demographics-p1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Cleaning the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/accounts/class/s159/s159-9/.conda/envs/demographics/lib/python3.6/site-packages/matplotlib/font_manager.py:279: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  'Matplotlib is building the font cache using fc-list. '\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import shelve\n",
    "import os\n",
    "from IPython.display import display\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.rcParams['figure.figsize'] = (15, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult = pd.read_csv(\"data/adult.csv\")\n",
    "adult.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This seems like a nice dataset. Let's look at the first few rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>native.country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>?</td>\n",
       "      <td>77053</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>Private</td>\n",
       "      <td>132870</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>18</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>?</td>\n",
       "      <td>186061</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>140359</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>264663</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
       "0   90         ?   77053       HS-grad              9        Widowed   \n",
       "1   82   Private  132870       HS-grad              9        Widowed   \n",
       "2   66         ?  186061  Some-college             10        Widowed   \n",
       "3   54   Private  140359       7th-8th              4       Divorced   \n",
       "4   41   Private  264663  Some-college             10      Separated   \n",
       "\n",
       "          occupation   relationship   race     sex  capital.gain  \\\n",
       "0                  ?  Not-in-family  White  Female             0   \n",
       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
       "2                  ?      Unmarried  Black  Female             0   \n",
       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
       "4     Prof-specialty      Own-child  White  Female             0   \n",
       "\n",
       "   capital.loss  hours.per.week native.country income  \n",
       "0          4356              40  United-States  <=50K  \n",
       "1          4356              18  United-States  <=50K  \n",
       "2          4356              40  United-States  <=50K  \n",
       "3          3900              40  United-States  <=50K  \n",
       "4          3900              40  United-States  <=50K  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also look at the unique values each column has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Values in Column age\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([90, 82, 66, 54, 41, 34, 38, 74, 68, 45, 52, 32, 51, 46, 57, 22, 37,\n",
       "       29, 61, 21, 33, 49, 23, 59, 60, 63, 53, 44, 43, 71, 48, 73, 67, 40,\n",
       "       50, 42, 39, 55, 47, 31, 58, 62, 36, 72, 78, 83, 26, 70, 27, 35, 81,\n",
       "       65, 25, 28, 56, 69, 20, 30, 24, 64, 75, 19, 77, 80, 18, 17, 76, 79,\n",
       "       88, 84, 85, 86, 87])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column workclass\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['?', 'Private', 'State-gov', 'Federal-gov', 'Self-emp-not-inc',\n",
       "       'Self-emp-inc', 'Local-gov', 'Without-pay', 'Never-worked'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column fnlwgt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 77053, 132870, 186061, ...,  34066,  84661, 257302])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column education\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['HS-grad', 'Some-college', '7th-8th', '10th', 'Doctorate',\n",
       "       'Prof-school', 'Bachelors', 'Masters', '11th', 'Assoc-acdm',\n",
       "       'Assoc-voc', '1st-4th', '5th-6th', '12th', '9th', 'Preschool'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column education.num\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 9, 10,  4,  6, 16, 15, 13, 14,  7, 12, 11,  2,  3,  8,  5,  1])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column marital.status\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Widowed', 'Divorced', 'Separated', 'Never-married',\n",
       "       'Married-civ-spouse', 'Married-spouse-absent', 'Married-AF-spouse'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column occupation\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['?', 'Exec-managerial', 'Machine-op-inspct', 'Prof-specialty',\n",
       "       'Other-service', 'Adm-clerical', 'Craft-repair', 'Transport-moving',\n",
       "       'Handlers-cleaners', 'Sales', 'Farming-fishing', 'Tech-support',\n",
       "       'Protective-serv', 'Armed-Forces', 'Priv-house-serv'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column relationship\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Not-in-family', 'Unmarried', 'Own-child', 'Other-relative',\n",
       "       'Husband', 'Wife'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column race\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['White', 'Black', 'Asian-Pac-Islander', 'Other',\n",
       "       'Amer-Indian-Eskimo'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column sex\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Female', 'Male'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column capital.gain\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([    0, 99999, 41310, 34095, 27828, 25236, 25124, 22040, 20051,\n",
       "       18481, 15831, 15024, 15020, 14344, 14084, 13550, 11678, 10605,\n",
       "       10566, 10520,  9562,  9386,  8614,  7978,  7896,  7688,  7443,\n",
       "        7430,  7298,  6849,  6767,  6723,  6514,  6497,  6418,  6360,\n",
       "        6097,  5721,  5556,  5455,  5178,  5060,  5013,  4934,  4931,\n",
       "        4865,  4787,  4687,  4650,  4508,  4416,  4386,  4101,  4064,\n",
       "        3942,  3908,  3887,  3818,  3781,  3674,  3471,  3464,  3456,\n",
       "        3432,  3418,  3411,  3325,  3273,  3137,  3103,  2993,  2977,\n",
       "        2964,  2961,  2936,  2907,  2885,  2829,  2653,  2635,  2597,\n",
       "        2580,  2538,  2463,  2414,  2407,  2387,  2354,  2346,  2329,\n",
       "        2290,  2228,  2202,  2176,  2174,  2105,  2062,  2050,  2036,\n",
       "        2009,  1848,  1831,  1797,  1639,  1506,  1471,  1455,  1424,\n",
       "        1409,  1173,  1151,  1111,  1086,  1055,   991,   914,   594,\n",
       "         401,   114])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column capital.loss\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([4356, 3900, 3770, 3683, 3004, 2824, 2754, 2603, 2559, 2547, 2489,\n",
       "       2472, 2467, 2457, 2444, 2415, 2392, 2377, 2352, 2339, 2282, 2267,\n",
       "       2258, 2246, 2238, 2231, 2206, 2205, 2201, 2179, 2174, 2163, 2149,\n",
       "       2129, 2080, 2057, 2051, 2042, 2002, 2001, 1980, 1977, 1974, 1944,\n",
       "       1902, 1887, 1876, 1848, 1844, 1825, 1816, 1762, 1755, 1741, 1740,\n",
       "       1735, 1726, 1721, 1719, 1672, 1669, 1668, 1651, 1648, 1628, 1617,\n",
       "       1602, 1594, 1590, 1579, 1573, 1564, 1539, 1504, 1485, 1411, 1408,\n",
       "       1380, 1340, 1258, 1138, 1092,  974,  880,  810,  653,  625,  419,\n",
       "        323,  213,  155,    0])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column hours.per.week\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([40, 18, 45, 20, 60, 35, 55, 76, 50, 42, 25, 32, 90, 48, 15, 70, 52,\n",
       "       72, 39,  6, 65, 12, 80, 67, 99, 30, 75, 26, 36, 10, 84, 38, 62, 44,\n",
       "        8, 28, 59,  5, 24, 57, 34, 37, 46, 56, 41, 98, 43, 63,  1, 47, 68,\n",
       "       54,  2, 16,  9,  3,  4, 33, 23, 22, 64, 51, 19, 58, 53, 96, 66, 21,\n",
       "        7, 13, 27, 11, 14, 77, 31, 78, 49, 17, 85, 87, 88, 73, 89, 97, 94,\n",
       "       29, 82, 86, 91, 81, 92, 61, 74, 95])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column native.country\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['United-States', '?', 'Mexico', 'Greece', 'Vietnam', 'China',\n",
       "       'Taiwan', 'India', 'Philippines', 'Trinadad&Tobago', 'Canada',\n",
       "       'South', 'Holand-Netherlands', 'Puerto-Rico', 'Poland', 'Iran',\n",
       "       'England', 'Germany', 'Italy', 'Japan', 'Hong', 'Honduras', 'Cuba',\n",
       "       'Ireland', 'Cambodia', 'Peru', 'Nicaragua', 'Dominican-Republic',\n",
       "       'Haiti', 'El-Salvador', 'Hungary', 'Columbia', 'Guatemala',\n",
       "       'Jamaica', 'Ecuador', 'France', 'Yugoslavia', 'Scotland',\n",
       "       'Portugal', 'Laos', 'Thailand', 'Outlying-US(Guam-USVI-etc)'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Unique Values in Column income\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['<=50K', '>50K'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for column in adult.columns:\n",
    "    print(\"Unique Values in Column \" + column)\n",
    "    display(adult[column].unique())\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are missing values in workclass, occupation, and native country. There do no seem to be missing values (N/A) for the quantitative columns. Exactly how many total rows are there where there is missing information?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_indices_with_value(df, value):\n",
    "    \"\"\"Return the row indices of a Pandas Dataframe where the row contains the given value.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    \n",
    "    df: Pandas Dataframe\n",
    "    value: value we want to see if row contains\n",
    "    \n",
    "    Return\n",
    "    ------\n",
    "    array\n",
    "        An integer array where each value is the row index of a row that contains the value\n",
    "        \n",
    "    Example\n",
    "    -------\n",
    "    \n",
    "    >>> row0 = [\">\", \"?\", \"!\"]\n",
    "    ... df = pd.DataFrame([row0])\n",
    "    ... find_indices_with_value(df, \"?\")\n",
    "    array([0])\n",
    "    \"\"\"    \n",
    "    \n",
    "    rows = []\n",
    "    for index, row in df.iterrows():\n",
    "        if value in row.unique():\n",
    "            rows.append(index)\n",
    "    return np.array(rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: We saved this function to a .py file for later notebook uses just through copying and pasting. We could not find a way to automatically save this function as a .py file to facilitate reproducibility, but maybe it exists!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2399"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_ind = find_indices_with_value(adult, \"?\")\n",
    "len(missing_ind)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 2399 rows where there is missing information. We believe this is an insignificant amount that will not affect our analysis if we delete them. Let's see if this is true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>native.country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>82</td>\n",
       "      <td>Private</td>\n",
       "      <td>132870</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>18</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>140359</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>264663</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>216864</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>150601</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
       "0   82   Private  132870       HS-grad              9        Widowed   \n",
       "1   54   Private  140359       7th-8th              4       Divorced   \n",
       "2   41   Private  264663  Some-college             10      Separated   \n",
       "3   34   Private  216864       HS-grad              9       Divorced   \n",
       "4   38   Private  150601          10th              6      Separated   \n",
       "\n",
       "          occupation   relationship   race     sex  capital.gain  \\\n",
       "0    Exec-managerial  Not-in-family  White  Female             0   \n",
       "1  Machine-op-inspct      Unmarried  White  Female             0   \n",
       "2     Prof-specialty      Own-child  White  Female             0   \n",
       "3      Other-service      Unmarried  White  Female             0   \n",
       "4       Adm-clerical      Unmarried  White    Male             0   \n",
       "\n",
       "   capital.loss  hours.per.week native.country income  \n",
       "0          4356              18  United-States  <=50K  \n",
       "1          3900              40  United-States  <=50K  \n",
       "2          3900              40  United-States  <=50K  \n",
       "3          3770              45  United-States  <=50K  \n",
       "4          3770              40  United-States  <=50K  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_adult = adult.drop(missing_ind).reset_index(drop = True)\n",
    "clean_adult.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adult\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education.num</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32561.000000</td>\n",
       "      <td>3.256100e+04</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.581647</td>\n",
       "      <td>1.897784e+05</td>\n",
       "      <td>10.080679</td>\n",
       "      <td>1077.648844</td>\n",
       "      <td>87.303830</td>\n",
       "      <td>40.437456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.640433</td>\n",
       "      <td>1.055500e+05</td>\n",
       "      <td>2.572720</td>\n",
       "      <td>7385.292085</td>\n",
       "      <td>402.960219</td>\n",
       "      <td>12.347429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.178270e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.783560e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.370510e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  education.num  capital.gain  capital.loss  \\\n",
       "count  32561.000000  3.256100e+04   32561.000000  32561.000000  32561.000000   \n",
       "mean      38.581647  1.897784e+05      10.080679   1077.648844     87.303830   \n",
       "std       13.640433  1.055500e+05       2.572720   7385.292085    402.960219   \n",
       "min       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.178270e+05       9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.783560e+05      10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.370510e+05      12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n",
       "\n",
       "       hours.per.week  \n",
       "count    32561.000000  \n",
       "mean        40.437456  \n",
       "std         12.347429  \n",
       "min          1.000000  \n",
       "25%         40.000000  \n",
       "50%         40.000000  \n",
       "75%         45.000000  \n",
       "max         99.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clean Adult\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education.num</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30162.000000</td>\n",
       "      <td>3.016200e+04</td>\n",
       "      <td>30162.000000</td>\n",
       "      <td>30162.000000</td>\n",
       "      <td>30162.000000</td>\n",
       "      <td>30162.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.437902</td>\n",
       "      <td>1.897938e+05</td>\n",
       "      <td>10.121312</td>\n",
       "      <td>1092.007858</td>\n",
       "      <td>88.372489</td>\n",
       "      <td>40.931238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.134665</td>\n",
       "      <td>1.056530e+05</td>\n",
       "      <td>2.549995</td>\n",
       "      <td>7406.346497</td>\n",
       "      <td>404.298370</td>\n",
       "      <td>11.979984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.376900e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.176272e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.784250e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>2.376285e+05</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  education.num  capital.gain  capital.loss  \\\n",
       "count  30162.000000  3.016200e+04   30162.000000  30162.000000  30162.000000   \n",
       "mean      38.437902  1.897938e+05      10.121312   1092.007858     88.372489   \n",
       "std       13.134665  1.056530e+05       2.549995   7406.346497    404.298370   \n",
       "min       17.000000  1.376900e+04       1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.176272e+05       9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.784250e+05      10.000000      0.000000      0.000000   \n",
       "75%       47.000000  2.376285e+05      13.000000      0.000000      0.000000   \n",
       "max       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n",
       "\n",
       "       hours.per.week  \n",
       "count    30162.000000  \n",
       "mean        40.931238  \n",
       "std         11.979984  \n",
       "min          1.000000  \n",
       "25%         40.000000  \n",
       "50%         40.000000  \n",
       "75%         45.000000  \n",
       "max         99.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Adult\")\n",
    "display(adult.describe())\n",
    "print(\"\")\n",
    "print(\"Clean Adult\")\n",
    "display(clean_adult.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adult Male Percentage: 0.669205491232\n",
      "Clean Adult Male Percentage: 0.675684636297\n",
      "Adult White Percentage: 0.854273517398\n",
      "Clean Adult White Percentage: 0.859790464823\n"
     ]
    }
   ],
   "source": [
    "males = (adult[\"sex\"] == \"Male\")\n",
    "clean_males = (clean_adult[\"sex\"] == \"Male\")\n",
    "print(\"Adult Male Percentage: \" + str(males.sum() / len(males)))\n",
    "print(\"Clean Adult Male Percentage: \" + str(clean_males.sum() / len(clean_males)))\n",
    "white = (adult[\"race\"] == \"White\")\n",
    "clean_white = (clean_adult[\"race\"] == \"White\")\n",
    "print(\"Adult White Percentage: \" + str(white.sum() / len(white)))\n",
    "print(\"Clean Adult White Percentage: \" + str(clean_white.sum() / len(clean_white)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary statistics did not change much, so we will indeed exclude these rows from our analysis.\n",
    "\n",
    "Now, let us visualize some of these columns. Because there are so many native countries, we will begrudgingly use a pie chart instead of a bar chart. We are not using it to make any inferences, so it is fine to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kflGOFKl56kTRBfQ/GBGR+qA+FRGcTifTpk1j//79mM1mXnjhBVq2bHnRMffffz+DBw/m\nrrvuuuR1lB9FROqH+pQjRWoTLS8SERGpgdasWYPNZmPp0qU8+eSTzJo166Jj5s6dS3Z2theiExER\nEZGyUNFFRESkBtqxYwf9+vUDoEuXLuzevbvE+H/+8x8MBgP9+/f3RngiIiIiUgZVUnRxOp1MnTqV\nMWPGkJCQQHJyconxZcuWMWrUKOLi4li3bh0AGRkZ3HfffcTHx/PYY4+Rn58PwNq1axk9ejRjxoxh\n2bJlVRGuiIhIjWOxWAgODna/9vHxwW63A3DgwAG++OILHnvsMW+FJyIiIiJlUCU9XS6cEp2YmMis\nWbOYP38+UFRcWbhwIStWrMBqtRIfH0+fPn145513GDlyJCNGjOD1119n6dKl3H333bz00kt88skn\nBAQEcNdddzFw4EAaNmxYFWGLiIjUGMHBweTm5rpfO51OTKaitL1q1SrS09O57777SElJwdfXl2bN\nmmnWi4iIiEgNUyVFl8tNid65cyddu3bFbDZjNpuJiYlh3759TJkyBZfLhdPpJDU1lVatWpGUlERM\nTAxhYWFAUTPA7du3M3To0KoIW0REpMbo1q0b69atY9iwYSQmJtK+fXv32NNPP+3++vXXXycqKkoF\nFxEREZEaqEqKLqVNiTaZTFgsFkJCQtxjQUFBWCwWDAYDdrud4cOHY7VaeeSRR0hNTb3ksVJzLd56\nrMLnxveKqcRIRERqtyFDhrBp0ybGjh2Ly+Vi5syZLFiwgJiYGAYPHuzt8ERqrCu5FymmexIREaks\nVVJ0udyU6N+O5ebmugsrvr6+fPnll2zevJlnnnmG5557rtRjRURE6jKj0ciMGTNKvBcbG3vRcY8+\n+mh1hSQiIiIi5VQlRZfLTYnu3Lkzc+fOxWq1YrPZSEpKon379kybNo1bbrmF3/3udwQFBWEwGIiN\njSU5OZmsrCwCAwPZvn0748ePr4qQ5QKV8YRIREREREREpL6rkqKLpynRCQkJxMfH43K5eOKJJ/Dz\n8yMhIYFp06bx5ptvYjQamTZtGr6+vkyePJnx48fjcrkYPXo0jRs3roqQRUREREREREQqVZUUXTxN\niY6LiyMuLu6i8YULF150rUGDBjFo0KCqCFNERERE6qHU7HyCzCZCA3y9HYqIiNRxRm8HICIiIiJS\nnT7YfJR3Nx7BZnd6OxQREanjVHQRERERkXrDYrVzrsDOaYuVf+9O9XY4IiJSx6noIiIiIiL1Rvq5\nAgBaRASw9cgZ9qWd83JEIiJSl6noIiIiIiL1RnHRZez1MTQJ9WfFjylYrHYvRyUiInWVii4iIiIi\nUm+kZRcQaPYhPNCX0d2ak2u188tJzXYREZGqoaKLiIiIiNQb6ecKaBzqj8FgIDrcH5PRQGau1dth\niYhIHVUlW0aLXIrT5SI7v5CzuTay8wspsDuxFTpwAf6+PoQGmGjVIIgOTUIw+ZSvHrh467Erii2+\nV8wVnS8iIiI1n9PlIj3HSreYCACMBgMRQWZOW2xejkxEROoqFV2kSlisdtKyC0g7V0Badj5p2QWc\nyrFid7pKPefzn08CEGj2oUuLcIZ2iub31zUlLMC3usIWERGROiwrrxCb3UmTUH/3ew2CzJzJVdFF\nRESqhooucsWcLhenzllJPpNLcmYeyZm5nM0rdI+H+JloEubP7xoGExXsR0SQLxEBZvzNPph9jBgM\nkF/o4MarGrI/LYcfk8/y/eFM/rpqNy98sZdhnaJ5+MZY2jUO8eKnFBERkdquuIluk1A/93tRwX4k\nZVhwuVwYDAZvhSYiInWUii5SIdn5hexLO8f+tByOZuZSUOgEigosMQ0C+V2bBkSHBdAkzJ9gP8//\nmvn6GOnQJJQOTUIZ3qUZLpeLPSfPsfSH46z88QSrElMYfl1THr+pPa2igqr644mIiEgdlHa+6NL4\ngpkukUFmCh0uzhXYNbtWREQqnYouUmbn8gvZlZLNzyeyOHE2H4CIQF86NQujZYMgWkYGEhlkrpSn\nRAaDgY7NwujYLIwnhrTn7fVJfLD5KP/alcof+rRmwqC2hPrrxkhERETKLv1cARGBvvj5+rjfaxBs\nBiAz16qii4iIVDoVXeSy8qx2dp88x88nsjh6OhcX0DTMn/+5pjFXR4fSKMSvyqfiRgaZeXbo1Yzv\n25p/fLWf/9twmJU/nuDpmztwR/fmGI2aCiwiIiKepWUXlJjlAtAgqGip0RmLjTZR3ohKRETqMhVd\n5CJ5Njt7T55jV0o2SRkWnK6i9c6DOjSic/NwGob4eb5IFWgU4s/sO67jnt+1ZPrqvTy9YicLtyQz\n7ffXeCUeERERqT3sTienLVaujg4t8X54oC8+BoN2MBIRkSqhoosAcNpiZe2+U3y5K5X1BzJwuopm\nmPRt25DOzcOIDvOvMc3lOjcP55MHe/P5zyd56ct9jJ7/Pdc1D+OWjtGaFiwiIiKXlJFjxemixM5F\n8Ou20Zm5Vi9FJiIidZmKLvVUvs3BtqNn2HToNBsOnuaX1HMANI8IoG/bKDo1C6dpeM0ptPyWwWBg\neJdmDLmmMfO/S2L+d0nsTT3HgPYN6deuIb4+Rm+HKCIiIjVI+rmiokrjMP+LxrRttIiIVBUVXWqw\nxVuPVfjc+F4xJV47nC52pWSz8WAGGw+d5sfkLGwOJ2YfI91bRvDUzVfRr10UnZqFsWTb8SsNvdoE\nmk08+T9X4W/y4d+7U1nzyym2J59laMdoOjYNLXPRqDJ/1iIiIlLzpJ8rwGiAqPONcy8UFWzmyOlc\nbRstIiKVTkWXOszpdLE5KZMvd6fy1e40Ms8/wbkmOpRxfVrRp20U17eKINBc+/81iAgyE9+rJYcz\nLHyxM5Ul247ROiqI2zpHEx0W4O3wRERExMvO5tkIDzRjMl48GzYy2A+bw0mO1a7dEUVEpFJVyV/b\nTqeTadOmsX//fsxmMy+88AItW7Z0jy9btoyPP/4Yk8nEQw89xMCBAzl58iRTpkzB4XDgcrmYMWMG\nbdq0YcGCBXzyySdERkYCMH36dNq0aVMVYdcZLpeLr/akMefrA+xPzyHA14dBVzfif65pTJ+2UUQF\ne6cRbnVo0zCYRwa2ZXvyGb7Zm84baw9xfetIbrq6McF+tb+4JCIiIhVjKbATUsq9QIOg89tGW2wq\nuoiISKWqkr9C16xZg81mY+nSpSQmJjJr1izmz58PQEZGBgsXLmTFihVYrVbi4+Pp06cP8+bN4557\n7uGmm25iw4YNzJkzhzfeeIM9e/bw8ssv07Fjx6oItc45bbGybPtxTpzNp01UEK+OuY5bro0mwOzj\n7dCqjY/RQK/WDejcLJxv96Wz5XAmPx/PYuBVjegd20D9XkREROohi9Ve6g6MxQ+kMi1WWkcFVWdY\nIiJSx1VJ0WXHjh3069cPgC5durB792732M6dO+natStmsxmz2UxMTAz79u3jmWeeISQkBACHw4Gf\nX1Hy27NnD++88w4ZGRnceOONPPDAA1URcp1wMD2HJT8cw4CB2Xd0ZlTXZpjqcYEhwOzDbZ2b0rNV\nJP/Zk8Z/9qSx5UgmN1/bhM7NwrRmW0REpB7JKbCXWlAJC/DFaMC9FFtERKSyVMlf5BaLheDgYPdr\nHx8f7Ha7e6y4uAIQFBSExWIhMjISX19fDh8+zMsvv8wjjzwCwK233sq0adP44IMP2LFjB+vWrauK\nkGu975NO8/7mo4QF+PLIwLbE9WhRrwsuF2oU6s+9vVvxxz6tCfD1YekPx/nf/yaRnJnr7dBERESk\nGjicLvILHQT7X/p5o4/RQESgWUUXERGpdFXyV3lwcDC5ub/+Qet0OjGZTJccy83NdRdhtmzZwiOP\nPMLs2bNp06YNLpeL++67j8jISMxmMwMGDGDv3r1VEXKt9vPxLFbvTOWqJiE82D+WyKCLu/ILtG1U\n1O9ldLdmZOUX8vb6wyzemqwtIkVEROo4i7Xo4d/l+rs1CDaTabFWV0giIlJPVEnRpVu3bqxfvx6A\nxMRE2rdv7x7r3LkzO3bswGq1kpOTQ1JSEu3bt2fLli28+OKLvPvuu3Tq1AkomhVz2223kZtbtIXf\n1q1b1dvlN5Izc1nx4wlaNQgkvmcMfr71p3dLRRgNBrq3jOTJIVcxuEMj9qfn8OqaA/xndxo2u9Pb\n4YmIiEgVsBQUFV1C/Epvktsg2I/MXBsul6u6whIRkXqgSnq6DBkyhE2bNjF27FhcLhczZ85kwYIF\nxMTEMHjwYBISEoiPj8flcvHEE0/g5+fHzJkzKSwsZPLkyQC0bt2aGTNm8MQTT3DvvfdiNpvp3bs3\nAwYMqIqQa6UzuTY+2pJMWIAv9/RqqeVE5WA2GRl8dWOubxXJ13vTWX8wg50nsritc1OuaRrq7fBE\nRESkElmshQClLi+Coh2MbHYnuTZHdYUlIiL1QJUUXYxGIzNmzCjxXmxsrPvruLg44uLiSox//vnn\nl7zWiBEjGDFiROUHWcs5XS6W/nAMh8vFvb1bEajtkCskNMCXO7o3p0fLCD77OYWPtibToUkIt3du\nSoSWaYmIiNQJOe6ZLqXfL0UEFuX9rDwtOxYRkcrjcWpEYWFhdcQh5bT50GmOn81n+HXNSt3+UMqu\nVVQQEwa2Y2jHJhzOyGXutwf47/5TOJyaYiwilUP5VMR73D1dLjPTJSygaOlRVp7+WxURkcrjsegy\natQoXnzxRQ4cOFAd8UgZnLZY+XpvOlc3CaFz8zBvh1Nn+BgN9GvXkMdvake7RiF8tTeddzce5qye\neIlIJVA+FfGeHKsdP5MR38ssxQ4PPF90yVfRRUREKo/Hostnn31G3759eeONN0hISGD58uUldh+S\n6uV0uVj5YwomHwPDuzTDYDB4O6Q6JzzQzD2/a0lcj+akZRfw+tqD7E7J9nZYIlLLKZ+KeI+lwH7Z\nnYsAAnx9MPsYydbDFhERqUQeiy5Go5H+/fszevRowsPDWbhwIePHj2fp0qXVEZ/8xo/JZzmamcuw\njtGEBpTegV+uXJcWETw6qB0Ng/1YvO0Y6w9kaEcDEamw8uZTp9PJ1KlTGTNmDAkJCSQnJ5cYX7Ro\nEaNHj+aOO+5g3bp11fERRGoti9V+2aVFAAaDgfBAX85qeZGIiFQij91XZ8+ezbfffkvPnj3585//\nTOfOnXE6nYwaNYoxY8ZUR4xyns3u5Jtf0mkREUD3lhHeDqdeiAwy8+d+bfjkxxP8Z08aZ/Ns3Na5\nKT5GzTASkfIpbz5ds2YNNpuNpUuXkpiYyKxZs5g/fz4AZ86cYfHixaxatQqr1cqtt97KjTfeqNmP\nIqWwFNhpHOq5B154oC/ZWl4kIiKVyGPRpVWrVnz66acEBga6mwAajUbeeOONKg9OStpwKIOcAjvx\nPWN0Y12NTD5G4nq0IDzAl/UHT2O1O7mje3OM+h2ISDmUN5/u2LGDfv36AdClSxd2797tHouMjOSz\nzz7DZDKRkpJCaGio8oLIZVisdmL9gzweFxZgJuWslhSLiEjl8bi8yOVyMXfuXAAeeOABVq1aBUDz\n5s2rNjIpIaegkA0HTtOxaSgtG3i+aZDKZTQYuKVjNEOuaUzi8Sz+vStVS41EpFzKm08tFgvBwcHu\n1z4+Ptjtdvdrk8nERx99xJgxY7j55purMHKR2s3ucJJf6CDYz/Oy7PBAX3JtDgoKHdUQmYiI1Ace\niy4ff/wxTz75JABvv/02S5YsqfKg5GJrfinavvjma5t4O5R67cb2Dekd24BNSZmsP3ja2+GISC1S\n3nwaHBxcotGu0+nEZCo5QfWee+5hw4YN/PDDD2zZsqXygxapA4q3iw7x0EgXIPx8v7yUrPwqjUlE\nROoPj9nHaDTi51e0BtbX11fTl70g02Jl+9Ez9I5tQINgz+uRa6vFW495OwSPDAYDt3aKJtdq56s9\naUQGmb0dkojUEuXNp926dWPdunUMGzaMxMRE2rdv7x47fPgwc+bM4fXXX8fX1xez2YzR6PE5iki9\nVFx08dRIF4p2MAQ4mZVPbMNgD0eLiIh45jH7DB48mPj4eDp37syePXsYNGhQdcQlF/juQAY+RgMD\n2jf0dihC0VKjO7o352yujZU/nuCB/m1oFaUlXyJyeeXNp0OGDGHTpk2MHTsWl8vFzJkzWbBgATEx\nMQwePJgOHTowZswYDAYD/fr1o2fPntX0SURqF0vB+aJLOWa6nNRMFxERqSQes8/DDz/MwIEDOXLk\nCCNGjKBDhw7VEZecdzbPxk9A539mAAAgAElEQVTHztKrTQNC/LVFdE1hMhq5q2cMr689xMOLfmTl\nwzfg7+vj7bBEpAYrbz41Go3MmDGjxHuxsbHurydMmMCECROqJFaRuqQ8M11CA3wxAClZBVUclYiI\n1Bce5yKnpqayceNGDh8+zJo1a7RrUTVbfyADAwb6t9Msl5omPNDMnT2aszf1HNNX7/V2OCJSwymf\ninhHjrXsM118jAZCA3xJOauZLiIiUjk8Fl0ee+wxLBYLUVFR7n+kepzLL2R78lm6tYwgLECzXGqi\nDk1CeXBALEu2HeOrPWneDkdEajDlUxHvsBTY8fc14utTtr5HYQG+Wl4kIiKVxmPJPygoiCeeeKI6\nYpHf2HAwA5fLpV4uNdykIe3ZeCiDZ1fuoltMBA1D6m6zYxGpOOVTEe/IsdrLtF10sfBAX05mq+gi\nIiKVw2PJv127dvzrX//i8OHDHDlyhCNHjlRHXPVeTkEhPySfpXPzcO2QU8OZTUZejetCrtXO5BU7\ncblc3g5JRGog5VMR77AU2Mu0tKhYeIAvqVkFOJ3K5yIicuU8ZqBffvmFX375xf3aYDDw4YcfVmlQ\nAp/+lILN7qR3mwYVOr82bL9cl7RrHMLkoR2YvnovH/9wnLt6xng7JBGpYZRPRbzDYrUTHeZf5uPD\nA83YHE5OW6w0Ci37eSIiIpfiseiycOFCcnJySElJoUWLFgQFaWvcquZyufhg81GaRwTQIjLQ2+FI\nGd3XuxXf/nKK57/YS+82DbSNtIiUoHwq4h0WayHBfsFlPr542+iUrHwVXURE5Ip5LLp89dVXzJ8/\nH4fDwS233ILBYODhhx+ujtjqrc1JmSRl5HJH9+beDkXKwWg08Pc7O3Pzq+uZtCyRZQ/0xlTGpn0i\nUvcpn4pUv0KHk4JCJyFl2C66WFhgUdHlZFYBXTVxVURErpDHvwgXLFjAsmXLCA8P5+GHH2bNmjUe\nL+p0Opk6dSpjxowhISGB5OTkEuPLli1j1KhRxMXFsW7dOgBOnjzJuHHjSEhI4J577uHw4cMArF27\nltGjRzNmzBiWLVtWkc9Y63yw+SiRQWY6NQvzdihSTtFhAbwwshM/Hsvif/+b5O1wRKQGqUg+FZEr\nYynHdtHFIgKLeumlZOVVSUwiIlK/eMxARqMRs9mMwWDAYDAQEBDg8aJr1qzBZrOxdOlSEhMTmTVr\nFvPnzwcgIyODhQsXsmLFCqxWK/Hx8fTp04d58+Zxzz33cNNNN7FhwwbmzJnDq6++yksvvcQnn3xC\nQEAAd911FwMHDqRhw7q7m09KVj5rfknnwQGxZd7aUGqW31/XlDV705m75iD92jXkuhbh3g5JRGqA\niuRTEbkyloLyF138fX3wMxlZty+jXLse/VZ8L02TERGRMsx06dGjB5MmTSI9PZ2pU6fSqVMnjxfd\nsWMH/fr1A6BLly7s3r3bPbZz5066du2K2WwmJCSEmJgY9u3bxzPPPMOAAQMAcDgc+Pn5kZSURExM\nDGFhYZjNZrp378727dsr+llrhaXbihrg3v27ll6ORK7E88M70jjUn4cX/cjZXJu3wxGRGqAi+VRE\nrox7pks5lhdB0bbRWfmFVRGSiIjUMx4z0KRJk1i/fj3XXHMNsbGxDBw40ONFLRYLwcG/Nizz8fHB\nbrdjMpmwWCyEhIS4x4KCgrBYLERGRgJw+PBhXn75Zd58803OnDlzyWPrKpfLxarEk/RpG0WzcD0B\nrc3CAn156+5u3Pm/3/P40kQWjLseo9Hg7bBExIsqkk9F5Mrkni+6BJVjpgtAeIC5zA9Nfj6RxYkz\neYQF+BIWaKZDkxDNVhYRETePGWHVqlWcOXOGqKgosrOzWbVqlceLBgcHk5ub637tdDoxmUyXHMvN\nzXUXVrZs2cIjjzzC7NmzadOmzWWPrYsSj2dx7Ewev7+uqbdDkUpwXYtwpt5+Df89kMFraw96OxwR\n8bKK5FMRuTLuoou5fEWXxqH+ZORYsTudlz2u0OFk1U8pbE7K5MvdaSzZdoytR85UOF4REal7PBZd\nkpKSSEpK4tChQ6xevZoNGzZ4vGi3bt1Yv349AImJibRv39491rlzZ3bs2IHVaiUnJ4ekpCTat2/P\nli1bePHFF3n33XfdU65jY2NJTk4mKysLm83G9u3b6dq1a0U/a433WeJJzCYjN3ds4u1QpJLc3SuG\nUd2aMe/bg3y5K9Xb4YiIF1Ukn4rIlcm1OfD1MWA2lW/mSZMwfxwuF6dzLj/b5WC6BavdyX03tOKv\nt15DRKAvR0/nXvYcERGpXzyW/Z988kn31y6XiwceeMDjRYcMGcKmTZsYO3YsLpeLmTNnsmDBAmJi\nYhg8eDAJCQnEx8fjcrl44okn8PPzY+bMmRQWFjJ58mQAWrduzYwZM5g8eTLjx4/H5XIxevRoGjdu\nfAUft+ayO5x8sTOVwR0aEepf8aZtUrMYDAZmjuxEcmYejy9NJCrYj56tI70dloh4QUXyqYhcmVyr\nvdxLiwCiw/wBSDuXT5PzX1/KrpQsAs0+xDYMxsdooFWDIA6k5+ByuSocs4iI1C0es5DN9muFPyMj\ngxMnTni8qNFoZMaMGSXei42NdX8dFxdHXFxcifHPP//8ktcaNGgQgwYN8vg9a7vvD2dy2mJleJdm\n3g5FKpm/rw/v3tuD0f+7mT998AMrHrqBdo3r7jI5Ebm0iuRTEbkyuTZ7uZcWAUQF++FjNJCaXUCX\nFpc+ptDh5Je0HK5rHobP+b5tMQ0C+el4Fplqoi8iIud5zEK33HILBoMBl8uFv78/48ePr4646p1V\nP50kxN/EjVfV3e2w67OIIDMf/KEno+Zv5r73trH8oRvULFmknlE+Fal+uVYHQX4+5T7Px2igUYgf\nadkFpR6zPy0Hm91Jp2bh7vdaNggCIDkzr/zBiohIneSx6LJ27drqiKNeKyh08NWeNIZ1aoK/b/lv\nDKR2aBEZyPt/uJ6xb28h4Z9bWf5AbxoE+3k7LBGpJsqnItUv12qnUUjFcm10mD8HT5W+a+aulGyC\nzD60jgpyv9coxA9/XyPJmerrIiIiRTwWXe69995Sxz788MNKDaa+2njwNBarnds6a9eiuu7apmH8\nc9z1JPxzK+MW/MDIrs0qXGiL7xVTydGJSFVSPhWpfrm2ivV0AWgS6s+Px7KwWO0E/+YaNruTfWnn\n6BoT4V5aBGA0GIiJDCT5jGa6iIhIEY9ZqF27dnTt2pVevXqxa9cuVq1axeOPP14dsdUb3x04RaDZ\nh15t1GC1PujZOpL593Tj/g93YLHaGXdDK3x9yrergojUPsqnItXLZndS6HARZK7Yw40mYUXLgNOy\nC2jbKLjE2P70HAodLjo3C7vovJYNgjiQnk5Wno3wQHOFvreIiNQdHosuhw4d4q9//StQ1NT2ww8/\npE2bNlUeWF2xeOuxy467XC7+tTOVlg2CWLEjpZqiEm8b1KEx/7jzOh5fmsjH244R36tliSdlIlL3\nKJ+KVK9cmx2g4jNd3DsYXVx02XUii2A/E60uWFpUrGVkIAA7ks8y+Oq6ueumiIiUncfH6y6Xi+XL\nl7N//34WL15MYGBgdcRVb2TkWDmbV0j7xsGeD5Y6ZUTXZtzeOZpf0nL49KcTOLW9pEidpnwqUr1y\nrVdWdAn2MxHiZ7qoma7V7mB/eg4dm4ViNFz8wKR5RCBGA2xPPluh7ysiInWLx6LLK6+8wt69e3nl\nlVdITU1l1qxZ1RFXvXEgPQeAq7SFcL3UOzaKwVc34sdjWazbf8rb4YhIFVI+FaleuVYHQIWXF0HR\nbJe07PwS7+1LK1padOGuRRcym4w0DQ9gh4ouIiJCGZYXNWzYkCFDhnDixAk6d+6Mn592W6lMB9It\nNArx05rfemzQVY04Y7Gx9pdTNAsPoEOTUG+HJCJVQPlUpHpd6fIiKGqmu/lwJg6ny70MeNeJbEL9\nTbRsUPpstZaRgWxPPovN7sRsUt82EZH6zGMWmDNnDqtWrWLp0qX88ssvPPvss9URV71gtTs4kpmr\nWS71nMFgYHiXZjQJ82fZ9uNkWqzeDklEqoDyqUj1utLlRVA008XhdHH6fG4uKHRwID2Hjs3CLrm0\nqFjzyECsdidJGaVvOS0iIvWDx6LLjh07mD17NoGBgYwcOZITJ05UR1z1wuGMXBxOF+2bqOhS35lN\nRu7p1RIDBj7amkyhw+ntkESkkimfilSvXKsDH4MBvyuYaeJupnu+r8u+tHPYnS46XWLXogtFBRXN\nZEvO1NbRIiL1nccs5HA4sFqtGAwGHA4HRqOmSFaW/ek5mE3Gy05PlfojIsjMmOtbkH7Oyn92p3k7\nHBGpZMqnItUr12YnyM8Hw2VmpHjSMMQPs8nIN7+kczjDws4T2YQF+NIi8vL3bpFBRcvGj53JrfD3\nFhGRusHjHd+4ceMYNWoUBw8e5M477+Tuu++ujrjqhQPpObRtGIxJN95yXvvGIdwQ24DvD2e6myyL\nSN2gfCpSvXKt9itaWgRgMhq5t3dLAN7deIT9aTl08rC0CCDA7EN4oK9muoiIiOdGuuHh4SxevJjk\n5GSaN29OZGRkdcRV553Ns5GVV0i/tlHeDkVqmJuvbcKhUxZW7DjBxMHtrviGUURqBuVTkeqVa7UT\nZL7yHNomKpiJg9rx7b50th89S9eYS+9a9FstIwNVdBEREc8zXV5//XXCwsLo3LmzbhArUXJm0XTT\nlg2CvByJ1DS+PkbGXN+CvEIHqxJTcLlc3g5JRCqB8qlI9cq1OQj0q/h20Rcym4wM7RjNX2+7huiw\ngDKdE9MgiGQtLxIRqfc8lv8NBgOPPPIIrVu3dq8/nzRpUpUHVtcdPZ2Hn8nobtAmcqHosABuurox\nX+1JY2dKNtc1L9tTNRGpuZRPRapXZc10qahWDQL5clcqhQ4nvj5aSi4iUl95zESjR4+ujjjqnaOZ\nubRsEOhxTbDUfIu3HquS6/ZtG8Wek9ms/vkkbaKCCPH3rZLvIyLVQ/lUpPrYHU6sdidBlTTTpSJi\nIgNxOF2knM2nVZRmNouI1FelFl2WL1/OnXfeyciRI3G5XFfU+V1KyrPaOZVjpUsLzV6Q0vkYDdzR\nrTlvrDvE5z+fJL5njP47FKmFKppPnU4n06ZNY//+/ZjNZl544QVatmzpHn///ff517/+BcCAAQOY\nMGFClcQvUhvl2hwAXu2LVryEPPlMnoouIiL1WKlzHVevXu3++r777ivXRZ1OJ1OnTmXMmDEkJCSQ\nnJxcYnzZsmWMGjWKuLg41q1bV2Ls/fff5x//+If79YIFC7j11ltJSEggISGBw4cPlyuWmujo+aZq\n6ucinjQK9Wfw1Y3Zc/Icu1KyvR2OiFRARfPpmjVrsNlsLF26lCeffJJZs2a5x44fP87nn3/Oxx9/\nzNKlS9m4cSP79u2r1LhFarNcqx3Aq8uLWjYo2lb6WKb6uoiI1GelZqILm3eWt5HnhTeKiYmJzJo1\ni/nz5wOQkZHBwoULWbFiBVarlfj4ePr06YPT6eS5555j586d/M///I/7Wnv27OHll1+mY8eO5f1s\nNdbRzFx8jAaaR5StEZvUb8XLjD7/+SSttcxIpNapaD7dsWMH/fr1A6BLly7s3r3bPdakSRPeffdd\nfHyKlk7Y7Xb8/PwqKWKR2i+vBsx0aRTih7+vsU7sYFQZS6nje8VUQiQiIrVPqTNdLpz+XN4lDZe7\nUdy5cyddu3bFbDYTEhJCTEwM+/btw2q1MmLECB588MES19qzZw/vvPMOd911F2+//Xa54qipkjNz\naR4RoKZqUiY+RgOjuzXHanfy+c8ntZuRSC1T0XxqsVgIDg52v/bx8cFuL3p67+vrS2RkJC6Xi5df\nfplrrrmG1q1bV17QIrXcrzNdvNfTxWAwEBMZSPKZ2l90uRSL1c57G4+Qmp3v7VBERGq0Usv/hw4d\n4sknn8Tlcrm/LvbKK69c9qKl3SiaTCYsFgshISHusaCgICwWC2FhYfTt25eVK1eWuNatt95KfHw8\nwcHBTJgwgXXr1jFw4MByf9CawmZ3kpKVT792Db0ditQijUP93bsZ7UrJprN2MxKpNSqaT4ODg8nN\n/XVZgtPpxGT6NW1brVamTJlCUFAQf/vb36omeJFaKtd2vujixZkuADGRQSTX0eVF6w9kcCjDwr92\npjK+b2v1nRMRKUWpmWju3Lnur8eOHVuui17uRvG3Y7m5uSWKMBdyuVzcd9997vEBAwawd+/eWl10\nOX42D6cLWqmfi5TTb5cZiUjtUNF82q1bN9atW8ewYcNITEykffv27jGXy8XDDz9Mr169uP/++ys1\nXpG6INdqxwAEeHGmCxT1ddl4KKPObUphsdrZeiSTED8Th0/ncijDQrtGl76fFxGp70otuvTs2bPC\nF73cjWLnzp2ZO3cuVqsVm81GUlJSifELWSwWbrvtNr788ksCAwPZunVrrd9y8+jpXAwUbSMoUh7F\ny4zeXHeIFT+e4M/92mA01p0bOJG6qqL5dMiQIWzatImxY8ficrmYOXMmCxYsICYmBqfTybZt27DZ\nbGzYsAGASZMm0bVr18oMXaTWyrU6CDT7YPRyoaNVg0AKCp2cyrHSONTfq7FUpo0HM7A7XPxhQGsW\nbjnKV3vSiG0Y7PWft4hITVQlcy4vd6M4ePBgEhISiI+Px+Vy8cQTT5Ta/C8kJIQnnniCe++9F7PZ\nTO/evRkwYEBVhFxtjp3Jo3Gov9efvEjt1DjUn2Gdovn855Ms2HyU8X3Vw0GkrjIajcyYMaPEe7Gx\nse6vd+3aVd0hidQauTa715cWAcQUbxudmVdnii65VjtbDp+hc/MwmoQVLX9evuMEu7X8WUTkkqok\nG3m6UYyLiyMuLu6S544aNarE6xEjRjBixIjKD9ILXC4XJ87mc23TUG+HIrVYr9aRHEjP4eV/7+N3\nbSK5tmmYt0MSERGpUXKtdgK9uF10sZbnZzYnZ+bSs3Wkl6OpHBsPnabQ4WTgVY0AuK5FOOsPZvDN\n3nQ6NgvTbBcRkd/wuH2OxWLh1VdfZcqUKXz99dckJydXR1x10plcG/mFDppHaGmRVJzBULTMKDzQ\nl0eX/EROQaG3QxKRMlA+Fak+uVYHQX7en1XcLCIAH6OBY3VkByOXy8UPR89wTdNQGp2fuWM0GOgT\nG0Vmro2MHKuXIxQRqXk8Fl2mTJlCixYtOHr0KFFRUfzlL3+pjrjqpBNZRVvqNY8I8HIkUtsF+ZmY\nN7YryZl5TFr2M06ntpEWqemUT0WqT01ZXuTrY6RpuD/JmXWj6JKVV0iezUHbRsEl3i/uVXjirLaP\nFhH5LY9Fl6ysLO644w5MJhPdunXD5dIfdxWVcjYfk9FQZ9b0inf1jm3Ac7dezTd703lt7UFvhyMi\nHiifilQPp8tFvs1BUA1YXgTQMjKI5Doy0yXl/APEpmElHyBGhfjhZzJy4mzd+JwiIpXJY9EFICkp\nCYC0tDSMxjKdIpdw/GweTcOLppmKVIZxN7RidLfmzF1zkP/sTvN2OCLigfKpSNXLtdpxAcH+NaPo\nEtMgkGOZud4Oo1KkZOVjNECTsJIPEI0GA83CAzTTRUTkEjxmo+eee44pU6aQlJTExIkT+dvf/lYd\ncdU5DqeLk1n59GhVN5qoifct3noMgM7Nw9h2JJMJi39kXJ9WtIkK9nAmxPeKqerwROQ3lE9FqkdO\ngR2AkBqwvAiKmumezSskO7+QsABfb4dzRU5m5dM41B9fn4uLxs0jAth0KBO7w4npEuMiIvWVx2x0\n7NgxlixZoidyVygjx0qhw0XzcPVzkcrl62Pkvt6teGfDYRZ+n8z4vq3VrFmkBlI+FakexQ3mQ2vI\nTJeWDYpy8rHMPDo1r707DrpcLlKy8rk6+tK7cDaPCMThOk3auQLdh4iIXMDjnd/mzZsZPnw4r776\nKsePH6+OmOqk4jWuLZSEpAoE+pn4Q5/WBJp9eH/zUdKyC7wdkoj8hvKpSPVwz3TxrxmzSlo2CAIg\n+UztXmKUlV/URLdZKQ8QizeKOK4lRiIiJXgsukydOpUVK1bQoUMHZsyYwbhx46ohrLrnxNl8/H2N\nRAabvR2K1FFhAb78sU9rTEYD72xI4ujp2n1zJ1LXKJ+KVI9z54suNaany/mdfWr7DkYp54sppRVd\nwgJ8CfIzkaJmuiIiJZRpjvPOnTvZuHEjmZmZ9O7du6pjqpNOZOXRLDwAo0FNdKXqNAj244EBsQT7\n+fLepiP8knrO2yGJyAWUT0WqXk5BIQG+PpfsO+INQX4mooL9OFbLiy4nS2miW8xgMNAiQs10RUR+\ny+MjgGHDhtGhQwfuvPNOXnzxxeqIqc4pdDhJyy6gX7uG3g5F6oGIQDMP9G/DB98f5aMtyQy5pjH9\n2zdUwU/Ey5RPRapHToGdkBoyy6VYywaBtX55UUpWPo1CLt1Et1iziAD2p+VgLXTg5+tTjdGJiNRc\nHjPSokWLiIiIqI5Y6qy07AKcrl/XuopUtSA/E3/q24aVP53g673pHD+Txx3dWxBg1g2QiLcon4pU\nj5yCQkJrSD+XYi0jA9lyONPbYVSYy1W0C2eHJpduolusRUQgLooKNG0aet5NUUSkPii16DJx4kRe\ne+01br/99ovGNm7cWKVB1TUnsi6/BlakKphNRsb0aEFMZCBf7krljXUHuaN7C1pHBXk7NJF6RflU\npHrlFNiJivLzdhglxDQI5NPEFKx2B36m2vcAJDu/kFybg6YeHiAW3+ueOKuii4hIsVKLLq+99hoA\ny5cvJzo62v1+UlJS1UdVx5w8m0+Qn4mwgJr11EXqPoPBwA2xUTSPCGTZ9uO8u+EwfdtFMbp7s1p5\n0ydSGymfilQfl8tFjrVmLi9yueD4mXzaNqp9xYiTxQ8QS+nnUizIz0REoK97104REblMI90DBw6w\nYcMGHnzwQTZt2sTGjRtZv349kyZNqs746oST2fk0C/fHoJ4a4iUxkYE8OqgtPVpFsuHgaYa/sUlN\ndkWqifKpSPXJyivE4XTVmO2ii8VEnt82OrN29nVJycrHADQJ8zxru1lEICezC6o+KBGRWqLUxwDn\nzp3jyy+/JDMzky+++AIoemoeHx9fbcHVBYUOJ+nnCriqsZroinf5mXwY2bUZV0eH8OWuNH7/xkYm\nDbmK+/u3wceogqBIVVE+Fak+p3KsADVupkurBrV72+jU7AIahvhhNnneEapJqD+7U7Jr7VIqEZHK\nVmpG6tGjBz169GDPnj1ce+211RlTnZJ+rqiJblP1c5EaokOTUB6/qT1TVu7i5f/s4/vDmcwd04XI\nILO3QxOpk5RPRarPqZyiGRY1baZLZJCZYD8Tx87UzqLLqRxrmXsTRp9fgpSeXUBMA/WRExHx+Bgg\nLS2NOXPmUFhYiMvlIisri9WrV1dHbHVCiproSg0UGWRm/j3dWLLtONM+38Otr23gjfhudG+pnVVE\nqoryqUjVSz9XNNMltIbNdDEYDMREBtbK5UV5Njtnc210jQkv0/FNzhddUs+p6CIiApfp6VLszTff\nZMKECURHRzNy5Eiuuuqq6oirzjiZlU+Arw/hgTXriYuIwWAgvlcMKx++AZOPgbve2cLqn096OyyR\nOkv5VKTq1dSZLlDUTDe5Fs50OXTKggtoHHL5JrrFwgN88fc1kqa+LiIiQBmKLhEREXTt2hWAUaNG\nkZaW5vGiTqeTqVOnMmbMGBISEkhOTi4xvmzZMkaNGkVcXBzr1q0rMfb+++/zj3/8w/167dq1jB49\nmjFjxrBs2bIyfaiaJCUrn2bhAWqiKzVWx2ZhfDGhH11ahPPokp94b+MRb4ckUidVJJ+KSPmcOmfF\nz2QsU++R6hbTIJATZ/JxOF3eDqVcDqRbAGgUWrZtuA0GA01C/VV0ERE5z2NG8vX15YcffsBut7Nh\nwwYyMjI8XnTNmjXYbDaWLl3Kk08+yaxZs9xjGRkZLFy4kI8//ph//vOfzJkzB5vNRkFBAf/v//0/\nFi9e7D62sLCQl156iffee4+FCxeydOnSMn3/msJmd5KebVU/F6nxwgJ9+XB8T265tgkzvtjL37/a\nh8tVu24KRWq6iuRTESmfjBxrjWuiW6xlZBA2h5O0c7WrGHEwPQcfo4EGQWUrukDREqO0cwW6lxAR\noQxFl+nTp2O323nooYdYtmwZEydO9HjRHTt20K9fPwC6dOnC7t273WM7d+6ka9eumM1mQkJCiImJ\nYd++fVitVkaMGMGDDz7oPjYpKYmYmBjCwsIwm810796d7du3V+RzesWB9BwcLhdNw8s2HVPEm/x9\nfXjz7m7c1TOGN9clMe/bg94OSaROqUg+FZHyOZVTUCOXFkHR8iKofdtGH0jPoWGwX7l2OowODcBq\nd3I2r7AKIxMRqR1KfRRw5MivSwyaNGkCwKRJk8p0UYvFQnBwsPu1j48Pdrsdk8mExWIhJCTEPRYU\nFITFYiEsLIy+ffuycuXKEte51LG1xe6UbEBNdKX28DEaeHFER+wOJ3PXHMTf14cHB8R6OyyRWu1K\n8qmIlM+pHCthATWz6BITWVR0OZaZxw21KLUeSLeUeWlRseJmumnZBdodUUTqvVKLLlOnTr3k+waD\ngQ8//PCyFw0ODiY399cqvtPpxGQyXXIsNze3RGHlcte53LE10a6UbPx9jUo2UqsYjQZmje5Mgd3J\nrH/vI8TfxN29Wno7LJFa60ryqYiUncvl4tQ5Ky0iAr0dyiU1DQ/A18fA0cza00zXYrWTkpXPNU1D\ny3Ve41B/DEDqufKfKyJS15RadFm4cKH765ycHFJSUmjRogVBQZ63fuvWrRvr1q1j2LBhJCYm0r59\ne/dY586dmTt3LlarFZvNRlJSUonxC8XGxpKcnExWVhaBgYFs376d8ePHl+fzedXuk+doGqYmulL7\n+BgNzIm7jlyrnb+u2k3T8AAGXtXI22GJ1EpXkk9FpOwsVjv5hY4a29PFx2igdVQQh07leDuUMjuY\nXhRr45DyzXQxm4oeOvJ5yDMAACAASURBVKqZrojIZYouxb766ivmz5+Pw+HglltuwWAw8PDDD1/2\nnCFDhrBp0ybGjh2Ly+Vi5syZLFiwgJiYGAYPHkxCQgLx8fG4XC6eeOIJ/Pwu/T9yX19fJk+ezPjx\n43G5XIwePZrGjRtX7JNWs0KHk19Sz9GzVaS3QxGpEF8fI6/f1ZW4t79nwqIfWfZgb65tGubtsERq\nrYrkUxEpu/RzVoAaW3QB6NAklB3JZ70dRpkddO9cVP7+hE3CtIORiAiUoeiyYMECli1bxvjx43n4\n4YcZPXq0x5tEo9HIjBkzSrwXG/vr4tW4uDji4uIuee6oUaNKvB40aBCDBg3yFGaNczDdgs3uVD8X\nqdWC/Ey8N+56Rr65iT++/wOfPdLXvU5bRMqnIvlURMruVE7RH/g1tZEuwNXRoXz+80my8woJC6y5\ncRY7kJ6Dn6liS+WbhPmz9+Q5bHZnjdzCW0Skunj8P6DRaMRsNmMwGDAYDAQEqIhQFjtPZAHQPEI/\nL6ndGof6894frsdSYOf+hdspKHR4OySRWkn5VKRqZeScn+niV3Nnulwd/f/Zu+/4KOr8j+Ov2d1s\nekiHQAi9SgdBpHqoqIgoHgoocFZA76eoKOAdiMDZ5eyFEzkPqYddvBNRKQIihA7SCZAASUhISDZt\nszu/PwJ79BKybELez8cjj2R3Zmc+M7vZz+xnv6VkbMLfDx31cSQXZltqDvVjQ7CUoqt8XFggJpBa\nwabIFhEpa+fNSu3ateOpp54iNTWVcePG0bx588sRV4W3PjmbsACbBtGVcmnmyn0X/Zg7WsczY+Ve\nRn22gTfubqWxikQukvKpiHeleboXld8WJE3iSgaV3XrwKNfUjfJxNOe3IzWXjvVKF+fxlrEHswuo\nGVk+BzcWEbkczlt0efLJJ1myZAlNmjShbt26FbKrjy9sTMmiRXy4PpjKFaNp9TBuaFqVr9YdoHG1\nMIZ3r0DzXYqUA8qnIt6VllNAgJ+FAL/y25UlNtSfyGA7vx8s/4PpZuc7OXS0gAZVQ0r1+IggPwL9\nrCQfyaN9HY1xKCKV11mzUnFxMQsWLODXX3+la9euPPjggzRv3pwRI0ZczvgqpAKni60Hc2gRr0FH\n5crSrWEMvVtW55Xvt7JwS6qvwxGpEJRPRS6PtJxCYkMDyvUXXoZh0CQutEJ0Lzo+y1LD2NBSPd4w\nDGpFBZGU4SjLsEREKpyztnQZOXIkVquV9PR0du7cSXx8PH/5y18YPHjw5YyvQvr94FGK3SYt4quQ\n6XD6OhyRMmMYBq/c2YKkww4en72WLx7tRMOqpbsYE6ksSptP3W4348ePZ9u2bdjtdiZNmkStWrVO\nWiczM5P+/fvzzTffnHUmQJHKIvVoAbEXObWxLzSuFsanv+6l2OXGZi2/rXK2HGuN06haKGnHxsu5\nWLWjgtl6KIecAl0Pi0jlddaiy759+/j8888pKirizjvvxM/Pj3/9618nzUIkZ7YxJRuAFvHhLNqW\n7uNoRMpWoN3KlMFt6f32Mh78ZDVfPdqJCI1dJHJWpc2nCxcupKioiDlz5rBu3Tpeeukl3n//fc/y\npUuX8vrrr3P48GFvH4JIhbAvI69CjJPSJC6MwmI3SRl51I8tXdedy2HD/iwig+2XNClE7ehgAPZm\n5JVVWCIiFc5Zy+shISVJwG6343a7+fjjj1VwuUDr92cTHeJPnKbWlStUXJVApgxuy6HsAh6ZsQan\ny+3rkETKrdLm08TERLp06QJAq1at2LRp00nLLRYL06ZNIzw8vOyDFqlg8otcHMguoM6xD/nlmWcG\no4Plu4vR+uQsWsRXuaTuWtXDA/CzGupiJCKV2gXNqRcVFaWLuouwoQySlEh5deLMR7e1rM68NckM\nmvobt7Wsft7HDuyQ4M3QRMq9i8mnubm5noINgNVqpbi4GJutJHV36tTJKzGKVER7Dpd8qK8TE8zR\n/GIfR3Nu9WNDsFkMfj94lN4XkDt9wVFYzM60XG5uFndJ27FZLNSM0LguIlK5nbXosnPnTp566ilM\n0/T8fdzrr79+WYKriByFxexMz6VXi0tLUiIVQZtaERw6WsAvOw9TLSxAsxOInEFp82lISAgOx/8+\nqLjdbk/BRUROdrzoUjc6hHX7s3wczbn526zUiwkp1y1dNqVk4zahZc1LnxSidnQwP29NI6fAWa6n\n8xYR8ZazXr298cYbnr/79+9/WYK5EmxKycY0oWW8WgZJ5XBTs2qk5RTw9foUokLs1Ispv/3TRXyh\ntPm0TZs2/Pzzz9xyyy2sW7eOhg0beiM8kSvC7vRcAGpHB5X7oguUdDH6dXemr8M4qw3J/xuf8FLV\njgrGBNbsy6Jbw5hL3p6ISEVz1qJL+/btL2ccV4zjSaq5pouWSsJiGNzdLoEPluxixsq9DO1aj6ph\nGs9I5LjS5tMbbriBZcuW0b9/f0zT5IUXXmDatGkkJCTQo0ePMo5SpGLbc9hBXJUAguwVozVYk7gw\nvlx3gCOOonI5GP365CxqhAcSHXLps0HVjAzEYsCqPZkquohIpVQxMlMFUpZJSqSiCLRb+dO1tflg\n0S4+WZHE8G711IRY5BJZLBYmTJhw0n1nGoD3p59+ulwhiZRbuw87qBtT/gfRPa5xXBgAWw4epVP9\naB9Hc7r1yVll0rUISrpTVQ8P5Lc95bdlj4iIN5119iIpnXX7SwbRFalsIoLsDO5YG0dhMZ+sSKLQ\n6fJ1SCIiUgmYpsnu9NwKMXPRcW0SwrHbLPywJdXXoZwm01HE/sz8MuladFztqGDWJWdRWKxrAxGp\nfFR0KUMHs/NJPpJPu9oaTFQqpxoRgQxsn8Ch7AKmr9yrqaRFRMTrMh1FHC0opk50xRlTLDTAjz80\nimX+xoO43KavwznJhuSSMXHK8kvEOtHBFBW7+WXH4TLbpohIRaGiSxk63myyvYouUok1qhbGnW3i\n2Z3uYM6q/eXuYlJERK4snpmLKlD3IoBbW8aRnlNY7rrdbEjOxjCgeY2yK7o0qBpCjfBA3v15J6ap\n6wIRqVxUdClDq5IyCbZbaRIX6utQRHyqdUIEt7aIY8vBo3yxNhm3LrBERMRLdqcfny66YhVd/tA4\nliC7lW82HPB1KCfZkJxFvZiQMh2bzWaxMKx7Pdbsy2L5rowy266ISEWgoksZWrXnCG1qRWCz6rSK\nXFsvmh5NYlmzL4sv1qao8CIiIl6x+7ADP6tBfESQr0O5KEF2G9c3qcp/Nh4sN91xTdNk3f5sr4xP\n2K9tPFXD/Hnrxx1lvm0RkfLMK9UBt9vNuHHjuPvuuxk0aBB79+49afncuXPp27cvd911Fz///DMA\nmZmZ3H///QwcOJARI0aQn58PwKRJk+jbty+DBg1i0KBB5OTkeCPkS5aVV8S21Bx1LRI5QY/GVflD\n41gS9x7hy7UpuNXVSEREytju9FxqRQVjtRi+DuWi9W5ZnSN5TpbtLB9jnexIy+VwbiFtEiLKfNsB\nflaGdq3Hyj2ZrNyt1i4iUnl4peiycOFCioqKmDNnDk899RQvvfSSZ1l6ejrTp09n9uzZTJ06lcmT\nJ1NUVMR7773HrbfeysyZM2natClz5swBYPPmzXz00UdMnz6d6dOnExpaPrvurE46AsDVdVR0ETlR\nj8axXNcohtV7j/D0vA0Ul5Nv80RE5Mqw57CjQs1cdKKuDaMJDbDx7YaDvg4FgC/XpmC1GPS8qppX\ntj+gfQLRIXZe/M9WdqSWzy9SRUTKmleKLomJiXTp0gWAVq1asWnTJs+yDRs20Lp1a+x2O6GhoSQk\nJLB169aTHtO1a1eWL1+O2+1m7969jBs3jv79+zNv3jxvhFsmViVl4mc1aFWz7KbXE7kSGIbB9U2q\n0qNJLJ+tSWb4jDUUaDppEREpAy63yd6MvAo3iO5x/jYrPa+qxn83HSL5SJ5PY3G7Tb5ad4BO9aOJ\nCfX3yj4C7VZG39yELQeOcsPfl3DXBysY//Vmnpizjof/tZqZK/fpGkFErjg2b2w0NzeXkJD/Tdtn\ntVopLi7GZrORm5t7UmuV4OBgcnNzT7o/ODiYnJwc8vLyuPfee7nvvvtwuVwMHjyYZs2a0bhxY2+E\nfUl+S8qkRXw4AX5WX4ciUu4YhkGPxlXp2iCG577ezJ+m/caHg9pRJbDsBukTEZHKJ+VIPkUud4Ub\nRPdEw7rVZcHmQwya+hv/HtaR6BDvFDzOJ3HfEVKy8hnZs6FXtj9z5T7P3yN7NmLN3iOs3pvJhpQs\nAv2sGIbBgi2p/G3+FjrVj6ZrwxgsxsldxgZ2SPBKbCIi3uSVoktISAgOh8Nz2+12Y7PZzrjM4XAQ\nGhrquT8gIACHw0FYWBiBgYEMHjyYwMBAAK655hq2bt1a7oou+UUuNiZn82CXur4ORaRcG3JtbaoE\n+jHy3+vp+94ypg65mtoV+EJZRER8a/fhXADqRIecZ83yq35sKB//6WrunbqSIR//xuyHrynTmYMu\n1JdrUwj0s3JjU+90LTpRiL+Nrg1j6NowxnOfaZrsOexg6Y7DLNiSSrHb5PomVb0ei4iIt3mle1Gb\nNm1YsmQJAOvWraNhw/9VzFu0aEFiYiKFhYXk5OSwa9cuGjZsSJs2bVi8eDEAS5YsoW3btiQlJTFw\n4EBcLhdOp5M1a9Zw1VVXeSPkS7J2/xGK3Sbt65T9oGMiV5rbW9fg0wc7kOEo4vb3lvGrBtMTEZFS\n8kwXXUG7Fx3XrnYkH9zblm2Hcujz7jI+S0y+rDMaFRW7mb/xIDc0rUqwv1e+kz0vwzCoGxPC4I61\naJMQwU9b09h68KhPYhERKUteKbrccMMN2O12+vfvz4svvsiYMWOYNm0aP/74IzExMQwaNIiBAwcy\nZMgQnnjiCfz9/Rk+fDjz58+nf//+rF27lnvvvZd69erRu3dv7rrrLgYNGkSfPn1o0KCBN0K+JL/t\nycQwoG0tDaIrciGuqRvFl490IirYzj0freSDxbs0s5GIiFy03/ZkElclgKhgu69DuWTdG8Xy0ZB2\n+FksPPXv9XR/dRHzL9MAu4u3p5OV5+T21tUvy/7OxTAM+rSqTvXwAOYm7icjt9DXIYmIXBKvlLIt\nFgsTJkw46b569ep5/r7rrru46667TloeHR3N1KlTT9vWQw89xEMPPeSNMMvMT1vTaBkfrvEpRC5C\n7ehgvni0E6M/28BL/9nKyt0ZvH5XKyKvgAtnERHxPqfLzbKdh+nVIg7DqHjTRZ9J90axdGsYw6Jt\n6Uz+YTuPzlzD4u3xPNf7Kq+1QDFNk1m/7SMy2E6XBjHnf8Bl4Ge1cE/7Wrzz805m/baPR66rf9r4\nLiIiFYVv2g9eQQ5m57MhOZtnbmrk61BEyr0TB9E7rlO9aKwWC99tPEi3V37m9tY1aBIXdtp6GjxP\nREROtHZfFjmFxXRvVD4KBWXFMAyuaxxL5wbRPPSv1fx7dTI//p7G/Z3rEBF08V9MnC9//n3hDn7a\nmsbTPRvhZ/VKI/hSiQi2c3vrGsz6bR+rkjLpUCfK1yGJiJRK+XlnraAWbkkF4MamGuhLpDQMw6Bj\n3Sge6V6PkAAb03/dy9zV+8krLPZ1aCIiUo4t3p6G1WJwbf1oX4fiFX5WCzc2rcYDXergKCpm2rIk\nHGWcG2es3MtbP+7grnbxPNK93vkfcJk1qx5GnehgftiSSn6RppIWkYpJRZdLtGBLKnWig6kXU3FH\nzRcpD+KqBDK8ez3+0DiWDclZvP7Ddn7dnYFLY72IiMgZLNqWTtuECMJ8MNPP5VQ3OoTB19QmK6+I\nT1YkUVh86cWHHak5vPLfrYz9chPXNYrhb3c0L5ddtAzD4NYWceQXuVj4e6qvwxERKRV1L7oERwuc\n/Lo7g/s61SmXiUqkorFZLFzfpCrNalTh2/UH+Hr9AVYlZXJrC98P7CciIuVHWk4Bmw8c5emelaN7\nd+3oYAa0T2DGyr3MXLmPwR1rY7Vc3LXn3gwH3244yDfrD7D1UA4WA3o0qcqb/VuVq25Fp4qrEkj7\nOpGs3JPBtkM5NKoW6uuQREQuiooul2DRtnScLlNdi0TKWLWwAB7oXIdNB47y3caD/GPpblKPFvDs\nLU2oViXA1+GJiMhZTPp2Cz9tTSMr30lsqD+xoQG0qx1BdIj/BT3+QsfvWrr9MADdGl5Z47mcS5O4\nMG5vVYPP16bw2Zpk/tg2/oIGl12/P4tJ87ewKukIAO1qRfD8bVdxc/NqxIZWjJx6Q5OqbEjO5i9f\nbGTu0I5YLrLgJCLiSyq6XIIftqQSFWyndUKEr0MRueIYhkHzGlVoVDWUxdvT+e/mQ3y/+RBDrq3N\nsG71NMuRiEg5sjMth7FfbmbF7gzCAmwkRAVzOKeQHam5rNyTwd1X16RxtdMHSS+txdvTiQ7xp+kZ\nBl6/krWrHUluYTELtqQSbLdyS/Ozz9yUd2y9v3yxkRB/GzddVY0W8VUIPzYY78ItaZcz9EsS5G+j\nV/M45q1JZvqvexlybW1fhyQicsFUdCmlomI3i7amcXPzahfdvFNELpzdZuGGplWZ0Ocq/r5wOx8t\n3c2MX/dyf+c6PNilrqZqFxHxsaU70nnk0zX42Sz0ah5H+zqRnu4qWXlFfPrrXqav2Mv1TavSvWHM\nJXfJdrlNluxI5w+NYytli4duDWPIKSxm2a4MTEpagfj7WT3L3aZJ4t4jfL/5EAVOF9fWi6JHk6oE\nnLBORdQ6IZy03EJe/u9WejSJJT4iyNchiYhcEBVdSmnx9nRyCou5sWk1X4ciUinUjAxi8l2teKR7\nPf7+ww7e/mknnyxPYmi3egzqWOuKH0hRRKSsud0mG1KyWbbzMMt2HsZiGFxdO5L2dSK5unYEtgsY\n5+PTX/fy3NebaRAbwtQ/Xc3ibeknLQ8PsvNw13p8uS6FH7akUlTspudVl3bt9PPWNLLynHRvFHtJ\n26moDMOgV/M43G6T5bsy2JCcTY8msQTZbWQ6ith8IJvkI/nUjgritpY1rphuuYZh8MIdzej59yWM\n+Xwj/7q/vcZUFJEKQUWXUvpo6W6qVwmgW6PK05dYpDyoHxvKu/e04ZED2UxesJ1Xv9/G+4t2MbBD\nAvd1qk1clUBfhygiUu7ty8hj5L/X81tSJlAyXkhWXhHLdh7GBKKC7fyhcSwta4afcdyQvMJivlp/\ngI0p2TSqGsrd7WqeVnA5zm6z0K9tPHabhcXb0wmyW+nSoHTXT47CYk+R56ZLLN5UZBbDoE+rGrRJ\niGD+xoN8te6AZ1l4oB/92sbTqmb4FVeUiI8IYtTNjRn31WZe/M9Wxtzc+Io7RhG58qjoUgrr92ex\nck8mf+3VpFyP9i5yJbuqehWm/ulqNiZnM2Xpbj5aupupv+yhR+NYBnRIoGuDGHX9ExE5hWmazFm1\nn4nfbsFiGEzscxU3N48jOsSfmSv3kV/kYkdaDou3p/PvxGR+2ppG64RwmtcIJyrETqajiH2ZeXy/\n+RB5hS5ubFqVrg1jzjugq2EY3NayOvlFLv6z6RCBflba1Y686Pj//sN2UrLymTesI3abrsFqRgYx\ntGtd9mXm4We1EBlsr/DdiM7n3g612JGay5Qlu8nOc/JC3+bK9yJSrqnoUgpTlu4m1N/G3VfX9HUo\nIpVe8/gqvD2gNc/0bMSnK/cyb3UyC7akUjXMn1uax3Fri+q0rhleKfv9i4icyFFYzKjPNvDthoNc\nWy+KV/u1pEb4ya0DA+1WWsSH06xGFbYcOMqyXYf58fc0Fv6ehtVi4HKbAFQN82dIx9pUD7/w1oUW\nw6Bfu3gKnC6+WJuCyzTpUCfqgh+/MTmbj5ft4Z4OCaUq2FypDMOgVlSwr8O4bCwWgwl9riI8yI+3\nf9pJak4BQzrWpmO9qCu+4CQiFZOKLhdpf2Ye/9l4kIe61iVUY0iIXDYzV+477zq1IoN5vEcDthw8\nyvrkbP61Yi/TliURGWynY90orq0fxbX1oqkdFaTmyCLiVS63yaqkTNbtz2L9/ixyC4tpWDWURtVC\n6dog5rKPs7EzLZdhnyayOz2Xp3s2Yni3eucsRlsMg2Y1qtCsRhWy851sPpBNdp6T2LAAqob5E1cl\nsFStC2wWC/d0qMWs3/bx1boD5Be56HYBg+vuTs/lybnriArx55mbGl/0fuXKYhgGT93YiMhgO69+\nv41F29IJ9LNSKyoIl9vE5TaxWQ0C/KyEBfjRoU4k3RvFclX1MH0JIyKXnYouF2nqL3uwGAb3XVvH\n16GIyBnYrBZaxIfTIj6cAqeLrYeO4nLD8l2Hmb/xIAA1wgPpUDeS1jXDaVUzgsZxoeoqKCKlcmpB\n2DRNNh84ysLfU0nLKQQ41uXDwopdGRS7TSxGSRfJTvWjSYgMYmCHhAveX4HTxYGsfFxuk2K3SVSw\nnZhQ/7MWLTJyC3lv0S6m/7qXUH8bnz7QgWvrR1/UMVYJ9OPaehf3mHOx2yzce00t5iXuZ8GWkvN0\nfZOqRAbbT1vX5TaZtmwPr36/jQA/K+8ObKNZ6yqxU//f/G1WRt3UmD2HHWw9dJSj+cXYbAaGYeA2\nTQqcLg7nFPLLzsO8/sN2wgP9+L8eDeh/dU2C/fUxSEQuD73bXISdabnMXrWP21pVv2JGghe5kgX4\nWWlVMwKANgnhZOQWsTM9l13puSzYnMrna1IAsFkMqocHUjMikJqRQdSMCCI8yA/DMC7qw5CIVF6m\nabI9NZcffj/EgawCYkL8uatdTRrEhng+3LlNk/ScQhL3HmH13kw2pmQTHxHIuv1HaFajCjbL6cVf\np8vN7nQHO9Ny2JuZx4GsfI718PHwt1loXC2UejEh1I0JJibUn5SsAvZmOFi4JZV8p4s728Qzsmcj\nqoaVj+sXq8WgX7uaRATb+WXHYTYkZ9EmIQKLAWGBfjhdbpbvzGDpjnQOZBfQo3EsL/ZtTmw5iV/K\nDz+rhYZVQ2lYNfSs6+QWFrM9NYfVSUeY+O0W3vpxB4M71mLItbWJDvG/jNGKSGWkossFyi9y8eiM\nNQTZbTzTU81aRSoawzCIDvUnOtSfa+pGYZomWflO9mfmkXwkn/2Zeazck8myXRkABPvbqBkRSKaj\nkFY1I2hRs4qmpRaR07hNk51pufy8NY29mXlEBPnxx2Mzx5w6uKzFMKgaFsAtzePo0SSWNfuyWLHr\nMHNXJ/PN+oPUCA8kLjwAu9VCdr7TM2htsdvEZjGIjwikS4MYYkP9sVpKvs3PLXCSnluEzWKwYncG\nn69NObYviKsSyPVNq/J/f2hA/dgQX5yec7IYBjc2rcY1daJYvD2dVUmZrN57xLM8NMBGp3rRPNsr\njl7N49QtVEotxN9Gm4QI2iSUtG79cPEu3vl5J1OW7ObOtvHc0KQqrWqGE3GstZVpmiQfyWfzgWw2\nHzhKUkYeyUfySDtaSHiQHzGh/tSOCubGplVpXyfygqZXF5HKS0WXCzTuq01sT8vhk/vaq5WLyBXA\nMAwiguxEBNlpER8OlDRjP3S0gP2ZeSU/R/J5bcH2Y+tDvZgQWsaH0yohnFbx4TSoGqJB+0SuAG63\nyd7MPDYkZ7Er3YGjsJi8omKsFoOoYH+iQuye32EBfmTlF5GeU8j6/dn8e/V+cgqLCQuw0adVddrW\nijhji5VT+dusdKwbRYc6kexIzWXTgWwOZuezfFcGLrdJaICN8EA/2teJpGHVUOpEB5+zG+TxVnmO\nwmIycouoViWgwszuExboR++W1bmpWTXyilzkO12YpklsaABWi8HR/GJm/bbf12HKFWLrwRy6NYyl\naVwVftmZzpxV+z3dloL9bRS73BQVuzneoMxiQHiQnYhjxZbIYDvpOYX8ujuDfy5PIirYzu2ta3Bf\np9rERwT57sBEpNxS0eUCTP91L/9OTOb//lCfrg1jfB2OiHiJ1WJQIzyQGuGBXFO3ZEaNXs3jWJ+c\nxbr9JT8/b0vjszXJQEkhJiEyiPoxIdSvGkL9mBBqRQUTVyWA2DB//G0qyIiUR6Zpsjcjj78v3M7O\ntFx2pzvId7oAMAA/mwW71YLbNMkvcmGeZTtWi0GjqqG0qhlOo2qlGxvKYhg0qlYywC6UFH9N0yz1\nN+fB/rYKO1aFn9VClUCLxmyRyyIm1J87WsfTq3l1krPy2J+ZT0ZuIXabBX+blbBAG9WrBFKtSsBJ\n/9vHC5x5RcUs3pbOtxsO8snyJP65PIlbW8TxcNe6XFW9iq8OS0TKoYqZlS+TowVOnvtqM1+sTaFL\ng2hGXN/Q1yGJyGV2fPDd6BB/rm9SlR6NYzmS5yxpZpxTSHpOIZsOZLNoWzou8+SPZlHBdqqGBVCt\nSgBRwXaiQvyP/bYTGWwnOqTkG7OSQTZVoBG5EIXFLpIO57HncC7puUUccRSRW1iMYYDVMPCzWgjw\nsxLod+y33YrFMDicW0haTiG70nJZs+8Ih3OLgJJBYptWD6NWZBA1IgI9rSuOc7lN8oqKcRS6cBQV\nk1/kIshuJSTARpVAvzIvrpbsW91oRC4Xu81C3egQ6kZfXBe8ILuNm5vHcXPzOA5k5TNt2R5m/baf\nr9YdoHP9aB7sUocuDWJKNcuXiFxZvFJ0cbvdjB8/nm3btmG325k0aRK1atXyLJ87dy6zZ8/GZrMx\nfPhwrrvuOjIzMxk5ciQFBQXExsby4osvEhgYeMZ1vS0tp4DvN6fywaJdHDpawOM9GvDnP9TXm6aI\nYBiGp1ByIpfb5IijiCN5RWTnOzlaUDK16qHsAlKPFvD7waNk5BZR5HKfcbsh/jZPMSYq+MzFmeO3\nQwP8CPKzXtS0l6Zp4ihykZ3v5IijiMxTfnIKnBS7TYpdJdNsBtmtBNlLYooN9ScmNODYb38ViC6T\n0uTSK0FRsZucAidH8pwcyMon+Ug+ezMc7ErPZWdaLvsy804bSNZ27H/BbZqnLTuR1TAID/IjITKI\nTvWjqRMVfM6Zf6CkCBIa4EeoxnQSkbOoHh7IX3o15c9/aMCs3/Yxbdke/jRtFbGh/vRqEceNTavR\nsmYVguz6vluks+pavQAAIABJREFUMvLKf/7ChQspKipizpw5rFu3jpdeeon3338fgPT0dKZPn85n\nn31GYWEhAwcOpFOnTrz33nvceuut9O3blylTpjBnzhx69ep1xnXt9tOnFLwUm1Ky+WlrGvsz89iZ\nnsu6/VmYJjSuFsrbAzvSJiGiTPcnIlceq+V/A/WeqNkJTYxN06Sw2I2jsJjcwmPfnBcW4yg6fruY\nnPxiDmUXkFtY8o168Vk+QRoGBNtthPjbCAmw4We1YDFKuioYBmQ6ijBNyHe6KDj2c7YPowYlMz1Z\nLQYWo+RbP0ehy9Pd4lShATZiQ/2JDQ0gJtSf2GPHHWy34m+z4u9nwf9Y82yrpWTaTj+rhQ4abPCi\nlCaXlnV+NE2TTSlHPUU5l2niPjZVsfvYbZfbpNDpPuG15qag+H9/FzpdFBS7yC86cdmx+50uCord\nOIvduMyS7RYVn16YtFst1IkO5qrqVbitZXXqxYaw/VAuoQE2gvytJ42h4jZLiodOlxuny02xqyTO\nYH8bQcdavYiIeEOVQD+GdavH/Z3q8MOWVL5en8KMlfuYtiwJq8WgYdVQ6seGEFclgGphAYQGlHQH\nPH6/iFyZvFJ0SUxMpEuXLgC0atWKTZs2eZZt2LCB1q1bY7fbsdvtJCQksHXrVhITExk6dCgAXbt2\nZfLkydSsWfOM67Zo0aJM4335v1tZuuMwVcP8SYgMYkSPhtzcvBoNYkM0Ur6IlBnDMAjwsxLgZyXq\nAqaoNE2TAqfbU5DJLSwmr8hF4bEPrUXFJR9YC50uXG4TlwnFpomJSZC9pDVKVIidQD9ryY+9ZN/B\nduuxD6A2gv1L7jvxg+jx/uout0mmo4i0nAJPV6r0nELSjhaQnltI2tFC1idnkXa08KwFmhP9/e6W\n3NE6vpRnr/IpTS4t6/y4Pjmb299ddtGPs1kMAv2s+PtZCTjWzSfAz0KAzUqw3UZBUSHB/jbCg+z4\nWY1jBb+SH38/CwE2C4F2K1UCSwavDAv0O+k16ih0USMi8Iz7thgGdptRYQaRFZErj91moVeLOHq1\niONogZPVSZms25fFuuRsNiZn8f3mgpMKzNEh/qz+6/U+jFhEvMkrRZfc3FxCQv5XrbVarRQXF2Oz\n2cjNzSU0NNSzLDg4mNzc3JPuDw4OJicn56zrnkliYmKp4x3RysaIVtVOuOcoOclHOTZW5iVppFaE\nInIp/IAzToZgOfZzsVzHforOukZiYvpp94UBYQbUCzt2AwD/Yz8XyJ1KYmLqha9fyZUml57JpeRH\ngM/6VTv/ShftfMnRBIqP/ciFONP/7cXSNUvZ0nNyZSrN81oF6BYF3aKsHMuoZ9jupb1Xi0j55ZW3\n8pCQEBwOh+e22+3GZrOdcZnD4SA0NNRzf0BAAA6Hg7CwsLOue6q2bdt64zBERER8pjS59FTKjyIi\nIiK+5ZW2t23atGHJkiUArFu3joYN/zfrT4sWLUhMTKSwsJCcnBx27dpFw4YNadOmDYsXLwZgyZIl\ntG3b9qzrioiIXOlKk0tFREREpHwxTNM8xzj/pXN8xoXt27djmiYvvPACS5YsISEhgR49ejB37lzm\nzJmDaZoMHTqUnj17cvjwYUaNGoXD4SAiIoLXX3+doKCgM64rIiJypStNLhURERGR8sUrRRe5ME6n\nk2effZaUlBSKiooYPnw49evXZ/To0RiGQYMGDXjuueewWDQY4IXKyMigb9++fPzxx9hsNp3LUvrw\nww/56aefcDqdDBgwgPbt2+tcXiSn08no0aNJSUnBYrEwceJEvSal3LtSpqk+U37t0aOHZ/m0adOY\nN28ekZGRADz//PPUrVvXV+Ge1+233+7pPhYfH8+LL77oWVZRnhOAzz//nC+++AKAwsJCfv/9d5Yt\nW0ZYWMn4FpMmTWLNmjUEBwcD8N57752x25wvrV+/ntdee43p06ezd+/ec76nFxQU8PTTT5ORkUFw\ncDAvv/yy5zVXHpx4LL///jsTJ07EarVit9t5+eWXiY6OPmn9c70Ofe3EY9m8eTPDhg2jdu3aAAwY\nMIBbbrnFs255fl5OPI4nnniCw4cPA5CSkkLLli35+9//7lnXNE26du3qOc5WrVrx1FNP+SJsETkf\nU3xm3rx55qRJk0zTNM3MzEyzW7du5tChQ81ff/3VNE3THDt2rLlgwQJfhlihFBUVmY888oh54403\nmjt37tS5LKVff/3VHDp0qOlyuczc3Fzzrbfe0rkshR9++MF87LHHTNM0zV9++cX885//rPMo5d73\n339vjho1yjRN01y7dq05bNgwz7K0tDTz1ltvNQsLC82jR496/i6PzpRfT/TUU0+ZGzdu9EFkF6+g\noMDs06fPGZdVpOfkVOPHjzdnz5590n39+/c3MzIyfBTR+U2ZMsW89dZbzX79+pmmaZ73Pf3jjz82\n33rrLdM0TfPbb781J06ceHkDPodTj+Wee+4xt2zZYpqmac6aNct84YUXTlr/XK9DXzv1WObOnWtO\nnTr1rOuX1+fl1OM4Lisry7ztttvM1NTUk+5PSkoyhw4dejlDFJFS0lesPnTTTTfx+OOPe25brVY2\nb95M+/btgZKps5cvX+6r8Cqcl19+mf79+xMbGwugc1lKv/zyCw0bNuTRRx9l2LBhdO/eXeeyFOrU\nqYPL5cLtdpObm4vNZtN5lHLvQqepDg0N9UxTXR6dKb+eaPPmzUyZMoUBAwbw4YcfXu7wLsrWrVvJ\nz8/n/vvvZ/Dgwaxbt86zrCI9JyfauHEjO3fu5O677/bc53a72bt3L+PGjaN///7MmzfPhxGeWUJC\nAm+//bbn9vne00/8f+ratSsrVqy4fMGex6nHMnnyZJo0aQKAy+XC3//kmenO9Tr0tVOPZdOmTSxa\ntIh77rmHZ5999rSZ3crr83LqcRz39ttvc++993qub4/bvHkzqampDBo0iIceeojdu3dfrlBF5CKp\n6OJDwcHBhISEkJuby2OPPcaIESMwTRPDMDzLc3JyfBxlxfD5558TGRnpSaKAzmUpHTlyhE2bNvHm\nm2/y/PPPM3LkSJ3LUggKCiIlJYWbb76ZsWPHMmjQIJ1HKffONk318WUXOk21r50pv56oV69ejB8/\nnk8++YTExER+/vlnH0V6fgEBATzwwANMnTrV855cEZ+TE3344Yc8+uijJ92Xl5fHvffey6uvvspH\nH33EzJkzy10BqWfPnp4ZxOD81xknPj/l7T3/1GM5/oF+zZo1fPrpp/zpT386af1zvQ597dRjadGi\nBc888wwzZsygZs2avPvuuyetX16fl1OPA0q6za9YsYK+ffuetn5MTAwPP/ww06dPZ+jQoTz99NOX\nK1QRuUgquvjYwYMHGTx4MH369KF3794n9QU+PnW2nN9nn33G8uXLGTRoEL///jujRo0iMzPTs1zn\n8sKFh4fTuXNn7HY7devWxd/f/6QLEp3LC/PPf/6Tzp078/333/PVV18xevRonE6nZ7nOo5RHZTFN\ndXlxan49zjRNhgwZQmRkJHa7nW7durFlyxYfRnpuderU4bbbbsMwDOrUqUN4eDjp6elAxXtOAI4e\nPcru3bu55pprTro/MDCQwYMHExgYSEhICNdcc025K7qc6nzXbCc+PxXhPf+7777jueeeY8qUKaeN\ncXKu12F5c8MNN9CsWTPP36f+f1ek5+W///0vt95662mt9QCaNWvmGauqXbt2pKamYmqoTpFySUUX\nHzp8+DD3338/Tz/9NH/84x8BaNq0KStXrgRKps5u166dL0OsMGbMmMGnn37K9OnTadKkCS+//DJd\nu3bVuSyFtm3bsnTpUkzTJDU1lfz8fDp27KhzeZHCwsI8H36qVKlCcXGx/r+l3LtSpqk+U349Ljc3\nl1tvvRWHw4FpmqxcudLzAa08mjdvHi+99BIAqamp5ObmEhMTA1Ss5+S4VatWce211552f1JSEgMH\nDsTlcuF0OlmzZg1XXXWVDyK8cOd7T2/Tpg2LFy/2LG/btu1lj/FCffXVV57rqJo1a562/Fyvw/Lm\ngQceYMOGDQCsWLHitNdRRXpeVqxYQdeuXc+47J133uGTTz4BSrp/Va9e3dPySkTKF81e5EOTJk3i\nP//5z0kzJvzlL39h0qRJOJ1O6taty6RJk85Y3ZazGzRoEOPHj8disTB27Fidy1J45ZVXWLlyJaZp\n8sQTTxAfH69zeZEcDgfPPvss6enpOJ1OBg8eTLNmzXQepVy7UqapPlN+7devH/n5+dx99918+eWX\nTJ8+HbvdTseOHXnsscd8GO25FRUVMWbMGA4cOIBhGIwcOZL169dXuOfkuI8++gibzebpvjJt2jTP\nsfzjH//gv//9L35+fvTp04cBAwb4NtgzSE5O5sknn2Tu3Lns2bPnjO/p999/Px988AEul4tRo0aR\nnp6On58fr7/+erkqVBw/llmzZtGxY0fi4uI8rT6uvvpqHnvsMZ555hlGjBhBdHT0aa/DNm3a+PgI\n/ufE52Xz5s1MnDgRPz8/oqOjmThxIiEhIRXieTnxOKCkK+SsWbNOao1z/Djy8/N5+umnycvLw2q1\nMm7cOOrVq+er0EXkHFR0ERERERERERHxAnUvEhERERERERHxAhVdRERERERERES8QEUXERERERER\nEREvUNFFRERERERERMQLVHQRERERETmPAwcO+DoEERGpgFR0EakAsrKy6Ny5M4mJib4ORURE5KyO\nT9cNcPjwYe6//37y8vK8tr/nnnuOsWPHUlxcDMDnn3/OwIEDGTlyJCNHjmTKlCknrT969GjS09NL\nta9x48YB8MEHH5CVlXVpgYuISKVh83UAInJ+c+bM4fHHH+fjjz+mVatWPP/889jtdnbu3Em/fv2I\niIjghx9+wOVy0bx5c/r16+frkEVEpBIaNWoUw4YNo3nz5rz22ms8//zz+Pv789JLL1FcXExOTg6j\nR4/mu+++Y8eOHaSlpdG7d28iIyN5++23adq0KbVq1SIpKYn09HSeeOIJatWqBcChQ4d46aWXiIyM\nxN/fn969e7Ns2TIefPBBbLb/XdLec8899OrVy3M7JyeHiRMnEhERwYYNGwB44IEHmDp1KuvWrWPp\n0qX069ePN998Ez8/P2rWrMn111/Pu+++S2BgIIWFhfz5z38mKSmJb775hqSkJJxOJ1OnTiUpKQmH\nw8FDDz3EwoULyczMJCQkBIvFwhNPPHF5T76IiJRLaukiUs45nU4WLFjAHXfcgcvlYtasWdSrV4+/\n/vWvdOzYEYAPP/yQ4OBgwsLC+PXXX30csYiIVFZ+fn5MmjSJoUOHcuedd1KzZk1++eUXdu7cSUBA\nABaLhfXr19OsWTNuueUWWrVqxZIlSwBo1aoVzz77LMnJyQQFBdG3b19iYmI8254+fToPP/ww48aN\nw+l04ufnR61atejfv/9JMcyaNYvRo0czevRoFixYwPz58+nZsydjxoyhYcOGZ4x75syZDBkyhAkT\nJtCxY0eCgoLo168fHTt2ZM2aNSQkJFCrVi169+4NgNvtZu3atUycOJGxY8fywQcfAHDjjTfy1FNP\nsX79em+cXhERqYDU0kWknPvPf/6D2+1mwoQJuFwu3nnnHR555BEALJaSuqnL5WL48OEEBATw+eef\n+zJcERGp5OLi4qhWrRpdunQBwDRNmjdvzuOPP86qVauIjIxkwoQJDBkyhObNm7N7924AwsLCALjp\nppsIDg7ms88+Y9++fdx7771ASaHjeN4zDAPTNM+4/wEDBpzU0mXOnDmev61WqycmwNNNyOl0era9\nd+9efvvtN/Ly8rj++usJDw8/bR9utxvDMICSXHx8e4GBgSftR0REREUXkXJu7ty5TJkyhZiYGEzT\nZMCAASxcuJCDBw+ybt06HnzwQYYOHcqYMWOwWq1cd911vg5ZRETEo3PnzsyfP5/nn3+ejIwMXnjh\nBSIjI1mxYgVFRUXk5uaetP7atWvZtm0bLpeLnj17eu6/9957mTx5MrGxsQQFBZ211cqMGTP4+eef\nAYiJieHRRx/lb3/7G6tXr2bnzp0ANG/enDFjxmCz2YiNjWXgwIG8+eabBAYGUrt2bapWrcqCBQvI\nysqioKCAI0eOULNmTaZOnQqAzWajRYsWTJgwgfz8fB599FEWLFjgjdMnIiIVnGGe7WsCESmXsrKy\neOutt/D398fhcPDMM88QEhLi67BERERERETkFCq6iIiIiIiIiIh4gQbSFRERERERERHxAhVdRERE\nRERERES8QEUXkQuQnJxMo0aNPDMonGj06NE0atSIzMzMi9rmm2++yZdffgnAO++8w8KFC8/7mLPt\nJy0tjREjRtC7d2969+5Nv379TtrehW7/QtcTERG5HBo1akTv3r3p06cPt99+Oz179uTOO+9k48aN\nvg5NRETkgmj2IpEL5O/vz549e0hJSaFGjRoA5OXlsWbNmlJt7/HHH/f8vXLlSurXr1/q2P76179y\n7bXX8sYbbwCwc+dOBgwYQJ06dahXr94Fb/9S4xARESlrn3zyCZGRkZ7bU6dOZdKkSSdNBS0iIlJe\nqegicoGsVis333wz33zzDcOGDQNgwYIF9OjRg48//hgAt9vNCy+8wPr163E4HJimyaRJk2jbti2j\nR48mKyuL/fv30717dzIyMmjQoAEBAQFs2rSJV155BavVSv369ZkwYQIOh4P09HQaN27MG2+8gb+/\n/1ljS09Pp6CgALfbjcVioX79+rz//vuEhYUxY8aMC9r+vHnzTlrvxx9/pEGDBjzwwANASYue47dn\nzpzJ7Nmz8fPzw9/fnwkTJqhYIyIiXldcXMzBgwepUqUKAIcPH2bcuHFkZGSQnp5OjRo1eOONN4iK\nimLPnj2MGzeOzMxMLBYLw4cP55ZbbiE1NZUJEyZw8OBBnE4nvXr18uR1ERGRsqbuRSIX4fbbb+er\nr77y3P7yyy+54447PLfXr19PWloac+bM4bvvvuOOO+7gH//4h2d5QUEB8+fP5+mnn/bcd88999Cs\nWTOeeeYZbrjhBubOncvtt9/O3LlzWbBgAcnJySxatOiccT3zzDN8+umndOzYkeHDh/PRRx9Rs2ZN\nYmJiLnj7p653Ni6XixdeeIGPPvqIzz77jLvuuovExMRSnE0REZHzGzJkCL1796Zz58707NkTgBdf\nfBGA+fPn06pVK+bMmcOPP/5IQECAJ08/+eST3HTTTcyfP58pU6YwefJkcnNzefrpp7nzzjv5/PPP\nmTdvHsuXL+e7777z2fGJiMiVTS1dRC5Cs2bNsFqtbNq0iaioKBwOBw0bNvQsb926NVWqVGH27Nns\n37+flStXEhwc7Fnetm3b8+7j6aefZtmyZfzjH/8gKSmJtLQ08vLyzvmYjh07smjRItatW8fq1av5\n+eefeffdd/nkk09o0aLFJW//RFarlZtuuon+/fvTvXt3OnfuTLdu3S748SIiIhfjePeizZs38/DD\nD9OhQweioqKAkoLM6tWrmTZtGklJSezYsYOWLVuSlZXF1q1b6devHwBxcXEsXLiQvLw8Vq1aRXZ2\nNm+++SZQ0lV469at3HLLLT47RhERuXKp6CJykW677Ta+/vprIiMj6dOnz0nLFi1axN/+9jfuu+8+\nevToQd26dfn66689y4OCgs67/SeffBKXy8XNN99M9+7dOXjwIKZpnnX9jIwM3n77bcaOHUu7du1o\n164dw4YN4y9/+QtffvnlaUWXC92+YRgn3e90Oj1/v/baa2zfvp3ly5czZcoUvvrqK8/Fq4iIiDdc\nddVVjBkzhtGjR9OkSRPi4+N59dVX2bBhA3feeScdOnSguLgY0zSx2UoucQ3D8Dx+9+7dxMTEYJom\ns2fPJjAwEIDMzMxzduEVERG5FFdE0UVdG8Tb0tPTcblcJCYmUqdOHaZNm0ZISAh//etfPa+/9evX\n8+WXX3LVVVfRqFEjioqK+Oyzz8jLyyMxMZGMjAwCAwM965942+FwsGPHDmJiYli8eDFjx46latWq\nbNmyhTVr1tC0adOT9hMWFuaJzeVy8dNPP2GxWLjlllswDIPCwkK2bNlC586dL2r7J65XVFTE0qVL\nad26NUeOHGHZsmWEhYXx888/8+yzzzJx4kSaN2+OxWJhypQp+j+Uy+ZCWoxJCf1fypXgxLwXFxdH\n7dq1GTVqFE8++SQ//PADd955JzVr1iQlJYVFixbRuXNntm3bRu3atXnzzTe57rrryMjIYMyYMbzy\nyivUr1+fv/3tb9xxxx04HA7GjRvHHXfcQefOnX18pCKXTjlSpPy5IoouoDcY8a7k5GSsVqvndTZz\n5kxCQ0NP6lbTsmVLEhISeOqppxg/fjzFxcV06tSJBQsW0Lp1a6KiooiPj/ds48Tbt99+O//85z+J\nj49n5MiRvPvuuwQFBRESEsI111yDYRiex7Vs2fKkWRwAZsyYwauvvsozzzxDUFAQhmFwxx13cP/9\n9wNc8PZPXW/kyJE8++yzxMfH06VLF+Lj47nuuut4/PHHmTx5MgEBAVitVl555RX9D8ploSLCxdP/\nplR0p+a9V199ldtuu428vDxGjhzJa6+9xrfffoufnx/XXHMNTqeTtm3b8sEHH/D888+zZMkSDMPg\n5ZdfpkePHjRq1IiJEycyfvx4ioqKuPPOO/m///s/Hx6hSNlQjhQpnwzzXP0WKojExERdVIqIVAJ6\nv784Ol8iIpWH3vNFyifNXiQiIiIiIiIi4gUquoiIiIiIiIiIeIGKLiIiIiIiIiIiXqCii4iIiIiI\niIiIF1wxsxddqtqj55fZtpJe6lVm2xIREfGlssyPF0I5VERERK4kaukiIiIiIiIiIuIFKrqIiIiI\niIiIiHiBii4iIiIiIiIiIl6goouIiIiIiIiIiBeo6CIiIiIiIiIi4gUquoiIiIiIiIiIeIGKLiIi\nIiIiIiIiXmDzdQAiIiJybk6nk9GjR5OSkoLFYmHixInYbDZGjx6NYRg0aNCA5557DotF36WIiIiI\nlCcquoiIiJRzixcvpri4mNmzZ7Ns2TLeeOMNnE4nI0aMoEOHDowbN44ff/yRG264wdehioiIiMgJ\n9JWYiIhIOVenTh1cLhdut5vc3FxsNhubN2+mffv2AHTt2pXly5f7OEoREREROZVauoiIiJRzQUFB\npKSkcPPNN3PkyBE++OADVq1ahWEYAAQHB5OTk+PjKEVERETkVCq6iIiIlHP//Oc/6dy5M0899RQH\nDx5kyJAhOJ1Oz3KHw0FYWJgPIxQRERGRM1H3IhERkXIuLCyM0NBQAKpUqUJxcTFNmzZl5cqVACxZ\nsoR27dr5MkQREREROQO1dBERESnn/vSnP/Hss88ycOBAnE4nTzzxBM2aNWPs2LFMnjyZunXr0rNn\nT1+HKSIiIiKnUNFFRESknAsODubNN9887f5PP/3UB9GIiIiIyIVS9yIRERERERERES/watElNzcX\nh8PBl19+SXZ2tjd3JSIiUmEoP4qIiIhUDl7rXvTMM8/QqVMn1q5di9vt5ocffuDdd9/11u5EREQq\nBOVHERERkcrDay1dUlJS6NOnD7t27WLChAnk5uZe0OMyMjLo1q0bu3btYu/evQwYMICBAwfy3HPP\n4Xa7vRWuiIjIZVHa/CgiIiIiFY/Xii5Op5PvvvuO+vXrk5mZSVZW1gU9Zty4cQQEBADw4osvMmLE\nCGbOnIlpmvz444/eCldEROSyKE1+FBEREZGKyWtFlwcffJDvv/+eoUOHMn36dEaMGHHex7z88sv0\n79+f2NhYADZv3kz79u0B6Nq1K8uXL/dWuCIiIpdFafKjiIiIiFRMZT6mS1FREQDdu3ene/fuAAwf\nPvy8j/v888+JjIykS5cuTJkyBQDTNDEMAyiZLjMnJ6eswxUREbksSpsfRURERKTiKvOiy0033eQp\nlEBJ4QTAMIxzdg/67LPPMAyDFStW8PvvvzNq1CgyMzM9yx0OB2FhYWUdroiIyGVR2vwoIiIiIhVX\nmRddfvrpp1I9bsaMGZ6/Bw0axPjx43n11VdZuXIlHTp0YMmSJVxzzTVlFaaIiMhlVdr8KCIiIiIV\nl9emjP7xxx+ZOXMmTqcT0zTJysrim2++uahtjBo1irFjxzJ58mTq1q1Lz549vRStiIjI5VEW+VFE\nREREKgavFV3effddxo4dy+zZs+nQoQPLli274MdOnz7d8/enn37qjfBERER84lLyo4iIiIhULF6b\nvSgiIoLWrVsD0LdvX1JTU721KxERkQpD+VFERESk8vBa0cXPz49Vq1ZRXFzM0qVLSU9P99auRERE\nKgzlRxEREZHKw2tFl+eff57i4mKGDx/O3Llzeeyxx7y1KxERkQpD+VFERESk8vDamC4ul4tatWoB\nMGbMGG/tRkREpEJRfhQRERGpPLxWdHniiScwDAO3201ycjK1atVi1qxZ3tqdiIhIhaD8KCIiIlJ5\neK3oMmfOHM/fR48eZdy4cd7alYiISIWh/CgiIiJSeXhtTJcThYaGsm/fvsuxKxERkQpD+VFERETk\nyua1li533303hmFgmiaZmZl07NjRW7sSERGpMJQfRURERCoPrxVdJk+e7Pnb39+f6Ohob+1KRESk\nwlB+FBEREak8yrzo8s4775x12Z///Oey3p2IiEiFoPwoIiIiUvmU+Zgu0dHRREdHs27dOg4fPkxC\nQgLZ2dls3bq1rHclIiJSYSg/ioiIiFQ+Zd7SpX///gD88MMPjB8/HoDbbruN++67r6x3JSIiUmEo\nP4qIiIhUPl6bvejIkSOeGRl2795Nbm6ut3YlIiJSYSg/ioiIiFQeXhtI99lnn+XJJ58kNTWVmJgY\nXn31VW/tSkREpMJQfhQRERGpPLxWdGnXrh3z5s3z1uZFREQqJOVHERERkcqjzIsujz32GG+99Rad\nO3c+bdkvv/xS1rsTERGpEJQfRURERCqfMi+6vPXWWwB8/fXXREZGlvXmRUREKqRLzY8ffvghP/30\nE06nkwHgrmg8AAAgAElEQVQDBtC+fXtGjx6NYRg0aNCA5557DovFa0O1iYiIiEgpeO3q7IEHHuDx\nxx9n2bJl3tqFiIhIhVOa/Lhy5UrWrl3LrFmzmD59OocOHeLFF19kxIgRzJw5E9M0+fHHH70YtYiI\niIiUhteKLl988QUPPvggCxcupG/fvrz//vve2pWIiEiFUZr8+Msvv9CwYUMeffRRhg0bRvfu3dm8\neTPt27cHoGvXrixfvtzboYuIiIjIRfLaQLoADRo0oFWrVuzbt4/Vq1d7c1ciIiIVxsXmxyNHjnDg\nwAE++OADkpOTGT58OKZpYhgGAMHBweTk5Hg7bBERERG5SF4ruowZM4b169fTs2dPnn/+eeLj4721\nKzmH2qPnl9m2kl7qVWbbEhGprEqTH8PDw6lbty52u526devi7+/PoUOHPMsdDgdhYWHeDFtERERE\nSsFr3YtuuOEGvv32Wx5//HEVXERERI4pTX5s27YtS5cuxTRNUlNTyc/Pp2PHjqxcuRKAJUuW0K5d\nO2+GLSIiIiKlUOYtXd577z0eeeQRvv32W+bPP7mVxeuvv17WuxMREakQLiU/XnfddaxatYo//j97\n9x2QVd3/f/x5sZdAOBBUECfumUhOnJkztSzNSuu22zK3Ke4iV0O7b7NhjjQrtVKzvC0VZw7CvSUR\ntywBFZB9fn/04/qKo9K4BOL1+Eeui3Odz/sMz+c6bz7n/endG8MwmDx5MuXLl2fSpEnMnj2bSpUq\n0bFjR0uGLyIiIiIPIN+TLm3atAHgmWeeye9Vi4iIFFl/t39844037nhv2bJlfysmEREREbGsfE+6\nnDx5kpMnT+b3akVERIo09Y8iIiIixU++J10iIyMBOHToEA4ODjRo0IAjR46QlZVFjx497vm57Oxs\nJk6cSFRUFNbW1syYMQPDMBg3bhwmk4mqVasyZcoUrKwsVoZGRETEYh60fxQRERGRoivfky6jRo0C\n4KWXXmL+/Pnm9wcOHPiHn9uyZQsAy5cvJywszJx0GT58OAEBAUyePJnQ0FDat2+f3yGLiIhY3IP2\njyIiIiJSdFls2EhCQgLXr18HIDExkaSkpD9cvl27doSEhABw+fJlSpUqxbFjx2jSpAkALVu2ZNeu\nXZYKV0RE5KG43/5RRERERIqufB/pkuvf//43vXr1wsXFheTkZKZPn/7nwdjYMHbsWDZu3Mh///tf\ntmzZgslkAsDZ2ZkbN25YKlwREZGH4kH6RxEREREpmiyWdOnYsSMdO3bk6tWruLu7Y21t/Zc+N2vW\nLEaPHs3TTz9Nenq6+f2UlBRcXV0tFa6IiMhD8aD9o4iIiIgUPRZLuuzcuZPPP/88T+Jk6dKl91x+\nzZo1xMTE8Morr+Do6IjJZKJ27dqEhYUREBDA9u3badq0qaXCFREReSjut38UERERkaLLYkmXGTNm\nMH78eMqWLfuXlu/QoQPBwcH069ePrKwsxo8fT+XKlZk0aRKzZ8+mUqVKdOzY0VLhioiIPBT32z+K\niIiISNFlsaSLl5cXjz322F9e3snJif/85z93vL9s2bL8DEtERKRA3W//KCIiIiJFl8WSLiVLlmTy\n5MnUrFnTXAy3T58+lmpORESkSFD/KCIiIlJ8WCzpUr58eQDi4+Mt1YSIiEiRo/5RREREpPjI96RL\nRkYGAIMGDcrvVYuIiBRZ6h9FREREip98T7o8/vjj5uHSuQzDwGQyERoamt/NiYiIFAnqH0VERESK\nn3xPumzevDm/VykiIlLkqX8UERERKX6sCjoAEREREREREZF/IiVdREREREREREQsIN+TLnPmzAFg\n06ZN+b1qERGRIkv9o4iIiEjxk+81XUJDQylTpgxffPEFV69ezfO7Pn365HdzIiIiRYL6RxEREZHi\nJ9+TLtOnT2fnzp1kZGQQFxeX36sXEREpktQ/ioiIiBQ/+Z50qVu3LnXr1qVZs2b4+Phw/vx5ypcv\nj4eHR343JSIiUmSofxQREREpfixWSPfixYv06dOHTz75hD59+vD9999bqikREZEiQ/2jiIiISPGR\n7yNdci1ZsoRVq1bh7OxMcnIyL7zwAt27d7dUcyIiIkWC+kcRERGR4sNiI11MJhPOzs4AuLi4YG9v\nb6mmREREigz1jyIiIiLFh8VGuvj4+DBz5kwaN27M3r178fHxsVRTIiIiRYb6RxEREZHiw2IjXaZP\nn06FChXYtWsXFSpUICQkxFJNiYiIFBnqH0VERESKD4uNdLGxsaFfv36WWr2IiEiRpP5RREREpPiw\n2EgXEREREREREZHiTEkXERERERERERELsNjjRTExMbz77rskJibSsWNHqlevTr169SzVnIiISJHw\nd/rHq1ev0rNnTxYtWoSNjQ3jxo3DZDJRtWpVpkyZgpWV/pYiIiIiUphY7NvZpEmT6NWrFxkZGTRu\n3Jhp06ZZqikREZEi40H7x8zMTCZPnoyDgwMAM2bMYPjw4Xz11VcYhkFoaKglwxYRERGRB2CxpEt6\nejqBgYGYTCYqVaqEvb29pZoSEREpMh60f5w1axbPPPMMZcqUAeDYsWM0adIEgJYtW7Jr1y6LxSwi\nIiIiD8ZiSRc7Ozt27NhBTk4OBw8exM7OzlJNiYiIFBkP0j+uWrUKDw8PWrRoYX7PMAxMJhMAzs7O\n3Lhxw2Ixi4iIiMiDsVhNl5CQEGbNmkViYiKLFi1i6tSplmpKRESkyHiQ/vG7777DZDKxe/duTpw4\nwdixY0lISDD/PiUlBVdXVwtGLSIiIiIPwmJJl5ycHMaMGfN/DdnYkJmZia2t7V2Xz8zMZPz48Vy6\ndImMjAwGDx5MlSpVVCRQRET+Ue63fwT48ssvzT/379+fqVOn8u677xIWFkZAQADbt2+nadOmFo1b\nRERERO6fxZIur7zyCjExMVSqVImoqCgcHR3JyspizJgxdO/e/Y7l165di7u7u3lGhyeffBJ/f3+G\nDx9OQEAAkydPJjQ0lPbt21sqZBEREYu73/7xXsaOHcukSZOYPXs2lSpVomPHjhaMWkREREQehMWS\nLuXLl2fJkiV4eHhw7do1Jk6cSEhICP/617/u+qXy8ccfz/OF0dra+o4igTt37lTSRUREirT77R9v\n98UXX5h/XrZsmSVDFREREZG/yWLP6ly9ehUPDw8A3NzciI+Px93d/Z6PBzk7O+Pi4kJycjJDhw5l\n+PDhKhIoIiL/OPfbP4qIiIhI0WWxkS61atVi5MiR1K9fn4MHD1KjRg3+97//UbJkyXt+5sqVK7z2\n2mv07duXrl278u6775p/pyKBIiLyT/Ag/aOIiIiIFE0WS7pMmTKF0NBQIiMj6datG61bt+bMmTME\nBQXddfn4+HgGDhzI5MmTCQwMBKBmzZoqEigiIv8o99s/ioiIiEjRZbGxzElJSdy8eZMyZcqQmJjI\np59+SqVKlXB0dLzr8p988gnXr1/no48+on///vTv35/hw4czd+5c+vTpQ2ZmpooEiohIkXe//aOI\niIiIFF0WG+kydOhQKlasSEREBPb29n/6ZXLixIlMnDjxjvdVJFBERP5J7rd/FBEREZGiy6JV+956\n6y38/PxYvHgx165ds2RTIiIiRYb6RxEREZHiwaJJl/T0dG7evInJZCI1NdWSTYmIiBQZ6h9FRERE\nigeLJV369evHkiVLaNasGa1ataJSpUqWakpERKTIUP8oIiIiUnxYrKaLt7e3ufBtp06dOH78uKWa\nEhERKTLUP4qIiIgUH/medNm7dy+nT5/m888/Z8CAAQDk5OTw5Zdf8uOPP+Z3cyIiIkWC+kcRERGR\n4iffky6urq7Ex8eTkZFBXFwcACaTiTFjxuR3UyIiIkWG+kcRERGR4iffky7VqlWjWrVqPPXUU3h6\neub36kVERIok9Y8iIiIixY/Farrs3r2bTz/9lIyMDAzDwGQyERoaaqnmREREigT1jyIiIiLFh8WS\nLp999hmffPIJXl5elmpCRESkyFH/KCIiIlJ8WCzpUqFCBXx9fS21ehERkSJJ/aOIiIhI8WGxpIuD\ngwMvv/wyNWrUwGQyATBy5EhLNSciIlIkqH8UERERKT4slnRp1aqVpVYtIiJSZKl/FBERESk+rCy1\n4q5du5KVlcWFCxfw9vbWl0wRERHUP4qIiIgUJxZLukyZMoXLly+zc+dOUlJSGDt2rKWaEhERKTLU\nP4qIiIgUHxZLupw/f55hw4ZhZ2dHmzZtuHHjhqWaEhERKTLUP4qIiIgUHxar6ZKdnU1CQgImk4nk\n5GSsrCyW35FiquK4dfm2rrMzO+fbukRE/oj6RxEREZHiw2JJl+HDh/Pss88SFxdHnz59GD9+vKWa\nEhERKTLUP4qIiIgUHxZLujRp0oTFixfj4ODAxYsXqVu3rqWaEhERKTLUP4qIiIgUHxYb0zx58mTW\nrFmDh4cHa9eu5e2337ZUUyIiIkWG+kcRERGR4sNiSZcTJ07w6quvAjBx4kROnDhhqaZERESKDPWP\nIiIiIsWHxZIuhmGQmJgIwPXr18nOzrZUUyIiIkWG+kcRERGR4sNiNV2GDBlCr169cHd35/r160yZ\nMsVSTYmIiBQZ6h9FREREig+LJV2uX7/Oxo0bSUxMpGTJkphMJks1JSIiUmQ8SP+YmZnJ+PHjuXTp\nEhkZGQwePJgqVaowbtw4TCYTVatWZcqUKZp+WkRERKSQsdi3s5UrV2JtbU2pUqWUcBEREfn/HqR/\nXLt2Le7u7nz11Vd89tlnhISEMGPGDIYPH85XX32FYRiEhoZaOHIRERERuV8WG+mSkZFBjx498PPz\nM//l7f3337dUcyIiIkXCg/SPjz/+OB07djS/tra25tixYzRp0gSAli1bsnPnTtq3b2+5wEVERETk\nvlks6TJ69OgH+tyhQ4d47733+OKLLzh37pyGTouIyD/Kg/SPzs7OACQnJzN06FCGDx/OrFmzzCNl\nnJ2duXHjRr7GKSIiIiJ/n8UyGDVr1mTnzp2sWbOGpKQkPD09//Qzn332GRMnTiQ9PR1AQ6dFROQf\n50H6R4ArV67w/PPP0717d7p27ZrnjxApKSm4urpaKmQREREReUAWS7qMHz+eChUqcPbsWUqVKsWE\nCRP+9DM+Pj7MnTvX/Pr2odO7du2yVLgiIiIPxYP0j/Hx8QwcOJAxY8bQu3dv4PfkTVhYGADbt2+n\ncePGFo1bRERERO6fxZIuSUlJ9O7dGxsbGxo2bIhhGH/6mY4dO2Jj839PPBmGoaHTIiLyj/Ig/eMn\nn3zC9evX+eijj+jfvz/9+/dn+PDhzJ07lz59+pCZmZmn5ouIiIiIFA4Wq+kCEBkZCUB0dPQD1WLR\n0GkREfknut/+ceLEiUycOPGO95ctW5bvsYmIiIhI/rHYSJcJEyYwfvx4jh8/ztChQxk3btx9r0ND\np0VE5J8mP/pHERERESkaLDLSJTk5GR8fH1asWPG31jN27FgmTZrE7NmzqVSpkoZOi4hIkZZf/aOI\niIiIFA35nnRZtmwZixYtwsbGhkmTJtGiRYv7+nz58uVZuXIlAH5+fho6LSIi/wh/t38UERERkaIn\n3x8v+vHHH/npp59Yvnw5S5Ysye/Vi4iIFEnqH0VERESKn3xPutjZ2WFnZ4eHhweZmZn5vXoREZEi\nSf2jiIiISPFjsUK6wF+aBlNERKS4Uf8oIiIiUjzke02X06dPM2rUKAzDMP+c6/3338/v5kRERIoE\n9Y8iIiIixU++J10++OAD88/PPPNMfq9eRESkSFL/KCIiIlL85HvSpUmTJvm9ShERkSJP/aOIiIhI\n8WPRmi4iIiIiIiIiIsWVki4iIiIiIiIiIhagpIuIiIiIiIiIiAUo6SIiIiIiIiIiYgFKuoiIiIiI\niIiIWEC+z14kIiIiIr+rOG7dQ23v7MzOD7U9ERER+WMa6SIiIiIiIiIiYgFKuoiIiIiIiIiIWICS\nLiIiIiIiIiIiFqCki4iIiIiIiIiIBSjpIiIiIiIiIiJiAZq9SKSQyc+ZLjSLhYiIiIiISMFR0kVE\nHhollEREREREpDjR40UiIiIiIiIiIhagpIuIiIiIiIiIiAUo6SIiIiIiIiIiYgGq6SIiIiIihU5+\n1gH7K1QrTERELEFJFxGRv0kFgkVERERE5G4KfdIlJyeHqVOncurUKezs7Hj77bfx9fUt6LBEREQK\nlPpHERERkcKv0Nd02bRpExkZGaxYsYJRo0Yxc+bMgg5JRESkwKl/FBERESn8Cv1Il3379tGiRQsA\n6tevz9GjRws4IhERkYKn/lFE/ohq4jw47TsRyU8mwzCMgg7ij0yYMIEOHTrQqlUrAFq3bs2mTZuw\nsfm/fNG+ffsKKjwREXnIGjVqVNAhFArqH0VE5HbqI0UKn0I/0sXFxYWUlBTz65ycnDxfKEEXFxER\nKX7UP4qIiIgUfoW+pkvDhg3Zvn07AAcPHqRatWoFHJGIiEjBU/8oIiIiUvgV+seLcmdniIiIwDAM\npk+fTuXKlQs6LBERkQJVlPrHy5cv4+3tXdBhiIjkuytXruDl5VXQYYhIIVboR7pYWVnx1ltvsXz5\nclasWFFov1BaimEYFPK82B1y442IiCA2NraAo7k/Fy5c4MCBAwBFbr8XRzpGxVdRvDbmt4LqH196\n6SXS0tIAeOaZZ/jqq68A+Pzzz6levXqeZT/55BOSkpKYPHnyQ4mtqFi1ahV9+/Zl9OjRvPjii3z3\n3Xd/eR+99NJLFo4ufyQlJdG8efO71hWKi4tj4cKF973OF154gdGjRzNmzBgGDRrEoUOH7uvzFy9e\nvOt+vtf7fya/j8Uf7bMHMXfuXA4ePPiXlpk1a9Z9r//W83j06NHMnz//jmXCwsLyvH/8+HHWrl17\n320VBoZh8N///pdx48YxatQoJkyYwKVLl/j444+BovN/U0QevkJf06W4M5lMBR3CH8q96bk1TpPJ\nxPXr1/nwww959tlnKVOmTEGFd0+GYdx13+7YsYOjR4/SoEGDey5T1Ny+HaGhobi6utKoUSOsrAp9\n3vUOt26PyWQiPDycmJgYunTpQk5OTpHcpoctdx9GRkaSkZFBjRo1isS+u/3YS8Fo1qwZe/fupUKF\nCjRp0oQ9e/bQt29f9u7dS7ly5QgJCSEqKoq3336bs2fPEhkZydmzZ/nhhx8oWbIkGzduJDs7mzp1\n6vDUU08V9OYUmH79+tG5c2eOHDnCBx98QLly5YiKimLevHk4OjqSnp7OO++8w+LFi7l8+TLR0dFM\nnDgR+D1JMGvWLN588008PDwKeEvubsWKFQwbNoxFixZRuXJlpk2bhru7O87OzvTu3Ztz587ddXuf\neeYZWrVqRUREBC+++CL16tUzr9PGxob33nsPgAMHDpjPpeXLl2NjY4Obmxtjx45lzpw5pKWlERMT\nw7Rp03B2ds4TW1JS0h3xAH8pHltbWxYuXIiHhwc3btwgOzubd999l6ysLG7cuMG4ceMIDg6mVKlS\ndO7cmcDAwAfaZ56engwfPpxWrVpx/PhxateuzYULF2jXrh21atXi448/xsbGBnt7e9544w0ef/xx\nGjduzNChQ/H09Myz3rlz55KQkICLiwtWVlYMGTKEN998ExcXF/MsaBEREQCEhIQAcPbsWSZMmMC6\ndevyfHbEiBF51p17Huc6f/48c+fOxdHRkerVq1OlShUAvv32Wy5dukTTpk2Jjo5m7ty5xMXFYWNj\nw82bN6lYsSL79+/n/fffZ9OmTYSFhZGRkUHPnj1p1qzZX96HlrRmzRo8PT0ZOnQoAHv37qVdu3bU\nqFGDyMhIoqKi7rj+3Xq98/HxYe7cudSsWZPx48cX8NaIyMOkpEshcK+b++TkZE6ePMmRI0do3rw5\nVatWLYDo8ro1yZKYmMgjjzwCQFZWFjExMZQrV45ly5Zx4cIFsrOzuXjxIunp6djb2xdk2Hfs49yf\n4+Li8PDw4ODBg3zzzTd4enpy/fp1gEJ/A3ovt988525rcnIyZ86c4ZtvviEgIIB69ephZ2dXUGHe\nl7vdbGdnZ3P+/Hk2b95MVFQUXbp0KcgQC7V7nf/nzp1j0aJFvPfee5QtW7agwrune8WdlZXFyZMn\n2bNnD25ubgQFBVGqVKmCCrPYadu2LStWrMDd3Z1OnTqxdOlSYmJicHBwoFSpUkyaNIk1a9bw66+/\nAuDj44Ovry9du3blhRdeoE6dOgDs2bOnWCddvv76a7Zv386hQ4cYMGAAx44dw8nJiaeeeoqrV68y\ne/Zs0tLSOHjwIP/5z3+IiYnBysqK69ev89JLL/HVV18V2oRLZmYmGzZsYMWKFYSGhnL69GkSEhJ4\n7LHHqFWrlnm527cXfu97Bw8eTHh4ODt27MiTdMnKymLcuHGYTCasra0ZMmQI6enp9O7dm+joaBYt\nWkRUVBRpaWkEBwdz7tw5cnJy7ojv5s2bDxxPVFQUU6dOxdnZmRdffJFffvmF06dP4+/vT0pKCocO\nHSI1NZUJEybg6Oj4wPssNTUVX19fXn/9dUaNGkX37t3Jzs5m8eLF5hG5Tk5OnDt3jtjYWNzc3Jg2\nbdo919+hQwcCAwN58cUX2bNnD9WqVeP555/n3XffzbN/O3bsSFpaGtevXzePJLr1s7f7+uuv2bFj\nBwBt2rShbNmyJCcnExQURM2aNYmJieGnn36iXLlyzJ07l7CwsDwxBQQEMHDgQGbMmMGcOXM4c+YM\na9euZdGiRWRmZvLvf/+70CRdDh8+zIABA8yvGzduTJMmTfD19aVy5cp3XP9Wr16d53rn4+ND/fr1\nGT16dEFtgogUECVdCsjdbiIzMjI4duwYZ8+eJTAwkLCwMH744QcAUlJSKFWqlDnJUdCxXrp0ia1b\nt9KyZUtOnjxp/oLQtWtXXFxc6NOnD/b29mzcuJEbN24USNLlbnEnJCTg4eHBpk2bOHz4MCdPnqRf\nv35s3bqVIUOGUL58ecaPH8/Jkyfx9/d/6DE/iNzHLKysrDh58iROTk74+PiQnZ3Nb7/9xoULF6hT\npw6nTp1i9+7dtGrVipiYGJKSkgrlKKS7yT1+MTExREZG4u3tjWEYLFiwgMDAQA4cOEB2djbW1tYF\nHGnhcK9kRVpaGocOHeLSpUtcuHCBNm3a5Jn9pqD9UZIlKiqKzZs34+/vj7e3NwsXLsTa2hobGxsc\nHBzo2rXrP2Z0WmHn6+tLfHw80dHRDBo0iDZt2jBv3jxatGhhfmzA1taW9PT0Oz6bnZ3N4MGDcXBw\nYNWqVQ879ELl2WefpXPnzqSnp9O0aVO6du3KunXrSE1NpV27dri7u5OVlWX+Y0dycjKZmZlYW1sz\nfPhwFi5cyBtvvFHAW3F369evJycnh7feeovs7Gw++eQTxowZw+XLl5k8eTLvvPMOwB3bC5gTFba2\nthiGwYcffkhkZCS9evXCxsaGmTNn5mkrJCSEqlWr0rhxY+zt7cnMzDT/4SExMZHMzEwmT56Mi4sL\nr7zyCgB2dnYPHI/JZDL/a21tjWEY1KlTh2HDhhEeHo6Hhwe2trb3lXC52z777LPPzCN0bG1tsbe3\nJz09nZycHHJycujUqROBgYGsWbMGNzc3SpQoAcBXX31FeHg4QUFBedafG4+1tXWe6+StM57FxsYy\nf/58Xn75ZapVq2Y+92797K+//srXX39tTqbmnse5Ll26xKhRo4iIiCAkJIRBgwbRqFEjEhMTOXHi\nRJ6YXFxcsLW1Nf8ByMbGJk+SrLBdz2vWrMnu3bvx8fEBfn9U6tZaVbnHIPf6d7frnaur68MPXEQK\nnJIuD8m9bibg9+GJsbGxODs7M2/ePPr27UtKSgpnzpyhadOmdOzYkVOnTuHg4FCgsSYmJnLy5Em2\nbduGm5sbdnZ2bN26laioKLy8vLC3t+f48eNUrVqViIgIypQpw549e2jWrNlD+Sv03eK+efMmiYmJ\nnDp1isOHDxMREcG4cePYvn07KSkpNG7cmOzsbGJjYzl58iTe3t7k5OSwb98+/P39C+1N3K2jWUwm\nkznG48ePs2HDBl599VViY2P59ttvMQyD3bt307JlS5o1a0aLFi348MMPSUhIKDRJl3vt54yMDAC+\n++47PD092bhxIwkJCfj5+WFtbU3fvn2pVasW4eHhHDp0iIYNGxbaY/Yw5W6/YRhERUVx/PhxvLy8\nOH/+PLt376Zv375s2LCBAQMG0KBBA/N+Lmi3xp2WlsaVK1c4duwY5cuXZ/HixZQtWxY/Pz9iY2OJ\njIzk+++/5/Lly+Zh8cX9uD9M3t7e5vOmefPmTJo0iVGjRt2zVkOFChVYuHAhr7zyCsHBwVhbW99x\nU1jcfPnll2zdupX09HReffVVLly4QJkyZdiwYQNJSUmkpaWRmZlJjRo1CAkJIS4ujgkTJuDs7Eyn\nTp3YuXMne/fupXHjxgW9KXdYuXIl8+fPp3Tp0hiGwRNPPMF///tfvL29qV27trn/un17ExMT71jX\nkCFDzD8vXrz4jt97eXlx4MABTp06RXp6OpUrVyYrK4u3336bhIQEQkJCmDNnDvD7Y1m5HjSel19+\nmbfffhs3NzcyMjJo3rw569at48033+Tq1atMnz49X/ZZy5Yt7zkj2XPPPcf06dNZv349jo6O9OjR\nw/y7vn370rdvX+D3x4ruJiAggNDQUN555x0OHDhg/r/o4OBATk4OW7du5eLFizRs2PCOzzZp0oQm\nTZoAv9d0+fLLL9myZQsApUuXpnfv3rz//vuUK1eOBg0amN8fPHgwI0aM4OWXX/7TfdGtWzfzo3T/\n+te//nT5h6VXr17MmTOHUaNGYWtri8lk4vXXX2fkyJEcPnz4juV1vRORXIV+9qKi6l43fufOnePm\nzZvY29uzaNEiQkJC2L17N+vXr6d79+7s3LmTRo0a8cMPP9CoUSPi4uLw9vZm165d5r/UPKxYU1JS\nuHr1Kra2tixYsAAHBwc8PDzYsmULy5YtY+vWrezbt4/IyEhmzpzJzZs32bRpE506dWLevHmUKVOG\nEsz2f/sAACAASURBVCVK0K1bt4ca9/Hjx/Hx8WHjxo2EhobSuXNnIiMjqV27Nq1btyYuLo4vv/wS\nHx8fOnbsyP79+yldujRhYWFERUVRvXp1Hn30UfNzyIXB3WrnwO9/+dy9ezenT58mIyODTp06MWPG\nDEaNGsWnn35K27ZtqVmzJjt37sTf35+9e/fi4+PDsmXLePXVVwvll4ADBw5Qr149FixYQHp6Os2a\nNePKlSts3LgRV1dXnn/+ec6fP8/+/fvx9fWlZMmSfP755zRp0oQhQ4YU+6RLTEwMO3bsoHnz5kRF\nRbF69WquX7+Op6cnVatWxcnJiZ49ezJ//nwqV65McnIyO3bs4PXXX8fX19fi8d3r+Jw+fZqYmBhK\nly5NZGQkqampNG/enM8++4z+/fuzevVqmjZtyq5du6hfvz6rVq0iMDCQ/fv3061bN1q2bFmsj7uI\niIiIFE4a6ZJPbr8pzv33xo0bpKWl4ebmxpQpU3B3d8dkMlG7dm1iYmIAqFOnDuvXr8fd3Z3mzZuz\na9cuMjMzad++PRs3bsTOzo5hw4bla+Libo/e5OTkkJiYiJ2dHXPnziUpKYnr16/z7LPP4uLiQseO\nHalZs6b5eVwXFxfs7e3p0KEDM2fOJCcnh9atW+Ph4cGECRMsVhPl+vXrODo6YmtrC/yeHEpISCAq\nKort27eTmppKw4YNOXr0KIGBgXTq1Ik1a9aQnJxMeno6Bw8epFOnTnz22WeEh4fj4+PDoEGDqFKl\nSp5htgXpXqON0tPTsbKyYvbs2VhbW9OqVSsOHz6Mq6srGzdupHfv3lSoUAF/f39cXFxwcXHh4sWL\nXLt2jdq1a5OamophGMyaNYtKlSoV1OYBsGTJEgICAihfvjxbt25l+/bt9OzZk2XLlvHKK6/g4uLC\n0aNHuXjxIv3796dJkyZcuHCBrKwszp8/T8+ePUlNTSU8PJyRI0dSo0YNoPiMdkhNTeXjjz+mffv2\nJCYmUrt2bQ4cOEBoaCh2dnZ06tSJI0eOkJaWxqxZs4iIiOD8+fOkpKRw6tQp6tWrZ36szs3NjdKl\nS1ss1rtdb65du8a5c+eoW7cumzZtYvPmzVSrVo3Vq1fTp08fjh07hqenJxkZGWRkZPDYY49x+vRp\nDh8+TLt27XjjjTeIioqiefPmDyVZJCIiIiLyIArHHWYR9Ec1E1JTU9m3bx979+6lbNmyREdH06VL\nF/z8/Bg0aBBnzpxh165dpKWlsWvXLlJTU4mPjyc2NpYzZ85QunRpevbsibu7e74VGbxXcdXLly9z\n4MABUlJSuHLlCmfPnmXOnDk89thj2Nvbs3PnTq5evYqDgwNxcXHA70Xljh49ip+fH0lJSTRt2pQO\nHTrg5ORkXn9+JlxyC9Pt378fFxcXzp8/T+PGjfH29iY9PZ0SJUqwfv16mjRpgre3NwMHDmT//v1s\n376dK1eucPPmTerVq8f8+fM5ePAgjo6OtG3blnHjxhWq4pv3mpklOTmZ77//nujoaM6cOcO8efMI\nDw8nJCSEatWqkZKSQoUKFbh48SLnzp3Dz8+PmTNn0q5dO65fv87ly5d5/PHHKVGiBG3bti2QbYuN\njeWjjz6iV69eVK9enfDwcNavX4+LiwsJCQmcOHGCESNG8Ouvv5KdnU1ycjJXrlyhYcOG7Nq1i61b\nt/Lqq6+ycuVKvv/+ex599FF8fHywsbGhdu3aBbJNlvZHBbajo6MpWbIkmZmZfPjhh9SqVYvY2Fji\n4uIoU6YMtWrVMidsnZycOHToEPv27aN58+Zcu3YNe3t7AgICCAgIAKB169YW3RaTyUR6ejqRkZFU\nr16dfv360bFjR27cuMG5c+f46aef+OCDD7C1teXf//43cXFx5vP+t99+4+zZszg4OODs7MykSZPM\nUyPnPlcvIiIiIlJYKelyH/5outLcaeEOHz7Ma6+9RlRUFJUqVaJPnz7MmTOHrVu3UqZMGY4dO0ZE\nRASlS5fmjTfeYOPGjXh4ePD6669TtWrVu04t+CCPS9w+8iY3CZKTk2MuAjdmzBj8/PxISUkhMzOT\n4OBggoODOXnyJKdPn8bd3R0PDw8uXrxIdna2+dnmYcOG4e3tjZubG+3atbu/nXifkpOTmTVrFsnJ\nyTz99NOYTCY2b95MYmIiQ4YM4ZtvvmHEiBFcu3aNUqVKERYWRnx8PMePH+eFF15g5cqVxMfH4+fn\nx9ChQylbtqx5nxSmhAv837G6cOECp0+fJiIigipVquDv74+joyOVK1dm69atnDhxgjZt2hAbG4un\npyfr1q3DwcGBmJgY9u/fT/fu3UlMTKR69ep3LWBs6cdvcgvarl27ln379vHaa68RHR3Ntm3baNiw\nIdeuXWP//v306NGD8+fP4+vrS2pqKl5eXlSrVo0zZ85QoUIFVq9ezZdffomXlxeurq5YWVnxzDPP\nWCzuwub2Y5SQkMD69esJCwvDMAweffRRWrdujYuLC0OGDGH9+vWYTCaqVq3K8ePH2bx5M+PHj8fP\nz4/IyEiaN29O48aN73rs8+ucyB2JUrFixTy1AObPn09sbCy2trbUrFkTd3d32rRpg6+vL19//TVX\nr15l8+bNlC9fnqpVq1KnTh1sbW2JiopizJgx5teWiltERERExFKsp06dOrWggyisbv1Cf+TIETw9\nPc2/27t3L6mpqebpGo8fP86OHTto1KgRFStW5OLFi5QoUYIKFSqYi+P5+vqyYsUKHB0dqVevHjVq\n1CAwMJD69etTunRpc2Lkj4ru/lW5xVUzMjKwtrbmk08+4euvv6ZGjRrmR5xCQ0Pp2rUr3bt3JyIi\nAl9fX65evUp0dDSOjo78/PPPPPLII1SvXp0OHTqYi/WVLl36oRX1tba2JjY2ltatW5sfRUlMTGTt\n2rWMGTOGCxcuEBoayqOPPmqexSY8PBxra2vat29P27Zt8fDwwDAMSpQoUShu0G7cuEF4eDjffPMN\nOTk5uLi4cPr0adasWYOnpydLliwhNjaW559/nm3bthEREUGNGjXw9PTEysqKs2fPUrduXTZu3Ejn\nzp1xcHAwzyAQFBSEm5sbnp6e5kel8uN8+qt+++03li5dSmBgINHR0ezYsYObN2/SpEkTNm/ezLVr\n13B1daVNmzY0b96cH374gfbt27Nz505Onz7N9u3bee655/Dx8aFJkyZUq1aNKlWqUKFCBYvFXBhd\nuXKFU6dOkZKSQsmSJYH/mz2ibt26lC9fnvXr19OjRw9CQ0MpV66c+fGsLVu2kJOTwxNPPIG/vz9+\nfn40aNCAcuXK5SlUm5/nRHh4OJMmTeLSpUvmx9tyZ90A+P777zGZTAQEBGAymcjOzubs2bOYTCbi\n4uIYOHAgkZGR7N+/nzZt2lCtWjUqV65Mo0aN8PLyMs9K9TDPZRERERGR/KCRLn/g1i/0//vf/zh8\n+DBubm4EBARw4sQJ7OzsqFKlCleuXCE7O5tBgwZx48YNvv76a0qWLElERARbt26lbt26NGnShLJl\ny951loEHvZG4/XO5rzMzMzly5Ai7d+/m3LlzjBgxgnPnzlGjRg18fX3ZvXu3+WZu37591K1bF2tr\na06cOEGrVq04evQonTp14sknnyyQqZ5vZW1tjYeHB9HR0eb3kpOTKVOmDMePH+fFF19k0KBBBAYG\nYjKZ+Ne//nXXmAvTzdlnn32GyWSia9eu3Lhxg2XLlnHixAkyMjJwcHDA3d3d/G+TJk34/vvv2bZt\nG+fOncPJyQlHR0caNGhgTkS0bNnyD9t7mNvu6+uLm5sbP/30E46Ojrz00kv8/PPPXLhwgaCgIHN1\n/y1btuDo6Mi+fftISkpiwIABpKen89xzz+Hi4kJOTs4/8rGhe43MyJ1hq0aNGhw8eJDVq1eTmZlJ\n6dKlmTp1KlFRUcTFxbFlyxaqVKmCj48PsbGxXLt2jbS0NHbs2EFAQACPPPIIL7300p+2+3fPiaio\nKN58801GjBhBvXr1yMrKonXr1rRo0YKEhARiYmIoU6YMkZGRJCUlmaeQX7lyJbt37+app57iX//6\nFwMHDqRy5cr4+vretS6LkiwiIiIiUtQV+9mLcmudREdHY29vzyOPPAL8Phph586d/Pzzz3To0IHU\n1FQuXbpEQEAAu3btonnz5ly6dAknJyc2btxIu3btyMrK4ueff8bf358uXbrw9ddf065dOxo1apSn\nzXvNRPNXZWVl5Sn4evXqVZKTk/H19eXjjz/G3t6erKwsTp48iZWVFUFBQdja2nL8+HGGDRvGokWL\nzIVWly9fzowZM4iOjsbV1TVPXZbC4rfffuPnn3/Gzs6OhIQEnJyccHBwICAggHr16pmXKwqPGqSm\npvLtt99SsmRJsrOzuXHjBpmZmcTFxfHcc88RHR2NlZUV8+bN47HHHuPs2bMMGDCAmzdvkp2dTbVq\n1Qr9YxYxMTEsXbqU6OhoJk2axJUrV5g1axbPPvssO3fupFu3bmRlZZGeno6/v3+eEWT/RLfXU8p1\n9uxZAHO9FRsbG9544w3Gjx9PlSpVCAoK4tdff8Xb25u9e/fy2GOPsX//fq5cuULFihVxc3Ojc+fO\nuLu737Hu/Dgf7nZtzH107Mcff2TlypV0794db29vGjRowHvvvcfZs2dp3Lgxbdu2xdXVlf/85z/U\nq1ePgIAA3nvvPapXr07dunVp1aoVL730EgsXLsz3uEVERERECpNil3S525f6+Ph4li1bRoMGDWjV\nqhXwe22C5cuX88orr+Dg4MCxY8dYu3Yt06dP5/333+fo0aPMmTMHJycn7OzsAIiLi8PZ2dmcuLhy\n5Yq5hsjfuZlISUnBMAxcXFz49ttvSU9PJzAwkF9++QUHBwfzjDSVKlXCzs6OM2fOmGuaeHt7s23b\nNlq0aMGgQYNYvnz539h7BSMlJYUlS5ZgZWVFYGAgtWvXNj9uUBSlpaWxb98+HB0dKVOmDO3atWPA\ngAHUqFGD0NBQZsyYweuvv06vXr0ICgoyP1aS61438YXJN998w3fffcewYcMIDAxk4cKF1KhRg/r1\n6xfKxF5++aOEanx8PA4ODixdupSXX36ZL774Aj8/P06cOEFmZqa51smnn35Kz549qVixIuHh4bRo\n0cJcODYtLQ0rKyvzNSdXbq2mv5Ow+CvXxtyky+bNm7lw4QI1a9Zk1qxZLFmyhB9//JGyZcuar6G3\nu3nzZp5zOTd5XBTOZxERERGRB/WPf7wo9wt9WloaDg4O5puKlJQUHB0d+e6778zTqt46vD23lkjp\n0qX57bffKF26NJGRkVy9epWRI0cyceJEDMMw3/xkZ2ebp1w1DAPDMPDy8jKv7+8kXIKDg3F0dCQ4\nOJhGjRrx6aefcuDAAQzDwNHRkbJly9K3b18mTJhA5cqVGTJkCM7OznzxxRdYWVnxyCOP0LlzZ6ZN\nm2aO7+/E9LA5Ozvz6quvFnQY+Wb79u2kp6cDsHHjRt58802ys7NxdHRk+PDhODk58fjjj9OoUaM7\nEi6QvzNDWUpAQACurq5UqVIF4K6PvPxT3KvAdlZWFtbW1nz66aecOXOGEiVK8MQTT5Cens6ZM2eo\nV68e586do1mzZri6urJhwwYSEhIYPHgwUVFRfPvtt7Rv3958XcrJyTHXUrpXoez78SDXRmtra65d\nu0Z4eDiXLl3i7NmzlC5dmmnTppln0kpJSclTzyU3IeTo6Jgn7tzRekXhfBYREREReVD/uJEud0so\nxMTE8OWXXzJy5EhWrlzJxYsXSUhI4NVXX+WDDz7gzTff5MyZM+zevRsXFxfCwsLo0aMHu3btwsHB\ngePHjzN+/HhiYmKoVasWJUqUeGjbk52dzRdffMGZM2fIycnh6aefZvny5Xz//ff89NNPeHp6smnT\nJp544gmWLl3Ktm3bePvtt/Hy8uL48eP4+Pjg4uLy0OKVP5ednc3KlSvJzMykQYMG1KlTp6BDknyQ\nk5PD0aNH2bdvH3FxcVSrVg2TyURqairPPvssP/74I5GRkdSpU4e4uDgqVqzImjVr6NmzJ7/++isp\nKSm0aNGCJk2aWGQkV35cG/fs2cOTTz7J4sWL6devH+3btwdg27ZtVK9enbJly+Z73CIiIiIiRVmR\nH+ly+41E7r9ZWVlERUWZi9/mTtW8c+dO3NzcMJlMxMfHk52dzdWrV81TIlevXp33338fKysr/P39\nSUlJ4fXXX8fGxgY/P797tmsp1tbWlC1blvLly+Pq6srXX39NdHQ0jzzyCJs3b+bYsWPUr1+f7Oxs\nevbsSbdu3XB3d8cwDGrWrGnR2OTBWFtb8+yzz97xvupZFF73qv0UHx9PeHi4uVbN5cuXOXToEB98\n8AH79u1jypQp5sdtvLy8OHXqFPXq1WPMmDHUqlWLhg0bUqNGDR599NE72vy715j8vjZWq1aNDz74\nwLz+unXrmtu51yNFIvLPdfHiRdq2bcvbb7/NU089ZX5/4cKF/Pbbb8ycOfMPP//hhx/i7+9Pu3bt\n+M9//oOvry89evTIl9j279/PvHnziI+PJycnBy8vL0aPHk21atXyZf23GzhwIO+99555Rkt5cBXH\nrftbnz87s3O+xJGcnExOTg6urq4WXdfly5fx9vb+220Ua1Pd/ubnr+VPHCJ/oEgnXU6fPm1+fAF+\nv6ilpKQwf/58srOzSU5Oxtvbm6FDhxIeHs6KFSuoVasW3bp1IyEhARsbG3r16sXSpUvJyMigcuXK\n1K5dGysrKwzDyFPgsyBn0ahcuTLbtm2jT58+1K9fn5CQECpWrEitWrUICAjA398fIM+IFt28F36a\nmaXwuv3YWFlZER8fz/Lly/PUfrp8+TKLFi2iW7dueHt74+rqyo4dO4Dfp3ju1asXrq6uBAcH88gj\nj9CiRQtKlixpLnh9e5twZ5LkQVjq2pjr1iSLzluR4svKyopZs2bRqFEjKlWqdF+fDQsLM1+nhg0b\nlm8xhYeHM2bMGD788EPzdWvt2rX079+f9evXWyQxsnPnznxfp+SvVatWYW9vT+fOvydlJk+ezKBB\ngyhfvnye5bZt24adnR379u2jefPm1K9f/y+vE+Dbb78lPDwcW1tbbGxsmDx5Mp9//vk91zV58mQW\nLFjwt7YtIyOD8ePH4+TkRIkSJRg6dCj29vYcOXKEd955hy+++OKOz6xevZojR44wefJkzp07x8yZ\nM/Hy8qJy5cr069fPvNyyZctISkoiJSWFsWPHsnjxYrp3715sEozz5s0jKiqKPXv20LRpUypXrszg\nwYPzLBMXF8fatWst9ij9yZMnmT9/Pg4ODgQFBZlHGee69Vjmmj17Ni4uLgwaNMj8XnE/lgWtSCVd\n4uPjKVmypHla5B49ejB69GicnJzYvn07lSpVIiAgAAcHB7p3786FCxeIjo7GxsaGevXqERoaSvPm\nzZk+fTouLi489thjdOnShVq1auHmljdLevuNREHeWJQtW5bSpUuTlJREhQoVCAkJKbBYJP/oZrXw\neNDaT15eXvTu3Zvq1asTEhLCiBEjSEhIYPr06Vy/fp0hQ4ZQvnx52rRpYx4dA2Bvb5+vSZaHeW0U\nEcnl4ODAgAEDGD16NMuXL7+jyHdUVBRvvfUWKSkpxMXF4e/vzwcffMC3337L0aNHeeedd7C2tiY0\nNJSqVavi4uLCli1b+OSTTwCIjIzkxRdfZOvWrZw9e5Zp06aRlJREdnY2/fv3p3fv3nfE9N///pdX\nX301T6K4W7du2Nvbk52dDcCKFSvMde9KlSrFpEmT8PPzY9y4cVStWtV883Tr6zZt2vDkk0+ye/du\nrly5Qvfu3Rk+fDjBwcEAvPDCC8yfP59+/fpRt25dTp06Rbdu3VixYgWbN2/GysqKmzdv0qZNG9at\nW6cbnULi3//+N7Vr1+bSpUu0bduW69evY2try+7du4mPj8fT05OPP/4YGxsb7O3teeONN5g1axaG\nYRAREXHHObhx40ZmzJiBh4cHO3bsICEhwbwuNzc35s2bh6OjI+np6QwZMoSzZ8/yww8/ULJkSTZu\n3Eh2djZ16tQhICCAuXPn4ujoSPXq1fMkQm53+vRpPDw8GD9+PKGhoaxdu5Z69erxyy+/3PVx5f/9\n73/ExcWRlZUFwJYtW+jVqxft2rVjxowZnD9/Hh8fHwCqVKnC4cOH8fLyIiIiAg8Pj2J17r722mvA\n77UJ33vvPeD3Y7xlyxbS0tKoW7cu7dq149y5cwwfPpzp06fz+eefk5WVxaBBg5g0aRIdOnS4Y/lR\no0bRunVrjh07xvTp0/9wRNWiRYsYO3Ysnp6eDBw4ME/S5fZjCbBu3TpSU1PvKC9R3I9lQSsyFQzP\nnz9P165d+eqrrwA4evQojz32GKmpqXh6etKmTRsyMjI4fPgwDRs25NKlS/j5+XHw4EEAatWqRbly\n5QgKCmLo0KFMnz6dLl26AJhvKgpreZsSJUrQvXt3KlSoUNChiPwj5Ba7zmVlZUVMTAwfffQRACtX\nrmT27Nnm6dTDw8MJDg7mhRde4OrVqyxfvpyRI0dy5MgRnJyc2LBhA7a2tnh6elK+fHm8vb2ZOXOm\n+S9ouQmXW9v8u7MN5SrO10YRKXiDBw/GycmJOXPm3PG7lStX0qNHD1auXMmGDRu4ePEiW7dupV+/\nftSuXZs33ngjzw1E586dzXWx4PeRBD179sQwDIYOHcqoUaNYtWoVy5YtY9GiRebr2K2OHj1Kw4YN\n73i/Y8eOlC5dmt27d7NgwQKWLl3K2rVr6dKlC6+99tpfus6lpqby1VdfsXz5chYtWsSFCxeYMWMG\nAEuWLDFPoFC1alXWr1/P4MGDcXNzM4+AXLduHYGBgbrRKQB3629NJhM3b95k0KBBvP7664SGhgK/\nfydo2rQpPXr0YNmyZQA4OTlx8eJFwsPDMQyD4ODgO0YcALz11lssW7aMkJAQwsLCcHZ2Nq/LycmJ\np556isDAQPbv34+Pjw++vr507dqVTz/9FGdnZ1xdXdmzZw9JSUkkJyfTtGlTmjVr9ofbVqNGDUqU\nKMHUqVM5cOAAV69epVq1agwePPiuSZcnnniCJ554wvy6R48ebN++nenTpxMfH098fLz5d02bNmXQ\noEE8+eSTrFmzBhsbG9555x1iYmL+2o7/B6pQoQI9e/YkMDCQLVu2mN9v1aoVYWFhXL58mfPnz7Nr\n1y5atmx51+V9fHwYPHgw/v7+nDp16g/bS0hIMD99cft5fPuxPHLkCBcuXLjrualjWbCKTNLF3d2d\nLl26EBERwebNm/H29sbe3p6kpCSioqKoXr06NWvWJCIiAnt7e06fPk2lSpVo3rw5GRkZ+Pr6MnLk\nSADzcNacnJw8bWjkgcg/0+1JltyER1ZWFr/99pt5JMut9U2SkpLuWd+kXLlyvPvuu7Rp04ZKlSrR\nqVMnZs+ezSOPPMLzzz9Pp06dzO3eyhLXGF0bRaQgWVlZ8e6777Jq1ao7HrMZM2YMHh4efPbZZ0yd\nOpXY2FhSU1PvuS4XFxfat2/P2rVryc7O5ocffqB3796cPXuW8+fPM378eLp3785zzz1HWloax48f\nv2s8t1/DbrVjxw6eeOIJc+KjZ8+exMTEcPHixT/d1rZt2wLg6elJyZIluXbt7rUgGjdubP65X79+\nrFy5Evh9hM3darqJ5bm7u3PlyhXz6ytXruDm5oaNjQ12dnbY2tre8T0Bfu8PO3XqxOjRo2nbtm2e\nEQm5s/DljnQ9dOgQn3/+OUOHDmXSpEn4+vqyYcMG87rWrVtHeHg4lSpVwt3dPU982dnZDB48mBEj\nRtC0aVNKlizJqFGjzOvPdfr0aUaMGMGkSZPM76WlpVG5cmWmTp1KpUqV7rtGTGxsLE899RTjx4/H\nzs7urp9fuXIlPXv25JdffqFXr1788MMP99XGP8lHH31EbGws9erVy3POBAUF8fPPP1OiRAlKlCjB\nunXrCAoKuuvyubOT2tnZ5bleffjhh4wYMYJffvnF/J6npyexsbHAn/8RbPXq1Vy6dInFixezadMm\noqKi7lhGx7JgFJnHi5ydnalevTqGYfDTTz9ha2vL008/zTfffMO2bds4f/48dnZ2+Pv7U61aNRo1\nagRA9+7dzeu4W50GEflns1R9E2trawzDoFatWuZ1G4ZB1apVza8fRrJC10YRKWheXl68+eabjB07\nNk8x3JEjR5KdnU2nTp1o3bo1V65c+dObhqeffppJkyZRuXJlKleuTIUKFTh16hQlSpTg+++/Ny8X\nHx9/19kk69evz6FDh+4omvvmm2/Svn37uyZkDMMgKysLk8mUJ77MzMw8y91ai+v2ZW/l5ORk/rlr\n167Mnj2bPXv2kJqaetfC6WJ5zZo148cff2To0KHk5OTQuHHjP5zd08fHhwULFhAcHMz06dNZv349\njo6O9OjRAzs7O2bNmsXp06fp0aNHngTI8ePHGTx4MCVKlCAnJ4fg4GB27tzJggULePzxx9mwYQNJ\nSUmkpaWRmJhIhQoVWLhwIa+88grBwcFYW1sTFBREWloa77//PuXKlaNBgwbm9VepUuWOUWWOjo6E\nh4cTFhZGTk4OU6ZMues2TZ48mbfeeuuO9z09PZk0aRLu7u5UrVr1jpkIo6OjuXbtGlWqVMHLy4sv\nv/ySbt26/aX9/k9UpkwZfv31V8LCwvJcA9zd3YmNjeXxxx8nPT2dVatW4eLics/l72bIkCF3vDdg\nwABmzZqFra0t/fv3B+59LHPruoSFhXHo0KE8k8CAjmWBMoqQ9evXG6tXrzbWr19vdOjQwbhw4YKx\nceNG4+effzZiYmIKOjwRKQTi4uKMnJwcwzAMIyMjw6hVq5axePFiY8WKFcZrr71mvP/++8Yvv/xi\nvPPOO8apU6eMTZs2GcuWLTMMwzDWrVtnjBw50ti8ebPx+uuvG8HBwcYPP/xgGIZhJCUlFdg2/Rld\nG0XkYbtw4YJRv379PO8FBwcbtWvXNsaOHWsYhmE0atTIOHHihGEYhhEREWE0bNjQWLlypWEYnMrM\n3AAAIABJREFUhvHCCy+Yr69jx441FixYYF5Ply5djN69exvr1q0zDMMw0tPTjaCgIGPNmjWGYRjG\n5cuXjYCAAGPv3r13xBUeHm60bNnSOHLkiPm97777zggMDDTi4uKMX375xWjXrp1x9epVwzAM49tv\nvzWCgoKM7OxsY9asWcawYcMMwzCM6Ohoo3nz5ua4goKCjMOHD5vXeevrGjVqmK+1ty9nGIYxbdo0\nIygoyFi6dOlf3LsiIvJPUmRGugD4+fkRGRlJq1atqFatGuXLl7+j6rihaXdFiq3z58/Tp08fhgwZ\nQr9+/fLUN/Hz86NNmzZERETcUd/kp59+ol+/ftSqVYuTJ08SFBREhQoV8oyQubW+SWG7xujaKCKF\nwcSJE9m3b5/59YgRI3jttddwcnLCxcWFRx99lPPnzwPQpk0bZs+efcdoEoCnnnqKjz76iHbt2gG/\nD8H/6KOPmDZtGgsWLCArK4thw4aZR+7dqnHjxv+PvfsOj6pKHzj+vdPSKyGFEgQJBjB0KQJKFARX\nsfATVJC10FdkjZQQmhjKBpWAFNEI4qoroEgUBZXeIUAIBOlESIP0NjPJtHvv748ss0aKhYRQzud5\n9CG3nPPem5NM5p1z38PMmTOZNWsW5eXl2O12QkND+fTTTwkICCAgIICXXnqJF198EUVR8Pf358MP\nP0Sj0TB48GDGjRtH7969adCgAZ07d/5D192nTx8GDx7MwoULr7i/X79+zvo2giAIwp1HUtVbv0Ki\neDMh3G4OHz7M3LlzKSkpQVVVgoODiY6OrvLoinC5srIyFi5ciM1m48EHH6Rly5bMnDmTkJAQ6tWr\nx3333UdaWpqzUv+JEycYNmwY3377LY8++uhlK2/A/1Y2uhWJ342CIAi1S1VVPvroI7Kzs3nrrbdq\nOxxBEAShFtwWSRdBuJ3YbDa6d+/Oxx9/7KwX8u233zJv3jw2b958xUr0QiVZlklMTERVVQ4cOEDf\nvn0B+Oqrrzh16hRdu3bFYDDg5+fH008/jZeXl7OY2SUiUSEIgiBUl4ceeojAwEDef/99sWqRIAjC\nHeqWerxIEO4EFRUVGI3GKis8PPHEE3h6eiLLMtu3b2fJkiXY7XZcXV2Jjo6mbdu2xMTEUF5eznvv\nvceZM2f4+9//zueff87dd99di1dzY2m1Wjw9PbFYLDz00EPMnDmTZcuW8cQTT6AoCm3atCEwMPCa\nbYiEiyAIglBdtmzZUtshCIIgCLVMzHQRhJvQ8uXLmT9/PgEBAbRr145OnTrx2GOPkZuby2uvvcan\nn36Kn58fZ86c4eWXX2bDhg0APP3004wcOZJly5YxfPjwO7Ii+alTp0hLSyMyMpKLFy/SpEmTy44R\ns1kEQRAE4SY03ec6z7/yMt7C7Svi3xHXdf7RF49WUySCcHUi6SIINymTycSBAwc4cOAAmzdvBmDg\nwIG8//77VZbzKyoq4qOPPiI8PJzjx48zYMAAnnjiCWbPnl1bod+URKJFEARBEG5yNZR0WbNmDe+8\n8w7bt2/HYDCQlZXFI488wvbt26lbt+41m7x0TpcuXf5yWFarlWnTpuHi4kJJSQmxsbH4+voCkJub\nS1xcHD4+PoSFhTFo0CBiY2NxOBwUFhYyceJEGjZsCEBqaiqrV69GlmUmT55MSkoKOp2OTp06/eXY\nbnU1mXS5tPTy8OHDr6uPXzt58iQJCQm4uroSGRlJr169quxPTEzk6NGjzuWfAeLj4/H09KwSx+ef\nf05JSQlms5no6GiWL1/Ok08+KR5jvEndFo8X/bpSviDc6k6dOsXp06fp27cv3t7ePPzww/To0YMJ\nEyZw+vRpwsPDGTNmjPP4wsJCjEYjycnJ7NmzBw8PD5KTk0lKSkKnuy1+xAWhiiutWCIIgiAI1xIR\nEcGmTZv429/+xpo1a+jYsSMA69atIyUlBbPZTK9evbBYLJw5c4aOHTuyZcsWmjdvjouLC3v37mXd\nunWYzWb69++PqqqsWbMGg8FA586defLJJ6/ad3FxMf3796dDhw58/PHHHD16lO7duwOwcuVKBg8e\nTLt27Rg2bBhPPvkk3bt3JzIykp9++ondu3fz3HPPARAQEEBgYCA6nQ5FUUhJSWH06NE1f/PucElJ\nSSxcuJAWLVpw3333sXXrViwWC61ataJnz56MHTuWHj16cOzYMWbPno23t/dV2/r444+Jjo4mKCiI\nV155pUrSZf369eTn5+NwOJzb1q1bR3l5OZ6enlXaadq0KampqYSEhHD69Gn8/f1FwuUmdtu8IxN/\nhAu3C39/f+bOncvjjz9Ohw4dALh48SIOh4PBgwczePBgfH19ufvuu9m+fTuTJ09m27ZtFBcXs2LF\nCj777DM+/PBDNm/ezKRJk2r5agSheokkuyAIgvBX9O7dm40bN9K7d28KCgqcs4aXLl1K165dcXV1\nZffu3UydOpVp06axdOlSlixZwvfffw/Ap59+yqJFi3A4HGRkZPDOO+/w/vvvo9PpePHFF6+ZdAkO\nDiY4OJjU1FROnjzJ3//+d+e+goICQkJCAPD29sZmsxEZGUl6ejrr169n1qxZzmPr1avnTLIkJCTQ\ntm1b4uLiiIyMvKNnu9wIbdq0Ydy4cZw8eZJ+/fpx7tw5vv/+e3r27EloaCijRo1i0aJFnDp1ivvu\nu++q7RQVFREUFARcXkfwb3/7G1lZWSQkJABw9OhRMjMz6dWrF0eOHKlybOfOnencuTOKovDuu+/S\nsmVL3n77bV588UVn+8LN47ZJugjC7aJx48YsXryYefPmkZOTg4uLC15eXsyePZvw8HBiY2N54403\nUFUVnU7HkiVLcHFxYezYsQwZMoRmzZoxbdo0+vbty/3330+PHj1q+5IEQRAEQRBqlaurK3Xq1GHl\nypX06NHDWQ9PkiTeeOMNysvL2bZtG6qqYjQakSQJs9nsPN/hcCBJEoqikJmZiaIoV3xs+ezZsyxe\nvBhPT09mzJjh3L5y5UpycnKYPXt2lZnIISEh5OTkEBISQmlpKd7e3mzatIm9e/cSFxd32SqLACdO\nnCAgIICNGzcSExPD9OnTRdKlhl2avfL+++/Tp08fWrduzXfffQfg/B4ZDAYURXGes2jRItLS0vi/\n//s/unXrBkBQUBB5eXkEBgbye1U+EhMTsdvtHD58mKKiInr16kXjxo2rHPPll1/Sr18/li1bxtCh\nQ/nuu+8YOnRotV23UD1E0kUQbkKXstdX8uijj/Loo49etn3VqlXOf/v4+LBjx44ai08QBEEQBKHa\n1XAh3GeeeYbXXnuNH3/80Zl0GTx4MOPHj8dqtTJo0CCWL19O9+7dadWqFdOnT+fBBx8EKuvqTZ48\nGYvFwoABA3jxxReZNGkSHh4e9O/f39lH06ZNmTdvXpV+9+/fz+LFi+nUqRMTJ05k4MCBpKWlcc89\n99C/f3/i4uJITEzkkUceITs7mylTptCtWzemTp1Knz596Nmzp7MtWZb5+uuvmTx5Mm5ubsTHx3Pv\nvffW6H27md3oQriBgYHs37+fpKSk302aXOnRr5dffpk5c+ag1+sZPHgwANOmTSM2NvayYy/VdblU\nW+a3CZecnBxKS0tp2rQpISEh/Oc//7kjF9G4FdwWhXSTk5PF40WCIAh3APH7XhAEQRAEQbiVaGo7\nAEEQBEEQBEEQBEEQhNuRSLoIgiAIgiAIgiAIgiDUAFHTRRAEQRAEQbilqKpKSbmdApOVfJOVsgoH\nDkVBVlSCPS00KLFQUEfF10WDZ4UvWq2KNkCHu1tdtFrxmaMgCIJw44iky3/dNXFdbYdwRzgf91ht\nhyAIgiAIwi1AUVTOFZo5cbGMkxeNnMwxcrG0gnyjhSKzHYdy5bKEfRufo8d5KyfvKaWJv0r9k81x\nFGeQ0+4gP5pfo0m7Ch4sOkUjYw/MmhzcgptTz1/CrVF9JI1IyNSmiH9HXNf5N7qoqlD7ToQ3v67z\nm588UU2RVLpw4QL16tWr1jaFW59IugiCIAiCIAi1rtBkZdfZAvamFXI0q4Sz+Sasjj+/3sPZk2fw\nSyvijMOM7Okg95gJ2XQKmyGDFCkdN49T/HyhFP+iZuzNWAGeL9A9KZY3X3XjA5c6bA74JwHN7qFd\niDd+evGn8u1gzZo1rF69mvr16+NwOGjcuDH9+vUjISGhyqoxc+bMITo6miFDhjB27FjOnj37h1aD\nuXRedXv77bex2WyYTCZiY2MxGAwAlJWVMWfOHPz8/JBlmejoaGJjY3E4HBQWFjJx4kQaNmwIQGpq\nKqtXr0aWZSZPnkxKSgo6nU4sMf0HXGncjBkz5prnTJs2jaVLl/7l/lxcXHjssat/SJ2bm0tcXBw+\nPj6EhYUxaNCgKvt37NjB6tWrWbBgAQBGo5FJkyYxZMgQ2rRp4zxu06ZNJCcnYzabiY2NJTExkdat\nW9OkSZO/FLtwbeKVRBAEQRAEQbjh7LLCwfPFbDuZy5YTOZwtqKA6ltTUqKCioqpaDHIFdtmOohjR\n28DqasfTlk5xkR9ao0qRsRg3yYjJUkK6XI7fqRSmpw4kyOFOh4KLPKt8QlnpMFrfcy916gUiaaRq\niFCoDYMGDXK+mR08eDD9+vUjJSWFt956i+zsbOLj4zl9+rTzeKPRSE5ODgsXLiQ3N5dGjRqh1+vp\n2bMnY8aM4fHHHycrK4uxY8c6z3v00Ud58sknOXbsGBMmTCAzM5ONGzciyzIRERF06tSJhQsX4ubm\nxj333HPZG+Zfy8zMdCZbvv76a3766Sf69u0LwFdffYWHhwfFxcV07doVs9lM9+7diYyM5KeffmL3\n7t0899xzAAQEBBAYGIhOp0NRFFJSUq64lLFwZb8dNw6HgzfffBM3NzeKi4t56623GDduHAEBAfTp\n04fz58+TkpLCokWLWLZsGYcPH2bnzp0899xzzJw5k4CAAJKSkli+fDkrVqygrKyM9PR0RowY8Yfi\nWblyJYMHD6Zdu3YMGzaMAQMGoNfrAdi3bx8ZGRmYzWbn8UuWLMHV1fWydho0aMDp06cJDg4mPz+f\n0tJSkXCpQSLpIgiCIAiCINwQDllh26k8vtx/nh1ni7D8hZksv0dFQpFUJFWDVtbgQEHVVOAp61Dd\n9ARr8ymtqE+xtRib4orBnMUvHjL+hVaOmnywaFROae08lLWaItdy9u7ZSZ1jYNN4c9ztIC0f+BvB\nTUORJJGAuZWsWLGCXbt2IcsyL774IgBNmzblzTffZPHixZw8efKq5z711FN06NCBf/7znzz44IM0\nb96cV155hY0bN7JhwwbncX5+fowcOZJvvvmG5ORkEhMTiYiofGRq37593HPPPZhMJiIjI2nRosU1\n483PzycoKAiA4ODgKvFlZGTQrVs3evbsydChQ4mMjCQyMpL09HTWr1/PrFmznMfWq1fPmWRJSEig\nbdu2xMXFERkZKWa7/AG/HTe7d+8mLCyMl156iQ0bNvDtt99SXl7O5MmTcXNzo1GjRrRt2/aydj7/\n/HNGjRpFeHg4Q4YMAeD+++/Hbrezbds29u3b94ceSyooKCAkJAQAb29vjEYj/v7+AHTu3JnOnTuz\ndetW5/ETJkxg4cKFl7UTHh5OeHg4APHx8bRt25Y5c+bw9NNP06xZsz9/o4RrEkkXQRAEQRAEoUad\nvFjG8p1nWHc0F5O9+hMtv6ZRVRRVRSuBKhuwqwpoynFX3JHdXfBVizDZtZTYilAlL/TlF8ioq6Fp\nhUyK4y50LmaQoJVhD8d+booqK/hma/nF7QRHTqwjO60hHh4XedD7IL7/1w+9qN9wS3j++eerPLaR\nlZWFl5cXAHq9HlmWr3quqlaOWYfDgUajQVEUACwWi3OWAYCbm5uzPavViizLjBo1CldXV9asWUOd\nOnWcM2NmzJjBsmXLADh79iyLFy/G09OTGTNmABASEkJubi4AOTk5BAYGOvupW7cunp6eSJKEt7c3\nkiSxadMm9u7dS1xcnDOOXztx4gQBAQFs3LiRmJgYpk+fLpIuf8Bvx822bducCVeNRoOqquj1+svu\n+aUxU1JSAoDNZnPuu3Tee++9x4gRI2jZsiXp6emX9f3FF19w4MABIiMjnY+5hYSEkJOTQ0hICKWl\npXh7e1/X9W3dupVOnTrx/fffM336dOLi4njzzTevq03hciLpIgiCIAiCIFQ7RVFZk5zOB1vPcLbI\n9vsnVBdJQpYUNIoWSXbBrtiwu4FWNqC663G12XGRdZTaitFo/PE2X+BkM+gg2zlIOHgo1C3LwMvX\nitHYCA+bAZ2k5dCpTdgkV0xF7vhXnKDgs8WcOneAVY97M6j5IDqHdL5x1yjcUJ999hkbNmygW7du\naLVajhw5wpw5czCbzUyaNInExMQrnjdixAhiYmLQarVERkZisViYO3cu9evXrzIbomnTpsybN6/K\nuSEhIfj6+jJz5kxMJhMzZsxg27ZtyLLMs88+y6xZs9iyZQutWrUiNzeXKVOm0K1bN6ZOnUqfPn3o\n2bOnsy1Zlvn666+dszHi4+O59957a+Zm3ea6devGxo0bmTNnDiaTiQkTJlSZWWKz2di+fTsRERHE\nxMSg0+kIDAzkhRdeID4+nsDAQH755Rfc3d1xdXVlx44dlJWV4eHhQf369av0NXDgQAYOHFhlW//+\n/YmLiyMxMZFHHnkEnU7HjBkziI6Odtb8+aPMZjMHDx5k/PjxpKWlMW/ePLp06fLXb45wVZJ6KQ13\nC0tOTqZ9+/bX1YZYvejGEKsXCYJwParj970gCDXLLiss336SD3eco9By4/uPqNhEs/MXKW6qoYWq\npzBNptw7hSZ1GrCow1DGG+OxHnscQ3E+RWoobc6sZe4z+Xxkzue5itnkNg2gnXcqf3f/ksMpzxBe\nFkhzTQM2ZSZgd22Bl1sfWhyaTXBZNjNH1CXVvxiAVZ5tadHsCWj5NGi0N/7ChRqxcOFCunfv7ixC\nmpWVdVkBXkH4PampqSQmJuLm5oZerycqKqq2QxJuIDHTRRAEQRAEQbhuFrvMkk3HWL4ngzJ77dU7\nkVQVRVLQoEWVtWhQsLhI5OODq9aKpcKDOpIXBfJpXNy74VeeR4mXBtdSDbmaQKyNgmlV+gn5eYGo\nqkprbRNOmY+hqqDXtUGx5RBcls15PxdnwuUuhzctjn4LR7+FLTOhRwxE9AexBPUt77XXXqvydYMG\nDUTCRfjTWrVqRatWrWo7DKGWiFcCQRAEQRAE4S9TFIWFPxyh3fT1vLc9s1YTLs6YUFA1WhS7Fq2i\nweIiUaDxxttejNXqRZDWH7OjFMVuJstDJaRMJcVaHx0mJC109D9BYWEIequEl9aDrNKfses8cdUH\nE1x6GIBdnb2c/T2XX/S/zovPQeJw+KArnP7pRl+6IAiCcJMRM10EQRAEQRCEv2TT4V+YnHiUXKuO\nm+azPLXyfzISiqxFUkF2VSmSfPBVCrHZvfCUDdhkLYbybDICJO6xyiTLd4OXTHDJWbQ+MpaKUBrY\nfCiSSrA68sGlNYpip8npzZh0Gn64txQAj3INz1suXB5H3nH4YgA0fhD+9g7UveeG3gZBEATh5nCT\nvDoKgiAIgiAIt4oLhUaem/8jQ1cc/2/C5SYiqYCKLGmR7Rp0SmXSJV/rSwAF2C3emOxGVMkTt/Js\nzgdDe4eFZF0r5LruNLOlUlDgC5ILETTiXPkZFBVc9e3RW3/B3V5Ocgsf7LrKsojPl2uu/Qf1ue2w\npCtHdqzFYr/6CjmCIAjC7ekme5UUBEEQBEEQblaKovB2YhIfH8jDhg6k2n+U6LckVQJJRdVqQdah\nUSVcdSp5+NNBcwy5ohEl/125qI7pHGl3SYy3ljNF0xLbXUG0qzhMQVYQWGXqG+pyMHc9dq0v7npf\nGqR/jQKs6+IAQG+VGGrK+N2YSg3BPPcTBCbtYPbTEXRtGlDDd+HWdCK8+XWd3/zkiWqKRLhVLB65\n5brOf/WDh6opEkG4OjHTRRAEQRAEQfhdpzJy6DXzGz44UFSZcLlJSQCqjF2VkBQJNBKeigabwQVf\npQSDrMNoK0ZvqEegMZtCH8i1+YBqQdJCa9+zlJaG4m/zoMRehsWRA7owHA4jd2WlkBboxnn/CgD6\nmPR4qI7fjen1ov+jQtaSXljOoKVJTE48Kma93ECqqjJv3jwmTJjA5MmTGT9+PAUFBbUdltPJkyd5\n4403mDRpEhs3brxs/8WLF3noocrkwPnz5xkzZgzTpk27rKDv/Pnz+eijj1i8eDEAixYtwma7gcu1\n32bWrFnD888/j8NR+TM+bdo0srKymDNnTi1HdmV79uxhwoQJjB8/nkOHDlXZJ8syH374IQkJCc5t\n6enpPPXUU5e1I8ZR9bt5XzEFQRAEQRCEWqeqKst+2M+7Oy5gwaW2w/ldlTNdHDgkHRpVRdaAi6xD\n8dTj5yjDIrtQIV9Er2+GUWeivlnikL0ReNmoX3oSi5seRQ2imVyPNMtpFBXcXdvhY0xFi8qOzu5A\nKZIDxpizfzeen8z3sFXbscq2/yRlcCijhPcHtaNxgEcN3Qnhkq+//pqgoCDnMr2ZmZkUFBRgt9tZ\nsmQJOp0OFxcXJkyYQJ8+fejQoQMNGzbkxIkThIaGkpmZSfPmzUlJSWHKlCmcOHGCrVu3YrFYaNWq\nFT179mTs2LH06NGDY8eOERsby7hx41i2bBnJyckcOnSI4cOHXzW+jz/+mOjoaIKCgnjllVfo1auX\nc19FRQVLliyhfv36ABQWFjJx4kTq1avHqFGjsFgsuLq6AhAeHk52djZhYWHs3LmT9u3bYzAYavDO\n3v7q1q3L3LlziY6Odm47ffo0APPmzcNisZCbm8usWbOIj48HKhNjkydPZt26dZw7d44OHTpQUFBA\nWVkZ6enpjBgxgkaNGjFz5kwCAgJISkpi+fLlTJw4kWXLlnH48GF27tzJ448/zuLFi3Fzc8NqtfL2\n229fM9bly5ezePFiFEUhKiqKJUuWOPetWrWKiooK3N3dAcjPz+err77Czc3tsnbEOKp+N1XS5cMP\nP2TLli3Y7Xaef/55OnbsyMSJE5EkibCwMN588000Yuk9QRAEQRCEG6LUaObVjzaxO1eLKt0if3RL\nEpU1XTRoZLBTgYtDh8YVdHYVP8mTfEcJYCLDD8KtMslSC9S67jSz7yavLADFLtPcpRHr8ndi1dbB\nTeNO47RNlOq1bAyvLKDbqUxPsFxxzVDK7Fre1I35b3Hfqk5cLKPvwl3M7hfBE63rVf99EJxSU1MZ\nNmwYUPmp/fnz52nYsKHz03t3d3fS09PJy8vDx8eHWbNmsWbNGvz8/Hj22Wd57rnnmD9/Pl999RWp\nqak0adKEfv36ce7cOb7//nt69uxJaGgoo0aNYtGiRaSlpdG1a1d2797Nd999R0xMzDXjKyoqIigo\nCADpV4/sqarKu+++yz/+8Q8mT54MQPv27QFYvXo1bdq0cSZcAPr06QNUJmoSEhIIDAxkz549jBgx\nAk9Pz2q6m3eW3r17c+TIkctmIJ07dw6LxUJMTAzp6ekoikLv3r2xWCyUlZVx5MgRAJ566ikeeOAB\nDh48iN1uZ9u2bezbt4+dO3cyatQowsPDGTJkyBX7dnd3p3///hQWFjoTOteiqqozOfLbmSkDBw4k\nKSnJGVfdunUZN27cFfsW46j63TQZjKSkJFJSUlixYgWfffYZOTk5/Otf/+L111/niy++QFVVNm/e\nXNthCoIgCIIg3BEOnviFh+PWsytPh3oT1m65Go2qgqRgV/WgqsiaclwUPQaDgqXcAz/Vk3KHFV35\nBc4HS3SSyzmobYutURCdPY5QWBCCp0WPyWGm3H4BjS4c1X6BQGMOB1r7oGgBFaIqin43lkX2p8hR\n/a6632R1MGZFCjFrxONGNally5Zs2VJZ+2P06NG8/vrrXLx4EUVRePTRRxk3bhwPP/wwPj4+eHn9\nbynwS28wPTwqZyNptVpUVeX9998nLy+P1q1bo6qVGbVLMwYMBgOKovDss8/y+eef4+Hhga+vr7PN\nRYsWERUVxa5du5zbgoKCyMvLA3C2B3Dq1Cmys7N5//33SUtLY/ny5djtdmbPno2rqysjRoy44vV+\n8sknvPDCC6SmpnLfffexc+fO676Hd7Lx48ezatUqMjMzndvsdrtzMkBxcTEnT54kISEBg8FAs2bN\nnN9Hb29vLBYL7733HrIs07JlS1RVrZIUudTOpXNKSkoAWLduHQcOHKBJkyZVxhDAjBkziIqK4vjx\n485tLi4u2Gw2LBZLtcxMEeOo+tw0M1127dpFs2bNePXVVzGZTEyYMIEvv/ySjh0rp2M+8MAD7N69\nu8p0O0EQBEEQBKF6KYrCv9ftZs6ufCySe22H8+epKpr/LhmtUTQ4pHLcHS4YXOxYij1wV7QoeOBl\nzuJcS4lhdoViyQ2tTiHMkMEeaydaOOqSZjmNjAZPt7bUvbAOGfiukxWAZqU6WtiKrxnGz+V1SNA8\n/YdCXrE/g+MXSvn4pfuo43nzP8JVU2qqEG7//v2ZP38+o0ePxsXFBbvdztChQ6lTpw6zZ8/mhx9+\nwM3N7Yr1La4kMDCQ/fv3k5SUVCVJ8muenp64u7szcODAKttHjx592bEvv/wyc+bMQa/XM3jwYABn\nzZYPPvgAgCFDhvDyyy+zaNEi9u/fT1FREdu2bWPq1Kn4+Pg42zp27BgBAQHUqVMHDw8PNm7ceM1H\nm251N6IQrl6vZ+bMmTz99P9+nps1a4bD4WDmzJkUFRUxfvx4FEVh27ZtZGVl0a5dO+exGo0GV1dX\nduzYQVlZGR4eHrzyyivEx8cTGBjIL7/8gpubGxEREcTExKDT6QgMDCQwMJANGzZQUlKCxWKhuLgY\nP7/KJO7UqVMvi/PFF19kypQpOBwO/vGPfyDLMjNmzGD69Ol/+prvtHFU0yT1ar8pqoHJZEKSJDZu\n3EhkZGSVXwi/NWXKFC5cuMAHH3xAVlYWo0aNwmw2O7PAe/fu5euvv+bdd9+97Nzk5GSLnMEYAAAg\nAElEQVTnVLu/6q6J667rfOGPOR/3WG2HIAjCLaw6ft8LgnB1Npud2H+vZ8VZkKWb5rO5P6W9cSMN\nL57gTHhzHjqrIdeRREBDT9a27UOfX5Jpd7Er+4tP0irrOO/93wWiLrgxzn00QS3LeZUETp54gr9b\nH2RD9pcUyg58vQfQdXc0vwTreOvvlUmXRRdtPGjJuWoMDkWiv/0tUtSmfyr2xgEefPpKRxr634LJ\nLqGKRYsWYTabq9QCEYRLUlNTSUxMxM3NDb1e76w3JNyeauzVdMKECXTt2pWUlBQURWHjxo3OCshX\n4uvrS5MmTTAYDDRp0gQXFxdycv73YmY2m/H29q6pcAVBEARBEO5oRpOZqA/WsjnfE1W6aZ5A/9NU\nFVQJtJKMBh1IFko09fBXCpErfCi1FaHTB6G1b6aeVSVFDUOt40oLZR/5BUHoK8COnXJ7NlqXh9CV\nn8JNtrKtiwdgJciovWbCBeDLio6kaP9cwgXgXIGZfkv28MnL99Gy3tU/rBRuflea0SIIl7Rq1YpW\nrVrVdhjCDVJjr6jZ2dk8+eSTpKWlERsbi8lkuubx7du3Z+fOnaiqSm5uLhUVFXTp0oWkpCQAduzY\nQYcOHWoqXEEQBEEQhDtWfmExL8WvYVOB1y2dcAGQ/juJW4eCggoaK/kab+pQiGr1xGQvQoc3F3xk\nWlgdHNREYAuty/3eRzAaQ6ln8+Ws8TQONHi5taJ+5nYKXXVsv7uygO7QimvXXsm1ujBb+9en4ecb\nrTz34T72nL15ljQWBEEQ/roae1W12+2sX7+epk2bUlRU5CwIdDWRkZE0b96cZ555hlGjRjFt2jSi\no6NZuHAhzz77LHa7nd69e9dUuIIgCIIgCHekrIu5DH0vkeRyf+DWKZh7NQoqqiShRcUu23C4QL7W\nF19HKW6yKyZ7CRpHORlBEvfJFk5qGqPXOwjQFGJ3hNDeEEaW6QQ2KQRFMdIk9zhJ7XxAI+FRrmGA\nOeOa/f9LfQkTly/D+mcYrQ5eWn6ADceuPaNGEARBuPnV2ONFQ4cOZd26dcTExPDZZ5/x+uuv/+45\nEyZMuGzb559/XhPhCYIgCIIg3PEu5Obz2vtrOWIPqe1Qqo1E5eNFOlRk2YrDVaVA5087xyl8VQ/y\nZBPe1mzOhUs8ZnUDg5VQ48/kVPhDhYIbBsrtWRhcH8Wz6BCKBN+1Lwfg+XLNNT+x3GUO5Rvtg9Vy\nHTZZYfSKFD556T7ubxpQLW3e7BaP3HJd59+IoqrCzWXus49f1/ljV31fTZEIwtVVe9Ll0vJXPXr0\noEePHgCMGjWqursRBEEQBEEQrkNuQSH/fP8bUuwh3A4zXJxUFQUNBkBWrMiuCgUGf7ztZXgoBuyq\nDl9TNkX+Kqn5jcBPTyvNzxQV1iPA4sEZ62ksqhYft3to+vMXHGvkRaFnBXqrxFDT1We5VMgaJuui\noBqXqLA5FIZ9epD/DOtMm4a+v3+CUGNMJhOKoogak4Ig/GnVnnTp06cPkvS/F+5LiyNJksTmzZur\nuztBEARBEAThTyooKuGNxWs4aL3NEi6/YpBUFNmGzVWHZNCAXYebrEHS+GGwJRMiQ7LUAnuDOnT2\nPsIZy8N00t7FCdMuHJoGyJYMAssL+bRL5RKtfUx6PFTHVfv7yPoI6Zqgar8Os03m5eX7+XJEF8KC\nvKq9/TvBmjVrWL16NfXr18fhcNC4cWPGjBnzp9r45JNP6NatG23atLnqMUlJScydO5fw8HBUVaWo\nqIiZM2eyefNm2rRpQ9Omf7648iWKojB16lQ8PDyw2WxVlgE+f/48c+fOJSAggIiICPr168fSpUsp\nLCzEZDIxdOhQGjVqBFSumrN69WpkWWby5MmkpKSg0+no1KnTX47tdlUd4+ZacnNziYuLw8fHh7Cw\nMAYNGlRl/44dO1i9ejULFiy45vd/06ZNJCcnYzabiY2NJTExkdatW9OkSZNqi1W4PtWedNmy5fqm\nBQqCIAiCIAg1p7i0jOj3v2JvRTDq7ZhwUVVUSYOLoqDKVmwGAwadDWuFJxpZRqP1p1xfQoTNzn80\nLXBxtSGZ7DgsBupp63DQnom7Wz8CcnaT46ln/11GJAeMMWdftcu0Cm/e0wy66v7rVVxuZ/Cy/Xw1\nsotYTvovGjRoEI899hgAgwcPJisri4SEBGJjY0lISKB169bIsszGjRuRZZmIiAhCQ0NZuHAhLVq0\n4NixYxQUFBAcHExcXBz+/v64uLhctiR0z549GT68spDyRx99xKFDh7h48SJNmzZl7dq1HD58mPz8\nfEaPHs2JEydISkrCZrPRr18/unbtetX4k5KSaNiwISNHjmTBggUcOnSIdu3aAWA0GpkwYQLBwcG8\n9tprPPDAA+zbt4+77roLV1dXQkL+9/hgQEAAgYGB6HQ6FEUhJSVFrLR0Db8dN7Is88477+BwODAa\njUycOJGYmBgCAgJo06YNqampVcbUtZJZK1euZPDgwbRr145hw4YxYMAA9Ho9APv27SMjIwOz2Qxc\n+/vfoEEDTp8+TXBwMPn5+ZSWloqEy02mxmq6bN68mS+++AK73Y6qqpSUlPDdd9/VVHeCIAiCIAjC\n7zCXVzArYSU7TUEoNbeeQi1TUCUJvexAllT0sh43gw251JsyWxF63EkPlGhvkyjTGbjb/DP5hXXx\nqjBwxnKGCkWHj0sQzTOT+PEBb6CUTmV6guWKK/emwpuaV5FVbY1eVU6ZhReX7+ebV7vi7aqv0b5u\nRytWrGDXrl3IssyLL754xWM+/PBDIiIigMo3vaGhobRp04Zx48axcOFCunfvzmeffcbw4cNp0aIF\nM2fO5MyZM4SFhTnb+PHHHzlz5gynTp2ie/fudO/enePHjwOwbt06PvzwQ0pKSjAajaxdu5aPP/4Y\nu93OyJEjr5l0uZTwAQgODiYvL8+5LyIigtzcXEaMGEHHjh3Jzs5Gr9czZcoUVq9ezdq1a3nmmWcA\nqFevnjPJkpCQQNu2bYmLiyMyMlLMdrmC346bXbt2cfbsWcLDwzGbzRw5coTy8nImT55MYWEhqamp\nf7jtgoICZ0LM29sbo9GIv78/AJ07d6Zz585s3brVeezVvv/h4eGEh4cDEB8fT9u2bZkzZw5PP/00\nzZo1q5b7IFyfGnu1Xbx4MaNHjyYkJER8wwVBEARBEGqZLMssXbWWDYV+2Gruc7dapwIKEnqHA0Uj\noXcYcHOzIpf7YLSXYLBXkBEMdrsfko+G1tqfMRobEUY9skwnUbSN0JT+jAYHa9sYQYWoiqKr9vdd\neQS71Igbcm2/5Jt5Y9Vh5+P7wh/3/PPP869//Yu3336bnj17otPpcDgqHxcrLi4GKn9GRo0aRVRU\nFJ07dwZw1nC5VD5BURQ0Go1zmyzLREVFMXXqVKCy1MI777zD2LFjyczMRKf738/apf6sVis5Of9b\nmerXpRkA9u/fT1RUFPPmzXNuCwkJITc3F4CcnBwCAwOd+06cOIHBYODjjz/m2LFjBAQE4OVV+Sia\nv7//FcfLiRMnCAgIYOPGjURFRfHNN9/8uRt6h/jtuFFVlYiICMaNG0e/fv1o2LAher0eNze3K46p\nS7744guioqJYu3atc1tISIhzHJSWll6zXtC1vv+XbN26lU6dOrFhwwZef/11VqxYcd3XL1SPGnvF\n9fPzo23btqxcuZJ+/fqxZs2amupKEARBEARB+B3rt+5ixQkLZZrbeyUcjQqqpEHvsCNrQa/o8dGW\nobX6YnJcwNtspjgADpWE4ajnQ3v3oxyz9CVc35A0+/d4eQ6k4cnPOdLUC7NbOc1KdLSwFV+xr2K7\nnunaG/toxqYTeczfdIaoXrffB5o3cvWhwMBASktLiY2NJTMzkx49ejBixAhiYmLQarVERkZWOT40\nNJSlS5cSExNDfHw8gYGBuLu7Ex4e7kyOJCUlOY9/8MEHOXPmDAkJCc5tffr04c0336S4uJhXX32V\nJ554gilTpgAwbNgw53EdO3akY8eOVfpv3749a9euZebMmQC0a9eOr776invuuQeAN998E19fXxo0\naED9+vUJDg4mNjYWq9VKTExMlbZkWebrr79m8uTJuLm5ER8fz7333nu9t7RW3OjVh7p168a6det4\n6623KCwsZPbs2c59VxpTlwwcOJCBAwdWaat///7ExcWRmJjII488gk6nY8aMGURHR2MwGKoce6Xv\n/6+ZzWYOHjzI+PHjSUtLY968eXTp0qWar174qyS1hlLlI0eOZMiQIaxcuZKnnnqKmTNn8tNPP9VE\nVyQnJ9O+ffvrauOuieuqKRrhWs7HPVbbIQiCcAurjt/3gnAnOnL8NOOWb+KMtlFth1Lj2hV9h78p\njwZ1JIyZElLjcvLDGtEutR4FOSdoVFJEUo90MkpfIKNzU8Ya3+Xiz73pUFGf/YVb8PEeQO8905j+\ndy+O169g0UUbD1pyrtjX1IpBfCbd+L9tNBJ88nJHHmhW94b3LQiCIPw5NfZ40VtvvYXD4WDUqFF8\n+eWX1VrpWRAEQRAEQfhjLuTmM+fz9ZzVhtZ2KDeEqkooaNDLdmQdSJIHPvYyXGUtSN7IykVaWGXO\n6kJoaj1OcWE9QpW6ZJpOgvZu3PL3k+XjwvH6FQQZtVdNuBwqD6qVhAtU1pGJWnWY3DJLrfQvCIIg\n/HE1lnSRZZlGjRrh7u5OTEwMbdu2ramuBEEQBEEQhCuosFhY+MmXpKqht+dKRVekgkZFdWiRsFKg\n9cHHbsLFAVqNJ7l+doKtrkg+Gtrqj2Iub0wr3V2U2tPx8epM88xd7LjPA4ChFfIVe7ApEpO1UTfy\noi5TaLbxxpeHazUGQRAE4ffVWE2XqKgoJElCURSysrJo1KiRKOYjCIIgCIJwA63dsI29RW6YJJfa\nDuWGURUJCQXFoUOijDydPy0c5zE4FPSKgcy64KPUQ63rQRhnOWMKJ9d8EbPDBZ/yXDzspayP0OJR\nrmGA+fwV+/i8ohsnboKZQ7vPFrLqQAbP3lf7sQiCIAhXVmNJl1WrVjn/XVZWxrRp02qqK0EQBEEQ\nBOE3Tv+SzuqdR0nXNq/tUG4oSZKQNCqKbECnKadE64VqccfsMOJqs1EaKHGo/B48PM2U5PkRZPMm\nw3QKrSGcgJw9HLrHG5urmb8XaK44JTzb4sbb2ldu+HVdzez1J4kMDyTQy7W2Q7luc599/LrOv9FF\nVYXalzVx53Wd3yCuezVFIghXd0PWC/Ty8iIjI+NGdCUIgiAIgnDHq7BYWLrqW07q7kZV75THii5R\n0EgyDsWARmvGojMgl3tjtBsx2Arx1yns0jUlzHESY1koXbV3kWRLwtNrMBEX1zCllw69VWKo6cp/\nu85gGBZunplDpRV2pq89xvuDRJHxq3E4HMydO5eSkhKMRiONGjVi/Pjx193ukCFDWLZs2Z86x2w2\nO1ca8vLy4p///Kdz36FDh/j3v/+Np6cnffr0oXv37owcORJfX18A/vGPfxAaWjmradOmTSQnJ2M2\nm4mNjSUxMZHWrVvTpEmT674uoZKqqsyfP5+LFy+i1+ux2WxER0cTEHDjVoDLzc0lLi4OHx8fwsLC\nGDRoUJX9O3bsYPXq1SxYsABFUZg6dSoeHh7YbDamT5/uPE6Ml9pVY0mXZ599FkmSUFWVoqIisWSV\nIAiCIAjCDfLdxh0cKHHHeAc9VuSkSqCRUa067HoJV4MNqcgPo8OEj3qBhhYd5Z4etNUfpaKsJdYK\nIybZnTolx8n215Fez0HfQgMequOypjeZm/KjtnMtXNS1rT+aw8bjufRqEVTbodyUVq5cSZMmTejf\nvz8A3377LUVFRfzwww+cOXOGvLw8+vbti7+/Px999BHt27fn5MmTxMfHs3jxYsrKykhPT2fEiBHU\nq1ePuXPnUqdOHTIzMwGYMWMGAOfPn2fy5MnXfCP7ww8/8OCDD9K3b18mTpxIbm4uQUGV3zer1cpb\nb72FoijExcXRqVMnMjIyCAwMxNPTk/r16zvbadCgAadPnyY4OJj8/HxKS0vFG+hq9vXXXxMUFERU\nVGX9pszMTAoKCtixYwc//fQTrVu3plGjRqSkpGA2m+nVqxfNmzdnyZIl6HQ6XFxcmDBhAvPnz8di\nsZCbm8usWbNYvnw5JSUllJaWMnr0aBo1uvqqcitXrmTw4MG0a9eOYcOGMWDAAPR6PQD79u0jIyMD\ns9kMVC5Z3rBhQ0aOHMmCBQs4dOiQc2lpMV5qV40lXeLj453/dnFxuaEZQUEQBEEQhDvVmXMZfLPj\nEOnae2s7lNqhSqgoSDKYPXW4GyqQKryxU47ZpYAAuz9SHT1B5TmUWO4jw3QKF9d7aZy2g00d9UgO\nB2PM2Zc1a3JomaZ7HdRauKY/YOo3P9Pl7jp4utyQiey3lNOnTzNkyBDn108++SQA9957L2FhYRw+\nfJgdO3bw1FNPce+99zJq1ChiYmLIzc3l/vvvx263s23bNvbt24fVamXYsGGEh4dz/PhxHA4HvXv3\nxmKxUFZWxpEjR675ZragoIAOHToAEBQURF5enjPp0qVLF06dOsW0adMYNGgQsizzr3/9i9atW/P5\n55+zfv16+vbtC0B4eDjh4eFA5fuutm3bMmfOHJ5++mmaNWtWI/fxTpOamsqwYcMAWLRoEefPn6dh\nw4Y0bNiQHj168Pzzz/P000/TtWtXXF1d2b17N8nJyQC4u7uTnp7OwYMHsVgsxMTEkJ6eTmZmJrt2\n7aJDhw44HA72799/zaRLQUEBISEhAHh7e2M0GvH39wegc+fOdO7cma1btzqPDQ4OBiA4OJi8vDxn\nO2K81K5qX71o0aJFLFq0iMTEROd/K1euZNGiRdXdlSAIgiAIgvArDoeDz9d8z3nDXSh3zGpFv6GR\nQFXRKQoVLgbcdHYMsoRWMVBQR0OachfeXkZKC4NpJtWnwPYLrtoGBJdnsLV1OZ3K9ATLFZc1u9j+\nOBdU/1q4oD8mp8zCRzt+qe0wbkrh4eHs2bPH+fVHH31EWloa8fHxmEwmIiIiUNXKbJq7uzsAer2e\niooK3nvvPWRZpmXLlqiqiiT97+dKq9WSl5dHQkICBoOBZs2aOdsB+PHHH4mKiuKTTz5xbgsJCSEn\np3IZ8tzcXAIDA537Dh48SOPGjVm1ahVfffUVhYWFZGVlAeDr64vdbr/s2rZu3UqnTp3YsGEDr7/+\nuli4pBq1bNmSLVu2ADB69Ghef/11Ll68CFQmQKCyhtQbb7zByJEjadu2LYqi8OijjzJu3Dgefvhh\nvL290Wgq33IXFxej1WoJDQ1l3LhxPPfcc4SFhTn7++KLL4iKimLt2rXObb8eL6Wlpc5+ryQkJITc\n3FwAcnJyqoytS8R4qR3Vngq/NKNl06ZNNGjQgHbt2nH06FHnABUEQRAEQRBqxr6UoxzLqeCC1qu2\nQ6k1KhKqpKB1KFhc9XgrFbjYDRhUHcYAlXSpKc04g6nsLtysUC57E5J/gL3h7sg6C1EVRZe1eaLc\njyWa/rVwNX/Osl3neOn+u/DzMNR2KDeV/v37M2vWLN544w0kSaJevXo0adIEf39/9u7di81mw2Qy\nXXaeRqPB1dWVHTt2UFZWhoeHB8OGDSM+Ph4/Pz8KCgpwdXVFURS2bdtGVlaW83EOgD59+tCnT58q\nbfbu3ZupU6dy6NAhQkNDCQoKYsmSJTz55JMYjUaio6Px8PDggQceICgoiB07dpCamorJZGLKlClV\n2jKbzRw8eJDx48eTlpbGvHnzREmHatS/f3/mz5/P6NGjcXFxwW63M3ToUM6ePes8ZvDgwYwfPx6r\n1cqgQYN44YUXmD17Nj/88ANubm489dRTOBwOZs6cSVFRETNmzMDb25tp06aRn59fpe7KwIEDGThw\n4GUxxMXFkZiYyCOPPIJOp2PGjBlER0djMFT9OW/fvj1r165l5syZAFXGIojxUpsk9dfp2Gr028JS\nL7/8MsuXL6+JrkhOTqZ9++srHnbXxHXVFI1wLefjHqvtEARBuIVVx+97QbhdlVdYmDRnAbvtd5Mv\n3/or2fxVbXJ/xF86xV2FwZwKu4BPQH3apjagtCSP9LZ7+FHzOi81WY2Sch/1imzkKkE8cPxL3hpY\ngbeLlq+Lq84WcSjwrP1NktV7aumK/pzhDzRh0t/urBWrBEEQbmbV/njRJcXFxc4Vi3755ZcrZo8F\nQRAEQRCE6rFt7wEyKlzu6IQLgIQKEigoaPDA12rGYrdgVy/ib/VC6yvhazRzl1KXvIrzGGQDed5W\ncoJUxlSUX9bemor2t0zCBeDTvefJK7PUdhiCIAjCf9VYpa1JkybxxhtvkJubS926dXnnnXdqqitB\nEARBEIQ7WpnRxLotOzmvCwe5tqOpXRKgaLVYJQda3PG1mTEpFuyaCxhsQfj7GinLbkBjmytnVB8a\n5O7lx7YQZNTyoCWnSlv5NgMztKNq50L+IotdYcGWM8x8KqK2QxEEQRCowaRLhw4dWL16dU01LwiC\nIAiCIPzX5t37yXe4UcQduET0b6iqDOiokKxI+IBFi9luwuFlIVvbhDDpDNbiBuSUn8PN9V5CSz5m\nXxuZSQWXFx5+W34Bo+R+4y/iOq06kMmoHk2p7+tW26EIgiDc8ar98aIxY8YA0K1bt8v+EwRBEARB\nEKpXeYWFTTv3kWOoX9uh3BQ0CqDRoGDFrHNBNnuhlWXK60CqWxjNrGcJtNXhYkU6ruYidodJuFu0\nDDBnVGlnr7kBX0k9a+cirpNdVvkiKb22wxAEQRCogZkuCxYsAGDt2rXONcQFQRAEQRCEmnEw9Rhl\nDolM+dabkVETVFVClSTQODDr3FDLNRiUciyerji8dLiWSQTZPMjBj4i8JN7t7+D5cn2VTyItsoYp\nuiiokeUmboxVB7J4vWcz9NoaK+FY7bIm7ryu8xvEda+mSIRbxa9X/6mN8wXhj6ixx4uGDBlCaGgo\nAwYMoGvXrjXVjSAIgiAIwh1LlmV+2LqLfJeGqLbLH4+5I0kKGhU0Ggm7xgW9w4ZZZ8fT5kOgXwnl\nv4RSVpGDm64JpYZdmDw1DL1YdZbLx9aHSdOE1NIFVI8Ck5Uff86hb+t6tR1KrVuzZg2rV6+mXr3K\ne9GsWTNat27NkSNHGD58uPO4bdu28d133+Hu7o7D4WDy5Ml4enpe1t5vV2n9PVlZWSQkJBAbG/uX\n4lcUhalTp+Lh4YHNZquSKDh//jxz584lICCAiIgI+vXrx/z58ykpKcFqtRITE4O3tzcA2dnZvPfe\ne7i5uTFixAjMZjMnT56kb9++fymu292Vxs2vx8tfMXHiRMaOHUvdunX/0vl79uzhm2++QVVVnn/+\n+SrLQi9dupTs7GyMRiOTJk0iIyODhIQEQkJCaNu2LY8//rjz2Pnz5zvH06uvvsqiRYsYPnz4ZctQ\nC9WjxpIuiYmJHD16lDVr1jB37lx69erFqFG3ViEyQRAEQRCEm9mpX9LJLSrjnLZJbYdy01CpTLoo\nGi1uGjs6hwa7NgdVDaKxlI6buQEXKs5Sx1zGT+1V+pgMeKgO5/nnKzyJ1wyuxSuoPp/tSxdJl/8a\nNGgQjz32mPPrpKSky475/vvvGT16NI0bN+bIkSMUFBRw+vRpVq5ciU6nw8fHh+joaACWL19OWFgY\n3bp1Y/To0UyfPp05c+bg4eFBbm4uCxYs4NtvvyU1NRWHw4FOpyMnJ4e4uDj8/f1xcXEhOjqa3r17\n06FDB8aMGUNQUNAVY09KSqJhw4aMHDmSBQsWcOjQIeebbaPRyIQJEwgODua1116jX79+hIeH06dP\nH5YuXcqxY8fo0qULAN7e3oSGhgLg6enJihUrGDt2bLXe59vNb8eNyWQiLi4OFxcX9Ho9kZGRzuTd\ntGnTGD58ONu3b+fMmTPk5eXRt29funXrxowZM/Dz8yM1NRWAZcuWcf78ecxmM8OGDWPTpk2cO3eO\nDh06MHDgwKvGs3z5chYvXoyiKERFRbFkyRIArFYrBw8e5IMPPmDfvn18+eWXFBQUMGbMGMLDw4mK\niuLhhx/Gza2yzlN4eDjZ2dmEhYWxc+dO2rdvLxIuNajGki4AYWFhtGnThoyMDA4ePFiTXQmCIAiC\nINxxNmzfQ5EhCKvj1nmEpOapoGhQNArukh1Z1qHR5pHq2o2HyvcTYG3KOfwILt7P0Zbw7oVs55mK\nCtM1o3CoNfon8g2z/1wRp3ONNAvyqu1Qat2KFSvYubPy8aWHHnoIHx+fy46ZNm0aK1asID8/H0mS\nGDVqFJIk/T97dx4XdbU+cPwzwzCAICAgA7grGrnlFRVNS80Kva7XJUtTUxM3ysifgguCgAsmbtGG\nSlZu2bXMbpqaaa7hdUmTRHJNvbIJww4DM/P7g+vciFxhHJXn/Xr1SuZ7vuc8fDkzMM+c73MYPHgw\nKSkpxMfHm9r279+fd955hwYNGuDl5YVKpWLw4MHk5OTw4YcfkpaWxs6dO4mLi+P8+fN88sknfPbZ\nZwQEBNC8eXOioqL47bffcHJyYt68ebeNPSMjAw8PDwA8PDxIS0szHWvVqhWpqamMHz+eDh06ANCz\nZ082bNjAtm3b6Nevn6ltzZo1CQwMBOCLL76gQ4cOREdH06ZNG/z9/e/zyj7e/jxvsrKy6N69Oz16\n9OD06dPk5+dXOKdly5Y0bdqUn3/+mX379pGdnY2/vz89evQgNTUVg8HAiRMniI2NJSsri/DwcLy9\nvRkwYADPPvvsbeMxGo2m5IhOpzM9rtVqTaU9bs6RMWPG8N577+Hs7ExBQQE5OTmmpEvPnj0BKCws\nJC4uDnd3dw4dOsT48eP/cnWXqByz/YaeMWMGAwcO5NKlS8ydO/eeluAJIYQQQojbu56Wwa/JF0i3\ncrV0KA+VUoUClVGJzqqEWqVFFOiyyHdRcMPeCSutPRTmUsPgwtGGeXTIscZDX2g6d3tBc/Ya/2bB\n6Kve+oTf79yoGnjllVdYuHAhCxcu5MUXXzQ9HhsbS1BQEAcOHCA2NpbXX3+d2XGxI04AACAASURB\nVLNn0717dzZu3Minn37KhQsXaN68OTY2/9sd7OYb3PXr1zNo0CASEhLYvn07Go0GLy8vjEYjCkXZ\nLX9WVlZA2W1CSmXZ2y+FQoHRaKRmzfIJsSNHjhAUFMTSpUtNj3l6epKamgpASkoK7u7upmNnzpxB\nrVYTHx9PYmIimZmZ/Pvf/+aVV15h/vz5rFy5ssK1SE1NRavVkpSUxODBg9m7d29lLu1j7c/zpqSk\nxPQzvHbtGlZWVpSUlABliQ+AJUuWkJeXR6tWrcrNAyibCwaDwfSYUqnEaCwrHnXzNrCbIiMjCQoK\n4tdffzU9ZmNjg06no6ioqNzKFFdXV9P4N+fI9evXmTRpEsHBwRiNRmrVqlXh+1uzZg2vvvoqp06d\non379qYEk6haZkvjv/DCC8ybN880KYUQQgghRNU5mXiWUqWK68WyTfQfKdCDQkWplQFNUSElJQVk\nODni7piJ8VwTrhZeo662kK//AfF5N0znZZeoCFO9+UgXz/0r2365Tljf5uXe+D2szFkId926dezZ\nsweA2rVr061bNwDTyg+AoqIiJk2ahKOjIzqdjrfffptdu3Zx4sQJzp49S3FxMXq93tS+X79+LF++\nnODgYLRaLdevX2fbtm2kpKSg1Wrp1asXc+fONZ3z6quvsmTJEtzd3alRowbNmjWrEGeHDh1MK1Zu\n8vX1ZevWrURFRQHQtm1bvvjiC5544gkAwsLCcHZ2pm7duri4uBAbG8u3336LVqtl3LhxFcZYs2YN\nU6ZMITExkbVr11K3bt1KXFnLMnch3D/PmwkTJhAVFcW+ffuws7Nj0qRJfPDBB0RFRZGeng6UJeQO\nHz6MTqcjLy+P3r17M2/ePI4ePcq5c+dQqVS0bt2aiIgICgsLmTx5Mjt37qwwdmhoaIXHRo0axezZ\nsyktLWXSpEno9XoiIyMJDw/Hz8+P8PBwcnJymDt3LlqtlujoaGrWrEmfPn0q3D6UmJiIm5sbrq6u\n2Nvbs2vXrkrXrBF/TWG8mVqrIu+//z6TJk3i7bffrvDiHhMTU5VDmRw7dgxfX99K9dEw5Nsqikbc\nzqWFve/cSAghbqEqXu+FeBwYjUZmv/MevxXW4EjhX9eBqK6eur4ZjaqQfOtMWqqcyL5sILtpMbqG\nGtqerEda5g002h/YPcCazVkXTOdFFA4lXtHfgpGbzz8ndKJdQ9lVVAghLKHKV7o899xzALz88stV\n3bUQQgghhKDs1qKMzCyu2zzaO+yYg9JgxKgHnaMKvdYGg3UO2VZ1eDLvKjaFGhxL1PzgB28WFpjO\n+bmgNvHKxzPhArD9dIokXYQQwkKqPOmSlJREUlJSVXcrhBBCCCH+63TSOUpRck1uLapAoTBSarTC\nWmlFaWFN1MpkLtm1oXVmCalFGbjnnUNbR0XXG1cAKDEoCLV667G7reiPdp9JJbRPc0uHIYQQ1VKV\nJ13Onz8PwMmTJ7G1teVvf/sbv/zyC6WlpQwYMKCqhxNCCCGEqFaMRiMH/n2CAjsPDIUPf52OB82I\nAoPCgJ3RGusSBVn2epwdCrG66k5RqZbkOim8XvS/moMbCjvxi1UjC0ZsfpduFHA+PY8mtWVXEiGE\neNCqPOlyc6/3sWPHEhcXZ3p8zJgxVT2UEEIIIUS1k5qRSUp6Bln2PpYO5aFkNIDKqhSDvgZWukJS\n6znjZczEodANa10J+9qpiM6+BMD1YlsWWL1u2YAfkD1JaQ990qWyRVHNXVRVPHx2/9CkUuf3eO58\nFUUixK2ZbfeizMxMcnJycHR0JCsry7SFlRBCCCGEuH8XLl9BAaTq1HdsWx3plVaoKSEbV7KMqahV\nbjTOySarWI1SeZz+BiU317lEGcdSiK1F431Q/n0pk9efaWzpMCwiISGBkydP3nZnlpttXFxcaNOm\nDd7e3vc0xqJFi0y71URERJh2isnMzCQ8PBx3d3c0Gg3jxo1j3bp1JCYmUlpaypQpU6hTpw4ABQUF\nhIaG4urqSp8+ffDy8mLbtm2MHDny/r95cd++/PJL/vnPf+Ll5QVA37596dq16wMbPykpibi4OGxt\nbenevTsvvPCC6diWLVs4evQohYWFBAYGolQqWbhwIZ6enjRp0oThw4eb2q5duxatVkt+fj7BwcF8\n/PHH9O/f37TtuTA/syVdJkyYwKBBg3BwcCAvL4/58+ebayghhBBCiGrj13MXUNjYkaUz259xjzSj\nUYG1oQQlCkqsi8mz9kCRpqS0qICf25bycd41APbmN+Jbq84WjvbBOXkl29IhWFxCQgIrV67E19eX\npKQklixZwqpVq0hPTyctLY2WLVty/fp1vL29WbduHb/99htpaWn07duXXr163bLfK1eumJItmzdv\nZseOHfTt2xeAnJwcJk2ahI+PD+PGjWPcuHG4uLgwf/58tm/fzuHDhxk8eDAAarWaJk2aYDAYcHd3\nZ926dUycOPGBXBvx14YPH07v3v/bfTUkJASVSsXTTz/NsWPHALh06RKzZs3i22+/JTMzEwcHB5RK\nJYGBgSxcuBClUkleXh5RUVG88847lJaWkpubS0hICLVq1brl2PHx8QQHB6PRaBgzZkyFpMuaNWu4\ncuUKcXFxNGnShEGDBvH888+zYMECfv/9d+rXrw+At7c3p06dwtPTk+TkZFxcXCTh8oCZ7be1v78/\n/v7+3LhxA2dnZ6ysrMw1lBBCCCFEtWA0Gvk1+SI6W1fQST2Xv6JQGMGgwMFQgMLagM7GFnWeEgX/\nwdtFhX1OKfmlVsxRPd7Fc/8sJaeIlOwiPJyqx8qeW2nZsiUTJ05kxowZpKamkpiYyIoVK/jxxx85\ne/ZsuXZNmzbl559/Zt++fbdNuqSnp6PRlG3d7uHhUW5TkYYNG1JUVERgYKBpxUSvXr3YuXMn77//\nPkuXLjW1ValUTJo0CYAff/wRHx8fli9fToMGDXjppZeq9DqIu7Nhwwb2798PQGRkJACjR4+mQYMG\nuLm5UVRURE5ODidPngTgxRdfpFOnTrz22mskJCTQoEEDRo4cSXJyMnv27OHcuXP4+PiQn5/PyZMn\n6dat2y3HzszMNM0rhaL8671KVfY23sPDg7S0NKZOncqSJUs4cuQIN27cICMjw5R06dixIx07dsRg\nMLB48WJatGjBokWLGDVqlKl/YV7KOze5PwcPHmTcuHEEBQUxevRoWRYnhBBCCFFJN7KyycvP54ah\nhqVDeaiVGq1xLComX11EPWMeJYUFpHmc4838/wDwka4XvxtrWzjKB+/nK1mWDsHiatQoe+5YW1tT\nWFhoejN7803sTUuWLCEvL49WrVphNP4vO3fu3DmCgoIIDQ01Pebp6UlqaioAKSkpuLu7m45dunSJ\nzMxMYmNjsbW15cyZMxw6dIgXX3yR9evXl0u63FRQUMDx48fR6/X4+flx4sSJqrsA4p688sorLFy4\nkIULF2JtbQ2Ao6MjaWlpxMXFoVaradasmWmO2NnZAWBlZUVJSQlKZdnb7bS0NAoKCmjVqhX/93//\nx8CBA6lXr55pnNjYWIKCgjhw4IDpMY1GQ1paGkC5OQiY+r0539LS0hgyZAgzZ85ErVabEnx/tGnT\nJgYOHMiBAwcYNGgQ33zzTVVdJnEHZlvpsmDBAmbOnImHh4e5hhBCCCGEqFauXk9FoVBIPZfbURgo\nNVphm1fKRXd72mbmUVJsRPekCo/8QpILnYhVDrV0lBZx4oqWni09LR3GQ8PGxoannnqK+fPnk5GR\ngY/P/4pTu7i4cPjwYVOdlpu8vb0rJEo8PT1xdnYmKiqKvLw8IiMj2bt3L3q9nmbNmrFgwQJcXV3R\n6/U0adKElStXsn37dgoKCnj55ZcrxBUfH8+IESPIz88nLi4OJycn810EcV9sbW0xGAzs3buXq1ev\n0rZt2wptunTpwpw5c4iKiqKwsJCwsDBmzZrF3LlzuXHjRrnyG4GBgRXOHz16NNHR0VhbWzNixAgA\n5syZQ0REBAMGDGDWrFnk5eUxffp0HBwcCA0NxdnZmaZNm1Z4D56SkkJ2djbe3t54enqybt06+vXr\nV8VXRdyKwvjntFkVGTduHCtXrrzn827cuMHAgQOJj49HpVIREhKCQqGgadOmhIWFmbJ6f3Ts2DF8\nfX0rFW/DkG8rdb64O5cW9r5zIyGEuIWqeL0X4lG26Zud7P3pKN8bWqEzmm3B8iOt9bV/UseYTc18\nG1KbGPC+6k6R8TSvtr5AM52W4brZ/GRsbukwLcKvkQufj+9k6TCEEKJaMdtva1dXV+bMmcPGjRv5\n/PPP+fzzz+94TklJCXPmzMHWtuxe0wULFvDWW2+xfv16jEYju3fvNle4QgghhBAPvbPnL6KyqykJ\nl9tQGI0YjQasFMXkURvr/GIKm2p5skTLloI21TbhAnA2NdfSIQghRLVjtt/YdevWxd3dnYyMDNLT\n00lPT7/jOdHR0bz88sum+yATExPp0KEDAM8++yyHDh0yV7hCCCGEEA81g8HA9bQMSqwdLB3KQ82A\nAoNBT5ayhDqGQrKtUuljl8MNnTWRVpMtHZ5FaQtKyC8utXQYQghRrVR5TRedTgdAQEDAPZ335Zdf\n4uLiwjPPPENcXBxQVjDoZnEre3t7cnMlOy+EEEKI6kmbk4vBaCTfIPVcbstgwGiloMQaPNNLMXqk\n8lxJBjP0o9Aq7C0dncVd0xbSTFPT0mEIIUS1UeVJl549e1bY0upm8uR2twdt3rwZhULB4cOHOXPm\nDMHBwWRmZpqO5+fn4+joWNXhCiGEEEI8EjK12SgUCnL1VpYO5eFmsMJgZaDEzoBVmoEGTW5wJN+L\nDVb+lo7soXA1q0CSLkII8QBVedLlhx9+uK/z1q1bZ/r3iBEjCA8P55133iEhIQE/Pz/27dtHx44d\nqypMIYQQQohHijYnD6PBIEmXO1AaSzAqFVgr3ShyTGWwPoP+qoVglq0jHj3XsgotHcIt7f6hSaXO\n7/Hc+SqKRDwqPPb8XKnzU7q3qaJIhLg1s20ZXRWCg4MJDQ1lyZIlNG7cGH9/+YRCCCGEENXTjSwt\nBiMUSNLltkrQo8cG52Ko73KeT4u6kmxV19JhPTSuah/epIu5lJaWEhMTg1arJTc3lwYNGjBt2rQK\n7RISEjh58uQ9l0m4yWAwEBoair29PTqdjvDwcNOxS5cuERMTg5ubG61atWLgwIHo9XpWrVqFQqEo\nN+a1a9dYvnw5dnZ2jB8/nvz8fJKSkujbt+99xSXuz93Mh/udM2PHjmX16tW3bbNo0SLTluURERGo\n1WW3lubn5xMWFoazszM1a9ZkypQpfPXVV5w5cwatVku/fv3o0qULAAUFBYSGhuLq6kqfPn3w8vJi\n27ZtjBw58p7iFZXzUCZdPvvsM9O/165da8FIhBBCCCEeDtfTMrC1UVNiVNy5cTVWqrTCWmHEsUhL\nF/ciBvCapUN6qFzXFlk6hAdu48aNNG7cmCFDhgDw9ddfk5mZybRp01i9ejXp6enExMTwj3/8g127\ndpGZmUlOTg6RkZG899575OTkcPnyZcaPH0+7du1uOU5CQgL16tVjwoQJrFixguPHj9O2bVsAcnNz\nmT59Oh4eHrzxxhsMHDiQzz//nMLCQmrUqFGuH0dHR+rXrw+Ag4MDGzZsYOrUqWa6OuJOEhISePfd\nd2nevDndunVj165d6PV6WrVqZfo5HT9+nI0bN6JSqXByciI4OJhevXrRv39/EhMTmT59OhcuXGD7\n9u04OTndccwrV66Yki2bN29mx44dpqTb9u3b6dq1K3379iUkJITU1FQOHjxIdnY2BQUFppgA1Go1\nTZo0wWAw4O7uzrp165g4caJ5LpS4pSrfvWjp0qUAfP/991XdtRBCCCFEtZWXX4BKZUWJQbaLvh2F\nwoi6CDztLhOjGIcOa0uH9FAp0OktHcIDl5ycbNoRFaB///64uLj8Zdunn36akJAQGjRoQEJCAk8/\n/TQ9evSgcePG/PTTT7cdJyMjAw8PDwA8PDxIS0szHWvVqhVqtZrx48fTpk3ZLS3Dhg2jU6dOFfqp\nWbMmgYGBBAYGsmPHDjp06EB0dDQ7duy45+9dVI02bdowc+ZMPvroI+zt7XF0dCw3H1xdXRk8eDAd\nO3bk8OHDANSqVYsJEybQo0cPjh07xoYNG5g3bx5vvvkmKtXt1z6kp6ej0WiAinMpIyMDT09PADQa\nDWlpafzjH//ggw8+IDIykg8++MDUVqVSMWnSJAIDAzl79iw+Pj4sX76cTZs2Vdm1EXdW5b+1d+/e\nzbp161i8eDGff/55uf+EEEIIIcT9KSgsQmVlJStd7sCgA6OxAPtaDuw23npVQnVVXFr9ki4+Pj4c\nOnTI9PXKlSs5f76s/ovBYECr1ZqO2dnZAWBtbU1JSQnLly9Hr9fTokULjMb/FQY6cuQIQUFBpg+c\nATw9PUlNTQUgJSUFd3d307EzZ86gVquJj48nMTGR7OzsO8admpqKVqslKSmJwYMHs3fv3vu7AKLS\nbm7ootfrmThxIkFBQeXqjX766adcuHCB5s2bY2NjA5SfSwaDwbTZjJWVVbmNZ86dO0dQUBChoaGm\nx243lzw9PUlJSQHK5oi7uzuxsbGoVCpq165NQUFBhfgLCgo4fvw4er0ePz8/Tpw4USXXRdydKr+9\naP78+Rw8eBCdTkd6enpVdy+EEEIIUS0VFhWhtFJK0uUODAYdGsdiPrCeKMVz/0JxqcHSIdySuQrh\nDhkyhHnz5vH222+jUCjw8vKicePGvPDCC0ydOtW0OgXg4MGDKJVK0tPTefXVV1m7di379u0jJycH\ne/v/bTneoUOHcqtnAHx9fdm6dStRUVEAtG3bli+++IInnngCwFSHo27dund1i8maNWuYMmUKiYmJ\nrF27lrp1pTbRnz3oQrjjx49nxowZWFlZ0b17d9Pjnp6enDhxgrNnz1JcXIxeXzG5OWLECEJDQ3Fw\ncCiXdPH29i6XvLvZn7OzM1FRUeTl5REZGcnevXvR6/X4+/sTGhrK8ePHqV+/PhqNhm7dujF9+nQM\nBgMTJkyoMHZ8fDwjRowgPz+fuLi4u5p/ouoojH9M2VahU6dOUb9+fX7//Xfq1q17yyV8VeHYsWP4\n+vpWqo+GId9WUTTidi4t7G3pEIQQj7CqeL0X4lE1Y+EKlEol6zMbYkQSL7fS8OwnPNnQg+02sgHD\nX3mqrhNfB3axdBhCCFFtmO2m4KtXrzJ06FA+/PBDhg4dytdff22uoYQQQgghHntFxTqMSitJuNxB\niWNtttu8YOkwHloP80oXIYR4HJlt96JPPvmEL7/8Ent7e/Ly8hg1ahT9+/c313BCCCGEEI+14uJi\nHB1rWjqMh941z79bOoSHmiRdhBDiwTLbSheFQmG679HBwcFUUEgIIYQQQtwbo9GIXq9HpVSgkEIl\nohIUslBKCCEeKLOtdKlfvz4LFy6kXbt2HD16tNx+4UIIIYQQ4u4pFArTDhgqhVGK6Yr7ZmdtZekQ\nbsljz8+VOv9BF1UVllfZupxSb1I8CGZb6TJ//nzq1avHoUOHqFevHpGRkeYaSgghhBDisWejVqP/\nb9JFiPtVQ/3wJl2EEOJxZLaVLiqViuHDh5ureyGEEEKIakWttkavl6SLqBx7G7P9+f/Q+vLLL/nn\nP/+Jl5cXAM2aNSMgIOCuzrOxsaF377tfDXH16lXi4uKIiIi4ZRuDwUBoaCj29vbodDrCw8NNx44f\nP86nn35KjRo18PLyIiAggJkzZ1KjRg1q1qzJm2++aSrbcOrUKf75z3+i1+uZNWsWJ06cQKVS4efn\nd9fxilv7888/JCSEqVOnUrt27SodJz8/37SdeM2aNZkyZYrpWFJSEnFxcdja2tK9e3deeOEF3nnn\nHQBycnJ4++23qVWrFgDff/89x44dIz8/n4iICL766iueeuopGjduXKXxintX/V51hRBCCCEeQbZq\nNUU6nSRdRKU421lbOgSLGD58eLnkSUJCAitXrsTX15ekpCSWLFnCZ599xoULF9DpdKjVatq0Kbtd\nadeuXezZs4eioiJat27N888/z9SpU+nWrRuJiYnMnz+fnTt3curUKUpLS1Gpbv8WKyEhgXr16jFh\nwgRWrFjB8ePHadu2LVD2RjoqKgoHBwfGjBnDuXPncHFxYebMmezevZutW7cyZMgQANzc3HB3d0el\nUmEwGDhx4gSBgYFmuoLipi1btpCQkIBOp2PgwIGkpqZy4MABGjduTEpKClFRUcTExJCfn09aWhqt\nW7e+bZJv+/btdO3alb59+xISEkJqaioajQaA+Ph4goOD0Wg0jBkzBg8PD37//Xc0Gg1ubm6mhAtA\n3bp1SU5OxsPDg/T0dLKzsyXh8pAw2+1FQgghhBCi6tjYlNV0sVHK7jPi/jnXUFs6BIvYsGEDISEh\nhISEsHPnTgBatmzJxIkTqVGjBqmpqZw4cYKIiAiGDh1a7tx69eoxcOBAOnXqxJ49e4Cy+pUTJ07E\nx8eHs2fPsnPnTiIiIhg7duwdY8nIyMDDwwMADw8P0tLSTMe6deuGvb09H3zwAX379uXJJ5+kZs2a\nhIeHc+LECW7cuGFq6+XlRWBgIBMmTGD9+vX87W9/Y+HChSQkJFT6eokyf5w3x44dA2Dr1q0sWLCA\nhQsXEh8fD0CnTp0IDAzk6tWrXL58meLiYubMmUPPnj3vOEZGRgaenp4AaDSacvMhMzPTlIBRKBRc\nuXIFjUbD7NmzATh8+LCprY+PD5MmTWLUqFF89tlnNGjQgOjoaJKTk6vmYoj7ZraVLqmpqbzzzjtk\nZWXh7+/PE088wVNPPWWu4YQQ4r5VtgibuDtSrE6IyrG1sUGvz6amlZ4USwcjHlmu9tUz6fLKK69U\nWOlSo0YNAKytrcnNzcVoLFtFZmVVvu7N+++/T8+ePXnqqaf45ptvALCzswNArVZjMBhQ/HdbqD+f\ne+TIETZs2ED9+vUJCgoCwNPT0/QGPiUlBW9vb1P7vLw85s+fT9++fenUqROFhYU0adKEN954gy+/\n/PIvV9GcOXMGNzc3du3axYwZMwgPD5dbjKrIH+dNSEhIuWOKP2wFdnMuWVlZodPpTMeUyvJrHL77\n7jt27NjBU089xWuvvQaUzYeUlLJX9dTUVNzd3U3tbyZh3N3dMRqNuLm54eDgAICLi4tpzv7Rnj17\n8PPz41//+hfh4eEsXLiQsLCwylwGUUlmS7qEhoYyevRo3n//fdq1a0dISAibNm0y13BCCCGEEI81\nN5danL98FQcbvaVDEY+wBm72lg7hlsy5+9C6detMq1Rq165Nt27dyh23t7enRYsWREVFkZWVhZub\nm+mYu7s7R44cISEh4S/f5AL06tWLuXPnoteXf3526NCBDh06lHvM19eXrVu3EhUVBUDbtm354osv\neOKJJ9iwYQOXL19m8+bNbNmyhejoaP7973+TkJCAwWCo8OZZr9ezefNmZs2ahZ2dHUuWLKFly5b3\ndY0eRZb4QKdfv36mlSbjxo3jP//5T7njTZs2xWg0smDBAi5evEiXLl1Mx3r27Flh9Yu/vz+hoaEc\nP36c+vXro9Fo+OCDD+jfvz+jR48mOjoaa2trRowYQfv27fnmm2+YP38+paWlvPLKK+X6ys/P5+jR\no0ybNo3z58+zdOlSOnXqZKYrIe6WwnirV45KGjVqFJ988gkjR47k008/ZcSIEXz22WfmGIpjx47h\n6+tbqT7kk+4HQz7pFg8jef4/GFXx/K+K13shHlU/HDzCxq3fUVyrMT9qa935BCH+wr/e6ELLOk6W\nDuOhtGzZMoqLi8nJyWHUqFE0a9bM0iGJR1BJSQmLFi1CpVKRk5PDm2++abpFSFRPZlvpolar2b9/\nPwaDgZ9//hm1unouZRRCCCGEqAouzk4oFQpqWslKF3H/Gj3EK10s7a233rJ0COIxYG1tzaxZsywd\nhniImK2QbmRkJF9++SVZWVnEx8eX2wpNCCGEEELcm1pONVEolNRUlVo6FPGIcrO3rpZbRgshhCWZ\n7VXXYDAwbdq0/w2kUlFSUoK1dfXcpk4IIYQQojKcnRwxGA3UUBqwUhjQG2UTSnFvGtV2sHQIQghR\n7Zgt6TJ+/HhSU1Np3LgxFy9exM7OjtLSUqZNm0b//v3NNawQQgghxGOppn0NlAolRqMBF1Up6SVy\n67a4N97uD3fSpbI11qR2YDUUXsn6ROHZVROHELdhtqRL3bp1+eSTT3BxcSE7O5vZs2cTGRnJuHHj\nJOkihBBCCHGPlEolHu6uFBQWUVutk6SLuGet6jhbOgSL+vLLL7GxsSm3dfT9iI6OJjg4uFJ95Ofn\nExYWhrOzMzVr1mTKlCmmY99//z3bt29HqVTSvn176tWrx9dffw3AwYMHWbNmDU2aNAFg7dq1aLVa\n8vPzCQ4O5uOPP6Z///64uLhUKj4BpaWlxMTEoNVqyc3NpUGDBuXu5Lgb7777Ls888wxt2tx5Z66r\nV68SFxdHjx49UKvV97zr0KpVq7h27Rq5ubnMnDnTNAcMBgOhoaHY29uj0+kIDw9n37597N69G7Va\njZ+fH88//zwABQUFhIaG4urqSp8+ffDy8mLbtm2MHDnynmIR5Zkt6XLjxg3TD9rJyYmMjAycnZ0r\n7FUuhBBCCCHuTrPGDTnw7xPUdiixdCjiEdShkbwRBzh+/DgbN25EpVLh5OREcHAwPXv2pE+fPpw6\ndYqnnnqKrKwsGjVqRK9evViwYAH29vakpqayYsUKkpOTAVi6dClFRUWkpqYyb948lixZAsClS5eY\nNWsWjRs3vmUM27dvp2vXrvTt25eQkBBSU1NNO9wUFxezePFiDAYD48ePZ9WqVXTq1Invv/+eli1b\nmhIuAN7e3pw6dQpPT0+Sk5NxcXGRhEsV2bhxI40bN2bIkCEAfP3112RmZrJ9+3Z+++030tLS6Nu3\nLy4uLnz44Ye0adOGc+fO8eSTT5KUlMTrr78OQHx8PG5ubnh6ejJ69GjCLD+eTwAAIABJREFUwsKw\ns7MjKyuLuXPn8t1333Hq1ClKS0tRqVTcuHEDGxsbdu3axZ49eygqKqJ169a89tprt4y1uLiYo0eP\n8uGHH/LTTz+xadMmJkyYAEBCQgL16tVjwoQJrFixguPHj7N+/XqeeOIJUlNTad68uakftVpNkyZN\nMBgMuLu7s27dOiZOnGi+i1xNmC0D0qJFC95++20+/fRT3n77bZ588km2bduGq6uruYYUQgghhHis\neTesR2lpKRq1ztKhiEeMk63VQ3970YPi6urK4MGD6dixI4cPHwbA2dmZwMBAnnzySdq1a0dISAh7\n9+5FqVQyePBgOnfuTFpaGmlpaQBcvHiRoqIiZsyYQVBQEAaDAX9/f7p27YqLiwsnT568bQwZGRl4\nenoCoNFoTP0C9O7dm9LSUhYsWMDo0aOBslUXGzZsYNiwYeX66dixIwEBAfzjH/9gy5YtqFQqFi1a\nRGpqapVdr+oqOTmZDh06mL6+uYKoZcuW/P3vf6dNmzbs27cPgFatWjFlyhSysrIICAjg5ZdfJiEh\nAYChQ4cyZ84cjh49ysGDB2natCmzZ8/G39+fr7/+mp07dxIREcHYsWPLjV+vXj0GDhxIp06d2LNn\nz21j1Wq1pmSbh4dHufmUkZGBh4dHuWPJyclMmDCByZMns2zZMlNblUrFpEmTCAwM5OzZs/j4+LB8\n+XI2bdpUiSspzJZ0CQsLo3fv3hQVFdGvXz/mzJmDj48PMTEx5hpSCCGEEOKx5qWpDYCjSk8NpWwd\nLe5e+4bVd/XDpk2bMBqN6HQ6VCoVn376KRcuXKB58+bY2NgAYG9ftpW2SqXCxsYGlUqFwWAgISGB\n7du3o9Fo8PLywmg0AlBSUmJawZ+VlUVSUhJxcXGo1WqaNWtmagfw3XffERQUxJo1a0yPeXp6kpKS\nAkBqairu7u6mY2lpacyYMYNBgwbRuXNnAH744Qd69ux5y7sGNm3axMCBAzlw4ACDBg3im2++qaKr\nV335+Phw6NAh09crV67k/PnzLFmyhLy8PFq1amX6OTs4lCU0b84dKysr0zE7OzugbBVJSUkJCoUC\nKLtl1Gg0mr62srIqN/77779PWloaTz31VLn5lJmZSVBQEEFBQeh0ZQl4V1dXtFotACkpKeXmk6en\npykJd/NYnTp1sLGxwdn5r285LCgo4Pjx4+j1evz8/Dhx4sT9XELxX2a7vUir1VJYWIi7uztZWVl8\n9NFHjB8/3lzDCSGEEEI89jxqu2KtUlHy39UuF4vsLB2SeEQ87V3b0iFYjEqlIiAgACsrK+bPn8+V\nK1c4ceIEZ8+epbi4GL3+1glMZ2dnrl+/zrZt20hJSTG9sW3WrBmlpaVERUWRmZnJtGnTMBgM7N27\nl6tXr9K2bVtTHz179qRnz57l+vX39yc0NJTjx49Tv359NBoNH3zwAf379yckJARra2tWr15N7dq1\nCQ4O5ueff2bAgAF/GWNKSgrZ2dl4e3vj6enJunXr6NevXxVcueptyJAhzJs3j7fffhuFQoGXlxeN\nGzfGxcWFw4cPo9PpyMvLu2M/X3zxBYcOHcLPz49u3boRFhZGdHQ0eXl5TJ8+HQcHB+bOnVthHrq7\nu3PkyBESEhLKJV1cXFxYunRpubYqlQo/Pz/Cw8PJyclh7ty5nD59msTERF566SW2bt1KVFQUAG3b\ntmX48OEEBwdjZWVlug3qj+Lj4xkxYgT5+fnExcXh5FTJgsXVnML4x59gFRo5ciQNGzYkOTkZGxsb\n7Ozs+PDDD80xFMeOHcPX17dSfVS2Wrq4O1JVXjyM5Pn/YFTF878qXu+FeNS9+/EGLvx+jQwbD/Zr\na1k6HPGI+NcbXWhZR944CSHEg2bWqrYRERE0atSIjz/+mOxs2Y5LCCGEEKKyWvs0Jb+ggAY2xSgw\ny2dn4jHjZGtFc09HS4chhBDVklmTLsXFxRQWFqJQKCgoKDDnUEIIIYQQ1cIT3o3ACDZKPR5SUFfc\nBf/m7iiVCkuHIYQQ1ZLZki7Dhw/nk08+oXPnznTt2vW2W6YJIYQQQoi7o3Fzwb22C/kFhTS0LbJ0\nOOIRMLBdA0uHIIQQ1ZbZCul6eXnh7+8PQK9evfj111/NNZQQQgghRLWhUCjo0u5vfLXjBxpqHDic\n4wjIKgbx15xtlXSoxjsXCSGEpVV50uXo0aOcO3eONWvWmPaVNxgMrFu3jn/9619VPZwQQgghRLXT\n0sebr777AXsrA7WtS0gvUVs6JPGQ8m+heXRuLQqvZKHf8KqvIXn9+nU8PT2rvF9RNVp90qpS5/8y\n6pcqikSIW6vypIujoyMZGRnodDrS09OBsk9kpk2bVtVDCSGEEEJUS3U83HFyrElBYRFN7Aol6SJu\naXD7hpYOweIyMzNZsGABNjY25OfnM3z4cNq1a3fH865du8ZHH31EREQEY8eOZfXq1Xc1XmpqKgsX\nLsTJyYmmTZsyfPhw07H169fzyy+/UFBQQP/+/XnuuecAWLJkCQ4ODgQEBJjarl27Fq1WS35+PsHB\nwXz88cf0798fFxdZufQgvPfee1y8eJGffvqJjh070qRJEyZOnGg6Hh0dTXBw8D31eTfz6Gab/fv3\n89VXX6HValm8eDHOzs5Mnz6dwsJCzp07R6tWrXjxxRd58cUXy53/7rvv8swzz9CmTZt7iu1eJCUl\nERcXh62tLd27d+eFF14wHduyZQtHjx6lsLCQwMBAXF1diY6OplatWuj1+nLXbNmyZdjb26PT6Zg8\neTKxsbEEBASgVj9ev9OqPOnSrFkzmjVrxpAhQ9BoNFXdvRBCCCFEtadQKHi63VN8t/cgzTR2HM2t\nSanRrPsjiEdQ7RpWtGsg24qfOXOGOnXqMGXKFEpKSti9ezelpaWEhYVhZ2dHVlYWc+fOZcqUKaxe\nvZr09HRiYmLw9fXl9OnTnD9/nosXLxIZGcnFixeJiorCy8vrluNt3LiRESNG0LZtW8aNG8dLL72E\ntbU1UPYB9YIFC8jKyiIyMpLnnnuOb7/9loKCAhwcHMr14+3tzalTp/D09CQ5ORkXFxdJuDxAkydP\nBsqSIIsXLwYgJCQElUrF008/TXJyMgkJCaxcuRJfX1+SkpJYsmQJ7733Hjk5OVy+fJnx48fj5eVF\nTEwMrq6uXLlyBYDIyEgALl26xKxZsygqKmL16tW4uLiQm5sLQFpaGt27d2f//v2UlJSgVCpZvHgx\nV69eJS4ujoiICABWr17NpUuXyM/PZ9y4cQDEx8fj5uaGp6cno0aNMs3133//nXfeeYfk5GQ+//xz\nHB0dSUpKYu3atRWeD3+ej38UHx9PcHAwGo2GMWPGVEi6rFmzhitXrhAXF0fDhg2xt7cnKyuLzp07\nl+vHx8eHa9eu0bRpU/bv34+vr+9jl3ABM9Z0OXz4MB999BE6nQ6j0YhCoWD37t3mGk4IIYQQolrp\n0KYl23bvw1phwNuukKQCe0uHJB4yw/3qo1A8IrcWmVHnzp3Jy8tj/vz5FBcX07NnTw4ePEjTpk15\n7bXX2LlzJ19//XWF8zp16sQvv/xCkyZNcHNzIzQ0lC1btnDkyBEGDBhwy/EyMjJMtyQ5OjqSm5tr\nSpb06dOH/Px8oqOjCQgI4JdffuHKlSu88MILnDx5slw/HTt2pGPHjhgMBhYvXkyLFi1YtGgRo0aN\nkg+3LWj06NE0adKEzZs3A9CyZUsmTpzIjBkzSE1N5emnn6akpIS9e/fy008/UVxczLhx4/Dx8eHX\nX3+ltLQUf39/ioqKyMnJ4eTJkxw4cIDw8HDs7e157bXXgLIVUz///DNt2rQhPT0dJycnbG1ty8VS\nUFDAiRMniI2NJSsri/DwcLy9vRk6dCidO3dm/PjxDB8+nAEDBlBQUMC6dev47bff+PTTT1mxYgV6\nvZ5hw4b95fPhjyu0/iwzM9M0B//8GqNSlaUYPDw8SEtLQ6lU0qVLF55//nlef/11unfvjp2dHQA9\ne/YEoLCwkLi4ONzd3Tl06BDjx4+/bdLnUWO2j0RWrlzJhx9+yPbt2/nuu+/Yvn27uYYSQgghhKh2\nvDS18fFuxI2sbJrb51s6HPGQUSmMvPaMt6XDeChs2bIFT09PZs2axZw5c/joo49MHwoDKJVKjEYj\nUFaLUqvVAuXfTNasWRMAa2trDAaD6fH169cTFBTE1q1bTY95enqSkpICQHZ2No6OjqZj58+fJyws\njMmTJ+Pj48NXX33FtWvX+Pjjj/n++++5ePFihfg3bdrEwIEDOXDgAIMGDeKbb76pqksj7sMff54A\nNWrUAMrmRmFhIcuXL0ev19OiRYty8wzAysqKtLQ04uLiUKvVNGvWzNTm5v+trKz49ddf0Wq1xMXF\nkZKSwqxZs7CxsakQi8Fg+Mt5fDOpoVar+e2339iwYQOOjo40bNgQo9GITqcznfPHsf/cD0BsbCxB\nQUEcOHDA9JhGoyEtLQ2gXNub5wOkpKTg7u5O7dq1cXBwQKFQ4Ojo+JeJ4DVr1vDqq69y6tQp2rdv\nz/79++/4c3iUmG2lS7169WjQQLanE0IIIYQwlxee7cSK+A00cCnFQ11Miq7iH+WienqhWS2cazxi\ny/TNUAgXylaszJs3D7VaTUlJCf369aNLly7s2rWL6Oho8vLymD59OiqViqlTp+Lh4QGAk5MTycnJ\nnDp16pZ9Dxs2jGHDhpV7bMiQISxcuJCvvvqKF198EZVKRWRkJMHBwUyYMAEfHx+WL19Os2bNmDNn\nDgAJCQmcPHmSRo0alesrJSWF7OxsvL298fT0ZN26dfTr16+Kr9Cj62ErhKtUKrG1tWXfvn3k5ORg\nb2/PuHHjWLJkCbVq1SIjIwNbW1sMBgN79+7l6tWrtG3bltdff52oqCicnJzQ6XR4e3uTk5PDnDlz\nyM/Pp3379uzatatC/RYHBwdat25NREQEhYWFTJ48mZ07d/LFF19w6NAh/Pz8cHBwIDc3l127dnHu\n3Dnat2/PiBEjmDlzJk5OTmU74v3F8+GmwMDACt/n6NGjiY6OxtramhEjRgAwZ84cIiIiGDBgALNm\nzTL1o1armTdvHj/88AOtW7eusFonMTERNzc3XF1dsbe3Z9euXeVqGz0OFMY/p6aqyFtvvUVeXh5P\nPvmkKZv19ttvm2Mojh07hq+vb6X6aBjybRVFI27n0sLelg5BiArk+f9gVMXzvype74V4nJSWljIj\negUqlYpUavFDltR7EKDAyK6gZ/HWON65sRCi2tmyZQu//vorBoOBNm3a0KdPH0uH9Fgz20qXrl27\nmqtrIYQQQghB2b3z/s925ot/7aChlx2OVqXk6M325514RPh62UnCRQhxSwMGDLhtXSJRtcxW06Vv\n376UlpZy5coVvLy87piEKSkpYdq0aQwbNozBgweze/duLl++zCuvvMKwYcMICwsrd/+kEEIIIYSA\nDm1aoFQq0ZeW4uuYY+lwhMUZCenT2tJBCCGE+C+zJV3CwsL4z3/+w8GDB017y9/O1q1bcXZ2Zv36\n9axcuZLIyEgWLFjAW2+9xfr16zEajbL7kRBCCCHEnzjWdODFrk+Tmp5BY9siXK11lg5JWFB7TzXt\nGte2dBhCCCH+y2zrT3///XfmzZvH0aNHee6554iLi7tt+549e+Lv72/62srKisTERDp06ADAs88+\ny8GDB8vtAS6EEEIIIaBH5w7sPpCArkRHu5q57Mh0tXRIwgKUGJg3pJ2lw7hvrT5pVanz76ao6vXr\n103bOYtH3xmfJyt1/pNJZ6ooEiFuzWxJF71eT2ZmJgqFgry8PNPWUbdib28PQF5eHm+++SZvvfUW\n0dHRpiK89vb25ObmmitcIYQQQohHlmNNB/o+35XN27+nfh01nupirstORtWOv7cDzbykmPKf3dwZ\nqHfv3nz00UdERERUaPPuu+/yzDPP0KZNm7vuNykpibi4OGxtbenevXu5D4djY2NJSUlBq9Uybtw4\nnJyciImJwc3NjVatWjFw4EBT22XLlmFvb49Op2Py5MnExsYSEBCAWv2I7T71mPnyyy+xsbGhd2/z\nbQSyatUqrl27Rm5uLjNnzsTFpez5azAYCA0NNc2L8PBw9u3bx+7du1Gr1fj5+fH8888DUFBQQGho\nKK6urvTp0wcvLy+2bdvGyJEjzRa3uDdmu73orbfe4pVXXuH06dMMHTqUyZMn3/Gc69evM3LkSPr3\n70/fvn3LJWry8/Mr7IkuhBBCCCHKPNuxLfZ2thQUFtFeartUO2qFnqiXOlo6jIfaoUOHOH36NOfP\nnycyMpLIyEjGjh3LhQsXgLLdwMaMGYPRaOTo0aN3XKkfHx9PcHAw8+fPZ8OGDabHjUYjjRo1Iioq\nigkTJvD999+Tm5vL9OnTmTlzJjt37izXj4+PD0qlkqZNm7J//358fX0l4fKQ+vO8OXPmDEFBQcyZ\nM4fVq1ej1WqZNm0a8+bNY9myZbftq7i4mKNHjxIWFsbgwYPZtGmT6VhCQgL16tUzJWKOHz/O+vXr\ncXZ2Jjc3l+bNm5vaqtVqmjRpgqOjI+7u7qxbt46XX37ZbNdA3DuzrXTp0KEDH3/8Mba2tly9epXW\nrW9f0CsjI4MxY8YwZ84cOnXqBEDz5s1JSEjAz8+Pffv20bGj/CIRQgghhPgrdra2/KNXD9Zu/hf1\n69rS1K6A3wprWDos8YC86qvB1dHO0mE81Dp16sQvv/xCgwYN8Pf3p6ioiJycHE6ePAmU7QbWuXNn\nDh48yDfffMOMGTNu219mZiYajQbAtDr/5r979+5NRkYGq1atYsaMGWg0GlJTUxk/frypfMJNPXv2\nBKCwsJC4uDjc3d05dOgQ48ePx8HBoSovgaiE0tLSCvNm3759hIWF4ezszOTJk/n73/9OZmYmTz/9\nNC1atLhtf1qt1rSyxcPDg7S0NNOxjIwMPDw8yh1LTk5m6dKlZGRksGzZMhYtWgSUzdtJkyYB8OOP\nP+Lj48Py5ctp0KABL730kjkuhbhHZlvpMmfOHLZs2YKLiwtbt24lKirqtu0//PBDcnJyeP/99xkx\nYgQjRozgrbfe4t1332Xo0KGUlJSUq/kihBBCCCHK6/i31ri61CI7Jxc/p2xslXpLhyQegNrqEmYM\neHRruTwoNxMjaWlpxMXFoVaradasGUaj0dRm6NChrF27Fnt7e5ydnU2Px8bGEhQUxIEDB0yPaTQa\n0xvlP/YBcOzYMWJiYpgzZw4ajYYzZ86gVquJj48nMTGR7OzsCvGtWbOGV199lVOnTtG+fXv2799f\npd+/qJy/mjcGg8E0rxQKBQaDgWnTpuHk5MScOXPQ68tegzMzMwkKCiIoKAidrqzYuaurK1qtFoCU\nlBTc3d1NY3l6epKamlruWJ06dbCxsSk3L/+ooKCA48ePo9fr8fPz48SJE2a7FuLemG2ly5kzZ0z3\nS86ePZvhw4fftv3s2bOZPXt2hcfXrl1rlviEEEIIIR43arU1owb3ZfFHn1DfwR4/xxx+1NaydFjC\njJRGA4sGtsJaZWXpUB56Tk5OJCcnk56ejsFgYO/evVy9epW2bdua2jg4OFCjRg2GDRtW7tzAwMAK\n/Y0ePZro6Gisra0ZMWIEUPbB87Rp03jjjTfo1KkT8+fPx8/PjyeeeMK0IqJu3bo4OTmV6ysxMRE3\nNzdcXV2xt7dn165dBAQEmOEqiHuxbt069uzZA8DUqVMrzJuAgAAiIyNxcXGhXbt22NraMm/ePLy8\nvGjZsiVWVmXPSxcXF5YuXVqub5VKhZ+fH+Hh4eTk5DB37lxOnz5NYmIiL730UrmFC23btmX48OEE\nBwdjZWXF66+/XiHW+Ph4RowYQX5+PnFxcRXmmLAchfHPadkqMnjwYFauXEmtWrXIyckhICCAjRs3\nmmMojh07hq+vb6X6aBjybRVFI27n0kLzFaIS4n7J8//BqIrnf1W83gvxuDMajazZ9DVHfj5NHU8N\nO2+48HuxraXDEmbSu5GK98bLavCqEhsbS35+PsHBwZYORQjxmDDbSpfAwEAGDRqEs7MzOTk5hIWF\nmWsoIYQQQgjxXwqFgkF/f56TvyaTl19AF2clm9PcKTaa7a5yYSFuqiLeGdnH0mE8Vv5qRYsQQlSG\n2ZIuOTk57Nq1i6ysLFxdXcsVlxJCCCGEEObjWNOBUS/1I/bjDTSoa0sXZy27s2Qr4ceJ0qhn6dC/\nUcNOtgYXQoiHmdk+8ti0aRNWVla4ublJwkUIIYQQ4gFr0/wJnm7Xhv+kptPIrohW9nmWDklUoSE+\ndjzTqrGlwxBCCHEHZlvpotPpGDBgAI0aNUKpLMvtxMTEmGs4IYQQQgjxBwqFgqF9/Tl36Qo3srS0\nd4aMEmuu62RlxKOumW0ekcMHWjoMIYQQd8FsSZf/+7//M1fXQgghhBDiLjjY12DyqKHMe3cVRUVF\nPFcriy0ZbuTrzfYnoDAzF/JYFdANtdra0qFUuTM+T1bq/CeTzlRRJOJR8d6EHyp1/uQPn6uiSIS4\nNbP9xm3evDkrV64kPT2dbt268cQTT5hrKCGEEEIIcQt1PTWMHTqADz77gvp1rHm+Vhb/ynBDj9z+\n/aixNRaz4pXW1PfSWDqUR0ppaSkxMTFotVpyc3Np0KAB06ZNY+zYsaxevdrU7scff0StVtOpU6dK\njZeUlERcXBy2trZ0796dF154wXQsNjaWlJQUtFot48aNw8nJiZiYGNzc3GjVqhUDB/5vBdOyZcuw\nt7dHp9MxefJkYmNjCQgIQK1WVyo+cXfee+89Ll68yE8//UTHjh1p0qQJly9fZurUqdSuXfuW50VH\nRxMcHFxhft2Nuzlny5YtHD16lMLCQgIDA2nUqJHp2KJFi9DpdOTl5REREcFvv/3Ghg0bcHR0pEGD\nBgwdOtTUdtq0aXh5edG6dWs6d+5MfHw8kyZNuqd4xd0xW9Jl5syZPPvss/z73//Gzc2NWbNmsXbt\nWnMNJ4QQQgghbsG3dXN6dnuanfsOU7+OJ12ctfyodQZJvDwylEY9/9fZjS5tKrcapDrauHEjjRs3\nZsiQIQB8/fXXZGZmYjAYiIqK4sqVKwwbNowbN25gY2NDSEgIHh4e5OXl0ahRI3r16sWCBQuwt7cn\nNTWVFStWYG1965VG8fHxBAcHo9FoGDNmjCnpYjQaadSoEYGBgZw+fZodO3bw4osvMn36dDw8PHjj\njTfKJV18fHy4du0aTZs2Zf/+/fj6+krC5QGaPHkyUJYIWbx4MQAhISEsW7aM3NxcnnnmGXr06FFh\nbiQnJ5v6yMzMrHD8jTfeoGXLlly7do0ePXrQvHlzYmJicHV15cqVK3eMa8uWLaxZs4YrV64QFxdH\nZGQkAFeuXDElWzZv3syOHTvYvXs3TZs25fLly/Tr169cPy1atKCgoIC6deuydu1aXn755aq6dOJP\nzFZIV6vVMnjwYFQqFW3btsVoNJprKCGEEEIIcRsKhYIB/s/xRJOGXE9Np2mNQjo65lg6LHGXFEYj\nAxsZGNOni6VDeSQlJyfToUMH09f9+/fHxcUFvV7P1KlTCQ8PZ8eOHeXOGThwIDNnzmT37t0olUoG\nDx5M586dSUtLIy0t7bbjZWZmotGUrUb644YiCoWC3r17k5GRwapVq3j11Vdp1aoVarWa8ePH06ZN\nm3L99OzZk7Fjx/LMM89w/PhxLl26RExMDHl5UhTbksaMGcOyZcv47rvv7jg3/up4YWEhAQEBvPHG\nG+zevZsNGzYwbtw4Zs6cibu7+x3HV6nK1k14eHiUGy89Pd00724eu3DhAkOHDmX27NksXbq0XD+v\nvfYakyZNwmg04uTkxCeffMKqVasqe3nEXzBb0gXg/PnzAKSkpJiK6QohhBBCiAfP2lrFuGGDcK3l\nTGr6DVo65NO2piReHn5GnnXOImp0L/l7+j75+Phw6NAh09crV67k/PnzWFtbY2dnh0qlwmAwlDvH\nzs4OpVKJQqEgISGB7du3o9Fo8PLyKvdhcmzs/7N352FVVesDx7+HMzAKiMjhkIKpqCWaV82pvEZa\nTqHklGKUhqIUXUNNUBRjSMGK1CyLyqw0vZrKtZTSvJpDRTknSjjkADKKiMwczvn94c99JZyFMHs/\nz9OTZ++11373Onuj+2UNiwgJCWHnzp3KNr1er7wM//EXz3v27OGtt94iIiICvV7PkSNH0Ol0LFmy\nhJSUFC5cuFAj/qVLl/Lss89y8OBBHn74YXbs2FEr7SJuj729vfIsXu/euNZ+jUaDTqdDq9ViNpur\nJebUanW140NCQggJCSErK0vZdvncWVlZ1ZI0BoOB7OzsavsaN26Mra0ttra2V+2dZTKZWLduHa1a\ntcLV1ZWCggLy8/PvsIXEH9XZ8KLw8HBmzJjB8ePH+de//sXs2bPr6lRCCCGEEOImODSw45VxzzJv\n8Sfk5p+noxNUmiz4tdiuvkMT19DJOo+3g57GyvLeX3WqribCHT58OK+//jqTJ09GpVLh5uZG8+Y3\nv9y2o6MjmZmZbNy4UZmPpUmTJgAEBwfXKD927Fji4uLQarX4+/sDEBERwauvvsrLL79M9+7dmTNn\nDl27dqV169bMnj0bR0dHmjRpgoODQ7W6UlJScHZ2plGjRtja2rJ582YCAwPvoDXuLfU9Ee7V7o1b\n2Q/g5+dHfHw8DRs2JC8vr9q+P/ZOAfD19SU8PJyioiKmTZtGdnY2q1evJjg4GEdHR2JiYigqKiI6\nOhqDwcCMGTOwtbVl9OjRNepasWIFQ4cOxdXVlRUrVqDVamvcg+LOqcx1MO6nqKgItVqNtbV1bVd9\nVXv27KFTp053VEezsA21FI24npOxA+s7BCFqkOf/z1Ebz39t/LwXQkB23jni3vsEsxkaNXRgZ4ED\nqSW29R2W+IMOumw+nPQ0jRs1rO9QhBBC3KZa76O4bNkyBg0axODBg6XrmxBCCCHEXUjv3IjJ4/0x\nVVVRcOEijzhc4EGb4voOS1zhIW0W773kIwkXIYT4i6v1pMvXX39Zc9JyAAAgAElEQVTNN998w8qV\nK/n0009ru3ohhBBCCFELmhj0TA70p6y8nILCQno4XuDhBoWALH5Qnyww8bAug8XBg3DTX3tZWiGE\nEH8NtZ500el06HQ6nJycqKysrO3qhRBCCCFELfFo4sa0oDGYTWZy8vJ5qEERjzkWYCGJl3qhw8g/\nrdNZ8NLTknARQoh7RJ1NpAs1Z+sWQgghhBB3F/f7DEwPDmDhki/IyMqhhb4xNuoqNuc7UWmW1XL+\nLLaU0d85n7AXRuHs5Fjf4dSLdyf+946Or+9JVcWf761nnrqj46f8++taikSIa6v1pMuxY8eYMmUK\nZrNZ+fNlb731Vm2fTgghhBBC3KHGjRoyLWgMiz9fxbHfz9D0Pld8nPPYnO/Exao6/R2dABqaC/Hz\nhJdG+2NjbVXf4QghhKhFtf636Pz585U/jxw5srarF0IIIYQQdaCBnS2TXhjN0tXr+Xn/rzR1c+Xp\nxrl8X+DIqbI/Z0XKvx8zTU1ZBHZ3Y5TPk2g0kuCqC2vXruXLL7/Ezc0No9HIww8/fNXlc6/n/fff\nZ+TIkTg6Vu+F9OWXX9KhQwdatmx52/Glp6czYcIEZXW+nJwcZsyYgbu7+23VlZCQQFRUlLItICCA\njz/+mLi4OEJDQ2+pvnnz5lFRUUFRURFRUVHodDoATp48SXx8PI6Ojmg0GiIiIoiKisJoNHLu3DnC\nwsJo2rQpAAcPHuTLL7+kqqqK8PBw9u3bh0ajoWvXrrd8fX+mmJgYOnXqRP/+/cnLy2PatGksWrQI\nGxubO6778ndyI7d7f13reysuLlaWKG/QoAGTJk1i0aJFpKWlYWNjQ79+/XjssccAyMjIYMGCBVhb\nWzNhwgSKi4tJTU3Fx8fnlq/3767Wf7J36dKltqsUQgghhBB/AktLHeNGPY2+cSO+2vw9jRs15Akn\nM4eKKvil0J4qVPUd4j3DSmXkAeMJXvZ9BO8eD6NSSdvWpdGjRzNw4EAAwsPDOXXqFPv27SM5OZmK\nigqGDBlCdnY233//Pe7u7pw5c4YHHniAffv2MXPmTE6ePEllZSUjR46kV69epKWlMWbMGDIzM2nZ\nsiXr169n//795ObmEhwczO7duzl69Cg5OTn4+Pjg5OTEhx9+SKdOnUhNTSU+Ph61Wq3E16lTJyVR\nsnHjRrZt28YTTzzB4sWL0Wg0WFpaMm3aNPr27cuwYcPIzMwkKCiI+Ph4pkyZQuPGjQkICCAyMpI9\ne/Ywd+5czp07x2uvvaacIy0tjaKiImJjY7G0tESr1RIWFnbNNjtz5ozy0r5mzRq+/fZb5YX7cmLF\nzc2NoKAgioqK6NmzJ97e3nz77bfs2rVL+QW8s7MzLi4uaDQaTCYT+/btIzg4uLa/4loXGhrKxIkT\nadeuHW+++SaRkZHk5+cTGxtLVFQUCQkJPPTQQwD8+9//xt7entTUVJYtW8bs2bOxtrbm/PnzREZG\nsmTJEkpKSsjMzCQyMlI5R3R0NHApiRUeHs7ChQuJjo7GwsKC8PBwWrRoQcuWLVm+fHm1+6l///7X\njPt631tSUhK9evXCx8eHsLAwsrOz2bdvH+7u7lRUVNC2bVulHnt7eyXxZ2dnx4oVK6qNYhE3T9Lp\nQgghhBBCoVar8e3rTQuPJnz4xRqKikto29iMwbKcrecbUmDU1neIf3l6Cuhqm0uw3xBaNfeo73D+\ndtq2bcvx48dZv349S5YsobKykokTJzJw4EB69OjBM888w8iRI5k/fz6rV6/m4MGDyrEWFhYEBQXx\nyy+/sGPHDmX7hg0b+OCDDygoKODixYt4eXnh6enJ/v372b59O76+vnh5eREUFMT06dPJzs7Gzc1N\nOT45OZnQ0FDS0tJo3749U6dO5f333wfAxsaGU6dOkZOTg5ubG4GBgRw+fJhVq1Zd9fratGnD9OnT\n+frrr0lKSqq2b8OGDXh7e9O7d28OHTp03XbKzc1Fr9cD4OrqSmpqqrLvcq+cyz0x7Ozs8Pb25tSp\nU2zcuJHXX39dKevm5qYkWRISEvjHP/5BbGws3t7ed3VvF61WS0xMDOPGjVN67qSnp9co99lnn7Fw\n4UKqqqrw8/Nj165deHp6MmbMGDZt2sR//vMfMjIyuP/++3nssceUnjJGo5G+fftSVlZGYWEhBw4c\nYNCgQSQlJaFWqxk4cKDS5n+8n66XdLne95aXl0fnzp0B0Ov15OTkEBgYSJcuXTh69KiS9AFo0KCB\n8r2tXr2aLl26EBcXR4cOHejbt28ttPDfh8yOJoQQQgghamjXxpOIVybSrIkbp9MzsacM38a5tLe7\nKKsb3SY1JtpUnSSgnRWvvzJeEi71ZP/+/bRo0UL5fGUvIzs7OwBsbW2BS0nIKxcHsba+NNROq9VW\n2240GgEoLy8nKyuL+Ph4ioqKaNeunVLu8su2VqslLy+PkJAQQkJCqKiooGvXrsTFxRETE8Pp06fR\narWYTCb69+/P1KlT6d27Nw4ODphMJgDKysrQarWo1WqMRiPFxcXKyrFXnufK3jQAlZWVWFhcegXM\nyMhQ4j527BghISHMmjVLKWswGMjOzgYgKysLFxeXavXMmTMHKysrJkyYAMB3333HZ599RmxsrNKO\nVzpy5AjOzs5s3ryZkJAQEhMTr/EN3T0MBgOurq707NkTAI1Go7TZ+fPnAaioqAAuJeRUKhVms1m5\npywsLDCbzYwcOZJHH32UdevWsWvXLuDSMLKEhAR0Oh2tWrXCbDbTq1cvfvrpJ3bu3Im3t7cSx9Xu\nJ7j1781gMJCVlQVAdnY2Li4uHDhwAJVKhaOj41VXH87OzqagoIDU1FSGDRvGtm3b7qxR/4akp4sQ\nQgghhLgqZydHXhk3mu92JLMmaQs2NlY83NCMp3Upuy44kFVhWd8h/mUYVBdoo8ogcPiTdOvYXoYT\nXUVdrj60fPlytm7dSkVFBR07dsTDw4NBgwYxc+ZMAMaPH8/Zs2dvu/5+/foxe/Zszp8/z0svvYST\nkxM//vijMq/GHzk5OfH2228DVOs90bZtW0aMGMGcOXOYMGECc+bMISkpCWtra3x9fTl9+jRz586l\nsLCQV199lQMHDhAZGYmHhwf29vYA/PrrryQkJJCRkUFYWBgbNmxQ6vfx8SEmJobt27djbW2t9Fho\n2bKlEs9lBoMBR0dHYmJiKCoqIjo6mm3btlFVVcWRI0f4+eefyc/PZ9u2bTz33HPMnDmTRx99lFmz\nZtGvXz/69Omj1FVVVcWaNWsIDw/H2tqa+Ph4vLy8bru9L/uzVx9ycXHhwoULREVFcebMGR577DH8\n/f2ZMWMGDg4OqFQqHn30UTZv3kxcXBxFRUVMmzaN999/n4sXL1JVVUXz5s0BsLKywmQysW3bNtLT\n0+nYsSNqtZpmzZpRWVlZbY6na91Pt/q99e3bl1mzZrF3717c3d3R6/VUVVUxe/ZsSktLCQoKqnHN\nS5cuZdKkSaSkpLBs2TKaNGlSR61771KZ74F1nffs2aN0cbtdzcI23LiQuGMnYwfWdwhC1CDP/5+j\nNp7/2vh5L4S4PaczMlm2dgMnTmfg4uyEjbUVx0qsSS60p9SkvnEFf1MO6nJaVPxOl2YNGfvMYPTO\njeo7JPEXdrMTsIo/T2JiIocPH8ZkMtGhQweeeurOlrEW9x7p6SKEEEIIIW7I/T4DoS+O5ae9v7Lq\nq2/JL7jA/S6NcXcpY8/FBqQW28pEu1ewVFXR3JxBa81FRg7tx8MPta0x1EOIWyUJl7uPr68vvr6+\n9R2GuItJ0kUIIYQQQtwUtVrNIw93wKtNS/7z7VZ2JO/F1taGbg1NtLcr4mCRHaklNlSZ/77TBmpV\nJtxVeTStOsugXt3o+1gPbG1kyW0hhPi7kqSLEEIIIYS4JQ4N7HhumA+PPNyB1V9v5vipM9jaWNOt\nYRUP2RXxa5EdR0psMP6Nki+WqiqaWeTjWnGGf7RpzoinAnHTN67vsIQQQtQzSboIIYQQQojb0sKj\nKaEvjuXo76f5z6atpJ04hbW1FV2cqmhvV0RKsS1pJTaU3MNzvjRQG2mmyqFxRRZt3Jsy6Ilnad2i\nmUyUexveeubO5sKorUlVz549W205Z3H3Sg/bceNC19EktmetxHHu3DkaNGiATqerlfrEpVWhCgsL\ncXZ2ru9Q7pgkXYQQQgghxG1TqVS0au7B1AnPc/zUGdZv/p4jaSfQWero0KghHRtcJL3ckrQSG06X\nWWG6B+Z90apMeFiW0Kg8C/vKC7Rt3YKnej9PC4+mkmy5CwUEBDBv3jwaNbo0iXFwcDAzZswgKSmJ\ngICAGuUjIiL46KOP6jSmjz76iIyMDC5evMiMGTNwcnICID8/n6ioKOzt7SktLeX1119Hp9Oxbt06\nfv31VyIiIpQ6SkpKmDVrFo0aNeKpp57Czc2NjRs38txzz9Vp7H8XZrOZd955h7Nnz1JZWYmVlRXT\npk3DwcGhRtk333yTyZMns3LlSnr27EmHDh2q7Y+LiyM0NPSWzr927VosLS0ZOPDSQgQREREEBgaS\nn5/P0qVLsbGxobS0lGnTpqHX60lMTKR169a4urry5ptvKsuLN2jQgOnTp9faz6b09HQSEhKIiooC\nYMOGDZSXl/PYY48xd+5cLC0tKS4uZvTo0Zw+fZqLFy/y/PPPA7Bx40bOnTtHQUHBVdvpShs2bECn\n0ynXf1lhYSGffvopL7/88nXjzM7OJjY2FgcHBzw9PRk9erSy74cffiAxMRGz2cyoUaPo2LEjzzzz\nDPfffz8A4eHhNGjQAIBly5ZRUFBAcXExoaGhfPLJJwwePFh5Zm+GJF2EEEIIIcQdU6lUtGzmTsi4\nZzl55ixbf/yFX/YfospkwqGBHb0b2lFmUnOs1JqjJTbkG7X1HfItUWHmPsty3DhHg9JstOUqund6\niN6PdKGJQV/f4Ynr8PPzY82aNQQGBnLq1CmcnJwwmUycOnWKzMxMFi9ejEajUV5wT548yffff8+K\nFSvw8vIiIyOD3r1706JFC959912sra0pLy9n3rx5jBo1im7dunH48GHatm1LVVUVJpOJKVOmXDOe\n8vJydu/ezfvvv89PP/3EqlWrmDhxInCpx8TEiRNp06YN0dHRpKenk5qaSm5uLkajsVo9Op2OFi1a\nYDKZcHFxYfny5Vdd8lfcnsTERPR6Pf/6178A2L17N3FxcQwePJgDBw4QGBhIREQEzzzzDCkpKSQm\nJgJgNBp54YUX+Pjjj9mzZw979+4lLS0NgP79+zN48GBSUlKYNm0aZ8+e5d///jf29vakpqaycuXK\nG8a1ZcsWhg0bRo8ePThx4gTZ2dlYWVmxd+9efH19CQ0NZfz48bRs2VKJu6CggLi4OKZMmULjxo0J\nCAhg8eLFzJ49G2tra06fPs0bb7xBXFwczs7O5ObmYmdnR+PGjTl06BDvvPPODZM2R44c4b777mPS\npElUVlayZcsWnnrqKcaNG6ckXdavX098fPxVJ4Revnw5v//+OxcvXsTPz49du3ZRVlZGr169iI2N\nxdLSEq1WS1hYGPb29jdc0XLlypX4+/vTsWNHxo8fz4gRI9BqL/2988knn/Duu+9iMpkICQlh9uzZ\nlJSUoNVqadKkiZJwgUvLch88eBCDwUBaWhpOTk63lHAB+PsMtBVCCCGEEHVOpVJxv/t9vPCML2/M\nnMyY4YOwb2DHmbNZ5Odk4ak9zxCXXJ5xyaa7/QXusyxDjbm+w74qa4sqWliX0NU6h178yoNlKXR0\n0RA4ypc3Z07m+WE+knD5C3j88cdJTk7GZDKxfPly5QUQLv0WG8DGxob09HQaN26Mh4cHvXr1orS0\nlMDAQF5++WW2bNmCjY0Nw4cPp3v37uzduxe41Bti0qRJ9O7dmyZNmhASEkJKSsp14ykoKFBe2lxd\nXcnJyVH2eXp60qZNG77//ntUKhXNmzdnwIABDBgwoEY9Go2GF198keDgYH777TfatGnDggULWLVq\n1R23mYCDBw/SvXt35XPnzp3JyMioUc7BwYEHH3xQWcFIo9HwyCOPsGvXLlavXs2IESOUsg0bNmTi\nxIn07t2bPXv28Nlnn/HGG28wY8aMGkm1qyU5VCoVL730EkePHiUmJobPP/8cR0dHfvnlF1q3bg1A\nbm4uLVu2pKKigrCwMJYsWaLcr1cyGo34+vrSs+elIVZHjx4FYOjQoQQFBVFcXMzEiROxsrIiPz//\nhnE98sgjPPDAA8yZM4eYmBgcHBzQ6XR069aN5ORkjhw5gqenJzY2NjWOLy4uZuXKlVhZWeHg4MCu\nXbvo0aMH/fv3Z8OGDXh7ezNr1ixlOe5OnTqxdevWGvVcKS8vD4PBAIC9vT0XL15U9pnNZnQ6HVZW\nVlRUVGBpackbb7xBdHQ0BQUF1dqrW7duBAYG8vTTT5OYmIhGo2HevHlkZ2df9/xXkp4uQgghhBCi\nTtjZ2tCjcwe6d3qIjKwcft53iJ2795Gblw8qaGrfgAedbKhCRXaFjoxyS3IrdORXaimvh0l4dSoT\nel0FBm0ZDYz5qEsLUJVCQwd7vHv14h/t2uDS6NZ+wynqn0ql4vHHH2fz5s2cO3eOFi1akJ6eDoDJ\nZKJ///50796dxMTEakNHNBoNOp0OrVaL2Wxmw4YNlJSU0KdPHxwdHQGwtbVVylpaWirnuyw/P5/o\n6Gjg0hATnU5Ho0aNKCgoACArKwsXF5dq8b777rvY2dkxc+bMm7q+kpIS9u7dS+vWrenatStJSUnV\nXvTF7XnwwQf58ccfcXd3B+Dw4cO4u7ujVquprKwEUL7HPyYinnnmGaZOnYqbm5tyrwBYW19ayUyr\n1VJeXk5FRQUAFhYWqFQqfv75Z1asWIG7uzsdOnTg+PHjyrGZmZk4ODjwzjvvEBQUhI2NDadOneKD\nDz7A29tb6cXh7OzMoUOH8PLyIjY2lrVr13LhwgXUajVGo5Hi4mIqKys5evQoK1aswN/fn2bNmmE2\nX0p+29nZUV5ersxPo9FoqKioICQkBICoqKhqicL09HRat25NYmIizZs3Jzw8HKPRSEBAAD169GDk\nyJG88cYbWFpa1uiJtWjRIo4fP87AgQNxcHBg6tSpnD17lqNHjyqJnsrKSiwsLv19kJGRQZs2bdBo\nNMo2gC+++IJffvkFb29vBg0aBIDBYCArKwuDwcCFCxewt7dXyltaWlJRUYHJZEKn03H27Fnlu3R0\ndFS+3yutWrWKIUOG8PHHHzNu3Di++uorxo0bd5U7pyZJugghhBBCiDqlUqloYtDTxKDHt583GVk5\nHE47we6DKZzKyESlUmFhYUFrWxs6NbRGrVZz0ajmXKWWc5Va8o0aCowaSqrUVNZCMkajMuGoMdJQ\nY6ShthIHiwosjUVUFp2HcrCoVNGqeTM6tu1Oi2ZNcdM3rvYPfPHXM2TIEHx9fYmMjKy2/dlnn2XO\nnDkkJSVhbW2Nr68vtra2ylCRK7m4uLBp0yYKCgooKyvj/PnzNzyvk5MTb7/9drVtGo2Grl278tpr\nr1FYWEhkZCSHDh0iJSUFS0tL1q9fT7t27Zg6dSovvfSSMs/EtSxZsgR/f3+Ki4tJSEi46pwj4tYN\nHTqUt99+mylTpqDValGpVEyfPh0LCwsWL15MTEwMubm5ADRr1owPPvhAaXs7OztsbGzw8/O77jn8\n/f2ZMWMGDg4OqFQqunTpQpcuXYBLE8l+/fXX/Otf/8JkMtG5c2fs7Ox47LHHCAkJwd7envLycsaN\nG8f999/PG2+8wYgRIwgLC2PevHmUlJSgVquxtrbmlVdeoWHDhkRGRuLh4YG9vT12dnZcvHiRzZs3\nc+zYMR5++OFrxqnRaKrdx15eXkycOBFra2t0Oh1jx47l/PnzyhxElZWVSvLDyckJOzs7jEYjen31\nnoHBwcHKn48fP8706dM5f/48r7zyCtbW1ixevJj58+cTExPD9u3bsba2pm/fvvz00088/vjjyrF+\nfn412nr48OHExsaybt06nnzySTQaDdHR0YSGhvL8888zc+ZMjEYjL774Ik2bNiUhIYEdO3ZgNBqV\n7+CyrKwsLly4QMuWLTEYDCxfvly5vpuhMl9Oaf2F3Wg8181oFrahlqIR13MyduCNCwnxJ5Pn/89R\nG89/bfy8F0LcXYqKS/j9TAZHT5zm2MnTnMrIpKqqClQqzGYztjbWWFrq0Gm1aNRqqswWlJgsKKlS\nU2KyoNRkgcmswgyYL////+u2ACwtTP/7DyMWxlKMZSWUlpUpv522UFnQxE1Phwdb06q5Bx73GdDp\n/lpzzggh7h6LFi1SJl69nsTERA4fPozJZKJDhw7K8JnbkZSUhIeHBw8++OBt1/FXUFhYyMqVKwkM\nDKzvUG6aJF3+n7x0/Tkk6SLuRvL8/zkk6SKEuBkmk4m88wVk554jIzOHoydPk3e+gIKCQkrLy/+/\nV4wK5V+wZvP/d4tXAeb/JVxUqkvbVP+rV6NW4+zUEI8mBpo3vQ+9izONnRri5GiPWn3vLmsthBCi\n/sjwIiGEEEIIcdewsLDApZETLo2caNfGk348ouyrqKikqKSEouISikpKKSsvx2y+NCni5f8wX0q8\n6HRarC0tsbK0xMbGiga2NlhZWsqSzkIIIf5UknQRQgghhBB/CTqdFiedA06OMmeFEEKIvwaZEUwI\nIYQQQgghhBCiDtz1PV1MJhOvvfYav/32GzqdjpiYGDw8POo7LCGEEEIIIUQtSg/bcUfHN4ntWUuR\n/I/JZCI3N7fGqivi7vDaa6/V6/FC3Iy7Puny3XffUVFRwb///W/2799PbGwsixcvru+whBBCCCGE\nEH8BZrOZ+fPnk5mZiVarpaKigtDQUJydnW947O7du9m/f/8dr5Tyzjvv0LNnTzp06HBLx82bN4+K\nigqKioqIiopCp9MBcPLkSeLj43F0dESj0RAREUFVVRUfffQRKpWqWrwZGRksWLAAa2trJkyYQHFx\nMampqfj4+NzRNd3r1q5di6WlJQMHXloIICwsjClTptC4ceN6jeta90RxcTGzZ8/G0dGRBg0aMGnS\nJAAyMzMZPXo0//3vf5U6SkpKmDVrFo0aNeKpp57Czc2NjRs38txzz9XLNd3r7vqky549e+jZ81LW\nukOHDhw6dKieIxJCCCGEEEL8VaxZswa9Xk9ISAgAZ86cIS8vj+3btysv1QEBAXz88ccsXLiQwsJC\nTp06xYQJE9i1axf79u1j8ODBfPLJJxiNRi5evEhYWBgzZsygVatWnDhxAnd3d2xsbMjIyGDOnDlE\nR0cDl5Ij4eHhSizLly/n6NGj5OTk4OPjQ//+/a8Z95kzZ5QX6zVr1vDtt98qiZJz584RFhaGm5sb\nQUFBlJWVsXbtWkpLS7GxsalWj729Pe7u7gDY2dmxYsUKpkyZUqtt/Hdy+V7Jzc3lrbfe4umnn+bD\nDz+kU6dOpKamEh8fz+eff86JEyeoqKhAp9MxduxY3n33XaytrSkvL2fevHmMHDmS5s2b07p1ayX5\nMWnSJGJjY7G2tr7qua93TyQlJdGrVy98fHwICwsjOzsbe3t7Fi9ezH333VetHp1OR4sWLTCZTLi4\nuLB8+XKCgoLqvO3+ru76pEtRURF2dnbKZ7VajdFoRKOpHvqePXvu6Dxrhrve0fHi5tzp9yREXZDn\n/88hz78QQoj6cPDgQcaPHw/AokWLOHnyJE2bNqVp06Y1yvbo0YPKykq2bdvGTz/9RI8ePbC1tSU1\nNZVjx47Rpk0biouLOXDgAMXFxbz44ovs3r2bvXv38tJLL/HCCy9gNBrp27cvZWVlFBYWcuDAAaV+\nLy8vPD092b9/P9u3b79u0uXKYU2urq6kpqYq+zp16gTAl19+SYcOHbCyssLPz4/k5ORq5wNo0KAB\nwcHBAKxevZouXboQFxdHhw4d6Nu372226t/DihUr2LHj0rC36/07xsvLi6CgIKZPn052djb79u1j\nwYIF7Nu3j3Xr1mFjY8Pw4cM5d+4c8fHxwKWha3PmzKGsrIyQkBBat26Nu7v7NRMucP17Ii8vj86d\nOwOg1+vJzs4mISGBF198sVriD0Cj0fDiiy8C8P3339OmTRsWLFiAh4cHI0aMuI2WEtdz1ydd7Ozs\nKC4uVj6bTKYaCZfLP3SEEEIIIYQQ4kpt27blv//9L88//zzBwcGkp6ezaNEimjVrRmVlJQAFBQWU\nlZWxYMECJkyYQNu2bTl16pSyxLjZbKZdu3ZMmjSJX375BScnJ7RaLZaWlmg0GiwtLQFQqVTk5OSQ\nkJDAuHHjaNWq1aWlzP9ffHw8zz//PO3atePEiRPK9mPHjvHuu+9iZ2en9JIxGAxkZ2cDkJWVhYuL\ni1K+srKSN954g/bt2zNhwoSbaofs7GwKCgooKChg2LBhfPTRR5J0uYFRo0ZVG150mclkoqCgQPl8\nuXeRVqvl4sWLyneuVqsB2LBhAyUlJfTp0wdHR0fgUjIMwMrKigceeIDY2FhiYmKUOm/1njAYDGRl\nZQGXvuvy8nIyMjJ47733OH78OJ988gljx46tdn0lJSXs3buX1q1b07VrV5KSkiTpUgfu+qRLx44d\n2bp1KwMGDGD//v20atWqvkMSQgghhBBC1LK6mAgXYPjw4cyfP5/g4GAsLS2prKxk3LhxNGrUiFmz\nZnHo0CGMRiMWFhZYWVmxfft2CgsLsbW1Ra/X8/3339OnTx82bNhAZGQk586dY86cOdc8n5WVFSaT\niW3btpGenk7Hjh2VfU5OTvz444/KnByXtWzZkrfffrtaPQaDAUdHR2JiYigqKiI6Oppt27ZRVVXF\nkSNH+Pnnn8nPz2fbtm3MmjULB4frL6W+dOlSJk2aREpKCsuWLaNJkya32aJ3j/qYCPeJJ55gypQp\nuLpevae0ra0tbdu2JSYmhvPnz+Ps7IyLiwubNm1Sknvnz5+vdszAgQP5/fffMRgMyrZbvSf69u3L\nrFmz2Lt3L+7u7jz88MM8/PDDwKUhUX9MuAAsWbIEf39/im3p0CAAABrASURBVIuLSUhIuOE9JG6P\nynxl6vUudHn1orS0NMxmM3PmzKFFixb1HZYQQgghhBBCCFHD/PnzKS8vp7CwkOeff/66HQfS0tJ4\n8803CQ0Nlffce9Rdn3T5O0pOTua5557j7bffZsCAAcp2Hx8f2rZtS2xsbI1j1q5dy4kTJ5g6deqf\nGaoQ4jalp6czaNAg2rZtq2zr2rWrMua6Nvj7+/Paa6/JX+BCCCGEEELUk7t+eNHfVfPmzfn666+V\npMtvv/1GaWlpPUclhKhNLVu25PPPP6/vMIQQQgghhBB1RJIud6k2bdpw8uRJCgsLsbe3Z/369fj4\n+JCZmcmyZcvYtGkTRqORBg0a8M4771Q79vPPP+frr79GpVIxYMAAWW9diL+Qt956i19++QWz2cyY\nMWPo378//v7+tG7dmqNHj2JjY0Pnzp3ZuXMnhYWFLFmyBLVaTXh4OBcvXuT8+fMMHz4cPz8/pc6L\nFy8SHh6ujB+eOXMmrVu3rq9LFEIIIYQQ4m9Dki53sSeeeILNmzczZMgQZam7jIwMCgoKWLp0KRYW\nFgQEBPDrr78qxxw7doyNGzfyxRdfoFKpGDNmDI8++ijNmzevxysRQlzNsWPH8Pf3Vz4PHz6c9PR0\nVq5cSXl5OSNGjOCRRx4BoH379sycOZOAgACsrKz45JNPCA0N5ZdffsFgMDBw4ECefPJJsrOz8ff3\nr5Z0ef/99+nWrRt+fn6cPHmS6dOns2LFij/9eoUQQojrudNJUetjUlVRv7b8986GUPd+/HgtRSLE\ntUnS5S7m4+PDa6+9RtOmTZU11y0sLNBqtUyePBkbGxuysrIwGo3KMWlpaZw9e5YxY8YAcOHCBU6f\nPi1JFyHuQn8cXvThhx+SkpKiJGKMRiNnz54FUOZ+sbe3p2XLlsqfy8vLcXZ25tNPP2XTpk3Y2dlV\n+5kAl34u/PTTTyQlJQFQWFhY59cmhBBC3G3Wrl2LpaWlsgRwXUpNTSUhIQErKyu8vb154oknlH2L\nFi0iKyuLgoICxo8fj6urK7GxsTg4OODp6cno0aOVsvPnz8fW1paKigpeeuklFi1aRGBgIDqdrs6v\n4e/uj/dLWFgYU6ZMoXHjxrddR11ITExk9+7dlJaWEhwczP3336/smzdvnrJSVlRUFDqdjsrKSl54\n4QWmTJlChw4dlLLR0dE0btwYBwcHRo4cyYIFC3jllVfqLO6/E0m63MWaNm1KSUkJn3/+OZMnT+bM\nmTMUFRXx3XffsXr1akpLSxkyZAhXzoXcvHlzWrZsyUcffYRKpWLp0qWyzLYQfxHNmzena9euREdH\nYzKZeO+9925qOcclS5bQoUMH/Pz8+Omnn/j+++9r1Dto0CB8fHw4d+4cq1evrqtLEEIIIe56e/fu\nZeXKlWg0GhwcHAgNDeXJJ59k2LBhZGZmEhQUxIEDB9i6dStlZWW0b9+ePn36MGXKFB577DFSUlKY\nM2cO9vb21zzHkiVLCA0NRa/X88ILLyhJF7PZzP33309wcDCHDh3i22+/RaPR4O/vT8eOHRk/fjwj\nRoxAq9UCl6YcyMjIwNPTkx07dtCpUydJuNSzxMREkpOTqaioYMiQIWRnZ7Nz506aN29OVlYWMTEx\nxMbGYjabSUtLY9iwYdUSNgEBAURGRvLyyy/j5eXF008/Xe1+nDJlCjNnzsTR0ZHS0lIiIyNvGM/S\npUs5c+YMCQkJREdHAyjvjlFRUaxZs4Zvv/0WHx8fFi5ceNXlrh944AHy8/Np0aIFa9euxcfHp07a\n7+/Ior4DENc3YMAAMjMzlYylWq3G2tqaIUOGMHbsWBo3bkxOTo5Svk2bNnTv3p1Ro0YxZMgQTp48\niV6vr6/whRC34PHHH8fGxgY/Pz+GDBkCgJ2d3Q2P8/b25rPPPmPUqFF8+umnqNVqKioqlP0TJ04k\nKSkJf39/xo0bh6enZ51dgxBCCHG3a9SoEcOGDaNbt278+OOPALi5uREYGMjw4cNZtWoVTZs2ZciQ\nIXTv3p2tW7cC4O7uTlBQEG3atOG333677jny8/OVf4OrVCplu0qlYuDAgeTl5fHRRx/x7LPPkpeX\nh8FgAC71Yr148aJSvl+/fgQEBNCzZ0/27t3LyZMneeuttygqKqrVNhFXt2LFCsLCwggLC2PPnj0A\nrF+/nrlz5xIbG8uSJUsA6N69O8HBwaSnp3PixAnMZjPTp0+v1sPpj5o1a0Z0dHSN+9FoNJKZmYmH\nhwfDhg27YYwazaV+FK6urtXeC3Nzc5V78PK+L774gi5duuDu7l6jnmHDhhEYGEizZs24cOECW7Zs\nYeHChVRVVd18g4mrkp4ud6GuXbvStWtX4NKSr5eHGvzzn//kn//85w2PHzduHOPGjavTGIUQd6ZJ\nkyasWrWq2jaVSsX06dNrlL1yCNLbb7+t/Dk8PFz58zfffHPd49577707ilcIIYT4q1q1ahXDhw+n\noqICW1tbPvvsMzw9PencuTOWlpYAmEwmAMrKytBqtbz33nv069ePhx56iK+++goAa2trAHQ6nVIe\nLg0XOn78OEOHDuXRRx8FQK/Xk5OTg4uLS7Ve6QB79uzhyy+/JCIiAicnJwwGA1lZWRgMBi5cuHDV\nHjRLly7l2Wef5c0336R///7s2LGD/v37135jiWpGjRpVbXjRla5MptnY2ACXfkF+5fbLCRG1Wo3R\naKS4uJjKykoAGjRoAFDjfjQajYSGhlJQUMC8efOYP38+jRo1AiAkJASA0NBQpbeKhcWlfhRZWVm4\nuLgo5zYYDGRnZ1fbt2bNGtzd3Tl06BC//fYbixYtqnHNn376KX5+fqxYsYL77ruPlJQU2rdvf1vt\nJy6RpIsQQgghhBDinqXRaAgMDEStVjNnzhzOnDnDvn37+O233ygvL6eqqorTp08zd+5cCgsLefXV\nV8nNzeXnn38mOTm5RtLkj4KDg2tsGzt2LHFxcWi1WuUXqBEREbz66qu8/PLLdO/enTlz5tC1a1eG\nDx9ObGws69at48knn1Re1C9LSUnB2dmZRo0aYWtry+bNmwkMDKy9BhK3ZNCgQcycOROA8ePHK/Pv\nXXb//fej0+mIi4vj2LFj+Pr60qdPHyIjI/Hw8KiRVDMYDNXuRwsLCz744AOcnZ1p0qRJtfJX/vLt\nMl9fX8LDwykqKmLatGlkZ2ezevVqgoODcXR0JCYmhqKiIqKjo5UhQ++88w49e/asUdeWLVvo1q0b\nrq6unDt3jvPnz/PUU0/dcZv93anMN/opIoQQQgghhBD3sICAAD7++OP6DkMIcQ+SpIsQQgghhBBC\nCCFEHZCJdIUQQgghhBBCCCHqgCRdhBBCCCGEEEIIIeqAJF2EEEIIIYQQQggh6oCsXiSEEEIIIYSo\nd1v+2+KOju/9+PFaikT8Vbhu3X9Hx2d5d6ilSP7n3LlzNGjQAJ1OV+t1i78mSboIIYQQQggh7llG\no5G33nqLgoICLl68iIeHB6+++mqNcmvXrsXS0pKBAwfedN1XOyYuLo7Q0NBbijE1NZWEhASsrKzw\n9vbmiSeeqLZ/3bp1/Prrr0RERHDq1CliY2MxGAy0aNGC0aNHK+WWLVtGQUEBxcXFhIaG8sknnzB4\n8GCcnJxuKR4B+fn5zJ07F0tLS4qLixk9ejSdO3e+4XFvvvkmkydPZuXKlfTs2ZMOHW4+sZOcnMyB\nAweuuyR4cXExs2fPxtHRkQYNGjBp0iRl37Xuo+3bt7N8+XI++OADpezBgwf58ssvqaqqIjw8nH37\n9qHRaOjatetNxytujiRdhBBCCCGEEPeslStX0rx5c4YPHw7Af/7zH/Lz89m+fTvJyclUVFQwZMgQ\npXxYWBhTpkyhcePGBAQEEBkZySuvvEKvXr04fPgwXl5enDlzhj59+gDw5ZdfcuDAAdRqNaGhoaSl\npSkv7La2tmRnZ7Nw4UK0Wu01Y1yyZAmhoaHo9XpeeOGFakmXjRs3kpubi9FoBGDr1q0MHTqUPn36\nMHfuXE6fPo27uzsALVu25ODBgxgMBtLS0nBycpKEy206cuQI9913H5MmTaKyspItW7ZQWVlJbGws\nFhYWFBUVERkZSWRkJNbW1pw/f54pU6aQkpJCYmKiUs/y5cs5evQoOTk5+Pj44OTkxIcffkinTp1I\nTU0lPj6ejz76iNzcXHJycvDy8rpuXElJSfTq1QsfHx/CwsLIzs5Gr9cDV7+Pjh8/zr59+5T75zJn\nZ2dcXFzQaDSYTCb27dtHcHBw7TekkDldhBBCCCGEEPeutLQ0unTpony+3PNj/fr1zJ07l9jYWJYs\nWXLdOjw8PHj55ZexsbFh8ODBBAUFsXPnTgD69+/PjBkzKC4u5tSpUwBYWFgwbNgwHnnkEXJycsjJ\nyblu/fn5+cqLs0qlqrZvwIABDBgwQPns6+vL9u3bmTNnDnl5eeTl5Sn7unXrRmBgIE8//TSJiYlo\nNBrmzZtHdnb2TbSUuNIjjzzCAw88wJw5c4iJicHBwYHk5GQ8PDwIDw9n7NixbN++HU9PT2bOnEnf\nvn3ZunUrDz74IL6+vko9Xl5eDBgwgA4dOrB9+3ZlW1BQEDY2NmRnZ5OSksLMmTMZOnToDePKy8vD\nYDAAoNfrq91bf7yPLly4wNKlS3nppZdq1OPm5kZwcDATJ07kiy++4B//+AexsbEkJyffUbuJmiTp\nIoQQQgghhLhntWnThh9++EH5/OGHH3L8+P/mf/ljkkOtVmM0GikuLqayshIAW1tbALRaLZaWlqjV\nakwmEwDW1tYA6HQ61Go1cGmYSFJSEnq9Hjc3N8xms1L/okWLCAkJUZI2UP3l+cqyV5OTk8Pw4cOZ\nMWMGOp0ONze3GmVWrVrFkCFD2LlzJ0OHDuWrr766QSuJP0pMTMRgMBAeHk5ERAQffPABlZWVWFhc\neoXOycmhpKREuX8sLCwwm8017qf4+HiKiopo166d8t3a2NgAl+6n0tJS5RiNpvpAlG+++YaQkBCW\nLl2qbDMYDGRlZQGQnZ2Ni4uLsu+P99F3331HSUkJUVFRHD9+nK1bt9a4ziNHjuDs7MzmzZsJCQmp\n1ktH1A4ZXiSEEEIIIYSod3U1Ee7w4cN5/fXXmTx5MiqVCjc3N5o3b86gQYOYOXMmAOPHj+fs2bMA\n9OnTh8jISDw8PLC3t79h/d988w1nz57FwcGBJk2aAODo6EhmZiYbN24kKyuLgoICZd/VhnCMHTuW\nuLg4tFot/v7+AERERBAVFVWjrF6vZ9asWTg6OuLp6Ymrq2u1/VlZWVy4cIGWLVtiMBhYvnw5gwYN\nuoUW++uoi4lwL+vevTuvv/46Op2OyspKBg0axKOPPkpERAQxMTGUlpYye/ZsIiMjiYuLo6ioiGnT\nplFcXMwHH3yAg4MDAE5OTvz4449UVFRQVFRU4zyWlpY89NBDSs+lNm3aKPv69etHv379qpXv27cv\ns2bNYu/evbi7u6PX61m8eDGDBw+ucR95e3srvWcCAgLw9vauVldVVRVr1qwhPDwca2tr4uPjbzi8\nSdw6lflGqVQhhBBCCCGEEEIIcctkeJEQQgghhBBCCCFEHZCkixBCCCGEEEIIIUQdkKSLEEIIIYQQ\nQgghRB2QiXSFEEIIIYQQ9c516/47Or4uJ1UVd6dmYRvu6PiTsQNrKRIhrk16ugghhBBCCCGEEH8B\nRUVFFBYW1ncY4hZI0kUIIYQQQghxz1q7di1+fn68+uqrhISEsHDhwquWCwgIuOW633nnHfbvv34P\nndTUVCZPnsyMGTPYvHlzjf3r1q1TloY+deoUQUFBREVFsXz58mrlli1bxqJFi4iLiwPgk08+IT8/\n/5ZjFjdn7dq1bNjwv540YWFh5Obm3rDczUhPTyciIqLatrCwMN544w3l87Xux6VLl3LixIlbPu/t\n3N+3c0xiYiIzZ85kypQp/P7779X2zZs3j5iYGMLCwqioqLjusxEdHc3777/PihUrMJvNzJ8//5Zj\nuVvI8CIhhBBCCCHEPW306NEMHHhpKIm/vz9Go5HZs2djbW3N+fPniYyMVMpGR0cDcPLkScLDw9mw\nYQP5+fnY2dlhYWFBcHAwkZGR2NnZsWfPHnr27Hndcy9ZsoTQ0FD0ej0vvPACTzzxhLJv48aN5Obm\nYjQaAdi6dStDhw6lT58+zJ07l9OnT+Pu7g5Ay5YtOXjwIAaDgbS0NJycnHBycqrVdhI3lpiYSHJy\nMhUVFQwZMkTZvnnzZrZu3UpZWRnt27enT58+TJkyhccee4yUlBTmzJnDpk2bOHjwIEajEY2m5qv4\nmTNn2LJlC71791a2LVq0iIKCAi5cuMCLL77Ijz/+SF5eHu3bt2fdunX88MMPqNVqoqKiqpUNDg5m\n8eLFaDQaevToAUB+fj5z587F1taW7OxsFi5cyMsvv4yXlxcZGRn07t2bBx98kLfeeotGjRpx5swZ\njEYjM2fOxNHRkdLS0mrPyrXaZ+nSpZw5c4aEhATleTpz5gxFRUVERUWxZs0avv32W3bs2HHNZ+OB\nBx4gPz+fFi1asHbtWnx8fO7oe6tPknQRQgghhBBC3NNWrFjBzp07qaqq4vnnn2fXrl14enoyZswY\nNm3axH/+8x8AjEYjffv2paysjMLCQg4cOADAk08+Sffu3RkzZgw//fQTrVq14rnnnqvWM+Fa8vPz\n0ev1AKhUqmr7BgwYQHp6OgkJCQD4+voSHx/Pzz//zLlz58jLy1OSLt26daNbt26YTCbefPNN2rZt\ny7x583j++eeV+kXtWrFiBTt27ABgz549AKxfv54lS5ZQWVnJxIkTlWRe06ZNGTJkCL///jtff/01\nffr0wd3dnaCgIBYtWsRvv/3Gpk2bSEhI4Pjx43z66ac1zhcWFsasWbNo1aoVAMePH2fnzp107twZ\no9HI7t276datGz179uTEiRP07t2bUaNG8cILL9Qo+/PPPwMwduxYWrRowZo1a7CwsGDYsGEUFhby\n/vvvk5OTQ2lpKYGBgeTl5fHOO+9w4MABxo8fT5s2bTh8+DBGo5HMzEweeughvLy8bthml5NJrq6u\n5OTkKNtzc3OV+9TV1ZXU1NTrPhvDhg0DICcnh0OHDrFlyxY2bNjASy+9hFqtvslv8O4gw4uEEEII\nIYQQ97RRo0Yxd+5c5s2bR58+fTCbzcpLnoWFBWazGbj0gpeQkIBOp6NVq1bKdmtrawDUanW1l8M/\n9lZYtGgRISEh7Ny5U9mm1+uVl8/L9V1LTk4Ow4cPZ8aMGeh0Otzc3GqUWbVqFUOGDGHnzp0MHTqU\nr7766labQ9ykUaNGERsbS2xsLJ06daq2749Jgvfee4+cnBweeuihGveNTqfDZDIpx1xOGnzxxReE\nhISwfv16ALRaLdHR0cyePZuKigpMJhPu7u5MnTqVkSNH4unpWe289vb2SixXK3tlGYDk5GSSkpLQ\n6/W4ublhNpvRaDTodDq0Wm215+JynEajkdDQUDw8PJg3bx7nzp1T9oeEhBASEkJWVpayzcLiUooh\nKysLFxcXZbvBYCA7O7vavpt5Nj799FP69u1LYWEhjRs3JiUl5arl7mbS00UIIYQQQghR7/7M1Yce\nffRRNm/eTFxcHEVFRUybNo2tW7diZWWFyWRi27ZtpKen07FjxxrHdu3alS1btjBv3jz27duHt7e3\nsi84OLhG+bFjxxIXF4dWq8Xf3x+AiIgIZR6XK+n1embNmoWjoyOenp64urpW25+VlcWFCxdo2bIl\nBoOB5cuXM2jQoDttjr+s+lh9aNCgQcycOROA8ePHc/bsWQBcXFz4+eefSU5OvmYCoX///kRGRlJV\nVQWAn58ffn5+APzwww8AuLm5MWbMGCIjI/H09MTe3p6IiAhyc3N57bXXcHd356OPPuLxxx+vVvfV\nyv6Ro6MjmZmZbNy4kaysLAoKCmqU8fPzIz4+noYNG5KXl4dWq+WDDz7A2dmZJk2aVEvivP322zWO\n9/X1JTw8XHmusrOzWb16NcHBwTg6OhITE0NRURHR0dE88MADNZ6NK23ZsoVu3brh6urKuXPnOH/+\nPE899dRV2/ZupjLfKN0qhBBCCCGEEEIIIW6ZDC8SQgghhBBCCCGEqAOSdBFCCCGEEEIIIYSoA5J0\nEUIIIYQQQgghhKgDMpGuEEIIIYQQot41C9twR8fX1qSqFRUVFBYW4uzsXCv1CSH+3qSnixBCCCGE\nEOKetXbtWjZs+F9CJywsjNzc3BrlDh8+zPr169mwYQPJycnK5+spLi5m6tSpxMTEsGDBghr7f/31\n1xqrsmzfvp0JEyZU23bw4EEiIiIIDw+npKSEXbt2kZycfCuXKYS4S0lPFyGEEEIIIcTfzvLlyzl6\n9Cg5OTn4+Pjg5OREVlYWaWlplJWVMWLECLKysq5bR1JSEr169cLHx4ewsDCys7PR6/UApKWlsXPn\nTtRqtVL++PHj7Nu3D6PRWK0eZ2dnXFxc0Gg0mEwm9u3bd9Xlp4UQfz2SdBFCCCGEEELc01asWMGO\nHTsA2LNnDwBeXl54enqyf/9+tm/fjq+vLwA9evTA0tISS0vLG9abl5dH586dAdDr9eTk5ChJl1at\nWtGqVSt2794NwIULF1i6dCmzZ8+u0dPFzc1NSbIkJCTwj3/8g9jYWLy9venatWsttIAQor7I8CIh\nhBBCCCHEPW3UqFHExsYSGxtLp06dAIiPj6eoqIh27dphNpuVsiqV6qp1fPPNN4SEhLB06VJlm8Fg\nUHrDZGdn4+Lics0YvvvuO0pKSoiKiuL48eNs3bq1RpkjR47g7OzM5s2bCQkJITEx8XYuVwhxF5Ge\nLkIIIYQQQoi/HScnJ3788UcqKiooKipStt93330sXryY0aNHVyvfr18/+vXrV21b3759mTVrFnv3\n7sXd3R29Xs/ixYsZPHgwbm5u1coOHTqUoUOHAhAQEIC3t3e1/VVVVaxZs4bw8HCsra2Jj4/Hy8ur\nNi9ZCFEPVOYr07pCCCGEEEIIIYQQolbI8CIhhBBCCCGEEEKIOiBJFyGEEEIIIYQQQog6IEkXIYQQ\nQgghhBBCiDogSRchhBBCCCGEEEKIOiBJFyGEEEIIIYQQQog6IEkXIYQQQgghhBBCiDogSRchhBBC\nCCGEEEKIOiBJFyGEEEIIIYT4v3bsWAAAAABgkL/1KPYVRgAD6QIAAAAwkC4AAAAAgwCq2UxwmLK+\nBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f28b6503400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3, ncols=2)\n",
    "\n",
    "ax1 = axes[0, 0]\n",
    "sns.distplot(clean_adult[\"age\"], bins = 20, ax = ax1)\n",
    "ax1.set_title(\"Distribution of Age\")\n",
    "ax1.set_xlabel(\"Age\", fontsize = 8)\n",
    "ax1.set_ylabel(\"Relative Frequency\")\n",
    "ax1.grid(False)\n",
    "\n",
    "ax2 = axes[0, 1]\n",
    "sns.distplot(clean_adult[\"education.num\"], bins = 18, ax = ax2)\n",
    "ax2.set_title(\"Distribution of Years of Education\")\n",
    "ax2.set_xlabel(\"Years of Education\", fontsize = 8)\n",
    "ax2.set_ylabel(\"Relative Frequency\")\n",
    "ax2.grid(False)\n",
    "\n",
    "ax3 = axes[1, 0]\n",
    "counts = clean_adult[\"marital.status\"].value_counts()\n",
    "percents = 100 * counts.values/counts.values.sum()\n",
    "ind = np.arange(len(percents))  \n",
    "width = 0.35  \n",
    "rects = ax3.bar(ind, percents, width)\n",
    "ax3.set_ylabel(\"Percentage of Individuals\")\n",
    "ax3.set_xticks(ind)\n",
    "ax3.set_xticklabels(counts.index, fontsize = 8, rotation = 15)\n",
    "ax3.set_title(\"Marital Status\", y = 0.95)\n",
    "ax3.grid(False)\n",
    "\n",
    "ax4 = axes[1, 1]\n",
    "counts = clean_adult[\"race\"].value_counts()\n",
    "percents = 100 * counts.values/counts.values.sum()\n",
    "ind = np.arange(len(percents))  \n",
    "width = 0.35  \n",
    "rects = ax4.bar(ind, percents, width)\n",
    "ax4.set_ylabel(\"Percentage of Individuals\")\n",
    "ax4.set_xticks(ind)\n",
    "ax4.set_xticklabels(counts.index, fontsize = 8)\n",
    "ax4.set_title(\"Race\", y = 0.95)\n",
    "ax4.grid(False)\n",
    "\n",
    "ax5 = axes[2, 0]\n",
    "counts = clean_adult[\"sex\"].value_counts()\n",
    "percents = 100 * counts.values/counts.values.sum()\n",
    "ind = np.arange(len(percents))  \n",
    "width = 0.35  \n",
    "rects = ax5.bar(ind, percents, width)\n",
    "ax5.set_ylabel(\"Percentage of Individuals\")\n",
    "ax5.set_xticks(ind)\n",
    "ax5.set_xticklabels(counts.index, fontsize = 10)\n",
    "ax5.set_title(\"Sex\", y = 0.95)\n",
    "ax5.grid(False)\n",
    "\n",
    "ax6 = axes[2, 1]\n",
    "counts = clean_adult[\"native.country\"].value_counts()\n",
    "percents = 100 * counts.values/counts.values.sum()\n",
    "patches, texts = ax6.pie(percents, shadow=True, startangle=90)\n",
    "ax6.axis(\"equal\")\n",
    "labels = ['{0} - {1:1.2f} %'.format(i,j) for i,j in zip(counts.index, percents)]\n",
    "ax6.legend(patches, labels, loc=\"right\", ncol = 2, bbox_to_anchor=(1.6, 0.4),\n",
    "           fontsize=8)\n",
    "ax6.set_title(\"Native Country\")\n",
    "ax6.grid(False)\n",
    "\n",
    "os.makedirs(\"fig\", exist_ok=True)\n",
    "fig.savefig(\"fig/columns.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a couple of interesting observations: \n",
    "* The distribution of ages is skewed to the right, which should not surprise us since as individuals get older, they leave the labor force. \n",
    "* The years of educations since 4th grade seem to have 2 peaks, one at 9 and one at 13. 9 years of education since 4th grade means the individual has graduated high school, while 13 years of education since 4th grade means the individual has graduated college. It is no surprise that most people in this dataset have a high school education or higher. \n",
    "* About 50% of the individuals in the dataset are married.\n",
    "* About 85% of the individuals in the dataset identified as White. This likely include Hispanics too. \n",
    "* About 2/3 of the individuals in the dataset were male, while 1/3 of the individuals in the dataset were female. Since it was the 1994, the female labor participation was still pretty low. Now around 47 percent of the labor force is female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(\"results\", exist_ok=True) #Make results directory if it does not exist yet\n",
    "adult.to_hdf('results/df1.h5', 'adult')\n",
    "clean_adult.to_hdf('results/df1.h5', 'clean_adult')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Testing Section**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import unittest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyTests(unittest.TestCase):\n",
    "    \n",
    "    def test_multiple_missing_indices(self):\n",
    "        row0 = [\">\", \"\", \"!\"]\n",
    "        row1 = [\"3\", \"1\", \"?\"]\n",
    "        row2 = [\"?\", \"10\", \"hello\"]\n",
    "        row3 = [\"my\", \"name\", \"is\"]\n",
    "        df = pd.DataFrame([row0, row1, row2, row3])\n",
    "        missing = find_indices_with_value(df, \"?\")\n",
    "        np.testing.assert_equal(missing, np.array([1, 2]))\n",
    "        \n",
    "    def test_no_missing_indices(self):\n",
    "        row0 = [\">\", \"\", \"!\"]\n",
    "        row1 = [\"3\", \"1\", \"4\"]\n",
    "        row2 = [\"6\", \"10\", \"hello\"]\n",
    "        row3 = [\"my\", \"name\", \"is\"]\n",
    "        df = pd.DataFrame([row0, row1, row2, row3])\n",
    "        missing = find_indices_with_value(df, \"?\")\n",
    "        np.testing.assert_equal(missing, np.array([]))\n",
    "        \n",
    "    def test_one_missing_index(self):\n",
    "        row0 = [\">\", \"\", \"!\"]\n",
    "        row1 = [\"3\", \"1\", \"4\"]\n",
    "        row2 = [\"6\", \"10\", \"hello\"]\n",
    "        row3 = [\"my\", \"name\", \"?\"]\n",
    "        df = pd.DataFrame([row0, row1, row2, row3])\n",
    "        missing = find_indices_with_value(df, \"?\")\n",
    "        np.testing.assert_equal(missing, np.array([3]))\n",
    "        \n",
    "    def test_multiple_missing_on_one_index(self):\n",
    "        row0 = [\">\", \"\", \"!\"]\n",
    "        row1 = [\"3\", \"1\", \"4\"]\n",
    "        row2 = [\"6\", \"10\", \"hello\"]\n",
    "        row3 = [\"my\", \"?\", \"?\"]\n",
    "        df = pd.DataFrame([row0, row1, row2, row3])\n",
    "        missing = find_indices_with_value(df, \"?\")\n",
    "        np.testing.assert_equal(missing, np.array([3]))\n",
    "        \n",
    "    def test_multiple_missing_on_multiple_indices(self):\n",
    "        row0 = [\">\", \"\", \"!\"]\n",
    "        row1 = [\"?\", \"?\", \"4\"]\n",
    "        row2 = [\"6\", \"10\", \"hello\"]\n",
    "        row3 = [\"my\", \"?\", \"?\"]\n",
    "        df = pd.DataFrame([row0, row1, row2, row3])\n",
    "        missing = find_indices_with_value(df, \"?\")\n",
    "        np.testing.assert_equal(missing, np.array([1, 3]))\n",
    "        \n",
    "    def test_one_row_df(self):\n",
    "        row0 = [\">\", \"?\", \"!\"]\n",
    "        df = pd.DataFrame([row0])\n",
    "        missing = find_indices_with_value(df, \"?\")\n",
    "        np.testing.assert_equal(missing, np.array([0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "test_multiple_missing_indices (__main__.MyTests) ... ok\n",
      "test_multiple_missing_on_multiple_indices (__main__.MyTests) ... ok\n",
      "test_multiple_missing_on_one_index (__main__.MyTests) ... ok\n",
      "test_no_missing_indices (__main__.MyTests) ... ok\n",
      "test_one_missing_index (__main__.MyTests) ... ok\n",
      "test_one_row_df (__main__.MyTests) ... ok\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "Ran 6 tests in 0.019s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.main.TestProgram at 0x7f28b1242be0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unittest.main(argv=[\"foo\"], exit = False, verbosity = 2)"
   ]
  }
 ],
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