problemframing-eda-featureengineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore the data\n",
    "\n",
    "This notebook is dedicated toward understanding the data, formulation of two prediction problem to represent customer understanding, and feature engineering from raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = './data/instacart-market-basket-analysis/'\n",
    "aisles = pd.read_csv(data_path + 'aisles.csv')\n",
    "departments = pd.read_csv(data_path + 'departments.csv')\n",
    "order_products_prior = pd.read_csv(data_path + 'order_products__prior.csv')\n",
    "order_products_train = pd.read_csv(data_path + 'order_products__train.csv')\n",
    "orders = pd.read_csv(data_path + 'orders.csv')\n",
    "products = pd.read_csv(data_path + 'products.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understand the structure of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 134 entries, 0 to 133\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   aisle_id  134 non-null    int64 \n",
      " 1   aisle     134 non-null    object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 2.2+ KB\n"
     ]
    }
   ],
   "source": [
    "aisles.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21 entries, 0 to 20\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   department_id  21 non-null     int64 \n",
      " 1   department     21 non-null     object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 464.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "departments.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32434489 entries, 0 to 32434488\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Dtype\n",
      "---  ------             -----\n",
      " 0   order_id           int64\n",
      " 1   product_id         int64\n",
      " 2   add_to_cart_order  int64\n",
      " 3   reordered          int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 989.8 MB\n"
     ]
    }
   ],
   "source": [
    "order_products_prior.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1384617 entries, 0 to 1384616\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Non-Null Count    Dtype\n",
      "---  ------             --------------    -----\n",
      " 0   order_id           1384617 non-null  int64\n",
      " 1   product_id         1384617 non-null  int64\n",
      " 2   add_to_cart_order  1384617 non-null  int64\n",
      " 3   reordered          1384617 non-null  int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 42.3 MB\n"
     ]
    }
   ],
   "source": [
    "order_products_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3421083 entries, 0 to 3421082\n",
      "Data columns (total 7 columns):\n",
      " #   Column                  Dtype  \n",
      "---  ------                  -----  \n",
      " 0   order_id                int64  \n",
      " 1   user_id                 int64  \n",
      " 2   eval_set                object \n",
      " 3   order_number            int64  \n",
      " 4   order_dow               int64  \n",
      " 5   order_hour_of_day       int64  \n",
      " 6   days_since_prior_order  float64\n",
      "dtypes: float64(1), int64(5), object(1)\n",
      "memory usage: 182.7+ MB\n"
     ]
    }
   ],
   "source": [
    "orders.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "0   2539329        1    prior             1          2                  8   \n",
       "1   2398795        1    prior             2          3                  7   \n",
       "2    473747        1    prior             3          3                 12   \n",
       "3   2254736        1    prior             4          4                  7   \n",
       "4    431534        1    prior             5          4                 15   \n",
       "\n",
       "   days_since_prior_order  \n",
       "0                     NaN  \n",
       "1                    15.0  \n",
       "2                    21.0  \n",
       "3                    29.0  \n",
       "4                    28.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prior    3214874\n",
       "train     131209\n",
       "test       75000\n",
       "Name: eval_set, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.eval_set.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3421019</th>\n",
       "      <td>2227043</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421020</th>\n",
       "      <td>1959749</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421021</th>\n",
       "      <td>3392839</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421022</th>\n",
       "      <td>731227</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421023</th>\n",
       "      <td>2936891</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421024</th>\n",
       "      <td>2072319</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421025</th>\n",
       "      <td>2061009</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421026</th>\n",
       "      <td>1923981</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421027</th>\n",
       "      <td>126826</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421028</th>\n",
       "      <td>2995770</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421029</th>\n",
       "      <td>1939529</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421030</th>\n",
       "      <td>922505</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421031</th>\n",
       "      <td>3097121</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421032</th>\n",
       "      <td>1569416</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421033</th>\n",
       "      <td>1949208</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421034</th>\n",
       "      <td>3049681</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421035</th>\n",
       "      <td>11859</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421036</th>\n",
       "      <td>447879</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421037</th>\n",
       "      <td>2467456</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421038</th>\n",
       "      <td>842771</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421039</th>\n",
       "      <td>2471606</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421040</th>\n",
       "      <td>2922221</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421041</th>\n",
       "      <td>3128968</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421042</th>\n",
       "      <td>2573371</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421043</th>\n",
       "      <td>2804556</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421044</th>\n",
       "      <td>2079142</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421045</th>\n",
       "      <td>183408</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421046</th>\n",
       "      <td>3038194</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421047</th>\n",
       "      <td>3412250</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421048</th>\n",
       "      <td>726186</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421049</th>\n",
       "      <td>2523609</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421050</th>\n",
       "      <td>756221</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421051</th>\n",
       "      <td>2254244</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421052</th>\n",
       "      <td>2017995</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421053</th>\n",
       "      <td>2789700</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421054</th>\n",
       "      <td>844592</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421055</th>\n",
       "      <td>1541132</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421056</th>\n",
       "      <td>2808240</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421057</th>\n",
       "      <td>3027766</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421058</th>\n",
       "      <td>3356245</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421059</th>\n",
       "      <td>442304</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421060</th>\n",
       "      <td>2675140</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421061</th>\n",
       "      <td>167903</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421062</th>\n",
       "      <td>2393201</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421063</th>\n",
       "      <td>3292671</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421064</th>\n",
       "      <td>3059777</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421065</th>\n",
       "      <td>2239861</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421066</th>\n",
       "      <td>1285346</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421067</th>\n",
       "      <td>1882108</td>\n",
       "      <td>206208</td>\n",
       "      <td>prior</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421068</th>\n",
       "      <td>803273</td>\n",
       "      <td>206208</td>\n",
       "      <td>test</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id  user_id eval_set  order_number  order_dow  \\\n",
       "3421019   2227043   206208    prior             1          1   \n",
       "3421020   1959749   206208    prior             2          2   \n",
       "3421021   3392839   206208    prior             3          2   \n",
       "3421022    731227   206208    prior             4          3   \n",
       "3421023   2936891   206208    prior             5          1   \n",
       "3421024   2072319   206208    prior             6          1   \n",
       "3421025   2061009   206208    prior             7          0   \n",
       "3421026   1923981   206208    prior             8          1   \n",
       "3421027    126826   206208    prior             9          5   \n",
       "3421028   2995770   206208    prior            10          3   \n",
       "3421029   1939529   206208    prior            11          2   \n",
       "3421030    922505   206208    prior            12          4   \n",
       "3421031   3097121   206208    prior            13          3   \n",
       "3421032   1569416   206208    prior            14          3   \n",
       "3421033   1949208   206208    prior            15          2   \n",
       "3421034   3049681   206208    prior            16          2   \n",
       "3421035     11859   206208    prior            17          1   \n",
       "3421036    447879   206208    prior            18          3   \n",
       "3421037   2467456   206208    prior            19          4   \n",
       "3421038    842771   206208    prior            20          0   \n",
       "3421039   2471606   206208    prior            21          5   \n",
       "3421040   2922221   206208    prior            22          1   \n",
       "3421041   3128968   206208    prior            23          5   \n",
       "3421042   2573371   206208    prior            24          5   \n",
       "3421043   2804556   206208    prior            25          2   \n",
       "3421044   2079142   206208    prior            26          2   \n",
       "3421045    183408   206208    prior            27          2   \n",
       "3421046   3038194   206208    prior            28          0   \n",
       "3421047   3412250   206208    prior            29          4   \n",
       "3421048    726186   206208    prior            30          0   \n",
       "3421049   2523609   206208    prior            31          4   \n",
       "3421050    756221   206208    prior            32          6   \n",
       "3421051   2254244   206208    prior            33          6   \n",
       "3421052   2017995   206208    prior            34          6   \n",
       "3421053   2789700   206208    prior            35          3   \n",
       "3421054    844592   206208    prior            36          6   \n",
       "3421055   1541132   206208    prior            37          2   \n",
       "3421056   2808240   206208    prior            38          0   \n",
       "3421057   3027766   206208    prior            39          2   \n",
       "3421058   3356245   206208    prior            40          5   \n",
       "3421059    442304   206208    prior            41          2   \n",
       "3421060   2675140   206208    prior            42          1   \n",
       "3421061    167903   206208    prior            43          4   \n",
       "3421062   2393201   206208    prior            44          6   \n",
       "3421063   3292671   206208    prior            45          2   \n",
       "3421064   3059777   206208    prior            46          1   \n",
       "3421065   2239861   206208    prior            47          3   \n",
       "3421066   1285346   206208    prior            48          1   \n",
       "3421067   1882108   206208    prior            49          1   \n",
       "3421068    803273   206208     test            50          5   \n",
       "\n",
       "         order_hour_of_day  days_since_prior_order  \n",
       "3421019                 15                     NaN  \n",
       "3421020                 14                     8.0  \n",
       "3421021                 15                     7.0  \n",
       "3421022                 15                     8.0  \n",
       "3421023                 10                     5.0  \n",
       "3421024                 18                     7.0  \n",
       "3421025                 20                    20.0  \n",
       "3421026                 10                     8.0  \n",
       "3421027                 10                    11.0  \n",
       "3421028                 14                     5.0  \n",
       "3421029                 16                     6.0  \n",
       "3421030                 15                    16.0  \n",
       "3421031                 19                     6.0  \n",
       "3421032                 15                     7.0  \n",
       "3421033                 14                     6.0  \n",
       "3421034                 15                     7.0  \n",
       "3421035                 13                     6.0  \n",
       "3421036                 23                     9.0  \n",
       "3421037                 13                    15.0  \n",
       "3421038                  8                    10.0  \n",
       "3421039                  9                     5.0  \n",
       "3421040                 12                     3.0  \n",
       "3421041                 16                     4.0  \n",
       "3421042                  9                     7.0  \n",
       "3421043                 15                    11.0  \n",
       "3421044                 16                     0.0  \n",
       "3421045                 14                     7.0  \n",
       "3421046                 17                     5.0  \n",
       "3421047                 18                     4.0  \n",
       "3421048                 16                     3.0  \n",
       "3421049                 11                     4.0  \n",
       "3421050                 18                     9.0  \n",
       "3421051                 13                     7.0  \n",
       "3421052                 15                     7.0  \n",
       "3421053                 22                     4.0  \n",
       "3421054                 15                    10.0  \n",
       "3421055                 12                     3.0  \n",
       "3421056                 15                    19.0  \n",
       "3421057                 14                     9.0  \n",
       "3421058                  9                    10.0  \n",
       "3421059                 14                    11.0  \n",
       "3421060                 19                     6.0  \n",
       "3421061                 14                     3.0  \n",
       "3421062                 16                     2.0  \n",
       "3421063                 11                     3.0  \n",
       "3421064                 10                    13.0  \n",
       "3421065                  4                     9.0  \n",
       "3421066                 11                     5.0  \n",
       "3421067                 22                     7.0  \n",
       "3421068                 11                     4.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders[\"user_id\"] == 206208]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1856451</th>\n",
       "      <td>2265286</td>\n",
       "      <td>111440</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1856452</th>\n",
       "      <td>1307373</td>\n",
       "      <td>111440</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1856453</th>\n",
       "      <td>1808064</td>\n",
       "      <td>111440</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1856454</th>\n",
       "      <td>1571988</td>\n",
       "      <td>111440</td>\n",
       "      <td>train</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id  user_id eval_set  order_number  order_dow  \\\n",
       "1856451   2265286   111440    prior             1          4   \n",
       "1856452   1307373   111440    prior             2          4   \n",
       "1856453   1808064   111440    prior             3          4   \n",
       "1856454   1571988   111440    train             4          0   \n",
       "\n",
       "         order_hour_of_day  days_since_prior_order  \n",
       "1856451                 10                     NaN  \n",
       "1856452                 14                     7.0  \n",
       "1856453                 10                     7.0  \n",
       "1856454                 14                     3.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders[\"user_id\"] == 111440]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14633bfa0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(orders.days_since_prior_order)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "=> It seems like `days_since_prior_order` is capped at 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    75000.000000\n",
       "mean        17.082640\n",
       "std         10.664071\n",
       "min          0.000000\n",
       "10%          4.000000\n",
       "20%          6.000000\n",
       "25%          7.000000\n",
       "33%          9.000000\n",
       "50%         15.000000\n",
       "66%         27.000000\n",
       "75%         30.000000\n",
       "90%         30.000000\n",
       "max         30.000000\n",
       "Name: days_since_prior_order, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders[\"eval_set\"].isin([\"test\"])].days_since_prior_order.describe(percentiles=[0.1, 0.2, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x146a0d8d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(orders[orders[\"eval_set\"].isin([\"test\"])].days_since_prior_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 49688 entries, 0 to 49687\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   product_id     49688 non-null  int64 \n",
      " 1   product_name   49688 non-null  object\n",
      " 2   aisle_id       49688 non-null  int64 \n",
      " 3   department_id  49688 non-null  int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "products.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem formulation\n",
    "\n",
    "Let's say we focus on predicting future behavior of the customers, including the two following problems:\n",
    "- How long will it be until the customer return (`<1week`, `1-2weeks`, `2-4weeks`, `>=30days`)\n",
    "- In the customer's next order, will the majority of the order  contains re-purchase products. (`<50%`, `>=50%`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prepare dataset**\n",
    "\n",
    "To also leverage customers from the test dataset, we use their last order (\"before the `test` order\") to create label. To do this, we change the `eval_set` status of those order to `train`.\n",
    "\n",
    "The models and algorithms built will be evaluated using k-fold validation method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prior    3139874\n",
       "train     206209\n",
       "Name: eval_set, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_users = orders.loc[orders[\"eval_set\"] == \"test\", [\"user_id\", \"order_number\"]].set_index(\"user_id\")\n",
    "test_users.rename(columns={\"order_number\": \"test_order_number\"}, inplace=True)\n",
    "orders = orders.merge(test_users, how=\"left\", left_on=\"user_id\", right_index=True)\n",
    "orders[\"test_order_number\"].fillna(-1, inplace=True)\n",
    "orders.loc[orders[\"order_number\"] == orders[\"test_order_number\"] - 1, \"eval_set\"] = \"train\"\n",
    "orders.drop(\"test_order_number\", axis=1, inplace=True)\n",
    "orders = orders[orders[\"eval_set\"] != \"test\"].reset_index(drop=True)\n",
    "orders[\"eval_set\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# At the sametime, we also need to move the last order (newly labeled as \"train\" instead of \"prior\") \n",
    "# of test users from order_products_prior to order_products_train\n",
    "\n",
    "test_users_last_order_id = orders.merge(test_users, how=\"inner\", on=\"user_id\")\n",
    "test_users_last_order_id = test_users_last_order_id[test_users_last_order_id[\"eval_set\"] == \"train\"][\"order_id\"]\n",
    "order_products_test = order_products_prior[order_products_prior[\"order_id\"].isin(test_users_last_order_id)]\n",
    "order_products_train = pd.concat([order_products_train, order_products_test], axis=0)\n",
    "order_products_prior = order_products_prior[~order_products_prior[\"order_id\"].isin(test_users_last_order_id)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check if the number of orders in order_products_prior and order_products_train is correct\n",
    "assert len(order_products_prior.order_id.unique()) == len(orders[orders[\"eval_set\"] == \"prior\"])\n",
    "assert len(order_products_train.order_id.unique()) == len(orders[orders[\"eval_set\"] == \"train\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(206209, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>next_order_id</th>\n",
       "      <th>days_to_next_order</th>\n",
       "      <th>reorder_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>1187899</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>1492625</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.387097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>1402502</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4</td>\n",
       "      <td>2557754</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5</td>\n",
       "      <td>2196797</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  next_order_id  days_to_next_order  reorder_ratio\n",
       "10        1        1187899                14.0       0.909091\n",
       "25        2        1492625                30.0       0.387097\n",
       "37        3        1402502                15.0       1.000000\n",
       "42        4        2557754                 0.0       0.000000\n",
       "47        5        2196797                 6.0       0.444444"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using order_products_train and orders[orders['eval_set']==\"train\"] \n",
    "# to create labels for each user: days_to_next_order, reorder_ratio\n",
    "\n",
    "labels_df = orders[orders[\"eval_set\"] == \"train\"][[\"user_id\", \"order_id\", \"days_since_prior_order\"]].copy()\n",
    "labels_df.rename(columns={\n",
    "    \"days_since_prior_order\": \"days_to_next_order\",\n",
    "    \"order_id\":\"next_order_id\"}, \n",
    "    inplace=True)\n",
    "reorder_ratio = order_products_train.groupby(\"order_id\")[\"reordered\"].mean()\n",
    "labels_df = labels_df.merge(reorder_ratio, how=\"left\", left_on=\"next_order_id\", right_index=True)\n",
    "labels_df.rename(columns={\"reordered\": \"reorder_ratio\"}, inplace=True)\n",
    "print(labels_df.shape)\n",
    "labels_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See the distribution of labels\n",
    "sns.displot(labels_df.days_to_next_order)\n",
    "sns.displot(labels_df.reorder_ratio)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To simplify the problem, we reduce to a classification problem. For days_to_next_order:\n",
    "# 1. If days_to_next_order < 7, label it as 0 (reorder within a week)\n",
    "# 2. If days_to_next_order >= 7 and < 15, label it as 1 (reorder in 1-2 weeks)\n",
    "# 3. If days_to_next_order >= 15 and < 30, label it as 2 (reorder in 2-4 weeks)\n",
    "# 4. If days_to_next_order >= 30, label it as 3 (reorder in 1 month or later)\n",
    "labels_df[\"cls_days_to_next_order\"] = labels_df[\"days_to_next_order\"].apply(lambda x: 0 if x < 7 else (1 if x < 15 else (2 if x < 30 else 3)))\n",
    "\n",
    "# Similarly, for reorder_ratio:\n",
    "# 1. If reorder_ratio < 0.1, label it as 0 (reorder product ratio < 10%)\n",
    "# 2. If reorder_ratio >= 0.1 and < 0.5, label it as 1 (reorder product ratio 10%-50%)\n",
    "# 3. If reorder_ratio >= 0.5 and < 0.9, label it as 2 (reorder product ratio 50%-90%)\n",
    "# 4. If reorder_ratio >= 0.9, label it as 3 (reorder product ratio >= 90%)\n",
    "labels_df[\"cls_reorder_ratio\"] = labels_df[\"reorder_ratio\"].apply(lambda x: 0 if x < 0.1 else (1 if x < 0.5 else (2 if x < 0.9 else 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.219884\n",
       "1    0.279707\n",
       "2    0.212430\n",
       "3    0.287980\n",
       "Name: cls_days_to_next_order, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_df.cls_days_to_next_order.value_counts(normalize=True).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.076699\n",
       "1    0.262113\n",
       "2    0.438283\n",
       "3    0.222905\n",
       "Name: cls_reorder_ratio, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_df.cls_reorder_ratio.value_counts(normalize=True).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>next_order_id</th>\n",
       "      <th>days_to_next_order</th>\n",
       "      <th>reorder_ratio</th>\n",
       "      <th>cls_days_to_next_order</th>\n",
       "      <th>cls_reorder_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1187899</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1492625</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1402502</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2557754</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2196797</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  next_order_id  days_to_next_order  reorder_ratio  \\\n",
       "0        1        1187899                14.0       0.909091   \n",
       "1        2        1492625                30.0       0.387097   \n",
       "2        3        1402502                15.0       1.000000   \n",
       "3        4        2557754                 0.0       0.000000   \n",
       "4        5        2196797                 6.0       0.444444   \n",
       "\n",
       "   cls_days_to_next_order  cls_reorder_ratio  \n",
       "0                       1                  3  \n",
       "1                       3                  1  \n",
       "2                       2                  3  \n",
       "3                       0                  0  \n",
       "4                       0                  1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_df.reset_index(drop=True, inplace=True)\n",
    "labels_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_df.to_csv(\"./data/labels.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis on customer behavior\n",
    "\n",
    "Regarding frequency, the following factors might affect cls_days_to_next_order:\n",
    "- Scheduling habits: Reflect in historical frequency (weekly or fortnightly; stable or spontaneous; etc.)\n",
    "- Types of product bought: (e.g. fresh foods need to be restock quicker, while frozen foods can be bought in bulk)\n",
    "- Loyalty and wallet share: If the customers get supplies mostly from instacart or elsewhere\n",
    "\n",
    "Regarding reordering behavior, the following factors might affect cls_reorder_ratio:\n",
    "- Number of products bought: The more products bought in the past, the more likely new ones will be a reorder\n",
    "- Types of products bought: Some products are meant to be restock regularly, while others are one-time purchases\n",
    "- Purposes of instarcart shopping\n",
    "\n",
    "Let's explore the datasets to see if there are bases for those assumptions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49668, 9)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_reorder_rate</th>\n",
       "      <th>product_reorder_std</th>\n",
       "      <th>order_count</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6433</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.237044</td>\n",
       "      <td>68</td>\n",
       "      <td>Raw Veggie Wrappers</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>prepared meals</td>\n",
       "      <td>deli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13875</td>\n",
       "      <td>0.931818</td>\n",
       "      <td>0.254972</td>\n",
       "      <td>44</td>\n",
       "      <td>Simply Sleep Nighttime Sleep Aid</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2075</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.256249</td>\n",
       "      <td>86</td>\n",
       "      <td>Serenity Ultimate Extrema Overnight Pads</td>\n",
       "      <td>126</td>\n",
       "      <td>11</td>\n",
       "      <td>feminine care</td>\n",
       "      <td>personal care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>700</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.266880</td>\n",
       "      <td>27</td>\n",
       "      <td>Jet Fuel Dark Roast Whole Bean Coffee</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>coffee</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43553</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.277350</td>\n",
       "      <td>13</td>\n",
       "      <td>Orange Energy Shots</td>\n",
       "      <td>64</td>\n",
       "      <td>7</td>\n",
       "      <td>energy sports drinks</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27740</td>\n",
       "      <td>0.920792</td>\n",
       "      <td>0.271410</td>\n",
       "      <td>101</td>\n",
       "      <td>Chocolate Love Bar</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4212</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.280306</td>\n",
       "      <td>36</td>\n",
       "      <td>High Gravity Lager</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>beers coolers</td>\n",
       "      <td>alcohol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14609</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>0.284029</td>\n",
       "      <td>35</td>\n",
       "      <td>Soy Powder Infant Formula</td>\n",
       "      <td>92</td>\n",
       "      <td>18</td>\n",
       "      <td>baby food formula</td>\n",
       "      <td>babies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>39992</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.294245</td>\n",
       "      <td>22</td>\n",
       "      <td>Energy Shot, Grape Flavor</td>\n",
       "      <td>64</td>\n",
       "      <td>7</td>\n",
       "      <td>energy sports drinks</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31418</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.302532</td>\n",
       "      <td>60</td>\n",
       "      <td>Sparking Water</td>\n",
       "      <td>115</td>\n",
       "      <td>7</td>\n",
       "      <td>water seltzer sparkling water</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id  product_reorder_rate  product_reorder_std  order_count  \\\n",
       "0        6433              0.941176             0.237044           68   \n",
       "1       13875              0.931818             0.254972           44   \n",
       "2        2075              0.930233             0.256249           86   \n",
       "3         700              0.925926             0.266880           27   \n",
       "4       43553              0.923077             0.277350           13   \n",
       "5       27740              0.920792             0.271410          101   \n",
       "6        4212              0.916667             0.280306           36   \n",
       "7       14609              0.914286             0.284029           35   \n",
       "8       39992              0.909091             0.294245           22   \n",
       "9       31418              0.900000             0.302532           60   \n",
       "\n",
       "                               product_name  aisle_id  department_id  \\\n",
       "0                       Raw Veggie Wrappers        13             20   \n",
       "1          Simply Sleep Nighttime Sleep Aid         6              2   \n",
       "2  Serenity Ultimate Extrema Overnight Pads       126             11   \n",
       "3     Jet Fuel Dark Roast Whole Bean Coffee        26              7   \n",
       "4                       Orange Energy Shots        64              7   \n",
       "5                        Chocolate Love Bar        45             19   \n",
       "6                        High Gravity Lager        27              5   \n",
       "7                 Soy Powder Infant Formula        92             18   \n",
       "8                 Energy Shot, Grape Flavor        64              7   \n",
       "9                            Sparking Water       115              7   \n",
       "\n",
       "                           aisle     department  \n",
       "0                 prepared meals           deli  \n",
       "1                          other          other  \n",
       "2                  feminine care  personal care  \n",
       "3                         coffee      beverages  \n",
       "4           energy sports drinks      beverages  \n",
       "5                candy chocolate         snacks  \n",
       "6                  beers coolers        alcohol  \n",
       "7              baby food formula         babies  \n",
       "8           energy sports drinks      beverages  \n",
       "9  water seltzer sparkling water      beverages  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_reorder_rate = order_products_prior.groupby(\"product_id\")[\"reordered\"].agg(['mean', 'std', 'count'])\n",
    "product_reorder_rate = product_reorder_rate.rename(\n",
    "    columns={\"mean\": \"product_reorder_rate\", \"count\": \"order_count\", \"std\": \"product_reorder_std\"}\n",
    "    ).sort_values('product_reorder_rate',ascending=False).reset_index()\n",
    "# product_reorder_rate.head()\n",
    "product_reorder_rate = product_reorder_rate.merge(products, how=\"left\", on=\"product_id\")\n",
    "product_reorder_rate = pd.merge(product_reorder_rate, aisles, how=\"left\", on=\"aisle_id\")\n",
    "product_reorder_rate = pd.merge(product_reorder_rate, departments, how=\"left\", on=\"department_id\")\n",
    "print(product_reorder_rate.shape)\n",
    "product_reorder_rate.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_reorder_rate</th>\n",
       "      <th>product_reorder_std</th>\n",
       "      <th>order_count</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>9292</td>\n",
       "      <td>0.860522</td>\n",
       "      <td>0.346505</td>\n",
       "      <td>2875</td>\n",
       "      <td>Half And Half Ultra Pasteurized</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>milk</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>45504</td>\n",
       "      <td>0.859631</td>\n",
       "      <td>0.347388</td>\n",
       "      <td>8955</td>\n",
       "      <td>Whole Organic Omega 3 Milk</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>milk</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>43394</td>\n",
       "      <td>0.858395</td>\n",
       "      <td>0.348665</td>\n",
       "      <td>8326</td>\n",
       "      <td>Organic Lactose Free Whole Milk</td>\n",
       "      <td>91</td>\n",
       "      <td>16</td>\n",
       "      <td>soy lactosefree</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5514</td>\n",
       "      <td>0.857946</td>\n",
       "      <td>0.349150</td>\n",
       "      <td>3914</td>\n",
       "      <td>Organic Homogenized Whole Milk</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>milk</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>47231</td>\n",
       "      <td>0.857240</td>\n",
       "      <td>0.349947</td>\n",
       "      <td>1471</td>\n",
       "      <td>Ultra-Purified Water</td>\n",
       "      <td>115</td>\n",
       "      <td>7</td>\n",
       "      <td>water seltzer sparkling water</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>29447</td>\n",
       "      <td>0.854092</td>\n",
       "      <td>0.353023</td>\n",
       "      <td>19855</td>\n",
       "      <td>Milk, Organic, Vitamin D</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>milk</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>38689</td>\n",
       "      <td>0.850618</td>\n",
       "      <td>0.356469</td>\n",
       "      <td>35011</td>\n",
       "      <td>Organic Reduced Fat Milk</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>milk</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>34197</td>\n",
       "      <td>0.849597</td>\n",
       "      <td>0.357501</td>\n",
       "      <td>5093</td>\n",
       "      <td>Goat Milk</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>milk</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>24852</td>\n",
       "      <td>0.842727</td>\n",
       "      <td>0.364058</td>\n",
       "      <td>461821</td>\n",
       "      <td>Banana</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>fresh fruits</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>31720</td>\n",
       "      <td>0.841301</td>\n",
       "      <td>0.365414</td>\n",
       "      <td>9685</td>\n",
       "      <td>Organic  Whole Milk</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>milk</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    product_id  product_reorder_rate  product_reorder_std  order_count  \\\n",
       "33        9292              0.860522             0.346505         2875   \n",
       "37       45504              0.859631             0.347388         8955   \n",
       "39       43394              0.858395             0.348665         8326   \n",
       "40        5514              0.857946             0.349150         3914   \n",
       "41       47231              0.857240             0.349947         1471   \n",
       "55       29447              0.854092             0.353023        19855   \n",
       "59       38689              0.850618             0.356469        35011   \n",
       "60       34197              0.849597             0.357501         5093   \n",
       "76       24852              0.842727             0.364058       461821   \n",
       "80       31720              0.841301             0.365414         9685   \n",
       "\n",
       "                       product_name  aisle_id  department_id  \\\n",
       "33  Half And Half Ultra Pasteurized        84             16   \n",
       "37       Whole Organic Omega 3 Milk        84             16   \n",
       "39  Organic Lactose Free Whole Milk        91             16   \n",
       "40   Organic Homogenized Whole Milk        84             16   \n",
       "41             Ultra-Purified Water       115              7   \n",
       "55         Milk, Organic, Vitamin D        84             16   \n",
       "59         Organic Reduced Fat Milk        84             16   \n",
       "60                        Goat Milk        84             16   \n",
       "76                           Banana        24              4   \n",
       "80              Organic  Whole Milk        84             16   \n",
       "\n",
       "                            aisle  department  \n",
       "33                           milk  dairy eggs  \n",
       "37                           milk  dairy eggs  \n",
       "39                soy lactosefree  dairy eggs  \n",
       "40                           milk  dairy eggs  \n",
       "41  water seltzer sparkling water   beverages  \n",
       "55                           milk  dairy eggs  \n",
       "59                           milk  dairy eggs  \n",
       "60                           milk  dairy eggs  \n",
       "76                   fresh fruits     produce  \n",
       "80                           milk  dairy eggs  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_reorder_rate[product_reorder_rate[\"order_count\"] > 1000].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using sns to plot the reorder rate of each department, sorted by the mean reorder rate\n",
    "sns.barplot(x=\"department\", y=\"product_reorder_rate\", data=product_reorder_rate, \n",
    "    order=product_reorder_rate.groupby(\"department\")[\"product_reorder_rate\"].mean().sort_values(ascending=False).index\n",
    "    )\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Dairy and Eggs` and `Bakery`, expectedly, are a mong the most departments with reorder products. Although, It is interesting to me that `Pets` are the second-most reordered departments, while `personal care` are the least.\n",
    "\n",
    "It is clear that there are differences in product types regarding reorder rate, thus, designing features to exploit this phenomenon might be beneficial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_days_since_mean</th>\n",
       "      <th>product_days_since_std</th>\n",
       "      <th>order_count</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10657</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Totally Fudge Premium Pops</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>ice cream ice</td>\n",
       "      <td>frozen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6786</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Anjou Pear Hand Soap</td>\n",
       "      <td>127</td>\n",
       "      <td>11</td>\n",
       "      <td>body lotions soap</td>\n",
       "      <td>personal care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10584</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Homestlye Cornbread Stuffing</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>instant foods</td>\n",
       "      <td>dry goods pasta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12051</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Aged Basmati Rice</td>\n",
       "      <td>63</td>\n",
       "      <td>9</td>\n",
       "      <td>grains rice dried goods</td>\n",
       "      <td>dry goods pasta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8679</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Asiago Beef With Penne</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>frozen meat seafood</td>\n",
       "      <td>frozen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id  product_days_since_mean  product_days_since_std  order_count  \\\n",
       "0       10657                     30.0                     NaN            1   \n",
       "1        6786                     30.0                     NaN            1   \n",
       "2       10584                     30.0                     NaN            1   \n",
       "3       12051                     30.0                     NaN            1   \n",
       "4        8679                     30.0                     0.0            2   \n",
       "\n",
       "                   product_name  aisle_id  department_id  \\\n",
       "0    Totally Fudge Premium Pops        37              1   \n",
       "1          Anjou Pear Hand Soap       127             11   \n",
       "2  Homestlye Cornbread Stuffing         4              9   \n",
       "3             Aged Basmati Rice        63              9   \n",
       "4        Asiago Beef With Penne        34              1   \n",
       "\n",
       "                     aisle       department  \n",
       "0            ice cream ice           frozen  \n",
       "1        body lotions soap    personal care  \n",
       "2            instant foods  dry goods pasta  \n",
       "3  grains rice dried goods  dry goods pasta  \n",
       "4      frozen meat seafood           frozen  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_days_since = order_products_prior.merge(orders, how=\"left\", on=\"order_id\")\\\n",
    "        .groupby(\"product_id\")[\"days_since_prior_order\"].agg(['mean', 'std', 'count'])\n",
    "product_days_since = product_days_since.rename(\n",
    "        columns={\"mean\": \"product_days_since_mean\", \"count\": \"order_count\", \"std\": \"product_days_since_std\"}\n",
    ").sort_values('product_days_since_mean',ascending=False).reset_index()\n",
    "product_days_since = product_days_since.merge(products, how=\"left\", on=\"product_id\")\n",
    "product_days_since = pd.merge(product_days_since, aisles, how=\"left\", on=\"aisle_id\")\n",
    "product_days_since = pd.merge(product_days_since, departments, how=\"left\", on=\"department_id\")\n",
    "product_days_since.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_days_since_mean</th>\n",
       "      <th>product_days_since_std</th>\n",
       "      <th>order_count</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>38300</td>\n",
       "      <td>18.076217</td>\n",
       "      <td>10.425000</td>\n",
       "      <td>1417</td>\n",
       "      <td>Tall Kitchen Bag With Febreze Odor Shield</td>\n",
       "      <td>60</td>\n",
       "      <td>17</td>\n",
       "      <td>trash bags liners</td>\n",
       "      <td>household</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>14419</td>\n",
       "      <td>17.688923</td>\n",
       "      <td>10.206169</td>\n",
       "      <td>1318</td>\n",
       "      <td>Fresh Scent Laundry Detergent</td>\n",
       "      <td>75</td>\n",
       "      <td>17</td>\n",
       "      <td>laundry</td>\n",
       "      <td>household</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>23662</td>\n",
       "      <td>17.616348</td>\n",
       "      <td>10.045406</td>\n",
       "      <td>1358</td>\n",
       "      <td>All Natural White Vinegar</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>oils vinegars</td>\n",
       "      <td>pantry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>1957</td>\n",
       "      <td>17.198366</td>\n",
       "      <td>10.417031</td>\n",
       "      <td>1346</td>\n",
       "      <td>Fabric Softener Dryer Sheet Outdoor Fresh 160C...</td>\n",
       "      <td>75</td>\n",
       "      <td>17</td>\n",
       "      <td>laundry</td>\n",
       "      <td>household</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1739</th>\n",
       "      <td>9358</td>\n",
       "      <td>16.911593</td>\n",
       "      <td>10.340143</td>\n",
       "      <td>1889</td>\n",
       "      <td>April Fresh Liquid Fabric Softener</td>\n",
       "      <td>75</td>\n",
       "      <td>17</td>\n",
       "      <td>laundry</td>\n",
       "      <td>household</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>26405</td>\n",
       "      <td>16.612717</td>\n",
       "      <td>10.610775</td>\n",
       "      <td>1038</td>\n",
       "      <td>XL Pick-A-Size Paper Towel Rolls</td>\n",
       "      <td>54</td>\n",
       "      <td>17</td>\n",
       "      <td>paper goods</td>\n",
       "      <td>household</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2078</th>\n",
       "      <td>32079</td>\n",
       "      <td>16.499763</td>\n",
       "      <td>10.026384</td>\n",
       "      <td>2107</td>\n",
       "      <td>Shredded Parmigiano Reggiano</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>packaged cheese</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2085</th>\n",
       "      <td>280</td>\n",
       "      <td>16.468599</td>\n",
       "      <td>10.158499</td>\n",
       "      <td>1035</td>\n",
       "      <td>Makeup Remover Cleansing Towelettes</td>\n",
       "      <td>73</td>\n",
       "      <td>11</td>\n",
       "      <td>facial care</td>\n",
       "      <td>personal care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2171</th>\n",
       "      <td>907</td>\n",
       "      <td>16.338737</td>\n",
       "      <td>10.153870</td>\n",
       "      <td>1789</td>\n",
       "      <td>Premium Sliced Bacon</td>\n",
       "      <td>106</td>\n",
       "      <td>12</td>\n",
       "      <td>hot dogs bacon sausage</td>\n",
       "      <td>meat seafood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2399</th>\n",
       "      <td>13259</td>\n",
       "      <td>16.089711</td>\n",
       "      <td>9.983705</td>\n",
       "      <td>2731</td>\n",
       "      <td>Organic Variety Pack</td>\n",
       "      <td>98</td>\n",
       "      <td>7</td>\n",
       "      <td>juice nectars</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      product_id  product_days_since_mean  product_days_since_std  \\\n",
       "1053       38300                18.076217               10.425000   \n",
       "1243       14419                17.688923               10.206169   \n",
       "1280       23662                17.616348               10.045406   \n",
       "1525        1957                17.198366               10.417031   \n",
       "1739        9358                16.911593               10.340143   \n",
       "1959       26405                16.612717               10.610775   \n",
       "2078       32079                16.499763               10.026384   \n",
       "2085         280                16.468599               10.158499   \n",
       "2171         907                16.338737               10.153870   \n",
       "2399       13259                16.089711                9.983705   \n",
       "\n",
       "      order_count                                       product_name  \\\n",
       "1053         1417          Tall Kitchen Bag With Febreze Odor Shield   \n",
       "1243         1318                      Fresh Scent Laundry Detergent   \n",
       "1280         1358                          All Natural White Vinegar   \n",
       "1525         1346  Fabric Softener Dryer Sheet Outdoor Fresh 160C...   \n",
       "1739         1889                 April Fresh Liquid Fabric Softener   \n",
       "1959         1038                   XL Pick-A-Size Paper Towel Rolls   \n",
       "2078         2107                       Shredded Parmigiano Reggiano   \n",
       "2085         1035                Makeup Remover Cleansing Towelettes   \n",
       "2171         1789                               Premium Sliced Bacon   \n",
       "2399         2731                               Organic Variety Pack   \n",
       "\n",
       "      aisle_id  department_id                   aisle     department  \n",
       "1053        60             17       trash bags liners      household  \n",
       "1243        75             17                 laundry      household  \n",
       "1280        19             13           oils vinegars         pantry  \n",
       "1525        75             17                 laundry      household  \n",
       "1739        75             17                 laundry      household  \n",
       "1959        54             17             paper goods      household  \n",
       "2078        21             16         packaged cheese     dairy eggs  \n",
       "2085        73             11             facial care  personal care  \n",
       "2171       106             12  hot dogs bacon sausage   meat seafood  \n",
       "2399        98              7           juice nectars      beverages  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_days_since[product_days_since[\"order_count\"] > 1000].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using sns to plot product_days_since_mean of each department, sorted by the mean reorder rate\n",
    "sns.barplot(x=\"department\", y=\"product_days_since_mean\", data=product_days_since,\n",
    "    order=product_days_since.groupby(\"department\")[\"product_days_since_mean\"].mean().sort_values(ascending=False).index\n",
    "    )\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shelf-stable products (canned goods, dry pasta, frozen foods, etc.), expectedly, seems to have the highest average days_since_prior_order. Meanwhile, snacks, alcohol, babies products, and bulk products have the lowest.\n",
    "\n",
    "However, the patterns are not as clear in this cases. This is understandable because one order can contains products from multiple departments and days_since_prior_order is only provided at order level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "0   2539329        1    prior             1          2                  8   \n",
       "1   2398795        1    prior             2          3                  7   \n",
       "2    473747        1    prior             3          3                 12   \n",
       "3   2254736        1    prior             4          4                  7   \n",
       "4    431534        1    prior             5          4                 15   \n",
       "\n",
       "   days_since_prior_order  \n",
       "0                     NaN  \n",
       "1                    15.0  \n",
       "2                    21.0  \n",
       "3                    29.0  \n",
       "4                    28.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_days_since_mean</th>\n",
       "      <th>user_days_since_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19.555556</td>\n",
       "      <td>9.395625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15.230769</td>\n",
       "      <td>9.867065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>5.573748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>3.055050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>4.932883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  user_days_since_mean  user_days_since_std\n",
       "0        1             19.555556             9.395625\n",
       "1        2             15.230769             9.867065\n",
       "2        3             11.800000             5.573748\n",
       "3        4             18.333333             3.055050\n",
       "4        5             13.333333             4.932883"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate historical days since last order for each user\n",
    "user_days_since = orders[orders[\"eval_set\"] == \"prior\"].groupby(\"user_id\")[\"days_since_prior_order\"].agg(['mean', 'std'])\n",
    "user_days_since = user_days_since.rename(\n",
    "        columns={\"mean\": \"user_days_since_mean\", \"std\": \"user_days_since_std\"}\n",
    ").reset_index()\n",
    "user_days_since.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns plot user_days_since_mean of each user\n",
    "sns.displot(user_days_since[\"user_days_since_mean\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering\n",
    "\n",
    "As we are trying to predict frequency (days to next order), and reorder behavior. The following information might have good predictive power:\n",
    "- Time between purchases for previous orders\n",
    "- Number of previous orders\n",
    "- Average number of products per order\n",
    "- Number of unique products bought\n",
    "- Average number of unique ailes per order\n",
    "- Number of unique ailes bought\n",
    "- Average percentage of reorder products per order\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_reorder_rate</th>\n",
       "      <th>product_reorder_std</th>\n",
       "      <th>order_count</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6433</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.237044</td>\n",
       "      <td>68</td>\n",
       "      <td>Raw Veggie Wrappers</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>prepared meals</td>\n",
       "      <td>deli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13875</td>\n",
       "      <td>0.931818</td>\n",
       "      <td>0.254972</td>\n",
       "      <td>44</td>\n",
       "      <td>Simply Sleep Nighttime Sleep Aid</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2075</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.256249</td>\n",
       "      <td>86</td>\n",
       "      <td>Serenity Ultimate Extrema Overnight Pads</td>\n",
       "      <td>126</td>\n",
       "      <td>11</td>\n",
       "      <td>feminine care</td>\n",
       "      <td>personal care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>700</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.266880</td>\n",
       "      <td>27</td>\n",
       "      <td>Jet Fuel Dark Roast Whole Bean Coffee</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>coffee</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43553</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.277350</td>\n",
       "      <td>13</td>\n",
       "      <td>Orange Energy Shots</td>\n",
       "      <td>64</td>\n",
       "      <td>7</td>\n",
       "      <td>energy sports drinks</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id  product_reorder_rate  product_reorder_std  order_count  \\\n",
       "0        6433              0.941176             0.237044           68   \n",
       "1       13875              0.931818             0.254972           44   \n",
       "2        2075              0.930233             0.256249           86   \n",
       "3         700              0.925926             0.266880           27   \n",
       "4       43553              0.923077             0.277350           13   \n",
       "\n",
       "                               product_name  aisle_id  department_id  \\\n",
       "0                       Raw Veggie Wrappers        13             20   \n",
       "1          Simply Sleep Nighttime Sleep Aid         6              2   \n",
       "2  Serenity Ultimate Extrema Overnight Pads       126             11   \n",
       "3     Jet Fuel Dark Roast Whole Bean Coffee        26              7   \n",
       "4                       Orange Energy Shots        64              7   \n",
       "\n",
       "                  aisle     department  \n",
       "0        prepared meals           deli  \n",
       "1                 other          other  \n",
       "2         feminine care  personal care  \n",
       "3                coffee      beverages  \n",
       "4  energy sports drinks      beverages  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_reorder_rate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>next_order_id</th>\n",
       "      <th>days_to_next_order</th>\n",
       "      <th>reorder_ratio</th>\n",
       "      <th>cls_days_to_next_order</th>\n",
       "      <th>cls_reorder_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1187899</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1492625</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1402502</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2557754</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2196797</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  next_order_id  days_to_next_order  reorder_ratio  \\\n",
       "0        1        1187899                14.0       0.909091   \n",
       "1        2        1492625                30.0       0.387097   \n",
       "2        3        1402502                15.0       1.000000   \n",
       "3        4        2557754                 0.0       0.000000   \n",
       "4        5        2196797                 6.0       0.444444   \n",
       "\n",
       "   cls_days_to_next_order  cls_reorder_ratio  \n",
       "0                       1                  3  \n",
       "1                       3                  1  \n",
       "2                       2                  3  \n",
       "3                       0                  0  \n",
       "4                       0                  1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_days_since_mean</th>\n",
       "      <th>user_days_since_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19.555556</td>\n",
       "      <td>9.395625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15.230769</td>\n",
       "      <td>9.867065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>5.573748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>3.055050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>4.932883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  user_days_since_mean  user_days_since_std\n",
       "0        1             19.555556             9.395625\n",
       "1        2             15.230769             9.867065\n",
       "2        3             11.800000             5.573748\n",
       "3        4             18.333333             3.055050\n",
       "4        5             13.333333             4.932883"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df = labels_df[[\"user_id\"]].copy()\n",
    "# Add user_days_since features\n",
    "features_df = pd.merge(features_df, user_days_since, how=\"left\", on=\"user_id\")\n",
    "features_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yh/0ppzrrgs7m77v8rxlkjbf2540000gn/T/ipykernel_53908/2948144647.py:12: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n",
      "  order_features.columns = [\"_\".join(x) for x in order_features.columns.ravel()]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>ord_product_count_mean</th>\n",
       "      <th>ord_product_count_std</th>\n",
       "      <th>ord_reorder_rate_mean</th>\n",
       "      <th>ord_reorder_rate_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>1.523884</td>\n",
       "      <td>0.705833</td>\n",
       "      <td>0.317544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>13.928571</td>\n",
       "      <td>5.717238</td>\n",
       "      <td>0.447961</td>\n",
       "      <td>0.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7.454545</td>\n",
       "      <td>2.161649</td>\n",
       "      <td>0.627801</td>\n",
       "      <td>0.331377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>2.362908</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>3.095696</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.277555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  ord_product_count_mean  ord_product_count_std  \\\n",
       "0        1                5.900000               1.523884   \n",
       "1        2               13.928571               5.717238   \n",
       "2        3                7.454545               2.161649   \n",
       "3        4                3.750000               2.362908   \n",
       "4        5                9.250000               3.095696   \n",
       "\n",
       "   ord_reorder_rate_mean  ord_reorder_rate_std  \n",
       "0               0.705833              0.317544  \n",
       "1               0.447961              0.315789  \n",
       "2               0.627801              0.331377  \n",
       "3               0.035714              0.071429  \n",
       "4               0.377778              0.277555  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add order-level features\n",
    "order_features = orders[orders[\"eval_set\"] == \"prior\"].merge(order_products_prior, how=\"left\", on=\"order_id\")\n",
    "order_features = order_features.groupby([\"user_id\",\"order_id\"]).agg({\n",
    "    \"product_id\": \"count\",\n",
    "    \"reordered\": \"mean\",\n",
    "})\n",
    "order_features.rename(columns={\"product_id\": \"ord_product_count\", \"reordered\": \"ord_reorder_rate\"}, inplace=True)\n",
    "order_features.reset_index(inplace=True)\n",
    "order_features = order_features.groupby(\"user_id\").agg({\n",
    "    \"ord_product_count\": [\"mean\", \"std\"], \"ord_reorder_rate\": [\"mean\", \"std\"]}\n",
    "    )\n",
    "order_features.columns = [\"_\".join(x) for x in order_features.columns.ravel()]\n",
    "order_features.reset_index(inplace=True)\n",
    "order_features.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>prod_reorder_rate_mean</th>\n",
       "      <th>prod_reorder_std_mean</th>\n",
       "      <th>user_reorder_rate</th>\n",
       "      <th>prod_nunique</th>\n",
       "      <th>order_cnt</th>\n",
       "      <th>order_dow_nunique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.437883</td>\n",
       "      <td>0.694915</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.634116</td>\n",
       "      <td>0.463857</td>\n",
       "      <td>0.476923</td>\n",
       "      <td>102</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.617979</td>\n",
       "      <td>0.472159</td>\n",
       "      <td>0.597561</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.550503</td>\n",
       "      <td>0.469545</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.581885</td>\n",
       "      <td>0.474626</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  prod_reorder_rate_mean  prod_reorder_std_mean  user_reorder_rate  \\\n",
       "0        1                0.725334               0.437883           0.694915   \n",
       "1        2                0.634116               0.463857           0.476923   \n",
       "2        3                0.617979               0.472159           0.597561   \n",
       "3        4                0.550503               0.469545           0.066667   \n",
       "4        5                0.581885               0.474626           0.378378   \n",
       "\n",
       "   prod_nunique  order_cnt  order_dow_nunique  \n",
       "0            18         10                  4  \n",
       "1           102         14                  5  \n",
       "2            33         11                  4  \n",
       "3            14          4                  3  \n",
       "4            23          4                  3  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add product_reorder_rate features\n",
    "product_features = orders[orders[\"eval_set\"] == \"prior\"].merge(order_products_prior, how=\"left\", on=\"order_id\")\\\n",
    "    .merge(product_reorder_rate[[\"product_id\", \"product_reorder_rate\", \"product_reorder_std\"]], how=\"left\", on=\"product_id\")\\\n",
    "    .groupby(\"user_id\").agg({\n",
    "        \"product_reorder_rate\": \"mean\",\n",
    "        \"product_reorder_std\": \"mean\",\n",
    "        \"reordered\": \"mean\",\n",
    "        \"product_id\": \"nunique\",\n",
    "        \"order_id\": \"nunique\",\n",
    "        \"order_dow\": \"nunique\",\n",
    "    })\n",
    "product_features.rename(columns={\n",
    "    \"product_reorder_rate\": \"prod_reorder_rate_mean\",\n",
    "    \"product_reorder_std\": \"prod_reorder_std_mean\",\n",
    "    \"reordered\": \"user_reorder_rate\",\n",
    "    \"product_id\": \"prod_nunique\",\n",
    "    \"order_id\": \"order_cnt\",\n",
    "    \"order_dow\": \"order_dow_nunique\",\n",
    "}, inplace=True)\n",
    "product_features.reset_index(inplace=True)\n",
    "product_features.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>prod_days_since_mean_mean</th>\n",
       "      <th>prod_days_since_std_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11.119767</td>\n",
       "      <td>8.668178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>10.761481</td>\n",
       "      <td>8.449797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11.235237</td>\n",
       "      <td>8.722556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11.515205</td>\n",
       "      <td>8.788381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>10.676956</td>\n",
       "      <td>8.507307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  prod_days_since_mean_mean  prod_days_since_std_mean\n",
       "0        1                  11.119767                  8.668178\n",
       "1        2                  10.761481                  8.449797\n",
       "2        3                  11.235237                  8.722556\n",
       "3        4                  11.515205                  8.788381\n",
       "4        5                  10.676956                  8.507307"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_features_days_since = orders[orders[\"eval_set\"] == \"prior\"].merge(order_products_prior, how=\"left\", on=\"order_id\")\\\n",
    "    .merge(product_days_since[[\"product_id\", \"product_days_since_mean\", \"product_days_since_std\"]], how=\"left\", on=\"product_id\")\\\n",
    "    .groupby(\"user_id\").agg({\n",
    "        \"product_days_since_mean\": \"mean\",\n",
    "        \"product_days_since_std\": \"mean\",\n",
    "    })\n",
    "product_features_days_since.rename(columns={\n",
    "    \"product_days_since_mean\": \"prod_days_since_mean_mean\",\n",
    "    \"product_days_since_std\": \"prod_days_since_std_mean\",\n",
    "}, inplace=True)\n",
    "product_features_days_since.reset_index(inplace=True)\n",
    "product_features_days_since.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(206209, 15)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_days_since_mean</th>\n",
       "      <th>user_days_since_std</th>\n",
       "      <th>ord_product_count_mean</th>\n",
       "      <th>ord_product_count_std</th>\n",
       "      <th>ord_reorder_rate_mean</th>\n",
       "      <th>ord_reorder_rate_std</th>\n",
       "      <th>prod_reorder_rate_mean</th>\n",
       "      <th>prod_reorder_std_mean</th>\n",
       "      <th>user_reorder_rate</th>\n",
       "      <th>prod_nunique</th>\n",
       "      <th>order_cnt</th>\n",
       "      <th>order_dow_nunique</th>\n",
       "      <th>prod_days_since_mean_mean</th>\n",
       "      <th>prod_days_since_std_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19.555556</td>\n",
       "      <td>9.395625</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>1.523884</td>\n",
       "      <td>0.705833</td>\n",
       "      <td>0.317544</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.437883</td>\n",
       "      <td>0.694915</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>11.119767</td>\n",
       "      <td>8.668178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15.230769</td>\n",
       "      <td>9.867065</td>\n",
       "      <td>13.928571</td>\n",
       "      <td>5.717238</td>\n",
       "      <td>0.447961</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.634116</td>\n",
       "      <td>0.463857</td>\n",
       "      <td>0.476923</td>\n",
       "      <td>102</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>10.761481</td>\n",
       "      <td>8.449797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>5.573748</td>\n",
       "      <td>7.454545</td>\n",
       "      <td>2.161649</td>\n",
       "      <td>0.627801</td>\n",
       "      <td>0.331377</td>\n",
       "      <td>0.617979</td>\n",
       "      <td>0.472159</td>\n",
       "      <td>0.597561</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>11.235237</td>\n",
       "      <td>8.722556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>3.055050</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>2.362908</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.550503</td>\n",
       "      <td>0.469545</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>11.515205</td>\n",
       "      <td>8.788381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>4.932883</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>3.095696</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.277555</td>\n",
       "      <td>0.581885</td>\n",
       "      <td>0.474626</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>10.676956</td>\n",
       "      <td>8.507307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  user_days_since_mean  user_days_since_std  ord_product_count_mean  \\\n",
       "0        1             19.555556             9.395625                5.900000   \n",
       "1        2             15.230769             9.867065               13.928571   \n",
       "2        3             11.800000             5.573748                7.454545   \n",
       "3        4             18.333333             3.055050                3.750000   \n",
       "4        5             13.333333             4.932883                9.250000   \n",
       "\n",
       "   ord_product_count_std  ord_reorder_rate_mean  ord_reorder_rate_std  \\\n",
       "0               1.523884               0.705833              0.317544   \n",
       "1               5.717238               0.447961              0.315789   \n",
       "2               2.161649               0.627801              0.331377   \n",
       "3               2.362908               0.035714              0.071429   \n",
       "4               3.095696               0.377778              0.277555   \n",
       "\n",
       "   prod_reorder_rate_mean  prod_reorder_std_mean  user_reorder_rate  \\\n",
       "0                0.725334               0.437883           0.694915   \n",
       "1                0.634116               0.463857           0.476923   \n",
       "2                0.617979               0.472159           0.597561   \n",
       "3                0.550503               0.469545           0.066667   \n",
       "4                0.581885               0.474626           0.378378   \n",
       "\n",
       "   prod_nunique  order_cnt  order_dow_nunique  prod_days_since_mean_mean  \\\n",
       "0            18         10                  4                  11.119767   \n",
       "1           102         14                  5                  10.761481   \n",
       "2            33         11                  4                  11.235237   \n",
       "3            14          4                  3                  11.515205   \n",
       "4            23          4                  3                  10.676956   \n",
       "\n",
       "   prod_days_since_std_mean  \n",
       "0                  8.668178  \n",
       "1                  8.449797  \n",
       "2                  8.722556  \n",
       "3                  8.788381  \n",
       "4                  8.507307  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df = pd.merge(features_df, order_features, how=\"left\", on=\"user_id\")\n",
    "features_df = pd.merge(features_df, product_features, how=\"left\", on=\"user_id\")\n",
    "features_df = pd.merge(features_df, product_features_days_since, how=\"left\", on=\"user_id\")\n",
    "print(features_df.shape)\n",
    "features_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>user_product_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>196</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10258</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10326</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>12427</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13032</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  product_id  user_product_cnt\n",
       "0        1         196                10\n",
       "1        1       10258                 9\n",
       "2        1       10326                 1\n",
       "3        1       12427                10\n",
       "4        1       13032                 3"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Beside the specificly-designed features above, I will attempt to extract useful information from \n",
    "# the large user-product matrix by applying dimensionality reduction technique - PCA.\n",
    "# Similarly to user-ailse matrix and user-department matrix.\n",
    "\n",
    "# count user_product interactions\n",
    "user_product_cnt = order_products_prior.merge(\n",
    "    orders.loc[orders[\"eval_set\"] == \"prior\",[\"order_id\", \"user_id\"]], how=\"left\", on=\"order_id\")\\\n",
    "    .groupby([\"user_id\", \"product_id\"])[\"order_id\"].count()\n",
    "user_product_cnt = user_product_cnt.rename(\"user_product_cnt\").reset_index()\n",
    "user_product_cnt.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sparsity:  99.87327261310125 %\n",
      "RAM requirement if convert to dense:  76.33948916196823 GB\n"
     ]
    }
   ],
   "source": [
    "# Create user-product matrix using compressed sparse row (CSR) format\n",
    "from scipy.sparse import csr_matrix\n",
    "user_product_matrix = csr_matrix((user_product_cnt[\"user_product_cnt\"], (user_product_cnt[\"user_id\"] - 1, user_product_cnt[\"product_id\"] - 1)))\n",
    "\n",
    "# Check matrix sparsity\n",
    "_matrix_density = (user_product_matrix.nnz / (user_product_matrix.shape[0] * user_product_matrix.shape[1]))\n",
    "print(\"Sparsity: \", (1 - _matrix_density) * 100, \"%\")\n",
    "print(\"RAM requirement if convert to dense: \", user_product_matrix.data.nbytes / 1024**3 /_matrix_density , \"GB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this level of Sparscity, there's the risk of OOM if convert to dense (require 76 GB)\n",
    "\n",
    "=> Settle for PCA on user-department and user-ailse matrices only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "del user_product_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sparsity:  79.69527800478909 %\n",
      "RAM requirement if convert to dense:  0.2058744877576828 GB\n"
     ]
    }
   ],
   "source": [
    "# Calculate interactions between users and aisles\n",
    "user_ailse_cnt = user_product_cnt.merge(products[[\"product_id\", \"aisle_id\"]], how=\"left\", on=\"product_id\")\\\n",
    "    .groupby([\"user_id\", \"aisle_id\"])[\"user_product_cnt\"].sum()\n",
    "user_ailse_cnt = user_ailse_cnt.rename(\"user_ailse_cnt\").reset_index()\n",
    "# Scale user_ailse_cnt to reduce disparity in variation between different aisles\n",
    "user_ailse_cnt[\"scaled\"] = user_ailse_cnt[\"user_ailse_cnt\"] / user_ailse_cnt.groupby(\"user_id\")[\"user_ailse_cnt\"].transform(\"sum\")\n",
    "\n",
    "# Create user-aisle matrix using compressed sparse row (CSR) format\n",
    "user_ailse_matrix = csr_matrix((user_ailse_cnt[\"scaled\"], (user_ailse_cnt[\"user_id\"] - 1, user_ailse_cnt[\"aisle_id\"] - 1)))\n",
    "_matrix_density = (user_ailse_matrix.nnz / (user_ailse_matrix.shape[0] * user_ailse_matrix.shape[1]))\n",
    "print(\"Sparsity: \", (1 - _matrix_density) * 100, \"%\")\n",
    "print(\"RAM requirement if convert to dense: \", user_ailse_matrix.data.nbytes / 1024**3 /_matrix_density , \"GB\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of components:  34\n",
      "Explained variance:  0.8042259688367953\n"
     ]
    }
   ],
   "source": [
    "# Apply PCA to user-aisle matrix, retain 80% of variance\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "aisle_pca = PCA(n_components=0.80)\n",
    "user_aisle_pca = aisle_pca.fit_transform(user_ailse_matrix.toarray())\n",
    "print(\"Number of components: \", aisle_pca.n_components_)\n",
    "print(\"Explained variance: \", aisle_pca.explained_variance_ratio_.sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sparsity:  49.20100711506519 %\n",
      "RAM requirement if convert to dense:  0.03226391226053238 GB\n",
      "Number of components:  6\n",
      "Explained variance:  0.8243843548092602\n"
     ]
    }
   ],
   "source": [
    "# Create user-aisle matrix using compressed sparse row (CSR) format\n",
    "# The apply PCA to user-department matrix, retain 80% of variance\n",
    "user_department_cnt = user_product_cnt.merge(products[[\"product_id\", \"department_id\"]], how=\"left\", on=\"product_id\")\\\n",
    "    .groupby([\"user_id\", \"department_id\"])[\"user_product_cnt\"].sum()\n",
    "user_department_cnt = user_department_cnt.rename(\"user_department_cnt\").reset_index()\n",
    "user_department_cnt[\"scaled\"] = user_department_cnt[\"user_department_cnt\"] / user_department_cnt.groupby(\"user_id\")[\"user_department_cnt\"].transform(\"sum\")\n",
    "\n",
    "user_department_matrix = csr_matrix((user_department_cnt[\"scaled\"], (user_department_cnt[\"user_id\"] - 1, user_department_cnt[\"department_id\"] - 1)))\n",
    "_matrix_density = (user_department_matrix.nnz / (user_department_matrix.shape[0] * user_department_matrix.shape[1]))\n",
    "print(\"Sparsity: \", (1 - _matrix_density) * 100, \"%\")\n",
    "print(\"RAM requirement if convert to dense: \", user_department_matrix.data.nbytes / 1024**3 /_matrix_density , \"GB\")\n",
    "\n",
    "department_pca = PCA(n_components=0.80)\n",
    "user_department_pca = department_pca.fit_transform(user_department_matrix.toarray())\n",
    "print(\"Number of components: \", department_pca.n_components_)\n",
    "print(\"Explained variance: \", department_pca.explained_variance_ratio_.sum())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(206209, 55)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_days_since_mean</th>\n",
       "      <th>user_days_since_std</th>\n",
       "      <th>ord_product_count_mean</th>\n",
       "      <th>ord_product_count_std</th>\n",
       "      <th>ord_reorder_rate_mean</th>\n",
       "      <th>ord_reorder_rate_std</th>\n",
       "      <th>prod_reorder_rate_mean</th>\n",
       "      <th>prod_reorder_std_mean</th>\n",
       "      <th>user_reorder_rate</th>\n",
       "      <th>...</th>\n",
       "      <th>apca_30</th>\n",
       "      <th>apca_31</th>\n",
       "      <th>apca_32</th>\n",
       "      <th>apca_33</th>\n",
       "      <th>dpca_0</th>\n",
       "      <th>dpca_1</th>\n",
       "      <th>dpca_2</th>\n",
       "      <th>dpca_3</th>\n",
       "      <th>dpca_4</th>\n",
       "      <th>dpca_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19.555556</td>\n",
       "      <td>9.395625</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>1.523884</td>\n",
       "      <td>0.705833</td>\n",
       "      <td>0.317544</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.437883</td>\n",
       "      <td>0.694915</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007117</td>\n",
       "      <td>-0.014654</td>\n",
       "      <td>0.000780</td>\n",
       "      <td>0.008540</td>\n",
       "      <td>-0.265929</td>\n",
       "      <td>-0.047494</td>\n",
       "      <td>0.079557</td>\n",
       "      <td>-0.257602</td>\n",
       "      <td>0.032950</td>\n",
       "      <td>0.028218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15.230769</td>\n",
       "      <td>9.867065</td>\n",
       "      <td>13.928571</td>\n",
       "      <td>5.717238</td>\n",
       "      <td>0.447961</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.634116</td>\n",
       "      <td>0.463857</td>\n",
       "      <td>0.476923</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.022066</td>\n",
       "      <td>0.034189</td>\n",
       "      <td>0.018054</td>\n",
       "      <td>-0.012521</td>\n",
       "      <td>-0.096470</td>\n",
       "      <td>0.102039</td>\n",
       "      <td>0.072159</td>\n",
       "      <td>-0.100331</td>\n",
       "      <td>0.050705</td>\n",
       "      <td>-0.006990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>5.573748</td>\n",
       "      <td>7.454545</td>\n",
       "      <td>2.161649</td>\n",
       "      <td>0.627801</td>\n",
       "      <td>0.331377</td>\n",
       "      <td>0.617979</td>\n",
       "      <td>0.472159</td>\n",
       "      <td>0.597561</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006983</td>\n",
       "      <td>0.002793</td>\n",
       "      <td>-0.020456</td>\n",
       "      <td>0.006397</td>\n",
       "      <td>0.167761</td>\n",
       "      <td>0.069432</td>\n",
       "      <td>-0.010328</td>\n",
       "      <td>-0.061908</td>\n",
       "      <td>0.042673</td>\n",
       "      <td>0.022836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>3.055050</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>2.362908</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.550503</td>\n",
       "      <td>0.469545</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021163</td>\n",
       "      <td>-0.032496</td>\n",
       "      <td>-0.019621</td>\n",
       "      <td>-0.010643</td>\n",
       "      <td>-0.164928</td>\n",
       "      <td>-0.127020</td>\n",
       "      <td>-0.003297</td>\n",
       "      <td>0.212055</td>\n",
       "      <td>0.007975</td>\n",
       "      <td>0.086597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>4.932883</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>3.095696</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.277555</td>\n",
       "      <td>0.581885</td>\n",
       "      <td>0.474626</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005851</td>\n",
       "      <td>0.008644</td>\n",
       "      <td>-0.009203</td>\n",
       "      <td>-0.002067</td>\n",
       "      <td>0.261500</td>\n",
       "      <td>0.059395</td>\n",
       "      <td>-0.035841</td>\n",
       "      <td>-0.019744</td>\n",
       "      <td>0.017033</td>\n",
       "      <td>0.005436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  user_days_since_mean  user_days_since_std  ord_product_count_mean  \\\n",
       "0        1             19.555556             9.395625                5.900000   \n",
       "1        2             15.230769             9.867065               13.928571   \n",
       "2        3             11.800000             5.573748                7.454545   \n",
       "3        4             18.333333             3.055050                3.750000   \n",
       "4        5             13.333333             4.932883                9.250000   \n",
       "\n",
       "   ord_product_count_std  ord_reorder_rate_mean  ord_reorder_rate_std  \\\n",
       "0               1.523884               0.705833              0.317544   \n",
       "1               5.717238               0.447961              0.315789   \n",
       "2               2.161649               0.627801              0.331377   \n",
       "3               2.362908               0.035714              0.071429   \n",
       "4               3.095696               0.377778              0.277555   \n",
       "\n",
       "   prod_reorder_rate_mean  prod_reorder_std_mean  user_reorder_rate  ...  \\\n",
       "0                0.725334               0.437883           0.694915  ...   \n",
       "1                0.634116               0.463857           0.476923  ...   \n",
       "2                0.617979               0.472159           0.597561  ...   \n",
       "3                0.550503               0.469545           0.066667  ...   \n",
       "4                0.581885               0.474626           0.378378  ...   \n",
       "\n",
       "    apca_30   apca_31   apca_32   apca_33    dpca_0    dpca_1    dpca_2  \\\n",
       "0  0.007117 -0.014654  0.000780  0.008540 -0.265929 -0.047494  0.079557   \n",
       "1 -0.022066  0.034189  0.018054 -0.012521 -0.096470  0.102039  0.072159   \n",
       "2  0.006983  0.002793 -0.020456  0.006397  0.167761  0.069432 -0.010328   \n",
       "3 -0.021163 -0.032496 -0.019621 -0.010643 -0.164928 -0.127020 -0.003297   \n",
       "4  0.005851  0.008644 -0.009203 -0.002067  0.261500  0.059395 -0.035841   \n",
       "\n",
       "     dpca_3    dpca_4    dpca_5  \n",
       "0 -0.257602  0.032950  0.028218  \n",
       "1 -0.100331  0.050705 -0.006990  \n",
       "2 -0.061908  0.042673  0.022836  \n",
       "3  0.212055  0.007975  0.086597  \n",
       "4 -0.019744  0.017033  0.005436  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df = pd.concat([\n",
    "    features_df,\n",
    "    pd.DataFrame(user_aisle_pca, columns=[f\"apca_{i}\" for i in range(user_aisle_pca.shape[1])]), \n",
    "    pd.DataFrame(user_department_pca, columns=[f\"dpca_{i}\" for i in range(user_department_pca.shape[1])])\n",
    "    ], axis=1)\n",
    "print(features_df.shape)\n",
    "features_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_df.to_csv(\"./data/features.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick analysis - sense check correlation with labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['user_days_since_mean',\n",
       " 'user_days_since_std',\n",
       " 'ord_product_count_mean',\n",
       " 'ord_product_count_std',\n",
       " 'ord_reorder_rate_mean',\n",
       " 'ord_reorder_rate_std',\n",
       " 'prod_reorder_rate_mean',\n",
       " 'prod_reorder_std_mean',\n",
       " 'user_reorder_rate',\n",
       " 'prod_nunique',\n",
       " 'order_cnt',\n",
       " 'order_dow_nunique',\n",
       " 'prod_days_since_mean_mean',\n",
       " 'prod_days_since_std_mean']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manual_features = [col for col in features_df.columns if col != \"user_id\" and \"pca\" not in col]\n",
    "manual_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>next_order_id</th>\n",
       "      <th>days_to_next_order</th>\n",
       "      <th>reorder_ratio</th>\n",
       "      <th>cls_days_to_next_order</th>\n",
       "      <th>cls_reorder_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1187899</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1492625</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.387097</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1402502</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2557754</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2196797</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206204</th>\n",
       "      <td>206205</td>\n",
       "      <td>1716008</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206205</th>\n",
       "      <td>206206</td>\n",
       "      <td>1904200</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206206</th>\n",
       "      <td>206207</td>\n",
       "      <td>1005822</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206207</th>\n",
       "      <td>206208</td>\n",
       "      <td>1882108</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206208</th>\n",
       "      <td>206209</td>\n",
       "      <td>272231</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206209 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  next_order_id  days_to_next_order  reorder_ratio  \\\n",
       "0             1        1187899                14.0       0.909091   \n",
       "1             2        1492625                30.0       0.387097   \n",
       "2             3        1402502                15.0       1.000000   \n",
       "3             4        2557754                 0.0       0.000000   \n",
       "4             5        2196797                 6.0       0.444444   \n",
       "...         ...            ...                 ...            ...   \n",
       "206204   206205        1716008                10.0       0.315789   \n",
       "206205   206206        1904200                11.0       1.000000   \n",
       "206206   206207        1005822                18.0       1.000000   \n",
       "206207   206208        1882108                 7.0       0.823529   \n",
       "206208   206209         272231                30.0       0.500000   \n",
       "\n",
       "        cls_days_to_next_order  cls_reorder_ratio  \n",
       "0                            1                  3  \n",
       "1                            3                  1  \n",
       "2                            2                  3  \n",
       "3                            0                  0  \n",
       "4                            0                  1  \n",
       "...                        ...                ...  \n",
       "206204                       1                  1  \n",
       "206205                       1                  3  \n",
       "206206                       2                  3  \n",
       "206207                       1                  2  \n",
       "206208                       3                  2  \n",
       "\n",
       "[206209 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_days_since_mean</th>\n",
       "      <th>user_days_since_std</th>\n",
       "      <th>ord_product_count_mean</th>\n",
       "      <th>ord_product_count_std</th>\n",
       "      <th>ord_reorder_rate_mean</th>\n",
       "      <th>ord_reorder_rate_std</th>\n",
       "      <th>prod_reorder_rate_mean</th>\n",
       "      <th>prod_reorder_std_mean</th>\n",
       "      <th>user_reorder_rate</th>\n",
       "      <th>prod_nunique</th>\n",
       "      <th>order_cnt</th>\n",
       "      <th>order_dow_nunique</th>\n",
       "      <th>prod_days_since_mean_mean</th>\n",
       "      <th>prod_days_since_std_mean</th>\n",
       "      <th>cls_days_to_next_order</th>\n",
       "      <th>cls_reorder_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19.555556</td>\n",
       "      <td>9.395625</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>1.523884</td>\n",
       "      <td>0.705833</td>\n",
       "      <td>0.317544</td>\n",
       "      <td>0.725334</td>\n",
       "      <td>0.437883</td>\n",
       "      <td>0.694915</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>11.119767</td>\n",
       "      <td>8.668178</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15.230769</td>\n",
       "      <td>9.867065</td>\n",
       "      <td>13.928571</td>\n",
       "      <td>5.717238</td>\n",
       "      <td>0.447961</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.634116</td>\n",
       "      <td>0.463857</td>\n",
       "      <td>0.476923</td>\n",
       "      <td>102</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>10.761481</td>\n",
       "      <td>8.449797</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>11.800000</td>\n",
       "      <td>5.573748</td>\n",
       "      <td>7.454545</td>\n",
       "      <td>2.161649</td>\n",
       "      <td>0.627801</td>\n",
       "      <td>0.331377</td>\n",
       "      <td>0.617979</td>\n",
       "      <td>0.472159</td>\n",
       "      <td>0.597561</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>11.235237</td>\n",
       "      <td>8.722556</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>3.055050</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>2.362908</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.550503</td>\n",
       "      <td>0.469545</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>11.515205</td>\n",
       "      <td>8.788381</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>4.932883</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>3.095696</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.277555</td>\n",
       "      <td>0.581885</td>\n",
       "      <td>0.474626</td>\n",
       "      <td>0.378378</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>10.676956</td>\n",
       "      <td>8.507307</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  user_days_since_mean  user_days_since_std  ord_product_count_mean  \\\n",
       "0        1             19.555556             9.395625                5.900000   \n",
       "1        2             15.230769             9.867065               13.928571   \n",
       "2        3             11.800000             5.573748                7.454545   \n",
       "3        4             18.333333             3.055050                3.750000   \n",
       "4        5             13.333333             4.932883                9.250000   \n",
       "\n",
       "   ord_product_count_std  ord_reorder_rate_mean  ord_reorder_rate_std  \\\n",
       "0               1.523884               0.705833              0.317544   \n",
       "1               5.717238               0.447961              0.315789   \n",
       "2               2.161649               0.627801              0.331377   \n",
       "3               2.362908               0.035714              0.071429   \n",
       "4               3.095696               0.377778              0.277555   \n",
       "\n",
       "   prod_reorder_rate_mean  prod_reorder_std_mean  user_reorder_rate  \\\n",
       "0                0.725334               0.437883           0.694915   \n",
       "1                0.634116               0.463857           0.476923   \n",
       "2                0.617979               0.472159           0.597561   \n",
       "3                0.550503               0.469545           0.066667   \n",
       "4                0.581885               0.474626           0.378378   \n",
       "\n",
       "   prod_nunique  order_cnt  order_dow_nunique  prod_days_since_mean_mean  \\\n",
       "0            18         10                  4                  11.119767   \n",
       "1           102         14                  5                  10.761481   \n",
       "2            33         11                  4                  11.235237   \n",
       "3            14          4                  3                  11.515205   \n",
       "4            23          4                  3                  10.676956   \n",
       "\n",
       "   prod_days_since_std_mean  cls_days_to_next_order  cls_reorder_ratio  \n",
       "0                  8.668178                       1                  3  \n",
       "1                  8.449797                       3                  1  \n",
       "2                  8.722556                       2                  3  \n",
       "3                  8.788381                       0                  0  \n",
       "4                  8.507307                       0                  1  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualization_df = features_df[['user_id'] + manual_features].copy()\n",
    "visualization_df = visualization_df.merge(labels_df[[\"user_id\", \"cls_days_to_next_order\", \"cls_reorder_ratio\"]], how=\"left\", on=\"user_id\")\n",
    "visualization_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8YAAAZbCAYAAABiiNp5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeVxUVf8H8M8wAoMioCiLsrqiBprgQpiamuTjVpCagalhWaIpWD3ylFtZaInYolZqaIJpWlpmihtiIfrgiqa5skgKKiqEOqBwf3/4Yx7HWQBnmDvL5/163deLe76Xc7/DAOfOPfecIxEEQQAREREREREREREREREREZGZshI7ASIiIiIiIiIiIiIiIiIiovrEjnEiIiIiIiIiIiIiIiIiIjJr7BgnIiIiIiIiIiIiIiIiIiKzxo5xIiIiIiIiIiIiIiIiIiIya+wYJyIiIiIiIiIiIiIiIiIis8aOcSIiIiIiIiIiIiIiIiIiMmvsGCciIiIiIiIiIiIiIiIiIrPGjnEiIiIiIiIiIiIiIiIiIjJr7BgnIiIiIiIiIiIiIiIiIiKzxo5xIiPTt29f9O3bV+w0dLZ3715IJBLs3btX7FSIiIiMEtt882Dpr5+ISBem2haaat5ERERiMdW201TzJlWrVq2CRCJBbm6u2KmQyNgxTkRERERE9P/Wrl2LxYsXi50GERFZsN9++w1z5swRO416sX//fsyZMwe3bt0SOxUiIiLRsc1/fEuXLsWqVavqpW4yb+wYJ6J60bt3b9y9exe9e/cWOxUiIiKqR+bW5rNjnIiIxPbbb79h7ty5YqdRL/bv34+5c+eyY5yIiAhs83XBjnF6XOwYJ9KjqqoqyOVysdMwClZWVpDJZLCy4r8ZIiIyP2zz/4dtPhERaXL79m2xU1BibPkQERGZC2NrY40tHyIyHrx7RWZj3Lhx8PHxUSmfM2cOJBKJYn/nzp3o1asXnJycYG9vj/bt2+M///mP0veUl5dj9uzZaNOmDWxtbeHp6Yl3330X5eXlSsdJJBJMnjwZKSkp6NSpE2xtbbF9+/Za5/zNN9+gdevWsLOzQ/fu3fH777+rHFNRUYFZs2YhMDAQjo6OaNSoEZ5++mmkpaUpjhEEAT4+Phg+fLjK98vlcjg6OmLixImKsi+++AKdOnVCw4YN0aRJEwQFBWHt2rW1zhsA1q1bh8DAQDRu3BgODg7w9/fHZ599poirW2+zb9++eOKJJ3Dq1Ck888wzaNiwIVq2bIlPPvlEbd5z5sxBu3btIJPJ4O7ujrCwMFy4cEFxTFVVFRYvXoxOnTpBJpPB1dUVEydOxM2bN+v0Wqp/R86ePYvIyEg4OjqiefPmmDlzJgRBwKVLlzB8+HA4ODjAzc0NCQkJKnXU9ncmKSkJ/fr1g4uLC2xtbdGxY0csW7ZMpT4fHx8MGTIEf/zxB7p37w6ZTIZWrVrhu+++q9NrIyIyR2zz2eY/bpv/zz//YNq0afDx8YGtrS1cXFzw7LPP4siRI4q8t27diry8PEgkEkgkEqXftYKCAjz//PNo1KgRXFxcEBMTo/K7QkRkro4ePYpBgwbBwcEB9vb26N+/Pw4cOKCIV6/bmJ6ejkmTJsHFxQUeHh6KeG3awtqo/qy0Y8cOdOnSBTKZDB07dsRPP/2kdFxN+SxdulTRprdo0QLR0dFqRzTVJm9Na1aqayMB4ODBg/jXv/6FJk2aoFGjRggICFC0rePGjcOSJUsAQNEWPXx9Uxva6q+2Z88ePP3002jUqBGcnJwwfPhwnD59WumY2l5zVec6efJkbN68GU888QRsbW3RqVMnpeulOXPm4J133gEA+Pr6Kl5bbdf6HDduHOzt7ZGfn48hQ4bA3t4eLVu2VPy8Tpw4gX79+qFRo0bw9vZWe81z69YtTJs2DZ6enrC1tUWbNm2wYMECVFVVKR23cOFCPPXUU3B2doadnR0CAwOxceNGlfpq87qJiEwN23y2+dXEavMLCwsxfvx4eHh4wNbWFu7u7hg+fLji+318fPDnn38iPT1dUffDa8H/+eef6NevH+zs7ODh4YF58+aptPVkuRqInQCRIf35558YMmQIAgIC8MEHH8DW1hbnz59HRkaG4piqqioMGzYMf/zxB15//XV06NABJ06cQGJiIs6ePYvNmzcr1blnzx788MMPmDx5Mpo1a6a2AVFn5cqVmDhxIp566ilMmzYNFy9exLBhw9C0aVN4enoqjistLcWKFSswevRovPbaa/jnn3+wcuVKhIaG4r///S+6dOkCiUSCyMhIfPLJJ7hx4waaNm2q+P4tW7agtLQUkZGRAIDly5fjrbfewosvvoipU6dCLpcjOzsbBw8exMsvv1yr3Hfu3InRo0ejf//+WLBgAQDg9OnTyMjIwNSpU7V+782bN/Hcc88hLCwMI0eOxMaNG/Hvf/8b/v7+GDRoEACgsrISQ4YMwe7du/HSSy9h6tSp+Oeff7Bz506cPHkSrVu3BgBMnDgRq1atwvjx4/HWW28hJycHX375JY4ePYqMjAxYW1vX6vVUGzVqFDp06ID58+dj69atmDdvHpo2bYqvv/4a/fr1w4IFC5CSkoK3334b3bp1U0wZW5ffmWXLlqFTp04YNmwYGjRogC1btmDSpEmoqqpCdHS0Uj7nz5/Hiy++iKioKIwdOxbffvstxo0bh8DAQHTq1KlOr42IyNKwzWebr84bb7yBjRs3YvLkyejYsSOKi4vxxx9/4PTp0+jatSvee+89lJSUoKCgAImJiQAAe3t7AMDdu3fRv39/5Ofn46233kKLFi2wZs0a7Nmzp1bnJiIyZX/++SeefvppODg44N1334W1tTW+/vpr9O3bF+np6ejRo4fi2EmTJqF58+aYNWuWYrRWbdvC2jp37hxGjRqFN954A2PHjkVSUhJGjBiB7du349lnn1U6Vl0+c+bMwdy5czFgwAC8+eabOHPmDJYtW4asrCyldkXfeQMP2tYhQ4bA3d0dU6dOhZubG06fPo1ff/0VU6dOxcSJE3H58mXs3LkTa9as0Xv9ALBr1y4MGjQIrVq1wpw5c3D37l188cUXCAkJwZEjR2p9jfOoP/74Az/99BMmTZqExo0b4/PPP0d4eDjy8/Ph7OyMsLAwnD17Ft9//z0SExPRrFkzAEDz5s1rfY7KykoMGjQIvXv3xieffIKUlBRMnjwZjRo1wnvvvYeIiAiEhYXhq6++wiuvvILg4GD4+voCAO7cuYM+ffrg77//xsSJE+Hl5YX9+/cjLi4OV65cUVpK5bPPPsOwYcMQERGBiooKrFu3DiNGjMCvv/6KwYMH1+l1ExGZErb5bPNro77b/PDwcPz555+YMmUKfHx8cPXqVezcuRP5+fnw8fHB4sWLMWXKFNjb2+O9994DALi6ugJ40Kn+zDPP4P79+5gxYwYaNWqEb775BnZ2do/1WskMCURmYuzYsYK3t7dK+ezZs4XqX/XExEQBgHDt2jWN9axZs0awsrISfv/9d6Xyr776SgAgZGRkKMoACFZWVsKff/5Zp1wrKioEFxcXoUuXLkJ5ebmi/JtvvhEACH369FGU3b9/X+kYQRCEmzdvCq6ursKrr76qKDtz5owAQFi2bJnSscOGDRN8fHyEqqoqQRAEYfjw4UKnTp3qlO+jpk6dKjg4OAj379/XeExaWpoAQEhLS1OU9enTRwAgfPfdd4qy8vJywc3NTQgPD1eUffvttwIAYdGiRSr1Vr+O33//XQAgpKSkKMW3b9+utlyb6t+R119/XVF2//59wcPDQ5BIJML8+fMV5Tdv3hTs7OyEsWPHKsrq8jtz584dlfOHhoYKrVq1Uirz9vYWAAj79u1TlF29elWwtbUVpk+fXuvXRkRkjtjms80XhMdr8x0dHYXo6GitxwwePFjt79fixYsFAMIPP/ygKLt9+7bQpk0blddPRGRunn/+ecHGxka4cOGCouzy5ctC48aNhd69ewuCIAhJSUkCAKFXr15K7UZd2sLaqP6s9OOPPyrKSkpKBHd3d+HJJ59UlGnK5+rVq4KNjY0wcOBAobKyUlH+5ZdfCgCEb7/9ts55V58rJydHKddH28j79+8Lvr6+gre3t3Dz5k2lY6vbPUEQhOjoaMU1TV3Utv4uXboILi4uQnFxsaLs+PHjgpWVlfDKK68oympzzVUNgGBjYyOcP39eqU4AwhdffKEo+/TTT9X+rGpj7NixAgDh448/VpRVf0aXSCTCunXrFOV//fWXAECYPXu2ouzDDz8UGjVqJJw9e1ap3hkzZghSqVTIz89XlD362b2iokJ44oknhH79+j3W6yYiMhVs89nmP0yMNv/mzZsCAOHTTz/VelynTp3U/k5NmzZNACAcPHhQUXb16lXB0dHxsa9ByLxwKnWyKE5OTgCAn3/+WePUGRs2bECHDh3g5+eH69evK7Z+/foBgNJ0pgDQp08fdOzYsU55HDp0CFevXsUbb7wBGxsbRfm4cePg6OiodKxUKlUcU1VVhRs3buD+/fsICgpSTPsJAO3atUOPHj2QkpKiKLtx4wa2bduGiIgIxZQnTk5OKCgoQFZWVp1yfpiTkxNu376NnTt31vl77e3tFSPZAMDGxgbdu3fHxYsXFWU//vgjmjVrhilTpqh8f/Xr2LBhAxwdHfHss88qvU+BgYGwt7dXeZ9qY8KECYqvpVIpgoKCIAgCoqKiFOVOTk5o3769Ur51+Z15+Mm0kpISXL9+HX369MHFixdRUlKilE/Hjh3x9NNPK/abN2+ucm4iIlKPbT7bfE2v5+DBg7h8+XKdX89vv/0Gd3d3vPjii4qyhg0b4vXXX69zXUREpqSyshI7duzA888/j1atWinK3d3d8fLLL+OPP/5AaWmpovy1116DVCpV7NelLaytFi1a4IUXXlDsOzg44JVXXsHRo0dRWFiodOyj+ezatQsVFRWYNm0arKyslI5zcHDA1q1b6y3vo0ePIicnB9OmTVNcq1Sr69Spj1v/lStXcOzYMYwbN05p5pmAgAA8++yz+O233x77/AMGDFDM9lJdp4ODg94/wz782b36M3qjRo0wcuRIRXn79u3h5OSk8tn96aefRpMmTZSuKQYMGIDKykrs27dPcezDn91v3ryJkpISPP3000rXZNUM9bqJiOob23y2+bVVn22fnZ0dbGxssHfv3jovnwY8+Ozes2dPdO/eXVHWvHlzRERE6JwbmQd2jJNFGTVqFEJCQjBhwgS4urripZdewg8//KB0w/zcuXP4888/0bx5c6WtXbt2AICrV68q1Vk9JVdd5OXlAQDatm2rVG5tba100VFt9erVCAgIgEwmg7OzM5o3b46tW7eqdKS+8soryMjIUNS/YcMG3Lt3D2PGjFEc8+9//xv29vbo3r072rZti+joaKVpZWtj0qRJaNeuHQYNGgQPDw+8+uqrtV4/y8PDQ6Xxb9KkiVIjd+HCBbRv3x4NGmhe7eHcuXMoKSmBi4uLyntVVlam8j7VhpeXl9K+o6MjZDKZYqqXh8sfzrcuvzMZGRkYMGCAYk2X5s2bK9a7ffT9fDQfQPVnRURE6rHNZ5uvzieffIKTJ0/C09MT3bt3x5w5c2r9wT0vLw9t2rRReU3t27ev9fmJiEzRtWvXcOfOHbX/7zp06ICqqipcunRJUfZoe1nXtrA21P0/rm6/H127UlM+j74eGxsbtGrVShGvj7wvXLgAAHjiiSce6/v1Ub+m1w88eD+vX7+umH62rgzxGVYmk6lMw+ro6Kj2ukPdZ/ft27erXE8MGDAAgPK136+//oqePXtCJpOhadOmaN68OZYtW6ZyTQbwszsRmQ+2+Wzza6s+2z5bW1ssWLAA27Ztg6urq2L5lEcfhNAkLy9P5b0E+Nmd/odrjJPZ0PSkVWVlpeJrOzs77Nu3D2lpadi6dSu2b9+O9evXo1+/ftixYwekUimqqqrg7++PRYsWqa3v0TVF6nttiuTkZIwbNw7PP/883nnnHbi4uEAqlSI+Pl7RAFZ76aWXEBMTg5SUFPznP/9BcnIygoKClP7pd+jQAWfOnMGvv/6K7du348cff8TSpUsxa9YszJ07t1Y5ubi44NixY0hNTcW2bduwbds2JCUl4ZVXXsHq1au1fu/DT+09TBCEWp27WlVVFVxcXJRGyz2sLmuUacutNvnW9nfmwoUL6N+/P/z8/LBo0SJ4enrCxsYGv/32GxITE1VGNOrrZ0VEZG7Y5rPNf1hd2vyRI0fi6aefxqZNm7Bjxw58+umnWLBgAX766SfFuudERKQbY1u/0RD51ObaxBTV9XUZ4jOspnPU9rP7s88+i3fffVftsdUdLb///juGDRuG3r17Y+nSpXB3d4e1tTWSkpKwdu3axzo3EZE5Ypv/P2zzH9BX2zdt2jQMHToUmzdvRmpqKmbOnIn4+Hjs2bMHTz75pF7OQZaLHeNkNpo0aYJbt26plFc/GVXNysoK/fv3R//+/bFo0SJ8/PHHeO+995CWlqaYAuT48ePo37+/XqY1Ucfb2xvAg6eVq6drBYB79+4hJycHnTt3VpRt3LgRrVq1wk8//aSUz+zZs1Xqbdq0KQYPHoyUlBREREQgIyMDixcvVjmuUaNGGDVqFEaNGoWKigqEhYXho48+QlxcHGQyWa1eg42NDYYOHYqhQ4eiqqoKkyZNwtdff42ZM2eiTZs2tf1RqNW6dWscPHgQ9+7dg7W1tcZjdu3ahZCQENEvwmr7O7NlyxaUl5fjl19+UXqq7nGmfScismRs89nm68Ld3R2TJk3CpEmTcPXqVXTt2hUfffSRomNc0++Ct7c3Tp48CUEQlI45c+aMzjkRERmz5s2bo2HDhmr/3/3111+wsrKCp6enxqU76tIW1tb58+dV/h+fPXsWAODj46P1e6vzOXPmjNIosIqKCuTk5ChGD9cl7yZNmgCAyvXJo9cm1VOOnjx5UnEedR73uqQ29T/8+h/1119/oVmzZmjUqBGA2l9z1UV9XXPVRuvWrVFWVqb1Zw88WOpFJpMhNTUVtra2ivKkpKT6TpGISFRs89nmP0rMNr9169aYPn06pk+fjnPnzqFLly5ISEhAcnKy1vq9vb1x7tw5lXJ+dqdqnEqdzEbr1q1RUlKC7OxsRdmVK1ewadMmxf6NGzdUvq9Lly4AgPLycgAPRhL9/fffWL58ucqxd+/efezpRR4WFBSE5s2b46uvvkJFRYWifNWqVSoNUPXTVw8/bXXw4EFkZmaqrXvMmDE4deoU3nnnHUilUrz00ktK8eLiYqV9GxsbdOzYEYIg4N69e7XK/9E6rKysEBAQAOB/P0ddhIeH4/r16/jyyy9VYtU/h5EjR6KyshIffvihyjH3799X25DXl9r+zqh7L0tKSvjhmoiojtjmP8A2v25tfmVlpcr0py4uLmjRooXSa2nUqJHaaVL/9a9/4fLly9i4caOi7M6dO/jmm29qdX4iIlMllUoxcOBA/Pzzz0pTlhYVFWHt2rXo1asXHBwcNH5/XdrC2rp8+bJSu19aWorvvvsOXbp0gZubm9bvHTBgAGxsbPD5558rtbkrV65ESUkJBg8eXOe8q29OP7xGdWVlpUob0bVrV/j6+mLx4sUqdTycS/VN6rr+fGpTv7u7O7p06YLVq1crHXPy5Ens2LED//rXv5ReV03XXHX1uK9NH0aOHInMzEykpqaqxG7duoX79+8DePA7L5FIlEbJ5ebmYvPmzYZKlYhIFGzz2eYbQ5t/584dyOVypbLWrVujcePGKp/d1dX9r3/9CwcOHMB///tfRdm1a9c0zkJHlocjxslsvPTSS/j3v/+NF154AW+99Rbu3LmDZcuWoV27djhy5AgA4IMPPsC+ffswePBgeHt74+rVq1i6dCk8PDzQq1cvAA9uMv/www944403kJaWhpCQEFRWVuKvv/7CDz/8gNTUVAQFBemUq7W1NebNm4eJEyeiX79+GDVqFHJycpCUlKSybsmQIUPw008/4YUXXsDgwYORk5ODr776Ch07dkRZWZlK3YMHD4azszM2bNiAQYMGwcXFRSk+cOBAuLm5ISQkBK6urjh9+jS+/PJLDB48GI0bN65V/hMmTMCNGzfQr18/eHh4IC8vD1988QW6dOmCDh06PP4P5v+98sor+O677xAbG4v//ve/ePrpp3H79m3s2rULkyZNwvDhw9GnTx9MnDgR8fHxOHbsGAYOHAhra2ucO3cOGzZswGeffYYXX3xR51xqo7a/MwMHDlSMups4cSLKysqwfPlyuLi44MqVKwbJlYjIHLDNf4Btft3a/H/++QceHh548cUX0blzZ9jb22PXrl3IyspCQkKC4rjAwECsX78esbGx6NatG+zt7TF06FC89tpr+PLLL/HKK6/g8OHDcHd3x5o1a9CwYUOdfw5ERMZu3rx52LlzJ3r16oVJkyahQYMG+Prrr1FeXo5PPvlE6/fWpS2srXbt2iEqKgpZWVlwdXXFt99+i6Kiolo9dNy8eXPExcVh7ty5eO655zBs2DCcOXMGS5cuRbdu3RAZGVnnvDt16oSePXsiLi4ON27cQNOmTbFu3TpFR2s1KysrLFu2DEOHDkWXLl0wfvx4uLu746+//sKff/6p6LANDAwEALz11lsIDQ1V+wCcOrWt/9NPP8WgQYMQHByMqKgo3L17F1988QUcHR0xZ84cRX21ueaqq+rX9t577+Gll16CtbU1hg4dqrh5Xp/eeecd/PLLLxgyZAjGjRuHwMBA3L59GydOnMDGjRuRm5uLZs2aYfDgwVi0aBGee+45vPzyy7h69SqWLFmCNm3aKHUYEBGZI7b5bPPFbvPPnj2L/v37Y+TIkejYsSMaNGiATZs2oaioSOlnExgYiGXLlmHevHlo06YNXFxc0K9fP7z77rtYs2YNnnvuOUydOhWNGjXCN998A29vb7bj9IBAZEZ27NghPPHEE4KNjY3Qvn17ITk5WZg9e7ZQ/au+e/duYfjw4UKLFi0EGxsboUWLFsLo0aOFs2fPKtVTUVEhLFiwQOjUqZNga2srNGnSRAgMDBTmzp0rlJSUKI4DIERHRz92vkuXLhV8fX0FW1tbISgoSNi3b5/Qp08foU+fPopjqqqqhI8//ljw9vYWbG1thSeffFL49ddfhbFjxwre3t5q6500aZIAQFi7dq1K7OuvvxZ69+4tODs7C7a2tkLr1q2Fd955R+l11WTjxo3CwIEDBRcXF8HGxkbw8vISJk6cKFy5ckVxTFpamgBASEtLU5T16dNH6NSpk0p96l7LnTt3hPfee0/w9fUVrK2tBTc3N+HFF18ULly4oHTcN998IwQGBgp2dnZC48aNBX9/f+Hdd98VLl++XOvXU/07cu3aNZW8GjVqpHK8utdR29+ZX375RQgICBBkMpng4+MjLFiwQPj2228FAEJOTo7iOG9vb2Hw4MFqz/3w7wcRkaVim/8A2/zat/nl5eXCO++8I3Tu3Flo3Lix0KhRI6Fz587C0qVLlY4rKysTXn75ZcHJyUkAoJRvXl6eMGzYMKFhw4ZCs2bNhKlTpwrbt29Xef1EROboyJEjQmhoqGBvby80bNhQeOaZZ4T9+/cr4klJSQIAISsrS+3316YtrI3qz0qpqalCQECAYGtrK/j5+QkbNmxQOq6mfL788kvBz89PsLa2FlxdXYU333xTuHnz5mPnfeHCBWHAgAGCra2t4OrqKvznP/8Rdu7cqbaN+OOPP4Rnn31W0R4FBAQIX3zxhSJ+//59YcqUKULz5s0FiUQi1PX2XU31C4Ig7Nq1SwgJCRHs7OwEBwcHYejQocKpU6dU6qrpmquapmslb29vYezYsUplH374odCyZUvByspK5bOwNnX5jF597kc/V//zzz9CXFyc0KZNG8HGxkZo1qyZ8NRTTwkLFy4UKioqFMetXLlSaNu2reL3KykpSefXTURkKtjms80Xs82/fv26EB0dLfj5+QmNGjUSHB0dhR49egg//PCD0nGFhYXC4MGDhcaNGwsAlN6n7OxsoU+fPoJMJhNatmwpfPjhh8LKlSvrdN1B5ksiCA/N20BEZiEmJgYrV65EYWEhRzERERGZMbb5RERkaXx8fPDEE0/g119/FTsVIiIiqkds84moPnCNcSIzI5fLkZycjPDwcN4gJyIiMmNs84mIiIiIiIiIiGqPa4wT6dmNGzdQUVGhMS6VStG8eXO9n/fq1avYtWsXNm7ciOLiYkydOrXOdVRWVuLatWtaj7G3t4e9vf3jpmlQZWVlatdkfVjz5s0hlUoNlBEREZkTtvnGg20+EZHpu3btGiorKzXGbWxs0LRpUwNmZFzEuu4whJKSEty9e1frMW5ubgbKhoiI6hvbfO3Y5rPNp/rFjnEiPQsLC0N6errGuLe3N3Jzc/V+3lOnTiEiIgIuLi74/PPP0aVLlzrXcenSJfj6+mo9Zvbs2ZgzZ87jJWlgCxcuxNy5c7Uek5OTAx8fH8MkREREZoVtvvFgm09EZPq6deuGvLw8jfE+ffpg7969hkvIyIh13WEIU6dOxerVq7Uew5UgiYjMB9t87djms82n+sU1xon07PDhw7h586bGuJ2dHUJCQgyYUe3J5XL88ccfWo9p1aoVWrVqZaCMdHPx4kVcvHhR6zG9evWCTCYzUEZERGRO2OYbD7b5RESmLyMjQ+sIoiZNmiAwMNCAGRkXU77uqMmpU6dw+fJlrccMGDDAQNkQEVF9Y5uvHdt8tvlUv9gxTkREREREREREREREREREZs1K7ASIiIiIiIiIiIiIiIiIiIjqk8WtMV5VVYXLly+jcePGkEgkYqdDREQE4MH6Of/88w9atGgBKys+t6YN23IiIjI2bMdrj+04EREZI7bltce2nIiIjE1d2nGL6xi/fPkyPD09xU6DiIhIrUuXLsHDw0PsNIwa23IiIjJWbMdrxnaciIiMGdvymrEtJyIiY1WbdtziOsYbN24M4MEPx8HBQeRsiIiIHigtLYWnp6einSLN2JYTEZGxYTtee2zHiYjIGLEtrz225UREZGzq0o5bXMd49fQuDg4ObLiJiMjocBqymrEtJyIiY8V2vGZsx4mIyJixLa8Z23IiIjJWtWnHuWAKERERERERERERERERERGZNXaMExERERERERERERERERGRWWPHOBERERERERERERERERERmTV2jBMRERERERERERERERERkVljxzgREREREREREREREREREZk1dowTEREREREREREREREREZFZayB2AqS73r17K77et2+fiJkQERHR42Bbbhr4Phk/vkemge8TmZuMjAwsXrwY06ZNQ0hIiNjpEBERERGJgtfFZAo4YtzEPXxTSd0+ERERGbcZM2Zo3Sfj8M4772jdJ/GNHz9e6z4ZB35+IXMjl8uRkJCAoqIiJCQkQC6Xi50SEREREZHB8bqYTIVRdYwvW7YMAQEBcHBwgIODA4KDg7Ft2zZFXC6XIzo6Gs7OzrC3t0d4eDiKiopEzJiIiIhIN/v379e6T8bh4MGDWvdJfBcuXNC6T0RUH5KTk1FcXAwAKC4uRkpKisgZEREREREZHq+LyVRIBEEQxE6i2pYtWyCVStG2bVsIgoDVq1fj008/xdGjR9GpUye8+eab2Lp1K1atWgVHR0dMnjwZVlZWyMjIqPU5SktL4ejoiJKSEjg4ONTjq6l/2kZXcErCxycIgt6fZhIEAeXl5QAAW1tbSCQSvdYvk8n0Xqex4/tkGvg+1Z45tU/1zZx+VmzLTQPfJ+PH98g0mPP7ZC5tk4+PD/Ly8lTKJ02ahCVLlkAul2P69OlYt24dysvLERoaiqVLl8LV1bXW5zCXnxUAFBQUYMyYMaisrFSUNWjQAN999x08PDxEzIyIiOrKnNqn+safFRE9itfFJLa6tE1Gtcb40KFDlfY/+ugjLFu2DAcOHICHhwdWrlyJtWvXol+/fgCApKQkdOjQAQcOHEDPnj3V1lleXq7oQAEe/HDMQU1TDvbu3dvkby6JRS6XIzQ0VOw06iQ1NRV2dnZip2FQfJ9MA98nIs3+/vvvGuMtW7Y0UDakSUFBQY1xfsgT17lz52qMt23b1kDZkCarV6+uMT527FgDZUOaZGVlKd3MOnnyJJ599lmMGDECABATE4OtW7diw4YNiofVw8LC6vSwurkQBAGJiYkayxcuXGhxD8USERERkeXhdTGZGqOaSv1hlZWVWLduHW7fvo3g4GAcPnwY9+7dw4ABAxTH+Pn5wcvLC5mZmRrriY+Ph6Ojo2Lz9PQ0RPpEREREWo0ePVqnOBnGyy+/rFOc6l9UVJROcTKMlStX6hQnw2jevDnc3NwU26+//orWrVujT58+KCkpwcqVK7Fo0SL069cPgYGBSEpKwv79+3HgwAGNdZaXl6O0tFRpMwd5eXkqDxIAD+5lZGVlqR15T0RERERkbnhdTKbGqEaMA8CJEycQHBwMuVwOe3t7bNq0CR07dsSxY8dgY2MDJycnpeNdXV1RWFiosb64uDjExsYq9ktLS9k5TlrJZDKkpqbqtU65XI7hw4cDAH7++WfIZDK91q/v+kwB3yfTwPeJSLPvv/9ea+f3999/b8BsSJO1a9dq7fxeu3atAbMhdVauXKm185sdrsYhKipK63vBBxiMT0VFBZKTkxEbGwuJRFLjw+qaZnGLj4/H3LlzDZW2wXh7e6Nbt244cuSI0k1AqVSKwMBAeHt7i5gdEREREZFh8LqYTI3RdYy3b98ex44dQ0lJCTZu3IixY8ciPT39seuztbWFra2tHjM0Di+99BLWrVunNU6PRyKR1Os0yjKZjNM06wHfJ9PA94lIs5qmSec06sahpmnSOY26+GqaJp3TqBuHsWPHau0Y5zTqxmfz5s24desWxo0bBwAoLCzkw+oPkUgkiImJwZgxY9SWc7pIIiIiIrIEvC4mU2N0U6nb2NigTZs2CAwMRHx8PDp37ozPPvsMbm5uqKiowK1bt5SOLyoqgpubmzjJiujYsWM6xYmIiEh8+/btq1M5iYPvk/Hje2Qa+D6ZlpUrV2LQoEFo0aKFTvXY2trCwcFBaTMXHh4eiIiIUNzsk0gkiIiI4MNtRERERGRReF1MpsToOsYfVVVVhfLycgQGBsLa2hq7d+9WxM6cOYP8/HwEBweLmKE4bt68qVOciIiIjMNTTz2ldZ+MQ48ePbTuk/hat26tdZ+Iai8vLw+7du3ChAkTFGV8WF29yMhIODs7AwCaNWuGiIgIkTMiIiIiIjI8XheTqTCqjvG4uDjs27cPubm5OHHiBOLi4rB3715ERETA0dERUVFRiI2NRVpaGg4fPozx48cjODhY41pm5qyoqEinOBERERmH+fPna90n4/Dpp59q3SfxJSUlad0n4/Do6HCOFjdOSUlJcHFxweDBgxVlfFhdPZlMhunTp8PV1RWxsbGQyWRip0REREREZHC8LiZTYVQd41evXsUrr7yC9u3bo3///sjKykJqaiqeffZZAEBiYiKGDBmC8PBw9O7dG25ubvjpp59Ezloc48eP1ylORERExuG1117Tuk/GYdasWVr3SXyjR4/Wuk/GoXfv3lr3SXxVVVVISkrC2LFj0aBBA0U5H1bXLCQkBBs2bEBISIjYqRARERERiYbXxWQKGtR8iOGsXLlSa1wmk2HJkiVYsmSJgTIyXrt27aoxzs5xIiIi43fmzBmt+2Qc9u7dq3WfxPf3339r3Sei2tm1axfy8/Px6quvqsQSExNhZWWF8PBwlJeXIzQ0FEuXLhUhSyIiIiIiIqK6M6oR41R7D09p9zhxIiIiEp+mkZIcQWlc+vTpU6dyMjz+LZkGvk+mYeDAgRAEAe3atVOJVT+sfuPGDdy+fRs//fSTRa8vTkRERERERKaFHeMmqry8XKc4ERERievChQs6xckwrly5AkEQ1MYEQcCVK1cMnBE96vTp0zrFyTBWr16tU5yIiIiIiIiISFfsGDdRSUlJOsWJiIhIXDUtecIlUYzDqFGjdIpT/Zs4caJOcTKMmpbNqilORERERERERKQrdoybqJrWceM6b0RERMaND7mZhvXr1+sUp/r39ddf6xQnw4iKitIpTkRERERERESkK3aMm6h169bpFCciIiJxtW7dWqc4GYa7uzskEonamEQigbu7u4Ezokd16NBBpzgZxtixY3WKExERERERERHpih3jJmrfvn06xYmIiOpDfHw8unXrhsaNG8PFxQXPP/88zpw5o3SMXC5HdHQ0nJ2dYW9vj/DwcBQVFYmUsbg0tddsx41Lenp6ncrJ8Pi3ZBr4PhERERERERGRmNgxbqIaNGigU5yIiKg+pKenIzo6GgcOHMDOnTtx7949DBw4ELdv31YcExMTgy1btmDDhg1IT0/H5cuXERYWJmLW4mrfvr3WfTIOffv21bpP4mvZsqXWfSIiIiIiIiIismzsGDdR9+/f1ylORERUH7Zv345x48ahU6dO6Ny5M1atWoX8/HwcPnwYAFBSUoKVK1di0aJF6NevHwIDA5GUlIT9+/fjwIEDImcvjuXLl2vdJ+PwwQcfaN0n8X3//fda98k4PDo6nKPFiYiIiIiIiMhQ2DFuooKCgnSKExERGUJJSQkAoGnTpgCAw4cP4969exgwYIDiGD8/P3h5eSEzM1NtHeXl5SgtLVXazMmj6+pynV3jNHXqVK37JL4pU6Zo3Sfj0Lt3b637RERERERERET1hR3jJurQoUM6xYmIiOpbVVUVpk2bhpCQEDzxxBMAgMLCQtjY2MDJyUnpWFdXVxQWFqqtJz4+Ho6OjorN09OzvlM3qJycHK37ZByOHj2qdZ/Ed/z4ca37RERERERERERk2dgxbqJsbGx0ihMREdW36OhonDx5EuvWrdOpnri4OJSUlCi2S5cu6SlD8WkaKckRlMaF75Px43tkGvg+EREREREREZGY2DFuooKDg3WKExER1afJkyfj119/RVpaGjw8PBTlbm5uqKiowK1bt5SOLyoqgpubm9q6bG1t4eDgoLSZg7Nnz+oUJ8PIz8/XKU71Ly8vT6c4GcbmzZt1ihMRERERERER6Yod4yYqPT1dpzgREVF9EAQBkydPxqZNm7Bnzx74+voqxQMDA2FtbY3du3crys6cOYP8/HyLe6hrwoQJOsXJMCIjI3WKU/0bM2aMTnEyjEWLFukUJyIiIiIiIiLSFTvGTdSMGTN0ihMREdWH6OhoJCcnY+3atWjcuDEKCwtRWFiIu3fvAgAcHR0RFRWF2NhYpKWl4fDhwxg/fjyCg4PRs2dPkbM3rBUrVugUJ8NITk7WKU71b82aNTrFyTBiY2N1ihMRERGRdnPmzIFEIlHa/Pz8FHG5XI7o6Gg4OzvD3t4e4eHhKCoqEjFjIiIiw2PHuInatWuXTnEiIqL6sGzZMpSUlKBv375wd3dXbOvXr1cck5iYiCFDhiA8PBy9e/eGm5sbfvrpJxGzFke7du10ipNheHl56RSn+uft7a1TnAzj+eef1ylORERERDXr1KkTrly5otj++OMPRSwmJgZbtmzBhg0bkJ6ejsuXLyMsLEzEbImIiAyPHeMm6tChQzrFiYiI6oMgCGq3cePGKY6RyWRYsmQJbty4gdu3b+Onn37SuL64udu3b1+dykkcfJ+MH98j08D3iYiIyDxkZGRgxIgRyMjIEDsVekSDBg3g5uam2Jo1awYAKCkpwcqVK7Fo0SL069cPgYGBSEpKwv79+3HgwAGtdZaXl6O0tFRpIyIiMlXsGDdRdnZ2OsWJiIjIODy6Dvuj+2QcnnzySa37JL7OnTtr3SciIiIi3cnlciQkJKCoqAgJCQmQy+Vip0QPOXfuHFq0aIFWrVohIiIC+fn5AIDDhw/j3r17GDBggOJYPz8/eHl5ITMzU2ud8fHxcHR0VGyenp71+hqIiIjqEzvGTVT1Wq2PGyciIiLjsHr1aq37ZBw+++wzrfskvi+++ELrPhmHR0eHc7Q4ERGRaUlOTkZxcTEAoLi4GCkpKSJnRNV69OiBVatWYfv27Vi2bBlycnLw9NNP459//kFhYSFsbGzg5OSk9D2urq4oLCzUWm9cXBxKSkoU26VLl+rxVRAREdUvdoybqO7du+sUJyIiIuMwatQorftkHCIiIrTuk/iio6O17pNx6N27t9Z9IiIiMl4FBQVISUmBIAgAHiyllZKSgoKCApEzIwAYNGgQRowYgYCAAISGhuK3337DrVu38MMPP+hUr62tLRwcHJQ2IiIiU8WOcRP13//+V6c4ERERGYcrV65o3Sfj8OioCI6SMD4nTpzQuk9EREREj08QBCQmJmosr+4sJ+Ph5OSEdu3a4fz583Bzc0NFRQVu3bqldExRURHc3NzESZCIiEgE7Bg3US1atNApTkREROLTNFKSIyiNC98n48f3yDTwfSIiIjJdeXl5yMrKQmVlpVJ5ZWUlsrKykJeXJ1JmpElZWRkuXLgAd3d3BAYGwtraGrt371bEz5w5g/z8fAQHB4uYJRERkWGxY9xEeXp66hQnIiIicZ06dUqnOBnGX3/9pVOc6l9ubq5OcTKMn376Sac4ERERicvb2xvdunWDVCpVKpdKpejevTu8vb1Fyoyqvf3220hPT0dubi7279+PF154AVKpFKNHj4ajoyOioqIQGxuLtLQ0HD58GOPHj0dwcDB69uwpdupEREQGw45xE3Xw4EGd4kRERCSuN954Q6c4Gcbrr7+uU5zq3yuvvKJTnAxj8eLFOsWJiIhIXBKJBDExMRrLJRKJCFnRwwoKCjB69Gi0b98eI0eOhLOzMw4cOIDmzZsDABITEzFkyBCEh4ejd+/ecHNz48OJRERkcdgxbqLatm2rU5yIiIjE9dVXX+kUJ8P45ptvdIpT/fvuu+90ipNhTJs2Tac4ERFZhoyMDIwYMQIZGRlip0JqeHh4ICIiQtEJLpFIEBERgZYtW4qcGQHAunXrcPnyZZSXl6OgoADr1q1D69atFXGZTIYlS5bgxo0buH37Nn766SeuL05ERBaHHeMm6ty5czrFiYiISFwdO3bUKU6G4efnp1Oc6p+Pj49OcTKMsLAwneJERGT+5HI5EhISUFRUhISEBMjlcrFTIjUiIyPh7OwMAGjWrBkiIiJEzoiIiIio9tgxbqIeXc+nrnEiIiIS3759++pUTuLg+2T8+B6ZBr5PRESkTXJyMoqLiwEAxcXFSElJETkjUkcmk2H69OlwdXVFbGwsZDKZ2CkRERER1Ro7xk1UZWWlTnEiIiIyDu7u7lr3yTh4enpq3Sfx+fv7a90nIiIi41VQUICUlBQIggAAEAQBKSkpKCgoEDkzUickJAQbNmxASEiI2KkQERER1Qk7xomIiIhEtH79eq37ZBweHbHEEUzGZ8mSJVr3yTg8Ojqco8XJHHBNZCLdCIKAxMREjeXVneVERERERLpix7gBCYKAu3fv6mWrDX2dix9AiIiI6s+j6+pynV3jNHr0aK37JL4hQ4Zo3Sfj0Lt3b637RKaGayIT6S4vLw9ZWVkqsx9WVlYiKysLeXl5ImVGREREROamgdgJWBK5XI7Q0FCDnU9f50pNTYWdnZ1e6iIiIiJl169f17pPxuHvv//Wuk/iKy0t1bpPRFQf1K2JHBUVJXJWRKbF29sb3bp1w5EjR5Q6x6VSKQIDA+Ht7S1idkRERERkTjhinIiIiEgkmkZKcgSlceH7ZPz4HpkGvk9kbrgmMpF+SCQSxMTEaCyXSCQiZEVERERE5ogjxg1IJpMhNTVVb/WFh4ejrKxMpdzR0RE//PCD3s4jk8n0VhcRERE9cPz48RrjnTt3NlA2pMnp06drjHfo0MFA2ZA6WVlZNca7detmoGxIk5rWfF+yZAmio6MNlA2R7mpaE3nhwoXszCOqAw8PD0RERGDNmjUQBAESiQQRERFo2bKl2KkRERERkRnhiHEDkkgksLOz09v222+/qT3Pli1b9HoefpgnIiLSvylTpugUJ8OYOHGiTnGqf9OnT9cpToaxfv16neJkOH///TciIyPh7OwMOzs7+Pv749ChQ4q4IAiYNWsW3N3dYWdnhwEDBuDcuXMiZiwOrolMpH/V/3sAoFmzZoiIiBA5IyIiIiIyN+wYN3GRkZFK++PHjxcpEyIiIqqLL774Qqc4GcbXX3+tU5zqX0JCgk5xMoxRo0bpFCfDuHnzJkJCQmBtbY1t27bh1KlTSEhIQJMmTRTHfPLJJ/j888/x1Vdf4eDBg2jUqBFCQ0Mhl8tFzNzwqtdElkqlSuVSqRTdu3fnmshEj0Emk2H69OlwdXVFbGwsZzAkIiIiIr1jx7iJGzNmjNI+O8aJiIhMQ03TpHMadeNQ0zTpnEZdfDVNk85p1I1DTdOkcxp147BgwQJ4enoiKSkJ3bt3h6+vLwYOHIjWrVsDeDBafPHixXj//fcxfPhwBAQE4LvvvsPly5exefNmcZM3MK6JTFQ/QkJCsGHDBoSEhIidChERERGZIXaMmxF9rl9ORERE9W/fvn11Kidx8H0yfnyPTAPfJ+P3yy+/ICgoCCNGjICLiwuefPJJLF++XBHPyclBYWEhBgwYoChzdHREjx49kJmZqbbO8vJylJaWKm3monpN5IdxTWQiIiIiIiLjxY5xIiIiIhE1a9ZM6z4Zh0c7OdjpYXwcHBy07hNRzS5evIhly5ahbdu2SE1NxZtvvom33noLq1evBgAUFhYCAFxdXZW+z9XVVRF7VHx8PBwdHRWbp6dn/b4IA3vxxRdhZfXg1oqVlRXCw8NFzog0ycjIwIgRI5CRkSF2KkREREREJBJ2jBMRERGJ6KefftK6T8bh+++/17pP4vv111+17pNxeHR0OEeLG5eqqip07doVH3/8MZ588km8/vrreO211/DVV189dp1xcXEoKSlRbJcuXdJjxuLbuHEjBEEA8GCq+R9//FHkjEgduVyOhIQEFBUVISEhAXK5XOyUiIiIiIhIBEbVMR4fH49u3bqhcePGcHFxwfPPP48zZ84oHdO3b19IJBKl7Y033hApYyIiIiLdREVFad0n49CvXz+t+yS+/v37a90n47Fv3z7FRsbF3d0dHTt2VCrr0KED8vPzAQBubm4AgKKiIqVjioqKFLFH2drawsHBQWkzFwUFBUhJSVHqGE9JSUFBQYHImdGjkpOTUVxcDAAoLi5GSkqKyBkREREREZEYjKpjPD09HdHR0Thw4AB27tyJe/fuYeDAgbh9+7bSca+99hquXLmi2D755BORMiYiIiLSzblz57Tuk3G4f/++1n0S371797TuE1HNQkJCVB5OP3v2LLy9vQEAvr6+cHNzw+7duxXx0tJSHDx4EMHBwQbNVWyCICAxMVFjeXVnOYmPDzAQEREREVG1BmIn8LDt27cr7a9atQouLi44fPgwevfurShv2LChxqfRiYiIiEzFw9c3j5ZzJKXx4Ptk/Pge1Q9BEPQ+3bAgCCgvLwfwYCSxRCLRa/0ymUzvdVqSmJgYPPXUU/j4448xcuRI/Pe//8U333yDb775BgAgkUgwbdo0zJs3D23btoWvry9mzpyJFi1a4Pnnnxc3eQPLy8tDVlaWSnllZSWysrKQl5cHHx8fwydGSmp6gGHhwoX8n0FEREREZEGMqmP8USUlJQCApk2bKpWnpKQgOTkZbm5uGDp0KGbOnImGDRuqraO8vFxx4wV48DQ7ERERkdjOnz9fY7xNmzYGyoY0SU9PrzHep08fA2VD6tTU8b1v3z6NHeeknVwuR2hoqNhp1Elqairs7OzETsNkdevWDZs2bUJcXBw++OAD+Pr6YvHixYiIiFAc8+677+L27dt4/fXXcevWLfTq1Qvbt2+HTCYTMXPD8/b2Rrdu3XDkyBFUVlYqyqVSKQIDAxWj7ElcfICBiIiIiIgeZlRTqT+sqqoK06ZNQ0hICJ544glF+csvv4zk5GSkpaUhLi4Oa9asQWRkpMZ64uPj4ejoqNg8PT0NkT4RERGRVq+++qpOcTKMmTNn6hSn+vf+++/rFCciZUOGDMGJEycgl8tx+vRpvPbaa0pxiUSCDz74AIWFhZDL5di1axfatWsnUrbikUgkiImJ0VjOUcjGofoBBqlUqlQulUrRvXt3PsBARERERGRhjHbEeHR0NE6ePIk//vhDqfz1119XfO3v7w93d3f0798fFy5cQOvWrVXqiYuLQ2xsrGK/tLSUneNEREQkum+//VZr5/e3335rwGxIkw8//FBr5/eHH35owGxInXnz5mnt/J43b54BszEvMpkMqampeq1TLpdj+PDhAICff/5Z76OMLW3UMonLw8MDERERWLNmDQRBgEQiQUREBFq2bCl2avT/qh9UGDNmjNpyPsBgfDIyMrB48WLFYBkiIiIiIn0yyo7xyZMn49dff8W+ffvg4eGh9dgePXoAeDDdqLqOcVtbW9ja2tZLnkRERESPq6Zp0jmNunGoaZp0TqMuvpqmSec06o9PIpHU67TkMpmM056TyYuMjMRvv/2G69evo1mzZkrTzpNx4AMMpkMulyM+Ph6lpaWIj4/Hxo0b+cATEREREemVUXWMC4KAKVOmYNOmTdi7dy98fX1r/J5jx44BANzd3es5OyIi8yYIAuRyudhp1OjhHE0hX+DBjX+ORiF1HBwcUFpaqracjEezZs1w/fp1teVERGTZZDIZpk+frhjhyk484xQZGYnNmzejtLQUDg4OfIDBSK1atUpxbVxaWorVq1dj4sSJImdFRERERObEqDrGo6OjsXbtWvz8889o3LgxCgsLAQCOjo6ws7PDhQsXsHbtWvzrX/+Cs7MzsrOzERMTg969eyMgIEDk7ImITJtcLkdoaKjYadRJ9VSsxi41NZUj4kjFrVu31HaKAw9uBN66dQtOTk6GTYpUlJaWqu0UB4Dr168rbrCTeC5dulRjnEspEVF9CgkJ4ZTPJkAQBABAVVWVyJmQOgUFBVi7dq1S2dq1azF48OAaZ5MkIiIiIqotK7ETeNiyZctQUlKCvn37wt3dXbGtX78eAGBjY4Ndu3Zh4MCB8PPzw/Tp0xEeHo4tW7aInDkRERFR3YwaNUqnOBnGyy+/rFOc6l9No/44KpCIiJKTk1FWVgYAKCsrQ0pKisgZ0cMEQcD8+fM1llc/1EBEREREpCujGjFe04Wup6cn0tPTDZQNEZHl+jcAG7GT0EAAcO//v7YGYKwTlFcAWCB2EqR3+lxyYNWqVVo7v1etWoW7d+/qfB5O5a+btWvXYsiQIVrjJK6UlBStnd/s/CAismwFBQVISUlR3HMSBAEpKSkIDQ3lSGQjkZubi+zsbLWx7Oxs5Obm1mq5RSIiIiKimhhVxzgRERkHGwA2RtvlDNiKnUCtcFSDOTLkkgP6GjHOqfx106hRI53iVP9atGihU5yIiMyXIAhITEzUWL5w4UI+QGgEahoowxHjRERERKQvRjWVOhERERGRMZk2bZpOcap/Y8eO1SlORETmKy8vD1lZWaisrFQqr6ysRFZWFvLy8kTKjB5W08MJfHiBiIiIiPSFI8aJiIiIakkmkyE1NVWvdSYlJWHdunWK/cjISIwZM0Zv9ctkMr3VZYmOHz+uU5zqX35+vk5xIiIyX97e3ujWrRuOHDmi1DkulUoRGBgIb29vEbOjaj4+Pmjfvj3OnDmjEvPz84OPj4/hkyIiIiIis8QR40RERES1JJFIYGdnp9dt/PjxSud4/fXX9Vo/R9johiPGjd/o0aN1ihMRkfmSSCSIiYnRWM7rJONhY2Ojttza2trAmRARERGROWPHOBEREZGR0PdodNLdCy+8oFOc6t/rr7+uU5yIiMybh4cHIiIiFJ3gEokEERERaNmypciZUbW8vDycOHFCbezEiROc8p6IiIiI9IYd40REREREGpw8eVKnONW/h5cieJw4ERGZv8jISDg7OwMAmjVrhoiICJEzoodVT3lvZaV8m9LKygrdu3fnlPdEREREpDfsGCciIiIi0uD999/XKU71b9WqVTrFiYjI/MlkMkyfPh2urq6IjY2FTCYTOyV6iKap7a2srDjlPRERERHpVQOxEyAiIiIi0hdBECCXy/VW3/Lly/Hiiy9qjd+9e1fn88hkMt70fUwbNmzAsGHDtMaJzN3t27cxf/587N69G1evXkVVVZVS/OLFiyJlRmQ8QkJCEBISInYapIGHhwdGjRqFtWvXKspGjRrFKe+JiIiISK/YMU5EREREZkMulyM0NNRg59PWaV4XqampsLOz00tdlsbe3l6nOJE5mDBhAtLT0zFmzBi4u7vzQRsiIqpXGRkZWLx4MaZNm8YHToiIiMiksGOciIiIiIhM1sqVK2uMT5w40UDZEIlj27Zt2Lp1KzsniMhkFRQUYP369Upl69evx+DBg+Hh4SFSVqSOXC5HQkICrl+/joSEBAQGBnJ5AiIiIjIZ7BgnIiIiIrMhk8mQmpqq93o3bNiAFStWKPbffPNNPP/883qr39JuJupzyvvi4uIa4/qY7h7glPdkvJo0aYKmTZuKnQaRUVuxYgWSk5MRGRmJCRMmiJ0OPUQQBCQmJmosX7hwIdtfI5KcnKy4/iouLkZKSgqioqJEzoqIiIhqy9JnfmHHOBERERGZDYlEUi9Tko8YMUKpY3z06NF6P4clMeSU99u3b8f27dv1UhenvCdj9eGHH2LWrFlYvXo1GjZsKHY6REbn1q1bSE5ORlVVFZKTk/Hiiy/CyclJ7LTo/+Xl5SErK0ulvLKyEllZWcjLy4OPj4/hEyMVBQUFSElJgSAIAB48vJCSkoLQ0FCO7CciIjIBnPkFsBI7ASIiIiIiU1IfI9KJiHSRkJCA1NRUuLq6wt/fH127dlXaiCzde++9h6qqKgBAVVUV3n//fZEzood5e3ujW7dusLJSvk1pZWWF7t27w9vbW6TM6GE1jeyv7iwnIiIi46Vu5hdLwxHjRERERERkUPUx5f1zzz2ndENWIpHobaR4NUt7ippMhz6XdiAyN4cOHcKJEyeUyrKzs3Ho0CEEBQWJlBU9TCKRICYmBi+//LJSuSAIiImJ4TTqRoIj+4mIiEwbZ355gB3jRERERERkUPUx5f2XX36J6Ohoxf6KFSs47TlZjNmzZ4udApFRqqqqwpw5c9TG5syZg19++UVllDIZD0EQOArZiFSP7D9y5AgqKysV5VKpFIGBgRzZTybN0tfbJSLzV9PMLwsXLrSYhxH1fvVfUVGBgoIC5OfnK21ERERERET1pU2bNoqvvby80LZtWxGzISIiY5CZmYnS0lK1sdLSUmRmZho4I1JHEATMnz9fbWz+/PnsHDcS1SP7NZVbys10Mj/V6+0WFRUhISEBcrlc7JSIiPSueuaXhx9uA5RnfrEUehsxfu7cObz66qvYv3+/UrkgCJBIJCo/bCIiIiIiovqwfPlysVMgMqjKykokJibihx9+QH5+PioqKpTiN27cECkzInH17NkTUqlU7T0pqVSKnj17ipAVPSo3NxfZ2dlqY9nZ2cjNzYWvr6+BsyJ1PDw8EBERgTVr1iju+UZERKBly5Zip2ZynnzyyVo/THDkyJF6zsayqVtvNyoqSuSsiIj0izO//I/eRoyPGzcOVlZW+PXXX3H48GEcOXIER44cwdGjR9l4ExEREREREdWTuXPnYtGiRRg1ahRKSkoQGxuLsLAwWFlZaZxGmsgSXLp0SeNAjcrKSly6dMnAGRGZvsjISDg7OwMAmjVrhoiICJEzMk3PP/88hg8fjuHDhyM0NBQXLlyAra0t+vbti759+0Imk+HChQsIDQ0VO1Wzpmm93YKCApEzIyLSL8788j966xg/duwYvv76awwaNAhdunRB586dlTYiIiIiIiIi0r+UlBQsX74c06dPR4MGDTB69GisWLECs2bNwoEDB8ROj0g01SNj1OnevbtFjYwxZj4+PmjXrp3aWPv27eHj42PYhEgrmUyG6dOnw9XVFbGxsZDJZGKnZJJmz56t2K5du4a33noLmZmZWLRoERYtWoT9+/dj2rRpKCoqeuxzzJ8/HxKJBNOmTVOUyeVyREdHw9nZGfb29ggPD9fpHKaspvV2uYwDEZmb6plfqjvBLXXmF711jHfs2BHXr1/XV3VEREREREREVAuFhYXw9/cHANjb26OkpAQAMGTIEGzdulXM1IhEpWkEjJWVlcWNjDF2fC9MS0hICDZs2ICQkBCxUzELGzZswCuvvKJSHhkZiR9//PGx6szKysLXX3+NgIAApfKYmBhs2bIFGzZsQHp6Oi5fvoywsLDHOoep43q7RGSJOPOLHjvGFyxYgHfffRd79+5FcXExSktLlTYiIiIiIiIi0j8PDw9cuXIFANC6dWvs2LEDwIOb4ra2tmKmRiQ6Dw8PjB49Wqls9OjRFjcyxpjl5ubizJkzamNnzpxBbm6uYRMiMjA7OztkZGSolGdkZDzWiPyysjJERERg+fLlaNKkiaK8pKQEK1euxKJFi9CvXz8EBgYiKSkJ+/fvt8gZZqpnFZFKpUrlUqmUs4oQkdnizC9AA31VNGDAAABA//79lcoFQYBEItG4phMRERERERERPb4XXngBu3fvRo8ePTBlyhRERkZi5cqVyM/PV7uOHOlXRkYGFi9ejGnTpnH0JBER1dm0adPw5ptv4siRI+jevTsA4ODBg/j2228xc+bMOtcXHR2NwYMHY8CAAZg3b56i/PDhw7h3757iPj4A+Pn5wcvLC5mZmejZs6fa+srLy1FeXq7YN5dBcNWziowZM0ZtOWeyICJzFRISYtGfW/TWMZ6WlqavqoiIiIiIiIiolubPn6/4etSoUYob3G3btsXQoUNFzMz8yeVyJCQk4Pr160hISEBgYKBFjrowZgUFBVi/fr1S2fr16zF48GB4eHiIlBUR0f/MmDEDrVq1wmeffYbk5GQAQIcOHZCUlISRI0fWqa5169bhyJEjyMrKUokVFhbCxsYGTk5OSuWurq4oLCzUWGd8fDzmzp1bpzxMRfV6u2vWrFEM8LPE9XaJiCyJ3jrG+/Tpo6+qiIiIiIiIiOgxBQcHIzg4WOw0LEJycjKuX78OALh+/TpSUlIQFRUlclZUTRAEJCYmaixfuHAhRwQaAR8fH/j7++PEiRMqsYCAAPj4+Bg+KdKKM2Xo38iRI+vcCf6oS5cuYerUqdi5c6deH9KKi4tDbGysYr+0tBSenp56q19skZGR+O2333D9+nWLXW+XiMiS6G2N8Wp37tzBX3/9hezsbKWNiIiIjEtpaWmtNyIiIjJua9asQUhICFq0aIG8vDwAwOLFi/Hzzz+LnJn5KigoUIzsq5acnIyCggKRMqJH5eXlISsrS2V5v8rKSmRlZSn+VkhcEokEcXFxamNxcXF8eMHIVM+UUVRUhISEBMjlcrFTMnmtWrVCcXGxSvmtW7fQqlWrWtdz+PBhXL16FV27dkWDBg3QoEEDpKen4/PPP0eDBg3g6uqKiooK3Lp1S+n7ioqK4ObmprFeW1tbODg4KG3mhOvtEhFZFr11jF+7dg1DhgxB48aN0alTJzz55JNKGxERERkXJycnNGnSpFYbERERGa9ly5YhNjYW//rXv3Dr1i1FJ6CTkxMWL14sbnJmqnrEsSAISuVVVVVqy0kc3t7e6NatG6yslG9/SaVSdO/eHd7e3iJlRo/y8PBAhw4dlMo6duzI6YyNUHJysqITt7i4GCkpKSJnZPpyc3NVHuABHqzt/ffff9e6nv79++PEiRM4duyYYgsKCkJERITia2tra+zevVvxPWfOnEF+fr7FzzQTEhKCDRs2cAYEIiILoLeO8WnTpuHWrVs4ePAg7OzssH37dqxevRpt27bFL7/8oq/TEBERkZ6kpaVhz5492LNnD7799lu4uLjg3XffxaZNm7Bp0ya8++67cHV1xbffflvrOvft24ehQ4eiRYsWkEgk2Lx5s1J83LhxkEgkSttzzz2n51dGRERkWb744gssX74c7733HqRSqaI8KChI7dTEmsyZM0elnfbz81PE5XI5oqOj4ezsDHt7e4SHh6OoqEivr8VUVI9EfrQDXBAEjkQ2IhKJBDExMWrfp5iYGI5ENiIFBQU4ffq0UtmpU6c4A4ORKSgoQEpKiuJvShAEpKSk8H16TL/88ovivnlqaqpi/5dffsGmTZvw4Ycf1mkpgcaNG+OJJ55Q2ho1agRnZ2c88cQTcHR0RFRUFGJjY5GWlobDhw9j/PjxCA4ORs+ePevpVRIRkbHJyMjAiBEjkJGRIXYqotDbGuN79uzBzz//jKCgIFhZWcHb2xvPPvssHBwcEB8fj8GDB+vrVERERKQHffr0UXz9wQcfYNGiRRg9erSibNiwYfD398c333yDsWPH1qrO27dvo3Pnznj11VcRFham9pjnnnsOSUlJin1bW9vHfAVEREQEADk5OWpnarO1tcXt27frVFenTp2wa9cuxX6DBv+7bRATE4OtW7diw4YNcHR0xOTJkxEWFmaRN1S8vLzg4OCgdskZBwcHeHl5iZAV1ZYgCBzVb0QEQUB8fLzaWHx8PL788ks+xGAEqmfKqKqqUiqvrKxEYmIiFi5cyPepjp5//nkADx7iefQzt7W1NXx8fJCQkKDXcyYmJsLKygrh4eEoLy9HaGgoli5dqtdzEBGR8apeEuX69etISEhAYGCgxS0hobeO8du3b8PFxQUA0KRJE1y7dg3t2rWDv78/jhw5oq/TEJEJEgTBJNacejhHU8gXeLAOEj94kj5kZmbiq6++UikPCgrChAkTal3PoEGDMGjQIK3H2Nraal2/jIiIiOrG19cXx44dU5kWevv27SpTE9ekQYMGatvpkpISrFy5EmvXrkW/fv0AAElJSejQoQMOHDhgcSPN8vPz1XaKA0BpaSny8/PrNMqP6kd1R5467MgzHrm5uRpntzhx4gRyc3Ph6+tr4KzoUdUzZTzq4Zky+H+vbqofMvD19UVWVhaaNWum93Ps3btXaV8mk2HJkiVYsmSJ3s9FRETGT92SKFFRUSJnZVh66xhv3749zpw5Ax8fH3Tu3Blff/01fHx88NVXX8Hd3V1fpyEiEySXyxEaGip2GnUyfPhwsVOoldTUVNjZ2YmdBpkBT09PLF++HJ988olS+YoVK+Dp6anXc+3duxcuLi5o0qQJ+vXrh3nz5sHZ2Vnj8eXl5SgvL1fsa7oJTUREZKliY2MRHR0NuVwOQRDw3//+F99//z3i4+OxYsWKOtV17tw5tGjRAjKZDMHBwYiPj4eXlxcOHz6Me/fuYcCAAYpj/fz84OXlhczMTI0d4+bajnPEuGlgR55pqGn0Pkf3Gwf+36s/OTk5KmW3bt2Ck5OT4ZMhIiKzpWlJlNDQUHh4eIicneHobY3xqVOn4sqVKwCA2bNnY9u2bfDy8sLnn3+Ojz/+WF+nISIionqQmJiIL774Av7+/pgwYQImTJiAgIAAfPHFFxpH2TyO5557Dt999x12796NBQsWID09HYMGDUJlZaXG74mPj4ejo6Ni03dHPRERkambMGECFixYgPfffx937tzByy+/jGXLluGzzz7DSy+9VOt6evTogVWrVmH79u1YtmwZcnJy8PTTT+Off/5BYWEhbGxsVG7Su7q6orCwUGOd5tqO12bEOInP29sb/v7+amMBAQEqsyyQOC5fvqxTnAyD//fqz4IFC7B+/XrF/ogRI9C0aVO0bNkSx48fFzEzIiIyF5pmUqout6QHEfU2YjwyMlLxdWBgIPLy8vDXX3/By8urXqaBISLTVDm0Uo//efRMAFDdNycFYKwz6t0HpFukYmdBZuZf//oXzp07h2XLluH06dMAgKFDh+KNN97Q6w3sh2/O+/v7IyAgAK1bt8bevXvRv39/td8TFxeH2NhYxX5paanZ3FQnIiLSl4iICERERODOnTsoKytTLHVWFw8vhxIQEIAePXrA29sbP/zww2PPUmSu7bi3tzfatWuHs2fPqsTat2/PDlcjwqnSjV9wcDCsrKxU1q4GACsrKwQHB4uQFT2KI8brz1dffYWUlBQAwM6dO7Fr1y5s374dP/zwA9555x3s2LFD5AyJiMjUaZpJqbKy0uJmUtJ791RFRQVycnLQunVrdO3aVd/VE5GpawDj7RgHAGuxEyASR35+Pjw9PfHRRx+pjdXXTY5WrVqhWbNmOH/+vMaOcVtbW9ja2tbL+YmIiMxNw4YN0bBhQ73U5eTkhHbt2uH8+fN49tlnUVFRoTK1a1FRkdo1yauZazsuCILGUax///03BEFgh6wRyMvLQ3Z2ttpYdna2Rd0ANGaXLl1S2ykOPFiD+dKlS3yfjEBtRozzfXo8hYWFiofGfv31V4wcORIDBw6Ej48PevToIXJ2RERkDry9vdGtWzccOnRIaXS4RCJBt27dLOrBXr1NpX7nzh1ERUWhYcOG6NSpk2L6nClTpmD+/Pn6Og0RERHVA19fX1y7dk2lvLi4GL6+vvV23oKCAhQXF8Pd3b3ezkFERGTuiouLER0djY4dO6JZs2Zo2rSp0va4ysrKcOHCBbi7uyMwMBDW1tbYvXu3In7mzBnk5+db5GjOzMxMlJWVqY2VlZUhMzPTwBmROtUj+9XhyH7j4e3tjYCAALUxTnlvPLy8vGBvb682Zm9vzxHjOmjSpAkuXboEANi+fTsGDBgA4MFDWNqWHSMiIqotiUSC0aNHq0yZLggCRo8ebVEP9ept3GZcXByOHz+OvXv34rnnnlOUDxgwAHPmzMGMGTP0dSoiIiLSM02jmsrKyiCTyWpdT1lZGc6fP6/Yz8nJwbFjxxQ35ufOnYvw8HC4ubnhwoULePfdd9GmTRuEhobq5XUQERFZojFjxuD8+fOIioqCq6vrY9/UePvttzF06FB4e3vj8uXLmD17NqRSKUaPHg1HR0dERUUhNjYWTZs2hYODA6ZMmYLg4GD07NlTz6/I+AUHB2ucUtjR0dEiHxYwRhzZbzo0rWtpSetdGru8vDytDwTl5eXV60PV5iwsLAwvv/wy2rZti+LiYsXSJkePHkWbNm1Ezs4yZGRkYPHixZg2bRpCQkLEToeISO8EQcD3338PiUSiMmJ87dq16Nq1q8VcF+utY3zz5s1Yv349evbsqfTD69SpEy5cuKCv0xAREZEeVa/5KZFIMHPmTKWpVysrK3Hw4EF06dKl1vUdOnQIzzzzjEr9Y8eOxbJly5CdnY3Vq1fj1q1baNGiBQYOHIgPP/zQLKdYJSIiMpTff/8df/zxBzp37qxTPQUFBRg9ejSKi4vRvHlz9OrVCwcOHEDz5s0BAImJibCyskJ4eDjKy8sRGhqKpUuX6uMlmBwrKyvMmTNHaf30anPnzoWVld4m6CMd1GZkPztAxJeXl4cTJ06ojZ04cYJT3pPZS0xMhI+PDy5duoRPPvlEMTL/ypUrmDRpksjZmT+5XI6EhARcv34dCQkJCAwMrNMAASIiU6BpjXFBELjG+OO6du0aXFxcVMpv375tMU8ZEBERmZqjR48CeHARdOLECdjY2ChiNjY26Ny5M95+++1a19e3b1+tozpSU1MfP1kiIiJSy8/PD3fv3tW5nnXr1mmNy2QyLFmyBEuWLNH5XOYgKCgI/v7+Sh16AQEB6Nq1q4hZ0cNqWpuXa/cah+op78+ePasS45T3xsPHxwdubm4oLCxUibm5uVnMzfT6YG1trfZzd0xMjNL+4MGDsWLFCi5FpmfJyckoLi4G8GB5mpSUFERFRYmcFRGRflWvMX7kyBGlZTqkUikCAwMt6npLb48wBwUFYevWrYr96s7wFStWcAoxIiIiI5WWloa0tDSMHTsW27ZtU+ynpaUhNTUVX3/9Ndq2bSt2mkRERKTF0qVL8d577yE9PR3FxcUoLS1V2qj+fPTRR4rR4VZWVpg3b57IGdHDDh48qFOcDEMQBBQUFKiNXbp0idOpG4nKykq1neIAUFhYyLWwDWDfvn16eRCO/qegoAApKSmK/zOCICAlJUXj/yQiIlMlkUhUHrh6uNySBjjrrWP8448/xn/+8x+8+eabuH//Pj777DMMHDgQSUlJ+Oijj2pVR3x8PLp164bGjRvDxcUFzz//PM6cOaN0jFwuR3R0NJydnWFvb4/w8HAUFRXp62UQERFZpKSkJDg4OCj2S0tLsXnzZvz1118iZkVERES14eTkhNLSUvTr1w8uLi5o0qQJmjRpAicnJzRp0kTs9Myak5MTIiMjYWVlhcjISDg5OYmdEj2kei14dbgWvPHIzMzEnTt31Mbu3LmDzMxMA2dE6qxatUqnOJGxEQQBiYmJGsv5UA4RmRsPDw9EREQoOsElEgkiIiLQsmVLkTMzLL11jPfq1QvHjh3D/fv34e/vjx07dsDFxQWZmZkIDAysVR3p6emIjo7GgQMHsHPnTty7dw8DBw7E7du3FcfExMRgy5Yt2LBhA9LT03H58mWEhYXp62UQERFZpJEjR+LLL78EANy9exdBQUEYOXIk/P398eOPP4qcHREREWkTEREBa2trrF27Frt378aePXuwZ88epKWlYc+ePWKnZ/YmTJiAvXv3YsKECWKnQo+oXgteHa4Fbzxqmhaa00Ybh3HjxukUJzI21evtPjrbQWVlpWK9XSIicxMZGQlnZ2cAQLNmzRARESFyRoantzXGAaB169ZYvnz5Y3//9u3blfZXrVoFFxcXHD58GL1790ZJSQlWrlyJtWvXol+/fgAejHDr0KEDDhw4gJ49e+qUPxERkaXat28f3nvvPQDApk2bIAgCbt26hdWrV2PevHkIDw8XOUMiIiLS5OTJkzh69Cjat28vdipEJqOqqkrsFOj/+fr6al1j3NfXV4Ss6FFSqRQtW7bE33//rRJr2bIlpFKpCFkRPT6ut0tElkgmk2H69OlYvHgxpk2bBplMJnZKBqf3R2OvXr2KkydPIjs7W2l7HCUlJQCApk2bAgAOHz6Me/fuYcCAAYpj/Pz84OXlpXFapfLycq6vRkREVIOSkhJFe7t9+3aEh4ejYcOGGDx4MM6dOydydkRERKRNUFAQLl26JHYaFmvFihXo27cvVqxYIXYq9IiqqirMmjVLbWzWrFnsHDcSEolE48j+OXPmWNSal8YsLy9Pbac4APz9998cXUsmh+vtEpGlCgkJwYYNGxASEiJ2KqLQW8f44cOH8cQTT8Dd3R0BAQHo0qWLYnvyySfrXF9VVRWmTZuGkJAQPPHEEwCAwsJC2NjYqKzZ5erqisLCQrX1xMfHw9HRUbF5enrWORciIiJz5+npiczMTNy+fRvbt2/HwIEDAQA3b960yCcHiYiITMmUKVMwdepUrFq1CocPH9bLg+pUO7du3UJycjKqqqqQnJyMW7duiZ0SPSQzMxNlZWVqY2VlZVy72oh4eHjA1tZWqUwmk1ncmpfGzNvbG/7+/mpjAQEBHF1LJonr7RIRWR69dYy/+uqraNeuHfbv34+LFy8iJydHsV28eLHO9UVHR+PkyZNYt26dTnnFxcWhpKREsfEpeiIiIlXTpk1DREQEPDw80KJFC/Tt2xfAgynWNd38ICIiIuMwatQonD59Gq+++iq6deumeED9cR9Up9p77733FKOOq6qq8P7774ucET2Ma1ebjm3btqG8vFypTC6XY9u2bSJlROpwBG392LdvH+7fv69Sfv/+fezbt0+x/5///Ecx0xvpD9fbJdKfjIwMjBgxAhkZGWKnQqSR3tYYv3jxIn788Ue0adNG57omT56MX3/9Ffv27YOHh4ei3M3NDRUVFbh165bSqPGioiK4ubmprcvW1lbliVMiIiJSNmnSJPTo0QP5+fl49tlnYWX14Nm5Vq1aYd68eSJnR0RERNrk5OSInYJFOnToEE6cOKFUlp2djUOHDiEoKEikrOhhvr6+aN++Pc6cOaMS8/Pz49rVRqKyshILFixQG1uwYAEGDhzI9auNQF5ensZZSLKzs5GXlwcfHx/DJmUmnnnmGVy5cgUuLi5K5SUlJXjmmWcU61/HxcWJkZ7Z43q7RPohl8uRkJCA69evIyEhAYGBgfx7IqOktxHj/fv3x/Hjx3WqQxAETJ48GZs2bcKePXtUPqAEBgbC2toau3fvVpSdOXMG+fn5CA4O1uncREREli4wMBAvvPAC7O3tFWWDBw9WWm/GwcHhsWaCISIiovrj7e2tdas2ePBgXLlyRcRMzUdVVZXWNZG5drVxkEgkmD17ttrY7NmzOfrVSGzevFnj30xVVRU2b95s2IRILW9vbwQEBKiNcSp13QiCoPb/UXFxMRo1aiRCRpbH0tfbJdKH5ORkFBcXA3jw/yslJUXkjIjU09uI8RUrVmDs2LE4efIknnjiCVhbWyvFhw0bVmMd0dHRWLt2LX7++Wc0btxYsW64o6Mj7Ozs4OjoiKioKMTGxqJp06ZwcHDAlClTEBwcjJ49e+rrpRAREZEGgiCInQIRERE9pn379uHu3btip2EWMjMzUVpaqjZWWlqKzMxM3lw3cryuNR6urq46xclwHp3uvqZy0i4sLAzAg4d4xo0bpzTraWVlJbKzs/HUU0+JlZ5FycjIUIwYZ/tNVHcFBQVISUlRXF8JgoCUlBSEhoYqzQpNZAz01jGemZmJjIwMtWv/SCQSxZQv2ixbtgwAFOuaVktKSsK4ceMAAImJibCyskJ4eDjKy8sRGhqKpUuX6pw/EREREREREVFt9OzZE1KpVO29DqlUyof3jYQgCFpH9i9fvpyjxo3AU089BZlMBrlcrhKzs7Njx6CRyM3NVbssAfBgRs/c3FwuT1BHjo6OAB78r2rcuDHs7OwUMRsbG/Ts2ROvvfaaWOlZDE7/TKQbQRCQmJiosXzhwoW83iKjoreO8SlTpiAyMhIzZ8587Cc5a/O0rkwmw5IlS7BkyZLHOgcRERERERERkS4uXbqkcQBAZWUlLl26xLV2jUBOTg7Onj2rNnb27Fnk5OSgVatWBs6KHmVlZYWPP/4YsbGxKrH4+HhYWeltJUjSQU2DnmozKIqUJSUlAQB8fHzw9ttvc9p0kaib/jkqKkrkrIhMR15eHrKyslTKKysrkZWVhby8PF4Xk1HR25VlcXExYmJiOL0REREREREREZk1rrVrGq5cuaJTnAwnKCgIzs7OSmXOzs7o2rWrSBnRo7Kzs3WKk2azZ89mp7hINE3/XFBQIHJmRKbD29sb3bp1g1QqVSqXSqXo3r07r4vJ6OitYzwsLAxpaWn6qo6IiAzs4Vk7KgBUQOCm06b+Z2vqOPURERER0QP//PNPncrJ8KqnvFeHU94bl4KCAsWIzWo3btxg55QR6dy5s05x0qyoqAhjxoxBixYt0KBBA0ilUqWN6kdN0z+b070covokkUgQExOjsZz3EsnY6G0q9Xbt2iEuLg5//PEH/P39YW1trRR/66239HUqIiKqB+Xl5YqvF4iYhzkqLy9Hw4YNxU5DL/jBkIiIiOjBWrs5OTlqYzk5OVxr10hwynvTwLVJTYOvr6/GteBlMhn/5+lg3LhxyM/Px8yZM+Hu7s7fdwPh9M9E+uPh4YGIiAisWbMGgiBAIpEgIiICLVu2FDs1IhV66xhfsWIF7O3tkZ6ejvT0dKWYRCJhxzgREZEJqKioQE5ODlq3bo0GDVQvE7Zt28aLWiIiIiOzb98+PPXUUypt9/3797F//3707t0bAPCf//wHTZs2FSNFs1PTKNaCggJ2EhkBLy8vNGzYEHfu3FGJNWzYEF5eXiJkRY/S1DkFgJ1TRiQ/P19tpzgAyOVy5Ofn8316TH/88Qd+//13dOnSRexULEr19M9HjhxReohKKpUiMDCQ0z8T1VFkZCR+++03XL9+Hc2aNUNERITYKRGppbeOcU1PShMRkWmwtbVVfP1vADbipWIWKvC/kfcP/2yN1Z07dzBlyhSsXr0aAHD27Fm0atUKU6ZMQcuWLTFjxgwAQK9evcRMk4iIiNR45plncOXKFbi4uCiVl5SU4JlnnlHc7I2LixMjPbNU04OCfJDQOOTm5qrtFAceXP/m5uaiVatWBs6KHuXl5QV7e3uUlZWpxOzt7fkAg5Hw9PSEVCpVOwuDVCqFp6enCFmZB09PT87OJoLqaZ7HjBmjtpwj94nqRiaTYfr06Vi8eDGmTZsGmUwmdkpEaultjfHacnBwwMWLFw19WiIiqsHDF/w2AGwg4abTpv5na6zi4uJw/Phx7N27V+nCdcCAAVi/fr2ImREREVFNqqcrfFRxcTEaNWokQkbmr6brO1O4/rMEly9f1ilOhpGXl6e2UxwAysrKkJeXZ+CMSJ0DBw5oXZrgwIEDBs7IfCxevBgzZsxAbm6u2KlYnOrpn6vbbU7/TKSbkJAQbNiwASEhIWKnQqSR3kaM1xaffiMiIjI+mzdvxvr169GzZ0+lG7mdOnXChQsXRMyMiIiINAkLCwPw4CbuuHHjlGapqaysRHZ2Np566imx0jNrPj4+8Pf3x4kTJ1RiAQEBnE7YSLRo0UKnOBlGTfcKeS/ROLi7u+sUJ81GjRqFO3fuoHXr1mjYsCGsra2V4jdu3BApM8vA6Z+JiCyLwTvGiYiIyPhcu3ZNZfpVALh9+zZHPBERERkpR0dHAA86jRo3bgw7OztFzMbGBj179sRrr70mVnpmTSKRYPz48YiNjVWJjR8/ntdPRHXAGRhMQ03rLXM95se3ePFisVOwaJz+mYjIsrBjnIiIiBAUFIStW7diypQpAP5382nFihUIDg4WMzUiIiLSICkpCcCD0ctvv/02p003IEEQ8P3330MikSiNZpVIJFi7di26du3KzjyiWvLx8UH79u1x5swZlZifnx9nYDASmZmZNcZ79eploGzMy9ixY8VOweKFhIRw6mcishizZ89GWloannnmGcydO1fsdAyOHeNERESEjz/+GIMGDcKpU6dw//59fPbZZzh16hT279+P9PR0sdMjIiIiLWbPni12ChYnLy8PWVlZKuWCICArKwt5eXnszDMCtVljvFWrVgbKhh4Hp1EnS5Cfn6817uXlZaBMiIjI3BUVFSEtLQ0AkJaWhkmTJsHV1VXkrAzLytAn5BPTRERExqdXr144duwY7t+/D39/f+zYsQMuLi7IzMxEYGCg2OkRERGRFkVFRRgzZgxatGiBBg0aQCqVKm2kf97e3ujWrRusrJRvq0ilUnTv3p1TChsJNzc3neJkGLm5uWpHiwPAmTNnkJuba9iESK3g4GCV/3nVrKysONOYDnx8fODr66txIyIi0pc333xTaX/SpEkiZSIeg48Y55OeRERExql169ZYvny52GkQERFRHY0bNw75+fmYOXMm3N3d+UC6AUgkEsTExCAiIkKpvKqqCjExMXwPjMTx48drjLdp08ZA2RCZtvz8fFRVVamNVVVVIT8/n524j+no0aNK+/fu3cPRo0exaNEifPTRRyJlRWRcMjIyFOvAc9p7osezbds2XL9+Xans2rVr2LZtGwYNGiRSVoan947xiooK5OTkoHXr1mjQQLX6bdu2oWXLlvo+LREREengt99+g1QqRWhoqFJ5amoqqqqqLOriiIiIyNT88ccf+P3339GlSxexU7E4jz78LwgCBwQYkWbNmukUJ8Pw9PTUKU6GoalTvLZx0qxz584qZUFBQWjRogU+/fRThIWFiZAVkfGQy+VISEjA9evXkZCQgMDAQMhkMrHTIjIplZWV+OSTT9TGPvnkEwwcONBiZhvT21Tqd+7cQVRUFBo2bIhOnTop1kaZMmUK5s+frziuV69esLW11ddpiYiISA9mzJiByspKlXJBEDBjxgwRMiIiIqLa8vT0ZGesgQmCoHSv42Hz58/n+0FUB1u2bNEpToZx+fJlneJUd+3bt0dWVpbYaRCJLjk5GcXFxQCA4uJipKSkiJwRken55Zdf1N77BR50mv/yyy8Gzkg8eusYj4uLw/Hjx7F3716lp3UGDBiA9evX6+s0REREVA/OnTuHjh07qpT7+fnh/PnzImREREREtbV48WLMmDGD6/AaUG5uLrKzs9XGsrOz+V4YiWvXrukUJ8MYPHiwTnEiU1daWqq0lZSU4K+//sL777+Ptm3bip0ekagKCgqQkpKieOhQEASkpKSgoKBA5MxInYyMDIwYMQIZGRlip0KPCAgI0CluTvTWMb5582Z8+eWX6NWrl9JaWp06dcKFCxf0dRoiIiKqB46Ojrh48aJK+fnz59GoUSMRMiIiIqLaGjVqFPbu3YvWrVujcePGaNq0qdJGRGTMtm7dqlOcDEPTKLPaxkkzJycnNGnSRLE1bdoUHTt2RGZmJpYtWyZ2ekSiEQQBiYmJGss5Q49xqZ7yvqioCAkJCZDL5WKnRA9p1aoVPDw81MY8PT3RqlUrA2ckHr2tMX7t2jW4uLiolN++fVupo5yIiIiMz/DhwzFt2jRs2rQJrVu3BvCgU3z69OkYNmyYyNkRERGRNosXLxY7BYvj4+ODli1b4u+//1aJeXh4wMfHx/BJkYouXbroFCfDGDZsGD7//HO1HasNGjTg5xEjcfbs2Rrjffv2NUwyZiYtLU1p38rKCs2bN0ebNm3QoIHebt8TmZy8vDy1ywlUVlYiKysLeXl5vOYyIuqmvI+KihI5K3pY06ZN1c620KRJExGyEY/eWtagoCBs3boVU6ZMAQBFZ/iKFSsQHBysr9MQERFRPfjkk0/w3HPPwc/PT/H0YEFBAZ5++mksXLhQ5OyIiIhIm7Fjx4qdgsWpqqpCYWGh2tiVK1dQVVUFqVRq4KzoUVeuXKkx3qZNGwNlQ5pIpVK8++67iI+PV4nNmDGDf0tGon379jrFSbM+ffqInQKRUfL29ka3bt1w5MgRpYenpFIpAgMD4e3tLWJ29DBNU96HhoZqHKVMhpWXl6d1KShLetBEbx3jH3/8MQYNGoRTp07h/v37+Oyzz3Dq1Cns378f6enp+joNkRJBEExiSo6HczSFfKvJZDLO+EBkIRwdHbF//37s3LkTx48fh52dHQICAtC7d2+xUyMiIqIa5Ofna417eXkZKBPL8csvv2icNriyshK//PILXnjhBQNnRY+q6fMsP+8aD39/f7XlHTt2NHAmpEnLli11ipN2Fy5cwOLFi3H69GkAD373p06dqpjRjcgSSSQSxMTEYMyYMWrL2Y4bh5qmvF+4cCHfKyPg7e2NgIAAtZ3jAQEBFvWgid46xnv16oVjx45h/vz58Pf3x44dO9C1a1dkZmZqvLgl0pVcLkdoaKjYadTJ8OHDxU6h1lJTU2FnZyd2GkRkIBKJBAMHDsTAgQPFToWIiIjqwMfHR+vNJq77qn/Dhg3DZ599hqqqKpWYlZUVp342Ej179tQpToYhCALee+89tbH33nsPq1at4g11I6BplNnDcc7A8HhSU1MxbNgwdOnSBSEhIQCAjIwMdOrUCVu2bMGzzz4rcoZE4vHw8MCoUaOwdu1aRdmoUaP4MI4R4ZT3pqN6RH9ty82VXhcpad26NZYvX67PKomIiMhAdu/ejd27d+Pq1asqN3m//fZbkbIiIiKimhw9elRp/969ezh69CgWLVqEjz766LHrnT9/PuLi4jB16lTFOuZyuRzTp0/HunXrUF5ejtDQUCxduhSurq66vASTI5FIYG1tjfLycpWYjY0NO/GMxP79+2uMc4Yk8V28eBE5OTlqYzk5Obh48SJHzRqBzp076xQnzWbMmIGYmBjMnz9fpfzf//43O8aJyKhxynvTkJeXhxMnTqiNnThxwqIeYNBbx/hvv/0GqVSqMno3NTUVVVVVGDRokL5ORaTWkt63YCs1zidbBAGo+P8+JhsrwJjvkZRXShC9z0nsNIjIwObOnYsPPvgAQUFBcHd3581cIiIiE6KuMyIoKAgtWrTAp59+irCwsDrXmZWVha+//hoBAQFK5TExMdi6dSs2bNgAR0dHTJ48GWFhYcjIyHjs/E1RZmam2k5x4MHDA5mZmYpRfySev/76q8Y4O8bFd/z48Rrj7BgXX00dG+z4eHynT5/GDz/8oFL+6quvKh5MI7JUBQUFWL9+vVLZ+vXrMXjwYK5dbSQ45b1p8Pb2Rvv27XHmzBmVmJ+fn0W143rrGJ8xY4bKU23AgyH4M2bMYMc41TtbqQCZVOwsNDOdCcmN8+ECIqpfX331FVatWqVyEUtERESmq3379mqnNaxJWVkZIiIisHz5csybN09RXlJSgpUrV2Lt2rXo168fACApKQkdOnTAgQMH1E5LXV5ertSBXFpa+hivxPi4uLjoFCfDaNeunU5xMoyalmDkEo3GYdOmTTXGR4wYYaBszEvz5s1x7NgxtG3bVqn82LFjbE/IonHtatPh4eGBiIgIrFmzBoIgQCKRICIiglPeGxFBEPD333+rjRUUFCjeN0tgpa+Kzp07h44dO6qU+/n54fz58/o6DREREdWDiooKPPXUU2KnQURERI+htLRUaSspKcFff/2F999/X+Ume21ER0dj8ODBGDBggFL54cOHce/ePaVyPz8/eHl5ITMzU21d8fHxcHR0VGyenp51zscYpaen6xQnw5BKtT89X1OcDOPKlSs6xckwbt68qVOcNHvttdfw+uuvY8GCBfj999/x+++/Y/78+Zg4cSJee+21WtezbNkyBAQEwMHBAQ4ODggODsa2bdsUcblcjujoaDg7O8Pe3h7h4eEoKiqqj5dEpBfVa1c/PD03oLx2NRmPyMhIODs7AwCaNWuGiIgIkTOih2VmZqKsrExtrKysTOPnOXOkt45xR0dHXLx4UaX8/PnzaNSokb5OQ0RERPVgwoQJWLt2rdhpEBER0WNwcnJCkyZNFFvTpk3RsWNHZGZmYtmyZXWqa926dThy5Aji4+NVYoWFhbCxsYGTk5NSuaurKwoLC9XWFxcXh5KSEsV26dKlOuVjrMaNG6dTnAyjplEvljIqxthdu3ZNpzgZhp+fn05x0mzmzJmYNWsWvvjiC/Tp0wd9+vTBl19+iTlz5uD999+vdT0eHh6YP38+Dh8+jEOHDqFfv34YPnw4/vzzTwAPlkPZsmULNmzYgPT0dFy+fPmxllshMpTqtavV6d69u0VN/WwKZDIZpk+fDldXV8TGxkImk4mdEj0kODgYDg4OamOOjo4IDg42cEbi0dtU6sOHD8e0adOwadMmxbo/58+fx/Tp0zFs2DB9nYaIiIjqgVwuxzfffINdu3YhICAA1tbWSvFFixaJlBkRERHVJC0tTWnfysoKzZs3R5s2bdCgQe0/9l+6dAlTp07Fzp079XYjy9bWFra2tnqpi6iugoKCdIqTYXTu3FmnOBmGm5ubTnHSrHod3piYGPzzzz8AgMaNG9e5nqFDhyrtf/TRR1i2bBkOHDgADw+POi+HQiQ2iUSC0aNHq10aaPTo0XzAzQiFhIQgJCRE7DRIDSsrK8yZMwexsbEqsblz58LKSm/jqI2e3l7pJ598gkaNGsHPzw++vr7w9fVFhw4d4OzsjIULF+rrNERERFQPsrOz0aVLF1hZWeHkyZM4evSoYjt27JjY6REREZEW1aPLqrenn34afn5+deoUBx5MlX716lV07doVDRo0QIMGDZCeno7PP/8cDRo0gKurKyoqKnDr1i2l7ysqKrK4DpGkpCSd4mQYH3/8sU5xMgxN613WNk6GsXfvXp3ipFlOTg7OnTsH4EGHeHWn+Llz55Cbm/tYdVZWVmLdunW4ffs2goODH2s5lGrl5eUqy7YQGYIgCFi1apXaWFJSEgRBMGxCRCYuKCgI/v7+SmUBAQHo2rWrSBmJQ28jxh0dHbF//37s3LkTx48fh52dHQICAtC7d299nYKIiIjqyaMjzYiIiMi0XLhwAYsXL8bp06cBAB07dsTUqVMVM7rVRv/+/XHixAmlsvHjx8PPzw///ve/4enpCWtra+zevRvh4eEAgDNnziA/P9+ipt4DoDKdfF3jZBh9+vTR2lnXp08fwyVDGnEqddPg6OioU5w0GzduHF599VW0bdtWqfzgwYNYsWJFnR46OHHiBIKDgyGXy2Fvb49NmzahY8eOOHbsWJ2XQ6kWHx+PuXPn1joHIn3Jzc1Fdna22lh2djZyc3Ph6+tr4KyITNtHH32E559/HlVVVbCyssK8efPETsng9NYxDjyY2mLgwIEYOHCgPqslIhOn9PTeffHyMBsP/Qz5ZCQRERERpaamYtiwYejSpYti6sKMjAx06tQJW7ZswbPPPlureho3bownnnhCqaxRo0ZwdnZWlEdFRSE2NhZNmzaFg4MDpkyZguDgYIubgrWmURWWNurCWF28eLHGeP/+/Q2UDWnCteBNA9+n+nP06FG1Uw/37NkTkydPrlNd7du3x7Fjx1BSUoKNGzdi7NixSE9P1ym/uLg4pal3S0tL4enpqVOdREQkDicnJ0RGRiI5ORmRkZEW+UCvXjvGd+/ejd27d+Pq1auoqqpSin377bf6PBURmZDy8nLF19ItUhEzMT/l5eVo2LCh2GmQiQoLC8OqVavg4OCAsLAwrcf+9NNPBsqKiIiI6mrGjBmIiYnB/PnzVcr//e9/17pjvDYSExNhZWWF8PBwlJeXIzQ0FEuXLtVb/aaCHUSmwcfHR6c4GUZAQIBOcTKMGzdu6BQnzSQSiWJt8YeVlJSgsrKyTnXZ2NigTZs2AIDAwEBkZWXhs88+w6hRoxTLoTzcCVKb5VBsbW1ha2tbpzyI9MHHxwf+/v4qMxoBD9oGtuNEj6dDhw5o3rw5OnToIHYqotDbGuNz587FwIEDsXv3bly/fh03b95U2oiIiMi4ODo6Km7aOjo6at2IiIjIeJ0+fRpRUVEq5a+++ipOnTqlU9179+7F4sWLFfsymQxLlizBjRs3cPv2bfz0008Wt744UPPMTZzZyTj8+OOPOsXJMK5evapTnAzj0UFQdY2TZr1790Z8fLxSJ3hlZSXi4+PRq1cvnequqqpCeXk5AgMDFcuhVLPU5VDUycjIwIgRI5CRkSF2KvQQiUSC8ePHq42NHz+eDyISPQa5XI6EhAQUFRUhISEBcrlc7JQMTm8jxr/66iusWrUKY8aM0VeVRGQmHn6qtHJopZ7nqrBA9/838p5P7JIukpKS1H5NREREpqV58+Y4duyYytqkx44dg4uLi0hZmbcrV67UGK/L+u5UP1q2bKn14ZCWLVsaMBvSpFmzZjrFiUzdggUL0Lt3b7Rv3x5PP/00AOD3339HaWkp9uzZU+t64uLiMGjQIHh5eeGff/7B2rVrsXfvXqSmpsLR0ZHLoWhQ3Ul0/fp1JCQkIDAwEDKZTOy0CA8eNPz+++8hkUiUHjqUSCRYu3Ytunbtys5xojpKTk5GcXExAKC4uBgpKSlqH7I2Z3rrnqqoqMBTTz2lr+qIyIwoXaA0ADvG9YgXf6Qvd+/ehSAIiqn58/LysGnTJnTs2BEDBw4UOTsiIiLS5rXXXsPrr7+OixcvKj6XZ2RkYMGCBUprgpL+tGjRQqc4GUb1Tb/HjZNhbNmypcZ4+/btDZQNaXLmzBmd4qRZx44dkZ2djS+//BLHjx+HnZ0dXnnlFUyePBlNmzatdT1Xr17FK6+8gitXrsDR0REBAQFITU1VLKnC5VDUYyeR8crLy0NWVpZKuSAIyMrKQl5eHqdTJ6qDgoICpKSkKB40EQQBKSkpCA0NhYeHh8jZGY7euqcmTJiAtWvXYubMmfqqkoiIiAxk+PDhCAsLwxtvvIFbt26he/fusLGxwfXr17Fo0SK8+eabYqdIREREGsycORONGzdGQkIC4uLiADzomJ0zZw7eeustkbMzT5xS2DTY2NjoFCfD6Nq1K3755RetcRJf+/btcfToUa1xenwtWrTAxx9/rPWYSZMm4YMPPtA4i8LKlSu1fn/1cihLlix57DzNDTuJjJu3tze6deuGw4cPK11bWVlZISgoCN7e3iJmR2RaBEFAYmKixvKFCxdazCA8va0xLpfLsWjRIvTp0wdTpkxBbGys0kZERETG68iRI4op2zZu3Ag3Nzfk5eXhu+++w+effy5ydkRERKSNRCJBTEwMCgoKUFJSgpKSEhQUFGDq1KkWc3PD0Pbu3atTnAzD0dFRpzgZBkcim4aHpzF+nDjpLjk5GaWlpWKnYTaqO4Me/d2tqqpSW06GV32N++h7IQgCYmJieJ1LVAfVMzBUVlYqlVdWVipmYLAUehsxnp2djS5dugAATp48qRTjPygiIiLjdufOHTRu3BgAsGPHDoSFhcHKygo9e/a0qAsjIiIiU5STk4P79++jbdu2ivYcAM6dOwdra2tOMVkPXnnlFXz33Xda4yS+mqYgrssUxVR/CgoKdIqTYZSVlekUJ92xo1a/NE3TXVVVxWm6jdyja44TUc2qZ2A4cuSIUue4VCpFYGCgRc3AoLcR42lpaRq3PXv26Os0REREVA/atGmDzZs349KlS0hNTVWsK3716lU4ODiInB0RERFpM27cOOzfv1+l/ODBgxg3bpzhE7IABw8e1ClOhsGOcdPg4uKiU5wMw9fXV6c4kbHx9vaGv7+/2lhAQIBFdRIZq+pR/eoGXnJUP1HdVM/AoKnckgY4661jnIiIiEzXrFmz8Pbbb8PHxwc9evRAcHAwgAejx5988kmRsyMiIiJtjh49ipCQEJXynj174tixY4ZPyAJwSmHTcOrUKZ3iZBgXL17UKU6GMXToUJ3iRMbIkjqCTFH1qP6H1xcHlEf1E1HteXh4ICIiQvG/TyKRICIiAi1bthQ5M8PSqWM8LCxMsa5JWFiY1q229u3bh6FDh6JFixaQSCTYvHmzUnzcuHGQSCRK23PPPafLyyAiIrJ4L774IvLz83Ho0CFs375dUd6/f38kJiYq9gsKClQ+kBAREZG4JBIJ/vnnH5XykpISlTXkSD+Kiop0ipNhcGS/abh586ZOcTKMjz/+WKc4kbHJy8tDdna22lh2djY7XY0AR/UT6V9kZCScnZ0BAM2aNUNERITIGRmeTh3jjo6OiicLHB0dtW61dfv2bXTu3BlLlizReMxzzz2HK1euKLbvv/9el5dBREREANzc3PDkk0/Cyup/lwfdu3eHn5+fYr9jx47Izc0VITsiIiLSpHfv3oiPj1fqBK+srER8fDx69eolYmbmq7i4WKc4GUZN96Pqcr+K6s+oUaN0ipNhqHsAqy5xImNTvd7uw/dAgAfr7Xbv3p2drkaCo/qJ9Esmk2H69OlwdXVFbGwsZDKZ2CkZXANdvjkpKUnt17oYNGgQBg0apPUYW1tbuLm56eV8REREVHucFpSIyDgJggC5XC52GjV6OEdTyLeaTCYz6ptyCxYsQO/evdG+fXs8/fTTAIDff/8dpaWl2LNnj8jZmafqURaPGyfDcHZ2xpUrV7TGSXzfffddjfHBgwcbKBvS5MKFCzrFSbP8/Hx4enqqXGsIgoBLly7By8sLwINRfg4ODmKkaJaq19UdM2aMSszS1ts1VrUZ1e/j42PYpIjMQEhIiNqluCyFTh3jD7t79y4EQUDDhg0BPPintWnTJnTs2BEDBw7U12kAAHv37oWLiwuaNGmCfv36Yd68eRo/zJSXl6O8vFyxXz31OxERERERkbmQy+UIDQ0VO406GT58uNgp1Fpqairs7OzETkOjjh07Ijs7G19++SWOHz8OOzs7vPLKK5g8eTKaNm0qdnpmqUuXLjrFyTBqugfEe0TGYcaMGZg6darWOIkvOjpa63Tp0dHRBszGvPj6+uLKlStwcXFRKr9x4wZ8fX0VM8IsW7ZMjPTMmoeHB5o2bYpr164pypydnS1uvV1j5enpCalUqnZpIKlUCk9PTxGyIm0yMjKwePFiTJs2zaI7Xo2dpb9POk2l/rDhw4crnvC8desWunfvjoSEBAwfPlyvjfZzzz2H7777Drt378aCBQuQnp6OQYMGaVw3LT4+XmlKd/6zJCIiIiIiInPTokULfPzxx9i6dSs2btyIWbNmqXSKT5o0CdevXxcpQ/Ny/PhxneJkGC1atNApToaxZs0aneJkGOfPn9cpTpoJgqB2dHJZWZlFTnFrSIcOHVLqFAeAq1ev4tChQyJlRA87cOCAxn6fyspKHDhwwMAZkTZyuRwJCQkoKipCQkKCSc0QZknkcjni4+NRVFSE+Ph4i3yf9DZi/MiRI0hMTAQAbNy4EW5ubjh69Ch+/PFHzJo1C2+++aZezvPSSy8pvvb390dAQABat26NvXv3on///irHx8XFITY2VrFfWlrKznEiIiIiIjJb/wZgI3YSGggA7v3/19YAjHmCygoAC8ROQs+Sk5Px9ttvo1mzZmKnYvJu3LihU5wMo7CwUKc4GYavr6/WTihfX18DZkOanDhxQqc4qaq+Zy2RSDBz5kzFTKzAg06/gwcPcgaSelRVVYU5c+aojc2ZMwe//PKLyvrjZFjBwcFwcHBQO8OLo6MjgoODRciKNElOTlY8hHv9+nWkpKQgKipK5KzoUatWrVL8TZWWlmL16tWYOHGiyFkZlt7+s9+5cweNGzcGAOzYsQNhYWGwsrJCz549kZeXp6/TqGjVqhWaNWum8alEW1tbODg4KG1ERET0eLjGFhGR8bMBYAOJUW62kMD+/zdbI8hH+2Z+BEEQOwWzERkZqVOcDKNz5846xckwaup4YseUcai+7/u4cVJ19OhRHD16FIIg4MSJE4r9o0eP4q+//kLnzp2xatUqsdM0W5mZmRqX1CgtLUVmZqaBM6JHWVlZaXx4Ye7cuWwfjEhBQQGSk5OVypKTk1FQUCBSRqROQUEBvv/+e6Wy77//3uLeJ73952jTpg02b96MS5cuITU1VbGu+NWrV+u1M7qgoADFxcVwd3evt3MQERHRA7yhTkRERPRg2TZd4mQYu3bt0ilOhnH58mWd4mQYFy9e1ClOqtLS0pCWloaxY8di27Ztiv20tDSkpqbi66+/Rtu2bcVO02z17NkTUqlUbaxBgwbo2bOngTMidYKCguDv769UFhAQgK5du4qUET1KEAQkJiaq3DOsqqpSW07iEAQB8+fPV/s+qSs3Z3rrGJ81axbefvtt+Pj4oEePHoppLHbs2IEnn3yy1vWUlZXh2LFjOHbsGAAgJycHx44dQ35+PsrKyvDOO+/gwIEDyM3Nxe7duzF8+HC0adMGoaGh+nopREREFq+0tBSbN2/G6dOnlcpPnToFb29vkbIiIiIiMg4PT3f7OHEyjJoGanBWQeNw7949neJkGE5OTjrFSbPFixfj/v37KuU3btzQOKKZdHfp0iWN61ffv38fly5dMnBGpMmsWbOU9mfOnClSJqROXl4esrKyVDpWBUFAVlZWvc4oTbWXm5uL7OxstbHs7Gzk5uYaNiER6a1j/MUXX0R+fj4OHTqE7du3K8r79++vWHsceDDCu6qqSmM9hw4dwpNPPqnoTI+NjcWTTz6JWbNmQSqVIjs7G8OGDUO7du0QFRWFwMBA/P7777C1tdXXSyEiIrI4I0eOxJdffgkAuHv3LoKCgjBy5EgEBATgxx9/VBzn6emp8YlqANi3bx+GDh2KFi1aQCKRYPPmzUpxQRAwa9YsuLu7w87ODgMGDMC5c+fq5TURERER1RcfHx+d4mQYNXUoscPJONy8eVOnOBlGTSPJLGmkmb699NJLWLdunUr5Dz/8gJdeekmEjCyDl5cX7O3t1cbs7e3h5eVl4IxIky1btijt//rrryJlQup4eXlpfNjQwcGBf0tkdBroszI3Nze4ubkplXXv3l1pv2PHjjh27BhatWqlto6+fftqvZBKTU3VPVEiIiJSsm/fPrz33nsAgE2bNkEQBNy6dQurV6/GvHnzEB4eXqt6bt++jc6dO+PVV19FWFiYSvyTTz7B559/jtWrV8PX1xczZ85EaGgoTp06BZlMptfXRET6IQgC5HK52GnU6OEcTSHfajKZDBKJROw0iKiO2JFnGmxsbHD79m2tcRKfl5cXzpw5ozVOZM4OHjyIRYsWqZT37dtX8Tmd9C8vLw9lZWVqY2VlZcjLy4Ovr6+Bs6JHFRQUICUlRaksJSUFoaGh8PDwECkrelh+fr7Ghw1LS0uRn5/Ph0aNgI+PD/z9/XHixAmVWEBAgEW9R3rtGK8NU3l6kDcA6xdvABIRGZeSkhI0bdoUALB9+3aEh4ejYcOGGDx4MN55551a1zNo0CAMGjRIbUwQBCxevBjvv/8+hg8fDgD47rvv4Orqis2bN/NJeCIjJZfLTW7Zour/MaYgNTUVdnZ2YqdBJi4/Px+enp4qn7EEQcClS5cUnUqRkZGcOlpPCgoKdIqTYdR0n8SU7qOYs+rlFB83ToZx9epVneKkWXl5udqp1O/du4e7d++KkBGRcaheu1pT+cKFC9nHYASqZ5dUtzSBVCqFp6enCFnRoyQSCcaPH4/Y2FiV2Pjx4y3qb8ngHeOmgjcA6xdvABIZtwoAgHE+yCQAqF5hzhqAsTbZFWInUEeenp7IzMxE06ZNsX37dsU0bjdv3tTbSO6cnBwUFhZiwIABijJHR0f06NEDmZmZGjvGy8vLUV5ertjnlJdERETKfH19ceXKFbi4uCiV37hxA76+voqbVMuWLRMjPbN069YtneJkGD4+Pjh9+rTWOImvps8bnFnKOPTv319lqaxH4/R4unfvjm+++QZffPGFUvlXX32FwMBAkbIyfz4+Pmjfvr3aGSv8/PzYRhiB6rWrH1VZWalYu5rvk/gOHDigtlMcePBeHThwACEhIQbOih4lCAK+//57tbG1a9eia9euFtM5zo5xIiJSsUDsBMjgpk2bhoiICNjb28Pb2xt9+/YF8GCKdX9/f72co7CwEADg6uqqVO7q6qqIqRMfH4+5c+fqJQci0k3l0Erj/QQhAKj+LC6F8T45BQD3AekWqdhZkBkRBEHtTYyysjJ2KNUTbdcutYmTYVy/fl2nOBlGnz59kJycrDVO4svJydEpTprNmzcPAwYMwPHjxxUPGOzevRtZWVnYsWOHyNmZN01LalhbWxs4E1LH29sb3bp1U+kcl0gk6NatG7y9vUXKjB7m7u6uU5wMQ9ODJgAs7kETY72tZVRud40ArIz0RyUIQNX/T7Vj1QAw5ic6qu6j0ZGUmo8jIiKDmzRpErp3745Lly7h2WefhZWVFQCgVatWmDdvnqi5xcXFKU3zU1paymmYiMTSAMb9CYL3r8jCVLePEokEM2fORMOGDRWxyspKHDx4EF26dBEpO/Pm5uamtVPVzc3NgNmQJteuXdMpToaxfv36GuOvv/66gbIhTW7evKlTnDQLCQlBZmYmPv30U/zwww+ws7NDQEAAVq5cibZt24qdntnKy8tTu9YuAJw4ccKiOomMlUQiQUxMDF5++WWlckEQEBMTYzGjW42dr6+v1tkXfH19RciKHuXl5QV7e3uUlZWpxOzt7RXLb1kCg9/WMsl/VlYNAKkx32VT/2QbEVFdyGQypKamip1GjeRyuWLpiJ9//tkkRkGZQo5paWl45plnEBQUpFQ+ePBgvZ2j+gZxUVGR0tOiRUVFWm/a29rawtbWVm95EBERmYujR48CeHBz8MSJE0qjnmxsbNC5c2e8/fbbYqVn1jp27IiTJ09qjZP4bGxsUFGheZEjTSMFybAcHR21Pmji6OhowGxIk8aNG+sUJ+26dOmClBQOKDIkLy8vODg4qF2uzcHBwaI6iYyZpocXsrOz0bJlSwNnQ+pIJBKEhYUhPj5eJRYWFmaafYJmKC8vT22nOPBgprG8vDyLeYjB4B3jgmCca9YSEVk6iUQCOzs7sdP4P/buOzyqMu3j+G8SkkwoCT20EEIRASkKggGkCBJRKeKKgi6IqLsCIl1ZlSZFqaJSRCkqumCj2ECNlBUBRUAQBAVCUwgIhACSBJLn/YM3Y4Z0MpkzM/l+riuXmfOcOeeeOcF7ztxPyRO73e51MXuqO+64Q1WqVFGfPn3Uu3fvAhmRHRkZqQoVKigmJsZRCE9ISNDmzZv1xBNPuPx8AAD4ujVr1kiS+vTpo5kzZyokJMTiiAqPX3/9NV/tcA8K497h8uXL+WqHe7A0QcE5fPhwtu0UaAvG4cOHMy2KS1e+qzh8+DAjxi2WkpKiyZMnZ9o2efJkdejQQf7+LFFltdTUVL366quZtr3yyivq0KGDY2ZKWCen2mxhqt0W2F9jQkKCli9frl9++cVp++7du1n7AQAAD/P7779rwIAB+vDDD1W9enVFR0fr/fffz/aLxMycP39e27dv1/bt2yVdWWdu+/btOnz4sGw2mwYNGqTx48dr5cqV2rlzp3r16qVKlSqpa9eurn9RAAAUEi+//HKmhaPTp09n+YUv8ientUdZm9Qz5PRZNq+fdVEwuE7eIS4uLl/tyFq1atUUGRmZ5Q8KRkREhOrXr59pW4MGDahheICVK1cqJSUl07aUlBStXLnSzREhMxs3bsx2JPLGjRvdHBEyQ2H8by4bMd69e3e1atVKAwYM0MWLF9WkSRMdPHhQxhgtWbJE9957rySxJigAAB6obNmyGjx4sAYPHqytW7dq4cKF6tevn/r166eePXuqb9++atiwYY7H2bJli9q2bet4nLb2ae/evbVo0SKNGDFCFy5c0OOPP674+Hi1bNlSq1at8orp5uF6xhglJiZaHUaupI/TW2K22+1MWQYUEg888IA6deqkfv36OW1///33tXLlSn3++ecWRea79u3bl692uEeVKlV04MCBbNthvUuXLuWrHe5RpUoVHT16NNt2XJu0pVHSXLp0Sdu2bdP06dM1YcIEi6IqHLhf8mwNGjTIVzvcI/1yidfSDvc4fvx4ju01atRwUzTWcllhfP369Xr22WclScuWLZMxRvHx8Xrrrbc0fvx4R2EcAAB4tptuukkVKlRQmTJl9OKLL2rBggWaPXu2oqKiNHfuXNWrVy/L57Zp0ybbHoY2m03jxo3TuHHjCiJ0eJnExERFR0dbHUaedenSxeoQcmX16tUsNwEUEps3b9b06dMzbG/Tpo3jPh2u1ahRI8dU9lm1w3pnzpzJVzvco169eo4Zp7Jqh/WKFMn+a+Sc2pG1zDqhN2nSRJUqVdKUKVPUrVs3C6LyfYcOHdKOHTsybduxY4cOHTrEVOoWq169usqUKaNTp05laCtbtqyqV69uQVS4WmRkpGrXrq29e/dmaLv++uuZ+cJDREVFKSgoSElJSRna7Ha7oqKiLIjKGi6bSv3s2bMqXbq0JGnVqlW69957VbRoUd1111367bffXHUaAABQQC5duqQPP/xQd955pyIiIrR69Wq99tpriouL0759+xQREaH77rvP6jABAMBVkpKSMp1K/dKlS7p48aIFEfm+7EYh56Yd7kFh3DtcvQxjXtvhHidPnsxXO/Kudu3a+uGHH6wOw2dVrVpVISEhmbaFhISwtrsHSE1NVXx8fKZtZ86cUWpqqnsDQqZsNptGjx6dadvo0aOZmcFDGGOyXJ4mKSmJqdSvRXh4uDZu3KjSpUtr1apVWrJkiaQr/4NielQAADzbk08+qf/+978yxuif//ynJk+erBtuuMHRXqxYMU2dOlWVKlWyMEr4slmt4hXk77kfwo2Rkv//njvQT/LU+7qkFJv6ry9pdRgA3Kxp06aaN2+eXn31Vaftc+fOVePGjS2KyrcxchJwncw69uSlHe6R1Tq/uW1H1hISEpweG2N07NgxjRkzRrVq1bIoKt93+PDhDO99moSEBB0+fJgR4xbLzRrj99xzj5ujQl4UpmKrp1u5cmWW18MYU6j+PbnsTm3QoEF68MEHVbx4cUVERKhNmzaSrkyxXr9+fVedBgAAFIDdu3fr1VdfVbdu3RQUFJTpPmXLls12ylAgP4L8jez+VkeRPe+YlJybTqAwGj9+vNq3b6+ffvpJ7dq1kyTFxMTohx9+0JdffmlxdL7J3z/7pJVTO4C/UXD1DlynglOyZMkMIyqNMQoPD3cMPoPrpY0Yz6w4zohxz9CpUyfNmDEj23ZYzxiT5fJNzz77rBYtWsSocQ+QfgDUtbT7EpcVxvv166emTZvqyJEjuv322+Xnd2WW9urVq2v8+PGuOg0AACgAMTExOe5TpEgRtW7d2g3RAACAvGjRooU2btyoKVOm6P3331dwcLAaNGig+fPnM9KsgOQ0Mx4z53kGu92uxMTEbNthvWLFiunChQvZtsN6xYsXz3b5geLFi7sxGt9ydQd0Pz8/lStXTjVr1mQGkgLEiHHPd+TIkRzbWb/aegcOHFBsbGymbbGxsTpw4IBq1Kjh5qhwtRMnTuTYXljuHV2WWdesWaO2bduqSZMmTtvvuusuV50CAAAUsN27d+vw4cMZ1pzp3LmzRRFdG2NMtl+CepL0cXpLzHa7nd6+AOBhGjVqpHfffTdfx5gzZ47mzJmjgwcPSpLq1aunUaNGqWPHjpKu5KmhQ4dqyZIlSkpKUnR0tGbPnq2wsLD8hu91AgMD89UO98jps5W3fPbydRcvXsxXO9wju6J4btqRNTqgWyMiIkI333xzpuu4N23aVBERERZEhfRyWkOcNcY9w08//ZRjO4Vx61WsWDFf7b7EZYXxO+64Q1WqVFGfPn3Uu3dvhYeHu+rQAACggB04cED33HOPdu7cKZvN5lhzJq346W3T4iUmJio6OtrqMPKsS5cuVoeQK6tXr1ZwsHdMLA4AhcHhw4ezbc/tVKBVqlTRiy++qFq1askYo7feektdunTRtm3bVK9ePQ0ePFifffaZPvjgA4WGhmrAgAHq1q2bNmzY4IqX4VW2bNmSr3YAfwsJCVF8fHy27YCvWblyZa739baO6t7CZrOpR48emRbGe/ToQWdwD3D8+PEc2ym4Wq9Bgwb5aod7REREOH3nm57NZitUnYFcVhj//fff9c477+itt97S2LFjddttt6lv377q2rUrPaUBAPBwTz31lCIjIxUTE6PIyEh9//33OnXqlIYOHaqpU6daHR4AAMhGtWrVsv3yNrcd3K5ep3HChAmaM2eONm3apCpVqmj+/Pl67733dNttt0mSFi5cqDp16mjTpk265ZZbrv0FeKFy5crp5MmT2bYDyJ2kpKR8tQPeqGvXrk6Pry5WpM/r3tZR3VsYY7Rw4cJM2xYsWKCbbrqJ4rjFbrnlFvn5+WU6MtzPz6/Qff70VMeOHcuxvWbNmm6KBlnZuHFjpkVx6cr/Dzdu3KiWLVu6OSpruKwwXrZsWQ0ePFiDBw/W1q1btXDhQvXr10/9+vVTz5491bdvXzVs2NBVpwMAAC60ceNGffPNNypbtqz8/Pzk5+enli1batKkSRo4cKC2bdtmdYjX7MJND0p+HrwumzFS6uUrv/sVkTz1xjv1soptzd8UvQCAgnF1nr506ZK2bdum6dOna8KECdd0zJSUFH3wwQe6cOGCoqKi9OOPP+rSpUtq3769Y5/rr79eVatW1caNG7P8YjIpKcmpqJXVWp7eJiAgIF/tAP7GVOoojNIX+r7++ms9/fTTmjhxoqKioiRduUd/7rnnNHHiRKtC9HkHDx7Uzp07M23buXOnDh48yPrVFjt8+HCW06Wnpqbq8OHDXCMPULly5Xy1A+5WIN8S33TTTapQoYLKlCmjF198UQsWLNDs2bMVFRWluXPnql69egVxWgAAcI1SUlJUokQJSVc6u/3xxx+qXbu2IiIitHfvXoujyye/IpK/p385zew6AIBrl1kn9CZNmqhSpUqaMmWKunXrlutj7dy5U1FRUUpMTFTx4sW1bNky1a1bV9u3b1dgYKBKlizptH9YWFi201xOmjRJY8eOzfX5vcWJEyfy1Q4AQJpBgwZp7ty5TiP1oqOjVbRoUT3++OP65ZdfLIzOd2U1cjK37Sh4rDEOuM7NN9+cr3Zf4ufKg126dEkffvih7rzzTkVERGj16tV67bXXFBcXp3379ikiIkL33XefK08JAABc4IYbbtBPP/0kSWrWrJkmT56sDRs2aNy4capevbrF0QEAgGtRu3btTNfNzOk527dv1+bNm/XEE0+od+/e2r179zXHMHLkSJ09e9bxc+TIkWs+licpUiT7cQY5tQMAkGb//v0ZOp5JUmhoqA4ePOj2eAqL3Ez/DGvlZo1xWO+PP/7IVzvc49VXX81Xuy9x2Z3ak08+qf/+978yxuif//ynJk+erBtuuMHRXqxYMU2dOlWVKlVy1SkBp557SSy34xLp30d6RgKFx3PPPacLFy5IksaNG6e7775bt956q8qUKaOlS5daHB0AICfpP7clX9liVSg+Iznd757+ufjq6cmNMTp27JjGjBmjWrVq5elYgYGBjjUAGzdurB9++EEzZ87U/fffr+TkZMXHxzt9eR8XF6cKFSpkebygoCAFBQXlKQZvkJycnK92AH/Lav3Y9O2AL7v55ps1ZMgQvfPOOwoLC5N0Jb8OHz5cTZs2tTg63xUVFSW73a7ExMQMbcHBwY5p7WGdqKgoFS1aVH/99VeGtqJFi3KNPER29wK5aYd7dO7cWStXrsy2vbBwWWF89+7devXVV9WtW7csb3rLli2rNWvWuOqUgNNadf3Xl7IwEt+UlJSkokWLWh0GADeIjo52/F6zZk3t2bNHp0+fVqlSpWTz1DWvAQAO6T8Xv2RhHL7K0z8XlyxZMkO+NsYoPDxcS5YsydexU1NTlZSUpMaNGysgIEAxMTG69957JUl79+7V4cOHC+WXkkztCbgO/55Q2C1YsED33HOPqlatqvDwcEnSkSNHVKtWLS1fvtza4HxcVv9/SUlhBJYnsNlsqlKlin799dcMbVWqVOH7Kg8RFxeXY3tax1tYJyAg+2Umc2r3JS4rjMfExOR8siJF1Lp1a1edEgAAFKDSpUtbHQIAAMiFqzug+/n5qVy5cqpZs2aepvQeOXKkOnbsqKpVq+rcuXN67733tHbtWq1evVqhoaHq27evhgwZotKlSyskJERPPvmkoqKidMstt7j6JQEAUGjUrFlTO3bs0FdffaU9e/ZIkurUqaP27dtT+CtA3333XZYzvCQnJ+u7775zWvcd7nfo0KFMi+KS9Ouvv+rQoUOqVq2ae4NCBmkzXVxrO9zj8uXL+Wr3JS5f9Gr37t06fPhwhqRSmIbhw33Sz04wq9UZBflbGIyPSEr5e/S9L055COBv3bp1y/W+H3/8cQFGAgDIr/Sf256WFGhdKD4jWX+Pvvf0z8Wu6oB+4sQJ9erVS8eOHVNoaKgaNGig1atX6/bbb5ckzZgxQ35+frr33nuVlJSk6OhozZ492yXnBlB4BQUFOc18klk74OtsNps6dOigDh06WB1KoZHTUjmevpROYVC1atUsc0RQUJCqVq1qQVS4Wm7WgmfEuPXWrl2bY3tel+HyVi4rjB84cED33HOPdu7cKZvN5kgcab3amH4EBSF9r8kgf8lOYdylCqRXqid3PDKS0v5X5S/JUzvlevJ7CK8SGhrq+N0Yo2XLlik0NFRNmjSRJP3444+Kj4/PUwEdAGCN9J/bAiUFeuwHGW/y95ehnjhaK7v14a6W247q8+fPz7bdbrdr1qxZmjVrVq7P7asCAgJ06dKlbNsB5E52RfHctAO+YN26dZo6dap++eUXSVLdunU1fPhw3XrrrRZH5rsqV66cr3YUvNjY2CxzQFJSkmJjY1WjRg03R4Wr8W/JO1x//fX5avclLiuMP/XUU4qMjFRMTIwiIyP1/fff69SpUxo6dKimTp3qqtMA8HL+n9B7AfAUCxcudPz+9NNPq3v37po7d678/a/8O01JSVG/fv0UEhJiVYgAPIDTSAk6Z7lGuveRkSi4Vl27dnV6nL6DetrjNHRUd73siuK5aQcAIM3ixYvVp08fdevWTQMHDpQkffvtt2rXrp0WLVqknj17Whyhb8pqffHctqPgffPNNzm2Uxi3Xk4j9xnZ7xlatGihwMDATJeQCAwMVIsWLSyIyhouK4xv3LhR33zzjcqWLSs/Pz/5+fmpZcuWmjRpkgYOHKht27a56lQAAMDFFixYoG+//dZRFJckf39/DRkyRM2bN9eUKVMsjA6AldL30KeDm+slJSWpaNGiVocBL5T+y9qvv/5aTz/9tCZOnKioqChJV+7Rn3vuOU2cONGqEH1aiRIldO7cuWzbAQDIjQkTJmjy5MkaPHiwY9vAgQM1ffp0vfDCCxTGC8hPP/2UYzvTP1urVatWeuedd7Jth/U2bNiQYzvXyno2m02lS5fOdOr70qVLe+QsaQXFZYXxlJQUx41f2bJl9ccff6h27dqKiIjQ3r17XXUaAF7Ibrdr9erVVoeRo8TERHXp0kWStGLFCtntdosjypk3xAjvcPnyZe3Zs0e1a9d22r5nzx56SQMA4OEGDRqkuXPnqmXLlo5t0dHRKlq0qB5//HHHtKxwneyK4rlpBwAgzYEDB9SpU6cM2zt37qz//Oc/FkRUOOQ0ow4z7lhv165dObZf/T0W3C+n+t/evXspjHuAAwcOZLke/PHjx3XgwIFCMwODywrjN9xwg3766SdFRkaqWbNmmjx5sgIDAzVv3jxVr17dVacB4IVsNpuCg4OtDiNP7Ha718UM5EefPn3Ut29f7d+/X02bNpUkbd68WS+++KL69OljcXQArBQUFOT4PaVTigvvIAqxy3+Pvk///gLXav/+/SpZsmSG7aGhoTp48KDb4wEAALkXHh6umJiYDKOTv/76a4WHh1sUle9LP2PetbSj4DVs2DBf7XCPtm3bZjuyv23btm6MBlnZvn17ju0UxvPoueee04ULFyRJ48aN0913361bb71VZcqU0dKlS111GgAAUACmTp2qChUqaNq0aTp27JgkqWLFiho+fLiGDh1qcXQArOQ0nVYRURh3scI0XRkKzs0336whQ4bonXfeUVhYmCQpLi5Ow4cPd3R4AwAAnmno0KEaOHCgtm/frubNm0u6MvXwokWLNHPmzFwfZ9KkSfr444+1Z88eBQcHq3nz5nrppZecRtQmJiZq6NChWrJkiZKSkhQdHa3Zs2c7Pj8UJl26dMn2/U2bVRLWiYyMVEBAgC5dupShLSAgQJGRkRZEhavRycQ7ZPbvKC/tvsRlX2tFR0c7fq9Zs6b27Nmj06dPq1SpUnzZAwCAh/Pz89OIESM0YsQIJSQkSJJCQkIy7LdhwwY1adKEEY4AAHiQBQsW6J577lHVqlUdI8uOHDmiWrVqafny5dYGBwAAsvXEE084Oqq///77kqQ6depo6dKleSrOrlu3Tv3799fNN9+sy5cv6z//+Y86dOig3bt3q1ixYpKkwYMH67PPPtMHH3yg0NBQDRgwQN26dctxjWBfdPTo0Rzbq1Wr5p5gkKnY2Ngsi3WXLl1SbGxsoRnh6skiIiJkt9uVmJiYoc1utysiIsKCqHC1I0eO5KvdlxToeI/SpUsX5OEBAEAByKwgnqZjx47avn07y6QAAOBBatasqR07duirr77Snj17JF35Qr19+/Z0VAcAwINdvnxZEydO1COPPKJvv/02X8datWqV0+NFixapfPny+vHHH9WqVSudPXtW8+fP13vvvafbbrtNkrRw4ULVqVNHmzZt0i233JKv83ubqlWrqmjRovrrr78ytBUtWlRVq1a1ICqk99NPP+XYTmHceocOHcq0KC5dmaXi0KFDjO73AHfeeac++eSTbNsLi3wVxrt165brfT/++OP8nAoAAHgAY4zVIQAAgEzYbDZ16NBBHTp0sDoUAACQS0WKFNHkyZPVq1cvlx/77Nmzkv4evPbjjz/q0qVLat++vWOf66+/XlWrVtXGjRuzLIwnJSUpKSnJ8Thtljlvd/DgwUyL4pL0119/6eDBgwwKsFjdunXz1Q73+P3333NspzBuvQULFuTYPm3aNDdFYy2//Dw5NDTU8RMSEqKYmBht2bLF0f7jjz8qJiZGoaGh+Q4UAAAAAABkbt26derUqZNq1qypmjVrqnPnzvrf//5ndVgAACAH7dq107p161x6zNTUVA0aNEgtWrTQDTfcIEk6fvy4AgMDVbJkSad9w8LCdPz48SyPNWnSJKc6QNqyLd4uNjY2X+0oePPmzctXO9yjfPny+WqHe9x44435avcl+RoxvnDhQsfvTz/9tLp37665c+fK399fkpSSkqJ+/fplOyUrAAAAAAC4dosXL1afPn3UrVs3DRw4UJL07bffql27dlq0aJF69uxpcYQAACArHTt21DPPPKOdO3eqcePGjvXA03Tu3DnPx+zfv79+/vnnfE/PLkkjR47UkCFDHI8TEhJ8ojie0wy3H3/8sdq1a+emaJCZm2++2WkgZmbtsN6KFStybB8+fLibokFWtm7dmmP7Qw895KZorOWyNcYXLFigb7/91lEUlyR/f38NGTJEzZs315QpU1x1KgAAAAAA8P8mTJigyZMna/DgwY5tAwcO1PTp0/XCCy9QGAcAwIP169dPkjR9+vQMbTabTSkpKXk63oABA/Tpp59q/fr1qlKlimN7hQoVlJycrPj4eKdR43FxcapQoUKWxwsKClJQUFCeYvAGTz75pB5//PFs22GtnP7ufPHv0htFRETkqx3u0alTp2w7mnTq1MmN0VgrX1Opp3f58mXt2bMnw/Y9e/YoNTXVVacBAAAWstlsVocAAACucuDAgUy/yOjcuTPTgAIA4OFSU1Oz/MlLUdwYowEDBmjZsmX65ptvMqzp27hxYwUEBCgmJsaxbe/evTp8+LCioqJc9nq8xXXXXZfldxw2m03XXXedmyPC1bp06ZKvdriHn1/2Zcac2uEe6TtKXUu7L3HZiPE+ffqob9++2r9/v5o2bSpJ2rx5s1588UX16dPHVacBAAAuZozRkSNHVL58ednt9hz3BQAAniU8PFwxMTGqWbOm0/avv/7aJ6Y6BQAAOevfv7/ee+89rVixQiVKlHCsGx4aGqrg4GCFhoaqb9++GjJkiEqXLq2QkBA9+eSTioqK0i233GJx9O536NChLL/jMMbo0KFDGToXwL1y+g6K76g8Q4MGDfLVDvf48ssvc2yvVauWm6KxlssK41OnTlWFChU0bdo0HTt2TJJUsWJFDR8+XEOHDnXVaQAAgIsZY1SzZk3t2rUrxw9A586dc1NUAAAgt4YOHaqBAwdq+/btat68uSRpw4YNWrRokWbOnGlxdAAAICcxMTGaMWOGfvnlF0lSnTp1NGjQILVv3z7Xx5gzZ44kqU2bNk7bFy5cqIcffliSNGPGDPn5+enee+9VUlKSoqOjNXv2bJe8Bm9z9OjRHNspjFvrzTffzLH93//+t5uiQVZ+/vnnHNuZgcF62U2jnpt2X+Kywrifn59GjBihESNGKCEhQZIUEhKSYb8NGzaoSZMmrP8AAICH8PPzU61atXTq1KlC0zMQniF97+6kvC2bhyykfx/pPQ8UHk888YSjo/r7778v6coX6kuXLmWKSXgdY4wSExPddr6LFy/m+xh2u50lhwBcs9mzZ+upp57SP/7xDz311FOSpE2bNunOO+/UjBkz1L9//1wdJzef/+12u2bNmqVZs2blK2bAHbZt25avdrhH/fr189UO9xg+fHi2HUmGDx/uxmis5bLCeHqZFcTTdOzYUdu3b1f16tUzbV+/fr2mTJmiH3/8UceOHdOyZcvUtWtXR7sxRqNHj9Ybb7yh+Ph4tWjRQnPmzOGLfAAA8uHFF1/U8OHDNWfOHN1www1Wh4NCIikpyfF7//WlLIzENyUlJalo0aJWhwGggF2+fFkTJ07UI488om+//dbqcIB8S0xMVHR0tNvO54pzrV69WsHBwS6IxnvQgQFwnYkTJ2rGjBkaMGCAY9vAgQPVokULTZw4MdeFceRNpUqV8tWOgnfDDTc4ZlHIqh3Wy81IZOp31vvpp59ybK9bt66borFWgRTGs5NTz7ULFy6oYcOGeuSRR9StW7cM7ZMnT9Yrr7yit956S5GRkXr++ecVHR2t3bt357guKgAAyFyvXr30119/qWHDhgoMDMzwxd7p06ctigwAAGSnSJEimjx5snr16mV1KAAKETowAK4THx+vO+64I8P2Dh066Omnn7YgosLh999/z7G9Ro0abooGmcmuKJ6bdrjHunXrcmzv0aOHm6JBVmJiYnJsLyzXye2F8Zx07NhRHTt2zLTNGKOXX35Zzz33nGM6uLffflthYWFavny5HnjggQzPSUpKchqNlDbNOwAA+NvLL79sdQgohNIvrTOr1RkF+VsYjI9ISvl79D1LFwGFR7t27bRu3TpVq1bN6lCAfLPb7Vq9erVLjpWUlKTOnTtn2b5y5UqX5EsGagDIj86dO2vZsmUZprFdsWKF7r77boui8n0nT57MVzsKXm46L8B6xYsXz1c73OP48eP5avclHlcYz05sbKyOHz+u9u3bO7aFhoaqWbNm2rhxY6aF8UmTJmns2LHuDBMAAK/Tu3dvq0NAIZR+Kssgf8lOYdylmCoUKDw6duyoZ555Rjt37lTjxo1VrFgxp/bsCoOAp7HZbC4b1RscHKw2bdpo7dq1GdratWunkiVLuuQ8hZErOzB88803eumll7Jsf/rpp3Xbbbfl+zyFsQMDU957h7p162rChAlau3atoqKiJF1ZY3zDhg0aOnSoXnnlFce+AwcOtCpMn1OuXLl8taPg9evXTxMmTMi2HdYrW7ZsvtrhHtdff72+//77bNsLC68qjKf1WAgLC3PaHhYWlmVvhpEjR2rIkCGOxwkJCQoPDy+4IAEA8FL79+/XwoULtX//fs2cOVPly5fXF198oapVq6pevXpWhwcAALKQ9qXg9OnTM7TZbDalpKS4OyTAY4wbN06tWrXKsH306NEWROM7XNmB4a677sq2MH7XXXe55DyFEVPee4f58+erVKlS2r17t3bv3u3YXrJkSc2fP9/x2GazURh3oZtvvjlf7Sh4Of2/oLD9v8JTVa9ePV/tcI/IyMhsC+ORkZFujMZafu4+obt7/AUFBSkkJMTpBwAAOFu3bp3q16+vzZs36+OPP9b58+clST/99BNfGgIA4OFSU1Oz/KEoDihD0ZVlhDzP+vXr87Qd8CWxsbG5+jlw4IDVofqUZ599Nl/tKHjJycn5aod7lCpVKl/tcI/0Ha+upd2XuGTEuDFGR44cUfny5XOclsgYc83nqVChgiQpLi5OFStWdGyPi4tTo0aNrvm4AAAUds8884zGjx+vIUOGqESJEo7tt912m1577TULIwMAAADyJ/13RiVKlNBNN91kXTDItcI2nXZBcOWU94mJierSpUuW7StWrHDJdPWFccr73AoJCdH27dsZfekiTZo00ZYtW7Jth7Xmzp2bY3v6ZXdhjQ8//DDH9ttvv91N0SArp0+fzle7L3HJiHFjjGrWrKkjR47kuO+5c+euOXlHRkaqQoUKiomJcWxLSEjQ5s2bHeuvAACAvNu5c6fuueeeDNvLly+vP//804KIAABAXsTExOjuu+9WjRo1VKNGDd199936+uuvrQ4L8Dg5fXkL61xdwF23bp1FkfiOtCnvXfFTqlQpNW3aNNPzREVFqVSpUi45Dx0ispafAWfIaNeuXflqR8G7++6789UO90hNTc1XO9wjqxye23Zf4pLCuJ+fn2rVqqVTp07l+1jnz5/X9u3btX37dklXppLZvn27Dh8+LJvNpkGDBmn8+PFauXKldu7cqV69eqlSpUrq2rVrvs8NAEBhVbJkSR07dizD9m3btqly5coWRAQAAHJr9uzZuuOOO1SiRAk99dRTeuqppxQSEqI777xTs2bNsjo8AMgzV41yhmtNnTo10+3ZrREPeKqiRYvmqx0F79NPP81XO9yjSpUq+WqHeyQmJuar3Ze4ZCp1SXrxxRc1fPhwzZkzRzfccMM1H2fLli1q27at4/GQIUMkSb1799aiRYs0YsQIXbhwQY8//rji4+PVsmVLrVq1iml2AADIhwceeEBPP/20PvjgA9lsNqWmpmrDhg0aNmyYevXqZXV4AAAgGxMnTtSMGTM0YMAAx7aBAweqRYsWmjhxovr3729hdJ7DGOPWL3wuXryY72PY7XZGTwLwKC+88IKef/55x+PJkydbGA1w7c6ePZuvdhS8evXq6cSJE9m2w3onT57MVzvcI6cZv3MzI7ivcFlhvFevXvrrr7/UsGFDBQYGKjg42Kk9t/PTt2nTJttpYWw2m8aNG6dx48blK14AAPC3tC/Nw8PDlZKSorp16yolJUU9e/bUc889Z3V4AAAgG/Hx8brjjjsybO/QoYOefvppCyLyTImJiYqOjnbb+VxxrtWrV2f4fgUArJR+qtXg4GDdcsstFkYDXDsK455v27Zt+WqHe/BvyTvkNOO3K2YE9xYuK4y//PLLrjqUR3Aqzqdcsi4QX5LufWRNHADwLIGBgXrjjTc0atQo7dy5U+fPn9eNN96oWrVqWR0aAADIQefOnbVs2TINHz7cafuKFStYexEAUGCWL19udQiFCjOIuJa/v3++2lHwmjRpoq+//jrbdlgvJCQkX+1wj9DQ0EyX0UzfXli4rDDeu3dvVx3KIyQlJTl+L7btPQsj8U1JSUms0wIAHig8PNwxanznzp06c+aMSpUqZXVYAAAgG3Xr1tWECRO0du1aRUVFSZI2bdqkDRs2aOjQoXrllVcc+w4cONCqMC1nt9tdtm7x4cOH9dhjj2XZ/sYbb6hq1ar5Pg/LxgEA0jDQyLUCAgLy1Y6Cd/To0Xy1wz0qVKignTt3ZtsO68XFxeWr3Ze4rDAuSfv379fChQu1f/9+zZw5U+XLl9cXX3yhqlWrst4DAAAebNCgQapfv7769u2rlJQUtW7dWt99952KFi2qTz/9VG3atLE6xDxh5pcCwMwvAOCx5s+fr1KlSmn37t3avXu3Y3vJkiU1f/58x2ObzVaoC+M2m81l05LXrl07X+0AAOTVF198ocqVK1sdhs8oUiT70khO7Sh48fHx+WqHe/z666/5aod71KlTR99991227YWFy/7vvm7dOnXs2FEtWrTQ+vXrNWHCBJUvX14//fST5s+frw8//NBVp3KLoKAgx+8Xbuwp+dNDLN9SLjlG36d/fwEA1vvwww/10EMPSZI++eQTHThwQHv27NE777yjZ599Vhs2bLA4wrxh5peCxcwvAOBZYmNjrQ6hUFq/fr1atWqV6XYAALIzZMiQXO87ffp0SVLLli0LKpxC6ciRI/lqR8FjTWTvkNMMR8yA5Bm2bduWr3Zf4rLC+DPPPKPx48dryJAhKlGihGP7bbfdptdee81Vp3EbpzVb/AMojLsYa+IAgGf5888/HVMbff755+revbuuu+46PfLII5o5c6bF0QEAAFcICQnR9u3bVb16datD8RkNGzbUTz/95Hh84403WhgNAMBbXF2A2Lp1qy5fvuyYceTXX3+Vv7+/GjdubEV4gEcoWbKkTp48mW07rHf99ddr79692bbDehUrVtSBAweybS8sXFYY37lzp957L+OIrPLly+vPP/901WmALCWl2CR55tSuxkjJqVd+D/STPLlfwJX3EUBhExYWpt27d6tixYpatWqV5syZI0n666+/5O/vb3F0ecfMLwWAmV8AwOuxFIbrTZ48WdHR0Y7HdCgEAOTGmjVrHL9Pnz5dJUqU0FtvvaVSpUpJks6cOaM+ffro1ltvtSpEn3f69Ol8taPgXbhwIV/tcI+1a9fm2D506FD3BIMs+fn55avdl7isMF6yZEkdO3ZMkZGRTtu3bdvG2idwi/7rS1odAgB4rT59+qh79+6qWLGibDab2rdvL0navHmzV/bsZOaXgsXMLwDgmyZNmqSPP/5Ye/bsUXBwsJo3b66XXnrJab3sxMREDR06VEuWLFFSUpKio6M1e/ZshYWFWRi5Z1i9erXVIQAAvNC0adP05ZdfOoriklSqVCmNHz9eHTp0oKBUQJKTk/PVjoKXU6dOOn16hosXL+arHe5x9uzZfLX7Epd1AXjggQf09NNP6/jx47LZbEpNTdWGDRs0bNgw9erVy1WnAQAABWDMmDGaP3++Hn/8cW3YsMExItjf318jR460ODoAHuOyB/9ckpT4/z+XPCCenH4AD7Ru3Tr1799fmzZt0ldffaVLly6pQ4cOTqNxBg8erE8++UQffPCB1q1bpz/++EPdunWzMGoAALxbQkJCptNFnzx5UufOnbMgosIhp9nxvHH2PF+TkpKSr3a4B51MvENSUlK+2n2Jy0aMT5w4Uf3791d4eLhSUlJUt25dpaSkqGfPnnruuedcdRrAid1u94pe+YmJierSpYskacWKFbLb7RZHlDveEieA/Bs3bpzj9wULFji1HTp0SJ07d3Z3SAA8kP8nfDkE+LJVq1Y5PV60aJHKly+vH3/8Ua1atdLZs2c1f/58vffee7rtttskSQsXLlSdOnW0adMm3XLLLVaEDQCAV7vnnnvUp08fTZs2TU2bNpV0Zfa24cOH0/msAFF09XxM/ewd/P39s/33QicTz3D+/Pl8tfsSlxXGAwMD9cYbb2jUqFHauXOnzp8/rxtvvFG1atVy1SmADGw2m4KDg60OI0/sdrvXxQzA9y1btszp8aVLlxQbG6siRYqoRo0aGjVqlEWRAQAAV8nrUhhp0+mVLl1akvTjjz/q0qVLjiVXJOn6669X1apVtXHjxkwL40lJSU6jDxISEq4ldAAAfNbcuXM1bNgw9ezZU5cuXZIkFSlSRH379tWUKVMsjs531alTR7/88ku27bBWYmJivtrhHnQy8Q6pqan5avclLiuMpwkPD3eMGt+5c6fOnDnjtD4KAADwPNu2bcuwLSEhQQ8//LDuueceCyIC4CmYoadgFUScVyaq88z19oyuzHQvSQGS8lamdS9fnPAvL+swpqamatCgQWrRooVuuOEGSdLx48cVGBiokiVLOu0bFham48ePZ3qcSZMmaezYsdccMwAAviwlJUVbtmzRhAkTNGXKFO3fv1+SVKNGDRUrVszi6HzbgQMH8tUOAPBOLiuMDxo0SPXr11ffvn2VkpKi1q1b67vvvlPRokX16aefqk2bNq46FQAAcIOQkBCNHTtWnTp10j//+U+rwwFgEWbo8T4vWR0APNYXX3yhypUr52rf/v376+eff9a3336br3OOHDlSQ4YMcTxOSEhQeHh4vo4JAICv8Pf3V4cOHfTLL78oMjJSDRo0sDokAMiTMmXK6NSpU9m2A57EZYXxDz/8UA899JAk6ZNPPtGBAwe0Z88evfPOO3r22We1YcMGV50KAAC4ydmzZx3TqAIAAM+Rvtick+nTp0uSWrZsmav9BwwYoE8//VTr169XlSpVHNsrVKig5ORkxcfHO40aj4uLU4UKFTI9VlBQkIKCgnIdKwAAhc0NN9ygAwcOKDIy0upQCpX0S71cSzuAK86cOZOvdsDdXFYY//PPPx03wp9//rm6d++u6667To888ohmzpzpqtMAAIAC8Morrzg9Nsbo2LFjeuedd9SxY0eLogIA5BZT3hcsT4zz6mVQtm7dqsuXL6t27dqSpF9//VX+/v5q3Lhxro9pjNGTTz6pZcuWae3atRm+oG/cuLECAgIUExOje++9V5K0d+9eHT58WFFRUfl8RQAAFE7jx4/XsGHD9MILL6hx48YZplAPCQmxKDIAyBlrV8PbuKwwHhYWpt27d6tixYpatWqV5syZI0n666+/5O/v76rTAACAAjBjxgynx35+fipXrpx69+6tkSNHWhQVACC3mPK+8FmzZo3j9+nTp6tEiRJ66623VKpUKUlXRmb06dNHt956a66P2b9/f7333ntasWKFSpQo4Vg3PDQ0VMHBwQoNDVXfvn01ZMgQlS5dWiEhIXryyScVFRWlW265xbUvEACAQuLOO++UJHXu3Fk2m82x3Rgjm82mlJQUq0IDAMDnuKww3qdPH3Xv3l0VK1aUzWZT+/btJUmbN2/W9ddf76rTAACAAhAbG2t1CAAA4BpNmzZNX375paMoLkmlSpXS+PHj1aFDBw0dOjRXx0nr4N6mTRun7QsXLtTDDz8s6UpnOj8/P917771KSkpSdHS0Zs+e7ZLXAQBAYZS+sxsAeBu73a7ExMRs2wFP4rLC+JgxY1S/fn0dPnxY9913n2MNMX9/f0aaAQAAAABQQBISEnTy5MkM20+ePKlz587l+jjGmBz3sdvtmjVrlmbNmpWnGAEAQOZat26t+Ph4zZ8/X7/88oskqW7duurbt69CQ0Mtjg4AspddUTw37YC7+bnqQOPGjdOuXbt07tw5LViwQOPGjdO4ceN06NAhbd++3VWnAQAAXmzMmDGy2WxOP8wsAwBA/txzzz3q06ePPv74Yx09elRHjx7VRx99pL59+6pbt25WhwcAALKxZcsW1axZUzNmzNDp06d1+vRpzZgxQzVq1NDWrVutDg8AAJ/ishHjy5Ytc3p86dIlxcbGqkiRIqpRo4ZGjRrlqlMBAAAvVq9ePX399deOx0WKuOzjCAAAhdLcuXM1bNgw9ezZU5cuXZJ0Jb/27dtXU6ZMsTg6AACQncGDB6tTp0564403HPfHly9f1qOPPqpBgwZp/fr1FkcIAIDvcNk30du2bcuwLSEhQQ8//LDuueceV50GAAB4uSJFiqhChQpWhwEAgE9ISUnRli1bNGHCBE2ZMkX79++XJNWoUUPFihWzODoAAJCTLVu2OBXFpSv3zSNGjFCTJk0sjAwAAN/jsqnUMxMSEqKxY8fq+eefL8jTAAAAL/Lbb7+pUqVKql69uh588EEdPnw42/2TkpKUkJDg9AMAAK7w9/dXhw4dFB8fr2LFiqlBgwZq0KABRXEAALxESEhIpvfFR44cUYkSJSyICAAA31WghXFJOnv2rM6ePVvQpwEAAF6gWbNmWrRokVatWqU5c+YoNjZWt956q86dO5flcyZNmqTQ0FDHT3h4uBsjBgDA891www06cOCA1WEAAIBrcP/996tv375aunSpjhw5oiNHjmjJkiV69NFH1aNHD6vDAwDAp7hsKvVXXnnF6bExRseOHdM777yjjh07uuo0AADAi6X/TNCgQQM1a9ZMERERev/999W3b99MnzNy5EgNGTLE8TghIYHiOAAA6YwfP17Dhg3TCy+8oMaNG2cYLR4SEmJRZAAAICdTp06VzWZTr169dPnyZUlSQECAnnjiCb344osWRwcAgG9xWWF8xowZTo/9/PxUrlw59e7dWyNHjnTVaQAAgA8pWbKkrrvuOu3bty/LfYKCghQUFOTGqAAA8C533nmnJKlz586y2WyO7cYY2Ww2paSkWBUaAADIQWBgoGbOnKlJkyZp//79kqQaNWqoaNGiFkcGAIDvcVlhPDY21lWHAgAAhcT58+e1f/9+/fOf/7Q6FFgsKcUmyVgdRpaMkZJTr/we6Celqzt5lCvvI4DCZs2aNVaHAAAA8qlo0aKqX7++1WEAAODTXFYYBwAAyMmwYcPUqVMnRURE6I8//tDo0aPl7+/PumlQ//UlrQ4BALxW69atFR8fr/nz5+uXX36RJNWtW1d9+/ZVaGioxdEBAAAAAOAZ/KwOAAAAFB5Hjx5Vjx49VLt2bXXv3l1lypTRpk2bVK5cOatDAwDAa23ZskU1a9bUjBkzdPr0aZ0+fVozZsxQjRo1tHXrVqvDAwAAAADAIzBiHAAAuM2SJUusDgEexG63a/Xq1VaHkSuJiYnq0qWLJGnFihWy2+0WR5Qzb4gRgGsMHjxYnTp10htvvKEiRa7c5l++fFmPPvqoBg0apPXr11scIQAAAAAA1qMwDgAAAEvYbDYFBwdbHUae2e12r4wbgO/asmWLU1FckooUKaIRI0aoSZMmFkYGAAAAAIDnYCp1AAAAAAC8WEhIiA4fPpxh+5EjR1SiRAkLIgIAAAAAwPMwYjw3Ui9bHUHWjPk7Pr8iks1mbTzZ8eT3EQAAAAC81P3336++fftq6tSpat68uSRpw4YNGj58uHr06GFxdAAAAAAAeAYK47lQbOu7VocAAAAAAECmpk6dKpvNpl69euny5SsdkgMCAvTEE0/oxRdftDg6AACA/DPGKDEx0a3nvHjxYr6PYbfbZfPkwWwu5u7r5IprJHGdChr/luBJKIwDAAAAAODFAgMDNXPmTE2aNEn79++XJNWoUUNFixa1ODIAAADXSExMVHR0tFvP6YrzrV69WsHBwS6Ixju4+zq56lxcp4LFvyV4EgrjWbDb7Vq9erXVYeQoMTFRXbp0kSStWLFCdrvd4ohyx1viBAAAAABvUbRoUdWvX9/qMAAAgEXWr1+vKVOm6Mcff9SxY8e0bNkyde3a1dFujNHo0aP1xhtvKD4+Xi1atNCcOXNUq1Yt64IGAGSKkf0Fg8J4Fmw2m9f1PrHb7V4XMwAAAAAAAAAg/y5cuKCGDRvqkUceUbdu3TK0T548Wa+88oreeustRUZG6vnnn1d0dLR2797t8QOZXD2QbdasWVq5cmWW7Z07d1b//v3zfR5Pf19dzZXX6dlnn9WWLVuybG/SpIkmTJjgknNxna7dvHnz9NFHH2XZfu+99+rxxx/P93kK2zWSGNlfUCiMAwAAAAAAIM+sWO/1WqSP0RvilbxjtA0Az9OxY0d17Ngx0zZjjF5++WU999xzjhlI3377bYWFhWn58uV64IEH3Blqnrl6INuwYcOyLYwPGzbMZecqTFx5naZPn65WrVpl245r48rr9NRTT2VbGH/qqadcch7AVSiMAwAAAAAAIM+sWO81v9KKQZ7OG0bbAPAusbGxOn78uNq3b+/YFhoaqmbNmmnjxo1ZFsaTkpKUlJTkeJyQkFDgsbrL+vXrMy28rl+/3oJokBmukXfgOhUMV47s//zzzzVz5sws25966indeeed+T6PN4zspzAOAAAAAAAA+CBvGdUvMbIfKGjHjx+XJIWFhTltDwsLc7RlZtKkSRo7dmyBxgYAyMiVI/vvvffebAvj9957r0vO4w28rjA+ZsyYDIm4du3a2rNnj0URAQAAAAAAFG6zWsUryN9YHUamjJGSU6/8HugneWodMynFpv7rS7r0mN44ql9iZD/gSUaOHKkhQ4Y4HickJCg8PNzCiFxr9erVTv+fZISr5+EaeQeuk+djZP8VXlcYl6R69erp66+/djwuUsQrXwYAAHCH1MtWR5A9Y/6O0a+I535T6+nvIwAAsFSQv5Hd3+oosuYdpUvP7FiAgsfI/oLFyP4rKlSoIEmKi4tTxYoVHdvj4uLUqFGjLJ8XFBSkoKCggg7PI7hqymIUHK6Rd+A6wZN5ZUW5SJEijkQOAACQnWJb37U6BAAAkEfeUiSiQARvktIpxbO/CTSSUv7/d39Jnvpnelny/8S1vUAY2V+wGNl/RWRkpCpUqKCYmBhHITwhIUGbN2/WE088YW1wAAC3YGS/Z38cztJvv/2mSpUqyW63KyoqSpMmTVLVqlUz3TcpKUlJSUmOxwkJCe4KEwAAAAAAXANvLBJRIILHKyLP/yYwwOoAAO92/vx57du3z/E4NjZW27dvV+nSpVW1alUNGjRI48ePV61atRQZGannn39elSpVUteuXa0LGgBgicI6st/TPw5n0KxZMy1atEi1a9fWsWPHNHbsWN166636+eefVaJEiQz7T5o0KcOa5AAAwLfZ7Xav+XCXmJjo+CJ9xYoVstvtFkeUM2+IEQAAAPBGT0sKtDqIbBhJl/7/9wB57sD+ZEkvWR2EBbZs2aK2bds6HqetDd67d28tWrRII0aM0IULF/T4448rPj5eLVu21KpVq7jHAwAUGl5XGO/YsaPj9wYNGqhZs2aKiIjQ+++/r759+2bYf+TIkY4PANKVEePh4eFuiRUAAFjDZrN55Ugou93ulXEDAFCQLtz0oOTnoV9fGCOlXr7yu18RyVOnKE+9zPIygJcIlBToseXmK7xjtWljdQCWaNOmjYzJ+rXbbDaNGzdO48aNc2NUAAB4Dg+9s8y9kiVL6rrrrnOaIia9oKAgBQV5x8c1AAAAAABwFb8ikr8nz6/syWM7AQAAAABp/KwOIL/Onz+v/fv3q2LFilaHAgAAAAAAAAAAAADwQF5XGB82bJjWrVungwcP6rvvvtM999wjf39/9ejRw+rQAAAAAAAAAAAAAAAeyOumUj969Kh69OihU6dOqVy5cmrZsqU2bdqkcuXKWR0aAAAAAAAAAAAAAMADeV1hfMmSJVaHAAAAAAAAAAAAAADwIl43lToAAAAAAHC99evXq1OnTqpUqZJsNpuWL1/u1G6M0ahRo1SxYkUFBwerffv2+u2336wJFgAAAACAPKIwDgAAAAAAdOHCBTVs2FCzZs3KtH3y5Ml65ZVXNHfuXG3evFnFihVTdHS0EhMT3RwpAAAAAAB553VTqQMAAAAAANfr2LGjOnbsmGmbMUYvv/yynnvuOXXp0kWS9PbbbyssLEzLly/XAw88kOnzkpKSlJSU5HickJDg+sABAAAAAMgFRowDAAAAAIBsxcbG6vjx42rfvr1jW2hoqJo1a6aNGzdm+bxJkyYpNDTU8RMeHu6OcAEAAAAAyIAR4wAAAAAAIFvHjx+XJIWFhTltDwsLc7RlZuTIkRoyZIjjcUJCAsVxH2KMcfyelGJhID4i/XuY/r0FAAAA4BoUxgEAAAAAQIEICgpSUFCQ1WGggKSfJr//+lIWRuJ7kpKSVLRoUavDAAAAAHwKhXEAAAAAAJCtChUqSJLi4uJUsWJFx/a4uDg1atTIoqgA5MRp5Pll6+LwKeneR0b2AwAAeBcK4wAAAAAAIFuRkZGqUKGCYmJiHIXwhIQEbd68WU888YTLz+dUbEq55PLjFzrp3kNXFvLSzwYwq9UZBfm77NCFUlLK3yPvXTXTQvpR/f6fcIFcjZH9AAAA3oXCOAAAAAAA0Pnz57Vv3z7H49jYWG3fvl2lS5dW1apVNWjQII0fP161atVSZGSknn/+eVWqVEldu3Z1eSzpi3nFtr3n8uMXZq4s5NlsNsfvQf6Snbqry6R/b+H70ndYSb6yxapQfEZyut8Z2e9+xhglJiZaHUaupI/TW2K22+3kCQC4RhTGAQAAAACAtmzZorZt2zoeDxkyRJLUu3dvLVq0SCNGjNCFCxf0+OOPKz4+Xi1bttSqVatkt9utChlADtKPPE/plMI3ga5w+e/R9wUxsv8llxwR6TGy3/0SExMVHR1tdRh51qVLF6tDyJXVq1crODg438fxlg4M3th5QaIDA+Cp+DgMAAAAAADUpk2bbEfV2Ww2jRs3TuPGjSvwWNIXmy7c2FPyDyjwc/q0lEuOkfeuKuTBOzh9IV9EfBPoYhQ8AHgzb+zA4C2dFyQ6MHgLOjAUPnwcBgAAAAAAHsXpyyn/AArjLsQXf4DnSd9h5WlJgdaF4jOS9ffoezoEWevCTQ9Kfh5chjBGSr185Xe/IpKn5snUyyq29V2ro4AF6MBQsOjA4Plc3XnBgzMSAAAAAAAAAPi29F/2BkoKlIcW5rzK3zOg0CHIYn5FvKCDG91RZrWKV5B/1jMHWckYKTn1yu+Bfp7bd0GSklJs6r++pNVhwAJ0YCg4ruq8kIbCOAAAAAAAAAAAQCEV5G9k97c6iqy5riRW0Aq2c0FKpxTPreoZSSn//7u/5NF9vC5L/p948B88CpSn/hMCAAAAAAAAAAAAIF2p6HlyVc/TJ4dwE09eFsVIuvT/vwfIc/svpF8SxdU8+Z8QAAAAAAAAAAAAAHgFT18WJcjqAHKl4GZf8CuwIwMAAAAAAAAAAAAA4AEojAMAAAAAAAAAAAAAfBpTqQMAAAAAACBfklJsKsgpD/PDGCk59crvgX6SzUNntrzyHgKA9zMmXT5IuZT1jsi9dO+j0/sLAMgTCuMAAAAAAMBzpV62OoKsGfN3fH5FPLfi6ob3sP/6kgV+DgCAd0hKSnL8XmzbexZG4puSkpJUtGjRfB8nfYE9KSXfh4Oc30c6MACeicI4AAAAAADwWMW2vmt1CIBv8OA+JpKuTDiQVlDwl+Sh/Uw8/n0EgFxK34Gh//pSFkbimwqiAwM5yEXSvY90YCh8KIwDAAAAAAAgz+x2u1avXm11GDlKTExUly5dJEkrVqyQ3W63OKKcFUSM/p/4u/yYAJCZoKAgx+8Xbuwp+QdYGI2PSLnkGH2f/v2F70vfgYFc7noF0YEh+cqWfB+zMEtO97urOy9QGAcAAAAAAB6FgmvBcWWMNptNwcHBLjueO9jtdq+LGYWLp3+ZbiSlrXQcIM8d2J+c8y4oQLb0S4v4B1AYdzGbi5ZuSV9gn9XqjIKoueZbUsrfo+/pwFC4pO/A8JKFcfgiV3VeSENhHAAAAAAAeBQKroBreEsnE4mOJmn4Mh2Au6QvsAf5S3YK4y5VEB0YUjqlUNVzhct/j76nA0Phwz8hAAAAAAAAwAd5YycTiY4mAACkcSqwFxFVPRcriA4MT0sKdMlRC69k/d1Z0NWdF/gnBAAAAAAAAAAWYWR/wfKGGAEA3i19gT1QUqDHLjbiLf5eVsZVnRfSUBgHAAAAAAAAAIswsh8+LfWy1RFkz5i/Y/QrIrm4AOMynv4+AoCXoDAOAAAAAAAAAABcrtjWd60OAbmQlGJT+hGansQYKTn1yu+Bfp7bd0FKex8BeDIK4wAAAAAAAAAAAIVU//UlrQ4BueHJEwcYSSn//7u/5NEziXvy+4gCR2EcAAAAAAAAAAC4hN1u1+rVq60OI1cSExPVpUsXSdKKFSu8Yk16b4gRBcP/E3+rQ0AuJEvy2BkYJF36/98D5Ln9F5IL8NgUxgEAAAAAAAAAgEvYbDavXH/ebrd7ZdzXyls6MHhj5wWJDgyF2UtWB4BsURgHAAAAAAAAAAAoRLyxA0Nh67wg0YGhoHlLnHAdCuMAAAAAAAAAAACAh6EDg3egA0PBcXWMFMYBAAAAAAAAAAAA4BrQgcF7+FkdAAAAAAAAAAAAAAAABYnCOAAAAAAAAAAAAADAp1EYBwAAAAAAAAAAAAD4NArjAAAAAAAAAAAAAACf5rWF8VmzZqlatWqy2+1q1qyZvv/+e6tDAgAAuUQeBwAAAADAGtyTAwAKK68sjC9dulRDhgzR6NGjtXXrVjVs2FDR0dE6ceKE1aEBAIAckMcBAAAAALAG9+QAgMKsiNUBXIvp06frscceU58+fSRJc+fO1WeffaYFCxbomWeesTi6rBljlJiY6NJjpj+eq4+dxm63y2azFcixPZE3XqfCdo0krpO34DohM96axyX+pr1BQVwjievkat74b0niOrkC/5ZgJf6mvQPXyTtwnbwD1wmZ8dZ7cu71vIM3/n9H4jq5Av+WXI/rVDBsxhhjdRB5kZycrKJFi+rDDz9U165dHdt79+6t+Ph4rVixwmn/pKQkJSUlOR4nJCQoPDxcZ8+eVUhIiLvCliRdvHhR0dHRbj2nK6xevVrBwcFWh+E23nidCts1krhO3oLrlHsJCQkKDQ21JD+5U17zuEQuz6/C9v8eb7xGEtfJW3CdPB953PNZ+V7xN+0duE7egevkHbhOuVdYcrk335N749+zVPj+38N18g7eeJ0K2zWSuE55kZc87nVTqf/5559KSUlRWFiY0/awsDAdP348w/6TJk1SaGio4yc8PNxdoQIAgKvkNY9L5HIAADwRa5MCAOB9uCcHABR2Xjdi/I8//lDlypX13XffKSoqyrF9xIgRWrdunTZv3uy0v6f0aJMKZtoDY4zj9QUFBRXIFAXeMPWBK3njdSps10jiOnkLrlPuFZbe6XnN4xK5PL8K2/97Cmp6Pa6Ta3njvyWJ6+SqY/riv6XCksfTLF26VL169dLcuXPVrFkzvfzyy/rggw+0d+9elS9fPtvnWvle8TftHbhO3oHr5B24TrlXWHK5N9+Tc6/nHbzx/zsS18lVx+TfkmtxnXIvL3nc69YYL1u2rPz9/RUXF+e0PS4uThUqVMiwf1BQkIKCgtwVXrZsNluBTCFQtGhRlx+zMOM6eQeuk3fgOuFqec3jErkceVNQ10jiOrkS/5a8A9cJWfHWtUn5m/YOXCfvwHXyDlwnXM2b78m51/MO/H/HO3CdvAPXqWB43VTqgYGBaty4sWJiYhzbUlNTFRMT49TLDQAAeB7yOAAA3i05OVk//vij2rdv79jm5+en9u3ba+PGjRn2T0pKUkJCgtMPAACwBvfkAIDCzutGjEvSkCFD1Lt3bzVp0kRNmzbVyy+/rAsXLjh6qwMAAM9FHgcAwHtltzbpnj17Muw/adIkjR071l3hAQCAHHBPDgAozLyyMH7//ffr5MmTGjVqlI4fP65GjRpp1apVGW7MAQCA5yGPAwBQeIwcOVJDhgxxPE5blxQAAFiDe3IAQGHmlYVxSRowYIAGDBhgdRgAAOAakMcBAPBOeV2b1FPWJQUAAH/jnhwAUFh53RrjAAAAAADAGqxNCgAAAADwVl47YhwAAAAAALgfa5MCAAAAALwRhXEAAAAAAJBrrE0KAAAAAPBGFMYBAAAAAECesDYpAAAAAMDbsMY4AAAAAAAAAAAAAMCnURgHAAAAAAAAAAAAAPg0CuMAAAAAAAAAAAAAAJ9W6NYYN8ZIkhISEiyOBACAv6XlpbQ8hayRywEAnoY8nnvkcQCAJyKX5x65HADgafKSxwtdYfzcuXOSpPDwcIsjAQAgo3Pnzik0NNTqMDwauRwA4KnI4zkjjwMAPBm5PGfkcgCAp8pNHreZQtYNLjU1VX/88YdKlCghm81mdTgukZCQoPDwcB05ckQhISFWh4MscJ28A9fJO/jidTLG6Ny5c6pUqZL8/FjpJDvkcliF6+T5uEbewRevE3k898jjsArXyTtwnbyDL14ncnnukcthBa6Rd+A6eQdfvE55yeOFbsS4n5+fqlSpYnUYBSIkJMRn/oh9GdfJO3CdvIOvXSd6pecOuRxW4zp5Pq6Rd/C160Qezx3yOKzGdfIOXCfv4GvXiVyeO+RyWIlr5B24Tt7B165TbvM43d8AAAAAAAAAAAAAAD6NwjgAAAAAAAAAAAAAwKdRGPcBQUFBGj16tIKCgqwOBdngOnkHrpN34DrB1/A37R24Tp6Pa+QduE7wNfxNeweuk3fgOnkHrhN8DX/Tno9r5B24Tt6hsF8nmzHGWB0EAAAAAAAAAAAAAAAFhRHjAAAAAAAAAAAAAACfRmEcAAAAAAAAAAAAAODTKIwDAAAAAAAAAAAAAHwahXEAAAAAAAAAAAAAgE+jMO4DZs2apWrVqslut6tZs2b6/vvvrQ4J6axfv16dOnVSpUqVZLPZtHz5cqtDQiYmTZqkm2++WSVKlFD58uXVtWtX7d271+qwcJU5c+aoQYMGCgkJUUhIiKKiovTFF19YHRaQL+Rxz0cu93zkce9AHoevIpd7NvK45yOPewfyOHwVedzzkcs9H7ncO5DLr6Aw7uWWLl2qIUOGaPTo0dq6dasaNmyo6OhonThxwurQ8P8uXLighg0batasWVaHgmysW7dO/fv316ZNm/TVV1/p0qVL6tChgy5cuGB1aEinSpUqevHFF/Xjjz9qy5Ytuu2229SlSxft2rXL6tCAa0Ie9w7kcs9HHvcO5HH4InK55yOPez7yuHcgj8MXkce9A7nc85HLvQO5/AqbMcZYHQSuXbNmzXTzzTfrtddekySlpqYqPDxcTz75pJ555hmLo8PVbDabli1bpq5du1odCnJw8uRJlS9fXuvWrVOrVq2sDgfZKF26tKZMmaK+fftaHQqQZ+Rx70Mu9w7kce9BHoe3I5d7F/K4dyCPew/yOLwdedz7kMu9A7ncexTGXM6IcS+WnJysH3/8Ue3bt3ds8/PzU/v27bVx40YLIwO839mzZyVdSQzwTCkpKVqyZIkuXLigqKgoq8MB8ow8DhQc8rjnI4/DF5DLgYJBHvd85HH4AvI4UHDI5Z6vMOfyIlYHgGv3559/KiUlRWFhYU7bw8LCtGfPHouiArxfamqqBg0apBYtWuiGG26wOhxcZefOnYqKilJiYqKKFy+uZcuWqW7dulaHBeQZeRwoGORxz0Yehy8hlwOuRx73bORx+BLyOFAwyOWejVxOYRwAMujfv79+/vlnffvtt1aHgkzUrl1b27dv19mzZ/Xhhx+qd+/eWrduXaFL4ACAzJHHPRt5HACQHfK4ZyOPAwByQi73bORyCuNerWzZsvL391dcXJzT9ri4OFWoUMGiqADvNmDAAH366adav369qlSpYnU4yERgYKBq1qwpSWrcuLF++OEHzZw5U6+//rrFkQF5Qx4HXI887vnI4/Al5HLAtcjjno88Dl9CHgdcj1zu+cjlrDHu1QIDA9W4cWPFxMQ4tqWmpiomJqbQrQkA5JcxRgMGDNCyZcv0zTffKDIy0uqQkEupqalKSkqyOgwgz8jjgOuQx70XeRzejFwOuAZ53HuRx+HNyOOA65DLvVdhzOWMGPdyQ4YMUe/evdWkSRM1bdpUL7/8si5cuKA+ffpYHRr+3/nz57Vv3z7H49jYWG3fvl2lS5dW1apVLYwM6fXv31/vvfeeVqxYoRIlSuj48eOSpNDQUAUHB1scHdKMHDlSHTt2VNWqVXXu3Dm99957Wrt2rVavXm11aMA1IY97B3K55yOPewfyOHwRudzzkcc9H3ncO5DH4YvI496BXO75yOXegVx+hc0YY6wOAvnz2muvacqUKTp+/LgaNWqkV155Rc2aNbM6LPy/tWvXqm3bthm29+7dW4sWLXJ/QMiUzWbLdPvChQv18MMPuzcYZKlv376KiYnRsWPHFBoaqgYNGujpp5/W7bffbnVowDUjj3s+crnnI497B/I4fBW53LORxz0fedw7kMfhq8jjno9c7vnI5d6BXH4FhXEAAAAAAAAAAAAAgE9jjXEAAAAAAAAAAAAAgE+jMA4AAAAAAAAAAAAA8GkUxgEAAAAAAAAAAAAAPo3COAAAAAAAAAAAAADAp1EYBwAAAAAAAAAAAAD4NArjAAAAAAAAAAAAAACfRmEcAAAAAAAAAAAAAODTKIwDAAAAAAAAAAAAAHwahXEgGwcPHpTNZtP27du96thwjzFjxqhRo0ZWhwEAyAa5HNkhlwOAZyOPIzvkcQDwbORxZIc8DqtQGAegtWvXymazKT4+3upQ8sxms2n58uVWhwEAgKXI5QAAeC/yOAAA3os8DngXCuMAkINLly555bEBAMAV5HIAALwXeRwAAO9FHoenoTAOSEpNTdXkyZNVs2ZNBQUFqWrVqpowYUKG/c6cOaMHH3xQ5cqVU3BwsGrVqqWFCxfm6hzff/+9brzxRtntdjVp0kTbtm1zak9JSVHfvn0VGRmp4OBg1a5dWzNnznS0r1+/XgEBATp+/LjT8wYNGqRbb71VknTo0CF16tRJpUqVUrFixVSvXj19/vnn2cZ18OBBtW3bVpJUqlQp2Ww2Pfzww5KkpKQkDRw4UOXLl5fdblfLli31ww8/5Or1pvWUi4mJUZMmTVS0aFE1b95ce/fuddpvxYoVuummm2S321W9enWNHTtWly9fliSNGzdOlSpV0qlTpxz733XXXWrbtq1SU1NVrVo1SdI999wjm83meJyTOXPmqEaNGgoMDFTt2rX1zjvvOLXbbDbNmTNHnTt3VrFixRx/Cy+++KLCwsJUokQJ9e3bV4mJiRmO/eabb6pOnTqy2+26/vrrNXv2bEdb2hQ/S5cuVevWrWW32/Xuu+/mKmYAQPbI5eTy9MjlAOBdyOPk8fTI4wDgXcjj5PH0yOPweAaAGTFihClVqpRZtGiR2bdvn/nf//5n3njjDRMbG2skmW3bthljjOnfv79p1KiR+eGHH0xsbKz56quvzMqVK3M8/rlz50y5cuVMz549zc8//2w++eQTU716dadjJycnm1GjRpkffvjBHDhwwCxevNgULVrULF261HGc6667zkyePNnxODk52ZQtW9YsWLDAGGPMXXfdZW6//XazY8cOs3//fvPJJ5+YdevWZRvb5cuXzUcffWQkmb1795pjx46Z+Ph4Y4wxAwcONJUqVTKff/652bVrl+ndu7cpVaqUOXXqVI6vec2aNUaSadasmVm7dq3ZtWuXufXWW03z5s0d+6xfv96EhISYRYsWmf3795svv/zSVKtWzYwZM8YRW1RUlOnatasxxpjXXnvNlCxZ0hw6dMgYY8yJEyeMJLNw4UJz7Ngxc+LEiRzj+vjjj01AQICZNWuW2bt3r5k2bZrx9/c333zzjWMfSaZ8+fJmwYIFZv/+/ebQoUNm6dKlJigoyLz55ptmz5495tlnnzUlSpQwDRs2dDxv8eLFpmLFiuajjz4yBw4cMB999JEpXbq0WbRokTHGOP6eqlWr5tjnjz/+yDFmAEDOyOXkcnI5AHgv8jh5nDwOAN6LPE4eJ4/Dm1AYR6GXkJBggoKCzBtvvJGh7erk3alTJ9OnT588n+P11183ZcqUMRcvXnRsmzNnjtOxM9O/f39z7733Oh6/9NJLpk6dOo7HH330kSlevLg5f/68McaY+vXrOxJfXqQl2jNnzji2nT9/3gQEBJh3333XsS05OdlUqlTJ6QNETsf8+uuvHds+++wzI8nxPrRr185MnDjR6XnvvPOOqVixouPx/v37TYkSJczTTz9tgoODneIx5kqiXbZsWa5fa/Pmzc1jjz3mtO2+++4zd955p9MxBw0a5LRPVFSU6devn9O2Zs2aOSXvGjVqmPfee89pnxdeeMFERUUZY/7+e3r55ZdzHS8AIGfkcnI5uRwAvBd5nDxOHgcA70UeJ4+Tx+FtmEodhd4vv/yipKQktWvXLsd9n3jiCS1ZskSNGjXSiBEj9N133+X6HA0aNJDdbndsi4qKyrDfrFmz1LhxY5UrV07FixfXvHnzdPjwYUf7ww8/rH379mnTpk2SpEWLFql79+4qVqyYJGngwIEaP368WrRoodGjR2vHjh25ii8z+/fv16VLl9SiRQvHtoCAADVt2lS//PJLro/ToEEDx+8VK1aUJJ04cUKS9NNPP2ncuHEqXry44+exxx7TsWPH9Ndff0mSqlevrqlTp+qll15S586d1bNnz2t+TdKVa5H+NUlSixYtMrymJk2aZHhes2bNnLalv4YXLlzQ/v371bdvX6fXM378eO3fvz/bYwMA8odcnjlyObkcALwBeTxz5HHyOAB4A/J45sjj5HF4LgrjKPSCg4NzvW/Hjh116NAhDR48WH/88YfatWunYcOGuSSOJUuWaNiwYerbt6++/PJLbd++XX369FFycrJjn/Lly6tTp05auHCh4uLi9MUXX+iRRx5xtD/66KM6cOCA/vnPf2rnzp1q0qSJXn31VZfEd60CAgIcv9tsNklX1p2RpPPnz2vs2LHavn2742fnzp367bffnD7orF+/Xv7+/jp48KBjjZSClvaBKLfOnz8vSXrjjTecXs/PP//s+LB1rccGAGSPXF6wyOXkcgAoSOTxgkUeJ48DQEEijxcs8jh5HK5HYRyFXq1atRQcHKyYmJhc7V+uXDn17t1bixcv1ssvv6x58+bl+Jw6depox44dSkxMdGy7+n/oGzZsUPPmzdWvXz/deOONqlmzZobeUNKVBL106VLNmzdPNWrUyNBDKzw8XP/+97/18ccfa+jQoXrjjTdyjC8wMFCSlJKS4thWo0YNBQYGasOGDY5tly5d0g8//KC6devmeMzcuOmmm7R3717VrFkzw4+f35X/PS1dulQff/yx1q5dq8OHD+uFF15wOkZAQIBT3DmpU6eO02uSrrz3Ob2mOnXqaPPmzU7b0l/DsLAwVapUSQcOHMjwWiIjI3MdHwAg78jl5HJyOQB4L/I4eZw8DgDeizxOHiePw+tYPZc74AnGjBljSpUqZd566y2zb98+s3HjRvPmm29mWAfl+eefN8uXLze//fab+fnnn83dd99tmjZtmuPxz507Z8qWLWseeughs2vXLvPZZ5+ZmjVrOh175syZJiQkxKxatcrs3bvXPPfccyYkJMRpnQ1jjElJSTHh4eEmMDDQvPjii05tTz31lFm1apU5cOCA+fHHH02zZs1M9+7dc4zv6NGjxmazmUWLFpkTJ06Yc+fOOY5XqVIl88UXX5hdu3aZ3r17m1KlSpnTp0/neMzM1lbZtm2bkWRiY2ONMcasWrXKFClSxIwZM8b8/PPPZvfu3ea///2vefbZZ40xxhw5csSUKlXKvPLKK077b9y40XHMWrVqmSeeeMIcO3YsV3EtW7bMBAQEmNmzZ5tff/3VTJs2zfj7+5s1a9Y49lEma6ssWbLE2O12s2DBArN3714zatQoU6JECafr88Ybb5jg4GAzc+ZMs3fvXrNjxw6zYMECM23aNGNMxnV1AACuQy4nl5PLAcB7kcfJ4+RxAPBe5HHyOHkc3oTCOGCuJMTx48ebiIgIExAQYKpWrWomTpyY4X+2L7zwgqlTp44JDg42pUuXNl26dDEHDhzI1Tk2btxoGjZsaAIDA02jRo3MRx995HTsxMRE8/DDD5vQ0FBTsmRJ88QTT5hnnnkmQ/I25sqHCH9/f/PHH384bR8wYICpUaOGCQoKMuXKlTP//Oc/zZ9//pmr+MaNG2cqVKhgbDab6d27tzHGmIsXL5onn3zSlC1b1gQFBZkWLVqY77//PlfHy03yNuZKQm7evLkJDg42ISEhpmnTpmbevHkmNTXVtGvXzkRHR5vU1FTH/k8++aSpUaOG4wPGypUrTc2aNU2RIkVMRERErmKbPXu2qV69ugkICDDXXXedefvtt53aM0vexhgzYcIEU7ZsWVO8eHHTu3dvM2LEiAzX59133zWNGjUygYGBplSpUqZVq1bm448/NsaQvAGgIJHLyeXpkcsBwLuQx8nj6ZHHAcC7kMfJ4+mRx+HpbMYY4/px6AAKUt++fXXy5EmtXLnS6lAAAMA1IJcDAOC9yOMAAHgv8jhQuBWxOgAAuXf27Fnt3LlT7733HokbAAAvRC4HAMB7kccBAPBe5HEAkuRndQCAL5g4caKKFy+e6U/Hjh1ddp4uXbqoQ4cO+ve//63bb78918/797//nWV8//73v68ploI4pivUq1cvy7jeffddy+ICAHg2cjm5HADgvcjj5HEAgPcij5PHAXdiKnXABU6fPq3Tp09n2hYcHKzKlSu7OSJnJ06cUEJCQqZtISEhKl++vEcc0xUOHTqkS5cuZdoWFhamEiVKuDkiAIA3IJe75piuQC4HAOQVedw1x3QF8jgAIK/I4645piuQx1EYUBgHAAAAAAAAAAAAAPg0plIHAAAAAAAAAAAAAPg0CuMAAAAAAAAAAAAAAJ9GYRwAAAAAAAAAAAAA4NMojAMAAAAAAAAAAAAAfBqFcQAAAAAAAAAAAACAT6MwDgAAAAAAAAAAAADwaRTGAQAAAAAAAAAAAAA+jcI4AAAAAAAAAAAAAMCnURgHAAAAAAAAAAAAAPg0CuMAAAAAAAAAAAAAAJ9GYRwAAAAAAAAAAAAA4NMojAMAAAAAAAAAAAAAfBqFcQAAAAAAAAAAAACAT6MwDmSjTZs2atOmjdVhuMyiRYtks9l08OBBq0MBAMCjkPPhCjabTWPGjLE6DADwWb6Y38gdAIDCirzu2XztexIgDYVxAF5h9+7dGjNmjE99UAIAABl5Ss7/7rvvNGbMGMXHx1saBwAAnmrixIlavny51WEAAIAc/PHHHxozZoy2b99udSiA5SiMA/AKu3fv1tixYy3/khwAABQsT8n53333ncaOHUthHACALFAYBwDAO/zxxx8aO3YshXFAFMZRyF24cKHAz2GM0cWLFwv8PJKUmpqqxMREt5xLyt/75473HgCANOT8/CHnAwDyw915KyfuzE3uPFdiYqJSU1Ov6bmedo0AAJ7L03IGeR1AXlAYh8/Ytm2bOnbsqJCQEBUvXlzt2rXTpk2bHO1pa5asW7dO/fr1U/ny5VWlShVH+7x581SjRg0FBweradOm+t///ndNcVSrVk133323Vq9erSZNmig4OFivv/66JCk+Pl6DBg1SeHi4goKCVLNmTb300ksZEtyFCxc0dOhQx361a9fW1KlTZYxx2s9ms2nAgAF69913Va9ePQUFBWnVqlWSpF27dum2225TcHCwqlSpovHjx2eZSL/44gvdeuutKlasmEqUKKG77rpLu3btctrn4YcfVvHixbV//37deeedKlGihB588MFcvSfZvfeHDh1Sv379VLt2bQUHB6tMmTK67777nEaJLVq0SPfdd58kqW3btrLZbLLZbFq7dm2eXkNO1q5dK5vNpvfff19jx45V5cqVVaJECf3jH//Q2bNnlZSUpEGDBql8+fIqXry4+vTpo6SkpAzHWbx4sRo3bqzg4GCVLl1aDzzwgI4cOeK0z//+9z/dd999qlq1qoKCghQeHq7BgwdnKKikve+///67unbtquLFi6tcuXIaNmyYUlJS8vT6AMBXkPPJ+fnN+ZL06quvql69eipatKhKlSqlJk2a6L333pMkjRkzRsOHD5ckRUZGOuJIizUpKUmDBw9WuXLlVKJECXXu3FlHjx7NcwwA4G3GjBkjm82mPXv2qHv37goJCVGZMmX01FNPOX1Bnl3eyimPp8lLfstNzLt371bPnj1VqlQptWzZ0tGem/s3Sfrggw8c+5UtW1YPPfSQfv/9d6d9ssuheckdv//+ux555BGFhYUpKChI9erV04IFC5z2Sbt/XbJkiZ577jlVrlxZRYsWVUJCQq7el+yu0dSpU9W8eXOVKVNGwcHBaty4sT788MMMz79w4YLeeustR558+OGH8/QaciPt89batWsdn7fq16/v+Gzw8ccfq379+rLb7WrcuLG2bduW4Rh79uzRP/7xD5UuXVp2u11NmjTRypUrnfY5ffq0hg0bpvr166t48eIKCQlRx44d9dNPPzntl/57gwkTJqhKlSqy2+1q166d9u3bl+fXBwBWIq/7Tl7/6quv1LJlS5UsWVLFixdX7dq19Z///Mdx7JtvvlmS1KdPH0feXrRokeP5rv6ehLwNT1bE6gAAV9i1a5duvfVWhYSEaMSIEQoICNDrr7+uNm3aaN26dWrWrJlj3379+qlcuXIaNWqUo4fX/Pnz9a9//UvNmzfXoEGDdODAAXXu3FmlS5dWeHh4nuPZu3evevTooX/961967LHHVLt2bf31119q3bq1fv/9d/3rX/9S1apV9d1332nkyJE6duyYXn75ZUlXRpt17txZa9asUd++fdWoUSOtXr1aw4cP1++//64ZM2Y4neubb77R+++/rwEDBqhs2bKqVq2ajh8/rrZt2+ry5ct65plnVKxYMc2bN0/BwcEZYn3nnXfUu3dvRUdH66WXXtJff/2lOXPmqGXLltq2bZuqVavm2Pfy5cuKjo5Wy5YtNXXqVBUtWjRP70tm7/0PP/yg7777Tg888ICqVKmigwcPas6cOWrTpo12796tokWLqlWrVho4cKBeeeUV/ec//1GdOnUkyfHfvLyG3Jg0aZKCg4P1zDPPaN++fXr11VcVEBAgPz8/nTlzRmPGjNGmTZu0aNEiRUZGatSoUY7nTpgwQc8//7y6d++uRx99VCdPntSrr76qVq1aadu2bSpZsqSkKx++/vrrLz3xxBMqU6aMvv/+e7366qs6evSoPvjgA6d4UlJSFB0drWbNmmnq1Kn6+uuvNW3aNNWoUUNPPPFEnl4bAHg7cj453xU5/4033tDAgQP1j3/8w/Glz44dO7R582b17NlT3bp106+//qr//ve/mjFjhsqWLStJKleunCTp0Ucf1eLFi9WzZ081b95c33zzje666648vUcA4M26d++uatWqadKkSdq0aZNeeeUVnTlzRm+//bZjn8zyVm7zeF7yW27dd999qlWrliZOnOjogJbb+7dFixapT58+uvnmmzVp0iTFxcVp5syZ2rBhg9N+UtY5NLe5Iy4uTrfccoujCFGuXDl98cUX6tu3rxISEjRo0CCn/V944QUFBgZq2LBhSkpKUmBgYK7fk8yukSTNnDlTnTt31oMPPqjk5GQtWbJE9913nz799FNHzO+8844effRRNW3aVI8//rgkqUaNGtf0GnKyb98+9ezZU//617/00EMPaerUqerUqZPmzp2r//znP+rXr5+kK/fy3bt31969e+Xnd2Us0K5du9SiRQtVrlzZ8bf0/vvvq2vXrvroo490zz33SJIOHDig5cuX67777lNkZKTi4uL0+uuvq3Xr1tq9e7cqVarkFNOLL74oPz8/DRs2TGfPntXkyZP14IMPavPmzXl6bQDgCcjr3p3Xd+3apbvvvlsNGjTQuHHjFBQUpH379mnDhg2SrtxPjxs3TqNGjdLjjz+uW2+9VZLUvHlzSa7/noS8DY9nAB/QtWtXExgYaPbv3+/Y9scff5gSJUqYVq1aGWOMWbhwoZFkWrZsaS5fvuzYLzk52ZQvX940atTIJCUlObbPmzfPSDKtW7fOUywRERFGklm1apXT9hdeeMEUK1bM/Prrr07bn3nmGePv728OHz5sjDFm+fLlRpIZP368037/+Mc/jM1mM/v27XNsk2T8/PzMrl27nPYdNGiQkWQ2b97s2HbixAkTGhpqJJnY2FhjjDHnzp0zJUuWNI899pjT848fP25CQ0Odtvfu3dtIMs8880ye3g9jsn7vjTHmr7/+yrD/xo0bjSTz9ttvO7Z98MEHRpJZs2aN0755eQ05WbNmjZFkbrjhBpOcnOzY3qNHD2Oz2UzHjh2d9o+KijIRERGOxwcPHjT+/v5mwoQJTvvt3LnTFClSxGl7Zq970qRJxmazmUOHDjm2pb3v48aNc9r3xhtvNI0bN871awMAX0HOJ+e7Iud36dLF1KtXL9t9pkyZ4vQeptm+fbuRZPr16+e0vWfPnkaSGT16dK7jAABvM3r0aCPJdO7c2Wl7v379jCTz008/GWOyzlu5yePG5D6/5SXmHj16OG3P7f1b2ueHG264wVy8eNGx36effmokmVGjRjm2ZZVD85I7+vbtaypWrGj+/PNPp30feOABExoa6sinafev1atXzzTH5iSra2RMxpydnJxsbrjhBnPbbbc5bS9WrJjp3bt3hufn9jXkRtrnre+++86xbfXq1UaSCQ4Odrp/fv311zN8hmjXrp2pX7++SUxMdGxLTU01zZs3N7Vq1XJsS0xMNCkpKU7njo2NNUFBQU7342nve506dZw+T86cOdNIMjt37sz1awMAq5HXfSOvz5gxw0gyJ0+ezHKfH374wUgyCxcudNpeUN+TkLfhyZhKHV4vJSVFX375pbp27arq1as7tlesWFE9e/bUt99+6zTlyGOPPSZ/f3/H4y1btujEiRP697//7dQD6+GHH1ZoaOg1xRQZGano6GinbR988IFuvfVWlSpVSn/++afjp3379kpJSdH69eslSZ9//rn8/f01cOBAp+cPHTpUxhh98cUXTttbt26tunXrOm37/PPPdcstt6hp06aObeXKlcswDepXX32l+Ph49ejRwykmf39/NWvWTGvWrMnw2vIzQvnq916SU8/AS5cu6dSpU6pZs6ZKliyprVu35njMa3kNOenVq5cCAgIcj5s1ayZjjB555BGn/Zo1a6YjR47o8uXLkq5MBZOamqru3bs7xVKhQgXVqlXLKZb0r/vChQv6888/1bx5cxljMp1C5t///rfT41tvvVUHDhzI82sDAG9GzifnuyrnlyxZUkePHtUPP/yQ+xf1/z7//HNJynDd8jr6DQC8Wf/+/Z0eP/nkk5L+/n+klDFv5SWP5za/5cXV91S5vX9L+/zQr18/2e12x/PvuusuXX/99frss88ynOvqHJrb3GGM0UcffaROnTrJGOMUV3R0tM6ePZshZ/bu3fuaR9xl9tlCcs7ZZ86c0dmzZ3XrrbfmKl9fy2vISd26dRUVFeV4nDYC8bbbblPVqlUzbE+7Vz59+rS++eYbde/eXefOnXPEcerUKUVHR+u3335zTJsbFBTkGK2WkpKiU6dOOaaizSzePn36OH2eTBt9x306AG9EXvfuvJ42wn3FihV5np6+IL4nIW/D0zGVOrzeyZMn9ddff6l27doZ2urUqaPU1FSndUQiIyOd9jl06JAkqVatWk7bAwICnJJ6Xlx9Dkn67bfftGPHDscUnFc7ceKEI55KlSqpRIkSTu1pU4imxZvduQ4dOuQ0lWyaq9+j3377TdKVpJSZkJAQp8dFihRxWqM1rzKL9eLFi5o0aZIWLlyo33//3WlN1bNnz+Z4zLy+htxIn6AlOT4EXD11TGhoqFJTU3X27FmVKVNGv/32m4wxGf6W0qQvth8+fFijRo3SypUrdebMGaf9rn7ddrs9w99NqVKlMjwPAHwdOZ+cL7km5z/99NP6+uuv1bRpU9WsWVMdOnRQz5491aJFixyfe+jQIfn5+Tmmi02T2d8lAPiqq3NpjRo15Ofnp4MHDzq2XZ0LcpvH69Wrl+v8lhdXx5Pb+7e0fJzZua+//np9++23Ttsyy6G5zR0nT55UfHy85s2bp3nz5mUaV9rniKxeV15k9dxPP/1U48eP1/bt25WUlOTYbrPZcjzmtbyGnOTlHl2S41553759Msbo+eef1/PPP59lLJUrV1Zqaqpmzpyp2bNnKzY2VikpKY59ypQpk2NMpUqVcjo3AHgT8voV3prX77//fr355pt69NFH9cwzz6hdu3bq1q2b/vGPfziKx1kpiO9JyNvwdBTGUejkZ+2S/JwjNTVVt99+u0aMGJHpc6677jqXnSu30nqQvfPOO6pQoUKG9iJFnP8Xkb4n1rXILNYnn3xSCxcu1KBBgxQVFaXQ0FDZbDY98MADuerhltfXkBtXj3DLaXvaF/upqamy2Wz64osvMt23ePHikq70Yrv99tt1+vRpPf3007r++utVrFgx/f7773r44YczvO6szgsAyB453zkmiZyfpk6dOtq7d68+/fRTrVq1Sh999JFmz56tUaNGaezYsbk+DgDgiswKpu7Iw3lxdTy5vX/Lq/zk0LRc99BDD6l3796Z7tOgQQOnx/l5nzN77v/+9z917txZrVq10uzZs1WxYkUFBARo4cKFeu+993I85rW8hpzk5x5dkoYNG5Zhhp80NWvWlCRNnDhRzz//vB555BG98MILKl26tPz8/DRo0KBMP6fkdG4A8Gbk9b95Q14PDg7W+vXrtWbNGn322WdatWqVli5dqttuu01ffvml279bJm/D01EYh9crV66cihYtqr1792Zo27Nnj/z8/BQeHp7lVJkRERGSrvQqSz8C6dKlS4qNjVXDhg1dEmeNGjV0/vx5tW/fPtv9IiIi9PXXX+vcuXNOI8j27NnjFG9Ox0gbVZXe1e9RWq+28uXL5xhXQfnwww/Vu3dvTZs2zbEtMTFR8fHxTvtl1TPdE15D+liMMYqMjMy26LFz5079+uuveuutt9SrVy/H9q+++sodYQKA1yLnZ34Mcv61KVasmO6//37df//9Sk5OVrdu3TRhwgSNHDlSdrs9yzgiIiKUmpqq/fv3O40IyOzvEgB81W+//eY0omnfvn1KTU1VtWrVsnxObvO4lPv8lh+5vX9Ly8d79+7NMGvJ3r17c52vc5M7ypUrpxIlSiglJcWyfP3RRx/Jbrdr9erVCgoKcmxfuHBhhn0zy5We8BrSpI10CwgIyDGWDz/8UG3bttX8+fOdtsfHx6ts2bIFFiMAeALy+t/xeGte9/PzU7t27dSuXTtNnz5dEydO1LPPPqs1a9aoffv22d7fSgX/PUlukLfhLqwxDq/n7++vDh06aMWKFU7Tu8TFxem9995Ty5Yts51as0mTJipXrpzmzp2r5ORkx/ZFixZl+KI2P7p3766NGzdq9erVGdri4+Md61TfeeedSklJ0Wuvvea0z4wZM2Sz2dSxY8ccz3XnnXdq06ZN+v777x3bTp48qXfffddpv+joaIWEhGjixIm6dOlShuOcPHkyV68tP/z9/TP0zHr11Vedpj+Rrnx5LSnDNfGE15CmW7du8vf319ixYzO8JmOMTp06JenvHmrp9zHGaObMmW6LFQC8ETk/I3L+3/LyGtJycprAwEDVrVtXxhjHsbOKI+26vPLKK07bX3755VyfHwC83axZs5wev/rqq5KUbe7KSx7PbX7Lj9zevzVp0kTly5fX3LlznaYV/+KLL/TLL7/orrvuyvFcuc0d/v7+uvfee/XRRx/p559/znAcd+Vrm83mlJ8PHjyo5cuXZ9i3WLFiGfKkJ7yGNOXLl1ebNm30+uuv69ixY9nGktnnlA8++MCxlikA+DLyunfn9dOnT2fY1qhRI0lyvMas7m/d9T1JbpC34S6MGIdPGD9+vL766iu1bNlS/fr1U5EiRfT6668rKSlJkydPzva5AQEBGj9+vP71r3/ptttu0/3336/Y2FgtXLjwmtfRyMzw4cO1cuVK3X333Xr44YfVuHFjXbhwQTt37tSHH36ogwcPqmzZsurUqZPatm2rZ599VgcPHlTDhg315ZdfasWKFRo0aFCGtUsyM2LECL3zzju644479NRTT6lYsWKaN2+eIiIitGPHDsd+ISEhmjNnjv75z3/qpptu0gMPPKBy5crp8OHD+uyzz9SiRYsMX9a72t1336133nlHoaGhqlu3rjZu3Kivv/46w1ogjRo1kr+/v1566SWdPXtWQUFBuu2221S+fHnLX0OaGjVqaPz48Ro5cqQOHjyorl27qkSJEoqNjdWyZcv0+OOPa9iwYbr++utVo0YNDRs2TL///rtCQkL00UcfsaYJAOQCOd8ZOf/aXkOHDh1UoUIFtWjRQmFhYfrll1/02muv6a677nKM3m/cuLEk6dlnn9UDDzyggIAAderUSY0aNVKPHj00e/ZsnT17Vs2bN1dMTIz27dvn2jcMADxYbGysOnfurDvuuEMbN27U4sWL1bNnzxxHFeU2j+c2v+VHbu/fAgIC9NJLL6lPnz5q3bq1evToobi4OM2cOVPVqlXT4MGDczxXXnLHiy++qDVr1qhZs2Z67LHHVLduXZ0+fVpbt27V119/nemX36501113afr06brjjjvUs2dPnThxQrNmzVLNmjUzvPeNGzfW119/renTp6tSpUqKjIxUs2bNLH8N6c2aNUstW7ZU/fr19dhjj6l69eqKi4vTxo0bdfToUf3000+SrnxOGTdunPr06aPmzZtr586devfdd136GREAPBV53bvz+rhx47R+/XrdddddioiI0IkTJzR79mxVqVJFLVu2dLw/JUuW1Ny5c1WiRAkVK1ZMzZo1U2RkpFu+J8kt8jbcwgA+YuvWrSY6OtoUL17cFC1a1LRt29Z89913jvaFCxcaSeaHH37I9PmzZ882kZGRJigoyDRp0sSsX7/etG7d2rRu3TpPcURERJi77ror07Zz586ZkSNHmpo1a5rAwEBTtmxZ07x5czN16lSTnJzstN/gwYNNpUqVTEBAgKlVq5aZMmWKSU1NdTqeJNO/f/9Mz7Vjxw7TunVrY7fbTeXKlc0LL7xg5s+fbySZ2NhYp33XrFljoqOjTWhoqLHb7aZGjRrm4YcfNlu2bHHs07t3b1OsWLE8vRdpsnvvz5w5Y/r06WPKli1rihcvbqKjo82ePXtMRESE6d27t9O+b7zxhqlevbrx9/c3ksyaNWvy9BpysmbNGiPJfPDBB7mKf/To0UaSOXnypNP2jz76yLRs2dIUK1bMFCtWzFx//fWmf//+Zu/evY59du/ebdq3b2+KFy9uypYtax577DHz008/GUlm4cKFjv2yet/Tzg0AhRE53xk5P+85//XXXzetsyS8RAABAABJREFUWrUyZcqUMUFBQaZGjRpm+PDh5uzZs077vfDCC6Zy5crGz8/P6f28ePGiGThwoClTpowpVqyY6dSpkzly5IiRZEaPHp3rOADA26Tdh+zevdv84x//MCVKlDClSpUyAwYMMBcvXnTsl13eyimPp8lLfstNzFfft6XJzf2bMcYsXbrU3HjjjSYoKMiULl3aPPjgg+bo0aNO+2SXQ/OSO+Li4kz//v1NeHi4CQgIMBUqVDDt2rUz8+bNc+yT1f1rbmV3jebPn29q1aplgoKCzPXXX28WLlyY6T3onj17TKtWrUxwcLCR5JTPc/MaciOrz1uZxR8bG2skmSlTpjht379/v+nVq5epUKGCCQgIMJUrVzZ33323+fDDDx37JCYmmqFDh5qKFSua4OBg06JFC7Nx48YMnxGzet/Tzp3+fh4APB153TfyekxMjOnSpYupVKmSCQwMNJUqVTI9evQwv/76q9N+K1asMHXr1jVFihTJkLMK+nsS8jY8ic0YVpcHAAAAAABA9saMGaOxY8fq5MmTrN8IAICXI68DKIxYYxwAAAAAAAAAAAAA4NNYYxzIpZMnTyolJSXL9sDAQJUuXdqNEVnv4sWLOnv2bLb7lC5dWoGBgW6KKHPJyck5rtcSGhqq4OBgN0UEAPBk5PyMyPkAAE9z/vx5nT9/Ptt9ypUrJ39/fzdF5BmOHz+ebXtwcLBCQ0PdFE3W+LwFAEiPvJ45T8nr5G34EgrjQC7dfPPNOnToUJbtrVu31tq1a90XkAdYunSp+vTpk+0+a9asUZs2bdwTUBa+++47tW3bNtt9Fi5cqIcfftg9AQEAPBo5PyNyPgDA00ydOlVjx47Ndp/Y2FhVq1bNPQF5iIoVK2bb3rt3by1atMg9wWSDz1sAgPTI65nzlLxO3oYvYY1xIJc2bNigixcvZtleqlQpNW7c2I0RWe/YsWPatWtXtvs0btxYpUqVclNEmTtz5ox+/PHHbPepV69ejh80AACFAzk/I3I+AMDTHDhwQAcOHMh2n5YtW8put7spIs/w9ddfZ9teqVIl1a1b103RZI3PWwCA9MjrmfOUvE7ehi+hMA4AAAAAAAAAAAAA8GmFbir11NRU/fHHHypRooRsNpvV4QAAIEkyxujcuXOqVKmS/Pz8rA7Ho5HLAQCehjyee+RxAIAnIpfnHrkcAOBp8pLHC11h/I8//lB4eLjVYQAAkKkjR46oSpUqVofh0cjlAABPRR7PGXkcAODJyOU5I5cDADxVbvJ4oSuMlyhRQtKVNyckJMTiaAAAuCIhIUHh4eGOPIWskcsBAJ6GPJ575HEAgCcil+ceuRwA4GnykscLXWE8bXqXkJAQEjcAwOMwDVnOyOUAAE9FHs8ZeRwA4MnI5TkjlwMAPFVu8jgLpgAAAAAAAAAAAAAAfBqFcQAAAAAAAAAAAACAT6MwDgAAAAAAAAAAAADwaRTGAQAAAAAAAAAAAAA+jcI4AAAAAAAAAAAAAMCnWVoYX79+vTp16qRKlSrJZrNp+fLlOT5n7dq1uummmxQUFKSaNWtq0aJFBR4nAAAAAAAAAAAAAMB7WVoYv3Dhgho2bKhZs2blav/Y2Fjdddddatu2rbZv365Bgwbp0Ucf1erVqws4UgAAAAAAAAAAAACAtypi5ck7duyojh075nr/uXPnKjIyUtOmTZMk1alTR99++61mzJih6OjoggoTAACgQLVq1crx+/r16y2MBNnhOnk+rpF34DrB12zYsEEvv/yyBg0apBYtWlgdDgAAyCNyOQAUHl61xvjGjRvVvn17p23R0dHauHFjls9JSkpSQkKC0w8AAICnSF8gyuwxPAPXyfNxjbwD1wm+JjExUdOmTVNcXJymTZumxMREq0MCAAB5QC4HgMLFqwrjx48fV1hYmNO2sLAwJSQk6OLFi5k+Z9KkSQoNDXX8hIeHuyNUAAAAAADg4xYvXqxTp05Jkk6dOqV3333X4oiQlQ0bNui+++7Thg0brA4F8Hpvvvmm2rRpozfffNPqUIB8I5cDQOHiVYXxazFy5EidPXvW8XPkyBHLYjHG6OLFiy79+euvv3TmzBmdOXNGf/31l8uPf/HiRRljLHvPAADwZVmNlGQEpWfhOnk+rpF34DrB1xw9elTvvvuu457ZGKN3331XR48etTgyXI3RgIDrxMfHa/HixUpNTdXixYsVHx9vdUjANSOXA0DhY+ka43lVoUIFxcXFOW2Li4tTSEiIgoODM31OUFCQgoKC3BFejhITE71yLfTVq1dn+f4CgBVY+8mzzJo1S1OmTNHx48fVsGFDvfrqq2ratGmm+7Zp00br1q3LsP3OO+/UZ599VtChepSvvvoqx/bbb7/dTdEgK4sXL86x/aGHHnJTNMjMxIkTc2z/z3/+46ZokJWcit+tWrVivXF4FWOMZsyYkeX2qVOnymazWRAZMpPZaMC+fftaHBUyw72e53v22WeVmpoqSUpNTdVzzz2n1157zeKogLwjlwNA4eRVI8ajoqIUExPjtO2rr75SVFSURREBANyN0R6eZenSpRoyZIhGjx6trVu3qmHDhoqOjtaJEycy3f/jjz/WsWPHHD8///yz/P39dd9997k5cuu98MIL+WqHe8ybNy9f7Sh4q1atylc7AFyLQ4cO6YcfflBKSorT9pSUFP3www86dOiQRZHhaowG9B7c63m+LVu2aOfOnU7bduzYoS1btlgUEXDtyOUAUDhZOmL8/Pnz2rdvn+NxbGystm/frtKlS6tq1aoaOXKkfv/9d7399tuSpH//+9967bXXNGLECD3yyCP65ptv9P7773vNCDO73a7Vq1e79JiJiYnq0qWLJGnFihWy2+0uPb6kAjkmAFwrRnt4lunTp+uxxx5Tnz59JElz587VZ599pgULFuiZZ57JsH/p0qWdHi9ZskRFixYtlIXx559/Ptvi9/PPP+/GaJCVxx9/PNvi9+OPP+7GaJCZO+64I9vi9x133OHGaAAUFhEREbr55pu1ZcsWp+XHbDabbr75ZkVERFgYHdIwGtC7cK/n2VJTUzVmzJhM28aMGaOVK1fKz8+rxmChkEvL5Vu3bnUqjvv7+6tx48bkcgA+q7DP0GPpp5UtW7boxhtv1I033ihJGjJkiG688UaNGjVKknTs2DEdPnzYsX9kZKQ+++wzffXVV2rYsKGmTZumN99802umJ7fZbAoODnbpT/qitd1ud/nxg4ODC91NojeuBc868CgsGO3hWZKTk/Xjjz+qffv2jm1+fn5q3769Nm7cmKtjzJ8/Xw888ICKFSuW5T5JSUlKSEhw+vEFOU2TzjTqniGnadKZRt16OU2TzjTqniGnadKZRh3exmazqUePHhnuxYwx6tGjR6G7j/ZUjAb0Htzreb6NGzdmeS+WkJCQ63tAwFPYbDYNHjw4y+3kcgC+iBl6LB4x3qZNm2wLeosWLcr0Odu2bSvAqFDYeeNa8KwDj8KA0R6e588//1RKSorCwsKctoeFhWnPnj05Pv/777/Xzz//rPnz52e736RJkzR27Nh8xeqp1q9fn+m6uxSIPAvXyfNxjbwD1wm+xBijhQsXZtq2YMEC3XTTTXw29QARERFq0KCBduzYkaGtQYMGjAb0ENzreYeoqCiFhIRkWhwPDQ1lqUt4pSpVqujBBx/UO++8I2OMbDabHnzwQVWuXNnq0HCVwj7C1VtwnTwfM/R42RrjAIDCi9Eevmf+/PmqX7++mjZtmu1+I0eO1NmzZx0/R44ccVOEAAAAmTt48GCGdXbT7Ny5UwcPHnRvQMhSVgMymHnNc3Cv5x38/PyynEp97NixTKMOr/XQQw+pTJkykqSyZcvqwQcftDgiXI0Rrt4hMTFRkyZNUlxcnCZNmsR18kDM0HOFpSPGAU/kjWvBsw48PJUxxmUfgsqXL6/GjRtr27ZtSk1NdWz39/fXjTfeqPLly+vixYv5Po/dbmc0Qi6VLVtW/v7+iouLc9oeFxenChUqZPvcCxcuaMmSJRo3blyO5wkKClJQUFC+YvVkV4+gZOSkZ+I6eT6ukXfgOsFX5FRUpejqGQ4dOpRtB4ZDhw6pWrVq7g0KGbDOr/do0qSJqlevrgMHDji2Va9eXTfddJOFUQH5Y7fbNXToUMcoV75n9TyMcPUOixYtcswqkpCQoLfeekv/+te/LI4KaZih528UxoGrpK0FX1DS1oIHCgN3LE2QkpKiLVu26I477nDJ8ViaIPcCAwPVuHFjxcTEqGvXrpKk1NRUxcTEaMCAAdk+94MPPlBSUhLrM/8/CkPegevk+bhG3oHrBF/wxx9/5NhevXp1N0WDrFStWlXFixfX+fPnM7QVL15cVatWtSAqXC1tPd/M7g1Y59fzpC+KZ/YYAFwpqxGu0dHRqlKlisXRIc3Ro0f13//+12nbe++9p7vuuovr5CHSZui5WvoZegpLh1HmuAEAANdsyJAheuONN/TWW2/pl19+0RNPPKELFy6oT58+kqRevXpp5MiRGZ43f/58de3a1TFdGQAAgDepVKlSvtrhHocOHcq0KC5J58+fZ4puD1KlShXVrl3baVvt2rVZ59fDjBo1Kk/bAW+QmJio559/XnFxcXr++eeZ/tmDpI1kvXqpjcuXL2vGjBnM0OMhjDF68cUXM1yPrLbDGmkz9FzNZrOpadOmhWqGHkaMA/BKrpyiO/0xk5KSJF2ZutnVvdIL4xTdBbE0wdmzZ9W9e3dJUpkyZbRgwQKXTnPFlFl5c//99+vkyZMaNWqUjh8/rkaNGmnVqlUKCwuTJB0+fDjDWnN79+7Vt99+qy+//NKKkAEAAPKtWrVqCgwMVHJycoa2wMDAQjPawtNldn3y0g73OXr0qH755Renbbt379bRo0cZaeYhkpKStHbt2kzb1q5dq6SkJJ9eAgu+67XXXtPly5clXSm4zpo1S0OHDrU4KkhZj3CVVOhGuHqygwcPaseOHZm27dixQwcPHlRkZKSbo8LVbDabevTokeHflDFGPXr0KFR1CwrjALySO6bodrXCOEV3QS9NMHDgQJUqVarAjo/cGTBgQJZTp2f2xUnt2rXpLQoAALzawYMHsyyqJicn6+DBg0yl7gEWL16cY/sLL7zgpmiQFWOMxo4dm2nb2LFjNW/evEL1Za2n+s9//pNj+7Rp09wUTeExa9YsTZkyRcePH1fDhg316quvqmnTppnuu2jRIsfsbWmCgoKcBpYYYzR69Gi98cYbio+PV4sWLTRnzhzVqlWrQF+Hpzp69KhWrlzptG3FihW6//776ZTjASIiIlS5cmX9/vvvGdqqVKlSqEa4erKcvuPjO0DPYIzRokWLMm1buHChbrrppkLzeYup1AEAXuuWW26xOgQAAAAUQrlZYxzWq1+/fr7a4R6xsbHau3dvpm179+5VbGysmyNCZvr27ZuvduTd0qVLNWTIEI0ePVpbt25Vw4YNFR0drRMnTmT5nJCQEB07dszxc/WSEZMnT9Yrr7yiuXPnavPmzSpWrJiio6ML5fThxhiNGDEi07YRI0ZQzPMAKSkpmRbFpSudGq6eYh3WyKmYWliKrZ4uNyP7CwtGjAPwSgUxRXdiYqK6dOki6UrvUFdPqc0U3QAAANKbb76pxYsX66GHHtKjjz5qdTjANWGNce9w00035asd7nHs2LEc25mBwXrfffddju1169Z1UzSFw/Tp0/XYY485RoHPnTtXn332mRYsWKBnnnkm0+fYbDZVqFAh0zZjjF5++WU999xzju+/3n77bYWFhWn58uV64IEHMn1eUlKSY+lBSUpISMjPy/IY+/fv19GjRzNtO3r0qPbv36+aNWu6OSqkl9Xo1vTt3E9Yr3Llyvlqh3tcuHAhX+2+hBHjALxS2hTdrvxJX7i22+0uPz694wAAKFgbNmzQfffdpw0bNlgdCrIQHx+vxYsXKzU1VYsXL1Z8fLzVIQHXJDIyUtddd12mbbVr12YdRQ/h7++fr3a4xy233JLl/bLNZmOmMA/Ru3fvfLUjb5KTk/Xjjz+qffv2jm1+fn5q3769Nm7cmOXzzp8/r4iICIWHh6tLly7atWuXoy02NlbHjx93OmZoaKiaNWuW7TEnTZqk0NBQx094eHg+X51n+PLLL/PVjoJXrFixfLXDPSZNmpSvdrjH0KFD89XuSyiMAwAAAPB6iYmJmjZtmuLi4jRt2rRCOR2kN3j22WeVmpoqSUpNTdVzzz1ncUTAtbHZbBozZkymbWPGjKFTrIeoWrWq/Pwy/+rLz89PVatWdXNEyMyhQ4eynLLYGJNhKmhYI6vpjHPbjrz5888/lZKSorCwMKftYWFhOn78eKbPqV27thYsWKAVK1Y4OiI2b97cMSo67Xl5OaYkjRw5UmfPnnX8HDlyJD8vzWOsX78+X+0oeFu3bs1XO9wju441uWmHe1y8eDFf7b6EwjgAAAAAr7d48WKdOnVKknTq1Cm9++67FkeEq23ZskU7d+502rZjxw5t2bLFooiA/KlSpUqG5ZLsdjvTRXqQTZs2OTrjXC01NVWbNm1yc0TIDAVX71C1alUFBgZm2hYYGEhHEw8QFRWlXr16qVGjRmrdurU+/vhjlStXTq+//nq+jhsUFKSQkBCnH19Qrly5fLWj4O3duzdf7XAPZuiBt6EwDgAAYDGmfwby5+jRo3r33Xcdo82MMXr33XezXDMQ7peamprt6NqsCleAJ/viiy8yzE6RmJioL774wqKIcLWrR0XmtR3A3w4cOKDk5ORM25KTk3XgwAE3R+TbypYtK39/f8XFxTltj4uLy3IN8asFBAToxhtv1L59+yTJ8bz8HNOX5LTsCcuiWO/SpUv5age8nTFGFy9edMlPbrjiPFnNAuRJilgdAAAAQGGWNv3zn3/+qWnTpqlx48YZRp8ByJoxRjNmzMhy+9SpU5nS2ANs3LhRCQkJmbYlJCRo48aNatGihZujAq5dSkqKXnrppUzbXnrpJXXo0IHRMR5g+/btObbXrFnTPcEgSxQ+vMNnn32WY/tTTz3lpmh8X2BgoBo3bqyYmBh17dpV0pWOhjExMRowYECujpHyf+zdeVhUZfsH8O8wLAMqoiKoiOCWZu4ohriUkpi+LmW9Jrjka75mmgtaSm5BKpYLaFGUSrmmVv5ccqEi0VRUxFwqxSSB8BUSjRCQbZjfH8bEyAwDs5xzhvl+rovrYp77zJl7GIWZcz/P/SiVuHLlCoYNGwbgYaG3WbNmiI+PR/fu3QE8fB929uxZTJ8+3RxPQ9JOnjypNx4SEiJQNqSNq6srCgoKqo2T+AoLC42Kk25FRUUIDAwU7PFM8VhxcXFwdHQ0QTbmwxXjRERERCJi+2ci46SnpyMpKQlKpVJjXKlUIikpifuSSoSfn5/OtpsNGzaEn5+fwBlRhejoaHh7e0OhUKBPnz44d+5ctcdHRUWhQ4cOcHR0hKenJ+bOnVtl1bQ12L9/f7Utuvfv3y9wRqQNV4xbhoMHDxoVJ2EcOHDAqDjVXkhICDZu3IgtW7bg6tWrmD59OgoKCjB58mQAwMSJExEaGqo+Pjw8HN988w1+++03XLhwAePHj0d6ejpeeeUVAIBMJsOcOXOwfPlyHDhwAFeuXMHEiRPRokULdfHdmtSvX9+oOJlfVlaWUXHSzZQrkR/9LP4opVJpNSuRyTJwxTgRERGRSHS1fw4MDETLli1Fzo4ederUKURFRWHOnDlc2SohXl5e6N27N86fP6/xQVkmk6F3797w8vISMTuqYGNjg7ffflvrqpuwsDDY2HDOthh2796NkJAQxMTEoE+fPoiKikJgYCBSUlLg5uZW5fidO3di4cKFiI2NRd++fXH9+nW8/PLLkMlkWLdunQjPQDxNmjQxKk7C0LffO/eDl4Y+ffogOTm52jiJLyQkRGenjIo4mdbYsWNx584dLF26FFlZWejevTuOHj2qntSTkZGh8R7qzz//xNSpU5GVlYVGjRrBx8cHp0+fRqdOndTHvPnmmygoKMB///tf5Obmol+/fjh69KhVdi1LS0szKk7mV1xcbFScdONKZMugUCgQFxdnknPduHEDM2bM0BmPjo42SSclS/h7wsI4ERERkQjY/tmysOW9dMlkMowbNw5JSUka4yqVCuPGjeP/Iwnp1asXunTpgitXrqjHunbtip49e4qYlXVbt24dpk6dql55FhMTg0OHDiE2NhYLFy6scvzp06fh7++PoKAgAIC3tzfGjRuHs2fPCpq3FOTk5BgVJ2HUZAUTic/WtvrLk/riJAx93UGssXuIEGbOnKmzdXpCQoLG7cjISK2fMSuTyWQIDw9HeHi4qVIk0qBSqQT9fVDTvZP1USgU/OxIkiOTyUw2GaBLly5GxesSvrMkIiIiEkFF++dHVW7/7O3tLXxipNX27dvVRY6cnBzs2LEDU6ZMETkrAh5eePn8888hk8mqrBjfuXMnevbsyQscErJixQqMHj0a5eXlsLGxwfLly8VOyWqVlJQgOTlZowWrjY0NAgICkJiYqPU+ffv2xfbt23Hu3Dn4+vrit99+w+HDhzFhwgSdj1NcXKyxmkfXXvOWhheWLMPx48f1xtu3by9QNqRL06ZNjYqTMDp37mxUnMhYQhdcAdMUXa2t4GqJK5EB61uNbMqVyKWlpfjXv/6lM/7111/Dzs7O6Mfh4gTjnThxAgMGDNA6bk1YGCciIiISQUX75wsXLmisVpLL5fDx8WH7ZwnJzMzE9u3bNca2b9/OlvcSoWuSiUql4iQTCXJxccH48eOxfft2jB8/Hi4uLmKnZLVycnKgVCqr7LHs7u6Oa9euab1PUFAQcnJy0K9fP6hUKpSVleHVV1/FW2+9pfNxIiIiEBYWZtLcpeCnn37SG3/ssccEyoZ0mTBhArZu3VptnMSn7/0U329Jww8//KA33qFDB4GyIWskdMEVYPtnqrtMuRLZ0dERL730Enbt2lUlFhwcDGdnZ5M8DplG27ZtkZqaqr5tjZNEWRgnIiIiEoFMJsPcuXOrXJCtGLemGeVSVtHavvJKZAAoLy9ny3uJ4CQTy/PKK6/glVdeETsNMkBCQgJWrlyJDz/8EH369MGNGzcwe/ZsvPPOO1iyZInW+4SGhmrsO5uXlwdPT0+hUjYbrhi3DPv379cb//e//y1QNqRLRkaG3njbtm0FyoZ0adOmjVFxIrIOlrgSGeBqZGO99tprWgvj06ZNEyEbqs6HH36oMeln8+bNImYjDhbGiYiIiETSsmVLjB07Fjt37lSPjR07Fh4eHiJmRZVxNbL0cZIJkWFcXV0hl8uRnZ2tMZ6dnY1mzZppvc+SJUswYcIE9cSGLl26oKCgAP/973+xaNEi2NjYVLmPg4MDHBwcTP8ERHbp0iW9cWtcfSE1Z86c0RtnYVx8e/bs0Rt/+umnBcqGdElJSdEbHzRokEDZkDUyZcEVePieZ+LEiTrjW7durdJZxxDWVnDlSmTrFR0djRkzZqhvW2PB1dKY8neqJan6qZWIiIiIiAAArVq10vlh29nZGa1atRI4I9KmZcuWCA4OVhfBZTIZgoODOcmEqBr29vbw8fFBfHy8eqy8vBzx8fHw8/PTep/CwsIqxW+5XA4AVTpr1HWPTiiobZyEsWLFCqPiJIxRo0YZFSdh5ObmGhUnMlZFwdVUX97e3ur3MY+Sy+Xw9vY2yeNwoq5xXnvtNa3jXIksPe3atVN/36pVK04SJcliYZyIiIhIJJmZmdi9e7fG2O7du5GZmSlSRvSojIwM5OXlaY3l5eXpbf1Jwhk/frx6NYZCoUBwcLDIGZEup06dwosvvohTp06JnYrVCwkJwcaNG7FlyxZcvXoV06dPR0FBASZPngwAmDhxIkJDQ9XHjxgxAh999BF27dqFmzdv4ttvv8WSJUswYsQInReW66rCwkKj4iSMs2fPGhUnYRw4cMCoOAnj7t27RsWJpOjYsWO1GidxREdHa9zmSmTp27hxo9gpEOnEwjgRERGRCCr2rtY1bm0r76SqYv9qbXx9fbl/tYQUFRWhqKioyvckLUVFRVi7di2ys7Oxdu1avk4iGzt2LNasWYOlS5eie/fuuHjxIo4ePapuG5qRkYHbt2+rj1+8eDHmzZuHxYsXo1OnTpgyZQoCAwPx8ccfi/UURMMVrpbh+++/NypOwhgxYoRRcRLG1atXjYoTSdXgwYM1bg8ZMkSkTEgXrkQmIlNiYZyIiIhIBBV7VyuVSo1xpVKp3ruaxFexT7U23L9aWhYtWqSeUKJSqbB48WKRMyJttm/frl5RdvfuXezYsUPkjGjmzJlIT09HcXExzp49iz59+qhjCQkJ+Oyzz9S3bW1tsWzZMty4cQMPHjxARkYGoqOj4eLiInziImMrdcvQt29fo+IkjP379xsVJ2EUFxcbFSeSqjfffFPjNj9HSBtXIhORsVgYJyIiIhJBxUrkR1vPyuVyrkSWGF3tO3mRVjrOnz+PK1euaIxdvnwZ58+fFykj0iYzMxM7duzQmMCwY8cObh9BFqnySnpD4iQMfZNvODlHGtzc3IyKkzCef/55o+JEliAuLk7sFIiIyMxYGCciIiISga6VyBXjXIksDaWlpdi1a5fW2K5du1BaWipwRvSo8vJyvP3221pjb7/9NsrLy4VNiLSq2Cbi0S4ZZWVl3D6CLNKdO3eMipMwCgoKjIqTMC5dumRUnITxxRdfGBUnIiIikgIWxomIiIhE0rJlS3Tq1EljrFOnTvDw8BApI3rU+vXrjYqT+SUmJiIvL09rLC8vD4mJiQJnRNpUbB+hDbePIEt0+fJlo+IkjM6dOxsVJ2FwAoNleHRyW23jRERERFLAwjgRERGRSDIzM/Hzzz9rjP38889sKywhs2fPNipO5ufn5wcnJyetMScnJ/j5+QmcEWnj5eUFb29vrbHWrVtz+wiyOPr2VbfGfdeliC3vLUOLFi2MipMwPD09jYoTERERSQEL40REREQiqGgrrK1lOtsKS4etrS3c3d21xtzd3WFraytwRkSWqby8HL///rvWWEZGBlvek8Vp3ry5UXESxo0bN4yKkzB0TZyqaZyE8ccffxgVJyIiIpICFsaJiIiIRFDRVvjRloNKpZJthSUkPT0d2dnZWmPZ2dl8nSQgMTERhYWFWmOFhYVspS4RBw4c0NliValU4sCBAwJnRGSc06dPGxUnYTz22GNGxUkY/P9kGfRNCOWEUSIiIrIELIwTERERicDLywu9e/fWGvP19WVbYYng6yR9fn5+cHZ21hpr2LAhW6lLxIgRI4yKE0lN/fr1jYqTMNLS0oyKkzD0dQ1hVxFp4F7wREREVBewME5EREQkAplMhp49e2qN9ezZU2uLdRKeTCZDQECA1lhAQABfJwmwsbHBsGHDtMaGDx8OGxt+5JGCzMxMo+JEUnPv3j2j4iSMBw8eGBUnYTRt2tSoOAnDw8PDqDgRERGRFPAqEREREZEIlEolNm3apDW2ceNGnS2HSVjl5eWIjo7WGvvggw+4gkkClEolvvjiC62x3bt38/+SRLRq1Urnyn5nZ2e0atVK4IyIjNO6dWuj4kT0j5ycHKPiJIxbt24ZFSciIiKSAm7+QkRERCSCmuy3+9xzzwmcFT0qMTEReXl5WmN5eXlITEyEv7+/wFlRZfy/ZBkyMjKq/b+UkZEBb29vYZMiMsKdO3eMihPRP0pLS42KExEREYlNpVKhqKhI7DT0qpyjJeQLAAqFwqQdG1kYJyIiIhLByJEjsWHDBq0FPVtbW4wcOVKErOhRFftXayvocf9qaeD/Jcvg5eWF3r17IykpqUrM19cXXl5eImRFZLjc3Fyj4kRERERENcGCq3mZquhaVFSEwMBAE2QknFGjRomdQo3ExcXB0dHRZOdjYZyIiIhIBHK5HG+++SYiIiKqxBYuXAi5XC5CVvQoGxsbvP322wgJCakSCwsL4/7VEsD/S5ZBJpNh7ty5CAoKqhKbO3euSWd/EwmhpKTEqDgRkRCELqY8ePDA6HOYelUYEenGgqt5seBqGUxddCXpY2GciIiISCRdunTROt6pUyeBMyFDcH9x6bh586bW8Rs3bmDIkCECZ0O6hIWFaR1/++23sXHjRoGzITKOvolRnDhFRFIgdDHFFI/FAkXdYCkFV8Ayi64suFoG/j6zXgsA2IudhA4qABWb1NgBkOpUtBIA75rp3CyMExERkVGio6OxevVqZGVloVu3bnj//ffh6+ur8/jc3FwsWrQIe/fuxb179+Dl5YWoqCgMGzZMwKzFp1KpEBkZCZlMBpVKpR6XyWSIjIzEmjVruFJCAsrLy/H2229rjb399ts4cOAAix8iKy0txa5du7TGdu3ahalTp8LOzk7grOhRhYWFSElJ0RpLSUlBYWEhnJycBM6KyHCFhYVGxYksHVciE1F1LLHgClhO0ZUFV+ulHKGUblVPBaBihzM5pFtxBYAyQH7QfN3l7AHYS/gH4CB2AjWi0n+IgaT6X4iIiIgswO7duxESEoKYmBj06dMHUVFRCAwMREpKCtzc3KocX1JSgmeeeQZubm748ssv4eHhgfT0dLi4uAifvMjS09O17rWrUqmQlJSE9PR0eHt7C58YaUhMTNS6vzgA5OXlITExEf7+/gJnRZWtX79eb3z+/PkCZUO6TJs2TW9827ZtAmVD1oqFPMvA18kycCWyZVAoFIiLizPJuUpLS/Gvf/1LZ/zrr782yWREhUJh9DmIqPaiB+TCQW6+QpQxVCqg5O+GbfY2gJT/5BYrZZhxwsV8D2ALaVf1OCedLICU/wsRERGRxK1btw5Tp07F5MmTAQAxMTE4dOgQYmNjsXDhwirHx8bG4t69ezh9+rT6oom1Fn+9vLzQu3dvXLhwAUqlUj0ul8vh4+MDLy8vEbOjCn5+fnB2dtZaHG/YsCH8/PxEyIoqmz17Ng4cOFBtnMT38ccfY+jQodXGicyNhTzLwNeJyHRkMpnJ/m06OjripZde0tqpJzg4GM7OziZ5HKp7CnoGAzYSLkOoVEB52cPvbWylW3UtL0O9CzvMdnoHuQoK8y2gNZrl/JWV5uQCIvqHhP8iERERkZSVlJQgOTkZoaGh6jEbGxsEBAQgMTFR630OHDgAPz8/zJgxA/v370fTpk0RFBSEBQsWQC7X/gmsuLgYxcXF6tu6Vu9aGplMhrlz52L8+PFVYnPnzrW6VUdSZWNjg7fffhshISFVYmFhYWyjLgF2dnbVXqRlG3VpcHR0hKOjo9aVmRUxIiKyHKZciXz37l0EBQXpjO/cuRNNmjQx+nG4Etl4r732mtb3XPo6w5CVs7EF5FJ/Ty7V3YCJoLH9HsrEy6NOqfRz1Pj5GqHyeUoejpjkvNaqpNL3pnqNKrAwTkRERAbJycmBUqmEu7u7xri7uzuuXbum9T6//fYbvv/+ewQHB+Pw4cO4ceMGXnvtNZSWlmLZsmVa7xMREYGwsDCT5y8FLVu2RIcOHXD16lX1WIcOHeDh4SFiVvSoZs2aaR3Xtl0AieO1117Dnj17UF5erh6zsbHhRVoJSU9P19mu+MGDB9w+ggRhykJeQUEBnn/+eZ3xvXv3ol69ekY/jjUW8kz5OuXn52PMmDE641999RXq169v9ONY4+tkypXILVu2hEKh0NpCX6FQoGXLliZ5HDKN6OhozJgxQ3178+bNImZDRMaoXGwqVlZzINVY5Z+jqYp5lReLmHNfbGtVXFwMJycnk5ynwrtGn40qM9VrVIGFcSIiIhJMeXk53Nzc8Mknn6hbht+6dQurV6/WWRgPDQ3VWK2bl5cHT09PoVI2q8zMTI2iOAD88ssvyMzM5AVAiVCpVIiMjNQai4yMxJo1a7i6XyIqF8W13SZxVWwfkZSUVCXm6+vL7SNIEKZuKdy6dWvcvHmzSqxdu3ZwdXU1yeNYI1O/Ti1btkRmZmaVmJeXF5o2bWqSxyHjffPNNxgwYIDWcZKWdu3aqb9v1aoV2rdvL2I2JFUaBUFlqXiJ1CWVfo7mKLjOONHIJOekf5i6mEdEpsHCOBERERnE1dUVcrkc2dnZGuPZ2dk6V9g2b94cdnZ2Gm3TH3/8cWRlZaGkpAT29lXblzk4OMDBwcG0yUuASqXSuRI+LCwMn3zyCQuuEpCenq61kAcASUlJXOUqEUuXLtU5Hh4eLnA2pI1MJtO5FUZeXh5/35FF2rJli9ZCXmxsrAjZkC47d+7U+jpt27ZNhGyoOqNHj8a+ffvUt1988UXxkqEa2bhxo9gpkERVLrjW+3GniJnUTSy4WpfK18SUI5Ss6plC2T+r7011zbHyeRaAGzQYqwT/rLw39XVh/hciIiIig9jb28PHxwfx8fEYPXo0gIcrNOPj4zFz5kyt9/H398fOnTtRXl6u3pv5+vXraN68udaieF128+ZNpKSkaI2lpKTg5s2baNOmjcBZ0aMe3SqgtnEyv+LiYiQkJGiNJSQkoLi4uE5OrrE0Dx480Pk779q1a3jw4AH3GSeLNG/ePKxdu1Z9OzQ0VMRsSJdZs2Zhw4YN6tvz588XMRvSZfr06RqF8ddff128ZIiIrEDlz0nRA/6EA7t0G61Y+c/qe1N9DtWYRGwLVvVMzFSTtCufxx6APTj52zj/dMYw9UR6/hciIiIig4WEhGDSpEno1asXfH19ERUVhYKCAkyePBkAMHHiRHh4eCAiIgLAw4tdH3zwAWbPno3XX38dv/76K1auXIlZs2aJ+TREcfv2bb1xFsbFp+/C+fz58xEdHS1QNqTNokWL9MbXrFkjUDaki64JU5Xj3KOULNGQIUPUhXEbGxs8++yzImdE2gwfPlxdGJfJZBg5cqTIGZE+ptpnnojEUbkgWNAjCJDbiZhNHaEsVa++N0fB1UEOKFgYNyl2xSKSJhbGiYiIyGBjx47FnTt3sHTpUmRlZaF79+44evSoehVtRkaGemU4AHh6eiIuLg5z585F165d4eHhgdmzZ2PBggViPQXR+Pn5oX79+sjPz68Sq1+/Pvz8/ETIih61du1aDBkypNo4iWvFihV45plnqo2T+D744AMEBgZWGyeydEeOHBE7BaqBo0ePip0CEVGdp1EQlNuxMG5iLLgSERmOhXEiIiIyysyZM3WuBNTW3tjPzw9nzpwxc1bSZ2Njg/DwcISEhFSJLV++XGNCAYlHoVDA19cX586dqxLz8/ODQqEQISuqzMHBAU899ZTW3zeDBw9mG3WJcHR0RNeuXXH58uUqsR49erCNOhERERERkT5lYidQDRUA5d/fywFJdxI388+xBEDlVuBSogJQ+vf3dpDuy1RixnOzME5EREQkEm1FcQCYM2cOTpw4IXA2pIu2ojgAJCYmCpwJ6aJrj/H4+HgsW7ZM2GTqEJVKhaKiIpOdb/Xq1VpXja9atQoPHjwwyWMoFAquoCEiIiIiqqVipQySLeSpgJLyh9/b2wBSfrv/8OdoPvKD7HdvCd4VOwGqliQK49HR0Vi9ejWysrLQrVs3vP/++/D19dV5fFRUFD766CNkZGTA1dUVL7zwAiIiIrhih4iIiCzG8OHD9cYPHTokUDaky4ABA/TGOYlBXHyNzKeoqKja9uemYsrHiIuL4+pzIiIiIikpl/ISVzysulbkaGMr3aqrmX+OM064mPX8RERSIXphfPfu3QgJCUFMTAz69OmDqKgoBAYGIiUlBW5ublWO37lzJxYuXIjY2Fj07dsX169fx8svvwyZTIZ169aJ8AyIiIiIau/+/ftGxYmIiIiIiIikrt6FHWKnQGTRFAoF4uLixE5Dr6KiIowaNQoAsH//fotZyGqqPPk6mY+pcxS9ML5u3TpMnToVkydPBgDExMTg0KFDiI2NxcKFC6scf/r0afj7+yMoKAgA4O3tjXHjxuHs2bOC5k1ERERkjAYNGlRb/G7QoIGA2RARVWWOD/bm/hBuCR/qpag2XdyeeuopHD9+vMr4sGHD2OmEiIiIyIKwkGdepspTJpNZXFcshUJhcTkbi6+T5RC1MF5SUoLk5GSEhoaqx2xsbBAQEKBzz8a+ffti+/btOHfuHHx9ffHbb7/h8OHDmDBhgtbji4uLUVxcrL6dl5dn2idBREREZIBDhw5V2wKaxQVp2Lp1KyZOnFhtnMR1/PhxDBw4sNo4GcbcH+yt9UO41NS2i9vevXtRUlKivn337l1069YNL774opBpExERkYRZSsEVsMyiKwuulpUzEUmLqIXxnJwcKJVKuLu7a4y7u7vj2rVrWu8TFBSEnJwc9OvXDyqVCmVlZXj11Vfx1ltvaT0+IiICYWFhJs+diIiIyFhPPfUUEhIStI6TNHh5eaF+/frIz8+vEqtfvz68vLxEyIoqS09P1xv39vYWJhkiC1TbLm6NGzfWuL1r1y44OTnpLIxzsjoREZH1scSCK8CiKxGRNbARO4HaSkhIwMqVK/Hhhx/iwoUL2Lt3Lw4dOoR33nlH6/GhoaH466+/1F+///67wBkTERERVVVcXKy1KA48fL9TuYhA4ikqKtJaFAeA/Px8FBUVCZwRPerRSba1jRNZs4oubgEBAeoxfV3cHrV582a89NJLqFevntZ4REQEGjZsqP7y9PQ0Se5EREREREREtSVqYdzV1RVyuRzZ2dka49nZ2WjWrJnW+yxZsgQTJkzAK6+8gi5duuC5557DypUrERERgfLy8irHOzg4wNnZWeOLiIiISGy6ut3UNE7CmDlzplFxMr958+YZFSeyZtV1ccvKytJ7/3PnzuGnn37CK6+8ovMYTlYnIiIiIiIiqRC1MG5vbw8fHx/Ex8erx8rLyxEfHw8/Pz+t9yksLISNjWbacrkcAKBSqcyXLBEREZEJ/etf/zIqTsJg0VX6xowZY1SciAy3efNmdOnSBb6+vjqP4WR1IiIiIiIikgpR9xgHgJCQEEyaNAm9evWCr68voqKiUFBQoN7fbOLEifDw8EBERAQAYMSIEVi3bh169OiBPn364MaNG1iyZAlGjBihLpATERERSV2/fv2MipMwcnJyjIqT+d29e9eoOJE1M6SLW4WCggLs2rUL4eHh5kyRiIiIiIiIyGRE32N87NixWLNmDZYuXYru3bvj4sWLOHr0qLqVW0ZGBm7fvq0+fvHixZg3bx4WL16MTp06YcqUKQgMDMTHH38s1lMgIiIiqrX9+/cbFSdh6GslXJNWw2Reubm5RsWJrJkhXdwqfPHFFyguLsb48ePNnSYREZHVio6Ohre3NxQKBfr06YNz587V6H67du2CTCbD6NGjNcZffvllyGQyja+hQ4eaIXMiIiJpEn3FOPBwb0Zd+zMmJCRo3La1tcWyZcuwbNkyATIjIiIiMo/y8nKj4iSMmzdvGhUn8+vXrx+2b99ebZyIdKttF7cKmzdvxujRo9GkSRMx0iYiIqrzdu/ejZCQEMTExKBPnz6IiopCYGAgUlJS4ObmpvN+aWlpmD9/Pvr37681PnToUHz66afq2w4ODibPnYiISKpEXzFOREREZI3S0tKMipMwPD09jYqT+W3YsMGoOJG1q20XNwBISUnByZMnMWXKFDFSJiIisgrr1q3D1KlTMXnyZHTq1AkxMTFwcnJCbGyszvsolUoEBwcjLCwMbdq00XqMg4MDmjVrpv5q1KiRuZ4CERGR5LAwTkRERCSCUaNGGRUnYehrVVjTVoZkPn369DEqTkQPu7ilp6ejuLgYZ8+e1fh/k5CQgM8++0zj+A4dOkClUuGZZ54ROFMiIiLrUFJSguTkZAQEBKjHbGxsEBAQgMTERJ33Cw8Ph5ubW7WT1xISEuDm5oYOHTpg+vTpuHv3brW5FBcXIy8vT+OLiIjIUkmilToRERGRtdmxY4feeHh4uEDZkC5//vmnUXEyvy1btuiNV7SEJqpLfv31Vxw7dgx//PFHle03li5dKlJWREREZAo5OTlQKpXqDi4V3N3dce3aNa33OXnyJDZv3oyLFy/qPO/QoUPx/PPPo3Xr1khNTcVbb72FZ599FomJiZDL5VrvExERgbCwMIOfCxERkZSwME5EREQkgoSEBKPiJAzuMS59jxYEaxsnskQbN27E9OnT4erqimbNmkEmk6ljMpmMhXEiIiIrc//+fUyYMAEbN26Eq6urzuNeeukl9fddunRB165d0bZtWyQkJGDw4MFa7xMaGoqQkBD17by8PG4pRUREFouFcSIiIiIRPPXUU9UWv5966inBciHd5HI5lEpltXEiIqEtX74cK1aswIIFC8ROhYiIiMzA1dUVcrkc2dnZGuPZ2dlo1qxZleNTU1ORlpaGESNGqMcqJoja2toiJSUFbdu2rXK/Nm3awNXVFTdu3NBZGHdwcICDg4MxT4eIiEgyuMc4ERERkQgWLlxoVJyEsXPnTqPiZH6VV68YEieyRH/++SdefPFFsdMgIiIiM7G3t4ePjw/i4+PVY+Xl5YiPj4efn1+V4zt27IgrV67g4sWL6q+RI0fi6aefxsWLF3Wu8M7MzMTdu3fRvHlzsz0XSxIYGCh2CqQHXyPLMHToULFTINLJ4BXj8fHxiI+P17qfWWxsrNGJEREREdVl7777rt4493ET37Rp0/TGDxw4IFA2pM0XX3yhNz569GhhkiESyIsvvohvvvkGr776qtipEBERkRamuHYeEhKCSZMmoVevXvD19UVUVBQKCgowefJkAMDEiRPh4eGBiIgIKBQKdO7cWeP+Li4uAKAez8/PR1hYGMaMGYNmzZohNTUVb775Jtq1a2fVxcY333xT4/bs2bOxfv16kbIhbR799zlgwACcOHFCpGxIl0OHDqm/V6lUOHDgAEaOHCliRqRPYGCgVf5fMqgwHhYWhvDwcPTq1QvNmzfX2M+MiIiIiPSbOXMmjh07Vm2cxNe1a9dqPyR07dpVwGxIm+DgYKxataraOFFd065dOyxZsgRnzpxBly5dYGdnpxGfNWuWSJkRERGRqa6djx07Fnfu3MHSpUuRlZWF7t274+jRo3B3dwcAZGRkwMam5g1h5XI5Ll++jC1btiA3NxctWrTAkCFD8M4771h1q/RLly5p3P7xxx9FyoTIsm3YsEHj9po1a1gYl6AxY8Zo3B4+fLjGpAZrYFBhPCYmBp999hkmTJhg6nyIiIiIrIK+91ETJkzA0aNHBcqGdNE3c9YaZ9ZKTXVF8Yr4sGHDBMqGSBiffPIJ6tevj+PHj+P48eMaMZlMxsI4ERGRiEx57XzmzJk6J00nJCRUe9/PPvtM47ajoyPi4uKMzkksKpUKRUVFJj2nrpXyAwYMMNnPSqFQWNXCQlO/TkK8RgBfJ2NVdLJ41EsvvYRPP/3UJI9hba+RueTn52vcvn//vkiZiMegwnhJSQn69u1r6lyIiIiIrEb79u2rzEx/NE7ic3JyQmFhYbVxElfTpk1x586dauNEdc3NmzfFToGIiIh04LVz8ygqKhK05bupHisuLg6Ojo4mOZclEPJ1MuXj8HUyj//973/8v2QES5xoYgkTGAwqjL/yyivYuXMnlixZYup8iIiIiKzCL7/8YlSchFFdUbwmcTK/6oriNYkTEREREZkSr50TEZEpWOJEE0uYwGBQYbyoqAiffPIJvvvuO3Tt2rXKfmbr1q0zSXJEREREdVWPHj1w7ty5auMkPnt7e5SUlFQbJ3G5uLggNze32jhRXZSZmYkDBw4gIyOjyu8pfiYnIiISD6+dm4dCoTBp6+yaFIFM8XgKhcLoc1gSU75OQr1GAF8nY/D/Elkagwrjly9fRvfu3QEAP/30k0ZM6kvkiYiIiKRg2LBh1RbGuSeyNNja2lZbGLe1NejtNJmQvv2wrHG/LKr74uPjMXLkSLRp0wbXrl1D586dkZaWBpVKhZ49e4qdHhERkVXjtXPzkMlkgq9ClPqqRykS+nXia2QYvk6WgRMYzMOgK3nHjh0zdR5EREREVuXChQt644MGDRIoG9LFw8MDv/76a7Vxqj1T7pPl5ORUbfHbyckJDx48MMljWcJeWWQdQkNDMX/+fISFhaFBgwb46quv4ObmhuDgYAwdOlTs9IiIiKwar50TEZEpcAKDeXCJCxEREZEIDhw4oDc+f/58gbIhXaoritckTtoJuU/W/fv3rWqvLLIOV69exeeffw7gYeeKBw8eoH79+ggPD8eoUaMwffp0kTMkIiIiIiIikh6DC+Pnz5/Hnj17tO5ntnfvXqMTIyIiItMJCQmp8bHc70wYMpkMKpWq2jgREZE29erVU38Ob968OVJTU/HEE08AAHJycsRMjYiIiMBr50RERFJlUGF8165dmDhxIgIDA/HNN99gyJAhuH79OrKzs/Hcc8+ZOkciIiIy0o8//qhx+8KFCygrK0OHDh0AANevX4dcLoePj48Y6Vml6oriNYkTWTJT7pOlUqmqbR199OhRk000sYS9ssg6PPnkkzh58iQef/xxDBs2DPPmzcOVK1ewd+9ePPnkk2KnR0REZNV47ZyIiEi6DCqMr1y5EpGRkZgxYwYaNGiA9evXo3Xr1pg2bRqaN29u6hyJiIjISJX3OFu3bh0aNGiALVu2oFGjRgCAP//8E5MnT0b//v3FSpGIrIip98nauXMngoKCqox//vnncHJyMtnjEEnFunXrkJ+fDwAICwtDfn4+du/ejfbt27PzCxERkch47ZyIiEi6bAy5U2pqKoYPHw4AsLe3R0FBAWQyGebOnYtPPvnEpAkSERGRaa1duxYRERHqojgANGrUCMuXL8fatWtFzMy66FspwJUE0tCwYUOj4iSMli1bavxOA4DGjRvDw8NDpIyIzKtNmzbo2rUrgIdt1WNiYnD58mV89dVX8PLyEjk7IiIi68Zr50RERNJlUGG8UaNGuH//PgDAw8MDP/30EwAgNzcXhYWFpsuOiIiITC4vLw937typMn7nzh313/faiI6Ohre3NxQKBfr06YNz587pPPazzz6DTCbT+LLW1sT/93//Z1SchPHXX38ZFSfhfPbZZxq3d+3aJU4iRALJzc3Fpk2bEBoainv37gF4uFXKrVu3RM6MiIjIuvHaORERkXQZ1Ep9wIAB+Pbbb9GlSxe8+OKLmD17Nr7//nt8++23GDx4sKlzFIVKpUJRUZHYaehVOUdLyLeCQqEw2V6PRERUO8899xwmT56MtWvXwtfXFwBw9uxZvPHGG3j++edrda7du3cjJCQEMTEx6NOnD6KiohAYGIiUlBS4ublpvY+zszNSUlLUt/n3gIhMofIkm7CwMKuddEPW4fLlywgICEDDhg2RlpaGqVOnonHjxti7dy8yMjKwdetWsVMkIiKyWtZw7ZyIiMhSGVQY/+CDD9RF2EWLFsHOzg6nT5/GmDFjsHjxYpMmKJaioiIEBgaKnUatjBo1SuwUaiwuLs6k+0oSEVHNxcTEYP78+QgKCkJpaSkAwNbWFlOmTMHq1atrda5169Zh6tSpmDx5svrchw4dQmxsLBYuXKj1PjKZDM2aNTPuSdQBCoWi2kltLOoRGe7JJ58UOwUiswoJCcHLL7+M9957Dw0aNFCPDxs2DEFBQSJmZjqcrG4+ppyoztfJfLiggMhyWcO1cyIiIktlUGG8cePG6u9tbGx0XvgmIiIi6XFycsKHH36I1atXIzU1FQDQtm1b1KtXr1bnKSkpQXJyMkJDQ9VjNjY2CAgIQGJios775efnw8vLC+Xl5ejZsydWrlyJJ554QufxxcXFKC4uVt/Oy8urVZ5Spe+irKVctCUiIuElJSXh448/rjLu4eGBrKwsETIyPU5WNx9TTlTn62Q+pnqdLGXyAsAJDFR38No5ERGRdBlUGAeA1NRUfPrpp0hNTcX69evh5uaGI0eOoFWrVtVe3LZEBT2DARuDf1TmpVIB5WUPv7exBaT8Zry8DPUu7BA7CyIiq/ef//wH69evR4MGDdC1a1f1eEFBAV5//XXExsbW6Dw5OTlQKpVwd3fXGHd3d8e1a9e03qdDhw6IjY1F165d8ddff2HNmjXo27cvfv75Z7Rs2VLrfSIiIhAWFlbDZ0dERFT3OTg4aJ0odv36dTRt2lSEjIhIqixx8gLACQxSxgkMNWNN186JiIgsiUHV3uPHj+PZZ5+Fv78/Tpw4gRUrVsDNzQ2XLl3C5s2b8eWXX5o6T3HZ2AJyO7GzqIa92AkQEZEF2bJlC1atWqXRehUAHjx4gK1bt9a4MG4IPz8/+Pn5qW/37dsXjz/+OD7++GO88847Wu8TGhqKkJAQ9e28vDx4enqaLUciIiKpGzlyJMLDw7Fnzx4AD7cpycjIwIIFCzBmzBiRszM9TlY3AQEmqkcPyIWDXGXWxzCUSgWUlD/83t5Gui9TsVKGGSdcxE6DRMAJDOYl9JaOVnftnIiIyIIY9Mly4cKFWL58OUJCQjQuqg8aNAgffPCByZIjIiIi08nLy4NKpYJKpcL9+/c19rBWKpU4fPgw3Nzcanw+V1dXyOVyZGdna4xnZ2fXeA9xOzs79OjRAzdu3NB5jIODAxwcHGqcl6WoX78+8vPzq40TERFps3btWrzwwgtwc3PDgwcPMHDgQGRlZcHPzw8rVqwQOz3T42R1i+AgV0EhFzsL3YQriRnDvBMLlCOURvSOFIAKgPLv7+UAJDqBAWWA/KCE/7GT6HjtnIiISLoMejt85coV7Ny5s8q4m5sbcnJyjE6KiIiITM/FxQUymQwymQyPPfZYlbhMJqtVy3J7e3v4+PggPj4eo0ePBgCUl5cjPj4eM2fOrNE5lEolrly5gmHDhtX4ceuK6oriNYkTEZH1atiwIb799lucPHkSly9fRn5+Pnr27ImAgACxUyMiKbOFtAvjACDlOTACWQBpT7dRASj9+3s7SHf+QgmAd0V6bF47JyIiki6D3g67uLjg9u3baN26tcb4jz/+CA8PD5MkRkRERKZ17NgxqFQqDBo0CF999RUaN26sjtnb28PLywstWrSo1TlDQkIwadIk9OrVC76+voiKikJBQQEmT54MAJg4cSI8PDwQEREBAAgPD8eTTz6Jdu3aITc3F6tXr0Z6ejpeeeUV0z1RIiIiK9GvXz/069dP7DSIiMiE7AHYS7bc/JBl9PMSb2sHXjsnIiKSLoMK4y+99BIWLFiAL774AjKZDOXl5Th16hTmz5+PiRMnmjpHIiIiMoGBAwcCAG7evIlWrVpBZoLNFceOHYs7d+5g6dKlyMrKQvfu3XH06FG4u7sDADIyMmBjY6M+/s8//8TUqVORlZWFRo0awcfHB6dPn0anTp2MzoWIiMiaJCUl4dixY/jjjz9QXl6uEVu3bl2NzxMdHY3Vq1cjKysL3bp1w/vvvw9fX1+dx+fm5mLRokXYu3cv7t27By8vL0RFRVll9xciIiJteO2ciIhIugwqjK9cuRIzZsyAp6cnlEolOnXqBKVSiaCgICxevNjUORIREZEJXb16Fb///rt6hVl0dDQ2btyITp06ITo6Go0aNarV+WbOnKmzdXpCQoLG7cjISERGRhqUN1FNqFQqFBUVCfqYDx48MPocCoXCJJNViMg6rFy5EosXL0aHDh3g7u6u8fujNr9Ldu/ejZCQEMTExKBPnz6IiopCYGAgUlJS4ObmVuX4kpISPPPMM3Bzc8OXX34JDw8PpKenw8XFxRRPi4iIqE7gtXMiIiLpMqgwbm9vj40bN2LJkiX46aefkJ+fjx49eqB9+/amzo+IiIhM7I033sC77z7cbe3KlSsICQnBvHnzcOzYMYSEhODTTz8VOUMiwxUVFSEwMFDQxzTF48XFxcHR0dEE2RCRNVi/fj1iY2Px8ssvG3WedevWYerUqeotUGJiYnDo0CHExsZi4cKFVY6PjY3FvXv3cPr0adjZPdwI2Nvb26gciIiI6hpeOyciIpIugwrjFVq1aoVWrVqZKheiWhNjVZghKudoCflW4Oo1orrp5s2b6tblX331FUaMGIGVK1fiwoULbINKRERkAWxsbODv72/UOUpKSpCcnIzQ0FCN8wYEBCAxMVHrfQ4cOAA/Pz/MmDED+/fvR9OmTREUFIQFCxZALpdrvU9xcTGKi4vVt/Py8ozKm4iIyFLw2jkREZH0GFQYV6lU+PLLL3XuZ7Z3716TJEekjxirwow1atQosVOoMa5eI6qb7O3tUVhYCAD47rvv1HucNW7cmBeryeIpFArExcWZ7HwFBQV4/vnndcb37t2LevXqGf04CoXC6HMQkfWYO3cuoqOjERUVZfA5cnJyoFQq4e7urjHu7u6Oa9euab3Pb7/9hu+//x7BwcE4fPgwbty4gddeew2lpaVYtmyZ1vtEREQgLCzM4DyJiIgsDa+dExERSZdBhfE5c+bg448/xtNPP11lPzMiIiKStn79+iEkJAT+/v44d+4cdu/eDQC4fv06WrZsKXJ2RMaRyWQmndTl6OiI1q1b4+bNm1Vi7dq1g6urq8kei4iopubPn4/hw4ejbdu26NSpk7qteQVzXXAvLy+Hm5sbPvnkE8jlcvj4+ODWrVtYvXq1zsJ4aGgoQkJC1Lfz8vLg6emp97FUKtU/N5SlRudu9Sr9DDV+tkREZHK8dm4ZHB0d8eDBg2rjRERU9xhUGN+2bRv27t3LdqskKdEDcuEgl+YHfJUKKPl7cqi9DSDl98PFShlmnHAROw0iMqMPPvgAr732Gr788kt89NFH8PDwAAAcOXIEQ4cOFTk7IunZsmULBgwYUGU8NjZWhGyIiIBZs2bh2LFjePrpp9GkSRODLri7urpCLpcjOztbYzw7OxvNmjXTep/mzZvDzs5Oo236448/jqysLJSUlMDe3r7KfRwcHODg4FDr/Cq3X6/3485a3590Ky4uhpOTk0nOVbnIXqw0ySmtWuWfIScwEFkuXju3DC4uLtUWxl1cXIRLhoiIBGNQYbxhw4Zo06aNqXMhMoqDXAWF9m3tJMFy5hjywzdRXdeqVSt8/fXXVcYjIyM1bq9atQqvvvoqPwxWolKpUFRUJNjjVfchvaYUCgVXKJjAvHnzsHbtWvXtynvyEhEJbcuWLfjqq68wfPhwg89hb28PHx8fxMfHY/To0QAergiPj4/HzJkztd7H398fO3fuRHl5OWxsbAA87DjTvHlzrUVxqvsqT2CYcaKRiJnUPaacwEBEwuK1c8uQk5NjVJyIiCyTQYXxt99+G2FhYYiNjWVLESIiojpq5cqV+Pe//83CeCVFRUUIDAwU7PFM8VhxcXF8v2YCQ4YMURfGbWxs8Oyzz4qcERFZs8aNG6Nt27ZGnyckJASTJk1Cr1694Ovri6ioKBQUFGDy5MkAgIkTJ8LDwwMREREAgOnTp+ODDz7A7Nmz8frrr+PXX3/FypUrMWvWLKNzeVTlVeYFPYIAuV01R5NeylL1yntDVvCT5dJYeV4mXh51SqWfI1f2kza8dm4ZnJyc8Ndff1UbJ3E5OTmhsLCw2jgRUW0ZVBj/97//jc8//xxubm7w9vausp/ZhQsXTJIcERERiYcXeYi0O3LkiNgpEJGVe/vtt7Fs2TJ8+umnRl0QHDt2LO7cuYOlS5ciKysL3bt3x9GjR+Hu7g4AyMjIUK8MBwBPT0/ExcVh7ty56Nq1Kzw8PDB79mwsWLDA6Of0KI1uJ3I7FsZNyJSdZCoX2aMH/AkHCXdxswTFyn9W3ptqAkPlVf3yg3yBTM1UK/srf/YqeThi9DmtXUml74X+bMtr55ZB3+9ZTiQTX0lJiVFxIiJtDCqMT5o0CcnJyRg/fjzc3d3ZnpOIiIisgkKhQFxcnEnOdf/+fbzwwgs6419++SUaNGhg9OMoFAqjz0FERNKyYcMGpKamwt3d3egL7jNnztTZOj0hIaHKmJ+fH86cOVOrfKnuqnw9yEEOSW9vZml4rc26VJ7A8K6IedRVQm9NwGvnlkGpVBoVJ/PTN6mFCzqkQaFQVLvtIK9LkdQYVBg/dOgQ4uLi0K9fP1PnQ0RERCRZMpnMZK3wHB0d0axZM2RlZVWJtWjRAm5ubiZ5HCIiqnsq9gQnItKn8opH5QilgVcCSUPZP6vvuaKUtOG1c8ugr1jHYp74ysvLjYqTMKoritckTiQ0g94Oe3p6wtnZ2dS5EBEREVmVPXv2YMCAAVXGd+3aJUI2RERkKZYtW1aj4z7//HOMHDkS9erVM3NGRCRVGitVbcHCuImZaiVw5QL7AgD2JjmrdSvBP6vvhZ7AwGvnlsHDwwO3bt2qNk7i6tSpE37++edq4yQ+uVxebYcFuZwthUhabPQfUtXatWvx5ptvIi0tzcTpEBEREVmX6dOna9yePXu2SJkQEVFdM23aNGRnZ4udBhER6VG5wG4PwB4yfhn9pf3nKwReO7cM169fNypO5lfdxIWaxEkY+n7HcjsJkhqD5omOHz8ehYWFaNu2LZycnKrsZ3bv3j2TJEdERETi6d+/v8nahpNuo0ePxkcffaS+PWbMGBGzISKiuoT7LhIREQmP184tg6OjI3Jzc6uNk7jy8/ONipMwWrVqhd9++63aOInPxsam2u0HbGwMWkdtkQwqjEdFRZk4DSIiIhJSamoqPv30U6SmpmL9+vVwc3PDkSNH0KpVKzzxxBMAgMOHD4ucpfWJi4sTOwUiIiIiIiIyAq+dW4YuXbrg9u3b1cZJXH5+fvjhhx+qjZP4GjZsaFSchOHo6IiCgoJq49bCoML4pEmTanTcqlWr8Oqrr8LFxcWQhyEiIiIzOH78OJ599ln4+/vjxIkTWLFiBdzc3HDp0iVs3rwZX375pdgpEhEREREREVkkXju3DI0bNzYqTuZ3+fJlo+IkjNatW+PHH3+sNk7ia9q0abWF8aZNmwqYjbjMujZ+5cqVbA1DREQkMQsXLsTy5cvx7bffwt7+n53XBg0ahDNnzoiYGREREREREZF14LVzcf31119Gxcn8OnbsaFSchJGcnGxUnITxxx9/GBWvS8xaGOd+ZkRERNJz5coVPPfcc1XG3dzckJOTI0JGRERERERERNaF187FxfbP0ldUVGRUnIRR3b7VNYmTMFq0aGFUvC6xnt3UiYiICADg4uKidR+tH3/8ER4eHiJkRERERObg5eUFOzs7sdMgIiIikpzc3Fyj4mR+d+/eNSpOwmjUqJFRcRKGXC43Kl6XsDBORERkZV566SUsWLAAWVlZkMlkKC8vx6lTpzB//nxMnDhR7PSIiIioBnJzc7Fp0yaEhoaq27BeuHABt27dUh/z008/wdPTU6wUiYiIiCSrSZMmRsXJ/B5//HGj4iQMtry3DNnZ2UbF6xJbsRMAgOjoaKxevRpZWVno1q0b3n//ffj6+uo8Pjc3F4sWLcLevXtx7949eHl5ISoqCsOGDRMwayIiIsu0cuVKzJgxA56enlAqlejUqROUSiWCgoKwePFisdMjIiIiPS5fvoyAgAA0bNgQaWlpmDp1Kho3boy9e/ciIyMDW7duFTtF0yovEzsD3VSqf/KzsQVkMnHz0UWAn2GxUgZAmm2BVSqg5O8unvY20n2ZHv4MiYhICAMHDsSOHTuqjZO4MjMzjYqTMPLz842KkzAUCoVR8bpE9ML47t27ERISgpiYGPTp0wdRUVEIDAxESkoK3NzcqhxfUlKCZ555Bm5ubvjyyy/h4eGB9PR0uLi4CJ88ERGRBbK3t8fGjRuxdOlSXLlyBfn5+ejRowfat28vdmpERERUAyEhIXj55Zfx3nvvoUGDBurxYcOGISgoSMTMzKPeBd0XrUk6ZpxwETsFIiKiGtu+fbve+PLlywXKhrTx8PDA1atXq42T+Cp/HjEkTsKwta2+HKwvXpeYtZV6//794ejoWO0x69atw9SpUzF58mR06tQJMTExcHJyQmxsrNbjY2Njce/ePezbtw/+/v7w9vbGwIED0a1bN63HFxcXIy8vT+OLiIjImoWHh6OwsBCenp4YNmwY/v3vf6N9+/Z48OABwsPDxU6PiIiI9EhKSsK0adOqjHt4eCArK0uEjIiIiKi2anLtPDo6Gt7e3lAoFOjTpw/OnTtXo3Pv2rULMpkMo0eP1hhXqVRYunQpmjdvDkdHRwQEBODXX3819ClYtCeeeMKoOJkf9xgnMp2ioiKj4nWJwVMAUlNT8emnnyI1NRXr16+Hm5sbjhw5glatWqn/aBw+fLjac5SUlCA5ORmhoaHqMRsbGwQEBCAxMVHrfQ4cOAA/Pz/MmDED+/fvR9OmTREUFIQFCxZo3Rw+IiICYWFhhj5NIjIBlUplEb9YK+doCfkCD1ucyKTaB5AkKywsDK+++iqcnJw0xgsLCxEWFoalS5eKlBkRERHVhIODg9ZJ39evX0fTpk1FyMj0FAoF4uLixE5Dr6KiIowaNQoAsH//fotoQWjKHPk6mY9ZcpTwrgQAHnbiV/79vRyAVD/qSv3nSKKozWIsZ2dnAPqvnde202qFtLQ0zJ8/H/37968Se++997BhwwZs2bIFrVu3xpIlSxAYGIhffvnFIn43mtLvv/9uVJzMr1mzZkbFSRj6JtdY6+QbqenatSu+//77auPWwqDC+PHjx/Hss8/C398fJ06cwIoVK+Dm5oZLly5h8+bN+PLLL2t0npycHCiVSri7u2uMu7u749q1a1rv89tvv+H7779HcHAwDh8+jBs3buC1115DaWkpli1bVuX40NBQhISEqG/n5eXB09OzFs+WiIxVVFSEwMBAsdOolYqLJlIXFxend3Yx0aNUKpXWCRWXLl1C48aNRciIiIiIamPkyJEIDw/Hnj17AAAymQwZGRlYsGABxowZI3J2piGTySzufa5CobC4nI3F18myyA9WXVBCRKbh4uJS44ULSqVS/0HQ7LQKADExMTh06BBiY2OxcOFCnecODg5GWFgYfvjhB+Tm5qpjKpUKUVFRWLx4sfq619atW+Hu7o59+/bhpZdeqlFedUXv3r3x9ddfVxsncembcMLuwNLg6upqVJyEwRXj/zColfrChQuxfPlyfPvtt7C3t1ePDxo0CGfOnDFZctqUl5fDzc0Nn3zyCXx8fDB27FgsWrQIMTExWo93cHCAs7OzxhcREZE1atSoERo3bgyZTIbHHnsMjRs3Vn81bNgQzzzzDP7973+LnSYRERHpsXbtWuTn58PNzQ0PHjzAwIED0a5dOzRo0AArVqwQOz0iIiKrc+zYMXz//ff4/vvvERsbCzc3N7z55pv4v//7P/zf//0f3nzzTbi7u+vcPvRRFZ1WAwIC1GP6Oq0CD7dOc3Nzw5QpU6rEbt68iaysLI1zNmzYEH369Kn2nHV1q9Lr168bFSfz07fAkQsgpeHOnTtGxUkYnGjyD4NWjF+5cgU7d+6sMu7m5oacnJwan8fV1RVyuRzZ2dka49nZ2TrbYDRv3hx2dnYabdMff/xxZGVloaSkRKNQT0TSoxyhNGITBzOzoLZtnOlPhoiKioJKpcJ//vMfhIWFoWHDhuqYvb09vL294efnJ2KGREREVBMNGzbEt99+i1OnTuHSpUvIz89Hz549NS50ExEBltPuHmDL+wolAB5eoJAmFYDSv7+3g3QvnZQI/HgDBw5Ufx8eHo5169Zh3Lhx6rGRI0eiS5cu+OSTTzBp0iS95zOk0+rJkyexefNmXLx4UWs8KytLfY5Hz1kR06aublXavn17o+Jkfvq2CKorWwhZusceeww//vhjtXES371794yK1yUGladcXFxw+/ZttG7dWmP8xx9/hIeHR43PY29vDx8fH8THx2P06NEAHq4Ij4+Px8yZM7Xex9/fHzt37kR5eTlsbB4ueL9+/TqaN2/OojiRJbCFdAvjwMNPdUR1VMWH79atW6Nv376ws+M/eCIiIku0detWjB07Fv7+/vD391ePl5SUYNeuXZg4caKI2RGRlFhiu3vAulvevyt2AmS0xMRErd1Ne/XqhVdeecUsj3n//n1MmDABGzduNHnb4rq6VemFCxf0xgcNGiRQNqSNm5ubUXESRn5+vlFxEkbjxo3xv//9r9q4tTColfpLL72EBQsWICsrCzKZDOXl5Th16hTmz59f6w/gISEh2LhxI7Zs2YKrV69i+vTpKCgoUO+dMnHiRISGhqqPnz59Ou7du4fZs2fj+vXrOHToEFauXIkZM2YY8lSIiIiszsCBA9VF8aKiojrZEo2IiKgumzx5Mv76668q4/fv31d/liYiIiJxeHp6YuPGjVXGN23aVOOCcm07raampiItLQ0jRoyAra0tbG1tsXXrVhw4cAC2trZITU1V36823VuBurtV6fDhw42Kk/nJZNX3pNAXJ2E899xzRsVJGKWlpUbF6xKD1m1WFKI9PT2hVCrRqVMnKJVKBAUFYfHixbU619ixY3Hnzh0sXboUWVlZ6N69O44ePapu6ZKRkaFeGQ48fGMRFxeHuXPnomvXrvDw8MDs2bOxYMECQ54KERGR1SksLMSbb76JPXv24O7du1XiSqVSy72IiIhIKlQqldYLgZmZmRpbpRARkWVgy3vzEjrHyMhIjBkzBkeOHEGfPn0AAOfOncOvv/6Kr776qkbnqG2n1Y4dO+LKlSsaY4sXL8b9+/exfv16eHp6ws7ODs2aNUN8fDy6d+8O4OHq77Nnz2L69OmGP2ELdeDAAb3xxx9/XKBsSBsWxi3Do797tMXZTl18np6eSElJqTZuLQwqjNvb22Pjxo1YunQprly5gvz8fPTo0cPgfTdmzpyps3V6QkJClTE/Pz+cOXPGoMciIiKydm+88QaOHTuGjz76CBMmTEB0dDRu3bqFjz/+GKtWrRI7PSIiItKhR48ekMlkkMlkGDx4MGxt//lIr1QqcfPmTQwdOlTEDImIyBBseV+3DBs2DL/++is++ugjXL16FQAwYsQIvPrqq7UqPISEhGDSpEno1asXfH19ERUVVaXTqoeHByIiIqBQKNC5c2eN+7u4uACAxvicOXOwfPlytG/fHq1bt8aSJUvQokULdfHdmuhb+V5XVsZbMn3b9tZmW18yH33bN5h6ewcyTHFxsVHxusSgwnh4eDjmz58PT09PjT/mDx48wOrVq7F06VKTJUhERESmdfDgQWzduhVPPfUUJk+ejP79+6Ndu3bw8vLCjh07EBwcLHaKREREpEXFReuLFy8iMDAQ9evXV8fs7e3h7e2NMWPGiJQdERERAQ87oHp6emLFihVaY61atarReWrbabUm3nzzTRQUFOC///0vcnNz0a9fPxw9etQiVv6bWm5urlFxMj99HQ3Z8VAa5HK5UXEShr6JbNY00c2gwnhYWBheffVVODk5aYwXFhYiLCyMhXEiIiIJu3fvHtq0aQPg4Qzoe/fuAQD69etnUPu06OhorF69GllZWejWrRvef/99+Pr66r3frl27MG7cOIwaNQr79u2r9eMSERFZm2XLlgEAvL29MXbsWKu8iE1ERCR1rVu3xu3bt+Hm5qYxfvfuXbRu3bpWxbzadlqt7LPPPqsyJpPJEB4ejvDw8BrnUFe1a9fOqDiZ3/Hjx/XGDe1iTKZTMVnH0DgJIyMjw6h4XVK7KWV/07Wf2aVLl9C4cWOjkyIiIiLzadOmDW7evAng4T5ke/bsAfBwJXlFq7Wa2r17N0JCQrBs2TJcuHAB3bp1Q2BgIP74449q75eWlob58+ejf//+Bj0HIiIiazZp0iQWxYmIiCRK17Xz/Px8/v2WkB49ehgVJ/ObNGmSUXESRk0mMJD4unXrZlS8LqnVivFGjRqp9zN77LHHNP7AK5VK5Ofn49VXXzV5kkRERGQ6kydPxqVLlzBw4EAsXLgQI0aMwAcffIDS0lKsW7euVudat24dpk6dqt7jLCYmBocOHUJsbCwWLlyo9T5KpRLBwcEICwvDDz/8oLc9WXFxscY+N3l5ebXKkYiIqK5RKpWIjIzEnj17kJGRgZKSEo14RTcYIiIiEk5ISAiAh6uylyxZotFtValU4uzZs+jevbtI2dGjbt++rTfOVePiyszM1Btv3bq1QNmQLhMmTMDWrVurjZP4OnfujN27d1cbtxa1KoxHRUVBpVLhP//5D8LCwtCwYUN1rGI/Mz8/P5MnSURERKYzd+5c9fcBAQG4du0akpOT0a5dO3Tt2rXG5ykpKUFycjJCQ0PVYzY2NggICEBiYqLO+4WHh8PNzQ1TpkzBDz/8oPdxIiIiEBYWVuO8iIiI6rqwsDBs2rQJ8+bNw+LFi7Fo0SKkpaVh37593NqMiIhIJD/++COAhyvGr1y5Ant7e3XM3t4e3bp1w/z588VKjx5RXl5uVJzMj3uMW4YDBw7ojb/44osCZUO65OfnGxWvS2pVGK9oTdG6dWv07dsXdnZ2ZkmKiIiIzKO0tBRDhw5FTEyMeh8mLy8veHl51fpcOTk5UCqVVfYKcnd3x7Vr17Te5+TJk9i8eTMuXrxY48cJDQ1Vz7wHHq4Y9/T0rHW+REREdcWOHTuwceNGDB8+HG+//TbGjRuHtm3bomvXrjhz5gxmzZoldopERERW59ixYwAedmlbv349nJ2dRc6IqnP9+nW98YEDBwqUDWnz/fff641zVb/4Tp8+rTfOwrj49HUJXbduHYYPHy5QNuIyaI/xgQMHqoviRUVFyMvL0/giIiIiabKzs8Ply5dFeez79+9jwoQJ2LhxI1xdXWt8PwcHBzg7O2t8ERERWbOsrCx06dIFAFC/fn389ddfAIB//etfOHToUK3PFx0dDW9vbygUCvTp0wfnzp3Teexnn32m3mKt4ov7pRIREf3j008/1fjcmpeXh3379umcQE7iePrpp42Kk/m5uLgYFSdhTJ8+3ag4CcPf39+oeF1SqxXjFQoLC/Hmm29iz549uHv3bpV4XWhhoVKp/rmhLBUvkbqk0s9R4+dLRESCGj9+PDZv3oxVq1YZdR5XV1fI5XJkZ2drjGdnZ6NZs2ZVjk9NTUVaWhpGjBihHqtoTWZra4uUlBS0bdvWqJyIiIisQcuWLXH79m20atUKbdu2xTfffIOePXsiKSkJDg4OtTrX7t27ERISgpiYGPTp0wdRUVEIDAxESkoK3NzctN7H2dkZKSkp6tsymcyo50NERFSX/Pvf/8aAAQMwc+ZMPHjwAL169UJaWhpUKhV27dqFMWPGiJ0iAfD29jYqTub3559/GhUnYbRp08aoOAmjRYsWRsXrEoMK42+88QaOHTuGjz76CBMmTEB0dDRu3bqFjz/+2OiL7FJRXFys/r7ejztFzKRuKi4uhpOTk9hpEBFZpbKyMsTGxuK7776Dj48P6tWrpxHX11qngr29PXx8fBAfH4/Ro0cDeFjojo+Px8yZM6sc37FjR1y5ckVjbPHixbh//z7Wr1/P9uhEREQ19NxzzyE+Ph59+vTB66+/rp70lpGRgblz59bqXOvWrcPUqVMxefJkAEBMTAwOHTqE2NhYLFy4UOt9ZDKZ1klw2hQXF2t8vmaXOSIiqutOnDiBRYsWAQD+7//+DyqVCrm5udiyZQuWL1/OwrhEHDx4UG/8ueeeEygb0mbgwIHYsWNHtXES3/79+/XG+XtPfIMHD8bOnbprnYMHDxYwG3EZVBg/ePAgtm7diqeeegqTJ09G//790a5dO3h5eWHHjh0IDg42dZ5ERERkIj/99BN69uwJoOqeWrVd8RUSEoJJkyahV69e8PX1RVRUFAoKCtQX1ydOnAgPDw9ERERAoVCgc+fOGvevaHv16DgRERHpVnlC+tixY+Hl5YXTp0+jffv2Gp1Z9CkpKUFycjJCQ0PVYzY2NggICEBiYqLO++Xn58PLywvl5eXo2bMnVq5ciSeeeELrsREREQgLC6txTkRERJbur7/+QuPGjQEAR48exZgxY+Dk5IThw4fjjTfeEDk7qjBixAhERkZWGydxVVcUr4i/8847AmVDulR0gzQ0TsLIysrSG2/fvr1A2YjLoML4vXv31O0PnJ2dce/ePQBAv3796sx+AZXbzxX0CALkdiJmU0coS9Wr72vb3o+IiEzn2LFjNTouMzMTLVq0gI2Njc5jxo4dizt37mDp0qXIyspC9+7dcfToUbi7uwMAMjIyqr0/ERER1U5paSmmTZuGJUuWoHXr1gCAJ598Ek8++WStz5WTkwOlUqn+u13B3d1d5z6oHTp0QGxsLLp27Yq//voLa9asQd++ffHzzz+jZcuWVY4PDQ1FSEiI+nZeXh67xBARUZ3m6emJxMRENG7cGEePHsWuXbsAPGz7rFAoRM6OKvz+++964xXvtUgcwcHBOH78eLVxEt+jnyVqGydh6Nve2Jq2PzaoMN6mTRvcvHkTrVq1QseOHbFnzx74+vri4MGD6pVflk5jxZzcjoVxE+MedERE0tepUydcvHhR715AM2fO1No6HQASEhKqve9nn31mYHZERETWyc7ODl999RWWLFkiyuP7+fnBz89Pfbtv3754/PHH8fHHH2tdsePg4MCJ0UREZFXmzJmD4OBg1K9fH15eXnjqqacAPGyx3qVLF3GTI7Igv/zyi954x44dBcqGdGFh3DLoq8lZU83OoCVckydPxqVLlwAACxcuRHR0NBQKBebOnct2MERERHWENc0UJCIisiSjR4/Gvn37jD6Pq6sr5HI5srOzNcazs7NrvIe4nZ0devTogRs3bhidDxERUV3w2muv4cyZM4iNjcXJkyfVXdTatGmD5cuXi5wdVfD29kbXrl21xrp16wZvb29hE6IqunXrZlSchFHdvtU1iRMJzaAV43PnzlV/HxAQgGvXriE5ORnt2rXT+ceEiIiIiIiIiIzXvn17hIeH49SpU/Dx8UG9evU04rNmzarReezt7eHj44P4+HiMHj0awMM9AOPj43V2g3mUUqnElStXMGzYsFo9ByIiorrMx8cHPj4+GmPDhw/XuO3s7FyjLm1kHjKZDAsXLkRQUFCV2MKFC61q9aRUtW7dGra2tigrK6sSs7W1Zat7iVi0aFG12zYuWrRIwGxIl+bNmxsVr0tqXRgvLS3F0KFDERMTo96I3cvLC15eXiZPjoiIiIiIiIg0bd68GS4uLkhOTkZycrJGTCaT1bgwDgAhISGYNGkSevXqBV9fX0RFRaGgoACTJ08GAEycOBEeHh6IiIgAAISHh+PJJ59Eu3btkJubi9WrVyM9PR2vvPKK6Z4gERGRFWCXNmmQyWQar8Wjt0k8N2/e1FoUB4CysjLcvHkTbdu2FTgrepStra3OCQx2dnawtTVofS6Z2O3bt/XG27VrJ1A24qr1v0g7OztcvnzZHLkQEZGIVCoVioqKxE5Dr8o5WkK+AKBQKDjTmIiIiEzm5s2bJjvX2LFjcefOHSxduhRZWVno3r07jh49qt4LMCMjQ90CFgD+/PNPTJ06FVlZWWjUqBF8fHxw+vRpdOrUyWQ5EREREZmbSqVCZGQkbGxsoFQq1eM2NjaIjIzEmjVreC1HZBcvXtQbZ2FcfImJiTonMJSWliIxMRH+/v4CZ0WPenT7rNrG6xKDpmqMHz8emzdvxqpVq0ydDxERiaSoqAiBgYFip1Ero0aNEjuFGomLi4Ojo6PYadQaPwASERFZtpq2aJ05c6bO1ukJCQkatyMjIxEZGWmqFImIiIhEkZ6ejqSkpCrjSqUSSUlJSE9P5z7jInNzczMqTsLo06ePUXESRuPGjY2K1yUGFcbLysoQGxuL7777Tut+ZuvWrTNJckRERCQetg4jIiKybPxbTkRERKSdl5cXevfujfPnz1dppd67d29uHSsB/v7+1bbo5ipkaTh79qzeOF8r8Z0/f15vfNCgQQJlIy6DCuM//fQTevbsCQC4fv26Royry0hIld+0FCurOZBqrPLPkRfSrNcCAPZiJ6GDCkDp39/bAZDqX50SAO+KnYQWpaWlcHR0xMWLF9G5c+dqj/3ll1/QokULgTIjIiIiIiIish68ji4umUyGcePGVVk1rlKpMG7cOL4+EqBSqVBeXq41plQqee1aIpo3b25UnIShrwOGNXXIMKgwfuzYsRodl5mZiRYtWmjsR0ZkSsXFxervZ5xoJGImdVNxcTGcnJzEToNEYA/AXrIlZ8BB7ARqRJpvzu3s7NCqVSuN/bN08fT0FCAjIiIiIiIiIuvDop64VCoV3n//fa2xDRs24LPPPmNxXGT79+/XWRgvLy/H/v378fzzzwucFT3K29sbTk5OKCwsrBJzcnKyqoKrlPXq1cuoeF1iUGG8pjp16lSj/cyIiIhIOIsWLcJbb72Fbdu2WdX+MUREdZlKpUJRUZHYaehVOUdLyLeCQqHghVEiIiLSq7S0FB07dsTXX3+Nxx9/vNpjjxw5Ag8PD4Eyo0f99ttvuHnzptbYzZs38dtvv6Ft27YCZ0WV6et0qC9OwsjIyNBaFAeAwsJCZGRksDguAa1atTIqXpeYtTDOWW9kbg4O/6wbjR7wJxzkIiZTRxQr/1l9X/nnS0R1xwcffIAbN26gRYsW8PLyQr169TTiFy5cECkzIiIyVFFREQIDA8VOo1ZGjRoldgo1FhcXB0dHR7HTqDUW84mIiIRlZ2dX48l//fr1M3M2VJ1Lly7pjbMwLq7jx4/rjT/22GMCZUO6eHl5oXfv3lW2JQAAX19feHl5iZAVPergwYN6488995xA2YjLrIVxInOrfKHHQQ4oWBg3KV5II6qbRo8eLXYKREREJABOViciIhLejBkz8O6772LTpk2wteXld6nq2rWrUXEyv/79+2Pbtm3Vxkl8MpkM48aN01oYHzduHGsMEtGhQwej4nUJ/zITERFZmWXLlomdAhERmdECAPZiJ6GDCkDp39/bAZDyJZISAO+KnYQWbNFKREQkbUlJSYiPj8c333yDLl26VOnStnfvXpEyo8pu376tN96uXTuBsiFtvv76a73xjh07CpQN6aJSqfD5559DJpNpTMyVyWTYuXMnevbsyeK4BMTGxuqNr1mzRqBsxMXCOBERkRXKzc3Fl19+idTUVLzxxhto3LgxLly4AHd3d15AJyKycPYA7CVccraczXqkudqaLVqJiIikzcXFBWPGjBE7DdKjZcuWRsXJ/EaPHo0DBw5UGyfxpaena10trlKpkJSUhPT0dO4xLgHLly/HkCFDqo1bC7MWxjkLhIiISHouX76MgIAANGzYEGlpaZg6dSoaN26MvXv3IiMjA1u3bhU7RSKSEJVKVeMinJgq52gJ+VZQKBT83ES1xhatRERE0vXpp5+KnQLVgJeXFxQKhdbPDo6OjtwXWQJat25tVJyE4eXlha5du+Ly5ctVYl27duX/JYn43//+pzfepk0bgbIRl1k/QXM/MyIiIukJCQnByy+/jPfeew8NGjRQjw8bNgxBQUEiZkZEUlRUVITAwECx06iVUaNGiZ1CjcXFxcHR0VHsNMjCsEUrERGRtJWVlSEhIQGpqakICgpCgwYN8L///Q/Ozs6oX7++2OkRHq5y1TWh9sGDB0hPT2fhVWRnzpzRG/f39xcoG6qOrloga4TSUZPtI1gY16G0tBSOjo64ePEiOnfuXO2xv/zyC1q0aGFwckRERGR6SUlJ+Pjjj6uMe3h4ICsrS4SMiIiIqDbYopWIiEi60tPTMXToUGRkZKC4uBjPPPMMGjRogHfffRfFxcWIiYkRO0Uii/Dkk09CLpdDqVRWidna2uLJJ58UISt6VHp6Oq5cuaI1duXKFbZSl4g+ffoYFa9Lal0Yt7OzQ6tWrbT+MnqUp6enQUkRERGR+Tg4OCAvL6/K+PXr19G0aVMRMiIiS6EcoTRzzykjqABUfESRAxLeYhsoA+QH5WJnQRaMLVqJiIika/bs2ejVqxcuXbqEJk2aqMefe+45TJ06VcTMqDJvb2+d7Z+7devGQp4E/P777zrrUGVlZfj999/5OkmAl5cXunTporU4zlbq0nH27Fm9cWvpwGBjyJ0WLVqEt956C/fu3TN1PkRERGRmI0eORHh4OEpLSwEAMpkMGRkZWLBgAVefEVH1bCX8ZQdA8feXnQTy0fdFZKSysjJ89913+Pjjj3H//n0AD/eFy8/PFzkz62Vp205YK75ORGRuP/zwAxYvXgx7e3uNcW9vb9y6dUukrOhRMpkMfn5+WmN+fn6QyaQ809Y6eHl5oXfv3lpjvr6+LLhKCP+/SJ+fnx+cnZ21xho2bKjz92FdZFBh/IMPPsCJEyfQokULdOjQAT179tT4IiIiIulau3Yt8vPz4ebmhgcPHmDgwIFo164dGjRogBUrVoidHhEREemRnp6OLl26YNSoUZgxYwbu3LkDAHj33Xcxf/58kbOzLosWLdK4/cYbb4iUCVUnKipK4/bq1avFSYSIrEJ5ebnWVa6ZmZlo0KCBCBmRNkqlEps2bdIa27hxY4065pJ5yWQyzJ07t0rRVdc4iSM9PV1r5wUAuHz5MtLT0wXOiLSxsbHBjBkztMZmzpwJGxuDysUWyaC1CqNHjzZxGkRERCSUhg0b4ttvv8XJkydx+fJl5Ofno2fPnggICBA7NbIyKpUKRUVFYqdRI5XztJScFQoFLxQQ1VFs0Sod58+f17itr0UhiePIkSMatw8ePMhJDERkNkOGDEFUVBQ++eQTAA+LePn5+Vi2bBmGDRsmcnZU4cCBAzqL30qlEgcOHMBzzz0ncFZUUyqVSuwU6G8VK/uTk5NRXl6uHpfL5fDx8eHKfolQqVQ4dOiQ1tjBgwcxZMgQq7mGZFBhfNmyZabOg4iIiATWr18/9OvXT+w0yIoVFRVZZDvTUaNGiZ1CjcTFxcHR0VHsNIjIDH744QecPn2aLVprydQTsnT9DRswYADi4uJM8hjWOMnJ1K/T8OHDtY4PGjRI58XB2rLG14mIdFu7di0CAwPRqVMnFBUVISgoCL/++itcXV3x+eefi50e/W3kyJHYsGGD1uK4ra0tRo4cKUJWVJlKpUJkZCRkMplGIVwmkyEyMhJr1qzh318JqFjBP2HCBK3jfI2kIS0trdqV/WlpaWjdurXAWYnD4N3tcnNz8eWXXyI1NRVvvPEGGjdujAsXLsDd3R0eHh6mzJGIiIiMtGHDhhofO2vWLDNmQkRERMZii1bDCDkhy1SPY42TnIR6ncrKyvg6EZFZtGzZEpcuXcKuXbvUXdqmTJmC4OBg/q6QELlcjjfffBMRERFVYgsXLoRcLhchK6osPT0dSUlJVcbLy8uRlJSE9PR0eHt7C58YVdGyZUt06tQJV65cUY916tSJtUKSJIMK45cvX0ZAQAAaNmyItLQ0TJ06FY0bN8bevXuRkZGBrVu3mjpPIiIiMkJkZKTG7Tt37qCwsBAuLi4AHk54c3JygpubW50ojLNFt3mZY1VU9IBcOMil2wpNpQJK/u4IZm8DSHXCc7FShhknXMROg4jMjC1aiYiIpM3W1hbjx48XOw3S49lnn8XGjRuRk5OjHmvatCmGDBkiYlZUoaJF9/nz56usGO/duzdbdEtIZmYmfv75Z42xn3/+GZmZmWjZsqVIWVFl3t7eeOyxx3D9+vUqsQ4dOljVJBODCuMhISF4+eWX8d5772nMRh82bBiCgoJMlhwRERGZxs2bN9Xf79y5Ex9++CE2b96MDh06AABSUlIwdepUTJs2TawUTYotus3LHKuiHOQqKCQ+Id8y1nZId3IBEZkOW7QaRqFQmKzFeU3eZ5jisRQKhdHnsDR8nQh4+NqdOHFC7DSIauzAgQM1PpYtuqVlwIAB2Lt3r8ZtkgaZTIZx48ZVWTWuUqkwbtw4tuiWiMot7x/FlvfSous9q4ODg8CZiMugwnhSUhI+/vjjKuMeHh7IysoyOikiIiIynyVLluDLL79UF8WBhzMDIyMj8cILLyA4OFjE7IiIiEgftmg1jEwmE/Tnw9fCMHydrNfy5cs1bi9duhTh4eEiZUNUO6NHj9a4/eieyBVjALRuh0LiyMzMxP79+zXG9u/fjzFjxnCVqwSoVCqdkz537tyJnj17suAqAbpa3iuVSra8l5D09PRq9xi3ptfJoMK4g4MD8vLyqoxfv34dTZs2NTopIiIiMp/bt2+jrKysyrhSqUR2drYIGZlXQc9gwMagtzzCUKmA8r9fDxtb6fboLi9DvQs7xM6CiIj+xhatRFTX/PDDDxq3ExISxEmEyADl5eXq77/77jssWLAAK1euhJ+fHwAgMTERixcvxsqVK8VKkR5RscpV1zhXuYpPV8EVAAuuElLR8v7ChQsaE3/kcjl8fHzY8l4iKl4nbf+nfH19rep1Mugq8ciRIxEeHo49e/YAeDjbLSMjAwsWLMCYMWNMmiARWT6NGbJVa3FUW5V+ho/OPiaqicGDB2PatGnYtGkTevbsCQBITk7G9OnTERAQIHJ2ZmBjC8jtxM5CD3uxEyAiIolji1YikhKVSoWioiKTnU9X2/sBAwaYrLW+QqFgkYsEMWfOHMTExKBfv37qscDAQDg5OeG///0vrl69KmJ2VIGrXKWvVatWcHZ21rpI09nZGa1atRIhK3qUTCbD3LlzMWHCBK3j/NsrDTKZDAEBAVp/7wUEBFjV62RQYXzt2rV44YUX4ObmhgcPHmDgwIHIysqCn58fVqxYYeocicjCFRcXq7+XH5T4BrIWpri4GE5OTmKnQRYmNjYWkyZNQq9evWBn97BgXFZWhsDAQGzatEnk7IiIiEgbtmglIikpKiqq0R7upmCqx4mLi2PrfBJEamoqXFxcqow3bNgQaWlpgudD2nl5eaFr165aWwt37drVqlZPSlVGRobWojgA5OXlISMjg5MXJKJly5YIDg7Gtm3boFKpIJPJEBwcDA8PD7FTo7+Vl5cjOjpaa+yDDz7AkCFDYGNjI3BW4jDoWTZs2BDffvstDh48iA0bNmDmzJk4fPgwjh8/jnr16pk6RyIiIjIRlUqFBw8e4KuvvkJKSgq++OILfPHFF7h69SoOHz4MNzc3sVMkIiIiLcrLy9Vf33zzDbp3744jR44gNzcXubm5OHLkCHr27ImjR4+KnSoREVkJoSZHWJrevXsjJCREY6uy7OxsvPHGG/D19RUxM3qUrk6M7NAoDRUrxrXhinHpGT9+PJo0aQIAcHV1RXBwsMgZUWWJiYnVTjRJTEwUOCPxGLXhZr9+/TRawhARaePg4KD+XjlCaeRvHkLZPyvvK/9siWpCpVKhXbt2+Pnnn9G+fXu0b99e7JSIiIioltiilYjEplAoTNbivCbFVVM8lkKhMPocBLzyyisatydMmIBt27aJlI00bd68Gc8//zxatWoFT09PAMDvv/+O9u3bY9++feImR2rp6em4cuWK1tiVK1fYSl0CuGLcsigUCsybNw9RUVGYM2cO/+5KjJ+fn86tCRo2bAg/Pz8RshJHjctTGzZsqPFJZ82aZVAyRFQ3aexPYQsWxk3Imvb+INOwsbFB+/btcffuXZMVxaOjo7F69WpkZWWhW7dueP/993XOgt+7dy9WrlyJGzduoLS0FO3bt8e8efOq7ENEREREurFFKxGJTSaTCdqWnC3QpeP333/XuJ2eni5SJtLVvn17XL58Gd9++y2uXbsGAHj88cetbg9XqfPy8kKXLl20FsfZSl0avLy80Lt3b5w/f15jFb9MJkPv3r35GkmQv78//P39xU6DtLCxscGMGTMQERFRJTZz5kyraaMO1KI8FRkZqXH7zp07KCwsVH8Yz83NhZOTE9zc3FgYJyIikrBVq1bhjTfewEcffYTOnTsbda7du3cjJCQEMTEx6NOnD6KiohAYGIiUlBStbdkbN26MRYsWoWPHjrC3t8fXX3+NyZMnw83NjW34iIiIaqiiReu2bdvg7u4OwLgWrbWZ5FbZrl27MG7cOIwaNYor4IiIJEilUqGoqMhk59P1mW3AgAEm6yCgUCgsunhcWloKR0dHXLx4EUOGDMGQIUPETomqYcn/1qyBTCbD3LlzMWHCBCiVSvW4XC7H3Llz+foR1YJKpcJ3332nNfbNN99gyJAhVvN/qsaF8Zs3b6q/37lzJz788ENs3rwZHTp0AACkpKRg6tSpmDZtmumzJCIiIpOZOHEiCgsL0a1bN9jb21dZfXHv3r0an2vdunWYOnUqJk+eDACIiYnBoUOHEBsbi4ULF1Y5/qmnntK4PXv2bGzZsgUnT55kYZyIiKiGTNmitbaT3CqkpaVh/vz56N+/vzFPhYiIzKioqEiwz1mmepy4uDiL7hBgZ2eHVq1aaRTxSJrS09Nx+fJlrbHLly+zlbpEtGzZEsHBwdi2bRtUKhVkMhmCg4Ph4eEhdmpEFiU9PR1JSUlaY0lJSVb1O8+gtfFLlizB+++/ry6KA0CHDh0QGRmJxYsXmyw5IiIiMr2oqCh88skniI2NRUxMDCIjIzW+aqqkpATJyckICAhQj9nY2CAgIACJiYl6769SqRAfH4+UlBQMGDBA53HFxcXIy8vT+CIiIrJmFS1aDx48iFmzZmHWrFn4+uuvceXKFbRr165W56o8ya1Tp06IiYmBk5MTYmNjdd5HqVQiODgYYWFhaNOmjbFPh4iIqE5ZtGgR3nrrrVpNOtclOjoa3t7eUCgU6NOnD86dO6fz2L1796JXr15wcXFBvXr10L179yr7v7/88suQyWQaX0OHDjU6T0tU0UpdG7ZSl5bx48ejSZMmAABXV1cEBweLnBGR5anYmkAul2uMy+Vy+Pr6WtXvPIN2+r19+zbKysqqjCuVSmRnZxudFBEREZnPpEmTTHKenJwcKJVKdQvXCu7u7up91LT566+/4OHhgeLiYsjlcnz44Yd45plndB4fERGBsLAwk+RMRERk6UzZorVikltoaKh6rCaT3MLDw+Hm5oYpU6bghx9+qPYxiouLUVxcrL7NCW5ERMJRKBQma3FekxXhpngshUJh9DnE9sEHH+DGjRto0aIFvLy8UK9ePY34hQsXanQec21dNnToUHz66afq2w4ODgY+U8tXUlKidbzyexcSn0KhwLx58xAVFYU5c+bUid8TREKrvDWBtnFraaMOGFgYHzx4MKZNm4ZNmzahZ8+eAIDk5GRMnz5dY9UYERERSZNSqcS+fftw9epVAMATTzyBkSNHVpk1aA4NGjTAxYsXkZ+fj/j4eISEhKBNmzZV2qxXCA0NRUhIiPp2Xl6eum0sERGRtTFli1ZDJrmdPHkSmzdvxsWLF2v0GHV1gpurqytycnKqjRMRiU0mkwnaltySW6Cb0ujRo01yHnNtXebg4IBmzZrVOI+6OsktLS0NKSkpWmMpKSlIS0tD69atBc6KdPH394e/v7/YaRBZNG5N8JBBhfHY2FhMmjQJvXr1gp2dHQCgrKwMgYGB2LRpU63PFx0djdWrVyMrKwvdunXD+++/D19fX73327VrF8aNG4dRo0bVeh81IiIia3Xjxg0MGzYMt27dUm+LEhERAU9PTxw6dAht27at0XlcXV0hl8urdIvJzs6u9kO2jY2Nus1r9+7dcfXqVUREROgsjDs4OFj1DHYiIqJHVbRo3bZtGxo3bizY496/fx8TJkzAxo0ba1z4rasT3KoritckTkREddeyZcuMPoehXV0qqFQqfP/990hJScG7776rEUtISICbmxsaNWqEQYMGYfny5eo21drU1UluRETWaPz48di3bx/y8vLg7OxslVsT1LowrlKp8ODBA3z11VfIzMxUrzTr2LEjHnvssVonUNuWMBXS0tIwf/589O/fv9aPSUREZM1mzZqFtm3b4syZM+qL6Xfv3sX48eMxa9YsHDp0qEbnsbe3h4+PD+Lj49Uz4svLyxEfH4+ZM2fWOJ/y8nK2KSMiIqoFU7Vore0kt9TUVKSlpWHEiBHqsfLycgCAra0tUlJSqkyw4wQ3ItJHJpNBpVJVGyeyRMnJyRpd2nr06FHj+5pr67KhQ4fi+eefR+vWrZGamoq33noLzz77LBITE3V2kKurk9y8vb3RpUsXXLlypUqsa9eu8Pb2Fj4pIiIBVLzvqvgsZ20MKoy3a9cOP//8M9q3b4/27dsblUBtW8IAD9u/BgcHIywsDD/88ANyc3ONyoGIiMiaHD9+XKMoDgBNmjTBqlWrat2WKiQkRN1FxtfXF1FRUSgoKFD/XZ84cSI8PDwQEREB4OFM8169eqFt27YoLi7G4cOHsW3bNnz00Ueme4JEZFIaF6rLxMujTqn0c6yuEFAblc/zcKdE05zXmlXecdJUr5OpmKpFa20nuXXs2LHKxePFixfj/v37WL9+fZ24SF5TCoUCRUVF1caJqGbs7Ox07vNbESeyJH/88QdeeuklJCQkwMXFBQCQm5uLp59+Grt27ULTpk3N9tj6ti576aWX1Md26dIFXbt2Rdu2bZGQkIDBgwdrPWddneQmk8kQGhqK4OBgjfd6FeOclENEddH27duRn58PAMjPz8eOHTswZcoUkbMSVq0L4zY2Nmjfvj3u3r1rdFHc0JYw4eHhcHNzw5QpU/DDDz9U+xh1dQ8UIiIiQzk4OOD+/ftVxvPz82Fvb1+rc40dOxZ37tzB0qVLkZWVhe7du+Po0aPqWe0ZGRmwsbFRH19QUIDXXnsNmZmZcHR0RMeOHbF9+3aMHTvWuCdFRGZT+b20/KD2VSRkuOLiYjg5OZnkPBXereY4MoypXidTMUWL1gq1meSmUCjQuXNnjftXXPB/dLyuKy0tNSpORP+orihekziR1Lz++uu4f/8+fv75Zzz++OMAgF9++QWTJk3CrFmz8Pnnn+s9h1Bbl7Vp0waurq64ceOGzsJ4XdayZUuMGzcOO3fuVI8FBQVZ3X67RGQdMjMzsWPHDvVkIJVKhR07diAwMBAtW7YUOTvh2Og/pKpVq1bhjTfewE8//WTUg1fXEiYrK0vrfU6ePInNmzdj48aNNXqMiIgINGzYUP1lTTPYiYiItPnXv/6F//73vzh79ixUKhVUKhXOnDmDV199FSNHjqz1+WbOnIn09HQUFxfj7Nmz6NOnjzqWkJCAzz77TH17+fLl+PXXX/HgwQPcu3cPp0+fZlGciIjIQMnJydi+fTu2b9+OH3/80aBzjB07FmvWrMHSpUvRvXt3XLx4scokt9u3b5sy7TrhySefNCpOwnB2djYqTkRkiKNHj+LDDz9UF8UBoFOnToiOjsaRI0dqdI7KXV0qVHR18fPzq3Eu+rYuy8zMxN27d9G8efMan7Ouefnll9GgQQMAD/8uTJo0SeSMiIhMT6VSITIyUue41LqkmVOtV4wDD2eMFxYWolu3brC3t4ejo6NG/N69eyZJ7lH379/HhAkTsHHjRri6utboPnV1DxQiIiJDbdiwAZMmTYKfn5+6LWFZWRlGjhyJ9evXi5wdWZPKb7qLlSImUodU/jma6kNN5baJyhFKAz9BkIayf1bfm6otZeXzLABQu/4fpE0J/ll9L7X2oaZu0Tpz5kytrdOBh5PcqlN5Apw1OX36tFFxEka9evWq7RxYr149AbMhImtRXl6udQsAOzu7Wu3nauqty/Lz8xEWFoYxY8agWbNmSE1NxZtvvol27dohMDDQBM/cMikUCrz11luIiorCnDlzuB0KEdVJ6enpSEpKqjKuVCqRlJSE9PR0eHt7C5+YCAy6rBUVFWWSB69tS5jU1FSkpaVhxIgR6rGKNxO2trZISUlB27ZtNe5TV/dAISIiMpSLiwv279+PX3/9FdeuXQMAPP744+p2a0RCqbxyYcaJRiJmUjeZqvWzxt56tmBh3MRMtXdh5fPYA7AH90Q0nuZek1JiihatZJwmTZogJyen2jiJT1+3A3ZDICJzGDRoEGbPno3PP/8cLVq0AADcunULc+fOrVW7clNvXSaXy3H58mVs2bIFubm5aNGiBYYMGYJ33nnH6q+f+/v7w9/fX+w0iCzeqVOn1JNM+H9KWry8vNC1a1dcvny5Sqxr167w8vISIStxGHRZy1TtRCq3hBk9ejSAf1rCaJut3rFjR1y5ckVjbPHixbh//z7Wr1/PleBERES14OXlhfLycrRt2xa2tnWr0qWxUlbJPT5NotLP0ZraKxERSdHRo0fx3XffaW3ROmTIEBEzsx76VpNxtZk0NG/evNritzW3DpYSBweHals9W3vBjizPBx98gJEjR8Lb21t9vfr3339H586dsX379lqdqzZdXZYvX47ly5frPJejoyPi4uJq9fhERDVVVFSEtWvXIicnB2vXroWPjw/fE0uMrut51nadz+Cr4EqlEvv27cPVq1cBAE888QRGjhwJuVxeq/PUpiWMQqFA586dNe5f0Tbu0XEiIiLSrrCwEK+//jq2bNkCALh+/TratGmD119/HR4eHli4cKHIGRqv8oW1ej/uFDGTuslUK5ErX+SMHvAnHGr3NpK0KFb+s/qeF5GJ6i5TtWglw5WVlRkVJ6J/uLu7IyMjo9o4kSXx9PTEhQsX8N1332l0aQsICBA5MyIi89m+fTvu3r0LALh79y527NiBKVOmiJwVVUhPT6+y8LjClStX2Epdnxs3bmDYsGG4desWOnToAODhHiaenp44dOhQlXbm1altSxgiIiIyTmhoKC5duoSEhAQMHTpUPR4QEIC33367ThTGyTJUbk3sIAcULIyblNRaPxOR6ZiqRSsZzsXFBVlZWdXGSXz29vZGxUkY+hbZ1HYRDpmHs7Mz8vLyqo3TP2QyGZ555hn0798fDg4OfG9ORHVaZmYmduzYoV55rFKpsGPHDgQGBqJly5YiZ0fAw86hvXv3RnJyssZkahsbG/Tq1Yut1PWZNWsW2rZtizNnzqBx48YAHs4AGT9+PGbNmoVDhw7V6ny1aQnzqM8++6xWj0VERGTt9u3bh927d+PJJ5/U+HD+xBNPIDU1VcTMTKfyStmCHkGAvOqqOqolZal69T1XIhMRicuULVrJMP/73/+MipMwHnvsMaSnp1cbJ/FVV2ytSZyEwdep5srLy7FixQrExMQgOztb3aVtyZIl8Pb25gpKCeK+yESGU6lUiIyM1Dm+Zs0aTg6SAJlMhrlz52LChAka4zY2Npg7d65VvUYGFcaPHz+uURQHgCZNmmDVqlX8w0FERCRxd+7cgZubW5XxgoKCOvMmSON5yO1YGDexuvLvhIjIUrFFq/ieeuopHDhwoNo4iS8nJ8eoOAmjXbt26taruuIkPltb22q3ibC1NXjHzjpn+fLl2LJlC9577z1MnTpVPd65c2dERUWxMC4x3BeZyDjp6elISkqqMq5UKpGUlGRVLbqlrmXLlggODsa2bdugUqkgk8kQHBwMDw8PsVMTlEE9yh0cHHD//v0q4/n5+WxDRURkoSpa3QBACYASqPhl1Jf2n60U9OrVS6O7S0WRc9OmTfDz8xMrLSIiIqqFihatU6dOxcyZM1kUF1hKSopRcRKGvouwvEgrDX/++adRcRJGdUXxmsStydatW/HJJ58gODhYYyuAbt26qSe0kXRo2xeZiGquokX3o1ufyOVy+Pr6WlWLbkswfvx4NGnSBADg6uqK4OBgkTMSnkFT+f71r3/hv//9LzZv3gxfX18AwNmzZ/Hqq69i5MiRJk2QiIiEUVxcrP7+XRHzqIuKi4vh5OQkdhpqK1euxLPPPotffvkFZWVlWL9+PX755RecPn0ax48fFzs9IiIi0oMtWsXn6OhoVJyEcfHiRaPiJIyGDRsaFSeSmlu3bmntdFBeXo7S0lIRMiJduC8ykfF0teiuGGfXQWlRKBSYN2+eevsIa+yQYdCK8Q0bNqBt27bw8/ODQqGAQqGAv78/2rVrh/Xr15s6RyIiIjKhfv364dKlSygrK0OXLl3wzTffwM3NDYmJifDx8RE7PSIiItJj+fLl+Oyzz/Dee+9pdG3r3LkzNm3aJGJm1qNNmzZGxUkYLVq0MCpOwvj111+NihNJTadOnfDDDz9UGf/yyy/Ro0cPETIibfTtiyy17n9EUtayZUuMHTtWY2zs2LFW16LbUvj7++OLL76w2q2xDVox7uLigv379+PXX3/V2M+Me/4QEVkuBwcH9fcLAHBjDOOU4J+V95V/tmIrLS3FtGnTsGTJEmzcuFHsdIiIiMgAFS1aBw8ejFdffVU9zhatwjlx4oTe+Jw5c4RJhnR65plncOrUqWrjJL5WrVohNze32jiJz97eHiUlJdXG6aGlS5di0qRJuHXrFsrLy7F3716kpKRg69at+Prrr8VOj/7GfZGJyFqdOnVKvWLcGovjBq0Yr+Dl5YXHHnsMzz77LIviREQWrnJbG3sA9pDxy6gv7T9bsdnZ2eGrr74SOw0iIiIyAlu0iq9Dhw5GxUkYf/zxh1FxEka9evWMipMwXF1djYpbk1GjRuHgwYP47rvvUK9ePSxduhRXr17FwYMHOSFHQrgvMpHpZGZmYvfu3Rpju3fvRmZmpkgZkS5FRUVYu3YtsrOzsXbtWhQVFYmdkuAMKowXFhZiypQpcHJywhNPPIGMjAwAwOuvv45Vq1aZNEEiIiIyrdGjR2Pfvn1ip0FEREQGYotW8Q0aNMioOAnjp59+MipOwmjdurVRcRLGnTt3jIpbi7KyMoSHh6N169b49ttv8ccff6CwsBAnT57EkCFDxE6PKqnY/1jXuJQWORBJGbclsCzbt29HTk4OACAnJwc7duwQOSPhGVQYDw0NxaVLl5CQkKCxMXtAQECVWSFEREQkLe3bt0d4eDheeOEFREREYMOGDRpfREREJG1Lly7FzJkz8e6776pbtE6dOhUrVqzA0qVLxU7PKmibmFCbOAmjY8eORsVJGOXl5UbFSRjNmzc3Km4tbG1t8d5776GsrEzsVKgGWrZsieDgYHURXCaTITg4mPsiE9VCxbYESqVSY7zytgQkDZmZmdi+fbvG2Pbt261uZb9Be4zv27cPu3fvxpNPPqkxc+qJJ55AamqqyZKTjHIJv5FRqf7Jz8YWkPJMNin/HImIrMjmzZvh4uKC5ORkJCcna8RkMhlmzZolUmZERERUExUtWsPDw9UtWnv27MkWrQJ66qmnkJCQUG2cxHfhwgW98fHjxwuUDemib1UmV21Kw/37942KW5PBgwfj+PHj3J/aQowfPx6HDx9GTk4OXF1dERwcLHZKRBalYluCCxcuaBTH5XI5fHx8uC2BROhawV9eXo7IyEisWbPGat5zGVQYv3PnDtzc3KqMFxQU1MkfXL0L1tdKgIiI6q6bN2+KnQIREREZqKysDCtXrsR//vMffPvtt2KnY7V+/fVXvXG2Uxefr68vzp8/X22cxNekSROj4iSMR/dhrm3cmjz77LNYuHAhrly5Ah8fH9SrV08jPnLkSJEyI20UCgXmzZuHqKgozJkzR6NDLhHpV7H9wIQJE7SO18WaoSWqWNn/KJVKpV7Zby0TugwqjPfq1QuHDh3C66+/DuCfmZubNm2Cn5+f6bIjIiIi0Tg7O+PixYto06aN2KkQERHR3ypatE6cOFHsVKwaW3RbhhYtWhgVJ2H8+eefRsVJGCyM19xrr70GAFi3bl2VmEwmq9JumMTn7+8Pf39/sdMgslgV2xJs27YNKpWK2xJIUKtWreDs7Iy8vLwqMWdnZ7Rq1UqErMRhUGF85cqVePbZZ/HLL7+grKwM69evxy+//ILTp0/j+PHjps5RFAqFAnFxcWKnoVdRURFGjRoFANi/f7/FzGizlDyJiKzZo611iIiISBrYopWoZlgYtwycaGIZuBd8zfFnQUTWiNsSSFtGRobWojgA5OXlISMjw2o+XxpUGO/Xrx8uXbqEiIgIdOnSBd988w169uyJxMREdOnSxdQ5ikImk8HR0VHsNGpFoVBYXM5ERERERERUO2zRKr6srCyj4iSMS5cu6Y23a9dOoGxIFxZcLYO7uzvu3LlTbZxqp0uXLjh8+DA8PT3FToWIyGjclkDaKvaC19ZO3dfX16r2gq91Yby0tBTTpk3DkiVLsHHjRnPkRGSQYqUMgDRXN6pUQMnfn+PsbQApb6vx8OdIRERERERSxRat4vvpp5/0xseOHStQNqSLvg5I7JAkDWfPntUbf/rppwXKhnS5ffu2UXGqKi0tDaWlpWKnQURkMtyWQLpkMhnGjRuntTA+btw4q9oLvtaFcTs7O3z11VdYsmSJOfIhMtiMEy5ip0BERFJUXiZ2BtVTqf7J0cZWurOnzPxzlPIEN8ByJrmZfYKblP87qQBU1ALlACT6GgGQ9s+RLAJXT4pv0KBB1W4lN2jQIAGzIV06depkVJyE8ddffxkVJ2E0bdoUd+/erTZORERE0qRSqfD5559DJpNpTA6VyWTYuXMnevbsaTXFcYNaqY8ePRr79u3D3LlzTZ0PERERSURdeTNU78IOsVOgGuAEN8sgPygXOwUiqgW2aDUffa0GrakVoZRt2rRJb1xb5wUSlrOzs1FxEsYff/xhVJyIiIjEk56ernW1uEqlQlJSEtLT07nHeHXat2+P8PBwnDp1Sut+ZrNmzTJJckT6KBQKxMXFiZ2GXkVFRRg1ahQAYP/+/Razv4al5ElE5sHWkkRERJaNLVrNh3siW4bhw4fj/Pnz1cZJfPz/ZBkKCgqMihMREZF4KvYYT05O1nhvZWNjg169elnVxF6DCuObN2+Gi4sLkpOTkZycrBGTyWQsjJNgZDIZHB0dxU6jVhQKhcXlTETW6ciRI/Dw8BA7DYNYysQpwDInT5kqR75O5mVtr5MlvkYAJyISWapLly7pjbdr106gbEiX1NRUvfHBgwcLlA3p0qRJE6PiJAw3Nzf8/vvv1caJiIhImmQyGebOnYvg4GCNcZVKhblz59aZzqE1YVBh/ObNm6bOg4iIiMwoJCSkxsdWtJPs16+fudIxO0ucOAVY3+Qpvk6WwRJfJ2t7jYhIeK6urkbFSRgNGjQwKk7CYGHcMvTu3bvawnjv3r0FzIaIiIhMxdq6hhpUGK8pZ2dnXLx4EW3atDHnwxAREZEeP/74o8btCxcuoKysDB06dAAAXL9+HXK5HD4+PmKkR0RERGRR9K2osKYVF1KWn59vVJyE0aNHD6PiJIxTp07pjc+ZM0eYZOqIjz/+GO7u7mKnQUREVkClUiEyMlJrLDIyEmvWrLGazzBmLYxb2ywDIiIiqTp27Jj6+3Xr1qFBgwbYsmULGjVqBAD4888/MXnyZPTv31+sFImIiIgsxp07d4yKkzAGDBiAbdu2VRsn8f3vf//TG+fWBOLz9/fH3r17q41bsw0bNtT42IptSIOCgsyVDhERkYb09HQkJSVVGVepVEhKSkJ6ejq8vb2FT0wEZi2MExERkfSsXbsW33zzjbooDgCNGjXC8uXLMWTIEMybN0/E7IiIiEho0dHRWL16NbKystCtWze8//778PX11Xrs3r17sXLlSty4cQOlpaVo37495s2bhwkTJgictbi6d+9uVJyE8dNPP+mNV3RQIvFkZ2cbFSdh2NnZGRWv6x5dhXfnzh0UFhbCxcUFAJCbmwsnJye4ubmpC+NERERCadWqFZydnZGXl1cl5uzsjFatWomQlThsxE6AiIiIhJWXl6d1FdOdO3dw//59ETIiIiIic6hJi9bdu3cjJCQEy5Ytw4ULF9CtWzcEBgbijz/+0Hp848aNsWjRIiQmJuLy5cuYPHkyJk+ejLi4OHM8Bcny8vIyKk7CcHNzMypORP84f/68UfG67ubNm+qvFStWoHv37rh69Sru3buHe/fu4erVq+jZsyfeeecdsVMlIiIrlJGRobUoDjy8VpyRkSFwRuLhinEiIiIr89xzz2Hy5MlYu3atejXY2bNn8cYbb+D5558XOTsiIjJWCQBAmttaqQCU/v29HQAp72BWInYCjzBXi9Z169Zh6tSpmDx5MgAgJiYGhw4dQmxsLBYuXFjl+Keeekrj9uzZs7FlyxacPHkSgYGBNc7R0u3fv19vfMyYMQJlQ7r4+/tDLpdDqVRWidna2lp962ep0LefpbXsdyl148aNw/Lly6uN00NLlizBl19+qdGRokOHDoiMjMQLL7yA4OBgEbMjIiJr5OXlhS5duuDKlStVYl27drWqib1mLYzzjSsREZH0xMTEYP78+QgKCkJp6cPyhK2tLaZMmYLVq1eLnB0RERnrXbETILMwR4vWkpISJCcnIzQ0VD1mY2ODgIAAJCYm6r2/SqXC999/j5SUFLz7rvZ/ecXFxSguLlbf1rVKwdKoVNVPPtEXJ2GUl5drLYoDQFlZGcrLy2Fjw2aKYuPKfsvw22+/GRW3Jrdv30ZZWVmVcaVSya0BJOrUqVOIiorCnDlzOGmKiOos1mwfMuu7f34QJCIikhalUonz589jxYoVuHv3Ln788Uf8+OOPuHfvHj788EPUq1ev1ueMjo6Gt7c3FAoF+vTpg3Pnzuk8duPGjejfvz8aNWqERo0aISAgoNrjiYiI6CFztGjNycmBUqms0m7d3d0dWVlZOu/3119/oX79+rC3t8fw4cPx/vvv45lnntF6bEREBBo2bKj+8vT0rHF+UtajRw+j4iSMzz77zKg4CaNly5ZGxUkYf/75p1FxazJ48GBMmzYNFy5cUI8lJydj+vTpCAgIEDEz0qaoqAhr165FdnY21q5di6KiIrFTIiIyufT0dFy+fFlr7PLly0hPTxc4I/GYdcX4kSNH4OHhYc6HICIiolqQy+UYMmQIrl69itatW6Nr165Gna9iX9KYmBj06dMHUVFRCAwMREpKitaVHQkJCRg3bhz69u0LhUKBd999F0OGDMHPP//M9wxEREZQKBQWscdzUVERRo0aBeBhq2mFQiFyRjUjtTzFbtHaoEEDXLx4Efn5+YiPj0dISAjatGlTpc06AISGhiIkJER9Oy8vr04Ux9n62TJMmjQJW7durTZO4vPy8oKNjQ3Ky8urxGxsbKyqtSfVDbGxsZg0aRJ69eoFOzs7AA+7VAQGBmLTpk0iZ0eP2r59O+7evQsAuHv3Lnbs2IEpU6aInBURkWl5eXmhd+/eSE5O1njPJZfL4ePjY1Xvt2pcGK/8QVafdevWAQD69etX+4yIiIjIrDp37ozffvsNrVu3Nvpctd2XdMeOHRq3N23ahK+++grx8fGYOHGi0fkQEVkrmUwGR0dHsdOoFYVCYXE5S4WpWrS6urpCLpdXuU92djaaNWum8342NjZo164dAKhXrkdERGgtjDs4OMDBwaHGOVkKtlK3DJmZmXrjpnhPTMY5c+aM1qI48LAd/pkzZ9jaWAJ8fX1x5MiRauP0UNOmTXH48GFcv34dV69ehUwmQ8eOHfHYY4+JnRo9IjMzEzt27FD/3VapVNixYwcCAwPZrYKI6hSZTIa5c+diwoQJWsetaWJvjQvjP/74o8btCxcuoKysTD1D/fr16+qZBUREOlW9ficdKgAV28/JAUj1b4GUf4ZkEZYvX4758+fjnXfegY+PT5X26c7OzjU6j7H7kgJAYWEhSktL0bhxY53H1NW9SYmIiAxV0aJ106ZN6NmzJwDDWrTa29vDx8cH8fHxGD16NICHRaj4+HjMnDmzxucpLy/X+FttDW7fvq033rZtW4GyIV10FVtrGidh+Pn5oX79+sjPz68Sq1+/Pvz8/ETIih6VmpqqNz548GCBsrEMjz32GNq3bw+AnUSkSKVSITIyUuf4mjVr+LoRUZ3SsmVLBAcHY9u2bVCpVJDJZAgODra6Lp41LowfO3ZM/f26devQoEEDbNmyBY0aNQLwcB+ZyZMno3///qbPkojqDPlBudgpEFm9YcOGAQBGjhyp8SGv4g2RUqnUdVcN1e1Leu3atRqdY8GCBWjRokW1F/EjIiIQFhZWo/MRERFZA1O2aA0JCVGfy9fXF1FRUSgoKFB3g5k4cSI8PDwQEREB4OHf5V69eqFt27YoLi7G4cOHsW3bNnz00UemfZISp+/ikbVdXJKqixcv6o1zAoP4ZDIZmjZtqrUw3rRpUxamJELfameuhta0detWrF69Gr/++iuAhz+fN954o8pKPRJPeno6kpKSqowrlUokJSUhPT0d3t7ewidGRGRG48ePx+HDh5GTkwNXV1ezb8MlRQbtMb527Vp888036qI4ADRq1AjLly/HkCFDMG/ePJMlSERERKZVebKbmFatWoVdu3YhISGh2r1b6+repERERIYyZYvWsWPH4s6dO1i6dCmysrLQvXt3HD16VD3xLSMjAzY2NurjCwoK8NprryEzMxOOjo7o2LEjtm/fjrFjx5rs+VkCLy8vKBQKFBUVVYkpFAqr2qNPyrp3725UnISRlpaGmzdvao3dvHkTaWlpbHkvAba21V9G1he3JuvWrcOSJUswc+ZM9TYAJ0+exKuvvoqcnBzMnTtX5AwJ+Ge/3QsXLmgsELDG/XaJyHooFArMmzcPUVFRmDNnTrXXZOsqg96x5OXl4c6dO1XG79y5g/v37xudFBHVLQqFAnFxcWKnoVdRURFGjRoFANi/f79F/FGwhBxJegYOHIjc3Fxs3rwZV69eBQB06tQJU6ZMQcOGDWt8HkP3JQWANWvWYNWqVfjuu+/QtWvXao+tq3uTEhERGctULVpnzpyps3V6QkKCxu3ly5dj+fLlBj9WXZGenq61KA48/FyRnp7OQp4EVJ7UYUichFGxt6+hcRKGm5ubUXFr8v777+Ojjz7CxIkT1WMjR47EE088gbfffpuFcYngfrtEZK38/f3VE7eskUGF8eeeew6TJ0/G2rVr4evrCwA4e/Ys3njjDTz//PMmTZCILJ9MJoOjo6PYadSKQqGwuJyJaur8+fMYOnQoFAqF+u94ZGQkVq5ciW+++Ua9V6k+hu5L+t5772HFihWIi4tDr169jH4+RERE1ogtWon009dliF2IpOF///uf3nibNm0EyoZ0OX78uN4426k/dPv2bfTt27fKeN++fXH79m0RMiJduN8uEZH1MWhqbExMDJ599lkEBQXBy8sLXl5eCAoKwtChQ/Hhhx+aOkciIiIyoblz52LEiBFIS0vD3r17sXfvXty8eRP/+te/MGfOnFqdKyQkBBs3bsSWLVtw9epVTJ8+vcq+pKGhoerj3333XSxZsgSxsbHw9vZGVlYWsrKytO4nSERERNqtW7cO06dPx7Bhw7Bnzx7s2bMHQ4cOxauvvorIyEix0yOSjIMHDxoVJ2G0aNHCqDgJo23btkbFrUm7du2wZ8+eKuO7d+9Wd3oh6Rg/fjyaNGkCAFa73y4RkTWp9YpxpVKJ8+fPY8WKFVi9ejVSU1MBPHzzU69ePZMnSERERKZ1/vx5bNy4UWMPOFtbW7z55pu1XsFd231JP/roI5SUlOCFF17QOM+yZcvw9ttvG/6kiIiIrAhbtIrP29sb3t7eSEtLqxJr3bo1vL29Bc+JqtK3ZY++OBH949GtNbTFBw0aJEwyEhcWFoaxY8fixIkT6la1p06dQnx8vNaCOYmL++0SEVmXWhfG5XI5hgwZgqtXr6J169b8EEFERGRhnJ2dkZGRgY4dO2qM//7772jQoEGtz1ebfUm1XTwmIiKi2mGLVvGpVCrk5ORojd25c0fdjpXE1aZNG3h4eODWrVtVYi1btmR7bqJaCAoKqrY4HhQUJFwyEjdmzBicO3cO69atw759+wAAjz/+OM6dO4cePXqImxxpZe377RIRWRODWql37twZv/32m6lzISIiIgGMHTsWU6ZMwe7du/H777/j999/x65du/DKK69g3LhxYqdHREREerBFq/gSExN1bgWTn5+PxMREgTMiXerXr691nF0PpUPfJBJOMpGGX375xai4tSgtLcV//vMfNGrUCNu3b0dycjKSk5Oxfft2FsWJiIgkoNYrxgFg+fLlmD9/Pt555x34+PhU+TDh7OxskuSIiIjI9NasWQOZTIaJEyeirKwMAGBnZ4fp06dj1apVImdHRERE+rBFq/j8/PxQv359rcXx+vXrw8/PT4Ss6FFpaWlISUnRGktJSUFaWhpat24tcFb0KG9vb3To0EHra9WxY0duTSAR3bp1MypuLezs7PDVV19hyZIlYqdCREREWhi0YnzYsGG4dOkSRo4ciZYtW6JRo0Zo1KgRXFxc0KhRI1PnSERERCZkb2+P9evX488//8TFixdx8eJF3Lt3D5GRkXBwcBA7PSIiItKjokWrq6sr9u3bh3379sHV1RXnzp3Dc889J3Z6VkEmk8HDw0NrzMPDgytciWpJ1+cQe3t7gTMhXWxsqr+MrC9uTUaPHq1uoU5EpnHq1Cm8+OKLOHXqlNipEJGFM2jF+LFjx0ydBxEREQnMyckJXbp0ETsNIiIiqoXS0lJMmzYNS5Yswfbt28VOx2qlp6dXuxI5PT2dq1wlwMvLC05OTigsLKwSc3JygpeXlwhZ0aPS09Nx+fJlrbHLly/z/xNZnPbt2yM8PBynTp3S2m111qxZNT5XdHQ0Vq9ejaysLHTr1g3vv/8+fH19tR67d+9erFy5Ejdu3EBpaSnat2+PefPmYcKECepjVCoVli1bho0bNyI3Nxf+/v746KOPuBULSVpRURHWrl2LnJwcrF27Fj4+PlAoFGKnRUQWyqDC+MCBA5Gbm4vNmzf/P3t3HhdVvf9x/D2gMKDihuKCAmmLlWa5hVjWzSJb1BYzw1xSW60Uu5X3lktWaknaqmmlllheb5v3VlqRZhkVaqaZ2aYoKqiUkiKgzPf3hz/mOjKsA3Nm4PV8PObxcL6fM2c+hyN85sznnO/R1q1bJUlnn322Ro4cqYYNG1ZpggAAAAAA4ASmaPUNbdu2LXUq9bZt21qQFU6Vnp7utikuSbm5uUpPT2cqdR8QFRVV6lTqnMDgG6Kiokr9u8d++p9XX31VjRo1ct5f/GQ2m63cjfGlS5cqMTFRc+fOVY8ePTR79mzFx8dr27Ztat68ebHlmzRpon/+858666yzFBQUpP/+978aMWKEmjdvrvj4eEnSU089peeee06LFi1STEyMHn30UcXHx+vHH3+k0QiftXjxYmVnZ0uSsrOzlZycrJEjR1qcFQB/Vak5btatW6f27dtr1qxZ+uOPP5zTr7Zr104bNmyo6hwBAAAAAMD/Y4pW66Wnp7ttDknS4cOHlZ6e7uWMAP9ljNHu3bvdxjIyMmSM8XJGcGfnzp2l/t3buXOnlzPyXdu3b3c+fv/9d/3+++8uz8vrmWee0ejRozVixAidffbZmjt3rkJDQ/Xaa6+5Xf6SSy7Rddddpw4dOqhdu3a6//771alTJ3355ZeSTvyuzZ49W4888oj69++vTp066fXXX9eePXv4XAGflZGRoeTkZGctMMYoOTlZGRkZFmcGwF9VqjE+btw4XXvttdqxY4feeecdvfPOO9q+fbuuueYajR07topTBAAAAAAARYqmaL3xxhs1bdo0Pffccy4PACdER0eXeOugTp06MT23j0hNTS214ZqamurljOBO27ZtFRYW5jYWFhbGTBmnePXVV3XuuefKbrfLbrfr3HPP1SuvvFLu1xcUFGj9+vXq06ePcywgIEB9+vQp1++EMUYpKSnatm2bLr74YkknGvaZmZku62zYsKF69OhR6jrz8/OVk5Pj8qhpuH+1bzLGaNasWSWOc+IUgMqo1FTq69at0/z581Wnzv9eXqdOHT344IPq2rVrlSUHAAAAAABcVdUUrai8oobr5s2bi8VouPoOm82ma665xu1+uuaaa2Sz2SzICqfq0aOHR3F4x86dO0tsiObk5Gjnzp387ft/EydO1DPPPKN7771XsbGxkk6cADJu3Djt3LlTjz32WJnrOHDggAoLCxUREeEyHhERoZ9++qnE1x06dEitW7dWfn6+AgMD9dJLL+nyyy+XJGVmZjrXceo6i2LuTJs2TVOmTCkzZ3/F/at9V3p6utLS0oqNFxYWKi0tTenp6fzdASph7dq1mj17tsaOHau4uDir0/G6Sl0xHhYW5nZ6nF27dqlBgwYeJwUAAAAAANyrqilaUXlFDVd3aLj6DofDoRdffNFt7IUXXpDD4fByRnDn66+/9igO72jbtm2JzUK73c4V4yeZM2eO5s+fr2nTpqlfv37q16+fpk2bpnnz5umll16q1vdu0KCBNm7cqLS0ND3xxBNKTEzU6tWrPVrnhAkTdOjQIedj165dVZOsj3B3/2r4hqioKHXr1k2BgYEu44GBgerevbuioqIsygzwX0UnA2VlZSkpKUl5eXlWp+R1lWqMDxo0SCNHjtTSpUu1a9cu7dq1S2+99ZZGjRqlwYMHV3WOAAAAAADgJJ5O0QrP0HD1D6mpqaVe4coU3b6hdevWHsXhHdu3by/xy/O8vDxt377dyxn5rmPHjrmdVbVLly46fvx4udYRHh6uwMBAZWVluYxnZWWpRYsWJb4uICBA7du3V+fOnTV+/HjnbVckOV9X0XUGBwcrLCzM5VFTcP9q32az2TRu3LgSxzkREag4TgaqZGN85syZuv766zV06FBFR0crOjpaw4cP14033qgZM2ZUdY4AAAAAAOD/TZw4Uffff7+uvfZaLVu2TMuWLdO1116rcePGaeLEiVanVyvQcPUPsbGxJTZwGjZs6JziGNaKjo5WTEyM29hpp53GNLk+4vvvv/coXpvceuutmjNnTrHxefPmKSEhoVzrCAoKUpcuXZSSkuIcczgcSklJqdDfLofDofz8fElSTEyMWrRo4bLOnJwcffPNN7Xy7yH3r/YPkZGRSkhIcDbBbTabEhISOGkKqAROBjqhUvcYDwoK0rPPPqtp06bpt99+kyS1a9dOoaGhVZocAAAAAABwVTRF68kztvXr10+dOnXSvffeW657l8IzF154oQIDA1VYWFgsFhgYqAsvvNCCrHCqgIAATZ48WYmJicViU6ZMUUBApa4XQTWoX7++2/F69ep5OROUpGPHjh7Fa5tXX31VH3/8sbMefPPNN9q5c6eGDh3q8jfpmWeeKXEdiYmJGjZsmLp27aru3btr9uzZOnLkiEaMGCFJGjp0qFq3bu28InzatGnq2rWr2rVrp/z8fH344Yd64403nE16m82msWPH6vHHH9fpp5+umJgYPfroo2rVqpUGDBhQTT8J38X9q/3HkCFD9OGHH+rAgQMKDw8v9wkmAP6nrJOBZs6cWWtmYahUY7xIaGgoH3oAoAYqkCT55pmxRtKx//93XUm+Wq4LrE4AAADUWFUxRSs8s2vXLrdNcenEF+q7du3iy3Qf0bVrV3Xs2FGbN292jnXq1EkXXHCBhVnhZOnp6S7752SbN2+mOeUjTp1+21389NNP91I2vu2HH35w/o0puqgsPDxc4eHh+uGHH5zLldWAGDRokPbv36+JEycqMzNTnTt31ooVKxQRESFJ2rlzp8sJPkeOHNHdd9+tjIwMhYSE6KyzztLixYs1aNAg5zIPPvigjhw5ottvv10HDx5Ur169tGLFihLvH1+TFd2/esOGDS41PTAwUF26dOH+1T7Ebrdr/Pjxmj17tsaOHVsr/78CnuJkoP/xqDFeVV588UU9/fTTyszM1Hnnnafnn39e3bt3d7vs/Pnz9frrrzs/RHTp0kVPPvlkicsDACqOm2IAAAD4rqIpWk+9yqwiU7TCM0Vfprv7cql79+58me5jnnjiCQ0YMEAOh0MBAQF6/PHHrU4JJ2nbtq3CwsLc3p4gLCxMbdu2tSArnKqsaaWZdvp/Vq1aVWXrGjNmjMaMGeM2tnr1apfnjz/+eJl/32w2mx577DFml9H/7lN96623uh2vLVdO+ou4uDjFxcVZnQbgtzgZ6H8snzNq6dKlSkxM1KRJk7Rhwwadd955io+P1759+9wuv3r1ag0ePFirVq1Samqq2rRpoyuuuEK7d+/2cuYAAAAAAFjj1Vdf1bnnnqtRo0Zp1KhR6tixo+bPn6+AgAAlJiY6H6geNpvNZSr7kw0ePJgv031Mo0aNNGTIEAUEBGjIkCFq1KiR1SnhJDt37nTbFJdO3P94586dXs4I7kRGRnoUB3wR968GUFsUnfRT0nhtOn6x/IrxZ555RqNHj3beG2Xu3Ln64IMP9Nprr+nhhx8utnxycrLL81deeUVvv/22UlJSNHToUK/kDAA1kd1u18qVK61Oo0x5eXnq37+/JOn999/3i+mT/CFHAADgP6pqilZUnjFGCxYscBt77bXXdMEFF/Dz9zEdOnRQs2bN1KFDB6tTwSm4Ytw/REdH68wzz9S2bduKxc4666xaM/0qah7uXw2gtig6GeiNN96QMabWngxkaWO8oKBA69ev14QJE5xjAQEB6tOnj1JTU8u1jtzcXB07dkxNmjRxG8/Pz1d+fr7zeUlnoAJAbWez2RQSEmJ1GhVit9v9LmcAAABPVeUUraicHTt2lHpP5B07digmJsbLWaEkeXl5mjZtmnJycjRt2jT9+9//5uRVH1KeK8ZpulrPZrPpjjvucDsbyR133MHJQPBb3L8aQG3CyUAWT6V+4MABFRYWKiIiwmU8IiJCmZmZ5VrHQw89pFatWqlPnz5u49OmTVPDhg2djzZt2nicNwAAAAAAAPzDwoULnY3XnJwcLVq0yOKMcLKie166071791p1z0tfZozRyy+/7DY2d+5c7jEOvxYXF6dly5ZxD2sANV7RyUARERFKTEyslScDWX6PcU9Mnz5db731lt59990Sd96ECRN06NAh52PXrl1ezhIAAAAAANQk0dHR6tSpk9vYeeedx9WtPiQjI0Nvvvmmy9ibb76pjIwMizLCqbjnpX/Yvn2722nUJWnbtm3avn27lzMCAACVUdtPBrK0MR4eHq7AwEBlZWW5jGdlZalFixalvnbmzJmaPn26Pv744xIPRiUpODhYYWFhLg8AAAAAAIDKstlsGj58uNvY8OHDaeT5CGOMpk+fXuxKVofD4XYc1jr198Zms7GPfMiePXs8igMAAPgCSxvjQUFB6tKli1JSUpxjDodDKSkpio2NLfF1Tz31lKZOnaoVK1aoa9eu3kgVAAAAAABA0omG65tvvum2kbdkyRKaeT5ix44d2rRpk9vYpk2btGPHDu8mBLeMMZo1a5YCAly/prTZbJo1axa/Tz6iVatWHsUBAAB8geVTqScmJmr+/PlatGiRtm7dqrvuuktHjhzRiBEjJElDhw7VhAkTnMvPmDFDjz76qF577TVFR0crMzNTmZmZOnz4sFWbAAAAAAAAapH09HSlpaUVa9gZY5SWlqb09HSLMgP8T9HvU2Fhoct4YWEhv08+JCoqqtjJC0UCAgK4FzwAAPALljfGBw0apJkzZ2rixInq3LmzNm7cqBUrVigiIkKStHPnTu3du9e5/Jw5c1RQUKAbb7xRLVu2dD5mzpxp1SYAAAAAAIBaJCoqSt26dVNgYKDLeGBgoLp3706DyEdER0erY8eObmOdOnXiXvA+gt8n//D111/L4XC4jTkcDn399ddezggAAKDiLG+MS9KYMWOUnp6u/Px8ffPNN+rRo4cztnr1ai1cuND5fMeOHTLGFHtMnjzZ+4kDAAAAAODnXnzxRUVHR8tut6tHjx769ttvS1x2/vz5uuiii9S4cWM1btxYffr0KXX5mspms2ncuHEljnOPcd9gs9mcMxKeasSIEewnH8Hvk3+48MILi528UKROnTq68MILvZwRAABAxflEYxwAAAAAAHjf0qVLlZiYqEmTJmnDhg0677zzFB8fr3379rldfvXq1Ro8eLBWrVql1NRUtWnTRldccYV2797t5cytFxkZqYSEBGfTzmazKSEhQa1bt7Y4MxThXvD+g98n37dr165i090XOX78uHbt2uXljAAAACqOxjgAAAAAALXUM888o9GjR2vEiBE6++yzNXfuXIWGhuq1115zu3xycrLuvvtude7cWWeddZZeeeUVORwOpaSkeDlz3zBkyBA1bdpUkhQeHq6EhASLM8LJuBe8f+H3ybcVTXnvDlPeAwAAf0FjHAAAAACAWqigoEDr169Xnz59nGMBAQHq06ePUlNTy7WO3NxcHTt2TE2aNHEbz8/PV05OjsujJrHb7Ro/frwiIiKUmJgou91udUo4Cfeu9i/8Pvm2oqnt3f0+MeU9gOq2du1aDRw4UGvXrrU6FQB+jsY4AAAAAAC10IEDB1RYWKiIiAiX8YiICGVmZpZrHQ899JBatWrl0lw/2bRp09SwYUPno02bNh7n7Wvi4uK0bNkyxcXFWZ0KTsG9q4GqFRkZqUGDBrmM3XzzzUx5D6Ba5eXlKSkpSVlZWUpKSlJeXp7VKQHwYzTGAQAAAABAhU2fPl1vvfWW3n333RKv7JwwYYIOHTrkfHAPWngb9672HzQ+AADuLF68WNnZ2ZKk7OxsJScnW5wRAH9GYxwAAAAAgFooPDxcgYGBysrKchnPyspSixYtSn3tzJkzNX36dH388cfq1KlTicsFBwcrLCzM5QF4G/eu9g80PnxfRkaGli5d6jK2dOlSZWRkWJQRgJouIyNDycnJMsZIkowxSk5O5u8OgEqjMQ4AAAAAQC0UFBSkLl26KCUlxTnmcDiUkpKi2NjYEl/31FNPaerUqVqxYoW6du3qjVQBj3Dvat9H48P3GWM0a9asEseL9h0AVBX+7gCoDjTGAQCAR1588UVFR0fLbrerR48e+vbbb0tcdsuWLbrhhhsUHR0tm82m2bNney9RAABQTGJioubPn69FixZp69atuuuuu3TkyBGNGDFCkjR06FBNmDDBufyMGTP06KOP6rXXXlN0dLQyMzOVmZmpw4cPW7UJQLlwL3jfRePDP6SnpystLU2FhYUu44WFhUpLS1N6erpFmQGoqfi7A6A60BgHAACVtnTpUiUmJmrSpEnasGGDzjvvPMXHx2vfvn1ul8/NzdVpp52m6dOnlzlFKwAAqH6DBg3SzJkzNXHiRHXu3FkbN27UihUrFBERIUnauXOn9u7d61x+zpw5Kigo0I033qiWLVs6HzNnzrRqEwD4ORof/iEqKkrdunWTzWZzGbfZbOrevbuioqIsygxATVX0dycwMNBlPDAwkL87ACqNxjgAAKi0Z555RqNHj9aIESN09tlna+7cuQoNDdVrr73mdvlu3brp6aef1s0336zg4GAvZwsAANwZM2aM0tPTlZ+fr2+++UY9evRwxlavXq2FCxc6n+/YsUPGmGKPyZMnez9xH7F27VoNHDhQa9eutToVlIL95LtofPgHm82mwYMHF7uC3xijwYMHF2uYA4CnbDabxo0bV+I4f3cAVAaNcQAAUCkFBQVav369+vTp4xwLCAhQnz59lJqaWmXvk5+fr5ycHJcHAACAL8jLy1NSUpKysrKUlJSkvLw8q1OCG+wn30bjwz8YY/Tmm2+6vWJ8yZIlTHkPoFpERkYqISHB+bfHZrMpISFBrVu3tjgzAP6KxjgAAKiUAwcOqLCw0DnVapGIiAhlZmZW2ftMmzZNDRs2dD7atGlTZev2NfHx8VanAAAAKmDx4sXKzs6WJGVnZys5OdnijOAO+8n3RUZGatCgQS5jgwYNovHhQ4qmvHd3xThT3gOoTkOGDFHTpk0lSeHh4UpISLA4IwD+jMY4AADwaRMmTNChQ4ecj127dlmdUpUaMmSIy/Mbb7zRokwAAEBFZGRkKDk52dkkMsYoOTlZGRkZFmeGk7GfgKrBlPcArGK32zV+/HhFREQoMTFRdrvd6pQA+DEa4wAAoFLCw8MVGBiorKwsl/GsrCy1aNGiyt4nODhYYWFhLg+rGGN09OjRKn3s37/f5T327dtXpetnSsOqx5X9AABjjGbNmlXiOPXXN7Cf/EdGRoaWLl3qMrZ06VJOYPAhTHkPwEpxcXFatmyZ4uLirE4FgJ+rY3UCAADAPwUFBalLly5KSUnRgAEDJEkOh0MpKSkaM2aMtclVk7y8PK80RavyPVauXKmQkJAqW19tdd1117k879u3rz766COLsgEAWK1oSuFTFRYWOqcUjo6O9n5icMF+8g9lncAwc+ZMmq4+ouhev2+88YaMMdzrFwAA+B2uGAcAAJWWmJio+fPna9GiRdq6davuuusuHTlyRCNGjJAkDR06VBMmTHAuX1BQoI0bN2rjxo0qKCjQ7t27tXHjRv36669WbQJQLrm5uS7Pjxw5YlEmAABfwJTC/oH95B+KTmAoLCx0GT/5BAb4Du71CwAA/BlXjAMAgEobNGiQ9u/fr4kTJyozM1OdO3fWihUrFBERIUnauXOnAgL+dx7enj17dP755zufz5w5UzNnzlTv3r21evVqb6dfYXa7XStXrqySdZXnqvCqeK/adu8tY4zy8vKqdJ0l7auLL764yv4/2O12roQCAD9SNHXwrbfe6nacv+m+gf3kH4pOYNiwYYNLczwwMFBdunThBAYfU3Sv39mzZ2vs2LG17ngDAAD4NxrjAADAI2PGjClx6vRTm93R0dF+fS9Hm83m1WnJmQK94rw13X2RqnovprwHAP/DlML+gf3k+ziBwf/ExcVxn18AAOCXmEodAAAAAACgEphS2D+wn3xf0QkMRU1wTmAAAABAdeCKcQAAANQYVTndvcSU9wCA0jGlsH9gP/mHIUOG6MMPP9SBAwc4gQEAAADVgsY4AAAAagxvT3cvMeU9ANR2TCnsH9hPvo8TGAAAAFDdaIwDAAAAAAAAsBwnMAAAAKA6cY9xAAAAAAAAAAAAAECNRmMcAAAAQI1SnnvDAwAAAAAAoHahMQ4AAADA7z344IMuz++//36LMgEAAAAAAIAvojEOAAAAwO99//33Ls+/++47izIBAAAAAACAL6pjdQIAAAAAahdjjPLy8qpsfSVNnX7xxRdr5cqVVfY+drtdNputytYHAADgj9auXavZs2dr7NixiouLszodAACAcqMxDgAAYIGzzz5bP/74Y6lxoKbKy8vz2n3Aq/J9Vq5cqZCQkCpbHwDAe2jkAVUjLy9PSUlJOnDggJKSktSlSxfZ7Xar0wIAACgXplIHAACwQGlN8fLEAQAAUD5FjbysrCwlJSVV6awlQG2zePFiZWdnS5Kys7OVnJxscUYAAADlxxXjAAAAALzKbrdX2RTn5bkivKrei6uhAMA/uWvkjRw50uKsAP+TkZGh5ORkGWMknbg9TnJysuLj4xUZGWlxdgAAAGWjMQ4AAADAq2w2m1enJGf6cwCovWjkAVXDGKNZs2aVOD5z5kzZbDYLMgMAACg/plIHAAAAAABAjVNWI6+oWQ6gbOnp6UpLS1NhYaHLeGFhodLS0pSenm5RZgAAAOVHYxwAAAAAAAA1Do08oOpERUWpW7duCgwMdBkPDAxU9+7dFRUVZVFmNduLL76o6Oho2e129ejRQ99++22Jy86fP18XXXSRGjdurMaNG6tPnz7Flh8+fLhsNpvL48orr6zuzQAAwGfQGAcAAAAAAECNQyMPqDo2m03jxo0rcZxp1Kve0qVLlZiYqEmTJmnDhg0677zzFB8fr3379rldfvXq1Ro8eLBWrVql1NRUtWnTRldccYV2797tstyVV16pvXv3Oh9vvvmmNzYHAACfQGMcAADAAi1atPAoDgAAgNLRyAOqVmRkpBISEpy/OzabTQkJCWrdurXFmdVMzzzzjEaPHq0RI0bo7LPP1ty5cxUaGqrXXnvN7fLJycm6++671blzZ5111ll65ZVX5HA4lJKS4rJccHCwWrRo4Xw0btzYG5sDAIBPoDEOAABggczMTI/iAAAAKBuNPKBqDRkyRE2bNpUkhYeHKyEhweKMaqaCggKtX79effr0cY4FBASoT58+Sk1NLdc6cnNzdezYMTVp0sRlfPXq1WrevLnOPPNM3XXXXcrOzi51Pfn5+crJyXF5AADgr2iMAwAAWKBu3boexeEdp069WtE4APi6ity7dMuWLbrhhhsUHR0tm82m2bNney9RwAM08oCqY7fbNX78eEVERCgxMVF2u93qlGqkAwcOqLCwUBERES7jERER5T6J+qGHHlKrVq1cmutXXnmlXn/9daWkpGjGjBn6/PPP1bdvXxUWFpa4nmnTpqlhw4bOR5s2bSq3UQAA+AAa4wAAABaoX7++R3F4R0BA6R+Xy4qj+p36ZWFF40BtVtF7l+bm5uq0007T9OnTueUH/AqNPKBqxcXFadmyZYqLi7M6FZRg+vTpeuutt/Tuu++6/M27+eab1a9fP3Xs2FEDBgzQf//7X6WlpWn16tUlrmvChAk6dOiQ87Fr1y4vbAEAANWDb/IAAAAsUNb0c0xP5xuOHTvmURzVr169eh7Fgdqsovcu7datm55++mndfPPNCg4O9nK2gGdo5AHwJ+Hh4QoMDFRWVpbLeFZWVpknp82cOVPTp0/Xxx9/rE6dOpW67Gmnnabw8HD9+uuvJS4THByssLAwlwcAAP6KxjgAAIAFzj33XI/i8I6i+5FWNo7qV6dOHY/iQG1VFfcuLQ/uSwoAQMUFBQWpS5cuSklJcY45HA6lpKQoNja2xNc99dRTmjp1qlasWKGuXbuW+T4ZGRnKzs5Wy5YtqyRvAAB8HY1xAAAAC4SGhnoUh3fQdPV9DofDozhQW1XFvUvLg/uSAgBQOYmJiZo/f74WLVqkrVu36q677tKRI0c0YsQISdLQoUM1YcIE5/IzZszQo48+qtdee03R0dHKzMxUZmamDh8+LEk6fPiw/v73v+vrr7/Wjh07lJKSov79+6t9+/aKj4+3ZBsBAN63du1aDRw4UGvXrrU6FUvQGAcAALBAXl6eR3F4R1RUlEdxVL8DBw54FAdQvbgvKQAAlTNo0CDNnDlTEydOVOfOnbVx40atWLHCeVLbzp07tXfvXufyc+bMUUFBgW688Ua1bNnS+Zg5c6YkKTAwUJs2bVK/fv10xhlnaOTIkerSpYu++OILbpECALVEXl6ekpKSlJWVpaSkpFr5/SOXuAAAAFggJCTEozi84/zzzy/1fnvnn3++F7OBOy1bttTBgwdLjQMozpN7l1ZEcHAwX7YDAFBJY8aM0ZgxY9zGVq9e7fJ8x44dpa4rJCREK1eurKLMAAD+aPHixcrOzpYkZWdnKzk5WSNHjrQ4K+/iinEAAAALBASU/jGsrDi8g3uM+77777/fozhQW1X23qUAAAAAAP+TkZGh5ORkGWMkScYYJScnKyMjw+LMvItvXAEAACzQuXNnj+Lwju3bt3sUR/XbunWrR3GgNqvovUsLCgq0ceNGbdy4UQUFBdq9e7c2btxY6swaAAAAAABrGWM0a9asEseLmuW1AY1xAAAAC1x//fUexeEdTzzxhEdxVL/mzZt7FAdqs4reu3TPnj06//zzdf7552vv3r2aOXOmzj//fI0aNcqqTQAAAAAAlCE9PV1paWkqLCx0GS8sLFRaWprS09Mtysz7uMc4AACABcqapigjI0PR0dHeSQYlyszMLDPOfrJW69atPYoDtV1F7l0aHR1dq64kAAAAAICaICoqSt26ddOGDRtcmuOBgYHq0qWLoqKiLMzOu7hiHAAAwAJFH0jd6d69e636QOrL2E++LyYmRjExMRWOAQAAoHLWrl2rgQMHau3atVanAgAAysFms2ncuHEljttsNguysgaNcQAAAAuU9IFUUq37QOrL2E/+oX79+hUaBwAAQOXk5eUpKSlJWVlZSkpKUl5entUpAQCAcoiMjNSgQYNcxgYNGlTrZtrzicb4iy++qOjoaNntdvXo0UPffvttqcsvW7ZMZ511lux2uzp27KgPP/zQS5kCAIBTUccrLzIy0u14bftA6uvYT74tPT1dmzdvdhvbvHlzrbpPFgAAQHVbvHixsrOzJUnZ2dlKTk62OCMAAIDys/we40uXLlViYqLmzp2rHj16aPbs2YqPj9e2bdvUvHnzYst/9dVXGjx4sKZNm6ZrrrlGS5Ys0YABA7Rhwwade+65FmxB+RljqvwsypPXV11naNrt9lp1NZQ/7qfato8k9pO/YD/VfLWpjleHiy++uMTxNWvWeDkblIT95NvKms6e6e4rjzoOAABOlpGRoeTkZBljJJ34rJCcnKz4+PgSTyYFAAC+ISMjQ0uXLnUZW7p0qa6++upaVcdtpuiTjEV69Oihbt266YUXXpAkORwOtWnTRvfee68efvjhYssPGjRIR44c0X//+1/n2IUXXqjOnTtr7ty5xZbPz89Xfn6+83lOTo7atGmjQ4cOKSwsrBq2qGRHjx5VfHy8V9+zKqxcuVIhISFWp+E1/rifats+kthP/oL9VH45OTlq2LChJfXJE9VdxyXfquVVqaRm68loulqP/eT72EfVhzpefv5ax63AzwoA/JMxRg888IA2bNigwsJC53hgYKAuuOACzZw5069PTqM+lR8/KwDwP9Tx/7F0KvWCggKtX79effr0cY4FBASoT58+Sk1Ndfua1NRUl+UlKT4+vsTlp02bpoYNGzofbdq0qboNAACgFvNGHZeo5QAAAABgtfT0dKWlpbl8mS5JhYWFSktL4/Y1AAD4MOr4/1g6lfqBAwdUWFioiIgIl/GIiAj99NNPbl+TmZnpdvnMzEy3y0+YMEGJiYnO50VXmVnBbrdr5cqVVbpOY4zzKrrg4OBqOaPDbrdX+Tp9mT/up9q2jyT2k79gP9Vs3qjjkm/VcgCoTajjAACgSFRUlLp16+b2SrMuXbpw+xoAAHwYdfx/LL/HeHULDg5WcHCw1WlIkmw2W7VM6xcaGlrl66zN2E/+gf3kH9hPqAq+VMur0po1a0qdApqpn30D+8n3sY+qD3UcAAAUsdlsGjdunG699Va34/48/SoAADUddfx/LJ1KPTw8XIGBgcrKynIZz8rKUosWLdy+pkWLFhVaHgAAVA/quOdKatjRyPMt7Cffxz4CAACofpGRkUpISHB+eW6z2ZSQkKDWrVtbnBkAACgLdfwESxvjQUFB6tKli1JSUpxjDodDKSkpio2Ndfua2NhYl+Ul6ZNPPilxeQAAUD2o4wAAAABQuwwZMkRNmzaVdOJk6YSEBIszAgAA5UUdt7gxLkmJiYmaP3++Fi1apK1bt+quu+7SkSNHNGLECEnS0KFDNWHCBOfy999/v1asWKGkpCT99NNPmjx5statW6cxY8ZYtQkAANRa1HHPnXpFK1e4+ib2k+9jHwEAAFQ/u92u8ePHKyIiQomJibLb7VanBAAAyok67gP3GB80aJD279+viRMnKjMzU507d9aKFSsUEREhSdq5c6cCAv7Xv+/Zs6eWLFmiRx55RP/4xz90+umn67333tO5555r1SYAAFBrUcerBg08/8B+8n3sIwAAgOoXFxenuLg4q9MAAACVUNvruM0YY6xOwptycnLUsGFDHTp0SGFhYVanAwCAJOpTRfCzAgD4GmpT+fGzAgD4IupT+fGzAgD4morUJsunUgcAAAAAAAAAAAAAoDrRGAcAAAAAAAAAAAAA1Gg0xgEAAAAAAAAAAAAANRqNcQAAAAAAAAAAAABAjUZjHAAAAAAAAAAAAABQo9EYBwAAAAAAAAAAAADUaHWsTsDbjDGSpJycHIszAQDgf4rqUlGdQsmo5QAAX0MdLz/qOADAF1HLy49aDgDwNRWp47WuMf7XX39Jktq0aWNxJgAAFPfXX3+pYcOGVqfh06jlAABfRR0vG3UcAODLqOVlo5YDAHxVeeq4zdSy0+AcDof27NmjBg0ayGazWZ1OlcjJyVGbNm20a9cuhYWFWZ0OSsB+8g/sJ/9QE/eTMUZ//fWXWrVqpYAA7nRSGmo5rMJ+8n3sI/9QE/cTdbz8qOOwCvvJP7Cf/ENN3E/U8vKjlsMK7CP/wH7yDzVxP1Wkjte6K8YDAgIUGRlpdRrVIiwsrMb8J67J2E/+gf3kH2rafuKs9PKhlsNq7Cffxz7yDzVtP1HHy4c6Dquxn/wD+8k/1LT9RC0vH2o5rMQ+8g/sJ/9Q0/ZTees4p78BAAAAAAAAAAAAAGo0GuMAAAAAAAAAAAAAgBqNxngNEBwcrEmTJik4ONjqVFAK9pN/YD/5B/YTahr+T/sH9pPvYx/5B/YTahr+T/sH9pN/YD/5B/YTahr+T/s+9pF/YD/5h9q+n2zGGGN1EgAAAAAAAAAAAAAAVBeuGAcAAAAAAAAAAAAA1Gg0xgEAAAAAAAAAAAAANRqNcQAAAAAAAAAAAABAjUZjHAAAAAAAAAAAAABQo9EYBwAAAAAAAAAAAADUaDTGa4AXX3xR0dHRstvt6tGjh7799lurU8JJ1qxZo2uvvVatWrWSzWbTe++9Z3VKcGPatGnq1q2bGjRooObNm2vAgAHatm2b1WnhFHPmzFGnTp0UFhamsLAwxcbG6qOPPrI6LcAj1HHfRy33fdRx/0AdR01FLfdt1HHfRx33D9Rx1FTUcd9HLfd91HL/QC0/gca4n1u6dKkSExM1adIkbdiwQeedd57i4+O1b98+q1PD/zty5IjOO+88vfjii1anglJ8/vnnuueee/T111/rk08+0bFjx3TFFVfoyJEjVqeGk0RGRmr69Olav3691q1bp7/97W/q37+/tmzZYnVqQKVQx/0Dtdz3Ucf9A3UcNRG13PdRx30fddw/UMdRE1HH/QO13PdRy/0DtfwEmzHGWJ0EKq9Hjx7q1q2bXnjhBUmSw+FQmzZtdO+99+rhhx+2ODucymaz6d1339WAAQOsTgVl2L9/v5o3b67PP/9cF198sdXpoBRNmjTR008/rZEjR1qdClBh1HH/Qy33D9Rx/0Edh7+jlvsX6rh/oI77D+o4/B113P9Qy/0Dtdx/1MZazhXjfqygoEDr169Xnz59nGMBAQHq06ePUlNTLcwM8H+HDh2SdKIwwDcVFhbqrbfe0pEjRxQbG2t1OkCFUceB6kMd933UcdQE1HKgelDHfR91HDUBdRyoPtRy31eba3kdqxNA5R04cECFhYWKiIhwGY+IiNBPP/1kUVaA/3M4HBo7dqzi4uJ07rnnWp0OTrF582bFxsYqLy9P9evX17vvvquzzz7b6rSACqOOA9WDOu7bqOOoSajlQNWjjvs26jhqEuo4UD2o5b6NWk5jHACKueeee/TDDz/oyy+/tDoVuHHmmWdq48aNOnTokP79739r2LBh+vzzz2tdAQcAuEcd923UcQBAaajjvo06DgAoC7Xct1HLaYz7tfDwcAUGBiorK8tlPCsrSy1atLAoK8C/jRkzRv/973+1Zs0aRUZGWp0O3AgKClL79u0lSV26dFFaWpqeffZZvfzyyxZnBlQMdRyoetRx30cdR01CLQeqFnXc91HHUZNQx4GqRy33fdRy7jHu14KCgtSlSxelpKQ4xxwOh1JSUmrdPQEATxljNGbMGL377rv67LPPFBMTY3VKKCeHw6H8/Hyr0wAqjDoOVB3quP+ijsOfUcuBqkEd91/Ucfgz6jhQdajl/qs21nKuGPdziYmJGjZsmLp27aru3btr9uzZOnLkiEaMGGF1avh/hw8f1q+//up8vn37dm3cuFFNmjRR27ZtLcwMJ7vnnnu0ZMkSvf/++2rQoIEyMzMlSQ0bNlRISIjF2aHIhAkT1LdvX7Vt21Z//fWXlixZotWrV2vlypVWpwZUCnXcP1DLfR913D9Qx1ETUct9H3Xc91HH/QN1HDURddw/UMt9H7XcP1DLT7AZY4zVScAzL7zwgp5++mllZmaqc+fOeu6559SjRw+r08L/W716tS699NJi48OGDdPChQu9nxDcstlsbscXLFig4cOHezcZlGjkyJFKSUnR3r171bBhQ3Xq1EkPPfSQLr/8cqtTAyqNOu77qOW+jzruH6jjqKmo5b6NOu77qOP+gTqOmoo67vuo5b6PWu4fqOUn0BgHAAAAAAAAAAAAANRo3GMcAAAAAAAAAAAAAFCj0RgHAAAAAAAAAAAAANRoNMYBAAAAAAAAAAAAADUajXEAAAAAAAAAAAAAQI1GYxwAAAAAAAAAAAAAUKPRGAcAAAAAAAAAAAAA1Gg0xgEAAAAAAAAAAAAANRqNcaAUO3bskM1m08aNG/1q3fCOyZMnq3PnzlanAQAoBbUcpaGWA4Bvo46jNNRxAPBt1HGUhjoOq9AYB6DVq1fLZrPp4MGDVqdSYTabTe+9957VaQAAYClqOQAA/os6DgCA/6KOA/6FxjgAlOHYsWN+uW4AAHACtRwAAP9FHQcAwH9Rx+FraIwDkhwOh5566im1b99ewcHBatu2rZ544oliy/35559KSEhQs2bNFBISotNPP10LFiwo13t8++23Ov/882W329W1a1d99913LvHCwkKNHDlSMTExCgkJ0Zlnnqlnn33WGV+zZo3q1q2rzMxMl9eNHTtWF110kSQpPT1d1157rRo3bqx69erpnHPO0YcfflhqXjt27NCll14qSWrcuLFsNpuGDx8uScrPz9d9992n5s2by263q1evXkpLSyvX9hadKZeSkqKuXbsqNDRUPXv21LZt21yWe//993XBBRfIbrfrtNNO05QpU3T8+HFJ0mOPPaZWrVopOzvbufzVV1+tSy+9VA6HQ9HR0ZKk6667Tjabzfm8LHPmzFG7du0UFBSkM888U2+88YZL3Gazac6cOerXr5/q1avn/L8wffp0RUREqEGDBho5cqTy8vKKrfuVV15Rhw4dZLfbddZZZ+mll15yxoqm+Fm6dKl69+4tu92u5OTkcuUMACgdtZxafjJqOQD4F+o4dfxk1HEA8C/Ucer4yajj8HkGgHnwwQdN48aNzcKFC82vv/5qvvjiCzN//nyzfft2I8l89913xhhj7rnnHtO5c2eTlpZmtm/fbj755BOzfPnyMtf/119/mWbNmplbbrnF/PDDD+Y///mPOe2001zWXVBQYCZOnGjS0tLM77//bhYvXmxCQ0PN0qVLnes544wzzFNPPeV8XlBQYMLDw81rr71mjDHm6quvNpdffrnZtGmT+e2338x//vMf8/nnn5ea2/Hjx83bb79tJJlt27aZvXv3moMHDxpjjLnvvvtMq1atzIcffmi2bNlihg0bZho3bmyys7PL3OZVq1YZSaZHjx5m9erVZsuWLeaiiy4yPXv2dC6zZs0aExYWZhYuXGh+++038/HHH5vo6GgzefJkZ26xsbFmwIABxhhjXnjhBdOoUSOTnp5ujDFm3759RpJZsGCB2bt3r9m3b1+Zeb3zzjumbt265sUXXzTbtm0zSUlJJjAw0Hz22WfOZSSZ5s2bm9dee8389ttvJj093SxdutQEBwebV155xfz000/mn//8p2nQoIE577zznK9bvHixadmypXn77bfN77//bt5++23TpEkTs3DhQmOMcf5/io6Odi6zZ8+eMnMGAJSNWk4tp5YDgP+ijlPHqeMA4L+o49Rx6jj8CY1x1Ho5OTkmODjYzJ8/v1js1OJ97bXXmhEjRlT4PV5++WXTtGlTc/ToUefYnDlzXNbtzj333GNuuOEG5/MZM2aYDh06OJ+//fbbpn79+ubw4cPGGGM6duzoLHwVUVRo//zzT+fY4cOHTd26dU1ycrJzrKCgwLRq1crlA0RZ6/z000+dYx988IGR5Pw5XHbZZebJJ590ed0bb7xhWrZs6Xz+22+/mQYNGpiHHnrIhISEuORjzIlC++6775Z7W3v27GlGjx7tMjZw4EBz1VVXuaxz7NixLsvExsaau+++22WsR48eLsW7Xbt2ZsmSJS7LTJ061cTGxhpj/vf/afbs2eXOFwBQNmo5tZxaDgD+izpOHaeOA4D/oo5Tx6nj8DdMpY5ab+vWrcrPz9dll11W5rJ33XWX3nrrLXXu3FkPPvigvvrqq3K/R6dOnWS3251jsbGxxZZ78cUX1aVLFzVr1kz169fXvHnztHPnTmd8+PDh+vXXX/X1119LkhYuXKibbrpJ9erVkyTdd999evzxxxUXF6dJkyZp06ZN5crPnd9++03Hjh1TXFycc6xu3brq3r27tm7dWu71dOrUyfnvli1bSpL27dsnSfr+++/12GOPqX79+s7H6NGjtXfvXuXm5kqSTjvtNM2cOVMzZsxQv379dMstt1R6m6QT++LkbZKkuLi4YtvUtWvXYq/r0aOHy9jJ+/DIkSP67bffNHLkSJftefzxx/Xbb7+Vum4AgGeo5e5Ry6nlAOAPqOPuUcep4wDgD6jj7lHHqePwXTTGUeuFhISUe9m+ffsqPT1d48aN0549e3TZZZfpgQceqJI83nrrLT3wwAMaOXKkPv74Y23cuFEjRoxQQUGBc5nmzZvr2muv1YIFC5SVlaWPPvpIt912mzM+atQo/f7777r11lu1efNmde3aVc8//3yV5FdZdevWdf7bZrNJOnHfGUk6fPiwpkyZoo0bNzofmzdv1i+//OLyQWfNmjUKDAzUjh07nPdIqW5FH4jK6/Dhw5Kk+fPnu2zPDz/84PywVdl1AwBKRy2vXtRyajkAVCfqePWijlPHAaA6UcerF3WcOo6qR2Mctd7pp5+ukJAQpaSklGv5Zs2aadiwYVq8eLFmz56tefPmlfmaDh06aNOmTcrLy3OOnfoHfe3aterZs6fuvvtunX/++Wrfvn2xs6GkEwV66dKlmjdvntq1a1fsDK02bdrozjvv1DvvvKPx48dr/vz5ZeYXFBQkSSosLHSOtWvXTkFBQVq7dq1z7NixY0pLS9PZZ59d5jrL44ILLtC2bdvUvn37Yo+AgBN/npYuXap33nlHq1ev1s6dOzV16lSXddStW9cl77J06NDBZZukEz/7srapQ4cO+uabb1zGTt6HERERatWqlX7//fdi2xITE1Pu/AAAFUctp5ZTywHAf1HHqePUcQDwX9Rx6jh1HH7H6rncAV8wefJk07hxY7No0SLz66+/mtTUVPPKK68Uuw/Ko48+at577z3zyy+/mB9++MFcc801pnv37mWu/6+//jLh4eFmyJAhZsuWLeaDDz4w7du3d1n3s88+a8LCwsyKFSvMtm3bzCOPPGLCwsJc7rNhjDGFhYWmTZs2JigoyEyfPt0ldv/995sVK1aY33//3axfv9706NHD3HTTTWXml5GRYWw2m1m4cKHZt2+f+euvv5zra9Wqlfnoo4/Mli1bzLBhw0zjxo3NH3/8UeY63d1b5bvvvjOSzPbt240xxqxYscLUqVPHTJ482fzwww/mxx9/NG+++ab55z//aYwxZteuXaZx48bmueeec1k+NTXVuc7TTz/d3HXXXWbv3r3lyuvdd981devWNS+99JL5+eefTVJSkgkMDDSrVq1yLiM391Z56623jN1uN6+99prZtm2bmThxomnQoIHL/pk/f74JCQkxzz77rNm2bZvZtGmTee2110xSUpIxpvh9dQAAVYdaTi2nlgOA/6KOU8ep4wDgv6jj1HHqOPwJjXHAnCiIjz/+uImKijJ169Y1bdu2NU8++WSxP7ZTp041HTp0MCEhIaZJkyamf//+5vfffy/Xe6SmpprzzjvPBAUFmc6dO5u3337bZd15eXlm+PDhpmHDhqZRo0bmrrvuMg8//HCx4m3MiQ8RgYGBZs+ePS7jY8aMMe3atTPBwcGmWbNm5tZbbzUHDhwoV36PPfaYadGihbHZbGbYsGHGGGOOHj1q7r33XhMeHm6Cg4NNXFyc+fbbb8u1vvIUb2NOFOSePXuakJAQExYWZrp3727mzZtnHA6Hueyyy0x8fLxxOBzO5e+9917Trl075weM5cuXm/bt25s6deqYqKiocuX20ksvmdNOO83UrVvXnHHGGeb11193ibsr3sYY88QTT5jw8HBTv359M2zYMPPggw8W2z/Jycmmc+fOJigoyDRu3NhcfPHF5p133jHGULwBoDpRy6nlJ6OWA4B/oY5Tx09GHQcA/0Idp46fjDoOX2czxpiqvw4dQHUaOXKk9u/fr+XLl1udCgAAqARqOQAA/os6DgCA/6KOA7VbHasTAFB+hw4d0ubNm7VkyRIKNwAAfohaDgCA/6KOAwDgv6jjACQpwOoEgJrgySefVP369d0++vbtW2Xv079/f11xxRW68847dfnll5f7dXfeeWeJ+d15552VyqU61lkVzjnnnBLzSk5OtiwvAIBvo5ZTywEA/os6Th0HAPgv6jh1HPAmplIHqsAff/yhP/74w20sJCRErVu39nJGrvbt26ecnBy3sbCwMDVv3twn1lkV0tPTdezYMbexiIgINWjQwMsZAQD8AbW8atZZFajlAICKoo5XzTqrAnUcAFBR1PGqWWdVoI6jNqAxDgAAAAAAAAAAAACo0ZhKHQAAAAAAAAAAAABQo9EYBwAAAAAAAAAAAADUaDTGAQAAAAAAAAAAAAA1Go1xAAAAAAAAAAAAAECNRmMcAAAAAAAAAAAAAFCj0RgHAAAAAAAAAAAAANRoNMYBAAAAAAAAAAAAADUajXEAAAAAAAAAAAAAQI1GYxwAAAAAAAAAAAAAUKPRGAcAAAAAAAAAAAAA1Gg0xgEAAAAAAAAAAAAANRqNcQAAAAAAAAAAAABAjUZjHAAAAAAAAAAAAABQo9EYBwAAAAAAAAAAAADUaDTGAXjF6tWrZbPZtHr1aqtTAQDAryxcuFA2m007duywOpVyi46O1vDhw61OAwAAn3fJJZfokksusToNAAB8jq/VSF/Lx2qTJ0+WzWazOg2gwmiMA0AZ9uzZo8mTJ2vjxo1WpwIAAAAAgE/Jzc3V5MmTOREeAAAAPo/GOACUYc+ePZoyZQqNcQAAymnbtm2aP3++1WkAAAAvyM3N1ZQpU2iMAwBQizzyyCM6evSo1WkAFUZjHPBxDodDeXl5lX59bm5uFWZTMmOMVwvhkSNHKv3avLw8ORyOKswGAIDiPK3h/iw4OFh169a1Og0AACznybFrRdXmzx4AAP9DjfRvderUkd1utzoNoMJojAOlGD58uKKjo4uNn3r/jE8++US9evVSo0aNVL9+fZ155pn6xz/+4fKa/Px8TZo0Se3bt1dwcLDatGmjBx98UPn5+S7L2Ww2jRkzRsnJyTrnnHMUHBysFStWlCvfSy65ROeee67Wr1+viy++WKGhoc48yvv+x48f19SpU9WuXTsFBwcrOjpa//jHP4otFx0drWuuuUYrV65U165dFRISopdfflmSlJGRoQEDBqhevXpq3ry5xo0bV+z1Rb755htdeeWVatiwoUJDQ9W7d2+tXbvW7c/7xx9/1C233KLGjRurV69e5fqZFN3b/K233tIjjzyi1q1bKzQ0VDk5Ofrjjz/0wAMPqGPHjqpfv77CwsLUt29fff/99y6v79atmyRpxIgRstlsstlsWrhwYYW2AQDgv4rq0E8//aSbbrpJYWFhatq0qe6//36XA+vSavh3332nvn37KiwsTPXr19dll12mr7/+uth7bdmyRX/7298UEhKiyMhIPf7445U6maso519//VXDhw9Xo0aN1LBhQ40YMcLlpLkdO3YUq2snb8/kyZMrvE7J/T3G3W3ba6+9Vuz+6ae+b2nrPHjwoMaOHas2bdooODhY7du314wZMzgBDgBQLcqq5wsXLpTNZtPnn3+uu+++W82bN1dkZKQzPm/ePLVr104hISHq3r27vvjiC7fv443vD6QTx7JXXXWVGjdurHr16qlTp0569tlnnfHhw4erfv362r17twYMGKD69eurWbNmeuCBB1RYWCjpxGeJZs2aSZKmTJniPGZ2V8sBADVXTauR5c1n3759GjlypCIiImS323Xeeedp0aJFLstccMEFuv76613GOnbsKJvNpk2bNjnHli5dKpvNpq1bt5Yrx6Lj+ZkzZzrzDQ4OVrdu3ZSWluaybEn3Rz+1/1GRdbq7x3h+fr7GjRunZs2aqUGDBurXr58yMjKKfTYob9+lyOLFi9WlSxeFhISoSZMmuvnmm7Vr166yf0iAG3WsTgDwd1u2bNE111yjTp066bHHHlNwcLB+/fVXl8aow+FQv3799OWXX+r2229Xhw4dtHnzZs2aNUs///yz3nvvPZd1fvbZZ/rXv/6lMWPGKDw83G2RKEl2drb69u2rm2++WUOGDFFERESF3n/UqFFatGiRbrzxRo0fP17ffPONpk2bpq1bt+rdd991ea9t27Zp8ODBuuOOOzR69GideeaZOnr0qC677DLt3LlT9913n1q1aqU33nhDn332WbFcP/vsM/Xt21ddunTRpEmTFBAQoAULFuhvf/ubvvjiC3Xv3t1l+YEDB+r000/Xk08+KWNMuX8mkjR16lQFBQXpgQceUH5+voKCgvTjjz/qvffe08CBAxUTE6OsrCy9/PLL6t27t3788Ue1atVKHTp00GOPPaaJEyfq9ttv10UXXSRJ6tmzZ6W2AQDgv2666SZFR0dr2rRp+vrrr/Xcc8/pzz//1Ouvv+5cxl0N37Jliy666CKFhYXpwQcfVN26dfXyyy/rkksu0eeff64ePXpIkjIzM3XppZfq+PHjevjhh1WvXj3NmzdPISEhHuUcExOjadOmacOGDXrllVfUvHlzzZgxw6vrrI5ty83NVe/evbV7927dcccdatu2rb766itNmDBBe/fu1ezZsyu9bgAATlXeei5Jd999t5o1a6aJEyc6r4Z79dVXdccdd6hnz54aO3asfv/9d/Xr109NmjRRmzZtnK/11vcHn3zyia655hq1bNlS999/v1q0aKGtW7fqv//9r+6//37ncoWFhYqPj1ePHj00c+ZMffrpp0pKSlK7du101113qVmzZpozZ47uuusuXXfddc4v/jt16lTJnzQAwN/UtBpZ3nyOHj2qSy65RL/++qvGjBmjmJgYLVu2TMOHD9fBgwed9fSiiy7Sm2++6XzdH3/8oS1btiggIEBffPGFs2Z+8cUXatasmTp06FChn/+SJUv0119/6Y477pDNZtNTTz2l66+/Xr///nulZ3Kr7DpHjRqlxYsX65ZbblHPnj312Wef6eqrr65UDkWeeOIJPfroo7rppps0atQo7d+/X88//7wuvvhifffdd2rUqJFH60ctZACUaNiwYSYqKqrY+KRJk0zRr8+sWbOMJLN///4S1/PGG2+YgIAA88UXX7iMz50710gya9eudY5JMgEBAWbLli0Vzrd3795Gkpk7d26l3n/jxo1Gkhk1apTLcg888ICRZD777DPnWFRUlJFkVqxY4bLs7NmzjSTzr3/9yzl25MgR0759eyPJrFq1yhhjjMPhMKeffrqJj483DofDuWxubq6JiYkxl19+uXOs6Oc9ePDgCv9MVq1aZSSZ0047zeTm5rrE8vLyTGFhocvY9u3bTXBwsHnsscecY2lpaUaSWbBggcuyFdkGAID/KqpD/fr1cxm/++67jSTz/fffG2NKruEDBgwwQUFB5rfffnOO7dmzxzRo0MBcfPHFzrGxY8caSeabb75xju3bt880bNjQSDLbt2+vcM633Xaby/h1111nmjZt6ny+fft2tzWuaHsmTZpU4XUac+JzwrBhwyq1bae+b0nrnDp1qqlXr575+eefXZZ7+OGHTWBgoNm5c2exdQAAUFnlqecLFiwwkkyvXr3M8ePHncsVFBSY5s2bm86dO5v8/Hzn+Lx584wk07t3b+eYN74/OH78uImJiTFRUVHmzz//dImdfGw7bNgwI8nl+NgYY84//3zTpUsX5/P9+/eXWL8BADVfTaqRFcmn6HvwxYsXu7w+NjbW1K9f3+Tk5BhjjFm2bJmRZH788UdjjDHLly83wcHBpl+/fmbQoEHO13bq1Mlcd9115c616Hi+adOm5o8//nCOv//++0aS+c9//uMc6927t0vuRU7tf1RknSf3SIz5X2/h7rvvdnmPW265pdjnhPL0XYwxZseOHSYwMNA88cQTLstt3rzZ1KlTp9g4UB5MpQ54qOiMpPfff7/EaTuXLVumDh066KyzztKBAwecj7/97W+SpFWrVrks37t3b5199tmVyic4OFgjRoyo1Pt/+OGHkqTExESX148fP16S9MEHH7iMx8TEKD4+3mXsww8/VMuWLXXjjTc6x0JDQ3X77be7LLdx40b98ssvuuWWW5Sdne3M6ciRI7rsssu0Zs2aYj/PO++8s0I/i5MNGzas2FVpwcHBCgg48WewsLBQ2dnZzqnwN2zYUOY6K7MNAAD/dc8997g8v/feeyX9r35KxWt4YWGhPv74Yw0YMECnnXaac7xly5a65ZZb9OWXXyonJ8e5ngsvvNBltpFmzZopISGh0jmfWjsvuugiZWdnO9/TW+usjm1btmyZLrroIjVu3Njl802fPn1UWFioNWvWVHrdAACcrCL1XJJGjx6twMBA5/N169Zp3759uvPOOxUUFOQcHz58uBo2bOjyXt74/uC7777T9u3bNXbs2GJXWbmbvtRd7f/9998r9J4AgJqpptXIiuTz4YcfqkWLFho8eLBzrG7durrvvvt0+PBhff7555LknIG06Bj1iy++ULdu3XT55Zc7p2g/ePCgfvjhB+eyFTFo0CA1btzY+bxoHZ7U6sqss+i7kfvuu89lfOzYsZXO45133pHD4dBNN93kss9btGih008/vdg+B8qDqdQBDw0aNEivvPKKRo0apYcffliXXXaZrr/+et14443Opusvv/yirVu3Ou+7dap9+/a5PI+Jial0Pq1bt3Yp2hV5//T0dAUEBKh9+/Yu8RYtWqhRo0ZKT08vM8/09HS1b9++2MH0mWeeWSwn6UTDuiSHDh1yKcCe/FzcvdbhcOjZZ5/VSy+9pO3btzvvkSZJTZs2LXOdldkGAID/Ov30012et2vXTgEBAS73xz613uzfv1+5ubnF6qAkdejQQQ6HQ7t27dI555yj9PR0lynmirh7bXm1bdvW5XlRTfrzzz8VFhbmtXVWx7b98ssv2rRpU7k/XwEAUFnlredFTv08UHQsfepnibp167o0ESTvfH/w22+/SZLOPffcMpe12+3FcmncuLH+/PPPCr8vAKDmqWk1siL5pKen6/TTT3f2AIoUTYVetK6IiAidfvrp+uKLL3THHXfoiy++0KWXXqqLL75Y9957r37//Xdt3bpVDoejUo3x0o7RK6sy6yzqLbRr185l3NPjfmNMsf1RpLJTxaN2ozEOlMLdmdKSXBqoISEhWrNmjVatWqUPPvhAK1as0NKlS/W3v/1NH3/8sQIDA+VwONSxY0c988wzbtd38r1JitZZWe5eW9H3L2m7y/Ne5VV0JfXTTz+tzp07u12mfv36VfZ+7l775JNP6tFHH9Vtt92mqVOnqkmTJgoICNDYsWPLdaV3ZbYBAFBzuKuXntSq6nDymfgnM8ZIKt9nnYqus7qcmpPD4dDll1+uBx980O3yZ5xxRrXmAwBASTw9VvbW9wflUVLdBwCgMmpSjayIXr16KSUlRUePHtX69es1ceJEnXvuuWrUqJG++OILbd26VfXr19f5559f4XWX5xjdZrO5PWYv6di/uo/7y/tdhMPhkM1m00cffeQ2J757R2XQGAdK0bhxYx08eLDY+KlXTgcEBOiyyy7TZZddpmeeeUZPPvmk/vnPf2rVqlXq06eP2rVrp++//16XXXZZuZvOVam87x8VFSWHw6FffvnFeWabJGVlZengwYOKiooq872ioqL0ww8/yBjj8l7btm0rlpMkhYWFqU+fPhXdpCrx73//W5deeqleffVVl/GDBw8qPDzc+bykn5kvbAMAwHt++eUXlzPOf/31VzkcDkVHR5f4mmbNmik0NLRYHZSkn376SQEBAc6D96ioKOdsJCdz99qqUnTW96mfd079rOOpimybu89fBQUF2rt3r8tYu3btdPjwYWowAKDalbeep6WluX190bH0L7/84pzuVZKOHTum7du367zzznOOeeP7g6Jj2R9++KFK6qgV33MAAHxDTauRFcknKipKmzZtksPhcLlq/KeffnJZl3RiKvIFCxborbfeUmFhoXr27KmAgAD16tXL2Rjv2bNntZ2Q1rhxY7fToFflsX9Rb+G3335zuUq8vMf97vJp166djDGKiYnh5HdUGe4xDpSiXbt2OnTokDZt2uQc27t3r959913n8z/++KPY64quHs7Pz5ck3XTTTdq9e7fmz59fbNmjR4/qyJEjVZy5q/K+/1VXXSVJmj17tssyRWfhXX311WW+11VXXaU9e/bo3//+t3MsNzdX8+bNc1muS5cuateunWbOnKnDhw8XW8/+/fvLfC9PBQYGFjvLbdmyZdq9e7fLWL169SQVbxr4wjYAALznxRdfdHn+/PPPS5L69u1b4msCAwN1xRVX6P3333eZcj0rK0tLlixRr169nNOPX3XVVfr666/17bffOpfbv3+/kpOTq3ArXIWFhSk8PLzY/bhfeumlKn2fimxbu3btiuUzb968YmeO33TTTUpNTdXKlSuLrePgwYM6fvx4FWUPAKjtKlLP3enatauaNWumuXPnqqCgwDm+cOHCYseZ3vj+4IILLlBMTIxmz55d7P0rcyVYaGiopOLHzACAmq+m1ciK5HPVVVcpMzNTS5cudY4dP35czz//vOrXr6/evXs7x4umSJ8xY4Y6derkvF/5RRddpJSUFK1bt65S06iXV7t27fTTTz+5fF/9/fffa+3atVX2HkXfjTz33HMu46f2GoryKavvIknXX3+9AgMDNWXKlGKfUYwxys7OrqLsUZtwxThQiptvvlkPPfSQrrvuOt13333Kzc3VnDlzdMYZZ2jDhg2SpMcee0xr1qzR1VdfraioKO3bt08vvfSSIiMj1atXL0nSrbfeqn/961+68847tWrVKsXFxamwsFA//fST/vWvf2nlypXq2rVrtW1Hed//vPPO07BhwzRv3jwdPHhQvXv31rfffqtFixZpwIABuvTSS8t8r9GjR+uFF17Q0KFDtX79erVs2VJvvPGG80C5SEBAgF555RX17dtX55xzjkaMGKHWrVtr9+7dWrVqlcLCwvSf//ynun4kkqRrrrlGjz32mEaMGKGePXtq8+bNSk5OLna/mHbt2qlRo0aaO3euGjRooHr16qlHjx6KiYmxfBsAAN6zfft29evXT1deeaVSU1O1ePFi3XLLLS5njLvz+OOP65NPPlGvXr109913q06dOnr55ZeVn5+vp556yrncgw8+qDfeeENXXnml7r//ftWrV0/z5s1znoVeXUaNGqXp06dr1KhR6tq1q9asWaOff/65St+jIts2atQo3Xnnnbrhhht0+eWX6/vvv9fKlStdZnORpL///e9avny5rrnmGg0fPlxdunTRkSNHtHnzZv373//Wjh07ir0GAIDKKm89d6du3bp6/PHHdccdd+hvf/ubBg0apO3bt2vBggXFjj+98f1BQECA5syZo2uvvVadO3fWiBEj1LJlS/3000/asmWL25POShMSEqKzzz5bS5cu1RlnnKEmTZro3HPPLdc9zAEA/q8m1ciK5HP77bfr5Zdf1vDhw7V+/XpFR0fr3//+t9auXavZs2erQYMGzmXbt2+vFi1aaNu2bbr33nud4xdffLEeeughSarWxvhtt92mZ555RvHx8Ro5cqT27dunuXPn6pxzzlFOTk6VvEfnzp01ePBgvfTSSzp06JB69uyplJQU/frrr8WWLU/fRTrxvfzjjz+uCRMmaMeOHRowYIAaNGig7du3691339Xtt9+uBx54oEryRy1iAJTq448/Nueee64JCgoyZ555plm8eLGZNGmSKfr1SUlJMf379zetWrUyQUFBplWrVmbw4MHm559/dllPQUGBmTFjhjnnnHNMcHCwady4senSpYuZMmWKOXTokHM5Seaee+6pVK69e/c255xzjttYed//2LFjZsqUKSYmJsbUrVvXtGnTxkyYMMHk5eW5rC8qKspcffXVbt8rPT3d9OvXz4SGhprw8HBz//33mxUrVhhJZtWqVS7Lfvfdd+b66683TZs2NcHBwSYqKsrcdNNNJiUlxblM0c97//79Ff6ZrFq1ykgyy5YtKxbLy8sz48ePNy1btjQhISEmLi7OpKammt69e5vevXu7LPv++++bs88+29SpU8dIMgsWLKjQNgAA/FdRHfrxxx/NjTfeaBo0aGAaN25sxowZY44ePepcrrQavmHDBhMfH2/q169vQkNDzaWXXmq++uqrYstt2rTJ9O7d29jtdtO6dWszdepU8+qrrxpJZvv27RXO+dTauWDBgmLrys3NNSNHjjQNGzY0DRo0MDfddJPZt2+fkWQmTZpUqXVGRUWZYcOGVWrbCgsLzUMPPWTCw8NNaGioiY+PN7/++qvbdf71119mwoQJpn379iYoKMiEh4ebnj17mpkzZ5qCgoJy/7wAACiPsup5UU1MS0tz+/qXXnrJxMTEmODgYNO1a1ezZs0at8ef3vj+wBhjvvzyS3P55ZebBg0amHr16plOnTqZ559/3hkfNmyYqVevXrHXnfydSJGvvvrKdOnSxQQFBRX7DAEAqPlqWo0sbz5ZWVlmxIgRJjw83AQFBZmOHTu6fG98soEDBxpJZunSpS7bExoaaoKCgly+XyiP7du3G0nm6aefLhZzV4sXL15sTjvtNBMUFGQ6d+5sVq5caYYNG2aioqIqtU53nweOHj1q7rvvPtO0aVNTr149c+2115pdu3a5zaesvsvJ3n77bdOrVy9Tr149U69ePXPWWWeZe+65x2zbtq3sHxRwCpsxlZgjCQAAAKglJk+erClTpmj//v1cgVzFFi5cqBEjRmj79u2l3qsdAAAAAAD4J5vNpkmTJmny5MlWpwJwj3EAAAAAAAAAAAAAQM3GPcYBP/DHH3+ooKCgxHhgYKCaNWvmxYysV1BQoD/++KPUZRo2bKiQkBAvZQQAQPU7fPiwDh8+XOoyzZo1U2BgoJcyAgAAvoTvDwAAcM9famRhYaH2799f6jL169dX/fr1vZQRULPQGAf8wPXXX6/PP/+8xHhUVJR27NjhvYR8wFdffaVLL7201GUWLFig4cOHeychAAC8YObMmZoyZUqpyzAtOQAAtRffHwAA4J6/1Mhdu3YpJiam1GWYlhyoPO4xDviB9evX688//ywxHhISori4OC9mZL0///xT69evL3WZc845Ry1btvRSRgAAVL/ff/9dv//+e6nL9OrVS3a73UsZAQAAX8L3BwAAuOcvNTIvL09ffvllqcucdtppOu2007yUEVCz0BgHAAAAAAAAAAAAANRotW4qdYfDoT179qhBgway2WxWpwMAgCTJGKO//vpLrVq1UkBAgNXp+DRqOQDA11DHy486DgDwRdTy8qOWAwB8TUXqeK1rjO/Zs0dt2rSxOg0AANzatWuXIiMjrU7Dp1HLAQC+ijpeNuo4AMCXUcvLRi0HAPiq8tTxWtcYb9CggaQTP5ywsDCLswEA4IScnBy1adPGWadQMmo5AMDXUMfLjzoOAPBF1PLyo5YDAHxNRep4rWuMF03vEhYWRuEGAPgcpiErG7UcAOCrqONlo44DAHwZtbxs1HIAgK8qTx3nhikAAAAAAAAAAAAAgBqNxjgAAAAAAAAAAAAAoEajMQ4AAAAAAAAAAAAAqNFojAMAAAAAAAAAAAAAajQa4wAAAAAAAAAAAACAGs3SxviaNWt07bXXqlWrVrLZbHrvvffKfM3q1at1wQUXKDg4WO3bt9fChQurPU8AAAAAAPxdWcfgxhhNnDhRLVu2VEhIiPr06aNffvnFZZk//vhDCQkJCgsLU6NGjTRy5EgdPnzYi1sBAAAAAEDlWNoYP3LkiM477zy9+OKL5Vp++/btuvrqq3XppZdq48aNGjt2rEaNGqWVK1dWc6YAAAAAAPi3so7Bn3rqKT333HOaO3euvvnmG9WrV0/x8fHKy8tzLpOQkKAtW7bok08+0X//+1+tWbNGt99+u7c2AQAAAACASqtj5Zv37dtXffv2Lffyc+fOVUxMjJKSkiRJHTp00JdffqlZs2YpPj6+utIEAACoVmvXrtXs2bM1duxYxcXFWZ0OAKCGKu0Y3Bij2bNn65FHHlH//v0lSa+//roiIiL03nvv6eabb9bWrVu1YsUKpaWlqWvXrpKk559/XldddZVmzpypVq1aeW1bfMnFF1/s/PeaNWsszASlYT/5h0mTJmnVqlW69NJLNWXKFKvTAQAAQA3jV/cYT01NVZ8+fVzG4uPjlZqaWuJr8vPzlZOT4/IAAADwFXl5eUpKSlJWVpaSkpJcrsoDAMBbtm/frszMTJdj7oYNG6pHjx7OY+7U1FQ1atTI2RSXpD59+iggIEDffPON2/XW9GPyk5ut7p7DN1xzzTWlPodvyMrK0qpVqyRJq1atUlZWlsUZATVPdHS0bDZbscc999xjdWoAAHiFXzXGMzMzFRER4TIWERGhnJwcHT161O1rpk2bpoYNGzofbdq08UaqAAAA5bJ48WJlZ2dLkrKzs5WcnGxxRgCA2igzM1OS3B5zF8UyMzPVvHlzl3idOnXUpEkT5zKn4pgcvuDUEzJq2gkaNcXdd9/t8pxGHVD10tLStHfvXufjk08+kSQNHDjQ4swAAPAOS6dS94YJEyYoMTHR+TwnJ8eyA3FjTJVfBWaMUX5+viQpODhYNputStcvSXa7vVrWCwCVxbTTqCkyMjKUnJwsY4ykE3U9OTlZ8fHxioyMtDg7AAA850vH5FWtpKvDL774Yqbq9iHsJ//w0Ucfaf/+/S5j+/bt00cffVSh2zDCOzgm91/NmjVzeT59+nS1a9dOvXv3rtL3qe7vwf1JdXxn78vf19MD8Q/+uJ9q2z6S2E/Vxa8a4y1atCg2jVJWVpbCwsIUEhLi9jXBwcEKDg72RnplysvL88t7oa9cubLEny8AeFvRtNMHDhxQUlKSunTpIrvdbnVaQIUZYzRr1qwSx2fOnOnzHyQBADVHixYtJJ04xm7ZsqVzPCsrS507d3Yus2/fPpfXHT9+XH/88Yfz9afypWPyqlTWlOk0XX1DWlpamfFu3bp5KRuUpLCwUE899ZTb2FNPPaUrrrhCgYGBXs4KJeGYvOYoKCjQ4sWLlZiYWOqxZ35+vktDujyzbvjr9+D+wpe/r/fXfe/LP9Pq4I/7qbbtI4n9VF38air12NhYpaSkuIx98sknio2NtSgj1ETGGB09erRKH7m5ufrzzz/1559/Kjc3t8rXX3SlIVAbMO00aor09HSlpaWpsLDQZbywsFBpaWlKT0+3KDMAQG0UExOjFi1auBxz5+Tk6JtvvnEec8fGxurgwYNav369c5nPPvtMDodDPXr08HrOQFnGjx/vURzesXz58mKfiYsUFhZq+fLlXs4IpeGYvOZ47733dPDgQQ0fPrzU5bgtCgCgJrH0ivHDhw/r119/dT7fvn27Nm7cqCZNmqht27aaMGGCdu/erddff12SdOedd+qFF17Qgw8+qNtuu02fffaZ/vWvf+mDDz6wahMqxG63a+XKlVW6zry8PPXv31+S9P7771fLGZq17axPzsIBfBfTTqMmiYqKUrdu3bRhwwaXLwIDAwPVpUsXRUVFWZgdAKAmKusYfOzYsXr88cd1+umnKyYmRo8++qhatWqlAQMGSJI6dOigK6+8UqNHj9bcuXN17NgxjRkzRjfffLNatWpl0VYBJUtKSiq1+Z2UlOTFbFCSfv36afbs2W5P+rfZbOrXr58FWcEdjslrlldffVV9+/Yts4Z7eluUIxckSAFV0IYwRnIc93w93hZQR6qK2eAcx1Vvg++fiEIPxD/4436qbftIYj9VF0sb4+vWrdOll17qfF5UYIcNG6aFCxdq79692rlzpzMeExOjDz74QOPGjdOzzz6ryMhIvfLKK37TxLTZbNXavLTb7TRHAdRYTDuNmsZms2ncuHG69dZb3Y7z/xkAUE0qn4oAAQAASURBVNXKOgZ/8MEHdeTIEd1+++06ePCgevXqpRUrVrh8uZGcnKwxY8bosssuU0BAgG644QY999xzXt8WoDzKavZwQodvMMaUOBNeaTF4F8fkNUt6ero+/fRTvfPOO2Uu6/FtUQLqSIF1K/96F0FVtB5UF3og/oH95B/YT9XD0sb4JZdcUuqH24ULF7p9zXfffVeNWaG24ywcwDcVTTt9qpOnnY6OjvZ+YoAHIiMjlZCQoDfeeEPGGNlsNiUkJKh169ZWpwYAqIHKOga32Wx67LHH9Nhjj5W4TJMmTbRkyZLqSA+ocoMHDy4zzr3grefu+79T46NGjfJOMigRx+Q1y4IFC9S8eXNdffXVVqcCAIBX+dU9xgFvKDoLpyofJzeui87CqcoHZ+SiNiiadjogwLV0BQQEqHv37kw7Db81ZMgQNW3aVJIUHh6uhIQEizMCAACoGd58802P4vCOsu5vXFYc3lF0TB4YGOgyHhgYyDG5n3E4HFqwYIGGDRumOnUsvW4OAACvozEOwC8ZY3T06NEqfeTm5urPP//Un3/+qdzc3CpfP9O/eaZoeulTf47GGKadhl+z2+0aP368IiIilJiYyCwgAAD4gXnz5nkUh3eEh4d7FId31KlTR3fccYfb2N13303jzkcUHZOXNM4xuf/49NNPtXPnTt12221WpwIAgNfxyRKAX8rLy1N8fLzVaVTIypUra+U9O7yBkw7g7+Li4hQXF2d1GgAAoJxuv/32MuNM0W29f/7zn2XGZ86c6aVsUJqi2wvl5uY6x0JDQ3XzzTdbmBVOxa2gaoYrrriC71EAALUWV4wDAPyCMUazZs1ye8W4u3FUvejoaNlstmKPe+65R9KJE1buueceNW3aVPXr19cNN9ygrKwsl3Xs3LlTV199tUJDQ9W8eXP9/e9/1/Hjx63YHAAAgErjinH/8MQTT3gUh3ed+nvD75Fv4lZQAADAn3HFOAC/ZLfbtXLlyipdZ15envr37y9Jev/996t8OmOmR/ZMenq60tLS3MbS0tKUnp6u6Oho7yZVy6SlpamwsND5/IcfftDll1+ugQMHSpLGjRunDz74QMuWLVPDhg01ZswYXX/99Vq7dq0kqbCwUFdffbVatGihr776Snv37tXQoUNVt25dPfnkk5ZsEwAAQGUUFBR4FId3lDUFN1N0+5bmzZurTp06On78uOrUqaPmzZtbnRLcKLoV1OzZszV27Fi+6wAAAH6FIwAAfslms1XrtOR2u51pz31M27ZtFRoa6jK1XpHQ0FC1bdvWgqxql2bNmrk8nz59utq1a6fevXvr0KFDevXVV7VkyRL97W9/kyQtWLBAHTp00Ndff60LL7xQH3/8sX788Ud9+umnioiIUOfOnTV16lQ99NBDmjx5soKCgty+b35+vvLz853Pc3Jyqm8jAQAAymHMmDFlxplK3XrLly8vM37dddd5KRuUZfHixc4TcQsLC5WcnKyRI0danBXc4VZQAADAXzGVOgDAL+zYscNtU1yScnNztWPHDu8mVMsVFBRo8eLFuu2222Sz2bR+/XodO3ZMffr0cS5z1llnqW3btkpNTZUkpaamqmPHjoqIiHAuEx8fr5ycHG3ZsqXE95o2bZoaNmzofLRp06b6Ngwoxdq1azVw4EDnLAgAgNrrhRde8CgO7+jXr59HcXhPRkaGkpOTnbfIMsYoOTlZGRkZFmcGAACAmoTGOADAL+zdu9ejOKrWe++9p4MHD2r48OGSpMzMTAUFBalRo0Yuy0VERCgzM9O5zMlN8aJ4UawkEyZM0KFDh5yPXbt2Vd2GAOWUl5enpKQkZWVlKSkpSXl5eVanBACw0F9//eVRHN5x7Ngxj+LwDmOMZs2aVeJ4UbMcAAAA8BSNcQCAX7jwwgsVGBjoNhYYGKgLL7zQyxnVbq+++qr69u2rVq1aVft7BQcHKywszOUBeNvixYuVnZ0tScrOzlZycrLFGQEArDRhwgSP4vCO8ePHexSHd6SnpystLc05jXqRwsJCpaWlKT093aLMAAAAUNPQGAcA+IVdu3YV+6KkSGFhIVcRe1F6ero+/fRTjRo1yjnWokULFRQU6ODBgy7LZmVlqUWLFs5lsrKyisWLYoCvYmpPAMCppk2b5lEc3pGUlORRHN4RFRWlTp06uY116tRJUVFRXs4IAAAANRWNcQCAX4iKilK3bt3cxrp3786XJV60YMECNW/eXFdffbVzrEuXLqpbt65SUlKcY9u2bdPOnTsVGxsrSYqNjdXmzZu1b98+5zKffPKJwsLCdPbZZ3tvA4AKYGpPAIA7derU8SgO7wgIKP1rr7Li8J6SPlPxWQsAAABViSMAAIBfsNlsGjx4sNvY4MGDZbPZvJxR7eRwOLRgwQINGzbM5Qvfhg0bauTIkUpMTNSqVau0fv16jRgxQrGxsc5p7q+44gqdffbZuvXWW/X9999r5cqVeuSRR3TPPfcoODjYqk0CSsXUngAAd/7+9797FId3PPHEEx7F4R3p6enavHmz29jmzZv5vAUAAIAqQ2McAOAXjDElTnU4c+ZMriTwkk8//VQ7d+7UbbfdViw2a9YsXXPNNbrhhht08cUXq0WLFnrnnXec8cDAQP33v/9VYGCgYmNjNWTIEA0dOlSPPfaYNzcBqJCi2SoCAwNdxgMDA5mtAgBqsaefftqjOLzjn//8p0dxeEfR561Tr+APCAjg85aPWrt2rQYOHKi1a9danQoAAECF0BgHAPiF3377Tbt373Yb2717t3777TcvZ1Q7XXHFFTLG6IwzzigWs9vtevHFF/XHH3/oyJEjeuedd4rdOzwqKkoffvihcnNztX//fs2cOZOpRuHTbDabxo0bV+I4s1UAQO3kcDg8isM7yjp5lpNrfQOft/xLXl6ekpKSlJWVpaSkJOXl5VmdEgAAQLnRGAcA+IXVq1d7FAeAyoqMjFRCQoLzS1mbzaaEhAS1bt3a4swAAFZ56KGHPIrDO7hi3H9ERkaqadOmLmNNmzbl85YPWrx4sbKzsyVJ2dnZSk5OtjgjAACA8qMxDgDwC0OHDvUoDgCeGDJkiPPL2vDwcCUkJFicEQDASjNmzPAoDu/gHuP+Y926ddq/f7/L2L59+7Ru3TqLMoI7GRkZSk5Ods62YIxRcnKyMjIyLM4MAACgfGiMAwD8wrfffutRHAA8YbfbNX78eEVERCgxMVF2u93qlAAAFqpfv75HcXhHWbfs4ZY+vsHhcGjy5MluY5MnT+bWBD7CGKNZs2YV2x+FhYWaNWsWtyYAAAB+gcY4AMAvlDWFHlPsAahucXFxWrZsmeLi4qxOBQBgsXvuucejOLxj+fLlHsXhHampqcrJyXEby8nJUWpqqpczgjvp6elKS0sr1gA3xigtLU3p6ekWZQYAAFB+nBoLAKg2xhjl5eVVybqaN2+u0NBQ5ebmFouFhoaqefPmOnr0qMfvY7fbnfcRBgAAANx58cUXS21+v/jii17MBiXp16+fZs2aVWoc1ouNjS31WC82NtaCrHCqqKgonXHGGfr555+Lxc4880xFRUVZkBUAAEDF0BgHAFSbvLw8xcfHV/v75Obmqm/fvlWyrpUrVyokJKRK1gUAAICa6dChQx7F4R0FBQVlxvnsD5SPMUZ79uxxG9u9e7eMMZxkDgAAfB5TqQMAAAAAAFTAP/7xD4/i8I4xY8Z4FId3pKamur1aXDpxEjRTqfuG1NRUHT582G3s8OHD7CcAAOAXuGIcAFBt7Ha7Vq5cWWXr2717t0aNGiWHw+EcCwwM1CuvvKJWrVpVyXvY7fYqWQ8AAABqrieffLLU5veTTz7pxWxQkhdeeKHUGaxeeOEFL2aDksTGxiooKMjtFf5BQUFMpe4jYmNjVb9+fbfN8fr167OfAACAX6AxDgCoNjabrUqnJmzfvr1uvvlmLVmyxDk2ZMgQtWvXrsreAwAAAChLnTqlf51SVhzeUda0zkz77BscDkeJ094XFBTI4XAoIIBJL61ms9nUqlUrt/cYb9WqFb9PAADAL/CpEgDgVwYNGuT8d3h4uBISEizMBgAAALXRgw8+6FEc3jF+/HiP4vCOhQsXehSHd6Snp7ttikvSzz//rPT0dC9nBAAAUHE0xgEAfuXkqc7vvfdepj4HAACA1z311FMexeEdSUlJHsXhHcOHD/coDu+IiopSt27dil0ZbrPZ1L17d0VFRVmUGQAAQPnRGAcA+K0LL7zQ6hQAAABQC5U07XN54wD+JyAgQEFBQW5jwcHBTKPuI2w2m8aNG1dsfwQGBmrcuHFMpQ4AAPwCnywBAAAAAAAq4JFHHvEoDu9gKnX/kJqaWuLJJPn5+UpNTfVyRihJZGSkEhISnE1wm82mhIQEtW7d2uLMAAAAyofGOAAAAAAAQAU8/vjjHsXhHUyl7h9iY2MVGhrqNlavXj3FxsZ6OSOUZsiQIWratKkkKTw8XAkJCRZnBAAAUH40xgEAAAAAACogPz/fozi84/jx4x7FYT1jjNUp4BR2u13jx49XRESEEhMTZbfbrU4JAACg3GiMAwAAAAAAVMDUqVM9isM7hg0b5lEc3pGamqrc3Fy3sdzcXKZS90FxcXFatmyZ4uLirE4FAACgQmiMAwAAAAAAVMCjjz7qURzesWjRIo/i8I7SplIPDQ1lKnUAAABUGRrjAAAAFlu7dq0GDhyotWvXWp0KAAAoh4CA0r9OKSsO77DZbB7FAQAAANQsHKkBAABYKC8vT0lJScrKylJSUpLy8vKsTgkAAJRhypQpHsXhHSNHjvQoDu9gKnUAAAB4C41xAAAACy1evFjZ2dmSpOzsbCUnJ1ucEQAAKMukSZM8isM7Xn31VY/i8A6mUgcAAIC30BgHAACwSEZGhpKTk2WMkSQZY5ScnKyMjAyLMwMAAKWpW7euR3F4B1OpAwAAADgZjXEAAAALGGM0a9asEseLmuUAAMD3PPLIIx7F4R133HGHR3F4B1OpAwAAwFtojAMAAFggPT1daWlpKiwsdBkvLCxUWlqa0tPTLcoMAACU5fHHH/coDu94+eWXPYrDO2JjY2W3293G7HY7U6n7oLVr12rgwIFau3at1akAAABUCI1xAAAAC0RFRalbt24KCHD9OBYQEKDu3bsrKirKoswAAEBZjh075lEc3uFwODyKwzuMMSooKHAbKygoYCYlH5OXl6ekpCRlZWUpKSlJeXl5VqcEAABQbjTGAQAALGCz2TRu3LhiX/QZYzRu3DjueQkAgA+bMmWKR3F4x7BhwzyKwzuWL19e4kkKDodDy5cv93JGKM3ixYuVnZ0tScrOzlZycrLFGQEAAJQfjXEAAAAfw1UxAAD4tkmTJnkUh3csWrTIozi8o1+/fgoMDHQbq1Onjvr16+fljFCSjIwMJScnO49XjDFKTk5WRkaGxZkBAACUD41xAAAACxhjNGvWLLexWbNm0RwHAMCH7d2716M4vKOsGXiYocc3BAYG6sEHH3Qbe/jhh0tsmsO7io5fTj1OcTgcHL8AAAC/QWMcAADAAunp6UpLS3M7lXpaWprS09MtygwAAJRl3rx5HsXhHSNHjvQoDu/p27ev6tSp4zJWp04dXXHFFRZlhFMVHb+cOu29w+Hg+MXP7N69W0OGDFHTpk0VEhKijh07at26dVanBQCAV9AYBwAAsEDbtm0VFhbmNhYWFqa2bdt6OSMAAFBet99+u0dxeMerr77qURzes27dOh0/ftxl7Pjx4zTrfEhUVJTOPPNMt7GzzjpLUVFRXs4IlfHnn38qLi5OdevW1UcffaQff/xRSUlJaty4sdWpAQDgFXXKXgQAAOCE3bt366GHHtJHH32k3NxctW/fXgsWLFDXrl0lnbjaedKkSZo/f74OHjyouLg4zZkzR6effrpzHX/88Yfuvfde/ec//1FAQIBuuOEGPfvss6pfv75Vm2WJnTt3Kicnx20sJydHO3fuVHR0tHeTAgAA5dKwYUOP4vCOgoKCMuP16tXzUjYoicPh0OTJk93GJk+erOXLlysggGt7rGaM0e7du93GMjIyZIzh9gR+YMaMGWrTpo0WLFjgHIuJiany93GZGa3wWJWvv1Y66edYVbcuMMYoLy+vStZVnU7O0R/yLWK326vk7yL7qXpV1X6C/6AxDgAAyqXozPJLL71UH330kZo1a6ZffvnF5czyp556Ss8995wWLVqkmJgYPfroo4qPj9ePP/4ou90uSUpISNDevXv1ySef6NixYxoxYoRuv/12LVmyxKpNs0TRFePumuNcMQ4AgG97+umny4xfe+21XsoGJbnpppvKjH/yySdeygYlSU1NLfWE0dTUVMXFxXk5K5wqNTVVhw8fdhs7fPgw+8lPLF++XPHx8Ro4cKA+//xztW7dWnfffbdGjx5d4mvy8/OVn5/vfF7S7+uprylS77vadazvDfn5+QoNDfV4PXl5eYqPj6+CjLynf//+VqdQbitXrlRISIjH62E/Va+q2k/wH5xuCQAAyuXkM8u7d++umJgYXXHFFWrXrp2kE2ewzp49W4888oj69++vTp066fXXX9eePXv03nvvSZK2bt2qFStW6JVXXlGPHj3Uq1cvPf/883rrrbe0Z88eC7fO+8pzxTgAAPBNf//73z2Kwzv+9a9/eRSHd8TGxpZ4i6GGDRsqNjbWyxnBHfZTzfD77787Z3VbuXKl7rrrLt13331atGhRia+ZNm2aGjZs6Hy0adPGixkDAFC1uGIcAACUS1lnlm/fvl2ZmZnq06eP8zUNGzZUjx49lJqaqptvvlmpqalq1KiRc+p1SerTp48CAgL0zTff6Lrrriv2vpU5O90fREVFqVu3blq3bp3LNGg2m03dunXjHn0AAFSxqpyGsk+fPqVeNd6nTx8dPXrU4/dhakfPlHXPXO6p6xsCAgJ00UUX6YMPPigWu+iii5hG3UcEBARo8uTJSkxMLBabMmUK+8lPOBwOde3aVU8++aQk6fzzz9cPP/yguXPnatiwYW5fM2HCBJf9npOTU2ZzPDg42PnvI+ffIgXWrYLsa7nCY86r70/++VaVFy8+qODAqpmivaoZIxU4Tvw7KEDy5Y9G+YU23bOmUbWtv/DaQt/t6hlJhf//70BJPryfdFwK/E+g1VnAIr76KwQAAHxM0ZnliYmJ+sc//qG0tDTdd999CgoK0rBhw5SZmSlJioiIcHldRESEM5aZmanmzZu7xOvUqaMmTZo4lznVtGnTNGXKlGrYImvZbDaNGzdOt956qwoLC53jgYGBGjduHF+CAwBQxbw5DWVVvQ9TO3omNze3zHhVTEULzxw/ftxtU1yS/vvf/yoxMVF16vAVpi/o2rWrzjnnHG3ZssU5du655+qCCy6wMCtURMuWLXX22We7jHXo0EFvv/12ia8JDg6ucCPW5Xg2sC6N8SpWHd8XBAca2X24T+g/n4aq+eSCOvLtrh6/6vADnMoHAADKxeFw6IILLtCTTz6p888/X7fffrtGjx6tuXPnVuv7TpgwQYcOHXI+du3aVa3v502RkZFKSEhwHtTabDYlJCSodevWFmcGAADg/+644w6P4vCOhQsXehSHd51zzjmlPodvi4uL07Zt21zGfv75Z2YsAwDUGj5xbsmLL76op59+WpmZmTrvvPP0/PPPq3v37iUuP3v2bM2ZM0c7d+5UeHi4brzxRk2bNk12u92LWQMAULuUdWZ5ixYtJElZWVlq2bKlc5msrCx17tzZucy+fftc1nH8+HH98ccfztefqjJnp/uTIUOG6MMPP9SBAwcUHh6uhIQEq1MCAKBGstvtWrlyZZWuMy8vT/3793c+r+r18z2HZ15++WVdeeWVpcZhveHDh+v1118vNQ7fkJGRUezK4rffflv9+/dXZGSkRVmhIsaNG6eePXvqySef1E033aRvv/1W8+bN07x586xODQAAr7D8ivGlS5cqMTFRkyZN0oYNG3TeeecpPj6+2JfmRZYsWaKHH35YkyZN0tatW/Xqq69q6dKl+sc//uHlzAEAqF3KOrM8JiZGLVq0UEpKijOek5Ojb775RrGxsZKk2NhYHTx4UOvXr3cu89lnn8nhcKhHjx5e2ArfY7fbNX78eEVERCgxMZEvwAEAliksLNSjjz6qmJgYhYSEqF27dpo6daqM+d+UkMYYTZw4US1btlRISIj69OmjX375xcKsy89msykkJKRKHyfX7aJpz6vywa1VPFNQUOBRHMD/GGM0a9Ysl5ognZhZzN04fFO3bt307rvv6s0339S5556rqVOnavbs2ZygDQCoNSxvjD/zzDMaPXq0RowYobPPPltz585VaGioXnvtNbfLf/XVV4qLi9Mtt9yi6OhoXXHFFRo8eLC+/fZbt8vn5+crJyfH5QEAACpu3Lhx+vrrr/Xkk0/q119/1ZIlSzRv3jzdc889kk582Tx27Fg9/vjjWr58uTZv3qyhQ4eqVatWGjBggKQTV5hfeeWVGj16tL799lutXbtWY8aM0c0336xWrVpZuHXWiouL07JlyxQXF2d1KgCAWmzGjBmaM2eOXnjhBW3dulUzZszQU089peeff965zFNPPaXnnntOc+fO1TfffKN69eopPj5eeXl5FmYOuDdo0CCP4vAOplL3D+np6UpLS5PD4XAZdzgcSktLU3p6ukWZoaKuueYabd68WXl5edq6datGjx5tdUoAAHiNpY3xgoICrV+/Xn369HGOBQQEqE+fPkpNTXX7mp49e2r9+vXORvjvv/+uDz/8UFdddZXb5adNm6aGDRs6H23atKn6DQEAoBYoz5nlDz74oO69917dfvvt6tatmw4fPqwVK1a4XE2VnJyss846S5dddpmuuuoq9erVi2nbAADwAV999ZX69++vq6++WtHR0brxxht1xRVXOI+/jTGaPXu2HnnkEfXv31+dOnXS66+/rj179ui9996zNnnAjaVLl3oUh3eUNVU6U6n7hqioKHXs2NFtrFOnTtyjGgAA+AVL7zF+4MABFRYWKiIiwmU8IiJCP/30k9vX3HLLLTpw4IB69eolY4yOHz+uO++8s8Sp1CdMmKDExETn85ycHJrjAABU0jXXXKNrrrmmxLjNZtNjjz2mxx57rMRlmjRpoiVLllRHegAAwAM9e/bUvHnz9PPPP+uMM87Q999/ry+//FLPPPOMJGn79u3KzMx0Obm9YcOG6tGjh1JTU3XzzTcXW2d+fr7y8/Odz5nFDd5UWFjoURzeUdYU3EzR7Tu4vQMAAPB3lk+lXlGrV6/Wk08+qZdeekkbNmzQO++8ow8++EBTp051u3xwcLDCwsJcHgAAAL7klVde0SWXXKJXXnnF6lQAALXYww8/rJtvvllnnXWW6tatq/PPP19jx451zg6TmZkpSW5Pbi+KnYpZ3GClgQMHehSHdzz77LMexeEd6enp2rRpk9vYpk2bmEodAAD4BUsb4+Hh4QoMDFRWVpbLeFZWllq0aOH2NY8++qhuvfVWjRo1Sh07dtR1112nJ598UtOmTSt2jxsAAABfd/DgQS1evFgOh0OLFy/WwYMHrU4JAFBL/etf/1JycrKWLFmiDRs2aNGiRZo5c6YWLVpU6XVOmDBBhw4dcj527dpVhRkDpVu2bJlHcXjH/fff71Ec3sFU6gAAoCawtDEeFBSkLl26KCUlxTnmcDiUkpKi2NhYt6/Jzc1VQIBr2oGBgZKYWgkAAPiff/7zn86T+xwOhx555BGLMwIA1FZ///vfnVeNd+zYUbfeeqvGjRunadOmSZLzBPaKnNzOLG6w0vHjxz2KA3DFVOoAAMDfWT6VemJioubPn69FixZp69atuuuuu3TkyBGNGDFCkjR06FBNmDDBufy1116rOXPm6K233tL27dv1ySef6NFHH9W1117rbJADAAD4g3Xr1mnz5s0uY5s2bdK6dessyggAUJuVdCJ60QlcMTExatGihcvJ7Tk5Ofrmm29KPLkdsBJTqfsHplL3D0ylDgAAaoI6VicwaNAg7d+/XxMnTlRmZqY6d+6sFStWOO9ZtnPnTpcD80ceeUQ2m02PPPKIdu/erWbNmunaa6/VE088YdUmAAAAVJjD4dDkyZPdxiZPnqzly5cXa04AAFCdio6t27Ztq3POOUffffednnnmGd12222STlwpOHbsWD3++OM6/fTTFRMTo0cffVStWrXSgAEDrE0ecGPZsmWlNr+ZSt033H///Vq+fHmpcVgvKipK3bp10/r1611uZxkYGKguXbowlToAAPALljfGJWnMmDEaM2aM29jq1atdntepU0eTJk3SpEmTvJAZAABA9UhNTVVOTo7bWE5OjlJTUxUXF+flrAAAtdnzzz+vRx99VHfffbf27dunVq1a6Y477tDEiROdyzz44IM6cuSIbr/9dh08eFC9evXSihUrZLfbLcwccK+goMCjOID/sdlsGjdunG699dZisXHjxjHNOgAA8AtchgQAAGCB2NhY1a9f322sfv36TEkLAPC6Bg0aaPbs2UpPT9fRo0f122+/6fHHH1dQUJBzGZvNpscee0yZmZnKy8vTp59+qjPOOMPCrIGSJSQkeBSHdzCVuv+IjIxUkyZNXMaaNm2q1q1bW5QRAABAxdAYBwAAsIDNZlOrVq3cxlq1asUVFwAAAB5KTk72KA7vKGuqdKZS9x3r1q3T/v37Xcb27dundevWWZQRAABAxdAYBwAAsEB6erp+/vlnt7Gff/5Z6enpXs4IAACgZgkODvYoDu8o64RQThj1DQ6HQ5MnT3Ybmzx5sst9xwEAAHwVjXEAAAALREVFqVOnTm5jnTp1UlRUlJczAgAAqFkGDhzoURzesXDhQo/i8I7U1FTl5OS4jeXk5Cg1NdXLGQEAAFQcjXEAAACLGGMqNA4AAIDyW7ZsmUdxeMfw4cM9isM7YmNjFRQU5DYWFBSk2NhYL2cEAABQcTTGAQAALJCenq7Nmze7jW3evJmp1H3Q2rVrNXDgQK1du9bqVAAAQDkwRbd/OHbsmEdxeIfD4VBBQYHbWEFBAVOpAwAAv0BjHAAAwAJt27ZVWFiY21hYWJjatm3r5YxQmry8PCUlJSkrK0tJSUnKy8uzOiUAAFAGplL3D2PGjPEoDu9gynsAAFAT0BgHAACwwM6dO0u9R9/OnTu9nBFKs3jxYmVnZ0uSsrOzlZycbHFGAACgLEyl7h9eeOEFj+LwDqa8BwAANQGNcQAAAAtwxbj/yMjIUHJysvPe78YYJScnKyMjw+LMAABAafLz8z2KwztCQkLUqVMnt7Hzzz9fISEhXs4I7gQGBioyMtJtrE2bNgoMDPRyRgAAABVHYxwAAMACXDHuH4wxmjVrVonjRc1yAADge2655RaP4vCekq4Kf/bZZ72cCUqSnp5e4omhu3btUnp6upczAgAAqDga4wAAABaIiooq8cqYTp06KSoqyssZwZ309HSlpaWpsLDQZbywsFBpaWl8AQgAgA9bsmSJR3F4T0n3Eef+4r6D4xcAAFAT0BgHAACwSElXG3MVsu+IiopSt27dZLPZXMZtNpu6d+/OF4AAAPiwunXrehSHdxw9elSbNm1yG9u0aZOOHj3q5YxQEo5fAACAv6MxDgAAYIH09HRt3rzZbWzz5s1ciewjbDabBg8eXOzLPmOMBg8eXKxhDgAAfMfAgQM9isM7yroqnKvGfQPHLwAAoCagMQ4AAGCBoiuRAwJcP44FBARwJbIPMcbozTffdBtbsmQJV8cAAODDli1b5lEc3lHS/cXLG4d3cPwCAABqAhrjAAAAFrDZbBo3blyxK44DAgLcjsMaRfcYd4d7jAMA4NsOHz7sURzewZT3/qHo+KWkcY5fAACAP6AxDgAAYJHIyEglJCS4jCUkJKh169YWZYRTRUVFqWPHjm5jnTp14soYAAB82IgRIzyKwzsWLlzoURzeExkZqXPOOcdl7JxzzuH4BQAA+A0a4wAAABa69tprXZ5fc801FmWCknD1CwAA/mnBggUexeEdw4cP9ygO78nIyNCPP/7oMvbjjz8qIyPDoowAAAAqhsY4AACAhSZPnuzyfMqUKdYkArfS09O1adMmt7FNmzYxlToAAD4sMDDQoziA/zHGaNasWTLGuIw7HA634wAAAL6IxjgAAIBF1q1bpy1btriM/fDDD1q3bp1FGeFUTKUOAID/Gjp0qEdxeAdTqfuH9PR0paWlyeFwuIw7HA6lpaVxwqgXHD9+XJ9++qlefvll/fXXX5KkPXv26PDhwxZnBgCA/6AxDgAAYAGHw6GJEye6jU2cOLHYF06wDlOpAwDgn15//XWP4vAOplL3D1FRUTrzzDPdxs466yxOGK1m6enp6tixo/r376977rlH+/fvlyTNmDFDDzzwgMXZAQDgP2iMAwAAWCA1NbXEM/sPHz6s1NRUL2cEd5hKHQAA/5Wfn+9RHMD/GGO0e/dut7GMjAymUq9m999/v7p27ao///xTISEhzvHrrrtOKSkpFmYGAIB/oTEOAABggZYtW3oUh3cwlToAAP5r9OjRHsXhHUyl7h84sddaX3zxhR555BEFBQW5jEdHR5d4wgIAACiOxjgAAIAFYmJiFB0dXWIsJibGuwmhREyl7j/Wrl2rgQMHau3atVanAgDwAfPnz/coDu9gKnX/EBsbq9DQULex0NBQxcbGejmj2sXhcKiwsLDYeEZGhho0aGBBRgAA+Cca4wAAABYJDg52O37qVQCwDlOp+4+8vDwlJSUpKytLSUlJysvLszolAIDFSmrilTcOAL7iiiuu0OzZs53PbTabDh8+rEmTJumqq66yLjEAAPwMjXEAAFAukydPls1mc3mcddZZznheXp7uueceNW3aVPXr19cNN9ygrKwsl3Xs3LlTV199tUJDQ9W8eXP9/e9/1/Hjx729KT5hx44d2rZtm9vYtm3btGPHDu8mBLeioqLUrVs3BQS4fmwODAxU9+7dmUrdhyxevFjZ2dmSpOzsbCUnJ1ucEQDAagkJCR7F4R1Mpe4fUlNTlZub6zaWm5vLVOrVLCkpSWvXrtXZZ5+tvLw83XLLLc5p1GfMmGF1egAA+A0a4wAAoNzOOecc7d271/n48ssvnbFx48bpP//5j5YtW6bPP/9ce/bs0fXXX++MFxYW6uqrr1ZBQYG++uorLVq0SAsXLtTEiROt2BSgXGw2m8aNG1dsOvWSxmGNjIwMJScnyxgjSTLGKDk5WRkZGRZnBgCwUlknSXESlW9gKnX/EBsbq7CwMLexhg0bMpV6NYuMjNT333+vf/zjHxo3bpzOP/98TZ8+Xd99952aN29udXoAAPiNOpV94RtvvKG5c+dq+/btSk1NVVRUlGbPnq2YmBj179+/KnO0hDHGL6ZfPDlHf8i3iN1u54tkAPBDderUUYsWLYqNHzp0SK+++qqWLFmiv/3tb5KkBQsWqEOHDvr666914YUX6uOPP9aPP/6oTz/9VBEREercubOmTp2qhx56SJMnT65104dHR0erY8eO2rx5c7FYp06dSrz/OLwvMjJSCQkJeuONN2SMkc1mU0JCglq3bm11atCJz+2zZs0qcXzmzJl87gSAWsrd/XgrEod3FJ3YVtk4vCMgIECTJ09WYmJisdiUKVOKzbCEqlenTh0NGTLE6jQAAPBrlWqMz5kzRxMnTtTYsWP1xBNPOA8kGjVqpNmzZ9eIxnheXp7i4+OtTqNC/OnnvnLlSoWEhFidBgCggn755Re1atVKdrtdsbGxmjZtmtq2bav169fr2LFj6tOnj3PZs846S23btlVqaqouvPBCpaamqmPHjoqIiHAuEx8fr7vuuktbtmzR+eef7/Y98/PzlZ+f73yek5NTfRvoRTabTRMmTNAtt9xSLDZhwgQaeT5myJAh+vDDD3XgwAGFh4cz9aoPSU9PV1paWrHxwsJCpaWlKT09nRNNAKCWGjp0aJnxNWvWeCkblOTZZ58tM/7AAw94KRuUpmvXrgoNDXWZUj00NFQXXHCBhVnVDq+//nqp8bL+3gEAgBMq1Rh//vnnNX/+fA0YMEDTp093jnft2pUPqgAA1FA9evTQwoULdeaZZ2rv3r2aMmWKLrroIv3www/KzMxUUFCQGjVq5PKaiIgIZWZmSpIyMzNdmuJF8aJYSaZNm6YpU6ZU7cb4iMjISIWEhOjo0aPOsdDQUK5E9kF2u13jx4/X7NmzNXbsWNntdqtTwv8rug/8hg0bXK78CwwMVJcuXbgPPADUYq+//nqpzaKyGk3wjvvvv1/Lly8vNQ7fsG7dumL3Gc/NzdW6devUtWtXi7KqHU79PTh27Jhyc3MVFBSk0NBQGuMAAJRTpRrj27dvd3tVV3BwsI4cOeJxUr7myP+xd9/hUVX5H8c/kzoJkABCEkoIvUlTEAgoNiSiUpSfIiAgi7iyAYUIIgoKCASQqiIoUiwgrAXbKsGNQlYM0hVUUGnBhdAhFFNI7u8PlpExZZLMZO5M8n49zzzPzP3eufOZXMiZzLnnnOv7ST7FnnW+ZBmGlHPp8n0fP8mTR5flXFK5bazfBQDeqmvXrrb7LVq0ULt27RQVFaV//vOfJToLyNixY+2m60tLS1NkZGSJvZ47ffHFF3ad4tLlL5a++OILu583gPxdWe+9f//+eW5n9gUAKLscLTnnTUvSlWY5OTlO1eEeOTk5mjBhQp61CRMm6JNPPmE69RJ0+vTpXNt+/fVXDR06VKNHjzYhEQAA3qlYn1bq1KmjHTt25Nq+Zs0aNWnSxNlMnsfHT/L198ybX4AUEHz55hdgfp6Cbp56cQEAoFgqVqyohg0b6rffflNERIQyMzN15swZu32OHj1qW5M8IiJCR48ezVW/UstPYGCgQkJC7G6lQXZ2tmbMmJFnbcaMGax56WHS09M1a9YsHT16VLNmzeKLdA9zZR34K53grAMPAJCkRx991Kk63OPZZ591qg73SE5OzndZq7S0NCUnJ7s5ERo0aKBp06YVaVaFCRMmyGKx2N0aN25cgikBAPAsxeoYj4uLU2xsrFatWiXDMLRp0yZNmTJFY8eO1VNPPeXqjAAAwAOdP39ee/fuVbVq1dS6dWv5+/srMTHRVt+zZ49SUlIUHR0tSYqOjtbOnTt17Ngx2z5ffvmlQkJC1LRpU7fnN9snn3ySb+d3dnZ2gdNJwv3eeecdnTx5UpJ08uRJLV/OLDie5qGHHtI111wjSawDDwCQJL3++utO1eEeU6ZMcaoO94iOjlZwcHCeteDgYNvffXAvPz8/HT58uEjPufbaa3XkyBHb7ZtvvimhdAAAeJ5iDeF95JFHFBQUpHHjxunixYvq27evqlevrnnz5unBBx90dUYAAOABRo0apW7duikqKkqHDx/W888/L19fX/Xp00ehoaEaPHiw4uLiVLlyZYWEhGj48OGKjo5W+/btJUldunRR06ZN1b9/f82YMUOpqakaN26cYmNjFRgYaPK7c79u3bppzpw5BdbhGX7//XctX75chmFIkgzD0PLlyxUTE6OaNWuanA5XsA48AOCvTpw44VQd7uFo+m2m5waU68JpwzB05MgRvfLKK+rYsWORjuXn51fgrG0oO678jStJGUxa5xJX/xyv/vmi9DMMwytmF7w6ozfklS5/3+PKZfKKPbd1v3791K9fP128eFHnz59XWFiYy0IBAADP8/vvv6tPnz46efKkqlatqhtvvFEbN25U1apVJUlz5syRj4+PevXqpYyMDMXExOjVV1+1Pd/X11efffaZhg4dqujoaJUrV04DBw7UpEmTzHpLpjp06JDDep06ddyUBvkxDENz5szJtbZldna25syZo5kzZ7KGtQfp2LFjkb8YBACUXs8884zDelJSkpvSID/z5s1zWB81apSb0iA/ycnJunjxYp61ixcvKjk5mc9hJahnz552jy0Wi6pWrarbbrtNs2bNKtKxfv31V1WvXl1Wq1XR0dGKj49XrVq18t0/IyNDGRkZtsf5TakP73P1eY1NqmRiktIpIyMj35k2UPqkp6crJibG7BhF0qNHD7MjFEpCQoKCgoJcdrxidYzfdttt+vDDD1WxYkUFBwfb/nOnpaWpZ8+e+uqrr1wWEAAAeIaVK1cWWLdarZo/f77mz5+f7z5RUVH6/PPPXR0NKDEHDx7U5s2bc203DEObN2/WwYMHVbt2bfcHAwAADk2dOrXAzvGpU6e6MQ3y88QTTxS4jFBR1k9GyYmOjlb58uV1/vz5XLXy5cszlXoJ++uFusXVrl07LVu2TI0aNdKRI0c0ceJE3XTTTdq1a5cqVKiQ53Pi4+M1ceJEl7w+AABmK1bH+Lp165SZmZlre3p6uv7zn/84HQoAAKC0i4qKUnBwcJ6jLoKDgxUVFWVCKvxVrVq1FBISkueoiJCQkAJHVgAAAHNVqlTw6DdHdbiHo9l3mJ3HM1gsFtWoUUN79uzJVatZsybnyUt07drVdr9FixZq166doqKi9M9//lODBw/O8zljx45VXFyc7XFaWpoiIyNLPCtK3tXL2s3vdFqBviaGKSUysv8cfV8Wlw3EZWMkBZgdIh+GpKz/3feX5Kmtd6ak6SV07CJ1jP/www+2+z/99JNSU1Ntj7Ozs7VmzRrVqFHDdekAAABKqYMHDxY4FeHBgweZSt0DpKSk5DtVYFpamlJSUhgxDgCAhxo6dKjDOlOpm2/ZsmUO64888oh7wiBfBw8ezLNTXJJ2797NTEol7OqOaUdmz55d6H0rVqyohg0b6rfffst3n8DAQDr4SqmrL2gJ9JWsdIy7FBcMlV0BkgI8tstZ8o7f6EaJHblIHeOtWrWSxWKRxWLRbbfdlqseFBSkl19+2WXhAAAASivDKPgDnqM63CMqKko33HBDntOpt23blpH9AAB4sAULFhTYOb5gwQI3pkF+Hn74Yb311lsF1mG+qKgo1axZU7///nuuWmRkJJ+LS9j27du1fft2ZWVlqVGjRpKkX375Rb6+vrr++utt+xW1I+78+fPau3ev+vfv79K8AAB4qiJ1jO/fv1+GYahu3bratGmTqlataqsFBAQoLCxMvr5cVgQAAOBIXl8o/bVet25dN6VBfiwWi/r06ZNnx3ifPn24AhwAAA929uxZp+pwD0drJ7tqbWU4Jzs7O9+/YQ4dOqTs7Gz5+RVr1U4UQrdu3VShQgW9+eabtmUgTp8+rUGDBummm27Sk08+WajjjBo1St26dVNUVJQOHz6s559/Xr6+vurTp09JxgcAwGMU6dPKlSv/+EAKT2EYhtLT082O4dDVGb0h7xVWq5Uv/AGghJw4ccKpOtzDMAy9++67slgsdqP4LRaLVqxYoeuvv562EgAAD/X00087rDOVuvmmTJnisD5x4kQ3pUF+mPLeXLNmzdLatWttneKSVKlSJU2ePFldunQpdMf477//rj59+ujkyZOqWrWqbrzxRm3cuNFuABwAAKWZU5fx/fTTT0pJSVFmZqbd9u7duzsVCiis9PR0xcTEmB2jSHr06GF2hEJLSEhQUFCQ2TEAuMhvv/2mvXv3qlOnTgoKCpJhGHTomahZs2ZO1eEeBw8ezHO0uGEY2rx5M2spAgDgwaZNm1Zg5/i0adPcmAb5efbZZ/X1118XWIf5mPLeXGlpaTp+/Hiu7cePH9e5c+cKfZyVK1e6MhYAAF6nWB3j+/bt07333qudO3fajZ658uV6dna26xICAACnnDx5Ur1799ZXX30li8WiX3/9VXXr1tXgwYNVqVIlzZo1y+yIZdIPP/zgsN6wYUM3pUF+rqwxvm3bNrvPuL6+vmrdujVrKQIA4MEuXrzoVB3AnywWS65ZlP5aQ8m59957NWjQIM2aNUtt27aVJH333XcaPXq07rvvPpPTAQDgPYrVMf7EE0+oTp06SkxMVJ06dbRp0yadPHlSTz75pGbOnOnqjEChzO90RoG+uT+cewLDkDL/twJBgI/kyX8rZGRbFJtU0ewYAFxo5MiR8vPzU0pKipo0aWLb3rt3b8XFxdExbpJTp045VYd7WCwWjRw5Uv37989zO18AAgDguSZNmuSw3rlzZzelQX6YSt07fPLJJ3l2ikuXZ1P65JNPdO+997o5VdmxcOFCjRo1Sn379lVWVpYkyc/PT4MHD9aLL75ocjoAALyHT3GelJycrEmTJqlKlSry8fGRj4+PbrzxRsXHx+vxxx93dUagUAJ9DVl95ZG3ID8pNODyLcjP/DwF3Tz14gIAxbd27VpNnz5dNWvWtNveoEEDHTx40KRUuP32252qw31q1qypxo0b221r3LixatSoYVIi5GfDhg26//77tWHDBrOjAAA8wHPPPedUHe7haKp0plL3DI6WzmRpzZIVHBysV199VSdPntT27du1fft2nTp1Sq+++qrKlStndjwAALxGsTrGs7OzVaFCBUlSlSpVdPjwYUmXp5rcs2eP69IBAACnXbhwQcHBwbm2nzp1SoGBgSYkgiTVrVtXERERedaqVaumunXrujkR8vP777/rxx9/tNu2a9cu/f777yYlQl7S09M1a9YsHT16VLNmzVJ6errZkQAAJitfvrxTdbjHldGvxa3DPRwtncnSmu5Rrlw5tWjRQi1atKBDHACAYihWx3izZs30/fffS5LatWunGTNmaMOGDZo0aRJf4gIA4GFuuukmvfXWW7bHFotFOTk5mjFjhm699VYTkyE/+U1RCPczDEPx8fF51uLj4zlXHuSdd97RyZMnJUknT57U8uXLTU4EADDbU0895VQd7jF48GCn6nCPwkx5D9e67777lJaWZrtf0A0AABROsTrGx40bp5ycywsmT5o0Sfv379dNN92kzz//XC+99JJLAwIAAOfMmDFDr7/+urp27arMzEw99dRTatasmZKSkjR9+nSz45VZ+/btU2pqap611NRU7du3z82JkJcDBw5o586dedZ27typAwcOuDcQ8vT7779r+fLltgsVDMPQ8uXLGdUPAGXcjBkznKrDPRYvXuxUHe7BlPfuFxoaKovFYrtf0A0AABSOX3GeFBMTY7tfv3597d69W6dOnVKlSpVsjTUAAPAMzZo10y+//KJXXnlFFSpU0Pnz53XfffcpNjZW1apVMztemZWYmOiwXq9ePTelAbyXYRiaM2dOvttnzpzJ3ygAUEZdunTJqTrcw9/f36k63MPR5yk+b7ne0qVL87wPAACKr8gjxrOysuTn56ddu3bZba9cuXKxPwDNnz9ftWvXltVqVbt27bRp06YC9z9z5ozty/zAwEA1bNhQn3/+ebFeGwCAsiA0NFTPPvus/vnPf+rzzz/X5MmT6RQ3WcOGDZ2qwz0cTZXOVOrmO3jwoDZv3pxrXcvs7Gxt3rxZBw8eNCkZAMBszzzzjFN1uAdTdHuHefPmOVUHAADwBEXuGPf391etWrVyffFUXKtWrVJcXJyef/55bdu2TS1btlRMTIyOHTuW5/6ZmZm64447dODAAb3//vvas2ePFi1apBo1argkDwAApU1SUlKBN5gjv886ha3DPRgZ4/mioqJ0ww035DoXFotFbdu2VVRUlEnJAABmmzp1qlN1uAdTdHuHJ554wqk6nHP06FH1799f1atXl5+fn3x9fe1uAACgcIo1lfqzzz6rZ555Rm+//bYqV67sVIDZs2dryJAhGjRokCRp4cKF+te//qUlS5bo6aefzrX/kiVLdOrUKX377be2qZRq167tVAYAAEqzW265Jde2qzuQXHWxG4rm1KlTTtXhHlFRUbJarUpPT89Vs1qtdLp6AIvFoj59+mjz5s122w3DUJ8+fbh4AQDKsHPnzjlVh3swQ493yMnJcaoO5zz88MNKSUnR+PHjVa1aNT7jAgBQTEUeMS5Jr7zyipKSklS9enU1atRI119/vd2tsDIzM7V161Z17tz5z0A+PurcubOSk5PzfM4nn3yi6OhoxcbGKjw8XM2aNdPUqVPz/VI/IyNDaWlpdjcAAMqS06dP292OHTumNWvW6IYbbtDatWvNjldmVa1a1ak63OPAgQN5dopLUnp6ug4cOODeQMjFMAy9++67eY4YX7FiBV+mA0X03//+Vw899JCuueYaBQUFqXnz5tqyZYutbhiGnnvuOVWrVk1BQUHq3Lmzfv31VxMTA/mLj493qg73YMS4d+A8meubb77R8uXLNXToUPXs2VM9evSwuwEAgMIp1ojxnj17uuTFT5w4oezsbIWHh9ttDw8P1+7du/N8zr59+/TVV1+pX79++vzzz/Xbb7/pH//4h7KysvT888/n2j8+Pl4TJ050SV4AALxRaGhorm133HGHAgICFBcXp61bt5qQCvfcc0+B6/Ddc889bkyD/Bw+fNhhvW7dum5Kg7xcWWP8rwzDsK0xzgxTQOGcPn1aHTt21K233qovvvhCVatW1a+//qpKlSrZ9pkxY4Zeeuklvfnmm6pTp47Gjx+vmJgY/fTTT7JarSamB3IbO3ZsgZ3fY8eOdWMa5GfKlCm64447CqzDfJwnc0VGRnLBJwAALlCsjvG8OqDz8u6776p79+4qV65ccV4mTzk5OQoLC9Prr78uX19ftW7dWv/973/14osv5plr7NixiouLsz1OS0tTZGSky/IAAOCtwsPDtWfPHrNjlFlvvfWWw/qQIUPclAb5qV69ulN1lLwra4xv27bNbhapK38rMN09vN3f/vY3zZs3TxUqVLDbfuHCBQ0fPlxLlixx2WtNnz5dkZGRWrp0qW1bnTp1bPcNw9DcuXM1btw42+i0t956S+Hh4froo4/04IMPuiwL4Apdu3YtsGO8a9eubkyD/AQGBiogIECZmZm5agEBAQoMDDQhFf4qMDBQVapU0YkTJ3LVqlSpwnkqYXPnztXTTz+t1157jYs+AQBwQrGmUi+sv//97zp69Gi+9SpVqsjX1zfXPkePHlVERESez6lWrZoaNmwoX19f27YmTZooNTU1zw/QgYGBCgkJsbsBAFCW/PDDD3a377//XmvWrNFjjz2mVq1amR2vzHL0mYTPLJ4hKioq3/X7LBYLna4ewGKxaOTIkfluZ/1FeLs333xTf/zxR67tf/zxh8OLrIrqk08+UZs2bXT//fcrLCxM1113nRYtWmSr79+/X6mpqXbLoYWGhqpdu3b5LofG8mYw0+uvv+5UHe5x7ty5PL/Tky4vw8ha8J4hIyMjz05x6fKsoBkZGW5OVLb07t1b69atU7169VShQgVVrlzZ7gYAAAqnWCPGC8vR9C4BAQFq3bq1EhMTbdOz5+TkKDExUcOGDcvzOR07dtSKFSuUk5MjH5/L/fq//PKLqlWrpoCAAJfmBwCgNGjVqpUsFkuudrl9+/YuHWWGojl79qxTdbjHxo0b8/1MaxiGNm7cqI4dO7o5Ff6qZs2a6tevn95++20ZhiGLxaJ+/fqpRo0aZkcDii0tLU2GYcgwDJ07d85umvLs7Gx9/vnnCgsLc+lr7tu3TwsWLFBcXJyeeeYZbd68WY8//rgCAgI0cOBApaamSlKey6Fdqf0Vy5vBTO+8847D+qOPPuqmNMjPQw895LD+8ccfuykN8lOYNcZnzpzppjRlz9y5c82OAABAqVCiHeOFERcXp4EDB6pNmzZq27at5s6dqwsXLmjQoEGSpAEDBqhGjRq2qa+GDh2qV155RU888YSGDx+uX3/9VVOnTtXjjz9u5tsAAMBj7d+/3+6xj4+PqlatyjqgJmvQoIFTdbjH1VNzF6cO93nooYe0evVqnTt3ThUqVFC/fv3MjgQ4pWLFirJYLLJYLGrYsGGuusVicXmHc05Ojtq0aaOpU6dKkq677jrt2rVLCxcu1MCBA4t1TJY3g5keeuihAjvHHXXIwj3eeecd3X333QXWYT7WGDdXcdthAABgz/SO8d69e+v48eN67rnnlJqaqlatWmnNmjW2K9BTUlJsI8MlKTIyUgkJCRo5cqRatGihGjVq6IknntCYMWPMegsAAHg0pnr2TJs3b3ZYv/XWW92UBigdmDYdpcnXX38twzB022236YMPPrCbJjUgIEBRUVGqXr26S1+zWrVqatq0qd22Jk2a6IMPPpAk25JnR48eVbVq1Wz7HD16NN/lWQIDA1l3FqbJyclxqg73uHTpklN1oCxISUkpsF6rVi03JQEAwLuZ3jEuScOGDct36vR169bl2hYdHa2NGzeWcCoAAEqHl156qdD7MgOL+7Rt21afffZZgXWY7+oLNItTh/u88847tjVIz507p+XLl2vw4MEmpwKK7+abb5Z0eeaXyMhIt/y+6dixo/bs2WO37ZdffrFdZFenTh1FREQoMTHR1hGelpam7777TkOHDi3xfEBRrVixwmH9sccec1Ma5OeBBx5wWP/yyy/dlAb5YSp1c9WuXbvAi0CZyQoAgMLxiI5xAABQcubMmaPjx4/r4sWLqlixoiTpzJkzCg4OVtWqVW37WSwWOsbdKL91qwtbh3s4ukCBCxg8w++//67ly5fb/t8YhqHly5crJiZGNWvWNDkd/mrDhg2aO3euRowYoY4dO5odx+NFRUXpzJkz2rRpk44dO5ZrhOuAAQNc9lojR45Uhw4dNHXqVD3wwAPatGmTXn/9db3++uuSLn9WGDFihCZPnqwGDRqoTp06Gj9+vKpXr66ePXu6LAfgKn379i2wc7xv375uTIP8/POf/1SPHj0KrMN8TKVuru3bt9s9zsrK0vbt2zV79mx+9gAAFEGRO8azs7O1YcMGtWjRwvblen6ioqLk7+9f3GwAAMAFpkyZoldffVWLFy9Wo0aNJEl79uzRkCFD9Pe//511eE3y73//22GdqdTN99ZbbzmsP/LII25Kg7wYhqE5c+bkupgkJydHc+bM0cyZM5li3YOkp6dr1qxZOnHihGbNmqXWrVvLarWaHcujffrpp+rXr5/Onz+vkJAQu3/PFovFpR3jN9xwg1avXq2xY8dq0qRJqlOnjubOnWv3WeGpp57ShQsX9Oijj+rMmTO68cYbtWbNGs4jPNLFixedqsM9AgICnKrDPfz9/eXn55fn1PZ+fn58B1zCWrZsmWtbmzZtVL16db344ou67777TEgFAID3KfJcbL6+vurSpYtOnz7tcN9du3YpMjKyWMEAAIBrjB8/Xi+//LKtU1ySGjVqpDlz5mjcuHEmJivbatSo4VQd7nFlKuPi1lHyDh48qM2bN+caRZuTk6PNmzfr4MGDJiVDXt555x2dPHlSknTy5EktX77c5ESe78knn9Tf/vY3nT9/XmfOnNHp06dtt1OnTrn89e655x7t3LlT6enp+vnnnzVkyBC7usVi0aRJk5Samqr09HT9+9//VsOGDV2eA3CFjz76yKk63MPR0icsjeIZkpOT813v/dKlS0pOTnZzIkiX/7bfvHmz2TEAAPAaxZpKvVmzZtq3b5/q1Knj6jwASiHDMJSenm52DIeuzugNeSXJarUyCg4OHTlyJM8vMLKzs3X06FETEkG6fLGhM3W4R2pqqsN6gwYN3JQGeYmKilLz5s21c+fOXLUWLVrY1kaG+Zjyvnj++9//6vHHH1dwcLDZUQCv07NnzwI7v1kCwDMsXrxYXbt2LbAO80VHR8tqteb5fYnValV0dLQJqcqOtLQ0u8eGYejIkSOaMGECf48AAFAExeoYnzx5skaNGqUXXnhBrVu3Vrly5ezqISEhLgkHoHRIT09XTEyM2TGKpKD1zTxJQkKCgoKCzI4BD3f77bfr73//u9544w1df/31kqStW7dq6NCh6ty5s8npyq569eo5VYd7OLp4hItLPAMXiXm+K1Pe57edKe/zFxMToy1btqhu3bpmRwG8TmBgoFN1uEdWVpZTdbiHYRjKzMzMs5aZmZlrWRu4VsWKFXN9VjIMQ5GRkVq5cqVJqQAA8D7F6hi/6667JEndu3e3a5ANw5DFYlF2drZr0gEAAKctWbJEAwcOVJs2bWzrvl26dEkxMTF64403TE5Xdr333nsO61y4YL7rrrvOqTpK3sGDB/XDDz/kWfvhhx908OBB1a5d272hkMuVKe//Kjs72zblPecpb3fffbdGjx6tn376Sc2bN8+1hmv37t1NSgZ4vlWrVjmsx8bGuikN8tO7d2+H9YSEBDelQX4++eSTXEvXXJGTk6NPPvlE9957r5tTlR1ff/213WMfHx9VrVpV9evXl59fsb7iBwCgTCpWq/nXhhgACiu7W3Yxf/O4gSHpynU9vpI8ddDSJcn3U6ZYRuFVrVpVn3/+uX755Rft3r1bktS4cWOn1wOdNm2axo4dqyeeeEJz586VdHmGiCeffFIrV65URkaGYmJi9Oqrryo8PNz2vJSUFA0dOlRff/21ypcvr4EDByo+Pr7M/TFfu3Zt/fzzzwXWYT5HI1gZ4Wq+qKgotWjRIs/OcaZS9xxRUVG64YYbtG3bNrsLqX19fdW6dWvOUwGurPE9adKkXDUuTAcK1rt37wI7xx11yMI9Vq1aVeBFPo4ucIB7dO/eXXPnzs1zZLjFYuFCrRJ28803mx0BAIBSoVjfQNMQAyg2P3lux7gk+TveBfBWDRs2dLoz/IrNmzfrtddeU4sWLey2jxw5Uv/617/03nvvKTQ0VMOGDdN9992nDRs2SLo8MvDuu+9WRESEvv32Wx05ckQDBgyQv7+/pk6d6pJs3qJr16764osvCqzDfIcPH3ZYZ3pj8+U3dSdTenoOi8WikSNHqn///nlu5yKT/OU3Og+AY4Vpx2E+Hx8fp+pwH4vFkm/HOErer7/+qq+//lrHjh3L9fngueeeMykVAADepdjdU//5z3/02muvad++fXrvvfdUo0YNvf3226pTp45uvPFGV2YEAABFFBcXpxdeeEHlypVTXFxcgfvOnj27SMc+f/68+vXrp0WLFmny5Mm27WfPntXixYu1YsUK3XbbbZKkpUuXqkmTJtq4caPat2+vtWvX6qefftK///1vhYeHq1WrVnrhhRc0ZswYTZgwQQEBAUV/s15q/PjxDuuffvqpm9IgP+3bt3eqjpJ38OBB7dy5M8/azp07maLbg9SsWVP9+vXT22+/bVuGq1+/fqpRo4bZ0QCUUv/5z3+cqsM9+vbt67D+2WefuSkN8sNU6uZatGiRhg4dqipVqigiIsLuYgSLxULHOAAAhVSsSy4/+OADxcTEKCgoSNu2bVNGRoaky1+Il7XRXgAAeKLt27crKyvLdj+/244dO4p87NjYWN1999251r/eunWrsrKy7LY3btxYtWrVUnJysiQpOTlZzZs3t5taPSYmRmlpafrxxx/zfL2MjAylpaXZ3UqDv//9707V4R7fffedU3WUvFq1aql8+fJ51sqXL69atWq5OREK8tBDD+maa66RJFWpUkX9+vUzOZHne/zxx/XSSy/l2v7KK69oxIgR7g8EeJGbbrrJqTrcY8WKFU7V4R6OpkpnKvWSNXnyZE2ZMkWpqanasWOH3d/127ZtMzseAABeo1gd45MnT9bChQu1aNEi+fv/Oe9wx44daYgBAPAAX3/9tSpWrGi7n9/tq6++KtJxV65cqW3btik+Pj5XLTU1VQEBAbbXvSI8PFypqam2fa7uFL9Sv1LLS3x8vEJDQ223yMjIImX2VI5GvTAqxjNER0cX2OkaHR3t5kT4q4MHD+r8+fN51s6fP6+DBw+6OREKYrVa9eSTTyo8PFxxcXGyWq1mR/J4H3zwgTp27Jhre4cOHfT++++bkAjwHtnZ2U7V4R6OloxgSQnPcOnSJafqcM7p06d1//33mx0DAACvV6yO8T179qhTp065toeGhurMmTPOZgIAAB7o0KFDeuKJJ7R8+XK3dmSMHTtWZ8+etd0OHTrkttcuSTVr1nSqDvewWCyqWrVqnrWwsDDWU/QAjtYRZ51xz9OxY0e99957eXb2IreTJ08qNDQ01/aQkBCdOHHChESA9/j222+dqsM9evfu7VQd7vHss886VYdz7r//fq1du9bsGAAAeL1irTEeERGh3377Lddafd98843q1q3rilwAAMBFLly4oGnTpikxMVHHjh3LNeJi3759hTrO1q1bdezYMV1//fW2bdnZ2UpKStIrr7yihIQEZWZm6syZM3ajxo8ePaqIiAhJlz9DbNq0ye64R48etdXyEhgYqMDAwEJl9CaVK1d2qg73OHDggPbv359nbd++fTpw4IDq1Knj5lS42uHDhx3W+RsF3qx+/fpas2aNhg0bZrf9iy++4N824ECHDh0K7Pzu0KGDG9MgP6tWrSpwGu5Vq1a5MQ3yM2XKFN1xxx0F1lFy6tevr/Hjx2vjxo1q3ry53Syu0uWlVwAAgGPF6hgfMmSInnjiCS1ZskQWi0WHDx9WcnKyRo0apfHjx7s6IwAAcMIjjzyi9evXq3///qpWrVqxR7jefvvt2rlzp922QYMGqXHjxhozZowiIyPl7++vxMRE9erVS9LlWWZSUlJs001HR0drypQpOnbsmMLCwiRJX375pUJCQtS0aVMn3qX3+f33352qA7isQ4cOCg4O1sWLF3PVgoOD6fSA14uLi9OwYcN0/Phx3XbbbZKkxMREzZo1S3PnzjU3HODhHM00VFpmIvJ2Z8+edVj/63JNcD8fn4InHnVUh3Nef/11lS9fXuvXr9f69evtahaLhY5xAAAKqVgd408//bRycnJ0++236+LFi+rUqZMCAwM1atQoDR8+3NUZAQCAE7744gv961//cnrK2goVKqhZs2Z228qVK6drrrnGtn3w4MGKi4tT5cqVFRISouHDhys6Olrt27eXJHXp0kVNmzZV//79NWPGDKWmpmrcuHGKjY0tlaPCC+JorfTSspa6t8vMzHSqjpJnsVgUHh6e58j+iIgIpruH1/vb3/6mjIwMTZkyRS+88IIkqXbt2lqwYIEGDBhgcjrAs9Ex7h369+/vsJ6UlOSmNMjPvHnzHNZHjRrlpjRlT36zWAEAgKIp1qV8FotFzz77rE6dOqVdu3Zp48aNOn78uO2PdAAA4DkqVarktmm558yZo3vuuUe9evVSp06dFBERoQ8//NBW9/X11WeffSZfX19FR0froYce0oABAzRp0iS35PMkt9xyi1N1uMfy5cudqqPkFWa6e8DbDR06VL///ruOHj2qtLQ07du3L1en+IYNG5SRkWFSQsAzcSGid3j77bedqsM9nnjiCafqcI+QkJBCL5cGAEBZVKwR41cEBASUuWlPAQDwNi+88IKee+45vfnmmwoODnbpsdetW2f32Gq1av78+Zo/f36+z4mKitLnn3/u0hzeaMWKFQ7rXHRovvDwcKfqAOBKVatWzbfWtWtX7dixg3XHgaucPHnSqTrcw8+v4K8nHdXhHtnZ2Q7rf133Gu5nGIbZEQAA8GiF/mR53333FfqgV48M81Z2HyKys8wLUppc9XPkQxoAuM+sWbO0d+9ehYeHq3bt2rm+rNi2bZtJycq2K2usF7cO92jevLlWrVpVYB3mql27tpo3b66dO3fmqrVo0UK1a9d2fyjABPyNBeR28eJFp+pwjz59+jisM5W6+Z588kmH9YIukAYAAPAEhe4YDw0Ntd03DEOrV69WaGio2rRpI0naunWrzpw5U6QOdE929RR05bYXPKILRZeRkeHyUYsAgLz17NnT7AjIw/bt252qwz0YMe75LBaL7rnnnjw7xu+55x7WGPdAGzZs0Ny5czVixAh17NjR7DgASrHg4OACO7/5XsIzvPvuuwV2jr/77rtuTIP8zJo1S126dCmwDgAA4OkK3TG+dOlS2/0xY8bogQce0MKFC+Xr6yvp8nQ5//jHPxQSEuL6lAAAoNief/55syMgDwcPHnSqDvf49NNPHdYbNWrkpjTIS05OjubMmZNnbfbs2erSpYt8fHzcnAr5SU9P16xZs3TixAnNmjVLrVu3ltVqNTsWgFKKEePeISur4JkaHdXhHjk5OU7VAQAAPEGxFulZsmSJvvnmG1unuCT5+voqLi5OHTp00IsvvuiygGYJDAy03b9wXV/JlzVynJadZRt9f/XPFwCAsqhVq1bavHlzgXWY75577tEnn3xSYB3m+vbbb5Wenp5nLT09Xd9++61uvPFGN6dCft555x3bmr4nT57U8uXLNXjwYJNTAQDMNGDAAId1plI339///neH9bfffttNaZAfZksCAKBgxRo6cenSJe3evTvX9t27d5eaqwPtPkT4+nNz1S2vny8AoET5+PjI19c33xvMcerUKafqcI9du3Y5VUfJO3LkiFN1uM/vv/+u5cuX29bCNgxDy5cv1++//25ystKBv7FQWhiGoT/++MMlt8Jwxetc+b2G4nnrrbecqsM9XnvtNafqcI+i/j6aNm2aLBaLRowYUTKBAADwMMUaMT5o0CANHjxYe/fuVdu2bSVJ3333naZNm6ZBgwa5NCAAAHDO6tWr7R5nZWVp+/btevPNNzVx4kSTUiEtLc2pOtzD0RdLfBFuPkedgXQWegbDMPKc8v7K9pkzZ3Ku8mAYhg4dOqSwsDCHU87z+wilRXp6umJiYtz2eq54rYSEBAUFBbkgTdnEFN2AY/v27VPdunUd7vfFF1+oRo0ahTrm5s2b9dprr6lFixbOxitYzqWSPb6zDOPPjD5+kqd+JvX0nyNKjN3nfP4ZuMZVP0dX/R119XEyL29xyXHLqsyr7rv6b91idYzPnDlTERERmjVrlm0ERrVq1TR69Gg9+eSTLg0IAACc06NHj1zb/u///k/XXnutVq1axRS2JnH05SlfrnqG48ePO1VHybv++uudqsM9Dh48mOfyEdnZ2dq8ebMOHjyo2rVruz+YhzMMQ/Xr19ePP/6oBg0aFLjvuXPn3JQKAFzr4YcfdlhnKnXzMZW6uerXr6+aNWvq5ptv1i233KKbb75Z9evXz7VfYZcQOn/+vPr166dFixZp8uTJBe6bkZGhjIwM2+OiXsRdbtvyIu0PwN7V//98P2XmR1fLyMhQcHCwS45zxXSnj4arueocXVHkjvFLly5pxYoVGjhwoJ566ilbQxgSEuKyUAAAoOS1b99ejz76qNkxyqzTp087VYd7HD582Kk6Sl7t2rUVGBho90foFYGBgXS2eoioqCjdcMMN2rZtm7Kzs23bfX191bp1a0VFRZmYznP5+PioQYMGOnnypMOOcaC0sFqtSkhIcOkx8xoV7srXcDSjAwq2bNmyAjvHly1b5rYsyN9rr72mO++8s8A6Ss6hQ4e0bt06rV+/XjNmzNCQIUNUvXp13Xzzzbr11lv1yCOPFOl4sbGxuvvuu9W5c2eHHePx8fHMNgcAKDWK3DHu5+enxx57TD///LMkOsQBAPBGf/zxh1566aVCT7EG1/Px8XGqDvcYOHBggSOUBg4c6MY0yMvBgwfz7BSXLl9VfPDgQdWpU8fNqfBXFotFI0eOVP/+/fPczjTq+Zs2bZpGjx6tBQsWqFmzZmbHAUqcxWJx+cw5CQkJdp3jjD72LJcuFTwvrKM63CMrK8upOpxTo0YN9evXT/369ZMk/frrr5oyZYqWL1+ulStXFqljfOXKldq2bVues/nkZezYsYqLi7M9TktLU2RkZIHPKYmLnEpKenq6baa9jz/+2CsudvKGjHCdwMBA2/3sbtnFnAcadi79Ofr+6p+vM64+zhhJAS45atmVqT9H3rvqHF1RrP9Cbdu21fbt27mqHwAAL1CpUiW7DgfDMHTu3DkFBwfrnXfeMTFZ2XbLLbfo448/LrAO8zn6wsdRHcCfatasqX79+untt9+WYRiyWCzq168fF2k5MGDAAF28eFEtW7ZUQEBArg7DU6dOmZQM8E7e0lFTljjq0HvkkUe4mMEDPPjggw7rX3zxhZvSlD0XL17UN998o3Xr1mndunXavn27GjdurGHDhhXpb8dDhw7piSee0JdfflnoztXAwMAid0qUxEVO7mC1Wr0yN0o3u4uI/UTHuIu56iLtq48TIClAXPztnD/XFXf1hfTF+i/0j3/8Q08++aR+//13tW7dWuXKlbOrt2jRwiXhAEcM48//HBnZBeyIQrv653j1zxeln2EYSk9PNzuGQ1dn9Ia80uU/rMwcCTd37ly7xz4+PqpataratWunSpUqmRMKWrNmjcP6k08+6aY0yM+zzz7rsD5r1iw3pUFeoqKiVL58eZ0/fz5XrXz58lzM62Eeeughvf/++7p48aKCgoJso56Qv7+24wBQ2rzxxhsFdo6/8cYbbkyD/KxcuVLdunUrsI6SU7FiRVWqVEn9+vXT008/rZtuuqlYf89v3bpVx44d0/XXX2/blp2draSkJL3yyivKyMiQry9rGAMASq9idYxfuULw8ccft22zWCy2q/6vXjMOKElXT5sZm0TnjqtlZGQoODjY7Bhwk/T09DzX3vNkV6a68nQJCQmmXnHMVM+eKb81ka+uw3zXXXddgVMMXnfddW5Mg7ykpKTk2SkuSefPn1dKSgrrjHuQ9PR0/fHHH5IuL+uRnp7OVJAO0I4DKO38/Ar+etJRHe7BUlDmuuuuu/TNN99o5cqVSk1NVWpqqm655RY1bNiwSMe5/fbbtXPnTrttgwYNUuPGjTVmzBg6xQEApV6xPlnu37/f1TkAAEAJOnPmjDZt2qRjx44pJyfHrjZgwACTUpVtjmblYNYOz/Djjz86VUfJq1mzplN1uNezzz5r+/1mGIbGjRunV155xeRUnm/v3r1aunSp9u7dq3nz5iksLExffPGFatWqpWuvvdbseADglIcffthhnanUzffQQw85rBe0VBSc89FHH0mSfvjhB61fv15r167V+PHj5efnp1tuuUXLly8v1HEqVKigZs2a2W0rV66crrnmmlzbAQAojYrVMc50hPAUV4+mm9/ptAK5qNFpGdl/jr5ntGLZNUaX10LxRIakrP/d95c8drWWTEnTzQ7xP59++qn69eun8+fPKyQkxG5ad4vFQse4Sa655hqdO3euwDrMd/LkSafqKHmffvqpw/q9997rpjQoyJYtW3KNUPrhhx+0ZcsWtWnTxqRUnm/9+vXq2rWrOnbsqKSkJE2ZMkVhYWH6/vvvtXjxYr3//vtmRwQApyxbtqzAzvFly5a5LQvy98477+juu+8usI6S17x5c126dEmZmZlKT09XQkKCVq1aVeiOcQAAyrpiz0W0d+9ezZ07Vz///LMkqWnTpnriiSdUr149l4UDHLm6cyfQV7LSMe5SZq6JDHMFSArw2C5nyTsu2fCc0b5PPvmk/va3v2nq1Kksj+BB6HD1Dp06ddLu3bsLrMNc3bt317x583LNhiFdntKze/fuJqTCX+Xk5GjChAl51iZMmKBPPvmEKVjz8fTTT2vy5MmKi4tThQoVbNtvu+02RtsDKBWOHz/usF63bl03pUF+mPHKXLNnz9a6dev0zTff6Ny5c2rZsqU6deqkRx99VDfddJNTx163bp1rQgIA4AWK9c1DQkKCmjZtqk2bNqlFixZq0aKFvvvuO1177bX68ssvXZ0RAAA44b///a8ef/xxOsU9zJU1dotbh3tcuQi0uHW4R16d4gVth/slJycrLS0tz1paWpqSk5PdnMh77Ny5M89ZD8LCwnTixAkTEgGAa40ePdqpOtyjb9++TtXhnHfffVcNGzbUW2+9pRMnTmjLli2aPXu2unfvrkqVKpkdDwAAr1GsjvGnn35aI0eO1HfffafZs2dr9uzZ+u677zRixAiNGTPG1RkBAIATYmJitGXLFrNj4C8uXbrkVB3uwch+z+doLUvWuvQM0dHRKl++fJ618uXLKzo62s2JvEfFihV15MiRXNu3b9+uGjVqmJAIAFzrxRdfdKoO91ixYoVTdThn8+bNmjlzpu655x6FhoaaHQcAAK9VrKnUf/75Z/3zn//Mtf1vf/ub5s6d62wmAADgQnfffbdGjx6tn376Sc2bN5e/v79dnWmGgfwdPnzYqTpKXpUqVZyqwz0sFouqVq2q8+fP56qFhYWxhE8BHnzwQY0ZM0bvvfeeLBaLcnJytGHDBo0aNUoDBgwwOx4AOM1qtTpVh3twYa/5zpw5o8WLF9stbTp48GA6ygEAKIJidYxXrVpVO3bsUIMGDey279ixQ2FhYS4JBgAAXGPIkCGSpEmTJuWqWSwWZWdnuzsSJFWrVi3PEYBX12G+ihUr6syZMwXWYa6aNWs6VYd7HDhwQPv378+ztm/fPh04cEB16tRxcyrvMHXqVMXGxioyMlLZ2dlq2rSpsrOz1bdvX40bN87seADgtOHDhzusJyUluSkN8nP//fc7rCcmJropTdmzZcsWxcTEKCgoSG3btpUkzZkzR1OnTtXatWt1/fXXm5wQAADvUKyO8SFDhujRRx/Vvn371KFDB0nShg0bNH36dMXFxbk0IAAAcA5r7HqmgjrFC1OHe1xzzTU6cOBAgXWYyzAMp+qApwsICNCiRYs0fvx47dq1S+fPn9d1112X60J1APBWL7/8coGd4y+//LIb0yA/7733nnr27FlgHSVn5MiR6t69uxYtWiQ/v8tf6V+6dEmPPPKIRowYwcUjAAAUUrE6xsePH68KFSpo1qxZGjt2rCSpevXqmjBhgh5//HGXBgQAAO7RvHlzff7554qMjDQ7CuAx7rjjDm3durXAOsyVmprqsF6vXj03pUF+atWqJR8fnzwv1vLx8VGtWrVMSOVdatWqxc8JQKmU1zIbRanDPXx8fJyqwzlbtmyx6xSXJD8/Pz311FNq06aNickAAPAuxeoYt1gsGjlypEaOHKlz585JkipUqODSYAAAwL0OHDigrKwss2MAHmX+/PkO63fddZeb0iAvjpZyYqknz7Bx48Z8ZzDJycnRxo0b1bFjRzen8lxFmYlt9uzZJZgEAErelUE3BdUZDWu+3r17O6wnJCS4KU3ZExISopSUFDVu3Nhu+6FDh/heHgCAIihWx7h0eaqWdevWae/everbt68k6fDhwwoJCVH58uVdFhAAAKA08vf3L/BCBH9/fzemQX6io6O1du3aAusw165duxzWmXLafNHR0Spfvnyeo/7Kly/P/6W/2L59u93jbdu26dKlS2rUqJEk6ZdffpGvr69at25tRjwAcKn4+PgCO8fj4+PdmAb5WbVqlbp3715gHSWnd+/eGjx4sGbOnGm3tOno0aPVp08fk9MBAOA9itUxfvDgQd15551KSUlRRkaG7rjjDlWoUEHTp09XRkaGFi5c6OqcAAAApQpTEXqHOnXqOFVHyWvRooVTdbiHxWJR1apV8+wYDwsLk8ViMSGV5/r6669t92fPnq0KFSrozTffVKVKlSRJp0+f1qBBg3TTTTeZFREAXIap1L1DWlqaw3rFihXdE6YMmjlzpiwWiwYMGKBLly5Junwx9dChQzVt2jST0wEA4D2K9Y3rE088oTZt2uj06dMKCgqybb/33nuVmJjosnAAAAClVUZGhlN1uMemTZucqqPk1a5d26k63OPAgQPav39/nrV9+/bpwIED7g3kRWbNmqX4+Hhbp7gkVapUSZMnT9asWbNMTAYArjFlyhSn6nCPhx56yKk6nBMQEKB58+bp9OnT2rFjh3bs2KFTp05pzpw5CgwMNDseAABeo1gd4//5z380btw4BQQE2G2vXbu2/vvf/7okGAAAAGC2n376yak6Sl5ycrJTdcDTpaWl6fjx47m2Hz9+XOfOnTMhEQC41rPPPutUHe7xzjvvOFWHawQHB6t58+Zq3ry5goODzY4DAIDXKdZU6jk5OcrOzs61/ffff1eFChWcDgUAAAB4gooVK+ro0aMF1mGuatWqOVWHe9SuXVt16tTJc9R43bp1GdlfgHvvvVeDBg3SrFmz1LZtW0nSd999p9GjR+u+++4zOR0AOC+v7xiLUod7+Pr6OlVH0RWlnf/www9LMAkAAKVHsUaMd+nSRXPnzrU9tlgsOn/+vJ5//nndddddrsoGAADc6LXXXlN4eLjZMQCP0qRJE6fqKHlHjhxxqg73MAwjz1HPknTs2DEZhuHmRN5j4cKF6tq1q/r27auoqChFRUWpb9++uvPOO/Xqq6+aHQ8AnOZofWTWT/YMffr0caqOogsNDbXdQkJClJiYqC1bttjqW7duVWJiokJDQ01MCQCAdynWiPGZM2fqzjvvVNOmTZWenq6+ffvq119/VZUqVfTuu++6OiMAACiil156qdD7Pv7445Kkvn37llQcwGtt377dqTpKXlhYmFN1uEdycrLOnz+fZ+38+fNKTk5Wx44d3ZzK82VnZ2vLli2aMmWKXnzxRe3du1eSVK9ePZUrV87kdADgGk8//XSBnd9PP/20G9MgP++++26Bnd98J+x6S5cutd0fM2aMHnjgAS1cuNA2Oj87O1v/+Mc/FBISYlZEAAC8TrE6xiMjI/X9999r1apV+v7773X+/HkNHjxY/fr1U1BQkKszAgCAIpozZ47d4+PHj+vixYu2aZ/PnDmj4OBghYWF2TrGAeRWqVIlnT17tsA6zLVu3TqH9YYNG7onDPLVvn17+fj4KCcnJ1fNx8dH7du3NyGV5/P19VWXLl30888/q06dOmrRooXZkQDA5ZhK3Ttwnsy1ZMkSffPNN3ZT1vv6+iouLk4dOnTQiy++aGI6AAC8R5GnUs/KylK9evX066+/ql+/fpoxY4ZeffVVPfLII3SKAwDgIfbv32+7TZkyRa1atdLPP/+sU6dO6dSpU/r55591/fXX64UXXij0MRcsWKAWLVooJCREISEhio6O1hdffGGrp6enKzY2Vtdcc43Kly+vXr165VqbOSUlRXfffbetU3706NG6dOmSy9434GqpqalO1VHy8upoLUod7pGSkpLvucjJyVFKSoqbE3mPZs2aad++fWbHAIAS46hDjw4/z/DQQw85VYdzLl26pN27d+favnv3bj7vAgBQBEXuGPf391d6enpJZAEAACVg/Pjxevnll9WoUSPbtkaNGmnOnDkaN25coY9Ts2ZNTZs2TVu3btWWLVt02223qUePHvrxxx8lSSNHjtSnn36q9957T+vXr9fhw4d133332Z6fnZ2tu+++W5mZmfr222/15ptvatmyZXruuedc92YBF2NkjOdr0KCBU3XA002ePFmjRo3SZ599piNHjigtLc3uBgDebvTo0U7V4R7vvPOOU3U4Z9CgQRo8eLBmz56tb775Rt98841mzZqlRx55RIMGDTI7HgAAXqNYU6nHxsZq+vTpeuONN+TnV6xD2Jk/f75efPFFpaamqmXLlnr55ZfVtm1bh89buXKl+vTpox49euijjz5yOgeAkmEYxp8PGBjqvKt+hnY/WyAfR44cyXNUdnZ2dq4R3QXp1q2b3eMpU6ZowYIF2rhxo2rWrKnFixdrxYoVuu222yRdXg+tSZMm2rhxo9q3b6+1a9fqp59+0r///W+Fh4erVatWeuGFFzRmzBhNmDBBAQEBeb5uRkaGMjIybI/pBIA7OZrRgBkPzJeUlOSwfvvtt7spDfLj6DMLn2nyd9ddd0mSunfvLovFYttuGIYsFgsX6ADweo4G4DBAxzOUK1fOqTqcM3PmTEVERGjWrFk6cuSIJKlatWoaPXq0nnzySZPTAQDgPYrVq71582YlJiZq7dq1at68ea4PPh9++GGhj7Vq1SrFxcVp4cKFateunebOnauYmBjt2bNHYWFh+T7vwIEDGjVqlG666abivAUAbnR1h5bvp74F7ImiysjIUHBwsNkx4OFuv/12/f3vf9cbb7yh66+/XpK0detWDR06VJ07dy7WMbOzs/Xee+/pwoULio6O1tatW5WVlWV3vMaNG6tWrVpKTk5W+/btlZycrObNmys8PNy2T0xMjIYOHaoff/xR1113XZ6vFR8fr4kTJxYrJ+AsHx+fAjudfHyKPAETXOyZZ57R119/XWAd8GYF/fsGgNLg5Zdfdli///773ZQG+XF0Du6//3599dVXbkpT9vj4+Oipp57SU089ZbtYPCQkJNd+GzZsUJs2bRQYGOjuiAAAeIVifZNXsWJF9erVSzExMapevbpCQ0PtbkUxe/ZsDRkyRIMGDVLTpk21cOFCBQcHa8mSJfk+Jzs7W/369dPEiRNVt27d4rwFAADKjCVLligiIsL2x3FgYKDatm2r8PBwvfHGG0U61s6dO1W+fHkFBgbqscce0+rVq9W0aVOlpqYqICBAFStWtNs/PDzctgZzamqqXaf4lfqVWn7Gjh2rs2fP2m6HDh0qUmbAGUyl7vkOHz7sVB3ucfVI5+LUy7Kbb75ZLVu21JYtW/T222/r7bff1tatW9WqVSvdfPPNZscDAKcNHz7cqTrc47333nOqDtcJCQnJs1Nckrp27ar//ve/bk4EAID3KNaI8aVLlxZqP0dXqGVmZmrr1q0aO3asbZuPj486d+6s5OTkfI87adIkhYWFafDgwfrPf/5TYAamXwXMd/XvgOxu2cX8zQObS3+OvOcKYBRG1apV9fnnn+uXX37Rzz//LIvFosaNG6thw4ZFPlajRo20Y8cOnT17Vu+//74GDhyo9evXl0DqP13pzAeAvHDxgneIioqSv7+/srKyctX8/f0VFRVlQirvsGXLFt15552yWq22JcfmzJmjqVOnau3atbbZYADAW+Xk5DhVh3ucOHHCYf2aa65xUxrkh+VpAAAoWInO/ejoCrUTJ04oOzs7z9Fj+Y0c++abb7R48WItWrSoUBni4+PtRrNHRkYW/g0AcAm7EUB+3Fxyy+tnCzjQsGFDde/eXd26dStWp7gkBQQEqH79+mrdurXi4+PVsmVLzZs3TxEREcrMzNSZM2fs9j969KgiIiIkSREREbnWNL/y+Mo+AFBUX3zxhVN1uMf+/fvz7BSXpKysLO3fv9/NibzHyJEj1a1bNx04cEAffvihPvzwQ+3fv1/33HOPRowYUaKvPW3aNFksFrvXSU9PV2xsrK655hqVL19evXr1ytW+A0BRzJ8/36k63GPIkCFO1QEAADxBiXaMu/oKtXPnzql///5atGiRqlSpUqjnMP0qAADSW2+9pebNmysoKEhBQUFq0aKF3n77baePm5OTo4yMDLVu3Vr+/v5KTEy01fbs2aOUlBRFR0dLkqKjo7Vz504dO3bMts+XX36pkJAQNW3a1OksAMqmjRs3OlWHezhaJ5t1tPO3ZcsWjRkzRn5+f14d6efnp6eeekpbtmwpsdfdvHmzXnvtNbVo0cJu+8iRI/Xpp5/qvffe0/r163X48GHdd999JZYDQOkXGxvrVB3u4WiQUmEHMQEAAJjJ1AmNq1SpIl9f3zxHj+U1cmzv3r06cOCAunXrZtt2ZTolPz8/7dmzR/Xq1bN7DtOvAgDKutmzZ2v8+PEaNmyYOnbsKOnyDCyPPfaYTpw4oZEjRxbqOGPHjlXXrl1Vq1YtnTt3TitWrNC6deuUkJCg0NBQDR48WHFxcapcubJCQkI0fPhwRUdHq3379pKkLl26qGnTpurfv79mzJih1NRUjRs3TrGxsbTVAIqtdevWBV782rp1azemQX4eeughvfXWWwXWkbeQkBClpKSocePGdtsPHTqkChUqlMhrnj9/Xv369dOiRYs0efJk2/azZ89q8eLFWrFihW677TZJl5daa9KkiTZu3Ghr86/G8mYAHCnMFN0wn6MBUEzhDQAAvEGJjhh3JCAgQK1bt7YbXZaTk6PExETb6LKrNW7cWDt37tSOHTtst+7du+vWW2/Vjh07mCYdAIA8vPzyy1qwYIGmT5+u7t27q3v37poxY4ZeffVVvfTSS4U+zrFjxzRgwAA1atRIt99+uzZv3qyEhATdcccdki6vd3rPPfeoV69e6tSpkyIiIvThhx/anu/r66vPPvtMvr6+io6O1kMPPaQBAwZo0qRJLn/PAMqOX3/91ak63OOdd95xql6W9e7dW4MHD9aqVat06NAhHTp0SCtXrtQjjzyiPn36lMhrxsbG6u6771bnzp3ttm/dulVZWVl22xs3bqxatWopOTk5z2OxvBkAR1atWuVUHe7x6KOPOlWHe7DkHgAABTN1xLgkxcXFaeDAgWrTpo3atm2ruXPn6sKFCxo0aJAkacCAAapRo4bi4+NltVrVrFkzu+dXrFhRknJtBwAAlx05ckQdOnTItb1Dhw46cuRIoY+zePHiAutWq1Xz588vcA3AqKgoff7554V+TQBwpFevXvrxxx8LrMN8t9xyS4Ejxm+55Rb3hfEyM2fOlMVi0YABA3Tp0iVJkr+/v4YOHapp06a5/PVWrlypbdu2afPmzblqqampCggIsP0dfkV4eLhSU1PzPN7YsWMVFxdne5yWlkbnOAA7vXv3LrDzu3fv3m5Mg/y8/vrrBXZ+v/76625MU7YYhqFDhw4pLCxMVqvV4b4AACB/JdoxXpgr1Hr37q3jx4/rueeeU2pqqlq1aqU1a9YoPDxckpSSkiIfH1MHtgMA4NXq16+vf/7zn3rmmWfstq9atUoNGjQwKRUAuEZhRoz/ddQr3M/R33T8zZe/gIAAzZs3T/Hx8dq7d68kqV69egoODnb5ax06dEhPPPGEvvzyS4dfvBcWy5sBcMTR77OS+H0HeBPDMFS/fn39+OOPDv+GP3funJtSAQDgnYrcMV4SV6gNGzZMw4YNy7O2bt26Ap+7bNmyQr0GAABl1cSJE9W7d28lJSXZ1hjfsGGDEhMT9c9//tPkdADgHEfrFbOesWdgXVLnBQcHq3nz5iX6Glu3btWxY8d0/fXX27ZlZ2crKSlJr7zyihISEpSZmakzZ87YjRo/evSoIiIiSjQbgNJr6dKlDutXZpaEeQozlXpSUpKb0pQtPj4+atCggU6ePMnF7QAAOKnIl+VfuULt0KFDDvc9d+6c6tatW6xgAADANXr16qVNmzapSpUq+uijj/TRRx+pSpUq2rRpk+69916z4wGAU2rXru1UHe6xfft2p+pwj9tvv107d+7Ujh07bLc2bdqoX79+tvv+/v5KTEy0PWfPnj1KSUlRdHS0ickBeDNHnd50insGR1OlM5V6yZo2bZpGjx6tXbt2mR0FAACvVuQR41yhBgCA98jKytLf//53jR8/Xu+8847ZcQDA5bKzs52qwz3CwsKcqsM9KlSooGbNmtltK1eunK655hrb9sGDBysuLk6VK1dWSEiIhg8frujoaLVv396MyABKAT+/gr+edFSHezBLj7kGDBigixcvqmXLlgoICFBQUJBd/dSpUyYlAwDAuxTrk+WVK9QWLFiQ649mAADgOfz9/fXBBx9o/PjxZkcBgBKxfv16h/W+ffu6KQ1Q+s2ZM0c+Pj7q1auXMjIyFBMTo1dffdXsWAC82KJFixzW+/fv76Y0yM+oUaMc1plKveTMnTvX7AgAAJQKxeoY5wo1AAC8R8+ePfXRRx9p5MiRZkcBAJcLCQlxqg73YGS/91q3bp3dY6vVqvnz52v+/PnmBAJQ6gwZMqTAzvEhQ4a4MQ3yM3PmzAI7x2fOnOnGNGXPwIEDzY4AAECpUKyOca5QAwDAezRo0ECTJk3Shg0b1Lp1a5UrV86u/vjjj5uUDACcd/LkSafqcI+NGzc6rN96661uSgNvYBiG0tPTzY7h0NUZvSGvdPniBovFYnYMwKZ///4FdowzWtwztG3b1qk6nLd3714tXbpUe/fu1bx58xQWFqYvvvhCtWrV0rXXXmt2PAAAvEKxOsa5Qg0AAO+xePFiVaxYUVu3btXWrVvtahaLhY5xAF7twoULTtXhHkePHnWqjrInPT1dMTExZscokh49epgdoVASEhJyzfwHmOn55593WJ84caKb0iA/33//vcN6y5Yt3ZSm7Fm/fr26du2qjh07KikpSVOmTFFYWJi+//57LV68WO+//77ZEQEA8ArF6hiXuEINAABvsX//ftt9wzAkiVFCxeTu0Wt//PGH08dgVBhKu8qVK+vIkSMF1mG+Bg0aaNu2bQXWAQBl09dff+2wTse4+YYPH+6wzhrjJefpp5/W5MmTFRcXpwoVKti233bbbXrllVdMTAYAgHcpVsc4V6gBAOBdFi9erDlz5ujXX3+VdLkDYsSIEXrkkUdMTuZd3D16zRWvxagwlHaXLl1yqg73KOjihcLUUbZduL6f5FPs6/pLlmFIOf/7PePjJ3nqxWg5l1Ru23KzUwB5uvXWWwvsHGepDc/w8ssvF9g5/vLLL7sxTdmzc+dOrVixItf2sLAwnThxwoREAAB4p2L9ZckVagAAeI/nnntOs2fP1vDhwxUdHS1JSk5O1siRI5WSkqJJkyaZnBAAii8sLEx79uwpsA7z3X777QWOIrv99tvdmAZex8dP8vU3O0UBAswOAHi18+fPO1WHeziazcoVs10hfxUrVtSRI0dUp04du+3bt29XjRo1TEoFAID3KVbHOFeoAQDgPRYsWKBFixapT58+tm3du3dXixYtNHz4cDrGi8BqtSohIcFlxytoRLirXsdqtbrkOICn+s9//uNUHe7h6O9E/o4EgLJr8+bNTtXhHk899ZTDOlOpl5wHH3xQY8aM0XvvvSeLxaKcnBxt2LBBo0aN0oABA8yOBwCA1/ApzpOuXKH2V1yhBgCA58nKylKbNm1ybW/dujVTDBeRxWJRUFCQy275fXGUlJTkstdgfXEAnoCOcQBAfm644Qan6nCPGTNmOFWHc6ZOnarGjRsrMjJS58+fV9OmTdWpUyd16NBB48aNK/RxFixYoBYtWigkJEQhISGKjo7WF198UYLJAQDwLMUaMc4VagBQ+hiGYbufeXmLWVFKhcyr7l/9szVD//79tWDBAs2ePdtu++uvv65+/fqZlAoAUJZcc801TtUBAKXX6dOnnarDPc6cOeNUHc4JCAjQokWLNH78eO3atUvnz5/XddddpwYNGhTpODVr1tS0adPUoEEDGYahN998Uz169ND27dt17bXXllB6AAA8R7E6xqdOnarY2FhFRkYqOztbTZs2VXZ2tvr27VukK9QAAJ4jIyPDdn+6iTlKo4yMDAUHB5uaYfHixVq7dq3at28vSfruu++UkpKiAQMGKC4uzrbfXzvPUfISEhLsplRn+kGgaFq1aqUdO3YUWIf5GDEOAMjPb7/95lQd7jF16lSH9TvvvNNNacquWrVqqVatWsV+frdu3eweT5kyRQsWLNDGjRvpGC/jMrIt8tRBMoYhZeZcvh/gI3nyxHiXf44lyJMnfjQkZf/vvq8kDz5PJf1z9ORBZ4akrP/d95fnnqZMx7sUW7E6xq9cofbcc89p586dxb5CDQAAlLxdu3bp+uuvlyTt3btXklSlShVVqVJFu3btsu3HlNvmc+X65UBZQYerdzh37pxTdQBA6VW/fv0CO7/r16/vxjTIzzPPPFNg5/gzzzzjxjRlw9UXsTtSnIvcs7Oz9d577+nChQuKjo7Od7+MjAy7wRRpaWlFfi14vtikimZHQCH4fuprdgQUAoPOPFuxOsaviIyMtI0a37lzp06fPq1KlSq5KhsAwI0CAwNt98dICjAvSqmQqT8/BF39szXD119/berrA0BJYvpV71C+fHmn6gCA0isrK8upOtzDx8fHqTqKbvv27XaPt23bpkuXLqlRo0aSpF9++UW+vr5q3bp1kY67c+dORUdHKz09XeXLl9fq1avVtGnTfPePj4/XxIkTi/4GAADwQMXqGB8xYoSaN2+uwYMHKzs7WzfffLO+/fZbBQcH67PPPtMtt9zi4pgmy/Hg+SkM4898Pn6ePY+IJ/8cAdiNFg6QFOCxE6l4iz+ny2EkNgCUnIoVK+rChQsF1mE+R20hbSUAlF0HDx50qg73mDx5ssN6ly5d3JSmbLj6IvfZs2erQoUKevPNN20D006fPq1BgwbppptuKtJxGzVqpB07dujs2bN6//33NXDgQK1fvz7fzvGxY8fajV5PS0tTZGRkMd4RPI3VavWKmevS09PVo0cPSdLHH38sq9VqcqLCcVVOzlPJ4jx5/nlydcZidYy///77euihhyRJn376qfbt26fdu3fr7bff1rPPPqsNGza4NKTZym1bbnYEAAAAAHn473//61Qd7vH77787VQcAlF5RUVEFdn5HRUW5MQ3yM27cuAI7x8eNG+fGNGXPrFmztHbtWrvZWitVqmS7IOHJJ58s9LECAgJsSxS0bt1amzdv1rx58/Taa6/luX9gYKDpM+GhZFgsFgUFBZkdo0isVqvXZXYW58k7cJ68R7HmuDlx4oQiIiIkSZ9//rkeeOABNWzYUH/729+0c+dOlwYEAAAAAHi36tWrO1UHAJRejBj3DmfPnnWqDuekpaXp+PHjubYfP35c586dc+rYOTk5dmuIAwBQmhVrxHh4eLh++uknVatWTWvWrNGCBQskSRcvXpSvr69LA5qFaQ9KlrfkBAAAAOC88+fPO1UHAADmevnllx3W77//fjelKXvuvfdeDRo0SLNmzVLbtm0lSd99951Gjx6t++67r9DHGTt2rLp27apatWrp3LlzWrFihdatW+cV34MDAOAKxeoYHzRokB544AFVq1ZNFotFnTt3lnS5MW7cuLFLA5qFaQ8AAAAAwDVq1KjhVB0AAJhr+PDhBXaODx8+3I1pyp6FCxdq1KhR6tu3r7KysiRJfn5+Gjx4sF588cVCH+fYsWMaMGCAjhw5otDQULVo0UIJCQm64447Sio6AAAepVgd4xMmTFDz5s2VkpKi+++/37bGiK+vr8aOHevSgEBhZWRbJBlmx8iTYUiZOZfvB/hIFou5eQpy+ecIAAAAuM7777/vsN6/f383pQEAAEWVmprqVB3Fl52drS1btmjKlCl68cUXtXfvXklSvXr1VK5cuSIda/HixSUREQAAr1GsjvFJkybZ7i9ZssSudvDgQXXv3t25VEAxxCZVNDsCAAAA3MzHx0c5OTkF1mG+jh076rPPPiuwDgAAPNd7773nsM6o8ZLh6+urLl266Oeff1adOnXUokULsyMBAOC1itUxvnr1arvHWVlZ2r9/v/z8/FSvXj0999xzLgkHAAAAAAUpqFO8MHW4x1dffeWw/tRTT7kpDQAAKKr777+/wM5x1hcvWc2aNdO+fftUp04ds6MAAODVitUxvn379lzb0tLS9PDDD+vee+91OhRQWFarVQkJCWbHcCg9PV09evSQJH388ceyWq0mJyocb8kJAAAAzxYWFqYDBw4UWAcAAJ6LixHNNXnyZI0aNUovvPCCWrdunWsK9ZCQEJOSAQDgXYrVMZ6XkJAQTZw4Ud26dWNtOLiNxWJRUFCQ2TGKxGq1el1mAAAAwBnnzp1zqg4AAMz1wQcfOKw/8cQTbkpT9tx1112SpO7du8tisdi2G4Yhi8Wi7Oxss6IBAOBVXNYxLklnz57V2bNnXXlIAAAAAICX++OPP5yqAwA8i2EYSk9Pd9vruaKdsFqtdh2KKJpevXoV2Dneq1cvN6Ype77++muzIwAAUCoUq2P8pZdesntsGIaOHDmit99+W127dnVJMAAAAABA6ZCVleVUHQDgWdLT0xUTE+O213PFayUkJDCDnxMyMzOdqsM5N998s86cOaPFixfr559/liQ1bdpUgwcPVmhoqMnpAADwHj7FedKcOXPsbi+99JLWrVungQMH6rXXXnN1RgAA4AHi4+N1ww03qEKFCgoLC1PPnj21Z88eu33S09MVGxura665RuXLl1evXr109OhRu31SUlJ09913Kzg4WGFhYRo9erQuXbrkzrcCAAAAACiCTz/91Kk6nLNlyxbVr19fc+bM0alTp3Tq1CnNmTNH9erV07Zt28yOBwCA1yjWiPH9+/e7OgcAAPBw69evV2xsrG644QZdunRJzzzzjLp06aKffvpJ5cqVkySNHDlS//rXv/Tee+8pNDRUw4YN03333acNGzZIkrKzs3X33XcrIiJC3377rY4cOaIBAwbI399fU6dONfPtAQBKkGEYTtUBAJ7FarUqISHBpcfMa1S4K1/DarW67FhlUbdu3Qrs/O7WrZsb05Q9I0eOVLdu3bRo0SL5+V3+Sv/SpUt65JFHNGLECCUlJZmcEAAA7+DSNcYBAEDptWbNGrvHy5YtU1hYmLZu3apOnTrp7NmzWrx4sVasWKHbbrtNkrR06VI1adJEGzduVPv27bV27Vr99NNP+ve//63w8HC1atVKL7zwgsaMGaMJEyYoICDAjLcGAChhjmYGYeYQAPAuFovF5dOSJyQk2HWO09HnWU6dOuVUHc7ZsmWLXae4JPn5+empp55SmzZtTEwGAIB3oWMcAAAUy9mzZyVJlStXliRt3bpVWVlZ6ty5s22fxo0bq1atWkpOTlb79u2VnJys5s2bKzw83LZPTEyMhg4dqh9//FHXXXddrtfJyMhQRkaG7XFaWlpJvSWUAoZhKD093a2v+ccffzh9DKvVKovF4oI0AAAA3s/Vo9HhvCuzgBW3DueEhIQoJSVFjRs3ttt+6NAhVahQwaRUAAB4HzrGAQBAkeXk5GjEiBHq2LGjmjVrJklKTU1VQECAKlasaLdveHi4UlNTbftc3Sl+pX6llpf4+HhNnDjRxe8ApVV6enqe03CWJFe8XkJCgstHXQEAAACu0rFjxwI7vzt27OjGNGVP7969NXjwYM2cOVMdOnSQdPlihNGjR6tPnz4mpwMAwHvQMQ4AAIosNjZWu3bt0jfffFPirzV27FjFxcXZHqelpSkyMrLEXxcAAAAAcJmj2Y2Y/ahkzZw5UxaLRQMGDLAtQePv76+hQ4dq2rRpJqcDAMB70DEOAACKZNiwYfrss8+UlJSkmjVr2rZHREQoMzNTZ86csRs1fvToUUVERNj22bRpk93xjh49aqvlJTAwUIGBgS5+FyitrFary6feLGhEuKtey2q1uuQ4AAAAQElwdFG0Oy6aLssCAgI0b948xcfHa+/evZKkevXqKTg42ORkAAB4FzrGAQBAoRiGoeHDh2v16tVat26d6tSpY1dv3bq1/P39lZiYqF69ekmS9uzZo5SUFEVHR0uSoqOjNWXKFB07dkxhYWGSpC+//FIhISFq2rSpe98QSiWLxeLyKcmTkpLUqVOnPLcDAAAAZcGNN95YYOf3jTfe6MY0ZVdwcLCaN29udgwAALwWHeMA3OuS2QEKYEjK/t99X0meOguYJ/8MUarFxsZqxYoV+vjjj1WhQgXbmuChoaEKCgpSaGioBg8erLi4OFWuXFkhISEaPny4oqOj1b59e0lSly5d1LRpU/Xv318zZsxQamqqxo0bp9jYWEaFAwAAAICHKmh98cLUAQAAPAEd4wDcyvdTX7MjACimBQsWSJJuueUWu+1Lly7Vww8/LEmaM2eOfHx81KtXL2VkZCgmJkavvvqqbV9fX1999tlnGjp0qKKjo1WuXDkNHDhQkyZNctfbAIolISHBbkp1RosDAACgLDEMw6k6AACAJ6BjHAAAFEphvuiwWq2aP3++5s+fn+8+UVFR+vzzz10ZDXArV69hDgAAAHg6i8VS4N+EFounTrsHAADwJzrGAZQ4q9XqFZ0I6enp6tGjhyTp448/ltVqNTmRY96QEQAAAAAAeDdGjAMAgNKAjnEAJc5isSgoKMjsGEVitVq9LjMAAAAAAAAAAADy5mN2AAAAAAAAAAAAAAAAShId4wAAAAAAAAAAAACAUo2OcQAAAAAAAAAAAABAqcYa4wAAAAAAACgywzCUnp5udgyHrs7oDXklyWq1ymKxmB0DAAAAKFXoGAcAAAAAAECRpaenKyYmxuwYRdKjRw+zIxRKQkKCgoKCzI4BAAAAlCpMpQ4AAAAAAAAAAAAAKNUYMQ4AAAAAAACnzO90RoG+htkx8mQYUmbO5fsBPpKnzlCekW1RbFJFs2MAAAAApRYd4wAAAADcyt1r0v7xxx8uOQ7rvQJA/gJ9DVl9zU6RP++YlNwzLywAAAAASgs6xgEAAAC4lbvXpHXVa7HeKwAAAAAAgPfyiDXG58+fr9q1a8tqtapdu3batGlTvvsuWrRIN910kypVqqRKlSqpc+fOBe4PAAAAAAAAAAAAACjbTB8xvmrVKsXFxWnhwoVq166d5s6dq5iYGO3Zs0dhYWG59l+3bp369OmjDh06yGq1avr06erSpYt+/PFH1ahRw4R3AAAAAKAorFarEhISXHa8gkaEu/J1rFary44FAAAAAAAA9zK9Y3z27NkaMmSIBg0aJElauHCh/vWvf2nJkiV6+umnc+2/fPlyu8dvvPGGPvjgAyUmJmrAgAG59s/IyFBGRobtcVpamovfAQAAAICisFgsLp2SPCkpSZ06dcpzOwAAAAAAACCZPJV6Zmamtm7dqs6dO9u2+fj4qHPnzkpOTi7UMS5evKisrCxVrlw5z3p8fLxCQ0Ntt8jISJdkBwAAAAAAAAAAAAB4B1NHjJ84cULZ2dkKDw+32x4eHq7du3cX6hhjxoxR9erV7TrXrzZ27FjFxcXZHqelpdE5DgAOZEqSDJNT5M2QlPW/+/6SLCZmKUim2QEAoIxJSEiwm1Kd0eIAAAAAAAC4mulTqTtj2rRpWrlypdatW5fven+BgYEKDAx0czIA8G7TzQ4AAIATXLmuOFCWxMfH68MPP9Tu3bsVFBSkDh06aPr06WrUqJFtn/T0dD355JNauXKlMjIyFBMTo1dffTXXBe8AAAAAAHgaU6dSr1Klinx9fXX06FG77UePHlVERESBz505c6amTZumtWvXqkWLFiUZEwAAAACAUm/9+vWKjY3Vxo0b9eWXXyorK0tdunTRhQsXbPuMHDlSn376qd577z2tX79ehw8f1n333WdiagAAAAAACsfUEeMBAQFq3bq1EhMT1bNnT0lSTk6OEhMTNWzYsHyfN2PGDE2ZMkUJCQlq06aNm9ICQOlmtVq9YoRdenq6evToIUn6+OOP850xxJN4Q0YAAIA1a9bYPV62bJnCwsK0detWderUSWfPntXixYu1YsUK3XbbbZKkpUuXqkmTJtq4caPat2+f65gZGRnKyMiwPU5LSyvZNwEAAAAAQD5Mn0o9Li5OAwcOVJs2bdS2bVvNnTtXFy5c0KBBgyRJAwYMUI0aNRQfHy9Jmj59up577jmtWLFCtWvXVmpqqiSpfPnyKl++vGnvAwC8ncViUVBQkNkxisRqtXpdZgAAAG9x9uxZSVLlypUlSVu3blVWVpY6d+5s26dx48aqVauWkpOT8+wYj4+P18SJE90TGAAAAACAApjeMd67d28dP35czz33nFJTU9WqVSutWbPGtj5ZSkqKfHz+nPF9wYIFyszM1P/93//ZHef555/XhAkT3BkdAAAAAIBSKScnRyNGjFDHjh3VrFkzSVJqaqoCAgJUsWJFu33Dw8NtF63/1dixYxUXF2d7nJaWpsjIyBLLDQD4k2EYSk9Pd9vr/fHHH04fw2q1ymKxuCANAABAbqZ3jEvSsGHD8p06fd26dXaPDxw4UPKBAAAAAAAow2JjY7Vr1y598803Th0nMDBQgYGBLkoFACiK9PR0xcTEuO31XPFaCQkJzAxXguLj4/Xhhx9q9+7dCgoKUocOHTR9+nQ1atTI7GgAALiFj+NdAAAAAABAWTFs2DB99tln+vrrr1WzZk3b9oiICGVmZurMmTN2+x89elQRERFuTgkAAIpq/fr1io2N1caNG/Xll18qKytLXbp00YULF8yOBgCAW3jEiHEAAABXcveUgc64Oqe3ZGZ6QwAonQzD0PDhw7V69WqtW7dOderUsau3bt1a/v7+SkxMVK9evSRJe/bsUUpKiqKjo82IDAAogNVqVUJCgkuPmdeocFe+htVqddmxkNuaNWvsHi9btkxhYWHaunWrOnXqZFIqAADch45xAABQ6rh7ykBX6dGjh9kRCoXpDYGygXVJy57Y2FitWLFCH3/8sSpUqGBbNzw0NFRBQUEKDQ3V4MGDFRcXp8qVKyskJETDhw9XdHS02rdvb3J6AMBfWSwWl39uT0hIsPtbKykpyaXHh3udPXtWklS5cuV898nIyFBGRobtcVpaWonnAgCgpNAxDgAAAADIhXVJy54FCxZIkm655Ra77UuXLtXDDz8sSZozZ458fHzUq1cvZWRkKCYmRq+++qqbkwIAPIGrR6PDvXJycjRixAh17NhRzZo1y3e/+Ph4TZw40Y3JAAAoOXSMAwCAUu3C9f0kHw/+yGMYUs6ly/d9/CRPHemYc0nlti03OwUAoAQZhuFwH6vVqvnz52v+/PluSAQAAEpKbGysdu3apW+++abA/caOHau4uDjb47S0NEVGRpZ0PAAASoQHf0sMAADgAj5+kq+/2SkcCDA7AADk4sp1SR999FEdPHgw33pUVJRef/11p1+HdUkBAAAcGzZsmD777DMlJSWpZs2aBe4bGBiowMBANyUDAKBk0TEOAAAAAMjFleuSvv322+rUqVOBdQAAAJQswzA0fPhwrV69WuvWrVOdOnXMjgQAgFvRMQ4AAAAAKHFJSUl5do4nJSWZkAaezm5a9+ws84KUFlf9DAszZT4AoHSKjY3VihUr9PHHH6tChQpKTU2VJIWGhrrsgkgAADwZHeMAAKBQkpKS9OKLL2rr1q06cuSIVq9erZ49e9rqhmHo+eef16JFi3TmzBl17NhRCxYsUIMGDWz7nDp1SsOHD9enn34qHx8f9erVS/PmzVP58uVNeEcAAMBTZWRk2O6X277CxCSlT0ZGhoKDg82OAQAwwYIFCyRJt9xyi932pUuX6uGHH3Z/IAAA3MzH7AAAAMA7XLhwQS1bttT8+fPzrM+YMUMvvfSSFi5cqO+++07lypVTTEyM0tPTbfv069dPP/74o7788kvbemaPPvqou94CAMBkf12znNHiAAAA7mMYRp43OsUBAGUFI8YBAEChdO3aVV27ds2zZhiG5s6dq3HjxqlHjx6SpLfeekvh4eH66KOP9OCDD+rnn3/WmjVrtHnzZrVp00aS9PLLL+uuu+7SzJkzVb16dbe9FwCA+f7aSQ5cLTAw0Hb/wnV9JV9/E9OUAtlZtpH3V/9sAQAAAKAsoWMcAAA4bf/+/UpNTVXnzp1t20JDQ9WuXTslJyfrwQcfVHJysipWrGjrFJekzp07y8fHR999953uvffePI+dkZFhN51qWlpayb0RuJVhGHYzCniyq3N6S2ar1SqLxWJ2DAAoFrvfX77+dIy7EG1D2cLnrZLF5y0AAADvQsc4AABwWmpqqiQpPDzcbnt4eLitlpqaqrCwMLu6n5+fKleubNsnL/Hx8Zo4caKLE8MTpKenKyYmxuwYRXZlVgRPl5CQoKCgILNjAAAAE/F5q2TxeQsAAMC7sMY4AADwaGPHjtXZs2dtt0OHDpkdCQAAAAAAAADgZRgxDgAAnBYRESFJOnr0qKpVq2bbfvToUbVq1cq2z7Fjx+yed+nSJZ06dcr2/LwEBgayFmYZML/TGQX6GmbHyJdhSJk5l+8H+EieOmNmRrZFsUkVzY4BAAA8UHa3bM/+JtCQlP2/+76SPPTzli5Jvp/6mp0CAAAAxeDJH4cBAICXqFOnjiIiIpSYmGjrCE9LS9N3332noUOHSpKio6N15swZbd26Va1bt5YkffXVV8rJyVG7du3Mig4PEehryOrh3y96xySZnntxAQAAMJmfPP+bQH+zAwAAAKA08/SPwwAAwEOcP39ev/32m+3x/v37tWPHDlWuXFm1atXSiBEjNHnyZDVo0EB16tTR+PHjVb16dfXs2VOS1KRJE915550aMmSIFi5cqKysLA0bNkwPPvigqlevbtK7AgAAAAAAAACUBXSMAwCAQtmyZYtuvfVW2+O4uDhJ0sCBA7Vs2TI99dRTunDhgh599FGdOXNGN954o9asWSOr1Wp7zvLlyzVs2DDdfvvt8vHxUa9evfTSSy+5/b0AAAAAAAAAAMoWOsYBAECh3HLLLTKM/KdptlgsmjRpkiZNmpTvPpUrV9aKFStKIh4AAAAAAAAAAPnyMTsAAAAAAAAAAAAAAAAliRHjAAAAAPJlGIbS09PNjuHQ1Rm9Ie8VVqtVFovF7BgAUCxXzyaUkW1ikFLi6p9hQTM1AQAAACgeOsYBAAAA5Cs9PV0xMTFmxyiSHj16mB2h0BISEhQUFGR2DAAoloyMDNv92KRKJiYpfTIyMhQcHGx2DAAAAKBUYSp1AAAAAAAAAAAAAECpxohxAAAAAIWS3S3bc/+CMCRdmYLWV5Inz05+SfL91NfsFADgtMDAQNv9+Z1OK5BfbU7JyP5z5P3VP1sAAAAAruGpX2sBAAAUm92ajNlZ5gUpTa76ObLmZRnmJ8/+C8Lf7AAAULZYLH9ehRToK1npGHeZq3+2AAAAAFzDk7/WAgAAKJar17sst32FiUlKJ9a8BAAAAAAAAOBt6BgHAAAAAAAAAJMYhqH09HSzYxTK1Tm9JbPVamUWBgAAIImOcQAAUApdvSbjhev6Sr7Mr+y07Czb6HvWvAQ8j7d8oe6NX6ZLfKEOAChZ6enpiomJMTtGkfXo0cPsCIWSkJCgoKAgs2MAAAAPQMc4AAAodew6L3z96Rh3MTqHAM/jjV+oe8uX6RJfqAMAAAAAUBrQMQ4AAABTGIZhu5+RbWKQUuTqn+PVP18AAAB4hzGSAswOUQBDUtb/7vtL8tRLZjMlTTc7BAAA8Dh0jAMAAMAUGRkZtvuxSZVMTFI6ZWRkKDg42OwYMIEnf6HuLV+mS3yhDgAwR4CkAI9uISXvWFiJi0QBAEBudIwDAAAAQCni6V+oe8eX6RJfqAMAAAAAULrQMQ4AAABTBAb+2T02v9NpBfqaGKaUyMj+c/T91T9fAABQNtktrXLJvBylylU/R5auAQAA8C50jAMAAMAUFsufI1oDfSUrHeMudfXP1xl8oV4C+EIdAOAmVy9d4/spH7ZcjaVrAAAAvAsd4wAAAADyxRfqJYsv1AEAAAAAANyDjnEAAAAAAOC5cjx4ugrD+DOfj5/kotk6XM6Tf4YoUVcvrZLdLZtvAl3h0p8XC7J0DQAAgHfh4zAAAACAfPGFegngC3WgSMptW252BMBr2S2t4ifacRdz1dI1AAAAcA8+DgMAgNLN00dIMdIMHo4v1EtWSawFn3l5i0uOW5ZlXnWfteABAAAAAPB+fK0FAABKNUaZASgLrl4LfrqJOUor1oJ3P6vVqoSEBLNjOJSenq4ePXpIkj7++GNZrVaTEzlWUhkzsi3y1ItyDEPKzLl8P8DHc69DvPwzRFnEBW6uxwVuAAAgL3SMAwAAwHSe/GW6xBfqAOBuFotFQUFBZscoEqvV6nWZXSk2qaLZEQCvxQVuJYsL3AAAwBV0jAMAgFLHW0aZSYw0u4Iv0wHnXL1W+RhJAeZFKTUy9WfnBGvBAwAAAADg/egYBwAApY43jjKTGGkGoPiuXqs8QFKAmD3AeX/OYuGqteCB0sZbLkbkQsT/ueT6Q7qUISn7f/d9JY9tykrg58gFbq7HBW4AACAvdIwDAADAFN7yZbrEF+o2nvyFurd8mS559s8RAIrAGy9GLMsXIvp+6mt2BOSDC9xKAhe4AQCA3OgYBwAAgCm88ct0iS/U4fkyJV39ZbAnMSRl/e++vzz7+oVMswMAAAAAAACXomMcAAAAAEqR6Y53AQCUEczQU7JKIqMnX+Amec9FblzgBgAA8kLHOAAAAIB8ecsX6t74ZbpUQlPeAwDwP8zQ4324wA0AAKDk0DEOAAAAIF/e+IV6WfwynQsYSpa35AQAAAAAAPmjYxwAAAAAvBwXMAAA4L285QI3yTsvcvOGjAAAwD08omN8/vz5evHFF5WamqqWLVvq5ZdfVtu2bfPd/7333tP48eN14MABNWjQQNOnT9ddd93lxsQAAAAAAAAA4DxvvMBN4iI3b5WUlKQXX3xRW7du1ZEjR7R69Wr17NnT7FgAALiFj9kBVq1apbi4OD3//PPatm2bWrZsqZiYGB07dizP/b/99lv16dNHgwcP1vbt29WzZ0/17NlTu3btcnNyAABQXPPnz1ft2rVltVrVrl07bdq0yexIAAAAAACUehcuXFDLli01f/58s6MAAOB2po8Ynz17toYMGaJBgwZJkhYuXKh//etfWrJkiZ5++ulc+8+bN0933nmnRo8eLUl64YUX9OWXX+qVV17RwoUL3Zq9qAzDUHp6ukuPefXxXH3sK6xWqywWS4kc2xN543kqa+dI4jx5C84T8nLloriFCxeqXbt2mjt3rmJiYrRnzx6FhYWZHa9A/Jv2fCVxjiTOk6t54/8lifPkCvxfgpn4N+0dOE/egfPkHThPyEvXrl3VtWtXs2MUGX/reQdv/L0jcZ5cgf9Lrsd5KhkWwzAMs148MzNTwcHBev/99+2maxk4cKDOnDmjjz/+ONdzatWqpbi4OI0YMcK27fnnn9dHH32k77//Ptf+GRkZysjIsD1OS0tTZGSkzp49q5CQEJe+H0f++OMPxcTEuPU1XSEhIaFMTYvkjeeprJ0jifPkLThPhZeWlqbQ0FBT2id3a9eunW644Qa98sorkqScnBxFRkZq+PDheV4U91dm/qz4N+35vPEcSZwnb8F58ny0456PdrxoytrvHYnz5C04T96B81R4ZbUtt1gsDqdS95Tv173x37NU9n73cJ68gzeep7J2jiTOU1EUpR03dSr1EydOKDs7W+Hh4Xbbw8PDlZqamudzUlNTi7R/fHy8QkNDbbfIyEjXhAcAAEWWmZmprVu3qnPnzrZtPj4+6ty5s5KTk/N8TkZGhtLS0uxuAAAAAACg5PH9OgCgNDF9KvWSNnbsWMXFxdkeX7mizQxWq1UJCQkuPaZhGLYr9gIDA0tkigKr1eryY3oybzxPZe0cSZwnb8F5wl8VdFHc7t2783xOfHy8Jk6c6I54DvFv2vOVxDmSOE+u5o3/lyTOkyvwfwlm4t+0d+A8eQfOk3fgPMEVPOX7df7W8w7e+HtH4jy5Av+XXI/zVDJM7RivUqWKfH19dfToUbvtR48eVURERJ7PiYiIKNL+gYGBCgwMdE1gJ1kslhKZQiA4ONjlxyzLOE/egfPkHThPcAVP+SNc4t+0NyipcyRxnlyJ/0vegfOEgsyfP18vvviiUlNT1bJlS7388stq27at2bEKxL9p78B58g6cJ+/AeYIreMr36/yt5x34veMdOE/egfNUMkydSj0gIECtW7dWYmKibVtOTo4SExMVHR2d53Oio6Pt9pekL7/8Mt/9AQCA5yjORXGBgYEKCQmxuwEAAPOsWrVKcXFxev7557Vt2za1bNlSMTExOnbsmNnRAAAAAADIl6kd45IUFxenRYsW6c0339TPP/+soUOH6sKFCxo0aJAkacCAARo7dqxt/yeeeEJr1qzRrFmztHv3bk2YMEFbtmzRsGHDzHoLAACgkIpzURwAAPAss2fP1pAhQzRo0CA1bdpUCxcuVHBwsJYsWWJ2NAAA4MD58+e1Y8cO7dixQ5K0f/9+7dixQykpKeYGAwDADUxfY7x37946fvy4nnvuOaWmpqpVq1Zas2aNbe3RlJQU+fj82X/foUMHrVixQuPGjdMzzzyjBg0a6KOPPlKzZs3MegsAAKAI4uLiNHDgQLVp00Zt27bV3Llz7S6KAwAAniszM1Nbt261u4Ddx8dHnTt3VnJycq79MzIybGvYSZeXRAEAAObZsmWLbr31VtvjK0uXDRw4UMuWLTMpFQAA7mF6x7gkDRs2LN8R3+vWrcu17f7779f9999fwqkAAEBJcHRRHAAA8FwnTpxQdnZ2rnY7PDxcu3fvzrV/fHy8Jk6c6K54AADAgVtuuUWGYZgdAwAAU5g+lToAACh7hg0bpoMHDyojI0Pfffed2rVrZ3YkAABQAsaOHauzZ8/abocOHTI7EgAAAACgjPKIEeMAAAAAAMDzValSRb6+vjp69Kjd9qNHjyoiIiLX/oGBgQoMDHRXPAAAAAAA8sWIcQAAAAAAUCgBAQFq3bq1EhMTbdtycnKUmJio6OhoE5MBAAAAAFAwRowDAAAAAIBCi4uL08CBA9WmTRu1bdtWc+fO1YULFzRo0CCzowEAAAAAkC86xgEAAAAAQKH17t1bx48f13PPPafU1FS1atVKa9asUXh4uNnRAAAAAADIFx3jAAAAAACgSIYNG6Zhw4aZHQMAAAAAgEJjjXEAAAAAAAAAAAAAQKlW5kaMG4YhSUpLSzM5CQAAf7rSLl1pp5A/2nIAgKehHS882nEAgCeiLS882nIAgKcpSjte5jrGz507J0mKjIw0OQkAALmdO3dOoaGhZsfwaLTlAABPRTvuGO04AMCT0ZY7RlsOAPBUhWnHLUYZuwwuJydHhw8fVoUKFWSxWMyO4xJpaWmKjIzUoUOHFBISYnYc5IPz5B04T96hNJ4nwzB07tw5Va9eXT4+rHRSENpymIXz5Pk4R96hNJ4n2vHCox2HWThP3oHz5B1K43miLS882nKYgXPkHThP3qE0nqeitONlbsS4j4+PatasaXaMEhESElJq/hGXZpwn78B58g6l7TxxVXrh0JbDbJwnz8c58g6l7TzRjhcO7TjMxnnyDpwn71DazhNteeHQlsNMnCPvwHnyDqXtPBW2HefyNwAAAAAAAAAAAABAqUbHOAAAAAAAAAAAAACgVKNjvBQIDAzU888/r8DAQLOjoACcJ+/AefIOnCeUNvyb9g6cJ8/HOfIOnCeUNvyb9g6cJ+/AefIOnCeUNvyb9nycI+/AefIOZf08WQzDMMwOAQAAAAAAAAAAAABASWHEOAAAAAAAAAAAAACgVKNjHAAAAAAAAAAAAABQqtExDgAAAAAAAAAAAAAo1egYBwAAAAAAAAAAAACUanSMlwLz589X7dq1ZbVa1a5dO23atMnsSLhKUlKSunXrpurVq8tiseijjz4yOxLyEB8frxtuuEEVKlRQWFiYevbsqT179pgdC3+xYMECtWjRQiEhIQoJCVF0dLS++OILs2MBTqEd93y05Z6Pdtw70I6jtKIt92y0456Pdtw70I6jtKId93y05Z6Pttw70JZfRse4l1u1apXi4uL0/PPPa9u2bWrZsqViYmJ07Ngxs6Phfy5cuKCWLVtq/vz5ZkdBAdavX6/Y2Fht3LhRX375pbKystSlSxdduHDB7Gi4Ss2aNTVt2jRt3bpVW7Zs0W233aYePXroxx9/NDsaUCy0496Bttzz0Y57B9pxlEa05Z6Pdtzz0Y57B9pxlEa0496Bttzz0ZZ7B9ryyyyGYRhmh0DxtWvXTjfccINeeeUVSVJOTo4iIyM1fPhwPf300yanw19ZLBatXr1aPXv2NDsKHDh+/LjCwsK0fv16derUyew4KEDlypX14osvavDgwWZHAYqMdtz70JZ7B9px70E7Dm9HW+5daMe9A+2496Adh7ejHfc+tOXegbbce5TFtpwR414sMzNTW7duVefOnW3bfHx81LlzZyUnJ5uYDPB+Z8+elXS5YYBnys7O1sqVK3XhwgVFR0ebHQcoMtpxoOTQjns+2nGUBrTlQMmgHfd8tOMoDWjHgZJDW+75ynJb7md2ABTfiRMnlJ2drfDwcLvt4eHh2r17t0mpAO+Xk5OjESNGqGPHjmrWrJnZcfAXO3fuVHR0tNLT01W+fHmtXr1aTZs2NTsWUGS040DJoB33bLTjKE1oywHXox33bLTjKE1ox4GSQVvu2WjL6RgHgFxiY2O1a9cuffPNN2ZHQR4aNWqkHTt26OzZs3r//fc1cOBArV+/vsw14ACAvNGOezbacQBAQWjHPRvtOADAEdpyz0ZbTse4V6tSpYp8fX119OhRu+1Hjx5VRESESakA7zZs2DB99tlnSkpKUs2aNc2OgzwEBASofv36kqTWrVtr8+bNmjdvnl577TWTkwFFQzsOuB7tuOejHUdpQlsOuBbtuOejHUdpQjsOuB5tueejLWeNca8WEBCg1q1bKzEx0bYtJydHiYmJZW5NAMBZhmFo2LBhWr16tb766ivVqVPH7EgopJycHGVkZJgdAygy2nHAdWjHvRftOLwZbTngGrTj3ot2HN6MdhxwHdpy71UW23JGjHu5uLg4DRw4UG3atFHbtm01d+5cXbhwQYMGDTI7Gv7n/Pnz+u2332yP9+/frx07dqhy5cqqVauWiclwtdjYWK1YsUIff/yxKlSooNTUVElSaGiogoKCTE6HK8aOHauuXbuqVq1aOnfunFasWKF169YpISHB7GhAsdCOewfacs9HO+4daMdRGtGWez7acc9HO+4daMdRGtGOewfacs9HW+4daMsvsxiGYZgdAs555ZVX9OKLLyo1NVWtWrXSSy+9pHbt2pkdC/+zbt063Xrrrbm2Dxw4UMuWLXN/IOTJYrHkuX3p0qV6+OGH3RsG+Ro8eLASExN15MgRhYaGqkWLFhozZozuuOMOs6MBxUY77vloyz0f7bh3oB1HaUVb7tloxz0f7bh3oB1HaUU77vloyz0fbbl3oC2/jI5xAAAAAAAAAAAAAECpxhrjAAAAAAAAAAAAAIBSjY5xAAAAAAAAAAAAAECpRsc4AAAAAAAAAAAAAKBUo2McAAAAAAAAAAAAAFCq0TEOAAAAAAAAAAAAACjV6BgHAAAAAAAAAAAAAJRqdIwDAAAAAAAAAAAAAEo1OsYBAAAAAAAAAAAAAKUaHeNAAQ4cOCCLxaIdO3Z41bHhHhMmTFCrVq3MjgEAKABtOQpCWw4Ano12HAWhHQcAz0Y7joLQjsMsdIwD0Lp162SxWHTmzBmzoxSZxWLRRx99ZHYMAABMRVsOAID3oh0HAMB70Y4D3oWOcQBwICsryyuPDQAALqMtBwDAe9GOAwDgvWjH4WnoGAck5eTkaMaMGapfv74CAwNVq1YtTZkyJdd+p0+fVr9+/VS1alUFBQWpQYMGWrp0aaFeY9OmTbruuutktVrVpk0bbd++3a6enZ2twYMHq06dOgoKClKjRo00b948Wz0pKUn+/v5KTU21e96IESN00003SZIOHjyobt26qVKlSipXrpyuvfZaff755wXmOnDggG699VZJUqVKlWSxWPTwww9LkjIyMvT4448rLCxMVqtVN954ozZv3lyo93vlSrnExES1adNGwcHB6tChg/bs2WO338cff6zrr79eVqtVdevW1cSJE3Xp0iVJ0qRJk1S9enWdPHnStv/dd9+tW2+9VTk5Oapdu7Yk6d5775XFYrE9dmTBggWqV6+eAgIC1KhRI7399tt2dYvFogULFqh79+4qV66c7d/CtGnTFB4ergoVKmjw4MFKT0/Pdew33nhDTZo0kdVqVePGjfXqq6/aalem+Fm1apVuvvlmWa1WLV++vFCZAQAFoy2nLb8abTkAeBfacdrxq9GOA4B3oR2nHb8a7Tg8ngHAeOqpp4xKlSoZy5YtM3777TfjP//5j7Fo0SJj//79hiRj+/bthmEYRmxsrNGqVStj8+bNxv79+40vv/zS+OSTTxwe/9y5c0bVqlWNvn37Grt27TI+/fRTo27dunbHzszMNJ577jlj8+bNxr59+4x33nnHCA4ONlatWmU7TsOGDY0ZM2bYHmdmZhpVqlQxlixZYhiGYdx9993GHXfcYfzwww/G3r17jU8//dRYv359gdkuXbpkfPDBB4YkY8+ePcaRI0eMM2fOGIZhGI8//rhRvXp14/PPPzd+/PFHY+DAgUalSpWMkydPOnzPX3/9tSHJaNeunbFu3Trjxx9/NG666SajQ4cOtn2SkpKMkJAQY9myZcbevXuNtWvXGrVr1zYmTJhgyxYdHW307NnTMAzDeOWVV4yKFSsaBw8eNAzDMI4dO2ZIMpYuXWocOXLEOHbsmMNcH374oeHv72/Mnz/f2LNnjzFr1izD19fX+Oqrr2z7SDLCwsKMJUuWGHv37jUOHjxorFq1yggMDDTeeOMNY/fu3cazzz5rVKhQwWjZsqXtee+8845RrVo144MPPjD27dtnfPDBB0blypWNZcuWGYZh2P491a5d27bP4cOHHWYGADhGW05bTlsOAN6Ldpx2nHYcALwX7TjtOO04vAkd4yjz0tLSjMDAQGPRokW5an9tvLt162YMGjSoyK/x2muvGddcc43xxx9/2LYtWLDA7th5iY2NNXr16mV7PH36dKNJkya2xx988IFRvnx54/z584ZhGEbz5s1tDV9RXGloT58+bdt2/vx5w9/f31i+fLltW2ZmplG9enW7DxCOjvnvf//btu1f//qXIcn2c7j99tuNqVOn2j3v7bffNqpVq2Z7vHfvXqNChQrGmDFjjKCgILs8hnG5oV29enWh32uHDh2MIUOG2G27//77jbvuusvumCNGjLDbJzo62vjHP/5ht61du3Z2jXe9evWMFStW2O3zwgsvGNHR0YZh/Pnvae7cuYXOCwBwjLactpy2HAC8F+047TjtOAB4L9px2nHacXgbplJHmffzzz8rIyNDt99+u8N9hw4dqpUrV6pVq1Z66qmn9O233xb6NVq0aCGr1WrbFh0dnWu/+fPnq3Xr1qpatarKly+v119/XSkpKbb6ww8/rN9++00bN26UJC1btkwPPPCAypUrJ0l6/PHHNXnyZHXs2FHPP/+8fvjhh0Lly8vevXuVlZWljh072rb5+/urbdu2+vnnnwt9nBYtWtjuV6tWTZJ07NgxSdL333+vSZMmqXz58rbbkCFDdOTIEV28eFGSVLduXc2cOVPTp09X9+7d1bdv32K/J+nyubj6PUlSx44dc72nNm3a5Hpeu3bt7LZdfQ4vXLigvXv3avDgwXbvZ/Lkydq7d2+BxwYAOIe2PG+05bTlAOANaMfzRjtOOw4A3oB2PG+047Tj8Fx0jKPMCwoKKvS+Xbt21cGDBzVy5EgdPnxYt99+u0aNGuWSHCtXrtSoUaM0ePBgrV27Vjt27NCgQYOUmZlp2ycsLEzdunXT0qVLdfToUX3xxRf629/+Zqs/8sgj2rdvn/r376+dO3eqTZs2evnll12Sr7j8/f1t9y0Wi6TL685I0vnz5zVx4kTt2LHDdtu5c6d+/fVXuw86SUlJ8vX11YEDB2xrpJS0Kx+ICuv8+fOSpEWLFtm9n127dtk+bBX32ACAgtGWlyzactpyAChJtOMli3acdhwAShLteMmiHacdh+vRMY4yr0GDBgoKClJiYmKh9q9ataoGDhyod955R3PnztXrr7/u8DlNmjTRDz/8oPT0dNu2v/5C37Bhgzp06KB//OMfuu6661S/fv1cV0NJlxvoVatW6fXXX1e9evVyXaEVGRmpxx57TB9++KGefPJJLVq0yGG+gIAASVJ2drZtW7169RQQEKANGzbYtmVlZWnz5s1q2rSpw2MWxvXXX689e/aofv36uW4+Ppd/Pa1atUoffvih1q1bp5SUFL3wwgt2x/D397fL7UiTJk3s3pN0+Wfv6D01adJE3333nd22q89heHi4qlevrn379uV6L3Xq1Cl0PgBA0dGW05bTlgOA96Idpx2nHQcA70U7TjtOOw6vY/Zc7oAnmDBhglGpUiXjzTffNH777TcjOTnZeOONN3KtgzJ+/Hjjo48+Mn799Vdj165dxj333GO0bdvW4fHPnTtnVKlSxXjooYeMH3/80fjXv/5l1K9f3+7Y8+bNM0JCQow1a9YYe/bsMcaNG2eEhITYrbNhGIaRnZ1tREZGGgEBAca0adPsak888YSxZs0aY9++fcbWrVuNdu3aGQ888IDDfL///rthsViMZcuWGceOHTPOnTtnO1716tWNL774wvjxxx+NgQMHGpUqVTJOnTrl8Jh5ra2yfft2Q5Kxf/9+wzAMY82aNYafn58xYcIEY9euXcZPP/1kvPvuu8azzz5rGIZhHDp0yKhUqZLx0ksv2e2fnJxsO2aDBg2MoUOHGkeOHClUrtWrVxv+/v7Gq6++avzyyy/GrFmzDF9fX+Prr7+27aM81lZZuXKlYbVajSVLlhh79uwxnnvuOaNChQp252fRokVGUFCQMW/ePGPPnj3GDz/8YCxZssSYNWuWYRi519UBALgObTltOW05AHgv2nHacdpxAPBetOO047Tj8CZ0jAPG5QZx8uTJRlRUlOHv72/UqlXLmDp1aq5fti+88ILRpEkTIygoyKhcubLRo0cPY9++fYV6jeTkZKNly5ZGQECA0apVK+ODDz6wO3Z6errx8MMPG6GhoUbFihWNoUOHGk8//XSuxtswLn+I8PX1NQ4fPmy3fdiwYUa9evWMwMBAo2rVqkb//v2NEydOFCrfpEmTjIiICMNisRgDBw40DMMw/vjjD2P48OFGlSpVjMDAQKNjx47Gpk2bCnW8wjTehnG5Qe7QoYMRFBRkhISEGG3btjVef/11Iycnx7j99tuNmJgYIycnx7b/8OHDjXr16tk+YHzyySdG/fr1DT8/PyMqKqpQ2V599VWjbt26hr+/v9GwYUPjrbfesqvn1XgbhmFMmTLFqFKlilG+fHlj4MCBxlNPPZXr/Cxfvtxo1aqVERAQYFSqVMno1KmT8eGHHxqGQeMNACWJtpy2/Gq05QDgXWjHacevRjsOAN6Fdpx2/Gq04/B0FsMwDNePQwdQkgYPHqzjx4/rk08+MTsKAAAoBtpyAAC8F+04AADei3YcKNv8zA4AoPDOnj2rnTt3asWKFTTcAAB4IdpyAAC8F+04AADei3YcgCT5mB0AKA2mTp2q8uXL53nr2rWry16nR48e6tKlix577DHdcccdhX7eY489lm++xx57rFhZSuKYrnDttdfmm2v58uWm5QIAeDbactpyAID3oh2nHQcAeC/acdpxwJ2YSh1wgVOnTunUqVN51oKCglSjRg03J7J37NgxpaWl5VkLCQlRWFiYRxzTFQ4ePKisrKw8a+Hh4apQoYKbEwEAvAFtuWuO6Qq05QCAoqIdd80xXYF2HABQVLTjrjmmK9COoyygYxwAAAAAAAAAAAAAUKoxlToAAAAAAAAAAAAAoFSjYxwAAAAAAAAAgP9n797jc67/P44/r81sYzY5H2YHhxQ5hJEmhxJJhMpplENHpQwVHYRk5Uvr24nkG8UopaQSOijFaMiXVCQ7mPN5DRt2fX5/+O76uezageuz67A97rfbdWvX+/XZ5/26rs/ms67X+wAAAEo0CuMAAAAAAAAAAAAAgBKNwjgAAAAAAAAAAAAAoESjMA4AAAAAAAAAAAAAKNEojAMAAAAAAAAAAAAASjQK4wAAAAAAAAAAAACAEo3COAAAAAAAAAAAAACgRKMwDgAAAAAAAAAAAAAo0SiMAwAAAAAAAAAAAABKNArjAAAAAAAAAAAAAIASjcI4AAAAAAAAAAAAAKBEozAOAAAAAAAAAAAAACjRKIyjxJo3b54sFotSUlKcPtfEiRNlsVicT8oDWCwWTZw40d1pAABgGu75jpX2e35pf/0AAAAAAACwR2EcAAAAgEdbt26dJk6cqBMnTrg7FQAAAAAAAHipMu5OAIBrnTlzRmXK8KsPAEBJV5Lu+evWrdOkSZM0ZMgQVaxY0d3pAAAAAAAAwAsxYxwex2q1Kisry91plFgBAQEl5kNyAIB3455fvLjnAwAAAAAAAP+PwjiKTe4enX/++af69u2r4OBgVa5cWU888YTdh+AWi0WPPfaYEhIS1LhxY/n7+2vFihWSpF9//VXdunVTcHCwgoKCdMstt2j9+vV5+tq+fbtuvvlmBQYGKjQ0VFOmTJHVar2ivH/++WdFRUUpICBA9erV0zvvvOPwuLlz5+rmm29WtWrV5O/vr0aNGmnmzJl2x9x3332qUqWKzp07l+f7u3TpooYNG9qef/PNN2rXrp0qVqyooKAgNWzYUM8888xl5b5x40Z17dpVVapUUWBgoCIjIzVs2DC7Yy7dbzP3Ou3atcs2CyskJERDhw7V6dOn8/SxYMECtW7dWuXKldNVV12l9u3ba9WqVXbHfP3117rppptUvnx5VahQQd27d9f27dsv67X88MMPslgsWrx4sSZNmqTatWurQoUKuvvuu3Xy5EllZ2dr1KhRqlatmoKCgjR06FBlZ2c7zLdly5YKDAxUpUqV1L9/f+3Zs8fumJ9++kn33HOPwsLC5O/vrzp16ig2NlZnzpyxO27IkCEKCgrS3r171atXLwUFBalq1aoaO3ascnJyLuv1dezYUdddd522bt2qDh06qFy5cqpfv74++eQTSdKPP/6oNm3aKDAwUA0bNtS3336b5xx79+7VsGHDVL16dfn7+6tx48Z677337I45e/asJkyYoJYtWyokJETly5fXTTfdpNWrV9sdl5KSIovFounTp2v27NmqV6+e/P39FRUVpaSkpMt6bQBKH+753POduedL0htvvKHGjRvb+mrVqpUWLlxoy/vJJ5+UJEVGRspisdjtKZ+dna3Y2FhVrVpVFSpUUM+ePZWenn7ZOVx8L3zrrbdUt25dlStXTl26dNGePXtkGIZefPFFhYaGKjAwUHfeeaeOHTuW5zxFeU+2bt2qIUOGqG7dugoICFCNGjU0bNgwHT161O64y71mAAAAAAAAyB9TSFDs+vbtq4iICMXFxWn9+vV6/fXXdfz4cX3wwQe2Y77//nstXrxYjz32mKpUqaKIiAht375dN910k4KDg/XUU0/Jz89P77zzjjp27GgrGkrSgQMH1KlTJ50/f17jxo1T+fLlNXv2bAUGBl52rtu2bVOXLl1UtWpVTZw4UefPn9cLL7yg6tWr5zl25syZaty4sXr27KkyZcroiy++0IgRI2S1WvXoo49KkgYPHqwPPvhAK1eu1B133GH73gMHDuj777/XCy+8IOnCh/x33HGHmjZtqsmTJ8vf31+7du3S2rVri5z7oUOHbLmPGzdOFStWVEpKij799NMifX/fvn0VGRmpuLg4bd68WXPmzFG1atX0yiuv2I6ZNGmSJk6cqBtvvFGTJ09W2bJltWHDBn3//ffq0qWLJGn+/Pm677771LVrV73yyis6ffq0Zs6cqXbt2unXX39VREREkV+TJMXFxSkwMFDjxo3Trl279MYbb8jPz08+Pj46fvy4Jk6cqPXr12vevHmKjIzUhAkTbN/70ksv6fnnn1ffvn11//336/Dhw3rjjTfUvn17/frrr7alWD/++GOdPn1ajzzyiCpXrqxffvlFb7zxhtLT0/Xxxx/b5ZOTk6OuXbuqTZs2mj59ur799lvNmDFD9erV0yOPPHJZr+348eO644471L9/f91zzz2aOXOm+vfvr4SEBI0aNUoPP/ywBg4cqH/961+6++67tWfPHlWoUEGSdPDgQd1www22IlPVqlX19ddfa/jw4crIyNCoUaMkSRkZGZozZ44GDBigBx54QP/884/+85//qGvXrvrll1/UvHlzu5wWLlyof/75Rw899JAsFoumTZumPn36aPfu3fLz87us1weg9OGezz3/Su757777rh5//HHdfffdtsEUW7du1YYNGzRw4ED16dNHO3fu1KJFixQfH68qVapIkqpWrSpJuv/++7VgwQINHDhQN954o77//nt17969yO/npRISEnT27FmNHDlSx44d07Rp09S3b1/dfPPN+uGHH/T000/b/iYZO3as3aC0or4n33zzjXbv3q2hQ4eqRo0a2r59u2bPnq3t27dr/fr1slgsdjkV5ZoBAAAAAACgEAZQTF544QVDktGzZ0+79hEjRhiSjP/+97+GYRiGJMPHx8fYvn273XG9evUyypYta/z999+2tn379hkVKlQw2rdvb2sbNWqUIcnYsGGDre3QoUNGSEiIIclITk4ucs69evUyAgICjNTUVFvb77//bvj6+hqX/rqcPn06z/d37drVqFu3ru15Tk6OERoaavTr18/uuFdffdWwWCzG7t27DcMwjPj4eEOScfjw4SLneqnPPvvMkGQkJSUVeJwk44UXXrA9z71Ow4YNszuud+/eRuXKlW3P//rrL8PHx8fo3bu3kZOTY3es1Wo1DMMw/vnnH6NixYrGAw88YBc/cOCAERISkqe9IKtXrzYkGdddd51x9uxZW/uAAQMMi8VidOvWze74tm3bGuHh4bbnKSkphq+vr/HSSy/ZHbdt2zajTJkydu2OrmVcXJxhsVjsfhbuu+8+Q5IxefJku2Ovv/56o2XLlkV+bYZhGB06dDAkGQsXLrS1/fnnn7bfh/Xr19vaV65caUgy5s6da2sbPny4UbNmTePIkSN25+3fv78REhJie03nz583srOz7Y45fvy4Ub16dbtrnpycbEgyKleubBw7dszW/vnnnxuSjC+++OKyXh+A0oV7Pvf8XFdyz7/zzjuNxo0bF3jMv/71L4fXeMuWLYYkY8SIEXbtAwcOzPP6C5N7L6xatapx4sQJW/v48eMNSUazZs2Mc+fO2doHDBhglC1b1sjKyjIM4/LeE0c/U4sWLTIkGWvWrLG1FfWaAQAAAAAAoHAspY5ilzuTKtfIkSMlScuXL7e1dejQQY0aNbI9z8nJ0apVq9SrVy/VrVvX1l6zZk0NHDhQP//8szIyMmznueGGG9S6dWvbcVWrVlVMTMxl5ZmTk6OVK1eqV69eCgsLs7Vfe+216tq1a57jL56ddvLkSR05ckQdOnTQ7t27dfLkSUmSj4+PYmJitGzZMv3zzz+24xMSEnTjjTcqMjJSkmwzlz///PMrXg429xxffvmlw2VcC/Pwww/bPb/pppt09OhR2/u8dOlSWa1WTZgwQT4+9v905M5q+uabb3TixAkNGDBAR44csT18fX3Vpk2bPMt3F8W9995rN1O5TZs2Mgwjz3Kxbdq00Z49e3T+/HlJ0qeffiqr1aq+ffva5VKjRg01aNDALpeLr+WpU6d05MgR3XjjjTIMQ7/++muR3qvdu3df9msLCgpS//79bc8bNmyoihUr6tprr7XNjsx9bZJsfRiGoSVLlqhHjx4yDMPu9XXt2lUnT57U5s2bJUm+vr4qW7aspAt7+R47dkznz59Xq1atbMdcrF+/frrqqqvsXtvFfQNAQbjnc8+/knt+xYoVlZ6efkVbd+T+bD3++ON27bkrp1yJe+65RyEhIbbnuffhQYMG2e3Z3qZNG509e1Z79+6VdHnvycU/U1lZWTpy5IhuuOEGSXJ4fy7smgEAAAAAAKBwFMZR7Bo0aGD3vF69evLx8bHtCynJ9mFxrsOHD+v06dN2+3Hmuvbaa2W1Wm37RKempubpQ5LD7y3I4cOHdebMmSKfa+3atercubPKly+vihUrqmrVqrb9QXM/JJcuFHbPnDmjzz77TJK0Y8cObdq0SYMHD7Yd069fP0VHR+v+++9X9erV1b9/fy1evPiyPjDv0KGD7rrrLk2aNElVqlTRnXfeqblz5zrcd9uRiwsDkmzF0ePHj0uS/v77b/n4+NgVMy71119/SZJuvvlmVa1a1e6xatUqHTp0qMivJ7+8cj+orlOnTp52q9Vqe+//+usvGYahBg0a5Mnljz/+sMslLS1NQ4YMUaVKlWz7hnfo0EGS/bWUpICAANvSrbmuuuoq2/t0OUJDQ/MslRoSEuLwtUn/fy0OHz6sEydOaPbs2Xle29ChQyXJ7vW9//77atq0qQICAlS5cmVVrVpVX331VZ7XJhX+cwAABeGezz3/Su75Tz/9tIKCgtS6dWs1aNBAjz76aJGXlk9NTZWPj4/q1atn1365PxMXu5y/PaT/f98u5z05duyYnnjiCVWvXl2BgYGqWrWq7XeD+zMAAAAAAEDxYI9xuNylhUBJV7Q3qDv9/fffuuWWW3TNNdfo1VdfVZ06dVS2bFktX75c8fHxdh9uN2rUSC1bttSCBQt07733asGCBSpbtqz69u1rOyYwMFBr1qzR6tWr9dVXX2nFihX66KOPdPPNN2vVqlXy9fUtNCeLxaJPPvlE69ev1xdffKGVK1dq2LBhmjFjhtavX6+goKACvz+/PgzDKOK7Itvrnj9/vmrUqJEnfvEsq6LKL6/C8rVarbJYLPr6668dHpv7fuTk5OjWW2/VsWPH9PTTT+uaa65R+fLltXfvXg0ZMiRPoaIo16KonHlt0oWZa/fdd5/DY5s2bSpJWrBggYYMGaJevXrpySefVLVq1eTr66u4uDj9/fffl903AFwO7vnc84vi2muv1Y4dO/Tll19qxYoVWrJkid5++21NmDBBkyZNKvJ5zOLs/bko70nfvn21bt06Pfnkk2revLmCgoJktVp12223ORwkwf0ZAAAAAADAeRTGUez++usvu9lhu3btktVqVURERL7fU7VqVZUrV047duzIE/vzzz/l4+Njm7UTHh5um6FzMUffW5CqVasqMDCwSOf64osvlJ2drWXLltnN4Mlv2dB7771Xo0eP1v79+7Vw4UJ1797dbrlq6cISrLfccotuueUWvfrqq5o6daqeffZZrV69Wp07dy7y67jhhht0ww036KWXXtLChQsVExOjDz/8UPfff3+Rz+FIvXr1ZLVa9fvvv6t58+b5HiNJ1apVu6yci0O9evVkGIYiIyN19dVX53vctm3btHPnTr3//vu69957be3ffPONK9K8IlWrVlWFChWUk5NT6Pv8ySefqG7duvr000/tClQvvPBCcacJoBTins89/0qVL19e/fr1U79+/XT27Fn16dNHL730ksaPH6+AgACHgyykCz8TVqtVf//9t90s8cv9mTBDUd+T48eP67vvvtOkSZM0YcIEW7ujn0cAAAAAAACYh6XUUezeeustu+dvvPGGJKlbt275fo+vr6+6dOmizz//3G751YMHD2rhwoVq166dgoODJUm333671q9fr19++cV23OHDh5WQkHBZefr6+qpr165aunSp0tLSbO1//PGHVq5cmedYyX6WzsmTJzV37lyH5x4wYIAsFoueeOIJ7d69W4MGDbKLHzt2LM/35H4QXdRlUY8fP55n1tDlnqMgvXr1ko+PjyZPnpxnJlNuv127dlVwcLCmTp3qcM/Tw4cPO51HUfXp00e+vr6aNGlSnvfFMAwdPXpUkuNraRiG/v3vf7ss18vl6+uru+66S0uWLNFvv/2WJ37x++zo9W3YsEGJiYnFnyiAUod7Pvf8XJdzz8+9J+cqW7asGjVqJMMwbOcuX768JOnEiRN2x+b+bL3++ut27a+99lqR+zdLUd8TRz9TkntyBgAAAAAAKE2YMY5il5ycrJ49e+q2225TYmKiFixYoIEDB6pZs2YFft+UKVP0zTffqF27dhoxYoTKlCmjd955R9nZ2Zo2bZrtuKeeekrz58/XbbfdpieeeELly5fX7NmzFR4erq1bt15WrpMmTdKKFSt00003acSIETp//rzeeOMNNW7c2O5cXbp0UdmyZdWjRw899NBDyszM1Lvvvqtq1app//79ec5btWpV3Xbbbfr4449VsWJFde/e3S4+efJkrVmzRt27d1d4eLgOHTqkt99+W6GhoWrXrl2Rcn///ff19ttvq3fv3qpXr57++ecfvfvuuwoODtbtt99+We+DI/Xr19ezzz6rF198UTfddJP69Okjf39/JSUlqVatWoqLi1NwcLBmzpypwYMHq0WLFurfv7+qVq2qtLQ0ffXVV4qOjtabb77pdC5FUa9ePU2ZMkXjx49XSkqKevXqpQoVKig5OVmfffaZHnzwQY0dO1bXXHON6tWrp7Fjx2rv3r0KDg7WkiVLPH7PzpdfflmrV69WmzZt9MADD6hRo0Y6duyYNm/erG+//dZWeLnjjjv06aefqnfv3urevbuSk5M1a9YsNWrUSJmZmW5+FQBKGu753POv5J7fpUsX1ahRQ9HR0apevbr++OMPvfnmm+revbsqVKggSWrZsqUk6dlnn1X//v3l5+enHj16qHnz5howYIDefvttnTx5UjfeeKO+++477dq1y+n34XIV9T0JDg5W+/btNW3aNJ07d061a9fWqlWrlJyc7PKcAQAAAAAAShMK4yh2H330kSZMmKBx48apTJkyeuyxx/Svf/2r0O9r3LixfvrpJ40fP15xcXGyWq1q06aNFixYoDZt2tiOq1mzplavXq2RI0fq5ZdfVuXKlfXwww+rVq1aGj58+GXl2rRpU61cuVKjR4/WhAkTFBoaqkmTJmn//v12H5I3bNhQn3zyiZ577jmNHTtWNWrU0COPPKKqVatq2LBhDs9977336ssvv1Tfvn3l7+9vF+vZs6dSUlL03nvv6ciRI6pSpYo6dOigSZMmKSQkpEi5d+jQQb/88os+/PBDHTx4UCEhIWrdurUSEhLslrV1xuTJkxUZGak33nhDzz77rMqVK6emTZtq8ODBtmMGDhyoWrVq6eWXX9a//vUvZWdnq3bt2rrppps0dOhQU/IoqnHjxunqq69WfHy8bY/SOnXqqEuXLurZs6ckyc/PT1988YUef/xxxcXFKSAgQL1799Zjjz1WaCHHnapXr65ffvlFkydP1qeffqq3335blStXVuPGjfXKK6/YjhsyZIgOHDigd955RytXrlSjRo20YMECffzxx/rhhx/c9wIAlEjc8y/gnn959/yHHnpICQkJevXVV5WZmanQ0FA9/vjjeu6552zHREVF6cUXX9SsWbO0YsUKWa1WJScnq3z58nrvvfdUtWpVJSQkaOnSpbr55pv11Vdf2Zbgd6WivicLFy7UyJEj9dZbb8kwDHXp0kVff/21atWq5fKcAQAAAAAASguLcekafoBJJk6cqEmTJunw4cOqUqWKu9Nxu88//1y9evXSmjVrdNNNN7k7HQAATMM93x73fAAAAAAAAMDzsMc44CLvvvuu6tatW+RlUgEAgHfing8AAAAAAAB4HpZSR6mQmZlZ6H7KVatWla+vr+l9f/jhh9q6dau++uor/fvf/5bFYrnscxw+fFg5OTn5xsuWLatKlSo5k6bLnD171rb/dX5CQkIUGBjooozMdezYMZ09ezbfuK+vr6pWrerCjACgdOGe7zk85Z6fk5Ojw4cPF3hMUFCQgoKCijUPAAAAAAAAuBeFcZQK06dPt+0xnZ/k5GRFRESY3veAAQMUFBSk4cOHa8SIEVd0jqioKKWmpuYb79Chg9fsF71u3Tp16tSpwGPmzp2rIUOGuCYhk/Xp00c//vhjvvHw8HClpKS4LiEAKGW453sOT7nn79mzp9C911944QVNnDixWPMAAAAAAACAe7HHOEqF3bt3a/fu3QUe065dOwUEBLgoo8uzdu1anTlzJt/4VVddpZYtW7owoyt3/Phxbdq0qcBjGjdurJo1a7ooI3Nt2rRJx48fzzceGBio6OhoF2YEAKUL93zP4Sn3/KysLP38888FHlO3bl3VrVu3WPMAAAAAAACAe1EYBwAAAAAAAAAAAACUaD7uTgAAAAAAAAAAAAAAgOJU6vYYt1qt2rdvnypUqCCLxeLudAAAkCQZhqF//vlHtWrVko8P49YKwr0cAOBpuI8DAAAAAOD5Sl1hfN++fapTp4670wAAwKE9e/YoNDTU3Wl4NO7lAABPxX0cAAAAAADPVeoK4xUqVJB04QOL4OBgN2cDAMAFGRkZqlOnju0+hfxxLwcAeBru4wAAAAAAeL5SVxjPXXI1ODiYD9MBAB6HpcELx70cAOCpuI8DAAAAAOC52PwMAAAAAAAAAAAAAFCiURgHAAAAAAAAAAAAAJRoFMYBAAAAAAAAAAAAACUahXEAAAAAAAAAAAAAQIlGYRwAAAAAAAAAAAAAUKJRGAcAAAAAAAAAAAAAlGgUxgHgImvXrtU999yjtWvXujsVAACAEom/twAAAAAAgDtQGAeA/8nKytKMGTN08OBBzZgxQ1lZWe5OCQAAoETh7y0AAAAAAOAuFMYB4H8WLFigo0ePSpKOHj2qhIQEN2cEAABQsvD3FgAAAAAAcBcK4wAgKT09XQkJCTIMQ5JkGIYSEhKUnp7u5swAAABKBv7eAgAAAAAA7kRhHECpZxiG4uPj823P/fAWAAAAV4a/twAAAAAAgLtRGAdQ6qWmpiopKUk5OTl27Tk5OUpKSlJqaqqbMgMAACgZ+HsLAAAAAAC4m0cVxuPi4hQVFaUKFSqoWrVq6tWrl3bs2GF3TFZWlh599FFVrlxZQUFBuuuuu3Tw4EE3ZQygJAgPD1dUVJR8fX3t2n19fdW6dWuFh4e7KTMAAICSgb+3AAAAAACAu3lUYfzHH3/Uo48+qvXr1+ubb77RuXPn1KVLF506dcp2TGxsrL744gt9/PHH+vHHH7Vv3z716dPHjVkD8HYWi0WxsbH5tlssFjdkBXinNWvWqEePHqpVq5YsFouWLl1qF//000/VpUsXVa5cWRaLRVu2bHFLngAA1+LvLQAAAAAA4G4eVRhfsWKFhgwZosaNG6tZs2aaN2+e0tLStGnTJknSyZMn9Z///Eevvvqqbr75ZrVs2VJz587VunXrtH79eofnzM7OVkZGht0DAC4VGhqqmJgY24eyFotFMTExql27tpszA7zLqVOn1KxZM7311lv5xtu1a6dXXnnFxZkBANyNv7cAAAAAAIA7lXF3AgU5efKkJKlSpUqSpE2bNuncuXPq3Lmz7ZhrrrlGYWFhSkxM1A033JDnHHFxcZo0aZJrEgbg1QYNGqTly5fryJEjqlKlimJiYtydEuB1unXrpm7duuUbHzx4sCQpJSWlyOfMzs5Wdna27TmD3ADAe/H3FgAAAAAAcBePmjF+MavVqlGjRik6OlrXXXedJOnAgQMqW7asKlasaHds9erVdeDAAYfnGT9+vE6ePGl77Nmzp7hTB+ClAgICNGbMGFWvXl2jR49WQECAu1MCoAuD3EJCQmyPOnXquDslAMAV4u8tAAAAAADgLh47Y/zRRx/Vb7/9pp9//tmp8/j7+8vf39+krACUdNHR0YqOjnZ3GgAuMn78eI0ePdr2PCMjg+I4AHgx/t4CAAAAAADu4JGF8ccee0xffvml1qxZo9DQUFt7jRo1dPbsWZ04ccJu1vjBgwdVo0YNN2QKAACKG4PcAAAAAAAAAADO8qil1A3D0GOPPabPPvtM33//vSIjI+3iLVu2lJ+fn7777jtb244dO5SWlqa2bdu6Ol0AAAAAAAAAAAAAgBfwqML4o48+qgULFmjhwoWqUKGCDhw4oAMHDujMmTOSpJCQEA0fPlyjR4/W6tWrtWnTJg0dOlRt27bVDTfc4ObsAQAAAACFmTNnjjp27Kg5c+a4OxUAAAAAAFCKeNRS6jNnzpQkdezY0a597ty5GjJkiCQpPj5ePj4+uuuuu5Sdna2uXbvq7bffdnGmAADAkczMTO3atcv2PDk5WVu2bFGlSpUUFhamY8eOKS0tTfv27ZN0YeUX6cJ2KWyLAgAl34kTJ7RgwQJZrVYtWLBAd999t902WQAAAAAAAMXFYhiG4e4kXCkjI0MhISE6efKkgoOD3Z0OAACSSs796YcfflCnTp3ytN93332aN2+e5s2bp6FDh+aJv/DCC5o4cWKR+igp7xUAlEaPPvqotm3bZnvetGlTvfnmm27MyBzcmwAAAAAA8HwetZQ6AADwbh07dpRhGHke8+bNkyQNGTLEYbyoRXEAKMjatWt1zz33aO3ate5OBQ5s3LjRriguSVu3btXGjRvdlBEAAAAAAChNKIwDAAAA8HpZWVmaMWOGDh48qBkzZigrK8vdKeEiVqs130FQEydOlNVqdW1CAAAAAACg1KEwDgAAAMDrLViwQEePHpUkHT16VAkJCW7OCBdLTExURkaGw1hGRoYSExNdnBEAAAAAAChtKIwDAAAA8Grp6elKSEiQYRiSJMMwlJCQoPT0dDdnhlxt27bNd+/tkJAQtW3b1sUZAQAAAACA0obCOAAAAACvZRiG4uPjbUXxXFar1WE73MPHxyffpdQnTZokHx/+1xQAAAAAABQvPn0AAAAA4LVSU1OVlJSUZ49qq9WqpKQkpaamuikzXKpVq1aqUqWKXVvVqlXVokULN2UEAAAAAABKEwrjAAAAALxWeHi4mjRp4jDWtGlThYeHuzgj5Cc9PV1Hjhyxazt8+DBL3gMAAAAAAJegMA4AAADAq2VmZjps/+eff1ycCfJjGIbi4uIcxuLi4ljyHgAAAAAAFDsK4wAAAAC8VnJyspKTky87BtdKSUnRtm3bHMa2bdumlJQU1yYEAAAAAABKHQrjAAAAQBGsXbtW99xzj9auXevuVHCR/fv3OxUHAAAAAABA6UBhHAAAAChEVlaW4uLidPDgQcXFxSkrK8vdKeF/2rZtq6CgIIexChUqqG3bti7OCI5ERESoYcOGDmPXXHONIiIiXJsQAAAAAAAodSiMAwC8DrM2AbjavHnzlJGRIUnKyMjQ+++/7+aMkMvHx0eTJ092GHvxxRfl48P/8ng69hcHAAAAAACuwKdEAACvkpWVpRkzZujgwYOaMWMGszYBFLv09HQtWrTIrm3RokVKT093U0a4VKtWrRQYGGjXFhgYqBYtWrgpI1wqJSVFO3bscBjbsWMHe4wDAAAAAIBiR2EcAOBVFixYoKNHj0qSjh49qoSEBDdnBKAkMwxDL7/8cp4ZrVar1WE73GPjxo06c+aMXduZM2e0ceNGN2WESxX2u8LvEgAAAAAAKG4UxgEAXiM9PV0JCQm2D88Nw1BCQgKzNgEUm5SUFG3dutVhbOvWrcxy9QBWq1UTJkxwGJswYYKsVquLM4IjFovFqTgAAAAAAICzKIwDALyCYRiKj4/Pt52ZZgBQOq1bt06ZmZkOY5mZmVq3bp2LM4IjOTk5TsUBAAAAAACcRWEcAOAVUlNTlZSUlOeD85ycHCUlJSk1NdVNmQHOmzNnjjp27Kg5c+a4OxVcIiIiQhEREQ5jkZGR+cbgOrVq1XIqDtdYvXq1U3EAAAAAAABnURgHAHiF8PBwRUVFydfX167d19dXrVu3Vnh4uJsyA5xz4sQJzZ8/X1arVfPnz9eJEyfcnRIuYhiGDhw44DC2f/9+VqvwACzR7R0aNmzoVBwAAAAAAMBZFMYBAF7BYrEoNjY233YKH/BW48ePtxVXDcPQM8884+aMcLF169YpKyvLYSwrK4tluj1AeHi4ypUr5zBWrlw5Bk55iBtuuMGpOAAAAAAAgLMojAMAvEZoaKhiYmJsRXCLxaKYmBjVrl3bzZkBV2bjxo3avn27Xdtvv/2mjRs3uikjwPukpqbq9OnTDmOnT59mqw0P8cEHHzgVBwAAAAAAcBaFcQCAVxk0aJAqV64sSapSpYpiYmLcnBFwZaxWqyZMmOAwNmHCBFmtVhdnBEduvPHGAmcj33jjjS7OCPBODRo0cCoOAAAAAADgLArjAACvEhAQoDFjxqh69eoaPXq0AgIC3J0ScEUSExOVmZnpMJaZmanExEQXZwRHfHx8NGXKFIexqVOnyseHP6fdLSwsLN/tNCwWi8LCwlycERypVauWU3EAAAAAAABn8UkeAMDrREdH6+OPP1Z0dLS7UwGuWM2aNZ2Kw3Vq1KjhsL1atWouzgSOJCYmyjAMhzHDMBhk4iG+++47p+IAAAAAAADOojAOuMjatWt1zz33aO3ate5OBQDgASIjIxUZGekwVrdu3XxjcC3DMPTyyy87jL388sv5FmThOsxE9g6FbQ/B9hEAAAAAAKC4URgHXCArK0szZszQwYMHNWPGDGVlZbk7JQCAB6hQoYLD9qCgIBdngvykpKRo69atDmNbt25VSkqKaxNCHjk5OU7F4Ro7d+50Kg4AAAAAAOAsCuOACyxYsEBHjx6VJB09elQJCQluzgj5YWa/d+A6oSRITU0tsOCamprq4ozgCLNcPd/nn3/uVByuUdjWA2xNAAAAAAAAihuFcaCYpaenKyEhwbbUqmEYSkhIUHp6upszw6WY2e8duE4oKcLDw9WwYUOHsWuuuUbh4eEuzgiO7N+/36k4it/IkSOdisM16tWr51QcAAAAAADAWR5XGF+zZo169OihWrVqyWKxaOnSpXbxzMxMPfbYYwoNDVVgYKAaNWqkWbNmuSdZoBCGYSg+Pj7fdvYl9SzM7PcOXCeUFIZhaM+ePQ5jaWlp3CM8RI0aNZyKo/j98ssvTsXhGhaLxak4AAAAAACAszyuMH7q1Ck1a9ZMb731lsP46NGjtWLFCi1YsEB//PGHRo0apccee0zLli1zcaZA4VJTU5WUlJRnb8ucnBwlJSWxTK4HYWa/d+A6oSRJTEzU6dOnHcZOnz6txMREF2cERw4ePOhUHMXv0KFDTsXhGocPH3YqDgAAAAAA4CyPK4x369ZNU6ZMUe/evR3G161bp/vuu08dO3ZURESEHnzwQTVr1oyZIPBI4eHhioqKkq+vr127r6+vWrduzTK5HoKZ/d4hv+thtVq5Th6ksJVfDMPQhAkTVLNmTQUGBqpz587666+/3JOsm1WvXt2pOFwjKirKqTiKX5MmTZyKwzV+/fVXp+IAAAAAAADO8rjCeGFuvPFGLVu2THv37pVhGFq9erV27typLl26ODw+OztbGRkZdg/AVSwWi2JjY/NtZ8lIz8DMfu+Qe52sVqtdu9Vq5Tp5kMJWfpk2bZpef/11zZo1Sxs2bFD58uXVtWvXUrlXPDORvcO8efOciqP4ffvtt07F4RqZmZlOxQEAAAAAAJzldYXxN954Q40aNVJoaKjKli2r2267TW+99Zbat2/v8Pi4uDiFhITYHnXq1HFxxijtQkNDFRMTYyuCWywWxcTEqHbt2m7ODLmY2e8dwsPD853117RpU66Thyho5RfDMPTaa6/pueee05133qmmTZvqgw8+0L59+/LMLC8N2rZtq4CAAIexwMBAtW3b1sUZAd5p27ZtTsXhGvv27XMqDgAAAAAA4CyvLIyvX79ey5Yt06ZNmzRjxgw9+uij+c4EGT9+vE6ePGl77Nmzx8UZA9KgQYNUuXJlSVKVKlUUExPj5oxwMWb2ew+uhXdLTk7WgQMH1LlzZ1tbSEiI2rRpU+B+2iV59ZdLV0DIdekKFnCfoUOHOhVH8XvsscecisM1/P39nYoDAAAAAAA4y6sK42fOnNEzzzyjV199VT169FDTpk312GOPqV+/fpo+fbrD7/H391dwcLDdA3C1gIAAjRkzRtWrV9fo0aPznSEI9wkNDVW/fv3s2vr168fMfg+SmpqqrVu3Ooxt3bqVpdS9wIEDByTl3Tu7evXqtpgjJXX1l3Xr1uns2bMOY2fPntW6detcnBEcKezfFv7tcb81a9Y4FYdrFDbghwFBAAAAAACguHlVYfzcuXM6d+6cfHzs0/b19c13xhXgKaKjo/Xxxx8rOjra3akAXil3yftLZ437+Piw5H0JV1JXfzEMw6k4XKOwZf5L4zYAnubXX391Kg4AAAAAAIDSweMK45mZmdqyZYu2bNki6cKyq1u2bFFaWpqCg4PVoUMHPfnkk/rhhx+UnJysefPm6YMPPnC4lykAFFV6ero+/PBDu7YPP/xQ6enpbsoIl8pvyXvDMFjy3kvUqFFDknTw4EG79oMHD9pijpTU1V8ojHuHkSNHOhVH8WvXrp1TcbjG+fPnnYoDAAAAAAA4y/TC+NmzZ5Wenq60tDS7R1Ft3LhR119/va6//npJ0ujRo3X99ddrwoQJki4UqqKiohQTE6NGjRrp5Zdf1ksvvaSHH37Y7JcCoJQwDEPx8fF5ilBWq9VhOzyLxWLhGnmJyMhI1ahRQ999952tLSMjQxs2bFDbtm3dmJl7UCTyDomJiU7FUfy++OILp+IAAAAAAAAoHcqYdaK//vpLw4YNy7MfpmEYslgsRd4zrmPHjgUWOGrUqKG5c+c6lSsAXCw1NVVJSUl52g3DUFJSklJTUxUREeH6xGAndwCDj4+P3T3FYrEoPj5e06dPZ9a4B8jMzNSuXbtsz3NXfqlUqZLCwsI0atQoTZkyRQ0aNFBkZKSef/551apVS7169XJf0m6yevXqQuM333yzi7JBfj755JNC4x07dnRNMnAoKipKX375ZYFxAAAAAAAAwLTC+JAhQ1SmTBl9+eWXqlmzJsUJAF4jLCxMwcHBysjIyBMLDg5WWFiYG7LCpfIbwJCTk8MABg+yceNGderUyfZ89OjRkqT77rtP8+bN01NPPaVTp07pwQcf1IkTJ9SuXTutWLFCAQEB7krZba677jr9+OOPBcbhfnv37nUqjuJXlEEmTz31lIuyAQAAAAAAgKcyrTC+ZcsWbdq0Sddcc41ZpwQAl0hLS3NYFJcuLPOclpZGwdUDhIeHKyoqSps2bZLVarW1+/r6qmXLlgoPD3djdshV2MovFotFkydP1uTJk12YlWcqbBYrs1w9Q2GDPRkM6n4VK1bUqVOnCowDAAAAAAAApu0x3qhRIx05csSs0wGAy+QWXC8tblgsFrVu3ZqCq4ewWCyKjY3NU3Q1DEOxsbEUp+B1Cvu3hX97gKLx9fV1Kg4AAAAAAIDSwbTC+CuvvKKnnnpKP/zwg44ePaqMjAy7BwB4qtyCq4+P/T+Jvr6+FFy9gGEYBc5QBjzV2rVrnYrDNRo3buxUHMWvRo0aTsUBAAAAAABQOphWGO/cubPWr1+vW265RdWqVdNVV12lq666ShUrVtRVV11lVjcAUCxCQ0MVExNjK4JbLBbFxMSodu3abs4MuQzDUHx8vMNYfHw8xXF4ncOHDzsVh2ukpaU5FUfx+/PPP52KAwAAAAAAoHQwbY/x1atXm3UqAHCLQYMG6bPPPtM///yjChUqKCYmxt0p4SKpqalKSkrK024YhpKSkpSamspe8PAqFSpUcCoO18jMzHQqjuJX2OpUrF4FAAAAAAAAycTCeIcOHcw6FQC4Dcume67w8HA1bNhQO3bsyBNr2LAh+zHD67z55puFxrt06eKibJCfs2fPOhUHAAAAAAAA4BlMK4znOn36tNLS0vJ8SNi0aVOzuwIAUy1YsED//POPJOmff/5RQkKChg8f7uaskMswDO3du9dhbO/evTIMg4EN8Cr+/v5OxeEaJ0+edCoOAAAAAAAAwDOYVhg/fPiwhg4dqq+//tphPCcnx6yuAMB06enpSkhIsO1TbRiGEhIS1LVrV4WGhro5O0hSYmJivksWZ2ZmKjExUdHR0S7OqmSxWq3atWuXDh06JKvVahdr3769m7Iqua666iodPHiwwDjcz8/PT+fOnSswDpRUhmEoKyvLZf2dOXPG6XMEBAQwUA4AAAAAADhkWmF81KhROnHihDZs2KCOHTvqs88+08GDBzVlyhTNmDHDrG4AwHSGYSg+Pj7f9unTp/MBqwdo27atgoODHe4VGxISorZt27ohq5Jj/fr1GjhwoFJTU20DRHJZLBYGuBWDtLQ0p+JwjYKK4kWJA94sKytLXbt2dVl/ZvS1cuVKBQYGmpANAAAAAAAoaXzMOtH333+vV199Va1atZKPj4/Cw8M1aNAgTZs2TXFxcWZ1AwCmS01NVVJSUp7CX05OjpKSkpSamuqmzHAxHx8fTZw40WFs0qRJ8vEx7ZZWKj388MNq1aqVfvvtNx07dkzHjx+3PY4dO+bu9EqkwgYbMBgBAAAAAAAAAMxj2ozxU6dOqVq1apIuLP15+PBhXX311WrSpIk2b95sVjcAYLrw8HBFRUVp8+bNdoUoX19ftWzZUuHh4W7MDhdr1aqVmjRpom3bttnamjZtqhYtWrgxq5Lhr7/+0ieffKL69eu7OxUApYA3LtEtlb5lugMCArRy5UrTzlfQjHCz+gkICDDlPAAAAAAAoOQxrTDesGFD7dixQxEREWrWrJneeecdRUREaNasWapZs6ZZ3QBea86cOVqwYIEGDRqk+++/393p4CIWi0WxsbEaPHiww/bS9AG4N3jppZfUq1cvWa1W+fj4aMqUKe5OqURo06aNdu3aRWHchbKzs52KA97MG5folkrfMt0Wi8XU17tmzRq1b9/eYTsAAAAAAEBxM60w/sQTT2j//v2SpBdeeEG33XabEhISVLZsWc2bN8+sbgCvdOLECS1YsEBWq1ULFizQ3XffrYoVK7o7LVwkNDRU/fr108KFC21t/fr1U+3atd2YFRypWLGiOnTooNWrV6tDhw78Lplk5MiRGjNmjA4cOKAmTZrIz8/PLt60aVM3ZQYAAAAAAAAAgPNMK4wPGjTI9nXLli2VmpqqP//8U2FhYapSpYpZ3QBe6dlnn5XVapUkWa1WPffcc3rzzTfdnBXgnbKysmxLqW/btk1ZWVksm2qCu+66S5I0bNgwW5vFYpFhGLJYLOx3DcBUZi7Rffr0afXu3Tvf+GeffaZy5cqZ0hf3G+etXLnSbgY/s8UBAAAAAICrmFYYz3X27FklJyerXr167PkKSNq4caPdfsiStHXrVm3cuFGtWrVyU1a4VHp6uj766CO7to8++kjdu3dXaGiom7KCIwsWLNCRI0ckSUeOHFFCQoKGDx/u5qy8X3JysrtTAFCKmLlEd2BgoG1bp0s1atRIlStXNqUfmM/M/csBAAAAAAAKY1ph/PTp0xo5cqTef/99SdLOnTtVt25djRw5UrVr19a4cePM6grwGlarVRMnTnQYmzhxopYtWyYfHx/XJoU8DMNQfHx8nhmx58+fV3x8vKZPn84+4x4iPT1dH3zwgV3bBx98oK5duzKAwUnh4eHuTgEwhWEYysrKcmmfZ86ccfocAQEB3Guc8O677zrcu3rWrFluyAYAAAAAAACeyLTC+Pjx4/Xf//5XP/zwg2677TZbe+fOnTVx4kQK4yiVEhMTlZGR4TCWkZGhxMRERUdHuzgrXCo1NVVJSUkOY0lJSUpNTVVERIRrk0IeuQMY8mtnAIM5fv/9d6Wlpens2bN27T179nRTRsDlycrKslum2RXM6G/lypWmzaAurcaNG6eXX37Z9nzChAluzAYAAAAAAACexrTC+NKlS/XRRx/phhtusCtMNG7cWH///bdZ3QBepW3btgoODnZYHA8JCVHbtm3dkBUuFRYWpnLlyun06dN5YuXKlVNYWJgbssKlGMBQvHbv3q3evXtr27Zttr3FJdnu6ewxfoGrZyMzExkouk6dOtkK42XKlFHnzp3dnBEAAAAAAAA8iWmF8cOHD6tatWp52k+dOsWHsSi1fHx8NHHiRI0ePTpPbNKkSSyj7iFSUlIcFsWlC9tEpKSkqG7dui7OCpcqbKl0llJ3zhNPPKHIyEh99913ioyM1C+//KKjR49qzJgxmj59urvT8xiuno3MTOTLFxAQYOq+xatXr7abhXypcePGqVOnTk73ExAQ4PQ58P+++uord6cAAAAAAAAAD2NaVa5Vq1Z2H0DlFsPnzJnDrFiUaq1atVLjxo3t2q677jq1aNHCTRnhUvv373cqDtdYtmyZU3EULDExUZMnT1aVKlXk4+MjHx8ftWvXTnFxcXr88cfdnR5QZBaLRYGBgaY9br/99gL7u/32203ph4GkAAAAAAAAQPEybcb41KlT1a1bN/3+++86f/68/v3vf+v333/XunXr9OOPP5rVDeCVGjdurO3bt9s9h+do27atgoKClJmZmSdWoUIFBvd4iGbNmjkVR8FycnJUoUIFSVKVKlW0b98+NWzYUOHh4dqxY4ebs/McZs5G/uabbwqcjT927FjdeuutTvfDTGTnrVmzRu3bt3fYDgAAAAAAAMA7mDZjvF27dtqyZYvOnz+vJk2aaNWqVapWrZoSExPVsmVLs7oBvE56erqWLFli17ZkyRKlp6e7KSNcysfHR5MnT3YYe/HFF1ny3kMUdh24Ts657rrr9N///leS1KZNG02bNk1r167V5MmT2UrgImbORu7Zs2eBffXs2ZOZyB7k0veRf3MAAAAAAAAA72LqJ3r16tXTu+++q19++UW///67FixYoCZNmpjZBeBVDMNQfHx8vu2GYbghKzhSo0YNh+3VqlVzcSbIT0RERL77iNepU0cRERGuTaiEee6552S1WiVJkydPVnJysm666SYtX75cr7/+upuzK7nym3HMTGTPs2LFCrvnP/zwg3sSAQAAAAAAAHBFTFtKPdehQ4d06NAh24fruZo2bWp2V4DHS01NVVJSUp72nJwcJSUlKTU1lWKeB8hvAIMkxcfHa/r06cy49ABWq1X79u1zGNu7d6+sVqt8fX1dnFXJ0bVrV9vX9evX159//qljx47pqquu4ucfuIRZy+kDAAAAAAAAcB3TZoxv2rRJ1113nWrWrKmmTZuqefPmtsf1119vVjdwYO3atbrnnnu0du1ad6eCS4SHhysqKirPcqu+vr5q3bq1wsPD3ZQZLpbfAAZJtgEMcL9ly5blGXSVy2q1atmyZS7OqGTatWuXVq5cqTNnzqhSpUruTqdUuLTIymxxAAAAAAAAADCfaYXxYcOG6eqrr9a6deu0e/duJScn2x67d+82qxtcIisrSzNmzNDBgwc1Y8YMZWVluTslXMRisSg2NjbPkulWq1WxsbHMwvQQYWFhCg4OdhgLDg5WWFiYizOCI4WtPMLKJM45evSobrnlFl199dW6/fbbtX//fknS8OHDNWbMGDdnV3owExkAAAAAAAAAiodphfHdu3dr2rRpatOmjSIiIhQeHm73QPFYsGCBjh49KulCUSMhIcHNGcGRSwvjhmGwv7gHSUtLU0ZGhsNYRkaG0tLSXJwRHImMjJSfn5/DmJ+fnyIjI12cUckSGxsrPz8/paWlqVy5crb2fv365dlbGQAAAAAAAAAAb2NaYfyWW27Rf//7X6fPs2bNGvXo0UO1atWSxWLR0qVL8xzzxx9/qGfPngoJCVH58uUVFRVVKgtX6enpSkhIsBVYDcNQQkKC0tPT3ZwZchmGobi4OIexuLg4iuMeIiwsTEFBQQ5jQUFBzBj3ECkpKTp37pzD2Llz55SSkuLahEqYVatW6ZVXXlFoaKhde4MGDdhOAAAAAAAAAADg9cqYdaI5c+bovvvu02+//abrrrsuz6y+nj17Fuk8p06dUrNmzTRs2DD16dMnT/zvv/9Wu3btNHz4cE2aNEnBwcHavn27AgICTHkd3sIwDMXHx+fbPn36dJbp9gApKSnatm2bw9i2bduUkpLCLFcPkJqaqszMTIexzMxMpaamcp08QO7S3gXF69at66JsSp5Tp07ZzRTPdezYMfn7+7shIwAAAAAAAAAAzGNaYTwxMVFr167V119/nSdmsViUk5NTpPN069ZN3bp1yzf+7LPP6vbbb9e0adNsbfXq1bv8hL1camqqkpKS8rTn5OQoKSlJqampioiIcH1iAFBMbrjhBvn4+MhqteaJ+fj46IYbbnBDViXHTTfdpA8++EAvvviipAv3bqvVqmnTpqlTp05uzg4AAAAAAAAAAOeYtpT6yJEjNWjQIO3fv19Wq9XuUdSieGGsVqu++uorXX311eratauqVaumNm3aOFxuPVd2drYyMjLsHiVBeHi4oqKi5ONjfwl9fHzUunVr9nX3EBEREWratKnDWLNmzRi8AFyGtLQ0h0Vx6cL9oTRuqWGmadOmafbs2erWrZvOnj2rp556Stddd53WrFmjV155xdS+/vnnH40aNUrh4eEKDAzUjTfe6HCwFwAAAAAAAAAAZjGtMH706FHFxsaqevXqZp0yj0OHDikzM1Mvv/yybrvtNq1atUq9e/dWnz599OOPPzr8nri4OIWEhNgederUKbb8XMlisSg2NjbPHtWGYSg2NpZl1D2ExWLRuHHjHMbGjRvHdfIQERERatKkicNY06ZNGcDgIQobZGXWIKzS6rrrrtPOnTvVrl073XnnnTp16pT69OmjX3/91fSVWe6//3598803mj9/vrZt26YuXbqoc+fO2rt3r6n9AAAAAAAAAACQy7Sl1Pv06aPVq1cX67LmuTMF77zzTsXGxkqSmjdvrnXr1mnWrFnq0KFDnu8ZP368Ro8ebXuekZFRYorj+bm0WA73s1gsdtfl0udwL4vFoqFDh9r9W5Fr6NChDGDwEFu3bi00Xr9+fRdlUzKFhITo2WefLdY+zpw5oyVLlujzzz9X+/btJUkTJ07UF198oZkzZ2rKlCnF2j8AlFSGYSgrK8vdaRTq4hy9Id9cAQEB/E0IAAAAAICXM60wfvXVV2v8+PH6+eef1aRJE/n5+dnFH3/8caf7qFKlisqUKaNGjRrZtV977bX6+eefHX6Pv7+//P39ne7b0xiGofj4eIex+Ph4TZ8+nQ9uPEDudXJUGOc6eQ7DMLRo0SKHsYULF6pFixZcJw/QrFkzp+IoXFZWlrZu3apDhw7lWba+Z8+epvRx/vx55eTkKCAgwK49MDAw33t5dna2srOzbc9LyrYoAGCmrKwsde3a1d1pXJY777zT3SkU2cqVKxUYGOjuNAAAAAAAgBNMK4zPmTNHQUFB+vHHH/Msa26xWEwpjJctW1ZRUVHasWOHXfvOnTtL3Z7aqampDvdjNQxDSUlJSk1NZflnD5DfdbJarVwnD5LfdZLEdfIgPj4F7/5RWBwFW7Fihe69914dOXIkT8xisZi2VH2FChXUtm1bvfjii7r22mtVvXp1LVq0SImJifnO+I+Li9OkSZNM6R/A5WMmcvFiJjIAAAAAAIBrmFYYT05ONuU8mZmZ2rVrl915t2zZokqVKiksLExPPvmk+vXrp/bt26tTp05asWKFvvjiC/3www+m9O8twsLCFBwc7HDWXHBwsMLCwtyQFS4VHh6uqKgobd682a6o5Ovrq5YtW5a6AR2eKjw8XE2bNnW4VHfTpk25Th4iIiJCDRs2zDM4SpIaNmzI4AUnjRw5Uvfcc48mTJig6tWrF2tf8+fP17Bhw1S7dm35+vqqRYsWGjBggDZt2uTw+NK4LQrgSZiJXLyKYyby05LKmnpG8xiSzv3vaz9Jnjwk4KykV9ydBAAAAAAAMI1phfGiCg4O1pYtW1S3bl2H8Y0bN6pTp06257kfhN93332aN2+eevfurVmzZikuLk6PP/64GjZsqCVLlqhdu3Yuyd9TpKWl5buUbEZGhtLS0igSeQCLxaLY2FgNHjzYYTuzgzxHfjPLvGnGWWmQmZl5We0ouoMHD2r06NHFXhSXpHr16unHH3/UqVOnlJGRoZo1a6pfv375/m1QUrdFAYDiUlZSWQ8uOXvPv+hG4YcAAAAAAACv4fLC+MX7LDvSsWPHQo8ZNmyYhg0bZmZaXocZrt4jNDRU/fr108KFC21t/fr1U+3atd2YFS6WkpKinTt3Oozt3LlTKSkpioyMdHFWuNTu3bu1d+9eh7G9e/dq9+7dqlevnouzKjnuvvtu/fDDDy59D8uXL6/y5cvr+PHjWrlypaZNm+ayvuEZvGWJbsk7l+kujiW6c3rkuOH/IIrIkJS7QI+vPHsq8nnJ9wtfd2cBAAAAAABQqnjqx1oogvwGEBQ2sACud/78+QKfAyjcli1bCo1TGL9yb775pu655x799NNPatKkifz8/Ozijz/+uGl9rVy5UoZhqGHDhtq1a5eefPJJXXPNNRo6dKhpfVBwLV5mFVy9cYluyXuW6S6OJbpVRp79fxB+hR8CAAAAAACA0smTP9ZCAVJTU7Vt2zaHsW3btik1NZWl1D1Eenq6Fi9ebNf20Ucf6c4771RoaKibssLFIiIi8l2BoVmzZvwueQir1epUHAVbtGiRVq1apYCAAP3www92RU+LxWJqYfzkyZMaP3680tPTValSJd1111166aWX8hTjnUHBtXgVS8EVAAAAAAAAAIoRhXEvFRYWpuDgYIf7jAcHByssLMwNWeFShmEoLi7OYSwuLk5vvvkm+4x7AIvFonHjxmngwIF5YuPGjeMaeYiqVas6FUfBnn32WU2aNEnjxo2Tj49PsfbVt29f9e3bt1j7gPd5q/0J+ft67qo3hiGd/d/4m7I+kqfeGrJzLHp0TUV3pwEAAAAAAAB4HJcXxikwmSMtLc1hUVySMjIylJaWxixXD5CSklLgzH72rvZ8bE3gOQ4fPuxUHAU7e/as+vXrV+xFcXc41SJG8vHgsYCGIVn/t8WGTxnPrbhaz6v85oRiO72/r6EAD99y2TvmyHPfAgAAAAAAABxx+afEFJnMER4erqioKG3cuNHuPbVYLIqKilJ4eLgbs0Ouwn7e+X3wDMzs9w5HjhxxKo6C3Xffffroo4/0zDPPuDsV8/mUkXw9fePhsu5OAICXu/jvyrMXWtyVSolx9qKv+bsdAAAAAADv5/LC+Ndff63atWu7utsSx2KxKDY2VoMHD1ZOTo6t3cfHR7GxsRTxPERh14Hr5BmY2e8dGGhSvHJycjRt2jStXLlSTZs2zbPf96uvvuqmzAAARZGdnW37+hU35lFSZWdnq1y5cu5OAwAAAAAAOMG0wnhOTo7mzZun7777TocOHZLVarWLf//995Kkdu3amdVlqRcaGqpGjRrZFfQaN27MwAMPQiHPO1z679XlxuEae/fudSqOgm3btk3XX3+9JOm3336zizGIByjd7P5eOe++PEqUi95H/h4EAAAAAABwDdMK40888YTmzZun7t2767rrruNDdBdIT0/PU7z47bfflJ6ertDQUDdlhYtRGPcOW7duLTRer149F2WD/DRp0kQ//fRTgXFcudWrVxfpuPT0dNWqVatE7kUOwLGLZyL7fuHhG8F7IbNmIvv7+9u+flps0GCGs/r/2fcXv78AAAAAAMA7mVYY//DDD7V48WLdfvvtZp0SBTAMQ/Hx8fm2T58+ncEJHmDLli2Fxim4ul+PHj0c/j5dHIf7XbxtxJXEYY5GjRppy5Ytqlu3rrtTQQlw8QCxbH6FTXHx+8gAvNLl4r/9y0oqK/5fwHn//zvE/1sBAAAAAOD9TCuMly1bVvXr1zfrdChEamqqkpKS8rQbhqGkpCSlpqYqIiLC9YnBTtWqVZ2KwzX27NlTaJw9xt3vm2++KTQeExPjomxKL28ptNnlmXPOfYmUJBe9j2b9HFw8E/nRNVeZck78v+KYiZzTI8fE/4Moxc7//+x7ZiIDAAAAAAC4hmkfa40ZM0b//ve/9eabbzKa3gXCwsIUHBysjIyMPLHg4GCFhYW5IStcqrAl7Vny3jOwx7h3qF69unbv3l1gHMh1ccG1/K8L3ZhJyWRWwRXewe5v+zKiMG4y/t8JAAAAAADANUz7WOvnn3/W6tWr9fXXX6tx48by8/Ozi3/66admdQVJaWlpDovikpSRkaG0tDRmjHsA9hj3Dr/++muhcZa8d7+wsDAlJiYWGAfgXS6eKftW++PyZ/tqp2Xn/P/se2Yil15nJV28DLgnMSTlrj/hJ3n0gu9n3Z0AAAAAAAAwlWmF8YoVK6p3795mnQ6FYMa4dzhw4EChcQqu7seMcaDkubggeOr6gZKvXwFHo0hyztlm35tVcL14pqy/rxRAYdxUxTIT+bz5pzSNISl3j3VfeXbFtZjfx1eK9/QAAAAAAABeybTC+Ny5c806FYqAGePeoUaNGk7F4Rp///23U3G4xv79+52KwxzesuSvXZ6+fhTGTVYcPwfZORZ56gxXSTIM6ez/xkmV9ZE89VfhwvtYfHL3xQYAAAAAAAC8DTsEeqnw8HBFRUVp48aNdstxWywWRUVFKTw83I3ZIZePj49TcbhG3bp1nYrDNRo3bqw1a9YUGEfx88otIKyePMVVFyquuTn6lPHcimsxv4+PrqlYrOcHSrqAgACtXLnS3WkUKisrS3feeack6fPPP1dAQICbMyoab8kTAAAAAADkz9TC+CeffKLFixcrLS1NZ8/a78i2efNmM7sq9SwWi2JjYxUTE5MnFhsb6zUz+kq6sLAw+fj4OFyK28fHhyXvPcSJEyecisM1fH0LnqVYWBzm+P3331WrVi13p3FZym9OcHcKgFej4Fq8zMrTYrEoMDDQlHO5SkBAgNflDAAAAAAAvJdphfHXX39dzz77rIYMGaLPP/9cQ4cO1d9//62kpCQ9+uijZnWDIvDK2Xwl1Pr16/Pdn9pqtWr9+vWKjo52cVa41FVXXeVUHK7x119/ORVHXn369CnysZ9++qkkqU6dOsWVDkohbym4St5ZdKXg6l05AwAAAAAAoHiZVhh/++23NXv2bA0YMEDz5s3TU089pbp162rChAk6duyYWd3gfwzDUHx8vMNYfHy8pk+fzqxxD8Ae496hWrVqTsXhGrt373YqjrxCQkJsXxuGoc8++0whISFq1aqVJGnTpk06ceLEZRXQPQUF1+JVmguuEkVXAAAAAAAAwBuZVhhPS0vTjTfeKEkKDAzUP//8I0kaPHiwbrjhBr355ptmdQVJqampSkpKytNuGIaSkpKUmpqqiIgI1ycGO4UNTmDwgmfYsWNHofGOHTu6JhnkKzU11ak48po7d67t66efflp9+/bVrFmzbMvS5+TkaMSIEQoODnZXileMgisAAAAAAAAA4GI+Zp2oRo0atpnhYWFhWr9+vSQpOTmZpb2LQXh4uJo0aeIw1rRpU4WHh7s4IzhCYdw7sJS6d2Bmf/F67733NHbsWLu92n19fTV69Gi99957bswMAAAAAAAAAADnmVYYv/nmm7Vs2TJJ0tChQxUbG6tbb71V/fr1U+/evc3qBhfJzMx02J47Wx/uFx4errJlyzqMlS1blgEMHqJq1apOxeEaOTk5TsVRsPPnz+vPP//M0/7nn3/KarW6ISMAAAAAAAAAAMxj2lLqs2fPtn1w/uijj6py5cpat26devbsqYceesisbvA/ycnJSk5OLjBWt25dF2eFSyUnJ+vs2bMOY2fPnlVycrLq1avn4qxwqV9++aXQeKdOnVyUDfKzf/9+p+Io2NChQzV8+HD9/fffat26tSRpw4YNevnllzV06FA3ZwcAAAAAAAAAgHNMK4z7+PjIx+f/J6D3799f/fv3N+v0uMS+ffsKjVMYd7/vvvuu0DiFcfeLjIx0Kg7XCAgIUFZWVoFxXLnp06erRo0amjFjhm2QQc2aNfXkk09qzJgxbs4OAAAAAAAAAADnmLaUuiT99NNPGjRokNq2bau9e/dKkubPn6+ff/7ZzG6gC8UKZ+JwjYYNGzoVh2sYhuFUHCgJfHx89NRTT2nv3r06ceKETpw4ob179+qpp56y23ccAAAAAAAAAABvZFphfMmSJeratasCAwP166+/Kjs7W5J08uRJTZ061axu8D8U8rzDxasoXEkcrrF161an4nCNgmaLFyWOgt188806ceKEJCk4OFjBwcGSpIyMDN18881uzAwAAAAAAAAAAOeZVpWbMmWKZs2apXfffVd+fn629ujoaG3evNmsbvA/27ZtcyoO16hRo4ZTcbgGA028AwNNitcPP/ygs2fP5mnPysrSTz/95IaMAAAAAAAAAAAwj2l7jO/YsUPt27fP0x4SEmKbgQbz3HHHHYqPjy8wDvdbtWpVofEGDRq4KBvkp3nz5gVu+dC8eXPXJYN8+fr6ymq1FhjH5bt4RYTff/9dBw4csD3PycnRihUrVLt2bXekBgAAAAAAAACAaUwrjNeoUUO7du1SRESEXfvPP/+sunXrmtUN/mft2rWFxjt27OiaZJCvv//+26k4XOP8+fNOxeEa586dcyoOx5o3by6LxSKLxeJwyfTAwEC98cYbbsgMAAAAAAAAAADzmLbu7AMPPKAnnnhCGzZskMVi0b59+5SQkKCxY8fqkUceKfJ51qxZox49eqhWrVqyWCxaunRpvsc+/PDDslja7sWgAABfSUlEQVQseu2115x/AV5m2bJlTsXhGq1atXIqDtf45ZdfnIoD3iw5OVl///23DMPQL7/8ouTkZNtj7969ysjI0LBhw9ydJgAAAAAAAAAATjFtxvi4ceNktVp1yy236PTp02rfvr38/f01duxYjRw5ssjnOXXqlJo1a6Zhw4apT58++R732Wefaf369apVq5YZ6Xud1q1ba+PGjQXG4X7sMe4d6tatq82bNxcYB0qq8PBwSSpwmXoAAAAAAAAAALydaTPGLRaLnn32WR07dky//fab1q9fr8OHD+vFF1+8rPN069ZNU6ZMUe/evfM9Zu/evRo5cqQSEhLk5+fnbOpeqXr16k7F4RqrV692Kg7X+Omnn5yKAyXB+++/r6+++sr2/KmnnlLFihV14403KjU11Y2ZAQAAAAAAAADgPNMK47nKli2rRo0aqXXr1goKCjL79LJarRo8eLCefPJJNW7cuNDjs7OzlZGRYfcoCdi72jvceOONTsXhGidOnHAqDpQEU6dOVWBgoCQpMTFRb775pqZNm6YqVaooNjbWzdkBAAAAAAAAAOAc0wrjWVlZ+te//qXbb79drVq1UosWLeweZnnllVdUpkwZPf7440U6Pi4uTiEhIbZHnTp1TMvFnTp06OBUHK7xn//8x6k4XGPq1KlOxYGSYM+ePapfv74kaenSpbr77rv14IMPKi4uztRVE3JycvT8888rMjJSgYGBqlevnl588UUZhmFaH97s/fffd3cKKIJevXq5OwUAAAAAAAAAl8m0wvjw4cM1bdo0hYeH64477tCdd95p9zDDpk2b9O9//1vz5s2TxWIp0veMHz9eJ0+etD327NljSi7uVqZMwdvDFxaHazRt2tSpOFxj9uzZTsWBkiAoKEhHjx6VJK1atUq33nqrJCkgIEBnzpwxrZ9XXnlFM2fO1Jtvvqk//vhDr7zyiqZNm6Y33njDtD68zcmTJ21fL1y4kFUqPNQvv/xi+/rMmTNav369G7NBYUaPHu3uFAAAAAAAAOBhTKuefvnll1q+fLmio6PNOmUeP/30kw4dOqSwsDBbW05OjsaMGaPXXntNKSkpeb7H399f/v7+xZaTu1z8HlxJHK7xzz//OBWHa+Tk5DgVB0qCW2+9Vffff7+uv/567dy5U7fffrskafv27YqIiDCtn3Xr1unOO+9U9+7dJUkRERFatGiRXdHxUtnZ2crOzrY9LynbouR65pln7J6PHTtWc+bMcVM2yM/zzz9v9/ypp57SmjVr3JQNHLl4AOz27duVlpbG38Qe7u6779ZXX33l7jQAAAAAAEApYVphvHbt2qpQoYJZp3No8ODB6ty5s11b165dNXjwYA0dOrRY+/Y0a9euLTTevn17F2WD/OzevdupOFxj165dTsWBkuCtt97Sc889pz179mjJkiWqXLmypAurtQwYMMC0fm688UbNnj1bO3fu1NVXX63//ve/+vnnn/Xqq6/m+z1xcXGaNGmSaTk4wzAMZWVlmXa+zZs35/k3ZufOnVq7dq1pW9EEBAQUeaWdksDsayRJzz77rMP20aNH66WXXjKlD66T8x599FG75/fff78+//xzU/sobdepOGzZssX29T///KPNmzebuvUWAAAAAABAfkwrjM+YMUNPP/20Zs2apfDw8Cs+T2Zmpt0HxMnJydqyZYsqVaqksLAw2wf1ufz8/FSjRg01bNjwivv0Rn/88UehcQrj7le3bl0dPny4wDgAeIKKFSvqzTffzNN+aUF6xIgRmjx5sqpUqXJF/YwbN04ZGRm65ppr5Ovrq5ycHL300kuKiYnJ93vGjx9vtyxyRkaG6tSpc0X9OysrK0tdu3Yt9n7Gjx9v2rlWrlypwMBA087n6Vx1jSRp48aNpvXFdfKOPkrbdSqOAQxPP/203fNRo0Zp5cqVpp2fwQsAAAAAACA/phXGW7VqpaysLNWtW1flypWTn5+fXfzYsWNFOs/GjRvVqVMn2/PcD8Lvu+8+zZs3z6x0vV7FihWdisM1goODnYoDgKdZsGCBxo4de8WF8cWLFyshIUELFy5U48aNtWXLFo0aNUq1atXSfffd5/B7Suq2KADg6Vw10MTMPkrb4AUAAAAAAFB0phXGBwwYoL1792rq1KmqXr36FY/S79ixowzDKPLxjvYVLw0Ke92l9X3xNJs2bXIqDgCe5nLu0Y48+eSTGjdunPr37y9JatKkiVJTUxUXF5dvYdyTBAQEmDaz8b333tNHH32Ub7xfv34aNmyY0/0EBAQ4fQ5vYuY1ki6sirRq1ap84126dNGYMWOc7ofrdOXmzZunRYsW5RsfMGCAhgwZYkpfpe06AQAAAAAAlCSmFcbXrVunxMRENWvWzKxTogCFzYJgloRnOHnypFNxAChpTp8+LR8fH7s2X19fWa1WN2V0eSwWi2n32MJm3VepUoX7+RUw8xpJUps2bQosjLdp04brdAXMvE6NGjUqNM41ujJmDmB49tlntXHjxnzjrVq10ksvveR0PwxeAAAAAAAA+TGtMH7NNdfozJkzZp0Ohbh4H/YricM1GjdurK1btxYYB0qy4tibtCBm3IfYm7R49ejRQy+99JLCwsLUuHFj/frrr3r11VdNmRntbS4dIHC5cbjGmjVrCo3feuutLsoGjhQ2sMZbBt54IjMHMHTr1q3Awni3bt0YwAAAAAAAAIqVaYXxl19+WWPGjNFLL72kJk2a5NljnL2UzXX8+HGn4nCN7du3OxUHvJ2r9ibNZUZf7E1avN544w09//zzGjFihA4dOqRatWrpoYce0oQJE9ydmssVNgCDARqe4brrrtOPP/5YYBzutXr16kLjnTp1clE2yM/u3budigMAAAAAADjLtML4bbfdJkm65ZZb7NoNw5DFYlFOTo5ZXUFSy5YtlZqaWmAc7lfYzz2/FwBKmwoVKui1117Ta6+95u5U3K5p06ZOxeEavr6+TsVR/MaPH68ffvihwDjczzAMp+IAAAAAAADOMq0wXthMDZiroJlLufFRo0a5JhnADVii2zuYuTfpP//8o7vvvjvf+CeffKIKFSo43Q97kxZs0KBBrAJjkqLcy6+++moXZYP8NGvWzKk4it+KFSsKjffu3dtF2SA/RdkLHgAAAAAAoDiZVhjv0KFDkY4bMWKEJk+erCpVqpjVdamUkZHhVBzwdizR7R3M3Js0MDBQNWrU0IEDB/LEatWqpWrVqpnST2n2008/6Z133tHff/+tTz75RLVr19b8+fMVGRmpdu3aSZJmzpzp5ixLjnvvvVfz588vMA73O3jwYKHxBg0auCgbOFK5cmWn4nCN2rVrOxUHAAAAAABwlo+rO1ywYAFFWxOcO3fOqTgAeKPFixc7bP/www9dnEnJs2TJEnXt2lWBgYH69ddflZ2dLUk6efKkpk6d6ubsSqavvvrKqThcg21RPF+7du3k5+fnMObn52cb2AP32r9/v1NxAAAAAAAAZ5k2Y7yo2DvOHLVq1dK+ffsKjAMlmZlLdB8/flz9+/fPN/7hhx/qqquucroflug2xyOPPGI3a/mJJ55wYzYlx5QpUzRr1izde++9dgMNoqOjNWXKFDdmVnJRcPUOR44ccSoO1/Dz83M4MDS/gjlcr3r16k7FAQAAAAAAnOXywjjMcfr0aafigLcze4nuoKAgZWZm5okFBQUx0MTD9OrVy64wftddd7kxm5Jjx44dat++fZ72kJAQnThxwvUJlQLsXe0dqlat6lQcxS8xMTHfv31Pnz6txMRERUdHuzgrXGrbtm2FxtmWAAAAAAAAFCeXL6UOcxRWpKCIAVye5cuXX1Y7PINZqwZAqlGjhnbt2pWn/eeff1bdunXdkFHJ9+OPPzoVh2sUVlCl4Op+N9xwg3x9fR3GypQpoxtuuMHFGcGRxo0bOxUHAAAAAABwFoVxL1WmTMGT/QuLA8jr0uXUBw8e7KZMANd74IEH9MQTT2jDhg2yWCzat2+fEhISNHbsWD3yyCPuTq9Euvnmm52KwzX27NnjVBzFb8+ePfluPXD+/HmukYf44osvnIoDAAAAAAA4i+qplzp//rxTceTPMAxlZWW5rL8zZ844fY6AgABZLBYTsindhg4dare38gMPPODGbADXGjdunKxWq2655RadPn1a7du3l7+/v8aOHauRI0e6O70SKTIyUmXLltXZs2fzxPz9/RUZGemGrHCpsLAwBQcHKyMjI08sODhYYWFhbsgKFwsNDXUqDtfo2bOnli1bVmAcAAAAAACgOLm8MD5o0CAFBwe7ulugyLKystS1a1eX9WdGXytXrjRtv21cwBLdKG0sFoueffZZPfnkk9q1a5cyMzPVqFEjBQUFuTu1EistLc1hUVySsrOzlZaWpoiICNcmhTzS0tIcFsUlKSMjg+vkAYoyE7l3794uygb5OXz4cKHxq6++2kXZAAAAAACA0sjUpdR/+uknDRo0SG3bttXevXslSfPnz9fPP/9sO2bmzJmqUqWKmd0CAAAnnTx5UseOHVPZsmXVqFEjtW7dWkFBQTp27Fi+RUE4Jzw8XE2aNHEYa9q0qcLDw12cERwJCwtTuXLlHMbKlSvHjHEP0LNnzwL3GGcmsmdo27atAgICHMYCAwPVtm1bF2cEAAAAAABKG9NmjC9ZskSDBw9WTEyMfv31V2VnZ0u68EH71KlTtXz5crO6AopVQECAabOFd+3apUcffTTf+FtvvaX69es73U9+HzICQFH1799fPXr00IgRI+zaFy9erGXLlnEfLybHjx932H7s2DEXZ4L8pKSk6PTp0w5jp0+fVkpKiurWrevirHAxX19f3X///XrnnXfyxB588MF8i+bwHIZhuDsFAAAAAABQCpg2Y3zKlCmaNWuW3n33Xfn5+dnao6OjtXnzZrO6AYqdxWJRYGCgKY/8ZgLmatKkiSn9sL84AGdt2LBBnTp1ytPesWNHbdiwwQ0ZlXzJyclKT093GEtPT1dycrKLM4Ij+/fvdyqO4mcYRr7/v5GUlETR1UOsW7dOWVlZDmNZWVlat26dizMCAAAAAACljWmF8R07dqh9+/Z52kNCQnTixAmzugG8zpo1ay6rHQDcITs7W+fPn8/Tfu7cOZ05c8YNGZV8W7ZscSoO12jTpo1TcRS/1NRUJSUlOYwlJSUpNTXVxRnBkVq1ajkVBwAAAAAAcJZphfEaNWpo165dedp//vlnlpf8H8MwdObMGVMeRWFWX8yycV69evXsnjdo0MBNmQCAY61bt9bs2bPztM+aNUstW7Z0Q0YlX2H7HrMvsmcobMUEVlRwv/DwcEVFRcnHx/5/bXx9fdW6dWuFh4e7KTNcrLAVjlgBCQAAAAAAFDfT9hh/4IEH9MQTT+i9996TxWLRvn37lJiYqLFjx+r55583qxuvlpWVpa5du7qsP7P6WrlypQIDA005V2n19ttv212P//znP27MBgDymjJlijp37qz//ve/uuWWWyRJ3333nZKSkrRq1So3Z1cy7dmzp9B4ZGSki7JBfmrWrOlUHMXPYrEoNjZWMTExdu2GYSg2NpaCq4eIiIhQ06ZNtXXr1jyxZs2aKSIiwvVJAQAAAACAUsW0GePjxo3TwIEDdcsttygzM1Pt27fX/fffr4ceekgjR440qxvA661cudLdKQBAHtHR0UpMTFSdOnW0ePFiffHFF6pfv762bt2qm266yd3pAW4TGRmphg0bOoxdc801DF7wYIZhsPKRB7FYLBoyZIjD2JAhQxjAAAAAAAAAip1pM8YtFoueffZZPfnkk9q1a5cyMzPVqFEjBQUFmdWF1wsICDCtKJqZmam77ror3/iSJUtMe+8DAgJMOQ8AwLM1b95cCQkJ7k6j1AgPD1dQUJAyMzPzxIKCglj+2UNYLBa98MILGjhwYJ7YCy+8QDHPAxiGofj4eFksFrtCuMViUXx8vKZPn8518gCGYWjRokUOYwsXLlSLFi24TgAAAAAAoFiZVhg/efKkcnJyVKlSJTVq1MjWfuzYMZUpU0bBwcFmdeW1LBaLaUuSBwYGKjQ0VOnp6Xli4eHhqlq1qin9AABKD6vVql27dunQoUOyWq12sfbt27spq5IrLS3NYVFcujAALi0tjaWFPRyzkT1DamqqkpKS8rRbrVYlJSUpNTWV3yUPkN91ksR1AgAAAAAALmHaUur9+/fXhx9+mKd98eLF6t+/v1nd4CILFy502D5//nwXZwIA8Hbr169X/fr1de2116p9+/bq2LGj7dGpUyd3p1cihYeHKyoqymGsdevWzBj3EIZh6OWXX3YYe/nllymOe4Dc3yVfX1+7dl9fX36XPEhYWFi+g6WDg4MVFhbm4owAAAAAAEBpY1phfMOGDQ4/OO/YsaM2bNhgVje4xOOPP273fOzYsW7KBADgzR5++GG1atVKv/32m44dO6bjx4/bHseOHXN3eiWSxWJRbGysfHzs/xzz9fVVbGwsSwp7iJSUFG3dutVhbOvWrUpJSXFtQsgj93cpv3Z+lzxDWlqaMjIyHMYyMjKUlpbm4owAAAAAAEBpY1phPDs7W+fPn8/Tfu7cOZ05c8asbnCJ7t272762WCzq2bOnG7MBAHirv/76S1OnTtW1116rihUrKiQkxO6B4hEaGqpBgwbZtQ0aNEi1a9d2U0aAdwoNDVVMTIytCG6xWBQTE8PvkgfJndl/6UAFi8XCzH4AAAAAAOASphXGW7durdmzZ+dpnzVrllq2bGlWNyjAihUr3J0CAMBLtWnTRrt27XJ3GqXSoEGDVKFCBUkXlhOOiYlxc0a4WEREhJo0aeIw1rRpU/ZE9iCDBg1S5cqVJUlVqlThd8nD5DeD38fHh5n9AAAAAADAJcqYdaIpU6aoc+fO+u9//6tbbrlFkvTdd98pKSlJq1atMqsbAABQDEaOHKkxY8bowIEDatKkifz8/OziTZs2dVNmpUPuqjvnzp1zcya4lMVi0fjx4xUTE2O3n3huO8U8zxEQEKAxY8botdde06hRoxQQEODulHCJ3FUyPvjgA1sbq2QAAAAAAABXMa0wHh0drcTERP3rX//S4sWLFRgYqKZNm+o///mPGjRoYFY3AACgGNx1112SpGHDhtnaLBaLDMOQxWJRTk6Ou1Ir8ebNm2fbdubMmTN6//339dBDD7k5K1wsNDRU99xzjxYvXmxr69u3L8U8DxQdHa3o6Gh3p4ECDBo0SMuXL9eRI0dUtWpVZvYDAAAAAACXMW0pdUlq3ry5EhIStH37dm3cuFHvvffeZRfF16xZox49eqhWrVqyWCxaunSpLXbu3Dk9/fTTatKkicqXL69atWrp3nvv1b59+8x8GQAAlDrJycl5Hrt377b9F8UjPT1dixYtsmtbuHCh0tPT3ZQR8lOmTJkCnwMomtyZ/dWrV9fo0aOZ2Q8AAAAAAFzG1E/0rFardu3apUOHDslqtdrF2rdvX6RznDp1Ss2aNdOwYcPUp08fu9jp06e1efNmPf/882rWrJmOHz+uJ554Qj179tTGjRtNex0AAJQ24eHh7k6h1DEMQ3FxcXbLc1/c/uabb7JMt4dIT0/XRx99ZNf20UcfqXv37goNDXVTVnBk7dq1tqXUmTnuuZjZDwAAAAAA3MG0wvj69es1cOBApaam5vmA93KWYO3WrZu6devmMBYSEqJvvvnGru3NN99U69atlZaWprCwsCtLHgCAUmjZsmXq1q2b/Pz8tGzZsgKP7dmzp4uyKj1SUlK0bds2h7Ft27YpJSVFkZGRLs4KlzIMQ/Hx8fm2T58+nQEMHiIrK0szZszQkSNHNGPGDLVs2ZLZyAAAAAAAALAxrTD+8MMPq1WrVvrqq69Us2ZNl31AePLkSVksFlWsWNFhPDs7W9nZ2bbnGRkZLskLAABP16tXLx04cEDVqlVTr1698j2OPcZRmqWmpiopKSlPe05OjpKSkpSamqqIiAjXJ4Y8FixYoKNHj0qSjh49qoSEBA0fPtzNWQEAAAAAAMBTmFYY/+uvv/TJJ5+ofv36Zp2yUFlZWXr66ac1YMAABQcHOzwmLi5OkyZNcllOAOCtDMNQVlaWu9Mo1MU5ekO+0oX9VD1xRunF255cugVKcYmIiFBqamqe9hEjRuitt95ySQ6eIjw8XEFBQcrMzMwTCwoKYnl7DxEeHq6oqCht3rzZboCIr6+vWrZsyXXyEOnp6UpISLCtXGUYhhISEtS1a1eWuwcAAAAAAIAkEwvjbdq00a5du1xWGD937pz69u0rwzA0c+bMfI8bP368Ro8ebXuekZGhOnXquCJFAPAqWVlZ6tq1q7vTuCx33nmnu1MokpUrVyowMNDdaVy2EydO5Lsiy5VKSkqyKy7+9ttvuvXWW3XPPfeY2o83SEtLc1gUl6TMzEylpaUxE9kDWCwWxcbGavDgwQ7bPXHQS2nDcvcAAAAAAAAoCtMK4yNHjtSYMWN04MABNWnSRH5+fnbxpk2bmtWVrSiempqq77//Pt/Z4pLk7+8vf39/0/oGcPmYiVx8PHUmMrzPK6+8ooiICPXr10+SdM8992jJkiWqWbOmli9frmbNmpnST9WqVe2ev/zyy6pXr546dOhgyvm9Se5MZEfLdLdu3ZqZyB4kNDRUMTExmj9/vgzDkMViUUxMjGrXru3u1CCWuwcAAAAAAEDRmFYYv+uuuyRJw4YNs7VZLBbbh4dm7U2aWxT/66+/tHr1alWuXNmU8wIoPsxELj7FNRP5aUllTT+rOQxJ5/73tZ8kTx0WcFbSK+5O4jLMmjVLCQkJkqRvvvlG3377rVasWKHFixfrySef1KpVq0zv8+zZs1qwYIFGjx5d4ACP7OxsZWdn255nZGSYnos7WCwWDRgwwGFBb8CAAQx68TCDBg3S8uXLdeTIEVWpUkUxMTHuTgn/w3L3AAAAAAAAKArTCuPJycmmnCczM1O7du2yO++WLVtUqVIl1axZU3fffbc2b96sL7/8Ujk5OTpw4IAkqVKlSipb1lPLOADgXcpKKuuxJWfJO9YBMdydwGU5cOCAbauRL7/8Un379lWXLl0UERGhNm3aFEufS5cu1YkTJzRkyJACj4uLi9OkSZOKJQd3MgxDixYtsg0kzGWxWLRw4UK1aNGC4rgHCQgI0JgxY/Taa69p1KhRCggIcHdK+J/cZe0HDRqUJ8Zy9wAAAAAAAMhlWmHcrJkYGzduVKdOnWzPc/cHv++++zRx4kQtW7ZMktS8eXO771u9erU6duxoSg4Aik9OjxwT/+UxmSEpd6KZrzx3KvJ5yfcLX3dngRLmqquu0p49e1SnTh2tWLFCU6ZMkXSheGvWqi+X+s9//qNu3bqpVq1aBR43fvx4298D0oUZ47lFfG+W3/LPhmGw/LOHio6OVnR0tLvTgAOhoaFq3Lixtm3bZmtr3Lgxy90DAAAAAADAxqny1LJly9StWzf5+fnZCtb56dmzZ5HO2bFjR7tZU5cqKIbSh72ri1ex7F9dRp5bGJcurM0NlEJ9+vTRwIED1aBBAx09elTdunWTJP3666+qX7++6f2lpqbq22+/1aefflrosf7+/vL39451Ai5H7vLPGzduzDNjPCoqiuWfgcuQnp6u33//3a7t999/V3p6ukJDQ92UFQAAAAAAADyJU+WpXr166cCBA6pWrZp69eqV73Fm7jEOXIy9q4tXce1fDcDzxMfHKyIiQnv27NG0adMUFBQkSdq/f79GjBhhen9z585VtWrV1L17d9PP7S3y22PcMAz2GAcug2EYio+Pz7d9+vTp/D4BAAAAAABAPs58s9VqVbVq1Wxf5/egKA4AgGfz8/PT2LFj9e9//1vXX3+9rT02Nlb333+/7Xn37t21f/9+p/qyWq2aO3eu7rvvPpUp48lLSBSvi/cYv1juHuOskuN51q5dq3vuuUdr1651dyq4SO62BJf+P0dOTo5tWwIAAAAAAACgWD+NPnHihCpWrFicXRQbluguXsWxRPdb7U/I39cziwiGIZ21Xvi6rI/kyZOWsnMsenRNRXenAcBDrVmzRmfOnHHqHN9++63S0tI0bNgwk7LyTuwx7l2ysrI0Y8YMHTlyRDNmzFDLli0VEBDg7rSg/9+WYPPmzXbFcV9fX7Vs2ZJtCQAAAAAAACDJxML4K6+8ooiICPXr10+SdM8992jJkiWqWbOmli9frmbNmpnVlUuwRHfxKo4luv19DQX4mnpKU3nPguSeObgAQMnRpUsXZkOLYp63WbBggY4ePSpJOnr0qBISEjR8+HA3ZwXpwioLsbGxGjx4sMN2llEHAAAAAACA5ORS6hebNWuW6tSpI0n65ptv9O2332rFihXq1q2bnnzySbO6AQAAKBFyi3b5tVPM8xzp6elKSEiwDegwDEMJCQlKT093c2bIFRoaqpiYGNvvjcViUUxMjGrXru3mzAAAAAAAAOApTJsxfuDAAVth/Msvv1Tfvn3VpUsXRUREqE2bNmZ14xanWsRIPh66B6phSNbzF772KePZa3Rbz6v85gR3ZwEAgMfILebNnz9fhmFQzPNAhmEoPj4+zyoHVqtV8fHxmj59OoMYPMSgQYO0fPlyHTlyRFWqVFFMTIy7UwIAAAAAAIAHMW3G+FVXXaU9e/ZIklasWKHOnTtLuvBh4sXLg3olnzKSr59nPsqUlcqWu/AoU9b9+RT08NTBBQAAuNGgQYNUoUIFSVJwcDDFPA+Tuxe81Wq1a7darba94OEZAgICNGbMGFWvXl2jR49mD3gAAAAAAADYMa1S2adPHw0cOFANGjTQ0aNH1a1bN0nSr7/+qvr165vVDQCgmFw8G/LshRZ3pVIinL3oa/bTRmFyf0YuLb7C/cLDw9WkSRNt27YtT6xp06bsBe9hoqOjFR0d7e40AAAAAAAA4IFMK4zHx8crIiJCe/bs0bRp0xQUFCRJ2r9/v0aMGGFWNwCAYpKdnW37+hU35lESZWdnq1y5cu5OwxTPPPOMKlWq5O40SpQFCxYoMzNTkpSZmamEhAQNHz7czVnhYiyVDgAAAAAAAHg/0wrjfn5+Gjt2bJ722NhYu+fdu3fXnDlzVLNmTbO6BuDh7GbLnndfHiXGRe8hM5Fhlvfff19VqlRR9+7dJUlPPfWUZs+erUaNGmnRokW2WbHjx493Z5olTnp6uhISEmy/y4ZhKCEhQV27dlVoaKibs4N0YSn1rVu3Ooxt3bpVqampioiIcG1SAAAAAAAAAC6byzd9XrNmjc6cOePqbgG40cUzkX2/8HVjJiWPmTOR/f39bV8/LamsKWctvc7q/2feX/zeeqqpU6dq5syZkqTExES99dZbio+P15dffqnY2Fh9+umnbs6w5DEMQ/Hx8fm2T58+nZnKHiA8PFxRUVHatGmT3VL3vr6+atmyJUupAwAAAAAAAF7C5YVxwEwXz5bNznFjIiXIxe8js5FLl4sLcGUllRUFOef8/++PNxQ39+zZo/r160uSli5dqrvuuksPPvigoqOj1bFjR/cmV0KlpqYqKSkpT3tOTo6SkpKYiewhLBaLYmNjNXjwYIft3vD7DQAAAAAAAIDCOLzcxTORH11zlRszKZnMmo188WzZnB45/MvjrPP/P/PeG2YiwzsEBQXp6NGjCgsL06pVqzR69GhJUkBAACu9FJPcmcibN29WTs7/j0piJrLnCQ0NVaNGjbRt2zZbW6NGjVS7dm03ZgUAAAAAAADgclCeyofdTNmcc+5LpCS56H1kJnLpYjebroz4l8dEzFSEWW699Vbdf//9uv7667Vz507dfvvtkqTt27cza7mYMBPZe6Snp2v79u12bdu3b1d6ejp7wQMAAAAAAABegvJUPi6eiVz+14VuzKRkKo6ZyG+1Py5/tq92WnbO/8++ZzYyUHq89dZbeu6557Rnzx4tWbJElStXliRt2rRJAwYMcHN2JVdoaKhiYmI0f/58GYYhi8WimJgYZiJ7kNw93x0NVGAveAAAAAAAAMB7UBiHV7v4g2h/XymAwripiuWD/vPmn9I0hqTc1Yx9JY/dYtsF7+FZSRfvke1JDEm560/4yXMv01l3J3CZKlasqDfffDNP+6RJk9yQTekyaNAgLV++XEeOHFGVKlUUExPj7pRwEfaCB8y3du1avfbaaxo1apSio6PdnQ4AAAAAACglXF4Yf+aZZ1SpUiVXd3vZLp4pe+r6gZKvnxuzKSFyztlm3zMTufTK3Rsbnu0VdycAl4uIiNCwYcM0dOhQ1alTx93plCoBAQEaM2aMrUgUEBDg7pRwEfaCB8yVlZWlGTNm6MiRI5oxY4ZatmzJv3sAAAAAAMAlTCuMv//++6pSpYq6d+8uSXrqqac0e/ZsNWrUSIsWLbJ9aDh+/HizuixWdjNlff0ojJusOGYiZ+dY5LEzXA3prPXC12V9JE9ecfXC+wigtBk1apTmzZunyZMnq1OnTho+fLh69+7NQCYXiY6OZtakh2IveMBcCxYs0NGjRyVJR48eVUJCgoYPH+7mrAAAAAAAQGlgMQzDlEpiw4YNNXPmTN18881KTExU586dFR8fry+//FJlypTRp59+akY3TsvIyFBISIhOnjyp4ODgfI87c+aMunbtKkk61SJG8vHQVecNQ7L+b11lnzKeXXG1nlf5zQmSpJUrVyowMNDpU158nWA+s66TYRjKysoyIaPilZWVpTvvvFOS9Pnnn3vF7KWAgADTijJcp+JTlOtU1PtTcdu8ebPmzZunRYsWKScnRwMHDtSwYcPUokULt+V0KU95r1C6zJkzx24v+HvvvZdiHnCZ0tPTNXjwYLvVF8qUKaMPPvhAoaGhbszMedybAAAAAADwfKZVe/fs2aP69etLkpYuXaq77rpLDz74oKKjo9WxY0ezunGL3GIugCtjsVhMKbC7UkBAgNfl7CyuEySpRYsWatGihWbMmKG3335bTz/9tGbOnKkmTZro8ccf19ChQ5khi1KJveAB5xiGofj4+Hzbp0+fzv0FAAAAAAAUK9MK40FBQTp69KjCwsK0atUqjR49WtKFosWZM2fM6gawExAQoJUrV7o7jUJ54wxXSV6TJwDznDt3Tp999pnmzp2rb775RjfccIOGDx+u9PR0PfPMM/r222+1cOFCd6cJuBx7wQPOSU1NVVJSUp72nJwcJSUlKTU1VREREa5PDAAAAAAAlBqmFcZvvfVW3X///br++uu1c+dO3X777ZKk7du3e+UHHBRci5dZeTLDFQDMsXnzZs2dO1eLFi2Sj4+P7r33XsXHx+uaa66xHdO7d29FRUW5MUvAvdgLHrhy4eHhioqK0ubNm+2WUvf19VXLli0VHh7uxuwAAAAAAEBpYFph/K233tJzzz2nPXv2aMmSJapcubIkadOmTRowYIBZ3bgMBVcAQGkSFRWlW2+9VTNnzlSvXr3k5+eX55jIyEj179/fDdkBALydxWJRbGysBg8e7LCdZdQBAAAAAEBxM60wXrFiRb355pt52idNmmRWFwAAoJjs3r270Nl65cuX19y5c12UEQCgpAkNDVVMTIzmz58vwzBksVgUExOj2rVruzs1AAAAAABQCviYdaKIiAhNnjxZe/bsMeuUAADARVjCFgDgCoMGDbKtLlalShXFxMS4OSMAAAAAAFBamFYYHzVqlD799FNFRkbq1ltv1Ycffqjs7GyzTg8AAIpRTk6Opk+frtatW6tGjRqqVKmS3QMAADMEBARozJgxql69ukaPHq2AgAB3pwQAAAAAAEoJUwvjW7Zs0S+//KJrr71WI0eOVM2aNfXYY49p8+bNZnUDAACKwaRJk/Tqq6+qX79+OnnypEaPHq0+ffrIx8dHEydOdHd6AIASJDo6Wh9//LGio6PdnQoAAAAAAChFTCuM52rRooVef/117du3Ty+88ILmzJmjqKgoNW/eXO+9954MwzC7SwAA4KSEhAS9++67GjNmjMqUKaMBAwZozpw5mjBhgtavX+/u9AAAAAAAAAAAcIrphfFz585p8eLF6tmzp8aMGaNWrVppzpw5uuuuu/TMM88UuofcmjVr1KNHD9WqVUsWi0VLly61ixuGoQkTJqhmzZoKDAxU586d9ddff5n9MgAAKFUOHDigJk2aSJKCgoJ08uRJSdIdd9yhr776yp2pAQAAAAAAAADgNNMK45s3b7ZbPr1x48b67bff9PPPP2vo0KF6/vnn9e233+qzzz4r8DynTp1Ss2bN9NZbbzmMT5s2Ta+//rpmzZqlDRs2qHz58uratauysrLMeikAAJQ6oaGh2r9/vySpXr16WrVqlSQpKSlJ/v7+7kwNAAAAAAAAAACnlTHrRFFRUbr11ls1c+ZM9erVS35+fnmOiYyMVP/+/Qs8T7du3dStWzeHMcMw9Nprr+m5557TnXfeKUn64IMPVL16dS1dutThubOzs5WdnW17npGRcTkvCwCAUqF379767rvv1KZNG40cOVKDBg3Sf/7zH6WlpSk2Ntbd6QEAAAAAAAAA4BTTCuO7d+9WeHh4gceUL19ec+fOveI+kpOTdeDAAXXu3NnWFhISojZt2igxMdFhYTwuLk6TJk264j4BACgNXn75ZdvX/fr1U1hYmBITE9WgQQP16NHDjZkBAAAAAAAAAOA80wrjhRXFzXDgwAFJUvXq1e3aq1evbotdavz48Ro9erTteUZGhurUqVN8SQIAUAK0bdtWbdu2dXcaAAAAAAAAAACYwrTCeE5OjuLj47V48WKlpaXp7NmzdvFjx46Z1dVl8ff3Z29UAAAcWLZsWZGP7dmzp2n97t27V08//bS+/vprnT59WvXr19fcuXPVqlUr0/oAAAAAAAAAAOBiphXGJ02apDlz5mjMmDF67rnn9OyzzyolJUVLly7VhAkTTOmjRo0akqSDBw+qZs2atvaDBw+qefPmpvQBAEBp0atXL7vnFotFhmHkaZMuDIAzw/HjxxUdHa1OnTrp66+/VtWqVfXXX3/pqquuMuX8AAAAAAAAAAA44mPWiRISEvTuu+9qzJgxKlOmjAYMGKA5c+ZowoQJWr9+vSl9REZGqkaNGvruu+9sbRkZGdqwYQPLvQIAcJmsVqvtsWrVKjVv3lxff/21Tpw4oRMnTujrr79WixYttGLFCtP6fOWVV1SnTh3NnTtXrVu3VmRkpLp06aJ69eqZ1gcAAAAAAAAAAJcybcb4gQMH1KRJE0lSUFCQTp48KUm644479Pzzzxf5PJmZmdq1a5fteXJysrZs2aJKlSopLCxMo0aN0pQpU9SgQQNFRkbq+eefV61atfLMegMAAEU3atQozZo1S+3atbO1de3aVeXKldODDz6oP/74w5R+li1bpq5du+qee+7Rjz/+qNq1a2vEiBF64IEH8v2e7OxsZWdn255nZGSYkgsAAAAAAAAAoPQwbcZ4aGio9u/fL0mqV6+eVq1aJUlKSkq6rD2+N27cqOuvv17XX3+9JGn06NG6/vrrbcuxP/XUUxo5cqQefPBBRUVFKTMzUytWrFBAQIBZLwUAgFLn77//VsWKFfO0h4SEKCUlxbR+du/erZkzZ6pBgwZauXKlHnnkET3++ON6//338/2euLg4hYSE2B516tQxLR8AAAAAAAAAQOlgWmG8d+/etiXOR44cqeeff14NGjTQvffeq2HDhhX5PB07dpRhGHke8+bNk3Rhr9PJkyfrwIEDysrK0rfffqurr77arJcBAECpFBUVpdGjR+vgwYO2toMHD+rJJ59U69atTevHarWqRYsWmjp1qq6//no9+OCDeuCBBzRr1qx8v2f8+PE6efKk7bFnzx7T8gEAAAAAAAAAlA6mLaX+8ssv277u16+fwsLClJiYqAYNGqhHjx5mdQMAAIrBe++9p969eyssLMw2I3vPnj1q0KCBli5dalo/NWvWVKNGjezarr32Wi1ZsiTf7/H397+s1WcAAAAAAAAAALiUaYXxS7Vt21Zt27YtrtPDgZdeeklTpkxxdxoAAC9Uv359bd26Vd98843+/PNPSRcK1p07d5bFYjGtn+joaO3YscOubefOnQoPDzetDwAAAAAAAAAALuVUYXzZsmVFPrZnz57OdIV8HDp0yPb1mjVrdPDgQVWvXt2NGaEw77//vh5++GF3pwEAeVgsFnXp0kVdunTJ95gmTZpo+fLlV7zPd2xsrG688UZNnTpVffv21S+//KLZs2dr9uzZV5o2AAAAAAAAAACFcqow3qtXL7vnFotFhmHkaZOknJwcZ7oqEQzDUFZWlqnnfPzxx+2eP/TQQ1q0aJGpfQQEBJg6W7A0OnnypO3rhQsXqn///qpYsaL7EgKAK5SSkqJz585d8fdHRUXps88+0/jx4zV58mRFRkbqtddeU0xMjIlZAgAAAAAAAABgz6nCuNVqtX397bff6umnn9bUqVNtS6gnJibqueee09SpU53LsoTIyspS165di7WPY8eOmd7HypUrFRgYaOo5PVlxDGB49tln7Z4/9dRT+ve//23a+Rm8YL7169erU6dO7k4DKJHuuOMO3XHHHe5OAwAAAAAAAABQipi2x/ioUaM0a9YstWvXztbWtWtXlStXTg8++KD++OMPs7oCipUrBjD8+eefpvZR2gYvFJeLB0RMnz5dbdu2VUBAgBszQmFeeuklTZkyxd1pAAAAAAAAAAAAD2daYfzvv/92uDR0SEiIUlJSzOrGqwUEBGjlypWmnOvzzz/X22+/nW98xIgRuvPOO03pi8IgPFFxzOx///33bV//888/mjNnjoYPH27a+ZnZb45Dhw7Zvl6zZo0OHjyo6tWruzEjAAAAAAAAAADg6UwrjEdFRWn06NGaP3++rUBx8OBBPfnkk2rdurVZ3Xg1i8Vi2qzeMmUKvnRlypRhBvEVMnMAQ2JioiZOnJhvfOLEibatB5xRGgcvuGJm/+LFi7V48WLTzlcaZ/YXxwCGxx9/3O75Qw89pEWLFpl2fgYwAAAAAAAAAABQ8phWGH/vvffUu3dvhYWFqU6dOpKkPXv2qEGDBlq6dKlZ3eB/mjdv7lQc+WMAA2AeVwxgOHbsGFsTAAAAAAAAAACAAplWGK9fv762bt2qb775Rn/++ack6dprr1Xnzp2ZeVcMDhw4UGi8fv36LsoG+Tl48KBTceTPzJn9qampevDBB/ONz549W+Hh4U73Uxpn9qPkeeedd1i6HgAAAAAAAADgdUwrjEsXZtp26dJFXbp0yfeYJk2aaPny5bZZ5bgy58+fdyoO1yhsUAiDRq6cmTP7y5YtW2icGcRXxswBDJ9//rnefvvtfOMjRozQnXfe6XQ/pWkAw+uvv17kY3OXsB84cGBxpQMAAAAAAAAAQLExtTBeFCkpKTp37pyruy1xdu3aVWi8Y8eOrkkG+br++uudisM19u3bV2i8bt26LsqmZDFzAIOfn1+hcQYwXJ74+Hi754cPH9bp06dVsWJFSdKJEydUrlw5VatWLc/e7gAAAAAAAAAAeBMfdyeAK3Pfffc5FYdr1K1bV6GhoQ5jderUodjqIW688UYFBQU5jAUFBenGG290cUZwpFevXk7FkVdycrLt8dJLL6l58+b6448/dOzYMR07dkx//PGHWrRooRdffNHdqQIAAAAAAAAA4BQK414qPT3dqThcp1KlSg7br7rqKhdngvz4+Pho8uTJDmNTpkyRjw//VHoCi8WS77L3ZcuWZWsCJz3//PN644031LBhQ1tbw4YNFR8fr+eee86NmQEAAAAAAAAA4DyqPV5q7969TsXhGqmpqdq6davD2NatW5WamurijJCfGjVqOGyvVq2aizNBftatW6ezZ886jJ09e1br1q1zcUYly/79+3X+/Pk87Tk5OTp48KAbMgIAAAAAAAAAwDwUxr1UzZo1nYrDNcLDw9W0aVOHsaZNmyo8PNzFGcERwzAUHx+fZ8axxWJRfHy8DMNwU2aA69xyyy166KGHtHnzZlvbpk2b9Mgjj6hz585uzAwAAAAAAAAAAOdRGAeKWX5FVYqtniM1NVVJSUl5rolhGEpKSmJmv4dgQFDxeu+991SjRg21atVK/v7+8vf3V+vWrVW9enXNmTPH3ekBAAAAAAAAAOCUMq7u8J133lH16tVd3W2Js3///kLj9erVc1E2yE9qaqq2bdvmMLZt2zalpqYqIiLCtUkhj9yZ/Y6WvWdmv+cobK939oJ3TtWqVbV8+XLt3LlTf/zxhywWi6655hpdffXV7k4NAAAAAAAAAACnOVUYf/3114t87OOPPy5JGjhwoDNd4n9q1arlVByuER4erqioKCUlJeWJtW7dmoKrB2Fmv+eLiIjIdwBDs2bNGGRikquvvloNGjSQpDzbCwAAAAAAAAAA4K2cKozHx8fbPT98+LBOnz6tihUrSpL+r707D46qzPc//jk0IR2BRCAQgYQQVhFkSwJCj3OLC46igliKXmgQvBlLEEUSXGsKZBVRWea6pASVLY0EZBl19OqILHfiQgOiwDDI2pifCIJIACcBOs/vD6WHJgkEbHK6m/er6lTRzzl9zqf7NPlW9befc3766SddddVVatCgQaAxjtBIS0tTWlqa9uzZU2Zds2bNlJaWZkMqnMuyLA0YMKDcxviAAQNoOoUJZvZHBsuyNHToUOXk5JRZN3ToUP4/hcD8+fP1wgsvaMeOHZJ+aZI//vjjGjx4sM3JAAAAAAAAAAD4bX7TdWf37NkTWCZPnqyOHTtq27Zt+vHHH/Xjjz9q27Zt6ty5syZOnBiqvDhLrVq1yh2vWbNmFSdBRYwxeuutt8pdt3DhQmYjh4kzM/vPvRR3tWrVmNkfRs78fzq3AW5ZFv+fQmD69OkaPny4br31Vi1evFiLFy/WLbfcomHDhpX5IRwAAAAAAAAAAJHGMiHqJDRv3lxvv/22OnXqFDS+YcMG3X333eXObLZDUVGREhISdPToUcXHx9sd55Lt3btX9913X4Xr58+fzwzXMMB5ihyFhYUaPHiw/H5/YKx69epasGCBGjdubGMynBHt/5/srk9paWkaP358mfd43rx5GjduXNjUccn+9woAgHNRmwAAAAAACH+/acb42fbv36/Tp0+XGff7/Tpw4ECoDoNfMcM1MjRp0qTCL8bi4+PVpEmTKk6EiiQnJ8vtdgdmI1uWJbfbTVM8jJz5u+dwOILGHQ4Hf/dCYP/+/erevXuZ8e7du2v//v02JAIAAAAAAAAAIHRC1hjv2bOnHnzwQW3cuDEwtmHDBg0fPly9evUK1WHwK8uylJ2dXeE499oND/v27VNRUVG564qKirRv374qToTzGTRokOrVqydJSkxMlNvttjkRzsbfvcurRYsWWrx4cZnx/Px8tWzZ0oZEAAAAAAAAAACETvVQ7ejNN9/UkCFDlJGRoZiYGEnS6dOndfPNN+v1118P1WFwluTkZLVt21abN28OjLVt25YZrmHkzAzX9evXB93/2LIsZWZmMsM1zDidTo0ePVozZ87UqFGj5HQ67Y6Ec5yZ2b9gwQIZY5jZH0Ljx4/Xvffeq7Vr18rlckmSCgoKtHLlynIb5gAAAAAAAAAARJKQ3WP8jG+++Ubbtm2TZVm69tpr1apVq1Du/jeLpnu/VXRP5Pnz5ys5OdnGZDhbYWGhBg0apNLS0sCYw+FQXl4ezTzgEhQXF2vgwIE6dOiQ6tevL4/HExU/YgiH+rRx40ZNnz5d27ZtkyS1adNGo0ePVqdOnWzJU5FweK8AADgbtQkAAAAAgPAXshnjZ7Rq1SpwyVUua3v5GGM0Y8aMCsdffPFF3v8wwcx+ILSY2R96p06d0oMPPqgxY8YoLy/P7jgAAAAAAAAAAIRcyO4xLknz58/X9ddfr7i4OMXFxal9+/ZasGBBKA+BX/l8Pnm93qDZ4pLk9/vl9Xrl8/lsSoZzFRYW6h//+EfQ2D/+8Q8VFhbalAiIfC6XS0uWLAlc8hu/TUxMjJYuXWp3DAAAAAAAAAAALpuQNcanT5+u4cOH69Zbb9XixYu1ePFi3XLLLRo2bFi5M5svld/v15gxY5SWlqa4uDg1b95cEydOVIivCB/2zty7+txZ4ZZlqUuXLty7OkxcaGb/lfa5BRC++vXrpxUrVtgdAwAAAAAAAACAyyJkl1J/6aWXlJubq/vuuy8w1rdvX7Vt21bjxo1TdnZ2SI4zdepU5ebmat68eWrbtq3Wr1+v+++/XwkJCRo5cmRIjhEJLMvSgAED5PV6g8aNMRowYACXUQ8TZ2b2n+vsmf1Nmzat+mAAcI6WLVtqwoQJKigoUHp6umrWrBm0/kqqsQAAAAAAAACA6BOyxvj+/fvVvXv3MuPdu3fX/v37Q3UYffrpp7rjjjt02223SZKaNm2qt956S+vWrSt3+5KSEpWUlAQeFxUVhSyLnYwxeuutt2RZVtCsY8uytHDhQnXu3JnmeBg4M7N/48aNQZe9dzgcSk9PZ2Y/gLDxxhtv6Oqrr9aGDRu0YcOGoHWWZdEYBwAAAAAAAABEtJBdSr1FixZavHhxmfH8/Hy1bNkyVIdR9+7dtXLlSn3zzTeSpK+++kp///vf1bt373K3nzJlihISEgJLSkpKyLLY6cxM5HMvxW2M4R7jYcSyrHKvlnBmnB8vAAgXe/bsCSy7d+/W7t27gx4DAAAAAAAAABDJQjZjfPz48br33nu1du1auVwuSVJBQYFWrlxZbsP8Uj311FMqKirStddeK4fDIb/fr8mTJ8vtdpe7/dNPP62cnJzA46Kioqhojqempqp9+/b6+uuvy6xr3749M5HDSHJystxutxYsWCBjjCzLktvtVuPGje2OBgBB3njjDc2YMUM7duyQ9Mvl1UeNGqU//vGPNicDAAAAAAAAAOC3CdmM8bvuukvr1q1TYmKiVqxYoRUrVigxMVHr1q3TnXfeGarDaPHixfJ4PFq4cKE2btyoefPm6cUXX9S8efPK3T42Nlbx8fFBS7Q4d7b4hcZhn7vvvjswO9yyLN111102JwKAYGPHjtWjjz6qPn36aMmSJVqyZIn69Omj7OxsjR07NmTHGTdunCzLClquvfbakO0fAAAAAAAAAIDyhGTG+KlTp/Tggw9qzJgxysvLC8UuK/T444/rqaee0n/9139Jkq6//nr5fD5NmTJFQ4YMuazHDic+n0+bN28ud93mzZvl8/nUtGnTqg2FCr399tsqLS2VJJWWlmrp0qXKysqyORUA/Ftubq5mz56tAQMGBMb69u2r9u3b65FHHtGECRNCdqy2bdvq448/DjyuXj1kF7ABAAAAAAAAAKBcIZkxHhMTo6VLl4ZiVxf0888/q1q14NgOhyPQdLxSpKamKjMzs8x7Ua1aNXXp0oVLqYeRwsJCeTyeoDGPx6PCwkKbEgFAWadOnVJGRkaZ8fT0dJ0+fTqkx6pevbquueaawJKYmBjS/QMAAAAAAAAAcK6QXUq9X79+WrFiRah2V6E+ffpo8uTJ+utf/6q9e/dq+fLlmj59ekgv1x4JLMtSdnZ24PLcZ1SrVq3ccdjDGKMZM2ZUOM5l7wGEi8GDBys3N7fM+KxZs+R2u0N6rB07dqhRo0Zq1qyZ3G639u3bd97tS0pKVFRUFLQAAAAAAAAAAHAxQnbt0pYtW2rChAkqKChQenq6atasGbR+5MiRITnOSy+9pDFjxuihhx7SwYMH1ahRIz344IMhvf9ppEhOTpbb7daCBQtkjJFlWXK73WrcuLHd0fArn88nr9dbZtzv98vr9XLJewBh5Y033tBHH32kG264QZL0xRdfaN++fbrvvvuUk5MT2G769OmXfIyuXbtq7ty5at26tfbv36/x48frxhtv1JYtW1S7du1ynzNlyhSNHz/+ko8JAAAAAAAAAIBlQjRlNS0treKDWJZ2794disP8ZkVFRUpISNDRo0cVHx9vd5zfrLi4WHfddZeOHTum+Ph4vf3223I6nXbHwq+MMXrssce0ceNG+f3+wLjD4VB6erpeeOEFZvcDkGR/ferRo0eltrMsS5988knIjvvTTz8pNTVV06dPV1ZWVrnblJSUqKSkJPC4qKhIKSkpUVPLAQCRz+46DgAAAAAALixkM8b37NkT+PeZXjsNv6rB+xy+zlzyfvDgweWOc+4AhItVq1bZctyrr75arVq10s6dOyvcJjY2VrGxsVWYCgAAAAAAAAAQbUJ2j3Hpl0uwtmvXTk6nU06nU+3atdPrr78eykPgHHl5eTp27Jgk6dixY/J4PDYnwrnOXPL+TBOcS94DwL8dP35cu3btUsOGDe2OAgAAAAAAAACIYiFrjI8dO1aPPvqo+vTpoyVLlmjJkiXq06ePsrOzr8j7f1eFwsJCeTyewAx9Y4w8Ho8KCwttToZzDRo0SPXq1ZMkJSYmyu1225wIAOzx2GOPac2aNdq7d68+/fRT3XnnnXI4HBowYIDd0QAAAAAAAAAAUSxkjfHc3FzNnj1bU6ZMUd++fdW3b19NmTJFs2bN0quvvhqqw+BXxhjNmDGjwvEQ3ToeIeJ0OjV69GglJSUpJyeH+8ADuGIVFhZqwIABat26te655x7Vq1dPn3/+uerXr293NAAAAAAAAABAFAvZPcZPnTqljIyMMuPp6ek6ffp0qA6DX/l8Pnm93jLjfr9fXq9XPp9PTZs2rfpgqJDL5ZLL5bI7BgDYatGiRXZHAAAAAAAAAABcgUI2Y3zw4MHKzc0tMz5r1iwuG30ZpKamKjMzUw6HI2jc4XCoS5cuSk1NtSkZAAAAAAAAAAAAAISXkM0Yl6Q33nhDH330kW644QZJ0hdffKF9+/bpvvvuU05OTmC76dOnh/KwVyTLspSdna3BgweXO25Zlk3JAAAAAAAAAAAAACC8hKwxvmXLFnXu3FmStGvXLklSYmKiEhMTtWXLlsB2NGxDJzk5WW63WwsWLJAxRpZlye12q3HjxnZHAwAAAAAAAAAAAICwEbLG+KpVq0K1K1yEQYMG6f3339ehQ4eUmJjIZesBAAAuk4KCAs2cOVOjRo2Sy+WyOw4AAAAAAACAixCye4zDHk6nU6NHj1ZSUpJycnLkdDrtjgQAABB1iouLNW3aNB04cEDTpk1TcXGx3ZEAAAAAAAAAXAQa41HA5XJpyZIlzFwCcMUoKChQ//79VVBQYHcUICT4TIe/vLw8HT58WJJ0+PBheTwemxMBAAAAAAAAuBg0xgEAEYVZm4g2fKbDX2FhoTwej4wxkiRjjDwejwoLC21OBgAAAAAAAKCyaIwDACIKszYRbfhMhzdjjGbMmFHh+JlmOQAAAAAAAIDwRmMcABAxmLWJaMNnOvz5fD55vV75/f6gcb/fL6/XK5/PZ1MyAAAAAAAAABeDxjgAICIwaxPRhs90ZEhNTVVmZqYcDkfQuMPhUJcuXZSammpTMgAAAAAAAAAXg8Y4ACAiMGsT0YbPdGSwLEvZ2dkVjluWZUMqAAAAAAAAABeLxjgAICIwaxPRhs905EhOTpbb7Q40wS3LktvtVuPGjW1OBgAAAAAAAKCyaIwDACICszYRbfhMR5ZBgwapXr16kqTExES53W6bEwEAAAAAAAC4GDTGAQARIzk5Wffee2/Q2L333susTUQsZiJHDqfTqdGjRyspKUk5OTlyOp12RwIAAAAAAABwEarbHQAAAOBKNmjQIL3//vs6dOgQM5HDnMvlksvlsjsGAAAAAAAAgEvAjHEAQMQoLCxUfn5+0Fh+fr4KCwttSgT8dsxEBgAAAAAAAIDLj8Y4ACAiGGM0Y8aMCseNMTakAkLD5XJpyZIlzEYGAAAAAAAAgMuExjgAICL4fD55vV75/f6gcb/fL6/XK5/PZ1MyAAAAAAAAAAAQ7miMAwAiQmpqqjIzM+VwOILGHQ6HunTpotTUVJuSAQAAAAAAAACAcEdjHAAQESzLUnZ2doXjlmXZkAoAAAAAAAAAAEQCGuMAgIiRnJwst9sdaIJbliW3263GjRvbnAwAAAAAAAAAAIQzGuMAgIgyaNAg1atXT5KUmJgot9ttcyIAAAAAAAAAABDuIrIx/v/+3/8LNEbi4uJ0/fXXa/369XbHAgBUAafTqdGjRyspKUk5OTlyOp12RwIAAAAAAAAAAGGuut0BLtaRI0fkcrnUo0cPffDBB6pfv7527NihOnXq2B0NAFBFXC6XXC6X3TEAAAAAAAAAAECEiLjG+NSpU5WSkqI5c+YExtLS0mxMBAAAACAcFBQUaObMmRo1ahQ/oAIAAAAAAECQiLuU+jvvvKOMjAz1799fDRo0UKdOnTR79uwKty8pKVFRUVHQAgAAACC6FBcXa9q0aTpw4ICmTZum4uJiuyMBAAAAAAAgjERcY3z37t3Kzc1Vy5Yt9eGHH2r48OEaOXKk5s2bV+72U6ZMUUJCQmBJSUmp4sTALwoKCtS/f38VFBTYHQUAACDq5OXl6fDhw5Kkw4cPy+Px2JwIAAAAAAAA4cQyxhi7Q1yMGjVqKCMjQ59++mlgbOTIkfJ6vfrss8/KbF9SUqKSkpLA46KiIqWkpOjo0aOKj4+vksxAcXGxBg4cqEOHDikxMVELFy6U0+m0OxaAMFJUVKSEhATqUyXwXgE4V2FhoQYPHiy/3x8Yq169uubPn6/k5GQbk+FKQW0CAAAAACD8RdyM8YYNG+q6664LGmvTpo327dtX7vaxsbGKj48PWoCqxgwmAACAy8MYoxkzZlQ4HmG/AwYAAAAAAMBlEnGNcZfLpe3btweNffPNN0pNTbUpEXB+hYWF8ng8gS9ljTHyeDwqLCy0ORkA2O+5556TZVkaNWqU3VEARCifzyev1xs0W1yS/H6/vF6vfD6fTckAAAAAAAAQTiKuMZ6dna3PP/9czz77rHbu3KmFCxdq1qxZGjFihN3RgDKYwQQAFfN6vXrttdfUvn17u6MAiGCpqanKzMyUw+EIGnc4HOrSpQs/oAUAAAAAAICkCGyMZ2Zmavny5XrrrbfUrl07TZw4UTNnzpTb7bY7GlAGM5gAoHzHjx+X2+3W7NmzVadOHbvjAIhglmUpOzu7wnHLsmxIBQAAAAAAgHATcY1xSbr99tu1efNmFRcXa9u2bXrggQfsjgSUixlMAFC+ESNG6LbbblOvXr0uuG1JSYmKioqCFgA4W3Jystxud6AJblmW3G63GjdubHMyAAAAAAAAhIuIbIwDkYIZTABQ1qJFi7Rx40ZNmTKlUttPmTJFCQkJgSUlJeUyJwQQiQYNGqR69epJkhITE7miFAAAAAAAAILQGAcuM2YwAcC/ffvtt3r00Ufl8XjkdDor9Zynn35aR48eDSzffvvtZU4JIBI5nU6NHj1aSUlJysnJqfTfGAAAAAAAAFwZLGOMsTtEVSoqKlJCQoKOHj2q+Ph4u+PgClFcXKyBAwfq0KFDql+//kU1hABcGa6U+rRixQrdeeedQbeY8Pv9sixL1apVU0lJSZnbT5zrSnmvAACRg9oEAAAAAED4Y8Y4UAWYwQQAv+jZs6c2b96sTZs2BZaMjAy53W5t2rTpgk1xAAAAAAAAAAAuRXW7AwBXCpfLJZfLZXcMALBV7dq11a5du6CxmjVrql69emXGAQAAAAAAAAAIFWaMAwAAAAAAAAAAAACiGjPGAQCArVavXm13BAAAAAAAAABAlGPGOAAAAAAAAAAAAAAgqtEYjwIFBQXq37+/CgoK7I4CAAAuAbUcAAAAAAAAAC4vGuMRrri4WNOmTdOBAwc0bdo0FRcX2x0JAABcBGo5AAAAAAAAAFx+NMYjXF5eng4fPixJOnz4sDwej82JAADAxaCWAwAAAAAAAMDlR2M8ghUWFsrj8cgYI0kyxsjj8aiwsNDmZAAAoDKo5QAAAAAAAABQNWiMRyhjjGbMmFHh+Jkv2AEAQHiilgMAAAAAAABA1aExHqF8Pp+8Xq/8fn/QuN/vl9frlc/nsykZAACoDGo5AAAAAAAAAFQdGuMRKjU1VZmZmXI4HEHjDodDXbp0UWpqqk3JAABAZVDLAQAAAAAAAKDq0BiPUJZlKTs7u8Jxy7JsSAUAACqLWg4AAAAAAAAAVYfGeARLTk6W2+0OfHFuWZbcbrcaN25sczIAAFAZ1HIAAAAAAAAAqBo0xiPcoEGDVK9ePUlSYmKi3G63zYkAAMDFoJYDAAAAAAAAwOVHYzzCOZ1OjR49WklJScrJyZHT6bQ7EgAAuAjUcgAAAAAAAAC4/CxjjLE7RFUqKipSQkKCjh49qvj4eLvjAAAgifp0MXivAADhhtoEAAAAAED4Y8Y4AAAAAAAAAAAAACCq0RgHAAAAAAAAAAAAAEQ1GuMAAAAAAAAAAAAAgKhGYxwAAAAAAAAAAAAAENVojAMAAAAAAAAAAAAAohqNcQAAAAAAAAAAAABAVKMxDgAAAAAAAAAAAACIatXtDlDVjDGSpKKiIpuTAADwb2fq0pk6hYpRywEA4YY6DgAAAABA+LviGuPHjh2TJKWkpNicBACAso4dO6aEhAS7Y4Q1ajkAIFxRxwEAAAAACF+WucJ+0l5aWqrvvvtOtWvXlmVZdscJiaKiIqWkpOjbb79VfHy83XFQAc5TZOA8RYZoPE/GGB07dkyNGjVStWrc6eR8qOWwC+cp/HGOIkM0nifqOAAAAAAA4e+KmzFerVo1JScn2x3jsoiPj4+aL5aiGecpMnCeIkO0nSdmmFUOtRx24zyFP85RZIi280QdBwAAAAAgvPFTdgAAAAAAAAAAAABAVKMxDgAAAAAAAAAAAACIajTGo0BsbKyeeeYZxcbG2h0F58F5igycp8jAeUK04TMdGThP4Y9zFBk4TwAAAAAAwA6WMcbYHQIAAAAAAAAAAAAAgMuFGeMAAAAAAAAAAAAAgKhGYxwAAAAAAAAAAAAAENVojAMAAAAAAAAAAAAAohqNcQAAAAAAAAAAAABAVKMxHgVeeeUVNW3aVE6nU127dtW6devsjoSzrF27Vn369FGjRo1kWZZWrFhhdySUY8qUKcrMzFTt2rXVoEED9evXT9u3b7c7Fs6Rm5ur9u3bKz4+XvHx8erWrZs++OADu2MBvwl1PPxRy8MfdTwyUMcBAAAAAICdaIxHuPz8fOXk5OiZZ57Rxo0b1aFDB9188806ePCg3dHwqxMnTqhDhw565ZVX7I6C81izZo1GjBihzz//XH/729906tQp/eEPf9CJEyfsjoazJCcn67nnntOGDRu0fv16/ed//qfuuOMObd261e5owCWhjkcGann4o45HBuo4AAAAAACwk2WMMXaHwKXr2rWrMjMz9fLLL0uSSktLlZKSokceeURPPfWUzelwLsuytHz5cvXr18/uKLiAH374QQ0aNNCaNWv0+9//3u44OI+6devqhRdeUFZWlt1RgItGHY881PLIQB2PHNRxAAAAAABQVZgxHsFOnjypDRs2qFevXoGxatWqqVevXvrss89sTAZEvqNHj0r65ctahCe/369FixbpxIkT6tatm91xgItGHQcuH+p4+KOOAwAAAACAqlbd7gC4dIcOHZLf71dSUlLQeFJSkv75z3/alAqIfKWlpRo1apRcLpfatWtndxycY/PmzerWrZuKi4tVq1YtLV++XNddd53dsYCLRh0HLg/qeHijjgMAAAAAALvQGAeAc4wYMUJbtmzR3//+d7ujoBytW7fWpk2bdPToUb399tsaMmSI1qxZw5fqAABJ1PFwRx0HAAAAAAB2oTEewRITE+VwOHTgwIGg8QMHDuiaa66xKRUQ2R5++GG99957Wrt2rZKTk+2Og3LUqFFDLVq0kCSlp6fL6/Xqz3/+s1577TWbkwEXhzoOhB51PPxRxwEAAAAAgF24x3gEq1GjhtLT07Vy5crAWGlpqVauXMl9+oCLZIzRww8/rOXLl+uTTz5RWlqa3ZFQSaWlpSopKbE7BnDRqONA6FDHIxd1HAAAAAAAVBVmjEe4nJwcDRkyRBkZGerSpYtmzpypEydO6P7777c7Gn51/Phx7dy5M/B4z5492rRpk+rWrasmTZrYmAxnGzFihBYuXKi//OUvql27tr7//ntJUkJCguLi4mxOhzOefvpp9e7dW02aNNGxY8e0cOFCrV69Wh9++KHd0YBLQh2PDNTy8EcdjwzUcQAAAAAAYCfLGGPsDoHf5uWXX9YLL7yg77//Xh07dtT//M//qGvXrnbHwq9Wr16tHj16lBkfMmSI5s6dW/WBUC7LssodnzNnjoYOHVq1YVChrKwsrVy5Uvv371dCQoLat2+vJ598UjfddJPd0YBLRh0Pf9Ty8EcdjwzUcQAAAAAAYCca4wAAAAAAAAAAAACAqMY9xgEAAAAAAAAAAAAAUY3GOAAAAAAAAAAAAAAgqtEYBwAAAAAAAAAAAABENRrjAAAAAAAAAAAAAICoRmMcAAAAAAAAAAAAABDVaIwDAAAAAAAAAAAAAKIajXEAAAAAAAAAAAAAQFSjMQ4AAAAAAAAAAAAAiGo0xoHz2Lt3ryzL0qZNmyJq36ga48aNU8eOHe2OAQA4D2o5zodaDgAAAAAAcOWgMQ5Aq1evlmVZ+umnn+yOctEsy9KKFSvsjgEAgK2o5QAAAAAAAMD50RgHgAs4depURO4bAAD8gloOAAAAAAAAGuOApNLSUj3//PNq0aKFYmNj1aRJE02ePLnMdkeOHJHb7Vb9+vUVFxenli1bas6cOZU6xrp169SpUyc5nU5lZGToyy+/DFrv9/uVlZWltLQ0xcXFqXXr1vrzn/8cWL927VrFxMTo+++/D3reqFGjdOONN0qSfD6f+vTpozp16qhmzZpq27at3n///fPm2rt3r3r06CFJqlOnjizL0tChQyVJJSUlGjlypBo0aCCn06nf/e538nq9lXq9Z2aurVy5UhkZGbrqqqvUvXt3bd++PWi7v/zlL+rcubOcTqeaNWum8ePH6/Tp05KkCRMmqFGjRjp8+HBg+9tuu009evRQaWmpmjZtKkm68847ZVlW4PGF5Obmqnnz5qpRo4Zat26tBQsWBK23LEu5ubnq27evatasGfgsPPfcc0pKSlLt2rWVlZWl4uLiMvt+/fXX1aZNGzmdTl177bV69dVXA+vOXHI3Pz9f//Ef/yGn0ymPx1OpzACA86OWU8vPRi0HAAAAAABAGQaAeeKJJ0ydOnXM3Llzzc6dO83//d//mdmzZ5s9e/YYSebLL780xhgzYsQI07FjR+P1es2ePXvM3/72N/POO+9ccP/Hjh0z9evXNwMHDjRbtmwx7777rmnWrFnQvk+ePGnGjh1rvF6v2b17t8nLyzNXXXWVyc/PD+ynVatW5vnnnw88PnnypElMTDRvvvmmMcaY2267zdx0003m66+/Nrt27TLvvvuuWbNmzXmznT592ixdutRIMtu3bzf79+83P/30kzHGmJEjR5pGjRqZ999/32zdutUMGTLE1KlTxxw+fPiCr3nVqlVGkunatatZvXq12bp1q7nxxhtN9+7dA9usXbvWxMfHm7lz55pdu3aZjz76yDRt2tSMGzcukK1bt26mX79+xhhjXn75ZXP11Vcbn89njDHm4MGDRpKZM2eO2b9/vzl48OAFcy1btszExMSYV155xWzfvt1MmzbNOBwO88knnwS2kWQaNGhg3nzzTbNr1y7j8/lMfn6+iY2NNa+//rr55z//af70pz+Z2rVrmw4dOgSel5eXZxo2bGiWLl1qdu/ebZYuXWrq1q1r5s6da4wxgc9T06ZNA9t89913F8wMALgwajm1nFoOAAAAAACA86ExjiteUVGRiY2NNbNnzy6z7twv0/v06WPuv//+iz7Ga6+9ZurVq2f+9a9/BcZyc3OD9l2eESNGmLvuuivweOrUqaZNmzaBx0uXLjW1atUyx48fN8YYc/311we+iL4YZ774PnLkSGDs+PHjJiYmxng8nsDYyZMnTaNGjYK+0L/QPj/++OPA2F//+lcjKfA+9OzZ0zz77LNBz1uwYIFp2LBh4PGuXbtM7dq1zZNPPmni4uKC8hjzyxffy5cvr/Rr7d69u3nggQeCxvr3729uvfXWoH2OGjUqaJtu3bqZhx56KGisa9euQV+mN2/e3CxcuDBom4kTJ5pu3boZY/79eZo5c2al8wIALoxaTi2nlgMAAAAAAOBCuJQ6rnjbtm1TSUmJevbsecFthw8frkWLFqljx4564okn9Omnn1b6GO3bt5fT6QyMdevWrcx2r7zyitLT01W/fn3VqlVLs2bN0r59+wLrhw4dqp07d+rzzz+XJM2dO1f33HOPatasKUkaOXKkJk2aJJfLpWeeeUZff/11pfKVZ9euXTp16pRcLldgLCYmRl26dNG2bdsqvZ/27dsH/t2wYUNJ0sGDByVJX331lSZMmKBatWoFlgceeED79+/Xzz//LElq1qyZXnzxRU2dOlV9+/bVwIEDL/k1Sb+ci7NfkyS5XK4yrykjI6PM87p27Ro0dvY5PHHihHbt2qWsrKyg1zNp0iTt2rXrvPsGAPw21PLyUcup5QAAAAAAAPg3GuO44sXFxVV62969e8vn8yk7O1vfffedevbsqcceeywkORYtWqTHHntMWVlZ+uijj7Rp0ybdf//9OnnyZGCbBg0aqE+fPpozZ44OHDigDz74QP/93/8dWP/HP/5Ru3fv1uDBg7V582ZlZGTopZdeCkm+SxUTExP4t2VZkn65D6wkHT9+XOPHj9emTZsCy+bNm7Vjx46gxsPatWvlcDi0d+/ewD1LL7czDYrKOn78uCRp9uzZQa9ny5YtgebHpe4bAHB+1PLLi1pOLQcAAAAAAIgGNMZxxWvZsqXi4uK0cuXKSm1fv359DRkyRHl5eZo5c6ZmzZp1wee0adNGX3/9tYqLiwNj537BWlBQoO7du+uhhx5Sp06d1KJFizKzk6RfvjDPz8/XrFmz1Lx58zIzplJSUjRs2DAtW7ZMo0eP1uzZsy+Yr0aNGpIkv98fGGvevLlq1KihgoKCwNipU6fk9Xp13XXXXXCfldG5c2dt375dLVq0KLNUq/bLn6f8/HwtW7ZMq1ev1r59+zRx4sSgfcTExATlvpA2bdoEvSbpl/f+Qq+pTZs2+uKLL4LGzj6HSUlJatSokXbv3l3mtaSlpVU6HwDg4lHLqeXUcgAAAAAAAFxIdbsDAHZzOp168skn9cQTT6hGjRpyuVz64YcftHXr1jKXZB07dqzS09PVtm1blZSU6L333lObNm0ueIyBAwfqT3/6kx544AE9/fTT2rt3r1588cWgbVq2bKn58+frww8/VFpamhYsWCCv11vmi9ibb75Z8fHxmjRpkiZMmBC0btSoUerdu7datWqlI0eOaNWqVZXKl5qaKsuy9N577+nWW29VXFycatWqpeHDh+vxxx9X3bp11aRJEz3//PP6+eeflZWVdcF9VsbYsWN1++23q0mTJrr77rtVrVo1ffXVV9qyZYsmTZqkwsJCDR8+XFOnTtXvfvc7zZkzR7fffrt69+6tG264QZLUtGlTrVy5Ui6XS7GxsapTp855j/n444/rnnvuUadOndSrVy+9++67WrZsmT7++OPzPu/RRx/V0KFDlZGRIZfLJY/Ho61bt6pZs2aBbcaPH6+RI0cqISFBt9xyi0pKSrR+/XodOXJEOTk5v/0NAwCUi1pOLaeWAwAAAAAA4ILsvsk5EA78fr+ZNGmSSU1NNTExMaZJkybm2WefNXv27DGSzJdffmmMMWbixImmTZs2Ji4uztStW9fccccdZvfu3ZU6xmeffWY6dOhgatSoYTp27GiWLl0atO/i4mIzdOhQk5CQYK6++mozfPhw89RTT5kOHTqU2deYMWOMw+Ew3333XdD4ww8/bJo3b25iY2NN/fr1zeDBg82hQ4cqlW/ChAnmmmuuMZZlmSFDhhhjjPnXv/5lHnnkEZOYmGhiY2ONy+Uy69atq9T+Vq1aZSSZI0eOBMa+/PJLI8ns2bMnMPa///u/pnv37iYuLs7Ex8ebLl26mFmzZpnS0lLTs2dPc/PNN5vS0tLA9o888ohp3ry5OXbsmDHGmHfeece0aNHCVK9e3aSmplYq26uvvmqaNWtmYmJiTKtWrcz8+fOD1ksyy5cvL/O8yZMnm8TERFOrVi0zZMgQ88QTT5Q5Px6Px3Ts2NHUqFHD1KlTx/z+9783y5YtM8aYMp8nAEDoUMup5WejlgMAAAAAAOBcljHG2NOSB3CpsrKy9MMPP+idd96xOwoAALgE1HIAAAAAAACganEpdSCCHD16VJs3b9bChQv5Ih0AgAhELQcAAAAAAADsUc3uAEA0ePbZZ1WrVq1yl969e4fsOHfccYf+8Ic/aNiwYbrpppsq/bxhw4ZVmG/YsGGXlOVy7DMU2rZtW2Euj8djWy4AQHijllPLAQAAAAAAEN24lDoQAj/++KN+/PHHctfFxcWpcePGVZwo2MGDB1VUVFTuuvj4eDVo0CAs9hkKPp9Pp06dKnddUlKSateuXcWJAACRgFoemn2GArUcAAAAAAAAlwONcQAAAAAAAAAAAABAVONS6gAAAAAAAAAAAACAqEZjHAAAAAAAAAAAAAAQ1WiMAwAAAAAAAAAAAACiGo1xAAAAAAAAAAAAAEBUozEOAAAAAAAAAAAAAIhqNMYBAAAAAAAAAAAAAFGNxjgAAAAAAAAAAAAAIKr9fwloQfdlo5SsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x2000 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of features, grouped by cls_days_to_next_order\n",
    "plt.figure(figsize=(20, 20))\n",
    "for i, col in enumerate(manual_features):\n",
    "    plt.subplot(5, 4, i + 1)\n",
    "    sns.boxplot(x=\"cls_days_to_next_order\", y=col, data=visualization_df)\n",
    "    plt.title(col)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x2000 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of features, grouped by cls_reorder_ratio\n",
    "plt.figure(figsize=(20, 20))\n",
    "for i, col in enumerate(manual_features):\n",
    "    plt.subplot(5, 4, i + 1)\n",
    "    sns.boxplot(x=\"cls_reorder_ratio\", y=col, data=visualization_df)\n",
    "    plt.title(col)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "\n",
    "`user_*` features and `ord_*` features seems to offer strong signals. Meanwhile `prod_*` features doesn't seems to differentiates much between different label classes for both tasks, except for `prod_nunique` number of unique products bought in the past."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare data for RNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare data for rnn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14084</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12427</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26088</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26405</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31653684</th>\n",
       "      <td>2977660</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14197</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31653685</th>\n",
       "      <td>2977660</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>38730</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31653686</th>\n",
       "      <td>2977660</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>31477</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31653687</th>\n",
       "      <td>2977660</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6567</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31653688</th>\n",
       "      <td>2977660</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22920</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31653689 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          order_id  user_id eval_set  order_number  order_dow  \\\n",
       "0          2539329        1    prior             1          2   \n",
       "1          2539329        1    prior             1          2   \n",
       "2          2539329        1    prior             1          2   \n",
       "3          2539329        1    prior             1          2   \n",
       "4          2539329        1    prior             1          2   \n",
       "...            ...      ...      ...           ...        ...   \n",
       "31653684   2977660   206209    prior            13          1   \n",
       "31653685   2977660   206209    prior            13          1   \n",
       "31653686   2977660   206209    prior            13          1   \n",
       "31653687   2977660   206209    prior            13          1   \n",
       "31653688   2977660   206209    prior            13          1   \n",
       "\n",
       "          order_hour_of_day  days_since_prior_order  product_id  \\\n",
       "0                         8                     NaN         196   \n",
       "1                         8                     NaN       14084   \n",
       "2                         8                     NaN       12427   \n",
       "3                         8                     NaN       26088   \n",
       "4                         8                     NaN       26405   \n",
       "...                     ...                     ...         ...   \n",
       "31653684                 12                     7.0       14197   \n",
       "31653685                 12                     7.0       38730   \n",
       "31653686                 12                     7.0       31477   \n",
       "31653687                 12                     7.0        6567   \n",
       "31653688                 12                     7.0       22920   \n",
       "\n",
       "          add_to_cart_order  reordered  \n",
       "0                         1          0  \n",
       "1                         2          0  \n",
       "2                         3          0  \n",
       "3                         4          0  \n",
       "4                         5          0  \n",
       "...                     ...        ...  \n",
       "31653684                  5          1  \n",
       "31653685                  6          0  \n",
       "31653686                  7          0  \n",
       "31653687                  8          0  \n",
       "31653688                  9          0  \n",
       "\n",
       "[31653689 rows x 10 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnn_data_prep = orders[orders[\"eval_set\"] == \"prior\"].merge(order_products_prior, how=\"left\", on=\"order_id\")\n",
    "rnn_data_prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnn_data_prep = rnn_data_prep[[\"user_id\", \"order_id\", \"product_id\", \"days_since_prior_order\"]]\n",
    "product_count = rnn_data_prep.groupby(\"user_id\").product_id.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    206209.000000\n",
       "mean        153.502946\n",
       "std         203.320222\n",
       "min           2.000000\n",
       "25%          36.000000\n",
       "50%          79.000000\n",
       "75%         183.000000\n",
       "max        3671.000000\n",
       "Name: product_id, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.displot(product_count, kde=True)\n",
    "plt.show()\n",
    "product_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yh/0ppzrrgs7m77v8rxlkjbf2540000gn/T/ipykernel_53908/2695112797.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rnn_data_prep[\"product_rank\"] = rnn_data_prep.groupby(\"user_id\")[\"index\"].rank(method=\"first\", ascending=False)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>product_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2539329</td>\n",
       "      <td>196</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2539329</td>\n",
       "      <td>14084</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2539329</td>\n",
       "      <td>12427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2539329</td>\n",
       "      <td>26088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2539329</td>\n",
       "      <td>26405</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31653684</th>\n",
       "      <td>31653684</td>\n",
       "      <td>206209</td>\n",
       "      <td>2977660</td>\n",
       "      <td>14197</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31653685</th>\n",
       "      <td>31653685</td>\n",
       "      <td>206209</td>\n",
       "      <td>2977660</td>\n",
       "      <td>38730</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31653686</th>\n",
       "      <td>31653686</td>\n",
       "      <td>206209</td>\n",
       "      <td>2977660</td>\n",
       "      <td>31477</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31653687</th>\n",
       "      <td>31653687</td>\n",
       "      <td>206209</td>\n",
       "      <td>2977660</td>\n",
       "      <td>6567</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31653688</th>\n",
       "      <td>31653688</td>\n",
       "      <td>206209</td>\n",
       "      <td>2977660</td>\n",
       "      <td>22920</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22769236 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             index  user_id  order_id  product_id  days_since_prior_order  \\\n",
       "0                0        1   2539329         196                     NaN   \n",
       "1                1        1   2539329       14084                     NaN   \n",
       "2                2        1   2539329       12427                     NaN   \n",
       "3                3        1   2539329       26088                     NaN   \n",
       "4                4        1   2539329       26405                     NaN   \n",
       "...            ...      ...       ...         ...                     ...   \n",
       "31653684  31653684   206209   2977660       14197                     7.0   \n",
       "31653685  31653685   206209   2977660       38730                     7.0   \n",
       "31653686  31653686   206209   2977660       31477                     7.0   \n",
       "31653687  31653687   206209   2977660        6567                     7.0   \n",
       "31653688  31653688   206209   2977660       22920                     7.0   \n",
       "\n",
       "          product_rank  \n",
       "0                 59.0  \n",
       "1                 58.0  \n",
       "2                 57.0  \n",
       "3                 56.0  \n",
       "4                 55.0  \n",
       "...                ...  \n",
       "31653684           5.0  \n",
       "31653685           4.0  \n",
       "31653686           3.0  \n",
       "31653687           2.0  \n",
       "31653688           1.0  \n",
       "\n",
       "[22769236 rows x 6 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnn_data_prep.reset_index(drop=False, inplace=True)\n",
    "rnn_data_prep[\"product_rank\"] = rnn_data_prep.groupby(\"user_id\")[\"index\"].rank(method=\"first\", ascending=False)\n",
    "rnn_data_prep = rnn_data_prep[rnn_data_prep[\"product_rank\"] <= 256]\n",
    "rnn_data_prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill missing values\n",
    "rnn_data_prep[\"days_since_prior_order\"].fillna(30, inplace=True)\n",
    "rnn_data_prep[\"norm_days_since_prior_order\"] = rnn_data_prep[\"days_since_prior_order\"] / 30\n",
    "# Convert list of product_id, group by user_id\n",
    "rnn_data = rnn_data_prep.groupby(\"user_id\")[\"product_id\"].apply(list).to_frame()\n",
    "rnn_data[\"norm_days_since_prior_order\"] = rnn_data_prep.groupby(\"user_id\")[\"norm_days_since_prior_order\"].apply(list).to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 206209 entries, 1 to 206209\n",
      "Data columns (total 2 columns):\n",
      " #   Column                       Non-Null Count   Dtype \n",
      "---  ------                       --------------   ----- \n",
      " 0   product_id                   206209 non-null  object\n",
      " 1   norm_days_since_prior_order  206209 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "rnn_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnn_data.to_parquet(\"./data/rnn_data.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>norm_days_since_prior_order</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[196, 14084, 12427, 26088, 26405, 196, 10258, ...</td>\n",
       "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 0.5, 0.5, 0.5, 0.5, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[32792, 47766, 20574, 12000, 48110, 22474, 165...</td>\n",
       "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[9387, 17668, 15143, 16797, 39190, 47766, 2190...</td>\n",
       "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[36606, 7350, 35469, 2707, 42329, 7160, 1200, ...</td>\n",
       "      <td>[1.0, 1.0, 1.0, 1.0, 0.6333333333333333, 0.633...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[15349, 21413, 48775, 28289, 8518, 11777, 3171...</td>\n",
       "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206205</th>\n",
       "      <td>[27845, 28745, 3896, 49235, 21137, 37067, 3873...</td>\n",
       "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206206</th>\n",
       "      <td>[27086, 41387, 38530, 47011, 8021, 38530, 1152...</td>\n",
       "      <td>[0.13333333333333333, 0.1, 0.1, 0.1, 0.1, 0.03...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206207</th>\n",
       "      <td>[47766, 3397, 3469, 2450, 45965, 20583, 30233,...</td>\n",
       "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206208</th>\n",
       "      <td>[23644, 21461, 31506, 31404, 27845, 20995, 276...</td>\n",
       "      <td>[0.13333333333333333, 0.13333333333333333, 0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206209</th>\n",
       "      <td>[6846, 9405, 41213, 40396, 14901, 40534, 23039...</td>\n",
       "      <td>[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206209 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                product_id  \\\n",
       "user_id                                                      \n",
       "1        [196, 14084, 12427, 26088, 26405, 196, 10258, ...   \n",
       "2        [32792, 47766, 20574, 12000, 48110, 22474, 165...   \n",
       "3        [9387, 17668, 15143, 16797, 39190, 47766, 2190...   \n",
       "4        [36606, 7350, 35469, 2707, 42329, 7160, 1200, ...   \n",
       "5        [15349, 21413, 48775, 28289, 8518, 11777, 3171...   \n",
       "...                                                    ...   \n",
       "206205   [27845, 28745, 3896, 49235, 21137, 37067, 3873...   \n",
       "206206   [27086, 41387, 38530, 47011, 8021, 38530, 1152...   \n",
       "206207   [47766, 3397, 3469, 2450, 45965, 20583, 30233,...   \n",
       "206208   [23644, 21461, 31506, 31404, 27845, 20995, 276...   \n",
       "206209   [6846, 9405, 41213, 40396, 14901, 40534, 23039...   \n",
       "\n",
       "                               norm_days_since_prior_order  \n",
       "user_id                                                     \n",
       "1        [1.0, 1.0, 1.0, 1.0, 1.0, 0.5, 0.5, 0.5, 0.5, ...  \n",
       "2        [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...  \n",
       "3        [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...  \n",
       "4        [1.0, 1.0, 1.0, 1.0, 0.6333333333333333, 0.633...  \n",
       "5        [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...  \n",
       "...                                                    ...  \n",
       "206205   [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...  \n",
       "206206   [0.13333333333333333, 0.1, 0.1, 0.1, 0.1, 0.03...  \n",
       "206207   [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...  \n",
       "206208   [0.13333333333333333, 0.13333333333333333, 0.1...  \n",
       "206209   [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ...  \n",
       "\n",
       "[206209 rows x 2 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnn_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49668"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_prior[\"product_id\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49685"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(np.concatenate([order_products_prior[\"product_id\"],order_products_train[\"product_id\"]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49688"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_prior[\"product_id\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import CrossEntropyLoss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  },
  "vscode": {
   "interpreter": {
    "hash": "0347152bab7395cf6d4d53b744fcb402ec7f725dba3cd9225265b4a9dcbbfed9"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}