glueberry/tutorials/gensim_w2v.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word2Vec Tutorial\n",
    "[GENSIM Tutorial](https://radimrehurek.com/gensim/auto_examples/tutorials/run_word2vec.html#visualising-word-embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-30 14:30:49,470 : INFO : loading projection weights from C:\\Users\\Francesco/gensim-data\\word2vec-google-news-300\\word2vec-google-news-300.gz\n",
      "2022-05-30 14:31:31,470 : INFO : KeyedVectors lifecycle event {'msg': 'loaded (3000000, 300) matrix of type float32 from C:\\\\Users\\\\Francesco/gensim-data\\\\word2vec-google-news-300\\\\word2vec-google-news-300.gz', 'binary': True, 'encoding': 'utf8', 'datetime': '2022-05-30T14:31:31.470006', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'load_word2vec_format'}\n"
     ]
    }
   ],
   "source": [
    "import gensim.downloader as api\n",
    "wv = api.load('word2vec-google-news-300')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word 0 / 3000000 is </s>\n",
      "word 1 / 3000000 is in\n",
      "word 2 / 3000000 is for\n",
      "word 3 / 3000000 is that\n",
      "word 4 / 3000000 is is\n",
      "word 5 / 3000000 is on\n",
      "word 6 / 3000000 is ##\n",
      "word 7 / 3000000 is The\n",
      "word 8 / 3000000 is with\n",
      "word 9 / 3000000 is said\n",
      "word 10 / 3000000 is was\n",
      "word 11 / 3000000 is the\n",
      "word 12 / 3000000 is at\n",
      "word 13 / 3000000 is not\n",
      "word 14 / 3000000 is as\n",
      "word 15 / 3000000 is it\n",
      "word 16 / 3000000 is be\n",
      "word 17 / 3000000 is from\n",
      "word 18 / 3000000 is by\n",
      "word 19 / 3000000 is are\n",
      "word 20 / 3000000 is I\n",
      "word 21 / 3000000 is have\n",
      "word 22 / 3000000 is he\n",
      "word 23 / 3000000 is will\n",
      "word 24 / 3000000 is has\n",
      "word 25 / 3000000 is ####\n",
      "word 26 / 3000000 is his\n",
      "word 27 / 3000000 is an\n",
      "word 28 / 3000000 is this\n",
      "word 29 / 3000000 is or\n",
      "word 30 / 3000000 is their\n",
      "word 31 / 3000000 is who\n",
      "word 32 / 3000000 is they\n",
      "word 33 / 3000000 is but\n",
      "word 34 / 3000000 is $\n",
      "word 35 / 3000000 is had\n",
      "word 36 / 3000000 is year\n",
      "word 37 / 3000000 is were\n",
      "word 38 / 3000000 is we\n",
      "word 39 / 3000000 is more\n",
      "word 40 / 3000000 is ###\n",
      "word 41 / 3000000 is up\n",
      "word 42 / 3000000 is been\n",
      "word 43 / 3000000 is you\n",
      "word 44 / 3000000 is its\n",
      "word 45 / 3000000 is one\n",
      "word 46 / 3000000 is about\n",
      "word 47 / 3000000 is would\n",
      "word 48 / 3000000 is which\n",
      "word 49 / 3000000 is out\n",
      "word 50 / 3000000 is can\n",
      "word 51 / 3000000 is It\n",
      "word 52 / 3000000 is all\n",
      "word 53 / 3000000 is also\n",
      "word 54 / 3000000 is two\n",
      "word 55 / 3000000 is after\n",
      "word 56 / 3000000 is first\n",
      "word 57 / 3000000 is He\n",
      "word 58 / 3000000 is do\n",
      "word 59 / 3000000 is time\n",
      "word 60 / 3000000 is than\n",
      "word 61 / 3000000 is when\n",
      "word 62 / 3000000 is We\n",
      "word 63 / 3000000 is over\n",
      "word 64 / 3000000 is last\n",
      "word 65 / 3000000 is new\n",
      "word 66 / 3000000 is other\n",
      "word 67 / 3000000 is her\n",
      "word 68 / 3000000 is people\n",
      "word 69 / 3000000 is into\n",
      "word 70 / 3000000 is In\n",
      "word 71 / 3000000 is our\n",
      "word 72 / 3000000 is there\n",
      "word 73 / 3000000 is A\n",
      "word 74 / 3000000 is she\n",
      "word 75 / 3000000 is could\n",
      "word 76 / 3000000 is just\n",
      "word 77 / 3000000 is years\n",
      "word 78 / 3000000 is some\n",
      "word 79 / 3000000 is U.S.\n",
      "word 80 / 3000000 is three\n",
      "word 81 / 3000000 is million\n",
      "word 82 / 3000000 is them\n",
      "word 83 / 3000000 is what\n",
      "word 84 / 3000000 is But\n",
      "word 85 / 3000000 is so\n",
      "word 86 / 3000000 is no\n",
      "word 87 / 3000000 is like\n",
      "word 88 / 3000000 is if\n",
      "word 89 / 3000000 is only\n",
      "word 90 / 3000000 is percent\n",
      "word 91 / 3000000 is get\n",
      "word 92 / 3000000 is did\n",
      "word 93 / 3000000 is him\n",
      "word 94 / 3000000 is game\n",
      "word 95 / 3000000 is back\n",
      "word 96 / 3000000 is because\n",
      "word 97 / 3000000 is now\n",
      "word 98 / 3000000 is #.#\n",
      "word 99 / 3000000 is before\n"
     ]
    }
   ],
   "source": [
    "for index, word in enumerate(wv.index_to_key):\n",
    "    if index==100:\n",
    "        break\n",
    "    print(f'word {index} / {len(wv.index_to_key)} is {word}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec_king = wv['queen']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The word 'cameroon' does not appear in this model\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    vec_cameroon = wv['cameroon']\n",
    "except KeyError:\n",
    "    print(\"The word 'cameroon' does not appear in this model\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'car'\t'minivan'\t0.69\n",
      "'car'\t'bicycle'\t0.54\n",
      "'car'\t'airplane'\t0.42\n",
      "'car'\t'cereal'\t0.14\n",
      "'car'\t'communism'\t0.06\n"
     ]
    }
   ],
   "source": [
    "pairs = [\n",
    "    ('car', 'minivan'),\n",
    "    ('car', 'bicycle'),\n",
    "    ('car', 'airplane'),\n",
    "    ('car', 'cereal'),\n",
    "    ('car', 'communism')\n",
    "]\n",
    "\n",
    "for w1, w2 in pairs:\n",
    "    print('%r\\t%r\\t%.2f' % (w1, w2, wv.similarity(w1, w2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('bicycle', 0.7798016667366028), ('scooter', 0.7793240547180176), ('motorcycle', 0.7453441023826599), ('bikes', 0.7298908233642578), ('vehicle', 0.6923801302909851)]\n"
     ]
    }
   ],
   "source": [
    "print(wv.most_similar(positive=['car', 'bike'], topn=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jar\n"
     ]
    }
   ],
   "source": [
    "print(wv.doesnt_match(\n",
    "    ['fire', 'land', 'water', 'sea', 'air', 'jar']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train your own model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.test.utils import datapath\n",
    "from gensim import utils\n",
    "\n",
    "class MyCorpus:\n",
    "    \"\"\" An iterator that yields sentences (list of str)\"\"\"\n",
    "\n",
    "    def __iter__(self):\n",
    "        corpus_path = datapath('lee_background.cor')\n",
    "        for line in open(corpus_path):\n",
    "            # assume there's one document per line, tokens separated by whitespace\n",
    "            yield utils.simple_preprocess(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-30 14:47:56,513 : INFO : collecting all words and their counts\n",
      "2022-05-30 14:47:56,515 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
      "2022-05-30 14:47:56,584 : INFO : collected 6981 word types from a corpus of 58152 raw words and 300 sentences\n",
      "2022-05-30 14:47:56,585 : INFO : Creating a fresh vocabulary\n",
      "2022-05-30 14:47:56,590 : INFO : Word2Vec lifecycle event {'msg': 'effective_min_count=5 retains 1750 unique words (25.07% of original 6981, drops 5231)', 'datetime': '2022-05-30T14:47:56.590672', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'prepare_vocab'}\n",
      "2022-05-30 14:47:56,591 : INFO : Word2Vec lifecycle event {'msg': 'effective_min_count=5 leaves 49335 word corpus (84.84% of original 58152, drops 8817)', 'datetime': '2022-05-30T14:47:56.591685', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'prepare_vocab'}\n",
      "2022-05-30 14:47:56,599 : INFO : deleting the raw counts dictionary of 6981 items\n",
      "2022-05-30 14:47:56,602 : INFO : sample=0.001 downsamples 51 most-common words\n",
      "2022-05-30 14:47:56,602 : INFO : Word2Vec lifecycle event {'msg': 'downsampling leaves estimated 35935.33721568072 word corpus (72.8%% of prior 49335)', 'datetime': '2022-05-30T14:47:56.602641', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'prepare_vocab'}\n",
      "2022-05-30 14:47:56,619 : INFO : estimated required memory for 1750 words and 100 dimensions: 2275000 bytes\n",
      "2022-05-30 14:47:56,621 : INFO : resetting layer weights\n",
      "2022-05-30 14:47:56,623 : INFO : Word2Vec lifecycle event {'update': False, 'trim_rule': 'None', 'datetime': '2022-05-30T14:47:56.623586', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'build_vocab'}\n",
      "2022-05-30 14:47:56,624 : INFO : Word2Vec lifecycle event {'msg': 'training model with 3 workers on 1750 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5 shrink_windows=True', 'datetime': '2022-05-30T14:47:56.624583', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'train'}\n",
      "2022-05-30 14:47:56,706 : INFO : EPOCH 0: training on 58152 raw words (35868 effective words) took 0.1s, 453792 effective words/s\n",
      "2022-05-30 14:47:56,780 : INFO : EPOCH 1: training on 58152 raw words (35915 effective words) took 0.1s, 495146 effective words/s\n",
      "2022-05-30 14:47:56,860 : INFO : EPOCH 2: training on 58152 raw words (35942 effective words) took 0.1s, 461119 effective words/s\n",
      "2022-05-30 14:47:56,934 : INFO : EPOCH 3: training on 58152 raw words (35913 effective words) took 0.1s, 496536 effective words/s\n",
      "2022-05-30 14:47:57,009 : INFO : EPOCH 4: training on 58152 raw words (35893 effective words) took 0.1s, 489118 effective words/s\n",
      "2022-05-30 14:47:57,010 : INFO : Word2Vec lifecycle event {'msg': 'training on 290760 raw words (179531 effective words) took 0.4s, 465875 effective words/s', 'datetime': '2022-05-30T14:47:57.009845', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'train'}\n",
      "2022-05-30 14:47:57,010 : INFO : Word2Vec lifecycle event {'params': 'Word2Vec<vocab=1750, vector_size=100, alpha=0.025>', 'datetime': '2022-05-30T14:47:57.010844', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'created'}\n"
     ]
    }
   ],
   "source": [
    "import gensim.models\n",
    "\n",
    "sentences  = MyCorpus()\n",
    "model = gensim.models.Word2Vec(sentences=sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\t'the'\n",
      "1\t'to'\n",
      "2\t'of'\n",
      "3\t'in'\n",
      "4\t'and'\n",
      "5\t'he'\n",
      "6\t'is'\n",
      "7\t'for'\n",
      "8\t'on'\n",
      "9\t'said'\n"
     ]
    }
   ],
   "source": [
    "for index, word in enumerate(model.wv.index_to_key):\n",
    "    if index==10:\n",
    "        break\n",
    "    print('%r\\t%r' % (index, word))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Word Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Francesco\\Documents\\XPUB\\w2v\\venv\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:795: FutureWarning:\n",
      "\n",
      "The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "\n",
      "c:\\Users\\Francesco\\Documents\\XPUB\\w2v\\venv\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:805: FutureWarning:\n",
      "\n",
      "The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import IncrementalPCA # initial reduction\n",
    "from sklearn.manifold import TSNE # final reduction\n",
    "import numpy as np\n",
    "\n",
    "def reduce_dimensions(model):\n",
    "    num_dimensions = 2 # final num dimensions (2D, 3D, ecc)\n",
    "\n",
    "    # extract the words & their vectors, as numpy arrays\n",
    "    vectors = np.asarray(model.wv.vectors)\n",
    "    labels = np.asarray(model.wv.index_to_key)\n",
    "\n",
    "    # reducing using t-SNE\n",
    "    tsne = TSNE(n_components=num_dimensions, random_state=0)\n",
    "    vectors = tsne.fit_transform(vectors)\n",
    "\n",
    "    x_vals = [v[0] for v in vectors]\n",
    "    y_vals = [v[1] for v in vectors]\n",
    "    return x_vals, y_vals, labels\n",
    "\n",
    "\n",
    "x_vals, y_vals, labels = reduce_dimensions(model)\n",
    "\n",
    "def plot_with_plotly(x_vals, y_vals, labels, plot_in_notebook=True):\n",
    "    from plotly.offline import init_notebook_mode, iplot, plot\n",
    "    import plotly.graph_objs as go\n",
    "\n",
    "    trace = go.Scatter(x=x_vals, y=y_vals, mode='text', text=labels)\n",
    "    data = [trace]\n",
    "\n",
    "    if plot_in_notebook:\n",
    "        init_notebook_mode(connected=True)\n",
    "        iplot(data, filename='word-embedding-plot')\n",
    "    else:\n",
    "        plot(data, filename='word-embedding-plot.html')\n",
    "\n",
    "def plot_with_matplotlib(x_vals, y_vals, labels):\n",
    "    import matplotlib.pyplot as plt\n",
    "    import random\n",
    "    \n",
    "    # random.seed(0)\n",
    "\n",
    "    plt.figure(figsize=(12,12))\n",
    "    plt.scatter(x_vals, y_vals)\n",
    "\n",
    "    # label randomly subsampled 25 data points\n",
    "\n",
    "    indices = list(range(len(labels)))\n",
    "    selected_indices = random.sample(indices, 25)\n",
    "    for i in selected_indices:\n",
    "        plt.annotate(labels[i], (x_vals[i], y_vals[i]))\n",
    "\n",
    "try:\n",
    "    get_ipython()\n",
    "except Exception:\n",
    "    plot_function = plot_with_matplotlib\n",
    "else:\n",
    "    plot_function = plot_with_plotly\n",
    "\n",
    "# plot_function(x_vals, y_vals, labels)\n",
    "\n",
    "\n",
    "plot_with_matplotlib(x_vals, y_vals, labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mZmLUqFGYP38+AKClpQWvvvoqbr/9dhQUFOAb3/gGjh8/DgAoLi7GXXfdhaeeegrBYAKju4lo0GHmmIj6XUxmU5Iw4qobcXLzQ8jLy0NRURGmTp0KAJibewlycnJQpDSgzvM05l51FZ599mdwOp14++tfx7Rp03DJJZfgmmuuidzdxLEOtKRZ/+6vzXyueGGvYTcICcCSWdkoLXSjuLLGcq9jfclFMlndoKJEHY/X54dsk2I2ttkkoGjSWBRNGouyjY0xJRR2mwQbxBsHRZW4aicKbSu9ts4uYTCvBtii56og/BppO2Usr25A1bb9aOvsMp28BwAvNR43vN+d2/6MG7Iz8Nwr/8LWNz/E7bOvxvHTZwEAkpwRObZ33jiGGyc7Y59/KASXy4WGhoaY3z3xxBPYvXs3tm7dihkzZqCurs7wGIho8GPmmIj6nTazGfSfhS1zJOzOMZjxnV9h7969+O1vf4u3334bkydPBgCkpaXhueeew9tvv43NmzfD6QwHNmvWrMHBgwexc+dO/Pa3v0VFRQUA4M3af+LR+xYBAOzOMbj0W7+xfGz+QBA79p2M2XS3ZFY2duw7iSnlW02zq2pLss113qjs57LqBhQ+tB1jHMbT+szYJSkmYAyElJja4JCCSBBZdXs+XJrHynLKeOT2fFTdnp/w46uZcfV/zQJj9XgBcW202kFi3eICdHSF0NzdQs7r8wvvVxtoi3pHt7ScxUUXXQRZlrHyV38U1prbxk/F1q0vob29HS0tLXjppZcAAKNHj8aUKVOwceNGAOHNe42NjQDCtcgzZ87EQw89hHHjxuHo0aMYNWoUzp07J3wdiGhwYnBMRHFpN88ZjQROlNrVoOvcKXzw7Pcx+tqFkBA+Xa5/rO1NHyT1GKWFbtPOD2bUjWlqANfa0YXndh2JBLvxbiuadtfcFkBrZ5ewJMGIQ7YnNKpaDSJLC92oWJAbCfCd6WmRy5N9XVTNbQHTjhbq8Rp1r9CWUCQyFdBKqciIq25EbW0t8vLy8N6u/0La2EvPH1PrGfgPNwAAMsZ/HGlTrsH06dPxuc99Dnl5eRgzZgwAYP369Xj66aeRn5+P3Nxc/PnPfw4/l7Iy5OXlYdq0abj++uuRn5+P2bNn46233uKGPKIhhn2OiciUaBxxT4crrPTsjRkPLNskQAICwfOXqo8FIGbogzqi2ehnNQCzOi1Py8o0OLPbHosTRGc5ZZz1d8UNetWyCasDNuIdu/a11P+uJ72a9VwOGSMy0uD1+WGTwhlt9fKKBbmRv5vJ5Vst3Z/+763woe2GGeYsp4z6B+cCiD8MRgq0Y93SWZibk4UbbrgBTz75JK6++upEniYRDXLsc0xESTHK7iXal9bIjn0nYwLIQEiJCozVx1r9YlPMJi1tJtfo5xUv7AWAuL2D9ZLNbGpvGy/L6WsL4JE78mOyqmo2NsspQ7ZJkdfHamAc79i1tbv6spGq28IlF9rLXEmUgMg2Ca2dXZHAVBtrn/EHsKy6AcWVNVjp2WuafVZLM9wuR8wXsVXzc2Hvzr5/+MKa7tZt9+K6znoEg0HcddddOPr/7sUHv7kPZ1/zAAA+2roOrfv+AQDoOP4vHPm/d2PhtZfBlTUWt9xyCwNjIorghjwiMiXaVJVsu7Bkbm9W3yqiBoI7y+dYylRKQMzmsEQmxdklKSqIM8s4T3A5ItczallWXFmT8HNWJ+Cp9ys6dm3ZhVHmX3tZopnzeBv1tBsf9WcN9IKKEgn2jY7TBiAI4ILP3Q+7YxTswU689McV+Oqt4R7ZR97ZB0+9F2s9dTjRoTmGYAAf/fmnuOj2CmSM/zguzgxh5crPRT1no8mCHEFNNHwwOCYiU6Keskb9fkXlDUaBhdVRwz2hDcA99V5heYK6iU4bCCZ6bOr0OuB8gKkNslSyXUJrRxemlG8Vvj7JfPFwpqdF7sdT743pZKGa4HJE/ZupXwh8bYGY4zF7HkZ2ls/BFIulElZy4fouFUD4uT2woTHy73iubgva/vVPAEDo7El0dnbi3XffxXe+8x3MmzcP/1x1C2w2G0ZuXQcACJz2wj5yLDLGfxwA8GG7DWlpaZH71nf5aG4LoGxTeFMeA2Si4YFlFURkymxTlVFPWn15w7LqBkwu34rJ5VtR+ND2yGY+o/uVbVK49lX3WMmc3geiA/iyTY2GgbH6XBItoRA9lqq00I2GVXPx6OKCSKlCllMGlHC3BVEPX6P7skLbp7lq237D4FPd9Kj9N/P5A1HdIso2NUYdj/55iEiAaY/oZOm/4Kx4YW/k37H9yBtoP9yIS770MCbc/QukXTQFHR0daGxsxI033ognnngCX/va1wAAznTj3tfa4xVNFgwElR6XERHR4MHgmIhMu1Go9anaADWze9JbogGlmoXz1HuN615vz0fVbfmRIExtYSZJ4qETItr629UvNsXUMgPhgE4thehJmYhskyKPpacdNe1MT4sJvozqt42+OFihBtuirLeCcK232b9ZIKjgexsaYgJ29Xk8urjAsFZYQfjvIZFjt/IvapOkyN9lxZamqGMPdbTBljkCNjkTgVNH0XlsPz766COEQiEsWrQIa9asweuvvw4AmOYeg/Q0G+SxbgRbTqPj+L/gkO247xNudHV1ATDP2Pe0jIiIBg+WVRANc/q6Uu1mNu1p5I6u2JHHyWRa1SycWvMqOlVdtqkxEtA2twVgt0mQpPCoaT27JGHWZVnY9W5zZFjFohnn7zteDSwgLvPQlyfINgkhAEFNkBsIKfjhn/aiYksTzvhjyxNUVuu3jeqRZ08dh+d3H427Oc8fCJqWj1gJ8kIKDP8G1J9Fo6vVFnjqsXt9/six6F9Hh2zHohnuuM9J/Z3Rv41jygycq/8rvE99E/IFlyLDPRV/3d2En/zkJwiFwn+va9euBQBkj3WiKDcbO4OjEfj8D3Bux1PIzAR+/rfRmP/3v2PkyJGmpT69nREnotTF4JhomIvX1cDsOom0GNOKF6AZZXqDJi3GgoqC14+ciRpWsbnOi6JJY+PWiapZ29aOrpjfqQGctoZatOGstTOI8BYx8RcMs/ptfe12WUkOdpbPibre+l1HTJ+LSt3Mpm/jppaPWKmnNqr3Vbnj1KGrX3q0zylTtqGjK4SQcn4c9prSPBRNGmv4RUtUMx11nTQZF9+xOuqylyUJjzz9tZjjfuaZZzQ/zQGwPOb+ykpyDCcLynbxmQEiGnoYHBMNc1aymaLrBBUFsl0yLFkwEy8Ll2inBqMJctrgzuWQhRvK1EBWf3tRlwKrG86Mgkv9pj8gHLSqdcDxsveJbGLMlG3ISLMZZrKtZv1F/+5mz6O4sgbHfH64nDJa2rsigaY/cP7Mg/7LS+17p2O6VyTbbTmoKMKsdzxGGxDZrYJo+GFwTDTMxetGAQAupywcutDSHp1xtQEIxVzzvJ5m4YwyoqJATw3uKhbkCksBjAJrVU+7bFgplxBtBrQaXIs0twXgkO1Yt7gg6j70ZQ9mRF9iRGUfm+u8kWOL9wVH+/yMel6LZDllONPTTI9d7Y1t1CYvHrNSHyIaHrghj2gYMNtwF2/Er6feGxMAq3xtgZhT0GaBMQBU3ZYfN/gQdadwOeSYTXxmQz60p/mXzsqO2QBmNpq5uS0QeZ20r19rR1dMRw0R9fFvvDE81lg9FnWDntqDOZFaZP3zXzorW/j89Zv91OexvPuLwqOLC/Do4gLD11v7N2BE/zzibfQze35WN7s5ZDtWzc+NbAw02/jX3BaIGQrT07HnRDQ8MHNMNMTF23BnNoxCvVw0UjiZU9/6wNio1rZiQW5M7adsk3BL/njhccY7zT/B5cCSWdkxPZjNMqirX2zC6heborKgPn8Ask2CU7ahLSD+KqAPLl/e/yHu/3uN4bFbyd6LXiv1PqaUbzX891ADT9HfwdqFeWhYNdf0vq0cQzLdHNTnJ3r+6hhqbXa6att+LK9uwASXw9KGPpU/EMQDGxojPyeTVSai4UFSkthM01eKiooUNbtCRL2juLLGMPBwuxwxG76MiIKuZOgf02gCmwRgyaxsFE0aGxXATL7AgVcPno7peKC2YjMaRqI9za+/vvYYRCUX8Z6LGlyrNbYdpz/AgWd/CNfEHGSceQ+zZuTj97//Pa694dNoyb8TGHc5Tm37JTo/OAB0daL01oWofnJd+Bh+sQkf/NcTCAXaIaXJmLS0EmvvKMKu6sfx8ssv46SvBe1XfBaZ00vQ1XIaH/35p0DAj4tGylj/26dQvjNg+u/c078D9bUy+hKydmFewhMFtZsdvT6/YTcL7b+V6LEXzXDH/Dubke0SoCDmi9fIzDTDQShENHRJklSnKEqR/nJmjomGuETHP+uDzDEmm9kSYXSa3qjWVkG4K0PRpLGRoM1T78Xy6oaYIF1bt6qvFS2urLFUx1ta6LY8AU5L37bsmM+PsRlp6Dr9Pjxb/oji4mLcfffd+NWvfoWjp/1wdoWQAcB1w5dhd4yCEgrivzY9iDfeeAM3507FN7f9DB8v/QHOjZqEizJD+Pd503Hq9f/CmDFj8Nprr+G6Nf+F13/5HVyYnQ//v16FY8rVGHP9YkwYnY6CggKUjTxrGDyqr3lvjAE3q41OpB7aIdtiglrtv63LIaNiQfQmONFj79h3MhKcqxP/zP4tjTaPBkJK5OyA1+fH8uoGLKtuiHwBYqBMNLwwOCYa4kSnrBUAl6/4S6QvcFBR4HLIaO3sigQQXp/fUn2tlbZb+owtIA7MFCBqQxUk8f0f8/kNT/UnEgxWLMhNuG+zTZIwWde54sTZdqSNHoeTzskAgKVLl+Kxxx5DR1cQzu7rtO37X7Q0boMSCiLYchpvvfUWJEnC5ZMuxc5f3gvg/BeUht/+DqFT7+HpZ5+H1+dHqKMNXc3HkD7+4zj1l59DCXWh84rrMP3HnZEyA9HobiulG/FKK8xeU/V6+jIUYxK2vnFc+Hpre2pbfWz18Ysra3r8ZU79WxN1DSGioY0b8oiGOLOJZdq+wEC4nlafWYvXps0h2+Fymo93drscws4PItoNVWbVXy6nHDPCesULe4XHNMYhx2xONNroFm9ktajOVQ3sVZIkISMt/PoHfB/g7J4/4aIv/BgT7v4FLrhyFtrb26Nurx3JDQCj59wD3Pp/cMU3foVLv/k0HFOuRubEabj4i5Wwj7wAH/1lHc69+d/w+vzYXOdFWUkO1i0uAAAsr26IPMd4Gy9XevZieXWD6SY20b+XduNj/YPRI7PtUuyXK38gaBpAG00NjPfYqt6eZGd0LEQ0tDE4JhritIFfb1O7RfhMAh2zrgdlJTmWRgiLSAgHzkan2xUFMcGgbJPQ2tllGADquy9ULMg1/FJhEOtFCZ49iQ8OvAEA+MMf/oBPfOITmDjWgYw0G5TONkhyBmwZTsgdZ9F5ODzaOCcnB8ePH8drr72Gqm370dpyDkooiMwpV+Ncw18RCnahtTMIxXcMoc52dJ35EPYRLowquAkjp89F5wcHI8979YtNhl8WABh2+lDrtfV9htX70waG8QJslfa1DCW5r0Uf5Bo9tmyT0NbZFfVFJ14PbdkuJTyKnKOjiYYXBsdEw4AarPQkEDWitiITBSR2SYrZVKXN2gLhzXfJWjIrG2cEp9DP+AMxweDIzLSYTLgoM2iUTX50cUHc+pG0sZfi3OtbceWVV6K5uRnf+ta3cOHIDNw352OY8vFcpF98OT58+ltw7Pwl5nzqkwCA9PR0VFdX4zvf+Q5e+9lXcaL6P6F0dWJk/lzIF0zE8Wfux7Gn78Wpbb/A+NEyOo7sxfHffgfHfvtdtO37X4wqWhB5/Oa2gLAuGAhPAlSD5tUvNkVKKczKVsxeE6NyGS3R34bLIZu2YtPfTv/YTtkWqRXWfgmYPXVczP2qf/dulwNVt+Wj6vb8yP24HHLc0iEro6PN2iUS0eDCbhVEw4ioY0Ey7JKEg2tvhqfea1hnatRtoGxTY1RwKtslLL5mIqr3HBW2ixNxOWQ0rJqbUBcGUecNCcChynmWHtfsNew6cwIfblqNq+57Cg2r5lq6v0TuX3ucyfxbyjbJcDSyWelMIt0sjJh1mXip8bhhfbBRVxH9fRpt0FSPV9tFRK2drn3vdKTtm12ScOfMiVhTmhe5P7XbRryuGUZWevbGZN6t3I6IBha7VRCRpY4C+rZWky9wYOfB0zHXu3PmRMOgAAgHrmpPYrUnbXNrh2E98x92H4FRXGy2yc8h21GxIFf4nESlHL2xKS3eayhBihxbMspKcoSBn/Y4Rc87I81mGHDapdjAGAj/G6gbMvWk7sexwuh1A853mVAfwy1os6eVkWZ+UjNeplvfuWSlZy+e23Uk8nNQUSI/rynNi7q+1X7P2udtVpLC4Jho8GFwTDSEGX3QGw1O0AYuRsHAkqf+GRUgF18+FkWTxgqDOABRwY9ZhlOUMFYQzgKq7bkCwRBaO8P3pw2e4g0x0YoXSMcbmGL0eC6nDEUJl3FMmjQZv9i+s0cBUWmhG7XvnTbMRGoDVdHzBowHoph9IQoqSsyXEbXftJXnYvS6LatugN0mIRg6v+lTfQ5Gbdm0fP6AaZcIsxpgoxKI53cfNbzu87uPRrLHWvrSE9FxAOaBure7kwoDZKLBhWUVREOU0els2SYBEmJKG0akp+GMP3YAgqfea9gD2CHbkSnbLLTsSp72dL6n3ms4Ma/q9uhR1FayfmbX6Y1BGb0l0QxmvNtaGdKhBsiJ9vdNpMRD/cJj5ZNH9LrHezz98etb7mkd1pTTGP2dqSQpvPlTf9/xhuSwvIIodbGsgmiYMcrOiU6rq8GvNlMKxGYgVf5AMKGewPHo6171WdKKLU0xxx4IKajY0iScoCbqUas/5a7VG4MyeovZcSZ7W1Hgp1ID40S/CCTy+qgBu5VgWnS/8Upb9P/2orIRfZs501HpivF9x3suLK8gGnzYrYJoiEo2oFM/zOOd+haRAGTF6XusJ9ukSKBilyQsmhEd3ImGOmgvN5veZpXVXrqDUWmhG1W358ft35zM300ir4+ayTbrVBHvfvWdK0S9lNV/+ztnTjS8n6CiRHWWsPrctfdt5bmwFRzR4MLgmGgIMGoj1ZOA7pjPH/cD3agVl1qnump+bkK9ZNsCoaiBJJvrvAm3wuqNrK/VPr6DVWmhGw2r5uJw5Txh3+tk/m6s9qtWX0t9cJvllGP+XuK97lZ6Kav/9mtK87B0VrZhj2ptr+tEnrt631b6iA+FL1dEwwmDY6JBTjtRLV6/V9kmWRoHPcYhw2Yy7ULtFrFohjsq47tkVnZk9//IzOSrtvQZX1EmWnt5b2R9k+njO1j15heB0kI3lszKjgmQZZuELKds+Fpqg9v6B+dG9R42et3N+ghb+bcvmjQWmWnGGV71762sJMfylzrtfavP5dHFBUP6yxXRcMGaY6JBTlROsGPfSaxdmCdsryWqk7RJQGtnl3A88oh0O2S7DcuqG6I6HKgZ36JJY1Fa6DadmmeFNuO7an6uYY/kVfPPt0xLpKWbmZ7U+g4miXT5sGJNaR6KJo1N+v7MXvd49eRG//YSgNlTx0V+jlcmpLaAA2C4CVVL9HfV268pEQ0MdqsgGuTMdstLCGe4Zk8dhx37TkY+sGdPHWfYmxU4vyvfiN0mwQbjjX0qdUOXlQ4GdklCepoEfyAkvB9VTztR0OBlpYuIUc9ttcxnTWle3K4SRsND1PeN1+ePaXcIMAgmGuxE3SoYHBMNQtog0CbYiW/GbMBGT6lT3IxayRmR7RKgRAfcbH9FWmaBrTaoNQqgJQDrFheYni1RJ/bpB5OI/g5FU//4N0s0uIiCY9YcEw0y+hrjRANjoO8CY+B8LaZavxuvc0UgqGBkZtqwqPOl5JjVjaslFqLAVwGwrLoBbZ1dhvXEWU4ZaxfmYce+k5a7nfRGZxQiSl2sOSYaZES1k3ZJQkhR+jTwjUc0xW31i02mA0N8bQHUPzi3z4+PBqd4fY2146lFmtsCkO0SXA7ZcODN8uoGw9sZdTux2hmFZT5EgxODY6JBRpQhCykKDlXOszytLJnSCv2wDi27JBl2GLBSWsFWV8NbvCBSu9FN9LdtNAJbLxBUMCIjDQ2r5kYec3l1Aya4HHA5ZcMvcDZJwpTyrVEbWkW0f8dWh9IQUephWQXRIOKp9wr7yaofzFaGEjhkO5bMyjYcnqDlcshRvWhHpIu/T4cUJeZD38ogEba6Gt5ErQj1fa7VdmmifsJul8OwnZzeMZ/f8DGb2wIw6uIW7D4b4/X5UbapEd+rbjAMwGWbFPV3LCq9WFbdENOKjohSC4NjokGkatt+4w4TCAfFajbMLCCVAGTKNqzfdcT0NLTay3hn+RysW1yA9kDItL2VUfY33gAO1hdTovW7Zv2Z15TmYd3iAtOBHDZJQsWWJsP3SEiB4aAQVSCoILavSphsl1C1bX+kD7PZ2RvRFwAiSg0sqyAaRMw2HQGwVMKgAKb1v0BsiUS8gFsNTvSnx0WnqoFwkK5t1UbDk+gLlLc7w6v/4hSvl7DaL1lU0hNUFNMveck2cGoLhNDW/Vy8Pn/cEg81i6wOH+EXRKLUweCYaJBQSyqMPnDdLocwG5Yoo5ZUZhlgbd9XfY2l2bQx1hkTEP47EH3pM6rRtbrJTb3sgQ2NSXV06SkF1ur6WYtMlHpYVkGUwrQjcx/Y0CgsqZg9dZxpNswKszZqokBWHcJQWug2zC4HQgocsi2mDpR1xqQyq5HXl1dYrU9Wr1u1bX+vBsayXUroQ1NBuG4/HraBI0otzBwTpSj9aWHRh7wCYMe+k8L7UVtcmbW60k+j0xO10pp8gSMShIiyf+2BUGQIA1takZ76d7DMQis1s/pktZRC/VvsrUE3aotE9e+29r3TwumSRjq6Qlg6KztmwIhevPp8Iuo/DI6JUpSVTg9AODNltvnnzpkTTT+YHbIds6eOQ3FljWkrrY21R7Dz4Omo2+48eBr/fPc0TKZJY4LLEakDJTKinnkw+jvWnrUw6y+s/zLZG4GxWmIEhN+Py6obEg66/YEgduw7ibUL80y/RLLMiCh1MDgm6kUrPXvx/O6jkUztnTMnomjS2KSyplYySbJNQmtnl/D3WU7ZcPKXyi5JuDp7TFTwLKqB3PVus+F9mAXGLJ8gq4zOTuj/fkT1yRNcDstfJq0S1dInE3R7ff7IGpDllNHS3hUzLp3vE6LUwZpjol6y0rMXz2naowUVBc/tOoLvbWiwVCOpJ8ok2SUpUh88MjNNOJTDIduxan6uaZAdVBS8evC0pVZaidZusk0bJUIdN242RtysjZuVL5P62nfZJkG2R1/qkO14dHGBaS19oiQgsgY0twUAKXzGh+PSiVITM8dEveT53UcNL9dnVrU1kmZEmTTtB+mU8q3C26vXMzuVC4gzYfpgI954Xq14NcxERuKV35i1cYv3d+6Q7Vg0w40d+07imM+PMQ4ZkhQOVtW/bbfBmZ2e1gIblWFoJ/URUephcEzUSxLJrFr5wI3XzxUQn2Z2d9f5AuLNdPG4nHJUHfKsy7Jiao4BwCZFfwHgKWLqS6IA2ujvXA1M9UGv0WZXteuLUacWK+PYgXA2+Jb88ZEA3Oy23IBHlLoYHBP1kkQyq1Y338TLpFmp09QG2aIPan12S7ZLaGnvigzw8Pr8ON3aieLLx2LXu829UlNN1JtKC92ofe90TM3/mtLwhjq1LeIxnx82g/eqAmD9riMomjQ2ppSjbFOjYfmS/n3T0RVC0aSxkccEIJyWxw14RKmLwTFRL7lz5kQ8t+tIzOWJZFatDjhQr6sf/OFyyKhYkGs4VUw0NUx/unmCy4HWjq6Yvsn+QBCHT/lxcO3NMcfCYJgGmqfei8113qia/811XhRNGgsAltsiGpY86a4u2ySMzEyLmf5oVDJl5QssEaUWBsdEvUTNFlnpVgEgpnUaEDthTjQhrGJLk+HQD6POFfqAWx8IGwXgolpmngqmVKL92zbKBms3llotK9L/jVdsaYrqLAGEh9uIxqJ7ff6Y97baxo1nV4gGB0kZgLGaIkVFRUptbe1AHwZRnzLK3sq28Ae7UVs07eY2o9smen2j8dB6olPB3GhHqcJT70XZxsaYwFVP7UVh9ZNO//4RDScxezxF9/OSWdlRpRZElBokSapTFKVIfzlbuRH1M9GYZdFnfLwJYXreONe3MqrWrGUWUSowyugasUlSQr2JZ08dF/lvs/eJyyHHvEeMOlOotcxq+0btSPjiyhpLbR2JqH+xrIKon1nd+a6yMiFMS0K45/KOfSeT3ilvpVMG0UAyKisyIqovFk26e3730cimPLP3yS354/FS4/HIl88spywstVBrmQFrpVNENLAYHBP1ongb6jz13oTGz+qztWMcctygQM1UmT2GlZ3yHPlMg5UEGNYgq9wmLdaCihIJWEWt2JyyLWYke3sgZBogH+uekic6k9Nf77UnnngCTqcTX/7yly3fZuTIkWhpaenDoyJKLSyrIOolan2v2TS8qm37LQfGdkmKqg321HtNR0VrmT2GPuDmaV4ajLKcsvDyQ5XzEDLJGO8snwO3yRdENWAVlRep19HfRlFip/CpbJKUEj2Pv/nNbyYUGBMNRwyOiXqJlfpeqx+CDtmOR+7Ij8omVW3bLxwVbZV+VK2VgJ4oFa2anxsz+lm2S1g1PxeA+OyIerlR4Kt1zOePGmn94Qtr8NGzy/HR7+7Didf+AgDwv1uH48/cj2O/uQ8n/vgfOOMP4Pb8C3Fq66M49vS3cew396F1/04AwNmml8OXPX0vml/+beRxjvzsNnS++nvk5ubiM5/5DPbs2YMbb7wRl112GbZs2QIAeOaZZ/D5z38eN954I6644gqsXr06cvvS0lLMmDEDubm5ePLJJyOXjxw5Ej/84Q+Rn5+PWbNm4cSJEwCAiooKPPzwwwCAgwcP4qabbsKMGTPwyU9+Evv27QMAHDp0CNdddx3y8vKwcuXKeP8UREMOg2OiXmIlK+QSZLtcDjmSybJLUiSo1gapVgNrUeZKzbQtr26IZIiT3bBHNNBKC92oui0fbpcDEsJf/KpuO/+FMt6mUjXwtUvG75gJLkdUmdS0L/wAz720A5d//XGcq9uCYGszTv3X47iwdAUm3P0LXPj5FZjgckBqeAE35k3CNQ/8Bu67f4ERk/PRde4Uml9+Bhff+ROM/7fH0XH8ANr+9U8AgBJox9133IKmpiaMGjUKK1euxN/+9jf86U9/woMPPhg5nj179mDz5s144403sHHjRqidnX7zm9+grq4OtbW1eOyxx3Dq1CkAQGtrK2bNmoXGxkbccMMNeOqpp2Ke4z333IPHH38cdXV1ePjhh3HvvfcCAO6//35861vfwt69ezF+/Pge/CsRDU6sOSZKkvaDc4zDOOgFwqdTp5Rvhcsp44xBPaJsl1CxIJzt0m/WWV7dgNr3TmNNaZ6w/tHlkDEiIy1S5zx76riYekijiXdmLeHYz5gGA7O6eKubSkdlpsXU8TtkOyZf4MDy6oZIidK/ajbgC7/+HkKKgq6zH+Fcw38hY2IuZNclAAC7YxTKSnLwo6+V4Y9//COuuOIKAOGe4Z1H3kRm9jTYnWMAACOuuhHtR9/EFdfOgVdOx0PfXgoAyMvLQ0ZGBmRZRl5eHg4fPhw5ps9+9rO44IILAAALFy7EP/7xDxQVFeGxxx7Dn/70JwDA0aNHceDAAVxwwQVIT0/HLbfcAgCYMWMG/va3v0U9x5aWFrz66qu4/fbbI5d1dHQAAHbu3InNmzcDAL70pS/hBz/4gdk/A9GQw+CYSMfKlDp9/2CzTXLqpiDRRp0R6WkoLXSjuLImJljVjrQVTdoymoinHzwimngnGnnN0bY0FJgFz6Ke4VlOGfOmj4/a1Np+5A20H27ERUurIKc74F3/A6RffBkCp9+Pul1poRs/0j3OBJcDBwwef2RGGnaWz8HINTL+3HAMVdv2o6nmHbhGj4a/u9vM2bYOFFfWYEZHMyRdhluSJLz88sv4+9//jn/+859wOp248cYb0d7eDgCQZTlyG7vdjq6u6P0KoVAILpcLDQ0Nhq+P/vGIhhOWVRBpWK3BtdJv2Koz3UGrKFurHWmr1j+qp5FFwzxKC93YWT4HhyrnYWf5nMhj6AUVhf2MaVgSvYed6WnYse9k1KbWUEcbbJkjYJMz0f7REXQe2w+lK4COo00I+D4AAHS1nYWn3ovPfvaz+OUvfxm57beuuxhjJl6J9qNvIth2BkooiPZ9r+Ari8Jj2DuDISyvboicFTrjD+C5XUciP3t9fmysex8v/mUbTp8+Db/fD4/Hg+LiYpw5cwZZWVlwOp3Yt28fdu3aZfn5jx49GlOmTMHGjRsBAIqioLGxEQBQXFyMP/7xjwCA9evXW75PoqGCmWOibp56Lx7Y0CgcQasNQnuz7EDN0orKJrSPl2x7NdF9u7sz4+xnTIOdlTM+WqL3sNHljikzcK7+r/A+9U2MviQbVxXMQLtrLGwl9+Hkn34CKCHYnS6skNfiwfl347+e/DGmTZsGu92Om750H0ZkTULWp+7Cief/A3Yb8LnP3Yy1y++Gp96LrqASt4NNIBiCdNHlWLRoEd5//30sXboURUVFyMvLwxNPPIErr7wSOTk5mDVrViIvGdavX49vfetbWLNmDQKBAL7whS8gPz8fP//5z/HFL34RP/3pT/H5z38+ofskGgo4PpqGPU+9FxVbmkxLIyQAhyrnRX4WjVdOlHa07ErPXjy364jh9czGNidTBgJYGyNNNBiISiRcDjmq7Ej7XhH1QVY3xhq9vyUA6xYXRMqgjK5jl6RIp5l47zur60jL3r+j84MDOFv3UtzrEpF1ovHRzBzTsOWp92L1i03CWmAtfZcJo/pf2SZhZGYafG0BTHA5cLq1A/5AyPR+FQDVe45i6xvHhcchdT+e6DlYmbjFiXc0lIlKJHz+QOT9AERveDUKjLUlRfr3t/pFVn3PiDLPQUXB8uoGLKtuMKzp9weCqNjShNJCd0JfsEek8+OaqL/w3UbDkijTJNLS3gVPvTfywWgl2PTUe1G2sRGBkPnZmUBIMQ3QFYhHyyYycYsT72ioMitz0rYmNHq/q9vOjN7DZu9vszIo9R0vmtDn8wfgqfcKN8Tqjbu6BGsX5sW9HhH1DgbHNCwluqEuEFJiAk4rwebIzLRI4CtJQDJVTGaTvBKpmyQaqswCVcD8/aAAeLS7VMJT70XhQ9sj71mXQ8a6xQUAwmvG8uqGSKBsdPYoEVXb9psGxm6Xg2d5iAYIg2MalpIJHtXbJFvjm0xgLNsk084RoqCArdhoOIkXqE4wqSMGEMksl21qjJpC6fMH8L3qBtjtUuRytXRp7cI8rF2YZ7iJ14pjPj/cJhtlRXsMiKjvsZUbDUvJBI/qxKx+bfUmAbXvnUZxZQ2mlG+NTLZTxZsCRjQcqG0OswwmUKrvB7P3xDGfXziePQTEXO4PBPHAhnDbs0fuyDcdQy2ifrHm+5co9bBbBQ1LZrvbb8kfHzNhTt1hXrVtv6Updb3RyUIlAVGtnvRdJhJtYUU0lJm9H7QlE1pqCUOin4bqexFAZG3Qv1/NbqeWcvD9SzQwRN0qGBzTsGX2oST63ZTyrZY+QEUfkK7uMdP6tnFWPlC1eNqVKHFmrdVEX3zj0b8X1bVDdF/aVm9ENLDYyo1IR7ShzixotpoVNgp0ZXt4X7xRP2UFsLxzHeCGO6JkxOsyo685tsLr86O4siZyP+r/ib5IhxQl4XWHiPoXg2MaFqx+8MTrG5zsDvUR6XZ0doVMB42oo5z1vVWNPmC54Y4oOWZdZkZmpFnqe67n9fkjvY3VqZOiL9IKEBVMA9b7lRNR/+CGPBryrG6iA8z7BgPnN/7YJSnmtmbaA6G4/Y7dLgfWLsyD2+WA1P3zklnZkYyzSrabd7AgIus89V4UrN6OZdUNSQXGKvXdra4vs6eOE27U069B8dYdIupfDI5pyEvkg8dK3+DSQjdCCdbqxyuXUHeolxa6sbN8Dg5VzsPO8jkomjQ2NnWcOtsEiAY19Yuz2RkdrSynbNp3XOUPBPH87qNYNMMtvL52DWK/cqLUwuCYhjxRjXAi/YH1l/dmWYNdkiIflEYt4fQZZ3UgCRH1TCItFx2yHavm52Jn+RxLAXJQUbC5zouykhyIzjOpwa/VdYeI+geDYxryRCUQRpdb7Ts6e+o4w/ssvnys4e3NqFllo3IPZpSI+k6895FdkiIlTtr2iUbrhBH1S2+84Jf9jolSC4NjGvJEJQ1Gl6s1xWpmSJTV3bHvpOF9Hj7lj6kbXjTDLcwc6enLPZhRIuo7LoOhISqHbMcjd+RHSpz0o+Otvq+P+fxxg1/tumMUjBNR/2K3ChryzEa0amk7WricMmSbFClp0O8eN8vo6ttFPb/7aEJlwtr7NuqOwYwSUe8QbQWQJMQNTnfsO2npfT3B5YjbQg4w76JBRP2LwTENefECTE+9FxVbmqI25RjtWteOjBW1adKOmFYfz2rvYu19qKx8qBJRcs6INuIp8VuoWSlt0meH+b4lGhwYHNOQZxZgisZIiwQVBcurG/Cxi0YY/n721HGWN/lkOWW0B0Jxs8L8UCXqG2Zfcs146r2wCYb22CUJIUXBBJcjsh4sr24QfrHl8A+i1MPgmIYFUYCZyG51lQLgwIethr/bse+k5YzSqvm5kWPgByNR/0umbEn9Qm0UGKujqI2+eOtLs4zOWHH4B1FqkJQET/n2paKiIqW2tnagD4MGsUSzMKIRr8mSAIxxyIZ9U6XucXfxjouZJKL+o77fvD5/ZIR7llOGooRHvauXqZPv1Ovq2SUJj9yRH3mvFlfWRK53tnYLztX/BaHWZlx64534+doK0zNWbpcDO8vn9N2TJiIAgCRJdYqiFOkvZ+aYhoxkRrCKTqsC4Ul0XSFFuGlHdH9tnV2Gv3M5ZKyanxs5zVq1bX9M4MsxskT9S31fad932j0H+laLooA2pChR71HtGaRz9X/BxYvXIG30hZAQ/4yVelt+USYaGGzlRkNGIpPwPPXeSGbHqB1TllNG1W35WHdHgeU2bOrpWJ9gBG1zWyDuGGuOkSXqf1bLq/yBoLBvur4tnFq3fGrbL9Dl+wAfblyFs6950P4/T+GYz4+Ptq7D6b//Gh88+314n/gqWvf9AwAQ6vSjedN/4vIr8/CFkk/gwGs7oAB4773DWPzZ6/DZhV9Ebm4u5s6dC78/HES/8847+MxnPoP8/HxcffXVOHjwYPh5VVXhmmuuwfTp07Fq1apkXx6iYYfBMQ0ZVgdmqNlZNWOsAJEA2O1y4NHFBah/cG6kTnnJrOy4j63tSyrazKP2TNbSB74c+kHU/xJ5fwUVBbI9NkBuae+K+qKr9ja+oOQ+2EeOxcV3/gQZI0ajMNsVWSOCLadx8dL/g3G3rYLvf34HAHA6HPjVb/+AS77yc4z7wo/RXPM01PLHztNenLz0RjQ1NcHlcmHz5s0AgCVLluDb3/42Ghsb8eqrr2L8+PHYvn07Dhw4gD179qChoQF1dXV45ZVXkn6NiIYTllXQkCEqkVAAFD60HYoSbt1ktMtcQXSdn/50ZvHlY/HqwdOG9cmyTUJbZ1ekVGL21HHYXOeN2eQjykxpP5iT3T1PRMkzK6/Sc7scaO3oitlXoI5115Y9ZKTZIu97l0PG7BmXInSyA58pycFXnpcgXzELkmRD+oXZCLb5kOWU8cObPo6Xf/8wXtuwFZAkBFtOIdTqAwCkuS7GGWf4/mfMmIHDhw/j3Llz8Hq9uPXWWwEAmZmZAIDt27dj+/btKCwsBAC0tLTgwIEDuOGGG5J/oYiGCWaOacgwG+na3BaAzx+AAnHfYa+mzk9f/vD6kTNYMivb8JRqIKSguS0QuW71a0exaIY7ZtpVlmAal/Z0LMfIEvU/q+Og1feiqD/yMd0aog2g27tCkf8uLXRjxqQsXDB6RGSNyLBLqH9wLlreehknT57EjPt/jQn/9jhsTheUYCcAQLLLkS/KdrsdXV3G+xsAQFEUrFixAg0NDWhoaMA777yDr371q3GfIxExOKYhRD/6OVF2SYKn3osHNjQalj88t+uIpYEegaCCrW8cR1lJDia4HDjm86Nq2360CzLH2rvkGFmi/qd/32U5Zbgc0V9m7ZKERTPCpVaisdMupyxcQ9oDQfz1zeORn7PHOvHjW/Mi46nttvAX7zNnzuCiiy7CD27ORcj7JoJnP4zcRpKkmC/Ko0aNwqWXXgqPxwMA6OjoQFtbG0pKSvCb3/wGLS0tAACv14sPP/wQRBQfyypoSFHrhJNp0RZUFGH/0kSpm++0XSdE9FkoDv0g6n/6953RpMvNdV4UTRor7GDTEQiariFGkzf1lixZgvnz5+Pll2/CFVOuwkfjwnseLh6dCdvoTMO14dlnn8U3vvENPPjgg5BlGRs3bsTcuXPx9ttv47rrrgMAjBw5Es899xwuuuiiuMdANNyxzzENSdoeo1bZBROv+hp7mhKlHtEa0pN1gu91otQi6nPMsgoakqzWEKocsj3hDzwJsNzmzexxWU9MlHpEHSySDYz5XicaPBgc05AkqiFU/9shn//Tz3LKCdcqu10OHKqch3WLCyDbokNk2SbF1CsacTlk1hMTpahkOsQIWiDDLkl8rxMNIqw5pkFH22ZtjEOGJAG+tkDMBCmj2t3zdYTnd463d/93WUkOyjY2IhAyzwxJAGZPHYeC1dsju9FtEhBSEBkxW/veaTy364jp/YzISOOHJVGKKivJMZ2IZ0RRYts2OmQ7A2OiQYbBMQ0q+k0y2lZJ2lHLAAzHrppNoCsrybFUJ6EAqN5zNCqIDinhcdPax4mHgz2IUpcazKrriFF/dD31yzFHPhMNbgyOaVCJN+bVHwhi9YtNaA+EojpFqEGzKCD1drdbCwTj1xPaJckwuxwInh8CYCXw5WAPotSkHwK0bnEBAJhmktWa4mS6zegfjwE10cBicEyDipWg06hdkpodFk3CkmDebk1lNukOmvuwMnFr9tRxcR+PiPqX/uyU+uV67cI8rF2YF1XSFQiG0NoZvl5GmrUtPPpAWD9RU/tlngEy0cDghjwaVHqSbT3m86OsJMewckIBDKff6WWk2YST7lSFD23H7Knj4nbL2LHvZNzHI6L+ZVZ6VVroxs7yOThUOQ8VC3KhPYHk84d7m3vqvcL7XunZi+XVDVHTN9fvOiJ8PCIaGAyOaVARBbcqh2wXdooY45BRtW2/cDiIlRZNPn8ALe1dsJkcRHNbAJvrvJER0iKsOSZKPaL3pf5ysyDaiKfei/W7jsSsP6JVh+sD0cBhcEyDSmmhG0tmZRsGyGpLtooFuTFZW9kmobWzy7TUQYK1U6OBkILRmebZY38giB37TmJn+RxhgMyaY6LUI3pf6i+3GkSrzL6YJ3IcRNT3GBzToLOmNA/rFhdEehi7XQ48urgAq+bnomrbfiyvbkCmbIv0NXa7HBiZmRZ3s50CoKMrZHod1Rl/IG5fZPVD0mggCQcCEKUmq+9Xq0G0yiwTrP+yz/WBaGAxOKZBSVv7p45jXfHC3kgtX3NbAGf8ASyZlY2d5XPgM9ik1xPqjnKzumL1Q1I/kMTtcrDvKVGKsvp+TfRLryholgAsmZXN9YEohbBbBQ0JRvV/CoD1u46gaNJYS90jrNJ+AGbKNsPuFbJNivqQTKa9ExENDCvvV30f5Hgt2IyGiqiB8ZrSvKguFtq6ZbZ4I+p/kpLknPi+UFRUpNTW1g70YdAgNKV8q7CeT23Mn+i0K5VNAsaPcUR9QAHmPU9lu4Sq2/L5QUZEEfo2bpMvcGDXu82Gm4FluwQoiOqpzml7RL1LkqQ6RVGK9Jczc0wpaaVnL57ffRRBRYFdknDnzIlYU5pneF1Pvdd0epWaMV40wx11n7Muy8LhU/7IB5UosxxSECndUBVX1pgG2tqBIEQ0PMRbt7QZ6ZWevaYj5o32SGhbyhFR32FwTClH/6ERVJTIz/oAeaVnr2F7JL2yTY2Acr5dW1BR8PqRM1FZmOLKGsMA2WjjnZU2S2zFRDR0xJtil8i6BQDP7z6a1HFwXSHqe9yQRylH9KGhv1zUN9RIIKjEjHzW9yRNZIONlTZLbMVENDSoU/O0wzv0Az+srlsqK33VjXBdIep7DI4p5Yg+NPSXJ9o31Ig2C5NIV4l4nSrYiolo6LAy8MPquqWKN5FTtkuQddOGuK4Q9Q8Gx5RyRB8a+st7o/uEPgujtohbt7gAALC8ugHFlTUxI2H1gXSWU47qq8xNM0RDh5WBH1bXLUDdJyH+au92OVB1Wz6qbs9nizeiAcCaY0o5d86caLhR5c6ZEyP/rQ9W9ew2CUFNGYXdJsGG2J3fahbGU+9FxZYm+Pyx/ZDVU6gAoj6Y2J6NaHgQbdjVfrmedVkWdh48HXOdWZdlRf3sqfeibGMjAgbzhoovH4v1X78u6jKjNSZe/TMR9Qwzx5Ry1pTmYems7EjGxS5JWNrdC1RVsaVJePviy8fG/GHbACy+diJcjvNjnzPl8LXUDyujwFilP4VKRMOHlf0Ih08ZZ5f1l1dt2x+z/yHefWhZqX8mop5h5phS0prSPGHrNgCmgezhU/6YD59AKLxzXHuCs7ktgBUv7EVGmk34YaXFXeJEw5OVgR9WSi/MrhfvdypR/fPqF5uYPSbqJQyOacgx+4DRh8D+QNDyYBDuEicavuKVUcUrvVBLIcy+hltZY0TrW3NbAJ56LwNkol7AsgoalEakG3eKyHLKfRLEcpc4EZkRlV587zMfiyqFEJHtkqU1xmx9Y+kXUe9g5pgGHU+9F51dsbtZ7DYJq+bnAjAf7ayX5ZTR0t4lLK1wc8MLERk4fPgwbrrpJsyYMQOvv/46styXYcwN30btI/+Giwtmw/5hEwK5P8SD24/g3W2/AYJdSHNdggtuXgZbugPNLz8D/zu7AZsdritmALf9HCdPnsQ3v/lNHDkS3pT86KOPori4GBUVFThy5AjOvPE2vO8cwqiiz2N00QIAQMub/42ze/6EYwC+1PQJPPvss8L7IaL4GBxTSrGyC1u0oWVURlrUdau27Y/b7s0h2yMBtbZbRZZTxqr5uQyIicjU/v378fTTT6O4uBh33303rnK+hQ+yHLh33tX493//Iz766CPcVXYjLl78Y9jSM3Fm1yacfc2DUVfPQ9uBf2LC156AJEkItbdgxQt7Ma72Cfzkh9/HJz7xCTz1lz347K234+K7f4Wu2kMYcbIJjXt2YsZ/bsHbj38VowpvRuC0F2dercYlS6uQPeES/PyeAgDA/fffj+XLl+MTn/gEjhw5gpKSErz99tsD+2IRDRIMjillqKce1YyvqIWaqObujD8QE1wvnZWNzXXeqCyyhHDtsT4jzECYiMx46r1Y/WITmtvCX6Kd7adw4SUTIhnZpUuX4rHHHgMALF68GACwa9cudJ0+ig/Wl4XvJNiF9AlTYcsYAcku49Rffw7n5dfC8bFr4A8E8c///R/cd99hnPEHcPxMO7r8LQh2+nGuPYD2sbn461sfYc2d12Pxb1wItvrQfuQNOKd+AiPHjEVZSQ7Gjh0LAPj73/+Ot956K3LsZ8+eRUtLC0aOHNlfLxfRoMXgmFKG2RQqbeAq2vjicsoxwfXmOi8WzXBjx76Twmw0e4YSUTyeei/KNjUiEDx/1upsRxd83RvhAGDl8/U49NYJBM+0438OnsWXJwGKomDUZVdjzLzvx9zn+C+vQ/t7DWjdvxNnX38Jl9z5E4RCIezatQuffvRVKLp1rgtpqNq2HzvL5+BilxPjRsk4jPBZM/2AEPV+MjMz++T1IBrKuCGPUobVVkiijS+KAsPgese+k5g9dRxskgSvz48HNjRipSeckWbPUCKyomrb/qjAWNV19iT+/VcbseKFvXhvz3ZkXHoVukIKfujZi8nlW/Ef//Dj3HtNCDQfAwCEOtsROO1FqNOPUEcrHJdfg7Gf/joCHx4CAIz9eBEef/zxyLrXeeLdqMdTLx+dmYbN37oerzz6HTi9r+GT2eEg+PTp8CCSuXPn4vHHH4/crqGhoXdfEKIhjJljShlWplAB4p6jy6sbDO/X6/NHTdwLKkrk5x37TlrKVhPR8Cb68p429lIce/XP6PjgEaRfmI2RhTfjbN1Lkd+fk0bggnnL8NGWKijBcDmG65NfgpTuwMkXfgSlKwAoCrLmfA0O2Y6HfvYotv56DT74n/+LYFcXMibm4oKS+yL353LKUY+fm5uLH/7wh/jUpz4Fu92OwsJCPPPMM3jsscfw7W9/G9OnT0dXVxduuOEGPPHEE33wyhANPZKixB9+YHoHkjQRwO8BXIxwKeeTiqL8XJKksQCqAUwGcBjAHYqiNJvdV1FRkVJbW9uj46HBS19zDIQzwvrThaIyiOLKmrgb8LTskoSQohj2HZUAHKqc14NnQ0RDidH60nXmBD7ctBoTvvqruLdX9zpoOWQbMmU7fG2BqLXMU+/F8uoGw7XJ5ZDRsGpu0s+DiM6TJKlOUZQi/eW9UVbRBeABRVGuAjALwLclSboKQDmA/1YU5QoA/939M5FQaaEbaxfmwe1yQEJ4w5xRYCwqgxCVW4gEFUXYM5QDP4hIq6wkB7JdirlcghQ1ll5EAWDT3dwfCKE9EMK6xQXYWT4nEhiveGGvcFjIGZPpoETUO3pcVqEoynEAx7v/+5wkSW8DcAP4PIAbu6/2OwAvA/hBTx+PhjajKVTaTLFNkhDUne1QyyB2ls8BEFtu8cCGxpjbAOHMcVlJjmG2mgM/iEhLXZe03SouvORS/GL7TgDxe6tLAIxaqevLuIw2JmvxiztR3+vVmmNJkiYDKASwG8DF3YEzAHyAcNmF0W3uAXAPAGRnZ/fm4dAQoC+1MApygXAGeUr5VsNuE7XvnY6qOVbdOXOisH6Z9cZEpNKXcol6oC/f0ABRpaJZAaO2XENU2wzwiztRf+m14FiSpJEANgNYpijKWUk6f/5IURRFkiTDtUFRlCcBPAmEa45763hoaIiXRdHSllkA5zM9a0rzAADP7z6KoKLALkm4c+bEyOVG2WoiIsB6//XSQjeWCTYFx2Pv/rz01HsNz46p19GXmRFR3+jxhjwAkCRJBvASgG2Kovys+7L9AG5UFOW4JEnjAbysKIrpV15uyCO9KeVbTTMuIm6XI1JmQUSULLONvvpBQpev+Ivw7FY8jy4uEJZmyDYJIzPTYjbuEVHP9NmGPCmcIn4awNtqYNxtC4CvdP/3VwD8uaePRcOPqL7OLkmI3RpzntmpSSIiq8zWEn1fdLPA2GxzsNvlEJ4lk7r/X3NbgL3YifpJb3SrKAbwJQBzJElq6P6/mwFUAvisJEkHAHym+2ciyzz1XrR2dMVc7pDteOSOfByqnAc3u00QUR+Kt5b4A0Esq25AcWUNRqQbB8ASgEUz3IZdLdQ6YlF2WgFiho+om/iIqG/0ODhWFOUfiqJIiqJMVxSloPv//qIoyilFUT6tKMoViqJ8RlGU071xwDQ8qHV+Pl3boiynHFV3N3vqOMPbN7d2MLNCRD1m1CLSiNfnR2un8f4IBcDWN45jREZ4m49aY6y2qwRgeiZM9HhTyreiuLKGax1RL+OEPEpJolOMzvS0qFq7HftOGt6+LRAy3DRDRJQIdf0QtYS0qrktEGkBF1SUSMZYHWAkGkbkcsqR2+mpZRYPbGxExZYmnPGzJpmoN/RGWQVRrxPV+amXe+q9cSfi8dQjEfWG0kI3Qr2weV1Luz6J1jsFwKr5uXEz18GQAp+fNclEvYXBMaUks8l12il58XBjHhH1hPpFvC/6jKrr0xjBhD2XQ46ZHGoFEwNEPcPgmFKSaBR0WUlOQr2PuTGPiJKVyBfxZKjrkySIetXLSwvd2Fk+B4cq51m+byYGiJLHmmNKCSs9e2OGdKxdmIeqbfvh9flhl6RINsTqBxWnSRFRT5h9EXe7HJg9dRx27DuJYz4/XE4ZHYEg2gIhAOGsr35DsZZ2ffIJaoqNLo93vyomBoiSx+CY+pyn3ouKLU2RBT3LKUeNX13p2Rs13jmoKHhu1xG8UPc+2gIhSDjfP9Tr80OC8SjWLKcMZ3oax0ATUa8wy75q15fzU/RCkd93dIVENwWAqK47E1wOwy/9RgFuxYJclG1sRCBk3lOZiQGi5DE4pj7lqffGLOTNbQGUbWoEED5d+Pzuo4a3VTMw+o8ABYgJkB2yPSrgJiLqKVHQCiCqG45Rhjle6VdpoRueeq/wbJhslwwDXHWN0yYctFwOGRULuBYS9QRrjqlPVW3bb5jhCASVyIaRZNojKUBkg4raK5QfBkTUm8x6HFvpNiHitrCxOBhSULGlybCXcWmhO9IzWW9ERhrXQqIeYuaY+oyn3mtaH6x+oNglKeEA2e1yYGf5nB4dHxGRGTXIXFbdYPh7dX0zyzAbmT11XNyNxSEFkcyw2p5Ne0yix+urzYNEwwkzx9Qn1KyIGbWe7s6ZExO6b9bTEVF/KS10C8fUA0DB6u2RvRBaZm3X1E18idC3Z7MLWlyILici6xgcU5+IlxXR1tOtKc3D0lnZkUVdkgCbbn1Xf2QJBRH1t7KSHGGwq2Z31b0QQHidWjIrW3h/6qbhRGkDatHZtp5M8SOiMAbH1CeMsiJdZ07g2NP3Isspo+q2/KgAd01pHg6uvRmHK+fh0Np5+NkdBZFsjV2SIjXG7EBBRP2ttNBtaQiIuk7tLJ+DokljhddTu+nEm3xndDuVKJttluUmImsYHFOfEGVF7JKE+gfnAgCKK2sMN5sA4Q+jspIcyDYpqo1b2cZGjkUlon5nNehUEwNmE+pmTx0XM/kuyynD1T0pT3/mDIgtJ5s9dZzwvomoZxgcU59QA1u9YCiIws8uwuK51+P1X38fwUAHDu1vwp3zP43JV1yJW2+9Fc3NzQCApbfejBPbn8Cx334Hx56+Fx3Hwp0v/nNTHe6++25ce+21KCwsxJ///GcAQFNTE6699loUFBRg+vTpOHDgQL8+ZyIauqxmetXEgFlN8Y59JwFET76rf3AuKhbkwiHboW/w43LIMeVk6n2I7puIksfgmPpEaaEbIzNjm6EETh/DiUtvxPiv/gq2jBFo+9er+GjrzzDmhrvg/uovkZeXh9WrVwMAukIhKIEOTPi3xzF27r049defAwCO1DyHOXPmYM+ePdixYwfKysrQ2tqKJ554Avfffz8aGhpQW1uLSy+9tF+fMxENXUaZXqMEQFtnFzz1XtOaYlHgLNqrYdSeTXQfHBtN1HNs5Ua9Rm1or242aTYYfZrmuhjpF18GAEi/5GPoaj6OUHsrMrPzcMznx1e+/hXcfvvtkes7r/oUACBz4jSEOvwItbeg/XA9KivfwsMPPwwAaG9vx5EjR3Ddddfhxz/+Md5//30sXLgQV1xxRT88ayIaLkoL3VFBqn76JxAecrTihb1YNMON9buOGNYq2yQJU8q3xkzyFLVhMwp4E5mqR0SJYXBMveL8+NRw1kO0yEt2WfODDaGO1siPLqeMO574J5qOnUVxZQ3skgRJv0dckmCXgM2bNyMnJ7qd25VXXomZM2di69atuPnmm/HrX/8ac+awFzIR9Q59AqCsJAcjMtJiJtX5A0FsfeM4MmVb1EhplXYfhbblpX7yp8oo4C0ryYlacwG2uSTqLSyroF4Rr3WbiC3DCVvmCHR5m9DS3oWD//wLMiZOg9fnR1BR0LrvFQBA+/tNsGU4keEciZtvugmPP/44lO4PmPr6egDAu+++i8suuwzf/e538fnPfx5vvPFG7z1BIhrWtBPtFJwPbEWJgOa2gGFgrKf2L67att8wMJYA4RhpbZkH21wS9R5mjskSo4yJdhHuSZ3bhfO+h1N/+yWCnR1Ic12CC25eFvmdZE/Hid/dj2BXAFPv+AEeui0fJVNnY9myZZg+fTpCoRCmTJmCl156CRs2bMCzzz4LWZZxySWX4D/+4z968pSJiCKMEgD+QDCpCZ96x7oDbiMKIAx49WUeRNQ7JCWFGoYXFRUptbW1A30YpKMvmQDCp++0WYriyppeH1v6wR/KkTX7q7jsyukcFU1EA2pK+VZhAOuQ7UmdOVOFe7krMV0q1N8dXHtz0vdNRGKSJNUpilKkv5xlFRSXKGOi7eOZTEN7q7j7mogGmsspG16e5ZRjyhsccmIfrUHFODBWf0dE/YtlFRSXlZZBagb5gQ2NSS3mWU4ZvrZAVGbmki9WAuDuayIaeKJlTVFiyxsKH9puqd7YCk68I+p/DI4pLrOWQfpa5DtnTsTmOm9Cpxgdsh3zpo/H5rr3Yz5QuPuaiFLBGX9sa0rR5T6DNpbJ0K9/8fZ+EFHvYFkFxWVUMuGQ7Zg9dVzM7u3NdV4smuG2nO1wuxxYNMPdHVBHB8bq6Uou/kQ00ERnsNSexcWVNZHR9sme7VLLMoy6T4i6ZaiPSUS9h8ExxSVqGbRj30nDWuTndx+Nm+11yHYUXz4WH5xpx3O7jhhmmp3psVOhiIgGgmhfRVBRYoLV2VPH6Tu0W6IAkXHSO8vnRK1/VvZ+EFHvYFkFWaLW1K307MXzu49iWXWD8LpBRcFyk98DwNXZY7Dz4GnT63AjHhGlCjVQVcsabAYt3PyBIFa/2IT2QEjY2cKM26BUTS2d4Lhoov7D4JgsW+nZi+d2HbF0XbMPBpdDxq53m+PeBzfiEVEq0W68m1K+1fA6zRbrjWWbhICmRYW2VE07aVSdoMdx0UT9h2UVZNnzu4/2+D5km4SKBblxO1pwIx4RpbKeBqUjM9Msl6pVbdsv3PvBdZKo9zE4JsuS7bfpcsiRD4Cq2/NRWuiGXRJX5HEMKhGlOqNgNZE6Y19bAGUlOZjgcuCYz4+qbfuFg5SO+fwcF03Ujzghjyy7fMVfDANkSQKgiEspXA4ZkhR9ujHdLqEzGHuLpbOysaY0r5eOmIio7+jrgxOZEprllNEeCEVliruX0hhul4NTQon6gGhCHmuOybI7Z040rDleMjMbAIT1yD6DPqCdQSWcZZHCTfTtkoQ7Z05kYExEg4Z++EdxZY2lANkh26EoiCmhUBAbILN0gqj/sayCLFtTmoels7IjJRESgBHpdqzfdQQ79p2EM8GRqQoAGyRIAC4Zk4miSWN7/ZiJiPqLqC546azsmHII0VARBeJex0TUP5g5poSsKc1D0aSxqNjSBJ8/gNbO87uqZbsUswM7HrVMQ7srmx8ERDQY6du9mU2xE9UYs4SCaOAxOCZDol6b6pQmo6EdgaCCEel2hAKhpDbvqbuyGRwT0WCjXzPXLS4wXctmTx2H9buOsISCKAUxOKYY+gBYm9U1mtKkpWaSk8WG9kQ02BitmWWbGiNn2OzdA0Pc3YkGANhc540KjCUAi2a4mRwgSgEMjimG2ZhSK8Gr3WBylJ7UvRFPjw3tiWiwMVozA0ElshlZXz6WKdsMN+Pt2HeyX46XiMwxOKYYZmNKrbQrCioKHLI9avF3yPaojSVG5Rk8pUhEg1EiZ7z8gaDw7BvPnBGlBgbHFMNsTGlZSY6w5lilnjpUM80upwxFAZZXN6Bq237MnjouMglKf7qRpxSJKJUZ7cdItMexCM+cEaUGtnKjGLOnjouZ9KRmdbVTmoDYiVDa6+0sn4N1iwvQHgjB5w9AQfi04nO7jkQ+SNQsMwNjIkp16hkvr88fWc/KNjXidGtHQvfjcsgcBU2Uwpg5piieem/cjSLaxvdGWRQg3Az/mM8Pm4X6Y3apIKLBQFRbHNBN+xyRbkdnV8iwraVDtqNiQW7k/uK1fCOi/sfgmKIYLf76jSKiNm8AsNKzN6o9kdWWbqy1I6JUZ7V0wuVMj5SWeX1+YfkYg2Gi1MTgmKKIglSvz48p5Vvhcspoae+KZES0bd4AxPTttIq1dkSUyjz13pjRziLHfP6Y0dKi+2T2mCj1MDimKGYbSxQAzW2xI0/9gSAqtjRhREZaUoExa+2IKNVVbdtveX2z8mXfrJ88A2SigcUNeRSlrCQnZqOIFT5/wPSUo12SICHcyWLprGy4XY7Iz9oWb0REqchq6ZfVL/tm/eSJaGAxczwMJHLqTr1cvX4imWDR8A8JwCN35DMAJqJBS3RWLcspw5melnBphFk/eSIaWAyOh7hkTt1pa+WKK2ssb0IxGv4hAVgyK5uBMRENakY93h2yHavm5ya1vpn1kyeigcWyiiFOdOquYkuTpdsb9TwWUUsktCUT6xYXYE1pXmIHTUSUIjz1XhRX1mB5dQMy0mzIcsqWS8LU204p34riyhp46r2R3xmVsDlkO2ZPHSe8DRH1D2aOhwhR6YToFJ3PH8CSp/6JXe82I6gosEsS7pw5MSqQNep5LKId5MEsMRENBfozbz5/AA7ZjnWLC+Kuc/q2lvqzdvoStgkuB2ZPHYfNdV5u0iMaYAyOhwCz0gmz7hM7D56O/HdQUfDcriMAEAmQjbLOItxUR0RDjdmmuXgZY6O2lvrb6pMJxZU1ST0eEfUullUMAWYL+Oyp4xK6r+d3HwUQXtyt1hq7XQ4u3EQ05CS7ac6s7ZvZbblJjyg1MHM8BJgtqNrJdlYEFQWeei/KNjVauj57FBPRUGVl05xRSZtZMGu24Y6b9IhSAzPHQ4Bo4ZzgciSccbBLEla/2IRAUFxpnMiGFCKiwUq0aU5NCKglbd7utpdqSdsYh2x4f1L3fSb7eETUP5g5HgJmTx0XU9+mLqhV2/YbZiJkGxAIxd7XrMuyomqRjdQ/OLeHR0xElNrUjLA/EIz0cHfr+hiLStoyZVtSbS2NNulxpDRR/2NwPMgZdZSQACyaEV5MfW2dMbdxyHZcnT3GMAhuOHrG9PHcPL1HREOcfpOz2sNdzeAWPrQdzW0B4e19bQGsW1yQVJDLjj9EA4/B8SBnlLlQAGx94ziqXztqWB6xaIY7svFOr7VT3J1Ctkk8vUdEQ4LZ5FCz/vCtnV2mZWdAuKRNH+SqPY+ZESZKfQyOBzlRTbFZVmPHvpOGY57NSACqbucIaCIa/OJNDjXrDx+PbJfQ2tGFKeVbI0EwgIQnlRLRwGFwPIh56r2wddfCJeKYzx+poTOir5VzyPaojXeeei9Wv9gUCcBdDhkVC5IboUpE1N9EmeEHNoS79Jj1hzeT5ZTR0t4VCaLVIDhTtrF/MdEgwm4Vg5Sa+TAKcB2yHU5Z/E87weXAnTMnGv7ObpOwaIY7agS0PjAu29QYlZn2+QMo29jIMadENCiIMsNBRcGKF/Zi9tRxhl0jspzGXSiA8FrpTE9DIBS9JvsDQeGZPPYvJkpNDI4HKdH0OrsUDm5FNXFq3fCa0jxkpMX+8wdDCnbsO4md5XNwqHIedpbPicpsVG3bb3jfgZCCqm37e/CMiIj6h1nfYH8giB37TmLtwryYJMGq+bmQ7VLMbdR1NdFsM/sXE6UmllUMUqKMQ0gJB7f67IUqEFLwwIZGLKtuEN631+eP2o2tLZtIZroTEVEqKSvJiaoB1vP6/KadJozKyoDw3gyrRW7sX0yUuhgcD1Jmk5TiBalWapSNyibMHlf9HRFRqlMD3Qc2NArXQ3Wd02+e03ahUPdfmCUbjGj7JZt1zSCigcGyikHKbJJSXwSpatlEWUmO6WlFIqLBoLTQjUfuyI9ZR42om/WmlG9FcWUNPPVew/0XVkhApFzNU+9F2cbGqAl73L9BNPCYOU5hZhmFeJOUzE4ZJuuYzx+5f3arIKLBTr+OmnX/US/XdqCI1+/YiDZ5UbGlKaYELhBSULGliesp0QCSlATbgPWloqIipba2dqAPIyXo+3ACsS3V9NfXBsqzp47Djn0n4y74WrJdMl3s3S4HdpbPSe4JERGluCnlWy3XDCdDtksYkZ6GM/5A3HZxhyvn9eGREBEASJJUpyhKkf5yZo5TlKgPp74vpqfei4otTVHN6b0+PzbXeSOBtFGgbWREepppk/vZU8cl+WyIiFKPPqkwxiFbGvSRrEBQieqBTESpiTXHKUq0qU57uRr0Gi3maiANhE8dqm2JRNwuB87E+VDYse+klUMnIkp56vqprfdt7eyCbIvdU9HfzPopE1HfY3CcokSb6rSXi3odq7SBdGmhGzvL5+DRxQVJb+RjqzYiGiqM1s9AUMHIzLRIf2OXQ4a9n4Nl2S5h1fzcfn1MIorG4DhFGXWjkBDObqi7peMFq0bBrjaLrJ+AZ/SY8e6PiGgwEq2fvrZAZAjSiIw0BAU943uLyyFHrcdVt+VzMx7RAOOGvBSm1sN5ff6Y5vIO2Y5M2SZsI2S3SRiVcX7jh9XemUY1zOrjiTYDEhENNsWVNYZ1v5IE9NfHItdVooEl2pDH4HgQEC3iLoeMM/6Apd3VRouwWas4NqYnoqHM6kbleCQJSLOZd/oxwhaYRAOPwfEg1pvthdTJTLXvncb6XUdistHMYhDRcKFNAlhteWlkRLodrZ3Wg2zZJmHxtROxY99JeH1+2Lsf281EBFG/YnCcgqxmZ0WZYwAx5RZWmPUzZi9jIhqO+qrHsWiNFl3OJAVR/xEFx9yQN0CM2ggtr27ASs/emOuabZRLZjE3O/3HjhRENBR56r0orqyJGgGtZbbhWAKwdFa2pVHTWm6XQ7hGiy7XtuEkooHB4HiAGLURUgCs33UkZtFWO0z0B3akIKKhxigZseKFvVFrbVlJDmS7cds2BeE+74tmuNEfjd2YpCAaWJyQN0BEi58CYFl1A6q27UdZSQ4AREov7AnWxCVaciEBkcckIhoqrEwcVf93WXWD4X0c8/mxY9/JhNbUZMvhmKQgGljMHA+QeIuf1+dH2aZGlG1sjGQ7Eg2Ml8zKxtJZ2ZYyHer1WedGREONlYmjQDhAFk0SneBy9EpG1+1yYInJuswkBdHAY3A8QMpKcuIGrYGggkCSDegVAH/YfQTP6TpSGHG7HFi3uABrSvundIOIqD+JkhE2SYqpQRbt8Wjr7MIYh/FYZ0mzmLsE11HtLJ+DNaV5prXITFIQDSwGxwOktNBtmj3oDVbiarU7BRdjIhqqRAFvUFFiapDVPR76ILe5LYBzHV0xH5qyTcK6OwpwuHIeDlfOQ8OquZaO6fTz/254uShz3RvuuusubNq0qc/un2ioYHA8gNaU5mHd4oKEF8Msp5zwrmkjDtnO03dENOSpAa86ptkuxaYltF0iSgvdMLgKgiEFIf2FSWY4fu/ZHrOOc00mSg0MjgdYaaEbO8vn4NHFBZYCXodsx6r5uVELvcshC3dZi7hdDvbSJKJhQ11rD1XOQ0iwf0NbU9zcFrB0v4GggtUvNkW1iXPKxh+tTtkWud6iWR/DohlujLO14YP1P8DJ39+P1ueX4YKWdxEMBnHXXXdh2rRpyMvLw7p16wAAN954I9RZAB999BEmT54MAAgGgygrK8M111yD6dOn49e//jUAQFEU3HfffcjJycFnPvMZfPjhh5aeE9Fwx24V/chs6If6v1Xb9sPr8xvuZpak89mNspKcqGEdVic9scE8EQ13E1wOw04SE1yOyFqaiOa2QCSY9vr8kO0SbFJ0aZtNAgIhJfK4igJsrvNiVvtelH7zTvzwhz9EMBhEW1sbGhoa4PV68eabbwIAfD6f6eM//fTTGDNmDF577TV0dHSguLgYc+fORX19Pfbv34+33noLJ06cwFVXXYW77747oedGNBwxOO4nnnovyjY2RjbYeX1+lG1sBGC8+cIotFXjXa/Pj2XVDVhW3QCXQ4YkAb62ACZ0b6wDgBUv7I1pXeRyyKhYkMvAmIiGtbKSnJg1UrZJaG7tELZyS0QgqCDLKcOZnhZJhrR1dsVko/2BIF5rHYv/3fILBAIBlJaWoqCgAJdddhneffddfOc738G8efMwd655HfP27dvxxhtvROqJz5w5gwMHDuCVV17BnXfeCbvdjgkTJmDOHE4/JbKCZRX9pGJLU0zniUBIQcWWJgDRTeoT4fOHMxbaTSUAosou3C4HHl1cgIZVcxkYE9Gwo5+OByCmNA0S0BaIqShOmq8tECnj2Fk+R1im0ZJ1BV555RW43W7cdddd+P3vf4+srCw0NjbixhtvxBNPPIGvfe1rAIC0tDSEQuFjbG9vj9yHoih4/PHH0dDQgIaGBhw6dChuQE1EYgyO+4nPb7wwqpcbNalPhraxvXZhZlBMRMORaDoegMgaOSIjDYGgtbaZsl2K264NiG0fZ7QJEABCZ0/i4osvxte//nVcW3Ibyn/9Z2R/9w/49MM7YL9sFtasWYPXX38dADB58mTU1dUBQFTXiZKSEvzf//t/EQiEP0/+9a9/obW1FTfccAOqq6sRDAZx/Phx7Nixw9JzJBruWFaRInpzXChHjxIRhVmZjhdvzVSnk9olCYGgghEZacKEBxDbdcJT7xXuA2l77w3k5+ejrQv4oA3Iunk5ulpOoWHDg/jCEwrGj8nErx59GADw/e9/H3fccQeefPJJzJs3L3IfX/va13D48GFcffXVUBQF48aNg8fjwa233oqamhpcddVVyM7OxnXXXWf+YhERAEBSEpi61teKiooUdSfuUFP40HbD02pZThn1D85FcWWNsKTC5ZBNF2I9tXcxEdFwN6V8q+EeDgnAocpwgGm2/jpkOxbNcGNznTcqyDYbAb10VnZkqJKauRadGVTXa9ExcD0n6juSJNUpilKkv5xlFf1k1fzcmHZrsl3Cqvm58NR70drRFXMbh2zHo4sLULEgFzaLndrYJ5OI6DzRdDzt5aIhIVlOGWsX5mHHvpMxwa0CcYvjzXXeyMQ9s5I57XptdcQ1EfU9Bsf9QG0NFAgqkbozt8uBqtvyAYQ7S+gzw1lOGVdnj8EDGxqxrLpBOO3O5ZCR5ZQjG+/Ypo2I6DyjwFefRNAPCVE3Mdc/GN7ELApQRZljfyAY2Wxttslau15bCeKJqH+w5riP6U+pBRUlsjCXFrpRXFljmFXoCASx8+Bp0/uWAMujSomIhiN9D3m7JMVMw1P/t7TQHUlmLK9uiPSUF/VFNuPzB7DSs1dYfuF2OaISGUbt5XgmkGhgMDjuY2abQQBxVsFKSyFmFIiI4lODUG3wqe1aof7eU+9F2abGSOcKr8+Psk2NWHzNxJiaYyue331UWO+sD3q1QbzRoCgi6j8MjvuY6HScdmFOBjMKRETWWelasfrFppiWboGggq1vHMfahXmR7LNVog4VCoyHP6nZayIaWKw57mOi7K56as+IQ7ZD0BITAGuLiYgSZZaoUDfPiQZ1NLcFIr3j3QmcsRP1Nk7kPoio/zE47mOzp44zvFyUUQDCmzSWzMw2/N3SWdkoK8lB1bb9kWlP6sJORETGzMrQVryw1/I6Kupsoe8o5JDtuHPmxLibAYko9bCsoo/t2HfS8HKbBMMOFOr6qvbIfH730Ujz+TtnTkTRpLExdXPLqxtQ+97pyG2IiChcQ7z6xSZhRlillleIesprJ+KVFrpR+95prN91JKqe2G6TMDo9DWf8gah64aJJY1lHTDTIMDjuY6JTeaLWbAoQqYFbU5oXE/AadbdQAKzfdQRFk8Zy0SUiQuzmuniO+fxYt7gA39sQ3TrTJgEVC3Kjrrtj38mYjXbq5Dx9ByHWERMNPiyr6GPJdJQwa/pu1m9T7YBBRDTcqb3lrVLXan2dsFHdMAd2EA1tzBz3Iv0pPJdDxi354/HcriMJ3c8Yh4ziyhrD03Bm/Ta5MBMRhSW6HrZ1dqFiSxMCutN6gZAS1dECEK/DbK9JNDQwc9xL1FN42to2nz+A6j1H4ZCNX+YspxyzWUO2SWjt7ILX54eC8y3f1M0iZSU5wpGlXJiJiMLM1sMspxxVRwyEO1IY1RsDsYG2lal7RDR4MTjuJaJTeIGQgkzZbriQrpqfGzOydGRmWsz96Kc5LZmVHRMgc2EmIjqvrCQHsj02lSDbJKyan4sRGdZPnOoDbaNx02yvSTR0sKyil5idwvO1BbBucUHMjmUgeqSpWXN57e/WlOZxBzQRkQm1q8Qfdh+JbLBzyDasXTgdpYVuLK9usHQ/osQDN9oRDV0MjnuJWS3wBJcjZiHV76Q263sMxG4K4cJMRHSep94blTCYPXUcNtd5dZ2Bzq+jojU7yynDmZ5mmnjQPxaTE0RDC4PjXlJWkmPYNki2SYZZB6MxpWbiBc9ERMOVp94b0/9d34cYiB4XbbRmy/ZwyYVZoGv0WCte2AvAeCQ0EQ0+DI57iboo6rtVVCw4v9Bqsw2JhrocN0pEZKxq237D/u9Gokrg9FcyuJGn3ouKLU2RzXqSBOhzFdqgm4gGP27I60WlhW6smp8b2aSh3fChZhu8SQTG3GxHRCSWSNs2dXNd1bb9wrZtKk+9F2UbG6O6WIhO4nl9fkwp34riyhrLo6iJKDUxc9yLzE63GWU2RGS7hBEGY0iJiCiWqH5YQnQyWJtosDLIw6jvsRlt+02AZRZEgxWD415kFACrp9usZjaynHLcmjciIjqvrCQnKjEBhAPhRTPc2LHvZEIDldTMsqfeK+x7HA/LLIgGNwbHvcgsE2HWzQIIZziWzMqOtGhbXt3ArDERkQVGez4y0mwomjQWa0rzDG8jCqi1bTbN2CXJdKM0J5YSDV6sOe5FoolMapCrHwSipQD40+teLK9uEE7HIyIisfZAKPLfPn9AuH76fD4c++efI4M8gudO4dzWn0YN8jALbmW7hEfuyI/aKP3BH8rRcfxA5GdOLCUavBgc9yLRSNHZU8dFSi70/Yq1WjuDwtZDREQkZlTW1tbZhe9V18cEyD6fD7/61a9Q+95pfHCmHfZRF8B1Szlq3zsduc4ElwNKKHafiE0Cqm7LR2mh2zSA5iZqosGLZRW9SM04aJvDT77AEdVvM6goMZtE4uHpOSIic+o62XXmBE5seBAZ4z+Ozg8Owjn1E7jz99/DxSPs+MoX78Dq1atRXl6O/Qfewf/5xufhmFyIUVfPw7FNq/Gc8is01vwZ0uE9OHv8FE5+eBYXLqrA6b8/gcBH70EKBbH8Bz9EaaEbfr8f5/76MM68/w7kCy6F0tUZORaXQ2Y5HNEgxuC4h/Q9MLUb6jz14TIJC600TfH0HBGROe2+jq7Tx3DhzcsR6mxD2/6duOhLP8OEMZmoe/VRvPLKK6isrMQL/70LE/7t8fD1z5yI3M/ug6eQ3vg63njjDbzynh/3fa8Mjkn5yP/iCtx7/SVY/bVSrLxnMX79618jf/LFOLbgBzjjfQfHn7kfQPhsYcWC3P5/AYio1zA47gG1B6a21U9zWwBlmxoBhDPIvTHXzuvzo7iyhpvziIgMeOq9aOvsivxsH3MRMtxT0VzzNPyH6nH8me/iOIDsUTYcOHAA2dnZEKUpQgA++9nPYuzYsSgdC6w59y+0n3wDrUf/jqo/Au3t7Thy5AheeeUV/PD+7+JsVg6qtqXj1EVTcNGoDKzW1C0T0eDE4LgHjJrIA0AgqCTUvs0K9s4kIgrTThsd45DR2tkVNQbaJmcAABQoGHPd7RhV8DkAwMUuBy64OgdAAOEeQbFsAEaMGBH5WVEUbN68GTk5xjXEpYVulBa6cfWG0XjyrmtQxPWZaNDjhrweMAt+k5mEB4Q3e0iA4cY9bs4jouFOP23U5w9EBcZajilXo+WNvyHUGV6r3zt6FGXP/i9eOdQCh9RpeJuZl18Q9XNJSQmWP7gW16/9b0wp34r87/4annovbrjhBvzhD38AALz55pt44403eu9JEtGAYua4B+L1LjbjMsh2AOGguOqOfCyvbjC8HTfnEdFwlsi0UceUqxE4dRQfPPt9AICUnokLb/k+fr37Q8z7zI34+x/uR+f4fIy8eh4ACUtnZeNjvpOorf0gch/58+/GL+/9Dvw1XwUUBSdcF6Ns5ESsmb8Ae/bswZVXXokrr7wSM2bM6IunS0QDQFJMmpj3t6KiIqW2tnagDyOG9hSedjCHUc2xFQ7ZjrUL87DihTfg1/TlVKm9M40Cb7fLgZ3lc5J7IkREg9yU8q2WzsrFG9LhtjhkqWD1dsNJeS6HjIZVcy0cCRGlKkmS6hRFKdJfzrKKOPSn8LSDOUoL3ai6PR8uh2zpviSEF+S1C8MTm4wCYyCcHRb1TGbvTCIazqx271HbZopYHbIkGiGd7GhpIkp9DI7jMDqFp9b+qhnlM/4A3C4HHl1cEDUxScvlkHGocl4k6/vAhkbhY05wOVBa6I5Mb9IG1dyMR0TDWbxpo1oKRNvuwriPg4iMsOY4DlGNr5p1UANn9edFM9yo3nM0ptSitbMrkqFY8cJe09N9anZY3QVNRERh2mFLVvZ8KAgnF0TX9fr8mFy+FUA4iVGxIDdq3c1yymhui80SZzmtnTEkosGHmeM4RKfw7JJkmFHese8kRmbGfucIBBVUbGnCAxsaTTeTZDk5WYmIyExpoRs7y+dg6azsuNdV92mIzupp+fwBlG1sjCq1WDU/F7I9Ov8s2yWsms9BH0RDFYPjOES1v6LM7zGfHz6DLAMQXnjNMsYO2c4Fl4hIw1PvRXFlDaaUb0VxZU1U4LqmNA9LZ2Ubtr4EovdpWC3HCISUqFKL0kI3qm7Ljypxq7otn0kMoiGM3SosMOpWsfrFJsNTbWadJszYJQmP3MEFl4hIpW6I1p5tk+0SRqSn4Yw/ENU9SL2+dm3Wl0lo1/J4n3yPLi7gekw0xIm6VbDm2AJ97a+n3ouW9q6Y68l2KZKl0C/oZtTWblyIiYjOM9oQHQgqkU4RRpND2zVdgHz+AJZXN6D2vdNYU5oXtZYXV9aYJjE4kZRo+GJZRRJEY6NHpKdFfu8PBCOn+twuh3Dzhl2SGBgTERmwMvRI23HCKJhWAKzfdSSmZVtZSU5MLbHofoloeGHm2AJ9WYUo2+DzB6IyxkFFiap502eT1YwxEM5i6IeMEBENZ1ankKpBtCiYVhAOnLXrqvrfohI5s/sjoqGNmeM4jIaAiHINog4WD2xoxPLqBmSk2ZDllGOGgYiGjBARDWdWN9GpXYXMBoR4ff6YDX2lhW7UPzhXeGbP6sARIhpa+jxzLEnSTQB+DsAO4P8pilLZ14/ZE/oscVtnV0zA2/yP9bDJDoyeuTBymUO2C2uM1Q4VPn8ADtmOdZqNHsWVNcIhI8weE9Fwpu1pfMznh8spo6W9K6qsTd+RYnl1g3Czndfnx7LqBqx+sQmr5oc36q307DXMHGv3kBDR8NKnmWNJkuwAfgngcwCuAnCnJElX9eVj9oRRllh0uk1tLK/NAlvpo6mvYxOdtuPpPCIa7vTJilXzc1F1e75wcmhpoRtLZmWbTsUDgOa2cAncSs9erN91xPA6I9LTmKAgGqb6OnN8LYB3FEV5FwAkSfojgM8DeKuPHzcpRps5VGderUbLm/8Nu9MF++gLkXHxx3Db5C5UP/ojnGtrw+92X45vLVuDH//9KA79rgwZE3LQfuQNhNpbccHnvovMidOghILw/c/vcPzIXkz/Qya+/e1vY4LrCsOaOp7OI6LhTN/GTS05W7swDzvL50RdT79no2jS2LgT9PyBIJ7ffVSYZT7jN06MENHQ19c1x24ARzU/v999WUoSZWs7PngHrW+/gvH/9hguur0CnccPQAHwH8u+iZbpi3Hu5rWoPTsS1b9eh0Uzwk9PCQUx/svrMPbTX8eZnc8DAFre+BukDCeK7n8Cr732Gp566il8eZrDcMgIT+cR0XBmlKzQn3kzOtu34oW9qH3vtKXHMBvKxAQF0fA14N0qJEm6B8A9AJCdHX8UaF8S7YzuONoE58evg03OBAA4PnYtlEA7Am0taBn7cQBA6GOfwvY/V2Jv9q0AAOfHrwcApF/yMXSd+RAA0H7odQQ+eg/HTryOmX+UcebMGUxJP4e1C/NihozwdB4RDWeiZIV2jRYF0Ot3HYk75AMIb6I2CpAlALOnjmMXIaJhqq+DYy+AiZqfL+2+LEJRlCcBPAmEJ+T18fGYKivJSWh4hxnJ3r37WbJBCZ2/v0X3rcTzq78Zc30uukRE54mSFRLCGePSQrdp67Z4HLIdi2a4sbnOG7XmSwCuv3xs1OVGw0aIaOjq67KK1wBcIUnSFEmS0gF8AcCWPn7MpJUWug031mVMzEXbgV0IBToQ6miD/+AeSHImbJkj0H70TQBAa1MNMiZOM73/zClX46U//h6b9hwGAPzrX/9Ca2trnzwXIqLBbPbUcYaXqz2LgeRLH7KcMtYuzMOa0rzImq9u8Fu3uACHT/njlnQQ0dDVp8GxoihdAO4DsA3A2wA2KIrS1JeP2VOlhW7sLJ8Ttds545KPYcTUT+L4b7+DDzdWIP2ScCnFhfO+h+Ydv8Wx39yHzg8PYUzxnab3PTJ/LmxjL8VXFszBtGnT8I1vfANdXbFjqImIhrsd+04Kf6dmjI36IMfrVAEALR3hdVffDUMtnWAXIaLhTVJMNiT0t6KiIqW2tnagDwNAuP+wlclMyZAAHKqc1yf3TUQ0FEwp3yosj1CHdqitNiWcb685e+q4mFIJ0X20B0KGU0tFnS7cLkdUpwwiGtwkSapTFKVIfzkn5AlYmcwk2yXTLIVN8EvugiYiMme2Tp7xB6J60CsAZJsUaeOWkRb/o625LSAsnTBa/9lFiGj4GPBuFalK3XTxwIZGw93MdklC1W35AICyjY1RE5tUBhdxgSUissBog7QEIFO2wR8IxVw/EFJQsaUJHV2hHm2qPubzx0zmY7cKouGFwbEJdSHUL9DqqTcgvHgaBcZGJACLZri5wBIRCWjrgMc4ZGTKNvjaApEAdXl1g/C2vl4Y3DHGES7ZKC3kWk00XDE4jkOUQQBig+Z4FADrdx1B0aSxXHSJiHT0U/F8/gAcsh3rFhcACK/Dfb1LRrKyo4+IhjQGxzrarIXLKaM9EIycwstyypFTa8WVNZYC464zJ/DhptWY8NVfAQgHyGUbGwEk1i/T4/Hg4x//OK666qrEnxQR0SAgGurRG+USVvnaODaaaLjjhjwN/SjS8IaN87VtzW0BfG9DAwof2t6jThaBkJJwv0yPx4O33nor6cckIkp1olZpPn/s5jmrZLsEV3ephBXcME1EDI41jLIWeiEFUbukrVBCIZx8sQrep76Jk3/6CUKBdhzatxef+tSnMGPGDJSUlOD48eMAgKeeegrXXHMN8vPzsWjRIrS1teHVV1/Fli1bUFZWhoKCAhw8eDDp50hElKoSDUwlmPc1drscqLotHxULciGL2gdpOGR7ZGz0lPKtKK6sgafeG/d2RDS0MDjW6K0G77JNivThBICu0+9jVOE8uL/+BKQMJ869vhUtO57Epk2bUFdXh7vvvhs//OEPAQALFy7Ea6+9hsbGRlx55ZV4+umncf3112PBggWoqqpCQ0MDLr/88l45TiKiVCJqoaZdT7XGOGTYBEXCak/i0kK3cOO0Q7ZFTcdTx0mrZw/VsdEMkImGF9Yca0xwOZIul7BLEoKKEmlCv2PfyUiG2T5qHDIvDdcKj8idjXO7NgCnjuCzn/0sACAYDGL8+PEAgDfffBMrV66Ez+dDS0sLSkpKeuGZERGlvkQ2QMs2Ca2dXYatNrUZ4GPdga6R9kAoMtTDU+81bN2p9j7mJmqi4YPBcTdPvRetHcmNctZOTdLvtgYQdd5vZHoapky5BNIlDvzzn/+Mua+77roLHo8H+fn5eOaZZ/Dyyy8ndUxERIORWQs1bdDc1tllWOJmkwAJCp7bdSTuY6llHOq6bRRoAxwbTTTcsKwC5xdGfY/MEel2OGTzl0gCooZ6GNUtB8+exOgz7+Jw5TzMlv+FW0tm4+TJk5HgOBAIoKmpCQBw7tw5jB8/HoFAAOvXr4/cx6hRo3Du3LmePE0iokFJ20VIzSaL9n6EFKDNYEiInnYgU7z9JtykRzS8MDiGeGF0OdPx9o8+h8OV84SbPhREt2QzKstIG3sp3n3lBVx55ZVobm7Gd77zHWzatAk/+MEPkJ+fj4KCArz66qsAgJtvvhkf+9jHUFxcjKlTp0bu4wtf+AKqqqpQWFhoaUNeRUUFHn744bjXIyJKZSs9e7G8uiGqDrhsU2PS96fWF69dmBdZu80yw5xqSjT8sKwC4oVRe7moHtntckSyGoaB8ZiL4f76E1GlFwBQUFCAV155Jeb6c+bMwejRo/GLX/wi6vLi4mK2ciOiYcVT78X6XUdiaoYDweRHgRiNghat73ZJigqiiWh4YOYY4lNm2stFu6hnTx0X6Y2s9eELa3D8mftx7P/di5aG/0JZSQ5GjhyJsrIy5Obm4jOf+Qz27NmDG2+8EZdddhm2bNkSue3Ro0dx44034oorrsDq1asBAIcPH8a0adMi13n44YdRUVEBAHjsscdw1VVXYfr06fjCF74Quc5bb70Vuf/HHnssuReHiGiA9MVEPKMOFKL1/ZE78hkYEw1DzBwjvDDqN9FpT6WpmWF/IAhJAtQ9GxlpNrzUeNywJOOCz90Pu2MUQoEOfPD75fhkdgVaW1sxZ84cVFVV4dZbb8XKlSvxt7/9DW+99Ra+8pWvYMGCBQCAPXv24M0334TT6cQ111yDefPm4cILLxQef2VlJQ4dOoSMjAz4fL7I5fv27cOOHTtw7tw55OTk4Fvf+hZk2XozfCKigZToRjiXQ7Y0SU/fgULUJYOBMdHwxOAY5gujvvuEdjOzfgOf1rm6LWj7V3jDXejcKRw4cADp6em46aabAAB5eXnIyMiALMvIy8vD4cOHI7f97Gc/iwsuuABAuO/xP/7xD5SWlgofa/r06ViyZAlKS0ujrjdv3jxkZGQgIyMDF110EU6cOIFLL700kZeGiGjAmLXXtAHQbruTbRIqFuQCiF7LRbfXB95mXTKIaHhhcNxNtDBamZqn137kDbQfbsQlX3oYaemZcP5tDdrb2yHLMqTuhvU2mw0ZGRmR/+7qOt9GTtI1tZckCWlpaQiFzn8UtLe3R/5769ateOWVV/Diiy/ixz/+Mfbu3QsAkfsHALvdHvUYRESpzuisngTg+svHYs/hZoS0tcfdy6Z+LS+urDEMkNmBgmhgGXWhSZUvqKw5jiOZ/pahjjbYMkfAJmei46Oj+Nfe+oRu/7e//Q2nT5+G3++Hx+NBcXExLr74Ynz44Yc4deoUOjo68NJLL4UfKxTC0aNHMXv2bPz0pz/FmTNn0NLSkvAxExGlmtJCN9YuzIuaYrducQEOn/LHbMoLBBWsfrEpZvSzUT0xAJxu7eCIaKIBop6VT9VplMwcm/DUe2HrnnxnZkS6Ha2dmnrlKTNwrv6v8D71TcgXXAqHe6rJrWNde+21WLRoEd5//30sXboURUVFAIAHH3wQ1157Ldxud6TNWzAYxNKlS3HmzBkoioLvfve7cLlciT1RIqIUZXRWb3l1g+F1m9sCkf7H6oft2oV5WDTDHdP1wt/dC1m9nvpYRNS3BsM0SkmJE/j1p6KiIqW2tnagDwOAYNKdgGyTMDIzTdiUXgJwqHJeLx8hEdHwYtY2U8TdXT4R7zb6dptE1LvCcdUbkS+mIof7MV6SJKlOUZQi/eXMHAskUmscCClQlHAQbPRVg7VtREQ9k0jCQstqIM0R0UR9x1PvRdnGRgRC5glZuyQauda/WHMskOhCecYfwJJZ2TGT9DhdiYgoPk+9N6ZeWMssYeF2OeBy9KxNJZMYRH2natv+uIExgLhlrP2FwbGOukCL/nlE32nGOGTs2HcSCs5/89GPKCUiolhWNueIEhYSgJ3lc1CxIBeyLfms0+yp45K+LRGZs3oGx50iX1IZHGtoF+hEnW0PRG4XVJRIxpiBMRGROaOssLo5R+VyGmeG1ctLC91ITzP/SDM7Zbtj30mrh0tECVjp2Wv5uqlypp3BsYaVOmNRRll/tkC/sBMRkTFRVli93FPvhU+w4Vl7FlbbNchISFGEZ/9Yc0zU+zz1XqzfdcTSdW1S6nSMYXCs0duLo9fnT5mefUREqUpU7zvB5Yhs5BElJs6YTCo1uj/RY9kkies1US+r2rZf+N7Vs1CS3G8YHGv0xYaMVGpqTUSUiowGdailaRVbmkw38riccmQjn1nFsXp/oqEgQUXhek3Uy5Jpu5gKGBxrWN2QYU9g0wfLK4iIzBlNwlM3M/viZIab2wKRjXyiEDrLKUfuT30so/pjrtdEvSeRL5qp1tmLfY41rG7IsAEY7ZThawtYmqDHWjYiInNGk/C0Qu0taH3rfzDqausDAtwuh+HG6NJCt3DKXk/X60cffRT33HMPnE5nj+6HaDBTGxxYlWqdvZg51rC6KAZCCpzpaThUOQ8hCz352D+TiCg5Wd3dKEIdrThXvzWh27Z2dAl/N0bQFzneeq0oCkIh8YSvRx99FG1tbdYOsFtXl/g4iQaj1S82WR7Ys3RWdkoFxgCD4yiJBLFqIB3vNql2qoCIaDBZNT8Xsl1C88vPoMv3AY799jto3vEbnNm9Gcd/txzHfnMffP+7HgDQdeYEvE99Ex9tXQfvk/fgwIaf4LsP/x5XFlyDK664Anv27AEAfOEb38O7mypx/NkH4H3y6zjX8F8AANkm4WMf1OCaa67B9OnTsWrVKgDA4cOHkZOTgy9/+cuYNm0ajh49im9961soKipCbm5u5HqPPfYYjh07htmzZ2P27NkAgJEjR0aey6ZNm3DXXXcBAO666y5885vfxMyZM/Hv//7vOHjwIG666SbMmDEDn/zkJ7Fv3z4AwMaNGzFt2jTk5+fjhhtu6PsXnKiHPPVeNAu6y+il2yWsKc3r4yNKHMsqunnqvaZZBj11F3VbZ+xt1DHSolN6RERkjbp+/qjrm9j7m/cw8e5foOXdOrTt34lLvvwzAApObv4R2o++ibTR49DVfAzjSsshX3g/PvjdcpzeW4NL76zED65qwU9+8hN4PB7sPHgK7ScO4ZIvPQIl0I7jz9wPx+XXwH7Oi4zMD7Fnzx4oioIFCxbglVdeQXZ2Ng4cOIDf/e53mDVrFgDgxz/+McaOHYtgMIhPf/rTeOONN/Dd734XP/vZz7Bjxw5ceOGFcZ/b+++/j1dffRV2ux2f/vSn8cQTT+CKK67A7t27ce+996KmpgYPPfQQtm3bBrfbDZ/P14evNFHvqNjSZPm6zvTUDENT86j6mVobY/UUAAA0t3agbFMjAsHosgqXQ0bFglwGxEREPeSp96Jq234c8/kxFsAlYxx45I58fOUb/w/+Q/U4/sx3AQBKZzsCp48hbfQ4pLkuRvq4yQAA+cJsZE7Kx/Ez7cjLy8Phw4cBAOfaA3BeMQs2OQOQM5CZnYfO4//CufffwvYPX0dhYSEAoKWlBQcOHEB2djYmTZoUCYwBYMOGDXjyySfR1dWF48eP46233sL06dMTen6333477HY7Wlpa8Oqrr+L222+P/K6jowMAUFxcjLvuugt33HEHFi5cmOQrSdQ/PPXeuJtotRJpxdifGBzD2vAPvbaAcc2ZJIXvb3l1AyYwc0xElBR90uLE2XacPNsOALhy/Ci8M/Z2jCz4XNRtus6cgGTX1BJLNkh2GWMcMm7/9S685W1GcWUNMmU72vUDQyQJIzLsWLFiBb7xjW9E/erw4cMYMWJE5OdDhw7h4YcfxmuvvYasrCzcddddaG9vN3wekqYrhv466n2GQiG4XC40NDTE3P6JJ57A7t27sXXrVsyYMQN1dXW44IILDB+LaKAl2u0lVfdkseYYvdtNQttWyOvzo2xTI/tmEhElqGJL9IYeKd2BYEcbqrbtx0PfXoKx3p24xBEuY7tQasGtU0dgdGZsvifNJqG1swsnugNrr8+P9kAQbe/sgtLViaD/LNqPvIkR7o/DMflqfHf1zzBr9VZ46r3wer348MMPY+7z7NmzGDFiBMaMGYMTJ07gr3/9a+R3o0aNwrlz5yI/X3zxxXj77bcRCoXwpz/9yfC5jh49GlOmTMHGjRsBhDf9NTY2AgAOHjyImTNn4qGHHsK4ceNw9OjRxF9Mon6SSDyVynuyGByjb7+5BIIKvrehAVPKt6K4soaBMhFRHEanZu2O0chwX4XXHvk3/O1vf8O3v3YXjj/3AD585jt48/er8ErT+1j2mY/jkjGOyDABCUBXSIkpfwOA9HGT8cHzK/DBsw9g3CfvRNrICxB0T8eIqz6F13/xbXyh5BOY87kFUYGuKj8/H4WFhZg6dSq++MUvori4OPK7e+65BzfddFNkQ15lZSVuueUWXH/99Rg/frzwOa9fvx5PP/008vPzkZubiz//+c8AgLKyMuTl5WHatGm4/vrrkZ+fn/DrSdRfrMZTkpR67du0JMVCK7L+UlRUpNTW1vb74xrVHMs2CZBguKj2hEO2p/QfBBHRQCuurBFO1nK7HNhZPsdw3VbXVwCm+0h8/1gPSXZgzMxwDa9d0K9efSwissZT78UyQQ9xLQnAoUrrPcv7iiRJdYqiFOkvH/aZY3XDhz8QjExMcrscqLo9H1W35ff6OENOYCIiMmd2alY9DWu0V0RdXxPdRyIa5MQBTkSJsZr4S9VaY9Ww3pCnzzwEFQUO2Y7ZU8dFdki7nDJkm4RAqPcyyFxwiYjEJrgchpnjLKeM0kJ3uB5YsI5aWV9dn1gS9bMoc5zqH+BEqUj0flKlcq2xalhnjkWZh/W7jkQ21TW3BXo1MAa44BIRmSkryYFDtkdd5pDtWDU/N+5Y2gkuR0JrrN0mISNNirl8MHyAE6WiO2dOFP7O7XIMitLSYZ05FmUY+rIKmwsuEZE59YNTPYOntsUEgAc2NAqzUtr1tWxjo6XERjCkoM3gerbYeJmITGj7kjtlG/xdIShKOJN858yJKTkJT2RYB8eiU3d9Qep+PPY9JiKKr7TQHSmhqNq2H8uqGyLTR0XUjJSn3htedHugtTOIsk2NkWMhIjFPvTdqMFpbIATZLqHqtvxB+f4Z1t0qkpmMlwzueCYiSlyia7Ra6xiv5jERXL+J4it8aDua22Kn3WU5ZdQ/OHcAjsgaUbeKYZ051p+6s/VwQZUApNmlqPZvEoDZU8f18EiJiIafZLtOWF3H42WiAW6gJrLCKDA2uzzVDesNeUA4QN5ZPgeHKuch1MNMgwIgqOuLrADYXOfl8A8iogT1RWBqlyRICGeEl8zKjnt9bqAmEvPUe1FcWTPQh9HrhnXmWK83apBDBpepvTcHY90NEdFA6Yt9ISFFiRo+8FLj8ZhpfCrZLnEDNZFAf5WmDoRhnznWMmof1Ft4ao6IKDF9sSbrM8EVC3INH2NEun3QbiYi6g+rX2yKGxi7HHI/HU3vYuZYQ1+D3JtbFXlqjogoMdo1uTcyyEatNEVt4xgUE4l56r1x64llm4SKBbn9dES9i8ExonvzaRfG4soa0wU5yymjpb0rbi9N9jYmIkqO2tIt3npshWj4gPoYRGRN1bb9ca+z+NqJg/Z9NeyDY33NjNfnx/LqBtS+dxplJTmmjeSb2wKQ7RIcsg3+gFG1cfiUQsWC3EH7B0JElApmTx2H9buORJ3Rc8h2ZMq2Hu+I99R7sfrFpsj9cN0mMmelVHTHvpP9cCR9Y9jXHBu1ClIArN91JPz72/NNa2YCQQWdXeLMcUeXcdBMRETWeOq92FznjQqMJQCLZrixar5xzbARfbbLU+9FwertWFbdEBVg+/wBLKtuQMHq7ew0RGTASqnoYN5rNeyDY7MR0mqHiYZVc3G4cp5w4JJZT021UwURESVHlMTYse8kSgvdWDTDbWkgnna9V88aijpVAOEgecULexkgE+lYmd8wmPdaDfuyCrNWQfrAWXTdeNOY+mtENRHRUCRKYqiX79h30tIGau2HtdUBI2zFSRSm3Z9lk8y/jsq2wd0GcdhnjstKcoQZB/23HqO2Qg7ZjjtnTjQ9rWeP80dERERiogyUermV07cSwomK4soarPTsTShpMZhPDxP1BvVMi7e7k1e8KZRVtw/uNojDPjguLXRjyazsmABZ1PJn7cI8uF2OyISltQvzsKY0D2sX5gkfoycjqYmIhjtRYkJdo0XBs5qY0I6J9vr8eK57T4lVg/n0MFFvSGSUu8shD+rAGGBwDABYU5qHdYsLooLeRTPcqNq2H1PKt6K4skZYc1b73mkUV9ZgeXWDMEPs5sJKRJQ0UWJC/QAWBc+P3JEPt8vRo571bMVJlNjZk6FwslxSUiirWVRUpNTW1g70YRiORHTIdiya4cbmOm9CoxIdsl3YW5OIiHqHqF/9lPKtSQfHdknCI3cM7tPDRL0hkT7jEhA1oj2VSZJUpyhKkf7yYb8hz4jR6QN/IIjndx+1VCJhlySEFIWTloiI+ol+kIen3oviyhrLgbG29AJgYoNIy6jPuMhQKENicGxAdPrAau1wUFHgdjlwzOePtHHjAktE1PuMMsYAYs7+mVHPDO7YdzJyP7OnjkPVtv1YXt3ARAcNays9ey0HxgCGRBkSg2MDybZs01Jv7/X5seKFvQAYIBMR9SajCacrXtiLTNmWUPnbpVmZMYGxtoSO6zgNV556b0KB8VDYjAdwQ56hZFu2iXAQCBFR7xOVwCU6TvrAh62RFlVqNwuj++U6TsNN1bb9CdXs35I/vs+OpT8xODYQr2Wb9vKls7ItdaNgn0wiot7V3+sq13EabhL9m9+x72QfHUn/YlmFgH5zh9nlnnovllU3mN7fGIeM4sqamJ3URESUHLMJp0Yb7GwS0NppvdzC6PGIhhOz95iRofIFkpnjHlJr3uI519EVddpuxQt7hb2TiYgoPqMSOJUCRIY7qb3rO7tCST8W+x3TcJTo3/xQ+QLJ4LiHrE6NCYaiq3ZYv0ZE1DPaEjgjCsKB8c7yOdix7yQCoeQ6HtsliW3diOIYSl8gGRwnSe2hmcjpBr2hcvqBiGiglBa6sbN8DkRDudR11my91e4d0d+POmmPgTENJ2qME69kVDS1crBjzXESjCboJWOonH4gIhpootpIdZ0d45Dh88d2sXA5ZBRNGosd+05C6r6eJAG+tgD3h9CwZDXGkSQM2fcHM8dJsFpKoZJtEmR7dD5iKJ1+ICIaaKIWnOo6KwlSy51dQax4YW9kT4jPH0B7IIR1iwuws3wOgPDo3CnlW1FcWcO9IjSkeeq9eGBDo6UYR1EwZPdPMXOcBLPTc+7uBvLahvLq4qyf4jQUv20REQ0EdT0VrbM+Qe/jtkDsJj3tnhCjISPaxyMaKtSMsdVhZ8D598pQez9ISgIvQl8rKipSamtrB/ow4ip8aLthk3l144cZo1GnQ+2Piogo1SS6R0SCuFTDylpPNNgku49KAnCocp6l6yqKAkVRYLOlRuGCJEl1iqIU6S9PjaMbRDz1XrS0d8VcLtuluGUSnnovyjY1RrV0K9vUOCRPSRARpRKjsgvRJj4gHBiLzhJyMzUNRaK/67N7/oRjT9+LY0/fi7Ov/RnNLz+Dc6+/FPl9V+0GPPzwwwCAqqoqXHPNNZg+fTpWrVoFADh8+DBycnLw5S9/GdOmTcPRo0f7/sn0EIPjBFVsaTJsBzQiPQ2lhe7IDk+j+rTVLzYhEIy+bSCoYPWLTX1+3EREw412Pa7ath+LZrijdtebnTctK8kRbprmZmoaioz+rjs+eActe/+OS770M1zypUfQ0rgNI678JFr3/QNAuK4f7/4Tixcvxvbt23HgwAHs2bMHDQ0NqKurwyuvvAIAOHDgAO699140NTVh0qRJ/fq8ksGa4wR46r2Gu50B4Iw/ELPD0+vzY3l1A5ZVN8DtchiWYgAQXk5ERMkxWo8313mj2k2JTiM7ZFvkOvpd+9xMTUNVWUlOTOu2jveb4Pz4dbClZwIAnB+/Dh1HmxBq9WGcrRVfnJaFTePHYeLEifj5z3+O7du3o7CwEADQ0tKCAwcOIDs7G5MmTcKsWbP6+ykljZnjBJgN7Zjgchh2sVAzE/HqeFhaQUTUe4zWY38giAc2nC9lKyvJgWyLLa7oCinw1HujhowMxV6uRFpW/65vvdqN8m//G750yQc42bgDixcvBhCuJ16xYgUaGhrQ0NCAd955B1/96lcBACNGjOiz4+4LzBwnwKzOrKwkB8vjNMs2wx3QRES9R7ReBxUlar1d/WJTzNm7QFCJ7MBX/49oOMq4NBen/vIoRs+6DVCAtgP/xLd/+QOkp6fj61//Oj766CP8z//8DwCgpKQE//mf/4klS5Zg5MiR8Hq9kGV5gJ9BchgcJ0C0c9kp21C1bb9p/Vo8Q7UdChHRQBCt10D0eitq8cZNdzTcGJ3BzrjkYxg57dP44PffAwC4Z94SKZs4d+4c3G43xo8fDwCYO3cu3n77bVx33XUAgJEjR+K5556D3W6Pud9Ux1ZuCTCaGiPbJEBCzEa7ZCTSDoWIiMTiTflS11tR3bHaro3tN2k48NR7sXxDA8xCQgnAusUFQ+rvn63ceoFR/dnIzDRhYGzWJsgId0ATEfUOdb22C0bjqeut2WQ9NcDWtt8cqhPBaPhS28zGy5UumZU9pAJjMyyrSJC+/mxK+VbD66nfsIzq2YxwBzQRUe8y6zgxe+o4FFfW4JjPjzEOGZmyDb62QFR2uLiyxnBTH0vgaCip2rbf0tnvNaV5/XA0qYHBcQ+J6tpcznARervBaFIj3AFNRNT7jMZKz546DpvrvJHA1+cPwCHbY04ZcwgIDQdW/p7dw+zMNssqeqisJAeyPfa0XUt7Fyq2NAnr3bQSLb8gIiLrSgvd2Fk+B4cq52Fn+Rzs2HfSMCOsH8jEISA0HFj5ex5uZ7YZHPdQaaEbI9JjE/CBkCIcGKKnwLyHMhER9R5Rpqy5LYDJ3dNNV3r2orWjK+Y6EoDZU8f18RES9Z+ykhzYDfp9q7Kc8rA7s82yil5wxmIQbOaYzw9PvRcVW5oiQXWWU8aq+bnD7o+SiKgvmbV5A8Ib757bdcTwdwqA6j1HUTRpLNdmGtTUTixm7wWHbMeq+bn9eFSpgZnjXtAbp9jGOGSUbWyMyjY3twVQtqmRO6OJiHpRT08RB0IKKrY0xb8iUYpa6dmL5dUNpoGxXZKG7X4oBse9wKgVUCIcsh2SFF5w9dRJTURE1DtKC91wOXo2uctq2RxRqvHUe7F+15G4g8tCijIsA2OAwXGvKC10Y9GM5P+AMtJspu3e1Po4T70XxZU1mNJdE8eMMhFRcioW5PYoqUE0WFmd6Kt23RqOWHPcCzz1XmyuSz5Q9fkDkADhH+sElyNm2pPajB7AsP1mR0SULHXdtNqLXs8pM7dEg5PVVoQdFrptDVV8d/eCqm37LbVsMyMKjGW7hLKSHMPHUJvRExFRcqz2otfLYNaZBimr+6TaknxvDAUMjntBbzaE19bBZTllVN2Wj9JCN5vRExH1sp4kNpLJNhOlArYijI/BcS/orYbwdklCw6q5OFw5D5l/XQVnehqWVzeguLIGY3SbRz549vsAgLHKGUybNq1XHp+IaDjpSXLBLnF8Ew1OO/adtHS9nm5aHcwYHPeCnnarUAWVcHGFp94LacEaeH1+KAjXF7d2dkHWNOm+5EsPwyHb8Y0bLuvx4xIRDUc9SWyo6zXRYGPlS6Fsk1CxYPj1N1ZxQ14vUDd2VG3bj2PdAW0y7JKEKeVbYZMkHHpkES6971mcfOFHCLW3Qgl24dLP3gX39BtwzOfHkXW3YfOud1CQFcBjJvepNvk+5vNjgsuBspIcbuAjIkI4saHd6JwIZo5psBrjkE1bEboZKzA47i2lhe7IH1JxZY1pY20RNROh/q+Ulo5xt66ELcOJYNsZHH32+zi4fhUkScLIX9hRWujG4cOHhffHDhdERGKlhW5srD2CnQdPJ3xbZo5pMPLUe9HaGTsWHQCKLx+L9V+/rp+PKDWxrKIPzJ46DvFyCrJNQpZThgSTDISiwPfK73DsN/fhRPVKhFpO4cSJEwCAYEhBcWUNPvHTGrz7Uathz2N2uCAiEvPUe/FqEoExAEjdtycaTKq27UcgGPvFLsspMzDWYHDcy9Sex9o/PQnhb2RulwMSwqcsFl87Ec70cOJelIFofetlBNvOYvxXHsXl9/wKF4y7CO3t7fDUe9HRFYpkp7uCIax4YW/MQs0OF0REYlaHIRhRum9PNJiIPv997L4ShcFxLzPK1ioADp/yY2f5HByqnIeykhxsrvNGNtyJhDpaYXeOwdhRDnxx4lmcPP5+5DH0jDLCos0mvdVdg4hoMOtpooCJBhpsGBdYw+C4l1nJ1lrqrSlJGHHVjej44ACaHr8Hjz/5G7gnf8zyY3jqvWgzqCtyyHaUleTEexpERENeTwMCBeE9JiyvoMHCqLsW44JY3JDXyya4HIab8bSLcLxsQ9B/FrbMkbA7x2D8lx6JXC7bJZT+7l9QAGR/bxMAIG3MxZjw1V9FPYZ+I57K5ZBRsSCXm/GIiBAOFJZXNyRdWgFwozMNLvruWuxiZYzBcS8zag2k/1YmCqABoOvcKZx4fgVGX7sw5neBoGLafqWtsyvSus0oMz0iI41vACKibqWFbtS+dxrrdx3pUYCslrVxfaXBQNtdi4yxrKKXlRa6sXZhXtTmu7UL86L+EI1Oa8g2CSPS7UgbdQHc9zyJ0TPmJ/zYzW0BlG1sFAberI8jIoq2pjQP6xYXRNbsLGdyU8G4vhINHcwc94F438pEpzUAoGJLk2l2OJ5ASIEEGGZBXEku+kREQ5l+zV7p2ZtwNtkhM9dEqYnDwBLH4HiA6BdjUZ1wMkQLuqhnPd84RETnrSnNQ9Gksajatt/yQCd/V6iPj4oocRwGlhx+1U0RojphuyRFTvXJtp6NK/X5AzG7qj313kgphoLwG6dsYyN3XxPRsFZa6MbO8jk4XDkPS2dlxx0XzYF5lIo4DCw5zBynCFG9WlBR8OjiApQWupM61adXtqkRFVuacMYfgMspo9mg8XcgpKBiSxO/VRLRsKcOdoo3Ljpe8Ew0EDgMLDkMjlOEWQeL5dUNqH3vNHbsO9mjwBiI7nhhFBirelL3TEQ0VFjqSw9g1mVZ/XA0RImx0l6WYrGsIkUYdbBQKQDW7zpiufYNAJzcHEJE1GNW1909h5pZjkYph0M/ksPMcYpQSxiWVTcY/l5B+LRdvFN7QPgPP0O2oS2Q/AaRdLuEy1f8BUFFgV2ScOfMiVhTmpf0/RERDWWBkMJex5RyOPQjOUwvppDSQjfcJqc6gooS8w1QrXJT693Uvso+k5IJI11nTuDY0/dG7rMzqEQC8aCi4LldR7DSE97h+t3V6zD+ulJMKd9qeXTqXXfdhU2bNiV0TEREgwnrOCkVqZtLD1XOw87yOQyMLWDmOMWYjTN1d3/jE30DVFuyLa9ugM1illkvyymH640Nbvr87qP4/+3de3xTZZoH8N+b9LRNKRDuQgBB5CJYoVIF7OoIzoAriBVFYFXAua2OuuJosTheyiw74FYHhtEZV0dFB0ZBLhHEGdABdxQEpyVgtwgqokC4X8qlTds0efeP5IQ0zUlO0qRJ09/38+FDc3JO8iZNkyfved7nybu0I94tO4SqunpkoHFZGJaFI6JUoveMHcA8TkoO/BxuOgbHSUarnalJMWL0oC4hA2P/WoZ638zVmedu7TJhaG+C7ZmxsNz/Gk6s+Q06jXsQp//+CmS9E4a0dHS6ZRZKNmTC6XKj/txJHP1LEVwXTqHN4NEoafMTHD98EA/fNxXdf/wSAGD3hmX46Qe1wO+ei9XTQ0TUrKaN6IWl2w7o2rdPJwbHlFisaxwbTKtIQoHtTC1mE+4YbsGqMnuDesRzVpf7UhrC1UkOZeGUYQCAo2cduPqxN3FyzW/QefwsKJ164ZK7/xs97luM9tffg8p/vOU7bVh35Ct0uf1JdL/vRVTv/RT7v/wC//OPbyEDgvJ6t5v1FImoxZpXkNOgzrFRCOT364h0Y+N31i37TvvSz4gSYfbKXaxrHAOcOU5SgR308hds0nzBF+RaNHPd3FJi4ZRhmqkaEJ5FgPXnauCqPofyJU+h6+1PQuncG/XnTuD0+t+i/sxhAALtMgR6mE3YCyCzzzAYTe0AAFkDrkPGqa9xLKtd0DEcrnSgfxTPARFRMphXkNNoQfJlc9YH3fcv2w9w8TIlxN2vfoY6V/CzxsyHjwyD4xYiXCHvULUMSzbs1dVS2pCRhbR2XVBzaDeUzr1R+clSZPa+Cu0mPYWeaefx+UuzLt6HX8H7NIPAmCu6Yps7A8f9blC66gAA5iwlgkeaOIF5Wn06mbDt2zOs2EFEjbg13lS1thPF25Z9pzWvYz58ZBgctxDhCnkXjhvYIM8I8OQTjx7UBct05ssJYxq63P4rHF/xDAzpmXDXVsHYthMA4P8+Xgu337t+zXc74XKcR4e2bYCjNjww5ZcoXXscruqzcDnOwaCY4PjmnzBddjXOVDvxQfkRdB1yCnci+GIBoPlLzfiPw5yl4EJNPZzex2ivdDR4vtWKHQAYIBMRUVIJVzWKdY0jw+C4hQgW/AYW8hYB88MSwNJtB2AQDWeIQzGkZ6Lrnc/g2PKnYep3DSr/902c3bocpn7XNNgvvfsAnLD+BierTqPo4Z8hLy8P51auR/vrpuLoW7+EMbsTlE49fftX17mwbPsBpFvLsarM3mCxQOG7uwDh6d6nbov3AoLARQuhugX6e3v7QQbHRESUVOauqwh5PRfjRUYELqBKpLy8PFlaWproYSQtrfIsgYFecxIA9i8YD8CTFx1JF79wLGYTthSNidnt+WvKWL/zPl4iar36FAXPOQb4HkHNy2qzazYQA4D8fh2x7Gejmm9ALYgQokxKmRe4nTPHLUjgIj2VVqWK5tDDbPIF7fZKBwSClkiOSrA861jVb4x2cYJRhKv9QUStgUUj1S1UIyeiePjVmtAVUibn9W6mkaQOlnJLAbFahdohwoVzikFg9KAumLO63PchIYGwpeP0ClxAoM6Qa5Wza8pt6zVtRK+Q11ttduQv2BRR90AiankKxw1s1LE0MNWNKN6sNjuq6kJPjrGMW+SYVpECYpHOYBDRrbLW6h5lNnk77emgGESDnGMAUIwCbdLTcNbh9M0Qq7PTwVginEUOloqi3qfWuNukG1Hx65sjuk2TYsT8STkNugfaKx2+5y3ScRNR8mAnMko0PZ///umP1BDTKlJYsMV6kXJLRJUSodWJ76zOwFgIoGTyUABoVDlCDVLVGeJQj89e6cCjy3ei9PvTjRbMhfoA86WDeINzrcDYpBjxX7eHXogXLL3Fv/h6sA6G7F5E1HJppboRNRc9E2Ms4xY5BscpwD/QUwPA0YO6YPOeExHNKKspEbE4l2DQmFEOlJlm8I1f/T9/waZG1SMcTpfmLLVKAli27QDyLu2o2VY7WDBauHJXg1nrQGaTAiGAR5fvRMmGvQ0WQvo/51rP9eFKB0o27EXV+bOo2v2/aHt1w2/wDqcLj63Y1WBMTWG1WjFgwAAMHjy4ybdFRETJK9xnNlN9osPgOEVozWBEmnIhoZ0qEQm9xzucblht9gZj18qhdkkJk2IMOYMsAV/XQEB7Nrd4bQUKci0o2bA3bGBcW+9uEFzPWr4Ts5bvbPCmFGoxYg+zCYcrHXDXVuG8bX2j4Fh9bIFBu5QSa3Ycwgsffh3RaVur1YoJEyYwOCYiSmFWmz1kYMy0vehxQV6K01o0orX4zmI2wd3MeeiBiwW0TgFZzCbMn5QTdjW4f3Ct9cWg0uGE1WYPu5ix0uHUDMYDn6VgixHVb+09zCac+XgJ6iuP4vAbD+P031/FsXeexJElj+Dwaw+i+uttcDhd+I9XNsLSpx+mT5+OSy8fhNlLNqFi/es49Oq/o+zFhzH9nrsx89GnAQD79u3DzTffjOHDh+P666/Hnj17sHXrVqxduxaFhYUYNmwY9u3bh8WLF2Pw4MG46qqrMHXq1JCPl4iIWoZQC+3UUqgMjKPDmeMUFyzlQj3FotVUJNTCt3iwVzrQt2i9b2yjB3XxdaPzN3pQF98M+VPW8qD7AJ6Ujr5F68O2rS7ZsDdkOkQ0JC6WeDIK4cs5Hj2oC+yj78Phk9+jx32/h3S7IJ21MGRkwVV9Fkf//DhMl4+AGxKHv9+PITctxL7BM/Htl7tQ/dVWzzGuehx58xF8vHcQAODnP/85Xn75ZfTv3x/bt2/HL37xC2zatAkTJ07EhAkTcOeddwIAFixYgP379yMjIwOVlZUxe6xERJQ4oT67mErRNAyOW4FQi0a0FqrpWeAnoD+3WE++sLqoTmuv9V8cweY9J3C40gFDiHrD6v2E63pnr3SgQ5YSdaWOYNTTWIF5zku3HUC9/+OXEpX/eBM1BysAIeC6cAruqkoAgLF9V7zzfSYqqx2oPfQlsi4fCZGWDpGWDlO/a3HO4cSFCxewdetWTJ482XeTtbW1Qcd01VVX4e6770ZBQQEKCgpi80CJiKjZWW12zF1XEfLzrUOWwhnjJmJw3IppBc2BlRyCUU/Z6OnOFy5P2F+oGPVMtdP3htDUnGj/21SMAtIlm7wQ0X/mPdzjrdr9MVzV59B9xiIIYxoO/fHHkK46AIBBycCZaifMJgVngxzbzqRgre0QXEoWKm/+rwZl4YLVVV6/fj1+89oqzH55BX78yBzk/fJ1zL5lMN88iYhaEKvNHnYBOQA8e+uQZhpR6mLOMQVVkGvBlqIxWDRlWMhC9wW5FsyfFLrEmZ484URyuiTamxQoxujbl6j50ID2qS6RboK7znOdu7YKxqz2EMY01Hz/BVznjjfav9LhREbPK+DY9zlkfR3cdQ449v0THdso+M+N38HQtiuq9nwKl5SQUmL/VxWYs7ocJ2oEzp8/DwBYXXYQOY8vw2vftoEy6l6466px6MRpFL67C7m/3shmJURELUS4BeQqTnw0XZNmjoUQJQBuBVAHYB+A+6SUld7r5gD4CQAXgP+QUm5o2lApEbRylv3/+NSqD6FaqVbV1je6LpatpptKb8OSQIpBoGTyUF9pN7XiRDBGUztkWAbj8Gu/QPolA+A8fRBHX38Qad36I61jz6DHZHQfANPl1+Lw6w/B2MaM9C59cPCCQBunC51ufRynN/4BZ7e+A7hdyLriBqR3vQwHzLkoKSnBfz73AjBmFg6vXQh3bRUAibbDb4UhMxtOt/TNwrPWMhFR8otVN1wKr0kd8oQQYwFsklLWCyGeAwAp5RNCiMEA3gZwLYAeAD4CMEBKGfJcMzvktVxa3eHuGG7BqjJ70DQDk2JAvVvq+iasFUgbhYBbSvQwm3C6qhYOp1vzNmKZWxxsDHrzr/0pRoE0gwg5bnedA4Z0E9zOGhz7SxE6jnsIGZdcHvJ2F00ZFvHCSjVVJhh2AiMiSqzcX28Mu5amQ5YC2zNjm2lELV9cOuRJKTf6XdwG4E7vz7cBeEdKWQtgvxDiG3gC5c+acn+UvLRmmEPl3zqcbigGgQ5ZCs5UOzUDYAHgun4dsePAWc3WzIAngPvl8p0IDDMNAjAahK4gPBpqQBxNHrTTJeFySygGAadG5H7qby/CeeoAZL0T2VeOQVb3/mHvK5qOiVqzEnoaqRARUfxYbfawgTHAfONYieWCvB8DWO792QJPsKw65N3WiBDi5wB+DgC9e/eO4XCouQVb4Pfo8p0hj3G6JbLS02B7ZqxvdjJwtlMC2HHgLO4YbvFVq9BK7wi2itctAXecAuNYcEvAaACyFAOqg8wgd5lY6PtZMQikpxlQVRc68NXTUTCQWgIvWIttrbbYDI6JiOLLarPj0RU7w+6XpXAZWayEDY6FEB8BuCTIVb+SUr7n3edXAOoBLIt0AFLKVwC8AnjSKiI9npKbnjrC6oylGlwH6+rncLqwec8JzdP+qkod36yTkdOlnV6izqibTQqq6urDBsYqlww9Ix1sf6DxzLDWjDLz34iI4ks9c6dnnqPa6eZZvRgJGxxLKX8Y6nohxEwAEwDcJC8mMNsB9PLbrad3G7UygTV/gwnsiNeUYCzSph4GAEZj5CkXkdR4bqr2JgXFE4egZMPeiBYOqsF0NBxOF+auq0Dx2grNRZMSnhw4KYGzDidzkYmIYkxPaVB/PKsXG02agxdC3AxgNoCJUspqv6vWApgqhMgQQvQF0B/A5025L2qZ1FJvatUKrfbK/rTaR2tt9xesXXYo7bMUlNw5FBazCQKeRWnhTk1ZzCbsXzAeL9w1NKL7ilalw4k5q8sjCvpNihFCoEl51meqnWGDcXUftYnLnNXlLAtHRBQj0XRw5Vm9pmtqzvGLADIAfCg8Hcu2SSnvl1JWCCFWANgNT7rFg+EqVVDq8s9F1lP1INhsc7AgWuu+gIsLA8OFhpXVzka50n2L1mvuH1jjWb2vSN/AIi1jpzeHWAC+5zVcvnc8cNaCiCg2op1o0DORRKE1qZRbrLGUG6liVTosWP6yv2Dly7SOMQqBF+4aGnQcfYvWR1yz2RJhCggQvtvgdwvG+34O99jjyWI2sewbEVETRPMeLgAsnDKM77k6aZVy49JGSkpqh779C8ZjS9GYqP/QQ6VZaM1GBzvGpBg1A2NA+5u6UQTvuqcG5cE6EGpRu/CFuk2V1WYP2ngllOj7AzZm987aM9WCiChyVps9qskNCS7GiwUGx5TSAnOe1cBSDTSDvYn4HyPC7KvSCqinjegVtv32HcMtmgFv4DEFuZaguc7+t6mubo5k8Z7FbMJ1/Trq3j8SaqoFERGFZ7XZUfjurqiOtTClIiZiWeeYKCkFq78c62NCtdnOu7SjZoqI1WbHqjJ7yFxis7dahXpMuJbeka5uNpsUFI4biMdWRPdmrAcXiBAR6VOyYa/uEpz+9K7NofCYc0yUQKFyyixR5utq5T+ruWjBGqWEy2VuqlCtqYmI6KI+IRaFa4n286K1i0v7aCJqGq0ZVQFEHUxq1XruYTahINeCkg17GwXH8QyMBcDZDCIinSKtZtSUzwsKjjnHRAnUlJrOWrTyn9UANdIUh4w0A8wmJerxXN61DWcziIh0sNrsEVc+Yum22GNwTJRA4QLZaIRbUBjpG6lBCEwY2j3qahZfH6/CU9byKI8mImo95q6riGh/5hnHB3OOiRIsVjWdI7m/cC29A1nMJowe1AVLtx2I6j6NQmDf/FuiOpaIqLWINN94EWsaNwlzjomSVDTVNJp6f0Bknf0OVzowryAn6uA4XGc/IqLWTJ0kiYTFu46EYo9pFUStkNpkRW8Tkh5mU5MaeYSr40xE1FqpZ/MiafrBdIr44swxUSsWWDO5vUlBVV09nK6LM70mxYjRg7pgzuro84anjejVaJs6U2KvdMAoBFxSshwREbU6xWsrQqa5KQaBKdf2wuY9J5ot/a61Y3BM1MoFpnUEy4GOtLGIyigEpo3ohXkFOQ22B+Y9q2kXartpdVxERKnMarOH7GbKCYPEYHBMRA0Ey4F+dPnOiG8nVOOPUMG22m6aHwZElOpC5RmzeVLiMOeYiMKKtPxbuHy4cLWW2W6aiFqDUO91zClOHAbHRBRWsHrMWgSAO4YHr8BhtdmRv2BT2CL36gLA/AWb0LdoPfIXbGrSgkAiomSkNfHQIUvh2bMEYloFEYWlvkk/tmJX2LJsEsDmPScabY+kvrK90oFHl+/0BdHMRSaiVOK/IDmwXbRJMeLZW4ckamgEzhwTkU4FuRa8cNdQXTPIwU4VRrqoLzAEdzhdmLV8J2eRiahFs9rsKHx3l690m/97XWBHU0oMzhwTkW6Bpd8M3hJsgYKdKoxVHjFnkYmopbLa7Hh0xU4EOwFnNilcgJckOHNMRBFRG4jsXzA+6Eyy1mK8SBf1heJwulC8tiJmt0dEFG9Wmx2FK3cFDYwBhCzpRs2LwTERRa0g14L5k3JgMZsgEPqUYCSL+vSodDiZXkFELcbcdRUNGixR8mJaBRE1SbC6yFr7AfAtQokF1kMmopbiTHXomeEOWUozjYTC4cwxETUbNSXDEqMUC9ZDJqKWINxZLsUoWKEiiXDmmIiaXayC2ljmMRMRxcNT1nIs23ZA83oBoOTOoTwLlkQ4c0xEzS4WQW24LnxERIlmtdmxdNsBzcZHikFg4ZRhDIyTDINjImp2heMGQjGIqI9nLVAiSnZWmx2PLt8Zcp+SyZwxTkYMjomo2RXkWlAyeSjMpugWoFTX1cd4REREsaM2+ghVm8JiNjEwTlJChmkF25zy8vJkaWlpoodBRM1MnWGJ5N1IbblqMZtQOG4gP2SIKGkMm7sxbN3iRUynSDghRJmUMi9wO2eOiSjhCnItEQXGwMWWq2rHPNY8JqJkYLXZdTX0YGCcvBgcE1FSaEp5N4fThZINe2M4GiKiyKnpFNSyMTgmoqTQ1A569koH+hStR5+i9cj99UbOJBNRsyvZsBdOd/jzYLGq9U7xwTrHRJQUYtlB70y1E4UrdzW4XSKieLLa7Lrfu1iGMrlx5piIkobaQS/6Im8XOV2SqRZE1CysNjvmrC7Xta/ZpPBLe5JjcExESSdWne/YXpqImkPJhr1wOF1h9xMAiieyTXSyY3BMREmnqfnHKraXJqLmoPeL+N0je3PWuAVgzjERJZ1Y5B8rRsG8PiJqFibFgGqnW/N6AU9gPK8gp/kGRVFjcExESakg14KCXIsvl0/PKUt/bdL59kZEzcNRrx0Ys1FRy8NPDyJKatHOIlc6nL4FMvxQIqJ4CtVseEvRmOYbCMUEc46JKOmpVSzuGdk7ouPYHISImoNRBK+xo7WdkhuDYyJqMTbvORHxMaxYQURNYbXZkb9gE/oWrUf+gk1BGwxNG9Er6LFa2ym5Ma2CiFqMaAJdVqwgomgFrnmwVzqCpmupC+3e3n4QLilhFALTRvTiArwWSshQiTLNLC8vT5aWliZ6GESUpPIXbIoo71gAkOCCGCKKjtZ7jtmkoE1GGg5XOtCD7y8tlhCiTEqZF7idaRVE1GLoqX8s/P5Xv/qrsz3BTocSEWnROltV6XDCXumABN9fUhGDYyJqMQpyLZg/KQeWEKkSEp5FMIHnxLg4j4gipTcti+8vqYXBMRG1KGrliu8WjIfWOnCXRroYF+cRUSRm5GThyOsP6to3VMrXkiVL8NBDD8VqWBRnDI6JqMXSmtXxL59Uf/YYDr/2i5D7RyI7O7vJt0FELcPYIZegfWaa5hdxfyzbljoYHBNRixUsB9mkGDHysg5B9x89qEtEt19fXx/12IgoNVyoqcPxdSWwv3o/Tqz5DdzOGlRueRtH3nwUh1/7BU797feQUsIlJW688UY88cQTuPbaazFgwAB88sknjW5v/fr1GDVqFE6ePJmAR0N6MDgmohbLPwdZwFOVYv6kHHx3quHpTel249RfF+O3/34rxo4di+WffYPLJz2GjO4D0KZ7P4y66RZUV1cDAGbOnIn7778fI0aMwOzZs7F//36MGjUKOTk5eOqppxLwKIkoUTZWHIXjxEG0zR0Py89ehsjIwvkdH6Dt1RPQfcZC9PjJHyDr6+DY97lvLUR9fT0+//xzLFq0CHPnzm1we2vWrMGCBQvwwQcfoHPnzol4SKQD6xwTUYtWkGtpVELp0eU7G1yuP3MYbSfORka3/0DVjpfx6HOvwHhpHroP8LR1/frTpXhk7gt49bmnAQCHDh3C1q1bYTQaMXHiRDzwwAOYPn06XnrppWZ5TETUfKw2O0o27G1Uls1qs+M367+EsW0XZPYcDABoM2Q0zpetQ5q5G85tXwXprIW75jyyuvZB4biBWPQ3YNKkSQCA4cOH47vvvvPdz6ZNm1BaWoqNGzeiXbt2iXiopBODYyJKOSbFgGqn23c5zdwN6d0ug0EI7K7rDFlzBJlZHXHikz/DXVMFt7MG65wXfPtPnjwZRqMnXWPLli1YtWoVAODee+/FE0880bwPhojiJliTj8J3d2HuugqcqXaiXkoESzg+vfGP6D5jIdLadcHZT/+CMf3NKMi1YBGAjIwMAIDRaGyQmtWvXz98++23+Oqrr5CX16i0LiURplUQUUp5ylreIDAGAGFUAHirWAgD4Hbh5AeL0PFH96PHT16COX8aLlRfTMVo06ZNw+O50IYoJZVs2OsLjFVOt8SZaqfvsuvcCdTavwQAVO/+X98sssHUDu46B9oeLcOg7uFngi+99FKsWrUK06dPR0VFRQwfBcUag2MiSilvbz+oaz9Z54CxTUdIVz2qKj5GVnrw5iL5+fl45513AADLli2L2TiJKPH0lHdM69gT53esh/3V++GuuYDs3FuQPXQcjrz+II6veAY3XT9K9/0NGjQIy5Ytw+TJk7Fv376mDJ3iiGkVRJRStGocBzJffw+O/PkxGLPawdRjIK7sEbzM2+9+9zv827/9G5577jncdtttsRwqESVYD7MpZH3itPbdYPnZy422d7jhXnS44V7k9+uIN352MTj++OOPfT937tzZl3M8c+ZMzJw5EwCQm5uL3bt3x2T8FB9C6vwgaQ55eXmytLQ00cMgohas35wPdAfIKrNJwc5nx8ZpRESUrAJzjiOR368jlv1M/6wxJR8hRJmUslECOGeOiSilTBvRC0u3HYjomLMOZ/idiCjlqJVu1GoVmYoBjoA1C/4EgIVThjWqkEOphTnHRJRS5hXk4J6RvSM6xiAE+hatR/6CTbDa7HEaGRElI7Ul/d0je6MmRGDsL3/BJr5npDAGx0SUcuYV5GDRlGGNuudpcUkJCU8Zpzmry/lhR9TKWG12LNt2AOESssxZCuasLoe90sH3jBTG4JiIUlJBrgV3DLcEK1EaksPpQsmGvXEZExElltVmDzrrW7y2ImxgbFKMkBKN8pP5npF6GBwTUcravOdE2A+8YEKtXieilkldfBc46/uj336MSh3rDuZPytFcn6CnJBy1HAyOiShlRfuBJQCeJiVKMcEafjicLnx9vCrssRazCQW5FvQwBy/5qLWdWiYGx0SUsqL9wJIAT5MSpZimzO4Wjhvo+z9wLYNJMfqup9TA4JiIUlawDzK9eJqUKLVE+2XZbFJ8pdsKci2YPykHFrMJAp4Z5fmTcljaLcWwzjERpSz/GqaR5hG3NynxGBIRJUjhuIEofHcXnG79KxEUo0DxxCENthXkWhgMpzjOHBNRSlNrmFoinDUSkZa5IKKkVpBrQXZmZHOCbdLTGAi3QgyOiahVGD2oS0T7n6l2ssg/UYqprI6sGya7Z7ZODI6JKOVZbXYs/+fBiI9jkX+i1JKVHtkaBFahaJ0YHBNRyiteWwGnK5qKxx4s8k+UGqrrXOF38mIVitaLC/KIKKVZbXZdBf7DYfUKopZP71fkDlkKnr11CPONWykGx0SU0mI148vTq0Qtj9Vm91WrMepYZWs2KSieyKC4tWNwTEQpLVYzvtV19bDa7PzQJGoh1HbRalc8l9SeN75nZG/MK8hprqFRkmPOMRGltFjN+J6pdnJhHlELEqxddDBZioGBMTXA4JiIUlpTuuQF4sI8opZD71kjh9Md55FQS8PgmIhSmtrutUNWbDreRdppj4gSQ2+XS64noEAMjoko5RXkWmB7ZiwWTRkWNEg2KUbdwbMAmFpBlOSsNjuq6urD7sdybRQMg2MiajX8g2Sz36xSpmLA+Ku660q/kIhdBQwiio+SDXvD1jY3CoH5k3J8i2xnzpyJlStXAgB++tOfYvfu3XEfJyUnVqsgolaptv5inuGZaieWbjsAk2JAhywFldVOmLMUnNFoNcuax0TJx79sWzgmxdggMHa5Gi7c+9Of/hTRfbtcLhiNsVnbQInH4JiIWh2tVewOpxs1TjckgHOOelyo2IzzZWshXfXI6D4Q6V37oP7scVx5+4MAgCVLlqC0tBQvvvgili5disWLF6Ourg4jRozAH/7wBxiNRmRnZ+ORRx7B+++/D5PJhPfeew/dunVr5kdMlHrUYPhwpQPtTQrO19bjbPmmBn+zHcc+gNMfvoy6o19DOuuQNTAfQ279KQrHDcSs2/Px2ZQp+PDDDzF79uwGt33jjTfi+eefR15eHjZu3Ihnn30WtbW16NevH9544w1kZ2ejT58+mOJ3/NSpUxP0TFCsMa2CiFqdUDO/6onYmhMHUP3lP3DJ3SXocd/vAYMBQjGh+qvPfDmKy5cvx9SpU/Hll19i+fLl2LJlC3bu3Amj0Yhly5YBAKqqqjBy5Ejs2rULN9xwA1599dV4PzyilKfWMLZXOiABVDqcqDne+G+2avfHMN8wHd1nLMJl//4H9Kzdjz/e0tk3Y9ypUyfs2LFDM7A9efIk5s2bh48++gg7duxAXl4efvvb3/quD3c8tUycOSaiVqeH2RT21Kvj+52oO7YPR956FAAg6+tgzGoPU6fuuKT2IE6dysSePXuQn5+Pl156CWVlZbjmmms8xzoc6Nq1KwAgPT0dEyZMAAAMHz4cH374YRwfGVHrEOzsj9bfbPWeT3Bh1wZ0b6ugqvIkdu/ejauuugoAMGXKlJD3s23bNuzevRv5+fkAgLq6OowaNcp3fbjjqWVicExErU7huIENOmdpaXPlGHT4wUzfZQFgfMYerFixAoMGDcLtt98OIQSklJgxYwbmz5/f6DYURYHwtq01Go2orw+/gp6IQtM6+xP4N+usPIrjy5/G1f/xR2yfextmzpyJmpqai/u3aRPyfqSU+NGPfoS33347+P2FOZ5aJqZVEFGro6f2cealQ1G9dwtcVZUAAJfjPJxnj+M/Z/0Y7733Ht5++23fqdSbbroJK1euxPHjxwEAp0+fxvfffx/3x0HUWgWrTRzsb9Z17gSM6Zl4YuLVOHbsGP76179GdD8jR47Eli1b8M033wDwpEl99dVXTR4/JTfOHBNRq1SQa0FBrkVzhXt6594wX38vjq14GpASwmDEwEmz0KFDB1xxxRXYvXs3rr32WgDA4MGDMW/ePIwdOxZutxuKouCll17CpZdemoiHRpTyRg/qgmXbDsC/WFuwv9lOYx/AdSPyUDTtJvTq1cuXHqFXly5dsGTJEkybNg21tbUAgHnz5mHAgAExfDSUbISUoesANqe8vDxZWlqa6GEQUSs1bO5GVDqCl28TAO4e2RvzCnKad1BE1IC6GC9cWhQALJoyzLf4jiiQEKJMSpkXuJ1pFUREXsUTh2g2ApEAVpXZ2R2PKMG0SjEGw8CYosHgmIjIT6ai/bbocLrYHY8ogaw2u64mHwBgCZKXTKQHc46JiKD/VC274xElxlPWcizbdkDXvibF6KtHThQpBsdERNB/qra9SbvCBRHFntVmx9x1FZrt3ANZzCYUjhvIlAqKGoNjIiLonxEWomHbWnOWAimBsw4nevBDmSimIll8B3gWzm4pGhPfQVHKY3BMRAR9XfMA4Ey1s8GHtf9slr3SgTmrywFwIRBRLESy+A4IXv+YKFJckEdEBE/XPK1KFf6MQoT8sOaiPaLYiGTxHcA8Y4odzhwTEeHiTK/aEEQIILAMvGIUcLrC14bnoj2iprHa7Ch8d5eufQXAlCaKKQbHRERewbrmGYWAS0p0yFJwoaZe1+3w1C5R08xZ/QWc7tBfRBWjQMmdQxkQU8wxOCYi8hO4AMglJUyKEXX17rAf1gBP7RI1ldVmh8PpDrsfA2OKF+YcExH5CbYAyOF0oapOO8/YbFIgvP9nKgY8unwn8hdsYjc9oijoydnvkKUwMKa4YXBMROQnkgVAgKem6s5nx2LhlGGorXfjTLUTEhcrVzBAJopMuJx9xSjw7K1Dmmk01BoxrYKIyI+aY6yXmkKhNeNcsmEvZ7iIQvCvG97DbII5S9Fs+GE2KSieOIR/UxRXDI6JiPxEEhinG4VvAZ/WjDMrVxBpU1tCq3919koHFINoVBlGALh7ZG/MK8hJyDipdWFaBRGRH0sElSbqXBJPWct9jT+CkQDzj4mCsNrsWOoXGKucbgnFIGAxmyDg+ZtcOGUYA2NqNpw5JiLyM3pQlwYzWeEs3XYg7D7snEfUWPHaCs3rqp1u/IZ1iylBOHNMRORltdmxqsyuOzCOBDvnEXlYbXbkL9iESkfwvGIV/14oUThzTETkFWxRXSwx/5haO7XznZ6a4fx7oUThzDERkVe8P4zZOY9au+K1FboCY4B/L5Q4DI6JiLzi8WFcf/YYDr/2i5h2zluyZAkeeuihkPsUFxfj+eefj8n9EcVKuFQKFTtNUiIxrYKIyKtw3MAGraNjJc1owPxJOS1qcZHL5YLRaEz0MKiFs9rsKF5boTsoBjzVKQq5GI8SiDPHREReBbkWzJ+U4yvnZhSiybf5zITBuKxzGxTkWvDtt98iNzcX27dvx6hRo5Cbm4vrrrsOe/d6Fh4tWbIEkyZNws0334z+/ftj9uzZvtt54403MGDAAFx77bXYsmWLb/u6deswYsQI5Obm4oc//CGOHTvmu2737t248cYbcdlll2Hx4sUXH2dBAYYPH44hQ4bglVde8W3Pzs7GY489hqFDh+Kzzz5r8mOn1k3NL9YbGAsA3y0Yjy1FYxgYU0Jx5piIyE9BrqXBB7PVZo96NtlsUjB2yCVYDGDv3r2YOnUqlixZgr59++KTTz5BWloaPvroIzz55JNYtWoVAGDnzp2w2WzIyMjAwIED8fDDDyMtLQ3PPvssysrK0L59e4wePRq5ubkAgH/5l3/Btm3bIITAn/70J/z3f/83XnjhBQDAnj17sHnzZpw/fx4DBw7EAw88AEVR8Prrr6Njx45wOBy45pprcMcdd6BTp06oqqrCiBEjfMcTNUXJhr2684sB5hhT8mBwTEQUghooq+1tDTrbSwt48ivv+ONWHDpyDLfddhtWr16NwYMH4+DBg5gxYwa+/vprCCHgdF6cWbvpppvQvn17AMDgwYPx/fff4+TJk7jxxhvRpUsXAMCUKVPw1VdfAQAOHTqEKVOm4MiRI6irq0Pfvn19tzV+/HhkZGQgIyMDXbt2xbFjx9CzZ08sXrwYa9asAQAcPHgQX3/9NTp16gSj0Yg77rgjJs8btU7+raAjKYmoGARzjClpMK2CiCiMglwLthSNwf4F4/HCXUNhUkLn4grAFxgcO1eDamQgs0M3fPrppwCAp59+GqNHj8b//d//Yd26daipqfEdm5GR4fvZaDSivr4+5H09/PDDeOihh1BeXo7/+Z//CXtbH3/8MT766CN89tln2LVrF3Jzc33HZGZmMs+YoqaeZbFHGBibTQpKJg9lKgUlDc4cExFFQP0An7uuAmeqg+dSNgoMDEZk/utsvPXWfGRnZ+Ps2bOwWDy3s2TJkrD3OWLECDzyyCM4deoU2rVrh3fffRdDhw4FgAa39eabb4a9rbNnz6JDhw7IysrCnj17sG3btrDHEOkRaZ1wk2JscQtVqXXgzDERUYQKci2wPTMW+f066j7mWDXw/vvvY+HChRg2bBjmzJmD3NzcsDPDANC9e3cUFxdj1KhRyM/PxxVXXOG7rri4GJMnT8bw4cPRuXPnsLd18803o76+HldccQWKioowcuRI3Y+BKJRI64QzMKZkJaSO3LnmkpeXJ0tLSxM9DCKisKw2Ox5dvlP36WOL2YQtRWPiOiaiRLHa7Hh0xU7oDSnuGdkb8wpy4jsoojCEEGVSyrzA7Zw5JiKKQsmGvboDYwHAXulA/oJNsNrs8RwWUbNTc431BMZGIRgYU9JjzjERURQiOYWsxgz2SgcKV+4CAJ5OphYv0gYfDIqppeDMMRFRFKKtyep0ScxdVxHj0RA1L6vNjl8u36k7MM7v15GBMbUYDI6JiKJQOG5g2JJuWrSqXBC1FMVrK+DWuW9+v45Y9rNRcR0PUSwxOCYiioLaajoWLaaJWpKuPXrh1KmTuvf/7pR2ClJ2dnbQ7TNnzsTKlSsjHhtRLDA4JiKKUkGuRVdTkEBCgAvzqEWy2uw4U10X0TGRlngjSjQuyCMiaqJMxeBrfmBSDKh3Szhd2kv3pQTmrC4HwIV5lNyqqqpw11134dChQ3C5XHBedTukBM6XvQ/Hvs8hXfXoUlAEpVMvuBznceqvv0N95VGItAx0uvkhpHfti/rSFXj++S/x+OOPAwCuvPJKvP/+++jTp4/vfqSUePjhh/Hhhx+iV69eSE9PT9AjJuLMMRFR1NQSVg1ziAWmXNMLFrMJAkCHLAXBEi8cThdKNuxtppESRedvf/sb6jLaQ0wqwYUJz8HRzbOozpDVDt1n/g5tc2/Buc/XAADOfroMGV0vQ48fvwjzD6bj5PrfwqQYkd+vU9j7WbNmDfbu3Yvdu3fjrbfewtatW+P6uIhC4cwxEVGUgrXLdThd2LznRIOGH32K1gc93s7TzZTkPjqajs1//zvaHK6Dqd81yOx1JQAga4BngV36JZej+qutMAqBzlX7Mbv4Ffy5ogaHMRSVtRfw9Ng++Orv7cLezz/+8Q9MmzYNRqMRPXr0wJgxbJhDicPgmIgoSlq5lPZKB4bN3airzFX+gk0oHDeQ6RWUVKw2O+auq8CZagO6z/wdHN+WovKTpci8dCgAQBgVz47CACHdeOGuoZj7NwVjh1yC+2+9DADQa1kGJgztgcX/mwa3+2Jti5qammZ/PESRYFoFEVGUQtU61lv/1V7pwJzV5VygR0nj7lc/w6zlO3Gm2on686dgUDKQPWQ02l07CXXH9jXYt2vbDFzWpQ0Kci24/vrrsWzZMgDAxx9/jM6dO6Ndu3bo06cPduzYAQDYsWMH9u/f3+g+b7jhBixfvhwulwtHjhzB5s2b4/9AiTQwOCYiilJTah37Y/4xJYunrOXYsu+077LzxHc48tYvcfiNh3F2y9toP2qK7zqL2YTXZl6DztkZAIDi4mKUlZXhqquuQlFREd58800AwB133IHTp09jyJAhePHFFzFgwIBG93v77bejf//+GDx4MKZPn45Ro1gXmRJHSD3N0JtJXl6eLC0tTfQwiIh0s9rsKNmwF4crHTAIAVeU76kCwP4F42M7OCKd1Nex3jx4xShQcudQpgNRiyaEKJNS5gVuZ84xEVETFORafAFCX42Fd3pE246aqKnUqiuBi0u1tEk34r9uz2FgTCmLwTERUYz0MJuirkBRVVsPq83OgIOa1VPWcizddkD3/oumDONrlFIec46JiGKkKTnIlQ4nF+ZRs4o0MLaYTQyMqVXgzDERUYyogUMkuZv+1IV5DEAoni6WadNXUQUATIoRheMGxnFURMkjJjPHQojHhBBSCNHZe1kIIRYLIb4RQnwhhLg6FvdDRJTsCnIt2FI0JmhXPD3YGITiyWqz47F3d0UUGFvMJsyfxBxjaj2aPHMshOgFYCwA/3Mz/wqgv/ffCAB/9P5PRNQqRJt/LADmHlNY/lVSephNuhvJzF1XAZdbX0UVk2JkUEytUizSKhYCmA3gPb9ttwF4S3rqxG0TQpiFEN2llEdicH9EREmvcNxAPLp8JyIt7CYBplZQSIHVJdRGMio1rcfoLS2o/t8hS9E9Y2yJIOAmSjVNCo6FELcBsEspdwnR4CSiBcBBv8uHvNsaBcdCiJ8D+DkA9O7duynDISJKGgW5FsxavjOqY+2VDs4ek6aSDXsblV1zOF0oXluB2nq37zq15rb6v57A+J6RvTGvICfGIyZqWcIGx0KIjwBcEuSqXwF4Ep6UiqhJKV8B8ArgaQLSlNsiIkomliaUditcuQsAGCBTI4c1XlN6W5ZrYZk2Io+wC/KklD+UUl4Z+A/AtwD6AtglhPgOQE8AO4QQlwCwA+jldzM9vduIiFqNYKXdFKPQtVjP6ZJsKU1BxaNhzD0jezMwJvKKulqFlLJcStlVStlHStkHntSJq6WURwGsBTDdW7ViJICzzDcmotamINeC+ZNyYDGbIOCZSS65c6ju47VmCKl1C/aly6QY0SFLifi2jEJg0ZRhTKUg8hOvOscfALgFwDcAqgHcF6f7ISJKav7tpVV66yAbhGDuMTXiX0/bv1oFgIjaQLMaBVFwQsrkSfPNy8uTpaWliR4GEVFcBVYbCMdsUlA8cQiDGArKv6ybWWdFCqMQeOGuoXxNUasmhCiTUuYFbmeHPCKiZhY485dmAJxu7f3V1tL+x1LrpgbEgWcg9JZqc0vJ1xKRBs4cExElWL85H/jKbYXDWWR6ylqOZdsORFxD25/FbMKWojExGxNRS6Q1cxyT9tFERBQ9vYEx4JlFLnx3F6w2FgBqjaw2u+7AWGuBnkkx+nKUiagxplUQESWY2sFML6dbsoteigvWHhoAHluxS/eMse2ZsZq3xdcOkTYGx0RECTZtRC8s3XYgomNY5i11BWsPHU23xfwFm3yBMINhIv2YVkFElGDzCnJwz8jeER0j4Ql+mF6ReoK1h46GvdKBOavL+RohihCDYyKiJDCvIAeLpgxr1NwhFAY/qcVqsyN/waaoW44H43C62GmRKEIMjomIkoTaUc8o9DSY9mDwkxrUVIpYBsYqpuAQRYY5x0RESUTNDQ1sEiIAzYVY8QioKL6sNjuK11ag0qGvLrEeWq+RHmZTzO6DqDXgzDERUZJRZ5At3qAmVGCsXs/UipbDarOj8N1dTQ6MDfCUaxPw1C2+e2TvRmk5LNtGFDk2ASEiSmKR5qAahcC0Eb0wryAnjqOipohFXrFFoyQby7YR6afVBITBMRFREutbtD6qTmht0o2ornMxQEoC/q2eI61pHWjRlGH8XRLFiFZwzJxjIqIk1sNsimqWsaruYo3cOavLAYBBVTOz2uyYu64CZ6ovpk80JTA2mxT+DomaAXOOiYiSWOG4gRGVdwvG4XRh1vKdrIvcjNTqE/6BcVOYFCOKJw6JyW0RUWgMjomIkpj/4jx14dU9I3v7LkdC7bSW++uNDJJjRK1N3LdofYMvH7Fo5KH+fi1mE+ZPyuGsMVEzYc4xEVEL9pS1POLW04BnJvKO4RZs3nOiweItAFzQpVNgm2cAUAwC2ZlpTZ4x1lpwR0SxwwV5REQpKtoAOVyJOMATRHPW0iOwEkR1XX3M0ib8KQaBkslD+ZwTxRkX5BERpah5BTnYvOdExAv39EyNqB34Wmug5l9pwv/LRDwbrzjdslU/50SJxpxjIqIUUDhuYMQ5yHq11vbDgS2dm/M8a2t9zomSAYNjIqIUUJBriVvwlqrth7UW06nmrqto8qK6QAa/bzBmkwKzSQm6X6o+50QtAdMqiIhSRFMbTARjUowYPaiLr6ubeh8tccGYf86wOUvBhZp6ON2e5yuwHrTVZo95PnGw/O1gi/rY8pkosThzTESUImIdGAsAV/duj1Vldl9qgXofajDZUkrC+adISABnqp2+wFj13V+ewnxrGZb9Yzd+WvRfvu01B77A8ZVzI75PAc/ssFqCb/6kHJjPfo2tW7f69glWqo8LIIkSizPHREQpwhJlNz0tEsCWfac1r3c4XXhsxS4AF2dbk7UMnJ66w90mz8WxWuBXy7fj3I71yM4dH/X9aVWcKC5+FdnZ2bjuuut82wpyLUnzPBERS7kREaWMYKfoVYpBAAJwumL/nq/WTF5VZm+UHhCrWdBwgbfW9f7VJs5uXwVhVNAubyJO//1V1B3fj0um/QaO73fhwhcbUXvoS3SfsRCnP3wZjm+2I62jBaY+uTD1y0Plp3+B0dQOdScPIP2Sfug84XEIIeD4bifObH4dcLuQ0X0AOo79BXp2boevF8/A7i9s6Ny5M0pLS/H4449jyZIlGDlyJIxGI7p06YLf//73uP7665v83BBRdFjKjYgoxanBohoMBuYH+1+np8axXg6nK2id5cAycIEB7OhBXXQ1ISn9/jSWbTvQoIzanNXlKP3+tK+EXWCZtTmry/Fu6QFs3Xfatz2z5xCc++caIG8i6o5+DelyQrrqUXuwApm9rkTtoS8BAB1unAnnye/R477fA/CkVdQd+xY9fvIHGNt2xNGlhai170bGJf1x6oNFuOpnz2PHCzMwffp0XN3zO8yaNQt9Xm7c8rtPnz64//77kZ2djccffzxGzz4RxRqDYyKiFBLuFL3/deoiu3hSS5I9ZS1vFOD6B9T2SgcK393VYHbbXulA4cpdQWe7AwPywD0cTlejlJD0Sy5H3dFv4K6thjAqSO/WD3VHv0btoQp0+OG/A3hX83FkdB+AtHadPbfT9TLUnz0OQ7oJ6eZL8Mw9PwQAzJgxAy+99BJmzZql45khomTF4JiIqBUIlnZQOG6gZhpGrPQwm2C12RsExloCF8gBsU0DEcY0pJm74UL5R8iwXAGlSx/UHCiH88wRKJ16hT427WLJNWEwAG4XurbNRIfObYJ+GUlLS4Pb7QYA1NTUxOwxEFH8sVoFEVGKC6zU4F+2zL9SgtmkQDHGtpVIZXUdHl2xs1kbaISS0XMIzn2+Bhm9rkRmryE4b/sr0rtdBiEuPm6RboK7zjPjHfhsCAADurXF85OHYttz9+L8ycP45ptvAAB//vOf8YMf/ACAJ4WirKwMALBq1Srf8W3btsX58+fj9wCJqMkYHBMRpbhglRr884G3FI3B/gXjsfPZsSi5c2hM77uqzoUkWveNjJ5D4Ko6jYweg2Bs0wEiTUFmzyEN9jGa2qFD3xxkv/8Ehh5eh87ZGQA81UAWThmGkZd1AgBkZmbijTfewOTJk5GTkwODwYD7778fAPDss8/ikUceQV5eHozGi/nHt956K9asWYNhw4bhk08+aaZHTUSRYLUKIqIU17dofdCZWwFg/4LG5cqaIxc52S2aMozl1YhSnFa1Cs4cExGlOK1WxFrbC8cNhElpXG1Bi4htJkbCmU0KA2OiVozBMRFRigsW7IZqURysa1uWEvzjwmI2Yf/88fhugeefRSPgThbh4niTYkTxxCFh9iKiVMZqFUREKc6//rHe7nWBJeGCNRgJFmA3RwUMPRSDaFD9Qm1IAiBsrWXOGhO1bsw5JiIiXfS2h/bfz5ylQEqg0uFs1rEumjIsaVtZE1Fy0Mo5ZnBMRERxF2zmOVhLa8UoABm85rFeFrMJW4rGNGm8RJT62D6aiIgSRiu1Q2vbYyt2wRVk8qZDloKs9DRPy2iBRmXiQuVSExHpweCYiIiahVZra610h2A5zs/eOqRRLjTTJ4golhgcExFR0tG7iFAr4CYiihaDYyIiSkoMfIkoEVjnmIiIiIjIi8ExEREREZEXg2MiIiIiIi8Gx0REREREXgyOiYiIiIi8GBwTEREREXkxOCYiIiIi8mJwTERERETkxeCYiIiIiMiLwTERERERkReDYyIiIiIiLwbHREREREReDI6JiIiIiLwYHBMREREReTE4JiIiIiLyYnBMREREROTF4JiIiIiIyIvBMRERERGRF4NjIiIiIiIvBsdERERERF4MjomIiIiIvBgcExERERF5MTgmIiIiIvJicExERERE5MXgmIiIiIjIS0gpEz0GHyHECQDfJ3ocCdIZwMlED4ISiq+B1o2/f+JrgPgaaF6XSim7BG5MquC4NRNClEop8xI9DkocvgZaN/7+ia8B4msgOTCtgoiIiIjIi8ExEREREZEXg+Pk8UqiB0AJx9dA68bfP/E1QHwNJAHmHBMREREReXHmmIiIiIjIi8ExEREREZEXg+MkIIR4TAghhRCdvZeFEGKxEOIbIcQXQoirEz1Gig8hRIkQYo/397xGCGH2u26O9zWwVwgxLoHDpDgTQtzs/T1/I4QoSvR4KP6EEL2EEJuFELuFEBVCiEe82zsKIT4UQnzt/b9DosdK8SOEMAohbEKI972X+wohtnvfC5YLIdITPcbWiMFxggkhegEYC+CA3+Z/BdDf++/nAP6YgKFR8/gQwJVSyqsAfAVgDgAIIQYDmApgCICbAfxBCGFM2Cgpbry/15fg+bsfDGCa9/dPqa0ewGNSysEARgJ40Pt7LwLwdyllfwB/916m1PUIgC/9Lj8HYKGU8nIAZwD8JCGjauUYHCfeQgCzAfivjLwNwFvSYxsAsxCie0JGR3Elpdwopaz3XtwGoKf359sAvCOlrJVS7gfwDYBrEzFGirtrAXwjpfxWSlkH4B14fv+UwqSUR6SUO7w/n4cnQLLA87t/07vbmwAKEjJAijshRE8A4wH8yXtZABgDYKV3F/7+E4TBcQIJIW4DYJdS7gq4ygLgoN/lQ95tlNp+DOCv3p/5Gmg9+Ltu5YQQfQDkAtgOoJuU8oj3qqMAuiVqXBR3i+CZHHN7L3cCUOk3YcL3ggRJS/QAUp0Q4iMAlwS56lcAnoQnpYJSWKjXgJTyPe8+v4LnNOuy5hwbESWWECIbwCoAs6SU5zyThx5SSimEYL3VFCSEmADguJSyTAhxY4KHQwEYHMeZlPKHwbYLIXIA9AWwy/tm2BPADiHEtQDsAHr57d7Tu41aIK3XgEoIMRPABAA3yYuFx/kaaD34u26lhBAKPIHxMinlau/mY0KI7lLKI950uuOJGyHFUT6AiUKIWwBkAmgH4HfwpFGmeWeP+V6QIEyrSBApZbmUsquUso+Usg88p0+ullIeBbAWwHRv1YqRAM76nWajFCKEuBme02oTpZTVfletBTBVCJEhhOgLz+LMzxMxRoq7fwLo712lng7PQsy1CR4TxZk3v/Q1AF9KKX/rd9VaADO8P88A8F5zj43iT0o5R0rZ0/v5PxXAJinl3QA2A7jTuxt//wnCmePk9AGAW+BZhFUN4L7EDofi6EUAGQA+9J5B2CalvF9KWSGEWAFgNzzpFg9KKV0JHCfFiZSyXgjxEIANAIwAXpdSViR4WBR/+QDuBVAuhNjp3fYkgAUAVgghfgLgewB3JWZ4lCBPAHhHCDEPgA2eL1DUzNg+moiIiIjIi2kVREREREReDI6JiIiIiLwYHBMREREReTE4JiIiIiLyYnBMREREROTF4JiIiIiIyIvBMRERERGR1/8DfqVWekMEVcwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_with_matplotlib(x_vals, y_vals, labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
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