklAoARKEXz+fxu/j5uY5QyxLJsyhYYdm3DHpOfYk7ebD/mmv2xXDb+ejO15l67pNfPXYSP5v0E2c2vc8/lq0kukfpF2dbXRqa375fj57k9Lqe2UOKculQ28DXIO5uaN+4JdJOb9my8fPpV6HZlwfvI++iHofnf9Gf768/VW2r9vM+EdHcvbgmzip37n8veh35gbvo3Z9zqZE+TJ0edB1W09JTub1bu7qbOHiRanTrjFj7hq237+v5ePnUr9DM26Y/Az7giVBIy54oz9fBPnGPTqScwbfRPt+57I2Kt+RXVrTpEc7kvcms2/3Hj65IXdf2LMzf8JsmnRoweOTXmRP0m6G9U8rDt06fCDD73iJzes20emyrnS59izKVirHA2OfYcGE2Qy/82VadzmODhefSnJyMnt37WHITc9m82zZC8NnLSezx8+kRYeWDJ78CruTdvNSvxdS77vrjXt4+fbBbFq3kWsevp5/Vq/j4U9dp/1pY6fy0Qvv/ycZpo2fTpuOx/DWlDfYtWs3T972VOp9j7z5EE/3f4YNf2/klkf78Peqvxk0yp2QTxkzhbeeeweAE047nlmTZrMraVeun3fF+LnU7dCUqye7fcyYfmk9TXq80Y+vb3+N7es2M+nR9+g++Eba9TuXvxetZEHw/s3u5wsXL0rtdo35+q7XMz1vw27H5jh1A2DphLkc3qEZt016lr1Ju/kkan906fDb+fSOoWxbt5mvHxvJBYNuonPfc/lr0e/M/MDla9ylDcdc3ImU4L38/k3u87Vq7q8sGjONG758hJR9yfy1aCUzRo7P9e8tIr+Oc7mlySlMuXsEpwfHkaXBceSo4Diy+O3x/BEcRy4MjiMT++bct6bNgPMpV68amqJsW7We7+8a7j3TCQ/2olDRwpzxrlt28+/Zy3OdKz9fpx4v3EDt446kZPky3PbTICY8+xFz3p+Uq1wAJKfwx92vcvg790JCITa8/x27fvmTShefCsA/b39NtVvPp3C5MtR6JLJSWjI/n94v98+RR7OCfeJLwT5xcNQ+ceAb9/BSsE+8NtgnPhrsE38aO5UPX3ifcpXK8eTnz1CidEk0JYUzrujOzZ1uIGk/pwNrcgrLBgyjyXsD3ZKgIyewc+kqql/qRi389ea3FK1UjpbfPEahMiUgRTnsmtOZ3u5Wkrcn0WhYP4qUL4Pu28cvA15jX9S00v01d/wsmnZowdOTX2JPsCRoRL83BvLa7e44e8plXTmj99mUrVSOR79+lnkTZvPaHa5I1+rUNiyYPI/dSbuzepr/TP97H2PGnPls3ryVk8+6mOuvvIQe3U7N9+ctCPKzoFbQif1y/AkaTn6hqo2D2/1w0zX+Bm4HfgcWAGVU9TIReQPYBLQCqgC3qeoXwQiIfqp6hogcDnwEpAA3ARWAZ3GFiZ+A1qraPuPjMvaViMhp+ka8jS1SwneETNru+m86Pv9XlhcNX61xZaFw/Y7Kavja6aT4DpDBCsn9l814uTCpiO8I6Qwt/t+vzvFvbUoO1+t2aqHKviNkskXC9mmDIhqzIYo3VVPClSeM1iaE6vQIgDOS//0X4P/SI4XC91m7edd/38D433iteLjOjwCGz3oq5wfFWZFD6h4QO6XixWt633Hs2vVHKH+X4fv2chAJGk42jrodvRd4OcbjL8vivzMRmBj8+xegSYaHjIrxM7EeZ4wxxhhjjDHmP5bbpqkHo/BdLjTGGGOMMcYYY8xBwYoSxhhjjDHGGGOM8cKmbxhjjDHGGGOMMfnIejlmzUZKGGOMMcYYY4wxxgsbKWGMMcYYY4wxxuQjGymRNRspYYwxxhhjjDHGGC+sKGGMMcYYY4wxxhgvbPqGMcYYY4wxxhiTj2zyRtZspIQxxhhjjDHGGGO8EGu4YeJBRK5R1aG+c0SzTDkLWx4IX6aw5YHwZQpbHghfprDlgfBlClseCF+msOWB8GUKWx4IX6aw5YHwZQpbHghfprDlMeFmIyVMvFzjO0AMlilnYcsD4csUtjwQvkxhywPhyxS2PBC+TGHLA+HLFLY8EL5MYcsD4csUtjwQvkxhywPhyxS2PCbErChhjDHGGGOMMcYYL6woYYwxxhhjjDHGGC+sKGHiJYxzyixTzsKWB8KXKWx5IHyZwpYHwpcpbHkgfJnClgfClylseSB8mcKWB8KXKWx5IHyZwpYHwpcpbHlMiFmjS2OMMcYYY4wxxnhhIyWMMcYYY4wxxhjjhRUljDHGGGOMMcYY44UVJYwxxhhjjDHGGOOFFSXMQUdESvnOkFEYM5nYRKSQiNzqO0c0ETk+N9tMeIhTw3eOWMK0PxKRYrnZZkxBERxDvvOdI5qIJIhIW985wix43d72nSPs7HzE7C8rSph8IyL1IiePItJeRG4WkXIe87QVkcXAz8HtpiLykq88YcwUnJgs9PX8sYjIuSJSJvj3/0TkExFp4SuPqiYDZ/p6/iwMyuW2uBGR22L8uVJEmnnMdIKIXB78u5KI1PGVRV2X6c98PX8sYdsfBabmcltciEiFGH+K+MoTxkwiskBE5mf4872IPCsiFQ/2TMExZKeIlI3n82ZHVVOAp33nyEhEDheRcZHzEhFpIiL/85EleN0qiUhRH8+fnaBgUl1Eakb+eIwTuvMRUzAU9h3AHNA+BlqJSH1gGDAaeBfo6inPs8CpQQ5UdZ6InOgpS0SoMqlqiojME5GaqvqHrxwZ3K2qH4rICbjf1VPAy0Abj5l+EJHBwPvAjshGVZ0dzxAichzQFneidFvUXYlAoXhmiaFV8Ofz4PbpwAygt4h8qKpPxDOMiNwb5DkCGA4UAd4GfF7B+UlEWqvqDI8ZooVmfyQiVYFDgRIi0hyQ4K5EoKSPTIHZQA1gEy5TOWCNiKwDrlbVWZaJMUAy7ngPcEHw91bgDaBbnPOEMdMuYIGIfEv6Y8jNcc4R7RsR6QF8ouFZmu9VoD/wCoCqzheRd4GHPOVZiTv+jyb96/aMpzyIyE3AvcDfQEokEtAkzjnCfD5iCgArSpj8lKKq+0TkbOA5VR0kInN8BlLVP0UkelOyrywRIcxUDVgkItNJf9Dt7ilP5PdxOvCyqo4Skfs8ZYmIDHN9IGqbAh3jnKMoUBq3Ly8TtX0r0DPOWTKqCLRQ1e2QWhT4CDgRmAXEtSgBnA00x32BQ1X/iozA8agDcK2I/I77rAluEEVcTyajhWh/dCpwGXAYEH3Cvw24y0egwFjgU1X9GkBETgFOAz4AXsJPsTRsmY5X1ehi3wIR+UFVjxeRi+OcJayZvgz+hMltQCkgWUSSSNsfJXrMVFJVp2fYJ+3zFQb4K/iTQPpjrk99gCNUdYPnHGE+HzEFgBUlTH7aKyIXAr1Iuwrhc5jrn8GcSQ2G391MMEzZMqVzv+fnz2i1iLwCdAIeD6YEeZ16pqodfD5/hKpOAiaJyBuq+rvvPBnUBPZE3d4L1FLVJBHZ7SHPHlVVEVEITd+ELr4DZBCa/ZGqjgBGiEgPVf3YR4YstFLV3pEbqvqNiDyiqreJv14XYctUWkTaqOo0ABE5BvdlBfx9oQxVJlUdEXzGDg82LVXVvfHOkSFTWL5kR1svIvVwRX9EpCewxlcYVb0/yFHG3XRFd8/+BLb4DhHy8xFTAFhRwuSny4HewMOq+lswf9tnk6DewPO4IcGrga+BGzzmgfSZVgHf4DmTqk4SkSpA62DTdFVd5zHSebirfk+p6mYRqYYbzulNMBf4XtxVf4BJwAOq6uvEoJiIDAVqE7VfV9V4j9yI9i5uesKo4HY3YGRQDFjsIc8HQXGrnIhcDVwBvOYhRypV/V1EmgLtgk3fq+o8j5HCuI8cJyLPEJ7P2kYRuQN4L7h9PrBJRAqRNnT6YM90FfC6iJTGXW3fClwVfPYf9ZAndJlEpD0wAjcdQIAaItJLVSfHO0tUJgEuAuqo6oPiGvFWU9XpvjLh9j9DgYYishr4LcjohYg0Bt4CKgS31wOXquoiX5mAFcBEEfkSSC34e5xSslNEngQaAcWj8vg8HzEFgIRn2pg5kIlIeaCGqs73ncVkT0TOA54EJuJOltoB/VX1I4+ZTgAaqOpwEakElFbV3zzm+RhYiDupBLgEaKqq53jKMw8YgpsWkTrc3tP89lQi0grXs0GAKao603OezsApQZ6vVfVbz3n6AFcDnwSbzgaGqqo1BQuE8LN2CK4geQLB+xo3umwLUFNVl1um1FxlceeZm308fyxhySQis4D/U9Wlwe3DgZGq2tJjppdxRayOqnpkcN72jaq2zuFH811QPEpQ1W2ec/wIDFTVCcHt9sAjqupt5ZJgamQmkVEd8SYi3+D6bfXDFbp7Af+o6h0+8piCw4oSJt+IyESgO+7K7VzgH2CSqt6WzY/lZ566uKuAx+KGAk4FblXVFT7yBJlGAH0iJ0jBScDTqnqFx0zzgM6R0RFBEeA7VW3qKU9qg0JVPVxEqgMfZpgfHO9Mc1W1WU7b4phnls+T2awEV2qrkH70hpcGqiJyN/CGqv4Zte0aVR3qI0/w/POB41R1R3C7FDDVV0+JkO4jQ/VZMzkLpoz0IPPIrQey+pmDLZOIzM/4OY+1Lc6ZZqtqCxGZo6rNg23zfB37g+evSFrBTXEFtwd89U+I9fvw/TuKyhGKKSWR85Ho97OITFLVk3zmMuFnS4Ka/FRWVbcC5wDDgy9NnTzmeRfX+KsaUB34EBjpMQ9Ak+grNqq6CdeMz6eEDNM1NuB3X3E2rri1A1yDQvw3mEoKRm8AqWtwJ3nM87mIXC8i1SRqWUCPeSIdwf8GvgW+wDV1+8JjpJuAr0Ukuh9I76weHCdC+kaSyaStMuFDGPeRofqsiVuicKiIfCMi4yN/fOUJaaZRuGWT9+H225E/PoUt00wRGSZuufT2IvIqbqSbT3uDQnKkf0Ml/E1JingPd0GrB65Z4j+4q/C+rBCRu0WkdvDnf7gpJd6ISGNxTeQX4pqUzxKRRh4jRXqjrBGR08WtnnSYxzymgLCeEiY/FQ7m/58HDPQdBjcy6K2o22+LyI3e0jgJIlI+KEYQfJH0/bkcKyJfk/Zl5HzgK495wtig8DpcE76yuC+RG3ErBfjSK/g7uteGAnU9ZIkIS0fwiNW4LyUfishHqvokfgsA4JYmnSYinwa3z8Itn+xLGPeRsT5rvbL/kXz1IW6q1Gv4XykpImyZDlPV03yHyCBsma7D9Uu4Gfe+noxbKcWnF4BPgSoi8jCuCPA/v5GooKoPRt1+SETO8hUG14voftyUu8jrdrnHPOB6btyWYUrJq6StEhZvDwX7677AINySoLd4ymIKEJu+YfKNiJwL3I2bS359MDT4SVXt4SnPY8BmXOVdcV+2iwEvAqjqRg+ZLgUG4JZKBDgX1xj0rax/Kv+JW6s80gtgsqp+msOP5GeWfkADoDOuIdkVwLthmHcvIokAwYggE0VEJuCmAflcvi1VZEiyiBQHXsZ13j9aVRt6ztWCtF4Ak1XV27LJYdxHRmULxWctjFOlwpZJXNPdQaq6wHeWiDBmCiMRaQicHNwcr6peVwMTkaeAmbgRXOAKJY1UNWYfhYNR2KaUiMjxqvpDTtuMyciKEuagISLZDbFTVfVyVTkYZtcB96VknKr6WJkg1CQkDQpFJNt+KL66XYtISdwa8zVV9RoRaYAbpeBtuoSIDAOOwE3b8N4RXEReVdWro27fAPT18bnPaWqNry//YdxHSshWuhGR+4B1uCvK0e9rnwWbUGUSkcVAfdyw9t24/bZ67pcQikwisoBgekQsPn9HkK5IqsAPqjrbc55tQCnSRgAVIm3ajapqYpxyfE72r1v3eOSIJRhpNxu3KgjAxbhlgs/ylGe2qrbIaZsxGVlRwuSb4IrklWReFshbE8ewEpHKpP8dxb0ZYHDwj7VDiJy8xeXgn+nJ3XSNXaqaLCJH4L7ojlEPa7pLWpfrI3BLpo4ObnfDXeW+Kt6Zglzv4+YjX6qqjUWkBK5hYjMfeYJMoeoIHibBl38l9vQRbwXSMJLwrb4Rq3Dj9TULWyYRqRVru6r+Hu8sEWHJlFWOCM+/o3twozU/xu2bzsI1lX7IV6awEJFsmzSq6qR4ZclIXIP0+4kacQfcF5kWHMccx+GmjNwCPBt1VyJwdhiagZpws6KEyTci8iGwBPg/4AHc2tI/q2ofj5kaA0eRvgDwpsc83YGncU3l1gG1cL8jn02KQkXc0mntgPLAT7ihnDtV1eda5d8APTRYnkxc1+sPfc1ZFpGZqtpKQtQ1PSxE5ANVPS+rK5S+r0yGTQj3kbb6RgEhIomqujWrkUCepkiGLlOEiFTBFbcBpmdoMO0jz89Ac1XdFdwuAcxW1SM9ZvoIeB0Yq6q+m24CICJFgcODm0t9XCAJo6Bw0x7XQHpI1F3bgM9VdZmPXKbg8N1QzxzY6qvquSJypqqOEJF3ga99hQmu3LbHnXB/BXTBLS/l7YQbeBC3/N53wXz3DsCFHvMAICJNcYUAcCMA5vuMo6o7ReRK3JzgJ4JO0z7VBPZE3d6DW2rOlz3BCWSkGWg9ooZxx5OIPKeqt2Q13NXDMNdIEfSMOD9vrgSFycjUhImep9yEcR+ZJCInqOoU8Lf6hoh0VNXxIhJzhIaqfmKZeBf3OZtF5pFAvhrvhjETInIe8CQwMcg0SET6q+pH2f5g/lqJK0buCm4XA371lsYZgmskOSi40PWGqi7xFSZoIjkC97sSoIaI9FLVyR6yhOpYG4wWmSQib/gc8WMKLitKmPwUqR5vDq6+rcXvF7eeQFNgjqpeHlyleM1jHoC9qrpBRBJEJEFVJ4jI4z4DiUgf4Gpcd2mAd0RkqMfGkhIMC7wINx0I/O+73gKmB3M5Fbfsrc8vbvcCY3EnSO/gmpRe5ilLZF7rU56ePx1VXRP8cz2QpKopInI40BAY4y9ZamPJ1sA7waY+QUOwAZ4ihXEf2Rt4M+gtAbAJP6tvnASMx03VykhJ21/GU6gyqeoZIiLAST6mIMYSxkyBgUDryOgIcctvfkda0+u4EZFBuPfLbtySkt8GtzvjipLeqOp3wHfB5/9C4FsR+RO3usTbHkYpPA2coqpLAYJjyUjAR6PZUB1rI0USYLAEq6VF89l3wxQMvk/szYFtaDDX7W7c3PvSwD0e80S+kOwT18l9HX6XTARXsCmNmwP4joisw62j7tOVQBtV3QEQFEmm4pZ28uEW3Aoln6rqInGruEzwlAUAVX1YRMYAp+NO3i5Tj6smqOq3IjIbN+pGgD6qut5TllnBP2cSfOYARKQQ7sqbL5OBdsE+aVyQ73xcscuXrkCzqN/RCGAO7v3uQ6j2kcF75mJVbSqeV99Q1XtFJAHXz+aDHH8gDkKaSYNibWhWAwljJiAhw3SNDUCCpywzg79n4ZqlRkyMf5TMRKQirnnjJbj94zu4/gm9cCO74qlIpCABoKq/iEiROGeIPHfkWNtMVZ+Pvi+4uBTvPhehKpKYgseKEibfqGrkCtsk/H/5B5gpIuVwFfZZwHZguo8gIlJMVXcDZ+KGSt6K+3JUFtd/wych/Vr3ycRuyBcXkSGBUbdX4NZ290ZEbiZtNIkAI8St7uBzmdJDcZ3JCwMnioiXIeVRxgGdcJ8zgBLAN/hbOz2M04AAygGRee1ls3lcPIRmHwkQNLdtGfzb+7K7QcHmRtKWJ/QujJmAn0SktarO8B0kStgyjRWRr3FX2cEVSL2M3FLVETk/yg8R+QQ3qu0toFvUyLf3RWRm1j+Zb2aKW1kq8gX8Ity+0qdewPMZtl0WY1u+ihRJVHVS0HejIe6izVJV3ZPtDxuDNbo0+UBCumxiNBGpDST66pUgwfJIIvKWql7iI0NWgtevF2lXTM7CzeN8Ls45QjVfMpqIzAeOixpNUgq32oWXpoki8jrQBFgERJqBqXpc6SZsDQqDAsT1uK7gVwajbhao6tE+8gSZLgQew438EVxviQGq+p6vTBG+95FROZ4GGgAfkrYUoLeCm4jcjetp8X6GPD4bJoYqk7jlNw8Hfg/yhGVJ0LBlOoeoFRNU9dMcfiS/85yB63NVC1fc9rryVpCpq6p+lWFb5KKOjzzFgBtIv9LFSz7yBMeP/wuyfB91VxkgWVU7xTtTkOt0XC+QX3G/ozrAtarqdbqkCT8rSpj/nKQtBRhryTtVVW8jAUTkUNIOuJFAPhoULcQ1uboH6J/xfs9XuKPXKo+cLMX9irKItFTVWZLFUlzqdwmuBbj5wJEu5cWBGb6+4IrIYlU9ysdzZ0VEfgBu0mCd++CK92BVPc5TnhOBfsAPqvp4MA3oFlX1PeqmGq6vhADTVHWt5zyh2EdG5RkeY7O3gpuEbPlNCF8mCcnym9HClklE6gBrNP1KF1VUdaWPPEGG5bj+SAs0JF8OIhdwctoWxzypS5QHtwsBxVR1p4cstXBf+B8F7oy6axswX1W9TAUWkSXAGaq6PLhdD/hSVRv6yGMKDitKmHwTzI/uo6qbg9vlgac9nkw+jhsiuZi06Qnq44q7iJyAG/Z3Hq7fRjTfV7iPBRZp+uUuj1LVab4yhU1YRpNE5RmG+2wt9vH8sYhIa+A94K9gUzXg/Kh5sN6ISFXfX/6DHGcD41V1S3C7HNBeVT/zlCc0+0hzYBCRa1R1qO8c0cKQKZh60DYyrD0Y7v6DqrbO/ifzNdME4GQNwdKbIlIVNyXxbdxogMgFrkRgiK8vuCLyE9BJVbcHt0sD36iqr2mJoSMik1X1xKjbAkyK3mZMLFaUMPlGROaoavOctsUxz1Kgia9hfxkFDcoGqOrDvrNEC4a5t4hcKQlyzvR4ZeI3Yk/f8NqnJAyjSaKynAh8jlvhZjchGJoc5CoCHBHkWaIhWc/d55W2DDliTXGxfWQWwvK6RYhblega3zmihS1T2F4zCEemLD7781S1qadIkULyg7geTqn7AB9TbkWkF64vQitgBmlFia3ACI/Tt0I1LTF4/mNxjciPBIriekvtiPe0G0lbmrgzbrTdB7hzt3NxfSX6xjOPKXis0aXJTwkiUl5VNwGISAX8vudWAEWIOtj6pK5B2RlAqIoSuGJlahEgyOnzdWsV9e/iuANcBU9ZUgXTEmb7zhF4HdeZfAFpPSXC4AjgKNzr1lxc802fS6dGeGvcmkGsbvu2j8xaWF63iFY5PyTuwpYpbK8ZhCPTPyLSXVVHA4jImbhli316GNfctjjuy6036ppvjhCRHqr6sc8sGewQkRYZpiUmec40GLgA13enFXApUN9Djuilif/GLVkM8A9QPv5xTEFjRQmTn54GfhSRj3DV0vPw8AVc0tbg3gnMFZFxpL8K4HNO+Tci0gP4JCxzOIEV4laXeDm4fT3uy4oXqrohw6bnRGQKfpeXDZs/Iie3YRH0lmmPK0p8BXTBrXkfhqLEq74DBGaKyDPAi7h91E146OQe8n1ktC99B8hgXc4PibuwZeqW80PiLgyZeuOWAR+MK5L8iSss+1RBVU/xnCGd6IKEiHyhqmf4zINbovxDEUk3LdFfHEdVl4tIoaDXxXAR+dFDhsvj/ZzmwGLTN0y+EpGjgI64g+44H3Peg2GAWVKPy2GJyDagFLAPtzRoGLpdVwZewL1uilva8RZNv6Z6PPNED7NNwF0JuM7nMNewEZGXcEtLfk76L5PeGqYGzUCbAnNUtamIVAFeU9W4fiEIRmhlSf2umlAKuBu3dCq4JVMf1mBVlzjmCPM+sotm6NouIr1VdYivTEGGUvF+nbIiInXVLZUcCsFn/RGguqp2Cc4DjlPVYR4z/Qr8hFulYHJY+u8EPQkk0sPJc5bHcD1uvvGdJRafU9sy5AjVtEQRmYw7hryGm8K5BrjM1zlS0Pj7SqARbtQNAD57pZmCwYoS5qAUNN2soZ6XuzM5C5pvRewDfsM1dVzqKVLohG2FAgARma6qx4jILKADriP4QlVtFOcckZ4k0UO2I7fVd2+SCBGppqprfOeICMs+Mrji9z9VHR/cvgPXDLSLpzxtcSf/pVW1pog0xS13d72PPEGmybimgDNwSxR+r6oLPOYZAwwHBgYFycK44qTP5XeLAW2AdsDxQENgnqqe7StTREhGAERfJNkN7CUEF0miicjrYfpiG5b+LcEqHOtwU+9uBcrilild7inPh8ASXIPSB3BN3X9W1T4+8piCw6ZvmIOGiEwEuuPe93NxczonqeptHjPF7EasHpfgi+a7IVjQZHOIqr7vK0NBENJhkzOD1SRexU1J2A5Mj3cIVa0T7+fcT18CvpvvTSRk+8ggzxci0h84Dfdl0udqIM8CpxKsmqSq87Laj8eLqp4YrN7QGjdl6ksRKa2qvnrvHKKqH4jIgCDfPhFJzumH8lky7ot2Mq7vzt+EZ6rLob4DAKhqGd8ZshOmgkQgFP1bNG1Z2yTgfp9ZAvVV9VwROVNVR4jIu8DXvkOZ8LOihDmYlFXVrSJyFTBcVe8VEd8jJfpH/bs4cAzuC1xHP3Ey8doQLGiyeQNgRYlsiEgl4GqgNlH7dZ8ncVFXjoeIyFgg0cdV9wzTfzKJNCwLgTA03wvdPlJV14tId+A73L6xp+/+O6r6p1vlLpXXL9zilphuF/wpB3yBm6bgyw4RqUiwalKwOsAWj3nArdqwAHgGeDVGryKfvK3cFC2MF0mCaYAZP+9bgJnAQ55fR69FrSx+N6nU3+pbkeksm0WkMW5KSW1PWUwBYkUJczApLCLVcA03B/oOA5Bxfr2I1ACe8BQnljA0lvtWRPrhChOpc7h99gIIoVG4LyHf4fkLUrRgibATcCdOUwAfX3CfzuY+JTwFwDA03wzNPjIYSp46zQa3GkBdoKeI+BxS/mcwhUOD0Qk3Az97yhIxCfcl7VHgK1Xd4zlPX9xIknoi8gNQCejpNxIX4vZF1wNXBdOCJqvqOL+xQjUCIIwXScbgjmnvBrcvCP7eCrxBnJuWisi5qvohgKqelnFbnHmf8pOFocH0v//h9gOlcb2TjMmW9ZQwBw0RORe3Y5yiqteLSF3gSVXt4TlaKnGX3+b7nHsb5KiKOyFRYIaqrvWYJdITIJ2w9AIIA/G8TnosQfPN+sDIYNP5wK+qeoO/VOEQ1uabBWEf6ZuIHAI8j2ssJ7jmpH18XrENpkkdD5yIm8KRAkxVVW9fBII+EpFmgEt9NwOMEJGGuJWAbgEqq2qJOD9/Vle3I/0bfF3dziRykURVL/SY4QdVPT7WNhFZEO9zpVhTWn1Pcw2boH9LD9zoiCLBZlXVB7yFMgWCjZQwB42gkv1h1O0VuB2nN1FL8YFbWaIZMM9bICAYun0PMB53ojRIRB5Q1dc9RToKd3UrcsX9e8Br5/0Q+kJEuqrqV76DRDkJaBwZai8iI3DDp+NKRDqq6vhg1EYm6meFkllk03wTNyIg7sK0jwzxtBtR1Ys8PXdMqrpZRFYANYDDgLakfRmIOxGZhxvZ9r6q/uorRzQR+Rh3fF2OG7V1KTDNQ5SwXt2OZRXQ2HOG0iLSRlWnAYjIMbgr7+AaX8eFiHQBugKHisgLUXclxjNHLFGjysCNKCsC7PA4mmwUborNLKJWAzMmJ1aUMAelEFW2Z0b9ex8wUlV/8BUm0B9oHrnyF8wN/hHwVZQYgRuqGTkRuDDYdp6nPKGRYYj7XSISpq7pS4GaQKQJVw38TN84CVdg60b6L/6Rv+NelCgIzTdDsI8M67SbH4PRW+8DH6vqZk85UgXLXS7FfdkeAlzueQpHd9zIqA9EJAX3u/pAVf/wmOkxYLaqep3eFtWUMHTCeJEEuAp4PbJ0Ku5c4Cpxyyk/Gsccf+HO17rjvmxHbMOteOFNxgalInIWbqSrL4dFprYYkxc2fcMclMKy3nW0EC3BNw7oEjmpDeZNf6WqnTzlmacZ1tuOtc2Ei4hMwg0lj6y40RqYCuwEUNW4rqAgIn1JPzpBCa7mqOrcOGcJ6yiAVGHcR4ZFcLX2AuAsYDHwnqq+7TFPgqqm+Hr+7IhIA9yUoItUtZDHHEWA63BTXMD14RgS72klIjJFVU+I0TPFeyFZRHpF3dwHrAzBRRIARKQs7jvLZs85ioRlKlJ2ROQnVT3W03MPBQapx2WJTcFkIyXMwSoMDRzDugTfamCaiIzCnSydCUwXkdsAVPWZOOeZIyLHqupPACLSBgjFiVJYiMjZwHhV3RLcLge0V9XPPMa6x+Nzx9ISt4TbaNwXgNOBGUBvEflQVePZYDasowCihWIfCRB0cD8K13wPAFV901ceVZ2O2yc+glvNYQTgrSgBVA+uch9PWlPZPqq6ylcgEamNG812Pq5R4e2+sgRexg1rfym4fUmw7ap4hlDVE4K/Q7f8pqqO8J0howz9CQpHVr3x2J/gGBG5D6iFO2+LFJO89bjKMDUxAXeci/sV56h+KYWBy4MpZbsJYb8UE042UsIcVMLUwDHIM0dVmwd9HGpEluDzufMWkXuzu19V47IOdtQBrgiuYdofwe1awGJV9T3XNTRiNbr0faU7GF6bpG5Z18OBhsAYX1eZRORroIeqbg9ulwY+As7GjZY4ykeuMBGRLqo6JsO23qrqrYdLsD9qjytKfIVrUjhFVb2s5iAiibj3zAVAPeBT3NSEWdn+YP5m+ha3OsFbwaaLcSMTOnvKMw233/4Q11dihY8c0cI44k7cUq4NVHV40EC1jKr+5jHP8cB9hOsL91jS+hOkTr1R1ewKu/mZZwluukbGPD4b3Q6PurkPWIlb9jauS5aKSK3s7g/z1CUTDjZSwhw0QtjAEUK0BF9EvIoOuVCQGoL5lhBjm+/9+2SgXTAtaRxuPu75gK8mgTWB6Hn2e4FaqpoU9OKIm5A23wS4W0R2q+p4ABG5A1cQ8NlYtifQFJijqpeLSBXgNY955gGfAQ+o6lSPOaJVUtXoLyZviMgtvsIAvVR1icfnjyVZROpFGm8GK8t46y8RFNta4Qruw3ENCt/GjXbxZRgxvnB7Frb+BFsyFm59U9XLfWcAKzqYf8/3Sasx8RS2Bo4ADwBf4678zQhOlJZ5zIOIVMINtW1E+uHScR1Sbge4PJkpIs8AL+JGk9xE+mZcPoiq7hSRK3HzS58Qkbke87wL/BRMSwLX+HJkMKJjcZyzhK75ZqA7biWX/sBpuNEtce39EUNktM2+YJTCOjytThKoq6oqImVEpHRk5I1n60XkYtKW370Q8HblFlgT7I+i+zc8EJle5kl/YEIwpFxwowF8fpk7G2gOzAZQ1b9ExPeUjtB94cY1lj06RP0JJojIk7h9dGox22cfoOC88XngWNzxYypwaxhGKBmTF1aUMAeTVbhOyRHbgD89ZQHCtQRflHdw3dLPAHoDvYB/vCYyObkJ10zufdwJ9zfADV4TgYjIcbiREVcG27w1ulPVB0XkK9zSsgL0VtXI6jdxHb2hqpEpUguJ0XxTRJrFu/lmkGu9iHQHvsMVtXqq/zmeM4MeKa/iMm0nrXmqD41E5C2gAu49/g9uZMBCj5muAAYDz+LeQz8G23x5HffejqyQdAluNEDMkUHxoKrjgqabR+A+b0tU1edyhXuC4lZkyeRSHrNEhO4LN25/fVmw4k0Y+hO0Cf5uFbXNdx+gd3EXJM4Obl+AK1C2yfInjAkh6ylhDhoi8iZwNG4N5dQGjsAv4KWBYzohWIIvkmOWqraM7m0RNN88yXc2U3CIyIlAP+AHVX08uJpzi6re7DlaaIjIu8RuvtkQiFvzzRgrARTFzU1W/C8tmyponpioHlcoEpEfgYGqOiG43R54RFXb+soUNln0uMm0LU5Zsi2E+JoqJSL9gAZAZ9zSllcA76rqIB95gkwTYmzWeI+SjJZVnwIbSZlGRKapapsM27ytvmHM/rKREuZg8mvwJyIyjNv3kMkIyfkhcRFpRLhGRE7Hrc99mMc8JgdhmXITTVUn4/pKICJVg1FAVpBIryLQIqr55r245psn4kYFxKUoEcaVACJEZJyqngygqiszbvOgVKQgEWSa6Osqd7DiRpZXljwWAJNE5ARVnQKpDRSTPGXpls19PqdKpQDfA1uBw4F7VPVbT1kAUNUOPp8/mogkqupW0o9u9UZELlbVtyOrkGXk+aLWBBG5E3gP954+H/hSRCoE2TZ6zGZMrllRwhw0QtTAMSthWYLvIXFrgvcFBgGJwC1eE5mchH3KzVeA91FAIRSK5psiku1r42P4togUB0oChwTNUiNF20SgerzzRFkhIneTfqULXysmzMz5IV5cB4wIjiMCbMTtk+IuLE0AYyiDm9a2Efdl0tvon1hE5AtV9dls+l0R6Qasx60mEX3RRol/X5lI4TGMBdzzg7+vzbD9Cvz8rozZLzZ9wxw0wng1GUK5TOkI3Br3m4PbFYCnVNXnHGWTjbBPufG9PGlYBV9uzyZt1FY33FSOp4GhqhqXXhdZDNuO8DJ8W0T64Iqh1YHVpE0t2Yb73bwY70xBrvLA/aT1JpkM3Keqm3zkiSYipVR1h+8cEUFjUoIr3r6zVAEeAaqrahcROQo4TlWHec7VBPelsgewSlU7+cwTEZZ9dlimtQKISCHgZlV91ncWYw5EsZaRM+ZA9Q6wBKiDO6lciZu/7U2wTOl0XAOwnrjVAXx/+W8SKUhA6tA/7ycnJlvpptyISHPCNeXmVd8BwkhVHwSuBjYDW3DNNx9Q1R3xKkgEOTpk88dL0VZVn1fVOsDDQLPg38OBFbju8l6o6qZgWkQH4ERV7eO7ICEix4nIYuDn4HZTEXnJY56KIvICMBE3tPz5YLUrn97ArXQVGWXzC+EYAbgOWItbLaWy5yzR5vgOEPhRRFr7DgGgqsn4X40oExEpIiI3i8hHwZ8bRaSI71zG5JWNlDAHjTBeTRaRpUBbzbBMqaoe4THTPKB95EQ7GCkxSVWP9pXJZE9EzsDNT65B2pSb+1T1cw9ZKmR3v81vDTcRaQwcRfrRZG96zDNfVZuIyAm4K91PA3dlbOwWxzytcatLRIZxbwGuUFVvS/CKyDRcUXt05Oq2iCxU1cae8nyLG0HydrDpItwxxdsoABGZoaqto0cA+Gq+GTz3dbgREpVwfWTeV9V4L00cekGx7XDgd2AHnlffEJGHgbK46ZKpo5J8rlAiIq8BRYARwaZLgGRVvcpXJmP2h/WUMAeTMDZwDN0ypbiT/h9F5CPccOnzcFcrTXhtUtUtuC9IHSC1uZwPs0i/zCVRt21+a4gFjTbb44oSXwFdgCmAt6IEkBz8fTowRFVHich9HvMMA65X1e8BgmLJcMDXEoUAqOqfIul6JSdn9dg4qBCMAop4SETO8hUmsCMo+keW4DwWt7/0pRZuNaK5HjOkIyILyNw4dQuud8lDkYsncdbFw3NmJ7LKzgNR23wvCdpaVZtG3R4fXFwypkCxooQ5mISxgeNqYJqIpFumNNLh2UdHZ1V9U0Rm4g6yApxjV3BCbxCZG0nG2pbvgmH2pmDqCTQF5qjq5cE8/Nc8Z1otIq8AnYDHRaQYfqeebosUJABUdUqwpKpPf4pIW0BFpChulZufPeaZICIXAB8Et3viv5Hzbbh+LfVE5AfcCIWevsKo6p2+njsbY3DFrHeD2xcEf2/FTX/JbiWTfBG2pT/DtEJJlGQRqaeqvwKIW37bZ1HSmP1i0zfMQSOMDRyDK5NZKgArhhiPROQ43JWbW4Do5luJwNkZrp7EK1PoVnIwuSMi01X1GBGZhRtxsw1YqKqNPGYqCZwGLFDVZSJSDThaVb+Jc47I+/oS3KogI0lbfm+Tqg6MZ54M2Q4BnscVbhJwvRP6xPvKdlCciYyKKoVb9pIg03ZVTYxnnoxEpDBwBC7fUlXdm8OPHFRE5AdVPT7WNhFZYFM4U6fY3otrdKu4kWQPeBpFEsl0Mmn9dgBqA5dr1NLFxhQENlLCHEwyNXAMGgJ6Y0UH8y8VBUrj9uXRS5Vtxd9VwKezuc/3MFeTvZkiUg7XmHQWsB3XiNcbVd0JfBJ1ew2wxkOUjO/r6IKy16s7qroe17fBK1UN43KJQOoSs9eT9mXyexEZoqq7/CYLldIi0kZVpwGIyDG44wvAPn+xQuU9XL+UHsHti3D9JXyumvID8ApwcnD7FTw2AzZmf9lICXPQCGMDx7AuU2oKFhG5XVWfyLDtXFX90FcmU7CJSG0gUVXn+85ishcM134eOBb3hXsqcKuqrsj2B/M3U3mgAemPa5M95vkAN/In0nzzQqC8qp7rK1PYRDVxLY0bTbIVuApYBJyuqh9k8+MHhUjD9AzbZqpqK4+ZPsC9Vu8Em+y9bQokK0qYg4aIXAoMwHW6Tm3gqKpvecz0Da7K3g/oDfQC/lHVO3xlMgVPrLXcfa3vLiIdVXW8iJwT635V/STWduOfiIxT1ZNz2nawCxolZywkP5D1T+R7np+AF3FTSsD1ArjJ4wolVwF9cI2k5+KKJVN9FttFZF7G6WyxthkIem9J9MhS44jIU7jGn9H9UhqparZTcfM5k723zQHBpm+Yg0ZIGzhWVNVhItJHVScBk0RkkudMpoAQkS5AV+BQEXkh6q5E/A23PQkYj2uKFr3qRuRvK0qETDC0vSRwSHCFO7KMQyJQ3VuwEBKRIbjfVQdcE9CeeJ7igvsCGV1cf1tEbvSWxhUkWgM/qWoHEWkI+J6qOEdEjlXVnwBEpA1u2LsJBE1ke+B6EhSOrObis+AWQtfimqa+hdtPJuBWdrkNt1Spj74p9t42BwQrSpiDSlCE8F2IiBbGZUpNwfEX7qpNd1wPgIhtwK0+AkVdMVpI+qVBFdgiIs3CtAyeAdyJ9i24AsQs0gpI24DB/mKFUltVbSIi81X1fhF5Gk+FtmAKIrjVLu7EzXePNN/0udrFLlXdJSKISDFVXSIiR/gIErXMZRHgUhH5I7irJuE6FwiDUbglQGcBuz1nCaWQ9k1pQ+b39s+R976qel2u2JjcsukbxngkImcA3wM1SFum9D5V/dxrMFOgiEiRsHWSF5F3gVa4ZfgEOB2YATQEPszYA8P4JyL3AM+p6lYRuRu3pOyDtmJKGhGZpqptgikT5wAbcCuUNPCQ5TfSF/6iqarWjXMkAETkU+ByXKGrI7AJKKKqXT1kqZXd/WFbctInEVmoqo195ygoROQ+Vb0vBDnsPW4OCFaUMMajMC5TagoeEWkAPAocRfp57l6+lASZvgZ6qOr24HZpXD+Xs4FZqnqUr2wmtuDqfxMROQF4BLfixF2+ehOEUVCsGYTrdP8irijwqqre4zVYSInISUBZYKyq7vGdB0BErlHVob5zhI2IDAUGqeoC31kKAl99m4w5UCX4DmDMQS7TMqWA12VKTYE0HHgZ10eiA/Ambs6rTzWB6C8he4FaqpqEDQ0Oq+Tg79OBIao6CrfsrAmo6oOqullVPwZqAQ3DVJAIvliGyRGqOjosBYlAb98BQuoEYJaILBWR+SKyQERs9Z2sxRqhZIzZT9ZTwhi/EkSkfIZlSu1zafKqhKqOExEJhmreJyLfA946ggPvAj+JyKjgdjdgpIiUwuZyh9VqEXkF6AQ8HjS+s4sXWRukqtf4DpGBt6UJs9AbCFuhxL5MxtbFd4ACpmXODzHG5JZ9+THGr6eBH0Uk3TKlfiOZAmiXiCQAy4Ku+6uByj4DqeqDIvIV7uqbAL1VdWZw90X+kplsnAechptCtllEqgH9PWcKs7AVAADW+Q6QgfcCQLBvnB/VL6GbzzxhIyKJqroV19jWZENEDseNSqyiqo1FpAnQXVUf8hzNmALPekoY45mIHEXaMqXjQrBMqSlgRKQ18DNQDngQ1zD1ycgSYcaY/56IjFXV03zniBCRUqq6w3OGdAUAETlMVVf5zBTkeAcYoKp/5Pjgg4yIfIEr1CQDK0lfSPLWMDWMgiXb+wOvqGrzYJs1CDXmP2AjJYzxLITLlJoCRlVnBP/cLiIDVHWt10DGHOCCAkAoChIi0hZ4DSgN1BSRpsC1qnp9vLOoaoqIzBORmqr6RxgKEoFqwCIRmQ6kFm5Utbu/SOGgqmcAiMhca9yYo5KqOl0k3QCgfb7CGHMgsaKEMcYcWL7CLeVojPmPhakAEOVZ4FTc8ruo6jwROdFjnjAWAO73+NwFxY8i0jqqyG0yWy8i9XDTbRGRnsAav5GMOTBYUcIYYw4s3udwG3MAC1sBgCDHnxmu3iZn9dg4CF0BQFUniUgVoHWwabqqhq3/hm8dgd4i8juumCS46RtN/MYKlRtwjVsbishq4DesR5Ix/wkrShhjzIHlVd8BjDmQhawAAPBnMIJDRaQocDOux4wXYSwAiMh5wJPARNyX7UEi0l9VP/KZK2Rs9Y1siEgh4DpV7RSsIpWgqtYc1Jj/iBUljDGmgAqWkM3ovch2Vd0Y50jGHOhCVQAI9AaeBw4FVgHf4K7oehHSAsBAoHWkOCIilYDvACtKBILlpE0WVDVZRFoG//baUNaYA5GtvmGMMQWUiPyGm9uarls6acNurWu6Mf8hETkEVwDohPucfQP0UdUNXoOFiIjMAzpnLACoalOPmRao6tFRtxOAedHbjMmJiDwNNAA+JH2/lE+8hTLmAGEjJYwxpoBS1Tq+MxhzMFHV9YRsDrmIjMAVRjYHt8sDT6vqFZ4iJWSYrrEBSPCUJWKsiHwNjAxun49rCmxMXlTAvZ87Rm1TwIoSxvxLNlLCGGMKKBHJdpUNVZ0dryzGHAxCWABAROaoavOctsUxz5NAE9IXAOar6h0+8kSISA/geNwIl8mq+qnPPMYYY9JYUcIYYwooEZmQzd2qqh2zud8Yk0dhKwAEzz8PaK+qm4LbFYBJPqcmWAHAHIhE5DBgEO69rcAUXJFylddgxhwAbPqGMcYUUKrawXcGYw4yCSJSPkMBwPe51NPAjyISadp4LvCwxzyo6sfAxz4zAIjINtyXx0x34Qq3iXGOZAq24cC7uM8YwMXBts7eEhlzgLCREsYYU0CJSEdVHS8i58S635pvGfPfEpFLgQGkrdpwLvCwqr7lLxWISCOgA+7L9jhVXewhgxUAzAFNROaqarOcthlj8s53dd8YY8z+OwkYD3QjatWNqL+tKGHMf0hV3xSRWaQVAM7xUQDISFUXicg/QHEAEampqn/EOUOZeD5fXolIU6BdcHOyqs73mccUSOtF5GLS+qVciGt8aYz5l2ykhDHGFHAi0pf0S4MqsAWYpapzfeUy5kAlIpUJCgAA8S4AZMjSHTeFozqwDqgF/KyqjTxmClUBQET6AFeTVqg9GxiqqoP8pTIFjYjUBAYDxwWbfsD1lPjdXypjDgxWlDDGmAJORN4FWgGjcYWJ04EZQEPgQ1V9wmM8Yw4YIS0AzMMtUfidqjYXkQ7Ahap6jac8oSsAiMh84DhV3RHcLgVMVdUmvjIZY4xJ43vdaGOMMf9eRaCFqvZT1b64AkUl4ETgMp/BjDnAPAgcC/yiqnWAk3FXS33aq6obcE04E1R1AtDMY54rgTaqeo+q3oP7fV3tMQ+4Ym1y1O1k0kaWGZMrIlJXRD4XkX9EZJ2IjBKRur5zGXMgsJ4SxhhT8NUE9kTd3gvUUtUkEdntKZMxB6K9qrpBRFILACLyuOdMm0WkNDAZeEdE1gH7POYJYwFgODBNRCJLk54FDPMXxxRQ7wIv4kb/AFyA6y/RxlsiYw4QVpQwxpiC713gJxEZFdzuBowMhih7b8JnzAEkNAUAESmmqruBM4FdwK3ARUBZ4AEfmQKhKwCo6jMiMhE4AVcguVxV5/jMZAokybDSztsicqO3NMYcQKynhDHGHABEpCVpJ9xTVHWm50jGHDAiBYCg0LcL9zmLFADeCaZPxDvTbFVtISJvqeol8X7+7IhIC9L2R5N9FwBE5FhgkapuC26XAY5S1Wk+c5mCRUQeAzYD7+EaSp8PFMONnkBVN3oLZ0wBZ0UJY4wxxphshLEAICILgSeBe4D+Ge9XVS9LAoexACAic3B9dzS4nQDMVNUWvjKZgkdEfou6GfkClbrqlapafwlj9pNN3zDGGGOMyV5REekFtBWRczLe6akA0Bs3WqMcbspWNCVt9Yt4exmI/rK/I8a2eBONugqnqikiYufAJq/uAMaq6lYRuRv3nn5QVWd7zmVMgWc7ZGOMMcaY7IWuAKCqU0TkR2CVqj4c7+fPRhgLACtE5GZccQTgemCFxzymYPqfqn4gIicAnXHLA7+MNbo05l+z6RvGGGOMMTkIhvwPCFkBABGZqqrH+c4RISKfABNJXwDooKpnecxUGXgB6IgrIo0DblHVdb4ymYJHROaoanMReRRYoKrvRrb5zmZMQWdFCWOMMcaYXAhbAQBARO4H5gOfaAhO6qwAYA5UIvIFsBroBLQEkoDpqtrUazBjDgBWlDDGGGOMyYWwFQAARGQbUAq3NGlkZRBV1USvwUIq0rTUdw5T8IhISeA03CiJZSJSDThaVb/xHM2YAs+KEsYYY4wxuWAFgLwJYwHAhtsbY0z4+G48ZIwxxhhTIKhqGd8ZMhKRE2NtV9XJ8c4Sg+T8kLj70ncAY4wx6VlRwhhjjDEmF0JaAOgf9e/iwDHALFxPB99CUQAQkS6qOgZAVf8XbOutqkP8JjPGGAM2fcMYY4wxJldE5POom6kFAFUNQwEAABGpATyhqhd6ev7UAkDUNq8FgGDp1P+p6vjg9h1Ae1Xt4iuTMcaYNDZSwhhjjDEmF1S1W/TtSAHAU5ysrAIae3z+u0Vkd8YCAOBzVEJ34AsR6Y9rVNgw2GaMMSYErChhjDHGGLN/fBcAEJFBuKU3ARKAZsA8b4FCWABQ1fUi0h34Dje1pWdYVk8xxhhj0zeMMcYYY3IliwLASlW92GOmXlE39wV5fvCVB0BEKpNWALjCVwEgWC1FCVZJAYrifkeKrZpijDGhYUUJY4wxxphcCGMBIJqIlAdqqOp8D89tBQBjjDH7xYoSxhhjjDF55LMAkCHHRNz0iMLAXOAfYJKq3uYxViiISIvs7lfV2fHKYowxJmvWU8IYY4wxJhdiFQBExHcBoKyqbhWRq4DhqnqviPgYKRHGAsDT2dynhGPZVGOMOehZUcIYY4wxJndCUQDIoLCIVAPOAwZ6zBG6AoCqdoj3cxpjjMk7K0oYY4wxxuROWAoA0R4AvgamqOoMEakLLIt3iLAXAESkMXAUUDyyTVXf9JfIGGNMhPWUMMYYY4zJBRE5F7gbVwC4PigAPKmqPTxHC5WwFQBE5F6gfZDpK6AL7jXs6SuTMcaYNFaUMMYYY4w5AIjIbFXNtrdDHDKErgAgIguApsAcVW0qIlWA11S1m69Mxhhj0iT4DmCMMcYYU9CISBhXbhDfAYCewMnAWlW9HFcMKOY3EkmqmgLsE5FEYB1Q13MmY4wxAespYYwxxhiTd2EoAGT0pe8ABAUAEQlTAWCmiJQDXgVmAduB6V4TGWOMSWVFCWOMMcaYvAtDAQAR6aKqYwBU9X/Btt6qOsRTpNAVAFT1+uCfQ0RkLJCoqr5XTTHGGBOwnhLGGGOMMbkQXQCI2uazAICI/Aj8T1XHB7fvANqrahdfmSJEpDYhKACIyDhVPTmnbcYYY/ywnhLGGGOMMblzt4h0jNwICgBneswD0B14RETaicjDwDHBNi9EZFzk36q6UlXnR2+Lc5biIlIBOEREyotIheBPbaC6j0zGGGMys+kbxhhjjDG50x34QkT6A6cBDfFYAABQ1fUi0h34Djddoqd6GAYrIsWBkgQFANJ6biTirwBwLXBL8PyzgkwKbAMGe8pkjDEmA5u+YYwxxhiTSyJSmbQCwBU+CgBBjm24L9iRL9pFgX3Bv1VVE+Ocpw9pBYDVpC8ADFXVF+OZJ0O2e4DnVHWriNwNtAAeVNUwrqBijDEHHStKGGOMMcZkI2wFgDALYwFAROarahMROQF4BHgauEtV2/jKZIwxJo1N3zDGGGOMyYaqlvGdISMRaZHd/R6LAD1V9YGgANAZVwB4GfBZAEgO/j4dGKKqo0TkPo95jDHGRLGihDHGGGNMNkJaAHg6m/sU6JjN/fkpjAWA1SLyCtAJeFxEimHN3o0xJjRs+oYxxhhjTDZEZEI2d6uq+ioAhI6IfIHrKdEJaAkkAdNVtanHTCVxjUkXqOoyEakGHK2q3/jKZIwxJo0VJYwxxhhjCjARaQwcBRSPbFPVNz1lsQKAMcaYPLGihDHGGGNMLoWpABDkuRdoH2T6CugCTFHVnr4yGWOMMXlhRQljjDHGmFwIYwFARBYATYE5qtpURKoAr6lqN1+ZjDHGmLywJj/GGGOMMbnTEzgZWKuql+OKAcX8RiJJVVOAfSKSCKwD6nrOZIwxxuSarb5hjDHGGJM7SaqaIiJhKgDMFJFywKvALGA7MN1rImOMMSYPrChhjDHGGJM7oSsAqOr1wT+HiMhYIFFV5/vMZIwxxuSF9ZQwxhhjjMkjEalNCAoAIjJOVU/OaZsxxhgTVtZTwhhjjDEmF0RkXOTfqrpSVedHb4tzluIiUgE4RETKi0iF4E9toLqPTMYYY8z+sOkbxhhjjDHZEJHiQEmCAgAgwV2J+CsAXAvcEjz/rCCTAtuAwZ4yGWOMMXlmIyWMMcYYY7J3Le6Lf8Pg71nATGAUngoAqvq8qtYBHgaaBf8eDqwApvrIZIwxxuwPK0oYY4wxxmQj5AWAnqq6VUROADoDbwAv+41kjDHG5J4VJYwxxhhjcieMBYDk4O/TgSGqOgoo6jGPMcYYkydWlDDGGGOMyZ0wFgBWi8grwHnAVyJSDDu/M8YYU4DYkqDGGGOMMbkgIl8Aq4FOQEsgCZiuqk09ZioJnAYsUNVlIlINOFpVv/GVyRhjjMkLK0oYY4wxxuSCFQCMMcaY/54VJYwxxhhjjDHGGOOFzTk0xhhjjDHGGGOMF1aUMMYYY4wxxhhjjBdWlDDGGGOMMcYYY4wXVpQwxhhjjDHGGGOMF/8PGm9OK1Hf8Q8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking Correaltion\n",
    "df_corr= df.corr()\n",
    "#Plottinh Heat MAP\n",
    "plt.figure(figsize=[18,12])\n",
    "sns.heatmap(df_corr,annot=True)\n",
    "plt.title('Corraltion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class                       1.000000\n",
       "gill-size                   0.540024\n",
       "population                  0.298686\n",
       "habitat                     0.217179\n",
       "cap-surface                 0.178446\n",
       "spore-print-color           0.171961\n",
       "veil-color                  0.145142\n",
       "gill-attachment             0.129200\n",
       "cap-shape                   0.052951\n",
       "cap-color                  -0.031384\n",
       "odor                       -0.093552\n",
       "stalk-shape                -0.102019\n",
       "stalk-color-below-ring     -0.146730\n",
       "stalk-color-above-ring     -0.154003\n",
       "ring-number                -0.214366\n",
       "stalk-surface-below-ring   -0.298801\n",
       "stalk-surface-above-ring   -0.334593\n",
       "gill-spacing               -0.348387\n",
       "stalk-root                 -0.379361\n",
       "ring-type                  -0.411771\n",
       "bruises                    -0.501530\n",
       "gill-color                 -0.530566\n",
       "Name: class, dtype: float64"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr['class'].sort_values(ascending =False)\n",
    "# All have little bit correraltion as no co has 0.00... values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split \n",
    "### Setting X and y axis and splitting the data into train and test respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Test Split \n",
    "# Setting X and y axis and splitting the data into train and test respectively.\n",
    "x = df.drop(['class'],axis =1)\n",
    "y = df['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cap-shape                  -0.247052\n",
       "cap-surface                -0.590859\n",
       "cap-color                   0.706965\n",
       "bruises                     0.342750\n",
       "odor                       -0.080790\n",
       "gill-attachment            -5.977076\n",
       "gill-spacing                1.840088\n",
       "gill-size                   0.825797\n",
       "gill-color                  0.061410\n",
       "stalk-shape                -0.271345\n",
       "stalk-root                  0.947852\n",
       "stalk-surface-above-ring   -1.098739\n",
       "stalk-surface-below-ring   -0.757703\n",
       "stalk-color-above-ring     -1.835434\n",
       "stalk-color-below-ring     -1.791593\n",
       "veil-color                 -6.946944\n",
       "ring-number                 2.701657\n",
       "ring-type                  -0.290018\n",
       "spore-print-color           0.548426\n",
       "population                 -1.413096\n",
       "habitat                     0.985548\n",
       "dtype: float64"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.skew(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding Best Random state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding Best Random state\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "maxaccu =0\n",
    "rs =0\n",
    "for i in range(1,100):\n",
    "    xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2,random_state=i)   \n",
    "    lr = LogisticRegression()\n",
    "    lr.fit(xtrain,ytrain)\n",
    "    predrf =lr.predict(xtest)\n",
    "    acc= accuracy_score(ytest,predrf)\n",
    "    if acc>maxaccu :\n",
    "        maxaccu =acc\n",
    "        rs=i\n",
    "rs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Random State = 21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2,random_state=21) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_LR 0.9649230769230769\n",
      "\n",
      " Classification Report_LR\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.96      0.97       842\n",
      "           1       0.96      0.97      0.96       783\n",
      "\n",
      "    accuracy                           0.96      1625\n",
      "   macro avg       0.96      0.97      0.96      1625\n",
      "weighted avg       0.96      0.96      0.96      1625\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#1. Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "LR = LogisticRegression()\n",
    "LR.fit(xtrain,ytrain)\n",
    "ypred =LR.predict(xtest)\n",
    "acc_LR= accuracy_score(ytest,ypred)\n",
    "print('Accuracy_LR',acc_LR)\n",
    "print('\\n Classification Report_LR')\n",
    "print(classification_report(ytest,ypred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Nearest neighbour "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_KNN 0.9649230769230769\n",
      "\n",
      " Classification Report_KNN\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       842\n",
      "           1       0.99      1.00      1.00       783\n",
      "\n",
      "    accuracy                           1.00      1625\n",
      "   macro avg       1.00      1.00      1.00      1625\n",
      "weighted avg       1.00      1.00      1.00      1625\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#2 K Nearest neighbour \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "KNN = KNeighborsClassifier()\n",
    "KNN.fit(xtrain,ytrain)\n",
    "yknn =KNN.predict(xtest)\n",
    "acc_KNN= accuracy_score(ytest,yknn)\n",
    "print('Accuracy_KNN',acc_LR)\n",
    "print('\\n Classification Report_KNN')\n",
    "print(classification_report(ytest,yknn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Desicion Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_DT 1.0\n",
      "\n",
      " Classification Report_KNN\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       842\n",
      "           1       1.00      1.00      1.00       783\n",
      "\n",
      "    accuracy                           1.00      1625\n",
      "   macro avg       1.00      1.00      1.00      1625\n",
      "weighted avg       1.00      1.00      1.00      1625\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#3 DT \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "DT = DecisionTreeClassifier(-)\n",
    "DT.fit(xtrain, ytrain)\n",
    "ydt =DT.predict(xtest)\n",
    "acc_DT= accuracy_score(ytest,ydt)\n",
    "print('Accuracy_DT',acc_DT)\n",
    "print('\\n Classification Report_KNN')\n",
    "print(classification_report(ytest,ydt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Randon Forest Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_Random_Forest 1.0\n",
      "\n",
      " Classification Report_RF\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       842\n",
      "           1       1.00      1.00      1.00       783\n",
      "\n",
      "    accuracy                           1.00      1625\n",
      "   macro avg       1.00      1.00      1.00      1625\n",
      "weighted avg       1.00      1.00      1.00      1625\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#4 Randon Forest \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "RF = RandomForestClassifier()\n",
    "RF.fit(xtrain,ytrain)\n",
    "yrf=RF.predict(xtest)\n",
    "acc_RF= accuracy_score(ytest,yrf)\n",
    "print('Accuracy_Random_Forest',acc_RF)\n",
    "print('\\n Classification Report_RF')\n",
    "print(classification_report(ytest,yrf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " CV score LR 0.8834389837196573\n",
      "\n",
      " CV score KNN 0.9260164076322857\n",
      "\n",
      " CV score DT 0.9212094211292287\n",
      "\n",
      " CV score RF 0.9185000615233173\n"
     ]
    }
   ],
   "source": [
    "#Cross Validation \n",
    "cvLR =cross_val_score(LR,x,y,cv=7)\n",
    "cvKNN =cross_val_score(KNN,x,y,cv=7)\n",
    "cvDT =cross_val_score(DT,x,y,cv=7)\n",
    "cvRF =cross_val_score(RF,x,y,cv=7)\n",
    "print ('\\n CV score LR',cvLR.mean())\n",
    "print ('\\n CV score KNN',cvKNN.mean())\n",
    "print ('\\n CV score DT',cvDT.mean())\n",
    "print ('\\n CV score RF',cvRF.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9649230769230769, 0.9975384615384615, 1.0, 1.0]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model to be Considered \n",
    "Accu_Sum =[acc_LR,acc_KNN,acc_DT,acc_RF]\n",
    "Accu_Sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8834389837196573,\n",
       " 0.9260164076322857,\n",
       " 0.9212094211292287,\n",
       " 0.9185000615233173]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CV_Score_sum =[cvLR.mean(),cvKNN.mean(),cvDT.mean(),cvRF.mean()]\n",
    "CV_Score_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8.148409320341955, 7.152205390617583, 7.879057887077135, 8.149993847668268]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x =[(Accu_Sum[0]-CV_Score_sum[0])*100,(Accu_Sum[1]-CV_Score_sum[1])*100,(Accu_Sum[2]-CV_Score_sum[2])*100,(Accu_Sum[3]-CV_Score_sum[3])*100]\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BEST MODEL after considering Cross Validation will be Random Forest as the difference for it model  is lower amongst all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HyperParaneter Tuning for RANDOM FOREST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters ={'n_estimators':[200,700],\n",
    "            'max_features': ['auto','sqrt','log2'],\n",
    "            'max_depth':[4,5,6,7,8],\n",
    "            'criterion' : ['gini','entropy']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=7, estimator=RandomForestClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [4, 5, 6, 7, 8],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'n_estimators': [200, 700]})"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSV = GridSearchCV(RandomForestClassifier(),parameters,cv=7)\n",
    "GSV.fit(xtrain,ytrain)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BEST PARAMS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini',\n",
       " 'max_depth': 8,\n",
       " 'max_features': 'auto',\n",
       " 'n_estimators': 200}"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSV.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RESEDIGING THE MODEL WITH RECOMENDED PARAMETERS by GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_Random_Forest_HYpertuned 1.0\n",
      "\n",
      " Classification Report_RF_MOD\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       842\n",
      "           1       1.00      1.00      1.00       783\n",
      "\n",
      "    accuracy                           1.00      1625\n",
      "   macro avg       1.00      1.00      1.00      1625\n",
      "weighted avg       1.00      1.00      1.00      1625\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "RF_MOD = RandomForestClassifier(criterion='gini',\n",
    " max_depth= 8,\n",
    " max_features= 'auto',\n",
    " n_estimators= 200)\n",
    "RF_MOD.fit(xtrain,ytrain)\n",
    "yrf_tuned=RF_MOD.predict(xtest)\n",
    "acc_RF_MOD= accuracy_score(ytest,yrf_tuned)\n",
    "print('Accuracy_Random_Forest_HYpertuned',acc_RF_MOD)\n",
    "print('\\n Classification Report_RF_MOD')\n",
    "print(classification_report(ytest,yrf_tuned))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=7, estimator=LogisticRegression(),\n",
       "             param_grid={'C': array([1.00000000e-05, 8.48342898e-05, 7.19685673e-04, 6.10540230e-03,\n",
       "       5.17947468e-02, 4.39397056e-01, 3.72759372e+00, 3.16227766e+01,\n",
       "       2.68269580e+02, 2.27584593e+03, 1.93069773e+04, 1.63789371e+05,\n",
       "       1.38949549e+06, 1.17876863e+07, 1.00000000e+08]),\n",
       "                         'class_weight': ['dict'],\n",
       "                         'multi_class': ['auto', 'ovr']})"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#HyperParaneter Tuning for Logistic Regression \n",
    "\n",
    "c_space = np.logspace(-5, 8, 15)\n",
    "\n",
    "pararm_lr ={'C': c_space,\n",
    "'class_weight' :['dict' or 'balanced'],\n",
    "'multi_class' :['auto', 'ovr']}\n",
    "GSV_LR = GridSearchCV(LogisticRegression(),pararm_lr,cv=7)\n",
    "GSV_LR.fit(xtrain,ytrain)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  As result came 1 , trting to next best one !!!\n",
    "#### HyperParaneter Tuning for Logistic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 2275.845926074791, 'class_weight': 'dict', 'multi_class': 'auto'}"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSV_LR.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_LR_Tuned 0.9723076923076923\n",
      "\n",
      " Classification Report_LR_Tuned\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.97      0.97       842\n",
      "           1       0.97      0.97      0.97       783\n",
      "\n",
      "    accuracy                           0.97      1625\n",
      "   macro avg       0.97      0.97      0.97      1625\n",
      "weighted avg       0.97      0.97      0.97      1625\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "LR_Tuned = LogisticRegression(C = 2275.845926074791, class_weight= 'dict', multi_class= 'auto')\n",
    "LR_Tuned.fit(xtrain,ytrain)\n",
    "y_LR_Tuned =LR_Tuned.predict(xtest)\n",
    "acc_LR_Tuned= accuracy_score(ytest,y_LR_Tuned)\n",
    "print('Accuracy_LR_Tuned',acc_LR_Tuned)\n",
    "print('\\n Classification Report_LR_Tuned')\n",
    "print(classification_report(ytest,y_LR_Tuned))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 1 1 0 1 0 1 1 0 0 1 1 1 0 1 1 1 0 0 0 0 0 1 1 0 1 1 1 0 1 1 1 1 0\n",
      " 1 1 1 1 1 0 0 0 1 1 1 0 0]\n",
      "[0 0 1 0 1 1 0 1 0 1 1 0 0 1 1 1 0 1 1 1 1 0 0 0 0 1 1 0 1 1 1 0 1 1 1 1 0\n",
      " 1 1 1 1 1 0 0 0 1 1 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "#Checking efficiancy in random \n",
    "\n",
    "print(y_LR_Tuned[:50])\n",
    "print(ytest[:50].values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Mussroom_Problem.pk1']"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Saving Model \n",
    "import joblib\n",
    "joblib.dump(LR_Tuned,'Mussroom_Problem.pk1')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LOAD and RUN \n",
    "MUSROOM_MODEL =joblib.load('Mussroom_Problem.pk1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "MUSROOM_MODEL_Results = MUSROOM_MODEL.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>690</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>959</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1010</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1013</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1015</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1020</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1036</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1037</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1038</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1039</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1044</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1046</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1047</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1059</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1060</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1062</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1116</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1121</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1134</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1143</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1144</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1145</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1148</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1155</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1156</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1157</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1158</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1161</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1163</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1164</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1168</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1170</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1172</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1173</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1177</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1187</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1188</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1189</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1191</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1209</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1211</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1212</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1213</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1214</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1217</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1218</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1219</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1226</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1237</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1252</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1264</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1311</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1322</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1347</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1352</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1356</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1359</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1370</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1372</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1373</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1374</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1375</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1376</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1378</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1381</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1388</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1390</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1391</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1392</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1394</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1395</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1401</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1402</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1403</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1405</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1407</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1408</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1409</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1410</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1411</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1412</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1414</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1415</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1416</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1419</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1420</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1421</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1422</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1424</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1425</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1426</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1431</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1434</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1445</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1446</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1470</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1471</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1472</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1473</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1474</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1475</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1476</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1480</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1482</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1483</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1484</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1486</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1491</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1492</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1500</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1504</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1505</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1506</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1508</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1509</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1510</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1511</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1512</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1513</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1514</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1516</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1517</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1518</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1519</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1520</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1521</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1522</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1524</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1527</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1530</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1539</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1541</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1542</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1546</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1549</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1555</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1557</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1558</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1559</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1560</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1561</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1562</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1563</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1567</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1568</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1571</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1572</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1573</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1574</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1575</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1576</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1577</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1578</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1579</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1580</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1581</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1582</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1583</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1584</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1585</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1586</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1588</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1589</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1590</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1591</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1592</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1593</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1601</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1603</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1605</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1607</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1608</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1609</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1610</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1611</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1613</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1615</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1616</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1617</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1618</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1619</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1620</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1621</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MUSROOM_MODEL =pd.DataFrame(MUSROOM_MODEL_Results)\n",
    "MUSROOM_MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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