pred_BTC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicción sobre los últimos $n$ días"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import pandas as pd\n",
    "import datetime\n",
    "from pandas_datareader import DataReader\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "plt.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.rcParams.update({\n",
    "  \"text.usetex\": True,\n",
    "  \"font.family\": \"Helvetica\"\n",
    "})\n",
    "plt.rcParams['mathtext.fontset'] = 'stix'\n",
    "plt.rcParams['font.family'] = 'STIXGeneral'\n",
    "plt.rc('font', size=12)\n",
    "\n",
    "horizonte_pred = 32\n",
    "\n",
    "moneda = 'BTC-USD'\n",
    "#Ventana de tiempo, en este caso uno, que es diario.\n",
    "window = 1\n",
    "\n",
    "# Primer gran crash de diciembre de 2017 - Bien\n",
    "#start = '2017-01-01'\n",
    "#end = '2017-12-10'\n",
    "\n",
    "# Mínimo en febrero de 2018 - Bien\n",
    "#start = '2018-01-01'\n",
    "#end = '2018-02-05'\n",
    "\n",
    "# Crash de noviembre de 2018 - Bien\n",
    "#start = '2018-01-01'\n",
    "#end = '2018-11-13'\n",
    "\n",
    "# Mínimo en diciembre de 2018 - Bien\n",
    "#start = '2018-01-01'\n",
    "#end = '2018-11-18'\n",
    "\n",
    "# Crash de julio de 2019 - Bien\n",
    "#start = '2019-01-01'\n",
    "#end = '2019-07-09'\n",
    "\n",
    "# Mínimo de julio de 2019 - Bien\n",
    "#start = '2019-01-01'\n",
    "#end = '2019-07-30'\n",
    "\n",
    "# Crash de marzo de 2020 - Bien\n",
    "#start = '2020-01-01'\n",
    "#end = '2020-03-02'\n",
    "\n",
    "# Mínimo de marzo de 2020 - Bien\n",
    "#start = '2020-01-01'\n",
    "#end = '2020-03-16'\n",
    "\n",
    "# Crash de enero de 2021 - Bien\n",
    "#start = '2020-01-01'\n",
    "#end = '2021-01-03'\n",
    "\n",
    "# Mínimo de enero de 2021 - Bien pero es más complejo por la alta volatilidad\n",
    "#start = '2020-01-01'\n",
    "#end = '2021-01-12'\n",
    "\n",
    "# Crash de mayo de 2021 - Bien\n",
    "#start = '2020-01-01'\n",
    "#end = '2021-05-01'\n",
    "\n",
    "# Mínimo de julio de 2021 - Bien\n",
    "#start = '2020-01-01'\n",
    "#end = '2021-07-20'\n",
    "\n",
    "# Crash de noviembre de 2021 - Bien\n",
    "#start = '2020-01-01'\n",
    "#end = '2021-11-05'\n",
    "\n",
    "# Mínimo de enero de 2022 - Bien\n",
    "#start = '2021-01-01'\n",
    "#end = '2022-01-22'\n",
    "\n",
    "#Ejemplo tesis\n",
    "#start = '2022-04-05'\n",
    "#end = '2022-05-05'\n",
    "\n",
    "# Actual\n",
    "start = '2022-01-01'\n",
    "end = datetime.datetime.now().strftime('%Y-%m-%d')\n",
    "\n",
    "cripto_m = DataReader(str(moneda), \"yahoo\")\n",
    "cripto_m2 = cripto_m   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "1024\n"
     ]
    }
   ],
   "source": [
    "cripto_m = cripto_m.loc[(cripto_m.index >= start ) & (cripto_m.index < end)]\n",
    "\n",
    "rolling = cripto_m['Close'].rolling(window = window)    \n",
    "cripto_MA = rolling.mean()\n",
    "cripto_MA = cripto_MA.tail(horizonte_pred)\n",
    "\n",
    "rolling_n = cripto_m2['Close'].rolling(window = 32)\n",
    "promedMol = rolling_n.mean()\n",
    "promedMol = promedMol.loc[(promedMol.index >= start) & (promedMol.index < end)]\n",
    "\n",
    "etiqueta = {0 : 'Compra', 1 : 'Incertidumbre', 2 : 'Vende'}\n",
    "color = {0 : 'green',1 : 'orange',2 : 'red'}\n",
    "\n",
    "s = pd.Series(np.arange(len(cripto_MA)))*2\n",
    "cripto_MA = pd.DataFrame(cripto_MA)\n",
    "cripto_MA = cripto_MA.set_index(s)\n",
    "df_aux = cripto_MA \n",
    "\n",
    "horizonte_n = 1024/horizonte_pred\n",
    "n = int(math.log(horizonte_n,2))\n",
    "print(n)\n",
    "\n",
    "for i in range(n):\n",
    "    s = pd.Series(np.arange(len(df_aux)))*2\n",
    "    df_aux = df_aux.set_index(s)\n",
    "    index = np.arange(len(df_aux)*2)\n",
    "    df_aux = df_aux.reindex(index)\n",
    "    df_aux = df_aux.interpolate(method='linear')\n",
    "\n",
    "cripto_MA = pd.DataFrame(df_aux)\n",
    "print(len(cripto_MA))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "def img(data):\n",
    "    tam = 32\n",
    "    tam2 = tam*tam\n",
    "    dataset = data\n",
    "    data_img = np.asarray(dataset)\n",
    "    mini = min(data_img)\n",
    "    maxi = max(data_img)\n",
    "    x = 0 \n",
    "    y = 0\n",
    "    df = np.zeros((tam, tam)).astype(np.uint8)\n",
    "    for k in range(len(data_img)):\n",
    "        df[y][x] = np.round((data_img[k] - mini) / (maxi - mini)*255)\n",
    "        x += 1\n",
    "        if(x == tam):\n",
    "            y += 1\n",
    "            x = 0\n",
    "    #print(df)\n",
    "    img = Image.fromarray(df)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-11 19:54:38.636954: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-07-11 19:54:38.637322: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n",
      "\n",
      "systemMemory: 16.00 GB\n",
      "maxCacheSize: 5.33 GB\n",
      "\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 28, 28, 32)        832       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 14, 14, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 12, 12, 128)       36992     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 6, 6, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 4, 4, 256)         295168    \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 2, 2, 256)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1024)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 2560)              2624000   \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 256)               655616    \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 3)                 771       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,613,379\n",
      "Trainable params: 3,613,379\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.load_model('CNN_BTC-USD_1_Sum.h5')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-11 19:54:42.110961: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2022-07-11 19:54:42.171670: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "data": {
      "image/png": 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5pUuXEgqFolbbrVixAr/ff1Zn3AULFpCbm9tiU41YtwGLiIiIiIhI/FLSTkRaJRgMRj1jLZKoq6ysjJpoa+05bRUVFeeUsIPTW3Uhtmq7JUuWUFlZGTXxV1VVRVVVFX6/v8m1lixZgt/vb3aLbU1NDTU1NZSXlzd5vby8nFAodMZZgY2VlJQA2kYrIiIiIiLSFSlpJyItqqqqoqKigltvvbXZpFtkK2njrZ4NNawMi7Z1tKqqiltvvZWVK1e2uRlDWVlZTJVo99xzD8uWLaOoqOiseSKNKnJzc6msrGx2jkjir3HiLRgMMmfOHEpLS5s9s8/v91NdXU1JSUmTZ/QVFxfX/28eSe6p8k5ERERERKTrSPI6ABGJXzU1NeTl5Z3xOSMjg9zc3LPOW8vPzyc3N5eFCxeeNU9xcTEVFRVnJJUKCgrw+/34/f76xFdNTQ1z5sw5I1GXkZFBeXl5i1tui4uLWb58+RnfZWdn4/f7KSoqanGra2M+n4/y8nJqamooLi4mFArVV7T5/f5WxeT3+9m8eTPFxcUUFhbi9/vr51m1alWLTTYi/zsXFxfXb6WNnBe4dOlSfD5f/bbckpISSktLWbVq1VlbdUVERERERKTzMdZar2OQziHmB6W6uvqMBJCIxB8v/n+6evVqZs6c2aFrijRHz6PECz2LEk/0PEo80fMo8cLFZ9E0d0HbY0VEREREREREROKMknYiIiIiIiIiIiJxRkk7ERERERERERGROKOknYiIiIiIiIiISJxR0k5ERERERERERCTOKGknIiIiIiIiIiISZ5S0ExERERERERERiTNK2omIiIiIiIiIiMQZJe1EROQsj76+iz88s5VDx056HYqIiIiIiEi3pKSdiIic5eGXd/D1+1/hwu+u4hv3v8ymXQe9DklERERERKRbUdJORETOsuyGqdz3qYspmDSIvz7/Nvk/eJyv/O0lr8MSERERERHpNpK8DkBEROKPMYbcERnkjsjgq1dPZMWatxmW0QOAw8dO8tunt7Jw+nAG9En1OFIREREREZGuSUk7ERGJqn/vVG6bNab+89Ob93LXw6/zo6qNXDV1MB+5aCS5IzIwxngYpYiIiIiISNei7bEiInJOCiYN4pEvXcHNF47kkVd3cePdz3D1T57kwNETXocmIiIiIiLSZajSTkREzpl/QG/++5rJ3D53PH9fv411b4Xom5YMwP3rtzFtmI9R/Xt5HKWIiIiIiEjnpaSdiIi0Wa/UJD50wUg+dMFIAN47XsdX//Yyh46d5IpxA/jwhSOZNWEgiQnaOisiIiIiInIutD1WRERc0yMlkUe+dAVfyB/HazsOcMvv13LFXY/y9KY9XocmIiIiIiLSqajSTkREXDWwbxqfyx/Lp2ZlU7lhJ79/ZitZ6WkAvLbjAMdOnGLacJ+3QYqIiIiIiMQ5Je1ERKRdJCcmcNXUwVw1dXD9dz97ZBP/fHE704al85GLRnH1eYOjzCAiIiIiItJ9aXusiIh0mO/eMJX/uWYSh46d5EvlL3DxnY/w7y3qOisiIiIiItKYknYiItJh+qYl8/8uGU3VF6/gT7dcwIxRGRyrswCcqDvFr54IsmP/UY+jFBERERER8Z62x4qISIczxnDJmP5cMqY/jz76KADVb9by7X+9yncefJWLs/txXc5QrpySRZ+0ZI+jFRERERER6XiqtBMREU8ZYwC40N+PR750BZ+ZPZa3973HlyteZPq3q9i065DHEYqIiIiIiHQ8Je1EpFlFRUVkZGQ0+aqoqGjVHMuXL8cYUz8u8ut4FQqFKCwsJC8vj+zsbPLy8rwOqVvxD+jNFwvG8diXZ3LvJy/mY5eOxt+/FwA/rNzIN+5/mXVv1WKt9ThSERERERGR9qWknYg0q7S0lNraWu655x5CoRChUIjFixdTW1vLggULWjXHkiVLqK6uJhQKkZmZSXV1NbW1te0cedv5fD6KiorIz88nGAwSCoW8DqlbMsaQNzKD4isnkJDgVOLtO3ycv655m+t/8TSzvreaH1VtZOuewx5HKiIiIiIi0j6UtBORFi1YsIDc3Nw2j8/NzSU3N5fKysqY5uko+fn5lJSU4PP5vA5FGvjWdVNY+7V8li84jyG+Hvx41RvcvXozANZaag8f9zhCERERERER9yhpJyKtsnTpUgDKysrOeWwoFMLv9+P3+90Oq11lZmZ6HYI00jctmYXTh/PnWy/k6Ttm8+nZYwB4edsBZnynilt+t4Z/vvguR0/UeRypiIiIiIhIbJS0E5FWiWyHDYVCrT7PLqKsrIyioqL2CEu6scHpPRie2ROAzN4pfPyy0by87QCf/vM6pn+7ii+Xv8Dug8c8jlJERERERKRtlLQTkVZbvHgx4Jx1dy4qKyvJz89vj5BEABjq68HS+RN56o7Z/PnWC7hqahZPbtpDn7QkAJ7evIdXtx/wOEoREREREZHWS/JycWPMDUAR4AdCwFpgpbX2US/jEpGmFRUVUVZWRlVVFcFgsFXbXWtqajrFOXbSNSQmGC7O7s/F2f2pO2VJDDex+NY/X+XV7QeYkNWH6wJDeX/OEAan9/A4WhERERERkea5WmlnjPmlMebhpl5N3PswUA4UANlABrAIqDLG/MLNuETEHZGGEtD6artly5Zpa6x4IpKwA/jTLRfwrfdPpmdKInf++zUuvvMRlj/0mofRiYiIiIiIROd2pd1ynGo5HxAEKoDK8K/rhRN2BeGPFcCt1tr94Wu5QJkx5hfW2k+5HJ+IxKioqKi+4q6kpCTqvaFQqL4JRXNqampYtmxZfdOHSAVfU0nBoqIi1q5dSygUIhgMUltbi8/no6KigjVr1tR/D05SsTWVgMuXL2fNmjVkZmaSnZ3N3r17KSoqanXTjHOJX7yT2SuFD180ig9fNIo39x7m7+ve5bxh6QC8G3qPux5+nf+6ZBTnDfN5G6iIiIiIiEiYq0k7a23QGFMFrLHW3tXUPcaYW3ESdhZYbq1d2miOGmC6MWaTMWaUtXarmzGKSGwWL15MUVERoVCIqqqqqGfVrVy5MmqVXUVFBcXFxZSXl5+xhbaoqIjs7GwqKyvPSJ4VFhaSl5d3xpzFxcUsWrSovlFGZHxeXh7V1dXNJt+CwSCFhYUsWrSI8vLyM64VFxeTnZ3d/P8IbYxf4sPIfr34XP7Y+s+bdx+icsNO/rZuG9NHZvCxS0czd9IgkhJ17KuIiIiIiHjH1aSdMWYOEGwuYRdWHH4PNU7YNfKJ8L2fdCs+6YQ+/3lYv97rKOJLTg786EeehrB48WLKysooLS2NmrQrLy+nsrKyyWsVFRUUFhZSXV191pl3paWl5OXlUVBQwObNm+u/j6xVWVlJRUUFZWVlLFq06KzxJSUl9ZWATVW8hUIhsrOzWbJkCUuWLDnreklJCcXFxVHP7WtL/BKfLhs7gGeWzqZ87Tv89umtfOpPNYzI7Ml/vnA5acmJXocnIiIiIiLdlNtlBIuB7zZ30RiTjtN0wgJl0Say1lYBma5GJyKuiFS6VVRUEAqFmrynpQYUt956KwsWLGj2nqVLlxIMBqmoqDjrWmQr6po1a5oc7/P58Pl8rFy5ssm5CwsL8fl8Ubf3trT1N5b4Jf70SUvmY5eO5tHbZ1L24TxuyB1an7D7wzNbCe4+5HGEIiIiIiLS3bh9pl2GtfZAlOsNS3KaLr85ky+2cKTT87iiTJqWm5uL3+8nGAxSVlbWZLVaaWkpxcXFTYx2zpELhUIUFBQ0eR1OV9WVlpaesfW1oWjjMzMz68+3a6iqqoqqqqpm52wNt+KX+JOYYJg7OYu5k7MA2HvoGN/+16scO3mKWeMH8LFLR3PpmP4YY1qYSUREREREJDZuV9qFWrje8G+4a11eW0Q6UKTarrmGC9G2lka2zE6fPr3Z+X0+X/08zYl2XlxkfGOR8+uiJdxa4lb8Ev/69U7lyeLZfD5/LC9t28+Hf/088370OC9v2+91aCIiIiIi0sW5XWlnW7geqbQLtVCRJyJxbvHixfXnvjVuSFFWVha1AcXatU7OfsWKFVRVVUVdZ9++fc1ei2yTPReRtdsytvEcscYvncOAPql8Pn8cn5yZzQMvbOfPz73JEF8PADa8e4DMXilkpad5HKWIiIiIiHQ1biftfM1daHSeXfS/5Z6+X6UMInHK5/OxYMECKioqzmpIEa0BBVB/Dt7SpUubrYgDmtx22ziGcxVZuy1jG88Ra/zSuaQmJbIgbxgL8obVf/e1v7/Ei+/s5+rzBvNfl4wmZ7jPuwBFRERERKRLcXt77H5jzKhmri1u8OsVrZhrJfDLmCMSkXbTVEOKaNtiG/OiCi2SZGuugca5UBWd/GhRgI9ePIpVr+7iup8/xQ2/eIrHNu72OiwREREREekC3E7alQJnnTwfrppbilNlF7LW3hdtEmPMMmC/tfYRl+MTERfl5+fXJ+ginVpLSkqabUARERnjRuLsXEXWjiXh5mX8El9G9OvJ1983iWeWzua/r5nE3sPHeaf2CADvHa8jdOS4xxGKiIiIiEhn5WrSzlq7CphhjPmFMaYPQLjyrorTW2dvbW68MSbHGLMGWGCtXehWXMaYXGNMuTFmszHGGmOqw59zWzneZ4wpMcZUhsdWh3+d3/Lo+jmWhNcsMcaUhl+tWj/W8bGuLRJNpNqupKQEaF2lXaSbauRsuGhqampijPBMixYtAqC6urrNc3gZv8SnPmnJ/Nclo3nkSzNZOH04ABXVb3PRskf42t9fYtOuQx5HKCIiIiIinY3blXbgNJu4AAgZY+qAzUBe+NodjavsjDGzjTHLjDFvANXhe/3GmIeMMX1jDcYYsxinyq/YWpttrTU4iUM/UG2MKW9hfD5QDlRaawustXnW2jygCCgyxkT9m78xxm+M2YxTYVhorS221hYBJUB5OL52GR/r2iKtsXix8xgFg0GKioooLCxscUxJSQk+n6/ZzrMNLVu2LOYYG1qwYAG5ubn1lYHNidb11cv4Jb4lJhiSE53/tF7g78c10wazcu075P/gMf7f/z3PYxt3Y21LPZtERERERETaIWlnrQ2Fk1qfBO4F1gEVQIG19q6G9xpjbgWWAwU4TSfWATXh9/7APbHEYoxZAGSHE1b1fwO31taEYywDFjSXuDPG+HGSfQXW2jOaZ1hrg9baQmCtMab5E/edhF+Ftbas8Xicn7u0hYq9WMbHurZIi3w+X30TirKysvokXkvKy8upqamhrKys2XuKi4vrK/iaEm17arRr5eXlhEKhqGtH1m1uG60b8UvXNm5QH5YvmMbTd8zmiwXjeOXdA/xk1RsYYwCoO6XknYiIiIiINK89Ku0AsNaWWWsXWmunh99XNXHPPeHrzb0WxRhGkbU22uFaxUAIJ3HXVKahBOecvmaFK9fym9puGp4zF2iy1CacPKtqbo1Yxse6tsi5iJxh19qEHTjn4VVWVlJcXNxk4mv58uXMmDGjya22kW2p0arhIsm2pu7x+/1UV1dTUlJCRUXFWdeLi4vrt/1GknuN54klfule+vdO5bNzxvJk8Sx+/IEcAPYcOsbFd66i5KHX2L7/PW8DFBERERGRuJTUUQuFz7bLBYLW2vVNXM9p6vsY1ssHolXAYa0NGWNW4nS2LcKpvGuo2aRXIzXA9PB7Q8U4P28oythynIq3XGutm+NjXVuk1SINKSKJrnMZt2XLFoqLiykoKDgjwVVcXHxWwisvL49gMFhfRVdYWIjP56OkpITFixcTCoUYPXo0cLrSLi8vj8zMTMrLy8nNPZ1bz83Npbq6muLiYlasWIHf76dfv34ALF26FJ/PV99ptqSkhNLSUlatWlX/XVvil+4tNSmRYRk9AadJRe6IDEof20zZ40HmT8nixtxhXDymH6lJiR5HKiIiIiIi8aBdk3bGmNtxkmEN/+ZaBcxrdF868BVjTACnCcULLiyfC5QYY7LD1XDNqW5wf2N+nDP6Wkpo+YAzynDCW2v9OD9vNJFxReFXzONjXVukLTZv3tymca09Gw5abh7h8/mora11be3WzHUu8YtEDM/syd035/H2viP8/pmt/HXN2/zzxe08fcdshvh68NbeI6T3TCa9R7LXoYqIiIiIiEfaJWlnjMkBVuEks0xL91tr9wMLw8mmXxpjHrbWfj/GMLLD75EquuY0fWCVowYn8VfT+Ey7iHDMmU1cj5wV1/z+vTOvT3dxfKxri4hIBxie2ZOvXj2J2+eN54W39zPE1wOAb/7zFVa/vpuLsvsxd9IgCiZlkZWe5nG0IiIiIiLSkVw/0y5cLVcNZOAk7CpwtmoW0kJyKNzcYS5wgTHm4zGGUhJeu6UKskgVYFMJrsjW2EpjTHOlNJU4P1tjkaRhS+VHkaRh40q/WMbHuraIiHSg1KREzh+dWf/5tllj+Phlo3mn9j2+fv8rXLhsFV9cud67AEVEREREpMO1R6VdOU6yrhgoC1fR1Yt0zYvGWrvQGLPJGFNurT3QliDCjRaaSqY1NiP8flYlnbW2whizHFgCLDbGLAQKrbVVxhgfzs9a1EwVnq+VcYaa+d8klvGxri0iIh4KjMggMCKDO66cwObdh3j4lZ0M6JMKwNETddzwi6e5bFx/5k7KIjDcR0KCfi8XEREREelqXE3aGWNuBTIBv7V2a4zT3QEsDb/aRTjxtiD8saSpe6y1xcaYNcA9OMmwSmNMFU5lXmGURg+RkonmrjcZT4P5Yhkf69oiIhIHjDGMGdiHMQP71H+359Ax+vVO4ddPbKH0sSAD+qRSMGkQH790NNkDensYrYiIiIiIuMntSrtC4FYXEnaRKrdi2jFp12Du5eHKvGixhHAq63w4Z8ZFPjfX7MHXhngyOZ1oi2V8rGsDYIxZjHMmIEuWLGH+/PltmPa0Pn36tHyTiHhu9erVHbreoUOHOnzNzu7j2fCBET14aXcdNbtOct/atxiXsIu30xPZur+OHYct5w1IpGeyKvDOlZ5HiRd6FiWe6HmUeKLnUeKFW8/izJkzm73mdtJutLX2Xhfn87k41xnCDSSWABXW2uIW7i0FQtbaDGPMAs6suitroTvtuYjWFKO9x5811lpbBpRFPsYwN9By508RiQ/R/qPRHlavXt3ha3YVV4ffj52sIyUxAWMM//vAK/zfi1tJTjRclN0/3MhiEIP6qpFFa+h5lHihZ1HiiZ5HiSd6HiVedMSz6HYjipDL82W2fEubVQJV1tpmz70zxviMMdVAZSSxZ62tAEbjNLkA56y7ynaMU0REJKrUpMT6M2O/dvUk7v3kRXzsktG8tfcwX/v7y1z7syex1vm3l32Hj3sZqoiIiIiItJLblXY+tyYyxowm9sqz5uauBILW2oIWbi0HSsOJunrhs98KjTH54XvyjTEljSr2QuF3X2vjanSmXCzjY11bREQ6qcQEQ97ITPJGZnLH/Als2nWId0LvYYzh1CnLVT9+gp6picydlMXcyYPIGaZGFiIiIiIi8cjtSrstxpgcl+ZaAtS4NFe98FZXWkrYhc9z84e3iDYp3DV2NE6SbEmjy5Ez8vq1sI6vmUuxjI91bRER6QKMMYwd1IdZ4wcCcPKU5VOzshmS3oNfPRHkhl88zYXLVvG3de94HKmIiIiIiDTmdqVdBU4X1nmxTGKMCeA0QGipEu5c512Ck4hrzbyFnN4C2yxrbSjcNbfcGJNrrY0kGjeH330tTOEPvzdOUMYyPta1RUSkC0pJSuAjF43iIxeNYv+REzz6+i7+s2EH/XqlArBp10FWrn2HG3OHMT5LzYNERERERLzkatLOWltmjLnTGPNXa+0H2jJHuFKvCqix1j7iVmzhyrkZzSXsjDG+RltE/ThbX1sU7i4LZ57Bt7bBPNFErq9t9H0s42NdW0REurj0nslcFxjKdYGh9d/VvBXiN09uoezxIFOHpnNj7lCuzRlKZq8UDyMVEREREeme3N4eC06F3EJjzEZjzPWtHWSMGWWMuRuoxqkQu9WtgMJnzxVEazqBUyHYUAjIbuX8vvAv65Nf4Yq7EDC9heGRxNkZCcJYxse6toiIdE8Lpw/n2a/M4Rvvm0TdKcv/PLCBK+56lGMn67wOTURERESk23F7e2yk6uwO4E4gUoFWhbMFcy/gN8bcEr7dh5MYmw7khr8zOAm29W7EY4zJBQpbSNjB2Z1qVwBFQHET9zaWj9OJNtTo+2VAiTHGb60Nnj0MgEU4TTGqmrgWy/hY124X1tr6DociEl8i3UWle+vfO5WPXTqaj106mle3H+D1HQdJTUoE4KO/eR7/gF7cmDuMyUP66vdzEREREZF25HrSDsBau9wYEwRWhr/KD78iSpsYZnCqw+ZYa9e5EYcxxg8UWWuLWrgvt/F34Z+hwBhTHi3hF16jBMhrZo6i8PWz5givm9vU2FjHx7p2e0hMTKSuro6kpHZ57EQkRnV1dSQmJnodhsSRiYP7MnFwXwCOnqijd2oSf3r2Lf7vqa1MyOrDjbnDuC4wlAF9Uj2OVERERESk62mP7bGAU3EHZAD34CTkWnotB0a7mLDzAZXAYmOMjfbC2ZK7pomfoQDwGWOqw1tsz5g/3NiiHKeSL9RMKAVAbvhMvYbj/cAqnKRitEYQsYyPdW1XpaamcuTIkY5aTkTO0ZEjR0hNVfJFmpaWnMjPP5TL81+dw7eum0JaciLfefBVHn19FwDvHa/j6AltoxURERERcUu7ljxZa/fjbDEtMsbMwUki+XG2xYaAIFBprV3VDsuX0HIjhoaaTF5ZawvCVWlLjTENz72LxB61Ui28NTXbGFNijCkPj/PhbMed01LSLJbxsa7ttoyMDGpra+nbt29HLisirVRbW0tGRobXYUic8/VM4cMXjuTDF45k065DDE5PA+Avz7/Fj6o2cs20ISzIG0bOcJ+2z4qIiIiIxKDD9imGE3PtkZxrbr0inIShG3PV0MQW03OcozVn47XL+FjXdktGRgavvfYaQ4YMITk52etwRKSBEydOEAqFmDBhgtehSCcyZmDv+l/njPAxe8JA7q15hz899xb+Ab0ozBvOJ67wK3knIiIiItIG7bY9VqSx1NRUBgwYwMaNGzlx4oTX4YhI2IkTJ9i4cSMDBgzQ9lhps9wRGfzoAwHWfDWfkhun0q9XCo9v3F2fsHt68x7eO67tsyIiIiIireV6pZ0x5m6cphPl1tqvuD2/dG5DhgwBYMOGDfh8PjIyMujZsyeJiYmqxBDpINZa6urqOHLkCLW1tYRCIQYMGFD//0+RWPRJS2bRjBEsmjGi/oy7PYeO8eFfP0+P5ESunjqYG/OGMWNUhn7fFxERERGJwtWknTHmTk5vSS02xtxprT3g5hrS+Q0ZMoR+/fpRW1vLtm3bOHbsGHV1qr4Q6UiJiYmkpqaSkZHBhAkTVGEn7SIt2elGnNkzhT9+/ALurXmHB158lxVr32ZEZk++VziN80dnehyliIiIiEh8crvSbgFOswM/EFTCTpqTmppKVlYWWVlZXofSba1evZqZM2ee+8ClS6GkBF58EaZMcT0uEel6EhIMF2X346LsfvzvtZN56OUd3FvzDkN8ThOLZ4N7eWvfEa6aOpjeqR123K6IiIiISFxz+0y7EJCH0yV2ustzi0g8uP126N0b/ud/vI5ERDqhXqlJ3Jg3jD/feiHDMnoCcP/6bSypeJG8b1Wy+Pdr+du6dzhwVGefioiIiEj35nojCmvtfmvtKmvt/ljnMsaMciEkEXFTv37whS/AvffC+vVeRyMiXcB3r5/KvZ+8iA+eP4IX39nPF1a8wAdKn62/HjkbT0RERESkO3F7D0qpMeZ2a+33Yp3IGBMA1gKJsYclIq76whfgJz+B//5vuP9+r6MRkU7OGEPeyEzyRmbyjfdNYv07IQ4ePQk4CbsLvruKKUP7cuWUwcybPIiBfdI8jlhEREREpP25Wmlnrb0HON8Yc4ML0+lkapF45fPBl74E//gHrFnjdTQi0oUkJBhyR2RwxbgBABw7eYqbLxzB9tBRvv73l7ngu6tY+MtnWLN1n8eRioiIiIi0r/bYHrsQmGuMudsYkxPDVH6cM/JEJB597nOQmelU24mItJP0Hsl8ed4EVn3pCh7+/OV8bs5YDhw9QVKCAWD92yFKH9vMW3uPeBypiIiIiIi72qVFm7X2E+HtrUuNMblABbAZp7NssBVT+IHi9ohNRFzSpw8sWQJ33AHPPAMXXeR1RCLShRljGJ/Vh/FZffh8/rj675/atIe7Hn6dZf9+jUmD+3LV1CyunDKYMQN7exitiIiIiEjsXE3aGWNOAbaJS0vaMh1QG1tEItKuPv1p+MEP4Otfh6oqr6MRkW7otlljuHbaEB5+ZQcPvrSd7/1nI//31Fae/2o+iQmGPYeO0a9XCsYYr0MVERERETknblfaBXGq5ESkO+jVy6m0++IX4bHH4IorvI5IRLqh4Zk9ueUyP7dc5mfH/qMEdx8iMcFgreX9P3uK5ETDlVMGM39KFucNS1cCT0REREQ6BbeTdiGcSrtVQCltP5POB5QAGW4EJSLt6BOfgLvugm98A1avBv1lWEQ8lJWeRla601325CnLbbPG8O+Xt3PPE0F++dhmhvp6sOTK8bw/Z6jHkYqIiIiIRNcelXYBa+3cWCcyzj+Dl8Yekoi0qx494Ctfgc98Blatgvx8ryMSEQEgOTGBmy4YwU0XjCB05DiVG3by0Ms76JuWDMCmXQf5/TNvcuWULM4flUlSouv9uURERERE2sztpN0+WtdoojVqcSruRCTe3XorlJQ41XZz5qjaTkTijq9nCoXTh1M4fXj9d69uP8jKtW/z+2feJLNXCvMmD2K4reOyU5bEBP0+JiIiIiLecvuflMuB5S7NFQTucmkuEWlPqanwta85XWQfesjraEREWuWaaUOo+XoBd38ol0vG9Ocf69/lB9VHOXz8JAAHjp7wOEIRERER6c5crbSz1q7COc/Ojbm2AHe4MZeIdID/+i+4806n2u7KK1VtJyKdQs+UJOZPHcz8qYM5eqKOP/xzdf322Q//6jmOnTzFDblDuXba0Pqz8kREREREOkJcHN5ijNlkjHmjwfvdxpi+XsclIucgJcVJ2K1dCw884HU0IiLnLC05kbEZiQBYa7kxbxhpyYl898HXuOjOVdz8q+d4bONuj6MUERERke4iLpJ21tox1tqxkXfgXqBciTuRTubDH4YxY5zk3alTXkcjItJmxhg+ctEo/n7bJTzypSv4zOyxvLXvCO+G3gOg9vBxHn19Fyfr9HudiIiIiLQPtxtRuMJaW2WMSQeWhl8i0hkkJcF//7eTvLvvPliwwOuIRERi5h/Qmy8WjOML+WOpO2UB+OdL2/n631+mf+9Urp02hOsDQ5kytC9GRwOIiIiIiEviotKuGX4g3+sgROQcffCDMHGik7yrq/M6GhER1xhjSEp0/ui0cPowSj+cx/SRGfzx2Te55mdPMu9Hj3Mk3MRCRERERCRWHVJpZ4wZ1YrbfEBm+H0GsAQoa7egRKR9JCbC//wPLFoEK1c6STwRkS4mNSmReZOzmDc5i/1HTvCvl7bz+o4D9Exx/mj1g8qNDElPY/7UwaT3SPY4WhERERHpjNotaWeMuQFna2tuW6cAStyLSEQ6zIIFMHWqk7wrLHS2zYqIdFHpPZO56YIR9Z9P1p3i4Zd38PrOg3zjH69QMHEQ1wWGcsW4AaQkxfMmBxERERGJJ+3yJ0djzDKgHCdhZ9rwWgfkWWu3tkd8ItLOEhLgf/8XNm6EP//Z62hERDpUUmICD33+Mu6/7RJuOn8Ezwb3cuvv13LPE0EATp2yWGs9jlJERERE4p3r5S/GmFuA4vDHGmAtUA3sC3+3FFjWxFA/UABsttZ+0u24RKSDXXcdBAJO8u6DH4RkbQ8Tke7DGMO04T6mDffx1asn8sQbu5mQ1ReA/2zYQclDr3NdzlCuCwxhZL9eHkcrIiIiIvHI1aRduONrGRAEiqy1q5q4Z6G19t5mprjLGDPHGLMGmG2tPehmfCLSgYyBb34TrrkGfvc7uOUWryMSEfFEcmICsycMqv/cNy2ZrL5p/GjVRn5YtZG8kRlcHxjKohnDSU7U9lkRERERcbj9J8PFQAjIbSphF+aLNkF4XAFQ4WpkItLxrr4aLrgAvvUtOH7c62hEROLCxWP685fFF/JU8WyKr5zAwaMnuOeJIEkJBoCXt+3n6Al13xYRERHp7tzeHrsQWGCtPRDlnv3GmFHRzquz1oaMMRXGmBustfe5HKOIdJRItd28efDrX8MntfNdRCRiiK8Hn5yZzSeu8LPv8HGMMRw/eYqbf/0cdXWWeVOyeH/OEC7O7k9iOKEnIiIiIt2H25V22dbaR1q4JwjktzSRtfYeoMiVqETEOwUFcMkl8J3vwNGjXkcjIhJ3jDH0650KQGKC4WcfzOXKKVk8/PIOPvzr57lw2SoefGm7x1GKiIiISEdzO2kXbMU9a4BCl9cVkXhljLM9dts2KCvzOhoRkbiWmGC4dGx/7iqcxpqv5XP3h3LJHeFjUF8nqffytv38sHIjW/Yc9jhSEREREWlvrnePbYUqYKUxZqS19s0W7vV3REAi0s5mzXJe3/2u05CiZ0+vIxIRiXtpyYnMnzqY+VMH13/3/JZ9/OSRN/jxqjeYNiyda3OGcs20wQzsk+ZhpCIiIiLSHtyutFtrjJkd7QZr7X5gHVAe7b5wJ1oR6Sq++U3YuRN+8QuvIxER6bQ+dulonrljDl+9aiInT1m+9c8NzPvh45ysOwVA3SnrcYQiIiIi4ha3K+2KgZVA/bl2xpjbgWyg1Fq7Pvx1GfBLY8wvrLWfamauO2nddlsR6QwuvRTmzoWSEvjEJ6B3b68jEhHplLLS07j1cj+3Xu5n066DbN59mKTEBKy1XP2TJ8ge0Jtrc4Ywc/wAUpMSvQ5XRERERNrI1Uq7cBXdvcaYh40xfYwxAWA5sBgnmRe5rwzYAhQZY543xkxrOI8x5s7wmKjVeCLSyXzzm7BnD/z0p15HIiLSJYwZ2Id5k7MAOHbyFBeMzuTZ4F6K/lDNjG9Xcce9L7Lh3QMeRykiIiIibeH29thIQu4enC2wd4a/NkC/RrcWhr/PA2qMMXuNMW8YY+qAL4fvWYmIdB0XXABXXw133QX793sdjYhIl5KWnMj/vn8Kz31lDr/9rxnkTxzEAy+8W9+0YtfBo7y8bT/WagutiIiISGfQLo0orLUVxphKYCEQ+Zt5aaN7aowxCzmdmPOFXyb8eYG1Vv80LNLVfPObkJcHP/4xfOMbXkcjItLlJCUmMHP8QGaOH8h7x+tITHD+aLVyzdt87z8bGTOwN9flDOHaaUMZ0U+NgURERETileuVdhHW2v3W2nustQvDr1VN3FOBc97dvTjJOgOEgCJr7X3tFZuIeCg3F66/Hn7wAzh2zOtoRES6tB4piaQkOX/c+9AFI/n2dVPI7JnC9/6zkcvvepSFpc+oeYWIiIhInGqXSrtzYa3dgrNVVkS6i5tugr/9DV5+2am6ExGRdpfRK4WbLxzJzReO5J3aIzzwwnZ2HzxWX4m37N+vMiGrD3MnZdEr1fM/IoqIiIh0e/oTmYh0vJwc533dOiXtREQ8MCyjJ5+cmV3/+cjxk/zrxe2UPhYkNeklLhnTnzkTB1IwaRAD+6R5GKmIiIhI96WknYh0PL8f+vRxknYiIuK5nilJPP7lWVS/Vcu/XtzOqtd28shru6g7ZfnIRaPYf+QEb9ceYfKQvhhjWp5QRERERGIWt0k7Y8xoYLG1dqnXsYiIyxISnGo7Je1EROJGQoJhxqhMZozK5L+vmcQbuw4xoHcqAA+/soMl975IVt80Zk8cSP7EgVyc3Z+05ESPoxYRERHputqtEYUL/MASr4MQkXYSCMALL0BdndeRiIhII8YYxg3qQ0avFADyJw3irgXnkTPcx9/XbeNjv11L4JuV1B4+DsDJulNehisiIiLSJcVtpR3gw+kkKyJdUSAAR47Apk0wfrzX0YiISBSZvVIonD6cwunDOXayjmeD+3jpnVB9Uu/Tf17HjgNHyZ84kDkTBzEhq4+20YqIiIjEKGrSzhgzu6MCacQHlHi0toh0hEDAeV+3Tkk7EZFOJDUpkSvGDeCKcQPqv5s+KoMHXtzO9/6zke/9ZyNDfT34yEUjKboiO8pMIiIiIhJNS5V2FUB6RwTSBAPUerS2iLS3iRMhOdlJ2n3gA15HIyIiMbjlMj+3XOZn18GjPPraLqpe3cXJUxaAoyfq+HLFi1w+tj+zJwykX/icPBERERGJrqWk3T6cqjcREXelpMCUKWpGISLShQzsk8aiGSNYNGNE/Xdv7zvC81v28sAL72IM5I7IYM7EgdyYO4xBfdM8jFZEREQkvrWUtAsCo4F7gFI67ow5H/ArYFQHrSciXggE4B//AGtBZx+JiHRJYwf14dmlc3h52wGqXt3Jqtd2svyh17nI349BfdN4Y+dBdh44xvmjM0lJiuceaSIiIiIdq6WkXQgIWWs/0QGxnMEYswwnUSgiXVUgAL/5DWzbBsOGeR2NiIi0E2MMU4elM3VYOl8oGMf2/e8xqI9TZffHZ9/kd8+8SZ/UJC4fP4D8iQOZNX4gvp4pHkctIiIi4q3WVNoFOiKQJtQCGR6tLSIdoWEzCiXtRES6jcHpPep/XTx/ApeM6c+qV3ex6rVd/OvF7Qzqm8qzS+dgjOHQsZP0Tm3pj6wiIiIiXU9LfwJaAVR2RCBNWAsUebS2iHSEadOcbbHr18M113gdjYiIeKBnShJzJ2cxd3IWp05ZXty2nx37j2KMwVrLvB8+jq9nMnMnZTF38iAmZPXB6EgFERER6QaiJu2stZ6dEG+t3Y9zlp6IdFW9e8PYsWpGISIiACQkGHKG+2C48/lEneUjF43kPxt28qNVG/lh1UaGZ/bgy/MmcO20IZ7GKiIiItLetNdARLyVkwPPP+91FCIiEodSkhIouiKboiuy2XXwKKte3UXlhp30Tk0E4I2dByl7PMjcyVlcNrY/acmJHkcsIiIi4p4OS9oZY/oC+YAf6AfsxWl0UWWt3dpRcYhInAkEYOVKqK2FDB1jKSIiTRvYJ40Pnj+CD54/ov67zbsP89ArOyivfoceyYlcNrY/cydncfXUwfRIUQJPREREOrd2T9oZY2YDJUBulHuCwJ3W2l+3dzwiEmcizSjWr4dZszwNRUREOpcrp2QxZ+JAngvu4z8bdvCfV3ayeuNurpySBcCL74TI7JXCsIyeHkcqIiIicu7aLWkXrqxbxelkXbQTg/1AmTGmGCiw1r7ZXnGJSJxp2EFWSTsRETlHyYkJXDq2P5eO7c//XjuZrXuP1Heb/cb9r7D+7RCTh/SlYNIg5k7KYuJgNbIQERGRzqFdknbGmNE43V8je92CQE34fW+DW7NxEnbTAR8wBthsjMmz1r7QHrGJSJwZOBCGDHEq7URERGJgjGF0/171n3+4KIfKcAXej1e9wY+q3uCG3KH8YGEOAKdOWRISlMATERGR+NRi0s4Yk2OtXd/aCY0x6UB1+ONyYFm4E2xL40YDdwC3AquMMdN11p1INxEIqIOsiIi4bnT/Xiy+PJvFl2ez++AxVr26k0HpaQDsPniMK3/0OLMmDGTupEFcNnaAzsETERGRuBI1aWeMWQsEjDEl1tqvtHLOe4B9QF5rknUR1totQJExphRnW+0vgStbOz4aY0wusBRnq66f01V/y6y1NW2cJ4TzcwaBYmttqIWxS4AZ4ft94a9LW7t+LONjXVuk3eXkwEMPwXvvQY8eXkcjIiJd0IA+qXygQROL947XcdnY/jz8yg4qqt8hLTmBy8cOYMmV4xkzsI+HkYqIiIg4Wqq0M+FXsTFmr7X2+1FvNiaA0yF2lLX2QFsCstbWGGPygE3GmGmxbpM1xiwGCnASa8Hwd7k4ycVqY0yFtbawFfOU4vxsRdbaqgbf+4F7jDG3NpW4C1+vBEoarhP5PpwQLYuybpvHx7q2SIcJBKCuDl5+GWbM8DoaERHpBkb068mPPhDgRN0pngvuo3LDDio37KRnivPH4yfe2M3WPYeZNzmLgX3TPI5WREREuqOWknazgUeAALDcGBNqocPrQpzkWJsSdhHW2qAx5g7gE8An2zqPMWYBkN04KReuMMsLJ+IWG2PKoyXujDGVQKa1NruJy8XAAmAFUNHE9XKgonFyLPwzFuCc4RdsmAh0cXysa4t0jIbNKJS0ExGRDtSwkcX/XDu5vknFQy/v4E/PvcU3/vEKM0ZmMn9qFldOyWJwuirCRUREpGMkRLtord1vrc0D7sKpuCszxlwfZUi+tfYel2KLVLbFoshaWxzlejHONtcF4Yq8sxhjSsJxNJfUmx5+z2xi7GKcrbTLmhoYrvyrwvlZm1q7zeNjXVukQ40eDenpOtdOREQ81bCr7Heun0rlFy7n83PGceDoCf73gQ381/+tqb8eOnLcixBFRESkG4matIsIJ77mAgeACmPMrGZuda391rmch9dkIMbk42wNjbZGCFgZ/ljUxBx+YAlQFtla24Q5QGEz20yLgWAL592VA/7wll03x8e6tkjHMcY5105JOxERiSNjB/Xhc/ljeejzl7PqS1fwreumAHD0RB2X3PkI1/z0SX6xehNb9hz2OFIRERHpilqVtAMIb6EcDawHqowx05q6zaW4Is6qXjsHuUBJeAtsNJFOt80lzSBKNZq1NmStPWtbbDjh58dp/hBN5PoZScNYxse6tognAgF48UXnbDsREZE4kz2gNzNGOX80PXnK8tk5Y0lMMCx/6HVmfW81V/7ocZ7atMfjKEVERKQraXXSDuoTVHnAr4BHjDEjG93iWqWdMSYdqI1hisj5c01ue21gX5Rri6H+DLxzFdna29rE2fRG38cyPta1RTpeIOB0j9240etIREREouqdmkTRFdn8/bZLeOqO2Xz9fZPok5ZE37RkAKrf3McP/vM6r24/gLVu/5u2iIiIdBctNaJokrW2KNycoQyY1+DSWmPM9dbav7kQ262croJrixKcSr2oW2RxKtKgUYIrvL0WoC0JOzidNNzcwn2RpGHjSr9Yxse6tkjHy8lx3tetg4kTPQ1FRESktYb6evDxS0fz8UtH139X/WYtP3t0Ez95ZBOj+vVk/tTBXDVlMFOG9j3j3DwRERGRaNqUtAMIbwltvC20ArjbGFNlrT3Y1rnDVXZLab75Q2viC7ZyfKRVZeMOqpFEVjAck58zt5H6gRVNbY0N87UyzlAzf3iLZXysa4t0vIkTITXVSdrddJPX0YiIiLTZ4suzuSF3GP95ZSf/fnk7ZY8Hqah+h2eXziHRwLuh98jqm0ZCgv4cJiIiIs1rc9KuKdbaKmPMVpwGB1e2ZQ5jTF9gLU4ThUdcDK+ptXzAgvDHkkaXI9Vq+8KNGhY17kRrjCk3xiyy1jaVHIycxxc6l3gaNI6IZXysa4t0vORkmDJFzShERKRL6N87lZsuGMFNF4yg9vBxgnsOk5hgOHXKcuPdTwMwb3IWV00dTN7IDBKVwBMREZFGXE3ahRUBm4wxG4Hic9kqa4y5HafCzgfktUNsjS0Nvy9vojtswyYYZyXsAKy1hcaYWmNMeROJO18b4snkdKItlvGxrg2AMWYx4XP9lixZwvz589swrcSrQ4cOsXr1aq/DOMO4rCwGPPEETz36qNNRVrqFeHwWpfvS8yjtafUWqDtluWakZc2Ok/zx2a389umt9E0xLBqfzCVDk+vv1bMo8UTPo8QTPY8SL9x6FmfOnNnsNdeTdtbaoDFmLvAfoMIYE8LZehoE9uIkhvbhJIl8OBVt0zm9HdUAhdba9W7H1lB4u+sSoKKphBynE18LiZ5ALAOWGGPywx12YxGtKUZ7jz9rrLW2DOfnA/c7A4vHVq9eHfU3B09s2AD/+hczx4yB4cO9jkY6SFw+i9Jt6XmUjjAn/H7o2EkefW0X/355O5fNGMHl4wawcedBfvPkFoaeOskn5l9OcuI59Y0TaRf6vVHiiZ5HiRcd8Sy2R6VdZJvsdJxtsqM5vQU1GoOT0Cu01q5qj7gaqQSqmtnaCqerzvY1UYXXeJ4lQDFnn4snIuciEHDe161T0k5ERLq83qlJXDNtCNdMG1L/3cadB3nghXc5fLyOX22oomDSIOZPyeLycQOUwBMREelm2u2//NbaGmttNnAHsB8nKRfttRwY3REJu3Dn26C1tiDKbZHKs2gJu4b3TW/0fSj87mttXI3OlItlfKxri3hj6lRnW6zOtRMRkW7qfecNofrrBXwuN5U5Ewfy8Cs7uO3PNRw/eQqArXsOc/REncdRioiISEdol0q7hqy1y4HlxpgAsAgnkeTHSSwFgcoOqqwDwBhTGo4rWsIOTie+QlHuaXjd16iZQyTZ16+FeHzNXIplfKxri3ijd28YN05JOxER6dbSkhMJDEziCzNzOH7yFBt3HqRXqvPH9s/8ZR2bdx9i1oSBzJ+SxazxA+uviYiISNfSYf+Ft9auAzz9m7gxZgngb0XCDmBNjMttDr/7WrjPH36vcXF8rGuLeCcQgKef9joKERGRuJCSlMCUoen1n4uvnMC/X97Ow6/s4F8vbic1KYFPzxrDZ+aM9TBKERERaQ/d5p/lwp1QZzSXsGtUJQenE1m5TdzekC/yi0bj14bf/UQXub620fexjI91bRHvBALw17/C3r3QL2qxqIiISLdz6dj+XDq2P998/xTWbN3HQy/vwD+gNwC7Dhxl6X0vceWULAomDcLXM8XjaEVERCQW3SJpZ4zJBwqiNJ0AKAGKIh/CXXBraDlpF0l8ndGEwlpbE+6c2/isu+bGl7s1Pta1RTwVaUaxfj3MmRP1VhERke4qMcFwob8fF/pP/wPXW/uO8NqOg6x6bRdJCYaLsvtx1dTBvO+8wfRJS/YwWhEREWmLuG1BZYy50xhztwvz5OJ0pI2WsAPIbOK70vAc+VHGLQq/FzdxbRnOWXfRKt4W4TTFaKrzbCzjY11bxBsNk3YiIiLSatNHZfJk8Szuv+0SbrnMz1v7jrD0vpc4fMxpXLFp1yF2HTjqcZQiIiLSWvFcaecH0lu8K4pwwqrIWlvUwn1NVtNZa8uMMUU4VXh5zYxbACy31p51Lpy1dnmD8WclDcPjc5uaO9bxsa4t4pn+/WHYMDWjEBERaQNjDNOG+5g23EfxlePZsucwWelpAHz3wVd59PVdTB+Zwfwpg7lyShZDfD08jlhERESaE7eVdkA+TVe/tUq4M2olsNgYY6O9gGqabzwxB6dirbxht9Vw0msVTsKuqSq7iAIgN3ymXsP4/OHxRU0l/FwaH+vaIt7IyVHSTkREJEbGmPrz7gCWzp/A5+eM4+DRk3zznxu4+M5HuL38BQ8jFBERkWiiVtq5sT31HEWSdPk4DR58McxVQsuNGBpqMnkVbi6RHe48W22MiVwK4my7jbq11FobDI8vMcaUh8f5cH7WOS0lzWIZH+vaIp4JBODBB+HIEejZ0+toREREuoSxg/rwuUF9+Fz+WIK7D/Hvl3cwsE8qAO8dr+PDv36OWRMGMn9K1hnJPhEREfFGS9tjFxHjFtU2imTG2lxpF94SG3Vb7DnOtxxYHsP4aNV47To+1rVFOlwgAKdOwUsvwQUXeB2NiIhIl+Mf0JvbZo2p/7zzwFFOnrLc9fDr3PXw60zI6sP8KYNZNGN4/fZaERER6VgtJe2CnO6eGgL2hd9bo+G41vCF3+05jBGRrijSjGLdOiXtREREOsCo/r34+22X8G7oPR56eQcPvrSdH63ayJyJA8lKT2PTroMcOlbHtGHpNNh5IiIiIu2opaTdPpwkWoG19pHWTmqMuTM8tshau+UcxuUDvwQWn8t6ItLFjBwJGRnqICsiItLBhvh68LFLR/OxS0ez6+BRBvR2ts/+6okt/HXN2wxOT2Pe5CzmTc5ixqgMkhLj+YhsERGRzq01lXZ555iwux2w1tq55xqMtbbKGDMdWGmM2Wut1cm4It2RMWpGISIi4rGBfU5vi106fyLnj87koZd38Jfn3+K3T29l4uC+/PtzlwFgrVUFnoiIiMtaStqFgHOplEsHzrfWLmxrQNbakDFmIbACmNfWeUSkk8vJgbvvhpMnIaml36pERESkPaX3TOaG3GHckDuMI8dP8tjruzl8vA6AU6cs+T98jMlD0pk3eRAzxw+kd6r+2y0iIhKrqP81tdbecY7z3QEsa3s49euGjDFlxphbrLW/inU+EemEAgE4ehRefx0mT/Y6GhEREQnrmZLE/KmD6z8fPn6SC0ZnUrlhJw+88C4pSQlcPrY/n7gim+mj2txXTkREpNtz+xAKv7XWlf1s1tp7gQI35hKRTqhhMwoRERGJW33Skll2w3k895V8VhZdxM0XjOTV7Qc5ePQkAJt2HeL3z2xlx/6jHkcqIiLSubhdt+5zeT4R6a4mTIC0NCdpd/PNXkcjIiIiLUhMMJw/OpPzR2fy9fdNxFrn+0df28V3HnyVb9z/CoERPq4MN7IY1b+XtwGLiIjEObcr7dyuf/e7PJ+IdBZJSTB1qirtREREOiFjDAkJTmOKWy/3U/XFy/nyvPGcrLMs+/drzP/xExw94ZyJV3v4ODaS4RMREZF6blfaGWPMKGvtVpfmy3BpHhHpjAIBKC8Ha52OsiIiItIpjRnYhzED+3DbrDG8ve8Ir+84SFpyIgAf+tVzHD5+0qnAm5JFzjBffcJPRESkO3O70q4KKHZjImPMLUCNG3OJSCcVCEBtLbz1lteRiIiIiEuGZ/Ykf9IgAKy13HzhSEb268VvntrCDb94movuXMWfnnvT4yhFRES853alXRmwyRiz0lr7aFsnMcaMBkpRIwqR7i0nx3lftw5GjvQ0FBEREXGfMYabLhjBTReMYP97J3j0tV089PIOeqU4f03Zsf8oP161kaunDuFCfyZJiW7XHIiIiMQvV5N21tqgMeYeoMoYs9ha++tzncMYMxsoB2qstY+4GZ+IdDLnnQcJCU7S7rrrvI5GRERE2lF6j2SuCwzlusDQ+u9e23GAf6x/l788/zaZvVKYN3kQV00dzEX+fkrgiYhIl+f6f+mstUXAVqDMGLPRGPNxY8yoaGOMMX2NMTcYYx4GKsNfF7odm4h0Mj17wvjxakYhIiLSTc0cP5Dqrxfwy5vzuHRMf+5f/y4f+c3z7Dt8HIBdB49ysu6Ux1GKiIi0D7e3x0YU4CTfxuBsmcUYEwKCwD4gBPhwus36ON0l1oSvzXGxmYWIdGaBADz+uNdRiIiIiEfSkhO5ckoWV07J4uiJOmreqmVg3zQAvlz+Ii9t28+8yVlcPXWwttCKiEiX0i5Ju/A22VzgV8CN4a99QG4zQyLtoWqAQmvtlvaIS0Q6oUAA/vxn2LMH+vf3OhoRERHxUFpyIhdnn/7zwM0XjuQfL7zL/eu38Zfn3yKzVwq3XubnkzOzPYxSRETEHe1VaYe1dj9QGE7elQBzotxeBZRYa1e1Vzwi0kkFAs77+vWQn+9pKCIiIhJfCiYNomDSII6eqGP167v410s7SE1yKu2OHD/Jdx98lflTBnPBaFXgiYhI59NuSbsIa20N4S6wxpgAzlbYTMLbZJWoE5GoGnaQVdJOREREmuBsoR3MlVMG13/36vaD3FezjT8++xb9eqUwb4qzhVYJPBER6SzaPWnXkLV2HaAT5UWk9fr1g+HD1YxCREREzkneyAxqvl5QX4H393Xb+PNzb/HgZy9j0pC+hI4cp3dqkhJ4IiIStzo0aSci0iaBgJJ2IiIics4aVuC9d7yOZ4J7mDi4DwDf/terPPraLlXgiYhI3FLSTkTiXyAADzwAhw9Dr15eRyMiIiKdUI+URGZPGFT/+eqpgzl6oq6+Aq9frxQ+dMEIvjh3vIdRioiInKaknYjEv0AArIUXX4SLLvI6GhEREekCZk0YyKwJA3nveKSJxXbqrAWg7pRl2YOvMnvCQC7w9yMxwXgcrYiIdEdK2olI/GvYQVZJOxEREXFRj5RE5k8dzPypp5tYbNlzmD899xa/enILg/qmcl1gKAtyhzF2UB8PIxURke5GhzaISPwbPhwyM3WunYiIiHSIMQN7U/P1An52U4DJQ9L51RNbKPjh4zwX3Ot1aCIi0o2o0k5E4p8xkJOjpJ2IiIh0mB4pibzvvCG877wh7D54jAdf2k7eyAwAflS1kVe3H+DG3GHMHD+QlCTVQoiIiPuUtBORziEQgJ/9DE6cgORkr6MRERGRbmRAn1Q+evGo+s+pSYlUvxni4Vd2ktkrhWunDWFB3jCmDE33LkgREely9E9CItI5BAJw7Bi89prXkYiIiEg398mZ2Ty7dDa/+X/TuSi7H39+/i1++djm+ut7Dx3zMDoREekqVGknIp1DpBnFunUwdaq3sYiIiEi3l5SYwOwJg5g9YRD7j5zg4LETAGzadZC5P3ycS8b0Z0HeMOZOyqJHSqLH0YqISGekSjsR6RzGj4cePZwOsiIiIiJxJL1nMsMyegLQNy2Z22aNIbj7MJ/763pmfKeKJRUvsPPAUY+jFBGRzkZJOxHpHBIT4bzz1IxCRERE4trAvml8ae54nlgyi7/ceiFXTsli1au76qvt1r1Vy5t7D3scpYiIdAbaHisinUcgAH/9K1jrdJQVERERiVMJCYaLsvtxUXY/TtSdIjnRqZf4nwc28MLbIWaMyuDG3GFcdd5g+qapyZaIiJxNlXYi0nnk5EAoBFu3ehyIiIiISOtFEnYAd38oly/PG8/ew8e5476XmPHtKn72yBseRiciIvFKlXYi0nk0bEYxerS3sYiIiIi0wRBfD26bNYZPzcxm/dsh7qvZxvBM5zy8fYePU/rYZm7MG8a4QX08jlRERLympJ2IdB5Tpzpn261bBzfc4HU0IiIiIm1mjCEwIoPAiIz672rerOXXT26h9PEgU4b25cbcYVw7bQj9eqd6GKmIiHhF22NFpPPo0QMmTFAzChEREemS8icN4tmvzOEb75sEwP8+sIGL7nyEXeo8KyLSLcVtpZ0xJgCstdYmeh2LiMSRQAAefdTrKERERETaRf/eqXzs0tF87NLRvL7jIM9v2cvAvmkA/OGZrZw/uh/js7R1VkSkO4jbpB2QCYS8DkJE4kwgAH/8I+zeDQMGeB2NiIiISLsZn9WnPkF34OgJlj/8OgePnqRg0iA+NTP7jK21IiLS9cTz9tgCrwMQkTiUk+O8a4usiIiIdCN905J5YsksvpA/jjVb93H9L57mQ796lk27DnkdmoiItJN2q7QzxtwALAJ8OFVzrRW534cq7USksYZJu7lzPQ1FREREpCP5eqbwufyx3HLZaP783Fv87pmt9Elz/kq37/BxfD2SSUgwHkcpIiJucT1pFz6Lrgon6Vb/tdvriEg3lZkJI0eq0k5ERES6rV6pSdx6uZ+PXzq6Pkn3qT9VU3v4BJ+alc3VUweTlBjPm6pERKQ1XE3aGWPSgVWcTtgFcarl9p3jVJlArmuBiUjXEggoaSciIiLdXiRhZ61l0Yzh/OLRzXzur+v5QeVGPnFFNjfkDiU1SX39REQ6K7cr7ZbiJOxKgDuttfvbOpExpgS4xaW4RKQrCQTg/vvh0CHo3dvraEREREQ8ZYzh+sAw3j9tKJWv7uTnj25i6X0vcexEHf/vktFehyciIm3kdtIuHyiy1t7jwlz/AW53YR4R6WoCAbAWXnwRLr7Y62hERERE4kJCgmHe5CzmThrEk5v21HeXffCl7WzadYiPXjSK9J7JHkcpIiKt5fZBBz6XEnbgbKnVWXgicrZAwHnXFlkRERGRsxhjuGzsAHqnOjUaa7bu4weVG7mk5BHu/Pdr7D54zOMIRUSkNdxO2gXdmshau85aq9NTReRsQ4dCv35K2omIiIi0wn9fM5l/f+4yZk0YSNnjm7m05BFKH9vsdVgiItICt5NiIZfnExE5mzFqRiEiIiJyDiYO7stPPxhg1Zdmcn1gKEMzegBw8OgJNu065HF0IiLSFNcr7Ywxo1yeU0TkbIEAvPwynDjhdSQiIiIincbo/r2488bzeN95QwD4w7NvUvDDx/jkH6t56Z029xEUEZF24HbSbhlQ6sZExpjRxpi73ZhLRLqgQACOH4cNG7yORERERKTTWjR9OLfNHMOTb+zhmp89yUd+8zzPb9nndVgiIoLLSTtr7X7gXmPMChem8wOLXZhHRLqiSDOK9es9DUNERESkM+vXO5Xb543nqaWz+fK88byybT8/feQNr8MSERHcr7TDWlsGlBtj1hhjZsUwlR+dkScizRk7Fnr21Ll2IiIiIi7om5bMbbPG8GTxbO688TwAtoXe47qfP8WaHSepO2U9jlBEpPtJcnMyY0xO+JebgDuA5cYYH1ADrKH13WUzgWI3YxORLiYxEc47T0k7ERERERf1SElkaIrTpGLH/qMceO8EP19/jIe2PcanZo7h2pwhJCe6XvshIiJNcDVpBzwCpDfxvR9YcI5zGaA25ohEpOsKBOBPf4JTpyBBf3gUERERcVPeyAwqv3gF31uxikd3JvCl8hf42aObeOjzl5GalOh1eCIiXZ7bf8vdh5Nsc+MlIhJdIAAHDsCWLV5HIiIiItIlJSYYLhicxL8/dxm/+sh0FuQNq0/YPfjSdo4cP+lxhCIiXZfblXZBYDRwD1COk8QLneMcPpztsSXhuUREmhZpRrFuHWRnexuLiIiISBdmjCF/0iDyJw0CYPPuQ3zqTzVk9EzmY5eM5iMXjyK9R7LHUYqIdC1uJ+1CQMha+4lYJzLG3AmUxhyRiHRdU6Y4Z9utXw8LznUHvoiIiIi0VfaA3tz7yYv5+aOb+H7lRsoeD/KRi0ey+PJsJe9ERFzi9vbYIE51nRtqgQyX5hKRrigtDSZNUjMKEREREQ/kjczgN/9vBv/67KVcPm4Af3jmTQg3mVW3WRGR2LldabcCp0usG9YCRS7NJSJdVSAAlZVeRyEiIiLSbU0eks7PP5TL/iMnSO+ZjLWWG+5+mkmD+/LJK7IZ0a+n1yGKiHRKrlbaWWvXWWvvdWmu/dbae9yYS0S6sJwc2L4ddu70OhIRERGRbi29p7Mt9uiJU0wd2pd7a95h1vdX84UV63lj50GPoxMR6XzcrrRrljFmFJALBK2165u4ntPU9y6tnQssDa/vB2pwtvIus9bWxDBvOVBqra1qxb1LgBnhdX3hr0tbu34s42NdWySuNWxGceWV3sYiIiIiIvRISeTb103ls7PHcs8TQf703Fv8bd02/vjxC7h0bH+vwxMR6TTcPtPuDMaY240xbxhj6oDNOB1lS5q4Lx34SvjeaS7HsBgnYVdsrc221hrgVpzkXXU48daWeRcALZ58b4zxG2M24zToKLTWFltri3D+dygPx9cu42NdW6RTyMlx3nWunYiIiEhcGdg3ja9ePYmnimez5MrxnD86E4DKDTtZs9Wto9BFRLqudqm0M8bkAKtwqrpMS/dba/cDC40xfuCXxpiHrbXfdyGOBUC2tbaw0Xo1QJ4xphRYbIwpb3xPC/P6gNZu3S0HKqy1ZY1iCBpjCoDNxphglGq9WMbHurZI/PP5YPRop4OsiIiIiMSdjF4pfGrmGACstfyoaiOvvHuA80dn8ulZY7hsbH+MafGvjSIi3Y7rlXbGmABQjdP51QAVQDFQCEyPNtZaG7TWzgUuMMZ83IVwiqy1xVGuFwMhYME5Vp2VAK3ZErsYZ0vusqauW2uD4XlK3R4f69oinUogoEo7ERERkU7AGEPFJy7mG++bxFt7j/CR3zzPdT9/iueCe70OTUQk7rTH9thynGRdMZBhrV1orb3LWntva89Qs9YuBJYaY/q2NQhjTD4QtaWktTYErAx/bFWn2gbzBltxezHOGX6hKPeUA/7wuXtujo91bZHOIxCAN96AgzrgWERERCTe9UhJ5GOXjuaxJTNZdsNUao+c4L0TdQAcOX6Sk3WnPI5QRCQ+uJq0M8bcCmQC/nCibn8M092BcxZdW+UCJeEtsNFUN7g/qvC22EJrbUUr7vXjnJvXUnIvcv2MpGEs42NdW6TTiTSjeOEFb+MQERERkVZLTUrkg+eP4JEvXcEV4wYA8OOqN5jzg8f46/Nvcfykknci0r25XWlXCNxqrd0a60ThxFh+DFNkh99b2vZ6LiegluBUsLVGJPbWJs4abx2OZXysa4t0LmpGISIiItJpJSUm1J9pd/7oTPqkJXHHfS8x865H+e1TWzgarsITEelu3G5EMdpae6+L8/liGFuCU/UXdYssTkUatJDgCm+LrW5hu2lDkaTh5hbuiyQNG1f6xTI+1rVFOpchQ2DAACXtRERERDq5ORMHMXvCQB7buJufP7qJ/3lgA69uP0jJgvO8Dk1EpMO5nbQLuTxfZlsHhhsttKYj7Izwe0uNJQqtteeyjdTXmpustaFmOiXFMj7WtUU6F2PUjEJERESkizDGMHP8QGaOH8hzwb30650KwCvv7ueHlW/woQtHcPnYASQm6O8yItK1uZ2087k1kTFmNOe2dbUta/iABeGPJVHuO5dtsRGRhGPoXOJpUMkXy/hY1xbpfAIB+MEP4PhxSEnxOhoRERERccEF/n71v36n9j3WvVVL1as7GerrwQfPH87C6cMZ2DfNwwhFRNqP22fabTHG5Lg01xKgVd1mYxBpdLE8XJl3lvC22M1tSGj52hBPw8rCWMbHurZI5xMIwIkTsGGD15GIiIiISDuYNzmLZ5bO4Wc3BRjZryff+89G5v7ocTWsEJEuy+1KuwqcirV5sUxijAngNJAocCOoZtbw4yQGK6y10aroznVbbCxirSyMZfxZY40xiwk38liyZAnz58+PYXqJN4cOHWL16tVeh+GaHsePcwHw2l/+wo5QyOtw5Bx0tWdROjc9jxIv9CxKPIm357E3sHgsXDukB+8cPMXTTz6OtZYf1xxjjC+By4Ylk56qrbNdVbw9j9J9ufUszpw5s9lrribtrLVlxpg7jTF/tdZ+oC1zhCv1qoAaa+0jbsbXSCVQZa1t9ty78LbYZrfNdnXW2jKgLPLRy1jEfatXr476m0Onc+oUfPKTTHjvPSZ0pZ+rG+hyz6J0anoeJV7oWZR40hmex/1HTpD2xloq3tjH3zefZO7kQdx0/kguzu5Hgs6+61I6w/Mo3UNHPItub48FpzJroTFmozHm+tYOMsaMMsbcDVTjbO+8tR1ii6xVCQSttc1W8jXYFhu1q2wUofC7r7UDGm3BjWV8rGuLdD4JCTBtmppRiIiIiHRD6T2T+evii6j64hV89OJRPL15Lzf/+jkeemWH16GJiLSZ29tjsdZWGGPuAO4EKsLdSatwzqfbC/iNMbeEb/cB2cB0IDf8nQEKrLXr3Y4NwBhTGo6zpa23sW6LjST7+kW7KdwMw+3xsa4t0jkFAvC73zlVdwnt8W8SIiIiIhLPxgzszdffN4kvzxvPQy/vYPaEgQD8+sktrH87xE3nj+BCfybhv6eKiMQ115N2ANba5caYILAy/FV++BVR2sQwg1MhNsda2y6lMsaYJYC/pYSdMWYBTrVgfpTb/OH3cmNM5Dy4mgbbbTeH330thBWZp3HTjVjGx7q2SOcUCMDPfw7BIIwZ43U0IiIiIuKRtORErgsMrf98ou4Uj72+iwdeeBf/gF7cdP4IbswdRkavFA+jFBGJrt1KUay1FUAGcA9OQq6l13JgdDsm7BYDM5pL2DWsOrPWVlhrM6y12c29cJpugFORF/m+4fl4a8PvfqKLXF/b6PtYxse6tkjnFAg479oiKyIiIiINfOKKbJ7/aj7fL5xGRs8Uvv2vV/lS+QtehyUiElW7VNpFWGv3A0VAkTFmDk43WD9OBVgIZxtnpbV2VXvGEa6YK4jWdAKn4YRrXWKttTXGmBDO1t9o6iv23Bof69oindbkyZCU5CTtCqP9311EREREupu05ERuzBvGjXnDeG3HAU7WOb323g29x3/93xoWzRjOjbnDSO+Z7HGkIiKOdk3aNRROzLVrcq4pxphcnGq4lv4Gn9kOyy8DSowx/igNLRbhNMWocnl8rGuLdD6pqU7iTpV2IiIiIhLFhKy+9b/ed/g4aSmJfPOfGyh56DWuPm8wH7pgBLkjMnT2nYh4qkuf1G6M8QNFLTWUCCf22srX3AVr7XKcasKSKOvmAk0mFGMZH+vaIp1WTo6SdiIiIiLSalOGpnP/bZfwr89eyoK8YfznlZ0sLH2WPYeOA2Ct9ThCEemuumzSLnxGXSWw2Bhjo72AamDNOS4RSfTNaOG+AiA3fKZew/j8OJWHRdbaaI0gYhkf69oinU8gADt3wvbtXkciIiIiIp3I5CHpfOf6qTz3lTn83/+bwYA+qQDc+vu1fLn8BarfrFUCT0Q6VIdtj/VACS03YmioxeSVMaYUpwtuJqcr7JaEk2L7gOJwA4564a2p2caYEmNMOU71my88x5yWkmaxjI91bZFOqWEzisGDvY1FRERERDqdXqlJXD5uAAB1pywD+6Zx/7ptlFe/w7hBvVk0YwQ3BIaq86yItLsum7QLb4l1rbFEgznbOrY4xrXbPD7WtUU6lZwc5339erjqKi8jEREREZFOLjHB8N3rp/KVqybyzxfe5a9r3uZb/9yAtZZbLvNzsu4UCcaQkKCz70TEfV02aSci3VTfvpCdrXPtRERERMQ1vVOT+MD5I/jA+SN4dfsBhqT3AOBv67bxk0feYNH04RROH86gvmkeRyoiXYmSdiLS9QQCStqJiIiISLuYOPh059ms9DSG+Xryvf9s5AeVG5k9YSAfmDGCORMHqvOsiMRMSTsR6XpycqCiAvbvh/R0r6MRERERkS7qsrEDuGzsALbuOcyKtW9TUf0OOw5sJH/SIABCR47j66mz70SkbZS0E5GuJ9KM4oUX4PLLvY1FRERERLq8Uf17UXzlBL5YMI6dB44CcODoCS6+8xECI3wsmjGCeZMHkZqU6HGkItKZJHgdgIiI6xp2kBURERER6SDJiQkMy+gJgLXwiSuy2brnCJ/9yzou/O4qvvnABt4NvedxlCLSWShpJyJdz+DBMGiQ00FWRERERMQD6T2S+eycsTyxZBZ/+Pj5XJzdnz88u5VDx04CsPPAUY4cP+lxlCISz7Q9VkS6JjWjEBEREZE4kJBg6s++23/kBOk9kwH49r9e5dHXdnHNtCF88PzhTB2aruYVInIGJe1EpGsKBKCqCo4dg9RUr6MREREREalP2AH8v4tHkpKYwN/WvcNfnn+LiYP7svjy0VwfGOZhhCIST7Q9VkS6pkAATp6EV17xOhIRERERkbPkjczk+wun8fxX8/nWdVNITICNOw8BUHfK8vyWfVhrPY5SRLykSjsR6Zpycpz3desgN9fTUEREREREmtM3LZkPXziSD184khN1pwB4fONu/uu3a/D378WiGcO5IXcYA/po94hId6NKOxHpmrKzoU8fnWsnIiIiIp1GcqLzV/QL/Jl8r3Aa/XqnsOzfr3HRslV84g/V1B4+7nGEItKRVGknIl1TQgJMm6aknYiIiIh0Oj1TkliQN4wFecPYtOsgK9a8zTPBvfTt4ZyJ99DLOxjUN5Wc4T41rxDpwjosaWeM6QvkA36gH7AXCAFV1tqtHRWHiHQjgQD85jdw6pSTxBMRERER6WTGDOzDV6+ehLUWYwzWWr774Ku8te8I/v69uD4wlOsCQxme2dPrUEXEZe2etDPGzAZKgGYPlTLGBIE7rbW/bu94RKQbCQTg8GHYtAnGjfM6GhERERGRNotU1Blj+OdnL+Whl3Zw37p3+H7lRr5fuZHb547j07PHehyliLip3ZJ24cq6VZxO1kWr2fUDZcaYYqDAWvtme8UlIt1IIOC8r1unpJ2IiIiIdBl905JZOGM4C2cM553aI9y//l0u9PcD4NXtB/jpI29wQ2AYV4wfUH9Onoh0Pu2StDPGjAbWAhnhr4JATfh9b4Nbs3ESdtMBHzAG2GyMybPWvtAesYlINzJpEiQnO0m7RYu8jkZERERExHXDMnpy26wx9Z/f2neEZ4P7ePClHWT2SuHaaUO4PjCU84al6/w7kU7G9aSdMSYdqA5/XA4ss9bub8W40cAdwK3AKmPMdJ11JyIxSUmByZPVjEJEREREuo15k7OYPWEgj72+m7+t28afn3+LFWvepvrr+fRMSeLoiTrSkhO9DlNEWqE9Ku3uAfYBea1J1kVYa7cARcaYUpxttb8ErmyH+ESkOwkE4J//BGtB/7IoIiIiIt1AcmIC+ZMGkT9pEPvfO8Er7+6nZ4rz1/8bfvE0fdKSuCF3KPOnDqZvWrLH0YpIc1zd3G6MCeB0iM09l4RdQ9baGiAPmGuMmeZmfCLSDQUCsHs3bN/udSQiIiIiIh0uvUcyF2f3B+Bk3SnmT8li18FjFN/7EjO+XcWn/1xDzVu1HkcpIk1x+0TKhUCxtfZALJNYa4M4W2U/4UpUItJ9NWxGISIiIiLSjSUlJvCZOWN55EtX8LdPXczC6cN5ctMetuw+DMC+w8d56Z39WGs9jlREwP3tsfnW2hkuzVWK08xCRKTtpk1ztsWuWwdXX+11NCIiIiIinjPGEBiRQWBEBl9/36T67++tfofvPPgqYwf25vrcoVyXM5Qhvh4eRirSvbmdtHPtwChr7X51thGRmPXpA2PGqNJORERERKQJKUmnN+AtnD6cnqmJ/K1mG8sfep27Hn6dS7L783//NYPkRLc36olIS9xO2rldQ5vp8nwi0h3l5MBaFe6KiIiIiEST3jOZD10wkg9dMJI39x7mb+u2sT10tD5hd/fqzYzP6s1lYwcoiSfSAeK20s4Ykw7oNEwRiV0gAOXlEAqBz+d1NCIiIiIicW9kv158Pn9c/eejJ+r49ZNB9hw6TmavFN533mDenzOU3BE+tEtOpH24nRpfa4y53qW5bgWqXZpLRLqzSDOK9es9DUNEREREpLNKS07k6Tvm8KuPTOfi7H6sWPM2N979NL9/5k0ANa8QaQduV9pVAHcbY6qstQfbOkm4ym4pUOhaZCLSfTVM2s2c6WUkIiIiIiKdVkpSAvmTBpE/aRCHjp3k4Zd3cMmY/gD844V3ueeJINflDOWaaUMY1DfN42hFOj9XK+2stVXAVqC8rXMYY/ridI0NWmsfcSk0EenOBg2CwYPVjEJERERExCW9U5O4MW8YWelOcq5XShKJxvDtf73KhctW8aFfPcvKtW9Td0oVeCJt1R4nRxYBc40xG891q6wx5nZgC+DH2R4rIuKOQEBJOxERERGRdpI/aRD3f/pSHvnSFXxm9ljeqX2Pex4PkhA+7u7lbfs5drLO2yBFOhm3t8dirQ0aY+YC/wEqjDEhoAoIAnuBELAPpzOsD8gGpgO54SkMUGitXe92bCLSjQUC8PDDcPQopKlUX0RERESkPfgH9OaLBeP4Qv5Y9hw6jjGGoyfq+EDZsyQmGK6amsX7c4Zy/qhMEhLUwEIkGteTduBskzXGTMfZJjsaWNCKYQYnoVdorV3VHnGJSDeWkwN1dfDyyzB9utfRiIiIiIh0acYYBvRJBSApwfCzmwLcv/5d7l//Ln95/m2GpKfxreumMGfiII8jFYlf7ZK0A7DW1gDZxpglwB04VXXRLAeWWWv3t1dMItKNRZpRrFunpJ2IiIiISAdKSkxg5viBzBw/kCPHT1K5YSf3r3+XgX2cHTAvvB3i6c17uTZnCEN9PTyOViR+tFvSLsJauxxYbowJAItwknd+nKq6IFCpyjoRaXejR0Pfvk4HWRERERER8UTPlCTenzOU9+cMrf/uyU17uOvh1yl56DXOH53JdTlDuWpqFr6eKR5GKuK9dk/aRVhr1wE6BV5EvJGQ4GyRVTMKEREREZG4ctusMVw7bQj3r9/G39e/y1f+9hI/feQNniqeTUKCwVqLMTr/TrqfDkvaiYh4LhCAe+5xzrZLTPQ6GhERERERCRue2ZNPzx7LbbPG8Mq7B3in9j0SEgynTlmu+skTTBmaznU5Q6k7Zb0OVaTDxHXSzhiToy6yIuKaQACOHIE33oAJE7yORkREREREGjHGMGVoOlOGpgNw+PhJpg5N56GXd1BR/Q59kmH+vhf4yEWj6u8R6aoSvA6gOcaYOUC113GISBeSk+O8a4usiIiIiEin0CctmbsKp7Hma/nc/aFcJvdP5N8v7WD7/qMAvLn3MPev38aBoyc8jlTEffFcaefDaVYhIuKOSZMgJcVJ2n3wg15HIyIiIiIirZSWnMj8qYPpsfd1Lr70ciJH3P3zxe3c9fDrJCcaLs7uz7zJWRRMGsSAPqneBiziglYl7YwxswGstY+05j4X+IClLs0lIuJIToYpU1RpJyIiIiLSiaUknd40+IkrsrlgdCYPv7KDh1/ZyVf+9hLf+ucG1n2jgLTkRI4cP0nPlHiuVxJpXotPrjHmTuDL4V+XWms/FeX2CsCtTeUGqHVpLhERRyAA998P1oI6UImIiIiIdGqJCYbpozKZPiqTr1w1kdd2HGTjzoOkJTuN5z5Q9iwn6izzJg9i3uQsJmT1USda6TRac6bdgvC7AYpauHdf+D43XiIi7gsEYM8e2LbN60hERERERMRFxhgmDu7L+3OGAmCt5dppQ+iVksiPV73B/B8/wRV3rWblmrc9jlSkdVpTI7oKuLXBr6MJAqOBu4AVtP1MOh/wK2BUG8eLiDQtEHDe162DYcO8jUVERERERNqNMYZbLvNzy2V+dh08StWGXTz8yo76DTe7Dhzlh1VvMG/yIC7O7n/GtluReNBi0s5aW2SMWRv+9T0t3B4CQtbaO2INzBizDCiNdR4RkTOcd56zLXbdOrjmGq+jERERERGRDjCwTxo3XTCCmy4YUf/dazsOcv/6bfzl+bfok5rErAkDmTc5izkTB9ZvrxXxUqtOY2xFsi5iC+6dQ1cLZLg0l4iIo3dvGDtWzShERERERLq5y8cNoObrBTy1aQ8Pv7KDyg07+ccL7/Ls0jlkpSeyZc9h0nskk9krxetQpZtyu4XKd4FfujTXWlo+Q09E5NwFAvDss15HISIiIiIiHktLTmTOxEHMmTiIk3WneOXdA2SlpwHw7X9u4NHXd3H+6EzmTc5i3uQshvh6eByxdCeubti21u631m5xca7WVviJiLReIABvvgm13bhB9dtvw5e/DDk5sHWr19GIiIiIiHguKTGBacN99Z+/UDCOT80cw95Dx/nfBzZw8Z2P8Lm/aseOdBy3K+3axBiTDoy21q73OhYR6QYizSjWr4dZszwNpcNVV8P3vw8rVzqfT52Ce+6B73zH27hEREREROLMlKHpTBmazu3zxhPcfYiHX9lJVnoqAEdP1PGllS9w9XmDmTNxIKlJOgNP3NdurVGMMTcYYx42xrxhjPlFK4Z8JXzv9e0Vk4gIcGYH2e7g1Cl44AGYOROmT4d//hM+9znYvBmuvBJ+/3uoq/M6ShERERGRuOUf0JtPzszm+sAwALbsOczaN/fxqT/VcP53VvHVv71EzVu1WGs9jlS6knZJ2oU7v34CKACygSJjzOzm7g9vhV0IzAA+aIy5uz3iEhEBYMAAGDoUVq2Cgwe9jqb9vPcelJbCpElw7bUQDML3vudsjf3+92HkSPjoR+Gdd+DRR72OVkRERESk05g4uC9P3zGH33/sfGaNH8C9Ne9wwy+e5pV3DwBw6pSSdxI717fHhhN2WGvnGmM2A6PDl/a1NNZaGwIWGmN+aYxZZq1d6nZ8IiIAvP/98ItfwMCBcPXV8IEPOO89usDBsjt3ws9/DnffDXv2ONV1f/kL3HgjJCefee/73w/p6fC730F+vjfxioiIiIh0QokJhsvHDeDycQM4ePQEq1/fzeQhfQH46t9fYuueI9yQO5T5UwfTOzUuTieTTsbVSrvw2XQFDZJt+cByYOG5nFdnrf0EkGeMGeVmfCIi9X76U3jqKbj1VnjySSgsdBJ4N9/sbB89ftzrCM/dhg1wyy1OBd23vw0XXwyPPQbPP+8kJRsn7ADS0pxr994LBw50fMwiIiIiIl1An7Rkrpk2BGMMANkDerN9/3t8ueJFZny7ii+sWM9zwb0eRymdjdvbY+8AlkU+WGu3WGvvsNbe24a5SoAi1yITEWkoIcFJav3kJ8720KoqJ3n14INwzTWQleUk9Fatiu/z3qx1YrzqKpg8Gf78Z/iv/4LXXoP774fLL4fwHxya9dGPOltpKyo6JmYRERERkS7ulsv8PHr7TO795EVcnzuUqld38u+XdwDO1tnNuw95HKF0Bm4n7XKBSjcmstauwqnUExFpX0lJMGeO00V1xw6n0u7qq+Gvf3W2jA4dCp/5jFOZd+qU19E6jh93GkgEAk6MNTXwrW/BW28522LHjWv9XBdeCGPHOltkRURERETEFcYY8kZm8t3rp7Lmq/l8bs5YANZs3cec7z/G+3/2JL9/Ziu1hzvhLh/pEG4n7fzWWjf3V/lcnEtEpGUpKU7C7g9/gF27nOqzyy6DX/0KLr0URo2CL38ZqqudKreOVlsLd94Jo0c7FXInT8Kvfw1bt8LXvgb9+5/7nMY4cz3+uNOsQkREREREXJWWnEhGrxQAxg7qw9eunsjxOss37n+F879bRdEf1rLr4FGPo5R443bSLjPO5xMRab0ePZzmDeXlTgLvj3+EadPgxz92mjuMGwdf/zq88kr7xxIMwmc/C8OHw9KlzlbYf/8bXnoJPvYx52y6WHz4w07y7ve/dydeERERERFpUmavFG65zM+/P3cZD372Mj560Si27jmCr4eT1Hv0tV28+E4I60WRgMQVt9uX1Bpj+rpRbWeMCdCKjrMiIh2iTx/40Iec17598Le/Odtnv/tdp+nDlCnOmXiLFsGYMe6t+/TT8P3vw9//DomJcNNN8MUvwnnnubcGwIgRMHu2k7T7xjecM/9ERERERKRdTRrSl0lDJmGtrW9i8e1/bWDz7sOMHdibG3KHcX1gKFnpMf4jvXRKbv+trAqnGYUb7gzPJyISXzIz4eMfh8pK2LbN6USbnu5sTx07FmbMgB/8wGlw0RZ1dc623IsugksugUcfheJiZwvsb3/rfsIu4qMfhS1bnG66IiIiIiLSYUyD5nH3feoSvnv9VNJ7JFPy0GtcdOcqfrLqDQ+jE6+4nbSrAIqNMdNimcQY82WcJhSlrkQlItJesrLg0592El1vvgl33eWcdfelLzlbWS+/HH7xC2d7bUsOHnS62Y4dC4WFzpif/hTeftup6BsypH1/lhtugN69ncSgiIiIiIh4Ir1HMjddMIKKT17M6ttn8pnZY8kbmQHA1j2Hub38BZ7evIdTp7R9tqtzNWlnra0CHgFqjDHXt2UOY8wvCVfZWWvXuxieiEj7GjECbr8d1q6FjRudbq5798Jtt8HgwTB3LvzmN04ziYa2bXMq6YYPh899zknO3XefM8enPw29enVM/L16OcnC8nI4fLhj1hQRERERkWaN6t+LLxaM45IxTsO513ce5KGXd3DTPc9x2fJHuevh19i065DHUUp7aY9DixYDB4AKY8zzxpjrjTF9ow0wxowyxiwzxuwFbgVCQKFbARljco0x5caYzcYYa4ypDn/O7Yjx4TmWhMeUGGNKw68OGR/r2iLSBmPHOttlX3nFaRaxdKnTTOLjH4dBg+Daa52urx/+sNOR9nvfc5J6zz7rVO1df71zhl1H++hH4dAh58w+ERERERGJK/MmZ7Hmq/n8+AM5jB3Um7tXb+aqHz/BwaMnAKhT9V2X4nYjCqy1W4wxc4C1QB7OllmMMUEgiJOQA/DhdIdtmDwy4evT3WhmEV53MVAAFFtrg+HvcoF7gGpjTIW1ttkEoQvj/UAlUNLwvsj3xpgSa21Ze4yPdW0RccmUKU6zim99C6qrnQYWK1bAAw8421Fvu82psBs92utI4bLLnDh++1u4+WavoxERERERkUZ6pCTy/pyhvD9nKLsOHGX92yH6pCUDcNM9z9K3RzI35g5l1oSBpCZ5UAggrnE9aQdgra0xxowByoFA+Gt/+NWYafDrKqDQWrvfjTiMMQuA7MZJNWttDZBnjCkFFhtjyptKvMU6PqwcqGicHLPWBo0xBcBmY0wwvLXY7fGxri0ibjIGpk93XsuXw4svOlV2Pp/XkZ2WkAAf+Qh885vOWXrDh3sdkYiIiIiINGNg3zTmTs4CnCq7acN9/G3dNio37CS9RzLvO28wH7pgJJOGRN0AKXGqPbbHAk5iyFqbBywEanCSc029wEnWFVhr57qVsAsrstYWR7lejFPZtyBcUefq+PB3ucCypgaHK/eqaKbhRizjY11bRNpZQgLk5MRXwi7iIx9xmmn84Q9eRyIiIiIiIq2UmGD4ylUTeeaO2fzuY+czc/wA7q15h5q3nDO1Dx49wdv7jngcpZyLdkvaRVhrK6y104EMnG2mhUBR+D3PWpsQTtatcnNdY0w+ztbQaLGFgJXhj0Vujg8rBoLh+5pTDvibOWMulvGxri0i3ZXf73S9/e1vneSdiIiIiIh0GkmJCVwxbgA//kCAtV8r4IbcoQD844V3uWz5oyz85TP89fm3OBA+B0/iV7sn7SKstfuttaustfdaa+8Jv69rxyVzgZLwFtZoqhvc79r48Llxfpxz/KKJXG+cNGzz+FjXFhHhox+FN95wGmOIiIiIiEin1Ds1iZ4pzslos8YP5MvzxrPn8DHuuO8lZny7ik//uYbjJ095HKU0p8OSduEOsTcYY3Kaud7k9zHIDr83te21oX3tND4//N7axNl0F8fHuraIdHcLFkCPHvC733kdiYiIiIiIuGCIrwe3zRrDqi9ewf23XcIHZgzn6Ik6UpKc1NDKtW/z8rb9WO22iRvt0ogiwhhzO04VV8MGFFXAvEb3pQNfMcYEgAXW2hdcWL4Epztt1C2uDWJrnOCKdXwk6be5hfGRpF/jSr9Yxse6toh0d337wo03Op1uf/QjSEvzOiIREREREXGBMYZpw31MG+6r/+7oiTq++cAGDh07ybhBvbkhdxjX5QwlK11/D/BSu1TaGWNyjDF7cRJf2ZzdeOIM4a2zC3GSeXcZY74UawzhRhiFjTunNmFG+P2MDqqxjgd8rYwz1MylWMbHuraIiLNFdv9+uP9+ryMREREREZF2lJacyJPFs/j2dVPonZrEnf9+jYvuXMXKtW97HVq35nrSLlwtV43TeMIAFThNEQppYRtmOFE2F7jAGPNxt2NrIlYfsCD8scTl8Znh99A5zufG+FjXFhGBWbNg2DBtkRURERER6QZ8PVO4+cKR3PepS3j09pl8ZvZYzh/lpBdWv76LL65cz1Ob9lB3SttnO0p7bI8tx0nWFQNl1tr9DS8a02Sx3RmstQuNMZuMMeXW2gPtEGPE0vD7cmttS+e/net4Xxvmy+R0oi2W8bGuDYAxZjHhM/2WLFnC/Pnz2zCtxKtDhw6xevVqr8OQODf68ssZ8de/8sy993K8X792WUPPosQTPY8SL/QsSjzR8yjxRM9jx8lNhq0vv8tWYPXbJ/j368e5r2YbGamGi4YkcdGQJIb36bBWCXHHrWdx5syZzV5zNWlnjLkVJ/njt9ZujXG6O3CSYktburEtwh1WlwAV1trijh7fjOaaWnTE+LPGhrcGR7YHK5XexaxevTrqbw4iAAweDH/+MxcHg84Zd+1Az6LEEz2PEi/0LEo80fMo8UTPozdmAnecqKPq1Z3cV7ONhzfu5sVQEk8suQJjDIePnaRXaru2TYg7HfEsuv2/aCFwqwsJO6y1FcaYYtopaYfTYKLKWlvo0XgRkfg3fjxceKGzRfb226EV1dIiIiIiItL1pCUn8r7zhvC+84aw99Ax3tx3BGMMJ+pOccVdjzJ2YB+uDwzlyqlZ9E1L9jrcLsHtOsbR1tp7XZzP5+Jc9YwxlUDQWlvQjuND4Xdfa+dt1BgilvGxri0ictpHPwqvvAI1NV5HIiIiIiIicaBf71RyR2QAcPzkKW6+cCTb97/HkntfZPq3q7jtTzW8vG1/C7NIS9xO2oVcni+z5VvOjTGmFCCGhF1rx0fOuIt6CFSUBhCxjI91bRGR0xYtgtRU+O1vvY5ERERERETiTK/UJD6fP45Hb5/J32+7hJvOH8Gzwb0cPnYSgK17DrN26z6s1alb58rt7bE+tyYyxowm9jPeGs+5BOe8vbYm7M5l/Obwu6+F+/zh98YlLLGMj3VtEZHTMjLg/e+Hv/wFvv99SEnxOiIREREREYkzxhhyhvvIGe7jq1dPJDF8tM7vn3mT3zy1hWEZPbguZyjXBYYwZmAfj6PtHNyutNtijMlxaa4luJhMCndCndFcwq2lqrM2jF8bfvcTXeT62kbfxzI+1rVFRM700Y/C3r3wr395HYmIiIiIiMS55MQEEhKcpN0X547jBwun4R/Qm1+s3kT+Dx7ng2XPqvKuFdxO2lUAJbFOYowJAIuB0pgjcubLBwpaaBrRbNxtGW+trcHZLjy9hfAiibNyt8bHuraIyFnmzoWsLG2RFRERERGRc9I7NYkbcofx+4+dz7NfmcPX3zeJS8f2xxiDtZbiihe5t/odDoW308ppribtrLVlwAxjzF/bOke4Uq8KqLHWPhJrTMaYXKCwFV1emzw/L8bxywCfMSZaxdsinKYWVS6Pj3VtEZHTkpLg5pvhwQdh926voxERERERkU5oYJ80Pn7paG6bNQaAvYeP89TmPXyp/AWmf7uSz/5lHY++tosTdac8jjQ+uF1pB06F3P9v777DpKiyPo5/DyAgBoJZMIE5C+qaI8Y1C2bFCGtYs2JG1wjmtIrhdXXNOWd3zeuas2sAc3YV06Kk8/5xbjPFMKFnpmeqZub3eZ5+eqa7qvo2nKnue+rec7czs/fMbOtydzKzBc3sEuAlohbbPk1tSEpYDXP3YfVs17859nf3UcSiEDWO4kv79QdqTAg2Zf+mvraIyHSGDIFJk+D66/NuiYiIiIhI++MOP/wAX30VP7cBs8/chaeOXJfb9l2VQQP68OT737LH317gwTe/AmJl2vY8jbbSC1Hg7rea2VHAGcCtFoUHHyXq0/0X6Gtme6fNewD9iGmcpcSXEVNRX21KO1KNuUfS6w0tY5fhldw/YwPgETMbmkYilo7fF3iMSArWVbuvKfs39bVFRKosvTQMGABXXw0HHZR3a0RERERE2pbx4+HTT+P2ySc1//zLL7Ftz56w1FLxHT17P8cc+b6HRjAzBizQiwEL9OKEzZbiife+ZY2FZwfgiqfHcvMLn7Ll8r3ZaoXeLDT7TDm3tmVVPGkHMcrLzMYCN6eHBqZbSU216oyow7a+u79SgWaMpP6FGLKqJ6+auj8A7j4W6GdmI83sFmL0Ww9iOu369SXNmrJ/U19bRGQ6Q4bAgQfC66/Dssvm3RoRERERkdZh0iT44ou6k3LffTf9fnPNBfPNB4svHnWm55sPZpgB3n4b3nwTbrwRxo2r2n7OOadN5JV+7t69xd5qU3Tu1IENlpxr6u+LzjkLvXvOyAX/eJ/zH3uf5ebrwaABfdh1lQVybGXLaZakHUwdcdcTGEV5U11HAae7+48Vev1hQJ3TWptz/xqOV9tIvGbfv6mvLSIy1Y47wmGHxWi7s8/OuzUiIiIiIvlzj4RbbaPjPvkkEnZTqtVp6949knDzzQcrr1z18/zzx32fPtClS/2v/cUX8NZbkcR78834+f/+D379tWq7Pn2mT+QtuSTMVOyRawOXnIuBS87FVz/+xt2vfc4dr3zBo29/raRdJaQE3DBgmJmtT0zX7EuM9hpHjPx6xN0fa852iIhIhcw+O/zxj3DddXDGGXGVT0RERESkPZg4EW66Cd57b9qk3Kefwm+/Tbttly5VSbj1158+ITfffDDrrE1vkxn07h23DTesenzKlGhjKYlXSug9/jj8/nvVdgstNG0ib+mlYbHFoGvXpretgubu3pWha/Vj6Fr9+N+E9rPKbLMm7bJSYk7JORGR1m733eHOO+Ghh2CzzfJujYiIiIhIyzj8cLjgAujQAeadNxJvK6wAW245fVJujjkioZaXDh1gwQXjlv3OPnkyjBlTlcgr3T/wQEzhLe27yCLT18tbZJFCXLTv1rnFUlm5az/vVEREKmOTTWLE3dVXK2knIiIiIu3D7bdHwu7Pf4ZzzoFOrTSd0rEjLLpo3LbeuurxCRPg/fenTeS98QbccUfVtN4ZZojaekstBbvtFv0CaVYVjzIzu4RYdOIWdz+m0scXEZGcde4MO+0El14K338PvXrl3SIRERERkebz4Yew556w0kpw1lmtN2FXl86dIxm31FLTPj5+PPznP9OOzHviiZgmfOGFsP/++bS3nehQyYOZ2RlEDbt+wHAzq8AEbRERKZzdd4+rcTfemHdLRERERESaz4QJsP328fNNN0Vyqz2ZccaYArzLLlHT+p574IMPYPPN4YAD4NhjYzEMaRYVTdoBg4jFJQDGuvtPFT6+iIgUwfLLwzLLxBRZEREREZG26sgj4YUXYjXWhRbKuzXF0K0b3HYb7LMPnHYa7LVXLNIhFVfppN04YACxSuyKFT62iIgUhRkMGQLPPw/vvJN3a0REmm7SJPjHP2DffaNTtssu8PnnebdKRETydOedcP75cOCBsM02ebemWDp1gtGj4cQT4aqrYjGOX3/Nu1VtTqWTdrj7j+7+mLv/2NRjmdmCFWiSiIg0h513jkK2Gm0nIq3VpEnw2GPwpz/FKoDrrw/XXBNFtm+9NYp0n3oq/PZb3i0VEZGW9uGHsMcesOKKMGpU3q0pJjMYMSKSdw89BOutB99+m3er2pRKJ+1Gm9nhlTiQma0AjKnEsUREpBnMPTdsvDH8/e+xdLyISGswaRI8+igMGwbzzAMDB8Z5bN114ZZborPxwAPw9tuw0UZw3HGw5JKxep5q9oiItA+lOnZTpkQduy5d8m5RsQ0dGqvrvv46rL56JDylIiqatHP3y4GVzawS40a1HKGISNENGQJffBEjVUREimriRHj44ehUzDMPbLABXHddJOxuvTUSdTfdBIMGRZ0egL59owPy6KPx2DbbwIYbRjJPRETatuHDq+rY9e2bd2tahy23jD7Bd9/BqqvCK6/k3aI2oTmmx24HbGhml5jZ8k04VF+iRp6IiBTV5ptDz56aIisixTNxYkzV2XvvSNRttBHccEMk7G6/PRJ1N9wA225blairyfrrw6uvwgUXwIsvwrLLwkEHwQ8/tNhbERGRFnTXXXDeefDnP8dnhJRvtdXgmWdihd21144LX9IknZrjoO7+pzS99Wgz6w/cSkx1HUvV6rJ16QsMb462iYhIBXXtCjvsEMVnf/wRunfPu0Ui0p5NnBhX+W+5JYqHf/89zDwzbLEFDB4cibsZZ2z4cTt1is7bjjvC8cfDRRfFSL1TT42kYMeOFX8rIiKSg48+gt13hwED4Mwz825N67TEEvCvf8Emm8Cmm8bF/R13zLtVrVZFk3ZmNgWoqdjHkY05HKBLmCIiRTdkCFxySXSS994779aISHszYcK0iboffoBZZpk2Ude1a2Vea/bZ43w3bFiMtvvTn+DSS2MU3pprVuY1REQkH9k6djffrDp2TdG7Nzz5JGy1Fey0E3z5JRx6aN6tapUqPT12LJFsq8RNRERag5VXjpUWNUVWRFrKhAlw//2xqt9cc8WV/Ntugz/+MaY1ffMNXHtt1NepVMIua/nl4fHH4cYb4b//hbXWilHHn35a+dcSEZGWcfTR8PzzcOWVqmNXCT16wIMPRr3Yww6L25Qpebeq1an09NhxxEi7x4DRNL4mXQ9gJNCzEo0SEZFmZBaj7Y4+Gj74ABZeOO8WiUhbNGECPPJIjKi76y4YNw5mnTUSc4MHxyIRLTkqwixGZGy+OYwcCaNGwd13w1FHwRFHNG4aroiI5OOuu+Ccc+CAAyLJJJXRtWtc4Dr44Pj3/fJL+NvfouadlKXSSbuxwAruvmFTD2RmRiT+RESk6HbZBY45Bq65Bv7yl7xbIyJtxe+/T5uoK9XOLCXqNtgg/+lL3brBSSfBnnvC4YfDiBGx2uBZZ0UBc9MEEhGRQvv446hj179/nLulsjp2jDISvXvHRf5vv43R8bPOmnfLWoVKT4/9nvIWmijHD8SIOxERKbo+fWDgwEjaadi7iDTF+PFwzz2w224w55wxku3uu2HrreHee+Hrr2M6/mab5Z+wy1pggUgu/uMf0REZPDhWnn3jjbxbJiIitVEdu5ZhFiPR//Y3+Oc/YZ114Kuv8m5Vq1DpkXa3AC9V6FhjAS3XIiLSWuy+O+y8MzzxBKy7bt6tEZGic49V+t54A15/ver2/vvReerZM0aqlZJfrWUqzbrrwssvw2WXxUqzyy8P++4bo5B79cq7dSIiknXMMfDvf0fCrl+/vFvT9g0ZEhfkBg2C1VaLmneLLpp3qwqtokk7d3+MqGdXiWN9CBxViWOJiEgL2GqrWLHx6quVtBORaf34Y1VyLnv/889V2/TrB8suGws6rLpqnEdaS6Kuuk6dYL/9YvTGiBGx4uwNN8DJJ8PQofG8iIjk6+674eyzYf/94wKRtIxNNonFnP74R1h9dbjvvljYTmqkbwwiIlIZ3brBdttFsdmLLoKZZ867RSLS0iZNipFy2ZFzb7wR9YJKevSI5NyQIXG/7LKw1FJt85wx22xxPhw2DA46KDqGl14atX3WWSfv1omItF+lOnYrrKA6dnlYaSV45hnYaKO4SHfrrZHMk+koaSciIpWz++5w5ZVRXHbIkLxbIyLN6euvpx059/rr8PbbsXgExGiyxRePq+h/+lNVgq537/a3OMMyy8Bjj8Htt8Nhh0UHZdCg6CgusEDerRMRaV8mToxR3ZMmxbTYrl3zblH7tMgi8OyzsOmmUb/2iiuiLyHTaFTSzsy2ATYA+gLjgBeAy9z9p8o1TUREWp3VV48pbldfraSdSFvx22+RjKueoPvmm6pt5pknEnIDB8b9MstEwk4FvauYRY2+TTeFM8+EM86IhTWOPBKGD4/RyiIi0vyOOQaeew5uugkWXjjv1rRvc88d9bC33Rb22AO++CJWmG1vF/fq0KCknZntDYxk+lVdBwEjzexIdz+7Qm0TEZHWxixWfBwxIqYdaASJSOsycSI89BC89lpVku6992Dy5Hh+xhljKutmm1WNnFtmGZh99nzb3ZrMOCOccEKMJjjyyFig4qqrIpG33XbqqIiINKd7741RzvvtF+dcyd8ss8T/y557wrHHRuLu/POhY8e8W1YIZSftzOwSYGhdmwCjzGwDd9+4yS0TEZHWqZS0+/vf4bjj8m6NiJTLPRJJ118fv/ftG0m5QYOqEnT9+ulLdKXMP3/UAN1vPzjwwJiq9de/Rkdl+eXzbp2ISNvzyScxE2T55WMBCimOzp3hmmti5P5ZZ8FXX8G112rqMtChnI3M7HRgGJGYy95+zPxMut/AzG6sfFNFRKRVWHDBKLB+9dWRBBCR1uH//i8SdsceCz/9BGPGwB13xEiwQYNg0UWVsGsOa60FL70UC1S89RYMGAD77ssMP/6Yd8tERNqOUh27iRNVx66oOnSIUefnnBP1sTfaCMaNy7tVuas3aWdmKwDD069jieRdP3fv4O693L0D0A8YDDxKJO4Gm9nWzdRmEREpuiFD4IMPorisiBTfm2/Cn/8cNelOOimmqkjL6dgxVph9/3044AC4/HL+sPPOMQrv2Wd1AUREpKmOPRb+9S+4/PJYAEGK65BD4IYb4v9rzTXhs8/yblGuyhlpd3m6H+3uC7v75e7+YXYDd//Q3W9z9w2B7YjE3cgKt1VERFqLQYNgpplitJ2IFNuvv8L228Oss8a0do2my0/PnjE99rXX+O8f/hC17koL/Bx/PPznP3m3UESk9bnvvhjBte++8XknxbfDDvDAA1Eje7XVYkGsdqrOpF0aZdcfuMXd9y3ngO5+K/AnoJ+Zrdf0JoqISKsz88yxCtRNN8H48Xm3RkTqcuCB8M47UTtm7rnzbo0ALLUU7xx/fKzQe/XVsbrhaafBEkvAiivCeedFvR8REanbp59GveXll49pl9J6rL8+PPlkTGleYw145pm8W5SL+kbabQ84sE9DDurulwEfAhs0sl0iItLaDRkSdbHuvDPvlohIba69NmrZHXtsTI2VYpllluhsPvxwTA8655yYKnvIIdC7d9T7ueYa+PnnvFsqIlI8pTp2Eyaojl1rtfzyUSZi9tnje0o77FfUl7QbCDzq7j814tiXpf1FRKQ9WmedWB1RU2RFium99+BPf4p6MSNG5N0aqc8880Sy7qWXYprQ0UfH/+GQITDXXLDTTjEFbOLEvFsqIlIMxx0XCR/VsWvdFloo/h+XWy5m8owenXeLWlR9Sbu+wCONPPYjaX8REWmPOnSIESKPPAKff553a0Qk67ffYLvtYtTB9ddDp055t0gaYokl4JRTYOxYePrpSNw99BBsthnMO28sZvHcc1rAQkTar/vvh1GjYpGfHXbIuzXSVLPPDo89BhtvHBccR4xoN59x9SXtehArxjbG2LS/iIi0V7vtBlOmxBQ8ESmOww6D116LkbB9+uTdGmkss1io4pJL4Msv4a67YN114corYdVVY2TJiBExIk9EpL0o1bFbbjk499y8WyOVMtNMMT12jz3gL3+BoUNh0qS8W9Xsylk9dlxjDuzuPzZmPxERaUMWWSRWfLr66nZzNUyk8G69Ff76Vzj8cPjjH/NujVRK586wxRZRt+mrr6JW4YILwsknw2KLwcorwwUXwNdf591SEZHmM3Ei7Lgj/P57nA9nnDHvFkklzTBDXJg69li44go4++y8W9Tsyknafd/srRARkbZr991jZcoXXsi7JSIydizstRf84Q9w6ql5t0aaS/fuMRLh0UdjxMmZZ0ZH9qCDYgGLTTaJEdC//JJ3S0VEKuuEE2KV0csug0UXzbs10hzMokTELbfAgQfm3ZpmV07STkREpPFKdbO0IIVIviZMiLo+ZnDjjTEyS9q+3r1jVOUrr8Cbb8KRR8ZCFrvuGgtY7LILPPBAu5hiJCJt3AMPwBlnxLTJHXfMuzXS3AYNahcjKQubtDOz7ma2Xt7tEBGRJureHbbaCm64IaYqiEg+jj46RryWpk1K+7PUUnDaafDhh/Dkk5Gwu+8+2HTTSO4deCA8/7zKGYhI6/PZZ3ExYtll4bzz8m6NSMUUNmlH01auFRGRItl9d/jhB7j33rxbItI+3XMPnHNOrCq6zTZ5t0by1qEDrLkmjB4d9e/uuCN+v+yymDq92GJw0kmR3BMRKbpJk1THTtqscpJ2vZq9FcV6XRERqbSBA2HeeeFvf8u7JSI1++03eP99bOLEvFtSeZ98AkOGwAorRG0zkawuXWI09K23RgLviiti1N1JJ8ViQgccAN9+m3crRURqd8IJ8PTTceFhscXybo1IRZWTtMtrfHx/GrlyrYiIFEzHjlV1k7RyoeTl++9jeuiNN8YiDHvtBeusA/PNB926waKLMmDffeGLL/JuaeWUVtGbOBFuuinqS4rUpkeP+Lv45z/h449h2DC49FJYeGEYOTKS2yIiRfLgg3D66bDPPqpjJ21SpzK2GWVmo2ncKrJmZocB1sD9+gHDgB8a8ZoiIlJEQ4bAqFFw/fVwyCF5t0baosmT4fPPYcyYaW9jx8b9uHHTbj/33NCvH6y3XtzPMgszHnMMrLpqdAKWWCKXt1FRI0bAs8/G390ii+TdGmlN5psPLr44RtoNHw5HHQV//Wt0jnfYIabYiojk6fPPo47dMsvA+efn3RqRZlFO0q4/MLqRx3dgVCP3FRGRtmTJJWGllWKKrJJ20ljjx0edreqJuTFj4KOPYoXUkk6dYsGFfv2iTle/flW3hRaCmWaa7vCvdOvGiiNGwOqrw913wxprtNhbq7iHHooEy957a/SBNN4SS8Tfwj/+EavQ7rxzFHk/55zW/fchIq1bqY7d+PFwyy2qYydtVjlJO2j4SDkREZHpDRkSozZefRWWXz7v1kgRucN//zvtCLnsrfrU1VlmiSTcMstEXa5sYq5Pn0jcNcAviy0G//oXbLxx1GK87jrYdtvKvb+W8uWXMfpgqaU0+kAqY7314MUX4e9/h2OPjYUrttkmps0uvHDerROR9mbECHjqKbj2WtWxkzat3G+yL9O46bGN1TfdRESkLdlhBzj0ULj6aiXt2rrffoOffoIffyz//quvIjH300/THmueeSIJt8EG0ybl+vWD2WYDq/C1xYUWgmeegS22gMGDY1TRgQdW9jWa0+TJMRrql1/g8cejXp9IJXToEBdfBg+OkXZnnBErE++3Hxx/fPw9iog0s57PPw+nnRYjyXfeOe/miDSrcpJ2l7n7vs3ekmpSHb1BLf26IiLSjGabDTbfPEYvjRoFM8yQd4ukusmTI2nW0IRb9cey01Rr07UrzDordO8e93PNBautNv001jySTrPPDo89BjvtBAcdBJ9+GiOKWkMdr1NPjYUE/u//Ylq6SKV16wbHHRcd5hNOgAsvjIsxxx0Xo6m7dMm7hSLSVn3+OUucdlqMsL/ggrxbI9LsyknaPdLsrajZzShpJyLS9gwZArfdFivJbrFF3q0RiOmgu+4aI91+/bX+7Tt2nDbZ1r07zDtv1L6q/nhd9507N/97a4oZZ4Rbb41RdmedBZ99FjUZi5yQePxxOOmkWK15993zbo20dXPPDZddFn8jRxwRNe/++tcYgTdoUOVHwYpI+/bCC3DAAXT8/Xe4+WbVsZN2oZyk3bjmbkQtvgd65PTaIiLSXDbeGOacM0ZlKGmXvylT4M9/jkLOw4aVl2zr1q39dMY7doSLLoL554/VM7/+Gm6/HXr0yLtl0/v22xgZuPDCcMkl7ef/SPK39NJxIebhhyNxt912sQrz2WfHvYhIY02aBHfeCeeeG6uhzzIL/znqKJZafPG8WybSIupL2o0CxrZEQ2owDjgzp9cWEZHmMsMMUX/kootiwQHJ1223wUsvwTXXxGg7mZ4ZDB8OvXvDHntEAf4HHoiFLopiyhTYbTf4/vto28wz590iaY823BBeeSVGpB53XEx33267GHm30EJ5t05EWpNx4+DKK2P6/ccfQ9++UWN2jz349uWX826dSIupszCLux/l7h+1UFuqv/aH7n5UHq8tIiLNbMgQmDgRbrihZV934sT44vfMM3DTTTHl8ZBD4KGHWrYdRTFxYqwCufTSMUJL6rbLLpEQ+/jjGD305pt5t6jKWWfBgw9Gh2a55fJujbRnHTvCXnvB++9Hvbt77oHFF48ReD/8kHfrRKToPvggptz36RPnjQUXhDvugPfeixqzs86adwtFWlS5q8eKiIhUznLLxe3qq+HMCg2q/v13+OKLWDDgs8+mvZUe+/prcJ92vw4d4O9/jw5mz56VaUtr8be/xfu+++7oaEv9Bg6EJ5+ETTeFNdaIKTvrrJNvm559Fo45Jlb0HDYs37aIlMw8c9RXHDo0VpY95xy46ioYMQL+9Kfi17QUkZbjHjVZzz0X7r0XOnWqWghqhRXybp1IrpS0ExGRfOy+OxxyCN0+/LD+pMf48fD55zUn4kq3b76Zfr/u3eNK7XzzwfLLx8+l23zzxf2HH8YXwlNPjdFK7cX48XDiiTF9bbPN8m5N67L88rF4xyabwEYbRfJ5hx3yacv338OOO0bNvcsvVx07KZ7evWMl4wMPjFEzBx0U5RFGjoSttlLMirRnv/0Wsy7OOw9efx3mmCOS/PvuGwvdiIiSdiIikpOddoIjjmDe++6D1VevPRn32Wfw3XfT79+zZ1XibcUVp0/G9e4Ns8xSfzuWWw723DNqpuy7L/TrV/n3WkQXXRQjE2+8UZ3mxlhgAXj66Ug67Lhj/FseemjLtsE9YvfLL2PKd/fuLfv6Ig2x/PLwyCMxxfzww2GbbWCtteJiyUor5d06EWlJX38dCyZdcklcdF1mmahft9NO0LVr3q0TKRQl7UREJB9zzgmbbEKf226LxRCyZp+9Kgm3yirTJuNKCbmZZqpcW04+OZJXRx0Ft9xSueMW1bhxcPrpMcVzzTXzbk3r1atXrJa5665w2GGRcD777Jhy3RIuvBDuuiumEynpIa2BWZx3NtwQrrgiat6tvHJ01E87LZLhItJ2vfZajKq7/nqYMCFG+h98MKy3ni4gitRCSTsREcnP2WczZp556LfmmtMm5GacsWXbMc88sTroCSfE6Kk11mjZ129pZ54ZBeFPOy3vlrR+XbtGwvfQQ6Mj8vnnsRJvc48UePHFGK20xRYx3VCkNenUKera7bRTTJM955y4eHPIIXHxRKNGRdqOyZPhvvviM/Kf/4Ru3WCffWLK/KKL5t06kcJroUvBIiIiNVhkET7dccdYlXOddWDhhVs+YVdy2GGRMDzsMJgyJZ82tIQvv4wvzjvtpFVGK6Vjx/g3PeusGKm50UbNu0rmjz/C9ttHvZ+rrtLoBGm9Zp016om++24spHLGGbDIIvDXv8KkSXm3TkSa4uefY0T4YovBllvGqrCjRkXZk4suUsJOpExK2omIiEBc+T3tNHj++Rg51VadckpMSfnLX/JuSdtiFgnf66+PRSpWXx0++aTyr+Meq3F+/HEU7+7Vq/KvIdLS5p8/VvF+4QVYYgnYf/+ocXXvvdOv+C0ixfbRR/F52KdPjKabc064+WYYOxaOOCJqEotI2ZS0ExERKdllF+jfP6ZnjR+fd2sqb8wYuOyySPq0lwU3WtqOO8JDD8XCFKuuGvV7Kunyy6Pzc8opkRgUaUtWXBEefxzuvDNGPG++eYzSGTIkCta//DJMnJh3K0WkOvcoLzJoUHy/OP/8qF/53HPw7LMxkraTKnOJNIaSdiIiIiUdOkRtpU8/jemObc0JJ0DnznDccXm3pG1bd1146qkYfbfmmvDYY5U57uuvR/26DTeEI4+szDFFisYsptK9+SZcemmMvHvwQdhvPxgwIOrdrblmjNi59daYaici+ZgwAa67LhaUWXNN+Mc/4vPpo49iNPgf/pB3C0VavXaR7jaz/sDRQH+gL/AyMBY43d1fLvMYRwIrpf16pIdHt4b9m/raIiLtytprw1ZbxVTZPfeEuebKu0WV8eqrMXXzmGNi4Q1pXsssE9NkN9kkblddBTvv3Pjj/fILbLcd9OgR0whbaoVakbzMMAMMGxY395gS/txz8O9/x/0FF0TCAGDeeSM5sMoqcb/iipVdYVykCM4/H048MS6+zTJLzbeZZ679uerbzTxz4z9L/vtfGD0aLr44RpYvtliMht11V/3tiVRYm0/amdlQYANguLuPTY/1By4HXjKzW919cB379wUeAUZmtys9bmYj3f2yIu7f1NcWEWm3Ro2CJZeMkWmjR+fdmso49tioI3PEEXm3pP2Yb76YLrTVVjH1+rPPYgRCYxaO2H9/eO+9GLU355wVb6pIoZnBggvGbYcd4rEJE2L6eTaRd8cd8VyHDpE4zybyFl9cyW5pvW66CQ4+OBbtWmyxWOShdPv661jkofT7L7+Uf9yZZiovwZf9+YknYpX0336Lkd9XXBELMOnvS6RZtOmknZkNAvpVT8qlEWYDzGw0MNTMbqkjcXcLcGv15Ja7jzWzDYAxZjbW3R8t4P5NfW0RkfZpkUUiSXLhhfDnP8PSS+fdoqZ58km4//5IRvbokXdr2pcePaLG3ZAhUSvx009jtETHjuUf4+qro4M0YkRMvRWRGG200kpx+/Of47HvvovFhEqJvJtuijqeECvVrrxyVRLvD3+AOebIr/0i5XrqKdhtN1hjDXjgAejate7tp0yBX3+tSuBlE3x13Urbfv75tI//73/THr9r1xhRd9BBsNRSzfe+RQRo40k7YJi7b1DH88OB7YBBZja0enIrjdLrD6xf084p+fUoMBqYrqJ3nvs39bVFRNq9E06IRMnhh0c9pdbKHY4+OqaPHXBA3q1pn7p0ianJffrA2WfHVKLrroMZZ6x/33feiVpe66wDxx/f7E0VadVmnz2K32+6afw+ZUqMUM2Oxjv9dJg8OZ7v23fa0XjLLx9/ryJF8e67UeNxoYXgrrvqT9hBjHgrjYyrhMmTp03+zT23VoAVaUFtdgyrmQ0kpobWyt3HATenX4fVsMlwYGzarja3AH3TlNsi7d/U1xYRad969YrE3UMPte6k3b33xsptJ55YXpJImkeHDnDWWbHAyZ13wsCBUROoLuPHRx27bt0iydeQ0XkiEn93iy8Ou+8e9bZeeQV+/DFGH48aBSusED8fdFAk7madNe4PPjiK6I8dGxc+RPLw9ddRE3WGGWK0fK9e+bSjY8dYAKZPn1gYRgk7kRbVZpN2xCizkWkKbF1eymw/Var71pdYvKEupeenSfrluX9TX1tERJL99oOFF47RdpMm5d2ahps8ORaeWHRR2GOPvFsjEMmBm26Cl16C1VeHDz+sfduDD44VNP/+9xgpKSJNN9NM068+++mn8fNBB8VIu8sug512gn79YlTRfvvBy1q/TVrQr7/C5pvDV1/Fxbe+ffNukYjkpC0n7UpTPofWs933tTw+MN2Xm/hasUD7N/W1RUQEombSqFHw1ltw5ZV5t6bhrr8+kj6nnAKd2npFjFZk8GB4+OEYRbHqqjUnA268MRIHw4fDxhu3fBtF2pM+fWDbbeN8/8QT8NNPMSrvkktgvfVi9ecBA6B/f/jrX2HcuLxbLG3Z5MmRNH7ppfgsWGmlvFskIjlqy0m7kcCt1D+KrHTZonqCq5T0G1PP/qWkX/Uppnnu39TXFhGRkq22grXWiqmyP/2Ud2vK9/vv0eYBA6IzKsWy1lqxsmznzrD22jENu+SDD2Do0EjonXxyfm0Uaa86dYr6dn/6U0yT/fJLuPjimCq7//4x8nXIkFggQNNnpZLcY5T13XfHokVbbJF3i0QkZ202aefuY919cPXFJWpQunRRfQXVHmW+zrhanspz/6a+toiIlJjF4gHffANnnJF3a8p32WXw0UdRdL1Dm/24b92WWioK4/ftC5ttBn/7WyRbt98+kgY33BC1jEQkXz16xBTZV16J0U9DhkRtyrXWipp5Z54ZI2dFmurcc+Gii+Cww7R4lIgAbThpVw4z6wEMSr+OrPZ0qdLnuAYerwj7N/W1RUQka8UVYddd4Zxz4OOP825N/X7+OUZorbdeLHggxTXvvFEIf+21o+7gGmvEdNmrroIFFsi7dSJSXf/+MW32iy8i0T7nnHDkkTHFdtCgWLiotDqtSEPcemsk6wYNiqnaIiJAey9wc3S6H+Xu1afH9mjE8XpRlSjLc/+mvjYAZjaUVBPwyCOPZJNNNmnEYaWofvnlFx5//PG8myHSamKxy2absfLNN/PdXnvxznHH5d2cOi1wzTUs9O23vDRoED8/8UTezWlV8opHGz6cxc2Y69FH+XTQIMZ07w6t4O9Cmk9rOTe2awssACefTLePP2ae++9nroceovNtt/HbnHPy1Sab8OXGG/P73HPn3cqKUDw2r1nffJPlDz2Un5daitf23pspTz6Zd5MKTfEoRVGpWFxnnXVqfa7dJu3SCqtHAre6+/AKHba2RS1aw/7T7ZumFpemF6tgRxvz+OOP13lyEGkprSoW33iDuU45hblOPRX+8Ie8W1Oz776Lq/XbbMOAfffNuzWtTq7xuP768NxzzLfyysynhUPavVZ1bpSYMjthAtx9N12vuIIFr7mGBa+5BjbaCPbeO1YC7dw571Y2muKxGb3/foyuW3BBuj/+OGvNPnveLSo8xaMURUvEYnueHvsI8Ki7D867ISIi0koMHw5zzw2HHlrc4uOnnw6//horxkrr0qEDrLaaVvoVaa06d66aIjt2LBx/fKzgPWhQTJ894gj4z3/ybqUUybffwiabRP3c++8HJexEpJp2mbQzs0eAse6+QR2bjUv3Pco9brWFHfLcv6mvLSIiNZl55qgV9+yzcNttebdmep98Eisc7r47LLFE3q0REWm/FlwQTjopFgS6/35Yc00477w4N6+1FlxzDfzvfzk3UnI1fnysDvv553DPPbDwwnm3SEQKqN0l7cxsNEA9CTuAUo272eo5Xo8C7t/U1xYRkdrssQcss0wUHv/997xbM62TTor7ESPybYeIiISOHWMk1W23waefwsiR8NVXMZ12nnliVdqXX867ldLSJk+GnXeGf/8brrsOVlkl7xaJSEG1q6SdmR0J9C0jYQcwJt33qGe7vum++qdtnvs39bVFRKQ2HTvGKrIffggXXph3a6q8806sZLj//jD//Hm3RkREqpt77rjg8+678MQTsOWWsVL0gAFxu+QS+PHHvFspLeHww+GOO+L7xDbb5N0aESmwdpO0SyuhrlRbwq6GUWcvpvu+1K30/IvVHs9z/6a+toiI1GXgQNh006gb9913ebcmHHcczDQTHH10/duKiEh+zKqmyH7xBVx0UYy82m+/GH03ZAg8/XRxa6dK05x/fkyVPuggOPjgvFsjIgXXLpJ2ZjYQ2KCeRSdGZn9x95eJ2nAr1nP4UuLrlqLs39TXFhGRMpx5JvzyS9WU1Dw9/zzcfnsUOVcRaxGR1qNnzxgh/cor8OKLkbC7446ogbfEEnDWWfDNN3m3UirljjvgkENg663h7LPzbo2ItAJtPmlnZv2BwWWsEturhsdOB3qYWV0j1rYnFrV4tGD7N/W1RUSkLksuCcOGxXSmPFcDdIejjoI55oiOgIiItD5mVVNkv/wyps3OPntcjJl//hjZPWFC3q2UpnjuOdhpJ1h5Zbj22ii3ISJSjzadtEsJq2HuPqye7frX9Li7jyIWdRhZ0/Npv/5AjQnBPPdv6muLiEgZTjwxpqQeeWR+bXj0UfjnP+H442N1WxERad1mmilWAX/6aXj77Vhh9PjjYYUVYvVyaX3GjIHNN4d554W774Zu3fJukYi0Em02aZdq1D0CDDUzr+sGvAS8UMuhNgD6p5p42eP3BR4jkoJ1LeSQ5/5NfW0REanLHHPAscfCPffAY4+1/OtPmRI17BZcEIYOrXdzERFpZZZYAm6+Ge69N0oyrL561L7TghWtx3ffxQrCU6bAAw/AnHPm3SIRaUU65d2AZjSS+hdiyKoxeeXuY4F+ZjbSzG4hRq/1IKbTrl9f0ivP/Zv62iIiUoYDD4zpTIcdBi+91LLTXW67LV7zmmugS5eWe10REWlZf/wjvPUWnHBCLGRw552xgvk228TUWimm336DrbaCTz6Ji3uLLpp3i0SklWmzSbs0JbbOabENPN7w1rp/U19bRETq0LUrnHEG7LBDJM/22KNlXnfixBjlt/TSUSNHRETatplnhnPOgZ13hn32gUGDYursRRfBfPPl3TqpbsoU2G03eOaZGC25+up5t0hEWqE2Oz1WRESkxWy3HayySiTRfvmlZV7zb3+D99+H005TMWsRkfZkwIBYNfyss6Ku6ZJLwgUXwOTJebdMsoYPh1tuif+nwSojLiKNo6SdiIhIU5nF6Icvv4Qzz2z+1xs/PhbBWG012Gyz5n89EREplk6doizDm2/CGmvAQQfBqqvCa6/l3TIBuPjiSNbtvz8cemjerRGRVkxJOxERkUpYdVXYfvtI2n3+efO+1kUXwRdfxLRc1TISEWm/FloI7r8fbrgBPv44RuEddRT87395t6z9uueeqHe7+eZRf1Cf0yLSBEraiYiIVMoZZ0QNm2OPbb7XGDcOTj8dNt0U1lyz+V5HRERaB7Ooq/rOO7D77jByZNQ7ffjhvFvW/rzwQvxf9O8fiVSVrxCRJlLSTkREpFIWXDCmKF19NbzcTAt0n3km/PBD1LITEREp6dULrrgCHn8cZpgBNtoIdtkFvvkm75a1Dx9+GCUr5pwT7r0XZpop7xaJSBugpJ2IiEglHXMMzD571Bpyr+yxv/wSzjsvVotdbrnKHltERNqGtdeO2nYnnBCrli6xRCxeVOnPJKny/fcxAn7iRHjgAZhrrrxbJCJthJJ2IiIildS9O/zlLzHS4e67K3vsU06BCRPi+CIiIrXp2hVOOglefTVWl91jD1h//Vh1XCrr999h661h7Fi4805YfPG8WyQibYiSdiIiIpW2zz4xsuGIIyLJVgljxsBll8HQodCvX2WOKSIibduSS8ITT8Do0VG2YZll4NRTK/fZ1N5NmRJ1BJ98MkpjrLVW3i0SkTZGSTsREZFK69QJzjorRjRcemlljnnCCdC5Mxx3XGWOJyIi7UOHDnHB5513YIst4nOkf3949tm8W9b6HXss3HhjLES1ww55t0ZE2iAl7URERJrDJpvABhvAiSdGrZumePVVuP56OPhgmGeeCjRORETanXnmiRp399wDP/0Ea6wB++0HP/6Yd8tap9GjI1k3bBgceWTerRGRNkpJOxERkeZgFqPtxo2LWnRNceyx0LNnTLcVERFpis02g7ffjgtBo0dHOYfbb9dCFQ1x//2R8Nx0U7joovjMFxFpBkraiYiINJdll4W99oov9B980LhjPPlkdA6OPhp69Kho80REpJ2aeWY45xz4979jpdNtt43FFD77LO+WFd/LL8N228Hyy8NNN0VJDBGRZqKknYiISHM6+eSoRTd8eMP3dY9k3bzzwgEHVL5tIiLSvq24IrzwApx5Jjz8cIy6u/BCmDw575YV08cfwx//CLPNBvfeG8lPEZFmpKSdiIhIc5p7bjjqqJh69OSTDdv33nujUPiJJ8KMMzZL80REpJ3r1AkOPxzeeivq3B14IKy2Grz+et4tK5Zx42I67PjxMQJeNWZFpAUoaSciItLcDj0U+vSBww6DKVPK22fyZDjmGFh0Udhjj+Ztn4iIyEILRTLq+uvhww9jhdmjjoL//S/vlrWciRPhm2/g3XfhuefggQfguuuizMVmm8Wq8HfcAUstlXdLRaSd0AR8ERGR5tatG5x2Guy2W3SGdtml/n2uvx7efDNW+lO9HBERaQlmsOOOsNFGsSLqyJFwyy3Ms9VW8O23Meq7a9e677t0yXdhhkmTYlTcDz9U3b7/ftrfa3vsl19qP26XLnDVVbDuui32VkRE1AsQERFpCTvvDOefHzXqttkmEnm1+f13OOEEGDAgioOLiIi0pF694Ior4iLTsGEsds45Ddu/a9f6k3sNue/aNaallpN4++mnuts244yxInuvXnG/4IKwwgrTPpa9ZR+bYYZG/5OKiDSGknYiIiItoUOHWKlv7bXh3HPh2GNr3/ayy+Cjj+K+gypZiIhITtZZB956i+duuolVllsOfvstkmdNvf/hh5of//338trVpcu0CbU+fWCZZcpLvHXp0qz/ZCIilaSknYiISEtZay3Yems4/XTYa69YpKK6n3+OFWfXWw8GDmz5NoqIiGR16sRvvXvD0ks3/2tNmRKJu+rJvPHjq0bI9eypxZlEpN1Q0k5ERKQljRwZq8KecEKMpKvuvPOibtDpp+dbE0hERKSldegQCblSgk5EpJ3TnBsREZGWtMgicMABcOWV8Prr0z733Xdw5pkxGm/llfNpn4iIiIiIFIKSdiIiIi3tuOOge3c4/HBwr3r89NPh11/hlFPya5uIiIiIiBSCknYiIiItrVcvGDECHnkEHnwwHvvkE7j4YhgyBJZcMt/2iYiIiIhI7pS0ExERycO++8ZU2cMOg0mT4KSTYtTdiSfm3TIRERERESkAJe1ERETy0LkzjBoF77wTibu//Q323x/mnz/vlomIiIiISAEoaSciIpKXLbeEtdeGCy6AmWaCo4/Ou0UiIiIiIlIQStqJiIjkxQzOOQc6dYKjjoI55si7RSIiIiIiUhCd8m6AiIhIu9a/P3z+uRJ2IiIiIiIyDSXtRERE8jbnnHm3QERERERECkbTY0VERERERERERApGSTsREREREREREZGCUdJORERERERERESkYJS0ExERERERERERKRgl7URERERERERERApGSTsREREREREREZGCUdJORERERERERESkYJS0ExERERERERERKRgl7URERERERERERApGSTsREREREREREZGCUdJORERERERERESkYJS0ExERERERERERKRgl7URERERERERERApGSTsREREREREREZGCUdJORERERERERESkYJS0ExERERERERERKRgl7URERERERERERApGSTsREREREREREZGCUdJORERERERERESkYJS0ExERERERERERKRhz97zbICI5MLOh7n5Z3u0QUSxKkSgepSgUi1IkikcpEsWjFEVLxKJG2om0X0PzboBIoliUIlE8SlEoFqVIFI9SJIpHKYpmj0Ul7URERERERERERApGSTsREREREREREZGCUdJOpP1SHQgpCsWiFIniUYpCsShFoniUIlE8SlE0eyxqIQoREREREREREZGC0Ug7ERERERERERGRglHSTkREREREREREpGCUtBMRERERERERESkYJe1EREREREREREQKRkk7kVbEzCzvNoiUKB6lSBSPUiSKR8mLJdUfy6s90n4pFqVISrGXuW81uTCtHivSSpmZuf6ApSAUj5IHM+sCzAbMCfzo7h/m3CRpxxSPkjcz6+DuU9LPC6WHu6N4lBamWJQiycZjLc8Xuh+jpJ1IgaUrAJsDawC9gZ+AJ4B73f3nPNsm7Y/iUYrCzDoCw4AhwPzAXMA3wIfAecAT7v5Vbg2UdkXxKEViZn2AUUA/FI+SI8Wi5C2bjDOzuYC9gR60sn6MknYiBWVm6wE3A/cCSwCzA3MAMwNPA9e6+2X5tVDaE8WjFIWZrQzcCPwDuJDoDMwGbA+sB/wKPOjug3NrpLQbikcpEjM7CDgKuI9p43E7YH0Uj9JCFItSJGY2Evgz0Zdpdf0YJe1ECiZdsT8D6AMckx1CbmbrApcDfdNDm7v7fS3fSmkvFI9SJOmq/aHAee7+SbXnOgPnA9sSX8aGu/uZLd9KaS8Uj1IkZrYgcCCKR8mZYlGKwsy6ARcDXWnF/ZhWU3xPpB0ZAPzq7ju6+4cpaYKZzeDu/wT2IUY7QVy5EmlOikfJXapl3QEYCrzr7p+YWcdSEeEUjxOIaTjXpt1OMLMe+bRY2jLFoxRJJh73BN5TPEpeFItSQEsDH7f2foySdiLFszHwIsQoJ3efnB6flO4fBy4FPgEWNLPdWryF0p4oHiV3qR7JjEQR6yvTY5NLRYXdfWK6/xC4CXgDmAnYOZcGS5umeJQiqRaPV6THFI/S4hSLUkBtoh+jpJ1IgZjZjMTc+mcgPuhKz5WKaKb7l4En01Orla4aiFSS4lHyYmZWw8PzAb+7+yQz61rH7mOAVwEHujRD86SdUTxKnkrxl7mv6TNW8SjNTrEorUlb6scoaSeSk8wHXvbvsCewuLv/UH27rLTS0ttVv/rkascRaRDFoxRJ6csUTNMp+JpY7Qt3/63ac9l9vwM+AAyYt9kbK22e4lHyUJpqWK1zOU3HU/EoLUGxKEWSmYY9zWPpvk32Yzrl3QCR9sTMOhHLnc9K/P29URoyDuDuX5jZM2a2JTH//tUajtEh7fNEeqh3teG+ImVRPEpRpMLU2wPLEAWBPwL+BdyT6t9AfLF/y8weBF5z9+HV4ywTj4+nh36o9rhIvRSPkjczs5QYcTNbBNiNGI1UisfngLtriMeHgFcVj1IpikUpkkyMuJnNTfRhOtLG+zGFyiCKtFXpisBg4J/Eh9tbwONm9oqZDTWzhdJ23YEJwB3Ay2a2SvYKP0DmZPQDMA54vYhXBKS4FI9SJGa2I/AFsC6wAbH4yf7ALcCdZrZD2nQ8MAXYEDjCYtWvaWTica50/261x0XqpHiUInB3N7MuZnYV8AIxcmkDoD8RjzdTczxugOJRKkixKEXi7lPMbEYzuxS4HXiM6Mc80Zb7MRppJ9LMzGxh4EbgS+DPxJWpmYDNgUHARcDTZra1u/9oZp8Sw8UXAvrUcehOQA+iiCtETQiROikepSgsat2MBn4HFilNX0gjQLckVvHaGFjHzP7j7q+a2avAdcDcRNHg6scsjQj4hOg0PFl9G5GaKB6lSMxsDuAy4FtgoXri8V13f0XxKM1BsShFki6snUJMZ92PdtKPsWrJRhGpIDObGfgLcLW7v1bD86cBOwILAFe5+15pn+WAFdN+42rYryMxbecpYHl3H9N870LaCsWjFImZbQos6u7npd87eRSuLt1vCRwKrAk87e5rlbYDerr7t3Uc+1Bgb3df0sw6Z6btiNRI8ShFUkY8bgUcQsTjU+6+dmk7FI9SQYpFKYo0cm4E5fVj/s/d924r/ZhCDfsTaSvMpha53BX41d1fM7MOpaG2ZjZDev480pLowB5mtpS7/+Luz7j7+bWcWCzNs+8LfFbEE4sUi+JRiiaNahoIPJ1+7+Duk9LTpToiDwKXAj8Da5jZ+unxKe7+bSaus8ctxfIipWMDk6pvJ5KleJQiKTMeH6AqHtdUPEpzUCxKEWSmqu5E+f2YPc1s6bbSj1HSTqQZZObLzwdcnh6bUpo37+4T0/03wG3EfHyA3bPHqemDLnPsjYBH0nYzZPbpYWbdKvVepPVTPEoBzUxMPXjZouBvtnBwaVW634HnqfpCv3p6fEp2u2pKnYilgIez2wOY2dZmtkxl34q0AYpHKZJSPL5URjw+k55SPEpzUCxK7jKxMR8xVbvcfsxu2eO05n6MknYizcTM5gG6ufsnFivR1eZj4FXiA6xz2rcD1PxBl4abQ9SJeDZtNzGzyXrAMU1tv7QtikcpmHmBedOXrrpW6PoQeD/9/DtMc8V1Oh4FiuckYveBGjb5hajlKJKleJQiKcWjlxGP76afFY/SHBSLUgiZfsynDejHdEn7tvp+jJJ2Is3E3b8E5jGzmUo1GtKc+anSkNzxwOvEctULpn1rXT0p1Y+YiTi5PF3D87cDC5pZ/0q9F2n9FI+Sp+pXN939deBNM9vEzJZM21SPxw4p9v6VHpov7Vvf6nILAF+4+6+lL3ZpGkUnd38E6JCZviPtkOJRWlpNIzxqe66B8fhcekjxKGVRLEqR1JXczWrv/RitHivSCOlDbX7AgB/c/cdqz3cgThZPAreY2UfAAR5LSJdWTMp6PN3/t7R/PR92iwNj3P2z0rbpQ65UD+J1YA3g5ca/S2ktFI9SFCkWNwWWJb6QPwU86O7TxJKZzQbMCNwH/GZmi7r7Z9ljZWLuK2AC8FrpNWq5WlqK06WB99LDEzOxOCVdqZ2T1KmQtk3xKHkrxYe7u5nNAuxPfFYrHqVFKRalSDLxOMVigYm+wA+oH1MjjbQTaSAzWxi4GNiBKEz9sJmdaWbLpec7eEyxmUgM794I+BOpPlj2xJL5uTdRgPW99Hh9V6cWIX0okob+lk4sFisxnQEsZQWYgy/NS/EoRWFmSwEnAHMAfwc+BYYTdcJOgmlq3PyXmMLwCTAFWKiOQ3clptC8mfatcdn7TJwuTXQwSF8Is7E4FtgM6F/9Cq20LYpHKYJSfJjZzsBRRGLjWiIejyJqhdUUj59QXjzOiOJRyqBYlCLJxONmwLnAhsBDRD9mlJktm56v3o/ZmHbYj9FIO5EypatSI4CPgBPd/RszO484eVwM7G1mGxLz6Esnh1eBK4EliSx9bcYQVxD+WU8bSlcT+lJ1Ihqfniud9PqlzV8AxjfgLUoroniUIjGz7YEVgJHu/kN6+FQz+z/gNOD4GPTE39z9w/T8bUSsLUbER226AN+TOgL1tKMTUcfkxcxjmwHnAAunh44Gzva66/NIK6Z4lCIxs72J/++GxOOtwAfUH4+dUTxKmRSLkrdS38FiuvVwIqaOqqEfs08t/ZgraI/9GHfXTTfdyrgRGf1tM793yPy8HnEi+QI4uIZ9563n2JsQQ3hnBDrVs20X4NLM74sD9xMntCnAzUCPvP+9dGvem+JRtyLciBH73YEbgd7pMSs9l+7nI0Y6TQGuzu5b37HT/XDgn9lj17HP7MCI9POyxFX8bCz2zPvfTLfmuykedSvSjeg0luKxj7viUbd8bopF3Yp2Q/2YBt00PVakDGa2AHAy6epRZr57qWDrE8QVoZmBU8xso7TdDADu/kX63aodtzTkezHgZY/sfo3DyTNmIhV7NbOLgLeJqxIfAku5+3buPq7x71aKTvEoReEx5WAF4krkN+kxzzyHu39KjAr9D7CrmQ1Pu0+Nv+qxmN0fWBu4K/3cMbPPnKX9Mvv3JopXH0t84duEaWOxNLJA2iDFoxSJxwihUjx+nR5TPEqLUyxKkagf03BK2omUZ2Hij/fjNJS29AFX+sCb7O4PAH8BugGj0+PZJaOnbl+DlUhFMz0zDNzM1rG0clLmRNQd2M/MfgT2I4aXbw0s7O7vNP2tSiugeJQiWQv4yd0nlr5QVefuY4HD06+nmtk82diqKRbNrKPFinIzA8+n7SZlNtkWOLC0ebofABxDfBlULLZPikcpEsWjFIViUYqiH+rHNIiSdiJ1yGTwZyQ+YCa4u9d0pQnA3c8i6jPMb2bH1Xd8j1VvOhJXnO6oYZOviNpkHTMnnfWBFYFZiA+7mdz9rjpOXNJGKB6lSCxW8wL4EljDzOZKnYEav1u4+/3A9cR3j3MsatrUKsXYPMQXtudr2OS69LrzZq70r0pc4T8VxWK7oniUPFhSw+OKR2lRikUpukx8dkP9mAZR0k6kmuyHU+YPthORKNmntFkN+5Uy9gen+xPNbMX0XI0no2Re4FfiakPnzPE6u/t/iCsRu2S2Xwx4FJjH3Ue4+4Qy35q0QopHKZpS/GS+fHcBZgD2rPZ4dp9SPB6Z7rcHBqbn6vousjTwb3efVDpG6W/C3X8i4nGtzGu8TcTi8YrF9kHxKC0pEztTi5hnO3c1xGNXyo/H0nTE7YmOpOJRaqVYlCKpLT7q6cfsXdqshv3Uj8lQ0k7aPTObx8x6m9kaMO2Q7swJY2y639bMuvu08+5J+01OH5rPAjcQf18Hp+dqGk5e2n8x4L/u/iswsfSa7j7BzLoShWHnyZx4Rrr7hu7+ddPfvRSN4lGKwszWMLMzzexKM7vezIaYWe9S/FjV9JpSAelNzaxPeq6meOzoUYdkRHp4RHpuuo5DxsLAv9LPpVomk8ysk5mdRkzjWdTMunpMpzhXsdg2KR4lT5kEyGQz62xmBwJnlxGP91J+PH5OVTyemJ5TPMo0FItSJKVkXeqLdDKzZc2sT5n9mMHqx5RHSTtpt8xsGTO7kyh2ORZ4zMzeNbORZrYCTD1hdHD314FniCKupZNQTcNmS39Tpav3O5nZmun1phlentl/OaqWpDZPw3XNbDfiasDOwLqkoq7u/l2T3rgUkuJRisLMljSzj4EDiLogawIbAlcBD5jZURC1RdKXoR+I2iFLACun52qKx9JjpwCfA38ws53Sa04Tj5kvXwsCY9Ixf0vP7UasKHZUOuYLpeek7VE8ShFkEiB/BsYBqwH9mTYeH1Q8SnNTLEqRlJK5ZnY48BhwKxEL5fRjlqe8fswR6b799mM85+VrddOtpW/EKjGXEasjDQT+SBRJPZuq5Z3fAdZL23ckhpSfQRSnPBvolZ6bbklzoGO6Py4d6+l62nMM0Dfz+yrE1apSWy4EZsz73003xaPise3fgP2Jq/Grpt87pPtliVo1k1IcHJIeN+Kq+okpHm8AFig9V8PxO6X7wek4X2dew6pt2wu4MvP7H6rF4vmKxbZ9UzzqVpRbiq1TFY+65X1TLOpWpFuKucdRP6Z5/53zboBuurX0jbjis10tz+0M/Dv9UY+t9tzGwEfptnEdx5/6oQZ8lo61efa5zLZdgFPSz/MDV2dOKv8C+uT976Vb894Uj7oV5Zb+z8+uIXZmSPfLEVfwSzExW2bbVYE3gP/WFs81vN4z6Ti7pt+rdwTmB7ZNP1+vWGxfN8WjbkW6lRGPy2ficTKpE5qey8bj4DJf71nFo261xIZiUbfC3Ki7H7MLkUSuqR+zCerHlH3T9FhpV8xsFWII+d3p907Ze+BG4DzgY2BBMzsgs/sTwD+Ik8AWZjZP2rf6HPwpqR6EA2elh7cuPVetSbMBXcxsGHHS2hX4H7CWu6/q7p819T1LcSkepWA2JKYOYGadSvHh7hPT/WvAaKpWhzuktKO7/wt4BOgJ7Ghmi9X2IlZVrPiKdL+2mXVJMZrVGzjWzKYAOwDjUSy2J4pHKZL64vFVquLRgENLO1aLx53KjMfL073iUapTLEohZPox96Tfq/djbgDOoeZ+zOPEdFr1Y8qgpJ20N4sCd3qqqeCpOKZHsdTSvPeHiPn4EF/2u6VtxqfHvyeG/a6VHq/+4YVXLSP9KPAJsJiZzV5De5YA9gUuSb/vD3R396eb+kalVVA8Su4yX5DmIr5ETY3FGryZtnFggJnNkvlifz3piimwgZl1qXZ80rFLX7L+Tawg18/df7fpVx77IzFiACIWZ1Ustn2KRykSxaMUhWJRCqjUjxkPtfZjHqb2fsxtqB9TFiXtpN2wWD1pQeLDZ7qlqUsnCXf/HniNGDreGZjBqlaeuR+4C5gD2N7MFq/nZT8DHiSm8UxdQjrzwbgM0A24ljipXJI5MUkbpniUonB3T1/aVyLqjgDTf4FP2/5CTK0xoKu7/0wqWu3uLwJ/J6Yn7ELUtanxC1jyCTHt5g9mNqtXFTMu/S30JToXPRSL7YfiUYpE8ShFoViUIlE/pmUpaSftRho23h2YN/0+3dLlmT/659O2ixPz5Sdb1TLVlxKre25KnGBmrrZv9jXHAe8RxV/nyTxe+mCcACzq7rulD1RpJxSPUhQWK3n9TlwNHWxmK8H0X+AzMfVoup/HzHqkjkR2Gs1TxMp0e5nZfKXXqP66qVPxFfCdu/9U2ibzt7C/u+/i7j9V7M1K4SkepUhqiMcaV91UPEpzUyxKkagf07KUtJN2IfMh9DhwgJndbGn56cxJo3QVy9z9PWJo+ZdAh/RBOTlt8wJxgvkfsDsxLLymD83Sa/6L+Fv7vPpz7v5Xd/+gsu9Wik7xKEXiqV4I8CNR1+vfZrYHTPulKfOFfyLwAfC6u49LMTol3X9G1C95i6g5skPpNbKvmYnHJ4DPzKxz9W3c/YfmeL9SbIpHKZJq8Xg58JziUfKgWJSiUD+m5SlpJ+1C5gPmG2BFYBBwfHpucrVt3cx6Eln8T939vz79UPCrgP8D5gP2NbOl0vMds4dK972JocNTX6emqxHSfigepWhS3L1PXMF0YqrCdF+aUqxMIWLt3fRw9e8S9xELqEwADjGzLWHqVIqph0r3uwAvufsERBLFoxRJJh7fR/EoOVIsShFk+g1fo35Mi+hU/yYibcqbwM3AmsC9KdM/3VUld/8hXbV6IfPYlNK27v5fM7uEWKVmCLGazSalE5XFak6l4rDzANd7KtIpkqF4lMJw9+fNbCiwBnB7TduYWWegI7E63DNpv9LV0tIV1Ulmdj1Ru+Ri4Aoze9Hdp14VTdv2IToLJzXn+5LWSfEoRVLheLwBxaM0kmJRCuRtoh+zFrX3Y6wB/ZjZgd1QP2Y65rXWnBRpW9JJw9PP87j7l3VsOxMxhHdLd/9ndt9q23UlVvdck1he/Rx3fz8914VYanodYF9vY3PrpWkUj9JamVk/4Dmgd31X3c3sYOAAolNwFTGl5ztgMLHq2N/TtAmRRlE8SpEoHqUoFIvSnBrZj9nK3f+hfkzDKWkn7VptI5uID6gHgJXc/bta9u3s7hPMbC5i2fRRwCTgGmIp9WWAq4EXazoxiVSneJSiqO0LVXruQOCP7r5RKe7S412BWdz922qPzwQcStTNeY4YEXoJ8IRiUcqheJQiUTxKUSgWpUjUj2k+StqJZJRONma2EzDM3deu9vz8wAbufmXmMUvDx2cBViVOMLMBd9V3ZUukLopHKRIz6+ix4tcFwPfufmL2C5qZDQCGuvuw6vtUO052moNIoygepUiqxeMP7j5C8Sh5UCxKUagfUzlaiEIkI3N1YGWiQGv1VXA+AdZNH3ilk5Gn534GHnH3f7j7Le35xCKVoXiUgild5VsEeBKmLf7r7i8Bv5nZ1jD1i9fk6Q6iToBUhuJRiiQbj0+A4lFyo1iUQlA/pnKUtBPJMJu6ZHoP4HmoKtyahpMD3EQsST3dajXtcbiuNB/FoxRJulo6D/Cbu/+j9LiZZRe1egZYua4pOyKVoHiUIlE8SlEoFqUo1I+pHCXtpN2wqmWla5WG485ODMN9sbRf+lD7LW22JNDDzOZovtZKW6d4lKIws46ZL1b16Qd8mvbrlmJxUvp9B+BKYEUiZkUaTPEoRaJ4lKJQLEqRNKAfMwfqxzSZknbS5lnoWD17X4fFgK/d/Rczm8FjSWo3sz+Y2ZPA6cBOwIzN1mhpsxSPUhSZWJycYmoJM+tW27bpx/mAzwHc/X9pv5XN7BngemAm4C3gh5Z4D9J2KB6lXNnpVc34GopHqZdiUYqknERaBV6jof2YRVE/psmUtJM2zaKIqnsUZJ3PzG4ws+Xq2a0nVfVxJprZnGZ2FfAvYA1ieG+/NA9fpGyKRymKarHY28weJL7A965p+8wUhaWJqQxkYvE5oljwi8AC7n5wTfVxRGqjeJRylBIkmTgp1eSqaH9G8Sj1USxKkWTicYpVTTttjtdRPyYnStpJm5Q5eZWGgp8JfEAUwpxYz+4LAY+k/YYDXwBDgN+Add19FXf/qHlaLm2R4lGKopZY/AToDuxGujJfy75dgI/c/SMzO5bpY3Fld/+0md+CtCGKRylHafSQV9VCOpKY+neJmVkDRnzU9zqKR6mTYlGKpIZ4PA14zqatX1iJ11E/JmcV/Q8VKZeZ9QR+BDp4BVcnKl3hypy8dgfOAn4HjgLuAD6u5zDzAGuZ2SlETQiAA4FLdEWqbVI8SlFYFI/eh7gi/qW7v5Eeb1Kx6Hpi8QjgNuCTel6jI7CjmR1HTL0BxWKbpniUPKUO6dREiJltBZwDLJg2eSJNs+rYlP9zxaPUR7EoRVJPPH5B1JD7ugKvo35MQVgTvnOJNJiZzQgcQmTdXwGWB05p6pDYdPLqmLkC8AfgUmAJ4PJ0+4/XsVx0OsbswFPE/HuA64D93f2nprRPiknxKEVhUZ/mBGBh4GRgILA3MNzd727CcZsci5ljrU7EIygW2zTFo+TNzDpkOqTLEB3S9dPTTwF/BTZ29z2a8BqKR6mXYlGKJJsYriEe3we+cPd1m/ga6scUjKbHSouwsAPwAvC5u+8DfEt0AkZYE1aMsar59ZPMrJeZ3UTMk/8C2BQ4xt1fr++DLl216k6cWMYCi7v7rjqxtD2KRykSM1sUeAwY6+6D3P01ol7NYsA2ZtalNAWigcetSCxmLAm8CSypWGy7FI+St1KSxMx6mNmFwGtEp/Q7YA93Xxt4KD3eqDpiikcph2JRisajntysZnYRVfH4XyIeFwMeM7PZG3t89WOKSdNjpaWslm4retUSz7Ok+xnc/duGHtCqVlIqXQUYARwLvEvUe3jY3b9p4GEXATZy90ca2h5pVRSPUiSHAee4+y2Zx+4nVnd7zN1/b8jBmikWAW5398sbsZ+0LopHyYWZrQFsA/Q0s++BP1G1ouAZwImZjmJfYiQo3oA6YopHKYdiUYrCzGYhpr3OStQy3Bo4hWnjcYTHIg+LAPO5+3eNeB31YwpM02OlWaUrTnMSV6HOcferzaxz6YPOzPoSyfgPzcqrkZOZX18aqj4YOB+YATiXWC3pwwZ+cDapPo+0DopHKRoz2xAYCawETCHizzPPlx0LzRGL0r4oHiVPZjYvcCtR3Lw0YukO4HB3/zBt04mIzY2BOd39b9npi3UcW/EoZVMsSlGk0e8XAKsAvxI14mD6eJyBGICwr7ufamYzuHt9i0SoH9NKaKSdNJv0BzvFYr797+5+NUB2OK27j838XOcfd5qOk51fvwwwmuhcXA1cArzZ0FEA5by2tH6KRymSNP1gEjAIeN9jGkKH6v/3DUiQdGqOWJT2QfEoeUuf0V+Y2T1E5/Q14Ah3fzQ93xFwYLK7u5mNB7qWPtvrObbiUcqmWJQicff3zOwNYENitN2bwCHu/hhUxWMaabcoMH/ar86EnfoxrYtq2kmzyfzB7gZ8BWCxDPl06qsBUcrYp45ENzP7P+JDdDywOXCYu7+kDzqpjeJRiqT0JYmYpj2m9HDp+eoxmK7o13k8xaI0luJRCqBUJ/Fi4BNgZ3d/1Mw6ZKZtTcl8lq8EfJ+SJnXWWFQ8SgMpFqUQMp+9lwOTiGmwy2YTdqV4TNt1BJ6otm9Nx1U/ppVR0k6aTcr8QwzjnQmg9Mdf/UMte2Wqpg+80gejmR0FfAOsDuwD7OLuD7r7j83xHqTtUDxK0ZjZnMRV056lUU2lePOqaQrd0++TMvtN99ltZjsBvxBJF8WiNJjiUfLkMRK+o0eR8rOAAaXHPa2UCNN8Jr9DSizXN6pD8SgNoViUokix2MHd3wOuAL4EMLPO6fnJ1XbpBSxT2reO46of08poeqw0m8yJZA7gczPrCkwgUyPHzFYGFgJ+JFa+eSXbGchKH3QjiA/QvxMr29W4rUh1ikfJU+mqZvYxd//GzCYB3dMXs+y0mSWIBQF+NLPFiXh8zN2vziRQssf8HDiOqMGjWJQ6KR6lpdUUc9Wfy3xOvwb0NrNu7v6/7LaZY6wNPFPfsZNSPN4GjFE8tm+KRSmSMmKm9NzlwEo1xWJGB+D5dNw66yuqH9O6aCEKaZQ0aqkPMQz3G3f/pYZtSsuk30oMHV8wkxyZA9iCuBrwArAVsB6xhPpfgPvcfVxp2G/mmAsBX3rVip8iikcpjBSL2xLJ3wWJaQr3ufvP6XnLxF0v4DFgcWB2d/81jWTaB+gK3O/uL5vZH4iiwCsDdwIXu/tj1b+QlfHFT9oZxaPkrVqMzQ7sD/xGVTzen0Y0ZT+n+xIrub9bw/FK22wJLODuFzS0HdI+KRalSKrF41zEQnm/Uns/ZlmgH/Cgu4+v5ZjbA6u4+yFltkH9mFZC02OlwcxsaeBSYAfgYeCfZnaCmS2Wnp9mFRoikTIfsHN6vg9wNHFSOtjdr3P3wen5/xLZ/istVr2ZnBl+jrt/qBOLZCkepSjMbABx1dKAW4AviJU4XzKzQ2DaqTPu/j3wMdCFSIwALAy86+6npARJJ3f/N7AdcBqRUL7OzPqmzkI2HtUJkKkUj1IE7jHN2sz2BA4F/kOMwCzF4wuZeJyS7sfWlCTJbkNML1wQ6q+xWGpHE9+KtHKKRSmSTDwOAs4ANqWqH3N8ph9TKu/zJvBobQm75Cei71Jvfe7UBvVjWgt31023sm7A7MBFwN7E9BmI5aD3JObYfwEsQaxEA9A53e9MLIl+d9p+HWCtzHFnSPcGLAe8mLY/o/R43u9dt+LdFI+6FekGDCUSJDNVe3wx4OYUQ0cA86THu6b7XdNzjwBzA38GFknPWbVjzUF0MKYQIwJyf9+6FfOmeNStSDfgkAbGY8c6jtUh3W8HfJX3e9Otdd0Ui7rlfcvEzUrAtQ3ox9Tb/0h9nC/yfo+6Vf6mkXbSEDsAd7n7Fe7+YxoWPtHd/4844fwPuA/YI21fWmp6DPARcXLaDJgX+E9mBNTEdO/u/hpxtQHgSDNb2d09c5VBpETxKLmzWE2uN7AucJ7HlEJLz5nHFfqjgXuJK/nHAHjVlc2xwLvA8kRMd3P399M21WuOfQucSsT2xma2TXod1acVQPEoxZKJx5VpWDxWL64+lVeNbvoB+NbMNmzO9yBtg2JRiiITN38Arm5AP6YczwMTzWw9qHkxPWmdlLSTspjZcsDhwBvp91Idh9LJ4GHiA25+4IxSciM99xHwMjFXfyBxkhpHVWHN6m4jpiQCnAR1f2hK+6N4lKJIX75WB+b2tNpWKdYy92OIzsCXwP5mlv0C9iYxmnM2oo5iVwAzm6GW13sFODv9WopHFQ4WQPEoxdKUeKwt+Zv5nP+cmJK4rBLFUh/FohRJY/sxZcRXF2A8MVNouott0nopaSflWgx4x92/skyx6cwH3UR3vxk4k1hu+q+lHd39K2Kqza/EsN3F3X1CbSeS9PiFxBWGjdKJTSRL8ShFsjbwLdSZ3HgLGJ5+HWVmM6fHfwTuAr4hRn4OSo9PrOk4yfXAh8C8ZrZwJd6AtCmKRymSRsVjbcnfzOf828AHxCJSvSrdaGmTFItSFIvSiH5MfRfF3P1NorzPhmY2Z3O+AWlZStpJnTIZ/1mBFWCaYb01GUF8ee9vZvtmHr8DeAWYCdjAzNZIx68tBkvFYScSKzuJKB6lUDLTpL8EBprZjO4+sbbpCO5+LfAoMYrpjEy83UmsXDceWCQzzbC2ePyKmALRCfi6Eu9FWj/FoxRJI+PxEVI8lnH8rsBTwBpA/8q0WtoixaIURSbmutO0fkxd7gXWBPo2tp1SPEraSZ0yo4+mAHOa2bZQ85d3M+vo7hOIFZkATjWzRdNxvgFGEyceiGLXdZ2ofk3bzgDMWIG3Im2A4lGKJDNNegagB7BbbdtmpjSU4nE/YEA6ziTgPOBVoDOwg5nNVG2qRPZ1fyQSKr8RCWwRxaMUSlPj0cxWSs/Vllj5DXgn/XpAU9srbZdiUYqiUv2YemrVvQt0A3YvY1tpJZS0kzpl/tBLyY0dzKxzTcmN0oeiu98FPEZ8MGavCtwE3E5MMxxoZuun15gmDtMwYQe+A34hruKLKB6lUDKxck+639zMeqW6I9N8SXL3SSmW3gQuTg+fkHn+X8ANxEiAdYnV6Op6zc+AF93988q8G2ntFI9SJI2Mx7eoFo81la7I7H8TMAHY1Mz+mJ7TQlEyDcWiFEUN/Zgdm9CPqe3YT6T7oWa2ZE1xLq2PknZSp8wH1LPAW8SqS6vUtn3mA+qQdH+QmS2ZjjWZKOh/J7AQMNzMeqar99kPttKJpR/weKpBJqJ4lELJfMn6DHgJWBZYMT1XV/HfY4lk8R/NbIPM49cDZxFTcs42s2XTl61OMPWqa+k1tyUKFesqqgCKRymWSsajVSu+nuKwo7t/D5yTHj45PTdZMShZikUpihr6MStRXj/m4HR/kJktVYq76sc2m7oScmkBvVMq1njJlZJ2Uq7OwNNAb2A9S0Wrq0sfUKWr9xelh0/NPP8WMT//n8TKnWemp6ZAfBhmhrHPBVxa6TcibYLiUYrkZ6KeTR9gkJnNBdMnL1JCuJO7/0RVoevzM89/D1xA1NDpAfzdzPqk6YpTr7qa2eZETcbR6XGtDiZZikcpkibHo9dQfD3z2Xw7MSJ0eTPbJz2m/o3URLEoRdHQfsxbVPVjTik9V8MupVi+EpgEbGVma2YvuEnrpBOJlMXdfyYSG/8lpsmsUMZuxxNXqLY0s9Vg6lTDMcAexPDdPc3sXFKxzDQsfTEzuxsYR7pyL5KleJQicfdfgQeBj4BNqKoNVlPyojTd4WLgfWBxM9sMpsbjZHc/BjiCSBQ/a2anmdnaZra4md0F7Akc7FFHR2QaikcpkgrGY20jlj4Abkw/H5BGPdXUmZV2TrEoRVHJfky145ZGlb4BXJd+Pjs9V+fKs1JsStpJvTIfTo8SV6gWJ4aKz1bteWDqFaqOHsWpz0sPD6r23CdEIdhjgMHAa2Z2p5ldSazSebK7768TjFSneJQiycTbP4gvYL2B7c1sgZq2T1c7S5+9l6f7tVOCZErpOXc/m1iJ7kJgM2AfYtTTae6+tbt/1zzvSFozxaMUSYXjscYRnO4+DriEGOW0q5IkUhPFohRFtX7MkzSxH1PLy/wI3EEk+brUNpJPWg/TLAYpR5oj72a2E/Hh9Q1wgLvfV89+qxBLT/8L2CldWahpu42JFTodeEbTa6QuikcpkkyCY33gWmKF4YOAa+v60m6xIt2twPPuPjh7Vb4U49W2n+4xkeoUj1IkzRGPIo2hWJSiaO5+TGb7fsBYfVa3fhppJw11C/AAsAAw2MwWhDqHio8lVrxZqaYTi6Uimu7+oLs/5e5P68QiDaB4lNyVrnS6+2PAzcCswK5A/3p2HQO8AKxVvRNQU9wpFqUcikcpkuaIR5HGUCxKAVW0H1Odu4/RZ3XboKSdlKU0TNzdJxJDv78CtgG2tViqusblpN39G6IWxIxmNm8Nz+uDTxpM8ShFYqH0eXop8DKwLjDEzGZP20z3eetR6P+/wJeeig23VJul7VI8SpEoHqUoFItSFM3Vj5G2SycdqZel1WaqXaG6koifvYAN0uPVp86U4uslYjXOL1uoydKGKR6lKDKx6Jl4fIeoOfIhsANRI3G6uiOZeHwK+KI0baeFmi5tkOJRikTxKEWhWJQiUT9GGkNJO6mVmXVMH06T0u/LmNkM6elLiXoQiwP7WSrkWppeWM08wDNATc+JlEXxKEVRQyz+2WJFzVJM3UYU6e8KHG5m66TtZkj3RtRLBNgJ+Jc6AdJYikcpEsWjFIViUYpE/RhpCiXtZDpp+HhHd5/sUbB1NTMbS9R/KNX8+pz4oLuTWDb9jPR4qWh1p8wH25xEkVetvCkNpniUoqghFlc3s9eA84FOpXhz9/HA/wGnAgsBV5pZ9zQNonS1381sEeBz4Ox83pG0ZopHKZJ64rFjHfF4heKx/SiNFqpp6l8FX0OxKIXRwH7MXagfIzVQ0k6mkU4K7lGzYS4zuwN4GniFWGHp99IHrbu/TQzjfY1YNv10SwU03X2Smc1iZn8G+hDJFJEGUTxKUdQSi08RU2vWAU7Obu/uv7j76cDxxFX8583s6LTvzGa2F3Hl/mR3/1/LvhtpCWbWwczmtmaof6R4lHI0R+zV8jr1xeMp2e2rxeOMKB7bvFIspqRFT2CRnM6NikVpMY3ox+yJ+jFSA3MtKCLE8FvPFOE3s1OA4cCbwEjgUXf/rqZ9zGw+YEtgBPA7McR3HLAIcIW7v9Ey70JampktD2wIvAU87+7fpsfNm3ByUTxKY1gz1JopIxYfK8V9bfuZ2RzAAcDWwOPEVdKz3P3FSrZVisPMBgDHAfcRhc5vAR529/815fyoeJTG0vlRiiAlHf4I3EqcG28FHtK5UdoS9WOk0pS0ayXMrBfwQxqmXbEvXtkrX+n3nYBz09PnEEN3P67r9UofsmY2K7AW8BswE3Cfhu62TRYrFp1NrKZ1BnAW0As4zt2fb8JxFY/SIClmLgbOcPePq39RauJxmxSLab/p2lOpNkrxWKxA+DfgB+BYd//EzF4GegND3f2uRh5X8SgNkkZvdAZuAA7R+VHykPlOtg6x8MMb6NwoOcnE46rAc+pXS2vRKe8GSNkeAx4EjgaaXAcifZnr6FXFMFcARgPLE/UdRgNvu/vv9R2rdFXM3X8C7m1q26TYzGxz4mrRCe7+j/RYb2B14AFiakGDrpYqHqUJlgWGAT2AHYkVtRqtkrEIVfVI6ntM2oxjgSvd/Q4AM+tGfGZ3AH5s6MEUj9JYqdO3BLAVMVpD50dpcZnvglsCJ2XOjTOhc6O0sEw8/pXosxyD+tXSCqimXcFZ1MQ5BFgOGG5mfdLQ2UavGGNV8+tL8+P/Tiwf/QOwGXCku79S7gedtB9pxOeexFXRf1hVPZKbgIuAf8H0y5TXc0zFozSIVRWyXhI4Mj28vZmtmTqqjbogpViUxsjE40HAmsA/LXTxqIG0LbAi8GoDj6t4lAYrfT/U+VHylonFms6Nv6Jzo7Swav3qo9SvltZCSbviWyHd35buz0r3Db5aWjohZa4CHA18S3xg7gkMcfeHU2ZfpCZ/Aia7+9vZL/7ufpG7H+juz5V7IMWjNJZHIWsjvvBfARycnjo/Pd+g6QOKRWmi0kWKTYhp2uPSF/jfAdx9rLt/7O7jyjmY4lGaInVAs+fHQ9JT56XndX6UllLqq2wCnK5zoxRAqV99e7pXv1paBdW0K7A0D35voiP6NFHsf05gbXd/KmX26/3ylUYBmFcVWt0SuBCYOR37emCsaxi41MLMZiA+0J4DrnH3C2vYpqwpsYpHaYrUGV2ciMUHgH3d/QczexHoT4wCvaIh50evqj2yBTFiVLEoDWJmiwAvA6sSU2Cm6wCUc45UPEpTpPPjYsC/ifPjfu7+vc6PkhedG6Uo1K+W1kxJu4IyszmBF4BD3f229Nh+xIfUK+4+oBHHXBS4HFgD+Dsxn/91d/+tYg2XNsvMuhNXkDZx98eyX7JKX6ZKj1kZxXsVj9JYqWO6hWcKV5vZRkQndRwwr7v/ZmUWF7ao+/RXYvrOtSgWpYHMbC1itcF13P3JzOM1dkbr6hwoHqUpdH6UItG5UYrAYmXgF6m5X/2qu/dvxDHVj5EWo+mxBZQSHt+4+wLufptVrUTzV+Jq1QpmtlfatqzaJGZ2NvCf9OuWwMHu/rxOLNIASxLFWheCqUWuLf08JcVpl/R7dpnz6Qq8Kh6lXNXrjJS+6Jc6pKVzoLs/BNxKLEhxcmnzMo7fmajJ2JUo2K5YlFrVUfemZ7rvmrYrfW67mXU0s13NbKCZbWZms1DLVBzFo9Skps/R2rbT+VGaU7mxmFHuuXFznRulOaR+9bd19KuXV79aik4j7QquNGKpdOWpsVdL08moA3C/u3/e/C2XtsbM+gCfAAe5+4XZq6Fmtg6xgudXwNLA28Cj7n5PLcdSPEqNzKwnkfxdBHjJo5B/ffuUzpOLErHXAVjU3T+oa9RnZmTovMBEd/+2gm9F2oBy49HMViambB/s7hdkHl8L2ID4zO4G/BFYCrgPONbdx6TtShdAFI9SqxRnnxHnuC8z07Nq/R6YOT8uRpwfDZ0fpYnKjcXMufFAd78o83j23DgTsCk6N0ozq6Nf/QPQW/1qKSol7VqYmXUDOrv7uHKmENZyjJuBQcDZ7n5EucepbSi6tF8NiUczW4YYWv6yu6+aHusH7Ep86boB+I2oW3IFMDcx7Pxyd3+r+pQHxaNkpeTIKcQ0gwWBWYCPiZi7GHg6fcGq8cuUVU3RPg04CrjX3bdosTcgbUoD4rHUAfgDsXr2+8DiqYO5BzAXcKu7f5A59gXAUOAL4AR3v7ax3wekfUgJt12IxO88RN26X4mFT+4vY3+dH6Uiyo3FTNJX50apuEx8LQ185e7fqV8tbZmSdi3MzG4l/t23begfe7WrpW9R5mgSkdo0NB7N7BtgdmA9d3/czAYBY9z9lfR8Z3efYGb9gSOA7YF/ufvqzfxWpJVK0xT2JqZsnQi8AcxBdAT2B3oTIzgvdPfTy0jazQS8A/QBNnX3B6snjEVq04h4nPrZa2Zvpe22c/fbzewIdz8zc+wu7v57SgjuCZwJ/Ags7+4fqwMgWWmalgGHA/MCN7j7s+nxjsBjwMrE6oenufsvtcVQ5vw4MzHarg9Rn/YhnR+lPo2MxezCEW8BixLnxjvqOTfuBYxC50Ypg5ndRgzEHNTEfnXZo5BF8qCadi3IzHYGtga2NrPV0xWCsubOQ9QJSx+C7xIfaADnlJ6rfIulLWtIPGYevzbdD7KoPbI88GZm+sKEdP8y8Bei3sOqZnZSOk5Da6FI27cJ0Mvd53L3S4Bn3f0Odz8D2Ay4nxi1eaqZLZ06ntPVFSs97u6/Aiekh0vnx0mKPSlTQ+NxspnNYGZdiCk2HYhz6gpEcg+L1bdx99/T/Q/A1Wn77sDpLfsWpTVIibSexGj4P7v7s+kpS7G0A3ABMXLucjObN32O13V+/IWq8+O5pdfR+VHq0shYnFLt3NgR2KaMc+Pf0LlRypD6MVsRcdXUfvXI9LD61VJISto1s3TVvjSNcC7g0vTU1M5kIw99GlFLYjOL+fhlF8+U9qux8Zh5/GXgJ2BtYCdiSfOJNV3Zcvd3qCp4fYyZLVhbh0LaJ4tVsnchatiUVo2bkkkCvwacATycdjktPV7bl6kp6fm/Ac8Ci5vZgek5xZ3UqQnxODF1Ol8C/gusQHRify09X/213P07qs6PO5jZKjo/Sg0uBmaEaRIcE9P9Z8D5wN3EqPZT0+M6P0pzaEws6twoFVVHP+Y8aFK/+lSq+tUbp9dQv1oKQ0m7Zpa+8K9HLAM9Efgz8AqwkpntDg07KehqqTRFY+MxE1cvEVOzFwYGkr541fFl6laiA9wRODq1QVevpKQ38KG7vwFVX7aqJYH/TVW9xDXTVfoaVbvKeky6P83Muqfzoz7zpC5NjccniAsbSwJ7AP+D2s+P7v4ccEn6db/0mM6Pgpl1sFgtsxdRD6y2BMenRCmKH4EhZrZ52r+m0XY6P0qDNTUWkyfRuVEqoI5+zIAm9qs1S0MKTR/QzSx9CbqIWF3mQo/6Dsenp8+0qAHW0C9LuloqjdLYeCx1WtPouQeIGNwW2Cg9XuOXqfTF7tK0/ZIW9XREShYm6s+ROgXTSTH0dmk7YIa6DphJtDwJ/J0olq0pNlKORsdjqqXzBTHS5Dui9udeaZ+6Opu3Eh3Y30rHafK7kFbP3ad4lJtYgpiOXWOCI8Xd+8CR6aFzzWzWOj6TdX6UBmlqLKZjfI7OjVIB6ldLe6WkXTNLJ5Ol3P18mFoM+H7gDmA2ou4XRPHLco+pq6XSKE2Jx0xc3QC8CjiwvpktUe356t4B3o2X918q9FakbZhCFLWeWg+xli/mrwDjiRo39SZ+Mx2KE4DfgT+Z2TKeqYen86TUoCnxWIqna4B/ABOAVcxsrXSc2uLtS6LkwMzpdVVsXQAws15EkmM3qDnBkbmgdjkxkqkv6XthbUkOnR+loZoai4nOjdJk6ldLe6UgbAHpZFD6ty6dRI5N90eaWV+PYphlZ/OrXS29hmpXS9Nwdv3/ynQaG4/pC725+xhi2sJYYB5g59LztbzkV0Rn+LfaRq9Iu/UWML+Z3Vea1kC1z6U0bWES8Ex66Pf6DupVxYU/Bk5JD59XerpUqyz7Gk14D9J2NDoeMzH3MzFt5zViuu2OVq02Xpa7/wfoBLyfjq/PbQHA3b8n6oCtlJn2WtMFtVJn85B0f1j6HPea4knnR2mopsZiOobOjVIR6ldLe6TgayGlL0CZL0v/oYkrwGZORiOIq/5/srS6YulmZgtV6C1IG1KBeLwNuAuYBGxpZmvC9F/u07H/B3wLPFMavSKSfEJ8ed8EOK+maV2Z32clauV80MDXGEV86V/XzLZI58ZJZraCmV2fYlS1cgSaGI+Z8+qTwJVEUeudgL3TJtUTgB3MrA9Rl+ef2WOIJNcSiYuBZtY1dVanSXCk85m5+1PAjcR0rhFlHl/nRylXk2NR50apFPWrpb1R0i5fpxLDv7cwsw2gwcUzs1dLS1cDzk3HmdnMrgKeSsPaRepTbzyWvqS5+3hitN2VwFLABWkEyuT0ZcsyV1D7AMsBj7Xs25GiSwnd64g6iX8n1RXJylzZ/AX4xN2/rm3aV7Vjl4oLTwROSg+fbWZrmtk1RIfifHUEpKQS8Zj5+Vqizs4swEVmtnipA2FmXdPrTQEWBJ4iCrWLVPc68AWwJjAAap0mWIrLI9L9rmY2IDvlNUvnR2mEJsVi5nmdG6W5qF8tbZaSdjnIfFn6maorUE1dAXYk8A1RY+wF4HPi6tR8aVi7SI0aGo+ZuiVjgOOI1WGXA64ws16Zq1+lZdcHA5e7+9PN/26ktXH3W4gr7od5DTUPM53G1YBH08/lfgnrkI5xPfAm0I84Lz4BLO7u/25C06UNamo8Zs6P/3P3q4mO62/AHWZWKiVQKqx+IHAxMFLJEanFq8T5anlgMzObBaafmliaCuZR8P/k9PCJpedqObbOj9IQr9LEWEzP69woFaV+tbQHStrlp5TYuAJ4jlhZc//0XFlz8NNJyNJxJhLFsSGuSs3t7tfUchVMpLoGx2MacfcjMb3hXKKj+7iZHQQsa2YDzexfwFxULaMuMh13/9HdJ9RUL8TMuqQfZyE6DKXzXfXtrPrvpe3MbBtiROhZwEzufqXOjVKbCsVjKZF3LrA+UQ/qIjN738zOMbNXiEUv/uDur1TfXwSmxtaFwDhgS2DF9HhN569ScmM0EW8rmtkKNR1X50dpqErFYmbkp86NUknqV0ubZoq9/KTpg5PMbB1iRaVfgXnd/ec0PLesq0tmtjBwKdAT2Nzdv2iuNkvb1Zh4TF/8Pf28DrAxUQ/qFWJlxQPS1VaRRjOzuYGngRXSldTS412B3YHRmanbnnm+H3Fu7Ars7O6ftGzLpS1qQDx2IPq0pdpP8wDLpM1fc/evWrjp0gql2LkF2IY4n/3F3b+qfr7LbN+ZSIgMBbZ197trOa7Oj9IglYzF0j46N0qlqF8tbZlG2uXIq1aqeZyoozMTcFpDjmFm+xE1H0529wE6sUhjNSYes1/S3P1xdz/K3ZcD9nT3rZWwkwpZFXg7ffGaofRgmkozJ/CnWvbbEzjV3ddUh1QqqNx49Mw5sgPwlbs/lG7qlEpZUgwdR4xw2gZYq7YkSdp+AlGXcTKwCNS68qbOj9IglYxFnRul0tSvlrZMSbucZYaJHw9MAPY3s6W8luLBNbgd6O3uTzRbI6XdaGo8lqYoqgaJVEJmyuv8wEvp59LIzs7p90uImiM9M6M+S52CY9OXN5Ema2w8QtR50nlRGstjZcQriaTwXqSpiXXUanqfSKz0SvtPjT2dH6UpKhmL6XedG6Vi1K+WtkpJu5x51Uo1H1F1NeDc7HOlk0xNH4ju/pU+7KRSKhCPmm8vlVSKsSWJ6YgApSLXE9LvKwCzAn1LO+mcKM2kUfEoUiGnAv8m6oDtZmbdM9Owp+Hu7wLjiZUUqz+n86M0VUViUaTS1K+WtkpJu2IZCXwIDDSzLSC+XKWTzJz5Nk3aIcWj5CpdGe0JzObuj5Wm4ZRi0MyuAB4EBhILnog0G8Wj5MndxwFHA2OAndNtuiScmXUysxmJRMnrLdxMaQcUi9JKqB8jbYaSdgXgVUtV/07UigA4p/S8mY0kPhh15V6aneJRCqY7Mb1m6khOMxsOfEHUZPofsKG7359bC6U9UTxKnp4ETga6AaPMbO3SE6X6iqmuU2fgLeD5PBop7YJiUQpJ/Rhpi5S0Kwh3n5zubwBeBPqa2ctm9gNRC6Knu4/JsYnSjigepUAWJNUPM7NtzGwMcDrx+XUw0N3dH82tddLeLIjiUXKSRolcCxxOJEuuMbN103MTAcxsFeAC4LK0OIpIxSkWpcjUj5G2plPeDZCQrghMTr/+myjs+h4w0N2/z69l0h4pHqVAFgI6mdntwFbpseuB/dz9p9xaJe2V4lFy5+4XmdkLwCjgWjN7n1gcYDngP0Q8/ppnG6V9UCxKEakfI22NqW58cZhZL+BiYDVgE3d/O+cmSTumeJQiMLMbgO3Tr+8BW7j7ezk2SdoxxaMUSSqkPh+xAMpMwLOpALtIi1IsStGoHyNtiZJ2BWFmmwFXA/sCt2gVTsmT4lGKwsy+AGYHtnH3e/Nuj7RvikcpkmqjSURyo1iUIlE/RtoaJe0Kwsx6AP9z9wl5t0VE8ShFkFad2xm4RrEoeVM8ioiIFJ/6MdLWKGknIiIiIiIiIiJSMFo9VkREREREREREpGCUtBMRERERERERESkYJe1EREREREREREQKRkk7ERERERERERGRglHSTkREREREREREpGCUtBMRERERERERESkYJe1EREREREREREQKRkk7ERERERERERGRglHSTkREREREREREpGA65d0AEREREcmHmY0EhjbX8d29Z3MduynMrD+wPdAX6JHuewHru/vLOTZNREREZCol7URERETar5uAF4iE1WBgYOa5scBI4Ps69u9FJL36pX37Zp80s77uPraC7a2UvsAgqtovIiIiUjjm7nm3QUREREQKwMx+oCqJNdzdRzVw//7A5UD/9NCAoo9cM7PRVI02LHx7RUREpP1QTTsRERERKcmOihvX0J3d/WV3HwCUEl9969q+IG7JuwEiIiIiNVHSTkRERERK6poK2xCD031rSNpV6j2LiIiIVJSSdiIiIiJSUamO3aPAbHm3pQzj8m6AiIiISE2UtBMRERGR5nALrWOknYiIiEghKWknIiIiIs3hRZS0ExEREWk0Je1EREREpOLSKqw98m6HiIiISGulpJ2IiIiIiIiIiEjBKGknIiIiIo1iZgPNbGAdm7xsZj1aqj0iIiIibUmnvBsgIiIiIq1Wf+pYfdXdB7dcU0RERETaFiXtRERERKSxZqOOpF1tzGwoMJhYqGIcUftuLHCLu1/WwGMNTMcaCPQCvk/Hehk43d0b3L503B7AyNTGXqmN44BHG3PczPFWTA/1Te28Cbisse0UERGRtkvTY0VERESkseqaGjsdMxtkZj8QSbbh7t7P3Qe4ez9gNDDSzMaYWf8yjtXXzB4BHiGSaQPcvWc61mDgBeAxMxvZwPeEmQ0CbgFGuvsG1dp4JPBhOW3MHG9oOt4t6VgD3L0nMBw4GvghbSMiIiIylZJ2IiIiItJgZtaXmB5b7vZHEomrR1Mi7OXs8+5+KzCAGIH2Ul1JsfTcS0TScIC7D8+OVHP3ce5+q7sPSNuXnbhLCbvtUxvHVmvjZcAoYtTdYw043uB0vEerHe9RYCFixN3oxiQYRUREpO1S0k5EREREypZGuA0lkmbl7jOImBo6tq46dylJNjz9WmNSLCULXyISZ4OrJ/9qOOZwoH9qQ336AkfXU4vv9HTfo75jZqbE1vWexwHD0q9HltlOERERaQeUtBMRERGRmow2M69+A8YQ00R7lHOQlLi6Jf06vI5NS0o17Xqk0XnVlY71chqdV9dr9zWzMcSIvGF1bZuMrK+NKclWGoG3fT3H2y5t27eeYz5K1OCDmC4rIiIioqSdiIiIiNRoFDFdtXTbIN2GAXUmy6qZOuWzviRb2mZc5vjTJNrSKLTStNnTqd8wqhJmdSbOMq//aP1bTV18o0c9221AJAxfqiUBmfViui97yrGIiIi0bVo9VkRERERqMqaOqaeXZRZrqE9pgYVykmElLwCDmD7Rlh2FVs7xXmjg9nVOtc34Pt33KnN7gH71PD91urGZ9a1eT09ERETaHyXtRERERKTB3P1WM6szsWRm2dVly02IQdX006kJrDTNtjQKbWx24Yl62jgA6FXmCLoX6t+kQUYTI+2+Tz/X5ft6nhcREZF2Rkk7EREREWms+hJhG2R+/m8DjptNBvZPv69Yy/N1qm+hijpet8lSorBnJY8pIiIi7YeSdiIiIiLSWGPqeT47vXWYmW1Q65bTKyUES4m0HpnnxjXgOIWSRgwOBFYi/n16EO+nrJp7IiIi0n4oaSciIiIijTWunud7ZH4e6e6X1bZhGRpSP65wzGwosTBGfyIheQswulS7rgE1AkVERKSdUNJORERERBqljCTcuAq+XLbmW48KHrdZmVl/IhnXl6jr10+LTIiIiEg5OuTdABERERFps7LJqR4VPFarGHWXRs+9RCTsLnP3AUrYiYiISLmUtBMRERGR5vJI5ud+TTlQtQUl+te6YUGYWV+qpru+7O7D8myPiIiItD5K2omIiIhIs0irp5asWOuGtUiLNmQ9mnmuQQs3mFmPGo7XnEZmft6nKQcys/oW/BAREZE2SEk7EREREWlOo9J9/4Ym2oDLU024kmwirKEj1y6nZVdoHZTux1UbJVibbNt6VL45IiIi0tooaSciIiIizel0qhakGF7uTinB1z+b8Eoj90qj7YY24Fg9qh+rBX1f/yYAbJD5uVXU7BMREZHmpaSdiIiIiJRUPFnk7uOA9dOvQ6uNnKvLaGBwDY8PJpKAPcxsdJnHupzaE4Y9yjxGQ/cdl+7rHd1nZgOp7KIdIiIi0gYoaSciIiIiJT0yPzdp4YisNMKtNJLssfoSd2Z2C/BITSPjUhJwAJEUG2pmR9ZzrNHA9+5+ay2b9Krl57r0qnZfk6lJQjMbWdtGaRTgyLRQRen9rpR5fiCZWn4iIiLSfpi7590GEREREclBmoLag0g+DWb6KafDiUTS9zDdCq6Neb3+xKi3/sBlRLJqbOb5QUTdupHuflk9x+pBrM5aSmoNz7YvJbuGEyu3Dq9h3xWJ9340VavRjk2vPxYYW61t/Yl/pw2AbKJwFLFK7vfV/33S+ymtIHtZauO4zPNDiX/3we4+Lr3GS+np4akdw0rP1/XvISIiIm2PknYiIiIi7VQahTaUqqmc1fWo9nu/bCKrCa87kEhGlRJh36c2PAqc3pAEVUp0DSOSd9WPNbqm9qZk2Wjqft+XpdFvpX1KX5pr3cfdrYbX6gFsRyTnSlNlS20aXX0EYEqkDieSig3+9xAREZG2Q0k7ERERERERERGRglFNOxERERERERERkYJR0k5ERERERERERKRglLQTEREREREREREpGCXtRERERERERERECkZJOxERERERERERkYJR0k5ERERERERERKRglLQTEREREREREREpGCXtRERERERERERECkZJOxERERERERERkYJR0k5ERERERERERKRglLQTEREREREREREpGCXtRERERERERERECkZJOxERERERERERkYJR0k5ERERERERERKRglLQTEREREREREREpmP8HL+C6GlugI4YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m Vende \u001b[0m \n",
      "Color:  red\n",
      "tf.Tensor([0. 0. 1.], shape=(3,), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "\n",
    "imagen = img(cripto_MA)\n",
    "plt.imshow(imagen,cmap=plt.cm.binary)\n",
    "plt.show()\n",
    "imagen = np.asarray(imagen).reshape(-1,32,32,1)\n",
    "imagen = tf.cast(imagen, tf.float32)\n",
    "\n",
    "etiqueta = ['Compra', 'Incertidumbre', 'Vende']\n",
    "predictions = model.predict(imagen)\n",
    "score = tf.nn.softmax(predictions[0])\n",
    "\n",
    "plt.rc('font', size=34)\n",
    "\n",
    "\n",
    "ax=plt.gca()\n",
    "for spine in ax.spines:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "#graph, = plt.plot(cripto_m.Close.tail(horizonte_pred),color = color[np.argmax(score)], label = etiqueta[np.argmax(score)])\n",
    "#plt.plot(cripto_m['Close'].tail(32),linestyle=':', alpha = 0.3)\n",
    "graph_MA, = plt.plot(promedMol.tail(horizonte_pred),linestyle='--', label = \"MA 30d\")\n",
    "\n",
    "ax = cripto_m.Close.tail(horizonte_pred).plot(color = color[np.argmax(score)], label = etiqueta[np.argmax(score)])\n",
    "ax.xaxis.set_major_locator(mdates.DayLocator(bymonthday=range(1, 30,5),interval=1))\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))\n",
    "plt.gcf().autofmt_xdate()\n",
    "\n",
    "plt.legend(loc='upper left',facecolor=\"white\",framealpha=1)\n",
    "plt.grid(visible=True)\n",
    "plt.xlabel('Fecha',fontsize=48)\n",
    "plt.ylabel('Precio en dólares',fontsize=48)\n",
    "\n",
    "plt.show()\n",
    "print(\"\\033[1m {} \\033[0m \".format(etiqueta[np.argmax(score)]))\n",
    "print('Color: ',color[np.argmax(score)])\n",
    "print(score)"
   ]
  }
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