1. Foundations of NLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\"> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>NLP with PyTorch</h1>\n",
    "<h1>Foundations of NLP</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "import gzip\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import string\n",
    "import nltk\n",
    "from nltk.corpus import stopwords  \n",
    "from nltk.text import TextCollection\n",
    "from nltk.collocations import BigramCollocationFinder\n",
    "from nltk.metrics.association import BigramAssocMeasures\n",
    "\n",
    "import sklearn\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by print out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.7\n",
      "IPython version      : 8.12.3\n",
      "\n",
      "Compiler    : Clang 14.0.6 \n",
      "OS          : Darwin\n",
      "Release     : 24.3.0\n",
      "Machine     : arm64\n",
      "Processor   : arm\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: f27953c25cb37eda249660943e02750136eeb23f\n",
      "\n",
      "nltk      : 3.8.1\n",
      "watermark : 2.4.3\n",
      "matplotlib: 3.8.0\n",
      "pandas    : 2.2.3\n",
      "numpy     : 1.26.4\n",
      "sklearn   : 1.6.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load default figure style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('d4sci.mplstyle')\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One-Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"\"\"\n",
    "    Mary had a little lamb, little lamb,\n",
    "    little lamb. 'Mary' had a little lamb\n",
    "    whose fleece was white as snow.\n",
    "    And everywhere that Mary went\n",
    "    Mary went, MARY went. Everywhere\n",
    "    that mary went,\n",
    "    The lamb was sure to go\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to tokenize the text. NLTK provides us with a convenient tokenizer function we can use. It is also smart enough to be able to handle different languages. We start by making sure that we have the punkt add-on downloaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     /Users/bgoncalves/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then move on to using it to tokenize our text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens = nltk.word_tokenize(text, 'english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Mary',\n",
      " 'had',\n",
      " 'a',\n",
      " 'little',\n",
      " 'lamb',\n",
      " ',',\n",
      " 'little',\n",
      " 'lamb',\n",
      " ',',\n",
      " 'little',\n",
      " 'lamb',\n",
      " '.',\n",
      " \"'Mary\",\n",
      " \"'\",\n",
      " 'had',\n",
      " 'a',\n",
      " 'little',\n",
      " 'lamb',\n",
      " 'whose',\n",
      " 'fleece',\n",
      " 'was',\n",
      " 'white',\n",
      " 'as',\n",
      " 'snow',\n",
      " '.',\n",
      " 'And',\n",
      " 'everywhere',\n",
      " 'that',\n",
      " 'Mary',\n",
      " 'went',\n",
      " 'Mary',\n",
      " 'went',\n",
      " ',',\n",
      " 'MARY',\n",
      " 'went',\n",
      " '.',\n",
      " 'Everywhere',\n",
      " 'that',\n",
      " 'mary',\n",
      " 'went',\n",
      " ',',\n",
      " 'The',\n",
      " 'lamb',\n",
      " 'was',\n",
      " 'sure',\n",
      " 'to',\n",
      " 'go']\n"
     ]
    }
   ],
   "source": [
    "pprint(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll note that NLTK includes apostrophes at the beginning of words and returns all punctuation markings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",\n",
      "'Mary\n",
      "'\n"
     ]
    }
   ],
   "source": [
    "print(tokens[5])\n",
    "print(tokens[12])\n",
    "print(tokens[13])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We wrap it into a utility function to handle these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(text, preserve_case=True):\n",
    "    punctuation = set(string.punctuation)\n",
    "    text_words = []\n",
    "    \n",
    "    for word in nltk.word_tokenize(text, 'english'):\n",
    "        # Remove any punctuation characters present in the beginning of the word\n",
    "        while len(word) > 0 and  word[0] in punctuation:\n",
    "            word = word[1:]\n",
    "\n",
    "        # Remove any punctuation characters present in the end of the word\n",
    "        while len(word) > 0 and word[-1] in punctuation:\n",
    "            word = word[:-1]\n",
    "        \n",
    "        if len(word) > 0:\n",
    "            if preserve_case:\n",
    "                text_words.append(word)\n",
    "            else:\n",
    "                text_words.append(word.lower())\n",
    "        \n",
    "    return text_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_words = tokenize(text, preserve_case=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mary',\n",
       " 'had',\n",
       " 'a',\n",
       " 'little',\n",
       " 'lamb',\n",
       " 'little',\n",
       " 'lamb',\n",
       " 'little',\n",
       " 'lamb',\n",
       " 'mary',\n",
       " 'had',\n",
       " 'a',\n",
       " 'little',\n",
       " 'lamb',\n",
       " 'whose',\n",
       " 'fleece',\n",
       " 'was',\n",
       " 'white',\n",
       " 'as',\n",
       " 'snow',\n",
       " 'and',\n",
       " 'everywhere',\n",
       " 'that',\n",
       " 'mary',\n",
       " 'went',\n",
       " 'mary',\n",
       " 'went',\n",
       " 'mary',\n",
       " 'went',\n",
       " 'everywhere',\n",
       " 'that',\n",
       " 'mary',\n",
       " 'went',\n",
       " 'the',\n",
       " 'lamb',\n",
       " 'was',\n",
       " 'sure',\n",
       " 'to',\n",
       " 'go']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get a quick one-hot encoded version using pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot = pd.get_dummies(text_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which provides us with a DataFrame where each column corresponds to an individual unique word and each row to a word in our text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>a</th>\n",
       "      <th>and</th>\n",
       "      <th>as</th>\n",
       "      <th>everywhere</th>\n",
       "      <th>fleece</th>\n",
       "      <th>go</th>\n",
       "      <th>had</th>\n",
       "      <th>lamb</th>\n",
       "      <th>little</th>\n",
       "      <th>mary</th>\n",
       "      <th>snow</th>\n",
       "      <th>sure</th>\n",
       "      <th>that</th>\n",
       "      <th>the</th>\n",
       "      <th>to</th>\n",
       "      <th>was</th>\n",
       "      <th>went</th>\n",
       "      <th>white</th>\n",
       "      <th>whose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <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>19</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</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>23</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</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>31</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a and as everywhere fleece go had lamb little mary snow sure that the to  \\\n",
       "0                                                    1                         \n",
       "1                                   1                                          \n",
       "2   1                                                                          \n",
       "3                                               1                              \n",
       "4                                        1                                     \n",
       "5                                               1                              \n",
       "6                                        1                                     \n",
       "7                                               1                              \n",
       "8                                        1                                     \n",
       "9                                                    1                         \n",
       "10                                  1                                          \n",
       "11  1                                                                          \n",
       "12                                              1                              \n",
       "13                                       1                                     \n",
       "14                                                                             \n",
       "15                           1                                                 \n",
       "16                                                                             \n",
       "17                                                                             \n",
       "18         1                                                                   \n",
       "19                                                        1                    \n",
       "20      1                                                                      \n",
       "21                    1                                                        \n",
       "22                                                                  1          \n",
       "23                                                   1                         \n",
       "24                                                                             \n",
       "25                                                   1                         \n",
       "26                                                                             \n",
       "27                                                   1                         \n",
       "28                                                                             \n",
       "29                    1                                                        \n",
       "30                                                                  1          \n",
       "31                                                   1                         \n",
       "32                                                                             \n",
       "33                                                                      1      \n",
       "34                                       1                                     \n",
       "35                                                                             \n",
       "36                                                             1               \n",
       "37                                                                         1   \n",
       "38                              1                                              \n",
       "\n",
       "   was went white whose  \n",
       "0                        \n",
       "1                        \n",
       "2                        \n",
       "3                        \n",
       "4                        \n",
       "5                        \n",
       "6                        \n",
       "7                        \n",
       "8                        \n",
       "9                        \n",
       "10                       \n",
       "11                       \n",
       "12                       \n",
       "13                       \n",
       "14                    1  \n",
       "15                       \n",
       "16   1                   \n",
       "17              1        \n",
       "18                       \n",
       "19                       \n",
       "20                       \n",
       "21                       \n",
       "22                       \n",
       "23                       \n",
       "24        1              \n",
       "25                       \n",
       "26        1              \n",
       "27                       \n",
       "28        1              \n",
       "29                       \n",
       "30                       \n",
       "31                       \n",
       "32        1              \n",
       "33                       \n",
       "34                       \n",
       "35   1                   \n",
       "36                       \n",
       "37                       \n",
       "38                       "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = one_hot.astype('str')\n",
    "temp[temp=='False'] = \"\"\n",
    "temp[temp=='True'] = \"1\"\n",
    "temp = pd.DataFrame(temp)\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here can easily generate a mapping between the words and their numerical id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 0,\n",
       " 'and': 1,\n",
       " 'as': 2,\n",
       " 'everywhere': 3,\n",
       " 'fleece': 4,\n",
       " 'go': 5,\n",
       " 'had': 6,\n",
       " 'lamb': 7,\n",
       " 'little': 8,\n",
       " 'mary': 9,\n",
       " 'snow': 10,\n",
       " 'sure': 11,\n",
       " 'that': 12,\n",
       " 'the': 13,\n",
       " 'to': 14,\n",
       " 'was': 15,\n",
       " 'went': 16,\n",
       " 'white': 17,\n",
       " 'whose': 18}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_dict = dict(zip(one_hot.columns, np.arange(one_hot.shape[1])))\n",
    "word_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Allowing us to easily transform from words to ids and back"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bag of Words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the one-hot encoded representation we can easily obtain the bag of words version of our document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>and</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>as</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>everywhere</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fleece</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>had</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>little</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mary</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snow</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sure</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>that</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>the</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>was</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>went</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whose</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count\n",
       "a               2\n",
       "and             1\n",
       "as              1\n",
       "everywhere      2\n",
       "fleece          1\n",
       "go              1\n",
       "had             2\n",
       "lamb            5\n",
       "little          4\n",
       "mary            6\n",
       "snow            1\n",
       "sure            1\n",
       "that            2\n",
       "the             1\n",
       "to              1\n",
       "was             2\n",
       "went            4\n",
       "white           1\n",
       "whose           1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(one_hot.sum(), columns=['Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'mary': 6,\n",
       "         'had': 2,\n",
       "         'a': 2,\n",
       "         'little': 4,\n",
       "         'lamb': 5,\n",
       "         'whose': 1,\n",
       "         'fleece': 1,\n",
       "         'was': 2,\n",
       "         'white': 1,\n",
       "         'as': 1,\n",
       "         'snow': 1,\n",
       "         'and': 1,\n",
       "         'everywhere': 2,\n",
       "         'that': 2,\n",
       "         'went': 4,\n",
       "         'the': 1,\n",
       "         'sure': 1,\n",
       "         'to': 1,\n",
       "         'go': 1})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(text_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more general representation would be to use a dictionary mapping each word to the number of times it occurs. This has the added advantage of being a dense representation that doesn't waste any space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = ['playing', 'loved', 'ran', 'river', 'friendships', \n",
    "         'misunderstanding', 'trouble', 'troubling']\n",
    "\n",
    "stemmers = { \n",
    "    'LancasterStemmer' : nltk.stem.LancasterStemmer(),\n",
    "    'PorterStemmer' : nltk.stem.PorterStemmer(),\n",
    "    'RegexpStemmer' : nltk.stem.RegexpStemmer('ing$|s$|e$|able$'),\n",
    "    'SnowballStemmer' : nltk.stem.SnowballStemmer('english')\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = []\n",
    "\n",
    "for word in words:\n",
    "    row = []\n",
    "    for stemmer in stemmers:\n",
    "        stem = stemmers[stemmer]\n",
    "        row.append(stem.stem(word))\n",
    "        \n",
    "    matrix.append(row)\n",
    "\n",
    "comparison = pd.DataFrame(matrix, index=words, columns=stemmers.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>LancasterStemmer</th>\n",
       "      <th>PorterStemmer</th>\n",
       "      <th>RegexpStemmer</th>\n",
       "      <th>SnowballStemmer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>playing</th>\n",
       "      <td>play</td>\n",
       "      <td>play</td>\n",
       "      <td>play</td>\n",
       "      <td>play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loved</th>\n",
       "      <td>lov</td>\n",
       "      <td>love</td>\n",
       "      <td>loved</td>\n",
       "      <td>love</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ran</th>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>river</th>\n",
       "      <td>riv</td>\n",
       "      <td>river</td>\n",
       "      <td>river</td>\n",
       "      <td>river</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friendships</th>\n",
       "      <td>friend</td>\n",
       "      <td>friendship</td>\n",
       "      <td>friendship</td>\n",
       "      <td>friendship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>misunderstanding</th>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trouble</th>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>troubling</th>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 LancasterStemmer  PorterStemmer  RegexpStemmer  \\\n",
       "playing                      play           play           play   \n",
       "loved                         lov           love          loved   \n",
       "ran                           ran            ran            ran   \n",
       "river                         riv          river          river   \n",
       "friendships                friend     friendship     friendship   \n",
       "misunderstanding    misunderstand  misunderstand  misunderstand   \n",
       "trouble                    troubl         troubl         troubl   \n",
       "troubling                  troubl         troubl         troubl   \n",
       "\n",
       "                 SnowballStemmer  \n",
       "playing                     play  \n",
       "loved                       love  \n",
       "ran                          ran  \n",
       "river                      river  \n",
       "friendships           friendship  \n",
       "misunderstanding   misunderstand  \n",
       "trouble                   troubl  \n",
       "troubling                 troubl  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "wordnet = nltk.stem.WordNetLemmatizer()\n",
    "\n",
    "results_n = [wordnet.lemmatize(word, 'n') for word in words]\n",
    "results_v = [wordnet.lemmatize(word, 'v') for word in words]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparison['WordNetLemmatizer Noun'] = results_n\n",
    "comparison['WordNetLemmatizer Verb'] = results_v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>LancasterStemmer</th>\n",
       "      <th>PorterStemmer</th>\n",
       "      <th>RegexpStemmer</th>\n",
       "      <th>SnowballStemmer</th>\n",
       "      <th>WordNetLemmatizer Noun</th>\n",
       "      <th>WordNetLemmatizer Verb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>playing</th>\n",
       "      <td>play</td>\n",
       "      <td>play</td>\n",
       "      <td>play</td>\n",
       "      <td>play</td>\n",
       "      <td>playing</td>\n",
       "      <td>play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loved</th>\n",
       "      <td>lov</td>\n",
       "      <td>love</td>\n",
       "      <td>loved</td>\n",
       "      <td>love</td>\n",
       "      <td>loved</td>\n",
       "      <td>love</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ran</th>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "      <td>ran</td>\n",
       "      <td>run</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>river</th>\n",
       "      <td>riv</td>\n",
       "      <td>river</td>\n",
       "      <td>river</td>\n",
       "      <td>river</td>\n",
       "      <td>river</td>\n",
       "      <td>river</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friendships</th>\n",
       "      <td>friend</td>\n",
       "      <td>friendship</td>\n",
       "      <td>friendship</td>\n",
       "      <td>friendship</td>\n",
       "      <td>friendship</td>\n",
       "      <td>friendships</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>misunderstanding</th>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstand</td>\n",
       "      <td>misunderstanding</td>\n",
       "      <td>misunderstand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trouble</th>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>trouble</td>\n",
       "      <td>trouble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>troubling</th>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubl</td>\n",
       "      <td>troubling</td>\n",
       "      <td>trouble</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 LancasterStemmer  PorterStemmer  RegexpStemmer  \\\n",
       "playing                      play           play           play   \n",
       "loved                         lov           love          loved   \n",
       "ran                           ran            ran            ran   \n",
       "river                         riv          river          river   \n",
       "friendships                friend     friendship     friendship   \n",
       "misunderstanding    misunderstand  misunderstand  misunderstand   \n",
       "trouble                    troubl         troubl         troubl   \n",
       "troubling                  troubl         troubl         troubl   \n",
       "\n",
       "                 SnowballStemmer WordNetLemmatizer Noun WordNetLemmatizer Verb  \n",
       "playing                     play                playing                   play  \n",
       "loved                       love                  loved                   love  \n",
       "ran                          ran                    ran                    run  \n",
       "river                      river                  river                  river  \n",
       "friendships           friendship             friendship            friendships  \n",
       "misunderstanding   misunderstand       misunderstanding          misunderstand  \n",
       "trouble                   troubl                trouble                trouble  \n",
       "troubling                 troubl              troubling                trouble  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NLTK provides stopwords for 30 different languages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dutch',\n",
       " 'catalan',\n",
       " 'german',\n",
       " 'slovene',\n",
       " 'hinglish',\n",
       " 'hungarian',\n",
       " 'romanian',\n",
       " 'kazakh',\n",
       " 'turkish',\n",
       " 'russian',\n",
       " 'README',\n",
       " 'italian',\n",
       " 'english',\n",
       " 'greek',\n",
       " 'tajik',\n",
       " 'norwegian',\n",
       " 'portuguese',\n",
       " 'hebrew',\n",
       " 'finnish',\n",
       " 'danish',\n",
       " 'french',\n",
       " 'swedish',\n",
       " 'azerbaijani',\n",
       " 'spanish',\n",
       " 'chinese',\n",
       " 'indonesian',\n",
       " 'arabic',\n",
       " 'nepali',\n",
       " 'bengali',\n",
       " 'basque']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('/Users/bgoncalves/nltk_data/corpora/stopwords/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can easily use them to filter out meaningless words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filtered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Original</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mary</th>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>had</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>little</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>lamb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>little</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>lamb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>little</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>lamb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mary</th>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>had</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>little</th>\n",
       "      <td>little</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>lamb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whose</th>\n",
       "      <td>whose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fleece</th>\n",
       "      <td>fleece</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>was</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>as</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snow</th>\n",
       "      <td>snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>And</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>everywhere</th>\n",
       "      <td>everywhere</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>that</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mary</th>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>went</th>\n",
       "      <td>went</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mary</th>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>went</th>\n",
       "      <td>went</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARY</th>\n",
       "      <td>MARY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>went</th>\n",
       "      <td>went</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Everywhere</th>\n",
       "      <td>Everywhere</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>that</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mary</th>\n",
       "      <td>mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>went</th>\n",
       "      <td>went</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lamb</th>\n",
       "      <td>lamb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>was</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sure</th>\n",
       "      <td>sure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to</th>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go</th>\n",
       "      <td>go</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Filtered\n",
       "Original              \n",
       "Mary              Mary\n",
       "had                   \n",
       "a                     \n",
       "little          little\n",
       "lamb              lamb\n",
       "little          little\n",
       "lamb              lamb\n",
       "little          little\n",
       "lamb              lamb\n",
       "Mary              Mary\n",
       "had                   \n",
       "a                     \n",
       "little          little\n",
       "lamb              lamb\n",
       "whose            whose\n",
       "fleece          fleece\n",
       "was                   \n",
       "white            white\n",
       "as                    \n",
       "snow              snow\n",
       "And                   \n",
       "everywhere  everywhere\n",
       "that                  \n",
       "Mary              Mary\n",
       "went              went\n",
       "Mary              Mary\n",
       "went              went\n",
       "MARY              MARY\n",
       "went              went\n",
       "Everywhere  Everywhere\n",
       "that                  \n",
       "mary              mary\n",
       "went              went\n",
       "The                   \n",
       "lamb              lamb\n",
       "was                   \n",
       "sure              sure\n",
       "to                    \n",
       "go                  go"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop_words = set(stopwords.words('english'))  \n",
    "  \n",
    "tokens = tokenize(text)  \n",
    "  \n",
    "filtered_sentence = [word if word.lower() not in stop_words else \"\" for word in tokens]  \n",
    "  \n",
    "pd.DataFrame((zip(tokens, filtered_sentence)), \n",
    "             columns=['Original', 'Filtered']).set_index('Original')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# N-Grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ngrams(text, length):\n",
    "    from nltk.util import ngrams\n",
    "    \n",
    "    n_grams = ngrams(tokenize(text), length)\n",
    "    \n",
    "    return [' '.join(grams) for grams in n_grams]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mary had',\n",
       " 'had a',\n",
       " 'a little',\n",
       " 'little lamb',\n",
       " 'lamb little',\n",
       " 'little lamb',\n",
       " 'lamb little',\n",
       " 'little lamb',\n",
       " 'lamb mary',\n",
       " 'mary had',\n",
       " 'had a',\n",
       " 'a little',\n",
       " 'little lamb',\n",
       " 'lamb whose',\n",
       " 'whose fleece',\n",
       " 'fleece was',\n",
       " 'was white',\n",
       " 'white as',\n",
       " 'as snow',\n",
       " 'snow and',\n",
       " 'and everywhere',\n",
       " 'everywhere that',\n",
       " 'that mary',\n",
       " 'mary went',\n",
       " 'went mary',\n",
       " 'mary went',\n",
       " 'went mary',\n",
       " 'mary went',\n",
       " 'went everywhere',\n",
       " 'everywhere that',\n",
       " 'that mary',\n",
       " 'mary went',\n",
       " 'went the',\n",
       " 'the lamb',\n",
       " 'lamb was',\n",
       " 'was sure',\n",
       " 'sure to',\n",
       " 'to go']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_ngrams(text.lower(), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collocations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigrams = BigramCollocationFinder.from_words(tokenize(text, False))\n",
    "scored = bigrams.score_ngrams(BigramAssocMeasures.likelihood_ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('little', 'lamb'), 20.789064969512467),\n",
       " (('mary', 'went'), 18.154919185356587),\n",
       " (('everywhere', 'that'), 15.777294140200038),\n",
       " (('had', 'a'), 15.777294140200038),\n",
       " (('a', 'little'), 10.232116695720478),\n",
       " (('as', 'snow'), 9.301260258907092),\n",
       " (('snow', 'and'), 9.301260258907092),\n",
       " (('sure', 'to'), 9.301260258907092),\n",
       " (('to', 'go'), 9.301260258907092),\n",
       " (('white', 'as'), 9.301260258907092),\n",
       " (('whose', 'fleece'), 9.301260258907092),\n",
       " (('mary', 'had'), 8.139124120662284),\n",
       " (('that', 'mary'), 8.139124120662284),\n",
       " (('and', 'everywhere'), 6.528671536667327),\n",
       " (('fleece', 'was'), 6.528671536667327),\n",
       " (('was', 'sure'), 6.528671536667327),\n",
       " (('was', 'white'), 6.528671536667327),\n",
       " (('went', 'the'), 4.802579101956642),\n",
       " (('lamb', 'whose'), 4.297236023525216),\n",
       " (('the', 'lamb'), 4.297236023525216),\n",
       " (('lamb', 'little'), 3.850143362325084),\n",
       " (('went', 'mary'), 3.065215528887738),\n",
       " (('went', 'everywhere'), 2.196764352889584),\n",
       " (('lamb', 'was'), 1.7502532877068806),\n",
       " (('lamb', 'mary'), 0.08819306486371048)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scored"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TF-IDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NLTK has the TextCollection object that allows us to easily compute tf-idf scores from a given corpus. We generate a small corpus by splitting our text by sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = text.split('.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 4 documents in our corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NLTK expects the corpus to be tokenized so we do that now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = [tokenize(doc, preserve_case=False) for doc in corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['mary', 'had', 'a', 'little', 'lamb', 'little', 'lamb', 'little', 'lamb'],\n",
       " ['mary',\n",
       "  'had',\n",
       "  'a',\n",
       "  'little',\n",
       "  'lamb',\n",
       "  'whose',\n",
       "  'fleece',\n",
       "  'was',\n",
       "  'white',\n",
       "  'as',\n",
       "  'snow'],\n",
       " ['and', 'everywhere', 'that', 'mary', 'went', 'mary', 'went', 'mary', 'went'],\n",
       " ['everywhere',\n",
       "  'that',\n",
       "  'mary',\n",
       "  'went',\n",
       "  'the',\n",
       "  'lamb',\n",
       "  'was',\n",
       "  'sure',\n",
       "  'to',\n",
       "  'go']]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We initialize the TextCollection object with our corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = TextCollection(corpus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This object provides us with a great deal of functionality, like total frequency counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAADmQAAAm0CAYAAAClMueuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAC4jAAAuIwF4pT92AAEAAElEQVR4nOzdebRdBX0v8N8+596bOSSBAIFARkhCyISUwUIRQUVEmRQEBWKX79nS1dW+Plefz+VzaGuH91zL1S5XLXQwgECxCKKllgISpYqUIXNCSEjCmDAkZLgZ7rDPfn9Ybank7HOTe/Y9+97PZy3X0uR37v7G9SP7/PPll2RZlgUAAAAAAAAAAAAAAAAAAAdVGegAAAAAAAAAAAAAAAAAAACtTiETAAAAAAAAAAAAAAAAACCHQiYAAAAAAAAAAAAAAAAAQA6FTAAAAAAAAAAAAAAAAACAHAqZAAAAAAAAAAAAAAAAAAA5FDIBAAAAAAAAAAAAAAAAAHIoZAIAAAAAAAAAAAAAAAAA5FDIBAAAAAAAAAAAAAAAAADIoZAJAAAAAAAAAAAAAAAAAJBDIRMAAAAAAAAAAAAAAAAAIIdCJgAAAAAAAAAAAAAAAABADoVMAAAAAAAAAAAAAAAAAIAcCpkAAAAAAAAAAAAAAAAAADkUMgEAAAAAAAAAAAAAAAAAcihkAgAAAAAAAAAAAAAAAADkUMgEAAAAAAAAAAAAAAAAAMihkAkAAAAAAAAAAAAAAAAAkEMhEwAAAAAAAAAAAAAAAAAgh0ImAAAAAAAAAAAAAAAAAEAOhUwAAAAAAAAAAAAAAAAAgBwKmQAAAAAAAAAAAAAAAAAAORQyAQAAAAAAAAAAAAAAAAByKGQCAAAAAAAAAAAAAAAAAORoG+gAQGP27NkTtVqt4fkkSaJS0bkGAAAAAAAAAAAAAAAAyqlWq0WWZQ3PVyqVGDNmTNPyKGRCSfSljBkRkWVZpGnapDQAAAAAAAAAAAAAAAAAraWvHay+cj4PAAAAAAAAAAAAAAAAACCHQiYAAAAAAAAAAAAAAAAAQA6FTAAAAAAAAAAAAAAAAACAHAqZAAAAAAAAAAAAAAAAAAA5FDIBAAAAAAAAAAAAAAAAAHIoZAIAAAAAAAAAAAAAAAAA5FDIBAAAAAAAAAAAAAAAAADIoZAJAAAAAAAAAAAAAAAAAJBDIRMAAAAAAAAAAAAAAAAAIIdCJgAAAAAAAAAAAAAAAABAjraBDgA0JkmSyLKsT5+pVqtNSsNQkWVZ7N69+y2/Nnbs2EiSZIASwcHZV8rGzlIm9pWysbOUiX2lTOwrZWNnKRP7SpnYV8rGzlIm9pWysbOUiX2lTOwrZWNnKRP7SpnYV8rGztJMaZr2ab7Ze6eQCSVRqVT69BdItVqN0aNHNzERQ0F3d3ds3LjxLb929tlnR0dHxwAlgoOzr5SNnaVM7CtlY2cpE/tKmdhXysbOUib2lTKxr5SNnaVM7CtlY2cpE/tKmdhXysbOUib2lTKxr5SNnaWZOjs7+9SpqlQqTUwT0dyfDgAAAAAAAAAAAAAAAAAwCChkAgAAAAAAAAAAAAAAAADkUMgEAAAAAAAAAAAAAAAAAMihkAkAAAAAAAAAAAAAAAAAkEMhEwAAAAAAAAAAAAAAAAAgh0ImAAAAAAAAAAAAAAAAAEAOhUwAAAAAAAAAAAAAAAAAgBwKmQAAAAAAAAAAAAAAAAAAORQyAQAAAAAAAAAAAAAAAAByKGQCAAAAAAAAAAAAAAAAAORQyAQAAAAAAAAAAAAAAAAAyKGQCQAAAAAAAAAAAAAAAACQQyETAAAAAAAAAAAAAAAAACCHQiYAAAAAAAAAAAAAAAAAQA6FTAAAAAAAAAAAAAAAAACAHAqZAAAAAAAAAAAAAAAAAAA5FDIBAAAAAAAAAAAAAAAAAHIoZAIAAAAAAAAAAAAAAAAA5FDIBAAAAAAAAAAAAAAAAADIoZAJAAAAAAAAAAAAAAAAAJBDIRMAAAAAAAAAAAAAAAAAIIdCJgAAAAAAAAAAAAAAAABADoVMAAAAAAAAAAAAAAAAAIAcCpkAAAAAAAAAAAAAAAAAADkUMgEAAAAAAAAAAAAAAAAAcihkAgAAAAAAAAAAAAAAAADkUMgEAAAAAAAAAAAAAAAAAMihkAkAAAAAAAAAAAAAAAAAkEMhEwAAAAAAAAAAAAAAAAAgh0ImAAAAAAAAAAAAAAAAAEAOhUwAAAAAAAAAAAAAAAAAgBwKmQAAAAAAAAAAAAAAAAAAORQyAQAAAAAAAAAAAAAAAABytA10ABho27dvjx/+8IexevXqWL9+fWzYsCFef/316OzsjM7Ozujt7Y3Ro0fHmDFjYvTo0TF58uSYOXNmnHTSSXHyySfHr/zKr8TRRx890H8MAAAAAAAAAAAAAAAAAJpIIZMhaePGjbFkyZL4/ve/H8uWLYssy+rO79y5M3bu3BkREevWrYsHH3zwLb8/c+bMeOc73xnnnntuXHzxxXHcccc1KzoAAAAAAAAAAAAAAAAAA0AhkyHlvvvuiz//8z+PpUuX5pYw+2Ljxo2xcePGuPXWWyNJkjj99NPjQx/6UFx77bUxffr0fnsOAAAAAAAAAAAAAAAAAANDIZMh4dFHH43/9b/+Vzz22GNNf1aWZfHEE0/EE088EZ2dnfGnf/qnTX8mAAAAAAAAAAAAAAAAAM2lkMmgtnfv3vgf/+N/xF//9V8PdBQAAAAAAAAAAAAAAAAASkwhk0HrqaeeimuuuSY2bNgw0FEAAAAAAAAAAAAAAAAAKDmFTAal+++/P6666qrYt2/fQEcBAAAAAAAAAAAAAAAAYBCoDHQA6G9/93d/F5deeqkyJgAAAAAAAAAAAAAAAAD9xoVMBpW77rorPvnJT0aWZX36XEdHR5x55plx3nnnxfz582PGjBkxefLkGDVqVAwfPjz27NkTu3btip07d8a2bdti5cqVsWLFilixYkWsW7cu0jRt0p8IAAAAAAAAAAAAAAAAgFagkMmg8cgjj8T111/fpzLm3Llz47d+67fi6quvjgkTJhx0bty4cTFu3LiYMmVKLFiwIN73vvf94vd27NgR3//+9+P++++PBx54IHbs2HFYfw4AAAAAAAAAAAAAAAAAWk9loANAf9i8eXNcfvnl0d3d3dD88ccfH3feeWesXLkyfvM3f7NuGTPPhAkT4mMf+1jccccd8eqrr8Y999wT733veyNJkkP+mQAAAAAAAAAAAAAAAAC0FhcyKb3e3t645pprYteuXQ3Nf/SjH42vf/3rMW7cuH7P0tbWFpdffnlcfvnlsWnTpti6dWu/PwMAAAAAAAAAAAAAAACA4ilkUnqf//zn4/HHH2949ktf+lKTE/3M9OnTY/r06YU8CwAAAAAAAAAAAAAAAIDmUsik1FavXh3/9//+34Zmv/SlL8XnP//5JicCAAAAAAAAAAAAAAAAYDCqDHQAOBy/+7u/G2ma5s4tXrxYGRMAAAAAAAAAAAAAAACAQ6aQSWndd9998fDDD+fOnXzyyfG1r32tgEQAAAAAAAAAAAAAAAAADFYKmZTWH/7hHzY097d/+7cxatSoJqcBAAAAAAAAAAAAAAAAYDBTyKSUfvCDH8RTTz2VO/fhD384zjnnnAISAQAAAAAAAAAAAAAAADCYKWRSSl/5yldyZ5IkiT/4gz8oIA0AAAAAAAAAAAAAAAAAg51CJqXz8ssvxwMPPJA7d9FFF8WcOXMKSAQAAAAAAAAAAAAAAADAYKeQSencddddUavVcuduvPHGAtIAAAAAAAAAAAAAAAAAMBQoZFI6d955Z+7M+PHj433ve18BaQAAAAAAAAAAAAAAAAAYChQyKZWXXnopnnzyydy5yy67LNrb2wtIBAAAAAAAAAAAAAAAAMBQoJBJqTzyyCMNzbmOCQAAAAAAAAAAAAAAAEB/UsikVBopZCZJEu9+97sLSAMAAAAAAAAAAAAAAADAUNE20AGgL5YuXZo7M2vWrJg4cWJDP2/Xrl2xevXqWL16dbz00kuxe/fu2Lt3b3R0dMTIkSNjwoQJMWXKlJg6dWosWLAgRo8efZh/AgAAAAAAAAAAAAAAAADKSCGT0ti1a1ds3rw5d27RokV1f3/btm1x6623xve+97147LHHIk3Thp5frVZj7ty5ccEFF8SVV14ZZ599dlQqjswCAAAAAAAAAAAAAAAADAUKmZTG6tWrG5o7WCHz2WefjT/6oz+Ku+66K7q7u/v8/DRNY+XKlbFy5cr46le/GlOmTInf+Z3fiU9+8pMxZsyYPv88AAAAAAAAAAAAAAAAAMrDeT9Ko9FC5qxZs97yv/ft2xef+cxnYt68eXHbbbcdUhnz7Tz//PPxe7/3ezF9+vS4+eabo1ar9cvPBQAAAAAAAAAAAAAAAKD1KGRSGuvXr29obvr06b/47xs2bIizzjor/uzP/qzfipj/1RtvvBGf+tSn4pxzzokXXnihKc8AAAAAAAAAAAAAAAAAYGApZFIaL7/8ckNz06ZNi4iIH/3oR3H66afHqlWrmhnrFx577LFYtGhRPPTQQ4U8DwAAAAAAAAAAAAAAAIDiKGRSGlu3bs2dGTNmTIwaNSoeffTRuPjii2P37t0FJPsPO3bsiEsuuSTuvffeQp8LAAAAAAAAAAAAAAAAQHMpZFIajRQyJ06cGBs2bIgPfvCDsXfv3gJS/bKurq646qqr4l/+5V8G5PkAAAAAAAAAAAAAAAAA9L+2gQ4AjXr99ddzZ0aMGBFXXHFF7Nq1K3f2iCOOiMsuuyzOP//8mDdvXkyZMiXGjh0btVot9uzZE1u2bIkVK1bED37wg/jud78bnZ2dDWft7e2Nj3zkI/GTn/wk5s6d2/Dn+lOWZdHd3T0gz2bw6Onpedtfe2VPxHFjBiAQ1HGwfYVWZWcpE/tK2dhZysS+Uib2lbKxs5SJfaVM7CtlY2cpE/tK2dhZysS+Uib2lbKxs5SJfaVM7CtlY2dppizLBjrCWyRZqyWCgxg2bFi/FAwnTZoUn/vc5+LXf/3XY/jw4Q19Zu/evXHTTTfFH//xH8f27dsbftapp54aTz75ZAwbNuxQ4/5CZ2dnpGnap/mNGzce9nPhP9veMyIe3TMlXuw6Ij4+cUWMbzsw0JEAAAAAAAAAAAAAAAAYpGbOnBmjR49ueL5arfZpvq8qTfvJ0I/669rjNddcE2vXro0bb7yx4TJmRMSoUaPi937v92Lt2rVxySWXNPy51atXxxe+8IVDiQotZV/aFj/YNS1uf2NBPN81PmpRiX/dPWWgYwEAAAAAAAAAAAAAAEBhFDIphQMHDv8K3xe+8IW44447Yty4cYf8M44++ui477774rd/+7cb/sxXv/rV2Lx58yE/EwZSb5bEk53HxS2vL4pV+46NLJJf/N6mrgnxYtfYAUwHAAAAAAAAAAAAAAAAxVHIpBR6enoO6/P/83/+z/jiF7/YL1kqlUr8xV/8RSxevLih+e7ublcyKaUXu8bGba8vjB/vmRLdWdvbzjy6e2rUsoKDAQAAAAAAAAAAAAAAwABIsixTpaHl7d27N0aPHn1In120aFH827/9W7S1vX2h7HAyLViwIJ577rnc2fb29njhhRfi2GOPPeTndXZ2RpqmDc9XKpUYNmzYIT8PNr8Z8dlHsrdcxXw7v/GOiHdNKSgU1NHT0xNPPvnkW37t9NNPj/b29gFKBPXZWcrEvlI2dpYysa+UiX2lbOwsZWJfKRP7StnYWcrEvlI2dpYysa+UiX2lbOwsZWJfKRP7StnYWZqpq6srarVaw/PVavWQe2iN6N+GGjRJR0fHIX/ulltu6fcyZkTEqFGjYsmSJXHeeefl/kPd09MTf/M3fxOf+9zn+j3HwSRJcsj/v0FExKxjIs49sTt+9EL9ubvWRPzatPYY3la/uAkDob293d+FlIqdpUzsK2VjZykT+0qZ2FfKxs5SJvaVMrGvlI2dpUzsK2VjZykT+0qZ2FfKxs5SJvaVMrGvlI2dpb90d3cPdIS3qAx0AGhEe3t7JEnfy17/7b/9t5g3b14TEv3MOeecE1dddVVDs3feeWfTckCzfHRuRFvUv8z65oGI76xr/HorAAAAAAAAAAAAAAAAlJFCJqVxKK34G2+8sQlJDu0Za9eujc2bNzc5DfSvCSMi3jH6ldy57zxTi+37sgISAQAAAAAAAAAAAAAAwMBQyKQ0Ro8e3af58847L0455ZQmpfkP5557bsyfP7+h2YceeqjJaaD/vWPUKzGqUv+8c3caccdKVzIBAAAAAAAAAAAAAAAYvBQyKY2jjjqqT/NXXHFFk5Ic+rOeeOKJJieB/tdeqcXZY17InVu6pRbP7agVkAgAAAAAAAAAAAAAAACKp5BJaRx55JF9mj/rrLOalOSXnXnmmQ3NPf30001OAs0xZ8TrMbFtb92ZLCJuWZ5GlmXFhAIAAAAAAAAAAAAAAIACKWRSGn25kDls2LBYuHBh88L8F2eccUYkSZI799xzzxWQBvpfJYk4d+yW3LnVr2XxxMsKmQAAAAAAAAAAAAAAAAw+CpmUxnHHHdfw7Jw5c6Kjo6OJad5qwoQJceKJJ+bO7dy5Mzo7OwtIBP3vhGG7Y/qwHblzt6zojZ5UKRMAAAAAAAAAAAAAAIDBRSGT0pg2bVrDs325ptlfjjzyyIbmtm7d2uQk0DznjH0+qjnHYLfuiXhgY62YQAAAAAAAAAAAAAAAAFAQhUxKY/r06Q3Pjhs3rnlBDmL8+PENze3du7fJSaB5xrcdiPc08I/it9aksafLlUwAAAAAAAAAAAAAAAAGD4VMSmPGjBkNzzZajuxPjT5z//79TU4CzXXl7IhR7fVnOrsj/mFNWkwgAAAAAAAAAAAAAAAAKIBCJqUxa9asqFarDc22t+e0xZqg0WemqZIa5TZmWMRH5ub/s/jPG2vxyh5XMgEAAAAAAAAAAAAAABgcFDIpjZEjR8acOXMamt25c2dzwxzGM0eOHNncIFCA959UiWNH15/prUXctqK3mEAAAAAAAAAAAAAAAADQZAqZlMrpp5/e0Nybb77Z5CSH/swRI0Y0OQk0X3s1iesXtOXOPf5SFmteqxWQCAAAAAAAAAAAAAAAAJpLIZNSOeOMMxqaG4gLmY0WMo855pgmJ4FinDk5iVMmJrlz31iWRi3LCkgEAAAAAAAAAAAAAAAAzaOQSalceOGFDc09++yzTU7yVj09PbFly5bcudGjR8eECROaHwgKkCRJLF5UzZ3b9GYWP9ziSiYAAAAAAAAAAAAAAADlppBJqZx00kkxffr03Lnt27fHxo0bC0j0M8uWLYuurq7cuWnTphWQBoozc0Ilzpua/yq5fWUaB3pdyQQAAAAAAAAAAAAAAKC8FDIpnYsuuqihuccff7zJSfr+rNNOO63JSaB4H5tXjY6cQ5k79kd89xlXMgEAAAAAAAAAAAAAACgvhUxK58orr2xo7qGHHmpykr4/64wzzmhyEijeUaOSuHR2/uvk3nVp7NjvSiYAAAAAAAAAAAAAAADlpJBJ6Zx//vkxZcqU3Lm77rordu7c2fQ8L774Ytx///0NzV544YVNTgMD47LZ1Rg3vP5MVxpx58q0mEAAAAAAAAAAAAAAAADQzxQyKZ0kSeL666/Pndu/f38sWbKk6XluuummSNP8ktns2bPj5JNPbnoeGAgj2pO4dl41d+4Hm2ux+c1aAYkAAAAAAAAAAAAAAACgfylkUkqf+tSnoqOjI3fuq1/9anR2djYtx+uvvx433XRTQ7NXX31103JAKzh/WiWmjEvqzmQRsWRZGlmWFRMKAAAAAAAAAAAAAAAA+olCJqV0/PHHxw033JA798ILL8SnP/3ppuW48cYb44033sida29vj0996lNNywGtoFpJYvHC/CuZq17L4slXFDIBAAAAAAAAAAAAAAAoF4VMSuszn/lMtLW15c7ddNNN8cADD/T782+//fa4++67G5r96Ec/GpMmTer3DNBqFhxbidOPq38lMyLiluW90VtTygQAAAAAAAAAAAAAAKA8FDIprenTp8fv/M7vNDR71VVXxVNPPdVvz166dGl88pOfbGh22LBh8Yd/+If99mxoddcvbItKTifzlT0RD2ysFRMIAAAAAAAAAAAAAAAA+oFCJqX2pS99KU488cTcud27d8f5558f999//2E/884774z3v//9ceDAgYbmP/3pT8eUKVMO+7lQFpPHJvG+mfmvl2+tTqOz25VMAAAAAAAAAAAAAAAAykEhk1IbNWpU3HTTTZEkOef4ImLPnj3xwQ9+MH7jN34jXnvttT4/6+WXX46Pf/zjce211zZcxly4cGF8/vOf7/OzoOyuPrUaI9vrz+zpjrh7TVpMIAAAAAAAAAAAAAAAADhMCpmU3kUXXRSf+9znGprNsixuuummmD59enzqU5+KpUuXRldX10Hn9+/fHw8++GAsXrw4ZsyYEbfffnvDucaOHRt33HFHdHR0NPwZGCzGDkviw3OruXP/tKEW2/a4kgkAAAAAAAAAAAAAAEDraxvoANAfvvjFL8bTTz8d999/f0Pze/fujZtvvjluvvnmGD58eMyaNSumTJkSY8aMiSzLYvfu3bFly5Z49tlno7u7u8952tvb45577ok5c+b0+bMwWHzgpEo8sCGNV/cefKa3FnHrit74/XNyzmkCAAAAAAAAAAAAAADAAFPIZFCoVCpx9913x4c+9KF48MEH+/TZAwcOxIoVK2LFihX9kqWjoyPuuOOOuOCCC/rl50FZtVeTuG5hW3zlx7115376UhZrX6vFKUc72gwAAAAAAAAAAAAAAEDr0n5h0Bg+fHh897vfjUsuuWTAMowZMybuv//+uPLKKwcsA7SSsycnMfuoJHfuG8vTqGVZAYkAAAAAAAAAAAAAAADg0ChkMqj8vJT5xS9+MSqVYtf7tNNOi6effjouvPDCQp8LrSxJkvjEomru3HM7snj0+VoBiQAAAAAAAAAAAAAAAODQKGQy6CRJEl/4whfi4Ycfjrlz5zb9eSNHjow/+IM/iMceeyxmzpzZ9OdB2Zx0ZCV+bUr+6+abK9Lo6nUlEwAAAAAAAAAAAAAAgNakkMmg9a53vSuWL18eX/va1+LEE0/s958/cuTI+M3f/M1Yv359/J//83+io6Oj358Bg8XH5lejI+dQ5vb9Ed9b70omAAAAAAAAAAAAAAAArUkhk0Gtra0tfuu3fis2bdoU9957b1xyySUxcuTIQ/55lUolzj333PiLv/iLePHFF+Mv//IvY/Lkyf2YGAaniaOS+OCs/FfOPevSeHO/K5kAAAAAAAAAAAAAAAC0nraBDgBFqFarcdlll8Vll10WXV1d8a//+q/x+OOPx7p162L9+vWxbdu26OzsjM7OzsiyLEaOHBmjR4+O448/PqZOnRqzZ8+OM888M84+++yYMGHCQP9xoJSumFONh56rxa6ug88c6I24c1UaN57h9QQAAAAAAAAAAAAAAEBr0XhhyBk2bFhccMEFccEFFwx0FBhSRrQnce38anz9ibTu3MObavH+k2oxbbwjzgAAAAAAAAAAAAAAALQObRcACvPuaZWYckRSdyaLiFuWp5FlWTGhAAAAAAAAAAAAAAAAoAEKmQAUplpJ4oZF1dy5la9m8dRWhUwAAAAAAAAAAAAAAABah0ImAIVaeGwlTptU/0pmRMQty3ujt6aUCQAAAAAAAAAAAAAAQGtQyASgcDcsbItKTifz5d0RDz5XKyYQAAAAAAAAAAAAAAAA5FDIBKBwJxyRxHtm5L+C7lqdxt5uVzIBAAAAAAAAAAAAAAAYeAqZAAyIq0+txsj2+jO7uyK+vTYtJhAAAAAAAAAAAAAAAADUoZAJwIAYNzyJK0+p5s7947O12NbpSiYAAAAAAAAAAAAAAAADSyETgAHzgZMrcfSo+jO9tYhvrnAlEwAAAAAAAAAAAAAAgIGlkAnAgOmoJnHdgvwrmT95sRbrXq8VkAgAAAAAAAAAAAAAAADenkImAAPqnSdUYtaRSe7ckmVp1LKsgEQAAAAAAAAAAAAAAADwyxQyARhQSZLE4kX5VzI37Mjixy+4kgkAAAAAAAAAAAAAAMDAUMgEYMDNOqoS55yY/0q6bUUaXb2uZAIAAAAAAAAAAAAAAFA8hUwAWsLH51ejPeet9Ma+iH981pVMAAAAAAAAAAAAAAAAiqeQCUBLOHp0Eh+clf9a+vbaNHYecCUTAAAAAAAAAAAAAACAYilkAtAyrjilGmOH1Z850Btx56q0mEAAAAAAAAAAAAAAAADw7xQyAWgZI9uTuGZeNXfu4U21eH5nrYBEAAAAAAAAAAAAAAAA8DMKmQC0lAunV+KEI5K6M7Us4pblrmQCAAAAAAAAAAAAAABQHIVMAFpKtZLE4oX5VzKXb8vi6a2uZAIAAAAAAAAAAAAAAFAMhUwAWs6iSZVYeGz9K5kREbcsSyOtZQUkAgAAAAAAAAAAAAAAYKhTyASgJd2wsBqVnE7mi7uzeGiTK5kAAAAAAAAAAAAAAAA0n0ImAC1pyrhKXDg9/zV156o09na7kgkAAAAAAAAAAAAAAEBzKWQC0LI+Oq8aI9rqz+zuirhnXVpMIAAAAAAAAAAAAAAAAIYshUwAWta44UlceUo1d+5762vxaqcrmQAAAAAAAAAAAAAAADSPQiYALe2SWZWYOLL+TG8t4psrXckEAAAAAAAAAAAAAACgeRQyAWhpHdUkrluQfyXzxy/UYv0btQISAQAAAAAAAAAAAAAAMBQpZALQ8n71xEqcdGSSO/eNZWlkWVZAIgAAAAAAAAAAAAAAAIYahUwAWl6SJPGJRflXMp/dnsWPX3QlEwAAAAAAAAAAAAAAgP6nkAlAKcw+qhLvPCH/tXXb8jS6U1cyAQAAAAAAAAAAAAAA6F8KmQCUxnULqtGW8+Z6fV/EPz7rSiYAAAAAAAAAAAAAAAD9SyETgNI4ZnQSl5yc/+r69po0dh5wJRMAAAAAAAAAAAAAAID+o5AJQKlceUo1xg6rP7O/N+Ku1WkxgQAAAAAAAAAAAAAAABgSFDIBKJVRHUl89NRq7tyDz9XihV21AhIBAAAAAAAAAAAAAAAwFChkAlA675lRiclj68/Usohbl7uSCQAAAAAAAAAAAAAAQP9QyASgdKqVJG5Y2JY79/TWLJZtdSUTAAAAAAAAAAAAAACAw6eQCUApnTYpiQXHJLlzS5ankdayAhIBAAAAAAAAAAAAAAAwmClkAlBKSZLE4kXVqOR0Ml/clcXDm1zJBAAAAAAAAAAAAAAA4PAoZAJQWlPGVeKCafmvsjtXpbGvx5VMAAAAAAAAAAAAAAAADp1CJgCl9tF51RjeVn9mV1fEPWvTYgIBAAAAAAAAAAAAAAAwKClkAlBq40ckccWcau7c99bX4rW9rmQCAAAAAAAAAAAAAABwaBQyASi9D86qxFEj68/01CJuX+lKJgAAAAAAAAAAAAAAAIdGIROA0hvWlsTH5udfyXz0+Vo8u71WQCIAAAAAAAAAAAAAAAAGG4VMAAaFc6dUYuaEJHfuG8vSyLKsgEQAAAAAAAAAAAAAAAAMJgqZAAwKlSSJTyzKv5K5/o0sHntRIRMAAAAAAAAAAAAAAIC+UcgEYNCYM7ESZ5+QfyXzthW90Z0qZQIAAAAAAAAAAAAAANA4hUwABpXr5rdFW87b7dW9Ef/0bK2YQAAAAAAAAAAAAAAAAAwKCpkADCrHjkniAyfnv97uXpvGrgOuZAIAAAAAAAAAAAAAANAYhUwABp0Pn1KNMR31Z/b1RHxrTVpMIAAAAAAAAAAAAAAAAEpPIROAQWdURxJXn1rNnXtgYy1e2uVKJgAAAAAAAAAAAAAAAPkUMgEYlN47sxLHj6k/U8siblnRW0wgAAAAAAAAAAAAAAAASk0hE4BBqa2SxA0L23Lnnnoli+XbagUkAgAAAAAAAAAAAAAAoMwUMgEYtN5xXBLzj0ly525ZlkZaywpIBAAAAAAAAAAAAAAAQFkpZAIwaCVJEjcsrEZeJfP5XVn8YLMrmQAAAAAAAAAAAAAAABycQiYAg9q08ZV49/T8192dq9LY3+NKJgAAAAAAAAAAAAAAAG9PIROAQe+aedUY3lZ/ZueBiHvXpcUEAgAAAAAAAAAAAAAAoHQUMgEY9CaMSOKy2dXcue+ur8Ube13JBAAAAAAAAAAAAAAA4JcpZAIwJFw6uxITRtSf6U4jbl/pSiYAAAAAAAAAAAAAAAC/TCETgCFhWFsSH5+ffyXzh8/XYsP2WgGJAAAAAAAAAAAAAAAAKBOFTACGjF+bWokZ45PcuSXL0siyrIBEAAAAAAAAAAAAAAAAlIVCJgBDRiVJYvGi/CuZ697I4qcvKWQCAAAAAAAAAAAAAADwHxQyARhS5h5dibMm51/JvG1Fb/SkSpkAAAAAAAAAAAAAAAD8jEImAEPOdQvaoi3nDbitM+L7G2rFBAIAAAAAAAAAAAAAAKDlKWQCMORMGpPE+0/KfwV+a00au7tcyQQAAAAAAAAAAAAAAEAhE4Ah6iNzqzG6o/7Mvp6Ib61OiwkEAAAAAAAAAAAAAABAS1PIBGBIGt2RxNWnVnPn/nljLV7a7UomAAAAAAAAAAAAAADAUKeQCcCQ9b6ZlThuTP2ZWhZx6/LeYgIBAAAAAAAAAAAAAADQshQyARiy2ipJXL+wLXfuyVeyWLmtVkAiAAAAAAAAAAAAAAAAWpVCJgBD2q8cl8SpRye5c0uWp5HWsgISAQAAAAAAAAAAAAAA0IoUMgEY0pIkicWLqpFXydyyM4ulW1zJBAAAAAAAAAAAAAAAGKoUMgEY8qaPr8T50/JfiXesTGN/jyuZAAAAAAAAAAAAAAAAQ5FCJgBExLXzqzGsWn/mzQMR9z2TFhMIAAAAAAAAAAAAAACAlqKQCQARMWFEEpfNyWlkRsR3nqnFG/tcyQQAAAAAAAAAAAAAABhqFDIB4N9dOrsSE0bUn+lOI+5Y6UomAAAAAAAAAAAAAADAUKOQCQD/bnhbEh+bn38lc+mWWmzcUSsgEQAAAAAAAAAAAAAAAK1CIRMA/pPzplZi+vgkd27JsjSyLCsgEQAAAAAAAAAAAAAAAK1AIRMA/pNKksTihflXMte+nsW/vayQCQAAAAAAAAAAAAAAMFQoZALAf3HqMZU44/j8K5m3Lu+NnlQpEwAAAAAAAAAAAAAAYChQyASAt3H9grao5nQyt3ZG/PPGWjGBAAAAAAAAAAAAAAAAGFAKmQDwNo4bm8RFJ+W/Jv9hTRp7ulzJBAAAAAAAAAAAAAAAGOwUMgHgIK6aW43RHfVnOrsjvrUmLSYQAAAAAAAAAAAAAAAAA0YhEwAOYsywJD4yt5o7988bavHKblcyAQAAAAAAAAAAAAAABjOFTACo46KZlZg0uv5MmkXcuqK3mEAAAAAAAAAAAAAAAAAMCIVMAKijvZrE9Qvbcuf+7eUsVr9aKyARAAAAAAAAAAAAAAAAA0EhEwBynHF8EnMnJrlz31ieRi3LCkgEAAAAAAAAAAAAAABA0RQyASBHkiSxeFE1d27zm1n8cIsrmQAAAAAAAAAAAAAAAIORQiYANGDGhEq8a2r+a/P2lWkc6HUlEwAAAAAAAAAAAAAAYLBRyASABl07vxodOYcyd+yPuO8ZVzIBAAAAAAAAAAAAAAAGG4VMAGjQUSOTuGx2/qvzO+vS2L7PlUwAAAAAAAAAAAAAAIDBRCETAPrg0tnVGD+8/kxXGnHHqrSYQAAAAAAAAAAAAAAAABRCIRMA+mBEexLXzq/mzi3dXItNb9YKSAQAAAAAAAAAAAAAAEARFDIBoI/eNbUS08YldWeyiFiyLI0sy4oJBQAAAAAAAAAAAAAAQFMpZAJAH1UrSSxelH8lc/VrWTzxikImAAAAAAAAAAAAAADAYKCQCQCHYN4xlTj9uPpXMiMiblneGz2pUiYAAAAAAAAAAAAAAEDZKWQCwCG6YWFbVHM6mVv3RPzLc7ViAgEAAAAAAAAAAAAAANA0CpkAcIiOH5vE+2bmv0rvWp3Gni5XMgEAAAAAAAAAAAAAAMpMIRMADsPVp1ZjVHv9mc7uiLvXpsUEAgAAAAAAAAAAAAAAoCkUMgHgMIwZlsRH5lZz576/oRZb97iSCQAAAAAAAAAAAAAAUFYKmQBwmN5/UiWOHV1/prcWcduK3mICAQAAAAAAAAAAAAAA0O8UMgHgMLVXk7h+QVvu3E9fymLNa7UCEgEAAAAAAAAAAAAAANDfFDIBoB+cOTmJUyYmuXNLlqVRy7ICEgEAAAAAAAAAAAAAANCfFDIBoB8kSRI3LKzmzj33ZhY/2uJKJgAAAAAAAAAAAAAAQNkoZAJAPznpyEqcNyX/1frNlWl09bqSCQAAAAAAAAAAAAAAUCYKmQDQjz42vxodOYcyd+yPuO8ZVzIBAAAAAAAAAAAAAADKRCETAPrRUaOS+NCs/Nfrd55JY8d+VzIBAAAAAAAAAAAAAADKQiETAPrZ5XOqMW54/ZkDvRF3rkqLCQQAAAAAAAAAAAAAAMBhU8gEgH42oj2Ja+dVc+d+sKkWm9+sFZAIAAAAAAAAAAAAAACAw6WQCQBNcP60SkwZl9SdySJiybI0siwrJhQAAAAAAAAAAAAAAACHTCETAJqgWkli8cL8K5mrXsviqVcUMgEAAAAAAAAAAAAAAFqdQiYANMmCYyvxjuPqX8mMiLhleW/01pQyAQAAAAAAAAAAAAAAWplCJgA00Q0L2qKS08l8eU/Ev2ysFRMIAAAAAAAAAAAAAACAQ6KQCQBNNPmIJN43M/91e9fqNPZ2u5IJAAAAAAAAAAAAAADQqhQyAaDJrppbjZHt9Wf2dEfcvTYtJhAAAAAAAAAAAAAAAAB9ppAJAE12xPAkPjy3mjt3/7O12LbHlUwAAAAAAAAAAAAAAIBWpJAJAAX4wEmVOGZU/ZneWsRtK3uLCQQAAAAAAAAAAAAAAECfKGQCQAHaq0lct6Atd+6xF7NY93qtgEQAAAAAAAAAAAAAAAD0hUImABTk7BOSmHVUkjv3jWVp1LKsgEQAAAAAAAAAAAAAAAA0SiETAAqSJEl8YlE1d27jjiwefd6VTAAAAAAAAAAAAAAAgFaikAkABTr5yEqcOyX/9Xv7yjS6el3JBAAAAAAAAAAAAAAAaBUKmQBQsI/Pr0ZHzqHMN/ZFfG+9K5kAAAAAAAAAAAAAAACtQiETAAo2cVQSH5yV/wq+Z10ab+53JRMAAAAAAAAAAAAAAKAVKGQCwAC4Yk41jhhWf+ZAb8Tfr0qLCQQAAAAAAAAAAAAAAEBdCpkAMABGtCdxzbxq7tzDm2uxZWetgEQAAAAAAAAAAAAAAADUo5AJAAPkgumVOPGIpO5MLYtYsiyNLMsKSgUAAAAAAAAAAAAAAMDbUcgEgAFSrSSxeGH+lcyVr2bx9FaFTAAAAAAAAAAAAAAAgIGkkAkAA2jhpEqcNqn+lcyIiFuW90ZaU8oEAAAAAAAAAAAAAAAYKAqZADDAbljYFpWcTuZLuyMefK5WTCAAAAAAAAAAAAAAAAB+iUImAAywE45I4j0z8l/Jf786jb3drmQCAAAAAAAAAAAAAAAMBIVMAGgBV59ajZHt9Wd2d0V8e21aTCAAAAAAAAAAAAAAAADeQiETAFrAuOFJXHFKNXfuH5+txaudrmQCAAAAAAAAAAAAAAAUTSETAFrEJSdXYuLI+jO9tYjbVriSCQAAAAAAAAAAAAAAUDSFTABoER3VJK5bmH8l8ycv1uKZN2oFJAIAAAAAAAAAAAAAAODnFDIBoIX86gmVOPnIJHfuG8vSyLKsgEQAAAAAAAAAAAAAAABEKGQCQEtJkiQ+sSj/SuaG7Vn8+AVXMgEAAAAAAAAAAAAAAIqikAkALWbWUZU458T8V/StK9Lo6nUlEwAAAAAAAAAAAAAAoAgKmQDQgj4+vxrtOW/pN/ZF3P+sK5kAAAAAAAAAAAAAAABFUMgEgBZ09OgkLpmV/5r+9to0dh5wJRMAAAAAAAAAAAAAAKDZFDIBoEVdMacaY4fVn9nfG/H3q9JiAgEAAAAAAAAAAAAAAAxhCpkA0KJGdSRxzbxq7txDm2rx/M5aAYkAAAAAAAAAAAAAAACGLoVMAGhhF06vxAljk7oztSziluWuZAIAAAAAAAAAAAAAADSTQiYAtLBqJYkbFuVfyVy+LYunt7qSCQAAAAAAAAAAAAAA0CwKmQDQ4k6bVImFx9a/khnxsyuZaS0rIBEAAAAAAAAAAAAAAMDQo5AJACVww8JqVHI6mS/uyuKhTa5kAgAAAAAAAAAAAAAANINCJgCUwJRxlbhgev5r+85VaezrcSUTAAAAAAAAAAAAAACgvylkAkBJXDOvGsPb6s/s7oq4Z21aTCAAAAAAAAAAAAAAAIAhRCETAEpi3PAkrjylmjv3vfW1eK3TlUwAAAAAAAAAAAAAAID+pJAJACVyycmVOGpk/ZmeWsQ3V7qSCQAAAAAAAAAAAAAA0J8UMgGgRIa1JXHdgvwrmf/6Qi3Wv1ErIBEAAAAAAAAAAAAAAMDQoJAJACVzzomVOOnIJHduybI0siwrIBEAAAAAAAAAAAAAAMDgp5AJACWTJEl8YlH+lcz127P4yYuuZAIAAAAAAAAAAAAAAPQHhUwAKKHZR1XinSfkv8ZvW5FGd+pKJgAAAAAAAAAAAAAAwOFSyASAkrpuQTXact7kr+2NuP9ZVzIBAAAAAAAAAAAAAAAOl0ImAJTUMaOTuOTk/Ff5t9emsfOAK5kAAAAAAAAAAAAAAACHQyETAErsylOqMXZY/Zl9PRF3rU6LCQQAAAAAAAAAAAAAADBIKWQCQImN6kji6lOruXMPPleLF3e5kgkAAAAAAAAAAAAAAHCoFDIBoOTeO6MSk8fWn6llEbcs7y0mEAAAAAAAAAAAAAAAwCCkkAkAJVetJHHDwrbcuae3ZrF8a62ARAAAAAAAAAAAAAAAAIOPQiYADAKnTUpiwTFJ7tyS5WmktayARAAAAAAAAAAAAAAAAIOLQiYADAJJksQNi6qRV8l8YVcWP9jsSiYAAAAAAAAAAAAAAEBfKWQCwCAxdVwlLpie/2q/Y2Ua+3tcyQQAAAAAAAAAAAAAAOgLhUwAGESumVeN4W31Z3Z1RdyzLi0mEAAAAAAAAAAAAAAAwCChkAkAg8j4EUlcMaeaO/e99bV4fa8rmQAAAAAAAAAAAAAAAI1SyASAQeaDsypx1Mj6M91pxO0rXckEAAAAAAAAAAAAAABolEImAAwyw9qS+Nj8/CuZP3q+Fhu21wpIBAAAAAAAAAAAAAAAUH4KmQAwCJ07pRIzJyS5c99YlkaWZQUkAgAAAAAAAAAAAAAAKDeFTAAYhCpJEp9YlH8l85k3snjsJYVMAAAAAAAAAAAAAACAPAqZADBIzZlYibMm51/JvG15b/SkSpkAAAAAAAAAAAAAAAD1KGQCwCB2/YK2aMt527+6N+L+DbViAgEAAAAAAAAAAAAAAJSUQiYADGLHjkni4pPyX/d3r0ljd5crmQAAAAAAAAAAAAAAAAejkAkAg9xH5lZjTEf9mX09EXetTosJBAAAAAAAAAAAAAAAUEIKmQAwyI3qSOLqU6u5cw9srMVLu13JBAAAAAAAAAAAAAAAeDsKmQAwBLx3ZiWOH1N/ppZF3Lq8t5hAAAAAAAAAAAAAAAAAJaOQCQBDQFsliesXtuXOPflKFiu21QpIBAAAAAAAAAAAAAAAUC4KmQAwRJx+XBLzjk5y55YsTyOtZQUkAgAAAAAAAAAAAAAAKA+FTAAYIpIkicWLqpFXyXx+ZxaPbHYlEwAAAAAAAAAAAAAA4D9TyASAIWTa+Eq8e1r+6/+OVWns73ElEwAAAAAAAAAAAAAA4OcUMgFgiLlmfjWGt9Wf2Xkg4jvPpMUEAgAAAAAAAAAAAAAAKAGFTAAYYiaMSOKy2dXcufueqcUbe13JBAAAAAAAAAAAAAAAiFDIBIAh6dLZlZgwov5Mdxpx+ypXMgEAAAAAAAAAAAAAACIUMgFgSBrWlsTH5+dfyfzhllps3FErIBEAAAAAAAAAAAAAAEBrU8gEgCHq16ZWYsb4JHduybI0siwrIBEAAAAAAAAAAAAAAEDrUsgEgCGqkiSxeFH+lcy1r2fx+EsKmQAAAAAAAAAAAAAAwNCmkAkAQ9jcoytx5uT8K5m3ruiNnlQpEwAAAAAAAAAAAAAAGLoUMgFgiLtuQVu05Xwj2NYZ8c8ba8UEAgAAAAAAAAAAAAAAaEEKmQAwxB03Jon3n5T/leBbq9PY0+VKJgAAAAAAAAAAAAAAMDQpZAIA8ZG51RjdUX9mb0/Et9akxQQCAAAAAAAAAAAAAABoMQqZAECM7kji6lOruXP/vKEWL+92JRMAAAAAAAAAAAAAABh6FDIBgIiIeO+MSkwaU38mzSJuXdFbTCAAAAAAAAAAAAAAAIAWopAJAERERHs1iRsWtOXOPfFyFqterRWQCAAAAAAAAAAAAAAAoHUoZAIAv/Arxydx6tFJ7tySZWmktayARAAAAAAAAAAAAAAAAK1BIRMA+IUkSeKGhdXIq2Ru3pnFD7e4kgkAAAAAAAAAAAAAAAwdCpkAwFvMmFCJd03L/4pw+8o0DvS6kgkAAAAAAAAAAAAAAAwNCpkAwC+5dl41hlXrz7x5IOI769JiAgEAAAAAAAAAAAAAAAwwhUwA4JccOTKJy+bkNDIj4jvP1GL7PlcyAQAAAAAAAAAAAACAwU8hEwB4W5fOrsSEEfVnutOIO1a6kgkAAAAAAAAAAAAAAAx+CpkAwNsa3pbEtfPyr2Q+sqUWz+2oFZAIAAAAAAAAAAAAAABg4ChkAgAH9a5plZg2PsmdW7IsjSzLCkgEAAAAAAAAAAAAAAAwMBQyAYCDqiRJLF6YfyVzzetZPPGyQiYAAAAAAAAAAAAAADB4KWQCAHXNO6YSZxyffyXzlhW90ZMqZQIAAAAAAAAAAAAAAIOTQiYAkOv6BW1Rzelkbt0T8cDGWjGBAAAAAAAAAAAAAAAACqaQCQDkOm5sEhedlP+14Vtr0tjT5UomAAAAAAAAAAAAAAAw+ChkAgANuWpuNUa115/p7I74hzVpMYEAAAAAAAAAAAAAAAAKpJAJADRkzLAkrjq1mjv3/Q21eGW3K5kAAAAAAAAAAAAAAMDgopAJADTsopmVmDS6/kyaRdy6oreYQAAAAAAAAAAAAAAAAAVRyAQAGtZeTeK6hW25c//2chZrXqsVkAgAAAAAAAAAAAAAAKAYCpkAQJ+ceXwSp0xMcue+sSyNWpYVkAgAAAAAAAAAAAAAAKD5FDIBgD5JkiQ+saiaO7fpzSx+uMWVTAAAAAAAAAAAAAAAYHBQyAQA+mzGhEq8a2r+14jbV6ZxoNeVTAAAAAAAAAAAAAAAoPwUMgGAQ3Lt/Gp05BzK3LE/4rvPuJIJAAAAAAAAAAAAAACUn0ImAHBIjhqZxKWz879K3LsujR37XckEAAAAAAAAAAAAAADKTSETADhkl82uxrjh9We60og7VqbFBAIAAAAAAAAAAAAAAGgShUwA4JCNaE/iY/OruXOPbK7F5jdrBSQCAAAAAAAAAAAAAABoDoVMAOCwvGtqJaaOS+rOZBGxZFkaWZYVEwoAAAAAAAAAAAAAAKCfKWQCAIelWkli8cL8K5mrXsviyVcUMgEAAAAAAAAAAAAAgHJSyAQADtv8Yytx+nH1r2RGRNyyvDd6a0qZAAAAAAAAAAAAAABA+ShkAgD94oaFbVHN6WS+sifigY21YgIBAAAAAAAAAAAAAAD0I4VMAKBfHD82iffOzP9qcdfqNDq7XckEAAAAAAAAAAAAAADKRSETAOg3V59ajZHt9Wc6uyP+YU1aTCAAAAAAAAAAAAAAAIB+opAJAPSbscOS+Mjcau7c9zfUYuseVzIBAAAAAAAAAAAAAIDyUMgEAPrVxSdV4phR9Wd6axG3regtJhAAAAAAAAAAAAAAAEA/UMgEAPpVezWJ6xe25c799KUs1r5WKyARAAAAAAAAAAAAAADA4VPIBAD63VmTk5hzVJI7943ladSyrIBEAAAAAAAAAAAAAAAAh0chEwDod0mSxOJF1dy553Zk8ejzrmQCAAAAAAAAAAAAAACtTyETAGiKk46sxK9Nyf+q8c0VaXT1upIJAAAAAAAAAAAAAAC0NoVMAKBpPja/Gh05hzK374/47npXMgEAAAAAAAAAAAAAgNamkAkANM3EUUl8aFb+141716WxY78rmQAAAAAAAAAAAAAAQOtSyAQAmuryOdUYN7z+zIHeiL9flRYTCAAAAAAAAAAAAAAA4BAoZAIATTWiPYlr5lVz5x7eVIvNb9YKSAQAAAAAAAAAAAAAANB3CpkAQNO9e1olphyR1J3JIuKW5WlkWVZMKAAAAAAAAAAAAAAAgD5QyAQAmq5aSWLxovwrmStfzeKprQqZAAAAAAAAAAAAAABA61HIBAAKseDYSpw2qf6VzIiIW5b3Rm9NKRMAAAAAAAAAAAAAAGgtCpkAQGFuWNgWlZxO5su7Ix58rlZMIAAAAAAAAAAAAAAAgAYpZAIAhTnhiCTeOyP/68ffr0pjb7crmQAAAAAAAAAAAAAAQOtQyKR0li5dGkmStOR/vvjFLw70/z0ALe/qU6sxsr3+zJ7uiG+vTYsJBAAAAAAAAAAAAAAA0ACFTACgUEcMT+LDp1Rz5/7x2Vps63QlEwAAAAAAAAAAAAAAaA0KmQBA4S4+uRLHjKo/01uL+OYKVzIBAAAAAAAAAAAAAIDWoJAJABSuo5rEdQvacud+8mIt1r1eKyARAAAAAAAAAAAAAABAfQqZAMCAOPuEJGYdleTOLVmWRi3LCkgEAAAAAAAAAAAAAABwcAqZAMCASJIkPrGomju3YUcW//q8K5kAAAAAAAAAAAAAAMDAUsgEAAbMyUdW4twT87+OfHNlGl29rmQCAAAAAAAAAAAAAAADRyETABhQH1tQjfacbyRv7Iv4x2ddyQQAAAAAAAAAAAAAAAZO20AHgGY477zzYunSpQMdA4AGHD0qiQ/OqsQ96+oXLr+9No0Lpldi3PCkoGQAAAAAAAAAAAAAAAD/wYVMAGDAXXFKNY4YVn/mQG/EnavSYgIBAAAAAAAAAAAAAAD8FwqZAMCAG9mexDXzqrlzD2+qxfM761/SBAAAAAAAAAAAAAAAaAaFTACgJVwwvRInHpHUnallEUuWpZFlWUGpAAAAAAAAAAAAAAAAfkYhEwBoCdVKEjcszL+SueLVLJZtVcgEAAAAAAAAAAAAAACKpZAJALSMRZMqsWhS/SuZERFLlvdGWlPKBAAAAAAAAAAAAAAAiqOQCQC0lBsWVqOS08l8aXfEQ5tqxQQCAAAAAAAAAAAAAAAIhUwAoMWceEQl3jMj/yvKnavS2NvtSiYAAAAAAAAAAAAAAFAMhUwAoOVcfWo1RrTVn9ndFXHPurSYQAAAAAAAAAAAAAAAwJCnkAkAtJxxw5O4cm41d+5762vxaqcrmQAAAAAAAAAAAAAAQPMpZAIALemSkysxcWT9md5axDdXupIJAAAAAAAAAAAAAAA0n0ImANCSOqpJXLcg/0rmj1+oxfo3agUkAgAAAAAAAAAAAAAAhjKFTACgZf3qiZU4+cgkd+4by9LIsgICAQAAAAAAAAAAAAAAQ5ZCJgDQspIkiU8syr+S+ez2LB57uYBAAAAAAAAAAAAAAADAkKWQCQC0tFlHVeJXT8z/ynLH6ojeLP+aJgAAAAAAAAAAAAAAwKFQyAQAWt5186vRnvOt5Y19Ecv3TiomEAAAAAAAAAAAAAAAMOQoZAIALe/o0UlcMiv/a8sTncfHvrStgEQAAAAAAAAAAAAAAMBQo7HAoLZt27b48Y9/HE8++WSsWbMmtmzZElu3bo29e/dGV1dXDB8+PEaMGBFjxoyJ448/PiZPnhwzZ86MRYsWxWmnnRbTpk0b6D8CAP/uijnVeHhTLXZ3HXymO2uLn3aeEO8+YnNxwQAAAAAAAAAAAAAAgCFBIZNB6ZlnnonTTjstli9fHlmWHXRu3759sW/fvti+fXts2bLll35/6tSpcfHFF8fll18eF1xwQSRJ0sTUANQzqiOJj55ajZufSuvOrd53TCwYuS2ObN9fUDIAAAAAAAAAAAAAAGAoqAx0AGiGV199NZYtW1a3jNmILVu2xF/+5V/Ge97znpgxY0b8yZ/8SezevbufUgLQV++ZUYnJY+vPZJHEo3umFBMIAAAAAAAAAAAAAAAYMhQyoUGbN2+Oz372szF9+vT4f//v/0VPT89ARwIYcqqVJBYvzD/w/XzX+Hi+64gCEgEAAAAAAAAAAAAAAEOFQib00fbt2+P3f//34x3veEc8/fTTAx0HYMhZNCmJhccmuXOP7p4aaa2AQAAAAAAAAAAAAAAAwJCgkAmHaNWqVXHmmWfG17/+9YGOAjCkJEkSNyysRiWnk7m9d2Q88nwxmQAAAAAAAAAAAAAAgMFPIRMOQ29vb9x4443x27/925Fl2UDHARgypoyrxAXT87/G/MPaiH09/n4GAAAAAAAAAAAAAAAOX9tAB4DB4Gtf+1r09PTEX/3VXw10lF/Isiy6u7sHOgYl19PT09CvwUC4clbEo89HHOg9+Myuroh/WNUT15xaXC5olL9jKRP7StnYWcrEvlIm9pWysbOUiX2lTOwrZWNnKRP7StnYWcrEvlIm9pWysbOUiX2lTOwrZWNnaaZWO6KXZK2WCHIsXbo0zj///IP+/hFHHBHz58+PuXPnxty5c+OUU06JiRMnxtixY3/xn97e3ti/f39s27YtXnrppVi9enU89dRT8fDDD8fWrVsPOdv//t//O/74j//4kD9fT2dnZ6Rp2qf5jRs3NiULQKt4ovO4+MmeKXVnqlGL6ycui7FtSuoAAAAAAAAAAAAAAFAmM2fOjNGjRzc8X61W+zTfVy5kUnpJksTChQvj/e9/f1x00UVx9tlnR1tb/dWuVqsxbNiwGDduXMyePTsuvPDCX/ze448/Hn/7t38bt912Wxw4cKBPWf70T/80zjzzzLj00ksP6c8CQN8sGrUtVu07Nvakww46k0YlfrxnSrx//IYCkwEAAAAAAAAAAAAAAINNZaADwKGaNGlSfPazn41NmzbF008/HV/+8pfj3HPPzS1j5jnzzDPj5ptvjk2bNsV//+//PZIkafizWZbF4sWLY9u2bYeVAYDGtCW1eOeYF3Lnnj1wVGzrbt6/4QIAAAAAAAAAAAAAABj8FDIpnRNOOCHuvffeeOGFF+LLX/5yTJ06tSnPmTRpUtx0003xyCOPxKRJkxr+3M6dO+PTn/50UzIB8MtmDX8jjmnfkzv3o91TIssKCAQAAAAAAAAAAAAAAAxKSZapJkCeV155JS6++OJYsWJFw595/PHH44wzzui3DJ2dnZGmacPzlUolhg0b1m/PZ2jq6emJJ5988i2/dvrpp0d7e/sAJYK3t357xBd+mD/3u2dEnDW5+XmgEf6OpUzsK2VjZykT+0qZ2FfKxs5SJvaVMrGvlI2dpUzsK2VjZykT+0qZ2FfKxs5SJvaVMrGvlI2dpZm6urqiVqs1PF+tVmP06NFNy9PWtJ8Mg8hxxx0XDz/8cJx33nmxZs2ahj7zZ3/2Z/Htb3+7yckOLkmS6OjoGLDnM3i1t7fbLVrOvEkRZx3fHT99uf7cnWsizprSHh3VpJhg0Ef+jqVM7CtlY2cpE/tKmdhXysbOUib2lTKxr5SNnaVM7CtlY2cpE/tKmdhXysbOUib2lTKxr5SNnaW/dHd3D3SEt6gMdAAoiyOPPDLuu+++GDduXEPz3/nOd2LLli1NzQTAf7jm1Ihq1P+3Xry6N+Kfnm3834wBAAAAAAAAAAAAAADwcwqZ0AczZsyIr3zlKw3N1mq1uOuuu5qcCICfO2ZUxMJRW3Pn7l6bxq4DWQGJAAAAAAAAAAAAAACAwUQhE/ro13/91+Md73hHQ7N33313k9MA8J/9yuiXY3jSU3dmX0/EXavTghIBAAAAAAAAAAAAAACDhUIm9FGSJPGZz3ymodmnn346du3a1eREAPzcsEoaZ415MXfuX56rxUu7XMkEAAAAAAAAAAAAAAAap5AJh+DSSy+N8ePH587VarX46U9/WkAiAH7u1JGvxfjqvroztSzilhW9BSUCAAAAAAAAAAAAAAAGA4VMOATt7e3xgQ98oKHZZcuWNTkNAP9ZNcni3LHP58499UoWy7fVCkgEAAAAAAAAAAAAAAAMBgqZcIjOPffchuY2b97c5CQA/FdTh+2MEzp25s7dsiyNtJY1PxAAAAAAAAAAAAAAAFB6CplwiObOndvQ3JYtW5obBIBfkiQR5459PpKcued3ZfGDza5kAgAAAAAAAAAAAAAA+RQy4RBNmzatobk333yzyUkAeDsT2/fF+VPz5+5clcb+Hlcy4f+zd+dRctZ1vvg/T1Wnu5OQhSSQBAghgSSErK2ODMimg6hXARfABYEIKm4/7+jVUeeOM3fmHpd7xrl6ZtRRdCQBQRFExWFcx4iyiEY6JIQ1ISEEOgkh+95d9fz+8IYJkK6nk3Q91VX1ep1T54x5Pqnvu+AZfeqcfvcHAAAAAAAAAAAAAIDKFDLhEI0YMaJPczt27KhyEgB6c8kpEe0tlWc27474wUOlfAIBAAAAAAAAAAAAAAB1SyETDlF7e3uf5nbv3l3lJAD0ZmR7xJumFzPnbnukHBt22JIJAAAAAAAAAAAAAAD0TiETDlFfi5Z9LW4CUB0XTCvE6MGVZ/aWIm5YYksmAAAAAAAAAAAAAADQO4VMOERbtmzp09zQoUOrnASAStpaknjnnOwtmXc8UY7Hni3nkAgAAAAAAAAAAAAAAKhHCplwiFauXNmnuZEjR1Y3CACZzpxYiBNHJZlz8ztLkaZpDokAAAAAAAAAAAAAAIB6o5AJh2jZsmV9mjvhhBOqGwSATIUkiXfNzd6S+dCGNH63RiETAAAAAAAAAAAAAAB4MYVMOER33nlnn+YmTZpU5SQA9MUpRxfiz4/L3pJ53eKe6C4pZQIAAAAAAAAAAAAAAM+nkAmHoLu7O26//fY+zc6ePbvKaQDoq8vntERLxtPPuh0R//FYOZ9AAAAAAAAAAAAAAABA3VDIhEPw4x//ODZu3Jg5lyRJnHbaaTkkAqAvxg1L4nVTsh9/bl5Wiq17bMkEAAAAAAAAAAAAAAD+i0ImHILPf/7zfZqbOXNmjBo1qsppADgYF88oxhGtlWd2dkd874FSPoEAAAAAAAAAAAAAAIC6oJAJB2n+/Pnxhz/8oU+zF110UZXTAHCwjmhN4q0zi5lzP11ejjVbbckEAAAAAAAAAAAAAAD+RCETDsLKlSvjox/9aJ/n3/a2t1UxDQCH6jUnFeKYYZVnymnEdYt78gkEAAAAAAAAAAAAAAAMeAqZ0EebNm2KCy+8MDZt2tSn+de85jUxderUKqcC4FC0FJK4Ym5L5tyip9NYsracQyIAAAAAAAAAAAAAAGCgU8ikLixatChuvfXWSNO0JuevW7cu/uIv/iKWLl3a57/zqU99qoqJADhcLzsmiVlHJ5lz8xeXolSuzf/+AAAAAAAAAAAAAAAAA4dCJnVhzZo18Za3vCVe8pKXxI033hjd3d25nX3XXXfFy172sujs7Ozz3zn//PPj7LPPrmIqAA5XkiQxr6MYWZXMVZvT+PUqWzIBAAAAAAAAAAAAAKDZKWRSVxYvXhyXXnppHH/88fHXf/3X8eCDD1btrHXr1sUHP/jBOOuss2LNmjV9/ntDhw6NL3/5y1XLBUD/mXRkIV45Kftx6IYlpdjVbUsmAAAAAAAAAAAAAAA0M4VM6tLatWvjc5/7XMyYMSNmz54dn/rUp+KOO+6I3bt3H/Z7L1q0KD7wgQ/E5MmT46tf/WqUywe3Fe1f//Vf4/jjjz/sHADk4x2zi9FWrDyzeXfEjx4u5RMIAAAAAAAAAAAAAAAYkFpqHQAO19KlS2Pp0qXx+c9/PgYNGhRz5syJjo6OmDp1akyZMiXGjx8fY8eOjSOPPDLa2tqitbU1uru7Y9euXbF+/fp48sknY9myZfHHP/4xfvnLX8ZTTz11yFk+8pGPxGWXXdaPnw6Aahs1OIk3TS/Gdx+oXLj84cPlOPfENMYMSXJKBgAAAAAAAAAAAAAADCQKmTSU7u7uWLRoUSxatCj3s6+88sr4whe+kPu5ABy+C04uxM9XlGLjrt5n9pYiblxSig//uccnAAAAAAAAAAAAAABoRoVaB4BG8MEPfjC++c1vRqHg/6UA6lF7SxKXzi5mzv16VTmWbyznkAgAAAAAAAAAAAAAABhotMfgMLS3t8e//du/xZe//OVIkqTWcQA4DGefUIjJR2b/d/n8zlKkaZpDIgAAAAAAAAAAAAAAYCBRyIRDdMYZZ8SiRYviyiuvrHUUAPpBIUniXR3ZWzIffCaNe59SyAQAAAAAAAAAAAAAgGajkEldGEjbJ6dMmRILFiyI3/zmNzFjxoxaxwGgH804uhAvPzb7f3OuX9wT3SWlTAAAAAAAAAAAAAAAaCYKmdSF888/PxYtWhSf//zn49xzz43Bgwfnen6SJHHOOefEd77znXjooYfi8ssvH1AlUQD6z+VzW6KY8V/xXdsjfrq8nE8gAAAAAAAAAAAAAABgQGipdQDoi0KhEC996UvjpS99aXziE5+IPXv2xN133x333ntv3HfffdHZ2RkrVqyINO2/bWVDhw6NM888M84777y46KKLYsKECf323gAMXMcMS+J1Uwrx749WLlzevKwU55xQiGFtCvoAAAAAAAAAAAAAANAMFDKpS21tbfHKV74yXvnKVz73Z1u3bo0lS5bE448/HqtXr44nnngiVq9eHV1dXbFjx47YsWNH7Ny5M3bt2hVpmkZ7e3u0t7fHiBEjYvz48TF+/PiYNGlSzJw5M2bNmhUzZ86MQYMG1fBTAlArF88oxq9XlWP73t5ntu+N+N6yUlz1Eo9TAAAAAAAAAAAAAADQDDQIaBjDhw+PM844I84444xaRwGgzg1rS+KSGcX4Vmep4txPHyvH605K45jhtmQCAAAAAAAAAAAAAECjK9Q6AADAQPSakwoxfljlmVIacd39PfkEAgAAAAAAAAAAAAAAakohEwDgAAYVk7hiTvYy8d8/lcbSdeUcEgEAAAAAAAAAAAAAALWkkAkA0Is/OzaJGUclmXPzF5einKY5JAIAAAAAAAAAAAAAAGpFIRMAoBdJksS8jmJkVTJXbkrjjlW2ZAIAAAAAAAAAAAAAQCNTyAQAqODEUYU454TsR6YblpRid48tmQAAAAAAAAAAAAAA0KgUMgEAMrxjdjFai5VnNu6K+NHDtmQCAAAAAAAAAAAAAECjUsgEAMgwekgSbzw5+7Hphw+V4tmdtmQCAAAAAAAAAAAAAEAjUsgEAOiDN04vxpHtlWf2lCJuXFrKJxAAAAAAAAAAAAAAAJArhUwAgD5ob0ni0tnFzLlfryzHio3lHBIBAAAAAAAAAAAAAAB5UsgEAOijcyYVYtLIpOJMGhELFpciTdN8QgEAAAAAAAAAAAAAALlQyAQA6KNCksS8juwtmQ+sT+MPTylkAgAAAAAAAAAAAABAI1HIBAA4CLPGFuLPjq28JTMiYsH9PdFdUsoEAAAAAAAAAAAAAIBGoZAJAHCQLp/TEsWMTmbXtoifryjnEwgAAAAAAAAAAAAAAKg6hUwAgIN07PAkXjsl+zHqpgdKsW2PLZkAAAAAAAAAAAAAANAIFDIBAA7BJTOKMXRQ5ZnteyNuebCUTyAAAAAAAAAAAAAAAKCqFDIBAA7BsLYkLplZzJz7yWPleHqbLZkAAAAAAAAAAAAAAFDvFDIBAA7Ra08qxPgjKs/0lCOuv78nn0AAAAAAAAAAAAAAAEDVKGQCAByiQcUkLpvTkjl375o0lq0v55AIAAAAAAAAAAAAAACoFoVMAIDDcOpxSZxyVJI5N7+zFOU0zSERAAAAAAAAAAAAAABQDQqZAACHIUmSmNdRzJxbsSmN36yyJRMAAAAAAAAAAAAAAOqVQiYAwGE6aVQhzj4h+7Hq20tKsafHlkwAAAAAAAAAAAAAAKhHCpkAAP3g0lnFaM1YlLlxV8SPHrYlEwAAAAAAAAAAAAAA6pFCJgBAPxgzNIkLT85+tPrBQ6XYuMuWTAAAAAAAAAAAAAAAqDcKmQAA/eSNJxdjZHvlmT2liO8sKeUTCAAAAAAAAAAAAAAA6DcKmQAA/WTwoCTeMauYOferleVYuamcQyIAAAAAAAAAAAAAAKC/KGQCAPSjV04qxMSRScWZNCLmd5YiTdN8QgEAAAAAAAAAAAAAAIdNIRMAoB8VC0nMm5u9JXPp+jT++LRCJgAAAAAAAAAAAAAA1AuFTACAfjZnXCFedkzlLZkREQsW90RPWSkTAAAAAAAAAAAAAADqgUImAEAVXD63JQoZncyntkX8fHk5n0AAAAAAAAAAAAAAAMBhUcgEAKiC44Yn8ZqTsh+1bnqgFNv32pIJAAAAAAAAAAAAAAADnUImAECVvHVmMYYMqjyzbW/ELctK+QQCAAAAAAAAAAAAAAAOmUImAECVDG9L4qIZxcy5/3isHGu32ZIJAAAAAAAAAAAAAAADmUImAEAVvX5KIcYOrTzTU464fklPPoEAAAAAAAAAAAAAAIBDopAJAFBFg4pJXDa3JXPunifTeOiZcg6JAAAAAAAAAAAAAACAQ6GQCQBQZacdl8TJY5LMuWs7S1FO0xwSAQAAAAAAAAAAAAAAB0shEwCgypIkiXd1FDPnlm9M47dP2JIJAAAAAAAAAAAAAAADkUImAEAOpowuxFkTsx+9vn1/Kfb02JIJAAAAAAAAAAAAAAADjUImAEBOLp1djNaMRZnP7or48SO2ZAIAAAAAAAAAAAAAwECjkAkAkJOjhiZx/rTsx69bHyrFpl22ZAIAAAAAAAAAAAAAwECikAkAkKM3Ty/GiLbKM7t7Ir67tJRPIAAAAAAAAAAAAAAAoE8UMgEAcjR4UBLvmF3MnPvPleVYtbmcQyIAAAAAAAAAAAAAAKAvFDIBAHL2qkmFmDgiqThTTiPmd5YiTdOcUgEAAAAAAAAAAAAAAJUoZAIA5KxYSOKKjuwtmUvWpXFfl0ImAAAAAAAAAAAAAAAMBAqZAAA1MHdcIV4yvvKWzIiI+Yt7oqeslAkAAAAAAAAAAAAAALWmkAkAUCNXzG2JQkYn86mtEb9YUc4nEAAAAAAAAAAAAAAA0CuFTACAGpkwIonzTsx+HLvpgVLs2GtLJgAAAAAAAAAAAAAA1JJCJgBADb11ZjGGDKo8s3VPxPcfLOUTCAAAAAAAAAAAAAAAOCCFTACAGhrRnsRbTilmzv37o+VYt92WTAAAAAAAAAAAAAAAqBWFTACAGnv91EIcPbTyTE854vr7bckEAAAAAAAAAAAAAIBaUcgEAKix1mISl83J3pJ595PleHhDOYdEAAAAAAAAAAAAAADACylkAgAMAKdPKMS00Unm3LX3laKcpjkkAgAAAAAAAAAAAAAA9qeQCQAwACRJEvM6srdkPrYxjTufsCUTAAAAAAAAAAAAAADyppAJADBATBtTiDOPz348+/aSUuzpsSUTAAAAAAAAAAAAAADypJAJADCAXDqnGIMyntA27Iz490dtyQQAAAAAAAAAAAAAgDwpZAIADCBHD03i/GnZj2i3PliKzbttyQQAAAAAAAAAAAAAgLwoZAIADDBvPqUYw9sqz+zqifju0lI+gQAAAAAAAAAAAAAAAIVMAICBZsigJN4+q5g598vHy/HE5nIOiQAAAAAAAAAAAAAAAIVMAIAB6NzJhZgwIqk4U04jFiy2JRMAAAAAAAAAAAAAAPKgkAkAMAAVC0nMm5u9JXPx2jTu67IlEwAAAAAAAAAAAAAAqk0hEwBggOoYX4iO8ZW3ZEZELOgsRamc5pAIAAAAAAAAAAAAAACal0ImAMAAdsXcYhQyOplPbk3jl4/bkgkAAAAAAAAAAAAAANWkkAkAMIAdP6IQ507OfmT7ztJS7Oy2JRMAAAAAAAAAAAAAAKpFIRMAYIB726xiDG6pPLN1T8StD5byCQQAAAAAAAAAAAAAAE1IIRMAYIAb2Z7EW04pZs79+JFyrN9uSyYAAAAAAAAAAAAAAFSDQiYAQB14w7RCHDWk8kx3OeLbS2zJBAAAAAAAAAAAAACAalDIBACoA63FJC6bk70l887V5XhkQzmHRAAAAAAAAAAAAAAA0FwUMgEA6sQrji/E1NFJ5ty1naVI0zSHRAAAAAAAAAAAAAAA0DwUMgEA6kSSJDGvI3tL5qPPpnHXk7ZkAgAAAAAAAAAAAABAf1LIBACoIyePKcTpE7If4b59fyn2lmzJBAAAAAAAAAAAAACA/qKQCQBQZy6bU4yWjKe49Tsibn/UlkwAAAAAAAAAAAAAAOgvCpkAAHVm7BFJvGFq9mPc9x8sxebdtmQCAAAAAAAAAAAAAEB/UMgEAKhDbzmlGMPbKs/s7I646YFSPoEAAAAAAAAAAAAAAKDBKWQCANShoa1JvG1mMXPuFyvKsXpLOYdEAAAAAAAAAAAAAADQ2BQyAQDq1KtPLMRxwyvPlNOI6xbbkgkAAAAAAAAAAAAAAIdLIRMAoE4VC0lcMbclc+6+rjQWd9mSCQAAAAAAAAAAAAAAh0MhEwCgjr1kfBJzxiaZc/MXl6JUTnNIBAAAAAAAAAAAAAAAjUkhEwCgjiVJEvM6ilHI6GSu3pLGr1bakgkAAAAAAAAAAAAAAIdKIRMAoM5NHFmIv5iU/Vh345JS7Oq2JRMAAAAAAAAAAAAAAA6FQiYAQAN426xitLdUntmyJ+LWh0r5BAIAAAAAAAAAAAAAgAajkAkA0ACOHJzEm6cXM+due7gc63fYkgkAAAAAAAAAAAAAAAdLIRMAoEGcP60QY4ZUnukuR9ywxJZMAAAAAAAAAAAAAAA4WAqZAAANoq0liUtnZ2/J/O0T5Xj02XIOiQAAAAAAAAAAAAAAoHEoZAIANJAzJxbipFFJ5ty1naVI0zSHRAAAAAAAAAAAAAAA0BgUMgEAGkghSeJdHdlbMh/ZkMY9axQyAQAAAAAAAAAAAACgrxQyAQAazPSjCnHahOwtmdcv7onuklImAAAAAAAAAAAAAAD0hUImAEADumx2S7RkPOmt2xFx+2PlfAIBAAAAAAAAAAAAAECdU8gEAGhA44Yl8fqp2Y96tywrxZbdtmQCAAAAAAAAAAAAAEAWhUwAgAZ10SnFGNZaeWZnd8T3lpXyCQQAAAAAAAAAAAAAAHVMIRMAoEENbU3irTOLmXM/W16ONVtsyQQAAAAAAAAAAAAAgEoUMgEAGth5JxXi2GGVZ8ppxIL7e/IJBAAAAAAAAAAAAAAAdUohEwCggbUUkrhibkvm3B+fTuP+teUcEgEAAAAAAAAAAAAAQH1SyAQAaHAvPSaJ2WOTzLn5i0tRKqc5JAIAAAAAAAAAAAAAgPqjkAkA0OCSJIkr5hYjq5L5xOY0Fq60JRMAAAAAAAAAAAAAAA5EIRMAoAlMOrIQr5qc/eh349JS7Oq2JRMAAAAAAAAAAAAAAF5IIRMAoEm8fVYx2lsqz2zeHfGDh0r5BAIAAAAAAAAAAAAAgDqikAkA0CRGDU7iTdOLmXO3PVKODTtsyQQAAAAAAAAAAAAAgP0pZAIANJELphVi1ODKM3tLETcssSUTAAAAAAAAAAAAAAD2p5AJANBE2lqSeOfs7C2ZdzxRjuUbyzkkAgAAAAAAAAAAAACA+qCQCQDQZM46oRAnHplkzs3vLEWapjkkAgAAAAAAAAAAAACAgU8hEwCgyRSSJOZ1ZG/JfPCZNO5do5AJAAAAAAAAAAAAAAARCpkAAE1pxtGF+PPjsrdkXnd/T3SXlDIBAAAAAAAAAAAAAEAhEwCgSV02pyVaMp4G126P+Mlj5XwCAQAAAAAAAAAAAADAAKaQCQDQpMYPS+J1U7IfB7+3rBRb99iSCQAAAAAAAAAAAABAc1PIBABoYhfPKMYRrZVndnZHfO+BUj6BAAAAAAAAAAAAAABggFLIBABoYke0JvHWmcXMuZ8tL8dTW23JBAAAAAAAAAAAAACgeSlkAgA0udecVIhjhlWeKaUR193fk08gAAAAAAAAAAAAAAAYgBQyAQCaXEshicvntmTO/eGpNJauK+eQCAAAAAAAAAAAAAAABh6FTAAA4s+OSWLm0Unm3PzOUpTKaQ6JAAAAAAAAAAAAAABgYFHIBAAgkiSJeR3FyKpkrtycxq9X2ZIJAAAAAAAAAAAAAEDzUcgEACAiIiYfWYhXTsp+PLxxSSl2dduSCQAAAAAAAAAAAABAc1HIBADgOe+YXYy2YuWZTbsjfvRwKZ9AAAAAAAAAAAAAAAAwQChkAgDwnFGDk3jj9IxGZkT88OFybNhpSyYAAAAAAAAAAAAAAM1DIRMAgOe58ORCjBpceWZvKeI7S2zJBAAAAAAAAAAAAACgeShkAgDwPO0tSVw6O3tL5sJV5VixsZxDIgAAAAAAAAAAAAAAqD2FTAAAXuTsEwox+cgkc25+ZynSNM0hEQAAAAAAAAAAAAAA1JZCJgAAL1JIkpg3N3tL5rJn0vj9UwqZAAAAAAAAAAAAAAA0PoVMAAAOaObYQrz82Owtmdct7onuklImAAAAAAAAAAAAAACNTSETAIBeXT6nJYoZncyu7RE/XV7OJxAAAAAAAAAAAAAAANSIQiYAAL06ZngSr52S/ch487JSbNtjSyYAAAAAAAAAAAAAAI1LIRMAgIoumVGMI1orz2zf+6dSJgAAAAAAAAAAAAAANCqFTAAAKhrWlsTFM4qZcz95rBxPb7UlEwAAAAAAAAAAAACAxqSQCQBApteeVIjxR1SeKaUR193fk08gAAAAAAAAAAAAAADImUImAACZBhWTuHxuS+bc759K44F15RwSAQAAAAAAAAAAAABAvhQyAQDok5cfm8SMo5LMuWsXl6KcpjkkAgAAAAAAAAAAAACA/ChkAgDQJ0mSxLyOYubcyk1p3LHKlkwAAAAAAAAAAAAAABqLQiYAAH124qhCnHNC9iPkDUtKsbvHlkwAAAAAAAAAAAAAABqHQiYAAAflHbOL0ZqxKHPjrojbHrYlEwAAAAAAAAAAAACAxqGQCQDAQRkzJIk3npz9GPmDh0qxcZctmQAAAAAAAAAAAAAANAaFTAAADtqFJxfjyPbKM3tKETcuKeUTCAAAAAAAAAAAAAAAqkwhEwCAgzZ4UBLvmF3MnFu4shyPbyrnkAgAAAAAAAAAAAAAAKpLIRMAgENyzgmFmDQyqTiTRsT8zlKkaZpPKAAAAAAAAAAAAAAAqBKFTAAADkmxkMS8juwtmQ+sT+MPTytkAgAAAAAAAAAAAABQ3xQyAQA4ZLPGFuJlx1TekhkRsWBxT3SXlDIBAAAAAAAAAAAAAKhfCpkAAByWK+a2RDGjk9m1LeLnK8r5BAIAAAAAAAAAAAAAgCpQyAQA4LAcOzyJ15yU/Vh50wOl2L7XlkwAAAAAAAAAAAAAAOqTQiYAAIftrTOLMXRQ5ZnteyNuXlbKJxAAAAAAAAAAAAAAAPQzhUwAAA7bsLYkLp5RzJz7yWPl6NpmSyYAAAAAAAAAAAAAAPVHIRMAgH7xuimFGHdE5ZmecsT19/fkEwgAAAAAAAAAAAAAAPqRQiYAAP1iUDGJy+e0ZM79bk0ay9aXc0gEAAAAAAAAAAAAAAD9RyETAIB+c+pxSZxyVJI5N7+zFOU0zSERAAAAAAAAAAAAAAD0D4VMAAD6TZIkccXcYubcik1p/GaVLZkAAAAAAAAAAAAAANQPhUwAAPrVlNGFOHti9mPmDUtKsafHlkwAAAAAAAAAAAAAAOqDQiYAAP3u0tnFaM1YlPnsrojbHrElEwAAAAAAAAAAAACA+qCQCQBAvxszNIkLpmU/av7goVJs3GVLJgAAAAAAAAAAAAAAA59CJgAAVfGm6cUY2V55ZndPxHeWlvIJBAAAAAAAAAAAAAAAh0EhEwCAqhg8KIl3zCpmzv3q8XKs3FTOIREAAAAAAAAAAAAAABw6hUwAAKrmlZMKMXFkUnEmjYj5naVI0zSfUAAAAAAAAAAAAAAAcAgUMgEAqJpiIYl5c7O3ZC5dn8Yfn1bIBAAAAAAAAAAAAABg4FLIBACgquaMK8RLj6m8JTMiYsH9PdFTVsoEAAAAAAAAAAAAAGBgUsgEAKDqrpjTEoWMTuZTWyN+saKcTyAAAAAAAAAAAAAAADhICpkAAFTdcSOSeM1J2Y+e311aih17bckEAAAAAAAAAAAAAGDgUcgEACAXl8woxpBBlWe27Y245cFSPoEAAAAAAAAAAAAAAOAgKGQCAJCLEe1JXDSjmDl3+6PlWLvNlkwAAAAAAAAAAAAAAAYWhUwAAHLz+imFGDu08kxPOeL6JT35BAIAAAAAAAAAAAAAgD5SyAQAIDeDiklcNqclc+6eJ9N46JlyDokAAAAAAAAAAAAAAKBvFDIBAMjVaROSmDYmyZy7trMU5TTNIREAAAAAAAAAAAAAAGRTyAQAIFdJksS7OoqZc8s3pnHnE7ZkAgAAAAAAAAAAAAAwMChkAgCQu6mjC3HmxOxH0W8vKcWeHlsyAQAAAAAAAAAAAACoPYVMAABq4p2zi9GasShzw86IHz9iSyYAAAAAAAAAAAAAALWnkAkAQE0cNTSJ86dlP47e+lApNu2yJRMAAAAAAAAAAAAAgNpSyAQAoGbePL0YI9oqz+zuifju0lI+gQAAAAAAAAAAAAAAoBcKmQAA1MzgQUm8fVYxc+4/V5Zj1eZyDokAAAAAAAAAAAAAAODAFDIBAKipv5hciONHJBVnymnEgs5SpGmaUyoAAAAAAAAAAAAAAHg+hUwAAGqqWEhi3tzsLZn3r0ujs0shEwAAAAAAAAAAAACA2lDIBACg5uaOL8RLxlfekhkRMX9xT5TKSpkAAAAAAAAAAAAAAORPIRMAgAHhirktUcjoZK7ZGvGLFeV8AgEAAAAAAAAAAAAAwH4UMgEAGBAmjEji1SdmP55+94FS7NhrSyYAAAAAAAAAAAAAAPlSyAQAYMB468xiDG6pPLN1T8T3HyzlEwgAAAAAAAAAAAAAAP4fhUwAAAaMke1JvGVGMXPu3x8tx7rttmQCAAAAAAAAAAAAAJAfhUwAAAaUN0wtxFFDKs/0lCOuv9+WTAAAAAAAAAAAAAAA8qOQCQDAgNJaTOKyudlbMu9+shyPbCjnkAgAAAAAAAAAAAAAABQyAQAYgF4xoRBTRyeZc9/qLEWapjkkAgAAAAAAAAAAAACg2SlkAgAw4CRJEu/qyN6S+dizady12pZMAAAAAAAAAAAAAACqTyETAIABadqYQpxxfPbj6nX3l2JPjy2ZAAAAAAAAAAAAAABUl0ImAAAD1jtnF2NQxhPrhp0Rtz9qSyYAAAAAAAAAAAAAANWlkAkAwIB19BFJvGFa9iPr9x8sxebdtmQCAAAAAAAAAAAAAFA9CpkAAAxob55ejOFtlWd29UR8d2kpn0AAAAAAAAAAAAAAADQlhUwAAAa0oa1JvH1WMXPul4+XY/WWcg6JAAAAAAAAAAAAAABoRgqZAAAMeOdOLsSE4UnFmXIasWCxLZkAAAAAAAAAAAAAAFSHQiYAAANesZDEFR3ZWzI7u9K4r8uWTAAAAAAAAAAAAAAA+p9CJgAAdeEl4wsxd1zlLZkRf9qSWSqnOSQCAAAAAAAAAAAAAKCZKGQCAFA3rphbjEJGJ/PJLWn88nFbMgEAAAAAAAAAAAAA6F8KmQAA1I2JIwvxF5OzH2G/s7QUO7ttyQQAAAAAAAAAAAAAoP8oZAIAUFfePqsY7S2VZ7buibj1wVI+gQAAAAAAAAAAAAAAaAoKmQAA1JWR7Um85ZRi5tyPHynH+u22ZAIAAAAAAAAAAAAA0D8UMgEAqDtvmFqIMUMqz3SXI25YYksmAAAAAAAAAAAAAAD9QyETAIC609aSxGVzsrdk/nZ1OR7ZUM4hEQAAAAAAAAAAAAAAjU4hEwCAunTG8YWYMjrJnJvfWYo0TXNIBAAAAAAAAAAAAABAI1PIBACgLiVJEu/qyN6S+cizadz9pC2ZAAAAAAAAAAAAAAAcHoVMAADq1sljCnH6hOxH2uvvL8Xeki2ZAAAAAAAAAAAAAAAcOoVMAADq2jvnFKMl46l2/Y6I2x+1JRMAAAAAAAAAAAAAgEOnkAkAQF0bd0QSb5ia/Vj7/QdLsWW3LZkAAAAAAAAAAAAAABwahUwAAOreW04pxvC2yjM7uyNueqCUTyAAAAAAAAAAAAAAABqOQiYAAHVvaGsSb51ZzJz7+YpyPLnFlkwAAAAAAAAAAAAAAA6eQiYAAA3hvBMLcdzwyjPlNGLB4p58AgEAAAAAAAAAAAAA0FAUMgEAaAjFQhJXzG3JnLuvK43FXeUcEgEAAAAAAAAAAAAA0EgUMgEAaBgvGZ/E7LFJ5tz8xaUoldMcEgEAAAAAAAAAAAAA0CgUMgEAaBhJksS8jmJkVTJXb0njVyttyQQAAAAAAAAAAAAAoO8UMgEAaCgnjCzEX0zOfsy9cUkpdnXbkgkAAAAAAAAAAAAAQN8oZAIA0HDePqsY7S2VZ7bsifjBQ6V8AgEAAAAAAAAAAAAAUPcUMgEAaDhHDk7izdOLmXO3PVKOZ3bYkgkAAAAAAAAAAAAAQDaFTAAAGtL50woxZkjlmb2liBuW2JIJAAAAAAAAAAAAAEA2hUwAABpSW0sSl87O3pL5myfK8diz5RwSAQAAAAAAAAAAAABQzxQyAQBoWGdOLMSJo5LMuWs7S5GmaQ6JAAAAAAAAAAAAAACoVwqZAAA0rEKSxLvmZm/JfHhDGvesUcgEAAAAAAAAAAAAAKB3CpkAADS0U44uxJ8fl70l8/rFPdFdUsoEAAAAAAAAAAAAAODAFDIBAGh4l89piZaMJ991OyJuf6ycTyAAAAAAAAAAAAAAAOqOQiYAAA1v3LAk/tuU7EffW5aVYuseWzIBAAAAAAAAAAAAAHgxhUwAAJrCxTOKMay18szO7oibHijlEwgAAAAAAAAAAAAAgLqikAkAQFMY2prEW2cWM+d+trwca7bakgkAAAAAAAAAAAAAwPMpZAIA0DTOO6kQxw6rPFNOI65b3JNPIAAAAAAAAAAAAAAA6oZCJgAATaOlkMTlc1sy5xY9ncb9a8s5JAIAAAAAAAAAAAAAoF4oZAIA0FRedkwSs45OMufmLy5FqZzmkAgAAAAAAAAAAAAAgHqgkAkAQFNJkiTmdRQjq5L5xOY0Fq60JRMAAAAAAAAAAAAAgD9RyAQAoOlMOrIQr5qU/Sh849JS7Oq2JRMAAAAAAAAAAAAAAIVMAACa1NtnF6O9pfLM5t0RP3y4lE8gAAAAAAAAAAAAAAAGNIVMAACa0qjBSbzx5GLm3I8eLseGHbZkAgAAAAAAAAAAAAA0O4VMAACa1oUnF2LU4Moze0sRNyy1JRMAAAAAAAAAAAAAoNkpZAIA0LTaWpK4dHb2lsw7VpVj+cZyDokAAAAAAAAAAAAAABioFDIBAGhqZ59QiMlHJplz8ztLkaZpDokAAAAAAAAAAAAAABiIFDIBAGhqhSSJd3Vkb8l88Jk07l2jkAkAAAAAAAAAAAAA0KwUMgEAaHozji7Eqcdlb8m87v6e6C4pZQIAAAAAAAAAAAAANCOFTAAAiIjL5rRES8bT8drtET9dXs4nEAAAAAAAAAAAAAAAA4pCJgAARMQxw5J43ZTsx+PvPVCKbXtsyQQAAAAAAAAAAAAAaDYttQ5AZT09PfHAAw/E448/Hl1dXbFjx44olUoxbNiwOPLII2PatGkxffr0GDp0aK2jAgDUvYtOKcbCleXYvrf3mR3dEd9bVoqrXuJRGgAAAAAAAAAAAACgmfgp8gFo8+bNccMNN8T3v//9+N3vfhd79uypOF8sFuPUU0+NCy64IObNmxdHHXVUTkkBABrLsLYkLplRjG91lirO/fSxcrz2pDSOHZ7klAwAAAAAAAAAAAAAgFor1DoA/2Xbtm3xsY99LCZMmBAf/vCH44477ojdu3dHmqYVXz09PXH33XfHJz/5yTj++OPj6quvjvXr19f64wAA1KXXnFSI8cMqz5TSiOvu78knEAAAAAAAAAAAAAAAA4JC5gDxn//5nzFt2rT44he/GDt27HiubJkkSZ9e++b37NkT3/zmN2Pq1Kkxf/78Wn8sAIC6M6iYxBVzshfJ/+GpNJauK+eQCAAAAAAAAAAAAACAgaCpC5nvf//7Y8iQIZmvj3/841XN8dWvfjVe+9rXxtq1a19UwoyIzA2ZEfGicubWrVvjqquuine/+91RKpWqmh8AoNH82bFJzDw6yZyb31mKUjnNIREAAAAAAAAAAAAAALXWtIXMUqkUt9xyS+zevbviq1QqxYc//OGq5fjXf/3X+NCHPhSlUqnXEmaW3sqZaZrGtddeG5dcckn09PRU7TMAADSaJEniirnFyKpkrtycxh2rbMkEAAAAAAAAAAAAAGgGTVvI/NWvfhXPPvvs8zZLvnAzZZIkcfHFF8eECROqkmHhwoXx3//7f3/urIjocwmzNy8sZqZpGj/84Q/jyiuvPPzAAABN5MRRhThnUvbj8g1LSrG7x5ZMAAAAAAAAAAAAAIBG17SFzJtvvrlPc+973/uqcv7WrVvjiiuuiJ6enueVMfvLvmLmvlLmDTfcEP/0T//Ub+8PANAM3jGrGG3FyjObdkf88KFSPoEAAAAAAAAAAAAAAKiZlloHqIVyuRw/+MEPDliE3FdgjIg4+eST44wzzqhKhk9+8pOxZs2a551XLfvO+J//83/GeeedF7NmzarqeY3iS1/6UmzevLnP8yeccELMmzevankAgPyNHpLEhScX4nvLyhXnfvhwOV59YhqjhyQ5JQMAAAAAAAAAAAAAIG9NWchctmxZPPvssxXLkEmSxNvf/vaqnP/www/HN77xjecKoX3V23ylQue+LZlJksTevXvjyiuvjD/84Q8HdW4zWrBgQXzkIx85qL9z9tlnK2QCQAN64/Ri/GJFOTbt7n1mbynixiWl+P/+vCkfrwEAAAAAAAAAAAAAmkKh1gFq4Xe/+12f5i6++OKqnP8P//APUSqVIqJymXKffYXKffP7v154/UD2P+O+++6Lb3/724cTv+GtW7cuPvrRj9Y6BgAwQLS3JHHp7GLm3MJV5VixsfImTQAAAAAAAAAAAAAA6ldTFjLvvffeA/75/qXGqVOnxrRp0/r97JUrV8bNN9/cp+2Y+4qW+8qXY8aMibPOOisuvvjiuOSSS+IVr3hFDBky5HnFzKz3S9M0/u7v/i7KZWWB3nzgAx+IjRs31joGADCAnDOpEJOOzH5+m99Z6tMv3AAAAAAAAAAAAAAAoP40ZSHzd7/7Xa/lxTRNI0mSePWrX12Vs7/+9a/3aTvm/hsxL7jggrjrrrti7dq18etf/zpuuumm+O53vxu//e1vY8OGDXH99dfHSSed9Fz2A9n/rFWrVsWPfvSjfvxUjeOWW26JW2+9tdYxAIABppAkMW9u9pbMZc+k8YenFDIBAAAAAAAAAAAAABpR0xUyt23bFg899FDm3LnnntvvZ5dKpViwYEHFTZb7b8VsaWmJm266KX74wx/GaaeddsC/19bWFpdeemncf//9cemll1YsZe7vq1/96mF9lka0cePG+NCHPlTrGADAADVrbCFefmz2c9aC+3uiu6SUCQAAAAAAAAAAAADQaJqukLl8+fLntkVW2lB56qmn9vvZv/jFL2LdunW9nr3/VsxCoRA333xzXHzxxX167/b29rj++uvjbW97W8VS5r6y58KFC2Pt2rWH+Eka00c+8pHn/v0AABzI5XNaopjRyezaFvGz5eV8AgEAAAAAAAAAAAAAkJumK2SuWrXqgH++f4Hx2GOPjbFjx/b72bfcckvmzL4y5dVXXx0XXHDBQZ/xb//2bzFlypSIiBeVMvcvgaZpGrfddttBv3+j+ulPfxrXXXddrWMAAAPcMcOTeO2U7Efo7y0rxbY9tmQCAAAAAAAAAAAAADSSpitkPvHEE71e21eGnDNnTr+fm6Zp/OhHP6q4uXKfIUOGxN///d8f0jmDBw+Oz3zmMxW3f+5z++23H9IZjWbbtm1x9dVX1zoGAFAnLplRjKGDKs9s3xtx87JSPoEAAAAAAAAAAAAAAMiFQuYB7Nsw2Z8WLVoUzz77bEREr2XJfYXQyy+/PMaMGXPIZ1100UUxa9as597vhZIkiTRN4+677z7kMxrJJz7xiVi9enWv108//fQc0wAAA92wtiQumVnMnPvJY+V4eqstmQAAAAAAAAAAAAAAjaLpCpmrVq3KnDnppJP6/dyf//znfZ5997vffdjnvec97zngn+9fBt24cWM8+uijh31WPfvNb34TX/va13q9fuSRR8b//b//N8dEAEA9eO1JhRh/ROWZUhpx3f09+QQCAAAAAAAAAAAAAKDqmq6QuXbt2syZcePG9fu5v/rVr3q9tm9jZUTEKaecEh0dHYd93vnnn9+nucWLFx/2WfVq165d8e53v7vXjaUREV/4whdi7NixOaYCAOrBoGISl81tyZz7/VNpLFtfziERAAAAAAAAAAAAAADV1nSFzB07dmTOHH300f16ZqlUinvvvTeSJKk4lyRJvOUtb+mXMydOnBgzZsx47n1788gjj/TLefXob//2b+Oxxx7r9forX/nKuPLKK3NMBADUk1OPTeKUoyo/30VEXNtZinKFXwABAAAAAAAAAAAAAEB9aLpC5s6dOzOLkaNGjerXM++7777YuXNnRETFbYwRERdccEG/ndvR0ZF5XqVCYiNbtGhRfPGLX+z1ent7e3z961/PMREAUG+SJIl3dRQz5x7flMYdq2zJBAAAAAAAAAAAAACod01ZyMzS1tbWr2fefffdvV7bvxx69NFHx0te8pJ+O/fkk0/OnHn66af77bx60d3dHVdeeWWUSqVeZz796U/HlClTckwFANSjE0cV4uwTsh+pb1hSit09tmQCAAAAAAAAAAAAANSzpitk7tq1K3OmvwuZ99xzT8XraZpGkiTxqle9ql/PzSpkpmka69ev79cz68FnP/vZWLp0aa/XZ82aFR//+MdzTAQA1LNLZxWjNWNR5sZdEbc9bEsmAAAAAAAAAAAAAEA9a7pCZl82ZO7du7dfz7znnnuetwmzN+ecc06/nnv00Uf3em1fng0bNvTrmQPdAw88EJ/97Gd7vV4oFOIb3/hGDBo0KMdUAEA9GzM0iQtPzn6s/sFDpdi4y5ZMAAAAAAAAAAAAAIB61XSFzHI5ezNRX0qbffXUU0/Fk08+GRF/2khZydlnn91v50ZEHHHEEZkzfdkY2ihKpVJcddVVFQu3H/zgB+PUU0/NMRUA0AjeeHIxRrZXntlTirhxSSmfQAAAAAAAAAAAAAAA9LumK2S2tbVlzuzYsaPfzrvrrrt6vbb/1szRo0fH1KlT++3ciIihQ4dmzuzZs6dfzxzIvvjFL8bvf//7Xq9PmDAhPvOZz+SYCABoFIMHJXHp7GLm3MKV5Vi5KfsXhAAAAAAAAAAAAAAAMPA0XSGzvT1jdVFEbNu2rd/Ou/POOyteT9M0kiSJ0047rd/O3Kcvn7XStshGsnz58vjbv/3bijNf+cpXYtiwYTklAgAazTknFOKEkUnFmTQi5neWMjenAwAAAAAAAAAAAAAw8DRdIXP48OGZMytXruy38xYuXPi8TZi9OeOMM/rtzH36UrYsFrM3OdW7NE3j3e9+d+zatavXmYsvvjjOP//8HFMBAI2mWEhi3tzsZ6ul69NY9LRCJgAAAAAAAAAAAABAvWm6QubRRx+duZFo+fLl/XLW2rVrY9myZRERmWeeeeaZ/XLm/ioVEPdpbW3t93MHmq997Wtxxx139Hp95MiR8c///M85JgIAGtXscYV42THZv4xjweKe6CnnEAgAAAAAAAAAAAAAgH7TdIXMsWPHZs70VyHzpz/9aa/X9t+a2d7eHi972cv65cz9bdu2LXNm8ODB/X7uQPLkk0/GJz7xiYoz//iP/xjjxo3LKREA0Ogun9sShYxO5tPbIn7Zf0vZAQAAAAAAAAAAAADIQdMVMidPntzrtSRJIk3TuPvuu/vlrNtuu63i9TRNI0mSePnLXx4tLS39cub+urq6MmdGjRrV7+cOJFdffXXFYupZZ50VV111VY6JAIBGd9zwJF5zUvZj9i0PRewuF3NIBAAAAAAAAAAAAABAf2i6Qua0adMO+Odpmj73f69fvz4WL158WOds3749fvrTnz5vE2ZvXvGKVxzWWb15+umne722rwzayIXM66+/Pn7yk5/0er2trS2uueaaPv07AgA4GG+dWYwhgyrPbN8b8fvtx+UTCAAAAAAAAAAAAACAw9Z0hcwZM2b0ae4//uM/Duucm2++OXbv3h0Rzy97HsiZZ555WGf1ZuXKlZkz48aNq8rZtbZu3br4y7/8y4ozf/M3f9NrQRcA4HAMb0vi4hnZ2y/v3zEuNve055AIAAAAAAAAAAAAAIDD1XSFzD/7sz+LQYP+tK7oQJsRkySJNE3jq1/9anR3dx/yOV/72td6vbb/ua2trXHWWWcd8jmVPPjgg5kzEyZMqMrZtfahD30oNm7c2Ov1GTNmxCc+8YkcEwEAzea/TSnE2KGVZ8pRiLu2HZ9PIAAAAAAAAAAAAAAADkvTFTLb29ujo6PjgFsr9/+zrq6uuPbaaw/pjIULF8Yf/vCH58qdB5KmaSRJEmeeeWYMHjz4kM7JsmzZsgOWTvd3/PGNVwC49dZb45Zbbun1eqFQiG984xvPFXMBAKphUDGJy+e2ZM4t3z06nto7LIdEAAAAAAAAAAAAAAAcjqYrZEZEvP71r694fV+R8pOf/GSsXLnyoN67VCrF//gf/6PP8xdeeOFBvX9fPfPMM7FmzZqIiF5LoRERU6ZMqcr5tbJp06b44Ac/WHHm/e9/f5x22mk5JQIAmtmfH5fE9DGVf0FGRMRvtp4QFR7ZAAAAAAAAAAAAAAAYAJqykPmmN72p12v7lxc3b94cb3rTm2Ljxo19fu+rr746Fi9e3Ot2zP03VhYKhbjooov6/N4H48477+zT3PTp06tyfq185CMfibVr1/Z6/dhjj43PfvazOSYCAJpZkiQxr6OYObe++4h4ZNeYHBIBAAAAAAAAAAAAAHComrKQOXPmzOjo6Ig0TZ9XkNxn358nSRJLliyJOXPmxB133FHxPbds2RKXX355fOtb3+q1jPnC9z/vvPNi7Nixh/15DqS3Qub+n7e1tTVOPPHEqpxfCz/72c9iwYIFFWe+8pWvxPDhw3NKBAAQMWV0Ic6amP3Yfde242NPTw6BAAAAAAAAAAAAAAA4JC21DlArH/jAB+I973lPr9f3L2s+9dRT8apXvSo6Ojrine98Z5xyyilx3HHHRblcjjVr1sTPfvazuPHGG2PDhg0HleF973vfYX2GSn7xi1/0em1fWXTWrFlRKDRGJ3f79u3x3ve+t+LMW97ylrjwwgtzSgQA8F8unV2M360px95S7zPby20x77aIQrI3v2BwENL01IiIaC/0xLhB2+OYzRFTjq5tJgAAAAAAAAAAAADIU9MWMt/5znfGP/zDP8SaNWt63Wi5fykzTdO47777orOz84Dvt+/vV9qOuf+1adOmxfnnn98fH+VFVq1aFQ888EBmlpe+9KVVOb8WPvnJT8bq1at7vT5ixIj4l3/5lxwT1V6aprF3r0IHh6e7u7tPfwYDgfuVgWzEoIjXnxTxg0cqz6URUep90TrU2J9+mcvOcms8vmdU/M2v0/gff7435o6rcSw4AM8F1BP3K/XE/Uq9cc9ST9yv1BP3K/XGPUs9cb9Sb9yz1BP3K/XE/Uq9cc9ST9yv1BP3K/XGPUs19daPq5UkHWiJcjR//vy48sorKxYXI+J5pczDndlX8rz++uvjHe94xyEmr+zLX/5yfPjDHz7g59o/wzXXXBNXXXVVVTLk6c4774yzzjqr4j/7r33ta3H11Vcf0vuvWrUqJk2alDl39tlnx69//etDOqMvtm/fHqVShbVaB5hfvnx51fIAAAdnb7kQC57piJ3l1lpHgX4zsrgr3nnU/VFMmvZrJQAAAAAAAAAAAABVdNJJJ8URRxzR5/lisXhQ8werULV3rgPz5s17rsi3r1B5IGmaPjfT22vfXG/2L0K+/OUvr1oZMyLipptu6tPcGWecUbUMedm9e3dcddVVFf/Zn3HGGfHe9743x1QAAC/WWijHacOerHUM6FebS4Nj9Z4RtY4BAAAAAAAAAAAAALlo6kJmRMS1114bw4cPj4ioWMqM+K9iZm+v3uz/vq2trfH1r3+9f8IfwOOPPx533XVXr9sx9znqqKNi2rRpVcuRl7/7u7+LRx99tNfrra2tcc0112T+uwUAyMMpg9fH6JYdtY4B/eqJPSNrHQEAAAAAAAAAAAAActH0hcxJkybFzTffHMViMSKyS5mHat92zP/zf/5PzJ49uypnRERcf/31fcpx1llnVS1DXv74xz/GP/3TP1Wc+eu//uuYPn16TokAACorJBFnDn+i1jGgXylkAgAAAAAAAAAAANAsWmodYCB49atfHdddd11cccUV0dPTc8Dtkodq/4LnBz7wgfjwhz/cL+97IKVSKb75zW/2qVR67rnnVi1HHrq7u+PKK6+MUqnU68z06dPjU5/6VI6pBpbhw4fHaaedVusY1Lnu7u5YtGjR8/7sZS97WQwaNKhGiaB37lfqxWkRsXFRxG9W1zoJ9I/NpcExadZpMe6IWieB/+K5gHrifqWeuF+pN+5Z6on7lXrifqXeuGepJ+5X6o17lnrifqWeuF+pN+5Z6on7lXrifqXeuGeppj179kS5XK51jOcoZP4/b3vb22LUqFHxjne8IzZu3Pi8UuOhlDNf+Pc/+tGPxhe+8IV+ydqbH/zgB/HUU0/1qVD6mte8pqpZqu1zn/tcLFmypNfrSZLEN77xjWhtbc0x1cCSJElTf36qZ9CgQe4t6ob7lYHqA6emUUy6Y+ETaURUZ0M75GnZs8U4flSx1jGgIs8F1BP3K/XE/Uq9cc9ST9yv1BP3K/XGPUs9cb9Sb9yz1BP3K/XE/Uq9cc9ST9yv1BP3K/XGPUt/2bt3b60jPI9C5n7OO++8WLJkSVx11VXxs5/9LCL+VGrrSznzQFsp0zSNUaNGxVe+8pV461vfWp3Q+/nnf/7nXq/tn2/q1KkxceLEqueplmXLlsVnPvOZijNXX311vOIVr8gpEQDAwWktJnH1SyMm7bovnukeGnvTPxXZppw0JYotSm0MPNcsKsWunt6vd3aV43VT3LsAAAAAAAAAAAAANDaFzBc45phj4ic/+Un88pe/jM985jPxm9/85rkS5gvLmQeyb3bEiBHx3ve+Nz71qU/FyJEjqx077rzzzrjzzjufy9dbQTRJkjj//POrnqdayuVyXHXVVRWbzcccc0x8/vOfzzEVAMChGVbcG8OK//Vcc9rxU6K1VamNgefu1aX4w9O9X39gfRrdpTQGFW18BQAAAAAAAAAAAKBxKWT24txzz41zzz03VqxYEbfeemssXLgwFi1aFBs2bDjgfJIkMXny5DjttNPiDW94Q7zhDW+IIUOG5Jb3f//v/x0RvW/w3N8b3/jGKqepni996Utx7733Vpz5l3/5lxgxYkROiQAAoPHNGRsVC5m7eyIe3pDGrLEKmQAAAAAAAAAAAAA0LoXMDCeeeGJ8/OMfj49//OMREbFly5Z4+umnY9u2bbF3795ob2+PkSNHxoQJE6Ktra0mGdeuXRtDhw6NCy+8MHN28ODBcfrpp+eQqv+tWLEiPv3pT1eceeMb3xhvfvObc0oEAADNYc7Y7JnOrnLMGluofhgAAAAAAAAAAAAAqBGFzIM0YsSIAbd9cdy4cXHrrbfWOkZVpWka73nPe2Lnzp29zgwfPjy+/OUv55gKAACaw1FDIka17IyNPUN6nensSuPyufllAgAAAAAAAAAAAIC8WV9CXbjmmmti4cKFFWc+97nPxbHHHptTIgAAaC4T2zZXvP7EljSe3ZnmEwYAAAAAAAAAAAAAakAhkwFvzZo18Vd/9VcVZ04//fR4//vfn1MiAABoPlmFzIiIxWvL1Q8CAAAAAAAAAAAAADWikMmA9773vS+2bt3a6/XW1ta45pprIkmSHFMBAEBzObZ1WxSjcuGys8uGTAAAAAAAAAAAAAAal0ImA9qWLVvi9ttvrzjziU98ImbMmJFTIgAAaE4tSTmOa9tScWbJunKUykqZAAAAAAAAAAAAADSmlloHgErSNPuHuR977LGYN29e1TJs3769T3MPP/xwn3N89atfjSFDhhxGKgAAyN/Ets3xxJ4je72+fW/E8o1pTBtjez0AAAAAAAAAAAAAjUchk7r33e9+t9YRIiJi3bp1sWDBgj7NfulLX1LIBACg7pzQtjl+kzHT2VWOaWMKueQBAAAAAAAAAAAAgDz5KVkAAAD6ZGRxdwwv7q4409mVveUeAAAAAAAAAAAAAOqRQiYAAAB9kiQRE9s2V5xZvjGNrXuUMgEAAAAAAAAAAABoPC21DlArkydP7vVakiSxYsWKHNP0r3Xr1sVpp51WcabePyMAAFAbE9s2x9Kd43q9nkbE/WvLcebEYn6hAAAAAAAAAAAAACAHTVvIXLVqVSRJEmn64s0tSZLUIFH/6enpqfj5Iur/MwIAALUxoXVLFKIc5Sj0OtPZlcaZE3MMBQAAAAAAAAAAAAA56P0naJtEkiTPezWaF36+RvyMAABAfloL5TimdVvFmcVry1Hu5ZfDAAAAAAAAAAAAAEC9avpCJgAAAAdnYtvmitc3745YtVkhEwAAAAAAAAAAAIDG0vSFzDRNn3s1ov0/Xz1+xpEjR77oM+T9WrlyZZ+ynn322X1+z5EjR1b3HxwAAFRRViEzIqKzq/6+fwAAAAAAAAAAAABAJU1fyAQAAODgjGnZGSPbKs90dpXzCQMAAAAAAAAAAAAAOVHIBAAA4KAkScScsZVnHtmQxo69tmQCAAAAAAAAAAAA0DgUMgEAADhoWYXMUhqxdL1CJgAAAAAAAAAAAACNQyETAACAgzbr6IgkY2ZxVzmXLAAAAAAAAAAAAACQB4VMAAAADtqwtoiTRleuZHZ2lSNNbckEAAAAAAAAAAAAoDEoZAIAAHBIOsZVLmQ+szPiqW05hQEAAAAAAAAAAACAKlPIbEC9baBJkuSA/zcAAMCh6Bif/ZWys6ucQxIAAAAAAAAAAAAAqD6FzAa0d+/ezJlBgwblkAQAAGhkJ41K4ojWyjMKmQAAAAAAAAAAAAA0CoXMBrRt27bMmdbWjJ+aBgAAyFAsJDFnXOWvlQ8+k8aenjSnRAAAAAAAAAAAAABQPQqZDWj9+vWZM0OHDs0hCQAA0Og6xiUVr+8tRSx7RiETAAAAAAAAAAAAgPqnkNmAVqxY0eu1NP3TD0KPHj06rzgAAEADmzs++2tlZ1c5hyQAAAAAAAAAAAAAUF0KmQ3ovvvuq3g9SZI46qijckoDAAA0slGDkzhhZOUtmYsVMgEAAAAAAAAAAABoAAqZDeiXv/xlJEnlH4g+7rjjckoDAAA0uo7xlb9/PLUtYt32NKc0AAAAAAAAAAAAAFAdCpkNZuHChbFq1aqIiEjT3n/gecqUKTklAgAAGl3HuOyvlovX2pIJAAAAAAAAAAAAQH1TyGwge/fujY997GN9mp02bVqV0wAAAM1i2pgk2lsqz3R2KWQCAAAAAAAAAAAAUN8UMhvEunXr4sILL4zOzs5IkqTidsyIiJe//OU5JQMAABrdoGISs8cmFWeWrEuju1T5ewoAAAAAAAAAAAAADGQZO0wYyNatWxf33HNP/PjHP47vfe97sXPnzoiIA5Yxk+S/fjh67NixccIJJ+QVs+6dcMIJmQVXAABodh3jC/H7p0q9Xt/dE/HIhjRmZhQ3AQAAAAAAAAAAAGCgqqtC5pVXXtmQZ/VFT09P7NmzJ7Zs2RLr16+P1atXx6ZNm567vq8wWGk7ZpqmkSRJvO51r8slMwAA0DzmjitERO+FzIiIzrXlmDm2kE8gAAAAAAAAAAAAAOhndVXInD9//vM2PR6uFxYX9/3nNE1jwYIF/XZOf6u0AbMvmxzf/OY393smAACguY09IoljhkU8va33mcVdaVw2J79MAAAAAAAAAAAAANCf6qqQuU9fSof1cMbhOFAxtbfM+89OmDDBhkwAAKAqOsYX4ult5V6vr9ycxsZdaYwa3H+/aAcAAAAAAAAAAAAA8lKodYBDkSTJYb/yOKOar4g/FTD3f1WSpmkkSRJ/+Zd/GYVCXf5rBwAABriOcdnfNe5f23thEwAAAAAAAAAAAAAGMhsya3hGHvaVN5MkialTp8aHPvShGicCAAAa1YyjkxhUiOiu0Lns7ErjlZPyywQAAAAAAAAAAAAA/cWqxAa2/ybN1tbWmD9/frS01GUHFwAAqANtLUnMODqpOLN4bTlK5cb4BTgAAAAAAAAAAAAANBeFzAa1fxmzpaUlvvWtb8Wpp55a41QAAECj6xhf+Wvm9r0RKzYpZAIAAAAAAAAAAABQfxQyG0ySJM8rYx511FHx7//+7/H2t7+9xskAAIBm0DEu+2tmZ5dCJgAAAAAAAAAAAAD1RyGzTu0rXr7wFfGnIuagQYPiPe95Tzz00ENx3nnn1TgtAADQLI4dHjFmSOWZzq5yPmEAAAAAAAAAAAAAoB+11DrAodhXPDwcaVp5I0t/nJGHF36O6dOnxyWXXBLvec974phjjqlRKgAAoFklSRId4wvxixW9ly6Xb0xj2540hrXVx/cuAAAAAAAAAAAAAIiow0JmVpGy3s45VEmSxPjx42PKlCkxffr0OPXUU+PMM8+MyZMn1zoaAADQ5LIKmeU04v515Tjj+GKOqQAAAAAAAAAAAADg8NRVIXPhwoX98j5pmsarXvWqSJLkecXLff85SZL41a9+1S9n9YckSWLQoEHR1tYWgwcPjjFjxsTo0aOjUCjUOhoAAMCLzDo6iWISUarwe246u9I44/j8MgEAAAAAAAAAAADA4aqrQubZZ5/dkGcBAAA0kqGtSUwbk8SDz/TeyOzsKj/3C3EAAAAAAAAAAAAAoB5YsQgAAEC/6xhf+evm5t0RqzZXWKEJAAAAAAAAAAAAAAOMQiYAAAD9bu647M2XnWsVMgEAAAAAAAAAAACoHwqZAAAA9LtJRyYxoq3yzOKucj5hAAAAAAAAAAAAAKAfKGQCAADQ7wpJEnPHV/7K+fCGNHZ125IJAAAAAAAAAAAAQH1o+kJmkiTPvQAAAOg/HeMqf8/qKUcsXa+QCQAAAAAAAAAAAEB9aOpCZpqmL3oBAADQP+aMK0TWr77p7CrnkgUAAAAAAAAAAAAADldLrQPUysqVK2sdAQAAoKGNaE/ixFFJLN/Y+y+/6ewqR5qmkSRZ1U0AAAAAAAAAAAAAqK2mLWROnDix1hEAAAAaXsf4yoXM9TsiurZFHDM8x1AAAAAAAAAAAAAAcAgKtQ4AAABA4+oYn/21s3NtOYckAAAAAAAAAAAAAHB4FDIBAAComimjkhg6qPJMZ5dCJgAAAAAAAAAAAAADn0ImAAAAVVMsJDFnXOWvng+sT2NPT5pTIgAAAAAAAAAAAAA4NAqZAAAAVFXH+KTi9b2liIeeUcgEAAAAAAAAAAAAYGBTyAQAAKCq5mZsyIyI6FxbziEJAAAAAAAAAAAAABw6hUwAAACqavSQJCaOqLwls7NLIRMAAAAAAAAAAACAgU0hEwAAgKrrGF+5kLlma8T6HWlOaQAAAAAAAAAAAADg4ClkAgAAUHUd47O/fi62JRMAAAAAAAAAAACAAUwhEwAAgKo7eUwSbcXKM51rFTIBAAAAAAAAAAAAGLgUMgEAAKi6QcUkZo1NKs4sXZdGTznNKREAAAAAAAAAAAAAHByFTAAAAHLRMb7yV9Cd3RGPblDIBAAAAAAAAAAAAGBgaql1gEawe/fuWL9+faxbty42btwYe/bsid27d8fevXujXC7XOl6vLr/88lpHAAAAmkjHuEJElCrOdK4txylH+91BAAAAAAAAAAAAAAw8CpkHadWqVfHb3/427r333njwwQfj4YcfjnXr1tU61iFRyAQAAPI0blgS44+I6Nre+0xnVxqXzs4vEwAAAAAAAAAAAAD0lUJmH9x7771x8803x/e///1YvXr1866laVqjVIcnSZJaRwAAAJrQ3PGF6Hqs3Ov1xzelsXl3GiPbfWcBAAAAAAAAAAAAYGBRyOxFT09P3HDDDfGlL30plixZEhEHLl/WY7GxXkukAABA/esYX4ifVChkRkQs7irHOZOKOSUCAAAAAAAAAAAAgL5RyDyA2267LT72sY/FihUrnlde7K18WU8Fx3oskAIAAI1j5tFJtBQieip0MjvXpnHOpPwyAQAAAAAAAAAAAEBfKGTuZ8uWLXHllVfGD3/4w16LmPVUvgQAABho2luSOOWoJJas6/271eKucpTKaRQLfqEMAAAAAAAAAAAAAANHodYBBoqlS5fGnDlznitjJkny3CtN0+deAAAAHJ6O8ZW/im7bG/H4Jt+/AAAAAAAAAAAAABhYFDIj4ne/+12cc845sXr16ufKmBGhhAkAAFAFHeOzN192dvkuBgAAAAAAAAAAAMDA0vSFzEceeSRe97rXxaZNm160ERMAAID+N2F4EqMHV57pXFvOJwwAAAAAAAAAAAAA9FFLrQPU0tatW+P888+PLVu2PG8r5sHa93cBAADIliRJdIwvxC8f7710+dizaWzfm8YRrb5vAQAAAAAAAAAAADAwNPWGzL/6q7+K5cuXH3QZc98mzX2vfX+3Xl4AAAC11jG+8tfRchqxZK3vLwAAAAAAAAAAAAAMHE27IfOee+6Jb3zjGwdVxtx/E+b+8xMnToypU6fG5MmT46ijjorRo0dHe3t7tLW12Z4JAABwALPHJlFI/lS87E1nVzlOP76pf48QAAAAAAAAAAAAAANI0xYy//7v/z7SNI0kSTLLmC8sYo4aNSre9KY3xfnnnx+nn356jBkzptpxAQAAGsrQ1iSmjk7i4Q29fx/rXFt+7nsbAAAAAAAAAAAAANRaUxYyOzs74+c///lBlTHTNI3JkyfHpz/96Xjb294WbW1teUQFAABoWB3jC/HwhlKv1zfuili9JY2JIxUyAQAAAAAAAAAAAKi9Qq0D1ML111+fOZMkyXOFzWKxGP/rf/2vePjhh+OKK65QxgQAAOgHHeOzi5adayv/Eh0AAAAAAAAAAAAAyEtTbsj83ve+99zmywPZfyvm8OHD49Zbb41XvepVecUDAABoCpOPTGJ4W8TWPb3PLO4qxxtPLuYXCgAAAAAAAAAAAAB60XQbMh988MF4+umnI+JPhcsX2r+M2d7eHrfffrsyJgAAQBUUkiTmjqv8tfTBZ9LY1W1LJgAAAAAAAAAAAAC113SFzLvvvjtzJk3TSJIk/vEf/zFe8YpX5JAKAACgOXWMType7ylHLFuvkAkAAAAAAAAAAABA7TVdIfOPf/xjr9eSJHmujPnSl740PvjBD+aYDAAAoPnMydiQGRHRubacQxIAAAAAAAAAAAAAqKzpCpkrV67s09zf/M3fVDkJAAAAI9uTOPHIylsyO7sUMgEAAAAAAAAAAACovaYrZK5atSqS5MU/7Lv/nx111FHxhje8Ic9YAAAATatjfOVC5trtEV3b0pzSAAAAAAAAAAAAAMCBNV0hc+PGjb1eS9M0kiSJV7/61VEoNN0/GgAAgJroGJ/9/cuWTAAAAAAAAAAAAABqrelahzt37sycOf3003NIAgAAQETE1NFJDBlUeaZzrUImAAAAAAAAAAAAALXVdIXMPXv2ZM5MmTIlhyQAAABERPz/7N15nNx1fT/w93c3mzshhBw74QoQyAGBjIDhUAhVUPgJ1noiKlZrW20VD7z6qEX9Ua1WPKpYj/oTiidq1RZaKpeICAbKJJBAOAyGI7M5Cbmz2Z3v7w+6cROy39mE3e/szDyfj8c8JDvv3XnN9/H1j88+9jXv1pYkjp+aZM4sWZVGZ3eaUyIAAAAAAAAAAAAAeK6mK2SOHj266szBBx+cQxIAAAB6FAvZx9Md3REPrlHIBAAAAAAAAAAAAKB2mq6QOX78+KozY8aMySEJAAAAPYrt1Y+npXIlhyQAAAAAAAAAAAAAsHcKmXtRqfgjXwAAgDxNGpPEoeOTzJlShw2ZAAAAAAAAAAAAANRO0xUyjzjiiEjT7D/i3bJlS05pAAAA6DGvkF3IfOKZNNZuVcoEAAAAAAAAAAAAoDaarpB53HHHVZ1Zs2ZNDkkAAADorViofkRdVK7kkAQAAAAAAAAAAAAAnkshcy+WL1+eQxIAAAB6mzM5ieGt2TOlDoVMAAAAAAAAAAAAAGqj6QqZL37xi6vOLFmyJIckAAAA9Da8NYnjpiSZM4s70uiupDklAgAAAAAAAAAAAIA/aLpC5uGHHx7HH398pGkaSfLcP/RN0zR+85vf1CAZAAAAxUL2MXXrzoiH1ylkAgAAAAAAAAAAAJC/pitkRkRccMEFe/16T0Hz3nvvjTVr1uQZCQAAgKheyIyIKJUrOSQBAAAAAAAAAAAAgN01ZSHzbW97W7S2tkbEH0qYafqHDSvd3d3xox/9qCbZAAAAmllhbMTUMdkzpQ4bMgEAAAAAAAAAAADIX1MWMqdPnx6vfe1rdyth9kiSJNI0jX/6p3+qQTIAAIDmliRJ1S2Zv1ufxobtSpkAAAAAAAAAAAAA5KspC5kRER/5yEeipeXZt7+3LZmPPPJIfO9736tJNgAAgGZWrZAZEbG4o5JDEgAAAAAAAAAAAAD4g6YtZB5//PHxzne+M3NL5qWXXhobN26sQToAAIDmddyUJIZVOa0uKtuQCQAAAAAAAAAAAEC+mraQGRHx6U9/Og455JCI2PuWzFWrVsXb3/72mmQDAABoVqPakpg9KcmcKXVUorKXD9gBAAAAAAAAAAAAgMHS1IXMsWPHxtVXXx2tra0RsXsps2dL5r/927/FJz7xiVrGBAAAaDrFQvZxdeOOiMeeVsgEAAAAAAAAAAAAID9NXciMiDjrrLPiK1/5ym6bMSN2L2V+8pOfjL//+7+vUUIAAIDmUyxkb8iMiCiVFTIBAAAAAAAAAAAAyE/TFzIjIv78z/88PvjBD+4qYe5tU+bf/d3fxZ/+6Z/Gjh07apwWAACg8R12QBITR2XPlMqVfMIAAAAAAAAAAAAAQChk7vKZz3wm/vZv/3bXpsy9lTL/9V//NYrFYtxxxx21jAoAANDwkiSJee3ZR9aH1qWxpdOWTAAAAAAAAAAAAADyoZDZyyc/+cn4/Oc/v6uMubdS5rJly+KMM86IV73qVVEqlWoZFwAAoKEVC0nm85U04v5VCpkAAAAAAAAAAAAA5EMhcw/vfe9748Ybb4ypU6fuKmJGxHP++9///d/jpJNOihNPPDG+8IUvxLJly2oZGwAAoOEcP7UlWrI7mVEqV/IJAwAAAAAAAAAAAEDTG1brALXyq1/9qs/nWltb40tf+lK8973vjY6Ojl3bMfcsZUZElEqlWLRoUVx66aUxderUKBaLceyxx8ahhx4ahUIhxowZEyNHjozW1tZc3te+OOOMM2odAQAAoE/jRiRx9MQkHlrX9xbMUkdlt7MaAAAAAAAAAAAAAAyWpi1kLliwoF9/sNtTvNzz372LmT1f6+joiBtuuCFuuOGGAU478JIkia6urlrHAAAAyFQstMRD67r7fH7t1ognN0YcekCOoQAAAAAAAAAAAABoSi21DlBrPYXKvh7Vvi9Jkt0e1X7eUHoAAAAMdcVC9Q/SKZUrOSQBAAAAAAAAAAAAoNk1fSFzz0Llno9q9iw4Vvt5Q+EBAABQL448MIlxw7NnFDIBAAAAAAAAAAAAyMOwWgeotYHcFFkPWycVMgEAgHrS2pLEvPaWuP3xvkuXS9eksb0rjZHDnHcAAAAAAAAAAAAAGDxNvyETAACAoW1eIbto2VWJWLp66H9ADgAAAAAAAAAAAAD1TSETAACAIW1ee/Wja6nc9wZNAAAAAAAAAAAAABgICpkAAAAMaQeOSuKIA7O3ZJY6FDIBAAAAAAAAAAAAGFwKmQAAAAx5xfbsQmZ5U0THpjSnNAAAAAAAAAAAAAA0o6YvZCZJ0lQPAACAelQsVD++2pIJAAAAAAAAAAAAwGAaVusAtZSmtqcAAADUg5mTkhg1LGJbV98zpXIlzj26Nb9QAAAAAAAAAAAAADSVpi1kXnzxxbWOAAAAQD8Na0li7tQkFj7V9wfrLFmdxs7uNNpakxyTAQAAAAAAAAAAANAsmraQ+e1vf7vWEQAAANgHxUJLLHyqu8/nt3dFLFubxtypCpkAAAAAAAAAAAAADLyWWgcAAACA/ii2Vz/ClsqVHJIAAAAAAAAAAAAA0IwUMgEAAKgLU8YmcfD47JlSR5pPGAAAAAAAAAAAAACajkImAAAAdaPalswVG9JYv00pEwAAAAAAAAAAAICBp5AJAABA3SgWqh9jS+VKDkkAAAAAAAAAAAAAaDYKmQAAANSNOZOTGN6aPVMq25AJAAAAAAAAAAAAwMBTyAQAAKBujBiWxLFTksyZ+1ZVoruilAkAAAAAAAAAAADAwFLIBAAAoK4U27OPsps7Ix5dr5AJAAAAAAAAAAAAwMBSyAQAAKCuFAvVj7KlciWHJAAAAAAAAAAAAAA0E4VMAAAA6sq0cRFTxmTPlMo2ZAIAAAAAAAAAAAAwsBQyAQAAqCtJklTdkvno+jQ27lDKBAAAAAAAAAAAAGDgKGQCAABQd4rt2cfZNCIWd1TyCQMAAAAAAAAAAABAU1DIBAAAoO7MnZpEa5I9UyrbkAkAAAAAAAAAAADAwFHIBAAAoO6Makti1qTsRuaijkpUUqVMAAAAAAAAAAAAAAaGQiYAAAB1qVjIPtJu2B6xYoNCJgAAAAAAAAAAAAADQyETAACAulQsZG/IjIgolRUyAQAAAAAAAAAAABgYCpkAAADUpekTkpgwMnumVK7kEwYAAAAAAAAAAACAhqeQCQAAQF1KkiSK7dnH2mVr09i605ZMAAAAAAAAAAAAAJ4/hUwAAADqVrGQZD7fnUbcv0ohEwAAAAAAAAAAAIDnTyETAACAunVCe0tkVzIjSuVKLlkAAAAAAAAAAAAAaGwKmQAAANStcSOSmHFQdiWzVK5EmtqSCQAAAAAAAAAAAMDzM6zWAWrlbW97W60j1FSSJPGtb32r1jEAAACet2J7Eo+s67twuWZrxFObIg4Zn2MoAAAAAAAAAAAAABpO0xYyr7rqqkiS7C0qjSpNU4VMAACgYRQLLXHt0krmTKlciUPGt+aUCAAAAAAAAAAAAIBG1LSFzB5p2vcWFQAAAIa+GROTGDs8YnNn3zOlciXOn6mQCQAAAAAAAAAAAMD+a/pCZjNuyVRCBQAAGklrSxIntLfEHY/3vSXzgTVp7OhKY8Sw5jsDAgAAAAAAAAAAADAwmr6Q2WzlxGYsoAIAAI2v2J7EHY/3/Xxnd8TSNWm8oOBMBAAAAAAAAAAAAMD+aal1AAAAAHi+5hWqH29L5b43aAIAAAAAAAAAAABANQqZAAAA1L2Jo5I4fEL29stFCpkAAAAAAAAAAAAAPA8KmQAAADSEYnt2IfOpTRGrN6c5pQEAAAAAAAAAAACg0ShkAgAA0BCKhepH3FKHLZkAAAAAAAAAAAAA7J9htQ5Qa0mSvUFlKEnT/m1yqaf3BAAAMFBmTUpi5LCI7V19z5TKlXjZjNb8QgEAAAAAAAAAAADQMJq6kNnfguNQkCTJbkXLrOz19L4AAAAGSltrEnOnJnH3U32fie5flcbO7jTaWn2QDQAAAAAAAAAAAAD7pmkLmY899litIzxHd3d3bNu2LbZs2RIrV66MJ598MpYuXRqlUinuvffe6Op6ds1L73JmT/my97+nTZsW3/rWt2LWrFm1eSMAAAA1Umxvibuf6u7z+W1dEQ+vS+PYKQqZAAAAAAAAAAAAAOybpi1kHn744bWOsE82btwYN954Y1xzzTXxn//5n9HV1bWrmJmmaaRpuuvf5XI5Xvva18bXvva1eOMb31jr6AAAALmZV2iJiL4LmRERpXIljp3Skk8gAAAAAAAAAAAAABqGv0CtE+PHj49Xv/rV8bOf/Sx+//vfx1/+5V9GW1vbriJmROwqZqZpGps3b443v/nNcemll9Y4OQAAQH7axyYxbVz2TKmc5hMGAAAAAAAAAAAAgIaikFmHpk2bFl/96ldj0aJFceqpp+62HTMidtuc+YUvfCHe/va3R5r6g2MAAKA5FAvZR93HNqSxfpszEgAAAAAAAAAAAAD7RiGzjs2aNSt+/etfxwc+8IHdCpc9/91TyrzqqqvioosuqlVMAACAXBXbqx91F3dUckgCAAAAAAAAAAAAQCNRyKxzSZLEP/7jP8ZXv/rVXf/uKWL2bM5M0zR++MMfxsc+9rEapwUAABh8x05Joq3KabdUtiETAAAAAAAAAAAAgH2jkNkg/vIv/zK+8IUv7LYpMyJ2K2V+6lOfiu9///s1SggAAJCPEcOSOHZKkjmzqKMS3RWlTAAAAAAAAAAAAAD6TyGzgbznPe+Jiy++eFcJs7eeUua73vWuKJfLNUoIAACQj2Ih+7i7uTPid08rZAIAAAAAAAAAAADQfwqZDebKK6+Mww47LCJiVymz99bMjRs3xl/91V/VJBsAAEBeiu3Vj7ulskImAAAAAAAAAAAAAP2nkNlgRo8eHZ///Od3K2FGxK6tmWmaxs9//vO48cYba5QQAABg8B08PmLS6OyZUrmSTxgAAAAAAAAAAAAAGoJCZgP6kz/5kzj22GMj4g9bMntL0zQ+85nP5B0LAAAgN0mSVN2S+ej6NDbtsCUTAAAAAAAAAAAAgP5RyGxQf/3Xf93nlsyIiFtvvTXuvffeWkQDAADIRbGQfeStpBH3rbIlEwAAAAAAAAAAAID+UchsUK95zWti2LBhEbH3LZkREVdddVWOiQAAAPI1d2oSLXs/Du1SKtuQCQAAAAAAAAAAAED/KGQ2qIMOOihOPvnk52zJjHi2oJmmafzHf/xHDZIBAADkY8zwJGZNym5klsqVvZ6bAAAAAAAAAAAAAGBPCpkN7LTTTnvO13r/ofHjjz8e999/f56RAAAAcjWvPfvY+/T2iBXPKGQCAAAAAAAAAAAAUJ1CZgM77rjjqs7cddddOSQBAACojWIhe0NmRESprJAJAAAAAAAAAAAAQHUKmQ3siCOOqDqzZMmSHJIAAADUxhEHJnHAiOyZUrmSTxgAAAAAAAAAAAAA6ppCZgObNGlS1ZmlS5fmkAQAAKA2WpIk5hWyj77L1qaxbactmQAAAAAAAAAAAABkU8hsYKNHj+7zuSRJIk3TePLJJ3NMBAAAkL9ie5L5fFcl4v7VCpkAAAAAAAAAAAAAZFPIbGBJkv1HxxERGzduzCEJAABA7ZzQ3hLVTkelciWXLAAAAAAAAAAAAADUL4XMBrZ169aqM5s2bcohCQAAQO0cMDKJoyZmVzJL5UqkqS2ZAAAAAAAAAAAAAPRNIbOBPfXUU1Vntm/fnkMSAACA2ioWsguZq7dElH1eDQAAAAAAAAAAAAAZFDIb2COPPFJ1ZtSoUTkkAQAAqK1iofrxt9RRySEJAAAAAAAAAAAAAPVKIbOB/eY3v6k6M3r06BySAAAA1NbRE5MY05Y9UyorZAIAAAAAAAAAAADQN4XMBtXd3R3//d//HUmS7PX5NE0jImLy5Ml5xgIAAKiJ1pYkjm/f+/mox5LVaezoSnNKBAAAAAAAAAAAAEC9UchsUNddd12sXbs2Iv5QvtxTkiQxffr0HFMBAADUTrE9+wjc2R3x4BqFTAAAAAAAAAAAAAD2TiGzQV1++eX9mjvqqKMGOQkAAMDQMK9Q/Qhc6qjkkAQAAAAAAAAAAACAeqSQ2YCuvPLK+J//+Z9IkqTP7Zg9TjrppJxSAQAA1Nak0UkcdkCSObOobEMmAAAAAAAAAAAAAHunkNlg7rjjjrj00ksjSbL/yLjHaaedNsiJAAAAho5iIfus9MTGNNZsUcoEAAAAAAAAAAAA4LkUMhvIL3/5yzj//PNjx44dERF73Y7Zu6h51FFHxZFHHplbPgAAgFortlc/Bi/qqOSQBAAAAAAAAAAAAIB6o5DZALq7u+Pyyy+Pl73sZbFhw4ZIkmSvZcweaZpGkiTx+te/PseUAAAAtTd7chIjWrNnSmWFTAAAAAAAAAAAAACeSyGzjnV2dsY111wTs2fPjssuuyx27ty52wbMPfV+rqWlJS6++OI8YgIAAAwZba1JzJ3a97kpIuK+VWl0Vfr+kBsAAAAAAAAAAAAAmtOwWgdg36xfvz5+/etfx3XXXRc//elPY/369bu2YfYULvuzHfNP/uRPYsaMGblkBgAAGEqKhZa4Z2V3n89v3Rnx8No05kzJLm4CAAAAAAAAAAAA0FyatpD5r//6r7WO0Kc0TaOzszN27NgR69ati9WrV8fy5cvjoYceihUrVuw2F7H75su+ypi9Z4YNGxaXXXbZIKUHAAAY2ortLRHRdyEzIqLUUYk5U1ryCQQAAAAAAAAAAABAXWjaQuZb3/rW3UqK9WDPsmV/iph7fn+SJPGBD3wg5syZM+D5AAAA6kH7uCQKYyPKm/ueKZXTuOj4/DIBAAAAAAAAAAAAMPQ1bSGzR3+KjEPF3gqk/cnf831JkkSxWLQdEwAAaHrzCi1RfqTS5/PLn05jw/Y0Joysrw/yAQAAAAAAAAAAAGDwtNQ6QK0lSVI3j4hnC5i9H/15fz3fN3Xq1Pj5z38eI0aMGNRrCgAAMNQVC9WPw4vKfRc2AQAAAAAAAAAAAGg+NmTW0YbMfdW7jNne3h433nhjHHzwwTVOBQAAUHvHTUliWEtEV0bnstSRxoIj8ssEAAAAAAAAAAAAwNDW9BsyG1XvMuasWbPi9ttvj2OPPbbGqQAAAIaGkcOSmDM5yZxZVK5Ed6VxP8QHAAAAAAAAAAAAgH2jkNlgkiSJJEkiTdNI0zTe8pa3xD333BNHHXVUraMBAAAMKfMK2UfiTZ0Ry59WyAQAAAAAAAAAAADgWQqZDaCnhNm7iDlnzpz4xS9+EVdddVWMHj261hEBAACGnGJ79obMiIhFHQqZAAAAAAAAAAAAADxLIbPO9C5f9jwiYlcR88QTT4zvfve7sXjx4njpS19a47QAAABD12EHJDFxVPZMqVzJJwwAAAAAAAAAAAAAQ96wWgeotZ5CYz1J0903tBx55JHx6le/Ol73utfFiSeeWKNUAAAA9SVJkigWWuLm5X2XLh9el8bmzjTGDq+/syMAAAAAAAAAAAAAA6upC5l7FhvrwZQpU2L27NkxZ86cmD9/fpxxxhkxffr0WscCAACoS9UKmZU04r5VaZx2qEImAAAAAAAAAAAAQLNr2kLmt7/97VpH6FOSJDF8+PAYMWJEjBgxIiZOnBhTp06NqVOnxujRo2sdDwAAoGGcMDWJluTZ4mVfSuVKnHZoS36hAAAAAAAAAAAAABiSmraQefHFF9c6AgAAADU2ZngSxxyUxLK1fTcyS+VKpGkaSWJLJgAAAAAAAAAAAEAzs+IDAACAplYsZB+N12+LePyZjBWaAAAAAAAAAAAAADQFhUwAAACaWrFQffNlqUMhEwAAAAAAAAAAAKDZKWQCAADQ1I48MInxI7JnFpUr+YQBAAAAAAAAAAAAYMhSyAQAAKCptSRJzGvPPh4/sCaNbTttyQQAAAAAAAAAAABoZgqZAAAANL1iIcl8vqsSsXS1QiYAAAAAAAAAAABAM1PIBAAAoOmdUGVDZkREqaOSQxIAAAAAAAAAAAAAhiqFTAAAAJrehJFJHHVg9pbMUlkhEwAAAAAAAAAAAKCZKWQCAABARMwrZBcyOzZHlDelOaUBAAAAAAAAAAAAYKhRyAQAAICIKLZXPyIv6rAlEwAAAAAAAAAAAKBZKWQCAABARBwzKYnRbdkzpbJCJgAAAAAAAAAAAECzUsgEAACAiBjWksTxU5PMmftXpbGzO80pEQAAAAAAAAAAAABDiUImAAAA/K9iIfuYvKM74sE1CpkAAAAAAAAAAAAAzUghEwAAAP7XvPbqx+RSRyWHJAAAAAAAAAAAAAAMNQqZAAAA8L8mj0ni0PFJ5kypbEMmAAAAAAAAAAAAQDNSyAQAAIBe5hWyC5mPP5PG2q1KmQAAAAAAAAAAAADNZlitAzSi7u7uePDBB+Oxxx6LlStXxsqVK6OjoyO2bNkS27Zti+3bt0dnZ2cMHz48Ro4cGaNGjYoxY8ZEe3t7TJs2LaZNmxZHHnlkzJo1K1pbW2v9dgAAAJpKsdAS//FQJXNmUbkSLz3KeQ0AAAAAAAAAAACgmShkDoAnnngibrrppvjlL38ZixcvjmXLlsXOnTuf989ta2uL2bNnx7x58+LMM8+Ml770pXHIIYcMQGIAAAD6MmdyEsNbIzq7+54pdShkAgAAAAAAAAAAADQbhcz99PDDD8fVV18dP/7xj+PRRx/d9fU0TQfsNTo7O2Px4sVx3333xb/+679GRMQxxxwTr3vd6+LNb35zzJgxY8BeCwAAgGcNb03iuClJ3Fvu+3y3uCON7koarS1JjskAAAAAAAAAAAAAqKWWWgeoJ2maxve///049dRTY/bs2fEP//AP8cgjj0SaprseERFJkgzYo+d1ex4PPfRQXH755TFz5sw4/fTT40c/+lEtLwkAAEBDKhayj8tbd0Y8vG7gPpAHAAAAAAAAAAAAgKFPIbOfrrrqqpg1a1a86U1vioULF+4qSPanRLm/j4i9lzt7nr/zzjvjDW94Q8yePTu+853v1PLyAAAANJRqhcyIiFK5kkMSAAAAAAAAAAAAAIYKhcwqFi1aFPPnz4+3v/3tu23DrFa+HAjVSpo9Mw899FBcfPHFcdppp8X9998/IK8NAADQzApjI6aOyZ4pddiQCQAAAAAAAAAAANBMFDL7UKlU4qMf/Wi88IUvjHvuuec52zAHunzZX71fd888d911V5x44onxsY99LPdcAAAAjSRJkqpbMn+3Po0N2529AAAAAAAAAAAAAJqFQuZerFmzJl760pfGZz/72ejq6tpVfoyImpQw+7Ln1syIiK6urvjUpz4V55xzTqxbt66W8QAAAOravCqFzIiIxR2VHJIAAAAAAAAAAAAAMBQoZO7hoYceihNPPDFuu+223bZQDqUi5p723JiZpmncfPPNcdJJJ8Wjjz5a63gAAAB1ae6UJIZVOTUv6hia50QAAAAAAAAAAAAABp5CZi9Lly6NBQsWxJNPPvmcrZj7q6ckuS+P/bXntswVK1bEmWeeGQ899NB+/0wAAIBmNaotiVmTss9oi8qVqAzRD+8BAAAAAAAAAAAAYGApZP6vRx99NM4666xYtWrVfm/FzCpX9vysrEd/fk41e27LLJfLsWDBgnjsscf6/TMAAAB4VrGQfWx+ZkfEY08rZAIAAAAAAAAAAAA0g2G1DjAUbN68OV75ylfG2rVr93kr5p5lyT2/r62tLQqFQkyZMiWmTJkSo0ePjhEjRsTw4cNj586dsX379ti6dWusXr06Vq9eHeVyOXbu3Pmc19iz3FlN71LmqlWr4pWvfGXceeedMWbMmH69LwAAACKKhSSuWZw9UyqncdTEfPIAAAAAAAAAAAAAUDsKmRHxpje9KR588MF9KmPurSDZ1tYW8+fPj9NPPz3mz58fs2fPjhkzZkRra2u/s3R3d8fy5cvjwQcfjN/+9rdxxx13xG9/+9vYsWPHrtftb86eUmZExNKlS+Mtb3lL/OQnP+l3FgAAgGZ3+AFJHDgy4untfc8s6qjEa47t/7kPAAAAAAAAAAAAgPrU9IXMa665Jv793/99v8qYaZpGS0tLnHfeefGGN7whzj///Bg/fvzzytPa2hpHH310HH300XHBBRdExLMbPK+//vr4wQ9+ENddd110d3fvKmb2t5SZpmn87Gc/i+9+97tx0UUXPa+MAAAAzSJJkigWWuKWxyp9zixbm8aWzjTGDE/6nAEAAAAAAAAAAACg/rXUOkAtrVu3Lt7//vf3u4zZuwQ5fPjweO973xu/+93v4j/+4z/ioosuet5lzL6MHTs2Xv/618dPf/rTeOyxx+KDH/xgjBo1alfZsve2zqzsaZrG+973vli/fv2g5AQAAGhExUL2mauSRty/qvqH+wAAAAAAAAAAAABQ35q6kHnZZZfFunXrIqJ/ZcyeuTe84Q3xyCOPxOc///k4/PDDBz1nb4ccckh85jOfiUceeSTe8pa37MqdVcrs/d7WrVsXH//4xwc7JgAAQMM4fmpLtFT5HJxSue8NmgAAAAAAAAAAAAA0hqYtZHZ0dMS3vvWtqtsle2/FPOigg+JnP/tZfO9734tDDjkkp6R7VygU4qqrrorrr78+pkyZsitrlp738S//8i+xatWqPGICAADUvXEjkjh6YvZ5q9RRqfpBPwAAAAAAAAAAAADUt6YtZH7uc5+LHTt2RETf2zF7b8WcO3du3H333XHBBRfklrE/zj333Fi4cGGccMIJkaZpn6XM3u9xx44dccUVV+QVEQAAoO4VC9nH57VbI57cmFMYAAAAAAAAAAAAAGqiKQuZXV1dcdVVV2VulOzZJpmmaZx00klx++23x+GHH55jyv479NBD41e/+lXMnz8/s5QZ8Yf3ddVVV0V3d3eOKQEAAOpXsZC9ITMiolSu5JAEAAAAAAAAAAAAgFppykLmf//3f8f69esjYu/bMXtKi0mSxMyZM+MXv/hFjB8/Pu+Y+2Ts2LFxww03xKxZsyIi9lrK7P1e161bF//93/+dWz4AAIB6duSBSYwbnj2jkAkAAAAAAAAAAADQ2JqykPmDH/ygz+d6FxlHjRoVP/7xj2PChAk5pHr+DjjggPjxj38co0ePjoi9lzJ7y7oOAAAA/EFrSxLz2rOP0EvXpLG967kf+gMAAAAAAAAAAABAY2jKQuYtt9ySWVbs2Y75sY99LI499tgckz1/c+bMicsuu2yvmz979GwAvfnmm3NMBgAAUN/mFbI/9KarErF0tUImAAAAAAAAAAAAQKNqukLm8uXLo1wuR0Q8p7TYu6Q5ffr0eP/7359rtoFyySWXxJFHHhkRz92S2fs9d3R0xPLly3PNBgAAUK9OqLIhMyJiUUclhyQAAAAAAAAAAAAA1ELTFTLvuOOOzOd7tmO++93vjra2tpxSDay2trZ497vfnbkls0e16wEAAMCzJo5K4ogJ2VsyS2WFTAAAAAAAAAAAAIBG1XSFzAcffHCvX++9SXLYsGHxp3/6p3lFGhRvfetbdxVK99yS2dsDDzyQVyQAAIC6VyxkFzJXboro2Fz9w3EAAAAAAAAAAAAAqD9NV8hcvnx5n8/1bMc87bTT4oADDsgx1cA74IAD4vTTT6+6JTPregAAALC7YqH6MXqRLZkAAAAAAAAAAAAADUkhcy8WLFgw+EFyUO19pGkajz32WD5hAAAAGsAxByUxalj2TKlDIRMAAAAAAAAAAACgETVdIXPNmjWRJEnmzJw5c3JKM7iy3kfPNVi9enVecQAAAOpeW2sSc6dmnynvX5XGzu40p0QAAAAAAAAAAAAA5KXpCplbtmypOnPkkUfmkGTwHXHEEVVntm7dmkMSAACAxlEsZB+lt3dFLFurkAkAAAAAAAAAAADQaBQy92L8+PE5JBl8/Xkf/bkeAAAA/EGxvfpRulSu5JAEAAAAAAAAAAAAgDw1XSGzs7Oz6szIkSNzSDL4RowYUXWmP9cDAACAP5gyNomDq3z+TanDhkwAAAAAAAAAAACARtN0hcxRo0ZVndm8eXMOSQZff7Zf9ud6AAAAsLtqWzJXbEhj/TalTAAAAAAAAAAAAIBG0nSFzDFjxlSdefrpp3NIMvg2bNhQdWb06NGDHwQAAKDBFAvVj9OlciWHJAAAAAAAAAAAAADkRSFzLx5++OEckgy+/ryP/lwPAAAAdjdnchLDW7NnSmUbMgEAAAAAAAAAAAAaSdMVMqdNmxZpmv1Hsffff39OaQbXkiVL+nwuTdNIkiSmTZuWYyIAAIDGMGJYEsdOSTJn7ltVie6KUiYAAAAAAAAAAABAo2i6QuaRRx6Z+XyapnHjjTfmlGZw3XTTTVVnql0PAAAA9q7Ynn2k3twZ8eh6hUwAAAAAAAAAAACARqGQ+b96NkZGRCxdujQee+yxPGMNuMcffzwWL14cSZJkbgRVyAQAANg/8wrVj9SlciWHJAAAAAAAAAAAAADkoekKmXPnzu3X3Ne+9rVBTjK4vv71r/dr7vjjjx/kJAAAAI3p4HERk0dnz5Q6bMgEAAAAAAAAAAAAaBRNV8h80YtelPl8z0bJb37zm7Fu3bqcUg2sDRs2xNe//vVdGz+zVLseAAAA7F2SJFGssiXz0XVpbNqhlAkAAAAAAAAAAADQCJqukDl58uQ4+uijIyKeU1hM0z/8kewzzzwTf/u3f5trtoHy8Y9/PNavXx8Ru7+niN3f84wZM2Ly5Mm5ZgMAAGgk1QqZaUQs7qjkEwYAAAAAAAAAAACAQdV0hcyIiJe//OXPKSr2SNN015bMb3zjG3H99dfnnO75ufnmm+MrX/lK5nbMnvd47rnn5pgMAACg8cydmkRr38eviIgolW3IBAAAAAAAAAAAAGgETVnIvPDCC6vO9JQyL7744li2bFkOqZ6/5cuXx0UXXRSVyrPbV/oqnfboz3UAAACgb6Pbkpg1KbuRWeqoRKXK+QwAAAAAAAAAAACAoa8pC5mnnHJKTJ8+PSJir5ske4qMSZLE+vXr46UvfWk8+uijeUbcZ0888US85CUvidWrV+8qk+6p93s94ogjYv78+XlGBAAAaEjFQvbResP2iBUbFDIBAAAAAAAAAAAA6l1TFjIjIt71rndlbpDsXcpcuXJlnHrqqXHbbbflFW+f/Pa3v4358+fHihUr9low7S1N00iSJN71rnfllA4AAKCxFQvZ57CIiFJZIRMAAAAAAAAAAACg3jVtIfOd73xnHHTQQRGx9y2ZEbuXMtetWxfnnHNOfOITn4iurq7ccmZJ0zQ++9nPxoIFC6Kjo2PX+6i2HfOggw6Kd77znbnlBAAAaGTTJyQxYWT2TKlcyScMAAAAAAAAAAAAAIOmaQuZY8aMife9732ZWzIjdi9l7ty5Mz75yU/GSSedFL/4xS/yiNmn2267LebPnx8f/ehHY8eOHZllzB492zHf//73x6hRo/KKCgAA0NCSJIlie/bxetnaNLbutCUTAAAAAAAAAAAAoJ41bSEzIuLSSy+NY445JiL63pIZsXspM03TuO++++Lcc8+NBQsWxM9+9rOoVPLbdHLdddfF2WefHX/0R38U//M//7OrZNk75556cidJEjNnzowPfOADueUFAABoBsVC32fKiIjuNOL+VQqZAAAAAAAAAAAAAPWsqQuZw4cPj2984xu7/t3fUmbPv2+//fZ49atfHYcddlhccskl8ctf/jK6uroGNGN3d3fcfvvt8YEPfCCmT58er3zlK+OWW26JNE37Xcbs/d/f+MY3oq2tbUAzAgAANLsT2lsiu5IZUSrn92E+AAAAAAAAAAAAAAy8YbUOUGtnnHFGfPCDH4zPfvazmYXMiL2XMiMiVq5cGV/5ylfiK1/5SowcOTJOPvnkeOELXxizZ8+OWbNmxaGHHhpTpkyJ4cOH9/mzOzs7Y/Xq1fHEE0/EQw89FMuWLYuFCxfGwoULY9u2bbu93t4yVMudJEl86EMfihe96EVV5wEAANg340YkMeOgJB5Z1/cZrVSu7PbBOgAAAAAAAAAAAADUl6YvZEZEfPrTn4777rsvbrjhhkiSpGrJcc9iZu+vbdu2LW6//fa4/fbbn/N9Y8eOjdGjR8eIESOira0tdu7cGTt27IitW7fG5s2bM1+rr9fL0vNekiSJ8847Lz71qU9V/R4AAAD2T7E9u5C5ZmvEU5siDhmfYygAAAAAAAAAAAAABoxCZjxbXPzBD34QZ555ZixevLhfpcyI3YuZ/SlLbtq0KTZt2rTP2fb2mvvyfcViMb73ve/t0+sCAACwb4qFlrh2aSVzplSuxCHjW3NKBAAAAAAAAAAAAMBAUsj8X+PHj49bbrklzj777Lj33nv7XcqMeO4Wyz1LlM9Xf3P0ztDzfS984QvjhhtuiHHjxg1opqFu3bp1sWTJklixYsWux+OPPx4dHR2xZcuWXY+tW7fGsGHDYuTIkTFmzJiYMmVKTJ06NY444oiYM2dOzJ07N174whfGmDFjav2WAACAIW7GxCTGDo/Y3Nn3zKKOSpw/UyETAAAAAAAAAAAAoB4pZPZy4IEHxi233BKvfOUr47bbbtut2NhfWbNZRc19LV1We400TeOP/uiP4qc//WnDlzGffPLJ+M1vfhOLFy/e9XjyySf7/f2dnZ3R2dkZGzdujHK5/Jznhw0bFieddFK88pWvjNe+9rVx1FFHDWR8AACgQbS2JHH81Jb4zRN9b8lcujqNHV1pjBg2sB/kAwAAAAAAAAAAAMDgU8jcw/jx4+Omm26KD33oQ/GFL3xht42Xz7c0OVCly73ZM+MHP/jB+PSnPx0tLS2D9pq18sgjj8SvfvWruP322+NXv/pVPPbYY4P6el1dXXHXXXfFXXfdFR/96EfjrLPOiksuuSQuuOCCAd+GCgAA1LdiIYnfPNH3853dEQ+sSaNYcJYAAAAAAAAAAAAAqDcKmXvR2toaV1xxRbz4xS+Ov/qrv4pyuTygxcyB1LsQmKZpTJs2Lf75n/85zj///BqmGjxLly6N4447rqYZbr311rj11lvjpJNOiiuuuCLOOOOMmuYBAACGjmKhJSK6M2dK5cr/zgEAAAAAAAAAAABQT/wFaIY//uM/jgcffDDe+c53RpIku4qYvcuZtbJnQbSlpSXe8573xLJlyxq2jBkxtMqw99xzT5x55pnxV3/1V7F169ZaxwEAAIaAiaOSOHxC9nmx1FHJKQ0AAAAAAAAAAAAAA0khs4rx48fHlVdeGffff39ceOGF0dLS8pxiZl7lzD1fL03TaG1tjTe/+c2xZMmS+OIXvxhjx47NJQt/8NWvfjXmz58fjz/+eK2jAAAAQ0CxPfuM+NTGiNWbh86HzQAAAAAAAAAAAADQPwqZ/TR79uz47ne/Gw899FC8733viylTpkSapnstZw5ESbOvn9fzmu3t7XHppZfGww8/HFdffXXMnDnzeb9H9t+SJUti/vz58eCDD9Y6CgAAUGPFQvWjti2ZAAAAAAAAAAAAAPVHIXMfHXnkkXHFFVfEU089Fdddd1287W1vi0MPPXRXUbJaSbO/j4h4zs887LDD4s/+7M/iv/7rv+KJJ56Iz372szF9+vQaXg166+joiHPOOSdWrFhR6ygAAEANzZqUxMhh2TOlskImAAAAAAAAAAAAQL2p8iei9KWlpSXOO++8OO+88yIi4pFHHonbbrstFi1aFPfdd18sWbIkNmzYsN8/f8KECTF37tw4/vjjY968eXHmmWfGjBkzBih942tra4tZs2bF0UcfHcccc0wcc8wxMWPGjDjwwANj3LhxMX78+Bg3blzs2LEjNm7cGBs3boxVq1bF4sWLY/HixfGb3/xmv7ZdPvnkk/G6170ufv3rX0dbW9sgvDMAAGCoa2tNYu7UJO5+Ku1z5v5VaezsTqOtNckxGQAAAAAAAAAAAADPh0LmADn66KPj6KOP3u1rGzZsiJUrV8bKlStj1apVsXnz5ti+fXts3749Ojs7Y/jw4TFy5MgYOXJkjB07NqZOnRrTpk2LadOmxYQJE2rzRurUxIkT49RTT43TTz89Tj/99Dj55JNj1KhRVb9v2LBhMWbMmCgUCjFz5sw444wzdj1XKpXiO9/5Tnzzm9+MTZs29TvLwoUL4/LLL49PfOIT+/VeAACA+ldsb4m7n+ru8/ltXREPr0vj2CkKmQAAAAAAAAAAAAD1QiFzEE2YMCEmTJgQc+bMqXWUhnT88cfH+eefHxdccEGcfPLJkSQD+4fMxWIxisViXHrppfGBD3wgvv/97/f7e//xH/8x3vGOd8QhhxwyoJkAAID6MK/QEhF9FzIjIkrlShw7pSWfQAAAAAAAAAAAAAA8b/7yk7oyceLE+PKXvxy///3vY/HixXH55ZfHC1/4wgEvY/ZWKBTie9/7XnzrW9+Klpb+/V9m27ZtccUVVwxaJgAAYGhrH5vEtHHZM6Vymk8YAAAAAAAAAAAAAAaEQiZ1Zdq0afHXf/3Xcfjhh+f+2m9729vimmuu6ff81VdfHTt27BjERAAAwFBWLGQfuR/bkMb6bUqZAAAAAAAAAAAAAPVCIRP2wRvf+MZ461vf2q/Zp59+Om677bbBDQQAAAxZxfbqR+7FHZUckgAAAAAAAAAAAAAwEBQyYR997nOfiwkTJvRr9qabbhrcMAAAwJB17JQk2qqcuktlGzIBAAAAAAAAAAAA6oVCJuyjgw46KF71qlf1a/a+++4b5DQAAMBQNWJYEnMmJ5kzizsq0V1RygQAAAAAAAAAAACoBwqZsB9e/epX92vukUceGeQkAADAUFYsZB+7N3VGLH9aIRMAAAAAAAAAAACgHihkwn6YP39+v+bWr18/yEkAAIChrFohMyKiVFbIBAAAAAAAAAAAAKgHCpmwHw466KAYNmxY1bnNmzfnkAYAABiqDhkfMWl09kypXMknDAAAAAAAAAAAAADPi0Im7IckSWLSpElV58aMGZNDGgAAYKhKkiSK7dlH70fWp7Fphy2ZAAAAAAAAAAAAAEOdQibsp02bNlWdOeigg3JIAgAADGXFQvbRu5JG3LfKlkwAAAAAAAAAAACAoW5YrQPsr8cffzwWLVrU7/mWlpY477zzoqWlvjuoW7dujZtuummfvuess86KcePGDVKi5rRly5bYsmVL1bkpU6bkkAYAABjK5k5NoiV5tnjZl1I5jdMPyy8TAAAAAAAAAAAAAPuubguZr3/962PhwoX9nv/0pz8dr3jFKwYxUT5Gjx4dN910U1x55ZX9/p53v/vd8cUvfnHwQjWhBx98sF9z8+fPH+QkAADAUDdmeBKzJiXxwJq+G5mlciXSNI0kSXJMBgAAAAAAAAAAAMC+qMt1kVdddVX89re/jTRN+/V4xzveER/60IdqHXvAfPGLX4zzzjuv3+//q1/9ajzwwAO1jt1Qfvazn/Vr7swzzxzcIAAAQF2Y1559/H56e8SKZzJWaAIAAAAAAAAAAABQc3VXyNy0aVN89KMfjSRJMh8REUmSxMtf/vL46le/WuPUA6ulpSV++MMfxrx586pehyRJoqurK97znvfUOnbD6Orqimuvvbbq3OjRo+Oss87KIREAADDUFQvVN1+WygqZAAAAAAAAAAAAAENZ3RUyv/SlL8WqVasiIva6DbK3adOmxXe+851oaam7t1nV6NGj40c/+lGMHTt219eyrsett94aN910Uy2iNpwvfvGL8cgjj1Sd+9M//dOYMGHC4AcCAACGvCMOTOKAEdkzpXIlnzAAAAAAAAAAAAAA7Je6aipu3rw5vvCFL+zagLmnnq+naRqtra1xzTXXxMSJE/OMmKujjjoqrrzyyucUUfcmTdP45Cc/mUOqxnbffffFxz/+8apzbW1t8d73vnfQ8wAAAPWhJUliXiH7CL5sbRrbdtqSCQAAAAAAAAAAADBU1VUh88tf/nI8/fTTERF9lhDTNI0kSeLDH/5wLFiwIMd0tfGmN70p3vjGN+5633vq/fU77rgjbrnllrwjNox77rknzjrrrNiyZUvV2b/7u7+LGTNm5JAKAACoF8X2vX+4UI+uSsT9qxUyAQAAAAAAAAAAAIaquilkdnV1xT/90z9V3Y4ZETFjxoy47LLL8opWc1/+8pfjoIMOiojo8/r0+Md//Mc8IjWUnTt3xmc/+9lYsGBBrF+/vur8qaeeGh/96EdzSAYAANSTE9pbIvvEFlEqV3LJAgAAAAAAAAAAAMC+q5tC5s9//vNYtWpVRFTfjvmlL30p2tra8oxXUwceeGBcfvnlfV6XiGeLmmmaxo033hjLly/PMV39WrNmTVx55ZUxd+7c+PCHP9yvzZhz586Nf//3f4/W1tYcEgIAAPXkgJFJHDkxu5K5qFzJPNsBAAAAAAAAAAAAUDvDah2gv77+9a/3+VxP2TBJknjFK14RL3/5y3NMNjT8+Z//eXzzm9+MUqm063r06Lk2Pf/99a9/PT7zmc/UKuqQ0tXVFRs3boxNmzbF008/HQ888EAsXrw4/ud//iduu+226Orq6vfPmjdvXtx4440xadKkQUwMAADUs2J7Er9b33fhctWWiPLmiGnjcgwFAAAAAAAAAAAAQL/URSHz8ccfj5tvvnlXqbC33l9LkiQ++9nP5hltyEiSJP7hH/4hzjnnnL1ep56ZNE3jqquuik9/+tPR0lI3C1L3y4te9KK44447Bv11kiSJd7/73fGZz3wmRo4cOeivBwAA1K9ioSV+/EAlc6ZUrsS0ca05JQIAAAAAAAAAAACgv+qikfdv//ZvuzY+9t782KNnA+QFF1wQM2fOzDvekPHSl740TjzxxN02Yvbofd3Wrl0bt956a97xGtIpp5wSN998c3zpS19SxgQAAKo65qAkRrdlz5TK2YVNAAAAAAAAAAAAAGqjLjZk/vSnP+3X3Ec+8pFBTjL0ffjDH47Xve51Ved+8pOfxEte8pIcEjWe4cOHx9lnnx3ve9/7hvQ1TNM0Ojs7ax2DOrdz585+fQ2GAvcr9cY9Sz1xvw6suVMifvtU388vWZ3G5m2dMdySzP3mnqWeuF+pJ+5X6o17lnrifqWeuF+pN+5Z6on7lXrjnqWeuF+pJ+5X6o17lnrifqWeuF+pN+5ZBtPeFjzWUpIOtUR7WLNmTRQKhb1uyEySZNc2yNNOOy1uv/32WsUcMtI0jRkzZsTvf//7Xf/u0bM1M03TaG9vj5UrV9YiYm5e9KIXxR133DFgP2/kyJHx4Q9/ON7//vfH+PHjB+zn9tfmzZuju7t7n+YfffTRQUwEAADsjyVbp8TNzxyVOfPHEx+Iw0c8k1MiAAAAAAAAAAAAgKFpxowZMXbs2H7Pt7a27tP8vmoZtJ88QG699daoVCoRkd1mffOb35xXpCEtSZJ44xvfuNdr1ftrq1atisWLF+cZre5t3749PvGJT0SxWIy//uu/joULF9Y6EgAAUIcOH7Gh6syKHRMGPQcAAAAAAAAAAAAA+2bIFzJ/9atf7fXrPdseIyLa2trita99bV6Rhrw3velN/Zq77bbbBjlJY1q+fHlceeWVMX/+/FiwYEH853/+Z60jAQAAdWRca2ccNGxr5oxCJgAAAAAAAAAAAMDQM+QLmbfffnufz6VpGkmSxMte9rI48MADc0w1tM2cOTOKxeKu69MXhczn77bbbov/83/+T7ziFa+Ijo6OWscBAADqRLUtmeu7RsfG7uH5hAEAAAAAAAAAAACgX4bVOkCWLVu2xJIlSzJLhRER5557bk6J6sd5550XpVJpr88lSRJpmsZvfvObnFPl67zzzosZM2ZkzmzatCmeeeaZ2LBhQ6xduzZWrFixX691/fXXx9y5c+P//b//F+eff/5+/YyBNn78+Dj11FNrHYM6t3Pnzrjnnnt2+9pJJ50UbW1tNUoEfXO/Um/cs9QT9+vAG7s64t5fZ88MO/jEOPWIfPI0Gvcs9cT9Sj1xv1Jv3LPUE/cr9cT9Sr1xz1JP3K/UG/cs9cT9Sj1xv1Jv3LPUE/cr9cT9Sr1xzzKYduzYEZVKpdYxdhnShcwHHnhg15bHNE37nDvzzDNzTFUfzjzzzPj7v//753y999bM1atXx9q1a2PSpEl5x8vF3/zN3+zz9zz99NNxzz33xN133x0//vGP+yy17s3atWvjVa96VXz3u9+N17/+9fv82gMtSZIYPtxGHQZeW1ube4u64X6l3rhnqSfu1+fn+EIaI1p3xo7uvmfuX5PEuTP9MmqguGepJ+5X6on7lXrjnqWeuF+pJ+5X6o17lnrifqXeuGepJ+5X6on7lXrjnqWeuF+pJ+5X6o17loHS2dlZ6wi7aal1gCxLlizZ69d7b8ycNGlSzJ49O69IdeO0006LYcOe7dtmbRjt6xo3qwMPPDDOPvvs+Ju/+Zu4995745577ol3vOMdu65lNd3d3XHRRRfFtddeO8hJAQCAetbWmsTcqX2f1SIi7luVRlel7w8nAgAAAAAAAAAAACBfQ7qQuXTp0j6f69n0eNppp+WYqH6MHj06TjjhhMzNohHZ15iIE088Mb7xjW/Eb3/72zjhhBP69T3d3d1x8cUXx7JlywY5HQAAUM+Khewj+dadEQ+vVcgEAAAAAAAAAAAAGCqGdCHz97//fdWZY445ZvCD1KmZM2dWnVm+fHkOSerfC17wgrj77rvjkksu6df89u3b4y1veUt0dXUNcjIAAKBeFdurH8lLHZUckgAAAAAAAAAAAADQH0O6kFkul6vOHH300TkkqU8zZsyoOrNy5cockjSGtra2+OIXvxgf/ehH+zV/9913xz/90z8NcioAAKBetY9Lon1s9kypbEMmAAAAAAAAAAAAwFAxpAuZHR0dkSRJ5kx/SofNqj9l1f6UXtndpz71qXjVq17Vr9kvfOELtmQCAAB9Khayj+XLn05jw3alTAAAAAAAAAAAAIChYMgXMqs5+OCDc0hSn6ZNm9bnc0mSRJqmCpn76etf/3pMmDCh6tyTTz4ZP/7xjwc/EAAAUJeK7dWP5Ys7KjkkAQAAAAAAAAAAAKCaIVvITNM0tm3btuu/+zJmzJi8ItWdsWPHVp3ZtGlTDkkaz+TJk+P9739/v2avvvrqQU4DAADUq2OnJDGsysm8VLYhEwAAAAAAAAAAAGAoGLKFzJ4yZjX9KR02q/6UVbdv355Dksb0jne8I1pbW6vO3XHHHdHd3Z1DIgAAoN6MaktizuQkc2ZRRyUqGR9UBAAAAAAAAAAAAEA+hmwhs79FQYXMvvXn2ihk7r/29vY45ZRTqs5t2rQpFi1aNPiBAACAujSvkH0037gjYvnTCpkAAAAAAAAAAAAAtTZkC5mdnZ39mrN5sG/9uTb9vc7s3emnn96vuVKpNMhJAACAelVsz96QGRFRKitkAgAAAAAAAAAAANTakC1kjhgxol9zW7ZsGeQk9Wvr1q1VZ/p7ndm7mTNn9mtu7dq1g5wEAACoV4cdkMTEUdkzpXIlnzAAAAAAAAAAAAAA9GnIFjLHjBnTr7n+lA6bVX+uzejRo3NI0rgmT57cr7l169YNchIAAKBeJUkSxUL28fzhdWls7rQlEwAAAAAAAAAAAKCWhmwhc/jw4dHS8my8JEn6nNu0aVNekepOf66NQubz09/rt2HDhsENAgAA1LVqhcxKGnHfKoVMAAAAAAAAAAAAgFoasoXMiP6V3R5//PEcktSn/lwbhcznZ9u2bf2aGz58+CAnAQAA6tkJU5No6fuziCIiolSu5BMGAAAAAAAAAAAAgL0a0oXMAw44oOrMY489lkOS+pR1bdL02c0q/bnG9G39+vX9mhs7duwgJwEAAOrZmOFJHHNQdiOzVK7sOssBAAAAAAAAAAAAkL8hXcg87LDDqv6x6e9///t8wtShatcmSZI47LDD8gnToB599NF+zU2aNGmQkwAAAPWuWMg+oq/fFvH4MwqZAAAAAAAAAAAAALUypAuZhx9+eNWZUqmUQ5L61J9r059rTN9++9vf9mtuxowZg5wEAACod8VC9obMiIhSh0ImAAAAAAAAAAAAQK0M6ULm9OnT+3wuSZJI07Tfhbhms2nTpnjggQciSbL/oDfrGpNty5Ytcccdd/Rr9phjjhnkNAAAQL078sAkxo/InllUruQTBgAAAAAAAAAAAIDnqMtCZpr+YSPIM888Ew888EBOierHXXfdFZXKs3+o2/t67emII47IK1LD+dGPfhRbtmypOjdx4sSYPXt2DokAAIB61pIkcUJ79jH9gTVpbO+yJRMAAAAAAAAAAACgFoZ0IXPu3Ln9mvvFL34xyEnqT3+vyXHHHTfISRpTd3d3/MM//EO/Zs8888xoaRnS/1cDAACGiGJ7kvl8VyViyWqFTAAAAAAAAAAAAIBaGNItsRe84AXR1tYWERFJ0vcfpf7whz/MK1LduPbaa/d6zXp/rb29PQ477LA8YzWMz33uc/HQQw/1a/bVr371IKcBAAAaxbxC9WN6qVzJIQkAAAAAAAAAAAAAexrShcwRI0bEvHnzIk2fu/0jTdNIkiTSNI2FCxfG448/XoOEQ9Odd94ZTzzxRERE5rWbP39+3tEawq233hof+9jH+jU7fvz4+JM/+ZNBTgQAADSKCSOTOPLA7C2ZixQyAQAAAAAAAAAAAGpiSBcyIyJOOeWUfs39y7/8yyAnqR/f+MY3+jXX32s7FFx//fXxz//8z9HV1VXTHLfddlv88R//cezcubNf83/xF38Ro0aNGuRUAABAIykWsguZ5c0R5U3P/fAdAAAAAAAAAAAAAAbXkC9kLliwIPP5ni2ZX/nKV2Lz5s35hBrCnnzyyfje974XSZL9B7wREWeddVYOiQbGmjVr4l3velfMnj07vvGNb8T27dtzff00TeNLX/pSnHPOObFx48Z+fc/48ePjIx/5yCAnAwAAGk2xvfpRfVGHLZkAAAAAAAAAAAAAeRvyhcyXv/zlMXr06IiI55QM0/QPG0GeeeaZ+NrXvpZrtqHoc5/73K7tjb2vT8Tu1++QQw6Jk08+OddsA+HRRx+Nv/iLv4jDDjssLr300li8ePGgv+Ztt90Wp512Wrz3ve+Nzs7Ofn/fpz/96Zg4ceIgJgMAABrRMZOSGN2WPVMqK2QCAAAAAAAAAAAA5G3IFzJHjRoV55577nPKhb31bMn81Kc+FatWrcox3dCybNmy+NrXvpa5HTNN00iSJF71qlflmGzgrVmzJq644oqYN29ezJ49Oy699NK46aabBmxL6sqVK+Of//mf46STTooFCxbEXXfdtU/f/7KXvSze9a53DUgWAACguQxrSeL4qX2f6yIi7l+Vxs7uvs/JAAAAAAAAAAAAAAy8YbUO0B+vec1r4ic/+clen+spGEY8uyXzkksuiR/84Ad5xhsy/uIv/iI6Ozt3FVSzvOY1r8kp1eBbtmxZLFu2LK644opobW2N4447LorFYsycOTOOPvromDZtWrS3t8eBBx4YI0eOjOHDh0elUonOzs7YtGlTrF69Op566ql4+OGH4/7774+77rorli5dWvUa9mXmzJnxve99b4DfJQAA0EyKhZa468nuPp/f0R3x4Jo0jm/PLm4CAAAAAAAAAAAAMHDqopD5ile8Ig444IDYuHHjXsuGPaXMNE3jRz/6Ubz+9a+v+w2Q++rKK6+M22+/vc8yZu+tmdOnT48XvehFecbLTXd3dyxevDgWL15ck9c/7LDD4oYbboiJEyfW5PUBAIDGMK+9JSL6LmRGRJQ6KnF8e0s+gQAAAAAAAAAAAACIuvjLzTFjxsQ73vGOqhsLe8qIb33rW+PBBx/MKV3t/frXv473v//9u5Uu96anuPqe97wnp2TN5fjjj48777wzpk+fXusoAABAnZs8JolDx2ef8Url7DMyAAAAAAAAAAAAAAOrLgqZERGXXHJJDBv27ELPvRUPe8qaSZLEpk2b4pWvfGWsX78+14y1sGLFinjNa14TO3fujIiouh3zgAMOiD/7sz/LLV+zeNOb3hS//vWvY9q0abWOAgAANIh5hexC5uPPpLF2q1ImAAAAAAAAAAAAQF7qppB58MEHx+te97rMLZm9S5mPPvpoLFiwIFavXp1XxNwtX75813vs2Q7al57tmO94xztizJgxOaZsbAcffHBce+21cc0118S4ceNqHQcAAGggxUL1I/uiciWHJAAAAAAAAAAAAABE1FEhMyLiYx/7WLS1tUXE3rdkRuy+IXLJkiWxYMGCWLlyZS758vTQQw/FmWeeGStWrOjzWkTsfp3Gjx8fl156aR7xBtycOXPilFNOiZaWoXHLTpw4Mf7v//2/8fDDD8drX/vaWscBAAAa0JzJSQxvzZ4pdShkAgAAAAAAAAAAAORlaLTb+mnmzJlxySWXZG6CjPjDNsgkSWLZsmXxghe8IG699dacUg6+n/zkJzF//vx46qmndhUu+7Md87LLLovJkyfnFXNAvfCFL4w777wzOjo64tvf/na8+tWvrslGypNOOim+/vWvx5NPPhl/+7d/G6NHj849AwAA0ByGtyZx3JS+P4AnImJxRxrdlewzMgAAAAAAAAAAAAADo64KmRERf/d3fxeFQiEi+t6SGfGHgmKSJLF69eo455xz4vLLL4/u7u5ccg6GHTt2xPve97543eteFxs3bqxaxkySZFcZc/bs2fGe97wnz7iDYvLkyfHWt741fvzjH8e6deti4cKF8aUvfSkuvPDCmD59+oC/3oQJE+K8886Lz3/+8/H73/8+7r777vjzP//zGDVq1IC/FgAAwJ6Khexj+9adEY+sV8gEAAAAAAAAAAAAyMOwWgfYV2PHjo3Pfe5zcdFFF2UWMiN235TZ3d0dl112Wfzwhz+ML3/5y7FgwYJ8Ag+Qn//85/G+970vVqxYset9RWSXMXv/91e+8pVoaam7/m2mtra2OPnkk+Pkk0/eVTZdvXp1LFu2LH73u9/teqxYsSKeeeaZ2Lx5865HV1dXjBgxIkaMGBGjRo2KyZMnx5QpU6JQKMSMGTNi1qxZcdxxx8Xs2bOr3mcAAACDpdjeEhHZHyxUKldi1qTGOu8BAAAAAAAAAAAADEV1V8iMiLjwwgvjF7/4RVx99dW7tkD2pXcpM03TWLp0abzkJS+JV73qVfGRj3wkTjrppByT77vbbrst/v7v/z5uvvnm3bZ+RvRdxuzR894//OEP110BdX9NmTIlpkyZEmeccUatowAAADxvhXERU8dErNrS90ypnMaFc/PLBAAAAAAAAAAAANCs6naFxle/+tWYM2dORES/NmX2nkvTNH7605/G/Pnz4+yzz47rr78+uruzN47kaceOHXHttdfGKaecEn/0R3+0q4zZu1iaVcbsmUmSJF784hfH5ZdfnmN6AAAABkqSJDGvkH10/936NJ7Znv2BPQAAAAAAAAAAAAA8f3VbyBw1alT86Ec/ijFjxkRE/0qZeys13nLLLXHBBRfE1KlT4y//8i/jlltuic7Ozjzewm62bdsW//Vf/xUXX3xxTJkyJS688MK4++67d8vd8z6y9L4OkydPju9///tVrw0AAABDV7FKITONiMUdlXzCAAAAAAAAAAAAADSxYbUO8HzMnj07fvrTn8YrXvGK6Ozs3FW0zLK3bZkREevXr49vfvOb8c1vfjNGjBgRp556aixYsCDmz58fc+bMiUMOOWRAs69YsSKWLl0ad911V9x6661x9913x86dO3fLtLecWXrPHnDAAfFf//VfUSgUBjQ3AAAA+Zo7JYlhLRFdGZ3LUkcaZ0zPLRIAAAAAAAAAAABAU6rrQmZExEte8pL44Q9/GK95zWuiu7u7X6XMiOcWM3t/bfv27fHLX/4yfvnLX+56bty4cTF79uw49NBDo729fddjzJgxMXLkyBgxYkSMGDEiIiJ27NgR27dvjx07dsSmTZuio6Nj1+Pxxx+PBx98MLZu3brXPH1lqqZ3GXPMmDFx3XXXRbFY7Nf3AgAAMHSNakti1qQklqzu+3y4qFyJSppGS6/zJAAAAAAAAAAAAAADq+4LmRERF1xwQVx99dXxlre8JSqVSr9LmRG7FzOzipAbN26MhQsXxsKFC59X1r5yJXv80Wx/8/f+3jRNY8SIEfFv//Zvcfrpp+9/SAAAAIaUYqEllqzu7vP5Z3ZEPPZ0GkdNVMgEAAAAAAAAAAAAGCwttQ4wUC688ML4+c9/HmPHjo2I5xYcq0nTdNej5/v3fPSe2d9HXz97bxn6o/f3Tp48OW6++eY4++yz9+m9AwAAMLQVC9XPuKVy/8+SAAAAAAAAAAAAAOy7hilkRkScd955cfvtt8chhxwSaZo+Z+tlf+1LkXJ/HlmvsS96/6zZs2fHXXfdFaeddto+/xwAAACGtsMPSOLAkdkzizoq+YQBAAAAAAAAAAAAaFINVciMiDj++ONj4cKFccYZZ+xWpHy+BmI75vMpX+6pdxnzggsuiDvvvDOOOOKI5/1zAQAAGHqSJIliIfsIv2xtGls6bckEAAAAAAAAAAAAGCwNV8iMiJg6dWr88pe/jM9//vMxcuSzK0T2d1vmUNPzPtI0jfHjx8e3v/3t+NnPfhbjx4+vdTQAAAAGUbGQfaatpBH3r1LIBAAAAAAAAAAAABgsDVnI7PHe9743SqVSnHLKKbtty6zHYmbvImaapvHyl788lixZEhdffHGtowEAAJCD46e2REuV42ypXMknDAAAAAAAAAAAAEATauhCZkTEMcccE3fccUdce+21MXPmzOcUM4dyObN3xp4i5gte8IK44YYb4j//8z/j4IMPrnVEAAAAcjJuRBIzJmafYUsdlV3nXgAAAAAAAAAAAAAGVsMXMnu85jWviSVLlsQ3v/nNmD59+q6CY8TQKmfumaUn55w5c+KHP/xh3HPPPXHOOefUOCUAAAC1UCxkH+PXbo14amNOYQAAAAAAAAAAAACaTNMUMiMiWlpa4u1vf3s8+uijcf3118f5558fLS0tfZYz8yho7u31evK0tbXFG97whrj11ltjyZIl8drXvnbQ8wAAADB0FQvVz6mljkoOSQAAAAAAAAAAAACaz7BaB6iFJEni3HPPjXPPPTdWrlwZ3/nOd+K6666LO++8M7q7u3fN9P7fPfUUOPflNfvS+2cNHz48XvziF8f5558fb3zjG2PSpEn79DoAAAA0rqMOTGLs8IjNnX3PlMqVOH9ma36hAAAAAAAAAAAAAJpEUxYye5s2bVp86EMfig996EOxYcOGuOGGG+KGG26IhQsXxsMPPxyVynM3i+zv9sy9lTiHDRsWs2bNilNOOSXOPffcOPvss2Ps2LH79V4AAABobK0tScxrb4lfP973Fsylq9PY0ZXGiGH7fm4FAAAAAAAAAAAAoG9NX8jsbcKECfGGN7wh3vCGN0RExNatW6NUKsW9994by5YtiyeeeCKeeOKJePLJJ2PdunX79LMnT54chxxySBx66KFx6KGHxpw5c+IFL3hBnHDCCTFy5MjBeDsAAAA0oGIhiV8/3vfzOyvPljJfME0hEwAAAAAAAAAAAGAgKWRmGD16dJx++ulx+umnP+e5HTt2xNNPPx3bt2+Pbdu27XokSRIjR46MUaNG7XoceOCBMXz48Bq8AwAAABrNCe0tEdGdOVPqqMQLprXkEwgAAAAAAAAAAACgSShk7qcRI0ZEe3t7rWMAAADQZCaOSuKICUk8tiHtc6ZUruSYCAAAAAAAAAAAAKA5WJcBAAAAdaZYSDKfX7kpomNz34VNAAAAAAAAAAAAAPadQiYAAADUmWKh+nF+kS2ZAAAAAAAAAAAAAANKIRMAAADqzDEHJTFqWPZMqUMhEwAAAAAAAAAAAGAgKWQCAABAnWlrTWLu1CRz5v5VaezsTnNKBAAAAAAAAAAAAND4FDIBAACgDhUL2Uf67V0Ry9YqZAIAAAAAAAAAAAAMFIVMAAAAqEPF9upH+lK5kkMSAAAAAAAAAAAAgOagkAkAAAB1aMrYJA4elz1T6rAhEwAAAAAAAAAAAGCgKGQCAABAnZpXyD7Wr9iQxvptSpkAAAAAAAAAAAAAA0EhEwAAAOpUsUohMyJiUbmSQxIAAAAAAAAAAACAxqeQCQAAAHXq2MlJDG/Nnil12JAJAAAAAAAAAAAAMBAUMgEAAKBOjRiWxJzJSebM4o5KdFeUMgEAAAAAAAAAAACeL4VMAAAAqGPFQvbRfnNnxKPrFTIBAAAAAAAAAAAAni+FTAAAAKhj1QqZERGlciWHJAAAAAAAAAAAAACNTSETAAAA6tjB4yImj86eKXXYkAkAAAAAAAAAAADwfClkAgAAQB1LkqTqlsxH16WxaYdSJgAAAAAAAAAAAMDzoZAJAAAAda5aITONiMUdlXzCAAAAAAAAAAAAADQohUwAAACoc3OnJtGaZM+UyjZkAgAAAAAAAAAAADwfCpkAAABQ50a3JTFrUnYjs9RRiUqqlAkAAAAAAAAAAACwvxQyAQAAoAEUC9lH/A3bI1ZsUMgEAAAAAAAAAAAA2F8KmQAAANAAioXsDZkREaWyQiYAAAAAAAAAAADA/lLIBAAAgAYwfUISE0Zmz5TKlXzCAAAAAAAAAAAAADQghUwAAABoAEmSxLz27GP+srVpbN1pSyYAAAAAAAAAAADA/lDIBAAAgAZRLCSZz3enEUtWKWQCAAAAAAAAAAAA7A+FTAAAAGgQJ7S3RHYlM6LUUcklCwAAAAAAAAAAAECjUcgEAACABjF+RBIzJmZXMkvlSqSpLZkAAAAAAAAAAAAA+0ohEwAAABpIsZBdyFy9JWLlppzCAAAAAAAAAAAAADQQhUwAAABoIMVC9aN+qVzJIQkAAAAAAAAAAABAY1HIBAAAgAYyY2ISY4dnz5Q6FDIBAAAAAAAAAAAA9pVCJgAAADSQ1pYkjp+afdxfujqNHV1pTokAAAAAAAAAAAAAGoNCJgAAADSYYiHJfL6zO+KBNQqZAAAAAAAAAAAAAPtCIRMAAAAaTLFQ/bhfKldySAIAAAAAAAAAAADQOBQyAQAAoMFMHJXE4ROyt2SWOhQyAQAAAAAAAAAAAPaFQiYAAAA0oGJ7diHzqY0RqzenOaUBAAAAAAAAAAAAqH8KmQAAANCAioXqR35bMgEAAAAAAAAAAAD6TyETAAAAGtCsSUmMHJY9UyorZAIAAAAAAAAAAAD0l0ImAAAANKC21iSOm5Jkzty/Ko2d3WlOiQAAAAAAAAAAAADqm0ImAAAANKhiIfvYv60r4uF1CpkAAAAAAAAAAAAA/aGQCQAAAA2qWiEzIqJUruSQBAAAAAAAAAAAAKD+KWQCAABAg2ofm0RhXPZMqcOGTAAAAAAAAAAAAID+UMgEAACABlZszz76P/Z0Gk9vU8oEAAAAAAAAAAAAqEYhEwAAABpYsVD96L+oo5JDEgAAAAAAAAAAAID6ppAJAAAADezYKUm0VTn9LyrbkAkAAAAAAAAAAABQjUImAAAANLCRw5KYMznJnFnUUYnuilImAAAAAAAAAAAAQBaFTAAAAGhwxUL28X9TZ8TypxUyAQAAAAAAAAAAALIoZAIAAECDq1bIjIgolRUyAQAAAAAAAAAAALIoZAIAAECDO2R8xKTR2TOlciWfMAAAAAAAAAAAAAB1SiETAAAAGlySJFFsz/4VwCPr09i0w5ZMAAAAAAAAAAAAgL4oZAIAAEATKBayfwVQSSPuW2VLJgAAAAAAAAAAAEBfFDIBAACgCcydmkRLkj1TKtuQCQAAAAAAAAAAANAXhUwAAABoAmOGJzFrUnYjs1SuRJoqZQIAAAAAAAAAAADsjUImAAAANIl57dm/Bnh6e8SKZxQyAQAAAAAAAAAAAPZGIRMAAACaRLGQvSEzImJRWSETAAAAAAAAAAAAYG8UMgEAAKBJHHFgEuNHZM+UypV8wgAAAAAAAAAAAADUGYVMAAAAaBItSRLF9uxfBTy4No1tO23JBAAAAAAAAAAAANiTQiYAAAA0kXmFJPP5rkrEktUKmQAAAAAAAAAAAAB7UsgEAACAJjKvvSWyK5kRpXIllywAAAAAAAAAAAAA9UQhEwAAAJrIASOTOHJidiWzVK5EmtqSCQAAAAAAAAAAANCbQiYAAAA0mWJ7diFz1ZaI8uacwgAAAAAAAAAAAADUCYVMAAAAaDLFQvVfB5TKlRySAAAAAAAAAAAAANQPhUwAAABoMscclMTotuwZhUwAAAAAAAAAAACA3SlkAgAAQJNpbUnihPYkc2bJ6jQ6u9OcEgEAAAAAAAAAAAAMfQqZAAAA0ISK7dm/EujsjnhgjUImAAAAAAAAAAAAQA+FTAAAAGhC8wrVfyVQKldySAIAAAAAAAAAAABQHxQyAQAAoAlNGp3EYQckmTOLyjZkAgAAAAAAAAAAAPRQyAQAAIAmNa89u5D5xMY01m5RygQAAAAAAAAAAACIUMgEAACAplUsVP+1QKmjkkMSAAAAAAAAAAAAgKFPIRMAAACa1OzJSYxozZ4plRUyAQAAAAAAAAAAACIUMgEAAKBpDW9N4ripSebMfavS6KqkOSUCAAAAAAAAAAAAGLoUMgEAAKCJFduzfzWwdWfEI+sUMgEAAAAAAAAAAAAUMgEAAKCJFQvVfzVQKldySAIAAAAAAAAAAAAwtClkAgAAQBMrjEuifWz2TKlsQyYAAAAAAAAAAACAQiYAAAA0uWpbMn/3dBobtitlAgAAAAAAAAAAAM1NIRMAAACaXLG9+q8HFndUckgCAAAAAAAAAAAAMHQpZAIAAECTO3ZKEsOq/IagVLYhEwAAAAAAAAAAAGhuCpkAAADQ5Ea1JTFncpI5s6ijEpVUKRMAAAAAAAAAAABoXgqZAAAAQMwrZP+KYOOOiOVPK2QCAAAAAAAAAAAAzUshEwAAAIhie/aGzIiIUlkhEwAAAAAAAAAAAGheCpkAAABAHHZAEhNHZc+UypV8wgAAAAAAAAAAAAAMQQqZAAAAQCRJEsVC9q8JHl6XxuZOWzIBAACA/8/enQbZcVAH/z5972i0y9oszdjyKu+ypLlhSYDwkkDC4rA4dkAG4w0qxCkISRFIlfMhIZUqkipwpYAiCRUnkYyNjRcgZguvDdgvcciCPSPZlizv8jYjWYu1a5bb/f9Awh+D1T2S5vbdnqeKD2gO6JjqL3TrpwMAAAAAANCdBJkAAABARETU+vJfE6RZxIatgkwAAAAAAAAAAACgOwkyAQAAgIiIWNWXRCXJnxkaTstZBgAAAAAAAAAAAKDFCDIBAACAiIiY05vEmYvyi8zB4TSyzJVMAAAAAAAAAAAAoPsIMgEAAICfqvXlvyrYcTDimT2CTAAAAAAAAAAAAKD7CDIBAACAn6r151/IjIgYHBZkAgAAAAAAAAAAAN1HkAkAAAD81PKFScybnj8zOJyWswwAAAAAAAAAAABACxFkAgAAAD9VSZJY3Zf/umDjC1kcmnAlEwAAAAAAAAAAAOgugkwAAADgJWp9Se7PJ9KIB7cJMgEAAAAAAAAAAIDuIsgEAAAAXmKgv/h1weBwWsImAAAAAAAAAAAAAK1DkAkAAAC8xPwZSZy+IP9K5pAgEwAAAAAAAAAAAOgygkwAAADgF9T684PM4X0Rw3uzkrYBAAAAAAAAAAAAaD5BJgAAAPALan3FrwyGRlzJBAAAAAAAAAAAALqHIBMAAAD4BWctTmLWtPyZwWFBJgAAAAAAAAAAANA9BJkAAADAL+ipJLFqaZI788DWLMbrWUkbAQAAAAAAAAAAADSXIBMAAAB4WQN9+a8NRusRm14QZAIAAAAAAAAAAADdQZAJAAAAvKyB/uLXBkMjaQmbAAAAAAAAAAAAADSfIBMAAAB4WUtmJ7FsXv7M4LALmQAAAAAAAAAAAEB3EGQCAAAAh1UruJK5ZXcWOw6IMgEAAAAAAAAAAIDOJ8gEAAAADqvWV/zqYGgkLWETAAAAAAAAAAAAgOYSZAIAAACHdd6SJHqr+TODwy5kAgAAAAAAAAAAAJ1PkAkAAAAcVm81ifOXJLkz60fSqKeiTAAAAAAAAAAAAKCzCTIBAACAXLX+/NcH+8cjHt0pyAQAAAAAAAAAAAA6myATAAAAyFXrK359MDiclrAJAAAAAAAAAAAAQPMIMgEAAIBc/XMjls7OnxkcdiETAAAAAAAAAAAA6GyCTAAAACBXkiQx0J//CuHxnVnsPiTKBAAAAAAAAAAAADqXIBMAAAAoVCsIMrOIWD+SlrMMAAAAAAAAAAAAQBMIMgEAAIBCK5ck0VPwFmFwxIVMAAAAAAAAAAAAoHMJMgEAAIBCM6clcc7iJHdmaDiNNBNlAgAAAAAAAAAAAJ1JkAkAAABMSq0//zXC7tGIJ3cJMgEAAAAAAAAAAIDOJMgEAAAAJmWgL/9CZkTE4LAgEwAAAAAAAAAAAOhMgkwAAABgUk6dn8T8GfkzQyNpOcsAAAAAAAAAAAAAlEyQCQAAAExKkiRR689/lbB5exb7x1zJBAAAAAAAAAAAADqPIBMAAACYtFp/kvvzehbxwDZBJgAAAAAAAAAAANB5BJkAAADApK1eWolKfpMZg8NpOcsAAAAAAAAAAAAAlEiQCQAAAEza3OlJnLEwv8gcHE4jy1zJBAAAAAAAAAAAADqLIBMAAAA4IrX+/NcJ2w9EPLenpGUAAAAAAAAAAAAASiLIBAAAAI5IrT//QmZExOBIWsImAAAAAAAAAAAAAOURZAIAAABHZPmCJOb05s8MDgsyAQAAAAAAAAAAgM4iyAQAAACOSLWSxEBf/iuFh7ZlMTqRlbQRAAAAAAAAAAAAQOMJMgEAAIAjVutPcn8+nv4kygQAAAAAAAAAAADoFIJMAAAA4IitLriQGRExOJKWsAkAAAAAAAAAAABAOQSZAAAAwBFbODOJ0+bnX8kcHBZkAgAAAAAAAAAAAJ1DkAkAAAAclVp/fpD5/N6IkX1ZSdsAAAAAAAAAAAAANJYgEwAAADgqA33FrxWGXMkEAAAAAAAAAAAAOoQgEwAAADgqZy9OYkZP/szQiCATAAAAAAAAAAAA6AyCTAAAAOCoTKsmsWppkjuzYWsW4/WspI0AAAAAAAAAAAAAGkeQCQAAABy1Wn/+q4VDExGbtwsyAQAAAAAAAAAAgPYnyAQAAACO2kBf8auFwZG0hE0AAAAAAAAAAAAAGkuQCQAAABy1pXOSOHFu/szgsAuZAAAAAAAAAAAAQPsTZAIAAADHZKA///XCUy9msfOgKBMAAAAAAAAAAABob4JMAAAA4JjUCoLMiIih4bSETQAAAAAAAAAAAAAaR5AJAAAAHJMVxyfRW82fGRxxIRMAAAAAAAAAAABob4JMAAAA4JhM70nivOOT3Jn1I2nUU1EmAAAAAAAAAAAA0L4EmQAAAMAxq/Xnv2LYNxbx2E5BJgAAAAAAAAAAANC+BJkAAADAMSsKMiMiBofTEjYBAAAAAAAAAAAAaAxBJgAAAHDMTpwbcfys/JnBERcyAQAAAAAAAAAAgPYlyAQAAACOWZIkhVcyH9uRxd5RUSYAAAAAAAAAAADQngSZAAAAwJQoCjKziFg/kpazDAAAAAAAAAAAAMAUE2QCAAAAU2Ll0iSqSf7M4LALmQAAAAAAAAAAAEB7EmQCAAAAU2LWtCTOXpxfZA6OpJFlokwAAAAAAAAAAACg/QgyAQAAgClT689/1fDioYinXhRkAgAAAAAAAAAAAO1HkAkAAABMmVp//oXMiIjBEUEmAAAAAAAAAAAA0H4EmQAAAMCUOXV+EvNn5M8MDaflLAMAAAAAAAAAAAAwhQSZAAAAwJSpJEkM9OW/btj0QhYHx13JBAAAAAAAAAAAANqLIBMAAACYUrX+JPfn9Sziga2CTAAAAAAAAAAAAKC9CDIBAACAKbW6rxL5SWbE4Ehayi4AAAAAAAAAAAAAU0WQCQAAAEypedOTOGNhfpI5OJxGlrmSCQAAAAAAAAAAALQPQSYAAAAw5Wr9+UHmtv0Rz+8taRkAAAAAAAAAAACAKSDIBAAAAKZcrb/4lcPgcFrCJgAAAAAAAAAAAABTQ5AJAAAATLkzFiYxpzd/ZnBEkAkAAAAAAAAAAAC0D0EmAAAAMOWqlSRWLc1/7fDQtixGJ7KSNgIAAAAAAAAAAAA4NoJMAAAAoCFq/Unuz8fqERtfEGQCAAAAAAAAAAAA7UGQCQAAADTEQF/xa4fB4bSETQAAAAAAAAAAAACOnSATAAAAaIhFs5I45bj8K5lDI4JMAAAAAAAAAAAAoD0IMgEAAICGqfXnB5nP7onYtj8raRsAAAAAAAAAAACAoyfIBAAAABqm1l/86mFo2JVMAAAAAAAAAAAAoPUJMgEAAICGOWdxEjN68mcGRwSZAAAAAAAAAAAAQOsTZAIAAAANM62axPlLktyZDSNZTKRZSRsBAAAAAAAAAAAAHB1BJgAAANBQtf781w8HJyI2bxdkAgAAAAAAAAAAAK1NkAkAAAA0VFGQGRExOJyWsAkAAAAAAAAAAADA0RNkAgAAAA3VNyeJ/rn5M4MjLmQCAAAAAAAAAAAArU2QCQAAADRcrS//FcSTu7LYdVCUCQAAAAAAAAAAALQuQSYAAADQcLX+4lcQQyNpCZsAAAAAAAAAAAAAHB1BJgAAANBwK5YkMa3gLcTQsAuZAAAAAAAAAAAAQOsSZAIAAAANN6MnifOOT3JnhkbSqKeiTAAAAAAAAAAAAKA1CTIBAACAUtT6819D7B2LeGKXIBMAAAAAAAAAAABoTYJMAAAAoBRFQWZExOCwIBMAAAAAAAAAAABoTYJMAAAAoBTL5kUsmpk/MziclrMMAAAAAAAAAAAAwBESZAIAAAClSJKk8Ermozuz2DfmSiYAAAAAAAAAAADQegSZAAAAQGmKgsw0i9gwIsgEAAAAAAAAAAAAWo8gEwAAACjNqqVJVJL8mcHhtJxlAAAAAAAAAAAAAI6AIBMAAAAozezeJM5elF9kDo6kkWWuZAIAAAAAAAAAAACtRZAJAAAAlKrWn/86YufBiKd3CzIBAAAAAAAAAACA1iLIBAAAAEpV68+/kBkRMTgsyAQAAAAAAAAAAABaiyATAAAAKNVpC5KYNz1/ZnA4LWcZAAAAAAAAAAAAgEkSZAIAAAClqiRJ1PryX0ls2p7FwXFXMgEAAAAAAAAAAIDWIcgEAAAASjfQn+T+fCKNeHCbIBMAAAAAAAAAAABoHYJMAAAAoHQDfZXITzIjBofTUnYBAAAAAAAAAAAAmAxBJgAAAFC642YkcfrC/CRzcDiNLHMlEwAAAAAAAAAAAGgNgkwAAACgKWp9+UHm1v0Rw/tKWgYAAAAAAAAAAACggCATAAAAaIpaf/FricHhtIRNAAAAAAAAAAAAAIoJMgEAAICmOGtRErOm5c8IMgEAAAAAAAAAAIBWIcgEAAAAmqJaSWLV0iR35qFtWYzVs5I2AgAAAAAAAAAAADg8QSYAAADQNLX+/FcTo/WITS8IMgEAAAAAAAAAAIDmE2QCAAAATVPrK341MTiclrAJAAAAAAAAAAAAQD5BJgAAANA0i2cncdJxSe7M4IgLmQAAAAAAAAAAAEDzCTIBAACApqr15QeZz+zOYvt+USYAAAAAAAAAAADQXIJMAAAAoKlq/cWvJwZH0hI2AQAAAAAAAAAAADg8QSYAAADQVOcen8T0av7M4LAgEwAAAAAASd9U4AABAABJREFUAAAAAGguQSYAAADQVL3VJM5fmuTObNiaxUSalbQRAAAAAAAAAAAAwC8SZAIAAABNV+vLf0VxYDzi0R2CTAAAAAAAAAAAAKB5BJkAAABA09X6i19RDA6nJWwCAAAAAAAAAAAA8PIEmQAAAEDT9c9Nom9O/szgsAuZAAAAAAAAAAAAQPMIMgEAAICWUHQl8/FdWbx4SJQJAAAAAAAAAAAANIcgEwAAAGgJtb7i1xTrR9ISNgEAAAAAAAAAAAD4RYJMAAAAoCWsWJJET8GbisFhFzIBAAAAAAAAAACA5hBkAgAAAC1h5rQkzl2c5M4MjaSRZqJMAAAAAAAAAAAAoHyCTAAAAKBl1PrzX1XsGY14cpcgEwAAAAAAAAAAACifIBMAAABoGbX+/AuZERGDw4JMAAAAAAAAAAAAoHyCTAAAAKBlnHxcEgtn5s8MDqflLAMAAAAAAAAAAADwMwSZAAAAQMtIkiRqffmvKzbvyGL/mCuZAAAAAAAAAAAAQLkEmQAAAEBLqfXnv65Is4gNWwWZAAAAAAAAAAAAQLkEmQAAAEBLWdWXRCXJnxkcTstZBgAAAAAAAAAAAOB/CDIBAACAljKnN4kzF+UXmYPDaWSZK5kAAAAAAAAAAABAeQSZAAAAQMup9eW/sthxMOKZPYJMAAAAAAAAAAAAoDyCTAAAAKDl1PrzL2RGRAwOCzIBAAAAAAAAAACA8ggyAQAAgJazfGES86bnzwwOp+UsAwAAAAAAAAAAABCCTAAAAKAFVZIkVvflv7bY+EIWhyZcyQQAAAAAAAAAAADKIcgEAAAAWlKtL8n9+UQa8eA2QSYAAAAAAAAAAABQDkEmAAAA0JIG+otfWwwOpyVsAgAAAAAAAAAAACDIBAAAAFrU/BlJnLYg/0rmkCATAAAAAAAAAAAAKIkgEwAAAGhZtb78IHN4X8TI3qykbQAAAAAAAAAAAIBuJsgEAAAAWlatv/jVxeCIK5kAAAAAAAAAAABA4wkyAQAAgJZ19uIkZvbkzwwOCzIBAAAAAAAAAACAxiv4I43Qvp544om4//774+GHH47NmzfHli1bYmRkJLZv3x4HDx6MsbGx6O3tjZkzZ8a8efOiv78/TjjhhDjnnHNi1apV8YpXvCLOOOOMZv9jAAAAdLWeShKr+pL4z2ezw848uC2L8XoW06pJiZsBAAAAAAAAAAAA3UaQSUfIsizuv//++MEPfhB33313/OhHP4qdO3cW/ucOHToUhw4dil27dsWWLVt+4ecnn3xyvPnNb45LLrkkfv3Xfz0qFUdlAQAAylbrq8R/Pls/7M8PTUQ8vD2LlUsFmQAAAAAAAAAAAEDjCDJpWwcOHIhvf/vb8c1vfjO+853vxLZt26b893j66afjuuuui+uuuy5OOumk+OhHPxpXX311zJkzZ8p/LwAAAF7eQH8lIg4fZEZEDA6nsXKpv0QHAAAAAAAAAAAAaBx/UpG2MjY2Fl/72tdizZo1sWTJknj3u98d69ata0iM+fOeeeaZ+MQnPhGnnHJKfOELX4g0TRv+ewIAABCxZHYSy+blzwwOZ+UsAwAAAAAAAAAAAHQtQSZtYe/evfGRj3wkTjjhhLjooovilltuif379zdll507d8ZHPvKRePWrXx2PPvpoU3YAAADoNrX+/FcYW3ZnseOAKBMAAAAAAAAAAABoHEEmbWHr1q3xhS98IXbs2NHsVX7qvvvui1e84hVx++23N3sVAACAjlfrK36FMTSSlrAJAAAAAAAAAAAA0K0EmXAM9u7dG+95z3vii1/8YrNXAQAA6GjnLUmit5o/MzjsQiYAAAAAAAAAAADQOIJMOEZpmsbVV18d1113XbNXAQAA6Fi91STOX5LkzqwfSaOeijIBAAAAAAAAAACAxuhp9gLQSH19ffGqV70qXvWqV8U555wTp556aixbtizmzJkTs2bNir1798aOHTtix44dcf/998c999wTd999d4yMjBzx7/X7v//7cfrpp8cb3/jGBvyTAAAAUOuvxP3D9cP+fP94xKM7szh9XolLAQAAAAAAAAAAAF1DkElHSZIkXv/618c73vGOeOtb3xrnn39+7vz8+fNj/vz5sXz58nj1q18dV199ddTr9bjtttvi05/+dNx3332T/r0nJibisssuiwcffDAWLFhwrP8oAAAA/JxaXyUiDh9kRkQMDqeCTAAAAAAAAAAAAKAhKs1eAKbCOeecE5/5zGfi6aefjnvuuSc+/vGPF8aYh1OtVmPNmjXx4x//ONatWxdz5syZ9H/2+eefj49//ONH9fsCAACQr39uxNLZ+TODw1k5ywAAAAAAAAAAAABdR5BJ20qSJC688MK46667YtOmTfHHf/zHsWzZsin9PS6//PK47777jijuXLt2bWzatGlK9wAAAOAn/z9woD//VcbjO7PYM1rSQgAAAAAAAAAAAEBXEWTSdnp6euLyyy+PjRs3xte+9rV405ve1NDf76yzzoq77747VqxYMan5NE3jr/7qrxq6EwAAQLeq9eW/ysgi4oFt5ewCAAAAAAAAAAAAdBdBJm0jSZK45JJL4uGHH45169bFOeecU9rvvWjRorjjjjti/vz5k5q/7bbbYs+ePY1dCgAAoAutXJpENcmfWb+1nF0AAAAAAAAAAACA7iLIpC0sWrQo7rvvvrjpppti+fLlTdnh9NNPj09/+tOTmj148GB84xvfaPBGAAAA3WfmtCTOPT6/yFy/NSLLSloIAAAAAAAAAAAA6BqCTNrCggULolarNXuN+MAHPjDpy5zf//73G7wNAABAdxroz3+dsXs04oWJWSVtAwAAAAAAAAAAAHQLQSYcgUqlEldfffWkZu+9994GbwMAANCdan35FzIjIraMzm/8IgAAAAAAAAAAAEBXEWTCEbr44osnNffEE0/ExMREg7cBAADoPqfOT2L+jPyZLaMLylkGAAAAAAAAAAAA6BqCTDhCy5Yti5NOOqlwbnx8PJ555pkSNgIAAOguSZJErT//lcbw2JwYTaslbQQAAAAAAAAAAAB0A0EmHIWzzjprUnO7d+9u8CYAAADdqdaf5P48jUo8M3ZcSdsAAAAAAAAAAAAA3UCQCUdh0aJFk5rbv39/gzcBAADoTquXVqKS32TGltH5pewCAAAAAAAAAAAAdAdBJhyFnp6eKZ0DAADgyMydnsQZC/OLzC2j8yPLSloIAAAAAAAAAAAA6HiCTDgKu3btmtTcnDlzGrwJAABA96r157/W2FufHrsmZpa0DQAAAAAAAAAAANDpBJlwFJ566qlJzfX19TV2EQAAgC5W68+/kBkR8dTo/MYvAgAAAAAAAAAAAHQFQSYcof3798fmzZsL5xYsWBCLFi0qYSMAAIDutHxBEnN682e2CDIBAAAAAAAAAACAKSLIhCN01113RZqmhXOrVq0qYRsAAIDuVa0ksbov/9XGc2PzYjzz+gMAAAAAAAAAAAA4dv5EIhyh2267bVJzb3jDGxq8CQAAALW+JPfn9ajEc6PzStoGAAAAAAAAAAAA6GSCTDgC27Zti1tvvXVSs295y1savA0AAAAD/cWvNraMzm/8IgAAAAAAAAAAAEDHE2TCEfjc5z4Xo6OjhXOnnXZavPa1ry1hIwAAgO62cGYSp87Pv5IpyAQAAAAAAAAAAACmgiATJunZZ5+Nv/mbv5nU7Ac+8IEGbwMAAMD/qvXnB5m76jNj2/6SlgEAAAAAAAAAAAA6liATJukTn/hEHDhwoHBu7ty58eEPf7iEjQAAAIiIqPUVv95Yv7WERQAAAAAAAAAAAICO1tPsBaAd3H777XHzzTdPavajH/1oLFiwoMEbAQAA8L/OXpzEjJ6IQxOHn/nekxEH6/XyloJJqtcjnt574kt+beThiGrV80rr8bzSbjyztJOffV5nVsajv3dfkzcCAAAAAAAA4OUIMqHAyMhIXH311ZOaXbZsWVxzzTUN3ggAAICfNa2axKqlSfzXc9lhZ57aHfHUA+ILWtXJL/l3P9oYEeF5pVV5Xmk3nlnayUuf12d/HPHhX8mip5I0aR8AAAAAAAAAfp4gE3LU6/W45JJLYvv27ZOav/baa2P27NkN3mpysiyLsbGxZq9BmxsfH5/Ur0Er8LzSbjyztBPPK+1g5fER//Vcs7cAAICpc8/TEbN6x+OKVc3eBF7KewLajWeWduJ5pd14ZmknnlfaieeVduOZpZ14XmknnlfajWeWRsqywx9raIYka7WNoIV84hOfiM985jOTmr3ooovi9ttvb9gu+/bti3p98n9z+759++Kxxx5r2D4AAACtZPfE9Fj7wi81ew0AAJhiWbxv8YY4ftqBZi8CAAAAAAAA0BRnnHFGzJkzZ9Lz1Wr1iOaPVKVh/83Q5m688cZJx5h9fX3xxS9+scEbAQAAcDjH9YzGgurBZq8BAABTLIkf7jkl/PWqAAAAAAAAAK1BkAkv4957740PfvCDk5qtVCqxbt26WLx4cYO3AgAAIM8pM3Y1ewUAAJhyz4zNj6dG5zd7DQAAAAAAAABCkAm/4LHHHosLL7wwRkdHJzX/yU9+Mt785jc3eCsAAACKDMwaiUqkzV4DAACm3A/3nBL1LGn2GgAAAAAAAABdL8myLGv2EtAqtm7dGq973evi8ccfn9T8BRdcEN/85jcjSRr/hyD27dsX9Xp90vOVSiWmT5/ewI3oBuPj4/HjH//4Jb/2yle+MqZNm9akjeDwPK+0G88s7cTzSjv51qMRNzyQRRb+sDoAAJ3lqtURb1ne7C3AewLaj2eWduJ5pd14ZmknnlfaieeVduOZpZ14XmknnlfajWeWRhodHY00nfyxhmq1GnPmzGnYPj0N+2+GNrN79+5461vfOukYc2BgIG666aZSYsyjkSRJ9Pb2NnsNOtC0adM8W7QNzyvtxjNLO/G80qp+e0XEqfPH4rYfb4tt43Ni4n+uCM2cMTNa9P++0eWyLOLgoYMv+TXPK63K80q78czSTg6MR+wezZ+5bVPEG5dPi9m9HmJaj/cEtBvPLO3E80q78czSTjyvtBPPK+3GM0s78bzSTjyvtBvPLFNlbGys2Su8hCATIuLAgQPxW7/1WzE0NDSp+TPPPDO++93vxrx58xq7GAAAAEdsxfERe+a/9C/bec1rXuPlHi1pbGwsfvSjoZf8mueVVuV5pd14Zmkn2/eOxUe+PRFj2eE/3e0di7htYz2uGPB5DwAAAAAAAKBZKs1eAJptdHQ03vWud8W99947qfmTTjop7rrrrliyZEmDNwMAAAAAALrBvOkRr57zXOHctx5JY2RvVsJGAAAAAAAAALwcQSZdbXx8PN7znvfEXXfdNan5pUuXxve+9704+eSTG7wZAAAAAADQTVbPHo551UO5MxNpxJc2TJS0EQAAAAAAAAA/T5BJ16rX63HppZfGHXfcMan5hQsXxp133hlnnnlmgzcDAAAAAAC6TU+Sxa/O3VI496Nnstj0QlrCRgAAAAAAAAD8PEEmXSlN07jyyivj1ltvndT8vHnz4rvf/W6sXLmywZsBAAAAAADd6owZO6N/2p7CubWD9UizrISNAAAAAAAAAPhZgky6TpZl8aEPfShuuOGGSc3Pnj07vv3tb8crX/nKBm8GAAAAAAB0sySJ+D/ziq9kProzi3/b4komAAAAAAAAQNkEmXSdD3/4w/GP//iPk5qdMWNG3HHHHfG6172uwVsBAAAAAABE9PXui7NmbC+cu2FDPUYnXMkEAAAAAAAAKJMgk67yh3/4h/F3f/d3k5rt7e2N22+/Pd74xjc2eCsAAAAAAID/3+vmbolpBV/xth+I+MZmVzIBAAAAAAAAyiTIpGt8/OMfj8997nOTmu3p6YmvfOUrccEFFzR4KwAAAAAAgJea1zMWF5xRPPfVTfXYddCVTAAAAAAAAICyCDLpCtdcc01ce+21k5qtVqtxww03xIUXXtjYpQAAAAAAAA7jXWdHHDc9f+bQRMTND9TLWQgAAAAAAAAAQSad78/+7M/ir//6ryc1myRJ/NM//VOsWbOmwVsBAAAAAAAc3qxpEe9dWS2c+96TaWx5MS1hIwAAAAAAAAAEmXS0v/iLv4i//Mu/nNRskiTxxS9+MS6//PIGbwUAAAAAAFDsTadX4uTjktyZNItYO1iPLMtK2goAAAAAAACgewky6Vh/9Vd/FZ/85CcnPf/5z38+fvd3f7dxCwEAAAAAAByBaiWJKwaKr2Su35rF4LAgEwAAAAAAAKDRBJl0pE9/+tPxp3/6p5Oev/baa+PDH/5wAzcCAAAAAAA4crX+StT6869kRkSsHZqIeirKBAAAAAAAAGgkQSYd57Of/Wz8yZ/8yaTnP/WpT8XHPvaxBm4EAAAAAABw9K4YqEaloMl8dk/EnY+n5SwEAAAAAAAA0KUEmXSUv/3bv40/+qM/mvT8Jz/5ybjmmmsatxAAAAAAAMAxOvm4Svzm8uLPejc/WI/9Y65kAgAAAAAAADSKIJOO8Q//8A/xkY98ZNLz11xzTfz5n/95AzcCAAAAAACYGmvOr8bMnvyZPaMRt2+sl7MQAAAAAAAAQBcSZNIR1q5dG7/3e78XWTa5v/X5Yx/7WHzqU59q8FYAAAAAAABTY/6MJC5eUS2c++YjaWzd50omAAAAAAAAQCMIMml7N954Y3zwgx+cdIz5B3/wB3Httdc2eCsAAAAAAICp9fazKnH8rPyZiTTihg2uZAIAAAAAAAA0giCTtnbLLbfEFVdcEWmaTmr+Qx/6UHz2s59t8FYAAAAAAABTr7eaxGWri69k3vt0Gpu3T+7bCQAAAAAAAACTJ8ikbX3961+PSy+9NOr1yf0tz1dddVX8/d//fSRJ0uDNAAAAAAAAGuN1J1firEXF3zr+abAeWZaVsBEAAAAAAABA9xBk0pa+9a1vxZo1a2JiYmJS85deemlcd911YkwAAAAAAKCtJUkSV9WKr2Q+uiOLe592JRMAAAAAAABgKgkyaTt33nlnXHzxxTE2Njap+TVr1sS6deuiUvG4AwAAAAAA7e/sxZV43cnF3z2uX1+P0QlXMgEAAAAAAACmikKNtnLPPffEu971rhgdHZ3U/EUXXRQ33HBDVKvFf1M0AAAAAABAu7hsVTWmFXzp234g4luPuJIJAAAAAAAAMFUEmbSNf//3f4+3v/3tcfDgwUnNv/Od74ybb745enp6GrwZAAAAAABAuZbMSeLtZxd/6rt9Yz1ePORKJgAAAAAAAMBUEGTSFu6777644IILYt++fZOaf9vb3ha33nprTJs2rcGbAQAAAAAANMdF51Zj3vT8mYMTEV95sF7OQgAAAAAAAAAdTpBJW/j85z8fu3fvnvT8d77znZg+fXokSdLUf1155ZWN+x8FAAAAAADoarN7k7jk/Grh3J2Pp/H07rSEjQAAAAAAAAA6myATAAAAAAAA2tRvLq/Esnn5M2kWsW7IlUwAAAAAAACAYyXIBAAAAAAAgDZVrSRx5UBP4dzgcBb3D7uSCQAAAAAAAHAsBJkAAAAAAADQxmr9SQz0JYVz64bqUU+zEjYCAAAAAAAA6EyCTAAAAAAAAGhjSZLEFQPVqBQ0mc/szuKuJ1zJBAAAAAAAADhagkwAAAAAAABoc6fMr8SbTi/+9HfzA/U4MO5KJgAAAAAAAMDREGQCAAAAAABAB3jvymrM6Mmf2T0a8dWN9XIWAgAAAAAAAOgwgkwAAAAAAADoAPNnJHHRudXCuW9sTmPbflcyAQAAAAAAAI6UIJO2sHbt2siyrO3+tXbt2mb/TwcAAAAAAHSRd5xdicWz8mfG04gb17uSCQAAAAAAAHCkBJkAAAAAAADQIab3JPH+VcVXMn/4dBqbt6clbAQAAAAAAADQOQSZAAAAAAAA0EF+9ZRKnLkwKZxbO1iPLMtK2AgAAAAAAACgMwgyAQAAAAAAoINUkiSurBVfydy8I4t/f8aVTAAAAAAAAIDJEmQCAAAAAABAhzn3+Eq89qTiT4E3rK/HWN2VTAAAAAAAAIDJEGQCAAAAAABAB3r/6mr0FHwN3Lo/4tuPuJIJAAAAAAAAMBmCTAAAAAAAAOhAfXOSePtZxZ8Db9tYj92HXMkEAAAAAAAAKCLIBAAAAAAAgA518XnVmNubP3NgPOIrD9bLWQgAAAAAAACgjQkyAQAAAAAAoEPN7k3ikpXVwrn/+3gaz+x2JRMAAAAAAAAgjyATAAAAAAAAOthvLq/EifPyZ9IsYt3QRDkLAQAAAAAAALQpQSYAAAAAAAB0sJ5KElcM9BTO3T+cxdBIWsJGAAAAAAAAAO1JkAkAAAAAAAAd7hX9SaxamhTOrRusRz3NStgIAAAAAAAAoP0IMgEAAAAAAKDDJUkSV9aqUZRkbtmdxfefdCUTAAAAAAAA4OUIMgEAAAAAAKALnDq/Em86vfjz4E0P1OPguCuZAAAAAAAAAD9PkAkAAAAAAABd4r0rqzGjJ3/mxUMRX9tUL2chAAAAAAAAgDYiyAQAAAAAAIAusWBmEr99brVw7o7Nabyw35VMAAAAAAAAgJ8lyAQAAAAAAIAu8s6zK7FoZv7MWD3ixg2uZAIAAAAAAAD8LEEmAAAAAAAAdJHpPUm8f3Xxlcz/tyWNR3ekJWwEAAAAAAAA0B4EmQAAAAAAANBlXn9KJZYvTArn1g7WI8uyEjYCAAAAAAAAaH2CTAAAAAAAAOgylSSJqwaKr2Ru2p7FfzwryAQAAAAAAACIEGQCAAAAAABAVzpvSSV+ZVnxlczrhyZivC7KBAAAAAAAABBkAgAAAAAAQJe6fHVP9BR8Mdy6P+Lbj6blLAQAAAAAAADQwgSZAAAAAAAA0KX65ibxtjOLPxne+lA99oy6kgkAAAAAAAB0N0EmAAAAAAAAdLF3r6jGnN78mQPjEV95sF7OQgAAAAAAAAAtSpAJAAAAAAAAXWxObxJrzq8Wzn33sTSe3eNKJgAAAAAAANC9BJkAAAAAAADQ5d5yRiVOmJs/k2YR1w9NlLMQAAAAAAAAQAsSZAIAAAAAAECX66kkccVAT+Hcj5/PYsNIWsJGAAAAAAAAAK1HkAkAAAAAAADEK09IYuWSpHBu7VA96mlWwkYAAAAAAAAArUWQCQAAAAAAAESSJHFlrRpFSeZTL2Zx91OuZAIAAAAAAADdR5AJAAAAAAAARETEaQsq8eunFX9CvHFDPQ6Ou5IJAAAAAAAAdBdBJgAAAAAAAPBT71tVjenV/JkXD0V8/eF6OQsBAAAAAAAAtAhBJgAAAAAAAPBTC2cm8dvnFhSZEfEvD6ex/YArmQAAAAAAAED3EGQCAAAAAAAAL/HOcyqxcGb+zFg94ssbXMkEAAAAAAAAuocgEwAAAAAAAHiJGT1JXLqq+Erm3U+l8djOtISNAAAAAAAAAJpPkAkAAAAAAAD8gjecWonTFySFc2sH65FlWQkbAQAAAAAAADSXIBMAAAAAAAD4BZUkiatqxVcyN76QxX8+J8gEAAAAAAAAOp8gEwAAAAAAAHhZK5ZU4tUnFl/JvH5oIsbrokwAAAAAAACgswkyAQAAAAAAgMO6fKAnqgVN5si+iH99LC1nIQAAAAAAAIAmEWQCAAAAAAAAh3XC3CTedmbxZ8VbHqzH3lFXMgEAAAAAAIDOJcgEAAAAAAAAcr17RTXm9ObP7B+PuOWhejkLAQAAAAAAADSBIBMAAAAAAADINXd6Eu9ZUS2c+9dH03h+jyuZAAAAAAAAQGcSZAIAAAAAAACF3nJGJfrn5s/Us4jr10+UsxAAAAAAAABAyQSZAAAAAAAAQKFp1SQuX91TOPdfz2XxwNa0hI0AAAAAAAAAyiXIBAAAAAAAACbl1ScmseL4pHBu7WA96mlWwkYAAAAAAAAA5RFkAgAAAAAAAJOSJElcWatGUZL55ItZ3POUK5kAAAAAAABAZxFkAgAAAAAAAJO2fGElfu3U4s+MN26ox6EJVzIBAAAAAACAziHIBAAAAAAAAI7I+1ZVo7eaP7PrUMS/POxKJgAAAAAAANA5BJkAAAAAAADAEVk0K4kLzyn+1Pj1TfXYccCVTAAAAAAAAKAzCDIBAAAAAACAI3bhudVYMCN/ZrQe8eUH6uUsBAAAAAAAANBggkwAAAAAAADgiM3oSeJ9q6qFc3c/mcbjO9MSNgIAAAAAAABoLEEmAAAAAAAAcFR+7dRKnDY/yZ3JImLdUD2yLCtnKQAAAAAAAIAGEWQCAAAAAAAAR6VaSeLKWvGVzAe3ZfHfzwkyAQAAAAAAgPYmyAQAAAAAAACO2sqllXjViflXMiMi1q2fiPG6KBMAAAAAAABoX4JMAAAAAAAA4JhcvronqgVN5vDeiO8+lpazEAAAAAAAAEADCDIBAAAAAACAY3LivCTeembxp8dbHqrH3lFXMgEAAAAAAID2JMgEAAAAAAAAjtl7VlRj9rT8mX1jEbdtrJezEAAAAAAAAMAUE2QCAAAAAAAAx2zu9CTevaJaOPedR9N4fq8rmQAAAAAAAED7EWQCAAAAAAAAU+JtZ1aib07+zEQa8aX1E+UsBAAAAAAAADCFBJkAAAAAAADAlJhWTeLy1T2Fc//5bBYPbUtL2AgAAAAAAABg6ggyAQAAAAAAgCnzy8uSOO/4pHDunwfrkWZZCRsBAAAAAAAATA1BJgAAAAAAADBlkiSJK2vVwrkndmVxz1OuZAIAAAAAAADtQ5AJAAAAAAAATKkzFlbiDacWf4q8cUM9RidcyQQAAAAAAADagyATAAAAAAAAmHKXrqxGb8GhzJ0HI/7lYVcyAQAAAAAAgPYgyAQAAAAAAACm3OLZSbzz7OLPkV/bVI+dB13JBAAAAAAAAFqfIBMAAAAAAABoiN8+txrzZ+TPjNYjbtpQL2chAAAAAAAAgGMgyAQAAAAAAAAaYua0JN63slo49/0n03hyV1rCRgAAAAAAAABHT5AJAAAAAAAANMyvn1aJU+YnuTNZRKwdrEeWZeUsBQAAAAAAAHAUBJkAAAAAAABAw1QrSVw5UHwl84FtWfz4eUEmAAAAAAAA0LoEmQAAAAAAAEBDre6rxCtPyL+SGRFx/dBETKSiTAAAAAAAAKA1CTIBAAAAAACAhrt8oCcqBU3mc3sj/u9jaTkLAQAAAAAAABwhQSYAAAAAAADQcMvmJfGWM4o/T37lwXrsG3MlEwAAAAAAAGg9gkwAAAAAAACgFO9ZUY1Z0/Jn9o5F3PZQvZyFAAAAAAAAAI6AIBMAAAAAAAAoxXEzkvidFdXCuW8/msbIXlcyAQAAAAAAgNYiyAQAAAAAAABK81tnVmLp7PyZiTTi+vUT5SwEAAAAAAAAMEmCTAAAAAAAAKA006pJXDbQUzj3H89msXFbWsJGAAAAAAAAAJMjyAQAAAAAAABK9ZplSZyzOCmcWztUjzTLStgIAAAAAAAAoJggEwAAAAAAAChVkiRxVa1aOPfYzix+uMWVTAAAAAAAAKA1CDIBAAAAAACA0p25qBKvP6X4c+UN6+sxOuFKJgAAAAAAANB8gkwAAAAAAACgKd6/qhq9BYcydxyM+MZmVzIBAAAAAACA5hNkAgAAAAAAAE1x/Owk3nF28SfLr26qx66DrmQCAAAAAAAAzSXIBAAAAAAAAJrmonOrcdz0/JlDExE3PVAvZyEAAAAAAACAwxBkAgAAAAAAAE0zc1oS71tVLZz73hNpPPViWsJGAAAAAAAAAC9PkAkAAAAAAAA01RtPq8QpxyW5M1lErB2sR5Zl5SwFAAAAAAAA8HMEmQAAAAAAAEBTVStJXFErvpK5YWsW9w8LMgEAAAAAAIDmEGQCAAAAAAAATTfQV4lf6s+/khkRsXZoIiZSUSYAAAAAAABQPkEmAAAAAAAA0BKuGOiJSkGT+dyeiDsfT8tZCAAAAAAAAOBnCDIBAAAAAACAlnDScUn85vLiT5hfebAe+8dcyQQAAAAAAADKJcgEAAAAAAAAWsaa86sxa1r+zJ7RiNs31stZCAAAAAAAAOB/CDIBAAAAAACAljF/RhIXn1ctnPvmI2ls3edKJgAAAAAAAFAeQSYAAAAAAADQUn7rrEosmZ0/M5FGfGm9K5kAAAAAAABAeQSZAAAAAAAAQEvprSZx2eriK5n//kwaD29PS9gIAAAAAAAAQJAJAAAAAAAAtKDXnlSJsxclhXP/fH890iwrYSMAAAAAAACg2wkyAQAAAAAAgJaTJElcWSu+kvnozizufdqVTAAAAAAAAKDxBJkAAAAAAABASzp7cSV+9eTiT5pfWl+P0QlXMgEAAAAAAIDGEmQCAAAAAAAALev9q6oxreCr5vYDEd98xJVMAAAAAAAAoLEEmQAAAAAAAEDLWjIniXecXfxZ8/aN9XjxkCuZAAAAAAAAQOMIMgEAAAAAAICWdtF51Zg3PX/m0ETEzQ/Uy1kIAAAAAAAA6EqCTAAAAAAAAKClzZqWxHtXVgvn7noijS0vpiVsBAAAAAAAAHQjQSYAAAAAAADQ8n7j9EqcdFySO5NmEeuGXMkEAAAAAAAAGkOQCQAAAAAAALS8aiWJKwaKr2QOjWRx/7ArmQAAAAAAAMDUE2QCAAAAAAAAbeGX+isx0Jd/JTMiYt1gPeppVsJGAAAAAAAAQDcRZAIAAAAAAABt44qBalQKmsxn9mRx1xOuZAIAAAAAAABTS5AJAAAAAAAAtI1T5lfiN04v/sx50wP1ODDuSiYAAAAAAAAwdQSZAAAAAAAAQFu5ZGU1Zvbkz+wZjfjqxno5CwEAAAAAAABdQZAJAAAAAAAAtJX5M5K4+Lxq4dw3NqexbZ8rmQAAAAAAAMDUEGQCAAAAAAAAbeftZ1fi+Fn5M+NpxA0bXMkEAAAAAAAApoYgEwAAAAAAAGg7vdUk3r+6+Ermvz2dxubtaQkbAQAAAAAAAJ1OkAkAAAAAAAC0pV89uRJnLkoK5/55sB5ZlpWwEQAAAAAAANDJBJkAAAAAAABAW0qSJK6qFV/JfGRHFvc+40omAAAAAAAAcGwEmQAAAAAAAEDbOmdxJV57UvFnzy8N1WOs7komAAAAAAAAcPQEmQAAAAAAAEBbu2x1NXoKvny+cCDiW4+4kgkAAAAAAAAcPUEmAAAAAAAA0NaWzkni7WcVf/q8fWM9XjzkSiYAAAAAAABwdASZAAAAAAAAQNu7+LxqzJueP3NgPOIrD9bLWQgAAAAAAADoOIJMAAAAAAAAoO3N7k1izfnVwrk7H0/j6d1pCRsBAAAAAAAAnUaQCQAAAAAAAHSENy+vxLJ5+TNpFnH9kCuZAAAAAAAAwJETZAIAAAAAAAAdoVpJ4oqBnsK5+4ezGBx2JRMAAAAAAAA4MoJMAAAAAAAAoGP8Un8Sq5cmhXPrhupRT7MSNgIAAAAAAAA6hSATAAAAAAAA6BhJksSVtWpUCprMp3dn8f0nXckEAAAAAAAAJk+QCQAAAAAAAHSUU+ZX4k2nFX8K/fKGehwcdyUTAAAAAAAAmBxBJgAAAAAAANBxLllZjRk9+TO7RyO+uqlezkIAAAAAAABA2xNkAgAAAAAAAB1nwcwkLjq3Wjh3x8NpbNvvSiYAAAAAAABQTJAJAAAAAAAAdKR3nF2JxbPyZ8bTiBs3uJIJAAAAAAAAFBNkAgAAAAAAAB1pek8Sl64qvpL5wy1pPLIjLWEjAAAAAAAAoJ0JMgEAAAAAAICO9fpTKnHGwqRw7p8H65FlWQkbAQAAAAAAAO1KkAkAAAAAAAB0rEqSxFW14iuZm7dn8aNnBZkAAAAAAADA4QkyAQAAAAAAgI527vGVeM1JxVcyvzQ0EeN1USYAAAAAAADw8gSZAAAAAAAAQMe7bFVP9BR8Hd26P+Jbj6blLAQAAAAAAAC0HUEmAAAAAAAA0PH65iZxwZnFn0dve6geuw+5kgkAAAAAAAD8IkEmAAAAAAAA0BXevaIac3vzZw6MR9zyUL2chQAAAAAAAIC2IsgEAAAAAAAAusLs3iTWnF8tnPvuY2k8u9uVTAAAAAAAAOClBJkAAAAAAABA13jzGZU4cW7+TJpFrFs/Uc5CAAAAAAAAQNsQZAIAAAAAAABdo6eSxBUDPYVz9z2fxfqRtISNAAAAAAAAgHYhyAQAAAAAAAC6yitOSGLV0qRwbu1QPeppVsJGAAAAAAAAQDsQZAIAAAAAAABdJUmSuGKgGkVJ5pYXs/jBk65kAgAAAAAAAD8hyAQAAAAAAAC6zmkLKvHG04o/l375gXocHHclEwAAAAAAABBkAgAAAAAAAF3qvauqMaMnf+bFQxFf21QvZyEAAAAAAACgpQkyAQAAAAAAgK60cGYSF55TLZy7Y3Ma2/e7kgkAAAAAAADdTpAJAAAAAAAAdK13nVOJhTPzZ8bqETducCUTAAAAAAAAup0gEwAAAAAAAOha03uSeP+q4iuZ92xJ49EdaQkbAQAAAAAAAK1KkAkAAAAAAAB0tf9zaiWWL0gK59YN1SPLshI2AgAAAAAAAFqRIBMAAAAAAADoapUkiStrxVcyN76QxX8+K8gEAAAAAACAbiXIBAAAAAAAALreiiWV+OVlxVcyr18/EeN1USYAAAAAAAB0I0EmAAAAAAAAQERctronegq+oI7si/jOo2k5CwEAAAAAAAAtRZAJAAAAAAAAEBEnzE3ibWcWf0K95aF67Bl1JRMAAAAAAAC6jSATAAAAAAAA4H+8e0U15vTmzxwYj7jlwXo5CwEAAAAAAAAtQ5AJAAAAAAAA8D/m9Cax5vxq4dx3H0vjuT2uZAIAAAAAAEA3EWQCAAAAAAAA/Iy3nFGJE+bmz9SziOvXT5SzEAAAAAAAANASBJkAAAAAAAAAP6OnksTlAz2Fc//9XBYPbE1L2AgAAAAAAABoBYJMAAAAAAAAgJ/zqhOSOH9JUji3drAe9TQrYSMAAAAAAACg2QSZAAAAAAAAAD8nSZK4YqAaRUnmky9mcfdTrmQCAAAAAABANxBkAgAAAAAAALyM5Qsr8WunFX9S/fKGehwcdyUTAAAAAAAAOp0gEwAAAAAAAOAw3reyGtOr+TO7DkX8y8P1chYCAAAAAAAAmkaQCQAAAAAAAHAYi2YlceG5BUVmRHz94TS2H3AlEwAAAAAAADqZIBMAAAAAAAAgx7vOqcTCmfkzY/WImza4kgkAAAAAAACdTJAJAAAAAAAAkGNGTxKXriq+kvmDp9J4fGdawkYAAAAAAABAMwgyAQAAAAAAAAq84dRKnLYgKZxbO1iPLMtK2AgAAAAAAAAomyATAAAAAAAAoEAlSeLKgeIrmQ+9kMV/PSfIBAAAAAAAgE4kyAQAAAAAAACYhJVLK/HqE4uvZF4/NBHjdVEmAAAAAAAAdBpBJgAAAAAAAMAkXb66J6oFTebwvoh/fSwtZyEAAAAAAACgNIJMAAAAAAAAgEk6YV4Sbz2z+DPrrQ/VY++oK5kAAAAAAADQSQSZAAAAAAAAAEfgPSuqMac3f2bf2E+iTAAAAAAAAKBzCDIBAAAAAAAAjsDc6Um8e0W1cO47j6bx/B5XMgEAAAAAAKBTCDIBAAAAAAAAjtBbz6hE/5z8mXoWcf36iXIWAgAAAAAAABpOkAkAAAAAAABwhKZVk7hsoKdw7r+ey+LBrWkJGwEAAAAAAACNJsgEAAAAAAAAOAq/fGIS5x2fFM7981A90iwrYSMAAAAAAACgkQSZAAAAAAAAAEchSZK4qlYtnHtyVxb3POVKJgAAAAAAALQ7QSYAAAAAAADAUVq+sBK/dmrxZ9cbN9Tj0IQrmQAAAAAAANDOBJkAAAAAAAAAx+B9q6rRW3Aoc+fBiDsediUTAAAAAAAA2pkgEwAAAAAAAOAYLJ6VxIXnFH96/dqmeuw86EomAAAAAAAAtCtBJgAAAAAAAMAxetc51Zg/I39mtB7x5Q31chYCAAAAAAAAppwgEwAAAAAAAOAYzZyWxKWrqoVzP3gyjSd2pSVsBAAAAAAAAEw1QSYAAAAAAADAFPi1Uytx6vwkdyaLiLWD9ciyrJylAAAAAAAAgCkjyAQAAAAAAACYAtVKElcOFF/JfHBbFv/9vCATAAAAAAAA2o0gEwAAAAAAAGCKrOqrxCtPyL+SGRFx/dBETKSiTAAAAAAAAGgngkwAAAAAAACAKXTFQE9UC5rM5/dGfPextJyFAAAAAAAAgCkhyAQAAAAAAACYQifOS+ItZxR/iv3Kg/XYN+ZKJgAAAAAAALQLQSYAAAAAAADAFFtzfjVmT8uf2TcWcetD9XIWAgAAAAAAAI6ZIBMAAAAAAABgis2dnsTvrKgWzn3n0TSG97qSCQAAAAAAAO1AkAkAAAAAAADQABecWYmls/NnJtKIL62fKGchAAAAAAAA4JgIMgEAAAAAAAAaYFo1icsHegrn/uPZLB7alpawEQAAAAAAAHAsBJkAAAAAAAAADfIry5I4d3FSOLd2sB5plpWwEQAAAAAAAHC0BJkAAAAAAAAADZIkSVxZqxbOPb4rix9ucSUTAAAAAAAAWpkgEwAAAAAAAKCBzlxUiTecUvxp9ob19RidcCUTAAAAAAAAWpUgEwAAAAAAAKDBLl1Vjd6CQ5k7DkbcsdmVTAAAAAAAAGhVgkwAAAAAAACABls8O4l3nl38efZrm+qx86ArmQAAAAAAANCKBJkAAAAAAAAAJfjtc6sxf0b+zKGJiJseqJezEAAAAAAAAHBEBJkAAAAAAAAAJZg5LYn3rqwWzn3/iTSe3JWWsBEAAAAAAABwJASZAAAAAAAAACV542mVOOW4JHcmi4h1Q/XIsqycpQAAAAAAAIBJEWQCAAAAAAAAlKRaSeLKWvGVzA1bs7hvWJAJAAAAAAAArUSQCQAAAAAAAFCi1X2VeMUJ+VcyIyLWDU3ERCrKBAAAAAAAgFYhyAQAAAAAAAAo2RWre6JS0GQ+tyfizsfTchYCAAAAAAAACgkyAQAAAAAAAEq27Lgk3ry8+HPtzQ/UY/+YK5kAAAAAAADQCgSZAAAAAAAAAE2w5vxqzJqWP7N3LOK2jfVyFgIAAAAAAAByCTIBAAAAAAAAmuC4GUn8znnVwrlvPZLGyF5XMgEAAAAAAKDZBJkAAAAAAAAATXLBWZVYOjt/ZiKN+NKGiXIWAgAAAAAAAA5LkAkAAAAAAADQJL3VJC5b3VM496Nnstj0QlrCRgAAAAAAAMDhCDIBAAAAAAAAmug1JyVx9uKkcG7tYD3SLCthIwAAAAAAAODlCDIBAAAAAAAAmihJkriqVi2ce3RnFv+2xZVMAAAAAAAAaBZBJgAAAAAAAECTnbWoEq8/ufjz7Q0b6jE64UomAAAAAAAANIMgEwAAAAAAAKAFXLq6GtMKvuBuPxDxjc2uZAIAAAAAAEAzCDIBAAAAAAAAWsCS2Um84+ziT7hf3VSPXQddyQQAAAAAAICyCTIBAAAAAAAAWsRF51XjuOn5M4cmIm5+oF7OQgAAAAAAAMBPCTIBAAAAAAAAWsSsaUm8d2W1cO57T6ax5cW0hI0AAAAAAACA/yXIBAAAAAAAAGghbzq9Eicfl+TOpFnE2sF6ZFlW0lYAAAAAAACAIBMAAAAAAACghVQrSVw5UHwlc/3WLAaHBZkAAAAAAABQFkEmAAAAAAAAQIsZ6K9ErT//SmZExNqhiainokwAAAAAAAAogyATAAAAAAAAoAVdMVCNSkGT+eyeiDsfT8tZCAAAAAAAALqcIBMAAAAAAACgBZ18XCV+c3nxJ92bH6zH/jFXMgEAAAAAAKDRBJkAAAAAAAAALWrN+dWY2ZM/s2c04vaN9XIWAgAAAAAAgC4myAQAAAAAAABoUfNnJHHximrh3DcfSWPrPlcyAQAAAAAAoJEEmQAAAAAAAAAt7O1nVeL4WfkzE2nEDRtcyQQAAAAAAIBGEmQCAAAAAAAAtLDeahKXDRRfybz36TQ2b09L2AgAAAAAAAC6kyATAAAAAAAAoMW97qRKnLUoKZz7p8F6ZFlWwkYAAAAAAADQfQSZAAAAAAAAAC0uSZK4qlZ8JfPRHVnc+7QrmQAAAAAAANAIgkwAAAAAAACANnD24kq87uTiT7zXr6/H6IQrmQAAAAAAADDVBJkAAAAAAAAAbeKyVdWYVvCVd/uBiG894komAAAAAAAATDVBJgAAAAAAAECbWDInibefXfyZ9/aN9XjxkCuZAAAAAAAAMJUEmQAAAAAAAABt5KJzqzFvev7MwYmIrzxYL2chAAAAAAAA6BKCTAAAAAAAAIA2Mrs3ifeurBbO3fl4Gk/vTkvYCAAAAAAAALqDIBMAAAAAAACgzfzG6ZU4aV6SO5NmEeuGXMkEAAAAAACAqSLIBAAAAAAAAGgz1UoSVwwUX8kcHM7i/mFXMgEAAAAAAGAqCDIBAAAAAAAA2lCtP4mBvvwrmRE/uZJZT7MSNgIAAAAAAIDOJsgEAAAAAAAAaENJ8pMrmZWCJvOZ3Vnc9YQrmQAAAAAAAHCsBJkAAAAAAAAAbeqU+ZV40+nFn31veqAeB8ZdyQQAAAAAAIBjIcgEAAAAAAAAaGPvXVmNGT35M3tGI766sV7OQgAAAAAAANChBJkAAAAAAAAAbWz+jCQuPq9aOPeNzWls2+9KJgAAAAAAABwtQSYAAAAAAABAm3v7WZVYPCt/ZjyNuHG9K5kAAAAAAABwtASZAAAAAAAAAG1uek8S719VfCXzh0+nsXl7WsJGAAAAAAAA0HkEmQAAAAAAAAAd4FdPqcSZC5PCubWD9ciyrISNAAAAAAAAoLMIMgEAAAD+P/buOzqK8v37+GcTCEV6D70IBBQUKdIhFBGQbuELih0QFUFQQRFBBWmCotgQEEUFUYqIUhJAQBAENfReQ+iETvo+f/js/jbJ7k6y2WzL+3VOzmFnJjPXkntnZ+65r+sGAAAAgAAQZDLpiXrGs2Tuv2jWppPMkgkAAAAAAAAAAABkFgmZAAAAAAAAAAAAABAgapUMUtMKxo+Bv4lKVkIys2QCAAAAAAAAAAAAmUFCJgAAAAAAAAAAAAAEkEfvClYugyfB525Ivx5glkwAAAAAAAAAAAAgM0jIBAAAAAAAAAAAAIAAUqaASQ/UMH4U/OOeZF2JY5ZMAAAAAAAAAAAAIKNIyESOER8fr7/++kuffvqpnn76ad19993KnTu3TCaT4c+6deu8HT4AAAAAAAAAAACQYb1qB6tgiPNtbiZKC3YleyYgAAAAAAAAAAAAIADk8nYAQHZISEjQrl27tG3bNuvPrl27lJiY6O3QAAAAAAAAAAAAgGx3W4hJvesEa+Z25wmXqw6nqGN1syoUNnkoMgAAAAAAAAAAAMB/kZAJv5eUlKRdu3Zp+/bt1uTLHTt2KCEhwduhAQAAAAAAAAAAAF7TvlqQfj2YrFNXHW+TYpbm/pukUa1yey4wAAAAAAAAAAAAwE+RkAm/FB8fr2HDhmnbtm2KiopSXFyct0MCAAAAAAAAAAAAfEquIJMevzuXxq9Pcrrd36fN+vd0iu4ODfJQZAAAAAAAAAAAAIB/IiETfunWrVuaMWOGt8MAAAAAAAAAAAAAfFr9UJPqljZpx1mz0+3m/pusOqVNCg4yeSgyAAAAAAAAAAAAwP9Q4hQAAAAAAAAAAAAAApTJZNIT9YJllGZ5/IpZa46meCQmAAAAAAAAAAAAwF+RkAkAAAAAAAAAAAAAAaxykSC1rWr8aPj7ncm6leh8Jk0AAAAAAAAAAAAgJyMhEwAAAAAAAAAAAAAC3P/qBCtvLufbXI6TFu9N9kxAAAAAAAAAAAAAgB8iIRMAAAAAAAAAAAAAAlzRfCb1qBVsuN3P+1N0/gazZAIAAAAAAAAAAAD2kJCJHMNkMql69erq3bu3pkyZorVr12rYsGHeDgsAAAAAAAAAAADwiK41g1Q8n/NtEpKlb3cwSyYAAAAAAAAAAABgTy5vBwBkB5PJpGrVqql+/fqqX7++GjRooHvuuUeFCxdOtd26deu8EyAAAAAAAAAAAADgYXlymfToXcH68E/nCZfrj6eoc40UVS9OfV8AAAAAAAAAAADAFgmZCAhVq1ZVgwYNUiVfFilSxNthAQAAAAAAAAAAAD6lRaUg/XIgRYcvmZ1uN+efZI1ra5LJZPJQZAAAAAAAAAAAAIDvIyETfilPnjyaMGGCdQbMokWLejskAAAAAAAAAAAAwOcFmUx68u5gjVqT5HS7fRfM+jParCYVSMgEAAAAAAAAAAAALEjIhF/Kly+fXnvtNW+HAQAAAAAAAAAAAPid2qWC1Li8SX9GO58l8+t/k9SgbG7lDiYpEwAAAAAAAAAAAJCkIG8HAAAAAAAAAAAAAADwrH535VIug6fFZ29Ivx5M8UxAAAAAAAAAAAAAgB8gIRMAAAAAAAAAAAAAcpgyBU3qWN34cfHC3cm6Gu98Jk0AAAAAAAAAAAAgpyAhEwAAAAAAAAAAAAByoIfuCFaBEOfb3EyUFuxK9kxAAAAAAAAAAAAAgI8jIRMAAAAAAAAAAAAAcqACISY9cmew4XYrD6Uo+iqzZAIAAAAAAAAAAAAkZAIAAAAAAAAAAABADtXh9iCVLeh8mxSz9PW/SZ4JCAAAAAAAAAAAAPBhJGQCAAAAAAAAAAAAQA6VK8ikx+/OZbjdthizdpxJ8UBEAAAAAAAAAAAAgO8iIRMAAAAAAAAAAAAAcrAGZU2qU8pkuN1X/yYrxeyBgAAAAAAAAAAAAAAfRUImAAAAAAAAAAAAAORgJpNJT9QLllFK5rHLZv1+3CMhAQAAAAAAAAAAAD6JhEwAAAAAAAAAAAAAyOGqFA1SeBXjx8cLdksJKTxmBgAAAAAAAAAAQM7EkzIAAAAAAAAAAAAAgPrUDVaeYOfbXI6Xtt8o55mAAAAAAAAAAAAAAB9DQiYAAAAAAAAAAAAAQMXymdSjlkFGpqS/r4fqWnKIByICAAAAAAAAAAAAfAsJmQAAAAAAAAAAAAAASVLXsCAVy+d8myQFa9O1ip4JCAAAAAAAAAAAAPAhJGQCAAAAAAAAAAAAACRJeXOZ1Leu8SyZ+26V1NmE2zwQEQAAAAAAAAAAAOA7SMgEAAAAAAAAAAAAAFi1qhykqkVNhtttuFZZZrMHAgIAAAAAAAAAAAB8BAmZAAAAAAAAAAAAAACrIJNJT9YzniXzVEIhHY4v5oGIAAAAAAAAAAAAAN9AQiYAAAAAAAAAAAAAIJU7SgWpUTnjWTI3Xq2kpBQPBAQAAAAAAAAAAAD4ABIyAQAAAAAAAAAAAADp9Ls7l4INcjKvJOfVysOeiQcAAAAAAAAAAADwtlzeDgAAAAAAAAAAAAAA4HvKFjSpY/Ug/XLA+RSYC/dKUecSPRQVkDlms3T5cq1Uy9ZslEwm2ix8D+0V/oY2C39Ce4U/ob3C39Bm4U9or/AntFf4G0ubLRCcoPuKUMkRgY2ETCBAmc1mJSQkeDsM+LnExPQX7PaWAb6A9gp/Q5uFP6G9wt/QZuFPaK/wJ7RX+BvaLPwJ7RW+rHsNae1R6YaTJhmXJO04a/ZcUECmFUn16uQ5SaLNwlcVSfWK9grfVyTVK9osfFuRVK9or/BtRVK9or3C9xVJ9Yo2C99WJNUr2it8W5FUr2iv8H1FVDg4zvqK511wF7PZt859JrOvRQR40JgxYzR27FjD7dauXavWrVtnf0BOXL9+XcnJyZna/tChQ9kYEQAAAAAAAAAAAHKCf26U0fqrVbwdBgAAAAAAAAAA8HGFg+P0RKl/vB0GAsztt9+uAgUKZHj74ODgTG2fWUHZtmcAAAAAAAAAAAAAgN+rm/+sigTf8nYYAAAAAAAAAAAAgNeRkAkAAAAAAAAAAAAAcCjYZFbzQse9HQYAAAAAAAAAAADgdSRkAgAAAAAAAAAAAACcqponVuVCrng7DAAAAAAAAAAAAMCrcnk7AADZo1ChQmrSpIm3w4CfS0xM1LZt21Ita9CggXLnzu2liADHaK/wN7RZ+BPaK/wNbRb+hPYKf0J7hb+hzcKf0F7hL8pdlkatMysxxeTtUAAAAAAAAAAAgI/jeRfcJT4+XikpKd4Ow4qETCBAmUwmhYSEeDsMBKDcuXPTtuA3aK/wN7RZ+BPaK/wNbRb+hPYKf0J7hb+hzcKf0F7hi6qXkp5rkKBP/kpWkjnY2+EAAAAAAAAAAAAfxvMuuEtCQoK3Q0iFhEwAAAAAAAAAAAAAQIY0LS/dOPavDsYVU2xSPqWYTSpdurSCgoK8HRpgV0pKis6ePZtqGW0Wvor2Cn9Dm4U/ob3Cn9Be4W9os/AntFf4E9or/I2lzeYLSvR2KEC2IyETAAAAAAAAAAAAAJBhBYITVO+2M9bXTe4prZAQHj3DNyUkJGjz5iOpltFm4ator/A3tFn4E9or/AntFf6GNgt/QnuFP6G9wt/Ya7NAoCI1HgAAAAAAAAAAAAAAAAAAAAAAAAAAwAAJmQAAAAAAAAAAAAAAAAAAAAAAAAAAAAZIyAQAAAAAAAAAAAAAAAAAAAAAAAAAADBAQiYAAAAAAAAAAAAAAAAAAAAAAAAAAIABEjIBAAAAAAAAAAAAAAAAAAAAAAAAAAAMkJAJAAAAAAAAAAAAAAAAAAAAAAAAAABggIRMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyRkAgAAAAAAAAAAAAAAAAAAAAAAAAAAGCAhEwAAAAAAAAAAAAAAAAAAAAAAAAAAwAAJmQAAAAAAAAAAAAAAAAAAAAAAAAAAAAZIyAQAAAAAAAAAAAAAAAAAAAAAAAAAADBAQiYAAAAAAAAAAAAAAAAAAAAAAAAAAIABEjIBAAAAAAAAAAAAAAAAAAAAAAAAAAAMkJAJAAAAAAAAAAAAAAAAAAAAAAAAAABggIRMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyRkAgAAAAAAAAAAAAAAAAAAAAAAAAAAGCAhEwAAAAAAAAAAAAAAAAAAAAAAAAAAwAAJmQAAAAAAAAAAAAAAAAAAAAAAAAAAAAZIyAQAAAAAAAAAAAAAAAAAAAAAAAAAADBAQiYAAAAAAAAAAAAAAAAAAAAAAAAAAIABEjIBAAAAAAAAAAAAAAAAAAAAAAAAAAAMkJAJAAAAAAAAAAAAAAAAAAAAAAAAAABggIRMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyRkAgAAAAAAAAAAAAAAAAAAAAAAAAAAGCAhE37tiSeekMlkcvln7NixGTpOeHh4lo7z1VdfZe9/BAAAAAAAAAAAAAAAAAAAAAAAAAAgW5GQCQAAAAAAAAAAAAAAAAAAAAAAAAAAYICETAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMkZAIAAAAAAAAAAAAAAAAAAAAAAAAAABggIRMAAAAAAAAAAAAAAAAAAAAAAAAAAMAACZkAAAAAAAAAAAAAAAAAAAAAAAAAAAAGSMgEAAAAAAAAAAAAAAAAAAAAAAAAAAAwQEImAAAAAAAAAAAAAAAAAAAAAAAAAACAgVzeDgDIiq+++kpfffWVt8MAAAAAAAAAAAAAAAAAAAAAAAAAAAQ4ZsgEAAAAAAAAAAAAAAAAAAAAAAAAAAAwQEImAAAAAAAAAAAAAAAAAAAAAAAAAACAARIyAQAAAAAAAAAAAAAAAAAAAAAAAAAADJCQCQAAAAAAAAAAAAAAAAAAAAAAAAAAYICETAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMkZAIAAAAAAAAAAAAAAAAAAAAAAAAAABggIRMAAAAAAAAAAAAAAAAAAAAAAAAAAMAACZkAAAAAAAAAAAAAAAAAAAAAAAAAAAAGSMgEAAAAAAAAAAAAAAAAAAAAAAAAAAAwQEImAAAAAAAAAAAAAAAAAAAAAAAAAACAARIyAQAAAAAAAAAAAAAAAAAAAAAAAAAADJCQCQAAAAAAAAAAAAAAAAAAAAAAAAAAYICETAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMkZAIAAAAAAAAAAAAAAAAAAAAAAAAAABggIRMAAAAAAAAAAAAAAAAAAAAAAAAAAMAACZkAAAAAAAAAAAAAAAAAAAAAAAAAAAAGSMgEAAAAAAAAAAAAAAAAAAAAAAAAAAAwQEImAAAAAAAAAAAAAAAAAAAAAAAAAACAARIyAQAAAAAAAAAAAAAAAAAAAAAAAAAADJCQCQAAAAAAAAAAAAAAAAAAAAAAAAAAYICETAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMkZAIAAAAAAAAAAAAAAAAAAAAAAAAAABggIRMAAAAAAAAAAAAAAAAAAAAAAAAAAMAACZkAAAAAAAAAAAAAAAAAAAAAAAAAAAAGSMgEAAAAAAAAAAAAAAAAAAAAAAAAAAAwQEImAAAAAAAAAAAAAAAAAAAAAAAAAACAARIyAQAAAAAAAAAAAAAAAAAAAAAAAAAADJCQCQAAAAAAAAAAAAAAAAAAAAAAAAAAYICETAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMkZAIAAAAAAAAAAAAAAAAAAAAAAAAAABggIRMAAAAAAAAAAAAAAAAAAAAAAAAAAMAACZkAAAAAAAAAAAAAAAAAAAAAAAAAAAAGTGaz2eztIAAYu3r1qjL7cQ0ODs6maJBTmM1mXb16NdWyQoUKyWQyeSkiwDHaK/wNbRb+hPYKf0ObhT+hvcKf0F7hb2iz8Ce0V/gT2iv8DW0W/oT2Cn9Dm4U/ob3Cn9Be4W9os/AntFf4E9or/A1tFtkpOTk5U9ubTCYVKlQom6IhIRPwG1euXPF2CAAAAAAAAAAAAAAAAAAAAAAAAADg0woXLpxt+w7Ktj0DAAAAAAAAAAAAAAAAAAAAAAAAAAAECBIyAQAAAAAAAAAAAAAAAAAAAAAAAAAADJCQCQAAAAAAAAAAAAAAAAAAAAAAAAAAYICETAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMkZAIAAAAAAAAAAAAAAAAAAAAAAAAAABggIRMAAAAAAAAAAAAAAAAAAAAAAAAAAMAACZkAAAAAAAAAAAAAAAAAAAAAAAAAAAAGSMgEAAAAAAAAAAAAAAAAAAAAAAAAAAAwQEImAAAAAAAAAAAAAAAAAAAAAAAAAACAARIyAQAAAAAAAAAAAAAAAAAAAAAAAAAADOTydgAAMiYoKEgpKSkZ3t5kMikoiJxrAAAAAAAAAAAAAAAAAAAAAAAAAP4pJSVFZrM5w9tndz6VyZyZaAAAAAAAAAAAAAAAAAAAAAAAAAAAAHIgps8DAAAAAAAAAAAAAAAAAAAAAAAAAAAwQEImAAAAAAAAAAAAAAAAAAAAAAAAAACAARIyAQAAAAAAAAAAAAAAAAAAAAAAAAAADJCQCQAAAAAAAAAAAAAAAAAAAAAAAAAAYICETAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMkZAIAAAAAAAAAAAAAAAAAAAAAAAAAABggIRMAAAAAAAAAAAAAAAAAAAAAAAAAAMAACZkAAAAAAAAAAAAAAAAAAAAAAAAAAAAGSMgEAAAAAAAAAAAAAAAAAAAAAAAAAAAwQEImAAAAAAAAAAAAAAAAAAAAAAAAAACAARIyAQAAAAAAAAAAAAAAAAAAAAAAAAAADJCQCQAAAAAAAAAAAAAAAAAAAAAAAAAAYICETAAAAAAAAAAAAAAAAAAAAAAAAAAAAAO5vB0AAAAAAAAAAAAA4Oueeuopu8tNJpNmzZrl4WgyZ/bs2dq4caPddf4QPwAAAAAAAAAAAAD4CpPZbDZ7OwgAAAAAgHt8/fXXDtf169fPg5Fk3qZNm3To0CGH6309fgAAAACBLSgoSCaTKdUys9ksk8mk5ORkL0WVMU8++aTmzp3rt/EDAAAAAAAAAAAAgK8gIRMAAAAAAoi9AcIWvj7A9sknn3SaUOrr8QMAAAAIbJb7rbSP1vwhodE2IdMf4wcAAAAAAAAAAAAAX5HL2wEAAAAAANzP3gBbf+CoZpC/xA8AAAAg8Nnen/hj3VN/jx8A7Dl69Kj++ecf/fPPPzp06JCio6MVExOja9eu6datW7p161aqc57JZFJSUpIXIwYAAAAA/3T48GGH910pKSnW7Uwmk2bNmuXFSAHAP12+fFk7d+7UsWPHFBMTo9OnT+vKlSu6deuW4uLilJKSorx58ypv3ry67bbbVKZMGYWGhqp8+fK64447VKlSJW+/BQDIEUjIBAAAAIAA5M8DbNMmX/pb/AAAIL2UlBRdvHhR58+f15UrV5Q/f36VKFFC5cqV83ZoAJBplnsUfy0c4+/xw3vi4+MNB1tKUr9+/bwUIXKalStXasmSJVqxYoVOnDiRbj19SvBXN2/edHiOrVixopeiAgDPS0lJUVBQkLfDAIAczWw2a+vWrVqxYoXWrFmjqKgoXbt2LUO/R0ImAGTMoUOHFBERocjISG3ZskWnTp3K0v4KFiyou+++W23atFG7du3UuHFjrqsBIBuQkAkAAAAAAcjfB9j6e/wAAED666+/9Msvv2j16tXatm2bkpOT022TP39+NWrUSJ06ddKjjz6q0qVLeyFSBKqkpCSdPn3aadJQy5YtvRQdAPi25ORkbdy4UWvWrLHOOhgTE5Oh3yUhE9np6tWrmj59umbOnKno6GhJjhMvHfUrZTVR84033tCcOXMcri9SpIh27NihXLkYjgHH4uPjtWnTpnQzu549e9buvZPEzK4IPDdu3NC2bdt07ty5dAWcatasqTp16ihPnjzeDhNe9O677+rHH39U27Zt1a5dO7Vq1UoFChTwdlgAkCMcO3ZMX375pebOnZuqP8BThW++++477dq1y+H6woUL67XXXvNILACQHc6fP6958+bp66+/1o4dO6zL3XGevXr1qjZs2KANGzZo7NixKlGihPr06aN+/fqpXr16Wd4/kFlxcXHpZnclSRiBwGSmLCQAAAAABIygoCCZTKZUCY2W6pOOBvL4iieffFJz58712/gBAMB/tm3bplGjRmn16tXWZc66oS0D5XPnzq1+/frp3XffValSpbI9TgSW48ePp0oaOnTokM6fP2/Y9hjQjszgfgs5QUREhGbNmqVff/1V169fty7P6CPlrLSn7du36+LFiw7X33bbbWrWrJlL+4b/S0xM1OTJkzVlyhRduXIlVZvMaEEvyzkvq+e+EydOqEaNGkpISLC73mQyadGiRerWrZtL+0fgunbtmr777jstWbJE69evV1xcXKr1RufarLTbpKSkdMVJbAUFBZFEDI+4fv26Pv30U/3yyy/6888/nd6T5cuXT+Hh4Xr66afVtWtXBmvmQGPHjtXYsWOt3/XBwcFq2LCh2rZtq7Zt26pp06bKnTu3l6MEgMBy7Ngxvf3225o3b56Sk5PTXaNm9v7L1evXFStWqFOnTk6Pt2PHDt1xxx0u7R85U3x8vI4dO6bo6GjFxMQ4LeY4evRoL0WJQHfixAlNnDhRc+bMUXx8vMvnWSOO9tu6dWuNGjVK4eHhbjkOIP2XBLxt2zbt2LFDO3fu1LFjxxQTE2MtnGtPSEiIypQpo9DQUJUvX1533HGH6tatq3r16qly5cqefQOAi0jIBIAAFh8fT8VIAAByGAYIAwAAi61bt+rMmTMO1xcsWNDtD9vefvttjR07VpIyPEg+7XaFChXS7Nmz1aNHD7fGhsBz6NAhzZo1S0uWLNGBAwdSrcvIo4+sXGNGR0fr5s2bDtfnyZNHlSpVcmnf8F3cbyFQmc1mffPNN3r33Xd1+PBh6zJbGRkIlNX29Nlnn+n55593uD5Xrlw6ceIEM2rnQH/++aeefvpp7du3L9U5zFZmkobdce4bOHCgvvjiC7txmEwmdevWTYsWLXJ5/wgsR44c0fjx4zV//nzdunVLkv02a3TflJV2+9prr2nKlCkO1xcsWFCnT59Wvnz5XNo/YCQlJUXTpk3TxIkTrQUYMnrfJkk1a9bUBx98oPvuuy9b44RvsU3ItHcNkC9fPjVv3tyaoHnPPfd4K1QA8HvJyckaP368xo8fr4SEBIf3XlLGCom4476rSZMm2rJli8NjDB8+XBMnTnR5/wh8Z86c0cqVK63FHPft25fhNkl/KdwtPj5e77zzjqZMmaLExESnz1Gzmt7j7NxtWde+fXt98sknqlq1apaOhZwrKipKCxcuVEREhLZv354usT0z/bVpVapUSe3atdMDDzygzp07Kzg42C0xA+5GQiYABLC3335bERERGjhwoB588EGFhIR4OyQAAJDNGCAMAACk/wY6hoaG6sKFCw63GTZsmCZNmuSW4yUnJ+vJJ5/Ut99+m6lB8o62MZlMevfddzVy5Ei3xIfAsn79er399ttau3atpMwPZrf8TlauMd977z2NGjXK4fq8efMqJiZGhQsXdmn/8E3cbyEQrV+/XgMHDtT+/fsNiykYzTqc1faUmJio22+/XSdPnnR4jMmTJ+vll192af/wTx9++KFeeeUV68wstm3TlaEO7jr3HTx4UGFhYXbXmc1mhYSE6PTp0ypatKjLx4D/u3TpkkaMGKG5c+cqKSnJpaI1lm2z2m4vXLigqlWrppr9OO0xvvnmG/Xp08el/cO/fPLJJzp06JDD9ZUrV9bgwYPddrxr167pwQcfVERERKZnOE67/YsvvqgpU6Ywo2sOkXaGTMl54ZCiRYsqPDzcmqBZvXp1j8UKAP7s6NGj6tmzp3bs2GG3fz+z917uuu9aunSpevTo4bCPIjQ0VNHR0W6bTQ6B4caNG/r+++81a9Ysbd261bo8M+04K2135cqVOn78uMP1hQoVUu/evV3aN/zXli1b1LdvXx09etQt59nMcnSsvHnz6t1336W/FRl269YtzZw5U7NmzdKuXbskOW6/mZlV29nvFy9eXH369NFLL72kKlWquBA1kH1IyASAAGbbOV20aFE98cQTevbZZ1WzZk1vhwYAALIJA4QBwH9cvHhRFy5c0K1btxQXF5euEqYnlS9fnuqXASYyMlLt27d3uD5Xrlw6fPiwKlSo4JbjDRo0SJ999pmk/3s44sogDQvL9//48eP12muvuSVG+L+DBw/q+eefV2RkpKT01XztcZSsmdVrzBs3bqhKlSoOk55NJpM+/fRT9e/f36X9wzdxv4VAkpiYqKFDh+qzzz6T2WxOl+gmZX6gmjva04wZM/Tiiy86HGxZt25d/fvvvy7vH/5lwIAB+vLLL9N952flvsmd574HHnhAv/76q8Nz60cffaRBgwZl6RjwXwsXLtSLL76o8+fP+8zMriNGjNCkSZMcXj+3a9dOK1eudHn/8A83btxQqVKlFBcX53CbadOmuS0h89y5c2rbtq327NmT6esNR7MQd+rUST/99BMFqXOAtDNkZqRoiO025cuXtyZntm3bVmXKlMn2mOE/Tpw44e0Q/ErFihW9HQKyydq1a/Xwww/r0qVLbimCI7nv+tVsNqtGjRo6cuRIqnhs9//rr7+qQ4cOLh8DgePatWuaNm2apk2bpqtXr0pyfp3gSFbb7vz589WnTx+nxzp8+LAqV67s0v7hfz755BO9/PLLSkhIkOTZREx70vavmUwm9ejRQ1999ZUKFCjg8XjgH+Lj463n2AsXLmTq/OpqwmbaAk3BwcH63//+p7feeouxLfAZJGQCQACzdE5bWC5eWrRooYEDB6pXr17KnTu3t8IDst2tW7e0d+9eRUdH6/Tp0zpz5oyuX7+uuLg4xcXFKTg4WHnz5lXevHlVrFgxhYaGKjQ0VFWrVuWCHR5z+PBhHTp0SNHR0YqJidG1a9d069Yt3bp1SykpKdbtTCaTZs2a5cVI4S8YIAwAvufcuXNas2aNtm/frp07d2rv3r06c+aMkpKSvB2a1UsvvaSpU6d6Owy40YABAzRz5kyHgxcffPBBLViwwC3HWrRokR588EG3DJCXUj8IDAoK0qJFi9S1a9csxwn/NmXKFL311luKi4vLcvVgd11jjh8/XqNGjXL4wLBx48b6448/XN4/fI8/32898sgjWrhwYbqBzGazWblz51Z8fLyXI4QnnTlzRj169NDWrVvdlujmrs/DzZs3Vb58eV25ciVVPLb7//vvv3XXXXe5fAz4h2effdbaH2rUPj0906DFzz//rO7duzv8bmjTpo1Wr16dpWPA/6SkpGjYsGGaPn2622a9cFe7jYmJUeXKla37SHtNEBwcrOPHj6ts2bIuHwO+7/vvv1ffvn1TnbtsFS5cWNHR0brtttuyfKzk5GS1atVKmzZtcsvnwPK7JpNJ3bt3108//ZTlGOHb0iZkppXZBM2wsDC1a9dObdu2VevWrVWoUCH3Bw2/YbnHhzGTyeRTzzPgPitXrlSPHj2shRoy0jeQ0XOvu+67pk6dquHDhzu873rsscf01VdfZekY8H/ffPONXn75ZWtisUVG2qstd7Rds9msO++8U3v37nV4jDFjxujNN990af/wLyNGjNDkyZOznPCe0WRiV/Zpia1evXpatWqVihUrlqn9IPCtXr1agwYN0pEjR5yeY92ZluZo3yaTSXnz5tUbb7yhV199Vbly5XLbMQFXkJAJAAHMtnNaSj9rQPHixa2zZlavXt1rcQLucuDAAa1du1Zr167V9u3bdfToUZcv8vPnz6/atWuradOmatOmjVq1asUDGWSZ2WzW1q1btWLFCq1Zs0ZRUVG6du1ahn7PHwZ3wjf48wDhPn36aP78+XYHCAcHBysxMdHLEQJAxkVHR+ubb77R/PnztWvXrlTrfK07zmQykZAZYMxms0qVKqVLly5ZX0uprwvWr1+vZs2aZflY169fV/Xq1XXu3LlUx8oq2+uA4sWLa/fu3SpVqpRb9g3/cvPmTT322GNasmSJzyUNxcbGqkKFCrp161aqeGz3v3//ft1+++0uHwO+xZ/vt9LO4mb7Obrtttsy1D+BwHD8+HG1bt1aJ06cSNcWnDEaXOHOz8Pw4cM1depUh5+3l19+WZMnT87SMeDbXn/9dU2YMMFwoJorA9nc2VaTkpJUrlw564zZaT9T+fLlU2xsLDO45SDx8fHq2bOnVqxY4dJgS0fnZHe227R9sGn3P2HCBL3yyitZOgZ8W48ePbR06VKHBZyGDh2qKVOmuOVY77zzjt566y23FXCSUrfXt956S6NHj87yPuG7jBIy08pMgmZQUJDq169vTdBs1qwZ39k5TFBQkLdD8Bv+0O+BzNuwYYM6dOiguLi4TBfByUgfgruuX8+dO6fy5cs7LCpStmxZRUdHZ+kY8F+xsbF67LHH9Ntvv7mlII672u4333yjxx9/3OG1SbVq1XTw4EGX9w//MHz4cE2bNi1LfbBpZbRIibPt7f2uZbs6depozZo1Kl68uNPfQ86QnJysoUOHasaMGW4rOuYKe8e0JBEvWrSImdzhVSRkAkAAS5uQaWGvQkXr1q01cOBA9ejRg4oR8CtHjx7VvHnz9P3332v//v3W5e4eBJwrVy7dd9996t27t3r16qW8efO6Zf/IGY4dO6Yvv/xSc+fOVUxMjHV5ZmdwcbWz77vvvkuXCGKrcOHCeu2111zaN3yPPw8Q7tq1q3755Re7A4Tz5cunGzdueDlCADC2a9cuvfPOO/rpp59kNpsz/VDE0yznWxIyA8uePXt05513prsmkP77m5cpUybVdWlWjB49Wu+++67hwDhXHyBa2mjv3r317bffZjle+JfY2Fi1a9dO//77r88mDdnORmvvGnzUqFEaO3Zslo4B3+HP91sNGzbU9u3b7d5vufN7Ab7t1KlTatmypY4ePZqpRDdn29lu767Pg6NrGcu/a9eu7bSvC/4tI7Ovp22/efPmVaNGjdSqVSs1bdpUoaGhKl68uIoVK6bbbrstW8/dL774ombMmOHwGKtXr1abNm2yfBz4voSEBPXo0UO//fabJN+d2XXt2rVq27atwzbbtGlTbdiwIUvHgO9KSEhQ4cKFlZCQIMl+UZkDBw6oWrVqWT7W8ePHFRYWlu5YjmQ00cP2s5UrVy5t2bJF9erVy3K88E32xrxk9vmqLWfXFXny5FGzZs2sCZr169f3qX5cuB8zZGaMv/R7IHNOnTql+vXr69y5cxm697JdV6FChVT3XcWLF9egQYOy9b4rbaGxtMfYvXu3wsLCsnwc+Jc9e/aoa9eu1gkb3JEk5K62m5iYqMqVK+vMmTOp4smOwqnwTZ988oleeOGFDLdLo+2KFCmi0qVLq0CBAsqTJ4+Cg4MVHx+vuLg4nT9/XufPn083m3Vmkult22bLli0VERGh4OBgw/eJwHXx4kV169ZNmzdvdts5NqvsxVCsWDH98MMP9L/Ca8i4AYAcwl4Spu3ydevWad26dSpRooSeeuopPfPMM2550AJkl82bN2vy5MlatmyZUlJSHFa2zArbAfSJiYn69ddf9euvv2rIkCEaNGiQXnjhBZUsWTLLx0HgOnbsmN5++23NmzdPycnJLrfTrN7EFitWLF1F+bQeeOAB3XHHHVk6DpBVsbGxDtflz5/fg5EgM06cOOHtEPwKldkC1/Xr1/XKK69o5syZ6RIxHX0He7tOGoNNAteff/5pd7nlYUnnzp3dcpxr167po48+MmxLGUnWdLSNZfmCBQs0fPhwBlnmIFeuXNF9992nf/75R5LrA9qz+1z7zDPPaObMmQ6PvXz5chIy4XVms1l79+51OACjRIkS3ggLHhYfH6/u3bunSsbMzIyDpUuXTjXY8ocffsjwTEWZVbt2bdWvXz9VErHlOsbSns+cOaMyZcq4/djwrgsXLqh///7W10YDgsuXL68hQ4aof//+KlCggMfitNW1a1fNmDHD4frIyEgGBOUQ/fv312+//Zap69a02+TKlUvFihWzDozPjnNseHi4KlasqJMnT9od0L5161bduHFDt912m9uPDe/7+++/FR8f77DoQVhYmNvGCLz55pvpjpWWo+tTSz+B7TLbbSzrkpKS9NxzzznsB4H/s8xYaa9tWDg7Vxo9m7Xtx42Li9OaNWu0Zs0aSf8VtG3durXatm2rdu3aqWbNmll7M/BZ3u6n92U8QwhcvXv3zlAypu2MaYMHD1bbtm1VuXLldNsOGjQoW+Pt2bOnfv31V4frIyIiSMjMYf755x+1b99ely5dkmT8/MB2G1vZ9R2QO3du9e/f3+5kKhbffvstCZkBauPGjRoyZEim26XlXqdBgwZq3ry5GjdurFq1aql69eoZmjzk5MmT2r9/v/7++29t2rRJ69at09WrV63HcRaPbd/r+vXrNWTIEH300UeZet8IHOfOnVO7du20e/fuTBXMtXD1GtJo//buCy9duqQHHnhAP/74ozp16uTScYGsYIZMAAhgttUCMzOwwnKxEh4eroEDB6pbt27MmgmfcfjwYQ0bNkzLli2TlPoi256sVLxyti+TyaQCBQpo5MiRevnll60PgwBJSk5O1vjx4zV+/HglJCQ4bacZqQjsjuprTZo00ZYtWxweY/jw4Zo4caLL+4fv8OcZW0qWLGntME/boVOzZk3t3bvXm+HBASoIZ5zJZEpXlRCBYfv27erZs6eio6Ptfu/7aveb7XcEM2QGlv79++vLL790eE2wePFide3aNcvH+fDDDzV06FDDfgez2ax77rlHTz/9tNq2bauyZctK+u/B4MqVKzVz5kxropCzmWAefPBBLViwIMtxw/eZzWZ16tRJK1eudClpyGQyqXDhwtakoa1bt2brNXLt2rW1f/9+axy2MQcFBen8+fMqWrRolo8D7/PX+61///1X99xzj8PY27Ztq1WrVnk5SmS3gQMH6osvvsjwzBchISF66KGH1LZtW7Vq1UpVqlRJtW12fx4mTZqkESNGODzG119/rb59+2b5OPAtTzzxhL7++muH14XS/7XP9957Ty+++KLh86vsbquJiYkqXry4bty4YY3P9niNGjUiUSgHSDvzRUZmF6pQoYLCw8NTzexaqFAhSdnfbl999VVNmTLF4TGWLVvGILYA9cEHH+jll192+Ld/5ZVXNGHChCwf59ixY6pevbpSUlIkZewzUaZMmVT9BefPn7e7ne3vW+JetGiRunXrluW44Zv279+vyMhIRUZGat26damKe2YlsSIjv2u7TWhoqNq2bWv9KVeuXEbfAnxU2u9bpOcv/R7InJkzZ2rAgAFOv2Mty+vXr6933nlH999/v9N9Zvf169mzZ63XCZbYbI/RtWtXLV68OMvHgX/YvXu3WrVqpUuXLmW6kKOnxmhJ/80YX7Vq1VTHto23atWqOnToUJaOAd8THx+vOnXq6NChQxkqTmNZX6tWLQ0YMEAPPvhgqvNdViQmJioyMlKzZs3S0qVLlZSUlKHPjKWtrlmzRq1atXJLLPAfV65cUdOmTVMV+czIuTOtzF5juzqu1rJdSEiIfvnlF7Vr1y5TxwWyioRMAAhgRgmZtuwNYLMsK1WqlHXWzLSDLgBPGj9+vN5++20lJiZ6fKC7swqtVatW1TfffKPGjRtn2/HhP44ePaqePXtqx44dbmmn7ursW7p0qXr06OHw5jU0NFTR0dEkVQUAfx0gfPLkSVWqVMlh7M2bN9fvv//u5ShhT1BQkLdD8Bu+/jmEa5YuXaq+ffvq5s2bkvwjEdOChMzAVbduXe3atSvV31j6v4cRly9fzlAl1YwcZ/fu3dZ927L9Tp80aZKGDRvm8FozKSlJr732mqZNm5auD8M29ty5c+v48ePMhpUDjB49Wu+++26mBrQ3a9bMmjTUpEmTVG08u6+R7fXB2R7jhx9+UK9evbJ8HHifv95vDR8+XFOnTnUY+4ABA/TJJ594OUpkp/Xr16t169YZGmxZsGBBPffccxoyZIjT79zs/jzs27dPtWvXdniMxx9/XLNnz87yceA79u7dqzvvvNP62tE1YdWqVbVgwQLVr18/Q/v1xLm7Q4cOWr16td3r79y5c+vatWsUdQxgx48fV+3atRUXFyfJ+Nq1UaNGevXVVx3210vZ3243btyoli1bOjwGfQSBq3fv3ulmubb922/YsEFNmzbN8nFGjBihSZMmOU2wl6Tg4GA9//zz6t+/v2rVqpVqux07dmj69OmaPXu2w/1YljVt2lQbN27MctzwfWazWdu3b1dkZKQiIiL0xx9/WM+/UuaTLmxlNkGzRo0a1uTM8PBwFSlSJMPHgm8gIdOYv/R7IONu3LihSpUqWZPbnfUPDB06VBMnTszQJA6euO+qV6+eoqKi7N53FS1aVBcvXnTLceDbLl++rIYNG+rw4cMZen5guz4kJET16tVTaGiotZhj2mtWd7fd8PBw/f777w6PcfjwYbuzzsJ/jRo1SuPHj89wMubdd9+tcePGqWPHjtka16lTp/T2229rzpw51rZt1H9RvXp17dq1S7lz587W2OA7zGazOnfurBUrVmQoGdPR2JjbbrtNNWrUUI0aNRQaGqrSpUurQIECypMnj4KDgxUfH6+4uDidP39eZ8+e1dGjR7V//37FxMQY7ttRDJbrga1bt6patWqZf/OAi0jIBIAAZjsYzMKVxEzLcpPJpHbt2mngwIHq0qWLgoOD3R80YMeZM2fUu3dvbdiwweOJmPbYO3auXLk0evRojRo1yqOxwLesXbtWDz/8sC5dupSqA1jK2myt7ujsM5vNqlGjho4cOZIqHtv9//rrr+rQoYPLx4Bv8NcBwlOmTNGrr77qMPbHHntMX331lXeDhF3MkJkx/vA5ROatXLlSXbt2VWJiYqY7hF3ZNqO/l5l9MdgyMOXLl08JCQmS0s/QU7duXf37779ZPsaOHTt09913Ox0UaTKZNHnyZL388ssZ2udrr72myZMn203KtOxv6tSpeumll7IcP3zXzp07Vb9+fYcPhG0frAUHB6tXr1569dVXdc899zjcZ3ZfI//9999q0KAByW45gD/eb+3Zs0cNGzZMlyRiG/u0adM0ePBgb4aJbGQ2m1WnTh3t2bPHaVKEJUlowYIFqlSpkuF+PfF5qFKlik6cOGGNzzbWihUr6tixY245DnyDo9kxbdvWnXfeqTVr1qh48eIZ3q8n2urbb7+tMWPGODzOX3/95fRaBf6tS5cuWr58ueE5tmjRovryyy/Vo0cPw31md7tNSUlRyZIldfnyZWt8tserU6eOoqKisnwc+J6qVavq+PHjktJ/txYsWFCXL1/Ocl9vcnKyypcvr3Pnzln3bWF7vMKFC+vXX39VkyZNnO5v2bJl+t///qdbt27Z3Z/lfURFRaVK7EfOkJCQoD/++EMRERGKjIzU9u3brefJjCRYOpOZBM2goCDVq1fPmqDZvHlztxRDQ/by1vOtrI5vcdbXn1379eV+D2TO1KlTNXz4cLv3XdJ/f+88efLou+++y9B1q4Un7rteeOEFffLJJyS25XA9evTQ0qVLM5SMaTabVapUKT377LNq27atGjdunO77Obvb7scff6zBgwc7PMYXX3yhp59+OsvHgW+4ePGiKlWqZPfeRUrdNvPnz69x48ZZ24enREVF6YknnkiV4J6WbRv99NNP1b9/f4/FB+969913NXr0aMNkzLRjY3Lnzq3WrVurW7duat68uerUqeNSuz537pw2bdqklStXaunSpTpz5kyq42UkyblOnTravn17hgpKAO7ANBYAEMDat2+vZs2ayWw2p7qhMxrUm3ZbywV2SkqKVq9erV69eqlixYoaPXq0dTAEkF12796te++915qMadsmbdurEdv27OwnI+x9TpKSkvTWW2+pT58+1oHPyFlWrlypzp076+LFi+keYju7GXSlDbrCZDLpueeec/qZ+f7777Pt+IAzly9f1kcffeT0M1CzZk0PRgRX2H4385P6B4Fp37596tWrV6pkTEd/c3vf9RlpH87aU2avIWifOcfZs2cVHx8vyf6Dvjp16rjlOD/++KPd5bYP6Ro3bpzhZExJmjBhgpo0aZLqejqtpUuXuhQv/MfAgQOVlJQkyfnD6ipVqmjz5s2aP3++1xMc7rnnHusscrZt1/J5WLNmjbdCQw63bNky3X///Q4HgFjce++9ngwLHvbTTz85Tca0XBcOHjxYGzduzFAypqc0b948Vcy2/z558iSzXwSQK1euaP78+emuAW1fly5dWitXrsxUMqanNGzY0On6nTt3eigSeNrWrVszlIx577336p9//snUoPbsFBQUZH2GbMsS8549e6zXDwgcycnJOnnyZLrllnvwO++80y3PqdatW6ezZ89a921h+5kICgrS4sWLDZMxpf+SnmfNmmXYj7VgwYIsRA1/FRISovDwcI0bN05//vmnLl68qMWLF+uFF15QWFhYun7QzPSp2utHTfv7lnXJycnatm2bJk2apA4dOqho0aJq27atxo8fry1btiglJSXb/y/g+9w1viWzv5OZ+BDYzGazPvjgA4f3XZbv6G+//dZnrlttGV03UFAk8C1evNhpMqbt93P58uX1ySef6Pjx43rnnXfUunVrrxRL6NKli9P1ERERHooEnjB16lTdvHlTkvPnW5UrV9bmzZv10ksvebw4xF133aWtW7fqiSeecPo81vJZmjBhAteyOcT+/fs1bty4TCU/li9fXhMmTFBMTIxWrlypQYMGqW7dui6361KlSql79+769NNPderUKS1btkwdOnRIdz+Wlm2su3bt0nvvvefS8QFXkPoLAAGsadOm2rBhg/bt26fPP/9c33zzjS5duiTJeHZBewN9bZefPn1a48aN03vvvaf77rtPAwYM0AMPPKCgIHL94T7btm1T+/btdeXKFUnGlU4sjJKOnW2fmUqZaS/0zWazFixYoHPnzmn58uXKkyeP0zgRODZs2KAePXooLi4uUxWCnG2XHR599FGNGDFCycnJdquv0dEHbzh37pz69OmjkydPOqy+JkkNGjTwcGQA4FhCQoL69OmjmzdvZrgCq0X+/PlVuXJlFSpUSHny5NHvv//usCpqqVKlFBYWJkm6deuWbt68qTNnzlgLQNgex96xbPd1++23q2zZsg7f0+233+7qfwd8jNFMUXXr1nXLcZYtW2a4zZgxYzK1T5PJpHHjxqlNmzZ215nNZm3atEnx8fHcbwWo1atXa/PmzU6ThiSpe/fu+uqrr1SoUCFvhGlX8+bN9eOPP6Y6H1v+ffDgQV29etWn4s3JsqvA3MmTJ70yeDElJUUJCQm6evWqzp49q6NHj+qff/5RRESEoqOjrW3R3mB4SSpQoIDq16/v8bjhORMnTrS73PZacdiwYZo0aZKHIzPWtGlTffvttw7X//PPP2rXrp0HI0J2WbJkiRISEuxeA1ja6dy5c60FEHxNrVq1nK4/dOiQhyKBpzka4GV7jg0PD9fy5ct9bqa0Zs2a6ZdffrG+tr1+TUlJUVRUlBo3buyt8JANoqOj0z0fsuWu/oKffvrJ4TpLO/vf//6n1q1bZ3ifjzzyiJYvX6558+Y5/K74+eef9c4777gaNgJEoUKF1K1bN3Xr1k3Sf+NaIiMjrT/R0dHWbe0N5nV2T+co6cN2vWWb+Ph4rVu3TuvWrdObb76pQoUKqVWrVtYZNGvXrp3l94qsq1ixoseTIK5fv66LFy86LeZg4ag9FipUSHnz5lWePHmUnJys+Ph43bhxQ3Fxcem2tddO0663nJsrVqzo6tuCn9iwYYOio6Od3ne98sor6tmzp5cidM5oJux9+/ZZz/8IPImJiRoyZIjTZEzL8h49emj27NkqXLiwx+NMq1KlSqpZs6YOHDhg93nwunXrvBsg3CY5OVmzZs2ye21h+7cPCwvTunXrVKpUKU+HaJU7d27Nnj1bJUuW1OTJk9N9L9j2Dxw/fly//fabOnfu7K1w4SEvvvii4uPjHfYZ2J5nCxYsqLFjx2rQoEEKCQnJlnhMJpM6d+6szp07a/PmzRo6dKi2bt2aaqy2vd8xm80aP368Hn/8ca5v4REkZAJADhAWFqZp06ZpwoQJWrhwoWbOnKkNGzZIcjxw15aj6hKWSn8rVqzQihUrFBoaqmeeeUZPP/20KlSokM3vCoFu79696tixo65cuZKhREyjzumQkBCVKFFCJUqUsNs5fe7cOcXGxjp9kOLsM2J7ob927Vo98sgjWrRoEUnKOcCpU6f00EMPGSZj2ltXoUIFNW3aVKGhoSpevLiKFy+uQYMGOU1Ky4pSpUrpvvvu06+//mp3kPDp06e1b98+a+IHkB1u3LhhHSC8atUqLV682O5sLbbn9Vy5cmWoUjYAeMqHH36of//9N8Pf/fXq1dPjjz+u++67L933rLPrxY4dO2r27NnplsfHx2vXrl3avn27Vq1apYiICF29ejXdcS2vzWazzp49q1GjRumxxx5z4R3Dnxw/ftzpeqNBCxlx9uxZRUVFOU3wuf3229W+fftM77t169Zq0KCBtm3blmqmAcu+ExMTtXXrVrVo0SLL7wO+Z8KECXaX2w4O6927t3UQri9p1qyZw5ljJenff/9Vy5YtPRgRHKlcuXKW2o+92fosVa19ie21gKP1lgfauXLxuDBQ7d27V9u3b7f7nW1pAx07dvTJZExJuvvuu52u37NnDwmZAcLeLOi27bRnz54uXVt6SqVKlZQ3b16Hg5aOHDnipciQnc6dO6dffvnF7gxDlrZbvXp1LVmyxOeSMSUZFmTYtWsXCZkBxqi/oE6dOm45ju0zKEdGjhyZ6f2+9dZbmj9/fqqkUtviI7t27dKlS5dUrFgxV0NHAAoNDdWjjz6qRx99VJJ04MABRUZGKiIiQuvWrVNsbKx126wkaBr97pUrV7Rs2TJrgbMyZcro1KlTrr8xuIVRYTt3W7RokZ5//nmHsxNK/9ducuXKpXvuuUdNmjTRXXfdpZo1a6py5coqWbKkw3v469ev6+zZszp48KAOHDigrVu3atOmTdb3aTT2pUaNGpo5cyYD1wPYDz/8kG6ZbfurVq2axo4d68mQMqVmzZrWeO3ddx09etQbYcFDvv76a7tFvtOOfZo6daqGDBnipSjta9asmfbv3293nNa5c+cUExPjtKgu/ENkZKTOnTvn9Nlp6dKlFRER4dVkTFsTJ07UuXPnNHfuXKdjFb/77jsSMgPcli1bFBERkaFkzNatW2vu3LkezRFo0qSJNm/erMmTJ2vUqFF2i03ZnlsTEhI0ceJEzZgxw2MxIufiCSsA5CB58uSxdjZnx6yZMTExeueddzRu3Dh17NhRAwYMUKdOnXxugBx837Vr19S1a1drZUApY7MNWrYpVaqUwsPD1aRJE9WtW1c1a9ZUaGio4XETExN1+PBha+f05s2btXHjRiUmJqY6lrPPh+VCf9myZRo5cqTDCvQIHL1797Z2qEiOEzIsy+vUqaPBgwerbdu2dgdrDho0KFvj7dmzp3799VeH6yMiIkjI9AFVq1b1q/0asZ2xxZJ8aZH2/Jl2nclkUsuWLVWgQAGPxQvXePqaL6uJ684GpmfHfhE4YmNj9d5772WoAmulSpX08ccfu/0BRZ48eVS/fn3Vr19f/fv3161bt7Rw4UJNnTpVO3bsSFcV0GQy6erVq3riiSe0atUqzZ49W7lz53ZrTPAdMTExTteXLl06y8dYs2aNw3WW7+9+/fq5vP/u3btr27ZtDtf//fffJGQGoOPHj2vt2rVOk4YaNWqkr776yie/b42Shnbv3k1Cpg/JjiJI3pgd0xmjvl6LJ5980hPhwEsWLFiQbplt2yhUqJBmzZrlyZAyxWjWQZLcAsfGjRudfr+/8cYbHowm80wmk8qWLetwALDtbFwIHD/++GO6AWC27TgoKEjffvutChYs6K0QnWJm15zHKCHTHTP27du3TydOnHB6X9esWTOXjlWtWrV0RUfT2rx5MwOF4VSNGjVUo0YNPffcczKbzfr7778VERGhyMhI/fHHH6meYRnNLGjLaPbMtNucOXMmq28FfiQuLk5PPfWU9f7MWSJR+/bt9fjjj6tTp04qUqRIpo5ToEABFShQQNWqVdP9999vXb53714tWrRIc+bM0ZEjR9KN97L8OyIiQnXq1NEnn3yivn37ZuUtw0etW7fO7neopR28+eab2TbLlTvkzZtXZcqUcXgO9XSSNTzLMoufPZY2PG3aNA0ePNjDkRlr2rSp3QK8FlFRUSRkBoBFixY5XGdpo99++63P/a0//fRTbdmyxZo0bO8+btmyZUpOTlZwcLAXI0V2eu+99xyus20Xzz33nKZPn+6VtmAymfTqq6+qQYMG6tWrl65evWp3rKFl2Zw5czRmzBiVLFnS47EiZyEhEwByqOyeNXP58uVavny5ypUrp2effVZPPfWUypUrl83vCoFiwIABOnz4cIaTMc1mswoWLKg+ffqoX79+aty4sUuDM3Pnzq2wsDCFhYWpa9eukv5LDl22bJlmzZqltWvXpjuuo7jMZrPef/99tW3bVvfdd1+mY4F/mDlzpv74448MJWTUr19f77zzTqqHH97QuXNnp1UDIyMj9cILL3gjNNg4duxYlmZKdTRjiy8+hDA6p0rioR9SycznwpXrgawmato+vEZg+uKLL3T58mWnnbuSdP/992vBggUeGXiZL18+9evXT/369dP8+fP18ssv68yZM3ZnC/juu+90+vRpLVmyhGT3AHXjxg2n6wsXLpzlY2zcuNFwm169erm8/44dO2rUqFEO1+/fv9/lfcN3GVVoz5Mnj7755hufTSg3GlB8+PBhD0WCjHD1es3ZNaEvXgM6KxplMplUr149n55xDlm3evVqu8stbWD48OFuKdaQXYoUKaJixYopNjaW2S8C2KFDh3ThwoV0SW2Wf9etW9ew8IEvCA0NtQ5wt7C8j/Pnz3sxMmQXyyxnaVnOsU888YThLJTeVK5cOd122226efMm59gc4uzZs07Xu2NmyXXr1hluY5mp0BVdunRxWnR0x44dJGQiw0wmk7Xo3WuvvaaEhAT98ccfioyMVGRkpLZt26bk5GTrtq7Onpn2932tmA+y19mzZ9WlSxdt377dYSJmUFCQHn30UY0ePVrVqlVzewy1atXSG2+8oTfeeENLly7VW2+9Zbewo/Tf+Jh+/frpwIEDPj1TIjIvNjZWe/fuTbXM9rxWrFgx9enTx9NhZVpoaKhOnz5t95x87tw5L0WF7LZt2zYdOHDAadGPxx9/3CeTMaX/+jWc2bt3rzp27OihaJBdNmzYYHcWbEsbfeSRR9SmTRsvRedY3rx59eGHH6pDhw7pxoFbXt+4cUN///23GjZs6K0wkY0uXrzosPCRbRt+8cUX9cEHH3g+wDTatGmjX3/9VR06dNCNGzdSfTfYttv4+HgtWLCAcbDIdiRkAkAOZztr5v79+/XZZ5+5ddbM6OhojRkzRu+88446d+6sAQMGeD0ZCb5t1apVmj9/vuFsg5Z1hQoV0tChQ/Xyyy9ny2B3S6Jnnz59tGPHDo0ePVo///yz3c5pS0yWdSkpKXruuee0d+9en64iB9fcuHFDI0eOzFAy5tChQzVx4kTlyuX9y+/SpUurbt26ioqKsjs4aP369V6MDmm5MqDX3wYIS44/P5JUpkwZEjJ9XMWKFT3etq5fv26dSdtZ+5EcfyYKFSqkvHnzKk+ePEpOTlZ8fLxu3LihuLi4dNsaVcK27YSsWLGiq28LfsBsNuvzzz837Izu2LGjlixZ4pXv/t69e6tjx47q16+fli1bZndQ89q1a3Xfffdp7dq1ypMnj8djRPa6efOm0/WFChXK8jH++OOPdMtsPxdVqlTJ0qzrd9xxh4KCglIlE9sisS0w/fbbb3aXW9rB888/r9tvv93DUWVcyZIlVbhwYYcVWRnQ7nvcPQjWHwbVpp21a8aMGV6MBtktPj5e27dvT9f/YxESEuIXgxHKli2rS5cu2b3XY2ahwHDgwAGH60wmk7p06eLBaFxXvHhxh+suXrzowUjgCWazWZs3b3baJzZixAgPRuSasmXLOpwJ89SpUx6OBtnNduY/e9zRX2CvgFPaa5Hu3bu7vP927do5XU8BJ2RFSEiIwsPDFR4ernfffVdXr17VunXrFBkZqYiIiFSJTK4maPrqczpkj/Pnz6t58+Y6cuSI3WKeZrNZd911l2bPnq169ep5JKZu3bqpa9eu+vjjj/XGG2+kGsRuO77B8hmYNm2aR+JC9tu5c6fd/nbLsm7duvnFzGf2ikpZ3tOFCxe8EBE8waiYY5kyZfThhx96MqRMMXpeduTIEQ9Fguxy6dIl7du3z+k2b775poeiybz27dvr3nvv1ZYtWxxOHrBx40YSMgPUjz/+qKSkJKdJ7/fff79PJGNaNGnSRLNmzdIjjzzi9B7ru+++84tnIPBv3h8RDgDwGTVr1tS0adM0ceJELVy4UF988YXbZs1MSkrSzz//rJ9//lkVKlSwzpoZGhqaze8K/iQlJUXDhg2zvjZKxuzSpYs+/fRTlS1b1iPx1a1bV0uWLNFvv/2mAQMGKDo62mlSpvTfLHcffvihXnnlFY/ECM/5/PPPrYPBHFWzzJMnj7777jv16NHDW2Ha1axZM0VFRVlf27bZy5cv69ixY6pcubKXokOgy+wsg++9957PzoKE/3h65tVFixbp+eeft1td0MLSznLlyqV77rlHTZo00V133aWaNWuqcuXKKlmypMNEuevXr+vs2bM6ePCgDhw4oK1bt2rTpk3W92l0XVyjRg3NnDmTxMwA9eeff9qdxTjtQ7958+Z5tRBD4cKFtXTpUr3yyit6//337SZlbtmyRU8++aS+++47r8WJ7GE0wDKrM2Rev35du3btsvtww/ahTFaEhISocuXKdhPYSL4ITMnJydq6davDpKHg4OBU/QW+qmzZsrp69arddadPn/ZwNIBzkyZN0r333uvtMJCNduzYoYSEBIeDLe+//363zJyd3cqUKaNdu3alWsZgy8BiVLSgSZMmHooka/LmzetwnVHRFPifvXv3pivEYTtQrUmTJtkyw5W7hYaG6uDBg3aLN3KODTxG5yJ3XBds2rTJYX+BJNWvX1+lSpVyef9Vq1ZVnjx57F7jSJ7vq0ZgK1SokLp27aquXbtKks6cOWNNzoyMjFR0dLSXI4Qvu3nzpjp37qzDhw+nKvZt+3zpmWee0YwZMzz+HNQyw1H79u3VrVs367WAbfFxs9ms6dOnq1y5cho+fLhH40P2MLrvatWqlYciyZr8+fM7XOeoXxb+LzIy0u5yy3lr5MiR2TKJg7sUKFBApUqV0vnz57mGDVB79uxJl/Ru+93atGlT1apVy8tROvfss89qy5YtDten7Z9F4FixYkW6Zbb39YULF9bcuXM9GVKGPPTQQ1q2bJnmzZvnMJl069atunz5sooUKeK9QBHwgrwdAADA94SEhKhv3776/ffftXfvXr300ksqVqyYzGZzqg44R5UlLNulTdC0LDtx4oRGjx6tSpUqqVevXlq1apUn3x582PLly7V7926Hs13Z3qhOmDBBS5cu9Vgypq2OHTvqn3/+UatWrexWMrSwxDtt2jQlJSV5OEpkJ7PZrA8++MBhMpDZbFZQUJC+/fZbn0vGlIwHMdkma8K7bL9TM/rj7v25+8eI7efqkUceUb9+/bL8/4jAEBcXpz59+uihhx7SuXPn0l1vSv83sKd9+/aaN2+ezp07pz///FPTpk3TE088oSZNmig0NNRpolyBAgVUrVo13X///Ro8eLDmzZunI0eOaPfu3XrnnXdUpUoVu9e5lhgiIiJUp04dffvtt9n53wEvWb58ucN1lnbw2Wef+UyH7uTJkzVs2LB0Az0s7XbBggX6+OOPvRwl3M1ogGVWq1xv2bJFKSkpkhwXWmjTpk2WjiFJlSpVsntfKP1XXR6BZdeuXda2ay9pKDw8XGXKlPFWeBlWpkwZu+3WbDbTbuE1tn25lv6KDz74QEOHDvV2aMhmRoMt27Zt66FIssbZgLrLly97LhBkm5iYGKfrq1ev7qFIsiZPnjwO1yUkJHgwEniCo1klLTp27OihSLKmaNGiDtddunTJg5HAE4z6C7I6Q+aZM2esg8kdFd3t0KFDlo5hMplUvXp1h/ddZ8+ezdL+AWfKlCmjvn37as6cOTpx4oT279+vGTNmqGfPntbzKTNgwmLIkCHatm2b3X556b9Zsr744guvFqUNCwvTn3/+qbvuusthEsmIESO0efNmr8UI9zl+/LjT9XfeeaeHIskaZ4Vw4uPjPRgJPOXGjRvasWOHw2KOBQoU0DPPPOON0DIlNDTU7jUyRUgDg9Esp507d/ZQJK7r1KmT0/XM5Bq4Nm7c6LQQ86hRo1SiRAkvRGZs0qRJ1j5Ze4X1zWazNm7c6JXYkHOQkAkAcMoya+apU6f0zTffqEWLFnaTLR2xt62l4y4pKUlLlixRx44dVbVqVU2YMIGHJDncJ598Ynd52iS3WbNm6dVXX/VkaOkUL15cK1eu1P333283KdP2wv7s2bP68ccfPR0istGGDRusVU/TdphZ2sMrr7yinj17eiM8Q0ad6fv27fNQJMD/STvrYM+ePfXVV195Nyj4jLNnz6ply5ZasGCBw8IfJpNJjz32mA4cOKCVK1eqT58+bk2Kq1Wrlt544w0dOnRIixcvVp06ddI9mLb8XLt2Tf369dNbb73ltuPDN0RERKRbZtsGW7ZsqS5dunghMscmT56szp07p7tmtcQ9atQoZm0LMEbFYG7cuJGl/W/atMlwm9atW2fpGNJ/D9EduX79epb3D99y+PBhp+vbt2/voUiyxtmMMrGxsR6MBBlh21eZ0R93788TP9L/9dG2aNFCf/31lwYPHuyJ/2J4mVFCZv369T0USdY4G2wZFxfnwUiQXYyuT0NDQz0USdY4uw63FDRB4DA6xzZu3NhDkWSNs3PsrVu3PBgJPMHob5qcnJyl/WckYSc8PDxLx5DktFgPM7vCkypXrqw77rhDtWrV8psCEvCMNWvW6Msvv7SbjGkymTRgwACNHTvWy1H+p2jRolq1apUqVqwo6f+eG1j+nZKSoqeffpoCIwHg2rVrTtdXqlTJQ5FkjbP+ucTERA9GAk/ZsWOH9TrVXjHHBx54wOl9ja8oXbp0umWW9sw1rP8zSnpv1KiRhyJxXZkyZVShQgVJqc+1lmuDEydOeCs0ZKNDhw7p4sWLkpTqGtCiQIEC6t+/v1diy4gyZcqod+/eTieI+PPPPz0YEXIiEjIBABmSdtbMIUOGuHXWzGPHjumNN95QxYoV9fDDD9sd6IzAdvnyZUVERDhtQyaTSePGjdPjjz/u4ejsCwkJ0eLFi3XXXXdJct7x98MPP3gqLHiAvb+n7d+/WrVqPvMQxZ6aNWta47XXbo0GkwBZYTRIuGDBgvrwww+1cOFChYSEeDla+ILz58+refPm2r59u93rTrPZrLvuukt//fWX5s6dq2rVqmV7TN26ddM///yjDz/8ULfddpskpTqvWq5z3333XWYeCiDJycnpKrCmNWDAAA9GlHGzZ89W8eLFJaUeUCH99xB+xIgR3goN2cDZzDyS8YwYRv744490y2w/FzVq1FCxYsWydAxJ1vOrPVS6DjxG9yD+8LBaImkI3mXb/2r7Exoaqueee06bN2/W77//rrvvvtvbocJDjGY2q1KliociyRpnM8YwKDgwGF2f5s+f30ORZI2z73p/GByKzDEqMHv77bd7KJKscXb/yH1XzpPd/QW5cuVSkyZNsnQMyXkBJxKJkd3+/fdfvf/+++rYsaOKFSum8PBwjR8/Xn/99Zck+7PDIuexfS6UNhmzbt26+vDDD70YXXolS5bU999/n+o5l21b3r9/v8Pi6vAfRoVwChYs6KFIssbZfRdjCwKT0ax87igQ6gnOrmGvXLniwUiQHYyS3sPCwjwUSdbUrFnT4fXs1atXPRwNPOHQoUN2l1uuXTt37uz0/OUL+vbt63T9wYMHPRQJcqpc3g4AAOB/atasqalTp2rChAlauHChZs6cqfXr10tKP7uVPY5m1zSbzUpMTNRPP/2kn376SVWqVFH//v315JNPqmTJktn8ruBtq1evVnJycrrOXdvO6TZt2ui1117zYpTp5cmTRz/88IPq1q2rhIQEh/GvWbNGycnJCg4O9mK0cJd169bZTcawtNU333zTpzt78+bNqzJlyujMmTN21x87dsyzAcEhoxlY7HH2sNeV/WWXtHHWqlVL/fr108CBA53OLISc5ebNm+rcubMOHz6cbjZM6b929Mwzz2jGjBlOB+lmB5PJpBdffFHt27dXt27ddPDgwVTxWf49ffp0lStXTsOHD/dofHC/vXv3Ki4uLl2FaItixYqpV69e3grPqZIlS2r06NF66aWX7Fa0nD9/viZMmOA3M8/AOaPB3hcvXnQ6m4QzycnJ2rRpk9Nr4ebNm7u077Ry5XLcdc3gtsBjVAG6atWqHooka5zdBzKg3XdUrFjR5Xuj48ePp7sWsJz/LLNJeFpwcLBCQkKUP39+lShRQqVKlVLVqlUVFhamRo0a+c3nB+5nNNiyaNGiHooka5wNtgwKovZwIDDqM09MTPTpvlaL8+fPO1znL0mlyDijc6y/PNcksT1nMSrgdOPGDRUpUsTl/dtLyJT+7x6+Xr16bklQd1bAiTYNdzt69KgiIiIUGRmpNWvWWGePkeifgn0rVqzQzp07040bsfDG86yMaNKkiZ588knNmjXL7jOEadOm6cUXX2S8ix8zmj3SX/62zhKC8uXL58FI4ClG45fq1avnmUCyyNl1MM8O/F+g9MM6K7ib1QI+8E2HDx92ur5Dhw4eisR1LVu2VJ48eeyO3TabzYbvEcgqEjIBAC6zzJrZt29f7d+/X59//rm++eYbayd02mTLtNImraVN5Dxy5IhGjhyp0aNHq3v37urfv7/atGmTnW8JXmRUNTUoKEgfffSRJ0PKsOrVq2vIkCGaOHFiunZveX3t2jXt3LmTWQgCQGxsrPbu3ZtqWdpkjD59+ng6rEwLDQ3V6dOn0w1CNZvNOnfunJeigq3seJDrCw+H8+TJo5IlS6YaINyiRQtVr17d26HBBw0ZMkTbtm1LdZ1oO+j9zTff9PqMxGFhYfrzzz/Vpk0bRUVF2U3KHDFihJo1a+aWCvDwHkcdtZa/d7Nmzdw+kCIpKclt+xowYIDGjRun8+fPp0tuTkpK0ueff64xY8a47XjwnhIlSjhdf/DgQd1xxx0u7Xvr1q26fv26wwFFktSiRQuX9p2Ws+QLBrQHHqOH1ZZZfn2ds4ETJA35jqwUIXL2dzSa6RXwNKOZef0hwU1y/h3BNUFgcJZYI/3XBvyhvZ48edJhwr+/zDaDjAuUmV2dzSbIzK6Bx6hdxsTEqFy5ci7t+/r169q+fbvD86A7Czg5w30XsurChQtas2aNNQnT9v4xbV+YvWesjvhSwVRkrxkzZqR6bdsX36JFCzVt2tRLkRkbOXKk5syZk+5ZnCRFR0dr8eLFevDBB70cJVxldN9169Ytw218QXR0tMNzqr9cgyNzjGaPrFSpkociyRpnSc9GCdPwfUaFYYyK4/gKZ/1vFL8JTI4m87CoU6eOhyJxXUhIiGrUqGEtimJhuZ41eo9AVpGQCQBwi+ycNTMhIUELFy7UwoULVa1aNQ0YMEBPPPGE3wzGQ8akTXCzsHTydu7cWWFhYR6OKuOGDh2qadOmKTEx0eHg5D179pCQGQB27tyZ6iGEhWVZt27d/KJ6YOnSpdMts7wno9lpkP1cHchrNptVtWpVhzO2HDlyxJ1hZpjtjC0FChTwSgzwP2vWrNGXX37pMBlzwIABXk/GtChatKhWrVqlhg0bWgdg2iZlpqSk6Omnn9a///7rF4NIYd+pU6ecrm/YsKHbj5mcnOy2fVkK6kybNs1uR/QPP/xAQmaAKFWqlNP1hw4dcnnfv/32m+E2LVu2dHn/tpwNcGZAe+BxNhBc8p/B4M7aLRXaAXia0XknLi7OL85NMTExDtf5Q/wwZjToNyYmxudnErhx44ZOnjyZbrmlb8DVBCf4LqOCTPHx8X4x4PLs2bMO1zGgPfAYzdx66NAhl/u3fv/9dyUlJXmkgJOz+0d/SCSBb7l586bWr1+vyMhIRUREWJ8DS8YJmPa2yei2vn5tA9fcvHlTERERDpPFnnnmGQ9HlDlVq1ZVeHi4IiMj7b6HpUuXkpDpx4yu7c6fP+/z36Nms9luoTXL+dXeGBj4P6NijoULF/ZQJFnjrHBarlykcvg7o3Ps9evXVahQIQ9F4zpnnzf6YQOT0Tm2SpUqHookaypXrqydO3faXWf0HoGs4lscAOBWtrNmHjhwQJ999plbZ808dOiQXn31VY0aNUo9e/ZU//791apVq+x8S/CQQ4cOOa0M+cQTT3guGBeUKlVKHTt21NKlSx2+j6wMfIbvMEqU85dzkrPOoKtXr3owEtiTXVX8/KU6ICD9V+zAIm0yZt26dfXhhx96Mbr0SpYsqe+//14tWrSwWz14//79+uSTTzRkyBDvBgqXnT592un67EjIdHdF1O7du2vatGnW12nb6MmTJ1WhQgW3HhOeZ/RgZNOmTS7ve+nSpemW2d7/lC1bVpUrV3Z5/7acVaukwEPgMRqsHh8f7xdJmc7aLQ+rAXia0UDKy5cv+8W56fjx4w5nH6JoY2AIDQ11uv7w4cMuz/DuKf/8849SUlIcJiLRJxd4jM6x169f94uEzBMnTjh8puUPg0WROWXKlHG6/sCBAy7v+9dff023LG1BMHclZF67ds3hOgo4wUhycrK2bt2qiIgIRUREaMuWLdY+2KwkYNrb3nbbvHnzqlmzZmrbtq3atWun+vXrZ+VtwEetXr1a8fHx6Z4PSf+1j44dO3oxuozp1KmTIiMjUy2zvJ+MFOuD7ypWrJjT9SdOnHBb33522bdvn27evGn3vstkMqlixYpeigzZyWhWPqNiOb7i+vXrDtf5Q/8cnDPqIzh9+rRf3GM7Gw/h60n7cI1RsqK/3GM7i9NZMV3AHYK8HQAAIHDVqFFDU6dO1alTpzRv3jy1bNlSZrPZ4WyYadnb1tKpEh8fr/nz56tNmzaqVauWPvjgA126dMkj7wvZ48qVK6le27aN4OBgtWvXztMhZdr999/vdH3a9wj/dPz4cafr77zzTg9FkjXOBjPHx8d7MBIASG/FihXauXOnw4GMM2bM8MmHK02aNNGTTz5p9yGg2WzWtGnT3DrjITzLqDO6fPnyLu3Xct1r797I2QAzV9x7773WAaH2jpd2oAX8U82aNe0ut5yL1qxZ49K5aN++fQ7PzZYBRs2aNXMpZntiYmIcDmTzh4eWyByj6sH+UjTGXtKQRZEiRTwbDIAcz2iwhL3Z/HxNdHS0td+fwZaBy6igyN9//+2hSFxndC9VtWpVD0UCTzEaiBgdHe2hSFx3+fJl6yzEtudYy/0dBZsCz+233+50/dq1a13et6OCtZa2FRYWZpgIklH2BglbjkMBJ9ize/duTZ8+XV27dlWxYsXUvHlzjRkzRhs3blRCQoLDcSnS/41Zsd3GwnbbtNsHBQWpYcOGGjlypCIiIhQbG6vVq1drxIgRatCggdNxMvBff/31l8N1FStW9IuCMg0aNEj12rbdx8bGUoTcjxndd0VFRXkoEtcZFZukEE5gMkpWdDZ7ui9xdo9o9HwEvs/oXmfPnj0eiiRr9u3b5/DZrLvu5+BbjO5LUlJSPBRJ1jgrnANkNxIyAQDZLiQkRH369NG6deu0b98+DR06VMWKFbN2Rmc0MTNtJ7hl2f79+zVs2DCVL19ejz/+uDZv3uyptwY3cjbYvFq1an7xAK1evXpO1zurdgX/YZQY4S+dvM7Ou+6ejQsAMmvGjBmpXttWE27RooWaNm3qpciMjRw5UkFB/3W3pE1aio6O1uLFi70VGrLI6IFe4cKFXdpvSEiIw3Xuvn4MCQlRtWrVHK7ftWuXW48H7yhXrpx1cI/tYDCLa9eu2Z25wsicOXMMt2nZsmWm92vPjRs3dPbsWbvrTCaTypUr55bjwHcEQtLQ+fPnde7cOUn2B7STNBRYjPozAV9g1EflDwOBtmzZ4nQ959bA4OweRcpagpCn2JtJ3tY999zjoUjgKUZFmfwhWWH79u1O1/vLsw5knFEBp82bN7vUF/X777/bTe613b87CzidOnXK7rW4yWSiEA4k/dcP/9VXX+nRRx9V2bJlVbduXQ0dOlTLly/XtWvXUo0/cZZUmZkETLPZrBo1auj555/XokWLdOHCBW3ZskXjxo1TmzZt/GLWZGSdvXssSztzdA72NTVq1HC6fvfu3R6KBO5mlJDpD2PtfvnlF6fr69Sp46FI4ElGxXD8ZQIPe8UcLdcaJUqU8EZIcCOjQlz+0LcVFRWly5cvS7JfGM/oewT+yegc6y8Fc53FyeyuyG4kZAIAPKpGjRp6//33082amVHOZs2Mi4vTvHnz1Lx5c91777364YcfqHzhR3LlypVumaVz2mhAhq8wurm29x7hf4xmxzIaSOwr4uLiHK5zlhgC/8EAYfirmzdvKiIiwmH7feaZZzwcUeZUrVpV4eHhDq9DjQZpwnclJSU5Xe/qgC9737uWe5wLFy64tE9nKleu7LB9HjhwwO3Hg3c0adLE4d/ZbDbrvffey9T+rl+/ri+//NLw2qJDhw6Z2q8je/futcZv730YzeoB/2M0+87+/fs9FInrnM2CIJE0FEiczZAC+BKjvkqj85Yv+O2335yur1WrlociQXa6/fbbVbRoUUmpi8jZJghdvHjRW+EZ+ueff/Tvv//anUne4t577/VwVMhuRgMRjZIdfcGqVaucrue+K/AULVrU2nbtFXBKSkrS3LlzM73fmTNnGm4THh6e6f3ac+HCBcXGxkqy319AInHOdPnyZS1atEjPP/+8atasqUqVKunpp5/W999/rzNnzmQqAdO2XRltGxoaqscee0xz585VdHS09u7dq48++kjdu3d3uXgf/NuBAwcc9p/6S8K4UZw8Q/BfYWFh1jFL9u67Vq1a5dOzYMXGxmrlypVOn1E0atTIgxHBU4zOSydOnPBMIFlw5MgRa7KQvUQ3rmH9n6N+WMs59ueff/b5ZwlLlixxut5fxu8ic4ySFQ8fPuyhSLLmyJEjDq8RSMhEdiMhEwDgFbazZk6fPl3BwcGZShwxmjXzr7/+0v/+9z+FhYXpm2++8fkbGjhPYitUqJAHI3Gd0YMVf0nUg3NGs0cGBwd7KJKscVYZKF++fB6MBNmFAcLwV6tXr1Z8fLyk/yvOYGEymdSxY0dvhZZhnTp1SrfMcq1qNKAYvstoxnZXO3Lz58/vcN358+dd2qczjq6tzWaz33Sow1j79u3TLbM9p27ZsiVDAyYtxo0bZ3fAo+05umbNmm57GBcVFeV0PQODA49R0tC2bds8FInrVqxY4XS90QwD8A9vvfWW3Z/Ro0d7OzQgHaNZsIzOW96WmJion3/+2ekzg4YNG3owImSnpk2bppth2iI5OVlfffWVF6LKmA8++CDdMtt2W6VKFZUuXdqDEcETqlev7nCd2WxWRESEB6NxzdKlS52eY+vXr+/BaOApzZo1cziLpdls1uTJkw2fg9k6duyYFi5cmK4t2b4OCgrSfffd53rQNoxmZqO/IGeIj49XZGSkRo4cqUaNGqlkyZJ66KGH9Nlnn+ngwYPZloBZqFAhdevWTR999JH27Nmj6OhozZ07V4899pjKli3rlf8L+BZL/6k9uXPn9mAkrjMqMm6ZOQv+J3/+/KpXr57D+67Y2FgtX77cG6FlyKxZs6xFx23HCloUKFBAd9xxh1diQ/aqXLmy0/U7d+70TCBZYDQDLQmZ/q927drWGdHtFb85efKk4Sy/3pSUlKSZM2c67SO45557PBgRPMXoPsbomb0vuHXrlg4dOpRuueUzyL0ashvTNAEAvOL8+fOaPXu2vvzySx05ckSS/SqWGZE2KdOyzGw26+DBg3riiSc0efJkvf/++3YHpcI3FCpUyOGA86Ag/6ghYZRQ7C+JpXDOKNni1q1bflFZJzo62mGbdZYYAt/XsmVLZsaEX3M2S0vFihVVvHhxD0bjmgYNGqR6bZsEFRsbq0OHDjE4yA8ZXcvFxcUpb968md5viRIldO7cOevrtO3l5s2bbv1utlc8wjLojsEUgaN79+566aWX7K6z/L0HDx6sChUq6P7773e6r4ULF2ry5MkOry8sbfbhhx/OctwWf/zxh9P1nEMDj9GAdqPZe7zNbDZryZIlJA3lAG+99Za3QwAyrHjx4qpRo4YOHjyYqpih5Vx17Ngxbd++3WcTbn788UdduHAh1ayDtufZXLlyMRAogISHh9sd/Gv5+0+bNk0vvPCCdXCbr9ixY4e+/fZbu9cAls9b165dvRAZslvFihVVunRpnTt3Lt15ymw26++//1Z0dLTKly/v5UjtW79+vXUWL0fPZ5lhKDB17NhR8+bNS7XM9vrg5MmTevHFF/XZZ59laH8vvfSSEhMT7bYly36bNm2qYsWKuSV+o9ln6S8ITGazWdu2bVNkZKQiIiK0adOmVEUdbTn6TnYk7fa22+bNm1dNmzZV27Zt1a5dO9WvX99vxi3AO65du+bSOl9y/fp1p+v95X3AvubNmzt9Djt9+nR16dLFgxFlzPXr1x0+o7Bcb7Rv354xEgHKMsO7I1u3btWAAQM8FI1rjJKda9eu7aFIkF1CQkLUsGFDbdy40eG5atSoUercubNPXk9+/PHHiomJcdpH0KJFCw9HBU8wKpi7cuVK9e/f30PRuGbt2rUO+yVMJpPhewSyyvfO6gCAgBYZGamHH35YFSpU0Ouvv67Dhw9nevYuZ4NA7VU6NJvN2rVrl+6//3716dNHly5dctfbgRtVrVrVYTvwl05dozi5uA8MRgkR2TGTlbuZzWYdO3bM7nJJVGz3c+vWrdPatWvt/gD+YM+ePemWWa7vHM3w4muMZsAyquQO32SUkHnr1i2X9luiRAmn90NHjx51ab+OOEu6NBpsAf9RoUIFhYeHp5tp2HaQcHx8vB544AG9/PLLOn78eLp9XLlyRa+//rr69u2rlJSUVL9v2Yftvx9//HG3xb9hwwangyfCwsLcdiz4htDQUFWsWFFS6urBln/v3btX+/fv91p8RpYvX67o6GhJ9iu0m0ymdAUbAMATHM2CZfHJJ594MJqMM5vNmjBhgsN1JpNJTZo08bnkPLjuoYcesn532ptJ4PTp05o0aZJXYnMkOTlZzz77rN1rZVs9evTwZFjwoLTn2LT/njlzpjfCypApU6akW2Y7cK127doqWrSop8OCB3Tu3DndzC3S/32/Wtru66+/bj2/OTJkyBAtW7bM6aBdSerdu7d7ghcFnHKiuXPnqkSJEmrcuLHeeOMNrV27VnFxcanGmGR0Bsy026bd3nLv/tprr2n16tWKjY1VRESERo4cqYYNG/rk4Hn4FkfPCMxms06ePOnhaFxj6d9yxDJDIfxT9+7d0y2zvQZYs2aNIiMjPR+YgTFjxljH4Ti65rD33hAYwsLCrN/BafvczWazVqxY4a3QMuTmzZtavnw5xRxzgDZt2qRbZvuca9euXQ77O73pyJEjGjNmTLo2avu6YsWKjHsNUI7GNtmeY319vH3aolNpGY3fArKKngIAQLY7f/68Jk6cqOrVq+u+++7TTz/9pISEhHSJk86k3c7od+x1wJvNZi1YsEB33XWXNm/e7L43CLe444477C43m81uH4SeXewluNmiolVgMKrie+LECQ9F4rp9+/bp5s2bkuxXjrUMhAYAb7BU57enSJEing3GRUZxHjhwwDOBwK2MEjJdLcpQrlw5p+vdncAbGxvrcJ3l+gCB4cUXX7S73HaQWUpKij788ENVq1ZN9erVU7du3dSnTx+1atVKpUuX1sSJE5WUlORwcKVlPx06dDCsUpxRhw8f1uHDh637l1I/9AsNDVXZsmXdciz4lubNmzsdxJvRGVq8wVGCiOX91K9fXwUKFPBkSAAgSerUqZPd5Zbv9m+++cb6vetLZs+erZ07dzpN8GCwZWCpUKGCmjRpYvfvbWkH48aNU1RUlBeis2/kyJH666+/0rVT22vXKlWqMINAAGvXrp3d5ZY28fHHH+vKlSsejsrY+vXr9csvvzg8x5pMJnXr1s0LkcETChUqpIceesjpPb7ZbNbEiRN177336ptvvknVjpOSkvTbb7+pefPm+uijj+wew/Y8mC9fPvXp08dt8aedcSbtv3kWG3iOHTum2NhYh8W57SVh2jLatnr16ho0aJB++uknXbhwQVu3btV7772ntm3bUvwDmWav78fS7vbv32+Y6O4Ldu3a5XS9UfFq+LYWLVpY+9btJd6YzWY999xzLhcgzQ5r167VBx984DRRKF++fOrataunQ4OHFCpUSHfccYfDYjhnzpzx6eLk8+bNs07wYO+ZV548eXTXXXd5JTa41yOPPOJwneUcO2bMGK1cudKDUTl37do1Pfjgg7p69aqk9GMILdfezt4b/FvlypWtE3fYK5IXFxenTz/91CuxZcSJEyf0008/Oc0laNKkiQcjQk5EQiYAINs4mw3TXoe3PY4qE9rrbLcnbWKm2WzWqVOn1LZtWy1fvjwb3jVcZa9zwfJ3PXjwoBISEjwdUqbt2LEj1eu0HSi1atXydEjIBkYDzX1pUJAjmzZtcrq+UqVKHooEANJzliyWO3duD0biuly5cjld72yGQvguo+/Hs2fPurRfo8r9f//9t0v7dWTPnj0O75+M2i78S/fu3VW/fn1J6QdXWFjuk1NSUhQVFaVffvlFCxYs0MaNG1MVUjLy+uuvuy3uZcuW2V1uOxsWAtN9991nd7ntLC2nT5/2cFTGlixZYh0UzIB2AL6mc+fO1kHB9gZUJCUl6bnnnvNKbI4cP35cw4YNczrY0mQyqWfPnp4ODdns+eefT7fM9vlOQkKCunfv7vK9lztNnz5dU6ZMcfpsymQy2X1PCBy9evVScHCwJPvn2MuXL2v06NFeic2Rmzdv6tlnnzXcjpldA9uQIUMcrrN9Fr99+3Y98cQTKlasmAoXLqyyZcsqX758euCBB7R58+ZU2zraz6OPPqrChQu7Je6tW7c6nR2rRo0abjsWfE9GZ8E0SsAMDQ3Vo48+qjlz5ujkyZPat2+fPv74Y/Xo0cNvCkLCdxUsWDDVa9u2efPmTcNn9L4gIiLC6XqjwpXwfY8//rjdhBuLw4cP66mnnvJ0WHbt27dPDz30kDWZ2VGiUJ8+fWibAa558+ZO18+YMcNDkWROQkKCJkyYYLfvwNJ+W7duzfPZAFGrVi3r+Ffbv7lt31ZSUpJ69uxp+H3rCdeuXVPnzp3177//Oi2KJ0l9+/b1YGTwNEcFc20LNvni81lJeuWVV5SYmCjJftJ7UFCQmjVr5pXYkHOQkAkAcKvz589r0qRJGZoN01Eipr3tcufOrd69e+v333/XuXPnNHXqVN155512Z8K0J20CZ1xcnB566CG/SJzKKe6///5UM6Dato2EhAStW7fOS5Fl3KpVq9Its7S7Nm3a+E0SCZwzSsj0hxl4f/nlF6fr69Sp46FIACA9S4XIzK7zJdevX3e63l/eB1KrWbOm0/VHjhxxab/Vq1d3uv733393ab/27N2715oQbO9ejOrWgce2cnTa+2V799L2Zhuw3dbCcs9mMpnUpUsXtz7I+Omnn5yuJyEzcHXv3t06+4S9tnfr1i0NGzbMK7E5cvHiRQ0aNMgwcZmkIQDekjdvXvXo0cPhoEXpv8KK77//vjfCS+fatWvq3r27YVX2Dh06qGLFit4IEdnokUceUdWqVSU5Hrh2/PhxNW3aVPv37/dKjJL0zjvvaOjQoXavV2zjLly4sJ5++mmPxwfPKVmypMLDw50OWpsxY4YiIyO9EJ19/fv318GDB+3O7Gp5XadOHWtxHwSme+65Rw8++KDDIky2yy19BNeuXdOZM2eUnJycroiyLdv95c6dWyNGjHBb3BRwgqNZMCXH/VuFChVS165dNX36dO3evVvR0dH6+uuv9fjjj6tcuXKefgsIcJUrV3aazLBw4UIPRpN5CQkJWrp0qdN+Lgo7+78hQ4Yob968ktLfd1m+23/44QcNGjTIaXvObrt371a7du106dIlw0ShwYMHezAyeEOXLl3SLbNts4sXL3Z7gVt3mDZtmo4dOybJ/nNZ6b/nIggcQ4YMcViwRvrvvHvr1i117tzZq4nEBw4c0L333qs//vjD7ve+7XPg8PBwxhEGuE6dOqVbZtuOr1+/rr59+3r1usCeefPmaeHChXavEyztt1mzZumKpgDuRkImAMAtbGfDHDlypOFsmBntJK9YsaLGjRunkydP6rvvvlOLFi1UrFgxDRkyRDt27NCmTZv05JNPKn/+/BlOzLRsExcXpyeffFLJycnZ8V+CTCpVqpQaNWrk8ML9u+++83BEmXPt2jUtW7bMYduz1zkE/xQWFmatTpa2Mr/ZbNaqVausFfp8UWxsrFauXOn0QUqjRo08GBEApHbr1i27y81ms06ePOnhaFwTHR3tdH1cXJyHIoE7VapUSfny5ZNkf7bBffv2ubTfe+65x+5y29kILly44NK+03I2cE2SypQp45bjwHc0a9ZMr7zyisP7LHtFjowKKdm2/4IFC2r69Olui/fIkSPWh3+OYmaAZeAqVKiQOnXq5PChmdls1oIFC3ymfyApKUm9e/fWmTNnJKVPxrDE3bRpU4WFhXkrTADQK6+84rBAg+V8NWLECH3//ffeCM/q+vXr6tq1q6KiogwHW7744osejAyeEhQUpLfeestw4NrRo0fVtGlTjxdxvHjxorp3764xY8ZY43E2K9zrr7/OLC05gL2ZBm3ba0pKih5++GHt3bvXw5GlN3z4cH333XdOz7Emk4lzbA4xdepU62ySzpIynf0462uwtKXKlSu7LeYFCxY4fbZFf0HOZWmPZrNZISEhCg8P17vvvqs///xTFy9e1JIlS/TCCy+oVq1a3g4VAc5RG7O00blz5+rKlSsejirj5s2bZ30W4egcX7t2bU+GhGxQsmRJPfPMM07vZcxmsz7//HN169ZNN2/e9HiMixcvVosWLRQTE2OYKNSrVy/deeedHo8RntW+fXsVL15ckuNr1wEDBvjUGNAdO3borbfestsXZ5ErVy4SMgPMo48+ap3owVmh3MTERA0ePFgdO3a0Ju16QlJSkqZMmaJ69epp//79Tvu3LN58801PhQcv6dmzZ7qCuVLqYk2///67BgwY4JX47Fm/fr0GDBhgWDC3T58+HooIORkJmQAAl7ljNkwpfSKmyWRSx44d9fPPP+vIkSMaOXKkSpYsafd3GzdurFmzZikmJkYffvihbr/9dsPETNs4oqKi9Nlnn2Xp/wHuY69atO2gy5iYGC9ElTGff/65dTastBVlCxQooN69e3srNLhZ/vz5Va9evVTnEtt/x8bGavny5d4ILUNmzZplTQSyPV9aFChQQHfccYdXYgMA6b/zUFqW89T+/ft9OundYteuXU7XMwuh/6pRo4bD+5qdO3e6tM+wsDDrIF17M6wkJydr/vz5Lu3bltls1qeffuqwU9pkMlHdOkCNHz9eXbp0SVcwyZZt8SSjQkqW7YOCgvTll1+6dXaqr7/+2uExJSlfvnzM1BLgXn75ZYfrLP0DzzzzjNavX+/BqNJLTk7WY489psjISCq0A/B5d955p8OEd+m/86vlvDZp0iRvhKjjx4+rZcuWWr9+veFgy3vuuUf333+/F6KEJzz22GNq2bKl3VnbbNtsbGys2rZtq759++rEiRPZGlNiYqKmT5+umjVratmyZRmaFa5q1ap66aWXsjUu+IZOnTqpbt26khzP7BobG6vmzZtrw4YNXokxKSlJzzzzjKZNm+bwHGtRpkwZPfroo54MD15Svnx5ffbZZ3afE1k46h8w6jMwmUyqXr263n77bbfFu3nzZh06dMh6fHtIyMx5bNttpUqVNHv2bF2+fFmRkZF6/fXX1ahRIwUFMTQSnmOv6LHtOevatWsaPXq0J0PKsBs3bujNN990mjgUEhKiu+++28ORITuMHTvWOg7P3n2X5X5n+fLlatSokVavXu2RuE6fPq1+/frpwQcf1OXLl+0+M7ONN0+ePF7rx4BnBQcHq3fv3g6LOUrS33//rVdffdUb4aVz5swZdevWTQkJCZLSX79a4u7Ro4dKlSrljRCRTYKDgzVp0iSnxWuk1JM+hIWF6YUXXrDe72SHuLg4zZ49WzVr1tRrr72mW7duOe3fsqzr2rWrWrVqlW1xwTcULlxYXbt2NSzWMGvWLD311FNKSkryQpT/Z8WKFerSpYu12L+j64T8+fPr4Ycf9nh8yHnodQAAZFpkZKQeeeQRt8yGaVutsHjx4nr11Vd16NAhLV++XA888IBhBQuLggUL6sUXX9S+ffv0ww8/qHbt2k4fIFmWm81mzZgxw8X/Cbhbv379rLPypL3hS0hI0CuvvOKt0Jw6d+6cxo0b57Cj8plnnrFWmUVgaN68udP17pwlyJ2uX7+uyZMnO6123L59+wyfewEgOxQsWDDVa9vrgZs3b2rTpk2eDinTIiIinK5nhgz/VadOnXTLLNetW7ZscWmfJpNJLVq0cHjfZDab9f7772e5Y/vzzz/X8ePHJTkeuGYZRIrAYjKZtHDhQnXv3t2wgJHRfqT/2k9wcLCmT5+uBx980G1xJiUlaebMmU6vVcPDwxUSEuK2Y8L3NGvWTM2bN0+XhGHbduPi4tShQwctWLDAKzFeuXJFDzzwgH744QfDAe233367evXq5cnwAMCuSZMmKXfu3JLSJwxZzrkpKSkaOXKknnzySbfN0J4Rs2fP1t13362oqKgMVWWfOnWqp0KDl3z22We67bbbJDmfTcBsNmv+/PkKCwvT888/r99//92tcRw6dEhjxoxRxYoVNXToUF26dMkwGdNyrTxnzhzrZw6Bb9y4cYYzu8bGxuq+++7T3LlzPRrb/v371aRJE82ZMydDM7u+++671lkREPgeeeQRvfvuu4bP1CXnz/1tf9dsNqtQoUL68ccflS9fPrfFOmfOHIfHlKQSJUpQbDQHsm2PJ06c0NNPP6169epp8ODBWrp0qa5everF6JATOSscY7mG/OSTTwyfIXnDc889p9OnT0tynDjUrFkzt57b4T1FixbVlClTDGe7NpvN2rNnj+6//3517txZf/31V7bEc/ToUQ0dOlQ1atTQt99+m6pv2Nm168iRI906Gzd827BhwxQcHCzJ/gxuZrNZH3zwgSZOnOitECVJMTExat++vY4fP04xxxyqV69e1uQ2Z5PJWNpHQkKCPv30U4WFhal9+/b67LPPFB0dneU44uLitGLFCg0cOFDlypXTs88+q6NHj6Ya4+2s2FjBggUZV52DjBw50uE62/Ps3Llz1bp1ax09etSD0f0nOTlZb7/9trp06aJr1645PMda4u3fv7+KFCni8TiR85CQCQDIEMtsmDVq1NB9992nH3/80S2zYZrNZjVt2lTffPONoqOjNWHChCx1lphMJj344IOKiorSRx99ZJ1hydFDe+m/h5GbN292+Zhwn5CQEI0ePTrdzIO2gyu+/fZbL0aYntls1mOPPaYrV65YX9u2t8KFC/tMBS64T/fu3dMts22ra9asUWRkpOcDMzBmzBidP39ekuOBbfbeGwB4UuXKlZ0+mFi4cKEHo8m8hIQELV261OkAJmYh9F8tW7ZM9dq2rV66dEn79u1zab9dunRJtyztgKKszCpw8OBBDR8+3DAB795773X5GPBtISEh+umnn/TOO+8oODg4XWKmvQRNe+vMZrOKFi2qX3/9VYMGDXJrjAsXLnQ46Meic+fObj0mfNPEiRNTzaxiYdtu4+Pj1bdvX40dO9ZaZdoT1qxZo7vuukurVq3K0ID2SZMmMRsHAJ9Qq1YtDR8+3Om9lqVf6+uvv9btt9+uyZMnZ+s5dunSpWrQoIGeffZZXblyJUNV2R9++GG1aNEi22KCbwgLC9PMmTMzPJtAXFycPvvsM7Vp00Zly5ZV//799dFHHykiIkIxMTEZOmZKSooOHDigRYsWaejQobr77rtVs2ZNvfPOOzp79qzTwWq2cVkGBRsV9ENg6dy5s3r06GE4s2t8fLyeeuopNW/eXFu3bs3WmM6dO6eXX35Zd911l/7+++8MnWPr1aunJ598Mlvjgu95/fXXNXXq1FT3YFkp4FSmTBmtWLFCd955p9tijI2N1Xfffee0gFPHjh3ddjz4H9uE4X379mnGjBnq2bOnihcvrsaNG2vUqFFat26dEhMTvR0qAly5cuXUoEEDp4XGkpOT9b///U+7d+/2VpjpvPfee5o3b55h4lDPnj09GBWy22OPPaa+ffs6TRiy/Y5fsWKFGjdurKpVq+q1117Tpk2bdO3aNZePHxUVpQ8++EDNmzdX9erVNX36dN24ccNpMqbttWvTpk01atQol48P/1O5cmW7s2RKqcdrvf766xo0aJDT81l22bp1q5o2bardu3c7LOZoibVNmzZq2rSpx2OEZ3zxxRcqX768JPuFb9JOfmM2m5WSkqI1a9bo+eefV6VKlVSlShX17t1bY8aM0bfffqs1a9Zo165dOnbsmGJiYnTmzBmdOHFCBw4c0KZNm7RkyRJ98MEHGjhwoO69914VLlxYnTt31syZMxUbG2t3sh17LNvNnDlTZcuWzb7/JPiUu+++W126dDG8LjCbzdq0aZPq1q2r8ePH6+bNmx6JLzIyUg0aNNDYsWOVnJxsWDA3f/78Pjv5DwKPyeyNqw4AgN+IjIzUF198oaVLlyoxMTHVhbi9DkR77D18LFCggPr27avnnnsuW2dhOXjwoDp16qQjR46ki9P2JnfcuHEaMWJEtsWBzGnVqpU2bNiQ6m8k/ff3y5cvn5YvX67WrVt7N8j/b9CgQfrss89SdU7bxv3ll1/yADtAlS9fPt2Acdu2evvttysqKirTVSKDgoIctqfk5GSX4127dq3at2+fbtCwbcz58+dXTEwMM7cB8KoBAwZYZ0izd64qVKiQjh8/7rOzT8+ePVvPPPNMugfXtufzjRs3qkmTJl6MEq46dOiQatSo4fC7esqUKRo6dGim93vmzBlVrFjR+l1vr+0HBQVp7ty56tu3b6b2feDAAbVv314nT5602y4t+w8JCdH58+fTzVKLwHPgwAGNGjVKixcvtrY5o1kwJClfvnwaOHCgXn31VZUuXdrtcdWvX1///vuvwxhMJpOOHz9ufYCJwGbvesDC9txlMplUqVIlTZw4UQ899JDD/WX1PisqKkpjx47V0qVL0+0jbWy2AypWr16dqfcNANkpPj5eTZs2tX7fOju/Wl4XLlxY3bp108MPP6z27dsrV65c1u1dObf++++/+vHHH/X999/r2LFj1uM5G2xpWV6+fHnt2LGDqtY5yIgRIzRp0iSnA8PttR1nBTot6y3trkGDBjpz5ozOnj2bKkkjM8/CbNd36dJFixcvznQyE/xfTEyM6tWrZ51hOCPn2A4dOuiRRx5R9+7d0/VzuXKOtQzenDdvnhYsWGAtrGv7+45iypcvn/766y/Vrl07K/8N8GMbNmzQk08+qSNHjmT6HGZpW02bNtUPP/zg9kG7kydP1muvvea0v3X+/PlO7wnhv8aOHauxY8caJoo5G1huWZc3b141b95c7dq1U7t27VSvXr3sCRo52pw5c/T0008bfveWKlVKP//8sxo1auSNMK3Gjh2bqhiko+cHBQsW1KlTp6xF8REY4uLi1LhxY+3YscPhedbonis0NFS1a9dWRESEw+vXjz/+WGfOnFF0dLT27t2rPXv2pErmtPdMzFkcpUuX1tatW1WhQoWs/hfAz5w6dUq1atXSjRs3JDm/3w8PD9fHH3+ssLAwp/t0xxitW7du6b333tPEiROtfQvOnskGBwfr77//Vp06dTL2xuGX/v77b7Vo0UJxcXGSHI+vdtbv5Gr/krP9OIvD0vZff/11vfPOOy4dG/7r2LFjuvPOO3Xr1i1Jjr+Pbc+XJUqU0MCBA/Xss8+6/Rl+YmKiFi1apI8//libNm2yxpSRwg2TJk3SsGHD3BoP4AgJmQCAdM6fP685c+boyy+/1OHDhyWl7/ywyGgipmW7O++8UwMHDlS/fv081lF36NAh3X333ekuFG0vwLp27arFixd7JB4YO3r0qBo3bpzqwXXapLF58+Z5dSa/xMRE9e/fX19//bV1mW0lGJPJpIceekjz58/3WozIXm+88Ybee+89pw+AH374YX3//feZ2m92JGTu27dPzZs316VLl5zG+/TTT+uLL75w6RgA4C6zZs3Ss88+6/R89cILL+jDDz/0YpT23bhxQzVq1NCZM2ck2X+AmCdPHl26dCnTCfvwHWmLMqR9uBcREeHSfnv06GGdXdVZ5/GwYcM0ZswY5c+f33Cfc+bM0WuvvaYLFy4Y7rdz5876+eefXYod/unMmTNauHCh1qxZo3/++UfR0dFKSUmxrg8JCVFoaKiaN2+uNm3aqEuXLipRokS2xPLLL7+oa9euTrepU6eOoqKisuX48D1XrlzRPffckypZx5a9Ae1hYWF6+OGH9fDDD6tWrVqptnflPuvSpUtatGiR5s2bpw0bNliPl5GkoaJFiyoqKooEYgA+59ixY2rYsKEuXbokyXiQo+3rfPnyqWbNmqpVq5Zq166tUaNGOTy37t69W6dPn7YOtty1a5f++OMPXb58Od1xM3JezZ07t1avXp1uxnoEvhdffFEzZszI0MBcC3cNf8joYDXL+mbNmikiIkJ58uRxy/Hhf9asWaMOHTpY76sycg0rSblz51bLli1Vp04d6zm2efPmTq9fU1JSdOrUKes5dsOGDdqwYYNiY2PtHsPZIDqTyaSPPvpIgwYNcvd/CfxMYmKiZs6cqU8++UR79uxJtc5ZW2rQoIHefPNNdenSxe0xxcfHq2rVqtb+1rQs1wnnz5+n2GiAsk3ItMjoGBV729quL1asmMLDw9WuXTu1bdtW1apVc1PUyMni4uJUrVq1dM+JLNI+L5oyZYqef/55j8d5/vx59e/fXz///HOq5xxpY7WsGzJkiN5//32Px4nsd+bMGbVq1UqHDh2S5HrCkLvOzUb9FAULFtTvv/+uu+++2+HxENimTp2q4cOHO00itizPlSuXnn32WY0aNUqhoaF295eVMVrXr1/XrFmzNGXKFMXExGQ4Uej555/X9OnTM/3e4X9Wr16t7t27GyZlSo5n0nSVK+O8zWaznn/+eX300UcuHxf+bdq0aRo2bFimCuSZTCZr4bsHHnhALVq0UMOGDTM0niWtvXv3atOmTVq9erVWrFhhLeBg1M9le45t2LChNm/eTME8eAwJmQAAq+yaDTMkJEQ9e/bUc889pxYtWrg/8AwYM2aM3n777XQ30JYYw8LC0j1Ygnf99ddfCg8PT5VIa/s3CwoK0tChQ/XOO+8ob968Ho1t7969euKJJ7Rt27Z0g/At8bVq1UqrVq1S7ty5PRobPOf8+fOqVKmS4uPjJTmegXfAgAGpBg0ZcXdC5u7du9WhQwfFxMQYPkiJiorSnXfemeljIHDduHFD8fHxiouLU1JSkrfDUcWKFb0dAjzg1KlTqlChgmHVyN9++03t2rXzVph29evXT/PmzXOaTJqVhD34hscee0zffvut3WvA4OBgRUdHuzR74Jo1a9SuXTvDB4gmk0lFixbVo48+qg4dOqhOnToqVaqUgoKCdP78eZ04cUIrV67UDz/8oL1792b44d+yZcvUqVMnF/5HECgSExN15coVxcfHq3Dhwh6tdr5w4ULDe/L69evrgQce8FBE8AVRUVFq2rSpwwfV9vqqLMuqVauWakD7o48+6vA+68aNG+mShjZs2KCdO3dat8/sgPYffvhBvXr1cud/BwLA9evXtWvXLu3du1cxMTE6c+aMLly4oFu3bikuLi5df6wnPfjggyRh5CAbN25Ux44ddfPmTUnGCW5GCUX2GA0gymiim+W8OmfOHPXr18/h8RDYhg8frqlTp7ql7WX0dzLze2azWeHh4Vq8eDHJQNDHH3+swYMHZyiJ2NF50d7v2Z4TS5QooYsXL6bbJjPn2bTPMD755JOMvkXkEDt37rQWcDp8+LBiY2Ot/QUlSpRIVcCpSpUq2RaH5TPlTOvWrbVmzZpsiwHeNX78eI0aNcr6OjPf9RnZ1nabihUrWpMz27Ztq5IlS7oaNnK4r776Sk899ZTTvn7p/8bANG3aVJMnT1bjxo2zPbakpCR98cUXeuutt3Tp0iWnyZiWGIsVK6ZDhw6pSJEi2R4fvCMmJkatW7fWoUOHDO+73JEw5Mq53Gw2q0CBAvr555/VunXrTB0PgcVsNqtDhw7pZmW1lfZZQFBQkJo3b66HH35Y3bp1SzWje2bHaF2+fFnr1q3Tjz/+qGXLlun69euZShSqVauWtm/fTjGnHGTt2rXq0aOH3ZmBnXFHMllmjmM2m/XKK69o4sSJWT4u/NvDDz+sH3/8MUNJmZL9/iiTyaTy5curevXqCg0NValSpVSgQAGFhIQoKChICQkJiouL04ULF3Tu3DkdO3ZMBw8etD4TdrRfo2vr0qVL66+//qJgLjyKhEwAyOGyczbMSpUqqX///nrmmWe83lkdExOj8uXLO0zILF26tHWWG/iO33//XT179rRWTrdl6aioUqWK3n33XfXu3Tvb47l06ZImTJigjz76SAkJCQ6TMdu0aaNFixYx+CIHGDx4sD7++GPDRMfOnTtr/vz5Gar8486EzMWLF+vpp5/W5cuXHQ7ysOy7V69e+uGHHzK1fwSGW7du6c8//9SWLVu0d+9e7d+/XzExMTp37pwSExO9HZ6VyWTyiaRQeEajRo20bds2p0mZxYsX17p163THHXd4K8xU3nvvPb3xxhuG3wnMOOD/vvvuO6eJPVOnTtVLL73k0r7Dw8P1+++/Gw6AsH3tSGYe/tWvX1//j737jorqePsA/l06KKBYEGMvWFGxV1RAYy+x9941amIseU3UmEQTW0zssfeaWGIH7D1qFAtiAQERRQFFurDvH57d38LerSx7d5fv5xxP4M4y82y43J07d56Z69ev6xUzEVFe+uuvv9C3b1/5vZC2E9oVj6v6OaHXqXq9NhORZNfUH374Af/3f/+nsj3KP9LS0nDs2DEEBAQgMDAQjx8/FjskJbLzdvLkyVi6dKnY4ZARnTt3Dh07dsy2GJ4QXZ4RaKLP8wapVIpff/0V06ZN07tdsgybNm3CuHHjso1VGWKag7b9BVU/J5VK0adPH2zZsgV2dna5jocsw+LFizF9+nSdJrMb+nzWpl2pVIru3bvLJ9kRmaK7d+/Kd35VpVSpUtzZ0MI9ePBAfl917tw5vH//Xl6WlwmaXl5e8gTNli1b6rXDC+VfLVq0wKVLl7RKypR97+vri/Hjx6Nz586wsbExaDyvXr3Cpk2bsGbNGkRGRio92xCKTzZmsHbtWowcOdKg8ZDpiY+PR8+ePXHmzBmtFsORyauFc3LW7e7ujmPHjsHb21vjz5Hli4uLQ926dREZGQlAu+cGiudhoUKF5Is5rl+/XuUz33/++QcxMTHZFnN88OCByoUchWLJmVR8+fJlLpSfD4WGhuKLL77AgwcPdLrG5iXFOBwdHbF27VoMHDhQ1JjINKSkpMDHxwc3b95Um5QJaL/LtSa6LhqZ8zVSqRROTk44deoUmjZtqlWbRIbChEwionwqKCgIa9euzdVumEKvtbKywueff45x48ahY8eOJvUAr3Llynj27BkA5d0WHRwc5CuCk2l59OgROnXqhKdPnwomPwKfzsNKlSph7Nix6N+/v147Eqlz48YNbNiwATt37kRSUpLgIIzs2IgRI7B69WqDD5CTaYqPj0fVqlXx5s0bAJpXOVu2bBnatGmjtk5DJGS+fPkSM2bMwI4dOwT/ZmT1yo45ODjgwYMHKFeunFb1k/lLSEjAnj17sH//fpw/fz5boqOp3iLqk5RM5mvTpk0YMWKExqS04sWL4/Dhw2jYsKEYYcrNmzcPP/zwg/x7VddbZ2dnvHjxwqi7zpHhJSUloXjx4tl2bFP8PXt6eiIkJESvum/fvo1GjRrplXgkRFOSkuwe7uzZs2jevLleMRMR5bXdu3dj0KBByMrKAmCcpKGc9WmbNDRp0iQsX748V+2S+bt//z6WL1+Offv2yScKm+p9FgAmZOZj165dwxdffIGXL1/qNYFS18mWmn5G8eek0k+7z69atQqjRo1S+zOUf9y6dQvDhg1DcHCwwRPZtJVzstqSJUswduxYo7VP5mPFihWYOnWqxj4soPsEdXXPXnW5zg4YMACbNm3i8ywiMitZWVm4ceOGPEHzypUrSEtLk5fnts+qahzV1tYWjRo1gp+fH/z9/dGoUSNYW1vn5q2QhYuMjIS3t7c8qVyXZCEXFxe0a9cOfn5+aNKkCapXr67z3KsPHz7g33//xaVLl3D8+HFcu3YNWVlZGhdylJXJnnv07NkTe/bs0altMl+ZmZmYOXMmli1blu3cEGNcS/Ecbdq0KXbu3IkyZcoYPQ4yXSEhIWjdujVev34NQPdkHkOMhWmbiCSVSmFnZ4cjR45onDNGlis5ORnTp0/HmjVrlM4XMca2ZO02btwYf/75p8kshE6m4d27d2jbti1u3Lih9+ILmn5Gmzq0fT5bsGBB/PPPP/Dx8dG6PSJDYUImEVE+8ubNG2zatAl//vmnwXfDLFq0KIYPH46xY8eabEJPp06dcOzYMYPtPEfGk5SUhJkzZ2LVqlWC5Yq/T4lEggYNGsgHp2vXrq3TFvTp6el4/Pgxbty4gUuXLuHEiROIjo5Waidn++7u7lixYgV69Oihz1skM7Zt2zYMGTJE4+qRsq/btWuHuXPnokGDBoL15SYhMywsDL///jvWr1+P5ORklcmYOeudO3cuvvvuO53fO5mf6Oho/Pzzz9iyZYt8IYLcrFBlLPyszn9SU1NRsWJFxMTEAFD/gMTe3h6LFy/GhAkTjB5nbGwsRo8ejcOHDyst0qAYq6xsypQpWLJkidHjJMPr3bu34A4Sst/10aNH0a5dO73qnjt3Ln744Qe1qw3mZiVB2c/zvCQic3Lw4EEMHjwYSUlJ8mN5nTSk6ecUf1YqleLbb7/Fjz/+qPb1ZNnCwsIwffp0/P3335BKpSoXwDMlsv4AEzLzr+joaPTo0QPXrl0TLcFNRvGa6ubmhi1btqBjx45Gj4NM28ePH/HLL7/g119/RWJiosEXZBAi9Lfh7++PP/74A1WqVDF4e2Q5Tp8+jb59+yI+Pt6gO2DoU1fOn5kxYwYWLFiQ61iIiMSWmpqKixcvyhM0b9++LU+Gz23Cu7qki4IFC8LHxwf+/v7w9/fnBHYSFBAQgE6dOsl3eddmZ5+cx4BPCcEVKlRAuXLlUKxYMRQpUgQODg6ws7NDVlYW0tPTkZSUhNevX+PVq1d48uQJXr58ma0NbXZxyxmLt7c3zp07x0VG86ErV65gxIgRCAkJMepYQc627Ozs8O2332L27NmwsrLK07bJPN2/fx++vr4qF9GX0XeXVm3q0ubabmNjg61bt6Jv3756tUmW5caNG5gyZQquXLkCwPCLjQoRaqNEiRKYO3cuRo8ebfD2yDIkJiaiT58+OHHihM5jUfo+E9O1fqlUipIlS+Kvv/4SfSF/yr+YkElElA/k1W6YANC0aVOMGzcOvXr1gp2dnYEjN6yBAwdi586dTMg0kOHDhxu9zWvXruHhw4dKv0MZVQmTjo6OKFOmjFaD0y9evJA/pFGsU6gtxfOnU6dOKFKkiGDcEokEGzZsyP3/ADJZgwYNwo4dO7ROygSAsmXLolevXujatSu8vLzg7OwMQPeEzDt37uDMmTPYv38/rl69mm3SpzbJmE2bNsX58+dNdnIoGUZWVhYWLlyIBQsWyJN1ZXIzAd0Y+Fmdf23evBnDhw9Xe20FkO16tmjRIjRu3DjPY/v48SPWrVuHOXPmIC4uTm0ypixGNzc3PHnyBIUKFcrz+Cjv/f333+jRo4fKa2izZs1w/vx5veqWSqXo2rUr/vnnH7VJmfpSPC/btGmD48eP80E2EZmF4OBgdO3aFeHh4Qad0K6PnCtbL1++HGPGjBElFjINv/32G7777rts91vGmEiRG4r3WkzIzN+ysrKwaNEizJs3D6mpqUZPzMzZnq+vL7Zu3YqSJUvmedtkvuLi4rBw4UKsXbsWiYmJAAw3wVJdPd7e3vjhhx+YLExae/HiBUaNGpVt8hpgvH5BzjaLFy+OTZs2oX379kZpn4jI2BISEnDmzBl5gmZoaKi8LC8TNIsXLy7fPdPf31+nBaPJsh04cAB9+/bVedfs3Cyoq+sucTlfI5VKUaVKFVy4cAFFixbVqk2yPJmZmdiwYQN++uknREZGAsibsS5Vdfbo0QO//vorypcvn+s2yLI9e/YMXbp0wYMHD7R+bqDtvZm+O74pxuHk5IR9+/bxHoyUBAYGYuHChQgKClL5TAHQ/Vqr6bytUKECpk6dipEjR8Le3l7HqCm/kUqlmDt3Ln766ads56LYz2dlMbRs2RJ79uxB8eLFRYmHCGBCJhGRRTt58iQmTZpksN0wZa8tWLAgBgwYgPHjx8PLy8vAUeedYcOGYcuWLUzINBBZ0pixabOalT47CWn7M7rWzfMrf0hNTUXjxo1x9+5drRKHch4DAA8PD1SvXh0BAQEqr1MrVqxATEwMoqKi8PDhQzx48EA+4UixbnWDjIpl7u7uuH79OkqXLp3b/wVkwl6+fInevXvj8uXLZjMxWBE/q/O3Fi1a4NKlS1pfWyUSCXx9fTF+/Hh07twZNjY2Bo3n1atX2LRpE9asWYPIyEila7VQfLLzd+3atRg5cqRB4yHxpKWlwcPDI9vncE7Hjx+Hv7+/XvUnJyejS5cuCAoKyrNdNFq1aoUjR46gQIECua6XiMhYPnz4gG+++Qbr1q1TKhMjaahatWrYuXMnateunedtk2lKS0vDkCFDsG/fPq13mzAVTMiknJ4+fYrvvvsOe/fuRVZWVp6OHQjVXbp0afz8888YMGCAwdohy5ecnIxdu3Zh06ZNuHLlitoJbLpQPN8LFCiAzp07Y8KECWjWrFmu6qX8a8eOHZg9ezaeP38uys6uVlZWGDZsGH7++WcUK1bM4O0REZmqFy9eIDAwEAEBAQgKCkJ0dLS8TJcJ79q8VvE1lSpVkidntm7dmgs15nNHjx5F3759kZycDEC3RCFFud2RSJu5YVKpFA0aNMCRI0c4sZ0AAOnp6di9ezfWr1+Pixcvyo8bcr6Y7Nx0dHRE//79MWnSJNSqVctg9ZPl+/DhA4YNG4YDBw6IPjareD2tWLEi9uzZg7p16xo9DjIfERER2Lp1K/766y/cuXNH6wX21cl57pcsWRKdOnXCwIED0bx581zFS/nT9evXMWbMGNy5c0eUOYc5r+2urq74+eefMXbsWG5CQqJjQiYRkQWbN28e5s2bJ/8+t7th1qxZE2PHjsXgwYNRsGBBA0eb95iQaVg5d/EzFbnZ6S23A9vq6uX5ZfliYmLQsmVLPHnyBIB+ycOa/qZ0WYlVU+KSs7Mzzp07hzp16qhsj8zf06dP0aZNGzx//lz+eSdjatdvVfhZnb9FRkbC29sb8fHxADRf2xS/d3FxQbt27eDn54cmTZqgevXqOg/EffjwAf/++y8uXbqE48eP49q1a8jKytI6AV527vbs2RN79uzRqW2ijIwMjBo1Clu3bs319TvnuTpkyBCsXr0aDg4OhgmWiMjILly4gBkzZuDq1asA8jYBTtViZTNmzMC0adO4gnA+lpqainbt2uHChQtmeb/FhExS5cGDB1i8eDH27duHpKQkALkbN9X0s5UrV8bEiRMxatQo9k8pV968eYMTJ04gICAA//77Lx49eqTXWJKNjQ28vLzQqFEjtG/fHm3btuXnPRlERkYG/vzzTyxbtky+mG5e7+xqbW2NLl26YO7cuWa1yC4RUV4JCQmRJ2ieO3cOCQkJ8rK8StC0srKCt7c3/P394efnh+bNm7NvkQ/dunULPXr0yLY4Q24TLNXRp26pVIo+ffpg48aNcHR01LlNsnxPnjzBoUOHcPz4cVy8eBHp6elKr9F1zlbBggXRpk0bdOvWDV26dIGrq6tBY6b8Zc+ePfjyyy8RGxtr9LHanNf2oUOH4o8//uDCuKSTN2/eIDAwENevX8fdu3dx7949vHr1Sqc6nJycUL16ddSqVQve3t5o3bo1qlevnkcRU36SlZWF9evX45dffkFYWBiAvNsQQtX9lpOTE0aNGoVZs2Zx8RAyGUzIJCKyYLKETG1vMIU6R3Z2dvjiiy8wfvx4s18dhQmZhmVqO2Sqo2uchmyD51f+Eh0djVatWuHJkycaH6QYYqKFPg8GZZOHDx8+jFatWunUHpmX2NhYNGzYEM+fPwegPnFMHVNYSYrX0vwtICAAnTp1QkZGBgDtrnM5jwGAra0tKlSogHLlyqFYsWIoUqQIHBwcYGdnh6ysLKSnpyMpKQmvX7/Gq1ev8OTJE7x8+TJbG9rueqQYi7e3N86dO2eWC5qQadi3bx++/vprREVF5eqaLJVK8dlnn2Hp0qXo1auXASMkIhLPsWPHsHjxYpw7d04pIU6RLn1gdfdZrq6uGD58OKZPnw53d3f9giaLIJVK0aVLFxw9elSrsdfc7E6hKQ5taKqLCZkkJDExEXv27MGhQ4dw5swZ+W4uMrr0TXOeq8WKFUOnTp3Qp08ftG3b1iDxEuWUmpqKhw8fIjIyElFRUYiJiUFSUhJSU1ORlpYGW1tbODo6wsnJCe7u7ihTpgzKli2LatWqMUmC8lxQUBA2btyIY8eOaUwG0pbitbZChQro3bs3xo4dizJlyuQmVCIii5WVlYWbN28iMDAQgYGBuHTpElJTU+Xlukwu1iVB097eHs2bN8epU6dyEz6ZocTERIwfPx47duwQfVGnnO07Ozvj999/x5AhQ4weC5mn9PR03L17F7dv38a9e/d0vu/y8vJCw4YNUaNGDVhZWYn9dsiCJCQk4Ndff8WKFSvw4cOHPN3JTeha3qBBAyxduhTNmjUzWDuUv6WmpiI6OhovX77Eu3fvkJqaitTUVGRlZcHe3h4ODg4oUKAASpQoAQ8PDxQuXFjskMnCZWZmYvfu3di4cSPOnTuHrKwsAIafX6h4va5YsSIGDx6McePGoWjRogZthyi3mJBJRGTBFBMytR0clr2ubNmyGD16NEaOHIlixYrleazGoJiQqYhJHvox1R0yTQkTfvOn+Ph49OzZE2fOnNEpAU6b1+qbxKlYt7u7O44dOwZvb2+NP0fmrVWrVjh//rzW56EpJF6qwmspHThwAH379pUP5Onat1VVro6mn9UmMbRKlSq4cOECBwQp19LT07Fu3TqsX78ed+/ezVamTf+gatWqGD9+PEaMGMGVrYnIIj19+hQbN27E4cOHcf/+/Wxlud1FwMbGBi1atECfPn0wcOBAODk55TpeMn8///wzZs+erdNiTMbYvVVfU6ZMYUImqZWeno7Lly/j33//zTbZUjGJSBVXV9dsky0bNWqE+vXrc8IlERE+TWK7cuWKfGfX27dvKy0Qpo3PPvtMfo1t3749d8MkItJDWloaLl26JE/QvHnzpvy5lK7PaDXdr/GZV/526tQpfPXVV3jw4EGeJgoJyTlOIZFIMHDgQCxYsAAlS5bM07aJiIzpzZs3WLVqFTZt2qS0iLkQfRbOU/wZPz8/fPnll+jcubOeERMRmZ+oqCj89ddfOH36NM6dO4cPHz4ovUbTc1qh66+VlRXq1q2LNm3aoHPnzmjcuLHBYiYyNCZkEhFZME0JmTkH2qysrPD5559j3Lhx6Nixo0knZuhDlpAphAPeumNCpmZMyMy/MjMzMXPmTCxbtizb34iYq1tKpVI0bdoUO3fu5IrY+cCqVaswceJEnRN9hV7n6OgIZ2dnk0jgCQsLEzsEEtHRo0fRt29f+a4s+iYZ53YXIW3+nqRSKRo0aIAjR46gePHiWrVHpK2HDx/izJkzuHr1KkJDQxEREYHExESkpKTA1tYWzs7OKF26NDw9PdGoUSP4+/ujZs2aYodNRGQ0UVFR2Sa037t3D4mJiVr/vKurK2rWrCmf0N6mTRuuKEzZ3LlzBw0aNJCP9WjaMR0AihYtCl9fX9SrVw/ly5eHi4sL7Ozs4Ovrm218TXEs6fPPP8eMGTMAACkpKUhOTkZMTAyioqIQHByMmzdvIiYmJlt7im0q1lW3bl38+OOPcHBwEHxPpUqVQsWKFQ3xv4fyGdl5qW73C2dnZ7HDJCIyK/Hx8TrvMFSoUCGxwyYisjjv3r3D2bNn5QmaDx8+lJfpkqDJBcNJSFZWFrZs2YKlS5fKFxfTd3FmdVTVaW1tjR49emDGjBlcyJmILJpUKkVgYCAOHTqE48eP49mzZ0qv0WaOrND12NvbG926dUPv3r1RpUoVg8RLRGSuMjMz8eDBA9y9exf37t1DWFiYTru7li5dGtWrV0etWrVQq1YtuLi4iP2WiLTChEwiIgsmlJApNDmnaNGiGD58OMaOHYty5cqJEapRvH37VnAFDpmyZcsaMRrzx4RMzZiQSVeuXMGIESMQEhKSp7ti5JSzLTs7O3z77beYPXs2dx/IB5KSklC2bFnEx8cD0G4XPwAoVqwYOnfujDp16sDLyws1atRA4cKFYW1tbZzAibRw69Yt9OjRA8+fP9dpF2Ig97tjaVu3VCpFnz59sHHjRpNIZCYiIiLg/fv3Wk9od3V1FTtcMnFt2rRBYGCgxkXwpFIp6tevj9mzZ6Njx46C91Y5x9cUx5KGDBmCjRs3qo3l3r17OHjwILZs2YKnT58K7nQh+2/16tWxc+dO7phFRERERESkh5cvX8qTMwMDAxEVFaVzHZw/QEJOnz6Nbdu24dChQ9kWFTPUIvqKYxeenp7o06cPRowYwUWciShfCg8Pz7aYo2wxnDdv3qj8mcKFC8sXw/Hy8kLDhg3RsGFDuLu7GzFyIiIiMkVMyCQismCKCZmKZJf+pk2bYty4cejVqxfs7OzECJHMmGzCGKnHByqUmZmJDRs24KeffkJkZCQA4ZVQc0tVnT169MCvv/6K8uXL57oNMg/Lli3D119/rTZpXrGsY8eOGD9+PNq2bcvkSzILiYmJGD9+PHbs2GHUZHchOdt3dnbG77//jiFDhhg9FiIiIiLKe2fPnlXa1VJGMRHT1tYWixYtwpdffqm2vtwmZCo6cOAA5s2bh3v37mWLRbH+AgUKYN++fWjXrp3W9RIREREREZGy0NBQeXLmmTNnEB8fr3FBayZkkjppaWkICgpCUFAQzp49i7t37yIjIyNXdbq5uaFJkybw9fWFv78/F2kiIlIhMzMTycnJgos52tjYiB0eERERmSgmZBIRWbCcCZlSqRQFCxbEgAEDMH78eA60Ua5wlz3t8YEKAUB6ejp2796N9evX4+LFi/LjhkxslnXtHR0d0b9/f0yaNAm1atUyWP1kHmrXro3g4GCNE4TLlSuHP/74Ax07dhQjTKJcO3XqFL766is8ePAgTxLd1RHaeWjgwIFYsGABSpYsmadtExEREZF4evXqhQMHDijdbyneazk5OeHIkSNo3bq1xvoMmZAJfJo4tGjRIsydO1c+aVNWn+xrW1tbbNq0Cf3799epbiIiIiIiIvqfjIwMXLlyBadPn8bp06dx/fp1JmSSQX38+BGhoaF48OABIiMj8fLlS8TExODDhw9IS0tDWloarKys4ODgAAcHBxQuXBgeHh7w8PBAhQoVULNmTXh4eIj9NoiIiIiIiCwWEzKJiCyYLCETAGrWrImxY8di8ODBKFiwoMiRkSXYsmWL2CGYFe6URYqePHmCQ4cO4fjx47h48SLS09OVXqMuUVOoC1+wYEG0adMG3bp1Q5cuXeDq6mrQmMk8hIaGomrVqiqTMWXHvLy8EBgYiKJFi4oRJpHBZGVlYcuWLVi6dCnu378PQPj6mduhD1V1Wltbo0ePHpgxYwa8vb1z1QYREVF+FxoaipiYGLWvqVWrFgoVKmScgIhyiI2NxWeffSafNJszIVM2qfbAgQPo1q2bVnUaOiFT5sqVK+jRowdevXoljzVnUuaJEye0SholIiIiIiKiT/777z8EBgYiICAAFy9eRHJysrxMm+cQTMgkIiIiIiIishxMyCQismALFy7E3bt3MX78eDRv3lzscIiISEB6ejru3r2L27dv4969e4iMjERUVBRiYmKQlJSE1NRUpKWlwdbWFo6OjnBycoK7uzvKlCmDsmXLwsvLCw0bNkSNGjW4cy3hzz//xJgxYwR3a5F9X7lyZVy+fBlFihQRK0yiPHH69Gls27YNhw4dQmJiovy4oXYiVvyb8vT0RJ8+fTBixAiUKVPGIPUTERHld506dcLx48dVlru5uSE8PBwFChQwYlRE/7NlyxYMGzZM5f2WRCLBgAEDsHXrVq3rzKuETODTYlB+fn6IiooCoJyU6ebmhuvXr6NChQp6t0FERERERGTJwsLCEBAQgMDAQAQFBeHt27fyMn2mXDIhk4iISHd///03goOD1b5m5MiRKFmypJEiIhLm6+sreFwikSAwMNDI0ejml19+wcmTJwXLzCF+IiKx2IgdABER5Z2ZM2eKHQIREWlgZ2eH+vXro379+mKHQhbg2rVrasslEgn++OMPJmOSRWrTpg3atGmDtLQ0BAUFISgoCGfPnsXdu3eRkZGRq7rd3NzQpEkT+Pr6wt/fH15eXgaKmojI9MTExODmzZsIDg7Gw4cPER0djZiYGLx58wYpKSlITU3N9XU1NyZPnoylS5eK1j7lnaioKJWTGSUSCUaNGsVkTBKVUMKw4uIfbm5u+P33340ZklqVKlXC8ePH0aRJE3z48CHbxF8AiIuLw5gxY3D69GmRIyUiIiIic/X27Vul8QKx9gUoVaoUFxuhXHvz5g2CgoLkSZjh4eHyspznds7FILknBhERUd5YtWoVgoKCVJZ7enri+++/N2JERMLOnj0r2Ec01CLieSkkJMSs4yciEgsTMomIiIiIiCzEkydPlI4pTrpt37492rZtK0JkRMZjb2+P9u3bo3379gCAjx8/IjQ0FA8ePEBkZCRevnyJmJgYfPjwAWlpaUhLS4OVlRUcHBzg4OCAwoULw8PDAx4eHqhQoQJq1qwJDw8Pkd8VEVHeunLlCnbv3o2AgACEhIQolZvKhDI+8LNsL1++FPwdy86/7t27GzskomwuX76s8hyV7WhZqFAh4wemRvXq1bFu3Tr069cv2+6YsvvEoKAg7Ny5E/379xc5UiIiIiIyZa9fv0ZQUFC2BZxiYmLw8eNHsUOT4wJOpI/k5GScP38egYGBCAgIQHBwsHwcQlMCptBr1OG4FhERkX4iIyPVLuY4ZswYI0dEpJ7sfDXX/p+5x09EZExMyCQiIiIiIrIQ4eHhagfEBg4caMRoiEyDjY0NqlevjurVq4sdChGRScnIyMD69evx22+/yRd1UDeJTOyHbqaSFEp558OHD/Kvc6646+joiAYNGogRFhGAT7ukREVFyRMZhZjqxJ8+ffpg+/btOHr0aLb4ZV/PmTOHCZlERER6Gj58uOBxiUSCDRs2GDka3WzcuBEXL14ULDOH+CnvRUVFYdu2bdi9ezfu3buXrczU7tHFHrMg85GZmYnr168jICAAAQEBuHbtGjIyMgAYJwHT1P52iIiIzMGbN2/Ufq526dLF2CERqWXu9yfmHj8RkTExIZOIyEJNmDABx48fV1luZWWFM2fOoHTp0kaMioiIiPLSu3fvsn2vOEhmbW2Ndu3aGTskIiIiMkF79uzBtGnTEB0drdVks5yMPXmMD/7yh/T0dKVjsnOtWrVqxg6HKJucE9ABZEtu9Pb2RuXKlQ3apiGvtT/99BOOHTsG4H9xy66tz549w7Fjx9ChQweDtUdERJRfbN68Wel+RfY5a+oJjRcuXMCWLVvMNn7KO/fu3cP8+fNx4MABSKVSwX6pKd2nM8GNNLl//758B8xz584pLQilSOiaqC1NCZjFihWDr68v/Pz84Ofnp3W9RERE+Z26xRzd3NxQoUIFMcIiUsncd5g09/iJiIyJCZlERBbq9u3bCA8PFyyTSCTo1q0bkzGJiIgsTHJystIx2UCZp6cnChUqZOSIiIiIyJS8e/cOQ4YMwZEjR1Q+TONERhJLgQIFkJiYqHRcIpGgZMmSIkRE9D9RUVEqyyQSCRo2bGjwNj9+/GiwumrVqoXWrVsjKChIcBLFunXrmJBJRvP8+XO8fv0asbGxePfuHZycnFC0aFFUqVIFRYsWFTs8EtnNmzexcuVKleXW1tb4/fff4ejoaMSohK1cuRI3b95UWd6yZUsMGTLEiBGRmMz9Psrc4yfD+PDhA7755hv8+eefSomYqibiin3ucIIwCYmKipLvgBkUFIRXr17Jywy5C6amny1YsCB8fHzg7+8PPz8/eHl5afsWiIiISIHQZ7MsMZOLORIREZGYmJBJRGShXrx4oXYAuH///sYOiYiIiPKYlZWV4HGJRIJixYoZORoiIiIyJU+ePEGnTp3w+PFjpRWExZ5ASQR8mqgolJAJAM7OzkaOhig7dQmZAEw+IRP4NB4cFBSU7Zhst8zAwEB8/PgRNjZ8bEiGJ5VKsX//fvzzzz8ICAhATEyMyteWK1cOHTp0wPDhw+Ht7W3EKMlULF26FLt27VKZYDNo0CCTSMYEgBo1amDSpEkqYz1+/DgGDhwIa2trI0dGYjD3+ytzj59y7+bNm/jiiy8QFRUluIATzwsyZQkJCQgKCpLvgvnkyRN5mSETMIV+XvFn7ezs0LhxY/kOmI0aNWI/gIiIyAAKFiyIhIQEwTIu7kVERERi4pNVIiILpeomFPg0SNyuXTvjBUNERERG4eLigjdv3giWubu7GzkaIiIiMhXh4eFo1aoVoqOjAfxv8hgnVJIpKVy4MF6+fCl2GESC4uLi1JbXrFnT4G0mJycbtD5/f/9s3ysm5ycnJ+PixYto1aqVQdskOnDgAL7//nuEhIQA0Nz3CAsLw6pVq7Bq1Sr4+flh2bJlqFGjhjFCJRMQFxeHv/76S54snpO1tTW+//57ESIT1qpVK/j4+OD8+fOC5a9fv8bBgwfRo0cPI0dGYhBKYDMn5h4/5c6hQ4cwYMAAef+TiZhk6tLS0nDx4kUEBAQgMDAQt2/fRlZWFoC8T8BU/HmJRAJvb295AqaPj4/JLBxBRERkSdQlZBYsWNC4wRAREREpYEImEZGFUpywk3Pni/Lly6NAgQJihEVERER5yNXVVWVCJnc6ISIiyp/evXuHtm3bIjo6WmMiprrJt+omq2k7aVebCW+q6uLEYMtXqVIl3L9/X/B3nZSUJEJERP+Tmpqqtrxw4cJ61WtjY4PMzEzB5KMPHz7oVacqZcqUgbu7O16/fi3Y3tWrV5mQacEOHTqE58+fqywvXrw4+vbta7D2MjMzMXr0aGzevBmAbok+stcGBASgbt26+OWXXzBlyhSDxUama8uWLUhLS4NEIlFKBpJIJOjatSsqVKggYoTKpk2bhvPnz6vcLWvNmjVMyCQik3by5En07t0bGRkZWidiqtshUNNrtf05XesiyyaVSvHvv//Kd8C8fPky0tLS5GWK8jIBEwAqV64sT8Bs3bo13NzctK6biMhchYWF4dq1a3j48CEePXqE6OhovHr1CnFxcUhLS0NqaioyMzNFjVEikeDjx4+ixkB5p1ixYoiMjBT8nM7IyBAhIiIiIqJPOCOXiMhC2dvbIyUlJdsx2UPrSpUqiRQVERER5aXKlSvjyZMnnMROREREcmPHjs3WPxCahJbb3S+0TbTUph3uvpF/eXp6Ch6XSqV48eKFkaMhyi7nOGtOhQoV0qteOzs7wbqlUinev3+vV53qVK5cGa9evRK8Z3z06JHB2yPTkJ6ejiFDhiAxMVHla+bNm2ew9pKSktClSxecPXtWMBFTU9KGYp8lIyMDX3/9NUJCQrBmzRqDxUimaefOnfKvhc6dyZMnGz0mTTp16oQKFSogLCwMwP+ew8kS38+cOYOYmBiUKFFC5EiJiJSFhISgR48e2ZIxdVnASZv7d3VjELokdmrbHlmeLVu24Kuvvsq2K5amJExDJmB6eHjA19dXnoRZunRpresmIjJXWVlZOH36NPbv349jx44hJiZG6TX8XCZj8vT0xK1btwTLDL2oHVF+xet6/nb+/HmEh4eLHYberKysYG9vDwcHBzg4OKBIkSJwd3eHu7s7N7CgPMczjIjIQhUsWFDlRCEXFxcjR0NERETGULNmTRw/flzpuFQqRVRUlAgRERERkZj+/vtv7NmzR6tkTKlUCjs7O3To0AFt27ZFvXr1UL58ebi4uMDOzg5WVlbZdlSTfS2RSDBkyBBs3LgRwKdd5JKTkxETE4OoqCgEBwfj5s2bCAwMRGxsrPxnFdtVrKtu3brYsWOHyuQ8sly1atVSOiY7N54+fSpCRET/Y2trq7Zc34RMoUX1ZOf9q1ev9KpTneLFi6ssY0Km5Tpx4gTev38vuDMqADg6OmLcuHEGa2/gwIE4c+YMAGhM7shJ8XWKP/vnn3/C1tYWf/zxh8HiJNPy9u1b3Lp1K1tShOLXFSpUQIsWLcQITaMhQ4Zgzpw5Sv1b2dcnT57EkCFDxAyRiEhJeno6+vfvj+TkZK3HDGScnJxQrlw5uLi4wN7eHufOnVM5XlC8eHFUrVoVwKdFTmTjBW/fvlX63BdqS7GuSpUqoWTJkirfExeltkzh4eGIj4/Pdiw3u2Bq+llXV1e0bNkS/v7+8PPzQ7Vq1XSMmIjIfKWkpGDlypVYsWIFIiMjAei2WINYmERk+apUqSJ4XCqV4vnz50aOhsgyJScnZ/te8Tqv6fkImb8//vgDf/31l9hh5InSpUujevXqqFGjBho1aoRWrVqhaNGiYodFFoQJmUREFsrZ2Vk+0TEnBwcHI0dDRERExlC/fn2lY5zETkRElD9lZmZi5syZ8u9V7R4glUphY2ODyZMnY/r06ShWrFiu2pWtPOnm5obq1aujbdu28rJLly5hw4YN2LVrF9LS0rLtHiSL59atW6hbty5+++03jBw5MlexkHnx9/fP9r3ieZGQkICQkBD5RF4iY9O0wJ2+E79cXV2z7fSiSNXYbm44OTkpHZNdh1++fGnw9sg07N27V/51zp0qJRIJBgwYgCJFihikrTVr1uDQoUM6J2IKybnT4KpVq1CrVi2MGjXKILGSaQkICMi2UIeM7Fi/fv1EjE69AQMGYM6cOSrLT5w4wYRMIjI5y5cvx3///ad1Mqa3tzeGDBmCtm3bKt2XWVlZqWynffv28gWcFKWlpeHevXu4efMmTp06hYCAAPkO8TljUlywZPbs2Rg0aJAe75jMnSF3wVT8WQcHBzRt2lS+A2b9+vXVntNERJZq586dmD59Ol6+fCm4WJImYiVFmlJiKOUdTfNgMjMzYW1tLUJkRJZD3W6z9vb2RoyExGKpCxxEREQgMjISJ0+elB+rWbMm+vTpg759+6JChQoiRkeWgAmZREQWqnTp0nj69KngwEPO1UyIiIjIMnz++eewtbXFx48flZIb3r17h4cPH3I1XyIionxi165dePz4seBuWIqTG0uXLo2///4bdevWzfOYmjVrhmbNmuGnn37CvHnzsH79esGJ98nJyRgzZgzCw8Px448/5nlcZBrc3d1Ru3Zt3LlzR/C8PXnyJBMySTSaEjJTU1NRsGBBnestVqwYwsPDBXdVS09PR3R0tNpdgHSVmpqqskzdhAsyX5mZmTh8+LBgYoPMhAkTDNLWmzdvMHPmTK2TMXMmhwrJmZQ5depU+Pn5cZKEBTp16pTa8q5duxopEt1VqFABNWrUwIMHDwR3iAsICBA5QiKi7OLj47FgwQKVn9mKx8uWLYsVK1agY8eOBo3B3t4e9erVQ7169TB69GikpKRg3759WLp0Ke7evZvt818W0/v37zF06FCcOnUKGzdu5C4t+ZAhdsG0trZG3bp14efnB39/fzRr1owTzMnkZGZm4vXr14iLi0NaWhpSU1Px8eNHscOCj4+P2CFQHvjw4QNGjRqFvXv3Co4ZAJaboEHmo1WrVrCzs0NGRobSPJjU1FT8+++/aNSokchREpm36OholWXOzs5GjITEZKkLHeTsywQHB+PevXv47rvv4O/vj1mzZqFVq1biBEdmjwmZREQWqkqVKjh79qxgWVxcnHGDISIiIqNwcXFB69atcerUKcFBkmPHjjEhk4iIKJ9YvXq14HHFPkLFihVx7tw5gyb7aMPDwwNr1qzB4MGDMWDAADx//lzpIbpUKsWCBQsQHx+PlStXGjU+Ek///v1x584dpeNSqRTbtm3D5MmTRYiKSPOEg/fv3+uVkFm8eHG15U+ePDHoNTo+Pl5lWVJSksHaIdNx9+5dfPjwQSmhQaZs2bKoXbu2Qdr64Ycf8P79e8GkekVC4xXqkjMV+wfJyckYP348Tpw4YZCYyXRcvXo123mg+LWbmxvq1asnRlha+/zzz3H//n3BBPu4uDjcv38fNWrUEDNEIiU5F/BV/LuzseFUIku2bt06JCQkqFzASXasXbt22LNnj1Em3zo6OmLw4MEYPHgwdu/eja+++goxMTHyeBQXc9q5cydevnyJgwcP6tUHJ8ujLgETAKpVqybfAbNVq1ZwdXU1ZnhEaj1+/BgXL17EtWvX8PDhQzx69Ahv3rwxuQQ4iURiEkmhZFhv375Fu3btcOvWrWz3MACTMMm0FChQAM2bN0dQUJDg5/6hQ4eYkEmUC5mZmQgJCVGZkF+0aFExwiKRWFofQLbgkyLZOAMABAQEICAgAD4+Pli9ejUXByadWYkdABER5Q2hToHsIcWzZ89EiIiIiIiMYdy4cYLHpVIpduzYYeRoiIiISAyhoaG4cuWK0uRKxQnidnZ2OHz4sNGTMRU1bdoUN2/eRIsWLZR2ypR9vWbNGixdulS0GMm4Ro4cCUdHRwBQStK9ffs2AgMDxQyP8jFNEw5evXqlV73ly5dXW/7ff//pVa8q0dHRKlc45sRKy3T16lXB47Lra+fOnQ3STmxsLNavX692BW3Fna5kn/eFCxdG4cKF5ccUd8QUihcATp8+jXPnzhkkbjINHz9+xJMnT5SOy37vzZo1EyEq3bRo0UJt+cOHD40UCZH21C3GYGdnZ8RIyJikUinWrl2rcoEE2bW3Q4cOOHz4sCg7ofTt2xcPHz5E586dlZJDZDGeOXMGbdu2RVpamtHjI/HJ+ouK/UbF/uRnn32GIUOGYOvWrXjx4gXu37+P33//HV27dmUyJpmE0NBQzJo1C5UrV0bVqlUxcuRI/Pnnn7h48SJev36NrKysbOe0qfwjy5KcnAx/f3/cvHlTaaFE/r7JFA0fPlzpmKxvuGPHDmRmZooQFZFluHbtGtLT0wEoJ+NJJBJRnyUT5ZZQv1bxflJ27Ny5c6hduzZ+/PFHsUMmM8Nl7YiILFTLli1VlkVGRiIuLg5ubm5GjIiISDwRERFih2BWypQpI3YIlAtdu3ZFlSpVEBoamm0ChVQqxZ07dxAQEAB/f3+xwyQiIqI8dPToUZVlsr7BnDlzTGLnbDc3N5w8eRIdO3bEmTNnsvVfZF/PmDEDderUga+vr9jhUh4rXLgwRowYgRUrVggm40yfPh3Xr1+HtbW1SBFSfuXp6am2PDo6Gt7e3jrXW6lSJbXl165d07lOVd6/f4+QkBCV5Q4ODgZri0yHqoRMGUMlZK5btw6pqakqd8dUnNjZtWtXjB49Gr6+vrC3twcApKSk4OTJk1i5ciUCAwPVJnYCwC+//KL2GQiZlydPniAjI0Pl+WPqu2MCmmNkQiaZInULSoiRhEfGcfXqVYSHh6tcwAkASpQoge3bt4u6U6qrqysOHTqEb775BkuWLBFcwOnatWsYNmwYdu7cKVqcZFyqdm5zc3NDq1at4O/vDz8/P1SuXFmM8Ig0un37Nr777jucOHFCZdKbpnshMTA5zzINHToUd+7cyXa/ro4pnpuUv/Tq1QvTpk3Dq1evlBZzjIqKwpYtWwSTNolIsy1btqgt1/Qcg8jcqFrQOiMjA3PmzMG///6L7du3o2DBgmKFSGaECZlERBaqTp06KF68OGJjY5VuQmWrOXTv3l3kKImIjKNcuXIcINaSRCLhrhgW4Ndff0XXrl0FJ7FPmzYNN2/e5CR2IiIiC3bixAmlY4r9gsqVK2PGjBnGDEktBwcH/P3332jYsCEeP36slJSZmZmJMWPG4N69e/LEDbJc8+fPx969e5XGtKRSKf777z/MmTOHq5OS0VWtWlVteUhICDp27KhzvdWrVxc8Ljvng4KCdK5TlUuXLintRqyIO8VYpmvXrintKiXj4OCAVq1aGaSdjRs3qtxpC/jf7tzbt29Hz549lV7n6OiIbt26oVu3bvjjjz/w9ddfy3c2yDk5QiqV4tSpUwgLC9O4yyyZB03JijVr1jRSJPorVaoUnJ2d8eHDB8HrrLqEeCIxSKVSPHz4UHD8GNC8OziZL20WcFqzZg0KFSpkvKDUWLRoEQBkS8pU7NPu2bMHTZs2xcSJE0WOlIxBdo1ycnJC8+bN4efnBz8/P3h7e/M5MJm0pKQkTJ06FRs3bsyWiKnuvDWVJEj+bVmmffv2Yf/+/VolY6pKhicyNltbW8ycORNTpkwR3EH922+/RdeuXVGkSBERoyQyPxcvXlQ5tiuj6jkGWSZV54Ku/QBd+5F5Vb+menP2zaVSKY4cOYIOHTrgxIkTcHJy0ikuyn+YkElEZME6deqksrO8e/duJmSSRXj+/DmuXbuGBw8e4NGjR3jx4gVev36Nt2/fIjU1FWlpafLJO2Jhgptp4OAw5SedO3dGr169sG/fPqVJ7MHBwZzEThYvJiYGDx8+RHR0NGJiYvDmzRukpKQgNTUVGRkZon0mtG3bFn379hWlbSLKX27cuCE4FiDrE4wePdrkJtO4uLhg165daNSoETIzM5UWl3r27BkWLlyIOXPmiBwp5TVXV1f89ttv6N+/f7aHX7JzYsGCBahUqRKGDh0qbqCUrxQoUACfffYZoqOjDZpo06BBA/l5nnOCOQC8fv0a58+fh4+PT+7eAIANGzYIHpe1V6pUqVy3QaZFKpUiLCxM8DjwaSKNIXa+unr1KsLCwlQm+8rOsS1btggmY+Y0adIkZGVlYerUqUoTPxU/F3bv3o1Zs2blOn4SX1RUlNrycuXKGSeQXCpXrhyCg4MF+9nPnj0TISIi1e7cuYPk5GTBa7dEIkGJEiVEiozyWkBAgNIxxftvHx8fg+2gbSiLFi1CSEgIjh49KrhT5uzZs9GjRw94eHiIHCnlFRsbGzRo0AB+fn7w9/dHkyZNYGtrK3ZYRFq5f/8+vvjiCzx58kQwEZNzGMjYMjIy8PXXX2tMxsxZbmtrixYtWqBOnTrw8vJCjRo1ULhwYbi4uMDZ2RkODg7GeQOUr02cOBGbNm3C3bt3lZ4hxcbGYujQoTh8+LDJPf8iMkVJSUnYuHEjvvvuu2zPZYU0adLEyNGRWHLTN9U3kVPxGZkuP2foehUXi5ZKpbh06RK6du2KkydPwsrKSm1blL8xIZOIyIJNnDgRGzduzHZM1lk4fPgwoqOjUbJkSZGiI9KPVCrF6dOnceDAARw9ehQvX74UfA1RThxw04x/O5Zl1apVuHHjBp4/f66UlLlgwQJUr14d/fv3FztMIoO4ffs2AgICEBgYiJs3byIuLk7skAQVKlSICZlElOfCw8ORkJCgNElRxt7e3mQT2by9vTFhwgQsX75ccIXj33//HdOnT4ejo6OIUZIx9O3bF7du3cLixYsFJ9yOHDkS8fHxmDp1qsiRUn5SrVo1vHjxQvDh7fXr1/Wq09XVFVWrVlWb0Ll27dpcJ2SGhYXh0KFDasdGPD09c9UGmZ7o6GhkZGSoTLapVauWQdrZt2+f4HHFsYj27dujT58+Wtc5efJknD59GseOHVM5GejQoUNMyLQQ79+/V1tuLs+xSpYsieDgYKXjUqlU43skMrbt27erLa9UqZKRIiFjyszMlE9eV2XMmDFGjEh7GzduRPXq1REXF6c0+T4xMREzZ87Eli1bRI6S8sKIESPw9ddfo2DBgmKHQqSz69evo0OHDoiPj8923QL4XJ7Es3XrVkRFRalNvFFMxqxduzZGjx6N3r17c+dBEp2VlRU2bNiAFi1aIDU1VWkezLFjxzB69GisW7eOc8RIb+fPn8+Tei9cuCDK539WVhbS09Px/v17vHr1CmFhYbh9+zauXr2K1NTUbH9DMop/P0WKFDHYODKZrtatW6u957p+/ToePnyo9lwBsvdxraysUKpUKbi7u6NYsWJwcnKCvb09JBIJUlNTkZycjNjYWLx69QpRUVHIysrKVq9Q31nxul+tWjU0aNAgW/vp6elITU1FQkICXr58iYiICCQnJwvWq2pxScV2goKC8N133+Gnn35S/T+P8j0mZBIRWbA6deqgWbNmuHTpktKDifT0dMyfPx+rV68WOUoi7SQnJ2PFihVYtWoVIiMjAWheqc0UcCDdtPD3oZop/d2QYRQpUgRHjhxBs2bNkJiYqLTrytChQ5GWloZhw4aJHCmRft68eYN169Zh69atePz4sfy4qV7reZ0lImNRldQjGxNo3Lgx3NzcDNpmZmamweqaNWsW1q1bp/QwHQASEhKwbds2jB492mDtken65Zdf8Pz5c/mu78D/zuOsrCxMmzYNFy9exKpVq+Du7i5ytJQfNGzYUGlHIdl16sGDB0hKSkKBAgV0rrd9+/byB9lCde/duxfffPMN6tSpo3fsI0aM0LjKdb169fSun0zT8+fP1ZZ7eXkZpB3ZblXqzJ49W+d658+fj+PHjwNAtnNX9vXNmzfx4cMHTs63AImJiWrLXVxcjBRJ7gjFKTtfNb1HImN68OABVq9erfbaXbVqVSNGRMby8OHDbPfaQPYxSzc3N/To0UOs8NQqVqwYvv/+e0yePFlwAafdu3dj4cKF3CXTApUqVUrsEIj08vTpU3To0EGeSK5pN8Kc+EyJ8sr69etVlimep66urpg/fz7Gjx/PXaHIpNStWxebNm1Cv379AGRPzpFKpdi4cSPevn2LzZs3m814ApmWVq1a5epzWPGzXva1VCpFq1atchuaQQndE+Ysl0gk6Nq1K/sl+cD48eMFjyclJWHmzJkICQkRfIYF/O9ccnd3R4cOHdCsWTM0bNgQnp6esLOz06r99PR0hIaG4vr167h06RKOHTuGV69eydsRej4QEhKC1q1bY+HChSqfzUmlUjx79gw3btxAYGAgjhw5gtevXwvGn5OsnV9++UX+voiEsKdMRGThlixZIh8YyZmI8eeff+bZii5EhrRp0yZUqlQJs2bNQkREBKRSabYt4nP+y0n2emP/IyISW40aNXDkyJFsA82y6+fHjx8xcuRITJgwIdtqUESm7t27d/jmm29Qvnx5fPfddwgNDc32+auqfyDmPyIiY3rx4oXa8oYNGxq8TUMmZBYvXhzdu3dXeU+1detWg7VFpk0ikWD37t0YN26c4MqnUqkUBw8ehKenJ3744Qe8fftW5IjJ0uWcMKF4ncrKysKZM2f0qrdz585KxxTrzszMxKBBg/TeXW3WrFk4e/as2mRMQPn9kfkLDw9XW16zZs1ct/H8+XOEhoYCyH7eKp5vtWvXRuPGjXWu29vbGz4+PoKTmIBPf3dXr17VN3QyIZqubw4ODkaKJHfUxfnhwwcjRkKk2pEjR9CuXTukpKQAUD3prFGjRsYMi4zk6dOngsdl91jNmjWDra2tQdv8+PGjweoaM2YMihcvDgBKfduPHz9i7dq1BmuLiCg3MjIy0L17d3kyJgCNc0jUPVcSa74L571YnsjISFy7dk1wjEjxXC1VqhT+/fdfTJw4kcmYZJJ69+6N3377Tf59zucGhw4dQp06dXDs2DHxgiSzZ+jPTLE/z3P+03bBiFGjRhn0/yuZj9DQUHh7e2PVqlWC545UKoWNjQ369u2Ls2fPIjo6Ghs2bMDw4cNRs2ZNrZMxAcDOzg41a9bE8OHDsWHDBkRHR+Ps2bPo06cPbGxs5O0C2RfvXbVqFby9veXPKHKSSCSoWLEi+vbtiz///BMvXrzA33//jUaNGmV79pyTYllWVhYmT56s6/8+yke4QyYRkYVr2LAhRo4ciXXr1mW7+ZR1FHr37o3r16+jTJkyYodKpCQuLg5DhgzBsWPHVHaAOQBMRKReixYtEBQUhHbt2iE2NlZpMHrNmjU4evQo5s2bh0GDBvGhCpm0gwcPYsKECYiJidFqcMwUMCGTiIxNU0JmgwYNDN6mISdYAp8epu/cuTPbMVnf5fr163j//j1XNs4nJBIJVq5ciRo1amD69OlITk5WWnAsMTER8+bNwy+//ILOnTujZ8+e8PX1NfhOsESyCeofP34UnLh27NgxdOrUSYQXpQIAAQAASURBVOd6fXx8ULZsWURERGSrV/G+7cGDB2jTpg0OHjyo9a4/Hz9+xNSpU7Fq1SrBPqnisTJlyqBWrVo6x06mLSYmRm150aJFc92GpkRkiUSCwYMH611/ly5dcO7cOZXl//33H/z9/fWun0yDpr5kRkaGThN4xJKRkaGyjAmZpi0iIiJP6o2MjBRljCorKwvp6el4//49Xr16hbCwMNy+fRsBAQGIiorK1seQUewXFCxYkDtnWygxxgsMuYCTnZ0dBgwYgGXLlgnukrl3717MnTvXYO0REelrwYIFuHfvnla77sjkfE2BAgVQqFAhuLi4wNnZGY6OjnkXMOUbZ8+eFTyueK6WKFECZ86cQcWKFY0YGZHuJk2ahOLFi2PIkCFIT09XmgcTHh6Ozp07o2XLlpg2bRo6dOggdshkZvSZ56Fp8QVTIxSv4t9S69at82ShXzJ9Z86cQc+ePREfHw8g+3kBfDp3+vTpg59++gkVKlQwePsSiQQ+Pj7w8fFBWFgYZs2ahb1792ZLBpX998mTJ2jcuDH+/vtvtGzZUm291tbW6Nq1K7p27Yp9+/bhyy+/xKtXrwSf+Sm+39u3b2PXrl3y3ZmJFDEhk4goH1i6dCmuXbuGu3fvKiVlvn79Gq1bt8apU6c4mEIm5dmzZ2jfvj2ePHmSrXMLmFaSBZkXUxzcIDIGb29v3Lp1C/369cPFixeVJrFHRERg+PDhmD17NgYPHoxu3brlyeQLotyYMWMGFi9eLJiIyb4BEdH/aNphqHLlygZv09ATzH18fJT6K7LvMzMzERgYiO7duxu0TTIN58+fFzxes2ZNLF++HNOnT0dCQgIAKD34S0lJwb59+7Bv3z5IJBJUqVIFNWvWRJUqVeDh4YHixYujYMGCsLe3F2UREh8fH6O3SYbl6OiIhg0b4tKlS4ITwI8cOYJVq1bpXK9EIsGIESPw/fffCy5EJqv/xo0bqF69OmbNmoURI0agSJEigvWlpaXh0KFDmD17Np4+fSqYdJGz/qFDh+ocN5m+5ORkteWurq65buPixYsaX/PFF1/oXf/nn3+Or7/+WmX5o0eP9K6bTIemyeVJSUlmkZCp7m/OxobTMkxZuXLlcvXsQPEzVnFhhXLlyuU2NINSt7iZrFwikaBjx448Zy3Uy5cv1ZbnxTMBdcnq+ujWrRuWLVsm/17xvvDRo0eIjIxE6dKlDdomEZEu3r59i0WLFmmdjCkrr1OnDr744gvUqVMHXl5eKFu2rHECpnzlypUrKstkn6lLlizh/EEyG3369EHVqlUxePBgBAcHCyYMnTt3DufOnYO7uzu++OIL+Pn5oXnz5ihWrJjI0ZOpM/QcFHOY06I4XuDg4ICVK1eKGA2J5caNG+jcuXO2RXKB/z0LK1q0KDZu3KjXAqX6KF++PHbv3o0BAwZgxIgRePv2bbZ5BACQkJCAjh074syZM1qPbfTq1Qs+Pj7o3r07rl69qvI5GvDp7/e3335jQiYJ4igqEVE+4OTkhMOHD6Nhw4Z4/fq1UlJmWFgYGjVqhI0bN6JLly5ih0uEFy9eoHXr1oiMjASgPBhNRESqbd26VWXZ0KFD8fr1a4SGhgoORr948QILFy7EwoUL4eLigtq1a6NatWooVaoU3Nzc4ODgAGtra2O9FSW52VWDzJdUKkX//v2xd+9eLtJARKSF1NRUteX6Jl+o2xUuKSlJrzpVKVSoEMqVK4fw8HDB8tu3bzMh00K1atVK42T4nBPeZeNbimVSqRQPHz5ESEhI3gWrA4lEYvCdZEkcvr6+uHTpkvx7xf5pdHQ0AgMD4efnp3O9EydOxJIlS/D+/Xul66xiQuW7d+8wa9Ys/N///R/q1q0LLy8vFC9eHFZWVoiNjUVERAQuXLiAlJSUbEkXOa/bOSdWjBkzRueYyfRpSsg0xG7TOROUgeznXPXq1VGmTBm96/f09ISNjQ0yMzMFz+Vnz57pXTeZDicnJ7XlL1++ROHChY0Ujf7UJTppeo8kvrwYYzK1cSttx9SGDRtmjHBIBJru3UuVKqVXvTkXVFKUmJioV52qNGrUCPb29tl2QVIUGBjIxUaISFQrVqxAUlKSygndimNY1tbWGDt2LMaPH49q1aoZO1TKh0JDQ5WOKc4ZaNKkCRMNyGT98MMPKss6deqEqKgoJCQkKM2LlV2LY2JisHr1aqxevRoAULRoUZNZzPH77783eptEOSn2UaysrLBx40ZUqVJF5KjI2MLDw9GpUyelZEzZtdTT0xPHjx9H+fLljR5b586dcfXqVXTo0AGPHz+WxyW73icnJ6Njx464du2a1vG5u7sjKCgIbdu2lW9woer53L///ot///0X9evXz5P3R+aLCZlERPlE6dKlceHCBXz++ecICwtTWrkiLi4O3bt3R48ePbBw4cI82UacSBsZGRno3LkzIiMjtU7E5K6HpEmZMmV4nlC+MXToUK3Od8XJ6kKT2N+9e4cLFy7gwoULeResjpiQmT+NGzcOe/bsAaB5kQZ1K/yrosvfiyaq6uJnEBEZk6aEzEKFCulVr729vWBCmVQqRVxcnF51qlOxYkWl8QsZ7oZl+XSZvK6YdJbz4aCpTYIn89ejRw/Mnz9fZfmWLVv0SsgsVKgQvvnmG8yePVvwupdzMZ3MzEzcuHED//77r+BrAc19Z1mdkydPRokSJXSOmUxfXu+QGRcXh0ePHqmdaNyuXbtctWFjY4OKFSsKThqVSqV49epVruon06CpfxoWFobq1asbJ5hcePbsmcr7/4IFCxo5GtKVvmM3uR1zMjZV12tZv8Db2xtt2rQRITIyhpSUFLXl+vYN7OzskJ6eLlj24cMHvepU11bFihXx8OFDwfJ79+4ZtD0iIl1t27ZN47MiqVSKBg0aYN26dahdu7Yxw6N8Ljw8XG0fdfjw4UaMhkg3c+fO1XkxR0D1wjSxsbF48+aNgaPUDxMySSw5/6akUimcnZ2xZcsWdOvWTZygSFTDhg1DbGys4PW2XLlyCAoKQsmSJUWI7JMKFSogMDAQzZs3R0REhNJmFG/evMGwYcNw9uxZret0cHDAkSNHUKdOnWx1Ctm3bx8TMkkJEzKJiPKRSpUq4fLly0pbbCuuCHTgwAH8/fff6N69O4YPHw5/f3/Y2PDjgozn+++/x3///adzwgUnWZI6qnbWIbJkhprEbipMcQIT5b0NGzZg3bp1OvULdD1vtZk4p239pvQ3Q0T5l6bdd/TdDcvBwUFpNw3ZWMLr16/1qlMdVbsgSaVS+aqXZLnU9f3UJZcp/ryp9B/ZP7AstWrVQtWqVZWS0GRf7927F4sWLYK7u7vOdU+fPh179uzBvXv3BB/4Ci2mo263DdlrVJVLJBJUrFgR3333nc6xknnQlHSRW1evXtX4Gl9f31y3U6pUKfnfnIzsbyQ2NjbX9ZP4ypYtq7b81q1b6Nixo5Gi0U9UVBTevn2rchV1MScqkfYM3W8zh36g4rXVysoKK1euFDEaymtCiywp0ncBJ6GETNn1MC8muZcrVw4PHjwQvOcTWsSBiMhY7ty5I1+kI2c/QPGYr68vjhw5AkdHRzHCpHws58KKOe+zTf2+iwjQfy6A0DMDU7hnM5XnGKRMn9+NuS3aBPwvZhsbG/Tr1w8LFizgOFY+tXbtWpw7d07wWmlnZ4cDBw6YxLnx2Wef4cCBA2jatCkyMjKy5UFIpVJcuHABq1evxrhx47Su09XVFVu2bEGrVq0E/1ZldR85cgS//PKLId8OWQBm2BAR5TPu7u64ePEifvnlF8ybNw8ZGRnyMlmnITMzEwcOHMCBAwfg6uqKFi1aoHnz5qhZsyaqVKkCDw8PDgxSnggPD8eyZcu0TrqQlRcvXhytW7dGvXr1UKVKFZQrVw5FixaFq6sr7O3tYW1tbZw3QERkQjiJncxdWFgYpkyZolO/wMbGBnXr1kW9evVQvnx5uLi4wM7ODsOHDxecMC+RSNCsWTOMGDECwKcJy8nJyYiJiUFUVBSCg4MREhKCzMxM+c/ljEexrrp162LixIkq35M57OhBRObP2dlZbXlGRgbs7Ox0rtfNzQ1v374VLHvz5g0yMzMNeu9lb2+vdCwvJ3SSaclt/89U+o+m0p8mw+rbt698Rfacv+OMjAysWLFC7S6aqtjY2GD79u1o1qwZkpKSVCZlAprv17TpOzs5OWHXrl0c57VgWVlZasuTkpJytUvm5cuXlY7lTOxp0aKF3vXLFChQQGWZpl1AyTxoSsi8ePGikSLRn6YYy5UrZ5xAiHLh119/RaNGjcQOg/KQpt161X3mquPk5KRyJ8y8WDxB1UJTUqkUT58+NXh7RETaunDhguDxnM+R/vnnHzg4OBg5OiL199CVKlXSa4ExImPTZ0xUqMwU5sOYynMMEpYfFm2ytbVF/fr10a1bN/Tr1w+lSpUSOyQSSUZGBubPn5/tGZJiH3b69Onw9vYWOcr/qVu3Lr755hv89NNPggs5zp8/HyNGjNBpToSPjw969OiBAwcOZHs+p7gD56NHjxAdHW0SialkOpiQSURkwSpUqKC23NbWFunp6UqT04H/3QAkJCTgn3/+wT///JPtZ52cnFCwYEHY29vDysoqD6JXTSKR8GGKhfrll1+UzklFORMu+vXrh9GjR6NZs2bGDpWIyORxEjuZu5kzZ6qciA5k7xeUKlUK06ZNw4ABA1CkSBGl1w4fPlxlO5UqVcKQIUNUlicnJyMoKAiHDh3C3r17kZiYmG2HecWByFu3bmHHjh3YvHkzB+CISDSadsBMTU3VKyGzaNGiCA0NVXoQA3xK+AgPD0fFihV1D1iFxMRElWWqJnoSERlDv379MHfuXME+qlQqxe+//46pU6fCzc1N57q9vLywe/dudO/eHR8/flTZF9bnfi3nuNr27dtRr149nesh8yG0uIGi3CZkqkpAk52fNWrU0Jj4oQ11daSlpeW6fhKfp6en4HHZNfD8+fN49+5drs7XvHbw4EG15YbsJxPlVs7dtK2trbFkyRJ8+eWXIkZFxqDNeIE+CUJFixbF69ev5d8rjhfEx8cjOTkZTk5OOteritBiULLPjISEBIO1Q0Skqxs3bigdyzlBfNWqVUzGJJMi+9xmMiaZC0PNYxF7PgznwZguHx8fvX8/st0FhRb39vHxMWSYWrO2toadnR2cnJxQtGhRFC9eHBUqVEDVqlVRp04d9ksIALBt2zZER0dnO2dl3NzcMHPmTBGjEzZz5kysXr0a8fHxSnG/evUKW7duxciRI3Wq89tvv8WBAwfUvubmzZucD0bZMCGTiMiChYeHq5y0AygnX8q+zrkCkNDPJyUlISkpycARa4c3pJYpNTUVu3btUvn7VTxfW7RogfXr16Ny5crGDJGIiIiM5NatW9i3b59WyZhTpkzBzz//nGcDxU5OTujUqRM6deqE5cuXY/Xq1Vi0aBFev34tuDpcYGAg6tWrh2PHjpnUCnFElH9ommAZFxen8TVCSpQoobb80aNHBp1orm4SpVjjEUREAFC5cmUsX74c79+/V/maiIgIvRIyAaBDhw44dOgQevXqheTkZI07xmtDaGfMzp07610fmQdNu5/GxsbqPXEgLS0N169fVzuWa4jdMQEYfUFIMr4iRYqgYsWKePbsmdLiRwCQnp6Offv26TyBxljevXuHI0eOqH121aBBAyNGRPrS5/mjus9nU36eKYu7RYsWWL58OerUqSNuQGQUmsYCUlJS9E7IzDlhU1FYWBhq1Kihc72qqBsv4AJOpMqzZ89w9+5dBAcHIzw8HNHR0Xj58iXevXuHlJQUpKamIisrCw4ODnBwcECBAgVQokQJeHh4oFSpUqhRowZq1aqFGjVq6LXQGeUPqhaWl10j+/Tpw34hicrZ2RlxcXGCZcWLFzdyNEREpuns2bN6/6y6ccwzZ87oXS9RXlu/fr3SMVkfdtCgQQZdZMlQChQogEGDBmH58uVK4xFSqRTr16/XeTzZ29sbdevWxa1bt1TOV7t16xafr1E2TMgkIsoHhB5+yB5oC1E8njM5U2xir05EeScoKAjv378X7MgqHuvVqxe2b98OW1tbMcIkIiIiI1ixYoXgccWJ5NbW1ti4cSMGDRpktLicnJzw9ddfY9SoUZg+fTrWrVuXbbdM2X9fvXqFVq1a4eDBg2jdurXR4iMiAgAPDw+15a9evUK5cuV0rldTsuXt27fRoUMHnetVRTYhn/In/u7J1E2aNClP62/Xrh2uXr2KgQMH4s6dO1otoJeT0ANoLy8v7Nq1C9WrVzd4zGR6ihUrprb88ePHqF27tl51X7p0CampqWoXhDRUQmZKSorKMkPswEmmoVGjRnj69KnStUt2ji1evNhkEzJXrFiBlJQUtX8PTZs2NXJURKr7CyVLlkTXrl0xePBgNGrUyMhRkZg0JWTGxsaicOHCOtf72WefqS2/f/++QRMy4+PjVZYlJycbrB0yb2/evMHBgwcREBCAoKAgvH37Vuk1QtdJxaTe0NBQpXI7Ozs0adIE/v7+6NSpE2rVqmXYwMmsRUREqB3T6t27txGjIVLm6uqqMiFT06JOREREZJlev36tdvHFnj17Gjki7fXu3RvLly/Pdkw2Rvvvv//i9evXOi860a1bN9y6dUtl+ePHj/WKlSwXEzKJiPIpbVdW17S7JpGhqFoFSDHBoUOHDti9ezfPPyIiLfBaSebq/fv32LNnj8pzWNYv+P33342ajKnIxcUFa9asQZs2bTBs2DAkJSVl67MAQGJiIrp27YpLly7By8tLlDiJKH+qUqWK2vLIyEi9Jt1WqlRJbfmlS5d0rlOV6OhoREZGqpzUzskhlo8LchEBNWrUwI0bN7B27Vr8/PPPePnyJQDdFtCT/S15eHhgxowZmDBhAqytrfMsZjIt7u7uastzM3Hg+PHjGl/j4+Ojd/2K1O2MzYRMy9G2bVvs3Lkz2zHFe+zHjx9jw4YNGDFihBjhqRQbG4ulS5eqTCQFPl3PixYtKkZ4pKUyZcroPZb6/PnzbL9vxfGhMmXKGDJMrVlbW8POzg5OTk4oWrQoihcvjgoVKqBq1apo2LAhKlSoIEpcJL6yZcuqLX/16hU8PT11rlfTeMGtW7cMmoT04MEDlX+zNjacBpffHT58GBs2bMCJEyfw8eNHAPrvZiz0c2lpaTh37hzOnTuH7777Dl5eXhg6dChGjBgBZ2fn3L8BMmvv37/P9r3i+WVvb482bdoYOySibMqXL69yIUR1995EpoTzYIiIDOvEiRPIyspSmvMEfHom37hxYxGjU69hw4ZwcnLKtlieYm7EiRMnMHjwYJ3q9Pf3x/fff6+yPCoqKlcxk+XhSBQRUT6QF5PYxJgYxxtqyxYcHKx0TPF3XqBAAaxevZrnARGRFjiBnczZyZMnBXeWUBw8a9u2LcaNGydilJ/06NEDpUqVQrt27bLt9C2bJP/hwwd06dIF169f17g7DRGRoVSqVAk2NjbIzMwUTGh8+PChXvWqWvFf1saFCxeQnp4OOzs7vepXFBAQoLa8SJEiuW6DTJOPjw/v+4kU2NjYYMKECRgzZgyOHDmC3bt34+zZs4iNjdX4s4UKFYKvry969+6N7t27w9bW1ggRkynRlHBz/vx5zJo1S6+6Dx48KJiApth2iRIl9Ko7J1kysiJZ/4aT3i1Hly5dVPZhZd9Pnz4dnTp10phsbExffvkl4uPjVS4kIpFI0KtXLxEiI12Eh4fr/bNWVlYqy8LCwvSulygvaFrA6dmzZ3rtcF25cmW15efOndO5TlUePnyIhIQEldddJycng7VF5mX37t346aef8ODBAwBQ6kvoQ92ikTJ3797F119/jR9++AGTJk3CV199BVdXV73aI/MnlNAmO18qV67MaxSJrmbNmggMDBQsi46ONnI0RLrjPBgiIsO7c+eO0jHFPqy6sS+xWVtbw9PTE//995/g/dt///2nc0Jm7dq1YWVlJZ/7lXMRNiZkUk5MyCQiIiKT8PjxY8FOsaxjO3HiRJQqVUqEyIiIzMuQIUPEDoEoV44ePap0LOciDevXrzdmSGo1atQIhw8fhr+/Pz5+/Ki06lpERAQmTpyIPXv2iBwpEeUXtra2KF++PJ48eSJYLpuYpitvb2/Y2dkhIyNDfq1TvN4lJyfj8OHD6Nmzp96xy6xevVrwuNg7zVDeO3v2rNghEJkkGxsbdO/eHd27dwfwabJ8aGgoIiIikJiYiJSUFNja2sLZ2RmlS5eGp6enxgn3ZPlUnQOyz/Hz588jLS0N9vb2OtV78+ZNPH36VDARQvZZ3axZM73jzik6Olpw3FgikcDFxcVg7ZC4ChUqBD8/P5w8eTLb71uxvxkfH48ePXrgzJkzJpFkvmLFCuzZs0dlUpBMnz59jBgVEZFqZcuWhaOjI1JTUwWvXSEhIXrVW7duXcHjsjZu3ryJN2/eGGS34CNHjggel70XQy0IQebjwYMHGDNmDC5fvqw2CdNQCRyyxRgV65VKpUhISMCPP/6ItWvXYunSpejfv79B2iPzYmdnh9TUVKXjEomEi3aSSfD29lY6Jvu8fvr0qQgREWlvzpw5YodApDUuPErm5P79+4LHJRIJypYta+RodFemTBn8999/gmWq3ps6jo6OKFOmDJ4/fy5Y/v79e53rJMvGhEwiIiIyCXFxcWrLv/jiCyNFQkRk3jZt2iR2CES5cvHiRbWLNPTr1w+fffaZCJGp1qJFCyxevBiTJ0+Wx664Wtr+/ftx6tQptG3bVuRIiSi/qF69utKiN7Jr0tWrV/Wq087ODvXq1cOVK1dUPkhctmxZrhMyz549i2vXrqmd2F69evVctUFEZO4qVKigcfdDouLFi8PDwwMxMTGCiymkpqZi//79GDBggE71btiwQeNrWrZsqVfMOb179w6xsbEq+wVcpMGyTJo0CSdPnlQ6rnh/feXKFfTt2xd79uyBjY14Ux12796NqVOnqkwWlsXs5+cHT09PESIkIhLm6emJO3fuCF6/goOD9aqzatWqcHFxQWJiomCfIzMzE7t378bEiRNzFbtUKsXq1atVjkmYy2RRMpylS5di1qxZ+PjxY7ZdS2TyYhetnHXmbO/169cYNGgQdu/ejW3btnG3zHzGxcVFMCET+HR/RiS29u3bK+34JLuOvX37Fs+ePeN4E5ksJmSSueBOrmRunj17pvI+29nZ2cjR6K5gwYJKx2T9nGfPnulVZ6lSpRAeHi74/yUlJUWvOslyme4eskREZDCyVfrM/R9ZtqSkpGzfK/7Oixcvjvr16xs7JCIiIjKy9+/fywfEVA1UjxkzxpghaW3SpElo0qRJtoeXMlKpFDNnzhQpMiLKj3LuSqV4TY2IiEBMTIxe9Xbq1EnpmOLkjatXr2Lv3r161Q0AiYmJGD58uMYxAN4fEhERaadZs2Yq762kUikWLFigU32xsbHYunWrxs/qzz//XKd6VdG0gnWlSpUM0g6Zhg4dOqBq1aoAhHe1kvU5Dx48iM6dOyMhIUGEKD/tjDlo0CBkZmbKY1Plm2++MVZYRERa8fLyUjomu75eu3ZNrzolEglatGgheD2U1b1kyRJ8/PhRr/pl1q5dK9+hQtW1t1atWrlqg8xDamoq+vTpg2+++QYZGRnyfoJiQrCxJsIrtqUYw9GjR1G/fn3cu3fPKHGQaShcuLDKMs55IlNQrFgx+bNMIadOnTJyRERElmXTpk2C/zZu3Ch2aEQqqdvxMSsry4iR6EfdvZ++u1mq291e1QIslH8xIZOIyMIpDjhbwj+yXHZ2dkrHZA9PypUrZ/yAiIiIyOiEVmFX3AmlWrVqqFu3rkHbNGQfc+HChUp1yx6y37lzB+fPnzdYW0RE6rRq1UptudDOQ9ro2rWryjLZ9Xrs2LEakyeEpKSkoGfPnggPDweg/vrMHYeJiIi0I5QYqXif8vDhQ8ybN0/r+mbNmoXk5GR5PTKK923e3t4oWbJkbsKW+++//9SWMyHT8sybN09tErHsXDt16hTq16+PCxcuGC22t2/fol+/fpg8eTIyMzMFd25V3GWmefPmaNOmjdHiI3FxcVkyFz4+Ptm+V7yOxcXFISQkRK96O3furHQs5+JQP/zwg151A8Djx48xbdo0jX9njRo10rsNMg/Jycno0KED9u/fL5iIqY28WDw8Z2KmVCrF06dP0bJlS9y8eTNX75nMh6enp8rz8MOHD0aOhkjY6NGjVZbt3LnTiJEQEVmeIUOGqPxHZKrUJS0mJiYaMRL9qItR3/gdHBxUlllbW+tVJ1kuG7EDICKivBMWFiZ2CERac3V1Vbmdu7u7u5GjIdJs69atKssGDx5sxEh0d/nyZTx58kRluanHT0SWKyIiQmWZRCJB48aNDd5mbldmV9SiRQs0bNgQ169fF5yYuW7dOqVJT0REeaFu3bpwcXFBYmKi4PXo+PHjej38q169uuB1TnECXEJCAnx8fLBt2zZ06NBBq3pDQ0MxZMgQlddPxYnt9erVw2effaZz7ERERPlRly5dMHbsWGRlZSl9xsq+nz9/Pjw8PNROygSApUuXYuPGjYKf1Yp19u3b12DxX7x4UW05EzItT69evbBq1SqcO3dO8FxTTMp89uwZWrVqhYEDB+K7777Ls/MhNTUVa9euxfz58xEfH58tqVmR4jFra2usXLkyT+Ih08MFZcmcaFrA6fjx4/LdinXRpUsXTJw4UTBhXfb9zz//jCpVqmDAgAE61R0aGoo2bdogOTlZsG4ZW1tb+Pv76xw7mY/09HR06NBBvvCh7Pev6Tos9LmtS/KmUB3qFpBQfH18fDz8/f1x5swZ1KlTR6s2yXzVrFkThw8fVjoulUrVPv8iMqZ+/fph9uzZiIqKyjbuLpVKcenSJfz777+oX7++2GESERGRkaiaMyWVSs0iB0G22LOQzMxMveq0sVGdYufk5KRXnWS5mJBJRGTBypYtK3YIRFpzdXVFTEyMYJmLi4uRoyHSbOjQoSpXRDX1hMY///zTrBNKichyvXjxQm15gwYNDN6mvgNwqgwcOBDXr1/Pdkz2IPP48eMqJ24SERmSlZUVWrRogaNHj2a75siuR8eOHUNKSgocHR11rnvs2LFK1zkg+4Sz+Ph4dO7cGZ9//jlGjx6NNm3aoECBAtlen5WVhYsXL2L37t3YsGEDPn78qNU1ctSoUTrHTERElF8VK1YMnTp1wqFDh7J9xip+bmdlZWHcuHE4ceIEpk6dihYtWmSr4+HDh5g/fz727Nkj2IZivTY2Nhg0aJDB4r9w4YLavkG1atUM1haZjlWrVqFBgwZISUlRm5Qp+3r79u3YuXMnunTpgsGDB6NDhw6wtbXNdRzBwcHYvn07Nm/ejDdv3iglWAglYshi++abb1CzZs1cx0Cmb86cOWKHQKSTSpUqoWTJknj58qXgNfbo0aOYOnWqzvWWKFECHTt21NjnGDx4MO7cuYO5c+dqNYlx06ZNmDFjBt68eaNyUQjZtbdt27ZwdnbWOXYyH2PHjsX58+e1SsRUl0BZuHBheHp6wsPDA+7u7ihYsCDs7e1hbW2NtLQ0pKamIjY2Fq9evUJYWBieP3+OrKysbHVrikFx8bJ3796hW7duuH79OooXL673+yfTJ7RLr+KOqXw+RKbAxsYGc+fOxYgRIwSvldOmTcPZs2fFCY6IiIiMztnZGQkJCdmOKfZh09PTYWdnJ05wGqSnp+Px48cq+9g55ydoKzU1VWUZEzIpJ4mUy/URERGRCejcubN8srDiCmwSiQTdu3fH/v37xQ6RKBsrKyuVu/cYOrnH0IYNG4YtW7YIlplD/ERkuWbOnIlff/012/VVsU9w+fJlwQfamuS8ZivW2aVLF/z9998Gew+PHz9GlSpVVLZ36dKlPNnpk4gop99//x1TpkxReT3avn07+vXrp3O9mZmZqFq1Kp49ewZAeeKZ4oQ02ddWVlYoW7YsihcvDisrK8TGxiI6OhrJycnZ6lDVv5a9pmTJknjy5AkcHBx0jpuIiCi/OnPmDPz8/FQmMSj2D4BPC+eVLl0aBQoUwIsXLxAVFQUg+86Eqn6+T58+2Llzp0HiDg4ORu3atZX6MrJYypcvj6dPnxqkLTI9mzZtkk8QVrcjK6C8E5WTkxNatGiBFi1awMvLC9WrV0fJkiVV9iGlUilev36NJ0+eIDg4GDdu3EBgYCAiIyMF69f0d+Tj44PAwEBYWVnl4v8AEVHeGTRoEHbs2KHUB5BKpbC2tkZUVBTc3d11rjcoKAj+/v5a9TkKFy6MgQMH4vPPP4eXl1e28YKIiAicPHkSe/fuxcOHD5ViVFXnkSNH0KFDBz3+j5A5WLduHcaOHasxEVIoIdjT0xNdunRB8+bN0bhxY52TItPS0nDnzh1cvnwZJ0+exJkzZ5Ceni7YllA8srKWLVvizJkzOrVN5iU1NRVFihSRT+DOOe/l33//hbe3t8hREn3i5+eHM2fOCM7RWr58OSZOnCh2iERERGQEFSpUwPPnzwEoP7OXSCQICAhA69atxQxRJaFnH4r3jGXLltVrl8+OHTvi+PHjguMmlSpVQmhoqOHeBJk9PgUgIiIik6Bu4PnVq1dGjIRIN7LVTc1xNUvF2M0xfiKyPCkpKWrL3dzc9KrX2toagPKq2ADw4cMHvepUpXLlyvI4hdq7dOmSQdsjIlKlZ8+e8kngQtejP//8U696ra2tsWDBArU7AMjalEqlkEqlyMzMxLNnz3Dt2jVcuXIFjx8/RlJSkrxc1h/VtKvA/PnzmYxJRESko9atW8PX11ftbiyKn9sJCQkIDg7GtWvXEBkZme3zWtXCCbKvv/32W4PFfeTIEcHjsliaNGlisLbI9AwbNgyjR49We94K9SWlUimSkpJw8uRJzJ49G127dkXlypVRoEABFC5cGGXKlEHlypVRtWpVlCtXDu7u7rCzs0PJkiXh4+ODCRMmYPPmzYiIiFCqX9ZmTorxlS1bFnv27GEyJhGZtPbt22f7XvHalpWVhd27d+tVr6+vL1q2bKlVnyMuLg5//PEHOnXqhLJly8LR0RH29vYoVaoUmjZtinnz5uHBgwdaJ2PWq1ePyZgWLDo6GtOnT9c6GVMqlcLJyQljx47Ff//9h5CQEPz666/o0qWLXjtU2tvbo2HDhpgyZQqOHz+O169f448//kC1atWUJv3mpHgOnz9/HmvWrNG5fTIfDg4O+Pzzz1Weo6rucYjEsHHjRhQpUgTA/65hss/WadOmMYGciIgon6hQoYLK/isAvccIjEFVbLL7sAoVKuhVb0xMjGCdwKcFLYkU8UkAERERmQShhEzZYN+TJ09EiIhIO7KJOebK3OMnIsuiKSGzUKFCetVrZ2cneFwqleL9+/d61alO5cqVVV5bHz16ZPD2iIiElCxZEs2bN892PVJMpjh37hyCg4P1qrtnz5744osvVE6yzDl5PeckeaGFQTTtntGmTRsMGzZMr3iJiIjyu+XLl8Pe3h6A8mRxoc9n2fGcn+NCZK8bMmQIatasabCY9+3bp7acCZmWb/Xq1ejVq5faxB4g+4IgOfudiv/evXuHqKgoPH36FKGhoYiIiEBsbCwyMzOVXiv096AuGVMqleKzzz5DYGCgXokeRETG1LVrVzg6OgJQXlxBKpVi9erVete9dOlS2NjYKNUNqE6kV/VPsQ5Ni0IsXbpU75jJ9H311VfycXxVn8eyc8rKygpffvklnj17hlWrVqFWrVoGj8fFxQUTJkzAvXv3sGPHDpQtW1ZpEQehGKVSKWbOnIm3b98aPCYyHV9++aXSMdnvf/v27SJERCSsbNmyOHDgAGxtbbMdl0gkSE9PR+fOnXH69GmRoiMiIiJjqVq1quBxWR92165dJnkP8/btW+zcuVPtuLGq96bJ8+fPBeuVSCQoXbq0XnWS5WJCJhEREZkEf3//bJOCFB+mvH79Wq+t44mIiMi8yHayVKVw4cJ61SvrYyiSDZ69fv1arzrVKVmypMoyJmQSkTH169dP/rXipDDZfxctWqR33X/++ScqV66crb6cck5czzkxLedES0U5dxratm2b3rESERHldzVq1MDChQvVLsqVM/lB20Q0AChRogR+/fVXg8UbHByMO3fuqE0EZUKm5ZNIJNixYwf69u2rMclBKIEnN/+E6hSKT/a68uXLIzAwEOXLl8+D/xNERIZVoEABdOzYUeXOfo8fP8aJEyf0qtvb2xvffvutxj6HrF1N/9Rdh2WfDZMnT0bz5s31ipdM34MHD7Bv3z61iY7Ap/OhWrVquHr1Kn777TcUK1bMKPH169cP9+7dw9ixY1X+TSmew4mJiUwgtnCtWrVCgwYNACjPe3n69Cn2798vVmhESnx8fLBt2zb5YgoyEokEycnJ6NixIxYtWsQFvomIiCxY48aNlY4pfvYnJSVh3rx5xgxJK3PnzkVSUhIA4YV7AOH3psmbN28QFxenst6yZcvqXCdZNiZkEhERkUlwdnZG+/btVXaOjx8/buSIiIiIyNicnZ3Vlqtb2UwdFxcXlWV5kZDp5OSkdEz24P3FixcGb4+ISJWePXvC2tpa5W4Tu3btQkhIiF51Fy5cGEePHoWHhwcA5WRLRep2uxCiOJnOw8MDJ06cMNpEOiIiIks1efJkjBkzRqfENm0S0ezs7LBz504UKVLEYLFu3rxZZZvAp3u8OnXqGKw9Ml02NjbYuXMn/u///k8wYViIrn1PVT+jjuLfQPPmzXH9+nV4enrq+O6IiMSjuICTItn17eeff9a77jlz5qBTp05qdzjWtDumNv0Q4NOCv7lZbIpMn+KiIkI7pcrOlc6dO+PatWuoV6+e0WN0cnLCqlWrsHnzZvlOc6p2U5FKpVi5ciUSExONHSYZkWLSreICeVKpFN988w1SUlLECo1ISa9evXD48GH57tkyEokEHz9+xMyZM9G4cWOcO3dOpAiJiIgoL7Vp00ZlmawPu2rVKgQFBRkxKvUCAwOxatUqjXPI1L03Vf777z+15eXKldO5TrJsTMgkIiIikzF48GDB41KpFFu3bjVyNERERGRs6hInASA1NVWveosWLZptsobi16mpqQZPyszIyFBZxokWRGRMRYoUwbVr13DhwgXBf2fPntV47VWnUqVKuHz5MqpWrar1BHl1cu5IVL16dVy4cIGT24mIiAxk9erVmDRpUq4+txU/qwsUKIC9e/eiZcuWBosxOTkZmzZtEoxLltjRtm1bWFnxMXd+Mn/+fBw5cgQlSpTQ+/zVNeFHiOKObdbW1pg9ezaCgoIMmpBMRGQMHTp0QKFChWBlZSX478qVKwgICNCrbolEgt27d8PX11fjQhD61A18uqa3atUKf/31F/sEFuzDhw84cOCA2uRGiUSCHj164K+//kLBggVFiPJ/Bg8ejN27d8Pa2loeo0zOXTK5S6Jla9asWbZdUxV//xEREZg0aZJYoREJ+vzzz3HhwgVUqlQp2/kqu9beuHEDvr6+aNKkCdavX58ni90SERGRONzd3dG0aVOlRZUUx2CzsrLQp08fvRd6NqSHDx+ib9++gn1txfvExo0bo0SJEjrXf/nyZbXl5cuX17lOsmw2ml9CREREZBzdunVD7dq1cffu3WydY9kA39mzZ9GqVSuxwyQiIqI8oikp6MOHDyhQoIDO9WraVe3p06coXry4zvWqEh8fr7Lsw4cPBmuHiEgbdevWzdP6y5Qpg3///RfTpk3D2rVrkZWVJTjZUpudLWSvs7KywtixY7F48WKllbmJiIgod5YvX46GDRti0qRJSEhI0CtJQiqVonz58jh48CC8vLwMGt/27dvlcanqP3Ts2NGgbZJ56NixI+7du4dp06Zh69atyMzMVFrQIy8ITURq1KgRVq5cmed9bSKivGJvb4+4uLg8q9/JyQnHjx/HqFGjsHXr1lxfr3P+7JAhQ7B69Wo4ODgYLmgyOQcPHkRKSopSv1BxHkH9+vWxc+dOeRKk2Lp164YlS5Zg8uTJavvYO3bswLBhw4wYGRnb4sWLceXKFdy5c0dp7sumTZtQuXJlzJgxQ+wwycSdP3/eqO0tWbIE06ZNQ2hoqNLurlKpFNevX8f169cxduxY1KhRA97e3qhWrRpKlSoFNzc3ODg4iHo99vHxEa1tIiIiczZq1CjBRETFRZbevn0LX19fHDlyBPXq1RMhSuDmzZvo0qUL3r59q/b5AQCMHj1arzY07QTK8WDKSSLNqycTRERERHr4559/0KVLF6VBaQCoVasWbt68aTIPVCh/s7KyynZ+Kp6zmZmZIken3rBhw7BlyxazjZ+ILNe2bdswZMgQldenW7duoXbt2jrXO27cOKxdu1ZlvStXrsTYsWMN9j7q1KmjtMAEAPkOGup20CQiMmf37t3D3LlzcfjwYXz8+BGAcsKlENm12crKCt26dcPs2bNRp06dvAyViIgo34uLi8PChQuxYcOGbIvKqNqZUqZkyZKYMWMGRo8eDXt7e4PGlJWVhapVq+Lp06eC5bKFG16+fKlx4R2ybA8fPsTs2bNx6NAh+YIgOek7DUJdXbVr18bcuXPRtWtXveomIsqP9u3bh6+//hpRUVG52ilTKpXis88+w9KlS9GrVy8DRkimqm/fvti7d6/SuD7w6XxwdHTE/fv3Ua5cORGjFNaxY0ccP35cZew2NjaIj4/XawFKMh8vXrxAgwYN8OrVKwDINv9FIpFg9uzZmDdvnshRkimTzUkxtpz3UqoWVRAjNlUkEon8mQQRERHpJi0tDZUqVUJ0dDQA9X0BJycnLFq0COPGjTNqjKtWrcL06dORnJws2DdRPPbZZ5/hyZMnOj+/iI+PR/HixZGVlSWvK2e9kZGRhng7ZEGYkElEREQmp3///ti9e7dSUqZEIsHUqVOxePFisUMkMuuETKG/MYCJQkQkvqtXr6Jp06Yqr6/Hjh3D559/rnO9S5YswTfffKOy3qFDh2LDhg0GeQ8pKSlwdXWVfxbkvM46OTlxl0wisnixsbHYv38/zpw5g6tXr+LFixcqJ8S7u7ujUaNG8Pf3R48ePeDh4WHkaMnSxMXF4fbt23jy5AmioqIQHR2NxMREpKSkICUlRf4QDfjUHwgMDBQxWiIi8aWnp+PkyZMICgrC7du38fTpU8THxyMtLQ2urq4oWrQoPDw80Lx5c/j6+qJ58+awtbXNk1i2b9+OwYMHq31Nw4YNcfXq1Txpn8xPZGQk1q1bh+3bt+P58+fy47mdGKzYd3V2dsYXX3yB0aNHo0mTJrmql4gov0pPT8e6deuwfv163L17N1uZNkn1VatWxfjx4zFixAg4OjrmaaxkOkqVKoWXL18CgOC4/rfffov58+eLGaJKoaGhqFGjRraJvED2+E+cOIE2bdqIGSYZQUhICNq0aZNtcrviedChQwesXbsWJUuWFDlSMkU556SITfEz21RikjGHeTpERESmbNOmTRgxYoTKvofivCeJRIJmzZrhl19+yfPx0suXL2PGjBm4fPmy0vyrnPHJyv/8808MHz5c57bWr1+P0aNHq5xb9sUXX2Dfvn25f1NkUZiQSURERCYnMTERtWvXlk+gyDkovXz5ckycOFHkKCm/M+eEzC5duuCff/4RTMh0dHREUlKSyBGSmNLT03Hv3j2dJrGr2rmCSFcJCQlwc3NTeX1dtmwZvvzyS53rPXbsGDp16qRyNeoyZcogPDzcIO/h7Nmz8PX1VdlWiRIl5A/eiYjyi9TUVERERMj7E7a2tnB2dkbp0qXh7Owsdnhk5t69e4cTJ07gxIkTCAoKQlRUlFY/Zy73b2T+wsLCcO3aNTx8+BCPHj1CdHQ0Xr16hbi4OKSlpSE1NVX085C7CJCpCAoK0rjCdNWqVdGoUSMjRUTmJDg4GEePHsWFCxdw69Yt+U5EuipQoABq166Nxo0bo3379mjZsiVsbGwMHC0RUf718OFD+QJOoaGhKscLPD095Qs41axZU+ywycgiIiJQrlw5lePsDg4OiIiIQNGiRcUMU62ePXvir7/+Uvm8Y86cOfj+++9FjpKMISIiAm3btkVoaKjgouTOzs6YOnUqpkyZAldXV7HDJRNiKjtk5mRKO2MCHOclIiIyBKlUihYtWuDKlSvy73PKmQwpkUjQtGlTjBs3Dp07dzbYc//ExEQcPnwYa9asweXLl+VtapOM2bhxY1y8eFGv/kqLFi1w6dIllfdwixYtwldffZXLd0eWhgmZREREZJKCg4Ph6+uLuLg4AMpJmbNmzcK8efNgbW0tcqSUX5lzQmbOm0fFm9UiRYogNjZW5AjJ2B49eoSDBw/ixIkTuHLliuAuqapuHc3hnCfzUqJECfl1KOfn/5gxY7Bq1Sqd64yNjYW7u7vSSm6KdV+/fh316tXLdfzDhw/H5s2bBT8jAKB+/fq4fv16rtshIiLK7y5evIg1a9bg77//RmpqKgDtV2Y3xP3bypUrcenSJZXlhQoV0qvfQuYvKysLp0+fxv79+3Hs2DHExMQovcbUHs3xvo6ILNHLly/x+PFjREVFISoqCvHx8fLFxjIzM+Ho6AhHR0c4OzujZMmSKF26NMqVK4fKlSub3ARjIiKi/CYgIABt27ZV+Sy2S5cu+Pvvv0WOUr0jR46ga9euKt9D3759sWPHDpGjJGP58OEDRo8ejd27dyslZQKfzg0HBwd069YN3bp1g5+fH9zc3ESOmsRmajtkmiJzmqdDRERk6sLDw1GnTh0kJiYC0D4pEwBsbW3RokULNG/eHA0bNkTVqlXli+yoI5VKER4ejpCQEFy/fh0XL17EhQsX5HMXhRboURWPi4sL/vvvP5QrV07n937v3j3UqlVL7byy4OBgVK9eXee6ybJxKUciIiIySV5eXjh16hT8/f2RkJCgNCi9YMECnD59Gtu3b0flypXFDpfIrISEhKi82TXllWTJsLKysrBz506sWbNGvroVoNuKl7l9+LNgwQIcOXJEZXmhQoVw7NixXLVB5qdq1ap4/fq10nVKKpXi5s2betVZrFgxlC9fHuHh4SofXG7YsCHXCZmxsbHyh+lCJBIJKlasmKs2iIiI8rtLly7hu+++w7lz5wBA6aGYsdSrVw+TJk0SbFM2fjFkyBDu4paPpKSkYOXKlVixYoV8hz91C9uYCk7qIyJL5eHhAQ8PD7HDIBPyww8/aP1abXcs06VOc8Nd24hITM+ePVNb3q5dOyNFoj9fX1/Y2NggMzNT8LmEpvdI5kld36Bq1arw9PRU2ikT+HRvnpKSgt27d2P37t0AgNKlS6NatWooVaoU3Nzc4ODgIOqC5ewbEBEREVm2cuXKYdeuXejatavK+xhVCZLp6ekICgpCUFCQ/LXW1tYoWrQoihYtigIFCsDOzg4SiQRpaWlITk5GbGws3rx5o7SogtCzX03Joba2tti1a5deyZgAsGzZMqV6FVWtWpXJmCSIO2QSERGRSbtz5w66deuG58+fCyYBOTo6YvDgwZgwYQJq1qwpVpiUD5nrDpmRkZEoW7asytibN28un9hMlmvnzp2YO3cunj59CkD/Sey5Pefv37+P2rVrCw6ayOo+deoU/Pz89KqfzNO0adOwdOlSwYfRdnZ2ePfuHezt7XWud/z48VizZo3S9U9Wt729PR48eIDy5cvrHXvv3r2xf/9+tSumLVy4EN98843ebRAREeVX7969w1dffYXNmzcDUH7gJ6PNIw9D3b916NABJ06cUNnG2LFjsXLlSr3rJ/Oxc+dOTJ8+HS9fvtTr/kqsR3XmMpZBRERkCLIxfW1o+7moS53mhn0DIhLT999/jx9//FHl88zLly+bxQJI1atXx6NHjwBA6XlHqVKlEBERIWZ4lAe06Ruo2tVHU5nY2DcwPu6QqRnHtvKP8+fPix1CnvHx8RE7BCKibLZt24Zhw4bJ+yDq+iLabuygy/NcbepUvLeSSCTYtGkTBg8erLJOdSIiIuDp6SnflVPI//3f/1n0wmykP+6QSURERCatdu3auHnzJvr06YPAwEB5R1o2qJaSkoJ169Zh3bp18PHxQfv27dGkSRM0aNAADg4OIkdPZHr27Nmjtjw3iUhk+sLCwjBq1CicOXNG7SRhbSex51aNGjXQq1cv7NmzR+XDpK1btzIhM59p1aoVli5dKv9ecZJCRkYGLl26BF9fX53r7dy5M9asWZPtmGLdaWlpGDx4MIKCgmBra6tz/atXrxZMxsyJD1SIiIh0d/36dfTo0QPR0dGCiZhiTUr65ptvcOLECZW7ZO7Zswe//fabXn0LMg8fPnzAqFGjsHfv3lwlCRMREZHxaPps1mfc09I+700p+YOI8qekpCS15ZUqVTJSJLlTsWJFhISECF5XP3z4IEJEZCy69A0UxxNMdUyBfQNx8f8/0ac5BJb4tyCRSPDx40exwyAiymbQoEFwcnLCoEGDkJaWpnYelLq+rOJr1O1wqa5eITkX39+yZQt69+6t8vWarFixAs7Ozmpf06tXL73rJ8vGHTKJiIjILGRlZWHZsmWYO3cukpKSBCdeKh6zsbFB+fLlUbhwYfk/fXbTMgSJRIINGzaI0jblHXPcITMhIQG1a9dGVFQUAAjG/uOPP2LWrFlihkl55ODBgxgyZAg+fPiQLQkN0O9hnqHO+Vu3bqF+/foqJ7EXKFAAr169gpOTk95tkHl5//493Nzcsq20pni+TZkyBUuWLNG53oyMDJQuXRqxsbHyemUU6+/YsSN27dqFAgUKaF334sWLMWPGDPn3QgnPUqkU7u7uePnypc6xExER5Wc7duzAqFGjkJqaCiD7Z6u+DHn/5u3tjTt37qi8P9y7dy969OiRqzbINL19+xbt2rXDrVu3DHKPJQZzGMsgIiIyFG13rNLlc9ESd8hk34CITMHYsWOxbt06lffaqampZrH40YABA7Br165ssQOfrrV2dnbysQ6yHPrskKmKqfQx2DcQD3fI1B7PUctnqX8PPHeJyJRdvnwZffr0wYsXL/R6Pqttf1afOqVSKUqWLIndu3ejefPmWv88kaFxh0wiIiIyC1ZWVvj666/Ru3dvjB49GidPnsy2WyaQvWOekZGB0NBQ0QepZYPTTMgksb1+/Rr9+/dHZGSk2kHK+vXrGzkyMoYFCxbgu+++Q1ZWFgDDTGI3lLp166JFixa4cOGC0oN1AEhOTsa+ffswZMgQMcMkI3JxcYG3tzdu3ryZ7XNcdn4cOXJEr4RMW1tbDBkyBIsWLRJcYVhW/9GjR+Hl5YWFCxeiR48esLa2Vlnn9evXMX36dFy4cCFbHTnJygYMGKBz3ERERPnZpk2bMGrUKGRlZWnVh1W38mpeGTduHMaOHauynAmZlik5ORn+/v64c+cOANO6xyIiIiL1VH1e5+Z5kqX0AcR+pkZEJJMXuxmLQd8dX8j8GeL3awrniLn8rVkqHx8f/g6IcrCkvwlTuM4TEanTtGlTBAcHY+TIkfjrr7+y7YKpzTXMkNe5nO1269YN69evh5ubm8HaINIHd8gkIiKVYmJi8PDhQ0RHRyMmJgZv3rxBSkoKUlNTkZGRIdpNYdu2bdG3b19R2iZxPX36FKtXr8amTZuQkJAAQHgHKkWm0NXhalaWydR3yExKSkJYWBhu376NU6dO4e+//0ZKSorS34TizaqtrS3evn2LggULihEy5ZEffvgB8+bNU1p5VxVtJ7Eb8pzfuXMnBg4cqPJvqmPHjjh8+HCu2iDzMn36dCxevFjlOXH58mU0atRI53qjo6NRqVIlpKWlAVB9Xsu+dnNzg6+vL7y8vFC8eHFYWVkhNjYWEREROHXqFMLDw+X1CCVjKv7N2djYICQkBBUqVNA5biIiovzowIED6NOnj8ZkTKGFFjQxZF/2/fv3KFGiRLb+hWK8RYoUke/QTZajd+/e2L9/v9YPnk15opApjWUQERHlJU07qujTR7S0XVpM7TkHEeVfU6dOxfLly1U+I3jz5g0KFy4scpSade3aFUeOHBHcIdPV1RXx8fEiR0iGxr4BEVHe4PWViEhcJ06cwNSpU/Ho0SO9ns3qQ7EdWRuenp5YsmQJOnbsmCdtEumKO2QSEZHc7du3ERAQgMDAQNy8eRNxcXFihySoUKFCTMjMZ96+fYuZM2di06ZNkEqlKjvwQsmZYk94s5SBIEuVV0kxYiXbZGVlIT09He/fv0dKSkq2spwPK3OWSSQS+Pj4MBnTwqxYsQJz584FoH7HFmMNlKjyxRdfwMXFBYmJiYIP18+dO4esrCxYWVkZNS4ST/fu3bF48WKV5du2bdMrIbNkyZIYP348li5dqnIhB8W/lbdv32L//v3Yv3+/4Gtl1D38kdU5fPhwJmMSERFp6cGDBxg6dKjWyZiyMnt7ezRs2BBNmzaFh4cHihQpAjc3N3Ts2DHPJmu4uLigc+fO2LdvX7Z4ZF/HxcXh9u3b8Pb2NnjbJI59+/ZpnYwp9LCYiIiIiIiI1CtQoIDa8vDwcLNIyHz+/LnKMk3vkYiIiIiIyFS0a9cObdq0wa5du7BkyRLcuXMHALLtmqlI12dimjaQ8PLywrRp09CvXz/Y2DAFjkwHz0YionzuzZs3WLduHbZu3YrHjx/Lj5vqBCGxk+vI+Hbs2IEvv/wSCQkJOp2XpnAO83w1feHh4bmalKv4c7KvpVKpfMc0U6LNRNEBAwYYKxwyggsXLuCrr77SOAE457lRpkwZtGzZUmkSe40aNfJsEruDgwO++OILbN68WXAS+4cPH3Dt2jU0adLE4G2TaWrSpAnKlCmDyMhIwSTdrVu34ueff4aLi4vOdc+dOxf79+9XqltGdu5pum5q+7cFAMWLF8fPP/+sc6xERLlx5swZxMfHo3Xr1mYxQY1I5uPHj+jduzeSkpJUfh7n7DN26tQJkydPRvPmzWFvb2/0mLt164Z9+/apLA8MDGRCpoXIyMjA119/rXVfUVZua2uLFi1aoE6dOvDy8kKNGjVQuHBhuLi4wNnZGQ4ODsZ5A0RERJQnz274PIiIyLBKlCihtjw4ONjk77MzMjIQGhqqclFUd3d3McIiI2HfgIiIiIgsjbW1NQYOHIiBAwfi8uXL2L59Ow4cOIDY2Fj5a3K7kY7ic7eiRYuiZ8+eGDBgAJo1a5a74InyCBMyiYjyqXfv3uHHH3/EmjVrkJycrHJimykxhQQ7Mq6ZM2di0aJF2ZIwZHg+kCHpc83TdgcMU6LuWl+iRAkmZFqQpKQkDBgwAB8/flSZRKk4QdjJyQnDhw/H5MmTUbFiRWOHCwDo2rUrNm/erLI8ICCACZn5TJ8+fbBo0SLB1dSSkpKwbt06TJs2Ted6CxYsiK1bt6JNmzYq/0YU+x7qrunaTMC3trbG5s2b4ebmpnOsRES5cf78efzwww+wsrJCnTp14OfnBz8/P7Ro0YKJP2TSFi1ahAcPHmiVjNm5c2csXLgQ1apVM3qcitq3bw8bGxtkZmYK9i0CAgL06reQ6dm6dSuioqLULlajeI7Wrl0bo0ePRu/evVGkSBFjhkpEREQC8uLZEp9XEREZXoUKFdSWnzp1CoMHDzZSNPq5ePEiUlNTBe8fJRKJxvdI5ot9AyKivGMK87F0uc6bQrxERHmhadOmaNq0KVauXInbt28jKCgIly9fRnBwMMLCwpCVlaVTfRKJBOXLl4eXlxeaNm0KX19f1K1bl9dRMnlMyCQiyocOHjyICRMmICYmRjDRTcaUBgnZqcp/Ro4ciU2bNmXbIQ0wrfOSyJRp+7ci+xtbsGABbG1t8zgqMpY5c+aonSSsOEF40qRJmDNnjujJYm3atIG9vT3S09MF4w4MDMR3330nUnQkhn79+mVbmEGRVCrF4sWLMX78eDg5Oelct4+PD1avXo2RI0fKky5VtaMv2c/+/vvv+Pzzz/Wuh4goN6RSKTIzM3Hz5k3cunULixYtgp2dHRo3bgx/f3/4+fmhYcOGsLKyEjtUIgDA27dv8dNPPwkmYyoes7Ozw6JFizBp0iRR4sypUKFCqFu3Lq5fv55tDEPWx7h8+bKI0ZEhrV+/XmWZ4jnq6uqK+fPnY/z48bzGkkVJSEhAcHAwwsPDER0djZcvX+Ldu3dISUlBamoqsrKy4ODgAAcHBxQoUAAlSpSAh4cHSpUqhRo1aqBs2bJivwUiysfmzJljFnUSERFQqVIlweOy++yjR48iOTlZr+cDxrJnzx615ZUrVzZSJGRM7BsQEeUtMecNqtr1TV1MnOdIRJZOIpGgbt26qFu3rvxYSkoKwsLC8PLlS7x8+RIJCQlITU1FWloaAMDe3h4ODg5wdXWFh4cHPDw8UL58eZO+vyNShQmZRET5zIwZM7B48WLuOEgm7eeff8bGjRuz7Uql6fxk0i7lhqGvf+ZwPVX8m+nTp4/JryJL2ouMjMQff/wheF1UvKYWLlwYmzZtQpcuXYwdoiAnJyc0atQI58+fF5zEfv36dWRlZXEycz5Sp04dTJ06Fe/fv1f5mrt376Jx48Z61T98+HBkZWVh3LhxyMrK0rrPoY5iHVZWVvjjjz8wbtw4vesjIjIE2Wep7PqWlpaG8+fP4/z58/j+++/h7OwMHx8feYJmjRo1RI6Y8rPly5cjOTlZabGEnIluJ06cQKNGjcQKU1CzZs1w/fp1+feKC0wlJSUhNDQUnp6eYoVHBhAZGYlr166p3N0E+PR7L1WqFM6cOYOKFSuKESaRQT158gQBAQEIDAzEtWvX8OLFi1zV5+zsjDp16sDX1xf+/v5o3Lgx7/PJ4NLS0hAeHo6oqChER0cjMTERKSkpSElJUVqZ/fvvvxcpShIDEzKJiMyHp6cnChcujISEhGxjW7J7r/fv32Pjxo2YOHGiyJEKe/v2LXbs2KF2DoOpjWuQYbBvQESUN3x8fESbG/jx40ekpKQgPj4eL168QHp6urxMaG6jrO8ikUhQunRplC9fXpS4iYjE4ujoiOrVq6N69epih0KU55iQSUSUT0ilUvTv3x979+7ljoNk0i5duoTvvvtOq6QIU9/ZlcgU5bz+f/HFF9i8ebN4AZHBLV68GBkZGWonsZcqVQpnz55FhQoVxApTULNmzXD+/Hn594p9lrS0NDx48AA1a9YUKzwSwZIlS/K0/pEjR6J8+fIYOnQoXrx4ke2BCaBdv0JoBcwSJUpg+/bt8PX1NXjMRET6ULda7/v373H06FEcPXoUAFC8eHF5koSfnx/KlClj1Fgp//r48SNWrVqldL7m3Bnzn3/+MclJi5oWibhz5w4TMs3c2bNnBY8rnqMlSpRgMiaZvdjYWGzfvh1bt27F3bt35ccNMe76/v17XLhwARcuXMC8efNQtGhR9O/fH4MHD4a3t3eu66f8KSYmBidPnkRQUBBu376NkJAQZGZmavWzTMgkIiIyXU2bNsXRo0cFxwmkUil++OEHDBo0CK6uriJFqNr333+PpKQkwQV9ZJo3b27kqIiIiMyXqrFZMTx9+hS3b9/G6dOncfToUURHRwPI/ixO1geIjY3FrFmzMGbMGLHCJSIiojzEhEwionxi3Lhx2LNnDwBoTHRTtZqQrolxuvy8NnVxB0TLl5WVhQkTJsgTcLQ5R2Wvsba2RtWqVVGlShWUK1cORYoUgaurK+zt7WFjwy4P5T/qrplSqRQuLi6YP38+Jk6cyOurBUlJSZHvMKxI8bPf2dkZp06dMrlkTABo2LCh2vK7d+8yIZMMzs/PD8HBwfjpp5+watUqpKSkAIBScqY6sv6Ig4MDxo4di7lz58LFxSXPYiYi0lXORRrUJWi+evUKu3fvxu7duwEAFSpUgJ+fH/z8/ODr64siRYoYJ2jKd06fPo24uDjB8QDZOMHChQvRrFkzkSJUT9PusqGhoUaKhPLKlStXVJbJztElS5YwGZPMVkREBH755Rds2rQJaWlpKneCzS3FemNjY/H777/j999/R6tWrTB79my0bt3aIO2QZUtKSsKuXbuwYcMGpR2qtZWbc/rkyZN4/vy5ynIXFxf07dtX7/qJiIgI+Pzzz+ULiMkoLuT59u1bjB49Wj4PxlScPn0aa9euVZlICgB169blGBsREZGZqlixIipWrIiePXsiKysLx44dw4IFC3DlypVsc3MkEglSUlIwfvx4XLhwAZs2bYKtra3I0RMREZEhMTuBiCgf2LBhA9atW6dTIqauq11rk6ypbf3c4TD/2rZtG+7evatVMqZUKoWrqyv69OmDbt26oWXLlnB0dDRmuGRB9Jl8k9skdWPJGWe1atUwePBgjB071iRXjKXcOXr0qMoVd2UDvqtXr0bVqlVFilC96tWrqy1//PixkSKh/KZQoUJYtGgRZsyYga1bt2LPnj24deuWVjtqSCQS1K1bF71798bw4cNRtGhRI0RMRKQ/oeQKdQmaT58+xbNnz/Dnn39CIpGgVq1a8gRNHx8fODk5GSVusnz79+9XOqbYr/X29sbkyZONHZbWKleuDGtra2RlZQn2x589eyZSZGQoQkm1st+1RCJBkyZN0K9fPxEiI8qdtLQ0zJ8/H4sXL0ZGRobSQg6Kcjt2r67fcfbsWZw9exZt2rTBqlWrTHIhKRJfYmIili1bhmXLluH9+/cAhPu3muT2XI6Pj8fYsWPVttW4cWOUK1cuV+0QERHlZ71798bUqVOV7rMVF3fev38/fvjhB5PZ9frBgwfo379/tlhzkkgkvHckIiKyEFZWVujUqRM6deqEAwcOYNKkSYiJick2biyVSrFr1y7Exsbi77//5nM1IiIiCyKRMuuFiMiihYWFoVatWkhOTgagORlTKpXCxsYGdevWRb169VC+fHm4uLjAzs4Ow4cPzzbQrXjj2KxZM4wYMQLAp925kpOTERMTg6ioKAQHByMkJEQ+oV0oMVOxrrp162LixIkq31P16tXRoEGDXP6fIVPUtGlTXL16VXDiouI56uDggG+//RZTpkxBwYIFxQiVLISVlZXYIeQZe3t7FCtWDBUqVEDVqlXRsGFDtGjRApUrVxY7NMpD/fv3x+7du1V+Xrdu3RoBAQF61W1lZaWyXm2S1rSRmZmJAgUKICMjA0D2h+oSiQQDBw7Eli1bDNIWkSaJiYm4ceMGQkNDERERgcTERKSkpMDW1hbOzs4oXbo0PD090bBhQxQuXFjscImIlMybNw/z5s1Tu+CNEKFJ5aruz2xtbdGoUSN5gmbjxo1hbW2du8Ap36pUqRLCwsIACI8XHTx4EJ07d9arbmP0ZQGgTJkyePHiBQDlvqyvry9Onz5tsLbI+DSdo3/++SeGDx8uZohEOrt27RoGDBiAsLCwbOe1TF4/RlbVloODA3788Ud89dVXedo+mZdt27bhq6++QlxcnNrEYUDzYnq57QdIpVLUrFkTDx8+VNnG3Llz8d133+lVPxEREX3Svn17nDx5UuX8Adln+vfff485c+aIFOUnd+7cQYcOHfDy5UuleBXnOtja2uL58+coUaKEWKESERFRHomNjUWPHj1w8eJFpXEv2RzbgIAA2NnZiRglERERGQoTMomILFyfPn2wb98+lRMwFQd+S5UqhWnTpmHAgAEoUqSI0mvVTV4bMmQINm7cqDKO5ORkBAUF4dChQ9i7dy8SExOVduxUrM/Pzw+bN29GyZIlc/3/gMxDSEgIqlevrjEZs2TJkjh16pTGXdSItPH8+XO9fk4qlaJChQoqr4li7XxibW0NOzs7ODk5MVk5nypdujSio6MBCH++njlzBj4+PnrVbaxJ7OXLl0dERAQA5UnsLVu2RFBQkMHaIiIismSKCZk55SZBU90ORAUKFICPj488QbNWrVo6Rk351atXr+Dh4aHU3wQ+nXOfffYZIiMj9a7fWH3ZRo0a4caNG0qrXwOAl5cX7ty5Y7C2yPjc3Nzw7t07AP+7V5F9bWVlhRcvXsDd3V3MEIl0smrVKnz11VdIT08HYNxETCFCzwu6d++OzZs3c5wrn4uPj8egQYNw/PhxgyQOG6ofsG3bNgwZMkRlf7tixYp4/Pix3vUTERHRp13UfX191c53kX2u9+jRA+vWrUOhQoWMHufWrVsxYcIEJCUlKfVrc8Y5dOhQbNiwwegxEhEBgK+vr+BxiUSCwMBAI0ejm19++QUnT54ULDOH+Cn/SE9PR7du3XDixAmlZwWynbK3b98udphERERkAEzIJCKyYLdu3UL9+vW1SsacMmUKfv75Zzg4OKisLzcJmYqSk5OxevVqLFq0CK9fv84Wh2KdxYsXx7Fjx+Dt7a3rWycztHr1akyYMEHtapGlS5fGmTNnUKFCBbHCJJIz1oReIm1FRESgXLlyKiexV6pUCaGhoXrXb6xzPuduyYpt1qhRA8HBwQZri4iIyJJduXIFv//+O86cOYPXr1/Lj+cmQVOX3TMBoGjRovD19ZUnaJYvX17b8CmfCQoKgr+/v8r+5vjx4/HHH3/oXb+x+rKdO3fG0aNHs9UP/G+BqaioKIO1Rcbn4OCAjIwMAMoJmZ6enggJCREzPCKdzJw5E4sWLcp2LgP6JbZpom+dsti8vb1x6tQpuLm56VQPWYYHDx6gS5cu8l1cDZE4bKh+QEZGBsqVK4eYmJhs8SjWf/78eTRr1kzvNoiIiAjw8fGR7zKlKSnT3d0dixcvRv/+/Y0S26NHjzBt2jQcO3ZM6T5RMT7ZMVtbWzx48AAVK1Y0SnxERDnJxkkVmcs8l2HDhmHLli1mGz/lL2lpaWjatCn+++8/AMrzYpctW4Yvv/xS3CCJiIgo16zEDoCIiPLOihUrBI9LJBL5DZ6VlRW2bNmCpUuXqk3GNCQnJyd8/fXXCA0NxejRowUfUkulUrx69QqtWrXCmTNnjBIXievatWsqy2TnxapVq5iMSUSkgqrJv7JraNeuXY0ckX6KFi2qdEz2UOXt27fGDoeIiMhsNWnSBLt27UJMTAzu3LmDJUuWoEOHDihQoACkUqn8H/C/cQLZP1UUf07VzyqWx8bGYu/evRgzZgwqVaqEChUqYPTo0dizZw9iY2ON8v+BzENYWJja8hYtWhgpktxxdHRUWZaYmGjESMhYFCf9EpmLadOmKSVjKn62C8n5eZ+zz5Czj5BzArq2fQ1ZXbKfk0qluHXrFlq3bs0xgXzo9u3b8PHxwbNnz7Q+X3U933LD1tY22zMuITt27Miz9omIiPKLP/74AzY2NgBULxYm6zvGxMRg0KBB8PLywqZNm5CamponMV27dg2DBw+Gl5eX2mTMnDF+9dVXTMYkIpOgaRzA1Jl7/GT57O3tsX//fvkzA1k/QdZn+b//+z88f/5czBCJiIjIAJiQSURkod6/f489e/aofNgsG/D9/fffMWjQICNH94mLiwvWrFmDffv2oUCBAgCyJ2VKJBIkJiaia9eu3A0rH7h7967SMcXzoUOHDujYsaMIkRERmQdNk9ibN29upEhyR90k9g8fPhgxEiIiIsvh5eWFqVOn4p9//kF8fDwuXLiAuXPnokWLFrCxsVGbZKmOrgma4eHh2LBhA/r3748SJUqgdu3a+Prrr3Hs2DEkJSXl+f8HMl2RkZFqy6tVq2akSHJH3WJn6enpRoyE8oKzs7PKsuLFixsxEiL9rVq1CkuXLgUAtRPGZRT7A0JJl66urvD09ES9evXQtGlTtGjRAg0bNkStWrVQokQJWFtba+wvCJG9VlZ+79499OzZk7td5CP3799HmzZtEBcXp9SvzCnn+aQqOTgvDB06NNukSsWYpFIpTp06laftExER5Qe1a9fGtGnT1H6uK84xkUqluH//PkaOHAl3d3f07dsX27dvz1XSQ1JSEgIDAzFz5kxUrVoVTZs2xY4dO/Dx40e1yZiK/YRKlSph7ty5esdARGRIeb2ATV4z9/gpfyhfvjxmzZol7x8o9hOSk5MxZcoUkSIjIiIiQ7EROwAiIsobJ0+eREpKinzAWUYxwa1t27YYN26ciFF+0qNHD5QqVQrt2rXD+/fvlZIyP3z4gC5duuD69esoVqyY2OFSHnn79q3awbKhQ4caLxgiHXCQl0xFVFSU2vIqVaoYKZLc4SR2IiKivGVtbY1mzZqhWbNm+P7775GcnIzz588jMDAQAQEBuHv3rlKyhCJNk98Uafr54OBg3Lt3D7/99htsbGzQoEED+Pn5wd/fH02aNJHvfkCWT9PukWXKlDFSJHknIyND7BAol1xdXREXFydYpm5hGSJTcfHiRUyZMkXrREwZ2Vh9/fr10bx5czRu3BjVqlVD5cqV1d7Dy0RGRuLRo0e4desWLl++jLNnz+L9+/fydrTZSUgqleL8+fOYMmUK/vjjD53eN5mfhIQEdOvWTZ6MCQifHznPUwCws7ODt7c3PDw8UKRIERQpUgS//vqr0rMyQylbtix8fHxw7ty5bLHKvg4LC0N4eDjKlStn8LaJiIjykx9++AGXL1/+f/buPDym6/8D+Huyy4bIYk3sQgShkhBLkkkpqqhaWlW1FKUtiu5qa21VqrRoi9JS2tCi9kkii4aofQtiSRAReyL7Mr8/+pv5zmS2ZDKZOzN5v57HU7ln5pzP6M2de885n3MQFxen8Xu9bFKmVCpFdnY2/vjjD/zxxx8AAA8PD/j6+qJFixaoV68ePD094ezsDDs7O1hZWaGwsBD5+fl48OABMjMzcfPmTSQnJ+PatWsoLS2VtyNTnnsVqVQKZ2dnbN++Hfb29gb/tyEi0ofiGIA5Mvf4qfqYMmUKvv76azx9+lRll8zdu3fj0qVLZrMgJREREanijBYiIgu1Z88elWOKnRBOTk746aefjBmSVkFBQdi1axciIiJQXFyslJQJAGlpaXjnnXewbds2gSOlqvL48WOlnxXPVzs7O7zwwgvGDomoXKp6lXWi8tK1q1SDBg2MFEnlaNvtori42IiREBERVQ+Ojo544YUX5M9cDx8+RHR0NCQSCaKionD9+nX5aw2doKm4a1FRURESExORmJiIL774Ao6OjujWrZs8QbNDhw6V/ahkwnTdyzo7OxspksrJy8vTWMZJl+avSZMmuH79utqJXtzll0xdQUEBxowZo9T3rk7ZCeWtW7fGhAkT8Morr6B+/fp6td2oUSM0atQIERERAP77zo+KisK6deuwc+dOeUy6JtZLpVJ8//33eOWVV9CzZ0+9YiHzMHr0aFy7dq3cCQ6enp546623IBaLERwcrJIovGTJkiqNd/DgwYiNjdVYHhUVhbFjx1ZpDERERJbOxsYG27dvR+fOnZGamqr13hFQv3ADAGRmZuL+/fuIj48vd9uadr3UVK74GqlUCmtra/zyyy9o27ZtudskIiIiy+Ds7Izhw4djzZo1KvNhpVIpli9fjh9++EHgKImIiEhfVkIHQEREVSMhIUHt5CDZQ92rr75qcokZ3bt3x9KlS5U6rBUnW0RGRuLgwYMCRkhVKT8/X+WY7Fxo1aoVnJycjB0SkVY9evRAjx490LNnT6U/PXr0EDo0qqZ0TQA2l+uouu8DmfLsvEFERESVU6dOHQwZMgRr165FSkoKbty4gR9//BHDhw+Hh4eHPImy7C6a6pI1y1J8r+KuBWV3L5BKpcjJycHBgwfx4YcfolOnTvDw8MDQoUM5MG2hZLtMmLsnT55oLOMOiuZP2+TZ9PR0I0ZCVHHz589HSkpKuZMxO3TogD179uDChQt477339E7GVMfW1hYvvPAC/vjjD9y4cQNvvfUWrK2tlWLQFJ9UKsX48eO567AF+/PPP7Fz506NyZiK940NGzbE999/j9TUVMyfPx+hoaGC9B31799fa7lEIjFSJERERJatTp06OHz4MJo0aaKUzKCOtr6rsv1Tuv6o6/sq2z+mSPE1NjY2+OWXXzBgwIAq+BchIiIiczB48GCVY7J7kh07dljM+AgREVF1xIRMIiILlJWVJd/BQtPkigkTJhgzpHJ799130aVLF7Ud6FKpFB999JFAkVFVc3FxUXtcJBLB09PTyNEQ6Xb48GHExMSo/UMkBF0JEObSifvo0SONZZzETkREZHw+Pj4YO3YstmzZgoyMDJw5cwbLli1Dv3794OzsbLAETXXvVSx/+PAhIiMj8fbbb1f5Zybj07V4iLadJ03JrVu3NJ735rJACmkWEBCgckx2rbp27ZoAERGVz8OHD/HNN99ovD4pfu/WqFEDy5cvx4kTJ9CnT58qj61BgwZYu3Ytjh8/jnbt2mmcWK84zpGSkoINGzZUeWxkfEVFRZg6darWZEzZ8UGDBuHs2bOYOHGi4LtQ+/j4oFWrVgCU++dkv1eHDx8WKDIiIiLL4+3tjdjYWLRv314pWVKTssmVgGr/k64/6urSRPF+xdnZGZGRkRg+fLiBPj0RERGZoy5dusDGxgbA//oKZB4/flyhnbuJiIjItDAhk4jIAp07d07lmOLDXOvWrdGxY0eDtqmt07miFi1apFK3rOP6zJkziIuLM1hbZDpcXV01ljEhk4hIN0dHR63lubm5RoqkctLS0jQOnjs7Oxs5GiIiIirL398fU6dOxe7du/Ho0SMkJCRg7ty56N69O2xtbbVOctOmPBPkyHLpSlbMyMgwUiT6Ky4uRmpqqspx2flcr149Y4dEBtanTx9YWf03rFZ24szDhw/lC+QRmZply5bJ+wS0Jbg1btwYiYmJmDJlitG/d9u3b4+kpCS8+eabWnc7kv3uLVq0yGwWnqLy27RpE27dugVA+VxVTBoG/junt2/fjpo1awoSpzohISFKMSv+PTMzkzspExERGVCDBg2QmJiIN954Q6UPSZeK7o5ZniTMsu1LpVL4+voiKSkJL730UuU/MBEREZk1R0dHNGvWTGM558ISERGZLyZkEhFZoLS0NI1lIpEIwcHBBm+zuLjYYHV1794dgYGBGide/PDDDwZri0xH/fr1NQ5klJSUGDkaIiLzo2sSuzlM/CooKFB7HyO7J6hfv74AUREREZEm1tbW6Nq1K2bNmoXY2Fg8evQI+/btw/Tp09GhQwcA0DvBsiKT3sj8ubu7ay1Xl+hoai5cuICCggIA6hOevL29hQiLDMjDwwNdunTReE06ePCgkSMi0q2kpATr1q1T+72ruBu1r68vjh49Cn9/fwGi/I+trS3Wr1+PmTNnqh0bUPzdS01Nxb59+4wdIlWxr776SuM9ouycWL58OaZOnWrcwMqha9euWsvPnDljpEiIiIiqBwcHB/z888/4/fffUa9evQonZhpK2URMa2trzJw5EydOnICvr6/R4iAiqg44TkDmrHHjxhrP4fPnzxs5GiIiIjIUJmQSEVmgO3fuaC3v3Lmzwds0dMLc66+/rnJMNkFk37597GSxQH5+fhrLnj59asRIiIjMk5eXl9bymzdvGieQSjhz5ox8kQd13/U+Pj7GDomIiIgqwNHREb1798ZXX32FkydPIjMzE9u2bcP48ePRtGlTjTtgEjVp0kRr+enTp40TSCXEx8drLW/cuLFxAqEqNX78eI1lW7ZsMWIkROUTFRWFzMxMAKo7Dsp4eXlBIpHA09PT6PGps3jxYowaNUrrTpkAf+cszb///osrV64AUD1XZefCqFGj8N577wkVolbt2rXTWn7p0iUjRUJERFS9vPLKK0hOTsZHH30EFxcXtYuCGVrZumVtDhgwAKdOncLixYtRo0YNg7dLRFTd5ebmKv2seI23tbU1djhEFVKzZk21x6VSKZKTk40cDRERERmKjdABEBGR4T148EBruWyXCkPKy8szaH29e/dW+llx8sWTJ09w7NixKtnpk4TTtm1blWOyyRY3btwQICIi7eLi4jSW9ejRw4iRVNyVK1eQkZGhsdzU4yf1dE1iP3nyJF544QUjRaOf2NhYreW6PiMRERGZljp16mDIkCEYMmQIACAtLQ0SiQRRUVGIjo7GvXv3BI6QTIWu+7wjR45g+vTpRopGP7t379Za3r59eyNFQlXp1VdfxWeffYbbt28rJQlJpVIcOXIE//77L5577jmhwySS27Fjh8Yy2fm7efNm1K9f34hR6bZ69WocO3YMly9flv+Oych+3r17N0pKSmBtbS1gpGQov//+u8oxxcm1devWxYoVK4wZUoXo2gHr+vXrRoqEiIio+nF2dsaCBQvw4YcfYtWqVfj5559x7do1ALoXA9O0ELiuRE7Z+1xcXPDKK69g2rRpauc7EBGR4Tx79kxjmb29vREjIao4dUnDsj4uXXN9iYiIyHQxIZOIyALpSo50c3PTq15ra2uUlpaqTIAAtHd66KNFixZwc3PD48eP1bZ35MgRJmRamOeff15j2bVr15CTkwMnJycjRkSkXWhoqNrBOJFIJN/hz1QtXLgQmzZtUltmDvGTek2bNtVarmvHHlOwa9cureUBAQFGioSIiIiqgre3N8aMGYMxY8YAAI4dO4Y5c+bgwIEDap/9qfpo3bo17O3tUVhYqHQuyP4ukUhQWFgIOzs7gSNVLz09HdHR0VonbAYGBhoxIqoqNjY2mDNnDsaOHavy/1sqlWLGjBk4fPiwMMERqREfH69yriomEw8bNgzh4eECRaeZg4MDVqxYgd69eyvFr7hwY05ODk6ePInOnTsLFSYZUFRUlNrjsv/nH3/8MVxcXIwcVfk5OzvD09MT9+/fV3tfe/PmTWECI5N248YNXL58Genp6bh37x4ePXqEgoIC5OfnC95HLxKJsG7dOkFjICKqqJo1a+LTTz/Fp59+iiNHjmD79u04dOgQLly4oPJa2T1leRMvFXl6ekIsFqN///4YMGAAd8OkKvPw4UM8ePAAeXl5yM/PR1FRkWD9pw0bNtQ5Fk1U1dLT0zWWmfLzIhHw3zVdk6ysLCNGQkRERIbEhEwiIgukKyGzVq1aetVrZ2eH/Px8leNSqbRKHgxbtGiBY8eOqe0Ev3z5ssHbI2G1bt0aTZs2xY0bN5QmBQFASUkJ4uLi0KdPH4GjJFJlrpPGzTVu0qxVq1ZwdnZGTk6O2knshw8fxrNnz+Ds7CxwpOpdvXoV//zzj9bBb06yJG0KCgpw6tQpXLp0yaQns8lW5iYiqo5KS0tx/Phx+S6ZiYmJKgl4VD3Z2tqic+fOSEhIkN8Plk26iYyMxGuvvSZkmBqtXr0aJSUlKvfhMm5ubmjRooVQ4ZGBjR49Gr/++itiYmJUdsmMj4/HqlWr8M477wgdJhEePXqE5ORkra+ZNWuWkaKpuOeffx5BQUHyMQJ19woJCQnsK7AAOTk5OHv2rNJ3p+LfnZ2dMW7cOCFCq5B69eohMzNTbcJ+RkaGQFGRKTly5Aj27t2LhIQE/Pvvv2rHXE2B7N6GCZlEZM5CQkIQEhICALh37x6SkpJw9uxZnD9/Hjdu3JCPHxQVFal9v0gkQp06dVCvXj00atQIbdq0Qbt27RAQEAA/Pz9jfhSqBjIzMxEdHY0TJ07g3LlzuHTpEjIyMgQf01I0ZcoULFu2TOgwqBorKSlBcnKy2uctAHB3dxciLKJyy8zM1Fhmqs+GREREpBsTMomILJC1tbXW8tq1a+tVr729vcoDoGwihLaHRn3Vr19fYxkTMi3TiBEjMH/+fLXJONu2bWNCJpmksqv0mxNNndVknqysrBAUFISoqCi1k9gLCwuxefNmTJgwQcgwNVq5cqXSRGZA+RytV68efHx8hAqPTNTly5cRGRmJvXv34sSJE2onT5jStU3XattERJYoOTkZEokEEokEsbGxSgs6mdI1moTXvXt3JCQkqC2TSqVYvny5SSZk3r9/H99++63a73nZ/S37MyzP+vXr8dxzz+HRo0dKO6vIdsn08/NDWFiYwFFSdXfx4kW1z9myY127dkXr1q0FjlK7t956C8eOHdNYfv78eSNGQ1Xl7NmzKgsbAP/7Hn3xxRfh4OAgYITl4+XlpXJM9pkePHggQERkCu7evYtVq1bh559/VkrM5bMQEZHxeHl5oX///ujfv79KWW5uLvLz85Gfn4/S0lLY29vDwcEBjo6OOufdEFXG7du38csvv2Dr1q0qzzWmdp/AsS0yBceOHdO4uKNIJNI6x5BIaKWlpfINKtQxhz4PIiIiUs9K6ACIiMjwXFxctJbr21nm6uqqsawqEjIdHR1Vjsk6Vu7cuWPw9kh47777LmrUqAEAKpPZfv/9d64iTSZJKpWa3KBIRZh7/KQsNDRU7XHZtXT58uUoLS01blDlcPPmTfzwww9aJ7G/+OKLAkRGpioyMhIhISFo06YNPv/8cxw9ehSFhYXya5riHxmRSCToHyKi6iI9PR2bNm3CqFGj0LBhQ/j5+WHKlCnYvXs3nj59qnSN5jWSFA0ePFjlmOICIydPnsTvv/9u7LB0mjFjBrKzswFonjA3cOBAI0ZExuDj44Pt27fD1tZW6bhIJEJhYSH69++PQ4cOCRQd0X+uX7+utbxfv35GikR/ffv21Vqu6zOSedD1/1FTf5epcXZ21lj29OlTI0ZCpuDJkyeYOnUqmjRpgkWLFuHu3btqn4VM8Q8RUXXi6OgINzc31K9fHw0bNoSHhwdcXFyYjElV5vz58xg2bBgaN26Mzz77DOfOnVMZ1xL6XoD3BWSKNm7cqLW8efPmRoqEqOISExPx+PFjAOrHEHTN9SUiIiLTxYRMIiILpC1xEoDKLpfl5e7urrI6sWKdhk7KVLfDkYxsohtZFnd3d0yYMEF+bimeYwUFBfj888+FCo2IyCy88sorKscUr6VXr17F2rVrjRlSuUyaNAmFhYUAOImdtEtISECnTp0wbNgwHD16tEID1EREVDWysrKwc+dOvPvuu2jTpg0aNWqE0aNH49dff0V6errWazWg+buf1/Hqp2PHjvKJM2X/n4tE/y0wMmXKFDx8+FCI8NTaunUrfvnlF3l8Morx16xZU2dCEZmnHj164JdffoGNjY3ScZFIhNzcXPTr1w9fffUVF0EiwaSmpmotDwwMNFIk+qtbty4aNWoEQPnaKrvupqWlCRUaGdDNmze1lgcEBBgnkErStqNFQUGBESMhoe3fvx++vr5YuXKlfAExTc9CpvaHiIiIqsazZ8/w9ttvo0OHDoiMjERpaan8u1dTPyjvC4j+k5CQgPXr12sdJ2jTpo0RIyKqmJ07d6o9LrvWNmjQwJjhEBERkQExIZOIyALpSsh89uyZXvV6eHhoLb927Zpe9WoiWxlIHX0/A5m++fPnw8fHB8D/JtbI/rtu3TruLkBEpEWrVq3Qrl07AJonsX/00Uc6J2Ua0/Lly7F//36tk9i9vLzw/PPPCxEemYiSkhJ8/PHHCA0NxenTp9Um9gDCD05z0JqIqoPCwkLExMTgs88+Q3BwMOrUqYOXX34Z33//PZKTk8uVgKnuGqnrtU5OTujbty+WLl0qyOcm4xg7dqzKd6fiz5mZmRg6dChKSkqMHZqKI0eOYMyYMRonAsl+B8aMGaM1QYPM25AhQ7Br1y7UqFFD6bhIJEJxcTE++ugjBAcHIzY2VqAIqTrTtaihr6+vkSKpnFatWml8rsrKyjJyNFQVdO0eKRsvMHXadtLStgApWZaFCxfixRdfRGZmptIzEaD5WYiIiIgs24kTJ+Dn54cffvhBnoip2A/KsSUi9XJycrBy5Uq8+OKL8v5gTb8bXbp0MWZoROV29+5drFmzRuM4gkgkQtOmTY0cFRERERmKje6XEBGRualdu7bW8oyMDHh5eVW43saNG2stP3PmjEE7OO7du6exTLaLFlkeJycnrFu3Di+88AJKSkrkHRKyjujhw4cjMTERLVu2FDhSIiLTNHHiREyaNEllBVXZz9nZ2RgwYACOHDkCJycnocIEAOzYsQMzZ87UOYl9woQJWie1kWUrKCjA4MGDsW/fPqXVggHNg25ERGRYJ0+ehEQiQVRUFBISEpCfnw9A9Tqs7jtd27W67OsVX2tra4ugoCCIxWJEREQgKChIZRc6sjyTJk3CokWLkJWVpbRgh+JiTYcPH8agQYOwdetWODo6ChJndHQ0Xn75ZeTn52tdWMTa2hrvvPOOECGSEfXu3Rvx8fEYPnw4rl69qtKXdfz4cYSHhyMwMBBjx47FSy+9BE9PT4GjpuogJydHa7mucQRT4ebmprEsNzfXiJFQVdF1rtasWdNIkVSO7B5ZHd7HVg+zZs3CggULlPpiAfZfERERVWc7d+7EiBEj5M8uvEcgQ4iLi6uSeuPj4wU5L0tLS1FYWIisrCzcu3cPN27cwKlTp3D06FHk5+cr9Q3LKP4u1alTR75oNZGpmTFjBp49e6ZyDivq1KmTkaMiIiIiQ2HPPxGRBWrRooXW8oyMDLRv377C9TZv3lxr+fHjxzFx4sQK16tOXl4eLl68qDFBw97e3iDtkGkKDw/HqlWrMHHiRKVzQCQS4fHjx+jZsycOHDjADjUiIjVGjx6NefPm4d69exonsZ87dw5hYWH4+++/BZsI/Msvv2DcuHEoLS3VOoDi4OCAt99+W4gQyQSUlJSgf//+kEgkAJiISURkLCkpKYiKioJEIkFMTAweP34sL9OVhFmRBEzF14tEIrRr1w4REREQi8Xo2bOnYMl2JBwXFxe89957mD9/vtpzS3bfuGfPHoSGhmLPnj3w8PAwaozLly/Hxx9/jMLCQo2TKBR3x9S1wBlVnaqanKbJ119/jRkzZuDKlSsqSZlSqRRJSUlISkrCxIkT4efnh4CAALRu3RoNGzaEm5sbHBwcBF2IpkePHoK1TVVD16KG5tLHbmdnp7GMCzdaBl3/H21tbY0USeU8e/ZMY1nZnZTJ8vz888/48ssvVXbE1EXTOCgRERGZvwMHDmDo0KEoKioqdyJmZftay/O+itZFpic0NLRS/88Uzw/FuQShoaGVDc2gyi7Uq65cJBJhwIABPIfJJH322Wf47bfftCZjAkC3bt2MGBUREREZEhMyiYgskK+vr9byy5cvo3fv3hWut3Xr1mqPyx4ao6KiKlynJseOHUNxcbHGB1JXV1eDtUWmafz48cjMzMTnn3+ukpR57949BAUF4csvv8T7778vYJRERKbH3t4eH374IaZNm6Z1Evu///6LLl26YO/evWjVqpXR4issLMTMmTOxatUqtatZlo11ypQp3D2mGps2bRokEkmFVgzmgBsRUcVlZmYiKipKnoR569YteVlldsHU9domTZpALBbL/7i7u+v7EciCfPzxx/j1119x8+ZNlXvFsvez/v7+mD9/PsaNG1fl9wCnT5/G1KlT5SvFq2tP8ZiLiwvmz59fpTGRdpWdnFYZ6hacUZzgdu7cOZw/f16Q2NQRiUQoLi4WOgwyMF0LGzx79sws+tm17Z7IJDfLoOv/Y15enlks1HH79m2NZeYQP+nvxo0bmDx5cpUkWhAREZF5Sk5OxuDBg5WSMfXtQ9VE3WsUF4iqSH28HzFPhv7/ZmrnQXnvr9966y1jhENUbvn5+fjwww+xatUqneMInp6e6NKlizHDIyIiIgNiQiYRkQWqVasWPD09cf/+fbVJDsnJyXrV27lzZ5VjihPQbt26hRMnTqBTp0561a9o06ZNao/LPkvDhg0r3QaZvs8++wxeXl6YNGkSSktL5cdFIhEKCgowc+ZM/P333/jiiy/QtWtXASMlMi9FRUVKPyt29llZWRk7HKoC77zzDtavX4/z589rncR+48YNBAQEYNq0afj444/h7OxcpXHt2rULM2fOREpKSrkmsXt6euKTTz6p0pjIdMXExCgNUnAiGxGR4eTk5CA2NhYSiQQSiQQXLlyQl1VlAqaHhwfCwsLku2A2adJE349AFszBwQGrVq1Cv379NJ5TsvvZzMxMTJw4EStXrsSsWbPw4osvGjw55/Dhw1ixYgV2794t3+lQ2/2JrHzx4sVG372T1BP63lDdav6yc4moKjk5OWktv3v3rlkkZN69e1djma7PSOZB1//HR48emUVCY2pqqsb+CS48YtkmT56MvLw8nbueqLuHdHNzg7+/P/z8/FC7dm24urrCxcWFCedERERmrLCwEK+99hpyc3O19iGpK3N0dETjxo3h6uoKe3t7xMbGKt1jyP4uEong6ekpX7A/Ly8Pubm5yMjIwMOHD1UWilLXlmJdzZs3R/369TV+pubNm+v7z0FVSJ+FyPTddVUomn53ZOduWFgYAgMDBYiMSL0///wT06dPR2pqarkWKB82bJhJ/u4RERFR+TAhk4jIQvn6+iIzM1Pt4O+JEyf0qtPDwwNNmjRRu0OBzLp16yqdkHn//n1s3bpV48OmSCRCs2bNKtUGmY+33noLzZs3x6hRo3D79m2lFf2kUiliY2PRvXt3tG/fHu+++y769OmDunXrChw1kWl79uyZxjI7OzsjRkJVxdraGj/88AO6d++OkpISjUmZwH+r8y1atAjr16/H+++/j6FDh8LHx8dgsTx79gyRkZFYsWIFzp49qzIZWVvn8+rVq6s8SZRMk1QqxeTJk5V+VkfdyqhNmzZFhw4dOJmNiEhBSUkJEhMT5TtgJiUlyXdCq8oETCcnJ/To0UO+A2b79u0r8zGoGunTpw9mzJiBpUuXqu2DKpsUef78eQwfPhyOjo7o27cvBgwYAH9/f7Rq1arCzzgPHz5EQkICYmJisGPHDty5c0feDqD5PlZxItBLL72ECRMm6PXZyfCMPaFF0zVT8RwypUk2TAy1XG5ublrLL168iFatWhkpGv0lJydrTHLT9RnJPNSqVUtreVpamskv0nn9+nVkZWWpvW8RiUQG7Wsj05KUlIT9+/drTcYse//YuXNnjBgxAi+//LLJn9tERERUcStWrMDp06fLnYwZEBCAUaNGoVevXvIESxltiyn36dMH69evVzleUFCA8+fP48SJEzh48CAkEgmysrJU2pX9LJVKce/ePXz22WcYOXKkHp+YhGLpO2Sqo9g/4ODggO+++07AaIj+mw9z+vRp7NixA9u2bUNGRobKNVaR4jlsbW2NqVOnGjNcIiIiMjAmZBIRWajnnnsOcXFxSsdkD3lnzpxBQUEB7O3tK1xv7969sWbNGpUJELK6N2zYgJkzZ1Zql4vJkycjPz9f6+Blx44d9a6fTFfZc1bG2toaK1eulO+qVjYpEwBOnz6NcePGAQB8fHzQpUsXtG3bFm5ubqhduzZq166t1zlvKD169BCsbaKynj59qrHMwcHBiJFQVQoKCsLixYsxffp0rTsLKQ60ffTRR/joo4/w3HPPySext27dukILIeTn5+PixYuIj49HTEwMDh48iIKCgnIlYipOYh83bhwGDhyo34cns7d9+3b5pNvyTGarW7cuRo4ciREjRqBdu3bGDJWIyGSlpaVh+/btkEgkiI+PR05OjrxMVxKmvrsS29raIjAwEGKxGBEREQgODoaNDbugST+LFi3CyZMnER0drTUpU1YmlUqRk5ODyMhIREZGAvhv0pquPqoPP/wQGRkZuH37Ni5duoR79+4ptSFTnkl0ANCiRQu1k+FIOKY2mcyU4jGlxFAyvKZNm2otj4mJwaBBg4wUjX7OnDmDJ0+eaJzAxt22LUPjxo21lp87dw5du3Y1TjB6SkxM1FrOhEzLtWLFCq3liveQnTt3xvfff1/phW2JiIjIdD1+/BgLFy7UuqCX7LiPjw9WrVqFfv36GTQGe3t7dOrUCZ06dcL48eORl5eHP/74A8uWLcPZs2eV+tNkMWVlZeHNN9/EwYMHsX79etja2ho0JiJDUPz9sbKywvr1681ioSkyjHnz5gkdAvLz85GdnY3s7Gw8evQIFy5ckO+ECehe1FFGNrYxZswYnX0iREREZNo4G4aIyEKFhoZi2bJl8p8Vdw4oKirCkSNHEB4eXuF6+/fvjzVr1igdU6y7oKAAb7zxBqKjo/XqoFu9ejUiIyO1Tr4HmNxmqUJDQ8s1EUzThEjZ8Zs3byI1NbVqgtSDSCSS7z5DZAouX76s8XetTp06Ro6GqtK0adNw4sQJbNmyReckdtnPAHD8+HH8+++/8tfp2lVo2LBh8knsih3OinUC5ZvELhKJEBQUpHMyE1m2svebisquGvnee+9h7ty5cHJyMkZoRERmY8OGDfIBakMmYCq+XiQSwd/fHxERERCLxejZsyevx2QwVlZW2LlzJ3r16oXExESN97OA+l2zgf92h01JSVE5rjhBYunSpWrfW7ZedeWKr5FKpahXrx4OHDiA2rVrl/+DEhFVEU0JmbLr6a5du7BixQqTTsz966+/tJZXZAEpMl26EmuTkpJMfufpPXv2aC1v06aNkSIhY8rLy8OuXbvUXkcV7xFFIhGWLFmiceE8IiIishw//PCD1kVlZMdeeOEFbNu2DS4uLlUeU40aNfDGG2/gjTfewNatW/H+++8jIyNDaZEz2d+3bNmCu3fv4q+//oKzs3OVx0aki7r+WRcXF2zcuJGLO1czc+bMMbnnKW3jCboWdvTy8sLixYurLjgiIiIyCiuhAyAioqrRo0cPWFn9d5lX9zCqa3BYk4iICHh6eqrUq5iU+c8//2Dw4MFKu2+Ux9KlS/HOO+9oHbgEAE9PTwQFBekTPpkJWaevuj+aXitLKCq7O4ap/CEyFffv30dGRgYA1QnJIpFIfo0nyyEbjCibfCmjeJ0qm5wp+1NQUCA/pvg+2X8jIyORkJCAGzduoLS0VOm96q7NZSm22apVK+zZs4e7tVZjmZmZiI2N1TmZzdXVFXFxcVi6dCmTf4iINCj7Ha/pu16Rrtf6+Phg7Nix2LJlCzIyMnDmzBl8/fXX6Nu3L6/HZHBOTk7Yv38/unTpovF+FlB/T1v2PNZE072rrt8VWVuy18iSMbkDFhGZijZt2sDe3h6A+gWSbt26hb///luQ2MqjuLgYP/74o9breMeOHY0YEVUVX19fteNZsr6k/fv3CxVaueTm5mLPnj1az9XOnTsbMSIylri4OPlYqKZF6aysrLBq1SrMmDHD5CYPExERkWFJpVKsXbtW4/iWrO+pb9++2LVrl1GSMcsaPnw4Ll26hP79+yvN81KMMSYmBr169ZKPD5PpUtcPquuPoeur6j/A//pnra2tMXLkSFy6dInJmNWY0PP/yjueUJZiuZ2dHbZt24aaNWsa9d+OiIiIDI8JmUREFsrV1RUBAQFqJ1ZKpVLs3r1br3ptbW0xatQojYlxsvr37NkDf39//P777ygpKdFaZ1JSEkJDQ/Hhhx8qJXdoqn/EiBF6xU7mQ5/OwbITJIXuFCxPZyaRELZv3661vHHjxsYJhIzG2toav//+OwYPHqx0jSxLW8dxeSex6xokUUfxNX5+fjh06BB3FKrmDh8+LL9/1DSZzdXVFQcOHEBwcLAgMRIRmSNNi8Zo++6WSqWoU6cOhgwZgrVr1yIlJQXXr1/Hjz/+iOHDh8PDw0Ooj0PViIuLC2JiYjBy5Eit97OA5vNcG23nv7Y6FF/fqlUrJCYmom3bthX5aGQkQvcNmfIfsmx2dnbo3LmzxmuZVCrFZ599htLSUiNHVj6rVq1Ceno6AM27eXfv3t2YIVEVcXV1hZ+fn9qFwAAgIyMDMTExQoRWLr/++iuys7MBqN+9297eHu3btxckNqpaR44c0Vgm6yudPHky3n77bSNGRUREREI5evQobt68CUD9+BYA1K1bF7/++itsbGyMHZ5czZo1sXPnTkyfPl1pnpfi348dO4bRo0cLFiOVjz4JZIaur6r/2NjYoEuXLli8eDGuX7+OjRs3on79+kb6FyZTJHR/qrq+VV2/Y4pjCdbW1tiwYQP7tIiIiCyEcE92RERU5cLCwnDixAn5z7LOMwC4du0ajh07ptdOk1OmTMHKlStRUFAg74wr24ZUKsXNmzfx6quvws3NDeHh4fD394enpyesrKxw//59pKWl4eDBg0odkmXrA5Q7J62trTFp0qQKx0zmpbyTJqvq/YbCiW1kagoKCrBy5Uqt52bLli2NGBEZi42NDf744w98/vnn+PLLLwGo3x1DRtt3sTplyysy+V32+u7du2Pnzp2oVatWud5LlisxMVFjmex+8YsvvkBgYKARoyIiMm/lmWgh4+TkhO7du0MsFkMsFqNDhw5VHB2RbnZ2dti4cSMCAwPx0UcfIScnR+v9rEx57ksr2odQdqLF0KFDsXbtWq5mbcJMpZ+ISAjh4eFISEhQOqbYD3/+/HksWrQIn3zyiUARqnf9+nXMmTNHpb9B8Wdvb280bdrU2KFRFenWrRvOnTunsfy7775DWFiYESMqn8LCQixatEht35nsdy00NFTQCfdUddSds4rnQp06dTBv3jxjhkREREQC2rNnj8Yy2b3hmjVrTGYs9KuvvgIAfP3112qTMrdt24auXbvinXfeEThSKqtHjx56z0eKjY1Vmpsn+7tIJEKPHj0MGWa5WVtbw87ODo6OjnB3d4enpyeaNm0KX19fdOjQAQ4ODoLERabJ3Pp6FccxnJyc8Ouvv2LAgAECR0VERESGwp5/IiILNmjQICxdulRj+S+//KJXQmb9+vUxadIkLFu2TOsgs+zvDx8+RGRkJCIjI9W+VkZdMmbZOseMGcNJFkREeigoKMDYsWNx6dIlrdfbjh07GjkyMqZ58+YhMDAQEyZMwN27d5VW7qvMRHZ9J7FLpVJYWVlh5syZ+OKLLzg5jQAAly5dUjmmeN3y8/PjAh1ERJWk+F1sY2ODzp07QywWIyIiAsHBwbC1tRU4QiL1Jk+ejL59+2L8+PGIiopSuxJ1VSnbjru7O5YuXYo33nijytqkyqnM5DQiSzFs2DCNyUCy56w5c+agU6dO6N27t5GjUy87OxuvvPIKsrKy1PZhycYKhg0bJlCEVBX69++P1atXKx1TnAz+559/4uTJkybXd7l8+XLcvHlTa3/rwIEDjRsUGc2NGzfUHpedu++88w4X7SAiEkBBQQFOnTqFS5cu4fLly0hPT8e9e/fw6NEjFBQUID8/H8XFxYLGKBKJcO3aNUFjIMOTSCQqx8omu/Xv31+AyDT76quvkJycjD179qhN0vvss88wePBg1KtXT+BISdHhw4f1fq+VlZXGspiYGL3rJaL/UbegeUBAALZs2YJWrVoJFBURERFVBZHU3JaLICKiCmncuDFu3boFQDVR0tnZGbdv34arq2uF63327Bnatm2rVHdZ5ZkQp+s1ivF6eXnhwoULcHNzq3C8ZB6srKy0TlwwN4qd6yUlJUKHQwZW9nw1xf/f2dnZOH36NA4ePIiff/4Z6enpGnc+lCXGZWZm8jpbDTx9+hQzZszAhg0bUFpaKtgkdl9fX3z//fcIDQ2tsjbJ/Pj6+uLq1asAoPYa++2332Ly5MlChkhEZBbmzp2LuXPnqn3GUryu1qtXD3379kVERATCw8Ph7u4uUMREFXPw4EHMmjULx48fB6D/ru2aaFqEzN7eHu+88w5mzZqlV58aEZGxBQQE4OzZswBUF0iUHatRowZ27tyJiIgIQWKUyc7ORr9+/ZCQkKDzHub06dPw9/cXKFIytJKSEtStWxePHj0CoNofAACdOnXC0aNHYW1tXaG6q6of9+zZswgMDERRUZFKzLKfbW1tcevWLXh6eurdDpkuLy8vPHjwAID6Pqzjx4+bXBIxEZGlunz5MiIjI7F3716cOHFC/v2syJTmIJjSeDIZRklJCVxcXFBQUABA/b3B5s2bMXz48ArVq+1edtSoUVi/fn2lY79//z7atGmjdC+u2M7rr7+OjRs3VrodMg3mMM+FSB1TnlOoaSwBAOrWrYvZs2fjrbfe0poQTUREROaJCZlERBbuww8/xFdffaV2UppIJMLixYsxY8YMveqOi4vD888/L189sDxJl+roep9UKoW1tTX+/vtvk1mlm6qGrPPEkrDj0rSFh4fr/d7Dhw9r7Kju2bOnoUKskNLSUhQWFiIrK0u+0quMuklMij+LRCIEBwfjyJEjRo+bhHP58mXMmjULO3bsUEnMlKnMI6OmSfFeXl747LPPMHHixApPoiPL5+7ujsePHwNQP2B9/fp1+Pj4CBkiEZFZ0JaQqUjx+1okEsHf3x8REREQi8Xo2bMnatSoYYxwifQWHx+PH374Adu3b0d+fj4A3X1RFSH7/WnQoAEmTpyI8ePHw8PDw2D1ExFVtY0bN2L06NEaExyB/yWOLVu2TLAFcK5cuYKBAwfi8uXL8mOa+rDCwsLU7j5D5u3dd9/Fd999p7X/curUqfj6668rVG9VTDjOyMhAly5dkJqaqjXeIUOGYOvWrXq1QabP2dkZeXl5AFQXxfX09ERGRoaQ4RERVQuRkZFYvnw5jh49Kj+ma1xL6PkInD9gmc6fP4927dqp3HcC//0/r1OnDtLT02Fra1uheo2RkAkAK1euxJQpU5Tql8Vua2uLmzdvcpdMC8GETDJX5jCnUPH3qmvXrhg3bhxee+21Cl/7iYiIyHwwIZOIyMKdPn1a6+qrnp6euH79OhwdHfWqf/369Rg3bpxSZ5yhKHYAfffdd3j77bcNVjeZJlNezaoy2HFpuirTYVfeSe1C0LXjcNnjsg72b775Bu+++65RYiTTkpqaip9++gk///wz7ty5Iz9uiHNZ8ZwLDg7G5MmTMXToUHY6k0Y1atRAYWEhANXJbE2aNMG1a9eEDI+IyGyUNyFTHdm119bWFsHBwfIEzaCgIK7gSyYrOzsbBw8exL59+yCRSJCWlqbxteXZIV4kEqFVq1bo06cPBg4ciG7dugn+rEdEpI+SkhK0bNkSN2/eBKC+b0h2XCQSoVevXli9ejUaN25slPiKi4vxzTffYPbs2cjPz1fajaVsnLKy6OhowRZEo6pz8+ZNtGjRAqWlpQA0J+QuWLAAH374YbnrNfSE4/T0dPTu3RsXLlzQea7Gx8eja9euFW6DzIOtra3S+ap4Pjz33HNISkoSMjwiIouWkJCAKVOm4PTp0wDU7wRvqpj4ZJl27tyJQYMGabzv7N+/P/76668K16vtXnbEiBHYtGmTQeIvLCyEt7c37t+/D0B1l8xZs2Zhzpw5BmmLhMWETDJXpjw2ZW1tjWbNmiEoKAg9e/bECy+8gPr16wsdFhERERmBjdABEBFR1erQoQOmTZuGrKwsja85e/YsgoOD9ap/zJgxKC0txdtvv620s5YhdtOSSqWwsrLCypUrmYxZTfTo0cPkB0jIMhk6CdgUkorLs9Oh4mtq1qyJMWPGVHlcZJp8fHwwf/58zJ8/HydOnJBPYj916hSys7P1rtfNzQ2BgYHySeyNGjUyYNRkqWQT2RTJBgK5+i4RkeGp29Fadt9YWFiI+Ph4xMfHY/bs2XBxcUHPnj0hFosRERGBNm3aCBEykVouLi4YPHgwBg8eDAB48OABTp06hfPnz+PWrVu4ffs2MjIykJOTg/z8fBQUFMDW1hY1atSAo6MjvLy84O3tDR8fH/j7+6Nz585wdXUV+FMREVWetbU1lixZgiFDhmjsLxKJRPJJmAcPHoSvry/GjRuHqVOnonnz5lUSV35+PrZs2YIvv/wSN2/eVJkMqkhxguhLL73EZEwL1bhxYwwfPhybN29We48qOw8++eQTpKamynfTNKakpCQMHToUaWlpattWPFfDw8OZjGnhnJyc1PadikQieHp6ChAREZHlKykpwWeffYavvvpKqQ9L3b2DKeJcCMuluOCtOp07dzZ4m4ZMnrOzs8OIESOwfPlypfNUdn/7+++/MyHTwvB6ROZm9uzZQocAa2tr2Nvbw8HBAW5ubvDy8kKDBg3QvHlzLkhORERUTXGHTCIiMoioqCi8+eabuHPnjl6d3ereU7duXfz6668IDw83aKxERDKWukOmjK7rr+IEoaVLl2LatGlGiozMydWrV/WexN6sWTOhwycz5O7ujsePHwNQXYF3wIAB2LFjh8AREhGZB8UdMsvStiOgrtcqvsbLy0uenBkREYEGDRpUMmoiIiKqKgMHDsSuXbs07p5ddrFFWZJmWFgYBg8ejBdffBENGzasVAz5+fk4fPgw/vrrL/zxxx948uSJyiR6bTt4urq64uLFi9xlwILduXMHrVu3Rk5ODgD154OsjyAsLAyrVq2Cr6+v1joNsQNMXl4eFi5ciMWLF6OoqEipHsXYZDFbW1vj5MmT8Pf3L98HJ7Pk7e0tT74o24dlyB2riIjoPwUFBRg8eDD27dun8x7SVHEnOss1a9YsfPnllxrvO/ft24devXpVuF5t97KDBw/G77//brDPEB8fj549e2ps7+bNm1x81wJo2mWQ1yUiIiIioorjDplERGQQYrEY586dw5dffonvv/8eeXl5AP43aaM8ZB16Dg4OmDhxIubMmcOdCIjIKCxxh0xdFK/NoaGhmDJlioDRkClr0aIFWrRoIXQYVI24urrKEzLLcnZ2NnI0RETmKyIiAufPn8fhw4fx8OFD+XF1z+my+1d1E961LbqUkZGBLVu2YMuWLQD+u2+QJWeGhYWhZs2aBv1MREREpL8ffvgBJ0+elC+qWPZ7X92kdqlUiujoaERHR2Py5Mnw9vZGUFAQfH190aJFC9SrVw+enp5wdnaGnZ0drKysUFhYiPz8fDx48ACZmZm4efMmkpOTcerUKZw+fRrFxcVq21M8VpZsAvCPP/7IZEwL16BBA8yZMwczZszQuqOr7Nxs164d3nrrLXz22WeoV6+eweN59uwZ1q1bh6VLlyI9PV3eviwWTfG9/fbbTMasBry8vHD79m2152pBQYEAERERWa6SkhL0798fEokEgPklYpLlky0ooom+i9vIznV1z3DqduqujKCgINjb26OwsFBte7KF+sm8bdiwQegQiIiIiIgsBnfIJCIig3vw4AE2bdqEbdu24eTJk+VaQUskEiEgIABDhw7FmDFj4O7uboRIiai6K7uiZHVQdpJbcHAw9u3bx8nyRGQyQkNDERcXp7Tqruy/gwYNQmRkpNAhEhGZFalUitOnTyMqKgpRUVGIj49Hbm6uvLyqdtC0srJCx44d5QmaISEhsLOzq8xHISIioko6efIkunfvjvz8fADl+87Xtlt2RWirR1scsufBTz75BPPnz9erbTIvUqkUvXv3hkQi0bqjq2JSr5WVFbp164ahQ4diwIABSom7Fd0h88mTJzh8+DAiIyOxe/duPHv2rFw7ucrqbN26NU6cOAF7e3vD/IOQyRo9ejQ2btyotg9LLBbj4MGDQodIRGQx3nvvPaxatapc95Ay+t63VjXukGmZ3n77baxdu1bjfWdaWhoaNGhQ4Xpr1KiBwsJCAKo7cnft2hXx8fEG/Rxt27bFpUuX1LY3bdo0LF261KDtERERERERmTMmZBIRUZXKzs7G8ePHceXKFaSlpSE7Oxt5eXmwtbWFi4sLGjVqhJYtWyIwMBC1a9cWOlwiqmYsPSFT00CjVCqFtbU13nvvPSxYsICTg4jIpLzzzjv4/vvv1U5mCwkJQVxcnNAhEhGZtaKiIiQmJsoTNJOSkuQ7VQGq95Da7pUrkqDp4OCAkJAQREREQCwWo1OnTpX5GERERKSnQ4cOYeDAgTqTMoGKLdxQHvrcZ0ilUkyePBkrV67Uu10yP48ePULHjh1x69YtAOrPlbLJkYrnV61atdC6dWu0adMGP/30k8aJ8X///TcyMjJw+/ZtXLp0CefPn8fFixdVdpAvT5KyVCqFs7Mz/vnnH7Rt29Yg/w5k2pYtWybfzbXs7qnNmjXD1atXBY6QiMgyxMTEQCwWl2tXzIrcbwqJCZmW56233sK6des03ndmZWXBycmpwvXWrFkTz549AwCV+41WrVrJkycN5cUXX8TevXvVjtH169cPu3btMmh7RERERERE5owJmURERERUbckSMvVR0clqQlKMtVatWhg2bBjef/99tGjRQsCoiIjU+/HHHzFhwgS1k9nq1auHO3fuCBwhEZFlefbsGWJjY+UJmufPn1c78VymMpPeFMtr166NsLAwiMViREREoHnz5pX5GERERFQBMTExGDRoELKzs+XHyjNkbIg+r4q0I5VKMXPmTCxevLjS7ZL5SU5ORlhYGDIzMwFoT8pUV26IpA1du3AptmFnZ4fdu3fj+eef19geWZbTp0+jY8eOavuwrK2t8fDhQ7i6ugocJRGReZNKpfDz80NycrLWRXbVfWc3bdoUHTp0gL+/P/z8/FC7dm24urrCxcUFNWrUMEr82vj4+AgdAhnQtGnTsGLFigrtzF4e9erVU7ofVrzfcHNzw4MHDwzzAf7fa6+9hq1bt6okZAJA69atceHCBYO2R0REREREZM5shA6AiIiIiEhIVbE+iamseWJlZYWGDRvC19cXgYGB6N69O0JDQ2Frayt0aEREGr3wwgtKPysOMGdkZCA9PR3169cXIjQiIovk7OyMfv36oV+/fgCA+/fvy5Mzo6KicPPmTflrRSKR1knrin/X9dpHjx5hx44d2LFjBwCgUaNG8t0zxWIxPD09DfYZiYiISFlYWBiSkpLw8ssv4+LFi/LvbV19WlXd56U4ubhGjRpYu3YtXn/99Sptk0yXr68vJBIJwsPD8eDBA7XnqOJkd312xNKV5KmtHsXz1cbGBj///DOTMauZDh06wNvbG7du3VJJyiwtLUVCQgL69u0rcJREROZt+/bt5U7GlEqlqFu3LkaOHIkRI0agXbt2xgyVqjldizDk5+fDwcGhwvW6u7vLEzIB5TGzx48fIzc3F46OjhWuVxNra2uVY7LfvydPnhisHSIiIiIiIkvAhEwiIiIiqrZiYmL0ep9UKkV4eLjGFS6jo6MNGWa5WVtbw87ODo6OjnB3d4e7uztsbHjLT0TmpVGjRggICMCpU6fUTrKIiorCyJEjBYqOiMjyeXh4YPjw4Rg+fDgA4Pr16/LkzJiYGNy/f1/+Wm0T39XtLqRtcntaWho2bNiADRs2AADatm0r3z2zZ8+ecHJyMtyHJCLSU3h4uNrjIpEIUVFRRo6mYhYvXowDBw6oLTOH+MnwWrZsiaSkJHzwwQdYs2aN0sRewLgLjpVtNzg4GD/++CP8/PyMFgOZJj8/PyQmJuKll16SJw8DquentnvPiuyQqak+Te+TSqVwdHTEH3/8gT59+mj/MGSRhg8fjiVLlqg9lyIjI5mQSURUSWvWrNFYpnjttba2xnvvvYe5c+eyD4kEoSshMy8vT++EzLLPaopu3Lhh0OcmbUmXz549M1g7RERERERElkAkNZXte4iIiIiIzIiVlZXGhMySkhKBoyMiMm+rVq3Ce++9p3Rtlf23d+/e2Lt3r9AhEhFVW2fOnJEnaMbHxytNxFE3MUjX7gXaXit7jY2NDQIDAxEREYGIiAiEhIRU5iMQEelN1hegyFz6AkaPHo2NGzeabfxUtY4fP46pU6ciMTERQPl3CKwMdW3UrVsXc+bMwfjx4w3eHpm3Z8+eYfTo0di+fbtgicMyismYzZo1w7Zt29CxY0ejx0Gm4e7du2jSpAmKioqUjkulUjg5OSE1NRVubm4CRUdEZN4yMzPRoEEDlJaWAlD+3lf8Pq5Zsyb27duH4OBgQeIkAoAff/wREyZM0Dh/4NKlS2jZsmWF63399dexZcsWjfX+9ttvGDp0qME+R7du3fDPP/+o7P4tlUphbW2tcs9DRERERERUnVkJHQARERERERERkaKxY8eiTp06AP43sUI2+Hvw4EEkJycLGR4RUbXWvn17vP/++9izZw8ePXqE+Ph4zJ49G926dYONjQ2kUqn8D/C/3YnUJV3oeq2srKioCP/88w/mzZuHnj17GvcDExGpoXjtMkfmHj8ZXufOnXHkyBEcOnQIYrEYADR+R2vamUWbsu8v+13fpEkTrFy5Ejdu3GAyJqnl7OyMP/74A7/99pt8lyBA/S7sVUXWluy8ffPNN3H69GkmY1Zz9erVw5tvvik/JxW/X3Nzc7FkyRKhQiMiMnuHDx+WLxyjKRnT1dUVBw4cYDImCc7Hx0dr+b179/Sqt3nz5lrLT548qVe9mijuSl+WjY2NQdsiIiIiIiIyd0zIJCIiIiIisiAxMTHYsWMHHj9+LHQoRHqrUaMGPvzwQ7WT2aRSKT799FOhQiMiIgU2NjYICQnB7NmzERcXh8ePH2Pv3r14//330b59ewDlT+Yom6CpLWmDiEhoxkwAqgrmHj9VHbFYjEOHDuHGjRuYN28eOnToAABaF1Eoz5+ydUilUtSrVw/jx49HXFwcUlJSMHnyZNjb2wv10clMDBs2DJcvX8ZHH30EJycnnYuAVJZivbJzt3PnzoiPj8f69evh5ORk0PbIPC1cuBCenp4AVBcWW758Oc6fPy9keEREZku2e7s6sr6jL774AoGBgUaMiki9Vq1aaS2/fv26XvW2aNFCa3lsbKxe9apz6dIlPHnyBID6negdHR0N1hYREREREZElEEk5g4WIiIiIqMKsrKzUTiIXiUTy1VqJhDB37lzMmzcPVlZW6NChA8RiMcRiMbp37w4HBwehwyMqt5KSEnTu3BlnzpwB8L9rrOy/W7duxZAhQwSOkoiItHn48CGio6MhkUgQFRWlNPFI8V5aVxe1YiIH77eJSEiyvgDFBCBzuTaNHj0aGzduNNv4STgPHjxAVFQUkpKScPbsWZw/f77Cu7s4OjqiTZs2aNeuHQICAhAWFoY2bdpUUcRUXTx48ADff/89NmzYgNTUVADQmpCp7Z5T0/sU3yMWi/Hee++hf//+ekZMliwyMhJDhw5V+m4F/juHWrZsiaSkJLi6ugocJRGReXnhhRdw8OBBtc8wANC2bVucPn0aVlbcj4JMg5OTE/Lz8wGojml98MEHWLhwYYXrvHTpEvz8/FR+D2Rt2NjYID09He7u7pWOf8mSJfjoo480/s61bt0aFy5cqHQ7REREREREloIJmUREREREemBCJpmquXPnYu7cufKfZeepnZ0dgoODERERAbFYjMDAQA5Sk8k7c+YMQkJCkJeXp3RcKpXCxcUFR44cQdu2bQWKjoiIKio1NRVRUVGQSCSIjo5GZmZmud/LpCEiMgVMyCT6T35+PtLT03H37l08ffoU+fn5yM/PR2lpKezt7eHg4AAnJyfUrVsX9erVQ+3atYUOmSyYVCpFVFQUdu7ciX379qndfag8O2eqmzYREBCAgQMHYujQoTp3PSL6+OOPsXjxYqXvV9l5FRwcjAMHDsDFxUXgKImIzIevry+uXr0KAGqfYb799ltMnjxZyBCJlAQEBODMmTMq9wIikQh9+vTB33//XeE6pVIpateujezsbPnPgPLvwooVK/DOO+9UKnapVIqmTZsiLS1NYzu9e/fG3r17K9UOEZGlyc3NxdOnT+Ho6AhXV9dy9T8QERGR5WBCJhERERGRHho3bqyxI+3GjRtGjobof2QJmYoTfmQUz1kXFxf06NFDnqDp5+dn7FCJyiUyMhLDhg1TOS6VSuHh4QGJRAJ/f38BIiMioso6duwY5syZgwMHDqi9d1HEpCEiMgVMyCQiMn03b97Ev//+i1OnTuH8+fO4desWbt++jQcPHmh8T+3ateHt7Q0fHx/4+/sjMDAQgYGB8PLyMmLkZAmGDh2KyMhIpX5Y2Xetn58fdu7ciSZNmggYIRGR+XB3d8fjx48BqE8Ou379Onx8fIQMkUjJG2+8gV9//VXtjtl16tTB/fv39aq3f//+2LNnj8ZdMn18fHD16lXY2NjoHfuaNWswadIklT5axc8yc+ZMLFq0SO82iIjM3d27d7F//34cOnQIiYmJyMzMlO+MDAC2trZo3rw5goKC0LdvX/Tv3x92dnYCRkxERERVjQmZREREREREFkQxIbMsbQmanp6eCA8Plydoent7V3msROX1zTff4P3331e7M7GzszPWrVuHIUOGCBQdERGVV2lpKY4fPw6JRIKoqCgkJiaisLAQgPodiRQxaYgqKicnBwUFBcjPz0dxcbHQ4fD+2kIwIZOIyHyVlJQgNzcX+fn5KCgogK2tLWrUqAFHR8dKTV4nUlRaWooxY8Zg06ZNKkmZAODs7IwFCxZg0qRJsLKyEipMIiKzUKNGDaV+I8UEtCZNmuDatWtChkek4qeffsL48eM1PndfuHABvr6+Fa73hx9+wMSJE7UmS3766aeYN2+eXnFfvXoVAQEByMvLAwCNbURGRmLQoEF6tUFEZM7u3LmD+fPnY8OGDfKxBl0LjAL/LS4xZcoUTJ8+Hfb29kaJlYiIiIyLCZlEREREREQWRNMOmRVN0GzatCnEYjHEYjHCw8NRp06dqguaqBzWr1+PCRMmoLS0VOm4bCB40KBBWLx4MZo1ayZQhEREpE5ycjIkEgkkEgliY2ORlZUlL6tI1zSThkidvLw8HD16FMeOHcOlS5dw+fJlpKenIzMzE0VFRUKHJycSiUwiKZQqjwmZREREVB6ffPIJFi9erHJc9r3btGlTzJw5E6+++ipcXFwEiJCIyPTZ29srJT0oPr906dIFCQkJAkdIpCwlJQUtW7bU+Ny9dOlSTJs2rcL1ZmRkwNvbW/7crm6XTCsrK2zcuBEjRoyoUN1XrlzB888/j1u3bmkcW5ZKpbCzs8P9+/d530JEgkpNTcXTp081ltvb26NVq1YGbXPTpk2YOHEiCgoKdM6/AdTPwWncuDF+++03BAYGGjQ2IiIiEh4TMomIiIiIiCyIpoTMsiqSoCkSidCuXTt5gmaPHj3g6Oho2MCJymHfvn0YNWoUHjx4oLLLgEgkgrW1NV555RWMHj0a4eHhsLa2FjBaIqLqKT09Xb4DZlRUFO7evSsv03SvUZ4uaiYNkcyTJ0+wbds2REZGIi4uTinR0VSHO3jeWg5zTsh888035bt1mWP8RERE5mb//v3yfixFit/DDg4OCA0NRUREBAICAtC6dWt4eXkJES4Rkclxd3fH48ePAagmZA4YMAA7duwQOEIiVQ0bNpT3h5Y9b8PCwiCRSPSqd9CgQdi5c6fa8V/FNqZPn445c+aUaxx3w4YN+PDDD+Vjbtrq7devH3bt2qVX7EREhuLj44Pbt29rLH/rrbewZs0ag7U3a9YsLFiwQCURXlF5F0m3s7PDunXrKpw4T0RERKaNCZlEREREREQWpLwJmWVVJEHT1tYWQUFB8gTN4OBgJr5VY2lpaUZt7/bt2xgzZgyuXLmikpQJ/O88dXFxQc+ePeWT2Ro2bAg3Nzc4ODgIer56e3sL1jYRUVXIyspCTEyMPAnz8uXL8jJtu3Freo0mTBqi9PR0LFiwABs3bkRubi4A9eePppWphcLz1rKYc0Lm0KFDERkZqRQz8L+dLvLz8wWOkIiIqGo1bdrU6G0+ffoUjx8/Vvn+BVT7smRsbGxQq1YtQfuwRCIRrl27JkjbREQyTZs2RWpqKgDVxLYRI0Zg06ZNAkdIpGrkyJHYvHmz2mdva2tr3L59W6/FF6KjoxEREaFx/Fexvdq1a+P1119H79694e/vD09PT1hZWeH+/ftIS0vDgQMH8Pvvv+PSpUsqMWqqc/fu3ejbt68e/yJERIZx9OhRdO3aVWO5SCTChQsX4Ovra5D2vvjiC3z++efyumX0nYMj2834559/xuuvv26QGImIiEh4TMgkIiIlhYWFOH36NC5evIjLly/jzp07yMzMxMOHD5Gfn4+CggKlnQeEwEFAIiIizRQTMsuqTIKmtoQKJycn9OjRQ56g2a5duwpGTeZMNind2Mqzy5qpJWSIRCLB76WJiCqrsLAQR44cQVRUFCQSCU6cOIHS0lIAhk3AVPd+c0l6IsMqLS3FokWLsHDhQuTm5pb7u94Uhj7MKVmPysecEzL79u2L/fv3q50U6uLigqdPnwocIRERUdUq+z1uDNqSJmRM4b61LHO4tyEiyxcaGoq4uDilZxjZfwcNGoTIyEihQyRSsWXLFrz++usa+w6WLVuGKVOm6FV3WFgYYmNjNe5mCWhe8KGssq/TlozZqVMnJCUl6RUzEZGhTJs2DStWrNB4fevVqxf27dtnkLZiY2MRHh6udKwyz22K11p7e3tER0ejS5culYqRiIiITION0AEQEZHwLl++jB07dmDPnj04ceIECgsLVV5jSoOBpjaxnoSXkZGBEydO4Ny5c7h06RLS09ORkZGBBw8eIC8vD/n5+SgqKhIsvilTpmDZsmWCtU9E1UuvXr2QnJyMmJgYZGZmyo+LRCKdSZaaynS999mzZ9i3b5+8g9vd3R3h4eHyBM0mTZpU6jOR6TOFe0XZwLCpT2gjIjJHJ0+elO+AmZCQIN9FraoTMMu+v0GDBiqD4GTZ7t69i6FDh+Kff/7ROKGM3/dE5ZOenq6xzMXFxYiREBERCctY44xSqVTjvariva2pjXvy/pqITEXbtm0RFxentkxx/IvIlAwYMAA1atRAfn6+2qTM1atX652QuWzZMgQFBaGkpEQlKVNXgqU62pIxFf/OuS5EZAoiIyPVJp/L5ghMnTrVIO0UFRVh3LhxSotBVJbiPIaCggK89tprOHfuHJydnQ0QMREREQmJCZlERNXY77//jm+//RaJiYnyY7oeIoUeFOQgIMkkJiZi69atkEgkSE5OVik3lXNF6N8ZIqp+unTpIl9N79y5c/Lkibi4ODx79kz+uookaKobiNP23vv37+P333/H77//DgDw8fFBREQExGIxwsPD4eHhof8HJJNkCjtklj1mahPaTOXehIioPFJSUuQ7YMbExODx48fyMl1JmIZMwKxduzZCQ0Plizy0atWq3HWT+bt27Rqef/55pKamKu3mB/B7laiiSkpKkJycrPGa7e7uLkRYREREFq08CRG6ds8kIqrO2rdvr3JMlhhx7do1ASIi0s3JyQn9+vWTJw6V/V6/evUq9u/fjxdeeKHCdQcEBOCTTz7BvHnzNN4vqFu8VBNd9ygikQhTpkxBt27dKhwrEZEhXbt2DXfu3FFJdJepVasWnn/+eYO0tWrVKly7dk1nMmZ5dyJW/Fn2nrS0NHz88cdYuXJl5QMmIiIiQYmknLlARFTtREdH4/3338e5c+cAqE5cN2Wyh9OSkhKhQyEBFBUV4aeffsI333yDlJQUANo7iYU+nxU7qblqIBEJraSkBEePHkVUVBSioqJw9OhRpd2DqzKZouxr2rZtK0/Q7NmzJ5ycnMrdFpkeKysrg60OaakUV+fkfSwRmaLMzEz5PYJEIsGtW7fkZVW9C6bie2vUqIGQkBB5AmanTp0Ef64jYdy/fx+BgYFITU0FULHV/RWZwvnDewDLUvbe11zu8/755x9069ZNY+y9e/fG3r17BY6SiIioarEPSzdzubchourh1q1b8PHx0fgcc+vWLdSvX1/gKIlU/fnnnxg8eLDGfqmQkBCNu7/qIpVKMWDAAPz9999Vcl+j2Af3/PPPY9++fbCysjJoG0REFbV582aMHDlS4z3B8OHDsXnz5kq3U1BQgCZNmuDevXsANI9H6Bqv0LUDsVQqha2tLS5evIhmzZpVOm4iIiISDnfIJCKqRgoKCjB9+nSsXr0aAJQeUBWZ6kCkKUyiI+Fs27YNM2bMQHp6erkmBJdl7POa5ysRmRpra2uEhIQgJCQEn3/+OXJzcxEXFydPvDh79qzSvUFF7g8quoPmuXPncP78eXzzzTewsbFB586dIRaLERERgS5dusDGho+qREREVSknJwexsbGQSCSQSCS4cOGCvKyqEzAV329tbY3nnntOvlBD165dYWdnV+66yXINGTIEqamp5U7E5DM4kXYbN27UWt68eXMjRUJERERERFQ+jRo1QkBAAE6dOqU28SwqKgojR44UKDoizfr27YtatWohOztbbXliYiIkEgkiIiIqXLdIJMLWrVvx0ksvITo6Wu9FzDTVLasrNDQUO3bsYDImEZmEo0ePai1/6aWXDNLOli1bkJGRoTHhXfE62aBBA7z22msQi8XyBSJu3bqFgwcP4tdff8XDhw9V6lHcJbO4uBiLFi3Cjz/+aJDYiYiISBjcIZOIqJp49OgRXnrpJSQmJqokYprLVwFXZa2enj59ilGjRmH37t1mlUSseL5yh0wiMgcPHz5EdHQ0JBIJoqKicP36dXlZVe6EVbbc0dER3bp1kydodujQodztkDC4u4BuvI8lIqGVlJQgMTFRvhBDUlISiouLARg3ARMA/Pz85DtghoaGwsXFpdx1U/Xw/fff45133ilXv5Xi+abudTVq1ICLiwtq1Khh+EAr6MaNG0KHQAZgjjtkJiQkICwsDKWlpQCgNvbvvvsOEydOFDJMIiKiKsc+LN3M4d6GiKqXVatW4b333lO6Psn+27t3b+zdu1foEIkEUVRUhLfeegubNm3S2T+mS9k+uFGjRmH16tVwcHAwTLBERJXUuXNnnDhxQuk+APjvumVjY4MHDx7A1dW10u107doVR48eVfvcqHhs0qRJWLJkCRwdHdXWk5WVhTFjxmDHjh0qdSnG7ujoiDt37qBmzZqVjp2IiIiEwYRMIqJqICsrC927d8f58+dVHkrNCQcBq5+UlBS8+OKLuHr1qtK5C5j++cuETCIyd6mpqfLkzOjoaGRmZsrLjJmg6ebmhrCwMERERGD8+PHlboOMRzaZjbTjfSwRGVtaWhq2b98OiUSC+Ph45OTkyMt0JWEaMgHT29tbvtBCeHg4vLy8yl03VT85OTnw8fHB48ePAWg+F8v2bXl4eKB///7o0KED/P394efnh9q1a8Pa2to4gVO1YU4JmTk5OVi/fj1mzZqFrKwstZN/ZLGfPHkS7du3FzBaIiKiqsc+rPIx1XsbIqqe8vLy4O3tjUePHikdl0qlsLKywvnz5+Hr6ytQdETC++OPPzB9+nTcvn27Uvc5st3eli1bhiFDhhgwQiKiynN2dkZeXh4AqMx99fX1xcWLFyvdRkpKClq2bKk1GVMkEmHq1Kn4+uuvy1XnyJEjsXnzZq39smvXrsW4ceMqHT8REREJgwmZREQWTiqVolevXoiKiip3IqYpD0ZyELD6uHnzJrp164b09HQA5r2jKxMyhZeWllbu13p7exu8TnNT3n8Dql7OnTuHqKgoREVFIS4uDtnZ2fKyyiRolve9/P43XZzMVj68jyUiY5s7dy7mzZsHwLgJmO7u7ggLC5PvgtmsWbOKhE3V3PLlyzF9+nStOxcplvXr1w+TJk1Cr169mHxJ5RYXF6f3e0NDQzUmZB4+fFiQfqvS0lIUFhYiKysL9+7dw40bN3Dq1CkcPXoU+fn5SrvIyCj2s7m7u+PevXu8pycisye7/p06dQopKSm4ffs20tPTkZ2djby8POTl5alcC2W7xlP1YGVlJXQIZoN9WERkSpYuXYoPPvhA7S6ZAwcOxPbt24UOkUhQhYWF+OGHH/DTTz/h7NmzSmXlGYf19fXFpEmTMHbsWNSoUaNKYyUiqqgHDx7A09NTY5/skCFDsHXr1kq3s2jRInzyyScakycBwM/PD2fOnCn3s2VBQQGee+45ecKouvhfeOEF7Nmzp9LxExERkTBshA6AiIiq1vLly8udjGlOuw+SZXv69Cl69eqF9PR0neeutsli5T3ftSnP74KmujiRzXQ0bty4XP8/KjIJp7x1mhtORCJN/P394e/vj6lTp6KkpATHjh1DVFQUJBIJjh07hsLCQvlrRSJRue8r1CWIVCZJhIzP29vbIq+HRESWQDagW5WLJzg5OaF79+7yBMwOHTroHS/Rzz//rLFMsX+gcePGWLlyJfr162ekyMiSyJIq9aV4DZT9XSqVIjQ0tLKhGZTiBB9N5SKRCAMGDOD9PGl048YNXL58Genp6bh37x4ePXqEgoIC5OfnC95/JBKJsG7dOkFjIOEdOHAAf/31F/bv3692AT32KZGiGzduCB0CERHpYdq0adiyZQvOnDmjkpT5119/4Y8//uCOflSt2dnZ4Z133sE777yDS5cuISYmBkePHsWVK1eQlpYmX6DE1tYWLi4uaNSoEVq2bImgoCBERESgbdu2Qn8EIiKNUlNTtZb7+/sbpJ1du3ZpLReJRJg9e3aFFvqxt7fH/Pnz8fLLL6v0v8ruZQ4fPozi4mLY2DCdg4iIyBxxh0wiIgt2//59NGvWDDk5OQB0J7TJym1sbNCpUyd06tQJrVq1QuPGjeHu7o6aNWvC3t7eJB4AfXx8hA6BqtCrr76Kbdu2aU3GNEYCsaESgqZOncodMgVW3g6xiqz6bKmraXPla9JHbm4u4uPjIZFIEBUVhTNnzmid/FvRXbi4syAREVHFzZ07F3Pnzi3XAk1laXsWsrW1RVBQkDwBMzg42CT6Ccj8XblyBb6+vmp3x1Q85u/vj6ioKLi7uwsRJlkA2Q7vlj48pq3vTPE5KzExEYGBgcYOj0zUkSNHsHfvXiQkJODff/9Ffn6+0CGpxX6C6i0rKwvffvstfvzxR9y+fRtAxRd0rOw59Omnn2LDhg0ay2vVqoWzZ8/yPpmIiMhAzpw5g5CQEOTl5Skdl0qlcHFxwZEjR5hURkREZIF27NiBV155ReMOmX/99Rf69+9fqTaePHkCDw8PlJaWAlBd6E4qlaJBgwZIS0vTa2E7Pz8/JCcnq9TN/lkiIiLzxxEAIiILtnTpUjx79kzrBCPFsu7du2P8+PHo378/XF1djRkqkdyff/5Z7mRMqVQKOzs79O3bF7169UKnTp3QpEkTuLq6ws7OTmWCnWJnxqhRo7B+/XoAQH5+PnJzc5GRkYHbt2/j3LlzOHHiBKKionD//n35exXbVayrY8eO2Lx5M1q2bFnl/z6kP12dYvpMxLS0HSQsfTIqVR1HR0f07t0bvXv3BgA8fPgQ0dHRiIqKQlRUFK5duyZ/bdnrKREREVU9Xd+52hZQEIlEaN++vTwBs0ePHnBycqqSOKl6i42NVXtc8bm+RYsWiI6ORp06dYwZGlkofZ7ptV1PTbGPQFO/mqxPKywsjJN9CHfv3sWqVavw888/IyMjQ36cz+xkaoqKivDVV19h6dKlePr0qdI5Wt5rsOz6V1kTJkzA119/jcLCQrXl9+7dw549ezBgwIBKt0VERERA+/bt8fPPP2PYsGFKx0UiEbKzsyEWiyGRSAy2SxYRERGZBtlCTJrUq1ev0m3ExMSgpKRE7RxbWT/CyJEj9e5PGDRoEBYsWKDx/SdOnGAfLRERkZliQiYRkYUqLi7Gxo0bNT7IKSZCtGzZEqtXr0ZYWJgxQyRSUVJSgo8++kj+s7rV+2XHbWxsMGXKFHzwwQfw8PCoVLsODg5wcHCAm5sb2rRpg169esnLjhw5gnXr1uG3335DQUGBPJFIceLGyZMn0bFjR3zzzTcYN25cpWKhqlXRldIrU6e5McWJo2S+6tSpgyFDhmDIkCEAgLS0NPnumdHR0bh3757AERIREVVvunawbtq0KcRiMSIiIhAeHs7kNzKKY8eOaS0XiURYuXIlz0cyGEM/z5tD/4Di9d/BwQHfffedgNGQ0J48eYI5c+ZgzZo1KCoq0tgXa4rM4feNDOvo0aMYO3YskpOTVXaqkDHmeeHt7Y0333wTP/zwg8Y4Nm7cyIRMIiIiA3rllVfw9ddf4/3331f6/hWJRLh//z5CQkKwbt06+dgUERERmb9nz55pLa9Zs2al20hISND5mldeeUXv+vv164cFCxZoLJftnklERETmx0roAIiIqGrExcUhMzMTgPqkNqlUCqlUim7duiEpKYnJmGQSfvvtN1y9elXtilOKyZiNGjXC0aNH8dVXX1U6GVOXkJAQrF+/HtevX8f48ePlcSj+HkmlUuTm5mLChAn47LPPqjQeIiJz5O3tjTFjxmDz5s24e/cuEhMT5btpmvIETyIiIkshW1im7E7Vsj+enp549dVX8dNPP+HGjRtISUnB2rVrMWTIECa/kdGkpKSoHFNcEKlPnz5KCygRUcUoXv+trKywfv16tGrVSuCoSCj79++Hr68vVq5cicLCQvm1Vtv9gqn8oepnxYoV6NGjhzwZU9M5amzTp09X268lu3/Zu3cvHj9+bPS4iIiILNnUqVPx008/wcpKecqjSCTCs2fPMHz4cLzyyiu4du2aQBESERGRIeXl5Wktr6qETMXn/Xr16qFjx45619++fXul+YZlqRsbISIiIvPAhEwiIgsVExOj9rjiRLYuXbrg4MGDcHV1NXJ0ROqtXr1a7XHFzohmzZohMTGxUh0d+qhXrx7WrFmDuLg4eHt7K+2Qqbhr5sKFCzF58mSjxkZEZOpKS0tx7NgxfPnllwgPD0doaCgOHjyoNgGfiIiIqobiZHVnZ2e8+OKLWL58Oc6ePYu7d+9i8+bNGDNmDHx8fIQOlaqpmzdval2s4/XXXzdiNESWQV1ynYuLCyIjIzFs2DCBoyOhLFy4EC+++CIyMzO1JrfxeZ1MwYQJE/D++++juLhYqU/eFM7RFi1aoE+fPvI4ysZUVFSE3377TajwiIiILNaYMWOwa9cu1KlTR+m7Vzbm9Oeff6J169Z47bXXcOjQIZSUlAgYLREREVVGbm6u1vLKznvNz8/H6dOn1Y5NyPohXnjhhUq14ejoiIYNG6otk0qluHv3bqXqJyIiIuHYCB0AERFVjVOnTqkcU3xwtLW1xY8//ggHBwdjhkWk0ZUrV5CYmKiSnKM4wcLe3h67du1C/fr1hQoTXbt2xYkTJzBo0CDEx8crJTnL/r5mzRo0a9YM77//vmBxknpVsRMfd/cjUi85ORkSiQQSiQSxsbHIysqSlwk9YY6IiKg6kT1LdenSBWKxGGKxGJ07d4a1tbXQoREpefr0qdLPis9a1tbWlZ70QFSWPs/z2p5lTLV/QBazjY0NXn31VSxcuFDQvjUS1qxZs7BgwQKlxDaAz+lkmt566y2sW7cOgPI4gTrarsFVeX6PHz8ee/fu1Vj+559/YtKkSVXWPhERkaVLS0tTe9zPzw9//fUXxowZgytXrigtpCyVSlFcXIxt27Zh27ZtcHFxQc+ePREQEIDWrVujYcOGcHNzg4ODg6D9Y97e3oK1TUREZC50JWTa2dlVqv5///0XRUVFWhcTDw8Pr1QbANCkSRPcunVLqf9C1mZmZmal6yciIiJhMCGTiMhCXb16VevKPePGjUObNm0EiIxIvT179mgsk523s2fPRuvWrY0YlXpubm44cOAA+vXrh5iYGLVJmR9++CE6dOhgkE4ZIiJzkJ6eDolEgqioKERFRSmt4le241rXJDoiIiKqvGbNmuGDDz6AWCxG9+7duSATmTx1Eytk94stW7ZErVq1jBwRWTpDP4+Y4vONra0tnnvuOQwcOBCvvvqqxpXYqXr4+eef8eWXX6rsiKmLqSYbk2X75JNPsG7dOp2Jw0InFvft2xceHh548OCB0uRN2d//+ecfFBYWVnqCKBERUXXVuHHjct2PalrwGQCysrLw999/4++//66aIPUgEolQXFwsdBhEREQmr6ioSGt5bm4unJ2d9a7/n3/+0fmasLAwveuXcXFx0ViWnZ1d6fqJiIhIGEzIJCKyUPfv39daPnToUCNFQlQ++/fvVzmmOLjSokULfPjhh8YMSSsHBwf8+eefCAwMlCdAKyZllpSUYMKECTh//jzs7e2FDrfa8/b2Nvjksaqok8icZGVlISYmRp6EefnyZXmZpgRMRfrsaEBERETl9/rrrwsdAlGFWFlZqT0uEong4eFh5GjIkvXo0UPv547Y2Fi1CTcikQg9evQwZJjlZm1tDTs7Ozg6OsLd3R2enp5o2rQpfH190aFDBybkEwDgxo0bmDx5crmT18r+jphiwjFZrh07dmDRokVaE4fLnssODg4IDAxEz5490bVrV9SrVw916tSBm5sbnJyctO50URk2NjYYOnQovvvuO6V4ZX/Pz89HQkICF24kIiKqhIp+hyuO2etbBxEREZkGXXPucnJyKpWQeeTIEZVjivcQPj4+qFevnt71y2iLsaCgoNL1ExERkTCYkElEZKFycnKUflZ8UKxduza6d+9u7JCItDp+/LjWXV3Hjx9vckk6rq6u+O233xAUFISSkhKlSXgAcP36dSxatAizZ88WOFK6efOmWdRJZMoKCwtx5MgRREVFQSKR4MSJEygtLQVg+ARMxdc6OzujR48eEIvFlQmfiIiIiEycq6srHjx4oLbMy8vLyNGQJTt8+LDe79WUOAwAMTExetdLVNUmT56MvLw8nUlp6hLg3Nzc4O/vDz8/P9SuXRuurq5wcXFBjRo1qjxuqn4ePHiA8ePHy3/WlowplUrRsGFDTJ06FePHj6/UBMzKeOmll/Ddd99pLI+KimJCJhERUSXomiOg7n6h7I6ZpjTPgMmhRERE5acrIfPJkyd6jx9IpVIcOXJE63zFkJAQveouy87OTmNZSUmJQdogIiIi42NCJhGRhbK2tkZxcbHSMVnHbtOmTU2qw5no5s2bePLkicoOAzL29vZ48803BYpOu4CAAEyePBkrVqxQiln2Wb799lt88MEHnKBERGbp5MmT8h0wExISkJ+fD6ByCZjqXq/4WltbWwQFBUEsFiMiIgJBQUGwseGjKxEREZGlq1mzpsaETN4PEhHpLykpCfv379eajFk2EbNz584YMWIEXn75ZTRs2NBosRLNmDEDjx49Unu+Kp6ndnZ2WLhwId59913B7xNCQ0Ph7OyMnJwclbilUimioqLw5ZdfChghERGR+atMEqMpJUByng4REVHFuLm5aS1PSUlBq1at9Kr79OnTGvsgZAy16Ylsro06jo6OBmmDiIiIjI+zGIiILFTNmjWRmZmpclwkEnFXATI5ycnJao/LVpsKDg7W2cFSUYZcXerjjz/GDz/8gPz8fJVdMp88eYJffvlFaVVvIiJTlZKSIt8BMyYmBo8fP5aX6UrCrEgCpuLrRSIR2rVrh4iICIjFYvTs2ZMdzkRERETVUIsWLZCSkqL23jEnJ0eAiIiILMOKFSu0lismuXXu3Bnff/89OnXqZIzQiJRcunQJv/zyi9p7AcXztGnTpti2bZvJnKe2trbo0qULDh06pHbRxlOnTqGwsFDrbhhEREREREREpMrT01NreUpKit5179+/X+drevTooXf9ivLy8jSWOTs7G6QNIiIiMj4mZBIRWShNCZkAULt2bSNHQ6TdnTt3tJYHBgYavE1DJmR6enpi0KBB2LJli9rJIps2bWJCJhGZpMzMTERFRcmTMG/duiUvq8wumLpe26RJE4jFYvkfd3d3fT8CEREREVmItm3bYt++fSrHpVIpbt++LUBERETmLy8vD7t27dKZ4CYSibBkyRJMnz6du/aQYBYvXiw/HxX7kRQXQfT390d0dDTq1KkjYKSqQkJCcOjQIfnPios2FhcX4/z58+jYsaNQ4VEVki1qFxYWxvFXIiIiIiIiA/Px8dFanpSUpHfdu3fvVjmm2C/m7u4OX19fvetXdO/ePY1lLi4uBmmDiIiIjI8JmUREFsrHxwdXrlxRO3kiPz9fgIiINNOVkNm5c2eDt1lcXGzQ+oYOHYotW7YoHZNNFElKSkJWVhZcXV0N2iYRUUXl5OQgNjYWEokEEokEFy5ckJdVZQKmh4cHwsLC5LtgNmnSRN+PQEREREQW6rnnnlM5JnuuvnbtmgAREWnGhDUyF3FxccjJyVGb4Ab89+xuZWWFlStX4u233xYqTCI8ffoUW7duVbm+Kv7s5eWFAwcOmFwyJqB7DOPcuXNMyLRQcXFxmDdvHqysrNChQwf5AnTdu3eHg4OD0OEREVkMPoMRERFVT61atVJ7XNbXpbg4UkXcunULx44d0zjXRSQSoWvXrnrVrU56erpKW7K+OiZkEhERmS8mZBIRWaiAgACND5wPHjwwcjRE2mVlZWktb9GihcHbfPbsmUHr69Gjh7zjRHHFbuC/3TijoqIwaNAgg7ZJRKRLSUkJEhMT5TtgJiUlyRPSqzIB08nJCT169JBPQGrfvn1lPgYRERERVQO9e/eGra0tiouLVZ6rnz59ikuXLqF169YCR0mk+TmJyBQdOXJEY5nsOjt58mQmY5Lg/vrrLxQWFqokDwP/O1c3btyIunXrChShdrruUVJSUowUCQlBKpWipKQEJ06cwMmTJ/HVV1/Bzs4OwcHB8gXqAgMDYWVlJXSoREREREREZsXHxweurq7Izs6W9xkojh08fPgQsbGx6NmzZ4Xq3bhxo7weTf293bt3r3T8AFBQUIC7d++qLROJRKhXr55B2iEiIiLjY0ImEZGFCggIUDkme4DkwC+ZGl27ttasWVOvestO5FSUk5OjV52a1KpVC40bN8bNmzfVlp86dYoJmURkFGlpadi+fTskEgni4+OVrne6kjC1TSzW9lpbW1sEBgZCLBYjIiICwcHBsLHh4yYRERERlZ+rqyvCwsJw8OBBtQuA7N27lwmZJLgNGzYIHQJRhZw7d07lmOI1tk6dOpg3b54xQyJSa+fOnSrHFBdoePnll/H8888LEFn5+Pj4wMHBAQUFBWrHI65fvy5QZGQsihODgf8m3MbFxSEuLg6ff/45XFxc0KNHD3mCpp+fn8ARExGZB29vb+6OSUREVI2JRCIEBQXh0KFDGu8JFi5cWKGEzMLCQqxZs0bnPUavXr0qFKsmycnJKC0t1Zj82bx5c4O0Q0RERMbHGbJERBYqIiICNjY2KCkpUdlV4M6dO7h79y5X1yGToSshs1atWnrVa29vL98JTpFUKsWjR4/0qlObZs2a4caNG2o7bC5fvmzw9oiI1NmwYYN8MqUhEzAVXy8SieDv7y+fQNSzZ084OTlVNnQiIiIiqubefvttHDx4UOW4VCrF5s2bMX36dAGiIvqfUaNGCR0CUYXcuHFD7XHZeME777yj92J4RIaUkJCgdSLkp59+asRoKk4kEqF+/foaf+du375t5IhICNr6XrOysrBnzx7s2bMHAODp6Ynw8HB5/6q3t7dRYyUiMheaFkMmIiKi6iM8PByHDh1SOqa4u+WhQ4ewe/du9O/fv1z1ffvtt0hPT1dJkFR8pmvUqBHatm1rkPjPnj2rtZwJmURERObLSugAiIioatSpUwdhYWEaEy3UTW4jEoqjo6PWcldXV73qdXBwUDkm6zzJzMzUq05tateurfa4VCrF1atXDd4eEZEmiomTin9kZYqrtcvoeq2Pjw/Gjh2LLVu2ICMjA2fOnMHXX3+Nvn37MhmTiIiIiAxiwIABaNWqFQCoLDB25swZSCQSIcMjIjI7d+/e1Zrk9uKLLxoxGiL1UlJS8ODBAwDKfVqyv7dr1w4dOnQQKrxyq1evntr+NqlUivv37wsUFRlT2X7Xsv2tiuX37t3D1q1bMW7cODRp0gQtWrTAxIkT8ccff+Dhw4cCfxIiIiIiIiLTMWjQII1lsmetN954Q2fiIwDExcXh888/19hfJhuTGDJkiN7xlvXPP/9oLWdCJhERkfliQiYRkQUbMWKExrJff/3ViJEQaefi4qK1vKioSK963dzcNJY9ePAAJSUletWrib29vcoxWQeObEIJEZGxKU70Kbu6n7YEzDp16mDIkCFYu3YtUlJScP36dfz4448YPnw4PDw8hPo4RERERGThlixZonaBMalUihkzZhj8WZ6IyJLl5OQo/aw42czDwwMdO3Y0dkhEKq5cuaKxTCQSlXuHC6HVqVNHYxkT7KofdX2y2hI0r127Ju979fLyQseOHTFz5kzs378fubm5An8aIiIiIiIi4bRs2RKdO3dWWsARUF7U6enTp+jSpQu+/fZbjc9QP/zwA/r374/8/Hyl96szatQog8WfkJCgdcG0li1bGqwtIiIiMi4mZBIRWbARI0agWbNmAJR3FZBKpYiOjsapU6cEjpDoP7p2wJR1hFSUu7u7UueJ4t9LS0tx8+ZNverVJDs7W2PZs2fPDNoWEVF5aOtAlpXL/jg6OuKFF17AV199hZMnTyIzMxPbtm3DW2+9haZNmxopYiIiIiKq7vr3748hQ4Yo9WPJJiucO3cOs2fPFjhCIiLzUVBQoHJM1lfg7e1t7HCI1Lpx44bW8i5duhgpkspxcHDQWMaEOqpIgmZpaSlOnz6NZcuWoV+/fnBzc0PPnj0xb948HDlyhAuUEBERERFRtTN58mS1x2XPUSKRCHl5eZg2bRrq1q2LQYMGYcqUKfjkk08watQo1K9fH2+//Tays7Plz1+KFMciunbtirZt2xok7vT0dFy4cEEeq6wtmTp16qBx48YGaYuIiIiMz0boAIiIqOpYW1vj888/x6hRo1RW2ZFKpZg2bRoOHz4sTHBECnQlZD569Ejna9SpW7eu1vLLly/Lk5YN4cmTJxrLyq5GT0QkBMWdMG1sbNC5c2eIxWJEREQgODgYtra2AkdIRERERAR8//33OH78OFJTU1UWGVu4cCHatGmD1157TegwiYhMnpOTk9pF5EQiETw9PQWIiEhVenq61vIWLVoYKZLKsbe311hWWFhoxEhIKOrGYjVRN/m37E4vstcUFhYiISEBCQkJmDt3LpycnNCjRw+IxWKIxWK0a9fOgJ+CiIiIiIjI9IwYMQILFizA1atX1SZUAv9Lqnz27Bl27dqlVKYuGVKTjz76yDBBA/j777/VHpeNeQQHBxusLSIiIjI+7pBJRGThRo4cCbFYrHZXgfj4eHz99dcCR0gE1KtXT2v5vXv39KpXV7KloXeJvX79erk6boiIhCS7F/Dw8ECbNm3g5+eH1q1bMxmTiIiIiExGnTp1sHv3bri4uAD43yQJWd/Wm2++iQ0bNggZIhGRWdC2yJ2bm5sRIyHSTNdihrrGD0xFcXGxxrLS0lIjRkLG1KtXLwwbNgweHh46d8DUpry7Z8omF+/btw8zZsxAQEAAvLy88Oqrr+Knn37SueMsERERERGRObK2tsZXX32lceGbss9Sis9QsjkyiguYK1KcUxsSEoJ+/foZLO4dO3ZoLe/SpYvB2iIiIiLjY0ImEVE18Msvv8DDwwOA6gS2jz76CH/99ZeA0REBrVq10lp+69Ytvept3ry51vIjR47oVa866enp8jjVdf7UqFHDYG0REelLdn2SSqW4e/cu1q1bh1dffRV169ZFQEAAZs6cif379yMvL0/gSImIiIiouvPz88Pu3buVkolkkyKKi4sxbtw4TJ48Gbm5uQJGSURk2ry8vDROVCsoKDByNETq6foud3R0NFIklZOfn6+xzMHBwYiRkDF16dIFv/32GzIyMnDmzBl8/fXX6Nu3L5ycnPRO0Cw7cVjdexXL79+/j99//x0TJkxA8+bN0bRpU4wfPx7btm3D/fv3jfLvQEREREREVNX69++PN998U2lDEkW6nr8Uy2UU67Gzs8P3339vsHgzMjIQFRWl9fmPCZlERETmjQmZRETVQN26dbFt2zaVAV+RSISSkhIMGzYMP/30k0DREf2XOGljYwMAajshLl26pFe97dq1U3tcNlgdHx+PwsJCveouSyKRaC2vU6eOQdohIjIUxUk7paWlOHPmDJYtW4Z+/fqhdu3aCA0NxRdffIHExESu4k9EREREgujevTuio6Ph7u4un2Sh+N81a9agTZs22LhxI+9ZiYjUaNu2rcayx48fGzESIs2sra21lhcVFRkpksrRlvhmLkmlVDn+/v6YNm0a/v77bzx+/Bjx8fGYM2cOunfvDhsbG61JltpUNEHz5s2bWLduHV577TXUrVsX7du3x/Tp07F3716dO9ISERERERGZstWrVyMoKEhjUiag+gylLhGz7OtFIhGWLFmitS+ton799VeUlJTI2wCU50Xa2toiMDDQYO0RERGR8TEhk4iomggNDcWOHTtgZ2cnPyZ7mCwqKsKECRPw8ssv4+HDhwJGSdWVra0tmjRporH84sWLetUbEBAgP+cVV7uSyc3Nxa5du/Squ6zVq1erPS77PfP29jZIO0REhqJtRcDCwkLEx8dj9uzZ6NatG9zc3DBgwAB8++23el+TiYiIiIj0ERAQgJMnT6Jbt25KkyxkE8/T0tIwZswY+Pj44NNPP8Xx48cFjpiIyHT4+/urHJNdP2/cuCFARESqnJyctJabSwLZrVu3NE4GdXFxMXI0JDRra2uEhITg888/R2xsLB4/foy9e/di+vTpaN++PQDdu7doUtEEzXPnzuGbb75B//794ebmhm7dumH27NmIj49HcXFx1f5DEBERERERGZC9vT327t2L4OBg+XiBrmcodcq+74MPPsC7775rsDilUinWrl2rcSdPkUiEbt26cQEnIiIiMyeSalv2gYiILM7BgwcxfPhwPH36VOm47EHPw8MD06ZNw7hx47ijHxnVoEGDsHPnTqXdLoD/zk0fHx+9JwiFhIQgMTFRXi8ApTaCg4Nx5MiRSsV++PBhhIeHK7VRtp2JEyfiu+++q1Q7RETlMXfuXMydO1djx6465Xmt4mu8vLwgFosRERGBiIgINGjQoJJRExEREZG52bRpk1HbKy4uxpIlS3DlyhW1fQfA/+5ZXV1d0b59e7Ru3RoNGzaEm5sbHBwcdO7AVZXeeOMNwdomourr9OnT6Nixo9rrprW1NR4+fAhXV1eBo6Tqbvbs2Zg/f77GPvyzZ8/Cz89P7/qtrKw01i3bqaKycnJyULNmTXkbZXf27tatG2JjYw3SFlmGhw8fIjo6GhKJBFFRUbh+/bq8rCL9uuqUfb+2fl5HR0d069ZN3tfboUOHcrdDREREREQklLy8PLz33ntYt25duZ+h1D0r2dnZ4fvvv8eYMWMMGt+ePXvQv39/rXMJly5dimnTphm0XSIiIjIuJmQSEVVD165dw6BBg3D+/HmlB03FwWh7e3sMGzYMffr0QZcuXdCoUSOhwqVqYunSpfjggw80Toy4c+cO6tatW+F6Fy5ciE8//VRrB8dvv/2GoUOH6hV3dnY22rdvj9TUVADQ2MZPP/2E0aNH69UGVZ6xJwqbO04UNm9HjhzBN998g8OHDyvtfF2VCZotWrSQJ2eGhYWhZs2a+oZPRERERGZCltxgbNruS8s+k5sSQyV8EBFVVOPGjXHr1i0Aqkliu3fvRt++fQWOkKq7NWvWYNKkSRrHBv7880+89NJLetdvjITMhIQE9OjRQ2M7I0aMYB81aZWamipPzoyOjkZmZqa8zJgJmm5ubggLC0NERATGjx9f7jaIiIiIiIiEEBUVhY8//hj//vuv/Ji2sQHFZ/aBAwdi7ty5aNu2rcHjCg8Px+HDh7XukHnp0iW0bNnS4G0TERGR8TAhk4iomsrNzcWHH36I1atXo7S0VGNipkz9+vXh5+eH2rVry//Y29sbPW6Zzz//XLC2qWr8+++/CAwM1DhhYf369Rg1alSF67148SLatm2rNiET+O98r1WrFuLj4yu8ynZeXh4GDhyIQ4cOqdRfNv60tDTuICcgoSYKmytOFLYMUqkUp0+fRlRUFKKiohAfH4/c3Fx5eVUlaFpZWaFjx47yBM2QkBDY2dlV5qMQERERkQkqm9wgNF0TzYVkyIQPIqKK+uijj7BkyRKlvkrZf0eNGoX169cLHSJVcwcOHECfPn00jg3MmjULc+bM0bt+YyRkzp07F3Pnzq2yz0DVz7lz5+T9unFxccjOzpaXVSZBsyI7x/D+lYiIiIiIzMXx48exdetWREdH48KFCyguLlZ5ja2tLQIDAxEeHo4hQ4ZUSSIm8L9Fm7Rp3rw5rly5UiXtExERkfEwIZOIqJr7999/MWHCBJw6dapCK6QKjYOAlqe0tBRubm7yQeWyk4OGDBmCrVu36lV3cHAwkpKStCZl1q5dG7/88ku5V4S/cuUKRo0ahaSkJHkdihRj79Spk/x1JAxTmyhsyjjRwnIVFRUhMTFRPpEnKSlJqRO6IhPYK5Kg6eDggJCQEEREREAsFqNTp06V+RhEREREZCJMZYdMdUypD8vQCR9ERBV19+5dNGnSBEVFRUrHpVIpnJyckJqaCjc3N4GiIwJSUlLQsmVLjcmM3bp1Q2xsrN71GyMhs2PHjjh9+nSV7fJJ1VtJSQmOHTuGqKgoSCQSHDt2DIWFhfLyyixMom23Ft6/EhFplpubi6dPn8LR0RGurq4m1Q9BRERU3RUXFyM1NRWPHz9GQUEBatasCXd3d7i7u8PGxqbK2//xxx/xzz//aH1N9+7dMWbMmCqPhYiIiKoWEzKJiKqx7OxsbNy4EatWrcKVK1dUBorLMpWvDA4CWq7+/ftjz549ShMVgP/OPWdnZ9y7dw81atSocL0///wzxowZozEhT7G93r17Y/z48Xj++efh5OSk9LrS0lIkJCRg69atWLduHYqLi5USRzXVuWbNGrz11lsVjpsMhztklg8nWlQvz549Q2xsrDxB8/z583rfB1RkUYfatWsjLCwMYrEYERERaN68eWU+BhEREREJhAvf6FYVCR9ERPqYOHEifvjhB7W7ZM6cOROLFi0SOkSq5urUqYMnT54AgMrYgI2NDe7evYs6deroVXdVJ2SeOnUKnTp1UrsgpKyd9PR0eHl5VbotIuC/JKD4+HhIJBJERUXhzJkzBhvf5f0rEZGqu3fvYv/+/Th06BASExORmZmJ/Px8ebmtrS2aN2+OoKAg9O3bF/3794ednZ2AERMRERERERGRMTAhk4ioGioqKsKSJUuwePFi5OTkANA9GGcqiUwcBLRs3377LaZOnapxcsSvv/6KV199tcL1lpSUwNfXF9evXwegOVFIcaKHlZUVfHx84OnpCSsrK9y/fx/p6enIzc1VqkNTMqbsNfXr10dKSgocHBwqHDcZDicK68aJFnT//n15cmZUVBRu3rwpL6vIRJ6K7J4JAI0aNZLvnikWi+Hp6annJyAiIiIiY+Jzlm58ziIiU/H48WO0bt0a9+/fVzoulUpha2uLEydOoG3btgJFR6S6WCOg/D26ZMkSTJ8+Xa+6qzohc9SoUfjll1/ULnoqlUrRtGlTpKSkVLodIk0ePnyI6Ohoeb/utWvX5GWK/bDl3Wme969ERP+5c+cO5s+fjw0bNqC4uBhA+RYvdXd3x5QpUzB9+nTY29sbJVYiIiIiIiIiMj4mZBIRVTOxsbEYN24crl+/bnYT5jgIaPnS09Ph7e0tPzfLrtbes2dPREdH61V3ZGQkhg4dqnWXTFmblTkuK5PF/NNPP2H06NF6xUyGw4nCuvEaS2Vdv35dPoknJiZGadJmVSZotm3bVr57Zs+ePVV2KyYiIiIi08DnLN34nEVEpqRs/6hi/2bLli2RlJQEV1dXgaOk6mrZsmWYMWOGxqTG+vXr49q1a3olNVRlQubZs2fRsWNHpTGNsm1MmTIFy5Ytq1Q7RBWRlpYm3z0zOjoa9+7dK/d7ef9KRKYoNTUVT58+1Vhub2+PVq1aGbTNTZs2YeLEiSgoKFDZAVsddeNejRs3xm+//YbAwECDxkZEREREREREpoEJmURE1ciaNWswZcoUFBcXK024AMq3KqrQOAhYPYSGhiIuLk7jBInTp0/D399fr7pfeeUV7NixQ+uE0bKDKGUnf6grK/t+WazPP/889u/fr1esZFicKKwbr7Gky5kzZ+QJmvHx8Xj27Jm8rKoSNG1sbBAYGIiIiAhEREQgJCSkMh+BiIiIiAxI9pxF2vE5i4hMyccff4zFixcr9QPJnsmDg4Nx4MABuLi4CBwlVUe3bt1C48aN5T+rGxuYM2cOZs2aVeG6qyohs6SkBF27dsXx48dV+p4V2zh8+DC6d++udztElXXs2DHMmTMHBw4c0DlOwnECIjJFPj4+uH37tsbyt956C2vWrDFYe7NmzcKCBQvKPU9AW7mdnR3WrVuHESNGGCw+IiIiIiIiIjINTMgkIqom5s6di3nz5pllIqYMBwGrh7Vr1+Ltt9/WOKlzxIgR2LRpk151P378GMHBwUhJSQGg/fwv7+qW6t4jlUrRuHFjHDt2DB4eHnrFSobFicLlw2sslVdxcTGOHTsmX2n92LFjKCoqkpdrSm5XR9drZeUikQjFxcWVDZ2IiIiIDMTKykroEMwGn7OIyJQMHToUkZGRKuMEIpEIfn5+2LlzJ5o0aSJghFRddevWDf/884/GXTLt7Oxw7NgxtG/fvkL1VlVC5gcffIClS5eqTcaUxdy0aVP5eASRsZSWluL48ePyvtvExEQUFhYC0D0uzLFYIjI1R48eRdeuXTWWi0QiXLhwAb6+vgZp74svvsDnn38ur1umIvNqyr7PysoKP//8M15//XWDxEhEREREREREpoEJmURE1cDGjRsxevRoAMoDwbqYYvISBwEt38OHD1G3bl2N/4+tra1x7tw5vQdVUlJSEBoairt378qPGeJ2SPF3q169eoiJiUHLli0rXS8ZBicKlx+vsaSP3NxcxMXFySf5nD17tsI7DMtoej3PTSIiIiLTIutrovLZsGGD0CEQEQH4L1FnzJgx2LRpk9pJ5s7OzliwYAEmTZrEPjUyqt9++w0jRozQmuDo4+ODo0ePwsvLq9z1VkVC5rfffoupU6eqHXNTrH/p0qWYNm2aXm0QVURycjIkEgkkEgliY2ORlZUlL6toEhH7YonIlEybNg0rVqzQOHelV69e2Ldvn0Haio2NRXh4uNKxyswjULxPsLe3R3R0NLp06VKpGImIiIiIiIjIdDAhk4jIwl24cAEdO3ZU2lFK06W/ogkTQuEgoOU7efIk8vLyNJY3adIE9evX17v+tLQ09OnTB5cuXar0jrFl39+mTRvs2rULTZs21Ts+MjxOFK4YThSmynr48CGio6PlCZrXr1+Xl1Xkuqs4WM3vfyIiIiIiIiLD+eSTT7B48WKV47Jn8KZNm2LmzJl49dVX4eLiIkCEVN2UlpaiZcuWuHHjBgDVJEfZsSZNmmDv3r1o1apVueo1dELm/PnzMWfOHPnPmuKsVasWbt68CVdX1wq3QaRLenq6vO81KipK6yKkFV2sl32xRGRKGjVqhPT0dABQ+12+d+9e9O7du9LtFBUVoU2bNrh27ZrK4hCVUXZhiXPnzsHZ2dkgdRMRERERERGRsJiQSURk4UJDQxEXF6e101hTYoSLiwtatGiBxo0bo06dOqhZsybs7e1hY2NT5XHrMnv2bKFDIDOXl5eHGTNmYO3atSgtLa1QQnLZ10qlUlhZWWHChAlYunQpatSoUSUxExGZq9TUVERFRUEikSA6OhqZmZnlfi8nARERERERERFVjf3792PUqFF48OCB0nHFye4ODg4IDQ1FREQEAgIC0Lp16wrtTkhUEb/88gtGjRqldkxLMaGhdu3a2L59O0JDQ3XWaaiEzIcPH2Ls2LHYvXu3/P3qYpSVLV68GDNmzCh3/UTaZGVlISYmRp6EefnyZXmZpt8VRRVdrJd9sURkCq5du4YWLVqofI8D/7sfuH//vkF2dV++fDmmT5+uMxlT006dMureq3h/MGnSJKxcubLS8RIRERERERGR8JiQSURkwf78808MHjy4XMmYsoSyiIgIDBw4EGKxGC1atDBmuESCOH/+PObMmYNdu3bJd5LVNZAC/G8wxcrKCgMHDsRnn32GDh06VGWoREQW49ixY5gzZw4OHDhQrsFtTgIiIiIiIiKi6qBp06ZGb/Pp06d4/Pix0vO3TNmJ7zI2NjaoVasWHBwcYG1tbdR4ZUQiEa5duyZI21S1tC00qjimJRKJMHz4cCxcuBDe3t4a66tsQmZRURFWr16NefPm4fHjx1qTMWWxNWvWDBcvXoStrW2FPjuRTGFhIY4cOSJf5O7EiRMoLS0FUDUJmDLOzs7o0aMHxGIxpk2bVpmPQERUaZs3b8bIkSM1fo8PHz4cmzdvrnQ7BQUFaNKkCe7duwdA9zW0ouWKx21tbXHx4kU0a9as0nETERERERERkbCYkElEZMF69+6NQ4cOaR0Ylhk9ejRmzZoFHx8fY4ZIZDLu37+PyMhIxMTE4OjRo7hz547GwRQvLy8EBQUhIiICgwcPRr169YwcLRGReSktLcXx48flK7gnJiaisLAQgOaBaxkmZBIREREREVF1UTZxzBg0tacuMdOUsJ/AciUnJ6Nz587Izc0FoD2pQSQSwd7eHqNHj8bQoUPRs2dPlfr0TchMSUnBr7/+irVr1yIzM1Pl/Zpisra2RkxMDLp166bnvwBVVydPnpT3nyYkJCA/Px9A5RIw1b1e8bW2trYICgqCWCxGREQEgoKCYGNjU5mPQURkMO+++y6+++47jd/jW7ZswbBhwyrdzoYNGzB27Fid98VSqRQNGjTAa6+9BrFYjPr16wMAbt26hYMHD+LXX3/Fw4cPde6iPWbMGPz444+VjpuIiIiIiIiIhMWETCIiC5Wamqq0mrbi5V6xw9jFxQV//vknwsPDjR4jkSnLz89HWloasrOzkZeXB1tbW7i4uKBRo0ZwcXEROjwiIpOXnJwMiUQCiUSC2NhYZGVlycsq8hjKhEwiIiIiIiKqLmSJY8ZSnudzY8ZTXuwnsHxbt27Fa6+9Vq7ECMWfvby88OKLL8Lf3x+tW7dGmzZt0LBhQ50JmaWlpUhJScH58+cRHx+PmJgYnDt3Tm0bmuKR1fnpp59i3rx5hvznIAuVkpIi3wEzJiYGjx8/lpfpSsKsSAKm4utFIhH8/f0REREBsViMnj17wtHRsTIfg4ioynTu3BknTpxQ2cldKpXCxsYGDx48gKura6Xb6dq1K44ePao1kRIAJk2ahCVLlmi8bmZlZWHMmDHYsWOHSl2KsTs6OuLOnTuoWbNmpWMnIiIiIiIiIuEwIZOIyEKtX78e48aN09rRW7t2bezfvx+dO3cWKkwiIiKyEOnp6fIV3KOionD37l15mbZdA3RhQiYRERERERFVF8ZOyJSp6HCx0Ema7CeoHj766CMsWbJE666x6vqYdCWuKfY1Pffcc8jIyMC9e/dQVFSk9j26dotVLO/fvz/+/PNPwX9HyDRlZmbK+04lEglu3bolL6vMLpi6XtukSROIxWL5H3d3d30/AhGRUTk7OyMvLw8AVBIyfX19cfHixUq3kZKSgpYtW+rc1XLq1Kn4+uuvy1XnyJEjsXnzZrVzdWT1rV27FuPGjat0/EREREREREQkHBuhAyAioqpx7NgxjWWyTt7ly5czGZOIiIj0kpWVhZiYGHkS5uXLl+VllZlARERERERERFTdmfozs5DxMdGt+li0aBFycnLw3XffaVzcSzbepStpsux7ZP89fvy4xteVp07FuEJCQrBt2zaeoySXk5OD2NhYSCQSSCQSXLhwQV5WlQmYHh4eCAsLk++C2aRJE30/AhGRYB48eIDc3FyVpEbZd3+7du0M0k5kZKTa44rttmnTBl999VW56/zpp59w+vRpXLx4UePCEn/++ScTMomIiIiIiIjMHBMyiYgs1KlTp1SOKa6417VrV7zxxhsCREZERETmqLCwEEeOHJGv4H7ixAmUlpYCMHwCJieuERERERERERHRypUrYW9vj2XLlskTL9UlZcqUTc4sW66Opn6o8r5PKpUiLCwMf/75J+zt7bW+hyxbSUkJEhMT5f2nSUlJKC4uBlC1CZhOTk7o0aOHfAfM9u3bV+ZjEBGZhNTUVK3l/v7+Bmln165dWstFIhFmz54NKyurctdpb2+P+fPn4+WXX1a5hsvuZQ4fPozi4mLY2HDqJhEREREREZG54lM9EZGFun//vtZkhvHjxxsxGiIiIjJHJ0+elO+AmZCQgPz8fADGScBUfH+DBg0QHh5e7vqIiIiIiIiIiMgyLF26FH5+fnj77bdRVFSkcbdMTce0qcjOmureJ5VKMWzYMGzcuBF2dnYVapssQ1paGrZv3w6JRIL4+Hjk5OTIy3T1oWo757S91tbWFoGBgRCLxYiIiEBwcDATeojI4uhKyDTEDplPnjzB8ePH1SZNytSvXx+DBw+ucN0DBw5E69atkZycLE/ClC2eDgD5+fk4efIkAgMDK/chiIiIiIiIiEgw7JUlIrJQjx8/VvpZsdPY2toaL774orFDIiIiIhOXkpIiX8E9JiZG6X6iMhOIytKVgFm7dm2EhobKV3Vv1apVuesmIiIiIiIiMnfaFlskqo5Gjx6N9u3bY/To0Th37pzSTpgVTcJUpG8Cp1QqRY0aNfD1119j4sSJerdP5m/Dhg2YN28eAMMmYCq+XiQSwd/fHxERERCLxejZsyecnJwqGzoRkUm7ffu21vJ69epVuo2YmBiUlJRo3IFbJBJh5MiRet+bDxo0CAsWLND4/hMnTjAhk4iIiIiIiMiMMSGTiMhC5ebmqhyTdSK3bNkStWvXNnZIREREZGIyMzMRFRUlT8K8deuWvKyqd8FUfG+NGjUQEhIiT8Ds1KkTJ58SERERERFRtVSZ5DIiS9axY0ecOHECixcvxpIlS5Cdna2UmAlUze+PuvojIiKwcuVKLiJGAP6XtFOR/lNdr23cuLG8r1QsFsPDw8NwARMRmYFnz55pLa9Zs2al20hISND5mldeeUXv+vv164cFCxZoLE9OTta7biIiIiIiIiISHhMyiYgslLOzM54+fapyXCQSwcvLS4CIiIiISGg5OTmIjY2FRCKBRCLBhQsX5GVVnYCp+H5ra2s899xz8lXdu3btCjs7u3LXTURERERERGSJbty4IXQIRCbNxsYGn376Kd5++20sWrQIa9euRXZ2NgCoTYjTJ0FTW59WQEAA5s2bh379+ukRPVUH+iZguru7IywsTN5f2rRp0yqLkYjIHOTl5Wktr6qETMXrdb169dCxY0e962/fvr28PnW7cKakpOhdNxEREREREREJjwmZREQWysXFRW1CJgAmZBIREVUTJSUlSExMlO+AmZSUhOLiYgDGTcAEAD8/P/mK7qGhoXBxcSl33URERERUPZSWlsLKykroMIiIBOPj4yN0CERmwc3NDUuWLMGcOXPw22+/YcOGDUhMTJT3RSkmP+hDsU/LyckJ/fv3x+TJkxESElL54Mli6epLLXtede/eXd5f2qFDhyqOjojIvOTm5motd3V1rVT9+fn5OH36tMaxLZFIhBdeeKFSbTg6OqJhw4a4ffu22jbu3r1bqfqJiIiIiIiISFhMyCQislBeXl64deuW2g5kfQegiYiIyPSlpaVh+/btkEgkiI+PR05OjrxMVxKmIRMwvb29IRaLERERgfDwcC4IQUREREQ6ffHFF4iMjJTfR/bs2RPOzs5Ch0VEREQmytHREWPHjsXYsWPx4MED7N+/HxKJBP/++y8uX76MkpKSCtdpY2MDf39/BAUFoU+fPujVqxfs7e2rIHqydLL+U6lUChsbG3Tu3Fl+nxscHAxbW1uBIyQiMl26EjLt7OwqVf+///6LoqIitTtXyoSHh1eqDQBo0qSJyrwdWZuZmZmVrp+IiIiIiIiIhMOETCIiC+Xn54d///1XbVl2draRoyEiIiJj2bBhA+bNmwfAuAmY7u7uCAsLk6/q3qxZs4qETUREREQEqVSK8+fP48KFC/j2229hbW0tn7guFovRtWtXTlwnIiIitdzd3fH666/j9ddfB/DfzleXLl3CrVu3cPv2bWRkZCAnJwf5+fkoKCiAra0tatSoAUdHR3h5ecHb2xs+Pj5o3bo1EzDJYGS7rHl4eKBNmzbw8/ND69ateU9LRKRDUVGR1vLc3NxKLeD0zz//6HxNWFiY3vXLuLi4aCzjvB0iIiIiIiIi88aETCIiC+Xn56f2uFQqxc2bN40bDBERERmVbKKPriRKbXS918nJCd27d5dPju/QoYPe8RIRERERlSWVSlFcXIyjR4/i6NGj+PLLL1GjRg1069ZNfg/asWNHocMkIiIiE+Xg4ICAgAAEBAQIHQpVU7K+VKlUirt372LdunVYt24dRCIR/P39ERERAbFYjJ49e6JGjRoCR0tEZFp0LY6Qk5NTqYTMI0eOqBxTHBfz8fFBvXr19K5fRluMBQUFla6fiIiIiIiIiITDhEwiIgsVHh6uckwkEkEqleLq1asoLCyEnZ2dAJERERGRMVVmF0zF99ra2iIoKEg++T04OBg2NnykJCIiIqKqIbs3Vbwnzc3NxaFDh3Do0CEAQO3atZV2aW/RooUgsRIRERERaaN4TyuVSnHmzBmcPXsWy5Ytg62tLYKDg+UJmkFBQbCyshIwWiIi4elKyHzy5Am8vLz0qlsqleLIkSMaFyYViUQICQnRq+6ytM3JKSkpMUgbRERERERERCQMzp4lIrJQHTt2RP369XH37l15IqasQ7mwsBCJiYno2bOnwFESERFRVdKVjKltF0yRSIT27dvLJ7f36NEDTk5OVRInEREREVFZmnZ9V7zHffToEXbs2IEdO3YAABo2bCi/fxWLxahbt65RYyYiIiIiUkfdPa3svrawsBDx8fGIj4/H7Nmz4eLigp49e0IsFiMiIgJt2rQRImQiIkG5ublpLU9JSUGrVq30qvv06dN49OiRfB6NOt27d9er7rLy8/M1ljk6OhqkDSIiIiIiIiISBhMyiYgs2JAhQ7BixQq1yRa///47EzKpUjZt2iR0CGbljTfeEDoEIiKtCZgA0LRpU/lEn/DwcNSpU8eY4RERERERKSk7MVJXguatW7ewceNGbNy4EQDg6+sr32koNDQUrq6uVR80EREREVVL2u5TFf+u6542KysLf//9N/7++28AgJeXl7zPNiIiAg0aNKiK8ImITIqnp6fW8pSUFL3r3r9/v87X9OjRQ+/6FeXl5Wksc3Z2NkgbRERERERERCQMJmQSEVmwadOm4bvvvkNJSYl8YE+2yt+WLVuwYMEC1KxZU+AoyVy9+eabahN7SD0mZBKREHQlYHp5eSE8PFy+g5CPj48xwyMiIiIiqpCKJmheunQJycnJWLVqFaysrNCpUyd5gmZISAjs7OyMEjcRERERWaaIiAicP38ehw8fxsOHD+XHtd2nVvSeNiMjA1u2bMGWLVsAAC1atJAnZ4aFhXGsl4gskq7xqqSkJL3r3r17t8oxxeuwu7s7fH199a5f0b179zSWubi4GKQN+j/27jy8qvreF/9nhykSEEEcQARxwgEHigrKTFI7OFSsU21lUlF6Tmuna09Pn3tabW89PfWc9mprW70ewKrVqqfW1pEEZBIB53mgOCEqgiDITLJ/f/jL6k7ISHayd5LX63nyNNnftdd6r5rubrPWe38AAAAgNxQyAdqw/v37x1e/+tWYNWtWUsSs/EPyhg0b4uc//3n87Gc/y3FKWrvqF47ZleIqkEuZr9Pdu3ePMWPGJAXMwYMH5zAZAADUrvLfpev6u0N9N7On0+lkm/Ly8li6dGksW7YsrrnmmujSpUuMGDEiKWgOHTrUv78DANAoI0aMiBEjRkQ6nY5nnnkmysrKoqysLBYsWBCbN29OtstmQfO1116L119/PX77299GQUFBfOYzn0kKmj50BGgrBg0aVOPjlfe9zJ49e7f2+84778SSJUtq/UDTVCoVp5xyym7tuyarVq2q9TVdIRMAAABaN4VMgDbuF7/4Rdx///2xdu3aXaZk/ud//mece+65MWTIkBynpDVzs2LdFFaBXEmn09GlS5c4+eSTkwLmiSeeGB06dMh1NAAAqFHlzeOV/y5d383o1dV0M3v19cpttm7dGnPmzIk5c+ZERESPHj1i7NixUVxcHCUlJbXe/AkAANWlUqkYMmRIDBkyJL73ve/Fjh07YvHixUlBc+nSpbFz584q21f/IJGavq9p28xtysvLY9myZfHEE0/Ev//7v0dhYeEuHzoC0BoNGDAg9txzz9i4cWNyf0vmB5CvXbs25s2bF2PGjGnUfmfNmpXsp7a/L4waNarJ+SMitm3bFu+9916Na6lUKvr06ZOV4wAAAAC5kUprCQC0eXfeeWd85Stf2WVKZjqdjgEDBsSSJUti3333zXFKWpuCgoI6L1QQVf43V15enus4QDtx6623xgsvvBDFxcUxatSoKCwszHUkAABosFdffTW5cf3RRx+NdevWJWu1TbBoiIY8N3ObPn36JB9sUlxcHAcccEBDTwEAAKr45JNPYt68ecn73BdeeKHKh5BUV9d73Po+sCRzvWfPnjFu3LjkQ0cOPfTQppwGQIv63Oc+F7Nnz65yT0Lm9ffPfvaz8dBDDzV4f9u3b4+DDz44KUlmvn5m7vfZZ5+NwYMHNzn/s88+G0OGDKk1/xVXXBH/9V//1eTjAAAAALmhkAnQTvzbv/1b/PSnP62xlHnEEUdEaWlp9O3bN8cpaU0UMuunkAkAAAC7L51Ox5NPPhllZWVRWloaixYtiq1btybrjZmeWV1jC5qHH354Us4cN25c7LXXXg0+FkAuzZ07N9atWxfjxo2Lnj175joOABHx4YcfJuXMsrKyePPNN5O1xhQ0G/ue9sADD0ymZxYXF/vAXiCv/fznP48f/OAHu9yTkHkN/t57740zzjijQfu79tpr48orr6xxfxGfvn7279+/ymtyU/zhD3+ISZMm1VrIvP766+PrX/96Vo4FAAAAtDyFTIB25NJLL42bb765xpvV9t577/j9738fZ599do7S0dooZNZPIRMAAACyZ/v27bFo0aIoLS2NsrKyePLJJ5N/327K9MyGPr9ym4KCghgyZEhyI/vIkSNNpgfy1lVXXRVXX311FBQUxPHHH5+8do0aNcprF0CeWLFiRVLOnDt3bnz44YfJWnMWNAcPHpxMzxwzZkwUFRXt7ikAZN1rr70WRxxxRJ0Fyh49esS8efPi2GOPrXNf8+fPj89//vOxbdu25LmZ+6u8pv+d73wnfvGLX2Ql//Tp0+P3v/99rYXMBx98ME499dSsHAsAAABoeQqZAO3MD3/4w7jmmmtqLGWmUqm46KKL4uqrr47+/fvnKCGtRWUhk7opZAIAAEDz2LBhQ8ydOze5ef3ll19O1lqyoNm5c+c45ZRTkpLTiSeeGAUFBQ0+FkBzuuqqq+Kqq65Kfs587Ro+fHgyKe2kk07y2gWQJ5599tnkPe6CBQvik08+Sdaaq6DZsWPHOOmkk6KkpCRKSkpixIgRTTkFgKwYNmxYLFu2rM5S5h577BHXXHNNXHLJJdG1a9dd9nHjjTfG//pf/ys2btxY4wdOZ5Ykn3322Rg8eHBWsh9zzDHx0ksvJTmrH+vvf/97HHTQQVk5FgAAANDyFDIB2qFbbrklvvGNbyR/cK5U+YffgoKCOPPMM+Of/umfYuzYsW7CoEZ+LxpOIRMAAACa33vvvZfcuF5WVhYrV65M1rJd0Kxr0tCee+4ZY8aMSQqaRx11VIOPA5BtlYXM2m4+r9S9e/cYPXp0UtA8+uijWzoqADXYuXNnLFmyJJkSv2TJktixY0eyXt/71EwNfU+bSqVi586dTY0O0GS33HJLTJ48ucb3shFVC45FRUVRXFwc/fv3j6Kionj33Xdj9uzZ8cEHHyTb1FXGPOWUU2LBggVZyb1q1aro16/fLtMxIz597e3du3esXr06K8cCAAAAckMhE6ANe/vtt2tdW7FiRUybNi2WL1++Sykz4h9/DO7atWuceOKJcfLJJ8fgwYOjV69e0bNnz+jZs2d06dKleU+gDiZ45t6UKVNyHaFVmTFjRq4jAAAAQLvy2muvRVlZWZSWlsajjz4a69atS9aaUtBszKShiIj9998/3n333YbGBsiqzEJmdXW9du27774xfvz4pKDpb/IA+WHz5s0xf/78pKD53HPP7XJ9N1NjCpqV2/ugUSBflJeXx9FHHx2vv/56RNT+/rW218GaypDVn1/5unfffffFaaedlpXcN954Y1x++eW7FDIrj3XaaafFfffdl5VjAQAAALmhkAnQhhUUFNR4IS1TfX+wznwsX/hUVgAAAAAaI51Ox1NPPZXcuL5o0aLYsmVLst6YyULV1XfjuxvagVyqbUJmYwuaBx98cDL5d/z48bH33ns3X2gAGmzt2rUxZ86c5H3uihUrkrWaPpS3NpnXiL1/BfLJX//61/jSl75U55TMSnW9n62rjDlixIiYP39+1jJ//vOfj0ceeaTWQuZPf/rT+MEPfpC14wEAAAAtTyEToA0rKCjYrec15Qa0luAiIAAAAABNsX379li0aFGUlZVFWVlZPPHEE8nfm5oyPbP6893QDuRabYXM6hpT0EylUnHssccmBc3Ro0dH165dsxucvHb11VfnOkKr8m//9m+5jkA78tZbbyVT4ufMmROrV69u8HMzy0LevwL5ZOrUqTFz5sw639PW9kHjdZU40+l0dOnSJZYtWxaDBw/OStb3338/DjzwwKioqKhy/MzX2LKyshg7dmxWjgcAAADkhkImQBu2OxMyq8u36ZguAgIAAACQbRs2bIhHH300uXn95ZdfTtZ2t6DphnYgHzS0kFldYwqanTp1imHDhiUFzeHDh0eHDh2aFpy81pDrT/yD9wHk0pIlS+LHP/5xPPzwww0q53v/CuSjbdu2xdixY2PJkiWNfl9bk8zXu1/96lfxjW98I0tJI6699tq48sord5mOGfHp++nOnTvHRx995ANNAAAAoJVTyARowyoviLeVl3oXAQEAAABoCe+//35SziwrK4uVK1c2eh/+lgXkg8xCZnVNKWjWVs6MiCgqKorRo0cnBc1jjz22kanJd23t+lNz8j6AllZRURHLli1L3scuXrw4tm/fHhEN+6Be71+BfLVu3bo47bTT4vHHH69ScGyMzPes6XQ6vv/978c111yTtYzpdDoOP/zwWLFiRZV8ma+v48aNi9LS0qwdEwAAAMgNhUyANqytXRB3ERAAAACAXHj99deTm9rnzp0b69atM2EIaBUWL14c1113XcydOzdWr16dPN6UgmZjpmdGRPTu3TvGjx+fFDQHDhzY0PjkKRMyG8b7AFrKK6+8EqWlpVFaWhrz5s2LDRs2JGuNLd/7vQXy2ZYtW+Kb3/xm3HzzzQ1+P1vTB4t07tw5brjhhpg6dWpW891///1xxhln7PL3gszX12uvvTa+/e1vZ/W4AAAAQMtTyARowxQyAQAAAKDpduzYEYsXL47Zs2fH7NmzY+nSpQqZQKvz/PPPJ+Xy+fPnxyeffJKstWRBc8CAAVFSUhLFxcUxfvz42GeffRp6CuSJtnb9qTl4H0BzWrVqVfJ6XlZWFu+9916yVtvrb0P+9+r3FmgtysrK4gc/+EE88cQTyWN1fVhE5qTKs846K6666qoYPHhw1nONHz8+Hn300VrfH6dSqXj55Zfj8MMPz/qxAQAAgJalkAnQhrXFTyh2ERAAAACAlvDMM89EWVlZlJaWxsKFC2Pz5s3JmhvagdauvLw8Hn/88aTM8/jjj8eOHTuS9ZomCTVUYwuagwcPTgqaY8aMiaKiogYfi9xQyKyf9wFk04YNG2Lu3LlJCfPVV19N1up6fa1tm9r4vQVam2XLlsUdd9wRc+bMiRdffDF27ty5yzadOnWKk046KcaPHx/nnntusxQxIyIWLlwYo0ePrnObQw89NF577bVmOT4AAADQshQyAdowhUwAAAAAaJg33ngjucl9zpw5sXbt2mRtdy6luKEdaE02b94c8+fPT4rozz33XJVJQtU1V0GzY8eOceKJJ0ZxcXGUlJTEySefHB07dmzMqdACFDLr530ATbF9+/ZYtGhR8pr85JNPRkVFRURkt4BZ0/P93gKt1c6dO+Ott96KdevWxbZt26JHjx7Ru3fv6N27d4u8n7zpppviscceq3ObUaNGxdSpU5s9CwAAAND8FDIB2rCDDjqozRUyK73xxhu5jgAAAABAK7ZmzZqYM2dOUsJ88803k7X6bnSv69JK5rZuaAdaq7Vr11Z5jVyxYkWy1hzln9rWu3btGiNHjkwKmscff3yDj0PzaYsfCNocvA+gMZ566qnkNXfhwoWxdevWiGj+Amb15x9wwAExfvz4mDVrVoP3CQAAAADQ3ihkAgAAAAAAbV716W/PP/98cvN5U250r2/bXr16xZo1a5oSHSDn3nrrrSpThFevXp2stWRBs1evXjFu3LgoKSmJadOmNfgYZFdBQUGuI7QaCpnUZvny5cn70rlz58a6deuStaZ8OEh19b1G9+zZM8aOHRvFxcVRXFwcgwYNavC+AQAAAADaK4VMAAAAAACgzSkvL4+lS5dGaWlplJaWxpIlS2LHjh0R0fRJQ3XdFF9YWBgjRoxIprkNHTrUFDGgzXn++eejrKwsysrKYv78+bFx48ZkrSkFzYY+V8ktt6666qpcR2hVfvSjH+U6Anlg9erVyetmaWlpvPPOO8lac0/BzHzuHnvskbxXLS4u9l4VAAAAAGA3KGQCAAAAAABtwosvvpjc5D5v3rz45JNPkrWmTBqq66b4Dh06xGc+85koKSmJ4uLiGDFiRHTp0qUppwHQqpSXl8eSJUuS198lS5bE9u3bk/XmmOymkAnku02bNsW8efOSDwd58cUXk7XmLmBmPr9Dhw5xwgknJO9VTznllOjcuXOD9w0AAAAAwK4UMgEAAAAAgFZp5cqVyU3uc+bMiQ8++CBZa8qN7vVtO2jQoOSm9nHjxkWPHj129xQA2pzNmzfHggULorS0NMrKyuLZZ59NXkOzUTpSyATyUXl5eSxevDgppy9dujR27twZES1bwIyIOProo5MJmGPHjo3u3bs3eN8AAAAAANRPIRMAAAAAAGgV1q9fH3PmzEludF++fHmy1pwFzL59+0ZxcXFSwuzbt+/ungJAu7N27drktbusrCz+/ve/J2uZr78NuWytkAnkk7fffjvuueeeKC0tjQULFsSmTZuStaZMZ6+uvveq/fv3T96rjh8/Pvbbb78G7xsAAAAAgMZTyAQAAAAAAPLStm3bYuHChcmUtaeffjoqKioionkLmD169IixY8cmBcwjjjiiKacBQIa33347eV2vPt24PgqZQD656qqr4uqrr46Ili1g9u7dO8aNG5dMwTzkkEMaExsAAAAAgCbqmOsAAAAAAAAAEZ/eaP7EE08kEzAfe+yx2LZtW7KWqTEFzJq2z9y2sLAwTjnllGSy0NChQ6OgoKAppwJALfr37x9Tp06NqVOnRkTEkiVL4sc//nE8/PDDSeESoLWoLIk39r1ppvqeW1RUFKNGjUoKmMcff/xu5wUAAAAAoOkUMgEAAAAAgJybNWtWfOc734n169cnjzVl0lBdN7YXFBTE0KFDkwLmyJEjo0uXLk1ID0BDVVRUxLJly5IpmYsXL47t27crY7YiGzZsqHWtc+fOUVhY2IJpIH80ZQpm5nM7deoUw4YNSwqYw4cPj44d3d4DAAAAAJAv/MUWAAAAAADIuTfffDPWrVtX5bHGTBqqb9vDDz88SkpKori4OMaNGxd77bVX0wID0GCvvPJKlJaWRmlpacybN69KoU8Js/XZa6+9avz/3YiISZMmxX//93+3cCLIvfpey+p6r5pKpeK4445LCpijR4+OoqKiZskJAAAAAEDTKWQCAAAAAAB5o6FTMOsrYPbt2zfGjx+fTME84IADshsUgFqtWrUqmYBZVlYW7733XrJW2/RjxczWxT8vqFt971UPPvjg5H3q+PHjY++9927JeAAAAAAANIFCJgAAAAAAkFfqKnlk3tyeuV2PHj1izJgxyRTMI488slkzAvAPGzZsiLlz5yYlzFdffTVZq62Amakx5XvyQ00foNCUf15Tp06t9Tg333zzbu8XWkp9r2377bdf8mEhxcXFMWDAgJaMBwAAAABAFilkAgAAAABmuxaSAACMq0lEQVQArUIqlUpubO/SpUuccsopyWShE044IQoKCnKcEKB92L59eyxatCjKysqitLQ0nnzyyaioqIiI7BcwM7ft1q1bjB49OoqLi5sSnyyp/GeTjeLszJkzay15KmTSWmS+XnXv3j3GjBmTFDAHDx6cw2QAAAAAAGSTQiYAAAAAAJDXMouYAwYMiB/96Efxla98Jbp06ZLjZADtx1NPPZVMwFy4cGFs3bo1IppWwKxp+8xtO3XqFMOGDUvK98OGDYuOHV3ibsvq+l2BfJVOp6NLly5x8sknJwXME088MTp06JDraAAAAAAANANXqwAAAAAAgLyWWc54++234+KLL47/+I//iJKSkiguLo5x48bFnnvumcOEAG3P8uXLkwmYc+fOjXXr1iVr9ZUwG1PAzNw+lUrFsccem7y+jxkzJrp27dqU06CVyfz9UM4k3x1yyCFx5ZVXRnFxcYwaNSoKCwtzHQkAAAAAgBagkAkAAAAAALQKmcWMV155JV599dX4zW9+EwUFBTF06NAoKSmJkpKSGDFiRHTq1CmHSQFan9WrV0dZWVlSwnznnXeStaZMwaxv24EDByYT5YqLi6N37967ewq0AZnlXMh3X/va13IdAQAAAACAHFDIBAAAAAAAWo3KgkY6nU5KG+Xl5bF06dJYtmxZXHPNNVFYWBgjR45MCppDhgzJZWSAvLRp06aYN29elJaWRmlpabz44ovJWnMWMPfZZ58YN25cMgVz4MCBu3sKAAAAAAAA0OIUMgEAAAAAgLySWbqsrrbJWZkFzS1btiQFo4iIXr16VSn/HHLIIc0ZHyAvlZeXx+LFi5MJmEuXLo2dO3dGRPMWMIuKimL06NHJBMzjjjuuKadBHqj8Z55KpXb5vajt9wQAAAAAAKCtUMgEAAAAAAByrlOnThFRtXBZU+mypu/r23bt2rVxzz33xD333BMREf3790/KmcXFxbHPPvtk92QA8sTbb78d99xzT5SWlsaCBQti06ZNyVp9Jcy6inV1bdupU6c46aSTori4OEpKSmL48OHRsaPL0m1Jly5dYtu2bTWubdy4sYXTAAAAAAAAtKxU2kdUAgAAAAAAeeCll16K0tLSKCsri3nz5sWGDRuStWxOa6u+zTHHHJMUNMeMGRNdu3bd3VMAyCtXXXVVXH311RGR3QJm5vapVCoGDx5c5XW0qKioqdHJY3vvvXesX78+Ij79PaiclJlKpaK4uDgeeeSRRu+zoKCgysTNzH2Wl5dnMz4AAAAAAECTKGQCAAAAAAB5p6KiIpYtW5YUNBcvXlxlGle2ikTVt+nUqVMMGzYsmew2bNiw6NChQ1NOBSBnrrrqqrjqqqtqfB2M2P1i+0EHHZRMGTZpuP0ZNGhQLF++PCL+Ucis/P7AAw+Mt956q9H7VMgEAAAAAABaC4VMAAAAAAAg723dujUWLlyYFDSffvrpqKioiIjGTc+safu6psZ169YtRo8eHSUlJVFSUhJHH310U04DoEVVL2TubgGzd+/eMW7cuGQK5sEHH9w8gWkVxowZEwsWLKi1QPn+++83uqSrkAkAAAAAALQWHXMdAAAAAAAAoD6FhYVJKTIiYv369TF37tykoPnaa68l26ZSqTpLl3UVMKuvb9y4MR544IF44IEHIiJi3333TaZnlpSURL9+/bJzggDNrL7P6c1cLyoqilGjRiUTMI8//vhmTkdrMmjQoFiwYEGt67fffntcccUVLZgIAAAAAACg5ZiQCQAAAAAAtHrvvvtulJWVRWlpacyZMydWrVqVrDVmgmZDts3c5tBDD03KmePGjYu99tprN88AIPsyJ2TW9lqWTqejU6dOceKJJyaF8+HDh0enTp1yEZlW4Lrrrotvfetbu0y0jPj096lfv37x7LPPRs+ePRu8TxMyAQAAAACA1kIhEwAAAAAAaHNeeeWVpKA5b968WL9+fbLWXAXNgoKCGDJkSJSUlERxcXGMHDkyunTp0oSzAGia+gqZlYW3Pn36xBe/+MUoKSmJ8ePHR+/evXOUmNbg6aefjqFDh+7ye5X5OzVixIi48847o0+fPg3ap0ImAAAAAADQWihkAgAAAAAAbVpFRUU8+eSTUVZWFmVlZbFo0aLYunVrsl69dFnXpZPGFDS7dOkSI0eOjEceeaQp8QF2W12FzEyZr22pVCqOOeaYpFw+ZsyY2GOPPVoiLq1EOp2OPn36xIcffpj8XCmzSNm9e/e48MIL46yzzorjjz8+9t1331r3qZAJAAAAAAC0FgqZAAAAAABAu7Jt27ZYtGhRUtB88sknk7JHY6Zn1rR9TeVMRRIgVxpayKxJ5etbp06dYvjw4UlBc9iwYVFQUNAccWlFrrjiirj++uvrnL5a+X2lwsLC6N69e3Tt2nWX/b355pu1FjIHDBjQjGfSvFKpVCxYsCD69u2b6ygAAAAAAECWKGQCAAAAAADt2scffxyPPvpoUtB8+eWXk7XGFDRrKmcqZAK5tDsTMiNqn/wbEdG9e/cYM2ZMFBcXR0lJSRx11FHZDU2r8Oqrr8bRRx+d/K7U9jvT3m9HSKVS8cYbb0T//v1zHQUAAAAAAMgShUwAAAAAAIAM7733XlLOLCsri5UrVzZ6H5mTvRQygVzJLGRW19ByeU3bZm6z3377JeXMkpKSOOCAA5qYmtZi4sSJceutt9Za+K3pd6k2jSkMtxaV7wMUMgEAAAAAoG1RyAQAAAAAAKjDa6+9lpQz586dG+vWrWvQtDmFTCDXFi1aFL/61a/i0UcfjbVr1yaPN2dB87DDDkvKmePGjYsePXrsbnzy3Jo1a+Loo4+ONWvWRET90zDrKlYqZAIAAAAAAK2FQiYAAAAAAEAdduzYEYsXL47Zs2fH7NmzY+nSpQqZQKuSTqfjmWeeScrlCxYsiM2bNyfrzVXQLCgoiM985jNJQXPEiBHRuXPnppwKeWbhwoXx2c9+NrZv3x4R9Zcy24vM9wEKmQAAAAAA0LYoZAIAAAAAAFRTWVwqLS2NhQsXVikuNeTSikImkM8qi+aVBc2lS5fGzp07k/XqpcvGTi+sraBZWFgYI0aMiJKSkiguLo6hQ4c25TTIE3Pnzo2zzz47Pv744yq/D+35VgSFTAAAAAAAaLsUMgEAAAAAgHbvjTfeiNLS0igrK4s5c+bE2rVrk7XduZSikAm0Jp988knMmzcvKWi+8MILyWtfY6Zn1rR9beXMiIiePXvGuHHjori4OEpKSuLQQw9tymmQQ2+99VZceumlUVpaGhE1/95EtJ+SpkImAAAAAAC0XQqZAAAAAABAu7NmzZqYM2dOUsJ88803k7W6ykM1rddEIRNozT788MOknFn9NbIxBc3GTM+MiDjwwAOT6ZnFxcWx77777uYZkCtlZWXxq1/9Kh5++OE6p65W19gprK2BQiYAAAAAALRNCpkAAAAAAECbt3nz5pg/f36UlZVFaWlpPP/880n5o74CZk3b1KXy+QqZQFuxYsWKpJw5d+7c+PDDD5O15ixoDh48OJmeOWbMmCgqKtrdU6CFrV27NmbPnh0LFiyI5557LpYvXx4ffvhhVFRU5Dpai1LIBAAAAACAtkchEwAAAAAAaHPKy8tj6dKlUVpaGqWlpbFkyZLYsWNHRDRfAbOm5ytkAm3Rs88+mxQ0FyxYEJ988kmy1lwFzY4dO8ZJJ50UJSUlUVJSEiNGjGjKKZAD5eXl8fHHH8fWrVtj586dVT4Y4eCDD06mS0dUnTS9YsWKXMZusn79+kWHDh1yHQMAAAAAAMgShUwAAAAAAKBNePHFF5MJmPPmzatSEKqvhJmtAmZExD777BPjx4+P4uLiKC4ujoEDBzZ43wCtzc6dO2PJkiVRWloaZWVlVQrwEY17va1v28r1VCoVO3fubGp08khBQUGthUwfbAAAAAAAAOQThUwAAAAAAKBVWrlyZTIBc86cOfHBBx8ka9mcglnfc7t16xajR4+OkpKSKC4ujmOOOaahpwDQ5mzevDnmz5+fFDSfe+65KiW76hpT0KzcXkmv7VHIBAAAAAAAWguFTAAAAAAAoFVYv359zJkzJ5mCuXz58mQtmwXMmp6f+dzOnTvH8OHDkwmYw4YNiw4dOjR43wDtydq1a2POnDlJQXPFihXJWuZrbX2v0ZXbKum1TQqZAAAAAABAa6GQCQAAAAAA5KVt27bFwoULkxLP008/HRUVFRHR/AXMzOenUqk4/vjjkwLm6NGjY4899mjMqQDw/3vrrbeSYv2cOXNi9erVDX6ukl7bpZAJAAAAAAC0FgqZAAAAAABAXkin0/HEE08kRZ3HHnsstm3blqxlas4CZkTEYYcdlhQwx40bF7169WrwvgFouCVLlsSPf/zjePjhh6sU8mqipNd2KWQCAAAAAACtRcdcBwAAAAAAAJg1a1Z85zvfifXr1yeP1VfCzGYBs0+fPjF+/PikhHnggQc2eN8ANFxFRUUsW7YsmX68ePHi2L59e71lTNqHmv7/GgAAAAAAIJ8oZAIAAAAAADn35ptvxrp166o81pQpmPU9t0ePHjFmzJgoKSmJ4uLiOPLIIxuZGICGeuWVV6K0tDRKS0tj3rx5sWHDhmRNCZNMfh8AAAAAAIB8p5AJAAAAAADkjWxOwcx8bmFhYZxyyinJBMwTTjghCgoKmhYWgBqtWrUqmYBZVlYW7733XrJW2/RjRbz2bfTo0aZjAgAAAAAArYJCJgAAAAAAkFeyMQWzQ4cO8ZnPfCaKi4ujpKQkRowYEV26dMlqTgA+tWHDhpg7d25Swnz11VeTtdoKmJmUMXn00UdzHQEAAAAAAKBBFDIBAAAAAIBWob4Sz5FHHplMwBw7dmz06NGjJeMBtBvbt2+PRYsWRVlZWZSWlsaTTz4ZFRUVEZH9AqapiQAAAAAAAOQThUwAAAAAACAv1Vfi6devX1LALC4ujj59+rRkPIB25amnnkomYC5cuDC2bt0aES1TwMx8/gEHHBDjx49v8P4AAAAAAAAgmxQyAQAAAACAvJJZxsks4fTq1SvGjh0bJSUlUVxcHIcddlgu4gG0C8uXL08mYM6dOzfWrVuXrNVXwsxmAbNnz54xduzYpHw/aNCgBu8bAAAAAAAAsk0hEwAAAAAAyCuVRZyuXbvGyJEjkxLOkCFDaizuANB0q1evjrKysqSE+c477yRrzT0FM/O5e+yxR4wYMSJ57R86dKjXfgAAAAAAAPKGQiYAAAAAAJAXOnbsGCeeeGIUFxdHSUlJnHzyydGpU6dcxwJokzZt2hTz5s2L0tLSKC0tjRdffDFZa+4CZubzO3ToECeccEIy/fiUU06Jzp07N3jfAAAAAAAA0JJS6cZcKQMAAAAAAGgGK1eujL322iu6deuW6ygAbVJ5eXksXrw4mYC5dOnS2LlzZ0S0bAEzIuLoo49OJmCOHTs2unfv3uB9AwAAAAAAQC4pZAIAAAAAAAC0QW+//Xbcc889UVpaGgsWLIhNmzYla/WVMLNZwOzfv38y/Xj8+PGx3377NXjfAAAAAAAAkE865joAAAAAAAAAANk3Y8aMuPrqqyOiZQuYvXv3jnHjxiVTMA855JDGxAYAAAAAAIC8pZAJAAAAAAAA0Eal0+lIpVL1lijrUt9zi4qKYtSoUUkB8/jjj9/tvAAAAAAAAJDPFDIBAAAAAAAA2oGmTMHMfG6nTp1i2LBhSQFz+PDh0bGjS88AAAAAAAC0fa6KAQAAAAAAALRx9ZUx65qCmUql4rjjjksKmKNHj46ioqJmyQkAAAAAAAD5TCETAAAAAAAAoJ2pq4AZEXHwwQdHcXFxlJSUxPjx42PvvfduyXgAAAAAAACQlxQyAQAAAACAnFuyZEn07t07DjnkkFxHAWiT6itg7rfffjF+/PhkCuaAAQNaMh4AAAAAAAC0CgqZAAAAAABAzj300EPxk5/8JMaPHx+XXXZZfOlLX4qOHV3GAMimzAJm9+7dY8yYMUkBc/DgwTlMBgAAAAAAAK2DOxkAAAAAAIC8UFFREWVlZVFWVhb77rtvTJkyJS699NIYOHBgrqMBtGrpdDq6dOkSJ598clLAPPHEE6NDhw65jgYAAAAAAACtSiqd+TGoAAAAAAAAOXDVVVfFVVddFalUKpnglkqlIpVKRXFxcVx++eVx5plnKg8BNMKtt94aL7zwQhQXF8eoUaOisLAw15EAAAAAAACgVTMhEwAAAAAAyCupVCoiPp3olk6no7S0NEpLS2O//faLiy++OC655JIYMGBAjlMC5L+vfe1ruY4AAAAAAAAAbUpBrgMAAAAAAABkqj4hs7KY+f7778fPfvazOOSQQ+K0006L++67LyoqKnKcFgAAAAAAAABoLxQyAQAAAACAvFNZwoz4RzGzspxZUVERDz30UEyYMCH69+8fP/7xj+Odd97JcWIAAAAAAAAAoK1TyAQAAAAAAPJWZTGztqmZq1atip/85Cdx8MEHxxlnnBF/+9vfkm0BAAAAAAAAALJJIRMAAAAAAGgV6pqaWV5eHg888EB86UtfigEDBsTVV18d7777bo4TAwAAAAAAAABtiUImAAAAAACQc6lUqsr3lV81qW9q5sqVK+Oqq66Kgw46KM4666x48MEHTc0EAAAAAAAAAJoslXYHAgAAAAAAkGNbtmyJ22+/PW666aZYunRpRFQtadZ3OaN6eTOzrBkRceCBB8all14aU6dOjT59+mQzOgAAAAAAAADQTihkAgAAAAAAeeW5556L3/3ud3H77bfHhg0bImL3y5mZ26ZSqejQoUOcfvrpcdlll8XnPve5LCcHAAAAAAAAANoyhUwAAAAAACAvbdmyJf74xz/GjTfe2CxTM/v37x/Tpk2LKVOmxP7775/N6AAAAAAAAABAG6SQCQAAAAAA5L3nn38+mZr58ccfR0T2pmZ27NgxzjzzzJg2bVp89rOfzXJyAAAAAAAAAKCtUMgEAAAAAABajcqpmTfddFMsWbIkIrI7NXPgwIExbdq0mDx5cuy7777ZjA4AAAAAAAAAtHIKmQAAAAAAQKvUnFMzO3XqFGeddVZceumlUVxcnOXkAAAAAAAAAEBrpJAJAAAAAAC0as09NfOQQw5Jpmb27t07m9EBAAAAAAAAgFZEIRMAAAAAAGgzmntq5tlnnx3Tpk2LsWPHZjc4AAAAAAAAAJD3FDIBAAAAAIA2p7mnZh522GFx2WWXxaRJk6JXr17ZjA4AAAAAAAAA5CmFTAAAAAAAoE1rzqmZnTt3jnPOOSemTZsWo0aNynJyAAAAAAAAACCfKGQCAAAAAADtwpYtW+KOO+6IG2+8sVmmZg4aNCguu+yymDhxYvTs2TOb0QEAAAAAAACAPKCQCQAAAAAAtDsvvPBC/O53v4vbbrst61Mzu3TpEueee25MmzYtRowYkeXkAAAAAAAAAECuKGQCAAAAAADtVuXUzJtuuikef/zxiMju1MwjjzwyLr/88rjooouiR48e2YwOAAAAAAAAALQwhUwAAAAAAIBo3qmZhYWFcf7558e0adNi+PDhWU4OAAAAAAAAALQEhUwAAAAAAIAM2ZyaWb2YGRExePDguPzyy+NrX/tadO/ePZvRAQAAAAAAAIBmpJAJAAAAAABQi8qpmbfffnusX78+Ij4tVjb08kpd5cyuXbvGV7/61fjmN78ZRx11VFZzAwAAAAAAAADZp5AJAAAAAABQj61bt8add94Zv//97+Pxxx9vVCkzov6pmZ/73OfiX//1X2PkyJHZCw0AAAAAAAAAZFVBrgMAAAAAAADku8LCwjj44INj4MCB0bFjx4ioWrKsTzqdTr5SqVTyVfnYww8/HGPGjInx48fHsmXLmus0AAAAAAAAAIAmMCETAAAAAACgFuvWrYtZs2bFjTfeGK+++mryeDYur9Q1NfMrX/lK/Od//mfst99+TT4OAAAAAAAAAJAdCpkAAAAAAADVLFiwIG688ca45557Ytu2bbtVwKytcFnXtpXbpVKp6NGjR/z2t7+N888/v9HHBgAAAAAAAACyTyETAAAAAAAgap6GmVmQzFTb5ZWatmtqMfOf//mf41e/+tUu+wYAAAAAAAAAWpZCJgAAAAAA0K7VNQ2zoWXKmrY79NBD4+ijj44HHnggduzY0eBSZ+b+KgudF110UcycObNR5wUAAAAAAAAAZJdCJgAAAAAA0O5UTsO86aab4pVXXomIxk/DrL5tOp2ODh06xOmnnx7Tp0+PU089NSIi1qxZEzNnzoybb745mbzZkKJn9VLm9ddfH1//+tcbe6oAAAAAAAAAQJYoZAIAAAAAAO1Gc03D3H///eOSSy6JadOmRb9+/Wp97sKFC+P3v/993H333bFt27Z6j5lZyuzWrVs8//zzMWDAgAacKQAAAAAAAACQbQqZAAAAAABAm1bXNMyIphUxx44dG9OnT48JEyZEx44dG5xp7dq18dvf/jauv/76+PDDD6sUL2s6buWUzOnTp8evf/3rBh8HAAAAAAAAAMgehUwAAAAAAKBNqpxGme1pmD169IiJEyfG9OnT44gjjmhSxi1btsR1110X11xzTWzYsCEpX9aUIZ1OR/fu3WPVqlVRVFTUpOMCAAAAAAAAAI2nkAkAAAAAALQZzTkNc8iQITF9+vS48MILo2vXrlnN/f7778fUqVPjoYceqrWUWTkl884774xzzjknq8cHAAAAAAAAAOpXkOsAAAAAAAAATbVw4cK46KKL4oADDojvfve78fLLL0c6nU5KjJVflY/VVMbM3C7i0yJmly5dYuLEibF48eJ48skn45JLLsl6GTMiYv/9948HHnggLrnkkiRzbR5//PGsHx8AAAAAAAAAqJ8JmQAAAAAAQKu0bt26uOWWW+LGG2/M+jTMQw89NC677LKYMmVK9OrVK9vRa7Vjx4444YQT4vnnn68yKTNzQuaIESNi/vz5LZYJAAAAAAAAAPhUx1wHAAAAAAAAaIyFCxfG73//+7jnnnti27Ztu1XCrGnbDh06xOmnnx7Tp0+PU089NfvBG6BTp07xwx/+MC644IJd1ipLmatWrcpBMgAAAAAAAADAhEwAAAAAACDvrV+/PmbNmtUs0zD333//uOSSS2LatGnRr1+/bEdvtB07dsSee+4Z27dvj4hIJmNWft+zZ89Yu3ZtLiMCAAAAAAAAQLtkQiYAAAAAAJC3Fi5cGDfeeGPcfffdWZ2GGRExduzYmD59ekyYMCE6dsyfSyadOnWK4447LpYuXVold6UNGzbkIBUAAAAAAAAAkD93FwAAAAAAAETzTsPs0aNHTJw4MaZPnx5HHHFEtqNnTd++fav8nDkls6KiIheRAAAAAAAAAKDdU8gEAAAAAADyQnNOwxwyZEhMnz49LrzwwujatWuWk2ffnnvumesIAAAAAAAAAEA1CpkAAAAAAEDO/fznP49//dd/jYimT8Os3LawsDDOO++8mD59egwbNizLiZtX9fMBAAAAAAAAAHJPIRMAAAAAAMi5rVu3RjqdjlQq1eRpmIceemhcdtllMWXKlOjVq1fzBAYAAAAAAAAA2h2FTAAAAAAAIO80tojZoUOHOP3002P69Olx6qmnNne8FmVaJgAAAAAAAADkB4VMAAAAAAAgr9RWxqxpGub+++8fl1xySUybNi369evXIvlaUn3FVAAAAAAAAACg5ShkAgAAAAAAea2mIubYsWNj+vTpMWHChOjYse1d7jjiiCNizJgxuY4BAAAAAAAAAGRoe3coAAAAAAAArV5NJcwePXrExIkTY/r06XHEEUfkKlqL+P73vx/f//73cx0DAAAAAAAAAMigkAkAAAAAAOSVyjJmZRFzyJAhMX369Ljwwguja9euuYwGAAAAAAAAALRjCpkAAAAAAEBeSafTUVhYGOedd15Mnz49hg0blutIAAAAAAAAAAAKmQAAAAAAQP445JBD4rLLLospU6ZEr169ch0HAAAAAAAAACCRSqfT6VyHAAAAAAAA2rcHHnggOnbsGKeeemquowAAAAAAAAAA1EghEwAAAAAAAAAAAAAAAACgHgW5DgAAAAAAAAAAAAAAAAAAkO865joAAAAAAADQvr322mvx/vvv17nNscceG3vttVfLBAIAAAAAAAAAqEEqnU6ncx0CAAAAAABov04//fR48MEHa13v1atXvPnmm1FUVNSCqQAAAAAAAAAAqjIhEwAAAAAAyKmVK1dGbZ8fmUql4tJLL1XGBAAAAAAAAAByTiETAAAAAADIqffeey9SqdQuj1eWNCdMmNDSkQAAAAAAAAAAdpFK1/aR0wAAAAAAAC2gqKgotm7dGhGfljAry5npdDq6du0an3zySS7jAQAAAAAAAABERERBrgMAAAAAAADt2/bt23d5rPLzJI888siWjgMAAAAAAAAAUCOFTAAAAAAAIKeKiopqfDyVSkXfvn1bOA0AAAAAAAAAQM0UMgEAAAAAgJzq1q1brWvdu3dvwSQAAAAAAAAAALVTyAQAAAAAAHKqZ8+euY4AAAAAAAAAAFAvhUwAAAAAACCnDj300Ein0zWubdq0qYXTAAAAAAAAAADUTCETAAAAAADIqcMPP7zGx9PpdLz77rstnAYAAAAAAAAAoGYKmQAAAAAAQE4de+yxuzyWSqUiIuLvf/97S8cBAAAAAAAAAKiRQiYAAAAAAJBTJSUlVX5Op9PJ9+vXr49XXnmlpSMBAAAAAAAAAOxCIRMAAAAAAMip/fbbL4477rhIp9PJZMxMDz/8cA5SAQAAAAAAAABUpZAJAAAAAADk3IUXXljj4+l0Ov7whz+0cBoAAAAAAAAAgF2l0ul0OtchAAAAAACA9m3dunXRr1+/2Lp1a0REMi2z8j8feeSRKC4uznFKAAAAAAAAAKA9MyETAAAAAADIuZ49e8bFF18cNX2OZDqdjiuvvDLKy8tzkAwAAAAAAAAA4FMKmQAAAAAAQF74yU9+Evvuu29ERJXpmBERzzzzTPzoRz/KZTwAAAAAAAAAoJ1TyAQAAAAAAPJCjx494le/+lWVKZmVpcx0Oh3XXHNNzJw5M3cBAQAAAAAAAIB2TSETAAAAAADIGxdccEF873vfqzIdM+IfEzMvueSS+OUvf5nDhAAAAAAAAABAe5VKZ37MNAAAAAAAQI6l0+m44IIL4q677kpKmZmTMlOpVJx11llxww03xH777ZfjtAAAAAAAAABAe2FCJgAAAAAAkFdSqVTccccdMX369Kj8XMnMMmY6nY577703Dj/88Lj66qtj7dq1OU4MAAAAAAAAALQHJmQCAAAAAAB564Ybbogrr7wyNm/enEzLjIgqRc3CwsI444wz4pxzzonx48dHr169chUXAAAAAAAAAGjDFDIBAAAAAICcmz9/fq1rr7/+elx55ZWxfv36iIhkUmbl9xGR/JxKpWLQoEExePDgGDRoUPTp0yf23Xff6NatW3Tp0iUKCgqa90RqMHr06BY/JgAAAAAAAACQfQqZAAAAAABAzhUUFFSZgFmT6pc0apqYWdNaLqVSqdi5c2euYwAAAAAAAAAAWdAx1wEAAAAAAAAqNeZzJDOnY1YvZ/o8SgAAAAAAAAAg2xQyAQAAAACAvFHXZMvaSpaZj1cvZ+aSUigAAAAAAAAAtC0KmQAAAAAAQN5oaokxX0qQ+VIKBQAAAAAAAACypyDXAQAAAAAAAAAAAAAAAAAA8p1CJgAAAAAAAAAAAAAAAABAPTrmOgAAAAAAAEClVCqV6wgAAAAAAAAAADVSyAQAAAAAAPJGOp3OdQQAAAAAAAAAgBopZAIAAAAAADk3evRo0zEBAAAAAAAAgLyWSvuoaQAAAAAAAAAAAAAAAACAOhXkOgAAAAAAAAAAAAAAAAAAQL5TyAQAAAAAAAAAAAAAAAAAqIdCJgAAAAAAAAAAAAAAAABAPRQyAQAAAAAAAAAAAAAAAADqoZAJAAAAAAAAAAAAAAAAAFAPhUwAAAAAAAAAAAAAAAAAgHooZAIAAAAAAAAAAAAAAAAA1EMhEwAAAAAAAAAAAAAAAACgHgqZAAAAAAAAAAAAAAAAAAD1UMgEAAAAAAAAAAAAAAAAAKiHQiYAAAAAAAAAAAAAAAAAQD0UMgEAAAAAAAAAAAAAAAAA6qGQCQAAAAAAAAAAAAAAAABQj465DgAAAAAAAJANH330UTz99NOxfPnyWLlyZaxatSo2btwYW7ZsiS1btkRFRUWybSqVirKyshymBQAAAAAAAABaG4VMAAAAAACgVfr444/joYceioceeijmzJkTK1eubNDz0ul0pFKpZk4HAAAAAAAAALQ1CpkAAAAAAECrsnDhwvjd734Xf/7zn2Pr1q0R8WnJsiGyUcT8zW9+E4sWLap1fa+99oobbrihyccBAAAAAAAAAPJLKt3QOxQAAAAAAAByaNGiRfG///f/jnnz5kVE1RJmY4qWlRMyy8vLdyvH448/HqecckqNx6zc92OPPRbDhg3brf0DAAAAAAAAAPmpINcBAAAAAAAA6vLxxx/HxRdfHKNHj4558+ZFOp1Oio+VXxGRPF7XVzYMHz48Pv/5z9e5/1tuuSUrxwIAAAAAAAAA8ocJmQAAAAAAQN5aunRpfPnLX45Vq1YlhcfMyZSNvcyRSqWaPCEzImLu3LlRXFxc65TMXr16xXvvvRedOnXa7WMAAAAAAAAAAPnFhEwAAAAAACAv3XbbbTF27Nh49913q0zEzPbEy90xbty4OO6445IM1fOsW7cu7rvvvlzFAwAAAAAAAACagUImAAAAAACQd2bMmBGTJk2KrVu37lLErEnlNtW/mtP06dPrXP/Tn/7UrMcHAAAAAAAAAFpWKp3Lj48GAAAAAACo5p577onzzz8/KioqklJlTZczqhcuG3LJo7LYmUqlory8vEk5N2zYEPvvv39s27YtOX5m3r333js+/PDDJh0DAAAAAAAAAMgfCpkAAAAAAEDeeOmll2LYsGGxadOmBpUxK9e6dOkSJ510UpxyyinRp0+f2HvvvaNXr15x2mmnJSXMyudlq5AZEXH++efHXXfdVesxnnjiiRgyZEiTjwMAAAAAAAAA5F7HXAcAAAAAAACIiNi5c2ecd955dZYxMx9PpVJx+umnxxVXXBEjR46MLl26tHjms846K+66665a18vKyhQyAQAAAAAAAKCNKMh1AAAAAAAAgIiIX/ziF/HSSy81qIx5xhlnxAsvvBD33XdfFBcX56SMGRHxhS98ITp27FglX6bS0tKWjgQAAAAAAAAANJNUuvrdDAAAAAAAAC1s7dq1MWDAgNiyZUtEVC1jZhYxO3fuHL/4xS/iG9/4RoP2W1BQEKlUKtlf5fepVCrKy8uzkn348OGxdOnSKvuuzNutW7fYsGFDVo4DAAAAAAAAAOSWCZkAAAAAAEDO/d//+39j8+bNEVF7GbNHjx4xb968BpcxW8qIESOq/JyZf9OmTfHaa6+1dCQAAAAAAAAAoBkoZAIAAAAAADm1c+fOuOGGG5LyZaXqkzH/9re/xbBhw3IRsU7Dhw+vc/3ZZ59toSQAAAAAAAAAQHNSyAQAAAAAAHJq9uzZ8dFHH0VE1emSlT+nUqn493//910mUeaLo48+us51EzIBAAAAAAAAoG1QyAQAAAAAAHLq7rvv3uWxVCqVlDOHDBkSV1xxRUvHarDDDjssOnToEBGxy5TPiIgVK1a0dCQAAAAAAAAAoBkoZAIAAAAAADk1b968GouMEZ8WHH/0ox/Vup4POnXqFH369Kl1/e23327BNAAAAAAAAABAc1HIBAAAAAAAcuaDDz5IJkhWTsTMLF/27ds3zjjjjJxka4w+ffok+TOl0+lYvXp1DhIBAAAAAAAAANmmkAkAAAAAAOTMiy++WOPj6XQ6UqlUnHXWWS0baDftu+++uzxWWSxdu3ZtS8cBAAAAAAAAAJqBQiYAAAAAAJAzb7zxRp3ro0aNaqEkTbPHHnvUurZx48YWTAIAAAAAAAAANBeFTAAAAAAAIGfeeeedOtePPPLIFkrSNIWFhbWubd++vQWTAAAAAAAAAADNRSETAAAAAADImfqmR/bv37+FkjSfHTt25DoCAAAAAAAAAJAFCpkAAAAAAEDObNq0qc71bt26tVCSptmyZUuta126dGnBJAAAAAAAAABAc1HIBAAAAAAAcqaioiLXEbJi/fr1ta7tscceLRcEAAAAAAAAAGg2CpkAAAAAAEDOFBUV1ble1+TJfPLOO+9EKpWqca2+cwQAAAAAAAAAWgeFTAAAAAAAIGfqKyu+//77LZRk9+3cuTPeeuutXR5Pp9MREdGnT5+WjgQAAAAAAAAANAOFTAAAAAAAIGd69+5d53pNRcd88+KLL8a2bdsi4h8lzEqpVCr69++fi1gAAAAAAAAAQJYpZAIAAAAAADkzcODAOtefeeaZlgnSBAsWLKhz/aCDDmqZIAAAAAAAAABAs1LIBAAAAAAAcqa+QuaiRYtaKMnu++tf/1rn+nHHHddCSQAAAAAAAACA5pRKp9PpXIcAAAAAAADapx07dsSee+4Z27dvj4iIdDodqVQq+b5bt26xZs2a6Ny5827tv6CgIFKpVFReDqn8PpVKRXl5eZPzr1q1KgYMGBAVFRVJ5urHeeWVV+Kwww5r8rEAAAAAAAAAgNwyIRMAAAAAAMiZTp06xYknnhiZnx+Z+f2mTZvi7rvvzkW0Bvntb3+bFDszy5iVevXqpYwJAAAAAAAAAG2EQiYAAAAAAJBTo0aNqnUtnU7HL3/5yxZM03AffvhhXHfddVUKmJUqp2N+4QtfyEEyAAAAAAAAAKA5KGQCAAAAAAA59eUvf3mXxyoLjRERTz31VPzpT39q6Vj1+t73vhcbN26MiKpTPTOdddZZLZgIAAAAAAAAAGhOqXRtdwgAAAAAAAC0kMMPPzz+/ve/R8Q/yo2Vhcx0Oh377bdfvPDCC7H33ns3ar8FBQWRSqWq7LOy7FleXr7bee+444648MILq+y7eua99torVq1aFYWFhbt9HAAAAAAAAAAgf5iQCQAAAAAA5NzFF1+8y5TJzJ9Xr14d5513XpNKlNmyaNGimDp1alK+rK6y8Dl16lRlTAAAAAAAAABoQ0zIBAAAAAAAcm7jxo3Rv3//2LBhQ0TELlMnK0uOp512Wtxxxx3RtWvXBu032xMy58yZE2effXZs2LChzumYHTt2jNdeey0OOuigRh8DAAAAAAAAAMhPJmQCAAAAAAA517179/jmN7+5y5TMiH9MnEyn03H//ffH2LFj48MPP2zxjL/85S/ji1/8Yo1lzOpZp06dqowJAAAAAAAAAG2MCZkAAAAAAEBe2Lp1axx99NHx5ptvRkTsUnjMLEHuu+++8ZOf/CQuueSSZDJlTbIxIfOZZ56Jb33rW7FgwYLkudXzZT625557xuuvvx777LNPw04cAAAAAAAAAGgVTMgEAAAAAADyQmFhYfz617+ucfJkRFQpQ65evTouv/zyOO644+Kuu+6KLVu2ZD3Po48+GhMmTIgTTjihzjJm9Xw///nPlTEBAAAAAAAAoA0yIRMAAAAAAMgrV155ZVx77bVVJltmql6KTKVS0bVr1/jiF78YX/rSl+KYY46JQYMGRefOnRs1IXPt2rWxcOHCmDt3bvzP//xPvPvuu7scJ/PnzDyV+zzzzDPjz3/+cxb/2wAAAAAAAAAA8oVCJgAAAAAAkFcqKiri1FNPjTlz5jS4lJn5WEREQUFBDBw4MJYvX15rIfN73/tevP/++7Fy5cp4+eWX44MPPkieX9N+68tx+OGHx+LFi6Nnz55NOX0AAAAAAAAAIE8pZAIAAAAAAHln06ZNceqpp8bixYtrLWVGVC1hNvaSR13PzVyrbd+ZZcw+ffrEY489FgMGDGhUBgAAAAAAAACg9SjIdQAAAAAAAIDqioqK4qGHHoqTTz45mWhZvSQZ8WkZMnP6ZfWvulQ+N3P/mc/LXK+uehnz4YcfVsYEAAAAAAAAgDZOIRMAAAAAAMhL3bt3j7lz58ZFF11UpXRZk8zyZEMnZdZVwKxrH5nbDxo0KBYvXhyDBw9uzKkBAAAAAAAAAK2QQiYAAAAAAJC3OnfuHLNmzYrrr78+unbtWue0zEwNKWY2tsRZvbx53nnnxeOPPx79+/dv+AkBAAAAAAAAAK1WKt3Qj4kGAAAAAADIoTfeeCOmTZsWZWVluxQym/NyR+ax0ul09O7dO6699tqYOHFisx0TAAAAAAAAAMg/JmQCAAAAAACtwsCBA2P27Nnx0EMPxQknnFBlsmXl9Mr6Jmc2ROa+Mididu7cOb773e/G8uXLlTEBAAAAAAAAoB0yIRMAAAAAAGiVFixYEDfeeGPcc889sXXr1oiIrBQyK1VeQjnggAPi8ssvj2nTpsU+++yTtf0DAAAAAAAAAK2LQiYAAAAAANCqbdy4MR555JF48MEHo7S0NN5+++1at80sbNZ2iSSVSsWgQYPiC1/4Qpx11lkxcuTIrBY9AQAAAAAAAIDWSSETAAAAAABoU9asWRNPP/10vPDCC/HOO+/EypUr4/33349NmzbF1q1bY9u2bdGpU6fYY489omvXrrHffvtF//79Y8CAAXHMMcfEiSeeGHvuuWeuTwMAAAAAAAAAyDMKmQAAAAAAAAAAAAAAAAAA9SjIdQAAAAAAAAAAAAAAAAAAgHynkAkAAAAAAAAAAAAAAAAAUA+FTAAAAAAAAAAAAAAAAACAeihkAgAAAAAAAAAAAAAAAADUQyETAAAAAAAAAAAAAAAAAKAeCpkAAAAAAAAAAAAAAAAAAPVQyAQAAAAAAAAAAAAAAAAAqIdCJgAAAAAAkHP3339//PnPf46dO3fmOgoAAAAAAAAAQI0UMgEAAAAAgJx74okn4pxzzom+ffvGt7/97XjmmWdyHQkAAAAAAAAAoAqFTAAAAAAAIC+k0+lYs2ZNXHfddTF06NAYMmRIXHfddbFmzZpcRwMAAAAAAAAAUMgEAAAAAADyRyqVinQ6Hel0Op599tn49re/HQcccECcffbZcd9990V5eXmuIwIAAAAAAAAA7ZRCJgAAAAAAkFdSqVSkUqmI+HRq5o4dO+Ivf/lLTJgwIQ444ID43ve+F88//3yOUwIAAAAAAAAA7Y1CJgAAAAAAkFcqJ2RWFjMzp2auXr06fvnLX8bxxx8fQ4cOjd/85jfx0Ucf5ToyAAAAAAAAANAOKGQCAAAAAAB5qbKEGRE1ljOffvrp+OY3vxl9+/aN8847L+6///6oqKjIcWoAAAAAAAAAoK1SyAQAAAAAAPJaZQGzejmzcm379u1xzz33xJlnnhn9+vWL73//+/HSSy/lMjIAAAAAAAAA0AYpZAIAAAAAAHmlsmxZk8xyZk1TM99///249tpr45hjjomTTjopfve738X69etbLjwAAAAAAAAA0GYpZAIAAAAAADnXrVu3iIhdpmA2pJxZffvKx5944on4p3/6p+jbt29ccMEF8dBDDyXbAwAAAAAAAAA0VirtzgMAAAAAACAPPPfcc/Hf//3f8cc//jE+/PDDiKg6LbMhlzRq277y8T59+sTEiRNj0qRJMWjQoGxFBwAAAAAAAADaAYVMAAAAAAAgr5SXl8f9998fM2fOjPvvvz927NgREdkvZ5500kkxderUOP/882PPPffMVnwAAAAAAAAAoI1SyAQAAAAAAPLWRx99FLfeemvMmjUrnn766YioWrSMqL+cWdv2lY8XFhbGWWedFZMnT47Pfvaz2YoOAAAAAAAAALQxCpkAAAAAAECr8MILL8SMGTPi9ttvjw8++CAisj8184ADDohJkybFpEmT4tBDD81WdAAAAAAAAACgDVDIBAAAAAAAWpXy8vJ48MEHY9asWfHXv/41tm/fHhHZL2eefPLJMXXq1DjvvPOiW7du2YoPAAAAAAAAALRSCpkAAAAAAECrtW7durjtttvilltuiSeeeCIiqhYtI+ovZ9a2feXje+yxR5x99tkxefLkGD9+fLaiAwAAAAAAAACtjEImAAAAAADQJrz00ksxY8aMuP322+O9996LiOxPzTzwwANj0qRJMWnSpDj44IOzFR0AAAAAAAAAaAUUMgEAAAAAgDaloqIiHn744ZgxY0b89a9/jW3btkVE9suZI0eOjKlTp8Y555wTRUVF2YoPAAAAAAAAAOQphUwAAAAAAKDN+vjjj+P222+PWbNmxdKlSyOiatEyov5yZm3bVz7etWvXOOecc2Ly5MkxZsyYbEUHAAAAAAAAAPKMQiYAAAAAANAuvPrqqzFjxoy47bbb4t13342I7E/NPOigg2LSpEkxceLEOOigg7KUHAAAAAAAAADIBwqZAAAAAABAu5JOp2P27NkxY8aM+Mtf/hJbt26NiOyWM1OpVIwePTqmTJkSF110URbTAwAAAAAAAAC5opAJAAAAAAC0Wxs2bIg//vGPccstt8TixYsjomrRMqL+cmZd26dSqSgvL89SWgAAAAAAAAAglxQyAQAAAAAAIuL111+PGTNmxG233RbvvPNORDR9aqZCJgAAAAAAAAC0HQqZAAAAAAAAGdLpdJSVlcWMGTPi3nvvjS1btkRE48qZqVRKIRMAAAAAAAAA2hiFTAAAAAAAgFps3Lgx7rzzzpg1a1YsWrQoIqoWMyNqLmcqZAIAAAAAAABA26OQCQAAAAAA0AArVqyIGTNmxK233hpvvfVWndsqZAIAAAAAAABA26OQCQAAAAAA0Ehz586NmTNnxh133BE7d+6MiKqTMhUyAQAAAAAAAKDtKch1AAAAAAAAgNZkzZo18dxzz8Wzzz4bO3bsiIiqZUwAAAAAAAAAoG3qmOsAAAAAAAAA+W7nzp3xt7/9LWbOnBkPPvhgMhUzQhkTAAAAAAAAANoLhUwAAAAAAIBaPP300zFz5sz44x//GGvXro2IqgXMVCqlkAkAAAAAAAAA7YRCJgAAAAAAQIbVq1fHrbfeGrNmzYoXXnghInYtYVZSxgQAAAAAAACA9kMhEwAAAAAAaPd27NgR9913X8ycOTMefvjhKC8vr7WEGaGICQAAAAAAAADtkUImAAAAAADQbj3xxBMxc+bMuOOOO2LdunURsfvTMKuXNgEAAAAAAACAtkUhEwAAAAAAaFfef//9uPXWW2PWrFnx0ksvRcTulzBr275Xr15xwQUXxJQpU7IVGwAAAAAAAADIMYVMAAAAAACgzdu+fXv85S9/iZkzZ8bs2bOjvLy81hJmROOnYabT6ejQoUOceuqpMWXKlDjzzDOjc+fO2TsBAAAAAAAAACDnFDIBAAAAAIA2a+nSpTFz5sy48847Y/369RGx+9MwayttHnnkkTF58uS46KKLYv/9989ScgAAAAAAAAAg3yhkAgAAAAAAbcqqVaviD3/4Q8yaNSteffXViNj9EmZt2++1115xwQUXxOTJk+Okk07KVnQAAAAAAAAAII8pZAIAAAAAAK3etm3b4s9//nPMnDkzysrKoqKiotYSZsTuTcMsKCiIU089NSZPnhxnnXVWdOnSJXsnAAAAAAAAAADkPYVMAAAAAACg1Vq8eHHMnDkz/vSnP8WGDRsi4h9ly2yUMCMiBg0aFJMmTYqJEydG3759sxUdAAAAAAAAAGhlFDIBAAAAAIBWZeXKlXHLLbfErFmzYvny5RERtU7DrK+EWdv2PXr0iPPPPz8mT54cw4cPz1Z0AAAAAAAAAKAVU8gEAAAAAADy3tatW+N//ud/YubMmTF37tyoqKiotYQZsXvTMAsKCqKkpCQmT54cEyZMiMLCwuydAAAAAAAAAADQ6ilkAgAAAAAAeWvRokUxc+bMuOuuu2Ljxo0R8Y+yZTZKmBERhx12WEyaNCkmTpwY/fr1y1Z0AAAAAAAAAKCNUcgEAAAAAADyyttvvx233HJLzJo1K1asWBERUes0zPpKmLVtv+eee8a5554bkydPjhEjRmQrOgAAAAAAAADQhilkAgAAAAAAObdly5a4++67Y+bMmTFv3rxIp9O1ljAjdm8aZiqVivHjx8eUKVPi7LPPjj322CN7JwAAAAAAAAAAtHkKmQAAAAAAQM79x3/8R1x99dUR8Y+yZTZKmBERhxxySEyaNCkmTZoUBx54YLYiAwAAAAAAAADtjEImAAAAAACQFyqnWGYWK+srYUZEjdt369Ytzj333Jg8eXKMGjUq+2EBAAAAAAAAgHZHIRMAAAAAAMg7uzMNM5VKxdixY2Py5MlxzjnnRNeuXZszIgAAAAAAAADQzihkAgAAAAAAeaW2MmZNJcyIiIEDB8bEiRNj8uTJMWDAgGbPBwAAAAAAAAC0TwqZAAAAAABAXsssYlaWMIuKiuLLX/5yTJkyJcaMGZOraAAAAAAAAABAO6KQCQAAAAAA5J2apmGmUqkYPXp0TJ48Oc4999woKirKUToAAAAAAAAAoD1SyAQAAAAAAPJKZRmzchrmgAEDYuLEiTF58uQYOHBgLqMBAAAAAAAAAO2YQiYAAAAAAJBX0ul0dO3aNc4+++yYPHlyjB8/PteRAAAAAAAAAAAUMgEAAAAAgPxxyimnxJQpU+K8886Lbt265ToOAAAAAAAAAEAilU6n07kOAQAAAAAAtG/Lli2LXr16xSGHHJLrKAAAAAAAAAAANVLIBAAAAAAAAAAAAAAAAACoR0GuAwAAAAAAAAAAAAAAAAAA5DuFTAAAAAAAAAAAAAAAAACAeihkAgAAAAAAAAAAAAAAAADUQyETAAAAAAAAAAAAAAAAAKAeCpkAAAAAAAAAAAAAAAAAAPXomOsAAAAAAAAAzWXz5s2xZcuW2LJlS1RUVFRZ69+/f45SAQAAAAAAAACtkUImAAAAAADQqm3bti0ee+yxePrpp+Ppp5+O5cuXx8qVK+ODDz6I8vLyGp+TSqVi586dLZwUAAAAAAAAAGjNFDIBAAAAAIBWZ+PGjXH77bfHvffeG/Pnz4+tW7dWWU+n08127J07d+4ybTNTQUFBdOzoEgwAAAAAAAAAtDXuBgAAAAAAAFqNFStWxM9+9rO44447YsuWLRFRc/kylUrVuo+mljV/+MMfxrXXXlvrevfu3eO9996LPfbYo0nHAQAAAAAAAADySyrdnB8RDQAAAAAAkAUfffRR/Mu//EvMmjUrdu7cWaVU2ZjyZSqVinQ6HalUKsrLy3cry5o1a+Lggw+OTz75pMb1VCoVf/jDH+LCCy/crf0DAAAAAAAAAPmpINcBAAAAAAAA6nLXXXfFUUcdFTfffHPs2LEjKVRWfkV8Wrys6as59O7dO77+9a9HRFTJkZln1qxZzXJsAAAAAAAAACB3TMgEAAAAAADyUkVFRXz3u9+N6667LilXZk7D3J1LHNmYkBkRsWrVqjjooIOSfVTus/L7Dh06xFtvvRV9+/bd7WMAAAAAAAAAAPnFhEwAAAAAACDvbNu2Lc4444ykjFnTNMy6ZG7fHPr27RvnnHNOlRyZ31dUVMRtt93WbMcHAAAAAAAAAFqeQiYAAAAAAJBXtm/fHmeffXY8+OCDu0yerKmIWVm+zPxqCZdeemmd6/fdd1+L5AAAAAAAAAAAWkYqXd9HSAMAAAAAALSgyZMnxy233FKliFmTzOJl9W06duwYvXr1itWrV0cqlUrWK79PpVJRXl7e5KwHHXRQvPPOO0mGzMydOnWKjz76KIqKipp8HAAAAAAAAAAg90zIBAAAAAAA8sYNN9xQbxmzcgpm5cTMfv36xcSJE+Pmm2+Ol19+OdavXx/bt2+P999/v9nznnfeeVUyZn6/c+fOmDdvXrNnAAAAAAAAAABahgmZAAAAAABAXnjrrbfiqKOOiq1bt0ZE7WXMyrWTTjoprrzyypgwYUKVaZmZCgoKmnVC5sKFC2P06NG1HuOKK66I//qv/2rycQAAAAAAAACA3OuY6wAAAAAAAAAREf/8z/8cW7ZsqVJurJRZxOzZs2f8v//3/2LChAm5iFnFKaecEj179oz169fvkjudTkdZWVkO0wEAAAAAAAAA2VSQ6wAAAAAAAABLly6N+++/v94y5rBhw+Lpp5/OizJmxKcTOEeMGFFr5pdeeim2bNmSi2gAAAAAAAAAQJYpZAIAAAAAADl3zTXX1Ph4ZkFz3LhxMXfu3Ojfv39LRqvXiBEjqvycWc6sqKiIZ599tqUjAQAAAAAAAADNQCETAAAAAADIqdWrV8ff/va3ZKpkpcoyZiqVisMOOyzuvffeKCwszFHK2g0dOrTO9RdeeKGFkgAAAAAAAAAAzUkhEwAAAAAAyKm77747ysvLI+If0yUzy5kFBQVx2223Rffu3XOSrz5HHnlknevLly9voSQAAAAAAAAAQHNSyAQAAAAAAHLqr3/9a42PV07HnDx5cr1TKHPpgAMOiKKiooiIXaZ8RkS88cYbLR0JAAAAAAAAAGgGCpkAAAAAAEDOpNPpWLx4cY1Fxkr/8i//0oKJdk/fvn1rXXv33XdbMAkAAAAAAAAA0FwUMgEAAAAAgJx5+eWXY8OGDRHxaTkz4tMpk5XTMU8++eQ45JBDchmxQfr06ZPkr1R5HmvWrMlRKgAAAAAAAAAgmxQyAQAAAACAnFm+fHmd61/4whdaKEnT9OzZs9a1jz76qAWTAAAAAAAAAADNRSETAAAAAADImTfeeKPO9eHDh7dQkqYpLCysdW3Lli0tmAQAAAAAAAAAaC4KmQAAAAAAQM588MEHda4feuihLZSkabp06VLr2rZt21owCQAAAAAAAADQXBQyAQAAAACAnNm0aVOd6/vss08LJWma7du35zoCAAAAAAAAANDMFDIBAAAAAICc2bx5c53rXbt2baEkTbNly5Za1woLC1swCQAAAAAAAADQXBQyAQAAAACAnOnUqVOd69u2bWuhJE3zwQcf1LrWWkqlAAAAAAAAAEDdFDIBAAAAAICcKSoqqnP9k08+aaEkTfP2229HKpWqcW3PPfds4TQAAAAAAAAAQHNQyAQAAAAAAHKmvkLmypUrWyjJ7lu/fn2sWrUqIiLS6XTyeDqdjlQqFQceeGCuogEAAAAAAAAAWaSQCQAAAAAA5Ey/fv3qXF++fHkLJdl9Tz75ZJ3rAwYMaKEkAAAAAAAAAEBzUsgEAAAAAAByZuDAgXWu11d2zAePPPJIneuHHnpoCyUBAAAAAAAAAJqTQiYAAAAAAJAzhx12WK1r6XQ6SktLWzDN7vnLX/4SqVSq1vWhQ4e2YBoAAAAAAAAAoLkoZAIAAAAAADnTv3//2G+//SIiqpQaK79/6qmnYuXKlTnJ1hDz58+P1157LSI+LZDW5KSTTmrJSAAAAAAAAABAM1HIBAAAAAAAcmrEiBFVyozVv7/ppptyEatBrr322l0eS6VSyTkcddRR0bNnz5aOBQAAAAAAAAA0A4VMAAAAAAAgp0pKSmp8vLLY+Otf/zo+/vjjFk5Vv/nz58ff/va3KgXMTKlUKr70pS/lIBkAAAAAAAAA0BwUMgEAAAAAgJz68pe/HB06dIiIT0uMEVWnZK5fvz7+7d/+LSfZarN58+a49NJL691uwoQJLZAGAAAAAAAAAGgJCpkAAAAAAEBO7bPPPjFu3Lhap0ym0+n4zW9+E2VlZTlIV7Np06bF66+/vst0zMyfjznmmBg6dGiuIgIAAAAAAAAAWaaQCQAAAAAA5Ny3vvWtXR6rLDamUqmoqKiI8847L15++eUWTrar733ve3H77bfvUsbMlEql4hvf+EYLJwMAAAAAAAAAmlMqXdudAgAAAAAAAC3o+OOPj+effz4iYpepk5WP9ezZM+69994YNWpUg/ZZUFBQpThZ+X0qlYry8vJG5du5c2dcfvnlMWPGjOSx2nL26dMnVqxYEV26dGnUMQAAAAAAAACA/GVCJgAAAAAAkBf+z//5PzVOnMwsU65bty5OPfXUmDVrVotme/XVV+Pkk0+OGTNmJHlqy5pKpeKnP/2pMiYAAAAAAAAAtDEKmQAAAAAAQF447bTTYsKECUmpMVNmKXPbtm0xderUGDlyZCxdurRZM61evTq+853vxHHHHRdPPfVUkq16GTNz8uaQIUNiypQpzZoLAAAAAAAAAGh5qXRNH98MAAAAAACQA6tWrYohQ4bEmjVrImLXKZSVRc3MgubnPve5OP/88+Oss86KHj16VNm+oKCgSoEyszhZXl5eY4aKioqYM2dO3HrrrXHnnXfG9u3bd3l+bZn22GOPWLZsWRx11FFN+a8BAAAAAAAAAMhDCpkAAAAAAEBemTNnTnzuc5+LioqKiGhYKTMiolOnTjF69Og45phj4sgjj4yjjjoqRo4cWWchs6KiIt599914+eWX44UXXogFCxbEggULYt26dTUeo6bLKpn7vP766+PrX/96tv8rAQAAAAAAAADygEImAAAAAACQd37961/HN7/5zXqLkNXXKh+rVFOZs7I82bt371i7du0u29S2v/rKmJdddlnccMMNDT1FAAAAAAAAAKCVUcgEAAAAAADy0rXXXhtXXnllnaXMiPpLk41VX6mz+nbpdDomTJgQd9999y7PBQAAAAAAAADaDoVMAAAAAAAgb/3617+Ob3/721FRURERdRcuaypDNnb7hjwv87npdDq++tWvxowZM6Jjx451PgcAAAAAAAAAaN0UMgEAAAAAgLw2e/bsuOCCC2LdunX1TstsjN3ZV/XnfP/7349rrrmmyVkAAAAAAAAAgPynkAkAAAAAAOS9d999Ny699NJ46KGHqky2bKnLHNWPue+++8aMGTPiC1/4QoscHwAAAAAAAADIvYJcBwAAAAAAAKjPAQccEA888ED84Q9/iP79+ydFzFQqlXw1h8x9p9PpSKVScfHFF8fzzz+vjAkAAAAAAAAA7YwJmQAAAAAAQKuyY8eOuOmmm+KXv/xl/P3vf4+IqLGQuTuXQGrbT4cOHeLMM8+MH//4x3HMMcc0PjQAAAAAAAAA0OopZAIAAAAAAP9fe/cZngV5vo//ChBGGALKkh02OEBEQHCgVRQVR+tAxYFYaautotU6KmArDrTaOmpdOFHRWgcKdTCUJRsUEJElS0FBWSFAyP9F//XX9qvkeZI8eRifz3Hwonrd93WGSDiOhpN7jzV69Oh48skn4+23345vv/32+39elBcz//NbJ9nZ2XHOOedEv379okGDBkWJCgAAAAAAAADs4RQyAQAAAACAPV5eXl5MmjQp3nvvvZg2bVrMnDkzVq9enfQ9devWjSOOOCI6duwYJ598stcwAQAAAAAAAIDvKWQCAAAAAAB7pfXr18fy5ctjxYoV8eWXX8bmzZtj69atkZubG5mZmVGhQoXIysqKWrVqRYMGDaJhw4ZRtWrVdMcGAAAAAAAAAHZTCpkAAAAAAAAAAAAAAAAAAAUole4AAAAAAAAAAAAAAAAAAAC7O4VMAAAAAAAAAAAAAAAAAIACKGQCAAAAAAAAAAAAAAAAABRAIRMAAAAAAAAAAAAAAAAAoAAKmQAAAAAAAAAAAAAAAAAABVDIBAAAAAAAAAAAAAAAAAAogEImAAAAAAAAAAAAAAAAAEABFDIBAAAAAAAAAAAAAAAAAAqgkAkAAAAAAAAAAAAAAAAAUACFTAAAAAAAAAAAAAAAAACAAihkAgAAAAAAAAAAAAAAAAAUQCETAAAAAAAAAAAAAAAAAKAACpkAAAAAAAAAAAAAAAAAAAVQyAQAAAAAAAAAAAAAAAAAKIBCJgAAAAAAAAAAAAAAAABAARQyAQAAAAAAAGAP1ahRo8jIyCjwR6NGjdIdFQAAAAAAYI+nkAkAAAAAAAAAAAAAAAAAUIAy6Q4AAAAAAAAAxSUvLy+2bt0aW7dujW3btkXp0qUjMzMzypYtGxUrVkx3PAAAAAAAAAD2YAqZAAAAAAAA7FGWL18es2bNirlz58bcuXPjiy++iNWrV8eXX34ZGzdu/NFzpUuXjv322y+qVasWtWvXjrp160bdunWjUaNG0axZs2jevHk0atQoSpcuXYIfDQAAAAAAAAB7CoVMAAAAAAAAdmubN2+OESNGxKhRo2LcuHGxZMmSQt2Tl5cX69ati3Xr1sWiRYt+cKZ8+fJx0EEHxaGHHhrt2rWLTp06xaGHHhplyvi2GgAAAAAAAMC+zneOAQAAAAAA2C2NGzcuHnnkkXjzzTdj8+bNJbJz69atMW3atJg2bdr3/ywrKys6dOgQxx57bBx//PHRqVOnyMzMLJE8AAAAAAAAAOw+MvLz8/PTHQIAAAAAAAD+7e9//3vccccdMX369HRH+UEVK1aM4447Lk499dQ45ZRTom7duumOBOzDGjVqFMuWLStwrmHDhrF06dLUBwIAAAAAANiLKWQCAAAAAACwW5g1a1b8+te/jg8//DDdUZIybdq0aN++fbpjAPsohUwAAAAAAICSUyrdAQAAAAAAANi35eXlxaBBg+Lwww/f48qYERHbt29PdwQAAAAAAAAASkCZdAcAAAAAAABg37VmzZo4++yz44MPPkh3FAAAAAAAAADYJYVMAAAAAAAA0mLJkiXRvXv3WLhwYbqjAAAAAAAAAECBFDIBAAAAAAAocUuWLIkuXbrE6tWr0x0FAAAAAAAAABKikAkAAAAAAECJWrduXZx88snFUsasWbNmdOzYMQ477LBo2rRpZGdnR40aNWL//fePChUqRLly5SIvLy+2bNkSOTk5sWbNmli5cmWsXLky5s2bF5988kl8/PHH8eWXXxbDRwYAAAAAAADA3kwhEwAAAAAAgBKTn58f5513XixYsKDQdzRu3DguvvjiOP3006Nt27YFzpcqVSr222+/2G+//aJ27dpxyCGH/J+ZxYsXx4QJE+KDDz6IkSNHxsqVKwudDwAAAAAAAIC9k0ImAAAAAAAAJeahhx6Kd999t1Bn27ZtGwMHDoyePXtGRkZGsebKzs6O7Ozs6N27d0REzJgxI95888146aWXYv78+cW6CwAAAAAAAIA9U6l0BwAAAAAAAGDfsGrVqrjhhhuSPlehQoX485//HNOnT4/TTz+92MuYP+Swww6LAQMGxLx582LGjBlx3XXXRY0aNVK+FwAAAAAAAIDdl0ImAAAAAAAAJeKPf/xjbNmyJakz9evXj4kTJ8avf/3rKFUqPd/aateuXQwZMiRWrFgRw4YNi2OOOSYtOQAAAAAAAABIL4VMAAAAAAAAUm7FihXx+OOPJ3Wmbt26MXbs2Gjbtm1qQiWpbNmy0atXrxg7dmzMnDkzzj///ChTpky6YwEAAAAAAABQQhQyAQAAAAAASLmnn346tm/fnvB82bJl49VXX43s7OwUpiq8tm3bxvPPPx+LFy+Opk2bpjsOAAAAAAAAACXAX9kLAAAAAABAyj311FNJzf/2t7+NI444IjVhilH9+vXTHQEAAAAAAACAEuKFTAAAAAAAAFJqwYIF8fnnnyc8X7Vq1bjxxhtTmAgAAAAAAAAAkqeQCQAAAAAAQEqNHj06qfmLL744KlasmKI0AAAAAAAAAFA4CpkAAAAAAACk1MSJE5OaP/3001OUBAAAAAAAAAAKTyETAAAAAACAlFqwYEHCs6VKlYrOnTunMA0AAAAAAAAAFE6ZdAcAAAAAAABg7/b5558nPNuwYcMoX758CtPs3XJzc2PevHnxySefxOLFi2Px4sWxfPny+Prrr+Prr7+OzZs3R25ubmzfvj3KlSsXFSpUiPLly8cBBxwQ9erVi7p160bTpk2jXbt2cdhhh8X++++f7g8p7WbPnh0fffRRzJs3L+bOnRvLly+PjRs3xsaNGyMnJycqV64c1apVi2rVqkWLFi2iY8eOccQRR0T79u0jMzOzxHJu2rQppk6dGrNmzYpZs2bFp59+Gt9++218++238d1330V+fn5UrFgxqlatGtnZ2dGsWbPo2LFjHHfccdGgQYMSy5lueXl5MXny5Jg5c2bMnTs35s2bF19++WVs2LAhNm7cGFu3bo1KlSpF5cqVo0aNGtGyZcto1apVdOzYMY4++mhfn0rAN998ExMnToyPP/44lixZEosXL44VK1bEpk2bYvPmzbFly5bIyMiIihUrRsWKFaN69erRuHHjaNy4cbRq1Sq6dOkSrVu3joyMjHR/KEWSl5cXH3/8ccyePTsWLFgQCxcujNWrV8fatWtj3bp1sXXr1sjNzY2dO3dGhQoVIisr6/sfFSpUiBo1akTDhg2jYcOG0ahRo2jYsGFkZ2dH7dq10/2hAQAAAADAHi0jPz8/P90hAAAAAAAA2Dtt27YtypUrl/B8hw4dYsqUKSlMtHdZsmRJfPDBBzFx4sSYNGlSzJ8/P3bs2FFs9zdv3jxOOeWU6NGjR3Tr1i1Kly5dbHfvziZNmhTPPvtsjBgxIpYvX16oO2rWrBkXXXRR9O3bN1q0aFHMCf8lJycnRowYES+99FK8/fbbkZOTU6h72rVrF3369ImLLrooqlSpUswp0y8/Pz9GjBgRw4cPj7fffjvWrVtXqHsqVKgQxx13XPTp0yd69uwZZcrsHn//caNGjWLZsmUFzjVs2DCWLl2a+kBJ2rlzZ3z44YcxfPjwGD16dHz66adFvrNatWpxwgknxHnnnRc9evRI6vehdFq1alW89tpr8cYbb8SECRNi06ZNxb6jXr160alTp+jYsWN06tQp2rdvHxUqVCj2PQAAAAAAsLdSyAQAAAAAACBl1q9fH9WrV094vkuXLjF+/PgUJtqzbdu2Ld5///0YMWJEvPPOO0m9PlpU9erVi8svvzwuv/zyqFOnTont3ZWBAwfGoEGDEpodOnRoXHLJJbucef311+Puu++OiRMnFkO6/+ecc86JP/3pT1G3bt1iuS8nJycefvjhuOuuu2Lt2rXFcmdExAEHHBA33nhjXHXVVSX6uud/Ks7P6fbt2+Ppp5+Oe+65JxYsWFBMCf+lbt26ce2118aVV16Ztp+rf9tTC5mrV6+OP//5z/Hss8/GqlWrUranSpUqccUVV0T//v1329chR40aFQ8++GCMHDkydu7cWaK7y5UrF927d49zzjknTjvttL2ylA0AAAAAAMWpVLoDAAAAAAAAsPfavHlzUvOFfblub7Zjx44YMWJEXHDBBVGzZs3o0aNHPPzwwyVaxoyIWLFiRQwYMCCaNGkSN954Y3z33Xcluj+Vli5dGt27d48zzjij2MuYERHDhw+Pli1bxn333Vfku5555plo0qRJXHfddcVaxoyI+Prrr+Paa6+Njh07xvz584v17pI2bdq0aN++fVx++eXFXsaMiFi5cmX0798/2rRpE2+//Xax3783W7ZsWVxxxRXRuHHjuOuuu1JaxoyI2LBhQwwZMiQaNWoU11xzTWzcuDGl+5Lx4YcfRufOnePkk0+Ot956q8TLmBERubm58cYbb8SFF14YNWvWjNNPPz1WrlxZ4jkAAAAAAGBPoZAJAAAAAABAypQqldy3o77++usUJdkzDRo0KA488MA47bTTYtiwYbtFCTInJyfuvPPOaNasWbz++uvpjlNkQ4cOjYMOOijeeeedlO7ZtGlT9O/fPy6++OLYtm1b0ue/++676NWrV1x88cWxevXqFCT8f2bOnBkdOnSIkSNHpnRPKuTn58eAAQOiU6dO8fHHH6d838KFC+OUU06Jq666KnJzc1O+b0+2ffv2uOuuu6J169bx6KOPlvjPV25ubtx///3RqlWrePXVV0t09//avHlz/OIXv4ijjz46Jk+enNYs/+nf5czly5enOwoAAAAAAOy2FDIBAAAAAABImaysrKTmv/76a6XM//Dyyy8X+yuIxWXt2rVxxhlnxC9/+cvYvn17uuMkLT8/P2666abo06dP0i+5FsUzzzwT3bt3T2rn3Llz49BDD40XX3wxhcn+2+bNm6Nnz54xfPjwEttZVNu2bYsLL7wwbrvttsjLyyvR3Q8++GB07tw5vvzyyxLdu6eYO3dutGvXLn73u9/Fli1b0ppl5cqV8dOf/jR+85vfxI4dO0p8/9KlS+OII46IRx55pMR3AwAAAAAARaeQCQAAAAAAQMpUrlw5SpcunfB8fn5+vPvuuylMRHH761//GqeffnraS1bJyM/Pj4suuijuuOOOtOwfO3ZsXHDBBZGfn1/g7KxZs+LYY4+NZcuWlUCy/7Zjx47o3bt3jBs3rsR3Jys3Nzd69OgRw4YNS1uGmTNnRteuXWPJkiVpy7A7evHFF6Njx44xd+7cdEf5L3/5y1/i+OOPj2+//bbEds6bNy86duwY8+bNK7GdAAAAAABA8VLIBAAAAAAAIGVKly4dBx54YFJnXnvttdSEIWVGjhwZJ510UuTm5qY7SkKuvfbaeO6559Ka4fXXX4+bbrpplzPTpk2L4447Lq2vxm7bti3OOuusWLVqVdoyJKJPnz7x/vvvpztGLFq0KLp27ZqWAu3u6NZbb41evXqV6Cu0yfjggw/ihBNOiPXr16d81/Lly6N79+6xZs2alO8CAAAAAABSRyETAAAAAACAlMrOzk5q/pVXXok5c+akKA2p8uGHH8Zll12W7hgFuvfee+O+++5Ld4yIiLjzzjt/tES4YsWKOPXUU0ukKFaQdevWRZ8+fdId40cNHDgwrS9j/q9Vq1ZFjx49SvTlxd3RDTfcEH/4wx/SHaNA06ZNi5/85CexadOmlO3Iy8uL8847L1asWJGyHQAAAAAAQMkok+4AAAAAAAAA7N3at28f48aNS3h+586dcfXVV8c777wTZcr4dlYyMjMzo2XLltGyZcto1apV1K9fP2rXrh21atWKrKysKF++fGRmZkZubm7k5OTEV199FatWrYp58+bFnDlzYsKECbFx48ZC73/++eejffv2cc011xTjR1V8Jk2aFDfccENCs9WqVYujjjoqunTpEs2aNYuGDRtGpUqVoly5crFly5ZYu3ZtLFy4MCZOnBijRo0q9AuSV111VcyePTsyMzO//2e5ubnx05/+NL766quE7jj44IPjqKOOirZt20aTJk2iRo0aUalSpcjPz4+NGzfGsmXL4uOPP44xY8bE2LFjIy8vL+mc//znP+OFF16IXr16JX02lcaPHx+33XZbwvNNmjSJ7t27x6GHHhotW7aM6tWrR6VKlSI3Nzc2bNgQS5YsiXnz5sWYMWNi4sSJsWPHjkLlmjdvXvzsZz+Ld955J0qV2vf+nuQbbrgh7r777iLdsf/++0fnzp3j8MMPj6ZNm0bDhg2jWrVqUaFChcjIyIjNmzfHV199FYsWLYpp06bFuHHj4rPPPivUrhkzZkTv3r3j1VdfjYyMjCLl/iH33HNPTJw4sdDns7KyokuXLtG8efNo2rRpNG3aNKpWrRoVK1aMihUrRvny5WPbtm2Rm5sbGzdujK+++irWrFkTixcvjs8++yzmzZsXCxYsiPz8/GL8qAAAAAAAYN+Uke//cQcAAAAAACCFXnnllTj77LOTPte7d+94+umnU1KO2VMcdNBBMXfu3B/992XKlIkjjzwyTjrppOjSpUt06NAhKlSoUOh9eXl5MWHChBg2bFg8//zzhXoxrkKFCvHxxx9HkyZNCp0jUQMHDoxBgwYlNPvAAw/E/fffH4sWLdrl3PHHHx9XXnll9OjRI8qWLZvQ3Xl5efH222/HrbfeGrNmzUrozH8aMmRIXHfddd//7759+8YTTzyxyzPVq1ePX/3qV3HxxRcn9XO9evXquOeee+Khhx6K3NzcpHI2atQoFixYkPDPS2Ek8zn961//Gvfdd1+BJbzMzMy46KKL4sorr4y2bdsmnGXdunXx5JNPxgMPPBBffPFFwuf+0+233x433XRToc4mqlGjRrFs2bIC5xo2bBhLly5NaZaIiCeffLLQr+VWrFgxLrzwwjj//POjS5cuUbp06aTOz549Ox5//PF4/PHHY+vWrUnvHzhwYAwYMCDpc7vyzTffRHZ2dmzYsCGpcxUqVIhLLrkkzjrrrDjqqKOiXLlyRcqxYcOGmDZtWowZMybefffdmDZt2o+WsydNmhSdOnUq0j4AAAAAANhbKWQCAAAAAACQUt99913UrFkztm3blvTZs88+Ox577LHYb7/9UpBs9/dDhczSpUvHCSecEL17945TTjklZT8369evj/vvvz+GDBkSOTk5SZ09/vjj47333ktJrv+UTHmvVq1au3xxsnXr1vHII4/EUUcdVeg8eXl5MXjw4BgwYEBSL9HVqFEjli9fHuXKlYtRo0bFySef/KOzZcqUieuuuy5uuummqFy5cqGzfvLJJ3HOOefE/Pnzkzr36KOPxuWXX17ovQVJ5nPaqFGjAguGJ554YjzwwAPRvHnzQmfKycmJu+66K+64446kv45lZmbGxIkT4/DDDy/0/oLsToXM8ePHx/HHH5/0z1NWVlZcffXVcf311xfL17TVq1dH//7948UXX0zqXOnSpWPy5MnF+vkaNGhQDBw4MOH5UqVKxTXXXBPXX3991KxZs9hy/K81a9bE3//+9xg+fHiMGzfuv75mKWQCAAAAAMCPK5XuAAAAAAAAAOzd9ttvv/jJT35SqLMvv/xytGjRIv72t78VqtC5N6ldu3YMGjQoVq5cGSNHjozzzz8/pUXVatWqxaBBg2LevHlx5JFHJnX2/fffjzFjxqQoWeHsqoz5q1/9KmbOnFmkMmbEv8pcv//975N+2XXt2rXx0ksvxZYtW+KXv/zlj87Vr18/Jk2aFHfccUeRypgR/yr7jh8/Ptq1a5fUuQceeKBIe4vTrsqFpUuXjsGDB8eoUaOKVMaM+NdLhQMHDowPPvgg6tevn9TZ7du3R9++fWPnzp1FyrAn2LBhQ/Tq1Svpr9VHH310zJ07N26//fZi+5pWp06deOGFF2L48OFRsWLFhM/l5eXFZZddFtu3by+WHDt37ownn3wy4flq1arFiBEj4p577klpGTMiombNmvGLX/wixowZEwsWLIjrrrsuqlatmtKdAAAAAACwN1DIBAAAAAAAIOV+8YtfFPrsV199Ff369YtGjRrF73//+1i0aFExJtv9NW7cOJ544olYtmxZ3HrrrVGrVq0S3d+oUaMYN25cXHrppUmdu+2221KUqHj9+c9/jgcffDDKli1bbHf27t076Y//wQcfjIEDB8aSJUt+8N8fcsghMXXq1GJ9ua969eoxYsSIqF27dsJnPv7445g4cWKxZUiF0qVLx3PPPRc33nhjUsXYgnTs2DEmTpwYTZs2Terc7Nmz44knnii2HLura6+9NlasWJH0mTFjxkSjRo1Skunss8+ODz/8MPbff/+Ez8yZMyceeuihYtk/bdq0+OKLLxKaLV26dLz22mu7fCE3VZo1axZDhgyJL774Iu6+++599lVqAAAAAABIhEImAAAAAAAAKXfKKadEq1atinTH6tWr449//GM0bdo02rdvH4MGDYrJkydHXl5eMaXc/dxxxx2xYMGC6NOnT7EWBpNVpkyZeOKJJ5IqZY4dOzbmzJmTwlRFd88998Svf/3rlNx94403RocOHRKenzp1atx7770/+O9atmwZ7733XkrKuAceeGA8+OCDSZ155ZVXij1HcXryySfjvPPOS8nd9erVizFjxkSdOnWSOnfLLbfEpk2bUpJpdzBmzJh4/PHHkzpz7733xj333BOlSqX2jy20a9cu3n///aRelb3zzjtjy5YtRd6dzEvBN910Uxx99NFF3lkUlStXjt/+9rdF/v0aAAAAAAD2ZgqZAAAAAAAApFxGRkbcc889xXbfjBkzYuDAgdG5c+eoVq1adO/ePQYOHBhvvvlmrFq1qtj2pNtpp50WmZmZ6Y4REf/6HP7tb3+Lzp07J3zm6aefTmGiornkkkvi2muvTdn9pUuXjrvvvjupMzt37vw//6xy5crx2muvRY0aNYor2v/x05/+NI488siE5//xj3+kLEtR9e/fPy666KKU7qhXr168+uqrSZWk16xZs1e/knn99dcnNT9gwIDo379/itL8X4ceemg8++yzCb+Y+tVXX8XDDz9c5L3Tpk1LaC4zMzNl5XAAAAAAAKB4KWQCAAAAAABQInr06BE/+9nPiv3ejRs3xjvvvBODBg2Knj17Rt26daNevXpx5plnxuDBg+Pdd9+N9evXF/vefVFmZmYMHTo0ypUrl9D8sGHDfrBkmG5NmjQplrJVQY499tikXsn8IQ888EC0aNGimBL9uBtuuCHh2aVLl8bixYtTmKZwDj744LjzzjtLZFenTp3illtuSerM/fffv1e+6PuPf/wj4eJhRETPnj1jwIABKUz0w04//fS44oorEp5/6KGHIj8/v0g7Fy5cmNDcSSedFAcccECRdgEAAAAAACVDIRMAAAAAAIAS89hjj0Xjxo1TvmflypXx2muvxc033xwnnnhiVK9ePZo1axbnn39+/PnPf47JkydHbm5uynPsjVq0aBF9+vRJaPbLL7+MGTNmpDhR8h5++OGoUKFCiey69NJLC332mGOOiYsvvrgY0/y4Hj16RK1atRKenzBhQgrTJC8jIyMef/zxEn1R9ne/+120adMm4fmlS5fG66+/nsJE6TFw4MCEZ2vUqBFDhw5N+KXK4nbXXXdFzZo1E5pdunRpvP/++0Xal+iLza1bty7SHgAAAAAAoOQoZAIAAAAAAFBiqlatGm+99VbUqFGjxHd//vnn8cILL8TVV18dnTt3jipVqsQxxxwTf/zjH+Ojjz4q8kto+5Jrr7024dmiFpqKW7du3eLEE08ssX09e/Ys9NnBgwcXY5JdK1OmTJxyyikJz0+aNCmFaZL3s5/9LI444ogS3ZmZmRm33357Umeee+65FKVJj/Hjx8ecOXMSnh8yZEhUr149hYl2rUqVKkm9Bjt06NAi7du4cWNCc/Xq1SvSHgAAAAAAoOQoZAIAAAAAAFCiWrVqFe+8807Url07rTm2bdsWH3zwQfz+97+PTp06Rb169eLXv/71bvfy3+6oSZMm0bFjx4Rmx4wZk+I0ybnxxhtLdF/dunUL9frdUUcdFUceeWQKEv24ZIqq8+fPT2GS5GRkZCT1SmNxOv3006N9+/YJz7/99tvx3XffpTBRyXrssccSnm3ZsmX07t07hWkS069fv6hSpUpCsyNHjoy8vLxC79q2bVtCczt37iz0DgAAAAAAoGQpZAIAAAAAAFDi2rZtGx999FEceuih6Y7yvVWrVsUDDzwQXbt2jTZt2sSDDz6Y8Otm+6Kf/OQnCc3NnDkzxUkS16BBg4RzF6fDDz886TOXXXZZCpLsWocOHRKeXbBgQQqTJKdbt26FKr0Wl1/96lcJz+bm5sYbb7yRwjQlZ8OGDfHyyy8nPH/DDTdEqVLp/yMKWVlZ0atXr4Rm169fHx999FGhd5UvXz6huRUrVhR6BwAAAAAAULLS/90OAAAAAAAA9kkNGjSIjz76KK699trdoqTzn+bNmxdXXXVVNGrUKG6//XbFzB/QqVOnhObWrFkTa9asSXGaxJx77rmRkZFR4nuTLR5nZmbGWWedlaI0Py47OzsqV66c0Ozq1atjy5YtKU6UmMsvvzyt+88999yEX1yMiHjvvfdSmKbkvPPOO5GTk5PQbOXKleOcc85JcaLEnXnmmQnPvvPOO4XeU7Vq1YTm/vnPfxZ6BwAAAAAAULJ2r+9sAwAAAAAAsE8pV65c3HPPPTFp0qTo3LlzuuP8H+vWrYtbbrklmjRpEk8//XS64+xWmjRpkvDsJ598ksIkievevXta9mZnZyc137lz54SLkcWtcePGCc/uDkXbcuXKxamnnprWDFlZWXHyyScnPD9mzJgUpik5I0eOTHj2pz/9aWRlZaUwTXKOOeaYhF+vnDp1aqH3NGjQIKG5OXPmxPjx4wu9BwAAAAAAKDkKmQAAAAAAAKTdEUccERMnToy///3vcfjhh6c7zv+xdu3auOSSS+LYY4+NFStWpDvObqFOnToJz37xxRcpTJKYMmXKRJcuXdKyO9FS1r8dc8wxKUpSsIYNGyY8uzsUMrt16xaVKlVKd4w4/fTTE55dvnx5LF68OIVpSsaoUaMSnu3Ro0cKkySvfPnyCb9cO2vWrELvadOmTcKzffv29RozAAAAAADsARQyAQAAAAAA2G2cddZZMXXq1Hj//ffj7LPPjrJly6Y70n8ZN25ctG3bNqmX4fZWFStWTHh29erVKUySmBYtWiT8Il5x23///ZOaT7QolgrJZP3mm29SmCQxxx13XLojRETyOWbMmJGiJCVj0aJFsWrVqoTnjz/++BSmKZy2bdsmNLdq1apYu3ZtoXYk8/LzggULolu3brtF0RkAAAAAAPhxCpkAAAAAAADsdo477rgYPnx4rFixIh544IE49thjo3Tp0umOFRH/KqGddtpp8cQTT6Q7SlplZmYmPLs7FDKTeamuuFWrVi2p+datW6coScGSybp169YUJklMMoW3VKpVq1Y0atQo4flPPvkkdWFKQDKF0uzs7KhevXoK0xROMp+vwr5o2qNHj8jIyEh4fvr06XHwwQfHE088ETt27CjUTgAAAAAAILUUMgEAAAAAANht1ahRI6688soYM2ZMrF69Op577rm4+OKLo27dumnNlZeXF3379o2HHnoorTl+zNKlS2PEiBFx//33x5VXXhlnnHFGdO3aNVq2bBm1atWKqlWrRlZWVpQuXToyMjIK/SNRu8NLiun8bybZl173lKy5ubkpTFKwjIyMOOyww9Ka4T916NAh4dk9vZA5c+bMhGfbtWuXwiSFV69evYRnV65cWagdderUSfp10DVr1kTfvn0jOzs7Bg8eHMuXLy/UbgAAAAAAIDXKpDsAAAAAAAAAJKJGjRpxwQUXxAUXXBAREUuWLIlJkybFRx99FFOnTo05c+bE5s2bSzTTr3/966hTp06cddZZJbr3fy1cuDBGjBgRo0ePjilTpsSaNWvSmud/7Q4vKdapUydtu8uVK5fwbFZWVlSpUiWFaXYtmazbtm1LYZKC1a9fP7KystKa4T81b9484dnPP/88hUlSb86cOQnPNmnSJIVJCi+ZX2crVqwo9J5rrrkm3nvvvaTPLV++PG6++ea45ZZb4sgjj4wzzjgjevTokdYXdAEAAAAAAIVMAAAAAAAA9lCNGzeOxo0bx/nnnx8RETt37owFCxbEzJkzY8aMGTFz5syYNWtWrFu3LmUZdu7cGRdddFEcfPDB0axZs5Tt+SFr1qyJoUOHxlNPPRWffvppie5O1u5QyExncS+Z10TTXTBMJuvOnTtTmKRgJf1rriAtWrRIeHb16tUpTJJ6y5YtS3i2QYMGKUxSeBUqVEh4du3atYXe06NHj+jWrVuMGTOmUOfz8/NjwoQJMWHChPjtb38bdevWjW7dusXRRx8dRx55ZLRu3TqpX7cAAAAAAEDRKGQCAAAAAACwVyhVqlS0atUqWrVq9X1JMyJi8eLFMWnSpJgwYUK89957sXDhwmLdu3nz5ujVq1d89NFHUbp06WK9+4csW7Ysbrvttnj22Wdj+/btKd9XHHaHQmb58uXTHSEhe0rO3UG9evXSHeG/1K1bN+HZtWvXxo4dO6JMmT3zW/bJvBh55ZVXxpVXXpnCNKmXk5NTpPOPPvpotGvXLjZt2lTkLCtXroznnnsunnvuuYiIqFq1anTq1Ck6d+78/Y9KlSoVeQ8AAAAAAPDDSqU7AAAAAAAAAKRSdnZ2XHDBBfHwww/HZ599FosXL4577703OnbsWGw7pk+fHo8++mix3fdDtm7dGr/73e+iefPm8eSTT+4xZcyIiLy8vHRHKJGybHHYU3LuDmrWrJnuCP8lmTz5+fnx1VdfpTBN6mzevDm+/fbbdMcoUUUtlTdt2jSeeeaZlPz6/vbbb2PUqFExYMCAOPHEE6Nq1arRvn376N+/f/zzn/8scpkUAAAAAAD4bwqZAAAAAAAA7FMaN24c/fv3j8mTJ8fcuXPjiiuuKJZXCQcMGFAsr5/9kFmzZsVhhx0Wd911V2zbti0lO2BPc8ABB6Q7wn+pUaNGUvMbN25MUZLUWrt2bbojlLjiKDWeeeaZ8dRTT0VmZmYxJPpxeXl5MWPGjLjvvvvipJNOiurVq0fPnj3jmWeeie+++y6luwEAAAAAYF+gkAkAAAAAAMA+q3Xr1vHII4/E4sWL47LLLouMjIxC37V27dp4+umnizHdv7z55pvRtWvXmD9/frHfDXuyrKysdEf4LxUqVEhqvqivLqbLli1b0h2hxBXXK78XXnhhjBo1KmrXrl0s9yVi69at8eabb8bFF18cderUiYsuuigmTpxYYvsBAAAAAGBvo5AJAAAAAADAPq9OnTrx+OOPx+jRo6N+/fqFvueBBx4oxlQRr7zySpxxxhmxefPmYr0X9gZly5ZNd4T/Uq5cuaTm99RC5p6ae3dx3HHHxZw5c+L8888v8d05OTnx7LPPRpcuXaJr164xatSoEs8AAAAAAAB7OoVMAAAAAAAA+P8de+yxMXXq1Gjbtm2hzi9YsCBmz55dLFnGjh0bF154YezcubNI9xx44IFx0kknxa9+9av405/+FMOGDYv3338/Zs+eHYsWLYo1a9bEpk2bIicnJ7Zv3x75+fkJ/4B0yszMTHeE/5Jsnm3btqUoSWrl5OSkO8Ier0aNGvH888/H+PHj4/jjj09LhgkTJsTJJ58cJ5xwQixYsCAtGQAAAAAAYE9UJt0BAAAAAAAAYHdSq1ateOutt6JTp06xfPnypM+/9tprceihhxYpwzfffBO9evWK3NzcpM9Wr149zjjjjOjevXscffTRUbt27SJlgd3V9u3b0x3hvySbZ3d74TNRytjFp0uXLvHee+/FtGnT4uGHH46XXnoptmzZUqIZ3nvvvWjbtm3ceeed8Zvf/KZEdwMAAAAAwJ7IC5kAAAAAAADwPw488MB46623CvUC39ixY4u8/5e//GV8+eWXSZ1p3bp1DBs2LFavXh1PPPFEnHPOOSktYyplkW672wuTyRaoy5cvn6IkqVWhQoV0R9jrHH744fHkk0/GmjVr4sUXX4yzzz47qlatWmL7t27dGldffXVcfPHFsWPHjhLbCwAAAAAAeyIvZAIAAAAAAMAPOPjgg+Pyyy+Phx9+OKlz06ZNi507d0apUoX7u1GnTJkSw4cPT3i+TJkycdttt8UNN9xQ6J2FsXXr1hLbBT+kpF8SLEhOTk5S83tqITMrKyup+Y0bN0alSpVSlGbvUrFixTj33HPj3HPPjby8vJgyZUp88MEHMWHChJgwYUKsW7cupfufeeaZ2Lp1a7zwwgsl+vsJAAAAAADsSRQyAQAAAAAA4Ef8/ve/j6eeeiqp4temTZti8eLF0bRp00LtvPXWWxOeLVeuXLzyyitx6qmnFmpXUSRbPoPitnbt2nRH+C9r1qxJar5y5copSpJayRYyv/zyy0J/PdyXlS5dOjp37hydO3eOiH+9Sjxv3rz48MMPY/z48TF+/PhYtmxZse8dPnx4NG/ePP7whz8U+90AAAAAALA38FcaAgAAAAAAwI+oXbt2nHDCCUmfK2xJZunSpfHOO+8kPP/QQw+lpYwZEfHtt9+mZS/82+5WyEwmT0ZGRtSqVSuFaVKnRo0aSc2vXr06RUn2LRkZGdGmTZvo169fPPfcc7F06dJYtWpVvPTSS9GvX79o3rx5se26/fbbY+LEicV2HwAAAAAA7E0UMgEAAAAAAGAXunfvnvSZ5cuXF2rXs88+G/n5+QnNnnjiiXHZZZcVak9xWLlyZdp2Q0TEihUr0h3hvySTp0aNGlGmTJkUpkmdrKysOOCAAxKe/+yzz1KYZt9Wp06dOOecc+Kvf/1rLFiwIBYtWhR/+ctf4phjjolSpQr/x0Hy8/PjqquuKsakAAAAAACw91DIBAAAAAAAgF3o1q1b0mc2bNhQqF0jR45MePa2224r1I7isruV4dj37G5Fv2Ty1KlTJ4VJUq9BgwYJz86dOzeFSfhP2dnZcdVVV8XYsWNj6dKlcdtttxX6v7UZM2bEqFGjijkhAAAAAADs+RQyAQAAAAAAYBfq1auX9JktW7YU6sy0adMSmm3Tpk107Ngx6R3F6eOPP07rflixYkWhfq2lSjKFzKZNm6YwSeplZ2cnPDtz5swUJuHH1K9fP37/+9/H0qVL409/+lNUqlQp6Tsee+yxFCQDAAAAAIA9m0ImAAAAAAAA7EKlSpWifPnySZ3ZsWNH0nvmzJkT27dvT2i2e/fuSd9f3GbPnp3uCOzj8vPzY8aMGemO8b2pU6cmPHvQQQelMEnqtW/fPuHZjz76KLZt25bCNOxK2bJl45prronp06dH3bp1kzo7cuTIyMnJSVEyAAAAAADYMylkAgAAAAAAQAGqV6+e1HzFihWT3rFw4cKEZ5MpQ6VCXl5eTJw4Ma0ZICJi0qRJ6Y4QERFfffVVLF26NOH5Pb2QecQRRyQ8m5OTE5MnT05hGhLRvHnzGDt2bFSpUiXhMzk5OUkVjQEAAAAAYF+gkAkAAAAAAAAFyM3NTWq+UqVKSe9YsmRJwrPZ2dlJ31+cpk2bFt9++21aM0BExOjRo9MdISKSz9GuXbsUJSkZHTp0iFKlEv/jBn//+99TmIZENW3aNAYOHJjUmWnTpqUmDAAAAAAA7KEUMgEAAAAAAGAX8vLyYsOGDUmdqVatWtJ7vvvuu4Rnq1atmvT9xekf//hHWvfDv40ZMyY2bdqU7hjx+uuvJzxbr169aNKkSQrTpF7lypWjY8eOCc+//PLLkZeXl8JEJOqXv/xlUn9pwOLFi1OYBgAAAAAA9jwKmQAAAAAAALALS5Ysie3btyd1pjAvWG7evDnh2dKlSyd9f3HZuXNnPPfcc2nbD/8pNzc3RowYkdYMW7ZsiZEjRyY8361btxSmKTlnnnlmwrOrV6+O1157LXVhSFi5cuWie/fuCc+vXr06hWkAAAAAAGDPo5AJAAAAAAAAuzBt2rSkzzRt2jTpM8mUPteuXZv0/cXltddei5UrV6ZtP/yvxx57LK37X3rppaRe0T3hhBNSmKbkJFPIjIi49957U5SEZLVp0ybh2WT+sgAAAAAAANgXKGQCAAAAAADALiTz8l1ERIMGDaJKlSpJ76lQoULCs+ksZA4ePDhtu+GHjBkzJubPn5+2/Q8//HDCs+XKlYuePXumME3Jadq0aXTq1Cnh+UmTJsUbb7yRwkQkqlatWgnP5uXlpTAJAAAAAADseRQyAQAAAAAA4Eds2LAh/v73vyd15phjjinUrsqVKyc8O2fOnELtKKoXX3wxpk+fnpbd8GPy8/Nj0KBBadn95ptvJvWK7sknnxz77bdfChOVrKuuuiqp+WuvvTZyc3NTlIZEZWRkJDybzO9NAAAAAACwL1DIBAAAAAAAIKXS+ZpjUf3lL3+JzZs3J3WmsIXMevXqJTz79ttvF2pHUXz33XfRv3//Et8LiRg+fHhSxcjisGPHjrj55puTOnPBBRekKE16nH322VGnTp2E5z///PO47rrrUpiIRKxcuTLh2WrVqqUwCQAAAAAA7HkUMgEAAAAAAEips88+Oy699NL44osv0h0lKUuXLo0777wzqTOZmZnRs2fPQu3Lzs5OeHbKlCmxYsWKQu0prL59+8bq1atLdCckKj8/P/r27Rs7duwosZ133313fPzxxwnPN2zYMM4888wUJip5mZmZccMNNyR15sEHH4yXX345RYlIxKRJkxKebdGiRQqTAAAAAADAnkchEwAAAAAAgJTasWNHPPXUU9G8efO45ppr9ohS39atW+Occ85J+nXMHj16RI0aNQq187DDDkt4dufOnXHrrbcWak9hDBkyJF555ZUS2weFMXv27LjppptKZNeUKVPitttuS+rM1VdfHaVLl05RovT55S9/Gc2bN0/qTO/evWP06NEpSlQ8tm7dGtu3by/0+TVr1sTOnTuLMVHxWLZsWXzwwQcJzx988MEpTAMAAAAAAHsehUwAAAAAAABKRG5ubtx///3RuHHj+PnPfx4LFy5Md6QflJubG2eddVZMnTo16bM///nPC723Vq1aSb2S+fTTT8f06dMLvS9RQ4cOTfoFPEiXIUOGxPPPP5/SHatWrYqzzjorcnNzEz5To0aN6Nu3bwpTpU9mZmYMGTIkqTO5ubnRs2fPGDFiRIpSFd7WrVvjL3/5S2RnZ8c333xT6HuGDRsWbdq0ieeeey7y8vKKMWHR3HzzzQm/JFu2bNk46qijUpwIAAAAAAD2LAqZAAAAAAAAlKjc3Nx47LHHomXLlnHmmWfGP//5z8jPz093rIiI+PLLL6Nbt24xcuTIpM927tw5evToUaT9p5xySsKzO3fujNNPPz2WL19epJ27ct9990Xfvn13m88PJOKSSy6Jl19+OSV3r1q1Krp16xYrV65M6twf/vCHqFSpUkoy7Q569uwZvXr1SurM5s2b44wzzoghQ4bsFi9Jfv311zF48ODIzs6O3/zmN8XymvOnn34avXv3jubNm8d9990X3333XTEkLbzHHnssqcJyt27d9ur/bgEAAAAAoDAUMgEAAAAAAEiLnTt3xmuvvRYnnXRSZGdnx+DBg2PZsmVpyZKfnx/PPvtsHHTQQTFp0qRC3TF48OAi5zj33HOTml+5cmV07949li5dWuTd/2nDhg1x6aWXRv/+/XeLohQkY8eOHdGrV6+kX20syNSpU6Nz587x2WefJXXu4IMP3mtfx/xPjzzySDRq1CipM3l5eXH99dfHcccdF59++mlqgu1Cfn5+jB8/Pvr06RP169ePm2++uViKmP9r8eLF0b9//6hbt27069ev0L/PFMV9990X/fr1S+pMsvMAAAAAALAvUMgEAAAAAAAg7ZYuXRo333xzNGrUKDp27Bj33HNPLFq0KOV7d+zYEa+++mq0b98+Lrroovjmm28Kdc8VV1wRxx57bJHzdOnSJdq2bZvUmfnz58ehhx4aL7zwQpH35+fnx4svvhgHH3xwPPXUU7ucPfzww4u8D4piV+W/fxf9Tj755Pj888+LtGfr1q3xhz/8Ibp27RpffPFFUmfLlCkTjz32WJQuXbpIGfYEVapUiRdeeCHKly+f9Nlx48bFQQcdFD//+c+TLrwmKz8/P6ZOnRq33HJLNGnSJI466qgYOnRobN26NaV7I/71Kujf/va3OPLII6NZs2YxYMCAmD59ekpfIZ46dWqccMIJSRfsmzdvHqeddlrKcgEAAAAAwJ5KIRMAAAAAAIDdypQpU+K3v/1tNG3aNLKzs+Pyyy+PF154IRYtWlQspZXc3Nx4//3345prron69evHT3/605g5c2ah72vWrFnce++9Rc71b9dff33SZzZs2BDnn39+dO7cOYYPHx47duxI6vz69evj4YcfjkMOOSR69epVYOmsQoUK8eyzzyadE4rT9ddfH82aNdvlzKhRo6JNmzZx+eWXx5w5c5K6f/369fGnP/0pWrRoEbfeemts27Yt6Yy33nprdOzYMelze6pOnTrFsGHDolSp5P8oQl5eXjz22GPRsmXLOP744+PRRx+NVatWFTlTfn5+zJ07Nx577LG45JJLok6dOnHEEUfE7bffHkuWLCny/YX1+eefx2233RaHH3541K1bN/r06RNDhw6NhQsXFvnu9evXxwsvvBCnnHJKHHHEEfHee+8lfcd99923TxSJAQAAAAAgWWXSHQAAAAAAAAB+zJIlS+Lxxx+Pxx9/PCL+9QLboYceGq1bt44GDRpEgwYNom7dulG5cuXIysqKrKysyMjIiG3btsXWrVvj66+/jrVr18bSpUvj888/j9mzZ8esWbMKVaz6ITVq1Ii33norKlasWCz3RUScd9558eCDD8bEiROTPjt58uQ499xzY//9948uXbpE165do3Xr1lG9evWoXr16lC9fPjZv3hzr16+PRYsWxfz582PcuHExderUpEqcd999d7Rs2TLpfFCcKlSoEE888UQcc8wxuyxrb9u27fuvI82aNYsTTzwx2rZtGy1atIj9998/KlasGLm5ubFhw4ZYunRpzJ07N8aOHRsTJkyI7du3FzrfscceGzfddFOhz++pzjzzzHj44YejX79+hTqfn58fo0ePjtGjR0dGRkY0b9482rdvH4ccckg0aNAg6tWrF9WqVYvy5ctH2bJlY/v27bF169bIycmJNWvWxJdffhmrV6+Ozz77LObPnx+ffvppbNy4sZg/yuK1evXqGDp0aAwdOjQiIqpXrx6tW7eONm3aRNOmTePAAw+MAw88MKpVqxYVKlSI8uXLx/bt22PTpk3ff03/7LPP4tNPP41PPvkkpkyZknQx/z9deOGF0aNHj+L68AAAAAAAYK+ikAkAAAAAAMAeY8OGDfHhhx/Ghx9+mO4oUaVKlXjrrbcKfKEvWRkZGfG3v/0tOnToEFu3bi3UHd9880288cYb8cYbbxRrtoiI3r17x69+9ativxcK46ijjopbb701Bg0alND8woULi+UFwoK0bNkyXn311X32hcErrrgiypYtGz//+c+LVAzMz8+PBQsWxIIFC2LYsGHFmHD3tm7duhg/fnyMHz++xHe3adMmHnnkkRLfCwAAAAAAe4pS6Q4AAAAAAAAAe5p69erFhx9+GB06dEjJ/QcddFA8+OCDKbm7KE466aR48sknIyMjI91R4HsDBw6M888/P90xvle7du14++23o1q1aumOklaXXnppvP7668X6gjCp1bRp03jnnXd8zgAAAAAAYBcUMgEAAAAAACAJXbt2jY8++igOOeSQlO657LLL4qabbkrpjmQce+yx8corr0SZMmXSHQX+jyeffDKOP/74dMeIxo0bx/jx46Nx48bpjrJb6NGjR4l8vaTo2rZtG2PGjIkDDzww3VEAAAAAAGC3ppAJAAAAAABASjVo0CDdEYpF+fLlY8iQITFu3LgSK6zcfvvtceONN5bIrl3p06ePV9PYrZUrVy7efvvt6NWrV9oyHHrooTF+/Pho0qRJ2jLsjtq0aRNTpkyJq6++OkqV2rP+iMK+8hrweeedFxMmTIh69eqlOwoAAAAAAOz29qzvdgAAAAAAALDHGTZsWCxcuDAGDx4chx12WLrjJK1UqVLRu3fvmD9/flx33XUlXigaPHhwPPHEE1G+fPkS3RsRkZWVFffff3888cQTkZmZWeL7IRlly5aN559/Pm655ZYoXbp0ie7u169fTJ482euCP6JcuXJx3333xbRp06Jbt27pjrNLFStWjEsvvTQmTZoUtWrVKvQ96fianayaNWvG8OHD44UXXoisrKx0xwEAAAAAgD2CQiYAAAAAAAAp17Rp07jxxhtj+vTp8cUXX8Rf//rXOO2003brAkhWVlZcdtllMWfOnHjmmWeiUaNGacvSp0+fmDFjRnTs2LHEdp5yyikxd+7c+M1vflNiO6GoMjIy4g9/+ENMnjw5Dj744JTva9q0abz55pvx17/+dY8o4KVbu3btYvTo0fHmm2/GUUcdle4438vMzIyTTz45nnrqqVi1alU8+eST0alTpyLd2a9fv/j000/jjjvuiE6dOu1Wr4NWrlw5br311li4cGGcffbZ6Y4DAAAAAAB7lN3n//EHAAAAAABgn1C/fv3o169fvPHGG7Fu3boYO3ZsDBgwII455pgoV65cWrNlZmbGcccdFw899FCsWrUqHn/88WjTpk1aM/1bq1atYtKkSTFs2LBo3rx5SnaUKlUqTjnllHj33XdjxIgRaS2hQlEcfvjhMX369Hj00UejRYsWxX7/gQceGPfee2/MnTs3Tj311GK/f2936qmnxgcffBCzZs2Kvn37RrVq1Uo8wwEHHBAXXHBBPP/88/HVV1/F22+/HRdffHFUqVKl2Ha0aNEifve738WkSZNi7dq18dJLL8Vll10WTZo0KbYdyWjVqlXcf//98cUXX8SgQYOK9WMFAAAAAIB9RUZ+fn5+ukMAAAAAAABARMT27dtj9uzZMWXKlJg6dWrMmTMn5s+fHzk5OSnZl5mZGYccckh07tw5unbtGt27d4+qVaumZFdxys/Pj3fffTeeeuqpGDlyZHz77beFvqtUqVLRoUOHOOmkk6J3795JFYVGjRqV0Fz16tXjiCOOKGxE9iEDBw6MQYMGJTQ7dOjQuOSSSwqc27lzZ4wYMSJeeumlGDVqVKxbt65Q2SpUqBDHHnts9OnTJ84444woU6ZMoe7h/9qxY0eMGzcuXn/99Rg9enTMnz8/du7cWWz3lypVKpo1axbt27ePLl26RNeuXeOggw5K66uVq1ativHjx8eUKVNi+vTpMXPmzPjuu++KdUf58uXj8MMPj+7du8fpp59eIq/GAgAAAADA3k4hEwAAAAAAgN3azp07Y8mSJbFgwYJYsmRJLFu2LL744otYs2ZNfP311/HNN9/E5s2bY9u2bZGbmxsREeXKlYuyZctGuXLlokqVKlGjRo2oUaNG1KxZMxo1ahTNmzePZs2aRcuWLaN8+fJp/giLZseOHfHRRx/F1KlTY9asWbF48eJYvnx5rF+/PrZu3Rrbt2+PrKysqFSpUlSqVClq1qwZzZs3jxYtWkTr1q2ja9euUb169XR/GBARqSlk/qe8vLyYPHlyTJ8+PebNmxfz5s2L1atXx8aNG2Pjxo2Rm5sbFStWjMqVK8cBBxwQLVu2jFatWkWnTp3imGOO2eO/XuwpNm3a9H1JcenSpd9/3f/6668jJycntmzZEjk5OZGRkfFfX+sPOOCA2H///aNu3brRuHHjaNy4cbRo0SIOOuigyMrKSveHVaDly5fHp59+GgsWLIilS5fG8uXLY9WqVfHNN9/EunXrYtOmTbFt27bYsWNHlClTJsqVKxfly5ePatWqRc2aNaN27drRtGnTaN68eRx00EHRtm3bKFu2bLo/LAAAAAAA2KsoZAIAAAAAAACwW0h1IRMAAAAAAACKolS6AwAAAAAAAAAAAAAAAAAA7O4UMgEAAAAAAAAAAAAAAAAACqCQCQAAAAAAAAAAAAAAAABQAIVMAAAAAAAAAAAAAAAAAIACKGQCAAAAAAAAAAAAAAAAABRAIRMAAAAAAAAAAAAAAAAAoAAKmQAAAAAAAAAAAAAAAAAABVDIBAAAAAAAAAAAAAAAAAAogEImAAAAAAAAAAAAAAAAAEABFDIBAAAAAAAAAAAAAAAAAAqgkAkAAAAAAAAAAAAAAAAAUACFTAAAAAAAAAAAAAAAAACAAihkAgAAAAAAAAAAAAAAAAAUQCETAAAAAAAAAAAAAAAAAKAACpkAAAAAAAAAAAAAAAAAAAVQyAQAAAAAAAAAAAAAAAAAKIBCJgAAAAAAAAAAAAAAAABAARQyAQAAAAAAAAAAAAAAAAAKoJAJAAAAAAAAAAAAAAAAAFAAhUwAAAAAAAAAAAAAAAAAgAIoZAIAAAAAAAAAAAAAAAAAFEAhEwAAAAAAAAAAAAAAAACgABn5+fn56Q4BAAAAAAAAAAAAAAAAALA780ImAAAAAAAAAAAAAAAAAEABFDIBAAAAAAAAAAAAAAAAAAqgkAkAAAAAAAAAAAAAAAAAUACFTAAAAAAAAAAAAAAAAACAAihkAgAAAAAAAAAAAAAAAAAUQCETAAAAAAAAAAAAAAAAAKAACpkAAAAAAAAAAAAAAAAAAAVQyAQAAAAAAAAAAAAAAAAAKIBCJgAAAAAAAAAAAAAAAABAARQyAQAAAAAAAAAAAAAAAAAKoJAJAAAAAAAAAAAAAAAAAFAAhUwAAAAAAAAAAAAAAAAAgAIoZAIAAAAAAAAAAAAAAAAAFEAhEwAAAAAAAAAAAAAAAACgAAqZAAAAAAAAAAAAAAAAAAAFUMgEAAAAAAAAAAAAAAAAACiAQiYAAAAAAAAAAAAAAAAAQAEUMgEAAAAAAAAAAAAAAAAACqCQCQAAAAAAAAAAAAAAAABQAIVMAAAAAAAAAAAAAAAAAIACKGQCAAAAAAAAAAAAAAAAABRAIRMAAAAAAAAAAAAAAAAAoAAKmQAAAAAAAAAAAAAAAAAABVDIBAAAAAAAAAAAAAAAAAAogEImAAAAAAAAAAAAAAAAAEABFDIBAAAAAAAAAAAAAAAAAAqgkAkAAAAAAAAAAAAAAAAAUACFTAAAAAAAAAAAAAAAAACAAihkAgAAAAAAAAAAAAAAAAAU4P8Dp02zs2p2590AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Samples', ylabel='Counts'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Individual tf/idf scores, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12602676010180824"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.tf_idf('snow', corpus[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the full TF/IDF scores for our corpus, we do"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "TFIDF = []\n",
    "\n",
    "for doc in corpus:\n",
    "    current = {}\n",
    "    for token in doc:\n",
    "        current[token] = nlp.tf_idf(token, doc)\n",
    "        \n",
    "    TFIDF.append(current)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'mary': 0.0,\n",
       "  'had': 0.07701635339554948,\n",
       "  'a': 0.07701635339554948,\n",
       "  'little': 0.23104906018664842,\n",
       "  'lamb': 0.09589402415059362},\n",
       " {'mary': 0.0,\n",
       "  'had': 0.06301338005090412,\n",
       "  'a': 0.06301338005090412,\n",
       "  'little': 0.06301338005090412,\n",
       "  'lamb': 0.026152915677434625,\n",
       "  'whose': 0.12602676010180824,\n",
       "  'fleece': 0.12602676010180824,\n",
       "  'was': 0.06301338005090412,\n",
       "  'white': 0.12602676010180824,\n",
       "  'as': 0.12602676010180824,\n",
       "  'snow': 0.12602676010180824},\n",
       " {'and': 0.15403270679109896,\n",
       "  'everywhere': 0.07701635339554948,\n",
       "  'that': 0.07701635339554948,\n",
       "  'mary': 0.0,\n",
       "  'went': 0.23104906018664842},\n",
       " {'everywhere': 0.06931471805599453,\n",
       "  'that': 0.06931471805599453,\n",
       "  'mary': 0.0,\n",
       "  'went': 0.06931471805599453,\n",
       "  'the': 0.13862943611198905,\n",
       "  'lamb': 0.028768207245178087,\n",
       "  'was': 0.06931471805599453,\n",
       "  'sure': 0.13862943611198905,\n",
       "  'to': 0.13862943611198905,\n",
       "  'go': 0.13862943611198905}]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TFIDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</center>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}