build_bigram_from_scratch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a28c4a6e",
   "metadata": {},
   "source": [
    "# build_bigram_from_scratch\n",
    "this is a character level bigram language model. \n",
    "1. predict the next char based on the current char.\n",
    "2. predict when the word ends."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d11299a",
   "metadata": {},
   "source": [
    "# 1. bigram char counting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a978f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "words = open('names.txt', 'r').read().splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9096a3ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['emma',\n",
       " 'olivia',\n",
       " 'ava',\n",
       " 'isabella',\n",
       " 'sophia',\n",
       " 'charlotte',\n",
       " 'mia',\n",
       " 'amelia',\n",
       " 'harper',\n",
       " 'evelyn']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8fe4a80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32033"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e74d6247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(len(w) for w in words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77d4ac13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(len(w) for w in words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "852d0785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 2), (2, 3)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1 = [1,2,3]\n",
    "l2 = [2,3]\n",
    "list(zip(l1,l2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63009fea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word:  emma\n",
      "<S> e\n",
      "e m\n",
      "m m\n",
      "m a\n",
      "a <E>\n",
      "word:  olivia\n",
      "<S> o\n",
      "o l\n",
      "l i\n",
      "i v\n",
      "v i\n",
      "i a\n",
      "a <E>\n",
      "word:  ava\n",
      "<S> a\n",
      "a v\n",
      "v a\n",
      "a <E>\n"
     ]
    }
   ],
   "source": [
    "# bigram\n",
    "# consecutive element\n",
    "# we have additional information for start and end TOKEN\n",
    "\n",
    "# a dict to count the bigram (ch1, ch2)\n",
    "bigram_dict = {}\n",
    "\n",
    "START_TOKEN = '<S>'\n",
    "END_TOKEN = '<E>'\n",
    "\n",
    "# check the first 3 words\n",
    "for w in words[:3]:\n",
    "    print (\"word: \", w)\n",
    "    # special start and end chr to tell which char is start and end\n",
    "    # <S> and <E> are special\n",
    "    chs = [START_TOKEN] + list(w) + [END_TOKEN]\n",
    "    # create bigram with (i, i+1), the last char will not have a pair \n",
    "    for ch1, ch2 in zip(chs, chs[1:]):\n",
    "        bigram = (ch1, ch2)\n",
    "        # if key does not exist, return count 0, else, +1\n",
    "        bigram_dict[bigram] = bigram_dict.get(bigram, 0) + 1\n",
    "        print (ch1, ch2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1cf17519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('<S>', 'e'): 1,\n",
       " ('e', 'm'): 1,\n",
       " ('m', 'm'): 1,\n",
       " ('m', 'a'): 1,\n",
       " ('a', '<E>'): 3,\n",
       " ('<S>', 'o'): 1,\n",
       " ('o', 'l'): 1,\n",
       " ('l', 'i'): 1,\n",
       " ('i', 'v'): 1,\n",
       " ('v', 'i'): 1,\n",
       " ('i', 'a'): 1,\n",
       " ('<S>', 'a'): 1,\n",
       " ('a', 'v'): 1,\n",
       " ('v', 'a'): 1}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigram_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "954bc2dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a dict to count the bigram (ch1, ch2)\n",
    "bigram_dict = {}\n",
    "\n",
    "START_TOKEN = '<S>'\n",
    "END_TOKEN = '<E>'\n",
    "\n",
    "for w in words:\n",
    "    # special start and end chr to tell which char is start and end\n",
    "    # <S> and <E> are special\n",
    "    chs = [START_TOKEN] + list(w) + [END_TOKEN]\n",
    "    for ch1, ch2 in zip(chs, chs[1:]):\n",
    "        bigram = (ch1, ch2)\n",
    "        # if key does not exist, return count 0, else, +1\n",
    "        bigram_dict[bigram] = bigram_dict.get(bigram, 0) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b14b2b7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('<S>', 'e'): 1531,\n",
       " ('e', 'm'): 769,\n",
       " ('m', 'm'): 168,\n",
       " ('m', 'a'): 2590,\n",
       " ('a', '<E>'): 6640,\n",
       " ('<S>', 'o'): 394,\n",
       " ('o', 'l'): 619,\n",
       " ('l', 'i'): 2480,\n",
       " ('i', 'v'): 269,\n",
       " ('v', 'i'): 911,\n",
       " ('i', 'a'): 2445,\n",
       " ('<S>', 'a'): 4410,\n",
       " ('a', 'v'): 834,\n",
       " ('v', 'a'): 642,\n",
       " ('<S>', 'i'): 591,\n",
       " ('i', 's'): 1316,\n",
       " ('s', 'a'): 1201,\n",
       " ('a', 'b'): 541,\n",
       " ('b', 'e'): 655,\n",
       " ('e', 'l'): 3248,\n",
       " ('l', 'l'): 1345,\n",
       " ('l', 'a'): 2623,\n",
       " ('<S>', 's'): 2055,\n",
       " ('s', 'o'): 531,\n",
       " ('o', 'p'): 95,\n",
       " ('p', 'h'): 204,\n",
       " ('h', 'i'): 729,\n",
       " ('<S>', 'c'): 1542,\n",
       " ('c', 'h'): 664,\n",
       " ('h', 'a'): 2244,\n",
       " ('a', 'r'): 3264,\n",
       " ('r', 'l'): 413,\n",
       " ('l', 'o'): 692,\n",
       " ('o', 't'): 118,\n",
       " ('t', 't'): 374,\n",
       " ('t', 'e'): 716,\n",
       " ('e', '<E>'): 3983,\n",
       " ('<S>', 'm'): 2538,\n",
       " ('m', 'i'): 1256,\n",
       " ('a', 'm'): 1634,\n",
       " ('m', 'e'): 818,\n",
       " ('<S>', 'h'): 874,\n",
       " ('r', 'p'): 14,\n",
       " ('p', 'e'): 197,\n",
       " ('e', 'r'): 1958,\n",
       " ('r', '<E>'): 1377,\n",
       " ('e', 'v'): 463,\n",
       " ('v', 'e'): 568,\n",
       " ('l', 'y'): 1588,\n",
       " ('y', 'n'): 1826,\n",
       " ('n', '<E>'): 6763,\n",
       " ('b', 'i'): 217,\n",
       " ('i', 'g'): 428,\n",
       " ('g', 'a'): 330,\n",
       " ('a', 'i'): 1650,\n",
       " ('i', 'l'): 1345,\n",
       " ('l', '<E>'): 1314,\n",
       " ('y', '<E>'): 2007,\n",
       " ('i', 'z'): 277,\n",
       " ('z', 'a'): 860,\n",
       " ('e', 't'): 580,\n",
       " ('t', 'h'): 647,\n",
       " ('h', '<E>'): 2409,\n",
       " ('r', 'y'): 773,\n",
       " ('o', 'f'): 34,\n",
       " ('f', 'i'): 160,\n",
       " ('c', 'a'): 815,\n",
       " ('r', 'i'): 3033,\n",
       " ('s', 'c'): 60,\n",
       " ('l', 'e'): 2921,\n",
       " ('t', '<E>'): 483,\n",
       " ('<S>', 'v'): 376,\n",
       " ('i', 'c'): 509,\n",
       " ('c', 't'): 35,\n",
       " ('t', 'o'): 667,\n",
       " ('o', 'r'): 1059,\n",
       " ('a', 'd'): 1042,\n",
       " ('d', 'i'): 674,\n",
       " ('o', 'n'): 2411,\n",
       " ('<S>', 'l'): 1572,\n",
       " ('l', 'u'): 324,\n",
       " ('u', 'n'): 275,\n",
       " ('n', 'a'): 2977,\n",
       " ('<S>', 'g'): 669,\n",
       " ('g', 'r'): 201,\n",
       " ('r', 'a'): 2356,\n",
       " ('a', 'c'): 470,\n",
       " ('c', 'e'): 551,\n",
       " ('h', 'l'): 185,\n",
       " ('o', 'e'): 132,\n",
       " ('<S>', 'p'): 515,\n",
       " ('e', 'n'): 2675,\n",
       " ('n', 'e'): 1359,\n",
       " ('a', 'y'): 2050,\n",
       " ('y', 'l'): 1104,\n",
       " ('<S>', 'r'): 1639,\n",
       " ('e', 'y'): 1070,\n",
       " ('<S>', 'z'): 929,\n",
       " ('z', 'o'): 110,\n",
       " ('<S>', 'n'): 1146,\n",
       " ('n', 'o'): 496,\n",
       " ('e', 'a'): 679,\n",
       " ('a', 'n'): 5438,\n",
       " ('n', 'n'): 1906,\n",
       " ('a', 'h'): 2332,\n",
       " ('d', 'd'): 149,\n",
       " ('a', 'u'): 381,\n",
       " ('u', 'b'): 103,\n",
       " ('b', 'r'): 842,\n",
       " ('r', 'e'): 1697,\n",
       " ('i', 'e'): 1653,\n",
       " ('s', 't'): 765,\n",
       " ('a', 't'): 687,\n",
       " ('t', 'a'): 1027,\n",
       " ('a', 'l'): 2528,\n",
       " ('a', 'z'): 435,\n",
       " ('z', 'e'): 373,\n",
       " ('i', 'o'): 588,\n",
       " ('u', 'r'): 414,\n",
       " ('r', 'o'): 869,\n",
       " ('u', 'd'): 136,\n",
       " ('d', 'r'): 424,\n",
       " ('<S>', 'b'): 1306,\n",
       " ('o', 'o'): 115,\n",
       " ('o', 'k'): 68,\n",
       " ('k', 'l'): 139,\n",
       " ('c', 'l'): 116,\n",
       " ('i', 'r'): 849,\n",
       " ('s', 'k'): 82,\n",
       " ('k', 'y'): 379,\n",
       " ('u', 'c'): 103,\n",
       " ('c', 'y'): 104,\n",
       " ('p', 'a'): 209,\n",
       " ('s', 'l'): 279,\n",
       " ('i', 'n'): 2126,\n",
       " ('o', 'v'): 176,\n",
       " ('g', 'e'): 334,\n",
       " ('e', 's'): 861,\n",
       " ('s', 'i'): 684,\n",
       " ('s', '<E>'): 1169,\n",
       " ('<S>', 'k'): 2963,\n",
       " ('k', 'e'): 895,\n",
       " ('e', 'd'): 384,\n",
       " ('d', 'y'): 317,\n",
       " ('n', 't'): 443,\n",
       " ('y', 'a'): 2143,\n",
       " ('<S>', 'w'): 307,\n",
       " ('w', 'i'): 148,\n",
       " ('o', 'w'): 114,\n",
       " ('w', '<E>'): 51,\n",
       " ('k', 'i'): 509,\n",
       " ('n', 's'): 278,\n",
       " ('a', 'o'): 63,\n",
       " ('o', 'm'): 261,\n",
       " ('i', '<E>'): 2489,\n",
       " ('a', 'a'): 556,\n",
       " ('i', 'y'): 779,\n",
       " ('d', 'e'): 1283,\n",
       " ('c', 'o'): 380,\n",
       " ('r', 'u'): 252,\n",
       " ('b', 'y'): 83,\n",
       " ('s', 'e'): 884,\n",
       " ('n', 'i'): 1725,\n",
       " ('i', 't'): 541,\n",
       " ('t', 'y'): 341,\n",
       " ('u', 't'): 82,\n",
       " ('t', 'u'): 78,\n",
       " ('u', 'm'): 154,\n",
       " ('m', 'n'): 20,\n",
       " ('g', 'i'): 190,\n",
       " ('t', 'i'): 532,\n",
       " ('<S>', 'q'): 92,\n",
       " ('q', 'u'): 206,\n",
       " ('u', 'i'): 121,\n",
       " ('a', 'e'): 692,\n",
       " ('e', 'h'): 152,\n",
       " ('v', 'y'): 121,\n",
       " ('p', 'i'): 61,\n",
       " ('i', 'p'): 53,\n",
       " ('y', 'd'): 272,\n",
       " ('e', 'x'): 132,\n",
       " ('x', 'a'): 103,\n",
       " ('<S>', 'j'): 2422,\n",
       " ('j', 'o'): 479,\n",
       " ('o', 's'): 504,\n",
       " ('e', 'p'): 83,\n",
       " ('j', 'u'): 202,\n",
       " ('u', 'l'): 301,\n",
       " ('<S>', 'd'): 1690,\n",
       " ('k', 'a'): 1731,\n",
       " ('e', 'e'): 1271,\n",
       " ('y', 't'): 104,\n",
       " ('d', 'l'): 60,\n",
       " ('c', 'k'): 316,\n",
       " ('n', 'z'): 145,\n",
       " ('z', 'i'): 364,\n",
       " ('a', 'g'): 168,\n",
       " ('d', 'a'): 1303,\n",
       " ('j', 'a'): 1473,\n",
       " ('h', 'e'): 674,\n",
       " ('<S>', 'x'): 134,\n",
       " ('x', 'i'): 102,\n",
       " ('i', 'm'): 427,\n",
       " ('e', 'i'): 818,\n",
       " ('<S>', 't'): 1308,\n",
       " ('<S>', 'f'): 417,\n",
       " ('f', 'a'): 242,\n",
       " ('n', 'd'): 704,\n",
       " ('r', 'g'): 76,\n",
       " ('a', 's'): 1118,\n",
       " ('s', 'h'): 1285,\n",
       " ('b', 'a'): 321,\n",
       " ('k', 'h'): 307,\n",
       " ('s', 'm'): 90,\n",
       " ('o', 'd'): 190,\n",
       " ('r', 's'): 190,\n",
       " ('g', 'h'): 360,\n",
       " ('s', 'y'): 215,\n",
       " ('y', 's'): 401,\n",
       " ('s', 's'): 461,\n",
       " ('e', 'c'): 153,\n",
       " ('c', 'i'): 271,\n",
       " ('m', 'o'): 452,\n",
       " ('r', 'k'): 90,\n",
       " ('n', 'l'): 195,\n",
       " ('d', 'n'): 31,\n",
       " ('r', 'd'): 187,\n",
       " ('o', 'i'): 69,\n",
       " ('t', 'r'): 352,\n",
       " ('m', 'b'): 112,\n",
       " ('r', 'm'): 162,\n",
       " ('n', 'y'): 465,\n",
       " ('d', 'o'): 378,\n",
       " ('o', 'a'): 149,\n",
       " ('o', 'c'): 114,\n",
       " ('m', 'y'): 287,\n",
       " ('s', 'u'): 185,\n",
       " ('m', 'c'): 51,\n",
       " ('p', 'r'): 151,\n",
       " ('o', 'u'): 275,\n",
       " ('r', 'n'): 140,\n",
       " ('w', 'a'): 280,\n",
       " ('e', 'b'): 121,\n",
       " ('c', 'c'): 42,\n",
       " ('a', 'w'): 161,\n",
       " ('w', 'y'): 73,\n",
       " ('y', 'e'): 301,\n",
       " ('e', 'o'): 269,\n",
       " ('a', 'k'): 568,\n",
       " ('n', 'g'): 273,\n",
       " ('k', 'o'): 344,\n",
       " ('b', 'l'): 103,\n",
       " ('h', 'o'): 287,\n",
       " ('e', 'g'): 125,\n",
       " ('f', 'r'): 114,\n",
       " ('s', 'p'): 51,\n",
       " ('l', 's'): 94,\n",
       " ('y', 'z'): 78,\n",
       " ('g', 'g'): 25,\n",
       " ('z', 'u'): 73,\n",
       " ('i', 'd'): 440,\n",
       " ('m', '<E>'): 516,\n",
       " ('o', 'g'): 44,\n",
       " ('j', 'e'): 440,\n",
       " ('g', 'n'): 27,\n",
       " ('y', 'r'): 291,\n",
       " ('c', '<E>'): 97,\n",
       " ('c', 'q'): 11,\n",
       " ('u', 'e'): 169,\n",
       " ('i', 'f'): 101,\n",
       " ('f', 'e'): 123,\n",
       " ('i', 'x'): 89,\n",
       " ('x', '<E>'): 164,\n",
       " ('o', 'y'): 103,\n",
       " ('g', 'o'): 83,\n",
       " ('g', 't'): 31,\n",
       " ('l', 't'): 77,\n",
       " ('g', 'w'): 26,\n",
       " ('w', 'e'): 149,\n",
       " ('l', 'd'): 138,\n",
       " ('a', 'p'): 82,\n",
       " ('h', 'n'): 138,\n",
       " ('t', 'l'): 134,\n",
       " ('m', 'r'): 97,\n",
       " ('n', 'c'): 213,\n",
       " ('l', 'b'): 52,\n",
       " ('i', 'k'): 445,\n",
       " ('<S>', 'y'): 535,\n",
       " ('t', 'z'): 105,\n",
       " ('h', 'r'): 204,\n",
       " ('j', 'i'): 119,\n",
       " ('h', 't'): 71,\n",
       " ('r', 'r'): 425,\n",
       " ('z', 'l'): 123,\n",
       " ('w', 'r'): 22,\n",
       " ('b', 'b'): 38,\n",
       " ('r', 't'): 208,\n",
       " ('l', 'v'): 72,\n",
       " ('e', 'j'): 55,\n",
       " ('o', 'h'): 171,\n",
       " ('u', 's'): 474,\n",
       " ('i', 'b'): 110,\n",
       " ('g', 'l'): 32,\n",
       " ('h', 'y'): 213,\n",
       " ('p', 'o'): 59,\n",
       " ('p', 'p'): 39,\n",
       " ('p', 'y'): 12,\n",
       " ('n', 'r'): 44,\n",
       " ('z', 'm'): 35,\n",
       " ('v', 'o'): 153,\n",
       " ('l', 'm'): 60,\n",
       " ('o', 'x'): 45,\n",
       " ('d', '<E>'): 516,\n",
       " ('i', 'u'): 109,\n",
       " ('v', '<E>'): 88,\n",
       " ('f', 'f'): 44,\n",
       " ('b', 'o'): 105,\n",
       " ('e', 'k'): 178,\n",
       " ('c', 'r'): 76,\n",
       " ('d', 'g'): 25,\n",
       " ('r', 'c'): 99,\n",
       " ('r', 'h'): 121,\n",
       " ('n', 'k'): 58,\n",
       " ('h', 'u'): 166,\n",
       " ('d', 's'): 29,\n",
       " ('a', 'x'): 182,\n",
       " ('y', 'c'): 115,\n",
       " ('e', 'w'): 50,\n",
       " ('v', 'k'): 3,\n",
       " ('z', 'h'): 43,\n",
       " ('w', 'h'): 23,\n",
       " ('t', 'n'): 22,\n",
       " ('x', 'l'): 39,\n",
       " ('g', 'u'): 85,\n",
       " ('u', 'a'): 163,\n",
       " ('u', 'p'): 16,\n",
       " ('u', 'g'): 47,\n",
       " ('d', 'u'): 92,\n",
       " ('l', 'c'): 25,\n",
       " ('r', 'b'): 41,\n",
       " ('a', 'q'): 60,\n",
       " ('b', '<E>'): 114,\n",
       " ('g', 'y'): 31,\n",
       " ('y', 'p'): 15,\n",
       " ('p', 't'): 17,\n",
       " ('e', 'z'): 181,\n",
       " ('z', 'r'): 32,\n",
       " ('f', 'l'): 20,\n",
       " ('o', '<E>'): 855,\n",
       " ('o', 'b'): 140,\n",
       " ('u', 'z'): 45,\n",
       " ('z', '<E>'): 160,\n",
       " ('i', 'q'): 52,\n",
       " ('y', 'v'): 106,\n",
       " ('n', 'v'): 55,\n",
       " ('d', 'h'): 118,\n",
       " ('g', 'd'): 19,\n",
       " ('t', 's'): 35,\n",
       " ('n', 'h'): 26,\n",
       " ('y', 'j'): 23,\n",
       " ('k', 'r'): 109,\n",
       " ('z', 'b'): 4,\n",
       " ('g', '<E>'): 108,\n",
       " ('a', 'j'): 175,\n",
       " ('r', 'j'): 25,\n",
       " ('m', 'p'): 38,\n",
       " ('p', 'b'): 2,\n",
       " ('y', 'o'): 271,\n",
       " ('z', 'y'): 147,\n",
       " ('p', 'l'): 16,\n",
       " ('l', 'k'): 24,\n",
       " ('i', 'j'): 76,\n",
       " ('x', 'e'): 36,\n",
       " ('y', 'u'): 141,\n",
       " ('l', 'n'): 14,\n",
       " ('u', 'x'): 34,\n",
       " ('i', 'h'): 95,\n",
       " ('w', 's'): 20,\n",
       " ('k', 's'): 95,\n",
       " ('m', 'u'): 139,\n",
       " ('y', 'k'): 86,\n",
       " ('e', 'f'): 82,\n",
       " ('k', '<E>'): 363,\n",
       " ('y', 'm'): 148,\n",
       " ('z', 'z'): 45,\n",
       " ('m', 'd'): 24,\n",
       " ('s', 'r'): 55,\n",
       " ('e', 'u'): 69,\n",
       " ('l', 'h'): 19,\n",
       " ('a', 'f'): 134,\n",
       " ('r', 'w'): 21,\n",
       " ('n', 'u'): 96,\n",
       " ('v', 'r'): 48,\n",
       " ('m', 's'): 35,\n",
       " ('<S>', 'u'): 78,\n",
       " ('f', 's'): 6,\n",
       " ('y', 'b'): 27,\n",
       " ('x', 'o'): 41,\n",
       " ('g', 's'): 30,\n",
       " ('x', 'y'): 30,\n",
       " ('w', 'n'): 58,\n",
       " ('j', 'h'): 45,\n",
       " ('f', 'n'): 4,\n",
       " ('n', 'j'): 44,\n",
       " ('r', 'v'): 80,\n",
       " ('n', 'm'): 19,\n",
       " ('t', 'c'): 17,\n",
       " ('s', 'w'): 24,\n",
       " ('k', 't'): 17,\n",
       " ('f', 't'): 18,\n",
       " ('x', 't'): 70,\n",
       " ('u', 'v'): 37,\n",
       " ('k', 'k'): 20,\n",
       " ('s', 'n'): 24,\n",
       " ('u', '<E>'): 155,\n",
       " ('j', 'r'): 11,\n",
       " ('y', 'x'): 28,\n",
       " ('h', 'm'): 117,\n",
       " ('e', 'q'): 14,\n",
       " ('u', 'o'): 10,\n",
       " ('f', '<E>'): 80,\n",
       " ('h', 'z'): 20,\n",
       " ('h', 'k'): 29,\n",
       " ('y', 'g'): 30,\n",
       " ('q', 'r'): 1,\n",
       " ('v', 'n'): 8,\n",
       " ('s', 'd'): 9,\n",
       " ('y', 'i'): 192,\n",
       " ('n', 'w'): 11,\n",
       " ('d', 'v'): 17,\n",
       " ('h', 'v'): 39,\n",
       " ('x', 'w'): 3,\n",
       " ('o', 'z'): 54,\n",
       " ('k', 'u'): 50,\n",
       " ('u', 'h'): 58,\n",
       " ('k', 'n'): 26,\n",
       " ('s', 'b'): 21,\n",
       " ('i', 'i'): 82,\n",
       " ('y', 'y'): 23,\n",
       " ('r', 'z'): 23,\n",
       " ('l', 'g'): 6,\n",
       " ('l', 'p'): 15,\n",
       " ('p', '<E>'): 33,\n",
       " ('b', 'u'): 45,\n",
       " ('f', 'u'): 10,\n",
       " ('b', 'h'): 41,\n",
       " ('f', 'y'): 14,\n",
       " ('u', 'w'): 86,\n",
       " ('x', 'u'): 5,\n",
       " ('q', '<E>'): 28,\n",
       " ('l', 'r'): 18,\n",
       " ('m', 'h'): 5,\n",
       " ('l', 'w'): 16,\n",
       " ('j', '<E>'): 71,\n",
       " ('s', 'v'): 14,\n",
       " ('m', 'l'): 5,\n",
       " ('n', 'f'): 11,\n",
       " ('u', 'j'): 14,\n",
       " ('f', 'o'): 60,\n",
       " ('j', 'l'): 9,\n",
       " ('t', 'g'): 2,\n",
       " ('j', 'm'): 5,\n",
       " ('v', 'v'): 7,\n",
       " ('p', 's'): 16,\n",
       " ('t', 'w'): 11,\n",
       " ('x', 'c'): 4,\n",
       " ('u', 'k'): 93,\n",
       " ('v', 'l'): 14,\n",
       " ('h', 'd'): 24,\n",
       " ('l', 'z'): 10,\n",
       " ('k', 'w'): 34,\n",
       " ('n', 'b'): 8,\n",
       " ('q', 's'): 2,\n",
       " ('i', 'w'): 8,\n",
       " ('c', 's'): 5,\n",
       " ('h', 's'): 31,\n",
       " ('m', 't'): 4,\n",
       " ('h', 'w'): 10,\n",
       " ('x', 'x'): 38,\n",
       " ('t', 'x'): 2,\n",
       " ('d', 'z'): 1,\n",
       " ('x', 'z'): 19,\n",
       " ('t', 'm'): 4,\n",
       " ('t', 'j'): 3,\n",
       " ('u', 'q'): 10,\n",
       " ('q', 'a'): 13,\n",
       " ('f', 'k'): 2,\n",
       " ('z', 'n'): 4,\n",
       " ('l', 'j'): 6,\n",
       " ('j', 'w'): 6,\n",
       " ('v', 'u'): 7,\n",
       " ('c', 'j'): 3,\n",
       " ('h', 'b'): 8,\n",
       " ('z', 't'): 4,\n",
       " ('p', 'u'): 4,\n",
       " ('m', 'z'): 11,\n",
       " ('x', 's'): 31,\n",
       " ('b', 't'): 2,\n",
       " ('u', 'y'): 13,\n",
       " ('d', 'j'): 9,\n",
       " ('j', 's'): 7,\n",
       " ('w', 'u'): 25,\n",
       " ('o', 'j'): 16,\n",
       " ('b', 's'): 8,\n",
       " ('d', 'w'): 23,\n",
       " ('w', 'o'): 36,\n",
       " ('j', 'n'): 2,\n",
       " ('w', 't'): 8,\n",
       " ('l', 'f'): 22,\n",
       " ('d', 'm'): 30,\n",
       " ('p', 'j'): 1,\n",
       " ('j', 'y'): 10,\n",
       " ('y', 'f'): 12,\n",
       " ('q', 'i'): 13,\n",
       " ('j', 'v'): 5,\n",
       " ('q', 'l'): 1,\n",
       " ('s', 'z'): 10,\n",
       " ('k', 'm'): 9,\n",
       " ('w', 'l'): 13,\n",
       " ('p', 'f'): 1,\n",
       " ('q', 'w'): 3,\n",
       " ('n', 'x'): 6,\n",
       " ('k', 'c'): 2,\n",
       " ('t', 'v'): 15,\n",
       " ('c', 'u'): 35,\n",
       " ('z', 'k'): 2,\n",
       " ('c', 'z'): 4,\n",
       " ('y', 'q'): 6,\n",
       " ('y', 'h'): 22,\n",
       " ('r', 'f'): 9,\n",
       " ('s', 'j'): 2,\n",
       " ('h', 'j'): 9,\n",
       " ('g', 'b'): 3,\n",
       " ('u', 'f'): 19,\n",
       " ('s', 'f'): 2,\n",
       " ('q', 'e'): 1,\n",
       " ('b', 'c'): 1,\n",
       " ('c', 'd'): 1,\n",
       " ('z', 'j'): 2,\n",
       " ('n', 'q'): 2,\n",
       " ('m', 'f'): 1,\n",
       " ('p', 'n'): 1,\n",
       " ('f', 'z'): 2,\n",
       " ('b', 'n'): 4,\n",
       " ('w', 'd'): 8,\n",
       " ('w', 'b'): 1,\n",
       " ('b', 'd'): 65,\n",
       " ('z', 's'): 4,\n",
       " ('p', 'c'): 1,\n",
       " ('h', 'g'): 2,\n",
       " ('m', 'j'): 7,\n",
       " ('w', 'w'): 2,\n",
       " ('k', 'j'): 2,\n",
       " ('h', 'p'): 1,\n",
       " ('j', 'k'): 2,\n",
       " ('o', 'q'): 3,\n",
       " ('f', 'w'): 4,\n",
       " ('f', 'h'): 1,\n",
       " ('w', 'm'): 2,\n",
       " ('b', 'j'): 1,\n",
       " ('r', 'q'): 16,\n",
       " ('z', 'c'): 2,\n",
       " ('z', 'v'): 2,\n",
       " ('f', 'g'): 1,\n",
       " ('n', 'p'): 5,\n",
       " ('z', 'g'): 1,\n",
       " ('d', 't'): 4,\n",
       " ('w', 'f'): 2,\n",
       " ('d', 'f'): 5,\n",
       " ('w', 'k'): 6,\n",
       " ('q', 'm'): 2,\n",
       " ('k', 'z'): 2,\n",
       " ('j', 'j'): 2,\n",
       " ('c', 'p'): 1,\n",
       " ('p', 'k'): 1,\n",
       " ('p', 'm'): 1,\n",
       " ('j', 'd'): 4,\n",
       " ('r', 'x'): 3,\n",
       " ('x', 'n'): 1,\n",
       " ('d', 'c'): 3,\n",
       " ('g', 'j'): 3,\n",
       " ('x', 'f'): 3,\n",
       " ('j', 'c'): 4,\n",
       " ('s', 'q'): 1,\n",
       " ('k', 'f'): 1,\n",
       " ('z', 'p'): 2,\n",
       " ('j', 't'): 2,\n",
       " ('k', 'b'): 2,\n",
       " ('m', 'k'): 1,\n",
       " ('m', 'w'): 2,\n",
       " ('x', 'h'): 1,\n",
       " ('h', 'f'): 2,\n",
       " ('x', 'd'): 5,\n",
       " ('y', 'w'): 4,\n",
       " ('z', 'w'): 3,\n",
       " ('d', 'k'): 3,\n",
       " ('c', 'g'): 2,\n",
       " ('u', 'u'): 3,\n",
       " ('t', 'f'): 2,\n",
       " ('g', 'm'): 6,\n",
       " ('m', 'v'): 3,\n",
       " ('c', 'x'): 3,\n",
       " ('h', 'c'): 2,\n",
       " ('g', 'f'): 1,\n",
       " ('q', 'o'): 2,\n",
       " ('l', 'q'): 3,\n",
       " ('v', 'b'): 1,\n",
       " ('j', 'p'): 1,\n",
       " ('k', 'd'): 2,\n",
       " ('g', 'z'): 1,\n",
       " ('v', 'd'): 1,\n",
       " ('d', 'b'): 1,\n",
       " ('v', 'h'): 1,\n",
       " ('k', 'v'): 2,\n",
       " ('h', 'h'): 1,\n",
       " ('s', 'g'): 2,\n",
       " ('g', 'v'): 1,\n",
       " ('d', 'q'): 1,\n",
       " ('x', 'b'): 1,\n",
       " ('w', 'z'): 1,\n",
       " ('h', 'q'): 1,\n",
       " ('j', 'b'): 1,\n",
       " ('z', 'd'): 2,\n",
       " ('x', 'm'): 1,\n",
       " ('w', 'g'): 1,\n",
       " ('t', 'b'): 1,\n",
       " ('z', 'x'): 1}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bigram_dict now has all the stats\n",
    "bigram_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "28497a1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " (('a', 'n'), 5438),\n",
       " (('<S>', 'a'), 4410),\n",
       " (('e', '<E>'), 3983),\n",
       " (('a', 'r'), 3264),\n",
       " (('e', 'l'), 3248),\n",
       " (('r', 'i'), 3033),\n",
       " (('n', 'a'), 2977),\n",
       " (('<S>', 'k'), 2963),\n",
       " (('l', 'e'), 2921),\n",
       " (('e', 'n'), 2675),\n",
       " (('l', 'a'), 2623),\n",
       " (('m', 'a'), 2590),\n",
       " (('<S>', 'm'), 2538),\n",
       " (('a', 'l'), 2528),\n",
       " (('i', '<E>'), 2489),\n",
       " (('l', 'i'), 2480),\n",
       " (('i', 'a'), 2445),\n",
       " (('<S>', 'j'), 2422),\n",
       " (('o', 'n'), 2411),\n",
       " (('h', '<E>'), 2409),\n",
       " (('r', 'a'), 2356),\n",
       " (('a', 'h'), 2332),\n",
       " (('h', 'a'), 2244),\n",
       " (('y', 'a'), 2143),\n",
       " (('i', 'n'), 2126),\n",
       " (('<S>', 's'), 2055),\n",
       " (('a', 'y'), 2050),\n",
       " (('y', '<E>'), 2007),\n",
       " (('e', 'r'), 1958),\n",
       " (('n', 'n'), 1906),\n",
       " (('y', 'n'), 1826),\n",
       " (('k', 'a'), 1731),\n",
       " (('n', 'i'), 1725),\n",
       " (('r', 'e'), 1697),\n",
       " (('<S>', 'd'), 1690),\n",
       " (('i', 'e'), 1653),\n",
       " (('a', 'i'), 1650),\n",
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       " (('l', 'y'), 1588),\n",
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       " (('<S>', 'c'), 1542),\n",
       " (('<S>', 'e'), 1531),\n",
       " (('j', 'a'), 1473),\n",
       " (('r', '<E>'), 1377),\n",
       " (('n', 'e'), 1359),\n",
       " (('l', 'l'), 1345),\n",
       " (('i', 'l'), 1345),\n",
       " (('i', 's'), 1316),\n",
       " (('l', '<E>'), 1314),\n",
       " (('<S>', 't'), 1308),\n",
       " (('<S>', 'b'), 1306),\n",
       " (('d', 'a'), 1303),\n",
       " (('s', 'h'), 1285),\n",
       " (('d', 'e'), 1283),\n",
       " (('e', 'e'), 1271),\n",
       " (('m', 'i'), 1256),\n",
       " (('s', 'a'), 1201),\n",
       " (('s', '<E>'), 1169),\n",
       " (('<S>', 'n'), 1146),\n",
       " (('a', 's'), 1118),\n",
       " (('y', 'l'), 1104),\n",
       " (('e', 'y'), 1070),\n",
       " (('o', 'r'), 1059),\n",
       " (('a', 'd'), 1042),\n",
       " (('t', 'a'), 1027),\n",
       " (('<S>', 'z'), 929),\n",
       " (('v', 'i'), 911),\n",
       " (('k', 'e'), 895),\n",
       " (('s', 'e'), 884),\n",
       " (('<S>', 'h'), 874),\n",
       " (('r', 'o'), 869),\n",
       " (('e', 's'), 861),\n",
       " (('z', 'a'), 860),\n",
       " (('o', '<E>'), 855),\n",
       " (('i', 'r'), 849),\n",
       " (('b', 'r'), 842),\n",
       " (('a', 'v'), 834),\n",
       " (('m', 'e'), 818),\n",
       " (('e', 'i'), 818),\n",
       " (('c', 'a'), 815),\n",
       " (('i', 'y'), 779),\n",
       " (('r', 'y'), 773),\n",
       " (('e', 'm'), 769),\n",
       " (('s', 't'), 765),\n",
       " (('h', 'i'), 729),\n",
       " (('t', 'e'), 716),\n",
       " (('n', 'd'), 704),\n",
       " (('l', 'o'), 692),\n",
       " (('a', 'e'), 692),\n",
       " (('a', 't'), 687),\n",
       " (('s', 'i'), 684),\n",
       " (('e', 'a'), 679),\n",
       " (('d', 'i'), 674),\n",
       " (('h', 'e'), 674),\n",
       " (('<S>', 'g'), 669),\n",
       " (('t', 'o'), 667),\n",
       " (('c', 'h'), 664),\n",
       " (('b', 'e'), 655),\n",
       " (('t', 'h'), 647),\n",
       " (('v', 'a'), 642),\n",
       " (('o', 'l'), 619),\n",
       " (('<S>', 'i'), 591),\n",
       " (('i', 'o'), 588),\n",
       " (('e', 't'), 580),\n",
       " (('v', 'e'), 568),\n",
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       " (('a', 'a'), 556),\n",
       " (('c', 'e'), 551),\n",
       " (('a', 'b'), 541),\n",
       " (('i', 't'), 541),\n",
       " (('<S>', 'y'), 535),\n",
       " (('t', 'i'), 532),\n",
       " (('s', 'o'), 531),\n",
       " (('m', '<E>'), 516),\n",
       " (('d', '<E>'), 516),\n",
       " (('<S>', 'p'), 515),\n",
       " (('i', 'c'), 509),\n",
       " (('k', 'i'), 509),\n",
       " (('o', 's'), 504),\n",
       " (('n', 'o'), 496),\n",
       " (('t', '<E>'), 483),\n",
       " (('j', 'o'), 479),\n",
       " (('u', 's'), 474),\n",
       " (('a', 'c'), 470),\n",
       " (('n', 'y'), 465),\n",
       " (('e', 'v'), 463),\n",
       " (('s', 's'), 461),\n",
       " (('m', 'o'), 452),\n",
       " (('i', 'k'), 445),\n",
       " (('n', 't'), 443),\n",
       " (('i', 'd'), 440),\n",
       " (('j', 'e'), 440),\n",
       " (('a', 'z'), 435),\n",
       " (('i', 'g'), 428),\n",
       " (('i', 'm'), 427),\n",
       " (('r', 'r'), 425),\n",
       " (('d', 'r'), 424),\n",
       " (('<S>', 'f'), 417),\n",
       " (('u', 'r'), 414),\n",
       " (('r', 'l'), 413),\n",
       " (('y', 's'), 401),\n",
       " (('<S>', 'o'), 394),\n",
       " (('e', 'd'), 384),\n",
       " (('a', 'u'), 381),\n",
       " (('c', 'o'), 380),\n",
       " (('k', 'y'), 379),\n",
       " (('d', 'o'), 378),\n",
       " (('<S>', 'v'), 376),\n",
       " (('t', 't'), 374),\n",
       " (('z', 'e'), 373),\n",
       " (('z', 'i'), 364),\n",
       " (('k', '<E>'), 363),\n",
       " (('g', 'h'), 360),\n",
       " (('t', 'r'), 352),\n",
       " (('k', 'o'), 344),\n",
       " (('t', 'y'), 341),\n",
       " (('g', 'e'), 334),\n",
       " (('g', 'a'), 330),\n",
       " (('l', 'u'), 324),\n",
       " (('b', 'a'), 321),\n",
       " (('d', 'y'), 317),\n",
       " (('c', 'k'), 316),\n",
       " (('<S>', 'w'), 307),\n",
       " (('k', 'h'), 307),\n",
       " (('u', 'l'), 301),\n",
       " (('y', 'e'), 301),\n",
       " (('y', 'r'), 291),\n",
       " (('m', 'y'), 287),\n",
       " (('h', 'o'), 287),\n",
       " (('w', 'a'), 280),\n",
       " (('s', 'l'), 279),\n",
       " (('n', 's'), 278),\n",
       " (('i', 'z'), 277),\n",
       " (('u', 'n'), 275),\n",
       " (('o', 'u'), 275),\n",
       " (('n', 'g'), 273),\n",
       " (('y', 'd'), 272),\n",
       " (('c', 'i'), 271),\n",
       " (('y', 'o'), 271),\n",
       " (('i', 'v'), 269),\n",
       " (('e', 'o'), 269),\n",
       " (('o', 'm'), 261),\n",
       " (('r', 'u'), 252),\n",
       " (('f', 'a'), 242),\n",
       " (('b', 'i'), 217),\n",
       " (('s', 'y'), 215),\n",
       " (('n', 'c'), 213),\n",
       " (('h', 'y'), 213),\n",
       " (('p', 'a'), 209),\n",
       " (('r', 't'), 208),\n",
       " (('q', 'u'), 206),\n",
       " (('p', 'h'), 204),\n",
       " (('h', 'r'), 204),\n",
       " (('j', 'u'), 202),\n",
       " (('g', 'r'), 201),\n",
       " (('p', 'e'), 197),\n",
       " (('n', 'l'), 195),\n",
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       " (('g', 'i'), 190),\n",
       " (('o', 'd'), 190),\n",
       " (('r', 's'), 190),\n",
       " (('r', 'd'), 187),\n",
       " (('h', 'l'), 185),\n",
       " (('s', 'u'), 185),\n",
       " (('a', 'x'), 182),\n",
       " (('e', 'z'), 181),\n",
       " (('e', 'k'), 178),\n",
       " (('o', 'v'), 176),\n",
       " (('a', 'j'), 175),\n",
       " (('o', 'h'), 171),\n",
       " (('u', 'e'), 169),\n",
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       " (('a', 'g'), 168),\n",
       " (('h', 'u'), 166),\n",
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       " (('u', 'a'), 163),\n",
       " (('r', 'm'), 162),\n",
       " (('a', 'w'), 161),\n",
       " (('f', 'i'), 160),\n",
       " (('z', '<E>'), 160),\n",
       " (('u', '<E>'), 155),\n",
       " (('u', 'm'), 154),\n",
       " (('e', 'c'), 153),\n",
       " (('v', 'o'), 153),\n",
       " (('e', 'h'), 152),\n",
       " (('p', 'r'), 151),\n",
       " (('d', 'd'), 149),\n",
       " (('o', 'a'), 149),\n",
       " (('w', 'e'), 149),\n",
       " (('w', 'i'), 148),\n",
       " (('y', 'm'), 148),\n",
       " (('z', 'y'), 147),\n",
       " (('n', 'z'), 145),\n",
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       " (('r', 'n'), 140),\n",
       " (('o', 'b'), 140),\n",
       " (('k', 'l'), 139),\n",
       " (('m', 'u'), 139),\n",
       " (('l', 'd'), 138),\n",
       " (('h', 'n'), 138),\n",
       " (('u', 'd'), 136),\n",
       " (('<S>', 'x'), 134),\n",
       " (('t', 'l'), 134),\n",
       " (('a', 'f'), 134),\n",
       " (('o', 'e'), 132),\n",
       " (('e', 'x'), 132),\n",
       " (('e', 'g'), 125),\n",
       " (('f', 'e'), 123),\n",
       " (('z', 'l'), 123),\n",
       " (('u', 'i'), 121),\n",
       " (('v', 'y'), 121),\n",
       " (('e', 'b'), 121),\n",
       " (('r', 'h'), 121),\n",
       " (('j', 'i'), 119),\n",
       " (('o', 't'), 118),\n",
       " (('d', 'h'), 118),\n",
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       " (('o', 'o'), 115),\n",
       " (('y', 'c'), 115),\n",
       " (('o', 'w'), 114),\n",
       " (('o', 'c'), 114),\n",
       " (('f', 'r'), 114),\n",
       " (('b', '<E>'), 114),\n",
       " (('m', 'b'), 112),\n",
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       " (('i', 'b'), 110),\n",
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       " (('k', 'r'), 109),\n",
       " (('g', '<E>'), 108),\n",
       " (('y', 'v'), 106),\n",
       " (('t', 'z'), 105),\n",
       " (('b', 'o'), 105),\n",
       " (('c', 'y'), 104),\n",
       " (('y', 't'), 104),\n",
       " (('u', 'b'), 103),\n",
       " (('u', 'c'), 103),\n",
       " (('x', 'a'), 103),\n",
       " (('b', 'l'), 103),\n",
       " (('o', 'y'), 103),\n",
       " (('x', 'i'), 102),\n",
       " (('i', 'f'), 101),\n",
       " (('r', 'c'), 99),\n",
       " (('c', '<E>'), 97),\n",
       " (('m', 'r'), 97),\n",
       " (('n', 'u'), 96),\n",
       " (('o', 'p'), 95),\n",
       " (('i', 'h'), 95),\n",
       " (('k', 's'), 95),\n",
       " (('l', 's'), 94),\n",
       " (('u', 'k'), 93),\n",
       " (('<S>', 'q'), 92),\n",
       " (('d', 'u'), 92),\n",
       " (('s', 'm'), 90),\n",
       " (('r', 'k'), 90),\n",
       " (('i', 'x'), 89),\n",
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       " (('h', 'h'), 1),\n",
       " (('g', 'v'), 1),\n",
       " (('d', 'q'), 1),\n",
       " (('x', 'b'), 1),\n",
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       " (('j', 'b'), 1),\n",
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       " (('t', 'b'), 1),\n",
       " (('z', 'x'), 1)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return tuples of key value, sort by value\n",
    "sorted(bigram_dict.items(), key = lambda kv: kv[1], reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae4cf62",
   "metadata": {},
   "source": [
    "# 2. bigram char counting in a tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3e7195aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d51c5cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = torch.zeros([3,5])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "78c2080d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "611c0d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0]], dtype=torch.int32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with count, we need int\n",
    "a = torch.zeros([3,5], dtype=torch.int32)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "baf78702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 1],\n",
       "        [0, 0, 0, 0, 0]], dtype=torch.int32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change the value inside the tensor\n",
    "a[1,4]=1 # row 1, col 4\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b53cdc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 1, 1],\n",
       "        [0, 0, 0, 0, 0]], dtype=torch.int32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1,3]+=1\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70fa2825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a',\n",
       " 'b',\n",
       " 'c',\n",
       " 'd',\n",
       " 'e',\n",
       " 'f',\n",
       " 'g',\n",
       " 'h',\n",
       " 'i',\n",
       " 'j',\n",
       " 'k',\n",
       " 'l',\n",
       " 'm',\n",
       " 'n',\n",
       " 'o',\n",
       " 'p',\n",
       " 'q',\n",
       " 'r',\n",
       " 's',\n",
       " 't',\n",
       " 'u',\n",
       " 'v',\n",
       " 'w',\n",
       " 'x',\n",
       " 'y',\n",
       " 'z']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we need to use dict from string to number -> embedding\n",
    "# concat all char, set of string, remove duplicats\n",
    "# we want list sorted\n",
    "sorted(list(set(''.join(words))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "99ec4f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we have char and special chars\n",
    "# we have 28 X 28 array with counts\n",
    "N = torch.zeros([28, 28], dtype=torch.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "54442106",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 0,\n",
       " 'b': 1,\n",
       " 'c': 2,\n",
       " 'd': 3,\n",
       " 'e': 4,\n",
       " 'f': 5,\n",
       " 'g': 6,\n",
       " 'h': 7,\n",
       " 'i': 8,\n",
       " 'j': 9,\n",
       " 'k': 10,\n",
       " 'l': 11,\n",
       " 'm': 12,\n",
       " 'n': 13,\n",
       " 'o': 14,\n",
       " 'p': 15,\n",
       " 'q': 16,\n",
       " 'r': 17,\n",
       " 's': 18,\n",
       " 't': 19,\n",
       " 'u': 20,\n",
       " 'v': 21,\n",
       " 'w': 22,\n",
       " 'x': 23,\n",
       " 'y': 24,\n",
       " 'z': 25,\n",
       " '<S>': 26,\n",
       " '<E>': 27}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# string to int mapping 1:1\n",
    "chars = sorted(list(set(''.join(words))))\n",
    "# string to int, int is the index \n",
    "stoi = {s:i for i, s in enumerate(chars)}\n",
    "# manually map the 2 specials\n",
    "stoi[START_TOKEN] = 26\n",
    "stoi[END_TOKEN] = 27\n",
    "stoi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6ae717f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for w in words:\n",
    "    # special start and end chr to tell which char is start and end\n",
    "    # <S> and <E> are special\n",
    "    chs = [START_TOKEN] + list(w) + [END_TOKEN]\n",
    "    for ch1, ch2 in zip(chs, chs[1:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        # store the count at the corresponding index\n",
    "        N[ix1, ix2] += 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fdbe66cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4c3b90e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x122f2d780>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the N, Display data as an image, i.e., on a 2D regular raster.\n",
    "plt.imshow(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f77e0c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'a',\n",
       " 1: 'b',\n",
       " 2: 'c',\n",
       " 3: 'd',\n",
       " 4: 'e',\n",
       " 5: 'f',\n",
       " 6: 'g',\n",
       " 7: 'h',\n",
       " 8: 'i',\n",
       " 9: 'j',\n",
       " 10: 'k',\n",
       " 11: 'l',\n",
       " 12: 'm',\n",
       " 13: 'n',\n",
       " 14: 'o',\n",
       " 15: 'p',\n",
       " 16: 'q',\n",
       " 17: 'r',\n",
       " 18: 's',\n",
       " 19: 't',\n",
       " 20: 'u',\n",
       " 21: 'v',\n",
       " 22: 'w',\n",
       " 23: 'x',\n",
       " 24: 'y',\n",
       " 25: 'z',\n",
       " 26: '<S>',\n",
       " 27: '<E>'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize N\n",
    "# int (index) to string \n",
    "itos = {i:s for s, i in stoi.items()}\n",
    "itos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "510d65bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(149, dtype=torch.int32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N[3,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "590b99f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N[3,3].item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6bf47d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 27.5, 27.5, -0.5)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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muP49r6muorqqkteH+pWfvv+GoOAQurrEnwjXVDve8ddGaFvG6NPEuEsyVr79/ANoa5uavk3e24K8t5n+5NUgU6ArPQ02G8jkot/Ta2mooaW+hlVbX0OhUHLm4E/YrBbWPv82zXVVlBfmUVtRzNxl60XRH2s8FXssq6utpq66khde3Y5SqWT3z99isVoYbl+AqGloqq+hsa6KDc+/iUKp5NieH7BarcQkpFJVVoS3rz+RcYkuf9fHG0dCwyPJv5OHr58/4RFR3L2TS2N9HctWutaEdDx9s9lMX69j08xkNqFSq12qC9DTWk9PSx2py59FrlBSdG4fNpuVwNhUGoty0Hj6EBgtzunu8Dz5uaF58t5fvsNqtVBbXUlZSRHPvvTGhJ9dU11FVWUFb25/D6VSyQ/ffo3VYmH9xi3k3rrJwX17SExOISMzi4CA+y1BfH398J3lR+aMWfznv/1fGAYGnFdC2lpbuXP7FqXFxURGRZM5Y+YD2lH+cqIDFOy42IfFBq8t0KOQw5HbBrKiVGyZoaWkycKdWhPtfbbHzpO/h4zpkSoSQxTUtlvJrTFNuHyGcUkvajIZUas1KJRKujraaWmqx2QyolCqUKk1DPT3U1td7gqpMbVVag1KpZLOIW1wXMSsKHWs9MuKCggJE2d3cPiyp0O/jebGeqxWCw11NfR0dwGIYsZpNhlRqRxl3tPZTntLA1arhZbGOoyDBmxWKzUVxS7XheH6Vjvru7WpAavFSktjPT1DkyKz2UR3p3i7k0ajEc1Qvbe3t9HYUCea1n2aQ3XdMaRpNpupq6lmcNCAzWajtKRQNH2r2YxMqUKmUGDs7WKws1U0rbGwmIwoVRrkCiV93R10tzVNmfZYZT8VjNW3ASDAsjWb6OpoJ/f6FVHTYLOYEJRqZHIF5r4ujF3i1bvF7L46HkYl2DDa5FjsMnwURoLV4pjBj8XI96y9vY2G+josQ218YGAAm81GYUE+kVExLtceOZa0t7fRWO94x+12OyVFBQAUFtwhPCLS5dpj59tCXU01hoEBrFYrxUNpEAPBasYuV4FMgWywB3m/uJulIzGbTCjVahQKJT1d7bS3NGIcNIDdTkRsEhmzF9HV1iya/ljjKTjuEYm1mWYyDt6bt7Q75i0AOr2ejvY27HY75WXizB8c+vf61e7OdlqbHXmOikuitrKMytJCYhNTXa473jgSERnFzWtXiIiMJig4hNrqKuQKBWq1Zkr0L5w9RUp6BgsXL+PE0UMu1RzGajYhV6qRK5QYejrp63C80x5+wZgMfbTXluIXmSiK9ljz5N6ebi6cPsa6zc+iUCon/Gzj4CAajdbRd7U5+i6AsPAINm7eyvb3P8TP358jB/ez46vPaWpsBKC8tMS5UdnR0Y5MEFBrNDQ1NrLjq885cmg/fv4BbH//QzZsfmbM00G1QsBotmOxgZ9eRpiPYxO0scvKkdsGvjrfR0eflQ3Ttby5SE+w18OXXMFeMt5cpGfDdC3tfVa+Ot/Hkdvjnw4+CS452YuMjqPw9i1++eZTvH39CAoJxz8giIDAYH755hP0nl4Eh4qz2IqMjqPg9i1+/uZTfIa0ARRKJS1NDeRcu4RWq2f1xm2i6EfFxHH3dg4/fv0JPr7+BIeGo9HqWLZmI8f278Jut6PV6djywmsu1Q2NjKW8II/DP3+Bp48f/kFhaHR6ps1ayMm93zsdtIhBRHQcRXdy2fXdZ3j7+BEYEoZGq2XJ6o2cPbYP69AANWv+Urx9/URJQ0xcPHm5N/nqs7/g6+dPqEiL+fs0Y+O5fesmX3/2F3z9HZoenp7MXbCYH3Z8iUajwc8/ALUIu3IA+qAIuqsLqTz9KyoPHzS+4jgrGI+AsGhqS+9w8cAO9F6+eAeETJn2WGXvRJxDe2Dsvm0YmUzGqg1bObZ/J0qVivTMWQ950sTRBkTQV1NMw7ndKD28UfuIV++BQ3V8Yf/U1/EwNYN60j06eDWkjE6zmmajuI5/RhIbF0/erRt88elf8PPzJyw8Ar2HB0uXr+LnH3Y4HbQkJrneQU5MXDy5t27w5ZB26NDkQqlU0thQz5VLF9Dp9WzZ9rzLtcfL98Ily/h+x5cOBy3B4r0LFq8QVG1l6AsOY9N4YdWLY0o2FiGRMZQX5nLk1y/x9PbDPygUw0AfZw7+DEOTwYy5S0TTH2s8BUhOz2LPj1/iHxjscgct0THx5Ofl8P1Xf3XOWwAWLFnBwT0/o9XpCAoOxWw2P+JJEyM8OpaSu7fY98NnePn4ExjsyLNao8Hb15/uzjYCgl1/7368cSQ8IpLe3h4iIqOQyWR4ennj6+/6d3As/bqaapoaG3jlje3IZDJKSwrJv53LtOlZLtX2Do6iteIud47/iMbDBw+/e74F/CLiGehqR6ESZ+4yPE/+4etP8B1633p7uhk0GDiyfycAer0nm597+YmfHRufwK2cm3z21z/j7+//wKJMpVKRmTWTzKyZtLXd2yjNz7/NqZPHUCqUyGQyNm99DplMhkKpYOOWrQ+cAo5FRauFrGgV7y/zoKPfRsOoRZnZCrdrzdyuNePvcf9Cb+SdPYBvhk4HD+cZnugU8HERHmaC09htEsce7DEQcf72SGQy96l39ovTuT4OOpU4njsfB1/9xHd2JstkrdBMJhMqlQqbzcb+3b+QPj3rse/zHCpsnJz4JInxFD++y3ikR3hN6vc2m42//s9/5cN/+O9P7HW2Tyz/xo/B0ZKpPy0bJs1vcmU+GZ75lxNu0678ywtu057sIPof/+Nf+Kf/5X+b0G9dOZLl386lqbHxsZ3D/O9HxTsZehTvz3b96efj4qFxX0Qrd47hPYPu61MBAjzE8/z9t8yFssmbWpdcOkxI4nS8gp5s03xOtO+ktSeKp9Z9c0bfBf/sNm3D9X+dULcuxdmTkJgkVy6eo6a6EovFQnRsHAmJ4rnFl7jHN1/8lWmZWaKFF5GQkJCQkHhasZiMFJzZhc7b/4kXehJ/X0xqsffDF39BqVIhEwQEmYznXnuHG1cuUJSfi3bII+ScRcuc8afaW1u4cOoIZpMJBIFnX92OQjGxJHz/xV9QqVQIQ9rPv3YvFkrezWyuXjjNWx/+E1qtjs6Ods4eP0hbazNzFy4jc9a8yWQbcJwq7Pr+K/Qenmx89iX2/LzDkS/AMDBAUEgYG7a+gN1u59KZE1RXlqNQKli5bguBT2AOk332CA3VFWi0Oja85MijcdDA5ZMH6O/tRu/pzaI1z6AaYVve3tLIyb3fs3D1FiLjkulsa+bGhROYzSYEQSB9xgKiEp78Iq7ROMilU0fobG8FQWDJqo3IFQounz2GxWzGw9OLZeueQaVSU19TyY3LZ7HZbMhkMuYsWkFYZMwTaw5z9NB+KspL0en0bH//IwAunT9DWWkJgiCg0+lZv+kZPDw9nb9pamzghx1fsnnrcySlpE1Y+9jhe9pvv/eR8/NbN6+Rm3MDmSAQG5/I0hWrsVqtHDu8n+amRuw2G2nTpjN3weOHnzAb+mjMOYfVaAABfKJT8I2bRlvRDXobqxEEAblaS+iMpSg0enobq2kruuG4yC7ICJo2H51/CIPd7TTfvoTNYgIE/JOy8Ap/tDvn/CsnaK2vRKXRsWjzvUvT1cW51JbcBkEgMCyW5JmLMfT1cPGgw9wPwNs/hPR5qwDobm8m/8oJrFYLgeExpMxa9kSX7ccq84P7dtHZcS/wqVqjYeny1XR3d/H15x/j5+cwuwkNC2f1uk2PrTUWY/VtAPm5N7ibdxOZICMyNp75S1Zis1o5d/IwbS3N2G02ElOnMWPu47kMtxj6aM+7gNVkQEBAH5mEV2w6XSW36K8tQaZytGuf5JlogyIxtNbTVXwTbFaQyfFNmY0mwGHu1N9QQU9ZHggCcrUO/6ylyFUPv3Ny58oJWusc9b14y4j6LsqlpuQ2giAQGO6o7662Ju5mn3J8wQ4J0+fdF3rDbrNx+chPaHR6Zq14PEdNb4SWYrbJsONwurKz+V4Ih0zPdhb5NPNlfRKDNgUxml7mebc4v3uxM4Qmk2OcWeDdTLS2DwE7tYMeXOwKZqLnWoODgxw7fIC21hYQBNZv3EJleRmlpcXOvmbj5q339TWu4sa1q9zJuwVAYGAQ6zdvJSExmS8++TMymYzQsHDWrN8kygbHWPn28w/gwN6ddHd34+3tzTPbJnFCarOiLz3teHexY/GJxBg6DW3VFWQDnSAIWHX+DEbNdjplkfe2oKm/BXYbdoWagcSV955nt6EvPoFNqcUQ//AYvtfOHaWxphy1VucMp1BbUczdm5fp6Wpn9bY38At0jMuO8XU/na1NxCSlM3PRvZiKNWWFFOZmgwBanQfzVmxE/YQxTccdR88cw2q1IMhkLFy2lsCQMO7kZFNefNdRfDYb3Z3tvPb+n1BPIKapxWJh9887sFqt2G024hNTmLdoGbXVlVw+fwq73Y5SqWLV+i34+PpRX1fDxTPHaWttYd3mZ0lImtwduoLc65QW5CEIAj7+gSxauZHs88dpb2nCDnj5+LJo5f2xeavLizl3dC8bX3yLgKCJh/14kjHcYBjgwN6dNDc2kJaROemYuWNptzQ3cfLYYaxWCzKZjJVrNhAaFk7h3Ttcz76M3W5HpVKzet0GAoMef75oHOij8sYpzIOOe86BsWmEJE7HYhqkPPsExv5e1HpP4uetRaFSM9DdhmXQwKC8m/yTv+AbHkd4qsMrZndTDTV5F7Hb7QTGphKa/KCDkkdhs9nYOTRP3vTsS5w+dojW5kbs2PHx9WPVui0oVSqK7t7m8vlTzrh3GVmzScvIemK9x6GivIxTx49is9vIzJrJ/IWuCQ+mVsCGTB2BnjLsdoc5ZkOXlVkxKmbGqLDZobzFwtnCe2FcvDQC7y/35GLJINcqHOuH2EAFq9M1yATIqzFztdzokvRN+mRvywuvOV39D5Mxc+4DCyqbzcaZY/tZsW4L/oHBDBoGkMkm5x9m8wuvOReVw/T19lBXXYmH5z1zJY1Gw6Lla6gqL52U3kju3LqOj5+/c4H37MtvOf92dP8uYhOSAKipLKerq4PX3v2I5sYGzp86yvOvbX9sndikaSSmzyT7zGHnZ4W52QSHR5M2Yx4Ft7IpuJVN1nxHbBSbzUZe9vn7XEXLFUrmr9yEp7cvhv4+ju3eQUhkzH0LxMch+/xJwqPjWLnxWaxWKxaLmWN7f2LO4pWEhkdRUpDHnZxsZs1fikarZc3mF9B5eNLZ3sqxfT/zyrv/8ER6I5mWkcmMWXM4cnCf87PZ8xayaKkjJk/OjWtcuXSeNes3Ocvh/JlTLolXk56RSdbMORw9dE+7prqK8tIS3nzndygUCgb6HW7gS4oLsFosvP3eR5jNZr75/GOS06bhPRSG41EIgoyg9HlofAKwWUxUnduLLjAc3/jpBKQ4OuHOinzaim8RkrkYfWAYHiHPIQgCg93tNN48TezKF5HJFYTOWIbKwxvLYD9V5/aiD4pArny4TX5YXBpRyZncuXzc+Vl7Uy0tdRUs3PgaMrkC4+CA8286Dx8Wbnz9gecUXD9D+vxVePuHkHNmH20N1QSGxzxWGcDYZb556727SudOn7jPY5mPjy9vvvO7x37+4zC6b2uoraa6vJQXXn8PuUKBYcBR5xWlRVitVl58831HzKodn5GQnIbnY9S5IMjwTZ2DyjsAm8VM08X9aAMc92c8Y9Pwisu47/tylYbA2atRaHSYejtpvXac8FUvY7fZ6CzIJnTps8hVGjqLrtNbVYjPI+IthselEZU0dn0v2nR/fXv6+LNgw6vIZDIGB/q5fOh7AiPinP14VVEuHt6+WMxP5jlsX2s0g6OCo3vIzUSq++i13DPVqTPqqWqOAwT8lYOs9a/jx6YEQlQDhKgH+LnJsVB8NqiKMPUADcaJmSifPnGU2Lh4tj73IlarFbPZTEBgEIuXOfqam9ezuXzpPGvXT25DYTS9vT3k3LjGOx/83uFZes9OigrySU3PYOMzzwJwaN9u7uTdImum6wN8j5Xvq5cvEB0Ty7wFi8m+cpHsq5cmHlBekNGfsBzkSsdCreQUFq8QzL7RWKLnA6CtuoqyrQJzYAJYTGjqbjIQvxS7So9gvj/Wnaq1FJvGC6yPvvoQm5ROYvoMss/eG0e9fQNYuGYrNy8ev++7crmcabMX0d3Rdp93a5vNxq0rp1n/4juoNTryss9RevcW02Y9WQDqscbRM0f2kjV3EZEx8dRWlXP98hk2Pvc6GTPnkTHTMZeqqSzlbu71CS30hvO17cU3UKlUWK1Wdv+0g+jYBM6eOsqmrS/i5x/Andwb3Mi+xOr1W/D09GLV+i3cupE9Ib2RDPT1UnT7Js+89h4KhZJzR/dSWVrI7MWrUA3dE7t+8RRFd3LImOV4F8wmI4V5NwhwQWzHJxnDFXIFi5Ysp621lba2FlG0L5w9xYJFS4mNT6CivJQLZ0/x0mtv4e3tw0uvvYVGo6WyvIwTRw/x2lvvPbaWIAhEZixE7xuI1Wzi7umdeAdH0FZdjFdQOKHJM2kszqGxOIfIjAUAeASEkrRo433PsdttVOdeIGnxFlQ6PQWnd+ETGoPW68l8MNy+dR1fP39MQ/PkxctXO8ftS2dPcif3BjOHNkYTktJYukqcWNjD2Gw2Thw9zMuvvYmnlxfffPkZCYnJzoDqk2F1upaKFjN7b5qRCaCUO7x1JgYr+fJ8H1Yb6FT3b0CuTNdSMSLIugCsnabhp+x+eg12ti/xoLTZ7JI7fOL6NB5BXXUFfgFB+Ac6LoVqtLpJL/bG4vK5k8xfsoKRu7panZ6gkDCX6fX19lBdUUbqGDsPJqOR+tpqYuMdi72q8hKS0zIQBIGQsHCMxkH6+x4/XkZQWCQqzf2LsvqqMmKTHHGXYpPSqa+6t4gtzc8hMjYR9YhJqpePH57ejtMXrd4DjUbn8Db2BJiMgzQ11JKUNh1wDBxqtYburk5Cwhz3I8IiY6ke8uDlHxiCbmiXxscvAIvFMilX+RFR0WhGDXIjHaEMn1oOc+vmdRKTU5wBNCdDRGT0A7ESb9+6wZz5C50n0zq9Y2IpIGA2m7HZbFgsZmRyuXMwexwUGh0anwAAZAoVak8fLIZ+5Mp7u522EeUoUyid+baP+Fzl4Y3Kw3vomXoUai3WxwgM7BccjnLUaVBt6R3i0mYjkzvy+qidbKOhH6vZhE9AKIIgEBaXSkvdk3njHavMh7Hb7RQXFZCSmv5Ez5wsBbdzyJwzH/lQnWt19xYTlhF1LpfLUD6mkx65RofKe7i+lSiHFufjofL2RzFU/koPH+w2C3anxz47dqsFu92O3Wx2fu9h+AWHoxy16VNbcofY9AfrWz50kR3AZrPcd3A22N9La0MlEQnTHivfj2KRTxNXuoPvu+9mscsYFlUI9wY/OyAX7MgEu/P/DdaJ7WMaBwepq60hI9OxSJbL5Wg0mlF9jXj3qu02GxaLxfEumc14eHgSl+BwQT88hvT2uj6u4Xj5ListIT0jE3BMWofj8E0IQXAs9ADsNsf/ELB4hzn+JghY9X7IzI7NBWVnNRbvCOwqRzuzK++9p4JpAEV3Ayb/uNEqYxIYGvnA5qaXrz9ePg9OXhVKFYEhEc52fg/H22gxm7Hb7ZhNJrQ6j8fSH2a8cVQQBOfGsclkRKd/8NS4oqSQuMSJW6gIgoBq6NTMZrNhs1lBcLQok8lxemA0GtHrHXny8vYhIDDYZeEPbHYb1uF322JBp/dwjo12ux2rxXJfhKzc7AtMmzkfuXzyt42eZAxXqlSER0RN2OrscbThXpmbjEb0Ho4yD4uIdM5zQsPD6e3tfSItlVaPfshxm1ypQuvpi8nQT1dDJf5Rjusl/lHJdA3F2huP/o4W1HpvNB5eyGRy/CIS6GyoeqK0jDVPHl7o2e12LBYzU+2do7GhHh8/P3x8fZHL5aSmpVM6FPNwMqgVEOmv4HatY2yw2cFogRnRKq6UD2IdGq4GRrhBSQxW0D1go63vnlOXUB85nf02ugccoYYK6s0kBrvmbuKk3mZBgEO7f0IQBFIzskjNcAwUd3NvUlp4h4CgUBYsXYlao3W64T+8+ycMhgHik9PImj1/UtqHd/8EQ9ppGTOoKi9B7+HpXFCKxaWzJ1iwdKVzt2IkleUlRERFO1/q/r6++04ZPTw86e/rdTbuiTBoGEA71CFrdHoGDY7BcaC/l7qqUlZueYX2s0fG/G17SyM2mxUPL58n0uzt6Uaj0XHh5CE62loICAph3tLV+PoFUFNRSnR8ElVlRfT1Pdg5VZUX4x8Y7JJOezQXz53mbv4d1Go1L732piOtvT2UlRTx0mtvcezQfpdrAnR2dlBfW8Ol82eQKxQsW7GGkNAwEpNTKS8t5pP/+jfMFjPLV651Blx/UswDvQx2t6PxdXhVbS28Tk9tGTKlisiF93biehuraCu8jsU4SMS8tQ88x9DZgt1mRamfmHOOgd5OOlvrKc27jEyuIHnmYrz9HaYlhr5uLh/+AYVSRWLmAnyDwhkc6EM9YhKk0XlgHJhcQNCR1NfVoNfr8fW75y2tu7uLb7/6FJVazaIlK4iInFzsu7H6tu7ODprqa7l+6RwKhYJ5S1YSFBJGXGIKVRWlfPfZf2IxW1iwbNUDGxOPg2WgF1NPB2qfQIydLfRWF9FfX47KOwDf1DnIRp3KGpqqUXr5IwyZ9PmlL6Txwl4EuQKlzgvfaRPrX/t7O+lsqac011HfKTMXO71ydrU1kX/lBIb+XqYvXOtc/BXePE/yjMVYJrAQ2hJYgx0o6POloN+XGE0v/VYl7eYHLQ9itT3M925BK7NwqM1Rx80mHQ2DOraHOQL25vf50WmZmFe5ru4utDodRw7tp7WlmeCQUFauXodKpeLCudPcvXMbtVrNy6+/9eiHPSGenl7MnreAT//87ygUSmJi44iJu2eZYLVaKci/zco1rt/9Hi/fA/19eAxt2un1Hgz0T7IdD5leyox9mAIS7ve6abeh7KhiMMJhLiYz9iLY7ehKTyNYLZgCEzH7xwKgqb/FYHgmwhTE2hxGJpMzc9Fqju36BoVCiYe3LzMXrXqiZ4w3js5bsopj+37h+qXT2O12Nr/w5n2/s5jN1FVXsGDZ5OK+2Ww2fvnuC7q7OsnImk1IaDgr127iwO6fUSgUqFRqXnwCy6PHRefhSXrWXHZ98zFyhYKwyFjCohx1eenUIeqrK/D2C2D2IoeZbntrE/19vUTExHP31uRPFsdivDF8Kli+ai27f/mBc2dOYrfbefWN7Q98Jz8vl9i4iVsmGft7GOhqw8MvGLPRgEo7tJjV6DAb723293U0kX/yF1QaHZHTF6L18sNk6Ec1YjNTpdXT3/Fkp5wXh+bJ5lHz5NPHDlJdWYavXwALl92zEqgoK6KhvgYfXz8WLV+Dp6frnYn19vbiNeK5nl5eNNbXT/q53joZAyYbmzK1BHnJaeq2cvKuAT+9nEg/BcuSNVhscLpgkKZuK0o5zE9Q89PVfubF3xurPLUCvYP3FoS9gzbCfF1jsj+p2fczL72J3sMTw0A/h3b/hI+vP2nTZzJz3iIEQeD65fNcOX+a5Ws3YbPbaW6oG7qnp+Tgrh8IDAohfILxiraO0D44pH3r2mU2PvfKZLL0SKoqStHq9AQGh1JfW/3A30uL7pI6LUvUNIzkvtOsy6fJnDf+3ShDfx9XTx9i3oqNT7xbZ7fZaG9tYv6yNQSFhHH1/Alu37zK4lUbuXr+BLnXLxEVm4hcfv/paWd7KzcunWXdtid3qfs4LF62ksXLVpJ95SK3bl5n0ZLlnD15nCXLV4kSfHgYm83G4OAgr775Lk2NDRzct4v3PvwHmhobEGQyfvfH/4ZxcJCff/iaqJhYfHyezGuVzWKm/vpJgtLnO0/1AlPnEJg6h/bSXLoqCwhIcbj69wyNwTM0hoH2RtqKbt63ELQMDtCYc47QGU92Z24kdpsds9HIvHUv093eTN6FIyzZuh21VsfSZ99FpdbS3d5M7vmD993zE4uigrskjzjV0+s9+OD3f0Kr1dHc1Mi+3b/w9nsfTSoExlh9m81uwzg4yLZX3qa1uZFTh/fyyju/p6W5EZkg8Mb7/4jROMj+X78jPCoGL+/Hr3ObxUxrzhl80+YiU6rwjE7BOzETEOguyaGz8Dr+0+/dLTD1dtJVfIPAOY7Fvd1mo7emiJBFz6DQedJZcJWe8tt4J2Q9cd7tNjtmk5H56x31nXvhCEu3bXfctQkIYfGWN+nr7uDO5eMEhMfQ3liDSqPF2z+Y9qYni3+4pyWGfqsSrczClsBqOi0qZnm1cqA1eszvVxq8qDR4EaruZ66343teChO+ShPfNDisKZ4JrCZU1U+j6cnNOO02G81Njaxas56w8AhOnTjKtSuXWLxsBUuWrWTJspVcvXyRnBvXWbx0+RM//2EMGgyUlRbzwR/+hFqt4cCenRTk3yZtmuMU6OSxw0RERhMROXbZTIbx8j0SYej0bVIIMvpT1oHFhK7yEjJDFzatDwCa2ptYPAKxejhOJgS7HdlABwMJKxDsVnQlJ7HqA5AZe7Er1Nh0fsh7J29m97jYbFbKC/NY+9xb6D29uXX5FEW52aTNXPDYzxhvHDUbjcxbspKYhBQqSgu5cOowG5591fm7msoygkPDJ2zCOYxMJuOVtz7AODjI4f07aW9rIffmNbY89zIhoeHkXL/CxbMnWLlu86R0RmMcHKS2spTn3voIlUrNuWP7qCi+S1xyOotWbcJms3HtwkmqygqJT8ngxsXTLFrlWjPp0Yw3hos5bxgmL/cmy1atJSk5leLCuxw/cpAXXrk3dtZUV5F/+xYvj7EIfBysFjNlV48RmbnoPqsguH/OqPcJJHPDm8gVSroaqym9fJTp6ycfJmx4nhw0xjx55brN2Gw2Lpw5TllxAanTMomJSyAxOQ25QsHd2zmcPnqArS8+eD3kbxWZIBDiJedE/iCNXVZWp2uYH69GJoBWKbDjUj+hPnK2zdLx19O9LE7ScL3ChFmcMJpjp3EyPx6+TKnV6YmJT6KluRGdXo9MJnPsiE/LdAbL1Ht4EhIeiUarQ6FUEhUbT1vLxF2Qj9SOjU+isb6Gnp5udn73Jd9/8Rf6+3rY/cNXk9+JHEVTfR1V5aV89/mfOXFoL/W1VZw87LDFNhgGaGlqJDruntMCvYcHfSPMbvr6ep1pnygarQ7DUL4M/X3Oe0Udrc1cPnmA/d9/Ql1FCTcunKSu0mHiaTYZOX90F9PnLplQ/Bqdhyd6D0+ChmIAxcSn0N7SjI+fP+u3vcLWV94hLikNT697E9z+vh5OHd7N0jWbn2jiOxFS0zIoLXYcxzc1NXJo324++8t/UlJcyMnjR1xyVD8SD08vEpJSEASB0LBwBEHAYBigqCCfmNh45HI5Or2esPBImhsbnujZdpuN+usn8YpIwDMs9oG/e4Un0NtY9cDnOv9QzAO9WIbMNa1mE3XZxwhMnY3Wb+IxF9U6D4Ii452TfQQBs9GATK5ApXZMPrz9g9F6eNPf0/XASd7ok77JYLPZKCspIjnl3mJPoVA47+4Gh4Ti4+PrdOQyUcbq2/QensQmJCMIgqMdCIJjgl50l4iYOGRyOVqdnuDQCFqbH79vs9tstOWcRh8Why4kBgC5WosgOPpRj8gkTCMCqVsM/bTdPI3/9CXO01pTjyO/Sr2Xw4lIaCzGzolNhDU6D4LHqO+ReHj7OQKvd7XT2dpIS10lZ/d8Sd7FI7Q31ZF38ehjafVbHSYqBpuCSoMnYeoBPBVmXgqp4I3QUjzkZl4MrkAru/8Ep9Gox0thQiOzEKftocmkxWKXYbHLqBn0mHAgdg9PLzy9vJyxmpJTUmluvj88Slp6BqXFhRN6/sOorqrE29sHnU6PXC4nMTmF+jrH4vnyhXMYBgZYsfrBk3tXMF6+dXoPp7VGX18vOt3E7kE+gEKFxSMIRY+jnaga8xEsRozh9+6Y2pQ6rF6hIFdgV6ix6gORGbqQ97eh6G7A4+4BtFVXUPS2oKm66pp0PYSudkd78vDycdyNikumreXJ+vbxxtHSonyi4x1mdrEJKbSNeucqSguIS5q4Cedo1BoN4ZHRVFeW09baTMhQnL3E5DQaGyZ/0jGaxroqPLy8Hdd35HKi4pJoabqnI5PJiE1Mpbq8BLPJRFdHG8f2/sCuHR/T2tzAmUO7aWtxbZii8cbwqaDgzm1neKaklDSaGu+VRWtLMyeOHmTr8y8/4JPicbDZrJRdOYZ/ZBJ+4Q4zZ6Vai8nguB5gMvSjHBq35UoVcoWjD/YJjcZutzlPAU0D964TmAz9KLWP3/Ybh+bJ337+Z44PzZNPHL53Z1Emk5GYnEZFqcMsXKPVOc2mU6dlPdH4+SR4enrSM2I+3tvT4xJHW72DNnoH7c7g50WNZoK95fQO2ihucli7NHZZHXG3VQJhPnJWpGr4/UpPZseqWZCgZmaMil6DHU/NvcW4p0ZGr8E1EfAmfLJnNpucHoPMZhP1NZXMnLeIgf4+dEMmhpXlJfj6O3bpIqNjybtxFYvZcY+psa6WjJlzXKJdN6T99of/5PzO91/8hede2z6hxvIw5i9ZMXQnEOprq8m7kc3qjQ7PcxUlRUTHJdxn6x0Tn8Sd3BskJKfR3NiAWqWelAknQHh0ApUld0mbMY/KkruExzgWl1teu+eg4uqZw4RHxxMRm4jVauXCsb3EJKYTGTexsAA6vQd6Dy+6O9vx9vWnoa4KHz9/DAP9aHV67HY7udcvkTJkn200DnJ8/6/MXrCcYJECnnd2tDtN+cpKi/EbCoL6we//0fmdowf3DQVBfnLvow8jITGZ2poqoqJj6Oxox2q1otXq8PTyora6irRp0zGbTDQ21DNz9uN7f7Xb7TTlnkft6YNf/D3nHKa+bucdvL6maud/m/q6nRP8wa427DYrcpUau81Kw/WTeEUkjrlgfBKCI+LoaK7DPySS/p5Oh0moWotpcAClSoMgkzHQ281AbxdaD29Uag1ypYqutka8/UNoqCgkKjlzUmkYprqqAl9/fzy97pliDAz0o9FokclkdHV10tnZgfcTnqSOZLy+TalU0lBXTVhkNF2d7disVjRaLR6eXjTUVpOUmoHZbKKlqZ6MGY/Xt9ntdtrvXETp4YNX3L37btbBAeRDd+UGmmtQejryYzMbab1xAp+UWahHBMWVa/SY+7qwGgeRqzUMtjWg9PCZUP6DIseu74G+bjQ6T2QyGYa+Hvp7OtHqvUiesYjkGQ4nFe1NdVQV3iRz8fpH6igEGwJ2zHY5CsFGpKaf6z2BfN1wr496I7SUnc2xDNoUeClM9FiUgECA0oAcO4M2OX0WJakeXeRgRwDC1P3k9U4sILKHhweenl50tLfh5x9AdVUl/gGBY/Q1ARN6/sPw8vKisaEes9mMQqGguqqSkNAwbufmUFVZzouvvinaqcN4+fYPCOTunTzmLVjM3Tt5JCQmTVhDMA9iF2SgUIHNgqK3CVNwKsq2chS9TQwkLL/v5NDiE46m9qbzfp98oB1TUBIW30iMYUN33npbULUUMRgz8Sshj4tW50lPZzuDhgE0Wh1N9dVj3vl7GOONo709XTTV1xAaEU1jXTVeI/ovk3GQpvpalq3dMqn0Gwb6kcnkqDUaLGYztdWVzJyzAJPR6Hy/a6srRQkmrvfworWpAYvZjFyhoLGuGv+gEHq6OvHy8cVut1NbWYq3rx8qtZqX3/uT87fH9vzArEUrJuWNcyzGG8OnAg8PD+pqq4mMiqG2ugofX8d71NPTzf49v7Jh09b7rik8Lna7naqbZ9F6+RCSdG/M9QmNob2mmNDkmbTXFOMzNCcwDw6gUGsRBIG+jmaw21GoNCh81Rj7ujD296DU6umoKyN+7uM7ZlqwZAULRsyTc29ks3rDM3R3duDt64fdbqeyvBSfoTz29/U558VV5aUTyvvjEBoWTmdHO11dnXh6elFYcJct256b9HP7jXZ6DDb89DI6+m3EBCho77PR2W8j2l9BTbsVX70MuUzAYLLz/ZV7C+nFSWpMFjs5VSYEAfz0cryHzDnTwpXsz3HNBsSEF3uGgX6OH9gNOHam41PSiIyJ5/TR/bS3tiAI4OHlzdJVDre1ao2W6TPnsufHr0GAyJh4Z0iGiWgfG6GdkJJGVMz4ts0D/X3s/vFrTCYjAgJ3bl3npTc/uM+bnysoKy5gxpz7TTqiYuOprizjhy8/RqFQsuIJzSMunzxAS2MtxkED+777mGmzF5E6Yx6XTuynoug2ek8vFq5+5qHPqC0vorWpDpPRQGVJPgDzlm/AN+DJ7jbOX7aGs8cPYLNa8fTyYcnqTZQV3aHwTg4A0XHJJKY6BuHC2zfp7e4i9/olcq87zIHWbX35PqcWT8LBfbupq6nGYBjgkz//OwsXL6OyvIyOjnYEQcDLy5vV6zc++kET4ND+e9qf/vnfWbB4GdOmZ3Hs8H6++eKvyOVy1m96BkEQyJo5x/H55x9jx+HUIDDo8cvZ0NFMT10ZKk9fqs463vGA1Dl01xRj6usGQKnzIHjIpK+3sYqeulLHKZBcQeislQiCQHddBQPtjVhNg/TUOu4yhcxYhsb74Z1o3sUjdDTXYTYOcnb3FyRMn0d4fDr5V09w6eB3CDIZ0xasRRAEOlrqKbt9dejelkDa3JVOJwhpc1Y4Qy8EhEUTEBYz6TLPyJxBceFdUlLvdwJSV1vDlQtnkcnlCILA6nUbJ3xPEsbv26xWK+dOHOLXbz9DJpOzfN1mRyiTzFmcPXGIX3d8hh07yWnT8Q98vJNUY2cLA/XlKD19abzg2Pn0SZ5Jf0Ml5p52EAQUWg/8pjk8lvVWF2IZ6KW7NI/u0jwAguauRaHR4Z2QRfPVwwgyGXKtx31mn+ORe+EInc11mIyDnNn9BYnT5xERn86dKye4eOA7ZDIZGQsd9d3Z0kDl3RsIMhkCAmlzV6CahFmZTmZhfUAtADIBSvu9qB0cfyMsXttDsr4bm93hrOV4ewQgUG7wIlzTzysh5diBmkEPqgcnvlu7au0GDu7fg9VqxcfHlw2bnuHokQN0tjvqw9vb2+n115WEhkeQlJzKt19+iiCTERwcwvSsmfzH//gXvLx9+GHHlwAkJqewcPEyl+uPlW+73c7+vTu5nZeL1yRDLwiWQXTV2WC3A3bMPlFYvMPwvPULdpUOfYkjrIfZOwJTaDo2jRcWr1D0Rcccn/vHOU0+n5Qrpw/S2uAYRw/88FfSZy5CpdZw68opjAYDF47txscviGUbHfk7+OOnWMwmbFYr9dVlLN3wAt6+AaTNXMCZgz8hk8nQeXgxd9mTu+UfaxyNiksk+/xJbDYbcoWCRSvvPbe6ooTwqFiUyskFDe/v7+PkkQMOB052OwnJqcTGJ7Ji7UaOHNiFIAio1RpWDc1RmpsaOLxvJ8bBQSrLS7l2+Tyvbf9wQtqBIWFExydz8Jevkclk+AUEk5SeyfG9P2EeclTi6x/EvOXinFw/yRgO8PnH/4nRZMRmtVJeUszzL7+Of8DEPDaOpb1mw2bOnDyGzWZDoVCwZr2jzK9eOs+gwcCpEw6fCzKZjNfffv+xtfram2ivKUHr5Uf+yV8AiEifR2jyTMqyj9NaWYRa50H8fEc5d9SV01JxF0EmQyaXEzdvjdNcOyprCcUXD4LdTkBMyhN74hyLU8cOYjIaATv+gcEsW+XYFLx96zpVFaXIBBlqjYaV611rRjyMTCZjzbqN/PLjd9htdjIyswh8zLH6UZy4a2DLDC1ymUDXgI1DeQOYLbAxU8t7Sz2w2uFQ7sMXbnY7HL9r4OV5egQBbteaaXOBJ04AwW4f/4iwsdvkmvPDCTC1PnruRyZzn3pnv3ie3h6FTuW+4NS+etd4HJoID2kConOo0LWmKU9KjKeLzLImQHqE6y9gPy59xqlz7DCaoyXimKg8Dml+7ivzZ/7lhNu0K/8yifhwk8SN3Ytbx9H//egkPHdOkvdnR7pN20Pjekdkj4s7x/CeQff1qQABHpNbDP+9cqGs7dFfEok50eJez3kYnlr3zRl9F/yz27QN1/91Qt36lIVekJCQkJCQkJCQkJCQkJg6pMWehISEhISEhISEhITEU4i02JOQkJCQkJCQkJCQkHgKkRZ7EhISEhISEhISEhISTyHSYk9CQkJCQkJCQkJCQuIpRFrsSUhISEhISEhISEhIPIU8NPRCR7/FbV6jtW50ISxW4NrHwWZzn6Puzn6T27T9PV0b8/DvhYe1v6nAZHFNDJeJoFa6r427E6sb27jcjWFl3IlU5lNPY9eg27RDfTRu03Yn7hxP3Dlv+i0j9W1Tj8XqvnmTh3pihS6d7ElISEhISEhISEhISDyFSIs9CQkJCQkJCQkJCQmJpxBpsSchISEhISEhISEhIfEUIi32JCQkJCQkJCQkJCQknkKkxZ6EhISEhISEhISEhMRTiEsWez3dXXz/1aeueNQT093VxZeffvzA53/9838wMDAwJfpffPoX0XXG0v3qswfzPZKa6ip2/fKjKPq9PV3s+u5zUZ79OIxX7rU11Xz+yV/46rO/Yjabp0x3KnD3uw7Q3d3Ft19+8sDnJ44cpL2tVVztccr+yMH9tLWKoz2e5sf/9e9TV+bjtPWfvv+GpsYGl2u56/3+W0jD4/SrYmq7M9/urPe+nm72//iFW7Tdnffx9K9fuyrKGDZSd6zxZCpw57zpb7Gup0r7aezbJvrs9vY2fvj2a7767K989tc/c/TQgQlpf/35X5/4d5cvnOOT//p3dnz5qfN/g4Piei9WiPp0CYkppiD/DgsWLiY9Y7q7k/KbYs2GzW7T3rD5GbdpS0hITA02mw2Z7LdjjHTj2lXSp01HqVS6OykSEk8NgwYDGq2Wk8ePMmfufBKTUwBobWkGwGQyIZfLkctdHxpqcNCARqMFYOacecyZt+C+v5tNJmQiabtssWez2zh2aC+tzU34+QeyZuMz3Lp+lcryUiwWC6Hh4axYs1GUWCw2m40D+3bT3NREQEAgm57ZBsC1q5eoKC9HoVCwZetz+Pr5uVzbqb93N01NjQQEBrL5mWdpa23h5PGjmM1mFHI5L7/+Fmq1a2PJ2Ww2Du7bTXOzI98bt2yjrqaa0yePoVAqiYiIcqneA/p2G2eO7aetpRlfvwCWr91MV2c7V8+fwmw2odHqWLZmEzq9hzj6o8o9MiqaosK7VFaUU1FexpZtz02J7uZnnqWmuorTJ4+hVKqIiIikq6uTF15+TRztMd71nBvXKCstwWazsfXZF/APCHC59sg0HDmwh5bmJvwDAlm3aSt7d/7I0uWrCQ4NE013WHt02f/60/esWLWW0DBxtMfSHMZsNrNn5y8kpaSQNWOWKPoAdpuNQ0Nt3X+orYvFWPn9/JM/k5Y+jYqyMmQyGes2buHc2VN0dXQwd/5CZsyaPSVpmJaRSXlpCVabjW3PvSjKez5WWTfU13Lu9ElsNhshoWGsXrcRhcL1e6Xj5TslNZ2K8jKUCgVbtj0vylg2lnZtTTWnThxFqVASERklWr82rH/h+AE62prw9gtg8arN7Pvxc2ISUmmsrSJ95lxiE9NE0x6d90sXzlFWWoxMJiMmNp6Vq9eKoj2WfmRUNH29vfz43TfodDpefeNt8XRHjSdffPoX3nrnA3Q6HY2NDZw9dUIU/dF5np45gzt5uWx7/kXAYZl07epll79vo3VnzZ7LzevZPPvCy5QWF7F/7y7+2//y/8Jut/P5J3/moz/+k6j6Gzc9w46vPue5l17B3z+A/Xt2ERUTI8p4Mrpvy5iexe3cHLa98DIAVZXl5ObcYNvzL7tce3S+Z8+Zx8ljRxx/s9tpa23h//n/+f+65Nmbn3n2vk2S/v5+8u/kkX87l5mz5jJj1mz6+3rx9PJyficwKBiAzo72oTE9lelZMwgICHyk9qH9e2hpbsQ/IJANm7fdpz3Q38/d/NvcvZPHjFlzyHxIvXZ0tLN/z04Sk1PImJ6F/yO0nwSXjVZdHe2sWreJsPBITh49wJ3cm0yfMZu5C5cAcPzwPqoqSomNT3KVpJOOjnbWb9pCRGQURw7u59bN6wCo1Rre/eAj8u/kcerkMV546VWXawN0tLezYdMzRERGcfjAPnJuXONWzg22PvsCoWHhGI1GUXbnOjraWbdpCxERURw5tJ8b166QdyuHl197Ex9fPw7s3eVyzZF0d3awZNVGQsIiOH/yEAW3c6gqL2HN5ufR6nSUlxRy48o5lq7eJIr+6HK3Wq0kJCUTn5BESqo4k4KxdK9nXyE35yavvbUdHx9f9u8Rr9zHe9e1Wh3b3/sdt25e51r2FTZs2iJaGjo72lmzfjNhEZEcP3KA27duiKY1mgfa2lD+3aFpNpnYv2cn0zIymTY9U9w0DLX18Igojh7aT26OePkeL79eXt6888FHnDpxlMMH9/L6W+9itVj44rOPXb7YGy8NOp2O7e9/SM6N61y7elmUU93RZX3j2hVu38rhxVffxM/fn8MH9pKXc4NZc+e7XnucfKvVat773e/Jv53HqRNHRVlwjdevvfLGW/j6+rFvz06Xa46kp6uDhSs3EBQawaVThynOzwFArdGy+eXtomqPzvvN69mUFhfx/kd/RBAE0U2sxhrLPDw9efWNt9HpdOLpjjOeTAWj89zW1kpDQx0mkwmVSkVhQT6padNE121sbKC5uQmA2toaAgKDaGyox2azERYWIbp+bm4Oq9dt4PCBfcyaM4/BQYNoG4ej+7a2tlY62tsYGOhHp9OTfzuPjOlZ4miPyndDQz3vfPARAGdOHScuPt5lz865eZ258xZQWVFOXm4O7W2tpKVn8OIrb+A1tMCbPXc+P36/g/DwSGLj4sjInIFGoyE4JJR3PviIooK7TtPO6VkzSElNR6VSPaDd2dHOuo1bCI+IHBqbbzB77nyqKsu5k3eL9rY2UtOm8fxLr923uMy5nk3h3TsAaDQaXnrtLYJDQnn73d9RVFTA8SMHQRDImJ5FckoayjG0nwSX2UR4eHoRFh4JQHJaBg31tdTVVvPLd1/xw9efUldTRXtbm6vk7sPTy4uISMcpVtq0DOrqagGcHUVq2jQa6utE0R6tn54xncqKcjw8PAkNCwccA7UY5ieeXl7O07u09AyaGhvx9vHB188fQRBIm5bhcs2R6D28CBnqDBOS06mrqaCzvZUje39i9w9fknv9Mv29vaLpjy73+toa0bQeptvU2ICPry8+Pr4ApKa7foAaS3vku56U4jBFCA4Jpae7SzR9AE9PL8IiHG09NW0a9UNpmArcUefjae769ScyMrNEX+gNpyF8RFuvrxWvzMfLb0JiMgCBgcGEhoWjVqvR6fXI5XKXT4bHS0NScioAIaGhdIv0no8u65qqSrx9fPDz93emp06k9268fKelO/ry1PRp1Is0lo3Vr3n7+OA3NJ6kTxPXNF7n4UlQqGM8iUtOp6WxHoCYhBRRdeHBvNfV1iBXKDhycD/FRYWim1L+LYxlI8eTqdYeznNcXILTQqW8rJSEpOQp0fXx9aOtrZXGhnrmzJtPbU01dbU1zu+JrR8bF09gYBAnjh1m/SbxriWM7tsa6mpJmzadgvw7DA4O0lBfR2x8omjaY73jhQX5NDc1sWzFapc+e/evP3Hk0H4ys2by3u/+wMLFS50LPYDpmTN4/8M/kpKaRk11Nd9+/TkWiwVwzNkzZ8zkjbffZf2mLeTdyuHP//F/j6sdPjQfSkvPoL6uhr27fub4kYNkZM5g+/sfMX/RkvsWeuAw43zr3d/x1ru/46XX3nJ+rlKrmZ45g1fffIe16zdxO+8Wf/2vf5tw2QzjspO90caZAnD25FFefuNdPL28yL50HutQQboaYZS68MB/PJg+MfVVKrXzpRGT0bpGo7i7jw/ojypUpVKFr38Az7z01tg/cLX+mG/d1OsajcYp0R1Le/hfcrmjKQsyGTabTexE3P9PEUyzx5ee+jofTzMiIpLK8nLS0jOmoAxGF7qYSmPnVz5ktigIAgr5vaFDEASXv3OPToOY7/n92mqNhkGDQSSt0crjvN9TMJa5s18bS38YxRTcWRutLZPJeeud96muqqS4sICcG9dEM6UcS99dY5kAyGQy7HY7gGhztrG0QSA1fRo5N66h1WoJDQ1z+dWX8XQjI6OoKC9DLpcTExPHoQP7sNttrFi1Zkr07XY77e1tKBVKjIMGGLUwcKX66H9Om57Fnp0/oVAoSE5JFe1e7Fj5bm1p4eL5c7z+5vZJ6Y717KUrVpJ3K4eTx44QExtHRmaW8wBmGE9PT6ZnzWB61gy++PQvtLW2EDJ0FaW7q4s7t3MpLMgnKCiExUuXj6s+Oi2Ll63gTt4tTp84RnRMLNOmZzmf+zh0d3VxNz+PooK7BAYFs3Dx0sf+7Xi4rFZ7e3tobHDsOJYU5hM6dMqn1WoxmUyUlRS5SuoBenq6nacLBXfzCR9a4RcV3HX8f+FdwsJdfxw/pn7+HcLCw+nr66WxwbEzaTQaRZmYjNQtLMgnOiaO7u4uOjs7HJ/dzXe55kj6entoHtp9LSsuICgknEHDgPMzm9VKZ7t4HhpHl3tEZKRoWg/TjY6Jpauzk+6uLuDeeye69oh3fSrp7elxnpQXFdx1nuhPBe6o8/E0Fy9bgUaj4cTRw6Knobenm4YRbT1cxPu47mpXfytpGF3WISFhjn61o+NeeqKiRdEeL9/OsUzE9jZWvzaV40l/Xw+tTY6xo7KkwHnKNxWMzntQcDBGo5H4hERWrVlHy5DzhqnSj4iMRKVSYzKJu+Aeazzx9vahucnh5be4qHBqtIfyHBkVTVNTI3m3ckgRwYTzYbo3rl0lLDwCnV6PwTBAR3s7AYFBU6J/PfsK/v4BbNn2nNOMVwzGGkc8PD3Re3hw9dIFpolkwglj53v/3l1sfmYbOr3e5c8ODAxi9dr1vPfhH4iMjub82dN8+dnHVFaUA1BRXuYs576+PgwDBjw8veju6uKn73ew+9ef0Gg0vPHWu2x97gVi48Y2M+3t6XbOhxxlGklAYBArVq9j+/sfEREVzcVzZ/jmi0+oqix/aD66u7r49cfv2Lf7FzRqDa++uZ0t254nJnbiJq7DuOxkz8fPnzu3bnLq6EH8/APIyJqF0TjI919/il7vQXBIqKukHsDPz59bN29w5NABAgICmDFzNjk3rjE4OMhXn/0V+ZCDFtH0/f3JuXmdwwf3ExAQyKw584iOieXEsSNYLBYUCgWvvP7WmPa+k9L18+dWzg2OHj6Af0AAq9asJyQklN2//Ohw0BIZhclkcqnmSLx9/Si4ncP5k4fx9fMnPXMWEdGxXDl3ApPJiM1mZ1rWbHz9XXfJdCSjy33GrDkcP3pIFK2H6a6et4CgoGB++ek7lEoVoSI6KRnvXZ9KfP38uX3rBieOHMA/IJDpM2ZRUV4yJdpj1XlZackDp8xia94cKvNVa9dz+OB+zpw6IcousDMNo9p61szZlJeJU+YPy+9U4c40jC7rlWvWExoezoG9O50OWh52yX5S2uPke3DQwJeffYxcruCZbc9PifbqeQsICQ1j588/OBy0REWJuvjw8vGj6E4Ol08fxts3gORpMyi6c1M0vZGMzvvipcvZ9fMPWKwW7HZEdc4ylv6MWXOQy+X8+uP3zrt7ouiOMZ6EhoZx9PABLpw7S1S0OJsaMHaeZTIZCQlJ3Lmd63Q+NhW64HCkETm0iRMUFEx/f58oFhuj9WNi49i3ZxdvvfM+arWaiKhoLl88z5JlK1yvPcY4Ag7zQ8PAgEsdgjygPSrfWp2Onu6u+0IeDN/hm+yzh+sUQC6Xk5o2jdS0aXR3d2EYCplUWVHOqeNHndYiK1atwcPDg56ebpYuX0VYePiYWqPx9fMnN+c6xw7vx98/kMyZ9+6vy+VyUlLTSUlNp6e7C8MIC5GRd/YAtj73EoLMcSo4+gTSFQjDx/Vj0dFvGf+PIqNVud716OMylWZpo7HZ3FbkdPaLtzB8FP6erjfXmEqGL5Xb7XZOHD2Mr5/fA251x+Jh7W8qMFlENvl8CGqla9r4F59+zPMvveK8M/m3jtWNbVwuc1/f5k7+nsv84//6d95+93eiOup4HJ7UO2Jj19ReKxhJqI/GbdruxJ3jiTvnTb9lXNG3nTx2hOCQEDIyZzzR736r44nF6r55k4d6YoUuxdmTkHABebdukn87D6vNSnBwqHPHTEJcfvrhWwKDgv5uFnoSEhISEhJ/K3z71WcolUqWi2iZIuF+JnyyZ7FY2PXTDqxWK3abjfikFOYvWsaxQ3tpaWpEJpMTHBrKijUbkcvldLS3ceroQVpamliweDkz5zzcbfXjnuxZLBZ++PZrrFYrNpuN5JRUFi9dTnVlBWdOnwS7HaVKxcbNWx87NtFEd6gOH9hHeVkJOr2e9373hwk940lO9gYHBzl2+ABtrS0gCKzfuIWqynJu595CO7QLvHTZSuISHs+70qNO9ozGQS6cPEJnRysgsHT1RuqqKyi+m4dG69Cbs3AZkTHx9PZ0sfPbz/H2dZR5UEgYi1euH/fZkznZqygv49Txo9jsNjKzZjJ/4eIJP2uqtZ90J9Zms7Hjq8/x8PTkhZdeJefGNW5cz6ars5N/+G//yxPv/o93stfb082xQ/sZGOgHICNzJjNmz6WkqICrl87T0d7Gq2++64yrV3T3DjeuX3X+vq2lmdfefp+g4JBxtSdysueKNjYZXPGuPWwn9uih/Y786fS888HvATAYDBzcu5Pu7m68vb3Zsu0FNFotZSXFXDx/BkEQkMlkrFi97pHe4ya6E+vONgaO9/6bLz/D09NzQmEHXFXmNdVV7N31M97ePgAkJqewcPGyh2r/PZa5K7QfdbJXkHud0oI8BEHAxz+QRSs3Ok2qrp0/SVnhbV778J+HvnuN0oLbCDIZGo2OhSs34OHlPe6zJ3qydz37Cnm5txAEh9fZjVu2ihJPcTQ9Pd0c2r+X/v4+QCBrxkxmTyC0x2THk8MH9lFbU+10jLJhy1aCH9KHj2QyJ3t/7++6O7Wf5GTvxrWr3Mm7BUBgYBDrN2/l5LHDNDc1Yrfb8fXzZ8PmrY995ei32rc97GTv6KH9VJSXotPp2f6+wyy1uKiAKxfP0d7Wxutvv/eAs5ae7m6+/vxjFixe9kiLsCk/2ZPL5Tz70huoVCqsViu7ftxBTGwCyanTWLtxKwDHDu2l4E4uGVmz0Gi0LF25loqy4olKjpuO4ftwVquVH779irj4BI4fO8xzL7yMf0Agt25e58qlC2zcstWl2qPJyMxi5uy5HDqwR1SdYU6fOEpsXDxbn3sRq9WK2WymqrKcWXPnMXfeQpfrXT13kojoOFZvehar1YrFYqauuoJpM+Ywfea8B77v5e3Dc6+96/J0jMRms3Hi6GFefu1NPL28+ObLz0hITCYgUDzbc3dq37yejb9/AMahuzPhEZHEJyTx4/ffuFRHJpOxdMVqgkJCMRmN/LDjC6JiYgkIDGLzthc5dfz+u5Ep6RmkDLmHb2tt4cCeXx660JsoU93GRjIV9Z2ekcmMWXM4fGCv87NrVy4SFRPLvAWLyb5ykeyrl1i2YjVRMbG8nZiEIAi0tjRzYM9O3v3wjy5LyzDubGPD3LiejX9AACYRPEQ+SZkDRERE8ZxIMVuHedr7tYG+Xopu3+SZ195DoVBy7uheKksLSUjNoK2lEdMoz9J+AcFsevFtFEolxfm3uHnlLMvWuXY87+3p4eb1a7z34R9QKpXs3f0rhXfzycjMcqnOWMgEGStWrSUkNBSj0cg3X35KTGy86PU9ejwBWL5yNckixqkdzdP+rv+taPf29pBz4xrvfPB7lEol+/fspKggnxWr1zkX92dOHuPWzWvMWyDe4utpL/NpQ+PJkYP7nJ8FBATyzLMvjuvM7ezp48TGJbgsDWMxYW+cgiA4V/82mw2bzQoCxMQlIAgCgiAQHBJGX28PADq9nuDQMGQy197FG50Oq3PFLThdRxuNRjw8PVyqOxaRUdFotVrRdQCMg4PU1dY4bazlcjkajXj3FEzGQRobaklOn+7UU6vdfy+isaEeHz8/fHx9hy7iplMqoudXd2r39vRQXlbK9Kx7dvXBIaF4+/i4XEvv4UnQkFMllVqNn38AfX29+PkHOOONjUdxYT5JKekuTxNMbRsbzVTUd2RUNBrN/fkrKy0hPcMRyy89I5OyEseGmUqlcu6mm02mB2OhuAh3tjGAnp4eKspKycyaKcrzn6TMp4rfQr9ms9uwWizYbDYsFgs6vQc2m42bl88yc+Hy+74bEhHtDMEQEBzGQJ848VuH02Kz2bCYzXh4eoqiMxoPT09CQh39rVqtxt8/kN6huZNYjDWeuIPfwrv+t6JtH/1+e3g6F3p2u30oZJi49/Ce9jKPGGM88Q8IxM8/YMzvl5YU4e3tK6pzHJjknT2bzcbP335Bd1cnGVmzCQm950HGarVSXHCHJSvF9WI1nI4dX35GZ2cHM2bNISw8gvUbN7Pzlx9RKBSoVWre2P6e6OmYSrq6u9DqdBw5tJ/WlmaCQ0JZuXodALduXufunduEhIaxYuUaNC6YHPf2dKPV6jh/8hAdrS34B4WwYJljp7sg7yalhfkEBocwb/Eq1EOLzt6ebvb88CVKlZrZC5YSIoLL8N7eXrw878Wk8fTyorG+3uU6fwvap04cY/nK1aJ6WB2L7u4uWpub7mvfD6OkqIAtz74kcqqmHne9awP9fXh4OCader0HA/19zr+VFhdx4ewpBgb6ee5FcU6b3NnGAE6dODrl7/3Dyryhvo5vvvgEvYcHy1euEcU9+9Per+k8PEnPmsuubz5GrlAQFhlLWFQshXk3iIxJQKcff3O2rPA24dFxLk0POPI5d/4CPv6f/4ZCqSQ2Nn5cd+ti0t3VRXNzo6jhomD88eT8uTNcvnieqJhYlq1YJboZ69P+rv+taHt6ejF73gI+/fO/o1AoiYmNI2bo/T5ycB+V5WX4BwSwfJW4c/bfUpk/CpPJxPWrl3nhlTe4kX1FVK1JtWKZTMarb3+AcXCQQ/t20t7agv/QwHf25FHCIqJEjQc1Mh3b3/+QwcFB9uz8mdaWFm5cy+aFl14lLDyC7KuXOX3yOBs2bRE9LVOF3WajuamRVWvWExYewakTR7l25RIzZs9hwaKlCILAxfNnOHP6BBs2PTNpPZvNRltLEwuWrSEoJIwr506Qd+MqaZmzmDF3EYIgcOPKebIvnmLp6k3odB688s4f0Gi1tLU0ceLgLp5//X1UIgRJ/S1QVuq4pxYSGkZNddWU6ZpMJg7t3cmyVWsfK8BtY0M9CoVSlAmwxNC9mBEneInJKSQmp1BbU83FC2d56dU33Zc4ESgrLUGvm/r3fiQjyzw4JJTf/fGfUKlUVJSVsnfXL7z/0T+4JV1/zxgHB6mtLOW5tz5CpVJz7tg+yovyqSovYt228e9kVhTfpb2lkXXPPvm9zUcxaDBQWlLMR3/8J9QaDft2/8rdO7dJz5jucq3xMJlM7Nn1C6vWrBcloPgw440nS1esRK/3wGq1cuzIQbKvXGLRkoffSZX4+2DQYKCstJgP/vAn1GoNB/bspCD/NmnTprNh81ZsNhunjh+lqPAuGSLG25O4x+WL55g1Z57Lw7KNhUuCqqs1GiIio6muqgAg+/J5DIYBlqyYWu8+Go2GqOgYKirKaG1pdu6MpaamOwNJPi14eHrh6eXlzGNySirNzY3o9R7IZDIEQWB65kyaGlyza6H38Bwy7XNcLI1NSKG9tRmdTu/US5mWSWtTIwByhcJ5ohgQFIKntw/dXR0uSctIPD096Rlh7tLb0zNlpjdTqV1fV0tZaTF//fN/cGDvLmqqKjm4T9x7a1arlYN7d5KSNo2EpJTH+k1J4V2SU8Ux4XQ37nrXdHoP+obM1vr6etHpHgxAGxkVTXdXJwNDMYRciTvbWH1dDaWlxXz8X//O/j07qa6q5MC+3aLrjlfmarXaOTDHJSRis1mfujKfCu3Guio8vLzRaHXI5HKi4pLIu3aR3q4u9nz3Cbt2fIzFYmbPt584f9NQW8Wdm5dZsfF55HLXnzZVVVXg7eODTq9HLpeTlJzqDNQ8FVitVvbs+oW0aRkkp6SKqjXeeOLh4YkgCCgUCjKmZ9HY2CBqOuDpf9f/VrSrqyrx9vZBp3O834nJKdTX1Tn/LpPJSElLp7SoULQ0wG+rzB9FU0M958+c4rO//Cc5N7K5duUit25eF0Vrwos9w0A/xkHHJWqL2UxNdSW+fv7cvX2LmqoK1m/aNiVxVwb6+xkcSofZbKa6ssJx4dg4SEd7OwBVlRX4B4xtL/v3ioeHB56eXnS0twGOhuwfEOicoIDDFthVJyw6vQd6Ty+6Oh1lWl9bhY+f/33mTVXlJc4A6oaBAWw2x/3Jnu4uero68RzyYOdKQsPC6exop6urE6vVSmHBXRKSkl2u427tZStW8Yd//O989Md/Ysu254mKiWXz1mdF0QKH/f7Jowfx8w94pOfckb8pKS58ahd77nrX4hOTuHsnD4C7d/JISEwCoLOjw+l9r7mpEavFKsp9Rne2sWUrVvPHP/0zv/+H/8Yzz75AdEwsW7Y+J7rueGXe39fnLPPGhnrsdvtTV+ZToa338KK1qQGL2YzdbqexrprUrDm89O4/8Pxbv+f5t36PQqHk2Tc/BKC9tZmrZ4+xYuPzaMfY7HAFXl7eNNTXYx5Kk2NMnZp5g91u58ih/fj7BzD3MeKzTpbxxpPh+YPdbqe0pJjAKXCa8bS/638r2l5eXjQ2PPh+d3Y4NuHtdjvlpcWPvJM/WX5LZf4oXnljOx/84U988Ic/MXP2POYuWHxfQHhXMuHQC22tzZw4cgC7zY7dbicxOZW5C5fwX//3/4Gnl7dz9zM+MYW5C5fQ39/Hz99+iclkRBAElEoVb7zz4bhmfY8beqGlpZnDB/Zht9mw2+0kp6axaMkySoqLuHj+LIIgoNFo2LDpGXx8Hy8W10QXqfv37KKmugqDYQCdXs/ipcuf2KnAk4ReaG5u4tjhA1itVnx8fNmw6RlOnThKS0szAN7ePqzdsMl59+RRPCr0QntrMxdOHcFqteLl7cPS1Zu4cu4E7W0tAHh6ebN45Xp0eg8qy4q4efWi89Rv5rzFRMeNHwJiMqEXystKOXXiKHabnYzMLBYuXjrhZ0219kSC4NZUV3Et+wovvPQqN69nk331Mv19fej0euLiE5/IXHm80Av1dTX8+sMOx2bBUHtYtGQFVquFsyePYTAMoFZrCAgK5rmXHCZVtTVVXDp3hlfefOextCcSesEVbWwyuOJde5ir7IN7d1FbU+3M36Ily0lITObA3p309PTgNRQGQKvVkn3lEgX5t5HJZCgUCpatXCNa6AV3trFhnjSo90hcVeY5N66Rd+ums8yXr1pLeMTD7yL/PZa5K7QfFXohN/sCVWVFyGQy/AKCWbBy/X0ndj988q/O0AvH9/1EV3srWp3jLp/e04uVm54f99kTDb1w4dwZigrvIpPJCA4OZf2mLVMSeqGutobvd3xFYFAQwpCDjKUrVhH/mGGThpnsePLT9zscJ9V2O0HBIazdsOmxTcwms7n/9/6uu1P7SUIvXDp/luLCuwgyGcHBIazduIVfftiByWTCbrcTFBTM6vWbHtuE+Lfatz0s9MLBfbupGzGeLFy8DI1Gy+mTRzEMOOZNgcHBvPDy6/f97vKFcyhVKtFCL0x4sSc2j7vYE4OpOJEcjydZ7LmaRy32xGQyi72/ZyYyOLuS8RZ7U8FEFntPA08yOLuaiQ7Of+9IZT71PGqxJyYTXez9vePO8cSd86bfMlLfNvU8bLEnNhNd7Lnkzp6EhISEhISEhISEhITE3xbSYk9CQkJCQkJCQkJCQuIpRFrsSUhISEhISEhISEhIPIVIiz0JCQkJCQkJCQkJCYmnEGmxJyEhISEhISEhISEh8RQiLfYkJCQkJCQkJCQkJCSeQh4aQKbHYJmqdDyATi1+bJvxcKf7YpkbXdn+Vt3oupP2PveFuwDwcGM7cycGk9Vt2kq5O9vZb7OND7qxvvWa32YbC/aSwulISEg8ffw9hhmRTvYkJCQkJCQkJCQkJCSeQqTFnoSEhISEhISEhISExFOItNiTkJCQkJCQkJCQkJB4CpEWexISEhISEhISEhISEk8h0mJPQkJCQkJCQkJCQkLiKeSpdBN26MBe4hOSSElNc3dS3Mq//p//B//8v/6/3Z2MKWOgv5+dv/yI1Wpl9dr1REZFuztJonP+xEEiYxKITUxxWxpu595EqVCSOm36lGvfunkDpVLJtOmZU6Z54vB+YuITSUxOnTLN0Vy+cA6lSsWceQvclgYJCQlxKC0uwtfPn4DAQHcnRULiqcNsNnP00AFaWpoBO2q1hpdefQOVSiW6dk11FXt3/Yy3t4/zs+Ur1xAdGyeq7lO52JP4bVJVVUlgYBAbNj/j7qT8ppieNctt2jNmzXabtoSExNRgs9mQyX47hkilJcXEJyRKiz0JCRdiMBjQarXcuHYVnV7Pe7/7PQDt7W3O/mXQYECj1YqmDRAREcVzL736wHfE0gYXLvbKi/K5m3sNAF//IGISUrh94zI2mxW1RsuSNVvQ6vSukruP/Nt5XMu+DAgEBQUjyASqKyvIvnwRo8nIytXrSEhMEkUbIP9OHtevXgFBIDAoiBUr13D86CG6uroAWLt+I+ERka7XHZXvxcuWc2DvbkwmE4lJyS7XG01JwR3ycrIBAf+AQBYsXc2F00fp6+0GYOGyNYSERYimPzL/Xl7etLQ0YbFYaPrsr7yx/T2USqXoukFBwSxauowDe3djNptJTErmxrWrop2olhbe4U7ONQTB0c5kMoGmhlryb13HMNDHnEUrRD/lK8y/zc3rVxEECAgMwtvHF6VSxay54p8yjS57H19flCoV8+YvFE2zMP82OUP59Q8MQibcm3ReuXiWvp4eVq3fLOpk9O6dPG5cu4oABAQF4+PjK5rWWP2pQqGguamJgYF+Nm7eSv7tPOrr6wgLD2fTlm1Tkga1Sk1jYwP9/X0sX7lGNMuNoru3uXUjGwQICAhi3qJlnDp2kEGDAa1Ox6p1m/H08hZFe8yylytoamwQfSx71HgiZr8GQ+No9hUEQSAwMAhBJkOhUNDS1EhYRCQrV68TT3tU3hOSkrh04RyCIEOtVvP6W++Ipg33zyF0Oh3NTY3U1lRz5dIFtj7/Ir6+fqLrBgY5+rb4hESSh9rWv/1f/8J//3/8b+JojyhzHx9fmpsa+egf/glBEDCZTHz+1z/z4R//hFwuF003KCiYutoaPvzjnzAajfznv/6fvPrG20RGRfP9jq/YsPkZ/Pz8RdU3Go0kp6QybXomuTk3qK2pYcu251yqOczdEW3M28eHluZm3vvwj8jlcoxGI9988Ynz365mdL5bW5qdf+voaOfFV14nKjrGJc/evPVZ598sFgvFRQXczr2Fh6cnW7Y+R39fH17e9/pwf/8A539/8+VnhIVHMD1rBlHRMY+MqXd3VL+18Zn7tUuKCrmTl4OHpxebRvxtLL79+nPCwsLJyJxB5GNoPwkuWex1trdy+8ZlNjz/BhqtDuOgARDY+MKbCIJASUEe+TnZzFm80hVy99Ha2sLlS+d54+330Ol0GAwGTp88Rnd3F2+9+wGdnR38+N03xMT+CYXC9QeZba0tXLl0gdffetepf/zoISKjonn2hZex2WyYTa4PnD1Wvg8f2MuMmbOZNj2TnBvXXK45ko72VnKuXWLry2+h1eoYHDRw6cxxMmbMITQ8kt6ebg7v+ZmX3/6dKPpj5b+spJimxgbWrN8oiuZ4uof272H23HmkpWdw6+YN0bQ721vJvX6ZLS++6Wxn2RdOMdDfx+YX36Cro50TB3eKuthrb2vl2tWLvPTadrQ6HYMGA7k54r5rw4xV9jevZ4uq2d7WyvWrF3lxRH4vnDkBwMWzJzGZTKzesEXUIKttrS1kX77IK2++48z3LZHa93j96eDgIG9uf4+ykmJ2/fIjb7z9LhsCg/jmy89obmoiOCRE9DT09fXxxtvv0t7Wxq5ffxRlsdfe1sr17Eu88Mrbzvo+eXQ/KenTSU2fTsGdXM6fPs6mbS+6XNudY5m7x5O21hauXrrAayPG0TOnjtPb08Nrb70r6kbKWHn/4duveemVN/D08mJwcFA0bRh7DnHm5PH7Fl1TqTsVjP2+7aOmuoromFjKy0qIjYt3+aJjLN0De3fR1tZKd1cXwSGh1NZUExoWTm9Pj8sXemPp26xWvtvxJd4+PlzLvsKb2993qeYww23s1RH1ffbUcSrKS0lMSqGoIJ/E5BRRFnpj5Xv4lKuspJjsq5cnfCAy1rMBWpqbyMvNoaK8jLi4BFasWktIaCgAGZkz+OXHbykuKiQ6JpZp0zOddf3B7/+BivIycm5c4/jRw0zLmM606Vl4eno+oD1WmQ5r38m7RWV5GTFxCSxftZbgkFDn7+rqavjmi0+c/9763Iv4+Prx3od/pLK8jFs3r3Py+BHSpmUwLSMLjzG0nxSX9KBN9TVEJ6Sg0eoAUGu0DPT3cuLAL+z78Qvu3rpGV2ebK6QeoKaqkpSUNHQ6h/bwC5SSmo4gCPj5+ePj40t7mzj61VVVJI/Sr6mqImumw7xMJpOh1mhcrjtWvuvqaklNnwZAeoa4d5gaaquJS0xFO1TnGo2WupoqLp05zs7vvuDY/p2YTEZRFrowfr2LzVi69fV1pKSmA5A2LUM07Ya6amJHtTOA6LgkBEHA1z+AwYEB0fQBamuqSExORTuUf7FMDsbCHXVeV1NFwhj5vX7lAkajkZVrN4q60AOHjX9SSuqU5Hu8Mk5IdLxjgUHB6PUeBAYFIwgCAYGBdHd3TUkaEpOTnZoD/f0u1RymrqaKhKSU++q7qaGepBRH+05Oy6CxvlYUbXeOZe4eT2qqq0hOfTDvySlpoptvjpX3iIhIDh3cR+6tm9htNlH1x5pDTAXu0oWxyzw1LZ2igrsAFN69S0pa+pToRkRGUVdTTW1NNfMXLqautpamxgZCwsKmRF/v4cGSpSv48btvWLlqrWj1UFNdRdKoNjY9awb5t3MByL+dy7TpWeJoj9O3dXS0c+b0CbY++8KEF5ljPfta9hV2fPU5fn7+vPe7P7Bm/UbnQg8gOCSED//4T8ydv5BBg4EdX35GW1sr4JizJyQm8ewLL/P6m9vp6uzk4//5bzTU1z+oPUaZ3rh2he+/+QJfP3+2f/B7Vq/bcN9CDxxmnG+/96Hzfz5DJ/cymYz4xCS2Pv8Sr7zxNt1dXXzy53+nseFB7SdFtDt72edPkJY1h6jYRJrqa8i9dlEsqbEZNf8SeT72N4PYE8+HYrez7ZW3RTlBlRifkZ2kHbsbU/LbISgkjJbmRlFt7P+WkMsdbVoQBOSKe++bIAjYRJ4Mj04DgN3+G3rP3TiWuXU8AZQqcUzxH8W6jZtpqK+jvKyUr7/8lO3v/s65AfA0I8hkzrZlt9uxWq1Tpp2QlMy5s6cwGAw0NTUQHRM7JbqRUdHcyrlBX28vS5at4NrVy9RUVxEZGTUl+gCtrc1odTr6+nqnTBMgPCKKnu4j1FRXYbfbCQwMmjJtk8nEvt07Wb9xi0tOrkaSPm06NquV3Fs3qamuImN6FnEJifdtHKlUKpJTUklOSUUQBCrKSgkIcNyRNQ4OUliQz53becjlcjZu3kpQcPBjaaemT8dqtZF36ya11VVMm55JbHziY29aGQcHKSq8S/7tXORyOes3PUNg0ONpPwyXbJmFhEdRXVbE4KDjCNM4aMBsMqLXOyqwrOiOK2TGJComlqKiAgxDJxrDx6jFhQXY7XY6Ozvo6urEb4RNriuJjomheJR+dEwsuTkOcz6bzYZRBDOQsfIdERFJ4d18AAryb7tccyRhkdFUlBYyaHDoDw4aiIiOJT/3nhlj2wibbFczXr2LzVi6YWERFBcVADjLXwzCIqKpLCti0HCvnU01kVExlBYXYhiu9ykqd3BPnUdExVA2Rn6jY+OZPW8h+3f9hMlkFDUNUdExlBTdS4OY+XZXu/pbSUNEVAxlJUX31XdIWASlxY72XVKYT5gI96/BvWOZu8eTqOgYigvdU+dj5b2zs4Ow8AiWLFuBTqenp6dHNP2x5hAqlQqTSFYxD9P19vamqakRcJjXibWRM1aZq1QqQkPDOXX8KAkJSaKc6I6lGxoWTn1dLYLguJscFBxMbs5NUbx5j6XfUF9PRVkZ29/7kGtXr9DV1elyXRgaR8ZoY2nTpnNo/27RTvVg7HwfPriPjMysSZfzWM/W6/XMX7iY9373B2bPnU9xUSGffvw/uZZ9BYC62hrnWG61Wmlra8VryDvmgX27+fqLT+nq6mLTM9t47c3tTJueOeYhxlhlqtfrmbdgEe988HtmzplHcVEhX3zyX9y4duWReTm0fw87vvqM7q5ONm7ZxitvbCc9Y2ztJ8UlRzC+/oFkzF7AsT0/OMxNAoPJnLOYs8f2olJrCA2Ppq+n2xVSDxAYGMSCRUv44buvEQSZ8+6Il5c3O778DKPJyLoNm0U7bQoIDGLBwiX8+N03CDIZwcEhrFqzjmNHDnI7LxeZILBGBActY+V71dr1HNi7m6tXLonuoMXPP5AZcxeyf+f3CIKMgMBgFi5fw8XTx/n1u8+x2WyEhkexdNV6UfTHyn9UVIwoWo/SXbV2HQf37eHKpQvExiWgVrvebBcc7SxrzkIO7foemUzAP3Dyuz1Pin9AIHPmL2LXT98OmfWF3HfRWUzGKntvb5/RBx8uZWR+ZUP5HSYxOQ2TycSB3b+w9flXUIjkECggMIh5Cxfz8/c7kAkCQcEhzoHJ1YzXn04l7kyDf0Ags+ctYs/P3yHIHPW9dNU6Th09wK3rV50OWsTAnWOZu8eTgMAg5i9cwk/fO8bRoOCpq/Ox8m4yGuno6ADsRMfEPvau/kQYaw4xPWsGxw4f5OaNa2x97gVRHLSMpbtsxSp27/yZrz7/hNi4eNGcnI1V5pu2bCMlLZ19u3/l1TfenlJdLy9vwsIdzuQiI6MpvJvvktOUR+kHBATS1tbKxs1b8fT0ZMXqtRw5uJ9XXn/L5afqjnHE0cZkQ21sw+atpKVncOn8GVLSprlUbySj863VaqmprqKzvZ07ubcAWL/pGUInYDo7Xp0OExkVTWRUNEaj0WkO2dXZyfGjh7DbHSfY8QmJJKc4QimlpKazacu2x9psGK9Mx9JuGmGKOfrO3vxFS0hOSSM5NY0Nm7eKstEhPMwcpqpt0G22MiE+4kyYHwd3mgi502ymq1/cncSH4aMXP76JWJjNZhQKBYIgUHA3n8K7+Tz/0iuP9du2XnFPhR6Fh9p9Jrca1eQvgp84dpjgkFCmZ854ot8ZTFNnnjQapdx9bVwh/+24rx9J/6DFbdp6zcTb2N9SzNgnjdtqs7lvHJXJ3NfGflMmxiNwt8nvbxXrJNtZcVEB5SXF93mRfFzkbmxn7mSyZT4Z9KqJNTTpcpWExCRpamzgxLEjDAfn3DhiZ0dCPM6fPU1jfT2Llyx3d1IkJCQkJCT+rjh1/AiV5WU899Jr7k6KhMhMarF3N/c6pQV5CIKAj38gi1dupKQgj8K8G/T2dPHyu//o9BzYVF/D6cO78PD0ASA6PonMOYsmnYHRVJSXcer4UWx2G5lZM5m/cLHLNUZjs9nY8dXneHh68sJLr3Jg326aGhuRy2SEhoWzdsMmUdzZjuTwgX2Ul5UMBYr8gyga33/xF1QqFYIgIMhkPP/aO9y4coHC/FynV865i5YRFZvAoGGAE4f20NLcSHJaBotXiBcjyR11DtDT082h/Xvp7+8DBLJmzGT23Pku1TAaB7l48gidHa2AwJLVGwkODedu3g0Kb+cgCDIiY+KZu3iF8zd9vd3s+u5zZs5bTMbMeRPS7e3p5vjh/QwMOLweTsucyYxZczm8fzedHe3OtKnVGl7f/gEGwwCH9+2iuamB1GmZrFgtjvmuxWLhhx1fYbFasdlsJKekutxxgsViYdePO7BaLdhsNhKSUpm/eBl5OdfJvXmN7q5OPvjjP9+nW1dTxfnTJ7DZrGi0Ol549a0JaR89tJ+K8lJ0Oj3b3/8IcOy8Xrl4jva2Nl5/+z1CQu+ZurS2NHPi6CFMJiOCIPD62++LYubnrjY2FdoWi4XdP+/AarVit9mIT0xh3qJl1FZXcvn8Kex2O0qlilXrt+Dj64fVYuHEkf20tjSh0WhZt/lZUcxqDx/YR0VZKU2NDaKf7I01fhQV3uXi+XO0t7Xy1jsfiBZjr6O9jf17dzn/3d3VyaIly5k9dz45N65x6+Z1BJmMuPgElq9cI0oahvn4v/4dlUqNTBCQyWS8/Z44oYNgqC/79musI/qyxUuXc/jAPmprqlGr1QBs2LKVYBebtbpTeyye5v5lPMYay5YsW/HoHz4BRw/td7RrnZ53PnAEEDcYDBzcu5Pu7m68vb15Y/v79zkZa2yo54cdX7J52/Mkp4jX79hsNr758jM8PT154eWpW2xOdX3fvJ7N7dwcAKZnzmDW3Pkc2LuTjvb751Fvv/ehqOmY8Kygv6+Xots32fraeygUSs4e3UtlaSFBoRFExiRwdO8PD/wmODSSVZtfmFSCH4bNZuPE0cO8/NqbeHp58c2Xn5GQmExAYKBomuCoTH//AIxDjhrS0jPYPHQkfmDfbm7n3mLGrNmipiEjM4uZs+dy6MAeUXU2v/Cac2E3zPSZc8mcdf+iQq5QMHvBUjrbW+lobxUtPe6qcwCZIHPGbjEajXzz5afExMa7VPvquZNERMexatOzWK1WLBYzDbXV1FSU8uyr7yJXKDAM3O+GPvv8aSKi4yalK5PJWLJiNUHBoZhMRn7c8QVR0bFsfOZesNfzZ044JwUKuYL5i5fR3tZKe5t49S2Xy3nljbdRqVRYrVa+3/EVcQmJhA/dt3CVxrMvv+HU2PnjN0THxRMaHklsfCK7fvr2vu8bBwc5c/Io2154FU8v70mFBZiWkcmMWXM4cnCf87OAgECeefZFThw9fN93bTYbhw/sZcPmrQQFh2AwDIhi6+/ONjYV2nK5nG0v3qvv3T/tIDo2gbOnjrJp64v4+QdwJ/cGN7IvsXr9Fgryc1FrNLz53h8oKbrL5fOnWb/F9UGQp6pPH08rIDCIZ194iWOHD4qq7ecfwPahiY7NZuPj//o3EpNTqKmupLS0mLff+xCFQkG/SOE2RvPqG287XamLiVwu55XX33K+dz98+xVx8QkALF+5WtQYe+7UHs3T3r+Mx1SMZelD48nhA3udn127cpGomFjmLVhM9pWLZF+9xLIVqwFHeZw/e4qY2HiXpWE8blzPxj8gAJNx6q6yTHV9t7a2cDs3hze2v49cLmfnz98Tl5DElm331kFnTh13zqPEZFIzA5vdhtXi2P22Wixo9R74Bwbj4TU1DhtG09hQj4+fHz6+vsjlclLT0iktKRJVs7enh/KyUqZn3bszFJ+Q6Dj9EgRHcM5e8Tx5DRMZFT2lcXIehVKpIjQ88j536WLgjjofxsPT0xm7Ra1W4+8f6NK6NhkHaWqoJSl9OuAYHNRqDUV3bjF91gLkQyc4Wp3e+Zuq8hI8vb3x9Zucxz69hydBwY68qVRq/PwD7nMLbbfbKS0uICnVcalbqVIRHhEletgNQRBQqRz3O202Gzar1eUOWh7UsCEwvmOU4sJ8EhKT8Rzq93R6/QPfeVwioqLRaO5vx/4BgWN6YKyqLCcwKMjpzEKr1Ymy2HNnG5sK7Qfq22YFwRHxYNjTqtFoRK/3AKCirJSUoTaZkJRKXU2VKPe0prJPH0srICAQf5G8WI9HdVUlPj6+eHv7kJtzk3nzFzn7FP0k2tXfIqPfO6t1akKYuFt7NE97/zIeUzGWRY4xnpSVljjjZqZnZFJWUuz8260b10hKTp3UGPY49PT0UFFWSmbWTFF1RjPV9d3R1kZoWDhKpRKZTEZkZDSlJYXOv9vtdkoKC0gV0TnOMBOemek9PEnPmsvObz5GrlAQFhlLeNTD46K0NtWz/6cv0ek9mLVwBb7+rl1N9/b24uXp5fy3p5cXjWMEQnQlp04cY/nK1WO6SbZardy9c5tVa8QzYZxKBAEO7/4JBIHUjCzSMhwL3Pzcm5QU3iEwKJQFS1c6g31PBe6o87Ho7uqiubnR6dHLFfT2dKPR6rhw8hDtrS0EBIUwf9lqurs6aG6o5eaVc8jlCuYuWUlgcChmk4nbN6+yYdsr3MnJdlk6erq7aGluIiQ03PlZQ10NOp2HKF7iHoXNZuObLz6ls7ODmbPnuLTMR2r8tOMLurs6mD5jNiFh4eN+t6uzA6vVyq6fdmAymciaOZfUadNdnqbRdHZ0AAI7f/4ew8AAyanpzJ2/0OU67mxjU6Vts9n45bsv6O7qJCNrNiGh4axcu4kDu39GoVCgUql58bXtgMOqxXMoTTKZDJVazaDB8JuIwyY2RYV3nROfjo526mpruHjuNHKFguUr1xD6kHboCgQEfvnhWxAEsmbMImvmLFH1bDYbO778jM7ODmbMcvRluTk3OX/uDJcvnicqJpZlK1aJsonmTu2R/Bb6l/GYirFsNAP9fXh4OMKi6fUeDPT3AdDb20NpSREvv/42Rw/tFzUNp04cHXfeLCZTXd8BgYFcPHcaw8AACqWSivLS+65h1NXWoNPr8fXzFy0Nw0y4FRsHB6mtLOX5tz5CpVJz9tg+yovvEp+cPub3/QKDef6t36NUqairKufMkT0894Z49vBTQVmp445DSGgYNdVVD/z9xLHDTterTwNbX3oTvYcnhoF+Du7+CR9ff9Kmz2TmvEUIgsD1y+e5cv40y9ducndSpxSTycSeXb+was16lx7H22w22luaWLBsDUEhYVw5d4LbN64OxW40sOWlt2hrbuT0kb289PZH5GRfZFrWHJQq13k2NZlMHNq3k2Ur196Xt+LCuySnjt3WxUYmk/HOBx8xODjInp0/09rSQmCQa4PBymQyXtv+AcbBQQ7u/ZX21hb8xwk4a7PZaGlu4rmXXsdisfDL918REhYuegdus9mor6vl9bffQ6lU8uuP3xIcEjplwYifJmQyGa+85ajvw/t30t7WQu7Na2x57mVCQsPJuX6Fi2dPsFKksAsSjs3R8tJili5fCYDdZmNw0MDrb79HU2MDB/bu4oPf/6OoXh9ff+sdPL286O/v5+cfvsU/IEDU8Vsmk7H9/Q/v68uWrliJXu+B1Wrl2JGDZF+5xKIly54qbQkHUzGWPQxBEBy7+MCZk8dYumK16F5Vy0pL0OvGnzc/TfgHBDJ3wSJ2/vw9SqWSoOAQBOGe9U1RQb6oIS9GMmGbn8a6Kjy8vNFodcjkcqLjkmhtGn+FrFKpnZPQiJh4bDarMyC3q/D09KRnhBldb08PHp6eLtUYSX1dLWWlxfz1z//Bgb27qKmq5OA+x52HSxfOMTAwwMrVa0XTn2r0Q7tBWp2e2PgkWpsb0en1yGQyBEEgdVomLc0NU5qmqa7z0VitVvbs+oW0aRnOOC2uQu/h6TCnDHHsBMUmpNDW2ozew5Po+GRHnLuQMAQEBg0GWpsbuH7pDD9/9Rfu5t4g9/oVCvJuTljfarVyaN9OklOnkZCU4vzcZrNRVlpMooiXtx8HjUZDVHQMFRVlommoNRoioqKpriwf9zsenp5Ex8ShVKnQ6nSER0bR1tosWpqG8fT0JCIyCp1Oh1KpJDY+gZbmRlF03NXGplpbrdEQHumo77bWZudpdmJymjNGk97D02mubbPZMBmN9zk4kJgYFeVlBAWHOs1lPTy9SEpOcV6HQBCcwe7FwtPLseuv1+tJSk6hoWFqTnmG+7LKijI8PDydAb4zpmfR2CjumOpObfht9S/jMRVj2TA6vYfzSkZfXy+6oWsgTY2NHNy3i0//8h+UFBVw8thhUUwc6+tqKC0t5uP/+nf279lJdVUlB/btdrnOWLijvjMyZ/DmOx/wyhvb0Wg0+Po5rKFsNhulxUWkTNGm+YQXe3oPL1qbGrCYzdjtdhrrqvH2HX8n29Df57zX0NrcAHa7y839QsPC6exop6urE6vVSmHBXRJEDAa7bMUq/vCP/52P/vhPbNn2PFExsWze+ix5uTlUVpSzZetzT03sGbPZ5Ly/YjabqKupxNc/YMgTpYPK8hL8XGya+yimus5HYrfbOXJoP/7+Acydt8Dlz9fpPdB7etHV6fDa1FBbha+fP9HxSTTWVwPQ3dkx5AFSy+YX3uDld/7Ay+/8gfSs2WTNWUBa5sTMkOx2OyePHsTPP4CZc+73MFpTXYmfn7/TlG0qGejvZ3BwEHDEN6yqrHD5vaKBgX6MQxoWs5naKse7Ph5xCck01Ndis9kwm800NTbgN8k7k49DTFw8ba0tmM1mbDYbdTU1+IvQ/tzZxqZC2zC6vqsr8fVzOA4Y9jxbW12Jr79jfIuNT6To7m0AykoKiYiKeWr6eXdSVJBPavq9Xe7EpGTnzn9Hezs2q/UB52CuxGQyYRxyFmEymaisKCdwnNN8VzC6L6uurLjvbrTdbqe0pJhAEZxHuFN7NE97/zIeUzGWjUV8YhJ37+QBcPdOHgmJSQD87g9/4nd/+Cd+94d/IikljdXrNpI4YpPXVSxbsZo//umf+f0//DeeefYFomNi2bLV9Q6uxsId9T3sWKqnu5vS4iJS0zMAhtqcv3ODSWwmFVQ9N/sClWVFyGQy/AKCWbhyPSX5ueTfysYw0I9GqyciOo6FKzdQePsmxfm3kMlkyBUK5ixaSVDo+PbJEw2qXl5WyqkTR7Hb7GRkZrFw8dInfsZELtvXVFdxLfsKL7z0Kv/Xv/z/8Pb2cV6+TUxOeWxTiIlOGvbv2UVNdRUGwwA6vZ7FS5c/8eXXhwVV7+nu5NgBx+6L3WYjISWNmXMXcfroftpbW0AATy9vlqza4NyZ/f6Lv2A2GbHarKjVGjY9+8q4k+bJBFV3RZ1PhLraGr7f8RWBQUEIQ1erl65YRXxC4mM/41FB1dtbm7l46ghWqxVPbx+Wrt6EQqnkwsnDtLc2I5fLmbt4BWGRMff9LufqBZQq1SNDL4wXVL2+roadP+7APyDI+U4uXLqC2LgEjh/eT0hYONOz7l9IfvnJ/8RkMmKzOup724uv4R8w/mRhIkHVW5qbOXRgL3a7DbvdTkpq+oTMjB4WVL2tpZnjR/Zjt9mxYycxOZV5C5eSe/MaN69dYaC/D51OT3RcAqvXO8z6bl67QkG+IwxNekYWM2aPX+4PC6p+cN9u6mqqne144eJlaDRaTp88imFgALVaQ2BwMC+8/DoABfm3uXb1EiAQG5/g9Ko2HhMNqu6uNuYq7YcFVW9rbebkkQPY7XbsdjsJyanMXbCE8tIisi+fRxAE1GoNq9ZtxtvHF4vFwokj+2hraUat0bBu07N4+/iO+/yJBlV3RZ8+GS2tRsuJ40cc751GQ1BwCC+/+sZjP/NJgqqbTCY++fN/8Lvf/yNqjWPst1qtHDm0n9bmJmRyOctXrnlsE+WJBFXv6uxk986fh9JuIy19mqjzh5aWZg4f2Ifd5ujLklPTWLRkGT99v4OBgQGw2wkKDmHthk3OuYSrEEN7Mhsef+/9y0Rw1Vj2sADfB/fuonbEeLJoyXISEpM5sHcnPT09eHl7s2XbCw84ZzpycB9xCYmPDL0w2aDqNdVVXLt6eUpDL7iivp8kqPqP336FwWBALpezfNUaomMcntKPHNxHaFg4WTOfzFP/RIOqT2qxJyYTXey5AjE8qz0u7twhfthiT2wms9j7e+ZRiz2xGW+xNxVMZLHnKh622BObhy32xGaii72/dx622BObiS72/t55ksWeq5nIYs9VuHP+4E6k02338CQLD1cz2cXe3yvuLPOJLvZ+myO/hISEhISEhISEhITEU4602JOQkJCQkJCQkJCQkHgKkRZ7EhISEhISEhISEhISTyHSYk9CQkJCQkJCQkJCQuIpRFrsSUhISEhISEhISEhIPIVIiz0JCQkJCQkJCQkJCYmnkIf6hO4xmKcqHQ/gztAL7nQhbLHa3Kbd0uO+MAC/1dALPjqlW/W7BtzXxt0ZekHrRm13uqT/rbqFl5h6+o3uC3fh4cZwF+6cP7jVDf9v0wu/2zFZ3DdndOc46k763BnKRzWxOaN0sichISEhISEhISEhIfEUIi32JCQkJCQkJCQkJCQknkKkxZ6EhISEhISEhISEhMRTiLTYk5CQkJCQkJCQkJCQeAqRFnsSEhISEhISEhISEhJPIaK4rCrMuYxCqSIxY7YYj38oF8+fRalSUV5awopVawkNC5ty7XnzF06Z5jCXL5xDqVIxZ94C52fdXV3s2fkT29//SFRtd9b3MMNln5CQxP49O0EQ2Pb8i/j6+omu6c76rigrZdnK1YSETt17PkzO1QsoVSpMJiMhYVGER8VMmfZw2ZuMRiKjov//7P1lcFzXuu8L/2aD1OpWi5mZbcvMzBwndhw7iSGJk7Wy4Oy97zm37n3rrbfqfjqXzjn77L0XhBOHE0PMzCyTLGZmZnWr6f3QUluSJVmSG7Kc+atKVdytnv85xxjzGeMZ8DxEREbZVdcR9gXg1g2zflNjI9ExscQnJNlU7+b1qzg5OTHHAW3813AP925fRy53YsbseXbXdqR9caQ2QNrt68idnJg+yzHl7qj2NlDuShclEVHRqNVqu+jeumF+5tlzfzttzdFt3NH3cPfWNZzkTsyYM//5f2xlbPXc471ud1cXZ04dp6OjA6PRgLu7BzveePOFtO/fMfcVqc+xWXnZGdy9cQmV69N3e+X6rXh5+76Q/vNwXHxiEREbUFiQR3xiEgsWLXH0rfxmmDnPcWW9eOlyh2mLiIjYD6PRiETy29iMlJmRjo+vn92cPRGR3wK9vb24uLhw4/oVIiKjmDXH7Jg11NcDYDAYMBgMODlZPxWYRtOLQuECQHRcEotXrB3yvcFgwGg0IJfbJg2Z1Zy9/PR7lBdl46xQolSp8fDxpzQ/g7L8TIwGA65uHsxcuh6ZzPp5xW7fvE5W5hOUShVubu74BwYCkJ31hLOnjmM0GVm/cStBwcF20W5taeHcmZP09PQgEQS22miF6e7tG+RkZuCiUqFWu+EfEEh9XS3nTh0HIDwy2uqaA4xU310dbTy5c4k+TS9SmZzpC1ej9rDdytrwsvf28eHxw/sIgoTyslJ2vbXX5pr+gYHU1lRz5tRxBAQioqIoKS7i3fc/tLr2SPUNUJCXw6XzZ9BqNKzZsJmQ0DCraw+Qfv82hbmZuLioUKnV+PgFcP3CSUIjYoiMTbCZLoxc9qdO/EJ0TBwJibZb4RrNvoA5b93pk8dQq91YsmyFTfTv3LpBduYTlAP1PkjfVnoDz6t2cyMgIJDvv/kKP/8Aqior0Ol0bNy8lbu3b9HY2EBiYhKLrfzso91DYFAwFeVlaLUa1m3YTGhYuFV1AR7cvUluTiZKFyWuajd8/QNpbKjj6sUz6HV63D08WLF2k6XjtiYjtbXvvv4SP39/KsvL7d6X1dXWcPqkuT+JtKFtA3hw7xb52Zm4KAfKPYBffvoGH19/amsqiY1PJnXWXJtoj9TeHt6/R/qjh0gkErx9fNmy7TWbaI9W7iePHUEmk/HWvneRy60/bro7gl354duvWLZiNQGBQfT09PDNl5/w/of/yeraw5/Zx9eXg59/wp53DgDmXUmHfvqed9//vU11/QMC+PKzj9n37vs01Nfxxacf8fs//hNu7u589Jd/4533f2/1sh+pvr/6/BOWr1xNWHgE165cREBgyfKVVtUFuH/nJrnZGbgolajVbvj4BfD9V5+ya+97ALS1tnDm+BHLv63JSOO1Lz75u+X7xsYGPvjDn3F393ih6w7vH7VaLbnZmWSkPyY8Moqly1fS3dVFZNTT8bGfvz8Amt5eDn75KZFR0UxLnUFg0Nh29uG9WxTkPLVZPn4BQ77v02opys8mN+sJIWGRzF20bNRraTW9HPnhK0LDI0lMScUvwLq7hqzi7LU21VNVmseKV97GZDRy5dg3ePj4ExQeS2T8VAByHt6ivCCL6KTp1pC0UFdbQ25ONvvf+x1Go5EvP/vIUtk6nY79B35HZUU5Z04ds3onNZr2iWNHmDd/IXEJiej1epskMq6vqyU/N5u333kfo9HIN198gn9AIGdPHWfl6nWEhIVz7fJFq+vC6PWdfusCqQtW4eruSUtDLel3LrF4/Q6b3MNoZZ86Y5bNtkWMpnn65DHWbdhMcEgoV21U5qPVN5iTdL+5911Kigu5c/M6O3a9ZZN7aGqoo6Qgl2273sFoNPLLD188Y9xsxVjvuaN0jSYjJ345go+vr81Wkutqa8jLzWbvux9gNBo5+PnHNn3ugefd16/31ecfE9DfzqRSKXvfOcCDtHscOfQje985gELhwsd/+3dmzZmHi1Jp83swGo3s2f8exUWF3L55nZ2737aK5gAN9bUU5OfwxtvvYTIa+fGbz/D1D+TimRMsWbGG4NBw7t26xv07N1i8fI1VtX+Nfdnpk8dYvXYDoWHhXLl03qqag2mor6UoL4fX334Xk9HIT998jq+/2bYYjAZ2vPmOzbRHa29379zigw//jEwmQ6PR2FR7eLkHBAbZdJv4gF3Z0//MX9vYrgzXHumZDQYDbW2teHh4kpuTRWJSsl109Xo9Wq2WyooKAgKDqKwsJ4QwlCqV1R290e5hw+at/HL4Z1avXU9JcTF79lvf2Wqoq6UgL5tdew9gNBr54eCn+PkH4uTsTGN9Hb7+AeRkppM0ZZrVtUd77v0HzEeMHj1Io6KifMKO3lg2s6qygifpj6iurCQuIZFNW1/Fy9sbgOkzZ3P86CEePbhPeEQkU6ZNR61Wo3J15cDv/khBfh7Xr16mp6eHKdNSSU6ZiovL0Mm9xvpaivNz2P6W2WYd+vZzy3iotrqS3Kx06mqqiIpJYOX6LXh4elt+W1yQQ11NpeXf297Yi1Llyq69H1BanE/arWv09vaQkDyV2MQUq0wsWsXZa66vJjA8xrJqFxBm9pg7W5u49+g2uj4Nep0Ov+AIa8gNobKygrj4BMtLGRMbb/kuKXkKAKFh4Wi1WjQaDQqFwqbaep2Ozs4O4hISAZDJbLNTtqqygpjYp9rRsXEAaLUaQvpnu5NSplBaUmR17ZHq22DQ09xQQ9qVk5a/MxgMVtceYKx6t7dmn7aP4JBQwNzmiosKrK49Wn0DxMabV9T8AwLpaG+zuvYAddWVhEfHIuu/h7DIWJtpDccR9f083XOnT5Jg4y3DVZUVxMbFj1jv9tCLGaQ38P++fn74+Pji2n/mwMPDk47ODqs5e2PdQ1x/Ww8ICKTdBm29pqqS6Jin2hHRseh1fWi1GoJDzXY1IXkqZ08ctrr2r60vA9BoNJbV0+SUaZQUW78/AfPgKComzqIfGf3UtsTEJdpEc4DR2pufnz8njx0lNj6e2Djb7FpwlF2rrqwgxo52ZTCjPXNCUjJ5OdnMW7CIvJxstry63S66wSEhVFVWUFlZzvwFiygpKQITNtkhM9o9+Pr6kTJlKod+/I63972LVCq1unZ1VQXRsfGD3jFznSdPTSUn6wmLff0oyMth59vWn1gZq51XVVbw5PEj3tw7cd3Rrnvx3BmysjJYs3YD6zdueWb7d1R0DB/84T9RWlxESXERX372Ee8e+D1KlQqZTEZScgpJySl0tLdz4dxprl66wAd/+E9DtlXXVlcSOchmRfTbrJtXzlOQm8XiFWtZtnrjiFvPR9rGCSCVyYiJTyYmPpnOjnZuXjnP3RuX2f3Oh0PO+E0Gm57Ze3jjHPNWbsXd25fywmyaaiuf/yMbIjhU/SXHZELupGDFK9adaRd5PgMdgyBIMBqNDr6b3w7BISFUlJcxZ94Cm03q/JqQ9j+jIAiW/x/4t73aneUeJL/ttv5b6stsdYblebz2+i4qK8opLirgzq2bvHPgdy/9mUGJRGLZiWTQ6+2qnZCUzLHDPxMXnwiCgJeX9/N/ZAVCw8Kpqqygo72d2PgE7t65hYBAdIz9JjMBGhsaUCgUdHd321U3Ji6RtNs3CAmLwC8gEBcX60zajYeuzk7OnDrOazt2WfWc3Oy583FydubWjWuUlBQxZWoqYeERCMJTy+ni4kJSyhSSUqZw6MfvqKwstwQ86+7uJjvzCVmZGajVbmx+5TVUKtW4tKfOmIOTkxMP7t6gsqyY+OSpBIWED9Eei96ebgpysyjIyUSlVrNy/VZclOPTHgurWC4f/2Bqy4sx6HXodH3UVZYAoNf1oVCqMBoNVBbnWkPqGUJDwyksyEOn06HVaikufLqqkpeTDZhnDpydFThbcSZ0NG2ZXI5a7UZBfh4Aer0enU5nVV0wzzoVFeaj0+no02opLioEwNlZQVVlBQC52ZlW14WR61sqk6NSu1Fdai5/k8lEe3OjTfRh7Hq3t6aTsxM11VUA5OZk2UR7tPq2JwHBoZQXF6LX6+jr01JZaptZ/pFwRH0/T3fqtBlExcRy7MjPNnM8QsPCKSywX72HhIVT1K+n1WopKrR/O3PkPQSFhFJSVIBeZ27jZcWFyOROOCsU1FSZ7WpeTiZBIdY/K/hr68sAFIqn/UlOdoZVNQcTFBxGSfGgci+xX52P1N5MJhOdHR2ER0SydPkqtFoNfX19VtcetU9xcqavT2t1vQEGP/Ngu+Lu7kF9XS1gPgtuC0Z7Zk9PLwSJhNs3r1t9C+dYuqGh4WRnZeDp5YUgCLi4uFBcXGiTlb3R7iE/L5deTS+7397PxfNnbLJtODg0jOLCp+9YabFZWyaTERYRxdULZ0hKsf4WThjluU0mfjnyM8uWr7Jsr7TKdQF3Dw+WLFvBux98SGJSCo8epPHp3/9CdpbZhpWXlVrG5VqtltbWVtzc3NFqNBz5+Qe++/oL9Ho9O3buZscbu4lPSHxmoicwOIzS4gLLeGjAZrm5ezBn4TJ27nmf6PgkstIf8MNXH1GQO/bYUKvVcPb4IY799DV6vZ7123ay4ZWdRMUmWGWSySrT0R4+/oRExnPpl69xVijx9DEfdkycsZCrJ77DWeGCp28gep31jWVAYCAJicl88enfUSpVBAza4y6Vyfji048wGg2s37jVbtqbtm7j3OmT3Lx+BYlEyiuv7sDD09Oq2v4BgcQnJvH15x/jolIR0L9Ped3GLeYALYJAuI3C0Y9W37OWbiD99kXy0u9iMhkJiYzH3UbhZMeqd1sxmub6jVs4e/oEAgKh4eE4O1t3IAaj17c98fELICoukaPffY6Liwoff/vdw1j1Pc4JM6vrAsyZOx+tRsPJY0fZ/Mqr4569Gy/+AWb9rz77CKVKRaCNU2wEBASSkJTMl599hFJpe71f2z34+QcSG5/I919/itJFaTkkv2rdZkuAFjd3D1au22R17V9jX7Zh01ZOnzyOAERE2S7gl69/ADHxSfz49We4KJX42dO2jNTeBIGTx4+i1WrBZGLmrDlW3TZr0R6l3KdMm8b5M6dsFqBlwK4c7LcrA6l7Zs2dz4mjh8hIf0RUdIxVNQcYq50nJCVz9dIFfvcH6weFGU3X3cMDTGbHAcwTq52dHShcrB+AabR7uHblIm+8uQc3N3dmzJrDpfNn2bjlFatq+/kHEpeQxHdffYKLUon/ILsan5RCcWE+YRG2GTOO9Nw6nY662hpuXr/KzetXAdj+xpsTikD7vP5ZIpEQHRNLdEws3d3dtLY0A+azfhfOnbasZE9LnU5gUDBajYaZs+c+swo4Er7+AUTHJfHzKDZLIpEQHhlDeGQMvT3dtLW2WL4bfmZv8Yp1eHr7MCV1FkGh418FnAjCWMFDMio7rR9ZZJzEBf42Qw7rDY7bmlTSYN/tA4P5R67vvr4+yxaEu7dv0tXVyao168f1W0fWN0Bbj/VXnceLj9r5ha9x6MfvmT13HuERkVa4I/tgNDrMrNrUMf4106O13fnh56FSvNic6ndff+mQnI7DMUdI/G7cwWE6ex1nW1xfsMxfBFsM1MaLwYG2RSr5jRoXB9Pb9+K27VHaHbR9WuaPES1yJFycrH+28B+BdgeOm/zd5JN60V7+gyYiIjamuKiAu7duYjQZcXPzYONm68+8izzL6RPH0Ot1Nk01ISIiIiIi8rJy8ujPtLe18upO20TxFvl1MCFn7+GNc9RVluCsULLqVXMOs5yHt6itKEYQBJwVSmYsWYuL0hWTyUTGvSvUV5YilcmZuXgtHj7+9HR1cPfScTCZMBqNRCelEplgvX3Cp08co7ioAKVKZbN8QCOh1+v57uAX6A0GjEYj8QmJNkn4fPbUcUqKC1EqVex773dDvntw7w7Xrlzk93/+X1AqlWg0vZw7dYK2tlZkMhlrN2zGx9dv3Foj1Xdm2jXqKkuQSKSo1O7MWLwWJ2cFRqOBRzcv0N5cj9FoIiwmifhpczDo9dw4/SMGgwGTyURwRCyJM6ybFuFv//GvODk5IxEEJBIJe99936rXH6Cjo51Tx3+hu7sLEEidPoNZc+bR3NRET08PSqWSrs4OamtrrHa4e6T6zs/L4c7NazQ3NfHm3nctW3AMBgPnz5ykob4Wo9FIUspU5s5fNGltrVbDzYtnaG1pBAQWr9pAdvp92vu3I/RpNTg5K9i2+2kUra7Odg5/8ykz5i5iygzb5MR6kHaXJ+mPMJlg2vQZNolcNhL2escHo9FoOHf6BE2NDSAIrNuwGS9vH078coj29nbc3d3Z8sp2q2850uv1fPf1l/2JXs3PumjJMtraWjnxy2F6e3vxDwhk05ZtVi//0bQfPUjjwf17tLW28sd/+s8orRT9U6/Xc+THg2YbZTQSHZvA3IVLOfzDQXT956Z6enrwDwhi4ys70Go1XDh9jM7ODkxGI6mz5tnkrEtJcRGXzp/FaDJSXlZi15W9kfpRdw8Pq/epRqORQ99+gcpVzcZtr3Ph9DEa62uRSCT4BQSxdNX6Ie2rvq6GI99/xZqNrxBthSidAza9p7sbBIFpqTOYNWcuDfV1nD97ir4+He7u7mza+irOzi++E+F5mFM/fIJarWb7zt021Xp4/x4Z6Y8AmDptOjPnzOPq5QuUFBYgkUrx8PRk3catNtm+OlL7OnbkEC3NTQBotBoUzgpLaH5rMlo/fv3qZYoK8xEQUKpUbNj8is2T2g9+x6elzmDegsn318PR6/Uc/v4gBoMeo9FITFwi8xYt5cmj+6Q/TKO9rZUDf/gXNm0zp8d6mHaH/P6YA0aTkdbmJg784V+s3rc4aow+gC3LfIBvPvsLTnInBImARJDw2pvvUFyQy4M7N2htaeLVXfvxC3i67bO5sYHrl87Qp9UiCAKv7t5v9aBvE7paeGwy0YmpPLh+1vJZ7JRZJM1cCEBx9iPyHt9l+sJV1FeV0t3exurt79DaWEv67Uss27IbhYuKpZveQCqVodf1cenoQQLConFRulrlgaZMS2XGrDmcOnHUKtcbL1KplDfe2ouTkxMGg4FvD35BVEwswcEhVtVJmTKN6TNnc+bksSGfd3S0U1ZWgtrN3fLZvdu38PX3Z+trr9Pc3MTl82fYsWv80TJHqm+/4HCSZy1GIpGQdf86BRlppMxeQnVpAUaDgZXb9qLX67h05CtCouJRurqxaP0OZHInjEYD10/+iH9IBF5+1h247Hprr9UGf6MhESQsX7mGgMBAtFotX33+MRH9ietnzZ1nk9x+I9W3j48vW7bt4MLZ00P+tiAvB4NBz953f4dOp+PLT/5GQmKK+UzCJLh77SIh4VGs3LgNg8GAXq9jxfpXLN/fu3EJJ6ehA6B71y8TEm6bff9gjlj2JP0Re/YfQCqV8tP33xATE4enl5fNNAew1zs+mMsXzhIZFc3WV3dgMBjQ6XTcvX2D8IhI5s5fxL07N7l39xZLl6+yqq5UKuWNN/dYnvW7r78gKjqG+/fuMmv2PBKTUzh35hQZ6Y+ZPnOWXbSDQ0KJjonj+2+/srreKzvesugd+eEg4ZExvPbGHsvfnD5+iKj+UOWZ6Q/x8vZl07ad9PZ0880Xfyc+McWqTq/RaOTC2dPs3P02ajc3vvr8E2Ji4/Hxtc0Z6OHYqx/NeHwfTy9vS/CTuIRkVq3fAsCF08fIzUonZdpMwFwmd29cIdSK9kUikbB81RoCAsw2/eAXnxARGcXZ0ydZtmIVYeERZDx5TNrd2zaf2AF4cP8e3j4+9GltF5wFzAmsM9If8da+95BKpRz68VuiYuKIiIhiybKVSCQSrl25yL07N61uW2Dk9rV1UKqFyxfP2eTsO4zej8+dv5Aly1YA5nq4feMaazdY/2zuALZ+x6VSKdt2PrVrh77/ivCoaAKDQ4mMjuXwD18P+fuZc+Yzc858AEqKCkh/eM8m5xYdNUYH+9rVzTveHBLZ1Mvbl7WbX+PapTPP3NOls8dYsW4LPr7+aHp7bBL1d0JX9AkIQT7sBZQPGuzp9XrLmZDaimJCY5IQBAEvvyB0fVo0PV1IpFKkUrOPObDaY01Cw8KfSX5oDwRBsJzbMhqNGA0Gm4THDgkLHzHB4tVL51mybOUQzebmRsLCzWeZvL19aG9v75/NGh8j1bd/cISlIXr5BtJruZ6AQa/DaDRi0OsRJBLkTk4IgoBMPqhcTEb+UQOHu6rVlsAozs7OeHv70tnZYVPNkerb28cXL2+fZ/9YEND1metAr9chlUpxmuRsdJ9WQ11NJXHJUwFzxzG48zWZTJQW5hEVn2T5rKy4ALW7O55eI9yblWhubiQwKBi5XI5EIiE0LJyCfNtE+h2Ovd7xAbQaDVWVFUyZNh0w14FCoaCosIDk/sS3yVOmUViQb3Xt4c9q6D9bWlFeSnyiuc5TpkylsCDPbtr+AYGTnriYiJ7RaBhiovq0WqoryomKeZobqq9Pi8lkQqfToVC4WL1zrq2pxsPLCw9PT6RSKYlJyTYp69GwRz/a1dlBeUkRiVNSLZ+FR8UgCAKCIOAfEERXZ6flu8z0B0TFxlstnyOAq6uagIDBNt2Hrq4OWlqaLfkFIyKjKMizvY3p6OigpKiQaakzbK7V0tQ01I6GhlNYkEtEVLSlLQcFhdDVYZv+baz2ZTKZyMvJITE5xSbao/Xjg1dudX19Nh+m2Podf7a/MiIg4OcfgNtzkpcX5GUTl2D9iKjguDE6ONauenr74DFCOpHK8hK8ffzw8TUHOlS4KG3i7FllnTD7wU0qi3OQyZ1ZvN68JNzb04WL6ukSuIvKld6eLhRKV3q6Orlz4SjdHW2kzF5itVU9R2M0Gvnqs49pbW1hxqzZBNlwxn8wRQX5uLq64ecfMORzXz9/CvPzCAkNo7ammo72Nro6O1GprFPe5YXZBEeaZ7uDI2OprSjmzA8fYdDrmDJnGU7O5hfaZDRy5fi3dHW0EZU4DS8/60ZaExD46buvQRBInT6T1BkzrXr9kWhva6O+vpag4BCqqyp59CCN7MwnBAQEsWLVGpvMiD2PuPhEigvz+fu//w90eh3LV66ZtFHt7GhH4aLkxsVTNDc24OMXwLylqyw5r+pqKnFRqnD3MK+o6fr6yHh4l/WvvEHmo3tWe6bh+Pj6cf3qZXp7epDJ5ZQUF9k1Mqk93/G29jZclErOnDpOY0M9/gGBrFi1lp7uLktCc5XKlZ4JTOBMBKPRyMHPP6G1tYXpM2fj4emFs0Jh6YjUbm5DBuO21La1LTUajfz0zWe0t7UyJXUWAYHBlu9KivIJCYuwTJxMnT6LU7/8zBcf/U90fX2s3bTN6kE5Ojs7cVO7Wf6tdnOjtrraqhqO5ubVC8xfssI8sB6GwWAgPzeTRctWA+Z8XKWF+Wx9/S0u1520yf2YbXodgUEh+Pj4UlSQT2x8Avm5OXTYeFIP4NKFsyxbscomKR6G4+Pry81rg+1ooeU4wACZGY9JSLTNgH8sqiorUKlUdsmzN7gfB7h+5RJZmRk4K5zZ9eZem2rb4x03Go38cPAz2ttamDp9FgFBwc/9jU6no7y0mGUr11n1Xn4N2MuuCsCpI98DAklTppM0dfqof2s+FiNw8sj3aHp7iI5LYvrs+Va/J6s4e8mzFpE8axH5T9IoyU1/7pkspaualdv20NvTxb2LxwiKjEXh8uJJAx2NRCJh/4HfodFoOHroRxobGvD1G/8Zucmg0+m4d+cm23e++cx3c+Yt5MrFcxz8/GN8fP3w8w+w2qAkP/0egiAQGm0+N9HaWIcgCKx/4310Wi3XT/2IX1AYKjcPBImEFa+8TZ9Ww71Lx+lobcLN03qrP2/u2Y/azY3u7m5+/O5rvH18LLOytqCvr4+jh39i5ep1ODs7M33GLBYsWoIgCNy4epnLF8+zwQFBWupqaxAECR/88Z/QajT88O2XhEVE4uEx8bQfRqOR5oY65i9djV9AEHeuXSDjwV1mzl8CQElBLlGDzsw8uneTlNTZyK2YGHUkfHx8mTt/IT9+/w1yuRw/f38EwX6Jju35jpuMRurralm5eh1BwSFcunCWtDu3hvyNIAg2C7EpkUjY994HlmcdOE9jD4Zr29qWSiQS3thzAK1Gw+njh2huasDbx6xXkJdN0pSnnXVFWQk+vv68suNN2ttaOXboO4KCwya9iv5bpKykEBelCj//QKory5/5/vqlcwQFhxEUYg6+dOvqBeYtXmGzSJd9fX38cuRnVq5ai7OzM+s3buHShbPcvnWDmNg4m58LLiosQKU0p0CoKC+zqRaYd4fMmb+QQz9+229HA4bY0bu3biCRSEhMnmLzexlOTnamzVb1BjO8HwdYsnwlS5av5M6tGzx8kGaXrbu2RCKRsHuf2a6d/OVnmhsb8H5O3IbS4gICg0MdMmH9srB15x5cXdX09nRz8vD3eHh5W2zZcIxGI3U1lf3n9OScPPwdvv4BhIRZN8K4VU8AhkYncPv8URJnLMBF6Upv99NZ397urmdW8FyUrqg9fWiuq7asEL0MKBQKwsIjKCkpsrmz19baQnt7Gwc//xiAzs4OvvnyE97c8y4qV1fWbTSffzCZTHz6t3/HfRID/+GUF2ZTW1nCovXbLZ1vZXEe/iERSCRSnF2UePkH0dpUj8rNw/I7J2cFvoGh1FeVWdXZU7uZZ2pUKhVx8QnU1FTbzNkzGAwcPfwTSSlTiE8wOzsq16ftetr0mRz66TubaD+P3JwsIqOikUqlKFUqgoJDqa+tmZSzp3JVo3JVW/KMRcYk8OThXcBsnMqK8nnljX2Wv2+sr6GsKI/7t66Yz5sIAlKpjKRp1l9lnZY6w7LV6dqVS6gHzdTZC3u8465qN9RubpZZ5/iERO7duYVS5UpXVyeurmq6ujpRKm07UTbwrNXVVWg1GoxGIxKJhM6ODlxtHMBgQLvUDrYUwFmhIDg0nPJS89aa3p4e6utq2bB1h+VvcrOeMHPOAgRBwMPTCzd3D1pbmvAPfP6s+XhRq9VDVpPsUdb2pLa6irLiQipKi9Hr9ej6tFw4fYzVG7Zy/84NNL09LFv9muXvG+pruXD6FwB6e3uoKC1GkEiGbK2dLAaDgV8O/0RScgpx/Tbd28eH13eZoxO2NDdbko7biuqqCgoL8ykuLsSg16PVajlx7Aibt75qM80p06ZbtojfuHoJ1347mpWRTnFRAa/v3mP3NBJGo5GC/Dz2vmObIGsDjNSPDyY5ZSo///itTZ09e77jzgoFIWHhlJcWP9fZK8jNId5GWzgdjb3KfGDnjYtSRURMHA11NaM6e65qNYHBYZbzfWER0TQ11Fvd2XvhKfGu9lbL/9dWFKPu39YVGBZNZVEOJpOJloYa5E5OKPodQIPenKOiT6uhub4aV3frJhx3BD3d3Wg0GsC82lZWWoL3SOeqrIyvnz8f/vl/4cCHf+bAh39GrXbjrX0HULm6otFoMBjMOVgynzwmJDTshSOK1VeVUph5n/mrtiKTPU30qnRV01hrThKp1+lobaxF7eGFtreHPq25XAx6HQ01Fbi6Wy+YRl9fnznxbf//l5YU4zuBiKMTwWQycebUcby9fZgz9+ky++CtbAX5uROKeGpN3NzcLLPCur4+amuqRz7bNw6UKldUajfaWs1JSGsqy/Ds31ZTU1GGh6c3qkFO1qbtb7Fz/4fs3P8hyamzSJ093yaOHkB3tzkfZEd7OwX5uSSl2Gf22d7vuKurK2q1m2VFrbysFG8fX2Ji48jOfAJAduYTYmKtP1E2/FnL+581LDyC/NwcALIyM4iNe/HB9ni0J9uOx0NvTzfafj29TkdleamlrRcX5hIRFTMkMprazZ3KirL+e+2irbUZNyv3YYFBwbS2NNPW1orBYCA3J5sYG5S1o5i/eDl73/8Tb7/3B9ZsfIXg0AhWb9hKTmY6FWUlrN6wdYij8fZ7f7D8Fx2bwJKVa63i6JlMJs6eOoG3jy+zB9n0ARtjMpm4c+uGzY8GLF2+ij/8+V/4/R//iS3bthMeEWlTRw+G2tHC/DwSk6dQWlxE2t3bbNvxhtUTuY+HAZvq5ma7CbzR+vGW/oTbAIUFeTa1OWD7d7xnuF0rK8XzOc+k1WqorionKublWXwZjD3sqk7XR19/FGedro+q8lK8fEYPABMaHkVLcwM6nTneQk1VhU3iHkwoqfr9K6dorKuiT9OLs4uSxBnzqa8spbO9FUEQULq6kbpgJS4qNSaTiSd3LtNQXYZUJmPG4rV4+gTQUF1OZto1yzWjElOJTJj6jPZkk2wfP3qYivIyent7UKpULFqyzC4Hnhvq6zl14hdMJiMmk4mExGQWLl464es8L8n2yWNHqKootzzfgkVLLbNzAJ/89d94c997KJVKaqqrOHvyGAjmbRtrN2weMbjLAMOTqo9U3wVP0jAaDZbzeJ6+gUxfuAq9ro+HN87R2dYCmAiLTSZuymzaWxp5eP0sJpMJk8lESGQcCdOf3Y882fpua23lyKEfAfOsYFJyCgsWLZnUtZ5HVWUF3x78Al8/P4T+09tLlq8kNzuL+vo6BAHc3T1Yu37TuGeLJlPfCoULly+epbenB2dnBb7+/mzf+SZ9fX2cO3Wc5uZGTCZImTqN2XPH3lI9VlL15sZ6bl46g8FgQO3uwZJVG3FWKLh+4SS+AcEkThl5H/qjuzeQOzk9N/XCZJOqf3vwC3p7e5BIpKxYtYaISNtF/xyMtd7xiSRVr6+v49zpExgMBjw8PFm/cQsmk4njvxyio6MDt/7UC+M9mzneifqGhnpOnziGyWh+1vjEJBYuXkpbayvHfzmMRtOLv38AG7dss3qI6NG0H96/x727t+nu6kKpUhEVHcv6jZvHdc2xkqo3NdZz8cwJi42KiU9kzvzFABz58WtmzllAeH/UXYCurk4unT1hDnZlMkexi08afcJhsknVi4sKuXThLCajiSnTUm1m10bCGv3oeJOqV1eWk/7gHhu3vc7f/sd/Re3mbtkOHhUTz+z+uhjg0tkTRETFjJl6YbxJ1asqK/ju6y/x9fWzOJeLl62gtaWFx4/uAxAXn2AOfjbOl+dFV8MqystIu3t7UqkXJpJU/fuvv6C3txepVMqylasJj4ji07/9OwaDwbKFLyg4hNXrNo7rehNJqj5a+zp14heCgkKsHuF3MKP14xnpj2lpaUIQBNzcPFi7fqNl15CtsMY7PlpS9aaGes6fOY7JaMKEidj4ROYuWEL6wzQept2hp7sLpVJFeFQMq9aZo47mZD2hvLSY9ZvHN9EwmaTqjhqjD2CNMh8rqXpHWyvnThwGzGPSmIRkZs5dSGlRPjevnKe3t8ccGMjXn02v7gKgIDeLx2m3QYCwiBjmL1kx6vUnm1R9Qs6ePZns4P8fnecN/m3JcGfPnoj17RjGcvZszWSdvX90JuLsWRs778r61TCWs2drJuvs/aMzXmfPFozX2bMF9t76OJiJOHvWZiLOnoj1GM3ZsweTcfZeBsZy9mzNZJ09+0U2EBEREREREREREREREbEborMnIiIiIiIiIiIiIiLyEiI6eyIiIiIiIiIiIiIiIi8horMnIiIiIiIiIiIiIiLyEiI6eyIiIiIiIiIiIiIiIi8horMnIiIiIiIiIiIiIiLyEjJmfGIvVyd73cczODokvaPQ6n6bz+3IkNFjpR952dEbHPfsjkxB4Mj29n9cKHCY9p8WRDhM+1R+ncO0d6eGOkz7txoO/3Flm8O0Y3xdHabtyHD07i72T4Q+QIcDU20AqB2YbsOR73h9u8Zh2sGe48vvagvkMsetVb3/Y7rDtI8dmD2p34kreyIiIiIiIiIiIiIiIi8horMnIiIiIiIiIiIiIiLyEiI6eyIiIiIiIiIiIiIiIi8horMnIiIiIiIiIiIiIiLyEiI6eyIiIiIiIiIiIiIiIi8hL62z92//7f909C04nNysJ1y7dNbRt2FX8nNz+Pzjv/Ljt185+lbswq+hnV888TN9WsdEBDv043doNPbX/vf/7thyV2WdQNBrHXoPIiIituFx2m1H34KIyEtLdXUVB7/4lC8++Tuf/P0v3Lx+1W7a4a1PSK6/QkLDDRIabhDXaJ933XGxakVEbEDmk8esWb+JkNAwR9/Kb4ZVm3c4THv7zt0O0xYREbEfJpMRQXhp56eH8Pj+babPWeDo2xAReWkwmUz0abU4KxScPv4LW1/djp9/AEajkZbmZgC0Gg1Ozs4IgpVT1phMSEx6jBJzapRqtwTaXAKH/InEqMMoyMDa2v1Yzdkryssi+8lDjAYDvgFBLFi2hjvXLtBUX4terycyJp4Z8xZbS24IOVkZPH54H4PBQGBQMCvXrAfgysXzlJcVo1K5snHrqyiVKrvpl5eVcPPaFUxGIy5KJTt2vW113fycTJ48vo/RYMA/MJilK9eRl5PJw7TbODs74+Prj1Rqm5w/FUU5lOQ8xmg04ukbQOr8lTTUVpD76A5GowGV2p2Zi9cik9suV2NOVgaPHqRZyt3VVU11VQXnTp8gOjaOZStW20RzeF1nZz7h/t3bOCsU+PqZy3ygDdpaG+DmtcuUFBUik8vZ+trrqFS2yzFVnJ9Nbkb/e+4fyNylazjyzUds2rEXhYvSZroA2cPqe/XaDXzy93/n7X0HUCptp52TbS53o8FAQGDwkLrt7enhl8M/MHfBYqKiY22iL2suw6mxEExGDCovtKEzbaIzQHF+NrlPHmIwmut43tI1fPfJvxKfMp3q8mJclK7MmL+EB7eu0t3VwZzFKwmLtO6za2qL6a3IxWQ0Inf3xTVxLk1XvkcZloi2sQpBKsV92gokztbP85SXnUn6I/N7FhAYxPLV6ynMz+H+3VsARETFsGjpSqvrwrM2bdXaDfz7f/+/mJo6g7LSElQqFZteec0mfVl2ZgYP798zawcHs2bdRrIyn3D39k0Uzgp8/f2RSWWsXrfB6toAjeX51BZmYjIacPX2J2rGEtKOfop/dDLt9VVEzliCm0/g8y80CUrys8nLfIjRYMTHP5A5S1Zz58pZmhvrEIDoxCkkTZtcfqvnUZibRVb6A8vYycnZGYNez+FvPsPT24cV67faRHcke/pv//3/4p/+8/8OQH5eDsVFhWzYZBv9gpwsMh/fx2A04B8QhLevHx3t7Szof7fysjNorKtl8cq1VtPMzsrg4f00jP3PHBYRSW1NFStWreVB2j0ePrjHBx/+mbbWVk6dOMqbe96xmvYAw/vxlKmpnD9zkjf3vovJaOTbg5+xaetr+Pj6WV27rDCHwqxHGI0GvHwD8fYLpK2lkRkLVgBQnJtBR2sz0xcst7r28H40MjqGOzevAaDX6zAYjLz3uz9N+Loj2S2J5OmkUHt7G5lP0snJymTl6rVEx8bR3dONylUNgEQiwcfXF4CqygouXThHUsoUpk6bjpu7+5jaXj3V+HaXIZiMdDt5UOmeMsRZc9L34t1TiWdvDVXuSXQoRq9T175WQtpzaHUJokkZik5m3b7NKs5eW0sTJQW5bN7+FhKplFtXzlGcn82s+UtwVrhgNBo5c/R7Wpoa8PKxbgNubmokPzeHN97ah1Qq5eK50+RmZ6LT6QgIDGT5qjXcuXmdOzev22QAPpr+zetXeePNPbh7eNLb22t13ZbmJgrzc3jtjb1IpVKuXjxDfm4Wabevs/Otd3ByVnD0p2/w9fO3unZHWzPVpQUs2fQGEomU9NuXqCzOo6wgk0XrtiOTyynISKMo6yEJ0+dbXR/M5Z6Xm82ut/cjlUq5cPY07h4e+AcGsWzFagICg2yiOVJd3711g7f2H8DJyYmfv//aJmU+VjsPDAph0dIVXLtykcz0x8xbaJtJlbaWJsqKctnw6ptIpFLuXjtPaUGOTbSGY37+bHYPqu+c7Ey76Bbk5vDGm+Zyv3T+NHk5Zt3u7i6OHf6RhYuXEx4ZZRN9SW8H8tZKeuJXgiDBueIBspZym2iBuY5LC3PZ8Jq5ju9cPU9JQQ56nY7AkDBmL1zO5dNHeHz3Bmu37qSttZkbF09Z1dnTd7WhrSvDY/YGBImEzty7aGtLwaBH5u6LKmYGXQUP6K0uQBU1zWq6YLarBfk57NhttquXL5whPyeL2zevsmvPuygULhz9+VuKC/OJjo23qvZINm3gHfcPCGL5qrXcvnmN2zeus2qtdfuypqZGcnOyeXPvO0ilUs6fOUV2VgY3r19l3zvv46xQ8P03X+HvH2BV3QF6OlporiwiZcU2JBIpJY+u0VRegNGgx9XLn4hpC22iC9De0kxZUR7rtpnb/L1r58l8eIee7k62vGEe7Ntqm3prSxPFBblsff1tJFIpNy+fxcvbF6lMxmtvvWsTTXCcPR2gtbmJooIcXnljD1KplOuXziKTO1FaVGBx9oryc5g513r13tzUSF5ONm/uMT/z+bOnMRj0VFVWAuaBvouLks7ODqoqKwgJDbea9uB7GN6PtzQ3ER0bx83rV9Dr9SQmT7GJo9fR2kxlcR4rt+5CIpHy4OYFBImEmvJiUuctRSKRUlqQxazF1p8kH6kf7dNqeHv/+wCc/OUQIWETL++R7FZOViaJySkUFuTxJP0xPd3dpEydxlv73rVMCs+eM49P/v4fhIVFEBUdTcrUVGQyGdGxcQQGh5Cd+YTDP32PytWVqanTiY1LeGbhRKHrwrO3lnyf+SBICG3Lwqu3mlaXINw19fj0VCIz9NGsDCbfZwEG6dOFj+COPAI6iwDQyNWUeabSofAjX+6Bd2810S0P0EmdaVaG0q7wx2SFHQ1WcfZqKstpbqzn2I/mc1IGvR4XFyUlhXnkZ6VjNBrp7e6itaXJ6s5eRXkZ9fW1fPvVZ4B5hkCpVCEIAvGJyQAkpkzh+JGfrao7ln5tTTUhoWG4e3gC4OJi/dnnqopSGurr+Pnbz/t19dTVVBEcGo5L/6xvbHwSba3NVtdurKmgrameq8e/A8z13dpYR09XO9dP/QBgmTmyFeVlpdTX1fLNl58C5udXqmy7sjRSXddUVxESFm6p47iEJFpbrF/mo7VzqVRKVIx5oO0fEEh5aYnVtQeorSqnuaGekz8fBMBg0Nt8NW+A8rJS6upq+dqO9Q1Py/27g0/L3UWpwmg0cuiHb1ixej2hk+ikxou0sx5JbwvKvAsACEYDJpnCZnoDdXxioI715jqWSKQEh5kdWk9vXyRSKRKpFE9vX7o62q16D7qWWvQdzbTeO2n+wGhA4qQAQYKTTwgAMjdvdM21VtUFqCwvpaGulh++HrCrOupqqggJDbespsUnplBdWWF1Z280myYIAglJ5r4sKXkqx478ZFVdgPLSUurrajj4+ScW7eqqSsLCI1CqzM+dkJRMa7P1bRtAe0M1Xa2NZF48BIDRYEDu7AKCgHeIbSZSBqitLqelsY7Th74GwGDQERQaRVdHO2k3LhIcHkVQaKRNtKsrymhqqOPo918CoDfoUbjYZgfSYBxlTweoqiijsb6Ow999YdF3cVHi5uFBXU01Hp6etLU0ExAUYjVNyzN/YX5mnV6PUqmkr68PrVZLZ2c7ickpVFZUUFlZQVx8gtW0BxitH5+/cAnffvkpUpmMFav2W10XoL66nJamei4c/Qbot+0KJf7BYdSUl+Dm6YXRaMDDy9fq2qP1owD3791GJpeTOmPiK+cj2S2lSsVXn3+M0Whkw6atBAU/24YWLl5KUsoUykqKycnOIic7i91v7wNAqVQye+58Zs+dT3VVJadPHuf2zeu8c+D3Q66h7mvCRddOQqN5x4fEZEQvcer/t4lyj6n0OHmMeN8jbeMEMEidaHCNpME1ElVfK+FtGQR2FpHr9+IT+FZx9kyYiElIYfbCZZbPOtvbOPPLD2zduQ9nhYLrF05i0OutITdU22QiOWUqi5cN3VZz9/YNq2uNV7+4sIC83Gwb60JC8lQWLH663F5SmE9xUb5NdQcIi00iedbTBlhbUUxVcR6zl2+0iz5A8pRpLBlW7z/YMDDLSHVdWJBHUYHty3y0dv4g7Y5lf7lEEDAZjTa9j+iEFGbOXzrks6I8+8wIp4xQ31mZT2ysaiIpZSqLlz5b7mbnutimzh6Y0HlF0hc8dcin8pZSW8kRk5DCzAVD6zg7PW3QOQYBqcQ8yykIAiaT9ducc1A0rrFDt6v2lGdb7sFWuiYgMWUqC5essHxWXJhPUUGe1bVGYiSbdvfW0L7M6udJADCRMnUaS5evsnxSkJ9HQX6uDbRGkjfhGxFP+JShO0Fq8tNtf07PZCIqPoUZw+xa6rzF1FSUUpCdTnlRPgtWWH9nEEBc4hTmLFo25LOMR/dsojWYkezp/bS7lv/X22C89hQT8UlTmLd46HbB3KwnFBfk4unlTWRMvFXbuslkfualy4c+c2dnB1kZ6Xh5+RASGkbmk3RqqqtYsdL6K1yj9eNdXZ306XRIjUYMej0SJ+sffzEBkXHJTJ2zZMjnzQ215Dy+i5uHF5FxKVbXHVAfqR8tLyuhIC+H13fvnfR1h9stgKjoGNIfP+TksaPExicwZVoqPj5DnVhPTy88Z3oxbfpM/u1//D/09vTg0r/y19TYSGbGYwrz8wkNC2fa9BkjSdOiDKbGbeikQLvCD5+eCiJa02lXBNCsDEEjH//RGoWuE++eKtw19XQ5e9GktE78CatY0aCQCMqK8unt6QZAq+mlq7MDuVyOk7MzvT3dVJbZZsUhPCKSgvw8errN2r29vXS0t2EymSjIM28xy8vOIjjENgE7RtL39fOjqrKC9rZWy2fWJjQ8guKCXHr6y1zT24uPfwA1leX09vZgMBgoKrBNR+0bGEZ1WSHa3h4A+rS9uHv50txQQ1eH+Zn1Oh2d7a020QcIi4ikIC+X7kHl3t7eZjM9GLmu/f0DqKooR6PpxWg0UmijwdFo7dyeBIaEU1487D238qrOaIRFRJJv5/oGCAuPpHCEcheANes309LSRFr/WS5bYFD7I2+rRND1byPTaxG03TbTCwwNp8xBdTyA3CuQvvpyjH1mu2nUaTH0dtlFOzQsgsL8XEt9a3p78fXzp7qqnN6eHoxGIwW52QTbIADUaDZtcF+Wm5NJcEio1bXDI6LIzx2q7R8QQGW5+bkNBgP5ubbbsu3uH0JLVQk6jblP0fVp0HZ32kxvMAEh4VSUDB+/tGMymQiPjid1zmJaGuttoh0cGkFJUZ5FW6PppbOjHYlEitFgsIkmjG5PVUoVzU2NmEwmCm04wREcFkFJYd6Q8UtnRztRMXGUFRdQmJdNTHySVTXDR3nmkNAw0u7dISQsDH//ACrKy5BKpTgrrL+DYrR+/MLZUyxcvJTE5BSuX71kdV0A/+AwKksK0PQ+befdne14+wXS09VJeVEe4TGJNtEerR+9fOEMm17Zjlwun9R1R7Jb7e1tBAWHsGHTVva99wFe3t6cOXmcg198Sl2teTdIcWEBJpMJgJaWZiSCgLNCQV1tLQe/+JQzp47j5e3Dvvc+YP2mLSOuDnY6e+PRW4fMYI6KLTX24aTvpcfJgwqPqeT5LkIjUxHWlkF8421c+sbuR1362olvvE1YWyYamSt5vouoGGN1cKJYZWXP09uHmfOXcPaXHzGZTEgkEhYsW4O3rz+Hvv4Ylasb/lZcjh+Mt48vC5cs49CP31q0V65Zj1wup662hru3b6JUqtj0yqt21V+zbiPHjvwMJhMuKhU73njLqrpe3r7MW7iM44e+w2QyHzJdunIdsxcs4dD3X1kCtNgCN09vkmYs5Na5w5ZnnjZ/BTMXr+X+1dOWTipp5kLU7p42uQcfH18WLVnOoR++wWQy2SwoymBGq+u5Cxbx7Vefo1Ao8PL2wdnZ2W7a9sTDy4fpcxdz4fhPgAlBImHeEuvPfo6Ej48vi5cs5+dB9b2q//ltE7vKjLePLwsXL+PwT0/LfcVqs65EImHD5lc5dvhHnJycSZ0xy+r6Rhd3tIFTcCm6Zp6aFiRowkaYZbQSHl4+zJi3mPPHfzJHEJNImLvUPnU8gMzVA2XMdNoe9m9dFQRcE+bZRdvbx5cFi5dx9Ofv+tuZhGWr1rFwyQoO/2je5hcRFWP1LZwwuk2Ty+XU1lRz59YNlCoVm195zfravr4sXracn777GhMmJBIpq9dtYOGSpXz91WconBX42ei8HoDSzYvQlDnkXD8BmBAEKZEzbHP2eDgeXj6kzlnMpRM/m99xqYRZC5Zz7dZR+seDTJ+3ZOyLTBJPbx9mz1/C6SM/WMp94fI1JKakcuibz/Dx87dJgJbR7OmS5Ss5/PMPKJVKAgKC6NP1WV0bzOOXOQuXcvLw9/12VcriFWtRBwXj6eVDS0sT/lY+d+/j68vipcv5+ftv+utZyuq16wkJDaOzo4PQ0HAkEglubm54eftYVXuAkfrxmNh4pBIpiclTMBqNfP/1F1SUlRIWYd2tw+6ePkyZvYhrpw9ZynzGwpWo1O6ERcfT2tSAk7NtjgiM1I9Gx8TR29vL8f5t6SpXNa/u2DWh645mt9zdPQBwcnJiWuoMpqXOoKmp0fK7rKwMLl08h1wmRyKRsGnrq0gkEmRyGRs2b31mFXAkNHI1tW5xxDSnIQAmQaDCPZk+zEd6jBIZzapQmlWhKHRDJysHn9kDyPddiEmQUu4xdUKrgBNBGPBuR6KqVTv6lzbGQzk5T/8fHa3OttvwxqK61forkOMl2t92ESSfx1jvwHjo6+vDyckJo9HIscM/kTI1lVgb7Pe3BU2dtunMx4Of24s5xUajkb/8z//Gh3/+lwlHnTUYHWba+D8uFDhM+08LIhymfSq/zmHau1Otvxo2XuSyF9tA8z//3//Kf+qPkDhRpBLrTYVkPkmnrrZm3NE4rxc0Pv+PbESMr+P6Excn20TAHg/uLo4bN3Vpbbnt8/moFY7LJObI/qSq5cXHbdfPHiF+ykz8gyd2JCHY0/rxKMbLi9rVF2HnF/cdpn3swOxJGXUxz56IyAty++Y1KspK0ev1RERGERNn/Vl/kWf54pO/MTV1us3Si4iIiIiIiLys9Gk1XPjlWzy8fCfs6In8Y/FCzp5Wq+HmxTO0tjQCAotXbSA7/T7trS2AuSE5OSvYtvsdDAYDty6fpamhDkGAeUtWERgyucZ19tRxSooLUSpV7HvvdwBcu3yR4qICpFIpHh6erN24BYVCQW9vDyeOHqKutobkKdNeeOvbSNq3b1wj88ljy+HORUuXExUdS21NNRfOnjL/0GRi/qKlL7Tio9frOfLjQQwGAyajkejYBOYuXEpleSm3r1/CZDIhlzuxct1mPDy9LL8rKsjj7InD7HhzP/4B498a8fDGOeoqS3BWKFn1qvkAbWbaNeoqS5BIpKjU7sxYvBYnZwWVxbkUZj6w/La9pZHlW9/Cw9uP7Ac3qSzOoU+rZcueiedRGc6DtLtkPnkMgK+vH+s2beXcqePU1dUikUjMOYPWbbSaEzCe9vb67j0oFAra29r4t//2f+Lp5Q1guRdrao/W3spKS7hx9RJGowGJRMrS5ateaCtIdvp9CnOeIAgCHt6+LFqxgYKcJ+Q+eUBnRxs73/mTJRpnn1bLjYsn6O7swGg0kjx9DrGJU5+jMH4++uv/xMnJnOxUIpGwZ/8BEpKSyUh/TGlJMQBLlq6wRCZ9Ec6dflrme981l/nJY4ctUVa1Gg3OCgVv738fg8HAhbMnqa+rxWQ0kpQylTnzF01M0GhAWXAZTAYwmdB7hNIXlIKi9A7SnlZMgoBR5Y0mbBYMClYh6W5GmX8JTeR89J7mlSvnqidIO2rAZMLgFoA2ZPq4k7S2tzZz9dxxy7+72ttInbuI5NTZ5D55SG7mIyQSgZDwaGYtXI7BYODOlQGbLjBn8SoCJ3A22qDppjPrpuVsniIkDmWY+ZxOb0UuvZV5IAg4+YTgGjcLTW0JPWVZT3/f1YrnvM3I1F60PTiLUdsL/YFjPGauRuI0vlnnzo52zp8+bjk/lDJtBtNnzgEg/dF9Mh4/QBAEIqNiWbRsJQaDgUvnT9FQV4sgCCxdsYaQsIhxP/dYjGTb4hOT+eqzjzCZTHh6ebN+01acbBDA4fSJYxQXFaBUqXj3/Q+HfJd29zZXLl3gT//8XyZ9/aL7l2mtLUfu7ELq2jcA6G5rouThNQx6HQqVmpi5q5HJndB0d5B+9ntc1B4AqL39iZq5DICmikKqch8iCCBXqIidu8ocuXMC5Dy5T1FOBggCnl4+LFixAYlUSvq9G5QX5yMIAnEp00mcOpP21mZuXz5DS2M9qXMXkzx9zqTLAOC7z/6K3MkJiSAgSCS8uns/TQ313Lx8FoNejyCRsGjFWvwCgjCZTNy+doHK0mJkcjnL1mzCx88622k1Gg3nTp+gqbEBBIF1GzYjl8s5f/YUOp0Od3d3Nm551arHEoxGI4e//QKVq5oN217HZDKRdusaxQV5CBKB5KkzmDpjNqVFBaTdvmax9wuXrSYw2Hqr83//i7k/GaiDve8cMG8pPHqI9vZ23N3d2bptOworRFIfqQ+/df0KRYUFCIKAUqli3cYtuKrV5GZnknb3NmBC7uTMqjXrJ7x9Ou3qWWoqinF2UbJ+hzmyp1bTy51LJ+nubEeldmfBqs04OSsoyctAJpPR1dHKmZ+/oLOtha1vf4izwoXaylIe376MyWQiKmEKialzJ3QfI/WjDfV1XDxnTnkxcCQiMCiYluYmzp0+TkN9HQsXL2fWXNuk7AIoKS7i0vmzGE1GpqXOYN6CCfbXIyCYDMQ13UUwGREw0aYIoNYtDldtEyEdeQgmIz1yd8o9poAgQWLUEdn6BLmhFwET9a6RtChDcdL3EtX6EEwmBEw0qsJpUlnHCX8hZ+/utYuEhEexcuM2DAYDer2OFetfsXx/78YlnJzMhiI/Kx2AV998l96ebs4d+4mtb+ybVLSllCnTmD5zNmdOHrN8Fh4ZyeJlK5BIJFy/cpG0OzdZsnwVMqmMBYuX0dzUaDZqL8hI2gAzZs9l9rAG6uPrx1v73kMikdDV1cnBzz8mOjZuSMLHiSCVSnllx1s4OTlhMBg48sNBwiNjuHrpLBu37sDL24fM9Ac8uHeLVes2A9DXpyXjUdqk9r+HxyYTnZjKg+tnLZ/5BYeTPGsxEomErPvXKchII2X2EkKjEwmNNh/ubW9p5N6l43h4m9NsBIZFEZ2UyvlDX0zquQfT2dnBowdp7D/we+RyOcePHiIvJ4vE5Cls2LINgFPHjpD55LHVzlBNpL0BuHt4sued922mDSO3NxcXF7ZtfwNXtZqmxgYO//gdH/zxnyal293VSV7GQ7bufheZTM7Vs79QWpiLX2AIoRExnP3luyF/n5f5CA9PH1Zu3I6mt4ej335CVFyyVVfddu7e80zy9Jlz5jJn7gKraYA5ImLqjNmcPfW0zDdtfXpO6trlCzj1D4AK8nMw6PXsffd36HQ6vvr0b8QnpVjODIwLQUJP7DKQysFkRJl/Cb17ADqvcDQR5rNqirK7yJtK0PnGmH9jMuJcnYHB7elAQNLVhLS7kZ5EcxJiZcFlpF2NGNTjS3fj7unN1jfMgwOj0chPX/6V8Kg4aqvKqSgtZOuu/UilMkswiYJscyTUV3abbfrFEz+z6fW947fpgoAqbhZyN2+Meh1t907i5BWEsa8XbWMlnvO3IEikT53BwCgUgeYw/PrOVtqfXEamfjqppU5ZjNx94mdtJBIJi5evws8/kL4+Ld8f/Iyw8Eh6eropKcxn994DyGQyS3CBrH5n7K39H9DT3c2xw9/zxtvvvnDkwNFs2/JVay0D7isXz/H4YRpzJzqhMA6mTEtlxqw5nDpxdMjnHR3tlJaW4OY2doLh5+EXkUBAzBSK0p4GoCh+cIXwaQtw9w2moTSXmvzHhKWYB5UKV3emrdk55Bomo5HS9Jukrn0DubML5Rm3qSvKJDR5/A5YT1cneRmP2LLrHWQyOdfPHaOsKBeTyfzd1t3vIQiCpZ07OSuYvWgllaWFL/T8g9m8ffeQtDX3bl5mxtxFhEVGU1FaxL0bV9i8400qy4rpaG1l577f0VBXw41LZ9m2a59V7uHyhbNERkWz9dUdGAwGdDodP3//DctWriI0LILMJ4+5f/c2i5ZaL8F25uP7eHh5o+szHx/Iz86gq7ODXfs/QBAEy4RLSFgEEdGxCIJAc2MD508eYdf+31ntPgDeeHNof3Lvzk3CIyKZt2ARd2/f5O6dWyxbsWqMK4yPkfrwWXMXsHCJuVwfPUjjzq3rrF63ETd3D3a+uQeFwoXS4iIunD3Fm3snlm8xIj6ZmJTp3Lty2vJZXnoa/sFhJKbOJTf9Hrnp95g2dykJ0+aQMM387lSXF1OQ+cCSG/vhzYss27gDF5WaC0e/ISg8GnfP8dvXkfrRG1cvMX/hEiKjYygpLuTG1Uv9E+UuLF+1jqJC20Y9NhqNXDh7mp2730bt5sZXn39CTGy8Jan6ZDEhodB7LkaJDExG4pvu0NHnS0RrBoU+c9DKXAnsKMC7p5pmVSi+3eX0yl0p9p6FzKAlqeE6rS7B6KTO5PvMxyRIkRj1JDbeoF3hj0764mcpJ73ptU+roa6mkrhk8+y9VCrFedDhTpPJRGlhHlH9EZXaWpotK3kuShVOzgqa6ieXJykkLByFYuiMS0RktMWJCgwKobPTHMlL7uRESGgYUql1dqyOpD0acrncck8GvR7hBUNJCIJgmdE1Go0YjQYQzAEq+vrMEYG0Wi0q1dPzCvduXWPGnPmTen6fgBDkww7s+gdHWJ7JyzeQ3u5no+RVleQTHPl0K6OXXxAKpfXOUJiMRvR6PUajEb1Oh6urmqgYc8cgCAIBQcF0dnZYTW8i7c3aTKS9+QcE4qpWA+YD0Xq97oXCZxtN5jDQxv5w0C4qV7x9/XEdYdAnCKDT9WEymdDp+nB2Vkx6UsPRhISGjzqjazKZyM/LIaE/h6eAgE6nM7dFvQ6JVGqZ4Bo3gmB29ABMRvN/CBjcg8zfCQIGpRdCX4/lJ/LGQvSeIZhkw7SMxqfXMBknnZOvtqocNzcPXN3cyc96zJSZ8yw2ZCA/Untr07M2vWH8Nl3qrETuZl4Bl8jkSFXuGLU9aKryUUakIPSv0o20QqepK0URYJ0ABipXNX7+5pxHTk7OeHn70NXVSWb6Q2bNXYBMZn7ugXxzLc2NhPav5ClV5ueur6uxyr2MZNsGHD2TydT/PtsmJFHooHyhg7l04RzLV6x6YVk33yBkw94NTWc7bj7miciBiJxjYcIEJhNGvR6TyYRB14fTJPLSmYxPbZs555crBdmPmTJ7gcVpH2jnLkoVPv6BNrVnAgK6/j68T6tF6WruL8uKC4lNTEEQBPwDg+nr09IzQp87UbQaDVWVFUyZNh0wj98UCgUtrc2WZOLhkVFWTb3R1dlBeUkRiVNSLZ9lP3nErHmLLGU+kMtS7uRk+Uyn6xv37oQXobCggJSp0wBImTqNQiulVBqpDx+8WqrT9VmeNTgk1PK3gcHBdE1iXOEXGDpkLA5QXV5ERJy5z4qIS6a6rOiZ31UU5RLWP2Hf0liH2t0TVzcPpFIpYdEJVJcVT+g+RutH+wa1c1V/O1eqVAQEBiGR2PZIRm1NNR5eXnh4eiKVSklMSrZO1FlBMDt6gNC/KmdCwCRI0MrMz9jh7IOH5um5dalRbw6CZjKgl8gtf28S+tMaYV4ltBaT9oA6O9pRuCi5cfEUzY0N+PgFMG/pKuRyszNSV1OJi1KFu4d55tXL14+K0kKi45Po7uyguaGOrq4OfLFuxCWArIx04hOtG7b3eaQ/vE9OVgb+AYEsW7na8sLW1lRz7vRxOtrbWb/plRfuMIxGIz998xntba1MSZ1FQGAwK9Zs5MSRH5HJZDg5ObNj9z4AGupr6ezsICIqlkf374594UlQXphNcGTcM59Xl+Yzb5X1I4gBqNVuzJo7n4//8q/IZHIiIqOIiIq2fG8wGMjJymDF6rU20R+J4e2tvb2Ng59/jLOzMwuXLCfEBmHaR2tvAxTm5+LnH2gZqE4Ulaua5NQ5HPrqb0hlMoJCIwkOG31wnTBlBpdPH+HnL/+Crq+PpWu3WjVHkoDAzz98gyAI5uha08052B4/vE92ZgYBgUEsX7HaKttuxqK6qgKVSmXZphsbn0hxYT4f/cf/QKfXsWzFmhEHzM/FZESZdwGJtos+3xiMKu8h38lbytCGmqNwCn09yNqq6Y1djrQ7zfJnRlcfDGo/XDOPgwnzdVzcJvWcpYW5RMb1r9S3tVJfU8mju9eRSmXMXrgcH/9APL39qCgtIjIuie6uDpoa6uju7GQyQYANvV3oO1uQufugL3iArq2B7qLHCBKpefVv2Iqdtr4U99QVQz7rzLkFCDj7h6OMnDqp9tfR3kZDfR0BgcHcvHqJ6qpKbt+8ikwqY9GyVQQEBuHj509JUSHxiSl0drSb7WxHBwGBwRN/8EGMZdvOnDxGaXER3j4+LFu55oV0JkJhfh5qtdpmkThd3D1prSnFKziK5qpitINSbGi7O3hy4SekMifCUubg5mseDEbNXMqT8z8gkclRuLoTOWNikTKVrmqSUmdz5ODfkcpkBIZGEBQWyY0LJygvzKOitACFi5LZi1bi5uH1/AtOEEGAU0d+QBAEEqekkjhlOvOXreL00R+5e8O8bW7rzj0A9HR34qp++g6rXNV0d3WiVL3Y5GlbexsuSiVnTh2nsaEe/4BAVqxai4+PL0WF+cTGJZCfl0OHFSdNb129wPwlK+jrexoUrL29jaKCXEqL8lG4KFm0fI3lCEpJYT73bl6ht6eHDdtet9p9gLk/+en7/v5k+gxSp8+kp7sLV1fzZKlK5WoVp3osbl67THZWJs7Ozry+++1nvs98kj5kbPMiaHp7cOmfcFe4qND09gz5Xq/XUVdVxoyF5jx4vd2duKjUlu+VKleaJzCRNxrLVq7hyE/fce3KRUwmE7ve2vfC15wInZ2duA16n9RubtRWV1vn4iYTCY03cTb00KgKp0fuDhhR9rXR4+SBp6YOJ4N5l0qjKoLolgdMqb+MxKSn1PPpcQu5oZeY5gc4G7qpckuwyqoevICzZzQaaW6oY/7S1fgFBHHn2gUyHtxl5nyz4S0pyCUq7mnOjrikqbS1NHHshy9xVbvhFxhsk2Spd2/fQCKRkJg8xerXHo1pM2Yyb+FiBEHg1vUrXL10gXUbtwDmM1v73vs9zU2NnD11nMjomEkPwMG85eiNPQfQajScPn6I5qYG0h+msfnVnQQEBvPo/h1uXr3A8jUbuXn1omU7p7XJT7+HIAiWrZsDtDTUIpXJcJvAcv9E0PT2UlSYz4EP/4yzs4ITRw+Rk5VBUop5hfniudOEhIZbZidtzfD2pnJ15f0P/4yLi5L6ulp+OfwT+977nVXPPYzV3gCaGhu4fvUy23funrSGVqOhsrSQ1/b8DicnZ66eO0ZxfjbR8ckj/n11RSmePn6s2foGne1tXDj+I35BIRNf5RqFXW/vQ612o7u7m59/+AYvbx9SZ8xi/sIlCILAzetXuHL5AusHlYMtyMvJJj7xaRnU1dYgSCS8/4d/QqvR8ON3XxIWEYmHxwRTjggS8/ZLfR8uJbeQ9LZhdPEAwLniIQZXXwyu5q0mzlWP0QZPfWa2W9B0ItF00JVifueVRdcwdDVafjdeDAYDlaVFzOxPMm0yGtFqNGzc/jZNDbVcPXuM1/Z8QGzSVNpbmznx01dPbfokoj+a9Do6nlzBNW42EpmTeeVGp8Vjzgb0HU10ZFzDa9GrT2f62xsRpDJkrk/LWJ2yGKlChbH/WlpFCYqgiQ2U+vr6OHXsEEtXrMHZ2RmTyYhW08vON/dTX1fDmROH2XfgjyRPSaWluYnvD36Gm7s7gUEhVln1Gcu2rd+0FaPRyKXzZ8nLzWbK1NQX1nseOp2OO7dvsnOXddMGDSZm1gpK029QlfMQz6Cnu0acFCpmbNyD3FlBV2sD+bfOMG3tLiRSKXXFWUxd/TrOKjdKH9+gOvcRIUnj37Kv1WioLCti29sf4OTkzLXzxyjJz8ZoMCCVSdm4Yy8VxQXcuXKWtdsmb0NHY8vrb6NyVdPb082pIz/g4elNSVEe85esJCo2geKCXK5fOM3G1yYWhn4imIxG6utqWbl6HUHBIVy6cJa0O7dYt3ELl86f5c6tG0THxCG10kpLWUkhLkoVvv6BVFeWWz43GPRIpTK2v/kOJYV5XDl/km39jm5UbDxRsfHUVFWQdvs6W7Zbry5273nan/z0/Td4D0u1IPTvqLAli5auYNHSFdy7c5PHD++zcPEyy3cV5WVkZTzmDRs4QyNNgtWUF+PjH4TzOHcRTZYn6Q9ZunINcfGJ5Odmc/7MSbZbOS2ZwxAE8vwWIzXqiGp5iELfRZnndEI6chFMRjqcfTD1b49w0zbSI3ej0HsuzoYeYprTyHXyxCiRo5O6kOu3GLlBQ1TLQ9oUgeilLz6OmnQPpXJVm7e/9Af8iIxJoKk/8ajRaKSsKJ+o2KeOgEQiYd6SVWzb/Q6rN2+nT6u1rPpZi6yMJ5QUFbJhyzarrio8D5XKFYlEgiAITJk2g7raZ7f0ePv4Ipc7WeXcIICzQkFwaDjlpcU0NdZbZpVj45Oorammr09LS1MjR3/6hq8++Q/qa6s59cvPVtluVF6YTW1lCbOWbXimnKtK8wmJsl3agfKyUtzdPVAqVUilUmLjE6iuqgLMgUt6e3pYvso+M98jtTeZTIZL/1kM/4BAPDw8LYE9rMVY7a2zo4PjR35m/aatQ4L0TJTaqjJc3dxRuCiRSKWER8XRWDf6DFhRXibhUXEIgoCbhyeubu60t1rvudX9s3EqlYrYuHhqa6uHlMPUaTOoq7HSDN0oGI1GigryiE946uzl5WQRERmNVCpFqVIRFBxK/Qjv/7iROWFQ+yHtMG/3cKrNQtBrzYFW+pH2tOJSegdV1glkbVU4Vz5E1laFrL0ag8rbvCVUKkfvFoi0q2nCt1BdXoK3r79lG5vSVU14tLluff2DEAQBraYXiUTCnMUr2frGflZufI0+rWbCNt1kNNKecRXnwCic/c0TNBKFEme/MARBQO7uCwKYdFrLb7R1pTgP28IpVZjvVSKTowiMQtcxsbD/BoOBU8cOEZ+YQkyc2X65uqqJjkswbw0PDEZAoLe3x5zTdMUa3tx3gM3bXqdPq32hd22AsWwbmPvQhKRkCvOst7VuLNpaW2hva+XzT//O3/7jX+ns6ODLzz6iq8t6Kx4ubp4kLdnC1NU78AmLxVll3iIukUotRwhcPf1QuLqj6Wyjp83cnhWu7giCgE9oDJ3NE0vpUVdVhqv6qW0LizTbNqWrmtAo806V0KhYWput01cPR9W/euSiVBERHUdDfS0FOVlExpiPPkTFJtBQb7YhSpWarkGra91dnZbfvwiuajfUbm6WZNHxCYnU19fi7e3D67veYs/+AyQmpeDhaZ08uXXVVZQVF/LNp3/hwqlfqK4s4+LpY+YjGP05KyNj4mlpfPa9DQoJo6O9jd5hq1EvwjP9SU01SpUrXV3mbZNdXZ2WLaW2JjFpCoX5T7cSNjbUc/7MSba+ttMylnhRFC5KenvM721vT9eQ86IAFcV5hA1KqO6iUtPb/XQLaU9315CVvsmSk5lBbL99jUtIoq7Wtn32cNRq9ZDV6s6ODsvRF2thkMjpdPbGTdtIt5MnBT7zyfddSJeTF1qZuU1591TRpggAQUArU9EnVaLQdw+5jk6qoFemxrWvxSr3NWlnT6lyRaV2o61/QFdTWWbZ2lRTUYaHpzeqQculep3OvPca8yqAIBHwtGLiytKSIu7fu80r23cil9s318yAgQBz5MuBw57tba0Yjea8eR3tbbS0NOE2kcANw+jt6Uar0QDm8qwsL8XTy4c+rdbiUFSWl+Lp7Y2zs4L3/vAv7D3wR/Ye+CP+gcFsfGXHhKJxjkR9VSmFmfeZv2orMtnQcjaZTFSX5hMy6LyetXFzc6O2phqdTofJZKK8rBRvHx8y0h9RVlrMxq2v2sXRH6299fR0W+q8ra2VttYW3Ce6yvMcRmtvGo2Goz9/z+JlKwgOebHIZSpXNxrratD3l3NtVTnunt5j/n1tlXnGtrenm/a2FtRuHi90DwP09fXRp9Va/r+stARfH78h5VBYkIeP7/gCkUyW8rISPL29UbsN3QZSWV4GgK6vj9qa6gkn5BV0GtD3b20y6pF21GFUuCFvKkbWUYcmct6QWebulE10p2ymO2Uzeo8QtKEz0XuEYJIrkXY1Ws7rSbsaMCgmvo2zpDCHyEETdWFRsdRVVwDQ3tqCwWjAWeEyxKbXVJQikUjw8Br/s5tMJjpzbiFTuaMMf+pAO/uGoWsxD+D13e1gNCLIn55b09aXofB/6uyZjEaMfRrL/2sbq5Cpxv/OmUwmLp49iZe3DzNmP03eHhUbT1VFGQCtLc0YjAZcXJTodDpLgInyshIEiYD3OJLwPo/RbFtrS4vlPosL8/HyHv09tCa+fv786Z//C7//4z/x+z/+E2o3N/a9+wGurtY7f63TmAfwJpOJqtwHBESb24FO24vJZLajmq52ejvbcXZ1w8nFld6OVnRa83aotvpKXNwmZl+Vajea6p/atrpqs20LjYylvr+d19dU4uZu/S2cOl2f5cySTtdHdUUpXt4+qFSu1FaZtWsqyy2TJhHRsRTmZmEymaivrcbJyfmFt3ACuLq6ola70dJsdp7Nbc2X7v4gRCaTiTu3b5Dav13+RZm3eDl73v8Tb733B1ZvfIXg0AhWbdhKZEy8ZaWvpqoC9/5Jk/bWFkv+28b6Oox6/bjPrj+Pvr4+tMP6Ex9fP2Ji48jKMAedysp4Qmzcs0dUrMXgCeCiQe90R3u7ZbLWy8t673lQeDRlBdkAlBVkExweY/mur09LY20VweFPd0J4+QbQ2d5KV0cbBoOBiuK8Id9PFldXV6r667uyvMwqk2QTITAomNaWZtraWjEYDOTmZFslVZbMoEVq1AHmyJxu2iY0MhUyg9bymX9XCY0q85GePqkLbtomy2+d9V1opUpzdE6TAQCpUYdrXwsamXUmHV4oqXpzYz03L53BYDCgdvdgyaqNOCsUXL9wEt+AYBKnPJ2N7uxo49wvP5kjsLm6smjlBtRjRPcaK6n6yWNHqKoop7e3B6VKxYJFS0m7cwu9wWA5LzM45P0nf/03+vq0GAwGnBUKtu98c9Kd80jalRXlNDbUAQJu7u6sXrcRV1c1OVkZpN29hUQiRRAE5i1cbJnVGI2xkqo3NdZz8cwJTCYTJpOJmPhE5sxfTHFhHvduX0cQBJydFaxcu+kZB+PIj1+zcOnKMZ294UnV7185RWNdFX2aXpxdlCTOmE/BkzSMRgNO/aGuPX0Dmb7QHLGqsbaS7Ac3WLZ56HaLrPvXqSzOQ9PThULpSkRcCokzhkZQnEhS9VvXr5Kfm40gkeDvH8CaDZv5n//vf8XN3cMSwCY2PoEFi5aO63rPS6o+kfZWkJfL7ZtXLXW+YNFSomMn32lMpL3dvXWDe3dv4TnIgG7f+aYlsMRIjJVUPf3eDUqL8pBIJHj5+LNgxToKstLJenyP3p5uFC4qQsKjWLBiPT3dndy8dNoSsCdlxrxRt3wOMN6k6m2trfxy5CfAvLqWmJTC/IWLOXX8KA0N5t0E7u4erFm/0XLm4nmMlQT31PFBZa5UMX/RUqZMm87ZU8cIDAqxnBcE82Dh3OnjtDQ1YsIcgWz2c6KDDk+qLulpQ1F+D0wmwITeM4y+wGRcH/2EyUmJqT94i94jhL7AoWWqKLuH3j3InHrBZMS58iHSzkZzUJeB1AuDeF5SdZ2uj0Nf/s28fbd/67HBYODWpdO0NDUgkUqZvXA5gSHhdHa0c+H4TwiCeQVi4Yr1IwbvsZTrsKTqutZ62h6cRTpoO6YqZgZO3oF0Zt9G39mCIJGgipuFk5c5gEpfSx3dRQ/xnPM0nYnJoKPt/lnze2wy4uQVhCp+1pBjAmMlVa+uquDQ9wfx9vGzTBQtWLKcsPBILpw5QVNjPRKJlMXLVhIaHklHextHf/4OQRBwdVWzat2mMSfxJpL8dyTb9tN3B+nrMwc/8vPzZ9W6jePeFj6RpOrHjx6morzMYmsWLVnGtNQZlu//9h//yt533n8mIu5oDE+qXnD3PB2NNei1GuQKF0KSZ2PU66grMqfT8AqOImzKPHMExqpiKrPTEATzyn1I8hy8giIAqCvOoq4wA0EiwVmpJnr2ymcCiT0vqfqTtJuUFeUhSCR4+fgxf/k6DHo9Ny+cpLurA5nciblL1+Dl40dvTxenfz5odvAFAblczuZd7466RX2spOod7a2cP3EEME9MRCckMWPOQuqqK7l97SJGoxGpVMqiFWvx9Q/EZDJx68p5KstLkMnkLFuzEd/+YEIjMZGk6vX1dZw7fQKDwYCHhyfrN24hOyuDxw/NCaNj4xNYsmzluCdPx5tUvbqynCcP7rFh2+toNRounjlGV0cHcicnlqxah4+vP4/T7pCfm4lEIkEmkzN/yYrnpl4Yb1L1ttZWjh5+2p8kJZv7k96eHo4dPURHRwfu7u5s2bZ93Oevx+pPRurDS4uLaGlpNu+EcXNn1boNqNVunDt9gsL8PNzc+1e4JRLe2vfemNrDk6rfuXSShppKtJpeFEolKTMXEhwRw+2LJ+jp6kCldmP+ys2WLZul+VnUVpayYNXQIz81FSU8vnMFk9FIVPwUkmbMYzhjJVUfqR/18vbmysVzGI1GZDIZK9dswD8gkO6uLr796lP6+rTmHR1yJ/a+9/sx7dxkk6oXFxVy6cJZTEYTU6alsmDRxM78wrNJ1V10HYS3ZvQHVDHR6hJInTqW4PZc3LUNYIJGVRiNruZJSrlBQ3hbBnKDeZKy3jWaFmUwak0jIR15mDDHw2pQhdOsGhrzYbJJ1V/I2bMlYzl7LzNjOXu2ZrizZ08m4uxZm+c5ey8zYzl7tma8zp4tGKtztjXDnT178jxnz5YMd/bsyVjOnq2Z7KDEGkzE2bM2w509e/I8Z8+WjOXs2ZqJOHvWZrzOnq0Yr7NnCxzZnwx39uzJWM6erXGkXR3u7NmTyTp7/5ix0UVERERERERERERERETGRHT2REREREREREREREREXkJEZ09EREREREREREREROQlRHT2REREREREREREREREXkJEZ09EREREREREREREROQlRHT2REREREREREREREREXkLGTL3Q1mtwWDxZhdxx4Yt/q+gNjkv74MgQ4fZIwv5rRad3XJ07MnSyyG8LowNDo0scaNsciSP7E5lUtC0iIiK2wZGpNlROkxuwihZRRERERERERERERETkJUR09kRERERERERERERERF5CRGdPRERERERERERERETkJUR09kRERERERERERERERF5CRGdPRERERERERERERETkJURmzYulP0wj4/FD/PwDWLd5mzUv/Vwe3L/H44cPCAgIxC8ggLnzFjhMf/Mrrzpc92//8a/sfed9lEqlzbQfPUjjyeMH+PkHsnGLfet7gIf37/H40UP8/P3p7emht6eHuQsWkZiUbDNNR9X1cG1HtHMw13vG4wd0d3cze94C5sxbaDftgefv7u5i3vyFzFuwyK66jrYvfVotcfEJrF63wS56jmjjv4Z7eHj/HumPH+LvH8CmrY57xx1pXxxR747sUxz97AP6nR3t7Nl/AB9fX7tpOvqZfy3jpt+C/suoPd7rmkwmLp0/S3l5KSAgk8nY+up2PDw8X0j/0aD+YuMY/UV7WxtffPJXPL28LZ/NmjOP5CnTXkh/PFjV2ct4/JBtO99ErXaz5mXHxeOH99m5ew8Z6Y/srj1Y383Nvs/uKF2A9EcP2PHGW6gdoD3A44cP2Ln7bTo7O7hx7Qr73vvADpqOK3NHt3OAJ48fsH2nY+rd0e+Zo+1LeWkJdbU1dtNzRBv/NdxD+qMHvL7r7d9UG3e0NozcpxiNRiQS229CcvSzD+jfuHaZpqZGuzh7v5Zn/i2Nmxyt/zJqP++6vb29uLi4kJuTRWdXJ+8c+D2CINDR0YGTXA6AprcXhYvLpPTTHz1gxxj9xYA+gLuHJ3vffXac+iL648FqefYunTtNTmY6nl7eJCRPobgwH4Nej0wmZ/X6zXh6ez//IoOYSJ69c6dPkvHkMV7ePnR0tBMbG09rawu9PT3Mmb+A1OkzJ6Q9UQbrJyQm0dbWRl1tDYIACxcvJT4hyea6U6ZOo7SkmM7OToKDQygrLWHvuxNb2ZtIXqQLZ0+RlZGOl7c38YnJtLW20tzUiMFgYMGipcTExU/oWSaTZ+/cmVNkPnmMh6cXrS3NyJ2c8HD3YOtrO/D09Br3dSaSZ29wmadMmUppaQldL1DmE2F4O4+PT6StrZWOjnZmzZnHrNlzJ3zNiebZu3jOXO+eXt6kTE2lra2VlavXT1gXJp5nb/DzT52WSltrq81XuIbrdnS0s2DREubOW0D644cU5OWxbfvryPs7DFvr2+O5hz9vXFyCuZ21t7Ni9VpqqqsoKS5CrVbz2uu7kEqtnxPV2m19Inn2zp812xXzOz6NqsoK2tpakcvlrFm/CT8//wlpTyTPniP7Mkf2JzC0T+no6CA6Jo72tlbUbu4TXl2daJ69wc8en5BIYX6e+QtBYPfb+3B2dp7Q9SbKgD6YnVt3dw+cnZ15ZfvrE+rLJqM53K4BfPbxX9n++m7cPTxsoj2S/u//+E8oFAoAPv7rv/Pmnv2oXF1tqtvU2MD/+v/5/6HVavm3//5/s+utvYSGhfPtwS9Yv2kLXl4TG7dOVL+jo505c+ezcPFSSoqLuHPrBrvf3mez3L8D2mq1G+3tbfzn/+3/i1QqRavV8sUnf+fA7/9oE3s+WNvL24e21hZL2ba1t7FqzTqmTE194etOnZbK7LnzAfN7VFxUSEb6I7RaLbvf3kfavTu0t7Wyeu2z/ed3X3+Js7MzU1NnEB0TO+YE0+A8excG9xdTU5k1Z55Fv6S4kMz0x2i1Gt54ax/tbW0c+fl79h/4/TPX/OGbL3F2VjAldTpR0aPrTzbPntVW9lau3UB5aTGvvfE2EqmUGbPnIZFIqCgr4daNK2x6Zbu1pJ5h7YZNlJQUseutvTx6kEZBfh5v73sXnU7Hl59+RHRMHGq12i76aXdv4ezszLvvmytT09trF93bN64REhrGwsVLKS4ssHQctmL1uo2UlRSzY9ceHt6/S1h4BOs2bkGj0fDdV58RHhGJ3MnJpvewdv1GSkuK2P32PpoaG0i7d4ftr++yreagMr91/Srh4RHMX7iYkuIim5f58HZeWlLMrrf20tfXxyd//w+mz5hlM0M9wKq15np/fdceSooLgVab6g1m8PMXFxbYTXt4uQM8vJ9GWWkJr+7YiUxm1Q0SY+rb47mHP29ZaQm73tpLc1MjX3/5Ga+89jrLV67myM8/UlxUSFx8gs3vwZ5tfc06s13ZuXsPt29ew88/gG3bd1JeVsrpE7+wb4RZWWvhyL7Mkf0JDO1T0h/ep7iogDfe2mfTiZQBBj/76RPHWL1uAyGhYfT19dn8/R6sv/ed97ly6TzRMXEkJNpmkni45mC7Zk+GtLeb1yjIz2XqtOnUVFfh5u5uE0dvuO7JY0doamqkva0N/4BAKivKCQwKprOjwyaO3nB9uVzOV59/QkhoGJfOn2X7G7tt5ugN1t7zzgGuXLpgsd+52VnEJSTYdPww+LkHJo3qams4ffIYcXGT70OGX7e1pYWMJ4/Iz80lOCSU2XPnExYeAUBCYjLfHvycqooKwiMjSU6Zin9AIAC73tpLZUU5GU8ec+XieeITk5g6bTqeXmNPtqzu7y9e373Hop+Z8ZiCvFyCgkOYNXceoWERlr9vb2vlq88+svx75Zp1hISGs/NNs35WRjpXL10gPiGRlKnP1x8vNrFifVoNF04fp621BTB7uPYkNi4euVyOXC4nLCKC2ppq1DYYkIxEWWkpW7a9Zvm3LZdlB1NZUc627TsBiI6Ns8yQ2YPy0hKKCwt4kHYXAL1BT0dHO94+tt+G4kiqKivYtsNc5lHRMXYtc4DomFhkMhkymQylUkV3d7dDt939VsjOfIJa7c6rO3ba3Ln+NRAVHYNUKsXXzx+TyURUdAwAvn5+tLe32eUeHNXWq6sq2bptBwDhEZFoenvRarU2X+kZwFF9mSP7kwGiY+Ps4ugNJyQ0lMsXz5OUMoW4+ETRptqBhKQUbt+4xtRp08nNySLBhmfuBxMSGkZVRTltbW3MW7CIJ48fERZeQ0BQkF305XI56zZu5ruDX7By9VqbreKOxLTU6dy7c5u4+AQyM9JZt2Gz3bQBenp6OHn8KFu37cDZSvYlPy+HY0cOMX/hYva++/4zdtrNzY0Dv/sj5WWlVJSX8sO3B9n66g4iIqMQBIGw8AjCwiPQarXcu32TT/7+H2x9dfu4d+cV5OVy4pdDzFuwmD37D+A0Qj8x2jbO4fppd2/x+cd/YfMr24lLSJxcgQzCJs7enZvXCAkLZ9O2HXS0t3Ho+69tITNubDdPIgLmQ69btm3Hy9vH0bfym0I6aMZZIhEw2XlS5beKr68/9fV1dHZ2vPDB7n8EBtqZIAhIJBLLzLMgCHabyBPbupnfUl8ml9t2Z8hozFuwiOiYWIqLCvn2q895fddbePu83H2bRCKBQUd69Hq9XfWDg0NobW2hp7ubwvx8FixcYhfd0LBwHj96QFdnJ4uXLift7m0qyssIDQ2ziz5AU0M9LkolXV2ddtMEs6N7/uxpKsrLMBmN+Pr52U3baDRy/OghFixaalXdiMhoVq1ZR+aTdGqqq5gyLZW4+MQhq/MymYzomFiiY2JRqlwpLMgjIjIKAJ1OR2F+HhlPHqPVaFi1Zh0RkdHj1g+PjGLF6nVkZZj1U6amEhufMO7dATqdjqKCPDIz0tFqNKxYvY7w/nt7UWxy6rlPq0Xlat5qkpP5xBYSY1JUkI9er6e3p4eK8jICgoLtph0RGcWjB/ct/7blNs7BhIaFk5OdCUBxUSEajcYuugARUdE8fnifgfOf9XW1dtN2JMGhoeTlZANQWlJs1zIXcRx+AQGs27CJwz/9QGenfTtoEfsSEhJmsasV5WW4KJV2W9UDx/VljuxPHE1rawu+fv7MW7CIgKAgmpub7Krv5OREX5/Wrpru7h7U9ffbdbW1tLe12VVfEATi4hO4fPE83j4+uNgwivhgAoOCqa6qRBDMkRn9/P1Jf/SQ0LBwu+i3t7eRdu8O+979gJKiImqqq+yiO0DKlKmc+OUwU6al2lX32pWL+Pr5k5ScYtXrOjs7M2PWHPa++z7LVqyiqrKCT//+F65cugCY2/ZAn20ymWhsqMfN3QOAK5cu8NlHf6W6qpLlK1ez9933mTFrzoTsvbOzM9Nnzubt/QdYsnwl1VUVfP7xX7l2+cJzf3vt8gW++ORvVFdVsXT5Kt7ef4DpM2dbrb+xycrezDnzOX/6OPfv3CSif8uPPfH19+f7b76it6eHBYuW2PS83nAWLFrChbOn+ezjvyIIkv4ALS++BPs8Fi5eyvFfDvPpR38lOCQENzd3m2sOMG/BYq5eOs/Bzz/CZDLh7u7Jth1v2E3fUSxcvIwTvxwmOyuDoOAQVCpXnGx8TlFkEA5c5ggJDWP5ytUc+vE7du5+26YpTkQcx4LFSzl76jhffPp35HI56zdttau+o/oyR/YnjuZB2l0qyssQBAEfHz/LtmV7kZiUwtnTJ3h4P41XJhhsbLLEJSSSlfmETz/6K0FBwUNCw9uLhMQUDn7xCRs22+8dk8lkuLm5ExQcAkBoaDi52Vn4TjAI06QwmThz8jjLV65BrVazftMWTp04xt53DtjlnChAUspUbly7QmLyFLvoDZB29w4+vr588cnfAVi0dDmxEwzq9zz8AwJZs24jer2e8rJSAHp6ujl7+gQGg3nlOjAwmJmz5gAQFh7B4qXLrVb2/gGB+AcEotfrqejXh2fP7E2ZmsqM2XMJDYtg4RLr6Q/HatE4rc1EonGKWIeJRk+zJpOJxmktJnsgWq/XI5FIkEgkVFdVcv7MKfYf+J2V7862TDQapzWZaDTOwaTdvY1Wq2Xx0uVWvCORl5WJROO0NhOJxjmYm9evIndyckguzeFMJm+rI/uTiUbjFBH5LZKXm0NRQT6btjomT/I/KgYH9icOj8YpIvJbo6OjnWNHDmEymZBKpazbaN8Dzr9VHj98QGbGE7Ztf93RtyIiIiIiIvIPx4VzpykpLmLHzjcdfSsidmDSK3udHe2cP3Wcnp5uAFKmzWB6/3IowKO0u9y4epH3//gvuCiV5GVn8iDtDphMODk5s3zN+jGXyie7sldSXMSl82cxmoxMS53BvAWLJnWd36L282Ziz546TklxIUqlin3vmVewrl2+SHFRAVKpFA8PT9Zu3IJCoaC2ppoLZ0+Zf2gyMX/RUmLHiCI30ZU9o9HIwS8+xVWtHpJu4eL5s2Q+ecw//5f/fdzXmuzK3ukTxyguKkCpUvHu+x9O6hqTxVraY63snTv9tL73vvt0xfLxwzTSHz1AIghERseyZPkqANLu3CQzIx2JRGD5ynVERI19sHmyK3uOes86Oto5dfwXuru7AIHU6TMsOXXsqdfb28uxo4foaGvDzcODV7Ztt3nUX0faNjC/7199/glqtZrtO3dP4vfjn4nVaDScO32CpsYGEATWbdiMl7cPJ345RHt7O+7u7mx5ZfxlPtmVvZe5PxmpL8nPy+HOzWs0NzXx5t53CQgcGhGxo72dLz/9G/MXLbXk0hqNya7saTQazpw6bq57BDZs2kJwSOikrjUeRrLj9XV1nDtzEkP/zpHV6zYSFGz9s5ojaTvCtoBj2/qL2pYXwZ7PPdaYIe3uba5cusCf/vm/2PxIgr370eFYo8wnsrL3IO0umf2pa3x9/Vi3aStdXZ2c/OUwmt5e/AMD2bB527gje9t9ZU8ikbB4+Sr8AgLp02r5/uBnhEVE4u3jS2dHO+VlJUOyybt5eLB919soFC6UlRRx6dwp3nj7ncnKj4jRaOTC2dPs3G3OZP/V558QExuPj6/tUwD8FrRTpkxj+szZnDl5zPJZeGQki5etQCKRcP3KRdLu3GTJ8lX4+Prx1r73kEgkdHV1cvDzj4mOjRszUeVEeHj/Ht7ePmgHHWSvra1Bo7FPQByAKdNSmTFrDqdOHLWbpj21k6dMI3XGbM6eelrfFeVlFBcW8Pb+95HJZPR0myd7mpsaycvNZu+7v6O7q5NDP37L/gMfWq2+B3DkeyYRJCxfuYaAwEC0Wi1fff4xEZHRNtMeTS8zI52IiEjmLVjE3ds3uXvnJstWrLbJPYBjy3yAB/fv4e3jQ5/W9oErLl84S2RUNFtf3YHBYECn03H39g3CIyKZO38R9+7c5N7dWyztn+SwBS97fzJSX+Lj48uWbTu4cPb0iL+5evk8kVG2PT936fxZoqJi2Pba65a6tyUj2fGrly+wcPFSS0TQq5cvsPvtfXbRvnv7pl1tCzjevtjTtgzG3s892piho6Od0tISu53LtXc/Ohh7l3lnZwePHqSx/8DvkcvlHD96iLycLEqKi5g1Zx4JSSmWpOypM2bZ5B4GmPRITOWqxq8/GaGTszNe3j6W0LHXL19g0bKVDI6gEBQcikJhniEKCAqmywZR7GprqvHw8sLD0xOpVEpiUjKFBXlW1/mtaoeEhVvqcICIyGjLgD4wKMQS6Ugul1s+N+j1CFaMptHZ0UFxUSFTU6dbPjMajVy9dIFlK2w3ABtOaFg4LnbKo+gI7ZDQ8GdmdTMeP2D2vAWWQ8RKlQqA4sJ8EhKTkclkuHt44uHhSV1tjdXvyZHvmataTUCg2eY5Ozvj7e1LZ2eH3fWKCvJJmTINMA+aC/PzbXYP4NgyB+jo6KCkqJBpqTNsrqXVaKiqrGDKNLNtkUqlKBQKigoLSO4v8+Qp0ygseHnL3B7aI/Ul3j6+o6bvKSzIw93d06a5W7UaDZUV5ZZ+ZaDubcmIdlwQLNE4tVoNrjYKyjOStr1tCzi2rdvTtgzH3s892pjh0oVzLF+xym4Bz+zdjw7GEW3NZDSi1+sxGo3odTpUrq5UlpcS15+7LzllKkV2aO9WObPX0d5GQ30dAYHBFBfm46pWj7lFMzsjfUK5K8ZLZ2cnbuqnq4lqNzdqq6utriNqj0xWRjrxiU+TT9bWVHPu9HE62ttZv+kVq63yXLpwjmUrVtHX12f57NGD+8TExePqar/Iq79FWltbqK6s4Nb1K0hlMpYuX01AYBCdXZ0EDgoL76p2o8sGBvzX0tbb29qor6+1RHGzp153d5dlAKhyde3fDmM7HF3mly6cfeZ9txVt7W24KJWcOXWcxoZ6/AMCWbFqLT3dXRbbolK50vMSl7mj63s4fX193L97m+1vvMWDe3dsptPW1oZSqeT0yWM01NcTEBDIyjXr7B5heeXqtfz0/TdcuXgBk8nEW/usuwNqLOxtW8Cx7c2etmU4v4b3rDA/D7VajZ9/gF11B7B3P2rvMler3Zg1dz4f/+VfkcnkRERG4R8QhLOzwjIednVzs0sKpxcefff19XHql0MsXbkGiUTC/bu3mLdo6ah/X1leRnZGOguXrXhRaZFfEXdv30AikQwJ4RsYFMy+937Pm3vfJe3uLaskai0qNO85H3yeo7Ozk/y8HEsIXRHbYTQa0Wg07Hr7HZYsW8XJY4cZ69zvy0hfXx9HD//EytXr7JJzbSw9QRBgkmdO/xEoKixApVQ9c37LVpiMRurrakmdPpO977yPXC4n7c6tIX/zspf5r43bN68xc/ZcmztdRqORurpaps+Yxf73PkDuJOfu7Zs21RyJ9IcPWLl6LR/++Z9ZsXotZ04et/s9wMvfzu1tW35t6HQ67ty+yeIljolobe9+1BFoenspKsznwId/5nd/+md0Oh1lJUUOuZcXWtkzGAyc+uUQ8UkpxMQl0NTYQEd7G99+8QkAXZ0dfPfVp7zx9juoXF1pbKjn0rmTbN2+CxcX6x8CVavVdAxaTejs6LDZFghR+ylZGU8oKSpkx663Rwx24u3ji1zuRFNjwwsb1uqqSooK8ykpLsSg16PVavn8k78hk0r5+G//DpiN2Md/+3fe//2fXkhL5Flc1W7ExCUgCAKBQcEIgkBvbw9qVzVdHU/bYFdnB66DZtCshaPbusFg4Ojhn0hKmWKX/Jkj6alUrnR1duKqVtPV2YlKqbLpPTiyzKurKigszKd40Pt+4tgRNm991SZ6rmo31G5ulpnm+IRE7t25hVLlSldXJ66uarq6OlG+xGXu6HdsOHU11RTm5XL9yiW0Wo0lAfb0mbOtqqN2G173Sdy9fes5v7I+mZlPWLlmHQAJiUmcPWU/Z8/etgUc197sbVuG4+j3rK21hfa2Vj7/9O8W/S8/+4g9+w/g6upqU21796MD2LvMy8tKcXf3sPQXsfEJVFdVotVqMBqN5pgWHR12yZ86aWfPZDJx8exJvLx9mDHbHEnHx9eP9//4L5a/+fzv/86uPe/iolSaI/D8cog1G7faLGFnYFAwrS3NtLW1ola7kZuTzeZX7PPi/la1S0uKuH/vNjvf3INcLrd83t7WitrNHYlEQkd7Gy0tTbi5e7yw3tLlK1m6fCVgDhaSdu/OkGicAP/j//mvoqNnI2Ji46msKCMsPILWlmYMBgMuLkqiYuI4feIoM2bPo7urk7bWFpvMmDqyrZtMJs6cOo63tw9znhMN0JZ6MXFxZGU+Yd6CRWRlPiHGysloh+PIMl+6fJUlEEpFeRlpd2/bdDDm6uqKWu1GS3MTXt4+lJeV4u3ji7ePL9mZT5g7fxHZmU+IiY2z2T3Ab7c/GYk33tpn+f/bN64hd3KyuqMH5rp3c3OnubkJ7/669/Ed+QyhLXF1VVNZUU5YeATlZaV2TXBub9sCjmtv9rYtw3H0e+br58+f/vm/WP49mVyak8He/ehg7F3mbm5u1NZUo9PpkMlklJeVEhAYRGh4BAV5OSQkpZCdlUF0rO3fs0mnXqiuquDQdwfx9vWzrOYsWLycyOin0bIGO3sXz5ykqCAPtbs54o9EkLBr77ujak829UJxUSGXLpzFZDQxZVoqCxYtmdR1fovaz0u9cPLYEaoqyunt7UGpUrFg0VLS7txCbzBYDv4GBgWzet1GcrIySLt7C4lEiiAIzFu4mNg466VegLGdPXukXjh+9DAVVJmPAgABAABJREFU5WWW8li0ZJndDnpbS3us1Aunjg+qb6WK+YuWkpQylXOnzeeZpFIpS5avIiw8EoB7t2+QlfkEiURg2Yq1Q2zBSEw29YKj3rOqygq+PfgFvn5+loBDS5avJDom1q56QUHB5vDo7e24ubuz9dUdNg/W40jbNsDAgMzWqRfq6+s4d/oEBoMBDw9P1m/cgslk4vgvh+jo6MCtP/XCeMt8sqkXXub+ZKS+RKFw4fLFs/T29ODsrMDX35/tw3KADTh7tkq9UF9Xx9lTxzEYzXW/YdNWm6YeGMmOe3v7cPH8WYxGIzKZjDXrNthk4mwk7bi4BLvbFnC8fXkR2/Ii2PO5nzdmsJezZ+9+dDjWKPOJpF64df0q+bnZCBIJ/v4BrNmwma7OTk4eM6de8AsIYMPmbZagd89jsqkXJu3s2ZrJOnsik+d5zp4tmYyzZy0m6+y9DIzl7NmayTp7IiITZSLOnrWZrLP3j44j+5PJOnsiIiIiz2Mizp61mayzJ1pEERERERERERERERGRlxDR2RMREREREREREREREXkJEZ09ERERERERERERERGRlxDR2RMREREREREREREREXkJEZ09ERERERERERERERGRlxDR2RMREREREREREREREXkJGTOxgyPDVYvYnz4HhuFXOo8vx8jLRrdW71B9uRii3O6Mle7G1jgyzYgjn1vsyeyPI4cPjmxrjsSRZe7I9Em/ZX6r/Ykjcax9mVyZiyM9ERERERERERERERGRlxDR2RMREREREREREREREXkJEZ09ERERERERERERERGRlxDR2RMREREREREREREREXkJEZ09ERERERERERERERGRl5CXNgTid19/yfKVawgMCnL0rTiM9rY2Dv30He++/6Gjb8VuNDc1cfzoIRAEXnltB56eXo6+JZty5IevWbhsJf4Bjmvnt29cJTg0jPCIKLtr37h2hdCwcCIi7avtaPty+sQxomNiiU9Mcoi+iIiI7cjMSCciMhq1Wu3oWxEReeno7urizKnjdHR0YDQacHf3YMcbb9pFOyvjCdevXMR10Lu9ccs2vH18bar70jp7Ir9NCgvyiE9MYsGiJY6+ld8MCxYvc5j24qXLHaYtIiJiP4xGIxLJb2MzUlbGE3x8/URnT0TEivT29uLi4sKN61eIiIxi1px5ADTU1wNgMBgwGAw4OTnZTBsgPjGJlWvWD/neYDBgNBiQ20AbrOjspd25QX5OFi5KJa5qN/z8A3Fydib7yWMMRgMeHp6s3rAVuVxuLUkLt25cIycrExelEjc3d/wDAwHIznrC2VPHMZqMrN+4laDgYLtox8UlcO7MSXp6epAIAltttMI0knZ4eASnTx4HIDLKdqsdI9V3dGw8Vy+epbe3B5lMzoo1G/Dy9rHZPQx/fm8fHx4/vI8gSCgvK2XXW3ttrukfGEhYWDhnTh1HQCAiKoqS4iKbrKbeH1bmvv7mdl6cn8e1i2fRarWsXLuRoJAwq2sPcPfWdfJyMnFxUaF2M9d7c1MDkdGxxCXYdpVppLJvamwgOiaOBBuucI1mX8Ccb+f0yWOo1W4sWbbCJvq3b14nOysTpVKJ2s2NgIDA5//oBRjpeYsLC/APCKCqooI+nY5NW17h7u2bNDY0kJCUbPVnH+mZi4sKCQwKpqK8DK1Ww7oNmwkNC7eqLsCdm9fJye7XVruZ7WpEFBfOnkKn0+Hh6cm6DVtQ9Hfc1mS0svfz96eyvNzufdnw/sRWtg1Gti2lxYX4+vlTXV1JQmIyM+fMt4n2SO1NKpWS/ughEokEbx9ftmx7zW7adbU1nDx2FLlMxpt737HJuGmkdl5cVMiyFasJCAyip6eHb778hPc//E9W1x7e1nx8fSkqyGfPOweAgV1J3/Pu+7+3qa5/QAC5Odnse/d9Gurr+OLTj/j9H/8JN3d3PvrLv/HO+7+3etmP9J7l5WSzfOVqwsIjuHblIgICS5avtKouPNvW/P2fPj9AS0szx48etvzbmow0XivIy7V839jYwAd/+DPu7h4vdF3/wEDmzltg+V6r1ZKbnUlG+mPCI6NYunwl3V1dREZFW/7Gz98fAE1vLwe//JTIqGimpc4gMGhsO3vn1nVyszNxUarM71BAILPnPrVRWq2WvJwssjLSCYuIZPHS0ftJjaaX7776nIioaKZMm05AoHV3DVnF2auvraG4II9dew9gNBr44eBnlsF/ytTpANy5eZWczHSmzZhtDUkLtTXVFOTlsv/A7zAYDHz12ceWwZhOp2P/gd9RWVHOmVPHrN5JjaZ94tgR5s1fSFxCInq93iYJGEfTPn3yGKvXbiA0LJwrl85bXRdGr+/L50+zfPV6PDy9qKut5trFs2zb+ZZN7mG050+dMQu5k9OQl93WmqdPHmPdhs0Eh4Ry9fJFq+vC0zJ/o7/Mfzz4mcXZMxqNvP7WO5SVFJF2+wavvG6b7Qh1tTUUFeTx1v4PMBoMfPvVp/j529bxGGCs99xRukaTkRO/HMHH19dmK8kW/fc+MOt//olNnb2xnlcqlbL33fd5kHaXIz//wN533kfh4sJHf/03Zs+Zh4tSad17GOGZjUYje/a/R3FRIbdvXmfn7retojlEOz+Xve+a2/jBLz4xv+MnfmHlmnWEhkVw8/oVbt+8zorVa62v/Svry+zRn8DYtsVgNPDm3vdspj1ae7t75xYffPhnZDIZGo3GrtoBgUEsW7maQCsP+IbojtDO7cFobc1gMNDW1oqHhye5OVkkJiXbRVev16PVaqmsqCAgMIjKynJCCEOpUlnd0RvtHjZs3sovh39m9dr1lBQXs2e/9dv7aG3N2dmZ+vo6/P0DyMpIZ8rUVNtpD3vu/Qd+B8CjB2lUVJRP2NEby2ZWVVbwJP0R1ZWVxCUksmnrq3h5ewMwfeZsjh89xKMH9wmPiGTKtOmo1WpUrq4c+N0fKcjP4/rVy/T09DBlWirJKVMtq3ID1NXWUJifx553zO/Q119+in/AU+2sJ4+prq4iNj6B9ZtfwcvL2/Lb/NwcqqsqLf/e9fZ+VCpX9r//IUUFedy8doWenm5SpqaSmDzlGe3JYBVnr7amisiYOGQyGSAjMjoWgJamRu7cvEafVoOur48wG5yrqaqqJCYuHplMhkwmIzo2zvJdUvIUAELDwtFqtWg0GhQKhU219TodnZ0dxCUkAvSXifUZ7bk1Go1ltjs5ZRolxUVW1x6pvg16PbU1VZw5ftjydwaDweraA4xV7/bW7NP2ERwSCpjbXHFRgdW1h5d5RP87BhAdFw+An38AnR3tVtceoKaqkqiBe5DJiBp0D7bGEfX9PN1zp0+SYOMtw9XD9GNibVvmYz1vTKy5nfn6+ePt62c5c+Dh4UlHR4fVnL2xnjkuPgGAgIBA2tvbrKI3mJqqSmJi4y1tPDomFl2fDu3/n73/jG7rStN10WcBJAESBJhzzlmkcs7BliVZkm05yNlyqqoO++xz9rlj3L93jO59uvt0d3WXs2XZspVzoHLOOVCimHPOAQQBggj3B0iIkkiJAcGW1jNGjZJBYL1zzjXTN8P39eiIiIwGID0j03Iv2Mb83sYycMx4Ak/vW5KSbTvpf5yh6ltgYBAH9+0hISmJhMRkh2rbm8HquaMYqq4lp6aR/yCXaTNmkf8gl1dfe8MhumHh4VRXVVJVVcH0GbMoLS0GM4RH2P6EzFBpCAgIJD1jHDu3beb9j9YhlUptrj1UXRuXNYH7d+8QsGgJ+Q8e8P5H62yu/bS+rbqqkru3b/Huh5/Y7Lknjh7m/v0clrz0CkuXvfrE8e/YuHi++Ms/UlZSTGlJMT+v/451n/0JD4UCFxcXUtPSSU1Lp7Ojg+NHD3Hm5HG++Ms/PnKsuqa6iriExCfa0KnjR3hw/x4Ll7zMkldWDHr0fLBjnGCxF5JT00lOtWifPH6Yc6dP8OmXf//IHb/RYNc7eycOH+CVVWsICAwi7/5dqqsq7Sn3TASnqj/fmM1mZDIZ73z4mbOT8sIh6RsYBEGCyWRycmpeHMLCw6msKGfKtBl2W9T5PSGVWvIoCAIuAyYjgiA4rN5J+8pZkLzYdf1FGstc7HCEcTi8/uY7VFVWUFJcyOWLF/jksy+f+zuDEonEehLJaDA4VDs5NY19u3aQmJQCgvDITog9iYiMorqqks6ODhKSkrly+SICgkMNYICmxkbkcjkajcahuknJKVy6cJbI4hiCgkNstmg3HLrUag5n7+f1Ne/Y9J7c5KnTcZPJuHj+LKWlxWSMyyIyKhpBeNhzuru7k5qeQWp6Bju3baaqqoKkvqsoGo2G3Ht3uX8vB6VSxYpVr6NQKIalPXHyNNxkMi5fPEd5aQlp47KIiIx6RPtpdGs0PLifw4P7OXiqVCx7dTUew9R+GjbpuUJCwykvKcJgMKDX6ykvtaz+6Xv1KBSeGI1GCvJybSH1BOHhEZQUFVq1B+6q5D+waFZXVSKTyZHZcCV0KG0XV1eUShWFBfkAGAwGent7bao7lDaAXC63GtUPcnNsrguDv28XV1dUXt4UFVjOYJvNZpoaG+yiD09/747WdJO5UVtTDUDeg/t20R6qjTmS0PAIygakobSkyGHaznjfz9IdlzmB2PgE9u3eYTfDIyw8guJH9O1b5s4q54E4Os8DCQ2PoKT4oXZpcRGubq7I5e5UV1UAkHs/xy53BX9vYxk4ZjwB5/Ytg9U3s9mMurOTqOgY5s5fRE+PDr1e7xBtADc3N/Q9PTbX62eweg7g5eVNQ30dAIX5D+yiPVRd8/HxRZBIuHThnM2PcD5NNyIiitz7Ofj4+iIIAu7u7pSUFNllZ2+oNBTk56HVaVn7/secOHbYLseGh6prLi4uxMTEcexINhmZmTbXhSHybTazd/cO5s1fZD1eaZPnAl7e3syZt4B1X/yZlNR0bt24xo/ffkXufUsfVlFeZp2X9/T00NbWhkrlRY9Ox+4dW9n86wYMBgNr3lrLmrfXkpSc8sRCT1h4BKXFRU+2IW9vZs2Zz0ef/omk1DRu37zGhh++Ji/33lPz0qPTsXfXNrZu+gWD0cDqN9/htTXvkJD0pPZosMlydFBIKDFxiWz55QfcPRT4+QfgJpMxbeZcdmzagNzDg+CQMLt0liGhYcQnJvHTD9+gUHgSEBCETCYDLKvAG378DpPJyNJlKx2mvXzlao4eOsiFc6eRSKSsem0N3j4+DtF+ZflKDh3cjwBED7iAakuGet9LXlnFmROHuX7lAiajicTkVAICg+yShqHy32uHOvYszaXLXuXIoQMICERERSGT2XYiBpYyj+4rc4++Mu+v544iOCSU2PhEfv3pOzwUCvwDAh2Whqe182EumNlcF2DK1On06HQc3LeHFateG/bq3Yj0ExLZ8OO3KDwUBAQE2qV+PaL3lPw6Akfn+XHtuIREfvnxW0sdD7RoL12+8qGDFm8fXl72ql20f29jmSPGE/gd9C2P1Te53J2D+/fQ09MDZjMTJ02x6bHZp2nLZHLSx2Vx7MghuzloGaqeT5o6nQN7dpJz5xaxcfE21RyoPVQ9T05N48zJ43z5F9s7hRlK18vbG8wWow8sxzfV6k67OGAaLA2YzZw9fYK33/0AlcqLCZOmcPLYEZa9usr22kP0q6npGRQW5hMdY582Pli+e3t7qa+r5cK5M1w4dwaAN95+d0QeaJ81XkkkEuLiE4iLT0Cj0dDW2gJY7tsdP3rIupOdmTWekNAwenQ6Jk6e+sQu4GAEh4QSF5/IxvWD91kSiYTYuARi4xLo1mho7dOGJ+/sLVyyFH//ACZMnELEMLRHg/A05yGtGsOwPYvo9Xrc3Nzo7e1l99aNzF/yypicN3jIhm+HDtTevHEDL72ygmAHXTZ+nrS7e4Z/bMOZ73uwNDiq7AfT9PXzsx5BuHLpAl1dahYNch57MDROLHMAV+nIVowGpmH75l9Y9NIy66XkkeLmMnrt/rK/cO4Mk6dOIyo6ZlRpGK3uWOrZSB02PaL/68+89MryUTtpGc4gYq92NZJ82zLPAKYRFPlA7a2//cySpctHXccBpJLhD9yDlf2pE0cdEtPxWe99pHFb9YaR7Xbbsm9xlY5ssmTr+uYs7T9yPXfEvMmZ8zV7pcEW/eq1K5fo6ekZcSijkRgl9ip7Z7xTg9H0hPa2Tb+w+OXR91nDxVM2goY2AJtdNDl97BCtLU0YjEZS0jIc5qUP4MihA7Q0NWEwGkjPyHRo431RtZ35vvtxRv4H08x7cJ8rFy9gMptQqbxZtsL2K+9gKfO2vjJPdlKZnzhykNaWZgwGA6np4+zesQ3k8bK/deMaBkOvXY7bPE3X0ZODo4cO0tLchMFgIH1cpt0noc7OLzg+zwM5dtiibTQYSMvIdGodf1HGE3Bu3+LM+uYs7Rexnju7jjs7DYPVtT07t9He1sZb735gV2175duZ5Xn8yEFami19VlqGY/uskTLqnT2DwcCurRsxGo2YTSbiEpOZNnMuJ48cpKGhDszg7ePLoqUrcHNzw2gwcOzwfpoa6pHL3Xl5xWpUT3GzOpqdHoPBwOaNGzAYjZhMJpKSUxwadLm0pJiTx45gMpvIzJrAtBmz7Kp36MA+SooL8VAorKutjQ31HD2cjV6vx8vLmxWrXhv2cZin7eyN9H33U1yYz+H9u3jzvY8JCh56ZXo07xsGLwN70dnZQfb+vWg0XYBA1vgJTJoyjQvnznD39i08+i42z5m/cNiXu5+2s2cwGNj9WJlPnTkXs9nMlQtnKC7MRyIIpGdNJHPCZMxmM+dPHaOirAQXF1cWLl3+TIPwaTt7xw7tp7SkCA8PBR+ss7hIbmqs5+TRQ+j1elRe3ixdsdpav65dvsD9nDtIJALzFr78zGNfI93Zg+ejjQ93JdZgMLD5158twVb78jprzjwO7NtNfV0dUomEkNAwlixdNmzvbaM9HqLT6TicvZ/mpkZA4JXlr1o90A6X4ea7v511azQgCGRmTWDSlKns27OTthbLURhdjw65TM5Hn34xrGeOZMfjxrUr3Lt7G7B4yXt5+UpOHD1EQ30dZrMZH18/li5fOWyHAiPZ8RiIo8cTW2s/bWdP3dnBkex9lncMZGRNYMKkqei0WrL37aKzswOVyotlq15HLnentaWZY4f209hQz4zZ85k0IJbVYAx3Z2+oNtbe3saBvbvQarUEBYew/NXVNveQOJT2rRvXuHH9Ku1tbfzd//i/rOPKcBhrPT9+JJuqygprn750+UoCg4KH9bzR1vPfwxgOcPP6VW71xemNi09g/sLFdk0L/D7Gk7HUt9GMJ45834NhizLv39kbjCPZD+dNH31qmTdptVoO7ttFZ0cHKi8vVvT1az06HYcO7EXd2YHJbGLSlOmkPyP0hcN39qRSKavffM9iyBmN7NqykeiYeGbPX4xbX0dx/vRxcm7fYNLUGeTeu4NcLueDT/9MYX4uF8+dYumK10YrP2Sa3n7vQ2uaNm3cQGx8AmFh4TbVGQyTycTxI4d4a+37KFUqfvnpB+ITkvAPCLCbZkZmFhMmTSH7wB7rZ4ezD1iDc+bcuc3VyxdtEvB4pO8bQK/v4e6tawTZKVYQDF4G9kIiSJi/cAnBISH09PTwy0/fW8+4T5o6zeax/aRSKasGlPnuLRuJiomntaWZLrWa9z75EkEQrBOmirIS2ttaeW/dn2ioq+Xs8SOsee/jUeunZmSSOWEyR7P3WT87fvggc+YvJjwyivs5d7h59RIz5synpbmJgrxcPlj3JZouNbu2beKjz/5sc+91L1Ibl0qlvP3uB9a8bv51A7Fx8aSmZbD81dUAHNi3m5w7txk/cZJd0tDPyWNHiI2NZ/Xrb2I0Gu3idKofiUTC/EVLCA62tLONG34gOiaWlasfumI/deKYXe50qdWd3LpxjY8/swRT3r9nJ/kP7jN/0UtWvdMnjnL75jWmTref8eWM8cSR2oJEwpz5iwkKDkHf08OmX34kKjqW3Ht3iYiOYcq0mVy7cpHrVy4ye94i5HJ35i16mZKifJulAYZuY9evXmHS5GmkpKVz9HC2XdrYUNph4RHExSeyZdMvNtUbyFD1HGDugkVWr4SO4Pcwhms0XRQVFvDxp1/i4uLiEI+Yv5fxxBH1bSCOfN+P44gyT8/IZPzEyRw++HDedO3KRSKjYpg6fSZXL1/k2uWLzJm/iDu3buDn78/qNW/T3a1hw/dfk5KWYZfQG6OeiQmCYF3ZNJlMmExGELBO/M1mMwaDweoiuqykiOS0cQDEJ6ZQXVlu82DjT6TJaHSYi+q62hq8fX3x9vFBKpWSkppGUaFtB6bHiYiMeiLYYmtri9VTXHRsLIV93jHHykjfN8CVC2eZMHk6LlL7uaUfrAzshadSaT0iIJPJ8PMLQK3utJveUGV+/+5NJk+fZV1V63fLW1ZcSHLaOARBIDg0jJ4eHZou9aj1wyOinrik3tbaSljfscmo6BhrHS8pKiApJQ0XFxe8vH3w9vahvq521NpD8SK18cfzauxbTYyLT0AQBARBICQ0zK51ECxewqoqKxiXNR6wTBrs4ayiH09PpfUom6Wd+dPV9TCPZrOZgrwHpKSl20XfbDJhMBgwmUwYenvx9FRaDb3+fs7ewQ+cMZ44UtvTU2k98uQmk+Hr50+XWk1pcQGp6ZZ5Qmr6OEqKCgBLHxccEopEYttJ0FBtrLKijKQUi8GTnjHOLmU/lHZQcIjFaYidGayeO4Pfwxh++9YNps2YZQ2hM1w3+2Ph9zKeOKq+9ePI9/04jijz8Mgo5PJH81dSVEBahqVfS8sYR3Ffv4ZgufdnNpvp1euRy93tFt5lTLNwk8nEtl/X09HeRkbWJIJDwgBLfL3yshJ8/fyZNW8RYImnoVSqAMvKrZubDJ1Wa/OYHiaTiV/Wf09bWysTJk0m1AEr/gBqtRpVX/4AlCoVdTU1DtEeiL9/AEWFBSQmJZOf9wB1p+0mgiN5340NdXSpO4mJS+D29Ss2S8PvhY72dhoa6ggNC6emuopbN66Re+8uwcGhLFi0xGaevEwmE9sfK/OO9naKCx5QUlSAu7sHcxa+hLePL11dajwH1EFPpYquLjUKGw7ifv4BlBQVEJ+YTGF+ntXQ6OpSExIa9qi2nYyQF6mNm0wmNv70A21trYyf+GhejUYjufdyWLj4Jbumob29HQ8PDw4d3EdjQwPBwSEsXPKyTeMiDYWlndUTEvow39VVlXgoFHaJw6VUqpg0dTrff/WfuLi4Eh0Taz2OfPjgPspKivHz92fewiU21x6IM8cTR2t3dLTT1FBPcGgY3RqN1ehQKDytpxbsyeNtzNvHF5lcbp10KVUqutSjXzQbibaj+rKh6nneg/tcOHuayxfOERUdw+x5C5/bGKIDx/AzJ49TVVnBuTOncHFxYf7CxY+MZ/bg9zaevAg4q18dql8bP2Eye3dt47u//Sd6fQ/LV75uF0+cMEZjTyKR8M6Hn9Gj05G9byctTY34BQSyaOkKTCYT504epSj/AakZ9ondMVSaPv7sS3Q6HXt2bqOpsZGAwECH6TubV5av5MSxw1y6cI74hERrwG1bMNz3nZI+jgunT7Bo6Qqbaf+e0Ov17Nm1nYWLX0YmkzF+wiRmzJqDIAicP3OKUyeO8YqNnLRIJBLe7ivzQ31lbjIakEpdeOv9dZQU5nPyyEFef8e+l6v7WfLKCk6fOMrVS+eJjU9EauOV9uHwIrVxiUTCR59+MWhejx89RERklF1ivg3EZDJRX1/HopeWEhoWzoljh7ly6YJNjoc/Db1ez97dO1g44AglQF7ufbvt6um0WoqLCvjsz/+ATCbnwJ6dPLifQ2r6OJYuX4nJZOLksSPk5+WS8Yy7FSLPRq/Xc3DPDuYuXPLEsVzLpMf++/aPt7HWlma7aw6l7ai+bKh6PnveAmts5GOHD3LtykVmzJpr9/Q4msfHcJPZhE6n5f2P1lFXW8u+3Tv54i//YLeJt7NwVn0TecjAfq28rISAwGDWvPM+7e1t7Ny6ibCISLtcUbDJfqFMLic8IoqK8tKHD5ZISEhOs56x91QqrbsAJpMJvb7HLnFM+pHL5URGRVPqoODTSqWSzgE7GerOTjxHEC/EVvj5+/PW2vf5aN3npKZl4ONt2/h+8Oz3rdf30NLSxO5tv/Hz93+jvq6G7D07aKi3/bE+R2M0Gtmzazup6RkkJacAoPD0RCKRIAgCmeMnUldn+5UimVxOWF+ZK5Qq4hKSAYhNSKKlqRGwHI0auJvWpe60+dEcXz9/Xn/rXd796DOSU9Px6osf6empfGQXuUvd+cguoz14kdp4f17L+vJ68fxZuru7WbDIvjtMYFn9VKpU1lXgpORUGurr7appNBrZu2s7qWnpJPa1M7CMHYUF+aSk2D7oMliC7Xp5eePhoUAqlZKQlExNdbX17xKJhOTUNIrybXM8fiicWdccpW00Gjm4ZwfJqRkkJFnesYdCQVff0fOuLjUeCtue/Hka/W2spqaaHp0Ok8lyzM0RZf94+7Y3Q9VzT08lgiDg4uJC+rgs6mv/+GP24ww2hiuVKhKTUhAEgdCwMARBQNvdbdd0/J7GkxcFZ5X5UP3a/Xt3SUhKRhAEfHx88fLyttti06iNPW23hh6dDgBDby+VFWX4+PrS3tYKWO42lJUU4tN31CYmLoH8XEv0+uLCPMIjbB84sFujQdeXpt7eXsrLSvHz87epxlCEhIbR1tpCe3sbRqORvAe5xCcmOUR7IP0Xi81mM5cuniNrgm0ulY/kfctkcj77y//ko8//jo8+/zuCQ8JYtnrNU71x/hEwm80czt6Pn58/UwZ4gxt4xKewIA//ANuslD1e5lUVZfj4+hEbn0h1VTkANVWVePv4Ag/bmNlspr62BjeZzKZHOAHr8QOz2czVS+cZlzURgNj4RArycjEYDHS0t9HW1kqwHRzzvEht/PG8VpSV4uvnz907tygrLWHFStsHcR8MT09PVCovWvoGoYryMvwD7FfmZrOZI9kH8PMPYPJjXhfLy0rx9fNDqbLPQoJKpaKutobe3l7MZjMV5WX4+fvT1vqwnyspKsDXz/ZHSAfizPHEEdpms5njhw/g6+fPxD5viACx8Uk8uG+ZJzy4n0NsvH3zPFgb8/PzJzIqmoK8BwDcv5dDgh3Kfqj27QiGquf9E1Kz2UxxYYFDHAI5kqHG8ITEZCorygFobWnBaDTa/IrR4/xexpMXCWf1q3HxSeTes/RrufdyiEuwaKpUXlSWlwGg0XTR1tqClx02aGAMoReamxo4fvgAZpMZs9lMQlIKk6fPYteWjej1PZjN4B8YyPxFS3GTyTAYDBw/tI+mxgZkcjkvL1/91EyNxhV/Y0MD2Qf2YjabMJvNJKekMXO2444glBQXcfL4EcwmMxmZWcyYNceuevv37KKyohytthsPhYJZc+bRq9dz6+Z1ABKTUpg7f+GwJ4RPC70w0vc9kN1bf2XmvIV2Cb0wWBlkZk0Y1bOeRXVVJZs2biAgMBChbxt+zvyF5OXep6GhHkEALy9vXlq6fNirRU8LvdDc1MCJAWUen5TClBmz6dHpOJa9F7W6E1dXN+YvXop/YBBms5lzJ49aQi+4urLw5eXPNLCfFnrh0P7dVFVWoNN24+GhYPqsueh79dy9dQOA+MRkZs1dYK1fVy+dJ/feXSQSgbkLXiImLv6p2qMJvfA8tPHhOqZqbGzg0IF9mE2WvCalpDJz9lz+9Z//f3h5eVvvzCUkJQ+7DEZrHDbU13Mkez9GkxFvbx9eWb5yxCczhpvv6qpKNv/6MwEBgdb0zp63gLj4BA4d2EdIWBjjR7iINRKX9BfPnaEgLxdBIiEoKJglr6xg++aN1ov0gYFBLHp52bCP2ozWJb2jxxNbaz8t9EJNdSXbN/2C/4B3PHPOfIJDw8jetwt1ZydKlRfLV76O3N0dTVcXm3/5Eb2+B0EQcHV144NP/zTkOxhu6IWh2lh7Wxv79+5Cp9MSFBTMsldX2/zu2lDaN69f5eqVS2i6uvBQKIiNS2DpsuFdiRhrPd+1fTPa7m5LPQ8KZvHLy+weYuT3MIZHx8T23UmuRyqRMn/REqKiY+yShoH8HsaTsdS30Ywnjnzfg2GLMn9a6IWD+3ZTXVlhzd+MWXOJT0zi4N5ddHZ2olJ5sXzV67i7u9OlVnMkez8ajRqzGaZMm2F1UDUUow29MGpjz96MdvIvMnqeZuzZmxf1fT/N2HMETzP27M1ojL3nAVt7IR4JzryD4sx8j2QSbGtGOwn+o/M0Y8/eDNfYe94Q6/mLx4s6njiTpxl79ma0xt6LOdsSERERERERERERERF5zhGNPRERERERERERERERkecQ0dgTERERERERERERERF5DhGNPRERERERERERERERkecQ0dgTERERERERERERERF5DhGNPRERERERERERERERkeeQp/q7P13c5Kh0PMGytBCnab+odOuNTtN+UUMvKJyc78bOHqdpB6qGF6fMHjjTXXWv0XnaEsGJvtmdiMGJZS51kzpNW8TxOLFrweTE2AvOjrzwooYBcGaIE5nri9m37b1f6zTt9yaGj+p34s6eiIiIiIiIiIiIiIjIc4ho7ImIiIiIiIiIiIiIiDyHiMaeiIiIiIiIiIiIiIjIc4ho7ImIiIiIiIiIiIiIiDyHiMaeiIiIiIiIiIiIiIjIc4hNjD29Rk3RyR22eNSI6WhvZ/33Xz/x+eZff6au1v4ec4bS/z3o2jNt6s52dv32o12ePRyGyltLczMbfviWDT9+R1tbq8N0HYGz67q6s509m55857eunKO2stzu+kPl//zZ05SXldpN86fvv3ni8y2//UJdnWM8cnV0tLNx/bdPfH40ex+F+Q9sq9Xezs8/PqnlSJyZhs6Odn7b8J1TtH+PfYvD9Ieo4w7RHqKNO1J/ww9P6t/PuUOXWm0fzY52fnFSeYPzytzp9dyZbbyjnV9/ev76ttE+W9PVxc5tm/nph2/58buv2LF104if0dutpvLsrhH/rrOqkLJjm6g6t8f6P726bcTPGQkvpr97keeWosJ8klJSmTFrjrOT8sIwYZpzy3r23PlO1RcREbE/JpMJieTFOYx0/95d/AMC8VQqnZ0UEZHnBq1Wi7u7O+fPnSY6JpZJU6YB0NjQAIDRaMRoNOLm5mZzbaO+B6mbJeSUZ0gMARkzHvm72WTEbDIhcXG1ubbtjD2zmZrb5+hubcBF7kHUtJdoryqirTwfs8mIm0JF+MQFSFxsb1+aTGYOZ++nproapVLJa2veBiD3/l2OZO/HZDaxdNlKQsPCbK49lH6XWs3Rwwfp7u5GIgisfH0NPj6+dtdtaW7i0MH9AMTExtpU73HMZjPnTx6msa4GD4Uni1e8TndXF5fOHEOn7cbF1ZVZC5bi7etnF/3H8z9p8lRuXLuCIEioKC/jnfc+dIjua2veprmpkcPZ+xEQiI6NpbSkmHWf/9kh2gAF+bkcP5KNrkfH0mWvEhEZZXNtsLzziycP01hveecLl7/O5dNHiYiJJzo+2S6aAxks/8eOZBMXn0hySqp9NM1mjhw6YNVc/cZb1r+ZzWYOH9yPUqlk9rwFdtEHMJtMHD98kNqaKjyVKla+9qb9tMwmjvVreapY+fqb7N6+hcCgYGqqK+nt1bN0+SquXr5Ac1MTSSmpzJpjW4N7qDSEhIZSVVlBj07HkldWEB4RaVNdsBgVJ44epK6mGk9PJStWv0lbawunjh+mt7cXb28fFi1djlzubgftJ+v3jq2bCAwKoqqiwq5j2XDGE3v1azB4Hd+zYwsBgUHU1FSRnJLGxCnT7aI9WBvPuXOLO7duIpFI8PMP4NXVr9tFu1//6KED1PTVubT0cdTX1XJw/x5cXFx494NPcHW17QTQbLK0sbq+8n61r7znzF9EcEgo2u5uNv3yI5/+6R9sqtvP42W+cPFLZB/YxwcffwpYdmx27djKJ599aVvdx+r5y8tWsGfndj5a9zmNDfVs+PE7/vR3/wOVlxffffVffPL5n2xa9o/rr3r9TbZu2sj8hYuJjIrm7OkTCAjMmb/QZpoPtU2cOHKQ2ppqPJVK5i5YwpGD+3j3I0uZt7W2cmj/but/21b70Xy/snwlO7dttv69qamRL/7yD3h5eY/52a+tefuRd9bT00Ne7j1y7twmKiaWufMXounqIiY2zvqdwKAgAHRaLRt//pGY2DgysyYQEvqMvtZspvHueXRtjbjIPQievBiJ9KGdY+rVo64tRV1ZgHtAGH7Jk4Z8lLFXT82F/XgEhKGMTELuHTDishgKmy2T9Wg68I1JJWHhGqSuMjpry1CFxhA3bzXxC95ApvShrSLfVnKP0NbawoSJU/j0iz8jk8spzM8DoLe3l48/+5IlLy/jcPY+u2gPpX9g324mTJzMJ599yXsfrcPT0/arc4PpHjq4j8UvLbV5BzkYne2tpI6bwOvvfYpMJqe8uIALp44wfd5iVr3zMVNmLeDSmaN20388/zqdjqwJk5g0dZrdDL3BdPvL/aWly/n4sy8RBPutPg9V100mEx988hkLF7/MxfNn7abf2d5K8rgJrH73U9xkciqKC+ymNRhD5d/emuMnTmbd539CJpNTWGDRNJtMHNi3Gx9fX7saegBtba1kTpjEh5/+CZlMRlGh/fLd1tpK1oRJfPTpn5DJZRT15VcqlfDeR5+SmTWRvbu2sXDJUj5c9wW59+6i1XY7JA0mk5l3P1zHvEVLuHzhnE01+2lvayUzaxLvf/IlMrmc4sJ8jh7az8y5C3jv48/xCwjg6qXzdtF25ljm7PFkqDpuNBl598NP7WboweBt/Mrli3y47nNLuS9dZjftfv2siZP55LM/IZfLQYDgkFCWv7qaj9Z9YXNDDyzlneWgPmVQ/cfKvL6+DpPRSHu75Thbfl6uXRbwHq/n1ZWVGAwGenp6qKqsJDgklKqqCjo62vFQKGxe9o/rFxcW8MqKlRw9nE15WSmlJSXMnDPPppr9tLe1Mm78JD5Y9yUymZzGhnpkMhmNDfUAPLh/h7SMTLtoP57vyopyPv7sSz7+7Esyx08gMTllVIbeYM/u7zerqyrJPrCXX9Z/T3t7O8tXvsbcPiN6/MTJHD64ny2//cKlC+dQ9x2ZVnh68tmXf0dkVAznzpxiw4/fceP6VbRa7aDavZpOvKJTiZz3OhJXGZq6cgC0rfU03jlH9fl9GLrVBI6f94ih11VX9sgxTpPRgIvMnch5byD3C6E1/wZV5/bQXpaLUd8zqnIZiM222dw8lLh7+wPg7u2PvltNT2crlXk3MPb2YDIY8AwcXeT3Z+Ht7UNQcDAAwcEhdHS0A5CalgFARGQUPT096HQ6S0dqZ/329jbU6k4Sk1MAcLHDbuZguh0d7eh0OuuuTlp6JqUlxXbRBlCqvPELsKyG+AUGo+7soLGuhlOH9lq/YzQa7aY/1Hu3N4Pp6nv0hIVHAJZ6V1Jc6DBtgMSklCc+sweej79zdYfdtAbDGe/cy9uHoKA+zZAQOtotmkcPZ5Ocksr0mbMdkobAvjQEBYfQ2WG/ch9KKy4hCQD/gED8/AOsC1heXj6oOztxd/ewexoSkpIHfNZuM72BqLy8CejTDgyy9Oc9PTrCIyz9akraOA7t320XbWeOZc4eT4Z650nJaXbTHKj9eBsPDAzi4L49JCQlkZBo31MLA/XtWbcf1+wv70A79ylD6T9e5kkpqeQ/yGXajFnkPci1y27qYPU8LDyc6qpKqqoqmD5jFqWlxWDGLicHBtNPyxhHesY4dm7bzPsfrUMqldpcF8DLy/uxd95O+rgsHty3HBkuzH/A2+9/Yhftofq26qpK7t6+xbsfjl53sGefOHqY+/dzWPLSKyxd9uoTR8Bj4+L54i//SFlJMaUlxfy8/jvWffYnPBQKXFxcSE1LJzUtnc6ODo4fPcSZk8f54i//iPKxY9Wu7kpkXpbTazIvPwxaNc33L6OuKcY/fQYBmbMG3QAY7BgngCCVogyLQxkWR6+2i+b7l2nNu07kgjW4yBWjLiObWSGC5GHlFAQBk9FE9a2zRE5dgruXH20VBWia62wl9whSlwHaEgkmg2HwNNpF/Un9nm6NnZSertvd1eUQ3X4kAzokiURA263DTSZj9Vr7dBaPM9z3bm9dR5b7UHmW9h0bECQSTCaT/fSlj7Zzsx21BtV3wjt3eSzP/eUbFhZOZUU5k6dOt9uCTj+PlrsEk8l++R5Kq/9zQRBwGXBMZWCZOC4N9qvnA9+lIAj09OjsojMYzhzLnD2eDPXOXeywq/U4g7Xx1998h6rKCkqKC7l88QKffPal3e4MPqIvETAZ7N+vDixviSDBYDIgkUgwm80AGOzctw5W5mkZ49i3eyeJSSkIgoCvHa6ADNbGIiKjqK6qpLOjg4SkZK5cvoiAQFx8gkP0AZoaG5HL5Wg09ps/Sgf0bRJBwGAyEZ+UwpVL54mIjCYwKMSmi3aPaj+Z7y61msPZ+3l9zTtjuiM32LMnT52Om0zGxfNnKS0tJmNcFpFR0QjCw97T3d2d1PQMUtMz2LltM1VVFSQlW3aTNRoNuffucv9eDkqlihWrXkehGMTYkj7sEwRBgsloxCs2HcHFlbbC23Q3VqOKSEDuF/KI9tMw9Gjpqi5GXV2Mi7sHgePnIZWN7dqAXW87mwx6XOUemE0m2qvttyI4FPkPcgHLyoFMJkdmh129wXBzk6FUqigssBxbNRgM9Pb22l1XLpcjl8uprqoE4EFujt01B+Lm5oZS5U1ZkSXfZrOZlqYGh6bBGcjlctxkbtTWVAOQ9+C+k1Mk4ggyssYTGxfPvj077WpgizgXmUyGXC6nptrSr+Y/uEeYHVb8n4YzxjJnjyfOxGw2o+7sJCo6hrnzF9HTo0Ov1zs0DW5ubuhtcHxrJKi8vGhssCzK9x+ddiQ+Pr5IJBIuXTxntzvYgxEREUXu/Rx8fH0RBAF3d3dKSorssrM3GAX5eWh1Wta+/zEnjh1Gp3PcApOLiwtRMbGcOnaYVDsd4RwMk9HI3t07mDd/Eb5+tjfqvby9mTNvAeu++DMpqencunGNH7/9itz7ln6sorzMOi/v6emhra0NlcqLHp2O3Tu2svnXDRgMBta8tZY1b68lKTll2Is9rh5K/JInETHvNTxDY+kof0DVmV2on2EHGXv11F0/Tu2lbMwmIyFTlhAy5SU8Q6LHfD3IrsvRgSmTKTm7Fxc3Oe4+gZgM9jd4BiJ1cWHDj99hMhlZumylQ7WXr1zN0UMHuXDuNBKJlFWvrcHbx8fuuq8sX8mhg/sRgOgBl08dxbyXVnDx9FHuXL+IyWgiNjHFeuzveWbpslc5cugAAgIRUVHIZI5ZWBCxMMwFM5szeep0enp6OLh/DytWvjbslTuRPxZLlr5qddDi5e3N4qUrHKrvrLHM2eOJszCbzRzcv4eenh4wm5k4aYpdroA8jbSMLI4dOWQ3By2DMWnKdA7u20XOnVvExtl+V2s4JKekcubUCb74s30cwwyGl7c3mC1GH1iOb6rVncjdbe+E6XG03d2cPX2Ct9/9AJXKiwmTpnDy2BGWvbrK7tr9JKemU1JYQFS0fZ36DaSmppr6ulounDvDhXNnAHjj7XefOCY5ViQSCXHxCcTFJ6DRaGhrbQGgvq6W40cPWXezM7PGExIaRo9Ox8TJU5/YBRwNgiBBERSBIigCQ4+WXs3Do9JddWXo2h5uhgSkz8BV6YNXTBruI9gFHHZa+rfsB2PX3bqh/2hnlqWFOEv6haVZ7dhVxIH4K2VO07YFer3eegzhyqULdHWpWbRkqZNT9WwaO533zgNVtnnnO7dtYfLUaURFxwz7N0/r9+xNr9F52pIX1BY1OLHM5W5ju3+z+defmb9wCSGhoTZK0ejoaG9n5/bNw/bGqXfAUcShcJU6r6I7sWvBaHKeuIsTyxx4YRfaenrH7hfh5rXL9PT0MGP2vBH9TuZqn7uFv3d23q12mvZ7E8NHVdHFOHsiIjagpLiQKxcvYDKbUKm8WbbCsTvJLyqHDuzDYOh12HEbERERERGR54UDe3bQ0d7G62+95+ykiNiRERl7+u4uam6dxtCjBQR8opPxj8ug/v4VOusrECRSSzy98XOtgQP7f1d8cjuByRPxT3h4JthsNlFyZg+ucgVR01+2SYauX73M3Tu3EQQICAjilRUr7e48AaCzs4Ps/XvRaLoAgazxE6zBGp2hd+3KJU6fPM7f/x//Cw8P21y47enRceHEYdpamwCB2YteIffOdTraWgHQ9+hwk8kfcdDSpe5g128/MmHqLDImTLVJOgbi6HL/vWgfOrCPkuJCPBQKu8S96mhr4cyRhy7e1R3tjJ82m+4uNVVlxUikUpRe3sxatAyZTI7RaOTS6SM0N9YjAFPnLCIk3D6x/m5cu8LdO7cwmyFz/AResaNh3f+OuzUaEAQysyYwacpUGhvqOXYkG72+Fy8vL5avfA2ZbOw7lccO7ae0pAgPDwUfrLO4u29sqOfk0UMYjQYEiYSFi5cSHBpGVWU5+3dttxxBAuITk5k2c/QB7o9kP9T+6FOLdkH+Ay5fOEtLczPvfriO4BDL7lJe7j2uX71s/W1TYwPvf/yZ1dObLbQvnjtNcVEhgiDg4aHg5WWv4qlU0qPTcejAXtSdHZjMJiZNmU76uKxR51vd2cGxQ/vp7nOslZ45gfETp9DUUM+p44cxGCyOK+YvfpngkDBuXrtMft+9XLPZRGtLM5//5X/a/MjXwDbu6F29wfoXL29vm/U16s4OjmTvs7QrICNrAhMmTUWn1ZK9bxednR2oVF4sW/X6I/EM6+tq2frrT7zy6mskJo/9TpfBYGDzrz9jNBoxmUwkJacwa848bt24xo3rV2lva+Pv/sf/ZbMxdCCtLc3s37vL+t8d7W3MnD0PpVLFxQtnaWlu4v2PPrW2OVtwdED/8uG6h+E0bt+8xp1bN5AIAjFxCcyZvwiwtOsTR7PR9/SAIPDuh5+OeT41VJlXlJdx+uRxTEYjQSEhg3pQtAWD1W2tVsu+PTvpbG9H5e3NqtVvOOQIZ2lJMSePHcFkNpGZNYFpM2bZ7Nnqzg6OZj/s1zIyJzB+0hQK8x9w5eI5Wluaeef9T1ixeg0A9XU17Nq+yfJjs5lpM+cQbyMvtEPNlZxR7vYoc4O2i4Y75zD2WMIzqCKT8I5Nt/69veQeLXnXiF7yLlI3ucWfRe4VNI1VSKQuBGbNQeZliWbQkncNTWMVAL4J4/EMtc3R2hG1WkEiITh9Ou7e/hh79ZSc2YNnQDiKwHCCUqcgSCTU516lqegOwWkPJ/b19y/jGRTxxPNaSu4jU3pjspHzEnVnJzevX2PdF3/G1dWVvbt3kJd7n4zMLJs8/2lIBAnzFy4hOCSEnp4efvnpe6Jj4vAPsF1QxOHqdXZ2UFZWikrlZVPNK2dPEB4Vy8JlqzEajRgMvSxYusr696vnT+Lm9uiE9+q5U4RH2e8cuKPL/feinZGZxYRJU8g+sMcuz/fy8WPlOxaj3WQysX3DV0TFJtLR3srEGfOQSCRcv3ianBuXmTxzPoW5dwBYvXYd2m4Nx/dvZ8VbH9n8aE1TYyN379zig48/QyqVsn3Lb8THJ+Lj62tTnX4kEgnzFy0hONjyjjdu+IHomFiOHDrIvAWLiIyKJufuba5ducTsuWMPLJ6akUnmhMkcHRBL7fyZk0ybOYeYuHjKSoo4f+Yka9Z+AEBYRCSr3nh7zLoA6RmZfbGHHmr7+wfw6uo1HD9y6JHvpqRlkNIXDqCpsYF9u3eM2tAbSnvS1BnM7AvWfuvGNS5fPMfil5dx59YN/Pz9Wb3mbbq7NWz4/mtS0jJG7a5cIpEwe/4iAoNC0Ot72LJxPZFRMVw4e5KpM2YTHRtPWWkxF86e5I23P2DilOnWmG+lxYXcvnnVLhMUe7dxZ2oLEglz5i8mKDgEfU8Pm375kajoWHLv3SUiOoYp02Zy7cpFrl+5yOx5FsPDZDJx4cxJomJsd3dQKpXy9rsf4ObmhtFoZPOvG4iNiycsPIK4+ES2bPrFZlqP4+vnz0frvgAsefvmb/9BQlIyht5eVr22hmNHsm2umZaRSdaEyRwZ0L9UVpRTUlTI+x9/jouLi9UAN5lMHD64l6XLVxIQGIxW220T42uwMo+JjePQgX28tfZ9fP38OH/2NPdz7jIua/yY9R5nsLp95dIFoqNjmDZjFlcuXeDK5QvMW7DY5toDMZlMHD9yiLfWvo9SpeKXn34gPiHJZvMHiUTCnPmLCOxrY5s3ricyOgb/gECWr1rDyWOP1i8//0DWfrAOiUSCpkvNbz//QGx8ok3e+VBzpXs5dxxa7nYrc0GCf+oUZF7+mAx6qs/vwyMgDDelDwZtF93NNbi4P/Tk2d1YjV7TSeT8NfS0N9F07xLhs15F01BJT0cLEbNXYzYZqb18CI+AcCSuo/dU2s+I3qKr3MMaS0/q6oZM6Y1Bp0EZGI7QVyE8fALp1T50HdtZW46bhxKZ8lHnJL3aLtT1lfhE2TZ+jclkwmAwWP6/txdPG1/2HApPpZLgEMs9Q5lMhp9fAGp1p1P0Th4/yvwFi2zqn1vfo6O+torEtHGApcMe6ITEbDZTVpRPbNLD1dbykkKUXl74+PrbLiGP4ehy/71oR0RG4e6AlUeAuuoKlF7eeKq8CIuMsXb+gcGhdHdZApG2t7ZYd/LcPRS4yeQ0N9g+1EpLSxMhoWG4uroikUiIiIyyBjm3B56eSoKDB75jf7q6OmltbbHGH4uOibVZcPfwiKgnDAcBrF75enp6UHh62kTrCe3IqEd2UQD8/APw9Xt6+7VF8OPBtAfulPb26h8uHAiWO7Jms5levR653H1MExKFp5LAIMs7dnOT4evnT1eXGgTBWu76Hh0KzyfHkoL8XLvFgXNkG3e0tqenkqC+duUm6ytztZrS4gJS0y1jTGr6OEqKCqy/uXPzOvFJyTbdZRMEwXrX2mQyYTRa7hkGBYdYd8wdQUV5Gd7ePnh5eQ+rzY2WwfqXnNs3mDxthnXHzqPPvXx5WQn+AYEEBFoWcdzdPWwy8R+szAVBQCqVWr0yRsfE2q1fH6xuFxcWkN7niTI9I5OigoLBfmpT6mpr8Pb1xdvHB6lUSkpqGkWF+TZ7vsJTSeDjbaxLja+f/6DeL/vHVLDsvgo2nEAONVdydLnbq8xd5B7WnTmJixuunt4YdN0ANOdexS9lMgMn5N0NFSjD4xEEAblPIKZePQZdN/quduS+wQgSCRIXV9xUvnQ32eZ+4Kj34/UaNbqOZtx9Ah/5vK2iAK9wy8qb0dBLU9Edomcuo7no7iPfq7t3meD0qRhtGJJAqVIxZdp0vvnv/8DF1ZWYmDhinOBBrKO9nYaGOkLD7BNE/ml6RQX5KJXKMa2yD4a6swO5uwfnT2TT0tSIf2Aw0+YuwrVvxaG+tgp3DwVe3pYdll69npybV1i66m3u3bpq07QMhaPL/feibW/KCh8Qk/DkZL7oQQ4xCZZg7r7+gVSVFRGbmIpG3UlLYz2ark4CsO3xM/+AQM6dOYW2uxsXV1dKS4qtg4i9sbzjekJCw/H3D6C4sICEpGQK8h7QaUcjf+7CJezZvplzp09gNpt5+72PrH+rq6nm15++w9NTyez5i/APCBz6QXaiIO8Bq15/0y7PvnD2FLn37yGTyXhz7fsAjJ8wmb27tvHd3/4Tvb6H5Stft9kOcmdHO40N9QSHhDF3wRL27NjM+TMnMJvhzbUfPvLd3t5eKspKmL/QNlcQXlQ6OtppaqgnODSMbo0Gzz6jWqHwtO4ydak7KS7KZ807H3Csbr9N9U0mExt/+oG2tlbGT5zslD48Py+XlNT0Z3/RDrS1tVJTVcnFc6eRurgwd/5igkNCaW9tRRAEdm3bhFbbTVJKGpOnPhkEejQ8XuYhoWGYTCbq6moJCQmlMD+Pzk7HLJwCaDRd1o0Bhadn33FD+6JWq1EpVdb/VqpU1NXU2EXL2sZCwp76vbraGo4fPoC6s4OXlq20yzHagXMlR5e7I8q8t1uNvqMFuXcAmvoKiyGoetS4Nui6H9npc5F7YNBpkKl8aSu8jSkuA7PRgLalDjdPb5uka1TGntHQS+W14wRnzEA6YHuxseAWSCR4hcdb/jv/Jv7xGUhdHnUZ3FlfgYvMHXfvALqaaseQ/EfRabUUFRbw5V/+EZlczr7dO8i9l0NaxjibaTwLvV7Pnl3bWbj4ZZvc4RmJnkQi4fKlC7z1ju0v2ppMJloa65k+dzGBwaFcPnucnBtXmDjdckeotDCP2MQU6/dvXb1AetZkXMcQKHMkOLrcfy/a9sZoNFJZVszEGfMe+fzu9UsIEgmxSZZdjYTUcbS3NXNg288olCoCQsLGHBdmMPz9A5g6fSbbtvyGq6srgUFBdtF5HL1ez97dO1i46CVkMhlLl73KyeNHuHTxPPEJiaM+Qjgccu7cZO7CJSQkpVCQl8uxwwd54+33CAwKYd2f/gE3NzfKSoo4sGcHH3/+F7ulYzDqamtwdXWxm5E5a+4CZs1dwNXLF7h98zozZ8+jvKyEgMBg1rzzPu3tbezcuomwiMgxtz29Xk/2vp3MXbAEmUzG5QtnmDN/MQlJKRTmP+DEkYO8NsCJQVlJIaFhEQ652/O8otfrObhnB3MXLnni/VkMeIsRf+bkMWbPXWgXj4sSiYSPPv0CnU7Hnp3baGpsJCDQcYsmRqORkqIC5sxb4DDNgZhMJnQ6He+8/wn1dbUc3LeLdV/8HSaTiZrqKt79YB0urq7s3PorQUEhRI7A6/FQPF7mzU1NrFj1GqePH8VgNBITE4vESd41BUFwXiwfO6DX68neu3PQNvY4IaFhfLDuS1pbmjmavZ/o2Hib+rx42lzpeSh3k6GX+psn8UubBhIJbcV3CZk6/MVAj4Bwetqbqbl4AKmbHLl3oM3KZMRv0WwyUXXtON4R8XiFPmz0bRUFqOsriZm53Noha9sa6awppf7+VYx9x3AEqZRebTeddRWo6ysxm4wYDXqqbpwiYtLYOrvy8lK8vL2txxASk1Koqa5ymLFnNBrZs2s7qekZJCWnPPsHNtZramygo72Nn378FrDcYfx5/Xd88PFneI7x6JfCU9l3LMCyUxMTn8zdm1cAy2BRXlzAqrc/sn6/qaGW8uJ8rl88bb3cLZW6kJo5cUzpGAxHl/vvRdsRVFeU4BcQhLvHw1WoorwcqsqLeXnVO9a2LpFImDp7kfU7B3f8ipePfe7RZWZNIDNrAgBnT59EOWClzh4YjUb27tpOalo6iX3v2M/fnzf7FlVaW1ooKS6ym/6DeznMW/gSAInJqZw4chB49JhjTFwCp44dRtvdjbsdnEkMRf6DXJJT7L8jkZKawe4dW5g5ex73791lyrSZCIKAj48vXl7etLY0ExL69FXrp2E0Gsnet5OklHSrU4K8+znMXbAEgISkFE4ePfjIbwrzHpBopyOcLwJGo5GDe3aQnJpBQpKlXXkoFHR1qfH0VNLVpcZDYanLDfV1HNq/GwCttpuy0mIkEonNHEiAJYh8ZFQ0ZaXFDjX2SkuKCQwKQaGwz/HsZ+GpVBGfmIwgCISEhiEIAlptN55KJeERkdb+JCY2noaGOpsYe/0MLPMp02aw9oOPASgrLaG1tdVmOs9CofCkS63GU6mkS61GMWC8sxdKpfKREyHqzk6bXzsyGo0c3LuT5NT0EbUVXz9/XN1caWlqJMhGDoIGmys5utztWeZmk4n6mydRhsXhGRJNT2crvd1qqs9Z7oYadBqqz+0lbNarlp28AdfdDLpuXOSWvPskZOGTkAVAw63TuCps43tjRMae2Wym5vZZZJ7e+Mc/NKDUDVU0F98lZtYKJANWAWJnv2r9d0PeDaQurvj1eagJTpsCQFdTLS3FOWM29ABUKi9qa2ro7e3FxcWFivIyhx3xMpvNHM7ej5+fP1OmTneKXkBgEH//f/wv63e++dt/8uEnn9vkjoOHwhOFUkV7WwvePn7UVpXj42vZmq6tLMfbxw/FgEn38jceroDfunIeVzc3uxh6ji7334u2oygrzCM28eERzuqKUu7dvMorr7+Ly4Agv4beXsyYcXV1o6ayDIlEwNtOdzU1Gg0KhYLOjg4KC/J4/6NP7aIDlnd8JPsAfv4BTB7wjvvTYDabuXzxPFkTbF+3+/H09KS6qoKIyGiqKsrx7jOiNV1deCgUCIJAfW0NZrPZobtMZrOZwvwHvPXeh8/+8ihoa22x9jHFRQXWeyYqlReV5WWER0Si0XTR1tqCl7fP0x71VMxmMyeOHMTXz58Jkx9601V4elJTVUF4ZDRVlQ/LHSyeiaurK3jJgQHOnyfMZjPHDx/A18+fiQM8GMfGJ/Hgfg5Tps3kwf0cYuOTAFj35d9bv3M0ex8xcQk2MfS6NRokUilyubzvWG4pU6bPHPNzR0L+g/ukpDnnCCdAfEISVZXlREZF09bagtFoxN3dg+jYOG5cu0xvby9SqZTqqkomTB67R+2hyry/TzUYDFy9fJHpM2fbIHfDIz4xkfv37jJtxizu37tLfGKS3TVDQsNoa22hvb0NpVJF3oNcVqx6zWbPH6pfG4qO9jaUKi8kEgmdHe20tbSg8vK2WVoGmys5utztVeZms5nGu+dx8/TGO9biuEym8iVmybvW71Sc3Eb47JVI3eR4BEXSUZ6HZ2gsPe1NSFxccZF7YDabMPXqkbrJ6elspUfdSmDA6BcxBzKioOqalnrKzu9HpvK1Xt4MSp1M3b1LmExGXFwtDjvcfQMJy3q0ofYbewNDL8BDY+/x0AujDap+/uxp8vNykUgkBAWF8PKyFQ4JvVBdVcmmjRsICAy0ls2c+QuJi09wmt5Ijb1nBVVvaWrgwsnDGI1GlF7ezFm0DJlczrnjBwkIDiMlY3DPWf3G3tNCL4w2qLqjy/33or1/zy4qK8rRarvxUCiYNWeedbdrJDwtqHpvr54dP3/NGx98iVufM56dG7/FaDRanWkEBIcyY/7LqDvbObZvu8VNvsKTWQtfwfMZ3mBHG1R908YNfZ7hpCxYtITomJF7ex1uUPXqqko2//ozAQGB1l3M2fMW0Nbayu1b1wFITEpmzrzhHzF7WlD1Q/t3U1VZgU7bjYeHgumz5uLj58eZE0cxmUy4uLiwYMkrBAWHcOfmde7evoFEIsHFxZW5CxYTGv6k1+OBPC2o+sF9u6murLDWqRmz5iKXu3PqxBG03d3IZHICgoJ44y3LAFZVUc75s6dY+8EnQz90mAymXVZSTGtrC4IgoFJ5sejlV1AqVXSp1RzJ3o9Go8ZshinTZlidegzF04Kq11RXsnPLRvz8H77jGXPm4+bmxrlTxzCZTEhdXJi/aKnVqciD+3epKCth6YpnTxRGG1TdVm3cWdpPC6peU13J9k2/4D+gXc2cM5/g0DCy9+1C3dmJUuXF8pWvP7GA0W/sPS30wnCDqjc2NnDowD7MJhNms5mklFRmzp7LzetXuXrlknVBJTYugaXLVgzrmSMJqq7X6/nuq7/y+Z/+Hpnc0scWFuRz8vhha5sLDApizdvDu5rxrKDq2fsHtLO+/iU1fRxHD+2nqbEBqVTKnPmLiIyy7N49yM3h+uWLIAjExMZbQzIMxnCDqg9V5qdPHqekuAiz2cz4CRNHHMZouP3vYHU7MTHZEgKgowOVlxcrX1vjEOdIJcVFnDx+BLPJTEZmFjNmjTx0zlBB1WuqK9mxeaPliH1/G5s9H6PRwJkTR9FqLfXLPzCI195cS15uDtevXEIilSIgMHXmbOITnm58DTeo+lBzpdDQMIeXuy3K/PGg6trWemovZeOm9LGWtW/SJBQDohAMNPbMZjPN9y/T3VSNROpCQOZs5N4BmIwGqs9bPOVKXFwJyJiJzOvR+36jDao+ImPPkYzW2BMZPc8y9uzJaI09kbHxNGPP3ozW2LMFwzX27MHTjD178zRj73nmacaevRmtsfdH52nGnr0ZrrFnD5zYtTzT2LMnwzX27IU97nP+ERjK2HMEwzX2njceN/YcyWiNPft7NhARERERERERERERERFxOKKxJyIiIiIiIiIiIiIi8hwiGnsiIiIiIiIiIiIiIiLPIaKxJyIiIiIiIiIiIiIi8hwiGnsiIiIiIiIiIiIiIiLPIaKxJyIiIiIiIiIiIiIi8hwiGnsiIiIiIiIiIiIiIiLPIU+NNt7da3BUOp7AYHRejB5Nj/PiljgzDlZ1i9Zp2j4KN6dpd/c4r557yJ7aBO2On6fzyt3kxJhQGie+89o2ndO0Q33kTtO+XN7iNO1JEb5O03Zzcd6aqjNDjzkzbmuXznnt25nxQz3lzhtPenqdN2cD58azdGZ8w/buXqdpe9k/7vyQOPd9O7eujwZxZ09EREREREREREREROQ5RDT2REREREREREREREREnkNEY09EREREREREREREROQ5RDT2REREREREREREREREnkNsauzVXDxoy8eNiM2/bgCgqqKcPTu2Olx/z9ZfHK4JsGvL4LonjxygpDDPrtpXj2636/OHw+aNP1n/febUcTb88A1nTh23q+ZQZe4o+vNcWVHO7u1bnKbfpVazb/cOh2pvcpJ2/zuvqaoge49z6v25g5Z3rVF3cHKPfeugs+s4QOGZvU7T3rd9o9O0Nw3o0xzNb784TxvgyK7fnKZ99qDj+9KB7N1mqXPqjnaK8nMdotk/b3IW2zf97BTdX39e7xTdfjY7sY1n7/zVadr2fN8jeac5d26z/vtv+OmHb1j//dcUFeSPSbvu0vBtn6rTO6g5t4ea8/uoOb+PltwrY9IeLoLZPLQXoV9vVDnNxdDqjLBR/a6qopwb166wes3bo9Z+HrxxnjxygOjYeOISU4b9m5IGjW3ER0FGpNeYn/Ff//7/8Hf/438hkYxsDeOP6o2zsqKcG1cv89qb79gwRY7DiY4CR+2Ns6aqgjs3rrJs9Zuj1h6rN06NuoMrJ/aycPWHI/6t6I3T8Xh7uDpN25neOOvabeN11mQyjbhP/yN746ytquDurassXTnyPsaZ3jgNRud5pARne2d0Xt6d6fXWy915fdtI37der0cqlaLRaNjy6898tO5zZHI5er2e7m4N3t4+6LRa5O7PdjG67XbliLRNhl4EiQRBIqXq9A5CZ65A6vboWGzs7UHq+uy+48PJkaPq1W26s1d2xLIyZTL0UnvlMNXn91F1bg+a+gpbygzKf/2//9v6756eHnbv2MJP33/F8SPZPM2gtRU//u1frf++ff0y2zf+wI5ff+TK+dN21f3+vy26ZrOZcyePsnnDt+zfuRltd7dddQFObPva+u+yBze5fHgLF7N/ozjnst21+/nrv/0zAHt2bKVXr+fXDT+Q/8C+q6IDy/zsySPWMj+4e5vdd1PhYZ4B9L169u3ewU/ffUX2vt0Oqev9+h3t7Wz44Ru76w3kP52k3f/OB9JQX8v2X9fT0d7mkDQc+PW/HaIDD/NbU1XB3m2/cmjfDn5b/zWXz5+mMO8+OzdtYOsvP9g173f3WVZq1U21FJ3bT9mVYzw4to3y6yftXs83fP1vgKWNXz1/ip2//cDO336kpPCBXXXhYR2vrChny28/s2v7Zn787iuOOWAs+49/tWibzWaOHz3MD99+xbbNv7Jz22YK8uyf9y3f/wcA9TWVHN2zmdOHdrF/i2N2YfZvtLQvXXcX57K3cWrvr5zY/QvN9dUO0V//laXOXb14hvqaanb+tp6cW9fsqtk/b3r8RNTJY4e5n3PXrtoAX//n/wPA4QO7KSspsn5+7NB+igrsN5b++7/8k0XnSDZFhQUA7N6xjUMH9gGWnZ9zp0/aTb9/DC0qyGf75o2YzWa6utSs//ZvaLq67KYL8Nt3/w7A+eMHqSgttH5+7tgBKkuLhvqZTeh/35cvnGHTzz+w6ecf+PGbv3Ls8P4xP7v/nT5ObU0NRw4dYP33X6PTaunWaHBzc8PVzRJ2ys3NDW9vHwCuXrnExp9+4M6tG/T0DN+Irjg6+G5pT3sTzfcuUnN+L6Ze/VOf0Vl6n9qLB1BX5j/zu6PBLstAgkRK8MSFSFzdMOp11Fw8gEdQJIKDlhnr62r46LM/oVJ5sWv7ZooK8khMTnWIdmVZCeUlhax+5yNcXV3R6RwTu660uID2thbe/vBztN0atvzyPSnp4xyi3VxXQbe6nWkvW3ZTb5/ZT2tDDb5Bo9udHQ2r17zNX//tn/lw3RcO0ywtykfd0cE7H31hKfOfHVfm/TQ21PPxp3/CU6lk88afqKmuIjwi0qFpeBGpq63mwqljLF35BkrV2Helf880NzfyzodfIJfL+W3916RkZPHGux9z99Y17t2+waz5i+2ehu72FlIWrcHVXUHh2b1oWurx9A+xu255cQEtTQ28tnYdOq2WvVt/JiQsEg+Fp921Aepqa/jksz+j8vJi57ZNFBbkkeSAsayoIJ/WlmbWff4nNBoNP33/NRnjsuyuO5CWpgZWvP0xSpW3Q3WrSvIJCosmKWsqZpMJg9Gxu4RTZ84b9c7eH5WEpFSKCh4QE5eA0WikqrKMBYuX2l03PCKK6qoKEhKT6FJ30tWlBqCqqpKU1DS76yckJVNYkMftm9cpLy1hxux5KDwd07ckpI4j984NomIT0ff00FhXw6xFyxyiPX3WPKbPmkePTseOLb+QOX6yTZ+v1WrJvZ/Dvbt38FAoGJeZxaIlS3FxccHdwwMPT0++/eqvREXHkpSUTHxiEgBz5y+kZVwm9+7e5uf13xEeEUVm1vgRzamM+h40tSV0VRchcZOjDE/AL3UagvThTmT9lcMgWPbaPMPj8YpJwydpIp5h8airC6m9uB+5TxCeEYnIfYNsUiZ22/NvLbiJrrUeBAGjrhtjjxYXuYe95B4hOCTMaqknp6RTU13lMGOvurKMpLRxuLpatrflcsdEnayrriIhOQ2JRILCU0lYRJRDdAFa6ipprqvg8uHNABh6e+lWtznU2HMGdTXVxCUmIwgCHgpPQp1gZIWEhKJUqQAIDAqms6MdRGPPrrS1NnP2+CFWvP4OCk+ls5NjdwKDQqwTEJW3DxFRMQD4+QdSWzWy4yyjReEbgJuHJQ0eXv7ou9WA/Y29+tpq4pJSkUgkeCgUhIRH0NRQR1Rsgt21AUJCw/D2sYxlKanp1FRVOcTYq6qqJCUtHYlEglKpJLLvnTsS/8Bghxt6AD4Bwdw6fxSTyUhIVDzefoEOT8OLRnRsPGdPHcNgMFBRVkJYeCQurvY/IhgREcmNa1dobmrCLyAAnVZHl1pNbU0Vi5a8bHd9gIWLX+bnH78lJCyMlLR0h2gCBIdFcuXsMXTabspLCoiKSxzxcemxYDabOZK9l/GTphIUbLu+XK1W891XfyU2LoHX33wb1WOLsRKJhDfffpe62loqyks5eeIo9fV1zJozDwA/P3/mLVjMnHkLycu9z45tm8nIyGTRS89efDDouqk+sxOPgDACJy7ExX1wwz142tInjnECuHp64Zs8GZ+kiWhqy2i4cRzPsHj80qaNvCAewy7GXldNCUa9jrBZKxEkEipPbcdsctw9uCc3EJ15O+j5x2w2E5s2mYiEDGcn5YVDKn3YhAVBgslkcmJqXgw8FJ4YjQaaGhteCGPv0TomWP9bEASH1TdBMuB+hiA45Ljy75IXaChzxGR/MPyDw5m97C3qq0q5df4o8WkTiUxwzGKxsxAkkkfalMHg2N1MFxcXwiOiqCwvpbDgAUnJ9t9VA1CqVPT06CgtLSYiIgqtTkt+Xi5urm7IZGO7ezlc1OpOBEGgW6PBbDY77AQcQFxSOiUFuZQV5TFr4SsO0wW4cvEcnkoVaRlZNn2uQqFgxarXyblzm13bt5KeMY7U9HEoFArrdwRBIDQsjNCwMKJj4jh0cJ/V2DObzVRWlJNz9zZ1tTVMnDSFzPEThqUtlckJyJpDV1URjTdPogiLxzM0FqlseJs+ZrMZXUsdXdVF9LQ3o4pORRmROOIyGAy7mPEmgx6pmxxBIkHbXIdBa98zyI9TX1dLR3sbZrOZgvxcwsIjHKYdHhVDQW4Ovb29AA47xhkSHkFxwQNMJhOari6HrbgD+IdGUVOSi6HvnLGuu4senf3vDDqb4LBwSosKMJvNdGscW+YizkMmk7Ns1ZtcuXCamir730cWcR7BYRGUFOZhMpnQdndTX1NFQJD9dxT7qa+rpb1vLMvPyyUs3DG79hERkeQ/yMVkMtHVpaaystwhur8Hurs6kcs9iEkaR1RiOu0tDQ7Vd3Vzo1dv+zs7T0Pl5UVLcxMGgwGdTkdlRblD9QESk1N5cO8utdWVRMXEOUw3NCycG9euEBEZRUREJNeuXCY80jHtzGQycST7AMtWvoavnz83rjnO3wFAfEoGD+7eAMDb199huqXFhVRVlDFv4Us2f7ZEIiEpOYU1b6/ljTffobe3l82/bmD3jq306HSo1Wrq6+qs329sqMfLy7L7l3s/hx+//YpbN66TmpbBp1/8hTnzFuDl5T0sbUGQoAiOJmjyYgInLcJs7KXuymEabp585j28rpoSas7tRl2RjyI0lrC5q/FJnDDk7uBIscvOnmdYHPXXj1N1bg8yL39cFY690xIUHMrJ40dob2slIjKahKRkh2lHRsfR0tjA7s0/IZFKiYyOZ+qseXbXjY1Poqaygq2/fI+nUkVQiOOOUPqHRKHpaLWGYpC6ujJuxkvgoGO7ziIuIZnqynK2/PwdnkoVAUHBuMmc5+3whcOJrgY9FJ4sW/UmB/dsY8GSZQ5tbyKOIzoukca6GnZvXg8ITJk132H39QCCQ0I5eewwbW1tREZFk+igsSwhKZmKinLWf/8NKpWKsLBwh+j+Hmiqq6Lo3g0kEgkurm5MnOOY43z9+PoHIggSdvy2nqTUDMZNmGJ3TZXKi8SUVH5Z/x1eXt4EBtnmntBIiIyO5eihfcTGJyKVOs6zZnhEJOWlJfj4+qIyeqHTaQl30DWYK5fOEx4RQXhEJIGBQfz284/ExiXg5x/gEH13DwVePn5EOuhYej+3b1ylq0vN1l8tIShi4xOYbod5slKlYsasOUyfOdu6gGEyGTl98hhdXWpcpC64KxS8tNRyV9HLy5t3P/gYjwG7gKPFRa7AOz4Lr7hMdC11j/xt4J09N5UPAZlzcHH3JGTaMqR2mkM+d6EXbMHzEHphNPzRQy+MlrGEXujV63F1c0On7Wbn5p957e0PRjQZHEvohT86Y6nq9XW1nD55jHfe+2hUvx9t6AVbMNbQC2NBDL3geEYbeqGyopzrVy/z+hhCq9hqPeTQgX3ExSeQlDL844y2Cr0wGv7IoRfGghh6wTn8UUMvGHp72bflJ1a89RFuozi2+kcKvWBLRhp6wZaMNvTCizvTFBGxAdl7t9PTo8NkNDJp2iyHrvq/qNTX1XJw327mzF/o7KSIiIiIiIj84aitKufiqcOkZk4alaEn8sdiRMaeQdtF451zGPWWFTtVZBJeMQ8v0raX3qM17zpRi9c+4mlG195E7aWDBI6fh2eIxatXafYG3FQWL2MucgXBk4fvvvtI9n5KS4rw8FDw0adfAlCQ/4DLF87S0tzMux+uIzgk1Pr9q5cvcP/uHQSJwIJFLxMdO7bz4L+t/wo3VzcEiYBEkPD6u5+g02k5nr0HdWcHSpUXS5atRiZ3p0en5fSxbDo72pBKXZi/ZBm+/mPz7mUymdi5aQMKT+UjwZ3PnzpGXu5dPv/7/wWA0WDgxJEDNDXUI3d3Z8myVaiGefYY4P7l4zTVlOEm92Dm8vesn1cU3KGqMAcEgYDQGJImzELb1cmFgxtR9L1TL79g0qZaJuM3Tu2lR6vBbDbhExBK6uT5CCP0+nQkez8lxYV4eCj4+LM/ARb3ugf37qSjowMvLy9WrHoDubs7LS3NHDm4j8aGembNnc/kqTNGpDUYj5f58UP7aGqoQyKREBgcytxFS5FKpfT06DhxeD9dnZ2YzCayJk4lJT1zzPoAN69fJefOLQDGZY5n4pRpXDx/hnt3buPuYTkyO3vuAmLj7XMk48a1K9y7exuAgIBAXl6+kqPZ+6mvt5RDSGgYi19eZvMjOK0tzezfu8v63x3tbcycPY/EpBRu3bjG7ZvXESQSYuPimbfANmEAhtvG8nNzuHTupNVRS0bWJFJHeOH81oWj1FeVIpN7WAOlP7h1kfrKEhAEZHIPJsx+CXePhwsJbU31nMvewqR5ywiLtlzg3vvzf6Dysdy78FAombZo1ZjzPVQ9v339CoX59wEwm0y0tbbw8Zf/Y1gBafvRd3dRceM0hp5uQMAvJoXA+Azaqkuoz7uJTt1G0vzX8PCxHGnqbKimNvcqZpMJQSIhLH0aysCwvnQbqb5zga7mOkAgNG0y3mGxw07Llp++xtXNDUEQkEgkrH7nY1qaGrhw6ggGgwGJRMLM+S8RGByKvkfH6aMH6FJ3YjKZGDdhKklptgm58t3Xf8XNTWZNxwcff8aFs6cpKiqweP31UPDK8pV4Km3vGOjbryzaEkFAkEj48JPPOH3yOCVFhda+LSomFrncNrvDD+5ep/iBZRzx8fVnxoJXkLpYpiTXzp+gJO8e73z+fwDQpe7g8qnD6LRaZHI5MxctH5FzpJvnH7axRa/1tbGbF6mrLEHob2NzLG1M3d7KzfNH6WhpJHXiTBIyJlmfo+/RcfvicTrbmgGBCbOX4BcYOoTq4Gxa/zVufXVNkEh4fe3H1r/dvXmVK+dP8cEX/4i7uwdtrS2cOXaQ5qYGpsyYS+bEqSPSepzB5k5arZaD+3bR2dGBysuLFateRy53R6fTcjT7AO3tbbi4uPDSKyvwDxjd3EXd2cGxQ/vp7racHkrPnMD4iZbjqXduXSfn9g0EQSAmNoFZ8xZiNBo5eSybxvo6BEFg7oIlhEdGjynvQ3H96mXu3rmNIEBAQBCvrFiJi4t99kEGG0Nrq6s4c+o4RqORoOAQXl72qk28Yna0tXDm6MP4dV0d7WRNnUVTfS0d7a3Wz4vz75OWNRmj0cjl00dobqxHEASmzF5EyCjvCA/1vg/t301bq+WER0+PDplMzrsffUb+g3vcvHbF+vvmpgbWfvApAUHBo9J/GqUlxZw8dgST2URm1gSmzZg15mcatF003z2PUa8FBJQRiagG2EYdpfdpy79OxKJ3kLrJMfXqabp7zuLPxGxGFZOOMsIyb2vNv4620RLb0zs+E0Xo8MexpzGyGi1I8EudgszLH5Ohl5oL+3D3D8VN6YNB24W2qRYX90fPuprNJlrzb+Du/+ixTEEqJXz2qlElOj0jk/ETJ3P44D7rZ/7+Aby6eg3Hjxx65LstzU0UPMjlw0+/RNOlZsfWTXzy+Z/H3JhWrHkXd/eHd9JuX7tMeEQ046fM4Pa1S9y+fplpsxdw69ol/AOCePnVN2hrbebCqaOseOPdMWnn3L6Oj68f+gGXuBvr6+jpefTYTN79u8jkct5b9yeK8nO5fP40Ly1fPWyd0NhUIpMyuXfpmPWzlvoqGqtLmfHKWiRSl0ccsXh4ejPjlSfzljV7KS6uMsxmM3fOZ1NfWURIdNJIskxa3zs/dGCv9bNrly8QGR3D1OmzuHr5AlevXGTu/EXI5e4sWPwyxUUFI9J4Go+XeWJyGouWvgrA8UP7yLt/h/TMidy/cxNfX3+WrXoTbbeGzRu+IzElfcwGUFNTIzl3bvHeR58ilUrZuW0TsfGWSf7EKVNtYtA+DbW6k1s3rvHxZ3/C1dWV/Xt2kv/gPilpGbzyqqVOZe/bzb27t8maMOkZTxsZvn7+fNQXP9FkMvHN3/6DhKRkKivKKCoq4MN1X+Di4oJGY7tjyMNtYwDxianMGcNF88j4NGKTs7h5/oj1s4T0SaROmAlAyYNbFNy5QtaMRYDFuMq9cZ7A0EfvlUilLixY+f6o0wHDr+fjJ09j/GSLO+jykiLu3ro2IkMPLB7RwjKm4eETgLFXT8Hp3SgDw3FX+RIzbQlVt8898n0XmZy46S/j6q5A29FKycVs0l+x5Lch/xYuMndSl7yN2Wy2LkiOhOWvr0U+oE+/euEUE6bOIiI6jsqyYq5dOM3yN94l9+4tvH39eenVNWi7u9mx8Tvik9Nstsjx1toP8PB4mI7J02Ywa+58wLLgc+niOZa8bJ94WG+/+6h2dEwsc+cvRCKRcObUCa5cusC8BYvGrNPdpSY/5xavvvMJLi6unDu6j/LiPOKSM2hprEP/WDu7eekMsUnpxCWnU1ddwe0rZ5m1aPmw9aIS0ohLyeLGuQFtLGMSqRP72ljuLfJvX2H8zEW4yeRkTptPbUXxE8/JuXqGoLBopi5YgcloxGDoHVX+l7+x9pH5A0CXupPqijI8lSrrZ3K5nJnzFlNeYptg14PNna5duUhkVAxTp8/k6uWLXLt8kTnzF3H10kUCgoJY+fqbtLQ0c+rYYda8M7r+RSKRMHv+IgKDQtDre9iycT2RUTF0d2soLSpg7Yef4eLiQndfH36/zyB67+Mv6NZo2LdrC2+/v87mnirVnZ3cvH6NdV/8GVdXV/bu3kFe7n0yMrNsqgODj6F5ufe4dP4sa955H18/Py6cO03uvbtkZI4fs56Xjx8r37YsJJhMJrb//DVRsYmkZT2MaXf9wilc3Sy7eoW5dwFYtXYd2m4NJw7sYPmbH46qzId636+8+pr1O+dOH7d6PE1OzSA51eLNvbmpkYN7ttvF0DOZTBw/coi31r6PUqXil59+ID4hCf+AMd6RFCT4pEy22ka1F/Yj9w/DTeltsY2aa5DKH9pGnRV5uHp6ETRpEcYeHTXnduEZFou2uRZ9Ryuhs1ZiNhmpv3oE94BwJK5uY8z5CL1xusg9kHlZVo4lLq64enpj6Jvstzy4hm/KJB6/idNZnociOMqmlw7DI6OeiF/n5x+Ar9+T3oSKiwpISk3DxcUFL28fvH18qK+rtVla+ikvLSQx1bK6m5g6jrKSQsASk6s/5p2Prz/qzg66NaP3Ttql7qSitJiUAbsHJpOJS+dOMn32gke+W1ZSaG1AcYkp1FSWj8hluW9QGK6PxQKpKrpHbOokJH3u12XDcMLi4mpp0GazCbPJxGhua0UM8s6LiwpJy7DsmqVlZFJcaDHuFAoFIaFhNosZM1iZR8XGW1ZmBYGg4FC61JZgrAgC+l49ZrOZ3t5eZHJ3m6SjtbmZkNAwXF1dkUgkREREUVSYN+bnjgSzyYTBYMBkMmHo7cXTU0lsfIK1HIJDw1CrO+2ahoryMry9ffDy8ubOrZtMnTbTugqrsMGlahhZG7MF/sHhuD7WP/YPwGDZoR9ISd4dQqMTcHO3rQOkEdXzARTl55KQNHLX9K7uCuuundTVDbnSm16tBrnKB7nS+4nve3j749q3mChX+WAyGjEZLferWyoKCEqyTJAEQcBlmK6un46AXm+5D6PX91iPaAsC9Op7+tq4Hplcbtf4VANdwPd7eXYUMbFx1ryFhoXbtH2bTSaM/f2JoRd3D09MJhM3L51hwvR5j3y3o7WZ4DDLLkNwWCTVZU8aYk/jWW3MYDBY7zfK3D3wCQh+4p326ntoqa8mKtESC00ildrUIdelsyeYNns+A8dHdw8FgcGhNqtfg82dSooKSMuwzF3SMsZZF0lbWpqs8RX9/Pzp6OhAM8q5i8JTSWCfF1s3Nxm+fv50dam5d+cmk6bOsPbh/Y4xWluaiOjbyfNQKHCTyWmot/28DeirfwPGNTvsmvfz+Bjq6uqGRCrF188PsCyuFObbflyvq65ApfLGc0DMObPZTFlxPrGJKQB0tDUTEm6Zq7p7WMq8ubFu0Oc9i6He90DtooIHJKY8GVewIO8+iSn2Cb1RV1uDt68v3j4+SKVSUlLTKCrMH/Nzn7SNvDDqLAsXrXnX8E2e/NgFagGzwYDZbMZk7EXiKgNBQm9XO3LfIASJBImLK25KH7TNNWNOH4zhzl5vt5qejhbk3gFo6iuQyj2Qqfwe+Y5Bp0FTX0HItKU05Zx/5G9mk5HqC/sQBAneceNQBNvH+1GXWk1I6MNdRaVSRdcYBywByN69BRBIzRhP6rjxaLs11sDDHgoF2r7taz//IEqLCwgJj6ShvhZ1ZweaLvWo73ZdOHOc6XMWPOKa+d6dG8TEJVr1+9F0qa2rhBKJBDeZDJ1O+8SK4kjoVrfR1lRD0d1LSKQuJE2YhZefZQVG29XBpUObcXF1IyFzOj6BD8v9xqk9dLQ04B8SRXBk/Kj1H0mLpgvPvqM8CoXnmIzopzFYmfdjNBopyLvHrHmW44MZWRM5tHcHv3z/X+j1epYsW22T1Uj/gAAunD2FtrsbF1dXSkuKCA4JRe7uzu2b18m9l0NwSCjzFiwe8Q7LcFAqVUyaOp3vv/pPXFxciY6JfeQ4tNFo5MH9HBYstr0r5YHk5+WSkmoZIFpbW6iuquTC2VNIXVyYt2DxI219tIykjQGUFudTW1OJt48vM+ctRjlgZX4sPLh5gariB7i4yZi1dA0AWo2auooiZi19k7YL9Y9832Q0cGb/JgSJQELGFEKjRtbORlLP++nt7aWyvJTZC8b23ns0arrbW1D4Du+YWHttGe7e/kikUgx9Blndg+t0NdUh81QRnjkT15F4Axbg0J6tCIJAcnoWKRnjmT53EYf3bOPq+VOYzWZeffMDAFIzJ3LswE42/fjf9PbqWbh0lc12HAQEdmz9DUEQyMyaQOb4iQCcP3uK3Hs5yGQy3nr3A5toDaa9fUuf9vgJZPVp93Pv7m2SU20zCfPwVJKaNZndG79F6uJCSEQ0oZEx5N29QXhM/BPjo49/IJWlhaRkTqKqtIjeXj09Oi0y+dj6utwbF6gqeYCLq4zZfW1sKDTqDmRyd26dP0pHaxPe/kGMmzp/xPEABQEO7d4KgkBKRhapGeMpLylE4anEL8DxXjC7NZrHxlHL3CUgMIiignzCIyKpq62hs6OdLrUaxRjvpXd2tNPYUE9wSBgXzpykprqKSxfO4CJ1Yda8RQSHhOIfGERpcRFJKemoOztobKhD3dlJsI29HitVKqZMm843//0fuLi6EhMTR8wYr/kMqTXIGJqUksrZ0yeor6slOCSUwvw8uyyYlhXlEdNn1PXTUFuNu7sClbfFeZWPXyCVZcXEJKai6eqkubEejVrNWKvkwPfdT211JR4envj4POk4qyj/AcsHXJ2wJWq1GtWA8VmpUlFXYxtjqp/ebjX6zlZk3gF0N1hsIzfVo/lURafQeOME1ae2YTL0EjB+HoIg4Kb0pb34DqrYdMxGA7qWOlw9vW2SrlEZeyZDLw03T+GfOhUkEtpL7hIy5Un3xC25V/FNnjToQBi54E1c5Ap6uzupu3IEN6UPrgrbTJLszcq3PsDTU4m2W8PBXVvw9n3UyLXk15Ln8ZOnc/HMcXb89iO+foH4BwYjCKNbpSsvLbKs9AWFWON7abrUlBTms+rN957xa9tgNpnp7elh6ktv0dHSwN3zh5m98iNk7h7MWf0JbjJ3OloauHPuIDOXv2fd1Zu0YDVGo4Gci0doaajCP8S2xr0gCHZxxT9YmQ/k3MmjhIZFEtp3tr2qvBT/wCBWrnmXzvY29u/aQmhYxJgvQPv5BzBl+kx2btuEq6srgUGWepQ1YRLTZ85BEAQunDvNmVPHeXnZq2PSGgydVktxUQGf/fkfkMnkHNizkwf3c0hNt6wInzh6iPCIKLu6rDYajZQUFTBnnmV3zWwyodNpeffDddTX1XJg7y4++9Pfj2niPdI2Fh0bT0JSKlIXF3JzbnHqyAFWrhnbMe1+UifOInXiLApzrlGad4eU8TO4d+0MaZNmD5rHJWs+xV2hRKNu58KRnXj5+KNQeQ9La6T1fODvgsPCx7TAYDT0Unb1GOHjpiMdxnEVbWcrtfevEj+zLwiw2USvVoPCL5jwcTNoLMqh5t4VoicPfxf21TXvo+jr0w/t2Yq3rx9lRflMn7OQmIRkSgrzOHfiEMtee4fqijL8/INY9tpaOjvaOLRnK8GhY2/jAO+8/xFKpQqNRsOOrb/h6+dPRGQUs+cuYPbcBVy5dIFbN65bAwDbkrUfPNTevuU3/Pq0AS5fPI9EIiE1LcMmWj06HVXlxax+/wvc3GScPbaPkvz7VJQUsGTVk95HJ86Yx7VzJygpuE9QSAQeCk+bGNhpk2aRNmkWBXf72tiEoY/Dm80m2lsaGTdtAb6BIeRcOU1hzjXrUdDhsvLNh3Xt4O6tePv4cfvaJV557e2xZmfMDJy7TJk2k9MnjrLxp+/xDwjsG3PGVuZ6vZ7sfTuZu2AJMpkMs9lEj07LW+9+TEN9LYcP7OKjz/6OtIwsWlua2bJxPSovL0JCw+2ye67TaikqLODLv/wjMrmcfbt3kHsvx7rTaWutx8fQvNx7rFj5GqdPHMVoNBIdE2vzo6pGo5GqsmImTp/7yOdlRQ8eMQATUsfR0dbCge2/4KlUERgShjBG9/CPv+9+CvJySRpk966+tgYXV9dR3w11NiZDL023TuObOsViGxXnEDzlyYVQbVMNbipfgqa+jKFbTcO1o8h9gnAPCKOno5m6S9lI3eTIfAJtNq8dsbFnNplouHkKz7A4FCHR6Dtb6e3uovr8XsCym1d9fh9hM1fQ09FM4+0zABj1Orobq/uCDkbh0nd+1dVDhdwvmJ7OFrsYe55K5SMrJWp15yNn4kf1zL5VMHcPBdHxiTTW1+LuoUDT1YXC0xNNV5fVYYabTMb8lyx3C8xmM5t++npETlIGUldTTXlJEZVlJRgMBnr1PWz95QckUimbfvoGsLjS/W39N7y37k8oPJV09eXXZDKh7+l54gjHSJF5eBIYEYcgCHj7B4Mg0NujxU3ugVvf0U4vvyDcPb3QdLbj5fdwWUgqdSEwPI7G6lKbGHseCk+6utR4eirp6lLj4WGbY3wDGazMjx/ax+JXVnL98nl02m7mLX7d+v283BwmTJ6OIAh4+fii8vKmrbWFoJCRXeIfjIzM8daz/OfPnMRTqXpklXVc5gR279gyZp3BqCgvw8vL21rGCUnJ1FRXk5o+jkvnz6Lt7mbJ68O/QzMaSkuKCQwKsebZU6kiMSkZQRAsO3qCgFbbPaZ6MNI2NvCOV0p6FpfPnR5bJgchPDaZy8f3kDJ+Bu3NDVw/a7mXrNdpaaguQxAkhEbF467oW51XeuMfHE57a+Owjb2R1vN+ivMfkJA0+t0es8lI2ZVj+EYkDMuhir67i7Irx4iaNB+Zp+VIktRNjkTqgneo5ciZd1gsLeUjO5qjGNinxyXSVF9HYd59ps+17GTGJiRz/qSl3Asf5JA5qa+Ne/uiVHnT3tZCYPDY23j/rrBCoSAhMYm6uhqrwQWQmpbBru2b7WLsPaFda9G+l3OHkuJC3lr7gc0movXV5XgqvaztJzImkbvXL2I0GNi76XsADIZe9v72Pave+xwPhZJ5Sy13g3t79VSWFtj0CGVEXDKXju15qrHn7qHEXaHEN9ByPC00OoHCnOsj1hpY12LiEqmrqaSzs4Odv1lijmm6Otm9eQOr3/7QId6dPRSKR8dRheWdyGQy68Kh2Wzmx2/+Gy9vn1HrGI1GsvftJCklnfhES7xIT08lcYmWPjw4JAyBh3343AVLrL/dvulnvAfZBRor5eWleHl7W4+PJialUFNdZRdj72lj6DvvW+7WlZeW0Nra+rTHjJiailL8AoJwHzAumkwmKkoKWfHWh9bPJBIJU2Y/9HCdvfNXvLxHX+aDve9+7eKiAt75YN0TvynIz7XbEU4ApVJJ50B7oLPTZsd2zSYTjbdOoQiNRRFssY0M2i5qLljuxxp1Gmov7Cdk5nK6qovwistAEARcFSpcPDzp1XQg8w7AOz4T73jL9aSm22dtZheNyNgzm8005ZzH1dML71jLUSo3lS/Ri9dav1N5ajths15F6iYncsHDrdjGu+fwCIxAERyFsbcHicQFQSrFqNeha23EO9Y2K4aPExefyKH9e5g4eRqaLjXtra2PeOocKb1997Hc3GT09uqprihj4rRZRMcmUPggh/FTZlD4IIfoWIvzjB6dDhdXV6RSKXn374xpl2f67PlMn225qF9TVcGdG1cf8RQI8P1//yvvrbN4q4yOSyD/wT2CQ8MpKcwjLDJqzIN1UHgsrQ3V+AVHoOlsw2wy4ipzR6/rxtVNjiCR0K3uoFvdjrunF4ZePUZDLzJ3BSaTieaaMrwDbXMUIy4hkdx7d5k6fRa59+4Sn5Bok+cOZLAyX/zKSh7cu0NleSkr31j7SJkqlSqqK8sJDY+kW9NFe2sLKm9vm6RFo9GgUCjo7OigqCCftR+usw7SAEWF+XZbEVOpVNTV1tDb24uLiwsV5WUEh4SSc+cW5WUlrHnnfZuvSD6OxSHMwzP+CYlJVFaUExkVQ2tLCyajcUxHlGHkbax/gQcsjkp8HtvlHy1dHW14elkmVnWVJSi9LIPukjWfWr9z8/wRgiNiCY2KR9+jQ+riglTqQo9OS2tDLQkZkwd99mCMtJ6DxZtabXUli14Z3U6y2Wym4tZZ5EpvAhOePbky6HsouXyY0LQpePo9vLwvCAKqkCi6mmpRBoahbqpBrhr+pPSJPr2yjAlTZqJQeFJXU0loeBS1VRXWiY+nUkVtVTkhYRF0azR0tLWMegFvIHq9Hsxm3GQy9Ho95WWlzJg5h7bWFmu9Ki4qGPRuui20zWYzsoHas+ZQWlLMtcuXeOe9D3Ed4XHFp+GhVNHcUIuhtxepiwv1NRWkZk4iedzDo6Nbvv8PVr33OQA6bTcyuTuCIHD/5hXiUsY+X3iijT1jYiv3UOCuUKLuaEXp5UtTbeUzf/M4g9a1qTP58It/tH5n0/qveW3tR2Puy4ZLXHwSufdymDp9Jrn3cohLsDhP0+l0uPbNXe7dvU14ROQjuzMjwWw2c+LIQXz9/JnQ59gJIDYhierKciIio2lrbcFosvThvb29YDbj6uZGRXkpgkSwS6BxlcqL2prHx7UQm+tYtAYfQ/vHdYPBwLUrF5k2Y7ZNdUuLHhCT8OgRztqqcrx8/FB4PjQkDL29mDHj6upGbWUZEokEb9/R9TVDvW+AyooyfH39nrjuYLnHl8ead+xzTB0gJDSMttYW2tvbUCpV5D3IZcWq1579w2dgNptpvncBV09vvAbYRpGLHp5SqDq9g9CZK5C6yXFxV6BtrkPuG4yxR0tvVycuHkrMZhOmXj1SNzn6zlb06lb8/W1TH0Zk7PW0NdBVU4Kb0se6k+ebNBGPwIgRifaq22m6fwkBMAPeceNwUw5/cD64bzfVlRVotd1899V/MmPWXORyd06dOIK2u5s9O7YSEBTEG2+9i39AIIkpqfz847dIJAILlywd03EArUbD0QMWN/Amk4n45DQio+MIDArhePYe8nLvolR6sbjP62VbazOnjx4EweJVcN5i+3hRG4yU9CxOHt7Pb+u/QS6Xs3jZqhH9/u6Fw7Q2VNPbo+PM7vXEj5tKWFwa968c5+LB3xAkEtKnL0EQBFobayjOudJXtgKpUxbgJpPTo9Vw68x+TCYjmME3KJyIhJEP1Af37qKq751/+7f/YObseUydNpMDe3dy7+6dPpfRbwCWCfivP/+AvqcHQRC4ef0qH3/251EPVINx9sRhlCovdm39BYDY+CQmT5/NpGmzOHn0IFt/+QEwM232ApsN2vt3b0er1SKVSln40lLkcjmH9h+msbEBAC8vbxYvtU/9CgkLJzEphV9/+h5BIiEoKJhxWRP467/9MyovbzZvtKxKJyQlM2PW3Gc8beT0T0AHeiHMyBzP4ez9bPjhGyRSKUuXr7S7wfk4ObevU15ahESQIJPLWfDyyHc3r5/Jprm+Gr1Oy5Ft35M8fjoN1WV0dbQhCALuniqypj89pqC6vZW7l45bjnyYzSSMm4zKe+yG51D1HKCsuJCI6BhcR+kpTNNST1tlEXKVL/kndwIQkjYFs9FI9d2LGPRaSi4dxt3Lj/hZy2guzUXf1Ul9/i3q8y0hSOJmLsNV7k5Y+lTKr5+iOucSLjI5URPnDTsd2m4Nxw/uBvr69KRUIqLjcHV14/K5E5hMJqRSKbMWWK4qjJ8yk7PHD7Lztx8BM1NmzX9kh3e0dGs07N293ZqOlNR0YuLi2bt7O20tLSAIeHl5sdgOnji7NRr27HqonZqWTmxcPN9/898YDUa2b/kNsPQDL9mgjwkICiUqLonsHb8gSCT4+geSkDZ0iJqG2ipuXzkLCASFhjNlzshCrFw/nU1TXxs7vPV7UiZMp6GqDHVfG/PwVJE1w9LGdN0aTu/fhKFXjyAIFOfeYtFrH+LqJmPctPncOHMYk8mIQunFhNkju6uq7dZw9IClrplNJuKTU4mMHvqOWLemi91bfkav70FA4N7t67z5/mejXjAebO40ZfoMDu7dxf2cO6hUXixfZdnBb+0LYYRguUbw0isrRqUJUFtTRf6De/j5B7Lp5x8AmDFnPmkZWRw/fIDfNnyHRCJlydJXEQQBbbeGPTs2IwgCnp5KXnpl5ai1n0ZoWDhJySn8vP47JBIJQUEh1nuytmaoMfTCudOUFhdhNpvJGj+RyOgYm2n29uqpqyxnxrxHr1kNdodPq+3m+P7tCIIldM/sEXi7fZyh3ndMbDyFeYPv3tVUVaBUqsa0e/wsJBIJi196he1bfsNsMpORmUWADRbIe9oa0dSU4Kr0oea8ZSfPJ2nCkLaRV3wWzTnnqTm3x/Ld5EmWkAxGA/VXLCdIBBc3/LPmjDhM2VAIT/PO+OuNquG7brQxqzNsexF3JGh6jE7THuMR6TFR0mA71/UjJSPS69lfshPdPYZnf8lOeMjsE8/nj4ATqzoaJ77z2raRhwWwFaE+tjv6NlIul7c4TXtShO2PgA0Xbw/b7YiNFAevfTxCXbvz6nmXznntO1DlvADVnnLnjScGo9OmiwDI3Wwb33UkGE3Oy3uzusdp2l7uzuvbnPm+t92udJr2h5MjR9Wr289ftIiIiIiIiIiIiIiIiIjTEI09ERERERERERERERGR5xDR2BMREREREREREREREXkOEY09ERERERERERERERGR5xDR2BMREREREREREREREXkOEY09ERERERERERERERGR5xDR2BMREREREREREREREXkOeWqcvZvlnU4LHpIWrnKW9AuLWtvrNG2lE+O1OJOntT9H4Mz4QC5Sca1JxDF0dDuvb/NyYpw9kRcLvcHkNG03F7E/F3kxMDlx3uThNrroqWLrFBEREREREREREREReQ4RjT0REREREREREREREZHnENHYExEREREREREREREReQ4RjT0REREREREREREREZHnEJsbexcPbbX1I4fNrz+vp7KinJ3bNjtN//eim31gL/l5D+yuvWvLL3bXeBb9+T998hg/fvc1p08ec5imM/jtl58s9Xz7FqelYfOvG+hSq9m/Z4fDtX/9eT1qtZo9u7Y7XNfZ/UtHezvrv//aYXrOxplp2LPVeX2bM/Pt7Pf+oua9v30/uH/Pobpbf93gUL3HcUaZO7uOOzsNz6P2cJ+bc+c267//hp9++Ib1339NUUH+mLU3bfxpWN/77uu/suHHb/l5/Xf8vP47Th47Mmbt4fLceeOsrCjn2pVLvPHWWhun6I9F9oG9xMUnkpySOuzf/NG9cf7Hv/1v/vF//t9IJH+cDevReuOsrCjn2tXLvPHmO2PSF71xjgxn9y8d7e3s3L6ZdZ//2Sn6f1REb5x/XEwm0x+qTx8LY+1fRG+cIiJPotfrkUqlaDQatvz6Mx+t+xyZXI5er6e7W4O3tw86rRa5u/uwnjcSb5z92lKplO++/ivvf/QZHh4ej3xnJNq/G2+chzf9DUOvnstHd3LuwCbO7ttIfWWJrWUG5d//5Z8A6OnpYcfWzfzwzd84euigw9zb9+tfuXShb+XgW86cOuEQXbPZzPEjh/jhm7+xddNGujUau+sCfP/f/wrA7etX2LFpA1s3/sC1S+ccot3Pv//LP7Fr+xZ69Xp+Wf89eQ/uO0TTbDZz7HA2P3z7N7Zu/pUdWzc5ZDf1P/71nwHo1evZu2sHP377FQf27XZoGIf/+n//Nx3t7fz847cO0+zn3//lnxy6wzVQdyB1tTVs+PE72tpanaLvCL3KinI2//ozu7Zv5duv/sqZUyfIvZ/Dxp9+YP3339g97wPTsGfXdn749m8c2OuYuv7j3/4Vs9nM5XMn2bbxe7Zv/IHiAvu3b3iY700bNzh8LBtsPHFU39av35/3Xdu38ON3XzlEt1+7S61m08YNbPjhW9Z//zVVlRUO0z5z6gRVVZVs+OFbrl+97BDdv/37/6aqspy9Ox+eyjp1/DC59+46RP/f/+Wf2LdnJyVFhdbP7H0y6d//5Z84diSbosICAHbv2MahA/sAy87PudMn7aY9MA2F+Xls3bQRs9lMl1rN99/8N11dXQ7RPrh/D4UDdrUO7N1tk12u4WifP3uaDT98y4YfvuWrv/472X1lP9bnPk5tTQ1HDh1g/fdfo9Nq6dZocHNzw9XNDQA3Nze8vX0AuHrlEht/+oE7t27Q09MzIu3//Ld/fuKzutoajh4+yIYfv0Gn0z7199euXuLXn3/kzu2bI9YeLi72eKhE6sKk+StwdZOh12m5cGgLQRGxCKMzSEdMXW0Nn37xF1ReXmzfuomC/LwR7XCNhZLiIooKC/jg409xdXVFq336S7YVhQX5tLS2sO6LP6PRaFj/3VdkZI53iHZleSkd7a28sfYjAA7t3UFtdSWh4ZEO0Qd4/c13+Pd/+Sc+/uxLh2kW5OfR0dHOp1/8BY1Gw48OLHOAhoZ61n32JzyVSjZt/Ima6irCIxxX5i8y1dVVnDh6mNfXvI3Ky8vZybErjQ31fPrFX5C7u/PdV//FuKzxfPDJZ9y4doWb16+xaMnLdk9DQ0M96z7/M0qlkt9+cVxdLysuoLmpgTXvfYpO283uLT8TEhaJwtPT7trgvLHMmeNJPw31dXzy+Z+skzFH8SD3HjGxccyYNQeTyURvr+N2hectWPRCnkxKSUkjP+8BcQmJGI1GKsrKWPLyMrtqhkdEUV1VQUJiEl3qTrq61ABUVVWSkppmV+1+EpNTKCjI49aN65SVFjNrzjw8HdS3jMscz41rV0hMSqZHp6Omuoplr65yiPbsufOZPXc+Op2OTRs3MHHSZJs9W6vVkns/h3t37+ChUDAuM4tFS5bi4uKCu4cHHp6efPvVX4mKjiUpKZn4xCQA5s5fSMu4TO7dvc3P678jPCKKzKzxIxpntFotD+7ncP/eXTw8PMgYN56Fi1/GxeWhqbVt80arLZSekcmkKdOYM28hrS3N3Mu5w8afvic8IpKMzJFpPwu7GHtgJv/WRVobaxAQ0HV30aPrRu6usI/cY4SEhuHtYxkgUlPTqa6qdJixV1FeSkZmFq6ulqM77sPcmh0rVZUVpKamI5FIUCqVREXHOEQXoKqijKqKMrb/Zjkz3avvpb2t1aHGnjOorqokKSUVQRDw9PQkKiraofohIaEoVZbjzoGBwXR0tIvGngNoaW7m6KEDvPnO+yiVSmcnx+4Eh4bh2ZdPbx8fYmLjAAgIDKKyotwhaQgJDUPVX9eDguhod0xdr6upIiEpDYlEgofCk5CwSJoaalF4JtpdG5w3ljlzPOknJDTM4YZev+6hg/swmUwkJCYTFBzs8DS8aMTGJ3Di+BEMBgNlJcVEREZZ51D2IiIikhvXrtDc1IRfQAA6rY4utZramiqHLGD1s3jJUtZ//w2hYeGkpmU4TDcyKppjRw7RrdFQkJ9HYnKKQ49Lm81mDu7bzeSp0wgOCbXJM9VqNd999Vdi4xJ4/c23UakeXYiVSCS8+fa71NXWUlFeyskTR6mvr2PWnHkA+Pn5M2/BYubMW0he7n12bNtMRkYmi15a+kxty87sfxETF8/qN956Qruft9Z+8MQxTgBfP3/mzl/E7LkLyHtwn107tpCenslCG9VFuxh7NaX56Hu0zF6+FolEysmd6zEZDfaQGpTHdxAdtKH4AmNmwpTppI2b4OyEvFBIB6wWCRIJJpPz7mu8SCg8PTEaDDTW170Qxp6LVGr9tyAISKUu1n87qs4NTINEeHHq+os8ltl7sj8UEZFRrH3/Y0qKCzl0YC+Tp04nfVymU9LiKCQSySNHhI0Gx83XAFxcXIiMiqastIS8vFxSUtPtrqlUqejp0VFaWkxERBRanZb8vFzcXN2QyWR21+9Hre5EEAQ0mi7MZrPDTsABpGeMI/d+DnkPcnll+UqH6QJcOHcGpVLFOBueGFAoFKxY9To5d26za/tW0jPGkZo+DoXi4UaTIAiEhoURGhZGdEwchw7usxp7ZrOZyopycu7epq62homTppA5fnjzWg+FguUrX+Pe3dvs2bmNtPRxpKRlPKL9NPq17+Xcob62hokTp5CRZbuysYsZ36vvQSb3QCKR0lxXhVbTaQ+ZIamrraG9vQ2z2UxeXq5DdzuiY+K4d/eO9eiHo45xRkRGkZeXi8lkokutpsJBK+4AEVGx5N3PoVevBywrHN3djrkz6EzCIyIozM/DbDaj6epy2C6HiHORy+W88dZazp45Kb7z55yQsAiKCx9gMpnQdmuoq6kkMNg2q9DDwVljmTPHE2fT0dGOQqEga/xExmVNoL6+zmHabm5u6PvGUUeiUnnR2tyEwWBAp9M5pV9LSUnj3t07VFdWEhsX7xDN0LBwbly7QkRkFBERkVy7cpnwSMfNF00mE4cO7ufV1a/j5+/PNQfd0+wnY1wWN65dBcA/IMBhusWFBVSUlw1rx2wkSCQSkpJTWPP2Wt548x16e3vZ/OsGdu/YSo9Oh1qtpr7uYXtubKjHq+8aRu79HH789itu3bhOaloGn37xF+bMW4CXl/ewtROTUnj9zbW8tuZtenv1bP3tZ/bs3EaPTvfU3z64f4/133/N7VvXSU1N55PP/8ysufOHrT0c7LKzFx6bwrVT+zi7byNefkF4evnaQ+YJ+ldEgkNCOX7kMO1trURGRZOYlOIw/di4eBob6vnlp++RSqTExicwd/5Cu+smJiVTWV7G+u++RunlRVhYuF01rdoIREbH0tbabA3D4OrmxqKlr4KHY47tOnIlbKBmUnIqFWVl/PjdVyhVXgQFhzhmRfD3sLzvxDQ8fN+OTcPAeqbw9OSNN9eyfesmXln+KqEOaG+OrufOaFe/pzQICMTEJ9FQV8OO335EQGDa7AV4KOx/p8aZY5kzx5N+fWchCILVI6ZEIsXNzc1h95gEQSAgMAhBEPjph2/JGJfJ5KnTHSGMUuVFQnIqv/70HSovbwKDguyva5W3vO/o2DgO7t9DfGIy0gE7+fbUDI+IpLy0BB9fX1RGL3Q6LeERUXbVHpiGyxfPEx4RSXhEJAGBQWzc8ANx8Qn4+9vX8OrPv8LTEz9/fxISk+2q97j29WtXUKs72fjTDwDEJyYxe+78MT93IEqVihmz5jB95mzr4oXJZOT0yWN0dalxkbrgrlDw0lLL3VAvL2/e/eBjPIa5E/eY+KPaShXTZ85h2ozZTyycDLyzFxAYxLIVq1B5ebH2/Y/wsOOc2abGnl6nxU0mx03uzqxX3rblo5+JtrsbudydyKho3v3gY4dqD9QHmDZjFtNmzHKoriAILH75FYdo9qPTdiOTywHInDCFzAlTHKoPj5b7//y//78O1RQEgfmLluDm5oa2u5uNG34kINC+g6S2uxv3vnoeOeCO4GIbr5A9NQ1aS/69vL356FPHOcSBh2Wv1XY77D7sQN2B5a7y8uLTLxwTAqFf38vb2yFhFwbLL8Da9z+y/vvxvzkqDY7o5/r7NkEQmD5nIdPn2HfBbiAD+zSZTOZQZx1DjSfZB/Y6VN/edetp2hnjssgYl+UUbalUyjvvfeg4Xe3DujZn/iLmzF/kMG14tK5LpVL+8f/8/zhUMzNrAplZE6z6jp5DzJw91/qZTCbjsy//zmHaAL29vbS1tpKaZv9jswO1bV3HB+bpcQRBsN45lsnlQ2qP9tRE/5zsWdoAX/z5H22qPRJsZuzpuru4fHQHsWkTbfXIYaNWq9ny289MmeaAVbDfkb4z863pUrN3+yayJk11uHY/zsj/45o7t22mR6fDaDIyY9Ycu3rSUqvVbN30i2NWe4egS61m++aNTJoyzeHa/WU/Lms8+/fuYq6DJiYvWv/i7Pw6Ow2aLjX7d2wic6Lj+zZn5tvZ7/1FzbuztLvUanZs2chEJ/Tl8PsYv53B76WulZeVcvjgfiZNnWZdtHeU9h/hucOh63cwJxsuIwqqfvfiMRqqS5HJPZi78oNHvluSe5O8G+dY8taXuMndKbl/g5pSS8wOs9mEuqOVJW99idTFhUuHt2MyGTGbTIREJ5CUNeMJ7dEGVT90YB8lxYV4KBROCTxsMpn45acfUCqVDnefPFbt4QRVN5lM7Ny0AYWnkmWr37R+fv7UMfJy7/L53/8vwHLB+8SRAzQ11CN3d2fJslWonnL+eCxB1UtLijl57Agms4nMrAkO21W1hfZI42aZTCY2bvgRT6WSN958h0MH9lFVWWE9Prp0xUqCgobvPe5pQdWPZO+ntKQIDw+FdQevIP8Bly+cpaW5mXc/XGf1omU0Gjl+JJuG+loEBOYveomIZ6zMjyaoemdnB9n796LRdAECWeMnOMzwdKY2wI1rV7h75xZmM2SOn8BkB2p/87f/xM1NhkQQkEgkfLjuc4fo2qrMhxNU3WQysWuzpW97ZdWb7N22kd5ey/0pbXc3gcGhvPzqG/TotJw+lk1nRxtSqQvzlyzD1z9wyOeONqi6M8cyR/epg+U1Py+XC+fO0tLcxAcff0ZIqH3vSra0NLN/907rf7e3tzFr7nyHtDNb1fOnBVVXd3ZwJHufNQZvRtYEJkyaik6rJXvfLjo7O1CpvFi26nXkcnfycu9x4+olzGYzbm4yFr60lIDAoceWsQRVd1b/AqDT6TicvZ/mpkZA4JXlrxIWHuEQbUe2s8HamFarZd+enXS2t6Py9mbV6jeGHdx7LPyR52ww/KDqrS3N7N+7y/rfHe1tzJw9D6VSxcULlr7t/Y8+HZE30tEGVR/Rzl54XCrRyZncuXD0kc+1GjXNtRW4Kx56potLn0Rc+iQAGqpKKH1wGzeZHLPZzPSX3sDF1Q2Tycilw9sJDIvBJyBkNOl/gozMLCZMmkL2gT02ed5IuXH9Kn7+/ujtFBjR2do5t6/j4+v3yCXyxvo6enoevYCad/8uMrmc99b9iaL8XC6fP81Ly1fbPD0mk4njRw7x1tr3UapU/PLTD8QnJDnksrEztG9ev4qfnz89+ofveN6CRSTZwR17ekYm4ydO5vDBhwFP/f0DeHX1Go4fOfTId3Pu3ALgw3Vf0q3RsGv7Zt776FOb37+RCBLmL1xCcEgIPT09/PLT90THxDnkfTtTu6mxkbt3bvHBx58hlUrZvuU34uMT8fF1zH1ogHfe+3BQl9H2xJFlfu+xvm3VWw8XNI8e2EV0nCXcwq1rl/APCOLlV9+grbWZC6eOsuKNd22eHmeNZc7o1wbLq39AIKvfeJOjh/7/7P1lcFvZuu+N/qYkS7ZlycwYs504zMyM3ekkzUmnacGm8979Vt2638+59Z579l57r9WrudOYDjMzMztmZrYlg2RbdD/IVmy3HdSUeiXzV5WKLU/N/xhjjjnwGc9zSDTdvgQGBjnitFqtVv7+3/9BcoprzjK5op4LMhkz5ywgNCyc7q4ufvnhG2Lj4snKvE903DAmTp7GjWuXuXntMjNmz8fX14833noPT08vSooKOXXsMG++t9lp6RmIO9oXgNMnjhEfn8ia19dhsVhcFlfR1e/ZYO/YtSuXiIsbxuSp07l25RLXrl5i9twFouj38iqN2QICg9i4+ROH9ud/+0+SUlIxm0ysfu0NThw7LIruYDzTUkxgWBQeqt9u92bdPEfauBkM5TChqiSPiGH2wIWCIKDwsEevt1mtTnehHR0T69KzPH1pbW2luLDAYQP+smm3t7VSVlxIWsZox2dWq5UrF04zZcbcfteWFOWTmm6PGZOQnEZVeekz72I9DTXVVfgFBODn749cLictfTgF+blO1/k9aLe1tlJUWMBIJ7rjfRxRMbG/sYMPDAomIDDoN9c2NTU6zth4q9V4enpSW1Pt9DT5aDSEhdsXhlQqFYGBwbS1ucbbrzu1m5oaCI+IxMPDA5lMRnRMLPl5OS7RdieuKvP2tlbKSwpJGzH6N3/r7uqiqqKMYT2TvZbmRiJ7nDj4BwTR1qrH0NHu9DS5qy9zR5s6WF6DgoIJHKStcQVlpSX4+Qc41Rve43BFPffx0RAaZtdQqlQEBAbR3tZGcWEe6SNGApA+YiRFBXkARERFO9r/8MhI2tranJqe3wNdnZ1UlJc5+lS5XI6nC0wawfXv2WDvWGF+HiMy7GFFRmSMoiAvTzT9Xl6lMVtfykpL8PPzx9fXb8hxlJi8cOiF2vIiPL190AYMPjO2mE00VJUSHpvk+MxmtXLhwM+c2P4lwRExTtvVczenTx5j9tz5bvEm5grtS+dOMmXm3H4amfduMSwhGfWAs2od7W34aOymuDKZDKVKRWen88NQtLW1odU8MvnVaLW0u6hTcrX26ZPHB33GF86fZcvXX3D65HHMLo6P1EtISChFBflYrVb0uhbqamtoaxV3IqTX6airq3GJJ0x3awcFh1BZUY7RYMBkMlFcVEhrq94l2mD3TLlj6098/+1X3Ltz22W6fRGzzK+cO8nkGXMH9TJbUpRPVHQsyh5T6cCgUIoL7YOiutpq2lr1dLS/PANhd7apvxdysh66JNbbYLiibdHrdTTU1RIWEYmhowMfH7tVllrt4zDz7MvD+/cYFp8gWnrc1b7odDq8vb05cmg/W775kqOHDrgs9MXv4T3r6GjHpydWrNrHp8eMWFxepTFbX3JdFD9yKF7IQYvFbKIw8waTFrw25DV1FcX4h0Sg7LMjKMhkzFz5DqbuTm6dPUhrSyNaf/es4DmLwoJ81N5qwsIjXB6jxhXapcUFeHmrCQkNp6qiDLBP6Iryc1m97h1RNCUeUVhgt7Uf+IxnzpmLWu2DxWLh+NFDXL96uZ+HL1cxYuRomhob+fn7b9BqfYmIjEaQibfw0N3dzd7dO5i3YLFLA+C6SzsoKJhJU6ax/def8fDwICQ0FEEQJUzqoLz93iY0Wi0dHR1s3/oTgUFBRMe4xkU5iFvmZcUFeHqrCe7TtvWlMC+r347fmAlTuHzuJDt//oaAwBCCQsJc+iwkxMVisVBYkCd6yKTBcEXb0t3dzaG9O5k1b+FvNOwLif3b7YqyUrIe3GXdOxtFSQ+4r32xWq3U1tYwf9ESIiKjOHXiKNeuXGLm7LlP/vJLhiAIv4+wTi8hFouFooI8t9arF5rsdbTpMbTruXDgZwA6DW1cOPQL05e9iaeXPV5EVUkekcMGt3v3UHoSGBZNQ1XpP/xkr6qynIKCPIqKCrCYzXR1dXFw/x5WrBp6IvyPpF1TVUlpUQHlJUWYzWZM3V1s++FrZHI5v3z3OQBmk4mfv/2cdzb/AbWPhva2Vnw0WqxWK91dXUO6xn0RNBoNrX3MXdpaWx0rVWLjSu2qygoKC/Io7vOMD+3fy/JV9nOQCoWCjJGjXR6UtReZTMac+Qsdv2/9aQsBAYGiaFksFvbu3kH6iAxSUl0TQ/P3oN3XTfj5s6fRaJ7PidXzoNHatdRqNckpqVRXV7lssid2mddWV1JWXMDPpUVYetq200f3M2/JKoxGA/W1NSxasdZxvVKlYs6i5YDdwdIv3/39sc6n/tFwZ5v6e6C4sIDQsPDfWKuIjSvaFovFwqG9O0lNzyCpJ2ajt1pNe3sbPj4a2tvb8FY/OjfXUF/HyWOHWPPGm3h5iXeezl3ti0arRaPVOnZRU1LTuXblsui68Pt4z9RqH9rb2vDRaGhva0PtgtjIr8qYrS/FRYWEhIajdkGM1qF4ocme1j+Ihesfxdk6vetbZix/C2VvDI/uLprrKhkz41EMsK5OAzKZDA+lJxazmcbqMhJGTHiRZPwumDVnvsMVfG9QVldM9FylPWXGHKbMsAe9rKoo496t6/28cQJ89df/zTub/wBAXEISudmZhEVEUZSfQ2RMrCgmpuERkbQ0N6HTtaDRaMnJzmLFateUuyu1Z82Z51hpLi8r5cb1qyxftcbRSdtsNgry8wh2wSHnwTCZTGCz4aFUUlpSjEyQEShCYFibzcbRwwcIDAxioovdHbtTG6CjowO1Wk2rXk9+Xg7vbvzQJbrd3d3YbDZUKhXd3d2UFBe5bPfYFWU+afocJk1/1Lbdv32deUtWAVCcn0vssEQUikddZVdnJwoPD+RyOTkP7xERGe0w8XwZcGeb+nsgO/shaS6KO9aLK+q5zWbj5NGDBAQG9Qu3EJ+YQvbDB0ycPI3shw+IT7T7V2ht1XNw704WL1uFv0gLd+De9sXHxwet1pempkYCA4MoKy0hKNg1Gw+/h/csMTmZh5n3mTx1Og8z75OYnCK65qsyZutLrhvalIE802TvzvkjNNVV0N3ZyamdX5M8egoxSUNnoLa8kOCIWBQej1xPdxk6uHf5uN1Zh81GeFwyodHxz5+DARzYu5vyslKMRgOf/fd/MH3mbLc4THnVSRsxmtNHD/Dzt5/j6enJgmWrRdGRyWQsWLSUHb/+jM1qI2PUaIKDh3aD/rJo93Jo/14MBgPYbISEhrFwyTIn3nsPleVlGI0GvvzsL0ydPgtPTy/OnDqG0WBg785tBIeGsnb92z0eOH9BEAR8fLQsXbHKaenoS1VlBVmZDwgOCWHL118AMHPOPBISk57wzX9sbYB9u3dgNBqQyeQsWLTUZY4EDB0d7Nm1HbCbPaUPH0F8QqJLtN1d5oX52YyZ0H/w3dLcyNnjh0Cwe1ubvcB571xf3NWXuaNdGyyvXp5enDxxFKPBwK4dWwkJDWP9m+IeGeju7qa0pJjFS5aLqjMQV9Tz6qoKcrIyCQoO4ectXwEwbeYcJkyeyuH9u8l6cA+N1pflq14H4PrlC3QajZw5eRSwH795+33nLzC5s30BmL9wCYf27cFiteDn58/S5eL0XQNx9Xs22Ds2ecp09u/dxYN7d9H6+rLqtTdE0+/lVRuz9bYpCxc/6ify83I5fdLetu3e8SshoaG8sUHctu2Z4uy5kueNsyfx/DxNnD2xeJE4e//IiOGh9Fl4XJw9sXmeOHsSEs/D08TZE4vnjbMnIfGsPC7Onti8SJw9CYl/JJ42zp4YPG+cPentlJCQkJCQkJCQkJCQeAmRJnsSEhISEhISEhISEhIvIdJkT0JCQkJCQkJCQkJC4iVEmuxJSEhISEhISEhISEi8hEiTPQkJCQkJCQkJCQkJiZcQabInISEhISEhISEhISHxEiJN9iQkJCQkJCQkJCQkJF5CHhtUvdNkcVU6JH4HtHWa3ab9qsbZ6zS5Ly4SuDcuk6/3q7nWZLa4r8xf1diGCvlzhSaSeAHcWc+F5wtF5RTkMvdpe7zC9dydMWvdWd863DhuU3s+dgrx0mJ049zIW/l8Zf5q9vwSEhISEhISEhISEhIvOdJkT0JCQkJCQkJCQkJC4iVEmuxJSEhISEhISEhISEi8hEiTPQkJCQkJCQkJCQkJiZcQp032jO2tXD38s7Nu90zodTq+/erv/T7LvH+Pk8eOuEXbVTyN9tafvqemutrp2u2teg5s+87p931ahsr7gb27+e7rz7l5/apLdV1Bq17Hz1u+7PdZ9sP7nDt1zKVp2P7jV/0+q6+t4dLZE6JrD1b2NdXVnDp+1KWarmpb+qbh+2++6PdZRVkpe3duE03PXXX896DfqtexdctXT75QBH7v/YnY+gPruat0t3z9uct1++oPLPe62lqKCgtE1/3uK/fk2111zZ157tV35/hh6/fuaddAvLw/732vXLrAN1/+ne++/pwtX39BdVXlM9/jefuKz/7P/2TbD187/t2+fuWZ7/GsvJqudCReStrb26mpqeKTP/6zu5PyShESFk5IWLhbtMMjIgiPiHCLtoSEhOuwWq3IZK+GMVJ9XS21NdUkJCa5OykSEi8NRqMRLy8vqiorKCosYOPmj1EoFBgMBiwWu4fNTqMRTy8vp2v3va9CoWDD+x/99ppOI56eztcGkSZ7hnY9Dy4eJm3CXPJvX8BiNSOXK0ifvAC11l8MSQe6lhb27t5B+vARtLa2svWn72lvayN9RAbTZ852ifaiJcvIzcmmpLgQQRAYNXos4yZMcon2/IWLuXP7JvV1dQQGBmE2i++Wt02v4/zxfUyatZAHNy7T2WlAofBg8uxF+PoHiq7fm/fmpkYAtnz9BfMXLSE6JtYluvMWLOLO7Zs0NtQTEBBEe3sbCxYtFXUSote1cHj/LlLSRtDR3sa+nVvR61pISEpl+ux5oun2pVXXwvFDe0hKHU51ZTlLV69ziS70f88rystYu/4tl2r2UlSQz5XLF3l93Zt4e3uLnwZdCwf27CStTxpE1euT56rKCrpNJlqam5g4aSoWq4WszAfI5XLe2PA2XiJ0kH31q6sqMZlM6FpaSEpJZc68BU7XG4he18LRA7uZs2ApF84cx2wy4+vnx9zFy0XrlHvpm/fKigq6ujpd3pcN7E9c0bZB/3peWVGOqduEzWZl/dvvi6vb0sKBvTuZM38h58+cwmKxYLPZWPXaG/gHuLYv81AqqawoZ/K06aSli/u+61pa2LdnJz4+PmSMHE1KWjoA//m//xf/9u//b9G19+7eQUN9HZs++pTg4BDAbpU0Z95C0epa3zzPnDOPkJBQvv/2K5KSU5g2YxYXz59Fq/Vl1Jixoun3vt9NjY0sXbGKhvo6DuzbzXubPsLDQ7xwVHpdCzt+/o6urk7+/H/9f3rS08zxQ3tY/+6Houn20pt3Ly8vjAYDAG3tbYwdN+GF2rbe+y5eupzwiEjAHpKjvKyU+/fuUFNVxSd/+mfa29vx8vJGobBPgfr23SdPHKW9rY2Ro8eQkpruuOZJ9O0rQsMjHNpVFWVkPbhHXU0V7330p8fe48LpE3S0t5GeMZqE5NSn1n4anD7Z62htIfPyUYZPXoCXj5ZxC9Yik8loqi2n8P4VRs1Y5mxJB01NjRzYu5tlK1ZRV1tLTXUVmz/+AwoPD37c8jUJicmiNRx9tasqK9DrdWz68FNkMhlGo1EUzcG0S0tL8FB48NGnf6K+ro7vv/3yyTd4AfQtTVw8eZCpc5dy+/IZJs1aiNYvgIa6aq5fOMnCVRtE1e+bd5XKk107trLpo09F1RyoW1JSjKenJx9+8ica6uvZIrIpUktzE0cP7mHBkpU01NfSUF/Hm+9/iFyu4Kdv/86osePRaH1FTYOuuYmTR/YxZ9Fyujo7qa4sF1WvL33LvrOzk4ryMpdq1tXW0qrXk5+bw80b13hj/VuirAQOpLmpkUP797B42Uq6OjupLBe3zAfmuaGhno2bP8FiNvPl539l9pz5bPrwE06fPMbDzPtMmDhZVP26ulo2bf4EuULB15//jXETJqIVsZ63NDdx/NBe5i9ewckjB5g5byGR0bFcv3Sem1cuMmPuQtG0fy99mav7E+hfz+vr6qivq+W9Dz4RZTFhKN3M+3cZO34S6SMysFgsWK3ixwwc+Mxra6pZsHipS3QP7tvD0uUruXXjuuh6A7V781xWWkJudhbBs0Job2ujo71d1Drem+fiokIqy8vx1foik8moqqwAoLKinIVLxBmv9s13cEgoW3/6nvzcHK5evsiiJctFnej1tmtr1r3DxXMnaaivJTgkjJyH90kdPko03V765j0kNAwAvV7Hjl9/IWPUaKfdt62tjYcP7vEw8wFBQcGMHDWa5SvXADAsPoErF8/z1ed/JS4untT04cTExgGwYtVr1NZU8+D+XS6dP0d8YiKjRo91pHUw+vYVQSGhtLe3kfvwAXnZmfgHBpE+YhQLlq50XG82m9n2w9eO38dNmkZSajoLl62ivraGnIf3uX75PLHxiQzPGE1QSOhzl0svTrWJMHUZuX/hECOmLkLjH4y5u5vMS0e4evhn8m9fpEPf7Ey5fhgMBvbs3MaKVa85HkpcfDxe3t54eHiQnJJGpUgD0oHapSUljB4zzmFyImYnNVC7sryM4RkjAQgJDSXECZVkKLqMBs4d3cv0+cvR+PrRUFvNheMHOLT9e66fO4Gxo100bRj8mbuCgbpVFeWOldfgkBBRy9xoMHBo7w4WLVtNcI9OdGwcKpUnCoWCgMBg2lr1oukDdBoNHDuwk3lLVhIULF5eB8Mdz3wwzbKyEq5dvcxaF030jAYD+3bvYNnKNS7J92B5jomNQ6VS4a1Wo1KpSExKBiA4OBS9Tie6fmzcMFSe9noeGBRMq168em40Gji8bycLl65C4+tLV1cnkdF2S4HUESNFXdz4PfVlruxPYPB6HhsXL/pEz2gwsG/Xdpb26EZERnH96iWuX71Mq14n6uAb3NuX7d25neUualcGavfNc2racPJycwDIzckiJTVNNN2+eY6KjqGioozKygriExIxmboxmUzo9ToCA4NE0e+bb0EQWLZiNYcO7CU6Npao6Bina/biaNeWrSIoJJT0jNHkPHyA1WqlIC+b5LThomnD4PXcbDazf/dOFixagq+vn1PuW11Vxed//U9a9Xrefm8Ta9auIyEp2TEmVyqVvL/5YxYvXYGXtzcH9u4i8/49x/3CwiNYuHgZmz/5I/7+Afy45RtuDOEHom9fERQSSl1NFT98+Vfa2vS8tuE9lq5aS1xCUj8T9F4zzt5/Sanpjr+FhIUza/5i3tr0CX5+/uz4ZQt3b734IoxTJ3sKDxWe3j7oGmoAKHpwDf/QKKYse4fRs1ZgtYhnUqhSqdBqfamseNQJCgj9rhEGfklEbVfhTm0PpQq1j5b6mkpsNhtKlYrl6zc6/q16S1xzAHfl3d3P20ejpaaqwvGZXP5og14QBNFXoZVKT3w0vtQ+x4HmF8UdZT+Ypp9fAN3d3TQ3Nbk0Db2rzq7S65tnxYB6Ju8xMREEAZuT69yT9GUyceu5UqVCo9FSXeWa8u7Lq9qX9dXvW8/Fnmj16mq0vlT15DtteAZr1q7HQ6Fg945fKS8tEV3fLX2Zpwqtr69j8UCQybDZbIDdBK33HJMo2gPyrNFq8fLyor6ujpzsLFJFMl0dmOfwiEjqamqorCgnOiaWkNBw7t+9Q5hI59AHe9bNzU0olUra28RdIFeqVGi0j9q1hKRUykqLKC0uICQ0HC8vcY8iDJb340cPkZyaRtyweKfdNyQ0lCXLV9LY2MCendu4d/c2XV1d/b4jk8mIiY1jxqw5zF+01LHQAPbzwQX5eRzYu4v79+4wfeZsho8YOaj2wL4iMDiUuYuW09LUyOF9O8l6cJfuAdqPw2q1UlKYz/FDe8l6cI9JU2eSkvbi74JTJ3uCTMaomcupKcmhtjQPs6kLlZcPANXF2c6U+g1yuZw1a9fzMPM+2Q8zASgtKcJoNGIymSjIzyUySpwVk4HaccPiuXf3tmMwIqYZ50DtqJhYR/4b6uupr68TTVsmlzNryWqK87KoKi3CR+NLWWEuYO8omhvrRdOGwZ+5KxioGxkVTW5OFgCNDQ00NIiXb5lczvLVb5CT9YC87Iei6Tw+DTIWrXyd/JxMCnKzXKrtjmc+mKavry9rXl/H4YN7RX3evcjkcla99gZZDx+QkyV+vt31bv1u9GVylq5aS152JqXFhag8PR27eblZmUREi3ce+PfUl7myPwHX1/N+uq+vI7tHV9fSgq+fP2MnTCIxKZkGkfM9sNyVKiXd3d2iaoK9nq9+fR1ZmQ/IzsrE19eX2lr7Yn1hfp6oCyqD1fO09OFcv3aZrq4uQkLF2UUemGe5XI5GqyUvN5uIyCiiomO4ef0qUSK94wPz3dXZyekTx3jr3U0YjQZyc8QbKzvataxM8nIeolAoiImN59ypY6QNMZlxqv6AvN+5dYPurm4mT53u1PsqFAoyRo7m7fc2sXTFKnQtLXz/zZcc3L8HsJt8Njc/Wqitr6vF19d+JODG9at89flfyc/NYcKkKWz++I9MnjodtVo9uHafvqK3TNNGjOS1De8xf8kK9LoWtv34DScO739iPu7eus7P335OYX4uY8ZP4q1NHzNu0lS8h9B+Fpzu2kqu8GD0rJWU595F4x9M0f0rXDu61bFaJCZKpZK169/i5o1rdHV3ER4Ryb7dO9jy9Rckp6SJeqi8r7aPRoNW68t3X3/Od19/Ifpgpa+2v58/3aZuvv7iMy5dOEtYuLgH6T08lMxd9jo5928Rm5hKYU4mh7Zv4eC276gsEdd1NPTPe0FBnuh6g+kGBARiMBj45svPuHj+DEFBwag8VaJpeyiVrHxtA3dvX3+mFSOnpsFDyZJV63hw5wambtemod973tWFePscQ2j25DcwKIgVq15j/56dtLSIZ6Lei4dSyZq1G7h987ojDWIyWJ5dibv1PZRKlq9Zz/1bN0hISuXy+dP8+v3XNDbUMWHKiw1OnsTvpS9zdX8C/et5twufu1KpZM0bb3LrxnWysx7w/Tdf8MO3X9LY0EB6hvhnmfqWu6nbRGNjA1u+/oIckRf1lEolr6+z51ur9aWivIwt33xJVVWl6Luq/frv/DxSUtPJyXpIalr6k7/8grq9eS7IzyMqOgZvbzUeHh5ERcfQ1tZKVIx45pR9873l2y8ZM34CAYGBLFm+kvNnT9HR0SGatodHT7t2+wYlhfmkpI1AEASiY59/Z+1Z6Jv3G9eu0tBQx5avv2DL119w9/Ytp9y3IP/RWDAgIJDZc+fz0R/+TGqPuaSpu5vDB/bxzZef8d3Xn9PU2MC0HscwISGhbPrwU5atXP3UTv769hUlhfmOz/38A5g6cy7vbP4DiSmpjs97z+z1/rty4QwAQcEhbHjvQxYsXUmEkxf0hMdNwi4XtIg/QxuCccPE9dop8VuqW8R1JPM4IvzFP/ckFlarFavVikKhoKWlme2//MRHf/gzcrn8id81dotnJvM0dJvFdzwwFL7eLz6QyMvNpjA/n2UrV794glyE2eK+MlfIXw3X9QPp6BLfK/FQqFUv5gct8/49lznreBLP4iHRnfVcEMRfABoKucx92q5YVB8Kd5Y5vLp57+h8sbbtzs1rdHd3Mnna7Gf+rtrz1Yze5s7+JFCteK7K9mo+KQkJJ2Iymdj28w9YrFbAxoLFS59qoifxYhTk53Hh3BmWLF/l7qRISEhISEj8Q3Fk/070Oh2r33jb3UmREJlnmuxlXTtFY1UJSk8vpix7B4DMS0fpaG0BwGzqQuGhYvLSt9A31pJz44zju/EZkwiJThjyPs6gtVXP4QP76OhoBwRGjxnLeCe7A3+VtbPv36Qw+wEIAv4BQUydu5T62iruXDmLxWolMDiUKXOWIJPJqK0q59zRPfho/ACIiU9i5IRpTk/TkYP7KSrMx1utZvPHf3T6/Z9EcVEhp08cw2qzMmr02Be2Pe+L2Wxm968/YrGYsVqtJCanMXn6LPS6Fo4d2kun0UhIaDgLl61CLpdz4cwJKnvCEJjNJgyGDj79539/oTT8/O1nKD2UCDIBmSDj9bc/oLPTyMnDe2lr1aPR+rJw2RpUnl50dRo5e+IwrfoW5HIFcxYuIyAoxBlF4WBgXR8zdjxRUdFO1XicXu+7ZTQa2b93F606HVo/P1avWesUr5zHDh+guKgAb281Gz+0hxC5fOEshQX5CIKAt7eaxctW4qPR0Nlp5Pjhg+h0LSgUChYtXUFQsHPLu5dbN65x/94dbDYYNWas08Ms9OVx7dntm9e5c/smgiAjITHJKbH2zGYze7b9aI+pZrWSkJzKpGmzHH+/cPo4OQ/v88m//N/9vleYn8uxA7t5451NhIY537xxYNviSoZq1956d6NT7j9YPTcajRzav5tWvR6try8rVr+Op6eX6PX89s3rPLh3B4CRo8YwbuJkLl88R+a9u3j1xN+aMWsu8SIEOB+s/9q/Z5cjZmxnVyeeKk9Rwgl98dl/oVSqkAkCgkzG+x98hNFo5MDeXej1enx9fVnlpHbtcbh67GQ2m9n60/eOkBopqWn9YrudOnGMzPt3RY8vCOKPH/Zs79OuJdnbtd3bfnQcvTAYDISGReDl7U1np5Ezxw+h1+mQK+TMW7ScQCf33+DesTKIW+YwdH9is9m4dukchfm5yASBEaPHMWrsBIoL87h+6QKCYPd7MmPOQiJEGNM802QvIj6N6OSRZF094fgsY/oSx8/5dy6i8FAC4OMXyMTFG5DJZHQZO7h2ZCtBkcOQyWSD3scZyAQZc+YtJCw8nK6uLn747ivihiUQFBzsVJ1XUdvQ3kbugzusfPMDFAoPLhzfT0lBNvdvXGbBqvVo/QK4d+MiRbkPSUrvcdUdHsXcZWudlobByBg1mrHjJ3L44F5RdQbDarVy8tgR1r/1Lhqtlh+++5rEpBSnlbtcLmfN+ndQKpVYLBZ2/foDsfEJ3L11nTHjJpGcNpwzJ46Q9eAeI8eMY2afuF/379ykoa7WKelY8cbb/bx03b1xlajoOMZMnMrdG1e4e/Mqk2fM5c6NKwQFh7J45Vpamhu5dOY4K9Y6d8XQ1e/ZUHqZD+4RFzeMyVOnc+3KJa5dvcTsuS8+8RiRMYox4yZw9NCjw9zjJ01l2sw5ANy5dYOrly+wYPEyrl+5THBoKKteX0dTUyNnThzljTfffeE0DKShvp779+7w3qaPkMvl7Pj1ZxITk/EPCHC6Fgxd5h0d7RTk57Hpw09RKBROO9cil8tZve7Re7bn1x+JHZZIWEQkdbXVdHV1/uY73d1dPLhzwxE819mI3ba4W3uwen7j2mViYocxaco0rl+9zI2rl5k5Z76o9byhoZ4H9+7wzsYPkcvl7Nr+C/GJ9pAi4yZOYsKkqU7RGYrB+q9Vrz3qM8+cOo5K5Sma/oa33+sXUPr61UvE9mvXLjN77nzR9MH1bbpcLmfD2+853vetP20hPiGRiMgoamqq6ex0zXEWV4wfVr/Rp13bZm/XXt/wnuOaIwd2EZ9gr++3r9v776Wr3qClqZHzZ46LsuPnzrGyK9q2ofqT5qZG2tvaeOeDTxEEAUNP/xUVM4xh7ycjCAKNDXUcO7iXdz5w/uLOMx3g8A+JxEM5eMNjs9moKy8gLDYFsDtq6Y0rYbWY6WvS/Lj7vAg+Gg1h4XZ3uSqVisDAYNraWp2u86pq26xWLGb7LpPZbEKh8EAml6P1sw/6wqPiKC/Of8JdnEt0TKzoMZiGoqa6Cr+AAPz8/ZHL5aSlD6cgP9dp9xcEAaXSvnhitVqxWqwICFSWl5KYYo9BlDZ8JMWFv3VMk5eTJVrMnNLifJJ7JvTJ6SMpKbI/85bmRkcsMv+AINpa9RicHGvR1e/ZUHqF+XmM6HHaMCJjFAV5znEOFBUTi6dn//qsUj1y9mMydTvOhzQ1NRATOwyAwMAg9Hp9z2qpc2lqaiA8IhIPD3ubHh0TS35ezpO/+JwMVeZ379xi8tTpKHpCPgzlHe1Z+c17ZrWAYP/5yvkzTJ059zffuX7pPGMnTOkX9sSZiN22uFt7sHpeVJDniOk3PGMkhT0Ot8Ss582Njf3rdnQsBfni1e2BPK7/stls5GZnkzZcnBAEg1GQn8+IkT3t2shR/RxdiIWr2/SB77ul52yp1Wrl3OmTok9ue3H5+KGnXeulu6uLqvIy4hPtY/bmpgaiYuIA8A8MolWvc3r/De4dK7uibRuq3B/ev82EKdMd/Xevh02lUun4zGQyieZuzmk9la6hGqWnN95aP8dn+sZasq+forOjjeFTFvYLKig2ep2OuroaIiKjXKb5Mmt7+2hIHz2BPT9+gVyhIDw6jtjEVO5cPU9TfQ2BIeGUF+VjaH/00jbUVnNo+xa81D6MmzoHvwDnByh1J21tbWg1WsfvGq2Wmqoqp2pYrVa2/fgtel0zI8eMx9fPH5XK0/Eu+Wg0tLe39ftOq15Hq17naLhfBAE4vOdXQCA9YwzpI8dgNHSg9rGHVPFWqzEa7CtUgUGhFBfmER4VQ11tNW2tejra2/BW+7xwOgbD1e9ZX72OjnZ8NBoA1D4+okyy+nLp/BmyHmaiUqlY95Z9VyM4JJSCvFyiomOoqa6iVa+jva0NtZPLOyg4hAvnzmA0GFB4eFBcVOjorMWmb5mfO32SivIyLpw7g0KhYM68BYRHRDpFx2q1suOnb9HrWsgYPZ6w8Eju377BsIQk1D6aftfW19XQ1tZKXEISd25ec4r+QFzRtvzetA0dHfj0lLVa7eNY+RazngcFB3PpfN+6XUBYeASeXl7cvX2TrMwHhIVHMHvuAtHNGQdSWVGOWq0mICBQlPsLCOz49WcEQWDUmLGMHjMOQ0f7gGcgbrs2EFe16VarlR+/+5qWlmbGjJtARGQUt25cJzE5xZF/sXHV+GHHz/3btV6KC/OIiolD2bOYGBQcSlFBHhFRMdTVVNHWqqddxP4bXN+Hu6ptG6w/0et0FOZlU1SQh5eXNzPnLcLP375RUlSQy9WL5zAaOlj+2nqnpwecONmrLc0nLDa532e+QWFMWfYOHfpmsq6eJDAiVrSV0L50d3ezd/cO5i1Y3G9V3BW8rNpdnZ1UlBay5t1PUCpVnD+xn5L8bGYsXMGty2exWMyERw9DEOyTkIDgUF5771M8PJRUlRVx7ugeVr/9sVPT9Cogk8l4a+NHdHV2cmjfTlqaG5/4nfzcbBKTU52yuLJq/Xv4+GgwGjo4tPtX/AYMPOwrUva1qDETpnD53El2/vwNAYEhBIWEOeqDs3H1e/Y4PUEQQGRvbNNnzWX6rLlcv3qJu7dvMm3GbCZOnsbZU8f58buvCAoOISQ0TBSvcEFBwUyaMo3tv/6Mh4cHIaGhoj3Xvgwsc6vNSmenkXc3bqamupr9e3bxyZ/+2Sl5lslkbHjf/p4d2b+LqopyCvNzWLO+v7mgzWbj0tlTzF+y4oU1JYamb7siZj0PDApm4pRp7Nr+S0/dtrdZo8eOZ8q0mQiCwKULZzl35iSLl610iubTkp2VKequ3lvvbUSj0dLR0cGOX38mMLD/Yqwr2rW+uLJNl8lkbPzwEzo7O9m7azsV5WXk5Wbz5jvvi6rramQyGRve62nXDuyiqbHecQ4vPzeL9IwxjmvHTZzKhbMn2Pbj1wQGhRAcIk5/0os7x8piM7A/aWqox2oxI5crWP/uZoryczl97BCvv2k3qU1ISiUhKZWqinKuXzrP6nXON591yszLarXSUFnIxMUbBv272jcAuYcHHbomtIHiBMrsxWKxsHf3DtJHZJCSmiaq1qukXVtZio/GF8+es1sxw5JpqK0iPmU4i9a8BUB1eQltOnu8MaXy0csbGZvA9Qsn6TQaHN9/GdBoNLT2MT9oa2117PY4G5WnJ1ExsdRUV9HV1YnVakUmk9He1vablcj83CzmzF/sFN3ee3t5q4lLTKa+thovbzUd7e32Ha32docTA6VKxZxFywH7oPiX7/6O1tfPKenoi6vfs8H01Gofe9lrNPZdBm/nmBQ+ibT0DPbs/JVpM2ajUqkcA1CbzcY3n/8VXz9xQtaMGj3W4STk/NnTaPqsjorBYGWu0WhJTklDEAQiIiMRBAGjweCUgLO9qDw9iYyOpaqiFH1LCz9983fAbl7z0zd/Z927H9Dc1MDe7T8DYOho5/DenSxb84ZTnbS4sm35vWh7q9W0t9vbM/uOgr1dEbueZ4waQ8Yo+6D34rnT+Gi0/XYNR44ay56dvzpN72mwWq3k5+Xy/gfiLZD2vsNqtZqk5BRqqqvwVvv0fwYuatfcNXby9PQkJjaO8rJSdC3NfPX5XwH7+/7V53/l4z/8k2jarh4/REbHUlZSTGBQCEaDgbraGpauesNxjVKlYv5i+yKWzWbjx28+w9dXnP7EXc/b1W2bo9xLi1FrtCQk2WPtxSelcPrYod9cHxkdw+ljOowGg2Nc5SycsjzbXFuOt9YfT+9HhWZs12O12m2hjR2tdLS24KkWd4Bgs9k4evgAgYFBTJw0RVStV03bW6Olsa4as8mEzWajtqoMX/9AhwmfxWIm6+51koaPBsBoaHfEvWmsq8Fms6HydM/ZOrEIj4ikpbkJna4Fi8VCTnYWickpTru/wdBBV6fdOYTZZKKitISAwCCiouMo7DkzlZP1wOFQAKC5qZGuzk7CIl7cLMJk6nYENTaZuqksKyEgKJi4+CTysx8AkJ/9gLh4u35XZycWiz1uYM7De0RERjtMRJyFq9+zofQSk5N5mHkfgIeZ95363AfS0tzk+LmwII+AQPvuamef8s68f5eo6BjRVkh7naG06vXk5+WQPiJDFB0YusyTklMpLysFoLmpCYvF4pQO0TjwPSsrITg0nA/++K+8//Gfef/jP+Ph4cG7H/4RlcqTD//0Pxyfh4ZHOn2iB+K3Lb9H7YTEFLIy7e1KVuYDEpLsmmLX8751uyAvl7ThGf1M4wvyc0XzcjsUpSXFBAYGodWKM2bq7u6mq6vL8XNpSTFBwSEkJiXz8EFPu/bgPknJyY+7jVNwdZtu6Oigs+d9N5lMlJUUExoWzp/+5f/i0z/9C5/+6V/w8PAQdaIH4r9ng7Vr/j2WOUUFOcTFJzrOP0P//js78x4RUTFO77/BvWNlV7RtQ5V7fGIylRWlAFRVlDtMOHUtzY6xcn1dDRaLWRST8Wfa2cu8fIyWukpMXZ1c3Pst8SMnE5kwnLqyR45ZetE1VFOafRtBkCEIAqnjZ6PsGewPdZ8XpaqygqzMBwSHhLDl6y8AmDlnHgkiuEx+1bSDQyOITUjh8M4fEGQyAoJCSBo+invXL1JZWgTYSB4+hvAou4OOsqJ88h/eRSaTIVcomLFgpSgmAQf27qa8rBSj0cBn//0fTJ8522VuymUyGQsWLWXHrz9js9rIGDWaYCcOCgzt7Zw4egCb1YYNG0kpaQxLSCIgMIhjB/dy9dI5gkPCSM8Y7fhOfm4WyanDnVLWxo4Ojh/cDdhXmhNThxMTl0BIaDgnD+8lJ+s+Go0vC5avAewOWs4ePwQCBAQGMXvBshdOw0Bc/Z4NpTd5ynT2793Fg3t30fr6suq1N55wp6fj0P49VJaXYTQa+PKzvzB1+ixKigppbm5CEAS0Wl/m9wTXbm5q5Nih/SDYzdEWLRXPtHDf7h0YjQZkMjkLFi3F01M8D4FDlfnI0WM4cmg/3371d+QyOctWrnZKPe/oaOfU0YP298xmI7HnPXMnYrct7tYerJ5PnDKVQ/t28/DBPbRaX5avfh0Qv54f2LMDo9GIXC5n3qIleHp6cuTAUerr6wDw9fVjwRLnt2UwdP+Vk/2QtHTxTDgNHR3s3b0DsLft6cNHEJ+QSHh4hL1du38PX19fVq4R15s2uL5Nb+9o58jB/disVmw2Gylp6SQmiT+pHYjY75mjXbP9tl3Lz81m3MT+nmabmxs5dewgAhAQGMzcReLUeXeOlV3Rtg3Vn0RERnPi8D7u3b6Bh4fSUb5F+bnkZWf2jJU9WLT8NVHGykLvjHIwLhe0DP1HkRk3TJztY4mhqW5xjcvhwYjwf7l2/Z4WY7fFrfrdZqvbtH29Pdym7U7MFveVuULuOidZvyc6usxu01arxD+n/nvEnfVczLNGT0Iuc5/248ZzYuPOModXN+8dnW5s2zxfzbbNnf1JoFrxXJXt1ez5JSQkJCQkJCQkJCQkXnKkyZ6EhISEhISEhISEhMRLiDTZk5CQkJCQkJCQkJCQeAmRJnsSEhISEhISEhISEhIvIdJkT0JCQkJCQkJCQkJC4iVEmuxJSEhISEhISEhISEi8hEiTPQkJCQkJCQkJCQkJiZeQxwbJeNikd1U6fsPYOD+3abszZoo7YxPtyKx2m/a/zkxwm7Y78VLK3ap/MNt9z3zd6Gi3abuToroOt2knhfm4TVvmxvhjhi73xbN8VePsuTOmo8XqvphrVjdqd5rcV889Pdzbl7mzfXEnhXXtbtMeFevnNm13UtXsvpjUgWrNc31P2tmTkJCQkJCQkJCQkJB4CZEmexISEhISEhISEhISEi8h0mRPQkJCQkJCQkJCQkLiJUSa7ElISEhISEhISEhISLyEOHWyV3fqF2fe7pn4z//9vwAoLytl145fXa7/H//P/3S5JsB//5//76CfHzu0n/zcbFG1uy5vE/X+T0Pfcj97+gTffPl3zp4+4TJNd9CrX15Wyq7tW12uX3rsRwDMnQbqbp9xqXZv3tva2ti7e4fLdd1V5gAHf/orAB1tek7v/UFUrb/8//6XqPd/Gtz5nv34xf9xm7Y78/17advcwX+5uc73vnN6nY7srEyXaH7xX/+PS3SGwl3tzKtcz09u+7vbtMXM97Pc+8G9u3z71ed89/XnfPvV3ynIy30h7QM//vWprz2+4xtO7/2BM/t+4sy+n7h/zTVjqFfTTZjES8m9u3f4l//xfyOTSRvWrkDh6U3ouLlu0dZoNKx5fZ1btCUkJFyH1Wp9pdp0vV5HTtZD0odnuDspEhIvDd3d3cjlcjo6Orh6+SIbN3+MytOT7u5uDAa7h+xOoxFPLy+na5tNJmQyGTK53WPt9CXrUHn21+nu6kSp8nS6di+iTPasZhO6u2ewmrrBZsUnaQyeITFiSA1Kd1cXu7ZvpaWlhZjYOBYuXurScArXrlwi62EmgiAQn5DI7LnzRde02WycOXmMstJiNBpf5HLXukE2V2ZhbSgDqxVZUDSK2FEu1d+941dM3d388O1XTJ42nbT0EaJr2mw2Th47QllZCRqtL3KZjIxRY0hNSxddu5fu7m727t5BY0M9YWERLF+1xmV13WRoo+7mSaJmveYSvb7odTp27djK5o//6HLtXmqqqzh25BCrX38Df/8At6VDTMrLSrl88Ryenp40NNSTkppOcHAot29dx2w2s/r1daLnvbyslEsXzuHl7e3yem6z2bh5+SyVZcUIgsCo8VOJT04TXRfs+b54/ixKpQpdS7O9L1uyzGX5PnX8KKUlxWi0WuRyuUvbtt68e3p60tTUyMd/+CeX6AK0t7dxaN9uurq6sFqtLFi8lKjoWJfpXzh3mqamRr7/9ktGZIxi/MTJomtWlpdx99Y1Vry2HoBzp44RGhZO2gjX9OMH9+0mfUQGCYnJABw5tJ+ExCRSUsWtbyeOHWZYfCJJySns2bkdT09Plq5YxYN7d9G1NDNzzjxR9fNzc7hz+ybr33qXjvZ2tv78PW+9uwkfH/FD5Dy4fJzQmERCo+1hr+5fOkZYbJLjdzG5eP4shfl5ABgMBuLiE1i2YpUoWtVVVTy4f4eS4iLe2/ghho4OlEolHkolAEqlEmXPz9evXaGspJiRo8eQNjwDlUr1QtrNDTWU5T+kvqqMWSvexNNLPeS1BZm3aKguIzY5g6j4FDyUL6Y9EFEme4JMjt+YOcgUSqzdnTRdO4IqONplg9Ca6io2f/xHtL6+7Nz2C/m5OaS4qJMqKiygID+P9zZ9iIeHB0aja+JxFObn0tzcxMYP/4Cho4Pvv/mcESNd01BbW6qxGdvwGL0EAHP2Oaz6OmS+oS7RB3h93Zv8x//zP9n00acu08zLzUGv1/HhJ3+io6ODb778jIxRY1ymD1BXV8vmj/+IRqPh5x++o6qygqho1y2svKpUVlZw6vhRXn9jA1pfX3cnR1Qa6uv44OM/4unpxdef/5WMUQre3fght29e5+6tm8xdsEj0NLirnpcV5dHcWM/qNz+gq9PIge0/EBYZjbfaNfEKa6qr+PCTP6H19WXHtl/Iy81xyYQrPy+XpuYmNn/yRzo6OvjWHW1bbQ0ffPwH/Pz8Xaqbk/WQuGEJTJ42A6vVitlkcqn+zNnzuHn9Kq+ve9Oluu4kJW04eTnZJCQmY7FYKC8tYcGipaLrRkXHUllRRlJyCu1trbS3twFQUVFOWvpw0fWTU9PIy8vhzq2blBQXMn3mbJdM9ACiEodTmnOX0OgETN1d6BpqyJi60CXaM2bNYcasOXR2dvLLj1sYN36CU+9vNBrJeviAzPv38FarGTlqNPMXLkGhUODl7Y23jw9ffPZfxMbFk5KSSmJyCgCz5syjaeQoMu/f5ftvvyQqOpZRo8c8U1/T3WWkojCHsoIsVF7exCaNYOTkOcjlj6Zbl47uQBDs1goxiekkjhjH8PHTadOnU5b/kLP7fyYwLIrYpBEEhUU6pUxEM+Nsz79Dd0s9CGDpMmDt7kSucv726GCER0Ti52/vINKGj6CyssJlk72y0mIyRo3Gw8MDAC8RtoQHo7KinNS0EchkMnw0GqJj41yiC2BtqcHaUoPp7hEAbBYTMmMbuHCy5w4qK8pJSUtHEAR8fHyIdWGZ9xIeEYlWqwUgJDQUvU4nTfZEpqmxkeNHDrLuzXfRaJ4vwOk/EmHhEfj42PPp5+9PXHw8AEHBIZSXlbokDe6q53U1lcQnpyGTyfDyVhMWGU1jXQ0x8Umia0P/viw9fURPOy9+X1ZRXkZ6ur0/0Wg0xMYNE11zIOERkS6f6IG9vh8/fACL1UJSciohoWEuT8OrRnxCImdOHcNsNlNSXERUdIxjDCUm0dEx3LpxjcaGBgKDg+k0dtLe1kZ1VQXzFy4WXR9gwcIlfPvV50RERrnUdDcgNIrsG2fp7jRQW15IaEyiS82lbTYbh/bvYcKkyYSFRzjtvm1tbXz52X8Rn5DE6+s2oNX2X4yVyWSs2/A2NdXVlJUWc/rUcWpra5g+czYAgYFBzJ67gJmz55GT9ZCd27eSkTGK+YuWPFHbaGjnxM5vCY2MY/L81Xj7DD4+GMyME0DjG8CICTMZPm46FcW5XD25l5ikdEZNfvHjMqJM9ow1xVhNXQROWY4gk1F/fhc2q0UMKYnfCYro4cjDk92djFcORR9zXZkgw2q1ujE1rwZqHx8sZjP1tTWvxGSv74qkIAgoen4XBMFl9e1VrecDrWFceBrB7bhisD8Y0TGxbHhnI8VFBRw9tJ/xEyczPMO1xxJcjUwmw2azOX63WMwu1VcoFETHxFFaUkReThapLthVA9BotXR1dVJcXEh0dCzGTiO5OVkoPZQvbML3tLS1tSIIAh0d7dhsNpceOYqIT6O6JJea0nwypixwmS7ApQvn0Gi0jHSyxYBarWbF6td5cO8uu3dsY0TGSNJHjEStfmRCKQgCEZGRRERGEjcsgSOH9jsmezabjfKyUh7cv0tNdRXjxk9k1JixT6Xt6enN+FlLKct/yLVT+4hJTCc6IQ2Vl/dTfd9ms9FYU0FZwUOaG2pJSB9DXIpzFgBEmcbbzN3IlJ4IMhldTTVYOzvEkBmS2ppqdLoWbDYbudlZLt3piBuWQOb9e5h6TD9cZcYZFR1DXm4WVquV9vY2KsrKXKILIPMPx1JXhM1iz7Oty4Ctu9Nl+u4iKjqa/NwcbDYbHe3tLtvlkHAvnp6erF3/FufPnZae+UtOaHg0JQW5WK1WjEYDtdUVBIWGu0y/prrK0Zfl5LiuL4uOiSUnp6c/aWuj7BWq53q9zm76NXosGaPGUFdb61J9pVJJd3eXSzU1Wl+amxqxmM10dXZS6YbnnZqWTuaDe1RWlDMsPtFluhGRUdy6cY3omFiio2O4ce0qUTGuec+sVitHDh1g5ZrXCQwK4sb1qy7R7SUyPo3S3HsA+PgFuky3MD+PstKSp9ote1ZkMhkpqWm8seEt1q57E5PJxNaftrBn5za6Ojtpa2ujtqbGcX19XS2+PUcxsh4+4JsvPuPOrZukD8/gw0/+xMzZc/H19XsqbUEmIzIuiakL1zBlwWrMZhMXjmzn2qn9mJ7wTlcU5XBq9/cU59wjKj6VBa9tJH3cNLx9tM9dFn0RZWfPKzyeljtnaLy8Hw9tIHK1a8+0hIVHcOr4UYeDluSUVJdpxyckUl9Xyw/ffYVcJic+MYlZIh/yBUhMTqW8rJTvv/kcjdaXiMgo0TV7kflHIDO0Yrp33P6BXIFHyjRAPM9CvwdSUtMpKynhmy8/Q6P1JTQs3GWrgRIA7tvmUPv4sHbdW+zY9gtLl6906fsm4TpiE5Kpr61i36/fIQgCE6bOcdl5PbD3ZSePHXU4aElOcY1zmOSUVMpLS/j2y7+j8fUl8hWq3xVlZdy8fgW5XI6HhwdLVqx2qX5wSCgymcylDlo0Wi1JKWn88v1XaH39CHKD6WrcsASOHNxHYlKKSx3MRUXHUFpchH9AAFqLL52dRpc55Ll6+SJR0TFERccQHBLKj1u+JiExiaCgYJfoq7zU+GgDCImOd4leLzdvXKOtrZUfv/sagMTkFGbMmuN0HY1Wy9TpM5kybYZjYdZqtXD29Ana29tQyBV4qdUsWrIMAF9fP95+bxPe6qEdqTwtXmoNqaMnkzJqEo01Ff3+1vfMntY/iPGzluCl1jJz2fqn3gV8VoS+W/cD+fJa6dB/FJn3xrnO+9VAXLmNPhCzxX3mSX+7XOI27X+dKb4HKDHo7u5GqVRiNBj4ccs3vP3+By47YO0MdtyrePJFIrFudPRzf7e2ppozp07w1rsbnZcgF5FX3eY27aQw99VNmcx97WpDq2t3SvoSrH2+BaDyslJuXLvC2vVvOTlFz87hg/tISEx2qafhF8FiddvQxY1LUNBpct9xGU8P13oAH4g72xd3cr9M99zftZhNXDr0C1OXvvlc3h9Hxfo9t/Y/Mvk17uvDR0ZrnquiS3H2JCRegF3bt9LV2YnFamHq9Jn/UBO9f1Rqqqs5uH83s+aIH9JEQkJCQkLiZaOxppyH104RlzrG6W7+JX5/PNNkz2LsQJ95CUu3EQHwik5GHWtf6esoy8FQkYuAgCo4Ck3KeLoaq2kruA1WK8hkaJLHowq0n3dovnUSa5cRbFY8/EPRpk9ybGs+K1989l8olSpkgoAgk/H+Bx9x9vRJigrykcvl+Pn7s2T5Kjw9nWtW2Nqq5/CBfXR0tAMCo8eMZfzEyVy6cI77d+/g7W3fjp05Zx4Jic7x3nbs8AGKiwrw9laz8UN7mIHLF85SWJCPIAh4e6tZvGwlPhoNXZ2dHDm4j7ZWPVablfETpzBi5Oin1rJ1dWDKuwLdnSCALCwJRWQq1vYWzIXXwWJG8FSjSJmGoFBibanBXHrX8bwVw8Yi87Obg5hL72GpKwZzN6ppG547/0OV+dnTJyjsfd5+ASxd4fznPRivvbGBo4cP0NhQz7WrlwkIDCQy6vl3rB7HkYP7KSrMx1utdsSXq6+r5fjRw3R3d+Pr68eK1a89tymp2dhOw70LWHrOW2piUvAd9uigvL44k+acm8QseAu50hOrqZv6e+exGO0Hy33jR6CJtjvpaassQFdwHwC/pFFoopznvdA/IIDgkFAunDvNhXNnWLp8pWhlPlR96+XGtSucPX2Sf/q3f3e878/KnUvHqa0oRuXpzbw17wOQfecyteVFIAioPL0ZO2MRXt6PFhJaGmq5cPhXxs9eRmScvcz3ff+faP2DAPBWa5g8f/XzZbqHzs5Ojh85SGNDPQgCi5euICAwiIP7dqHX6/H19WXl6rVOD0I7WD3vxRnlPRhdXZ1cPn2UlqYGEARmzFuKXKHgyrnjmE0mfDRaZi1aiVKpoqq8hFtXzjmCfU+YNoeI6LgXTsPAfMfExlFeVspn//UfovQlQ2E2m9n64xbMFgtWq5WU1DSWiWzKONgzr6ut5fjRQ1jMZmQyGQsWLyMi0jluyPty++Z1Hty7A8DIUWMY1+f9vnn9KufPnOSP//L/cmp9A2huauTAvt2O3/W6FqbNmE11dSXNTU2AvV6qVJ5s3PzJC+uZzWZ2b/sRi8WCzWolITmVydNmcfzwPupra5DJ5ISGhzNnwdJ+ZpR1NdXs3Po9i5evIdEJZsRD5bu9vY2ignxkvWO2ZeL04UO1L7dvXufO7ZsIgoyExCTmzBPfYUlxUSGnTxzDarMyavRYJk+d/sL3zLx6kobKEpSe3kxf8Q4ABfevUVn4EGWPB8jk0VMJjhzGzFXv8/DaaS4d+hmb1UpEfBoJI+xhEEpz7lBZmAUI+PgFkjF1QT9nXc/CYGVuNBrZv3cXrTodWj8/Vq9xfl8yGGKU+e2Lj/rw+a/19OG3L1NTXoTQ24fPtPfhbbpmbl88jr6pnvRx00jKGN/vXjarlbMHfsFT7cPUBWteOG29PNuTkwloUsfjoQ3EajbRdPUQqsAILF1GuuorCJq6EkEmx9Jld0oiU6rwHzMPuac3prYWWm6fJGT2OgD8Rs9CplBis9nQ3TtHZ20ZXuHP7955w9vv9WuM44bFM2vOPGQyGefOnOLalUtOD24uE2TMmbeQsPBwurq6+OG7r4gbZjdHHD9pMpMmT3WqHsCIjFGMGTeBo4f2Oz4bP2kq02ba7Z3v3LrB1csXWLB4Gffu3CIwKIg1b2zAYOhgy1d/J214xtPbwwsCivixyHwCsZlNmO4dweoXhrngKoph45D5hWKpLcRSmY0ibjR4qPBIn42g8sbaocP08DSqSa8DIAuIRB6RTPfNAy+U/6HKPG5YArPmzO953ie5duUis+eK31ifPnGM+PhE1ry+DovF4nDMIwYZo0YzdvxEDh/c6/js6OGDzJm3gJjYOB7cu8v1q5eZOfs53fQKMgLSJ6LyDcJqNlF1aT9eQREoNf6Yje0YG6qR9wkK2lqWg9LHj4AJC7B0Gak8vxufyASsZjO6/LtETF8FAlRf3I93aAxyD+esHrqyzIeqb0HBwbS26ikpKf6Na+dnJSZxOPGpo7l98Zjjs6QR40kfOw2Aouw75N27xuip9vbLZrWSdesiIRH9Td3lcgVzV737Qmnpy5mTxxgWn8Cq195wlPO1KxeJjRvGpCnTuX71EtevXXb6Dutg9RxwWnkPxvULp4iMjWfu0jVYLBbMZhPH921jwvS5hEfGkJ99n8w71xk3eSaeXl4sWL4Wbx8NLU0NHN+/nQ0f/PmF0zBUvsXqS4ZCLpez4Z33USqVWCwWfvlxC/GJSaKe2xss7+fOnGTajFkkJCZRVFjAuTMnnW623dBQz4N7d3hn44fI5XJ2bf+F+MRk/AMCaG3VU1ZShEaE+gYQEBjkmMRZrVY+/9t/kpSS2m8x6ezpE047By6Xy1mz7h3Hc93964/EDUskJW0EC5faA1ofP7yP7Mx7ZIwe50jXlQtniIlz3pmuofLd3NTEzNn2Mdv5s6e4fvWSKNYbg9W1stISCvLz2PThpygUCjo6xHcqaLVaOXnsCOvfeheNVssP331NYlIKQcEvdl4vMj6dmORRZF450e/zuLQxDEsf1++z2rICrBYL05e/g8Vs4uLBnwiPS0Emk1GWe5/pK95FrlBw78IRakrziUp4PjPuwcr82pVLxMUNY/LU6Vy7colrVy+JPmYTq8xjk4aTkDaaWxf69OEZ40kf19OHZ90h9+41xkybj1LlyajJc6guKxz0XoXZd9H4BWAydb9QmgbyTFtpcpU3Hlq7xx6ZwgOF2hdLpwFjRR7q+BEIMnnPdfbZuYc2ELmnfQKm8PHDZrE4QjDIFPaI9dhsYHP+ObVh8QmOmCERkVG0tbU6XcNHoyEs3L5TqVKpCAwMFkWnL1ExsXgOiM/RtzMwmbofnTkU7GfKbDYbpu5uPD29nimOiqD0RuZjf96CwgPByxe6jdiMbQi+IYDdE6e10X7uS+YTgKCyP2/B2xesfZ63NhhB+eIro0OVeb/nHRFFW6v4NtVdnZ1UlJcxcrTddbBcLhd1NzE6JvY3cRubm5uIjrEP+uPi48nPy3nu+ys8vVH52neGZAoPlD5+WDoNADRl38A/bTzCgBMpVrMJm82G1WJG5qECQYaxoRKv4EjkShVyDxVewZEY6yufO119cXWZP+4dP33yOHPmzn/hQzpBYVF4qPrnoa9ZjcXc3w16Uc49IuKSUIp0kBvs5VxZUe4IpN1bzoUF+Q439MMzRlGQn+d07cHqOTivvAfS3dVJbXUFyekjAXteVSpP9LoWwiLsO8YR0cMoK7TnNTA4zBE/yS8gCLPZ7BRX9UPl29UIgoBSae+frVYrVotF9HNog+ZdEBxeKbu6OvERIcxJc2Mj4RGReHh4IJPJiI6OpSDf3oaePXWCmXPmuyTcRVlpCX5+/v28/tlsNvJysklLH+EUjd88V6sFBIiLT0QQBARBIDQsgvY+Y5gHd2+RkJyKl/eLO6wYjL757tuHh0dE0dYqzlhqsLp2984tJk+djkJh3/9QO8FBx5Ooqa7CLyAAP39/5HI5aenDKcjPfeH7BoRG/qY/GRoBi9mE1WrFYjEjk8lReNjriM1m/8z+NxOeXs9fJoOVeWF+HiN6+pIRGaMoyHN+XzIQscr8SX242Wx2tCMqL2/8g8MGHYsbO9qoqygmLtn58Raf+8ye2diOqa0ZD78g2vJv0d1ST3vBXZDJ0aaMx6Nn0NhLV10ZHtpAx4QQ7KacJn0jqqBIPMOe3yGLgMCOX39GEARGjRnL6DH9Vy8y798VPW6LXqejrq6GiMgoqioruHPrBlmZ9wkLi2Du/IWib09fOn+GrIeZqFQq1r1lX90fM3YC+3Zv58u//YXu7i6Wr3r9uZ3P2DrbsXY0o9AEInj7Ym2qRB4UjaWhDFv3b1fBrI3lCD4B/Z63s+lb5n15cP8eaS6I06PT6fD29ubIof3U19URFhbOvIWLHR2qKwgKCqYgP4/klFRyc7Kd1kGaDG106ZtQ+QXTUVtmnwhq+7tm1salU3fzJBWnt2E1mwgZMwdBELB0GpB7PuoY5J5qx6TxRXFnmfetbwV5uWg0GlEDLmffvkRFYTYKpYrpS94A7J1BTVkB05eso+VSf5fwVouZcwd+QZAJJGVMJCL2+d2X6/Q6vLy9OXr4AA31dYSGhTN3/iIMHe2OAOtqtQ+Gjvbnz+AzIGZ5t7Xq8fT05uKpwzQ31hMUEsakmfPxDwiivLiA2IRkSgtzaW//7QJSaVEegcGhz23e9DS4ui8B+2Tgh2+/oqWlmbHjJ7jF2+y8BYvY8evPnD11EpvNxjsbP3C6RlBwMJfOn8FoMKDw8KC4qICw8AgK8/NEf7/7kpuT9ZtJXWVFOd5qNf4BznOJb7Va2f7Tt+h1LWSMHk9Y+COzWIvFQl52JjPmLgSgva2VooI8Xlv/DnXHDjktDX0ZLN8ADx/cJSXNNbH2AFqamqgoL+PCuTMoFArmzFtAeITzTYb70tbWhlbzyK2+RqulpqpKNL2yvPtUFefgGxhK6tgZeKg8CYtNpL6ymLO7v8FqNpE6fibKnklLXPpYzu/9DplcQVB4DEERznWa2NHR7ljAUfv49ByVEBdXl3nWrUtUFGWj8FAxo6cPfxwPrp9j+ISZmJ28qwfPGWfPajahu3cWbeoE+w6dzYbN1EXApKVoksehu3++X4BOU3sLbfm30Q7v70I4YPwCQmavw2a10N30/LFs3npvIxs3f8za9W9x9/YtKsofxZi7evkiMpmM9OHOnyn30t3dzd7dO5i3YDEqlYoxY8fzyR//mU0ffoqPjw9nTp148k1ekOmz5vLJn/6FtOEjuHv7JgClJUUEh4TxyZ//lXc/+JjTJ4/R1fXsXulsFhOmnAso4scjKJQokqdgqcmn++4RsJhhwFlLa4cOc+ldPBInOSVvgzGwzHu5cumC/XmPEO9592K1WqmtrWHM2PFs+vATPJQeXLtySXTdvixdvoq7t2/y/bdf0d3VhcwJLqutZhP1t88QmD4JZDL0RffxT/5tUFFjQyVK30Ci520gcsZqmrKuYhWhkeqXNjeVed/6JpPJuHrlEjNmOt9VdF/Sx01n0fqPiU5IozjnHgCZN84xfPyMQRdtFr7xIbNXvs34WUvJvHGOjlbdc2vbrFbqamsYPWYc73/wMR4eHty4ernfNYIguCTKt8lkErW8bVYrTQ21pGaMZfWbH6Dw8ODB7WtMn7eUnMw77N+2BVN3N3J5/3aupamBW5fPMW3uYlHSBbilLwF7rKpNH33KH//5f1BTXU1Dfb1LdPty7/Yt5i1YxB//+d+Yu2ARRw+92BGAwQgMCmbilGns2v4Lu7f/QkhoGBaLhWtXLjJtxmyn6w2GxWKhqCCPlAGeTnOyHzptV68XmUzGm+9/xKZP/pm62mqaGh4913OnjhERFUNklD3G3MWzJ5k2c65o3smHyvfVyxcRRB6zDcRqs9LZaeTdjZuZPXcB+/fs4nGe6v/RiEnOYNaqjUxb9jYqLzW5dy4CoG+sQxAE5ry+mZlrNlGSfQdDmx5TVyf1FcXMWr2ROa9vxmI2UV384jtgQ+GqvsTVDB8/ncUD+vChqCm3n/nzDwoVJS3PPNmzWa3o7p3DKzwez1D7TF+m8kYVEmM3E/Cz277aTPZJhaWzA93dc/hmzEDh/dvggIJcjmdIDF315c+dCU3PTF2tVpOUnEJNtX2mnvngHkWF+Sxf9ZqoDdbe3TtIH5FBSqr98LLaxweZTNaz0ziOmhrxVg4GkpaeQUGe/aV8mHmfpJRUBEHA3z8AX18/mpsan+l+NqsVU/YFZMFxyIPsnYDM2xdlxjyUY5YiC45D8HxkXmPr6sCccx6P5KkIXs43u4HByxwg8/49igoLWLFavOfdF41Wi0ardax6p6SmuzwAb2BQEOvfepeNmz8mfXgG/n7+L3Q/m9VK/e0z+EQmoA6Pw9zRisnQTtXFfVSc2YG5s4Oqi/sxdxpoqyhAHRaLIAh4qLUovDWYOvTIPb2xdD7a7bV0djjMuV8Ud5T5wPqma2lGr2vhu2++4PO//YW21la+//ZL2tvFWZmMik+lurQAAF1jHTfPH+H4zm+oLi3g/tXTDtt/L3XPKqnGj6CwKHTNzz9A99EMLOc06upq8Fb7OHa42tvb8BbJvKsvYpe3t48GtY+GkLAIAOISUmmqr8MvIJDFqzewasMm4pPT0WgfvVsd7a2cPrKHmQuWo/V9sXfucbizLwHw9PQkJjaO4uLBz5eISWbmfUdcwdS0dEe/7mwyRo3h3U0fseGdjXh6ehIYFIxer+OH777kq7//F22trfy05Ss6RHq/i4sKCQkNR90nfqPVaqUgL5dUkXa3VJ6eREXHUlZaDMD1KxcwGg3MmPPozFR9bQ3HDu3l+6/+RlF+DudOHaOowHmmdoPl+2HvmG2la/rwXjQaLckpaQiCQERkJIIgYDQ4xxplaE0NrX1MZttaW0UxVQZ7PD2hpx2JShyBvrEOgJrSPIIiYpHJ5PaJRkgE+uY6mmor8PLRovT0tjvuiUmkpbHaqWlSq31ob+vpS9raULugL3FlmfclOiGVqp4+fCia66uoKS/i+I5vuHnuMI3VFdw6f8RpaXgm2xObzYY+6zIKtS/quEeNkGdoDN3NtagCwzF36LHZrAgeKqymblpun0aTPBalf4jjeqvZhM1iQq7yxma10tVYiYdfyGCST6T3TJpKpaK7u5vSkmKmTp9JcVEhN65e4c133sfDw+O57v0kbDYbRw8fIDAwiImTpjg+b29rc1Sg/LwcgoKfL29PS0tzk8PUo7Agj4BA+89arS/lpSVERcfQ0dFOS3MTvs8wGbDZbJgLriLz9kUR9Wj1zdbdiaD0xGazYanIRB5u9w5nM3djyjqLPG4MMl9x8jxUmRcXFXL92mXeemejaM97ID4+Pmi1vjQ1NRIYGERZaQlBwUFP/qIT6ejoQK1WY7PZuHL5AqPHjn/yl4bAZrPR+OAiHj6++MbbV5SV2gBiFzyK81VxZgcR01ciV3qi8PLB2FiNZ0AYli4jpnY9Cm8NCm8tLXm3sfQs+BgbqvBPef509cXVZT5YfQsOCeWf/u3fHdd8/re/8P4HHzvVW1+7vgWfnklETXkRGt8AwL5718vti8cIi44nIjaR7q5O5AoFcrmCrk4jzXXVJGVMeG59Hx8fNBotzU2NBPSUc2BQMIFBwWRl3mfSlOlkZd4nMSn5xTL6FIhd3t5qH9Q+WvQtTfj6B1JdWYpfQCBGQwde3vZ3697Ny6RmjAbs58dOHNjJ+CmzCY0Q17zR1X0JgKGjA1nPGU2TyURpSTGTpkwTXXcgPj4aKsrLiImNo6y0xKnmjH3pbUNb9XoK8nJ56/3NjJvwyCrlq7//F+9s/Mjp3jh7yc1+SNrw/jt4ZSXFBAQGotH+doH8eTEaOnoG9Z6YTSbKy0oYN3EKWQ/uUl5azJo33u43wXr/40dOh04ePciw+EQSklKclp6B+S4pKuTGtStsEHHMNhRJyamUl5USGzeM5qYmLBYLXiI9717CIyJpaW5Cp2tBo9GSk53FitWviaLVaejAs2cyVV9RiI+f/V3yVGtorq0gMj4Ns9mErrGWuNTRWCwW9I21WMwmZHIFTbUV+AY4t+1JTE7mYeZ9Jk+dzsPM+yQmO69uDYUry/w3fbhfwGOvHz5+BsPHzwCgoaaCgoe3GD9rqdPS80yTPZOuns7qYhQ+/jResZtUaJLG4hWZiP7hFRov7wdBhu+I6QiCQEd5DhZjG+1F92kvsrth9x9nXzlquXPG7qIfG8qAMLyjn+9BGzo62Lt7B2BfDUsfPoL4hES++vyvWMwWdvz6MwDhkVEsWrLsuTSGoqqygqzMBwSHhLDl6y8Au2vsnKyH1NXVIgjg6+vHoiXLnaZ5aP8eKsvLMBoNfPnZX5g6fRYlRYU0NzchCAJarS/zF9sryOSpMzh2+AA/fPsFNhvMmD33mTosW2sD1voSBG8/uu8cBkAeNxqbsQ1rjX2FTxYYgyzU7oHUUp2HzdiGpTwTS3kmAB4j5iEoPTGX3MFSXwpWM13X9yAPS0ARO+qZ8z9UmZ86cRSL2cL2rT8Bdqc8i5Y6r9yHYv7CJRzatweL1YKfnz9Ll68STevA3t2Ul5ViNBr47L//g+kzZ2Pq7uZOj9luckoaGaNGP/f9u1rqaK8qwkPjT9XFfQD4p4zDO2TwsAZ+SaNpuH+Bygt7wWYjIHU8cqWn42/Vlw44fpY7MY6PK8t8qPrmTPf3N88dprG2ku5OI8e2f0XqmCnUVZbQrm9BEAS8fLSMnjLvsfdo0zVz/8pJuymMzUbSyAlo/V5scDxv4RIOHdiLxWIv5yXLVmKz2TiwbxcP7t9D2xN6wdkMVs9Hjf6tGbEzmTxrAedOHMRqsaDR+jFj/jIKczPJybS75I+NTyEpze7AJefBbdr0Ou7dvMy9m3bT1kWr1r+wE4vB8l1RViZaXzIU7e3tHD64D5vNis1mIzVtuOiT+sHyvmTZCk6dOIbVakWhULBYpPb8wJ4dGI1G5HI58xYtcUnInl56F6gXLu4/NskZ4izbi9DR0c7JowexWW3YbDaSUtIYlpDE3/7P/0Sj9WXn1u8BSEhKZeLUGU7VHshg+T514igWy6MxW0Rk1G/KxRkMVtdGjh7DkUP7+farvyOXyVm2crXoO4symYwFi5ay49efsVltZIwaTbATFnPuXTxKS10l3V2dnN3zLUkjJ9FcV0VrSwMAXmotwyfZ+5OY5JFkXj3JpYM/YQOi4tPR+Nut80JjErly5FcEQYY2IJjopOevj4OV+eQp09m/dxcP7t1F6+vLqteefKbtRRGrzG+ePUxDTx9+dNtXpI2dQl1FCW09fbi3j5bRU+1l3mno4OyBXzD3OFMszLrD/NfeFz3WofA4u+Qvr5W6zWj5vXHOPQz6LLjSfGAgZovzPZM+LX+7XOI27X+dmeA27VeZHfcq3Ka9brQ4sfF+7+RVi+8pdiiSwnyefJFIyGTua1cbWp/9rLKzCNZKAYtdjcXqvvNW7jx51GmyuE3b00M8Z2xPgzvbF3dyv0znNu1RsX5u03Yn+TXu68NHRmueq6I/XxRzCQkJCQkJCQkJCQkJid810mRPQkJCQkJCQkJCQkLiJUSa7ElISEhISEhISEhISLyESJM9CQkJCQkJCQkJCQmJlxBpsichISEhISEhISEhIfESIk32JCQkJCQkJCQkJCQkXkKkyZ6EhISEhISEhISEhMRLyGODqsvcGDHGnXFy5G6cAivcKL5hZKTbtCXcw5gwf3cn4ZUjJULjNm2T2X1xPN0ZB0vl4b529XGxbMXGnTFj3dmHuzPkmjvLXKlwXz1/VePcuZuUcPf1J68qob6e7k7CMyPt7ElISEhISEhISEhISLyESJM9CQkJCQkJCQkJCQmJlxBpsichISEhISEhISEhIfESIk32JCQkJCQkJCQkJCQkXkIe66DlZeDKxfN4KJVMmDTF3UlxK5//7S+8/8HHeHt7uzspomM2m9m1fStGg4HJ06aTlj7C3UkSnUsXzuGhVDJp8lS3paG6rJA2XRMpoya5XLsgP4+mxgYmT53uUl13l3vm/XvU1lSzYPFSt+hLSEiIR3lZKTK5nKioaHcnRULipcNms3H6xDHKykoAAYVCwarX1uLnJ77jula9ju0/fIVfQIDjs5FjJ5GSniGK3ks/2ZN49airqwVg00efujklrxYRsYkQm+gW7aTkFJKSU9yiLSEh4TqsVisy2athlFReVoqHUilN9iQknIjRaMTLy4uc7Ie0tbfxwUd/QBAEWltbUXp4ANBpNOLp5eV07c5OI56e9vtq/fx4450Pf3NNV6cRladztV94smc2ttF8+yQevsF06xpQ+gbhHZlIa+E9rN1G/DNmAqDPvYHNakGQy/EfMR2F2veFE6/X6di9YysREZFUV1USGh7BiJGjuHLxPAaDgaUrVgPQUF/H1h+/w2g0MmHSFEaOHusU7Z3bfiEiMoqqqgrCwiPIGDmayxfP0dFhYMWqNQQGBXPqxFFqa2oQgKkzZpGSmuYU7R3bfrZrV1YQHh5JxqjRXLpwDkNHB8tXvYa/vz8H9u2mra2NyMgocJIX7PZWPScP7iA4LIKGmioCQ8JJTMvg3o1LdBoNzFiwHL+AIG5cOEVLcyM2q4VRE6YTE5/kFP2nyfuh/XswGAxs+foLVq9dh79/wJNv7ARdPz8/DuzfQ3tPmZeWFPP+5hffTX0abYCmhga2/vQ9ra16xk+czPgJztth62jTc/n4bgJCImiuq8Y/OJTYpBFk37lCV6eBCbOX0tbSREtjHaOnznOaLjxd/psaG5y+w/W05d7Lvbu3yc/NZc3adXj0dBiu1H9hPb2OPTu2Et7TnoaFRzA8YxRXL9nb0yXLV1NSXEirXode10Jrayuz5y6gprqKkuJCfDRaVr++HrlcLlp+i4sKaNXr0elanF7PW/U6Du/ZRmh4JLXVVYSEhZM6fCQ3r17EaOhg3pJV+Pr5c/bEYVr1LSgUHsxesJTA4JAX1n6a/qSosACdrgVdSzMGg5FJk6cyaoxz+rJn7U+c1bb16u/e/gvhkVFUV9rzPqIn7waDgWUr11BcVIC+pQWdrgWt1pflq193iu6TytxkMnHm5HH7FwSBN995H5VK9cLavfpPKvd7d24hCDKyMx8wf9ESomNinaL7pHFTSVFhP4uo77/5gjVrN+Dr5yd6nvfv3cmmDz/F09Pu3v6rv/+Vt9/bhNrHR1Tdo4cP8PZ7m1CpVPz3f/5v5s1fxIiRozh0YC/DR4xkWHyCqPrZWQ/w8vJm2oxZFBcVcvXyRd56d+MLh/Jo1evYt+tXwsIjqamuJDQsnPQRo7h2+QIGQweLl6/m2KF9rHt7I97eamw2Gz9883fH7y/K0+T98sVztLe1AaDT65i/cDEZI0e/8H0jIh+FE7NarRQVFvDg3h26urp4692NtLe34+Pj4yhjrVbruH7Pru2oVCpGjh5LQmJSvwWmVr2OI3u3ERIeSV11FcFh4aSm9/QVRntfERoW0U+7vKSQnIf36e7qZNW6dx+bt2MHdqFUeZI2YhQxwxKdsrjllOUxs6ENn7gRhExfg7lDj6GmmKCJS9CmTKC95AEKH1+CJi4hZOpKtIljaM2/4wxZAHQtzYybOJlNH/+R5qZGcrIesuGdjcyaM5/rVy8B0Fhfxxtvvsub727i6uWLjkr1orS0NDNh0mQ+/ORPNDc1kZP1kLfe3cSceQu4euUSVy5dQKXy5IOPPmXTR58SGxvnFF2AluZmJk6awkef/pmmpkayH2by9nt27WtXLnL54nmiomP48JM/kpySSmur3mnabfoWho+eyOq3P0Kva6IkP5slr73N+KlzyLx9jcxbVwmLimX5G++xaPWb3L5yFpOp22n6T8r7kmUriY6OYdNHnzplove0upcvnic2No4PP/kjKWnpTi3zJ2kDNDU1su7Nd3hv00dcvngei8XiNH2AjlYdSSPGsWDtJtp0zVQU5TBr+QYyJs4i7951p2oN5Gny707d2zdvUFRQwGtvrHfKRO9Z9Z2Foz39yN6e5mY/ZP3bG5k5Zz43rl3quaaFtRveZfXr6zh6aB/RMXG8v/lTFAoFJUUFL6Tv7nqu17Uwatwk3tz0CS3NTRTkZrF6/btMmTmPOzeucPPqBYJCQln/3kdMmj6b08cOOE37Sf0JQEN9Pevfeo933v+AK5cu0OasvsyN/QnY8z5+4mQ++ORPNDc3kZP9kDff3cTsuQu43pP3pqZG3njzXadM9PrqPq7Mb16/yvxFS9j44Se89e5Gp77b8ORyHz12POMnTWbTR586ZaLXy9OMm8TiSXlOSk4hPy8HgOqqSrS+vi800Xta3aioaKoqymlsaMDPz5+KijJ7GioriXTCzuqT9GfNmU9OdhZlpSWcPnGMpStWOS1mo66lmbETJvPe5j/Q0txEXk4Wb7z1PjNmz+fmtcukpmeQl/0QgPLSYoKCQ50y0evlSXl/Y8PbbProU5YsX4mvry/JyalOuW/vNefPnuKbLz4jPzeHCZOm8Na7GwFITRtOYUE+W77+gjOnjlNXW+O495vvvM+ESVPIy83mmy8+4/zZ07Q0Nzv+3ttXbNj4CbrmJgryHvUVd29c6bmmmeuXzrLthy8pLshj1LiJ/SZ6rTodO3/+xvGvprIcgJVvvMPIsRMpLshl2w9fcv3SOfS6R9rPg1Mme3IvHzw0/giCgMLHD1VAOIIg4OHjj9nYjs1kouX+Oeov70OfewNzR4szZAHw9fMnOCQUQRAICgomNm6Y/eeQEFr19g4pISkFDw8PvL29iYmJpaamShTtmB7t4OAQWnU6ykpLGDNuvON6Z24J+/XVDg4mdliPdkgoep2OivIyho8YCUBCUrJjlcwZ+Gj98A8MRhAE/AKCCI+KRRAE/AODaW/VU11RysM71ziwbQvH9v6KxWKmo63VafpPyrtYPEm3sqKctOH284HxCYlOLfOnyXNCYhIKhQJvb2+8vdV0dHQ4TR/AW+OLb4D9uWv9gwiOsD93X/8gDO3Oe76D8Xt95gBZmfcpLipk9etvoFA41zLe1fn29fMnONiuFxgUTExsT3sa/Kg9HRafgFwuJyg4FJvNRlzPinffa54Xd9dzra8fgcEhCIJAQGAQkTFxjrJoa9VRW1VJSpr9HY+KiaPTaKS7q8sp2k/qTwASk5If9WWxcdRWO6cvc2d/Av3zPrDe6fU6u25istMnW08q88ioaM6ePsHtm9fp6ux0uvmou9q1pxk3icWT8pyaPoLc7CwAcrIfkpo+3CW6UTExVFSUUVFexpix42mor6ettRVPT0+USqXo+h4eHixetoLtW39i7PgJTl2o1vr6EeRo14KJjo3r164PzxhFTlYmANkP75OeMcpp2vB09dxgMHDowF5WrHod1VO2L0+6b15uNl9/8TdkMjnvb/6YZStXE9Nn00Wr1fLRp39m5px5CILAtl9+pLSkGABBEIiJjWP5yjW8v/ljBODrL/5GXm42ABpfPwKD+vQV0T19RaC9ryguyGXb918ik8lZ+/YHzF28goio/gs2vWacvf/Co2Ic2pHRscxdvJK1b30AAmz7/kuKC3Kf+xk4ZWQiyOSD/y4ANhuthXdQBoQRMGYuZmMbTTeOOUMWoJ/JkCAIjt8FBKxW66N09E3fwA+eE8UA7d7fBcGuLRfxXIFcMTDfikfaNisyQUTtAfmW9cm3zWbFhsDsxavx9Q8UR/8JeRcLt5b5U+RZ3meiIZMJ2KzOLQt53/e8z7uG4Hyt32j/Tp85QHBwKHV1tbS1tTr9YLer8z1keyo8ak/7pkEmkzlWn/te89z6bq7nsiHyj6NNfz4T1afhSf1J78/9cNLKvzvbNhik3il+m3cPpXMnevDkMp88dToJiUkUFxXyy49beGPD2wQGBTlN323t2hPGTTKZDJvt0dkPi9nsPO0n5DkyMoqWlmYMHR0U5OUxddpMl+hGx8Ry59ZNfH1bmTl7Lvl5ueTlZhMVE+MSfbBboXl5e9Pe7pwd+176LkIO1LbZrGi0vnir1VSUlVBbU82iZaudqv+kvFutVg7s3cXU6bMIDnl6s/gn3TduWALzFy4m8/49qqsqyRg1muSUtH7loVAoSEhMIiExCW+1DwX5ucQNiwfAZDJRkJfLg/t36ersZP7CxcQNS6C+ubX/cYVB+oqomGFMm72AvKwH1NVUkTJ8JMMSU556QdhsNlFSmE9uj+nntNkLiIoZ9tRlMxCXnHK2mU3IVXbbfkNVoSsk+1FUkI/ZbMZoNFBRUUZYeMSTv+QE4oYN4+7tW47fO41Gl+gCRMfEkt2zUlNUWEBnZ6fLtCOjh5GTecfRWTQ11LlM251ERkc7ViRLiotcWuYS7iMkLIzFS5eze8c2p5nVSfw+CY+MJj/X/o5XVZTh5eWN0klnuJ6Gwvw8e19mMFBeXuqyvsyd/Yk7aWlpJjgklElTphEWHkFzU6NL9ZVKpdN2jp8Fra8v9XV2k7a62hrH7qorEASB5JRUzpw6QWBQEF4u8iCu1fpiNBpoaW7Cz9+fqOhobly7SnS088xnH4der+PG9ats3PwJxYWFVFdVukS3l+EZozl+eD9JKWkud4B0/uwpgkNCSR/uXM/pKpWKseMn8v7mj5k9dz6VFeV888VnnD19EoDamhpHn22z2Wior0Pr6wfA2dMn+fbLv1NVWcGceQt4f/PHjB0/8anP7CpVKkaMHs/rb3/A5BlzqK2uYPsPX3L1wpknfvfqhTNs/+EraqsrmTJzLq+//QEjRo9/ob7GJd44fYaNoCXzEm3FD/AMjnKFZD+Cg0PYsfVHjEYjk6fOwEejcYnulGkzOXn8CN999TmCTMa06TNJdoKDlqdh2oxZHNi3m2++/DuRUVFotS/uEOdpGTlhKjcvnubAtu+w2WxotH7MW77WZfruYtqM2Rzct5ushw+IiIxCrfZxivmHxFPinE2O5yIqOoY58xawa/tW1r/17isR4uRVZPyUGZw9cZjtP36NQuHB3MUrXKofHBLKtl9+wGAwMnXaTDQu6svc2Z+4k9s3rlNeVmo3zwoOZliCa70NJyalsG/PDgrz85zmoOVpSEpJI/thJt9/8zlh4ZH4BzjPpPBpSE0bwY9bvmbpilUu1Y2IiHLsskXFxHL+7Gmiop2zs/c4bDY4eugAc+YtRKPRsGT5Sg4f3M/7H3zk9KMBQxGfmMypYwdJH+FcE86n4ca1qwQFB7Pl6y8AmD5rjtO9a4eGhbNw8TLMZjNlpSUAGAwdHDtyEIvFvnMdHh7JuPETAYiJjWPGrDlOKf+gkDBmzF2M2WymqqLU8Xnvmb1eUoePImPMBCKiY5gwdaZTn73Qd6t+IF9fK3OSD8dn580x7nM1LJe5b9TorAO5z0Otzn2rtWF+zj0D4g7MZjMymQyZTEZVZQUnjh7+3Yd/KKhtd5t2UtiLH7rv5ca1K3R1dTFj1hyn3fNlxGQW19z2cXgo3Ocuv9Vocpu2xtM5HfalC+dQKpVMfIaYjmL1J08Tt9ViddvwATd24W7tw80W973fCvmrEQ7j90Znt3McVNXVVnPhzEneeOv9p/6Op1I8s/bfM3qD+/qTUK3HczUwUpw9CQkn0dqqZ/+eXdhsNuRyOYuXuXbV/1Xl7u1bZD64z5q169ydFAkJCQkJiX8obl6/TOa9204/qyfx++GZJnsWYwctmRexdhsBAe/oZHxi02ktvIuhsgCZ0m5Pqk0ah2dwFDarBV3WVUytjYCAb9pEVAHhAHTrG9E9vITNYsEzOApt6sSnXhE7dvgAxUUFeHur2fihfeckLzebq5fO09TYyNvvb3acZSgtKebiudNYrRZkMjmz5swnJu75DzkO5IvP/gulUoVMEBBkMt7/4CNyc7K5fPE8TY0NvLvpQ8JFOFfR2qrn8IF9dHS0AwKjx4xl/MTJjr/fuHaFs6dP8k//9u9OMynLuneTguz7di+cgcFMn7uU/Oz75Ny/RVurjvUf/BOeXnat7q4uLp46SEdbK1arleFjJpKUNtIp6ejLkYP7KSrMx1utZvPHf3T6/Z9EcVEhp08cw2qzMmr0WDZ9+InLtD//218cdU8mk/H+5o9f+J63LxyjtqIYlac381/fCEDmjfPUlBchk8lRa/0YN2MRSpUnHW16Tu7+Ho2v3SlJQEg4Y6YtAMBqsXDv6mkaaypBgOHjphM5LPmF09fU1MiBPbscv+t0LUyfNYfNH//hhe/9JNxd18R43n05fuRRu/r+5kc70ndv3+DenVvIBIFhCUnMnDMfi8XCyWOHqKutwWa1kj5iJBOnTHdKOoZq24xGI/v37qJVp0Pr58fqNWud6uHYarWy+5ctqH00LF2zjjPHDlJdWe44JzF30QqCQkLJz3nI3ZtXwQYeSiUz5y8mKDj0hfXNZjNbf/oei8WC1WolJTWN6TNnU1ZSzNkzp8Bmw0OpJCk5VXSzuoHt2uSp/Z/tH/78r07Vu33zOg/u2UMyjRw1hnETJ3Nw3y6am5oA6OrqRKXy5P3Nzm1fhyrzIwf3U1Fe5jifs2TFKkJDw5yqPRhWq5UfvvsajUbD2vVvOf3+g42dLl84S2FBPoIg4O2tZvGylfhoNHR2Gjl++CA6XQsKhYJFS1cQ5ISYkgMxm81s/XEL5j7PwFUWGk8aR4nNk96zF6GtVc+JIwcwGOzeikeMGsuYcRNpqKvlzMmjDkukOQsWM2HSNGSCjHOn7DElbTYrzU2NfPyn/+GUNnawvjM3J4tLF+zj5Pc2fUR4hGvOH4tZ5r38/O1nKD2UCDIBmSDj9bc/oCg/h1tXL9LS3Mhrb24iJCzccX1TQz0XTh+lu6sLQRB47a1NTjfffba7yQS0qRNQagOxmk00XD2IKtD+gHxi0/EZ1v9wpaEyH4CQaauxdBlpvnOKoMnLEQQBffY1/IZPxcM3mOY7p+hqrHrq83wjMkYxZtwEjh7a7/gsKCiYlWve4OSxI/2u9fLyYs3aDfhoNDQ21LN7+1Y+cXJHteHt9/pNqIKDg1n9+hucOHrYqTp9kQky5sxbSFh4OF1dXfzw3VfEDUsgKDiY1lY9JSXFTj1X0dHeRu6D26x6azMKhQfnju2jpCCHkPAoouMSObZva7/rczPv4OcfxLxla+k0Gtj7y9fEJw9/7oDLQ5ExajRjx0/k8MG9Tr3v02C1Wjl57Ajr33oXjVbLD999TWJSCkHBwS5Lw5vvvO/U844dYVgAAQAASURBVGGxSSOITx/D7fNHHZ+FRMQyfPwMZDIZD29cIP/+DUZMtHtI89H4Mm/Ne7+5T+79a6g8vVn4xgfYbDa6u5zjnCgwMMhhGmu1Wvn7f/8HySlPF5PnRXFnXevF2c+7L8MzRjF67ASOHX7UrpaXlVJUkM+7mz5GoVBg6AlzkJ+XjcVs5v3Nn2Iymfjhm89JSR+Bb8/h9hdhqLYt88E94uKGMXnqdK5ducS1q5eYPXfBC+v1knn3Jn4BgZi6H8UEnTJzLgnJ/c9Za339WL3uHVSeXpSVFHH+5FFef2vjC+vL5XI2vP0eSqUSi8XC1p+2EJ+QyInjR3ht7XoCg4K5e/smVy9fFPUsk6vbtYaGeh7cu8M7Gz9ELpeza/svxCcms2L1o3PeZ0+fcFow874MVeYAs+fOJyUt3emaj+PWzesEBgWJ5pRlsLHT+ElTmTbTPrm6c+sGVy9fYMHiZVy/cpng0FBWvb6OpqZGzpw4yhtvPj4Y9PMgl8vZ8M77jmfwy49biE9MIjJSfP8OjxtHiY3Y75lMJmPGnPmEhIbT3d3Frz9+S0zsMC6dP82kqTOIi0+kpLiQS+dPs3bDe4ybOIVxE6cAUFyYz93b1522mDZY3xkUHMKates4fuSQUzSeBle2bSveeBsvr0d9dUBgMItWvM7500f7XWe1Wjl9bD9zF68kKDiUTqNBFAc5z3RHucobpdbuTl+m8MBD7Yul0zDk9aZ2ParA8J7veiEolJj0jVi6DNgs3Sj97DEqvCIS6Kwvf+p0RMXE4unZvxIGBgUTEPhbl8ihYeEOhyyBQcGYzSbMTnQjPBiBQcEEDpIWZ+Kj0RAWbi9blUplj+3RE8vu9MnjzJk73+kOK6w2KxazGavV/r+X2ofA4FB8BplUCgKYTN3YbDZMpm5UKk9RKnB0TCxeTlzdfxZqqqvwCwjAz98fuVxOWvpwCvKfPw7K74Gg8CiUqv7nJ0Oj4hzPzj8kHKPhyR4ny/IfkjJqEmA/w6LydP4Epay0BD//AKdMMJ4Gd9Y1VxAVHfubzv3B3VtMmDzVscrorbYH2hUQMJlMWK1WzGYTMrkcpdI5g/Gh2rbC/DxG9MR/GpExioK8PKfoAbS3tVJWXEhaxugnXhsWEYWqp/8JC49wWgxRQRAcDp2sVisWx/krga6ewX9XVxc+GueddR0MV7drzY2NhEdE4uHhgUwmIzo6loL8HMffbTYb+TnZpKU711MfPK7MXU9rayvFhQWMGj1WNI3Bxk59J9EmU7fDwqqpqYGYWLsVVGBgEHq9vmcHzLkMfAZWi8VlvrYeN44SG7HfM7WPhpBQe96UShUBgUH2kA6CQHe3vT3p7upE7fNbJ095uVmkpDonviEM3ncGuWCcPBB3jtn8A4PwC/htOLKKsmICg0Ic1iGeXt6ijJWfe5/QbGzD1NaM0i+Ibl0dHeU5GKqL8PANxDdlAjIPFR4afzrry/EKG4alswNTayOWzg4QBGQqteNeck81lq6hJ43OoiAvh5DQcOd6uEFgx68/IwgCo8aMZfSYcU6799Oi1+moq6shIjKKgrxcNBoNIU42OVH7aBg+eiK7fvgcuUJBRPQwIh8T8yM1Yyxnjuxh5/efYeruZtaiVW49uC4GbW1taDVax+8arZaaKucEOX4aBAR2bP0JBIHRY8Yxeqz4da8s/yFR8Y+8ZHW06zm990c8lCrSx00jKCyK7i67o5/s25dpqK3AR+PHqKlz8fRSD3Xb5yIn66EoA8DfK+543i0tzVRVlHP5wlnkCgWz5iwgLDyCpJQ0igry+PJv/4nJbGL23IWiTIT7tm0dHe2OhTu1j49TB56Xz51kysy5dPfZ1QO4fvk8t65dIiomjsnT5/SL8QeQ8/A+0cMSnJYOq9XKj999TUtLM2PGTSAiMorFS5eza8evKBQKVEoV72zc7DS9wXB1uxYUHMyl82cwGgwoPDwoLiroF1KisqIcb7Ua/0EGSs5gsDK/d+c2F86f5cqlC8TEDWPWnHmie0U8ffIYs+fO/00ddAWXzp8h62EmKpWKdW/Zd++CQ0IpyMslKjqGmuoqWvU62tvaUKudv9hgtVr54duvaGlpZux4+zNwNX3bGlfgyvesVa+jvq6WsPBIZs1dyN6dW7l47hQ2G6wb4JDFZDJRVlLEnHmLRUmLO3FVmQvA4T2/AgLpGWNIHzlmyGv1Lc2AwKE9v9JpNJCQnM6YCVOcnqbnar2sZhMt986hTZ2ITKFEHZ2KJmEUINBWeBd93k38R0zHOzIJc4eehmsHUXj6oPQLcVoQ2GelsaGeC+fOON0O/q33NqLRaOno6GDHrz8TGBjkMvfIAN3d3ezdvYN5CxYjk8m4euUS6998x+k6XZ2dVJQU8Pp7n6JUqjh3fD9FeVkkpAy++lNVXoJ/UAgLV22gTa/j5IHthEREOW31XwLefm8TGq297m3f+hOBQeLWvdx71xBkMqIT7GZtnt5qFq//GJWnFy2NdVw7tY/5r23EZrNi7GgnMDSCkZNnU5B5i8zr55kwe6nT0mKxWCgsyGPWnHlOu+fvHVc/b7APwjo7O3nz3Q+oranm0P7dbP7kz9TWVCPIZHz8p3+lq7OT7Vu/JyZumFODyvdt2waa8AmC4LS+pLS4AC9vNcGh4VRVlDk+nzR9Dt5qNVaLhXOnjnL35lXGT5nh+HtVeSk5D++zZr3zTNtkMhkbP/yEzs5O9u7aTkN9PbduXGftujeJiIzi+rUrnDl1giUvkfOnwKBgJk6Zxq7tv+Dh4UFIaBhCnyDuudkPSRVxUWewMp85Zy5qtQ8Wi4XjRw9x/eplps2YJVoaCgvyUXurCQuPoLysVDSdoZg+ay7TZ83l+tVL3L19k2kzZjNx8jTOnjrOj999RVBwSM9zEWf8JpPJ2PTRp/2ewbME135RHtfW/KPT3d3N4f27mDV3ISqViquXzjFzzgKSUtLIz83m1LFDvLb+0ZixpCifiMhop56HftVYtf49fHw0GA0dHNr9K34BgUREDR7Cw2q1Ultd0XNOz4NDu7cSHBr2QgHUB+OZ9wptVist987iFR6PV6h9oCFXeSEIMvsB36gkTHp74FFBJsM3dSIhU1cRMHYeVnM3CrUvck9vrF0djntaOjscQdfFoK21lQN7drJk+Sr8/J17sF3Ts0qgVqtJSk6hptp1OzsWi4W9u3eQPiKDlNQ0dC3N6HUtfPfNF3z+t7/Q1trK999+SXv7i6+A11SW4qP1tW8xy+XExifTUDt0XgtzM4mNT0YQBLR+/vhofdG3NL1wOn5PaDQaWvuYfLS1troshiPYV6XAXveSU1KpFrHuleU/pLa8mAmzlzo6fLlc4TBn8w8KRa3xo13fglLlZd/9jUsCIHJYMrqmeqemp7iwgNCwcNQ+4pq0/Z5w5fPuxUejJTE5FUEQCI+IRBAEjEYDudkPiRuWgFwux1utJiIymrqaaqfpDmzbANRqH9p7AuC2t7Wh9nbOTnFtVSWlRQX8/M1nnDy8j6qKUk4d2Y/axwdBEJArFKQOH0l9bY3jO00N9Zw7eYQlq9Y6nFI5E09PT2Ji4yguLqShvs6x25CWNpzqygqn6/XFHe1axqgxvLvpIza8sxFPT0+HAxqr1UpBXi6pac4zKRuK3jIvKS7Ex0eDIAgoFAoyRo6mxol1ezCqKsspKMjj87/9hQN7d1FWWsLB/XtE1RyMtPQMCvLsZm0qlYrFy1by3gcfs2T5KowGA75OXMwZjL713lUM1ta4Ale8ZxaLhcP7d5GSNoLEZPvZ9pyHDxw/J6WkUVfbv27n52ST7EQTzt8TrmrbfHpMY7281cQlJlNfO3T74aPREB4Zg5eXNx4eHsTEJdBYX+f0ND3TZM9ms6HLuoxC7YtP3KPK0NcEs7OuHIWPHwBWixmr2R6PorOxGkGQ4eHjh1zljSBX0q2rx2azYawuwjNEnMCVnZ2d7N35KzNmzyUyyrmx+7q7ux1nKbq7uyktKRbFW9Vg2Gw2jh4+QGBgEBMn2bd8g0NC+ad/+3f+8Od/5Q9//lc0Wi0bN3+CjxMGxGofLQ211ZhNJmw2GzWVZfj6D21Wo/bRUlNpXyU3GjrQ65rRaP1eOB2/J8IjImlpbkKna8FisZCTnUWikwOBDsXAuldSXESwSHWvtrKE/MybTFmwGoXCw/F5l9GAzWo/49LRqqO9VYda62ufGEQn0FBjH5Q2VJej9XOuCVZ29kPShr86JpyufN59SUxKoaK8FICW5iYsFgteXt5otFoqenYgTN3d1FRXDXpm+nkYrG0DSExO5mHmfQAeZt532rs2ecYc3vv4n3jnwz+xYNlqIqPjmL90FR09i2Q2m42SwnwCguyH+Nta9Rw7sJt5S1bi95g28FkxdHTQ2Wk3gbabUhUTGBhEV1enwytlaUkxgUHinnNxR7vW0eP4p1WvpyAvl7ThGQCUlRQTEBjoWOhwNoOVueNsE/ZnX5CfR7DIDjtmzZnPn/75f/CHP/8rK9esJTZuGCtWvSaqZi8tzY8WYQsL8ggItNfpzs5OLBZ7DLfM+3eJio4RZddr4DMo7an3rmCotsYViP2e2Ww2Th07REBgEGMnPPIwqvbxcVgwVJSX9tsA6erqpLKyjITEF/ec/XvEFW2bydTtOBNpMnVTWVbi6DsGIzo2nuamescZ+OrKcvwDnF//nymoeldLHU03jqLw8Xc4/9AmjcNYU4ypzW53KvfywW/4FOQqb8zGNppunUQQBGQqb/xGTEPhZZ949A29oAqKxDdtUj8TgccFVT+0fw+V5WUYjQa81WqmTp+Fp6cXZ04dw2gwoFJ5Ehwaytr1b3Pt8kWuX7uMf58KvXb92w5HA4PxtEHVdS0t7N29A7CvQKYPH8GUaTPIz8vl1ImjjrSEhIay7ilNK5/WTKKyopxfftxCcEgIQs/DmDlnHgmJSY5rnibwbV+eFFT93vWLlBTmIpPJCAgKZercxeQ/vMfDu9cxGjrw9FITFRvP1LlLMHS0cen0EYw952pGjJ08pMknPH9Q9QN7d1NeVuqoC9Nnzhb1gPtAigoLOH3yGDarjYxRo5k6faZLdHUtLezZtR14VPeeR3tgUPUbZw/RUFNJd6cRlZc36WOnknf/BlarGaXKvovXG2KhqiSf7DtX7IeJBYH0sVMJj7GfXzK0tXLz/BFM3V2oPL0ZN3MR3j79B2zPG1S9u7ubz//2Fz794z+j8ny+evM8uLOuOet5Py6o+uEDfdpVbzVTps8ifcRIjh85QEN9HXK5nJlz5hMTO4zu7m6OHzlAc2MDNuyePCdMenyw76cNqj5U2xYREWkPvaDXo/X1ZdVrbzz1OcGnDapeVVHG/VvXWbpmHft3/kKnwYANG0HBocyavwQPpZKzJw5TXJDnmIDIZDLWvv3BkPd82qDq9fV1HDm4H5vVis1mIyUtnWkzZpGfl8ulC+cQBAFPT0+WLFuJn//T7bA8r9mdM9q1Zwmq/utPWzAajcjlcmbPW0BsXDwARw/tJzwiktFjxz+T9tMGVR+qzLf98iMGgwFsNkJCw1i4ZJnDiciTeFFTx/KyUm5cu/JcR06eFFR9sLFTSVEhzc1NdiscrS/zFy9Fo9FSXVXJsUP7QbCb2i5auuI3zl368rxB1evr6jh8cB82m/0ZpKYNF9Vkti9PM44SE2e8Z0MFVa+qLGfXrz8SGBTiqJNTZ85BqVRy4cwJrFYrcoWCOfOXENoTBiD74X3KSopYsuLpFhqeNqj6YH2nl6cXJ3vHyZ6ehISGiXIEaSDOKPPHBVVv1bVw/OBuwN5XJ6YOZ9ykaZQU5nHp7AmMRoPdGVBwKMtfexPAHs7nxhUQICYukSkz5w55/+cNqv5Mkz1X8rjJntg87WRPDNzpxORJkz0xed7JnsSLMXCy50qed7In8fw8brInNk872RODp53sicHTTvbEwJ39ybNM9pyNG7twt5b5kyZ7YvK8kz2JF2OoyZ4reNrJ3svG4yZ7YvO8kz3p7ZSQkJCQkJCQkJCQkHgJkSZ7EhISEhISEhISEhISLyHSZE9CQkJCQkJCQkJCQuIlRJrsSUhISEhISEhISEhIvIRIkz0JCQkJCQkJCQkJCYmXEGmyJyEhISEhISEhISEh8RIiTfYkJCQkJCQkJCQkJCReQh4bAKiu3X2xJNwZs6XL5L64Ja1Gs9u0dW6MHeLOOHuvcpwad8Yfe1yMT/G13SaN2Y3xx5RujHV3qaDRbdoZkb5u03Zn3DV34s5Yd10m98WbM7kx1p23yn0xHa1ubNcAZO6scG6k252xFS3uK3N3zhHiZv2b27SNd//2XN+TdvYkJCQkJCQkJCQkJCReQqTJnoSEhISEhISEhISExEuINNmTkJCQkJCQkJCQkJB4CZEmexISEhISEhISEhISEi8h0mRPQkJCQkJCQkJCQkLiJeSlnext/el7aqqr3Z0Mt9LWqmPPL9+4OxkupamxkS1ff8GWb76kpaXZ3clxCe6u60UPrtFUW+42/Yvnz1JaUuxSzW2//EBtjXvbl+OH95Ofm+3WNEhISIhDbtYDOtrb3J0MCYmXko72dnZt38p3X3/BN19+xs5tv7hMOyNGxT8vCWTTHH/Hv0CNuJ7Z3eenV0JCBAryc0lJS2fq9JnuTsorQ8LIyW7VnzFrjlv1JSQkxMdqtSKTvbTr078hN+sBAYHBqH007k6KhMRLg9FoxMvLi4sXzhI3LJ7xE+3jl/q6OgAsFgsWiwWlUul0bU8PgU6TPURJTlUXJx+09/u7TAC5DMSI/uaUyZ6tsx1r9lkEbTC21kZQeSFLnYWtoRRbXQFYreClQZY0FUHu3PmlXqdjx7ZfiIqOpqqyEo1Gw2tvbAAg6+F9jh0+gNVmZcmyVURERjpVG0Cv17Fv569ERkVTXVWJj0bDyjXraG9v4/SJIxgNBgRBxrJVr+HnH+A03bZWHScO7CQ0PIr62iq81T7MX/46uuYmLp46DEBkzDCn6Q3E0Kbn6sm9BIZG0Fxfg6e3D5PmraTT0MGDa6fp6jQiV3gweuoCNH7Oy3cvgz338RMmcevGNQRBRllpCW++877TdVv1Ovbt+pWIqGhqqirx8dGwYs06GhsbOHXsEIIgEBM3jLKSIt7Z9IlTtR9X1/Nyszh57DCdXZ0sWbaS6JhYp2oDGNtbuXtuP37BEegba1B5qRk1cwW5N88SFBlHaEyS0zX7otfp2Ll9a7/8r1m7npPHjpCQmERKWroomrt2bCUqKpqqnue9Zu16x99tNhtHDx9Ao9EwY9Zcp+tDTxuzYysRUTFUV1Xgo9Gy6rV14mgNUcd2bvuF0LAwKsvL6TaZWL5yNdeuXKKhvp7U9OHMnO28vHd2tJJ94RDaoHBam2pQefmQOm0p2RcPogkIRd9Qhbm7i8QJc/ENjnCaLtjf7yP7thMWEU1ddSVqHw2LV61F19zMhdNHMZtNaH39mbNwGSpPL6dqP67sQ0JDqSgrE60vG0q7qbGBI4cOADAsPp7iokI2f/xHp2r36g/2bu/avpWQ0DAqK8pJGz6CiZOmOF17qDb94YO7ZN6/gyDICAwKYsmK15yu3at/aO92wiOiqa2x17mUtBHU19Vw6uh+5AoFr294H4WHh9M09Todu3dsfTRu8dGweu16du/Yyuy5CwgLj8BgMPDz91/z8R//xWm6ffUHtqvzFiziyKH9vLvxQ8c1e3ZtY9OHnzpVd2A9X7xsBXt37WDj5o+pr6tlyzdf8oc//ytaX1++/Oy/+eDjP+DhpLIfTH/16+vY9suPzJm3gJjYOM6fPYWAwMw585yi2UurXsfhAfVs2qz5nD52gDfe2QyArqWZk4f3On53Fnqdjj07txLZ24f5aFm0bAV7d25zXNPYUM+Hn/4Zra/fM997sLar7zPr6uoiJyuTB/fuEjssnllz5tHR3s6w+ATHNSGhoQB0Go38+P03DItPYNTosfz/2fvL6Laydd8b/C1JtmzZkpmZGcPMjJXipCqVVAr3Pmfvc+89b/f7dvcY/eUdfcfo+/Y9dPcuZkyFwWFmBjtmZkjMJMuW1B9kK44ju2JbS6qTrN8YNcqR5fWfc67J85nPExA4el/rppLx6iw3apr7CfJ0oEtrYM+1dgaGhT50VAgkBitJDXOioqmfC/ndoz7PyVHgnQUelDXquF+ppaHNenG3rbdN1tuJ4B+LPHMtgtwRY3M1glcI8rRVyDPWIDhrMDaWWk1uOK0tzWROmc57H/4JpZMTRQX5APT397P9/Y9YvnINR7MOiKINpgaSmjGVrTs+Qql0oriogKOH95OWMZW3tn/A629tE2V3rqOthYTUTDZteQ9HpRMVJYVcPJXFrAXLeGmzdRurJbo7WomIT2fxS+/g4KikrrKYe1dOkjJjMQvXv0XStPlkXz0tmv7I967VaknPnMrUGTNFWegN0dbaQlr6VN5+9yOUTk6UFBVw6tghlixfzZZt7yMI4u0+j1bXDQYDW999nyXLVnL54nnR9Hs72wiJTWXWmrdQOCppqi4RTcsSrS3NZEyZxo4PPkapdKKoMN8mmulTpvHu+x/j5PRY02AwcPjAXjw8PEVb6JnT0NpCWuZU3nnvY5RKJcVF4uV7tDoml8t5Z8cHZGROYe+uX1m2YjXvfvAxOdn36O3psWoaerva8I9OJnPlZuQOjjTXmsYOo9FA2tJXiciYS3XuTatqDtHe2kJy2hRef+cDHJVKyooLOXP8IDPnLeK1t9/Hy9uHW9cuiaJtz7HMkvaRwwdYtmIV775vvQn3WPqW2rZer+edd98XZaE3hKU+/db1K7y59T3e2v4Bi5etFk0bTHUuJX0Kb77zAUqlEhDw9Qtg6aoNvP72e1Zd6A0xVN7b3/8YpZMTxTboS0fqD+9XGxrq0ev1tLW1AlCQn0u8CBt4I+t5TVUVAwMD9PX1UV1VhX9AINXVlbS3t6FycbHaQm80/ZKiQlav28Dxo1lUlJdRVlrKnPkLrao5RHtrC8npU3hjsJ49amrE0dH0fzCdJscnpYqi3drSQnrmVLa99zFKJyXVlRVsffcDtr77ASlpGcTExY97off42Zb7zZrqKrIO7ee7rz6nra2NtRs2sWBwEZ0xZRpHDx/klx+/48qlC3R2mkymXVxdef+jfyA0LIIL587wzZefcevmdXp7ey1qe7rIuVPey1dnWtH2G4gLVAIQ7KlgTaaabQvdcVPJOXSr84mFXkKQ8gkzToUMevqMfH6qhapH/SxIdGH7QnemRDrj5DD54PXWO2ZzckVwHTzBcfWEvi7oaUNfdR8GdKAfQHAPsJrccNzdPfDz9wfA3z+A9vY2ABKTUgAICQ2jr68PrVaLk5OT1fXd3Nzx9TPp+/oH0NHeRndnJ9Gx8QAoFOJYy6o17nj5mHYjvH396epsR9fXh39QKADR8cnUVIp3l0nl6oably8A7l6+9HR20NJUx81zh83fMehFOI8eZLT3LjYaN3d8ht63XwAdHW3odH0EBAUDEJeQREVZsSjao+U5Ni7hqc/EwMlFg9rDBwCNpy+93R2iaVnCzd0Dv8Gy9w8IoL2tzaaafoPtG+DEsSzi4hOZNWeeTdLg+0Qa2kXTGq2ORcfEAeDj64eXjy+uarX5+x0dHTirVFZLg5OLBtfBeubq4Utft2kg9gqOevxZjzh1T+3mjrevqV/18Qugo60VXV8fgcGm0/LYxFROZu0VRdueY5klba1Wa7YSSEpOo6xUvM2d0dq2GBP+kVjq0719fDmetZ+o6DgiB+u+mPrmOucbQGdHm6h68HSfYqvxc7j+yH41PiGRwvxcZsyaS0F+Lus3vmx1XUv1PCg4mJrqKqqrK5k1ey5lZSVghOCQUJvoJ6WkkpySyu6dP/P2th3I5eLc37JUzxJS0inIvc9s76WUFubx8uZtomiPNobV1lSTc/8ub7w1cV1LZXrq+FEePMhm+YrVrFqz/ikT8MioaD78818pLy2hrLSEb7/6jB3vf4zKxQWFQkFiUjKJScl0tLdz8vgRzp0+yYd//itq9ZMHN209epraTfPchrYB3FRylqa4kBzixInsLo7c6cRoIc2WzDgB9AbT7/Jr+9A4y1iW6sqiJBc+PdlCl9Zg4UnPhvVWIcMLUhDAYMRQfBVZwgIEFw8MjaXQ0WQ1ueHIFY8bhiCTYRiwfPQ5+bXxaPqPi1EmCPRqtSIpPYlsWIcgCALaUXYebKMvo1/Xg4OjE4s2vG0T/Wd979Zm+OJdEIRRd3zEYLQ8ywfNowWZDINh4h3C7zHynRsNtinzIRQj6ryYebWoKRMwDNpoBAYFU1VVwbQZs0Tb0BlCPqLcDSKW++/WMUEQ/T3IZCOerzcMpkdu/sxosDSETh75iLz19dmmPwf7jmUjtXu6np6IiMlodcpBhLszT2mP6NMNBgPrX36D2poqykuKuXHtEm9t/1C0O4PyUfoYMRmuKZMJDAyY7kQajaZ2pRd5PLXUryYmp3Jw325iYhMQBAEPTy+r61pqYyGhYdRUV9HR3k5MXDzXrl5GQCAq2vpXE0Zr4w+bmnBycqK7e3Qzv8kis1DmkTHx3Lp6iaCQEnz8/HFytt6m3XAsjWFdXZ0cP3KIja+8Pqk7cpbKdNqMWTgqlVy+eJ6yshJSUtMJDQtHEB73ns7OziQmp5CYnMLunT9TXV1JXLxpc6m7u5vcnPs8yMlGrdawbuPLuLi4PKWtH9ZUjUaQyeFmaS99A0bmxKmI9HUku0pL1aP+Z86PylEgOdSJ5BAnOnr1HLzVQfckFnogtjdO/QA4OGM0GDA+rBBVyhIFebmA6ShXqXRCKcKpniUcHB1xVWsoKS4EYGBggP7+Z3/RE8VRqcRRqaShrhqA0sJc0TWHo3BwROWqoba8CDDdZ2pveWjTNNgDpVKJo6OShrpaAIoKbFvuEvYhNS2DyKhoDu7bbZNFp4R9cFQqUSqdqK8xeZwtys8hIMj6O/5jYY+xzMnJCScnJ2qqTfnOy80WXfOPgtFopKuzg5DQcOYsWIyur49+nc6maXBwdKRf12dTTTc3dxob6gHs4unXw8MTmSDj6uULotzBHo2QkDByH2Tj4emJIAg4OztTWlosysmeJQoL8unV9rL57e2cOnEUrY0ODMC00RESHsGF08eIS0qzma7BoOfQ/j3MX7gETxEW9W7u7sxfuJgdH/6JhMRk7ty6wZef/o3cB6Z+rLKi3Dwv7+vro7W1FY3GjT6tlr27fuXnH75hYGCAV1/fzKtvbCYuPuGZN3vaewxczO/hy9Ot5NX2MSXSmfeXeJAYrBzz75QKgU0zNGyZ545cJvDb1XZ2X+ugqF5n8XRwPIi6HS2EpmLIPgYOTghqL9Piz4bIFQq++fIzDAY9q9ZssKn2ijUbOH0ii2uXziOTyViz4WXc3D1E1523dA0XT2UhCIKoDlpGY8qC1WRfPUXR/WsYDAaCI+Nw8/SxeTpszdIVazl13FTuwSGhODqO3aglrIwg1rn92EybPos+bR9ZB/exdsOmJ3YNJZ4fFq1Y95SDFltir7Fs9doNHDl8EAEIH+bM4HnHaDRyLGs/uj7TYittyjSbbRYPEZ+YyvnTx0Rx0DIaU2fM4tC+3WTfu0NkVLToepaIS0zk/JlTfDD/LzbTdHN3B6Np0Qcm883Ozg6cnK3rhMkSvT09nD97ije2bEWjcSNz6nROnzjGmvUbRdceIiY+mfKSIkLCbDdnrKutobG+jiuXznPlksnPwKZX3zRfD7AWMpmMqOgYoqJj6O7uprWlGYCG+jpOHj9iPs1OS88gIDCIPq2WKdNmPHUKOBGMQFmjjrJGHSpHAU/XxyeQCUFKgr0et+kT9zt51KHndmkvleM4BXxWhKEje0v8n6dKxLGTeQb+eaF9OhqAPjH8nj4jHb22XRAPp7nLtjuXw4kPtJ97aa1u8u9bp9OZzRBuXr9MT1cXC5as+N2/c3IUN7bK73G7vNVu2pnh7lZ5zp7ffmHq9JmEhT/7QDVGtyc6AyKZHz4Ljgr7ua6/VPzIbtopQW5203ZTTW6S/vMP37JoyXICAq3reXS8DHlQfFZvnGPNLcSmr99+J+39evtpq5T2i6Zl720umczeKbAPHb2TXxjcu3UNXV8f0+csGNffqew4f1HI7TeWeUz7B7tp9979XxOq6FKcPQkJK1BRVszNa1cwGg2oNW4sW7XO3kl6ITh6+CD9/f02M7eRkJCQkJB4Xjh2YDft7a2sf3WLvZMiISLjWuwZ+7oxFF2F/l5AQPCPRhYYj7G7FUPpDdD3g9IVWewcBIUDhqZyjHXD3Pl2tyJLWwVOagwPTj7+vK8HwSccWeTUSWfo5vWr3L93F0EAHx8/Vq/bYFXnCZ0d7RzPOkhPj+kSbUpaJhlTp1NUkMe1yxdoaX7Em2+/i1+AaRe2ob6WU8ePmP7YaGTmnPlmL53jpb21mbPHHrvd7mxvI3PmPKLjkzl77ABdHe24atxYtHLjEyYnDxvrObzrexau3EBE9LNr3710nIbqMpROKha/ZAplkH/nMg1VpSAIKJ1UZMxbgbPKlUf11Vw/fQCV2rSDHhgWTVy6yVV2ae4dKotyAAiLTSEqKXNC+R/iyKEDlJYUoXJxMe8yF+TncunCeZofPWTr9vettgve2dHOiSOP33dyWiYZU6Zz5OBeszlAX58WpdKJLdvep6G+ltPHj7Dvt58BIzNmT/x9j6Sjo52sg/vp7u4CBNIzMs0BQW/fvM6d2zcRBJPJwqIlyyakkXvtFI9qy3F0cmbWmrcAyLl0lO4O0wngQH8fCgclM1dvprm+ipJ7l83BjmMy5uLpHwLArVN70PV2Ixt06pG5eCOOThO/+G0wGPj+my9xVat55bU3zZ87ODpSX1crmvcygM/+/m84OioRBAGZTMbW7e/T2NjAyWNZDAwMIJPJWLZi9ZjxeJ6VE0cOUlZajErlwtYdJnf3D5saOH38CDqdDo2bO6vWvYRSqaS9vY3vvvzEfN/BPzCIpSusY15oqY01NTZw/GgWOp0ONzd31m3cNOgmfmIU3zhNa30lDkpnMlaa3ml32yNKb59DP9CPUqUhduYyFA6mE/Oa/Ns0lueBICMyYx4e/qYF/oCuj5JbZ+lpbwYEoqctRuPtP+70GAwG9vz8DS6ualZvfI3aqgquXjyNXq/HxzeAhcvXmO9s1FZXcuX8SQx6A07Ozmx4zfpOqT75X/+Ko6MSmSBw4uhh3tnxgdU1LDFaP+Pm7i5KjL0hRmvjp04cI+f+Xf7L//Z/WEVnvH26Xq/n9PHDNDU2YDAYSEhKZdrMOZNKg8FgYPdPprq25qXHMTMvnjlBfu59PvjH/w2AB/fv8ODebQSZgIODIwuXrcLTyzrXIm7fvE72vTuA6f7xlMGx5M6tG9y7fRNBJiMyKpoFiyc2loyFpT718sVzZN+7a/bqO3/BYiJFcJBiy/nD76HVajmadZBHD5sAgdVr1xMUHGJVDYPBwJ7Burb6pdcwGo3cuHye0qICBJlAUmomKze8Qm11JccO7EbtZprDRUbHMXXWxD1NH8t6PJZtGxEr8db1q5w/e4qP//LfUKlUVFdWsH/vb7gNhl6IiY1n1tz5E9Yei7LSEk6fOIbBaCAtPZOZs+dO+plKB4FV6Wp8NHKMwJE7nUyLcsZTbZr/DAVT/+asaR7lo5GzMl2No0LAaITvzreiN8Brs9xwdZIhCFDT3M+J+12Tvqs3xPhWQYIMWUQmgqsnxoF+DPePYnQPwFByDVl4JoKbH4bGUoy1eQhhach8I8DXZFpl7G7FUHDBHJ5Bnv44bo3+3lEEr8lX8M6ODm7fvMGOD/+Eg4MD+/fuIj/3ASlp6ZN+9hAymYz5i5bi6x+Arq+Pn7//itDwCLx9fFm78VVOn8h64vte3r5s3roDmUxGd1cnP377BZHRsRPy6uXm4cXGN98FTA145zd/Iywyluzb1wgIDiNt6izu37pK9u2rTJuzyPy9W1fOTuj+Xkh0EhHx6dy5eMz8WXTyVBIyTQNdad4diu5dI232UlNe/YKYueylJ57R0fqIyqIc5q/bjEwm5+qJvfiFROCqmfj9xZS0dDKnTifr0D7zZ94+vrz0ymscP3J4jL8cPzKZjHmLluLrF4BO18cv339FaFgEq9c/DrB74exJ86TXy9uXN4e975++m/j7fiotgoxFS5bjHxBAX18f3339OeERUXR3d1FcVMj29z5CoVBMyptXYGQCIbGp5F49Yf4sZe4q889Fdy6aJ98OSifSF6xDqXKlq62Zu2f3M++lx/Edk2evQOPlN+G0DOf2zet4eXnTN8xhQX19HVqtbTyhvr55K6phoQXOnznF7LnziYyKoaykePDOxeRjOyampJGWOY3jw2KpnTx6mPmLlhEcGsaD7Hvcvn6F2fNN7dvd3YO3tlt/EWCpjR3NOmQO/Jt97y7Xr16eVDB134gEAmJSKb5+yvxZyc2zhKfNxs03iMayPGoL7hKWMoOe9hYeVhWTsWIzut5ucs8fIHPVFgSZjLK7F3H3DyV+9koMej2GCd4Nz7l7Ew9PL3Q6HUajkTPHD7Hulc24e3hx88p5CvOySUhOp0+r5dKZY6x+6Q3UGjd6e8TznvfmW+88Ue9swWj9jLePuHevbdXGx9unFxfmo9freWv7h/T39/PD158Sl5A04ZhgANnD6toQTQ31T3l+jY1PIjnNtDlaXlrE5XOnWffyGxPWHeLhwyay793hrW3vIZfL2b3zJyKjY+nsbKekuJCtOz6c9Fjye4zsUwGmTJ/B9BmzRdME284ffo/TJ44RGRnNSy+/hl6vF8WRX87dm7h7epkdDBXmZtPV2cGb2z9EEATzpgdAQFAIq4dtPkyG5JS0wVh2T8YF7ehop6KiDLXmSdP64OBQXnp18nV7LAwGAyePHeH1zW+j1mj47usviI6Jm3TftjTFlbImHftvapEJ4KAQOHCr0/z7xcku9PWblm2CAOumaDh8u4OmDj1ODgJD/t323+xAN2D63kvTNcQHKcmvtY6TpnHNQAVHZ/NiTVA4gMoNdD3Q2wkaU7w1wd0fY3PVU39rfFSJ4B329Oe9HdCvNf/9ZDEYDAwMDJj+399v9cueLq5qfP1N8QIdlUo8vbzp6urE08sbT6+nPQo5ODiYJ/oDAwMIVrJsr6+pRO3mjqvGjcqyYmISTHGYYhJSqBwW4y0/+zZhUXETcqfr7R+Mo/LJS+kOwxyPPItr5s62Fjx8/FEoTOXg7R9MfeXkYjWFhIbhPOLitLe3D15e3pN6riVcXNX4+g2+b8fH73sIo9FIcWEesQnJwNPv25o3GVzVavwDTGlRKpV4efnQ2dnB3Tu3mDl7rvkE25J74GfFwzcIB0fLjgiMRiONVcX4h5liTmk8fVGqXE2abp7o9QMTnmiPRWdHB6UlxaSmZ5g/MxgMnDt9koWLl1pd71kQBND1mQbPvr4+XF2t088Eh4Q95RSgtaWFoEEz1bDwCIqLCqyiNRaW2lhLS7M55lp4ZOSkA9q7+QSiGOHIqLerDY2PaVfd3T/EHEy9pa4cn9AYZHI5Tq4anFzd6GxpYkDXR8ejOvwiTHEmZXL5U898Fro6O6gqLyEhOR0AbW8Pcrkcdw9Tnx4cGkHZoHfl4sJcIqLjzJMVZ9XE29sfkdH6GTGxZRsfb58uCKag9qa5RT9yuXxSDri6OjuoLCshISXd/JnBYODKhdPMmvfk5onjsJPzgf5+q/mhann0iIDAIPN4FRISRnFRPvfu3GbGzDlWGUv+qNhy/jAWfVot1VWV5jovl8utHjvTUl3LvX+HqTPnmp2PqETqv4JDw3ByetrBzbnTJ5i/cIld7njW19Xi7umJu4cHcrmchMSkSY+nSoVAiJcD2ZWmjRqDEfPCboj4QCV5NaZFW4SvI00dAzR1mHxFaPuN5tO7oYWeTAC5DKud6sEk7uwZtV3Q1QKu3qZFX0sNeIVgfFQFfT1Pf/9RJbL4py9/Gh+aFoHW8GKn1miYPnMWn/zHv6BwcCAiIooIET2Itbe38bCxAf+Asc236utqOXn0EJ0d7axYs8EqpzxlRXlExphcE2t7ulG5mCbdzioXtIM7Nd1dnVSWFrFq02YuNmaN+qzxknf7EtUleTg4Kpmz6lXz5y0P6zm7/3ucVK4kTZuPxsMbjYcX+XcuodP2IlMoaKwpx93bOqc9tqajvY2mEe+7rqYKlcoVDw9P82cNdbWcPGZ638tXW+d9j6S9rY3GxnoCg4I5d/ok1VWVXDh3BoVCwaIly6xiUjiStod1ODqpUGncn/pdU3UJGg9fs9kmmExCBUHANySaiORpE27jp08eZ+HipU/sgt+5dZPo2DirLbLGQkBg168/IggCaemZpGVMYfHSFeza+RPnzpzEaDSyeet20fS9vH0oLS4kOjaeooL8Jybe7e1t/PjN5zgqlcyet0jUu4ve3j4UFxUSGxdPQX4enR3WXwCoNJ601JXjFRTJo+pS+npMsd76ertRDzsldlS5ouvtQiaX46B0puTmGbrbHuHi4UNkxjzkivE5Rbly7iQz5y021zEnZxUGg4Gmhnp8/QMoLS6ge7Dc21tbMBj0HNj1I/06HSkZ04hLTLFSCTxGQOC3n38AQSA9YwrpmVOsrvF7DO9nxMRebfxZ+vTo2ATKSor48u//Sv9AP/MXLZuUl8ZL504ya/7iJ0I55Ny7RURULC6urk99P+feLe7fvoFer2eDle5Vefv4cOn8GXp7elA4OFBWWox/QCCtLc3UVFdx8bxpLFmwWJyxxFKfCnD39k1yc7LxDwhk0eLJlfMfnba2NlQqFUcOH6CpsRF//wCWLF85qXhzI7k8WNeGt6v29jZKivIpLynEyVnF3EXLcR+s6w31tfz2/ZeoXF2ZPX8Jnt7WPc0vKSrE1VVjDq4+nLraGr7/6jNc1GoWLFqKt491DoCG09nZiUatMf9brdFQX1s7qWe6ucjo0RlYk6nGVyOnoW2AUzldDPl5DPFyoLvPQGu36YMhj5yvzXJDpRTIr+njeslj64XXZrkR6KGgtFFHoZVO9WCCiz2jvh9DwUVkkVMQFA7IomdiKL8F1Q8QPIOeDLAOGDsfgUyO4OL+9LMeVSCLtc6xvba3l+KiQj76819ROjlxYO8ucnOySUpJtcrzh6PT6cjav5sFS5b/7r2VgMAgtu74iJbmRxzPOkh4ZPSk7hHq9XqqykuYOnvhU78TBMF8mHT94immzllodXfwiVPmkjhlLkXZNyjPv0d8xmzcvHxZ/up7KBwcaawu48bpgyx95V3U7l7EpEzjyok9KBQOuHn6/Kd0T6/T6cg6sJsFi59834X5ucQlJD3xXf/AIN5+1/S+TxyZ/Pu2lJZ9e35jybKVKJVKDEYDWm0vb2/bQX1dHQf27ubDP//F6uXcUFGEf1jsU593tTVTcu8yGYs2mj9Lnr0CJ5UrA/06si9mUV+uJjAyYdyaJcWmuxX+AYFUVVYApg67sCCPN9+avNnks/Dm29tQqzV0d3ez69cf8fTypqggn0VLVhAXn0BBfi7Hjhzi9Tetf28LYPnqdZw9dZzrVy4SGR2LfDCwuIuLK+99/BecnVU0NtRzcO9vbN3x0aTu0Y3F6rUbOHXiKFcuXSA6JvaJAL3WInraYsrvXqQ67xaegeG/u1FiNBrpan1IZMY81F7+lN29SE3+HcJSZjyzZmVZMU4qF3z8AqitrgRM/ejS1Ru5cv4ker2ekLAIhEFvfwaDgYeNDax7ZTMDAwPs+/U7/AICzaeA1mLL1u2oNaZ6t/PnH/Dy9jafrNqCkf2MWNirjT9rn95YX4cgCOz4+K/0abXs+uU7QsMiJhRKqaKsGGeVC77D6lp3VyelRQVsfO0ti3+Tkj6VlPSpFOXncvv6ZZasnLzzLy9vH6bPmsPunT/h4OCAr5//YKBr01iy5Z0dNNTXcWj/Ht7/+B+tPpZY6lPTM6cya858BEHg0oWznD1zklVr1ltV94+EwWCgoaGepStWERgUzKkTR7l25dKkTOOHM1TXhvdrAHr9AHK5gle2vEtZcQFnTxzmpde34uPrz9vv/RkHR0cqy0o4dnA3m9/92CppAdPp+PWrl3jl9ac3LHz9A3j/T3/B0dGRstJiDuzdxY4P/2w1bTGRCQL+bgpOZndR3zrA0hQXZsaquJhvOvRKCH7SFFMmQLCnA9+db6Vfb+TNOe40tA2Ywy38drUduQzWT9UQ5uNAxUPrmPaOewZqNBgwFFxE8AlH8DLtIgsqN+RJS0y/7+3A2Fr35N88rETwDn/6Wd2tYDQiuFpnkKyoKMPN3R3VoOlBbFwCtTXVVl/s6fV6Du/fTXxi8ricb3h6eePg6EDzwyazA5eJUFNZipePn9l8yEnlQk93FyoXV3q6u3ByNn3+qKmBc4MOXbTaXmoqy5AJMsKinp6wT4TgyHiundxHfMbsJ8w7/UIiuX/tDH3aXpROzoTFphAWa9r5zrt9CWfV07uXf2T0ej1ZB3YTl/Dk+zYYDJQUF/Lm1h0W/878vh814edvnQvfer2efXt+IzE5hbh40+JJrdYQG5eAIAgEBgUhCAK9PT3mdmANDAYDD2tKmL7ySZt6bU8n2RezSJq1HJXa3fy50+A7Vjg44h8eR0dz44QWe7U11ZQUF1JWWox+YIC+vj6+/uITFHI5n3/yH4BpEPn8k//gg4//ceIZHAP14E6gi4sLMbFx1NfX8uDBfRYvM4XWiItP5PiRQ6Jog6kevTw4QLa2NFNeZjKDVigU5k0EP/8A3N09aG1pxn8SfctYeHl78/pm04K2pbmZspLi3/mL8aPSeJC0wDTB6+1so7XeNElROrugGzzlA9D1dOHo7IrS2QWlsytqL9NOsVdwFLUFd8al2VBXQ2VZMT9WlKIfGKBf18fpowdYsmoDG1/fCkB1ZRltrS0AuLqqcXJyxsHBEQcHRwKDQml+2GT1xZ5a87jexcbFU1dXa7PFnqV+Rizs0cbH06cX5j8gLCIKuVyOysWFwKAQGhvqJ7TYq6+toaK0mKryUgYG69qv332BTC7np68/AUzmmj9+9Qlv7Xhyoh0Tn8iF08csPXZCpKRlkJJmMiG8eO40rmoNLc2PiImLRxAEAgIHx5LeHqub+lnqU4fX7dS0TPbu+sWqmn801BoNao3GfGoeF5/ItSuXrfb8Bgt17dSRA7i6qomMMV3FiIiO4+xxk9XXcJPhsMhoLp45Tm9vD84TuAJkibbWFtrb2/j+688B6Ozs4Mdvv2DL1h1PnGhHRsVw+vhRenp6rH5fWa1W0zHMMqazo2PSV706e/V0ag3Ut5qusBTU6ZgZYzqRFgSIC1Dy7bnWYd83UN3cT6/OZKRZ2qjDz13xRGw9vQGK6/uICVDaZ7FnNBoxllxDcNYgC3o8ABh1WgRHJ9Pvqx8g+Mc8+TfNlchSnvboZHxYgeATPvHUj0CjcaOutpb+/n4UCgWVFeXmuwfWwmg0curYYTy9vMmcNvN3v9/e1opa44ZMJqOjvY3W5uZJXewGKCvKJzI20fzv0IhoivNzSJs6i+L8HMIiTeX/2juPB4sLJw8TEhE96YVeV3srrm6mQa6hqhRXN9Pxv7anG6WzCkEQaH1YD0aj+b5fX28PSmcVPV0d1FcWM3/Nm6M+/4/GWO+7qrIcT08v88AFo7xvC2aPE03L0ayDeHl5M33GLPPnMbHxVFVWEBYeQUtzM3q93uzRzFq0NFSh0njgpHrcMfbr+rh37hDR6bNx93m8wDAYDAzo+nB0csZg0POothxP/4mZFy5YtIQFi0wbSVWVFdy4fvUJT30A//I//rtoCz2dTjdYl5XodDoqysuYPWc+rq5qqqsqCQ0Lp6qyHA9P6070h9PT3Y3KxQWj0cj1KxdJTTeZPPX0dOPk5IxMJqOtrZXW1hbcJzABfVa6u7txGUzHlcsXSM+cvPfkkei0PTg6qTAajVTn3cI/0nTC4hkYTuG1kwTGpqPr7aa3qx21py+CTIZS5UpPRysqjQftjTU4j9P504y5i5gx1+Twpra6kvu3r7Nk1QZ6e7pxVrmgHxjg3s2rZE43OaYKj4rl0tnjGAwG9Ho9jQ21pGZOs245DDqJUQ7Wu/KyUubMG18MrIkyWj8jFrZu4+Pt09UaN6qrKkhISqVfp6Ohvpb0KdMnpD1r3iJmzXtc1+7duv6EN06Az//jf5gXem2tLWYTu8qyEtw8rNe+h9pzR3s7xYUFbH5nB4IgUFVZQWiYaSwx6PVWm+wPMVqf2tXVaTbZLS4qEMWM74+Eq6srGo0bzc2P8PLyprKiHG8f690bnDlvETOH1bX7t66zdPUGrl08S211JRo3d+pqqnAbrF893V04q1wQBIHG+jqMRqPFO3cTxcfXjz/95b+Z//3F3/+dLdveQ6VS0d3VhcrFpF1fV4sR41P3Kq1BQGAQrS3NtLW1olZryM/LZd3GTb//h2PQ3Weko8eAp6ucli494T4ONHeaTDbDfRxo7hqgU/s49mZZk44ZMc4o5KZFXaiXAzdLe3GQm+LgdvcZEASI8nOkutl6DnvGd7LX+RDjw3JQuaO/ZwonIAtNw6jtxFBfBIDgFYLgG/n4bzqawFGF4PT06tn4qApZ4sIJJ34kgUHBxMUn8O1XnyGTyfDzCzDbgluLutpq8nNz8Pbx5cdvvwBgzrxF6PUDnDtl2gk5sGcn3r5+bHptM3W11dzcsxOZXI6AwKLlqyY1Ee/v11FXXc6cRY8DdqdOmcXZY/spzsvGRa1h8aqNk80mALfOZfGooQadtpfjOz8nPmMWjTXldLW3IggCzq4a0maZBum6iiIqCrMRBAG5QsHUBWvMph83zh4y3dmTyUiduQQH5eQuIR/ct4eqygp6e3v427//T+bOX4izkzMnTxylt6eH3b/9jK+fP6+/adksZjzU1VZTkJeDl7cvPw2+79nzFxERGU1Rfi6xI0w462qrubV3JzKZHEEQWLRscu97OLU11eTmZOPj68s3X3wKwPxFS0hNz+DI4QN89fnfkcvkrFm/ccJmNzmXj9HaWEN/n5aL+74iMnUmQVFJNFY+dswyRHXRfXo62yjLuUFZzg3AFGJBrnDg7tn9GI0GjEYjnn4hBEUlWZL7w9PT3c3+vb8BpkVsQmIyEVHRrHB05Mwp04RfIZezfKWVQh4c3Et1VSXa3h6++Nu/MmvuAnT9Ou7fuQVAdGw8SSlpANRWV3Hl4jnkclNdW7JitdXuuFhqY/06HXdu3wRMVhOT9XJcePUE7Q9rGejTcvPQt4QmTUc/0E99iSlMi1dwFL6DjldUbl54h0Rz99jPIJMRlTkfYdDEMyJjHkXXT2I0GHBy0RAz3TpmUPduXaOyvASj0UhSaiZBoeEAeHh5ExIexa4fvgBBICE5HU9v605Me7q72bt7J2Cqd4lJyURGRVtVYzRG62eiRHCDbw/G26enZkzl5NFD/PC1qSwSk9Pw8bXNvfOce7eoqapAJpOhVDqxZIX14rce3Psbvb29yOVylqxYhZOTEylpGRzLOsg3X3yCXC5n1doNVjfhHK1PzTq4j6amRgDc3NxZvso6fepIbDl/+D2WLl/F4f170Rv0uLt7sHrtBtE1M6bN4tTRA2TfvoGDoyMLl5s845cWFZCbfQeZIEOuULBszcTnEQCHD+ylpqqS3t4ePvvbvzJ77gLzSfJIigrzuX/3FjJBhsLBgTXrN4ly3WcoTNJvv/yI0WAkJS0dHytsKpzM6WTdFDVymUBbj56sOyaHT4nBTmbHLEP09Ru5WdLLOwtMGzeljTpKG3WolAKvzNQglwkIAlQ96uduhfYprYkiGI2j+3v5P0+VWNMZzLj454W2Gdgs0Td0s9IOdPRa35vhs9Lcpfv9L4lEfKD4jjZGQ6uz3/t2chQvPtyzcLu89fe/JBKZ4e520x6j2xOdAYP9xB0V1ncW9KxcKn5kN+2UILff/5JIuKnG5yzmeWGsuYXY9PUbfv9LItGvt5+2Smm9u+Hjxd438WUye6fAPnT0Wj9cw7OisuP8RSG331jmMe0f7Kbde/d/Taii26+0JCQkJCQkJCQkJCQkJERDWuxJSEhISEhISEhISEg8h0iLPQkJCQkJCQkJCQkJiecQabEnISEhISEhISEhISHxHCIt9iQkJCQkJCQkJCQkJJ5DpMWehISEhISEhISEhITEc8iYoRe6+uzoI/wFxZ4lbk/PxfIX1G3ygN6+Tcye5W5PV9kGOza01h77ucp2UdrPVfbhvHq7aW9MDrSbthjxop4Ve7bviofddtP2cHG0m7Y925g9xxN7j+F2bGZ2zbs954z2fOP2nD/UtfbaTTvSx1kKvSAhISEhISEhISEhISFhQlrsSUhISEhISEhISEhIPIdIiz0JCQkJCQkJCQkJCYnnEGmxJyEhISEhISEhISEh8RwiLfYkJCQkJCQkJCQkJCSeQ57LxV57WxvffvmpvZNhd65eOs+t61ftnQybcvvmdb787O8cOrDX3kkRnfb2Nr77yv71/Kfvv7ab9g/ffmVzzfa2Nr754hOb6w7n27//X3bVl5CQEAetVsu9O7fsnQwJieeWkuIivvnyM77+4lO+/OxvNm9vJ/b/woGfv+Dwzm85vPNbzh/bL7qmQnQFCQkbcvf2LV7f/DZqjcbeSXlh2LL1Xbtpv71th920JSQkbMNQiCh7hrGwFX2Di730zKn2ToqExHODXq9Hr9cjl8s5fuQQb29/H41Gw8DAAO3tbQBoe3txcnYWTd9g0OPgYAoLM3fpWrx8A574Tp9Wi9LJSRR9qy32bt24xoPsewCkpGUwZdoM9u/ZSWdHB3r9AJlTZ5CanmktuTG1o2PiMBgMZB3cR1NjPV7ePqxauxEHBweb6E+ZNoPcnPvcunENAfD29WP1uo1W17194xq5OSbd5NQMMqfN4PqVi+Q9yEalckGt0eDrFzD2QyaIpTznPcjm7u2b6PV6AgKDWLJ8FTKZeIfHN69fJee+KQ2p6Rk0NzfT1tbKrp0/k5KWzrTpM0XXnDp9JlcuXSD3QQ4qlQq1RoO/fwDTZ862uvbtG9d4MPi+U1IziIqNw2gwcOLoYeprq3FVa1i/6TXR6jnAzRtXeTCY/5Q0U/7/9f/67/zTP/8fomkOceP6VXLu3wUgNT2TadNn8j//v/8f/uv/7f8hqu7IPMfExpt/19bayoF9u1i+cg0BgUGi6OfcuUFR3n0A4pLSSM6YLorOEHduXiMvx6SXlJpOVEwc+3f9gn9gEPW1Nfj5B5CYksa1yxfo6e5m5bqN+AdYN+9tZTl0VBcBoAmJw8U/jPobx3Hy8EPb2oTCSYX/tGXI5Nbfr7TUt1n6TAxu3RjWv6RlEB0bz56dP+HnH0BjYwNe3j6sXifOWGapfV25dIEHOfdRqVzQaNzwCwhghgh9G0Bh9i3KCnMAiIxPJTg8mvNHduPpG0Dro0bmr9yEi9pNFO37t69TkGuq8wnJ6SSkpHMyax/dnZ0YjAamzJhLdFyiKNoj61Z9XQ3tba18//XnhIVHsmDxUlF079y8Rt6DwXaeYmrnB/fu5K3tHwJw+8ZV+vt1zJyzQBT9keNZd3cXao2G9MxpAFy5dB5HB0emzphlVd2R5W0w6JHLFWROnc7ZUyd42NTIa5vfpqqinJzse6xZ/5JV9UfOIYzAo6YmVq1dz8OmRg7t38vb298TpY2P7F96tb04OzkzZXCudPH8GVQqF1H6t5HjqEKh4N7d2wDo+vrQuLnxxpZ3xv1cS/3WcB49ekj2vTsUFRTw0iuvoXFzx2Aw4Dy4qFMoFHh5eQOQn5fLnds3SElNJzklDZWLy+/q5927SUmBqd+KSUglIe3JTZr2lmaK8+9TVVbMwpUb8fTxG/VZlSX5FDy4S3R8MpFxyTg5q569IH4Hq4yUjQ315ObcY8vWdzECP3//FcEhoaxYvR5nZ2f6+/v56buviImLx9mKiR9Lu7WlmRWr1xEUHMKxrIPcu3OLaVbuNEbT9w8I5PqVS7zx9nZUKhW9vdYPwDik++bbJt1fvv+KoJBQCvNzeWv7BxgMBn769gtRFnuW8hwQGERhfh5vvLUNuVzOqeNHyM/NISklzer6AA31deRk3+etwZOdH779irXrN1JeVsIbW7aiUlm3no2mGRQcQlFBPtvf+xC9Xs93X3+Bv784Zf4g5x6bh73v4NBQWltbWL1+E8tXreXw/t0UF+WTmJRqdX0w5f9B9n22vGPK/4/ffUVIaJgoWpa0c+7f4+1t7wHw/TdfEmoD7bHy3NL8iEMH9rJqzXp8/fxF0X/UWE9RXjbrX38HjHBg53f4B4WKogWmepaXc5/X39qOEdj5w9cEhYTR1trC6g0v47XKh1+//4rC/Fxe3fwOZSVF3Lx6mXWbXrNaGvraHtFZXUzwnPUA1Fw6iLOXP/3dHfhlLMI3bR4Nt8/QXV+BOjjaarow+nhi6TM/K7fzkXXtp+++Ijg0jJaWZlasWUdQcOjgWHaTaTOsu+Cy1L5CQkLJz8tl+3sfYTAY+Parz/ALEGfzsOVhA+WFD1i2cQtG4NT+n/ANCKazvZXpC1fh7Rcoii7Aw8Z6CnOz2fTmNjDC3l++xWg0onJRs3rj6wD09WlF0bZU31at3cijhw/Z+u4HomgO6eY9uM/rWwbb+Y+mdm4rLI1nq9Zt5OzpE+bFXlFBHi+/ttnquiPLe/mqddy+cY3MqdNpbKgznwDV1FQRHGLdvtbSHGLNug0UFxZQVFjA1csXWb5qjSgLPUv9y+p1Gzl+5BBTps/EaDRSkJfLW+9Y31rG0ji6Zt1G0jOnotfr2fnz90ydwOb8aPMCD08vCvJzyb5nWgSmpKWz/f2FKJVKAKJj4vj0f/0rYeGRRMXEkJiUgiAIZEyZSlRMDDn37/HTD9/i7eNDWnomEZFRFq0KmpsaKC3IYdXLbwFwdPcP+AWGoHb3oLKkkJL8bACi4lNY9/ocHByV5r+9dOowcrnpPQeEhDFl9iJikzMICouitPABx/f9jLunN9GJqQSGREzaqsEqi73a6iqiY+JxcDQdT8bExlNbU01pcRElRQUAdHZ20NrSgnOQdSfho2mrNRqCgkMASExK4c7tG6Is9izpN9bXERufYF5wOItwLFxbU0V07JO65aXFps8GO4qo6Fir64LlPDfU19HYWM9P35nuUA0M9KNS/f6uyESpqa4mJjYOx8E0xMbFU1NdJZreaJplpSVEx8ahUChQKBREx8SIoj3yfUfHxlNTXY2bu4d5oeHrH0BHe7so+qY0jMh/rPhlPkRNdRWxcfFPlH21DbRHy3NPTw/7du9kw8uv4e3tI5p+Q10N4VGxZtOP8OhYGuqqRdOrq6kmKibuiXpWV1OFxs0dbx9fADy9fQgJDUcQBLx9fOnosG6d621pwMU/DJnC1I+5BoTT29KIg7MapZsXAEo3LwZ6O62qC6OMJ6OMMdZe7NXWVBM9rK4NaZvGMtOkMzEphTu3bmDtjXdL7au0pJjYuMfjSXRMnHVFh/GooZagiGgUg/U8ODyGhw21uKg1oi70AOprq4mIftzGImLikMlk1FSVc+3iGcIiogkIFmeDZbS6JTZ1tZbbua0YbTzr7e6mq7OT3t5unJycUGuse5Jrce5SV0tjQz19fX3I5Qp8/QJobKintrqaxctWWFXf0hyitqaa1Ws38M2Xn5KWMcXqC8whRutfnJ2daWyop6e7G18/f5xF2CgfbRz18w/gzMnjhIZFTKh/GW1e8PMP3+Lj68eqNevx8vZ+6u+GTlErysu4ce0qFeVlrBm0vtNo3JgzbwGz586nrLSEo4cP4B8QyMuvvfnUc5rqawiJjDH3HaGRsTTW13Bi/y+4e/kwa9FK3Dy8LKbdkhkngItaQ+rU2aRMmUVdVRlXzxzFy9efRatfHnf5DEfUO3uVleW8ufVdHBwc2PnT9+gHBsSUG4Ew4l/Pv62/XTEaSUpOZd7CJfZOyQuFXC43/ywTZAwYbNnGXlyUSiVqjRu11VWiLvb+KCgUj4cKQRCQD/5bEASMBoNtEiF/bBIuCDIMer1tdO2OMOY/n2fkCvFM0n+PV7a8S1V5KTeunCcoNJypM+fZLS22oK9Pa74bCaDX234siYlPpKgwn57uLmLjk2wjKgi4ubuTm3OfwKBgfHx9qa6soK21BU+vpxcKYtDa2oyjoyNdXdbfwPo9UtIyyc25T3d3Fymp6TbVfpB9j46ONpauWGXV5258+TWy791l356dJCQmk5yahpub+xPf8fH1w8fXj6SUND7727+ZF3sAdbW15GTfpaK8jLiEJNIyxncFbf6KDZTkZ3P+2H7Co+OJjE/GdRwm6I8a6yktyKG+uoKw6HhiEidvIWeVC1VBIaGUFBfS399Pv05HcVEhWm0vTkonHBwcaG5+RH1djTWknkk7KDiEzo526mpNmvl5D8ynfLbQ9wsIpKggn97eHgBRzDiDgkMpHaZbUlxIRFQMpcWFDPT3o+vro6y02Oq6MEaeCwvo6e4GTHnuGLz0KgbBoaGUFJnSoNPpKC4sEG1HbCzNyKhoSoqLGBgYQKfTUVoiUplbeN/BIeLU6dEIDgmleHj+i8Qv88faYRQXFTyhHWID7dHyLJfL2fjya+Q+yCYvN0c0ff+gECpKixjo76e/X0dlaRH+geK996DgEEpLisz1rLS4kECRTjVGw9nLn+6GSgz6AQwD/XQ1VODsOfo9B2ticTwZZYyxNsEhI/qXogKCQkJNY9ngaY9pLLP++7DUvqKiY8yf9fX1UVpcZHXdIbwDgqmtKGFgoJ+Bfh01FcX4+ItzB3YkAUGhlJcO1vl+HeUlhXj7+qNQOBCbkEz61Jk8amoURXu0uqXT6UTRM+sGh1Ba/GQ7D4uIpqenm97eHgYGBigvLRFR3/J4FhefSGF+LkWF+cTGJ1hfd5TyDgoO5daNqwSHhhIUEsr9e7fx9fO3ukMgS3MIH18/Tp04zptvbUPb20thfp5VNc3ao/QvMXHxlJeV0lBfR3hklGjaI8fRoOAQbl6/ypp1L024nEebF0RERrFh0ytseXs7SqWSvb/9yq8/fU97Wxs6nY6qygrzM5oaG9C4mRZi5WWlfP3FJ1w8f4bQsAje+/DPLF2+Ep9Bq5aR+AYGU11WYh6fq8qL8QsIJjA0gvkrNrDipc04OCo5d2QfJw/upOt3rGDqqso59Os33Lt+Eb+gUNa9uYNpc5fg7jn5TQernOz5+QeQlJJmNuFLScsgLWMKB/b+xjdf/B0PTy8CAoOtIfVM2k5Oznh4enHvzk2OHzmIl5cPaSJ5trKkHxQcwozZc9n50/fIBAFfP39Wrt1gdd3E5DR++d6km5yagZ9/ALHxifzwzeeoVC5WNzMarm0pz3PmL2T3zp8wGo3IZDKWLF+FZsRuirXw9w8gOTWNH775EjBddBYrv2NpBgQGER0TyzdffoqLygUfH1+USut7U/LzDyApOY2fB993SmoGSidxvEaNlYbk1DR+/NaU/5Q08ct8CP+AAJJT0/n+my8A00VsP/8A0b3zWcqz02C5Ozo6sunVN9n1y484OjqKYubm7etPbGIqB3Z+C5gctHj7inM/EEymwInJqez8wRROIyk1HSeRvIONhtLNG3VIDDWXDgAmBy0yB+Xv/JV1sNS3jfaZGNrD61rqYF3z9PTi7p1bHDtyCC9vb1G8NFpqX/4BgcQnJPHNl5+iUrngHyieOaWntx8Rscmc3PcjYHLQ4ihCP2oJHz9/4hJT2fvLN4DJQctAfz97f/kGQRCQyeTMW7xSFO3R6lZQcDDffvkpEZHRojho8fUbbOc/DrbzlHT8AwKZMWseO3/4Ghe1Gg9Py+Zn1sDSeDbkX0Cn68NVrcbVVS2OroXy1mq1XL96icDAYBwcHVHIFQSJsJloaQ6Rm5NN5pSpeHp5sXLNOn796XuCQ8NweQbnIOPBUv8y1I+FhoWjVCpFc6hnaRy9e/smvdpefv35ewD8AwJZuXrduJ472rxgCGeViqnTZzJ1+kzqamsRZAJGo5HrVy9z/MhhFAoFDo6OrF670fR9Z2defu3Np04BR8PLx5+o+GSO7PkBMDloGe6ARenkTELaVBLSpvKosf6J+crwO3tKZ2eWrX8dpZMzi9ZsGtcp4LMiDD+2H0lXn2H0X0qIgj1LXGZH8yC5PcUniU6nw9HRkf7+fn7+4VtWrF77zE5aBvT2bWL2LHfZJLV7e3r49qvP+fgf/2ncf2uwY0Nr7em3m7aLUv77XxKJw3n1dtPemCzuva+xmMyGRHtbG3t3/cL29z+e0N9bs31funAOB0fHZ/bGWfGw22ra48XDxdFu2vZsY/YcT+w9htszKoc98z7ZocxoNPL915+z/qVXxr3At+cbn+z8YTLUtVrfWu9ZifRxnlDGpTh7EhKT5PiRwzQ/esjAwADJqWmieOOUeJLOzk5++fFbps+0vtMlCQkJCQmJ551Hjx6yb9cvRMfGi3qSK2F/JrzYO5Z1kLLSYlQqF7a999ETv7t1/Srnz57i47/8N1QqFVptL8ezDtHW1opCoWDF6nVmz25iaxuNRs6eOk55aQkKBwdWrlk/KRMcS9qFBXlcvXSe5keP2PLODvwDntxF7mhv59svP2HW3AWT9gh64shj/a07TPoPmxo4ffwIOp0OjZs7q9a9ZHYxe+PqJR5k30MmE1i4ZOWkbLIt5f3yhbOUFBchCAIqlQsr16zHVa0mPzeHG9euAEYcHJUsXb7Kau7pBwYG+PmHbweDVBqIi09g7vyF5t+fOnGMnPt3+S//mzix3wwGA99/8yWuajWvvPYm6zZuMutevXyRmbPnWk3r+LD3/c7g+z58YA+tLc3A4yCcb2//gN7eHg7t301jfR2JKWksWWbdS89arZbjRw7x6GETCAIrV6/DwcGBE8ey6O/vx83NjTXrN5nrnrU4cugApSVFqFxc2PHBnwBQq9VMmTqdO7dvcvfObaKiY1i0ZJlVdYewlG+FgwMnj2UxMDCATCZj2YrVVouz19en5eKpI7Q2PwQE5i9bTXdXJ3euXaKt5REb3tiGz6DJk16v59LpozxqakAQBGYuWEpg8MRcqHd2tHMi6yA9PaYTmeS0TDKmmmL63bt9k+y7txAEgYioGOYuXEJvbw9H9u+hsaGOhOQ0Fi0bn6nbQG8XjfcuoO8z7ZJqQuNwj0w2/76tNIfm/BuEL9+C3NEJXVcbTfcu0NfRjFfcVNyjUszfbbp/ge7GauRKJ0IXjM9r2Xj69Pa2Nr798hPzxCggMIhlK9eMS28sbt+8Tva9O4DJzGrK9JnExifw6X/8i9lL3rwFi4mMFsfrLwz2r99/w8Cw/nXegkVP9LET4ca5Y9RVlaJ0VrHq1e0A9Gl7uXr6MN2d7bio3Zi9dB2OSiea6qq4dHw/LoPeGIPDY0iaYjpRLMq5TWmByaV5ZHwqcSlTxp2WH7/6G44OjggyAZkg4+Ut76LV9nIyax+dHe2oNW4sX/PSE6byTQ117Pv1O5au3khU7MTvk42nvun1ek4cPUxTYz0Gg4HE5FRmzJrY+NLZ0c6JIyPa95TpHDm49/F40qdFqXRiy7b36Whv4/uvP8Vj0Jugf2AQS5avnnC+xzOWATxsauTU8Sx0fX0gCGx5570nHEWNB3vP24Yzcv5w5NABqqsqzePmqnUb8LPSXOlY1kHT2KlyMVsH9Pb2cnj/btrb23Fzc+Ptbe/j5OxMSVEhly6cHTRdlrFo6Qqr3sn/7O//hqOj0vz8rdvfN//u5vWrnDtzkj//9Z9FCZs1nLLSEk6fOIbBaCAtPdOq87Uh8u7fpCQvGwQBD09vZi9ezbXzJ2isq8ZxMPzC7CWr8PT2o6G2inNH9+KqdgcgNDKG1GlzrJ6mCS/2klPSyJgyjaOHDzzxeUdHOxUVZU+4zL1+5TI+fn5sePk1mpsfcebEUV598+0JJ3o82uVlJbS2tvDuh3+mvq6WU8ePmGN9WEvb29uH9S+9ysljRyz+zbkzJ4iItE48qMSUNNIyp3E867H+yaOHmb9oGcGhYTzIvsft61eYPX8RzY8eUpify9YdH9Hd1cmenT+x7f0/Tdgu21Lep86YzZz5iwC4c+sGVy9fYNnKNWjc3Hl9y1acnJwpLy3h5LGsSZX7cORyOW9s2YqjoyN6vZ6ff/iGyKhoAoOCqa+vQ6sV94j99s3reHl506frM38mlm5SShrpmdM4Nux9r93weDJ7/sxJHAcHCYVcwZx5C3n08CGPHjVZPS1nTh4bvPj8Knq9nv7+fnb98iMLlywlJDScnPt3uXntCnMXLLKqbkpaOplTp5N1aJ/5s8qKcoqLCtn+3kcoFAq6u8UzGbOU74P7djN77nwio2IoKynm/NlTEwoIa4lr508SHBbJ0jWb0Ov1DAz046h0YunaTVw6feyJ7xY+uAfAy2+9R29PN8cO/MbGN7ZNyHRQJpMxb9FSfP0D0PX18cv3XxEaHkFPdzdlJYVs3vY+CoXC7IRJIVcwc94Cmh8+pPnRw/FnVJDhnTgdpZs3hgEdNRcPoPIJwlHtwUBvFz2PalE4P763InNQ4p08i+6GyqcepQ6OwS08kcZ758edjPH26W7uHqLEQHv4sInse3d4a9t7yOVydu/8icjB8DlTps+wemy90ZDL5bzx1jvm/vWn778hMjqGoKDJ3bsPj0siOjmD62cfl2nBvRv4BYWSkD6D/HvXyb93nbQZpiDe3gHBzF+56YlntLU8pLQgm2UvvYVMJufC0d0EhkaidvMYd3rWvbrlibi/d29cJTgknIzps7l74wp3b15l5rzFgGmCfu3SWYLDIieS9ScYT30rKshDrx/gnR0f0d/fz7dffEJ8QjJu7u7j1jW3b78AdLrB9h0Wwer1j8v4wtmTT2zWubt7sGXb+5YeN27GM5YZDAaOHt7PqrUb8PH1p7e3Z1L3yew9bxuOpfnDwsVLiUtItLpW0mC+jxzab/7sxtVLhIZHMGPWXK5fvcT1a5dZsGgpoeERvBMTiyAIpuDu+3bz7od/tmp6Xt/8dAzkjo52KspL0Vg5zIYlDAYDJ48d4fXNb6PWaPju6y+IjonD28d6HrV7ujopyL7D+jffRaFw4MLxA1SU5AMwZfZCwqKevtvvGxDM4jWvWC0Nlphw6wkODTM7KhjOudMnmL9wyRO2vM3NDwkNiwDAy8ub9vZ2uru7Jio9Lu3S4iISk1MRBIHAoGD6+rSTcm9rSdvL22dUF73FRQW4uXngZSX37MEhYTiNiNvX2tJivkwcFh5B8WBsw9LiQuISklAoFLi5e+Du7kFDfd3EtS3kffjA0N+vM08yg4JDzN8NCAqiq9N6LoUFQTDHVTEYDOj1BvPP506fZKEIF9qH6OzooLSkmNT0DPNnYupaet9DGI1GCgvyiE8wuah2cHQkKDh0wrufY9Gn1VJTXUVKminfcrkcJycnWlqbCR4MxhsWEUlRYb7VtUNCw56KVXn3zi1mzp5rzqu1L7MPMVq+BQF0fSaPeX19fVZzJqDr01JfW01cUppZT6l0wsPTG3cL8XpaWx4ROFj+zioXlI5KHjZO7G6ci6sa30GrB0elEk8vb7q6Osm5d5upM2aby1o1WNaTrW8KJxVKN1O/KVM44uDqzoDW5MH4Ue51vBKmMfxWiELpjJO7D4Lw9LDl7BUwYScu4+3TxaLl0SMCAoNwcHBAJpMREhJGcZH129PvMbJ/Nej1Vrmb4xsQ8pTzqtrKEsJjTf1XeGwStRVje4DsbGvByzcAhcJURj4BIdSUW8cDckVZEbGJqQDEJqZSXvrY++iDe7eIjI6zSgyycdU3QaBf14/BYGBgoB+5XG5eEI0XF1e12QmKo+Pj9j2E0WikuDCP2ITk0R4xKcYzllWUl+Lt44vPoDMqZ2fVpBZ79p63DWFp/iAmIRbyXVJcRFKKaXxJSkmjpKgQMDkcG5q/9et0NrsEefbUCRYsWmqTC4D1dbW4e3ri7uGBXC4nITHJPF+2JkaDAf3AgLndOqtcra4xXqw6KywpKsTVVfOUuZ6Pr5/ZNX59XS0d7W10dXbi4mK9AhhNu6uzE7VaY/63Wq2hq7NTFE9PI9HpdNy8doVX3niLW9eviqbj5e1DaXEh0bHxFBXk09nZAUBXV+cTpmWuag1dg7+zJpfOnyH3QQ5KpZLXNj99Yptz/57VXfoaDAa+//oLWltbyJgyjcCgYG7duE50bJyo7/b0yeMsXLz0CdfYd27dFF3XErU1Vbi4uNjE1r6tvQ1nlYqjWQd52NSIn38Ai5euwNvbh5LiQmJi4yksyKNDhPplidbmZqqrKrlw7gwKhYJFS5ZZzYxyOKPle/HSFeza+RPnzpzEaDSyeet2q+h1drTj7KziwsksWh414eXrz6wFS81BW0fi5e1LVVkJUXFJdHd28Kipge7ODvCfnEOSjvY2mhob8A8I4tK509TWVHPl4jkUcgVzFy19yuRpsvT3dKJrb8bJ3YfuhkrTQlDzx7xD0t7exvdff45SqWTO/EVWM3Xy9vHh0vkz9Pb0oHBwoKy0GP+AQJycnbl7+ya5Odn4BwSycPGyUSfN1sJgMPDdV5/T2tpC5lRT/yoG2t4e80TIydkF7WC4IoDmxjqO7f4OZxcX0mcsxM3TGzcPb7JvXqJP24tcoaC+qgwPn/GbvAlA1t5fAIHElAwSUzPo7enGxdWUFpWLC72D5o5dXZ2UlxSy/tW3aDpxeNJ5Hg+xcQmUFhfy6X/8C/0D/Sxasvypja+JMLx9D1FXU4VK5YqHh6f5s/b2Nn7+7gscHZXMmrdQlLAf8PRY1tbSgiAI7Nn5E729PcQlJNnsZFvMeZul+QPAhfNnuXLpAqHhESxYtESUDdsherq7zHMVFxdXeoYdvBQXFnDx3Gl6errZ9OrTQcQng4DArl9/RBAE0tIzScuYQnFRIa5qtdWu+PwenZ2daIavBzQa6mtrraqhclWTmD6Nvd9/ilyhICAknMDQCMqL87l3/QLZN6/gHxxK5qwFyOWm9/ywoY7DO7/B2cWVKbMXWSXUwkisVqP6+/u5fvUSr7y+5anfTZ85h7OnjvP915/j7eNr9fglY2nbkyuXzjNl2gzzLqlYLF+9jrOnjnP9ykUio2ORy2zrDWzugsXMXbCY61cvcff2TebMW2j+XVVlBQ+y7/LGW9usqimTydj23odotVr27d5JdVUlhQV5vPmWdUzpLFFSbLo35h8QaI7T0tnZKbruaBTk5RKXYJvAs0aDgcaGepYsW0lgUDCnTx7jxtXLrFyzntODdxWjbFj3DEYDWm0vb2/bQX1dHQf27ubDP//F6qEYRst3X18fi5asIC4+gYL8XI4dOcTrkzBNH8JgMPCoqYFZC5fh6x/E1fMnuX/rKlNnLbD4/dikNNpamtn/yze4atzwDQhCmKT7bJ1OR9b+3SxYshylUonRYKBP28vrb22nsaGOowf3sO2Df7BaWRsG+mm4fRqvpJkgk9Facp+AGeK4up8sLq6ufPCnv+DsrKKxoZ79e35j23sfWeWeqpe3D9NnzWH3zp9wcHAYHCdlpGdOZdac+QiCwKULZzl35iQr16y3Qm5GRyaTsf39j8z968OmJnx8J37P/lkYXp88vP1Yu/kDHBwcqasq49KJ/ax54z00Hl4kpE3n/JHdKBQOuHv5Tqgebnh9K66uanp7ujm85xfcR2yYmZ5peu6VcyeZOW+x6GFeLNFQX4cgyPjwH/6JPq2WX3/6ltDwCNzdx2+2OoROpyPrwG4WLF7+RL0tzH9yPFG5uPLuh/9oruuH9//GW9utU9dHMnIsMxgM1NZUs2XrDhQODuz+9Qf8/AIIDY+wuvZIxJq3WZo/AMxftBgXF1f0ej3Hjx7m+tXLzJlnub+3NoIgPHGCFxMXT0xcPNVVlVy6eI7XrDCmDfHm29tQqzV0d3ez69cf8fTy5vqVi7z6xltW0/gj0KfVUl1Rwktvf4ijo5LzJw5QVphLxsz5OKtcMBj0XDt3nNw710mdNgdPHz82bf0IBwdHaitLOXd0Lxu3WP+agNUWe22tLeYdT4DOzg5+/PYLtmzdgYurq3lwMhqNfPnJf+A2ic5qPNquarX5pGvod65q25zANNTVUlyQz4Wzp+nr0yIIAgqFgowp06yq4+nlzcuDC93WlmbKy0ymMK6uajo7Hue9q7MD12G7GtYmITGFvbt+MS/2HjY1cuLoYTa99uYTdyOsiZOTE6Fh4VRVVtDW2sLnn/wHYNoA+PyT/+CDj//Ralq1NdWUFBdSVlqMfmCAvr4+vv7iExRyuai6ljAYDJQUFbDlnfdE1RnCVa1BrdGYd/jj4hO4fvUycxcs4rU3TZ11S3MzZaXiBJUfiVqtITYuYdA8OwhBEOjt6TGbGFqL0fJdU1PN4mUrBj9L5PiRQ1bRc3FV4+KqwXcwoHREdDz3b42+uyyTyZi54LH58MHfvsfN3XPU7/8eer2erP27iUtMJjo2HgBXtZqomHgEQcA/YLCse3tQqSZf1kaDgYbbp1EHReEaEE5fRwv9PZ3UXDDdzxzQdlNzYT9Bc9ejcBL34v6zoFAozLvufv4BuLt70NrSbLWTzpS0DLPJ8MVzp3FVa56wgElNy2Tvrl+sovUsDPWvZWUloiz2nJxV9PZ04axypbenC6fBccLB8fGCIjA0ktuXTtGn7UHppCIyPoXIeJNznuwbF3GegIXQ0MmGs8qF8OhYmhrqcFa50N3VhYurK91dXWaTzYeN9Zw8sh8wnURWlZcik8mIiLZ+XM2R5Oc9ICIyCrlcjsrFhcCgEBrr6ya82NPr9WQd2E1cwuP2DYPjSXEhb259fK9+ZF13c/egrbUZv0laDYzE0ljmqlYTHBJqfgcRkdE0NtbbZLEn1rzN0vzh8IF9rN3wEmAq75TUdG6IaAUGpkV8V5fJuq2rq9NiPx4SGsaxrFZ6enqs5jBlyMLOxcWFmNg4qqsqaW9v49uvPwNMJq7ff/M5b73zHq6u4pg9qtXqJ6yPOjusvx5oqKnAVe1m7stCI2J52FBLZJxpM0MuVxAVn0LevRsAZoctAEFhUVy/cBJtb4/5762F1RZ7Pr5+/Okv/8387y/+/u9s2fbeoDdOLQ4ODsjlcnLu3yU4JNSqu0NjaUdFx3L3zk3iE5Kor6tFqXSymbnd8NOsKxfP4+DoaPWFHkBPdzcqFxdTsMgrF0lNN3kni4yO5eihfWROm0l3VyetrS1WN79qbWk2m16UFBfi6WX6uaO9nYN7d7Fq7QY8rWxm2NPdjWzw7lR/fz+V5WVMnzWHP//1cR34l//x362+4FqwaAkLFi0BTCeWN65f5ZXXnjR1EEPXEpUVZXh4eaHWiLd4H46rqytqtYaW5kd4enlTWVGOl7cP3d3duAzWvatXLpKeMX7PeBMhJjaeqsoKwsIjaGluRq/XW+U+zUhGy3dbWxvVVZWDGw3lVjOlVbm44qJW09bajLuHF7XVFWOadAz092PEiIODIzWV5cgEGR4TvGtmNBo5dewwnl7eZE6baf48MjqOmqoKQsLCaW0ZLGsrDERGo5Gm+xdxdHXHPdI0eVdqPIlY/thCo/L0ToLnbUDuaNvA7qPR09ONk5MzMpmMtrZW2lpbrLpxOdSeOtrbKS4sYPM7O8wTMzDdJZqMJ+tnYWT/WlFexoxZ1vcOBxAYFkVFUS4J6TOoKMolKMzkEKO3pxsnZxWCINDcVA9GI45Kk/mitrcbJ2cXurs6qCkvZunGzePS7O/XYTQacXRU0t+vo6aynCkz5xIeGUNRXjYZ02dTlJdNeKTJOc6WHY+dVJw5foiwiGibLPQANBoNVZUVJCan0q/TUV9Xy5RpMyb0rNHaN0BVZTmenl5PXHkZXtfb21ppa23FbQKOcH4PS2NZeGQUt25cpb/fdE+xprqKzAnme7yINW+zNH9Yu+Elc/s2Go0UFxXiY0VnIZaIioklN+c+M2bNJTfnPtExpnre2tKCu4cHgiDQ2FCPfkBvFZNhMJ0mm9qwEp1OR0V5GbPnzGf2X//Z/J3P/v5vvL3tfVG9cQYEBtHa0kxbWytqtYb8vFyzN3VroVJreNRYx0B/P3KFgobaSrx8/Onp7kLl4orRaKS6vBh3T9N7Nm1yuSAIAo8a6zEajU94AbYWE17sHT6wl5qqSnp7e/jsb//K7LkLzDuSI2lpfsSxwwdAMJmqrFi9bsIJHq92RFQ0ZWUlfPXZ33BwULBi9eTMXyxpOzk5c+bUMXp7eti361d8/PxEMyk9cnAv1VWVaHt7+OJv/8qsuQvQ9eu4f+cWANGx8ebLt94+vsTGJ/L9V58ikwksXrZqUpecLeW9vLSElpZmBEFAo3Fj6UqTa+arly/Q29vL6RNHAdMJxFvbrHMK1dXdxZFDBzAaDBiNRuISEs0d1vNG1sHHZf754PtOScugMD+XeAsX6b/85N/p0/Vh0OspLSrk5de3WO2S+ZLlqzh8cB96vR53dw9WrVlP7oNs7t6+CZhMQJJT062iNZyD+/ZQVVlBb28Pf/v3/8nc+QtJTc/gyOEDfPX535HL5KxZv1E0MytL+Y6OiePMqeMYDAYUcjnLreh+f/bC5Zw7dhC9Xo/GzZ35y9ZQUVLIlfOmHb/jB37Dy8ePVS+9QW9vN8f27QRBwMVVzYIVE+9b62qrKcjNwcvHl5++/cKUlnmLSEpN5+TRQ/z49WfIZHKWr15vLuuvP/0PdIP1ray4kI2vbX7m+qZtbaSrtgRHtQfVgyd5nnFTcfELsfj9AW0PNZcOYBjoR0CgrfwBoQteRubgSOOds/Q216PXaak49QuesZloQp9tQj6ePr2mqoorl84hk8kRBIGlK1ZbbUIEcHDvb/T29iKXy1myYhVOTk4cOXiUpqZGANzc3Fm2ynp1zRJdXV1kHdqP0WjqX+MTkqzSv149fZimumr6tL0c/OlTkqfMISF9BldOHaKsIAcXtYZZS0z1t6askJL8+wiCDLlCwawla8117vLJg+i0vQgyOVPmLsFROb6NgN7ubo4f2gOYTpWi45MIDY/C1y+Ak1n7yM+9j1rtxrK1L006z5YYT31Lz5zG8ayDfPvlJxiNkJyaho+v34R062qrKcjLwct7WPuev4iIyGiK8nOJHXEloLa6imuXz5vr+uJlqyZ1V3Q8Y5mTkzOZ02bw83dfgiAQERlNZNTEw43Ye942dtr20dPTA0Yjvn7+LLdi+z68fw/Vg/n+9H/9C3PmLWTGzDkc2r+bnPv30Li5sW6jyQtkUWE+eQ+ykclkKBQK1m582Wpjak93N/v3/gaY2lxCYjIRUdb3dPp7DIVJ+u2XHzEajKSkpeNj5c0zH79AwqLiyNr1HYJMhqe3LzFJaZw+vJu+3h6MgKeXLzMWLgegsrSIogd3kclMfd28ZetFmcsIRqNx1F929RlG/6WEKNizxGW2v5JgRm5PcTsyoLdvE7NnucvsqG2wY0Nr7em3m7aL0rb3eYdzOG9iXkKtwcZk61o0jAd73PUawp7tu+KheOFQfg8PF3HvyY+FPduYPccTe4/hdmxmds27PeeM9nzj9pw/1LWKG95rLCJ9nCeU8cnd5JeQkJCQkJCQkJCQkJD4QyIt9iQkJCQkJCQkJCQkJJ5DpMWehISEhISEhISEhITEc4i02JOQkJCQkJCQkJCQkHgOkRZ7EhISEhISEhISEhISzyHSYk9CQkJCQkJCQkJCQuI5ZMzQCx1a+zl1dVRI61Bboxsw2E37RX3f9ixzAMULGnpB4sXCnqE27OkS3p5hHwb09uvbFPIXczyRkJAQn7HWTWLj7DCxTl3qESUkJCQkJCQkJCQkJJ5DpMWehISEhISEhISEhITEc4i02JOQkJCQkJCQkJCQkHgOkRZ7EhISEhISEhISEhISzyHSYk9CQkJCQkJCQkJCQuI55Lld7H3yv/6Vnp4eeyfDrlRVVrB758/2ToZNqa6q5MvP/s43X3xKf3+/vZNjE7765N/ptXNdP3bkEI8ePbSL9tHDB3n00Lbaf4T+5ecfvqW+rs6uaZCQkBCHmzeuvTBjmISErWlufsTPP3zLN198yhef/o1jWYdspn3pwjn+/u//wrdffmb+T6vViqqpEPXpEhI2Ju9BDrNmzyUpJdXeSXmhWLl6nd20V61dbzdtCQkJ22AwGJDJntv96ae4deMaScmpODg42DspEhLPDdreXpycnTl14hjTps8kJi4egIdNjQDodDrkcjlyuVw0bYCp02cwfebsJ34vprZVFnvt7W3s++1n/PwDaGqox9Pbh5VrN3L7xlXKSooYGBggICiYpSvWWD3uT3tbG7/9+iP+/oE0NNTj7ePD2vUvAXD96mXKSktwUChYt/FlPDw9rao9lv6jh02cOnGM/v5+FHI5r2/ZilKpFF23uqqS0yeP4aBwIDgk1Gp6T+mP8s5bmh9x/swJ+nU6nJ1VLF+zHldXtfX1LeQ/JDSMgvxcystKKSstYd3GTTbRXbv+JaoqKzhz6jgODo4EB4fQ1tbKK69vtq72KGUOcPf2DcpLi9Hr9azd+AqeXt5W1Tanoa2N3Tt/ws8/gMbGBry9fVi9biN7fvuZhYuX4R8QKIrukLalst/1608sWrKcgEDra4/VvwD09/ezb/dvxMbHk54xxer6z5IGW2h9+dnfSExKpqykBJlMxorV6zh/7jRtLS1MnzmbjClTbZKG5JQ0SouL0BsMbNz0Kl7e1q3no9Xvutpqzp05hcFgwD8gkGUrVqNQWHevtL2tjV2//oRfQACNDSbtNes38tXnfyc+IZGy0lIUCgXrNmyy+lj2rOOJGP3akP6eob6tsR4vbx9Wrd3It198QlxCIpUV5UybMYv4xGRRtC3l/fLF85QUFyKTyQiPiGLx0uVW1x5NPyQ0jK7OTn758TtUKhVvvvWOTXSH2tk7736ASqWivq6Os6dPsPntbaJrp6ZlkHP/HhtffhUwWSbduHbFqvXNku6UqdO5ffM6L73yOsWFBRzcv4d/+uf/HaPRyJef/Y2P/vxXUfVXr1nP9998yabX3sDLy5uD+/YQGh5u9fFktDK/f/c2m159A4DyslLu3r7FpldfF1176rQZnDp+FACD0cijh0383/+f/2+rPHvt+pee2CTp7u7mQc59HmTfI3PKdDKmTKW7qxO1RmP+jo+vHwCtLc2DY3oCqekZeHv7jKltqc8eqZ2bc58H2ffJmDqNjMzRx8rWlmb279lFbHw8qWkZeI2hPV6sNlq1tjSzbNU6goJDOHHkIPfv3CI9cxoz58wH4Ojh/ZSVFhMVHWstSTMtzc2sWrOe4JBQjhw6wJ3bNwFQKpXs+OBjHmTf5/TJY6IMUhb1b93g7p1bbHjpFQICg+jr6xNld26k7s3rV7l35zZvvLUVDw9PDuzbbXXN4Tz9zm9SUlTI+pdfQ6VyoTA/lysXzrJ8tTgnLyPzr9friY6NIyo6lviERFE0LekOlfvmrdtwd/fg4L49omlbamcAzioVW7a9z/07t7h94yrLVol30tbS0syKNesIDg7laNZB7t65KZrWU9qjtHV7aPbrdBzct5vklDSSU9PskgZbamk0bmx//yNOnzzGkcP72bL1XfQDA3z1xSdWXeyNlQaVSsW29z7kzq2b3Lh2RZRT3ZH1+9aNq9y/e4fX3nwbTy8vsg7t596dW0ydPlMU7ZVr1hEcEsrRwwe5ax7LnHj3/Y94kHOf06eO88prb1pf+w8wnqxYberbjmUd5N5g3+bkrOLt7e+Lqj0y77dvXqe4sID3PvozgiCIbmJlaSxzVat58613UKlUNtO1RX86mvajRw+pq6tBp9Ph6OhIft4DEkRY3I/Ura+vo7GxAYDq6iq8fXypr6vFYDAQGBgsuv69e3dYumIVRw4dYMq0GWi1vaJtHD5V5g+baG5+RE93NyoXF3Ky75Galm4T7bq6Wra//xEAZ0+fIDIqymrPvnP7JtNnzKK8rJT79+7Q/OghiUkpvPrGW2gGF3hTp8/kl5++JygohIjISFLSMnBycsLPP4Dt739EQV6u2bQzNT2D+IQkHB0dn9a20GdPG9TOvneX5kcPSUhK5tU3tjyxuLx14zp5D3IAUDo58eZb7+DnH8C29z6kID+XY0cOAQKpaRnEJSRa1B4PVrOJUKs1BAWHABCflEJdbRXVVRX88v1XfP/Vp1RXVtAs0p0etUZjPsVKSkmltroKgMSkFAASkpKpra0RRduSfnlZKa6uagICgwDTolMM85ORug31dbi5u+Pp6YUgCCQli2vKOPKdV5SX0fyoib07f+LHbz7n+pWLdHZ2iqc/ynsXG0vl7u7hgbu7B2Cqb6JpW2hnADGxJlMEX/8A2tvbRdOHwfwHm/KfmJRCbXW1qHpPadv4nY+muWfXr6SkpYu+0BsrDbbUio6JA8DHx4+AwCCUSiUqFxfkcrnVJ8OjpSE2LgEA/4AA2tvbrKr5hPaw+l1ZUW7qV728AEhOSaVGpPIfnu/E5BRqakxta2jSm5CYTJ1IY5ndxxPN474tMSmF2hpTGYu5cTdce3jea6qrkCsUHD18kMKCfNFNKf8oY5mtdEfTjoyMpqS4CIPBQGlJMdGxcTbRdffw5NGjh9TX1TJtxkyqqyqpqa4SxTrKkn5EZBQ+Pr6cPH6ElWvEu5bwlHZNNUnJqeQ+yEar1VJXU0NkdIxttAfrWn7eAxobGliwaKlVn713168czTpIWnomOz74E7Pnzjcv9ABS0zJ478M/E5+QSFVlJT98+yUDAwOAac6elpHJW++8y8o167h/9w5/+7f/3+9qD/XZe3fv5NiRQ6SmZ/DuBx8ze+78JxZ6YDLj3Pbeh2x778MnTu6VSiVp6Zls2fouK1ev5f69O/z93//nhMtmCOvZoTxlnilw5sRRNr+zA7XGjauXzqMfLEhrI/C09vD/jfhRdH1HR6W50ojJSN2+vj7RNZ9MwMh8O+Ll7cMbb79rG/nR3ruNdW1a7hbaGYBcrhj8tYDRYBA3CSPL2TbFblnbBuKjaQYHh1BeWkpiUorVzdOfNQ221JIrHtcxhfzx0CEIAgYr17nfT4PM6pqjaSudnND29oqi9XvawlM/iPfm7T6ePJV3078VDpPb0X425Se1ZTI5W7e/R2VFOYX5edy5dUMUU8rR9O01loGATCbDaDQCoNeLN4+xpJ2QlMydWzdwdnYmICDQqldfxtINCQmlrLQEuVxOeHgkWYcOYDQaWLRkmU30jUYjzc2PcFA40KfthRELAzG1U9Iy2PPbLygUCuISEkW7F2tJ+2FTE5cunGfL29smpWvp2fMXLeb+3TucOn6U8IhIUtLSzQcwQ6jValLTM0hNz+Crz//Oo4dN5qso7W1t5GTfIz/vAb6+/sydv/CZtAVg/sLF3L93l1Mnjpm0U9Oe0h6L9rY2HuTcJz/3AT5+fsyZt+CZ/3Y0rPZWOzvazTuOhXkPzDt0zs4qdDodxYX51pJ6io6OdmoHd0DzHuQQHGLSLsjLNf8/MCjEZvqBQUF0dXVSX1cLmAZNMSYmI3XDwiNob2+jtbUFgPzcB1bXHM7Idx4QGERPT4/5M71ez6OHTaLpj/bexcZSube1ttLe1gY8rndiMFo7syXD85+f98B8CmJrbVu989E05y5YhJOTEyePHbFbGv6za/0R0zCyfvv7B5r61RZTv5r7IIeQ0DDRtfNyHxA0uGNsHsvycwkMsr5p2VPadh5P8m3ct43Mu6+fH319fURFx7Bk2QqaBp032Eo/OCQER0clOp24C25Lum5u7jTUm7z8Fhbk2VQ7JDSMhoZ67t+9I8r9zLF0b924RmBQMCoXF3p7e2hpbsbbx9cm+jevX8XLy5t1GzeZzXjFwJK2Wq3G1VXNlUsXSRHJhHM07YP797B2/UZULi5Wf7aPjy9Ll69kx4d/IiQsjAvnzvD1F59QXlYKQFlpibmcu7q66O3pxVWtob2tjV9/+p69u37FycmJt7a+y4ZNrxARadnM1FKf7e3jy5JlK9jxwceEhIZx8fxZvvniU7P2aLS3tbHz5x/Yt3snSqUTW7ZuZ8NLo2uPB6ud7Hl4enH/zk1OHjmIp7cPqRlT0Wq1fP/1Z6hcXPDzF89xg6eXF3du3+TI4YN4e/uQMWUat2/dQKvt5esvPkEuV7B+48s2058ybQZh4RGcPH6UgYEBFAoFb2zZOmmb29/TXTpjFv4Bgeze+bPpQn1oqKiDxch3nj5lOmERUZw7dZy+Pi0Gg4HMqTNE6TDB8ns/cSxLFK2xdJfOmIWvrx+//fojDg6OBIjopMRSO7tnw3sWAJ6eXty9c4tjRw7h5e1NeuZUSkuKbKNt4Z2XFBc9feApsubtWzcAWLJ8JUcOH+Ts6ZOi7AKPlYaSYnHKfKz82gp7pmFk/V6ybCWBQUEc3L/b7KAlTaQ7NZ6eXty9fYujWYfw9vYmI3Mqd27dQKvV8s0XnyIfdNAiivYfYDy5d+cmx48cxMvLh7TMqeY7i2IzMu9z5y9kz86fGdAPYDQimnOW0fQzpkxDLpez65efzHf3bKUbEBjE0ayDXDp/lpCwcFF0R9OWyWRER8eSk32PNes32kwXoKe727yJ4+vrR3d3lygWGyP1wyMiObBvD1u3v4dSqSQ4NIwrly4wb8Ei0bWH8p6YnEJPT/eYzkisre2sUtHR3vZEyIOhO3yTffZQvgDkcjkJickkJCbT3t5mDlNVXlbK6RPHzNYii5Ysw9XVlY6OduYvXEJg0LOdxFnqs5/UTiIhMekJbXjyzh7AS6+8jiATmL9w8bhOAZ8Vqy32ZDIZq9Y96SFuzvxFzJlv/QprSXvkAPjxP/wTAAsXizcBG0s/IDCIrdvfs7luZFQ0kVH/IKrucP2R79zXz5/Xtohn7jJSf2T+16zbaBfd0PAI3v/oHzAajZw8dgT/gADRtEeW+Y6P/2L+2T8gkFc3bxVFe3gaRnqDfMOO77x3mDtjW2kO9S8Aa9ZtEE17rDRY20PeWFrD85uSlv7EDvDw39kqDQGBgaLmf2T9DguP5J13PxBF7yntDU97Wp0+czYLF0/8Tsuzao81ngx5RxRTf/WIvu39P/1llG9bX3tk3re+K65TmN/TnzJtBlOmzbC5bkhoGB98/I+i6o6mDbBs5WqWrVxtc91//t//X+afV64Rz8GZJf33P/qz+ecly1bYVBugtrqKtIxM0XRH005JTRft2ZZwc3PHzc0dMJWzpbLWaNzQaNzGpW2pzx5Le+78haOahY5HezxIcfYkJKzA/bu3eZB9H71Bj59fAOljuNeVsB6//vwDPr6+Zuc4EhISEhISEs/Gt199joODA4tEPr2WsC/C0EVcS3RoDaP+srOjnWNZB+jp7gYgJT2TzKkz0Pb2knVgDx0d7Wg0bqzZ+DJOTs5otb2cOHKI9rZW5AoFy1etG9O8z1Ex/uuEHR3tZB3cT3d3FyCQnpEpinvs0Thy6AClJUWoXFzY8cGfbKYLJvvj0yeOYTAaSEvPZObsueN+hm5g7HuFJ44cpKy0GJXKha07TMftD5saOH38CDqdDo2bO6vWvYRSqSQ/N4fbN66a//ZhUyNbtr2Pr5+/xWdP5H2DfcscJl/uY5X5eNvYEA31dfz6w9esXr+J2PixPdkpZM9upqLVajl+5JDpHqYgsHL1Oro6O7l86TzNjx7y9rb3xhVnTzYO7SHs+b5trT2a3u2b17lz+yaCICMqOkZU89Gx0mELrKVtGH0oe4rP/v5vODoqEQSTs4qt29+nqbGBE8ey6O/vx83NjTXrNz2z84hntQQbGBjg5x++Ra/XYzAYiItPYO78hVRWlHP29EkMej1+AQGsWrP+mZ0ZTNQMzRrjyYB+9L7tWNbjsWTbe6ax5MrF8+Tcv4vzYKiBuQsWERkVQ29vD4f27aahvo6klDSWLF/1u9oK+bOPJ/ZsZ5a0D+zdTUvzIwC0fVqclE4TNm8bDwaDge++/gK1Wi1amKrRsEZ9k7THxlJdO3v6BCXFRcjlctzdPVm9bgNOTk6ipWGI/+xlPta6aTij9elHsw7SUF8PRiMenl6sXrfhma95OTtMrFOf8MmeIJMxf9Ey/PwD0PX18dN3XxIWHkluzn1CwiOYPnMON65d5ua1y8xbuJQbVy/j4+vH+k2v0dL8iDMnj/LKG29PVN4iMkHGoiXL8Q8IoK+vj+++/pzwiCi8fcSzQx5OSlo6mVOnk3Von030hjAYDJw8doTXN7+NWqPhu6+/IDomzur5TkxJIy1zGsezDpg/O3n0MPMXLSM4NIwH2fe4ff0Ks+cvIiEphYTB0BePHjZycO+uURd6k8FeZQ7il/t429hQmi6dO01YxOQv9I7kzMljRERGsWHTq+j1evr7+3FycmLjpldtclcS7Pu+ba1tSa+yopziokK2v/cRCoWC7sGNAFunw1bYS/v1zVufiG92/MhhFi5ZSkhoODn373Lz2hXmWvlOjVwuN9/t1uv1/PzDN0RERnHk0AFe32yK8Xfx/FkeZN8nNT3DqtrDscV4kpySRsaUaRw9fOCJzzOnzWDajFlPfKaQK5g9byHNjx6K4vDLnu3MkvaGTa+Yfz5z6jhKpfiTb4BbN6/j5e2NzsZeWG01f3nRtS3VtfCIKBYsWopMJuPcmZNcu3JR9KtPL1KZW+rTI6OiWbx0hXmz8Myp49y5dUP0Be+EvXG6uqrx8zfdS3JUKvH08qars5OykkISB+PxJCanUlpcCEDLo4eEhEUA4OnlTUd7++AJnPVwVavNd6WUSiVeXj50dnZYVWMsQkLDcBbx7tBo1NfV4u7pibuHh/lCaHFRgdV1gkPCnrob1drSYvYYFxYeYVG3IC+XOJFiJdmrzEH8ch9vGwO4d/sm0XHxVg/E26fVUlNdRUqaaZIpl8txcnLCy9sHTy9vq2qNhT3ft621LendvXOLmbPnohi8VO4ySS9mE02HrbCn9nBaWpsJDjE5bwiLiKRIBO/SgiCYd3cNBgN6vQFBEJDL5eYYf+EiaQ/HFuNJcGjYE9YIY+Hg6EhwSKg5vIy1sWc7G6t+G41GCvLyRI3bOkRHRwdlJcWkpYt7b8sStpq/vOjalupaRGSU2UogMDCYzg7x4iIP8SKVuaU+HTAv9IxGIwP9A6KHbgIr3dlrb2/jYWMD/oFB9HR34+qqBsDFxdVsgubt60dJUQHBIaE01NXS0d5GV2cnLi6u1kjC02lqa6OxsV40N9V/JDo7O9GoH8dlUWs01NfW2kTby9uH0uJComPjKSrIt7i4LirIY/2m12ySHltiy3J/ljbW1dlBSXEBr765lRP1B62q39behrNKxdGsgzxsasTPP4DFS1dY3cOsxNi0NjdTXVXJhXNnUCgULFqyTBTPXS8yAgK7fv0RQRBIS88kLWMK3t4+lBQXEhMbT2FBHh0ibSIaDAa+//oLWltbzJ4RDQYD9fV1BAQEUlSQT0eHuBuY9hxP7t2+Sd6DbPz8A1i4ZNkzLwitzR+hndVUV+Hi4oKnp5foWqdPHmPh4qXodDrRtUZiz/r2ompbIvv+PRISk0TXedHKfGSfPrQmOXL4AGUlJXh5e9vkvuSkF3s6nY7D+3axYMnyp+4wmFarphXrtJlzOHfqOD9+8zlePr74+vmLtprV6XTs2/MbS5atFCUop8Rjlq9ex9lTx7l+5SKR0bHIZfInfl9fV4tCoRAt/MKLwLO2sXOnTzBvwRJR2pXRYKCxoX7QDX0wp08e48bVy1Y3ZZMYG4PRgFbby9vbdlBfV8eBvbv58M9/scnO4IvCm29vQ63W0N3dza5ff8TTy5uVa9Zz+sQxrl6+SJSFfs5ayGQytr33IVqtln27d/Lo4UPWbdzE2ZPHGdDriYiIRPacvuu0zCnMnDMPQRC4fOEs506fZOWa9XZJyx+hneXl5tjkVK+kuAgXlQv+AYFUVVaIrifxx+PKpQvIZDISk1PsnZTnjpF9+sOmJnx8fVm9dgMGg4FTJ45RkJcraoxDmORiT6/Xc3jfLuITU4iJSwBA5eJCV1cnrq5quro6UbmYzMmUSiUrBjtuo9HI15/+B24ieNDT6/Xs2/MbickpxMUnWP35f0TUavUTO82dHR24qtU20fb08ubl17cA0NrSTHlZyRO/L8zPFS04qr2xRbmPp401NtRz5OBeAHp7eygvKzHFLYqNn3Q6XNUa1BqNeVcqLj6B61cvT/q5EuNDrdYQG5eAIAgEBgUhCAK9PT2TDkor8Rj14M6vi4sLMbFx1NfXMn3GbF578y0AWpqbKSstFjUNTk5OhIaFU15WwvSZs9m8dTtgig3VMhjcXSzsNZ4Mt/JJSctk3+5fRdccDXu3M4PBQFFhgU3CfdTWVFFcXEhpaTH6gQH6+vo4dGCvaPEcR2LP+cuLqj2cnPv3KC0p5o0tW22ymfGilvnwPt3H13T4IZPJSEhM4vq1K6Iv9iZ8Z89oNHLy6CE8vbyZMszjZWR0HHkPsgHIe5BNZHQcYPLkNxSt/sH9uwSFhFr91M1oNHI06yBeXt5MH3HJ+3kmIDCI1pZm2tpa0ev15OflEh0bZxPtIRNCo9HI9SsXSU1/HGzYaDRSVJBHbIL4pgH2QOxyH28b2/HRP7Lj47+w4+O/EBOXwOJlq6yy0ANwdXVFrdaYvcRVVpTjJWIAVgnLxMTGm3ffW5qb0ev1Zu+FEpNHp9OZHVTodDoqysvw8fY1O+gwGo1cvXKRdBGCqvd0d6PVagHo7++nsrwMTy9vs/bAwADXr14mPVOcgO5D2Gs86ep6fF+opKjAZo7VLGHvdlZRXoaXlzcajeb3vzxJFixayp//8l/5+B/+ifUvvUJYeITNFnpg3/nLi6o9RFlpCdevXeblV9/AwcHBJpovUplb7tO9aB3csDMajZQUF+LlJb6p9oRDL9TWVPHbT9/h7eNr3g2YM38R/oFBZB3YQ2dHB2qNG2s3vIyTszN1tTUczzqAIJjueS1btW5Me/yJuOKvqa7ip++/wcfXF2HQtG3+oiVERceM+1kT4eC+PVRVVtDba9oBnDt/oc0uPJeWFHP65DGMBiMpaenMnjt/3M/4vdALRw7upbqqEm1vDyqVC7PmLkDXr+P+nVsARMfGM3fBYnN9qK6q4NK5M7y59d3f1Z5o6AV7ljlMvtzHKvPxtrHhHM86QERUjFVDLzQ2NnD8yCH0ej3u7h6sWrOeqqpKTp88Sm9PD0qlE75+frz6xlvP9LyJhF6w5/u2tbYlveSUNI4cPkBTYwNymZxFS5cTFh4hWhpGS8d/tjJ/1tALba2t7N/72+DfGEhITGbWnHncvnmdu7dvAhATF8/8hc9uLv2sm+VNTY0cOXQAo8GA0WgkLiGROfMWcPb0SUpLijEajWRkThlXOKGJ7tRbYzwZK/TC4QN7qamqNL/X2XMXUF1VycOmBkBA4+bGspVrzHeTv/j7v6PT9aHX61E6OfHK61vG3GwaT+gFe7az0ep31qH9BAYGkzHFtvFaqyoruHHtis1DL1ijvknaY2Oprl27cgn9gN7suCUwKJgVq9eKloYh/rOX+bOGXrDUp8+eO5+ff/iGvj4dGI34+PmxfOWaZz78mmjohQkv9sRmopN/iYnze4s9MXlR37c9yxzGt9izNhNZ7ElITITxxNmzNva8YmfPu5xjLfbEZjyLPQkJCYnx8KyLPTGY6GJP6hElJCQkJCQkJCQkJCSeQ6TFnoSEhISEhISEhISExHOItNiTkJCQkJCQkJCQkJB4DpEWexISEhISEhISEhISEs8h0mJPQkJCQkJCQkJCQkLiOURa7ElISEhISEhISEhISDyHjBl6QTuA/fyLSkhISEhISEhIPBNS+CQJCfGRQi9ISEhISEhISEhISEhI/CGQFnsSEhISEhISEhISEhLPIdJiT0JCQkJCQkJCQkJC4jlEWuxJSEhISEhISEhISEg8h0iLPQkJCQkJCQkJCQkJiecQhb0TIBY///Ati5YsJyAw0N5JsRvtbW3s/u1ndnzwJ3snxWY0P3rEwX27QRDY+PKreHh42jtJovJHqOcXz58lJDSM8IjIF0bb3uWedWg/UdGxxCck2kVfQkJCPHLu3yM8Mgq1Wm3vpEhIPHd0d3VxNOsgHR0dGAx63NzcefWNLTbRzsm+x7nTp55o22s3bMLbx0dU3ed2sSfxYlJcVEBcQiKz5863d1JeGOYtWPRCaktISNgOg8GATPZiGCPlZN/D28dXWuxJSFiR3t5enJ2duXjhLOERkUydPhOApsZGAPR6PXq9HkdHR9G0AeITk1i2YtUTvxdTG6y42Lty6QIPcu6jUrmg0bjhFxCAUqnk/t076PV6PDw8WbvhJRwcHKwlOaY2QO6D+xzLOojBaGDVmg0EBgXZRDs2Np7jRw/T09ODTBDYINIJkyXtsLBwjhw+CEBEpHinHRbzHRfPyWNH6OnpwcHBgZWr1+Hl7W2zNHh5e3P39k0EQUZlRTlvvvWO6Jp+AQGEhoZxNOsgAgLhkZGUlZaIcpo6Wj0vLMjl5LEstH1aVq1ZT0homNW1x0rDo4dNNjllspf2aOUOpng7Rw4fQK3WMH/hYpvr20qvtLgIP39/aqqq0PX3s3b9Rq5ducTDpibiE5OsnvfR0hAYFERVRYWodd2Sdnh4JMePHmZgoB93d09Wr12P0+DALbZ2aXERvn5+VFdW2nwsGzmeiNW3jaZvyrs/tdVVJCQlM33mbJtpy+Vy7t25hUwmw8vbhw0vvWIz7Yb6Og4f2ItCoeCtbTtEmTddv3KRvAfZqFQuqDUafP0CKC8tZt6ipfgHBNLb08PP333Jjo//YnXtkXn29vGhpKiQre++DwxZJf3Cjg8+FlXXz9+f/Lxctu34gKbGBr758jM+/od/QuPmxmd/+3fe/eBjq5e9pfddkJfLoiXLCA0L5/zZUwgIzF+0xKq6lrR9/fwoyMtl23sfAtDS0szBvbvN/xZT28vbm6KCfPPvHz5s4sM//wU3N/dJPdcvIIAZw/qJvr4+8nNzyL53l7CISBYsWkJ3VxcRkVHm7/j6+QGg7e3l+2+/JCIyirT0TAICx+5nr16+aNZWazT4+wc80UeZtB+Qff8u4RERzF84+jvV9vbyw3dfERERRWp6xu9qjxerLPYa6uvIz8tl+3sfYTAY+Parz/ALCCAuLoH0jCkAXDh3hux7d5gybYY1JH9XG6C/v5/t739EdVUlR7MOWH2QGk370IG9zJw1h9j4BAYGBkQJwDia9pHDB1i2YjUhoWGcPX3C6rpjaR8/cpjlq9bg6elFXW0NJ45libLgGisN6ZlTcXB0fKKxi6155PABVq5eR1BwCOfOnLK67ljaYNrx3vru+5SWFHP54nne2LLV5mkQG3tpj1nuRgOH9u/F28dHtJNkW+d7LD25XM47Oz7g1o1r7N31K++8+wFOzs589vd/Z9r0mTirVKKnQey6Ppp21qF9LF2+itCwcC6eP8uli+dZunylTbTBfmOZLcaTsfQBDHo97+z4wOba169c4sM//xWFQoFWq7Wptn9AoKhm4o0N9RTm5/LW9g8wGAz89O0X+PrZty/X6/W0tbXi7u5Bft4DEhKTbKI7MDBAX18f1VVV+AcEUl1dSTChqFxcrL7QGy0Nq9dtYP+eXSxbsYqy0lK2bn/PqrpjaSudnGhsaMDP35+c+/dISUu3mfb29z8C4M6tG1RVVY57oTdWv1FTXcX9e3eora4mNj6BtRs24enlBUDGlGkc3LebO7duEhYeQUpaBmq1GhdXV97/6B8oKizgwrkz9PT0kJKWTlJyqvlUbqT2th0fYjAY+O7rz/H3f6ydfe8uNTXVxMXHs3b9S2ZtgIK8XGqrq8z/3vLOu7i4uvLeh3+muLCAi+fPmrRT00i0oD0RrLLYq66uIjYu3twwomPiANNK/eL5s2i1Wvp1uidW0tZiNG2AxKQUAEJCw+jr60Or1eLk5CSq9kB/P52dHcTGJwCgUIhjKTtavrVarXm3Oyk5jbLSEpto6wcGqK2p5sCeXebvDej1VtceKw1iM5qmrk9HUHAIYKpzpSVFNtMGiI0z1TV//wDa29usrv0saRAbe2mPpXv8yGHiRTYZtnW+x9Ib+tnH1w8vH19cB03M3N096OjosNpiz5513ZJ2v06HVqslNCwcgOTUtCf6OTG1h7DHWAa2GU/G0geTyZOYjKbt4+vHoQN7iY2NJyYu3qbaYlNbXUV07GPdqOhYm+jC6HmOT0yiIC+XmbPnUpCXy/pN1j1JHU03KDiYmuoqqqsrmTV7LmVlJWCE4JBQq+qPlQYfH1+SU1LZvfNn3t62A7lcbjPt1PQMcrLv4uO7goK8XFEWmmPV85rqKu7fvcOWd9612nNPHT/KgwfZLF+xmlVr1j9l/h0ZFc2Hf/4r5aUllJWW8O1Xn7Hj/Y9RubigUChITEomMSmZjvZ2Th4/wrnTJ/nwz399wqy6prqKmNi4YdqmNnTqxDFyH2SzbMUqVq5ZZ9H03JIZJ5jWCwlJySQMaZ84yrkzp/jgT3+ZtEm3qHf2jhw6wKZXX8fXz7RjUFVVIabc7yLYVf35xmg0olQ6mXdqJGyHXG5qxoJMhsFgsHNqXhyCgoOpqqxg+szZom3q/JEw1zNBQDFsMiIIgs3qnVTXTbxIY5mDgzh3WH6PV17fTHVVJSXFRVy5fJEdH3z83N8ZFGQyGLREGhgYsKl2fGISB/bsMm3oCAKenl6//0dWICQ0jJrqKjra24mJi+fa1csICERFx9hEf4iHTU04OTnR3d1tU924+EQuXzxPWFgEfgEBVtu0exa6Ojs5mnWQl19906p31abNmIWjUsnli+cpKyshJTWd0LBwBOFxz+ns7ExicgqJySns3vkz1dWVxMWbroN0d3eTm3OfBznZqNUa1m18GRcXl2fTnj4TpaMjly9eoLy0lOTUtKe0x6K7u5vcB9nk5mSjVqtZt2HTM2uPhVV6rpCQMIqLCujv76evr4/SYtPJhk7Xh4urGr1eT25ujjWknlkbTEelYFqBK5VOKK24EzqatsLBAbVaQ1FhAWDqMPv7+62qO5o2gJOTEzWDx8N5udlW1x1NW+HggJu7OwX5pjI3Go00NTaIoj9aGsRmNE1HpSN1tTUA5Oc9sKm2LbFnGuylPZZualomkdExHNi7S7SFh63zLdWzp7UdHB1xcnKmuqoSgNycbELCrH9X8I82loFtxpOx9G2BRW2jkc6ODsLCI1i4eCm6Pi06nc422oCjoxKdrs/qekMEhYRSWlzIQH8/ur4+ykqLAXBzc6OxsR6A4sL8sR4xYUbLs4eHJ4JMxpVLF6xuwjmWbkhIGLkPsvHw9EQQBJydnSktLRblZG+0NBQW5NOr7WXz29s5deKoKGbDo2krFAoiIqM4cSyLlNR0q+uOqm00sn/vLhYuWvqEieOknwu4ubszf+Fidnz4JxISk7lz6wZffvo3ch+Y+rDKinLzvLyvr4/W1lY0Gjf6tFr27vqVn3/4hoGBAV59fTOvvrGZuPiEpzZ6gkPDKCkqNGuXFBebtectXMyODz4mPjGJO7du8uVnfyf3wdhroD6tlr27d/LLD9+iHxjgldff5JXXNxNrQXsiWGU72j8ggPiEJL758lNUKhf8B+3M5y1YxA/ffIlKpSIgKEiUznI0bQC5QsE3X36GwaBn1ZoNNtNeu+Eljh85zKULZ5HJ5Gzc9CruHh420V69dgNHDh9EAMJFMJsdS3vdxk2cOJrFlUsXMRj0JCQm4+vnb9M0iMlomqvWrOfYkUMICISEhaFUWnciNpa2LbFnGsbSfsYNM6vrAkyfMYs+rZbDB/axbuOmZ969s5a+tZHqmWXtNes2DnPQ4sHqtbYbT8B+Y5ktxpOx9G3BaNqHDuylr68PMDJl2gyrms3+nnZKWhonjmaJ5qDFzz+A2PhEfvjmc1QqF/wG7xpNmT6LrAN7yLl3h4gocU62xnrX8YlJnDt9ko/+/Feb6bq5u4PRtHAAk/lmZ2eHKA6YRkvD+bOneGPLVjQaNzKnTuf0iWOsWb/RJtoAicmpFBUWiHLVajTt/v5+GurruHThHJcunAPglTe2jMtc8ff6DZlMRlR0DFHRMXR3d9Pa0gyY7tudPH4EmUyG0WgkbdAhSp9Wy5RpM57pJM7fP4D4xCS+/eozVCoXAgKeTRuevrO3bOVqvL19mDJ1+rhOAceDMJbzEO0AE/IscunCOdGcZEjakvYfJQ1DmhmZU80mCNeuXKKrq5Oly5+2xxZD+0Ur85Ha1ZWVTJsxk7DwCJvq2qvcba1v7/zaOw1/BO3S4iKbx3S0lG9bxm39I5T7f0Zt3cDELAyuXjqPg4MjU2fMmtDfAzgqJnb6YK/ylvq2x9rXr12hT6sVzaP0WNr/GZ47EkvrpksXzuHo6Ciax+AhnB0mthJ8/i+aSEiITGlJEdcuX8JgNKDRuLNmnfV33iWe5tzpk4SFR4hibiMhISEhIfG8s3fXTtraWnhjizie0yX+GFjtZO/IoQOUlhShcnEx7/xdOHeGkuJCBARULi6sXrdR9CChZaUlnD5xDIPRQFp6JjNnzxVVT9K2/O5tyYta7rbU/qO0b3hxynw49mxjHR3tZB3cT3d3FyCQnpFpDkZrC+xV5i9qvu2tba+6/jy877FO9jo72jmWdYCeQQcgKemZZE6dgba3l6wDe+joaEejcWPNxpdxcjKZMFZXVXD+9An0ej3OKhWvbR59QTDRkz2wX30bGBjg5++/YUCvx2AwEBefwLwFi2yiDS9uG//Prv2s4dSG+pSe7m4QBNLSM5k6fQaNjQ2cOJqFfmAAQSZj+crVzxxXz+4neylp6WROnU7WoX3mz2bMmmM+Fr518zpXLp5nxeq11pJ8CoPBwMljR3h989uoNRq++/oLomPi8PbxEU3zRdcGy+/eVryo5W5r7T9C+4YXq8yHY882JhNkLFqyHP+AAPr6+vju688Jj4h67sv8Rc33izqePO/vW5DJmL9oGX7+Aej6+vjpuy8JC48kN+c+IeERTJ85hxvXLnPz2mXmLVyKVqvlzImjvPTaZjQaN/Mi0drYs77J5XLeeOsdHB0d0ev1/PT9N0RGxxAUFCy69ovaxl8kbZlMxqKly/H3N/Up33/zBeERkZw/c4o58+YTGRVDaUkx586cEi0mtTkt1npQSGjYU4H/lEql+ed+nU50f9H1dbW4e3ri7uGBXC4nITGJ4qICcUVfcG2w/O5txYta7rbW/iO0b3ixynw49mxjrmo1/oOBapVKJV5ePnR2dthE255l/qLm+0UdT5739+3qqjY7YnFUKvH08qars5OykkISk1MBk6OO0uJCAArzHhAdG49G4waAygru3y1hz/omCIL5vr3BYMCg19ssrMmL2sZfJG1XV7U50LqpT/Gmq8vUp/T16Qb/34erq/gWUaLf2btw9jQPcrJROil5U2Sb4M7OTjRqjfnfao2G+tpaUTVfdG1786KW+x/lnduyfYNU5vamva2NxsZ6Am2w8w1/nDJ/kfL9Rylze/K8v+/29jYeNjbgHxhET3e3ebLp4uJqPsFrbWnGYDCw6+fv0en6yJg6ncTkNKunxd71zWAw8N1Xn9Pa2kLm1GnP7TuXtO2rbepTGggIDGbJshX89utPnDt9EqPRyJZ3touuL3qE0PmLlvCnv/wXEpNSuH3rhthyEhISNkRq3y8OOp2OfXt+Y8mylU+c6j7vvKj5flF53t+3Tqfj8L5dLFiy/Kn8mVy+m862DEYDjQ31bHzlDTa9toXrVy494T7+eUEmk7H9/Y/401/+K/V1dTxsarJ3kiSeM3Q6Hfv37mLJ0hUolUru3rnN4qUr+Pgf/4nFS5dzLOuQ6GkQfbE3RFJyKkUiBekcQq1W0zHM7KKzowNXGziMeJG17c2LWu5/tHdui/YNUpnbC71ez749v5GYnEJcfILNdO1d5i9ivu1d5vbkeX/fer2ew/t2EZ+YQkycKX8qFxe6ujoB6OrqROWiAsBVrSEsIhIHR0ecVSqCgkN52NRo9TT9Ueqbk5MToWHhlJWV2ETvRW3jL5q2Xq9n/57fSExKJnawT3mQc5/YuHgA4hISqa8T/3RR1MVey7BdoOKiAjy9vMWUIyAwiNaWZtraWtHr9eTn5RIdGyeq5ouubW9e1HL/I7xzW7dvkMrcHhiNRo5mHcTLy5vpk4jDNRHsWeYvar6lev58vm+j0cjJo4fw9PJmyjAvo5HRceQ9yAYg70E2kdEm3ajoWOpqqzEYDIMBsGtF6ePtWd96urvRarUA9Pf3U1FehpcNxjF4cdv4i6RtNBo5lnUIL28fpg3rU1xd1VRXVQJQVVGOh6eXaGkYwmqhFw7u20NVZQW9vT2oXFyYO38hZSUltLQ8Qvj/s/efwW1d654n/ANAEiQCc845U6RypkTlHCxbli1bwbJsH59w7+2enn7rnbdqvk1XzXT3vbfnBB9n+zjKyjnnSEXmnHPOAQABvB9AQiRFSiSFDfjK+1flsggS+7/WXvFZ4XkkEpydXVm9dj1qZ+cXP+wlKCku4tKFsxgNRpKSU1iwKFVQPVF77LJPTplhNf3f6nu3pvavpX3Db+edD8eWbay6qpLvv/0KL29vJINHvFLTlhMRGWUVfVu9899qvm2tbau6/iqU9/NCL9RUV3Lg+2/w9PIePK4JC1PT8PUP4NSxQ3R1dqJ2dmHD5m04DjrIeXDvNjlZGUgkEhKnTWfG7LnjPv9lQi/Yqr41NjRw6sRRjEYDRqOR2LgEFi5eYhVt+O228f/o2hMNvVBdVckP//gar2FtbvHSZcjlci5dOIfBYMDOTsbK1evw9fOf0DOnGnrBYsaeiIiIiIiIiIiIbXiesSc0L2PsiYj8R2Kixp4QTNXYE1uniIiIiIiIiIiIiIjIK4ho7ImIiIiIiIiIiIiIiLyCiMaeiIiIiIiIiIiIiIjIK4ho7ImIiIiIiIiIiIiIiLyCiMaeiIiIiIiIiIiIiIjIK4ho7ImIiIiIiIiIiIiIiLyC2D3vl0cyq62VjmfYkhRgM22DDQNO6G0onl3VYTPtGWFuNtM22PCdG2zowhfgf1wrsZn2f11mnfhVY2HLdtan1dtMW25D9+hTcxhtGRo7NTbT9ndzspm2Lenq09lMW24vs5m2LbHleGLLPhVAJrVhB2NDGjr6babt4+JoM21bkl/bZTPt6SFTi2Us7uyJiIiIiIiIiIiIiIi8gojGnoiIiIiIiIiIiIiIyCuIaOyJiIiIiIiIiIiIiIi8gojGnoiIiIiIiIiIiIiIyCuIaOyJiIiIiIiIiIiIiIi8grySxt6//j//zdZJ+FVw5+Y1Hty7Y+tkWI38vBw+++Qv/PjdN7ZOilXoaG/n688/sXUyIO8KDGhtIv3LT9/T329db2T//t9t3798+9mf6evttXUyREREBCD9zk1bJ0FE5JWlpqaab7/6nK8++4TPPvkLN69ftZr24xvnuHjwC64e+46rx77jxqmfrKL73NALU8U46P5XYgNf20aj0axvC2yd99+i9pB+xuNHrF2/kcCgYKvqgq3ybdu6bkqCEWKX2sSvvtFo5PU337bqu7d1/2KLNPwa2vZvUf+33J+PToM102PrvBuNRu7dvsGc+Yusrgu/rfr2ayhrW+m/qnkf77lGoxGtRoPc0ZHTx4+y+bXX8fbxxWAw0NrSAoCmvx8HuXzKaXqe9oBOi72DHID4WYvxD40e8Tc6rQY7ewfBysNixp6ut4vqu+dwdPNC095s+n9nG0b9ACr/MDxjZlhK6hk62ts58NP3+PsHUF9fx8DAAJcunKO8rBSlUsmmLdtQKJWC6h/8+Xv8/ANoqK8jJjaewoI8JBIJYeERpKatEE67o50jB77H18+kHR0bT35uNgqFErWzM94+foJp93V38ujKUVw8fOlsa8QnOIrmmjIMBj3egRFETJsnmDYMlvuP3+EXEEBOViYAnR0dREZHk7Z8laC6v4wq77zcbBQKBWq1Mz5+fsyZu0Aw7UMHvsfPL4DCwnwUCiXnz5yktqYKlcqZzdu2Y29vL4i2mf5u026eygN62qCvA2a9BvbCx9wZXub1dXW0NDfxx3/5LygUCkE1h7fvgYEBAHp7eznyy4/MW7iYiMjoFzzl5ejsaOfEoR/x8QugsaGO7q5OQfU6Oto5PNivFA3Ws8CgYGprqvH18ychKZk7N6/R29vL2g1b8PO3bFzUsep5SGiY1ep5d2cHl04ewNPbn5amejx9/GhurEcCJM2cT2hUnCC6Y9XvWXPmUlZaikqlEnQsG60dGxdPbk4WCoUSZ2cXfPz8mDtPmH5tiM6Odk4e/glvX39KiwpwUijwCwiiqbGeDVvfRO3sIojuiHG0rhaFSmXeOU+clsKM2QKPZcP0a2uqGRgY4LuvPsXD04u1G7cKptvZ0c6RAz/g6x9AY30drS3N/NP//v8DoKggj7KSIlat2ySI9vB+NS8ni/jEaazdsBmA7Mwn1NfVsWL1WkF0h9dzP/8A1m/czIP0uzy4f4+Pfv9PtLe1cfL4Ed7Z/Z7g+kqlkh07d9HT3c0P333N2+/uRaVSWVwXoKuzgwvHD+Dl40d5cQFhUbEsWrEegMKcJ7S3tjBn8XJBtGFk3qsrKwGQy+UMDAyg1+v56A//9NLPra+r440db+Pi4mr6XUc7WRlPyM3OYvnK1URERdPT24NSpQZAKpXi6eUFQHVVJZcunCM+MYlpydNxdnlxf9Pb1cGdC0dw8/Klo6WBuSu2olCZYuH1dndSWZRDTWk+iXOW4BMUPu5zWhtqyE6/RkB4LMFRCeZnWAqLHuPU9XTgGhpHaNo2vOLnEpK6mZClW+lrqUPT2WpJqWdoa21h+sxZ7PvgdwD4+vmz74PfERQcwq2b1wXVHtJPmTGLJctWUF5Wws7d+9i970Nmz1toBe1WkqfPYs2GLRQV5PHO3g/Y8sZb1NfVCq7d29VOYHQSMTMWo+ntZs7qN5m39m06Wxtpa6wRXL+1tYXpM2fzX/+P/5Og4BA2bH5NUENviLbWFqbPmMXa9ZsoKsxnz74PeX3729TX11lBu5XkGbPYs+8jujo7SJkxiz3v/w65o5yigjzB9QHo7wLfaEhZD3LhFlLGYqjM3//w4wl1xpZgqH3v3f877O3t6enp5vCBH1iYulRwQ2+I9rZWEpNn8vaeDwWb9A5nqF/ZPVjPZs6Zx979H9Pa0kx+bjZv7txDatoK0u8Kc+TM1vW8s72N6MTpJM9ZRE93Fxu272HFpjd5eOcqvT3dgukOr99gGsve//Bj01h245pgusO1N2zaQkF+Hnvf/4g3duykrk74vnyIoXq+Y/d+ujo7SEyeyVu7PxC8zpvH0Y1bkUqk7Nr3Ebv2fUR8UoqguqP13/vwD9jb2/PO3g8ENfSGaG9rZdr0mby77yPhFwpHMdSv/u5P/4maqkrz5/l5OcTGJwimO1TPd+zcRXNTIwBVVZU4OSno6uykqqqCoGDhTggNb+MqtZpHD+5z9vQJFqUuFczQG6KzvZWYpOns2PdHGmqrMej1ABTlZREVN01QbXia94/+8E989Id/Yu/+j/Dy8WHOvPkWee77H36MSqUmPy+Hn3/8jsO//Izc0ZF39uwjIso0Vs+eM4/PPvkzh3/5mSePHpgXcCOionlnzz7kcjmHDvzIgR+/Iz8vB/3gOxqPns42QmOSSduyG0cnJbXlhdw5f5j0y8exd5CzaP2OEYZe7oMb5mOcD6+fAcAnKJxF63dg7yAn/dJx7pw/TG15obl8XhaLHuO0c1Lh5OYNQFdtKR0VBRiNBvSaPjRdbcid3S0pNwIXF1f8AwIB0xZq3GBHkZA4jaOHDgimO4TzoP6VS+dJnJZi7jSdnJysou0XEMij+/eIjI41a1tjEuqodMbV04/CRzdoqa/k7pkfAdAP6OjtasfN27Ir/qNxcXElYLDcrclQfXuQfpfIqBjs7OzAzo6IyCjBtYfqWkd7Oy6ubnj7+ALg4+tHZ0eH4PqAycBTe1pHaxS2KHPnYf2LwWDgwA//YMXqtQQFh1otDWpnF3wtvIP2PMz1rMNUz7y8fADw8PQiOCQMiUSCp5e3YHXO1vVcqXbBy9ef+zcvERYVh1QqxUmhxMc/iJbGOhRhwrT14fXbNJYlAqax7MjBnwXRHK19P/0u0TFPx5LIqBhBdYczVM87O9qtWueHxtH+/j46Otq5cuEsYRGRhIRFWFXf2ji7uODnb31dk/bTftXF1Y3ammrc3NxpbWkhIDBIMN3hbUyr1aLRaOjq7CQ+IZGqqgqqKyuJjhVm9360/spVa/ni07/hHxBIfEKSYJpDqNQuePua2pRvYDBV5SW4uHtgMBhw8/QSXH/0+H3vzi3s7eyZMWuOxZ77zZefYjAYWLdhs7l+DWfh4iXEJyZRXlpCbk42uTnZvP3uHgAUCgWz585n9tz51FRXcfrkcW7fvM57+383rrZC5Yy7t+kU3bWTP2A0GJi+aBVuXmOfrBvrGCeA3NGJiIQZRCTMoLWxlie3LlCQcY+0ze9O9nU8g0WNPamd6XG63i7aSrIIXrwZmYOc+sfXMRosY52Ox3NXpKxwJtnaK2K/Fm3ZYJkbgbD4WQRGCd9ZDcfewTZ5/7WUt0wmM/9bIpFiMAxYJxFSQa77TghblPnwdy6RSvHx9aO8tMSqxp69vYPVtEx649UziflniUSCwWCwsr516rmdjdr4c+u3wGOZrfrTEWkY9t6t2c8OaTk6OvHO3g+oKCsh88lDCvNzBTvKOJa+tRnZrzytX/oB4dvY8DzHxidQkJeDu4cnUdExgt4lG17PAwIDycp8gruHB4FBIWRlPKamppplK4Q7ITRcv6urE4lEQk9PN0ajUfA7dMP7tej4ZDIf3sHFzYOoOOvM3YbnvbyslPy8XLOhZannrlm3kSePH3Ly2BGiYmJJSk7Bc5Qh6+bmjttMd5Knz+R//ev/Q19vL06D10Gam5rIynxMUUEBQcEhJE9//jU0md1T7ZQFK6gozOLR9bP4hkQQHJmA2tVjwvnoam+hsiiH+soSPHwDCYlOnPB3n4cg3jgNOi0SO3uk9g4MaProaawSQmZcjEYjBXm5AOTmZBEo4ArRaEJDw8nOfIJOpwOgr6/PatoBQcGUFBUwoNOh1WgoLSmymranXzA1pbkM6ExeGft7u9H2v/reAgMCgygpLmRgYACtVktpsfXeuYhtkABr1m+itaWFe3du2To5IgLj4xdEeXE+BoOB/r5eGmur8RTwLvRwjEYj+cPHsiDrjGVBQSEUFeaj0+nQaDSUFBVaRffXQF9vL0ajkaiYOBYsTqOxod7qaZBKZS88OiYECqWS1pZmjEYjxUUFVtWOio6luKiQ/NxsYuMtM8GdCEFBIaTfvU1QUAg+vr5UVJRjJ5MhdxT+DrrBYOD0yeNs2roND09P0q3sPd3L15+e7i5KC3MJi4q3qnZHRzsXzp5my2tvWHyhwz8gkHUbNrPn/Q9x9/DgzMnjfPvV59TXma7ZlBQVmp2ptLa2IJVIkDs6Ul9Xx7dffc6ZU8dx9/Bkz/sfsnbDpjF3B8fDzcuPlIWrWLJpJypnd57cusCNkz/S3tLw3O+1tzRw4+SPPLl1AZWLO0s27SRl4cpxdwcniyDL83IXDxydPSi/chA7JyVO7j5CyIyLvb09dXU13Ll1A8WggxZrERYRSWNjPf/46jNkMhnhEZEsXirchdfh+Pj6ER0bzz+++hSFQomPr3UmJAAefiH0dLRx//wvgGmlI3HBKhwchXOc8WvAzz+AyKhovv78ExRKJZ7e3sjlwg8SIk+xhS8xqVTK+s2vcfTgTzg4ODB95mwbpELEGgSFR9HUUMPJA18jAWbMX4KTQth7NUPY29tTV1vDnVvXUSiUbN76ulV0ff38iI1L4KvPP0GhUOLr728V3V8D3d2dnD99wjwZXJi6zOppSEqZzndf/h1vXz+r3NsbYuGSZRw/9BNOTkq8ff3Q6awXUsfRyQkPD09aWpos7vDpeQQGB9PV2UlQcAhSqRRnZ2c8PKxzReHOrRsEBgUTGBSMl7cP3371GRGRUc/sQglJaGQsrc0NVjFuh5OV8YS+vl4O/2IKPaBSq3ljx06Lajg4OJCcMoPklBk0NzeZP8/OzuTSxXPY29kjlUrZsPk1pFIpdvZ2rNu42SLv387egZDoREKiE+lqbxnxu9wHNyjMSDf/nLrhLWQyO1IWrZrULuBkkDzPhfePj6pt5mN8S5L1GvtoDDb0rK63oXh2lZXue43BjDA3m2kbXvKda7VaHBwc0Ol0/Pjd16xeu2HChrbBxm78/8e1Eptp/9dlL3fnyWAw8P/+23/nD//0n0cc85sItmxnfVrrr9gPIbezXWhVG3rxp7FTYzNtf7ep39v+n//3/8V/+t//vxZMzdS4ef0q9g4Ok/LG2dWnEzBFz0duP7n+4FXBluOJvcy2YZtlUht2MDakoePl4sxePHGQ+JRZ+AeFTvq7Pi6/zYXtvBphvWE/j+khzlOq6La7eCMi8opw/sxJmpub0A8MkJCUbNUd1d8yX3z6V5JTpk/a0BMREREREfkto9H0c+rAt7h5ek/J0BP5j8WkjD1dXzf1j6+j15juobmExOAWnkhz/kO66ytMF/cdHPGdnoqdo5Le5jpq71/AXmGKZ6HyC8UjejoAep2GhoybaDrbkEjAJ3nxSx33NBgMfPvV56jUal7f/haPHqTz4P492tva+MM//2+CxeF6eP8emU8eATAteToz58yjsaGeC2dPodPpcHZxYf2m15DL5RbRO3/6OKUlRSgUSnbt+wiAxoZ6Lp07jV4/gEQqZfnKtfj6B/Dg3m3yc7MBBgNHNvPRH/8zjhP0EJpz9yJNNWU4ODqxYP075s8rCzKoKso0eePzDyV6+iIMej159y/T2dIIEgkxM1Nx9wlkQKfl/oWD5u9q+rrxC40lZmaqRd7Hg/S7ZDx5hNEIydNnMHuOsPGQWluaOX70kPnnjvY2Fi5eyobNr/HoQTqPH94nNyeL8IhIli5b+dJ6Z089Le8975vK+9rli5QUFyKTyXB1dWP1+k04OjrS19fLiSMHqa+rJSEpmeWrJhmjyKCH7AtgNJiCpXsEQdA0U1y9olug04DKHSLng1QG9UVQX2jaspHZQ/gcULhAVzOUPj2iQGCS6VlTpLOzg1PHj9LT0w1ISJk+g1lz5hEXn0jG40eUlZh2J1PTlgviDXWsNt7X18fJowfp6OjAxcWFjVten3C7ehEGg4FfvvsSpVrNhq1vkvn4PhmP7tPZ3sZ7v/sX8yXy0uIC0m9dB4npWOmipavwf4n7yeeG9S27B/sWgMcP03ny6AFSiYSwiChS01ZQUVbKjWuX0Ov1yGQyUtNWEBwSNmXtser5EA/u3eHalYv87k//GYVCgdFo5MrFc5SVFGNnb8+a9ZteeoElN+M+xbmZIJHg5u7JgmXrkMpkPLl3g4qSAiQSCdGJ04mbNhNNfz93rpyhq6MdmZ2M+WlrcfN4+WM/p08co6S4EIVSyb4PTGEXhnb10u/e5sqlC4LFlBxLG0x1/9HD+0gkUkHalsFg4OD3X6FUqVm/dTsXTh+jqaEOqVSKt68/S1asRSaTUVNVwZljB1EPhloJj4xh9vzFU9IcawxtajSNoVqtFmcXV9Zu3Goes9Pv3CQ78wlSqYSly9cQGj5175xdnR2cPXWM3p4eAJJSZjBj1lz6+/o4dewQnZ0dODu7sH7LNhwdndBo+jlz4ihdnR0YDAZmzZlPwrSUKWufP3Wc3l6TdmLyDKbPmsPdm9fIznxi7lcWLE4jLCKSivJSbl+7bG7ji5YuJ+gl2vhwHqTfJSvjMQBeXt6s2bCZ7u4uTh49RH9fHz5+fqzbuNXiC3njjSX5eTncvH6NluYmdu3dj58Vji0PDAzww7dfMaDXYzAYiImNY/GSNItqdLS1cPXccfPP3R3tpMxdRFN9LR3trcjs7WlurOPYT1+xecdemhpquX3lnOmPjUZS5iwiJOLlvbqP996vX71McVEBEiQolErWbdyCWq1+ab3nUVpSzKXzZzEYDSSnzGDegkUv/czHN8/TUF2K3FFB2pZdAOQ/uk1dVQkSJMidnJi+aDWOChXF2Q+oLskHwGg00NXRypodH6Ht7+XB1dPmZ/Z2dxCTMp+IBMvEKJ+UsSeRSPGKn4OjqyeGAS0V14+h8ArALSIJz9iZALSV5tBS+ASfaab4ck7uvgTMfdarUVP2XZRegfjPWo7RoMegfznPTw/v38PDwxON1nRcJyAwiIjIaH78/puXeu7zaGpqJPPJI97Z8z4ymYyDP39PeGQ0506fZOnyFQQFh5KV8Zj7d2+zyEKNOD4pmeQZszl36pj5sxtXLzFvYSphEZGUlRRx4+ol3nh7F7PmLmDWYHDvkuJCHt+/N6kJqX94HEHR08i+c978WWtDFU3Vpcxf+xZSmZ3ZCUtNicmonL9+J9r+Xh5dOcbcNTuws3dg/rq3zd+/e+ZHvIMs48q6qbGRjCeP2LV3PzKZjAM/fkdkZDRu7sKF+HD38GTPvg8B00Tlb3/+V6JiYqmsKKOoqIDd+z7Ezs6OnsHB/GVJTEpm+szZnDn5tLxDwsJYvHQZUqmU61cukn7nJqlpK7CT2bFg8VJampvMsYMmhUQKCctNhpvBADkXwNUf6vLBLwY8Q01GXGMp+EaZfvYdnAC2VkP5I4hPA4UrTFtjep62DzJOg3uA6ecpIJVISVu+Cl8/PzQaDd98+Smhg+7QZ82dJ2ig5/HaeOaThwSHhjF3/iLu3bnJvbu3WJK2wiKamY/u4+bhiXawL/PzDyI0PIqjB74b8XeBwWGERUQjkUhobmrg3Ikj7Hzvo7EeOSESkpJJmTGbs8P6lsqKckqKCnl37wfY2dmZJ6lOCie2bNuBSq2muamRQwd+4MPf//OUtceq52CaJJSXl46IsVZWWkxbWyvvffh76mpruHjuNDt375uydm93F/mZj9j01nvY2dlz/dwxyovzMBpNv9v89vtIJBL6BifJ2Y/u4ObpzdK1W+loayH9+gVWbt4xZf0hkpJTmDFrDqdOHBnxeWdnB2VlpTgLGGduLO2K8jKKCgvY+/5HFu3ThpP5+D5u7h5otaa7YdGxCaxYa/J8eeH0MfKyn5CYbJpb+AUEsX7r9pfWHGsMvXDmJKlpKwkMDiE78wkP791mQWoaLc1NFOTlsGvfR/R0d3Ho5+/Zs/9jpNKp9WUSqZTUtJX4+Pqh1Wj4/pvPCQkNJycrg6DQMObMW0j63Vvcv3uLxUtXkPHoAR6enmx5fQe9vT18/dlfiU1ImpIRJJVKWZy2Au9B7R+//YLgUJPxNn3WHGbOGRnnzMlJwcbX3jS38aO//Mj7H08t6PVwuro6efQg3Ryz9PiRg+TnZlNaUsysOfOIjU/kwtlTZGU8JmXGrJfWG854Y4mnlzdbX9/OudMnLar3PGQyGTve2Y2DgwN6vZ7vv/2K8Mgoi4YVcnHzYPOOvcBgyKCv/0pIeDQJKU/vmN+/eRl7B9PChpu7Fxu370YqldLb083xn74iKCxyyvV9iPHe+9z5C0ldaroT++D+PW7fuMbqdRteSut5GAwGLpw9zZtvv4va2ZlvvvyMyKgYc1D1qRIcGU9YXDKPb5wzfxaROJPYGaa5SWnuYwqe3CV5wQoiE2cRmWiq1/VVJZTmPMZB7oiD3JGlm02bKkaDgfMHPsMvJPKl0jWcSZWgnaMCR1fTxVWpnQMOKlcG+nuRDXPda5yA0abXaeltqcc52LRiIJHKkNlPfeerq7OTkuIipqVMN3/m4+uHi6vrlJ85EVqbm/HzD8De3nTJ0+TBLI+2thYCg0IACAkLp9CCAYADg0KeMdgkYJ4YajQalGME5SzIzSYmbnJBSt28A7B3GHkmu7ooi9CEmUhlpnWCIQcs3R2tuPkEmj+zd5DTOcr7UE9nG1pNH65ellk1G7rIbX7/wSEWfdcvoqK8DFdXN1xcXHny6CFz5y00xdsDlErLBBoPDA7B0XFkeYeGRZg7Xz//QLq6ugCwd3AgMCgYmWyKp7OHduhgcHdv0KV+RwN4DAaY9QqD1kHvusPcDWMYeOopRWb31LAz6F/6spZKrcbXz7RzI5fL8fDwoqvLOmfmx2vjxUWFJCQlAyYjqbjQMp7rurs6KS8rHhHI2cvHF2cX12f+1sHBweyme0Cne+k7cWP1LZmPHzB73gJzvVYM1mtvHz9UgyuwHp5eDAzozIFpp6Q9Rj0HuHrpPKlLl49wwlNSVEh84jQkEgn+AYFoNP10d3dNWRtMg6t+YACDwcDAgA4nhYrCnMckzV5gfsdOClPeO1pb8A0wtQcXNw+6uzrNhuDLEBQcMmZc1ksXzpG2bIWgnojG0n786AHzFiyyeJ82RHdXJxWlxcQNq+sh4ZFIJBIkEgk+vv50d71cuY7FWPW8rbWVgCBTmYaEhlFUaFp5LykqICYuATs7O1xc3XB1daO+rnbK2iqV2rwL7SCX4+7hSXdXF6XFBcQnmgJaxydOo2SYJ0ytVovRaESn1eLo6DTlibdSpcZ7tPZz2o23j69F2/hwjAYDA0PtTadDqVJRVVFGdKzJI2RC4jSKB8vAkow3lnh6elnNKcsQEokEBwfT3NlgMGDQ6wV1NlZXXYGzsyuqYYtGRqORsuJ8wqNNMQXtBsc5AP1LbsAMZ7z3PvzEm06rFdzbWl1tDa7u7ri6uSGTyYiLTzC39ZfBwzcQh1Fz5SEDGkxxp8caoGtKCwgIfzaGaVNdFQpnFxQq55dO2xBTvrOn6+1C09GCo6vJIm7Oe0BndTFSe3sC568z/11fWyPl145gJ1fglTAHudoNXW8XMrkjDU9uoOlsQe7qiXfCPKR2U3O/eunCOZYuW2FeHbQWnl5e3Lx2mb7eXuzs7SktKcLXzx9PTy+KiwqIio6lMD9X8InpkuWrOHLgB65fuYjRaGTHO3tG/F6n01FeVsKylZM81jcGPZ3ttDfWUpxxB6lMRvT0xbh4+KB286KpugzfkBg0vV10tjbS39vN8LXo+opCfIOjLBZHxtPLm+tXh7//YnOHYg3y83LMAY9bW1uorqrk5rXLyOzsWLpspVU8imVnPiEmzoIuk40GyDxrOrrpGwWOKpMBOGS8OShMu3VD1BdCbb7pe/HDvNZ1NUPJPdD0mI59TnFXbzQd7e00NNThHxBITXUVjx6kk5OVga+vP8tWrLLYUcohxmvjvT3dqFSmiZBSqaK3p9siejevXGBB6jLTwDcBSovyuXPjKn19PWzY+qZF0jCctrZWaqoquXX9CjI7O5akrcTXb+RiTVFBHj4+fmajwFIUFxagUjmbg6kP0d3VhVr9dBBUq53p7uoyl8dkUajUxKfM5vC3nyCzs8MvKBT/4DBuXDhBRVE+lWWFODopmL1oOc6u7rh5elNZWoiPfxDNDXX0dHXQ291lNgYtSVFBPmq1+pl3YA3aWlqoqqzg+tXL2NnZkbbcsn3azasXmD9OXdfr9RTkZbFo6dOj8PV1Nfz87ecoVSoWpC7H3YIeCz08vSgpKiAyOpbC/DzzmN3d3TUizyq1M90WGs87OtppaqjH1z+A3p6eUf2JafEgZcZsjh3+mU//8m/otBrWbd5mkfGzs6OdxoZ6fP0CqKuuIuPRA/JysvDx9WNx2opnFl6KC/Px9vG1SBtXq52ZNXc+n/7l37Czsyc0LBwfX3/kckezoaFydjYvYgrF8LHEVhgMBr754lPa2lqZMWu2oGkpK8ojLHpkoPiG2mqcnJQ4uz49DdVUX8uty6fp7upk8YoNL72rN5rR7/36lUtkZ2Uid5Tz1s7dFtUaTVdXF87Dxw5nZ+pqagTTy3t0i6riXOwd5CxYM9KT8sCAjsaacpLmPevtt6asgMCwWIumZUqlaBjQUfvgEl6J88y7ep5xswhfuQPngEjay027K3IXD8JXvEnokq24hsVTe/+i6QFGA5qOFlxCYwlZshWpzI7W4swpZaC4yHTPYPQExBp4eHoxZ/5CDv78PYd+/h5vH18kEimr12/iycMH/OOrz9BqtMikwjqQyHzykCXLV7H/439iybKVnD8z8ihCaXEh/gFBFpkIG40GdNp+5qzaTnTKIjJvnsFoNOIfHo+jQsW9sz9R8PA6Lp5+zwxKDRWF+IY+u4oxVTw9vZg7fyE///gdB378Dm8fHyQWMipehF6vH1z1NRlaRoOB/v4+du7ex9JlKzlx9BDP83RrCe7evoFUKiUuwYLBUCVSSF4HM7dAdwv0vWBi4xsNMzZBcArU5Dz9XO0JKeshabXpc8PLe6DUarUcOXSA5SvXIJfLmT5jFh9+/Cf2vv8RKpWKyxfPv/ghk2S8Nj4ciURiEVeT5SVFOCkUeE8ihlt4VCw73/uIdZvf4N6tay+dhtEYDAb6+/t56933SF26gpPHRtbr5qZGbly7zIrV657zlMmj0+m4d+cmCxcvsehzx0LT309VeTFb3/2Q13d/zMCAjtKCHAx6PTI7Gevf2E1UXDJ3rpwFIGHGXHRaDSd//pr8rIe4e/ogEcAToE6n487tmyxOtew9noliMJr6tHf3mPq0Y4cPWqxPKy8twkmhHLeuX790Dv+AYPwDTbttXt6+7Hr/97y5632SUmZx5vjBMb83VVat20jG44d8//VnaLUawcdsrVbLySO/sGT5qmfu85vGTVN9Ki8rwcvblw9+/8+8s/cDrlw4i0bzcp5ltVotp44eNGsnTZ/Jng9+z849+1EqVdy4cnHE37c0N3Hr2iWWrbJMG+/v66O4qID9H/+Jj/74L6bF6NJiizx7ooweS2yFVCpl7/6P+PhP/4m62lqaGqdw/WIC6PV6qsqKCY0caUCUFeU+YwB6+fqz5e332fDGLrIe3rXYbi6M/d5T05bz8Z/+hfiEJB4+SH/BE/5jETdjIau27ycwPJayvCcjftdQVYq7tz8Oo0J1GfR6GqpK8Au17B3pSS/TGA0Gah9cwjkgArVf6DO/VwdEUJN+Ds+YGSOOd6p8gmjMuo1e04+doxI7RyVObt6m3/mF0VacMaUM1FRXUVxUQGlJEfqBATQaDSePHWHDZuvEpklKnk5Ssun46I2rl1CpTTFa3njLdPa2taVF8ODmuVmZLF2+GoDo2Hgunh1p7BXk5RAbP7kjnOPhqFDhHRSBRCLBxdMXiQR0mj4cHBUjnK6knz+AwtnV/HNXWxNGoxFnd2+LpGOIoRgqANeuXBqx4i8kpSXFePv4oVSajsyq1M5Ex8QikUhMK8ESCX19vSgEWO0HyM7MoLS4iDfeetdiO6UjsHMAZx/TDp1eZ9q5k0hB2wsOYywaeIZA2f1nP1e4mHYGe9tBNfX4MXq9niOHDhCfmERMrGlwGn5cOXn6TA4e+GHKz38eY7VxhVJFd7dpN6m7u8si5VxXW01ZSREVZSUMDAyg02q4cPoYK9dtfuF3/QOD6exop6+31+xowRKo1M5ERj+t15Jh9bqrs5PjR35hzfrNuLpZ9p5se1srHR3tfPvlp4Dpns93X3/Gzl37UKnVI05LdHV1mo+bTYX66nJUahccnUzvLTgsmqb6GhQqNUHhpqsGQeFR3L5iujzv4CBnwTLTxNdoNHLku7+jGtbXWYr2tlY62tv48vNPANN1ha+/+Du79u5HNcZRfUujVjsTHRM3eFx2sOx7e81HeV+GuppqykuKqByjrt+/c4P+vl6WrnwaH9dh2IQ8JDyS65fP0dfXi5OTZeq6u4cn2940xfhqa22hbND4UKnUdHU+rWvdXZ2oXnKM0ev1nDzyC7HxSUTFmPoyhVI5sj9RmvKVm5XBrHkLkUgkuLq54+LiSltLM75T3GHV6/WcOnqQmPhEIqNNE/+hMQwgMXk6xw/9bP65q6uTk0d+YdU6y7XxivIyXFxczX1mVEwsNdVVaDT9GAwGpFIp3Z2dgjnqGGsssTWOjo4Eh4RSWlqMl7dl50gANRWleHj5jDh9YDAYqCgpZOObY++mubp7YmdvT3tLE56TWIAcjxe994TEafzy8/cWd1IzHLVaTefwsaPz5caOiRIQHsu9i0eJnf7Uv0BNWQEB4c/u3jXUlOPi4Y2jk2XnjpPaBjEajdRn3MBB5YpbxNPdBG330/hs3Q0VOKhcARjo7zWvBPa1NYHRiNRBjp2jAnsnJdrudgB6m2txUE8tztqStOV8/Md/4aPf/xMbt2wjODTMaoYeYL603tnRQVFBPnEJSebPjEYjd2/fIHn6TEHToFKpqK6qAKCqonxEp6zR9FNdVUFEpGV21LwCI2htMG1793S2YTAYsJc7oR/Qmc4lAy11lUgkUlQuTyf39RWF+Ia8vFen0Qx//4UFecQnWnCX6znk52YTl5Bo/jkqOobKinLAZOAb9HqLTURGU1ZazP17t9ny+pvY20/t6POY6PphYPBIlX4AOupNxpqzN7RUmj5vKgP3waMmw3f92mrAcbDT7O9+et9P02P6O/nUOy6j0ciZU8fx8PBkztynTgSG3+cpLMjD08vygySM3cYjoqLJyTItUOVkZRAZ9fJ1e/7iNPZ8+Cd27f8DqzdsJSA49LmGXntbq7l/bWqoQ68fsPgx1sioGKoqywHTJFg/WK/7+/s5cvBHFi9ZRsBLeAAdDy9vHz7+039m/8d/Yv/Hf0KtduadPftRqlREREaTm52J0WiktqYaudxxykc4ARRqZ5obahnQ6UxjXE0FLm4eBIVF0VBjqvcNtVU4u5j6Va2mH73etFNdnJeJj18QDg6W3x3w8vbhj//yX/jdH/6Z3/3hn1E7O7Nn34dWMfQAoqJjR/Rper3eYgsJ8xensfuDP/Lu+79n1fotBASZ6npu1hMqy0tZuW7ziEWs3p5uc11vqKvFaDSOecdzqvQOG7Pv3b7BtBTTmB0eGU1BXg4DAwN0tLfR1tb6UqeIjEYjF86cwN3Dk5nDPEeHR8aQm2063ZSbnUn44HitdnahqqIMgJ6eblpbW3BxndpcyWg0cvHsSdw9PJkx+6l2z7B7e8WFBXgMHo/V9Pdz/OBPLFyy7KW8/I7G2dmZutoadIPtraK8DA9PL4JCQinMzwUgJzuTiCjLnQIaYryxxBb09vTQ32+KkWe6alMq2L3B0qJcwqJGGli1VeW4uHmgHHYvrKuzHYPBNHZ3d3bQ0dY64o7fVBnvvbe2Pg02XlSYj7vA9yb9/ANoa22hvb0NvV5PXm4OkdGWr2cA3Z1t5n/XV5WgcnnabnVaDS311fiO4aywpjSfAAsf4YRJBlXva6mn6vYpHNRu5o7YI3YWnZWFaHvaAQn2ChXeSQuxd1LSVpZLR3keSKVIpTK8Euaawyv0d7TQkHETo0GPvUKNb0oqsmED5lSCqldWlJN+7w6vb3+Lh/fvce/ubXq6u1EolYRHRLF2/cYJPWcy8ZZ//MdX9PX1IZPJWLp8JSGh4Ty8f48nD027HFExsSxeunzCuy8vCvZ8+vhhqior6B9cXZ+/aAluHh5cvXgOg8GAnZ0dy1atM18Cz8nKoLy0mPWbtz33ufBsUPXMW2dpa6hGp+nHwdGJiGnz8AuNJefeRbrampBKZURPX4S7bxB93Z08unIUiUSC3ElF/LzlOCmfdiI3j33N9KWbULqMvTo41aDq33/7FX19vUilMpatWEVoWPiknzHZoOparZa//+Xf+eB3f0TuaNqC1+v1nDl1nKaGeqQyGUuXrSQk9MVuql8UBPfkscNUV1aYdlOUShYsWkL6nVsM6PVmhwp+/gGsXLMegM/++r/QajXo9Xrkjo68/uZO8+A9FiOCqve0QfFdwDgYeiEYgpJMxlvhTZMhqHSDqAWm0AtlD0zOWyQS005g2CyTJ86mMqjJNX0ukUBgIrg/O1mYaFD16qpKvv/2K7y8vZEMHm9KTVtOXk42DQ31SCTg4uLK6rUbJrxKN5mg6mO18b7eXk4cPUhnZyfOg6EXxnKuMRYTCapeU1XB4wd32bD1TTIe3efx/Tv09nTjpFASEhbBstUbeJR+m/zcLKRSKXZ29ixIXf7CSdnzgqqfOj6srg32LfGJ0zh3+jhNjQ0jQizcvX2D9Lu3cBu2sLRt+87n7vo8rwscq54P7aaCqV7v3PO+OfTCpQtnKS8twd7ejtXrNr1wAv6ioOoZ6TcpL85HIpXi7unN/LQ16AcGuHnhJD3dndjZOzB3ySrcPb1pqq/h1qXTpnrn5sn8tLXmfmAsJhpU/fiRQ1RWlJvfwaLUpeZTCwB/+/O/sfu9DwQJvTCWdmJSMqdPHqOxoR6ZVEbailUT6tOGmGhQ9ZqqCp48uMf6rdv527/+N9TOLtgPOq4YCrGQ9fgB2ZmPkEqk2NnZsWDpCvz8x7/f9Lyg6mONoVqdloxHDwCIjI5l0ZJl5jH73u0b5GRlIJVKWLJsNWERU/eQV1NdyYHvv8HTy9v8/IWpafj6B3Dq2CG6OjtRO7uwYfM2HJ2c6O7q4tzp42aDbPa8BcQlTBv3+c8bT2qqKzn4w7d4DNNesDiNwrxsmhpN/bizswvLV69DqVKTfvsG9+/dHrF4vPWNt8dt45MJqn7r+lUK8nKQSKX4+Piyat1Guru6OHnMFHrB29eXdRu3TuqO4ESCqo83lugHBrhw/gx9vb3IHR3x9vHlzbfeecHTXo7GhgZOnTiK0WjAaDQSG5cwpWPrLwqqrtNpOfj139i266MRO+Q3Lp7Cy9ef2MSn/WxJfjZZj+4ikcqQSCQkz15ASPj4C5kTDao+3nvPfPKY1tZmJBIJzs6urF67HrWzsKezSoqLuHThLEaDkaTkFBYsmnwYsNFB1R9eO01zfRXa/n7kTgpiUubTWFNGd0cbSCQolGqmzV+B0+AuemVRDo015cxaun7EcwZ0Oi4c/JwV294b4eBlOFMNqj4pY8+aTMXYsxSTnPtblMlMQi3NaGPPmkzV2LMEkzX2LKot8L2+FzHC2LMyEzX2hMCW7Wwixp5QPM/YExohThtPlBcZe0IyUWPvVWOixp4QPM/Ye5Wx5XgyGWNPCCZi7L2KvMjYE5KJGnuvGqONPWsyVWPPtq1TRERERERERERERERERBBEY09EREREREREREREROQVRDT2REREREREREREREREXkFEY09EREREREREREREROQVRDT2REREREREREREREREXkFEY09EREREREREREREROQV5LlBTKb5uFopGc8y0bh0gmjb0H2xgw1do/u6/jbd6Ept6LK5uK7bZtoA+2YH21T/t4idDeubLeu6LV2jKxxs54r/eeGNhMaW46hSPvEYaZamWzNgM+1+G4ZWcVU62Ez7txr6wNa4KuxtnYTfHLacp0+V/3gpFhEREREREREREREREXkhorEnIiIiIiIiIiIiIiLyCiIaeyIiIiIiIiIiIiIiIq8gorEnIiIiIiIiIiIiIiLyCiIaeyIiIiIiIiIiIiIiIq8gr6yx97c//xu9vb22ToZNqawo5+DPP9g6GVajqrKCz//+V7767BN0Op2tk2MVzh34HE1/n03TcOPiadpamm2ifebkcZqbmqyq+elf/93mfcvBn76lob7WpmkQERERhpwn9xn4jYxhIiLWpqWlmR/+8TVfffYJn33yF86eOmE17ZyHtzn5w9+5cPhb839aTb/gurbzjSwiYmFys7OYv2ARCUnTbJ2U3xSLV6yzmfbaDZtspi0iImI9DAYDUukruz49gtyMB0TEJGBnL7rVFxGxFP19fTg6OXHx/Flmz5lHVEwsAE2NDQBotVpkMhkymeVD9Wg1/TjITeHNohJnEDNt9ojfD+h0SGVSpFJhwgRZzNgb0Om4e/kEfT3dGI0G4lLm0dXRRl1lCXr9AB7e/sxYtFKQuD9arZZjhw/S1dWJ0WhgwaJUAO7duUVpSTH2dnZs3LINN3d3QbRPHD1IV1cXRoOB+QtTcXF15fLFc+h0OmQyGW++9S4Ocrkg2qPzLZc7cunCWezt7AkMEi6Gmk6n5ca54/T2dGEwGpk2cz5qFzce3L7MgE6H3NGJBcvWoVCqBNEfnfeExGnk5+VQVlpCaUkxG7e8ZhXdBYtScXCQc/niOeztHQgMDKK9vY3X33zb4toDOh3pV07S19OF0WgkNmUeAKW5j6mrKsVo0DMnbSNqV8vX8yF0Oi2Xzxyjt7sTg8HI9DkLyct6xJxFy/Dy8RNMF8Z+948fPiBt+Sr8/P0F0zxx9CDdXV0YBtv3EDqdjmOHDxAdE8e0lBmC6APotFpOnzhMd1cnRqOROfMXCaYFY+f5+tWLxMYnUlZSjFQqZdXaDdy4eom2tjZmz51PyoxZFtUfXc5XL18kMSmZkqJC9AYDW157Aw9PT4tpDken03Lx1FG6u7swGg3MmLMQRycFd29cxmgw4OXjx+Jlq5HZWX6tVKvVcvzIsPFkUSrXrlwkNi6e0pIS7Ozs2Lj5NcHGsheNJ0L1bUP6o8fSa1cvEhuXQHlZKXPmLSAuPtHiujqdlvMnj9DT1YXBaGDWvEU0NzZQXlKIRColKCScBUuWW1x3uP7Vs8fo6Tb166GRMfT2dHPmyI84OilYu/UtYXS1Wk4dOzRY3kbmLljMzauXeHv3+zgpFNTX1XLjykXeeHuXxbVH17U58xZQUlTElm1vAKZTSel3b1u8ro01b6irrWHr629SVJDP8aOH+Of/7f+D0Wjk87//hY9+/0+C6s+Zt4C7t27y2vYdeHh4cvzIIYJDQ0mZPtOiujBY3sdHjiMFeTlsem07ABXlpWQ+fsjGrW9YXBvGfvd5OdkAGIxGmpsa+a//x//50s9dsCh1RD/R09NDdlYG2ZlPmDFzDtNnzqKnuwu1s7P5b7y8fQBoa23hyMEDRMfGMS1lOp6eXs/VHsvuCYqINf++v6+XiqIcKopyiIifTkRc8rjP6upo5c7F4wSERREWnYSzm8ek38XzsNhoVV9dhpNCxaLVpkm2TqvBJyCE+BnzAUi/epq6ylL8QyIsJWmmrLQYlVrFGztMHYOmv5+rly8il8vZ98HvyM7M4NKFs4IMUmWlJahUarZtf6r9zVefsnHzNvz8A9BoNIKtzo2V7y8+/Rs73tmFm5s7x44cFEQXoLayDCelimUbXgdAq9Fw+eQvLF33Go5OCsqL8nhy7wYLlq0VRH+svLe0NBMRGU1sXLwgmuPpfvHp33h71x5cXd04fuSQYNoNNWU4KpQsWLUVMLWxnAc3cHB0YtnmdyjNe0JR9gNmLFolWBqqK0pRKFWs3mQaELSafvKyHgmmN5yx3v3jhw8E1Swfo31fv3oRnVbLyWOHSEicRkLS+B24RdJQVoJSpWLzth2mNGj6yXzyUDi9cfLs7OzC7n0fcuXiOc6cPMbb7+5lQD/A1599YlFjb7z+XKFQsOf9D3n04D7pd28LtqtbVV6KQqVi7RbTJEij6eeXf3zOhm1v4ermweVzJ8jJfMS0GXMsrl1WWoJKrTaPVZr+fq5duYhc7sh7+z8iOyuDSxfP8fp2yxsAthxPTPrP1rtrVy/i6OTE7vc+EEy3sqwUpVLN+q1vAtDV2UH67eu8tedDJBIJmn5hj1jVVJShUKpYuXGoT9VQnJfF2q1v4eikEEzX1K+o2fKGqS5pNP3cvHpJML3hjFXXbly7glarxcHBgbzcbEEM+7F0nzw29aVVVZV4enlTV1uDwWDA3z/QKvoqlZrTJ44xc/Zc+vv7BDH0wFTeKpWKLa8/HUfu3rpOb28PCoWS3KwMQceysfI+b4Fp4fLKpfOER0zNNhjruUajkbLSEjKePKKluYn4hCTe2PEOzoMG3qw58/jx+28JCAgiLDycpOTpODo64uPrx979H5Gfm2M+2jktZTqxcQk4ODg8oz2W3WM0GmmoLqesIIvO9haCI+JYtGYbCqXa/L2i7EdUFucB4CB3ZMn67bh5+rDytd1Ul+bz8MZ5kEBYTBKBYTEWsSEsdibCxd2ThpoKMtOv01Rfjb2DnMa6Ki4d+57zh76hsbaKznZh7vV4eflQXlbK1csXqKqsQO5o2iqNT0gCIC4hkZqaaoG0vSkvL+XalYtUV1XQ2dmBUqnCzz8AALlcLtjRk9H5bm9vx8XVFXd3DyQSCQmJwh1ndPPwoq66nEd3rtJQW0VPdyftrc1cPH6Akz9/TdbDO/R2dwmmP16ZC81Y79zVzQ1XVzfAVNeEwtnNi6baSrLvX6d5sI0B+IdEAuDq4UNvV6dg+gDuHt7UVpaRfusK9TVV5mMJ1sAWZe7p5U3FsPY9pHn00M8kJqUIbugNpaGyvIyb1y5RU12JXOB3Pl6eI6Kizb/38w/AQS5HoVAis5PRb8EJ8XjlHB0TB4Cvnx8dHe0W0xuNu6cX1RXl3L1xhbqaKro7O1C7uOI6uNIaHZdEXU2VINpeXt6Deb84Iu9DE9+4+ERqBRvLbDeemPRHjqVDeY+NSxBU18PLi+rKMu5cv0xtdSVKlRo7mR1Xz5+itChf8KOUbh5e1FaVc//2VeprqwQ5BTQWpn6llBtXL1JdJXy/Mpyx2nh4eCTFRYUYDAZKiouIjI6xiq6rmzvNzU3U1dYwe+48qiorqK6qFORk1Fj6YeEReHl5c+HcadasF+5agqlfL+PG1UvUDJZ3XEIS+TnZ9Pf3U1dbTWh4pGD64/XrebnZNNTXsyRthcWee/iXnzhz6jjJKTPY98HHLFiUajb0AKYlT+f9D39PbFw8lRUV/OPrzxkYGABMc/bk6TN4Z/d7rFm/kYzHj/jLv/+PMbXHsntuXzjKwxvnCYtJYtW2PcRNnzfC0APTMc6Vr+1i5Wu7WLJ+u/lzewcHwmKnkbbpLWYuWkVpfiYnf/hkSu9lNBbb2VO7uLNiyzvUVZWR8+AW3v7BlOQ+YfmWnShUzuQ8vI1+QG8puRG4e3iwZ9+HlBYXcePaFUJCw0y/GHZi1PKHR59q79r7AaUlJu3gkDCBlMbWHjPfVsDZ1Z31b+ympqKUJ+k38A0IwcXdk7Xb3rGKvq3ybst3rnZxI23zTuqrysh9dAtvP9NgJJWZmrFEKsFoNAiaBhc3d7a8tZeq8hIe3rmOf1CIoHrDscW7d/fw4N29H1BWUsTNa1cIHtT0DwiivLSYuIREQY6mD8fN3YO3d71PeWkxd25cJSgkVFC98fIsG6pnEon530M/GwyWq3fjlfPQsUmJRGpRvdG4unmwbedeKstKuH/7mtXr+O73no4n1h7LbNW3DekPH0uH9O3tn11RtySubh68vvM9KstKSL91jcDgULa9vYfqynJKi/LJevKQzW/sFEzfxc2dTW/uobqilEd3b+AfaJ365ubuwc49+ykrLeL2DdPcRSKVYjQaAdDrBwTTHquuxSUk8uhBOk5OTvj5+SMXwOgdSzcoKJjSkmJkMhmhoeGcOnEMo9FA2vKVVtFfsCiVlpZm7O3sTc7WhhkllsTN3YOdu9+nrLSY2zdM40jitOkcP/wzMjs7omLiBb0TO1beo2PiuHn9Gjvf3TNl7bGem5q2nIzHj7h47gyhYeEkJaeYN2CGUKvVTEuZzrSU6Xzx6V9pbmrE1890HaSjvZ2szCfk5Wbj7e3LotSlY2qPZfckzl5MWX4WT+5cxjsghNDoRNy9fCecn56uDiqKcqgqycfF3Yv4GQum9F5GYzFjr6+nGwe5IyFR8djL5ZQXZAEgd3RiQKelpryQgNBoS8mNoKurCycnJxKSpiF3dCTzielYWX5uDvMWLCI/Nwf/gCBBtLu7unB0ciIhcRqOckceP3pAT083dbU1+PkHoB08xilEIxqd70cP0unoaKetrRU3N3fzeWgh6O3pQi53IjwmAQe5nILsJ2j6emmqr8HLNwCDXk9nRxuu7sLcqxmrzK2x0zNa9+GDdNrb2ugYXAXPz80RTLuvtxsHB0eCI+Oxd3CkojBLMK3x6OnuQu7oRGRsIg5yRwpyMqymPV47F5Kh9h2fOA253JGsjMcALExdyp2b17l47gwr1wjroKa7uwtHRydiE5JwcHQkJ/OxsHrj5Nla2KKchzNUx6PjEpHL5WRnPKS7s4OO9lZcXN0pysvGP1CY+9DmvA+++8zBd28ey/Jy8A+w/PGyEdo2GE/g2bE000r1zlze8Yk4OMrJfHSf+OQZhIRH4hsQyPdf/E1Q/d7uLhwcnYiIScDBQU5hbiZ29g7odFpBj3EOve+4BFNdy858jLOLKw31dYRFRFJUkC+Y9lhtfP7CxZw+eYyMx4+IFeAI53i6s+bM4+TxIyQmJaNQKunr66W3pwdPL2+r6N+/dwcPD09Sly7j9IljvLNnnyAOQp6Wd5K5vFVqNUqVmvQ7N9n2pnALGjB23vPzctmwaQsKpdKiz124eAkrVq1Bv3wlhQV5XL96mZ6ebtKWryIsPILSkmJCQsOQyWR0d3fT19uHSu1MR3s7Z04dp6+3l6TkFN7Z9R5OivHb4Fh2T/yM+aTMT8MwJ5Wa8iKyH9xE09tD0twl+AaGjvusnq4OHt44j6a/j9DoRJZufAu5o9OU38toLGbsdbQ1k3XvGkgkSKVSpi9cQW1FMecPfYOjkxI3z4lbtpOlqbGBq5cvIEGCVCZj1Zr1HD18gP7+Pr787G/IZHZs2rJNGO2mBq5evmha7ZbKWLlmHUajkUsXzjIwMICdnR3b33p3zPO+L609Rr77+no5+PMPpgv1wcFotRqL6wK0tzTz8PZVJIPlPXfJSiRSKfdvXEKn1WAwGIhLniWYsTdW3h89TBdE60W6Pd1dHPjpO+ztHfDzE8ZRCEBnazPZ968jkUiQSKWkLFhO+uWTgumNRVtLE+k3r5jLfUHaatJvXraK9ljv/sql8wi5sdbU1MC1wfYtHWzfx4/8AsCylas5e+o41y5fYMkyy68CD9HS1MiNq5dMaZBJWbZyLTeuXhRM73l5tgbj9efWorW5ibs3Lw/qS1m8bA0ajYYLp46aHbTEJ00XRLu5qYGrlwbfvcz07o8d/oX+/n6++uwTZIMOWoTAluMJjD2WHrNCvWtpbuLO9Uvm+r5gyXLOHDnAgH4AjAjqnAVMfer9YWPp/CWraKyv5fzxX1AoVYI5aGluauTG1Ytm3WWr1jEwMMCFMye4ffMqQQLuaI9V16RSKZGR0WRlPmH9pi1W0/X08qK3p4egYFN+vb196OnpFuTExmj9lavXcurEMXbtfR+5XE5gcAi3b15n8ZI0i2s3Nzea7mRKJMikpnEEIDY+kb7eXtw9hJmrDTE67xGRUVSUl40Ie7B3/0cv/dxVa9abfyeTyYiLTyQuPpGOjnb6BkMmlZWWcOn8WfNpkbTlK1GpVHR2dpC6dDn+AQFjao1mLLtnCKlMRlBELEERsfR0daLVPA2RNfzOHsCClZuRSCQkzlqEu7cwju4kQ1v2Y5FT0z3+LwUmwkcYL44TwWCwWbaRSoU9EvY8attsF6/N381yKxjWZuhSudFo5MLZ07i5uzN77vwJfbewTrh7jRPBXSXsEann4al++WM6X3z6N7Zt32G+MzlR9DZs47oBYY/aPg97O9u5rpfZsG9r79HaTNtF8XJ3vj75y7+za+9+FM9ZYR4PS05YJ+sh0ZbjaLdGuCOIL6JfK8x1lYngqrRdf+5gw77lt4xG93L17cqFs3j5+JA4bfILWHJ7YcIE/Nopaei2mXZCgGpKnboYZ09E5CXJePyQ7MwM9AY9Pj5+FvVMKDI+P/3wD7y8vSdt6ImIiIiIiPzW+eGbz7G3t2fxFJ2jiPzHYVLG3oPrZ6mrLEXupGDVtj0AZN67Rl1lCVKpDKWzK7NSV5s99OU/uUdZYTYSiYSU+cvM51XHes5U6ezs4NTxo/T0dAMSUqbPYNaceeTn5XDz+jVampvYtXe/YHG4+vv7OXf6BM1NjSCRsGbdRtw9PDlx9CAdHR24uLiwacvrODoJu3P1IP0uGU8eYTRC8vQZzJ4zz+IauRn3Kc7NBIkEN3dPFixbR2N9DY9uX8FoBDt7exYsX4ezy9PJd0VJAdfPHWPd6+/iIcD29Hjlbw2e1Z5pEe2HN85RX1WK3FHBitd2A5CVfo36qlJTO1O7MGOxqZ0ZDHoe3bxAR0sDBoOR4Mh4YpJNLuHPHfgcO3t7JBIpEomUtM2TP5Ov0fRz8+IZ2lqbAAmLV6zDzs6OW5fPodcPmI5yLl2Fl+/T9tXUUMeJA9+StmYzYVGx4z98Cox+55GRURZ9/lg8SL9rvrfm5eXNmg2bOXfqOPX1dUilUvz8A1i5Zr1F7ll0dXZw/vRxent7AEhMnsH0mXM4ffwwba0tgKlM5HJHdu7Zj16v59L5UzTW1yGRSFiybBWBwaEvnQ6Ah/fvme/LTUuezsw58+jr6+PksL5towB924vadPrd21y5dIE//st/mdJu11h8/8VfcXBwMB+R3vb2Xu7fvkZ5aRESJDgpFCxdtQGlSk1bawtXz5+kuamBOQuWkDxzrkXSAKZdPAcHOdLBdOx+bz9Ll63kx+++oaW5iXf3vi/oUfEhTp84RklxIQqlkn0ffAxAcEgowQI5B/r7X035HjpSuGvvfm7duErmk8fmOzOpS5YRbsH2bjAYOPT9VyhVatZt3Y7RaCT91jVKCvORSCUkTJvBtBmzaWtt5sq5UzQ11jN34RJSZk29j+9oa+HquePmn7s62pk+dxEJKbPJzXhIftYjJFIJgSERzF6YRldnB0e+/xwXN1NcRS8ffxakrZ6y/vnTxyktKUKhULJrn+nIXGNDPZfOnUavH0AilbJ85Vp8/QOoqizn+KEDuLi6AhAZHcu8YXFGX4ax6tcQQrTv4diifxmP0pJiLp0/i8FoIDllhjkUgSXo6uzg3KmnY0lS8gymz5pDYX4ud29dp7WlmbfefQ+fIackHe18+8UnuLmbvA77+QWwfLVl7qOPVd59fX0cO3KQzvZ2nF1d2bJV+HkyCPPOx7JpqksLyH10h872FpZt3jnCSUt7SxOPbl1gQKsFiYTlm3cis7OjqiSf/Cf3MBqN+AaHM22OZdobTNLYC4lKJCJ+OvevnTF/5h0QQuLsxUilUjLTr5Ofkc60Oal0trVQVVrAqm276e/t4frpX1jzxntIpNIxnzNVpBIpactX4evnh0aj4ZsvPyU0LAJPL2+2vr6dc6eFvdN0+cJZwsIj2PzaG+j1enQ6HXdv3yAkNIy58xdx785N7t29NWW3shOhqbGRjCeP2LV3PzKZjAM/fkdkZLRFA+/2dneRn/mITW+9h52dPdfPHaO8OI+sh3dJW/saLu4eFGQ/JuvBHRYuN3UQOq2G/MyHeAoYbHv88n9+MMxfs3ZIVAIRcSk8uH7W/Jl3QAgJs0ztLPv+dQoz00mcnUpNWSEGvZ7lW3czMKDj0uFvCAyPQal2AWDR2u0vdcn37rWLBIaEs3z9VvR6PQMDOi6fPsr0uQsJCo2gqryE9FtXWL/NZEgaDAbu37pCQLAwnvysXd5dXZ08epDO3v2/w97enuNHDpKfm01cQhLrNpniHZ46dpisjMcW2dGVSqUsTluBt48fWq2GH7/9guCQMNZtenpP6/qVC2ZPddmDRug7ez+kt6eHY4d+ZMe7+176+F5TUyOZTx7xzp73kclkHPz5e8Ijo8l88pBggfu255VxZ2cHZWWlODu7WFQTYMPrb+M0zCFG8sx5zF6wBICsx/d5eO8WqcvX4OjoyMKlKykvKbJ4GgB27Nw1YpLp5eXFlm1vcP7MKUH0xiIpOYUZs+Zw6sQRq2m++fauZybXM+fMZc5cy3ijG03W4/u4unug05qO+BbkZNLd1clbe03x9YYmyXJHJxalraSsuPClNV3cPNi8Yy9g6isPfP1XQsKjqauuoLKsiM1v7UUms6NvUBtA7eJq/s7LEp+UTPKM2Zw7dcz82Y2rl5i3MJWwiEjKSoq4cfWSOZB6QFCwOS6bJRmvfgnZvoewVf8yGoPBwIWzp3nz7XdROzvzzZefERkVY7GxTCqVkpq2Am9fP7QaDT98+wXBoWF4enmzYcsbXDr/bH/i6urGO3v2W0R/OGOV993bNwkNDWPegkXcvX2Tu3duslTAu+8g3Dsfy6ZxdvNk/opNPLx54Zk03L96mtlL1+Lq4Y2mvw+pVIqmv4/M9Ous2PIOcicF96+doaGmAp8Ay9ydndQhay+/wGfiavkGhpo9TXp4+9HXY7qDVFtRTFB4DDKZHUq1CypnV1qb6sd9zlRRqdX4+pmMCblcjoeHF11dnXh6euEh8IVTTX8/1VWVJCWbzjrLZDIcHR0pLio0x99KSEqmqLBA0HS0tDTh5x+A/aDXz6DgEAoL8l78xUliNBjQDwxgMBgYGNDhpFAhkYBWZ7q0r9VoUCif3rV8kn6ThOlzR7hptzTjlb81EErb0zcQ+1HtwyfgaTtz9/Kjr2fozLgE/YAOw2DZSKRS7C3kDEir6ae+toroBFN8LZlMhlzuiEQiMU+QTGX+NIZMbsZDQiNinuvB6mWwRXkbDQYGhuq9TodKpSY8Msq0CySR4OsfYLE0KFVqvAcXRxwc5Lh7eNI9LF6l0WikqCCX6DiTt7rWliaCBnfyFEolDnJHGuprXzodrc3NI/uUoBCKCvOe6duKBejbnlfGly6cI23ZCuHiDwxjeLyzAZ3OLOmkUOLt6y+om/LheFhhLBtNUHAITlZYZbcV3V2dVJQWE5eUYv4sJ+MRs+YtMi+UKBRK8/+FKO+66grUzq6onF3Iz37MtJnzzGOlk2Lq3gmfR2BQyDO7JxIwO97RaDQoVcL7Sxivflmjff9a+pe62hpc3d1xdXMbdCSSQFGh5TygKlVqvH0HxxL507HE3cMTdw8Pi+lMhLHKu7iwgMTBsSQxKZmiAmHnySDcOx/LpnF280Dt+uyGS0NNOS7uXrh6mLy9yh2dkEil9HR1oHJxQz644OjtH0JNueUWFC06Cy8vyCYw3BQIs6+3G3evpzs6Tko1fb3CXmrsaG+noaFOMNfUo2nvaMdJoeDMqeM0NTbg4+vHshWr6e3pRqUyTYCVShW9PcLm29PLm+tXL9PX24udvT2lJcXmzsxSKFRq4lNmc/hbk0c4v6BQ/IPDmLd0DZdPHsTOzg57BzlrBuPstTTV09vdRWBoBLlPhPeSCdYvf1tpVxTlEBBmCmMSEBZFXWUJZ376O/oBHUlzluIgf9qp3jp3CAkQGjONsNjJBUXu6uzA0UnBjYunaGlqxNPbl3lLVjAvdTlnjx4g/eZljEYjG954FzC5MK8oKWTdtre5cVH4XQhrvHO12plZc+fz6V/+DTs7e0LDwgkNjzD/Xq/Xk5udybKVUz9aNR6dHe00NtTj6/fUM1htdSUKhQq3wWNdnt4+lBYXEROXSFdnB40NdXR1do74zlTw9PLi5rXhfUoRvn7+Vu/bhpdxUUE+arUabx/Le3aWSOD04Z9AIiEuKcXsbTP91jUK87JwkMvZuE1Y1+QAEiQc+PE7JBIJydNnkDJ9puCavwYkSPjlp8F8p8wgeTDfjx/eJycrE18/f9KWrbTYMa9bVy8wP3UZWu1Txz0dHe0UF+ZRVlyAo5OCRWmrcHWz3OmY0ZQV5REWHQdAZ3sbDbVVPLx7HZnMjtkL0/AaXPTp7uzg2E9fYe8gZ8a8xfj6WzaM1JLlqzhy4AeuX7mI0Whkxzt7zL+rq6nmH1/+HZVKzeK0FYKEIxhCyPY9HtbqX8aiq6sLZ/XTmHpqZ2fqamoE0eroaKdp1Fgy3t99//VnODjIWbB4KQECBJYfoqenG5V6cCxRqQaP1QqLNd/5eHR3tIEEbpw5iKa/j6DwGGKS56BydqW7vZWerg6clGpqK4ox6C3n7Mlixl7e47tIpFKCI+Ms9chJodVqOXLoAMtXrhEkGOdYGA0GGurrWL5yDf4BgVy6cJb0O7dG/I1EIkFQ3/CAp6cXc+cv5Ocfv8Pe3h5vHx8kEsuuQmr6+6kqL2brux/i4CDn2vljlBbkUFlayLINr+Pl40/O43s8vHWZeUvX8PDWFRYsEzb+2HBsUf620C54cg+JREJQhKmdtTXVI5FIWLvjA3QaDddP/Yy3fzBKZ1dS17+Jk1KNpq+Xm2cPonZ1x9N34oaRwWCgpbGe+UtW4u3rz51rF8h8cBetVsPc1GWERcZSWpjHzUunWbv1Le5ev8jshUsFDzIO1nvn/X19FBcVsP/jPyGXO3LiyEFyszOJTzQZzhfPnSYwKIRAC7sp12q1nDp2kCXLVo3IX0FeDjFxCeafE5JSaG1p5sdvv8DZxQU//0CL7EB4eHoxZ/5CDv78/WCf4vtMnyJ03za8jKVSKXdu3+TNt94RRGvz9ndRqtT09fZw8vBPuLp54B8YzJyFS5izcAmP02+TnfGA2fMtd4diLN7etQe12pmenh4O/PgdHh6eZpfwrzJvvfs037/89B3uHp6kzJjF/IWpSCQSbl6/wpXLF1i7ftNLa5WXFuGkUOLl40dNVYX5c71+AJnMjtd3vkdpUT5Xzp9k65u7XlpvLPR6PZVlxcycbzombDAY0PT3s+H1d2lurOPq2WO8vutDFEolb+z+HY5OTjQ31nPp9GG2vr0PBwfL9XmZTx6yZPkqomLiKMjL4fyZk7y+4x28ffzY97s/4eDgQFlJESeO/MLeD35vMd3h6HQ6Qdv3WFizf7ElWq2WU0cPsmT5queOlUqlin0f/REnJwUN9XWcOHKAd9/7yCrzKWvMk38tGAwGmutrWL5lJzI7e66f/gVXTx98AkKYvnAFdy+fRCKR4OHtT09Xu8V0LWIRlBdmU1dVypy0deaJnpNCZT7SCdDX04WTQpjjAXq9niOHDhCfmERMrPWMTZXaGbWzs3lnISY2joaGOhRKlfnoVXd3l/k4iJAkp8xgz74P2LlrL46OTri7W3abvr66HJXaBUcnBVKZjOCwaJrqa2hracLLx3TBNzQyjqb6WnRaLe2tzZw/9iOH//EJTQ21XDl9mJbGOoumaQhblb+1tSuKcqirKmXW0qftrKokH5/AUKRSGXInBe4+/rQ1NwCm3XQAuZMC/5BI2gaPUU8UpUo9eBTEVL5hkbE0NzVQlJdNaIRpBz8sKpamelO5NjfWc+XsMX7+6q+UFRdw++p5ykte/p7LaKz6zsvLcHFxRaFQIpPJiIqJpaa6GoDbN67R19tL2opVFtXU6/WcOnaQmLhEIqOfOrgxGAwUFxUQFRtv/kwqlbJk2Sp27tnPxq3b0Wo0FtuNSEqezrt797PjnT04Ojri5u5utb5tdBm3t7XS0d7Gl59/wt/+/G90dXby9Rd/p7vbMqvBysHdSieFkrCIaJoaRvZVkbEJlBULf8xIPbjqrFQqiYqOoa7WuqvOtuKZfNfVoFSqkEqlSCQSpiXPoN5C76K+pprykiK++/wvXDh1lJqqci6ePmY6nh012K9FxtDa1GQRvbGorijFw8vHfFxTqVITEhGNRCLBy8cfiUSCpr8PmczOvJvp6e2Ls7MrnW2tFk1LblamuZ+Jjo2noc70nuVyuTk+cFhEFAa93hynzNII3b5HY+3+ZSzUajWdw47/d3V2mne6LIVer+fk0YPExo8cS8bCzs7OfGfZx9cPF1c32gedggmBUqmiu2twLOnqQmmFebI13vmLUCjVePkFIndUYGdnj29QGO0tjQD4h0SwfPNOlm16G7WrOypny3kaf2ljr76qjILM+yxcuQU7u6fxhPxCIqgqLUCvH6Cnq4PuzvYR3mgshdFo5Myp43h4eDJngrHNLIVKpUKtdqa1pRkwTQw9PL2IjIomJysDgJysDCKjogVPS0+P6UJ3Z0cHhQV5xCcmWfT5CrUzzQ21DOh0GI1G6msqcHHzQKfV0NluGnxqq8pxcfPAQS5n+3t/5LV3P+K1dz/Cy8eftHWvCeKN05blb03thuoyirLuM3/F5hHtTKFS01RXBZjuFbU11aF2dWdAp0On05o/b6ytwNltcgsACqUKpdqZ9jZTh19bVY6buwcKpYr6mkrAdO/EeTD0wZt7fsebez/mzb0fExYZw4KlqwiNsGzdt3Z5Ozs7U1dbg26w3pvauCeZTx5RXlbC+s2vWXQn02g0cvHsSdw9PJkxe6TXv8qKMtzdPcwTYzCtiA/dn6woL0UileDhaZkL/sP7lKKCfOISkoiwQt82Vhl7efvwx3/5L/zuD//M7/7wz6idndmz70NUFrhfpNNpzXeWdDot1ZVluHl40jFsUl1RWoTrJNvPZNFqtWg0GvO/y8tKBT0292tBq9WiHZVvL0/vEXdViwrzLfYu5i1OY9cHf+Sd93/PyvVbCAgKZcW6zYRFxph3+mqrK80eMIWgrCiX8KinC1XB4VHUDfapHW2t6A165I5O9Pf1YjCY4nJ2dbTT2dGG2sXVomlRqVRUD+a7qqLcvFjU093NUBzm+toajEajYN4ShWzfo7F2/zIefv4BtLW20N7ehl6vJy83h8joGIs9/3ljyVj09vaY61pHexvtbW24CBjWKDI6muzBsSQ7K8OieR8Pod/5RPAJDKWjtZmBQV8LzXXVOLuaxpb+PtNiilbTT0nuE8JiLTePn1RQ9XuXT9JUV42mvw9HJwXxMxeQn5GOQT9gvifk4e3HjEUmjzp5j+9SXpiNRColeV4afkFh4z4nLGZkpiYaVL26qpLvv/0KL29vJIO3alPTlqMfGODC+TP09fYid3TE28d3wlv0kwkG29BQz7nTJ9Dr9bi6urF2/SaMRiPHjx6ks7MT58HQCxO97D7VoOrff/sVfX29SKUylq1YRWhY+KSf8aKg6hnpNykvzkcileLu6c38tDXUVJSSkX4TiUSCg9yR+WlrnxmMzh/9kZkLlj7X2JtqUPXxyj/CCi75LaE9VlD1+1dO0VRfjba/D7mTgrgZ8ynMSMdg0JvbmZuXH9MXrmBAp+XhjXN0tbcCRoKjEohOmk1PZzt3L5lcfBuNRoLCY4lJedZF/IuCqrc0NXDz0hn0ej1qF1dSV6ynrbWJu9cuYjQakMnsWJC2Gk/vkQs51y+cJCg08rmhF6YSVN1S5T2ZoOq3rl+lIC8HiVSKj48vq9Zt5N//+3/D2cXVvPIdFRPLgkVLJvS85wVVr6mu5OCP3+Lh6W02IhekphEWHsn508fx9Q9gWsrTe1ydHe0c+eUHJBIJKpWaFWs24PycyeBkgqr/+I+v6OvrQyaTsXT5SkJCw+nr7eXEsL5t4yT6tokGVZ9IGf/tz//G7vc+mLBr9OcFVe/saOPcicOA6Wh+ZGw8M+Ys5PzJw7S3tZjerdqF1OVrUKrU9PZ0c/jHr9FqNUiQYO/gwPZ3949w6DKciQZVb29r48ihA4BpFzc+IZH5CxdTWJDPxaGxTO6It48P2yc4lk11IeL4kUNUVpTT19eLQqlkUepSklNmTOoZEx1H29vaOHr4ab7j4k35PnX8CI2NplMKLi6urFq73nxf9EVMNKh6TVUFGQ/usW7rdjT9/Vw8c4zuzk7sHRxIXbEGTy8fenu6Ofj9V6bylkiwt3dgx+4Pxi3vFwVV1+m0/PL133h910fmZ+j1em5eOk1rcyNSmYzZC9PwDwyhvLiAx+k3kEplIJEwfc4igsMix332i4Kqnz5+mKrKCvr7elEolMxftAQ3Dw+uXjyHwWDAzs6OZavW4ePrx5OH98l4/ACpVIqdnT1Llq3EP3D8+4KTCar+ovo12fY9GYToX6ZKSXERly6cxWgwkpScwoJFkz8mPl5Q9ZrqSn754VvTIslgP7BwcRp6/QBXL56jr8/Un3h6+/Da9rcpKsjjzs1rSGUyJEiYvyiV8MjnL+ZNNKj6WOUdHR1rCr3Q0YGziwubX3vDKk6hLPHORwdVH8umcZA78uT2ZTT9fdg7yHH18GLx2tcBqCjKpSAjHSTgGxjGtLlLzM9pbzWdKIifPp+giGfnT1MNqj4pY8+aTNTYE4LJGHuWZqrGniV4kbEnJFM19v6jM5axZ01eZOwJyVSMPUsxGWPP0jzP2BOayRh7lmaixp4QPM/YE5qJGntCYI37s+Nhy3F0osaeELzI2BOSFxl7QjIZY0/Ecoxn7FmDiRp7rxqjjT1rMlVjT2ydIiIiIiIiIiIiIiIiryCisSciIiIiIiIiIiIiIvIKIhp7IiIiIiIiIiIiIiIiryCisSciIiIiIiIiIiIiIvIKIhp7IiIiIiIiIiIiIiIiryCisSciIiIiIiIiIiIiIvIKYve8X9rLfpu2oC3DH9gSu99oedsSDxuGPgDbusO3JbbMt1FmO+3fankP2DAMgC3DH9iSPhu6hFc7PndqIygyG5a33W+0ff+WsWUYIVtq23Iss2XYqKkizu5FREREREREREREREReQURjT0RERERERERERERE5BVENPZEREREREREREREREReQURjT0RERERERERERERE5BVENPZEREREREREREREREReQSxm7Gk1/RTlPLbU4yZFf38/jx7cB6CyopyDP/9gE21r8zztUyeOkp+XK5i2RtNPXuYjwZ4/EUbn/8ql83z+979y5dJ5q2laE42mn9zBd15bXcG5479YPw39/eRkPASgp7uL8ycPW017+Lvv6uriyKEDVte1dv8ypP/k0QMAOtrb+frzTwTXs1Ud/zWkYXg7sza/1vHEGmj6+8l6/MAm2v39/Tx+aKP61t9P1hNTvjs72inIy7aKri3zbKu6Zus6bus0aPr7yXzF2thk32fmk8d88enf+PKzv/HFp3+lqCB/ytqa/n6yB+dDE+G7L/7CgW8/45fvPueX7z7n5hXh5qqjsZyxp9VQlPPEUo+bFJr+fh4/sl3j+S1qazX95GfZ1tgbnf8njx/x3v6PSFu+ymqa1kT7KzCwtZp+cgbToFSpWbXhNatpD3/3arWardu2W13XFmiGGXvW0rNlfm2dBlu2s9/qeAImIzvrybPv3WAwCK/d38/jh7aZBA/Pd2dnB4X5OdbRtXK/MlrbFnXN1nXc1mkYr41ZRVug+jaR96nVatHr9XR2dnLn1g3e2bWX9/b/jnf3vI+Xjw8A/X19k9fWPF38Hg+dzqQ9xMY3dvLGO+/zxjvvsyht1WAeJq89WSRG4/hxMgrreyccROPWxRPUlBejdnXDxz+Y9pYmtNp+DAYD02YvIjA0alIJC/ZUTPhvjx05SHFhAe7uHkhlMuzs7HBwkNPe1kpwSCir1q4XLNbRcO3Q8HCcnBTkZGchkUgIj4hk6bIVguiOpT2gG6C8rBS1szMymYyk5OnExsVP+HmNnZoJ/+3Vs8eoLC3Cxc0d/6BQHBVKyovy0Ov1hIRHM33e4knlxdt58nFLhuffSamksrwMLy9v5i1cRFx84qSfN1nN0PBwdFodFRVlqJ1dkEmlk37nLV0Tf+eXzxyjYvCdS6VS7OztcXRU0NbahKe3L0tXbZx0PZ9srJqLp49SXlKEq5s7Lm7utLU2s/3d/ZN6xhCuysnFGBz+7t3cPWhpaWLfBx9PSXuqulKZDKVSyetvvk1dbQ1nT59ky7Y3cHNzn/DzBvSTm7yePHaIkqJC3Nw9cHNzp6WlmT3vfzTZbAATi6U5Or/29vbI5Y40NTUQG5eAl7c3D9PvoRsY4LU33pxU3ifKWGlwUihobmrE19efDZs2bLOCAAEAAElEQVS3TqquN0+ynQ31bQHBoQBUlZcikUhImb2A8Oi4SeVlMjGZfk1j2cuOJz2agUnpnztxhNKSQtzc3JFKZcjs7JA7OtLW2sK7+343qWcpHGST+vvjRw5RXGTKu6+/P60tLWg1GgwGAyvXrCMoOGTCz+rVTC6+4LmTI/Pd3taKs4sLsQnTSJk5d1LPcppEvk8cNeXZzd0DmVSGQqlk2/a3ALh47gy+fn4kTkuZ8PMmE594dF+eNC2ZiKhowHQqKSIyelJ1bSq63r5+RMfEEhUdw+FffsbR0ZF1GzeT+eQx7W2tpKYtt7j+6DTodDqcXVx48+136enu5ofvvubtd/eiUqkm/LzeSbSzs8PamKubO9FxiURExQBw7tRRomLiCI+MmfDz5PaTq28lg/UtJCycirJSAPp6ewkJC2fths0TfhY8nbuM7reGL/bX1tSQmfGIstISdu15n66uLs6cPMbufR8glY4cC69duURFWSnTUqYTl5CEXD5+v93RqwPgwqkjpvmQuzuBwWHMT31aZxrqa8nPfkJVRRmv7diNQqniuy/+wra39+LkNNK+uXfzKtWVZcQlJhMZk4DDc7R9nO2nNABYLPJo8txUOlqbWfv6HgwGA/oBHfYOcjR9vZw/+j0BIZGCDVJL01bQ3NTI3v0fUVlRzoEfv+P9D3+Ps4sLB376noL8PEE6jtHaJcVF3L55nV1738fe3p6+KawUTFW7ID+Px48esO/Dj+np6eGLv/+FpOTpgmnPXLCEtpYmNr/1HjWVZZQX57Nh+24ALp48SH1NJb4BwYLpw8j8A/zP//v/Mv/bGpr5eblkZTzm/Q9/T09PD58L/M5nLzS989fefo/a6gounDzM6zv3oVCpOfHLP2ioq8bXP0gwfYC5C5fS2tLE6+/so6ujnTNWPEo6/N13tLdz8IB1jlOO7l/S796murqKi+fOsO2NHTi7uAiqv3jJcpqbmtj13gd0tLdz5OBPguqNzu/hX37i/Q9/j6OTE3//y/9iWsp0dr23nwfpd3l4P50Vq9YInoZDv/zEvg8+Rq1W8903X1JTXUVgkDD9y1A72/r2e5QV55Of9YStb7+Hpr+PYz99g29AEArlxCdjk+HXMpZZezwBmJ+aRktzEzt276e6soKTh3/m7T0f4OzqKqguwJK05TQ3NbLn/Q9Jv3cHFxdX5i9cjMFgQKfTCao9f/Fgvnftp7qqgscP7rJx65uCagKkLh3M874Pqawo5/69O4JrDjG8rhXm55Gfl0tEVDR6vZ6KsjJWrVkvuG5uTjbVVRVERcfQ3dVJd3cXAFVVlcTFJwiiPzoNACeOHebRg/uUlRazKHXppAy9ybJgsI29tXs/NVUVPH6YTkRUDBpNP/U11axcu0kw7aH6tnvfh+bP+vv7+em7r5k+c/aUnzv6ffb19ZGTnUlWxhMUSiXTklNYsWotdnZ2OCkUKFQqPvnLvxMSGk5MTCyR0SbjdknaclqmJZOV8Zivv/g7gUEhJKdMf+44M3dRGq0tTbzxzvuD+emjKC+b/JxMnBQKYhOSWbR0FTK7p6bWiV++RzJoqEbHTyN5xhzmLlpKTGsS+TkZHPz+S/wCgohNTMYvwHLzOYsZeyMwGslIv0FjXRUSiYS+nm76+3pwUghXiYfj5x+Aq5sbAPHxiVRXVQo2QA6noryUpOQU7O3tAXBychJcc4iqygri4xORSqWo1WpCQsOspl1TWUZtZRnHf/oKMG1bd7a3CW7s2Zrqqkpi4uKRSCSoVCpCQkKtqu/l44dS7QyAu5cPXZ0dght7ItDS3My50yfY/ta7qNVqWydHcHz9A1AN5tPVzY2w8AgAvLx9qKwot0oa/PwDcHY21XVvHx862tsFM/aG01BbTUR0HFKpFCeFEt+AIJoa6ggJn9xJlaliq7HMluPJED5+/lYx9Ebj5+fPmVPH0ev1RMXE4uPja/U0/JYIj4zi4oWzDAwMUFZSTFBwiHkOJSRBQcE8SL9Lc1MTHl5e9Pf1093VRW1NlSALWOOxctVavvj0b/gHBBKfkGQ13YCgEK5ePEtfbw/FhflERMc+s9slJEajkdPHjzBzzjx8/fwt8syuri7+/pd/Jzwiim3bd+DsPHIhViqVsn3HTupqa6koL+XSxXPU19exKHUpAB4enixdtpLUpcvJy8nml59/ICkpmRWr175Qu6e7ix++/CtBoRGs3fwGqsG52Wg2vrHzmZ09AFd3D+YtXsachUspLsjlzNEDRMcnmY96viyCGHvlxXlo+npZ89oupDIZx7//+4gzq0IzegdRoA1FkSGMRpJmzSc2UdiVX5GRyGRPj1BIJRKMVrjbIgJKlQr9wACN9XW/CWPPblg9k0gkyGR25n9b4z7V6DRIJVKr6dqa3/JYZmeFCf9YBAWH8NY7eygtLuLMiWPMmjuPxKRkm6TFWkilUoZf6RnQT+4I7stgZ2dHcEgoZaUl5OXlCHYFYzRqZ2c0mn5KS4sJCgqhr7+P/LwcHOwdnnuEz9J0dXUikUjo6enGaDQKdgJuLGLjk8jPzaYoP5cVazZYTRfg9o1rqJ2dSZrEUeEXoVQq2bhlG5lPHnPowE8kJk0jPnEaSqXS/DcSiQT/gAD8AwIIDYvg9MljZmPPaDRSWVFOZsZj6mprmDlrDsnTZ0xI20mhZPnazeTnZHDm2C9ExycRHZuAk0L54i8PatdWVZCfk0FjfR2J02cRl5gy2VcwLhYz4+3tHdDptADotBrkTgqkMhkNNZX0dHdaSmZMHBwc0Gq05p/ramtob2/DaDSSl5cj6ArwcO3QsAiyMp6Yj30IfYxzuHZQcAh5eTkYDAa6u7qoEHjF3d5ebi7vgJBwinIz0WlNP/d0d9HX2yOoPjxb7tZguGZgUBCF+XkYjUZ6ursF3+UY/s5thb2Dg7mcrY0tynssXUdHR15/822uXb1klZ0tB7kDWiu+c1u9519LGoa3M1//IEqL8jEYDPT19lJfW4WXj59g2r+Wscza4wkM9S0Tv1tpSRwcnraxjo52lEolydNnMC1lOg319YJqD8+3gxX71+F5dnZxoaWliYGBAfr7+6ksLxNee1g9j4tLICvjCdWVlYRHRFpN1z8gkAfpdwkKDiEoKJj0u3cIDBb2xMDwNBgMBk6fPM6mrdvw8PQkXeCjtKPbWFziNDIepgPg7uklqPbw+lZSVEBFeSnLVr78Durw9ymVSomJjeONHW/z+va30Ol0/PCPrzj8y09o+vvp6uqivq7O/N3GhnpcBq9h5GRn8vknf+HRg/vEJyTx/oe/J3XpMlxcXJ+rPdRepVIp4VGxrNvyJmu3bGdAp+XYgX9w9vhBNJr+5+ahMC+bn775O9kZD4mMTeDN3R8wZ8ES1M6WuyJisZ09uaMTXr4BnD7wFe5evnS2t3D6l69w9/TF2dXyF/iH46RQEBAUxBef/hU7O3t8/fy5cPaM+VJ7dMzkLtRPVTs8IpKo6Bi++fJTZFIZ4ZFRLBHoku9Y2u5u7nzx97+idnEhICBQMF0ARycnfPwCOfL95wSGhBMeHc/Jg/8AwN7entRVG3FiYisaU2V0/q3BaE212pnP//4X1M4u+Pj6CboiOPTOD333OTI7uwmvGFk2DQp8/QM58I/PcHP3tKr28Hfv4eEFWGcFdHT/olQqUapUvL79bQ789D3rNmzCX8D25uSkICAwkK8//wQPD+Hf+Vj5tTa2TMPwdhYUGo67pxdHfvgSiUTCnIVpgt3Xg1/XWGbN8QRM9dw3IIgfvvrUdL/GimXupFAQEBjEl5/+DZ1Oh729PVKZDAd7e9Zt2iKs9lC+v/6UoJAwJBIJP377GXFTcNAyKd3BPH/12d8Ii4gkJjaerz7/BBcXV7wFPro6uq6lLl3OyeNHiIyOHXFiRWjdwKBgyktLcHN3x1nvQn9/H4FBE3fG87JpaG5qYtaceQQGBePl7cO3X31GRGQUngIZXk5OCvwCgvj+q08JCYtg0dLluHl4Eh4ZLYjeCO1h9U2hUNDd3cV3X38OQERUNItS06b+3GFlOuSgRe3szIJFqcxfuNi8KGsw6Lly6Tzd3V3YyUx9zOq1pvuhLi6u7Ny1F8Uk+p2h+dDP335KcGiE2UGLSqVm5txFzJizkNqqihHfGX5nz8PTm2VrNqF2dmHL9ncFndNZzBunpZmMN04RyzAZb5yWZireOH8NaLVaHBwc6Ovt5duvPmfn7vcmdcF6Mt44hWCy3jgtyWS9cQ6nvq6WyxfP8/a7eyyXICsxWW+clmQi3jhfRSbjjdPSTMYb53CGnAG9/ubbFk7R5JmKh8TJeuO0JJP1xmlJJuuN05JMxhunpZmMN04RyzEZb5yj0el0/PD1p+zYtQ+53HHS35+MN05LY8u5y5A3Tltgc2+cIiK/RQ7+/AOa/n70Bj0LFqUK6klLxERdbS0njh1iSZpwYU1EREREREReVSoryrh89iQps+ZOydAT+Y/FpIy9u1fPUFtRiqOTgnXb9wKmYIC3Lp6gp6sDpdqFRSs34SB3pLwol9wn9wDTfb5Zi1fi5uFNZ3srty4eNz+zu7ODpFkLiZ02yyIZOn3iGCXFhSiUSqvE4BpL79jhg7S2NAPQr+nHUe4oeEiA0pJiLp0/i8FoIDllBvMWLLLo8zvaWrh69pj5566OdqbPW4xCqeZJ+k3aW5vZuH03nsPus7Q2N3L7ylnTmWaJhI3bd2NnZ9n1hc7ODk4dP0pPTzcgIWX6DGbNmWdRjedpSyQS9AY9IHnhueypoNH0c+PiGdpamwAJqSvW4eMXAEDmo3uk37zCO/v/hKOTgsyH9yguMAXkNRoMtLe1sHP/n3B0nLpX2O+/+CsODg5IJBIkUinb3t7L/dvXKC8tQoIEJ4WCpas2oFSpKcrP5smDu2A03Q1YvGw1Hl4+lngNZgYGBrhw9hR2dvbcuHaFxoZ6Fi+Z2vGPqSB0Ozt76jilJUUoFEpzLL1b169QXFSIRCJBoVCyZv0ms3dMMO1y/vDtl2zY/BrRscJ5ajQYDHzz5Weo1WpBd5vGa9NXLp2nuKgQmUyGq6s76zZuxtHRMpMUjaafm8Pa2eLBdpaT8YC8zEdIJFKCQiOYsyiN4vwcsh7dM3+3tbmRLW/tFaSut7W28ve//i9B6trzGF3P12/cYtHnDwwMcPinb9Hr9RgNBiKiY5m7cAkXz5ygpqrCfBx++dqNeHn7UpCbzaP0Oxgx4uDgwNIVa/H0fvn3PTAwwA//+Bq9Xo/BYCAmNo5FqUs5feIYVZVP07F242aLeOUcGBjg8M/D8h1lyvehn74136Xq7e3Fx9ef9VveMH+vob6Wgz98zeoNW4mcZJzHsWhtaeb40UPmnzva21i4eClBIaFcOHuKgYEBpFIpK1evw88/4KX1RjPWvOnm9atkPH6EQmE61ZWatpyISMt7vR1LOz8vh5vXr9HS3MSuvfvx87eMd8jnYY25y8DAAIdGtbN5C5cAEBwSRlhkNHdvXiVl5hwAaqoquXHlPM1NjazZsJXIlzg2fvbUcdN7VijZu98UJ7Ovr4+TRw/S0dGBi4sLG7e8jqOTE5UV5Rw99LP5flxUTCwLFi15ucyPg9BjOJjGykM/fIVSpWbdlu1UV5Zx98ZljEYj9vYOpK3egIurOzkZj8jJeIhEKsHe3oHUFWtx9xDmCO+kZt7h0YlEJ8zg7pXT5s9yn9zDNyCE+OlzyX18j9zH90iZtwSl2oUVm97CQe5IbWUp96+fZ9XWd3B2dWft63sA0ws59t3fCAqzXINOSk5hxqw5nDpxxGLPnKze5tdeN//78sVzgq+aGAwGLpw9zZtvv4va2ZlvvvyMyKgYPL0sV2lc3DzY/NZ7Zr0DX/2FkPBoBgZ0LFu3ldtXzj6TpuvnT5C6cgPuXj709/UJ4tZXKpGStnwVvn5+aDQavvnyU0LDIiyad1tq3712kcCQcFas34per2dgwHR8oLurk5rK8hHufafNnMu0wTseFaVFZD+5/1KG3hAbXn97hKvg5JnzmL3A1BFnPb7Pw3u3SF2+BrWzK5te34nc0YnKshKuXzzD1rf2vLT+cGQyGTve2Y2DgwN6vZ7vv/2K8Mgoq9wpskY7S0xKZvrM2Zw5+XRhZdbcBSwcvM/w6EE6d25dZ+VgHCqDwcD1K5cIDYuwWBrG48H9e3h4eqLVCHskcrx2FRoWwZK0FUilUq5evsDd2zdYumylRTSH2tnyYe2stqqCytIitr71HjI7O7PTqcjYBCJjTXG4WpsbuXjysMUNPWvUNVtqy2Qytmx/x9yOD//4LSFhpnvXC5csf2aS6eziytYd7+Do6ERFaTFXzp/mjXf2WiQdO3buMqfjh398Zb7/vXTZCmIsHOZCJpOx5Y1h+f7JlO9tO3aZ/+b08YOERzy9Q2UwGLh9/TLBoeEWS4e7hyd7BuOdGQwG/vbnfyUqJpZzp0+yYFEq4RFRlBYXce3KRXbs3G0x3SHGm6fNmjuPufMWWFzvRdqeXt5sfX07506fFFR7ONaYP8hkMrYOa2eHfvyW0LBIfP0DaKivfWaBWu3szIq1G3l0/944T5w4CYNj2ekTR82fpd+5SXBoGHPnL+LenZvcu3vLfEInMDCY17a/9dK6z8Na/WrW4/u4uXuYHdLcuHSONZtex83Dk+yMhzy8d4tlqzcSFZtAQrLJ22d5SSF3rl1i/Ws7LJqWISY1+/b2D8Jh1EpqTXkxYdGmgS8sOoHq8iIAvHwDcBg0cjx9/OkdDFg5nIaaClTOrijVlvM4ExQcYtX4ds/TMxqN5OfmEpcgrCvhutoaXN3dcXVzQyaTERefQFFhvnB61RWoXVxRObvg6u6Ji5vHM39TU1mGm6c37oOTIEcnJ0GMPZVaja+faTdRLpfj4eFFV5ew3l+tpa3V9FNXW0VMwjTA1HEPLRzcvX6JOQuXjvvd0sI8IqKF2eVxGOaEZkCnM7tJ8fUPRD5oXPr4+ZuD1FoSiUSCg4Pprp/BYMCg11vJTYt12llgcMgzBvpwpz86nXaEa+7HD+8TFRNrXg0Xis7OTkqLi0hOmZgb6pdhvHYVFh5h7kP8/QPp6rRM/dJq+qmvrSJ6VDvLz3rMtJnzzQFxx7o8X1qYR7gFdlpGY+0+3draz7Rjg/65/pb8AgLN7cLHP4BuC3n4Hp0OvcD3aV+Ub61GQ01lBeGRMebPMh8/ICIqVjDnDRXlZbi6uuHi4opEgtmzoUajQaUSJrSMtedpL9L29PSyivOr4Vhj7jJefTMYDNy6dpmFqctG/L2ziyueXj4WCf8QNMZYVlxUSMJgGJOEpGSKCwteWmcyWKNv6+7qpLKseGTYBAloB3futRoNSqWpXTmMGNt1gvqce+kzdf19vTgNeidzVCjp7+t95m9K8jPxC342KGtFST4hkcJ5F7M11VWVKJVK3N2fNYYsSVdXF87DdnjUzs7U1dQIpldWmEtY1PMNic72ViTAuWM/09/XS3hUHEkzhT1e2dHeTkNDnaCeEa2p3dXZgZOTgusXT9Ha1IiHty/zl6ygtrIcpUo17m7CgE5HdUUp85e+/K6HRAKnD/8EEglxSSnEJ5liKabfukZhXhYOcjkbt+185nv5OZkEhwqz22QwGPjmi09pa2tlxqzZVitva7ez4dy8dpmc7Czkcjnb3353MD2dFBfms/3tXZw7dfwFT3g5Ll04y9JlK6waAgLGb1eZGU+Ii0+wiEZXZweOTgpuXDxFS1Mjnt6+zFuygo72Vhpqq3h45xoymR1zFi97JuxCaWEeKzZus0g6RqTJhnXNWtoGg4ED//iCjvY2klJm4esXQPaTR9y9eZX7d24SGBLKgsVpZmN7iNysDEIsuJNtMBj49svPaGtrZfpMU3/y5NFDrl+7wu2b1wkODWNJ2nKLXUEwGAwc+G5kvocoLS4gMDjUPAns7uqktLiArdvf4dI5YXad8ofFtlu2YjW//Pw9Vy9fwGg08vaul989nQyPHqSTk5WBr68/y1aswtFGBqG1EXLuYjAY+HlUO3vyMJ2wiCiUAhnz49Hb021eQFAqVfT2dJt/V1tTzTdf/B2lSsXSZSvx9PK2uL41+rbbVy8wb/GyEWPl0hXrOH30AHZ2djg4OLB1xx7z77KfPCDzUTp6vZ6Nrz87l7IUFr1ANdZqQENNJaX5WazYPPKOh16vp6aihOQ5qZZMwq+K3JwswXf1rI1er6eyrJiZC5Y+9+8MBgMNddWD9/TsOXv0Rzy8ffEPChUkXVqtliOHDrB85RqrBkQVUttgMNDcWM/8JSvx9vXnzrULPLp3k/qaKtZueXPc71WUFePtF2CRI5ybt7+LUqWmr7eHk4d/wtXNA//AYOYsXMKchUt4nH6b7IwHzJ7/tB3XVFWQn53B5u3vvLT+WEilUvbu/4j+/n6OHPyZpsZGvLwtPzD8mli0ZBmLlizj3p2bPH54n4WLl3L14nkWL10ueBDe4qJClAolvn7+VokrOMR47er2zetIpVLiE5MsomMwGGgZ1c4yH9zFYDCg6e9j4/ZdNDfUcfnMUbbv/sj8vhvra7GztxfsjsWrjlQqZcfu/Wj6+zl97CAtTY3MX7wUhVKFQa/n8vnTPEy/w5wFi83fqa4sJy/rCa+9tes5T558Ova8/+GI/iQ1bRlKpQq9Xs+5Mye5d+cWCxdb5g6RVCplx67BfB8/SEtzIx6epv6rMD/HvKAGcOPqBRYsXiZYG9fr9ZQUFZC61LTD8+TRQ9KWryYmNo78vBzOnj7Bm2+9K4j2aKbPmMWCRalIJBJuXL3M5YvnWbdxs1W0bYnQcxepVMpbg+3s1LGD1FRVUlyYx2tvWqdcx0MikZhWkwEfXz8++P0/4eDgQGlxEUcPHeD9j/5g0/RNhYrSIhwVSrx8/KgZFnIh83E667Zsx8cvgCcP7nL7+kWWrjRdxUhMmUViyiyK8nN4dO8Wy9ZsFCRtL32uztFJQd+gdd7X043jsLs9bS2NpF8/y+LVW83Hu4aoqyrF3dPbJrHCrIHBYKCwIJ/YeOGNPbVaTeew7f+uzs4RDhwsSXVFCR5ePi8sN6VKjY9/EI5OCuzs7QkMiaClqUGQNOn1eo4cOkB8YhIxsdbdKRZSW6lSo1Sp8fY1XRYPi4ylpbGBrs4ODv/wJT999Vd6urs48uPXI1bISgtziYixzBHOoZU/J4WSsIhomhrqRvw+MjaBsuKnRzFamhq5fvE0qzdtG9EXCIGjoyPBIaGUlhYLqjOENdvZeMTFJ1FUYDp2Ul9fx6ljh/nsr/+LwoI8Lp4/I8hRv5rqSoqKCvjbn/+N40cOUlFexoljhy2uM5zx2lVWxhNKiovYuOU1i02Ax2pnzU0NKFVqQiJikEgkePn6I0FCf1+f+XulhbmCHOEE29Y1a2vLHR0JCAqhorwUpUqNRCJBZmdHXGIyDfW15r9rbmrg8rlTrNvyxog7xJZiqD8pKy1GNZgOOzs7kqalUFdX++IHTBJzvstKAejr7aWhvo7Q8KcxYxvr6zh36gjffPZnSgrzuHbxLKVFljv6VlpSjLePH8rB01nZ2RlEx8QCEBMbT32tdXaTAZQqFVKpFIlEQvL0mdTVWU/bVlhz7iJ3dCQwKITqqnI62tr49vO/8vWnf0an0/Ht538VVHsIhVJlvt7R3d2FYnAeKZfLzcdNwyOjMBj09PY+e0rwZRG6b6uvraaitIjvvvgLF08fpbaqnNNHf6alqdHsWC8iOo6G2upnvhsZE095SaHF0jKalzb2AkIiKSs0eQAsK8whINTUUfV0dXLz/DHmpa0fM6h6RXE+IRGv7hHO8rJSPDw8cXZ2fvEfvyR+/gG0tbbQ3t6GXq8nLzeHyOiYF39xCpQV5hE+gbtgAcHhtLU0MaDTYTAYqK+pxHWMu30vi9Fo5Myp43h4eDJn7nyLP9+W2gqlCqXamfa2FgBqqsrx8Pbhnf1/Ysfej9mx92OUKjVb39pjDvSs1fRTX1NFSPjLOz3S6bTmc+Y6nZbqyjLcPDzpaGs1/01FaZG5XLs6Ozh/8hBpqzcKUtYAvT099Pf3D6ZJZ25n1sCa7Ww4ba0t5n8XFxXg7mF6t/t/90f2f/wn9n/8J6Jj4lixai1R0bEW11+StoLf/+k/8bs//DObtr5OSGgYGze/ZnGdIcZrV6Ulxdy7e4ttb+zA3t7eYnqj21ltVTlu7h6ERERTV2Nane1oa8Vg0JuPlRmNRsqK8ifUF04FW9U1a2n39fagGWzHAzodVRVluLl70DM4ETQajZQVF+AxGFy6q7ODM8cOsXLdZtwseC1idH9SUVaKu4eneUJqNBopKizAy0IOHMbLN0BJUR6h4ZEjjovu3v8H838R0XEsWbGG8CjLlUV+bvaI00cqlZqqSlOdrxyWNmvQ3fX0Dm5hQZ4gx/h+TVhj7jK6vlVWlOHt48e+j/+ZPR/8gT0f/AF7e3t2vS+853owBU/PycoAICcrg8gokyOinu5uhmJ+19XWYDQaBbnTKXTfNndRGu/u/yPv7Ps9K9ZtwT8olDWb3kCr0ZjHl+rKMlzdTXOW9hFzqWJcXN0slpbRTOoY562LJ2isq0LT38fR7/5G0qyFxE+fy60LxynJz0Spdmbhik0AZD+6jaa/jwc3LwAmz0Ort5mOXgzotNRXlzN78SoLZweOHzlEZUU5fX29/OV//U8WpS4V1KnAeHp5udnmc/BCM+Qi+cCP32E0GElKTsFLgI5Sp9NSW1XGgrTV5s8qSgq4e+0i/X29XDjxC+5ePqze/CZyR0cSU2Zz4sA3AASGRhAUFjneo6dMTXUVOVmZeHl789VnnwDCuWy2hfaCJSu5eu4Eer0eZxdXUlesf+7fl5cUEhAchr391AOWD9HX28O5E6YdHKPBQGRsPMGhEZw/eZj2thYkEgkqtQupy9cA8OjeLfr7+7l5+RyAOVSDJenu7ubUiaMYjQaMRiOxcQnmAUNorNHOTh47THVlBX19vfz9L//GgkVLKCspprXV9L6dnV1YsWadRTV/bYzXri6eP4N+QM/PP/wDAP+AQFav22ARzflLVnJtsJ2pB9uZnb09Ny6e5tB3nyOTyUhdud68m1hfU4lS5YzzoKtwS2OtPt1W2j093Vw8cwKjwYjRaCQyJo6wiCiO/PwdfX29YARPbx+WrlwLwP07N+jv6+PaxTOAqW958919L52O7p5uTp84htFg6k9i4uKJjIrmp++/Ne0sGI14+/iyau3z+92JYs63cWS+AQrzc5k5R1hPlMPRarWUl5Wyas3TvK1eu4HLF89hMBiwk8lG/M6SjDVvqqqooKGhHokEXFxcWb3WMm17ItpOjk5cOH+Gvt5eDh74AW8fX958S5hrCENYY/7Q09PNhWHtLGpYfRuLhrpaTh07iKa/n/KSIu7dvs7OvR9OSfvk0UNUDY5ln/z5X1m4eClz5y3kxNGDZGU8wXkw9AJAQX4uGY8fIpVKsbOzY8PmbYIcXbZFvyqVSlmych3nTxw2OcxxdCRt8Ahn9pMH1FSWI5VJkcsdSVstzBFOAMmQNT0WhfW94/9SYII9hT0CJvIsjZ3CulR/Ht7O1r1n92uhpct27xxAJrWWL8tncVW+vEH6H5EBgT3+PQ87meU94v5HoNmG7cxT/dvs23o0AzbTVjjIbKbdq9HbTNvJhvmW2nAs+S3Ta8N2Jre3XX2z5dylo1dnM20fZ/spZfy3OfKLiIiIiIiIiIiIiIi84ojGnoiIiIiIiIiIiIiIyCuIaOyJiIiIiIiIiIiIiIi8gojGnoiIiIiIiIiIiIiIyCuIaOyJiIiIiIiIiIiIiIi8gojGnoiIiIiIiIiIiIiIyCvIc0MvdPUbbBZ64bfqxvd55SG8ts2ksbez3brDb/WdAxhsmABbhgHQ265rQztgu9AL9jLb9asDetu9c0cbuqT/rdLdbzuX8Lbs15TySYUvtii2dEdvy3EUwIZdOracrgoRj26i2LLMbZlvW4ZPUsmnVtvEnT0REREREREREREREZFXENHYExEREREREREREREReQURjT0REREREREREREREZFXENHYExEREREREREREREReQURjT0REREREREREREREZFXENu5jRKYn77/hqXLVuLr52/rpNiMjvZ2jhz8iT3vf2TrpFiNluZmjh85CBIJW7a9gZubu62TJDi2ruu3rl8lMDiYkNBwm+jfuHaFoOAQQsOsp2/rdw5w4fRxQiOiiIqJs1kaREREhCE78wmhYRGo1GpbJ0VE5JWjp7ubM6eO09nZicGgx8XFlTd27LSKdnZmBtevXBzRttdv2oqHp5dgmq+ssSfy26SoMJ+YuHgWLEq1dVJ+MyxMXWpT/cVL0myqLyIiIjwGgwGp9LdzGCk7KwNPL2/R2BMRsSB9fX04OTlx4/oVQsPCmTVnHgCNDQ0A6PV69Ho9Dg4OgmkDxMTFs3zV2hG/1+v1GPR67AXQtoix19HRztGDP7F7n2kH6cG9O2h1WlQqNVkZj9Dr9bi6ubN2wxbs7e0tIflUu72dw7/8yN79vwPg/r3baLVaAHKzMzl3+gQGg4E16zfh5x9gUe3n6ccnTOPCuVP09fYikUjYtPV1XC24yzR61+7+vTvotFoioqI5d+o4ACFhERbTe0Z/nDKPT0ji0oWz9PX2YG9vz8o1G3D38LS8fns7Bw/8wL4PPgbg3t3bFOTm0NnZgUQipaK8jLfe2S2I7qEDP/LeB6byTr9rKu+IyCjOnjqBRCIhJCycspJi899YUnt4XUu/dxvdYF0vyM/lwrnTaPr7WbN+I4FBIRbVHtIfq851drQTHhlFdGy8xTVH648uc51WS0dHOxGR0cTGWV7/ef0LmOIMnT11HLVazaIlyyyuD9DZ0c7xwz/zzt4PAXiUbmprQjBeGVdVVuDt40tNdSU6nZa1G7Zw785NmpuaiImLZ1Gq5Qzu0fl9mH4HnU5LdVUFvn4BVFdWoNH0s2LNBgICgy2mC+PXsajoWM6d+f+z99fvUV/t3jf+mplkMslkIsTdPSHB3d0KNazQQoFSuWTvfT/3fRzf5w+4n+N4ZO997X1VaaGlLW1xd3crEiHuHoj7ZOT7wyRDCJNAks9npoXP6xeIzXvpuda55FxH0em6cHMbweKlb6DqHrTF1i4pLsLbx4fS4mIMRgOLlizHP0DYsaw/7cioaI4fNY0nYeHhFOTnmX9HSJoaGzh2YDdrNnwEwP07N+nq0lJeWoyntw+V5aVExSYwauxEUbSPH9zN6g9M2g/u3qRLq8VB5cij1HvI5HJGeHgyb8mbgmuDZRuTnvrQVCaHD2BnZ8fa9z8UdO40UFubNWc+fv7+tLW18cP2b/jkL/8imG5v/b7jaFVlJQ0N9by/cfPT39nzKx9uEe5kkqWybqhvoKqynPUbt1BTXcXO7d/w0ad/x8XVlW1f/jcbNn8sWNlbyndnRweFhQXMnD2X4JBQLl04h0wmY/pMYccTS3X+uLqaJ49r2LDZZGvr6mo5vH+v+Wshtfvmu66ujqqKcvPvPH5cw0ef/hVXV7dBf7altjy11yJ0Z2cnmRlppD64T0hYODNmzaG1pYWw8KdzZG8fHwA62tvZ+f23hIVHkJwyekC/ob/xcvK0Gc9oZz1KJz31AcGhYUwbYJ7Q0dHOrh+2ExoeQVLyKEFPDom6sxcVE8vIlNEAXLt8gfTU+4waM15MyWfo6urig01bKS0p5uSxw+YObg2OHd7PhElTiYqJRafTWe3xyZPHDjNn3kICg0O4dP6sVTR7c+bUMebOX4z7CA8qK8o5d/oE765ZbxXt8MgoAOyVSiZMnGwVzR5OHD3MgsVLCQgM4tIF65e7wWBg/YbNFOTlcu3qZVZZqcxfZwwGA8cO7cfTy5uJU6bZOjmio1DIWbdhM/fu3OLgvt9Yt2EzKpUj3339T8aMm4Cjo5PoaTAYDKxe/yGFBXncunaZt1atE10T4NiRA8ydv4jgkFCuXLrA1SuXmDt/oVW0wTSWbdzyMaUlxZw4dkgUh8sSx48eYt6CxQQFh3Dh3GmraPbFoNezct0mq+vev3ODdZs+RWFnR2dHh1W1o2PjKC0ptvlRcWsywsODutonNDTU4+bmTlZmhigLeH1xUjuh0+no7OykrLQEH19/ykpLCCAIJycnwTco+iKTy1m89A0O7t/L3PkLKSzIZ/0G67R3N3d3mpubqK6qwsfXl7SHD0hKTrGKtrOzs9mpvHf3DqUlxYN29F5EWWkJDx/co7y0lOjYOJYuf4sRHh4AjBozjsMH9nLv7h1CQsNISh6FRqNB7ezMlo//Qk52FpcvnqetrY2k5BQSEkead+VeVjv94X3Ky8uIioll0bIVjBjhYf55duYjystKzV+vWb8RtdqZjR99Sl5OFlcvXaCtrZXEkSnEJSQNStsSojp7Tx4/5vqVC3R0dNDV1WXVOzUAcfGJAAQFh6DVdtLR0YFKpRJdV6vV0tLSTFRMLAB2dtY5LdvZ2UFnZweBwaZdnfjEJAoL8qyiDdCl1VJRXsbRQ/vM39PrdFbTtxWdnZ1otVoCAoMAiEtIIj8316pp6GlrPn5+NDU0WFX7deXMyWPExMa/Fo4eQERUDACeXt54eHrh7Gw6Xubq6k5zU5NVnL3IKFM79/bxpampUXQ9MNnVjo4OgkNCAUgcmcyhfXusot1DfEISYBrLOjutM5Z1dJjyHdQ9niQkJlOQb73xpIfIGPEn/Jbw8PTm7IlDhEXEEBYZbZM0vG7ExMWT9SiDiZOnkvkogzfefNsquv4BgZSXlVBWWsLEyVO7501GAoOEPTnQH55e3iQkJrFv9y+s++BDFAqFVXQBRqaMIi31Pl7eC8h6lGHeWbUWZaUlpD64x9r3Nwr6uWdPnSA9PZX5CxazaMkbzx0BD4+IZOtnf6cwP4+C/Dy+/+5rNm35BCe1Gjs7O+ITEolPSKSpsZEzp45z8dwZtn72dzQvcaz6/JmTPEpPY878hcxfvMzi8XNLxzjB5C/ExicSG2/SPnfmBJcvnGXzx38d1pFuQbwQuUz+zM6VTm+a4J86fpjlb72Ll7cvGWkPKS0pEkLuWW15H+3ezoXs2d/t86X4+iLSV9faTpWlOjcajagcVKzf+JHo+jIb5d9W9W1Ju3ee7RSmriyTyTEYDVbXtwa2qPOB6ts/IJCSkiLGTpgk6oJO33z32FcxGKiOeyYgMpnM3N56vjYYhGtzz9Vzr/wq7ExpkMvkgmr2q23FNj4YbaHHMlvmGyy0u151LvbOilwuBwt5X/zmSirLSigqyOP329dY9f4WUe4M2mJM6a++e6dFzHT0l+fY+AQO7d9LdEwcMpnsmZ0QMXUDg0IoLy2hqbGByOgYbt+8hkwmIzwi0ir6YDrGqFKpaGtrFVSzh/7qPCY2nmtXLhESEoaPnx+OTsIv2vWX75aWZk4eO8Jb764e8h25/vI1bsIklA4OXLtyiYKCPJJGphAcEopM9tR6Ojo6Ep+YRHxiEnt/20VpaTEx3ddRWltbyUh7SHpaKhqNC8tWvI1arR4wXz3aY8ZNROngwI1rlykqyCdhZApBwSHPaA9EW2srj9JTeZSeirOLC0veeBOnPtqDRRDL5aRW09bWSnt7GzqdjoI8066GVtuJWq1Br9eTmZEmhJRF7fa2VtrbntUG0zYpmFYOHBxUOIiwEmpJX6lUotG4kJuTBZgadldXl+C6z5R5fq4pjw4qykpLAEQrc4v6ebkoHRxwcXUjJ8tU7kajkcc1VaLoq9Vq2lqflntebo4oOn0x57tbNz8vFwcHB5RKJRXlZQBkPUq3mrY1sdTmrIkt6nwg+5KUPIrwiEiOHNgriuNhToNTdxq6y71QxN0VW9cxmPLbZqX89sVSG3NwUKFSOVJaUgxARloqQSHC34kdqH1nPcoAxBvLLGmrVCpUqqfjyaOMVEE1e+PY3cY72tvQ63QUFViv3Zm023pp52E0GmlpbiIgOJSJ02ah7ew0348Wmv5sjFKpRKvtFEWzv7bm6upGdVUFYLoHLhb9jWXu7iOQy+Vcv3ZZlCOc/ZV1YFAwjzLScB/hgUwmQ6VypCA/V/A7wf3lOycrk472dtas28DZ0yfpEOHYcH91bmdnR1h4BKdPHiNpZIrgumA53waDgUP79zJj9lzz0cqh0G9bdnNj+szZbNr6KXHxidy7e5tvv/qcjHSTHSsuKjTPyzs7O6mvr8fFxZXOjg727/mVXT/uQKfT8e6qtby7ei0xsXHPLfb0N166urkxdfosNmz+hJj4BO7/fpsd27544Zy8s6ODg/t+49eff0Cn1/HmyjW89e4aomKe1x4sgixHKxQKJk6ezq6d23F21pgrbsq0mez6cTuOTk74+QWIYrgUCgWTpkznpx++Q6PRPBMMRKGwY+f2b9Dr9Sxc8obg2gPpL162gtMnj3Ht8kUUCgXLVryDm7u74Lo//9Bd5t0rYAuXvGEK0NIdKEQs+qvzxctWcPb0CW5ev4LBYCAmLgEvb19R9CdPm8HOHd/irNHg4Sl8EJh+dadO58fvvzPpdtf3wiXLOHX8KDKZjKDgEBxUDqJoT5oynR8ttHVr0F+bs6Z+f3X+kgtmQ9Lsz74AjB0/ic6OTo4fPsCS5W+99MrdYNMwftI0fvtxO84accvd1nXck4YJ3flVazS4WzEN/bWxJctW9ArQ4s7ipcutpg2gsLNjx7dfYzDoWbTEetqLly7n+NHDyIDQcPECfikUCsZOmsaen3egdtbgPsJ6tk2hUDBm4lT27fq+W9sDo9HIuROH0XZ2YsRI0qixoiwW9+hbsjGJSSmcOXlclAAt/dX3+ImTOXRgDw/u3yOi+w68GPQ3jgLExsVz8fxZtn76N1F0LZW1q5sbGDEf2wwICqK5uUnwIEyW8t3e3s6li+dYtXY9Li6ujB4zjnNnTrJk2QrhtfuxL/GJI8nJznomYIng2n3yXV5WSlVlBdcuX+Ta5YsAvL1q7Usdk3zusweYC8rlciIio4iIjKK1tZX6uloAqiorOHPquHl3LjllFH7+AXR2dDBm3ITndgH70x5ovJTL5YRHRBEeEUVbayt13drw/J29OfMX4enpxegx4wl6Ce3BIhsocEhzh8E6UUUsIJeLNHv7g2OtQC6WtW0mjb2d7UJqC1HmWq3WfAzh5vWrtLa0MOclAjjYsswBDDZMgJ1CmDrf+9svjJswkZDQsJf+G73tTBtanXi7gC/CXmE7u6rT267MVUrr3YERml0/fm+OkGhL+ka9exEtHba7r21Lu6Z2sN2LVgobzptsOXcBsKFJx5bTVSGcglvdUUEHGwHUlnUuxsLqy6LT224Md3YYWmuT3tmTkBCA/Lxcbl2/isFgwMXVVZSVf4nnOX7kEDpdl9Uu0ktISEhISLwq7N/zGw0Ndax+T/inqiT+OAzZ2Tt1/DAF+bk4OanNb60B3P/9Ng/u3UUukxEWEcX0WXOprCjn7KljgGklYNLUGURFxw450SePHSY/LwcnJ7X5OYX29naOHtxLY2Mjrq6uLFvxDipHR/Jysrl6+QIymQy5XM6suQuGPTG0pf7JY0/Lvedtj2uXL5CXm4NMJsPJSc3CJW+Yo/aUFhdx4dxpDAY9jo5OrBpGhx5Mnev1es6ePEZVVQUymYxZcxcQFBw6ZO3eHD9yyFT+arV5lbmmuopTJ46h1WpxdXVj2Yq3cHAQ/iglmMK/9xwZeGflGnbt3GF+e629ox23ESOGfZm2P77+4h8olQ7m9vT+xi1UV1dx5uQxdDodcrmceQsWC/KmpKW2dun8WfLzclAoFLi5ubNgyRuoVCra29s4cmAvVZUVJCQlW4wyNVQs1XcPnl5epKU+oLOzEycRLpYD/H7nFqkP7gEwMnkUY8ZP5OqlC+TlZpv73KKlywV5/Fin07Hvl53o9ToMBgOR0XFMnDqDxoZ6Th49QEd7O94+fsxfshyFQkF5aTGXz5/hyeNqFi57i6iYuCFrW6rv61cukfbwvvnS/tQZswiPeHq8q6mxke+//ZJJU2cwbsKkIWs3NzVy+vhhc3CCxOTRjBoznuOH95uP3XR2duDgoOK9DVtoamxg5/avcHc3HZvx9Q9gzvzFQ9bvwVJba29v59CBvTQ1NODi5saKN98R/HhXf9oAMbFxHD28H5nMdBxp1px5gmv3p9837++tFzZinsFgYM9PpuO6S99cRer9Ozy8d4emhno+/ORfnwsWUV1Vwb5d3zN/6ZtERg+9rfdo7+s+Nrr4zZUc+G2n+V5ee1sb3r7+LFr+jvn3a6oq2P/LD8xbsoKIYWr3cPf2TdIe3gfAy8ubhUuXc+rYYaqqKpHL5fj5BzBv4RLRozPqdDp27dyBTq83Xb+IjWPaDOHeznxO68fvTQ9Id2tNnT7zmXG0ta0VP/8A3npnleD6lspcoVBw9fIFcjIfIZPLSRk1htHjJgiu3V/eezh7+iRpD+/zr//z/ye4tqX+ffnieRoa6pAh48jBfSxetmLQxyhflr7zpuNHDlFaUmyepy1athwfH+Gv/PSlID+Pc6dPYjAaSE4ZzcTJUwX9/MGMo40NDXz/7Zfmqwo9/V0MhuzsJSQlkzJ6HCePHTJ/r6S4iPzcHNZv/Ag7OzvaWk0Dt6eXN+99sBm5XE5LSzM/7viGiMjoIV84TEhKZtSYcRw/ctD8vds3rhIcGsaESVO5deMqt25eY8asuQSHhvFBVDQymYzHNdUcObCXD7d+NtRs21w/sVv7xNGn5T52wmSmdD9qfO/ubW5cu8y8hUvo6Ojg7OkTvL1yLS6urub6GCqDqfO0h6bJ8QebPqattZX9e3bx3gebBdl6T0pOYfTY8Rw7csD8vRPHjjBrzjyCQ0JJfXCfWzeuCf4oaQ+/37mFh4cnnd13UHuHDD64bzeR0TGi6Pawau37zzg2l86fZfLU6YRHRFGQl8ulC2cFWaWz1NZCwsKYNnM2crmcyxfOcvvGVabPmoudwo7J02ZS++QxTx7XDFu7N5bqG6CpqZHCwgJcXFwF1evN48c1pD64x7oNm1EoFOz97WfCI6MZN3EyU7snQ/fu3DL3ueGiUCh4c9U6lEoler2evb/8QEh4BPfv3mLUmAlExyVw/vRxMlIfMHLUGDQursxbtIx7d24OW9tSfQOMHjehX0fu4vnThIUPP2KdXC5n2qy5ePv4odV28svO7wgOCWPxG2+Zf+fyhTPPLOC4ubnz3oYtw9bujaW2dvP6VUJDw5g4eSo3r1/l5o2rzJwtvMNlSbu4qJDcnGw2bv4YOzs7WodpwwerL3beU+/dwd3D03yf388/iNDwKA7u/um53zUYDNy4fJ6gUGHuoqfdv4PbCA+zg/fmqvfNPzt5eN8zzy0YDAZuXLlAUIhw9+Cbm5u4d/c2G7d8gr29PYcP7CXrUTpxCUksfsP0gPuxQ/tJe3iflNFjBdO1hEKhYPW6D8x25+edOwiPjCIgIFAcrffeN2vt+nEH4RGRVhlH+ytzI9Dc1MSHWz9DJpOJ1s/6y7t/QCCVlRV0dLSLoguW+/eESVPM86S7d25x/colFixeKop+33kTwMzZc4mxwluKPRgMBs6cPM6qtevRuLjww/ZtREbF4OnlJZjGYMdRVzd33v9Q/Aj2Q740ExgU8tzqZur9u4ybONkchrxnd8Pe3t7s2Ol1OmTDDBwdFByCSvWsdl5uDglJyYDJKcnLyQZMEa16HIwurVaQSA621A+0oN17AtTVpTXrZT1KJyomFhdX02R4uLtNg6nz2idPCOp+l8pJrcZBpaKqsmJY+j0EBYc898BkXV2t+T2o0PBwcrIzBdHqS3NTE/l5uYxMGfXczzo7OykuLhrWrvVQkMlA26k1p6Hn/bPhYqmthYZFmPuyn38gzc3NgOkh+8CgYBQK4U+GW6pvgHNnTjFr9lxx3lTppu7JE/z8A8w2LCgohNyczD59TrhIuzKZzHz302AwYNAbkCGjrKSIyO5du7iEkRTkmeyLi6sbnt4+giyiWKrvgcjNycLV1R0Pz+EPlGpnDd4+fgAolQ6M8PCkpaXZ/HOj0Uhu9iOi4xKHrTUQltpaXk42id22PTEpmdzsbKtp3793l4mTp5rta9/Q32Lri5n3luYmigrziE9KMX/Py8cXl34eVk67f5eIqFicnIZfBi3NTRQX5BHXS7sHbWcn5aXFhEU8dfbSHtwlIipG8LD0RoMBnc60i6/r6sLZWUN4ZBQymQyZTIavfwDNzU2CalriebujF82s9tXS97n/JPY4aqnMH967y6Qp0812VKx+1l/eDQYDF8+dYebsuaLoguX+/cw4ptWKNpYONG+yJpUV5biNGIGbuzsKhYK4+ARz1HyhGOw4ai0EnZnV19dRXlrCtcsXUNjZMWPWPHz9TJfKKyvKOX38ME1NjSxcukLw92raWlvMk1y12pm21hbzz3Kzs7hy8Rxtba289e4aQXX/KPpXL50nIz0NBwcHVq5dD0B9XS16g4Hfft6JVtvJ6LHjzQ6pUPRX517ePuTn5RAbn0hzUyM1VZU0Nzfhx/CPF1rC09OL3JxsomNiycp8RHOTOAPkuTOnmDl7rvm4SW9yc7IICQkT7fgogAwZe379CZlMRnLKaJJHjWH23AXs+e1nLp4/g9FoFPxx0v5IT31g1VW53uRmZ6HRaPAW+diHp5cXVy+dp72tDTt7ewryc8027cql8zxKS0Xp4MCq995/wSe9PAaDgV93fkdjQx0jR43F1c0dBweV2WY6azTPOEJi8+D3OzxKT8XH14+Zc+ahUjmi1Wq5c/M676xex91bNwTVa2psoKa6Cl+/p7aioqwEJydn3N1HmL/X2NjArh+2oVQ6MGnaTMHDpPfQ2tpiPqKrdnamtZdtF5v62lpKS4q5fPE8dnZ2zJozT5Aj2i+LmHm/euEMk6fPfqknDVqamyjIy2bFynWcP3V02NrXLp5h0vTZFu14YX4OgcEhKLvteEtzM4W52SxfuY6aquFr96DRuDB2wiS++fw/sbOzJzQs/JmIp3q9nkfpqcyet0AwzYEwGAz88N031NfXMXrsOPxF2NXrrbVz+zbq6+sYNeZZLTHH0f7K/Oih/WRnZpCbk4WjkxNz5i0ULQqwpbzfvX2LyOgYwRZqB8PlC+dIT0vFQeXAGpHu7fU3b7p86QLXr14mODSMGbPmiPpeLUBzczMuGhfz1xoXFyrLy0XV7MHSOAqmcWzn9m9wcHBgyvRZosUfELRkDQYDHR0drFn/IVWVFRw9tI9NW/+CTCbDzz+ADzZ/Qu2Tx5w8fpiw8EjRKlYmkz2zgxYVE0tUTCylJcVcvXKRlWvWi6JrS/2pM2YzdcZsbt24yv3f7zBl2kwMBgM1VZW8u3odXTodv/y4A7+AQEHDqfdX54kjU6irfcLPP3yLi4srfgFByEWMnrR46XLOnj7B9auXiYyKRi7CHYe8XNN5d18/f0qKi577eWZGOiNTRguu25s16zeg0bjQ2trKnl9/YoSHJzlZmcyas4CY2DiyMjM4efwIq0Ru4zevX0EulxOXkCSqjiW6urq4cf0qq9asE13Lw9OL8ZOmsPe3n7G3t8fbxxeZzOR0TZsxm2kzZnPr+lXu373DlF53L4aDXC5n7YYtdHZ0cPTgHurrngjyuUMhefQYJk6Zhkwm49rlC1w8d4aFS97g+tVLjBk3YcgP4faHVqvl2KG9zJg9/5nJXnZmBjFxCeavndTOfLj1rzg6OlFdVcnRg7tZt/FjURda4HnbLjYGo4GOjnbWb9hEZUUFh/bvZetnf7NJJDoh816Un4ujkxPePn6Ulxa/8PevXjzDpGmzBcl3UUEujk5qvPrRzs3KIC4xxfz1tYtnmCiQdm862tvJy81my6d/w8FBxZEDe3mUnkp84kgAzp46TmBQCIFBwr/paAm5XM7GLR/T0dHBgb2/8bimBi9vb9G0NmzealFLzHG0vzLX63Uo7OxYv3ELOdmZnDx2mDUC30/toW/eS0uKyc56xJp1tgmQMn3WHKbPmsONa1f4/e5twe9q9jdvmj5rNmq1M3q9nlMnjnLrxjWmTJshqPYfhf7GUbWzMx99+jfzOHZw3242bBZnHBPU23LWuBAZHWt27mQyGe3tbc8cu/Dw9EJpr+TJ4xrzCrkQOKmdaWlpxtnZtOpt6ahHUHAIJ4/V09bWJngwB1vr9xAXn8T+Pb8wZdpMnDUuqBwdsVcqzcfsHtdUC+rsDVTnM+fMN//eLz/uEPW9LA9PT1Z172jW1dY+8/i1UJSXlZKXm01Bfi56nY7Ozk6OHjrA0uVv0tbWRmVlBW+KcKG8N5ruVSm1Wk1UdAyVleWkpz80r/7GxMZz6vgRUdOQnvqQgrxc3l2z3iaTzob6Ohob6tn+7VeA6YjI9999zfsbt+Ds7Cy4XlLyKJKSTcdPrlw8h3OvlUGAuIQk9u3eJZiz14ODSkVgcAiVFeV0dnZgMBhM956bm622AqxWPy3PpOTRHNj7KwBVFeXkZmVy+cI5Ojs7kMlk2NnZMWrMuCFr6fV6jh3aS0xcIpG9jnAZDAbycrNZ8/4m8/fs7OzMi4U+vn64urnTUF+Lj6/wzxOo1c6mMtdoaGluRi3AMcKXRaNxITomDplMhn9At31taxMtAFRfxMp7ZUUZhfm5FBfmo9Pp6NJ2cub4IeYtthzFuKaqktPHTHeN2tvbKC7IQy6TEx41+HtdVeVlFOXnUtJL++zxQ8xdvJz29jZqqipZ+MbTwCyPqys5e/zgU+3CfORyOWGRw7tTVlxUiKurm3muEBUTS3lZGfGJI7l+5RLtbW3Mf1uc+1MDoVKpCA4JpaAgTzRnr69WYbeW2ONof2Wu0bgQFWOyOVHRsZw8dlgU/d705L2kuIiG+jq++fK/AdNi5jdf/jcfffJX0dPQm4TEkez57WfBnb2B5k1gsuVJI1O4LfAJEUtoNBqaeh2Lbm5qEiSw2ovobxztO465ublTX1crqG/Ug6DOXmRUDKUlRQSHhJqOEOpNESAbG+rRuLgil8tpamygru4Jrv2cyx8qEVHRZKQ9ZMKkqWSkPSQyynTevr6uDjd3d2QyGdVVleh1eov3f/7M+vV1tWZHKi832/zAeWRUNOfPnOw+G66nsqKcMQJHmOqvzru6usBoxF6ppLiwALlcLsjdnv5obW1FrVZjNBq5fu2yKBfaZ8yaw4xZcwBTYJrbt26YDVZ21iMiIqNEPYag1WrBaETp4IBWq6WosIDJU6bj7KyhtKS4e+AoFNWpLizI486t66x6731BH/kdDF7ePvz1X/+n+esv//mffPDhR6ItoPS0rabGRnKzs1j7wSYLfU6YR6Db2lpRyBU4qFTourooLSpkzITJBAaFkpedSXRcApkZqYT3Ch4hJj0LWAB5OVnmi+yr120w/871K5ewVyqH5egZjUbOnjzKCA9PRo+b+MzPSooLGTHCw7zQAaZyUqkckcvlNDbU01Bfj6ur+5D1ByIyOpr0tIdMnDyV9LSHogdg6k1UdCwlxUWEhIZRV9ttX0Vq55YQK++Tps1i0jTTpLK8tJj7d2/26+gBvL/lL+b/nzt5hJDwyCE5egATp81iYi/th3dvMbdbuyAni5A+p47WbX4aUO18t/ZwHT0AFxcXKivK6erqws7OjuKiQnz9/El9cI+iwnyrLqa1tbYiVyhQqVR0dXVRVFjAhElTrKJVXFjA+G4tscfR/srcwUFJaXERbm7ulJYUm6P8Ck1/ef/s7//D/Dv/8f/8X1Zz9Orqas2L/7k5WYKNY73pb97UM7YYjUZyc7LxEjBISn/4+QdQX1dLQ0M9Go0LmY8yWLbirRf/4TDpbxztPY41NNTTUF+Hq5s449iQe9Sxw/spKymmvb2Nbz7/TyZNnUHiyBROHT/MD999hUKhYOGSN5DJZJSXlXLn5q/IFQpkMhlz5i0a1oB19OA+Sru1v/rnfzBl2kwmTJzCkYN7SXv4AJfupw8AcrIzeZSeilwux87OjqUr3h62AbWl/tFDT8v968//k8lTZ1CYn0ddXS0ymQwXF1fmLjSFIPfw9CI0PIIfvvsamUxGUvIoPL2GvlI3mDpva2tl/+6fkSHDWePCIgHfnTt8YB8lxUW0t7fx+X/9O1Onz6RLq+Xe73cAiI6JIyk5RTC9lyHrUYZog2MPba2tHNy/GzDtdsTFJxIWEckCpZLzZ09hMBiwUyiYL1DoXktt7faNa+j0evb++jPwbKjgbV/8F1ptJ3q9nrzcbN5Z9Z4gDr6l+k4W+bjsM/r7d9Pe3o5CoWDOgkWoVCpOHT9MXW13n3N1FSxccltLC6dPHMZoMGLESFRMHGERUYzw8OTkkQPcuHoRL29fc1CL6soKjh7cQ2dnB4X5udy6dol1H348sEg/WKrv0pJiHtdUAcLmsy8V5aVkPUrDw9Obn7/fBsDk6bMIC48kJzOD6F5HOAHKS0u4ee0ScrlpTJk9b5EgzyFYamsTJ03l0IG9pD64j4urK8vfenfYOi+rPTJlFMePHuK7b75AIVew5I0VojkAtsx7Dw/v3eH+nRu0tbbw685thIRFMHuB9Xa38rIfMWrc0J8QGQx+AYFEx8Tx4/ZvkMnl+Pj4MjJlNP/4f/8vXFzd2LVzO2DafZo8VdzjbS0tLRw7chCj0YDRaCQ2LsG8YC24VmsLx48cwmgwacXExZu1xB5H+ytznU7HscP7+f3OLezt7UWLSDlQ3sXGUv8uyMujru5J99zRjQWLxLHvljh66ABtbW1gNOLt48t8K2j3PE21+5efMBqMJCWn4DWMObElBjOOlpWUcP3qRfM4NnfBYlE2owBkRqOx3x82dxj6/6HIyIf2SPyfnoHqQ3xtm0ljbydswJ7B8LqWOYDBhgmwU9iuzvW2M21odYYX/5JI2CtsZ1d1etuVuUop7jtlEs/T0qGzmbYt7ZraQdwgEwOhsOG8yZbjKIANTTq2nK7a4ipFD7asc1vmW6e33Rju7DC01ma72ZaEhISEhISEhISEhISEaEjOnoSEhISEhISEhISExCuI5OxJSEhISEhISEhISEi8gkjOnoSEhISEhISEhISExCuI5OxJSEhISEhISEhISEi8gkjOnoSEhISEhISEhISExCvIgE8vlNd32iyuqofGwVbSry31rVqbaburlTbTfp0pr2u3mXbACHHek5GQ6IstQ2Xb8okRW/K6hmWXkLAmkm2zPrZ8ukmtHJpxez1rSkJCQkJCQkJCQkJC4hVHcvYkJCQkJCQkJCQkJCReQSRnT0JCQkJCQkJCQkJC4hVEcvYkJCQkJCQkJCQkJCReQSRnT0JCQkJCQkJCQkJC4hXEztYJEIOS4iJu37zOO6vW2jopNuXYkYNEREYTGxdv66RYjQvnTpOfl0dEZCSz5sy3dXJE5Y/Qzttam7l79RzTF6ywunZzczNnT5/gzbdXWlXX1uXe2NDA3t272PTRpzbRl5CQEI/GhgbKy0qJT0yydVIkJF5JUh/c587tm8hkpqjB02fMJiom1ira33zxD5RKB3PE4MCgEObMXyi67ivp7Em8vjy4f4+//9v/Qi6XNq2tgZNaYxNHD0Cj0Vjd0ZOQkLA+BoPhtbHpjY0NPMpIk5w9CQkB0Wq1KBQKWltbuXHtChs2fYSDSoVWq6WtrRWAjvZ2VI7CPwnVo61QKABYufZ9nJycnvkdsbR7EMTZu33tIs7OGuKTxwDw+80ryOVyKstK6OzswGAwMHbiNEIiooWQe4aL58/i4uLC6LHjAbh6+SL2SiWdnZ3s+XUXDfV1BIeEMn/REsHf3ulP22gwkJGehkwmIzwikpmz5wqq26+2vT1NjY0UFRagcXExNyyhuXX1As4aFxK66/vujSvYK5VgNFKQm4leryc0Ipqxk6aLog+W83/rxjX0ej0/fPcNE6dMJS4+UXRNe3t7GhsaKC4uROPiikIuJyl5lOC7qf21Na1Wy4F9u3nyuAZfX3+WLn9TtDem7t+8hJOzhpjE0QA8vHMVe3sl+VnpLFv9oSiaPfSX//TUB6LucPWn20NlRTknjx9lxdvv4u4+wur61tLLz83BwUHF48fVxMYl4OXtze+3b9Gl0/HWu6sEzftAaXB0chK1rV++eA6NxoVRY8YBcP3KJeyV9rS1tlFYkIdMBhMmTyM2LkFQXeg/33k52SiVDtYfyyyMJ2LYNoBLF86i0bgyeuw4s76yO+8Ojo7U1T5hy8d/EVwXLOddJpNRVFiAtrMTg9HA/IVLCAoOsYr29auXsVcq2bHtKxJHJjNuwiSr6NorlZQWF5lPLJw5eRxfP3+SklNE175+9TJvv7uaiCjTHFGMU0mWdG/fvM6yFW8TFR3D/j2/oVKpWLxsOakP7tNQX8f0WXME0+8vDfb29hQW5LNq7XpaW1rY9dP3rF2/EWdnZ0G1Ldm2O7eus/iNN4mKNu1qHTt8gJjYeCKjYwTV7m++NmKEBwBtbW2EhkewZNnyYX+uvVLJhImTzb9TUV5O6sN7FBbk8/6GzbS1tqJUKs3jqFKpRNn9/1s3r1NcWMDIlFHEJSTh4ND/e9+XL5xF4+JqLs9rV0w2a9yEp9qVFeWkPbxPUWE+732wCbW6/zq9c+s6xUWFJhsbnzig9lAQZKksPCqWgtws89eFuVlExSUxd8lbvLlmI0veWsOtq+dFeWQ1Lj6BrMxH5q+zMjNQq9VUVpQzb8EiNm39lPqGerKzMq2mnZuTzfsbN/Phlo+ZMGmK4Lr9aTup1dTW1bJp66cseeNNystKRdGOiI6jIOdpeRbkZuLo6EhjQz0rVm/g7fc28aSmisqyElH0wXL+t3zyF+zs7Ni45WPBHb3+NNXOzjQ2NrB562csfeNNysvLBNftV1utprq6ijnzFrJ562c0NNSLVucAIRGxFOc97ecl+dl4evuJptcbS/n39w+wia5arQagrKyUUyeO8fa7q0Vx9PrTFzPf/eW3prqKBYuWsHnrZ2SkpVJXW8v7H24hOWUUv9+5bZU0WKOtx8TGk5P1VDs76xFOTmpqaqp4/8OPeGf1Oi5fOEtLS7Pg2n+0scxa4wlAbFwCWZkZ5q+zMx+hVjt31/kC0Rw9sJx3o9FIWHgEG7d8zMbNH+Pt42s17cXLlhMUFMzGLR+L4uj1p9tj18Smvzz3fE+v11NcWEhEZJTouvMXLaGstBiAluYmnjx5DEBpaQmBIjj3ltIQl5CI2tmZe3fvcPL4EaZOnym4oweWbdtbK9eSkfYQgM6ODirKywgXuNyh//naxi0fs2b9BlSOjozpXugZ7ufGxSfQ3t7O3Tu32PHt11y5fIGQ0DC2fPwX1M7OePv44OTszFef/4NjRw6Rl5Nt/vsZs+awZPmbNDTU8/13X3PsyCHKSi3PY2PiEsjuY7Ni4kza9+7cYuf2b7h2+QLBIWF8+NFnzzh6u3ft5IfvvuaH777m7u2bAEybOYfFy1bQ0FDPj9u/4cTR/rWHgiA7e57evrS3t9Ha0kxHextKlQonJzU3r5yjsrwUmUxGa0sL7W2tOA3g2Q4FH18/WltbaW5upr2tFZXKERcXV/z8A3BzdwcgPj6RstISwVckLWnXVFeRlJyCvb09AI4ibcta0q6uqiI+PhG5XI5GoyEkNEwUbU9vX9rbnta3g4OKuiePKSsuZP/P2wHo6tLS2FCPX2CwKGnor97FxJJmVWUlMXHxyGQynJ2dCQkJtZp2Tzt3cXEBwNvHh8aGBgKDxCnzEV4+dLS30dbaTGd7O0oHFU7OLqJo9cUW9T2Qbu2TJ5w6foSVa9aj0Wisrm9tPV//AJy78+nm7k5YeAQAXt4+lBQXWSUN1mjrPr5+tLW10tLcTFtbKyqViprqamLjTHZVrXYmMCiEqsoKIqOEXf3+o41l1hpPevTb2tqe6juq0Li44Ovnj5ubu2i6Pdp98x4SGsbxo4cwGAxERcfi4yuOs/dHs2vWwJJ2bFwCly+eR6fTUZifR1BwiHkOJaZuSEgY9+7e4cnjx3h4edHR3kFLczMV5aXMFeEeVX/lPm/+Ir775kv8AwKJTxDn+K4l2xYUHMK508dpa2slNzuLqJhYUY5L95dvo9HI0UP7GTdhIr5+/oJ8rkwm5/N//H+ER0Tx9srVz7VruVzOytXvUVlRQXFRAefOnqKqqpKp02cC4OHhyczZ85g+cw6ZGens+W0XSUnJzF2w6Dnttra2Z8pTLpPz1X//O2ERkax4Z1W/fcrSMU6AER6ezJg1l2kzZpP1KJ39e34hITFZkDt9gt3ZC4+KoTAvm/a2VsKj4sjLzqC9vY03V29ArlDw644v0Ot1Qsk9Q2xcPNlZj2htaSE23nS8pu8xF5FOtj2n3dTYII7QS2g31NdbTTs8OpbC3Cza2lqJiI6jubmJlHGTiB85ymppsFTv1ta0Zplbyq9dr6O6cpkcg8EgahpCImIoyc+hva2VkAjrXGjuwRb13Z+u2tkZvU5HTVWlqM5ef/rW1uvdzmQyGQqFnfn/YrQ5W7b16Jh4crIzaW1tISYugcaGBlF0LPFHGsusadsAYmLjyMl6RGtri/mYrJhHlnvTN+9BwSGsXb+R/Lwcjh85yLgJk0gcmWwVbWvRV1culz9z+kqnE2e+Zknbzs6O4JBQCgvyyczMEOVkjiVdjYsLnZ0dFBTkERQUQntHO1mZGSjtlYIfo+svDQDNzU2mTZHWFoxGo2hXMfraNoD4xJFkpqeRlZnBwiVviKILlvN99fJFNBoXRiYPfd7Y93PVajXLVrxN6oP77Nv9K4lJI4lPHPnMzrVMJsM/IAD/gABCwyI4fvSQ2dkzGo2UFBeR+vA+lRXljBk7nuRRoy1qR/exWU5qNUuWv0Xaw/sc3Psb8YkjiUtIeuldc6PRSGlxEWmpD6iqKGf0mPEkpQgzpxbMhQ+PMh3tK8zLIjwqFq22E0dHJ+QKBRWlxbQ0Nwkl9Ryx8QlkZqSTnfWI2FjTimdlRTkNDfUYjUYyMzNE2+3oqx0aFkHawwd0dXUB0N7eLoquJe2g4BAyMzMwGAy0NDdTLPCKe2/Co+PIz8mkMDeL8Og4gkLCyM54SJdWC0Bri2mlRUws1bvY9NUMDAoiJysTo9FIa0uL4LscA2nbgpDIOIryMikpyCY4QtidjRdhq/xb0lWpVLyzai2XLp4Ttc7703+V9P5oaYiJiycrM4PcrEyiY+MIDAoiO8tkV9vaWikrLcHPT5yjtH+kscya4wmYjmRlPsogOyuTGCtHkO6b98bGBtRqNSmjxjAyZTRVVZVW01Z238UWm766Lq6uPHnyGJ1OR0dHB8VFhVbTBoiLSyDt4QPKSkoIj4i0mq5/QCB3b98kKDiEoKBgbt+8QWCwOH3MUhoMBgPHjx7mjTffxsPTk9u3boim3de2ASQkJfP7XdNRfA9PL9G0++Y7Lyeb4qLC53bMhvu5crmcmNg43l29lndWrqGrq4tdP+5g/55f6ezooLm5marKp/25proKV1fTDlxGeirffvU59+7eIT4hic1bP2P6zNm4urpZ1u4+fp6TlUl0nEk7OiaOt1eu5c13V9PVpeW3n77n4N7f6OzoGDAfj9LT2P7NF9y/d4e4+EQ2fvQpU2fM6ld7sAi2s+fu4UVXlxa1WoOT2pnImAROH9nLvp+/w9PbF1d3D6GknsPLyxutVotG44KzRkNdXS2+fv6cOXnCfKk9OibOKtrOGg011VX8sP0bFHIF4ZFRzBD4km9/2tExsZQUFfLd11+gcXUlICBQFF2AER5eaLVanJxN9e2kdqa+rpaDv+0EwN7entkL38DRSbx7AH3zbw36asbExlNcWMi3X3+OxsUVH18/0VYELbVza+M2whNdl9Zc5y1NjVbT7pt/046LSNscA+j2lLva2Zl3Vq5l968/s3jpG/iL1N8s51s8/gjtzJZp8PTypkvbabLnzhoio2OpKC9n5/ZvkMlg+qw5qEW4UwN/rLHMmuMJmMpdq9Xi7Gwq97pa69V537ynpT7g9s3ryOUKlEolS95YYTVtRycnZDIZ27d9RZJIAVos6YJp8rr9my9xdXMT7ehqf9qh4REcPXyAyOhY0YLLWdINDAqmqCAf9xEjcNG70tHRTmCQ8Pf1+kvDtSuXCAwKJjAoGC9vH3bu2EZEZBSeIjhefW0bgFrtjIeHp+BBWfrSN993Dt2kubmJndu3ARAZHcO0GbOG/bm90bi4MHnqdCZNmWZelDUY9Fw4d5qWlmbsFHY4qtUsWLQEAFdXN957fyNOL7kTZ7ZZvcrTrK1xYdKU6UycPI3SPgtlu3ftNO/eenn7sHjZClxcXVmzfgNOIs2ZZQMFTSmv7xQ+ospL4qERZ8Is0T/1reKvJvaHu9o6x3XEQKvVolQqaW9rY+eOb3nvgw9FuWAtBuV14u08v4iAEcO7z1pVWcH5s6dZu36DMAmSeGXR6cU93jwQdoqhH6Cx9ZuOvRlshEQxArK9LGIdg5OQ+KMxXNvW1dXFD999zfoNm3FQqQb1t8OxbX9m9Abb2Ta1cmjGTXpnT0JimOz9bRedHR3oDXomT53+p3H0/sxUVlRw5NA+ZswS/lkTCQkJCQmJV53iogJOHT/KmHETBu3oSfy5GJaz19nZwZWzJ6ivewzImD53MT7ddxlS793i9tULrNvyN1SOTuRlZfDwd1OIUXt7JVNmzcfDy2fYGejL8SOHyM/LwUmtFvX9LUsU5Odx7vRJDEYDySmjmTh5qtW0rZHvXd99gb1SiVwmQyaX89bajQCkP7hLxsPfkcvkBIVFMHHabAx6PZfOHudJTTVGg4GouERGjZ/8AoWhYctyL8jPo7Wlxawt9JtE/WGN+s5KvUvuo1TASGRcMnHJYynOzyL1zjUa62tZ9PZ6PLqfXujsaOfyqYPU1lQRHpvI+GnzREkTPK1vo9FIvRWP9+l0Onbt3IFOr8dgMBATGzekYycvy0B1fPvmdS6cO8Nf//V/WozqJTS27GN3bt3g4YP7yGTg5eXD4mXLsbMTbp3y5LHDFOTn4uSkZsPmjwHTG1RpD+/j2F22U2fMIjwiisqKcs6cPGb6Q6ORSVNnEBUjTqCi3mV+8/pVq9u1vvW9ZNkKUTUNBgM7d3yLs0bDOyvXUFxYwIXzZ8FoxF6pZPHS5biPEOeJkx5sPZbYSru29gmH9+81f93QUM/UGbMYN37iK60Nr3adW7Jtl86fJT8vB4VCgZubO+9/+BEqlYr29jaOHNhLVWUFCUnJzJk/vLt0A/Eql3lf7t6+SdrD+4DpyOnCpcspLyvh0vmzGI1GlEolC5eIb9uGNWLevHSWwJBw5i55E71ej05nCkrS0txEeUkRzpqnYdk1rq4sffs9HFQqSovyuXr+JMtXfTC81FsgKTmF0WPHc+zIAcE/eyAMBgNnTh5n1dr1aFxc+GH7NiKjYvD0Eu/Ca2+sle9l76xF5fh0cllRWkxxfi7vvLcJhZ2dOShLQW4Wer2ed9dvRtfVxe6d24iMiUcj0GXTHmxZ7rbUFru+G2ofk/solUVvr0euUHD+6B4CQyNwG+HF9AUruHX59DO/r1AoSB4/jYa6xzTUPRElTWDbMlcoFKxe9wFKpRK9Xs/PO3cQHhkl2l2m/uq4qamRwsICq4VKt2WZNzc18fud22za+in29vYc3L+HzIx0QRdVEpOSGTVmHCeOHnrm+6PHTXjuvpSnlzfrNmxGLpfT0tLMzu3fEBEVLXi48tfRrv1+5xYeHp50ajsBOH3qOG+9swoPTy/u/36HG9eusHiQjy4PhtexzHvw8PBk45aPzWn54r/+nWiRFjH+SNqvep1bsm0hYWFMmzkbuVzO5QtnuX3jKtNnzcVOYcfkaTOpffKYJ49rBEtDX171Mu9Nc3MT9+7eZuOWT7C3t+fwgb1kPUrn1vWrrOhl225ev8KipeLZNhhGNE5tZweVFaXEJIwETBMhBwfTNvDNy+cYP2XmM7/v4xdo3ib29g2gVYRHaQGCgkNEe9tuICorynEbMQI3d3cUCgVx8Qnk5mS9+A8Fwlb5fpR6j+RxE1F0r7T3Dsii6+rCYDCg03WhUMixFyFwiS3L3ZbaYtd3Y0Mtnj5+2NnbI5fL8fYPoqQgB1d3D4vBluzslXj7BZpD8ouFLctcJpOh7A4FbzAYMOj1ooaH6a+Oz505xazZc60RmwawvW0z2RCd6d+uLsGDMQUGh6BSvVxfsu/uDwB6nQ6ZSJXwutm15qYm8vNyGflMmHEZnZ0mx6+zsxNnjbjH41+3Mu+P4qJC3NxHCBYF8I+s/arXuSXbFhoWYbZhfv6BNDeb5uL2SiWBQcGv9BhuC21j3/HLWQMyGdpu26bt7LTK1Z8h12pzUyOOjk5cPnuMusc1eHj7MmnGXCpKilA7Ow94RDP70UMCQ8KHKv2HpLm5GZfeO5kuLlSWl9swRcIjk8Gx/b8ik8mIS0ohLmkUjfV1VJWXcufaJezs7JgwbTbevv6ER8VSVJDLT9v+C12Xjkkz5rz0hGow2LLcX+U6dxvhxYNbV+jsaEehsKOipAAPL/Gis70sti5zg8HAD999Q319HaPHjhMtAmd/5GZnodFo8PaxXl3Yssw1Li6MnziJL//7P7CztycsLML8oLvYPPj9Do/SU/Hx9WPmnHlm+1VZUc6p44dpamxk0dIVojxC/LrZtXNnTjFz9txnnhxYuHgpe3f/gp2dHQ5KB9Zt2CRqGl63Mu+PzIx00d65+6Npv+51np76wOrPnLxOZa7RuDB2wiS++fw/sbOzJzQsnNDwCBYsXsq+3b9gZ2+HUunAex+Ia9tgGM6ewWDgSU0Vk2bMw9vXnxuXznDv1lWqyktZtGJVv39XUVpMdkYqy95ZN1RpCRvxxsr1qJ01tLe1cmz/r7i5e2AwGujs6GDF6g94XF3JueMHWb3xE2qqK5HLZKzb/Fc6Ozs4vOcnAoJDcXF1t3U2JF4CV3cPEkZN4NyR3djZ2+Pu4S1FuAPkcjkbt3xMR0cHB/b+xuOaGry8va2i3dXVxY3rV1m15vWxnR3t7eTmZPPxZ3/HQaXi0P49ZKSlkpA0UlTd5NFjmDhlGjKZjGuXL3Dx3Bnzg8N+/gFs2PwJtU8ec/LYYcIiIgW9Q/i6kZdrupfq6+f/zJuVd2/f4p2Va/APCOTWzeucP3uaRUuW2S6hrwF6vZ683GzRnov6o2q/jty8fgW5XE5cQpKtk/LK0tHeTl5uNls+/RsODiqOHNjLo/RUcrOzeHvlGvwCArl98zoXz51mwWJxbduQlyTVzhrUzhq8ff0BCIuMpbammuamRvbv2s6vO76gtaWZA798T1trCwC1T2q4cu4E85e+jcoGRw7FRKPR0NTr4fjmpiarvf1mLdTd74g4OqkJjYimproStbOGsMgYZDKZqS3IZKYGnpVBYGg4coUCRyc1Pn6BPK6uEjxNtiz3V73OI+NGsvjdD5i/Yi1KBxUaN3EvEL8Mf5QyV6lUBIeEUlCQZzXNhvo6Ghvq2f7tV3z5z/+kuamJ77/7mpaWFlF1bVnmRUUFuLq54aRWo1AoiI6Jo7ysVHRdtdoZuVyOTCYjKXk0VZUVz/2Oh6cX9vZKUe63vE52rbyslLzcbL76/B8cObiPkqJC9v62i8c11ead87i4BCpErvfXqcz7oyAvFx9fP9Hekfyjab+udZ6e+pCCvFwWv/Gm1RdxX6cyLy4qxNXVDScn0/gVFRNLeVkpNTXV+HXbtti4BKuMaUN29pzUzqg1LjTUm6LhlZcW4eHtw7otf2P1xk9ZvfFT1M4a3lyzwfT4cnMj547tZ+aCpbi6237SKDR+/gHU19XS0FCPXq8n81GG6I9UWpOuLi3a7ovzXV1ayksKGeHhSWhENBVlxQA01Ndi0OtROTrirHGhorTY/Ps1VeW4WbjrNVxsWe6vep13dAfbaW1uorQwh7AocR5zHgy2LPO21lY6OjoA0y5bUWEBHh6eVtEG0+Orf/3X/8knf/kXPvnLv6BxcWHDpq2in/e3ZZm7uLhSUV5OV1cXRqOR4qJCPDzFL/OWXnfK83KyzBf4GxvqMRhM71o1NTZQV/cEFxHuF71Odm3GrDl8+td/5ePP/s6yFW8THBrGW++uprOzw/ywelFhgej1/jqVeX88epROXIJtjnDaQvt1rPPCgjzu3LrOindWYW9vL7peX16nMndxcaGyou/45YW2l20rLhLftsEwH1WvfVzNlXMn0Ov1uLi6MX3ukmfe6vh1xxesWL0BlaMTl88epyg/G2eNKYKcXC5nxeoN/X72UB9VP3xgHyXFRbS3t+GkVjN1+kySU0YP6bMGS35eLufOnMRoMJKUnMLkqdOtogvC5HugR9WbGus5fWQ/YLpwGhEbz+jxU9Dr9Vw6c4zax9XI5QomTp9NQFAoXVotF88co6H2CUaMxMSPJHls/6GUh/Ooui3L3VbaQrXzgR5VP3VgF9rOdmRyOWMmz8YvMISSghzuXj1LR3s7SgcH3D29mbN0JQAHfvqKLq0Wg16P0sGB2UtX4jaifyM21EfVbVXmNdXVHDtyEKPRgNFoJDYugSnTZoim96I6/vKf/8kHH35klacXbNnHrly6QFZmBnK5HB8fPxYuWTboY5MDPTx89NB+ykqKzeU8eeoMSkuKeVxTBchwcXVl3sIlODtreJSeyu2b15DLFchkMiZOmUZU9MCRA4f68PCf3a4N5VH1kuIibt+6wTsr15CTncXVyxeRyWSoVCoWLXkDN/eXuwYw1N2KP3uZDwetVsuX//xPPv70b1Z/c82W2n/2Oh+sbbt94xo6vd4c/MvPP4B5C5cAsO2L/0Kr7USv1+OgUvHOqvfw8Ow/UuXratsG86j6tcsXyc7MQCaX4+Pjy/zFyyjMz+PaFZNtc1CpWLj45W3bUB9VH5azJyZDdfYkhs5Azp7YDMfZkxg6Azl7YjNUZ09CYrAMNCESm6FOiP7sDMXZEwrpfrHE64Jk26zPYJw9oRmqs/d61pSEhISEhISEhISEhMQrjuTsSUhISEhISEhISEhIvIJIzp6EhISEhISEhISEhMQriOTsSUhISEhISEhISEhIvIJIzp6EhISEhISEhISEhMQriOTsSUhISEhISEhISEhIvIJIzp6EhISEhISEhISEhMQryIAv06odBvdwrcSfG41Kqu/XDR9X6T1LCetgsOHbRAq59O6atZHeupOQEB+51M+szp9xOJF29iQkJCQkJCQkJCQkJF5BJGdPQkJCQkJCQkJCQkLiFURy9iQkJCQkJCQkJCQkJF5BJGdPQkJCQkJCQkJCQkLiFeSVdvb+/f/+37ZOgs1Je/iAMyeP2zoZViUrM4NtX33OLz/9YOukWIU/Qjvfv/sXOjo6bKK959efbaJt63L/8p//SVtbm03TICEhIQ43rl2xdRIkJF5ZysvL2LnjW3Zs+4ptX33O1csXraZ9/Mghvv78v/j+26/5/tuv+emH7aJrSuEXJV45Uh/cZ9GSZQQGBds6Ka8Nb61cYzPtd1e/ZzNtCQkJ62EwGJDLX+k1ajM3rl1h0pRptk6GhMQrg9FoRNvZiYNKxfHDB1n+1jt4+/hiMBioq60FoLOjA6WDg+DRhHtrA8ycPZeYuPhnfkcsbRDA2fv99g0UCgUpY8Zz6fxpnjyu5u1V6yktLiQj7QFKpQPVVZXodF1ERscxaeoMIdINwK0b11DY2TF23ATOnTlJTXU1a9Z9QHFRIakP7gNw7sxJCgsKcHZ25o0Vb+OkVltFOyFpJJcvnMNgNOLk5MTq994XRPdltEPCwrh5/SoqBxVePj7YKYTz6e/cuo5CYcfoseO5cPY0j2uqWbl2PSVFhaSlPiAhaSTXr1xCr9fj5ubOgiVvoFQqBdOHgfP/6887sbe358TRw0RGRzNrznzRNVMf3Cc4NJRbN649U+bzFi62ijbA5QvnyMvLxc7OjrffXY3a2VkQ7R5eVO8VZaW8t2EzTk5OgurCi/NfVlbCBx9+JLj2y5Q7QFtbG/t2/8LkKdOIiIq2ur619HJzshg1Ziz5eXk4OzszfeYcLp4/Q1NTI3PmLSQqOmbYabh98zoKhYIx4yZw/uwpHtdUs2rt+xQXFZL28D55udmMGTeB/O62/uY7q1CrhWnrt25ex65b+9wZk/bq90zaqQ/vExEZzc3rV8FoJDwyipmz5wqiCy9X9smjRttkLLM0nghl215GPzcni5TRYygqLGT+wsWCLuINpP3w/j0UdgqqKisAGSOTUxg3YZJVtO/dvYNOp2PHtq/w9PJm2Yq3rKLbU97/9r/+TwCyMh+Rn5fDkmUrRNc+uH8PI5NTzGN22sMHVFVWCNLWBtLdt/sXkkeNYc68Bdy9fZO7d27x8Wd/p6G+nqOHD7Dugw+Hrf+iNFy/epnmpibWbdiEo6Mju378nslTpxMWHiGI9kB29djhA8TGJTB73gIAHj64R+2Tx8yeu0AQ7YHyfWDfblxdXAHQ6XTo9Xo+/svfh/25qQ/uP9NnGhsbSHv4gEfpacyZt4CIqGha21pRO2sAkMvleHp5AVBWWsK5M6eIT0xiZPIoXFxdn9d+wVixbLkF7Yx05sybT0Rk/3OEsrISzp05TXxCYr/aQ2XYS2T+gUGUl5UCUFNVSZe2C71eT3lZKQGBwUyeNos172/ivQ0fUV5WzOOa6mEnuofA4BDKSkoAqKqspEurRa/XU1pSTFBwMF1dXfj6+bN566cEBYdw7colq2h7eXtz8tgRVryzkg+3fMzyt94VTPdF2u4jRnD18kXWvf8h733wIbVPngiqHRAYTHmpSbu6qoKuLpN2WVkJXt7e3Lx2lXdXr2P9xi34+Pnx++2bgurDwPlfsGgJvn7+LF3+lmCO3os03UeM4PrVy6zfsJn3PvjQvEJkDe2edu4fEMiHWz4mKDiEhw/uCaoPA9e72DuoL8q/LXVbW1rY+9supk6fKaij97L61tTr6uoiOCSMzVs/Ral04Mql86xau5633lnF1UsXhElDUDBlZT1pqEDbnYay0hICg0Po6urCzz+ADZu2EhgUTKqAbT0oKJiynjbeR3vECA8uXTjL6rXr2bB5K1WVFeRmZwmm/Ucdy8QeT16k35N3P3+TfRPa1gyk7e3jQ3NzM5s++pRNH31CUvIoq2mHhYdjZ2fHxi0fC+rovUhXTHv6Iu0JEyeT06tPZT7KIC4hUXTd8RMnm/t9aWkJjo5ONDc1UVoqbHkMlIa4+AQmTJrC6RPHuH3zOh6enoI5ejCwXZ0waQp5eTno9XoA0lMfkDQyRTjtAfI9c9YcNm75mI1bPsbLx4fxE19+MeVF7Viv15OVmcFvv/zE/j2/4aBSsW7DJvM4PW78RLZ99U/27/mNB/fuotPpAIiIimbdhk04ODiwb/cv7P7lJ7IyM8zlAwOPFUFBId3aj9j9y08c2LsblUrFug8+fMbRu3j+rPkY55FD+03akdGs++BDHFQq9u/5lT2//kxW5qNntIfKsJ09bx8/aqor6ezsRKFQ4OcfQE1VJRVlJfgHBpOT/YhdP3zLrh++pe7JE+pqhRssfH39qKqqMGv7BwZSVVlhmhgEhSCTyYiLNxmLhMSR5soRW9vOzo6g4BDc3NwBcHR0FEz3RdoKhYLgkFCc1GoUCgWx8QmCavv4+lFd1VPfdvj5B1JdVUl5aSl2dvbU1j7ml5++Z+f2b8hIS6WpqVFQfXhxvYvBC8s8OBRHR0cUCsVzW/NiagcGhaBQKMwGzNfPj8aGBkH1YeB6F9vZs0V9v4yuwWDg1107mTl7rqAD88vqW1tPoVAQHhEJgJe3N0HBpu95efvQ2NggSBqeaWd2dvgHdKehe1FBoVCYB0xfX38aG4WzLz6+flT10g4IeJp/BwcVwcEmuyqXy4lPSKK0tFgw7T/qWCb2ePIi/Z68x8TGCa77Yu1gGuvrOXPqOAX5eTg4OFhR23Z2TUwG0o6MisHNzZ3y8jLa29qoq31CQGCQ6LqxcQlotVo6OztpbmoiPiGR0tJiykqELY8XlXvyqNF0ajt5cO93QReqYWC7GhIWTkhIKPl5udTWPsGgN+Dl7SOY9su0t1s3rmFvZ8/oseMF+9wftn/D1csXmTZ9Jhs3b2Xc+InPnP6ZMm0GH3y4hbDwcB5lpLP7l5/MP3NycmLchEls3PIxU6bN4Mqli/yw/RvzzwcaKwKDgtm5fRvXLl9k6vSZbNj0EWP7aIPpGOeGzVvZsHnrMzuBTk5OjBs/kQ2btzJ56nSuXr7Izu3bBl3ufRn2+T6FQoGLqxuZ6Q/xCwjE08uH0tIiGhrqsbOz496dm6xe/yEqlSOnjx9Gr9cNO9G9td3c3ElLfUBAYBBe3j6UFBdRX1+Hh6fn838g4DnYgbTd3NyprKgQTGsw2qPHjKNWQIfakrarmxsZaQ/xDwjEy9ub0uIiGurrcHV1IyQ0nKXLhV2JtJSGQdW7yJqjx4wTfDfvZbU9PD2Ry+XmM94ymRyD0SBKGvqr9xEe4pR5b21r1/fL6Mrlcnx9/SksyCc4JNTq+tbWe7adyVDY2Zn/bzAI0+YUCgWurm6kpz4gICAQL28fSktM7czDo08a5MLp9tX2DwjEu1f+Xd3cqK6qFEzLkvYfcSwTezx5kb6Hpyd2dnai3dMbSDsgMIiNWz6msCCPB/fukvUog8XLlltF25Z2rfd9Ib1OuPnay2jHxSeS9SgDDw9PomJiBbu79CLdgMBA0lIfMMLDg8CgENIe3qe8vIzZc4Vzul6Uhq6uLpqbmgDo0moFXVx4kV1NSh7FrRtXGeHhSeLIZMF0e7QHyndRYQFZmY9Yu36DoJ+7cPEyHtz/naOHDhAVE0tScgqenl7PfIa7+wjcx4wgedQY/us//h/a29pw7HbKnjx+TFrqfXKzswkKNjnjvbX7Gys8PD1ZsHgpDx/c4+jhg0THxJA0MgWPPtoDYdJ+QG5Ot3bK8E8VCGJBAwKD+P3OTQICg/EPDCLtwT28vX3RarXY29vj4KCitbWFosJ8IeSeITAomNs3rxMUHEJQUDD3793Fx8cPmUyG0WgkK/MRAI8y0ggMEmaV6EXa/gGBlJYU09BQD0B7e7ugui/ULi6mva0NvV5Pdnf+hSQgMJi7t28QGBxMQFAwDx/8jrePL34BAVSUl1JfXweYDFZdnThO0ED1Lhb9afr5B1BSUkRHezsGg4GcrEyraYuZ3770V+/WSIOt8v8i3UVL36Cu9onpLpcN9P/sev2l4c7tGwQGhRAYFMyD+1ZuZ7duEBT8VNvHxxc/f39KS4ppa2vDYDCQ+SidoOBQ4bX/iGOZyOPJQPq2tC3t7e0YjUZiYuOZNmO2KM7+QPmWKxSCHN8arK6TWs2TJ48xGo3PHKu0hnZ0TCx5OdlkPko372RbQzcoKMT0s6AQfHx9KS4uwk6hMAfTsEYaLp4/Q0JiElNnzOTk8SOC6vZo92dX/QMCaWpqIjND+HLv0baU76amRs6cPM6Kt97F3t5esM/tydPipcvZsHkrIzw8OHH0MDt3fEtVpakf5+fmYDQaAairq0Uuk+GgUlFVWcnOHd9y4thhRnh4smHzVhYtfQP/gMDntS2MFT3ai5a8wYZNHzFihCcnjh3hx++/o+oFNqSqqpIfv/+Ok8eP4OHhyYZNH7FoybLntIeCIJE7/AODuXPzGn7+gdgrldjZ2eHf7Wl7efuy87svcda4CJLgvgQGBXPj2hX8AwJRdmsHdp+ztre3p7KinBvXLuPkpGb5m+9YRdtJrWbB4qUc2Lsbo9GIk1rN6rXrraLtrNEwZfoMfvzhO1QOKrx9fAXVBQgICubWjav499S3wo6AoGCcnNQsXPwGxw7tNw9SU6bPZMQID8HTMFC9i0V/mhoXFyZNnsbOHd+icnTEw8NT8CM/tshvX/qrd2vwovyLNSV8ka5cLmfZirfZt+dXlEoHRo8dZ1V9ofkjtLPAoGBuXr/6NA0KO6tF1g3qq21n0nZ21jB91hx+/XmnOUCLEAFpevNHHMusMZ4MpG8N+tNuaW7i+JFD5gnh9FlzrKYNkDJqNNu3fYWvr5/w9/YG0J0xay77fvsFRycn/Pz80XZpraatcnTEw9OTJ08e4x8QYDXdwOBgmpuaCAoOQS6X4+LigocIJ1b6S0NJcRGVFRWs++BD5HI5OVmZpD68z0gB74m+yK7GxsVTU12NSuBrRz3alvKd9vAB7e1t7N/zKwDOGs2gomu/jN1QKpUkp4wmOWU0T548Nn8/PT2Vc2dPYW9nj1wuZ+nyt5DL5djZ27F42fLndgH70t9Y0Vd7ZMooRqaMoraXNpju7PV+XmX9xs3Y29mxeOkbg9oFfFlkPYbMEg1t+v5/KDIqpcJW0q8tOr3wx/9eFjvFnzuctlarRalUYjAY2L/nN0YmpxAt0j0TIfmz17nBYOC///P/5S9//x8oFJLN+CNjMNhsOBHy1OMQtIcv/u//9/82R0i0JUJGSJSQkBg+QtnVfbt/Yez4CYSEhr/038jlNjSsNmQgv0lsHO2HNqBI7+xJSAjA1csXKS4sQKfXERYWQVRMrK2T9Frw3TdfkJwySnL0JCQkJCQkBklHRwc/ff8tXj4+g3L0JP5cDHlnr7mpkdPHD9PW1gpAYvJoRo0Zz+PqKs6fOYFOp0MulzNr3kJ8/QIwGo1cOn+aooI87Ozsmb94Gd4+fv1qD3Vn7/iRQ+Tn5eCkVrPpo0+H9BlDpSA/j3OnT2IwGkhOGc3EyVOtpn3n1g0ePriPTAZeXj4sXrYcO7vB+fIv2uU5eewwBfm5ODmp2bD542d+dvfWDS5dOMsnf/sfODk50dHRzqljR8yBehYsXoanl3e/nz3UXZ6mpkaOHT5Ia2sLICNl1GjGjp84pM8aLNZua/3l9fLF8+TlZiPDdN9i8bIVaDSal/rMgep8MPXd2dHB8SMHaW5qxGA0MHb8JBJfEL55qHVuy37W0dHBiWOHefK4BpCxeOkbgkWM64ul9jWcuh4qQvWxwaxAf/3FP1AqTY/LyuVy3t+4herqKs6cPGYeW+YtWIyf/8sd9XrZtVCdTseuH79Hr9djMBiIiY1j6vSZGI1Grly6QHbWI2QyOaNGj2HMuAkvqT201W9btnNravfXvi6cO01ebk53IIYRLF62HJXAd6j6025vb+fQgb00NTTg4ubGijffEeV4W2+EGMNflv7yffXyRR7ev2eOHDh91hwiIqNESUNvbNXWbTl/AGHy/bJ2ta72CYcP7jN/3dhQz5RpM+lobyc3N9t0X9NJzeKly3F+yTFlKDt7tpyjgzBlPpidva8+N41lcpkMmVzOBx9uISvzEdeuXKL2yWPWb9yMn5//S3+e1Xf25HI502bNxdvHD622k192fkdwSBhXL51jwuRphIZHUliQx9VL53hn9fsUFebTUF/HB5s/paqynPNnTrB6nTCPVfYmKTmF0WPHc+zIAcE/eyAMBgNnTh5n1dr1aFxc+GH7NiKjYswPNYpJc1MTv9+5zaatn2Jvb8/B/XvIzEgnKTlFUJ3EpGRGjRnHiaOHnvl+U1MjRUUFaFyePgB56/o1vHx8WP72Smprn3D+9AneXSPsvUUAuUzOrDnz8fXzo7Ozkx+2f0NoWIRVyt3aba2/vE6YNIXpM2cDcPfOLa5fucSCxUuHrTeY+n5w7y4enp68+e5q2tpa2fHNF8QlJAm+42bLfgZw7vRJwsMjefPtlej1erq6ukTTstS+xKrrgbBVH1u19v1nwlVfOn+WyVOnEx4RRUFerundu/c+EFRToVCw+r33USqV6PV6dv24g/CISGqfPKG5qYnNWz9DJpPR2toqqG5fbNnOra3dX/sKDYtgxqy5yOVyLp4/w83rV5g5e55VtNNSHxAaGsbEyVO5ef0qN29cFVy7N9Yaw3voL98AYydMZMLEyaLoWsKWbd2W8wdr53uEhycbNm01a3/5z/8gKiYWlcqRqTNmAfD7nVtcv3aZ+QuXiJIGsN0cHWzX1la/9+xY5uXlxYq33+X0iWOi6vZmyJdm1M4a886cUunACA9PWlqaQSZDq+0EQNvZYX6hviA3m7iEJGQyGX7+gXR2dNDa0ixAFp4lKDhE8HftXobKinLcRozAzd0dhUJBXHwCuTnCR7LqD4PBgE6nM/3b1fXSKzODITA4BJXq+bK9eO4002fOeSZIRm3tY4JDwgDw8PCksbGxe/VMWJw1Gnz9TO3QwcEBDw8vmpubBNexhLXbWn957R0MpkurFSxayWDqG5np3qLRaKRLq0WlchQlXLot+1lnRwelJcWM7A6DrFAoBN9p6I2l9iVWXQ+ELftYb2Qy0HaagkV0dnbi7Cy8jZPJZCiVSsBkU/XdO9/3791l8tTp5l06tVotuHZvbNnOra3dX/sKC48w2xB//0Cam4SfL/SnnZeTTWKSKQR9YlIyudnZgmv3xRpjeA9/lD4Ntm3rtiwHW+a7uKgQNzd3XF3dnh1TRFy87MFWc3Sw/Ty9Bw9PL1ECAA2EIGcEmhobqKmuwtcvgBmz53Ngzy6uXDyL0Qgr15pWXltamnHWuJj/xlnjQktLs9kZ/LPT3NyMS6/8aVxcqCwvt4q2xsWF8RMn8eV//wd29vaEhUWI8sizJfJysnF2dnkuSpuXtw+52VkEBgVTWVFOU2MDLc3NqNXOoqWlsaGB6upKUaK+/tHom9fLF86RnpaKg8qBNQLvdvSmv/oeNXocB/f9xtf//E+02k6WLn9blLDptuxnDQ0NODk5cfzoIWqqq/H19WPO/IVm58BaWKuuLWGtPiZDxp5ff0Imk5kiqY0aw+y5C9jz289cPH8Go9HI2vc3iqJtMBjYuX0b9fV1jBozDv+AQBoa6snKzCAnOwsnJyfmzF8oSpThHmzZzm2p3V/7Sn34gDgRHnTvT7u1tcXsbKmdnUVZqOyNLcfw3vkuLyvl3t3bZKQ9xNfXn9lz54t+fNWW7a031p4/2DLfWZkZzzyxcOXSeTLSUnFwcGDVe+9bJQ22wBZlLkPG7l+6x7JRo0kZNUZUvf4Y9tK7Vqvl2KG9zJg9HwcHB1If/M70WfPY9PHfmT5rHmdPHhUinRID0NHeTm5ONh9/9nc++9u/0dWlJSMtVXTdrq4ubt24ypRpM5772fiJU+js7GDn9m+4//sd0d/J0mq1HNi3mznzFgr+7MEfDUt5nT5rDp/+7V+JT0ji97u3RdEdqL6LCvPx8vZl61/+hfUffsS5Myfp7OwUJR22wmAwUFVVyajRY9m4eSv2SnvR3tcbCGvUtSWs2cfWrN/ABx9+xNsr13L/3l1KS4p5cO93Zs1ZwMd/+RdmzZ0vyltUYLqisGHzVj75679SWVHO45oa9DodCjs7PvhwC8kpozl59LAo2q8z/bWv61cvI5fLiU9Msro2dN+5FDmcq63G8L75HjV6LFs//RsbN3+Ms7Mz58+eFj0NfwRep/mDXq8nPzebmLh48/emzZjNx3/5F+ISkrh3944NU/fqsfb9DWzY9BHvrFrL/d9NY5ktGJazp9frOXZoLzFxiURGm6IPZqanmv8fFRNHdVUFAM7OGlp6bY+3NDeJcgzHVmg0Gpp65a+5qUnUYxi9KSoqwNXNDSe1GoVCQXRMHOVlpaLrNtTX0djYwM7t37Dti/+iubmJn77fRmtLCw4ODixc8gbvf/gRi5Yup72tDVc3d1HSodfrObBvN/GJScT8CZ47GA4vymtC4khysoV/1B0Gru/0tIdExcQik8lwdx+Bq6sbdbVPBE+DLfuZxsUFjcvT90JjYuOprqqyirYlxKzrvli7j2m6V1/VajVR0TFUVpaTnv6Q6O4otzGx8VRViLsiq1KpCA4JpbAgD43GxawdFRNLzeMaUbVt2s5toN1f+0p7+ID8vFyWrXhLtMVCS9pqtTMtzaZjoy3NzaidxD22a4sx3GK+nZ2Ry+XduxBjqKwUf6fJlm0dbDd/sFW+C/Lz8Pbxs3jKKj4hiVwrjSm2wBZl/txYJvK41R9DdvaMRiNnTx5lhIcno8c9jV6kdnamvNTkuZaWFOHmPgKA8MhoMjPSMBqNVFaU4eCgemWOcAL4+QdQX1dLQ0M9er2ezEcZRAr86G5/uLi4UlFeTldXF0ajkeKiQjw8xT8P7OXtw6d/+x9s+fRvbPn0b2g0LqzbsAW1szMdHR3mh9XTHt4nMChYlBUzo9HIiWOH8fDwZPyESYJ//h+J/vJaV1dr/n9uThYjRDoLPlB9u7i4UlJUCEBrawv1dbWiOPe27GfO3fms7XZii4sK8fSy7rl7a9V1b6zdx7RaLdruXWGtVktRYQFent44O2vMq6IlxYW4i3CMsq21lY6ODsC0k11cWMAID0+iYmIoKS4CoLSkWNQjnGDbdm5t7f7aV0F+HrduXuPtd1djb29vVe3I6GjS0x4CkJ72UPSyt/YY3l++exxcgJzszAEjaAuFLdu6LecPtsp31qN04hKeHuGs7zWm5OVmW2VMsRXWLnOtVms+4dQzllmjT1liyE8vlJeVsPeXnXh4eptX3CZPn4VSqeTy+dMYDAYUdnbMmrsIH18/jEYjF8+epLgwHzt7e+YtWoaPb//hRof69MLhA/soKS6ivb0NJ7WaqdNnkpwyekifNVjy83I5d+YkRoORpOQUJk+dbhVdgCuXLpCVmYFcLsfHx4+FS5YJ/vTC0UP7KSspNpft5KkzSEoeZf75ti/+i/c2bMbJyYmK8jJOHj0EMtNl1AWLl1kM9tHDUMPwl5WW8PPOHXh5eyPrjlZhrXDR1m5r/eU19cF96uqeIJPJcHFxY8GiJWhcXF7waSYGqvPB1HdLczMnjx2mtbUZoxHGT5xMfOLIAbWHWue27GfVVVWcPHYYvUGPm5s7i5cuF+1Oi6X2VZCXN+S6HipC9bGXDRHeUF/Pwf27u//GQFx8IpOmTKOstITzZ09hMBiwUyiYu2Axvi8ZsvplN4Vqaqo5fuQQRoMBo9FITFw8U6bNoKOjg6OH9tPU1IRSac/8hUueu7fav/bQdqRs2c6tqd1f+zp7+gR6nd4czME/IFDwyLP9afv7B5ieXmhsxMXVleVvvSt6UAkhxvCXpb98Z2akU11dhUwGrq5uLFi01Cq7TbZq67acP4Aw+R7MkzZarZavP/8HH33yVxy6g4sd3L+b+tpakMlwdXVl3sIl5t2oFzGUpxdsOUcHYcr8ZZ9eaKiv58C+p2NZfIJpLMvJzuLs6RO0t7Xh4KDC28eHlWvWvdRnDvXphSE7e2IzVGdPYui8yNkTk6FO/CWGh1TnEtZiMJMSoRH5ytULtG0oLiEh8UpjS7s6FGfvVWAw7+wJzVCdPWm2JSEhISEhISEhISEh8QoiOXsSEhISEhISEhISEhKvIJKzJyEhISEhISEhISEh8QoiOXsSEhISEhISEhISEhKvIJKzJyEhISEhISEhISEh8QoiOXsSEhISEhISEhISEhKvIJKzJyEhISEhISEhISEh8Qoy4Iud9naSL/g6oXhN30x5nZHeALM+tnyjx5b1bcs3mWxZ5hLWx5b1bcum9rq+e2ZrbGvTbSb92qKz3RPkYD+0Cpe8OQkJCQkJCQkJCQkJiVcQydmTkJCQkJCQkJCQkJB4BZGcPQkJCQkJCQkJCQkJiVcQydmTkJCQkJCQkJCQkJB4BRkwQMuflZLiIm7fvM47q9baOik25diRg0RERhMbF2/rpFiNC+dOk5+XR0RkJLPmzLd1ckTlj9DOW5qbOXfmJMvfetfq2s3NzZw9fYI3315pVV1bl3tjQwP7dv/Chx99YhN9CQkJ8WhsaKC8vJT4hCRbJ0VC4pUk9cF97ty+iUxmCq4zfcZsomJiraL97Zf/hb1SiVxu2msLCApm9tyFouu+ks6exOvLg/v3+Pu//S9zR5IQF2eNxiaOHoBGo7G6oychIWF9DAbDa2PTGxsbyMxIl5w9CQkB0Wq1KBQKWltbuXHtChs2fYSDSoVWq6WtrRWAjvZ2VI6Ogmt3abXIFQoUCgUAK9e8j6OT0zO/09HRjkolvHYPgljPyopyvv/2K3Q6HVqtlh3bvuTx4xp279rJzu3f8P23X5GXky2ElEXt7du+NGt/+/UXPK6pobOzkz2/7mLbl//k1PGjooXGtaRfU13F+bOn+e6bL9i+7Ut+v3PLKrqPa6o5c/I42778J7/+vJO21lbBdXu0d2x7Wt/fffMlNdVVnDh6mJ07vuX7774hV6T67p2Gvvn/5acf6NJq+eG7b8h8lG4VzZrqak6fOMa2r/7Jr7t+ZM+vP5OV+cgq2o9ratBqtRzYt5ttX/2TIwf3ixoCur9+vmPbl6Jp9ta2lP/vvvnCJrq9f77j26+pr68TTb9vX9NqtaJo9ej1ze/vd26z68fv2bf7V776/B9cPH+WjPRUdm7fxnfffCl43gdKg9ht3VJ5l5YUs3/vb+zY9hU/fv8dNTXVguv2aFvK9887d4g+lr3MeCKWbetPvyfv+3b/wrdffy6Kbo+2pTrf9eP3fP/t12zv/lo07V42dfu2Lzl8YA9lZSV8/93X3L19UzRdS+W997dd5t85c/I4aQ8fWEX7wL7d5OfmmH/n2JGDgrc1S7q//rzTPFfZv+c3jh85BJh2fi5fOCeofk8a+ra161cv8+vPOzEajbS0NLPtq3/S0tJiFe0De3eTm51l/p0jh/aLMnezWOfdNnXHtq/4/B//zrHush/u5/YenwEqyss5efwI333zBR3t7bS1tqJUKrFXKgFQKpW4ubkDcOvmdXZu38aDe3fp7OwcULuqsoKd279Gp9OZ5p3ffsmTx89qV1aUc+bkMX747is6OtoH/Ly7t27w887vSH3w+wu1h4JsoIGjVfvyo8rVS+fR6XTodDo0GhfGTZxMV1cXDg4OtLW1seuH79j08V9e+p2nwbz5dvlij3YXGo0LAYFB7P7lJzZv/QwXV1d2//ozKaPGiHacsa++SqWiqKiQ5W++g1wup729HUcRVgv66o7w8OT+vbusXP0era2tfPf15yxc8sZL53swk4grF8+j0+vQdenQuLig1Xbi4elFQuJIOjo6+PH7b/ngw49QdneoFzGU97/65n/SlGn8+//9v/m3//V/DvqzhqrpPsKDtIf3eWfVWlpbW/n2689ZuHiZKG3NUjvft+dXNn30KRqNhp9+2M6sOfMIDAp+6c/UGwY3cezbz2PjE9m/5xc2bhn8kcLBvuvYN//xCUns3b2LTR99Omjt4egGBAZx++Z1Jk6ZxtlTJ3jrnVW4uLq+9OcNdrLet6/FxScO+Rjny/QzS/ndv+dXNm/9DJWjI19//l+MTBnFtBmzuHv7Jg0NDcydL+wxFKHb+nBsW0tzM45OTkyZNoPiokIunD3Nhs1bX/rzBmPbbDmWCT2eDFc/IDCIvb/t4sOPPjFPxl6W4fYxuUKBXqdj0pRpGAwG81zm5bQHJc2VPjbVPyCQO7du8PbKNYP7IAb3zl5/dq3nePqZk8fx9fMnKTll0OkYrLaHhye5OdkseWMFer2erz//L7Z88hfs7e1F1XV1c6e6qoJZc+azc/s2kMl4f+Nmjh05RFx8AuERkS/1ucOxLxMnT+XooQP4BwRQUJBPfEIS8QmJQ83ioLT9AwK5e+cWb72zis6ODr7/7hu2fPKXl95FH45tmzRlGgAdHR38vHMHS5Ytx9fPf9B5svS57e3tZKSnkvbwAU5qNSOTU4iKjsXOzg6DwcCe33ZR++QxIaHhxMTEEhkdY/682tonpD28T3ZWJoFBISSnjHpunOnSGQC4dvlCr76rYfykqbS3t5OZkUZG2gOcnNQkjkwhIioGOzvTQcq+xzjjE0cyZtxEAOpqn5Ce9oDc7CwCA4NJTE4hIPBZbY1qaI9pCnYuYtJU00BYVVnBuImTMRqNXLl4nu+//Yo9v/xIS0uzaDtNU6bNoKiwgKrKSiZMmgKAn38Abu7uyOVy4uMTKSstEUXbkn5RYSEpo8aYK1MMR8+SbmlJMfHxicjlcjQaDSGhYaLoAkyeNoOiwkKqqioYP3EyhQUF3Lp+je+//Zpff/oBnU5Hc1OjaPpgud7Fpq9mWWkJMXHxyGQynJ2dCQkJtZo2mNq5i4sLMpkMbx8fGhsaRNOH5/u5NbFFffenW/vkCaeOH+HtlWsG5egNhb59TWws5dfXPwBnjQY7Ozvc3N0JC48AwMvbh6bGBqukwVptvW95l5WVkpA4EoCQ0DDa29tFWXkF245lthxPLOlDd94H6egNhb517ufnT1rqA65evsjjxzUv7egNSbvbplZXWqd/92Are2pJOzwyiuLiQnQ6HQV5uQQFhwju6FnSDQoKprSkhCePH+Ph5YVa7UxLczMV5aUEBAYJrg+W7fnc+Qu5ef0adgqFaI6eJe3gkFDq6+poa23l0aN0omNjRTsubam9GY1Gjh7az7gJE4fk6Fn63ObmZj7/x/9HSVERb69czao164iLTzQ7W3K5nJWr32PFWysZMWIE586e4urli+bP8/DwZObseWz5+C+Ehoax57ddnD11wqL2xCnTKS4qoLqqgrETJtPS3Mw3n/8HpSVFrHh7FW+veo+YuASzdg8r17zP+o0fsX7jR2ZHD2CEhyfTZ85l45ZPCQoN5cCeXzl/9uSQyqUvgt3Za29vM5+J1el05GQ9or29lfUbt6BQKPjmi3+g0+mEkntWu62NLq0Wg0Fv1ui74jCEjaNh6VsDW+mCqb67tFoMeoVZe/nb7+Lh4Wm9NPTJ/8vuIgqpaU0sadt1nwEHkMvkGAwGcdPQp59bkz9SP1M7O6PX6aipqkSj0Yirb6Gviar3gnYmk8lQKOzM/xejzdmyrVu7vJ/RtuFYZkvb1p++GBN+i9p96jwoOIQ16zZQkJfLiSOHGDthIolJyaJpa7VaDFa2qX3LWy6XP7NDJWZaLI3dwSGhFBbkk5mZQVy8OA5PX12NiwudnR0UFOQRFBRCe0c7WZkZKO2Vojn4fduaUqmkubkJmUxGa2srRqNxSCedhqqdmDSSjIw0sh6ls2jJclF0wXKdX718EY3GhZHJowT7XLVazbIVb5P64D77dv9KYtJI4hNHolarzX8jk8nwDwjAPyCA0LAIjh89xNTpMwGTA1pSXETqw/tUVpQzZux4kkeNtqzd3kZXVxcGgwG9ToeTWs3iZW+SnvqAg/t+Iz5xJHHxSTj10h4Io9FIaUkR6akPqKqsYNSYcSQNo2x6I5gLf+bEMaZOn0l8QhKXL5yjs7MTJyc1CoWCkuJCmhrF2+U5deIo02bMIj4hiUvnzwKms7INDfUYjUYyMzMGdbRtuPqhYeE8uP+7eTLS3j7wWV2hdIOCQ8jMzMBgMNDS3ExxcZEouibtY0ydMZP4xCQuXThHWHg49+7eMQ8W1VWVomk/TcPz9W5tzcCgIHKyMjEajbS2tFAiaplbP7996dvPrYmt8m9JV6VS8c6qtVy6eE7UOjfpP9vXxOaP0M5smYa+5R0YFMyjjDTAFInV0clJtImgLccyW44nlvStSd86b2xsQK1WkzxqNCNTRlFdVSWa9ulumxrXbVOVSiVarTg7x73pW94urq48efIYnU5HR0cHxUWFVtMGiItLIO3hA8pKSl76+KQQuv4Bgdy9fZOg4BCCgoK5ffMGgcFizhefbWsGg4ETx46wbMVbeHh6cufWDatpAyQmJfP7bVNcCU8vLxG1ny37vJxsiosKmbtgkaCfK5fLiYmN493Va3ln5Rq6urrY9eMO9u/5lc6ODpqbm6mqfDo/ramuwrX7dE5GeirffvU59+7eIT4hic1bP2P6zNm4urpZ1D576hiTp80gLj6Ry5fOIZfLiYqJ481317DindV0dXXx264fOLT/Nzo7OwbMR2ZGGt9/+wUP7t0lNj6RDZs/Ycr0Wbj0oz1YBNnZy0h7iFwhJy4hCYPBwK6d2wkMDibrUTrff/sVPr5+jBBpxyc99SFyuYL4RJP2Tz9sJzo2Dl8/f86cPEFDfR3BIaFEx8RZTT8yOgaXOle2b/sSuVxBcspoxowbL7puVEwsI9xH8N3XX6BxdSUgIFBQTbN22kMUcjnx3fX9887tTJ46nbycHHZ8+zVGoxFXNzfeGcKdg5dOg4X8izk49acZHRuHRuPCt19/jsbFFR9fP1Emgv1pWxNL/bykWNwy76H/+hZxy74f3Z5yVzs7887Ktez+9WcWL30DfxH6m6W+Viximf8R2pkt02CpvCdMnEJ6eio7tn2Fnb09i5eJs/pty7HMluNJf/q2rPPS4mJu37yOXKFAaW/P4jdWiKYtVzzV3rVzOxFRUcjlcr7/7msSk5IZO37iiz9osLoWyru+ro7YuAS2f/Mlrm5u+Pj6Cq7bn3ZxUSGh4REcPXyAyOhYc9RCa+gGBgVTVJCP+4gRuOhd6ehoJzAoRHB9sNzWbly7QmBQEIFBwXh5+/Djjm+JiIzCw1NYx8viWFJUSEhoGB6ens/cWxMaS2V/7/c7uLi6mu5KApHRMUybMWvYn9uTJwCNiwuTp05n0pRp5kVZg0HPhXOnaWlpxk5hh6NazYJFSwBwdXXjvfc3vtRO3KN0k3ZcvEn71592UFJcSHBIt7bGhYmTpzFh0lRKS4qe+dvdv+w0H5f19PJm0dIVuLi6suq9DTg5vdwu4GARLECL0Aw2eIPE8BEzkuOLEOvYgjXQarUolUra29rYueNb3vvgQ5ydnW2drJdisAFahGS4fbyqsoLzZ0+zdv0GYRJkJaR+Zn3+rGVu6zcde/NnerfVlvVtQ+lBBWiREA5btrfh0tXVxY5tX/HBh1twUKkG9bev63jSE6DFFgw1QIv0zp6ExDDZ+9suOjs60Bv0TJ46/U/j6P2Zqayo4MihfcyYNdfWSZGQkJCQkPjTUVRYwMljRxg7fsKgHT2JPxdD3tk7eeww+Xk5ODmpzWHX29vbOXpwL42Njbi6urJsxTuoHB3Jy8nm6uULyGQy5HI5s+YueOG9g6Gu+h8/csiULrVa9JDsljAYDPywfRsajcbqq7Ff/vM/USodkHeX8webPhrU37/s6pROp2PXj9+j1+sxGAzExMYxdfpMUwTWSxfIznqETCZn1OgxjBk34aU+c6grRDqdjl07d6DrlZbBHgUYDgX5eZw7fRKD0UByymgmTp5qFV2h2vlAO3uD6eOP0tO4ffMaYHq3Zu6CxXj7DHwMaKh93FZlLpT2cPtZcVEhF86dwaDX4+Pnx6Ilb4gSKrsHW9pUobQHu/JuMBjYueNbnDUa3lm5hnt3b3P3zi0a6uv5y7/8Hzj1eRB3IIZq2/7s7dyW2sPtY8Op75dtanW1Tzh8cJ/568aGeqZMm0lCUjJHetnYN7pt7MswnJ294c4fhoOt2ptQ84dh2/TCAi6cPwtGI/ZKJYuXLsd9xIhBp2Mo2g0N9Rw5uI/29nZ8fP1Y+sabL32M9mVtmyU7XlNdxakTx9Bqtbi6urFsxVuiRr3tQYi2NtDO3qnjhynIz8XJSc0Hmz42f//+77d5cO8ucpmMsIgopvdaqG5qauSHb79k0pQZjJ0waUBtq+/sJSQlM2rMOI4fOWj+3u0bVwkODWPCpKncunGVWzevMWPWXIJDw/ggKhqZTMbjmmqOHNjLh1s/G6r0gCQlpzB67HiOHTkgyue/iLt3buHh6YlWpNDcL2LNug8GNTANBYVCwer33kepVKLX69n14w7CIyKpffKE5qYmNm/9zBxZSmwUCgWr131gTsvPO3cQHhkl6v2SHgwGA2dOHmfV2vVoXFz4Yfs2IqNiRL3k3IM12vlg+rirmxur3/sAlaMjBfm5nD5xlHUbNgueJluWubW1LfWzsPAIjh85xKq16xnh4cGVSxdIT33IyBRhInZZwpY21Vbav9+5hYeHJ53dgTICAoOIiIzml59/sIr+69TOband31hmjfoe4eHJhk2m9xoNBgNf/vM/iIqJ5daNq4RYsLHWwBrzh77Ysr1Ze/7QX3s7feo4b72zCg9PL+7/focb164Ifje4P+07t24ydtxE4hISOXXiGKkP7jNqzFhBtS3Z8RPHjjBrzjyCQ0JJfXCfWzeuMX3mbEF1+2KNtpaQlEzK6HGcPPb0kfiS4iLyc3NYv/Ej7OzsnnuG7tK504SGixOUqIchR+MMCg5BpXp2tSkvN4eE7vDECUnJ5OVkA6aV/p4VgC6tVtR3EIKCQ0R71+5FNDU1UZCXS3KK5TCtrwoymcz8zIHBYECvN61y3L93l8lTp5vrWv2S4WaFTItBrxc5ZMdTKivKcRsxAjd3dxQKBXHxCeTmZFlF2xrtfDB9PCAwyLz67O8fSEtzsyhpsmWZW1vbUj8zPXugYISHBwChYeHkZGeKlgawrU21hXZzUxP5ebnPONA+vn64urlZLQ2vUzu3pXZ/Y5m167u4qBA3N3dcXd2es7G53Tb2VcWW7c3a84f+2hvIzG93dnZ24qwR/ipIf9olxYXEdN/DTUwaKUrZW7LjdXW1BAWbAuGEhos/joF12lpgUMhzO/Gp9+8ybuJk83t7vQPA5OVk4eLmLnhAnr4IemevrbUFZ2fTm1NqtTNtrS3mn+VmZ3Hl4jna2lp5613xojTaknNnTjJz9ly0Wq1N9GXI2L3rR5DJSBk1hpTRY0TTMhgM7Ny+jfr6OkaNGYd/QCANDfVkZWaQk52Fk5MTc+YvZMQID9HS0DstP3z3DfX1dYweO06UqIiWaG5uxkXjYv5a4+JCZXm5VbRtxUB9vIe01PuEiRQ625Zlbgvtvv3Mzz8Ag8FAZWUFfn7+5GRl0tTUJGoaXjfOnTllUzsOr187t6W2pbHM2mT1elvuZWysGFhz/tAbW4+j1p4/WGpvCxcvZe/uX7Czs8NB6cC6DZusou3mPgIHlcp8DUDj4iLaQm1fPD29yM3JJjomlqzMRzRbYRyzVVurr6+jvLSEa5cvoLCzY8asefj6+aPVarlz6zpvr1rH3dviPbkBIgZokclkz+zgRcXEEhUTS2lJMVevXGTlmvViSduEvNwc1E5qfP38RX93qz/ee38jGhcXWltb+W3Xj3h4eppXToRGLpezYfNWOjo6OLD3Nx7X1KDX6VDY2fHBh1vIycrk5NHDrH1/oyj6fdOyccvHz6TFy9tbdN3Xnb59HEyrhGkPH7Bm3QbbJOoVo28/e/L4MctWvMWFM6fQ6fWEhYUjf00joolBXq7pXokt7biEdbE0lllz/NDr9eTnZls8wmbJxoqFNecPfySsPX+w1N7u3r7FOyvX4B8QyK2b1zl/9jSLliwTXbuu9ongGi/L4qXLOXv6BNevXiYyKhq5CM9t/FEwGAx0dHSwZv2HVFVWcPTQPjZt/Qs3rl5i9NgJ5h1XMRHU2XNSO9PS0oyzs4aWlmaL70UEBYdw8lg9bW1tVj8bLiblZSXk5maTn5+LXqejs7OTI4f2s2z5W1ZLg8bFtGKhVquJjomloqJcdGOtUqkIDgmlsCAPjcaF6JhYwOTcHz92WFTt/tJSUJBnlcFao9HQ1Px0Naq5qQlnjUZ0XVsyUB9/XFPNqeNHeXvlWhxF6tu2LHNbavfuZ+MnTjYvohQW5FNXV2eVNLwOlJeVkpebTUEvO3700AGWLn/Tqul4Xdv5H6WPWdPZK8jPw9vHD7XadHTvZeZRYmCL+QP8ccZRa88feus9rqk27yjGxSWw59efraJdXl5GZ0cHBoMBuVxu1bL38PRk1VrTpk9dbS0Febmia9qqrTlrXIiMjkUmk+HnH4BMJqO9vY2qynJyszO5cvGc6dF1mQyFnR2jxowTPA1DvrNniYioaDLSHgKmB5gjo6IBqK+rM0crqq6qRK/T2+wOiFjMmDWXz/72b3zyl3/hjTffISQ0zKqOnlarNZ/51mq1FBbk4+UljsFqa22lo6MDML3RUlxYwAgPT6JiYsyr4aUlxVY5wtk3LUWFBXh4eIquC+DnH0B9XS0NDfXo9XoyH2WI+jDpH4H++nhTYyOH9u1m8bIV5vtkYmDLMre2dn/9rCfwkU6n49aNa1Y7bvU6MGPWHD7967/y8Wd/Z9mKtwkODbO6owevVzu3pXZ/fcyaZD1KJy4h0fx1ZD82VkysOX/oiy3bm7XnD5bam4eHJ52dHdTV1gKm5xA8PIVPQ3/awSGhZGc+AiA9LZUoK5V9zzhmNBq5fu0yKaOFDQpjCVu1tcioGPPD6vV1tej1ehwdnVj13gY2f/I3Nn/yN0aNncCEiVNFcfRgGE8vHD24j9KSYtrb23BSq5kybSaRUTEcObiXpqYmXLrDsjs6OnLrxjUepacil8uxs7Njxux5oj29cPjAPkqKi8zpmjp9ptUDptjiIdyG+nr27/0NMG0ZxyckMnnq9EF9xsuGD66pqeb4kUMYDQaMRiMxcfFMmTaDjo4Ojh7aT1NTE0qlPfMXLnlh+P0ehhqevKa6mmNHDmI0mtISG5fAlGkzhvRZQyE/L5dzZ05iNBhJSk4ZdJkPFaHa+UBPLwymj586foSc7ExcXFwB03GR9Ru3DKg91D5uqzIXSnu4/ezCuTPk5+ViNBoZNXoMY8dPfGntofQzW9pUobSH8uhxSXERt2/d4J2Va/j9zi1u3bxOa0sLTmo14RFRL33Maqi27c/ezm2pPdw+Npz6HkxT02q1fP35P/jok7+a3zlrb2vjcC8b+0a3jX0Zhvr0ghDzh+Fgq/Ym1PxhuO0tJzuLq5cvIpPJUKlULFryBm7u7oNOx1C0G+rrOXxwHx0d7fj4+LLkjTfNgURexMvaNkt2vEur5d7vdwCIjoljxqw5VnmkXYi2NtDTC8cO76esZ97kpGbS1BnEJ47k1PHDPK6pRqFQMH3WXIJDwp75u+tXL6G0V4r29MKQnT2xGepEUGLoDGVCJBTW6OQSzzOQsyc2r2sfl/qZ9ZHK/PXClvVtQ+lhvbMnMXRs2d5syetq2wZy9sRmqM6eoMc4JSQkJCQkJCQkJCQkJP4YSM6ehISEhISEhISEhITEK4jk7ElISEhISEhISEhISLyCSM6ehISEhISEhISEhITEK4jk7ElISEhISEhISEhISLyCSM6ehISEhISEhISEhITEK4jk7ElISEhISEhISEhISLyCDPhy4tmsamul4znmx/nYTNuWb4/p9DZ8H8hmyuCoVNhM+3V+g+t1fWfPlvkurW2zmXbgiJd7nFkM7hbV20w7JcjNZtoqG9o2W9Ku1dtM25b925ZjmU5vu/e/bI2d4vXcu7DlHMJgw35myzclr+XX2kx7YYLXkP7u9ewdEhISEhISEhISEhISrziSsychISEhISEhISEhIfEKIjl7EhISEhISEhISEhISryCSsychISEhISEhISEhIfEK8ko6eyXFRezd/Yutk2FzTh8/TG52pq2TYVUunDvNt19/wYVzp22dFNEpKS5i72+7bJqGluZmjhzYYxPt5uZmDuzbbXXdkuIi9tvQvrQ2N3Jizw6b6UtISIhHY0MDmRlptk6GhMQrS+qD+3z3zZds3/Yl333zBbnZWVbTvn/8R1JP/0ramd9IO/MbRQ+uWEV3wGicEhJ/Nh7cv8ff/+1/IZe/kusYfzicNRqWvfmuTbQ1Gg1vvr3SJtoSEhLWw2AwvDY2vbGxgcyMdOISkmydFAmJVwatVotCoaC1tZUb166wYdNHOKhUaLVa2tpaAehob0flKHzEar2uC5lcjlxuitQbN2M59g7P6ui0HdgpVYJr9yCIs1f56A4KpQPekSNNX2fcxs7BkcaqIvRaLUajAb+4cbj6hwoh9wxXLl3A0dGRseMnAnD54nmcnNRoOzvZ+9su6uvrCQ4JZf7CxYKHqL12+SIqR0fGjJsAwNVLJm2dXkdmRhoyZIRGRDJ95hxBdQFuXL2ISuXIqLEm7etXLuDopKahvo6SogI0GhfkCnFCQN+8ehGHPtpOTmr0ej252Y/Q6/RERMUwceoMUfTBVO8qR0fG9dT7hXPcuH4VmUzGD999w8QpU4mLTxRc01Jbq6urpaSoEI2LCwqFgqSRKcTExQuu3Te/Tmo1Wq2WA/t28+RxDb6+/ixd/qZooZivXzG1udHd7f3a5fM4OqlJf3if9zd9LIpmD/3l/+GDe2z66FPRdK9evoCjypEx3bpXuvt4D5UV5Zw5eYw33nwHN/cRguun3b2K0sGRmKQxAKTevoLK0UlwnR76s2m5OVk4OKh48riG6Lh4vLy8uXf3NjpdF8vfWilo3gvTbmGvdCAwJsX0deoN7B2ceFKWj72DI62NtWhGeBM7cZ7gbb0/u9rS3ERxYT4A4ydNJTo2QVBd6L+NZ2dlolQ60FBfZxrLFi0RPN/9adfX1VFUWPDUtiWPIlZg2wb9jyl52Zk4qFTU19Xy/mZx+vmta5dQqRxJHjO+Oy0XkMsVlJUUodV2YjQYmTF3If6BwYJrW7IvD+7dxWg08sN3X5OQlGweb4Skv35eXFTIm++uBuDc6RP4+PqTODJZdO0H935n8bIVhEdGAXDy6CHCI6OIjhWurfU3Z3jr3dVERcewf89vqFQqFi9bTuqD+zTU1zF9lrBzN0tzCIVCQWlJMavWrqe1tYVffvqBNes24OzsLLh23/zfu3eXxUuXEx0TC8CRg/uJjYsnqvtrobh6+QIq1dN8X7l0nvv37uLi4gpAe1sboWHhLFq6fFCf25/d6t1nKsrLSX14j8KCfN7fsJm21laUSiX2SiUASqUSZff/b928TnFhASNTRhGXkISDg0O/2mUZt1EoHfCLMvWP0vSb2Ds44hv1tL+01FXzuDCThupSEme/jVzV/9hdmf2AxpoyvMLi8AiKws5eOaiyeBGCLJWNCImlviQXML1ZVl+ej1tgBGETFhAz+20ipy6lPP2GKO+ZjUweRXpaqlk761EGGhcXKivKmTt/EZs++oSG+jpysoQ/zpg4MoVH6b20Mx/h6OREXm4Oa9/fxPubtjJuwmTBdQESklLMRz2MRiM5mRk4O2uor6tl/YcfM3/JciorykTRjk9KIauPtpPa5GiuWvchazdsoaa6kvLSYlH0wVTvGakPzWnIfJTB3/71f2JnZ8fGLR8L7uj1aPZta2pnZ5oaG9i09VOWvPEm5eXilLml/GpcXKiurmLOvIVs3voZDQ31lJeViqIPkDAyhcyMp/nPznxEgAgTIEtYyn9gkPjaSSNHkZHex75oXAAoLyvl7KnjrHh7lSiOHkB4TBJFORlm/ZL8LDx9A0TRAss2zVnjwuOaauYuXMyGLZ+QmZ5GXV0t732wiaSRo7j/+x1B0+AXFkd1UbY5DTXFuTg4qWlpeELk6GmMW/we7S2NND2pFFQX+rerj2uqWfvBFt5c+R5XLp6jtaVZcO3++nhlRTnzFixi09ZPqW+oJ1uEsaw/7dq62qe2TUTbYmlMcXbWUFNTxfTZ80Vz9ADiEpPJevS0zedmPcJeqSQ4NJzV729h1fub8fQW591fS/ZlwaJlBAYG88GmraI4etB/P7cGlrTnL1pKdtYjAPR6PcXFRYRFRAmqa6mNL13+JmXd85SW5iaePHkMQGlpCYHBIYLq96Sh7xxi9JhxODtruP/7HU4eP8qUaTMFd/R6tPvm/613VpGe+gCAzo4OystKiYiKFly7bzvPfJTBlq1/YcOmrax+7wNUjo6MGjNu0J9rKU8JiSNpb2/n7p1b7Pj2a65cvkBIaBhbPv4LamdnvH18cHJ25qvP/8GxI4fIy8k2f96MWXNYsvxNGhrq+f67rzl25BBlpSUWtb1CY3lS/HScqi3NwyM4Bp22g6rcVNLO7qYs4zYu3gEkL1iLfS9HL/PSIfMxzsocU/qDkiYSMX4OnS1NpJ/dTf6d8zQLOMYJsrPnoNagUDrQ1vAEXWcbjq4e2CkdKE+7QcuTSpDJ6GpvRdfZ/kyGhcDVzQ1HR0eqqyppbW3F28cXR0dH/PwDcHN3ByAuIZGyslLBd1tc3dxQdWu3tbXi7eNDdVUViUnJ2NvbA+AowpYwgIurSbumuoq2tla8fHypKC8lJi4BuVyOs7OGoOBQq2lXV1ZSUlTALz98C0BXl5aG+joCgoQ3mNBd9k5OT+vd1xdHJ/F2PHo0+7a1ivIyYmLjkclkODs7EyxSmVvMr6MTfv4BuLiYBmlvHx8aGxpEc4JcXd1QqRypqa6krbUVL28fUY48WNTuJ//W0O2p87buOlc5OlJb+4TTJ47y7up1OGs0oumrNa44qBypf1JNR3sb7p7eOKjEK3NLNs3R0REfP3+cnTXdv+NOaFgEAJ5e3pSUFAmaBpWzC3ZKFc31j+nqaMPZ3Qt7pQrNCB8cnEyTIGd3Lzpam3Ed2vuy/fIiu6pWOxMYFEx1VSXhkcLW+0B9vGcsi49PpKy0RPDdNUvapSUlxMcnIpfL0Wg0hISGCarZG0vlrnJ0xMfXH1c3d9F0zdoqJx73aHv74uPrz7lTRzEYDIRFRuPl7SuKdn/2RWz66+fWwJJ2eGQUF8+fRqfTUVSQT2BQsHkOJahunzYeHBzK73du8+TxYzy8vOho76CluZmK8lLmzl8oqH5PGp6brzo5MXf+QrZv+wr/gADiE4RfqO7Rfi7/IaGcPnmcttZWsrMyiY6NE+W4dN927tOdb6PRyLHDBxg7fiK+fv6C5Emn1/P15/8gPCKKt1euNu8e9iCXy1m5+j0qKyooLirg3NlTVFVVMnX6TAA8PDyZOXse02fOITMjnT2/7SIpKZm5CxY98zkOatM41Vr/mK7OdpzcPDEa9Nw/8RNuvsFET16Eg5PlMcLSMU4AR407wSMnEZQ0gdqSXLKvHsMzNIbQlGmDLpu+CHZnzyM0lrqSbHQdbXiExFJXmoeus52YWW8hkyvIOPUzBr1eKLlnGJkymvTUh7S0tpCUnCKKRn8kJY8iIy2V1tYWEkemUFJUZDXtxJEpZKY/pLW1lfikFEqKCqymndCt3dbaSkJSCqXFhYydMJmklDFWS0NyyijSUh/Q2tLKyORRVtHs29aKCq1X5pbya9frqK5cJsdgMIiahsTu9t7W3d6tiS3qGyApeTQZaQ9pbW0hqTvPzs7O6HQ6qqsrRXX2AMJjkyjMyaCjrZWwGPHv8fS1aQB2iqdDhUwmQ9Hd7mQyGUaD8Cc2/CLiqS7MRNvehm94HAByxdNJiElXnLZuS7tqqY33PbIp0int57QLC/LFEeqHvmMKIPiEvz9MO4smuxabmIx/YDBvrVpPUUEe504eIWXMBGITRoqibcm+WIO+/Vwmlz9z+kqn01lN287OjqCgEIoK88nOyiAmTvhj0vB8G9e4uNDZ2UFBQR5BQSG0d7STlZmB0l454BG+4WBpvtrc3IRMJqO1tRWj0SjaVQxL9iUxaSQZ6alkPspg8SCPUQ6GkcmjSe/Tzq9duYTGxWVY7b5vntRqNctWvE3qg/vs2/0riUkjiU8ciVr99PqFTCbDPyAA/4AAQsMiOH70kNnZMxqNlBQXkfrwPpUV5YwZO57kUaMtanuHxfG4OIuujja8Q+OwVzkSOWEujwszybl+As/gGDyDo156k8toNNL0uJzHRZm01tXgE5mEd5gwC3uCufCu/mE0V5fSVv8YjU8ghi4tdg6OyOQKmh+X09XWIpTUc0THxFJQkE9VZQVh4aYV56rKChoa6s1b5WLtdkRFx1JUmEd1ZQWhYRGEhIWRnvaQrq4uANrb20XRBYiIiqWoMJ/qqgpCQsMJCAwmJ+sRBoOB1pZmykrEO0YZERVLcbd2cGg4waHhPEp/iFarBUxHItpaW0XTB4iOiaMwP5/KynJzvYtN37YWEBhEdnYmRqOR1pYWSgXe5XhW2/r57UtkdCzFhXlUVVYQEmbdNNgq/1ExsRR213lot66Dg4q3Vq7hysXzlBQXiaofEBpFVWkhdY+r8A0MFVULnrdptsAzIJy6yhKa62oY4Wudo8I9DGRX29paKS8rwcd38KvQL4OlNl5ZUW4eyzIzxRvL+moHBYeQmZmBwWCgpbmZYpHbed8xxZqER8VQUpRPTXUlwaHhNDU14uikJmHkKOKTUnhcUyWadl/7olQq0Wo7RdMz6/bp5y6urtQ+eYxOp6Ojo0NUu2bJxsTEJZCR+pCy0lLCwiNF0bXUv/wDArl7+yZBwSEEBQVz++YNAoPFszl95xAGg4ETx46wbMVbeHh6cufWDRG1n89/0sgU7t6+BYCnl8BHJXrRt53n5WZTXFTAnHnD20Htmye5XE5MbBzvrl7LOyvX0NXVxa4fd7B/z690dnTQ3NxMVeXT45E11VW4upp2/zLSU/n2q8+5d/cO8QlJbN76GdNnzsbV1c2itntAOI1VpbTWP8bVNwiZ7P/P3n9Gx3Vkeb7o76Q38Eh4772h96D3TpSjvCupVFU9U9M98+6sde+HV6veW2/m3p6Znp6e7uqqUkkqUY4SRdGToPfeAoT33nsPZObJ9yETCZcAATATqFbl74tEZOb5n4gTsSP2iYi9JXgFRRG3ehdxq3YgGvXkXT1K0a0zGPRT9+mWqiKyz35LY2kOupBYUre+TkjyMpRa+7xMttvKnkQixUUXiFSuRBAkeIZEU3Y7k4KLh9B46FC6eNhLagJSqZTQsHBUKqV1Cdo/IJALZ89YA7TE2vnA6WjtkNBwlCoVEomEiMhomhob+erPf0IqlRIRFc2atRscph0cGo5SadaOiomjuqqCLz/7Pa5u7vgHOu5sz3jtsIgo2ttaOfS1OSS8XK5gy869aEa9TXHEPYSGjdT9XDC+rcXFJ1BZUc6nf/gdrm5u+PkHoFQ55o3gfJTX1j2Mfu5zrT2x/A5a5rClq1SOKbNW68KLr7zO4e+/YduO3QQEBTtM3zcwFLlCOSd1Pt6mzQcSqRQP3yBkCiXCPLSz8Xa1vq6Gb774BIDVazeidcCZmmHt8W3cPyCQ85lnrAFaYuMS5kQ7Ni6eqmHb5u5OkIPa92j9+bQtQSFhVu266koe37+DRCJBrlCwafseh2qPti8+vn4IEolDA7QM647u525u7sQmJPLFp3/A3d0DXz/HnFO0pQ0QFhHJmZNHiYqJs+4ccITu+P4VHBJKRVkpnl5euBndGRjoJ9hBx09G38PwHOLm9asEh4QQHBKKj68fX37+J6KiY/DW2d/xslV+rYsL3jodMbGOmSNP0La08wf37tDT082XfzYf/YmOiWV1xvrZX9fGeOXq5sbK1RmsWLXG+vJCFI1cvniOnp5uZFIZaq2Wrdt3AuajKm++8/60560SiRQ33xG/ZzQKtQtBCYsJjF9EV3PtmM/yrx6zrt5q3L2JWroJpcaVxPX7bG7vtAd2c/ZMJhO97U1ELN1svrBSTey6ffa6/DO162tr2PPiywCEhoXzxtvvzZ12XS27X3jJ+rdlK1axbMWqOdFuqKtlx16ztiAIrN9k/33m09EGSF+0lHRLRLO5uoe62hr2vjQS+v8//uf/y+Gao9uaIAis37gZhUJBf18fX/75U3Q+jhkkx5c3NCyc0LBw6+ebt+1wiO74e2ioq2Wn5bm7u3s4PBLnaO3R5e/v75uTcybDunv2jdiX4Xp3c3fn/Y9+6XD91qY6Vm4yTzi1ru5sf+V9h+qNtmkhYeGEjGpn+998x/r/4z+z5z10tTaSuMpszzz8gvHwG3E2YhY5LtKvLbu6Zt0m1qzb5DDN0drjbZpSqeTl/W/MubYgCGNsyqkTRx2uP7reg0PDCXbQGWib2vW1bNtt1o5PSnXYtk1b2qPti1QqZf8b7zzjV/bRHT93Wbt+E2vXz007H68tlUr5m7/9PxyuO75/paUvJC19ofUe5noOsWrNiC1TKpV8+Iu/caj2+PLr9Xra29ocdlZwgralnb/25rv2ve5Lk6eAEgTBeuZYqVLx+lu2tWe6a8JkMtHT2kjM8q1Tarv7joxdC3a8bfN7rrqAGWnPFLs4ewNd7ZTdPoN7YARKF/dn/8COtDQ3c/jQt8TExuPl5T2n2q0tzRw5dJDo2Hg850H7+I/fERUTh6eDIgFOpX3Cou2oKITPoqW5mR++/4bYuLl77pO1tcPff8vgwABGo5EVq9c4JJLWfJR3PK0tzRz9YX7a+/jy19fVceLYYYdPTFpamjly6Nt5KTNAZ3sL1zOPEBQejau7YwNVwPzatGF6O9vIuXYSXXAkGlePOdWeT7s6n318vu3LfI4pba3NnDryPRHRc689X/Zlvucu86E93218+B7ma75qq/wV5WWcOXmcxcuWo1Q5LsdbS0szP1rKbc9nPp/PtK+rjaKbp/EMjEA1x+PUbBCmSodwLLthzIdDfT1UPbyMfrAPAQHv8AR8olPoqC2lIf8hA93txK57EY3n2OXnob5uCi58j3/CYnxj0hCNBkquHzcHbDGZcA+KICBhbNjVLQnTWx3p6urk1PGj5vNhgkBa+kIWL13GsSM/0N7aCsDA4AAqpYr3Pvx4Wtc0ThFwIPPUccpKi9FotLz3oXlFo7Agj9s3rtLa0sKb7/7MGlWos6ODP//pX62NOyAwiM3bdk6pbTBOrt3d1cm508etCSCT0xayYNFSTh//kfY2c1kHBwdQKlW8+d5HVFaUcevaJYxGI1KplNVrNxISNnlEtanCLBgMBg5/ewCj0YAoikTHJrB89Vo6O9rJPHmEgf5+fP0C2LJzL1KplNrqSq5dOk9LcyPbdr9IzDO2HqkV09+2cfrEMUpLitBotdYca5cvnqOkuAipVIqHhxc7du9FNU3jNZOUIAaDgW++/DNGoxFRFImLT2B1xjo6Oto5cfQw/f39+PkHsGvPvmltRZnJQezhtt7b2wMIpC9YyOKly5+r7EOGyYNcnDs90taHV++amxq4ePY0Q0NDuLl7sH33PpRKJZXlZdy4etHa1tas30ToFG0NQCGb3latycp97colSooLERDQaLXs2P0CrtMMljJVHx/Pg3t3eJr1GAAfH1+27TK38RvXLlOUn4cgkZC+YJE1/+CzqG7tG/Pve1cyqasqRanWWFfsqssKyXl4i672Vjbvewsvn5FogHmP71Je+BRBEFiwcgMBISP1LIoi5498hVrrQsa2FydoB3tNvhJqy7ZZ6+Duba5evsAvf/2f0Gg0DA4McPrEUbq7OhFNIouXrnhmwJ4HFe1j/l149yKtdRXIVWqWbDevXPW0N1P04Aqi0YggCMQsXoebtx+NFYVU5z8CTEhlCmIWr8PFUweAYWiQwnuX6O1sRRAEYpduwH3cW9L0EI9J72umdrW/v4/Txw7T2FBHQnLaM3dTqKZp22zZtRvXrpD1+BEaS6ThjPUbiYq2b0h6WwwMDHDm1HFampsAgR279hAUHDKja/QPTR6QbXSdC5jrPH3RUpqbGrl87jR6/RCu7h5s3fkCSqXSWudNljpf94w6n07/FkWRQ199htbVlV379tPV2cHZk0cYGOjH19efTTvM/byuporrl8/R2tzEll37iI59vrEs89Rx83PWaK27Avr7+zl59Ac6Oztxd3dn9wsvj4nKWV9XyzcHPmPXCy8RN0XuuWeNZTPp462tLZw9dZymxgZWZaxnybIVU177WdjS7u/v5+Sxw3R1duLm7s7uF15CZYk2XF1ZweWL5xBFI2q1hv3PWAmSSWe39VcURb747BNcXV1nvYI+3TnEZPPVxsYGzp05hdFgQJBI2LJtBwHTPIYz3TmEI8ZRcZrjaFtrC8ePHrb+u7OjnVVr1hESFs75zFMYDAYkEgmbt06/3BLJ7I5wlJWWcPFcJqJJJC19IctXrp7xNa4UNo/592BfN6X3L6If6EcQwDciEf+YNAxDAxTfOcdgXzdKjSsxy7cgU6gwmUxUZt2go74SiUxG1OKNaD196GyqpSrrhvW6/d0dRC/bjFfQyBnmbUk+syr4jFb2BIlAYMpyNB4+GPVDFF3+EVffYFSuXoQv20L1k2s2f1f79DaufqGjriMlavVupDI5JtFI8bXjuPmFovWa+fY3iUTC+k1b8PcPYHBwkAOff0J4RKR1qRjg0oVzdouslJySxoJFSzhz8pj1bzqdD3v2vcL5zNMTvu/u4ck7H/zcLtoSiYQ16zfh6xfA0NAg3x74lNCwCHbsGZnUXbt83lpWtVrD7hf34+LiSktzE0d/+JYPf/kfZqUtlUrZt/8tFAoFRqORH779grDIKB4/uMuCRcuITUji0rnT5GY/IXXBIlzd3Nm8fTeP7t+xS9lHk5KWzsLFSzl14oj1b+ERUaxdvwmJRMKVS+e5c+s66zZstru2VCrltTffsdbDN19+TmRUNPfv3mHxkuUkJCVz9swpsp88ZsGixXbVlggS1m/cgn+Aua1/8dkfCY+IcljZE1PSSFu4hLOnRtr6+TMnyVi/meDQMHKyn/Dw7i1WZqxHrVGz96XXcHE1t7Ufv/+Gn//N3z73PcDk5V62YhUZ68znYR/cv8ut61fZumOXXTSH6e7u4tGDe7z/0S+Ry+UcP/IDBXk5mIDuri4++PhvrFHUZkt4XBLRyQu4e3nEfrh76li1eS8Prp8b893O9haqSgvY9sp79Pf2cOXUIXbs/5n1rEJxziPcPLzQ64dmfB+2bBuYJwkVFWW4jgph/eTRA7x1Ova98hp9fb18/sffkZCUMqOzNn4R8QTGpFBw94L1b2VPbhGWtBTvwDBa6yooe3KT9I0vonJxI23jPuQKFa11lRTdv8zCLeZtOyWPruEVEErS6u2IRiOicWZRBGdqV2VSGctXr6W1pZnWlubJLjtjbNk1gMXLlrNsuWPytU7GxXOZREZGs++lVzEajdaAY/ZifJ0fPPApIWERXDx7ktXrNhEcEkbu0yc8un+bFavXIZPKWGHnOs9+dB9Pb501GMqta5dIX7SUmPgkrpw/Tf7TJySnL8LF1Y2N23bz5MFdu+gmWfrZ6VHbYu/dvkFoeATLVqzm7u0b3L1z07prQRRFrl25aJdgSTPp42qVmg2bt1FSVPDcupNp37tzk9CwCJatWMXd2ze5d/smGes3MTAwwIVzZ3jp1Tdwc3d3aLC3B/fv4q3TMTTo+KA4k81Xr166wKo1GURGxVBaUsyVSxcm3Wo4a+15HEe9vHW89zPzYosoivzrP/9PYuLiOXv6JCtXm8tdVlLM1csX7La90xaiKHI+8zT733gbVzc3vvjsE6Jj4p47MI0gSAhLXYXW0+wb5Vw8hJtfCC0VBbj7BhMYv5C6gkfUFTwmNHUFnQ1VDHR3krbtTXraGil/dJXkjS/j7htEyub9ABiGBnhy5mvc/Wb2km0yZvQqRK7SovEwV4pUrkDp6oF+oBeVm+eky5gddeUoNK6o3Ea2IAmCgFRmDqlsEsXnCqHt4uKKv7/5La5SqcTbW0dPT5f18+Hkzwl22o8cHBpmffM0jLfOBy9vnV2uPxVaF1d8/cxlVSiUeHnr6BmV3NdkMlFcmEdsgrmsvn7+1txY3jofDAb9rMMpC4KAQqEAzB1GNIoICNRUVRBtWbVLSEqlrMScZNLN3QOdr59DQgiHhIZNOKs1HIUJIDAwmO4u+yc9hon1YDSa225VZbk1j2NySirFdhogR+Pi6op/wOi27kN3d5fDyh4cEjYh51N7WxtBln3tYeER1nL6+gVYUxA8b1sbz2TlHv0CRz805LBYLSZRxGAwr2gb9HpcXFzJevSAFasyrO1b+xyBiHwDQlAqx67Eunl64+YxcUtZbUUpoVHxSKUyXNw8cHX3pK3ZHCmwr6ebuqoyIuNnd9bIlm0DuHLxHBnrNo6tXgGGhoYwmUzoh4ZQqdQzDqrh4RuEXDFuBVoAo8HsqBr1QyjV5np11wVYv+um82Ow3xzd2TA0SGdzHf6R5r4nkUqRKWb2Ym+mdlWuUBAUHIpMZrcj74BtuzYfDA4MUF1VSWq6OTS7VCqd9k6B6TK+zj29dfT2dNPR1kZQsNm+hIZFWB0NuUJBYHAoUjvVeU93FxXlJSRaUjyYTCZqqyqIsqzaxSelUlZSBFjGMh/7jWUhNvpZSXERSSlpgNkZHJ3o+fGDe8TGJdgl2NlM+rhGq8U/IBCJxD7BUmxplxYXkpRitldJKamUFJvLXZCXQ0xcPG6WKImOCvTW1dVFWUmx9cyeo5lqvjo4OGT576B13mZX7XkeR4eprCjHw8MTd3cPBAGGHFzu0dTX1eLh5YWHpydSqZSExCS7zNUUai1azxHfSOXqib6/l/a6CnRhcQDowuJorysHoL2uHF1YHIIg4Ortj1E/xFD/2BcabTWlePiHWn2l52XWlnOwt5v+zlY0nr6Tfsdo0NNU9ISo1btoKs4a85nJJFJ4+UeGejrRRSbNalVvPJ0dHTQ2NhAQOHIYsqa6Co1WO297tDs7Ozjw2R9RKpWsylhvt7DZXZ0dNDU24B8wsuRdV1OFRuNi86xJSVEBvr7+zzVBEUWRgwc+pbOjjdQFi3H38BwTOc3F1XXMJGm+yM56QkKiY/L0gLkeDnz2Ce3tbSxYtAQPT68xkaBc3dzo6XZsPZjbej2B4yLkObrs3jofSosLiY6Np6ggn+7urgnfKS7Mx9cvwO6TYZhY7muXL5LzNBulSsnrDngj6OrqxuJlK/jjv/wjMpmc8IhIwiOjOHnsRwrzcykuKkCt0bBx87Y5OX/S39uNt99I2H+11oX+XnNbe3z7EmnLMjDMYlVvMkqKCnFxccPXb2xS6QULl3D08Hf84Z//kaGhQXbtfckuk+GoBWt4evU4ZY9vYsLEgk0vTfhOQ1keXgHmaHkDvV3IlWoK716kt6MFFy9foheumfUAOVO7Ohc8enCP3KdZ+PsHsmHTFocn3e7o6ECj0XD65DGaGhvx9w9g45Zt1pdc9qars4Pmxgb8AoLw0vlQVlJEVEwcxYX59HRNtC/24Mbl86zM2GCe3AID/f0oxkQodKN3Dseyvt4e60RXq3Whr9f8MqO7u4viogL2v/kumaeOO0R7sj4+F/T19o4rt3nC297WilEU+e7rAwwNDbJw8VKrM2xPLp7PZN2GTdaUUXPJ6Pnqxs1b+f7g11y5eB6TycSb7zouANeI9tyNo6MpyM8lIdH84mzDpq0c+u5rrlwyl/uNdxxb7u7ubtxc3az/dnVzo762dopfzJzB3i76OlrQevmhH+xDYXlhKVdp0A+aj3AM9fei1IzEdlCotQz191q/C9BaXYJ/jP3a/Kw2ORsNeirunSMoZQVS+eQDQEP+A3yiU20OvIIgIX7DyyRue4u+9mb6u9pmcytWhoaGOPrjITZu2jrmTUV+bo7dVvVmitbFhZ//6te888HPWbdxC6eOH2HQDlsFhoaGOHXsB9Zu2DKmrIX5tpORtrY0c/PqRTZseb5ojRKJhDfe+4gPfvEfaKivo72t5bmu5whu3biGRCIhMdlxyaclEgnvffgxv/z3f0d9XS1trXNbD0NDQxw5/D0bN28b8/znouxbduwm6/FDvv7zJwwNDSId99a3pbmJG1cvsWmr/SOD2ip3xvqN/OrXf0diUgoPH9yzu+ZAfz8lxYV89Ktf84t//3fo9XrycrIxGg1IZTLefv8jUtMXOmwiNl3qKs1n/kaf7Xte9Ho9d2/fGBMtbpiK8lJ8fP35+N/9LW9/8HMuns+0i22rL8khasFqlu99j6gFqym8d2nM5+2NNTSU5ROZZj4/ZDKJdLc3ExiTzKJtryGVyajKezgr7Zna1blgwcLFfPyrX/P+h7/AxcWFSxfOPftHz4koijQ01LNg4WLe//Bj5Ao5d27dePYPZ8FwnWdY6nzTtl1kP37Atwf+hH5oyCEh+CtKi1FrNNaVxb80BEEAy4uTyxfOkrF+k8OSbE/Vx+cacxnN5RRFkaaGel585TVe2v8md27doM1yftZelBQXodVorTEW5pLx89XHjx6yYdNWfvnv/5YNm7aQeeqEQ7Xnehwdxmg0UlpcaN0F9eTRQ9Zv3Mov/t3fsn7TFjJPO67cc4HRoKfo9lnC0lchG+cbjW7fz2Kov5e+zlbc/e2zhRNm4eyZRCMVd8/hGRyDx6hDg7boa2+iLvcOuWe/prn0KY2Fj2kuzRnzHZlCiYtPIN2N1TO9FStGo5Gjh78nMSmZ2PiRw9OiKFJUWEDCPA3UMpkMtdp8sN7PPwAPD0/rgf/ZYjQaOXXsB+ISkokelRdFFEVKiguJGXd4u7u7i5NHD7Flx167RRpTqlQEh4ZRX1fL4OAAomUbbk93t8OX4afiadYTSkuK2f3Ciw4bHEejUqkIDQuntraGwYGReuju6rJuabQ3RqORI4e/JzE5hbhRbX2uyu7lreOl/W/y5nsfEZ+YjLvnyPbs7q4uThw5xNad9mtrw0xW7mGSklMpKsy3qyaYt5y4u3ug0WiRSqXExMVTW1ODq6sbMZbcnTGx8TQ3N9ld2xZqrSt9o1Yc+nt7UGtdaWmspa6ylBPf/JHbF0/SVFvFnUunnkuro73NujPhk9/9E93dXXz150/o7ekh52kWMXHxCIKAp6cX7u4ednnp0VBRgC7YfDbJJySa7tZG62c9HS0U3btE0uod1lxESrULSrULbt5mJ1cXHE1P+8zPdM3Urs4VWhcXJBIJgiCQtmAR9fX2fQttC1c3N1zd3Kxv/ePiE2lssH9ScaPRyOlxde7lrWPfq2/y+jsfEpuQhLuH/SPQ1tfVUF5azIFP/pmzJ49QW1XB9cvnGBplw3t7utDO4Vim0bpYd8X09HSj0Zjf8DfU13Py2GH++Lv/RVFBHhfOnrbrEYGp+vhcoNFqx5Zba54vubi6ERYRiVyhQKPREBwSSnNT41SXmjG1NVUUFxfyr//8jxw/8gOVFeWcOPajXTVsYWu+mvM0y5oLOi4hkfo6x/Tz+RpHhykrLcHXLwCt1ryqlZMzqtzxiTQ4qNzDuLq60jVqN5I952qiaKT4dia60Bi8gsxjmFypsW7PHOrvtY5bCrWWwb6RPjZ+Va+tpgTPoEi7baGGGW7jNJlMVD26itLVA9+YZ58LicnYa/3/+vwHSGVyfKKSMQz2gyBBplAiGg10N9XgG5M+45sfvqfMUyfw1vlMiBZVUV6Gl7c3rm5uk/zasfT19VrPsnR0tNPR3vZcg5fJZOJC5km8vHUsXDI20WpVZTleXt64jlqiHhwY4Pjhg6zK2EDgDCOpjaevrxepRIpSpcKg11NdUc6iZSsJDgmnpDCf2IQk8nOziYyOfS6d2VJWWsLdOzd54633kMvts8fZFn29vUgsZ1j0ej2V5WUsXbGK0LBw69nQnKfZxMTG2V3bZDJx5tRxvL11LB3V1ueq7GAuv0arxWQycffWdVLTFwHm6H1Hf/iW1Ws3zDhq37OYrNxtba3W7dnFRQUOOTfr5uZGfV0ter0emUxGZUU5/gGBKJUKqisr8PDwpLqqEk/PudkmHhQWxe1Lp4hLXUR/bw/dne14+fij8wskdWkGAE11VRRkP2D5hqkj/z4LH18/fvXr/2T99ye/+yfefO9DNBoNbm7uVFWUExwSSm9vD+1trXaZmCvVWjqbavHwC6ajsQa15Sz4QG83uTfOEL9iM5pR578Vai1KjQt9Xe1o3DzpaKxG4z6zFw0ztatzSU93t3UyUlSYj85n8mMT9sLFxQU3N3daW1vw9tZRWVGOzse+fctkMnHRRp2Pti/3b98g2QFnqVasWc+KNebkzbXVlTx+cIctO18g88RhSovyiYlPoiA3m4g5iHo6TFRMLLlPs1i2YjW5T7OIjjGPoz//1a+t3zlz8hiR0TF2TX49VR+fC6Ki48h9ms2yFavIfZpNVIx53IyOieXS+UzLuXgj9XW1LJpmtOPpMjqnYFVlBffu3GL33okRjO3JZPNVFxdXqqsqCQ0Lp6qi3CFHAuZzHB2mIG/sTrsx5a50TLlHExAYRHtbKx0d7bi6upGfl8vuF57/mZtMJsofXEbt6klAbLr1756B4bRUFhIYv5CWykI8A8MB8AgMp7EkB++QaHraGpHKFWOcvZbqEkKSl2NPZpR6oaelnpLrx1G5eVm3GQQmLkUUjdRm3cQw1I9UrkTt7k3UqrETjWFnzzcmjf7OVqoeXjaHqzWZ8AiOwj9+0ZjvTzf1Qk11Fd98+Wd8fHytKxpr1m0gKjqG0yeOERAUxIKFM4uKOFXY5pPHfqSmqpL+/j40Wi0rV69FpVJz6UIm/X19KJUqfPz8eHn/mxQV5HPrxhUkEimCILBy9VqiYqZ2hqZKvVBbU8UP3x7AWzdS1pUZ64mIjObc6eP4BwZZJ98A925f5/7dW3iMCvSw75U3Jj3sPFUQ3ZamRs6dOY5JNGHCRExcAstWZphTL5wwh6v28fVny869yGQyGuvrOHn0EIODA8ikMjRaLW99MHny7ZmkXjh+5DBVlRXWZ7A6Yx13bt3AaDBaAxwEBgVPO6LUTFIvNDU1cvrEMXNgIZOJuIREVq1ZS0d7O8ePHmZgoB8/P3927tk3rTNrM1mFq6mu4usDn+Pj64tg2Q6QsX4jF86dmXXZp0q9cPr4j1RXVTLQ34dGo2XF6rUM6YfIevQAgOjYeFav3YAgCNy9dZ17d26OOdf04qtvTnmwfrqpFyYrd/aTx7S1tSAIAm5uHmzdvnPaL3Zmknrh5rUrFObnIkgk+Pn5s2XHbgwGA6eO/0h3VxdyuZzN23ZO+8zL+NQLty+epKmumsGBflQaDcmLVqFQqnh06yKD/f3IlUo8vX1Zu8McYTjv0R3KCp8ikUhYsGI9AaFjd1gMO3szTb1gy7alpC2wfj56ItjT3U3mqeP09nZjMsHS5StJTJ76BeD41At5t87S2VSLfnAAuUpNePIyNG4elDy6jskkIpHIiFm8FlcvXwrvXaKluhSl1uz4CILAoq3mqGU97c0U3ruESRRRubgRt2zjhMAvU6VemKldBfjsD/+boaFBRKMRpVLFC6+8gbfOdkS36aZesGXXqisraWxsQBDA3d2Drdt3OWzXwGgaGxrIPHUco2jEw8OTHbv2zvis4FSpF+omqfOO9jayH5vtS1RMPCsz1ls//3xUnSueUefT7d/Dzt6uffvp7Gjn3KkjDAwM4OPrx+bte5HKZDQ21HHm2A8MDgwglZnHsjfemzyN07PGspNHD1M9qp+tWrOO6Jg4Thz9ga6uLksKgpcnBOsZdvaeJ/XCTPp4b08PX33xJ4YGBxEEAblCwXsf/nLWkc1taUfHxnHy6GFzud3c2fXCS9Zy3797i5zsLARBICVtwTOdvdmmXoARZ8/RqRcmm68qlUounj+LKIrIZFI2b90x7e2l051DOGIcnW7qBTBvH/3Dv/wvfv7Lf2/N6VdTXcWlC5ZyS6VsmkG5Z5t6obSkmIvnMzGJJlLS0lm5OmPG1xifeqG7pZ68K0dQu3tZ6zYkeTlaLz9K7pxlsL9nQuqFiifX6WyoQiKVEbl4Ay5e5hd5g71d5F4+woKd79h8trNNvTAjZ28uma6z5whmMhG0N1M5e45m/pRn5uzZm5k4e/ZmLrabTsVUzp6jma6z5wjms4+Pd/bmkqmcPUcz3tmbS6Zy9hzNdJ29nxpTOXuOZj7791/rWDbfPI+z97z8tc4hZuLs2ZvZOnv2YLyzN5fM1tmbv97hxIkTJ06cOHHixIkTJ04chtPZc+LEiRMnTpw4ceLEiZOfIE5nz4kTJ06cOHHixIkTJ05+gjidPSdOnDhx4sSJEydOnDj5CeJ09pw4ceLEiRMnTpw4ceLkJ4jT2XPixIkTJ06cOHHixImTnyBOZ8+JEydOnDhx4sSJEydOfoJMmWevrLl/3pJoBHrOXz6ov1Z6Bwzzpq1VPTsB+U+R+c6L1DePubC0yr/OZ24wzl9uQ+k85iaaz3xQ81nn85n/az75a809Np/8teY9+2vGOZ7MPfp5zE/sqppdpf91jkJOnDhx4sSJEydOnDhx8hPH6ew5ceLEiRMnTpw4ceLEyU8Qp7PnxIkTJ06cOHHixIkTJz9BnM6eEydOnDhx4sSJEydOnPwE+Uk6e//w9/9lvm/hL4KnWU84n3l6vm9jzijIz+WT3/8L3371xXzfypzxP//bf53vW+DE4YMMDgzMi/ahg18zMMfafwn25ZPf/RN9fX3zfRtOnDhxALdvXp/vW3Di5CdLbW0NBz7/E59/8ns++f2/cOPalTnTzjx1jD/9/n/z5ed/5MvP/8i3X34+J7p/neHwnPwkyX7ymO07dxMcEjrft/JXxe6XXps37Vdee3PetJ04cTJ3iKKIRPKTfD89gds3r7Ni1Zr5vg0nTn4ymEwmhgYHUapUnD5+lL0vvoyvnz+iKNLW2grA4MAACqXS7pE+TSYTQ0ODKJUqADLWbSQ2PnHMdwYHB1Ao7K89jF2cvaKcxxTlZgEwNDSIi6sb7p7etDTVYzQYCIuKI23pantITeDxwwc8efQAgMHBQdw9PAC4eD6T8rIyXFxc2PPCS2i02jnTX7ZiFdcuX0Q0mdBoNLz25jtzpp2UksqdWzdQKVX4+PkhkzrGn8/JekhO1iOrtpu7B4uWruTurWuIRgNuHp5s3LobhULhEP3xZe/s7EAul3Pm5HGiY2NZv3HLnOi6e3iQmJzC3ds3x9T55m077K/96AFZjx5atd3c3QG4duUSpSXFyGQyXnx5P1oXF7trD5PzZOJz7+rs4NW3PkCt0ThMF2zXfUdHO+9+8HM0DtKezL4A9PX1cfj7b1m5ag1RMbEO0QfIevyQrMcjz93d8twdga021lBfR/rCxZSVluDi4kLGug1cuXSBrq5ONmzaSkxsnH3vwUad19fVsnjJMkos7fylV15zSDu3Vdcp6Qu5d/sGJhNERkWTsX6T3XVh8nKnLVjo8LFsumOJI+ya9R4maXtpCxZRWVHO5q3bHfIiz1bZh7VBIDUtnSXLVthddyp9g8HA55/8Hp2PL7tfeNEhuk8ePeCJpa0Pjarvv/1//Z8AFBbkUVpSzI5de+2ubWv8Xrp8hXXcfpr1hIb6Oru3N1u6i5cuZ+PmrTy4d4cH9+/yi7/5D3S0t3Py+BHeevcDh+q7e3jQ3dXFW+/9DLVazTdf/pmVqzOIiIyyqy5MtG1dnR0sXLyU9Zu2ApD95BGtLS2s3+SgudO4/t3Z2YGvrx8AeoMB0Wjk47/59cyva6NOX3/rXevnnZ0dPM16Ql7OUzZu3kpUTCy9fb1oXVwBkEgk6Hx8AKipruLi+bMkJqeQmrbAOseajKzHD8l+MtZmvfr6yHy/q7ODnKdZFOTlsG7jFiKjYia9Vm1NNVcuniM+MZnk1HTc3Ow7zts1z55oNHL++HckLliKr38QSpUaURS5cPw7lqzeiKfOd9rXmmmePaPRyMGvD7Bs+UoOHzrIrr37SEpO5eb1q/T19jp0kBqtn7ZgIdcuX+KNd97Dw8OT/v5+1GrH5gwco33lEu998HOUKhXffvUFfn7+0y77bPLsGY1Gjh76mqTUBeRlP2b3S68hlyt4eO8WRqORpSum93Zytnn2Rj/3e3dvs37jFgICA2d1rdnopqYv4Ma1K7z3s49RKBQc/PoAvr5+067z2eSiMhqNfPf1AZauWMmPh77jxVf2Ex0Tx5VL51EolKxcnTHta802z57RaOTo91+zcOlyrl08Nytnb7Z59kY/8/PnzjjU2bOlefzoYT7+1a85fOgga9aun/HAPNu8SEajkUPffsmSZSu5dD6TN9/7cMblnm5epPFt7OX9rxMZFcORH75Dr9fz0quv09rSzOkTx3jvw4+ndc2ZvrEcb9NfeuU1omPjuHzxPErlzNr5TOt8uK5T0hZw8/oV3nrvQ1QqNT8c/JoFi5cQExs/7WvNNM/efI5l9hpLYPZ59sa3vT0vvER8YtKMrjGbt+PDZQ8Lj6CmpprX3ngbgIGBAVQq1YyvN1v9YRvzH//z/zXja8wmz57RaOS7bw6wdPlKTh77cdbO3mzy7I1ubzevX+XjX5kn+99/+zUrV69x2C6d0boP79/j3Q8+4sjh7+nq7OTFl/dTUVFGW2sLax30Ymf0s+7t7aW8rJSAwEDa29vYtmP3jK41W9u2ZNlKLl88x/sf/RKpVMo3X37O5q078LE4YNNhNnn2Rvfv6Bjzi8JjP/5ASGgYCxcvmfZ1xvfx0XUaERVNcVEBWU8e09fbS3JqGknJqdbx8ub1q9y/d4fQ0HAio6JITk1HJjPPR/r6+sh9mkVOdhZaFxdS0xcQExuPVCq1ao3Ps2c0Gvnh4JcsXraS8IgoSosLeZr1mL6+XpKS00hISrHOjzJPHaOmugqlUgmAt86HHbv3AdDf10debjZ5OdlotC6kpKYTFRM3Rnu2efbsuuxz/8ZF/INCCQmPpijnMcV5WYiiif6+HjraW2fk7M2Ui+cyCQ0LJzo2DkEQSEhMBiApOZUjP3znMN3x+iqlipDQMDw8PAEc7uiN1lYolISGhVvf/MYnJtFuWZ52FNcvnyM4JAylUkVbawuHvzWflzMaRfwDgxyqDWOf+727tx2uN15XqVQRGhpufc5xCYkOr/OL588SGh5BtMUIREWbV5X8/AOpKC9zqPYw1y+dIzg0jIioWK5dPDcnmsOMfubnz52Zc01RFDn4zQE2b91BaFj4nOgDXL5wlpCwcKJiYrl0PtOhWuPbWERkNAA6H19kMhlSqRQfXz86Ozscdw+j6lwqlVpXT/0DAqgoc2w7H65rpVJFSEgYGo3ZpiYkJVNbXTUjZ2+mzOdYNp9jifUeRrU9QRCIjU+YG11L2RcvWUbu02zOnz1NVHSsQ1ZZptKPtvNK+bO4dP4soWER1on3XDJc5uSUNHKfZlNbW4OXpxdtrS0EBYfMie7tmzcYHByku6uLxKRkqqsrqamqcmi7G/+sCwryePLo4bRfnD0Po8eR0pIiykqK8dLpEI3GGTl6s2V0/wa4e/smMrlsRo6ezeuOqtPPPvlXRFFkx669BAYFT/juqjVrSUxOoaKslLzcHPJyc3jj7fcA0Gg0LFm2giXLVlBbU83pk8e5deMaH3z0y0m1r1w8S0hoOFHRsRz47A+IosjWHXsImGQObGsbJ4Bao2HRkuUsWrKcutoazp0+zp1b13nng+dvF3bbAF9a8JTe7i5Sl6yiu6uDvCf32bTnNXa/9j5BYVGIxpmvGk2Xp1lP6OzsZHXGOttfcNAe2Gnr/0S183Oy6O7qZOnKDMBESFgEr73zEa+98xFvvv8xG7fucqj+fJV9Xp939hO6OjtYtWYtYN6CMPyGSyIImMTZrRrNhLHPfW6Zj7ofrymRSPD3D6S8rHTO7iEnO4uurk5Wrl7rcK2p2pggCNa3jIIgIDqovdmq85F7kCCaHNfO57KuxzOfY9l82jXrPYxrezKZbE7O6Y0uu0qt5v2PfkFoWDhPHj3gzMnjc6o/l+RkP6Gra6S+R7cvg8FxczaYWOaExGQK8nIpLMgnJi7eYWeXxusGBQfzNPsJXt7eBIeEUVNVRW1tDcEOcjbH6+v1erq7usz/PzTkEM1hxtu2lLQF5D7NIjf7Ccmp6Q7Vhon9u6K8jMKCfLZs2/l81x1Xp9t27CYwKJiTx45w+eJ5WlqaJ/zG09OLBYuW8Nqb79DU1Ej/qIBnLc3NXL54jlPHjxIcHDLlamvu0yy6OjtZYanTzdt2ERAYzJmTR7l2+QKtNrSnorWlmauXL5B56hiBwSFs3mafebRdrGhrUwO5T+6zatMuBEFAPzSETC5HoVTS39dLXZXj3sI21Ndx7+4tdu/dZzUOJpOJgvw8APJynxIc4rg3ROP1A4OCqa6qpKOjHYD+/v650w4Morqykv6+PoxGI4WWOnAETY31PH5wl8079iIIAv4BQdTX1dDR3gaAXj9Ee5vj3gTbeu5zwXjdgIBAqqoqGOjvRxRFigryHap9/85tdu2Z2zKPpqmhnsf377J55945v4f5eOaTaW7ftYe21hbu3Lrh8HtobKjnwb3b7Nj9gsPL/ZfQxuarb8PEuvYPDKS6uoq+vj5EUaQgL5fgkDCHaM/nWDafY8noe5iPtje+7H19fZhMJuLiE1mzdgONDfVzqg8gkUoxGme3xX4muvfv3mbn7hFdrUZLa0szJpOJ4qICh2qPL3NsXDwlRYXk5+VYV7PnQjckJIx7d24REhKGn78/lZUVyKRSlA7YumtL/8ql8yQlp7B67ToyT5+wu+YwtsaRgMAguru7yM/LnfFW6Zkyvn93dnZw/uwZ9u57Gblc/lzXHV+ngUHB7Ni1l/c+/Bgvb2/OnDzOgc//REO9uS+XFhdZt5m3tbUiEQSUKhUN9fUc+PxPnDl1HC9vHe99+DHbd+2xuToII3W6fVydbt2xm7fe+whPL2/OnTnJNwc+e6YdaWyo55sDn3HuzEm8vLx5672P2LJ996SrgzPFLts4C3MeMTTQz/ljBwHw9vXDU+fHsW/+hNbFFR9/x23ne/jgPgP9/dZw+/4Bgcjlcurrarl98xoajZa9+16eU/2tO3Zx5IfvMZlMaLRa697/udBelbGWL7/4FJVSha+fv0N0AbIfP2BgoJ+j338FgK9fAJu27ebcqaPWQWr5qrV4enk7RN9W2ecCW7orVq7hwOd/QqVW4+2ts+7FtjePHt6nf6Cfg18fAMBvjso8Gutz/87y3P0D5kx7smfuyGnhZJoSiYTdL7zE4UMHUSiUz70FZSoeP7zPwEA/33/zJQB+Dqzzv4Q2Nl99G2zX9Zp1Gzj07QFrgBZHbbObz7FsPseSYear7Y0vu0qtZnBgwDoZzFi/cU71/QMCSV+wkM8++T3+/gEOC9DyeLi+vzlg1c1Yv5HDhw6i0Wjw9w9kSO+YlSZbZd6+aw/eOh0tLc0EBjlmzmhLd/mq1XR3dRESGoZEIsHNzQ1vb92c6KvUavR6PW+9+wESiYSignyysx6TmrbA7tq2bNvWHbuJjU+kuakBlcqxR47G9++mpkbUarV1a7qLqysv739jxtedrC0BKBQK0tIXkpa+cMzqXk5ONhcvnEUukyORSNi190UkEgkyuYwdu/ei0/lMS/uJpU4PfTtSp1u277Zqp6QtICVtwYTVvWtXLnL39siL4jfe+RkymYytO3bjPU3tmWLXAC32ZKYBWpw8P7MJ0GIvZhug5S+BoaEhFAoFoijy46HvSE1Ln/Z+/9kGMbAXsw3QYg9mG6BlGFEU+d//+N/5d//hP405wPyXzmwDtNiD2RyotxfztVII81vnMw3QMpp/+Pv/MqtAHfZmNtER59O2zWdbm09mE6DFXswmQIuT5+d5bduRQwdZuGQZYeERM/7tX+t4Mj5Ay1zyFxGgxYmTv0ZuXLtCZXkZBqOBiIgoYuIcF7zByQif/vF3pKUv+Dfl6Dlx4sSJEyfzzcDAAN988Sk+vn6zcvSc/NviuZy9vKz7lORlgyDg6aVj5YYdSKRSnty9TmVpoTmSVvICElIXYTKZuH/jInWVZUhlclZu3I63j2O2hoiiyBeffYLrLJeFZ0NXVyenjh+lt7cHEEhfsJDFS5f/ZLQNBgM/fncAo9GISRSJioln2aq1HD54AP3QIGAOWevnH8jOF16hrKSQuzevIQggSCSsWbeFQAcdeC4rLeHiuUxEk0ha+kKWr3RMTsfxnD5xjNKSIjRaLT/7+a/mRFMURQ58/ifzlodXX+fMqePmfegmE55e3uzYvdduuQ0NBgM/Hhz1zGPNz9xkMnHnxhVKigqQCALJ6YtIWziyjbGxvo4fvvkzW3ftIzrOvhHNRrd1QZDgYsmVMxfMRT/LPHWcstJiNBot7334CwCuXrpAaUkRUqkUDw9Ptu7cg0qlor6ulvOZp8w/NJlYsXqt3V40GAwGvvnyzxiNRkRRJC4+gdUZ6zh94hjVVZXWrcrbd+/Fbw62+DnSptuq88KCPG7fuEprSwtvvvuzMVtJ796+QU7WEwSJwIZN2wh3QLTG4bbm5u7On/7wuzkdT8C2TU1JS3eI1mRt7cSxH2mor0cqkRAQGMSW7Tvn5MXOfMwf5lq7rbWF40cPW//d2dHOqjXrGOjvp7jYPHfTaLTs2LUXF1fH2tj5nDsZDAa+OfA5hlFtb83a9XOiPRfzB1u2bZicrMe0t7fx2tvvA3D/7i3yc3MALInGW/jlr/+T3SLKj5+7zNdY4uj54tnTI3X+7s9G6vzxw3s8efQAiSAQERVjzdd67/YNnmY/QSIRWL/RMeMJPIez19fTTUH2I/a8/gEymZxrZ49RUZKPyWT+bO8bHyIIAv19vQDUVZXR3dnO3jc/oqWxnrtXz7PjZcecZXtw/y7eOh1Dg4MOub4tJIKE9Ru34B8QwODgIF989kfCI6KsyRr/rWtLpVJeeOUtFAoFRqORHw8eICwimpdeG0kgefr4D0RGmcOjB4dGEBEViyAItDQ3knniCG998IvJLj9rRFHkfOZp9r/xNq5ubnzx2SdEx8TNSb2npKWzcPFSTp044nCtYR7ev4u3t45Bi4O9YdNWq7G8dOEsjx7cs5vxkkqlvPDqqGf+rfmZt7W20NPdzVsf/MIc0KC31/obURS5de0SoeGRdrmH8fzU+1lyShoLFi3hzMlj1r+FRUSwZt0GJBIJ1y5f4N7tG2Ss34TOx5e33vsQiURCT083Bz77I1ExsXaJXiiVSnntzXesz/6bLz8nMsqcfmHdhk3EJUwMG+1IHGnTbdW5TufDnn2vcD7z9JjvtrY0U5iXy7sf/oLenm4OHfyaD37+K7tHjJzPdj7XNnWytpaYlMKuPeb8UyeO/Uj2k8csWLTYIfcwmvmYP8y1tpe3jvd+Zg7nLooi//rP/5OYuHhUKjWrLc7Ow/t3uXXz2nNHSnwW89nWpVIpr731rrXtfX3gcyKjYwiaJCCHPZmL+YMt2wZmB7uiogzXUYm7lyxbyZJlKwFzAJOH9+/aNXXY+LkLzP1YMhe2LSkljfSFS8g8NVLnVZUVlBYX8fb7P0cmk1nnTK0tzRTk5/Luz8zjyQ/ffc37H9l/PIHnjMZpEkWMBgOiKGIw6FFrXCjKfUzKkpXW/bRqS36i6vISIuOSEAQBH/9A9EMD9PX2PH8JxtHV1UVZSTFp6Qvtfu2pcHF1xT/AHDhBqVTi7e1Dd3fXT0ZbEATripEoioiicUxkjKHBQWqrKomMNgcvUCgU1jag1+sdFjG8vq4WDy8vPDw9kUqlJCQmOTSK2GhCQsPmJI/iMN1dXZSWFJOaPnJ4e9jRM5lMGPQGu+5jn+yZ52Q9ZMmK1Vat4VxcYA7gEhUbb+339uan3s+CQ8MmHJQPj4iyGv+AwGC6u7sBkMvl1r8bDQYEO4aqGf/sjfN45s3RNt1WnXvrfPCyEaShpLiQuMQkZDIZ7h6eeHh60lBfZ/d7ms92Ptc2dbK2FhUdgyAI5qjHlqiBjma+5g/zqV1ZUY6Hhyfu7h5jgovp9fo50Z/Ptj5hjDMaHRrwazRzMX+wZdsArlw8R8a6jZOWtSA/x67ROW3NXeaDubBtwSFhqMY91+zHD1iyfKU1cfvwnKm0uJD4hFHjiYdjxhN4jpU9jYsrielL+PHA75HKZASEhBMYGsH18yeoLC6gqrwIlVrDktUbcfPwoq+3G62L28jvta7093aj0brYpSDDXDyfyboNmxhycL6Sqejs6KCxsX7ScK3/VrVFUeT7rz6ls6OdlPTF+AeMRMwqKykkODQcxajBorS4gNvXr9Df38uuffvtfj8A3d3duLmOtCtXNzfqa2sdojXfXDx/1mbbPn3yGGUlJXjrdKzftMWumqIo8v2XY595Z0cHJYV5lBYXolZryNi4FQ9PL3q6uygrLmTf/re4mHnSrvdhi59qP5uKnOwnY96E1tfVcvb0cbo6O9m+6wW7vhEURZEDn31Ce3sbCxYtITAomCePHnLt6mVu3bhGaHgEa9dvtA5gjuIvwaYP09PdPSYUtqurGz0OnpjOdVubD5tqq60NYzQayX2azcbNWx16DzC/bW2+tAvyc8ekOrh+9RK5T7NRKpXsf/OdKX5pf+bDroqiyBef/pH29jYWLl4yL+PJXFJSVIiLi9ukEXb1ej0VZaVs2LzdbpqTzV3meiyZr/lie3sbtdVV3Lx2GalMxtr1m/EPCKS7Z+x44uLA8WTWM4PBgQGqK0rY9/bHvPzurzAY9JQV5iIajUhlUna+8i4xCWncvpxpz/udkpLiIrQa7ZyG6h7P0NAQRw5/z8bN2xwWgn++tCUSCa+98xHv/fzXNDbU0drSZP2sqCCXmPixb4KiYuJ564NfsGPvK9y9edXu9/PXREmxeW+/rba9Y9defvXrv8Nb50NBXq5ddSUSCa+9+xHvfWx55s1NiEYDUqmM/W//jKTUBVbH7vrl86zM2DAnUbJ+yv1sMu7cuo5EIiEhKcX6t4DAIN778Je8+e7PuHfnpl0TIUskEt778GN++e//jvq6WpqbmshYv4EPP/4Vb7//IQMD/dy9fdNuerb4S7Dp88l8tvO5xFZbG+b82dOEhIYREuqY/IbDzGdbmy9to9FIaXHhmBdIa9Zu4Bf/7m9JSErh0YP7c3Yv89XWJRIJ73/0C3716/9IfV3dmLb3U0Ov13P39g1rUnNblJYUERgUYrdVx8nmLnM9lswnoigyMDDA629/QMa6TZw8dnjOoxXP2o1uqKnAxdUdlVoDQGhELM0NtWhcXAmJNJ/bComM4dZl85kHjdaV3p4Rj7Wvtxu11r4Hf2trqiguLqS0tBijwcDg4CAnjv3I7r2OyVMzHqPRyJHD35OYnELcNEPv/1vUVqpUBIWEUVlehrfOl/6+Phob6tmx9xWb3w8KDuViZwf9fX2oNRq73ourqytdo96EdHd1OfxA+XxQW1NNSXEhZaPa9sljR9i113ymRSKRkJCYxN07txwSSMH6zCvK0Lq6ERVjDgQSGRNndfaaGuo5e9J8/mCgv4/KshIkEgmRMfbNS/bX0s9Gk5OdRVlJMa+8/rZNZ9pb54NcrqClucnuE0aVSkVoWDjlZSUsXW4+0yGTyUhJTefe3dt21RrPfNv08bi4uo7ZYtbd3YXLqDfF9mS+2tp82tTRbc3H15eb16/S19fHvpd2OVx7PtvafGmXlZbg6xeA1sYOq8SkFA5//w2rM9Y59B5gfm36MMNtr8zS9n6KdLS30dnZwYHP/giY7ddXf/6EN9/5GVoXcxsotHOC9WfNXeZqLIH5s20urm5Ex8Zbt6QLgkB/fx+uLq70dI3cT48Dx5PZb+N0daOlsQ6DXo9UJqOhthJvH3/kCiWNtVW4unnQWFeNm7sXAMHh0RTmPCI8OoGWxnrkCqXdt3CuXb+JtZYIN1WVFdy7c2vOJgUmk4kzp47j7a1j6bIVc6I5l9r9fb1IJFKUKhUGvZ7qynIWLjFrlRbnEx4ZPWYJvqO9DXcPTwRBoKmxHqPRMGEfsz0ICAyiva2Vjo52XF3dyM/LdVgS2vlk7fqNrLUk962qrODe3dvs3PMC7W1teHp5YTKZKCkuxNvbfknsbT7zpSuIjI6lprqCRI90aqur8PA09/F3f/7vrL+9cOYE4ZHRdnf0fur9zBblZSXcv3uL/W++g1wut/69s6MdVzd3JBIJXZ0dtLW14ObuYRfNvt5eJFIpKpUKvV5PZXkZS1esoqenGxcXV0wmE8VFhfg4OIjCfNp0W0RFx3L6+BEWLVlOb083HW1tDlmNmc92Ptc2dbK2lvXkEeVlpex/w/YLDnszn21tvrQL8nJISBrZwtne1oqnl3kMKSkutHlu1d7MZ1sf3/YqystYtmLVnN7DXOLj68evfv2frP/+5Hf/xJvvfYjG8hJ+cGCAmupKdux+wW6atuYuu/bum/OxBOZvvhgdE0d1VQWhYeG0t7ViNBpRqzVERsdy+sQRFg6PJ+2OGU/gOZw9H79AwqLiOHXoCwSJBC+dLzFJaRgNBm6cP0l+1gNkcgXL128DICgsktqqMo5+/QkymYyVG+y3H/gvgdqaanKfZuPj68vnn/wegIz1G4mKjvlJaPf29nDhzAlMJhMmk4nouAQioszXLyrIY9HSlWO+X1pcQGHeUyQSCVKZnK07X3TIgC2RSNi8dQfff/sVJtFESlo6Pj5z81bu+JHDVFVW0N/fx7/80z+wOmPdnB+uP33yKIODQ2Ay4ePnZ9fIadZnLo595oFBIZw7dZQnD+8hlyvYsNWx0dpG81PvZyeP/UhNVSX9/X384V/+kZWr13Lv9k0MRiM/HPwaMA9Ym7ftpLammnt3DiKRSBEEgY1btlsH7eelp7eH0yeOYRJFTCYTcQmJRMfEcvDrA/T19YHJhK+fP1u2z92zdxS26lylUnPpQib9fX0cOXQQHz8/Xt7/JjofX2ITEvnzn36PRGKuc0dETpvPdj7XNnWytvbf/uv/F3d3D77+4jMAYuLip9x+5mRmDA0NUVFeNmbMuHrlIu2trSAIuLu7s9nBkThhftt6T08Pp04cxWQyt734hCSiY2IdrgtzM3+wZdtS0iYPklJcVEhYRCRyO6Vvmvrejsz5WDIXtu3U8ZE6/+O//CMrVq8lOTWds6eP88Wnv0cqlbJt5x4EQUDn40tcfCJffGoeTzZsdsx4AiBMtW+0rLl/bjeVjiLQc+6iHDox0ztgv/M+M0WrcuzB3L9U5nrf9nj6hozzpq1V/nU+c8M8RraUSuYq1txE5mJ1ZjLms85lUscM3n/pzKdtm8+2Np+I4vzVuWQebctfM87xZO7RG+avzl1Vs6v0v85RyIkTJ06cOHHixIkTJ05+4jidPSdOnDhx4sSJEydOnDj5CeJ09pw4ceLEiRMnTpw4ceLkJ4jT2XPixIkTJ06cOHHixImTnyBOZ8+JEydOnDhx4sSJEydOfoI4nT0nTpw4ceLEiRMnTpw4+QnidPacOHHixIkTJ06cOHHi5CfIlHn2Vv/36/OWtOV3+9PnS5pB/fzl0DDMY54cuXT+8paE+2jnTXtAP3+55obmMV8LgEounTftS6VN86Zd0T4wb9pbIn3mTTunpWPetJcH6+ZN289NOW/a82nTXeYxf+nTqs55024bGJo37WD3+csRHO3nMm/a/fOYsxXmN8/ffM6deuYxP3LP4Pw982Cvv85c3CoZzjx7Tpw4ceLEiRMnTpw4ceLEjNPZc+LEiRMnTpw4ceLEiZOfIE5nz4kTJ06cOHHixIkTJ05+gjidPSdOnDhx4sSJEydOnDj5CWKX09s6QwtR+jIEQDCJVMlDqJUH2ePSz6S+qoz8RzcxmUyYTCJRiQuJiE995u8qi3PpaGkgbcXG59JvrCmj+MltTJgwiSLh8emExj5b/3lprimjJPuOudyiSGh8OiExKTO+zv3zPxC7cA3u3n4z+l1jTRlFj0eVOyGdsDko9zAVpcXcvXkVEyZEo0jqwiUkpy2c1m/PnTxCW2sL8cmppC9aNiPdqvISHt6+BhbdpPTFxKcsmEUJZk5NRQlP7t0AkwlRFIlPXURsUvozf1da8JTWpgaWZmx+Lv3KsmIe3rmOyWRCFI0kpy8hYRplb26spzj/KSvXbZm1dm9jFW2FD8EEJpOIe0QS7mHxz/zdQEczPTUl6JJXzErX1FaDWJUNJhOYRITAeCT+Mc/8ndhYCj1tSKKWzEp3NE01ZRQ9uQ2Yn/t0bUxrQzXleY9YvGHvtLX6m6rpLHoMln7tGp6IS2jc7G9+FtRVlpL78Ka1ncUkLyIqIW1OtMvH2ZW0GdiV56WyrJh7t65Z+reRlAVLSEydG+3S4iJuXr+CyWTCaDSyaMky0hYsmhNtMLfx0eNZ2CzHs9nQUV9Bbe5961jmF52Cb2TSnGjD7OcwHa1NDPT14B8SOWPN0uIiblw3P2/RaGThkmWkz+Hznq9+Vl5azO0bV8BkwiiKpC9cQkr63JW7tKSIm9dG9BcuXjpn/ayyrJj7t0fG75T0JSSmzt3cJfv+DYu2SHzKImKmMXeZDiXFRVy/etlarsVLlpG+cLFdrn3rxjXycnOQSAQEBLbu2EVgUPCcaP/9f/n/4OPja/13QlIyy1eunvX1Zu3sCSYRARMmBBKHCrirWsygRIVgElGb+s0XN+kxCPJZ39xkiEYjoigikUp4cvM86/a8gVrritFooK+nC4ChwQEUSpXdtcfr59y5yMrtr1n1+y36+sEB5HbWF41GRJOIRCIh794llm3bj0rjimg00N/b7TDdMfqWcj+9fZFVO+am3MMYjUZE0YhEIuXy+dO88ub7uLi6YTQY6OoyR34bGOhHpZo8SlNvbw+NjfW8/bNfzVhXKpFy81Ime159B61Ft7vbrDs4MIBSZf9yj9a+c+Us219+B62Luc57uhyrPV7/xqVM9u5/11rnI2XvRzlFnfv4BeDjFzBjbZNoxCSKCBIpzdk3CV69B5lai8loRN9vbu/GoUGkiskjLao8fFB5zCz6pUk0mp07QYJYcg9J2jYEpcb894Fe83cMgwgyx0V4HG9jVjjQxoyu57acW/it2GWtZ0N/j/l+9INI5I4urxGJVMrDG+fYuPctNJZ23tftWJs+3q68Ogu7Yg/tqxfO8OLr7430r67p9a/n0TYajUilUs5lnuStdz/E1c0Ng8FAV2cHAAP9/ajUjol6N348W/6c45komq81ve8Ot3kJFY+ukrj+JRQaF0SjkcE+c3szDA0im8K2PA/2mMN0tjXT0dIwbWdv9PM+m3mSt+f4ec9XPzMajYhGs225dPYUr779Aa6u5nJ3O7h/j9c/n3mKN9/52cR6nwP7cu1iJvtee3dO7YvJon3v6lm2vfSO1ab3DmsPDqCchU03Go0MDQ2Z2/LpE7z9/ke4Weq08znbcn9/P2q1mtqaakpLinnvZz9HJpPR19dn7UN9vT1otC4O0waQyWS8/9EvJnznt7/9redvfvOb9plee8bOnlbsJUhfh6+xmSxlCv0SFYLJhN7i1JkECX2COYy+v6GJEH0NdfIA6mT+6AXFmGupxH6SB/OQYg7fWqCIo1PqPql2V0crlYU51FUWs2zjHjQurogm0WoQpVIZru5eANSWF1Ka94SwmCRCoxNRqjUTrtfX28P109/T39dDSFQCCQumfvvf09lGdXEODVUlLFy3C7XWDZMoolCqrfouFv26yiIqC7IIjk4kKDIBpWqsvkkUKXh8g+baSgRBICQmmfD49El1a0tyaKwuJT1jJ2qtG6IoIleYdSVSGVo3TwAaKouoKsomKCqRwIh4FON0jQYDuXfO093ejNbNC6Px2WF7uzvaqC4xl3vR2l2oXdwwmSYpd0URFYVZhEQlEhQ1Sbkf3aS1sQbRaCQsPvWZK4JtrS3kPX1CWXEB2/e8jKubOyZRtBpHqUyGp5c3ACWFeTx9/JD45FTiE1NQa8amdDjxw7f09nRz8MAnZGzYSmBw6KS6HW0tFOZmUVFaxMYd+3B1c0cURZTqEV0PT7NuWXE+eVkPiU1MITo+BbVmbLkNej3Xzp+ivbUZd08v+np7WLFuy6Qrqp1trRTnZ1FVVsy6bS+gdXVDNJmsTp1UKsPdol1Zkk9BzmOi45OJjEtGNUlbv3jiEN1d7YRExLJo5bop67y9rYXCnCzKSwvZvPNFXCxlV9koe2lRPnnZD4lNTCXGRtnrairJfniXbXtfnVJzmKHuDrqqC+mtr8B/8UZkaldMJhGJZeIlSKUoXDwA6Kkvo7MiD7fgWFyDo5EqxxrY/pZ6OsqeErD02auKpr5OTI0lmFqrkcRngFILiCAz2y1BIgWNm/m7zZWIDUUIvlEIvhEI8skHLFNbLWJNDpKEtVN+D0ZsTGNVCQueYWPqR9mYQBs25lnoezroqS6mv7ES3YL1SNUuYDKNqWe5i9ke99WX011ZgDY4Gm1gNNJxA3RvTTH9jVWIRgOGvi5cI5JBFOmtLUWQSNAttr2q3NXeSnlhNjXlxazcvBeti7m8o9u5q4e5vNWlBZTkPSY8NpnwmKQJNr2iKIe6ylIMBj29XR0EhceQumztpOUftiulxQXsmIZdyX78kIRJ7IrBYODKhTM0NdYjESSsXreJ4NDwSbXbW1vIz3lCeUkhW3e/ZLt/WbXzycl6QHxSGrEJyRO0C3KzqSgpZHBokN6ebmLjk1m8Ys2k2q0tzWRnPaa4sIC9L76Cm7vHGG2ZTIaXtzlNRkF+Lo8f3ic5NZ2klFQ047T1ej2Zp47T3NSIl7c3Pd3dbNq6A/+AwEn1ezrbqCnJockynqksbXw241ltaR6N1SUYDXpMJhNLN788qS5Af1c7zRX5tNeWEb1iK0qN2bbILO1ZIpWidjVrt9WU0FT6FF1YPN5hccjH25auNsofXEa0vByKXr4VlavHlPr2msOIRiP5j25hNBpobawjNnUpwZG2V+BtPW/TLJ93TvYTGurr2bR1OwCHv/+WJctWEBoWPmmZ7dnPDn3zORu27MJbZ36B9+N3X7Jq7Ub8/Ce2t7bWFnKzH5t1976Cm5s7oklErRop97BucUEeWY8fkJicRnxSyoRyHz98kJVr1qPz9eObLz4hKiaOZSszuHPjCi6ubixYOHF1rrWlmadZTyguKmDPvpen7GeF+Xnmek9JI9FGvT+8d4fm5ia27dxDc1Mjp44f4c13f4Zcbnsxpb2thYKcLMpLCtmya5Lx22tk/M7JekhcYiqxCRPH72OHvmLV2s3ofM1zlWPff8nq9Vvw9plk7tLeSkl+FtVlxWRstcxdRBOKUTbdbdTcpejpY6Lik4mwMXe5c/kMbc2NAPT1dhMaFY9UJqW6rJhXXt1vrdPRDpK3pU7z83J59PAeKanpJKekodFOnt7LZDJRVVlB1pNH1NfW8vHf/Jqenh7Uag0ymdlN6uvrJfvJI4oKCvDS6RgaHGRoaMj6DOyp/Qz+929/+9tA4FPg8G9+85tp5ZGaVp49icmIv6GRQEM9AHWyABplvhgFcyUkDubjY2ihTepJs0xHg9QPBHMqCKU4QKChHn9DI70SLbWyQFqlXiAISExmJ08UpGjEPlIGc7mrNm97Gs6zZ9DrqS0vpKIoB4Cw2CSCIuKQy80TsEc3zlFfVYpvQCj+oZEER8YjWLT7erqpKsmlurQAN09vwmOT8Q0KRxAEKotzyX1wg4373kEmk3H5+DcsytiKp85/TJ49g15PfWURNSW5AARHJRIQHovMop99+zxN1WV4B4TgGxRJYEScVb+/t5ua0jzqygtw9fAmJDoZXWCYWb8wi9aGatLX7EAikVjf4g3nZDIY9DRWFlFbatYNjEzEP2xEN/fOBZpqyvD2D8EnKAL/8BHdgd5uasvyaKgoROvuTXB0Et4BZt2K/Ef0dLSSvGIz3e3N3DnzLUu37sfd229MrpjhclcXW8odnUjg6HLfOk9jTRk6/xB8g22Xu7a8AFd3b0JikvGxlLuq6CmDA33EpC7DaDRwO/N7FmbsJDFyrLHW64coKcwn/+kTAOKT04iOS0BhmYheOnuSitJigkPDCYuKITY+yarf3dVFQW4WxQW5eHn7kJCSTmh4JIIg0NXZwakj3/P6ez+3ao3Os6fXD1FeXEBRbjYAMYkpRMTEW3WvXzhNVXkJgSFhhIRHExWXaNXt6e6iOP8ppYV5eHrpiEtKIygsAkEQeProLp0d7azesI221maOfvMZu199Z4yzp9cPUVlSSEm+WTsqPoXw6DjkFu3bl89QXVFKQFAoQeFRRMSMaPd2d1FamEN5UR4eXjqiE1MJDDFrlxY8JfvBLXa+8h5SqZRj3/yJrfveQOvqNibPnl4/RFlRAYW5sN/C1wAAU+hJREFUWQDEJqYSGTtS9msXTlNZVkxQSBihEdFExSWNKXtRXra57N7msgeHmet8MmdvdJ490aCnp76c7qoiAFxDYnAJjEBicbSasq7T21iFRheIxjcEl6Aoq7ahv4eu6mJ66kpRuHjiFhqH2icIQRAmdfaG8+yZjAZMLZWYGksBEPwiEbzDEGRmAy4W38HUXoPg7g+eQQg+4VZd02AvpqYyTM0VoHFH4hcNHgEIgmDdxil4+CPW5ZsdPctK4Pg8ewa9noZRNiZonI15OsrG+NiwMbUWG+MyzsbY2saZ3dBMX0MFvTXFAGiDotEERCCxlLft6U36m6pReQeg8glGExg5qp576a0toa+uDLmLB9qQGFQ6cz331hTTVZqN36o9mIxG6q8dxiNuES6h8bTn30Om0rJ5zVpLeYeoLiukvNBs0yNikwmOjEOuMJf3wbWz1FWW4BsURkBoJKFRCaNsehcVRblUlebj5ulNZFwqfsHmZ1JRlEPeo9tsfvEdJBIpmYc+Zf3u19G4uFnz7On1QxSPsisJNuxKucWuhE9iV4osdiVxlF15/OAObS3NbNy2m/bWFo4d/pa3PvglMpnMatP1+iFKi/IpyDH3r7ikVKJiR7SvnD9ltWmhEdHExI/tX4W52RQX5uLlpSM+JZ0QS/8qyM3m3o3LvPrOR8jkcg5/8znrt+zG1z/AmmdvaGiIwoI8crIeA5Ccmk5cfCIKpVn77OkTlBQXEhYWQWR0LAlJyVbtrq5OcrOzyM/LwVvnQ2raAsIjzf3vwb3btDQ3WyehBz77I2+++zP8AwLH5NkbHs9qLONZ0LjxLOfOBZpryvCyjGcB4WPbeF1ZHvUVhbi4exMUnYTOMp7VluZRnHWblTvfHLMKNjrPntGgp62mlJaKfAB0YfF4BUchtWiXP7xMR30Fbj7BeASE4RUSY9Ue7OuhtbKA1upi1G5e+IQn4OYXYh7Dn1zHxcsP79BYq8Mnkcom5Nlz5Bxm/FGU4Tx7w8/76ajnHT/qeWeePkFpcSGhYRFE2XjeOZbnrRv3vKdy9kbn2XNUP3vy8C6DAwMsW7WW3p5ujnz/FW998EvAnGdPPzREcWEeuRbdxOQ0YuITrboXMk9SXlpEiEU3LiF5lG4n+TnZFObn4KXzISl1AWEW3Qd3byKXK4lPSubI91+jUql54ZU3OHzwSzZs2YGvj3mSrx+u92yzfnJK2oR+VlpSZKn3GOITx/Wzp1kUWPpZStoCwiPM9W4ymfju6wMsWrKMu7dvsH7TVoKCQ4CRPHtm+zIyfseNG7+vnj9NhWX8DouMJnrc+F2Yl01JgXn8jk9Os9qXwrxsWpoaWbVuMx3trVw8c5yX3njf/DtLnj2DfojK0kJKLXOXyPgUwqJG5i53Lp+hpqIU/+BQgsKiCB89d+npoqwgh4riPNy9dEQnpBJgmbsMX7sw9zFP793E3UtHTFI6YVFxRPh7AHDm5HGz7QqPJComhsSklDF1+jTrCXm5Oeh8fEhLX0hE5Mjcobu7m5zsJ+Q8zba09XQioqLNc/KhIb7686f09vUiIKDWaFi8dBnxCUkolUoa6us4eewIbW2teHh6kpKazvKVq+2iDRO3cS5ftZqExGRUMoTf/va3i4APgG3AaeBPv/nNb7KYgmk5e+t7r9ItcSFPGU+fxLaH6iL24GVsI8DQQI/EhVxl4tgvmEzojK0kDhXQJXHjiSoVmclA3GAhrmIPCAIasY9L2nXAiLN34st/xs1Tx8LVW6xveMfT2dZMc10VVSV5uHv5sChj2zhpE4015Ty+cR4PnR8rNr9AZXEuzXVVLF5rNlp5j26iUKqJTlo4xtk7d/B3uHroSFmxyfpGfTxd7S201ldRW5aPq6eOtFVbJ+g311Xw9PYF3L39WLx+Dw+vniQ0JgWfwLAx3x2eGFz87l9x9fQmadkmtJPodre30NZQZXYmPXUkrxg7oTWZTLTUVZB39yJuXr4sWLeHx1dPEBqXjre/2VDcPv0Nics2TnD2zn5rLnfqyqnL3VJfRW1pPm5ek5S7toJsS7mXbNjDwysn6W5vQWp5W6LXD5GyfCNL0sa2lz/+7/+GTufL+i078fS2nYy5tbmJ6qpyCnOfovP1Y+O23RP0K8tLuXzuFL5+Aezc9+oznb0D//oPeOl8Wb1xu/XN13jaWpqoq66gOD8Hbx9fMjbvmqBbU1nGjYtn0Pn6s3n3y5w/eZiktMUEhpif99FvP2fVhm1jnL2Dn/wjHt4+rFi/zbpqN5721mbqayooK8zF09uXVRt3TNCuqyrj9uVMvH39Wb/jJUoLntJUX8uK9eZ+cfHkIVIWrcA3IHiMs/fnf/0feHn7krFpx5Rlr62qoLggBy+dL+u2TCx7dUUp1y1l37rnlWk5e2WZB1C6euGTttq6ajeewa42+lvq6K4pRunmjW96xgTtvqYamrNvoPTQEbBk8zOdPeOd70DjiSR6GYLG9q4CU287po4GTM3lCFpPJDFjdwCYTCZor0MsuQsuXkgT1yE2lmKqzQOpHEnSRqvzCBOdvXMHf4ebh47kKWxM93BfK8vHzVNH6iQ2JsfS1xat32PT2cv85l+Qu3nilbwS+ST1PNTdzmBLHb21pcjdPPFOHbtKZDKZGGiupS3nJgp3HT6LNtJbU8xgexNeKasAqLt8CN8VO5CptPRUF6PvbmP7lp0AHPnzP+Hu5cPijC24edhuZ51tzTTWVlJZbLbpS9dtn3APDdXlPLh+Fk+dP6u37qOiKIeWhloWZ5jr5vqZH0hYsBydf7DV2fuDxa5smMKutDQ3UVNVToHFrmyaxK5cstiVXfte5fSxQ6QuWGJdzTt88ABrN25F5+Nntemf/st/x1vny9rNO/D0msSmtTRRU1VBUd5TvH182bB1onZVRSlXz5/Gxy+A7XtfoSA3m7rqCjZs2wPA/VtXUarUpC5canX2/ul//N/4+PqxZcdu69vn8TQ3NVJZUU5eTjY+vn5s3zX2rKfJZKK8tISzZ07g7x/Ivlde4+gP37Fw8VJCwyMAOPDZH9myfdcEZ+/CqPFsqjbeOmo8S5lkPMu1jGcL1+2htjSPtqaaCd8d7ew9PPYnNO7ehC9ch9qyYjievs5WuppqaK0sQu3hTeTiDRO0OxuqqHh0Ba2nDzErd9BaVURdwSN0YXF4BkZYV/XGO3uOnMNM5uz9L8vz3jqN551red47JnnemZbn/eIrr03b2XNUP+vp7uL44W95472PyXp0j76+XlasXg+Ynb1//V9/j87Hl41bd1lXzWzpVleWk5+bjY+PH5t37LGpezHzJL7+Aex+cT91NdVkPbpPQnIqDfW1VFeU88Krb/LVZ7/n/Y//vXXu9L//4f9B5+vHlu27pqz3Kku9+/r6sc1WvZeVcO7MSfz8A9j38msAdHS0c+DTP5Cavoh1G0d2Sgw7e5/97n/grTOP356TjN+tlvG7KD8Hb50v67faHr+vXjiDj58/2/a8gl6v54ev/sSr7/ycB7evoXVxJTndfC5t2Nn77k//iKe3D8vWTT13aaipoLzIPHdZscH23OXulUy8fPxZt+Ml67XBxKJVG4lOGNkFNjqpenNTIxXlZeQ8zcbXz4+du1+YcO2y0hIyTx3HPyCQl159nbraWr764lPS0heyZt0GNJqJO2P+53/7r7i5exAcEkpxYT5r128iJS19zHfq6+u4feMaxUWF+AcE8u4HH9lF+x/+/r/wH//z/zXh76OTqv/2t79VAR8Dfw/8n7/5zW/+YcIPLExrg3uWMplBQUnawFMih8pRif0TvtMjcaFKHsojVTq+huYxn7kZu4gfKiJuqJgGqS/FCvMe81B9FUOCgjvqpdxVLUZgouO5dMMu1FoX7l46TsHj29b97KNx9/IhOnkRq7a9TF1F8ZjP2prrybp9kew7lwmKiCVp8agDjsKzE9EvyNiJSqPl0dWTFGffsZ6XGVM+Tx0RiQtZuulFGqpKxnzW0dJA7r1L5N2/QkBYDHELVj1TEyAtYwdKtQtPrp2i9Oldm7qunjrCEhayaMM+GsfpdrY0kH//MgUPruIXGkNM+vR0h1m41lzuh1dOUpx1x2a9u3nqiExcyLLNL1JfObHcOXcvkXv/CgHhMcQvHNFPWrqONbvfYs3ut9jw4gcTHF6AbbtfQuviypnjh7l/+7p1T/9ovH18SV+0jD0vv0FpUcGYzxrra7l6MZPrl88RHZfA8jXrp1XuDTv2oXFx4eKpH3l894Z1X/tovHS+JC9YyvZ9r1FRUjTms+aGOm5dOcftq+eJiIln8crJt5GNJ2PrXjRaF65mHiX7/k16uidqe3r7kJi2hE27X6WqrHDMZy2N9dy7dp771y8SFh3PguUj2lLpiFMnCAKiKDKeTTv2oXVx5fypwzyaouwpC5eyY99rVJSM1W9qqOPm5bPcvnqeyJh4lqxaN+2y+y/agFSloeHBRdqKHqPv657wHaWbFx6RyQQu305PfcWYzwbam2nJuUVL7m1cAiPwjp/eIWlJXAaCQo1YcA2x6immgZ4J3xG0nkiCEpAkbcDUUjXmM1N3C6ay+4jlDxB0oUjC00c+VLmC0QD9E/vOaBZk7ET5DBvjOg0bk3//Cv5hMcROYWN0C9YhVWpoeXSZzuIn1jN5o1G4euIakYTP0i30N1SO+Wywo5n23Dt05N9F4x+OR+xIUAVBMtLGEEb+LQiYz0BaWLFpD2qtC7fOHyPv0S16bbRzdy8fYlMWk7HjZWorxvaxtqZ6Ht28wOPblwiJjCNlyYgzKhnTziUT2vl2i105ffww9yaxKzqLXdn7DLsSE5fAimnaFYAtu15E6+LKuROHeXDnus3+5a3zJW3hUna99DrlxWP7V2NDHdcvZXLz8nmiYhNYNqZ/TT2W7dn3Ci6urhw7/D23bly1ni0ZjY+vH4uXLufl196iqDB/zGf1dbVcOHuaS+cziYtPYs26DRN+PxXpGTtQWcazkinGs/CEhSy2MZ51WMazfMt4FjtqPJPKpo4LEL18K3K1lpI7Z6nNf8Bg70TbonH3xj8mjdg1u2ivLRvzWU9bI5WPr1GVdQOv4CiCk5cD4B0aS8zK7UikUopunqarqcamvkPnMJMwk+f9yiTP+/zZ01w8n0l8fBIZluctSCSMXiAwTHIMxFH9zMXVDZVKTUtzI8UFecTEjX1JvGPPS7i4uHHq2A/cvXXNeiZuvO6CxcvY98qblIzTbaiv5cr5M1y9eJaY+ERWZpjL7RcQSGNDHbU1VQQFh+Lj509u9mN8x51F373vZVxcXDn+4yFu37Ct7+Prx6LhfjZOv76ulovnznD5/Fni4hNZs3akn3W0tSFXKOjpmdh+ATbv3IdG68q5k4d5eMf2+O2t8yV14VJ2vvga5TbG7xuXznLzinn8XmqxL3K5nODQCCrLiiktKiAmfmLwooyte1FrXbh29ihPH0w+d0lIW8KGXbbnLvevnefBjYuERsWTPmruovMLwMXVg/ys+5Ne28fXjyXLVrD/jbcpKhjblutqazmXeYoL584Ql5BExnrzyxFfPz+279pDS0szPx46yJPHDxkcHBzz2xdeehWdzofqqgpCQsPJeZpt/UwURYqLCrl94xrt7W2sXLWG1pZmu2lPxW9/+1vZb3/72z3AQeAj4P8NfDXVb6Z1Zq9N5k2bzBu5SY+/oYH0wacMISdPGY9ekOMmdtMuNb8xcxV7GBDM2ym8DK3EDpUwJCiolQdSqIjBJIz4lzKTkUGJEgSBAH09EhvOnl9QOH5B4QwO9FNdms+dC8dQKNUsWL0ZpUpNe0sjPgHmVarO1ibULuYzNY21FeTcu4ZKrSUsNpnUZevHTAQAmmsrGRrsRyqVUV9ZysI1E8/1+ASG4RMYxtBgP7VlBTy4chyFUk3Kik0olGo6Wxutq2Rd7c2otWb95rpKCh5eR6nWEBKdTOLidWP0dQGhVBc/xds/ZMw2zpHPw9AFmHXrywt4cu0EcqWapGWbkCtVdLU14eVnjgrU3d6MyqLbUl9J0aPrKFVagqKTiF+0doyup28QDRWFePuH0N3RQk9Hi81nbi33QD+15QU8vHwcuVJN6kob5W5rttZ7c10l+Q8s5Y5JJmnJ2HL7BIZRWZSNd0AIEomUnq52VGqXCfqh4ZGEhkcy0N9HYX4Op49+j1qtYf2WnajUGpob6wmyrJK1NDfi6mZelamqKOPW1YtotFoSUtJZs37LGEfnWQSHRRAcFsFAfz8lhTlcOHkYlVrN6o3bUak0tDTVExBs1m1tbkLrai53TWU5925cQq3REpeUxvKMTWN0/QKCKS8pIDAkjPbWFtpamydoB4ZGEBgaweBAP2WFuVw5fQSlWs2KddtQqtW0NjXgH2Q+Y9je0oTW1VzmuqpyHt66glqjJToxlcWrN86ozCNljyQ4zFznJQW5nDvxAyq1hoxN5rI3N9UTaC17Iy5uw2Uv4+71S6i1WuKS0lmxdvOM9TU+wWh8gjEODdBdU0LDgwtIFSp8UlcjVagY7GhBrTMPrIOdrcgsZxr6mmtozbuHVKnGLTQOXdLysU7HMxA8AxA8AzDpBzE1lyMWXAOZEkn0MpArzVsx3S2rr73toDLrmtrrESsegUKF4BeNJGLRBF1BqUUIX4BYcA1J/BoEjYfNexhtY+rKCnhosTHJ07AxhQ+vo5jExthC5ROEyicI49AAfXVltDy8iEShwit5JRKFiqHOFlTe5nrWd7WZz/EBA821dBQ+QKJQ4xISg2fi0hnV82j8g8PxDzbb9KqSPG6eO4pSpWZxxlaUKjVtzQ34BprbeUdrMxqLbWmoqSD77hVUai0RcSksWLHhmeUdz7Bd6e/vo8hiV1RqDRssdqWpsZ7gSezKTYtdSbRhVwKCQinMzyE4NJz2tlZ6ujrxHPeGOyQskhBL/yrKzyHz+CFUKg1rN+9AbdEetmmtTSP9q7qyjNvXLqLRuBCfnMaqdRNtWk1VOQMD/chkMspLi1g3brdBeGQU4ZFR9Pf1kZf7lKM/fIdao2Hr9t2oNRoa6uus566aGxtws5S7oqyUK5fOo9W6kJK+gA2bt43RDg4NJT8vh9DwCJqbm2huarRZ7+PHs8eW8Sx5kvFMPWo8K7SMZ8E2xrPp4O4XgrtfCIbBAVqqiii+fQa5QkX4onXIlGp625tw8zFHD+/raEWpMbf5zsZqqrNvIVdp8IlIIDR9NZJRbX6gpwul1g2/6FQG+3ro62zFzTd4gr6j5jAyuRy9Xm+zzBGRUUSMet5HLM97m43n3dTYgLvleZePet6p6QvYOO55u7t78OTRA0wmE93dXTTU1drUd1Q/A4iOS+TR/TsMDQ2iG3duLCwiirCIKPr7+yjMy+HkkUOo1Wo2bttl1m2os66+Nzc14Opu1q0sL+XGFbNuUuoCMjZuHaMrlUpxdXOjpDCfZSvW0N/fx40rF1i4ZOwuj/CIKMIt+vk5Tzl62Dxv2bJ9F2qNhsb6OkLCRvSt/ay8lKuXLpj7WVo66zeN1R8cGODShUz2v/kul85lUlSQR2z8WEd3tH0pLsjl7HHz+L1203brnGl4Z9Ho8bu6sow71y+h0WiJT05n5bqJ43d8chqZx3/APzDEZjCXgJAIAkLMc5fyolyunTmCUqVmmWXu0tbUgN/w3KW1Ca3lLHh9dTmPbl1BpdESnZDKonFzl8KcR8jlSjbu3z/h2i+88ILNtuzmPtKWL188Z2nLC9m0ZfuYa8tkMlJS00lJTaetrZXsJ4/585/+QGBwMLv3vkhrawvuHh7sffFl+vv6OH70MM1NDRz8+gABQcHk5WTj5aVj5eo1hISGUVFeZnXen1d7Kn7729/+R+DfAdeB//Gb3/zm+pQ/sDCtbZy2cDN2MSgoMAgyUgdzUYv9iIIEI1IKFTF0Sd1wNXahF+QMSGxHpdGIfaQOPAVBoEXqRYi+lstas0c/vI3TFm3N9ajULsiVSu5fPklvVydSmQypTE7q8nV46vxpb2lEqVJbJwrjqSzOpb6yBP3Q4IQALaO3cdqio6UBpVqLXKHk8bXT9PV0IpFKkcnkJCxZh4e3H52tjSiUaqvhHo8oihQ8uk5LXSWCREJItDlAy/CWH1t0WnRlCiXZN07T120pt1RO3OK1uHv70dXaiHwK3TEBWty9GOjrIWHJ+gnbOJ9V7kfXLPpSKVKZnMQl6/DQmcstV05e7yaTicLHt2iqKcMEKJRqFq/fTUyQ7e0to2msr0WjdUWpUnL25BG6OtqRymTI5QrWrN+Mr38gTY31qNQaqxEdz7O2cdqiuaEOtdYFhVLJ5TPH6O7sQCqTIZPLWZ6xCR+/AFqaGlCq1NZBazx6/RDXzp2io60Fdy9vujs72LDjBdSTbKUbpqWxHrVGi0Kp5Nq5E/R0tSOVypHJ5SxZvQFv3wBamxpQqtW4uNrWHp964dKpH0hMX4p/UOiYbZy2aGqoQ2Mp+6XTR+mylF0ul7Ni7WZ8/AJottT5ZGWfzjZOWwy0NyNTqZHIFTQ+vIy+rxtBKkUileGdtByVhw+DHS1IFErkGleb13jWNk5bmLpbQKEGqQKx6Ab0d4NUBhIpkojFCK7emHpaQaZEUE18UQFjUy+YetoQi24iSViHoHadsI3TFqP72pNRNma4r7lPw8bY2saZ09Ix4XuDHc1IlRokcgWtT65g6OtGkEgRpHI8E5eicNcx1NmCRKFCZuPFDJgDtAx1tuKZZF71qLtyCL+Vu5EqVNbPto/bMjSatqZ6VBotcoWKO5dO0NPVgVRq7mPpKzbg5eNPe3MDCpXa+pJjPBVFObQ1N7Bw1SYAbmT+SGzqYnwDQ63bOG0x3q50drQjk8mQWeyK3zTsylQBWqay6Y0NdWgt/ev8qaN0dVq0ZQpWrduMr/+z+1dBbjYVpUUMDQ7QMy5Ay/A2TlvU19WidXFBpVRx4uhhOizllsvlbNi8Df+AQBrq61BrNLi7e9i8xugALd7eOrq7u6wBWkZv47TF6DaeNW48i7eMZ89q47WleXS2NZK4ZOwK6+htnLboaWtErtIikysovXuewd5OBKl5DhGatgqtpy+97c3IFCqUWtu2pb7wES2VRQgSCXKVhqilm5ApVBO2cdrCHnOYocF+bp79EZMoWgO0DG/jtHm/o5738XHPe+M0n7fJZOLU8SM0NtTjpdMxODDAytVrJ2zjtIU9+hmYg419/od/Ysny1SxdObKVXyKxPX9pqK9Fq3VBqVJx5viPFl05crmcjI1bzLoN9ajUatwmKTfA7RtXqK6s4NU336Onp5vP/vV/8do7P8PXL2DKudPoej957Ec6Otqs+us3bcU/IJDGhnrUU+hnnjqOr58/Cxcvpaurk0PffMnrb7+PRqu1buO0xejx+4Jl/JZZ7OqqddMbv4f57os/sGLtJkLDo6x/G97GaYuWxnrUWi0KhZIb50/Q3Wmud6lczuJVlrlLs3neNNnc5ehXf0AikSCzrN5HJ6URm7SAlsZ6IoO8USpVHD/yAx3tlrasULBx8zYCAp/dlm0hiiKlxUXExMXTUF/H+bNnGBwcQCKR4OnpxdYdu+lob6ejvY2QsDAyT51wiDZMPLMXERXNug2b+H/+f7/dDNz7zW9+M/WWoXHM2tlzNFM5e47mWc6eI5lqYuBonuXsOZJwn8mjFTmaZzl79kAURURRRCaT0dXRzpmjB3n57Z9jNM1fnQPPdPYcybOcPXvQU19Ob2MVfuljt9NO5ew5muk4e47ClrM3VywPtn2GZS6YytlzNI626QW52TQ31rNmw9YJn03l7DmCg19/wboNm6fl7DmSZzl7jmQ6zp6jmMrZczTPcvYczWTO3lwwn3OnqZw9e9Hb082JH75m/7sfWwOMwNTOnqMZfWbvr4nRZ/ZmwtyOBE6c/JViMOg58+O35vxOJlhp2YplNMzfi4WfOr0NlbQVPMQ3bfIw9E6cOHHixIkT2xTlPeXerausyNg4xtFz8m+L2Tl7JhPLBu4zKCh5okqz/jlusIhAQ711K6aHsZ24oWJcxF6eKpNoko0sSQbo64nUVwBQJg+nXj510uWH18/SUF2GUqVh04vvApD38Cb1VaUIgoBSpWFhxlbUGhdMJhPZdy/TWF2OVCZn0ZqteOjMe7v7erp4fOM8fb3dCAKs2Lxv0m1Bw2TfOkdTTTkKlYaMPW8DUJR1m+riHBSW/ctxC1bhGxRh/U1/bxfXjn9JTOpyIpMW0d/bTdbNswwN9AEQEpNCRMKCZ1Z1zu3zNNeatVftesv696rCJ1QVZSMIAj6BEcQuXI1oNJJ37yJdrU0gCMQvXms9B9HV2kjO7fMYjQZ8gsKJW7T2mR036+Y5mizaa4fL/eQ2VcU51n3bcQtW4RscQUdLA09vXwDABMSmLcc/NHrS68wGURQ59NVnaF1d2bVvP9mP75P16D5dHe188Mu/s+aHeXT/NkX55jDXJtFEe1sLH/zy72adLHZwcIAbF87Q3tYMCKzZtAO/gCBysx6Qn/0IQZAQEh7F0tXrMRqN3LyUSUtTA4IAyzM2ERAchkKhZO9r781YOy/rPiV52SAIeHrpWLlhBxdOfI9+yPzmeqC/F2+/ANZvH9nn3dJYT+aPX7Fmyx7ComznXpoO3372O+QKBYIgIJFI2Pf6+7Q2N3LjUiZ6vR5XN3fWb92DQqlkoL+PC6eP0NxYT2xCCqvWT1xlmApDfw+NT65hHDQHf3ILjcMjMtn6eUfpU1rz7xG+5U2kChXdNSV0lJoPSwsyOT4pK1G6eU+8Tlg8Ki/b+YCGMQ32IhbdBn0/ICD4RyMJjMfU245Yeg+MelC6IIldZY2qaf3MoAcBJGnbESRSjLmXYKgfTCYENx+EqCUIwtRxsLJvnaPZYl/WWPpH8Tj7EmuxL7VlBZTnPbD+tru9hVU730Dr5snja6fo6+5EEAR8gyOJW/jsQA6G/l7asq+b60sQcAmJxTU8kc7ix/RWF1tz7rnHLkLtG2wO0JJzy/p7t+h0NP7mcyDd5bn0WFI6KFw98UpZhSCdfIi5fzWT+qpSlGoNW182h/KuLisk7+Etujpa2fjCW3j5+I/5TV9PF5mHPidp0UriUs1peoYGB3hw/Sxdba0gwJKMbXj7TZ7nbUIdGAz8+N0BSxJgkaiYeJatWktNVQU3r17AaBTx8fNn49ZdY5J2NzbU8cM3f2brrn1ExyZMW288X336LyjkCgSJgESQ8NKbH1BalM+D29dpb2vhxdffx9ffPD52dXbw3Rd/xMPLvO3dzz+IjE3biU+aOlfpdHh4/y7ZTx4BkJq2gEVLl3Pl0nnKiouQSKV4eHqybedeVKrJc0W+9ua7k3422VhWWfiE6iKzjfMJjCBu4Wo6WhrIu3cRMMf2iU5dhl+IeTypLHhMTUkuJkwERycTHv/scXSwr4fyBxfRD5jtgk9EIv4xqRiGBizbOLtRal2JWrYFmUJJV3MtJbcyUVi2cHoGRRKUYA76lHXmK3NQGEFAECQkbZw6t5+95i4596/RUF2OyWTCNyiU1GXrZz35bmtt4fjRw9Z/d3a0s2rNOkLCwjmfeQqDwYBEImHz1h0EBAbNSmM0k/WxwwcPoB8yB6bo6+vDzz+QnS+8AkBNdSU3Lp+z5IfT8OL+2c0dDAYDh789gNFoQBRFomMTWL56LZ0d7WSePMJAfz++fgFs2bkXqVTKtUvnqKmqtPxWT19fL7/49f8x67JnnjpOWWkxGo2W9z40J8m+ee0yJcVFCIKARqNl2849uLiObBduqK/jmwOfsWvvixPO5s2Urz/9HQrLOC5IJLz0xvvcv3WVirJia0qBdVt2oXVxpa66krMnDlu3dEZEx7Fo+WpiE1NmrFuQ/cA8d8FEdEIa8WmLeXTrMrWVpUgkUlzcPVixfjsKpQqj0ci9q2dpbW5AEAQWr9poPeNnb8pKS7h4LhPRJJKWvpDlK589Tv4UtGfl7IUaqukVtMgYWT52M3YhM409LDwgqMhVJhKmHxvBTmbSE6kvt+bUW9Z/n2aZDoMweVStsJgkohLSeXAt0/q3mJTFJC4yR+UqzX1EweM7LFi1icaacno7O9j88ge0N9fz5NZF1u15A4CH1zKJS1uGb1AYBv3QtCJyBkclEhaXTtbNs2P+HpGwkMikick0AfIfXMMnMNz6b0GQkLAoA3dvXwz6IW6c+gZdQCiuk4QeHyYwMpHQuDSe3jpn/VtbQzVNNWWs3PEGEqmMQYsDWVNidnBW7nqLwYE+Hl0+xvJtryEIAnn3L5O4fCPu3v48unyMlrpKfILCbUmOlDs6kfD4dJ6ML3fiQqLGldvVw5tVO99AIpEw0NfL9ZNf4RsciUQimfQ6MyX70X08vXUMWQaHgMAQwiNjOPr92CBEC5essB6cLi8tIuvhvVk7egB3rl4gOCySjTv3YTQaMRj01FVXUlVWzL7XP0Aqk9Hf1wtAYc4TAF5882f09/Vy9tj37H3tvVkNyn093RRkP2LP6x8gk8m5dvYYFSX5bN33hvU7VzOPEhwebf23KIo8unOVgJAIW5ecMbteemNMotNrF06zfM1GAoJDKczNIvvRHRavWItUJmPx8gzaWptptxF85pkIEnSJS1G66xANQ9RcP4bGJwiFqyeG/h76WmqRqUe2+so1rgSu2IlUoaS3qZrm7JsEr94z5XWm0pZELERw8cJk0CNmncHkEYBYcgdJ+EIEdz9rKgUhLA2TSUQsuoUkdiWC1hOTftBqRyRxaxBkckwmE2LhdWipQvAJn7Low/Yle1z/CLdhX4Ii4wmKNO/p725v4eGVE7h5+WI06IlIXIS3fwii0ci9C4dpri3HJ2jqdiAIAh7xS1C4eyMa9DTePIHK2+wouYQn4jbK4QaQu3rit3I3gkSCcaCPhpvHUfuGYBzqp7syH/81LyCRymh5fIW++nK0wTGTaofHJhGdtIB7V05b/+buqWPl5r08vHHO5m+e3Lk8oW0/uX0J/+AIVm7ai2jpnzNBKpXywitvoVAoMBqN/HjwAKHhUVw4c5y9r7yJp5c3d29epSA3m8SUdMDcz25du0RoeOSMtCZj9ytvoh7Vz7y8fdi6+yWuXjwz4btuHh688taHdtEdprm5iewnj3jrvQ+RSqX88N3XREbHEh4eSca6jUgkEq5evsDd2zdYu37TrDRsjWWtk4xlrh7eLN/2OhKJhMH+Xm6d+hqfoEh6u9qoKcll+bb9CBIpDy8fxScoAu0zkpkLgkBIykq0nj4Y9UPkXvoBd79gWioLcfMNIiBuIfWFj6gvfERIinnscNEFELtqh83rxWXsmZBkfTLsMXdpbayjtbGOjS+YHZ6rp76jpaHGGthlpnh563jvZx8D5rb8r//8P4mJi+fs6ZOsXJ1BZFQMZSXFXL18YUoHfrrY6mNhEdG89No71u+cPv4DkVGxgDkgydULmex56TVc3dzps4yvs9Xet39E+4dvvyAsMorHD+6yYNEyYhOSuHTuNLnZT0hdsIiMDSPnu7Me3ae5sWH2Bceca2/BoiWcOXnM+rfFy1ayKsN8zvTRg3vcvnmNzdvMqWlEUeTa5YuER0TZvN5s2PXyG2PsS9qi5SyxRAp/+vg+D+/eJGOjOd2Hf1Aw28edr58pHa3NlORls+2lt5FIpVw+eYig8CgCQsJJX74WiUTC49tXyH10hwUr1lGSb04Rt2v/Bwz09XL51A9se/kdu68kiqLI+czT7H/jbVzd3Pjis0+IjolD5+P4oxXzqQ3TTL0wGqU4gM7QSu3olTiTiZihEooV0WO+OyBR0yOZuIdcZ2ylTeqFQZBjEOS0Sb3QGVun1NX5ByNXjn2jOJywEcxvb4bbRX1VKSHR5qSNXr6B6IcGGejroau9FVEU8Q0yv4mWyRXWg59T4eUXjFw5/TMfDVUlqF3ccRmVU0el0eLu7WvVdbEER3m2dhByxdhyVxc/JSJxMRLLW3OlytyJezvb8PILsf5NLlfQ1drIYH8vBv0QHjpzwufAyASaa0qfqe09g3JLZXLrW2/RaGB0KPCZXGcyerq7qCgvsU62AHz8/Kc8UA2YwzPbCBU8XYYGB2ioqybW8uZcKpWiVKooePqY1EUrrPkC1ZbokB1trdZonebAKipaGutnrW8SRYwG8xtJg0GPWjPSn4aGBmmorSQkcmRCXfj0EWGRsWMcNHvS2dGOf5C5jQWFRljDN8vlCvyDQpDJZrdZQKbSoHQ3n+mSyBTIXTwwWCZ+Lbl38U5Ywug2pfLyQ2rp/yoPXwz9vc+8zmQICjWCi7mvCjI5aNxhqM8cmMXN3GcFD39MrZaXVu31CFoPBK3ZgRTkSuvqnTWfnskENtJb2GKm9mWYuopCAsPNkyOpTG6N2CmRSnHz8p2WfZGqNCjczS+cJDI5Mhd3jIOT15dEKkOw9HOTOO68hknEZHlzbzIakCqnboM+ASFjIhADuHl6T5qLrLaiGK2rO26jolzqhwZprq8hIi7Fcn/SCdd8FoIgoLAkdDefrTWaV9mkUmu+qpCwCEqLR8KkZz9+QFRMvLXf2xtPb92kuS4dQVtLCwGBQcjlZjseEhJGcVE+4ZFRVrseGBhMT9eMYgKMYbKxLNLGWDZ6PDEaDdau39vZjrvOz/q5l28QTePSNNhCodai9TRPqqRyBWpXT4b6e+moK8c71Lz7wTs0jo668lmXbzLsMXdBMI+romjEKJr7mNJONr6yohwPD0/c3T0QBBgaNO8aGRwcxMXFdnCamWKrj40+eTQ0OEhtVSWR0eZnUVSQQ1RMnHWFSfMc/WyCtlFEQKCmqoLoOPOKfEJSKmXjUhEAFObnEpsw+/kDQHBoGKpxESyVo+y9Xj80xql5/PA+MXHxNnOv2QvFKH2DXj+7Q2BT0NnRis4vAJnFnvgGhlBVVkRASIS1X+v8AumzpEHpbGvFzzIvV2m0yJVKWpuez8m2RX1dLR5eXnh4eiKVSklITKJ4XPoLRzGf2jCLlb24oWKKFVHIGBnoQww1NMt0DEmmN2FRmoYYEEa+OyAoUZpmd6A698ENqkvzkMmVrNluXv7v7+tBPSqCllrrQn9fD/29PciVSu5cPE5fdyc+gaEkL15jnbzMlMrCJ9SW5ePu7UvCogzkShUG/RBluQ9YuulFyvIe2vxdX08nXW3NeOj8bX7+LPq622lvrqU46xZSqYzYhatx9/bH1VNHc20Z/uFxDPR109XWxEBfNwgCqlFOgkrjMq2J4GRUFjyhttRc7sTFGdaBrL25nuxb5+nv7SZ99dYxW56elxuXz7MyY4N1++J00Ov1VFWUkmEjcMF06e7qRKXWcP3CKVqbm9D5+rN87SY6O9porKvm4e2rSKUylq7ZgI9fAF4+vlSVFxMVl0hvdxetTQ309HThw/S3lQ2jcXElMX0JPx74PVKZjICQcAJDR1Y1qsuK8Q8ybxEF80pgVXkRW/a+TsuliSsCM0aA00cOIggC8cnpJKQswNNbR2VZMeFRsZQVF9DbbTvnz/Og7+tmqLMVlYcPvQ2VZgfObfKJb3d1ERobIc9HX2e6mAZ6oKcNXHRmp6+tBrxDzPn1LE6QacBcZmPuJdAPIOjCkASPTAiMuZegu9Wc1kE3+60oVYVPqCvLx22UfRlNfUURi9btnvA7/dAATTVl09reNhpDXzf6rjYU7joG2xvpqcqnr64UhZs3HglLkMjN7Wywo5m2pzcx9vfglWq2nzKVFteIZOqvHEKQSFHpzCke7IVBP0RB1j3W7niFwuz71r/3dneiVGu4fzWTzrZmPHV+pK9Yj0yumNH1RVHk+68+pbOjnZT0xfj5B2ISRRob6vDzD6SkqIDubrOj09PdRVlJIftefYuLZ08+d9kE4NSP3wICiSkLSEyd+rl1d3Zy6KtPUSgULF25loDg59/upPPx4cbVS/T39SGTyykrLcY/YKzNepr9mPjnnPiOZ/RYJpHKiLOMZWDJ1XrnPAO93aSs3IJEIsHFw5virFvWlEnNdRW4P2Ob9ngGe7vo62jBxcsP/WA/CsuOAblKg35wJIdwT1sDORe+R6HSEJK6ErXbyEuIohvm5+4TkYRv5Oy22c1k7uLtG4guIIQzB/+IyWQiMiEdt2fsCpouBfm5JCSaV/A3bNrKoe++5sql85hMJt545327aMDEPuYfMGIfykoKCQ4NtzohHe1tiEaRH7/7Ev3QEGkLlzzXVmVRFDl44FM6O9pIXbAYdw9PlEqVdY7i4uo6IX9dV2cHXZ0d1qi69ubG1Uvk5jxFqVTy6hvmFdvu7i5Kigp49Y13OHvquF10BAFO/3gQBIGElHQSU8z25d7NqxTlP0WhVLL7pTet32+sr+XQV5+i1bqwPGMDXt4zX3ny8PIh6+51BgfM/bSuqmzClvzSgqeERZt3qXjqfKitKCE8JoG+ni7amhvN+Sj9pj7eNVO6u7txcx2Jbuvq5kZ9re30IfZmPrVhhs6eztDCkKCgW+qGp7EdAKU4iJ+hiYeqmU0s7EXS4tUkLV5NYdY9yvKfkLBw5aTfNYkirQ21bNj7FmoXN+5fPkllSS7hsTPfjxwWm0pMyjIQBIqe3CL/4TVSV26hOPsOEQkLJ51sGPRDPLp6isQla8e83ZsJomhCPzjIsq376WptJOv6GdbsfY/AqCR6utq4m/ktKq0bHj4BzzwvNFPC4lKJSTWXu/DJLfIeXCNtlXnbg6dPAGv3vkN3RxtZN8/iExSOdIozO9OlorQYtUaDr18AtdWVz/7BqN8FBAY/1xZOURRpbWpgxVpzaofbV8+T/eAOoigyONDP7lffoaWxnktnjvLqu78gNjGVjrYWjh38My6ubvgGBM36GQwODFBdUcK+tz9GoVBy9dwxygpziYwzT7gqSvKJThgZAO/fvMTC5evstvVhzytvo3Vxpb+vl9NHDuLh5c3aTTu5dfUcj+/dJDQiGonUvu1LNOhpeHgR76TlIJHQXpJFwLJtk36/v6WOrupCglaODes/+jqSaU78TUY9YsF1JJGLEGRyJNHLEcsfQHUOglcQDL+8MImYupqRpG0DiQwx9yImF28ED/NgJk3agEk0IhbdROhsBI+ZD1ihsalEW+xL8Sj7MkxHcz1SmQxXz7ERLkVR5Mn1M4TFp6N5xlnkMb8z6Gl5fAWPhKVI5ApcQuNxi04DBDqLH9ORfx+vVPP5AqWHDwFrXkDf00Fb9g3UPkGYRCP9jVUErH3ZnMLh8WV6a0vRBtlnK1Luw1vEJi+aYFdFUaSjpZEFKzfi7RvA41uXKMi6R/I0Ek+PRiKR8No7HzE4MMDp4z/Q1trMll37uHHlAkajgdCwSCSWfnX9ynlWrtlgt362d/87uFj62cnD3+Lh5U3gJA6cVuvCWx/+jTVvVubxH9j/zs/HvKmfDd46H5auWMUP332NXC7H189/jN26c/M6EomEhKSZj5VTYRo1lnWOGssEQcBD58/qXW/T09nG09vn0AWG4+LuRUTiIh5eOopUKsPN02daRzGGMRr0lNw5S0jaKqTj2tLo56n18CFt+9tIZXI66ispvpVJ6jbz9vmEdS+gULugH+ij8MZJ1K4euPrM/GXeTOYuPV3tdHe0sW3/RwDcyDxMS0MNOv+JL7lmgtFopLS40Jo0/cmjh6zfuJW4+AQK8nPJPH2C/a/P/pz9aMb3sdaWJrx15p0TRQW5VicEzP26qameF155E4PewA/f/hm/gCDrSvtstN94z6x98ugh2tts5xceTVFBHtGx8XZ9aT2a1Ws3sHrtBu7evsHjh/dZtWYdVy6cY806+wZC2fvqyDh+8seDeHia7cvSVWtZumotj+/dIifrAUtWZKDz9efND/4GuUJBVXkJZ08c5vX3fjFjTXdPbxIXLOPSie+RyuV4evuOKVPOw9sIEgnhMeYXJVHxqXS1t5L5wwG0rm74+AfNehHGiW1mNBP3EDvxMbag62tFgojMZGBF/11EQcKq/jsASDGyqu82NzUrJr3OoKDAU+yw/ltlGqRd4jGrAgwTEhXPrXNHSFi4ErXGhf7ekbc0/b095sPPooi7tw9aN7NWQFg0bU31EDtzPeWo80MhMck8uGR+C9PR0kBDZTEFj66jHxo0B7eQSgmPT0cUjTy6epLAiHhr8JLZoNK44BcShSAIuOv8EQTB/IZSpSF+0UiI+btnv0fj5oFcoRqzkjfQ1zNmpW8mjC53aEwy9y9NfPvk6uGFTC6nu73Verj8eaivq6G8tJjK8lIMBgP6oUHOnz7G5h17p/xdcWHuc23hBNC6uKJ1ccXX3zyYR0THk/XwDloXV8Ki4swBcvwDERAY6O9HrdGwPGPkTMuJ77/EfZJtac+ioaYCF1d365bM0IhYmhtqiYxLYqC/j5bG/397ZxYdxZUm6C8yUykptS9oRzuLEIsszGKMwRQ7XnB5t6tc3tp2u7u6H/phzpl5GA5zZubMzOmu00utbbu8UcamDDYGgzGr2Q3GCCGB0L6gHWVKylSmpFRmzEOKRFgrUobSyP/3JJ3MuF/cyIi48ce997+NPLjh597vt7U0ceKg5/focTior61EUXSkZg4/b2q0uoNnOGp61kxamxqZv3AJm37+HADtljbqqkcfDjxWVLebpguHCUvOIjQxnZ5OM067levHPwOgr7uL68c/J3n5oxiCTPR0mmkpPEni4vXoBwwP+2E5Y3W7S06gTEtHifE8aCumCPS5qz2fOzpRLQ2eLxtNKOFxKAEepxKVhGoze4M9wLNOXXQKatt1lHEEewOvs5QZc7nwg+ussbqUpPTByXeKzh4iJCyKjJz8MbtUt5u2i0cJScr0JlvRD5iPFJoyg9YLhwdtFxAaiaI34LS102e3YTCFoe/vfQxOSKOnvcVnwZ65pZHrVaUUnjvuSeigKOj1elIyZhEcEkZMnOcYp2TMpOTSt+P2BAYFkTw9jZqqSvIXLfXOKaqtrqTdYgagpamRA196zsluh52aynJ0io7MGeNLhhQ68DrLnklLU8OwwZ5nLTZPsz0tPpHwyCjaLWZvApeJMG/BPcxb4HngPnHsMKH9b6GLCguoKC/l6ed9P4cm0BRKXH9bFhmbAAPaspuERkRjMARga28jIiaelOy5pGR7eqJKC06NuS1zu12UnzlAzPSZRCd75loGBAbT6+jCGBxCr6PLOw9vYCAYmZhGTcEJnD0OAgKDMfavNRkQZCIqKQObpWVcwd5NxvLsUltxlei4RO/LjoSUdMwtjRMO9ioryomLTyQkxFOnoqJL/GytZyTMrNlzOLBvz4TKH4qB11hMbBwOu53mpkY2bX7K+53Q0HCCgoIJCDASEGAkKSWVttaWcQd7A90pqWk0NtTT09ON2+1Gp9Nhs1oHDVktLSlm1ZrhXzT6ipw589j11+3c/8CDNDU18uXuXQA4HHYqK8tRdDpmzJw97vIHtuMZWTM9i6sPuL9kz85l/+4dLLpvxW0vjVIzsjlx5GscDvtt8/3GSnbOfO/L6IKzxzH170dFyWXqaypY/cgz3vuJTqdj4f2rvdse2LWN8IgR5tmPk7CwMDqtt4aiWzs7b0uMoyX+dMMdBnvlxizKjZ7GO8plIc1Ze1s2ToBVXd+MGOgB3NDHkN1b6U3oEuMyU2a884cCW4eF0P4TorG2wjvXIzE1i8orBaRkzsLS2kiA0UiQKZTAIBPOnh56HHYCg020NtYRFTO+YKTb3kVQ/zjy5toKb6KV+9bfmthaeukMBoOR9Nl5qKrK5TOHCI2IJnPO2B/EhiIuJRNz83WiE6bT1WnB7XYREBiMq8+JChgMAbQ11qAoCqH983EMAUbabzQSEZNAQ+VVUmctGFkyhno3Dai33dpBUEgYOp0Ou60TW4dl2MVg75T7HljFfQ94JjPX19Vw8buzowZ6PT3dNFyvHfV7o2EKCSUkLJx2SxuRUTE01FUTFR1DWEQUjfU1JE1Po8Nixu12ERQcTJ/TiYpKQICR+toqFJ1CVMz41hczhYVzo7mBPqcTvcFAU30NMf1DIWorrpGSnuV98AN4/IU3vH+fOryPlPSscQd6TmcvqqpiNAbidPZyvbaK/MX347B3EWwKQVVVLp47Tc483/Toq6pKy6UTGEMjicz09B4EhkeTse7W8JKaw5+Q8sBm9MYgnA4bTd8dIj5vJcbQiBHLGYtbLT+LEhyOLvlWVkW1txvFGOT5vK4IJcFzLJWoRNz1V1BdfaDToXa0eLJ3upzg6kMxBqOqbjDXQ0TccNoRGe7+cnN/G2tKWbr+9kn0pRdP0+fsZd59a8fsUVUV8+VTGEIiCMsYMBS1246+/4Hb0VxLQH8CjD67FX1QCIpOR5/DhrOrA32wJ4tgT3srblcfik5Pd1sjxnDfrau36tHnvH8XXziFIcBIdq7nPmoKCcPabiYsMpqWhprb5vSNBYe9C51OT2BQEH1OJ3U1VeQvug+7vQuTKQRXXx/fnz/DvUs8CTVefO3X3m0PfbWH9MzscQd6g66zmioWLh2+V9Jh7yIwKBidTkdnu4UOi5nwyMhxuX9IV1cXISEhdHZ0UHathOdffJWqinLOnT3Ns798kYCA0ee33ynx/W1ZTH9bpva3ZXZbB0EmT3visHXS1WkhOMTTnvR02wkMMuHo6qSlroIl658Z1aOqKtUXjhEcHknCzFttX2RiOm2110iclU9b7TUikzzD5J3ddgyBwSiKgs3cDKqKwRiEq88Jqoo+wIirz0lHc503S+edcKfPLqaQcKqvXcY93w2o3Gi6TlbuxJ4jAEquFJGTeysJU2hoGHW1NaSmpVNbUzXh4Oomw11jABVlV0nPzL5tvndG9kyOHzmA2+3G5XLR3NhA3sLF43Lb7V3oB7qrq1i4ZBkp09Mpv3aVmTm5XC0uJDP71lt/c9sNerq7SUiaWDA9HBZzm/fYlpddIzrG8/drb/6D9ztf7d1NZvaMCQV6Q7bjS+6nw2ImIspzztVUlhHZf8+0d9kINoWgKAotTQ2AOmi+4Vi52YZ1WTupqypl/eO/pKG2kisF51i7+TkMA+4nfU4noGIIMNJYV42i0xER7ft1WROTkrGY22hvtxAWFs7VK8U88tjjo294l7tB43X2wl2dLOi5TIDqJLbvBlm9VZwxLaFPCaAyIJ0lDk8K8cqA9BEzcQKcP/olrU3X6e12sP/j/yQn/z6a66qwdlg86WtDw8lb5nkzEJ+SQVNdFQc//TN6g4H8BzxvqhSdjnmLV3Lyq08BlciYeNJnjf5AePHEPszN1+nt7ubIzreZMX8pbc3X6bS0elLXhoYzd8nqEcuwtDZQX3mVsMhYTuz1ZI/84XINQ1F4cj/m5us4e7r5Ztc7ZM1fQnJWLsVnD3Jq7zZ0Oh1z71uHoij0dju4cOQzTzpnUyjzlt2aq5azaBVFZw7idvURm5RG7IBMocPW+/g+2vrrffjTt5mxYCnm5ut09i9BEBwazrylnnqbWxqoKDrvGfKgKMxdssqbNn6oclJnzB3BPDYufX+ei+fPYO+y8fEHb5GWkcXP1nuG81WWXWN6WiYBdzh3ZyjuW7mWbw7sweVyERYRyYo1D2EICODEoX3s3PY2er2eFWsfQlEUHI4uDny+AxSFkNBQVq4bPKdqrEyLTyItaxZf/vV9FJ2O6Ng4ZuR6HlSqy0vIzV8y4boNh8PexcG9njeMbreb7FlzmJ6eRdHF8xQXeuaiZmTNYuacW8NIt//59zh7e3C5XdRUlrHxsWfHHOh2W5qx1ZdjDIuirr8nL3rWvYTED51tzlJ6Ebezh9b+ZQAURUfKA5vvuBwArK2orVVgisRV4MkMqUtdgNptxd1Y6ik/ZjpKnKc3QDEEoiTl4L70FSienj0lOhm114H76rH+xCwqSkS8N0AciYIh7i/mH9xfcgfcX8zN1wkKCbttmKajy0pF0TlCwqM49eVfAEiblcf0Ua6zXksL9oYKAsKiaDrpyRYXMXMh9sZKnJ1mTw9acCjRuZ4Hsx5LC52Vlz1vZBWFqNyl6I1B6I1BmBLSaD71BSg6jOHRhE4fecjE2SN7aW2oo6fbwd6P/khu/v0YA4O4eOYwPQ4HJw/sIjI6jhWbRk5tf8/9q/n26Je43S5CwiJZtPLO3sZ3ddk4tH+PJ6hXVbJn5ZCRNYNT3xymurIMVVWZu2ChJnN3HF1dHNjjSYHvdrvJnp1LanoWVeXXOHnU81Z9/+5PiJkWz8OPP0djfR3nTx9Hp9ehKAorVm8c98PYD/li1w4cDgd6vZ7V6zcSFBTE4a/343K5+Ot2T5uVlJzizRp4p1wa0JYd2/UO2f1tWVF/W6YMaMvaWxqovPJd/xA6hZxFt9qTguNf4uzpRtHpyFn04JimQ9jammirLSU4PJqiQzsASMldQuKsfMq//ZrWqhICTaFkLfUMlTZfr6ClshhFp0On15O5ZK1nBE23g/KznqyaqttNTOoMIhJGnjPpi2eX5PQZtDbWcvizD1AUiEtOJzF1Yr3mvb29VFdVsm7A77l+48McOeQJsgx6/W2fTYThrjHwDJdcuPj2IazRMbGkpmey/f23UBSFOfPyvEM+7xS7zcbX+79AdauoqMzod0fHxPLVns84c/IY0+ISbkv+VlpSzMzZuT7pyd67exfXa2twOOz86Xf/yrLlK6mqKMdsbkNRFMLDI1izYeisrxPFYe/iwB5PO6663WTPnkNqehZf791Fu8XjDw2L8GbirCwr4UrhRc88bIOB1Rs3j/sYHD+wm54eBzqdjkUPrMUYGMT5E4dwu1wc2eO5BmPiE1mycj3dDjtH9u7wXA8hYSxb7Zvz7ofcXE5kx/ZtqG6VeQvymDZtfOfV3eQGUFRVHfbD5f98YvgPNeb3z+T5S02P038LXfe5/XbICdD7b8HM9GnaZLUbC91O1+hf0ohePy+qHhSg95v7SEWL39zVlm6/uddlTk6q5aEoutHuN/fSFN+/qR0r8eETm9M2Efx5Tw8N0vR97ohcru3wm9vcPb6Eb74gJcI3wfd4yI4f3/QMX+Do9V87CqDT+e/5xZ/PTrbuvtG/pJW7x3+/eUq0/64zfxJkGF/yVJkBKQiCIAiCIAiCMAWRYE8QBEEQBEEQBGEKIsGeIAiCIAiCIAjCFESCPUEQBEEQBEEQhCmIBHuCIAiCIAiCIAhTEAn2BEEQBEEQBEEQpiAS7AmCIAiCIAiCIExBRlxnr7sP/y0QJAiCIAiCIAiCIMg6e4IgCIIgCIIgCMItJNgTBEEQBEEQBEGYgkiwJwiCIAiCIAiCMAWRYE8QBEEQBEEQBGEKYvBFIR99+B5dNhsGg6e4yOhofv7E074oWjP35UsFNDU2sHbDJr/4J4qvvB99+B6rVq8jMSnJL/7xMhH/F5/t5MaNFubNz2PRkvsmzTtR7tZzvbGhgeLLl1izfuNd5/b3MZ/IPtTWVHPu7GmefOZ5zV2+5G68xsR99/rv1ro3NzVhs1nJyp4xaU5fcbc/N92N/qnq1rLs0yePc6W4CJ1OQUFh/aaHSUpOmRT3//vf/4Np0+K8/+fkzmXpsuXjLm/cwZ7L5cLlcmE0GgF4ePPjgwKGboeDoODgce/cj9HtT/9Ptd6+9NtsNhob63nj7/5xUr3jZSoc88SkpDt+meBPt7+P+WTvw0+tvuL2v1sLv9vtRqcb22ClqVD3luYmmhobxhzsTYU6303eH4N/qrq1LNvhcBAcHEz99Toqyst46dXXMRgM2O12r9feZSMsPEIzN4DBYODl1/520He2bt0atWXLFsudln3Hwd6NG60UFnxPaUkJP3/yaeITEof97tUrxXx/4Rzz5ucxd94CTCEht33e0d7O3i8+w9nbC8CaDZtISZk+KW6Azs5OPvrwPWxWK3PmzmP5igcnre5ut5tjRw5RVVmOoigsyMtn4aIlmnudTif79u6mpbmZmJhY+vr6RqyzFvX+5ughamtqcLn6yF+4iLz8eyfNv2P7h9isVt5964+sWb+R6alpk+J1Op18uedzbrS2EB0di81mZe36TcMGIr4+1202Kzu2b6PdYmHGrNmsWr122PJ87b/THiZ/uX19zG9SUVbK6VMneOLp5zCZTH7ZB61dly8VUFZaQq/TicXcxuIly3C5XRRfLkSv1/PUs7/wNmJa7kN52TWcTueYznNfuvv6+jiw/0uaGhvQ6XT8bM060tIzJsV9+VIBpddK6OnpHlNbpmV7Mtp9TZu6X6W3txdVVXn+hZeGLcvX7tbWFvbv2Y3L5UJF5bEnniY6OmZS/C6XixPHj9LX18f1ulqW3r+cnDlzNa/zD0csfPrJRyxeuozUtHTN6wzwwbtvs/HhR709HiONTPKl968ff8TKVauJi4/n3bf/xMxZs7n/gZWc+OYoYeHh5N2zUFP/+W/P0NrSwqZHNtPa0swXn+/kVy+/RkBAwJDl+dL9lw/eZc26jcQnJACw7f0/s27DJuLiEzR379/7BU2NDQBYbVZm58zBYDD4vH1UVZXammouFXxPY309b/z9P2Kz2QgONnl77ez2Lm+9omNjcfX10dXVhcs1+Bl6ou5R+I+tW7cmAe8AO7ds2dI92gYwxnX2ent7KblaTGHBRQDmLchjdk4ugYGBwOCuzPTMTFatXgdAZ2cHly8VcKW4iNhp01iQl09GZhaKouB0OlEUBYPBgNncxp7PdvLiq6/ftg9auS9fKuCbo4d59fU3MQQE8MG7b7Hp4ccG3TS08l+8cJ7q6io2//xJdDrdbRG9lt5z357hRv9No6W5mffe+RMvvPQ3k1bvgu8vYLd3sWz5Cvr6+vjL+39m8xNPERkZNSn+jvZ2Pt3xEa++/ncMhVbeb8+exmJuY8OmR2htaeHdt/846Lhrea6fOvkNL7/6BnqDgbf+8Ft+8eLLhPe/mdLaP5aAy19uLY95U2MDaekZnD93lieeenbYN32TWXct63v61HFeevUNXH19/OkP/8GDq9Zwz8J7OXzwK8IjIlm0eKnm+zDaea7ZffXsaW60trLpkc203bjBJ9s/5PU3/8Fbjtb1Hq0tm6rtyeVLBRz/5givvPbmkC8TtHQfPLCPpOQUcufOx+Vy4Xa7h3z41voeM9RQ8clyDhfsaeU//+0Zuru7eWDlKmxWK9u3vc9rb/5ac+/Z0ycxGo3kzp3Pxx99SFBwMM8890u2b3ufdRsfIiYmVlO/qqp89OF7LFq8lDOnTrB63QZSpqdOzm9eWEBzUxNr1m3A3NbGns8n7zn9ZtkXL5zn5PFjxE6bRl7+vT4r22q1UlRYQNHlQmJjpzF/QR4ZWdnodDp6e3vZ9t47dNm7UFAINpm4d/ESr7upsYGdO7Zjt9sJDAwkONhE9syZPnHD4GGcN1/oBBlQtm7duhB4BdgA7APe3rJlyyVGYEw9e7/7t39hWlw8Gx96lJjY2CG/M1RXJkB4eAT3P7CSZctXUFlRzv69u0lITOKJp5/D7XJx8MB+mpub0CkKZnPbpLnBc1IE979pnzkrh+vXaweVo5W/uqqKvPyF3h/2hw2VVt7rtTXeHsS4+Hji4uKHLFsrf1VVBa0tzVy7egWAnp4eLGbzoGBPy999JLTy1tfVeo/7tLi4IY+7lnVOS88gMCgIgJjYaXR2dAwK9vx1zP3p1tJbU1NFY2MDzzz/grdh8nfdtXSlpqV76hkYSGBgINkzZgIwbVo8LS3Nk7IPo53nmt1X6+pYuGhxvzeWiIhIzG1txMXfus792ZZN1fYEID0ja9hAT0t3cvJ0Tp86gbWzk5mzc4bt1fPHvc2f93It/bNzcvlk+zYeWLmKkqvFzJqdMynelOmpXDh/jojIKLKyZ1BdVekZQdBu8QZ6WvoVReGhRx7jz2/9gbz8hYMCPS3ds3NyOX3yOKtWr6Xw0kXmzs+bNPfNslUV1q7fxPy8e3xWdkN9Pdvef4cFefn84lcvDxp1YzQa6ehoJzwikpTpqZRdu4pO0Xnb8oTEJKKiY9j8+FM0NTZw7MghQkLDfOKG4YdxAmzZsuUCcGHr1q1BwBvAua1bt/7XLVu2/GbIDRhjsPfYE09TWHCRz3Z+Qs6cucydv4CIiMixbApAQ309lwsvUl1VyaycXBbckw/A+XNnMYWE8Mprf4uqqvzz//mfk+YGUH6wEP1Qy9Jr6R8Jf3k196uwZt1GMrOy/eMfBX8edy3dBv2tS12nU3C73ZPqH42p+HtHRkbT3m7B3NY24rC2yaz7ZJ1jiqKg73/TqigK6oDzzZ/n+VQ9x0dry6ZsewIYhxnKprV7ztx5JCYnU1Fexqcff8T6TQ8POXTXH8deK6dOp2PgaLDhpoFo5Q8LDyc4OJiW5mauXilm/caHJ8WbmJRMU2MDkVGRpGdk4bDbuXTxAgkJt9/XtfytzeY2jEYjNqttyG21cgcEBJCekUlZ6TVKrhbz0iuvD9pWy3onJqdg77Jx7tvTWK2dPis7Lj6ejQ8/SmHBRXb99WPmzl9Azpy5t72YvVmvutpqpqemU3S5kHkL8gDPlCSHw87xY0dwOOwsX/EguXPn+8w9Elu3bjUAm/D07mUD/x3YNtI2Ywr2MjKzyMj0nODFRYXs2vExwSYTGx96lIjIyGG3q6qs4OjhrwkJCWV+Xj5r1m1Er9d7P+/p6SYsLNzbVTzUkFKt3ADVVRU4HA4MBgNlpSVsfGjzpPnTMzIpuHiBtPSMIYdxauVNSU3jStFl0tIzaG1pue2N+2TUOyMzi4LvvyMtPQO9Xo+5rY3QsDDvRFut/aOhlTc5ZTolV4tJS8/gRmsrra0tk+b2d91/zG4tvREREaxavZbPdn7C5sefum1Ihr/q7u9zzN/7oN19NZXiokLS0jMwt7XR2dFBdMztPT3+bMumansyFrRyt1ssREZGce+iJXR2dNDS0jxksKeV3xhopLc/38FkOSMiI/n+wnlUVcVq7aSxoX5S/QA5c3L59uwpenp6bus519Kr1+sJCw+n5OoVli1fid3exdFDB1m89PZs3po9K3d3c/jrr3j+hZc5eGAfJVevMDtnzqS4ARbk5bNzx3ZSpqcNOR1BK/f3350j0BjIs8+/4POyDQYD8+bnMW9+HmZzG4UFF3nv7T+RlJLCI5sfp63tBhGRkWx+/EkcdjtffL6T1pYmPv7LByQmp3C1+DJOp5OFi5YMmrM5UfdIbN269Z+AXwMngH/ZsmXLiRE36GdMc/aGoqG+ntCwUMLDIwaNmQ02mXj2F7+iqbGBYJNp2CjcbG7j8507AIXMrCy+/+48//Rf/tuoO+0L982EAj09PVg7O8eUoMWXfrfbzdHDB6mqLEen0/cnaFmsuXfghPrY2Fis1tEn1PvSr6oqx48dobysFFAxmUJ4/MlnvEOvtPaPNmdPK29vby9f7vmcthutxMTE0m6xsPmJp0ad0O+rc/1OJ9X70j+eJQB86z7Dk8+MfQiSr495c1Mje3bv4omnnyMqKnqS6z76cdfiHPvDb/+VF195HZPJNKZlKPx5nvvCfacJWnxd7/G0ZVOhPRnvEie+cJ89fZKiy4XodTpCQkN55LEnRhxO6mu/w+Fgx/ZtuF2uERO0+NKpqip7d39GU1MDMTHT6O52sHzFg5PWlgB02Wz87t9/w7LlKybtPAc4fuwINdVVvPDSq1itVn7/77/hxVdeJyFx+CQhvvLv27ObuIQEz4uFzg62b3ufX774KiGjJOXyVd0B3vrjb1m9dsOoo7J86f7jb/8NnV5HgMHTe5+Xfy/3LLzXp/UaiNvtpqKslBmzZtPU2MDBA/vp6elGp9MRFRXN+k2P0G6x0G4xkzVjJjt3bNfEDYPn7GVkZfPgz9bwf//X1rXAuS1btnSOuXAmEOwJgjB23G43brcbg8GAxWLmk798yGtv/nrcvSPC2LhWcoXy0lIeevQxf++KIGiCL9d0nCjjXbdVEIQfL1arle3b3uO1v/21N8GI4B+CDEPOOBuVca+zJwjC2HE6nXy87X1cbjegsnbDJgn0NKas9BrHjx1h48ODh2cLgiAIgjAyRYWXOH7sCD9bu04CvbsYTXr2KivKOfz1V7hVNwvy8ie06ru4xf1j9otb3OIWt7jFLe670y9ucd9N7vH27OnGs9FIuN1uDn61j6ee/QV/88bfc6W4iButrb7WiFvcfveLW9ziFre4xS3uu9MvbnH/FNygQbDX2FBPZHQ0kVFR6PV6cubkUlZa4muNuMXtd7+4xS1ucYtb3OK+O/3iFvdPwQ0aBHtWq5XwsHDv/2Hh4disVl9rxC1uv/vFLW5xi1vc4hb33ekXt7h/Cm7QINgTBEEQBEEQBEEQ/I/Pg72wsDA6rbeWf7B2dhIaFuZrjbjF7Xe/uMUtbnGLW9zivjv94hb3T8ENGgR7iUnJWMxttLdbcLlcXL1STPbMWb7WiFvcfveLW9ziFre4xS3uu9MvbnH/FNyg0dILFeVlHD74FapbZd6CPJYtXzHuHRS3uH/MfnGLW9ziFre4xX13+sUt7rvJPd6lFzQJ9gRBEARBEARBEATf8KNZZ08QBEEQBEEQBEHwPxLsCYIgCIIgCIIgTEEk2BMEQRAEQRAEQZiCSLAnCIIgCIIgCIIwBZFgTxAEQRAEQRAEYQoiwZ4gCIIgCIIgCMIURII9QRAEQRAEQRCEKciI6+wJgiAIgiAIgiAIdyfSsycIgiAIgiAIgjAFkWBPEARBEARBEARhCiLBniAIgiAIgiAIwhREgj1BEARBEARBEIQpiAR7giAIgiAIgiAIUxAJ9gRBEARBEARBEKYg/x9jn3XTuDAUrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,16))\n",
    "plt.imshow(N, cmap='Blues')\n",
    "for i in range(28):\n",
    "    for j in range(28):\n",
    "        # get the text from index\n",
    "        # add text to the graph\n",
    "        chstr = itos[i] + itos[j]\n",
    "        plt.text(j, i, chstr, ha='center', va='bottom', color='gray')\n",
    "        plt.text(j, i, N[i,j].item(), ha='center', va='top', color='gray')\n",
    "plt.axis('off')\n",
    "\n",
    "# <E> can never be the beginning, <S><E> is also not possible => we waste space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aa2cca33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix special tokens with the same token for start and end\n",
    "\n",
    "SPECIAL_TOKEN = '.'\n",
    "\n",
    "N = torch.zeros([27,27], dtype=torch.int32)\n",
    "# string to int mapping 1:1\n",
    "chars = sorted(list(set(''.join(words))))\n",
    "# make the special char index 0\n",
    "stoi = {s:i+1 for i, s in enumerate(chars)}\n",
    "stoi[SPECIAL_TOKEN ] = 0\n",
    "itos = {s:i for i, s in stoi.items()}\n",
    "\n",
    "for w in words:\n",
    "    chs = [SPECIAL_TOKEN] + list(w) + [SPECIAL_TOKEN]\n",
    "    for ch1, ch2 in zip(chs, chs[1:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        # store the count at the corresponding index\n",
    "        N[ix1, ix2] += 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8746daf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 26.5, 26.5, -0.5)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,16))\n",
    "# this is the same as above\n",
    "plt.imshow(N, cmap='Blues')\n",
    "for i in range(27):\n",
    "    for j in range(27):\n",
    "        # add text to the graph\n",
    "        chstr = itos[i] + itos[j]\n",
    "        plt.text(j, i, chstr, ha='center', va='bottom', color='gray')\n",
    "        plt.text(j, i, N[i,j].item(), ha='center', va='top', color='gray')\n",
    "plt.axis('off')\n",
    "\n",
    "# this just makes the graph nice, because . is small "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f78b932",
   "metadata": {},
   "source": [
    "# 3. compute bigram probability and sample from the probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1140745a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([   0, 4410, 1306, 1542, 1690, 1531,  417,  669,  874,  591, 2422, 2963,\n",
       "        1572, 2538, 1146,  394,  515,   92, 1639, 2055, 1308,   78,  376,  307,\n",
       "         134,  535,  929], dtype=torch.int32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if we start with dot, what's next?\n",
    "# first row for .X, 1D array\n",
    "N[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f319ce5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ". has count of 0\n",
      "a has count of 4410\n",
      "b has count of 1306\n",
      "c has count of 1542\n",
      "d has count of 1690\n",
      "e has count of 1531\n",
      "f has count of 417\n",
      "g has count of 669\n",
      "h has count of 874\n",
      "i has count of 591\n",
      "j has count of 2422\n",
      "k has count of 2963\n",
      "l has count of 1572\n",
      "m has count of 2538\n",
      "n has count of 1146\n",
      "o has count of 394\n",
      "p has count of 515\n",
      "q has count of 92\n",
      "r has count of 1639\n",
      "s has count of 2055\n",
      "t has count of 1308\n",
      "u has count of 78\n",
      "v has count of 376\n",
      "w has count of 307\n",
      "x has count of 134\n",
      "y has count of 535\n",
      "z has count of 929\n"
     ]
    }
   ],
   "source": [
    "for idx in range(27):\n",
    "    char = itos[idx]\n",
    "    print (char + \" has count of\", N[0, idx].item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "49e5c646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([27])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N[0, :].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2f8267eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([   0, 4410, 1306, 1542, 1690, 1531,  417,  669,  874,  591, 2422, 2963,\n",
       "        1572, 2538, 1146,  394,  515,   92, 1639, 2055, 1308,   78,  376,  307,\n",
       "         134,  535,  929], dtype=torch.int32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "75e735cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.0000, 0.1377, 0.0408, 0.0481, 0.0528, 0.0478, 0.0130, 0.0209, 0.0273,\n",
       "        0.0184, 0.0756, 0.0925, 0.0491, 0.0792, 0.0358, 0.0123, 0.0161, 0.0029,\n",
       "        0.0512, 0.0642, 0.0408, 0.0024, 0.0117, 0.0096, 0.0042, 0.0167, 0.0290])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize the count to get the probability distribution of all chars in a tensor\n",
    "p = N[0].float()\n",
    "p = p/p.sum()\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "342d0cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.7081, 0.3542, 0.1054])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample from a distribution multi-normial , make it deterministic with Generateor \n",
    "g = torch.Generator().manual_seed(2147483647)\n",
    "# randomly generate 3 random numbers from a uniform distribution [0,1]\n",
    "p = torch.rand(3, generator=g)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "27c36de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.5996, 0.0904, 0.0899],\n",
       "        [0.8822, 0.9887, 0.0080]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.rand(2,3, generator=g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8b685387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.6064, 0.3033, 0.0903])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we need to normalize the sampled number! \n",
    "p = p/p.sum()\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4544b71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 1, 1, 0, 0, 2, 2, 0, 1, 1, 1, 0, 2, 1, 1, 1, 2, 0, 0, 1, 0, 0, 1, 0,\n",
       "        0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 2, 0, 1, 2, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "        1, 0])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sample from a distribution multi-normial , make it deterministic with Generateor \n",
    "# draw 20 samples with replacement, there are 3 samples, 60% is 0, 30% is 1, 10% is 0\n",
    "\n",
    "g = torch.Generator().manual_seed(2147483647)\n",
    "torch.multinomial(p, num_samples=50, replacement=True, generator=g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6b2d134d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we use the same seed to sample index from the distribution in the 1st row \n",
    "\n",
    "p = N[0].float()\n",
    "p = p/p.sum()\n",
    "g = torch.Generator().manual_seed(2147483647)\n",
    "ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n",
    "ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c1e10291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'m'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is the first char following .\n",
    "itos[ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "de307478",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m\n",
      "o\n",
      "r\n",
      ".\n"
     ]
    }
   ],
   "source": [
    "# sample more char in the seq, and look for the row that starts with m\n",
    "# ma, mb, mc ...\n",
    "\n",
    "g = torch.Generator().manual_seed(2147483647)\n",
    "\n",
    "ix = 0\n",
    "while True:\n",
    "    p = N[ix].float()\n",
    "    p = p/p.sum()\n",
    "    ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n",
    "    print (itos[ix])\n",
    "    # this is the index to be the next\n",
    "    if ix == 0:\n",
    "        # we sampled the start or the stop token (which is the same .)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e262eddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([27])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "abc78629",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qvsaayxbqrqmyqwuznivanukotdjvdhd.\n",
      "qnoymtzduqkatdetkpfjdgigvlejfkrsqlwnirghhzwlu.\n",
      "idcx.\n",
      "cekmzucjnjoeovjvrggqrjr.\n",
      "cfbhabkslpokc.\n",
      "xtxwbpmknuusxdgzfexhwqpldpdnwzvkyxsqjforqqpfxstwkfoufhvwfhmsuyyotvcvvqpfcbydjcouhkajkhqnnpqmmllaordqy.\n",
      "gszpw.\n",
      "zlgijinangzzuulsyvqrufuawavsdbnwvlmrypvgrsfgpshgnmwafqmsjdvbhngvoiigxhkwdltrdkwnagzyknqv.\n",
      "lfstdqigvncdoidetsukgdp.\n",
      "cfpjsxeqjcsmjwguzes.\n",
      "woflfjxflylgbegpjdpovdtw.\n",
      "dlzysqtrbhxhcdneiuum.\n",
      "xtyslfbmaboaanyjpojuujflcsaucqcgtjmlzqtbaisvxrtgupkppigxudejdzsroqeigovuxmvt.\n",
      "jlxfolkozci.\n",
      "tkhdivkdifaxcevlpktkwwvuxlymtwylgpzauwdvxfvbooflddphmjeomjgjcqeqwt.\n",
      ".\n",
      "wlxclcjbm.\n",
      "quuyijtnzmycshclormjyrerqslomdrlbuwqnlmitbrmqhtbdwbyvlsmwnborwcdhjotezwnsxuvffvinrmedelubhdfgtavxqfgmnyqrygyevxaapbjtnwfnwewqxerdytttvfo.\n",
      "iauarz.\n",
      "tynoqkyp.\n"
     ]
    }
   ],
   "source": [
    "# untrained probability, uniform \n",
    "g = torch.Generator().manual_seed(2147483647)\n",
    "\n",
    "for i in range (20):\n",
    "    out = []\n",
    "    ix = 0\n",
    "    while True:\n",
    "        p = torch.ones(27)/27.0\n",
    "        # uniform distribution\n",
    "        ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n",
    "        out.append(itos[ix])\n",
    "        if ix == 0:\n",
    "            break\n",
    "    \n",
    "    # this is a randomly generated word with uniform prob distribution \n",
    "    print (''.join(out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "705a2b56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mor.\n",
      "axx.\n",
      "minaymoryles.\n",
      "kondlaisah.\n",
      "anchshizarie.\n",
      "odaren.\n",
      "iaddash.\n",
      "h.\n",
      "jhinatien.\n",
      "egushl.\n",
      "h.\n",
      "br.\n",
      "a.\n",
      "jayn.\n",
      "ilemannariaenien.\n",
      "be.\n",
      "f.\n",
      "akiinela.\n",
      "trttanakeroruceyaaxatona.\n",
      "lamoynayrkiedengin.\n"
     ]
    }
   ],
   "source": [
    "# \"trained\" probability distribution from counting \n",
    "\n",
    "g = torch.Generator().manual_seed(2147483647)\n",
    "\n",
    "for i in range (20):\n",
    "    out = []\n",
    "    ix = 0\n",
    "    while True:\n",
    "        p = N[ix].float()\n",
    "        p = p/p.sum()\n",
    "        ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n",
    "        out.append(itos[ix])\n",
    "        if ix == 0:\n",
    "            break\n",
    "\n",
    "    # bigram launguage is bad lol\n",
    "    print (''.join(out))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee25c42b",
   "metadata": {},
   "source": [
    "## sum values in tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "31c43ca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.tensor([[1,2],[3,4]])\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0ed1c773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x12327f0f0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARUAAAD8CAYAAABZ0jAcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAP1UlEQVR4nO3df6xkZX3H8feniwtBCyxshQ1ahEhVDLroBkSMoiK//gASaV3S1qWBbLXSJhobISRqsKZo/6AxVesGqagtULHq2kItgsQmuMjaAitYYFmbyoqCLK6lILr47R9zMMfrnd177zzM3Ll5v5KbOXOe88z9Pln45MyZOfebqkKSWvmNSRcgaWkxVCQ1ZahIaspQkdSUoSKpKUNFUlMjhUqSA5PckOS+7nHFkOOeSnJ797Oxt//wJLcm2ZrkmiTLR6lH0uSNeqZyIXBjVR0J3Ng9n80TVbW6+zmjt/9DwGVV9ULgUeC8EeuRNGEZ5ctvSe4BTqyqB5OsAm6uqhfNctxjVfWcGfsCPAwcUlW7khwPvL+qTllwQZImbq8R5x9cVQ922z8ADh5y3D5JNgO7gEur6ovAQcCPq2pXd8wDwKHDflGS9cB6gGUse+W+7Ddi6RqnXSufPekSNA8/+98d7Prp/2Uhc/cYKkm+Chwyy9DF/SdVVUmGnfYcVlXbkxwB3JRkC7BzPoVW1QZgA8B+ObCOyxvnM10T9qM3Hz/pEjQP93z+sgXP3WOoVNVJw8aS/DDJqt7bn4eGvMb27nFbkpuBY4DPAwck2as7W3kesH0Ba5C0iIx6oXYjsK7bXgd8aeYBSVYk2bvbXgmcANxdg4s5XwPO3t18SdNl1FC5FHhTkvuAk7rnJFmT5PLumJcAm5PcwSBELq2qu7ux9wDvSrKVwTWWT45Yj6QJG+lCbVU9AvzaxY2q2gyc323fAhw9ZP424NhRapC0uPiNWklNGSqSmjJUJDVlqEhqylCR1JShIqkpQ0VSU4aKpKYMFUlNGSqSmjJUJDVlqEhqylCR1JShIqkpQ0VSU4aKpKYMFUlNGSqSmnrG254mWZ3kG0nuSnJnkrf0xj6V5Lu9lqirR6lH0uSNo+3p48Bbq+qlwKnAXyc5oDf+572WqLePWI+kCRs1VM4Eruy2rwTOmnlAVd1bVfd1299n0Bvot0b8vZIWqVFDZa5tTwFIciywHLi/t/uD3duiy57uDyRpeo2r7SldB8PPAOuq6hfd7osYhNFyBi1N3wNcMmT+L3sp78O+eypb0oSMpe1pkv2AfwEurqpNvdd++iznySR/B7x7N3X8Si/lPdUtaTLG0fZ0OfAF4NNVde2MsVXdYxhcj/n2iPVImrBxtD39PeC1wLmzfHT890m2AFuAlcBfjFiPpAkbR9vTzwKfHTL/DaP8fkmLj9+oldSUoSKpKUNFUlOGiqSmDBVJTRkqkpoyVCQ1ZahIaspQkdSUoSKpKUNFUlOGiqSmDBVJTRkqkpoyVCQ1ZahIaspQkdSUoSKpKUNFUlNNQiXJqUnuSbI1ya+1Pk2yd5JruvFbk7ygN3ZRt/+eJKe0qEfS5IwcKkmWAR8FTgOOAs5JctSMw84DHq2qFwKXAR/q5h4FrAWe7rP8se71JE2pFmcqxwJbq2pbVf0MuJpBj+W+fs/la4E3dr1+zgSurqonq+q7wNbu9SRNqRahcijwvd7zB7p9sx5TVbuAncBBc5wLDNqeJtmcZPPPebJB2ZKeCVNzobaqNlTVmqpa8yzs4y4tVi1CZTvw/N7z53X7Zj0myV7A/sAjc5wraYq0CJXbgCOTHN71TV7LoMdyX7/n8tnATVVV3f613adDhwNHAt9sUJOkCRmp7SkMrpEkuQD4CrAMuKKq7kpyCbC5qjYCnwQ+k2QrsINB8NAd94/A3cAu4B1V9dSoNUmanJFDBaCqrgOum7Hvvb3tnwK/O2TuB4EPtqhD0uRNzYVaSdPBUJHUlKEiqSlDRVJThoqkpgwVSU0ZKpKaMlQkNWWoSGrKUJHUlKEiqSlDRVJThoqkpgwVSU0ZKpKaMlQkNWWoSGrKUJHU1Ljanr4ryd1J7kxyY5LDemNPJbm9+5n5B7MlTZmR/0Ztr+3pmxg0A7stycaqurt32H8Ca6rq8SRvBz4MvKUbe6KqVo9ah6TFYSxtT6vqa1X1ePd0E4P+PpKWoHG1Pe07D7i+93yfrp3ppiRnDZtk21NpOjRp0TFXSf4AWAO8rrf7sKranuQI4KYkW6rq/plzq2oDsAFgvxxYYylY0ryNq+0pSU4CLgbOqKpfnmpU1fbucRtwM3BMg5okTchY2p4mOQb4BINAeai3f0WSvbvtlcAJDLoVSppS42p7+lfAc4DPJQH4n6o6A3gJ8Ikkv2AQcJfO+NRI0pQZV9vTk4bMuwU4ukUNkhYHv1ErqSlDRVJThoqkpgwVSU0ZKpKaMlQkNWWoSGrKUJHUlKEiqSlDRVJThoqkpgwVSU0ZKpKaMlQkNWWoSGrKUJHUlKEiqSlDRVJT42p7em6Sh3vtTc/vja1Lcl/3s65FPZImZ1xtTwGuqaoLZsw9EHgfg15ABXyrm/voqHVJmoyxtD3djVOAG6pqRxckNwCnNqhJ0oS0+Gv6s7U9PW6W496c5LXAvcA7q+p7Q+bO2jI1yXpgPcDy5+7Hzitf2KB0jcu3Vn980iVoHo79xsMLnjuuC7VfBl5QVS9jcDZy5XxfoKo2VNWaqlqz1/77Ni9QUhtjaXtaVY/0Wp1eDrxyrnMlTZdxtT1d1Xt6BvCdbvsrwMld+9MVwMndPklTalxtT/8syRnALmAHcG43d0eSDzAIJoBLqmrHqDVJmpxxtT29CLhoyNwrgCta1CFp8vxGraSmDBVJTRkqkpoyVCQ1ZahIaspQkdSUoSKpKUNFUlOGiqSmDBVJTRkqkpoyVCQ1ZahIaspQkdSUoSKpKUNFUlOGiqSmDBVJTY2r7ellvZan9yb5cW/sqd7YxplzJU2XsbQ9rap39o7/U+CY3ks8UVWrR61D0uIwiban5wBXNfi9khahFqEyn9alhwGHAzf1du+TZHOSTUnOGvZLkqzvjtu8a+fjDcqW9Exo0qJjHtYC11bVU719h1XV9iRHADcl2VJV98+cWFUbgA0Az/6dVTWeciXN11janvasZcZbn6ra3j1uA27mV6+3SJoyY2l7CpDkxcAK4Bu9fSuS7N1trwROAO6eOVfS9BhX21MYhM3VVdV/6/IS4BNJfsEg4C7tf2okafqMpe1p9/z9s8y7BTi6RQ2SFge/USupKUNFUlOGiqSmDBVJTRkqkpoyVCQ1ZahIaspQkdSUoSKpKUNFUlOGiqSmDBVJTRkqkpoyVCQ1ZahIaspQkdSUoSKpKUNFUlOt2p5ekeShJN8eMp4kH+naot6Z5BW9sXVJ7ut+1rWoR9LktDpT+RRw6m7GTwOO7H7WAx8HSHIg8D7gOAadDt+XZEWjmiRNQJNQqaqvAzt2c8iZwKdrYBNwQJJVwCnADVW1o6oeBW5g9+EkaZEb1zWVYa1R59My1ban0hSYmgu1VbWhqtZU1Zq99t930uVIGmJcoTKsNep8WqZKmgLjCpWNwFu7T4FeBeysqgcZdDU8uWt/ugI4udsnaUo16VCY5CrgRGBlkgcYfKLzLICq+lsG3QtPB7YCjwN/1I3tSPIBBv2YAS6pqt1d8JW0yLVqe3rOHsYLeMeQsSuAK1rUIWnypuZCraTpYKhIaspQkdSUoSKpKUNFUlOGiqSmDBVJTRkqkpoyVCQ1ZahIaspQkdSUoSKpKUNFUlOGiqSmDBVJTRkqkpoyVCQ1ZahIampcbU9/v2t3uiXJLUle3hv7727/7Uk2t6hH0uSMq+3pd4HXVdXRwAeADTPGX19Vq6tqTaN6JE1Iqz98/fUkL9jN+C29p5sY9PeRtARN4prKecD1vecF/FuSbyVZP4F6JDXU5ExlrpK8nkGovKa3+zVVtT3Jc4EbkvxX1/B95tz1wHqA5c/dbyz1Spq/sZ2pJHkZcDlwZlU98vT+qtrePT4EfAE4drb59lKWpsNYQiXJbwP/BPxhVd3b2//sJL/59DaDtqezfoIkaTqMq+3pe4GDgI8lAdjVfdJzMPCFbt9ewD9U1b+2qEnSZIyr7en5wPmz7N8GvPzXZ0iaVn6jVlJThoqkpgwVSU0ZKpKaMlQkNWWoSGrKUJHUlKEiqSlDRVJThoqkpgwVSU0ZKpKaMlQkNWWoSGrKUJHUlKEiqSlDRVJThoqkpgwVSU2Nq5fyiUl2dv2Sb0/y3t7YqUnuSbI1yYUt6pE0OePqpQzw712/5NVVdQlAkmXAR4HTgKOAc5Ic1agmSRPQJFS6joI7FjD1WGBrVW2rqp8BVwNntqhJ0mSMs+3p8UnuAL4PvLuq7gIOBb7XO+YB4LjZJvfbngJP3nbapUux6dhK4EeTLuKZsGzprm2prutFC504rlD5D+CwqnosyenAF4Ej5/MCVbUB2ACQZHPXjGxJWarrgqW7tqW8roXOHcunP1X1k6p6rNu+DnhWkpXAduD5vUOf1+2TNKXG1Uv5kHS9TZMc2/3eR4DbgCOTHJ5kObAW2DiOmiQ9M8bVS/ls4O1JdgFPAGurqoBdSS4AvgIsA67orrXsyYYWdS9CS3VdsHTX5rpmyOD/bUlqw2/USmrKUJHU1FSESpIDk9yQ5L7uccWQ457q3QqwaC/47unWhCR7J7mmG781yQsmUOa8zWFd5yZ5uPdvdP4k6pyvOdyGkiQf6dZ9Z5JXjLvGhRjl9prdqqpF/wN8GLiw274Q+NCQ4x6bdK1zWMsy4H7gCGA5cAdw1Ixj/gT42257LXDNpOtutK5zgb+ZdK0LWNtrgVcA3x4yfjpwPRDgVcCtk6650bpOBP55vq87FWcqDL66f2W3fSVw1uRKGdlcbk3or/da4I1PfyS/iC3ZWy5qz7ehnAl8ugY2AQckWTWe6hZuDutakGkJlYOr6sFu+wfAwUOO2yfJ5iSbkpw1ntLmbbZbEw4ddkxV7QJ2AgeNpbqFm8u6AN7cvUW4NsnzZxmfRnNd+zQ6PskdSa5P8tK5TBjnvT+7leSrwCGzDF3cf1JVlWTY5+CHVdX2JEcANyXZUlX3t65VC/Zl4KqqejLJHzM4G3vDhGvScAu6vWbRhEpVnTRsLMkPk6yqqge708qHhrzG9u5xW5KbgWMYvM9fTOZya8LTxzyQZC9gfwbfQF7M9riuquqv4XIG18qWgiV5u0lV/aS3fV2SjyVZWVW7vYFyWt7+bATWddvrgC/NPCDJiiR7d9srgROAu8dW4dzN5daE/nrPBm6q7srZIrbHdc24znAG8J0x1vdM2gi8tfsU6FXAzt7b9am1m9trdm/SV6DneJX6IOBG4D7gq8CB3f41wOXd9quBLQw+ddgCnDfpunezntOBexmcRV3c7bsEOKPb3gf4HLAV+CZwxKRrbrSuvwTu6v6Nvga8eNI1z3FdVwEPAj9ncL3kPOBtwNu68TD4Y2P3d//trZl0zY3WdUHv32sT8Oq5vK5f05fU1LS8/ZE0JQwVSU0ZKpKaMlQkNWWoSGrKUJHUlKEiqan/B71Iwdc0Rs1wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3fce4be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(10)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0b36dc47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4, 6])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum by column\n",
    "x.sum(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3449c6df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3, 7])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum by row\n",
    "x.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4d02f8e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c1da5049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(1, keepdim=True).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d34071",
   "metadata": {},
   "source": [
    "## broadcasting semantics\n",
    "\n",
    "https://pytorch.org/docs/stable/notes/broadcasting.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ce8a78b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5b59b8ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e5c71b29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3, 7])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3a23abfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = x/x.sum(1)\n",
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "68aa5cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1, 2],\n",
       "        [3, 4]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8e29880e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.3333, 0.2857],\n",
       "        [1.0000, 0.5714]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrong_sum_broadcasted = torch.tensor([[3, 7], [3, 7]])\n",
    "x/wrong_sum_broadcasted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "29048ceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.3333, 0.2857],\n",
       "        [1.0000, 0.5714]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [2,2]\n",
    "# [  2], wrong\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "207af3a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = x/x.sum(1, keepdim=True)\n",
    "res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "668953b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(1, keepdim=True).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "28bb1d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[3],\n",
       "        [7]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sum(1, keepdim=True)\n",
    "# should be [[3],[7]], not [3,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2717bcd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.3333, 0.6667],\n",
       "        [0.4286, 0.5714]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6bfbfbfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.3333, 0.6667],\n",
       "        [0.4286, 0.5714]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correct_sum_broadcasted = torch.tensor([[3, 3], [7, 7]])\n",
    "x/correct_sum_broadcasted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62cf8b68",
   "metadata": {},
   "source": [
    "## compute bigram probability matrix and sample from the probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0c1c6620",
   "metadata": {},
   "outputs": [],
   "source": [
    "smoothing_factor = 1\n",
    "# the bigger smoothing_factor is, the closer P is to uniform \n",
    "\n",
    "P = (N+smoothing_factor).float()\n",
    "P = P/P.sum(1, keepdim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8ed3e54d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mor.\n",
      "axx.\n",
      "minaymoryles.\n",
      "kondlaisah.\n",
      "anchshizarie.\n",
      "odaren.\n",
      "iaddash.\n",
      "h.\n",
      "jhinatien.\n",
      "egushl.\n"
     ]
    }
   ],
   "source": [
    "g = torch.Generator().manual_seed(2147483647)\n",
    "\n",
    "for i in range(10):\n",
    "    out = []\n",
    "    ix = 0\n",
    "    while True:\n",
    "        p = P[ix]\n",
    "        ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n",
    "        out.append(itos[ix])\n",
    "        if ix == 0:\n",
    "            break\n",
    "\n",
    "    print (''.join(out))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b0ccc2",
   "metadata": {},
   "source": [
    "# 4. evaluate the bigram performance with maximum likelihood estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6d8814fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-559951.5625\n",
      "nll=559951.5625\n",
      "nll/n=2.4543561935424805\n"
     ]
    }
   ],
   "source": [
    "# P is the parameter of bigram launguage model\n",
    "# evaluate the quality of the model\n",
    "\n",
    "log_likelihood = 0.0\n",
    "n=0\n",
    "\n",
    "for w in words:\n",
    "    chs = [SPECIAL_TOKEN] + list(w) + [SPECIAL_TOKEN]\n",
    "    for ch1, ch2 in zip(chs, chs[1:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        prob = P[ix1, ix2]\n",
    "        # summarize the stats model into a single number\n",
    "        # MLE: maximum likehood estimation -> log likelihood\n",
    "        # wolframalpha.com\n",
    "        # log(x) from 0 to 1\n",
    "        # log(a*b*c) => log(a) + log(b) + log(c)\n",
    "        logprob = torch.log(prob)\n",
    "        log_likelihood += logprob\n",
    "        n+=1\n",
    "#         print(f'{ch1}{ch2}: {prob:.4f} {logprob: .4f}')\n",
    "        \n",
    "# we just add all log prob\n",
    "print (f'{log_likelihood}')\n",
    "\n",
    "# negative_log_likelihood\n",
    "nll = -log_likelihood\n",
    "# the lowest is 0, the higher the value, the worse\n",
    "print(f'nll={nll}')\n",
    "# make it avearge instead of sum. hmmmm normalize\n",
    "# THIS IS THE SMALLED NEGATIVE LOG LOSS WE CAN ACHIEVE FROMDATA \n",
    "print(f'nll/n={nll/n}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "114debe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.037037037037037035"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the probability of the pair \n",
    "# if everything is the same, uniform distribution of all pairs \n",
    "# known words is training set\n",
    "1/27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ea5c4ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluate the prob of a single word, not likely!\n",
    "# jq is unlikely, just get 0, loglikilihood does not apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4bafb700",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".j: 0.0756 -2.5826\n",
      "ju: 0.0694 -2.6685\n",
      "u.: 0.0493 -3.0091\n",
      "-8.260233879089355\n",
      "nll=8.260233879089355\n",
      "nll/n=2.753411293029785\n"
     ]
    }
   ],
   "source": [
    "# bigram combination\n",
    "TEST_WORD = 'ju'\n",
    "\n",
    "log_likelihood = 0.0\n",
    "n=0\n",
    "\n",
    "for w in [TEST_WORD]:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2 in zip(chs, chs[1:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        prob = P[ix1, ix2]\n",
    "        logprob = torch.log(prob)\n",
    "        log_likelihood += logprob\n",
    "        n+=1\n",
    "        print(f'{ch1}{ch2}: {prob:.4f} {logprob: .4f}')\n",
    "        \n",
    "print (f'{log_likelihood}')\n",
    "\n",
    "nll = -log_likelihood\n",
    "print(f'nll={nll}')\n",
    "print(f'nll/n={nll/n}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6d670a86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".j: 0.0756 -2.5826\n",
      "ju: 0.0694 -2.6685\n",
      "uq: 0.0035 -5.6611\n",
      "q.: 0.0970 -2.3331\n",
      "-13.245343208312988\n",
      "nll=13.245343208312988\n",
      "nll/n=3.311335802078247\n"
     ]
    }
   ],
   "source": [
    "# bigram combination, more unlikely than ju\n",
    "TEST_WORD = 'juq'\n",
    "\n",
    "log_likelihood = 0.0\n",
    "n=0\n",
    "\n",
    "for w in [TEST_WORD]:\n",
    "    chs = ['.'] + list(w) + ['.']\n",
    "    for ch1, ch2 in zip(chs, chs[1:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        prob = P[ix1, ix2]\n",
    "        logprob = torch.log(prob)\n",
    "        log_likelihood += logprob\n",
    "        n+=1\n",
    "        print(f'{ch1}{ch2}: {prob:.4f} {logprob: .4f}')\n",
    "        \n",
    "print (f'{log_likelihood}')\n",
    "\n",
    "nll = -log_likelihood\n",
    "print(f'nll={nll}')\n",
    "print(f'nll/n={nll/n}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f668df",
   "metadata": {},
   "source": [
    "# 5. train a neural net for bigram "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f1451d5",
   "metadata": {},
   "source": [
    "goal is to \n",
    "- maximize the likelihood of the data  w.r.t. model parameters (stat modeling), the probability learned\n",
    "- equivalent to maximize log likelihood\n",
    "- equivalent to minimize negative log likelihood\n",
    "- equivalent to minimize average negative log likelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "235ae237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SPECIAL_TOKEN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "852be26b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ". e\n",
      "e m\n",
      "m m\n",
      "m a\n",
      "a .\n"
     ]
    }
   ],
   "source": [
    "# create training set of all bigrams\n",
    "\n",
    "xs, ys = [], []\n",
    "\n",
    "for w in words[:1]:\n",
    "    chs = [SPECIAL_TOKEN] + list(w) + [SPECIAL_TOKEN]\n",
    "    for ch1, ch2 in zip(chs, chs[1:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        print (ch1, ch2)\n",
    "        xs.append(ix1)\n",
    "        ys.append(ix2)\n",
    "\n",
    "# torch.Tensor or torch.tensor \n",
    "# hmmm\n",
    "xs = torch.tensor(xs)\n",
    "ys = torch.tensor(ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "16dfa986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xs.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9a1134b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0,  5, 13, 13,  1])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8f56939b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 5, 13, 13,  1,  0])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6a4c529f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0],\n",
       "        [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# input 0, output is 5\n",
    "# input 5, output is 13\n",
    "# ...\n",
    "\n",
    "# use one hot encodding to pass in integer tensor\n",
    "\n",
    "import torch.nn.functional as F\n",
    "# it will know the desired output is 27\n",
    "xenc = F.one_hot(xs, num_classes=27)\n",
    "xenc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "af259cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 27])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xenc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "7874f29f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1233fc710>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(xenc)\n",
    "# each row is one-hot, a bit is 1, everthing else is 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "895d2cfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# still int, one_hot\n",
    "# cannot change it to dtype float 32\n",
    "xenc.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "53b03a66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cast this tensor to float \n",
    "xenc = xenc.float()\n",
    "xenc.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "edf347ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "W = torch.randn??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "059a2dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "W = torch.randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "af608a12",
   "metadata": {},
   "outputs": [],
   "source": [
    "W = torch.randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "35afb761",
   "metadata": {},
   "outputs": [],
   "source": [
    "W = torch.randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "5eda1166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.4877],\n",
       "        [-1.4306],\n",
       "        [ 1.4439],\n",
       "        [ 0.7164],\n",
       "        [-3.1612],\n",
       "        [ 0.7941],\n",
       "        [-1.6766],\n",
       "        [-1.3760],\n",
       "        [-0.2385],\n",
       "        [-0.5276],\n",
       "        [-0.7115],\n",
       "        [ 0.2031],\n",
       "        [-0.1815],\n",
       "        [ 0.5241],\n",
       "        [-0.8217],\n",
       "        [ 0.7072],\n",
       "        [-0.8245],\n",
       "        [-0.5039],\n",
       "        [-1.3716],\n",
       "        [-0.0141],\n",
       "        [ 0.5177],\n",
       "        [ 0.4381],\n",
       "        [ 0.2916],\n",
       "        [-2.1168],\n",
       "        [-0.0626],\n",
       "        [-0.2190],\n",
       "        [-1.7570]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normal distribution (0,1)\n",
    "W = torch.randn([27,1])\n",
    "W"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e34a0b",
   "metadata": {},
   "source": [
    "## matrix multiplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3506abad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.4877],\n",
       "        [ 0.7941],\n",
       "        [ 0.5241],\n",
       "        [ 0.5241],\n",
       "        [-1.4306]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X*W matrix multiplication\n",
    "# [5, 27] @ [27, 1] = [5, 1]\n",
    "\n",
    "xenc @ W\n",
    "\n",
    "# AB = A.mm(B)\n",
    "# AB = torch.mm(A, B)\n",
    "# AB = torch.matmul(A, B)\n",
    "# AB = A @ B  # Python 3.5+ only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b8ee8704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 6.7366e-01, -1.6251e+00,  3.3043e-02,  1.6559e+00, -5.6355e-01,\n",
       "          6.7864e-01,  8.1665e-02,  7.5858e-01,  1.5930e+00, -6.3549e-03,\n",
       "         -1.6644e+00, -2.5768e+00, -1.2786e+00,  3.3031e-01, -4.5170e-01,\n",
       "         -7.2214e-01, -1.8827e+00,  7.8235e-01,  1.1566e+00, -8.4164e-01,\n",
       "          8.4719e-01, -6.1289e-01, -6.2672e-01, -1.5248e+00,  1.8524e-01,\n",
       "         -5.9706e-01,  4.5993e-01],\n",
       "        [-1.9348e+00, -7.5474e-01,  1.2555e+00,  1.3161e+00, -4.5045e-01,\n",
       "         -5.7690e-01,  2.3186e-01, -2.8714e-01, -1.5449e+00, -1.2750e+00,\n",
       "          7.4916e-01,  6.0161e-01,  7.0086e-01, -1.0388e+00, -5.5108e-01,\n",
       "          4.3476e-01, -1.0778e-01,  5.5963e-01, -5.0879e-01, -7.0088e-01,\n",
       "          5.8301e-01, -1.5363e+00, -9.5728e-01,  1.1673e+00, -4.4890e-01,\n",
       "         -1.4149e+00,  2.4459e-01],\n",
       "        [ 8.5552e-01,  1.9392e-01,  5.9796e-01,  2.5886e-01, -1.0865e+00,\n",
       "         -4.1909e-01, -2.1069e+00, -4.0672e-01, -1.5447e-01, -3.1003e-01,\n",
       "         -2.6841e-01, -1.7356e+00,  8.0922e-01,  5.5645e-01, -5.2474e-01,\n",
       "          5.9820e-01,  1.0203e+00, -9.0834e-01,  3.4733e-01,  1.3715e+00,\n",
       "          1.3045e+00, -5.9457e-01, -6.1425e-01,  5.8100e-02, -6.2866e-02,\n",
       "         -3.1745e-01,  1.1547e+00],\n",
       "        [ 5.9592e-01, -1.2932e+00,  1.1758e+00, -3.1761e-01,  1.9808e-01,\n",
       "         -1.3568e+00, -5.7797e-01,  9.5890e-02,  1.6989e+00,  3.2520e-01,\n",
       "          1.5768e-01,  9.0405e-01,  8.6104e-01, -2.0646e-01, -8.5332e-01,\n",
       "         -1.4609e+00, -1.3647e-01, -1.1577e+00,  3.8683e-01, -1.2180e-01,\n",
       "         -9.9886e-01,  3.3089e-01,  1.5644e+00, -1.4327e+00,  5.5671e-01,\n",
       "          8.3331e-01, -1.4558e+00],\n",
       "        [ 4.2251e-01, -4.7857e-01, -1.1855e+00,  4.5884e-01,  1.7059e-01,\n",
       "          2.5021e+00,  9.5742e-01,  1.9329e-01,  3.9694e-02,  9.5058e-01,\n",
       "          1.5036e+00,  4.9381e-01, -1.1339e+00,  1.1239e-01, -2.7575e-01,\n",
       "         -2.3565e-01, -1.0534e+00, -2.3986e-01,  1.8142e-01, -1.0111e+00,\n",
       "         -7.6648e-01,  4.8173e-01,  8.6051e-02, -1.1486e+00,  1.4843e-01,\n",
       "          1.3736e+00, -1.8897e-01],\n",
       "        [ 1.8695e-01, -2.4639e-01, -2.6120e+00,  6.9936e-01,  1.1110e+00,\n",
       "          1.4868e+00, -3.0976e-01,  1.1148e+00, -3.6452e-01, -2.9878e-01,\n",
       "          7.2817e-01, -7.6368e-01, -9.7288e-01,  3.3623e-01, -1.1741e+00,\n",
       "         -9.0328e-01,  1.3201e-01, -9.3973e-01, -8.7832e-02, -1.6727e-02,\n",
       "         -1.6069e-01,  1.1609e+00,  1.1186e+00,  1.6045e+00, -7.9328e-01,\n",
       "          2.5878e-01, -2.3090e+00],\n",
       "        [ 1.5891e-01,  1.2129e-01,  3.7438e-01, -6.2416e-02,  1.3744e-01,\n",
       "          1.4865e+00, -1.5351e-01,  2.2391e-02,  1.0016e+00,  3.4622e-01,\n",
       "         -5.0713e-02,  1.4396e+00,  9.3556e-01, -3.2392e-01, -8.0059e-01,\n",
       "          1.4630e+00,  1.0813e+00, -4.5412e-01,  7.5783e-01,  7.5070e-02,\n",
       "          7.7848e-01,  5.3888e-01, -9.0371e-01, -1.2781e+00, -1.6092e+00,\n",
       "          1.5843e+00, -8.9792e-01],\n",
       "        [ 1.2053e+00,  2.0837e+00, -3.4478e-01, -9.1324e-01,  9.7935e-01,\n",
       "          1.4024e+00, -6.8647e-01, -7.3052e-01,  4.6044e-01, -1.8558e-01,\n",
       "         -1.4008e+00, -3.5331e-01,  1.2172e+00, -6.9884e-01, -4.6410e-01,\n",
       "          2.4232e-01, -1.1189e-01,  9.8715e-02, -6.0214e-01, -8.7609e-01,\n",
       "         -1.8004e-01, -4.7486e-01, -1.7727e-01,  1.3586e+00, -2.8319e-02,\n",
       "          8.2898e-01, -1.0118e-05],\n",
       "        [ 1.1711e+00, -7.3792e-01, -5.0166e-02, -9.2999e-01, -2.4889e+00,\n",
       "          2.1269e-01, -6.0397e-01,  2.4360e-02,  6.1304e-01, -9.0033e-01,\n",
       "         -3.3854e+00,  3.1690e-01, -1.5723e-01,  1.0576e-01,  2.9974e-01,\n",
       "          4.9790e-01, -7.2950e-01, -2.0975e-01,  4.3120e-01, -2.1985e-02,\n",
       "         -5.6910e-01,  2.0567e+00, -1.0886e+00,  2.3643e-01, -1.4496e+00,\n",
       "          1.0589e+00,  4.6330e-01],\n",
       "        [-1.5148e+00,  2.7480e-01, -8.3565e-01, -1.9388e+00,  1.4436e-02,\n",
       "         -7.6294e-01,  3.7624e-01, -8.1611e-01, -2.6823e-01, -1.3993e-01,\n",
       "         -6.1949e-01,  1.8922e-01, -1.9853e-01, -2.7180e-02,  1.4542e+00,\n",
       "         -9.0964e-01, -2.0772e+00,  1.1193e+00,  1.6884e+00, -1.5543e+00,\n",
       "         -2.5125e-01, -9.0721e-01, -6.1710e-01,  1.1257e+00,  1.0483e+00,\n",
       "          1.6801e+00, -1.0383e+00],\n",
       "        [ 1.8098e+00, -6.3203e-01,  6.2397e-01, -2.2912e-02,  3.0817e-02,\n",
       "          6.4446e-01,  6.9180e-01, -1.4723e+00, -1.2899e+00,  1.0202e+00,\n",
       "         -5.8240e-01,  1.3207e-02,  8.9164e-02,  7.9113e-01,  2.2767e-01,\n",
       "          4.3525e-01, -2.1950e-01,  2.2122e+00,  1.0683e+00,  3.7381e-01,\n",
       "         -1.6204e+00,  2.7938e-01,  8.0237e-01, -6.8072e-01, -1.4612e+00,\n",
       "          3.8040e-02, -2.9058e+00],\n",
       "        [-4.0428e-01, -8.2777e-01, -1.0235e-01, -6.6608e-01,  1.0411e+00,\n",
       "         -5.0253e-01, -1.3336e+00, -2.0359e+00, -9.1133e-01,  1.1645e+00,\n",
       "          1.2499e+00,  8.7750e-01, -2.3666e-01,  1.9865e-01,  1.5671e+00,\n",
       "         -6.2722e-01, -1.3866e+00,  1.7942e+00,  3.8908e-01,  7.6188e-01,\n",
       "         -3.6236e-01,  4.7424e-01,  8.4565e-01, -1.4394e+00, -4.5363e-01,\n",
       "          3.8641e-01, -2.0225e+00],\n",
       "        [ 5.0828e-01,  8.0430e-01, -9.1308e-01,  1.0504e+00, -3.4082e-01,\n",
       "          2.1026e+00, -5.9096e-01,  8.9654e-01, -9.2375e-01,  1.5205e+00,\n",
       "          2.5154e+00, -7.5762e-01,  8.0465e-01,  9.7785e-01, -1.0485e+00,\n",
       "          1.7111e+00,  3.7002e-01, -8.6138e-01,  2.0091e+00, -2.1850e+00,\n",
       "         -6.8967e-01,  5.3903e-01, -1.0775e+00, -3.9219e-01, -2.4099e-02,\n",
       "          5.7787e-01,  5.2209e-01],\n",
       "        [ 1.7277e+00,  1.0395e-02,  8.2521e-01,  5.5002e-01,  1.2095e+00,\n",
       "          9.5051e-01,  5.3905e-01, -8.1839e-01,  1.2746e+00,  4.5979e-01,\n",
       "          1.1903e-01,  4.4113e-01,  1.4398e+00, -8.8192e-01, -1.7236e+00,\n",
       "          3.3548e-01, -1.0976e+00,  1.2797e+00, -1.5019e-01, -1.0081e+00,\n",
       "          3.3529e-01,  7.0154e-01, -3.8981e-01, -1.5657e+00, -1.4764e+00,\n",
       "         -1.7376e+00,  7.1248e-01],\n",
       "        [ 2.6856e-01, -1.7930e+00,  1.6102e+00,  5.6291e-01,  1.1752e+00,\n",
       "          7.9614e-01,  6.0347e-02,  8.6508e-01, -5.2358e-01, -3.3562e-01,\n",
       "         -6.9178e-02,  1.7077e+00, -3.3839e-01,  1.1232e+00, -3.2236e-01,\n",
       "          5.9799e-01, -2.1598e+00,  1.8946e-01, -2.2041e-01,  6.8097e-01,\n",
       "         -2.0035e+00, -2.7089e-01,  9.4519e-01,  1.3800e-01, -7.5490e-01,\n",
       "          1.4426e+00, -1.4576e-01],\n",
       "        [-1.2890e+00, -5.6142e-01,  2.2526e-02, -1.3520e+00,  2.9318e+00,\n",
       "          3.4027e-02,  1.6249e+00,  2.0974e+00, -1.3137e+00,  5.1168e-01,\n",
       "          5.1861e-01, -9.8732e-02, -2.0337e-01, -1.1845e+00,  4.8448e-01,\n",
       "          9.6336e-01,  1.1468e+00,  6.6260e-02, -7.0931e-01,  8.0811e-01,\n",
       "          5.4651e-01, -6.7645e-01, -1.0663e+00, -2.1309e+00, -2.6915e+00,\n",
       "          2.0674e-01,  3.1117e-01],\n",
       "        [-8.7364e-02,  2.8807e-01,  8.6676e-01, -1.2226e+00, -2.0274e+00,\n",
       "          7.9849e-01,  1.1325e+00,  6.7774e-01, -2.9776e-02,  3.7495e-01,\n",
       "         -1.4242e+00,  5.1585e-01, -4.6582e-01,  5.3997e-01,  1.5799e+00,\n",
       "         -2.3044e-01, -1.4685e+00, -1.8574e-01,  8.8535e-01, -3.8044e-01,\n",
       "         -2.7406e-02, -1.3049e-01, -1.2182e+00, -2.0039e+00, -5.1911e-01,\n",
       "          1.1293e+00,  6.5212e-02],\n",
       "        [ 8.2069e-01,  3.8598e-01,  4.9207e-01, -3.4454e+00, -5.2592e-01,\n",
       "         -5.0446e-01, -1.5951e+00, -5.1583e-01, -1.4402e+00, -3.1281e-01,\n",
       "         -5.5371e-01,  8.8093e-01,  1.3749e+00, -1.8441e-01,  8.8916e-01,\n",
       "         -1.4474e+00,  7.0724e-02,  8.5681e-01, -2.3074e+00, -2.9328e+00,\n",
       "         -1.6311e-01,  3.5729e-01, -4.5497e-01, -8.8182e-01,  3.2250e-01,\n",
       "         -1.7007e-01,  7.0106e-01],\n",
       "        [ 5.9563e-01,  8.1328e-01, -1.1920e+00, -1.0808e-01, -3.0865e-02,\n",
       "          3.2732e-01, -5.1413e-01,  9.4256e-01, -1.9303e+00,  2.6270e-01,\n",
       "         -1.3823e+00, -6.6171e-01, -2.7330e-01, -1.1252e+00, -1.6105e-01,\n",
       "         -1.2618e+00,  2.9021e-01,  6.2307e-01,  3.4974e-01,  9.3941e-01,\n",
       "         -1.6548e+00,  1.5351e+00,  1.0708e+00, -1.8975e+00, -1.4047e+00,\n",
       "         -2.8294e+00, -1.4771e+00],\n",
       "        [-1.4408e-01, -6.5134e-01,  3.0794e-01,  6.0442e-01, -1.8332e+00,\n",
       "          7.6490e-01,  7.8579e-01, -6.6934e-01, -1.1751e+00,  2.6339e+00,\n",
       "         -2.2631e-01,  1.1130e+00, -4.9176e-01,  1.3079e+00, -3.2231e-01,\n",
       "          1.6362e-01,  2.3703e-01, -1.3086e+00, -4.4626e-01,  2.3344e+00,\n",
       "          9.2601e-01,  1.4286e-01, -2.2446e-01, -3.2011e-01, -4.0894e-01,\n",
       "          1.2481e-01, -4.4619e-01],\n",
       "        [ 7.1376e-01, -9.5865e-01,  3.3858e-02,  1.9940e+00,  2.8355e-01,\n",
       "         -7.6035e-01,  5.9378e-02, -2.9588e-01,  1.6448e+00,  6.1379e-01,\n",
       "          4.4148e-01,  1.9789e-01,  1.0039e+00, -4.3889e-01,  6.8278e-01,\n",
       "         -2.1814e+00, -7.3512e-01,  2.7261e-02,  1.1956e-01,  2.0176e+00,\n",
       "          1.5915e+00,  9.5286e-01,  7.7493e-01, -1.5108e+00,  6.2177e-01,\n",
       "         -9.4888e-02, -7.8738e-01],\n",
       "        [ 3.7958e-01, -7.1091e-01,  2.3584e-01, -8.0025e-01, -1.5559e-01,\n",
       "         -3.1500e-01,  8.9816e-01,  5.0782e-01,  4.0067e-01,  2.6006e+00,\n",
       "          1.5152e+00, -8.2036e-01,  1.4834e+00, -3.4415e-01, -4.6556e-01,\n",
       "          2.0659e+00,  1.0475e+00, -1.8154e+00, -9.1862e-01,  9.6945e-01,\n",
       "          1.4039e+00, -1.0116e-01, -1.1514e+00,  2.4344e+00, -6.3829e-01,\n",
       "          1.9498e+00,  1.8181e+00],\n",
       "        [-2.3019e-01,  6.4471e-02, -8.7969e-01,  4.9525e-01, -1.4215e+00,\n",
       "          8.8811e-01, -8.1021e-01,  5.8585e-01,  1.4072e+00,  5.5616e-01,\n",
       "          4.1052e-01, -1.0400e+00, -1.9184e+00,  2.3957e-01, -9.7865e-01,\n",
       "          4.6212e-01,  6.2121e-01, -1.4015e+00, -2.0688e-01, -2.3406e-01,\n",
       "         -3.5601e-02,  4.7945e-01,  4.3024e-01, -5.6733e-01,  1.7954e+00,\n",
       "         -1.1243e-01, -6.2580e-01],\n",
       "        [-6.0013e-01, -3.0229e-01, -1.4313e-01,  3.6234e-01,  1.0747e+00,\n",
       "          1.2112e+00,  7.0896e-01,  1.2028e+00, -1.6324e+00,  1.4475e+00,\n",
       "         -4.2763e-01,  9.5844e-02, -2.6225e-01, -1.9063e+00,  3.1461e-01,\n",
       "         -2.2683e-01,  6.9043e-01,  8.5755e-01, -3.2605e-01, -1.7437e+00,\n",
       "          9.1532e-01,  5.5016e-01,  1.5163e-02,  3.3318e-02,  9.8207e-01,\n",
       "         -1.6814e+00, -2.6592e-01],\n",
       "        [-4.1348e-01, -3.5500e-01,  4.6218e-01, -2.3696e-01,  9.0620e-01,\n",
       "          3.0214e-01,  2.2964e-01, -2.2136e+00,  1.8205e-01, -1.0420e+00,\n",
       "         -5.8272e-01, -1.0897e+00, -9.1334e-01,  9.3932e-01,  3.8619e-01,\n",
       "         -1.8673e+00,  1.7994e-01, -9.7477e-01, -1.4137e+00, -1.3256e+00,\n",
       "         -2.1090e-01,  9.3449e-01, -8.8768e-01, -1.8150e-01, -1.8996e+00,\n",
       "          1.9117e-01, -5.4307e-02],\n",
       "        [-5.6649e-01, -1.9077e-01,  8.7103e-01, -3.2863e-01,  5.7662e-01,\n",
       "          6.1658e-01,  5.1068e-01, -6.4802e-01, -9.4705e-01, -5.1729e-01,\n",
       "         -9.7946e-01,  1.2867e+00, -8.3852e-01,  3.0814e-01, -1.1346e+00,\n",
       "         -1.1359e-01, -4.5308e-01,  2.6874e+00, -1.5490e-01,  8.1863e-01,\n",
       "          5.0291e-01,  1.4398e+00, -4.0718e-01,  1.2517e+00,  1.6919e-01,\n",
       "         -8.4097e-01, -1.6242e+00],\n",
       "        [ 2.5005e+00,  8.9059e-02,  1.9510e+00,  4.7837e-01,  4.4233e-01,\n",
       "         -1.6209e+00,  1.9583e+00, -6.5056e-01, -3.6724e-01, -1.3535e-01,\n",
       "         -6.2131e-02, -7.2377e-01, -5.2916e-01, -1.0447e+00, -1.1420e+00,\n",
       "          2.9941e-01,  3.0026e-01, -1.6693e-01,  8.9528e-01,  1.8431e+00,\n",
       "          2.2649e+00,  1.3751e+00, -1.0478e+00, -1.4452e+00, -1.3361e+00,\n",
       "         -7.7888e-01,  7.1542e-01]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W = torch.randn([27,27])\n",
    "W\n",
    "# 1st hidden layer, 27 neurons, each gets from 27 input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e3a50541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.6737, -1.6251,  0.0330,  1.6559, -0.5635,  0.6786,  0.0817,  0.7586,\n",
       "          1.5930, -0.0064, -1.6644, -2.5768, -1.2786,  0.3303, -0.4517, -0.7221,\n",
       "         -1.8827,  0.7824,  1.1566, -0.8416,  0.8472, -0.6129, -0.6267, -1.5248,\n",
       "          0.1852, -0.5971,  0.4599],\n",
       "        [ 0.1869, -0.2464, -2.6120,  0.6994,  1.1110,  1.4868, -0.3098,  1.1148,\n",
       "         -0.3645, -0.2988,  0.7282, -0.7637, -0.9729,  0.3362, -1.1741, -0.9033,\n",
       "          0.1320, -0.9397, -0.0878, -0.0167, -0.1607,  1.1609,  1.1186,  1.6045,\n",
       "         -0.7933,  0.2588, -2.3090],\n",
       "        [ 1.7277,  0.0104,  0.8252,  0.5500,  1.2095,  0.9505,  0.5391, -0.8184,\n",
       "          1.2746,  0.4598,  0.1190,  0.4411,  1.4398, -0.8819, -1.7236,  0.3355,\n",
       "         -1.0976,  1.2797, -0.1502, -1.0081,  0.3353,  0.7015, -0.3898, -1.5657,\n",
       "         -1.4764, -1.7376,  0.7125],\n",
       "        [ 1.7277,  0.0104,  0.8252,  0.5500,  1.2095,  0.9505,  0.5391, -0.8184,\n",
       "          1.2746,  0.4598,  0.1190,  0.4411,  1.4398, -0.8819, -1.7236,  0.3355,\n",
       "         -1.0976,  1.2797, -0.1502, -1.0081,  0.3353,  0.7015, -0.3898, -1.5657,\n",
       "         -1.4764, -1.7376,  0.7125],\n",
       "        [-1.9348, -0.7547,  1.2555,  1.3161, -0.4504, -0.5769,  0.2319, -0.2871,\n",
       "         -1.5449, -1.2750,  0.7492,  0.6016,  0.7009, -1.0388, -0.5511,  0.4348,\n",
       "         -0.1078,  0.5596, -0.5088, -0.7009,  0.5830, -1.5363, -0.9573,  1.1673,\n",
       "         -0.4489, -1.4149,  0.2446]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [27, 27] output\n",
    "xenc @ W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "6fb830bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-0.8819)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dot product of the 3rd input, for the 13th neuron weight? \n",
    "\n",
    "(xenc @ W) [3,13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3fc137dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "        0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xenc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "22ffa446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.3303, -1.0388,  0.5564, -0.2065,  0.1124,  0.3362, -0.3239, -0.6988,\n",
       "         0.1058, -0.0272,  0.7911,  0.1986,  0.9778, -0.8819,  1.1232, -1.1845,\n",
       "         0.5400, -0.1844, -1.1252,  1.3079, -0.4389, -0.3442,  0.2396, -1.9063,\n",
       "         0.9393,  0.3081, -1.0447])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W[:,13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d9cf09a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-0.8819)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dot product: element wise and then sum up\n",
    "(xenc[3] * W[:,13]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "da1fd3db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1.9614, 0.1969, 1.0336, 5.2378, 0.5692, 1.9712, 1.0851, 2.1352, 4.9183,\n",
       "         0.9937, 0.1893, 0.0760, 0.2784, 1.3914, 0.6365, 0.4857, 0.1522, 2.1866,\n",
       "         3.1791, 0.4310, 2.3331, 0.5418, 0.5343, 0.2177, 1.2035, 0.5504, 1.5840],\n",
       "        [1.2056, 0.7816, 0.0734, 2.0125, 3.0375, 4.4231, 0.7336, 3.0489, 0.6945,\n",
       "         0.7417, 2.0713, 0.4659, 0.3780, 1.3997, 0.3091, 0.4052, 1.1411, 0.3907,\n",
       "         0.9159, 0.9834, 0.8516, 3.1928, 3.0605, 4.9752, 0.4524, 1.2954, 0.0994],\n",
       "        [5.6278, 1.0104, 2.2824, 1.7333, 3.3519, 2.5870, 1.7144, 0.4411, 3.5774,\n",
       "         1.5837, 1.1264, 1.5545, 4.2200, 0.4140, 0.1784, 1.3986, 0.3337, 3.5957,\n",
       "         0.8605, 0.3649, 1.3983, 2.0169, 0.6772, 0.2090, 0.2285, 0.1759, 2.0390],\n",
       "        [5.6278, 1.0104, 2.2824, 1.7333, 3.3519, 2.5870, 1.7144, 0.4411, 3.5774,\n",
       "         1.5837, 1.1264, 1.5545, 4.2200, 0.4140, 0.1784, 1.3986, 0.3337, 3.5957,\n",
       "         0.8605, 0.3649, 1.3983, 2.0169, 0.6772, 0.2090, 0.2285, 0.1759, 2.0390],\n",
       "        [0.1445, 0.4701, 3.5097, 3.7287, 0.6373, 0.5616, 1.2609, 0.7504, 0.2133,\n",
       "         0.2794, 2.1152, 1.8251, 2.0155, 0.3539, 0.5763, 1.5446, 0.8978, 1.7500,\n",
       "         0.6012, 0.4962, 1.7914, 0.2152, 0.3839, 3.2133, 0.6383, 0.2430, 1.2771]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# xenc: input layer\n",
    "# W: 1st hidden layer , single layer lol\n",
    "# output -> prob, weights come from randn\n",
    "# we want count, which then can be normalized to get probability\n",
    "# W = log count\n",
    "# log count => exp (x) to get count \n",
    "\n",
    "# element wise make it bigger exponetiate\n",
    "(xenc @ W).exp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e8f1ce57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.0544, 0.0055, 0.0287, 0.1452, 0.0158, 0.0546, 0.0301, 0.0592, 0.1363,\n",
       "         0.0275, 0.0052, 0.0021, 0.0077, 0.0386, 0.0176, 0.0135, 0.0042, 0.0606,\n",
       "         0.0881, 0.0119, 0.0647, 0.0150, 0.0148, 0.0060, 0.0334, 0.0153, 0.0439],\n",
       "        [0.0308, 0.0200, 0.0019, 0.0514, 0.0776, 0.1130, 0.0187, 0.0779, 0.0177,\n",
       "         0.0190, 0.0529, 0.0119, 0.0097, 0.0358, 0.0079, 0.0104, 0.0292, 0.0100,\n",
       "         0.0234, 0.0251, 0.0218, 0.0816, 0.0782, 0.1271, 0.0116, 0.0331, 0.0025],\n",
       "        [0.1259, 0.0226, 0.0511, 0.0388, 0.0750, 0.0579, 0.0384, 0.0099, 0.0800,\n",
       "         0.0354, 0.0252, 0.0348, 0.0944, 0.0093, 0.0040, 0.0313, 0.0075, 0.0804,\n",
       "         0.0193, 0.0082, 0.0313, 0.0451, 0.0151, 0.0047, 0.0051, 0.0039, 0.0456],\n",
       "        [0.1259, 0.0226, 0.0511, 0.0388, 0.0750, 0.0579, 0.0384, 0.0099, 0.0800,\n",
       "         0.0354, 0.0252, 0.0348, 0.0944, 0.0093, 0.0040, 0.0313, 0.0075, 0.0804,\n",
       "         0.0193, 0.0082, 0.0313, 0.0451, 0.0151, 0.0047, 0.0051, 0.0039, 0.0456],\n",
       "        [0.0046, 0.0149, 0.1114, 0.1184, 0.0202, 0.0178, 0.0400, 0.0238, 0.0068,\n",
       "         0.0089, 0.0672, 0.0579, 0.0640, 0.0112, 0.0183, 0.0490, 0.0285, 0.0556,\n",
       "         0.0191, 0.0158, 0.0569, 0.0068, 0.0122, 0.1020, 0.0203, 0.0077, 0.0406]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits = xenc @ W # log counts\n",
    "counts = logits.exp() # eq to the N matrix \n",
    "\n",
    "probs = counts / counts.sum(1, keepdims=True)\n",
    "probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4c0511b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 27])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for each input, we have a prob distribution of all possible output\n",
    "probs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d8b4db7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.0544, 0.0055, 0.0287, 0.1452, 0.0158, 0.0546, 0.0301, 0.0592, 0.1363,\n",
       "        0.0275, 0.0052, 0.0021, 0.0077, 0.0386, 0.0176, 0.0135, 0.0042, 0.0606,\n",
       "        0.0881, 0.0119, 0.0647, 0.0150, 0.0148, 0.0060, 0.0334, 0.0153, 0.0439])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs[0]\n",
    "# nn assignment of how likely each char will come next\n",
    "# with W tuned, the output prob will change \n",
    "# we backpropogate with ys"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb93d13e",
   "metadata": {},
   "source": [
    "## forward pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f1171efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0,  5, 13, 13,  1])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3b33a7db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 5, 13, 13,  1,  0])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "97c0d599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 27])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "d1d6f984",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------\n",
      "bigram example 1: .e (indexes 0, 5)\n",
      "input to the neural net: 0\n",
      "output progabilities from the neural net: tensor([0.0607, 0.0100, 0.0123, 0.0042, 0.0168, 0.0123, 0.0027, 0.0232, 0.0137,\n",
      "        0.0313, 0.0079, 0.0278, 0.0091, 0.0082, 0.0500, 0.2378, 0.0603, 0.0025,\n",
      "        0.0249, 0.0055, 0.0339, 0.0109, 0.0029, 0.0198, 0.0118, 0.1537, 0.1459])\n",
      "label (the actual next char): 5\n",
      "probability assigned by the neural net to the correct next char 0.01228625513613224\n",
      "log likelihood: -4.399273872375488\n",
      "negative log likelihood: 4.399273872375488\n",
      "--------\n",
      "bigram example 2: em (indexes 5, 13)\n",
      "input to the neural net: 5\n",
      "output progabilities from the neural net: tensor([0.0290, 0.0796, 0.0248, 0.0521, 0.1989, 0.0289, 0.0094, 0.0335, 0.0097,\n",
      "        0.0301, 0.0702, 0.0228, 0.0115, 0.0181, 0.0108, 0.0315, 0.0291, 0.0045,\n",
      "        0.0916, 0.0215, 0.0486, 0.0300, 0.0501, 0.0027, 0.0118, 0.0022, 0.0472])\n",
      "label (the actual next char): 13\n",
      "probability assigned by the neural net to the correct next char 0.018050700426101685\n",
      "log likelihood: -4.014570713043213\n",
      "negative log likelihood: 4.014570713043213\n",
      "--------\n",
      "bigram example 3: mm (indexes 13, 13)\n",
      "input to the neural net: 13\n",
      "output progabilities from the neural net: tensor([0.0312, 0.0737, 0.0484, 0.0333, 0.0674, 0.0200, 0.0263, 0.0249, 0.1226,\n",
      "        0.0164, 0.0075, 0.0789, 0.0131, 0.0267, 0.0147, 0.0112, 0.0585, 0.0121,\n",
      "        0.0650, 0.0058, 0.0208, 0.0078, 0.0133, 0.0203, 0.1204, 0.0469, 0.0126])\n",
      "label (the actual next char): 13\n",
      "probability assigned by the neural net to the correct next char 0.026691533625125885\n",
      "log likelihood: -3.623408794403076\n",
      "negative log likelihood: 3.623408794403076\n",
      "--------\n",
      "bigram example 4: ma (indexes 13, 1)\n",
      "input to the neural net: 13\n",
      "output progabilities from the neural net: tensor([0.0312, 0.0737, 0.0484, 0.0333, 0.0674, 0.0200, 0.0263, 0.0249, 0.1226,\n",
      "        0.0164, 0.0075, 0.0789, 0.0131, 0.0267, 0.0147, 0.0112, 0.0585, 0.0121,\n",
      "        0.0650, 0.0058, 0.0208, 0.0078, 0.0133, 0.0203, 0.1204, 0.0469, 0.0126])\n",
      "label (the actual next char): 1\n",
      "probability assigned by the neural net to the correct next char 0.07367686182260513\n",
      "log likelihood: -2.6080665588378906\n",
      "negative log likelihood: 2.6080665588378906\n",
      "--------\n",
      "bigram example 5: a. (indexes 1, 0)\n",
      "input to the neural net: 1\n",
      "output progabilities from the neural net: tensor([0.0150, 0.0086, 0.0396, 0.0100, 0.0606, 0.0308, 0.1084, 0.0131, 0.0125,\n",
      "        0.0048, 0.1024, 0.0086, 0.0988, 0.0112, 0.0232, 0.0207, 0.0408, 0.0078,\n",
      "        0.0899, 0.0531, 0.0463, 0.0309, 0.0051, 0.0329, 0.0654, 0.0503, 0.0091])\n",
      "label (the actual next char): 0\n",
      "probability assigned by the neural net to the correct next char 0.014977526850998402\n",
      "log likelihood: -4.201204299926758\n",
      "negative log likelihood: 4.201204299926758\n",
      "======================\n",
      "average negative log likelihood, i.e. loss =  3.7693049907684326\n"
     ]
    }
   ],
   "source": [
    "g = torch.Generator().manual_seed(2147483647)\n",
    "# 27 neuron weight, each neuron receives 27 input\n",
    "# the loss depends on W\n",
    "W = torch.randn((27,27), generator=g)\n",
    "xenc = F.one_hot(xs, num_classes=27).float()\n",
    "# forward pass with the input\n",
    "logits = xenc @ W # log counts\n",
    "\n",
    "# softmax\n",
    "counts = logits.exp() # eq to the N matrix \n",
    "probs = counts / counts.sum(1, keepdims=True)\n",
    "\n",
    "# we can back propagae through W and exp (softmax)\n",
    "\n",
    "# calculate nll\n",
    "nlls = torch.zeros(5)\n",
    "\n",
    "for i in range(5):\n",
    "    # i-th bigram\n",
    "    x = xs[i].item()\n",
    "    y = ys[i].item()\n",
    "    print('--------')\n",
    "    print(f'bigram example {i+1}: {itos[x]}{itos[y]} (indexes {x}, {y})')\n",
    "    print('input to the neural net:', x)\n",
    "    print('output progabilities from the neural net:', probs[i])\n",
    "    print('label (the actual next char):', y)\n",
    "    # the probability of the real label\n",
    "    p = probs[i, y]\n",
    "    print('probability assigned by the neural net to the correct next char', p.item())\n",
    "    logp = torch.log(p)\n",
    "    print ('log likelihood:', logp.item())\n",
    "    nll = -logp\n",
    "    print ('negative log likelihood:', nll.item())\n",
    "    nlls[i] = nll\n",
    "    \n",
    "print('======================')\n",
    "print('average negative log likelihood, i.e. loss = ', nlls.mean().item())\n",
    "\n",
    "# the network currently thinsk 0.012 prob in generating e after ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0cab04",
   "metadata": {},
   "source": [
    "## backpropagation\n",
    "start with a random guess, commit to this one, and update W with gradient\n",
    "single number calculation loss "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a226ae74",
   "metadata": {},
   "outputs": [],
   "source": [
    "g = torch.Generator().manual_seed(2147483647)\n",
    "# 27 neuron weight, each neuron receives 27 input\n",
    "# the loss depends on W\n",
    "W = torch.randn((27,27), generator=g)\n",
    "xenc = F.one_hot(xs, num_classes=27).float()\n",
    "# forward pass with the input\n",
    "logits = xenc @ W # log counts\n",
    "\n",
    "# softmax\n",
    "counts = logits.exp() # eq to the N matrix \n",
    "probs = counts / counts.sum(1, keepdims=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d4b600a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0123)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we are interested in the index 5 for the 1nd example\n",
    "probs[0, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "e72b1ac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.0181)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we are interested in the index 13 for the 2nd example\n",
    "probs[1, 13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "2d84ccf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we need to pass the index to get probs\n",
    "torch.arange(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "29a64e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 5, 13, 13,  1,  0])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4c628e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.0123, 0.0181, 0.0267, 0.0737, 0.0150])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get [0,5], [1,13], [2,13], [3,1], [4,0] from probs\n",
    "# the forward pass of the correct char\n",
    "probs[torch.arange(5), ys]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "0a671953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(3.7693)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss = - probs[torch.arange(5), ys].log().mean()\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "1d3f7562",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we get the forward pass loss\n",
    "# now we will do backward pass\n",
    "\n",
    "g = torch.Generator().manual_seed(2147483647)\n",
    "# 27 neuron weight, each neuron receives 27 input\n",
    "# the loss depends on W\n",
    "# need to require grad = True to track the gradient \n",
    "W = torch.randn((27,27), generator=g, requires_grad=True)\n",
    "xenc = F.one_hot(xs, num_classes=27).float()\n",
    "# forward pass with the input\n",
    "logits = xenc @ W # log counts\n",
    "\n",
    "# softmax\n",
    "counts = logits.exp() # eq to the N matrix \n",
    "probs = counts / counts.sum(1, keepdims=True)\n",
    "loss = - probs[torch.arange(5), ys].log().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "ed54b8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.7693049907684326"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "70f0ba10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. reset all gradients to 0, or none (more efficient)\n",
    "W.grad = None\n",
    "\n",
    "# 2. backward, this build a computational grad with all the op\n",
    "loss.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "57b7a89d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.5674e+00, -2.3729e-01, -2.7385e-02, -1.1008e+00,  2.8588e-01,\n",
       "         -2.9643e-02, -1.5471e+00,  6.0489e-01,  7.9136e-02,  9.0462e-01,\n",
       "         -4.7125e-01,  7.8682e-01, -3.2843e-01, -4.3297e-01,  1.3729e+00,\n",
       "          2.9334e+00,  1.5618e+00, -1.6261e+00,  6.7716e-01, -8.4039e-01,\n",
       "          9.8488e-01, -1.4837e-01, -1.4795e+00,  4.4830e-01, -7.0730e-02,\n",
       "          2.4968e+00,  2.4448e+00],\n",
       "        [-6.7006e-01, -1.2199e+00,  3.0314e-01, -1.0725e+00,  7.2762e-01,\n",
       "          5.1114e-02,  1.3095e+00, -8.0220e-01, -8.5042e-01, -1.8068e+00,\n",
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       "         -3.4762e-01,  3.3244e-01, -1.3263e+00,  1.1224e+00,  5.9641e-01,\n",
       "          4.5846e-01,  5.4011e-02, -1.7400e+00,  1.1560e-01,  8.0319e-01,\n",
       "          5.4108e-01, -1.1646e+00],\n",
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       "         -7.2390e-01,  1.7507e+00,  1.7530e-01,  9.9280e-01, -6.2787e-01,\n",
       "          7.7023e-02, -1.1641e+00],\n",
       "        [ 1.2473e+00, -2.7061e-01, -1.3635e+00,  1.3066e+00,  3.2307e-01,\n",
       "          1.0358e+00, -8.6249e-01, -1.2575e+00,  9.4180e-01, -1.3257e+00,\n",
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       "         -8.7462e-01, -2.9771e-01, -1.3707e+00,  1.1500e-01, -1.0188e+00,\n",
       "         -8.3777e-01, -2.1057e+00, -2.6044e-01, -1.7149e+00, -3.3787e-01,\n",
       "         -1.8263e+00, -8.3897e-01],\n",
       "        [-1.5723e+00,  4.5795e-01, -5.6533e-01,  5.4281e-01,  1.7549e-01,\n",
       "         -2.2901e+00, -7.0928e-01, -2.9283e-01, -2.1803e+00,  7.9311e-02,\n",
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       "         -1.6335e+00,  3.8761e-01],\n",
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       "         -2.1259e+00,  9.6041e-01],\n",
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       "         -1.2464e+00,  6.8305e-01, -3.9455e-01,  1.4388e-02,  5.7216e-01,\n",
       "          8.6726e-01,  6.3149e-01],\n",
       "        [-1.2230e+00, -2.1286e-01,  5.0950e-01,  3.2713e-01,  1.9661e+00,\n",
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       "         -4.9578e-01,  1.2648e+00,  1.4625e+00,  1.1199e+00,  9.9539e-01,\n",
       "         -1.2353e+00,  7.3818e-01,  8.1415e-01, -7.3806e-01,  5.6714e-01,\n",
       "         -1.4601e+00, -2.4780e-01,  8.8282e-01, -8.1004e-02, -9.5299e-01,\n",
       "         -4.8838e-01, -7.3712e-01],\n",
       "        [ 7.0609e-01, -1.9295e-01,  1.2348e+00,  3.3308e-01,  1.3283e+00,\n",
       "         -1.0921e+00, -8.3952e-01,  1.9098e-01, -7.1750e-01, -3.8668e-01,\n",
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       "         -1.4367e-01, -2.7737e-01,  1.1634e+00, -6.6910e-01,  6.4918e-01,\n",
       "          5.8243e-01,  1.9264e+00, -3.7846e-01,  7.9577e-03,  5.1068e-01,\n",
       "          7.5927e-01, -1.6086e+00],\n",
       "        [-1.6065e-01,  1.3784e+00, -2.7804e-01,  2.0710e-01,  1.0033e+00,\n",
       "         -5.9772e-01, -3.9771e-01, -1.2801e+00,  9.2445e-02,  1.0526e-01,\n",
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       "         -1.5100e+00,  1.0930e+00,  1.0797e+00, -8.6681e-02,  1.3423e+00,\n",
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       "         -6.4308e-02, -8.5528e-01],\n",
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       "         -1.2773e-01, -2.5679e-01],\n",
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       "         -1.8798e-01, -1.2647e+00],\n",
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       "         -1.0786e+00, -1.8384e-01,  7.1622e-01,  1.8175e-01,  1.1053e+00,\n",
       "          1.7003e+00, -1.6965e-01,  1.6293e-01,  1.3413e+00, -2.6301e-01,\n",
       "         -7.5521e-01,  8.1911e-01],\n",
       "        [ 7.4140e-01, -5.8787e-01, -4.6505e-01,  5.3112e-02,  2.2190e+00,\n",
       "         -3.5158e-01,  3.6381e-01,  2.5769e+00,  1.4544e+00, -6.1003e-01,\n",
       "         -5.9961e-01, -5.8392e-01, -1.8104e-02, -9.5177e-01, -9.6400e-01,\n",
       "         -2.8183e-01,  1.0597e+00, -7.2370e-01,  1.4755e-01, -3.2667e-01,\n",
       "          2.4958e+00,  1.1088e+00, -8.5476e-01,  1.8443e+00, -1.3881e-01,\n",
       "          1.3096e+00, -2.5802e-01],\n",
       "        [ 1.0669e+00,  2.1363e-01, -7.6603e-01, -1.6977e+00, -1.5023e-01,\n",
       "         -5.2150e-01, -6.3730e-01,  2.6214e-01,  7.6539e-03,  1.3067e+00,\n",
       "         -6.3482e-01, -1.1042e-04, -6.6158e-01,  1.4723e-01, -6.6036e-02,\n",
       "          5.2851e-01,  5.7950e-01,  2.1438e-01,  9.2200e-01,  5.2919e-01,\n",
       "          7.7070e-01,  4.2899e-01,  3.4330e-01,  2.0698e+00,  1.3405e+00,\n",
       "         -2.1746e-01,  8.6273e-01]], requires_grad=True)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "803e8225",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,\n",
       "          0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,\n",
       "          0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,\n",
       "          0.0000,  0.0000,  0.0000]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W.grad\n",
    "# the influence of that weight on the loss function\n",
    "# positive 0.0121, means the loss will be bigger "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "d12bee6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([27, 27])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W.grad.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "5a6520df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use W.grad to update the weight\n",
    "# we only have 1 param\n",
    "\n",
    "W.data += -0.2 * W.grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "fcc8a862",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8476314544677734"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W = torch.randn((27,27), generator=g, requires_grad=True)\n",
    "xenc = F.one_hot(xs, num_classes=27).float()\n",
    "# forward pass with the input\n",
    "logits = xenc @ W # log counts\n",
    "\n",
    "# softmax\n",
    "counts = logits.exp() # eq to the N matrix \n",
    "probs = counts / counts.sum(1, keepdims=True)\n",
    "loss = - probs[torch.arange(5), ys].log().mean()\n",
    "# loss will get smaller \n",
    "loss.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6f2b1688",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7693049907684326\n",
      "3.6690573692321777\n",
      "3.5698916912078857\n",
      "3.47186541557312\n",
      "3.375035047531128\n",
      "3.2794578075408936\n",
      "3.185190439224243\n",
      "3.092288017272949\n",
      "3.0008041858673096\n",
      "2.9107913970947266\n",
      "2.8222999572753906\n",
      "2.735377788543701\n",
      "2.650071859359741\n",
      "2.566426992416382\n",
      "2.484485149383545\n",
      "2.4042882919311523\n",
      "2.3258750438690186\n",
      "2.249284029006958\n",
      "2.1745505332946777\n",
      "2.101710319519043\n",
      "2.0307953357696533\n",
      "1.9618362188339233\n",
      "1.8948619365692139\n",
      "1.8298975229263306\n",
      "1.7669651508331299\n",
      "1.7060835361480713\n",
      "1.6472663879394531\n",
      "1.5905228853225708\n",
      "1.5358566045761108\n",
      "1.4832653999328613\n",
      "1.4327411651611328\n",
      "1.3842686414718628\n",
      "1.3378269672393799\n",
      "1.29338800907135\n",
      "1.2509180307388306\n",
      "1.2103769779205322\n",
      "1.1717193126678467\n",
      "1.134894609451294\n",
      "1.0998480319976807\n",
      "1.0665206909179688\n",
      "1.034851312637329\n",
      "1.0047760009765625\n",
      "0.9762290716171265\n",
      "0.9491443634033203\n",
      "0.9234551191329956\n",
      "0.8990948796272278\n",
      "0.8759980201721191\n",
      "0.8541001081466675\n",
      "0.8333381414413452\n",
      "0.8136513829231262\n"
     ]
    }
   ],
   "source": [
    "# ======== optimize with gradient descent ======== \n",
    "\n",
    "learning_rate = 0.5\n",
    "\n",
    "g = torch.Generator().manual_seed(2147483647)\n",
    "W = torch.randn((27,27), generator=g, requires_grad=True)\n",
    "xenc = F.one_hot(xs, num_classes=27).float()\n",
    "\n",
    "for i in range(50):\n",
    "    # forward pass with the input\n",
    "    logits = xenc @ W # log counts\n",
    "\n",
    "    # softmax\n",
    "    counts = logits.exp() # eq to the N matrix \n",
    "    probs = counts / counts.sum(1, keepdims=True)\n",
    "    loss = - probs[torch.arange(5), ys].log().mean()\n",
    "    \n",
    "    W.grad = None\n",
    "    loss.backward()\n",
    "    \n",
    "    # W upgrade\n",
    "    W.data += -learning_rate * W.grad\n",
    "    \n",
    "    print (loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "144646e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.758953332901001\n",
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      "2.7380728721618652\n",
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      "2.65293025970459\n",
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      "2.6253881454467773\n",
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      "2.4735772609710693\n",
      "2.4733383655548096\n",
      "2.47310471534729\n",
      "2.47287654876709\n"
     ]
    }
   ],
   "source": [
    "# ======== optimize with gradient descent with all words ======== \n",
    "\n",
    "xs, ys = [], []\n",
    "\n",
    "for w in words:\n",
    "    chs = [SPECIAL_TOKEN] + list(w) + [SPECIAL_TOKEN]\n",
    "    for ch1, ch2 in zip(chs, chs[1:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        xs.append(ix1)\n",
    "        ys.append(ix2)\n",
    "\n",
    "xs = torch.tensor(xs)\n",
    "ys = torch.tensor(ys)\n",
    "\n",
    "# number of training data, xs.shape[0]\n",
    "num = xs.nelement()\n",
    "\n",
    "# we can see as we change learning rate, it may blow up\n",
    "learning_rate = 50\n",
    "\n",
    "g = torch.Generator().manual_seed(2147483647)\n",
    "W = torch.randn((27,27), generator=g, requires_grad=True)\n",
    "\n",
    "xenc = F.one_hot(xs, num_classes=27).float()\n",
    "\n",
    "for i in range(100):\n",
    "    # forward pass with the input\n",
    "    logits = xenc @ W # log counts\n",
    "\n",
    "    # softmax\n",
    "    counts = logits.exp() # eq to the N matrix \n",
    "    probs = counts / counts.sum(1, keepdims=True)\n",
    "    loss = - probs[torch.arange(num), ys].log().mean()\n",
    "    \n",
    "    W.grad = None\n",
    "    loss.backward()\n",
    "    \n",
    "    # W upgrade\n",
    "    W.data += -learning_rate * W.grad\n",
    "    \n",
    "    # when we optimize by counting, the negative log likelihood is 2.47\n",
    "    # we achieve the same results with gradient descent without counting the bigrams \n",
    "    print (loss.item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad826c9",
   "metadata": {},
   "source": [
    "# 6. regularization\n",
    "\n",
    "In counting, we use N+1 to smooth the count matrix so that bigram with 0 count will have non-zero probability. The bigger we set the smoothing factor, the more uniform the probability is.\n",
    "\n",
    "In NN, we smooth the W by penalizing bigger W to avoid bigger W having too much weight on the model. Regularization helps the model to generalize better so that it performs better with unseen data. By incentivizing smaller weights, regularization essentially makes the model \"unlearn\" and introduces uncertainty or randomness to the learning algorithm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "91421746",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.768618583679199\n",
      "3.3788065910339355\n",
      "3.16109037399292\n",
      "3.0271859169006348\n",
      "2.9344840049743652\n",
      "2.867231607437134\n",
      "2.8166542053222656\n",
      "2.777146100997925\n",
      "2.745253801345825\n",
      "2.7188305854797363\n",
      "2.696505308151245\n",
      "2.6773722171783447\n",
      "2.6608052253723145\n",
      "2.6463515758514404\n",
      "2.633664846420288\n",
      "2.622471570968628\n",
      "2.6125476360321045\n",
      "2.6037068367004395\n",
      "2.595794916152954\n",
      "2.5886807441711426\n",
      "2.5822560787200928\n",
      "2.576429843902588\n",
      "2.5711236000061035\n",
      "2.566272735595703\n",
      "2.5618226528167725\n",
      "2.5577263832092285\n",
      "2.5539441108703613\n",
      "2.5504424571990967\n",
      "2.5471925735473633\n",
      "2.5441696643829346\n",
      "2.5413525104522705\n",
      "2.538721799850464\n",
      "2.536262035369873\n",
      "2.5339579582214355\n",
      "2.5317976474761963\n",
      "2.5297679901123047\n",
      "2.527860164642334\n",
      "2.526063919067383\n",
      "2.5243704319000244\n",
      "2.522773027420044\n",
      "2.521263837814331\n",
      "2.519836902618408\n",
      "2.5184857845306396\n",
      "2.5172054767608643\n",
      "2.5159904956817627\n",
      "2.5148372650146484\n",
      "2.5137407779693604\n",
      "2.51269793510437\n",
      "2.511704921722412\n",
      "2.5107579231262207\n",
      "2.509854555130005\n",
      "2.5089924335479736\n",
      "2.5081682205200195\n",
      "2.507380485534668\n",
      "2.5066258907318115\n",
      "2.5059032440185547\n",
      "2.5052103996276855\n",
      "2.5045459270477295\n",
      "2.5039076805114746\n",
      "2.503295421600342\n",
      "2.502706289291382\n",
      "2.5021398067474365\n",
      "2.5015945434570312\n",
      "2.5010693073272705\n",
      "2.500563383102417\n",
      "2.500075101852417\n",
      "2.4996049404144287\n",
      "2.499150514602661\n",
      "2.4987118244171143\n",
      "2.49828839302063\n",
      "2.4978787899017334\n",
      "2.497483015060425\n",
      "2.4970998764038086\n",
      "2.4967293739318848\n",
      "2.496370315551758\n",
      "2.4960227012634277\n",
      "2.4956860542297363\n",
      "2.4953596591949463\n",
      "2.4950428009033203\n",
      "2.4947361946105957\n",
      "2.494438886642456\n",
      "2.494149684906006\n",
      "2.4938690662384033\n",
      "2.4935967922210693\n",
      "2.4933321475982666\n",
      "2.493074893951416\n",
      "2.4928252696990967\n",
      "2.492582321166992\n",
      "2.4923462867736816\n",
      "2.492116928100586\n",
      "2.4918935298919678\n",
      "2.491676092147827\n",
      "2.491464376449585\n",
      "2.491258382797241\n",
      "2.491058111190796\n",
      "2.4908623695373535\n",
      "2.4906721115112305\n",
      "2.4904870986938477\n",
      "2.4903066158294678\n",
      "2.4901301860809326\n"
     ]
    }
   ],
   "source": [
    "# ======== optimize with gradient descent with L2 reg ======== \n",
    "\n",
    "xs, ys = [], []\n",
    "\n",
    "for w in words:\n",
    "    chs = [SPECIAL_TOKEN] + list(w) + [SPECIAL_TOKEN]\n",
    "    for ch1, ch2 in zip(chs, chs[1:]):\n",
    "        ix1 = stoi[ch1]\n",
    "        ix2 = stoi[ch2]\n",
    "        xs.append(ix1)\n",
    "        ys.append(ix2)\n",
    "\n",
    "xs = torch.tensor(xs)\n",
    "ys = torch.tensor(ys)\n",
    "\n",
    "# number of training data xs.shape[0]\n",
    "num = xs.nelement()\n",
    "\n",
    "# we can see as we change learning rate, it may blow up\n",
    "learning_rate = 50\n",
    "\n",
    "# how much do we penalize bigger W\n",
    "regularization_strength = 0.01\n",
    "\n",
    "g = torch.Generator().manual_seed(2147483647)\n",
    "W = torch.randn((27,27), generator=g, requires_grad=True)\n",
    "\n",
    "xenc = F.one_hot(xs, num_classes=27).float()\n",
    "\n",
    "for i in range(100):\n",
    "    # forward pass with the input\n",
    "    logits = xenc @ W # log counts\n",
    "\n",
    "    # softmax\n",
    "    counts = logits.exp() # eq to the N matrix \n",
    "    probs = counts / counts.sum(1, keepdims=True)\n",
    "    \n",
    "    # penalize for bigger W, bigger W, bigger loss ; add a gravity force to pull W to 0\n",
    "    L2_reg = (W**2)\n",
    "    # L1_reg: absolute value\n",
    "    loss = - probs[torch.arange(num), ys].log().mean() + regularization_strength * L2_reg.mean()\n",
    "    \n",
    "    W.grad = None\n",
    "    loss.backward()\n",
    "    \n",
    "    # W upgrade\n",
    "    W.data += -learning_rate * W.grad\n",
    "    \n",
    "    print (loss.item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82b139b",
   "metadata": {},
   "source": [
    "# 7. use the trained NN to generate new names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ffb225ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mor.\n",
      "axx.\n",
      "minaymoryles.\n",
      "kondlaisah.\n",
      "anchshizarie.\n"
     ]
    }
   ],
   "source": [
    "# with count matrix\n",
    "\n",
    "g = torch.Generator().manual_seed(2147483647)\n",
    "\n",
    "for i in range(5):\n",
    "    out = []\n",
    "    ix = 0\n",
    "    while True:\n",
    "        p = P[ix]\n",
    "        \n",
    "        ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n",
    "        out.append(itos[ix])\n",
    "        if (ix==0):\n",
    "            break\n",
    "    print (''.join(out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "93f67d4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mor.\n",
      "axx.\n",
      "minaymoryles.\n",
      "kondmaisah.\n",
      "anchthizarie.\n"
     ]
    }
   ],
   "source": [
    "# with NN\n",
    "g = torch.Generator().manual_seed(2147483647)\n",
    "\n",
    "for i in range(5):\n",
    "    out = []\n",
    "    ix = 0\n",
    "    while True:\n",
    "        # prepare input data with the same encoding\n",
    "        xenc = F.one_hot(torch.tensor([ix]), num_classes=27).float()\n",
    "       \n",
    "        # forward pass\n",
    "        logits = xenc @ W\n",
    "        # softmax\n",
    "        counts = logits.exp()\n",
    "        p = counts/counts.sum(1, keepdims=True)\n",
    "        \n",
    "        # sample using predicted p\n",
    "        ix = torch.multinomial(p, num_samples=1, replacement=True, generator=g).item()\n",
    "        out.append(itos[ix])\n",
    "        if (ix==0):\n",
    "            break\n",
    "    print (''.join(out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d260092",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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