data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "books = pd.read_parquet(\"books_data/books.parquet\")\n",
    "users = pd.read_parquet(\"books_data/users.parquet\")\n",
    "ratings = pd.read_parquet(\"books_data/ratings.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num books: 138700\n",
      "Num users: 92107\n",
      "Num ratings: 1031175\n"
     ]
    }
   ],
   "source": [
    "print(f\"Num books: {len(books.index)}\")\n",
    "print(f\"Num users: {len(users.index)}\")\n",
    "print(f\"Num ratings: {len(ratings.index)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# books['Category'] = [l[2:-2] for l in books['Category']]\n",
    "# books.drop(index=books[books['Category'] == '9'].index, inplace=True) #remove 9 in category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>location</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>stockton, california, usa</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>stockton</td>\n",
       "      <td>california</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>timmins, ontario, canada</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>timmins</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11400</td>\n",
       "      <td>ottawa, ontario, canada</td>\n",
       "      <td>49.0000</td>\n",
       "      <td>ottawa</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11676</td>\n",
       "      <td>n/a, n/a, n/a</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41385</td>\n",
       "      <td>sudbury, ontario, canada</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>sudbury</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                   location      age      city       state country\n",
       "0        2  stockton, california, usa  18.0000  stockton  california     usa\n",
       "1        8   timmins, ontario, canada  34.7439   timmins     ontario  canada\n",
       "2    11400    ottawa, ontario, canada  49.0000    ottawa     ontario  canada\n",
       "3    11676              n/a, n/a, n/a  34.7439      None        None    None\n",
       "4    41385   sudbury, ontario, canada  34.7439   sudbury     ontario  canada"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# books.drop(columns=['category_clean'], axis=1, inplace=True)\n",
    "# books.Category[:1].values.item(0)[2:-2]\n",
    "# books.year_of_publication = books.year_of_publication.astype('int32')\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_user_rating_times = ratings.groupby('user_id').min()['rating_timestamp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_user_rating_times = min_user_rating_times.to_frame().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2022-08-04 16:03:16.862\n",
       "1       2020-08-04 07:22:59.858\n",
       "2       2020-04-01 12:05:52.654\n",
       "3       2020-07-04 18:18:19.970\n",
       "4       2020-07-23 04:19:17.666\n",
       "                  ...          \n",
       "92102   2020-03-06 11:36:34.759\n",
       "92103   2020-05-11 14:32:33.494\n",
       "92104   2020-01-03 15:06:18.502\n",
       "92105   2021-03-15 17:04:45.672\n",
       "92106   2020-03-19 06:20:28.966\n",
       "Name: signup_date, Length: 92107, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_user_rating_times.rating_timestamp.rename(\"signup_date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>rating_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-04 16:03:16.862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>2020-08-04 07:22:59.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>2020-04-01 12:05:52.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>2020-07-04 18:18:19.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>2020-07-23 04:19:17.666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92102</th>\n",
       "      <td>278846</td>\n",
       "      <td>2020-03-06 11:36:34.759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92103</th>\n",
       "      <td>278849</td>\n",
       "      <td>2020-05-11 14:32:33.494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92104</th>\n",
       "      <td>278851</td>\n",
       "      <td>2020-01-03 15:06:18.502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92105</th>\n",
       "      <td>278852</td>\n",
       "      <td>2021-03-15 17:04:45.672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92106</th>\n",
       "      <td>278854</td>\n",
       "      <td>2020-03-19 06:20:28.966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92107 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id        rating_timestamp\n",
       "0            2 2022-08-04 16:03:16.862\n",
       "1            8 2020-08-04 07:22:59.858\n",
       "2            9 2020-04-01 12:05:52.654\n",
       "3           10 2020-07-04 18:18:19.970\n",
       "4           12 2020-07-23 04:19:17.666\n",
       "...        ...                     ...\n",
       "92102   278846 2020-03-06 11:36:34.759\n",
       "92103   278849 2020-05-11 14:32:33.494\n",
       "92104   278851 2020-01-03 15:06:18.502\n",
       "92105   278852 2021-03-15 17:04:45.672\n",
       "92106   278854 2020-03-19 06:20:28.966\n",
       "\n",
       "[92107 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# min_user_rating_times = min_user_rating_times.rename(columns={\"rating_timestamp\": \"signup_date\"})\n",
    "min_user_rating_times"
   ]
  },
  {
   "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>user_id</th>\n",
       "      <th>location</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>signup_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>stockton, california, usa</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>stockton</td>\n",
       "      <td>california</td>\n",
       "      <td>usa</td>\n",
       "      <td>2021-09-12 06:14:27.197750400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>timmins, ontario, canada</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>timmins</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "      <td>2020-03-28 08:33:46.118505600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11400</td>\n",
       "      <td>ottawa, ontario, canada</td>\n",
       "      <td>49.0000</td>\n",
       "      <td>ottawa</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "      <td>2021-04-28 17:26:12.129878400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11676</td>\n",
       "      <td>n/a, n/a, n/a</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-05-26 23:27:34.657603200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41385</td>\n",
       "      <td>sudbury, ontario, canada</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>sudbury</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "      <td>2022-09-12 22:51:16.743340800</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031170</th>\n",
       "      <td>278851</td>\n",
       "      <td>dallas, texas, usa</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>dallas</td>\n",
       "      <td>texas</td>\n",
       "      <td>usa</td>\n",
       "      <td>2020-01-15 00:52:00.962140800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031171</th>\n",
       "      <td>278851</td>\n",
       "      <td>dallas, texas, usa</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>dallas</td>\n",
       "      <td>texas</td>\n",
       "      <td>usa</td>\n",
       "      <td>2021-05-17 03:49:13.677052800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031172</th>\n",
       "      <td>278851</td>\n",
       "      <td>dallas, texas, usa</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>dallas</td>\n",
       "      <td>texas</td>\n",
       "      <td>usa</td>\n",
       "      <td>2022-12-08 17:21:07.617916800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031173</th>\n",
       "      <td>278851</td>\n",
       "      <td>dallas, texas, usa</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>dallas</td>\n",
       "      <td>texas</td>\n",
       "      <td>usa</td>\n",
       "      <td>2022-07-19 22:00:24.133795200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031174</th>\n",
       "      <td>278851</td>\n",
       "      <td>dallas, texas, usa</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>dallas</td>\n",
       "      <td>texas</td>\n",
       "      <td>usa</td>\n",
       "      <td>2021-04-14 18:43:07.366972800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1031175 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id                   location      age      city       state  \\\n",
       "0              2  stockton, california, usa  18.0000  stockton  california   \n",
       "1              8   timmins, ontario, canada  34.7439   timmins     ontario   \n",
       "2          11400    ottawa, ontario, canada  49.0000    ottawa     ontario   \n",
       "3          11676              n/a, n/a, n/a  34.7439      None        None   \n",
       "4          41385   sudbury, ontario, canada  34.7439   sudbury     ontario   \n",
       "...          ...                        ...      ...       ...         ...   \n",
       "1031170   278851         dallas, texas, usa  33.0000    dallas       texas   \n",
       "1031171   278851         dallas, texas, usa  33.0000    dallas       texas   \n",
       "1031172   278851         dallas, texas, usa  33.0000    dallas       texas   \n",
       "1031173   278851         dallas, texas, usa  33.0000    dallas       texas   \n",
       "1031174   278851         dallas, texas, usa  33.0000    dallas       texas   \n",
       "\n",
       "        country                   signup_date  \n",
       "0           usa 2021-09-12 06:14:27.197750400  \n",
       "1        canada 2020-03-28 08:33:46.118505600  \n",
       "2        canada 2021-04-28 17:26:12.129878400  \n",
       "3          None 2020-05-26 23:27:34.657603200  \n",
       "4        canada 2022-09-12 22:51:16.743340800  \n",
       "...         ...                           ...  \n",
       "1031170     usa 2020-01-15 00:52:00.962140800  \n",
       "1031171     usa 2021-05-17 03:49:13.677052800  \n",
       "1031172     usa 2022-12-08 17:21:07.617916800  \n",
       "1031173     usa 2022-07-19 22:00:24.133795200  \n",
       "1031174     usa 2021-04-14 18:43:07.366972800  \n",
       "\n",
       "[1031175 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# books[books.year_of_publication <= 1678]\n",
    "# books.drop(index=books[books['year_of_publication'] <= 1678].index, inplace=True)\n",
    "def random_dates(start, end, n, unit='D', seed=None):\n",
    "    ndays = (end - start).days + 1\n",
    "    return pd.to_timedelta(np.random.rand(n) * ndays, unit=unit) + start\n",
    "\n",
    "start = pd.to_datetime('2020-01-01')\n",
    "end = pd.to_datetime('2023-01-01')\n",
    "rating_dates = random_dates(start, end, n=1031175)\n",
    "# ratings['rating_timestamp'] = rating_dates\n",
    "users['signup_date'] = rating_dates\n",
    "users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = users.merge(min_user_rating_times, on=\"user_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged['clean_signup_date'] = merged[['signup_date', 'rating_timestamp']].min(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-01 01:29:42.993    11141\n",
       "2020-01-01 00:45:44.257     6456\n",
       "2020-01-01 00:04:25.161     5814\n",
       "2020-01-01 00:58:52.487     5779\n",
       "2020-01-01 07:00:45.819     5644\n",
       "                           ...  \n",
       "2021-04-27 09:18:41.137        1\n",
       "2020-10-21 17:35:06.109        1\n",
       "2020-08-07 08:09:08.231        1\n",
       "2021-01-05 18:25:04.158        1\n",
       "2022-01-04 08:51:21.480        1\n",
       "Name: clean_signup_date, Length: 123345, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged['clean_signup_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged.drop(columns=['rating_timestamp', 'signup_date'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = merged.rename(columns={\"clean_signup_date\": \"signup_date\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>location</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>signup_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>stockton, california, usa</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>stockton</td>\n",
       "      <td>california</td>\n",
       "      <td>usa</td>\n",
       "      <td>2021-09-12 06:14:27.197750400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>timmins, ontario, canada</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>timmins</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "      <td>2020-03-28 08:33:46.118505600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>timmins, ontario, canada</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>timmins</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "      <td>2020-08-04 07:22:59.858000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>timmins, ontario, canada</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>timmins</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "      <td>2020-02-10 07:22:09.616627200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>timmins, ontario, canada</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>timmins</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "      <td>2020-08-04 07:22:59.858000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031170</th>\n",
       "      <td>278636</td>\n",
       "      <td>irvington, alabama, usa</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>irvington</td>\n",
       "      <td>alabama</td>\n",
       "      <td>usa</td>\n",
       "      <td>2022-04-18 21:21:45.025516800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031171</th>\n",
       "      <td>278659</td>\n",
       "      <td>vancouver, washington, usa</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>vancouver</td>\n",
       "      <td>washington</td>\n",
       "      <td>usa</td>\n",
       "      <td>2021-09-28 02:22:09.675494400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031172</th>\n",
       "      <td>278713</td>\n",
       "      <td>albuquerque, new mexico, usa</td>\n",
       "      <td>63.0000</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>new mexico</td>\n",
       "      <td>usa</td>\n",
       "      <td>2020-12-28 16:58:04.785312000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031173</th>\n",
       "      <td>278714</td>\n",
       "      <td>silverton, oregon, usa</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>silverton</td>\n",
       "      <td>oregon</td>\n",
       "      <td>usa</td>\n",
       "      <td>2022-02-22 01:46:43.967481600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031174</th>\n",
       "      <td>278729</td>\n",
       "      <td>großostheim, bayern, germany</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>großostheim</td>\n",
       "      <td>bayern</td>\n",
       "      <td>germany</td>\n",
       "      <td>2022-01-04 08:51:21.480000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1031175 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id                      location      age         city  \\\n",
       "0              2     stockton, california, usa  18.0000     stockton   \n",
       "1              8      timmins, ontario, canada  34.7439      timmins   \n",
       "2              8      timmins, ontario, canada  34.7439      timmins   \n",
       "3              8      timmins, ontario, canada  34.7439      timmins   \n",
       "4              8      timmins, ontario, canada  34.7439      timmins   \n",
       "...          ...                           ...      ...          ...   \n",
       "1031170   278636       irvington, alabama, usa  34.7439    irvington   \n",
       "1031171   278659    vancouver, washington, usa  33.0000    vancouver   \n",
       "1031172   278713  albuquerque, new mexico, usa  63.0000  albuquerque   \n",
       "1031173   278714        silverton, oregon, usa  34.7439    silverton   \n",
       "1031174   278729  großostheim, bayern, germany  15.0000  großostheim   \n",
       "\n",
       "              state  country                   signup_date  \n",
       "0        california      usa 2021-09-12 06:14:27.197750400  \n",
       "1           ontario   canada 2020-03-28 08:33:46.118505600  \n",
       "2           ontario   canada 2020-08-04 07:22:59.858000000  \n",
       "3           ontario   canada 2020-02-10 07:22:09.616627200  \n",
       "4           ontario   canada 2020-08-04 07:22:59.858000000  \n",
       "...             ...      ...                           ...  \n",
       "1031170     alabama      usa 2022-04-18 21:21:45.025516800  \n",
       "1031171  washington      usa 2021-09-28 02:22:09.675494400  \n",
       "1031172  new mexico      usa 2020-12-28 16:58:04.785312000  \n",
       "1031173      oregon      usa 2022-02-22 01:46:43.967481600  \n",
       "1031174      bayern  germany 2022-01-04 08:51:21.480000000  \n",
       "\n",
       "[1031175 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "books['created_at'] = [pd.Timestamp(year, 1, 1, 12) for year in books['year_of_publication']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "users = users.sort_values(by=\"signup_date\", ascending=True)\n",
    "users = users.drop_duplicates(subset=['user_id'], keep='first')\n",
    "users = users.sort_values(by=\"user_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "books = books.sort_values(by=\"created_at\", ascending=True)\n",
    "books = books.drop_duplicates(subset=['isbn'], keep='first')\n",
    "books = books.sort_values(by=\"isbn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# books[['isbn', 'book_title', 'book_author', 'year_of_publication', 'publisher', 'Summary', 'Language', 'Category']].to_parquet(\"books.parquet\")\n",
    "# users[['user_id', 'location', 'age', 'city', 'state', 'country']].to_parquet(\"users.parquet\")\n",
    "\n",
    "# books.to_parquet(\"books_data/books.parquet\")\n",
    "# ratings.to_parquet(\"books_data/ratings.parquet\", coerce_timestamps ='ms', allow_truncated_timestamps=True)\n",
    "# users.to_parquet(\"books_data/users.parquet\", coerce_timestamps ='ms', allow_truncated_timestamps=True) # this takes random signup dates + mins with ratings\n",
    "\n",
    "books[:100].to_csv(\"books_data/books_sample.csv\")\n",
    "users[:100].to_csv(\"books_data/users_sample.csv\")\n",
    "ratings[:100].to_csv(\"books_data/ratings_sample.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore data"
   ]
  },
  {
   "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>isbn</th>\n",
       "      <th>book_title</th>\n",
       "      <th>book_author</th>\n",
       "      <th>year_of_publication</th>\n",
       "      <th>publisher</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Language</th>\n",
       "      <th>Category</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>948518</th>\n",
       "      <td>0000913154</td>\n",
       "      <td>The Way Things Work: An Illustrated Encycloped...</td>\n",
       "      <td>C. van Amerongen (translator)</td>\n",
       "      <td>1967</td>\n",
       "      <td>Simon &amp; Schuster</td>\n",
       "      <td>Scientific principles, inventions, and chemica...</td>\n",
       "      <td>en</td>\n",
       "      <td>Technology &amp; Engineering</td>\n",
       "      <td>1967-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826650</th>\n",
       "      <td>0001055607</td>\n",
       "      <td>Cereus Blooms At Night</td>\n",
       "      <td>Shani Mootoo</td>\n",
       "      <td>1994</td>\n",
       "      <td>Fairmount Books Ltd Remainders</td>\n",
       "      <td>When Mala, old and notoriously crazy, arrives ...</td>\n",
       "      <td>en</td>\n",
       "      <td>Adult child abuse victims</td>\n",
       "      <td>1994-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908526</th>\n",
       "      <td>0001061127</td>\n",
       "      <td>CHESS FOR YOUNG BEGINNERS</td>\n",
       "      <td>William T. McLeod</td>\n",
       "      <td>1975</td>\n",
       "      <td>HarperCollins Publishers</td>\n",
       "      <td>A step by step guide to playing chess</td>\n",
       "      <td>en</td>\n",
       "      <td>Chess</td>\n",
       "      <td>1975-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025127</th>\n",
       "      <td>0001374362</td>\n",
       "      <td>When It's Time for Bed (Collins Baby &amp; Toddler...</td>\n",
       "      <td>Nick Butterworth</td>\n",
       "      <td>1994</td>\n",
       "      <td>Collins</td>\n",
       "      <td>Shows baby and his animal friends preparing fo...</td>\n",
       "      <td>en</td>\n",
       "      <td>Animals</td>\n",
       "      <td>1994-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935227</th>\n",
       "      <td>0001711253</td>\n",
       "      <td>The Big Honey Hunt</td>\n",
       "      <td>Stan Berenstein</td>\n",
       "      <td>1942</td>\n",
       "      <td>HarperCollins Publishers</td>\n",
       "      <td>Father Bear takes Small Bear on a honey hunt. ...</td>\n",
       "      <td>en</td>\n",
       "      <td>Bears</td>\n",
       "      <td>1942-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               isbn                                         book_title  \\\n",
       "948518   0000913154  The Way Things Work: An Illustrated Encycloped...   \n",
       "826650   0001055607                             Cereus Blooms At Night   \n",
       "908526   0001061127                          CHESS FOR YOUNG BEGINNERS   \n",
       "1025127  0001374362  When It's Time for Bed (Collins Baby & Toddler...   \n",
       "935227   0001711253                                 The Big Honey Hunt   \n",
       "\n",
       "                           book_author  year_of_publication  \\\n",
       "948518   C. van Amerongen (translator)                 1967   \n",
       "826650                    Shani Mootoo                 1994   \n",
       "908526               William T. McLeod                 1975   \n",
       "1025127               Nick Butterworth                 1994   \n",
       "935227                 Stan Berenstein                 1942   \n",
       "\n",
       "                              publisher  \\\n",
       "948518                 Simon & Schuster   \n",
       "826650   Fairmount Books Ltd Remainders   \n",
       "908526         HarperCollins Publishers   \n",
       "1025127                         Collins   \n",
       "935227         HarperCollins Publishers   \n",
       "\n",
       "                                                   Summary Language  \\\n",
       "948518   Scientific principles, inventions, and chemica...       en   \n",
       "826650   When Mala, old and notoriously crazy, arrives ...       en   \n",
       "908526               A step by step guide to playing chess       en   \n",
       "1025127  Shows baby and his animal friends preparing fo...       en   \n",
       "935227   Father Bear takes Small Bear on a honey hunt. ...       en   \n",
       "\n",
       "                          Category          created_at  \n",
       "948518    Technology & Engineering 1967-01-01 12:00:00  \n",
       "826650   Adult child abuse victims 1994-01-01 12:00:00  \n",
       "908526                       Chess 1975-01-01 12:00:00  \n",
       "1025127                    Animals 1994-01-01 12:00:00  \n",
       "935227                       Bears 1942-01-01 12:00:00  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['en', 'de', 'fr', 'es', 'ca', 'it', 'da', 'nl', 'pt', 'gl', 'ro',\n",
       "       'no', 'el', 'ms', 'la', 'ru', 'zh-CN', 'ga', 'pl', 'tl', 'th',\n",
       "       'ja', 'cy', 'fa', 'eo', 'gd', 'hi', 'vi', 'ar', 'zh-TW', 'ko'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.Language.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>location</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>signup_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>stockton, california, usa</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>stockton</td>\n",
       "      <td>california</td>\n",
       "      <td>usa</td>\n",
       "      <td>2021-09-12 06:14:27.197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>timmins, ontario, canada</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>timmins</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "      <td>2020-02-10 07:22:09.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32775</th>\n",
       "      <td>9</td>\n",
       "      <td>germantown, tennessee, usa</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>germantown</td>\n",
       "      <td>tennessee</td>\n",
       "      <td>usa</td>\n",
       "      <td>2020-04-01 12:05:52.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193428</th>\n",
       "      <td>10</td>\n",
       "      <td>albacete, wisconsin, spain</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>albacete</td>\n",
       "      <td>wisconsin</td>\n",
       "      <td>spain</td>\n",
       "      <td>2020-07-04 18:18:19.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196326</th>\n",
       "      <td>12</td>\n",
       "      <td>fort bragg, california, usa</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>fort bragg</td>\n",
       "      <td>california</td>\n",
       "      <td>usa</td>\n",
       "      <td>2020-07-23 04:19:17.666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id                     location      age        city       state  \\\n",
       "0             2    stockton, california, usa  18.0000    stockton  california   \n",
       "3             8     timmins, ontario, canada  34.7439     timmins     ontario   \n",
       "32775         9   germantown, tennessee, usa  34.7439  germantown   tennessee   \n",
       "193428       10   albacete, wisconsin, spain  26.0000    albacete   wisconsin   \n",
       "196326       12  fort bragg, california, usa  34.7439  fort bragg  california   \n",
       "\n",
       "       country             signup_date  \n",
       "0          usa 2021-09-12 06:14:27.197  \n",
       "3       canada 2020-02-10 07:22:09.616  \n",
       "32775      usa 2020-04-01 12:05:52.654  \n",
       "193428   spain 2020-07-04 18:18:19.970  \n",
       "196326     usa 2020-07-23 04:19:17.666  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>location</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>signup_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186657</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186723</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186719</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186716</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>93044</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93043</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93041</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93039</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278854</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92107 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         location  age  city  state  country  signup_date\n",
       "user_id                                                  \n",
       "2               1    1     1      1        1            1\n",
       "186657          1    1     1      1        1            1\n",
       "186723          1    1     1      1        1            1\n",
       "186719          1    1     1      1        1            1\n",
       "186716          1    1     1      1        1            1\n",
       "...           ...  ...   ...    ...      ...          ...\n",
       "93044           1    1     1      1        1            1\n",
       "93043           1    1     1      1        1            1\n",
       "93041           1    1     1      0        1            1\n",
       "93039           1    1     1      1        1            1\n",
       "278854          1    1     1      1        1            1\n",
       "\n",
       "[92107 rows x 6 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.groupby('user_id').count().sort_values(by=\"age\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>location</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>signup_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>timmins, ontario, canada</td>\n",
       "      <td>34.7439</td>\n",
       "      <td>timmins</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "      <td>2020-02-10 07:22:09.616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                  location      age     city    state country  \\\n",
       "3        8  timmins, ontario, canada  34.7439  timmins  ontario  canada   \n",
       "\n",
       "              signup_date  \n",
       "3 2020-02-10 07:22:09.616  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[users['user_id'] == 8]"
   ]
  },
  {
   "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>isbn</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>0002005018</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-08-15 16:00:36.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8</td>\n",
       "      <td>0060973129</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-03-26 10:25:04.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8</td>\n",
       "      <td>0374157065</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-01-21 13:10:34.477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8</td>\n",
       "      <td>0393045218</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-12-29 12:19:41.604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8</td>\n",
       "      <td>0399135782</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-08-04 12:31:50.798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>8</td>\n",
       "      <td>0425176428</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-12-28 04:19:48.895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>8</td>\n",
       "      <td>0671870432</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-08 18:51:55.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>8</td>\n",
       "      <td>0679425608</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-07-15 02:44:56.907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>8</td>\n",
       "      <td>074322678X</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-07-01 03:02:06.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>8</td>\n",
       "      <td>0771074670</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-02-13 12:58:11.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>8</td>\n",
       "      <td>080652121X</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-12-05 03:56:53.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>8</td>\n",
       "      <td>0887841740</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-08-04 07:22:59.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>8</td>\n",
       "      <td>1552041778</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-03-08 21:25:25.406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>8</td>\n",
       "      <td>1558746218</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-11-03 11:22:49.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>8</td>\n",
       "      <td>1567407781</td>\n",
       "      <td>6</td>\n",
       "      <td>2020-12-04 10:12:55.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>8</td>\n",
       "      <td>1575663937</td>\n",
       "      <td>6</td>\n",
       "      <td>2021-08-16 16:01:37.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>8</td>\n",
       "      <td>1881320189</td>\n",
       "      <td>7</td>\n",
       "      <td>2022-06-04 07:26:55.243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id        isbn  rating        rating_timestamp\n",
       "1         8  0002005018       5 2020-08-15 16:00:36.000\n",
       "15        8  0060973129       0 2022-03-26 10:25:04.125\n",
       "18        8  0374157065       0 2022-01-21 13:10:34.477\n",
       "29        8  0393045218       0 2021-12-29 12:19:41.604\n",
       "30        8  0399135782       0 2020-08-04 12:31:50.798\n",
       "63        8  0425176428       0 2022-12-28 04:19:48.895\n",
       "68        8  0671870432       0 2021-01-08 18:51:55.942\n",
       "71        8  0679425608       0 2021-07-15 02:44:56.907\n",
       "72        8  074322678X       5 2022-07-01 03:02:06.010\n",
       "73        8  0771074670       0 2022-02-13 12:58:11.005\n",
       "79        8  080652121X       0 2020-12-05 03:56:53.049\n",
       "80        8  0887841740       5 2020-08-04 07:22:59.858\n",
       "82        8  1552041778       5 2022-03-08 21:25:25.406\n",
       "83        8  1558746218       0 2020-11-03 11:22:49.083\n",
       "91        8  1567407781       6 2020-12-04 10:12:55.667\n",
       "92        8  1575663937       6 2021-08-16 16:01:37.436\n",
       "95        8  1881320189       7 2022-06-04 07:26:55.243"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[ratings['user_id'] == 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6447"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.Category.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fiction                          0.383504\n",
       "Juvenile Fiction                 0.074160\n",
       "Biography & Autobiography        0.038789\n",
       "Religion                         0.025415\n",
       "History                          0.025299\n",
       "Juvenile Nonfiction              0.021132\n",
       "Social Science                   0.016474\n",
       "Business & Economics             0.015162\n",
       "Body, Mind & Spirit              0.012307\n",
       "Health & Fitness                 0.012206\n",
       "Family & Relationships           0.012098\n",
       "Cooking                          0.012033\n",
       "Humor                            0.011464\n",
       "Computers                        0.010216\n",
       "Psychology                       0.008421\n",
       "Self-Help                        0.008025\n",
       "Science                          0.007823\n",
       "Travel                           0.007743\n",
       "Poetry                           0.007015\n",
       "Literary Criticism               0.006770\n",
       "Art                              0.006460\n",
       "Sports & Recreation              0.006035\n",
       "Philosophy                       0.005883\n",
       "Nature                           0.005631\n",
       "Political Science                0.005479\n",
       "Drama                            0.005299\n",
       "Reference                        0.005278\n",
       "Performing Arts                  0.005148\n",
       "Language Arts & Disciplines      0.005076\n",
       "Crafts & Hobbies                 0.004816\n",
       "Education                        0.004614\n",
       "Comics & Graphic Novels          0.004052\n",
       "Music                            0.003778\n",
       "Medical                          0.003691\n",
       "Pets                             0.003619\n",
       "True Crime                       0.003396\n",
       "Literary Collections             0.003374\n",
       "Detective and mystery stories    0.003360\n",
       "Gardening                        0.003122\n",
       "Children's stories               0.003057\n",
       "Foreign Language Study           0.002588\n",
       "Animals                          0.002336\n",
       "House & Home                     0.002170\n",
       "Technology & Engineering         0.001983\n",
       "Adventure stories                0.001795\n",
       "Games & Activities               0.001665\n",
       "Photography                      0.001622\n",
       "Friendship                       0.001593\n",
       "Games                            0.001593\n",
       "Law                              0.001471\n",
       "Name: Category, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.Category.value_counts(normalize=True).sort_values(ascending=False)[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8645061283345348"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "books.Category.value_counts(normalize=True).sort_values(ascending=False)[:100].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABMYAAAImCAYAAAC4mYZ0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1gU5/c28JsivVooKgqIvaFiN8aCwV6SGDVWNPGXxJYQaxK7EXuwRWPFEmOLGo0JxqBYsYsaewGxgSUBIkYUOO8fvrtfVkDdnRlS9v5c11zK7OzZZ3dnZ5458xQLEREQERERERERERGZGcu/uwBERERERERERER/BybGiIiIiIiIiIjILDExRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWmBgjIiIiIiIiIiKzxMQYERERERERERGZJSbGiIiIiIiIiIjILJmUGJs/fz58fX1hZ2eHunXr4siRI6/0vLVr18LCwgIdO3Y0WC8iGDNmDLy9vWFvb4/g4GBcvnzZlKIRERERERERERG9EgsREWOesG7dOvTq1QsLFy5E3bp1ERERgQ0bNuDixYvw8PDI93kJCQlo1KgR/P39UbhwYWzZskX/2NSpUxEeHo4VK1bAz88Po0ePxpkzZ3Du3DnY2dm9tEzZ2dm4ffs2nJ2dYWFhYczbISIiIiIiIiKi/xgRwZ9//onixYvD0jL/dmFGJ8bq1q2L2rVrY968eQCeJaV8fHwwaNAgjBw5Ms/nZGVloXHjxujbty/27duHlJQUfWJMRFC8eHF8+umnGDp0KAAgNTUVnp6eiIyMRNeuXV9apps3b8LHx8eYt0FERERERERERP9xN27cQMmSJfN93NqYYE+ePMHx48cxatQo/TpLS0sEBwcjNjY23+dNmDABHh4e6NevH/bt22fwWHx8PJKSkhAcHKxf5+rqirp16yI2NjbPxFhGRgYyMjL0f+tyezdu3ICLi4sxb4mIiIiIiIiIiP5j0tLS4OPjA2dn5xduZ1Ri7P79+8jKyoKnp6fBek9PT1y4cCHP5+zfvx9Lly5FXFxcno8nJSXpYzwfU/fY88LDwzF+/Phc611cXJgYIyIiIiIiIiIiAHjpkFuazkr5559/omfPnli8eDGKFi2qWtxRo0YhNTVVv9y4cUO12EREREREREREZB6MajFWtGhRWFlZITk52WB9cnIyvLy8cm1/9epVJCQkoF27dvp12dnZz17Y2hoXL17UPy85ORne3t4GMQMDA/Msh62tLWxtbY0pOhERERERERERkQGjWozZ2NigVq1aiI6O1q/Lzs5GdHQ06tevn2v7ChUq4MyZM4iLi9Mv7du3R9OmTREXFwcfHx/4+fnBy8vLIGZaWhoOHz6cZ0wiIiIiIiIiIiI1GNViDADCwsLQu3dvBAUFoU6dOoiIiEB6ejpCQ0MBAL169UKJEiUQHh4OOzs7VKlSxeD5bm5uAGCw/uOPP8akSZNQtmxZ+Pn5YfTo0ShevDg6duxo+jsjIiIiIiIiIiJ6AaMTY126dMG9e/cwZswYJCUlITAwEFFRUfrB8xMTE2FpadzQZcOHD0d6ejr69++PlJQUNGrUCFFRUbCzszO2eERERERERERERK/EQkTk7y6EUmlpaXB1dUVqaipnpSQiIiIiIiIiMnOvmivSdFZKIiIiIiIiIiKifyomxoiIiIiIiIiIyCwxMUZERERERERERGaJiTEiIiIiIiIiIjJLTIwREREREREREZFZsv67C6A135Hbjdo+YUobjUpCRERERERERET/JGwxRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWmBgjIiIiIiIiIiKzxMQYERERERERERGZJSbGiIiIiIiIiIjILDExRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWmBgjIiIiIiIiIiKzxMQYERERERERERGZJSbGiIiIiIiIiIjILDExRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWTEqMzZ8/H76+vrCzs0PdunVx5MiRfLfdtGkTgoKC4ObmBkdHRwQGBmLVqlUG2/Tp0wcWFhYGS8uWLU0pGhERERERERER0SuxNvYJ69atQ1hYGBYuXIi6desiIiICISEhuHjxIjw8PHJtX7hwYXz++eeoUKECbGxs8OOPPyI0NBQeHh4ICQnRb9eyZUssX75c/7etra2Jb4mIiIiIiIiIiOjljG4xNmvWLLz//vsIDQ1FpUqVsHDhQjg4OGDZsmV5bt+kSRN06tQJFStWRJkyZTBkyBBUq1YN+/fvN9jO1tYWXl5e+sXd3d20d0RERERERERERPQKjEqMPXnyBMePH0dwcPD/AlhaIjg4GLGxsS99voggOjoaFy9eROPGjQ0ei4mJgYeHB8qXL48PP/wQDx48yDdORkYG0tLSDBYiIiIiIiIiIiJjGNWV8v79+8jKyoKnp6fBek9PT1y4cCHf56WmpqJEiRLIyMiAlZUVvv76a7Ro0UL/eMuWLfHmm2/Cz88PV69exWeffYZWrVohNjYWVlZWueKFh4dj/PjxxhSdiIiIiIiIiIjIgNFjjJnC2dkZcXFxePjwIaKjoxEWFgZ/f380adIEANC1a1f9tlWrVkW1atVQpkwZxMTEoHnz5rnijRo1CmFhYfq/09LS4OPjo/n7ICIiIiIiIiKi/w6jEmNFixaFlZUVkpOTDdYnJyfDy8sr3+dZWloiICAAABAYGIjz588jPDxcnxh7nr+/P4oWLYorV67kmRiztbXl4PxERERERERERKSIUWOM2djYoFatWoiOjtavy87ORnR0NOrXr//KcbKzs5GRkZHv4zdv3sSDBw/g7e1tTPGIiIiIiIiIiIhemdFdKcPCwtC7d28EBQWhTp06iIiIQHp6OkJDQwEAvXr1QokSJRAeHg7g2XhgQUFBKFOmDDIyMvDTTz9h1apVWLBgAQDg4cOHGD9+PN566y14eXnh6tWrGD58OAICAhASEqLiWyUiIiIiIiIiIvofoxNjXbp0wb179zBmzBgkJSUhMDAQUVFR+gH5ExMTYWn5v4Zo6enp+Oijj3Dz5k3Y29ujQoUKWL16Nbp06QIAsLKywunTp7FixQqkpKSgePHieOONNzBx4kR2lyQiIiIiIiIiIs1YiIj83YVQKi0tDa6urkhNTYWLi4vBY74jtxsVK2FKGzWLRkREREREREREBexFuaKcjBpjjIiIiIiIiIiI6L+CiTEiIiIiIiIiIjJLTIwREREREREREZFZYmKMiIiIiIiIiIjMEhNjRERERERERERklpgYIyIiIiIiIiIis8TEGBERERERERERmSUmxoiIiIiIiIiIyCwxMUZERERERERERGaJiTEiIiIiIiIiIjJLTIwREREREREREZFZYmKMiIiIiIiIiIjMEhNjRERERERERERklpgYIyIiIiIiIiIis8TEGBERERERERERmSUmxoiIiIiIiIiIyCwxMUZERERERERERGaJiTEiIiIiIiIiIjJLTIwREREREREREZFZYmKMiIiIiIiIiIjMEhNjRERERERERERklpgYIyIiIiIiIiIis8TEGBERERERERERmSUmxoiIiIiIiIiIyCwxMUZERERERERERGaJiTEiIiIiIiIiIjJLTIwREREREREREZFZMikxNn/+fPj6+sLOzg5169bFkSNH8t1206ZNCAoKgpubGxwdHREYGIhVq1YZbCMiGDNmDLy9vWFvb4/g4GBcvnzZlKIRERERERERERG9EqMTY+vWrUNYWBjGjh2LEydOoHr16ggJCcHdu3fz3L5w4cL4/PPPERsbi9OnTyM0NBShoaHYsWOHfptp06Zhzpw5WLhwIQ4fPgxHR0eEhITg8ePHpr8zIiIiIiIiIiKiF7AQETHmCXXr1kXt2rUxb948AEB2djZ8fHwwaNAgjBw58pVi1KxZE23atMHEiRMhIihevDg+/fRTDB06FACQmpoKT09PREZGomvXri+Nl5aWBldXV6SmpsLFxcXgMd+R2415e0iY0sao7YmIiIiIiIiI6J/lRbminIxqMfbkyRMcP34cwcHB/wtgaYng4GDExsa+9PkigujoaFy8eBGNGzcGAMTHxyMpKckgpqurK+rWrZtvzIyMDKSlpRksRERERERERERExjAqMXb//n1kZWXB09PTYL2npyeSkpLyfV5qaiqcnJxgY2ODNm3aYO7cuWjRogUA6J9nTMzw8HC4urrqFx8fH2PeBhERERERERERUcHMSuns7Iy4uDgcPXoUX375JcLCwhATE2NyvFGjRiE1NVW/3LhxQ73CEhERERERERGRWbA2ZuOiRYvCysoKycnJBuuTk5Ph5eWV7/MsLS0REBAAAAgMDMT58+cRHh6OJk2a6J+XnJwMb29vg5iBgYF5xrO1tYWtra0xRSciIiIiIiIiIjJgVIsxGxsb1KpVC9HR0fp12dnZiI6ORv369V85TnZ2NjIyMgAAfn5+8PLyMoiZlpaGw4cPGxWTiIiIiIiIiIjIGEa1GAOAsLAw9O7dG0FBQahTpw4iIiKQnp6O0NBQAECvXr1QokQJhIeHA3g2HlhQUBDKlCmDjIwM/PTTT1i1ahUWLFgAALCwsMDHH3+MSZMmoWzZsvDz88Po0aNRvHhxdOzYUb13SkRERERERERElIPRibEuXbrg3r17GDNmDJKSkhAYGIioqCj94PmJiYmwtPxfQ7T09HR89NFHuHnzJuzt7VGhQgWsXr0aXbp00W8zfPhwpKeno3///khJSUGjRo0QFRUFOzs7Fd4iERERERERERFRbhYiIn93IZRKS0uDq6srUlNT4eLiYvCY78jtRsVKmNJGzaIREREREREREVEBe1GuKKcCmZWSiIiIiIiIiIjon4aJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWmBgjIiIiIiIiIiKzxMQYERERERERERGZJSbGiIiIiIiIiIjILDExRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWmBgjIiIiIiIiIiKzxMQYERERERERERGZJSbGiIiIiIiIiIjILDExRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWmBgjIiIiIiIiIiKzxMQYERERERERERGZJSbGiIiIiIiIiIjILDExRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMySSYmx+fPnw9fXF3Z2dqhbty6OHDmS77aLFy/Ga6+9Bnd3d7i7uyM4ODjX9n369IGFhYXB0rJlS1OKRkRERERERERE9EqMToytW7cOYWFhGDt2LE6cOIHq1asjJCQEd+/ezXP7mJgYdOvWDbt370ZsbCx8fHzwxhtv4NatWwbbtWzZEnfu3NEv3333nWnviIiIiIiIiIiI6BUYnRibNWsW3n//fYSGhqJSpUpYuHAhHBwcsGzZsjy3//bbb/HRRx8hMDAQFSpUwJIlS5CdnY3o6GiD7WxtbeHl5aVf3N3d8y1DRkYG0tLSDBYiIiIiIiIiIiJjGJUYe/LkCY4fP47g4OD/BbC0RHBwMGJjY18pxqNHj/D06VMULlzYYH1MTAw8PDxQvnx5fPjhh3jw4EG+McLDw+Hq6qpffHx8jHkbRERERERERERExiXG7t+/j6ysLHh6ehqs9/T0RFJS0ivFGDFiBIoXL26QXGvZsiVWrlyJ6OhoTJ06FXv27EGrVq2QlZWVZ4xRo0YhNTVVv9y4ccOYt0FERERERERERATrgnyxKVOmYO3atYiJiYGdnZ1+fdeuXfX/r1q1KqpVq4YyZcogJiYGzZs3zxXH1tYWtra2BVJmIiIiIiIiIiL6bzKqxVjRokVhZWWF5ORkg/XJycnw8vJ64XNnzJiBKVOm4JdffkG1atVeuK2/vz+KFi2KK1euGFM8IiIiIiIiIiKiV2ZUYszGxga1atUyGDhfN5B+/fr1833etGnTMHHiRERFRSEoKOilr3Pz5k08ePAA3t7exhSPiIiIiIiIiIjolRk9K2VYWBgWL16MFStW4Pz58/jwww+Rnp6O0NBQAECvXr0watQo/fZTp07F6NGjsWzZMvj6+iIpKQlJSUl4+PAhAODhw4cYNmwYDh06hISEBERHR6NDhw4ICAhASEiISm+TiIiIiIiIiIjIkNFjjHXp0gX37t3DmDFjkJSUhMDAQERFRekH5E9MTISl5f/ybQsWLMCTJ0/w9ttvG8QZO3Ysxo0bBysrK5w+fRorVqxASkoKihcvjjfeeAMTJ07kOGJERERERERERKQZCxGRv7sQSqWlpcHV1RWpqalwcXExeMx35HajYiVMaaNm0YiIiIiIiIiIqIC9KFeUk9FdKYmIiIiIiIiIiP4LmBgjIiIiIiIiIiKzxMQYERERERERERGZJSbGiIiIiIiIiIjILDExRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWmBgjIiIiIiIiIiKzxMQYERERERERERGZJSbGiIiIiIiIiIjILDExRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWmBgjIiIiIiIiIiKzxMQYERERERERERGZJSbGiIiIiIiIiIjILDExRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWmBgjIiIiIiIiIiKzxMQYERERERERERGZJZMSY/Pnz4evry/s7OxQt25dHDlyJN9tFy9ejNdeew3u7u5wd3dHcHBwru1FBGPGjIG3tzfs7e0RHByMy5cvm1I0IiIiIiIiIiKiV2J0YmzdunUICwvD2LFjceLECVSvXh0hISG4e/duntvHxMSgW7du2L17N2JjY+Hj44M33ngDt27d0m8zbdo0zJkzBwsXLsThw4fh6OiIkJAQPH782PR3RkRERERERERE9AIWIiLGPKFu3bqoXbs25s2bBwDIzs6Gj48PBg0ahJEjR770+VlZWXB3d8e8efPQq1cviAiKFy+OTz/9FEOHDgUApKamwtPTE5GRkejatetLY6alpcHV1RWpqalwcXExeMx35HZj3h4SprQxansiIiIiIiIiIvpneVGuKCejWow9efIEx48fR3Bw8P8CWFoiODgYsbGxrxTj0aNHePr0KQoXLgwAiI+PR1JSkkFMV1dX1K1bN9+YGRkZSEtLM1iIiIiIiIiIiIiMYVRi7P79+8jKyoKnp6fBek9PTyQlJb1SjBEjRqB48eL6RJjuecbEDA8Ph6urq37x8fEx5m0QEREREREREREV7KyUU6ZMwdq1a7F582bY2dmZHGfUqFFITU3VLzdu3FCxlEREREREREREZA6sjdm4aNGisLKyQnJyssH65ORkeHl5vfC5M2bMwJQpU/Drr7+iWrVq+vW65yUnJ8Pb29sgZmBgYJ6xbG1tYWtra0zRiYiIiIiIiIiIDBjVYszGxga1atVCdHS0fl12djaio6NRv379fJ83bdo0TJw4EVFRUQgKCjJ4zM/PD15eXgYx09LScPjw4RfGJCIiIiIiIiIiUsKoFmMAEBYWht69eyMoKAh16tRBREQE0tPTERoaCgDo1asXSpQogfDwcADA1KlTMWbMGKxZswa+vr76ccOcnJzg5OQECwsLfPzxx5g0aRLKli0LPz8/jB49GsWLF0fHjh3Ve6dEREREREREREQ5GJ0Y69KlC+7du4cxY8YgKSkJgYGBiIqK0g+en5iYCEvL/zVEW7BgAZ48eYK3337bIM7YsWMxbtw4AMDw4cORnp6O/v37IyUlBY0aNUJUVJSicciIiIiIiIiIiIhexEJE5O8uhFJpaWlwdXVFamoqXFxcDB7zHbndqFgJU9qoWTQiIiIiIiIiIipgL8oV5VSgs1ISERERERERERH9UzAxRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWmBgjIiIiIiIiIiKzxMQYERERERERERGZJSbGiIiIiIiIiIjILDExRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWmBgjIiIiIiIiIiKzxMQYERERERERERGZJSbGiIiIiIiIiIjILDExRkREREREREREZomJMSIiIiIiIiIiMktMjBERERERERERkVliYoyIiIiIiIiIiMwSE2NERERERERERGSWmBgjIiIiIiIiIiKzxMQYERERERERERGZJSbGiIiIiIiIiIjILDExRkREREREREREZomJMSIiIiIiIiIiMksmJcbmz58PX19f2NnZoW7dujhy5Ei+2549exZvvfUWfH19YWFhgYiIiFzbjBs3DhYWFgZLhQoVTCkaERERERERERHRKzE6MbZu3TqEhYVh7NixOHHiBKpXr46QkBDcvXs3z+0fPXoEf39/TJkyBV5eXvnGrVy5Mu7cuaNf9u/fb2zRiIiIiIiIiIiIXpnRibFZs2bh/fffR2hoKCpVqoSFCxfCwcEBy5Yty3P72rVrY/r06ejatStsbW3zjWttbQ0vLy/9UrRoUWOLRkRERERERERE9MqMSow9efIEx48fR3Bw8P8CWFoiODgYsbGxigpy+fJlFC9eHP7+/ujevTsSExPz3TYjIwNpaWkGCxERERERERERkTGMSozdv38fWVlZ8PT0NFjv6emJpKQkkwtRt25dREZGIioqCgsWLEB8fDxee+01/Pnnn3luHx4eDldXV/3i4+Nj8msTEREREREREZF5+kfMStmqVSt07twZ1apVQ0hICH766SekpKRg/fr1eW4/atQopKam6pcbN24UcImJiIiIiIiIiOjfztqYjYsWLQorKyskJycbrE9OTn7hwPrGcnNzQ7ly5XDlypU8H7e1tX3heGVEREREREREREQvY1SLMRsbG9SqVQvR0dH6ddnZ2YiOjkb9+vVVK9TDhw9x9epVeHt7qxaTiIiIiIiIiIgoJ6NajAFAWFgYevfujaCgINSpUwcRERFIT09HaGgoAKBXr14oUaIEwsPDATwbsP/cuXP6/9+6dQtxcXFwcnJCQEAAAGDo0KFo164dSpcujdu3b2Ps2LGwsrJCt27d1HqfREREREREREREBoxOjHXp0gX37t3DmDFjkJSUhMDAQERFRekH5E9MTISl5f8aot2+fRs1atTQ/z1jxgzMmDEDr7/+OmJiYgAAN2/eRLdu3fDgwQMUK1YMjRo1wqFDh1CsWDGFb4+IiIiIiIiIiChvFiIif3chlEpLS4OrqytSU1Ph4uJi8JjvyO1GxUqY0kbNohERERERERERUQF7Ua4op3/ErJREREREREREREQFjYkxIiIiIiIiIiIyS0yMERERERERERGRWWJijIiIiIiIiIiIzBITY0REREREREREZJaYGCMiIiIiIiIiIrPExBgREREREREREZklJsaIiIiIiIiIiMgsMTFGRERERERERERmiYkxIiIiIiIiIiIyS0yMERERERERERGRWWJijIiIiIiIiIiIzBITY0REREREREREZJaYGCMiIiIiIiIiIrPExBgREREREREREZklJsaIiIiIiIiIiMgsMTFGRERERERERERmiYkxIiIiIiIiIiIyS0yMERERERERERGRWWJijIiIiIiIiIiIzBITY0REREREREREZJaYGCMiIiIiIiIiIrPExBgREREREREREZklJsaIiIiIiIiIiMgsMTFGRERERERERERmiYkxIiIiIiIiIiIyS0yMERERERERERGRWTIpMTZ//nz4+vrCzs4OdevWxZEjR/Ld9uzZs3jrrbfg6+sLCwsLREREKI5JRERERERERESklNGJsXXr1iEsLAxjx47FiRMnUL16dYSEhODu3bt5bv/o0SP4+/tjypQp8PLyUiUmERERERERERGRUkYnxmbNmoX3338foaGhqFSpEhYuXAgHBwcsW7Ysz+1r166N6dOno2vXrrC1tVUlJhERERERERERkVJGJcaePHmC48ePIzg4+H8BLC0RHByM2NhYkwpgSsyMjAykpaUZLERERERERERERMYwKjF2//59ZGVlwdPT02C9p6cnkpKSTCqAKTHDw8Ph6uqqX3x8fEx6bSIiIiIiIiIiMl//ylkpR40ahdTUVP1y48aNv7tIRERERERERET0L2NtzMZFixaFlZUVkpOTDdYnJyfnO7C+FjFtbW3zHa+MiIiIiIiIiIjoVRjVYszGxga1atVCdHS0fl12djaio6NRv359kwqgRUwiIiIiIiIiIqKXMarFGACEhYWhd+/eCAoKQp06dRAREYH09HSEhoYCAHr16oUSJUogPDwcwLPB9c+dO6f//61btxAXFwcnJycEBAS8UkwiIiIiIiIiIiK1GZ0Y69KlC+7du4cxY8YgKSkJgYGBiIqK0g+en5iYCEvL/zVEu337NmrUqKH/e8aMGZgxYwZef/11xMTEvFJMIiIiIiIiIiIitVmIiPzdhVAqLS0Nrq6uSE1NhYuLi8FjviO3GxUrYUobNYtGREREREREREQF7EW5opz+lbNSEhERERERERERKcXEGBERERERERERmSUmxoiIiIiIiIiIyCwxMUZERERERERERGaJiTEiIiIiIiIiIjJLTIwREREREREREZFZYmKMiIiIiIiIiIjMEhNjRERERERERERklpgYIyIiIiIiIiIis8TEGBERERERERERmSUmxoiIiIiIiIiIyCwxMUZERERERERERGaJiTEiIiIiIiIiIjJL1n93Af7tfEduf+VtE6a00bAkRERERERERERkDLYYIyIiIiIiIiIis8TEGBERERERERERmSUmxoiIiIiIiIiIyCwxMUZERERERERERGaJiTEiIiIiIiIiIjJLTIwREREREREREZFZYmKMiIiIiIiIiIjMEhNjRERERERERERklpgYIyIiIiIiIiIis8TEGBERERERERERmSUmxoiIiIiIiIiIyCwxMUZERERERERERGaJiTEiIiIiIiIiIjJLTIwREREREREREZFZMikxNn/+fPj6+sLOzg5169bFkSNHXrj9hg0bUKFCBdjZ2aFq1ar46aefDB7v06cPLCwsDJaWLVuaUjQiIiIiIiIiIqJXYnRibN26dQgLC8PYsWNx4sQJVK9eHSEhIbh7926e2x88eBDdunVDv379cPLkSXTs2BEdO3bEb7/9ZrBdy5YtcefOHf3y3XffmfaOiIiIiIiIiIiIXoHRibFZs2bh/fffR2hoKCpVqoSFCxfCwcEBy5Yty3P72bNno2XLlhg2bBgqVqyIiRMnombNmpg3b57Bdra2tvDy8tIv7u7u+ZYhIyMDaWlpBgsREREREREREZExjEqMPXnyBMePH0dwcPD/AlhaIjg4GLGxsXk+JzY21mB7AAgJCcm1fUxMDDw8PFC+fHl8+OGHePDgQb7lCA8Ph6urq37x8fEx5m0QEREREREREREZlxi7f/8+srKy4OnpabDe09MTSUlJeT4nKSnppdu3bNkSK1euRHR0NKZOnYo9e/agVatWyMrKyjPmqFGjkJqaql9u3LhhzNsgIiIiIiIiIiKC9d9dAADo2rWr/v9Vq1ZFtWrVUKZMGcTExKB58+a5tre1tYWtrW1BFpGIiIiIiIiIiP5jjGoxVrRoUVhZWSE5OdlgfXJyMry8vPJ8jpeXl1HbA4C/vz+KFi2KK1euGFM8IiIiIiIiIiKiV2ZUYszGxga1atVCdHS0fl12djaio6NRv379PJ9Tv359g+0BYOfOnfluDwA3b97EgwcP4O3tbUzxiIiIiIiIiIiIXpnRs1KGhYVh8eLFWLFiBc6fP48PP/wQ6enpCA0NBQD06tULo0aN0m8/ZMgQREVFYebMmbhw4QLGjRuHY8eOYeDAgQCAhw8fYtiwYTh06BASEhIQHR2NDh06ICAgACEhISq9TSIiIiIiIiIiIkNGjzHWpUsX3Lt3D2PGjEFSUhICAwMRFRWlH2A/MTERlpb/y7c1aNAAa9aswRdffIHPPvsMZcuWxZYtW1ClShUAgJWVFU6fPo0VK1YgJSUFxYsXxxtvvIGJEydyHDEiIiIiIiIiItKMhYjI310IpdLS0uDq6orU1FS4uLgYPOY7crtRsRKmtDFqe2PiGxubiIiIiIiIiIiM96JcUU5Gd6UkIiIiIiIiIiL6LzC6KyUVHLZGIyIiIiIiIiLSDluMERERERERERGRWWJijIiIiIiIiIiIzBITY0REREREREREZJaYGCMiIiIiIiIiIrPExBgREREREREREZklJsaIiIiIiIiIiMgsMTFGRERERERERERmiYkxIiIiIiIiIiIyS0yMERERERERERGRWbL+uwtAfw/fkduN2j5hSpt/RGwiIiIiIiIiIrWwxRgREREREREREZklJsaIiIiIiIiIiMgssSsl/esY01WT3TSJiIiIiIiIKD9sMUZERERERERERGaJiTEiIiIiIiIiIjJLTIwREREREREREZFZ4hhjRDlw/DIiIiIiIiIi88HEGFEBMSbpBjDxRkRERERERKQ1dqUkIiIiIiIiIiKzxBZjRP8BWrdG07KL6T8ltinxiYiIiIiI6N+NiTEiIhMw6UZERERERPTvx8QYEdE/ECeCICIiIiIi0h4TY0REZoZJNyIiIiIiomeYGCMiItVo2cWUY+kpj21KfCIiIiKi/zImxoiIiChPTEYqj21sfCY6iYiIiAqWSYmx+fPnY/r06UhKSkL16tUxd+5c1KlTJ9/tN2zYgNGjRyMhIQFly5bF1KlT0bp1a/3jIoKxY8di8eLFSElJQcOGDbFgwQKULVvWlOIRERERUR7+KQlDc0pG/lM+FyZRiYiI8mZ0YmzdunUICwvDwoULUbduXURERCAkJAQXL16Eh4dHru0PHjyIbt26ITw8HG3btsWaNWvQsWNHnDhxAlWqVAEATJs2DXPmzMGKFSvg5+eH0aNHIyQkBOfOnYOdnZ3yd0lERERERK+Mych/dmxj4/MzVx7blPhE9O9gdGJs1qxZeP/99xEaGgoAWLhwIbZv345ly5Zh5MiRubafPXs2WrZsiWHDhgEAJk6ciJ07d2LevHlYuHAhRAQRERH44osv0KFDBwDAypUr4enpiS1btqBr165K3h8RERERERFRgWIyUnlsY+PzM1ce29j4/5XkslGJsSdPnuD48eMYNWqUfp2lpSWCg4MRGxub53NiY2MRFhZmsC4kJARbtmwBAMTHxyMpKQnBwcH6x11dXVG3bl3ExsbmmRjLyMhARkaG/u/U1FQAQFpaWq5tszMevfobzCfGixgT/98a29j4/MyVxzY2Pj9z5bGNjc/PXHlsY+PzM1ce29j4/MyVxzY2Pj9z5bGNjc/PXHlsY+PzM1ce29j4/MyVxzY2Pj9z5bGNjc/PXHlsY+Nr/ZkrpXs9EXnxhmKEW7duCQA5ePCgwfphw4ZJnTp18nxOoUKFZM2aNQbr5s+fLx4eHiIicuDAAQEgt2/fNtimc+fO8s477+QZc+zYsQKACxcuXLhw4cKFCxcuXLhw4cKFC5d8lxs3brww1/WvnJVy1KhRBq3QsrOz8fvvv6NIkSKwsLB46fPT0tLg4+ODGzduwMXFRdWyaRlb6/iMXfDxGbvg4zN2wcdn7IKPz9gFH5+xCz4+Yxd8fMYu+PiMXfDxGbvg4zN2wcc3l9gigj///BPFixd/4XZGJcaKFi0KKysrJCcnG6xPTk6Gl5dXns/x8vJ64fa6f5OTk+Ht7W2wTWBgYJ4xbW1tYWtra7DOzc3NmLcCAHBxcdFkB9Y6ttbxGbvg4zN2wcdn7IKPz9gFH5+xCz4+Yxd8fMYu+PiMXfDxGbvg4zN2wcdn7IKPbw6xXV1dX7qNpTEvbmNjg1q1aiE6Olq/Ljs7G9HR0ahfv36ez6lfv77B9gCwc+dO/fZ+fn7w8vIy2CYtLQ2HDx/ONyYREREREREREZFSRnelDAsLQ+/evREUFIQ6deogIiIC6enp+lkqe/XqhRIlSiA8PBwAMGTIELz++uuYOXMm2rRpg7Vr1+LYsWNYtGgRAMDCwgIff/wxJk2ahLJly8LPzw+jR49G8eLF0bFjR/XeKRERERERERERUQ5GJ8a6dOmCe/fuYcyYMUhKSkJgYCCioqLg6ekJAEhMTISl5f8aojVo0ABr1qzBF198gc8++wxly5bFli1bUKVKFf02w4cPR3p6Ovr374+UlBQ0atQIUVFRsLOzU+Et5mZra4uxY8fm6o75T4+tdXzGLvj4jF3w8Rm74OMzdsHHZ+yCj8/YBR+fsQs+PmMXfHzGLvj4jF3w8Rm74OMztiELkZfNW0lERERERERERPTfY9QYY0RERERERERERP8VTIwREREREREREZFZYmKMiIiIiIiIiIjMEhNjRERERERERERklpgY+4fr3bs39u7d+3cXw+ykpKRgyZIlGDVqFH7//XcAwIkTJ3Dr1q2/uWT527t3LzIzM3Otz8zMVH0fSktLw5YtW3D+/HlV4+aUkpKiWWyi/7KHDx8iLS3NYCEi46l1Hvrrr7/w6NEj/d/Xr19HREQEfvnlF1Xia+nfXPZ/M63qoTdu3MDNmzf1fx85cgQff/wxFi1apCgu5W/s2LG4fv36310MInoJJsYUSk9Px+jRo9GgQQMEBATA39/fYFEqNTUVwcHBKFu2LCZPnvyPTswUpMuXL2PRokWYNGkSJkyYYLAodfr0aZQrVw5Tp07FjBkz9BXjTZs2YdSoUYpir1ixAtu3b9f/PXz4cLi5uaFBgwaKT5pNmzbVV55ySk1NRdOmTRXFfueddzBv3jwAzyrJQUFBeOedd1CtWjV8//33imIDwNSpU7Fu3TqD1ytSpAhKlCiBU6dOKY7/b/VfqcCKCP6pEyCfPn36lZd/svj4eLRp0waOjo5wdXWFu7s73N3d4ebmBnd3d0WxV65ciYyMjFzrnzx5gpUrVyqKDQDZ2dm4dOkS9u/fj7179xos5mzVqlVo2LAhihcvrj8/RERE4IcffvibS/b3OXHiBM6cOaP/+4cffkDHjh3x2Wef4cmTJ4pia3ke6tChg/63kpKSgrp162LmzJno0KEDFixYoCi21v7NZf+30rIe+u6772L37t0AgKSkJLRo0QJHjhzB559/rrgOrWUdV2talv2HH35AmTJl0Lx5c6xZsybP86mpCqKeqNW5KL+61pkzZ3D58mVFn5OW36eW5yHK29OnT2FtbY3ffvtN2xcSUqRr167i7e0tw4cPl6+++koiIiIMFjXcvXtXZs6cKdWqVRNra2tp2bKlbNiwQZ48eaJKfBGRjIwMuXHjhly/ft1g+SdatGiRWFlZiaenp1SvXl0CAwP1S40aNRTHb968uQwbNkxERJycnOTq1asiInLgwAEpXbq0otjlypWT6OhoERE5ePCgODg4yDfffCPt2rWTTp06KYptYWEhd+/ezbX+4sWL4uzsrCi2p6enxMXFiYjIt99+KwEBAZKeni5ff/21BAYGKootIuLr6ysHDhwQEZFffvlF3NzcZMeOHdKvXz9p0aKF4vipqal5LmlpaZKRkWFy3CdPnoi/v7+cO3dOcRnz0qhRI1m5cqWIiNy5c0dcXFykfv36UrRoURk/frzi+Lt27VIc40WWLFkilStXFhsbG7GxsZHKlSvL4sWLVYm9cuVKadCggXh7e0tCQoKIiHz11VeyZcsWo+JYWFiIpaWlWFhY5LnoHrO0tFSl3Fpp0KCB1K9fX9auXSu7d++WmJgYg0UJS0tLSU5OzrX+/v37ij+X2NhY8fPzy/M7UOsz13o//+OPP2Tx4sUycuRIefDggYiIHD9+XG7evGlyzK+//lqKFi0qkyZNEnt7e/15aPny5dKkSRNVyl0QMjIy5MKFC/L06VNV4gUFBcnGjRtFROTq1atiZ2cn3bp1k4CAABkyZIii2Fqeh4oUKSK//fabiIgsXrxYqlWrJllZWbJ+/XqpUKGCotjPU7s+V5Bl/7dLTU2VzZs3K64TaFkPdXNzkwsXLoiIyOzZs6VBgwYiIrJjxw7x8/NTFFvLOq6IyKlTp/JcTp8+LZcuXZLHjx//Y8t+4sQJGTRokBQtWlTc3Nzkgw8+kCNHjiiOq3U9Uctzke48n99ia2srvXr1kr/++svo2Fp+n1qeh3R0n/O/zc8//yz79u3T/z1v3jypXr26dOvWTX7//XdFsf38/PTXoloxm8RYUlKS9OjRQ7y9vcXKyirXj89Urq6usn//fhVL+mLHjx+XgQMHip2dnRQtWlQ+/vhjuXTpksnxLl26JI0aNcr1eahxUdK4cWNZsWKFPHr0SFGc55UqVUqmTJmiasycXFxc5MqVKyJiWCFJSEgQW1tbRbHt7e31FdThw4dLz549RUTkt99+k6JFi5oUs1OnTtKpUyextLSU1q1b6//u1KmTtG/fXnx9fSUkJERRue3s7CQxMVFERHr27CkjRowQEZHr16+Lo6OjotjPxx88eLD0799fRJ4l9dzc3BTHf9nJt1SpUjJmzBjJysoyOnbx4sU1S4xpWYEVEbGxsRF/f3+ZOHGi/vNXy+jRo8XR0VFGjhwpP/zwg/zwww8ycuRIcXJyktGjRyuKrWZFLSEh4ZUXNWh1XHR0dNTvK2rLL+keFxcn7u7uimJXr15dOnfuLOfOnZM//vhDUlJSDBY1aLmfnzp1SooVKyYBAQFibW2t3xc///xz/fHdFBUrVpTNmzeLiOF56MyZM1KkSBHF5RYRyczMlOnTp0vt2rXF09NT3N3dDRYl0tPTpW/fvmJlZSVWVlb68g8cOFDCw8NNjpvz/DxlyhR54403RERk//79UrJkSUVl1vI8lPPc37lzZxk3bpyIiCQmJoq9vb2i2Dpa1ee0Lvv27dslKioq1/qoqCj56aefTIqZ382wvBYlOnfuLHPnzhURkUePHknZsmWlUKFCYm1trb9wNoWW9VBHR0eJj48XEZF27drp69PXr18XOzs7RbG1qOPmpGUiReuy6zx58kS+//57adu2rRQqVEiqVq0qERERJp/vtK4nanku2rJli5QvX16WLFkip0+fltOnT8uSJUukYsWKsnbtWlm9erWULFlSPv30U6Nja/l9anke0rGwsJAmTZrIqlWrTNqfXyYzM1M2bNggEyZMkAkTJsiGDRtUuYFVpUoV2b59u4iInD59WmxtbWXUqFFSr1496dOnj6LYS5YskdatW+tvQGrBbBJjLVu2lEqVKsnXX38tmzdvli1bthgspvL19dXsovh5t2/flilTpkj58uXF0dFRevXqJc2bNxdra2uZNWuWSTEbNGggjRs3lp9++klOnjwpcXFxBosSQ4YMkWLFiomLi4u89957EhsbqyiejrOzs6aZ9GLFismJEydExPAk8Msvvyg+4OWMHRgYqL/Lc+XKFZMTTH369JE+ffqIhYWFdOnSRf93nz59pH///jJ58mS5d++eonKXLVtW1q1bJw8fPpRixYrp78LExcWpcpHm7e2tv1Nfrlw5Wb9+vYiIXLhwQXFrNxGRFStWSMmSJeWLL76QrVu3ytatW+WLL74QHx8f+eabb2TSpEni5uYmX375pdGxv/zyS+ndu7dqLSJy0rICKyJy7949mTVrllSvXl2sra3ljTfekHXr1ilqRadTtGhRWbNmTa71a9asUbzPFETSQCtaHRebNGkiO3fuVCWWjq4VrqWlpVStWlVq1KihX6pVqybOzs7SuXNnRa/h4OAgly9fVqnEedNyP9eqZYednZ0+GZsz7qVLl1T57Ys8S157e3vLjBkzxM7OTiZOnCj9+vWTIkWKyOzZsxXFHjx4sNSqVUv27dsnjo6O+vJv2bJFUStjZ2dn/c3A4OBgfct8NY6JWp6HqlatKrNnz5bExERxcXGRgwcPiojIsWPHxNPTU1FsHa3qc1qXvWrVqvoLqZx+/vlnqVatmkkxX5ZAUesGsFat6bWsh9apU0dGjBghe/fuFTs7O335Y2NjpUSJEopia1HHzUnLRIrWZdfJyMiQtWvXyhtvvCHW1tbSuHFjCQgIEGdnZ1m7dq3R8bSuJ2p5Lqpdu3a+SfHatWuLiMjmzZvF39/f6Nhafp9anod0Tp48KYMHD5ZixYqJq6ur9O/fXw4fPqxK7N9++038/f3FwcFBX6dzdHQUX19fOXPmjKLYOffHsWPHyltvvSUizxr2KD1fBAYGipOTk9ja2kq5cuUM6qRq9BgTMaPEmJOTk5w8eVL1uKtWrZK3335b0tPTVY8t8uzOwsaNG6VNmzZSqFAhqVWrlixYsMDgLtemTZtMvpvp4OAg58+fV6u4uTx9+lS+//57ad++vRQqVEgqVqwo06dPl6SkJJNj9u3bVxYsWKBiKQ3169dPOnbsKE+ePBEnJye5du2aXL9+XWrUqKG4iey7774rNWvWlH79+omDg4Pcv39fRER++OEHqVy5sqLY48aNk4cPHyqKkZ/58+eLtbW1uLm5SfXq1fUtq+bMmaNKt54BAwZI6dKlJTg4WIoUKSJ//vmniIh89913qhzsmjVrJuvWrcu1ft26ddKsWTMRedY1r3z58kbH7tixozg7O4u3t7e88cYbBi32lDbX1rIC+zxda9QiRYpIkSJFZNCgQYouplxdXfNszXrx4kVxdXVVUFJtK2pqddF8ES2Oi1euXJHg4GCJjIyUY8eO5epqYopx48bJuHHjxMLCQoYOHar/e9y4cTJ58mRZs2aN4uRS06ZN5eeff1YUwxhq7+dateyoWLGifp/LGXfOnDmqVQD9/f3lxx9/1L+G7n3Mnj1bunXrpih2qVKl9EnfnOW/fPmyoiRT06ZNpVevXrJy5UopVKiQPqkaExOjuIuZluehDRs2SKFChcTS0lKCg4P16ydPniwtW7ZUFFtHq/pczrLn7FKqVtnt7Oz0F1I5xcfHi4ODg0kxn+9K/qJFadm1aE2vZT109+7d4ubmJpaWlhIaGqpfP2rUKMV1Fi3ruCLaJlK0LvuxY8dkwIABUrhwYfH29pYRI0YY3BSaM2eOeHh4GB1X63qiluciOzu7PI9Z58+f19fl4uPjTWqZquX3qeV56Hm6+mK7du2kUKFCUrlyZZk5c2aerfhfVb169aRdu3YGXRt///13ad++vdSvX19Red3d3eXs2bMiItKwYUP55ptvRMT07zGnnHXQvBY1mE1irGLFivrMsZoCAwPF2dlZnJycpEqVKqpnL4sUKSLu7u7y0Ucf5ZvY++OPP8TX19ek+EFBQQZ9gbWUnJwsEydOFDs7OylUqJB06NBB3/LIGJMnT5aiRYtK7969ZcaMGTJ79myDRamUlBQJDg4WNzc3sbKyEh8fHylUqJA0btxYceLpjz/+kAEDBkj79u0NLgbHjBkjkyZNUlp0TR09elQ2bdqkv1gQEfnxxx9V6Ur85MkTmT59ugwePNjgdzpr1ixVxqSys7PLM0lz6dIl/YH62rVrJh20c7bQy2tRQssKbF5u3bolY8eOFVtbW3F0dBQrKytp1KiRfnwZYwwcOFA++eSTXOs//fRT+eijjxSVU6uK2t8xrpNax0XdWF1qj4+WmZkpkZGRcvv2bZNjvMimTZukUqVKsnz5ctUSei+j5n6uVcuOxYsXS4kSJWTt2rXi6Ogo3333nUyaNEn/fzU4ODjou5p4eXnJ8ePHReTZ2CYuLi6KYuf8/eT8XOLi4hTFjouLkypVqoiLi4tBRXjgwIGKk3lan4fu3LkjJ06cMOiyf/jwYdWSWVrW57Qsu6enZ57HvJ07d0qxYsUUx9eSVq3ptayHijw7rj8/3k98fHyeY0kaQ+s6rpaJFC3LXqVKFbG2tpbWrVvL5s2bJTMzM9c29+7dEwsLC6Nja11P1PJcFBgYKL179za4wfbkyRPp3bu3vsXl/v37TbrG1fL7PHXqlGbnofw8fvxYZs2aJba2tmJhYSG2trbSs2dPk+pmdnZ2edZ3zpw5o/jmcrt27SQkJEQmTJgghQoV0o+1umPHDilbtqyi2AXBbBJjO3bskDfeeCPPu1JKaJ29XLlypSZ9i3Wio6Olfv36snv3brl//76qYy/kdPjwYfnggw/Ezc1NP55Tv379xN7e3ugmz76+vvkuavSn19m/f7/Mnz9fpk6dqnoXJbXUqFFDX7nRdXvKb1FTZmamnDx5UvFAigWlbNmy+ju5OY0YMULKlSsnIs8Sf8WLFy/oouUrOztbrl+/LmlpaZpUYHWePHkiGzZskFatWom1tbXUq1dPFi9eLA8fPpT4+Hjp3r27VKxY0ei4AwcOFBcXF6lcubL069dP+vXrp69I6JJmusVYWlXUCrqLpprHxYoVK8qbb74phw4dkvj4eFXHR7O1tZVr164pipGfgprwQKv9XMuWHatXr5aAgAD951KiRAlZsmSJopg5lStXTg4dOiQiz+7s6sb+Wrt2reKExGuvvSZz5swREdF/LiLPjgtKx7zMy19//aVJd3a1Xb58WaKiovRjDGZnZ6sWu6Dqc2rr37+/VK1aVd9iUeTZ51StWjXp16+faq+Tnp4u58+fVzX5rnVr+n379v3j66EFSctEipYmTJigaDKWl9Eq0amj1bnowIEDUqRIESlWrJg0b95cmjdvLh4eHlKkSBF9i+OVK1fKtGnTFL9WQfjrr79UnRhP5Nm1yYcffiju7u5SsmRJ+fzzz+XatWuyd+9ead68ub6lpDGqVauW582I6OhoqVKliqLyXr9+Xdq0aSPVqlUz2Ec+/vhjGTRokKLYItpMdpST2STG3NzcxMbGRiwtLcXJyUnVQWb/zXJeiKg99kJycrLMmDFDPyvdW2+9JT///LNBRVA3/oi5WLZsmX7ckpzWr18vkZGRRscbN26cvhuvlknaIUOG6A9wmZmZ0rBhQ7GwsBBHR0fZvXu3otg6Fy5ckAEDBkizZs2kWbNmMmDAANUGE//hhx/ExsZGX9Hu16+fVK9eXWxtbWXbtm0i8qy1kClJGp27d+/Kvn37ZN++fYqaOOtkZWVJoUKFFE2u8TK6LmWFCxeWIUOG5Dm2wJ07d0y6i9mkSZNXWpo2bWpS2bWoqBXEuE5aHRe1HKurVq1a8uuvv2oSuyAmPNByP9e6ZYfIswt6tS5wchoxYoR+XMW1a9eKtbW1BAQEiI2NTZ43Eoyxb98+cXJykg8++EDs7OxkyJAh0qJFC3F0dJRjx46ZHNfPz0/fJSanP/74Q5UbYytXrpSGDRuq3pX6/v370qxZM33dSndsCQ0NlbCwMMXlFlG3Pvf8kAAvWpRKSUmRevXqibW1tf4mp7W1tTRt2lT++OMPxfHv3r0rbdq0yXesMaWOHTuWZ2t63Xh1/zRaTUams3fvXunevbvUr19ff7G6cuVKVVozap1I0bLs/wVanIvS0tJkwYIF+hulCxculLS0NFVia/l9ap2gmTlzplSpUkXfk2Dbtm25Jgi7ceOGWFlZGR17+/btUrlyZdmwYYPcuHFDbty4IRs2bNCP9/hPvaGi1WRHOVnDTERERGga//jx4zh//jwAoHLlyqhRo4YqcdPT0zFlyhRER0fj7t27yM7ONnj82rVriuLv3r1b0fNfpGTJkihTpgz69u2LPn36oFixYrm2qVatGmrXrm3ya4gIAMDCwsLkGM8bPHgwAgICMHjwYIP18+bNw5UrVxTtS+Hh4fjmm29yrffw8ED//v3Ru3dvo+KNHTsWAJCVlYWmTZuiWrVqcHNzM7l8+dm4cSN69OgBANi2bRvi4+Nx4cIFrFq1Cp9//jkOHDigKP7333+Prl27IigoCPXr1wcAHDp0CFWqVMHatWvx1ltvKYrfvn17XLhwAd988w0uXboEAGjVqhW2bNkCX19fAMCHH35oUuz09HQMGjQIK1eu1P8+rays0KtXL8ydOxcODg4mxbW0tETZsmXx4MEDlC1b1qQYL3Pu3DnMnTsXb775JmxtbfPcpmjRoiYdJ7Q8tgBA9+7d0b17dzx69AgPHz6Eh4eH4ph+fn6Ii4tD6dKlDdZHRUWhYsWKiuMD2h0XmzVrhlOnTiEgIECVcuY0adIkDB06FBMnTkStWrXg6Oho8LiLi4vJsZ//rLWg5X7u6uqKnTt34sCBAzh16hQePnyImjVrIjg4WFGZ4+PjkZmZibJly8LBwUF/HLl8+TIKFSqkP24pMWXKFP3/u3TpglKlSiE2NhZly5ZFu3btFMVu1KgR4uLiMGXKFFStWhW//PILatasidjYWFStWtXkuAkJCcjKysq1PiMjAzdv3lRSZCxYsABjxozBxx9/jC+//FL/Om5uboiIiECHDh1Mjv3JJ5+gUKFCSExMNDiWdOnSBWFhYZg5c6aisgPqHnNdXV1Vi/Uqr3Xw4EHs3LkTp06dgr29PapVq4bGjRurEv/jjz9GSkoKDh8+jCZNmmDz5s1ITk7GpEmTFH/uEyZMwNChQ1GrVi2D9c2aNcP06dPRoEEDk2MfPXoUu3fvzrPuP2vWLJPj9unTB4mJiRg9ejS8vb1VrT9///336NmzJ7p3744TJ04gIyMDAJCamorJkyfjp59+UhS/QYMGiI+Px7fffquvy3Xu3BnvvvsunJ2dAQA9e/b8R5Q9LCzslbc19vusUaPGK39vJ06cMCr2i+Q8F6nF2dkZH3zwgaoxAW33xdOnT6N58+Zwc3NDQkIC3n//fRQuXBibNm1CYmIiVq5cqbj8CxYs0NcTvb2989zGw8MDS5cuNTp227ZtAQDvvPOOfj/SXU/rzv0iAgsLizzPty9z9epVLF++HFevXsXs2bPh4eGBn3/+GaVKlULlypWNjqcTFhaGPn36YNq0afrfOwC0bt0a7777rslxc7IQ3SdBJrl79y66du2KmJgYfUIiJSUFTZs2xdq1a/O86DFGt27dsGfPHvTs2TPPE9iQIUMUxdeKiGD//v0ICgqCvb296vFXrlyJ6dOn4/LlywCAcuXKYdiwYSafEHMqUaIEtm7dmquic+LECbRv315R5dvOzg4XLlzIdVGTkJCAihUr4q+//lIU+/z58/Dz8zM5xotiX7lyBSVLlkT//v3h4OCAiIgIxMfHo3r16khLS1MUv0yZMujevTsmTJhgsH7s2LFYvXo1rl69qii+lv7v//4Pv/76K+bNm4eGDRsCAPbv34/BgwejRYsWWLBggcmxt23bhmnTpmHBggWoUqWKWkX+18uZNMhJadJgyZIlGDduHGbOnIl+/fphyZIluHr1KsLDw7FkyRJ07dpVUbm1PC4uWrQIkyZNQt++fVG1alUUKlTI4PH27dubHNvS0lL//5znICUVp5yuXr2KiIgI/c2lSpUqYciQIShTpoyiuFp7+vQp7O3tERcXp/rv8/XXX0ffvn1z3SxZvXo1lixZgpiYGFVf759u69atAICOHTtixYoVBombrKwsREdHY+fOnbh48aLJr1GpUiVMnjwZHTt2hLOzM06dOgV/f3/89ttvaNKkCe7fv29ybC8vL+zYsQPVq1c3iH3t2jVUq1YNDx8+NDk2vZi3tzd++OEH1KlTBy4uLjh27BjKlSuHrVu3Ytq0adi/f7/Jsa2srHDnzp1cN2YePHgADw8Pk4+NkydPxhdffIHy5cvD09PT4LhrYWGBXbt2mVxmZ2dn7Nu3D4GBgSbHyE+NGjXwySefoFevXgb7+cmTJ9GqVSskJSWp/ppqUbvsTZs2Nfj7xIkTyMzMRPny5QEAly5dgpWVFWrVqmX09zl+/PhX3lZ3A91UDx48wJgxY/JN0v7++++K4l++fDnf2GPGjDE5rpb7YnBwMGrWrKlP0OhiHzx4EO+++y4SEhJMjl0Q9uzZ88rbvv7660bHbtWqFRo2bIi9e/fi/Pnz8Pf3x5QpU3Ds2DFs3LjR2OLqubq64sSJEyhTpozB5379+nWUL18ejx8/Njm2jtm0GAOeVZ62bNli0LKrffv2sLKyMjnmoEGD8Oeff+Ls2bP6u4Dnzp1D7969MXjwYHz33XeKyvzzzz9j+/bt+gtuLaSkpGDp0qUGn0vfvn0V3TEUETRv3hxnz55VvbXLrFmzMHr0aAwcONAgEfHBBx/g/v37+OSTTxTFf/DgQZ7v3cXFRVHFGHiW3T99+nSuC/dTp06hSJEiimJXqVIF165d0yQx5unpiXPnzsHb2xtRUVH6ZM+jR48U/X507ty5g169euVa36NHD0yfPl1xfODZfn7kyJE8T755vfar+v7777Fx40Y0adJEv65169awt7fHO++8oygx1qtXLzx69AjVq1eHjY1NrmSK0gqJ7mLzeRYWFrCzs0NAQIBR+9Obb775yttu2rTplbd9Xp8+fdC3b99cx5bDhw8rShq89957sLe3xxdffIFHjx7h3XffRfHixTF79mzFSTFA2+Oi7o7r88llAIqTVy9qiXLmzBmT4wLAjh070L59ewQGBuqP5wcOHEDlypWxbds2tGjRQlF8HS0q34UKFUKpUqUUJwbzcvLkyTzP+/Xq1cPAgQNVe53bt29j//79eX4uz7eaNsZPP/0EKysrhISEGKzfsWMHsrOz0apVK6PidezYUf//55OFumS40tY/8fHxebb2t7W1RXp6uqLY6enpeba0+P333/NtxWgKLepzAJCZmYmYmBhcvXpV3zrn9u3bcHFxgZOTk9Hx5syZg/79+8POzg5z5sx54bZK9kPg2WevS1y5u7vj3r17KFeuHKpWraq4NY3u5sDzTp06hcKFC5scd/bs2Vi2bBn69OmjoHR58/HxgVZtIi5evJhnSz9XV1ekpKSo8hpaJVLULnvO8+asWbPg7OyMFStWwN3dHQDwxx9/IDQ0FK+99prRsZUmu4zRs2dPXLlyBf369cuVpFVq8eLF+PDDD1G0aFF4eXnlSgD/k77PnI4ePZpn758SJUqomvzV6rrF2GSXMUaOHIlJkyYhLCzMoFVXs2bNMG/ePEWxbW1t82yIcenSJcUNkfRU6ZD5L3D58mUpW7asODg46Acjd3BwkPLlyxsM+GksFxcXOXLkSK71hw8fFldXVwUlfsbX11fOnTunOE5+jh49KoULF5YSJUrox4soWbKkFClSRD9DlakqVaqk7/OvJl9fX1mxYkWu9ZGRkaoMuFm5cmWZO3durvVz5swxaXDmnIYPHy6lS5eWXbt2SWZmpmRmZkp0dLSULl3a6MG2n/fzzz9LYGCgbNu2TW7fvq3qwLtjx44VV1dXqVChgpQqVUoeP34sIiJLly6VevXqKYotItKqVStZtmxZrvXLli2TN954Q3H8rVu3irOzs1hYWIirq6u4ubnpF6VjDNrb2+f5G/3tt99MnmpeJzIy8oWLUjkHOs9v8PPGjRu/8iQLL5uhU63ZOp2dnfMcT+vy5cuqHHdFtBvXSavjYkFKS0uTb775RmrXrq14TJrAwMB8J8ZQa9KQRYsWiZWVlXh6ekr16tUlMDBQvyh9jSVLlkjr1q31Y4yoxcXFJc+ZtI8dOyZOTk6qvMby5cvFxsZGnJycpHTp0qpOZKMbq+R5P//8s1SrVs3kuL6+vnmOMaYGrWa7FXl2jvviiy/0sa9duyZZWVnSuXNneeutt5QV/P/Tqj6XkJAgFSpUEAcHB7GystJ/LoMHD5b/+7//Mylmzu9R6wmVgoKCJCoqSkSezZjWs2dPuXnzpgwfPlz8/f1NiqmrO1haWur/r1tcXFzE0tJS0ezLXl5emo0vqtVkZCLPxgDUTRKQ8ze0YsUKxfVnEW2P5VqWvXjx4vnOAujt7a0ottacnJwkLi5Ok9ilSpWSKVOmaBJby+9Tq9moc1L7uuXUqVP6Mcqen4BEzQlJHB0d9ZPt5Pxs4uPjxdbWVlFsLSc70jGbxFirVq2kZcuWBpXX+/fvS8uWLaV169Ymx3VycpKTJ0/mWn/ixAlxdnY2Oa7OqlWr5O2339YPsK62Ro0aSZ8+fQxmc3r69Kn07t1bXnvtNUWxt27dKo0aNcpzoGMlbG1t87wovnTpkuIfncizZI+9vb2MGTNGYmJiJCYmRkaPHi0ODg6yaNEiRbEzMjLknXfeEQsLCylUqJAUKlRIrKysJDQ01GCWHVM8n9hQcyIFEZENGzbIrFmz5MaNG/p1kZGRigclFhFZsGCBFCtWTAYMGCCrVq2SVatWyYABA8TDw0MWLFggP/zwg34xRdmyZWXIkCGa/I6aNWsmnTt3Npg99tGjR9K5c2dp3ry56q+npl9//VXq1q0rv/76q6SlpUlaWpr8+uuvUr9+fdm+fbvs379fKleuLH379v27i2pAq6TBxIkTNZt9UUer42Je1Bi4Oqc9e/ZIr169xNHRUT/Ta143hoxha2ub5wXgxYsXVTmei2hb+Q4MDBQnJyextbWVcuXKqTYbcNu2baVz586SmZmpX5eZmSlvvfWWtGzZUo2iS8mSJWXSpEm5BvRVg52dXZ4X3vHx8SbfMHjy5Ik0a9ZMs4SBVrPdijy7APbw8JCWLVuKjY2NvP3221KxYkXx9PRUdHM2J63qcx06dJAePXpIRkaGwYXO7t27JSAgQHG5tbZq1SpZvny5iDw7RxQtWlQsLS3Fzs5O1q5da1LMyMhIWb58uVhYWMjs2bMNblitWbNGDh48qKjMU6dOVe1C73laTkY2efJkqVSpkhw6dEicnZ1l3759snr1ailWrJh+lloltDyWa1l2JyenPCeq2rVrl+IbHZmZmTJ9+nSpXbu2eHp6qj65XFBQkGY385ydnfXHE7Vp+X0WRIJG7esWCwsL/c3e/G6Kq3G9WKJECf3EIznPF5s2bTL5RoROQUx2ZDaJMQcHBzl9+nSu9XFxcYpmRWzfvr00btxYbt26pV938+ZNef3116Vjx44mxdTd+dAtzs7O4uTkJFWqVFGt0q1jZ2cn58+fz7X+7NmzYm9vryh2zpOvnZ2dagfrypUr62fSymnixImKp5nV+frrr6VEiRL6A4Wfn1+erdRMdfHiRVm/fr1s27ZNtZnXdEm8/Ba15EwAqSWvg7OaB2wHBwfNTr5nzpyR4sWLS5EiRfQzahYpUkRKlCiR5x1CY125ckU+//xz6dq1q/6k9tNPP6kSu3LlynnOnLV//36pVKmSiIjs3LlTfHx8TIr/9OlT2blzp8EsQ7du3TKYvcsUWiUNqlWrJpaWllK/fn2ZP3++3Lt3T1E586LVcXHKlCkGF3lvv/22WFhYSPHixRXd7b1z546Eh4dLQECAeHh4yMCBA8Xa2lrOnj1rcsycSpYsmedMvevWrTN5v3uelpVvrWYDPnv2rBQpUkTKlCmjb2VZpkwZKVasmGpJ1cKFC6uWlHmep6dnntPB79y5U4oVK2Zy3KJFi2o6U68Ws93qpKSkyKRJk6Rz587SqlUr+fzzz+X27duqxBbRrj5XuHBh/QzRz7cAUFpPzEtmZqacPHnylVsqGys9PV2OHz+uyvE9JibGIBGplqysLGnZsqX4+/tL27ZtVZ0JVMuW6NnZ2fpksu43ZGdnp28tqZSWx3Ity96zZ0/x9fWV77//Xj8L4MaNG8XPz0969eqlKPbo0aPF29tbZsyYIXZ2djJx4kTp16+fFClSRGbPnq247EeOHJFmzZpJTEyM3L9/X9XeKH379pUFCxYoLmNetPw+CyJBo/Z1S0JCgn72cy1nA//000+lUaNGcufOHX3vjv3794u/v7+iOlFO+/btk/nz58vUqVP1rQLVYjaD7xcuXBg//vhjrhliDhw4gHbt2pk8Vs+NGzfQvn17nD17Fj4+Pvp1VapUwdatW1GyZEmjYxbkoIqenp5YtWoV3njjDYP1O3bsQK9evZCcnGxy7BUrVrzwcWNnYNT5/vvv0aVLFwQHBxuMSRMdHY3169ejU6dOJsXNy71792Bvb2/SGBr/JVlZWZg8eTIWLlyI5ORkXLp0Cf7+/hg9ejR8fX3Rr1+/v7uIL/Tmm2+ia9eueOeddzSJ/+jRI3z77be4cOECAKBixYro3r274gHWtRzEEgDs7e1x9OjRXAOHnzlzBnXq1MFff/2F69evo2LFinj06JFRsa9fv46WLVsiMTERGRkZ+n1myJAhyMjIwMKFC00u97lz59C4cWO4ubnpx+fYt28f0tLSsGvXLkUDoZ89exbffvst1q5di5s3b6JFixbo3r07OnbsqMpsTJGRkS8co8PU46Kfnx++/fZbNGjQADt37sQ777yDdevWYf369UhMTMQvv/xidMx27dph7969aNOmDbp3746WLVvCysoKhQoVwqlTp1CpUiWTyprThAkT8NVXX2HkyJH68/OBAwcwdepUhIWFYfTo0Ypfo1+/fqhdu7YmM19p6fbt25g3b57BTH0DBw5UNHZRTsOHD0fhwoUxcuRIVeLl9H//93+IjY3F5s2b9ZMoXLlyBW+99RZq166NJUuWmBT3k08+ga2trcGMmlpQc7bbgqJVfc7d3R0HDhxApUqVDAY83r9/P9566y1F9UTg2ayRVatWRb9+/ZCVlYXGjRsjNjYWDg4O+PHHHw3G7/wn0mIGtoEDB2LJkiVo2rRpnuM6LV++XI2ia+bJkye4cuUKHj58iEqVKqlWhy6IY7kWZX/06BGGDh2KZcuW4enTpwAAa2tr9OvXD9OnT88107MxypQpgzlz5qBNmzZwdnZGXFycft2hQ4ewZs0aRWW/fPky3n333Vzj8YkKk++Eh4dj1qxZaNOmTZ6TBikdXxDQbl8Eno1vffr0adVmo85J6+sWrTx58gQDBgxAZGQksrKyYG1tjaysLLz77ruIjIxUZVxqLZlNYqxXr144ceIEli5dijp16gB4NlDz+++/j1q1aiEyMtLk2CKCX3/91eCiWM0fh5YGDx6MzZs3Y8aMGQYXJcOGDcNbb72FiIiIv7eA+Th+/Di++uor/QCzFStWxKeffprnwLl/t7CwMEycOBGOjo4vnb7Z2CmbT58+jSpVqsDS0hKnT59+4bbVqlUzKnZOEyZMwIoVKzBhwgS8//77+O233+Dv749169YhIiICsbGxJscuCEuXLsWECRMQGhqq+ox9Wqpfvz46d+6sH8RSd0Fy5MgRvPnmm4pmSAWARo0awdnZGStXrtQPXHnv3j306tUL6enp2Lt3L3799VcMGDDA6NnedDO7LV26FEWKFNGXPSYmBu+//75+RllTaZ00AJ4dC9esWYMNGzbg8ePHimdf1ZK9vT0uXboEHx8fDBkyBI8fP8Y333yDS5cuoW7duvjjjz+MjmltbY3Bgwfjww8/NJgsQM3EmIggIiICM2fOxO3btwEAxYsXx7BhwzB48GCTB/rNOaB3enq65pXvf6OsrCy0bdsWf/31V56fi7Hno5xSU1PRsmVLHDt2TH+D8ObNm3jttdewadMm/Szexho0aBBWrlyJsmXLolatWrkuKJWUWW0FdX7W0ao+16VLF7i6umLRokVwdnbG6dOnUaxYMXTo0AGlSpVSnKQpWbIktmzZgqCgIGzZsgUDBgzA7t27sWrVKuzatQsHDhxQFP+tt95CnTp1MGLECIP106ZNw9GjR7FhwwaTY2t188rZ2Rlr165FmzZtTC7bi2gxGVlBKIhEipbS09P1s6yXKVNGUUJMx9HREefPn0epUqXg7e2N7du3o2bNmrh27Rpq1KiB1NRURfHr1KkDa2trDBkyJM8krZKB3F80uZOFhQWuXbtmcux/o5wTYt27d0/T6xatJrHQSUxMxG+//YaHDx+iRo0aqk04FR0djejo6DzLvWzZMsXxzSYxlpKSgt69e2Pbtm36nSszMxPt27dHZGSk4hl7tHbs2DGDqexr1aqlStwnT55g2LBhWLhwITIzMwE8u+j58MMPMWXKFMWzJf0bT77JyckYOnSo/of3/E/E2LsjTZs2xebNm+Hm5pZr+uacTJmC29LSEklJSfDw8IClpSUsLCzynG1I6V2dgIAAfPPNN2jevLlBgubChQuoX7++SRfdBTkrlaWlZb6PKf1sAO1mdnNycsKZM2fg5+dn8LknJCSgQoUKiqcmvnjxIjp06ID4+HiDFq/+/v744YcfUK5cOWzZsgV//vknevbsaVTsIkWK4ODBgyhfvnyusleqVMnoFmh/h7i4OKxevRpr167FgwcP8NdffymOaWVlhTt37uRqifLgwQN4eHiYvC8WL14cGzduRIMGDVC+fHlMmjQJnTt3xsWLF1G7dm2TknqHDh3C0qVLsW7dOlSsWBE9e/ZE165d4e3trVpiLKc///wTAAxmMjLVq86mqrTyrTvu5kfJsUWrGal0Jk2ahDFjxqB8+fK5LnhMOR89T0Swc+dOg+R1XrOEGeNF51DgxTOovoza5/6COj/raFWfu3nzJkJCQiAiuHz5MoKCgnD58mUULVoUe/fuVdyqzs7ODleuXEHJkiXRv39/ODg4ICIiAvHx8ahevbriGxLFihXDrl27ULVqVYP1Z86cQXBwsKIWb1rdvCpdujR27NiBChUqmFy2/Fy5cgWtW7fGrVu3UL58eQDP6gI+Pj7Yvn27voXnq3rzzTcRGRkJFxeXl85MrWQ2akD9REpBll0r5cuXx8qVK1G3bl00atQIbdu2xciRI7Fu3ToMGjQId+/eVRTfwcEBJ0+e1O8r/2Rafp8Fcc3yomuVnJSeM142G6jS2Xq1Mn78eEyYMAFBQUHw9vbOVffavHmz4tewVhzhX8LNzQ0//PADLl++bNCyKyAgwOhYBXlBf/PmTXTr1g0HDhzQ32FNSUlBgwYNsHbtWpO6auZkY2OD2bNnIzw83OAuhhpdhvI6+YaHh5t08k1LS4OLi4v+/y+i285Uffr0QWJiIkaPHp3nD89YOSvqSirteYmPj9e39ImPj1c1dk63bt3K87eSnZ2tbxZurK+++grdu3eHnZ0dvvrqq3y3s7CwUPw7ev6iUk2RkZH4v//7P9jY2KBIkSK5TjBKyu7m5oY7d+7kqgyePHkSJUqUMDmuTvny5XHu3Dn88ssvuHTpkn5dixYt9Cfojh07mhQ7Ozs7zxP3zZs3VUl8aJU0iI+Px5o1a7BmzRpcvHgRr7/+OsaPH4+3335baZEBIM8LYwDIyMiAjY2NyXHffPNNvPvuuyhbtiwePHiAVq1aAXi2r5hyngOAevXqoV69eoiIiMC6deuwbNkyhIWFITs7Gzt37oSPj48q36WOmrG0PB7m9HxF7OnTpzh58iRWrFhh1LAIz9u2bRu6d++Ohw8fwsXFJddxRY3E2MyZM7Fs2TL06dNHcay8WFhY4I033sjVtU8Jtc+hOal97i+o87OOVvW5kiVL4tSpU1i7dq2+21C/fv1UGS4AeNYF9Ny5c/D29kZUVBQWLFgA4Fn3MzVuoj58+DDPY2uhQoUUJ93OnDmTZ1c1Dw8P3L9/3+S448aNw9ixY7F8+XJV6uM5DR48GGXKlMGhQ4f0LawfPHiAHj16YPDgwdi+fbtR8VxdXfW/Fa0bGaj9Oyqosjdt2vSFxxMlNyE6deqE6Oho1K1bF4MGDUKPHj2wdOlSJCYm4pNPPjE5rk5QUBBu3Ljxr0iMafl9FsQ1i5bXKjlNmjQJX375Za5WtGoQEWzcuDHf1mhKEswLFy5EZGSk0TfqjWE2LcbU5Ofnh2PHjqFIkSKaNwNt2bIlUlJSsGLFCoM7O6GhoXBxcUFUVJSi+Fpq3bo1RATffvttrpOvpaWlUSffnK0s8rtDr0Z/d+DZxdm+ffsQGBioKM5/Sa1atfDJJ5+gR48eBndFJ0yYgJ07d2Lfvn1/dxH/Nj4+Pvjggw8watSoV77b86qGDh2Kw4cPY8OGDShXrhxOnDiB5ORk9OrVC7169VI8xqCWtOx+87KkgaljRtarVw9Hjx5FtWrV0L17d3Tr1k2VBCTwv659n3zyCSZOnGgwzkVWVhb27t2LhIQEnDx50qT4T58+xezZs3Hjxg306dNH3638q6++grOzM9577z3lbwLPzj9Lly7FqlWrkJKSghYtWhg0/38VNWvWRHR0NNzd3VGjRo0XXjCofedSV+VRmvR4mTVr1mDdunX44YcfTHp+uXLl0Lp1a0yePFn1C2MdLy8v7Nu3T7UuDs/TusuDjoggKioKS5cuVTTuIs/9eXv8+DHs7Ow0iz9u3DhERETA29sbjx49wqVLl2Bra4tly5Zh8eLFiodqqFOnDtq2bZure9C4ceOwbds2HD9+3OTYJUuWxPr169GgQQODutHmzZsxdOhQfYLSWDVq1MDVq1chIvD19c3VjUrJcdHR0RGHDh3K1YLu1KlTaNiwIR4+fGhSXBHBjRs3UKxYMVUSpgVJ67I/n6B6+vQp4uLi8Ntvv6F3796YPXu2aq8VGxuL2NhYlC1bFu3atVMcb8OGDRg3bhyGDRuWZ5c+Y7uBazm8jM6/eV8sCC4uLoiLi4O/v7/qsYcMGYJvvvlGk/ERixQpgiNHjhjdqtUY/+kWY1r9+HLesdD6LuCePXv03ZF0ypcvj7lz5+oHnTZWQTUd3rNnj8EdKeDZTj1lyhT9oPmvateuXfo4Wt4xBp4lOrTKFz9+/Bhz587NN5Ou9CLw4sWLmDt3rsHYa4MGDVJ8p2fMmDHo3bs3bt26hezsbGzatAkXL17EypUr8eOPPyqKrZWCatn56NEjdO3aVfWkGABMnjwZAwYMgI+PD7KyslCpUiX9IJZffPGFKq+xZ88ezJgxw6Cr9rBhw0w+vujMnDkTISEhqFSpEh4/fox3331X3/3mu+++UxT7008/Rd++fVVPGjRv3hzLli1TvYsgAP0dRhHBwoULDVpC2NjYwNfXV9GEBIUKFcLQoUNzrVfjjnFO5cuXx7Rp0xAeHo5t27aZlODo0KGDvltXhw4dNE9SAc/GGfzqq6/0Y9uVLVsWH3/8sWoJw+fVq1cP/fv3N/n5t27dwuDBgzVLigHPKrBz58596fHRFC/r8qCG+Ph4LFu2DJGRkbh3757isV21PPcD2pyfC6I+5+HhgU6dOqFHjx5o3ry56ue6cePGoUqVKrhx4wY6d+6sPzZYWVmpMjHE6NGj8eabb+Lq1ato1qwZgGdJ2++++07R+GIA0LVrV4wYMQIbNmyAhYUFsrOzceDAAQwdOlRRq05TW2q/CltbW3239Zzya1n3qkQEAQEBOHv2rKrJ9oJKpGhRdp38WhiNGzfO5ERkfurXr4/69eurFq9Lly4AgL59++rX6bqFm9IY4eTJk/qeJidOnMj33KDknKH19zlhwgQMHTo01/n5r7/+wvTp01UZowt4dpx6fjztjz/+WPG5rnPnzvjll180mcRi1apV2LRpE1q3bq167Pfeew9r1qxRZVKm/PynW4y96thOgOnJFq1/HOXKlcPq1av1EwboHDlyBO+++y6uXLlidMzQ0FDMmTMHzs7OCA0NfeG2SjK7Ws0EmpiYCB8fn1wHTd0dglKlSplcZgD45ZdfMHPmTHzzzTfw9fVVFOt53bt3xy+//IK33347z0y6khZA33//Pbp27YqgoCD9SfHQoUM4evQo1q5di7feektR2fft24cJEybg1KlT+hlYxowZo0o3maysLERGRubbusCUZuYF1bJTy5nddG7cuIEzZ86oPojl6tWrERoaijfffFOfrN6/fz+2bNmCyMhIvPvuu4riZ2ZmGnS/qVmzpirdbxwdHXHmzBlN7nZprWnTpti0aRPc3d0Vx9q6dStatWqFQoUKvbTV1j91gomCMGbMGMyaNQuDBg3SHxtjY2Mxb948fPLJJ5gwYYKqr/fXX39h1KhR+Pnnn42etEKnIGak6tSpE3bt2oUiRYqgcuXKuVoCKLkx5u3tjWnTpqne5SEjIwMbN27E0qVLsX//fmRlZWHGjBno16+f4mEUtDz3a3V+zlmf69OnzwsvJk2tz23evBlr1qzB9u3b4erqii5duqBHjx4ICgoyKd7fYfv27Zg8eTLi4uL0492NHTtW0cDhwLNx3QYOHIjIyEhkZmb+K2Zg03IyssqVK2Pp0qWoV6+eSqXVdpzenLQo+8tcuXIFderUMfl6SEercW6BZzOMv0jp0qUVxdeKlt+nVmPF5vT1119jyJAhePvttw3OGRs3bsRXX32FAQMGGBWvoCYk8vPzw88//6za+Ig5k+HZ2dlYsWIFqlWrhmrVqqk6YZDOfzoxVhC0/nH88MMPmDx5MubPn6+vhBw7dgyDBg3CiBEjNL2rpJRWJ1+tP3N3d3c8evQImZmZcHBwyPXDU3ICc3V1xU8//WR0i7lXUaZMGXTv3j3XRd7YsWOxevVqk5v0FwRdxbJNmzZ5ti54UX/+v5uWM7tpnXivWLEi+vfvn6tV0axZs7B48WL9Xap/Gi2TBjdv3sTWrVuRmJiIJ0+eGDym5ox3T548QXx8PMqUKQNra9Mabz8/uHd+1BrcWwv+/v44evQoihQpYrA+JSVFP7OWUsWKFcOcOXPQrVs3g/XfffcdBg0apGgsIHd3d4PjlYjgzz//hIODA1avXm1yQrIgZtLV8saY2l0ejh8/jqVLl+K7775DQEAAevbsiS5duujHv1KjlaeW5/5/8/lZ588//8TGjRvx3XffYdeuXfD390ePHj0Un4delphWq/WFmrKzszF9+nRs3boVT548QbVq1fDWW2+pfvNKC1pORrZt2zZMmzYNCxYsQJUqVdQqcoH4O8q+atUqjBgxQj8jsyleNs7tP3Vmx6dPn8Le3h5xcXGafN5afp+WlpZITk7WjyGps2vXLnTp0gX37t1T/BolS5bEyJEjMXDgQIP18+fPx+TJk3Hr1i2j4hXUhEQrVqxAVFQUli1bpko31pc1bMpJjR5lZpMY69u3L2bPnp1rcN/09HQMGjTI5PEutP5x5Kyo6S6edP9/fppfpXcc1Paik+/y5ctNnq49v8/8+vXrqFSpEtLT0xWVe8WKFS98vHfv3ibHrlSpEtauXavK1OzPc3BwwOnTp3MNtH358mVUr15dlVkAnzx5kucdKaWt9IoWLYqVK1dq0vQWAH777bd8T4xbtmxRlGDWcmY3rZPAtra2OHv2bK595sqVK6hSpYpJs16+6nhTSi7stUoaREdHo3379voZV6tUqYKEhASICGrWrKl4lj7gWVJz4MCB+uPMpUuX4O/vj0GDBqFEiRKatjz8J8qZ3MspOTkZPj4+uZKTpnBzc8PRo0dzXaxeunQJderUQUpKismxIyMjDX7zlpaWKFasGOrWrauoVeC/NdGpM2LECDg5OanW5cHa2hqDBg3CBx98YND1sFChQqolxrQ89xfE+blZs2bYtGlTrrpVWloaOnbsqMrxS+fcuXPo3r07Tp8+rXhf1I2HqPP06VPEx8fD2toaZcqU+UfOkDZx4kSMGzcOwcHBsLe3x44dO9CtWzdFY+cVLlwYly5dQtGiRXMl3J+nRn1fjcnInpfzmsXGxibXhfE/7TolJy3L/nw3ZxHBnTt3cOzYMYwePVpRbxEtx7nVuXr1KiIiIgyG3BgyZIjiGx+68fiqV6+uRjENaPF96n6Xqampuca3zcrKwsOHD/HBBx9g/vz5isvv5OSEuLi4PM8ZNWrUUL0Lrlr++usvdOrUCQcOHFB9fMSC8J8eYyynFStWYMqUKbkSY3/99RdWrlxp9MlM9+OwsLBAuXLl8v1xKBUREaE4xovkN/CxhYUF7OzsEBAQgD59+hiVsdXRzQR65coVg/7Rpp58dc0pLSwsMHr0aINWNFlZWTh8+LAqg+Yqqfy+zMyZMzFixAgsXLhQ9ebHTZo0wb59+3J9vvv371c8XtTly5fRt29fHDx40GC9WhMe2NjYqFIpy09ISAj279+f647J999/j169eilKpmo5s5vu833eqVOnDMbuM5WPjw+io6Nzffa//vorfHx8TIr5fJJRNxbF8+uU7DPvv/8+gLxbGiiJPWrUKAwdOhTjx4+Hs7Mzvv/+e3h4eKB79+5o2bKlyeXNaeTIkTh16hRiYmIMYgYHB2PcuHFmkxjLmUDdsWOHQSuFrKwsREdHv/Idzpfp2bMnFixYkKvF36JFi9C9e3dFsZs1a5Zn137gWbd/U28aFNTsVFp5/PgxFi1ahF9//VWVLg/NmzfH0qVLcffuXfTs2RMhISGqj1um5blfy/OzTkxMTJ6J5MePH6syQc7jx4+xdetWrFmzBlFRUfD09MSwYcMUx81rwpG0tDT06dMHnTp1Mimm1kmmlStX4uuvv8b//d//AXh2zmzTpg2WLFlicnJCN1EKoH3dH3g2zqLaLdu0Lnd6ejqmTJmS77AbSlq6aFn255MolpaWKF++PCZMmKB4OBItx7kFnp2f27dvj8DAQH1vlwMHDqBy5crYtm0bWrRoYXLszz//HJ999hlWrVqlSp02Jy2+z4iICIgI+vbti/HjxxvUW3Rjxao1vlv79u2xefPmXMfYH374AW3btlXlNXSysrJw5swZlC5dWvEwH71798bx48fRo0ePPIcMUkKrRk45/edbjKWlpUFE4O7ujsuXLxu0MsrKysK2bdswcuRIo5uxrlixQv/jiIiI0PTHoaVRo0ZhwYIFqFq1qr6749GjR3H69Gn06dMH586dQ3R0NDZt2oQOHToYFVvtbmC65NyePXtQv359g0FCdZ/50KFDVTnRX716FcuXL8fVq1cxe/ZseHh44Oeff0apUqVQuXJlk+Peu3cP77zzDvbu3at6V42FCxdizJgxeOedd/R96g8dOoQNGzZg/PjxKF68uH5bY1vUNGzYENbW1hg5cmSeXR2V3u2ZOXMmrl27hnnz5mkySLOuu8qBAwfg5eUFAFi3bh369u2LyMhIdO7c2eTYWszsVlB3pRYsWICPP/4Yffv21Y8FeODAAURGRmL27Nn6Sr8SOWfq+qdzdnZGXFwcypQpA3d3d+zfvx+VK1fGqVOn0KFDByQkJCh+jdKlS2PdunWoV6+ewWdz5coV1KxZE2lpaSbFHTx4MAICAnKNDTFv3jxcuXKlQC60jKGrxOeVOC1UqBB8fX0xc+ZMVSqAgwYNwsqVK+Hj46M/Nh4+fBiJiYno1auXwXHY2IRNQYw1ouWsgBs3bsT69evz7Dqs5M6uFmMB3bhxA8uXL8fy5cvx119/oUuXLvj6669x+vRpVKxY0aRypqWl6ccme9lvz9gxzHImf2/fvv3C87OSG6mnT58GAAQGBhpMVAQ8O19ERUXhm2++Mfn4tWPHDqxZswZbtmyBtbU13n77bXTv3h2NGzc2ucyv4syZM2jXrp1J5V6xYgW6du0KW1tbTVoC2tra4sqVKwY3kOzs7HDlyhWULFnS6Hg5ZWZmYs2aNQgJCYGnp6eiWDoFMYB9QejWrRv27NmDnj175lkXHTJkyN9Usr+P1uPc1qhRAyEhIZgyZYrB+pEjR+KXX35RdJ6oUaMGrly5gqdPn6J06dK5ekL9U1sX7dmzBw0aNMh1DaemSZMmYcaMGWjYsKHBGGMHDhzAp59+anA+MnZMsI8//hhVq1ZFv379kJWVhcaNGyM2NhYODg748ccf0aRJE5PL7ejoiB07dqBRo0Ymx8hPfvWt+/fvw8vLC5mZmYpf4z+fGLO0tHzhhbaFhQXGjx+Pzz//3KT4e/bs0ScNtPb48eNcFVelg82+//77KFWqVK7uDpMmTcL169exePFijB07Ftu3b8exY8eMiq3VBUNoaChmz56t+L3nZ8+ePWjVqhUaNmyIvXv34vz58/D398eUKVNw7NgxRdPBBwcHIzExEf369cszk67kjvWr3i0ypUWNo6Mjjh8/rtpgikDupuW6Cr3ag0DrDBo0CLt378bevXsRFRWF9957D6tWrVI8KUF4eDju3Lmj6sxuBZl437x5M2bOnGnQqnPYsGFGJ8Lzo3ViTM2kgZeXF3bv3o2KFSuiUqVKmDJlCtq3b694GvucHBwc8Ntvv8Hf39/gszl16hQaN26M1NRUk+KWKFECW7duRa1atQzWnzhxAu3bt8fNmzcVl10Lfn5+OHr0KIoWLarZa7xqi2dTEjb5dQVV2rU/KysLkydPxsKFC5GcnKzvcjt69Gj4+vqiX79+JsXNac6cOfj888/Rp08fLFq0CKGhobh69SqOHj2KAQMG4Msvv1T8GlrZuXMnli9fjs2bN8PHxwdvv/023n77bdSsWdOoODnrKfnVF01tGa3lOfn519GVO68qvb29PebOnWswq5wxHBwc0LZtW3Tv3h2tW7fW9GIwp/3796Ndu3b4448/CuT1jGFlZYWkpCSDm+3Ozs44ffq0Ki1dHRwccP78edV6FhTEZGQ6WVlZ2LJli75OUblyZbRv316ViQjc3Nywfft2TcbpBbQru5ZjaWo5zi3wLOF75syZPIciqFatmklDbuiMHz/+hY8r6WIKqPt9ankTJS9ajglWsmRJbNmyBUFBQdiyZQsGDBiA3bt3Y9WqVdi1axcOHDhgSpEBABUqVMD69etVHTJIq0ZOefnPd6XcvXs3RATNmjXD999/b3AnzcbGBqVLlzZoSWOs9PR0REdHIyQkxGD9jh07kJ2djVatWpkcWxd/xIgRWL9+PR48eJDrcaV3o9evX4/jx4/nWt+1a1fUqlULixcvRrdu3Uw6sGrVDSwiIiLPrPDvv/8Oa2trxQekkSNHYtKkSQgLCzNortmsWTPMmzdPUeyDBw8iNjZWk/70Wna9qVSpkqIBqvPy/ACvpnaZeFVz585F9+7dUa9ePdy6dQvfffedKsmfI0eOYNeuXfjxxx9VS+rpEqR+fn6a35Xq1KmT5p+92rRKGtSrVw/79+9HxYoV0bp1a3z66ac4c+YMNm3apNrMRkFBQdi+fTsGDRoE4H9Tki9ZskRRsvPBgwd5Dprs4uKi+m9XTfHx8Zq/hhoDsj4vZ9f+MWPGqN61/8svv8SKFSswbdo0fddhAKhSpQoiIiJUSYx9/fXXWLRoEbp164bIyEgMHz4c/v7+GDNmjGpjAV25cgVXr15F48aNYW9vn2+9wFgtWrRAixYt8Mcff2D16tVYtmwZpk6danSdKGcLK7X3k4LqDhsfHw8Rgb+/P44cOWJw0WBjYwMPDw9FF/bJycm5uq6o6fmbSrrxl1atWmVyHdqYlrem1BlFBH369IGtra1+3ePHj/HBBx8YtHox9aZenTp1cPLkSdUSYzn3bS2OhzpXrlxB69atcevWLf1YgOHh4fDx8cH27dsVj0nl7u6uepc7HS3LnpCQkOexKSMjw+hB1J8XHh6OHTt26Mv8/Di3ShUrVgxxcXG5EmNxcXG5bggZS2ni60XU/j7d3d31N1Hc3NxUvYmSFy3rRroWVgDw008/oXPnzihXrpy+q6ISM2fOxPDhw7Fw4ULVZnfWfd664auep2vkpIb/fGJMNxVzfHw8SpUqpXo3rZEjR+ZqXgo8+3GMHDlScWJs+PDh2L17NxYsWICePXti/vz5uHXrFr755ps8X9dYdnZ2OHjwYK5xLw4ePKhvhZGdnW1Uiwytx1/r2rUr2rVrh48++shg/fr167F161b89NNPJscGnjXfX7NmTa71Hh4eii8wK1SogL/++ktRjL/D1KlTMXz4cEyePDnPO1KmVCyVzHj2KvIaBP7NN9/Evn370K1bN1hYWOi3UTIQvJubW67Wb2rx8/PDnTt38n1c6aQH/1ZaJQ1mzZqlbxU2fvx4PHz4EOvWrUPZsmVV614yefJktGrVCufOnUNmZiZmz56Nc+fO4eDBg9izZ4/JcQMCAhAVFZVrBqOff/75H9+NNT09HXv27MmzO5/Sqeafp2s5p7S7k25cJBHBmTNncnXtr169OoYOHWpy/JUrV2LRokVo3ry5wfmyevXq+gGzlUpMTNR3oba3t8eff/4J4NmYbPXq1VN0I+jBgwd45513sHv3blhYWODy5cvw9/dHv3794O7ujpkzZ6ryHtzd3TFo0CAMGjTIpG43ujri8///N9ElT7RKxDk7O+dqeVGpUiV06NBBlVZAz886rZvAonfv3hg1apRJMfO7cM2LKRexebXu79Gjh9Fx8vPRRx/h008/xc2bN1GrVq1cXcyUtMbQcpyewYMHo0yZMjh06JA+gfXgwQP06NEDgwcPxvbt202ODTyb9GDMmDFYsWJFrmFalNKi7K8ylqbS5IGW49wCz3oW9e/fH9euXTMYcmPq1Kkv7Zb7KlJSUrBx40ZcvXoVw4YNQ+HChXHixAl4enqiRIkSJsdV+/vMeRNl165dmgz7UlA8PT1x7tw5eHt7IyoqCgsWLADwbLw6pcf0Hj164NGjRyhTpoxqQwZp3cjJgJiJZcuWyfr163OtX79+vURGRpoc187OTuLj43Otj4+PFwcHB5Pj6vj4+Mju3btFRMTZ2VkuX74sIiIrV66UVq1aKY4/ceJEsbe3l8GDB8uqVatk1apVMnjwYHFwcJBJkyaJiMisWbMkODj4lWNGRkbK8uXLxcLCQmbPni2RkZH6Zc2aNXLw4EFFZXZ3d5dz587lWn/+/HkpXLiwotgiIiVKlJADBw6IiIiTk5NcvXpVREQ2bdok/v7+imLv2LFDGjRoILt375b79+9LamqqwWKKgwcPyrZt2wzWrVixQnx9faVYsWLy/vvvy+PHjxWV28LCQiwsLMTS0tJg0a1T6tGjR5Kenq7/OyEhQb766ivZsWOH4jK/bFGj/FrJ6zPPuZjCzc1N3N3dX2lRg7Ozs1y7dk2VWDplypSRX3/9VUQMf6Pnz58XNzc3k2JmZmbKnj175I8//lCrmPm6cuWKvPfee1K7dm2pWLGidO/eXU6fPq0o5tKlS8Xe3l7GjBkjMTExEhMTI6NHjxYHBwdZtGiRSiVX34kTJ8TLy0tcXFzEyspKihUrJhYWFuLo6Ch+fn6qvEZWVpaMHz9eXFxc9L8dV1dXmTBhgmRlZSmK3adPH5OP3S9iZ2cnCQkJImK4j589e1YcHR1VeQ0/Pz85ceKEiIjUqlVLFi5cKCLPzlNKf/89e/aUkJAQuXHjhkH5o6KipFKlSsoKrqJTp0698qLUr7/+Km3atBF/f3/x9/eXNm3ayM6dO1V4F4bOnj0rP//8s/zwww8Gi6kuX74sZcuWFQcHB6lRo4bUqFFDHBwcpHz58nLlyhUVS64e3TEwJiZGIiMjxcvLS0aOHKn/LEaOHCne3t6K6v9ayq+uokadxdLSUpKTk3Otv3fvnlhZWSmK7eDgkOe5LC4uTpXjVmBgoDg7O4uTk5NUqVJFvz/qFiW0KPvz313OxcbGRsqVK5er/m4sT09PuXTpkqIYL5KdnS2zZs2SEiVK6MteokQJiYiIkOzsbEWxT506JcWKFZOAgACxtrbWnyc+//xz6dmzp6LYWu+LBeHGjRsyf/58GTFihHzyyScGixJjx44VV1dXqVChgpQqVUp/jbh06VKpV6+eotg5r/nzWpRISEiQ33//XWbMmCH9+vWTfv36yaxZsyQlJUVR3Jz+8y3GdMLDw/HNN9/kWu/h4YH+/fubPLaTq6srrl27livjf+XKlVx3eEzx+++/6+/2u7i46DOtjRo1wocffqg4/hdffAE/Pz/MmzcPq1atAgCUL18eixcvxrvvvgsA+OCDD4x6rZzdwLQYfy0jIyPPrpRPnz5VpTVW165dMWLECGzYsAEWFhbIzs7GgQMHMHToUPTq1UtRbN0sdM2bNzdYLwqa306YMAFNmjTRD1J95swZ9OvXD3369EHFihUxffp0FC9eHOPGjTO53Fo2vQeADh064M0338QHH3yAlJQU1KlTBzY2Nrh//z5mzZpl0r7+b5/VDcg9W9fTp09x8uRJzJo1y+Txf7QehP35GcAePnyIGjVq5BpvR0lXrVu3buU5i2l2djaePn1qUkwrKyu88cYbOH/+PNzc3Ewu26soU6YMFi9erGrMvn37IiMjA19++SUmTpwIAPD19cWCBQsUH7e09Mknn6Bdu3ZYuHAhXF1dcejQIRQqVAg9evRQbSDlzz//HEuXLsWUKVP0Y9Ps378f48aNw+PHjxWNpaVVy9dKlSph3759ubpSbdy4ETVq1FDlNZo1a4atW7eiRo0aCA0NxSeffIKNGzfi2LFjilvB/vLLL9ixY0eulnlly5bF9evXFcVWU2BgYJ4TQDxPafeYr7/+GkOGDMHbb7+t368PHTqE1q1b46uvvsKAAQNMjq1z7do1dOrUCWfOnDF4T7rjsanl16oV0KuOeWZKC6acrf8mTJiAWbNmoVu3bvp17du3R9WqVbFo0SJNZyM1lRbdqHTj9IgI/vzzT4OeIFlZWfjpp58Ud42ztbXVtzzN6eHDhwatak31/KzXatKi7Lp6qJZjaQ4ZMgRz585VdZzbnCwsLPDJJ5/gk08+0X8+anWtDgsLQ58+fTBt2jSDmK1bt9Zfg5pKy32xbNmy6N69O7p37676zK460dHRaN++Pfz9/XHhwgVUqVIFCQkJEBGjx9J83rhx41ClShXcuHEDnTt31ncJt7KyUjyJg5bH03v37qFWrVqws7PTTxioux765ZdfFH8uAMynxZitrW2+Lbvs7OxMjtu/f3+pWrWqwV2zy5cvS7Vq1aRfv34mx9WpWrWqxMTEiIhI8+bN5dNPPxURkdmzZ0uJEiUUx9fS8ePHDbL1W7ZskQ4dOsioUaMkIyPD5LhNmjSRgQMH5lr/0UcfSaNGjUyOq5ORkSHvvfeeWFtbi4WFhRQqVEgsLS2lR48ekpmZqSh2zruYeS2m8PLykqNHj+r//uyzz6Rhw4b6v9evXy8VK1ZUVO7r16/neWcoOztbrl+/rii2iEiRIkXkt99+ExGRxYsXS7Vq1SQrK0vWr18vFSpUUBxfS76+vuLn55fvooUff/xRXn/9dU1iK/Wyu0Vq3DWqWbOmrFq1SkQMW9OMHz9e0TGgVq1a+pZoWsrMzJSNGzfKxIkTZeLEibJp0ybFx5ac7t69K3/++adq8bTk6uoqFy5c0P9f1xr40KFDUr58eVVew9vbO88WM1u2bJHixYsrjn/06FEZNmyYdOnSRTp16mSwmGrLli3i6uoqU6ZMEQcHB5k+fbq89957YmNjI7/88oviMos8a0n39OlT/d/fffedDBo0SObMmaPoHC3y7Hepa8WQ8zd69OhRVVp2qyUhIeGVFyVKlCghc+fOzbV+3rx5quyDIiJt27aVDh06yL1798TJyUnOnTsn+/btkzp16sjevXtNjqtVywsLCwvx9fWVTp06SceOHfNdlLK3t8+zRc3FixfF3t5ecfx/i5e1QLeystL3FDFVz549pXLlynLo0CHJzs6W7OxsiY2NlSpVqkjv3r3VeSMa+beWvWPHjuLi4iJ+fn7Stm1b1c5BBcHFxUV//ZzzPJGQkCC2traKYmv5fc6aNUuCgoLE0tJSgoKCJCIiQu7cuaMo5vNq164tY8aMEZH/fTZ//vmntG/fXr7++mtVX0srf/31lyo9o3QaNWokffr0Mai3PH36VHr37i2vvfaa0uKKyLM7B2bBx8cn34qxkgRTSkqK1KtXT6ytrcXX11d8fX3F2tpamjZtqkqXnFmzZsns2bNFRGTnzp1iZ2cntra2YmlpKREREYrjaykoKEg2btwoIiJXr14VW1tb6datmwQEBMiQIUNMjrt//36xs7OT1157TcaNGyfjxo2T1157Tezs7BRV/p53/fp12b59u6xbt07TZspK2draSmJiov7vhg0bGlRu4uPjxcnJSdFr5Nf0/v79+6p0RbS3t9cn2Dp37izjxo0TEZHExETVKq4xMTHStm1bKVOmjJQpU0batWunyv4SERFhsEyfPl3effddKVy4sISHh6tQ8twuX76sSldtEe2TNFrQKmnw888/S2BgoGzbtk1u376t6gld5/Lly1KuXLl/VbckLRUtWlR/fC1btqxERUWJyLNusWrt47a2tnLx4sVc6y9cuKDoxpjIs2RSoUKFpG3btmJjYyNt27aVcuXKiaurq/Tp00dR7L1790pwcLAUK1ZM7O3tpWHDhoq6l+f09OlTGT9+vNy4cUOVeM9r1aqVfPHFFyLyrFJ/7do1ycrKks6dO8tbb72lyWv+kzk6OuqHwsjp0qVLqnXrKVKkiL7Lp4uLiz7hHB0dLYGBgSbHdXd31w8vkdP+/fsVdbn96KOPxN3dXQIDA2X27Nny4MEDk2O9SLly5WTYsGG51g8bNkzKlSunyWua6tixY9KkSZM8zzcpKSnSpEkTiYuLMyl2TEyM7N69WywsLGTTpk0GN2UPHjwot27dUlp8+eOPP6R9+/b6roI2NjZiaWkpHTt2VG2Ygj/++EMWL14sI0eO1O8zx48fl5s3byqOq3bZC2Kokz59+rxwMVVgYGCurqp5LUoUK1ZM350/Z2Lsl19+kZIlSyqKXRD74sWLF2XMmDFStmxZsba2lhYtWsiKFStUie3k5KSvE7q5uekbD8TFxUnp0qWNjjd79uxXXpR4+PChDBgwQIoVK6baEDA6dnZ2cv78+Vzrz549q9q1otkkxoYPHy6lS5eWXbt2SWZmpmRmZkp0dLSULl1a3wrLVNnZ2bJjxw6ZNm2azJ07V/bs2aNSqXNLSEiQ77//XtF4F+7u7nLv3j0Refl4Q0rkvBMwZcoUeeONN0TkWWVK6QHv5MmT8u6770qlSpWkVq1aEhoa+o9OXuX0xx9/qNo/ulSpUvp9LiMjQ+zt7Q1avZw+fVrxd2lhYSF3797NtT4hIUGVi9eqVavK7NmzJTExUVxcXPTj0B07dkw8PT0Vx1+1apVYW1vLO++8oz/wv/POO1KoUCH59ttvFcfPy7x58xRfGD+fmElJSZHz589Lly5dpHr16orLWFBjx3z44Yf6Y45a1EwajB8/Xh4+fJhrPBe1x9ITeZYwaNmypcFF4P3796Vly5bSunVro2LVqFFDfv/9dxF5eSX2n6pFixb63+B7770nderUkdWrV0tISIjUqVNHldeoU6eODBo0KNf6gQMHSt26dRXFrlq1qsybN09E/lexz87Olvfff19/t/efytHRMc+W9Go4c+aMeHh4SMuWLcXGxkbefvttqVixonh6ev6jE8AXLlyQAQMGSLNmzaRZs2YyYMAAfYJJiW7dusm0adNyrZ8+fbp06dJFcXyRZ/U53XiO/v7+smvXLhF5NqahkosGLVtePH78WNasWSPBwcHi4OAgnTt3lqioKMVjF+W0fft2sbOzkypVqujrXVWrVhU7OzvZvn27aq+jhm7dusmECRPyffzLL7+U7t27K3qNhIQEVT/fvFy+fFm2bt0qW7duzTMhbCotx6TSUbPsLVu2lClTpuj/Pn36tFhbW8t7770nM2fOFC8vLxk7dqzCEmtD1/Bg3LhxMnbsWLGxsZHBgwcbrNfdxDZVv379pGPHjvLkyRP9DZTr169LjRo1FDWgyEmrffF5sbGxEhgYqFpd0dPTU9+CvmLFivrGPaa21NU13tEtjo6OYmFhob/eV2ts148++kgqVqwoGzduFHt7e1m2bJlMnDhRSpYsKatXr1YU28PDI896flRUlHh4eCiKrWM2ibGMjAx555139F3jChUqJFZWVhIaGqq4y4BWnjx5Is2aNVM94RMZGam/Q6FldydnZ2d92YODg/Ut3K5fv674Lr3aPvnkE3n48KH+/y9alNB1IylRooS+mXPJkiWlSJEicvz4cZNifvDBB1K/fn3Zu3evhIWFSZEiRQz26dWrV0tQUJBJsXXv2dLSUv7v//7P4HMYPHiw1K1bVxo0aGBS7Jw2bNig77LaokUL/frJkydLy5YtFcevUKGCzJo1K9f6mTNnatZV8+rVq+Ls7KwoRn4THpQqVUrxJBYi6iZpXsTZ2Vlfgf0n0rWI1KKr8/PU7JY0btw4/aQVY8eOzVVhVavyqqWjR4/qL+CTk5MlJCREnJ2dpWbNmia3jHheTEyMODo6SsWKFaVv377St29fqVixojg5OSluNerg4KBPLhUuXFj/3Z47d068vLyUFl1T7du313Tw8ZSUFJk0aZJ07txZWrVqJZ9//rncvn1bUcz8EsA1a9aUBg0aSK9evfT7k7E2btwo1tbWUq9ePf15rn79+mJtba1v/W6MnHfgJ06cKK6urtK6dWt969w2bdqIm5ubTJw40aTyPq9Ro0ayefNmEXmWYGnZsqXs379fevXqJZUrVzY57otaXqg56HFCQoKMGzdO/P39pVSpUqp2B09MTJRRo0bp612fffaZQUv7fwp/f/8X3vg+ffq04gtXrSYjE3l2kynnREo6jx49kvHjxyuKLfJsSBld67+cLYwOHDhgUiuanLQoe0EMdVJQcn7eaklJSZHg4GBxc3MTKysr8fHxkUKFCknjxo3112Sm0npf1Dl8+LAMGTJEvLy8xMHBQbUbHR06dNBPnPTpp59KQECATJo0SWrWrCnNmzdXFPvbb7+Vhg0bGtz0uXDhgrz22muKk1daTho4aNAgKVmypKxdu1YSExMlMTFRvvvuOylZsqRqiVQLkZeMOPofc+nSJZw6dQr29vaoWrVqroFtX8WcOXPQv39/2NnZvXSwQ6VTzRcrVgwHDx7UbHA/LTVr1gw+Pj4IDg5Gv379cO7cOQQEBGDPnj3o3bs3EhISXjlWWloaXFxc9P9/Ed12xmjatCk2b94MNzc3NG3aNN/tLCwssGvXLqPj67z22msICAjA4sWL9ZMSZGZm4r333sO1a9ewd+9eo2Pev38fb775Jvbv3w8nJyesWLECnTp10j/evHlz1KtXz6QBpnWfxZ49e1C/fn2DASttbGzg6+uLoUOHqrJ/JiUl4c6dO6hevbp+oPYjR47AxcUFFSpUUBTb1tYWZ8+ezTVg+5UrV1ClShU8fvxYUfy8TJs2DV9//bVR+/nz9uzZY/C3bhr7gIAAVSa1cHR0xKFDh1C1alWD9adOnULDhg3x8OFDxa/x/9g787ia1u+Pf06TSiOipFFIRCXDxW2SIUMyU0hlJim6ck1lSMZCCEmDq5ApU4bIUDKUSpJGlSkZMhSNz++Pfu1vp1PU2fs0cN6v13ld59nd9axT++z97PWs9VlApVBrQkIC1UikuSEgIIC3b9/SFh6uD23atMGFCxeotudVREVFYcyYMbSaEvCpm9evX2Pv3r1ISUkBAHTv3h0LFy6k3eK7U6dOuHz5MrS1tdGrVy+sXLkS06ZNw7179zBixAh8/vy53rbatGmD1NRUtGvXjqOJRU2YOE98fHzg5uYGKysr9OnTh6NhkLm5OVd2S0tLMWLECPj4+DC+dlm5ciX2798PbW1tSnz34cOHSExMxKxZs5CcnIyIiAicPn0aY8eObZDtzp07w8rKCuvXr2cbX7duHY4ePYqMjIwG2VNTU6vXz7FYLGRmZjbIdm1cuXIFhYWFGD9+PNLT0zF69Gikpqaibdu2OH78OExMTGjZT0tLY/v+1NYAhQ65ubk4cuQI/P39UVJSgpSUFEhISDA6R3NHVFQUz549q/PcycrKgpaWFq1GU127dsWBAwc41rq3bt3C3Llz8fz5c65tCwoK4s2bNxz30g8fPqB9+/a0GlgAlQ3P4uLi0LlzZ7Z1RXZ2Nrp160ZrLccL30VFRZGWlgYlJSUAlY3TzMzMsGrVKgDAixcvoK2tXatI/M/Q09NDREQEZGVloaur+9N7RVxcXIP9rg1eruPu3r2LxMREfPv2DXp6ejA1NaVtk5fnYmpqKv777z8EBwcjKysLJiYmsLKywvjx4xm7ZmVmZuLbt2/o1asXCgsLsWzZMioesHPnTq7iF1V07ty51kY+sbGxmDhxIq3mHxISEkhOToaysjI6deqE06dPo1+/fsjKyoK2tjat54qSkhI4OzvDx8eHasInLCyMBQsWwMPDg2oiQIc/pitlFV27dkXXrl1p2fD09ISVlRVERUXh6elZ58+xWCzagbHp06dT3bSY4leBpepwE2SqwsvLC1ZWVjh79ixWrVpFLaJCQ0M5Hgp/haysLHWBk5GRqfUmQGh0dqzedZGXHRgfPXrEFhQDACEhIfzzzz/Q19fnyma7du1w+/ZtfP78GRISEhAUFGQ7fvLkSa4v1FW/CxsbG+zatYvW+fAr5OXlIS8vzzZW9eBDFyUlJURERHAs5K9fv04tWLil5qKEEIK3b98iPz8f+/bto2W7emctXsB0156wsDCYmZlBWFiYCffY4HXQ4Gf2mGT06NGYO3cuDh8+TJ3f9+/fx/z587kORACAuro6Hj58iLZt27KNFxQUQE9Pj5EHb15RVlaGyMhIZGRkwNLSEpKSknj9+jWkpKQYW2R27NiRVvfJujAwMMC1a9egra2NSZMmwcHBATdu3MC1a9c4ug//Ck9PT6ozl6enJ8/PyYULFwKo7OpUEzpdGIWFhZGYmEjLt7p4//49li1bhjVr1rCNb9y4EdnZ2bh69SrWrVuHDRs2NDgw9ubNm1o7uE6fPh3btm1rsK+86Cz4M4YPH079W0NDAykpKfj48eMvr5f1pUuXLowHOouLi3H69Gn4+fnh7t27GD16NLy9vTFixAiOTsZ0uHPnDg4cOIDMzEycPHkSioqKCAoKgpqaGgYPHszYPHSRk5PD8+fP6wyMpaSk0O5smJOTU6t9FRUV5OTk0LJdtQ6vSUJCAtXRlA6tWrWq9RkmNTUVcnJytGzzwvcOHTogKysLSkpKKCkpQVxcHNzc3KjjX79+5Wq9NHbsWCoIwMtOnY3F4MGDoa+vj1atWjF23+PluaipqYm+ffti0aJFmDp1Kjp06EDLXk3Ky8vx8uVL9OrVC0DlJraPjw9j9t+8eUMFlmrOm5eXR8u2uro6srKyoKysDE1NTZw4cQL9+vXD+fPnaXd8FxERwa5du7B582Zqo6pz584QFxenZbc6v3VgzMnJCRs2bEDr1q3h5OT005+tbWFYF1lZWVQLXl4vfMrKyuDn54fr16/XuqPbEL+rqCuwVB06QaYqevXqhSdPnnCMb9u2jSN48ytu3LhBXch4GbgCgM+fP6O8vJzjwvnx40cICQnRCg5JSUkhJyeHIwMqNzeXdvtjaWnpWseZWIwcOXKEto2fUVhYCA8PD0RERODdu3fU96sKug/1y5Ytw5IlSxAfH08FZaOiouDv749du3bRsj127Fi271NVVpeRkRHtTDcAeP78Ofbs2YNnz54BqNypX7x4MSO2mQ7SjBs3Dm/fvoWcnBzbbl1Dd0Nrg9dBg65du/7SJhNZOrt378asWbMwcOBAtqxRc3NzWufiixcvar1eFxcX4+XLl1zb5TXZ2dkYMWIEcnJyUFxcjKFDh0JSUhJbtmxBcXExI4vB8PBwSEhIUA/Ae/fuxaFDh6ClpYW9e/dCVlaWa9ve3t5UlsKqVasgLCyM6OhoTJgwAatXr26QreptzmfNmsW1T/Wl5nWWSXixqQcAJ06cQGxsLMf41KlT0adPHxw6dAjTpk3jam1kZGSEO3fucGyg3L17F3///TfXPtekqlCD6WtYbWuXNm3acL12qZk5Vxdr165tkN0qFi5ciJCQECgpKcHW1hbBwcG0gz61cerUKcyYMQNWVlaIi4tDcXExgMrfl7u7Oy5dusT4nNxiamqKTZs2YcSIERzHCCHYtGkT7Wya9u3bIzExEaqqqmzjCQkJHBsr9aUq+MpisTjupeXl5fj27Rvmz59Px20AlVms69evx4kTJwBUfodycnKwYsUKTJgwodn5PnLkSLi4uGDLli04e/YsxMXF2a4liYmJ6Ny5c4Ptrlu3rtZ/tzQqKiqwadMm+Pj4IC8vD6mpqVBXV8eaNWugqqoKOzu7BttsjHPx+fPnPK3kEhQUxLBhw/Ds2TPawaTaGDJkCObNmwdfX1/o6ekBqMwWW7BgAe3ri42NDRISEmBoaAgXFxeMGTMG3t7eKC0t5eq+XBvi4uIclS5M8VsHxh4/foyUlBTo6uri8ePHdf4cN4sTYWFhthRNZ2dnrFy5kpEgRHWSkpKokzY1NZXtGLeLKl4Hln6FqKhog/+fXbt2QVdXF1JSUsjOzsaUKVMYSZmsjalTp2LMmDHUbnoVJ06cQFhYGK1F1JQpU2BnZ4ft27ezBWicnZ0xbdo0Wn4zzfjx4+Hv7w8pKSmMHz/+pz97+vRpWnPNnj0bt27dwowZM6CgoMD4A8OCBQsgLy+PHTt2UAuq7t274/jx4w3OKqiiatfyZ0H36iXA3HDq1ClMnToV+vr6+OuvvwCAKn0MCQnheiFYxe7du2FtbY2//vqL2rWkE6SRk5NDTEwMxowZU+duHbfwOmjg5uZWZ3CZCSoqKrBt2zaEhYWhpKQEFhYWsLa2BovFolWWFBYWRv37ypUrbJ+hvLwcERER9S7pagocHBygr6/P8VA2btw4zJkzh5E5nJ2dsWXLFgDAkydP4OTkhGXLluHmzZtwcnKiFfivfs8XEBCAi4sLbX8B4NKlSxAUFGTLAgKAq1evory8HGZmZrTsV1RUwN/fH6dPn8aLFy/AYrGgrq6OCRMmYMaMGbS/u7zY1AMq1w/R0dEc35fo6GhqbVFRUcHVOsPc3BwrVqxAbGwsBgwYAKDyenvy5Em4ubmxfde42TgIDAzEtm3bkJaWBqAyGO/s7IwZM2Y02FZtML12OXPmTJ3HWCwWnj9/jh8/fnAdGPPx8YGysjLU1dVx69YtDumAKuiuLzZu3AgfHx/MnDkTISEh1PigQYOwceNGWraZZvXq1ejTpw/69++PZcuWoVu3bgAqM8V27NiB1NRU+Pv705pj2rRpWLJkCSQlJWFgYACgsozSwcEBU6dO5cqml5cXCCGwtbXluJdWyW5UrWHosGPHDkycOBHt27fH9+/fYWhoiLdv3+Kvv/7iOiOYl75v2LAB48ePh6GhISV1Uj0b38/PD8OGDePKdk1KSkpq3VhWVlbmyl5NqaCysjL4+/tzBK/pVEZt3LgRAQEB2Lp1K9v9vmfPnvDy8uIqMNYY52JjyBv17NkTmZmZPFm/+fn5wdraGvr6+mxr/+HDh8PX15eWbUdHR+rfpqamSElJQWxsLDQ0NKgMuObMb68xVrPGeMqUKdi9ezfttMeamjRSUlKIj49vtho6jQWvyp1ERESQnZ0NBQWFOuvGmaJNmzaIiopC9+7d2cZTUlIwaNAgfPjwgWvbjVEfzRQ2NjbYvXs3JCUlYWNj89OfpZtRJiMjg4sXL2LQoEG07DQmAgICPz2/mci6ZFrzpi6Y0o5xdXXF+vXr6/VQTef3wnTQoDE0xjZs2ABXV1eYmppCTEwMV65cwbRp0+Dn50fLblW5EYvFQs3bubCwMFRVVbFjxw6MHj2a1jy8om3btoiOjka3bt3YNExevHgBLS0tFBUV0Z5DQkICSUlJUFVVhaurK5KSkhAaGoq4uDiMHDkSb9++bbDNX33/gcq/SW3lCvWhV69e8PDwwMiRI9nGw8PDsWLFCiQkJHBlF6i8No0ZMwaXLl1C7969oampCUIInj17hidPnsDc3Bxnz57l2j4Anml1bty4Ee7u7pgzZw769u0LoFJjzNfXF//++y9WrVoFT09PXLp0CdeuXWuQ7fqW7nFzXd+5cyfWrFmDxYsXU/e5u3fvYu/evdi4cSPbwwS38HLtUp34+Hi4uLjgxo0bsLW15Tqrc9asWfW6V9BdX4iLiyM5ORmqqqps15jMzExoaWnxRGOUDo8ePaL08qp+P4QQaGlp4ciRI9R5zy0lJSWYMWMGTp48SWUtV1RUYObMmfDx8eFKRqGKW7duYdCgQYxooP4MXmhS8dL3uqROPn78CAkJCVq/89TUVNjZ2SE6OpptnO4atD4BGbr6iBoaGjhw4ACGDBnC9t1MSUnBX3/9hU+fPnFtm+m/Z2NrgIaHh2PlypXYsGFDrZtLTEjbpKamUmt/TU1NWlJT379/R0REBLXWXLlyJZWdC1TKBq1fv56rTavG5LfOGAPA8aBw+fJlFBYW8nyelgST2gu8KnfS1NTEypUrYWxsDEIITpw4UedFoTaNkIZQXFxc68NMaWkpLcFToHHqo5niyJEjuHHjBgwMDHheSikrK8t4tmVtxMbGUiWJPXr04BCebAjVMy8JIRg5ciR8fX2hqKhI288qmNa8qQumtGNcXV0xdepUpKenw9zcHEeOHOFJGriLi0utJVoVFRVwcXFpcGCsMfTFAgMDsW/fPsybNw9Apb7dqFGj4OvrS0tLp2p3WE1NDQ8fPuRJKRIvqaioqHXh/vLlS9rl5VWIiIhQAbbr169T36k2bdo0SHOzOj/Lprl37x52795Nq1QxLS0NWlpaHOOamppIT0/n2i4A+Pv74/bt24iIiOAIYN24cQMWFhYIDAykdS/lVWb66tWroaamBm9vbwQFBQEAunXrhkOHDsHS0hIAMH/+fCxYsKDBtnlZWrpnzx7s37+f7Xdqbm6OHj16wNXVlZHAGC/XLkCldMiaNWtw/PhxjB8/Hk+fPqV136Cb+VRf5OXlkZ6ezlE6ePfu3Wa5ma2vr4+kpCTEx8cjLS0NhBB07doVOjo6jNgXERHB8ePHsWHDBtrNyGoiKSmJZ8+eUWVO586dw5EjR6ClpQVXV1daAaDqDB48mHFtOF76zkupExsbGwgJCeHChQuMVlw0hkbiq1evat2IraioQGlpKS3bTP89G1sDtGpTzNzcnEPHmO6mexVM6K5XERAQgIsXL1KBMW9vb/To0QNiYmIAKjdoFBQUGLnX8RRGels2Y1gsFsnLy6PeM9Vulld2a2JhYUG1l67+Gj9+PLG0tCRr165la7faUEJDQ4mYmBiZPXs2adWqFfUZ9uzZw3Vb1c+fP9fr1RCioqJI//79Sbt27YiAgACRlpYmMjIyHC9ZWVmufK6OkZERWbx4Mcf4woULyeDBg2nbbwp+/PjB1f8nICDAdp7379+fvHz5kim3KIKCgsjEiRNrba3MBHl5ecTY2JiwWCwiKytLZGVlCYvFIiYmJuTdu3eMzMGLa4CZmRnx8/PjGPfz8yPDhg2jbb+srIz4+vqSadOmkSFDhhBjY2O2Fx1cXV159vcUFRUlWVlZHONZWVlEXFy8wfZqXs95gYiICMnJyWEba9WqFcnNzeXpvM2dyZMnkzlz5hBCKr9DmZmZ5OvXr8TExITMmjWLkTnGjBlDhg8fTtavX0+EhYWpa9iVK1dIly5dGJmDkMp25xYWFkRQUJDMnDmTvHjxgmtbHTp0IBERERzj165dI3JycnTcJEOHDiWbN2+u8/imTZsYub4QQkhaWhoJDw8nRUVFhBBCKioqGLHb0mjVqhXVtr46qamppFWrVozMwau1S35+Plm8eDEREREhJiYm5MGDB3TcbHTc3d2JlpYWiYmJIZKSkuTOnTvk6NGjRE5Ojuzevbup3WsyiouLSUpKCiktLWXMpr6+PgkNDSWEEJKRkUFatWpFpk2bRjQ0NIiDgwNXNnft2lXvV3PzvTEQFxcnz549a2o3uEJPT48EBQURQtjX0G5ubrSft1rq37OKyMjIn77owIu1/+DBg0lYWBj1vuYzUVBQEBkwYAAtvxuD3z5jrEqAr+YYE6xdu5bK9CkpKcGmTZs4dgXoCs1JS0vj7NmzkJGRQZ8+fQBUtt4tKCjAsGHDcPz4cWzZsgURERFclaHxQnuhPuL+QMNKqQYOHIiYmBgAleUOqampPCt72rhxI0xNTZGQkEB1FYuIiMDDhw9x9epVWrZ5LTIPVO4uGBgYwMrKCgoKCsjPz8fYsWM50qzrA6mRCfn06VO21Fim2LFjBzIyMtChQweoqqpydOmh227a3t4eX79+xdOnT6kyk+TkZFhbW2PJkiUIDg6mZZ9X8FrzxsHBAf7+/hg1ahR69uzJ6A4YLwVhpaWlkZmZyZEBkJ6ezpFuXh94mSlSRVlZGUcKubCwMK1d0d27d2Pu3LkQFRXl0AOpCd0Oybxi+/btGDFiBFXSZGlpibS0NLRr146x76W3tzcWLlyI0NBQ7N+/n8rqvHz5cq0i1w3l9evXWLduHQICAjB8+HDEx8ejZ8+etGyOHTsWS5cuxZkzZyhx5vT0dCxbtoxW91KgUvB569atdR43MzP75fn0Kz58+IDJkyfj5s2bYLFYSEtLg7q6Ouzs7CArK4sdO3bQss+Unk5jfYc0NDRw4sQJ/Pvvv2zjx48fZ0yvhum1S2FhIbZv346dO3dCQ0MD58+fZ0wPqTFxcXFBRUUFhgwZgqKiIhgYGKBVq1ZYvnw57O3tm9q9RqeoqAj29vYICAgAAErw3N7eHoqKirR0ElNTU6nMtpMnT8LQ0BDHjh1DVFQUpk6dCi8vrwbb9PT0ZHufn5+PoqIiKhu9oKAA4uLiaN++Pa3vKC98bwy0tLTw/v37pnaDK9auXQtra2u8evUKFRUVOH36NJ4/f47AwEBcuHCBlm1e/j3rkvT58OED2rdvz0g2Fy+70vNi7Z+ens4miC8qKspWDdGvXz8sWrSI9jy85rfXGBMQEICZmRml3XT+/HmYmJhwPDw1VNzTyMioXvoi3OpoVOHi4oIvX77A29ubOsEqKirg4OAASUlJbNq0CfPnz8fTp09x9+7dBtvnhfZCdQFV8pMSM26/9NnZ2VBWVuZpGmt8fDy2bduG+Ph4iImJoVevXli5ciXtBey0adN+KjLv4OBAyz5QuXh/8uQJ7t+/j3nz5iEsLAxSUlI/bUBRFzW1l6qfI0xSvX11bdANskhLS+P69esc2hwPHjzAsGHDUFBQQMs+wJvfDS81bwCgXbt2CAwM5NAx4hZdXd16fy/pBDvnzZuHe/fucQQNJkyYgL59+9IWD+UFNe9FQO33o4bci9TU1PDo0SO0bdsWqqqqdf7u6eqA8JqysjIcP34cCQkJlGaMlZUVlYLfXKnqardnzx7o6Ohgy5YtjHUv/Pz5M0aMGIFHjx6hU6dOACrLS//++2+cPn2aVolydc3O2nj9+jXU1NRobYLMnDkT7969g6+vL7p3705dG69cuQInJyc8ffqUK7tpaWmwtbVlTE+n+nfoZ5o6dL9Dp06dwpQpU2BqakptYkZFRSEiIgInTpzAuHHjuLZdHSbXLvLy8vj69Svs7e0xbdq0Oq8vLUFQGagMpqanp+Pbt2/Q0tKChIREU7vUJDg4OCAqKgpeXl4YMWIEEhMToa6ujnPnzsHV1ZWrtWIVUlJSiI2NRZcuXTB06FCMHj0aDg4OyMnJQbdu3WiX9B47dgz79u3D4cOHqcYEz58/x5w5czBv3jxYWVk1W995xY0bN7B69Wq4u7tDW1ubY2OZCS0qXnLnzh2sX7+e7f6/du1a2kF4Xv4969Klff36NTp37szIuZKYmFjrOIvFgqioKJSVlbnWpWZ67Q8AYmJiiI+Pp76XNUlJSYGOjk6z03SsyW8fGPuVaHgVvNZQ4hY5OTlERUVx1ACnpqZi4MCBeP/+PZ48eYK///6bq4d7dXV1HDx4EKampmwP9oGBgfDw8EBycjLtz8CLgEFBQQEePHhQ644xXY0xXtKYIvMhISGwtLSEhIQEsrOzISsr22AbgoKCePv2LeTk5ABU3mgSEhKadZe72pCUlMSdO3c4NDoeP34MQ0NDrnWGas6RmJjYon43HTt2RGRkJGMaA78KcFaHTrCztqBBbm4uDAwMaAcNeEVLvxfxgtLSUmhqauLChQscguFMU15ejrNnz7JpDJqbm3OIIdeXrVu3YsuWLZCXl4e7uzvX3W1/BiEE165do3SAevXqRXWRo0PN63pN8vLy0LFjR1q73vLy8rhy5Qp69+7NsenWq1cvfPv2jSu7VWLKLi4utW4u9e7dm2ufeU1sbCw8PT2pc7B79+5YtmwZLa1LXlJ9Y6Zmc4+q90xp3fACW1vbev0c3QYoLQ0VFRUcP34cAwYMYPtupqenQ09Pj9Z6yMTEBEpKSjA1NYWdnR2Sk5OhoaGBW7duwdraGi9evKDle+fOnREaGsrxnYmNjcXEiRNp6WLx2ndeUb0BT3Wa+/eT1/Di71mVVezo6IgNGzawBdfLy8tx+/ZtvHjxglZwuYpfNfgRFhbGlClTcODAgQYL2jO99gcqtYo9PDwwYcKEWo9XZUzT1UjlNb99KWVLf8goKytDSkoKx8mbkpJCXexERUW5zp6aM2cOHBwc4OfnBxaLhdevX+PevXtYtmwZ1y24ec358+dhZWWFb9++QUpKiu2zs1gsrgNjZWVlKC8vZ4vA5+XlwcfHB4WFhTA3N6ct9skLkfnFixdDR0cHs2fPpsYyMjLg6OgIW1tbZGRkYM+ePVz9PQkhGDJkCNXVpaioCGPGjOEQraRb6lgFk+L41TExMYGDgwOCg4PRsWNHAJWin46OjlTJSUMZP3482/sfP35g/vz5tLNRG5Nly5Zh165d8Pb2ZiQDk5flk9WRlpZGdHQ0W9Cgd+/ejGXr8AJe3osaM8DEJMLCwo2ye5ieno6RI0fi1atX1G7m5s2boaSkhIsXL1JZhw3BxcUFYmJi0NDQQEBAAFWWVBM6338Wi4Vhw4YxXr5GCMGsWbPq3G1moly+sLCw1qYyHz9+pNV9OT4+HrGxsdDU1KTjXpPQp08fHD16lGf2c3Jyfnq8oWWmjSG+zUv8/f2hoqICXV3dFt0gi2ny8/NrlSIpLCykvQ7w8vKClZUVzp49i1WrVlHC6qGhoRg4cCAt20BlQ6LaGkyUl5cjLy+Plm1e+x4UFAQfHx9kZWXh3r17UFFRgZeXF9TU1GhtrPys0cmTJ0+4ttuYMFUaXx1e/D2rynoJIfDx8WHbWBMREYGqqirXXXprcubMGaxYsQLOzs7o168fgMoqlx07dmDdunUoKyuDi4sLVq9eje3btzfINtNrf6CyWcDatWsxatQojkDd9+/f4ebmhlGjRjEyFy/57TPGWjpV+kf//vsvW2tyd3d3WFpaYteuXfD19YW/vz9XpZSEELi7u2Pz5s1U165WrVrB2dkZK1euZKSUhemMsa5du2LkyJFwd3dntJujjY0NREREcODAAQDA169f0aNHD/z48QMKCgpITk7GuXPnaKWeHj16FOfOnUNAQABjvlc93FWVM7x58waDBg3C2LFj4enpievXr2PBggVIS0trsO36ZgDRDYi8e/cOU6dORWRkJJtuhLGxMUJCQurMbKgvubm5MDc3x9OnT6GkpESN9ezZE2FhYVTWUUNorAyghw8f4ubNm7UuGrjRMKwZ0Ltx4wbatGmDHj16cKTg0w3qFRQUIDQ0FBkZGXB2dkabNm0QFxeHDh06cNW98969e/jw4QPV9Qao7ISzbt06FBUVwcLCAnv27KH14N1SUVRUxPXr11tUYAwA3N3dkZqaCl9fX8baqtdk5MiRIITgv//+ozYmPnz4gOnTp0NAQAAXL15ssM1Zs2bVa0HZ0O9/bed4YGAg1q1bh8LCQkbO8ca4do0cORJ9+vTBhg0bqGxaFRUVTJ06FRUVFQgNDeXKbt++feHp6cl4RzoAmDBhAvr164cVK1awjW/duhUPHz7EyZMnG2yzvtk3TJQ7/SrD4E/LHFm0aBGCg4OhoqICGxsbTJ8+vVG6XzNFVcf4jIwMhIaG0uoYXx0DAwNMmjQJ9vb2bJnu9vb2SEtLQ3h4OIOfopIfP35AUFCQY43RUMaMGYNXr17B19cXenp6ACo3VOfOnQtFRUU23VWmYML3/fv3Y+3atVi6dCk2bdqEpKQkqKurw9/fHwEBAYx28f369SuCg4Ph6+uL2NhYxr73O3fuhIGBAfT19QFUPj86OjrS0upiujS+PjDx9zQ2Nsbp06e5qsSpL/369cOGDRswfPhwtvErV65gzZo1ePDgAc6ePYtly5YhIyOjQbbHjRuHmzdvMrr2z8vLg46ODkRERLB48WIqoef58+fw9vZGWVkZHj9+jA4dOjTYdqPSqFL/fBpMWVkZ2bhxI5GXlycsFouwWCwiLy9PNm3aRMrKygghhGRnZ9PubFZcXEyePn1K7t+/T75+/Uq2b99OOnTowMRHoDqNMYW4uDhPOoB26dKFXLlyhXrv7e1NOnbsSAoKCgghhPzzzz/EyMiI1hw6OjpEUlKSSEhIkJ49exJdXV22FzeIiYlRv4+PHz8SbW1tsnbtWup4RkYGERMTo+U3r5k8eTLR19cnycnJ1NjTp0+Jvr4+mTp1KiNzVFRUkKtXr5Ldu3eT3bt3k2vXrjFil5ds2rSJsFgsoqmpSQwNDYmRkRH14rZzzKxZs+r9okNCQgKRk5MjGhoaREhIiDpHV61aRWbMmMGVzREjRhAPDw/qfWJiIhEWFiazZ88mO3bsIPLy8mTdunW0/G6pbNq0iVhbWzPaYawxsLCwIJKSkkRBQYEMGzaMowMzE4iLi5PExESO8fj4eNK6dWtG5mCK2s5xISGhFneOP3nyhLRv356MGDGCiIiIkIkTJ5Lu3buTDh06kPT0dK7tRkREkL/++ovcvHmTvH//nla365q0a9eu1vMkMTGRtG/fniubLBaLCAgI1PmqOs4E8fHxbK+HDx+SgwcPEk1NTXLq1ClG5mhp/Pjxgxw7doyYmpoScXFxMmnSJBIeHt7su6PyomN8FXfu3CESEhJk/vz5RFRUlDg4OJChQ4eS1q1bk0ePHjHhPs949+4dMTMzIywWi4iIiBAREREiICBAzMzMeN5Zmg7du3cnZ86cIYSwd+t78uQJadu2LSNz3Lp1i8ycOZO0bt2adOnShaxYsYLRDrLa2tpETEyM9OzZk5w9e5aYm5sTaWlpWjYHDhxIDAwMyKVLl8jjx485rmF/MqKiorV2G3327BkRFRUlhFR2Yufm+Y5Xa//MzEwyfPhw6t5WdX8bPnw4T57beQE/Y6wFUbXzyMTOYnFxMVxdXXHt2jUqQ8zCwgJHjhzB6tWrISgoiEWLFnHsnNaHmhkpTDU8qG5/6tSpmDx5Mlf/f120bt0aSUlJlEbU+PHj0alTJ6qmPDk5GUZGRnj37h3Xc/BCZF5PTw86OjqwtLTEP//8g1GjRmHDhg3U8aCgILi7u1Mlis0RXonj37hxA4sXL0ZMTAzH9+bz588YOHAgfHx8mm0JXocOHbBlyxbMmjWrqV1pMKamptDT08PWrVvZskajo6NhaWnJlb6DgoICzp8/T+1Yrlq1Crdu3aKyZU+ePIl169Yxoo3Y0hg3bhwiIiIgISEBbW3tFlPS+6vsJSZKUNu0aYMLFy5wlE9ERUVhzJgx+PjxI+05mOJ3Osc/f/4Mb29vNlHlRYsW1Sn6Xx94qadTl3hwSkoKdHV1uRJU5nUzovpw8eJFbNu2DZGRkTyboyWQnZ0Nf39/BAYGoqysDE+fPm22Avy6urpwdHTEzJkz2e6fjx8/hpmZGd6+fUvLfkZGBjw8PNi+mytWrGDrKtfcIIQgNzcXcnJyePnyJbWm1dTUZFQriReIiYkhJSUFKioqbH/PtLQ09OrVi2ux9rdv38Lf3x+HDx/Gly9fMHnyZPj4+CAhIQFaWloMf4rKbKtdu3Zh5cqVkJCQwMOHD+sUW68PrVu3bpGl8bzILq6Jrq4uevfujYMHD1LyNaWlpZgzZw4SEhLw+PFjREVFYfr06c2u7P3jx4+UlpiGhkaLytT97TXGfhfy8/Px/PlzAJU3gXbt2tGyt3btWhw4cACmpqaIjo7GpEmTYGNjg5iYGOzYsQOTJk3iWpRYWlqa7f306dNp+VqTUaNGwdnZGcnJybV2YOG2lb2oqCjbzSkmJgbbtm1jO86tYHAVvNBgcnd3x4QJE3Dq1CkMGzYMISEhGDx4MHR0dHD79m0sW7YMTk5OjM/LJBUVFbWmNQsLC3OUDzYELy8vzJkzp9ZgsrS0NObNm4edO3c228CYgIAATxs1ZGVloaysjKNjWVpaGoSFhaGqqsq17YcPH1JlydVRVFTkelH/6dMntjTsW7duwczMjHrft29f5ObmcmW7pSMjI1On6GlzpjF0QEePHo25c+fi8OHDlFbH/fv3MX/+fK7vF7yiJZ/j48ePh7+/P6SkpBAYGIgpU6Zg1apVjM7BZMlRTbS1tXH8+HEOPc6QkBCuHzJrBrwEBQUxYMAAxjs7/4xu3brh4cOHjTZfc6Wq1JQQ0uzLSp8/f15row1paWlGumh37twZhw4dom2nMSGEQENDA0+fPkWXLl1od4lvTNTU1BAfHw8VFRW28fDwcK7lD8aMGYPbt29j1KhRVIdRQUFBxjSufH19IScnx6Z/RgjB2bNnoauri48fPyI2NpZWYExLSwvv379nwt1G5fbt23B1deUYNzMzw44dOxiZY+/evTA3N0enTp0oqZwnT56gvLwcFy5cAABkZmZi4cKFXNkvKytDZGQkMjIyYGlpCUlJSbx+/RpSUlK0NwzatGlDrbVaHE2XrPb7cPv2bWJlZUUGDBhAXr58SQghJDAwkNy5c4e27W/fvhEbGxsiKChIpSUKCQkRW1tbUlhYyLVdNTU1cu7cOUJIZSovi8UiNjY2zT69nBBC/R5qe9EpSTAxMSEuLi6EkMq/qYCAAHn9+jV1/OrVq6Rz5860/SeEkEePHpGgoCASFBRE4uLiaNv78eMH+fHjByGEEFdXVyIqKkqlsk6bNq3Zl1eZm5sTAwMD8urVK2rs5cuXxNDQkFhYWHBtV1lZma08sybPnj0jSkpKXNvnNVu2bCEODg48s29gYED8/f05xoOCgoihoSEt23JyctS5Xb104OrVq6RTp05c2VRWVia3bt0ihFSWf4uJiZHr169TxxMTE4msrCwtv/k0LpmZmSQ1NZVjPDU1lWRlZTEyx6dPn4i5uTlH+Y2FhQVVKt9caMnnuLCwMHXPFBAQaNalTbURFhZGhISEyMyZM4m/vz/x9/cnM2bMIEJCQlQZFF2qXwuZpmZZaUFBAXn27BmZMmUK6d27N0/mbO5UL6UUFRUlEydOJBcvXiTl5eVN7dpPUVNTo+Qeqp8zAQEBpHv37g22V/Pc+NmrOaOlpUXu3bvX1G40mEOHDhFFRUUSEhJCWrduTYKDg8nGjRupf3ODoKAgcXR05Lh/CgkJkadPn9L2WVNTk0RFRVHvS0pKyPDhw8ngwYPJ58+fSXBwMNHX16c1By9L43mJqKgoSUlJ4RivXubIBF++fCH79+8njo6OxNHRkfj4+JAvX77QtvvixQuiqalJxMXFiaCgIHV9WbJkCZk3bx5t+y0ZfmCMJrzUASCEkLlz5xJ1dXVy6dIl6kJx8eJF0rlzZzJ//nyu7QoLC1NBPEIqv+S1aWv8SURGRhIxMTGirq5OxMTEiK2tLdvxBQsWkJkzZ9KaIy8vjxgbGxMWi0VkZWWJrKwsYbFYxMTEhLx7946W7ep8+vSJxMTE0NaeayxycnKIjo4OERYWJurq6kRdXZ0ICwsTXV1dWp+hVatWJC0trc7jaWlpjN7EmKa8vJyMGDGCqKurk9GjRzOuvyQpKVnr7yctLY22doSdnR2xsLAgJSUllM5gdnY20dXV5TrYN3/+fPLXX3+R27dvEycnJ9K2bVtSXFxMHT969CjthVpLo6ioiJw7d67WxdLnz5/JuXPnqKB5c4SXwdmapKamkrCwMBIWFvbT60JT0pLPcW1tbWJtbU38/f0Ji8Uie/bsIQEBAbW+GkJCQgIVyEhISPjpiy4XLlwgAwcOJOLi4qRt27bE2NiYREZG0rZbBS8DY7XpmbFYLKKsrEyio6O5ttsYwWtesGDBAiIrK0t69epFvLy8SH5+flO7VG/c3d2JlpYWiYmJIZKSkuTOnTvk6NGjRE5OjuzevbvB9n6ldceU3t2NGzdo/f+/IiwsjAwePJg8efKEp/MQUqnx/PjxY/Lx40dG7B09epRoaGhQG/mKiorE19eXa3v37t0js2fPJpKSkqRfv35kz549JD8/n7HAmJiYGMnOziaEVGr0Tp06lQwdOpRKykhPT6etX1w9qYFX2ovFxcUkJSWF0QSBvn37Ejc3N47xdevWET09Pcbm4RVjx44l06dPJ8XFxWz3pJs3bxINDY0m9q5p4WuM0YTXOgDt2rVDaGgojIyM2MZv3ryJyZMnIz8/nyu7goKCePv2LdXtr3pnmj+ZZ8+e4erVq5CXl8ekSZMoPRMAOHjwIPr16wcdHR2u7U+ZMgWZmZkIDAyk0qeTk5NhbW0NDQ0NBAcH0/0Ijc7Hjx8ZqR8nhOD69etISUkBAHTv3h2mpqa0bHbu3Bk7duyAhYVFrcdPnz6N5cuXIzMzk9Y8vGLx4sXw9fWFsbExOnTowKGrQ7cMTVpaGpGRkdDV1WUbj42NhZGREb5+/cq17c+fP2PixIl49OgRvn79io4dO+LNmzf466+/cPnyZQ4NrPrw/v17jB8/Hnfv3oWEhAQCAgIwbtw46viQIUMwYMAAbNq0iWu/Wxq7du1CWFgYIiIiaj1uamoKCwsLLF68uJE9qx9SUlKIi4ujWqlXkZ6eDn19fUbKhloSLfkcj46OhpOTEzIyMvDx40dISkrW2iWRxWI1SNdNQEAAb9++Rfv27dnK4Wqz29xL5Hi51qquZwZU/t7k5OSgoaFBq+OroaEhbG1tYW1tzTZ+9OhR+Pr6NlvtMgEBASgrK0NXV/en3Tqbo/4iqaNj/PLly9k0ZOtLzXPjZ9DRu2vVqhU6deoEGxsbWFtbU13AmUJWVhZFRUUoKyuDiIgIxMTE2I7T0YtcunQptLW1YWdnh/LychgaGiI6Ohri4uK4cOECx3MYtxQVFeHbt29o3749I/YKCwtx/Phx+Pn54cGDBygvL8fOnTtha2sLSUlJru2qqanB2dkZtra2WLhwIQoKChASEkLpXUVERMDW1hbZ2dlcz/Gr85LOuVhUVAR7e3sEBAQAAFJTU6Gurg57e3soKirCxcWFa9vnz5/H+PHjYWlpCRMTEwCVv4/g4GCcPHmyzmeOhpKRkQEvLy9KT69Hjx5YsmQJOnfuTMtu27ZtER0djW7durHFLl68eAEtLS3qmvMnwg+M0URcXBzJyclQVVVlO7kyMzOhpaWFHz9+0LYfGxvLUYP+9OlT9OvXD4WFhVzZFRAQgJmZGdXynWmBfF5z69YtbN++nbpYaGlpwdnZudlqRVXBK5H5xkJbWxsGBgawtbVFnz59kJqaitGjRyM1NbWpXasVe3t7REZG4uHDhxAVFWU79v37d/Tr1w/GxsZUg4XmhqSkJEJCQjBq1Cie2B8zZgzExMQQHBxMaQqWl5djypQpKCwsxOXLl2nPcffuXSQmJuLbt2/o06cPhgwZQtvm58+fISEhwaGD+PHjR0hISFALtz+Bfv36Yc2aNRgzZkytxy9cuID169fjwYMHjexZ/eBVcLYhuoo7d+7kag5e0tLP8erBLLpkZ2dDWVkZLBbrlw9hNTV8GkJubi5YLBY6deoEoPK+fOzYMWhpaWHu3Llc2eR1M6LGoKUGr2fNmvXTgFgVjaFzyC0lJSVIT0/Ht2/foKWl1WybBVTx/v17BAUFISAgAE+fPoWJiQns7OxgYWHByDWrKshRFzWDtw2hU6dOOHv2LPT19XH27FksWrQIN2/eRFBQEG7cuIGoqCiubTcWz58/x+HDhxEUFISCggIMHToUYWFhXNnasWMHnJ2dISQkhI4dO0JHRwcBAQGQlpbG69evMWrUKPTt2xcHDx5k+FMwg4ODA6Kioij9tcTERKirq+PcuXNwdXXF48ePadm/ePEi3N3dER8fDzExMfTq1Qvr1q1jrJHKlStXYG5uDh0dHUprOCoqCgkJCTh//jyGDh3KtW1ZWVlERUVBS0uLLXZx9+5dTJgwAXl5eYx8hpYIPzBGE3V1dRw8eBCmpqZsJ1dgYCA8PDxod44aMmQI2rZti8DAQOrB/vv377C2tsbHjx9x/fp1ruz+qhNYFc1xwXD06FHY2Nhg/PjxbBeLM2fOwN/fH5aWlk3sYd1ISkrizp07HFlnjx8/hqGhIdV5tLmyc+dOPHnyBKdPn4aJiQnu3LmDvn37chVA2b17N+bOnQtRUdFfBqaWLFnClb95eXnQ09ODoKAgFi9eTImEpqSkYO/evSgvL0dcXByb2HVzQkVFBVeuXOFZx57k5GQYGBhARkaGCirfuXMHX758wY0bN9CzZ88G27x37x4+fPiA0aNHU2MBAQFYt24dioqKYGFhgT179lBBeT70kJWVRUJCApSVlWs9npOTg969e+PTp0+N7Fn94FVw1tjYuF4/x2KxcOPGDa7m4FM31YNZLYW///4bc+fOxYwZM/D27Vt07doVPXv2RFpaGuzt7TlE+esDr9daDXno5bbRBC8zi/k0DomJifX+2Sqhb7rExcXhyJEjVCWEpaUl7Ozs0Lt3b0bsM42oqCjS09PRqVMnzJ07F+Li4vDy8kJWVhZ69+5Na31eV9Yii8WCqKgoNDQ0MGvWrHrft35FeXk5zp8/Dz8/P64DY0DlelBAQAA6OjoYO3Ys7t27B2VlZWRlZUFZWRnR0dENbgaXmJiInj17QkBA4JfnJZ1zUUVFBcePH8eAAQPYns/T09Ohp6fHs+etpKQkrtbONdHV1cXw4cPh4eHBNu7i4oKrV68iLi6Oa9tTpkyBtLQ0Dh48SGUxVzVaUFZWbpbP/o1GkxVx/iYwrQNQkydPnpCOHTuStm3bEhMTE2JiYkLatm1LFBUVSVJSEgOfoOWhqalJdu7cyTG+Y8cOoqmp2QQe1R9eiczzivLy8lpFakNCQgiLxSKSkpLkw4cPXNlWVVUl79+/p/5d10tNTY3WZ3jx4gUxMzOjdAuqtAvMzMxIZmYmLdu8xs/Pj0yePJlWo41f8erVK7Jy5UoycuRIMmHCBOLm5sb135QQQkaMGEE8PDyo94mJiURYWJjMnj2b7Nixg8jLy5N169Yx4DkfQio1ix49elTn8UePHhEJCYlG9KhhPH36lLRt25Z07tyZzJo1i8yaNYt07tyZyMnJNYqODB/ewavGRK9evSLHjx8ne/bsIbt27WJ70UFGRoYSVN61axcZOHAgIYSQK1eu0L4P8YraGhDVfF/14pbRo0eTSZMmkbKyMmqsrKyMTJgwgYwYMYKJj8GnBkZGRsTY2LjOV0Op7dxguoFVbbx69YqsW7eOtGrVirRu3ZoICgqSwYMHM/L88v37d0bF2pWVlcmVK1dIWVkZUVJSIhcuXCCEEJKUlERkZGRo2XZxcSHS0tJk8ODBxMnJiTg5OZG///6bSEtLEwcHBzJ06FAiICBAzp49S2seXlJRUUEuXrxIPDw8SFBQECkqKuLKDovFohqz/Oy8pHsuiomJUdpZ1XW04uPjiZSUFC3bNfny5Qs5cOAA6du3L2PfoVatWtWq7fj8+XPSqlUrWrZzc3OJlpYW6d69OxESEiIDBgwgbdu2Jd26dWtxTXOYhnvRAT4AKiO3FRUVGDJkCIqKimBgYEDpANjb29O2X7Vb+d9//1HaS9OmTYOVlRVHbf2fQmZmZq1lQ+bm5vj333+bwKP64+3tDXNzc6iqqlLaC7m5uejZsyeOHj3axN5xMnXqVBgbG2PBggXU2IMHDzBnzhysX78e8fHx2LRpE1ftibOysmr9N9OoqKjg0qVL+PTpE9LT00EIQZcuXSArK8uzOZli9+7dyMjIQIcOHaCqqgphYWG243R2jKro2LEj3N3dadupIj4+nk0DJSQkBP369aNawyspKWHdunW1trrm03B69OiB69evo0+fPrUev3r1Knr06NHIXtUfLS0tJCYmwtvbGwkJCRATE8PMmTOxePFiRrQL+TQNp06dwowZM2BlZYXHjx+juLgYQGWJqLu7Oy5dusSVXX9/f8ybNw8iIiJo27YtWxYGi8XiOrsYAEpLS6lM1uvXr1MZVpqamnjz5g3XdnlJRUUF9e/r169jxYoVcHd3x19//QWgMoN39erVtK7xW7ZsgYGBAbp161ZrZjEf5qlZVVBaWor4+HgkJSVxVS7IyzVWTUpLS3Hu3Dn4+fnh2rVr0NfXh7e3N6ZNm4b8/HysXr0akyZN4qqiprCwECtWrMCJEyfw4cMHjuN0NAZtbGwwefJkKCgogMViURq39+/fp521//79eyxbtgxr1qxhG9+4cSOys7Nx9epVrFu3Dhs2bMDYsWNpzcUrWCwWRo4ciZEjR9Kyk5WVRelb8/K81NfXx8WLF6ln8ap7ha+vL3V9pMvt27fh6+uL06dPo2PHjhg/fjz27t3LiG05OTnEx8ejS5cubOPx8fG0JQo6deqEhIQEhISEUFIndnZ2f3RsoQp+KSVDtDQdgJaMhoYGnJ2dMW/ePLZxHx8f7NixA2lpaU3kWf0gDIrM8zo9vkOHDoiMjKQ07p49ewYDAwOsXr0aDg4OuHPnDqZPn05LfBMA1q9fj+XLl0NcXJxt/Pv379i2bRtXJSy/A25ubj89vm7dOlr2VVVVYWtrCxsbG8ZEckVFRZGWlkbZGzx4MMzMzLBq1SoAwIsXL6Ctrc0vv2GIgwcPwsnJCSEhIWzlq0ClntG0adOwc+dOrjWSWirjx4+Hv78/pKSkOHSeatKc9Z1aKrxqTKSkpIT58+dj5cqVbM1xmKB///4wNjbGqFGjMGzYMMTExKB3796IiYnBxIkT8fLlS0bnY5qePXvCx8cHgwcPZhu/c+cO5s6dS2mycsPr16/Zgte9evXiB6+bAFdXV3z79g3bt29valdqxd7eHsHBwSCEYMaMGZg9ezZHWdnbt2/RsWNHtqBufanS/dqwYQNmzJiBvXv34tWrVzhw4AA8PDxgZWVFy//Q0FDk5uZi0qRJlNZgQEAAZGRkaAWspKWlERsbW6tOX58+ffD582ekpKSgb9++/LURQ9y9exdmZmaYPn06taGSnJyM6Oho3Lp1q87NxF/x9u1b+Pv74/Dhw/jy5QsmT54MHx8fJCQkQEtLizH/169fD09PT7i4uGDgwIEAKmWDtmzZAicnJ44gKx9m4AfGmiFhYWEwMzODsLDwL2vDudWMaMns378fS5cuha2tLdvFwt/fH7t27eIImP3OVO/Q9SstF2520iQkJBAXF4euXbsiOzsbhoaGWLt2LWxtbQFU3tR79+7NdROIKgQFBfHmzRuOXZAPHz6gffv2zb7TWEvFy8sL/v7+SEpKgrGxMezs7DBu3Dha+l8qKioICgqCgYEBSkpKICMjg/Pnz1Oi+0+ePIGhoSGt7lF82Jk+fTqOHTsGTU1NNh291NRUTJ48udl3u71z5w4OHDiAzMxMnDx5EoqKiggKCoKamhrHQ359sbGxwe7duyEpKflLnac/Wk+DR/CqMVHbtm3x4MED2l25aiMyMhLjxo3Dly9fYG1tDT8/PwDAv//+i5SUlGYfQBUTE8PDhw85AhGJiYno378/vn//3kSe8WGK9PR09OvXj/b9s2a3Oy0tLTg4OND+Xg0ZMgSzZ8/G+PHj61xHlJWVISoqiiuRcmVlZQQGBsLIyIitKURQUBCCg4O5zkTlNR06dMC2bdswc+ZMtvHAwEA4OzsjLy8PycnJMDQ0RH5+fhN52fjU9YxbXXuNTgffzMxMbN68GQkJCfj27Rv09PSwYsUKaGtrc2VvzJgxuH37NkaNGgUrKyuMGDECgoKCEBYWZjwwRgiBl5cXduzYgdevXwOorPBwdnbGkiVLGqzfyY8t1A9+KSUX/Gr3uTrcLKQsLCyobk4/a/naElqT84IFCxZAXl4eO3bswIkTJwBUZl0dP36c6x0dWVnZel9k6CxIlixZAg0NDY5yD29vb6Snp8PLy6tB9qqnIT9+/BjLly+Hs7MzWxnFjh07sHXrVq781dHRwdKlSzF+/Hhs3LgRCxcupIJiABAeHs6xA8YNdQX2EhIS+DvSqBQ5rt6uuaYIMrcsXboUS5cuRVxcHPz9/WFvb4+FCxfC0tIStra20NPTa7DNkSNHwsXFBVu2bMHZs2chLi7O1i02MTGRJw+1fzJHjx6Fubk5jh07htTUVBBC0K1bN7i5uWHy5MlN7d5PqV5yFxcXx1jJXfVgV0sJfDWGkHpjIS8vj/T0dKiqqrKN3717F+rq6lzbtbOzw8mTJ+Hi4kLTQ06MjIzw/v17fPnyha3UvkqIu7nTt29fODk5ISgoiGook5eXB2dnZ/Tr169BthpLIJtPw7h37x5Hh+2GUle3ux49etDqdldaWgoVFRUMGDDgp5trQkJCXHfu+/jxI3X9kJKSotbjgwcPZpP8aAiBgYFs72sGr5jA3t4e8+fPR2xsLNWV/uHDh/D19aUkYK5cucJRQvu7Y2FhQW3uV6f6hv/gwYNx9uzZBsmflJaWYt68eVizZg0l48EEly9fxpIlS7BgwQKOEkemYbFYcHR0hKOjI5VFKCkpybU9fmyhfvAzxrigvl2GgJazIP/T+VUL6OrQaQetqKiIsLAwjhTeuLg4mJub0yrV6NevH1xdXTnq/y9duoQ1a9YgNja2wTYfPXqEKVOmQFBQEObm5ggICMDGjRuho6OD27dvw83NDZ6enpgzZw5XPlcFJD9//gwpKSm24Fh5eTm+ffuG+fPnM1az39J49+4dpk6disjISMjIyAAACgoKYGxsjJCQEEqngSlKS0uxb98+rFixAqWlpdDW1saSJUtgY2NT78Dx+/fvMX78eNy9excSEhIICAjAuHHjqONDhgzBgAEDsGnTJkZ959My4VXJXUukvqWBLWHhunnzZhw9ehR+fn4YOnQoLl26hOzsbCxduhRr167lWoO1vLwco0ePxvfv36Gtrc2hu7hz504m3G9UiouLGenSm56ejnHjxiE1NZVNw7RLly44c+ZMgx7kBAQEqIeo6pnpNWkJ52JLpOYGPCEEb968waNHj7BmzRpaMgq87HYnLS2N+Ph4Wlk+P6NXr17Ys2cPDA0NYWpqCh0dHWzfvh27d+/G1q1buVpDV+8Eycsuxf/99x+8vb3x/PlzAEC3bt1gb28PS0tLAJXSIVWZUn8KERERWLVqFTZt2kQF7x88eIA1a9Zg9erVkJaWxrx589C/f38cPny4QbZ5cS7GxMTg8OHDOH78OLp3744ZM2Zg6tSpUFBQYDxjzMTEBKdPn6bW/lV8+fIFFhYWfH1HHsEPjPHh04iIiooiKSmpVp2Bnj17cl1eAlSWUcTFxVF6YFU8e/YMenp6jJRRBAQEYOXKlXj79i3ExMTg4OBAS9Q3ICAAhBDY2trCy8sL0tLS1DERERGoqqoyJpLZEpkyZQoyMzMRGBhI/V2Tk5NhbW0NDQ0NxkrkSktLcebMGRw5cgTXrl3DgAEDYGdnh5cvX2Lv3r0wMTHBsWPHGmTz8+fPkJCQgKCgINv4x48fISEhAREREUZ859Oy4VXJXXXy8vKwfPlyRERE4N27dxwP+PwHe+YhhMDd3R2bN29GUVERAKBVq1ZwdnbGypUruRb43bhxI9auXYtu3bqhQ4cOHOL7DX1Y0NPTQ0REBGRlZaGrq/vTDQAmmp2Ym5vDwMAAVlZWUFBQQH5+PsaOHYvo6GjatoH/aZhWZRhXaZg2tOwmOzsbysrKYLFYv9QQVVFR4dpfPrVTcwNeQEAAcnJyMDExwbBhw2jZFhUVxZMnTzgCpampqejVqxeta661tTV0dHTg6OhIy8e68PT0hKCgIJYsWYLr169jzJgxIISgtLQUO3fuhIODA0/m/dMpKChAaGgoMjIy4OzsjDZt2iAuLg4dOnSAoqIi13Z79uyJgwcPUrI4VURFRWHu3Ll4+vQprl+/DltbW+Tk5DTINi/PxcLCQhw/fhx+fn548OABysvLsXPnTtja2tLK6qpO9c2J6rx79w6KioooLS3lym5paSlGjBgBHx8fnme9tUT4pZQtgIiICHh6erItdJYuXcq1YHtLRU1N7ZeLOxaLhYyMDMbm/PHjB0pKStjGpKSkuLanoaGB8PBwLF68mG388uXLtMpLgMrzYvPmzfD19aWCDiUlJdi8eTNHsIxbrK2tMXPmTLx79w6ysrK0gxtV2XdqamoYOHAgx+7/n054eDiuX7/O9vfT0tLC3r17aS+OgcoHvSNHjiA4OBgCAgKYOXMmPD092TowjRs3jkr9bwjVg5zV4ZfG8qkOr0ruqjNr1izk5ORgzZo1VMcxPryFxWJh1apVcHZ2ZmtMdODAAaipqXGdCbhjxw74+flh1qxZjPg5duxYKlvrZ+UlTKGqqoqLFy9i7dq1mDdvHsLCwmitKYDK8vXg4GBIS0uDxWIhNjYW8+fPpzINPnz4gL///rtBXQCrB7v4ga/Gpby8HDY2NtDW1uZJ92xedrvr0qUL1q9fj6ioKPTp0wetW7dmO06naywAtiCHqakpUlJSKFF7fkkvb0hMTISpqSmkpaXx4sULzJkzB23atMHp06eRk5PDUYraEDIyMmq9/klJSSEzMxNA5Tn1/v37Btvm5bnYunVr2NrawtbWFs+fP8fhw4fh4eEBFxcXDB06tEGyCDWpXrqenJzMdq8sLy9HeHg4rWCksLBwgxq3/WnwM8a4oDF3GPft2wcHBwdMnDiRypyJiYlBaGgoPD09sWjRIlr2WxK7du2q89iLFy9w4MABFBcX097952U7aD8/PyxevBjOzs4wMTEBUBn43LFjB7y8vLguSQQq04+rds+qFgiJiYlgsVg4f/58gzVGmgqmg5EtGUlJSdy5c4dDd+Lx48cwNDTEly9faNkXFBTE0KFDYWdnBwsLi1oDk4WFhVi8eDG/LJwPT6ir5M7R0RFr1qzhuuSuOnV9j5o7hYWFuHXrFnJycjiuiXQfMHlFcXExXF1dce3aNSpDzMLCAkeOHMHq1ashKCiIRYsWYcWKFVzZl5eXx507d1r8TndISAgsLS0hISGB7OxsWgGQms1rpKSkEB8fTwWW8/Ly0LFjR1prl7S0NNy8eRPv3r3j6Cb4p3aN5iWioqJ49uwZT0oSednt7mf+slgsKtjRUL5//46IiAiq8/LKlSspPUqgUrds/fr1tMoQy8vL4e/vT2UW1zzP6ZSulZeXw9PTEydOnKj1et6cmxGZmppCT08PW7duZcvqjo6OhqWlJV68eMG17cGDB0NSUhKBgYGUNEh+fj5mzpyJwsJC3L59G9evX8eiRYuoEtT6wqtzsS7Ky8tx/vx5+Pn50QqMVZWuA6i1fF1MTAx79uxh03tuKI6OjmjVqhVHOTUffmCMK9zc3ODs7AxxcXG4ubn99Gfp6AAAQKdOneDi4sKRYbR37164u7vj1atXtOy3dD5+/IgNGzZg//796N+/P7Zs2YIBAwbQssnrdtD79+/Hpk2bqC4jqqqqcHV1ZUTws7CwEP/99x9SUlIAVGaRWVpacuyUNDeKiorwzz//8CQY2ZIZO3YsCgoKEBwcjI4dOwIAXr16BSsrK8jKyuLMmTO07GdnZ/OzAfg0KXWV3C1fvhwbNmxgZA4tLS38999/jDWtaAweP36MkSNHoqioCIWFhWjTpg3ev38PcXFxtG/fnvFFPVOsWLECBw4cgKmpKaKjo5Gfnw8bGxvExMTg33//xaRJkzjKqxvC5s2b8ebNG+zevZtBryshhCA2NhYvXrwAi8WCmpraLzc/f8XixYuho6OD2bNnU2MZGRkYPHgwRo0ahYyMDBgbG9MKLtUsuan+8ArQD4wdOnQICxYsQLt27SAvL89RvspEiSkfdvT19bFlyxaqmzOTMN3trjHw8fHBxYsXcf78eQCV53iPHj2okuyUlBT8888/tMrmFi9eDH9/f4waNarWzGJPT0+uba9duxa+vr5YtmwZVq9ejVWrVuHFixc4e/Ys1q5d22w3OoDK7P+4uDh07tyZ7dqSnZ2Nbt260Sq9ff78OcaOHYusrCw2XUR1dXWcO3cOXbt2xdmzZ/H161fMmDGDqY/UrMnOzgYhBOrq6njw4AGblrCIiAjat29P6x4KVDaDCAwMRJcuXWrNpmuJWp2MQfg0a1q3bk3S0tI4xlNTU0nr1q2bwKPmQVFREdm4cSORkZEhvXv3JhcvXmTMtpKSErl58yYhhBBJSUnq9x8YGEjMzMwYm+fdu3fk69evjNlrySxcuJB0796dhIaGEjExMeLn50c2bNhAOnXqRI4ePdrU7jUZOTk5REdHhwgLCxN1dXWirq5OhIWFia6uLsnNzWXEfnU79+/fJw4ODuTAgQO0bfNpXPz8/EhhYWFTu8E1xcXF5OnTp+T+/fvUdbGoqIgR21euXCHDhg0jWVlZjNhrDAwNDcmcOXNIeXk5kZCQIBkZGSQnJ4cYGBiQU6dONbV7daKmpkbOnTtHCCHkyZMnhMViERsbG1JRUcGIfQsLCyIlJUXU1NTI6NGjybhx49he3HLjxg2ipqZGBAQECIvFIiwWiwgICJDOnTuTW7ducW23U6dOJCEhgXr/+vVroqamRpYuXUoIIeTatWtEQ0ODa/uEEMJisUheXh71vup8qeLt27dEQECAa/vKysrEw8ODlo98Gsbly5eJjo4OOX/+PHn9+jX5/Pkz24spvnz5Qr58+cKYvSqKi4tJSkoKKS0tZcTe4MGDSVhYGPW+5jkeFBREBgwYQGuOtm3bMvosUR11dXVy4cIFQkil7+np6YQQQnbt2kWmTZvGkzmZQk5OjsTFxRFC2H/vV69eJZ06daJtv7y8nFy+fJns2rWL7Nq1i4SHh5Py8nLadvlwkpGRQcrLy4mRkVGdL2Nj46Z2s0nhZ4wxRElJSa2pt8rKyrTsWlpaQldXF87Ozmzj27dvx6NHjxASEkLLfkujvLwchw4dgpubG0RFRbF+/XpMnz6d0R0uCQkJJCcnQ1lZGZ06dcLp06fRr18/ZGVlQVtbG9++fWuwzXfv3v1Uu6GsrAxxcXG0yx1barmDsrIyAgMDYWRkBCkpKcTFxUFDQwNBQUEIDg7GpUuXmtrFJoP8v5hy9SxApvQF//77b8ydOxczZszA27dv0a1bN/To0QNpaWmwt7dv1ucMH3Y6dOiA79+/Y9KkSbCzs+MQs21JFBcXY+/evdi6dSvXWlRVHW+rKCwsRFlZGcTFxTlKhptjGYuMjAzu37+Pbt26QUZGBvfu3UP37t1x//59WFtbU9eD5oaIiAiysrIoDRQxMTE8ePAA2trajNj/VVdwbkq+09PT0bt3b/Tv3x8ODg7Q1NQEIQTJycnYvXs3Hj16hMTERK4078TFxZGUlAR1dXV8+vQJhoaGGDduHFVtkJmZiZ49e1LZktwgKCiIt2/fUpkFkpKSSExMpEqJ6GaM1SzN5MM71q9fj2XLlrEJeFe/jhFCuO4E+v37d1y7dg3GxsYcAuFfvnxBZGQkhg8fTqtDalFREezt7alO76mpqVBXV4e9vT0UFRXh4uLClV0FBQXcu3eP0qKUk5PDw4cPqfepqano27cvPn/+zLXvHTt2RGRkJLp27cq1jbpo3bo1nj17BmVlZSgoKODixYvQ09NDZmYmdHV1afnNa2bPno0PHz7gxIkTaNOmDRITEyEoKAgLCwsYGBjAy8urqV2slV+VGvr5+TWSJ9yTkZEBLy8vSmNcS0sLDg4O6Ny5M1f2apbdT5kyBbt370aHDh0Y87mlwxffp0lqairs7Ow4OgrRuXlVLxHQ0tLCpk2bEBkZyaYxFhUVhWXLltFzvoVx4sQJrF69GgUFBVi1ahUWLFjAk8526urqyMrKgrKyMjQ1NXHixAn069cP58+f52ibW18UFBTYLkba2tq4dOkSlTr84cMH/PXXX7RKBn9V7tCcgxwfP36kFt1SUlLUg+rgwYOxYMGCpnStSbhx4wYWL16MmJgYSElJYejQoRg6dCiAym6PPXr0gI+PD/7++29a8yQlJVHB2BMnTqBnz56IiorC1atXMX/+/GZ9zvBh59WrVzh//jz8/f1hZGQEdXV12NjYwNraGvLy8k3tHgfVtahERETwzz//UFpUq1atgqCgIK2ymOa6WK8vwsLCEBAQAAC0b98eOTk56N69O6SlpZGbm9vE3tVNeXk5231ZSEgIEhISjNguKyuDsbExhg0bxug57eXlhQEDBiAiIoJtXFNTE+PGjYOpqSk8PT2xZ8+eBtvW1NTExo0bYWlpiX/++Qdjx45lk+CIioqiXc5OCMGsWbOogMaPHz8wf/58qjymuhYTN0yaNIm6J/DhLW5ubpg/fz5u3rzJuO2DBw8iLCwM5ubmHMekpKSwe/du5Obm0tIuXrlyJRISEhAZGYkRI0ZQ46ampnB1deU6MFZQUMB2Hufn57Mdr6iooH2eL1u2DLt27YK3tzfj5aSdOnXCmzdvoKysjM6dO+Pq1avQ09PDw4cPaQUiG4MdO3Zg4sSJaN++Pb5//w5DQ0O8ffsWf/31FzZt2kTbfkRERJ26bnSCV58+fWJ7X1paiqSkJBQUFFAaz82ZK1euwNzcHDo6Ohg0aBCAyvtFjx49cP78eeqZoCHUzIW6fPkyCgsLGfH3d4EfGKOJjY0NhISEcOHCBca6XdWsY5eVlUVycjJbRyEZGRn4+flh9erVtOdrKUydOhViYmKYNm0asrOz67zB0q2NtrGxQUJCAgwNDeHi4oIxY8bA29ubagfNDTUvRi9evOBotUs3eXPjxo3YtGkT16LGv4KX2Wi8CEa2ZKoaMdTWdEBaWhrz5s3Dzp07aQfGSktLqUXZ9evXqQWzpqYm3rx5Q8s2n8ZFSEgI48aNw7hx45CXl4ejR48iICAAa9aswYgRI2BnZ4cxY8ZQwZamZu3atWxaVJMmTaK0qHbu3Elbi8ra2hrl5eXYvn07wsLCUFJSgiFDhmDdunWULk1zRldXFw8fPkSXLl1gaGiItWvX4v379wgKCkLPnj2b2r06+VWQporTp0832LaQkBDmz59P7Z4zRWRkJDZv3lzrMRaLhaVLl2LlypVc2XZ3d8eECRNw6tQpDBs2DCEhIRg8eDB0dHRw+/ZtLFu2DE5OTnTcp7o7VzF9+nSOn2mohmn1DVoNDQ2sWbMGMTEx0NbW5si4bM76SC2NqnWgoaEh47b/+++/nwrrL126FOvXr6cVGDt79iyOHz+OAQMGsD0P9ejRg1bH+E6dOiEpKQndunWr9XhiYiI6derEtX2gshPyzZs3cfnyZfTo0YPjPOfmmlXFuHHjEBERgf79+8Pe3h7Tp0/H4cOHkZOTQ2sDqDGQlpbGtWvXEBUVhYSEBHz79g16enqMVC64ublh/fr10NfXZ7xjdG0avBUVFViwYAHXGVf1obi4mJFgp4uLCxwdHTkE8l1cXLBixQquAmM14RcNcsIvpaRJ69atERsbC01NzaZ25bfHyMjolxdNFotFq3NMbWRnZ9NuB81rcVyAt+UOvBbf9fT0hKCgIJYsWYLr169T3TWrgpEODg50P0KLQkVFBeHh4ejevXutx1NSUjBs2DDk5OTQmqd///4wNjbGqFGjMGzYMMTExKB3796IiYnBxIkT8fLlS1r2+TQd9+/fh5+fHwICAqCgoIBPnz5BVlYWR44cgZGRUVO7B3V1dXh5ecHc3BxJSUno1asXZs2ahcOHDzO2ON6wYQNcXV1hamoKMTExXLlyBdOmTWsRJRSPHj3C169fYWxsjHfv3mHmzJmIjo5Gly5dcPjw4WbbYfNXpY5VcNvl1sjICEuXLoWFhQVX/39tSElJITExkSrLqklWVhZ69eqFr1+/cmW/KpOlVatWcHNzg4eHB0pKSkAIwdSpUxEYGAghoea1T13fboi86O72JyMgIIC8vDw2wW2mkJWVRUJCQp0SLzk5OejduzdHpk1DqF46XH2dm5CQAAMDA65LBh0cHHD9+nXExsZydJ78/v079PX1YWpq+tPu9b+CF2XadXHv3j3cu3cPXbp0wZgxYxiz21gUFBQwsmmtoKCArVu3Nqqw/vPnz2FkZMTI5q+5uTkMDAxgZWUFBQUF5OfnY+zYsRxVZNwgKiqKJ0+ecHRgTk1NRa9evbhqevCrsns+4Ivv00VfX5/cuXOnUeaqqKhgTMCWT+PCa3FcQgixtbUl+/fvp2WjLhpbfPfFixfk1KlTbKLFfxKtWrWqtelGFWlpaURUVJT2PJGRkURGRoYICAgQGxsbanzlypW0hKz5NA1v374l27ZtI1paWkRUVJRMnTqVXLt2jRBCyLdv38g///xDlJWVm9jLSoSFhcnLly+p96KioiQxMZHROTQ0NIiPjw/1/tq1a0RERIQv7NuCOX78OFFXVyd79uwh0dHRJCEhge3FDTXvzzVh4v5cnU+fPpGYmBhGGqjw+b1gsVhERkaGyMrK/vTFDRISEuTRo0d1Hn/06BGRkJDg1nVCCCF///032b17NzVfZmYmIYSQxYsXk+HDh3Nt9+3bt0ReXp4oKyuTrVu3krNnz5KzZ8+SLVu2ECUlJaKgoEDevn1Ly3c+tePh4UFCQkKo95MmTSICAgKkY8eOJD4+npbtNm3aUI0IGouLFy+Sdu3aMWLL3t6eGBkZETExMbJ06VKirq5OdHR0GLHdqVMncuLECY7x48ePEyUlJa5sslgsMnLkSKpZjZCQEBk2bBhjTWx+B5rXFlULZMuWLfjnn3/g7u5ea4p5baVQDSUwMBDbtm1DWloaAKBr165wdnb+Y1rXNga7d+/G3LlzISoq+ss28NyUDbBYLHz9+hWioqKU/ty3b9/w5csXAKD+Swdeljt8+vQJkyZNoutivVFRUaGtu9KSUVRURFJSEjQ0NGo9npiYCAUFBa7tV1RUYNu2bQgLC0Pnzp3Rp08fNk2muXPnQlxcnGv7fBqfMWPG4MqVK+jatSvmzJmDmTNnok2bNtTx1q1bY9myZdi2bVsTevk/eKlFVUVOTg5GjhxJvTc1NQWLxcLr169pl93wmqysLJSVlXHsFqelpUFYWLjO7KbfnalTpwJgv5+xWCxauq4AkJycXGejh/fv33Nlsy5kZGTQv39/Rm02FuT/i0yY1mDi8z/c3NwgLS3NuN0ePXrg+vXr6NOnT63Hr169ih49etCaw93dHWZmZkhOTkZZWRl27dqF5ORkREdH49atW1zb7dChA6Kjo7FgwQK4uLiwnYdDhw7Fvn37GBEQLysrQ2RkJDIyMmBpaQlJSUm8fv0aUlJStO5PgYGBPz3e0FLnxsTHxwf//fcfAODatWu4du0aLl++jBMnTsDZ2RlXr17l2vbs2bNx7Nixn5b4ckvNEnVCCN68eYOLFy9ylJ9zS9XzYkhICCwtLSEhIYFHjx4xYnvOnDmYO3cuMjMzqWZKUVFR2LJlC9fl9/Upu//T4ZdS0qRKr6XmIoHuIq2KnTt3Ys2aNVi8eDElvnf37l3s3bsXGzdubPa16S0FNTU1PHr0CG3btv1pSim3ZQMCAgK1dhaq+Z7O+cILv6uws7ND3759GRXf/VUAsjp/moaJvb09IiMj8fDhw1rLBvr16wdjY+MG/Q6r05JLzPjUjp2dHWbPnk01aakNQghycnKaRdBZQEAAZmZmlBbH+fPnYWJiwogWVRU1ywaAllM6YGhoCFtbW46F7NGjR+Hr64vIyMimcayJyc7O/ulxbs7tqvtzbcthJoJuvwOHDx+Gp6cntUHbpUsXLF26FLNnz25iz34vaspuMMnBgwfh5OSEkJAQjB49mu3Y+fPnMW3aNOzcuRNz586lNU9GRgY8PDzY9KhWrFjBWGfajx8/Ij09HUDlhnD1DSA6ZGdnY8SIEcjJyUFxcTHVUdPBwQHFxcXw8fHh2rasrCzb+9LSUhQVFUFERATi4uLNsjNyFWJiYkhNTYWSkhIcHBzw48cPHDhwAKmpqejfvz+t0lsHBwcEBgaiV69e6NWrF8eGPh3NaGNjY7b3AgICkJOTg4mJCWxtbbkqX1+8eDF0dHTYrnsZGRkYPHgwRo0ahYyMDBgbGzPSuIoQAi8vL+zYsQOvX78GUNk51dnZGUuWLOFvTvAIfmCMJr/aAaEroKmmpgY3NzeO3YSAgAC4uroiKyuLln0+jUN9d8p4IbjKLdWDLoWFhdi5cydGjRrFWDYaX8OkbvLy8qCnpwdBQUEsXryYEpxNSUnB3r17UV5ejri4OK53SLt06YLly5dj3rx5ACqF90eNGoXv3783G3F2PvWntLQUI0aMgI+PD0eGUXOF11pUAGfwDag9AEcn+MYrpKSkEBcXx5E1mp6eDn19fRQUFDSNY78hvwq2VdEcAspNwdq1a7Fz507Y29tTgfd79+7B29sbjo6OWL9+fRN7+PsgKCjI1sGcaaZPn45jx45BU1OTbV2RmpqKyZMnIzg4mCfztgQsLCwgKSmJw4cPo23btpQ+WmRkJObMmUMFhZkiLS0NCxYsgLOzM4YPH86obSbp2LEjQkNDMXDgQHTr1g0bN27EpEmT8Pz5c/Tt25dWxUvN4FV1eKEZTRclJSVcvHiR0pt+8+YNBg0ahLFjx8LT0xPXr1/HggULGD9XqvQtJSUlGbXLhxN+YKyZIyoqWmtJVVpaGrS1tbkS3+PDpz7wA1dNS3Z2NhYsWIArV66wlQ0MHz4ce/fupZXx0qpVK6Snp0NJSYkaExUVRXp6erMvMeNTO3JycpQ4O59KGiP4xiukpaURGRkJXV1dtvHY2FgYGRlxLQT/O5CRkQEvLy+qO6WWlhYcHBx42mnsT0ZOTg67d+/GtGnT2MaDg4Nhb2/PeKnpnwwvM8aqOHHiBI4dO4a0tDQQQtC1a1dYWlpi8uTJtG3XFdj78OED2rdv36yzLtu2bYvo6Gh069aNrXHAixcvoKWlhaKiIsbnfPToEaZPn46UlBTGbTPF4sWLceHCBXTp0gWPHz/GixcvICEhgZCQEGzdupV28y1eYWJigtOnT3M0Cfjy5QssLCy4CrpVby7x6dMnGBoaYty4cXBzcwMAZGZmomfPnjw5V/g0DnyNMQa4c+cODhw4gMzMTJw8eRKKiooICgqCmpoaBg8eTMu2hoYGTpw4gX///Zdt/Pjx4/wHIB4xYcIE9OvXDytWrGAb37p1Kx4+fIiTJ082kWecODk5YcOGDWjduvUva84bmpLcFNmIJSUlyMrKQufOnZtdl67GRkVFBZcuXcKnT5+Qnp4OQgi6dOnCkZLPDWVlZRwlmsLCwigtLaVtm0/TUNX+vWZr7z+Z5hjwqi8GBgbYvHkzgoODISgoCKBSl23z5s201xUtmStXrsDc3Bw6OjqUvERUVBR69OiB8+fPM9LCng87paWl0NfX5xjv06cPysrKmsCj35eKigqezzF58mRGgmC1UVeuRXFxMZumZHOkoqKi1sDdy5cveZapIyQkRJXJNVc8PT2hqqqK3NxcbN26ldJae/PmDRYuXMized+9e0crQBwZGYmSkhKO8R8/fuDOnTtc2dTU1MTGjRthaWmJf/75B2PHjqWCYkDlvYipzOK8vDwsX74cERERePfuHcd3qzkHmVsyf/aTJwOcOnUKM2bMgJWVFeLi4qi23J8/f4a7uzsuXbpEy76bmxumTJmC27dvsy0CIyIicOLECdr+t1SqgpEZGRkIDQ1lNBh5+/ZtuLq6coybmZlhx44dtGwzzePHj6lgxuPHj+v8Obq16OvXr8fy5cs5BNm/f/+Obdu20a6nLyoqgr29PQICAgCA0nawt7eHoqIiXFxcaNlvycjKyqJv376M2iSEYNasWWwlZj9+/MD8+fObfYkZn9opKyuDn58fJa5cU6uLjlYHn8Zny5YtMDAwQLdu3fD3338DqLzvffnypdmVlzQmLi4ucHR05AgAu7i4YMWKFc02MBYeHg4JCQlqfbJ3714cOnQIWlpa2Lt3LyMbHrxixowZ2L9/P8c15ODBg7Cysmoir/g0J6qkN1gsFnx9fdmE6svLy3H79m1oamo2lXv1YtiwYfDy8sLBgwcBgGqStW7dOrYmLtwQFhbG9r5KCN7b25t6tmuuCAsLY/ny5RzjdDSuxcXFkZ2dTel/jho1Cr6+vlRTqby8PHTs2JGr4E9iYiL175pNVcrLyxEeHg5FRUWu/HZ3d8eECRNw6tQpDBs2DCEhIRg8eDB0dHRw+/ZtLFu2jGth/JrMmjULOTk5WLNmDRQUFPiaYo0Ev5SSJrq6unB0dMTMmTPZUm8fP34MMzOzOrscNYTY2Fh4enpSZQPdu3fHsmXLOEos/hSqByODgoKQnJwMdXV1eHt749KlS7SDkWJiYoiPj6f0F6pISUmBrq4uvn//Tst+S4TX6fEODg6IioqCl5cXRowYgcTERKirq+PcuXNwdXX9adCPT8NpySVmfGqnpWl18Pk1r1+/hre3NxISEiAmJoZevXph8eLFjIlNt0RERUXx5MkTjoz51NRU9OrVq9nKS2hra2PLli0YOXIknjx5gr59+8LJyQk3b96EpqZms77W2tvbIzAwEEpKShgwYAAA4P79+8jJycHMmTPZNEf5Afg/kypph+zsbHTq1InKcgUAERERqKqqYv369c26G2tubi5GjBgBQgjS0tKgr6+PtLQ0tGvXDrdv36aVvVRTu5XFYlFC8Dt27KDVZZzX8KKjZs2S4erPz0BlYExBQYGrDMrqzc5qC3GIiYlhz549sLW1bbBtAFQCTKtWreDm5gYPDw+UlJSAEIKpU6ciMDCQkYoXSUlJ3LlzBzo6OrRt8ak//MAYTcTFxZGcnAxVVVW2L3ZmZia0tLSa7SKtJcPrYGS/fv0wevRojiwoV1dXnD9/HrGxsbTst0QEBASQl5fH1t0NAG7cuIEpU6YgPz+fln0VFRUcP34cAwYMYPubpqenQ09Pj5a4Jx8+fPjw+T1QUlLCzp07MWnSJLbxEydOYPny5cjJyWkiz36OhIQEkpKSoKqqCldXVyQlJSE0NBRxcXEYOXIkI5uovOJnQffq8APwfIyNjXH69OlmnQH5M8rKynD8+HG2jppWVlYQExNrateaDF501KxPYIzbjLHs7GwQQqCuro4HDx6wPbeIiIigffv2bIFbuhQUFOD58+dQVFRkVKNXS0sL//333x+bBNNU8EspaSIvL4/09HSoqqqyjd+9e5f6gtOhJQtZ8ornz5/DwMCAY1xaWpqRTl1r1qzB+PHjkZGRARMTEwBAREQEgoODGdMX+/jxI8euf0xMDLUb2xDqu+vh5+fXYNuysrJgsVhgsVjo2rUrWypveXk5vn37hvnz5zfYbk3y8/Nr3Y0rLCzkpw/z4fOHUlxczFbu+7uTmJiInj17QkBAgK0cpDaqumL9acyZMwdz585FZmYmBg4cCKBSXmLLli2MlbDExcWhd+/ebA9PZ86cwbhx47i2KSIiQgkyX79+ncqyaNOmTbPf+Ll582ZTu8CnhVDzXCkvL8eTJ0+goqLSrINlpaWl0NTUxIULF2BlZcUvEa7Gp0+fOMaqd9RsblRpfDWGXh8AyMjI8CQT0svLCy4uLjhw4ABHjIEP7+AHxmgyZ84cODg4wM/PDywWC69fv8a9e/ewfPlyrFmzhrb9lixkySt4HYwcM2YMzp49C3d3d4SGhlIlLNevX4ehoSFt+wDQrl07dO/eHba2tli0aBHCwsJgY2ODwsLCBtvy9/eHiooKdHV16zxfuMXLywuEENja2sLNzQ3S0tLUsar0+Kr27XTQ19fHxYsXYW9vD+B/mmi+vr6M2OfD50/g0aNHOHHiBHJycjhEZ5u7Xpy5uTkMDAxgZWUFBQUF5OfnY+zYsYiOjm5q1xoNHR0dahddR0cHLBar1ms6i8X6IzfFgMqNK0lJSezYsQMrV64EAHTs2BGurq5YsmQJI3Po6+tDSkoK06dPx5o1axAYGIh///2XVnOSwYMHw8nJCYMGDcKDBw9w/PhxAJUloPxOwHwakyNHjmDKlCkcmrFMsHTpUmhra8POzg7l5eUwMDDAvXv3IC4ujgsXLsDIyIjxOZlAWFiYpxU+dQXtWSwWREVFoaGhgbFjx7aYMvkuXbrAw8OD646aVRvudb1nirS0NNy8eRPv3r3jCJTR1UbmBVXJCFUUFhaic+fOEBcXZytZB8BVph6fX8MPjNHExcUFFRUVGDJkCIqKimBgYIBWrVph+fLl1EM+N/wOQpa8gtfBSKBSCHLUqFGM2KqNR48eITExEYcPH8bOnTuRn59fq+B/fViwYAGCg4ORlZUFGxsbTJ8+nbGbq7W1NYBK/YiBAwdyXJiZwt3dHWZmZkhOTkZZWRl27dqF5ORkREdH49atWzyZkw+f34mQkBDMnDkTw4cPx9WrVzFs2DCkpqYiLy+PVqZLY6GqqoqLFy9i7dq1mDdvHsLCwiAlJdXUbjUqWVlZVNlHU3QFbq6EhYXBzMwMwsLCYLFYcHR0hKOjI75+/QoAjHeMe//+PRITE3Hw4EFKO8nf35+WTW9vbyxcuBChoaHYv38/Jf58+fJljBgxgq7LPKclB935sOPi4gIHBwdMmjQJdnZ2VOYlE5w8eRLTp08HAJw/fx4vXrxASkoKgoKCsGrVKkRFRTE2F9MsWrQIW7Zsga+vL+Nd0R8/foy4uDiUl5dT+sWpqakQFBSEpqYm9u3bh2XLluHu3bvQ0tJidG5eQaejJiGErQrl27dv0NXVpbTYmNjkP3ToEBYsWIB27dpBXl6eIxDXHANjXl5eTe3CHw9fY4whSkpKkJ6ejm/fvkFLS4stkMUNv4OQJa8ghMDd3R2bN2+mShOqgpEbNmxgbJ7Y2Fiq4UGPHj1o1XmnpaUBAIdg8KZNm7Bhwwa0atUKDx484BD8ry/FxcU4ffo0/Pz8EB0djVGjRsHOzg7Dhg3jehemIeUdTDzAZmRkwMPDg03bYcWKFdDW1qZtmw+f351evXph3rx5WLRoEaXXoaamhnnz5kFBQYGtpXhzJiQkBJaWlpCQkEB2dnazLr/hJbdv38bAgQM5HtDKysoQHR1dq5zA74qgoCDevn0LOTm5OuUl6BATEwMhISHo6+uzjc+fPx8nTpxAeXk5rl27hn79+jE2Z0viV0H35tw4gA8nZWVlOH/+PPz9/XH58mWoq6vDxsYG1tbWkJeXp2VbVFQU6enp6NSpE+bOnQtxcXF4eXkhKysLvXv3btZlw+PGjUNERAQkJCSgra3N0dmZTgDYy8sLd+7cwZEjR6j18ufPnzF79mwMHjwYc+bMgaWlJb5//44rV67Q+hxM87OOmkpKSrh8+XKDbVZ1oP8VVZvz3KCiooKFCxdixYoVXNtoCsrLy7F9+3aEhYWhpKQEQ4YMwbp16/5onbtGhfChRVBQECksLOSZfSMjI/Lx40ee2W/JFBcXk6dPn5L79++Tr1+/MmY3Ly+PGBsbExaLRWRlZYmsrCxhsVjExMSEvHv3jiubxsbGJDQ0lG3Mx8eHSEpKkmvXrpHVq1eTyZMnM+E+efHiBXF1dSXq6upEWVmZ698Ni8UiAgICP31V/QwvOXnyJE/t8+HzOyAuLk6ysrIIIYS0adOGJCYmEkIISU5OJvLy8k3oWe0sWrSIHDp0iG0sPT2dyMvLEzs7O2JkZETc3NyayLumR0BAgOTl5XGMv3//nufX3OZGhw4dSFhYGCGk8r7E7X24Lvr3708uXLjANrZ69WoiLy9PEhISyPbt28nIkSNpzdGS/57a2trE29ubEEKIhIQEycjIIBUVFWTOnDlk7dq1TewdHzq8ffuWbN++nWhraxNhYWEyZswYcvbsWVJeXs6VPWVlZXLlyhVSVlZGlJSUqO9VUlISkZGRYdJ1xpk1a9ZPX3To2LEjefr0Kcd4UlIS6dixIyGEkNjYWNK2bVta8/ACFovF9hIQECAdOnQg06ZNI69fv25q9+pEUlKSZGRkNLUbDWb9+vVEQECADBs2jIwdO5aIiooSGxubpnbrj4EfGKNJu3btSOvWrcm0adPIxYsXSVlZWVO7xIcmkydPJvr6+iQ5OZkae/r0KdHX1ydTp07lyqaUlBTJzMyk3p88eZK0bduWREVFEUIIefz4MWnTpg09x/+fnJwc4ubmRtTU1IiioiLXgbHIyMh6v+hQWlpKnjx5Qp4/f842fvbsWdKrVy8iIiJCyz4fPn8CioqKVDBMW1ubHDt2jBBCSHR0NJGSkmpK12qlU6dOJCEhgXr/+vVroqamRpYuXUoIIeTatWtEQ0OjqdxrcuoKAD1//pxISko2gUdNx7p16+q1UcNtgElCQoK8ePGCer9r1y6irKxMUlNTCSGV93+63yEWi1VrYOzVq1dEVFSUlm1e09KC7nwaRkxMDJk7dy5p1aoVUVVVJdLS0kRVVZXcvHmzwbbWrVtHpKWliaamJlFWViY/fvwghBBy+PBhMmDAAIY9bzm0bt261t/nzZs3iYSEBCGEkIyMjD/u2s5LbG1tyf79+3li+/Lly+TOnTvUe29vb9K7d28ybdo02sksGhoaxMfHh3p/7do1IiIiwnWwmk/D4GuM0eTNmzcIDw9HcHAwJk+eDHFxcUyaNAlWVlaM1O3/quMgN50GWyLjx4+v98/S1bsIDw/H9evX0b17d2pMS0sLe/fuxbBhw7iyKSgoiLy8PKipqeHKlStwcnLCtWvXqPJMYWFhWh1UqpdS3r17F6NHj4a3tzdGjBhB1ew3FKYaDfyMpKQkjB49Grm5uQCAsWPHYv/+/Zg8eTKSkpIwZ84cXLx4ked+8OHT0jEwMMC1a9egra2NSZMmwcHBATdu3MC1a9cwZMiQpnaPgw8fPlCSA58+fcLw4cMxY8YMquRTXV0dr169akoXm4Sqex2LxcKsWbPYunKWl5cjMTGRUU2gloCrqyumTp2K9PR0mJub48iRI5CRkWHMvpiYGNLS0qCiooIjR45g3759uHPnDpSVlQHQa3b0O+jFysrKUnpuioqKSEpKgra2NgoKCig5Cz4ti7y8PAQFBeHIkSPIzMyEhYUFLly4AFNTUxQWFmL9+vWwtrZGdnZ2g+y6urqiZ8+eyM3NxaRJk6jrl6CgIFxcXHjxUVoEY8eOha2tLXbs2IG+ffsCAB4+fIjly5fDwsICAPDgwQN07dq1Cb38NeT/1ZdaQrd4DQ0NrFmzBjExMdDW1ubQSKbTrMXZ2RlbtmwBADx58gTLli2Dk5MTbt68CScnJ1rl5Tk5ORg5ciT13tTUlNLT5jdq4T38wBhNhISEMHr0aIwePRpFRUU4c+YMjh07BmNjY3Tq1AkZGRm07Ndsk1taWoqkpCQUFBTAxMSElu2WRPVuiLymoqKiVpF5OsErExMTWFpaYuDAgQgNDcX69evZNMv279+P3r17c2V74cKFCAkJgZKSEmxtbREcHIx27dpxZetXFBUV1Sq+26tXL67srVixAhoaGvD29kZwcDCCg4Px7Nkz2NnZITw8nF9Tz4dPPfH29qa6aq1atQrCwsKIjo7GhAkTsHr16ib2jhNNTU1s3LgRlpaW+OeffzB27Fg2HbSoqCiq7fqfRNW9jhACSUlJtmugiIgIBgwYgDlz5jSVe02GpqYmunXrBmtra0yYMIG2jmt1zM3NMXnyZGhpaSEmJgYuLi5UUIwQAg8PD671xTw9PSk7Pj4+terF+vj40P8QPKSlBd35/JwxY8bgypUr6Nq1K+bMmYOZM2eyNWxq3bo1li1bhm3btjXYdmZmJiZOnMgxTkcripfo6urWO8gTFxfH9TwHDhyAo6Mjpk6dirKyMgCVz4/W1tbUNUJTUxO+vr5cz8FLAgMDsW3bNkovuWvXrnB2dsaMGTOa2LO6OXjwICQkJHDr1i2OJl4sFotWYCwrK4tqknDq1CmMHj0a7u7uiIuLYwtqcUNZWRlERUXZxoSFhWl1ReZTf/ji+wzz/v17hISEwMfHB8+ePeNJS/WKigosWLAAnTt3xj///MO4/T+dsWPHoqCgAMHBwejYsSMA4NWrV7CysoKsrCzOnDnTYJvv37/HP//8A0FBQYwdOxaWlpYYOXIkdHV1cefOHYSHhyMiIoKrLC0BAQEoKyv/8gZPJ5MuPz8fNjY2dYpscnuet2/fHlevXoWOjg4+f/4MWVlZBAQENOubLR8+zY2ysjIcO3YMw4cPR4cOHZranXoRHh6OCRMmQEhICMOGDUN8fDy8vb2ho6OD27dvY9GiRXBycvpjswzc3NywfPlyDgHoP5mKigqIiori6dOnHI1s6PDjxw9s3boVgoKCGD16NMzMzKCiogIdHR1ER0cjPT0d9+7da/AGUFhYGEaMGAEREREYGxvj9OnTLbKZxMePH/Hjxw907NgRFRUV2Lp1K6Kjo9GlSxesXr26RX6mPxk7OzvMnj0bf/31V50/QwhBTk5OgzcnBAQEYGhoCDs7O0ycOJHjAb+5UX0z5sePH9i3bx+0tLSo301MTAyePn2KhQsXYvPmzbTn+/btGzIzMwFUZkUzGeDnFTt37sSaNWuwePFiDBo0CABw9+5d7N27Fxs3boSjo2MTe9j4tGnThuogOnjwYMycORNz587FixcvoKWlRSuTVkBAAGZmZmzZ4ufPn4eJiQnbeoDfDZg38ANjDFCVKfbff/8hIiICSkpKmDZtGqysrHiWIv/8+XMYGRnhzZs3PLH/J5Obmwtzc3M8ffoUSkpK1FjPnj0RFhbGSCprcnIy3NzckJiYCEVFRTg7O2P48OFc2Zo1a1a9drzopPZaWVkhOzsbXl5eMDIywpkzZ5CXl4eNGzdix44dGDVqFFd2BQQE8PbtW6rDmKSkJOLi4hh96OHD509AXFwcz549a1FZVsXFxQAquwq7ubnBw8MDJSUlIIRg6tSpCAwM5OjKyOfPpkePHjh8+DAGDBjAsznevXuH7du348mTJ1BUVMSSJUu4yormdTfNxqAlBt351E1paSlGjBgBHx8fnqyz4uPjceTIEQQHB6OkpARTpkyBnZ1di+joOnv2bCgoKHB0t1+3bh1yc3P/GOmamqipqcHNzQ0zZ85kGw8ICICrqyuysrJ4Mm9GRgY6d+5M205JSQmysrLQuXNnxtYT5ubmKCkpwaBBg7BhwwZkZWVBUVERV69exeLFi5Gamsq1bRsbm3r9HL8bMG/gB8ZoMnXqVFy4cAHi4uKYPHkyrKysfroLwxSXLl2CtbU18vPzeT5Xc0BPTw8RERGQlZX9ZWYUnXTnKgghuH79OlJSUgAA3bt3h6mpKW27LRUFBQWcO3cO/fr1g5SUFB49eoSuXbsiLCwMW7duxd27d7myKygoiNTUVMjJyYEQAiUlJdy9exeqqqpsP1fV3poPHz61Y2RkBEdHR4wdO7apXeGagoICPH/+HIqKin+klkZj3+daIufPn8fWrVuxf/9+9OzZs6nd+Sny8vI4dOgQxowZAwEBAeTl5UFOTq6p3WowLTHozqdu5OTkqIw/XlFWVoawsDD4+/sjPDwcXbt2ha2tLWbMmNFsvwPS0tJ49OgRx+8lLS0N+vr6+Pz5M9e2CwsL4eHhgYiICLx7945DlqUqi6w5IioqiqSkJGhoaLCNp6Wl9/SBugAAWbdJREFUQVtbm5JwoIOUlBT+/vtv2NraYsKECYiKisK4cePw7t07rm0WFRXB3t4eAQEBAIDU1FSoq6vD3t4eioqKtLLRc3JysHDhQuTm5mLJkiWws7MDADg6OqK8vJzSluTT8uBvxdJEUFAQJ06cwPDhw9m0I5jCycmJ7T0hBG/evMHFixebbc0+Lxg7diyVVlolVMlLWCwWhg4diqFDh/J8rpZAYWEhtdMtKyuL/Px8dO3aFdra2rQe0AghbGKjhBA27TVCCFgsFk9Kkvnw+Z1YuHAhnJyckJubiz59+nCU4HGrA9iYyMjIoH///k3tRpPR2Pe5lsjMmTNRVFSE3r17Q0REhEOH8uPHj03kGSfz58/H2LFjwWKxwGKxIC8vX+fPNud7XL9+/RAfH88PjP0mTJ8+HYcPH4aHhwfP5hASEsL48eMxatQo7Nu3DytXrsTy5cvx77//YvLkydiyZQsUFBR4Nj83iImJISoqiiMwFhUVRbskdPbs2bh16xZmzJgBBQWFFiFeX4WGhgZOnDiBf//9l238+PHjjAVXjxw5gqSkJCxfvhybNm1CSkoKpk+fTsvmypUrkZCQgMjISIwYMYIaNzU1haurK63AmLKyMi5cuMAxXqUXx6flws8Ya+YYGxuzvRcQEICcnBxMTExga2vLLzNhkHv37uHDhw8YPXo0NRYYGIh169ahsLAQFhYW2LNnD1vd959C3759sXHjRgwfPhzm5uaQkZHB5s2bsXv3boSGhnLdZKKmIGZdNEaHTD58WjK1dZ9lsVj84DKf34qq3f+6aG4bhikpKfXqptmcMz1PnDiBlStXwtHRscUG3fn8D3t7ewQGBqJLly61/j137txJe45Hjx7Bz88PISEhaN26NaytrWFnZ4eXL1/Czc0NX758wYMHD2jPwyQeHh5wc3PDnDlzqNLP+/fvw8/PD2vWrKEVSJGRkcHFixcpja6WxKlTpzBlyhSYmppS/kdFRSEiIgInTpzAuHHjGmzzw4cPIIRwNAo7fPgw5s6di9atWyMlJYXSeeYGFRUVHD9+HAMGDICkpCQSEhKgrq6O9PR06Onp4cuXL1zbBipLPY8cOYKMjAzs2rUL7du3x+XLl6GsrIwePXrQss2n6eAHxmiyfv36nx5fu3ZtI3ny51FSUlJrSnJVN6mGYmZmBiMjI6xYsQJAZQtePT09zJo1C927d8e2bdswb948uLq60nW9xXH06FGUlZVh1qxZiI2NxYgRI/Dx40eIiIjA398fU6ZMaWoX+fD5o8nOzv7pcX62Bx8+TYebmxucnZ0hLi7e1K40GH7Q/fei5oZ7dVgsFm7cuMG17Z07d+LIkSN4/vw5Ro4cidmzZ2PkyJFs59DLly+hqqpKdWdsTpw4cQK7du3Cs2fPAFTKqDg4OGDy5Mm07KqpqeHSpUvo3r07E242OrGxsfD09GT7vSxbtoytwqMhjBo1ClOmTGHTLbt48SImTZoEHx8f3Lt3D6WlpbS6dIqLiyMpKQnq6upsgbGEhAQYGBjQKo29desWzMzMMGjQINy+fRvPnj2Duro6PDw88OjRI4SGhnJtm0/Twg+M0aTmRaG0tBRZWVkQEhJC586deaID8uXLF/z33384fPgwHj16xLj95k5qairs7OwQHR3NNk53kaagoIDz589DX18fALBq1SrcunWL0s86efIk1q1bh+TkZHof4DegqKgIKSkpUFZW5tjx4cOHDx8+DUdWVrbeJTbNqWSwseHv1Dcu/KA7n/rSpUsX2NraYtasWXWWSpaUlCA4OLjZZXfykqNHj+LcuXMICAhokcFxpmnbti1iYmKoUsy7d+9izJgxOHz4MMaPH4/79+/DwsKCVoM5AwMDTJo0Cfb29pCUlERiYiLU1NRgb2+PtLQ0hIeHc237r7/+wqRJk+Dk5MQWdHvw4AHGjx+Ply9fcm2bT9PCr8OjyePHjznGvnz5glmzZnGVXvozbt68CT8/P5w+fRrS0tKM228p2NjYQEhICBcuXGC0Vv/Tp09sXZeqdgSq6Nu3L3JzcxmZq4ry8nI8efIEKioqLaLlefXuLnp6ek3tDh8+fKoRFBQEHx8fZGVl4d69e1BRUYGXlxfU1NSadakWn0q8vLya2oVmT82d+k2bNqF9+/ZISEjA4cOHGd2p//LlC27cuIFu3boxkukRGhqKEydOICcnByUlJWzHmnMzBX7gi099SUtL++XPiIiI/FFBMQDYsWMHMjIy0KFDB6iqqkJYWJjteHP8/te31JCb5lhlZWX4/v07gMrn6ClTpiA4OJjSApORkcG3b98abLc67u7uMDMzQ3JyMsrKyrBr1y4kJycjOjq63jIudfHkyRMcO3aMY7x9+/Z4//49Ldt8mhZ+YIwHSElJwc3NDWPGjMGMGTNo2Xr16hX8/f1x5MgRFBQU4NOnTzh27BgmT57cosQbmSQ+Ph6xsbHQ1NRk1G6HDh2QlZUFJSUllJSUIC4uDm5ubtTxr1+/ctzMGsrSpUuhra0NOzs7lJeXw9DQENHR0RAXF8eFCxdgZGRE81PUzu3bt9G7d29IS0tz9f/zsrsLHz586LN//36sXbsWS5cuxaZNm6jMWRkZGXh5eTXbwFh4eDgkJCQwePBgAMDevXtx6NAhaGlpYe/evS1iw4Ap/rSHRW5wcXHBxo0bqZ36KkxMTODt7U3L9uTJk2FgYIDFixfj+/fv0NfXx4sXL0AIQUhICCZMmMC17d27d2PVqlWYNWsWzp07BxsbG2RkZODhw4dYtGgRLb95RUVFBZ4+fQptbW0AgI+PD1tAT1BQEAsWLKi11JJP8+bRo0d1BmlPnz5Ny/aPHz+QmJhYq9SJubk5LdtM01hZui2xmYqMjMxPfzd0qnQGDBgAOzs7DB8+HPv27cPq1avZBPKPHz9OezNi8ODBiI+Ph4eHB7S1tXH16lXo6enh3r171DWNW2RkZPDmzRuoqamxjT9+/BiKioq0bPNpWviBMR7x+fNnWvXLp06dwuHDh3H79m2YmZlhx44dMDMzQ+vWraGtrf3HBsUAQEtLiycR+ZEjR8LFxQVbtmzB2bNnIS4ujr///ps6npiYiM6dO9OaIzQ0lOq0cv78eWRlZSElJQVBQUFYtWoVoqKiaNmvCyMjI8jKyuLff//FsmXLGvz/87K7Cx8+fOizZ88eHDp0CBYWFmzdxvT19bF8+fIm9OznODs7Y8uWLQAqd2GXLVsGJycn3Lx5E05OTjhy5EgTe9h0lJeX4+zZs5SuS48ePWBubs6TDtgtBV7u1N++fRurVq0CAJw5cwaEEBQUFCAgIAAbN26kFRjbt28fDh48iGnTpsHf3x///PMP1NXVsXbt2mZbFhsSEgIfHx/cvn0bQOV3VUZGhmr69P79e4iKisLOzq4p3eTTQEJCQjBz5kwMHz4cV69exbBhw5Camoq8vDzalSjh4eGYMWMGPnz4wHGsOerRNVaW7rp16xplHia5efMm9W9CCEaOHAlfX19GAj/79u3DnDlzcP/+faxfvx4uLi7Iz8+Hjo4Obt++jYMHD+L48eO05+ncuTMOHTpE205Npk6dihUrVuDkyZNgsVioqKhAVFQUli9fzqabxqcFQvjQYteuXWwvLy8vsmLFCtKxY0cybdo0ru0KCgqSf//9l3z58oVtXEhIiDx9+pSu2y2aiIgI8tdff5GbN2+S9+/fk8+fP7O9uCU/P5/8/fffhMViEUlJSXL69Gm24yYmJuTff/+l5XurVq1Ibm4uIYSQOXPmEAcHB0IIIZmZmURSUpKW7Z/x4sULcuPGDeLs7MzV/6+srEzu3btHCCFEQkKCZGRkEEIISUtLY9TvDx8+cIxVzcuHD5+6ERUVJS9evCCEsH9HU1NTiaioaFO69lNat25NsrKyCCGErFu3jkyYMIEQQkhsbCzp0KFDE3rWtKSlpZEuXboQcXFxoqurS3R1dYm4uDjp1q0bSU9Pb2r3mgxFRUUSFRVFCGE/z0+fPk3U1dVp2RYVFSU5OTmEEEJmzJhBVqxYQQghJDs7m7Ru3ZqWbTExMer7KScnR+Lj4wkhld/PNm3a0LLNK0xNTUlISAj1vvrvmxBC9u/fT4yMjJrCNT400NbWJt7e3oSQ//1NKyoqyJw5c8jatWtp2dbQ0CALFy4kb9++ZcJVPs2Imt9/JomIiCD9+vUjoqKipHPnzuTAgQO0bQ4ZMoQcOXKE1nNhXRQXF5PZs2cTISEhwmKxiLCwMBEQECDTp08nZWVljM/Hp/HgZ4zRxNPTk+29gIAA5OTkYG1tjZUrV3Jt187ODnv37kVkZCRmzJiBKVOm/FElJT/D1NQUADBkyBC2cUJTfL9du3a4ffs2Pn/+DAkJCY5d+ZMnT0JCQoI7p/+fDh06IDk5GQoKCggPD8f+/fsBVJYq8jILQEVFBSoqKj/tRvQz8vPz0b59e47xwsJCRrMX27Vrh+7du8PW1haLFi1CWFgYbGxsUFhYyNgcfPj8jqipqSE+Pp5DDyg8PLxZd8ISERFBUVERAOD69evUbmubNm1ot1NvySxZsgSdO3dGTEwM2rRpA6Cyxf306dOxZMkSXLx4sYk9bBp4uVOvpKSEe/fuoU2bNggPD0dISAiASv1RUVFRWrbl5eXx8eNHqKioQFlZGTExMejduzeysrJAmmkPrJSUFKoZUW0YGhri33//bUSP+DBBRkYGRo0aBaDy+lu1jnN0dISJiQmbhEhDycvLg5OTE5teb3Pmy5cvlEbWr+433GhpVVFeXg5PT886y1eba9ZoY2FiYoL79+8zarNHjx5YuXIlFi5ciFGjRmH69OkYOXIkbUkcoPJ7c+jQIaxZswZJSUn49u0bdHV1qWYCfFou/MAYTbKysnhi98CBA/Dy8sKJEyfg5+eHpUuXYvjw4SCEcNTs/2lUT+/lBXXpcFU9nNDBxsYGkydPppoGVAX57t+/T1szzdraGnZ2djAwMKDtZ0309fVx8eJF2NvbAwAVDPP19cVff/3F2DyPHj1CYmIiDh8+jJ07dyI/Px+urq6M2efD53fFyckJixYtwo8fP0AIwYMHDxAcHIzNmzfTannOawYPHgwnJycMGjQIDx48oMonUlNT0alTpyb2rum4desWW1AMqOzk5eHhgUGDBjWhZ02Lu7s7Fi1aBCUlJZSXl0NLSwvl5eWwtLTE6tWradleunQprKysICEhARUVFUrz8/bt27Q1aUxMTBAWFgZdXV3Y2NjA0dERoaGhePToEcaPH0/LNq/Iz89ne5+ZmYm2bdtS74WFhfmbVi0QWVlZfP36FQCgqKiIpKQkaGtro6CggNqk4JaJEyciMjKStuxIYyErK4s3b96gffv2dWpq0d10BwA3Nzf4+vpi2bJlWL16NVatWoUXL17g7NmzWLt2LZ2PwKcOdu3aBU9PT1y/fh3Hjh3DzJkzISgoiIkTJ8LKygqGhoa051BWVoaysjID3vJpLrBIc92qaubUZyEjJCQEeXl5DB06FGPGjKE1X1paGo4cOYKAgAB8+/YNo0aNwsSJE5vtgopP3YSGhiI3NxeTJk2iHvwCAgIgIyNDSyDbwsICly5dgoqKCmxsbGBtbc2YCOTdu3dhZmaG6dOnw9/fH/PmzWPr7tKnTx+u7FZ1MKq5y7Jp0yZs2LABrVq1woMHD9CtWzfan4EPn9+d//77D66ursjIyAAAdOzYEW5ubs1aAygnJwcLFy5Ebm4ulixZQvnq6OiI8vJy7N69u4k9bBratGmDCxcuYODAgWzjUVFRGDNmzB+fYZCbm4snT54wvlMfGxuLnJwcDB06lMoQv3jxImRlZTn+Fg2hoqICFRUVlD5XSEgIoqOj0aVLF8ybNw8iIiKM+M8kKioq2L9/P0aOHFnr8fPnz2Px4sXIzs5uZM/40MHS0hL6+vpwcnLChg0bsGfPHowdOxbXrl2Dnp4eLfH9oqIiTJo0CXJyctDW1ubIzlmyZAld9xnl1q1bGDRoEISEhH7ZqZBOIKVz587YvXs3Ro0aBUlJScTHx1NjMTExteomNjckJSWRmJjIITjfUvjx4wfOnz+PTZs24cmTJ7QCnYQQhIaG4ubNm7U2maDbwIJP08EPjHGJjY3NL3+moqIC7969w61bt7B8+XKsX7+e9rwVFRW4ePEiDh8+jMuXL6O4uJi2zZbInTt3cODAAWRmZuLkyZNQVFREUFAQ1NTUqO5mLYWCggLIyMgwYis/Px9BQUEICAhAcnIyTE1NYWdnh7Fjx9JOH87IyICHhwcSEhLw7ds36OnpYcWKFbR20k1MTLBo0SI2UeMDBw7A2dkZp0+fxq1bt5CamsqICCcfPn8KRUVF+PbtW63lz3xaBjNnzkRcXBwOHz6Mfv36AajMLJ4zZw769OkDf3//pnWwkamoqMC2bdsQFhaGkpISDBkyBOvWrYOYmBhjc6xfvx7Lly+HuLg42/j379+xbds2rjM7ysrK4O7uDltb2xaVBWlra4vnz5/X2hSIEIJBgwZBU1MTfn5+TeAdH275+PEjfvz4gY4dO6KiogJbt26lgrSrV6+mJdty+PBhzJ8/H6Kiomjbti1bBhaLxUJmZiYTH6HF0bp1azx79gzKyspQUFDAxYsXoaenh8zMTOjq6tJq1sYraiZenD9/HiYmJmjdujXbeEsIAr19+xYhISE4evQo4uLi0K9fP8TExHBtz8HBAQcOHICxsTE6dOjAkWn4JzcNaunwA2ONwIULF7Bw4ULk5OQwavfdu3d/5IPPqVOnMGPGDFhZWSEoKAjJyclQV1eHt7c3Ll26hEuXLjW1i3WyZcsWqKqqYsqUKQAq28OfOnUKCgoKuHTpEnr16sXYXHFxcThy5Ah8fX0hISGB6dOnY+HChc2qBl5aWhrx8fHUDlRoaCjmz5+PsLAwDBw4EPHx8RgyZEitHY748OHzP0xMTHD69GmOIPuXL19gYWGBGzduNI1jv0BQUJAqZanOhw8f0L59+2bXxayxKCgogLW1Nc6fP09tapSVlcHc3Bz+/v51lvz/rmzYsAGurq4wNTWFmJgYrly5gmnTpjEalOHluSghIYGkpCSoqqrS9LLxyMjIgJ6eHjQ1NbF8+XJ07doVAPD8+XNs374dz58/R2xsLDQ0NJrYUz71paysDMeOHcPw4cN5ogMmLy+PJUuWwMXFBQICAozb5zUFBQV48OBBrVlAdDQMu3XrhsDAQPTv3x+DBw/G6NGj4eLiguPHj8Pe3h7v3r2j6zrj1CcBBGi+QaAvX77g1KlTOHbsGCIjI6Gurg4rKytYWVnRLvVt06YNjh49Wmc2LZ8WTJNI/v9hfPr0iYwbN66p3fht0NHRIQEBAYQQ9i4pcXFxzb6LmaqqKtVR6+rVq0RGRoZcuXKF2NnZkaFDhzI2z+vXr4mHhwfp1q0bad26NZk5cyYZMmQIERISIjt37qy3nZodP+t6cYusrCzVdTI8PJwoKSmRuLg46nhSUhKRkZHh2j4fPn8KLBaL5OXlcYzn5eURISGhJvCoftTl96tXr5p1N83GIi0tjYSFhZGwsDCSlpbW1O40GRoaGsTHx4d6f+3aNSIiIkLKy8sZm4PFYpF3795xjEdERJB27drRsm1ubk78/f1p2WgK7t+/T7p3705YLBYREBAgAgIChMVike7du5OYmJimdo8PF1TvkMo0srKyLbZrblhYGJGUlCQsFotIS0sTGRkZ6iUrK0vL9ooVK8imTZsIIYSEhIQQISEhoqGhQURERKjut38ivOpySUhll2EFBQWydOlS8vDhQ0Ztq6qqkmfPnjFqk0/zgC++3wjIyMi0iFTTlsLz589rFZiXlpZGQUFB4zvUAN6+fQslJSUAlZmEkydPxrBhw6Cqqor+/fvTsl1aWoqwsDAcOXIEV69eRa9evbB06VJYWlpS3XTOnDkDW1tbODo61stmXWKkVRCaoqQmJiawtLTEwIEDERoaivXr10NXV5c6vn//fvTu3Zsr23z4/AkkJiZS/05OTsbbt2+p9+Xl5QgPD2dMa5BJqrTDWCwWldVaRXl5OW7fvk27IUlLpDFKBlsiOTk5bLvzpqamYLFYeP36Ne3yRFlZWbBYLLBYLHTt2pXtnldeXo5v375h/vz5tOYwMzODi4sLnjx5gj59+nCUI5mbm9Oyzyv69euH5ORkxMfHIzU1FUClJmj1+zSflkW/fv1q7WDMBNbW1jh+/HiL7Fa6bNky2Nrawt3dnaOcmi4eHh7Uv6dMmQIVFRWqfJWuBnVLRkNDA4aGhrCzs8PEiRNpd/+tTlhYGIYMGcKTzEVXV1e4ubnBz8/vj783/27wA2N8Whzy8vJIT0/nKEm4e/cu1NXVm8apeiIrK4vc3FwoKSkhPDwcGzduBFAZYKJbMqSgoICKigpMmzYNDx48gI6ODsfPGBsbN0jPrHoHUEIIRo4cCV9fX8YetH18fPDPP/9AUFAQoaGhsLS0RFxcHHR1dXHnzh2Eh4cjIiKCkbn48Pkd0dHRoR7qTUxMOI6LiYlhz549TeDZz/H09ARQeV3x8fGBoKAgdUxERASqqqrw8fFpKveajE2bNrGVDO7atQvv3r3743WcysrKOB6ahIWFUVpaStu2l5cXCCGwtbWFm5sbW5lq1blIt/vywoULAQA7d+7kOEa3411joKOjU+uagk/LY+HChXByckJubm6tQVo6kh7l5eXYunUrrly5gl69enFo29Z2/jcXXr16hSVLljAeFKuNAQMGYMCAAXj37h3c3d1bZCCRCaokX5ycnLB48WJMmTIFdnZ2lK4mHYYOHcqAh7UzefJkBAcHo3379lBVVeU4z+Pi4ng2Nx/ewtcY49Pi2Lx5M44ePQo/Pz8MHToUly5dQnZ2NhwdHbFmzRrY29s3tYt1snjxYly4cAFdunTB48eP8eLFC0hISCAkJARbt26ldTENCgrCpEmTGN1xqYmkpCQSEhJ4FoBMTk6Gm5sbEhMToaioCGdnZwwfPpwnc/Hh8zuQnZ0NQgjU1dXx4MEDyMnJUcdERETQvn17tqBTc8PY2BhnzpxhrAFJS6dLly5Yvnw55s2bBwC4fv06Ro0ahe/fv7dIzR6mEBAQgJmZGVq1akWN1SYGzW12fllZGf777z+YmJhQWd18+PyO1HYdYbFYtCsAgMrreV2wWKxmq3UJVIrNT506FZMnT260ORMSEqCnp9fsA+O8pqysDGFhYfD390d4eDi6du0KW1tbzJgxg21N8yt0dXV/WuVSHTrPW5MnT8bNmzcxceLEWsX3161bx7VtPk0LPzDWQjh9+jQGDRrEJpbp5eWFpUuXNp1TTQQhBO7u7ti8eTOKiooAAK1atcLy5cuxYcOGJvbu55SWlmLXrl3Izc3FrFmzqHIET09PSEpKYvbs2U3s4c/hdWCMDx8+fw6lpaXQ1NTEhQsX0L1796Z2p1nQqlUrpKenswVnREVFkZ6e3qI6GjJNYwhBi4uL49mzZzwpMePDp7mQnZ390+N/0vkfFhZG/Ts/Px/r16+HjY0NtLW1ObKAeFHuzA+MsVNcXIx9+/Zh5cqVKCkpgYiICCZPnowtW7ZAQUHhl/+/m5sb9e8fP35g37590NLSojJ+Y2Ji8PTpUyxcuBCbN2/m2s/WrVvjypUrGDx4MNc2+DRP+IGxFoK0tDQKCwsxYsQIbNy4Eb6+vjh06BCKi4ub2rUmo6SkBOnp6fj27Ru0tLTYNGr+FGq2U/4ZTOjc8QNjfPg0H8LCwmBmZgZhYWG2BX5tNFcNI0VFRVy/fp0fGPt/BAUF8fbtW7ZdcklJSSQmJlLde/nwBiMjIyxd+n/t3XlYzWn/B/D3OUXRpqhGEaVQSPbBWLJvJcxgbGXfebKNeewZfhhLY3lkC4Visk3DI0v2YWqKwkiirBVTkjZavr8/+jm/OVPZWu6T835dl+vSfZ/r25sxp+/5fO/7c/8LLi4uJXbN4OBgTJkyBVevXlX0+nzr5cuXaNOmDTZv3lxo31QiKl0fugq3tLY7szCW748//oC3tzf8/f2ho6MDV1dXjB49Go8fP8aSJUuQmpqKkJCQj7rmmDFjUL169QILJhYtWoRHjx4Vqz1B/fr1ceDAgWJtOybVxMJYORIXF4cNGzbA09MTlStXxrFjx9TyZmrPnj3o379/mfQBKA2+vr7YsmUL7t+/jytXrqBWrVrw9PSEpaUl+vbt+1HX+vtTdEmScPjwYRgYGKB58+YAgLCwMKSkpKB///4lcqQyP6ARqQ65XI6EhASYmJi88wZflXsYLV++HNHR0di+fTs0Ndn2tLS3DFLRDhw4gO+//x7u7u4l1nvJ2dkZjo6ORR54s379epw9exaHDx/+pMxEn8LX1xdeXl6IjY0t9n0ofTp1L4ytXbsWO3fuxJ07d9CrVy+MGTMGvXr1Urqfefz4MWrXro2cnJyPuraBgQH++OMP2NjYKI3fvXsXzZs3x8uXLz8597Fjx7BhwwZ4eXkV6HdN5RvvQlVUYGAgdHR0lJopW1hY4O7du7CwsEBaWhoyMzMFJhTH3d0dEyZMgLOzM4YNG4bu3burdA+dv9u8eTMWLlyIf/3rX1i2bJnih2GVKlXg6en50Tckfy92fffddxg4cKBSI+vc3FxMmjSpwJPqD/XPFWlZWVmYMGFCgQ8M/JBGVPby8vIK/X15EhoaijNnzuDkyZNo1KiR2r+3uLq6FhgbNmyYgCTqZ/DgwQCAadOmKcaK23spIiICK1euLHK+W7duWL169ceHFWDatGlo3749nJycoKWlhczMTAwbNgwHDx4UHY0+Qknfh1LRZsyY8c7558+fl1ES1bR582aMGjUKbm5uRW6VNDExwY4dOz762pUqVcLly5cLFMYuX75c7F7Mw4YNQ0ZGBurUqYPKlSsX2HabnJxcrOuTOFwxpqIcHBywdu1apcLYyJEjcfXqVZw6dQonTpzAnj17cO7cOXEhBcnJycGJEyfg5+eHo0ePonLlyvjmm28wdOhQtGnTRnS8d7Kzs8Py5cvh4uKitC3x5s2b6NixI/76669PvraxsTEuXbqEevXqKY3fuXMHbdq0QVJS0kdfsyz6uhBR6Xn8+DE8PDywdetW0VEK9b73GL63UFkpjd5L2trauHnzJqytrQudj4mJQaNGjcrFg05nZ2fcvHkTaWlpmDdvHvbt24eEhIT3/r2RainN+9DyqDS3O7/rMIK/+/sJ8FQyVqxYgSVLlmDs2LGKUy5///13eHt7Y8GCBZg7d+4nX3v37t3vnC/sAReVD1wxpqKio6OVbqRmz56Na9eu4cKFCzA2Nkb79u3VsvE+AGhqaqJPnz7o06cPMjIycPjwYezbtw+Ojo6oUaMG7t27JzpikWJjYxUN9/9OS0sL6enpxbp2Tk4OoqKiChTGoqKiPnk1SVl9KD179uwH30AQ0YdLSkrCjh07VLYwxsIXqYqiCl95eXk4fvz4JxXGzM3N31kYi4yM/KCm0qrgbR/D7du3Y9y4cdDV1UV0dLTgVPSxSvM+tDzy9PTE2LFjC91ZYWBggPHjx2PdunWfVBhjwevdIiMjCx2XyWTQ1taGhYWFUluBjzF37lxYWVnhp59+wp49ewAAtra22LlzZ7FPHmXh6/PFwpiKMjQ0xO+//w4LCwssWrQIly9fxrlz5xRH2iclJX3y9rjPSeXKldG9e3e8ePECDx48wO3bt0VHeidLS0tcv369wA32iRMnit18euTIkRg9ejTu3bun9HRkxYoVH7zyS5QePXqgRo0aGDlyJFxdXZVOZCOiz9/z589x584dAEC9evU+6oh2otIQExMDb29v7Nq1C8+fP0d2dvZHX6NXr15YsGABevToUWD7TmZmJhYtWoQ+ffqUVOQStWzZMlhaWmLIkCGKsb/++gs//vgjunfvjqdPnyIgIABTpkwRmJI+Vknfh77v4Je/U8VDYD6n7c7ljYODA2QyWZHzFSpUwKBBg7Bly5ZP2v44cODAQotgN2/eRMOGDT/6eoXJysrCmzdvlMb4+bwck0glzZkzR6pYsaJUo0YNSVdXVxo/fryUm5srSZIkpaWlSZ06dZIGDhwoOKU46enp0p49e6SePXtKFStWlOrUqSPNnz9fun37tuho77Rt2zbJ3Nxc8vf3l3R0dCQ/Pz/phx9+UPy+OHJzc6WVK1dKZmZmkkwmk2QymWRmZiatXLlSysnJKaE/Qel4/vy5tHbtWqlx48aSpqam1K1bN2n//v3S69evRUcjKteuX78uyeVy0TGKlJaWJo0cOVLS0NBQvG9pampKo0aNktLT00XHIzWTkZEh7d69W2rXrp0kl8ulDh06SJs3b5YSEhI+6XoJCQmSmZmZVLNmTWnlypXSkSNHpCNHjkgrVqyQatasKZmZmX3ytUubpaWlFBoaqvg6NTVVatq0qfTNN99Ib968kY4ePSo1bNhQYEL6FCV9H/r2ffvtL7lcXuDrt79UkZaWlnT37t0i5+/evStpa2uXYSL1ceTIEalevXrS9u3bpcjISCkyMlLavn27ZGtrK/n7+0t79uyRatSoIc2cObPY3ys1NVXasmWL1KJFi2L/W0xLS5MmT54sGRsbK/37VuV/5/RhWBhTUXl5eZKPj4+0Z88eKT4+XmrYsKFUs2ZNqUePHtIXX3whGRsbS7GxsaJjCjFo0CBJR0dHMjY2liZPniz99ttvoiN9lD179kjW1taKmwZzc3Np+/btJfo9Xr58Kb18+bJEr1lWwsLCpClTpkhVq1aVqlatKk2dOlW6fv266FhE5ZKqF8bGjRsnWVlZScePH1e8bx07dkyqU6eONGHCBNHxSE2EhIRI48aNk/T19aUmTZpIq1evljQ0NKRbt24V+9pxcXFSz549lQoGcrlc6tmzp3T//v0SSF86tLW1pbi4OEmSJCkrK0tydHSUXF1dFQ9p7927x4JBOVVa96GnTp2SmjZtKp04cULxfn7ixAmpefPm0smTJ0sgecmzsrKSDh8+XOT8wYMHJUtLy7ILpEZatGghnThxosD4iRMnpBYtWkiSJEmHDx+WrKysPvl7nD9/Xho+fLiko6Mj2djYSN99950UEhLyydeTJEmaNGmSZGtrKwUEBEiVKlWSvL29paVLl0o1atSQ9uzZU6xrk1hsvl9OZGZmYufOnbhx4wbMzc0xevToctOXoqQNHToUQ4cOLVenURYmIyMDaWlpMDExKdHr/n1LUv369VGtWrUSvX5ZePr0KbZu3YoVK1ZAU1MTWVlZaN26Nby8vNCgQQPR8YhUxj9Pjv2nlJQUnD9/XmWPg69WrRoCAgLQsWNHpfGzZ89i4MCBan9qF5U+e3t7pKamYsiQIRg6dKjiZ0yFChUQEREBOzu7Evk+L168QExMDCRJgo2NDQwNDUvkuqXF1tYW/fv3x8iRIzFlyhRYWVnhP//5j2L+6NGjmDlzJmJiYgSmpOIo6fvQhg0bwsvLC1999ZXS+MWLFzFu3DiVbHcydepUnDt3DqGhoYVud27ZsiUcHR2xfv16QQk/X5UqVcK1a9dQv359pfGoqCg0adIEmZmZiIuLg52dHTIyMj74ugkJCdi1axd27NiB1NRUDBw4EF5eXiX2fm5hYQEfHx907NgR+vr6CA8Ph7W1NXx9feHn54fjx48X+3uQGHLRAejDVKpUCZMmTcLmzZsxf/58tS2KAcDevXvRq1evcl0UA/L7o5VkUSw9PR2jRo1C9erV0b59e7Rv3x7Vq1fH6NGjP+oHiijZ2dkICAhAr169UKtWLQQFBWHjxo1ITExETEwMatWqhW+++UZ0TCKVYmBg8M5ftWrVwogRI0THLFJGRgZMTU0LjJuYmJSL9y0q/+7cuYP27dvD0dGxxIpghTE0NESLFi3QsmVLlS+KAcC8efOwYsUK2NraIiUlBeHh4YqHbqGhoXB3d39vYZ5UT6dOnZCSkgJA+T40NTUVnTp1Kta17927p+iF/HcGBgaIi4sr1rVLy/z585GcnIy6deti1apVOHr0KI4ePYqVK1eiXr16SE5Oxrx580TH/CzVr18fK1asUOrRlZ2djRUrViiKZU+ePCn0HqEoTk5OqFevHiIjI+Hp6YmnT59iw4YNJZo7OTkZVlZWAPL7iSUnJwMAvvrqK1y4cKFEvxeVLa4Yo3LHw8PjnfMLFy4soyQfLzExEbNmzcKZM2fw7Nkz/PN/v+Ks6hg/fjxOnz6NjRs3om3btgCAS5cuYdq0aejatSs2b95crOylaerUqfDz84MkSRg+fDjGjBlToDFmQkICzMzMPvmETSJSPZ07d0bVqlXh4+OjeFqfmZkJV1dXJCcn4/Tp04IT0ufuyZMn2LVrF3bu3InMzEx8++23GDp0KFq1aoXr16+XarFM1T148AAymQzm5uYYM2YMdu/eDW1tbbx+/RqtW7dGUFAQdHR0RMekjyCXy5GQkFDgweyzZ89gbm7+SYdMvNW+fXtoa2vD19dXUcxITEzEiBEjkJWVhfPnzxcre2l58OABJk6ciKCgIMV9uUwmQ/fu3bFp0yZYWloW+3ukpKQgJCQEz549K3Afq8oPr0rTb7/9BmdnZ8jlctjb2wMAbty4gdzcXPz666/48ssv4evri4SEBMyePfuDrqmpqYlp06Zh4sSJsLGxUYyX5Apge3t7bNiwAR06dECXLl3g4OCA1atXY/369Vi1ahUeP35c7O9BYrAwRuXOP4+Zzs7ORmxsLDQ1NVGnTh2Eh4cLSvZ+PXv2xMOHDzFlyhRUr169wGksffv2/eRrl+ctSZ07d8aYMWPQv3//Io9mzsnJweXLl9GhQ4cyTkdEpeXmzZvo3r07Xr9+jcaNGwPIPyVMW1sbQUFB3DpNZSo4OBje3t44dOgQsrKyMGvWLIwZMwZ169YVHU0l3L59G5GRkTA3Ny+wXY5UW2RkJID8kwCDg4NhZGSkmMvNzcWJEyewZcuWYq3siomJQb9+/RAdHa04XfzRo0ewsbHBkSNHYG1tXaw/Q2krre3OgYGBGDp0KNLS0qCvr6907y+TyRQrjtTRq1evsHfvXkRHRwPIP5V6yJAh0NPT+6TrXb16FTt27MD+/ftha2uL4cOHY/DgwahevXqJFcbWrVsHDQ0NTJs2DadPn4aTkxMkSUJ2djbWrl2L6dOnF/t7kBgsjNFnITU1FW5ubujXrx+GDx8uOk6R9PT0cPHiRTg4OJT4tStXroywsLACx23funULLVu2RHp6eol/TyKi4srIyMDevXsRFRUFIL+30dChQ1GpUiXByUhdvXz5Env37oW3tzfCw8PRsGFDRWGBqDySy+WKgkxhH/0qVaqEDRs2YNSoUcX6PpIk4dSpU0rv5126dCnwIFid1K1bF7169cLy5ctRuXJl0XHUQnp6Ovbv3w9vb2+EhIQgNzcXa9euxahRoz656FaUBw8eICwsDNbW1oqVb1Q+sTBGn40bN27AyclJZfsYAICdnR327t1bYNVbSShvW5J++eWXD36ts7NzKSYhIiIq3PXr1+Ht7c3m21SuPXjwAJIkwcrKCiEhITA2NlbMVaxYESYmJuW+d6+q0tHRwY0bNxR9qej/3bt3D56enoqDGRo0aIBp06ahTp06JfY97ty5gx07dsDX1xcpKSno2rXrR30GIfXBwpiKW7RoEUaNGoVatWqJjqLyLl26BCcnJ7x48UJ0lCKdPHkSa9aswZYtW1C7du0SvXZ525Ikl3/Y2R8ymUxlT9QjouK7c+cONmzYoLgxtrW1xZQpUwqcVEVERKrtzJkzij66/+yl5e3tLSiVWP3798fgwYMxcOBA0VFUSlBQEJydneHg4KDojXz58mVEREQgMDAQXbt2LdHvl5ubi8DAQHh7e39SYSwzMxNnzpxBnz59AADff/89Xr9+rZjX0NDA0qVLC5xuSuUHC2MqzsHBATdv3kSHDh0wevRoDBgwoMgeTOrin09tJUlCfHw8fH190aFDB+zbt09QsvczNDRERkYGcnJyULlyZVSoUEFpvrh9BrgliYjKk4MHD2Lw4MFo3rw5WrduDSC/R0hoaCj8/f0xYMAAwQmJiMq3X375BT179kSFChXeWxAozgr9JUuWwMPDA82bNy+0j+7hw4c/+drl2Y4dO+Dh4YGRI0eiUaNGBe791XVXRJMmTdC9e3esWLFCaXzu3Lk4efKkyvWM9vLywrFjxxAYGAggvz1OgwYNFJ+xoqKiMGfOHLi7u4uMScXAwlg5cO3aNezcuRN+fn7IycnB4MGDMWrUKLRo0UJ0NCH+eTqMXC6HsbExOnXqhO+//77E946XpN27d79z3tXVtYySEBGJV6dOHQwdOrTAacOLFi3Cnj17cO/ePUHJiIg+D38/ifJdq/WLu0K/evXqWLVqlUr3+hWhNP/OyzNtbW3cuHFD6fRIAIiOjoa9vT2ysrIEJStcu3btMGfOHDg5OQHIL4xFREQotsju2bMHmzZtwpUrV0TGpGJgYawcyc7ORmBgIHbu3ImgoCDUr18fo0ePhpubGwwMDETHI8GSkpJQtWpVAPmnAG3btg2ZmZlwcnJC+/btBacraP369Rg3bhy0tbXf27tl2rRpZZSKiMpS5cqVERkZWeC0srt376Jx48bIyMgQlIyIACA8PBwVKlRAo0aNAABHjx7Fzp07YWdnh8WLF6NixYqCE5KqqFq1KkJCQkq0PxR9vmrWrIm1a9fim2++URo/cOAAZs2ahYcPHwpKVrjq1avjypUrilY4xsbGCA0NVXwdHR2NFi1a4OXLl+JCUrFoig5AH+7tUbBv3ryBJEkwNDTExo0bsWDBAmzbtg2DBg0SHbFU9e/f/72v0dTUxBdffIGuXbsqKvqipaamQl9fX/H7d3n7uo/x9tCBt0di+/v7o0ePHkhPT4dcLse6desQEBAAFxeXT4lfatatW4ehQ4dCW1sb69atK/J1MpmMhTGiz1THjh1x8eLFAoWxS5cuoV27doJSkbrLyspin5j/M378eMydOxeNGjXC/fv3MXjwYPTr1w8///wzMjIy4OnpKToilYDHjx/Dw8MDW7du/eRrjBkzBvv27cOCBQtKMBl9rsaOHYtx48bh/v37aNOmDYD8HmMrV67EjBkzBKcrKCUlRamn2PPnz5Xm8/LylOap/OGKsXIgLCxMsZVSS0sLI0aMwJgxYxQfJDZs2IAffvgBiYmJgpOWrpEjR773NXl5eXj27BnOnz+PWbNmFdieI4KGhgbi4+MVS9gLO7JakqRPXk7ds2dPaGpqYu7cufD19cWvv/6K7t27Y9u2bQCAqVOnIiwsDFevXi32n4WIqCR5eXlh4cKFGDhwIL788ksA+T3Gfv75ZyxZsgRmZmaK16prHxYqG3l5eVi2bBm8vLyQmJiI6OhoWFlZYcGCBahduzZGjx4tOqIQBgYGCA8PR506dbBy5UoEBwcjKCgIly9fxuDBg/Ho0SPREakEREREoGnTpsXa1jd9+nT4+PjA3t4e9vb2BXpprV27trgxy6WidkXIZDJoa2vD2toa7du3V7tTQSVJgqenJ9asWYOnT58CAMzMzDB79mxMmzat0M9LItnY2GDFihVF9j49cOAA/v3vfyMmJqaMk1FJYWFMxTVq1AhRUVHo1q0bxo4dCycnpwJvnH/99RdMTEwKnP6izn799VdMmjRJJZbhnj9/Hm3btoWmpibOnz//ztd26NDho69frVo1BAcHw97eHmlpadDX10doaCiaNWsGIL8Z5JdffomUlJRPiU9EVGp4Oi2pCg8PD+zevRseHh4YO3Ysbt68CSsrK+zfvx+enp5q2zdGX18fYWFhsLGxQdeuXdGnTx9Mnz4dDx8+RL169ZCZmSk6IpWAkiiMOTo6Fjknk8kQHBz8ydcuzywtLfH8+XNkZGTA0NAQAPDixQtUrlwZurq6ePbsGaysrHD27FnUrFlTcFoxXr16BQAq3Sd6+vTpOH36NMLCwgqsKM7MzETz5s3RpUsX/PTTT4ISUnGxMKbili5dilGjRsHc3Fx0lHIlJSUFo0aNwqFDh0RHKXV/b6oKFGwGmZiYCDMzM5X+UClJEgICAnD27NlCj/hWh/+OREQkjrW1NbZs2YLOnTsr/RyNiopC69at8eLFC9ERhejUqRNq1qyJLl26YPTo0fjzzz9hbW2N8+fPw9XVFXFxcaIjUgkoicIYFc7Pzw9bt27F9u3bFf3XYmJiMH78eIwbNw5t27bF4MGD8cUXXyAgIEBwWipKYmIiHBwcULFiRUyZMgV169YFANy5cwcbN25ETk4Orl27BlNTU8FJ6VOxx5iK4z79T1OlShWVLKacOHECurq6+OqrrwAAmzZtwrZt22BnZ4dNmzYpniR9rH8uN1a15cfv869//QtbtmyBo6MjTE1Ny11+IiIq3548eVKg1x2Qv8UyOztbQCLV4OnpiaFDh+LIkSOYN2+e4u8oICBA0ReIiIo2f/58HDx4UOlQAmtra6xevRoDBgzA/fv3sWrVqiK36H1OmjRp8sH3+OHh4aWc5uOYmprit99+w8SJEzF37ly8XVskk8nQtWtX/Oc//2FRrJxjYUzF5ebmYteuXThz5kyhK2nUdVlyeTV79mysXLkSQH7T/BkzZmDmzJk4e/YsZsyYgZ07d37Sdd3c3KClpQUgv2nwhAkToKOjAwDlohGkr68vDh06hF69eomOQkRl4MqVK0hKSkKfPn0UYz4+Pli0aBHS09Ph4uKCDRs2KN7XiEqbnZ0dLl68iFq1aimNBwQEoEmTJoJSiWdvb48bN24UGP/xxx/VridSefa+A6xKqt3GH3/8gQMHDuDhw4d48+aN0pwqPrAuC/Hx8cjJySkwnpOTg4SEBAD5vbXebif8nKnaQWAfy9LSEidOnEBycrKil5i1tTWMjIwEJ6OSwMKYips+fTp27dqF3r17o2HDhlxJU87FxsbCzs4OAHDw4EE4OTlh+fLlCA8P/+SikKurq9LXw4YNK/CaESNGfNK1y4qBgYFi6ycRff48PDzQsWNHRWHsxo0bGD16NNzc3GBra4sff/wRZmZmWLx4sdigpDYWLlwIV1dXPHnyBHl5eTh06BDu3LkDHx8f/Prrr6LjCRMaGoq8vDy0atVKaTwiIgIaGhpo3ry5oGT0MQwMDN47X9x7RX9/f4wYMQLdu3fHyZMn0a1bN0RHRyMxMRH9+vUr1rXLM0dHR4wfPx7bt29XFNmvXbuGiRMnolOnTgDyfwZaWlqKjFkmFi1aJDpCiTAyMkLLli1Fx6ASxh5jKq5atWrw8fHhSprPhJGRES5dugQ7Ozt89dVXGDFiBMaNG4e4uDjY2dkhIyNDdEQhdu/ejRMnTsDb2xuVKlUSHYeISln16tURGBio+FA9b948nD9/HpcuXQIA/Pzzz1i0aBH+/PNPkTFJzVy8eBEeHh6IiIhAWloamjZtioULF6Jbt26iownTsmVLzJkzB19//bXS+KFDh7By5Ur8/vvvgpKRqrG3t8f48eMxefJkRZ8+S0tLjB8/HtWrV8eSJUtERxQiISEBw4cPx5kzZxQndebk5KBz587w9fWFqakpzp49i+zsbLV8rwkLC8Pt27cBAA0aNFDrFbokFgtjKs7MzAznzp1TNPij8s3Z2Rlv3rxB27ZtsXTpUsTGxsLc3BwnT57ElClTEB0dLTqiEJmZmejXrx8uX76M2rVrFzjiW9X6DBBR8Whra+Pu3buKE7i++uor9OzZE/PmzQMAxMXFoVGjRmqxtYRIlenq6iIyMrLAqu7Y2FjY29vz/1FS0NHRwa1bt1C7dm1UrVoV586dQ6NGjXD79m106tQJ8fHxoiMKFRUVpbjPr1evHurVqyc4kVjPnj3D4MGDce7cOVSpUgVA/pZeR0dH+Pv7w9jYWGxAUjsfdk46CTNz5kz89NNPYP3y87Bx40ZoamoiICAAmzdvVpw2+t///hc9evQQnE4cV1dXhIWFYdiwYRgwYAD69u2r9IuIPi+mpqaIjY0FALx58wbh4eH48ssvFfOvXr0qUCAnorKnpaWFxMTEAuPx8fHQ1GRHFvp/hoaGikKpubk5bt68CSC/2KGuOyL+rn79+nB2doazs7PaF8UAYOrUqXj16hVu3bqF5ORkJCcn4+bNm0hNTcW0adNExyM1xBVjKuifDTKDg4NhZGSEBg0aFPigoK6NLOnzoqOjg6CgIMVpnUT0eZs4cSIiIiKwcuVKHDlyBLt378bTp09RsWJFAMDevXvh6emJ0NBQwUlJXRgaGhbax1Umk0FbWxvW1tZwc3PDyJEjBaQT59tvv0V8fDyOHj2q6FOVkpICFxcXmJiY4MCBA4ITkqoYMmQImjdvjhkzZmDp0qXYsGED+vbti1OnTqFp06Zq9Znl7d+Bjo4OZsyY8c7Xrl27toxSqRYDAwOcPn0aLVq0UBoPCQlBt27dSuxACKIPxUc9KuifDTLVuWHl5+bhw4fvnLewsCijJKqlZs2a0NfXFx2DiMrI0qVL0b9/f3To0AG6urrYvXu3oigGAN7e3mrZa4XEWbhwIZYtW4aePXsqmiqHhITgxIkTmDx5MmJjYzFx4kTk5ORg7NixgtOWndWrV6N9+/aoVauWovfP9evXYWpqCl9fX8HpSJVs3LgRWVlZAPL7RlaoUAG//fYbBgwYgPnz5wtOV7auXbuG7Oxsxe+Los6HquXl5RW6MrxChQrIy8sTkIjUHVeMEZUhuVz+zh+Cubm5ZZhGdRw7dgwbNmyAl5cXateuLToOEZWRly9fQldXFxoaGkrjycnJ0NXVVSqWEZWmAQMGoGvXrpgwYYLS+JYtW3Dy5EkcPHgQGzZswNatW3Hjxg1BKcVIT0/H3r17ERERgUqVKsHe3h7ffvsttzsT0Sfr27cvUlJS4OfnBzMzMwDAkydPMHToUBgaGuLw4cOCE5K6YWGsnHj27Bnu3LkDIL9ho4mJieBE9CkiIiKUvs7Ozsa1a9ewdu1aLFu2rMA2WnVhaGiIjIwM5OTkoHLlygVutpOTkwUlIyIidaCrq4vr16/D2tpaaTwmJgYODg5IS0vDvXv3YG9vj/T0dEEpiai8iomJwb1799C+fXtUqlQJkiSp9YqxR48ewdnZGbdu3VIcxPPo0SM0bNgQv/zyC2rUqCE4IakbbqVUcampqZg8eTL8/f0Vq4k0NDQwaNAgbNq0qcC2S1JtjRs3LjDWvHlzmJmZ4ccff1Tbwpinp6foCEREpMaMjIwQGBgId3d3pfHAwEAYGRkByF85paenJyJemfrll1/Qs2dPVKhQAb/88ss7X+vs7FxGqYjKp6SkJAwcOBBnz56FTCbD3bt3YWVlhdGjR8PQ0BBr1qwRHVGImjVrIjw8HKdPn0ZUVBQAwNbWFl26dBGcjNQVV4ypuEGDBuHatWvYsGEDWrduDQC4cuUKpk+fDgcHB/j7+wtOSCUhJiYGjRs35lNoIiIiAbZt24aJEyeiV69eih5joaGhOH78OLy8vDB69GisWbMGISEh2L9/v+C0pUsulyMhIQEmJiaQy4s+wF4mk6ltCwiiDzVixAg8e/YM27dvh62tLSIiImBlZYWgoCDMmDEDt27dEh2RiMDCmMor6rS+ixcvokePHiyklDOpqalKX0uShPj4eCxevBhRUVG4fv26mGAqJCsrC2/evFEaY2N+IiIqbZcvX8bGjRuVWldMnToVbdq0EZyMiMqrL774AkFBQWjcuDH09PQUhbH79+/D3t4eaWlpoiMKc+bMGZw5cwbPnj0r0HDf29tbUCpSV9xKqeKqVq1a6HZJAwMDGBoaCkhExVGlSpUC/QQkSULNmjXVevVfeno6vvvuOxw4cABJSUkF5vlEmoiISlvbtm3Rtm1b0TGIyjX20lKWnp6OypUrFxhPTk6GlpaWgESqYcmSJfDw8EDz5s1RvXp1tf43QqqBhTEVN3/+fMyYMQO+vr744osvAAAJCQmYPXs2FixYIDgdfayzZ88qfS2Xy2FsbAxra2toaqrv/45z5szB2bNnsXnzZgwfPhybNm3CkydPsGXLFqxYsUJ0PCIiUiNcuawsNDQUZ8+eLXRVx9q1awWlIlWTlJSEQYMGITg4mL20/qZdu3bw8fHB0qVLAeRvQc7Ly8OqVavg6OgoOJ04Xl5e2LVrF4YPHy46ChEAbqVUeU2aNEFMTAxev34NCwsLAMDDhw+hpaUFGxsbpdeGh4eLiEhUbBYWFvDx8UHHjh2hr6+P8PBwWFtbw9fXF35+fjh+/LjoiERE9BnLyMjAnDlzuHL5H5YvX4758+ejXr16MDU1VVrVIZPJEBwcLDAdqRL20irczZs30blzZzRt2hTBwcGKkxiTk5Nx+fJl1KlTR3REIapWrYqQkBC1/fOT6lHfJSrlhIuLi+gIVIKSkpJQtWpVAPlHEm/btg2ZmZlwcnJC+/btBacTJzk5GVZWVgDyn8onJycDAL766itMnDhRZDQiIlIDs2fP5srlQvz000/w9vaGm5ub6Cik4k6ePImgoCDUqFFDadzGxgYPHjwQlEq8hg0bIjo6Ghs3boSenh7S0tLQv39/TJ48GdWrVxcdT5gxY8Zg37593AFFKoOFMRW3aNEi0RGoBNy4cQNOTk549OgRbGxs4O/vrzg8QS6XY926dQgICFDbQqiVlRViY2NhYWGB+vXr48CBA2jZsiUCAwNRpUoV0fGIiOgzFxgYqFi5PHLkSLRr1w7W1taoVasW9u7di6FDh4qOKIRcLmffNfog7KVVUHZ2Nnr06AEvLy/MmzdPdByVkpWVha1bt+L06dOwt7dHhQoVlOa5TZvKWtFnMBNRiZkzZw4aNWqECxcuoGPHjujTpw969+6Nly9f4sWLFxg/frxaP5EeOXIkIiIiAABz587Fpk2boK2tDXd3d8yePVtwOiIi+ty9a+XyhQsXREYTyt3dHZs2bRIdg8qBt7203mIvLaBChQqIjIwUHUMlRUZGwsHBAXK5HDdv3sS1a9cUv65fvy46Hqkh9hhTcbm5uVi3bh0OHDiAhw8fFmgG+/bGjVRbtWrVEBwcrDiWWV9fH6GhoWjWrBkAICoqCl9++SVSUlLEBi1j9+/fh6WlZYGTaB48eICwsDBYW1vD3t5eUDoiIlIX9vb22LBhAzp06IAuXbrAwcEBq1evxvr167Fq1So8fvxYdEQh8vLy0Lt3b0RHR8POzq7Aqo5Dhw4JSkaqhr20Cufu7g4tLS21fgBOVB5wK6WKW7JkCbZv346ZM2di/vz5mDdvHuLi4nDkyBEsXLhQdDz6QMnJyYpTRXV1daGjowNDQ0PFvKGhIV69eiUqnjA2NjaIj4+HiYkJAGDQoEFYv349atWqhVq1aglOR0RE6uLtyuUOHTpg7ty5cHJywsaNG5Gdna3WW3qmTZuGs2fPwtHREVWrVi3wIIvoLfbSKlxOTg68vb1x+vRpNGvWDDo6Okrz6vz+QqRKuGJMxdWpUwfr169H7969oaenh+vXryvGrl69in379omOSB9ALpcjMTERxsbGAAA9PT1ERkbC0tISAJCYmAgzMzO1O/VKLpcjISFBURjT09NTnGJEREQkClcu59PT04O/vz969+4tOgpRufSubaTqfLJreno6VqxYgTNnzuDZs2fIy8tTmr9//76gZKSuuGJMxSUkJKBRo0YA8lcavXz5EgDQp08fnuJRzri5uSmaj2ZlZWHChAmKp0avX78WGY2IiIj+hiuX8xkZGantFjj6ONbW1hg2bBiGDh0KGxsb0XFUxtmzZ0VHUEljxozB+fPnMXz4cFSvXp2rUUk4FsZUXI0aNRAfHw8LCwvUqVMHJ0+eRNOmTREaGqq2J7yUR66urkpfDxs2rMBrRowYUVZxVIZMJivwg5A/GImIqKx5eHi8c15d21csXrwYixYtws6dOws9cZDorcmTJ2Pfvn3w8PBAs2bNMGzYMAwaNEjRSoSg6FVYo0YNwUnE++9//4tjx47x1FtSGdxKqeLmzp0LfX19/Pvf/8b+/fsxbNgw1K5dGw8fPoS7uzsbOVK5JpfL0bNnT0WRNzAwEJ06dSrQf4HNfYmIqDQ1adJE6evs7GzExsZCU1MTderUQXh4uKBkYjVp0gT37t2DJEmoXbt2geb76vr3QkWLjo7G3r174efnh9jYWDg6OmLYsGFq+QAYyD/A4ocffsCaNWuQlpYGIH+L8syZMzFv3jzI5XLBCcWwtLTE8ePHYWtrKzoKEQAWxsqdq1ev4rfffoONjQ2cnJxExyEqlpEjR37Q63bu3FnKSYiIiJSlpqbCzc0N/fr1w/Dhw0XHEWLJkiXvnF+0aFEZJaHy6OrVq5g4cSIiIyPVro/uW99//z127NiBJUuWKFZHXbp0CYsXL8bYsWOxbNkywQnF2LNnD44ePYrdu3dzNSqpBBbGVFh2djbGjx+PBQsWKJq0ExEREVHZuHHjBpycnBAXFyc6ClG5ERISgn379mH//v1ITU2Fk5MT/P39RccSwszMDF5eXnB2dlYaP3r0KCZNmoQnT54ISlb2mjRpotQyJSYmhqtRSWWwx5gKq1ChAg4ePMgm+0REREQCvHz5UnHwkToLCwvD7du3AQANGjQosPWU6J9bKDt16oSVK1eif//+0NXVFR1PmOTkZNSvX7/AeP369ZGcnCwgkTguLi6iIxAViSvGVJyrqyscHBzg7u4uOgoRERHRZ2n9+vVKX0uShPj4ePj6+qJDhw7Yt2+foGRiPXv2DIMHD8a5c+dQpUoVAEBKSgocHR3h7+8PY2NjsQFJZcjlcrRo0QJDhgzB4MGDYWpqKjqSSmjVqhVatWpV4D1m6tSpCA0NxdWrVwUlI6K/Y2FMxb1t1ti5c2c0a9asQFPyadOmCUpGRERE9Hn4Z8sKuVwOY2NjdOrUCd9//z309PQEJRNr0KBBuH//Pnx8fBRNsv/880+4urrC2toafn5+ghOSqrh79y5sbGxEx1A558+fR+/evWFhYYHWrVsDAK5cuYJHjx7h+PHjaNeuneCEYoSGhiIvLw+tWrVSGv/999+hoaGB5s2bC0pG6oqFMRX3rt5iMpkM9+/fL8M0RERERKQuDAwMcPr0abRo0UJpPCQkBN26dUNKSoqYYETlyNOnT7Fp0yZERUUBAGxtbTFp0iSYmZkJTiZOy5YtMWfOHHz99ddK44cOHcLKlSvx+++/C0pG6oo9xlRcbGys6AhEREREpIby8vIKNMUG8vvg5uXlCUhEqsTIyAjR0dGoVq0aDA0NlRqr/5O69dO6f/8+LC0tIZPJYGZmpranTxblzz//RNOmTQuMN2nSBH/++aeARKTuWBgjIiIiIrXWr1+/Qj/Uy2QyaGtrw9raGkOGDEG9evUEpBOnU6dOmD59Ovz8/BSrW548eQJ3d3d07txZcDoSbd26dYptxuvWrXtnYUzd2NjYID4+HiYmJgDytyWvX7+evdf+j5aWFhITE2FlZaU0Hh8fD01Nliio7HErpYqbMWNGoeN/v1Hr27cvjIyMyjgZERER0efBzc0NR44cQZUqVdCsWTMAQHh4OFJSUtCtWzdEREQgLi4OZ86cQdu2bQWnLTuPHj2Cs7Mzbt26hZo1ayrGGjZsiF9++QU1atQQnJBINcnlciQkJCgKY3p6eoiIiChQCFJX3377LeLj43H06FEYGBgAyD/Yw8XFBSYmJjhw4IDghKRuWBhTcY6OjggPD0dubq7iKWV0dDQ0NDRQv3593LlzBzKZDJcuXYKdnZ3gtERERETlz9y5c5GamoqNGzdCLpcDyN9GOH36dOjp6WHZsmWYMGECbt26hUuXLglOW7YkScLp06eV+iN16dJFcCpSNRoaGkorpN5KSkqCiYkJcnNzBSUTg4Wxd3vy5Anat2+PpKQkNGnSBABw/fp1mJqa4tSpU4pCPFFZYWFMxXl6euLixYvYuXMn9PX1AQAvX77EmDFj8NVXX2Hs2LEYMmQIMjMzERQUJDgtERERUfljbGyMy5cvo27dukrj0dHRaNOmDf766y/cuHED7dq1Y8N5okL8sxD01tOnT1GnTh1kZmYKSiaGhoYGEhISYGxsDCC/MBYZGfnOg9XUTXp6Ovbu3YuIiAhUqlQJ9vb2+Pbbbwvta0hU2riBV8X9+OOPOHXqlKIoBuSfELR48WJ069YN06dPx8KFC9GtWzeBKYmIiIjKr5ycHERFRRUojEVFRSlWumhra6tND6Xg4GBMmTIFV69eVboHBfIf0LZp0wZeXl5o166doISkKtavXw8gv83L9u3boaurq5jLzc3FhQsXUL9+fVHxhJEkCW5ubtDS0gIAZGVlYcKECdDR0VF63aFDh0TEUwk6OjoYN26c6BhEAFgYU3kvX77Es2fPCmyTfP78OVJTUwEAVapUwZs3b0TEIyIiIir3hg8fjtGjR+Pf//43WrRoAQAIDQ3F8uXLMWLECADA+fPn0aBBA5Exy4ynpyfGjh1boCgG5D+gHT9+PNauXcvCGGHdunUA8gtBXl5e0NDQUMxVrFgRtWvXhpeXl6h4wri6uip9PWzYMEFJVJevry+2bNmC+/fv48qVK6hVqxbWrVsHKysr9O3bV3Q8UjMsjKm4vn37YtSoUVizZo3SjdqsWbPg4uICAAgJCSnwhJOIiIiIPsy6detgamqKVatWITExEQBgamoKd3d3fPfddwCAbt26oUePHiJjlpmIiAisXLmyyPlu3bph9erVZZiIVFVsbCyA/L7Ihw4dgqGhoeBEqmHnzp2iI6i0zZs3Y+HChfjXv/6FH374QbEy19DQEJ6eniyMUZljjzEVl5aWBnd3d/j4+CAnJwcAoKmpCVdXV6xbtw46Ojq4fv06AMDBwUFcUCIiIqLPwNsV+YWtllIX2trauHnzJqytrQudj4mJQaNGjdSubxQRlQw7OzssX74cLi4uSgcT3Lx5Ex07dsRff/0lOiKpGbnoAPRuurq62LZtG5KSknDt2jVcu3YNSUlJ2Lp1q2KPuoODA4tiRERERCVAX19frYtiAGBubo6bN28WOR8ZGYnq1auXYSJSdQMGDCh0leGqVavwzTffCEhEqiw2NlZxGuXfaWlpIT09XUAiUncsjJUTurq6MDIygpGRkVJTSyIiIiIqnsTERAwfPhxmZmbQ1NSEhoaG0i9106tXLyxYsABZWVkF5jIzM7Fo0SL06dNHQDJSVRcuXECvXr0KjPfs2RMXLlwQkIhUmaWlpWLX09+dOHECtra2ZR+I1B57jKm4vLw8/PDDD1izZg3S0tIA5B/3O3PmTMybNw9yOWubRERERMXh5uaGhw8fYsGCBahevbranD5ZlPnz5+PQoUOoW7cupkyZgnr16gHIP6Vz06ZNyM3Nxbx58wSnJFWSlpaGihUrFhivUKGCYnsykYeHB2bNmoUZM2Zg8uTJyMrKgiRJCAkJgZ+fH/7nf/4H27dvFx2T1BB7jKm477//Hjt27MCSJUvQtm1bAMClS5ewePFijB07FsuWLROckIiIiKh809PTw8WLF9ma4m8ePHiAiRMnIigoCG8/LshkMnTv3h2bNm2CpaWl4ISkSlq2bIk+ffpg4cKFSuOLFy9GYGAgwsLCBCUjVaKhoYH4+HiYmJhg7969WLx4Me7duwcAMDMzw5IlSzB69GjBKUkdsTCm4szMzODl5QVnZ2el8aNHj2LSpEl48uSJoGREREREnwc7Ozvs3bu30J436u7FixeIiYmBJEmwsbHhqYNUqMDAQPTv3x9DhgxBp06dAABnzpyBn58ffv75Z7i4uIgNSCpBLpcjISEBJiYmirGMjAykpaUpjRGVNRbGVJy2tjYiIyNRt25dpfE7d+7AwcGBpwERERERFdPJkyexZs0abNmyBbVr1xYdh6hcOnbsGJYvX47r16+jUqVKsLe3x6JFi9ChQwfR0UhFyOVyJCYmwtjYWHQUIiUsjKm4Vq1aoVWrVli/fr3S+NSpUxEaGoqrV68KSkZERET0eTA0NERGRgZycnJQuXJlVKhQQWk+OTlZUDIios+HXC6HgYHBe/s48j2Xyhqb76u4VatWoXfv3jh9+jRat24NALhy5QoePXqE48ePC05HREREVP55enqKjkBU7qWkpCAgIAD379/HrFmzYGRkhPDwcJiamsLc3Fx0PFIRS5YsgYGBgegYREq4YqwcePr0KTZt2oSoqCgAgK2tLSZNmgQzMzPByYiIiIiISN1FRkaiS5cuMDAwQFxcHO7cuQMrKyvMnz8fDx8+hI+Pj+iIpAIK6zFGpApYGCMiIiIi+j9ZWVl48+aN0pi+vr6gNETlQ5cuXdC0aVOsWrUKenp6iIiIgJWVFX777TcMGTIEcXFxoiOSCvj7qZREqoRbKVVQZGQkGjZsCLlcjsjIyHe+1t7evoxSEREREX2e0tPT8d133+HAgQNISkoqMJ+bmysgFVH5ERoaii1bthQYNzc3R0JCgoBEpIq4JodUFQtjKsjBwUGxxNTBwQEymazQNxGZTMYbNSIiIqJimjNnDs6ePYvNmzdj+PDh2LRpE548eYItW7ZgxYoVouMRqTwtLS2kpqYWGI+OjuYJhKSQl5cnOgJRobiVUgU9ePAAFhYWkMlkePDgwTtfW6tWrTJKRURERPR5srCwgI+PDzp27Ah9fX2Eh4fD2toavr6+8PPz44FHRO8xZswYJCUl4cCBAzAyMkJkZCQ0NDTg4uKC9u3b84ALIlJpLIwRERERkVrT1dXFn3/+CQsLC9SoUQOHDh1Cy5YtERsbi0aNGiEtLU10RCKV9vLlS3z99df4448/8OrVK5iZmSEhIQGtW7fG8ePHoaOjIzoiEVGRuJVSxSUlJaFq1aoAgEePHmHbtm3IzMyEs7Mz2rVrJzgdERERUflnZWWF2NhYWFhYoH79+jhw4ABatmyJwMBAVKlSRXQ8IpVnYGCAU6dO4fLly4iIiEBaWhqaNm2KLl26iI5GRPReXDGmom7cuAEnJyc8evQINjY28Pf3R48ePZCeng65XI709HQEBATAxcVFdFQiIiKicm3dunXQ0NDAtGnTcPr0aTg5OUGSJGRnZ2Pt2rWYPn266IhEKs3HxweDBg2ClpaW0vibN2/g7++PESNGCEpGRPR+LIypqJ49e0JTUxNz586Fr68vfv31V3Tv3h3btm0DAEydOhVhYWG4evWq4KREREREn5cHDx4gLCwM1tbWPAGc6ANoaGggPj4eJiYmSuNJSUkwMTHhgWFEpNJYGFNR1apVQ3BwMOzt7ZGWlgZ9fX2EhoaiWbNmAICoqCh8+eWXSElJERuUiIiI6DP1+PFjeHh4YOvWraKjEKk0uVyOxMTEAidQRkREwNHREcnJyYKSERG9H3uMqajk5GR88cUXAPIbwuro6MDQ0FAxb2hoiFevXomKR0RERPTZS0pKwo4dO1gYIypCkyZNIJPJIJPJ0LlzZ2hq/v/Hy9zcXMTGxqJHjx4CExIRvR8LYypMJpO982siIiIiIiJR3vY7vn79Orp37w5dXV3FXMWKFVG7dm0MGDBAUDoiog/DwpgKc3NzUzSwzMrKwoQJExRHHb9+/VpkNCIiIiIiUnOLFi0CANSuXRuDBg2Ctra24ERERB+PPcZU1MiRIz/odTt37izlJERERETqKSIiAk2bNmXjcKIPkJKSgoCAANy7dw+zZ8+GkZERwsPDYWpqCnNzc9HxiIiKxMIYEREREaml/v37v3M+JSUF58+fZ2GM6D0iIyPRpUsXGBgYIC4uDnfu3IGVlRXmz5+Phw8fwsfHR3REIqIicSslEREREaklAwOD986PGDGijNIQlV/u7u5wc3PDqlWroKenpxjv1asXhgwZIjAZEdH7ccUYERERERERfTIDAwOEh4ejTp060NPTQ0REBKysrPDgwQPUq1cPWVlZoiMSERVJLjoAERERERERlV9aWlpITU0tMB4dHQ1jY2MBiYiIPhwLY0RERERERPTJnJ2d4eHhgezsbACATCbDw4cP8d1332HAgAGC0xERvRu3UhIREREREdEne/nyJb7++mv88ccfePXqFczMzJCQkIDWrVvj+PHj0NHRER2RiKhILIwRERERERFRsV26dAmRkZFIS0tD06ZN0aVLF9GRiIjei4UxIiIiIiIiIiJSS5qiAxAREREREVH5lJeXh127duHQoUOIi4uDTCaDpaUlvv76awwfPhwymUx0RCKid+KKMSIiIiIiIvpokiTByckJx48fR+PGjVG/fn1IkoTbt2/jxo0bcHZ2xpEjR0THJCJ6J64YIyIiIiIioo+2a9cuXLhwAWfOnIGjo6PSXHBwMFxcXODj44MRI0YISkhE9H5cMUZEREREREQfrVu3bujUqRPmzp1b6Pzy5ctx/vx5BAUFlXEyIqIPJxcdgIiIiIiIiMqfyMhI9OjRo8j5nj17IiIiogwTERF9PBbGiIiIiIiI6KMlJyfD1NS0yHlTU1O8ePGiDBMREX08FsaIiIiIiIjoo+Xm5kJTs+i21RoaGsjJySnDREREH4/N94mIiIiIiOijSZIENzc3aGlpFTr/+vXrMk5ERPTxWBgjIiIiIiKij+bq6vre1/BESiJSdTyVkoiIiIiIiIiI1BJ7jBERERERERERkVpiYYyIiIiIiIiIiNQSC2NERERERERERKSWWBgjIiIiIiIiIiK1xMIYERERERERERGpJRbGiIiIiIiIiIhILbEwRkREREREREREaomFMSIiIiIiIiIiUkv/C08EQE3wzRq6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "prob = books.Category.value_counts(normalize=True)\n",
    "threshold = 0.001471\n",
    "mask = prob > threshold\n",
    "# Filter for values above threshold\n",
    "prob = prob.loc[mask]\n",
    "# Add other bucket\n",
    "tail_prob = prob.loc[~mask].sum()\n",
    "prob['other'] = tail_prob\n",
    "f = plt.figure()\n",
    "f.set_figwidth(15)\n",
    "f.set_figheight(4)\n",
    "prob.plot(kind='bar')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.9 64-bit ('tecton')",
   "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": "3.9.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "02fc557da4a3ce4b9a7967a2b3cdedcfc966d2f035e590f38f27d61f458124be"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}