STAR_Protocol.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6bb9ddd6-e96d-4cdf-8f1b-9a197b46a730",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "normWFs = np.load(\"full_data.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "11bea910-8418-4248-9a28-2d474c3fe468",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from wavemap_paper.helper_functions import RAND_STATE, set_rand_state\n",
    "set_rand_state(RAND_STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4a98efe7-6528-4b31-8226-45d7a50adc51",
   "metadata": {},
   "outputs": [],
   "source": [
    "from umap import umap_ as umap\n",
    "reducer = umap.UMAP(n_neighbors=15, random_state=RAND_STATE)\n",
    "mapper = reducer.fit(normWFs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c01efadf-4699-4f41-86ba-9ad07d935646",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "G = nx.from_scipy_sparse_array(mapper.graph_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "63e1754a-bbf3-4382-b033-3ab701c27016",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kenjilee/Library/Caches/pypoetry/virtualenvs/star-protocol-wavemap-S8Yf6mBx-py3.8/lib/python3.8/site-packages/networkx/linalg/graphmatrix.py:187: FutureWarning: adjacency_matrix will return a scipy.sparse array instead of a matrix in Networkx 3.0.\n",
      "  return adjacency_matrix(G, nodelist, dtype, weight)\n"
     ]
    }
   ],
   "source": [
    "import cylouvain\n",
    "clustering = cylouvain.best_partition(G,resolution=2)\n",
    "clustering_solution = list(clustering.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "849d6319-719d-4f35-8c56-d5545ac9af32",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(normWFs.T);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1a04d04c-c726-47a3-9d5c-95d6ca58af5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f8eac923fd0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import cm\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "embedding = reducer.fit_transform(normWFs)\n",
    "umap_df = pd.DataFrame(embedding, columns=('x', 'y'))\n",
    "umap_df['waveform'] = list(normWFs)\n",
    "umap_df['cluster_id'] = clustering_solution\n",
    "cmap = plt.get_cmap(\"turbo\")\n",
    "colors = cmap(np.linspace(0, 1, len(set(clustering_solution))))\n",
    "umap_df['cluster_color'] = [colors[i] for i in clustering_solution]\n",
    "plt.scatter(umap_df['x'].tolist(), umap_df['y'].tolist(), marker='o', s=5,c=umap_df['cluster_color'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "47ab6453-00f8-4cc1-a2ce-5bfb44b16db3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAGdCAYAAADaPpOnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9d5Bk6XmfCz7fd1x6U95Xe9/TPd7BDIABhjAk6Cka8JISqdVqNzYkXklX3LihXV3FFTdCoqQQlyK01AVJUQQBgheEBwaDAca77pn23pT3VenN8d/+cbJqujEGg8G0mzlPREb6zO/kyarzy9f8XqGUUsTExMTExMTE3CLIG72AmJiYmJiYmJgfh1i8xMTExMTExNxSxOIlJiYmJiYm5pYiFi8xMTExMTExtxSxeImJiYmJiYm5pYjFS0xMTExMTMwtRSxeYmJiYmJiYm4pYvESExMTExMTc0uh3+gFvNOEYcj8/DzZbBYhxI1eTkxMTExMTMxbQClFvV5naGgIKd88tvKuEy/z8/OMjo7e6GXExMTExMTEvA1mZmYYGRl508e868RLNpsFoo3P5XI3eDUxMTExMTExb4Varcbo6OjGcfzNeNeJl/VUUS6Xi8VLTExMTEzMLcZbKfmIC3ZjYmJiYmJibili8RITExMTExNzSxGLl5iYmJiYmJhbili8xMTExMTExNxSxOIlJiYmJiYm5pYiFi8xMTExMTExtxSxeImJiYmJiYm5pYjFS0xMTExMTMwtRSxeYmJiYmJiYm4pYvESExMTExMTc0sRi5eYmJiYmJiYW4pYvMTExMTExMTcUsTiJSbmOqCU4phbZSGwb/RSYmJiYm55YvESE3MdmA3afM9e5u9a87gqvNHLiYmJibmlicVLTMx1YDFwAGirgJNe7QavJiYmJubWJhYvMTHXgeXQoRy4tEOfw26ZQKkbvaSYmJiYW5ZrKl6eeuopfvqnf5qhoSGEEHzlK1/5kc954oknuOOOO7Asi23btvHnf/7n13KJMTHXhdNujcfsZb7rrHDYKfOiW7rRS4qJiYm5Zbmm4qXZbHLgwAH++I//+C09fmJigk9+8pN86EMf4ujRo/yTf/JP+J3f+R0effTRa7nMmJhriqtCLgVNFGAgOO81+JP6Zb7RWqAUuDd6eTExMTG3HPq1fPGPf/zjfPzjH3/Lj//sZz/L5s2b+cM//EMAdu/ezTPPPMN//I//kUceeeRaLTMm5pqyGjo0Ax9NCEa1JNN+i4XA5nmnxHm/wU4jy31mkW7NutFLjYmJibkluKlqXp5//nkefvjhq2575JFHeP7559/wOY7jUKvVrjrF3FqUQ5eLXuNGL+OasRw4NFWAgWRcT7HfzJMUGsuhTagUZ706f9Gc5hvtBVY7hb0xMTExMW/MTSVeFhcX6e/vv+q2/v5+arUa7Xb7dZ/zB3/wB+Tz+Y3T6Ojo9VhqzDvIo+0lvtpeYNJv3uilXBOWA4cWPqYQ3G4W+FRygITQKIc+t5k5tukZFHDOa/DfOyLGi9upY2JiYt6Qm0q8vB1+//d/n2q1unGamZm50UuK+TFQSrESRnUfE37rBq/m2rASutgqxEAyqCV4ONHHATOPQvF3rQU+mOjhN9NjbL9CxBx1qzd62TExMTE3LTeVeBkYGGBpaemq25aWlsjlciSTydd9jmVZ5HK5q04xtw424YZp2/S7ULyESrHs29gqxBSReBFC8I8ym8lIneXQ4S8b02Slzs+kBnm/1Q3AXPD6kcaYmJiYmJtMvNx///08/vjjV9322GOPcf/999+gFcVca6qht3F5NXRphf4NXM07Tyl0qSoPlMIUgiGZAKBHs/iZxCAmkpNejW+0FwmVYliLRPpCYKNiL5iYmJiY1+WaipdGo8HRo0c5evQoELVCHz16lOnpaSBK+fzmb/7mxuP/0T/6R1y+fJl/8S/+BWfPnuW//Jf/wt/8zd/wT//pP72Wy4y5gVwpXgBm3mURh6XQoR76GEKSlQa6fPVP7gOJHnYZWcrK5axX5/vOCn3SRCJoqYCqencJuZiYmJh3imsqXg4fPsztt9/O7bffDsDv/d7vcfvtt/Ov/tW/AmBhYWFDyABs3ryZb37zmzz22GMcOHCAP/zDP+S//bf/FrdJv4up/VCkZepdljpaCRzqyscUkqIwrrqvV7PYb+bYrmVYCGyOuVVO+HX6Oy3T8RDHmJiYmNfnmvq8PPTQQ28a+n4999yHHnqII0eOXMNVxdxMVDqRl17NYiVw3nWRl+XAoRn6GEh6XsfH5R6ziwm/hRNEtT9P2CsMalFqaT5os9vIvuY5YRBw/AtfwHddtj38MIW4wy4mJuY9xk1V8xLz3qOqIvGy18giicRM7YdSSbcqSimWQ4eWCjCEoEear3nMsJZgSEswIC0SQqKAM16ddhi8YeRl7vBh1i5dpDozzct/9jnOfP3ruK13V8QqJiYm5s2IxUvMDWVdqPRKi/5OxGHaf3dEX2rKx1EhNlGb9Pr2XYkQgrvNIkIIJIJeaWIJyUrosBI4r/F78WybiaefAiA/EkVc5o+8wgv/5Y+Ze+WVuMg3JibmPUEsXmJuGKFSVDs1L3lpMNrptJkO3h1RhJWOW66vFELAwBvY/2/V03RLEx9FqMAJAwKlCInSTlcy/dyzeK0Wqe4e7vit3+LO3/ptMn39eK0WZ7/xdV7+sz+j8UN2AzExMTHvNmLxEnPDaCifEIUEskJnXE8BUeTl3RBBWAodgjAkJNqWEe31vYqEENxjFQF41l3jpFen1DHum78idWTXaky/8AIA2x5+GCklhbEx7v7d32X7Rz+GZppUZ2d46U//f1z47qP4TjxqICYm5t1JLF5ibhjrnUZZaSCFYFBLoCFoKp/yu6DuZaUz00hHoAlBn3zjwYs79SymEJRCl5byKYUu9dC7SrxcfuIJQt+nMDZOz44dG7dLTWPs/vu57x//3+jbvQcVhky/8AIv/Jc/ZunUqXeFEIyJiYm5kli8xNww1juN8jJqITaEZGi97uVdkDpaDh3qysMQkpy42uPlh9GEICOi5r+2CtERnPcbG2Z1jaUlFo4dBWDrwx9hgZeYDL/HkjpCXc3iK4dELsf+X/olDv7ar5MsduHU65z8P/+WyU6NTExMTMy7hWvaKh0T82asdxrlxatfwzE9xUzQZspvc9As3KCV/eS0w4B66FPvTJPuksabPt5TYWRmh6RXs2iGHquBy4zfoq58Lj/+OChF3569qH6bV774Z4SBYvCRfVhdaQBMlSMlekht7WXf//XnWXr6FNNPP8/kM88wePB2EvHojJiYmHcJceQl5oZR+6HIC8CYHtWFzAatWzrdsRxG9SahUkgBXa/TJn0lp706HopNeopRmSAtDQQwGbQ4MX2RtYsXEFKy5cMPcfQH/4PauUW8izZzf3qCxtE1lFK41Kioy8yrF5mUj9L+4Bzqwy3cdJ2JJ5+8DlsdExMTc32IxUvMDaP6OuJlQCYwhcRW4YYAuBVZ7xJaL9Z9s3oXpRSvuBUA7re6yGoGFgJDSOww4BsXjwIwfNddrNRPsPDCcYTSGOy7m6w3TPsbLcIvWYy0P0C/uIMcY+gkCZWHvlejsXuSmdMv0lxZuabbHBMTE3O9iMVLzA2jckWb9DpSiI2unFvZ72VdePkd8fJ6Hi/rTAYtSqGLKSQfsHpICw2bkAFpYbcanEyG2NkUI/fexZGv/AWB7cGygSZ0xh54EKFplM5f4tR//Sr6pQyb5MPsFn+PnBiHXIAacFnZ+TLPPf4fWVPn8NStX08UExPz3iYWLzE3BF+FNDuDB6+seQHeFX4v6x4vXif1Nfgm4mU96rLPyFHUTHo1i4TQyIcCltZomTrTD93BiWe+QHOtROtyme7urTSWlyhdusTB3/gM6Z5e3EaDo5//K859+1vUvTkazJFjM/lNo5APWHaOcWH665xRX+Ri+HWW1XFsVSYIPQJ160a5YmJi3nvEBbsxN4T1NmlDSJJCu+q+cT0FDswFNoFSaELciCW+bTwVUgpdvDDAI3LIHdJfX7ysBQ6TfgsBHDTzAAxrSdJCwz9yiu52g4XxXuaCNuKlJwnPLtA3vI9EvoAQgsbSIpNPPMEdv/3bTDz5BLMvvcTMSy9x7vLX6P70ZvRByXD6Prwxl4ZcZPnFC4yNFKgyxYo6iUudEJ8EXexWv0xBbrleH1NMTEzM2yYWLzE3hMoVnUbih8RJjzRJCo22ClgMbIb11zd3u1lZCR0UoDrbZQj5unONAF5xq0DksluUJq16ndaxo+TmLhJ8/btY+7bTnU6w9tjf4Z2bZMBu07BXyGYq5IdH8D2P8tQkZ7/+dfb94i/Ss207h7/6p9Qb86w8dYz0PXmy44P4ow5BsU5zd5P5ZoCZzhDiEeAi0PBUizPiC2xXn6aLXbjUcajiUMGhSj2cZZ6XyDDEAf4BSa37en2cMTExMa8hFi8xN4TX6zRaRwjBqJ7kvNdgKmjdeuIliNxx1yVZTujo4rUZ2rYKOO3XALij0xb+31/+PkdqS/DyEVJTcxjdefqebBJMTBFUWjR6B9k6uAndNGmuLDN2/wPMvPQiK2fPcPZb32Dg43tJ/GMdecFFrIa0pqO0UGJrBpIQhi4VNUGSbhIUSNAFQI1J2mqNhlokJXrJqKENUdlWZZY5go9NnVlWOcmm4GEG5T3k2YQu3jglFhMTE3MtiMVLzA3h9TqNrmRMS3HeazDjt+GNG3VuSpY6rrjrjd7FN4i6nHCr+ErRq1mMaEkueU3OenVkELK8aZD++SUSw8MYp46iFsuIvVtRH/0Zbrv3ozgnTjN76BCV6Wl2/9zPcOy5v+Ci/BqTk99C2yQw9hioiRzt55sgdZJDfRTNLlaXTxNqPpaXJ5MbxKNFnRk8bATQYAGlApQI6FW3YVOiwkV0kiTpwaGMS5NLfJNSeI4CW8iKMQpiMznG0cSb+9nExMTEvBPE4iXmhvB6nUZXsu73Mh/YuCrEfJ3Ixc3KSmcu0Xqx7uuljAKlONJJGd1hFPBQfLcxh2fb2JUy+kAfyx97gC3Hz8H0HHKsl5GHP4798Ac5ZBj89Pvez+zpl1gxjyEG2mQ+XmT5/AL1y2vojkF6ZzeJLSHpF0zUBdB7MvTs2UnC72Lp8kn8tM/I7e9nTZ7Ep42kgo+LRQaTLCk1QIlztCkh0ckyygHxDxBoHFWfpcwlKlymTQlXRQJo/XFFtpJl9DXpwJiYmJh3ilvniBDzrqLWqXnJidfXzwVhkJU6IYr54NZpmQ6V2ug0clQAQO/rTJO+4DdoKJ+U0NhlZHjOWWO1VsaqN9l26BymplPPpuDUUWRKR/vg3dz7yV9BF5Jpv8mxxCHEJ23ckQql2cv0DO6kkBvHfcGl9KVZmqdKdLOHHQ9/guziOOGzEqdWIzXehZ4xCJo+M3PPYIg0PexlkHvJ0E+Ih02VZY6xwnHqzJJjjNvlPyQt+0nJHu4V/wtbxMdJMwBAhUs4qkKgXCrhZS6F32aRw9fvQ4+JiXnPEUdeYm4I62mjwhtEXoQQjGkpToU1pv02m/T09Vze26YUugQoDCE3WsH7X0e8rLdHHzDzrIYur7gV3HqdLc+fplhus+bB6vICK1uHGBnMkPrZX6ElQu6xirzoXuRUcIS9I8O0zlRQp3SyA5vIHVhCWxSIQxq1r66hkpIt2x5GHTBYOHoE9wch1qd1CiObWVo4zpJ7HBW4bNZ+ii6xjQvhV5nlWRrMoVDoWJjkcGkyGT6OEgE5Mc6weIBd/BIFNnNBfQ2HCk2WMcjRYokyF1gKX2GeF+gXt2OSR25EzgQCAQiSdJMSvddnx8TExLyriMVLzHWnrQIcFbUQ595k5s+onuSUV7ul/F7Wzel6pckrnejSkLy6oHU+aLMQ2EgE+/UcX7EXUMDAUg1rbpGlrSN0ffdZtH09rNy/l+EP7sdM5FkIbH4xNcy5YA5XhZSNTYx5/SytneLkc19E/HSL4iMjiJZAnEqx9LcXufyZx0h8MEf75Br28Qrd+7eS3eyzVj2LnVulWp3BKKZxaeLSJMDFpc66yDDJYrPGHM+RV5vxsdFFggFxJ4PibtJqgPPhlylzkQkeJWDdL0bQZo1ldRyLHJYqkKCATuqqdFIXOxnkbjTx5uMTYmJiYq4kThvFXHfWO43SQnvTWpaxjlndcuDQ7qRgbnbWxwLk0GiFAYLXerysR112G1nO+Q2WAwdLSHacmuT4jkFe6E9yZmc/ImcRPLiFS9YmAqVYDByUCthmlpGizGW/TP59e2ixyGrtDC1nFUMk2PzpD5LbMoTnNjn++b9hzT2LdY9FlQlOfvtLtMMy4bhNaPq0/BVeCf6IQ+o/UOESIS46SQQCiY5Fhm6xmwwj2JRpqWWW1TFK6ny0nWKUTXyUNms4VPBoINHQSSDQCbARaIR4tFimKRYIlIeh0iilKKlznFdfpqZmruduiomJucWJxUvMdWc9ZfRmUReArDTokiYKmLtFRgWsixdNyih9hLyq26geelzwGgBs1zM855YAuD9MU59fYCFn4TsOfkKQuncri9Yg00FII/QIUEyqi1iiQkpW0eQip3q/h/PxZfw9Ndx5B4MMWW2YHb/0MfRBA7tVpfLNJba972Mkk134JYfyxARJsxtdJQh1n2o4RYVJHGr0sI9e9jLAXWiYtCljqTyIEJsSZS7QVEvMqWephtOcD77K8/xbfBx0kqQZwiSLSZY0fVgU0EkwwF0UxXYyDKEJA080kUJDKgOPFpPqMWbUU/ix029MTMxbIBYvMded6o/oNLqS0U7X0dQtkDpSSrESXj0WIC+NqzxejrhVQmBYJjjuVfFUyLCWYGy5xmRK4vgeVq3KfQWH8aEcQqUphx6H3AqV0GM67BTRih4ClWfWX0HsCpBF8EQDwhCHGrZZxvxFQUtbZHLyB1QWZzj4wd+iK7+VYNmlN9jPnuAzSNtAqYAgtAFJWvRzj/if6ecAJlnqzHCBr6KUwiKHRpISF1hUR3la/a+c4r/jY5OgwD38M+4Q/5hudqNhItERSGzKLPAiQ+o+NomHKYptaJgoQhCQYRgQlNVFzqsvU1WT133fxcTE3FrENS8x152qenOPlysZ05Icoxr5vdzk1JSPrUIk0AqjNFeX9mrUxVMhJ7yoPbpHMznm1ZAIPmD1cGjqcQ6lFUHNpzA/Td+vP8Cn01u55Pic9uuUQpfT3gJF7xW2SJf2n5xheXqC+ke6ue1uDSOwQEpajRIqp6gyhZdvYT2YoPFUicNP/gkDv7qbWnMRPIE8rdO9fwuyJgk0hcKnxSINtcACL+GJJrpKINAIcGgwx238Dmf4PE2WaDBLSICGxSB3cS//HFPLolSIo6oE2Lg0STNAiyUcahzjT9msPsaweJABcReT6jHarNFkkV72U2MKhyoT4aMYIkeKLhzqpEQ3BbaSYRj5Q6MkYq49SikcH5oOtBxoOmrj8vp1PwBdA0MDTQoMLbr+6m3ReTYpKKYhn4oeFxPzdonFS8x1Z8Og7g3apK9kVEshgLXQpRH6ZOTN+5Vdb5Hu1ixW1y9fkTI67dWxVUhKaFzwm/gqpEua/F17nqXFy9QMgdQUPStL1Lf00i/3ca81w3LHN8aSF6iurvHi48v4j5UxFudgrYepF03Gi0n0HgN3n4Nz20QUhUGSubcHf9WnfbpGdXUa9iicY22WH3UJNtmEGZfO+CWC0GVBHqLEebJqhBS9WBSoMIFLg6N8FosCPm1CAiQaJjlSDCBF1FElhGRcPoQTllnmBB5NethHgzmaLDGpvk9FTYAQaMqkziw2FRZ5GYs8LnVaLIMCgUaKHnJqnIqYQMMkzybybCHDAOIW8v65lfADxWIF5sqKpYqi4UDwFkrOHG/9knqTR0X3CREJmGJaUMxE511pSJixoIl5a9y8R4KYdy0/yl33SpJSo1ezWA4cZoI2u2X2Wi/vbbPeadQnLSb8JgC9MjqoK6U2CnV9pbjoN6gqj716DikEVdfGbDnkVlfJDORw2cE532GHkaF72UacPc/oxa+hJhdpL6VoBwL6ukgt1GhZfSB1ymfmCc42SMykydzVTX5gCDORx9rfTVXNoxYCzAMJ7GabdmaNtTkba1cS6ZuEQQBmSICDQxWJRpJuJBoWWSpMEuJtFONaFEkzgIZGiTO8ov6/HAz/IYZI49GkTxykpVapcIkG8+QYxyRPmYvM8iyGSndeu9CZn1SjwTwGKUyy+LQRSFwaVLhMTo2SoEiJ85Q4j06SPJspsoWE6kYIEYuZn4Cmo5grKWZLsFhRrytWEiakLUHKhHQCUlbnugWmBl4AQRid++unMIrKeAG4PtTaUG4oXB8qTag0FRPLsC5qEiZ0ZwRDRRjuEmSTsZiJeX1i8RJzXVFK/Vg1LxCljpYDh2m/xW7jJhYvnWhLn2ax1plvNNDxeFkMHZYCm0m/yWrooVDsNXL0aha3Ozp/kUggfJ/e6Vm67huhVk3x4vknGDg/w9Cpw5jOBHq2jmr6ZE6Xad02jCo3aMkMRiaPO56DxBRh1aP1fBXjSAFvSJI92ENh1xa8oInvOqSrvaj3C2onFgg9F+UaSM1Cn0kTdrcQOYmQEhCUuYhBmhar+LSBgBCJSZYk3WjoWORosEiZi7ys/oisGiUUHYdhGgS4rHCMGjNIDBosENCmwRwaCSzypOhFYuDTRsMiSRcFttFihRqT+LRwaWJRwFQp6mIeV82yok4S4CCApOqlnzsoyC3kGI1br38EoVKs1ekIFkW5cfX96UQkHoaKgnwK0tbbTfO89jlKKVoOlDripdSIzus22G60prkSHLqkyKdguFsw0iXozYGMXZtjOsTiJea60lA+IQoJZN9C2ghgVE9x2K3c9H4v65GXHmFuRJcGtahNet5v87JTpqw88tJgm57hN9KjbNcznJg/TV2GCNelMLOItqmI8ydfoRUqjOUauYVTpO8LsXf0skwfxeoyva0ZlrQcvq1oH59i2VSM/k4XQTVL8HUTzU1QW5inOV/CWkni31MnDF2qZQdzcwK9yyK0PZwLLRJ7dbSiT+LlYdTdTcgFCDQg6jAK8bg6FSDIMU6TBWzKpOmjzEXqzGJwlILaSg97yTGOj0NIQItlFCEBHj7RfgxpofAxSDPAnfi0qDKJS4M1zpKmD4lJk2UaLFDmPEl6SKsBQOFSw6FKgEuNWapM0hPuwRRZMmqYvNhEjjF0cYsNx7qG+IHi4qLi1GwkINYRAnqyguEuGOkSFNJcs/EOQgjSiUggjXa/+h5eoKg0YbmqmCtH59UWVFuK0zMK04ChYiRkhopgGbGQeS8Ti5eY68p61CUrjbf8K2pESyKBWuhTCb03dOW9kbTDgHpn2xJCYqsAgWCwY1A36beoKA8QHDDy/LPsdlJa9Od3rLqA17YpTswg6g6mlqTLUSwtL9JIaHT/5gCJ7hUqHqxO51i9L88DExdouAVacwFNPUHruxdZmquw/TMPI+8X0AXCFSy9chp/WGDoBkHbI6QNJZfESI7miVW8motMtTBGA9zROnIiibUvQVtb7WyZRBEg0QkJAIVDhTYlhriPKhMEOCTpxqOJQzVKE4lxbhN/Hzes8zz/Ow3m8LERSAxSSLSOhA1pscIKJxnmPvJspsJlGsxT4gIWOXSSBDi0WMGmTJMlUvSRZZgCW/BpU2cOjybzvEhaDdCmRE1NIYVGWg2SF5vJM4Yubq0J5e8UXqC4sBCJFjsKjGHqMFgUDHcJhos3vt7E0KLoSm9OsHcUHE+xUIbZkmKurHA9mFxWTC4rhID+gmBrv2CsG3QtFjLvNWLxEnNdqfwY9S7rmEIyqCWYC2xm/BYFM3+tlve2WY+6FKRBVXn4KBJCI69F2zkbtKkpn2EtyS+khzeEi69CzthV3Ead/qlZhCYJa2GUjPE8/K1LJNpN1IKDMetQ3/wRggGHqt3Pjv0hZ76jY8/5rA2Pkzy9xPwfnsK82yT9iRzmvgTJzRa+7eKtOaiqIqh7qLogc1sWcyhJ61gN50wbfdxAjII/3UY6ISIlEUggQHW6irJ002SFAJtVTiDRyLOJJgtYFOkmjU+LBkvMqedYU6dxqOFQIcRDABoGGYYY4wO0WGONM9hUaLLAEq+QYYQqUziUOq6/NbrYiUOVOnMEOAQ4uNTw6WKQe1AiIKMGWeBwp3ZmoVO3I5HKxCTHvP88xmqDfGofW3N/D0O+N0SMFyjOzytOz70qWtIJ2DcaHfhv5o4fyxBs6oNNfYJQKVZrHSFTiiI0i2XFYlnxkg6beqPt6cleu4hRzM1FLF5irivrAxnfSqfRlYzqKeYCm6mgxX5uQvESrI8FsJj3bSASaEaniHTab6FUVMy7R3+1bueS16TRbCBWSuRWV1FhyOJzJyj0byY1HqAPeKi2jYdGU9/C5oN3cvnM01zq28lo8iTDHzGZ/r5OO+iiXQ9pllZovOBTLxnkfq2b5MEssmlgP+lghjmCQh1Cge+00TcZGGUTWpJwNUTr0dC2KMKGRzLZgyEiMRIJAZ1hHsSmyjTf79SyRJ4z64W3u/l7rHGaJo/RYKFzn4FJmjSDCHQUPjlGqXfmJ3WxEx+bKhNUmKDCJBINjxaKAJ8WKxynix0McCdNFnBooPApc5EKFymq7WQZpY8DlLlIi2UcqhiOSXq+gj5zBnO+jvShzQuczP4NxaH30zX8ITL9dyDNd19ayfUV5+YVZ+bURhdQJgH7xySb+269NmUpBH156MsL7tgM9bbi8rLi0pKiacOFhSiylE/B1n7Bln5BMu5celcTi5eY68qP02l0JaNakheAxeDmdGDd6DTSLKb8qKajKF7dxpVOu/OQlrjql+Hx2jJeu03hzASogDAMkdIk1VdE/7BNaWaVxLYcdnGQyV3/iL2Tl5mot6g4KSZeMBlIrJCVvfguzH/qPgqPPo7CgbxG0HRpH62hL+TILozjhU0Im6hdIYFwELrA6EviHGvhHGuT/HAaoQHpELfVgLTa8HKR6Kxykr38Jm1WWeE4Pg4tltFIIDFY4ghNFjZqZAQSiY5OGpNsJ/piUuIcOmkUPj4OPm0CXHxsQKGRIElXJ1Xl49OmwgQJqhtRlwCXqA5HssppAlx28PMMNvdSnX2a9vQRxPICqBANAyjgJcB3S8h6hcq5b9E+9wyW7CLZv5fc0H0kRvYh8z239C9311ecnYtEixtlMcklYd+YZFPvrSda3ohsUnBgXHDbmGKpChcXFdNrUY3MKxOKI5OK4a4oGjPSHRf6vhuJxUvMdeXtipdMJ1Jj36Qzjlau6DQ65JQB6L3CoK7cES8j2qtzjtphwLnqMoHr0nNhktAPQSiCpkv240Wa4SqaqaMKAzS6tiGePk71qUfp1kzcbJKVWoLifJtcaY5acQiVmqX8/lEKly5g7UhBUuBOt2l8r0F+myTY1UDrEmhpHeUrhNBID3Xj/N0Cdr2GMZzA2KmDpgho0cbuxFS2EBJ1SF3gq2zmozSYp8kiIJAIbGpc4htomIDAIN3pBBI4VFB4ZBgmxEVgYlNCJ4FHA78zzHFdrKiO+NnER8kzykW+Q4ULNFns1L94iE4CyiSN11jGu/wCC7NH2VS6iwFhYTNKTSmCYhpGRkiN3km9q4VyyzSWX8GYLxPMVQiai/gLLRoLL2O+nCWZGSM9fDfJAw8hk5nr8t15p1isKJ49F24U4uZSUaRlU++79+AthGCgAAMFgesrplbg4pJitaaYXYtO6QTsHBRsHxSY+rvzc3gvEouXmOvKesFu7sc0m7M66RdXhSilbqpfx54KKXXESZ+0WF3vOuq0SXthSKOz3eN6euN55/w67UaNxNIaiXIZP/RACsIeFztXwXmuRvq2LE7Txju1SHJuBTk5SV/JozYygNElaTUTJEptCnMXsGeSVLf3kJOK+ndKyF4NIQTWaA5/Rw2kAmXitxzEmoRGgLHbRXtAoR4N8B73SW/vwRXl6D+DCgmEjc0au/hVlnkFhyozPINJBpcUihCLAm1KeLTxaGKSwyKPQBASYJIlxzg6FjZrGFhAQIBDgi4UPgq14e/i0STA4zLfQhGik0QngYaFhkkXO2lTwqGMtlii78kSnlfCQzApKgz3/Sz5kV/AHE0ykX2etiqxzBEsCoSmgz6yHX/ExVFt/JqOPb9CasGDpTpu4xS1c2fITj9P14N/H2N460/8/VBBgHJtlGeD60SXnTbK61x2bYRhoY/uQCv0/tivH4RRpOHMbNQRlk3CwU2SsZ53r2h5PUxdsH0Qtg8KKk3FpWXF5U5a6ZUJxfFpxdZ+wa5hQS72j7nlicVLzHXDVyFNFR3EC+LHi7ysixcFOIQkuHls4ldCBwWkhEZaaKx1oksDnSjLYmjjdXprxrVXC0VPe3WceoPc8fNgCmgpQi8g+ZE0y4+ewSBHUmo4iTxOPUfYp0gWHNxSgCl9xI4x/D2bKDw5hTd7FkeFWCtl7M1pUu0GwYqHSEhai2Xo8ZG6hnfWIXlPBuYVzkkHfbOBdbtB49mAZmWV5Kksco+F0pzowxaKJivM8Ry7+GWO8lkaLFBgCwA2ZRyqGwW5CvBoomEgO6ck3TRZJMQl7IgWRQBoCDz0jijRSbGVn2aZYyzyEjZlRMfH1yCFj41Ex6UZzbyemCP9/ApOGOJ2J6nusGgPW1xOPEOeOQpsIVAONaaxqaA6Qkog8GgihEYlH5LOD+DsHiTl9yKXVjGOnKNWOYv9+L+lZ98vkTr4sY73zVtDKUWwMod34Qj+zDmU+9ZSnc6RJ5CFXoxNu9HHdr0lIVNuKJ45F1KJPBHZPii4c4vAeI933xTSgjs3Cw6ORyZ4Z+aiz+jcvOL8QpRS2j0s6M/HBb63KrF4iblu1EIfBRhCkvwxZ9ToQqILga8UjgpJ3EQzblY6hnR9moWPotYRL8OdNumFwMZHYQmNYieVVApc5v02br1Oz9GzBDI66Gl9Gr4d4E95JA4U6N+zB6OwH+uuu/Ef/RPUk22k0jBzAueB2/F7biP3wJcw6h72Hy3hDaSwPzyEWVmh0N2m8WgF+3QT+2ydxB1Zir/TR9gMaB9uovcYeJNtrF1JUvdlaXyvQun5abr3D0YjA9bViAhY4RhRPUq0focKI3yAy3ybFiudT0J0/GHApY5OEoMsVSY7gxpNLHK0WcPHQeES4uKioWNiUuAy344GNnaIam4ECoWGhUcDFHSd9igecfHQqI9brDzQBRqdGpkKKxynxFkSFEjSR5o+fFwUHjpJNEzsThdUmQtUMbD0POnhfsz+HRgvnyJzYR7vxOcoLJ2i6wO/g0y/eaG4ctp4l0/iXjhCWFl5zf3CMBFmAmElwbQQRiK6blqE9TL+/ARhZQXn6ArO0adeFTLju9HyPVe/l4o6iI5OKsIwcqa9b7u8yjclJqrx2TYAW/slixU4M9dxEu6klIoZ2D387qoHeq8Qi5eY68b6QMac0N/Wrx0LDR8fWwXkuXm8Xq7sNKqHPo4K0RAbowFm/RahUiSkJNup9Tnj1/Ftm66VKiyvEloeSoWIbgN72kUYgxQfHGFg/37G+SiT9Tba0eOosoumJ9AdHzdII7t0ZrpSDFQMcvuT1CbaNI/UuPSzd3Kv9jKZTwn8FQ/vso23YKMNGIhl8OZswpKPuT2J1ZsjdWeR1nN1ZFrirzpoPSYEsN4UFmCzxCvkGCNFHwLJIi/jUuuIjRCJhU6ikwYCEHjUMchikiLPZhQKiyIlzuLTxsdDoqMIOhGcYCM9ZJIhxEcAEoMiW1BhgDx8lOT5alQQvOsA9p06hljDx0VDI8TpGOK5uDSRlCmyDZ0UIR4peunjAD5tZnmGMpfxaOBQxcdG0w0K9+7FG5gm9cIlnOVHaX79HAP3/z+wxvddte+jKMss3vkjeFNnIIgii0I30Dftwdh2IBIehvUjozfKtfFmzuFPnsFfmLxayBT7MMZ3YWw7SFtmePZ8NHcIYKRbcP92ccN9Wm5mhBAMFiNfm2orKmi+vBw5Cz93LuTIJOweFuwYjKNWtwqxeIm5bqwX675dk7mEkDQVOCr80Q++jqx0alz6NYul0MEnGr6Y63i8THUmYuelgSkkSinOeHWcRp2+6WUqoU9oe4QokBIlNBr3jxDkk7QmSty2dYziwtO4UwsoJ0QmAjQ7JH1kionGAqluk9y5Jsmswm20qMy6eEpnJjVO7rEXMbcnwFOoQFH/4irZX+xGSklQ8XGea5FY7Sb3S71k7mvhO228GRety0CFCkKJiKYFoPCpMc0oD1HmIm2WCOkcrNHQSWKSIXqkTYiPREMAGYZxqHWiLu2NCI1EQ2IgOtGVSHQ46J2OI4XCoUaATcU/T8+zNbSZeVxsyncWaewudbqhDEz0aKHk8GkQEnbqbnzWOINJgURnllIoPHaIn2NMPEQpvMBl9R3qzFBnpuPqO09+fAvtrn78Zw7jrZ2i8cTvUdz1aQbu/F1EEOBdPtGJsqxufBdksQ9zxx0Ym/cizFeLs98Kwkxgbj2AufVAFMWZPd8RMhOE5WXs8hKLhw9xrOcTVAvb0TW4a4tk20Cc+vhxyKcE920XHNwUtVefW1C0HXjlsuLkjGL3kGDnkIgdfG9yYvESc914u51G66zXvdxs4qV2xaym014NgFxHqAAshJHvS1dnwvR8YFMNPcJag/SZy5S1kMAOUEmBUgIvkUTe28eM1iT/wireYIveM0dYXqkTln30QQP6eyBcwl/KsToNazJNMemDgK7hNt6ZRS7tGWHzhRfRJ8qkP1EgXPBwztmIv66Suq2L5jNl6l8pI+9KUfzACNm7e6gemsdf9HDTDsaYFRVHG4L1EiOFzwxPIZEEuB3H3HSnU8jruPEaaFhY5NFJ4lJjldMoAkI8QnxMcvRx4ArflgCDqJh5vTU7ShO1kWhguxSfnESsNFBSo/TgELVxicBFw8Ik06lpid6zyeJGJCfEw6QABNSYwiLPsjpBQy2wjZ/BkjkGuQuBQCidCpcI0LCpUcxuQ35sG62j34Ezcyyf+TzV098grzaT0vuR6FGUZfNezO23I7sH3xEhIazkVUKmNXmOC08dorG4SNfclyjuuIfbPvlh8pmbJ316q5EwBPvHBHtGFJeX4dRMSL0Nx6ailNyOwaguJvaLuTmJxUvMdePtdhqt86p4uXnapZVStDtFyGmhsRhEQqVbvtomvV4T098p4D3t1QHoWyzTmpnF0xVhqBC6RGkCWcjib7Jorjmcv9hm/Ikf0PjBdwnnbBCgZyTqwXH8i7MURJvy2BiLwQi5CyukDiax2k1ap2aoJIrMPHgX24OnCaYcUu/PYx9q0n6iSerXejF1j9Z8ndVvXIYtPj3/fAhzOEH9FRt/oU7htyyCcgAFDSnFxow9hUu0ByQSiwRFfFodr5YWKQbJMIhDGYsiPu1Owa5CQ0MnRZ5x7uGfcYq/ZIofEOCRoECGIRyqaEQptzT9+I1VUt8/jlZro5k56h/chtXfQ5Y1XKLPMiTAooBHA0VIhiFA0GYVhxoerUicYODQQCBos0KFy/SHB0mKHvTAxGoKUiJJI12lLM4T4NCn7Wf4zv87la4n0L70t2irCzTlJZq799Dz4D+ksPkjiGtodOeIBE+2bqO0fTdd+g/Y3jhMn3MI/YlZgg/8LFqu+5q993sBTQq2d+piplfgxExU3HtqJvLM2TYg2DsiSCdiEXMzEYuXmOtGdcNd9+1GXqJfmfZNFHmxCTfKS5NCY7kjVLpfx+NlSEvgq5Dzfp1W6KPOXaS6PE8YRoZrWkKimRrJ+8Ywc3lqpQQTWUHhi19g7uhhwmaAMWqhf6QP87uHUE1Fz5489YPbKDFE66kjFH8xQ+ulOrnD81GXzyd20C6s0jU5g9alI9ISZ7VJ5dFZej+2g8bxNeypGq0LJdoTaczhBGErJKx4eJdd9FEz0iyK1xkQLAlxaLOCxCTEJySa6RTQRgEVLuJhAxI6PVcmaVL08xJ/iEu9E3Fp4mNTZwaBxGYVkwJaqU32+6dQdhMvLVn8UB6jYJEh03HqnaHBfEc82Qg0fFr4OFjkcaji4XSiRBoCGy0wka5GIBsI32HVeYaBy130XUzSFxjUc4LZLQ7N3pBq1yVsvUKzfoncWpP84IO07UO4mRBXrtLgSfqNbgbUncgf0zX6rdCwFY+fDKm1wLJ07vqFRyjWtmI/93WC0iKtb36OxD2PYGy97R1/7/caUkTjCMZ7JbMlODGtWKurjQ6lLf2CfaNxm/XNQixeYq4bP2nNy0bkhZtHvLTDKAZhCYku5IbHS19nkrGjAuqdyMyYluQJe5WXnQqtZoPdi4t49Qaa8EGBlpZYmkHf/j7a6RzLxSFE+gJnTh3CqDUQmiB5fw6nrtCmVlG5AoVEjXS9Si1MsrZ7lK7EGtauJMGfLZE7dZrWviFmt+xjpKdB2HSRpo5yoXmsAskL9P6voyz/x0nMbQka36tQ/JUBkndlaD5XoflMncL/1AOoyPlXyB8SMD4KreOMGwkUiU6VKVyK+NidyIhAJ4lP1I9k0+Qy34LOPZH7rk/QmTatYyIxCFZmSD6+BL6OX7BY+FAeP+0BE7RYJMMQSXpI0UeAg0d7o/DXo4VHkgJbaLOK61cI/BbCV+h2E6SG2RQE6QBfGKwNVkBKhpb6GWyMkTvXzyX7LI3FNbxMhao3hWMmqd3Vw/in/j9oF15AnX8K98VnWRIBzW2LjPEQlnjnRldUmpFwaTnRPKKP7JPkUwJy29E++Tu0n/0qweI07We/jr8wQeKen7qmEaD3CkIIRrthpAsWK4KTM4rFiuLSYuQbs7VfcNtYHIm50cTiJea60FbBRq1K7u2KF26+mpdWJ4WV6kSF1s3q1j1eSoGHo0IE8LJbYSpoUVUeA6U6qtFECwNEEE1rlgkNczzNyOAAFzDImlvRpp4mrFcBsHYmEXmT4IU1ZNUhdUcPWn+SofYCpeYYy9vH2C7XUG5I0ArJyRLt8zPUerdR1VPkUh6yVyB7Bf5sSPPQGn7DJv/bfaiKR/twHe+0R9//NkrrUIWwGeCveBj9b7a/rkzhhShCFD4t1jqDHUEREGCjY+HSJMCGTg+RidVJPkUpHwCDJNJVdD8zg+4b+P0Fqh8cB7OMot7xkmnRYokEPWQZZY3TnUJgSYIiNmVAIFs+d5y+hwX9KOXuMm2rTqCBCBRuXoKUYAjaWZ3KQBZNG8ZVW+ibyrH7yPe4kPwu9VyTULYITEVzVHC68DV67t5HjvehnX8J+4WXqIuQC1urDHE/Rbb9WHUvQagIQq5yf12uKX5wKsT1IJ+Cj+yXpK1X75fpHKmHfx335LM4x57Gu3ySYGWO5Ad+Dq178C2/d8wbc2WH0kpNcWI6arO+uBh1Km0fiCIxKSsWMTeCWLzEXBfWvU9SQtsoZP1xWfd2uZlqXlqdqEpKaNgqoNGJxAzqV3u8ALidKMywluQDSy4n5xYRMgRHQSKKW6R3FCkWeulu97L2xW8j6jbKd5BpibUvhTfhQdVBqIDisI/WaGE9fRr948M4vQWqooC5OgNJDaOo033iEsH+LhqD3WTKdaQhkUUJcwLVCnGn2tiv1LF2JGmfaZL/ZILQ98j+XAF/0cNfctH7o9Z2FbLRefT6iI75HKhOCkdgAGpjvIBAdh6jkEgMsvRzB1vkx0AJLqlvUuYimZcuYzV1/IzO2gcHwQxJ00dAfsNwLsCjxCmsTlfSOjoWfa2dtFdPEVZWmJQz9Jc2Q28PolAn0DwCLegIKQfVmdIUMIOHjRIhTb1Kv+uyrXQnl6yzNIo2SigSF5bxsmusda9ROTjIKHcizx/Hfv4otgiZ3eLTEHMMqwfQhHnVpxOEinobKq0oqlJtQaUV3aYUWAZkEgLPV1xcUmgSBguC9+0UJK9+qejTlhLrtvej9Y/TfvorhPUyzW//BYk7P4yx6+64A+kdpDcn+PA+wXJNcWwyisScm4+EzM4hwd5RQSLuTrquxOIl5rrwk3Yawc3ZbbQeeUkKnWoYRVkMJD2dgt2ZjseLIQQNFVAUBvuNHJMTFwkXlwEfghDN0pEJneyWflSoSH39FGG5gpyfRw6ZCE+hfIV3sYG/5pB5Xx7N83BmmtiHq/R9cplKfhtL+iijTCM0gey1SCxU6Dk9ido6xMJ8L93OBN6Ui7HNQKYkylZRpKbu0/t7wxgDJmgCfcgkaISEXoDyoo4joXiD2pdompHC7zwgQhF0hIronEIkWifpF6JQ2JSwWSWnNmGRY4ansSbKpCZrBCJg6cE8rtkgSTdb+SQD3MVlvs00P6BNiQAfKNPNXhQB7WAZY7lB+pUZMs0WlTGf+oCNv0fSn7gHmKPMRTQ00gzSZgWHOp6qEzg13EqFlcUTFFaKOGN9bNY/xh13/L856X+OtfLzeKU6uXKOdquOt1phZtdmxtUdiAtH0Z47S0soypsULbVEV/l2VmYt1oxBqo5GrQ3hm3x1HQ8WyyEzpagQPJcUFFKKbx4BKRUZC4a6Ii+SfOrVnaD3j5H56d+l/dw38GfOYx96jLBZw7rzI7GAeYfpywk+eptgsRIZBK7UFKdno5qY3cOCPSPx/KTrRSxeYq4L1Svaid8uN7N4SQmNlcDFJyQj9I3U2HQQebwYQlIKPYrSYNm3UYtLiFIF6fuEgJbWMbIJkoN5Fo4dxVrsp7vQxYpwEb6HMWLgnGkTND1EKLC2JVFBQOVZm+wH86QTi5S9rcwt5kl9pUb2rhyO0lBzDtmpBdpOP5fscbQfnEJUfXyhEErQ/b+M4F1sE6z6BCs+5liCsO4jcxpaTiLzJngKTDbapV9PwETDFDWuTiO9em/k6xKiEB0juxAfhwCPJY7ysvrPbBOfQms4dL1UIkBQ3Z/F6TWwSFNkG6FwWVHHEQgyDKN1BjsaZGiHSwyf7aa9skqj2KIxJMhXhgm2QqO3QZ1zNCiRogeBxKVKGLoUVrI4zRqe4+OrNk7Sw1cBbXeG9FmH5dFnEJe62TvyG5weSLHcd5RKtUXXdBdNewZv6iIzIy3Gy1uQZ17B/OuXaA+fp+5nWW1/CexBArkDd8vDhN3bMYxIeORTUEhFNvb5FBhaNH/n8CXFQAG6MpKRbmjZ0HAi0VNrQ20u6oAZKAp2Dr46MVlYSZIP/SLumZdwDn8P9/SLoGlYBx+KBcw1YKAgeOQAzJcFRydDSo2owPfcvGLPSNRircdmd9eUWLzEXBeudNd9u6yLl5tpsvSGeJEaCx2hkpP6RmpsvnObRFBXHijoKTeRno9eqeMGISiQGZ3EYI5WvUlzyWZU384dtx/k0e/8NcoLQNPx6wHhkkdifwoUeCUffyyP9YE06WQDa65MZc1k2etn8MwMoSkxN1tI5RG+vIbXO8T81p2MrL5CWAnIfKpI8rYUWkZS/dIa7ZcbNH9QJfW+LIX/qR992EI1Q4KaT+iG6HkD3jTj93r7RUPDIsABgs4gxyIWGXxsWqwS4DLF91kOjrL5WYX0FM1enfq+LrIMoghZ5TQNNU+BLaREH5t5hDrTtNQqlfpxjNlFmvOTZBYFzRzUtyaoZhWW6MIgwFYVWu0pQmcFs21C2MANPexpl+6XW7jpgGafQgxY1LdYBDkTw6tQWLzIyuKfkjv0GFu6d2KMFFgacaj0Vclc1KG5TLhYYk4mGGinCVZWkYtNwqFuVK4Ho7hCMbDZvlIhndxK9r6H0Yt9V31CSkWDFS8sKPIpwe6RaC7PuugIlaLlQLkJFxejuovFcnRKWbBjULBtIPIjsfbci5Aa9kuP4p54DqHpWLe9/yf5ise8AUIIhrtgqCiZWYNjU1GL9dFJxYVFxV1bJKPdsYHgtSIWLzHXhZ+00whebZW+mbqNWuGrkZeJMJqO13WVx0vUfSQ6j00Iib64wnAz4KJjR66yGghDQ4zkWJlZwpnxWVi9TPf5swivCjp4Mw7Bgo/ywsgx1xIIpTD/5x1Io4p06/Qvz7A6Ncryls30fv8y5o4Eibuz+KebJF6aRX1kL6t3bqfv9DmK79PI/1w3KPDLPo1vlxEpGY0RmHdJ3psheSBL0AhpPl1DhJD/5V6U/+rIgDdGopHoFOaqzvn6PlM4rNHFLkyStCmxwnECbMxTEzgrHhgGKw/0IWUSnzYhDj4ubUIyDLOVT5CSvSx5r3Bx/rNozRUc2cYblng7tqKNjBDK850hkG36lkbRXlpCvzxHdWcCoVsE2QROHlq9EmvMoOepCqkpjbU7NJz+JM1BSbtbpz5Rp2fSI6i0CVZdBuYsEl8+Tq3Hxh3OYuR6cexl2rrLhTtNkqfvx3ImkRmT5MMHMDb3Ik5eRp2ZRa4IWt/8PzB33ol14AMIM4FSihcvRsIF4PbNkafIlQc8KQSZBGQSMNotaNhRmuLiYiRqjk5GE5PHeqJoTO/OO1GBj/Py4zhHnwJNx9p7/0/+ZY95XYQQjPXASLdkagWOTIY0bXjydMhQl+DuLZBLxQLmnSYWLzHXhdo7kTa6CbuN2lekja6ccQTRL+a10OuYswkqymNUJNm51qY2OU0gfPAUWl5DJQ1sM0RregQTLby1aebPnkM6dWRGEJR8lB0gshrGuIU0BVqfRneyRjDv4ASKnulpVLkfZ6wb765BkrKONawTTEjkiQpdqXMsbd7C2q/cw9i2CYztScKqT/0rJbBAmoLQVXjzLn7Zx5t1kYFFeEIQZl2Cho/MaISuQJq8SeEuV9S6hKznmSQWCo+QgAWe60RgcmQZw1m9SOF4gwAo3V1AZPNEYxbbG0MZJToBDtM8gbUmcJ/+BmZ6DbdHwxvuwSlahBK2czfb+RnON/4Kb2kKeXKZ7HNtlC8JA5vSXSaBbhEaAwRJh8ruJla9D8OV6KOjpJw5wkYFJ6OobtdpjAWkyw7dp07QNdkitSJQjqAmXeyqQ63vbshMgebAA8sMVd4HK8dRh47hmnfg3b6dyjYbXlklNwPumUN4EyexDn6I0/p+LiwIhIB7t0u2D/zog1wmIbhjs+DAuGJyBc4vKFZrisnl6NSVgbu33ks+DHCOPIHz8vcRUsPcfc9P8E2P+VFIIdjcF4mYk9ORS+98SfH1SlQPs38snpv0ThKLl5hrjlKKSify8nbbpCGabQTgqpBQKeRNEI69suZl9Yrp0gAN5dNSflTpodgY1ji8Umfy8mWQ0TQfkdShmEJoPprjYroZErU2q8srBJ5DsApKCoQGiQMppCnxK5E3jPuX04gtGTxLEZ6q0VtYxv61fdgP3k7Po4dQ9QDlhoQ+DF68QPVT+6h2baVRX0Gbc/DrPsYOi6SdwV/1oBKgj5h4FxxkKCj951lSfTnMDyTxZz2s3ToYRJrkDZ3pQ0KcjnHdeiop6i5KMkaT+c70ozIOFQzPYuhZG6UEzXGL2mZJBh0NAxCYpAkIaLHEEkdwp84w9KyLERh0OwNod24lm09R5hJe6HGx/Bi9j29HtAyczTrp2SlaGYtW737mP5XF762ACDHWXPSGTzNpcvnBXuz2ZyB3GSH3Ia0LSP0ygaiDBi3Hxt/t08xDYTZFuLAV251FUSN/eQKZShOM2rhdDWZzL9FTv52gepbw8ZdpPXg3jPfgPDhKsJKk53CNsLrG/Pe+yZpziMTWj3Lwnk1vSbis4wdR1CXdSRtlE3BhUTG3priwCC9eDOjL3csu0yU3+QzqK49SPg314TtRKupu0jSwdLAM0Tnn1fPObQkTsgniOT8/BoYmuH2zYGu/4vDlKM13akYxsay4c7NkvDdOJb0TxOIl5prT6BzAJT9pzcurR0uHkOQbHz2vG+ut0km0DY+X/o54qYYediddooTCUIJBYVJfXKK+tIRSUURCZHU0I0lSD/CckEaqD6U1EHpI2Oh07wgFCYE5YoEhCFY8QBA2FPoeDT9r4l1aYvu/bHG6qLO00s/okAVBSNgKCMo++fs1NmWXmC9s4lJ9C3uPHcZb9tByOubORNRRtEuiJTTsI02az1Rpn22iPA/d6AVNEDoB0up87r4Ouv+Gn02I27kkNs5D2gxxNyucxKWOQpJ9eQFRbxOkk6zdkwMhaLDQKcrVaVNG4RMENqLRoOl71DXI9O4meN8dNIVguXkOr1VDb89hrdiU0+dITO0kX9GR5gjhFg3t4Q8yWAgpy1NoU5fRG6sEholT6CNIJUnYh5Erj2CnXyKsbyMIdKR1AcIaJHUQktJug5X+JOZkDb86SDqskq0uQ60X7XSCRHKF5rjLTNdhEkt7MdQk6gcvM3/vB3G7kkjZIrFrC6nj+wkuPouhltju/w+60vsJuz+BNF7bXr3WgJWqYrUeOe42nagz6YfRJQx3w0IZSo0oKjOvPcgdXR4DSy+SOf0o7UCnMXCgs/+g7cCVHWKvcvVtSSsaaFhIRb4z60XHcXvwG5NLCT60F2ZLgsOXQho2PH025Pyi4J6tUbF2zNsnFi8x15z1lFFWGj9RtEQTAkNIPBXiqJCkuLHiJVBqY1SBElEKSQCDGwZ1Lm7n/kApDCEp1to0yiVsuw0iBCmQaZ2UE4IICXQdCqME+llC34eEAB8IFUqBe8lB6CI6tLgh5qiJP5LFDhIUPjNGeqSJeH6JxpzGatcQI6llvFWf5B1p/BWX/udOs/DpTdS2DVNrTmKcmAalELrA2GqR/nAe77JD65kaze9XkV06qYfTmNtNRFIQ1kNkQkMJEOqNhcuVSEwSdBMS1fgs+5N4oYmSFqmZOpkLTQKhWL63gK4N4alVFD4NZhGdfWzYGj0vt6iPhfhpjfkPmeS7+3CqFtUZh/xUk6BvgTCpCLOKViEgo05TrGRxTZvqwzvR8w0Kqpv0GYE81qBZ8KjekUH2KExRw0pP0Z95mu6VYabFZdrnwW+51HaBlxLUzQHcMIE2ZCO6LfKLPkHVoDy6ifTSGunpACW76Dq1gFeo4Wx2yV0Yw9GrDB99lrm7fwo361PTpjnbP0698Q/YU3meVOMoZ547gXu2RuuuX0IYJqEC14dWNDniddG0KPKSsgRpa/2yJGnAal1xdCqk7QhWez7EUCFkU+Uw2+vfxthroG3ehx9GIsjxwPEVjhe9p925LXr/aOJydFIsltffPVpUwoRCSlDMRG3E/fk4SnMl6269Q0XJqZloavVSRfGNVxR7RwQHNombIoJ8KxKLl5hrTuVHdBqVApeV0GGHnvmR4VQLiUfY6Th6+ymod4L1ehcJOGGArUJMISl2CnZngzaBiv7J+0qRkAJtcYWX5y6hVIAIFCQkptQJpUfgaHh+irXFZXInTkCg0DIaWBLVDNAHTFAK+2SLoORjbLbI3pvF77ZY8oaxViq4/36OjK9RvfsAq9u3sDVfJ/OpHoRSeCcaeC+v0DN0guW79jGzfQ87Li2iVnyUUhhbE1hbUxhdJtUvriEsiT5gkH6kgNFnoXyFX/UQaYlMaqhOu/Wb1b5EKDzVALcbO3RQegWUQGt5dL9YRilFdU+Gdr+BchXYfYjkIogAwoDEfECi4iPMBKMTQ0zeW8MxPRbsKcTk/Qx/7yK9iVFEmKFyh0ezP0B/5Qx6qYStmvg79tH25wlWJvEut+l+dhXf9GCwC99SaCsVQrVKS59n3plATgwx/OQUTmkxih3ZedZuT2DJCqKRQ8kERhacTQbSHyFXXcUodiFGDZKXV9ErWbx2k/TZWYJClYHaKHbGpXficZrv/1lOVCr09k8xVFQMiY8yf3kPqcNfIpiaorz0ec5u+WWCjgAWArJJwUABRroEm3sF/YVIrJj6G6cfRrojz5Ejk4rz84pL/Q/Ttn12tY+SfOXrJNMaxqY9VzzjjXei60eGetHpVXO9pg22C4uuYrECZ1AIAYU09OcF/XlBXz6OzkA0/PG2ccGWTippZjUSMuUmvG8XsTfM2yAWLzHXnDfrNFJK8dX2AqXQ5RPJAXYb2Td9LUtIGurmKNp91aBOY1VFHi8pjI2p2VN+NKtHQ+AJhacUJyfP40xMYQoFCvSUhgg0ZEGidJ36mkI+/zJBtYpMCERKErYV+oBB9ue7UJWA1ot1gmWPsBmQ/mAeudgg/cVDeOVV/LJDb3qa0m334uzIYrtdpHeGON1J3PM2/mKL7m8dZ3XfTtZqKdZUD13WMsIPSd2dJWwFtA+1yezqwe6tYGxJ4E+6GAMWYTXAnbQJVnzS9+d4C6oFFITKI1SgRAmlUkhlIvQ6vU+tonk+TrdJZV8PAoUwS9GPej+DUi7FQ1WyE3XqO9NUi5Lq9i7MxgdpZx4FvUIy9af09XUxkNyFNfxRjNpx5NyL2LYNoaJ8ME9P22DziWHm8+fwwwZOHnTHxJhfY+SsT3vQoDmWpN2r0zJtJrvKDAYBSUfS6u9CXMiSyrfxxlzS+irJqkFQl7TzBoFss5LKYeSga62NvmUU129iXF5C+D5aqUbNO0dhYoTGeEjtsW8hd/0yqfwMucI0S+UnCQsPwT2/Ss+xL5J25uld+QLze38VX0tg6q+KlLU6rNUVhbRivEcw3hulbt4IUxfcu00w3qN44ULI/PhP4Z4PGVs9ztCTXyEjNYyxnT9yF5q6oDcHvTmu2ude8KqoWakplqqKWgvKDSg3Ij8aiMTMQCESMwOF9/aBOpMQPLRHMLmseO58yFxJ8Z2jiof2ynjg449JLF5irjlv1mk0F9gbtSIvOSV2/YjoS2LDqO7Ge71s1LsIjaVOp1FW6hu1OXOBTYjCROIpRQ0fLk1hLiwDCgQIS8NzFXpe4Zga9oKNqNQQCYHMayg7RAgwNicwBk1CK0AkNbSijgJaT9TQF3z056uEXYrEFovez/Ri3+6wYGeZqfWxNbOKO+Hg9abxllZJDZsML11iMrOVqf6dFCrLZB8uIDMaQTlyyc38fA6radI+XKf5gyodjzkqX1jBGLDQB02sTVF04HVdd9UVZ0IBLkL3ELqNIU3Sp9skl1qEumD1fUUsK4mPC/hoJNA0DaucwqSORMNLZ2mMp/D9BVotDRp7KBQP4ffVWetZxfXr9F2oUZhdpr65SlBIUH1fN35vhhIa2x9PMjRhsDqWwN5TpGvKRLZLtFIlkDpK1wmFHpUadwnmfj5J9/cOsrpbolJZNKmTkZfwMlUCU5CoCoyWRysD5pqNbAeU8j2UB5MMzqfwDySgVsA4dQkqbZr2DP5LOdQmn0TwdRq7f4WqOo+wpkkOPcHm0Q8xsOczpF78PJq3jAz+muSHfpUWaUoNKDcVK3VYqigqzWi8wLEpRSENYz2C8R7xhjUUAwXBp+6QHJ1SnBUfR5wNqM+dZOTRv2Pw534TrWfobX3/DU3Qk4WeLGztj9677SqWqrBUjdIj1RYb6z07p5AymhU01h2lU96rKaZNfYJsUvKD0yHVFnz7aMgHd0sGCu/Nz+PtEIuXmGtO9U06jU55tY3Lq6HLZNBis55+w9fa8Hq5iSIvKamzGNgAdF/h8bIcOARKoUuBF4YEYcBUSiPfkydbLyMBpUl8AaJHwxcCf7aN1WwhC1E6Rnkheq+JdVsK1VQE7YBgxcPYmiCoBITNgPZTVfQsGDtSmDsT+Cse/bU5FoxuFs1+Nsvz6C0bvztD8qECMgzpPXyG6TtG8Pb3Ih7cRrLYJKgHuBfbGFtNwqSPMWxhH2/gnLcJvrCGNmjgnGzjnrHxZ1yGPrsVWdSv1i3qqjOgo2sEHRHjoBpN8kfXAEH5zgJ+XkfSJkUPPh4iVGh1m5bZxN6r0djdS2BIzDUHI6yTqZXoe6pM7R6d1qjJ0h065reXaE7PkK4lGQwGWPzl7RhjXVTDCYJT5yhdXqa4kCE7sgm7v4g3PsCw9n4udb/CgjyEnF2leHyR9qBGc2uCcCSget85Br+TpfEApAwLfTVLZUDRyraxC2l0G9INB6/og2aSmJhEr+ksj+0i1EcpZix4UMc8dxnvdAWxJuk62STltCh1f41k36+Q7D5OwpwhL55kUHwI1fUZWo/9FWF5mfZ3/wfpj/462d4s473Rp+x4ipk1mF5VLFwhZI5PKfKpSMhs6n2tkNE1wV1bBGM9gucTn6J22GZy8SLtv/sbtvzqb6Nl3plp2ElTsKkXNvVeLWaWq9F6ay2YW4u6ol4QkbAa64GxbkHCfG8duLuzgk8clDxxWrFWV3zvRMi92yTbB99bn8PbJRYvMdecysZco6u/bo4KOOdHk4RHtCSzQZtDTvlHiJd1l90bL16aVxjUTQaRQV2vFokXRwVUlLvRZRWgUI5DzdIo1JsIL4jESaAAn7AnQSg0zJPLSHyEoRNWA5ACa18SXEXYCvBnXPR+A61HJ/W+HO3jbfyVBsZ4muRdWWReohxFwV0lQYtmy2C+VCB/7iLJlEP+7/fR+PIa3ok6A/IUqwMH0cbS4LSxT9Twlz2CZQ/SGuaYhbUnhTfp4K95NJ6tEvoKq9/Em3dY/c/zdP+TIbTc1QJGqahWY4Mf+l+cP9lAhAF2f4rW9iKCgBAPmyqWypE9PI9ec1jbZ+FlBIHv0PVkDbPm0tiZRoQBVlBl+BsuE785hFkKcfQWwUwTG42E1Oj7yybl1AKGaCFcF2yFnZIMlDdRWQtoj2pcVIvU/6qHXqNOfvoiIvQoiyzNzQn8dEhjB6RXVhh5rs3anRae1MhdUIhxA3uoia8nEGkTnQxhv0I3utEv1eg/fhSbfhb3DuCIYVLbfDI5i76XV0lfSpCfXaQ3eBK336Rn6FdZ4BA1ppnlGUbzHyD1yGdoP/Z5wuoarUf/ktTHfh2ZjsSFZQi2DcC2AYHjKWZLMLWqWChHUY4T09H04758ZFg32hPVW6zTlxN86i6dl7M/S+Pbf87S4iqtL36JvZ/5TXTzdaY//oT8sJipNBXTq4qp1Uh4LZQVC2V46WK05nUh816Z1JyyBB+7DZ6/AJPLUXqv0hLcuSUu5P1RxOIl5priq5BmJ71SEFdHXi54DTwV0iVNPp7s5/9oTDITtFkI7I2OnR9mY77RTeCye6VB3Won9dUno3XXQp+2ClFEowECFKrWIHH6IumFqMaEhEQYGka/ga90fBfkagsSoEKFChR6UUc5IVpWi9xwFxy0Lh29T8fanQJT4l1uo8kQ8/48qieJPlNFswRDaoEzpSEuT3dxUAvo+uki+rCJPWTiTtn0nruE9RtjaKFPo27gz7rgKPxagFrzkJbE2GJh7U5S+0qJsORj7klF3U6uov1Cndrflsj/YjdaTovEGFcIl9f536vXfTKXopEJ5YMZlAiASPwE2CTP2eSPVZAtFyMYZ+6gxE+4lO7PUzzWwmwFhJqiuSWNDHRGvl1BKR+94eN1BThotL15MhcCkukA1R3ipyXSDfGdVZpPfwnrpR7ayRKGoeirGSSX2jhbNNzhLHZ2IBooqRyCHKx+0KJgDNO16lDqqRIYHumJBqIpaY/YkM7jawGap+Hldby9RQySGJfK9L4Q0tjqU+3qJRz10TIaWq5E5giEy3XEX3+Dcq3N4Pt+hwXxPBV1CV0kGMzeQ+qRTgSmXo4EzMO/hsx1XfVZWoZga3+UsnH9VyMycyXFcjU6JczIB2b7wKuCQJOCe3YnOW/8Mot/+2fU5xd48Yvf5PZf/jQp6+1NfH+rFNJRVOi2cai1IyEzvRrV8ixVFEsVOHxJMVgUbOuPZjddKb7ejeia4H07o1lXRyej9FqlBR/Y9d5Nq70Vru03NeY9Ty30UYAuxGtam092UkZ7jSw5abCrU6x7yCn/8Mts8Gra6GaoeYnWYCGpdMTLuuiqht6GuAkBVylErU7x+AU0PxJzUpNoho61PYMQEjHrIJRCSAjrAQqByOmoEIQhI0ETgjFmoWzw51z8aRshIKx4rP0/J1j6xxdRZY/QDumvzkDdo57IE3xsDHOThZbTse7NoWU1zB7JeGIe5SvmnAHCjIm36tL8QQWj14ymTS+66KMWYdmPVEkzRGQ0QjvEX4vGFbjnbMJQXV368gb/cwvHGwilaA9ZOL0GEh2TBCY5UtMOxuQyqek29uYuSj0WTB1EuGlE0qR6Z5rkZIue58p0nWihC53chE9yyaM9ZLF2T5YwGSJbDWyjCWstvFyC5s4iyx/ool0IsIMS4eXTpC6VyMyUSTRWSGg+hUYX7eRe/HA3oX0vhlkEQ8PNaVz4mI3/kYfo2v5JjC270boGMUKLxFqIqlfAdfE0H6cSUPdt1naHVA72kyjqFCZ0uhtLmPUkzaEcy7d1sXCHgdZSaHUH/RuPU/3yv6e/GhXOrqpTrHACmSmQ+thnkLkuwkaV5nf/kqC6+obfRVMXbO0XfGiv5Ofvkdw2LkiYUTfQ8SnFl18KefpMyHJVdfyFYMe2brb//C8gdYmaPMmzX32WUkO94Xu80+SSgn2jkk/cLvm5eyR3bhH05gRKwXxJ8dSZkC+/FPLy5ZBq6/qt60YghGD/mOSDeyS6BotlxXeOvvu3+ychFi8x15T1gYx5YVxViFsKXOYCGwnsMXIA3GUWAbjoNyiH7mteC26uEQHrBbuIKI0lEfR1al4qoYfdibz4KpqyY1YbpGcWNw7smqmjJzREv0loGGgXyqBHERdchUChj1gYw2Y0iHHeRctoKAlan4EKFEEpIPmBLFpex1vw0OttZEYiEhK53KJrYRaZlFy+2It7wcYv++j35DF2pxFJiT5dQYWCuVoXs+YY7RMt0o8UEUlJ6IT4ix7+nEPgR0IsKPsE8y4iIdEKEpRC6QpvykF4IYlpm8zFJriv3T9G1Sc92Ym63BYJVYMURkuSf2mV/LEqoQardyQoD/dTH8iQ4BSjh9v0PbXK0FfmMasORt3HT2u0xlK0N+VIripCS8MeNKnclSUopAktA3swibasE/gaKoClh3op35ajOZpE8yUilUAVs3g9aZpeltyhCbb+3fe554++zY5vKRJOBkSIr9mcH3qWxrY01m3vx/rwz5Lb+3GkkcSsQlBv4DQdHEuAk0KXArW1RvmAhmZlGLmg6PbqmGVJcyzFyt4sC7vSaA3QGm3ky0dofeWz9E1F3/9FdZiSuoBM50g98hlkoRfVatB69C8Jyks/8nuZsgQHxiMR8/5dkr58JAgmVxSPHgv51pGQi4sKP1AM7tjM7k/+FJYByXNP8NTjZ5levf4HzExCsGdE8lMHJZ++W7JvVJC0IvF1elbxtcMhjx4LubSk8IJ37wF9rEfwyAFJOhFNEf/O0ZCV2rt3e38S4rTRewil1HW3pX6jNun1qMsmPU2mUwvTq1ls1lNM+C0OOxU+mrx6+i5c2W10M4iXzgFdKRwVYglJvlPzshTYOCpAAp4KCYIQs9rAKNUgjIpCpKlB0UBlDJSuoaYaIAU40bBGY8RCJiTG1gS4EJZ8tD4D++UmWlpDpjXMO9Ok78rg7m3T/OMyyX0G7nmbxG1ptB6D7ekyq2vjrLYGqE9OkpivI8ZDjPsLeI+vYr/cRA63aLdMptxh7v10P+kdOv6aR7gWohoh1b9aQe8z8V2XsBkicyB9RWJXCs0QiGaAWnJJfK/EQEqAUBRerrP84SJujxltE1A4XkcoRWskgdtlgh/iNZcwaiay0QJDwxlMsLw9hTVdZvixCyQbLslqJNictMLusqge7EKZkRgOZUDuhKR4SdDcalDeZOHuGmHs5SI11aDS1yB/vIyz2aSZz1L5+VF6L1UZ+9IsibrO8sEszWwffmWY5EKePv9FpNug+NQcO84FTP1MjupWcEWD2eBJBuu7EY0qZjVFoD1I0zqK0ShhWDa68tDTeZKpIVbDNVR/g+bdBTafGSd18jLabT71lkZrzGTFB1HWKc5WQbRQF8/gY9B95wOs7YU5nkHHIpccI/Wx36D9vb8mKC3SeuyvSH30N9CKr/3b+GE0KdjUF3W2rDUiv5eJZUWpAc+fDzkyCftGBTv23cGuxgqXnjtMeOprPG8WqO3rf82AyOtFLhnZ6x/YpJgrvTpJez0VduhSVEOzfVDQnXn3pVW6MlEh75Nnou194nTIxw9KMol337b+JMTi5T1CPfT4YmuOjND46eQgaXl9dn01eG2nUagUp706APs6UZd17jG7mPBbnPJqPGB1vWad1k3VKh2twQ59PELS4lWPl5mgRaCigYw+ClwPq1RDa7ejLmkpwJKEGQ2Vt9D9EHu2BZ6K8kwJQfZTRYKSjxQCv+njTjvotcjqX8tqUcrmSAPnxTrKUWRut9CLGv6SR+3La1h3Zege8xgc9VgtpVlYHGdL7TzBmQayz0BYEn/Vwzq6jDm3TDOTY/mOPXQPzODNutBWeHNuNKAxp2NmJaIWIlY9Er06liEwtyQIZh2Yd6g8XcP4qQLZEZPQFFglj9AU+Ekdo/Fq1KWyL4Ns+BgND0KB57vUNploYTftviSJyUV6Xi7RdawGhkaQT2I0BMFoDm9IJ0iFBKFLerZFoEmW3p8n2UwRdKXwch6VXB19R0D3SQenraiPp2nYI9gJgR7UaBYVM78wyNhTgsTFFOX7HMLRGbJDWbpG/zlLl/9PzOePUbjgI/58mpV706zcmwOvTX1pifzpDE2nQcJPE45mozqjWgNPCYLQo1GvgerBG0xibw2Z8H0Gju4nf/wU3m6bcFijPW6w8v4Q8fUC6dU18GqE+imSIkWxPkL5nm6mtB+whZ8inegn9dFfo/W9vyZYW+gImF9/SwJmne6M4P4dgts3Ky4tKs4tRCZzhy8pTs8q9o8/zJb6KnNnJglOfoljxm9Ra2W4d/uPrjlpOoqGDZ4PXhD5v7x6uXPqXJcyGmOgy8ghWJegdU66BroUGFo0jiBpwlARRrslLUdxaSk61dtwYSGaxN2XF+wejmpj3k0FrglT8OF98OgxRbkBPzgV8lMHZTzY8QquyxHsj//4j/l3/+7fsbi4yIEDB/ijP/oj7rnn9Sec/vmf/zm//du/fdVtlmVFplMxb5vnnBLV0KOKx1+3Zvn55BBd2jvfXXAl1bk5Xnjqa6xtHiK9+x7o1OBO+C2ayicpNLb8UGfRsJZgUEuwENi84lZ4f6LnqvvXa15udLeRUopWp9tovZsqKw0SnfXN+g4hioSQOEohPJ/iqcsQhCBBJDREt4HeaxDqJtZqnWDNjYSLAGtPCnPcol31sU+1cE61IsNZO8QcNkndn8VbclG2wp93cS63SRZ1QqkIKgH+iofyFJoh6Vk6y9qWu1jbu5XtK7N4h5qYEsxtCdov1qn991X63Je5/PMPMSuG2dSc77RiN2mdaqB1G5EbsAvmmIVXCXAtCRUfueQSLHu0vlpBZQ2qiyHFT+dIpSTSU1jLHpb0KBxrIPyQxuYUoQDdDgCJWXII0jq+GeC3G+SfniM/0SCx5oGm8BJgrjQxm4JC0yHTk6OyK4EnbMySS3VflkY+RbpmMfidNVZuEzQ3WVR6HExVo+sHLl4hS795CW9JUdsa0Nxs0DbhzCM5EieGSJ8vE+xepeI/RfJ/PEa6reF0a/iuR35OR71YxVqxWb03T8tM0d4RYKyNYIoKxVYdmwxOSkOrtvHxCfU2VnuaxAUTb0Cy0lOk0T9E/txt6OfPogcVvHFobrVY/YBN8M0i6fIadlChGhzFqCUQK2XaHx5nMvMYW/kkCatI6uFfpfW9v8Zfm6f53b9C//Cv42d6N8SB3xEKQfiqODC0SBisn+sa7BgS7ByCiWXBiZmQpg0vXhJkMj/Lwa6/YLxeJjjzZS7pv0bd1vngbjbamIMw6hJaqSlWarBSj0TQO/iX9ZpbLCMSMmlL0JeLZiut1KDUgIVyyHJVkE3CriHBln7xrjHBMzTBh/ZIvnU0pNKEZ88qPrgnHuq4zjUXL1/84hf5vd/7PT772c9y77338p/+03/ikUce4dy5c/T1vf4vh1wux7lz5zauxzvrJ2MlcDb8VNJCpxp6fKE1y8+lht6wq+cded8zp6lJRXNlhcnjX2LLBz5G765dGymjPUYW7Yf2rRCCu80iX2svcMyrco9VvGogo3WTpI1cQoLOP9pKp/alW0RiUCnFUhCNZNSFpKE8NNejcG5i4/nS1KDHhKKBQODOtoDIdRcJyYdyNB+tRKZxAsJGEHUXCUjdl0UkNWQrIHFnitANMM8kozRKCGE5xDvVIph3kQlJanoVa3APXibLUmKI/KY59D6B3qtT/2oJ+3SL7j2KkqhTm9WYaGbYvrWFudUi8/4C4v0hrb8t4dcC2kse2iYDlTYJpaT1XB1/1cMPwBwyCNsh7rk2+r05RE6AUnS/UCEz0UZp4GYkeitABKA0CFM6xVdqBGkN6YXkzrRAKbyMQWl/jp7ny5glByUlMkgi2jaFIzb4Nna3hpczcAYNDNsje6lJ9liDqV/oprkpRfnONMpN0XfeRSWqpGZdlJVG6RqNUUlqbonCwiW6zrjUSVMfN1jaHzD0WBldZfD60tDTTb5k4y8u0/eDMo2tNl7SQVM+vupien8aJ5dCBgGyq41abKPlSuhpB6tiI0sGXm4ZOfg0idUPoJe2oKemIVWiPejQuj1Bsu5gfT+DUa8RLq5RE0cJ6nfhLpzg3P07OZT6NmrxkxCkSaR+if4LX8CsL+JP/hWLt/0aXrqHt0PCVKRN8A1YrCjKJCjnf5FdS/+dIWOG8PKjzG3+Kb74vGTnINRswWpdEfxQ0FMIyCSiomGjI5gMnddc1rWo9sYPo9fww+i0fnn93PUjf5iWG922Pn+p0rxa2BiaYrkWDas0tCi9lE0K9o/C/rHIBO5WJ50QPLRH8tjxkJk1xdFJuH1zfDyE6yBe/sN/+A/87u/+7kY05bOf/Szf/OY3+dznPse//Jf/8nWfI4RgYGDgWi/tPcPTzioK2K5n+Eiily+351kOHP6mNcvPJAff1FflJ6EyM0urqCN1DaNS5/jffJHC7Qe58OAOhK6/JmW0zlY9TVEalEOPE26Nu6zixn03S6v0esrIEHLDIXjd46WhfJpEk7SF6gxlbLRITy1GTxYCkTOQhkbQnSTUBGKu9Wr5vADvQhv3vI0xZCGTArM/RfLeHK3HqwhLIkxBYn+KxO40ftlDH02S2JWgfdGDFxo4NQ9/1aP0J4voWyxG5AwXlrdz/kyO+6wLaDvyhCUP5YfoAwbmsMn23BRHk93IHUW85TXC+Sb+gou15pGzoNQK8BRoZoLMI3mcszZBPcB+pQmmwHADkndkQUH7SIPE3VmcXgs/pROkJG5OJ7XoYtYClC6w+038pEb59iyD317Fqvj4WR17IMXK+/vR6w5GM0S+UqG8N4vSNYqnHAzXxbAFIp0iUVKEZhvPCFg+oFE8miB9UdLcpNMcN3AHDPLf0yg8NYMvIDfj43RLvEWF22VS25MkueKQPFOnNdSLX0yz9FCGvscr2H0azaE0jeJuxPIKaf8EmQmbwHRwsi6yUmPguI4/ZNLYmac6ksHd2ofl7yRjnkflG1hLbVTg4fU5tHY8wZ5L98JSgQW/zHzGpNnt0HifRr6eRj/koBoOwdIiq32nqE3sp9A4wsxdu6mlHmXt0schTFIc/Hvc5nyevLvM8MnPs3bnryOy3Rgd8SBlFH3xA/ADFYmEKwTCOrYbnQDySVhrwPlmN7PJn2HH5b9BhUeYWOxmpe9uTkzDjkFImhLTgN7s+sgAQXeWHzOd8dYeq5SKhkM60YDKlhsNh2y50W2VVjQ6IQijGUGrdVishFxYgK+/IhjuCtkzLNg2IOnLccua4PXmBPftkDx7NuTkjCKfVmzpuzW35Z3kmooX13V5+eWX+f3f//2N26SUPPzwwzz//PNv+LxGo8H4+DhhGHLHHXfwb//tv2Xv3r2v+1jHcXAcZ+N6rVZ73ce9V5n2W0z4LSTwPqubtNT55dQwX28vMuW3+Eprno8l+9n7BkLi7RIGAWtLC7g9fQwfOMhuc4T5557n0NxFZg7X2LtjLz0563WfK4XgLrPIY/YyL7sVbjcLGxGaBFEUxlNhVFNyg6JyrSsM6ubCqJajpyNeqqFPM4ziMgGKEIVeb5FY67SAawKVlvhKIXozCNfHn2m9GjGXUfREJjUS+1OETogxbKLaISIlEBooJwBP4k7YNJ6rk3ukiHIV5hYLbUsK/0CS1f/XJO4lG3/BRf5fnsX55SLS9AnuLBCuudR/UEHpgrDiY7/UIDu0wJZPLJEY0KinB0ldrGMNmGAJwj4Da5uPOtXCHDIxRk1UqKh9YY2wHaJKIe5lG63bACFoPtfAW1T0vS9Jet7GT2osv79AasGl+7kKjR1pnKJCaiFdxxoEeZ1m3qC6P0dra57kTBMvA6X78thbunAtl/yJGq0+ibViYhckqidD70tNVvdBY1uKyv40+goEYgi9rGMlKhC6LA+4GJtSpCcFbjsgeaaJcyCP6rFo7i1yZu8YQTOBPq8jh6pkzDrc3kXuSIMgUKztzXOs6x/SkznPwdbnMJttkqsNAjOJyKTJTrSg7UO1htc9jzK6sRObCbuW8PpqJKoBzd4G5W1wsfky2xs76akfxHnuRbx7oNHrM/1+xfB8F+npJWi06NamCfvSaPUtHDh1jLXbbWp3WrTnPoYUSUpdv0rm9BfoU8tsm/s8vT/96+iF3h/5vVUqEjO2qyg1oVSHUkOx1gAakdi5yBbqmQ+zc+VxRmceZ97vYTq3meUajHaH9OcFbUdRaQmWqgpDE1iGIpuEfFKQS0Im+ZPXoAghsIwobfTqz5erX9PxIuGy1lCU6orJFbi8DPW2YnI5Mn5LW4q+fFQ/05+X9OYi4ZVP3TpR/S19gkpTcGpG8cL5kGwi2o73MtdUvKyurhIEAf39/Vfd3t/fz9mzZ1/3OTt37uRzn/sct912G9VqlX//7/89DzzwAKdOnWJkZOQ1j/+DP/gD/vW//tfXZP23OkopnnIib4j9Zn6jxsUSGj+XHOJRe4kzXp3vtJdohj53m8V37I+5vrhIQwPN0Clk8+z+yO0MbN/BU8efwHccxLe+z7nNi2x7+KNoxmvHBuwxsjznrNFQPmf9+oa4Wo+8ALgqfI13zPXiSoO6eidt1CMjMVYJPRrKRxKVsAQorNUKmu1F/3stDZGRCFMSZC2stTLenA3rv4pNiRKK5H0ZtLxOsOAgTIk75aAPmEQ2HQKZ1bDPtEjcnkcfsRBJgbQkfkvSfKaKv+QisxoECjXdYuALz5P43/bS6usiMT1L+5ka3pKPMCWhHVL7Soke/yjNg6NMav1s680zvC8aAFmf8wgu2ciiTvJAGn/Bo/1ig9APkVkNpQsSB1IEyz6yqOGdb+PO6GxaaUFS0B40SM/YBGmd5tYk6Yt19LqHUQtQpsAtGqw+WERJjYFvLiCUorI/TWsshddnMPatEFELSM82UQoqW/PUxxTFswED360x3WXSHrRY+bBk7EsXGX7eZunBFPUtFk7RYHm7Td+aQqwJDKFILfsE+Rx2SqGkhq/naGt9mO0l2tsSOMkS4YpJ9nKDLfYp+sf/Hbb5OzQ/+mt4l75K+lwZvelBso29aRS56GDNe5CRIJbRJ2v4i0XamwV+MkSs5bF760zfoVFrV8gdLuIn3kfq1CsEB+o4fbD2EYfs13vIVpYw7Dp64yL1/ix6czPbzp6G21uE91nYSw8zuZxmbt+vEh7/PKXJZSY+91ekP/brbNrSQz716t+wUlGkotyZj1RpQqkZFb0GYVRoW2lCta0IO8HM3jzIwt241grFleM8WP4a30/8FrbKM7MGTVuha0BninTCiOpRsskofSSFQMrocj4pyKWiDqJcCrrSUfroncIyomGP6zOB3r87EmBTq4ojE4qLi4qGo5hYhsWKoC8XbogWU4eenKA3+2oE6Waul7l9k6DWgpm1qAPpEwcl6fdwB9JN1210//33c//9929cf+CBB9i9ezf/9b/+V/7Nv/k3r3n87//+7/N7v/d7G9drtRqjo6PXZa03O+f8BkuBgyEk95tXu3NqQvDxRD9poXHYrfC0s0ZDBTxk9bwjVfu12VmapoaVzW20STvD/WSy9xJcusTI8XlmVw9RunSZ3Z/+NIUf2me6kNxhFnjaWeOQU2aPnkWIyDLbFBL3/8/efwZJlt7nveDvPTa9L2+62vvx3gAz8EbwpOBoN0RJu5ehjcu4G/tBIW2ENjYUd6UblBR7V3d1jSiKAGhEAgQxhJsZYAbju8e2N1XV5U1W+szjz3n3w5vV3QMMIJECAQw1/4iOKZdZp3Iyz3ny+T9GJnjEpPn5gJebA+oGyZvBS0eGODJWybqJYl6qr19CSKlWQ1kDLWvCWAYpEnTHI9r0EcOLh5YTyEGMOWZBJDFHTbScjn/FI/tAHnTQcjrGqEl6YhRxKIdp+UgvJljz8U459P6kgZbWyX60SLQdElxxGS10iM+s4RzZSzqTxdyfQVQjsvdl6f95i+Cqi9b1sRabxE90uZbSKSxdJj+To5MF+2ia8icqRGsBwYKHe6pH3IwwZmyMWQvZT4h2QtzH2xhFnXKrjf1cgjiWISVANkK6h7O0T+axGiFWI0QPJZ29OXbuLiBTOoWLA0SSoHsJmXkHb9QmsSXr9wn2/oFEaII4pTE4lCdOJQz2mqTndcp/4eD/qo03abPydyvs+4Ntxr7fIrbLeDFEKcHmnRr26RKmJTEaEqvQQ8Ym3oxLvt8k27xG99mjeCd7OAfSbHzCZM/XWlQ6DoWNTZL+v8Zeu52LH5+hbcdUX2pidAYIMY9Mz1AOSniVHN3SgCTXoXi5DvUqTjlEyAFsmURjEY3bWrR7FcrXshTdT2O2vkszs0H/iElzpcv0Szmybhej0wT7HL3CPTQ358i8tIDdd5l4MM/te+5nu5Pj2ugX8Z/4EkZvm9Zjf8Brx78I+SrVnNKatB0Iwjc/d/1IstODrio9x9BVzko+BTNV1X80UhCk9I8SP7HD1rUNRvga3y9/ESc2sC04MgF+JPBCVQcRxdAawHZXaVFyKaVT6TpAA3ZpRV2H8aJgsgyTZUEh89O/+Bq6CuzbPwaOL7mwJjm/Juk6Uq2g2pC1FVO03oSVHUkUqyPMp6GYEepfWj2Guyu4OFGu/113lK4LJYK+yTG1K45OWzfawH9aI4TgwSPwrdcUCP3e+YQP3vrfrgPpbxS81Go1dF1na+vNwUpbW1v/xZoW0zS5/fbbuXr16lt+37ZtbPut1w//LU8sJc/4DQDutkpvaY0WQvDu1AhZYfCUv8OrQZuBjPhwagxD/GSx25WwjyNjTpqFtwQ7nbU1+ikDu5C/3iZ9Nuyi6Tr3H7+Te8pHuPD1r+M0G7z8e/+euYceYt8jj77pxX6LVeTFoEUjCViIHPabSptj74KXn6Nodxe8GEIQIhFAefh37sQ+XhIjGGpzJFTOLVwnvDVLI7FVHL0RJ1B3EeGNv0XLaGhZHRKIB7Fqk3YSonUfLVVEaOr74UaAZZmIcRdv2cE756ClBOFrDlIKsr9cY+S3R4mWAjpfqhO3ItytHr3/uIpzV42Dn/GJN3w0U2DOWMS9iHDZJ9rYQfRGiPoJq6s6djTAfKgIkUQvGJjTFr1vtzBqpjqORoie00mcmP43W/gXXMoHLCb2WdjtEP28R1IpIeyQMKdTeaULuiAYsWnengNA9xL0TkRYMGgfz6D1JLXXuqS2t9n42AhhUWP70TK113I0T2Ypnm8Q2wmD6TRbD5XJfzdk5Ks9tj9XxJ1Ns/AbE0x+z6W86LMxaeKOaqS8GPeumKB5EqPaJ2X2IReg9TUG1QFJEJJbfw3riWl6Wg5jtEn90TQ8HpLd7KGvtgm1HaYXC8z/n0tsPqQx+lQDsx2SihYwNjKUL5tED9TYvrNA+44YfT4GyyLJCExngLUS409K4rs26Psm3rxGt/URtI8+iZVfYuORLNmlFsUFidULKLGJnnmdlnE/3cY0xmvLOO4fMrgvT56TjI5m2Hr08ziPf5nBdh2e/hJn936RJFvGNmG0qLJQChl1kW32lSB2NA+TJUjbqshxbkQ1RL/5YmuSfOSXKD72vzPR2SSjP8n3Mx/E8SVLO4K/e79gT01jpwcbbZXF4viK7QljpaeRpiRrq/WPWlfBWlPlt4ACEJNlwWRZMFb6z2tngkjS86DvQhAr9ihOIEkUuIjljY+TZGjbjqGYkfRd6LgQhKosMk7A0lW7exirn9cEaEIxSkIo8JKxBFkbMkP79o3z3U8Oj9M09fNpS5A2h/Zvc/i5pT7P2mD/FUCOqasU5W++ltDqw7OXJO8++vZZf/00528UvFiWxZ133skTTzzBJz/5SQCSJOGJJ57gt3/7t/+L7iOOY86cOcNHPvKRv8Ej/ds3r4cdOklIVujcaZV/4s/eZZfJaQbfdLe4HPZxkphPZibe5PK5eZpxwF+4G0hgMRrwkfQ41g+Bnc7KCp2yiV0oMKrZhDLh4k3ZLtX949z7D/8hl7/1LTbPvMG1H/yA6v4DlGZnr99HSujcYhY4HbQ5HbRugBc0evx8s152wYuOIJIJBhoZTT1eG5FLhMRAEAhlqSgsrKobCsDWkKZOXM2RdT1Y9d60ypcSzClbhdkhkRL8MyqYTpgCrahjTlrEvYSw6RL+ZQf/zABjxISKQdSNMT88RvEfjGGUwCiYhB+MwI2Jn/Zw1xJotYj+jsSYsAkuOMSeRC8ZJF6CdqVHanoCxxc0qjUq11ZIMn2K/48ZhC3wzjqY+yySdkS4EmDndGQgCa74yDhGODGWBoVuSCaSNJHESwHZOZj8doMorxMVDDbfVyUsGpiNkNyiS1jUCbMGQc4mk3ikll3QNLLzAc5eG2fSZDtlMf7dBsVXtgkKBv7HbJy5ItufmMJtZci/soh/MsAbMVj/UJkkepSGvkpWLIDepXTZJV26wHjtI6zmVnDkAC/rEIQZ+vtcOlWNUtZg5rmExkMmMh3g74vJP+sjdEAfYHs5Jp+ULH88y/LH04x/d4d00wPpEtdjso9vUexncfYZGElEfDlH/3gemdVJdbukVz3C2jbBHSaJZ2GvbhD/2UF6vxmQqta5+ssl9v/rJtlmn8QPMbxFwjmLtd6d2N1R0v01Lq79O05l/q/0W3MIkcZKfY7b5ZfJ+jscnf8Sr+75FUSphBdKep7ECzVMXaJrgnxKMFVVxY0T5Z+sTdFyJdIPfQL55B9zp3yVialp/tPmcTqO5PeegncfSXjPSY09IxpSKv3JWlMVRjZ66rm7m/Vim5KpiiBlCvqepN6DnguXXMmldYmuwUgByjlBNQdRLOh5arXV96DnyR9hkW6eOJG4Abi74t5ACX7lTRgjkcPv+8OXFwqwZFMKaNx8vFKCLtSarDV0Tlk6lHNQy6vjTJkQJ4phjYagKYwUQEwSGHjq9m+eN3+uawoYZW1BxoaMdePzrK30QzeDulxK8O5dB9KO5PUluG3uHfDyU5/f+Z3f4dd//de56667uOeee/hX/+pfMRgMrruPfu3Xfo2pqSn++T//5wD8s3/2z7jvvvs4cOAA7Xabf/Ev/gVLS0v8vb/39/6mD/VvzXgy5gW/CcD9dvVHgMVbzREzT1rofN3dYDV2eclv/UjGyu68GDSvv/zmowF/5KzyqfTk9aRcv9/H67RpT41RyxcY0S2uRgN8mVDQDGb1NABmOs3xT32K0HFozF9lUK+/CbwA3GGVeCXosBq7rMcuk3r6epbKz9MuvVsNIKXStFhCu35ca7GnvoZGLBO0MCJdb1+/rZZS3LNu6aQcH3/du3E+ExB3IqxZW4XZWTpEEn/ewz6Ywpg0Sd2RRUbAICaREd6ZAeEVD3OPDbpAr1kYv7Ifu+YACSSCzINFwksO2YMh8nUDEUu2vx0y/kEdcaiAvuCh5zTcl/oUo4Rae4XHH34f2vIO2foO2TGTZCcilOCdd+j8/g5aTsPam0a6CXEnIm5FyJ0IraQzE0SkWtCXGg0jwb3Y5eiWwNxj4U7ZbL2vRljUSa151F7o0N+XJjF1QCKkIE7rrH1mFPuaj/QSchf6uLMpnFGNtfdlMbfSaA4IR8eZTMOkw+g3HIrPN9iRKZp3p9gZzxEE8zSW72Z0T4Sc2AZrB/3VLsuL36bJh2gcDKFxDb3QRvgGWs5HP7pNbllSeFVj826dwb4ymm9SutRGmgkaTWovRbSmCvROmmy/u8LIU23S0kFPIuzVgKlnBjSMLFFJw+52mPlKyM6DZcKMjeGG6FFIVLyGfaJB3t9DuJWh+UejDH51QHjQ4upvmez7l5D1Olhxj8lLZ3H2mmy3TyCFRy5Z57aRf8OL9v8d4jGsVJbl45/n5OJXyDg73LP2JZ6Lv8hSvYSUYBkJGRtOzkoeOSaoFTR0Ta11bFNiDa8Ejq+AQt+HRjehOYCx4l4q0w+QuvoMYwvf4h+9d4wvn6sxvyl56gKstmIePqJxZEpQyal/J2cVeFhvwWpDst6S+KG4zriUsnByRl24r+3AwpYSDb++JIfrGUEpC9UcP9IwnbKUzibctVT7gr4v8UOuMyapIcMhUAyIZSh9jGUoAKJrkq4LWx0II0kybBUt55TzStcEiVRgaOCrx2XgS9wQ3Jb6uwwd8ikYL8NMRXBwQmlvcil1XtgFUu4QSF3/OLzhmvICBXh6rhIZv3lufJ6xlW4on1arLdULJXjtWsKZZcUs7f1vzIH0Nw5ePvvZz1Kv1/mn//Sfsrm5yW233ca3vvWt6yLe5eVlNO3GxbXVavFbv/VbbG5uUi6XufPOO3nuuec4duzY3/Sh/q2ZU34LV8ZUNIuTfwUX0R4jw/tTozzmbnIh6vGQrP4IHdlOwusMyntTIzznN9mOfb40WOHTmUlGdJvOygqhJgjLBTTDYFSzecxTq8PjZuFH7jNVLgHgddo/ckx5zeSYmeds2OWU3+YTmfQvRNbLrtsoGgpVTKGRQq2z6nFAIiVo6p2n6fkYvvKkSqGYkKiawYwjLDegv+G/CbxY4zZCFyR+glZUgEjokH20iDmqYvV7f9JEZgxEECOdGH3cREvraAUdc0ZiaQPcH3SJljwMW0Pfn0abMPD6Grm90HALdJyY7J+ukqQMMp8ehVeb+I818XsxOWtA7toK9cN7Wcvcy/Thq8Q7Ed3HGoQrAdFOCC2BltOx9qfRyjoyksRuQiqS9LZiwjHB4lZM0JfsywgCV+Adz7P53hpR0cBshqS3AnQ/QcQSZ8bGaoZYjYA4Y+CZGoMJm8nHGkRFgzCj4U/bhFmdhV+bQlvO0tk7SvnaMtLokuyJiDZL1N3DNIUkFXUQWo/K1MtogwMEZoi3X+CmutR+0MbsPU766Vtp3HUAvdpmJLyKobUR2Yj+rQmzV8YxBgXq+1z6D/lAncobC2iRj4wc9v5Bg8v//RRh1aR3JI12PiEbO8g5nXRdUFsQ1O9K408bpNp9aq90ad+WJtJMrO0IREBQ69Df/wbFBYuZ0wZtW9D+WED/iMX8r+9h379fIxftYPkuh6+eIjsb0XBuQbN9RptrPFz7n1iu/WMyVglNy7Oe/QK1V7+E7DY4vvgVnp/4AgO9SDS8aD51Qa0acqmISm53DbIbbieIEqX/CKIbGo+UBbOV+znhr5LZXCT+wz9l6v7fwCnbLO1I3liGjhOzuK1x3yHtemR/2rrReh0nko2W5OyKSsZdacCLV5T+Jp8WFNKSUka9BDoDCBIVgtccQCEN40VJxgY3EGx11doIoVZhmkiuN5mnTAUeCilBLq0cRbNVGCtpbynGTaRkqQ5vLCfXNUCmAfvGBLNVQRQL3FC5swa+ZLMNay3JTleBmdZACaEvDIlV21TAYroqmKpAaaifqRWUC+uH12JxIofAaGgH92/63JcMfKUdcoafb7Z3b6lOGFsd6DgJSzuS950QjJc1ylkFcP42JQ6/1fxMBLu//du//WPXRN///vff9Pnv/u7v8ru/+7s/g6P62zm9JOSVsA3Aw3b1r/wE3m9ksYRGL4lYiz2mjfSbvv+S3yQB9hoZbrNKzBlZvuqs00wCvuKs8rH0OPHaKp20iVUskhMGIZKVSJ0Z3sqSnS6WAPA6b21zv8sqcTbsMh/1acbBLwZ4Ga6NwiEnnRE6Qgi6cUBbBojhHihGkh54iEAxNSKlI3IWSSVNxojQwpDwZuZFgr1fPeYylOg1k8RJ0MsGpDW8Ky6D/7kNpiAJE5LN3fsVJLdkiNciwvUAsbxCbIBMwBcCccFFJhB1obBxha1b7qGfLjLY2UZf6qM93iK7z6ZiCDpBwspiwGRykfbJvaQ/Ogp2C3mpyeBbbUQ8dJQUdYqfG8H5Xgd/3sOctTGygtxKwADJq9diummNO+IYK9JwcxbLS2AHgsLFPlY/JrY0mncW6E/blF/pEpRMwoqF5sUktiDVCAlGDLRujB4m5C8OcKZt+lNZon1Zpl5qUD4f0J0OcKZzrHx+FD97HCvoIrsLGKkOdlRHS1oE60X0gkZQsth5qED1+R1Gei9RfHKSXLlDeDLCn7DwygmNQwa4bUqvgHAcGIvo7EnhtQ9Q3V7G9H1EkDD1xw2WfmuUcMSkfWSUbhhTutikWcoSd4p4OzrerMX2hyWZVY9Ms0s4CdI2EC2JHE9w92nwrojcdyT57yf4Exra7R7RrRqNe6qYpwLSQRtN+sxeO401ElE3bkHXlijWlxgP/kdOmf+EWKbxoiyZ8c9zT/glJpIWvxz8IQtHP8+qW2C7oxiV3cyUeu9GkJwQgFSrG9O4kcara+rCeW5NY9H4GA/5v0febRK++BjZY59kJC/YbMO5FVhvJryxLLljn+CRoyrptucqPcxGG7baEidQepIwlnQ9tVZp9dXX0hZU83B4Qr0U1loqSbfeuQEODH3X6fRm8awKwVN6lesvpOEIoW5Xyqp8mvGSsk1X8xpZW308WxMs7wjeWFZNzueWJRdWBXtHYbqqgJiuKTB0bFqFQQ58pZ3ZbEm2uipEbxdk1LuSM8sKvJSyijkRCLIpBW6KGShlBZUclDKQT++ep3/0fO2HiiXqDtmZngtdV31tvAh9T9AeSH5wEcZL6pyo6+p3l7OKTSpn1ce/yG6qv+r8wrmN3pn/unnebxJJyZSeYv9fI3zOFBqHjBxnwy7nw+6bwEs3CTk3ZF3uHbqXSprJ57LT/IWzwUrs8jVnnYnuFt20SaqQZ1S3ORd2kcCskbku3r15UsUiAG67/ZbHVNVt9htZ5qMBp4PWTRUBPz/Ni3sdvKiTRWZ4TO0kpJtEaELt1CVgNVpoQ5AjUjphxiQppciHMWHPR3ZvAmESrENp0EEfM9ByOt4bAxInYfBYC2ELkk5E1IpJuhFJPyFxEkRKEFz10HI6Wl5HrxqYh7LItE7umE3SiYmueNi9kLioMXrqAt1shYFnUuzHBM+2GHldMPVInrPf7uI3IjKNPlNrCxQ+PsGgMEnm+W0MXSNyY6QAczqFMWpiztr0v9lGzrtM35tjZI/B+lJEM28wG8bousBJ4I2NBCFCrH+yyfTtwKhB494STsFg9k+2qL7coX1vgcZdBaK8gdkOseoBwYiFlouxGwFB0SRyNfRBiOU06U/EFF7rUTgV40zYmFGduXPfwlyRDPYb+KUELxOouigGSNfESAuELejdkiZvdZn6/hqphYDk9Yj1j47gHSwS2D6dqo+ZtBEvTtB7X5EkE9E8nGN59hBj4gKljSbZJY/ycw7Nd2eJag5cKbJ23ySjL28h0TCv5YiyEREWO2NlenNzVPqbpJM2mp0gWwayFtI/CK5v4T2/n9rXV/Gn+8jxkNYngZ1pRq4Z5MI6ZuIztfkyue4O28ltMNpirDfPnZn/keeMf0wxY1JM5+lPfJ6RC1/GClocuvKHlG77AsaePE4gWd6B5R0FSnaFrbmUujDb5nAVUhJMVpTmYn5LsrAFcZLhwuwnuWvpDyg2L9FfPo01eg+5lErN7bjghZK1huQvTsNoQa05QIGlvieJEzHUdAgOjUs0oUS0jZ5aqaw24Oqmeu3sOnkSeUOfEic3En1LGbUeEppASolkV6wriIZC3SAEL4I4lmy14dJNoMbQE2xD/c0pUwEMUwcvhJ6nWJEzy8q9VMsrnctb9TyZhmC6AhMlpctp9tXfFEaw01POLkNXq51yVul9NlqCXYClDUFRJSeoZNV/y7kbLI1tCkZMpQe6GdzI4Vrr4prkqQsJUQKl7PDYY6U5avRunFiEgHsOCA5NvP2Th+Ed8PK3anZi/3r0/sN27a+tQD8yXNNcifq8V45eD4I7FbRIkMzoaaZuAjVpofOZzBTf9bY567d5JhUgZ4pM5fLUNOt6NcGPS9RNlUoA+J3Ojz2mO6wS89GAhWjASUuBnZ9Xym4i5XXwsnsM2SF46cjdjBdxHdgUrqxdv62W1ZGaQLNM7NDF3fC4HggDw/WPRhJLdEPHebzN4Pke1t4UIq0RbgRgaSRugAwkMkwQtkBY6oQkQ4n0E+JWTPhiHy0t0Ptpsu8uYNsCmc0h8xryKYfuik5fFqhVemiLfTrzEZnjaQqfnmTw++uEYcLUYBE/NUpfz9HNjaPnmohYnQiTTkTvz5vEgxgZSUppwciyiz1iYM5YlNYDXFNjewAXHINGKov1RsCBsstgy6D4QIqa18AaJBidCLsVUfp+B/2Mjxwz0DWJNYhAE0NNjIY7ZaH7UH6pRTBuEoxYbD6Sp/rMgPLpAe27Icy75JsR6R3J1oMVSHJEaYlMx+iaJNBsLFNgWBFxSdJ80GLscR/hpqg+1yfMm/T25entMYkeCSn+5YDq07DzwQx2pUfmio0vp3FGfPx8itxrLoMDNt4MGGNN8q9a1O8bZeT0DgKNzFmb+G6PnBYRuynqtf1k8ptkvSb2dgANG1EO0Y74FJwLrL96B9mvbBD/9hbBpMT9LZvt37uXfVfPUY2X0GRMYbCEPd+m05gkmNQQUcwjhX/Jq+J/wAtNFv0ia2Of5+Til0k1G6Se/wr1279AbTTH/jE4NCnZaMJiXa2INKHcQKMZwWxNFSR2HaWJGSsKDo5LVhqCVn+STd7H5NJ3mFn7HqnRcTKVGXXBd6HjDIFGAkt1iTZkRSxDMTopQ+IF6n6TRAGFQaBWI7urql2gIobMim1CdrjeCoYrLRWup+47Zck32ZUtQ2l40haYOfW1aFgzoFZA6vfufi0Zrou0obvIGrI4MCyTFKposusK9o1KZmoCgbh+rHGy63oS5NNKbBzGwwwdB7qOWsU1huzMLsNUyYKmCWUz7ysGal69ihFCgchKTrmwxosKCN58ThdCCXxv2wsrDUHXhQPjgsOTgr6rVm7twY3V1sCDlxcks1X5tk0bvnneAS9/i+YHfgMJHDBybwIXf9WZ0dNkhcFARixGAw6YOQZJxJlAgZD77MqP3EYXgg+mRrGaHRbjhKXRAoGVcI+M6SYRttA48GOYoF3mxet1SeIYTf9Rl9PIMENlIGMMqV54P6+1kSdVeq4A/GGyV06ol1IniXDkTTZpoHrm8o33S1kdMibZMCGWLuGq/ybwYu61CRY8wkaEXtSRntrnp27JIDIa4aJP1IqQnkSkNKwxC2PSpPDLVcwpm3A9IJz38Od9/I5AG/i4L/cJrrrEGyG1fzpD8c4c0/sytL4/wVpUorWa4bB/noGbsHYlINvqUXu4SvtCF1NPsPw2l5399EsH2VNeQBu1sTMJshPhPN8j3ggpZqCaFWRtgVuPKKcS/LJOu5+wYBkkSEqhz9GxhCkRkndDrFcjMmciBvszJKbAm7DQmzGpRoBvgpwwCHMGZjcid8VBGhr5C33aJ/JEOZ3y812aDxRwpjMsfSJD7jJIQxKMavR+ySLzgxRGvYc965Dzc4jOACcDUR40EnQvIMroeKMWV/7hDKOvGuR3YgrbCUE1xJmw6e830N43YPyPd0h9V6f+cIFk2qD2bB9DD9EyLsGUTfFZn+CTBlEFqAyYenLA1gMFihccdExqbwh69wbYyRraUoqdqb2IkZiw0kfupEjtRIhsm+BEwqR3iuDVCfyvlog/s0M05hF/uMnF79zH9EKJce0Slu9hxl0KjQTPy5Ap7JDLbBFlujxe+yfkSxlyxRLrt3yBE/NfohzukD7/FV7qfIHQyJBLCe45IPiVhyUL24KnLqgclK4jubwOhycFY0VJoy+GYlKBGD7rF/O3E6ZXKdbP4Xzva7y+5zfpypzqIho+j3ef7zf3F/khdBIlZk1Q/5U3AZVdca05JAYSlTJAGEErGgKZIbiIEgUsolgxSPrQCbS78tKHwMc0FBDZ7VjaBQQZS62fEinwA4kTCgxNkkgFjpRLSVC1FKMzGAp2z67AtTocm4bjMyqxt5wF/SbdZiIlXUfVLTT7ksYw+XenJ+m6u+4ppbXJpyXTVUEloxikXbdSGDNcFanbgtL8jBUF4yW1+soNA+o0ITg2rfHClYQLa5JDEyo/p5ABRtTPSCn55mvqWM6uSu7a9w54eWd+QWYlcliIBmgorct/zWhCcMTM8XLQ5kLU44CZ43TQIkYyqaeY0d8aGAkhOLja4q5rTRb3jtCUId/wNhnTUtxqFTF/jOvJyuUQuo6MY/xej/SQibl5UkLDFJqqBRhe6X9eVuldvUtK6AyGrqNdp9VO7BMMQVWCOjsXryxfv63IGejVFHlDJ/Q8wmWPmwkkoQnCVR+trKPZGtIUpB8poFdN+n/RQiKJdzuJaiky7y1R+odj2CMmEkkWAbEkCiWD1YTk9Q7O9zq4z/WxbYH2x014ekAymeX+Yos/HZ1iPX+Y29avYacNti66OBsupYKgcixNYmsUX9liefYovqmjzU4wkttBvz9H/6kO/rfbEEiMtKCQUoLPjCWI3QTDS+iULAwBEyTMjgZUgpicLXCjhLAekMmBjCVb76sQ5XRSyx4ylhTPO7QPZnCmbaytkMK8Q6oRoicw8lKboGziS43gLwJ2vlhEmzVo3abjDvLkSgGGJmm9u0T6tTLV5jw6bfTthEwcEkwaOLMmQVEnzKaIsxZkNFb226RPFSmdbZC95gA+Xk2ncbLCIK4y+p+6pC9EeEcSWvdlmXw8QApBod0mERrhSwk77y7SP2FiLzmMPN2ie9ImHbZxwxGslzz8OxOK1lW8iyk68iijU2cxJ/tY4yPkBlPI/BVMM2A81yR71mLxFY3ObRHxoTbZKxFNZxJtR1KsLWO5HqIfYTkesdTIxQ2Ou99hdHueH4z9A1YnHmB2osS5fV9g/PU/QLTq7Ol+hcsHP4+RzXBxDRa3oZKTfOCkZKuj8cqiHF6kExbrgiOTCWlTRe5f3VKMiRcKzhsf4v54m7xbZ27xz3ll+vPkU5q6+A5flqbO9c/9GEQMCBBD5C9QwCJrq7VJJqVAxy6bISX4kVonBeGws1QooFIe6kh2QYylqxVTpQCltArQ2wU3QTS0L8fQDxS4aAxfb7ousXTF1sSxyr1R2hAFpPxQ4AUqeK81uKE7qXclp+dhupJQzSv3Vq2gNDWjBaVnKWWVWBkUeOh5Sr9zZVMl/6611H1dWJWkLLUWKmXU+dcwJJau4GLPlfQ8QduRbHUUgNI1KGQEU8Owv/GSElYPPFjagX0/1HkshODWPfDkWcnldcnRKZW/83aed8DL34JRNQDq5XhzDcB/zRw187wctJkPB7StkNeHq5977cpPXEd11tYouSGHRJpNNNYiD0eL+Y3cnh97GyEEqUIRt9XEa7ffErwIISgIg4YMCIZv135ezItzUzXAdqx6tfJCaXnWY48IydD0iyYl6dVhSKMBwtQQGYtUOqHXDYm23hxcIaVE6BrZdxfRx0y6f9ZEE9D/Tpu4HxE3Q5JAIgwNkdaInQTnsTbcm8U+nEK6kqgdksSQGTcxZ6tUqzZRUxJECc41j2g9YPDqgNR0l7kPVVjRRlgd28Px/Ca5aobVUw1a6y7afpvosovZTzj5vqu8kTpA+PG7mLVO09BimE7jpT2syMUPEzQBeUvQC1Qex2YnYcbz8PImWIJ2T5KUderNGMOWuD1JvSUYOwS9fRmiskn3SI7cpT6jr/YYeanDerVGMJuiXdQRQUKqHpFZ87BbIekkppa4JP+/OsmtFq378khT0LoGtemQQtKj0guxV316dxgEWYPis5KMHyKloH3CIiqaxIGO3tcwAw8vH1KngLUlMOweFgm21iSqZGncNkvlQoNozCeowvKDY4w+tYMciUh1XcZ/sEM4IegdytJ6NMXEVzoUziQ4h0wsb5OeP0XujQ6DWxMmsmdpX7qdvnEL+YmXkUad0BLk8vfSt95A6CGWhL2vjHN5ZAVvPEbc32JkzSEIRxg4AWGpiZny0RoaUZAijiWWdKgZC7x79d8y33iF+bX7OVO9m1L1i9y3/iVK4Ta3LP8Rr/M5toy0WpXo8OqiwDISLEMBhq220q+cmr8hjB1qetVaR7N4dfxTPLT2HxjxljnYeIqF0UeVbmQYRucPn9pRrMLjBEPxrAa2BWlDARBNU19PEshloJwRjBaVy8gyBIYuCCLJRluy3lT6mTBSq5pKTpCzJc2BoOdKttsw8ASHJ+DEjGIgNKGYk74PfVfSGghafcWA7K6hHF/ZmTs32ZVNXbU6p23IZ1R9gKEr7cx6W62ttjqQMiWjxZhSVoXRZWwYL2nM1GC2qlZJQii3USEt2D/MZ+25kteXEs6tqGPpuSqlOGdLqnmIhu/1DE1QTEkGgQInfU89BlImnBo+prsrNS+AsysJt8yCZd54s6iYLQlCsTtnVyT3HngHvLwzP+e5HPXZjL23rAH4686oZlPRLJpJwDe8DUKZMKrb7NUzP/F23dVV2mmTaq5I0cxxJugQkFAVPyrUvXlSxSF4+Qm6l6Jm0kgCguvMy88XvKRvYl4KmoGUktVoQIJElxoJEjNOSDWGLipDgC4wsIkTh9iJSLrxDY7dEmi6QJ8yMcYt4nZEvB0Q+qgGaF8iY6kqEo6kSd2RQ0aScNmjs+KhpTWMMRNzfxp7n03GEJiRZMKyGfnCFItrA5Yv9Rlc8/DmPZKcoPaDcwTmJKuFCvuuXGPuRAkzL1hZ6NNr+ggE3TMDsmuvkPriJMF0gV7mAEd3Flm41CQ9aVDAolcPKKQEuoA4At2Q7KlpaAKKYcR6otOetKlLgX4ohXnVoZAW9DMmjTciKt/vEn6kTKodka6HJCmdKK1jNgPCgoE3aWP0IrzJFN2jWewdn9ROSHrN58DpNp1TNrlWQPekSzdtkj7vY01r+HtBvG7gySp6ycP5QJbyqzU8OSC92WewL0GkEoxGhF2PwDaQJ2IGZ6vEgzyZ3jZJIcRKekT7Es5576N2ZhXjwVVEMWTjgQNEG0WqM68yemmTmT/dYuHvTxGUTLoPm4z/eQMjytA7nKUULDPoTVO40KF/PKYsX2fw0l2s33ob43OnQN+m6Vv4zVsZKZ5lZa9Fqp6Dx4ronz9HnIrx7utS/rMWPaYwvIig5KKnQ8yNBKMrGEzmMOOAQnqdA4MXsJMBc93TXC3cywsjn+Hhxn8i52xybPGPeG32cwxkCi+AIFY5Kbs6jpsTR2IUuyG4sX5JEnCsKq+PfIQ7Nr/KXPMFtvQpNrOHgDffXheKGQGuMy7IYfu1ShRQ66EY2n3oOZLtrqCUgYmyZKKsVj17aiqZtjWA1WbCVlsMs1IUW1LKCBp9BQpOL8BrSzBWlExVYbqsMVkWHJ9WIXmg0nodXwlcu66k3pEsN2C9qViWMJa0h31QUg41O6Zy7pyckex0BS1H5dI0+0qsa5uQJIKNVsJaE14xlPh2dli7cHMZZD4teOiIzt37FRtzcV3i+gqUBbHKm6nkFXgJYjEMv9tNL1bFmD1XgRkvBCGVtbrvKbaqmFE1DbmUYrd0TSi3F3BlA45NyZtcTm+/eQe8vM1HSnk9kO6uH1MD8NcZIQRHzTxPeXWe9hocNnPcZ/1k1sXv93HbLTpTRex8gUESsZ0o2/DlqM+JodD2rWZXtOt1fzx4KQz/tl2X0c+rHuA686Lp1z/OawYDGVNPFL+dIJFILC9A91TGC6ZASxnkzBT+oEV41X3T/QpbndlTRzIkXkLnyzuEqwEylAhLqHTdrIG5z0JL61jTFnrNAE0QXHKJNgPcV/pYMw7WnXni+/NkMjaDKIuccIjvLrN3PoP7Jw26uk4jiREX62SrNmz1uNiwcPttDn1mjGYV5IZBvOAT7EQ4qz4TX/kem3//URZqcxxqX2Nq1KJQ0/ETnWhZkHUihFTv8Ewh0E11IWoNJHESEkcgjmdVeumJPI3NACOjkR3E+F9tMe5FeCcySF3glXUGcxm0GFJbPt1KjqBmkZl3iUoGrTtK6G5MetvHmU4x8dgO1asuvdDFHbNpjqTpujrBmE14j8bI1xqUb0uQ4zrO0ZjZhTE6iWTL7NOfkcRlgzACuxEiywnBgyEbzduIdY0xcYZcaRtDJpT2vcZflP8NR9uvse/AnyLyEV0xyWXrbvY88jgHXjtD7btdNj9RoX8gQ/+4Q/X5NmYU0t+XoeLNM9iZIH/Bo380YU/3FZZfvZeGfZza1BnS+Q18L826c5jRyav4t/r0XzyA//Iscw9/l/5+SN/RYeJ75+nq4wjLIq5YGKmEzIYks+PSmkljOwP6tYCxnatsy30ca30fv5vlXPYIs4OzpAYb7HX/iOdGP4snU6gs558ceC9R7+7D+IY1ead4hLXwHmZbL3Fn8zGeSo3gmmX0mzbEhj4MVkup+xh4XE+jLdo3yhotY7hecQV+pGzIWx04uwL5lKSYURdiXRPD4D3FEu0Ke9OmZKYq0DSNpbpqzd5oSZp9QauXML8lEEIBod1updpwxQMChr2/qsxStVIvbEuWd1TmjBfe0KukLEEhJZmtMbQuK/eQlIJ8WmKZsNZUbdthpAS5ry9JCmmYqQlmqjfqGGxTBc4dnVL6lgtrKvp/N/vl6BTcPyd+xOm0W8MQRuAE6hifv5JweUMJmGdruz8HYayAVspQ4MY2JWeWJQ8cfge8vDM/p9lMfHaSAB3B7Vbpp3rfR8w8fzRYpZkE5IT+YwW3u9NdVWEM3mgFXddZDLqqh4iE/61/jd8pHGTWeGvmJlVUTiSv/RPAy5C92QUt/vUa5p/tXGde0K+7jkrCpJOEdJIQTQhiKREIcq0+YhhoJ2wNUU1hCQ3X9wmu+aBzvU1apDTQQUaS1r/dJFwNIJboVQNhCmQiwRDEnQQtA8GCBwugpTT0SQvqAea0RU7TcM45DM45DMopUseK7B01mNuBdMam8IkJVhounWUPfRCzea3H+kAnssHfdlh7vs7Ur46w9b0Wzb5gUAc5CMg0W6QeP4NTn+MNY5YPHPMRQYTf1JEdHyE1ttoxQQSNQULbU3t4P5I4LuheSDHqEd2WJzIEcjaF3AkQzQCRBKS+GxFXDIJJm/7+LKGpkZQMoqyu9A6hxJu2iSwdqQvivEE/q+FXTQw3ZuzJJqVFB93Q0IROnKQIcwkTzoBSpUfqzx06j5ag5tJxG+SfiYlnbbR3mfQP2CRlQRJI7IZLrnWVAxfOYa7oeFWdzgNpZC6hpDX47fX30VndQ2e5AHd1yFeaaG/ssHj8Xpr3TnLslVPoSxHRQcHW+6vYmyHll3tggDOdonx1EVZTpEjTPRZzYuElVl+9l25mlkx5iUJ1hXYyRyOapTK7Ri5aRL50mCsnH2XP+HPU3wOplYjqxWX8jSyDJE+rlqVbBmvDJr3YpD+XZsS5xsbIcax+gC8y2PGAfd2XkYAdNshGCXeFf8hzY58l1NNKMAsIbTeFVrEw0TAqP06GQlvU14RQUfbXJh6hFq1R8dd4T+9rXD3yqxyfM7nnALyyKLi8keCHMFVR4GGnJ9lsKdal56iVzWhBkjIFpazg1jnVx9QaqA6lgadYodZgN5tGMQqFNORTAlNXoXFLA4GuKzdPJSs5OqXhDTNtOi6EiSRnK1t2owdnlpUraawkmKoo/UjGViuerC04PqNEuaCYjtWm5PKGZGF7V4eijsc2BaWMxAtVxkurrwBFraAen0EgyAgwNEnXFZxbkZxbUb1OhyYEB8ZVJo6uqVC/faMam224sKb6os6vqoTiBw7fCACEG83YlqHWWyMF2FMTfPVUQhBJbp/T8ELYbCvQZegwvwWjecl6S6BrkuMz8k0t5G+neQe8vM3nTKAu9ofMHOkf00X015200OjLCImkptv/Wet1Z3UFCXhjFbqxhyNjippJGp2N2OOPnTXusyvcb1Wu2693J3U9qO4nrY3ezLxEUqpeof+C+oOf5jjDFmkDcT2krqCZtKXKeBEMxbpAaXHj+ttZzRZohRRe2CMJhim1moBomPeQ1ZASel9rkgxi0FRInQykch2ZGpqtIzRB7sNlpXlZC4gaEcEll3A5ZHQ2BVmdREqsZkx2y8W65NIrmRRO5CjsyxAbAvNohtKIQfRCl5ERjWQiR2vRIYpt2gsO2h9sc9sHRxhYHjvlFNfOtHFFTGpzjU5b40Ipz/GqYLYgyHR8ZAJJXqdg6szXXXb6yh7qhEMXyVC82avHZF7sEt2ex9yXQlZM2ppA23JZ7SbsXfRoFnSStI7VCWmMpYgqJul+RGILgpKJTGmIOAFPWakTS2PzPRViU1A91SW97mI4EamBx54lyWA2RXjAJGnr5F/s0H24SDMnoOxRPt/F6thsfLLIYH8Gf9xGJCBN9U660h9QWJfoL0c0788y2G9htXtM1M8wddagJXL0T9oUDtaZ+sFFNu6fYuPEDHPPXKY5YRLUDFY+O8ae39+m9JJL8qBGb3+K/BWP/KqH6Q/o7vOYfeNxFrv34H+iip1tUa1tkXgjGGIUgx1scZnMY/vp/dZe4tFNln/VwvzXCfmNHuaGhxU4NMfG8CtpoqZBdq2OM5Nion8WXcT0xd1cyx1n1L1GJuoSaSlmBmdJRx0elF/hufHPEemZYcO0WjOYhipvLOcVW9J1YbujLthBOGRhXDB1nVdGP8mjG/+esWSTeOO7XMt+BEMXvOeEuhhf3pD0fbU+mSwry/FSXYlWvQBWGsqZo+uK7ZgoCw6OCz5ym7qorzZVzUDPBYkkilQtgMpqEUyUJIWMAiXNvmS7A3ItUasuQ4HoMFbRkXMjCpTVexLXUSujhU11ca8VNKYqMF1RAXK757yUpUDGgXG1ullpwNKOZLUh6XvqWMIYNE0BhURCLiVp9VXNQCGt1kBTVUgZsNVVjM3LC5LXlyT7xlTXVCmrwNNEGSbKgpWG5IUrCe0BfPPVhFv3CI7PiB8bPppNCfaOCha21DrsXUfVubHvSZ69JHGDhLWmAnztgWKD3nX0HfDyzvyMJ5AJF6M+wF+pBuC/dN4IuhQ1E1fGDIYX7J80ndU1PFODUoGl2MFE4w6rRFGYXIh6dJKQF/0mK5HDR9Ljbwqsu26X/jFBdaAAAnDdiixRj8HPHLzsljIKQUSCjiCr6WxG3vXOo3jYNJ1bWFY7fg2wdQh0Iq1HvB0i+7H6+nC0nEbSiZF+AqZAWoKkE6NlNNA0tKKOfTRN5p48hb9TAR2ELojqIZ1vdhm3TWIvJmvpjFZtKMU4Vzx6rZC2lDRebJJ6rcPEsTzdR7NkzYBKKqSb0lh+Y0CzXCGTtsmtr+NfHtCvRxx+oMzElIUd59lYHBAQw8Y2zabLd6/BR+1tRuIIQwNzMsPEwRKpWzy+88ebbG9E15NaEw0Y4rRuO0F7pksOSXwwQwTUMzkqliR6tsPoIGL73WWcvRny2y7ahqds1H3lndUiidkIqD3bwh9L0TuSJSgZtO4uktga499rkVl10CIbv2aSW3KJsgbe0QypwYDojE/vRI71wyXiLYc5J6L0kos/miKomLhTaZINnzitkxgapZccZNvE75fxpxPcR9JEcZfMKcieC4lNA+9QgnGbyy3fWqU3XaA/Uqb8jQ47f1cjqJqsfLbG1FebFJ8fEL6/TPdwmvwlD7MZUAw6ONMeJ1/7Js1kPzufNRG5Lqado++axJNFMjhUU0v0vzlC+1M92pM65/4veQ7/fxYobTbINTrocUSnPILvF5DuOJn6Nu6ITa1/Ea0foMc+p0c+gS4jDneeRSKY7Z+hGG5jJB5PT/46AVk6rgIraUtpW7a7N4LcUpayG7ua0lnsOoOaWoFTtY9z3+Yfkd94jYV4mu9sn+T5yyrczY8UW9PswT0HlAvn0LhgvSV5bUnSdpTAN4wUMNpoSS6sSZ6/AnNVwdyYYKQgEUL1I221BQP/zWm61lCTMpJXQKvRvyEaThL1hkIXcGpeUsnBdEWxJk6ggIQbqOC63dThrA2TFbXima1BMa0Sci3jRu3BzUBmo6X0L9e2VffRwhZMVRQT1HFgsw2LdanYpVnBeFmFBbYHcHlduYDGywrETFeV62imKhjJa7xwVbKyI3ntmiq9fPCQchu91RybEixsqXVX15UU0mJY5ggDX5Vddgew0pCk7YQTM6qP6u0274CXt/FcDnuEMqGkmUz/GPvyX3dCmXA6aFHTLAwE9SSgGQc/1smUxDHdjXXaaQuyWZrJgIpm8V57hK6MCEioaRY9GbEee/z+YJn3p0Y5YuZJpMTYXRt1O8px8xbvLIrDtdFAxpgIQiSeTPjJEuKf/uyCF6QkkqqUMS10mnGARzLMsZBoaKQvD23SGiRZm36io41oyFcjkkEy9I2qEVmdpBmTtCMoa4iBROQ0zBkbY85CT+uItEb63jzSUCc3GUm0CCaPFLjnmEY/K/A7EfFOhDOI6fZCVkSEjA00B+yrDhurLvopnYmcRmU2w/H3VpnIu/zxVZNtu0A45aBfadBshji9gJkDWYyshmHpGG7C0fGYup2Q1AfMX/FJ8gmFokGmksbOmpQO23z4H2W59IMGrz6+Qxgqe6sc2lyFAC2RmKd7pJ2YwW05gm6C04vpJpL4+22sE1ncSRsKBsVTXYQm8UsmhfN93JkUmBrBWIrckoc0NQZ7U8SWYLAvzWpOZ+ypFukVlzCvE2d0tDAhWw+I91mMbURoLZ9BxULcZdL4RoBYj8gkDuEnikRFi3jcwNrxiffpCAQjT3WZe9xhJTtK75YU65+sUExHJKf3kFn1SYl1/LmEzn0G1ed75BsukWFT+l5E+xEDf8Jm40Mlaj8wqD3ZZuvDZTonMhQuOGiOYsjCPQYTr53HNiqsfbTIwFzD6c+h2RCMm7iDGD3TgKeqxI9sMdij8dpv3cGx//08lc1tMu0eIGlngIFBv18jlW7gFW3KXMNoh9iJw8tjn+bp2b/P0dbT6LrGVPcNbm18m1KwxV/M/g/4Zp4Y8F1ou1x3JBm6AjRZS+lUEqlWOR1X5bgsir1Y6Yc42vkBRza/zbYYY9UaHTpyhpkvseTcqhKTFlJgmmolFcVqvRPFwxRdFABZb8G5FUnaUvH+kyXJWBEOjGvXs1D6ntKHgHIOCQGFDOwdVbBmsy3pOAqc9D2l2Wn2FWjIp9T95oZrl+sliZ66zXpLWaJV87RyDI2X1MV+VwybSwlOTAvumIOdnmC6LHnqomTgScIE8sPVTjwM5dtoSbY7ElMX7BtTPUpJwvWqgc2WJJuCw8OVUsoSvPsoLNYFp64m7HQl33hVcsdeBXR++FxZzqk12FpTAcBdV1HKFLznuIYXJFz0wA0lKzvw2rWE95z46bL2P4t5B7y8jefM0L588i3KDn8a9+3ImKpuccDIsRQ7nI96PKS/dYZMf2uLJAzpTxTYSAkIlTvoTrvMSuRyNuziyYRfyczwTW+L9djjMXeTpcjBkzGL+oB9tkHWjwgGA+xc7kd+R0poGEIQDaPAAbyfg+5lF7wkSBIkBoKU0FmOHGKphLoJ6sWVvrqimBddg5QGekLkSUJXxy0W0DwXOwgU0xInJIMYkRLIdoJeM8g8VEAv6kQ7EeasrVwPYybEkiSS4CfkQ8EdCAxbR9hgTFh09lsYXQt9v0nxmR6DEOJWDEIjKepEzZBrzzksWj2Kf9klKeVJfX6aztmIdrvG2EQPrRHRXwu4lhXEFQMrCklWQsrViL3HMsR1h37OZDWIKXgCHt9Enyqw7z2jlMsGd39whD235HnyyxvsrHpoQr0DT6XUxVAiCM45iE5E+oECOILFjEVqNkPt5T72dAqpCeKcTmbNJxECmQjKp7u07yjQOZZD66siR2/UIDEt9F6MN26z8slRas91yC04+GWLOKOjBzEiAn/GRrezpIoC307gAUnytIP3RJdivY/7SxWcWoZeNYOMNQblDE4uxcS3mkx8q44uS/ROZhh8QKOmzVN6WuBsjaHnIqhFdB+sUHijjhAhpTcMgj0a7h5BVDFxJw2ipEL5qT7N9+boHMuRv+xiODEGIe6YoHhqm9Dy2X6kQiG4QrxewRmz6c5pWG6EcU1DXrFJDrvEBySv/vrD3P4fn6Ow1SDVH1Bmk82RveS3BsjtDGbKwa2lyclNDrRCcgw4LT/Fa6X3sZg+zt36n3Ki9ST7uqf51Su/w9f2/mN20nPXn+/JMG/FjxSTEQ0Fu+MVODSpgMv8tmIWrtUeZCRYZdRb5L7G13hm+teJNZtEqoA5JR5VnUXNngKzu8m4giFDktywVu8CGz9UwKPRhYUUTFUTpstKR1NMq96hRl9F4jcH6jZbbXX8KVNQHlHrHinV/dR7XGdvEwnFtAITQoN9I4JcWhBEClBstNQqyPGV1mWtqdiUQloFwaUtrneZ6ZoCNHfvU3ofL5JEiTpOTaj72eyov10iubAGl9YlhYxgtqqEzX1PVQi8sgivL0tu2yM4OiXYNyoYK2g8d0UBnFNXJasNuP8QP5LZcmxagZf5TcmtszcSdUtZwcNHNdwg4dyqyqt5ZVG1jY8U3l7syzvg5W06jdhnPfbQgGM/5ZVRJBNO+S0A7rEqWEJjyXW4GPZ48Mc4jjq7Yt3JMVZiF1NoHDZyfHmwwnEzjznUz7gkfDYzzXNBk5f8JqeCFueCLrN6mvJYhezyNl6n85bgRQhBUZg0ZHA91+3nYZfeBS+7eTOW0DARLMfO0GWkTmamhNT6bsaLCmOwRIC24BMFGr25CbS+i3VxCS0lkK0EGSnHgpbXsQ5mSN2ZI7jgomV1Yich+0AeKdRjocWS6Y5gytfJaZKdLAxs2CyDHkN4xsHLaxTelce9FOH2PfyshelH6AY47YgwjmlfC0jMLrmqh//JR4nmbfJuE/NaHWkJwnqIlddwFzzMVkizDWwOmL21QrZgsNONcZOY1EiGwDZ447FN7rgzR35/gcp0hg/9wz288fgOV1/rMmiHeIGyx+bshL4nSBYCpqI28T152p7GIBQEWZO5F7toRzO40ymyCy6pdkiYM/CqFqlVn97RLPX31Ci90sXciPDGUoR5Axkn+LNZvFGLkWfblN7o4U7YOJM26TUf2wvJrGyz4dUI7i4SjoDjD/BfcRmZ9yl/tUX8CTCKSl8TYVC/pcDmVJmRJ9tkzkWksxHenE7jUQvNMNj7/CbJ1QH9wCBJ21h3HqaVh/amh3hmgKi6yLzEn7PJX2uTWDr55wJaj2TpHMmRn3ewOiFJGpJCTPH1HqQSdu4ogbCoXG0T1nTcgxmSuIB5IY+suoS1gHivwzOf+QAPfPVxilsC03cZMZbZqh2h0tzBXpVYU126U2VSSYfx1jnuC3qYtQ6v1P4Oj8/8d2zkjvG+tX/LSLjOr175v/Hk5P+JM5X3k/yQezFKhpH3Lmx0YKIMd87BFx/WiELJE+fgivExiov/npTX4Nb6N3ll9BNEicDUYayo8lbCYRVAxh4Kg4dpumkbggDcSJWKXv+9Q1eNHyk2aLMDZwzFnGTtoYA3oy7iI3lJEEHPE3QcJazdnXDI7Jjajb+l7ymQkk9LimkFrFKGOpa5EcH9h7Th1yXX6mrVtVtn0Oip485YEtsUZG1Jx1HupUJG0m0onZClSwXWIwV25kYkbqjYHz9W1QBdZwi0sgk5W61YteGaa72l8cAhxf687wRc2oBXFtWq6hsvS+49oDE3euO8PFaEal7Q6CkL9m1zN743UxXce1DQ95V4eLkh+cHFhE/f8/ZiX94BL2/T2e0w2mtkr6e7/rTmfNijLyOywuC4mSdBFTZ2kpD12HvL6oFd8LIyWaSfRFQ1S9kiG2s8m8mRTuUJSbga9plIpXjIrrJHT/NvuvOsxC4L0YDMVInp5W28dpvi1NRbHltBM2gkgXLziJ89eAllcr2zaDf+PyN0IiRbsa9cGUPTaToWWG1VSil0gYwlthkgrnXRt0Li8REKF7cBEDmdeCAhlCRxgp7VKH6xiveqQ9yOSN2aJaqHWHMphKlRdGDPpsRGx/QStjOwmROEtjIvDdY9dp5sYx7JECKJyhbhqIVwQoxBQrzsYZVNEmHjbSXYs5Jky2P866dpjk2xU5pgyt8iMUHTdSxfMlGxcfsh4SCmnwgWL/fJv/cQZhwjFjdwQknYD5nZm6G+HdHvNbHnCqSzOnd8eIRs1WT1Qp/Wpo/bCen4AsuQJInAr0cUn+vi3FMkCBIGSwHLcxaTfkwykyYoGVSe7WBlY5zpFF5Gx+hHeKM27TsKFM70kG2Bf0QBDhJJWDXZ/EANb8SifLqLuROpELwFBzuRzM5v0YkG7NxZJfueAtIaobnRo9fvUX3WQzwokJGJZQfYMcSGoH1fhtZVSeXMAExBMGWw+R6LQnGGkR+sY2zVcYpd/PoraA0L58EZ6tMHiOdtKifPICc1Bgc8iuGA7LKL+b2A7fdU6OzPkl0KSDdd4qyG0QrJnXdIbOjuiwgbZcxmjJG0Cff0iYMC4ekixoM72LkO/r4cz3/0g9z3nScZ3VonHQWMZRZpjt+KtrOEvdag6m1RnxsnHXmMDZZ4T/MrHEht8sbcF1jLv5c/Lhzgo1f+X1T8Nd63/r8x413i9OgnqafmlP3/pvyXRKpVzdVNmN8E81RCMasamkdHs1yKP8nJhS8x0rnApDnDcvFOnET1GKVNtdqxDJUBs7uuSXZfysM9sDsUe++WNHqB+p1eMEzWjdTaqudB2lWroFxKuXgKacHcCNTymuoOGkg2O+D6qkRyNxQuZQ71OOGN9ZimKTFrEClWxDRU+NxIQTA3Cg8eFkSJCrrb7KjjUI+JVEF7w8yZfaMS09BYayRsdwUHx5XNO4yV66jryeuW8l39kBeqjBnLgFpepf8uNwQrjYSrm4L33yLYP6ZxZFKJlJ+9pCL/n7mUYBoaUxUFUoQQHJ+Gpy9ILm0oV9Fu0SPA8WlxPaxvsQ4vXE64e79gpvr2KW18B7y8DSeW8nq7848rO/yvue8XA5Ubc7ddui6GPWBkuRD2uBj13hK8dFdXCTTBUsHClwllzSS3cpUDzz9BohusTc+RHDjB/KjFw6gAggk9hS00HBnjypjXx3M8xE92HO2KdhMSdLSfOXjZZV00BO7QAp3VDLrDNulESJJdB5IboAeRYkpMDWLQE49oEEM3ZGL17PVSOGEKEndY+pIy0cctnKe7xI0IvarKGlPHVb9RJoTDGxI9EgSGZJOI9ohJ1YetNPhhQueVPvqhNElWQzoJuiWRk3nSdxSoXaoTLLlsSR1nKyLJmUz8oxpyJ8Re7jNoDhB9sMaKyIxDuBUSbDlUygLXhm4CWz1J+5qD8/w60afuQsvnyT91Dl3A8mWH6qhFKWtRP9NlMIiZPpHn4F1F7JTG6uUBTjciDmMSL6YmJIQJg26C8VSb8i1Z2lmT3lLA6phF5ZhOftnH9GP0nQB3yiapWcQ6WO2QOK3TO5ZT7dqdGBFL9CAhfXFAOG7TP5QhyujUXuqQvzKgdzSDXNJAQmHNwRCSxi0l4tvS1AaTJPTQ15fRXhjg350lyhjYltIfFSIfOxsQb+lo5yOEDVHV4eWDW7x6+ZeY3oBb5NcoaysYpYCDT5ynetc6mzN72OreSbl2gehBgyTRKMse2csDxr4bsf1IFWfOJNE1MjsucUqgOzG5Ky5JSsOrJESrWbXm0CX6ngbpqy3Ci2m0W1zMyjb6HXla1oPY3z9NcWEeqzcgV7jAxdJ72ds6Rb6zycTVNQZ7auR2TKL+Dqn1b6P3d+hP/jpb6T380ZH/Nx+89q/ZN3iNk92nKSUtrhTv543yewk1Ezlc6dw8u/kv9S7sDMW9OXua5an3MLf2OCeaT9BLj9Mwp4ZZLko/E8UKoDR68K4jcHhSo+fBVkcSxYI4kWy0FYioZGG6CgfGBBfW4fxqwnpLCWGlVABCYygKHig79fwWZFOSfArSlspVmSyrBY8XqiPvuYKN1lAwHKn7M3VlkY4Tje2OpOPI6z1D80MiNWur+P+xkhL9esO1VpKocLuBK9E0wYExMHUNXSgH1dEpjZ6rGri7jmSlqULxeq7qVfICCIeVB/1hHk4uJek5uyJmODSR8NARwZ6axgdvhRevCuY3FYD56O3a9c6jmZpaQ/VcmN+UHJl6s836gUMa7QFsdxO2u/CnL8b8ow+Bpr09AMw74OVtOPNRH1fGZIXBvv9M9srNM0giteL4Ce6ci1GPbhKRETq3mDdC5Y6aeS6EPS6FfR6xR95kdd4Np7s6nqdv6egyRhMa49euMJEIXAEsXUFfOEt2ZIadkx+guv9WLsUOi5FDIGN8GXM1b7BWTDHdaf/Y49sV7YbDCP6fdb/RzdUA/aGzKCt02klET+4G1Clwk9tsIuJhIIYBIkzAVSbqKBRo7g2zkXQUCJMGxJaNN11BO9tEy+ukTlqEWwH5+ypopmD/GtixoJOBlkxwqhaTfeiZEteAyJXoEuxHSsi0QdwJSDdigodmSA9cKoGDd2/CyhsJqYFL7RYD88UBufcWMf9OlejlFM1nE5r7xxlLVolWA/RtnzgWzJQ11ko2LRHS24yIztTpHGrTvv0AH9u/irHZpdGK2N70cQPJyIQFlsbZ7zWojVuM7c8gpWB7ycHpReRnM+SiiLDp0+7FBP0YXh5QOJ6lHRkMTg0QhsC8NUPz9jy6E9M9kCG1HZBxYiQSd9wmyhukugGlp7u07y5BWoOsTu2ZFu5cmv7+NFvvqVC8MCB/2aV7JEOc0jCcPoX5PqYX0TqewzzcIvd9iLKCcNmlY0n6x7M4aZ2UHxBXdYL9JtnYo/xEn7afo3d/inTZ4faH/5Tv/d6D/GXvd3jXu1/n3uaTjMoVKq+2sfo+eX+HhjWGXyiy8aBJULQolnpUTnWY/O42G+8bwd2ro0mLVMPHaCcMIpvMFZ/kmEAvQLKRQmZi4rxOPCfJrXRgQ+DNRgSpDEt799JbPsJYoDO5coV0r8M++2nO1j7O3s5pxoLzZK7V6RU8Ar8GcY9p/0UecVqcnv0Vlgu38/iB/577V36fPd3XmOq+ARJKzjKvjH6cjjWOkDdqAn441E6igIEXQlu7Czu7yrR7kbu3v8YLe36TvswQJZDRoJxRTIzjw3fPwNWthIePaHzkNkFzoHF1U6JrKrJ/tSFZawmubEoeOiJ430mdyxvwxnLC4pZiK4JYsSeVNCDU/bb7ClAZuipCtIwbbIcxdMIhoJRVDquOC6sNtU564BD8/fcKvEhnsyVZ2JIsNVSmjBfCWlOy1oRsSnUSFdLqMek4aq1lDBkcISRX6sr2bOoJ9x/SSA81KFKqFdRKQ3JmRWlUBr68LkRODQ2Zun5DaPzCFeU6Gi8ljBUFsyPqcfdDxbR88FbF8GhCaWVeuio5vyY5NCnfZLHWNcF7T2hsdSRPX5CcWYbnr0gePPxTPWX+jc074OVtOLtC3eNm/sf6/W8eKSWngzbP+DvkNIPPpKd+rGvoZb9NJwl5IFV5E8jZo2fICJUouxQ7bwJNu+F08wfG8ZFkhE4+DCkuX2F0YR798J3UR8bQ60u49VW2nvkz7Nee4rFbjtMspNCGfbWODk8dqFEI+/y4189uym4oJSnxs0/Zda6zLTqNWPkwc8KgmfgMhsBGDp1G1uIqIpFIXQNNoOUkyU4ICUQBWLtnfgEySBCmgJQgzpj4jkk2r2OMm2hZnXApJN6MGMtY1AaC0IRGBnrdmLEBSKnRSksiIRCWIPOZMTwEWS0mY1ogYgKzixXEZH2T9IPjPHA0xYjlMz9o0Vv26Dzbxd4MODkV87333U3b8ii+5lNZjTHrPuuthI1uwuQxm5Mzgjd6CX0vQf/26zBVY358Px/oncYwDK6thLQCjzBMGJ9LM3kgi9eP2F5wSZUN8lULoQv8fkymajJxIIu2HtBc9+k1A9zzDuVxk/aoxeBZST2jUbgrR7oTsu/31jB7Ec27inijFoYTI1MaQcXEnLSovtSmeX8Jf8ymfYuEGJJY4k/ZeBWLzLpH7oqDO2WrduqXe5itkPLZPoM9aby7IPOSQJgZqlsDbDPBmUrhlCzkVkw8aSIP2RhhzOh3mqT8LI1HC2hTkkd/+VnO/oc2L/zlYZofvZf91UMcv3iekZUlRpw6WhzSPlFEpiStvVUiYSEiQfFCn4kn62y9u0r3WBrtnESLPGoXm6y9awz7Wsxgf4KRdREXMni3RMRlDW9Sw6zHCDtGqyxCqLFwyxxG30HokvHVBXJakxPBNzlf/BAtd4T9/IBMp49WkPj1IrYWMuNcILf0v/DyyCd4rfIBnhz7dR6QNmPOVWrdywRZeGDt97lYfjfzhXswdHEdBISxEu3+8CsxkoLnCx/mUWeLnNvi4Mo3ODX+y0hd0Bwo1qWQVjksjg8X1mCjlfD4Gbh9Dt59THDvAcHCtuDSugqI2+5I/uxFyVhJ8J4TGp+9T2O5ITm9IJnfVA6aOIFCSnB8Wn3cd2/qNhr2Au3mv1iGehEGoTqeKAIvgq4HXzuV8NQF+Ojtkved1Lhlj9KEDHzJcj3h6pYSxQbhsA+qw/XQuGpOrcLajmrkjhPJdhcee0WyuC2ZrmrMDruPsinBwQnBwQkVhnd5Q616HE+y3VXWaNtQtQRzNfX5wFcFjI2+SuXdPZWMFBIMXfD+kxpCCPaPCd5Ylqqwsa4cWDdPxhZ86m6dxe2Y5R3JnzyvrNPFzC8++/IOeHmbTTcJWYrUruGE9Z9fGbky5tvuFvPRYHj7iD92VvmlzBQ13X7Tzy6FA57wthnImFvN4pssy5oQHDbzvBq0uRD23gReOmuqz2h7pIgrY/KaydzGOlOtFkYqg2VnuNUJ+Xd3PoTXa2KurGDHAS/7LYKdhPvrOzy/dw7HMtkp2DwVxuwJ2m+ZGLy7NvKJyWP8zNdG7pBtyQidZani/fOawWrsEd7kgtIAc15lvEhN2RnNgkY8iEl8iREEN+7UGAIeW0ezBWR1zMaAuCDI356DRGLtT5HVBLNbEoGkoUtamyHZMQvTgXYmwc3r6IYgSQR9V8PsJsi6g7ANyArGGz3MHozGAjIGOTthZ9Siuq9GdK6P90Sb/gWHa19rUPhknq1H72I9ezsnv/V1REljoxGz3U1Yf77P0WmDO46YvHApJhp4FP/j06z/5sO0aiPMJH2srMGVqx6NNR+nGzF3Ik+2YhFHkpwJFASBqxF5Cb1ujB8F7DmWozBisXbFIXBi9EFEddGlGUoGT3eRto6cs+geyVJ5pQdxQn82TZwzkJZA6NA/kidrOdgrPu5cmrBikT/Xx26ERNd83LkUzmgKdyRN7tqAoGRSf1eJ/DkHfB3RE3gTJp0Plak+E2O0JanRCDcBoxWTZDW0RZ9wn0XzZIbIhNmv1rFij633j2DsS7j3s+cY//0dTn33OHxAp3H0CEcuTzDnXKB8tqd0LIdzaHZAVLJoHS9jtGPS2x7VF1rI+8s0b8tReVWihwlz31xj6ZPjpNYD+hNFkBrpVyTubZJ4MiY0BEYvRppQKV4l1XTY2D+B5nrIJKK2uUFa32F/60nm0/dyxvwAh+ynyfY7iAIE7SJGtEMxirjL/zIZZ5MXxn+Z58Y+zwObX8bTspT9DRBjHG8+yai7wKu1j+IZBQRDx5BQK45d4e3u6yDSU7xY+xTv2vh9qv159jSe40rpQaVXC5Xwt5BWupeBp1Y+PQ822glPnlcX24cOCz5wq8ZDhwXPXpa8sijZakv+8NmYWl4l8r77qMbDRyTPXhJcWJM4vuT8KoyXBXfvUxbn7a6g3pUkicSP5PVeoDhR61fDUMfihSrOf9eC/b8+IfnKszG37om576DGkSnB4SmNo9Mqa2Z5R7E69a76qxOpOpMMQ3UujRSUgPjCmtLovHRVUu/GLNUFaUtQzQtmqkpIW8zALXs0js9IFrdVVUA5J1neUaBJ1wTvOQ5hJFlqKmbJj1Xigh/CUn3Y0bQjuWufxtyIslO/viQ5v5owN6L9iOGimhf86sOCf/kNSaMP/+GphP/ugz9aR/CLNu+Al7fZnAuVAHRaT1PWfnJ79Ebs8Q13g24SoSF4yK5yIepRj33+yFnlM5kpxvUUAM044P/bX6Qvldj2WuzwfNDkAfuGNfqImePVoM3VqE8gE6whM9NZWeXqaA4nayvJhtA4sLxEtdVEG5vFPHgbxfk3+NTCMv/Po3tY2LOPNxITL+qRam1z+9WLtHXJ2bkD+IZG1Pd50qsTyIR7rPKbXmxFcYN5SaT8mVcE3FgbGdeD+3LCYD32CEmuu41MNMyFFeQwnA1dARopQQoNLbyJcJcgNIGW0ZFhgt3pE08X0asWRs1EWJB9sMD+awmltqBRgu1OgGVoGIYgyECzJEBA6MR0vt7CGLExCwY1VxKWJaEOWiuhsBWjBZLWVZfNbZ/G+228JYm2x6T0hRqDP2ug65B97QqZTJW5+RamZTJT8cnnoH1VEjqS+dWQjbqgWjJwNyOM9oD4Pz7HDz58mM9kFimnTMYiwcaCQxQkXH25w9jeDOP7M4TdkOlxC8vWaexobCy6BIFk8Wyf2UNppg5maG/6RAONMEwoNkO6ZxO8vI7QC0TH8rTvLaG3Q/JXBnhVS1nNMxpx1iAYs8ldc5F1n7Bi4k+lqD3XwuwnRK916ZzMM5hMMdibRncj7HbE4GCK3GWHdNPHcE2sVMjKe/fQfvEhUtEquepVhJ0QbUmkBuJqRLLfpHskw9JnR5n4+g6jxg7bj1bRDvU58smAvX/R4fTTe+g+YvDqkQKNi7dxMj5P7jWXsOQzmMggRn2MtqB1WxHzeyFmN2Tk+RbJoxqNu4twCoQvmfvzDVY/ViPX6tAvVWhrY9iv++hxh3hGEnYFuowxPEl2agOjHxOXI9xBjtZIhWx7QM6us693mpXsSRZS9zE99grZbhcr6OH4VXL+NlY84Lb6NyjGO5ya+SKv7/k8d618CaEbWHjoesJ0eI2R7f+DVyofZiNzWD2nh11DoDQqcXIDwHTtMd6ofoDbd/6SI+0f0LbG2c7sBxRj0+irdUjGhnL2Rs7LwIOzy3BhVVLOxuypwZEpjV97CE4tCua3FGB48qzkzJJgsqJSeW+fU6WM51ZUbso3XoGJEjx0BO7eL+h7GustFbk/uO5EEoRxghcoMOMGitVY2hmukxx45iKcmk8oZKCaE+wbhYMTgumK4I69AktXbdPX6srlA4r1TiQU0hp37lOrGT9UwMkLFfMTRJJGT/DatTd3H6kgPI2NNpxbTXh1Aba7kmcuQS2v+pDajipy3OkpALnaVO6qS+sSmSScWRZkUwqUBJGys0+Uf/S8dmRK573HJd98Xa2kLm8kHJ36xXYfvQNe3kaTSHkj2+UnsC5SSl4J2zztNUiQFDWTj6XHGdNTnLAKfNVZZyP2+BNnjU+nJxEC/nSwzrVoQErofDg9xkLk8LzfpKxZHDXzAExoKUqaSTsJuRr1OWYWSOKYrfomK0eqtG2dBMl4BEeW5omBzUMnOHTfh8gevpPDz/8ldza7vCgl381mSGWzZK1pgsICc32Hi0lEN21y6OwmbhTxDA08GfMuu3YdwKSFjiEEOgKf5Ocm2M0I/fqaqKgZvBp0iKW8XguQ0XSs1U2kpiGECmjDH66VnPDNYgGhwAt5AXWJaSSIKCR9T4kkbZK712asBeORDpakYUhsV+IVIenHLJYEDCQyoxFsBBjbPiOHsqQzJkEqRZIJiKRGSsbIax6bZopL13QGqy1K764gTB3D14kNQbGooR+yCGO474mXyfQNzFIKZ7uP0DUOzhnsrIU0BoKuK+kFEUVL0vMDBo0B9cfn+YtiwqjlM74vw8SMzdIlB2FodHcC3F7EnpMFPAvGSpKUbWGmda6dH9CpB1wZRBw8kaVc1OkAWiLI6BK94dN5podfM0ETRN2YcT8itx2QaoS0j2RJfImWRMRFg8GeNLl6RFeCO2Wz8YERMosemU2f7BUXggTHTxFMWAQlg1RBR4sk2XkXu+WR6vjYm1fIjnlcvfYIQdHGfP8KzIX0rgqi7YTCVZ9kn0G0L0vyS4LJx+pUTrVp3lsiud2j0An44FN91l/Kc+neGovH82yfO8kjvE7mxYTgAxFOrkQ856Bf6tG6u8jIUy3MTsTEN7dZ+9gojXuKSE1DC2DiiRZrHyiT1RsIO6ZZmSO4PE1eXkOb7RM7GtgxWgjp6W28aIRiw0H0IMgZENjYSZcp5zxbyX62cwcZLV/FyIaYKw6OViMTNhnxrpHbbpMLW7w8/VlOT3+eu1e/hAw7+DKNNNKUkiYPNv6MlfBWzlTeR4iFpikdSXHoFtq9QMcJrORvpeyvM9d7jbvqX+epyd9gYN64ikoUyzHwlQ4lZ6luID9S4XKbbcXKLGwnWIbKK5muqBVP31WMxk4/YbOthLnZlOCDt0qubgoW6yrx9qsvwWRFcmxacGRS4579yk693pJstGCzrWEOr9elrLrI37VPhdy9vqzYmF17dN9Vt3npqgqUq+ZgpKiqDyo5xaD0XNjpKct3EKkX+r7RhNWmilIYLSrnVd8XaBroP9R9lLFhuqratN97QuPuffDs5YRnL0p2epIfXITJsrJoV7KSMFat2dd2VDt1cyCZsGDgCdXN1JB4YcxvvFsnm/pRVuXT92q8OB/T6MGLVyRH39rw+Qsz74CXt9Esxw69JMIWGgeNH81BAdX78213m6vD2oCDRo4PpEdJDXuP0kLnM5lJvuZssBq7/Lv+onLOyJiU0LnbLvOJ9CTP+A1OBS2+5W5REAZTRhohBEfMPC/4TS6GPY6ZBfpbW1wt2QQpG1cDHcHRrS22+002Jse5cuw4F50N/m55isyHf4P3XH2eU94yYeiD0+NdFy8wce0qFSH5zsGDtHI2B7f6ZAOLl4yE00EbXya8LzWKJsT1rJdtfHz58wMvaaFdrwLIC4ON2CW+KeMlh05qq6HAiyYRsQptEALineDN4EUDrWSgJRABWl7HroGxJ01csLCWYiYSQd4VbJcESS+ErIHMCNZbHrg65owKsEuFcPT9IzhjJkFi09QypMMW+k5MxgezZvLE5EFeb5f45McE6QMSqQkiXSe57OC/0Mf6B6NMnneofKtJL8qw7OtMFItEoQ99h/GiTj6VcHkLtFgSATkTgjjE3exyVZapL23TuNAmP2KTMnT8MGFn2aE0keLyqTb7jmapHs6gDyJaA4/9txfYmB8waEVcfLXHzLRJNqfR2QmRls7e40XqGx7bj7WJqiaMmqw4ku6JIukRHb9o4pkakbZreRVEo5JsJ8YMoTduU59JKyDgJ4itGOFGRC2BNqbjjOsERZPYEGTWPMxuTHrDZXbrAhPBIueffpTIGsF4aJPSwRivqLM5nye1HFOa9ommcvifMRh/tknp9S6t2wt474opNDY5+mqTsdQOS7eXWTw6xtfm7+PD4QvkTnvED2oM7DLhngwFOmh3wdgzOyQWzPz5Fst/d4zW7VmkFGQv+ow+22HzPWXS2Q7j+iXa5gz1K8ex9DqFyWW0AEQmRpCQKe3QOlHEfDHA7oTouo9TLiIaCTVvmd7KKM1jU4wMVpFzGuk1iWtPk3E2yMZtju98h1Kwyenpz/HK1Oe4c/XLWGGPnpbjXOoO9vVfpbbzOvd2Vnh57OO0rAnEMARO01S0US51w9Z8vvp+SkGdcrDGvfU/4+nxXyUeRircPFGsslw6nspbyaWVcDWRCgyAcv5sdxTQ0YRau1q66jXa6amcE1VxIDg6KdnsCHquWu80erCwpVZOhyZg/7gStkaxarLeZWW6jnIRlbKCBw6qPJTtjgJYu5d+IZRD6NoOLG5LbEtSzipmppoDywQ3EHihAhS6rvQvA08xMvvHVN2CrglMQzBZEkgU2HL8G7UBtqnWSrft0bh3f8LXX4bLG6qaIJ+W+BHkbFWNsG9EcHFDHf/RaTgyKbi0Bk9fVMf4h8/H3L1P49i0wLjJPm2bGocnEp7rSS5v/vD/lV+8eQe8vI1ml3U5Ogx9++HZjD2+4W6qZmMEj6Rq3GYWEUKVg20lPlmhU9BMPp2Z5H/uLVy3XNc0i+NmgTutEpoQPGxXaSUBV6MBX3c3+Hx2hpJmctRQ4GUpcnCSiJ3VFRZHsuzU8vRljAZUlq/QjH1aI7MYmTyt9assOpeZ6LSZq68iZnJkY5/DS9eorCxiDXpM9fukPYd+Mc3ZiTy/3okoV8b5jrfNmbCLT8JHUuPoQlDQDHQh8GT8cwMvptBuCqnTaSfhMF1XndjsOMFudpFCgAZCKkGusDWiVsKb/u/pAmPEIG7FCFtDK+nYe00MGZH4GlORRskUJAJ2spLMNrRGNbxRDekI9FgiI4mtCw7kU/g6BCYEjo7pOgSWRrod4Cy7LJZsWs+5HDmaQdyaQ2gOcQyWLrAGCdEDRYxEo7bjY3RC4iSgR5qNIMeecZOMZmCvdghj1XIdCWWNlUjSMsLHwK63iWpF4l4Hv+5j6OD5EOsam/2YTMVkvhcSdQL2313ixINlVi476Ab0mhFbCw4raxGTIzoFS9LzIrbmB4wezFKzddae7eN8uARFg+3X++RuyZEp25iGUCXdsSTQILZAFnQyvZhyPSTWBf20ILANkmmTMMwTeDphLyBX8YlzGjv3lshfGZBd9fDLBoWrLvnLXe51vkb9KxW2GpM4j5qkRmMmT2h0ljVa6xaZSRiIFIP7TUY2eqRXXdzpNPWP55jqNJi80EPPxpQOO9T3Znhx5RDHu4vkrgzgCDjZEjsjU8S6DY5B7cwOcSVm6mt1Vj43Tu8WCyMISZYylF53aN2ZhXJALVggP2iw+foR1ow7GKldwEp66JkIbdLD7MLO/SVSTzpo0qPg79AbqeLXS2SCNv7FNI1bRqg0tgjKAfF2ho3K7Yx1zqMnPns7p6mF61ze/8ssnPw0s5f/FNOtIyKDl8c/zS3N75CLmjy49h85X3kv1wp3kEhBHEMoQRvG9QMkusGpsU/yrrXfI+9vc3vzm7w68nFiKX4EwIBaRbmh0nJo2o0clrR1I+l3dwxd6W2iRAlxB74kYykGZayo/u2aG8JYMTKtgbJBv74klDZkatguPcxK6XsKxKw3YaMN+0aV9Xi5rtxAmgaVnHr+WwYMXHW8PRfaA8nVTfW9QlpSzQtGixInUF1KzYFalyltjFIJ5Wwl+h0tCO7Zr5qmV4aFlH4IVzclVzdVE/ah4brq/JpiYaJYrdCafYY6G+WGevaiarz+2F06uhbz0rxKHn59SXJ1S3LnXiUc3mW2b58TPHdZstUGJ0jIWL+4wt13wMvbZNwk5mqoRLcnb7Iwg1oTvRZ2+L63Q4KkoBl8LD1BUTO5GPVZiAYsRgN8qVpW9+lZdCEIZExZM0mQ1GOfimZyfJgbI4Tgw+lx/shZZTv2+Zqzzuey01R0izHdZiv2eT3o8Jy/yaWxHKuVFLFMGIkFUyuLVLyA44nN0lPfou90WEWjahV5uVqgn0qhk5BG8MqxWxCXz1M++xq1Tpd6qcbpuTKf7XQ4YR3GEhqPuVtcDvsEcp2PpyfICxMDMWRefj6aFw1VyqghiGSCK+PhykiqdN1mFy0MQQiEJtFslSsSuzEiePOpWpgCUdWhHiF1SXjFo/jpKnpBZywQlA2DdCRo5gEf9IJBVNSJioL83jRBP0ZGkvCMw6AraN+fR+oSc7lP04txqiZRIyDT8gln03zhhRd48dMPIbM6YWhiigjLiRg912bzkQnKr3eJlyKMMGEm7bFeHaNhZDCaDfbtBEwUBfN1dWIWKI1A2pJEIdhJhI8g9GI6mokMA8wEChZs9xK8KMFvh4iCxqWmz/pWyP0frDF7KEO7ZVFfdsmWDOrLHmtbAXsPpzGihOY1H+f8gJG5NHOTafrrCc39Os6MTfd7bfxNn/xDJbS0QDcEZgihKeinJKHQKXQkuhMz0kgIjICkGRAWTGJhEYUW8UaEnEmw3IiwZDLQBWYnROqCxBBUXuky5W0z8pdN2pdzNB8tE4yYlDBxPZ3OloEoCzpjebbiNGOyTz5MiDM661+oMPu/bjP10gqmNYK5H9J7duhdTCMWY6yqQ2ZEEhWy9FpjeCey0NcorLUIDgjG/nKb9U+P074zQzXsYtYTsvMBneMW4bjETjrs2zpF56lxrrzrOMXRZXKFTWQuxNrnYJyPWf3QBHN/uUSchqzfQi9FtJp7IQDtfIrGrSPUNraIcwZmb4uz+YeY8S9R9DbIOhvccuHfMdK9TPjob3J0+zusb20wJV7g9ZO/wtzGk4x0L3Hf4DtMscYbtQ/hS7V6TIZP9V0mJhYFTo99kgc2vsJk7zwtc4LF0j2KkUx4SxCTcCPALhr2AyHUiiptq5VN2lS26yhUa6T2YNjI0VLC15GCKigcKShbtR9KGj1oDQQTpYQoFsxvCUaLioWZrqrOokMTip2JE0m9q7JWrmxKXruW0HZUaWXGVCWOYyUYLYLjC7a7u7kvEjdQQtplMUzxzQhmqgpo7HYxSWCjBYvbYJuSU/MK8BwaF9eLLbuuCtvbDdgzDfV7w0iBv56rWKdEQnGoG2oNJH/2EhhazF37BTt9lW1jGZKBJ3j6QsJ4SXDXPtWJdMsegWWo4zqzLLn3wE//HPrTmnfAy9tkzoVdEiRjus3oD7mEnvZ3OB20ARjXbOaMLE95O6zH7pvsi7bQ8JKY7/rbbMYeeWHw/tQo65HLC0mL7SRgKXI4aSlwZAmNT6Yn+NJglUYS8Ji7yafSkxwZsi9ngg71TEhXM4g1lR/z+abDbTstYgyeGalyIZ+hF9c4EkK5doAnR3MEwscXkpdHpohkghG4HL1whsn6Jhfn9nOtlsXdVvUEh4Ys09fdDa5FDt/xthnRLAyhwMvPyyotgEiqXqNGEhLKRJ1kARuBtraJ1FUSl9BA2OoMFTci3rRtFqBXDUQgwBLIeoQ2Y6OXTYplgxkvRSVICIFmAXKOINAkbhG0BGxdQxvRCJsR8XLAxUUPMy+x+pLmn3fx7sxgj1m4Z/usLzukl11cLaCg75BEFiQCTYJeD9kxTEqvNbHbCdkumFkDUybkJ9N41VmazztMtTe5qkv2jehkcgYL2wmBH9NxVHJp2ghJNB0RQ1+zMLwAS1e6BdtUWRwTRY2MBav1kMXlJjvXHO79UI2pw1mSqTS+F6MbGt6oxcqCw+SJLDN3Wlw73ePaG12mvIRKlGZk0sIfsegdg8apHp3VgOzfqWBXTdAgPUgIbQGJpGdKCl5MlEhS2yrRLPNSGy+rgw9GO0GEkqiqEWehvzcDQhDULOJVn/6eNIV5FyuMGLnSobg+YOs9ZbzxFGk0StsaTT2Lm04wxwT1yyk6nYDRQ5KkoLP8myPs+V/WGX9mDWEFpCbzuIey6Oc8zBcCBu8BUjFaNcY4a7N8+wHG5QYjC2u078tTfrFL4+ESzXvyVF/skq57cAWaR0uEkzFCxowsL1P6ToP5hw7Q2TNFobKBrIbY+wOyFyKWPzrN1BPrpOljJQNKuWXcTg3PyWNf8tg5MsposonwdCacSyxlj5MzJpgYXCIXNhhf+i47f7LAn+z5NeaieTJijSPxY1w79MsY7WmONL/HTHKOff06L9U+xVZSwY+UlVom6qJqGCCrs1zjvRzY/C7H29+jmxqjmd6j2JOh+Fe+RYYM3ABDSAV2ggh6jrqQl7Mqc6WcGXYhDe3RHVcF6NmG0qfkbMWUeIHKYFncVuutkYLqJdvuqPbow5NKAGwZynkzXlKljLfvhQ/fKvjuGcm51YSNNrQcFd633RWM5OHQpCRrCQa+oNlnmO4r8QJlh97Nwqnl4e79Spey1ZEMfCVe3ump1dhWW3J4UlBIq7NGxlJszU5fhdwlUmBoam2EVHqj1kCdV2o5BfYaPcn/8X3Je4+rYD0BHBhXf9P5VclmW/LYq5JDE4Jb9ygtzmpD1Q+8A17emf+qkVJerwP44UTdndjnWb/JRuyqSH4p2Uz869+vahb7jCz7jSw1zeKPnTUWogEagppusx57nI26mAiqmsl3vG0CEu60lJgur5l8KjPBHzqrXIscvufXGddSbMY+gyggDkNMQyOnm+R0k0fWF3D8Ad+5/S62M7Z6Z374Lr5tSE4Li7oMINZIJ5IdQlJobNQm2MnnmdxYISShnk/x+32X25w1LDRSQmNMs3nRbyGR7E9PXBfsxkgFgH5C8N5P8//DrlVaSEmE5P/P3n/GSJKeeZ7g731Nm2v30CK1LK2rWCzqZrPZWk3P9PTo3ZnFYrEfBjhgcXef7g4HHA64vRVY3GExN9OY3d6Z6Z5mKzabzSLZZLFYLC0ysyp1ZkRk6HAtTNv73gfzyEyyyR7OTJPLAeopeEWUV7i5h4XZa397nr/whGRfRaTo6T+F0khsFuBFaApOi13EA6hB9t0blWDMW+hQISSgwH3Qxz/mctK1qccKU0NQ4AykhpHQpBKcRBc8mRwIcnq3Q/JQ465FDC/E9HJN47iNPdHIOyGRKTAuTNh8vka2OSK7kBFcm2CXJe5mTK1i4AcJcivFHEeUbIGQkqO9TV46+SDO2SXi7S3yScSVXc3iosPT5yUXrkV0Jxn5dExQkjED20cqRei6EEbYZiGBlQJmy+A6gp2BQivYvh3x9X+5yZnzJU5+tEnu2cwf8+hsxRx7xKC/l2A4cPbZGutvD1l7d0D3TsiRPMf62Tqzcw6zjwn2P5jQ/+d7yM/VcY87WIMMf5QzWXFQUjBpmpTWQ6TUWMOccNmlcXlCYkDkmeSWQyYM0hmJjBTxjI30NZ2PNqi8O2TSsKmuRbiDFDNSLH+5zd4nGoTLLjLRHP/SkO6zdQYzLvKUQXDdYPN9RfMB8Fo26/94ieP/0x0WvrHH7ucyzHpIesonWfOx3xqjnwVRyeCoQ+2CzWC1QWT6zF9cY/y0Te3CiP4jVTrPVGm9McDpJtRvDBicKpPNQIygdL3H+a9dYPMTK8SRD8sTkgUQcYa3HtB7oYx+U1EejRGVHJEamEFG1rYorYUcHJlnLttF3NEsTS6z45/hevVZFoLrzETrzI6u88K1/5GrjY8yNmzkaANv9AW+uvjrvG0s8HTvj2ga+7wQ/za3jv48dypn6E1gGNzzg4lTGHhP4pa3WR6/z5P7f8TLy3+fyKrhWEXuUJpPE6anIOUHnpNMfyYtRjuSgmfi2QVIqTWKSIAgLpxru+OC82IZ3HX7VYAzKXxYZqvQKmmiVPD2LbiwrjkxLzi3JKj59247Kr7kV57RPHpM8voNRXuk2exqJJr2GAbBve5NzYdjs8WxPwyLz7nbL0ZPu3145WrxvvN1wUm/UDqNQsXO1EF4u6c5OltsJ0okQayp+lOH4qR4r0GgaZahOxJYhuZgUPy/U/PF79gdw1cvaY60im6VIQW/8ZEitfqtW4W0+uq2Zu1AM1sRbHYKU76f5PoQvPwnUDsqoqMSzClh9v76arTPxWRARZpUpIVEsGJ6nDRLnDBL1Ke+KKlW/H6wxb6KOWNV+C+cGSbkfDXcZ6zzu2OYtWzCl8OCvHs4Qpo3XH7WXeBPwh3eiftEKKrSZJKMsXOFY1mEUrKQgdi4xu8/9CCTep2o26Vx8RZWbRUWaryWdTGQzEmbDR2QTVOZN2dneemJZzh3+xZWnpMYkgueojH1swFItGIjD2irhL/lr2AgSA4zhn5M4CVC3QuEnEIVE0knT4i1usd3ERK5sVUojNCFCYMpyMeFx8t3lSUwaiaql6MyjTCg9HSFhapNKYJyApkBnZrAig2UzujXQE1ywpsRYctEVwyCnQQ5EJSeLpEkmv5mTu1vN5h52KHyfoT7WInN4xWav32Hvc81sS1B9nYMmSbdS/FvR1R9TZjB5GaMsBQ4oEqSmpPyZH8NS6W4Dy8TfOcW7VASbyYsNwRPnTQRpsFeOyXJFFmskUZOYlqoDDzbIE3zIj1YQC/UzJkFCVNS3E2PArh5ecLm7ZD6UY+lR+q0VjzSWBZqjVgR7MU8+GSVNUfQ3om58Ed7zA1iWr85j79sczYTDPdSNr8+YnwyofFcmTTKabwzQguIWxbRgoveixGTHDNV9M6XaF6ZgCNpP2QTaRvlGIQnTFRZgCkQUc7gnI+1FbO5kWEdZBx9tY8/SJh5ZcjgMcXwhEe6YrH6xV1aR12Cqsu4YbGb2XTWLMonJPGcwZX/4jgn/ocN5r7e5eBzLYxSTnk2x3pH0a5D+JCDmA1RRyZUr9mY5ARelfL7PYZPOlRuTJgc8+g+UaFxcYzdT6neGDM8W8aoKKITBuUrE45+4zYbn1pFp5AvSaJlG60y5J7L6MEMeSXD68XQbOMGA5KkAmsaxxjSXpljNt7H2M6ZDW/Rdo+xWX6QwKrRiu/QiHZ4oPsNOt4qY6tJUyke2/kCb87/Ci/O/QOe3PtDFuJNlq/8PsbCRzAWPs5cVRauu0FBeE0zwYWZz1NO2lSiPZ7Y/UO+tfBbpLmJIaYclyKEHVFEVd3rukxLMB253NelUdzn8DsptmEZxbF26FabZIc5QsV/SwHDFPJJwTlxLKi6Gt8pyLfDQHB1G1aahcfLYr0YqxcmcEV+0stX1F2FUZKBZRbKoUwV8QTdseCQ5rvUgIdW4cIGrO1rEAVI6U00tqlplgW+LZipFAnZwwC2OooolZxfgtOLhSoqzgpQdmFDs9FWzFcFrQq8egM+2Czk2BfW4chMAQB7k2LMVfM17WGRjP3ZRySfeECy29e8cVPRnxQ3abkqnIkHQU7N/8mUTH8IXv4TqEtJ0XU5Y5bvqoYAbqcT/jzcI0XzqFXjp7w5jpk+jvjLB9srcYetPMIRkl/2lliZ5hNtZyFjnWEjqUuLjTzknanC5/9YO4s33dZpq8zH1Ay/H2xxI5tQEyYqSehUXRYNl6owOdY54AsLdUZosvdvsPrtCwSLRyl/6y2s3/gMoVakulg50rt9CkHoOGSGSWJb1OKEruOQZik/486RoIm1YpCnvJn0iHSOg4EQgpyp14tW/PAhCf/hdZhl5AjJZNqBcYXBgUpI7oqkoSxNzO19xLT/LREIV6A6OSTffRtp1AykLMi4ZOAsOVSPusxnEq/wwCNyIDEgH5sE/YAg18hYoFKNcCUq07hSsvRsg+55n6CXk4uA6oqB7qRYV4YkZ6sYXpmtf3ye5qkYC43+rRZZAs7bY2bTlChW7G4pFkyN0LDbV/QHkIxSWtcvkZ8/RtissnHuJPnuCLe9x/qBYhCbnF2UuDqjPRLEuUYFEVHLQyHpp5qmDPFkwUNIMs21vaLVXfMLN1Olis5MrhTD9yesXQlYfbDK6adrlBsWk0GGZUvaBxlHH61ilkJGvZTRexM6YofFX5hFnfSYLZmUdgxG3Zzev+4w3xA4QpFOr3TJjM34TIlkmONtBtipoPNMndaVgLmNiO5RTTYG84YkPOKSlSTaFoUia8XB9CRykPHG315i+eURy2tD/FsxItdMjnoMHy3TeGOAcVxhDyzKOia6rRlvOWQP2vTnXd7+r88x9991WPlaB/W5MnlLYB/LOPGnO2zZswzP+OiVHHnQwd3Mccaa1HLxL2YMnnJwDlKSus3ggTLVy2OsMMe/ljI+56LzjMkpKF+NOPrNddY+s4ph5oiyImnYSEOQtJtwXCBFH/cgJlhxMTdiSCX2Wo7HBpOVEuWgjdMJMHRO2z1Cxz1KJiyG1hwrkw+YC27hWiMa8TZeNsIl4q3FX+OdI3+b892/4OToDVYPvkM93mHv7C/iNEu4VjHK2B/A3tDiqvWrPL3228xk2zzW+wrvtH62ACp5MRaVUyCD+sujJD39lzkFOvk0d2nKoy+6Mqp4RNOQRzkF0FLc27bmXlRAkhadmjAuQM9uHy5v6WIs5ee0KoXx3YPLkvPLhRKp5gt+5jHJ27c1V7Y0udKMokJt5FhFmvaRWUGawU6/IN/u9AS2UZBvPVtwZlHTGQviVDMIinyniiuKXKMQLt6BeklzfUdT9+HRY4XyqFkWPLwKd9qC9hg+8YDkqZOCm3uK3/mWYu2gcOBtTBfIflB8n+bw1i3NOMo5MS95cEXwc09IXr5SABc5Td1++zZ86sG/3nX0r6s+BC8/4RXrnCtT2fP9RF2tNf9svMZQZ6waHr9ZWv2Blv9bWchbU07Mz7jzd4HLSKVs5CHzhss/LB2hrzNej7r8abTLB+mQPw62+Zul1bvbedyq8W/YRKMZ6oxhnqIFhK6F1pp2PCYOQ+TrVzl2awttljHSeZLNNuM0QekCtPRJEQhMBBOK4MLQcZi4HuXJiJ7t0LcN6tpg2Snf3Q//fLJOhiLROQaF10tCQZb9cdT9Hi+DqUFdSRoc5DHJtPMiKeToRn+EyPICwEx9XHSmIf3ubRotc7qKFqMl/6zHsaqDocDOIUPT7acYPUmWaO7EGfaciczAOumSNU0MpVlZ17QNSS+C6CDnob9XoeGaNIcalTtkbYFuucweifA6AiPWZFWJjjWljQi54JLXbY7Pp1h3JEEnJtmNEaYkVjA7CtDb+9w68wCvffIJPvG7f4QSkt1hzlY/YX1XU3H1lEgpmK1pxGRIuz4DAQyynIZKqLiQ5IIw0YRpcYHwLQjSQy+M4iISh5qrbwzYvD7mgReazB31UVLgVU2GA8XMUQ8sieFK5AFMrgQMxhnR6RInsbCvZtQxibcz0jLkz5bpPlYmcSQGgrBkkjdtpIAsV0xO+SzsZNRjxXBeIiY5eTdBYpJmBjiQ2xJaFuK5Eg//b7sMyw43Hq6zsDGmuhVT1hDO2XSeq1G5NCF81MEe5Fga3F5I+q0Iuz4kKNsMftHl3f9tnsrXFI3PpehVF5lrFl5so0ozBMsu0TkbrzciiSTGSCFTg9pbCb2PepihIq44jE5rKjcm+GJCfsVk8oCLwkCcVPg3U479xQZbn50jtQSWjDACg3jOoO/MIBOBFn38/QGTpQbBTpNq3EXs+rgyJjkOlXGH1fGAUtan467Qs5dACG5WnmQpuEYt2WNi1lgevIsftyHo89rS3+ad2k/RsZd45ODPqA3XKF/6F6yd+BW2/eW7HcojLYExW6ft/xJHr/wbToXvMYmWuOk/djc3Sel7IEbIApzc34HRFOMgpj9jiHvqJk0BZA67M1CAZDX9f4edGyheBwW4NmTxfK4hTZgeI8UYZqtXgJnvXMspu7BQK+TOczWDqgdHZ+DaTkFgV6pIiYYiOPHkguCXnhKMwiJWYLOjudMpzoVJLKj7ECQC35kSedF3VVVF5EBBMu6M4Oae5kvvaE7OCx4/VrxmEhcKoodWi+Tp/9OvwL/5juatW4o4vbcPwukaNAwLEjIUI6KVlmCuCusHEs9WhEmhSvoQvHxY/0F1LR2TTlOal6duuAB/HO5wJRthIvjHleM/ELikWvGVaB8NPGBVOGXd84d5Px3ddettGg5NHI6XfCJy/iTc5Q/CHR636pyxi1HVxWzIvHToy5R2FtE3NfVIMXYMsiyCi+9T/sorzAwnmGaL9PjPw9p1Rs0qvUGXrOpgoMkRVIRkpPOpYgcix6VXrdFs77NVn2HsGrzX32N5vvi8NhJXSsZK0dcZNWkVvBetiP9SqsqPpoL7ogGGulgBSsJgTWdk05VBIjEAazBC5gqhNVoLdAaYAhXdB7QkSN8ArRGuwK6YLD1WoWQbuBNF3svpdRMipbDnHQ4MhV4p4S1pLEMS2RphCEp9TXgDdrQi3I54yolozXmAAVlGnEHSy6nKDuaMST7tJuSGwL42JEs1VCyU79IyMuSxMv6xMg1LsFKRjK5MqOwGBIMB32mWmT/ikHzkLM2X3yLJM8JJwjjSxJnAswuFBVowb+dEhmJccokEjIYpIixAjjsl7+YKtLx3Vx1nxeOwxr2c1//kgNljLueebeCWTExbkqdQqRkopYjHitKbIf4Jj2wrZCPMOFqCyJDkJYfcMxD7koU2DJqaoVP8LWJHYEeaai7ptwSjlsF8W9PoKoYts+AXKVCZJpcCoRSZb6ClYPvvLrD0e/s0twKGNYeDGYvaZoijEwzfZPRgGf96yO7PNiGXODsJ3mZMHmhKxPhZQvQsBK+aBF+WWC9IshWfihI0vz0k/SmLbNFi9LhP/dURzjjA3lPooYk3Ctn6xSZSKKKajzyqKd0Jqeo+4kqV4KTH0K4j1IDSrYjFrx+w/YtNVFki5jLq77cJl8q0z80jlUZojXcwRLQkO/1z+GGfRu82aM3wbJnqxQkz4RpSxdjZhMiqYqmYtneEWrKPm09IpUMr2sDPepSSDt9a/Ltcc0+wX/s1nm7/MdVRl2b3d+jMf4aD2SewTIFAF2eufRy5+AmO736DJ/pfIXRn2TOXyfJ7nBZ0cWNgioL79f3GSIfP3T9OOvz+MHNITbeVT2XV+ZRInE23lani5w9fJygMJjUFiGF6jB6OpXb78MEWNEo58zVolgSmodntFxlKBbdG45jQHgkub8ELZyWPHhM8ftyk5OZcvFN0aDybu3lRcVrIqW2z6PrkU3D23GlJb6K5sQeTqDCz+2CzeF2WF7EKaa6Zr0lmKoLf/Kig5sFrN4qO0DAqiMqz1UIN1R6BY2sMIbjTUdzpwFa3CJocR4UvzP0xMT9J9SF4+QmvQ6Luw1O/FigyiH4v2ALgZ7z5u9yU71evxB26KqEkTD7pzt59XmvNxXQAfHdGkhCC3yod4XI65kY25n8Y3+T/WX8IU0hejbtIIXjGbvBvojWMTDPxbNIk4vSfvcTqiy/RGg4xnVWS0z9Pq+pQctd5e+lxknYXWV1CYiDRRFPZdg6YCFK7GB3Vhz2UFKSG5NvRAZ/XJ+7Ol31hMCajoxKq0rxPLv3jAi+HBnUmu3nhK+5iEOiMfBoMYCBIwwh7NAE0UhQA427wYnTfBh0BjkD4BiJW2LZgbtUreCDbCXEIg0mGveRA1aB7qkLZzMHISEVObghkDlZXsLamsU5rHvUV9i2FnhGkAiZJzPC0Qf/ihKyVkbct0quK8scrlLKEhTsTspLJ5ceWefa3L5PkOWbNRlYtspaLWTZYmXORcy4VJE+V2szhEzy9yny2x6l2n+HtDjf2FGFaGG+5pkAKTa4FDyRt3jrzAPmtA8K8BIMx47DortT94m52Ek8vUH9FHaxFdDZ3OPZQmbnjJWozNpZrUqvDwTCAXkb2e21Kf2cWMe+xbaeUTvoEuxnmTooc5nivTCifdWksmeyXNEFJEHsCFWpaXUVQluzMQa9usLgLdqLRsWBiQaJycjRynJNXDWLDZvM35pl9qUvjwpjIMWk/VqF6O8Ka5Pg3FWnTZuZrA/ofrRKc8wlOu/g3IjJHIDONY6boLCF6Jaf0NXBOGeQVAxUKqhfGdJ+tEq66mJMcazJAZ+D1Isqx5NS/CNj8hRniJY9kqYGRatyDmNpwiLWVE855jI430HpE+faEhT8fsPXrTTLPJDgBpUsTrOWE4flKESuQ9XGHIxbT6+wnZ9maNJn3L2ISMj7pUbka0Yq30UhyYRJYDSwVMTZqoBVeNkYLiZ1PcNIx1eSAry7/E7bKD/L1hX/AE+0/ZWlyldPrf8Bc523W5j5OXJoDr07ZF6Qnn0MZuzS7V/iZ8Au8dvQfspeUGU85JIcdl8NRkeAemPh+46T7R0eH/i9RWhx3plGAirJXAJo0K4BGmhePu/lM94Efcfg9xUhFTLedTH1ogqjwTym5moZfkGLz6SjUlMX7GKJIob68mdMsC86vCMwpcDck/Moz0J9ItrqFSZ1jFdsI08LN9/1NGISapQb86tPFmvjW7UKGPYk0B6Oi+/LylWK0BMVYtlEuPGqGIeig+PxHZ2CzJxgGGksWqqtReAhQFGWnIFf3J9AeKmZrP3m8lw/By09wHeQx23mEpOiaQDHq+Z/Ha4Q6Z9nw+C1/9Qe+/v5x0Wfdubv8FYCNPGQ4des98z1uvZaQ/NPKSf4P/Uvs5BH/83iNZ+wGgc6pCpOxyoiTBCUFeRBy5Pde4syVayy0uzDzGOm5n2ZGtTk/fIOvH2tiGyHuepulk8eIheZAJaiiuX1XNpxU6rCzSSkMSAyBVIIb6YQdFbFkFGOuytRZd6BSqsK6z6juxz82Gk27MFIUEuHDT2AiyXf3MTJ1tzddgJe/rP2UrkQ6EuEKZMnAzhSlkollCNxU0HM1zrESVtVkrySo2jHkJqk2wS7umGUG2e2cuRnBiVMGeSdlHGt2REbmSmY7MfaDFVY8we6CzWQ7pX8lxWiOWL4zgUTTfahGNPFRY4NsMCbfC0lLkq3VORbfGaISDRWbtGRzrLvPTNVgVC4jn1ti6cKIpWqV+XbCxasR7X5OnGk0goYPvi35hXCdLz7/GPnrN8jSQsOaKUjHU2XJD4k9VQa33h2zdT3gyAMVFk761Gsmx0767FyfMLwVMPxne5R+cxZv3sHfyllYcdmtSMYXA+rKwLwYUh441FZtBr5muwWJJ+hoaPYVdlkQOnB7VVMfCur7GjHQjBoSLSA3gIFC1QySlsXBp5oEKy7VawH13ZDBwyXs/YxSN8bZjzESjfnFAfufbxCv2ARnPEq3AsIlh+4zVYydlKSRMPqTmLk8onoKHJHjXo7JagajB0tMjnnISNH6Vo+gLCkNUsxccOQLe+x9tkW4nJIulDAyA2sQUtoLkIYmaUFyusrEdPBvT1j4swE7v1ojmrcxe1C+HsBJzeiES542aSZdDDFg5uAGA+MY49FRSjPrmFZMvCTxNhXz0RqWitkwSoRmldjwSWQJzxhST/YAQTPZpZz3+Fu31vnG0j/k7dbP8+rsr3HSfYsHet/AHe9xLP5zLs98GuF2kTIAS3DDeoAH5R1qesxPDX+fOw/9Fu3QZLdfEGkPM5PyaXdEqfsAjSx4MvefYmKKMO5TV98FKBEFaJYSbKOQSte84mZqFBbdiTi7L2xyuhEpADXtkBwimGnn5hAgdcYFOKq6BUCKpwBHTA0d07wYJ3XHmtWZgv+y2VFEiWClpZmpFBlOvq0Zx4L5uuYvLinGUSGPHgZwZUtTLxW+LE8eF6y34Z3bmt1BkR5dOVKMwgZB8YACjORTxVWUwsl5QRAVrr/2lEDfHhU+OJIpj0jD6zc1P/fED3eO/jjrQ/DyE1paa74eHQBwwixTkiapVvzbKWHWFwb/oHQUW35/RPy946KT1ndTWi9Muy4/yK13wfT4u6Uj/P/Ga7wSd7iRjjlm+iyaDl9LRwzyBGuvjbu9T6kzYG4QoE7+PHnzOHP5Nk/U9gg33ufOuU9jRBnn33yf/UdOYC2u0ElSMjQahUExEvJsn8D1qUxGKCAxJAdZwtV0fB94KQ7Xvko5azn/u3VefGncDWUUU8n2If3YFgV4kXGCVAoMELZAx7rQOd9XwpEIRyA8iZNoKr6F4RoYgFqxyJsSO4cEGNQ1bq7Ic83ENHFIsTLNwraG7YzqwxJdkey3bG5fHFFrCByhyfcStDEhPuZgz1uoio3fEbT2Q8ROQphA4kke/PYOriuQho10DLoPVan3NV6kyAYJ+UFE6pvMVizKSUb/kQeIfZdgtYWzELNk5sw9HLF+I+DKByG9g4idAZQdhSbg00d2+M4nzqBeu4l1cwedKLLpheTfpzQQToqgR/MgIDteYu50mdkzFeRBwu6tgIP/7w6LvzlHfsxFXgs5cdyj8xGLq2/18XqaGaWwBxmNkx7VCWzOw9CBAy1o9jV+TeBKSepoNo5BvS2oHsBwHtIUckeQjzJ02SBu2OjTAuUbmMMMZycmWbVJ50yqtwIYZriDiGP/bJv9zzRpf7LO6IEylcsThNYEx32M/9yme8rn/f9byjEj4PRDMTWRUrk0IitJgpMe8ZLN+KEy9bdHRI6BHWVIJZh/sc32z82S1nLiqkCODLShcDcj8lyjmhnxkonIyvjbEa1vhLQ/4zN+wIWOQ+Nym9Fpj+QkjCclSrsTmAvwd/Y4EKcI0jKz3gfoloE5DjF7CbPJHaqDLrdqTzF0FpnYZXRqM7GazIZrRE6ZStqmEW3za7f/7zzb/mNeXPon7HvHeMX+DR7vfBk/G/Dw3p/yQeNTXKw8hqUyfD1hZD7Ls7u/h7W/h9yPCM7+BsutBifnYKNbdDeitDhuzPtIumiwrOJrdghqpt0SQ97rwNwPZHJdXNDTKZ+lPSoSsl0TqqVi++HUKybN7iMA62Jbhx0VuPdcrgoQFStoT4HMIYfGkAVAKDkFOBhHcGWryKwDWDvQKKXYH3A30dk24dyy4IVzgp1+YYLXHQmu72r6E81LH2jesAsF1HJLsD/UZAqyTLE0Izk6KwpfmJFgGObsj4rfdaMNp0xdgDcTPv+YYO2g4PPs9AvDP98u+DGX7vAhePmwfvh6Nx2wmYdYQvIJdwatNV+N9nknGSCA550Wj9m1H/j6HzQugr/arTfVis08ZMXw+Iw7y3vpgK+Ee3RVwrzh0M4T3k8GLHzxm0S1MsqzMRaP0St/gsok4YixzaPiGmJrm5ePHiUuudTabU7fuMnXrt+kM9OgJS32VJEHbSCohinV7oTMtslMGy+OiByXXUNxMR7wyWkwY1UWh+tAp1SlhSkE4x9jRMChQZ0vDMbTzgtaE6sMRbFQekKiu32MJEHkCiyBMEGnqiDs3lfSl5gLTnF3uJ3S+GgZ0y4cYiO3MI/TGvYqAsMAmYOZKEwnY2agme1D1tbYWUp/xWd3UTK5njD30Qq4EnOQMpGC4df7WP90GW0Lsp7B4sc95r80wlIS8UCF+Q/6ONsBblViLswgHKjNeXi9GHOpRCaKDpk/4yItiYMiH8V80FrlzlKZWjjgyd01qo7BiQcrrJwts91T7G9HHGzF7OzGzLx6m+VfX2br2RNIQ2Nf3yZKigvNv08JijvlZkkQTzIGVwes3ww59lSd6owDQlAdpmz/2wOGz1fIH6kQvD1g5ukajz7XZGMt4OqbQxpDi7lxTuW0xyomBw1B4MC4BP6+RrbAUVAJBWEZJmWN1wbqAmKQliSPNMoVJE0LLQXuTkLSsBGxwhrkdB6vUFmPEGshTp6y/KU9qtfGrP3NRbqP1/FvTPBvBwTHfRY+KVio5uz+XxWvXq0y9ynJShpQ3kxwHEm04iAijTnMqV6bIFJRODVrmPtah52fm0HZBnFT4G8Xtq2l7ZBQaeJZh3QJnMxk/kYGMzGdJ32iFwTxnx2nun6HaBHi8xI3NCntjOgu16nttdkLHySbMZmNrpIddVCZR2XUwctGnO19mwP/OL3SMUbmDI4Yc1A5QyO6Q89Yxsv6uNmEmcktfmnt/8FB9Syx9NhwzmDohFLa56HuV2nFG7zd+ll6Roue02R/bobn9/41dnsLK3uRtfqjYHkIx6PmlXEt7y6AyaYuvnFagAc5BQhpfs8fRuuiY2JOOS+HHRu4R9w9HBPlWQFUxnHRuTAMcEywneJYTdJ7I6xM3Rv5QPFzpnGvO3TIqbm/WxRN4wMOJdyHX7PpuKriafRk2lnSxbG+Nyj4KlA4e/+jT0nGkebV65p31wsQc2u/UD7VfTgYwTvrRQL19V3Ns6cEn35Icn7Z4PpOzlYPbu7B6UVNmBRqqPUDeOK4pOzCRlsRxgVPZxTBevsnk/fyIXj5Cay+SnkpbgPwMadFXVq8lfR4J+mzm0ectSr8tDt/N6vje+uvGhfBD3brHauML4TbHOQxZWHygtvip+05vh4d0MsTtrOInSyic2eD49c3KM812H/h04ztGvkHXU4f/DkP2puIPGaXjPdPnCGt+Dy0vs1MMqJ+bYvhw2cwKzVcJBNyvL0uJ//1l2m2u+x+9nFix6ExGrLtusQSXo7b/HK2xBHLpzr1rBmqlIow7xJ2ox/T2Ci8b2x02IXRwKGP8aHSiE4fI8kQSk1ZhhJiRT66j4kqQbgSc9EiuR2R3YlpHfOwnSJpLrSKRk1owKSqcZTEyjQ5GbMTyfxQkytBPkr4YMkgXzVQuca9lWGcNQqw0c8INWRPVYoF905KLco59t6IUiZRxyvELQf73QGyFxE0fIaxhe0prHZIvh+RThQq1phpSi6BkgUlkycO1rmyeIz3q8d4uL1G7LqoJEZKjVMyOeIoquUS9QW3aPcnisaty2TPPkV3vIQVR+jNLsTffTH5d9V8FWqeZBzrImivA4mK2b6zx7GHK5x8vIpWmiO2pH81YqOX0V91yF7sUvtsg/nTJSrzNte/2WP/1oiZfsyR02Vqp12MssA3BIanCXY1ol6MlPwUWpGg39CQaiwhyIUBGWhDo21IayZocNop2jMYH3Xx1xKGZyTRnEXlWkANTe3ymPP/4xq7P92k+2wNq53ibQSERz140mP1/yU48t926Lzqsv5smbqV0ggVRjdjsuqidKHWqq2HWO0UO81x2orFP++w/bOzaEeSmwIjzJCGxN8JMNKUcMElWFbYsWL5TY2qC0Znykw+l1D549Pk/Q3SVkLwkImVxCxs3uKg5VKPOvR2T7L9oGBh9zrD4x7BrTrNYAs3HzE/uY6TjjDLJwndWSr5gNBp4qUDUioEVpNWuIGdBzRGt3hr7peoyZCFdINWcAszi1gMr7MSXOHlhb/DgXuUA/8E78z8HI+1v0RzcIN18zR75VXcLMKddDEFaMMnEx7KsEAYWOa9zkeiCyCh9dTThXsdE3Pq+XI4QjrsxsjDEZMo3ID1dBR06OJ7KKs2DDD0FGxQAJRM3QMwh89LUYyiLFkQ06OkGI8e8m+Eutc9inVBQo6myqJGCVy7ILWPs2JkdhhK+cGmphfknFuSLDUFJ+YFW70ivPEwxfuQnBynhXvw2gF85ULOM6cEv/os/E9fKT7Tm7fh3GLh6nttFx46ojk6UyiOdvvA1FRwFMJmR7E685PFe/kQvPyEldaaPw/3yLRmxfB4zKqxngW8FLXZyEJWTY+HrConTP/7vv7fNS663633/q5LJ4/5QrjNcDoOGeuML4d77OcxTWFhGYK1fMJ2HtF67ypumGIGy2zVjqHjHZ7d/0OORx8gYpNJqcR7p84xac5QjiLOtjcIdcjZS1dY+8WPopMQ37Jxbtzh2J+9jDme0PE97L0uYsGnOhmzMzNHDuyqmN8J7vDfVM/cNdwbqUJtZApJon/8UmkHeRcw5ULfNcszEEgEZn+ITNLpFVnA4Tkf3btCC1sgHIGsmWQ3IqpVE2fWQnoScjBzyAXsNQSmASIDqw+2r2mMFFrAWtWh99YY/mEJR0DezZkzcmKnUDDZnRQ16+A9VQbTYPZqwqxOsNspSEFwrkR+dYi7NibupQSdjL07CmNRckIHMFQoaWIGMVmmyA9icgwiYZFUbB6I3mb42NNcLy/yfHa5IDQqyKKcRArqFUnF0ewNNCMpMWybh+5cpf+RVfbEHKafMenEhKOUKFCkyQ/e90LAcl2wUC2IimGq2RkXFx6DYlffujBi51bA+efrOCWT8jjDG0K0k/JBHDHzz0JO/L0F3JbFAz8/y+13B+y8P2Z3kLC87zP7QhVdNpCWYMXUxF3oVjWJLcgdmBlCLotwzDBVWFIghoK0BtqGrGEitMbqZrj9nNEjHgzA3U2Jax7hosfMm3382wHLf3xA9dqEnc+1yA0D71ZAeNwnPeXi/J9nWPpXfaKLPcaPV9gfmTR9heUo4jkb8UyZSdlE7mdULwW0RIK7mzD3rS7t5xskSzb1V0NEXoAqSxVX4XjGZrCqqH7QYeGPJOmvzxGulOj8FJz66jHazh0mTYPBExorGbKwcxG/kiFrRwnaj9I9m9G6cYt8UbK3e5JGtE05adOMNnGyEV3vCLuls5g6wRIWDjHNZIeRM0813kXqnGd3f493536Ory79Z1SSNkcHb/Ng7xusti/zTOePudN6lrcXfpXbpXPcyp7l5OB1Hut+he/YTTruMmNdwVA5Vp7giCFSGOTCRmCgpUWuDYSQhOk93oakAAeHo6MwvyeJtozi2MqmnRpBIck+5LgctmX0FMgcZiwd8mkO7x/z+7owWhdAaTiVOJtGwXUxFGTZfaMnXXR5hCwIwbmehjUmRZCk0kX3xzKK8Y3WRUfo21c1mx3FYh1sU9x9/1FUfKbDLtSJucIM8DAN+8/eKfKSal6RgbTXg7JdhE36TiGXPrskObMo2ejkHAwLsJbl8Or1gp/zk1QfgpefsHpvOi4yheCnvTkGOuOL4Q4jlSEELEqXj7szP7CF91eNiwC28/vdegui7lYW8ofhNtFUkv1L3iK3soCvTQMRI51jCsFmFjJOQlaurWENK4yffh4/CPnEjS8xH14lkOC6Pgeex6Xjp7Asl4X9PfpzTRYHBzzWuckb+22uuQ6z79+m/o03cOIIbQpkHKO7I8S8RzUs3Nk0mrHKuJAM+INgm/bUT2Wsc0rCwBYSjWag0r/0e/4o6lAqXYQx6mko4aHhHnc7QaV2HyNJKTh9hYeL1hruTwawBMashepniEAxc66CVTLJLPAjUBImJkQ+WMXOwN3XVOoCZWnaZclNq8KV1QU+Xl0HDfWbCYYPMi/M+0RmUF11kE0He6JphBqzHRCamr2TLkNLc/zKBDvRCFuQJTmOiNi9kpNY8ICTYVs5Yjrb0UIQ2SVit0ol3OX4Gx9ww1tkd2aWV0YzfPr6JtIz6HgujZqBbRUSzKUabIwTbm+k2JUE39hm9qkFhjpjptRBSEG5aTHs5rTXJ3S3Q/L79pUpi7tRpWB/pBlHim7Ad9Uhaysa57z9lQ7zxzyOPlQh6GaUhInfsFBK8cF/v8Hxf7xMZd7h9FN1mkse177dZW19wvZOyJFfmcVrWvTqgrmOptWFjq8ZODCwoJLAYh/6Zc1YAUpjHEiiGYF2IG1ZCKUxBxnlmxGjkz6juse4mxM0JJN5k7nXBjRfH1J5f4LTSek9WWV41MNZi4iPOMSrDuK36lS/NcK4NSE55TEcm5gSSrMFv8Y45+KGY7aea/DeiwlPNsZUb8VUWxNGp0sMnqgy940udj8ldwzSPQNOaeJ5m9EZj9oHYxa/uMf2z80ymUu5dX7A3J9oskcF4YrD/serzH+tS333AuX2bQLvGJ1gleCxKjVxgD+JCHSTVDpU431K2QB7fI1K0ma/fIauu4xlxrSdVZaCa0ycFrV4Fz8f8NzO77IQXOcrK/8l20vnuNz8JI/tf5HF4BqlwTqfHP+/+bzvkZZmCGxBGGk+uf+/8NWl/5KR3URpg1ibxNpF6hxDZwidIVWOIwWZttBIFAK0RAt5l7irmIKLaVfhMK1aTuXUh4Z3yWEmk743UpKSu6QZNZ0TC+51bw67h4fACO51ZQyj+GrLKbk3hzgv+DH3c3EOx1ZBDL5T+MjAvQTrvQEMJrBnFR2T1SbUSoUj79EZzcFQ0BkX0QPvrBVOvueXYb1d8IX6QeE/I0TxO97Yg4pbbEspzbE5xckFwbtrgoOBxpya1V3e+g9aMn+k9SF4+feoicooyR/dLhuolJfiDgAvOC1KwuRfTe4QaUVfpxw3SpyzKyze5/dyf/27xkVwT3p91qzgCINr6YgvhXvkaJYMlyesOl+LDhjolBv5hKFOybUmUDkDMmrX1rHHEJUXCI4v8wvv/rcs7q0zdB1y0yHXksvHTqJLFYw04dbKMTafd/gbt9epZhG/8Af/hn977mmc69uUlCZdbnA6OWDn6oRoX5M+uIiXFNlMaqom2sgC/sVknYa06KqEWe0ghKA2JfAeGsb9KCvX+m4IpBCFeZxJ4bSbc28eHeQJS90BxqH3uCgAjM4099vRCFtgr9qkmzEy0cw/XMawi3RYPcWlu5XC8E0oEKFiMYexGd7rlwABAABJREFU1kw8wU4LOhsVxk8ukBvbGGFCqZ/CrMAQguZY0Co57Bx1QQisNqRBwMTXhC2LvTmb5ut9SndCUtNCSZOymRJVoLOpGQ80V5KcM/Ma1y4+UCZscmHgZQN0xaGkFR+58A5/8omf4sbiEU6srzOQPnKYY6xtIqoGVtNBNhwWVjwsz+S1v+gxfn9CcjzDfeYI9iil3B8w6mQsnKqwcrZEGmV0tyK2rk9Q4xRX50ghSHPN/uiv5skcylkP1kI6WyHHH69hiWKhM33J7PES4Z/10J+sUpl1aM07PP1zc1z/dp/9/Yjrv79H/dMNZs6WyGYFS23N/ASaIQxcGJhQVlAbgVHSREYho/Y2NP1ViXIF8XxxfNqDjOrVCZOTHumswajsMZ5zGK6UmD3qs/JHB1jbCTNxH28rZrzgMJ5khKd8wmUX9UlB7Z0RyUGMEJq8ZDHa09hzFtUkJ37UZ+W1EY3nTL76UpOH5gOOXAiw6ybxvEPn+TpzX+1gJBqRa4x3J4zPKKJVh+Fpn9rFCbPf6tJ+vs7kqE3/IxFzXx7Te9pn+JDL9udnWHixQ2lzQKX3AXJwwE52kp0njjAnNqm8HyGwGNpH8cN9nHREJT7AySfUom02Sw+CaXO7/gyNcIOx2aKUdalH25zpvcJicIPvLP4trtee56tH/iuODd/k4c6LuNkElUiGxhIzosPJ6F3OyPd5Yus93l/6eW5bZ1k3jtEWs+TaIlWq4GToHKlyBEU7QwpRAElycmGRaYmkADMaUYyGpseSFCDyIopD3keuPeTIfD+p9qGXjJT3zO8Ox0LFelBsJxdguQUw0hTbNab5Tf2giMY45M8cAqwkhzQo5M2eXYD3Q7O8TBVeLELArT1NydGcX4GfflhQLxVS6997VTOJNQcDTewXGUZLDc36QeHga8YFqTjJiviBYQjDQDEM4ZEjktkarLULUnGQwJ2uJlcKQ/7oY1h+2PoQvPyQFeuc/254gxXT46xV4YxZ/oHGcP8hpbXmK9E+qVYsT0HES3GbtkqIdc6MtLGE5AXn+/fuUq3482gPDTxoVf/SuOjwd7iSjQB42KrydtLnG9EBGjhlllg2PL4U7aIoeCU9ldKQNg9bFb4YFtuevXATe+gRnj3P45u/y7nbH9B3HV5/+HE+cu0KYbXFtcVFbMcn0IrRqMeZfMLluSd54M4rmK9f46lrXfabLSbPPszDVsDsZp8DpbAnIUmUIdziwq9EwTNp6WJko/MCRE1UhtKahiz2/6Fh3I+y7nVdir9VphWWkIx0djcYwBKSdDDAGgbIfDrKkhQr3/dIpYUtMZcc4nfGeFWT2oqLaUmsBGITBrYg8wWG1KgYTm4X3ZjYFmzMFXyYYCOn+XBMT9c5sXaHSskjqBs4EkphxrhhMGoaRFogvzpG1jJ0RdI/V0IMNIsXB8h+woFfg6pDMx5S9xQnHnXZfjPGoDDE8hJNtWQUC3WaMrEMwthiPg2YH3Q5u3Gby8eO8ycf/RQr+3s8dfMKroBskDIaKOL3hwzLLjPLHs99doY3XhvT3uyxO9TkJxZY6Cf4B0NuHkSsPlTFr9u0Vjzmlh3yUUJ7K2Yyypn0Uow8Jwv+Mno5JF3eXyqFm68PqC3ZzD5SwUwknUizMOdgvReiHxIYY4VXMTj/mSatSxPW3h3S+1qPaJBRebrCeFVyZFfTGEMrhLqEsaHROdQygWqAtMDwoHQ1Ye+ERVIqeC7aAG8/ofXOgHDeIW5YyCBHJorgfIlNV7L4hX2sbk4lDbCHGaWdiOhWRPepCumMzeDxKtWLI6rkRANNvOSQ9xTdukt9N2TyeIn62yN+5omQ4fWcvpZUXx6hPmMQtyzaH6sz/2IHbQpwoXwlIDMl6ZLF6KxP840hZpAzeLDM+LSPE5dY+GaApaD9tMfOz80w/2IXfyvFH45YuLXDQJfYPneWsjmg+fYII8sI3Bkyq4QfHWCrkFa0QTnt0HOWOfCO07GX8dUYoXMGlXmWJpcpJW2e3/xfqEdb3Kw9S2JVeXv+lznd/TZ+1seLu1zzz/H2zEd5bv8LzHS3eWD8Lzk7c5y8PENslth3j3NbHOe2OMIui2TY5Gqq/tMCgSJHUGaMR0ogPGJtkWOgpt0ZjUDpok+aT/ktgoLga007MSqfnvvT8ZOYOvkecmWkvMetgXsgRKnCpXc8Naw7DIT0nCI4crUJ9ZVCznzroOi4HBKMD1VTQVwAnapX+LZE6b2fCZMis+iVq/DBHc3JBcWpeXhgpfCUMQxYrINhaD56VvDJBwQvXlAkG4VTr2PeU/xt94vtHQw1D60Wo6si6wjGIVzf0Zxb/o9cSP8a60Pw8kPWzXTMF6NdHCRNaTMjbR6wqjzt1DlrVWhJ+z+KjX0xHbKRBRgIftqdp6MS3k76aK1xhYEGHrNrd3kf31uvxB16KqUkTD7h/mWAo5MBe9tf4oRKGTSe5Ho65q20DxRAxkDwzSlJ+LxZYSMPsIXkSatOX6d8LTqgdjCgcbOLmZY5ZW5ybOcdKsMB7xx/gr1Gi51Gk9R2oNSgM0lRX3mJ+a1tvIUqJf84H+woZBpQdiY8kMYceXWNzHHolEo4XgMVZKTdEZ5nTW31i06EZRg8alW5lU6IUSRaM1YpzUPw8mPovNwzqDMItCoSpREMdX63oWIhyNo9rNEEkefTWADQiS58Xu4ro2ZgNE30WNFccBENA+1JnBgCF9pljSkESsN8XzM7gK4Pt+chNwQiUsyoHVLToTLWzIcOnqEZGBKTjGGs2D1lkZQlvK2pTfqkM5L++RJRyaX1Fx3K7YQ8UfSThOUoRFY1ac2kFOQcX3HI+ookykkyxcFI43sa084YdzNmrBSDYnT25OWL3FxeYac5y9B0+anXv42dZ8SOA7ZNXq9gDkJGV/qEyqTueuRBTDaZMLq1z97CIovjmLKZsfnBCITEMTXlhkl9waF1wiS6HVKzDWrzYFqCySBl3MuIRhlZqv8ScLm/BtsJg16H5U818VLF+iRjrm4zXzKJVzQ6yKn3TKrHyzTmbG58s0vvOyOGk5zkqTLDYzazfc2xPXBSqGbFLbfRy6nv5mwtSmKruNid+faItYddJnNWoUIyBK4pqd4OiPsZwYIDFYHoK6LjLpv/aJHGV3rYuwl2P8MPcoxU47QTRud8BudK9B+t4l4OMMsCOU5IZhxUohm2XKK9BHGuROODMbOrGfF2zmRkYr86Iv2pOuGCy8HHmsx/rQsNk2zeobIRMnAFUcui+2SV1lsDrFFO/eKYqGyxe8pk5rUAM8toP11h52dnWPhaF6+d0drdobldo2F1CcoekwcX8K6l6CxGGzapXaEU7iFVhpdPsOJ16ukBA3eBPf8UB6WTtIJ11iuPsTi5Rjnr8mDna1SSNq8s/E1GzjzxjM/Z3kvMBrdZnlyhly3yrfnf5JHui7j5hMmwjjWKWYqvck6/yHnLQTtlQm+GTf8ct6xz3DJOsWWukgiHFIMeFgOtcVREmSG21ATCJ9EWKQa5PuzGaBAaLQzSVKJkofQ77Jqo7+G+HB53h90aa9rsVqqIMdP3HZiH6qgoKbode6Lgw9hGIZ+ue/c6OiUbEPc8WdKpYZxrF+/R8OFj54quzDvrcH2n6J5c39F0x8V2g3ianK00SheRBCfmBH/3Y5L8mwXJtx8UgChXRYclSouAyI12YSJpTdNL0rzwe/kQvPwnWBOds2y4jFVORyVs5SHvpgO+EG7TkBbHTZ8XnBYvODPMG86/F5AZqvQucHjBadGQFr8bbKEoLpYRCkdInrOb3/f1/65xkY47qJ0v0w33qOqMTu8t3iyfBGeWjzhNdvOYtbwgEXzMadGSNpezESVhcs6q8N/0LzHME46+dZly28SerbGQXWNlex2lFM1OD9WZYK+v8/pHXiDuB4RXbtPaO+Do9ib+ekYgbjGMBb50WZ0zOSECsjBHDUIm8RhHJgTNJqWdDulinVIUMfI8NDBWKVVpFuGFQI6io1Jaxj0C749aync/eDns9FhC0s+jezkpCOxuH3sSIg6ZpIZAZQqd3reKmWC0LMg1btWkteRieAa5DcSQGoLELxbDRh9m+qBCuL0MkScwADnWfGQuZhJvkkUGQVZFyQOCkUQ4OZZU5DMWfl9x5I/3MBoQtCz6J8uY2xlHL3SxY0UoBEtEmFqQuQaRJXHaGaUkRZUNMkcyGaZkmeKDzYRmTXK0luNoUbTNAT+OePDGTfbKc1TiCZbKi3Z9lKCkxDENWKjixg6lcYjRjZFuiawTMZYKtd5jf/UIXLuFIzUtN2WubvDB1RFX3+hjuAaVWRvLKRRVWQp+1aK55BKNc+IgJxxlpJFiMkhR34+/HcLWV7vUf7pBJRHsHMS0/zzi+At1xEmPzijG66Usr7rMfnaOm68P2PnOhChSRA/47J3zOKgKlg40i32NH4NyJK4WPLCeszkr6PuCYNnhxNWETS0YtAzSmknuGmQlg8paSGk3IViyGZ+2kZEmP+PTeb6Od2FC/Y+7uIbCH2dYiaZ8PcDbTRid8Bk/5CG3MkSqCwl+xcBINDQNBsJBryhmJjmlVo7TS9hte+iLEfHn6iSPmchEMfdyHyNRBMsu1fWIXtlkvOSgRY36B2O0KfDyBDEOCStgvDhiZitg8GSJvU/Umf+LHmlJUb/ZpX5TYBw1qcg9bFsiY5dIeOSYTPwlvHAfZZSwVICnxljJJvVkl653hI3ak1TifQ6qZyhHBzSiTU4O3sDPhrw+/2us1Z7gW97fZ2X8Pg91v0Yl63Jq+DrrpUc4MrmEoTKu1p7h9dYv0kh2WZ1cYmV8lZnuRc7JC5yxPaRlM7Tn2HDP8o73EW65DzEx60TCIcbGUDmOiinRAyGIDJ8Eh2zakUErQE0DInOkofEck1xaJOk95dAhoFFTmTXTtWBKdUMYP0BNN12qsimgCRLu6bYpOiBlF2YrBVm3Pym2ExQTdYYBtN+EY7NwekFQ9zW3Dwopt1JQ9otuzjgquDN1vyDt7g80l+4IWmWN7xZ+LmlevJdnF+/bGU/BVKl4//ao2O6VrR9SEvhjqg/Byw9ZT9kN/j+Nx7mYDrmUDriajridB0xUzm4esZNHvBJ3+e/FTeYNh6NGidNWiZNmibq08aWBL4yCPCiK76UokP6L0T6JVgXnxK5zJRuzmYfFxGGK7Z+xG3jfx5BuMwv543DnB46LdLiL2v0K3XTMplnmVatCBYEZbPLRtMcVTtERBqYQ/Ky7wCmzxL+cbADwqFXlD8JtdvII4oiZN9ZxEsExd8iDt94lGAZs7ofUL7/O7FyFmydPsF2qMVSaVZlztqkIty2SOz38UkTgLnKkMqFyMOTr1QVqRsx8NqAqMhazCYORi33QB62pBRNGnkeOJtCKnawg6x6SZbsqoSULmXdETobG4kcPXnxp0p8ShF0hGd53pdRo3PYApz+6u4AJE4QWf4nvYiwUp54BNFZdTFsic4hsGLkghcCLFHMDgR/CzhB6NVF4T+SF8qXsgFIh+3GNC3qJFT1i0QxwI4XbsnBGiuqVCQtBSG/Wpv9sDTvXHP9mGzfX2CUb/4ESWSdBG4LgpE+WKMSViDTIwDUxLJNyy6B9kKJ0RmeQo2I4MSOxrcL/JTFs5jp9qsEIYcK7Z8/zxAfvk6UKOYoILYfUdujPz9BdnmWxn+Ldianc6FPdidierZJuj2g3ZlnY2GBvoIlTzdmPNdhcSzjYjJj0MyxH4VVNQJOnmngC9bkiKbm/n6CVBjws1yh4M9sxk35GlmlUriGB/lf6ZD9Vw06hYkqu/Hmb+ceqVB8r0WtHjPspjRMe555tMDfjcOs7faKBJunk6Ec8thdM9uow19PM9zUVIchtk9WhpiQE3arJpGqzMFSYJgzKmsSBcN4mdyWlnZjKRow9UYxXHKTWyEFO+JBPvOKw8OU2umNgaIXVKyxjK7cCyrdDgiWHQdkuRh2GQNYkyi7ymYKjHp1E04rHlMKY43HA4IOUvYrB8FdnWP8bXuG4/NIA/05EtOTQvDSi91CF/gmfzJGYiQJbYg0y/Fsh+qjEea+P1YnI5i2SWRPrZkhv1cDZThH7OVlLEtc05YMOlUihtEALk0TYGAIUBpFwsfMJQpg046s0h9c48E+w7x9HWw1Co8TC5DpHR+9SynpcSj7HW/O/yu3W8+yXz/Lk3h9STg5opnvse8fwswHLwVU63jFu1J5lvfIYTj6hmh6wOLnKanCZ+eAW5dFVzsmbPCi/RMc7wiuNX2LPPsrQaKA1DJx5ekYTkww7i/AJ0cIgxiXDIpMGORKlLbJMkaYJddmmaWYIyyGVLonhEWYWYTZVEmnuqpM0hfrue4MkD3kz5tTdV0wJwvc7Bkfp1K1XFIqjQ6+Zw4TsNL9ncndjV1N2p4640+VI6wLYdEYFAdgyBaszsNvXjCNNOFX2OWbBu4nTosvCVPXUHUN7CLO1whMmSuBOB5JMYZs/GbyXD8HLD1lCCGZNh0+bs3zaK1Q8Q5VyIRnwbtLn7XTAWhYwURkbWchGFvKtuI0pBBVh0pAWs9Jl1rDvAhdfmAQq42Y+oSJMPuvO0VMJ3wj3AXCnfiJlYfK4Xf9Ln+lSMuTFaB+FZsFw+eT3jIt0sIna/SphHvO64fJ71QeoSpc5FfL0+BZvpWOC/kXK3hK/3HycRdPnUjKkrRIcUdihvx73CPKUmcu3ER2BJ+HRyfu4t7bZDiRmljFfgo5j8dZHnuPG2bPMj/s4skV96xLDgxFZrIlVSjDroOMuMYrRoseG0SD1XOaSMXOjiM3RmNS3EHFKNUkwlSYzYKIzNrKAaBrimKHp65RzRhkBd112v59T8F9X3W9QNzoEL0jCKbyUFPjEafexxoVa6pCxpw7ZfNMSrsRasBGZxhMCq2khHIGdQmzD2CtydZbbAiPT6BACA6omhBRqIkfBpARMEpqDgIdVH6tqIn2J1c6JFiyMUUbjWx3ikqD7oE/UsGm8OaRybYKRapj10Ekx+tEzDk7LR040pack6V4A/ZgsUgyki2w4zBtj+t2UUaS5uJ1ztClplA0G9iLbtYc5f+sW108f5cKpc3TsMkfvbGLd6pIHwCDiO6eeJTq9Quw2OL3XwV90aN28RX75HfZbFSZJQnduDvP2Pu2JYnscc+K5RY4sJuy8v0s0zsm7KX7NwHZNskTT241xPIPlMyXiMCccFre/lYbHw5+Ywa+bhKOMnRsB+2sh4Thj8PqY9BGfrqEplyWd98fYChaeq9K7NGK4FeIsOsw/XuGhuTlu/UWXYRKRRRrjqIM+ZrM9JzmoQ2OimR9C3RTUJhong4kHoS2ZG2hMXYQ7JqYmqZsoS+B2UvzNCCPIiZYd4pqBPcyQmWL3F2bwroW03h6ROkCo0CUwLE1pO6Kch0S+SfekT+Q56KpJ7kiyKxHZA2VEqkhMSfVOSC1MKb/SZstW7P3KPHf+5iKTOYelFzs4Owli1qZ1aYSZlxgc9bA7CVRMWHaYHHGpvDcmqRrU3h0TC43MFVnNpHQ7JJeCoCcZVjxKdYU+oqncCJC5Ah3j6mQ6VBR4jMgRaCRaGuTaYmH0AbOTG2w6JwnNKpvWCrPJDvOjK7hJj8b4Fm/P/SKb5Yf51sJvcb73DY4N38HLx5SyAbEscab/bV5d+Fv0/WUsFdPXq+yXTnMl/xR+NqAZb7EYXGchvE5rssnHw9/mvcbnGNhzxNKj2X8RoRX77lHazgoDZ4HEcLFEgkVCqixS4ZAji4dw2NMz9JOYVtbHFRG+6FGVGmlIMscmECVGyiPOzbucF/E9aqTDOvSIkRRjntlq8XO9yT3p9aHfTJYUXRIpwLOKr7kCpl2fYXgv62kSF+Cj7AKicMqNEk2rLHjqhCBIoDsqwh1na2Bbha/L/QCqp4ptWmYxVhqGBVi6sKF56sRf48L6H1Efgpf/iKpKixfcGV6YgoZJnvFO2ufbcYcb2YSdPCKcOsBu5xGbeYRKNY6Q1IVFTVh0VIIlJI7p82K0z+1swkGeYAlBWZiUpMGvecsY93UVtNZ8K+7wRtID4IxV5mfc+e+6eOvxbdT+N8hVxptmiT+rnEcC5+0qj1qrvGZVySYbzKU9fml0hUpywEHrOb6eFc67Jwyfb0cdNvMAYzKk9uoaVgIrNcXxfpst6VDSAUdnJWmjwu/9+q9w58lnMJOA2kaP0995i9c/CJi0FvDMMY3JEM/K2I/LHDFCjsxKbnziea4P++xs7vPId17HzVJSwOgMKYchXpIzck1yNLsqxpkKYjOt6OUJNdvGRBJrRagyyj9CJdj9BnUHuujdmkKSTEGNABKd4x70MKP43gsNCon0fcNvWTGwVhzoZVRcE6NukHsSK4WxCaGtOX4gcFKNH4EOQbZA2dO7thyMXGDkGiPW+OMxt6tNRJaTa0ngGIQyp3l5gN4IOHikyvaTVYyNhPLLfeglZL2MtJ1hosESBE2DSCha2zHKk1iqSFI2aha1mk0501jbkrhicf1AMwpybnbAyXyy1jJH9q8wO7lDUja5sXKEbz39EeB1jgUK62BE/yDF++03WP9Mk7njEcbBmDw0KZdNXjhV5r1hyu16mXbNo+o46Ms79HZD3v7iJqUjszROPUD46iXyKGHUEViuxHYFpmUQ2SnDToLtG5TrBahJIsWtd4dUZ2xWzpU491yDE49X2boyYefmhM5aTLxoMpKCcaJJ3xmQhzmnP9lCDjKCJOfgzSHGWZ+FvzFL6TtDdq5GhSt0N2P2uEdWkfTKgr4PzVDTGgkWOprqROCkkEuB14WDDIYVKMWacdkgWpBknqS8HlK+HmAtOMTzDrmncXdjhCm4/dlZ6m+NKO3FqC6kxyzsqsTbjXGCjJWXeoxOePQer5DVTOLHfNSlgK3nWzTtIbFhUL4Z4Cc5y1/tYhqw+7kZup9okJuC1psj/O0IM9fUrkyQuWa86mIMUuLlQqEWnXBxN2J2PzfDzNfbxDMWWcVEn/SoXA+pWgnWrT7jZY+R55Cs2lS3Itw8RWo1HeMWx72p1TQQpHgIpSAPOJe9RWz4dOwlBs4sOulQjzZ5OOngT7a52vgY7838DBeqL3BgL/No90XGVoPF4BqjvMXTe7/Pywu/SWC3CPCRRguDHJeIfX2Wzexx/LRHOe0yG63TDDaQOmVozRJLn1Z0hyfHFwnMGolwiI0SY6vFwJmjYx+h78wTmRUyYZHgoIRBJH228DF0RlmPmGFERUbYeYypJ/jKQKKIcUgxUVKSCodIeoSZSa7E/fcyd9VFu4N7cQa+XfBashz6U/O5w/HU5D4/JCnA0t/t1hum01ymoNjeMCg6NettjWfDY0cEqRI4libL4eNn4YPtosMSp0UEhlKw2S04NitN2O4Wn+X1G4qnTvxkmNV9CF7+GqtkmLxg3AMzsc65nQZcSodczUasZQE9laK1JkdzPR8TqBxHSGwh6KiE3TzCRCCFIBQ5tvD4TtLl7bTPEcNn2XC5no3ZmqYaP+c0ed5ufhffQw+vog5eRmvNO3aTr1fO0skCHrGqLBkOryZdkDanGo/y+WyE2XmdKG7zh3vfIPEWWfaPsp1HXJscoEdt8v6QytUBbpLwESck6sSkcc6JGQPXFrw/v8irZx/BjDMqa9us/O7XuKZlEb1oGGx8+incty5inl/m4JbFSnaNY/s7NIIx14+dwGnMce6td/BlTh9BaeeAcjDBUBpDabRRgIdYKIQuOi8DlVISBpYQpFrRUQmzfH8J+V9HfVco47TzIihUXoeVaoXb6SOT+wjEBar5rs6L0TQx5yyy2zHNBQejbGJYgjyH2ICT++CkGkOBnBTmU+kxiN1C7WAoMDWMtYIo5yD06FYiKghkbEFN4A8Sqlcn5KZk97kGWgnmXutT2okLqWdWJF5LrUk9k6AqydcnjK+GaK0pqaRQV5QLq1KzEyAsidPyOPOYS2c74c7NiHYqYe06s7UYk4zn33ub2wurSCG4fvIEbpYwk+xwc6AwLMln3/oLkvUZguXTRLaHbfa4NfsQR+bGZFaPg2FEXF3CMlvID65AGBBubBMcjOD0s+jrF2HQJ40zggFA8beQU5rC/Te3QoJbNrj8isX8cY/ZIx6mI1k5W6bStGnvRcQNg0mcsT1IOfhKm/ZaxPmfblF1TeZMQbiRErYz7PM+R7sWd24EZCcdhhcn1BZtFhZsEkMw9ASBC90yzA1gtl+MBYSClW7hUnpQA7OvmZQFqmETuCbubkRpPcQYZyTLHvmiC+WUlVf7xC2T/mKZ8sUA81ZKtGAzfrJG5U6IKxNKNwL8rZjho2UGp3yyp0t410K6H6uxV/doug7zFwaUZE7zz3sox2D4QInwgRL9cU7qm1g7MV47o0SAyGF8zKV0OyQ46hIsOdi9lMpOxOjpGqU3hkyeqdJ9tML4pE/r5T6mpfHbEZNFl8CTJFUDf19RkgpTFMnxhmFMk5lzDJVhkIFhokQx5/DzMX54neX4NoldITdN/HzIE5OXWM7XORG+yxvNn2W99Agvzfw6j3e/gnASVieXKacdUDnfWP5HhGa1UPxgEosyiDJtawbDUpikeLWYshpQyXvY2YRYmQz8ZcLJVc72XyESDqmwqSX7LAXXkFNuW2BU6TnLdOwlDrzj7LvHiKwqOSYD0WSoGhiJwpMJJRnjiWJtzpHY5AhSHB1TT/YROqVPnVB4pNggDRJlkU9vzDRFN2YUFQ/LKIBMxS0AyCC4T7LNNBohKx5ySgD27EIldZhYPUlgf1DwWAYBvJZojs4V/Jb+BL7+ftGBqXqQWJCNi1HTMID+GObrRQcmTODCxn/sSvrXVx+Clx9hOcLgnF3hnF0kQmut2c9jLqVDvhG3eTXuoiTMSodUa7byCaHKSVFUpMWsdGhKi0hlaGFwSQ35/WCLsc7whcGn3VnmpUOCwpnauKr+RXTnNQBu+Mt8wz/GzWzMabOEEIKbWUHMfdpu8DGnhRBLKG+FLx98m37aoxpu4wd73BibrMuMUKfMvbGHkQqOuC716BZBNKFqFSf2QWjwXljm2Muvs3P8CLI/YO3hc7S++jauVshWBbvisH7yBA/s7xE3H2Owt8lMb8RPvf5t/ofTDzB/cICtcjxXoNIMe7+HF0cYSuGmObFlkFPka6ToArzoDCEEZWkS5DntH7FR3aFUusg1usdzSe+7XOowwmsPvut1Gl14vGhIfAdD5RgNE1mSGIOcylEb05WFXbgB9QkoY2pS54IbwUGiGc0Vygc05Llm00vRqUJGisSClYNthoslkswEy2Tu2gC7mzI8WmFywmPuOz2cTowV5Igov6f11DlpzSSet5n7SgcVFXybwtdCEw819mBE5Lsoz8QomZQQLK66GBWHG7cyjHDCfqSwGg4Ldcnja7fZb81x0Gpx+dRpvPkllt+5jjupkuY+sagTBHPU4l0Cp8So3OS1Mw/jRDbZ5gXS7X2sY4oV3yK7eZ1wr48IR3D1Hag1i7066H/Xfj40DbsfvWgF4TAnHOZ0tiI8f0Br2WX+pE+15eCVDfqDlPKMQVKx6N0OufnOgPX3hxx5usaxs2Vc18Cbtcl7AbEBTcvk4GJIetqhvxbh7STUT5fwMklkQ+QIdpvQK2vme1COCll7KQI3Kf6m41DTq0HiGcSrPqJqU12PUJcn9I+5ZHWL8adnKF+dUHlnyOSch7mbYO3GJN9MCZ4rM1l0Ka+HOLcjqm8OKa2FDE/5jM54WO0M5xmLoFrmbduj9I0hp7yQ6p92yTwDa5ihGyZZJ4YVh3w7xd7KMHVESWnGpz3c/YSsZDI+4WP2h3jdFHXSpfb6ADSMHirT/mSDxhtDAmFx4DvkVYuSiJlMBJO+oGElWEKTT9WASggMIQtKrMqRqCJNMc8hzxB5jhfGKGGgjEJxeHx8idXwOk+NX2ZQXuVS7Xmu+2fIpIWdBywHVznf+yZWPuHLy/8ViVFCi6nMD41CoASk2ITY9EQFYS4jLY1JhtSK69VneW/+FzkTvkUpbJPlinLSxk96lNM2fj6ikvU4MrmE6KRoDT17kX3/BD1nma69yNCZZ2I1CaSNKcq4psI2YKI0aQ4ChSVTTJHjiIQkA4uYuuoRK5MMkxyDCT4xNrmwQAjSXDAICyAjp6es4J4p4/cCmWRqcndovmeb96ISDsMqewGY3XuZSqP8nqooywuOTUYBljZ7hdx6pgIbnQIEjUJFxfvfn/fyIXj5MZYQAk8aZGga0uJnvHmesupUpcWL0R4vxR0mOsdGFhdrNBfTYTHXRDPUGRpwEJw0fTp5wh+FO0hg0XA5GmxybHiZWeCgcoYvOQus5wFz0uZAJxzHQlIokh66L9TxVRWxVj6OlTR4cO9dXp4MuCZLJE6VTFjMvnNAJVacq7WJ9rbJ0wgjjelFkqtOnf3VVew8x4kjwmqZ2Vu7lAdDjmzeYcZo8FXzJKOVGTpb+/itnP3dKkfiA07dWWdhZ4uHb13HJ2eOnJthgshj7DjCVjkiLeLri75S0bHKtGY0XRDLwmKfmK6Kv3d3/7XWvc6LeS+UkXvgRQBWf4Tb7n8XbVgg0DkoKUlKHraRky7XSSYmDSGRVRPlFPk5h2KDyIKtBjTa0FYQ1jVh+e5OINJQRiMjTex5VPYjtKtxRMLErhILm9LtCFLoPFGlciugtB7i7SbknsTd1WSGgZulaEcQLDkYsYJ+hkSCURCDQ9vGzRJiyyIybG7OLlOKE85euklsObQby6weC0m7iuFBxN4gp5tIFuQOz995n8tnTpOfaNAIctLZM8y9N2EyaaIyn3raxpoE7Fs1rtclB9mQeOYoSfUf4ZZfZfbSt7ldXqK8MIfTOECsvY/OMkSvTaUhiXODaJJ/d6tl+v2hedj3Vhgotm8EbN8IMGzBzIrHzIrL7JzDxATLkYxLJtFezO6FMd07EYtPlGl2EoymhV03ERaUXcloLSZatkhTRfjmgNlZB/esh2kJEhu0IdhvQjDRlOMCuAgKEOPFUB9rOlVFuwlh3STzfKq7KctrGd0FmMyYjB4o4y+5NN8ZETqQnHcobcb43xkweazM5KhLNGvjXgkw2zn1PKByK2RwpkS4YGM9IlgtRSSmw2t/alMdp5z+kwH8eqPI1bINjCBFLZvk2xp7LSZSEjeD4KESMlPkjmR8xqf5xgCRC7IjLjOv9zE09B8tMXikTOOdEWaguLNqs/9YA/1+wuRPQkq9lDOlgLqVYguNMb0BibTAEEWivM5zcmmSG2UckWGqBAONkceFQy5g6RxzvE4p3GOhf4lPuA1ip85YVsltjRceUO9+mYXwFr+/8k8Z2vPkwkQJcwpimH4tODdaSJQQZNoGAQEefVljo7SK4wVUsh62jrF1TDVpM5Nucjy4SDntFtlFuWIuXmcm2SQwqygMcmGSSJe+s0jfWWRstZjYLQK7ibQ8DNMklw6xNhgBqZCFB42wcOTUlFMblEhp6BFRbpLnOamwyWQxrtJCFvIlBPldfWNxriLuqZ800wgCmAKnaS5TVgAZxy3GQyWnADdhWqidtC5iDEyjACl66jHTGRdeMQVhF16+nPP5Jz4EL//JlNaaPRVTk9b3da79d1Woc96Ie7yd9O8qiM5bFT7mzpCgeDXpcs6qcNooUZUWp6wS+3nCrXzCehbQzmMUhTy3LC2u5hNu5AGOkAUPI77JrbSHaZRwvUVumlXyLGCiU/rAabNESRr8orfI6n25SDfSMd+JuwA8VDrCK+MPyKIxG3PzJMKidSmiNAhoqZR6vIZKx4gsIdeCtnYY/9Iv03/mQXoyJpnE1N+6xvLrl1je2GC5ZRL0Y47s7XBrZpHtM0dZXl+jp33Guc2cCvj7X/oChpBYWUwuoDYckjfL2J1+obhIYKhASX03uTnVitEUQFSnLru9H3nn5VBtZDA5VBhNQSVMybqdAe5B996LDIHIiyupMorcG+lL1JxHNygz50WYFRO8Qmlk5gXZM3AEgaVxOnBpAuVHNKY1RS5KY4wylC0gkeR9AzlIMOwCzIg6lDdDwshF+JrgiM3StzqIWKFzsEZFzISpi0tDWjIIVl3MSxNyZGFljkYjcdKUwHUQCALLphYEhI7LhTPniTOX/cpJPnL7mzQnGXUr504PdgIXO+jw4F6bfd9nsDBDVzt8cuMKVB1aRoLWmnF1hv1yg8qgw9MH7xPkLbaPBATk9OZX2Gk8Q/PVS4SJQXM7xj3xBMadD3DNCVmU4NVsEAnROL/reDr9k3yXmdj3gpi7acKJZudmwM7NAGkIZh4vMf/ZJnbDZHxdMtqISHoZu6+O0E9VmJs1sTxJ3jJozNr4WtMfJIzSnFgINndDStcCmh+r4dZMwrLAzgppu5C6uOuVkJsFkDGUYKlXqMYOGpp+WdA5YhOXcxo7KaWJortqE9Us2h9rMLOR4K9P6J3zEJHGWwsxFh2SGZvwsRLezQi9l2KUJY1LI6pXJYNzJSbnfUoGPMWA4KWYd275zL2cUfuMQ77gUu4r/L2YtG5Cppi5OWYgfKLUIHiuhJPnCGHTf6QyBSk5yYJD8/UeoOk/WmH4YIn6eyPOXOywXWsy+ohPvalwf2dMZ2gwSgV1K6dk3hsl6cIHF1NoTJUh1YhQmUyUhWfkOMZhNpFAopBCInWCzgUy6mJmAZ5sI1SGUBEyDaln+6zeusXLK3+fHfckQ6PG2GySI9EKMiQ6F0WXQ1poaZJLszhihIHGJJJVIqcCWiG0QjgKU8W8WfkMzXiL4+O3mU13UaZNrg3IEgyV4uRB0aEZdzk+fJvU8MimACo1XAKrQWDWGNnzdLwj9O0FQrNCJqAs0oIfo01SYaJEGW0UANghopSOMVWMm48QQhBYdYayTkaxfa2LMARXJ0id3TXfU0gyw0UjihBXCjATJALPvkcQFvqeOd4wLDg3Nb9QKuVAb1yMoAxZgKKvXYLPP/HXtrT+B9eH4OWHrLHO+F/HGwghcIWkLm3q0qImLerCoiEt6tLCF8Z38U8yrXgnGfBa0iWe8iNWDI+PuS2WDA+AV6Iue3nMfh7zsFXlU+4sTzkNQp3zXjLg6+E+HZWghcbDZFdFDFXh7JqgsMIdttIRGug7dTpKQdQnoLBVn5MOmCU+786zMn1PgG6e8GfRHgBHDJ9LB7eJ97ZYb1WZ2C7kBsde3QUN9kKA3tjFzhMmuaaXGcz+1t/j6n/2T9js3YK1WzTevMoDf/BVZtfvUCmbXK/Msd1o8cR7H7D7+UV6MxW4cBUrbLFnNzgS9Vk52COs1OmVPIw4oJ6EDJVPbWMHL45ITB9HQSQLrotkCl6mnZf6lKTb/xGCFz2NRwDwkIRT4JShv4t4Z3f6uL3h3f8WhkBRKI20KRG5wtYZTkkTdnPK8z5J2cMwDUSmkBpSCzpVEIHgg64GR2O1ita3AMjBHSaoukXumJQvDsn9HNuCXAt8I6B8LaaX1xk/7DD3Zr/wBUk00tA4/WxqmV5cRCZHXTJLUrqZgpRFu1lrhMqJHIdetUYqTY7tbZH2TS6fOM3LDz3FfnmFf/jn/wotIPJdSpGmdnSenfAoYvA2WXvEI//6q7z4s58jrNZ41V7hSJaTr56BSgvsIRYHiBWbXJyjNRizGHXQ7tskXsadOZdrn5+n9M0rlD2LWn9Aa2mOYGOTNCv2l+N6jOWYcJQUqo7pfr+/lf6D/6b3vle5Zv/NMb1uSu1vtrBmStieIL4cEo0yNr7Zp3NpwvHPNln0SngTTV4xKDddEpUz0ZrBKGNi5Ey+0Kb6eAn/lEfSMEk8Udzlm9AcaFQOO03wU6iNwctgqQMzAxh60K4bjEuCxnbK/LWI9qpNWjE4OOZQa1os7SQM3IxxwySVGpHkKMcgPO1hVSRWL8OKNWaiaF0ckXsGwxMenU83mIu7fPbdLusXXLbOzFE9b5Ofk6TvgN9L0LZE+ZLG5TGW0FivKHY+2cBtaDLXwBrneNsxdjcjr1nMvNZHaBg8WGLwQJn6pTEnvrTH+q8sMHnEQ3stlv/ZLjLVWKKQzyg0poBYFRdYgcaXOYYAX2Y4IiPVkKTy7vhSA5bMEVIi8pTclKSmgWHa6NI8lmWihwe4/S284Dq/fOP/wl7lPDcqT7PlnGTgzDO2WoRmteiQ6gRyhZnlCKEJjCpjs0FglEm0VZjVCVmMn7RBYtgkZoWBs8jtypMInWFnY/ysRyntYKLQWBhCU0v2qKV7zIZrlNMuVj4kUS5eNqIhTOAypo4ZWy2u1F8gkw6rk8uUCOi7iwysObr2Ah1zkdAoEWqX2DARUmPKCpWsRyluU5Yd+tY8gSyTYVJQgIvbO0dF2PkEqRIMFAN7gdDwi9Vzuj+DEPbimLKIEMLBkgqpc3qqTCIslBZFMvWUTzMIiliDcVTEBiitkT9CX60fpj4ELz9kdfOEl5MODpKKNPGFiSckrjDwhHFX6WMJSU2YVKXFQKXczibkaFwMFg2XT3mzHDf8uwBnL494I+5yNR2yZPi4wuAgj/kX43W6qqCVO9Lg59xFPunMFBkvWrGTRVzJRtwc3+YgHTICDtx5htIlVOnUtr6YtxsI3op73EzHeNLEFQYOkvU8INeasjS4oPske1c4Hoxo18s4cYy/V4bdLZSIObV3kZoKSVJFrCS6NU/nH/9T3g43EXs7VF56j+f+7Z/ibHWxPZM7zVl25+apX9mg2++w/PAmammZ4aklGq+ss1OdZUiJ1bSPSCK2G030IME2oToaIda3qE5GbDfLlDOITH3XcjtF3+WgHDoOD3+E4CVB3e2WCSHujopife95AO+gh5HcyzTCAHJdLIaAkeWYHpTdiFY7wmnUkBWJyi162qZsTkhMzcAHfUMTI5g/mqNs4256LanG0AKVFlQBe2OM+ZxD5phoR9K4M0KOoGfU8B3NzMGQ3ACZgzXKEam6e5HPHcHgmI8+yPDiDEdOeQlTe/WR53N7cZXF7j5Cg5um1HsBfWuRZ669TWQ7VMdjItMFR6FLOY/a16iTECUWcSg5/uoNvvncR7glVqi/t4Y2+4THnudIq8fpqMdu4yFiT+EpC99MKQcDKjubzC8l9E802fn5x9BfewdPZ9Afc+axWfLxmN5+AkIyszrL8GBEdyciGCR3UYs0KDpjOX81krmPJ5Peihn8bofab8wgPl5BzxgM/7iLKQXxJGf0O7vsnCtx9FN15h6rUnMNTGkR55q5sklc13RKFsPrAcH1kNLHqxhVk17LpGwJElPQHGqO7sNBHTbmoBpAcwAyA2MMtQBCR9KZdxh6MZWNALtuE85a9KuS1HEpjxRzQc5QagKZE9SKzp22JLqckCQaa5xT2ooxw5zmJCNzDQYv1NBKc/LSmNWvb3FjaYn+UZ8QSfb6kFKUkdVt/FBRuzBGPqwxv6HZ+2SD7qrHeNZm7tUe5a0Ep5OQuwatV/ugYXjeZxT7VK5OOPGvttn4G/OMT5fo/deLHPkXu8hxkT9U9Kw19vTMybVgkhvYUmEKkELjiKKjqXTxnNYCpTUqVxh5hk4SsknIjbjMRIzoiipBaZ5j5RYPpLdwdUwj3OAxNeBh+zU69iJbzik2ndP0nXlCWSGWHhkGbh7SSLc4nr1NRQ2ZWC1u+Y8wMFrEwi0gvhTFaAgDJSRa28R2g9hu0NPHEVohdYpSOXgPYJFh6pRSNmAm3qCUD7CzEC8fYecT3GyMnYe0ojscuMfYd4/xcP+rnOq9gq2CYnRslnlx8T9nxz+DwiCRDjkmY6NGhomtApxkQGoZIAuhQo5JJgS5kCSGhcuYUnrA8d7rjNwW+95pkLJQQCFJtM1QFx0aI1c09ZCZrE9fNIhVGdezCvn0tDNzmPUUpfDmzYxnTn1/t/cfV30IXn7IGqiEnSzCQmBNiWcSgaRwGjUQ2ELiU1j593WC0rp4XhosSZcUxe8HW1SFRVWaGMBLUYf30+EUMwvmDJsPpvlDAA1p8bTd4OH7OCqWkByxfFbJ+Mz4Glon9OuP8HV3ia+Ee1zJRqSqcFmYkTa2kAx1TjdPsXKJjSDQOclUzmikOaeuXOCZd98gcD3sVoMTN9dpvmSSxCHL6RbHJruF/C+D3czh5rmneOePfoeo4dN45S2e+ddfxB2MsA3Yc2u8+9mPc/aPvo4/HDKOofmtC+z+5hLD40u0r3dw+wGDqssJrcmSCBtF4FhYJUE4yWhu7dHqd9mYWWI+Sum5BTFZw9S4LidTivo0ImBwHw/lr7sOR0aWkOS64NwYCCbfk6nkb7eRubpnUGcISEFLiUZipBlG00Z6kkaSUvdjTMfDUoIot7lTksSlEK0yBgON72uMpiazREHQ0yAnOdoW5LbEfDfCrmm0oZGpIvUt6pfGiBAOFms8trOHlhC0bGY2Rzj99C5wEUA0Y5O4BjPf6ePIgitiTs0jEsNi7HqYKqM16JOaJlJpJvk8n37tbUy3z8ivIFOT4/u3QeQIMcJQGaljs37kGF989NfYGK+i410Sb0znUc3RL73G7JVXsRsWl08/QetmH9V4jIE02BUJXfcYieNjrXVh6zqdJwXysy7B11+DC32yix3m5ks0Zww2dzKIEsrzZYx6nd56h/F+gFKFusu0JVal4AWkacFWVJku4gQOW2b6vh2iIb0Z0/vne9T+zizOgz6NlkXvf90nG+dE45zxd/psXxqz8HSFk59v0jhfwTGgrCTVRDNTcUkedZiMUjpfHxE+4kFsM5yxsP2CdD1xYWagqYQwLMHWLNTHxZjEToEYVmLILIfRosWkGyLXUqhZjFoWaU3iehZOrDCVQSnQtJuC8YyNObRx9mNUKSeZtXB3k6JbMsrx92L0rEn7kTK1C2OO/dkBW782R3DeYyirRK9N8K3iHEvvRJTenWA8pDH/QrH7sQbxjM3WR5pUrgaUwozq/5+9P4+1Nb3vesHPM7zjGvc8nLnOOVV1anDZ5bIdD2QixJlvuK1cGsSF0IJuWsoVdCTUBAEtxB8RQlyBAClNq3PV3ZAOlwskZITEjhPHdmyXXVWuuerUmffZ49prfsdn6D/etfc5FZKLScpJX8mP9J69zhr2etde7/u83+f3+w7XM+q2avgwzjN5PEVWnu5bcy78z3vc+6F1plda3PqL62z83AGtezVeepRftPZo2kaRtKcVlpP7Bb7hciy+pJOFi8ATC08oKp6UQwZ1gJ/nlJMxvzjr8iuixydXjtiKp2S+YMSMY5kzjabEwSusRz3yzkWm7YvMOxeoomVmao1Dd4Fg0ZrZnL3BBZeTBT1qFVLIDghFpTvMVIcZbSoRLaozTSaSQ50as1QkVHiyoM9hfI7QFYQ2J7VTAldipUZ6h3Ilme4hgHGwwqXZi/TqI9p2RELB5fxlOm5C4mYA3I2ukqkutQhJzYTITulWB9QiRHpL6Auc0MyCZXLVaQz01CWG4RkCXxHXY/KgS2onWBliRIyWjpoAKyRj0UVpi/cSaSvyQhNoQWkaR2G54Pk7B7/4Ffjwlfdsiv0DjW+Cl69zzJ1FIsi9ZeYXrG/f9Natb9oHdrESb6bLZj6MhKLlFIeiJKgFkVBEQi4ufoZdW1D5Rl1U4LhtczZVxBmVcF6lC1M7zaEt6UhNhEQIgfcWv/8b4CpkvEnZfZJb2T1qPF0RsKZjPhz2+Vi0wn1XcNdkjJwh95Ydm1PagtB71g/2uPLWq1y88RatLOMr154mT9ts38xJb7zO2vAOH5l+iSi1zDuaO+dafK11iV1bMCwnxF+5wZP/719ETiZUHiZWMNzucOY3niceTIiFp+zGTN45ZuXmDu6Rc4yvrJF+9hbHxyVvPXeRLzz5NOO0xbOf+Tw+hmI6gVnG0y+/yPOPPk1rXhEtJeSLK07zt25cdpflg4iAb1Qp82GDusbN16GFaGIJFs/xQGv36F1OVB4PhoZsJyXSVIhEIiJBB0mSasLQE7iCSoVo6bjeTtgtVpAc8ujZcZNjpB68iZ7UeNm0IpZuz1FtQR00MoF4VBNMLbIyXJzcR2rL5HxKeisnnBmCsTk9Lj0wupISjCzp2KC8x3hB0LhwMG116eQ5z7z1OlZp6iBgt7vFXPW4dHCbPBXkLUW3GhHaEukctQrJ4pSD/gafvfrdfPHqn0DWIdhr4Ep0cRevQ67+u1+lOp4Tf/43mbsAVf9HdNSD3jmWl65Qd7dJxICkP+M7f+4/cevaI+x+4DLXI830d17n+u0ZRRUQFjUikOjDGJUukZLR6hXMx64JxCsd1gh0IIgSiQwkAoE1jQ9MVdjTKAEpQWqBMx57YBj+3/fpfP8S4ZWY5b+8yfhfHWIHBm+hGBlufWrInS+P6V9LOPfnNlh9tEUfSZJBEkniTsjKBwLM0JBPDPm2Z7iiONrSdILmO1see3oz6MqGpC1qOOo23Ke0gsBA30qWWi2K0jDMa9y9grKtqNqaOFUEtWgUi0PPxMGsH5D1A1zlkENDFmmS1YBobGjdKwmPK8La4s5HyKOalU8P8X9KUDwRE4QW/7WMwXabeClEvjghfDGDwnHWeQ4/1md6NmL+SEJ1vSR/LiC9X5JtR6x8foRwMLmaIIyj+2bG+f9ln/1vW2LyeIvD71mhfHFG+/kJwjURF0p65EPg8YQCKuG00ugfuo33nOj8NKClZzOqWAtrplbzdHvCnSLlRhZjnGM5qJFmjqgrAjfgrbzF1IVI9RYyiHFhi3m8TtnaxrU3CdI2MmqzE8bIYAnpEwhbtH1Jv9rnyvzTJHbMNFjlODzLIDr7gLsiY9zpieoW+960nyrVopIJM73UgDIMga9R3lCJECsjRtEme8mjaFcgcEQuJ7JzStVG+5JLsxc5m7/JYXiOSiZUMmKiNtCuIrTZ4pxWhL7iXPY6rXpIUo8IXEUpEyoRkasWu+0nqGVMpz4CBPNoBZRmIvvNAguLkSleCJyr8TwIH67MArx4eHsfJrmnm/zxtY6+CV6+zrGkQt4XdjmwFUNfUS+AisWjEQghFiXFJg05QKCFOL1QGO+o4YHMVsDcGWrv6QjNY0GbK7pNa8GZGbmakRuf2FicjlBIOkLTyW7Tqqd0VYtw6QP8wuwWr5sZ2cKs7UPhEn+9e+W0nWW8Y9+WfLUa8kvjO7SODlDDI1SVc5ykDK49xfVzFymW1lie5BzqgC3xEleTN0mfSzg82+f+WPDF2SPsqE0mZzaIqxln3n6d1miMDiWzAqbLfdQkozs/xgmBCBXpSo88r4g/8zW4eAZxeY389R0+9+z7iR9fpxWDspbD7Q2SO/scrq6yeXiXjes3uXL3BmXvDH0RMF6orfxCcTR0NT0ZIBHUOGbe0BXvfSnzYYO63DmM9yRCMfQP3KJEVZMcDREP22iKE+8R0fizaI9KFCq39HoBuqVQWhDUkCQTRm2NCdrsjtaYrp9Bd2+xpe4SPKSLVLmj7gek9wvCzGAuhXghMB1F550cUTtqqTgzOSDbiJlupiy/NCUc1KerWoA6kWQrIUtvF4SFxXuHai4bVDpESEiLkqQqqHTAqN3j3vom5/duktoJ6dRTWEFaFGRxhPEB97qPE6kRu+2L3Ii/jeU3z+C0wgmQRjG+otn9rpjBBx7l4i9/lvaNfdr3ZmhfIqspeu9NOrtvEfiCFQYsiwFaGj7w+Zdw3/OdvHHtg9xaOs/ZT32R9u59rJDoEiqdIsuCer0H9xU+VYjpMQKDq00TJZA5hIK4pWj1NEEsiSpJkVmqvAE7rvIoBUEs8JVj/ovHmA+0iR9PWPpLa4z+9QBzd/GdO3Ajx/EX54xevUnruTbd/26N/rmYeN+w3Q9ZVpqor0kzR3rXsTGXuCPDbFkx6QqOu5AWkJYnJF7YPmoclndXIDIPPZ5otsuGLD6qHfVBSd7W1IEkdIqsI4hrCAeOyZLEhgq3LqmXAvIDR73sSK45enczVr86Idkt0ULT38lJfvGA/Y/3mTzdphUKeu9klFcj9jZWWPqVY7pvFIjCswqoZzuMryZwLsDsVszPx9hQUnx/wNpvDuk4z+zRFOGg8/aczU8Pab9TML8Q4RPB4SdXiD83xw8MLeWIlEM3p0pzyV9I7qR/kO58wk9qKtwPjcVztPCEoqavas7HOVOryJ0mkpbVsGbLSiZWcyHOuFtEvDzpMC8KvBgTyD1i9SpeRUyjDSadC/hkBaE0iGYZuq80MmjxcniGFmtsmrtsm//IZQ0+6jJtX+Ko8xh5vEEtE7Kgv5A6NzvvZPMJPR65WGSVQoMI8DLALxRETjXKK+VKnAzIdA8jApzQHEdnEItFshNBA4J8o84S3qJ8fXpf4OuGsuvdKdE3NjNiM2Wp2GEUbyK85+r4cwSupAp77LSv4YQitDlT2eVe6wnmwRLGn2ggH8QaeBrfopdvWz7++B8fhPgmePk6x4qKOHY1qzLkGd0l847CWwpviYUiFpIYybIKOSMTVlREABy4in1XsG9LjlzFxBlybzi0JVMPWgh6UjN3hheqEYEQdGVAVwR0hKatNDGy8TrDU3nHUXnIYb6LFZr9eJvrs9sc2qap0rSvmqyif5/dpy01baFpS42bj7n/ymf41ttvYoqcHMO9OGa4tMKbFy5gO3225xMeHd2meGyEfyLgneqjXPea0Z7kbqfHRHWhhqjIWF7LuTS/SdTRzOYwX+5ikATTDNNtUa336Nzbgemc0Xc8Q+fXXiC+fp+j73iOnb/6Qyxdv0t8VHDGDxmfWWOwtsrFm/excciw06U9mPLxF7/Eb37ku9iWF7jtGtt9DxgcA1+yIRPCRTtn4gzd3yd1+w8zfrdBncUjEWTenYKBYDwjGM8evMjTGCYIgfcCWVtEKpEdTbeQxEri2xLlm4tWHcC4a+n5Ke3phDJZ4y19kV2xzAd5FY2D2iErjyocemLRGqpEImrfkCKnFgpHPLLUGyGTJ2I6NwsKFRMOGxWUZOHSuR6RTCzJQYnwblEvBIdkHiV47whNhfCewBiG0QrX7lwnsAaNoQw0vUlFHkfMk5DPPfkBVgclW0cFR/IJBsEjBFmEnDTldRcXdI9Ljrsw3d7klR/9c6SDHq2dNnq+i568TPfOLueuv8OFw1dpZxNUWVNVit2qRfgf3qFTrjA7u8LsA+fpHewQTuZ4r/DiABfFuJsz6u4G0ubQX0fNj5GmQtqqqZLaB74vJ3kOcuFM6nwDNBvLEd9wZqynfH6GyyzBakD3u3qUX5qSvVFwavXjwE0809+ckr2SMf6WDu1v67Nb1yglWVoOWNWKZTztfYduKbolLI0lNpUY1fj7BLaptHgB60NYH8G4BYdLMGo3fjFtIehYTVQ6Rm2FqRx1ZiiVhVTjQ4mJoTtwZF1BFQlcLNFnBelEUB9KDp5e4uCDfZZembLx20PSuwVq7jjz6WP6b2ccv7/D+FqL1v0KtSTIf3gZ/28HtG5UpGXTUlSZ4/h9bSgcfmLJtiPsIex+1worXxzTeX3O9PFWA2CuZ3RuZETHFdVyQH+UkZ8LsP2A7EZFZgNC2bDCQukRwqO8R/jGX0TxUBXmRFW2+P9JMOjJ7UCAlo6WctS+xnqB9YKuhnXfMNWuted8x/KIozrgfhExs5qZUQyNZpYfUc/fpPAKqxNMukqRblLGa5S2T1lHFAgGYp03/ApRNSedjmnvvkpiv4CSIXWyhYg3qKM1XNhG6gAtJTbugU6wYUqgBEngkUrjfUAuO4v2UxND4ERMKcSDD7qIVBDCNoGv+GahLBo/GOlrvGjjkAhvSc0I5S2VTjAyXvxuj/QO6SoQCoHlMD7PenFz4T58lyxdRycKX8zZzN/hgEtM9BpWRe+a1qDxg/mVFz0fuerR6o+n+vJN8PJ1jtfLCa/WEyIk92zME0GXZ4IeHakovWdgSw59xZGrGLkaZRoDtcu6zXdG6zwStAiFZG4NPz27xX8sm/yibZVwQaccuZLxQkE0cjWjk5LLgsohEXSFpg3o2U2crbgddLnnLXld4QWnOUoCuGNy9mxJKBqOSz4+YHrrNSpnmG2vU0Qxo1aLltQkeUbXC67u7HCmep1xS1CgGKk1ZLBGcbtkPoFBb4XW4ZDO7hFn336HK+cP6DwuuPuCop5YxCwnUI3Etrz0CPLRZcrsADWu6IwmHHziGQ62thksLaMDRevzL3L+l7/Acqdm9y//N1TdLlYpWsaws7zGxnCHVj7n2VefZ//CY4iA08pLTVN5uaBaxEJSeMfY15wl+b2+vj/UeBi8nBCDpYDyoVDGaDQlHM9/1yt90xcHVFUj1xSyI1mqBUEgoavQFmoNWdw4tKrSc3nwOuc3D/maeBSjQ2auRUtmRHkFCoKJBetxkWg8WaY14aSZVvy8UWSVqwGuJ0leqMhtzFvbj/DkrXea9FsPxfmY9KgmHlaIhaxS4CmDiFGvTW82Q7qGvzMLllgbj5GyxmjBjY1tzh3sMez06GRzDvtd3tm+zNV7v8lUb/DayicQJgET4KQEbZBqho+GtA47qExRtB0eSbZuUPk2wYWAJzqf48JGn6h8GpsV3JQ9BgON2bfIMqP/6n3qtiPvtbn/Jz/MIz//W/hxBbaGuqCOY9T0EFkbpKkRNGnAQml8bU5djj2N8y2yIYK+q7q5mJ1Pv1rrsS9m2HMhZmJQWxEJgvLtHFs/eD4W7IFl/p/G1K/nJI+ltD7QYjixjLcCrqfAnYru8xVbWwnr11okvYCwrYhVQ+g2asF5kQ2QWRs3UuoshL0VOOo1PjEtKbk0gUFLMm9rqBzq2GAF1N2AKpaEBajKUbQERkuqvkInjtV9S9ZXZO/rcuOJDsE7OcufH7H81px4r2L9C2OqnmZ+IUIYcG2B+aEl1C8dIu5XtBbp6DK3HH2w25gkjj3lWkIyrjHfvkz3pQnJa3Om11rgPG0B0aAmmBtsqOjezrGxwm5LqmNHlkGoBLkFKSRz2yiN6gpi2cjwI/kgu+cExHhxennnoQIBSpzI5RuvLOvBCof1glAIUuXoBoaLScHcSnIrqZ0kc4pRrRnXmswqJuUhs/ytpk0fdhmtvZ/B6ofJwiWsc5g6Y1Z0mOoVRJ01vJb5iPZ0h64QTKMNMt2nECHS1RipqXSMU00lVkiH0DGoNlLFiCBA6AShE1QQNI8FKSJdhyBpDgzZqB89zaIICdY1qi0vFEjNTK4R12PWZjcJfcY0XGcarlLqNlanCzQoyPQSB8klJA5tKyI/Z80e0Bd3iV3GZv4OMvEcqy3874IKnsa0rrbfBC//fz9KVxPMc4ow4Ka3HLiS36mOWZIBkZBoxCmhs1xUZYSA3+AQzQnBFw5sSeEbzsRZlfDD6TZLMiAUEuc8A1dx5JoqzcBVHLuamTeUznLdFRRmQikDcp1QCoV0ZUMa9oLKe2ocfvF7pPeUwtM6uM/2vdtYKRm3Wow2LlIJOJ8XnBuNObYl4fERgS6402tTGUFwZ8LKaIi/M0Yejrn7yEWe+OwrdMYTLrgZ3bUEaXscRSH+ag2zCao0KKFIegHtbEp9+wj6kkImDFa7TD/4FOa4pHNrl56v2IhA5FCZAn9ngLmywWR9hfZhxrAOyDKLd46kmPPEl36TX/rYB0E0fgXGe8bWEIeSSDTl9G+U4ugEvCRCnUqyAwTFQ0670fGEYPYQeFENURcA1yiNVC8kSCU9K1A9iUwk2sI0beTRCEGjE4Wt9IBETNgJzqCEpXAR94oNHs1v0DIlqnTYFd1Mgh6isYHKE09q6qWQ42c6dO7khGXF7XCL20+d4+z+fVp5gUslynl0YVGVO6nUY2TALEkwStPNZkjvqGSIkSGRKTCB4ObmWfrzKbOkyzTqMkn73O9tce36XaLSc2t9nXutJ/BOYYVAaE/VLRhf2aE12ELNW0iVEzmHiWvi3PL07gt8yysvIKIJVbvitcuXefncM1TZGqXqYFWJzvaIhreJB3sUqzFHj5zj8PxjXPv/fI7OrVcRtkDPC7wS1ElAUFuCvITaNORcCQQKZx4ytnPNVdAHCmFOVrW/x3BQ365wuUP1DLKjEJsB3HvoeFv0PXzpKW+U1IeG4q2M1oWE1cdT9PtayMdT6rMRL/+7AdUv7rHx7ctsf6jL2pmEuKUILFjVbEZBUjT8l04B7fuQRU07adCBcQq9uaA18xx3FT6QxIVj+56hThRVoqhCQT2DSeoY9QR1LPHbgmTsKIXAa0F4PsF0Eq5/wtD/2pDlt2cEc0v3jRwfCoTzlKshgz+1wvKvHBMdVagvT/EIVOnY/2Af7Sta94umRSUFxQeWaN3NCW6VjB5vPKW8hPCoRmIbzsvE4DSonqIdecgsxgsKBz3tmVtF5RW3Zy3ulzHX2hnbcUE/sMjFFyh54DZ7Up05aTOJh4CNFBDQ8OQq56kdKCmQeNrKEUtJ4RQpntWwRgqPtZA5ydwqBlXIflUQ7XyGs3u/Qb50laML30u2cok6eRwvFKLOcbakNjV1NUPkQ2Q5IXWOWmlqoRAcI+0dvFnC+wBEgHM5iAnOxQ81ZxTgkLJoqi3hCnr1CYQMYHYfkfSbLe4johWQAdB4vVg0XmjycIU7wRJ4i/QV2tVoWyBFSS3TxuxOSBCNZL3SKbWPqWRC5jQr5X3wsJ29Rag8u/osD1hJzRjPwdj/kinBN258E7x8nSMaZ0Qvvk5sHV4KbBhQxTGDOCZOW/STFi4KIYrQYUhbaTIsU2cYe0PhLfXC50AB6yomQvDF8pieCGjJhuviTsFPsxYOEWTWcOQqnMupvaUUCiM0EtHkINF4jpxUJQa+wtiGj9OezdDlnDvLS2RJi+PlVdKy5M+8/hY/+M4t7uQDDsOQWlq4NGFHPcLoaxXXfuXTRLd3IDe88Cc/wforb6MPx6ze3yO91IMAzNTDOxPC1ZjpnzvL/LfGJKMpSZ2jhgdUZ1bYff8HUK8PobIIci5/5SWSn9nl9o9+L4MLW5z52EepXvgt0us7DM+uMVlbpns8ozYCIwQ3euusC0Pv/i22j85xd23jtG009jWRUITI5v/2GwVeFtEAUnHXNK0rJRqezcmIB2P0vDidMJulX3OyC9v4Yeq+ptPWaC/wLUmCRHjIo6ZFgAebQxyBiQWpqbgS3MIITeYSDqoVcpnwZPQ2q2aA7SiEh3BYN5ya0iItjB9vUfYV61/OifIaWyjMhubVS1f54GuvUK9ogokh3GucPT1iQTaUtKqM3u4EbQxOSHLdJnQFTkJUZ6yOhggpuL51mbAyLOUDbpw5w3Nvv4pRki8/8iHy5X3S2+cRXmJaBfONEd6lVEGMMor0/gr55pBeNuCR8Us8vfcmdilHFiHPn/0YX7v0PowPIBUkR56orjBxyvzso4yvrVMnAqwjX1a88D9s8ci/vcDa175MODlA2oIwm0MSUicxQlSoskZ4D8b+rul3caEzFi9Fc3vBWToBdA+vKe2BaVa+FoSSqPUAu7c45jyLK2SzuamlrBz1kWX2Tk765Qnpoymtaymbf3oVuVNx8G8HvPC5MZ3vW2LtfW1WLiZ0FiAmNA2Jt+HDNMqAuIRLu1CEMG01j+ehIC0aY8OirdgPBWt7ltWxZd5VZG1JWgpWJp69tSa+wHQkSe6oHdhEUCnPRi3hqRUOPrpCeH1K68YMPTNI5+m+OcMGktmzbcQXM+IDy9LzY0Yf6rL1O0N23tej8IrOQUEaOYQGfybEpBK1UzN4fwevBElYEB5VCLngtFQQ1hYvwaWCoGzS0h2WVFmWPWxFJY/bnJt5xJuzFpEy9HRDxgUIpW88SvCE4oGF/kOFmNOfWjStKO95YH0ABMISCUvlBYWV1L7xeEmVI1GOfmA5nxRUTlI4iTdfwd16hWHvMYadq0xVn2GwTukkWbhMFS9Rt9eowgQTaJzSIBOUr4nKQ5b2voifFlRFDtMRtp6RhcsYFSEpsd2zON0Cp/HlAJ/fwbe2IIjw87uw/yWcCJuNGKJlVG+b5PzjSJXgRJsGAHkkHumbyp50NVJFaF9jTttSDyj8XihK1eIgeYRpsM5SuYPwnrXx66gVyT3OvOuMMI6m1/puJtIf2fgmePk6x2s7N9n47AvUrYSqm1J1WlTdFnUcYeweubFo4wgRaCQ2CqhaMVUrQYYBOtSIMIAoJIpjyrDkFWewNe/yChFCLKSEYiEXlBhnKTHgDAhFS4Z0VMySDCicY+4bM4tm4e4pvEN5R3s8IM7meKkY9paYJwnn793ifW+9gRgd86vOIJVjFinWNo7xrS6toeHMz3wKu3sPNFz/0DOMohbRrXt03ryDdlP4kMNerZg/XyFvZKQf77L2JzRvTleYvhFi84zRdz/O8PFLmLFl5dU5S4M9zvz7XyHGUNiS5NU7zJ5+hOGTK7Sv91i5s8te9T5Gq6tsvn4DryRHS8tEhyO+9tFnWX7nBlfu3eW41WaetjALjkskJPGiNTP03yDwcqo20qfOvsILHhZnx0cjdPnQ+0vANiQ94RwiEgQtRSdQKCvwfUVkmxX2QR+8FEgDRQ5hB5wWYDxCCgJhafs5rUGGFJ6d/iZj0eL8eI/0ICfIHV6CmFjqjmbyVIt0r0IXDmE9H3rpZe5+5xZOKUb9PnGraEDPdJE5QyP5VK5GFc3K1iGbFZpXSF8RuZx5mJLWnhceeYqvPXKV73v+1/EIpBdEdcFhe4uXH30U6wzOHNHeWcGGBVYXRNOkuaDoKdtHYy7dfp69qzlWa968coajdJ3Xt58iGaygB5p6dUrdrsjXa5yShOM+XkwRnRCddfD1MuWyJV+3HD/+CKtvfCuXf/UXWHnxeWRtccIzO7tBttIn2T9aVGUkdTthdmadqtcBASqv6Ny6T/veflOpAUwcYtKYut1C1DXReIbOClRRo2cFkfAEKxKhBdKDO6oXXuyL711zWoVxylEdOczUMr1bEt/Iic5GxMsB/e9ZovVazuG/PCR7Ys7et3aIr8asXEzotwM6VjC2nrAWdOcNkNGu+RlWkKXN8YOHdt6E6s1jydG2YDK2rN8tiKaa2XLjAbR+BIfLUEZgQ0la+kYpqQTZpqR17NjcseQXOowf7SAHOembM3xUEu+XhLMKf0FT71qKY0/7xTniA3D+K0PuP9Zlf7OFnlp6s4JeYFEdiQwDWjdyxo+1mmMtkPjM4i20hjXKeLCNMSJqIYd2jZur8hDiSKSjqw1To8itJJKNMk4BpRPkVhJKzxyQwjXfj2zapw/nAZ1uD3n7nMy8SkAiPYm0OG8bYuyCnOoQsAAAZsGjseScm38RW7/KuP0IVdVqWjnzGlFPmIZ9jpKL7CWXONbrjMNt8uQ8ZXCRweY6OnoHYQqs0AhrEKYiKYf0xm+RHHyJ4cVPUsZn8MUy7L9EdXwbv3YN17qMHQ6wzuHMBOeHeA6pD96hvPkl1JkrqN5ZcArV3kDqGCEkPp8ibAEmB+8ROsK31hBxB1QIQi6qVxInAuZBHysUiRkzDddozYYstROGLHECYIwTHEyg88Cw/Y90fBO8fJ1jNa955Od/k7qdNhNbGlGnCVV3AWI6zf1VKyFvJdQ+xkiHq0u8lE2ZUkqEXORqKNkoMcIApyRIiVcKrxUu0PhA48IQF2oQoJ1tbPSFRAiNB3ZNcap6UghWdERbCLCW577yeTb3dijjmFeefBYmOc/98s+TTMfkSjGLY1658AivX7pMJ5zyLfolatej+OwhzpUEK33eefIKbz33DL1Pv0hr/4j2pmHlh7dRiWQ4dOTHOQQ1m0czWgdHXHs64kvlKtefuIpLNK7WyGFBOMuIbwzwHQvrEtXXrL32GoOr59kJNJutkJX799HTHLuWMOt1SI4Mw06P7bt7fOn7lnhj+xxRbTi3v8cbly5jvGPiazSCeCFRPDH1e6/Huwm7C/AiBOYh0JkcDpH2QRsJLfHOn5J1VV8hpKCrFFJLwp5GuUZZctxdrGYsVLkn7TYTQ4Nim2Wkriyf/OpnuX3hHIfdZabdDm+0W1y+cxf0DB9Ad1Ry9Ik+9VLA8quzJjm6cPTyOR995SWyJOH2mW22oj3W7kyQtqnWGRkt5P0VTe1ONKsw2SayWVPR85oi2OB28gRmvMXWXYM0krsr2zz91i10JXl15RMwX8L2MuYbY8r+HCcdutIEfg7BhDOHUx4vXiNyOe4g4kvvv8Le8lNUqUeYirp1SHzcBiepk4Kq3wSJnvzEdknqDumwjc8dxdoA0orxsymvPfUDtO58jPVf+QLR/jFhkYEQ7PzJDxMfDum/fgtZGzq3dql6U2Zn16l6LQbPXGV85Szt23t07h0QZDnxYEwwy6nbCdnmCnUao2qDnucI50lETmc5R6w7/HlPdWCxtcBEASaNKZa7BEVFundE5EqUs/jKU7zj0eOa8KLg6Fjggoj6qQ42A/fzlvaHPIOxIz5v0ZshcdQidRGRDPBJgPaWXl2yXJVEpUEqiw0tCI+qPb2qCfUUSpJd0rTHlvVdw7wjmXYk7UBShJJKC4QHXYGPQEiBaCnkJiwfG1qxZB4GHH/bCjIztN+as/TSlPiwIloTmIGn3LfEr87hyRbnXhnTupiw10447HXY36lZ9wUrqoa1kOioYvJoSs954kOPmlj2nunQez0jyUxTEVu095xoqlun0mkBofCsBIZSCTLbXGilaM4TJRoQE8jG4M4LgfGghKdyovH9AULZpC2fABgPC3+ZhYmk98iGRnZaSzipZp/AnJNww1NRoR+xNP0qRdBnHq1TyxgXhmyYHT54+OUmH0j1mOs+o3CDQXKRme6Tqza5UEyDNabtdebBGrV6lKp6GnPwOsoUDaE36UH/IuHBWxS9S3gZ49QqrhjgZAcRhAgcrpjiiwxz+xXc8i5y80nIfKOcChNku4u3LXw5A1PjqwJ3/1VE2gOhsdNDhJSo7hYi6aB62xSq0xCpbcYsWKauQGh7qo6KxZz1TusPPb/+Qcc3wcvXOczbt4j3j0kOjheppTT9Qi0xcYRpJdStmLqVYNIYG4U4rTBJhI0jbBxio2ZzocaGAaYVN5WbNMGmMVUrbsCMVs17wOky4aQf77RiHmqmQYANNC7UuCDABYpDapS1XN65y1vasbPSZ7y0zGOvfolrb71BK5uivee4u8TzVx/nzsYW2lQ8k7zGPOzgfuuIrV//Alp7dt53jd/5oe9m5TMvIiNIPhDT/USfMquZfG3G/HNjoqsxfMcmv73xGJ2rfYZyiWzJoUcVwa0DVj7/GlU7JdtYwr8vIChLVrctnbUj2nfmLO/dY7B9jrcun0e+fpP+7R3KC0uMlpdpDQ6Y6AQ1OCIpS56/coWnbrxFVDer4wrPzDXJ0j35IN/oG+H18rBUenZS3fH+QcXMWpLBqKmwLF7jQ413NHlGgIwb6/1W0ADZNGwkxIMuGN3MpK4CcmCLhqgdPvgcalKTDCq2t/dpDWbsr6zhp5JoasiDmC9ffpL5Uoq+ZliZH7MxHCKsJ94p8EJzbf+At9bXeOfSOXIRszV9oZm8CfBItCsWpfamhZSrHsqXWDSxm3OYXOIousCUNa63P8y1wX9iZ/ksr55/jPXV+8yiK3z6ysewrRKko+oX5GGNdoZo4JHRnLXxkKBT8tbjPYpgi3tLF5j0NFU3wwsPWuCMw26NmnMuLh84ly1Wy9KohgegS0JT09sfUy3PsKGlpMvh1kWG/923sPXZn2frC1+gf/s+y2/fpt5epmpHRPtzovsDhPOsvfAGsrZ4Z1HGNa0l65DO4aRqsvz2wStFncbU7YSq08bEAZlLqVxMf7nh2kQKyqnATSzRaIbOSupOwuDRCyhraQ1HxFWOzCrM0DXhmk9JyC0iMmSk5CJg+AqEb1QsfwQ6z0jqM1MmacE8baPCmDBPWC9DjqIU6T2RtbRtRSwLZFCDg3BhUOm8IO9CkHj6U8fayLE885wdWO5uCI47ME8FqpYUMVQaKinp+RBZaDSK/p4hi0qOPpww+NAy/a/NWPnyiDgqSaeeegLyjYryXEDf5oTrnr2zHfInE+4ctLh55NjyOcsrFcpbBk/3WH5lQiwrNt6eM7qWYoaGdK8iqCzSLbqtmncRqU9k/rGCKHAYBM6JhWkkBN7jvGRsJZGESDq8FwTCI3Tzt6gdFEYQKXd6akka/lzpJNZLvBcI4U/WDCjxcHXGo/FI4U9ff0IIThe+KpVKsSrFCM00WCMwOe16QLc+YCt7m3L2Ajudp5kFqxgZkE6PsEJTq4R5uMI4XGfUWycTMRMx5ji9DP1V+ksdkBPyOCYJVtncfwNha7ypsN4z7bTYLUJG4yl+tos9vI3cfh9+6RyuvYpL+s2iN4ohShCtHqq3jkCBCpCddczxHex4FzE/wmUjgs1r5KpNZGYI7wikp3IlRjWApSTiaAZ/XPjlm+Dl6xyvTA4WTG8eUNudRTpLWBnC6XyR+slpw9XLprriF5sNA1wYND+jsAEfgVqAmgAbBAuwE2IXz7NxeLqZOMYFCk+z7HCyScvyocYriVCS0Fu8EuwHASJoszoYciNpc+MDH2LY6XLY7WO9xApQteHJ+XUKpQlfG7Hy/3qeflrTOtvhxnc+zvZXXmF59yab9S7hiiT7TxOK1zKK+3XzOUPF+Psf4eCxp9gdO+JlR1sPWP/1L9H56k3IIW/FjOc5+dlVqpUekyfOs3m2IrF32bwxYbQcMpJnOF5eJnnrPtVHHmOwscb67QNGpSF3imQ0Zm91g8gYlHNIa6mVOvXMaRRWTWzC3Bs676HXi/P+XeBlfmKU591pKGMwzdDz4oEhBeAChShtoykEVCqJQ4WSAhE1fJdawv0VHihcTJPo6gIBMw+JOCWCpncL1MRiU0WQGC7t3aX1VoGqPIONPkUWMd1uUXVCWm8V3HPbTDZSonHN6rohHR4TVTmD5R6XXr5HKUNScmoZNq2fhb0iCIyMmMcxk1bEvB2z2z/HzvIF9rtbjFp9dtNl7PE6sMqklVK31znsbFEFEUGmiOYKX9TkyzUuqGnJI67e3UfUCY6A+53z7LbOYaTBqxlqHuEDi7ASLzw2rHHJoim3WPTq3LN+T6FqT7C3ia4lKjgmWNtjWgoO1GU8CcpqUJr9T/wI80s9Hv/Zn2f97VuIlwa4foui08KdX0XtDpGzAhdodGYQ1qIq0wAY75ELkHqyaAgnv1tJ1hQKfCAJriTozYBIeExeU+3b5sF96N28j9MKqxUuVAQdkNbDOKf4zYzWn+gQrXqiwZBqYCiOASEo367xvyxwzyakz7VZff8UX97HGMndfYUbK5bSkOVY4bRkrCVaB3RSQxU0bUgXABJiLSgDBWVAUEqCzLKxY1hJaiotyIVilEqybs2sbxmlkpUDhStiCpHgLOjRnKJdsfuhFnc+cIalt6ZsfuGA9s2MoPQEt2paYUlrtyIcOK5/dJvw8ZCyVrw+3KDMNMuBoY4j2lcyrn32Fq3lArXvmCyliC605nM645woq1DGgYJKK+xpE71ZDCjn0L4Js9LeEyzaO7WUDalFCHLnkRZqbwnrmsjZBd+lUR1V3hMsqixNtcVhEU1baKGjbyhMgtpJSi+Z2ICZkQtfmaZeIxbGJ1J4Trx2HXOMFxjfSJ+N72K8wDuDxCNGr2CiVdAh1s6IXEaoI0LdoqU7rKoULwOc1NRBh3m4SqG7GGvJzBqViHCtVagLSplinScxM66EJaW2HB0e4o7eRgy+SjcJONtVyDih7F6kSNaYBl1Gepl55wp1vIYJE1y8TNTqIqoZZrRHZSrqvTfRG1cZh+usFXdwXrFkcgbxuYV3jWRr+Y9HaQTfBC9f9+ieOctBGDZJo4vaofCA84jFBU2cqE8elu3JxmXRaYmSCqcVSIEXopkoxcILBECI01bSCYjxCwBTpwkmCXFKNeRPawmsRVqDrmqCsiSoaqS1DUoOJDLU1GFIEUWM45RlpemFAVUSY5IInQj8WclQauTNgtG3PsvNXsTgynn0zoCt51+jdXTEqB/j3lH4w8aDwF1s9nHw1GWK0Tri83skw4yL+TusH97FHBZQGEIlCSYz4nnGfD7GxymzRPL6tY/TuR9z7tl9Otspw9dh57uf44l/9xuY0uKWEoJYY1zING3Rv7uHuvQI0jV/59DUWBUwdwbvPYnUREKe8mA676HXS+Ht6dcZI8kWbaPyIY+XaDQlmOfN90kj4UQ2E62om2NCthVpSyE9tFab/RunkKUnpF4wlSdug1OiAT1KnR5PretzfCgaYqmCYGxoHxYIPLWa8snrn+bOc2fYibe5fHsH4wL2+mv4MwF3pMSbBnBOei1++Vu+jbCsWRsM6I8Lnrj7BnEZMktT5nGLG1sXuLe+jsDjhGKul5hEa9QiZhCfYzO7TW86xdcpl+/cIDJTfvF9HyJWju03YuIipssxVX/AmnqRlfEIUUeM/SW+uvqd5OUZeuOa8fkDqrYirDThNKXqz6nSAheb00VAfzbmibdv07+5QV2tMXUXMFKStWHngweMly4Q5BGiVnhZU3YznLYQOGaPvY/q8iaP/0//jvO/8wLhYEI0myOEwEYh1WqKGVfYNEaWNQKBNBa8byIdhEBYA96jXNMreHiqlgC1o359jh9ookdi9IYkXBaUr2WQPaTEeEjmK3tN69QrT/2pQ/x6SOt7+qSXYup7FbPPzqiHjmIaUo5Lincqol8XJO9LCR9L6PQ0hZPs38jZGdakyrGawkoqCVNJ0NXoviIMNEGkqFPJJJS4GITVBNOYVm3QQqAjQ0c4egeS6iChSC3zSDIMFYUMCYwjmSuSeYqWiooKE3jm55d45ewG6v6MM1/eZ+nWFGVrguOSteMhy29NOHxmif33LdNaMQzThMNhiyCzmF7C177zMtd++w5hWjOfptw8t02U1yxNxmwMjmnPc4LKEFQG6SxFGDFJW+RRzDxOmKRtjJS0i4J2kS1UR83fuwxCRp0Oo26XadqUBZRtQExQGwJTE5clG8dHbBwPaOdzlHU4IXCymZeTokA7h/OeBAMeVqkxQlKgmXpFhWxiKLxDucX7e49znhg4YcWdHDPON+CodgJjDjC1pPKCqQdT5EiZocUhqWzIxYEEpKKUSQNShGo2GS7I5QaHwDtP5TVGpZigxda5FSZVl+l4Sm0tt2YN6TsYvg7yLTZDy7VYosyIaWuL+ysf5e7m9+JUTGomiLbE1hWmPKQ4nFGuXGMQnaNX7YOETnXIOFoH5zk4Fpzf+MPNsX/Q8U3w8nWO1bTF3eUuqqwaz47aNiEP0jcH0gLUnFzATlfhi6wYbS3e16f+BAhwqqm8OB3gtcQr2chrjSOczBHjGcL5hWhFIL1DaImPAlzaABCTxo18WCpcokEKhBIYa4nKEjXNSYYZHXOEF4pJu00WBUgcS8sF3JbY+xVGhmSdFmW7Rfvl+3Tf2SEajCgDzVz1yNI2xfkOZa+N1+qU9KYO5my88Dprr7xN/EiAfDwl/dY+g50xsQJXN0AmvntE1ApJEok4s8Th8grX39xgpRwg+ltMHj/D8ZUt0nsH1L0Y102gcORRRLI3IKrLJqEZ0MZQR57SWzJviYRswAuesa858x56vZzwXWKhsKIJaQSY8zB4mRFMs9MKig2DZoUFp94isi1ptxqeS6sd4AUcLDWPiaZ/g8khboumn64eYhQaR+tGQbUWYkOJrCG5XyJEI2Ol9Ew3Y1qtgmu719msh3ghWHprgNvpM9GaWZow7neQM5gGCXfXNhl0l7h66x75QYQXgkG3z721be6tbRHaik42RxQhUm6xnk8QleHz/Q/y51/5q/SrA/bjR1gr7zKMNyifPEPocw6vwGMvH/LI4Rts33qNcdpittzn+cee4dWLl2ntJkSzHJMWmNCg85D0uIcXnkJNqXsZsbH0xiOWJmPiokYen6Osl5jJM8xbEfM44fjKPqXosfLmEiauMUlF3SqwYd3Mah4q79k7v8bof/izTLZWOffll0lv7qKrGlXUTcW0m1AaQR0GCCmgrJHVQp2Ex4dBU/wxzaJA1m4BINWDFqGS1LkivyOJHwmRIahnQuxOgdmtkMa+iyzqJg6fVYhOw4OqD2tG/9MB8QfbRE+lLP/FVbKXMmafHuNnBfmBwj8Z48o55WsZIlUE5yN6SxHVVkhOwv2OZqgNgavplxWde0VD+l3SyEzQiSU6EKgaagS+kphcka0H2ATC0JJMBb2poGUraikocMzKNh5BpiKqeRvrSmy7ZB4pjqolZltXeP37wVWG/s6A9bv7PP7WdZKyYOkrU4KbhuSRPvFZR7AVsF+ukB96ZJTx1gfP8vQXrnM22+PMy/vcXlljXidcP7tJais2joasHk0ICk9UVWwWBQ5BEYbkUUwZRozabYZJmxpBYCy9ck5QlKyVFatHA2qlOer02O/0OUoSdEzTSurCvbUN4jzn4t591kbHdLIMB4yiFvfPLnO7u8ZEhiRZwfp4yLnxAf0ya1LarWfuJEMfMq8F1oIRjZloLB2pNKTC0hKGRDZqJo0nEZ4WDsXvym7yYhF70gAcw4IY7B0xFdIOUYvFrpNRk0PmitNjyrLIV3IaX0mEUrAiqWsHvmlnKymRYUwhUjIbUqmA9eo+13b/JYPxF7m//Cy5XiKuhuRBj0mwwpw+8+nb5OkWs3CZk1VF4CpqERJSwkMRAn+U448EvPzzf/7P+Yf/8B+yt7fHM888wz/9p/+UD3/4w7/v8//Nv/k3/J2/83e4desWV69e5R/8g3/A933f9/1R7OrvO0ZvvMH1//Y7Gstoa1FlTTDLCbIclZXoogE1J5uwtpm0anN6n6wtwizutw5pzCKFuGjeRIhTYONVQ9x1gUIJCBalUrUolzpE05ZCYKKQqp1g+m2K1SXKpQ51GJEnEXUcY+IQHwTovCQ6OKZ7d4/z4S5yJeF4L2IctXFKI/KaYHpEZzShfe8AVRvkepfW6IhyPCa7pzA6QKQNz4ZhxurLb6OVRa8G6FCQPtcmdBL/eEDxauPQ6j3IymFcSftrt4gmGfGjl4jLktbnSlafOuadYJXZmXW6R1MUnqzbYenmPaokgUmBsga3qLxoa6lofBuGtmrAy6LxMXHvbUDjw2TdwlsMjRft/KFco2g4IZjnp69xoULWbsHsa2wYwk5AlCqSQKDDpmW0t/TgfXwNvvaIWDQGdEHDkQFQuSMcW8rzChMJVOFIRjXCwfxsjKnAJAH1Ssjq7xyBEFR9zfnfOqZ/+x5u8TexrQCnPVOf8LPf+QNsHx1z7fZbbBwPKIOQ9jzn8myXj7/0Ip35lHmwxNu9j5N0NKoueX3pWS5M3qBfHeCQtO0QieVe+2kuvKm4/kRALA64KH6bzfqIXK1xLK/yQvjd7OoCXUwo1iaYVk6+MkbagPSgj6wUZu2Qi/MXWH3piFmSNhygOmHGJQ7PdnGrS7gqIZqlFEszvLScfeUysgwoo4LRlV3yVtZocJ1/YPwBFMtdXvzRH+LowjZnnn+V/s17tG/uIsMQPc0hCBBxSJ72CYczVKAQxqHKCmEcNlBkW2uMrpxj8sgZvFakewOSg2PCyQw9LwhnOaqomb9ZE18M0WshKlKIRFPfKRDlomXgFrtWgxtaRCIQutnX/LcnlC/NiR6PiR5LiP/7Zaa/OMTs5Jgv5rAR4i9FBNsh9XWLEDlCQBoIZE+htkLSj3TwF1LmiWJuHOzWtDJPxzjaBcShRHlPEDqisqZ7PaNcEszWNdMVSVIIgkpTpxBay6YvEHlEZTWVlYgZyJlBxjVOjZm7hJHpMJMBxVbMZPsRPv3xCzz5pZc58/YunUFGmBtag5wzr+4zW24xWOqxW25yKM7wmStbXKuus8qQ7SzDdC2te1OYzxkJz0FlScqaXl6jjCP0ntYJ6ZYmFNcuvvLMKSZW4r0iko5EWZQEJyVWSDIZMpQRZRwRpBofKlwQkCvNKEypvKRVFvSzGd08Y2My5G5nmVf7Z3ll+SxeSHrVnMvjA7bnw0UEL2Rhwo3uBrthG1kZhLFNy19rnFRYJZF4Wq6iZUrapqRdF7RsRdtXtHxN4g2B8qjT9pM/rSQJv1gI+ZN/FHiBtAZl61MGsXeQ64BZGDMPY7Iwpggad1yFI8DRKXN6ZU6nKFjKJ6RZTivLuVx/AXP4AkZqrA+Yqh7joDHZq2RMpdtUurWoAAVYqfEIdPxX37O59r92fMPBy7/+1/+aH//xH+enfuqn+MhHPsI//sf/mE9+8pO8+eabrK+v/2fP//znP8+f/bN/lp/8yZ/kB37gB/iZn/kZfviHf5ivfvWrPPXUU9/o3f19x/DlVzn76iuYNMFFC1KtatpALg4puwk+DvCxglDiKweVxZceYR3CWWTtmlVfVTUl6AU5UDgP7kHl5sSu/UR55Bbv47V8wJ9RCumbnqv2jkg4gromLkrU3YzCQugWOFmphlMTh1SdFoNPPM7s2z9GeeCxt3LkpEKVFe0b99g42qe1t4efWVzpaZkj+j5GxwopBbFwHL+Rs3f4UOusIwmuxMhUYT8zJ488hGCKAqw/lSpKms/X3z9ipS7IgxArBbbIWRWH1EspB0vLLAnFfHWFy7OXuakD3KxEl+UpQVY5i8Nj8By5iojGqM54954b1c0fMqjLvcV4h0YwfEgoHQ2nhNOGE+FEsxIXzi2qbqBiSaAFOpKkiYZAMGpDHTboxHswFSjn8VpCaZHiwQU4OKwwHU3d1WA96X4JEupYUaWKOhK4VBPUNcn9EhdLTKqIBxXONWGMTiq8bv5+S5MZ3/mVLzFsLbEyHtHJ5rR8QZh7lBsQm4JahhzFF3lx+09zdvhVKpVwu/00P3zrJxHeUQRd2maIESE3Wx/A2Yj3v3iHi+I/IVTBznafV7vfxlHxMaQP6N8wyFoy3x4w3xwSTFqEWUgQTtiUe3zo7a+yND1gTMn9lR4vX7rGKLmIq/p4JTDdAkxNPSkJs5jld85hraRKc/LHbhL0R3RcU7WyQKkUVshTyYqLQu58z8cYXrvEhf/4BXrnt1h6/SYRsqmmTjJ0GjM7s4YLNeFohrCWeDhFFyXpUaMmk9YyfPwixUpvwSvRxGKMsk27SRcl9msZsi+IzgSIFUHUD6nedrjjh9rKLCpumW++l1AgAoGfW/IvzihfzVF9RXg1Rm+FFF+eYXYqzKCiXgkJLkdEl2O8aS5udtdR3yjJ/uMIkQiSD3dp/aku4XbMHM9cCvCesILW1NEuoTWGSAjCzNG9b7CpQigIy5pOIThcV4y6ACXrx7A18wTGofZqilBQdBTL0ZhLdo84dwQl+LAJiqyuCcqzfeytGa17OUv3DqlTSVGM2do74HFxk0na4m77DDcvnYUbUMgMrOT5DzxBoUKWJyO62YR0PqWeT+jmGe2sRJc1yjTzqXcevYg6DoGed5R+UYnwCgto6wmdIa4rVsQcUUE1aoi5oXBI0TBq3IJf5QVYoViVAy6rHT4UvsVR0uXm8gb30iXGOmLS32Ajn7CRT1jNxqxkI0oZcLOzwu32ClY4BGYBRhRGaYYq5TBoYxqrPpDiVMqtvSW1NW0qurL52V6AGrkAMgqPxqGkQugIhUNVc5R3xFVJmudIPF08bZ/jqxxTK6ZJyrSVMk267CWbeK2IXMMfPLnmJEVBJ8/ozJutP5mwNX2NeG4oZYeZ6pOpLqVKKWWbWsZMwhWGewNWe1vv6Zz79Y5vOHj5H//H/5G/8lf+Cn/pL/0lAH7qp36KX/qlX+Knf/qn+Zt/82/+Z8//J//kn/A93/M9/I2/8TcA+Pt//+/za7/2a/yzf/bP+Kmf+qlv9O7+vkMpy/rL14EFG15KfCjxsWraOGHT/nFSYaUCJXGLNpDzEucFTjUqIxto/OKodVI8WEWcABpjGl6MtciqSRH2ctFSekiF5CLdvHccMQ9jTLtLFYWNgimJcFo38mslG6M077FLbVqPh9THFf6gJL1zTHQ0Irm+Q7+b0zoeIYDKNYz7MvfsvpPT3w64/L4Og3sl4VqL5z6UgJYc3iuYhIZaCezEMHphQvupBLGkiN/Xwn9lig4AKUhCGpJdqllPLaOiYMfE1DpAOU8F6N1jdv7EU6xORtj1HsuHI46idaKjCVYqlHUEpql61DhGvj7Nlpr7xhDwvRwPG9Tl3lLTrIwK95BB3XCCnjV998YYjaa0vGgdqlSipSCJFelSiBdw/8Qu4YSs6yBQzcSP8ci4AUJ4aN3McYmkWAoQlad1rwAlyM5EGCmxscS2NMuvT/CBpO4EyJknnJyY0IGQHheoBlAjubCzR7ReIvCMOl2iicHKkLQe4/EMoy0+c+4vszS7iQdudJ9lqbrPuflrSG8JfYX0jsP0IsN4i8vjL3ImexOvNK9feIbPPvktFGGKNDu07/eJhyn9owi7XRPva4J6TloNWLEDVuMZ+5tnuNW9TLm/yW55lunEYXszhG58P+rAohANkbf0CCsoliYcPXkLl5Zopwho7AiklISAo1GEOU67d0wvbPHaj/4gW597ibzdJqoKWncPSO4fobOCaDan6HWYXNxC5xVVJyU9HBFO5qSHQ+LjMStfe5s6jQnmGdE0R1oHzp2ep8J53IGjnNVEV2JkSxE/1cIMaqrreYOuBI2izC9+Go8PfXO/EriZxeUOMzAILdCbIWZYQ+YxWYU5rqjfKQifSNHrGtXWoAUyFrjMMfuVY0b/zz1EIkk/1iH51i7ps23KlqJakoxCCQqi3BMfWuLdml7mSCOFU4Lo0LI6sPTXNHUiqSMYdwSypQi6kt7QYivHUcsjtCQW0K4d8ahpbWuvUJ0Y/2iAX0oQpYPC4gtL4Rx1UdOfDTmbD3EHklmY4B2UTvPo7Rtcv3CJ/dV19vx6A+6FQJuapWzMaj7m7OEBm8dDuvkcWTn8YqGAELS8b8oxEoSWCN2EJAlLU6Kx7lT8LHzzGmTzdxey4ZB443GlwztDzxu28jlP7u1Teygrzyz3zEvPvAS0Ig2b116VitpLRnWzlaY5+IxpdOBaCwLZtHWmVnJUxRzUKdNakdcwcJ492wAbJRraWxBCGAvilkLFzR3p5Y8QdyLi6duk2W1UIFEioOUrOr6k6yo6riLCsikX+V1CUktFFieMul0mSz0mK0tMuh0KWozcMsI1GUjae6Sx6NrQnWW0JiXprCaZzGjN5jgTE8oxdw89Vx97T6fcr3t8Q8FLVVV85Stf4Sd+4idO75NS8l3f9V184Qtf+D1f84UvfIEf//Eff9d9n/zkJ/m5n/u53/P5ZVlSluXp/yeTyR9+x3+P0RaeITQnxMKUQFQOagOLi0QjP+CBvPPELOChi9SJA6enUQR4IU8JvH5hXuDhwc+HXr4QTTcXR9mcLCwmTCsltQ4eyLDjENNKqdoJNokwUYiPQ6Sc0n3b0Hn5Fsmnb1NWgjxNUYnE71dMSbFhDEuW1mCENx4vIB9ZDu9VbD/WIe4GhGdD1IWU5blFDQr290tufWlEqTzTNzPiZ9vIAGwkKLMmcM1YGhfSqWV9NWJ9K2F8PWNmHDIUCOsJDsfUtedgc4P1rXX6d16jzOaIwYRiLSGqSrRpTKROkqW3VUwoJGPn3/PKywODOkXuLMZ7IiSVfwBewtEUnTXHgAs1CvvA4QqQkUJLQbet0bEgD2DcbcpRwtNMlrVApwtgbDxenZQMPPH9CqSg6mnCQU04s7hAkm1FkHvckkKGnvbdAiQUq5reSzkPH0EmEphIEk1Ms47zkrMHexz3euytrKG6iiu7d1DeMNHLvLj2XRzGFzkzfZVaxhxF53h89Nt06sEpA9EKzRu9j/HBw1+gXQ0wKualle/lLfFR4ns55swRJqqYnD+kXjdcO7zDR//DgOtnt7n/iCNWA4hG1PEad/wlBkEL2/YEtefRewFu0uKd8wWzdoEuNa2DJUwno14ZwcYebvkYHSsMCqsX5waN43SjtpULjOCpcJxmKYYBO9/xHIfPPk7n5g7RLGPp7i5nf+N5em/eIdnZp39vr6l0Go/3nqrdWCFE4xnRLEOXFSaNGV/YIhqOiSdzpPUI29QHrVL4wpO9WhKeCwjPR+j1ALWsqW8XmJFFb2jswOKntpEFVyc7vgAx1uMzj9cCX7qmvZQIfO5gDqaoMcMJ0aMJeiNEtgSqq9EbAXo9wG6G2KFh9mtjxv/qCBEIgosRySe6tD7eIX66he9ryvOaycWAvcqjRpZk39AxjqUDRy93yGVF0FJILYhrT1hDHjey4vWZY39VMGkL8giWsxo3qZHHYFJF0ZaIQJHWHh2q5nag8LGkrg1uXCOGJb0yh9QTHNeI+REffPMe4zNrvLN5lp1oiUHYwfmAKQlTNrl57dEGsGSW1nBGezyjnc1p5RmtIictclr5nKQswUmM1NRR0LTYgwCjNJXS1LoRNSAl2ltC5Qg1hMoRSIfKK+QwR1WG0BlSV9ONPButoOEfPjRXiBMCCmCdpzZQ1J5ZCbPC42xjSGudxzuL8w7PHCHmC86LoLSSudVMjGZUhWRWUVhBZSW59RgcBZrZ/VtM+o8h7Hns4S5ONIRgL6ImtNFDlddEeUXPFSyLitWgYkVXBKJEyyE9BevKoQNJ3U2p+inFcodsqUveazPpLOHSgNnS8uKUdwuep0NXlv5sxodXivdknv2DjG8oeDk6OsJay8bGu+nIGxsbvPHGG7/na/b29n7P5+/t7f2ez//Jn/xJ/t7f+3vvzQ7/rwxhFwBlQa48HQ+HvT38ggVIWfRLFvGoNH/xxWpSWL+Isf2v3BfxwC3S0SzwNRD9Ps93ctFuSjTxWYUYlriJxc0cMRBxDJFaSDoDbBIhpSTvd4lMxcULAeeupgSLlhgbAUerimxvxnIBaSBZaYf0n1vmeD3n3isz5vs10WaAvpIw/WpjdEYNlM2Nu7slV3otji4/gr95B/VkipqXdPYOOBqMOL54jVuPX+WZL77G2nDArcMR5ZkucVkSWLNYsDYhlpd0ekrYnSwUSOI98np5WCY99SchDFCeKMusJZxm6No0K3yt0NY04OVkMRhJQinodwJ8IJnGTSQA0JSpjcDVHtWRGOdRucWtNMhXlg5VWPL1EGE8rUEFAsoljbcCH0tcoOjsZqjMYjoapxXpYf7Qp/AUyxFeuCZsD0lATeAMcVWxs7xBNysxhx5Rh+y0r/HpM/9Hnjv4BaS33Gtd40z2Nk+MfhOHwklF4ErmqsvF6Yt4BLNojc9v/jmOovN4IJomhG9sYZbvU20d0Ar2GC5lzK5us7ITcgXB9Y9tcnN7k7IShANLuu9RStBxNVUvxfoevbsFojPBao/SFZ2Rxl04wC0bTNjiERVR48mdYYalWEjYm29fnJ5+ASep2f7BOqLXZvz0VZbeuIV/Z4fJxW1Wd/ZId8ZgLI7GjsDTrFpNoKk2lqAyRIMJ4SwnyEqsVszOrBGMM+LJDOE92thT/lp1p8KOLPGjCbIlCB9NUSNDdackvBIjNJiBwQ5q/Mg1k4kWD5zQbEPopF4cUCFNtcYCc0/5SkZ9q0StauSSRrUUIpHoFU14NSa8EmOPDMWrGdUbOeWLGaOf3ic4ExI/0yL5cIf4fS3CSxE+lMwuB0xLz53ViuqrM3p7ns2thM5WRDuQBEIQzT0B4CvBxh3LrC2opGe0GtIrDco47MDAviMPYCRB+Jp4WqOUANlkF4W9gOiJpOEBjmt8KAmPa4K8Zu3GfTbu7KH6Mb4XMVvtMYja3NdddoIeg6iFjQLqlZiRXWFYWXzp8LXD1Q5XOUzpqCqHtQvXXiWQi5DHk5YNQrzLdffkoBFCwLKA882E29ANG/FFqy5puZqWqmmJmrYtSU1Fqy6JzLvNMmMgWhSDKusbgrhbcHUQi3WOQAhBDIQI+kJw3tNUYLxf5E03idvKZ0h3hHAvoxUEVxxaeaQEo1QTAKk0tdJUUlHLNhWC0iusF8iyRhc1Oq8I8wqcwwmJkxZfTLB7c/yhwsmT7kHDxXSiUTu6haN5rTT3D3MuXvuvnVXfm/G/ebXRT/zET7yrUjOZTDh37tx7/j763gHhtQSfWVzm8KXH1w5fezAP/CD+s3ECdizvThKDBtCcuC/9LlXlSWJqU2VpHj+xkUE0J+GpcFM8WMVD402Ca4iyJ94IYBAdgd2rcKXHzRtCjGDxutIiK4umIp5miFCwfDZl9UKHuBdSC09VOw4HBcMjh/2ipXgjR3QU/aWAM1dbrJ6JWeu1WbmcUk1qZk/G7N7Pmb5dwPQhlAccHFVk1+dEvUPYn+IfNxSVxKYxZz77ApNnrnLnkUucW11l/eiQq197jVefu8JanqNN08ox3jF2Tb5RhMTRZEfN3kOvlweEXc2BLU6/n/qEf5M3fCG8p1zuLvhLDR8I75v8s1gQKkGcKOqgCdY7tfj0YAyNOZcGbMMrKHTDUVAzi6o848cjxLEhOazwSpCvh3jnMUsKJT2912f4QFJshuiZJTqqTwG1CRVlN6C9k4FrKnzal3gE6bxgaZKhqXj9kcvEY8VnVv5PLNUHdMyASil65pBMdlkp76OFJSOlVR+TyRYCz1F6id/a/lFy2QFXEbkK6WsSM0LvVawMRsxWpry1eYnifJtbF1M+qtb48yLhn++8QdGbULQLVBGyvtdh2a2Q+ZpJMCZrNferuCIKaj54Yc5TZy7xui94x8ypcaQI1lRI4T1TV1M4y8wbChwVD/xR/eKMaaoyAoVAKIV98lGGK8uEv/YF3v6uFuFwwsrX3qK9d0hQlKi6RpmaIC+JsxwXKvzVDezRDDmeowtDtF82rdxeiq8NMiub5GrX8NLcyJB9eUr8/jbJkwmyrVB9TX1YI60g2AhRHY3LLXZmcWODbGnwAj9rLspEAhlIfOXx0jchnhaowI0sbm4RezXiJORHgV4PCLYiVFeSfLRN8pE29Y2C4qWM+s2S+u2S2S+PCB6JiB5NiJ9qEVyJCS9G6M2Q6KMdxq9nDLMKuVuTnIvoBYqVJcX62BOFEHhBMvXItLm4me2AcOjoVZolAS4UuEhQKM9oVGB2ZlBWSCvInWVwVFJLj98KsU91aGWWlVsZ7Z0CXVnk7gx1b0YkjtlOA872QsRyjOuG1FFIqQMqrSm9ZkbINIgYhjHDXspEpQjn0WYhmsgrmFeoeSOyCMuKsCxRtcUK3XhvaYUJNEYF1Fpj9KJCo5v/18CIiBERCxV1MyQQP5ijT+fmkyHe/diJsy+LqcAt/v+u24KHDEsXt0+q9KJRoErbcH+M0s3Czfummnta5fEN/e7k4E+BtPmd3nvSuqSXZ3TyjF6WEVUlnFhBnHw47xf8TId2i5a4kPhnbwNP/gFn1z/c+IaCl9XVVZRS7O/vv+v+/f19Njc3f8/XbG5u/lc9P4oiouj3qzm8d6N9tc/SD25iZrbZpoZ6ZDAji80dft70qH3hcLnDFoutfEjy2hyVpzbYp9vveuz0eOFdmOZdoz559OS4XvgzSS0QCuJEI4OmkO4F+LMR1nlcqbE7JUpL6gpsvUgVlgu78EDSWtK0+wFCCYpBwWQnYzyqyAqL7Gp85XAHdbNzezWHccloUNN7ts2ZJ9psPJISA/1AsL2e8Mp31Nz5D4PT/RSAM47sOCMta2o82lmEkJhOwplPP8/hj3w7d7fP8OL3fzvf8//4/9IaT7h44xZVv01gmtZQ5T3TRb6REE2mFPCeer08rDaaLPgv6qFogCAv0HmJcJ662yIaTVG2SRT3XhAEEHYVcapQSlBFgnGHhyY38FVDZnZSQO7wyQNuUzAyOC2oWpru/hxVWOq2ou4ovBfY5YB4vyIe1JieJtuO6L6ZER3Xp23GyXYXZStU1nzX0jdToyXAiYCn3nmbna0VvvLoU7z67Mc5mjzD97z6s4RuRoDnXvQUa/ldAl8RmIy2KHBosmCJ+92n+dzWn8d6eGT0BebBMoVu4/EoZeiHOUpdZO/421ClwZ0/Qq9mvL58i5dupKTDFexqgI0NInDMtqeU8zliGmOcRtaaNE+J6xZLzw5xax0OpOMTwQrfLzb5xWKPPVtQOsuWiliXIfdsjnFNIF0TOeQQXpwoqKlx1PjTYM0KQbm5xPzPfi8bX3qVpa+8yu63PUd6d4/O23cI5jlhlhNmBTIvG7Xh2zu0ezHtS23m45piWmEKg8gcXghcGuOdRxZV47y8OF2Lr86o3snp/uAy8VMpdmSo71VUN4rmehCrBlR50YCTQBBciXHW48YWnzuCMwG+Antc4wuPz9yCpQy+cgtOHqAE1chSvV0gE4nsa/SyQnYVybe08cZT3Skxtyqql3Oq6wX5C3OixxOCsxGqrdCbAWpJY6fNcTj3HnM+ZqAdN9cFfaVYK6FbSRSCKgRtBTYWlCNHMnao3IMSJFqR9Fu4JCab5Nhpja0tHSRZoKkPHO64JD8fcOMDHcI1Tf+1GdHEImaWYGoJ4wq9nzUcj0gjE0XQCnFLMfRjOi3JY8ISe4/Mmgu4sA5vTxQ5Hq8EritwHairhofjSocvDG5eY4c11cxQTw1mZqB2p1UaqxTzOKGMY8o0pkoT8jghj2PmcUwWxeQ6PJ24Hy7m/O5xaqtBI3x41//dg/m/XnBVatkINaxUD10jTgCPwJ+ICLxHG0NgDampCYxBW0NQ12hjmoT72qDqGl3VCGNQ5UIpWxtEbTB5RZEZ6swgK4OWjiDWyFSTLoUkvcZBfPPi1T/ItPqejG8oeAnDkA9+8IN86lOf4od/+IcBcM7xqU99ih/7sR/7PV/z0Y9+lE996lP89b/+10/v+7Vf+zU++tGPfiN39b846nKI/1+OUT1FvBygVjT6iRixFiAiic3dKbCxc9sAl8xhpgYzNNQTi5kZzMxhFo9T+wdA5uToPuHJnPBn/kvj5CQ5MXOtFm2N6YL7IUFfCFG1QwSCetCUsCVNgUCGEPY0mxcSNrZjWr0AU3vq0jM+KNm5PmdmHTKQ6OUAO3WYKbg4ROBRxkLmKN8uOLpVMLuRc//DbTYupGwsh7S3Ep740+vs/9qQMnenFSQAVxpkIgmkIx+XiDTAhCG29Dz7qc9y77//3zM5u8XOuTMsj0acefsGx9cuEdbNZ6txTL0hWoAWvbjgv5deL+8CL+6B6dSJ8imY5+iiBDw2jhB2jHB2kUnkUIEiaCuStgYhMKFgehJkdlKgsRBqgZcQHFfY5SbiHgvRUUV2ISY8NrRGzTKvWA+b/JZUQQBLL49xoaBua7AQjgyibvbPCUW1oeneyRHG45FILIaGPV3LmLSe8si9nC9d+gS3+u8nab9JsTfi8etv89WN7yaLEtLZEbEZEdgMKTWD6Bxf3f6TvLHx3QT5kMvjr+G9YBifoZIxSheo9jqzsMfhuOkYxlnIxaNt5ltvc/0AXGhQq1OUbMJIRe0po5wyCXAR9O6usTxcJWo5eqsFF1Ykc+F5oR5z3xZcCzr8aOs8P5/v8nI14Y7JMXgSIbmgUxKh8L4xGsy9ZY6lcJYCR+0sJY1izS6mfaHh5seeZOepi2z+ziusCCg3Vlh+/RbicITrVshZjh5NkNZSTQzDsmJ+5SzTP3UFVRvaX7tOemcXURmk9/i4IWiLurFNEK4BIaN/eUj8dErne/skz7aJPtCifDWnvpsjWxrV1bjMUu9XVHdL9HJAsBI0C4faITuK9JMrkHuq6wXlGwXusH6wOFpwZk6ugK60uJHF7DSqJrRARAKpBcG5popnRzXmZom5W6I3Q8InYsIyQe42Kw4/d9ixoVgNiJ9u4bYCDjqKPelob8T0tKZTgVdQGghbknYLWocWNakIjEJFCiUEWobIviYuLAhYEgLXU42J29BS7BumyzH2uRCuz7BblpmD6T2DeidD5xZvHFoJwlAQhBIVKfI0YNgKoBUStCQ6VoSSJjJANxVrsdhQAh1IfKSaLDLAWY81HmU8kXE447GFoRjVVOOaYlxRjmrK3RyMJ3CetvKkanExXczjxnqKGvLKY04WpIvW1Mm0LuE0o0k8vMkFr1E0rSQW7ZrmfG7aTGYBZLyWjTDDuUamXRqoLd416qlGyOoX2oEFePMNdcW5piqpg0YJqUKFjiRhLAlWNUEaErRayFRDqDBKUtZQlq55vYH+xtn3ZJ79g4xveNvox3/8x/mLf/Ev8txzz/HhD3+Yf/yP/zHz+fxUffQX/sJf4MyZM/zkT/4kAH/tr/01vu3bvo1/9I/+Ed///d/Pz/7sz/L888/zL/7Fv/hG7+r/6rDzjMNXp5wcnScXYCEEMhLIjkIvafRKQLAeEG2HtJ5IUZ0mI8XbxsfDVw5feWzhqKZN5aY6qqgHhnpUYzLbgJ5FJYeCrw/E/H4jENgjgxs2ZeXTvBAJa2djNq6mrGzHzUoPsMYz3Ku498aM4aQiPBuhvER1JP6oRpQeGTfuo7ZseqUilEhrGz+atwr0Uclge8rR4y2e/t9tka6HXP3OZV75paOTbkrDfrceV1sCLVDDOaLVxilJDWx/5Q3WfmDAbn+FnSuPIN58h960Yuv4iNetQTiHkY6ZNySLYMYT27Dpe+j1kj0klZ49lFp9Al70NEdnJUWvjajrRTijb2YhByoUBIEkbWukhlkMTi9cdT1Y6/FWoJLmd8bDmvl22PThjSM4rBk+1mL5+Qnh2OAVZOsRsrC4MxpVOrpvZ1TrIfPzEfGgJjpq9tMjyLsJUV6ip+a0heGRWKGoZULk5ggclehQ7H+M6Owam+oF7j2iqIOnGc8uMU7Wed/hp2lVIwqVMolW+dq555guJQTzOUFVklbHvLn0rcyDJaQypK0mrXY0B7OQ7C+34GPtXX77t5dYUR2qdo7pFNjuBAzYyFJ7Td2t0TVMH90jKlps2R5rWzWlckydoSc0d0zGa/WEf5/d50nd4bJuseMKQgQrMmRNRlwJ2qypkENb8ko94ZbJFgVOT+Ua4HtsKwa+onrIMVl1Oxx998cZPXuN1c+9SB0GxIMRS2/cQkUBup0SDcbIeYbMS9qv3iS5vsPs0hkOv+UZ8r/037L82y/Q/+JL6GnWeDtJj0sivGfhA+UoXs6orue0vrtP+v42yVMJ8bWE4qU51W6BPhcSPpGAheK1HDOx6DWNFgo3tcx/YYxe00SPJ0RPpVDB7NeHmLtVo5wJm82XHl8t2kyFxxceAvCZOFW04ZsEcxGLRt1zv8Lcq8g+NyV6NEUtN6Q9IcFOLNU7ObKn0V2FaCnylmTQVgQXI4KVkPZqQLSiyTuKSVfQ3mvAqQ59Y1MSCNQdS6Sh5TxaAoVpqin9gISQlcVcwWMRYlphraO8BLMf2MTvVfBORjWoMNOacl5TZ4bquOGEyaBRnjnjEELgA4WPNGEs6bckrVgRxgIVKWQoIVSIsCEXB6EkCiWi3ZByWXA+8Jy6q3vrcaXF5habG1zZWCPIRdvmpJLunV8AIkdVNyReY1yz0Fz4QAmx+J3uBFRw2upx3jf3+aZlJGgquhIQXmA9sHCfIlWwCE0U4iRgckEpWPCvdCBRC68pHTa3G2S6ADULB+DaQVZDkTnKI0Odl9SZwdUOWzuq3CK05Ae7fzwGdfBHAF7+zJ/5MxweHvJ3/+7fZW9vj/e///386q/+6ikp986dO8gTNy7gYx/7GD/zMz/D3/7bf5u/9bf+FlevXuXnfu7n/lg9XgA2u10ufPc6pnTUv2s7SS2lBO4b2Kmpv5JRWg8adKpQHXVq2x30NeGSprUV4S8IvGgvklQ9dd6QzNwJiJlazHFNPTCYicHOLWbmsIsqzu9L9lVAvOhrGo9fcE6iVNHfCFk7n9DqN8Zpwgumg4r92xn7t3PqwiFaEtVR2JlFdRTF2wV+6pARhB1NGCtcGpCHMZNul9opwrIgnE8ph3P8vETcqngtVXzgO1bZ/nif+1+dcrxbnoIXgDyzdHoadTRFnNnAO0GlNfX+hDN7u8xFwHhzldb9I+L5hPU8Q3hPYGrqUDP3Tdvo5CN77xm/R4qj2jvqhaoolYrZAhQ550+lt+F0hs4Lss1VwlnWSNKVOGVTy0QSKkmUKggF06TpYzdfDDgrGk8XCZRNedpFquG7FBaXSEQF7f0CrwXlUoAPBGRgO5rWzQxVWLySFNsR/RdnhEfV4tcLqk1FOC6R9QkDplEJWREgcChvcEje6T3HG6t/ih96/l8RpV/ha49e5gtPfojlQ8snv/zT9IpDpK/Jgg7H6QahmpNVF3AuwGL56uoPYlVEKGpacYoQkqKGYvFVJIHjmegdXroTMbR9EqBXhbiZoNzrUK9PmIUQ9DJU4aBT0l4rySh53Q/Je5q+04QI9lzFoS2Z+sbDZuoNHwuX+b91HsMK+Hx1zKEtedvMuG0lHwj7/F+Sq3jv2XclB7Zk35YcuJKxq7HOcdNmvG1mTJwh940jrlxbYfjD38Xs3h4rn3uBqtuiffM+6e4RRbqGKA3J0bAhbGc5/ddv0L1xj3x7jeOPf4DX/snfov+FF1j6zJdJdg5QeYkqm5R5F+hGmVRYpv9hSPbZKelH28TXEuL3p0TXEvIvzSjemhGcj2l9rIM6H5DftMhpjaocOrPYqSX/6gzZUshIEj+aYs+GFK9kTTtJSsILMXJD46YWu19jdusGyNTNcSdi8UA5c2KUczIlZ57yq3OImkqN0A+4Ws2JLBCxbB4LG18dmUpkohCRQPU1KpKEgaTjBJ1+QLgWoDsKFQj8oaU0liir6NaeKJREqzH6QpsgkSgnUEKggohgUBJaaE0s9YUEcz7FTwwcm4aoWzhEVmMnFXZSUWeWunJUhQPjEKbCTWE8hmPjUYDCI5VAao1SLNpREpUoVKqRsWpuRwoZK0SokKoBcU0Sd4DshEi/ACm1w5bNBd6bBchZbN55rAFrGwJxE5H3wJhUiub3KtmQisXi/ywqMScLv2ZrwM7p7dNZS5xMLQ94kCccG++bqnrtqYxgUnmKiafIDFnmKeYWU1hcbRuPqtrhjcXbZp+98biTz+Q8SgsOb91g8/IfT+voj4Sw+2M/9mO/b5voM5/5zH9234/8yI/wIz/yI9/gvfqvG+2w5NK39JvUW+NxhlNEXRaOcmYo5pZybikzR5Ubytw1B23RlB7t4YOKwEmJUIYSnUh0qtCppN1S6LZCpBLZDpGrEvEoiEA25knGYSuHKTx1ZqkmlnpYUx4b6rHB5g7Mooe6QP3CiQVYaMrEOhBMj2tG+yXzkWE+qpsT/GSENBfWzCFjSXWzAAMybMqLUgiSVLF+PqEuLXf2Jgx9jPKOOQGZSpB5hcwtk//5iBffLHjq+9a5+qE+X/7FfZxrmP/4ZmUxn9RQ141BW+Wol9vMJyPO3b7DMEjY7XfRpmYgY85XNdI1Xi829OTOnnbcQqEal933yOvlpOoiEUTIU8O64iHEGE7m6FlOtr5EcjxGOtugKAdSA6EgUoI4VRgtyOIHv98D3iw6TEqgRwbb0Sxy4dAzy2w7ZumtGWHuMB3N/GKMqD31SoBwnuWvTjD9kGItaFZxjkXlpUmHloFBH1qoLQKJE4pSxHipaZkRAk+uOvz62f8zHxn+B57e/xWkq7kTfRD9VI3o7HHzsZAPHNbkqoORMcorsvBbOJBXyYLOaaFJCU+SxIgm4YJ52UyuSjiu+OscDnLuum28hFTW9EXGxPVQiWQ6CugYQXoPzNaY1V6GshUDUeATw0iU3K3nNC41AounLwJq0RwDv1Ue8aVqyPuCHv9NvMnjYYvXzIyBq/hiecwL1Yhnwz7PBn0u6QcxuLm3HC7AzC0z57fLATs2X1zDPZGQBJeuMrlwkez6Tfxnn6d+8yatm/dQqmJ+YZN8njdGhZM5MitIb+4Q3T9k6Te+xMG3P8fr/9f/AyovWfn8i6x+/kXi/WN0lqG8b/KqPLiJZ/rLY7LPT4keSwjORIQXI4JHIvKX5sx+4Ri9GRBciok/kOLXQ8KVALdbUb2RU98rMPsG7y0CQfxUC3tYU90uKd/OUEea6GqMuhgRXIyodyvMrbKxbag8vvSQQHApQS9pqD3m2OCmFl+4hjBsm4oAcFpVAA8L8v/JvOZP6HiKpj2lmzLAER5XNAA97Gp0LAki1eAkCVJBUDukAqkE9APkSogOJYGAKJDEM0OgIEg0aiUiiFTjnSIaThNS4zoKl0RQOWRpadUWUVlE2bSa/KJlYuvFXG49tXXkNZjMURuPqZtKSVU0QgVnF2BlASpUKNGRRC8qNyqSDagRApR/0PLxJ1WPEyE/C2C44CK6hkjepFkvrglKLt6Hd6kmnX9g+Om9bz6Lb9o3J60haxuAYayjNJKigtJ4qtJTV755fGHud2IDwMPVosVP4U+CJ1m0IpvnKJr9lUKiNLz1G7/E5uW//oecaf9g43/zaqM/qpEvz3jpN7MGlYcSGQpkKFCBQAUS3deka5qWF43KgEZWZytPnVuquaXKHGVmqXJLXTQgxJYWk1s4XixRHzpYhQCxOPlFsNh0s8LRaXPCRKEk6YeojRgZCLAeW3vq0lGNa6qRPVVF+dpj5pbJcUU2M1S5g999nV+wGr1tyMB+aIhjSXszIOprysxRzyz5qOZ+ZuksBXSqGjkeUzlFpYLGOC2JwDt0XrH74oyV7ZhYwublFvffnlPVEOimRGrdieqpOVnqTsr8cMQj12/yytXHiesKLSFXIbfSHtK5RUSAp/SO3DsUgkicRAS8N5WXE/DSkgohBLOF50vu7elKJ5xmCGPwgUYa2yQGBwKcR2r4/7H3Z0+Wpvd9J/Z5lnc7e26VtVd1dVU3GkADjcYOECQkiuSQFCkPLSnGYTk0M7qww770RPjG4Qv/ARPh8J0jHI65cDgshSRrKFGiFpLiBhLE3ui9u/Yl98yzvduz+eJ5T2ZhE4sASCmk/kWcOpmnMs85ed7t+/x+3yWkgkQKsp7CpIJyRcVZXQMMpAGCCqRHBrOddCOngCw98wsZV/7ZbqdGEpRbKfle5MGo0jF+Y0F5o8fyek520EYX51l8n3YokFVAth7p46nICU0jC3p+icThULw3+iyXm3d4cfonKN+ym99ge69h68++znc+ss1yCHcuX2Brt2GWXmR/+wv8zuBvY1GdnNcThKTIIzfCB2hMnPsLPOfsQ5JqlzfTV/BSkyvPuSHst9u4IKCG3AdaaUnGLdsXBLPzc/yb21zpB7ZvLXiHGZXwhOBJheSm7rMuU+bBcs+W7LqGuW/4d26P32v2GYuEbZGyrlMqorHg6+2MfyQecln1uJn0uKkG5DJyMLZVxkWV88Vsg4eu4g+bQ+6YJWVwVHi2dc7Gh18hf+kTNG+/y/yP/gT/2tvIh0/wSUpb9DBrJWpZkx3N0FXN4OEuxT/6N5z791/n5KPPM795ldv/3d8iWZSsf+ttJq+9R7Z/hGxahAt4JfBLsF+v0XcbsmsZspDovkJ/qKC5XdG+X1P98Qy5nVB8ckDxxRFr/4fzYKH+7pL2zYr2YUOYOmSmUJsJ9lGL2W0pv74kuZyiNhL0WCNvSeyjFj+LvBNqMG9WGAVyXdP74pDeZ0eoLU2Ye+yBwS8sYeExBwa3a3BzRyjjKDyYeEEUPkSvopWhsA0rXTCi40rUO6ZDvCBlRy0XAqm60QwgqTudu4xmcyp2bJWJuUBCRtM8UhU7FVKQJIIsFaSJIs3iCCjNJUmmSQYJuZYkwiNah6wdqnWIDtDEk27HQSHerxQ+Tkbyt+syiAKddLgb54QuU66LqI7fC6Ii6JTUEo0/oaMbiO5E4EUEU97HhocB0zja2mPagG091sQuv20jsHJmxV0JMQPJd64b3YLV247nElYAJX4dAVQ4HSsJIdAyoKSI8QQqDpatkzSO6FKtBToQryMiLoTTgSIfCJ7c+85P5Vz749QH4OUZyxSexdSckpw6NXIHVFeEKFCZIE0VaS5Je4okl7EVOdLkm4JBIlFZ3KFDCPhu53TdTtlUjrbytI3HtC76FBiPNcTVkYzdEzHv5rqpRGYyApc0gpug4kFFX8BagrVnr9EeC5rG4GpOgQoQ28eJOP07sAGqSL5ra8/JTgM7TTxQQzwxSQGmdmRFJKSq2iJkDPNpVR4BzHSBsA1v/tYBF6720Gns/Nhu/islp6+pZ0t8r4fPU1qt6d17EmWtEsZtRdlLOQwJqgMvMcjMc+Qa8lU4YwhMf0peL08b1LkQqEN0F6ye4keksyUh0ZGp37TdyS7Oo3UCuogXx2wQbfyr7Ow9OR/ACZSO+1I6M8yv53QtKawSDB439PdamgsZ1YUU6QLSeHxPMnhjiao9tq+oz6WsfW1OemhOJfNuLNAL973kXZEhhSfzSwJQywF7/ZtcWL5Lr97nKL2EFJ7N5hGulnz4W8cMhvcps4KdrSH7xYt8M/8CFtntOx6kIk/iHD4u5BxNF7kycDMu2ru8m38cJ3KSRHLtnOLeYSSY646gLoLgXJ7w6RcgfKzhd/c09zb2ILPsYvlEMuHlZEQdPAe+ocFTB89zqs+X8y32bc13zIw37Jy5Nxz7lpmw9E3DSGiUENwNZWcueBRfU0APxUhqekKTCBlHCUKSImJKebAsvWUuBHuuZSg0oxeu4W9dYbG/T/vmO+Rf+SbZoz1wDrEoWQL5zj7Z432SZc3gwS69J/u0X3uDw1deZPncJRaXzjG/cp5kUTF+6w6DOw/Ri4oVm9LPHO13Ddl5RXpBIxPInitwGw7zsMG8UWHeqVn8k0OOr2QMfnWN0a+v0/viEHu/pb3T4PYN7sQQqoDZa6m/usA8bvHHnuT5DL0VhQd212DuNlCvdhzwO5bFPz1m8U+PEYUgvZGTvdwn+2iP9NMZej2BLDoBu5kFJKGNx0tYeMzjJqqo3q5i98aBzGRU/JjIwQk27ufBRZWU79SZQsULu18RjmsXvQRWRFbZuZETYGZBS0TSjay68YtU0RD1lATbHXMdnkBpiZQCqQRKxv1QS9BKkIgQuzvd44kWaCXivirFKdFWrewORCfCD5wRZV1ghdmME/FrF8m7tgMZbRvBibMhBjtafzZiCrEr47uLjQ9nMuoziXXHvxRn2UeroZFCxFiB7u9QcoWr4v+JTo0WlMQrjVEKozRLpSIZONXROG9WIRc1Inh0EpB45HM5fHGA3dJc+tlP/0Tn2J+kPgAvz1jpOOHSr23gGo8p4xzV1i6ObiqPqTzOBZoQKL2Pc8OphVlcWUjRzTJF/F4pQVIokkySdjc10PS2UwaJQKWxNam0iOAklXgdqAgsG09ZOcrGUTUe08mdQx0IS4eYRUmlkCISdJWARCJzSIcKva2xexZ3Yk9PIkKL6HLnwB873F5LcN1c1Z3Zqz/NHnbA7MCS5J6ir0gyTd4YJm1NolpK1ePg1jbceYwTnkXr2Ohrzt/o8fDt5Q98xvp4SdvrEaTCCEl50jJezFgUY+pBLxo8zmpSY9BuFREQOA5nXi8LLJ7AMjgG4ifbvatVNMBToYwBaJ8aGyXzZTT1aw2qMR05LvbOk7RzJgXSXDErBHYFGEP8XEWI7XIqi3KBkEZQIE2gHSec/9YRycJRJYLFtQLVBuxYoyrP6J2Sdj2hvJihmoAyIYKXbhsFoVC1Rdo4+PYojMzI3VnXZb+4ihSB1MxYqjEbzX12e7eoZJ+7g1d4Ze83uXHvDe6dP8/O5DKzYs6ffbyPLPdJ5znpoiBzmlQD3hFMS+k0PkiS0HKTd7g7+hSN76GU4MoG3DuIfmtKxO7bwhvEdkn/xZLyVows6JmMsWqpigYTPG+bBblQ/Fy+ySfEmLfsgoeuogmeXdfwkXTE3xlcZozmd+oD/mW9wz1XUnZjlBTFZZWiESyDZRYsbYiS6UNvOMLQE4qe1GRBIDuGYyIkPamYessyWA5pUE4wkSnnz53jwrmL+E+8SvW1b8HvfQWpNN4YzNWLNLeuU9x5SP7kALUoyY9mXPrdr9F+8y3mH3qOxc2ruH7B9JMvcfzKi6RP9hjefkTvyQGqir4x5VGgKgP9F1Kyi4pkPYaguhODedjG0dCbFUfvVBz/P3bJP9yj+NyQ5FqG2tQwkISZQ64nFK8OMA8aFn8wpX3QkL1YkD1fkF7N8R/p0b5T0bxZnXVjO9VSKAPNdyua71YRIIwV6fUcfS1DjVU01LMB2Y/cPlFI9HZKcjmneHVA+25Ne7tG5gJ9K6f4cA/fBNyeof52iW98vA7bEDs5bTx3CRtIphZRdaofE2i7RSM2evVIiN0F42PnqjtHxc0Xyboi0B0T3cUeEN0xvAIzrExYQjj9WrD6vjtmn5IKidPHOP1/Ic5+5nu4Kavj/anJzOp5w1PPs+rU+NU3slMZiZg+LgEhA1LKCEa6MZbsxjwq+AgufEDhkYTT/D2rFTZJaPo5bZFjtcYpiZUxOcn6SAzGedJ5SbIfuXx5iNvDuYA4r+n999sMfm5MciUjfX9J3T9+xrPpT78+AC/PWO5WH/pxZZYowaBD7aLbiaQkmr9VLs5NO8KtWTrM0mFLh112jzceHwK1h9K5GII4ja1t6btbiPcqdF52Z7AbqQSJlqwncYXhRcwimjnH8cziRYhkOSlIhSLxAmFiK9FNFH6g0T1NkGC7NrAvo1dNcAF9PoVLKa50ce69Z7s2cHf0aSJqb+L3xnn8MiBagUwUlQroQpL2DDfULvtf2MY8OKbOBYnW3Lw1oC4dBw9qvD+blOmjOeHqNi5N4ih96dg6PGD38oiDC+dJH+wjpstTzssqIuDIm1Ovl7TTmk+9YSB/st17lZM0EDrmGoV4km1WuUbek84WeKXQVYuwLvKFOj6P1JHrkuQKkQiWPThbA8aVlVTxI02PLT6T+KQ7wZqATyXr350TNJi+ot5OGdytKc8n6KllcLvEbCSUVzKSw0jSzfbjvUtEVDEZj3CdwkgmSG9JfQUIjMh4OHoFEQyFmXO+fIdpep693nO8Of4SmSvZMHuk1jM+kjzsDbmz/iJtJiFbUK8tEAIKq6hmCek8Rc4KXIgExufyA3aKT7OsBaSe0SDwsPJUqccrT7pes5OVhEHD5XXBtRsRNKR1xs1Fj5/zfT7zguEftg951yz4ljnhia+5rnpc0DkvJQMOnWHPN7xmZrxmZqzJhA8nI/5P4xf5ZnvCV5pDHruaNnguqpwLKueiynlBDxhKzfuu5G0zj8RdAj4EtJCMu25Mr1OylcFx4BoeuppDH63vDn3LUji2xiMu/+LfIPnMZ2n+yW9h//W/h2VJOHkMUiJfehFfVoSHTwiLBXpa0vvq62y+dZ/6Yx+ifOk6dpiDTimvXOLEW7JHe4zeuE22f4xctJRfKylHmsGn+hSf7pP6QFE77E5L/a0F5k5DmDmqP51TfXtJei0jvZYhRxpRiDjCcQGpJf1Pj7DHhub1knKvpfj0kOKzQ4rPDXEzy/LfTKm/vaQ7wOIuq4hjkQB+4ai/s0TcqUjOpZGw24+cDzlSyLFCDnXstHTO4vp8gn1iqP9oTvOnS/TFNEqWU4G712IetafjcTovKJFJxIdyNi+mjA5avAnYpWeeaxZ9iS896rBl1HqEEjgEVWf0J0zA6ehBIVxAuLggkG08HiLnJC7OViOt4IAgzngoLh6vKwCyup1imKfBy2rBEJ7+Nn4jWF0nOieMrhUkpUTqjnaQalAC2XWEoh+W6MZZ4ZRMLVdzdtdF07h4AvWJwmcptkhpejl1r6DNMyyi6wRBsC6S0bo3mlSGtGlRbYs2Bt1G7xfbdnyg1uOsp38zZfR/uYL46BA51KBBDhTuYyN60+/5EP5K6wPw8qx1teDkKigHqgXagGg92kBqA5kTcVSUSJJhh4ZPuRzEi1kH6oOJ3ZvQeFwdIlO+9djWY0zA2MgIX805fRM9Y0LjUd3zRLTNKaLXAXKnWJcwnxmO7zYsFzZ2grtE48FawoW8x/jlPtXNDCfB1J7mXs3JV2Y0JhCGAjnQyE2N10SSngu4xw3+QYs7svi2O5jzgG87xZOOB5pro4SuWTjaJrBcBrKdHfTLY+z9mkfGkCSKm58cc/ykwdmnRm7TEtfP4mxVS05ax5V3bvPWlec4OrfJpTuPwUuks6cuuwbPiW9PFUer+1mwXPoJN/mhi0BgXabUwa8Eiae5RrHbYglSkFQ1qwXcSjkAkOWKbKDwCVR5J3XsiHp4gdQBF2BwZLBDxcoLwgO9/Ybe/Roz0Syv5mdsSCXoParRC8f8hR7tRLN2P7r/Zvudgd+wh17aGBAJeGLKsvaGSGsWHGWXMSpnrX7EpcUbIASvbf4C3974ZaxMuLJ8g7HZxSFZyg3S6Yijo19g7b0t2mFNO6igX2KUwaw1iInFiAzVJAwSw3dzTe3uYolt+AMBbbeqzzS0LrbqL4wF//WtHi9mUfL89QeKR3XgpcuCj/V6fKQY8QfNAf+m3mPPRaBSB8eejLaEY6FxwMJbjr3hj5poiHhZFfxifo57tuK2W7Ljap60NQ9Uxl1X0hOK53SfX823yYTiPbfkHbM4jYQog6MMjkxILqmCTyQT1lVC7T1fM8e8bRaUwVIFy3umpRhqRv/tbzD+O7+G+4f/M+Zf/3vC0TH2zv145bq0Dek15P4+fv8QNZ3T/6Ov0f/OW4jnrhI++TF48Qa+VzB/ccb8wy+weLxL8niP7O5j0uMpi99fUP2BpfhEj/6n+6Qf6pN/qIevHeXXZlR/uoDS075d0d6rSW/kJBezOCZIZQx/XMSNkN0scCeW6s8WVF9dkFxMkUNFci1Db2nq10rs4VlOV5AC8qigE5JTVZLeTuL5oPG09xrkQCFzgxwq1EhFcKMk+lJKaFQ08zQeoSRCC7IXCtS5BHNvFVviorQbaO81PMgk+XMZIwVZ49GNoTd3zBA0WlA7mARPmgoGJ5ZFIWmkgJlFFYIwjNEoXsSRFXWAA4M4NkgTicVCCYTueCwKnADbLSxwnQGoXM04xSkvZsWRORvZdMevjHlKVmmsVFidRofeROPokMzKOb0LQ5Q+xHwla0mdIekM5hIb06lDovB5is1T2jylzXParFMAyLNFUdS6e3zrkDYCktwYtDGoZY1YNITKYE3Hj+lCcU13+goSxGZC/+cnDP7OBunHhnHhKgU+hNMQ0EBg0rv545xafyr1AXh5xlo8BPW8jG3/rAMOqHjvI4s72HhwSBcdEjWgvUB7SMLZ18orkqBQHhIHuhsfnCrmz7qTp21NQZyjGhvnpMZ2BK7aQ+URc4c68biFY7h0nFs6bOtpfaBpPW3t8CZwODecfGdO9toCn0ucjDydc7f69KceMzW0HlSSoD9SYPqRn2JdwDQOM3M079VU35hj7zfYJnZ5xKYmDYJsTeMHCpcJnPXYo0hQzt6dI5/vs9z17D2sufR8j2svD7n9zdnpZyx9TDIFsKOCk1rx4u4e48Wco2KN4EM8aRpH4lYRATHP6KKOB3EmJE0XG/CT1pHvwItKqUIMZRQImm7r6KpBGoNPNMm87E5m8SQiRQzUS7Uk6yt8GoPrVtvVB/AtJAqE8yRTy/JCwUpi7VLJ8J0l6dRSXc5ZPFdEaWwmUSYwuF/hM8nyWoFeevTcRy+XOuBlbAln9ZmUPgiBchYdYpSxQ/Nk9BHG5UOuLb+LDIY/2/47fO3836aWfSDwmYN/RuoqlmrESXqBw+wSx+kV0qUgXeakDEBazKDGDhbMhoGQG0Rh8JnAeYflTBZvrUB5SYbEloJBm/DxQZ//88tDNvLoiDyvAo+PIuB64Xz8LJUQfDnf4obu81vVLie+5dgbkiBJEEy7OYcjquxaPCYEHoSSh0IgA1xSOX0R7dMPQ8s7dkGC5MC3vGcW5FLxgh7wq/l5FPDI1zx0FY9cTRM8t+2S28RRpxaCi6rgl4ttlsGx62qOfBtXyiHghxmX/7f/Pef+3v+G2T/+TXZ/9/dZ3LlLu3MYyQfDPvLjL+OmU+zOLn6+JHznTXj3DmJtTHr5Atsv3eLCJz5K+bnPsZsEZjt75F/7Ltnbt9FHU8x+xfIfzeldh96rGXqU0P+ZNfqfGVN9Z0H99SVuz9B+t6K9Xcf8oheL2IXrgIdfemRfoc+nuEOLm1qCC6iRAinIP9LDPGpp79Txou8DLMAvfNclENHWofGk1/JIQs0l7e0IpNUwdmH0VoIa60hkD7G7AJFsqgqJ6Guy8wnpCzmhiiuyYMAfttjjGJXQHhmm2ymj6zmTuaHfKIZ7hsWJ46gOHCDYLAL9BCa1Z+kFhwiYBcKjFhKBGmvURCMSibiQEs4nuGOHedTgd75XubA67wY6Dpsi+nXFg4kAOK1xWYJbBeJmKS5LsVmKVxLEKheoC9HllLeLblp005LUTbxvGlTbgJT4PKXpxQ6KHWaYLMVL2WXhdczcuYdZhByCgKpbRNkiqxZRt8jGojtTRESnjH+KiKw6jo9SoFPI1zXpVoL7+ADxmSHprQKxmSDPJ9EnyAbc0pJsJ6gA53/rgOXVDHH9Px6E+AC8PGP19moGdxU2kXglTq39vThjnwNgBc4GrIAqk9DrNPvOdwFrT81QV9JDF1AOEtvdOlCTBEiDiAAndLNbKUgyQZpJGHCaLr2S8MUODKRtXGG0VeTl1JVn2bjIlVnY6BJ5aGmFx2iJqRyLJJD0JaLyhIc1PGwoLmcMLyTo6xny5QF+TcWZuAnYRzX3/od3mWtF8akB44/3SXKN0BIpoTkwHL6zZPmVBU3pSB6VpOsFeycJ6YOK89cLdm+XLKdnJ45kWmKVxmcpbe3gaMlkMUMO1rFZinYeGktinu68GJ4jyl8LoWiC59B/bzjaX7R8iInVABsy5W4n0xWA6ybrelmjWoOXAl11wZ0itntVCIhUkkhB3te0uaR+ys8p+NBZ1gtkaRHB4wp1unyTNrD+zRmYQHUhw6ylFDsNLpfouSPbN5ixpjqfUezE104ODA5BrUdoF/1w5Cl4gVoNWTMx4LRMJmRmzlr9CIB7k0/zx9f/AQu5jrYV15bf4fz8TUKApZ6wSDe4O3yVVYKd6tJXZFCk8xwz66FIIXEMNywJgvlSIkMkv0ofW/hKgHHxaV66CP/DzylG+ZnP07s7sRN3YU0w6n0v4fqq7vH3+1f57XqX920EElsy46LOeWQrDrptnqMQOPZ8iw0eJQQTko4T5UmRbMmMFMHMGZ5QMxYJc295zcwYSs0N3efjyZhfybeZBstDW/HIVTx0FXXw3Lcl9ylP35sJgTpY5t7iCBz5ljupYv3v/RrX/5v/Gv3NN5j9s3/B0Te+xfzJE8S8IhsO6b3wErZtWe7v0y6XmL0jxMEJybv3Sf/g62xdu8ZLVy7SXr3Ezhc+z/Fv/E183SDefBdeextzMqN+vSQfzchfMIg1z+ALOf3PTWjvV9TfWlB/t6T6vRn1H89Jb+akN4voxbKmkBdiDEj4EOi16AVj77fR52mgyT7iTk3p2rfrSL5tw6nYAAfBQlNXUYq9kZCvaZp3K8xOiziQmDsNoifRGwkiX82EiCPnVfr6052DTnkUPQRC5zicQAjM9g0LJcgqyLSEsWCiPbUJHLaBRQsjFVAK1gMcGxFN30qPn7aY+21UbaYicnVCJBKL8+I03oUmviUfwKUJLk9xeYYtUlyWYfMUl6dnmUNPv3MpCCqCP9m0qLJF1zW6rNFlg2wapPWgJS5JCImiUYpa55AUnFJzKhBVS4wa78p5pLVR1Wgdylqkc0jnT7s+AU7z8KSOWFnrM2qD0oJkI0Gf0+gLKepySnIlj1YcfYl5rogjwi4B3S8c5jD66CSXM2RfM3ivJDts0VPD5oXtH/gM/qrqA/DyjFU9DKjPhugkGwRKCwIy8kuEiLk0AZwSBCFP2d2uJcrspESqQDZvGd2r6N8t0U3A5pKgiB4cGkIqCanESzASKilxCqwUBC0QUnWhcjEpVyNIECQCpI7t3KUQ6FSQa0HRizlF46cwU50EjntQJlAbz3xpmbso5W7nDn9osfcb3MwyrQ3yjkG+tUDa6M9QPJeTXM3Irmbc+p9e4u4fTvG1xwuBqS3DA4d8ZPHvVPQTj/tIQfV6idmz5L2W7ELBdMejHjbc+OSY137n8PRz1kdz7Lk1fKJRzlHODZOTKcn5QN3r0V8sEY0hNfGgbkN02V2Ni3KhAMPBKgX8x6wTb/AEtBCMhGYWTJw8h7NQxqSqkMYhnEd2YGrlvKxTRTpSKAlpT9JmsfOyQrnBrVZ2gWRmYxfsqYt4emIY3qnxWjC/URCS6LgrtCDfbZCtZ3GjIGSSfCeGLLJvsXqA0i2yXdlyxvyJRvTp2RPiK0r28mtMmicEKXjc+zCvXf4NDuQ22pakvuJT+7+J8oZKD5knWxiZ8bD/4fg3EtAiRCJhsIgQaLvc5p5IGLcJJ8s4ylQCso4HJEQ0rQsBrm7C/+4XFaPi7G92PvDeTvyAXrjwgxcGgEIq/lZxgW+ZKf++PmDfN5TG8kvFNlsy464tueuW3LcVmVCEEMnbT1wdR0BEAq5CcOIN8+DoC4UJnru2RIvo0Dtzhm+LKRK4qAqu6x6fzdb5NZFyFAwPXcWeazjy7SloSoVkKDTL4Nj3DXddiTWeBEn2sS2yj/59xvd/meJPv4364z+j/e67VPfvUwwGrF2/jilL2sWSZjHH1jXm8IjZ8QmLN96kv7HBrRs3KC6cZ54qTjLFySc+jg+BcDKlPjrBLTwTKvT2jHa7Qd5qyL8wJUznNN+aUX51Tvt+TXu3iZLprSRe9E65JiLyYgbRYM4qgz6fkD6Xk79UEH5N0LxX0rwbrfH93OOnnRO4C5gDS+9THYD5aI/mnZrm9TLOuC3YfYMYSnqfGCAnOjYZq2jIGarOt6TqOtjdoi6ScbuvoZMhB0olqXJBYi1Sx32lJwUGOHSBtIq+JHmIJuUWWDnaBhsIq9B1CS7RuCzHjlPsdobNM2yRY7IMH93iCB1pJawERkKA9yRlzDaL4KRGtS2qMQgb1VFeyUiaVQpfpPje9+Xwie+5i3YRPoYtCu+iLYS1KGeRdIorCUGDyEX0hNEKsRJ2dIaDsi+jQeBGgtzQ6PWYUZVM1Cm38vRklArIxOm1SG7q2I2bWuxBPK8lF1PUWCNCYPTmguTY0Fwu6I9u/NDj9K+iPgAvz1jtWsZiTSHlmVZfdsoSIehSPmE1/JRBgA+kS0v2sGH4bkn/TkW+311YAvFgEgKfClwucXkct/hUfs9OvQLjdE/vE4lNRccWh5Lol2KJHUVROUQTD/xUSvr9hMkko9/TZEqRSNgUgrmzTIUjUYJxImg2EuqbRWyxhgC7juVuS7nXstxvqXYMCx8QDyqSRxXZVyX5hYztKxn7taV9q6T3pGV5p2akFJdvDXj4dknVBsKNnOb9hvpOw+Sqp9Q5tXGoyrF5LefgXmw1p4dTltfPY4ucrDEcypSNnV2K52pckmCVwjeR8yK8x6jAwlvyFXjprEGPvMGHcMo9+YvWqnOzLlOEEExXuUbirMuWzKoY0tethE43mAworSgmcRSiC4ktBK4bmUcZZJReehtIj00Mi0vl6XMM75SkJ4Z6pFleyWOUQCJR1lM8iUGQ1aUcWTmEDTghCYcKJ1O0XMT2vO06RULhRUIaokTYyJzM1xxml0AqZuMbvNb/IniL8pbr5Xc5V76PxLHQGyySCQ/7H8aqOJrTQuCRSG/QWEpyArHLNMhhWsbutpTQz+NozDqYd1EX2xP4b78suLh2BlwgqpAaA70MLm/86G0jhOAT6YTLquBfVDsc+pZ/Uj7mqu7xhXSdj6YX8J2b7l275J4tGTmN64DM1BsOQ8vMWxRw3GUf2RAYSs3CG5SQFCiGXSdv5f3SE4qrusd13eNz2TrDTtG2DI5D33LgGw5dy5Fv2XMN+77lxLeceMOBcBxc24DLX8Z/6aOE2/dI37lL7/5jBo8O0IM+xQuX6JUN4v4j5GxBWJQoawmzKeW3voX6riYbDFi7fJkrFy6wLFKOg+EIgakb9o5beF8i1yb0bgrWnzvP6Koi+RlBme9TLQ8pvzNl/q+OaO/UyDzaLIQ2kuhEKvBLTziI/WRztwYEciBjh8aDaME+srgTE2MIVPSeCieO+b88ZvALE3qfG5I+n5N/qGDxOyedKRr4qWX+uyf0Pz8if6VPOtGIfow7wAbkpkJqgZ87/NLjZx47NZ2goAu+rT3UnuAEpp+SWs+o9ijjO4M2KIUgcwHtA6MAC6U5TnKMSrAqxeoEl6bYNCEIeepkGwnG8ZyhARE8qmlRiwZRmpgdZGO6MoEISrTGa007Hjy1k3I67/daR0JtoglaIYlBtNpZUmvIbUNmKnLbkAgbP08BK8QvRAIiORM8hYDoqdgdG2vUWKHWYqdErWnkUMbR3NP+LpIIcIrohqwA1ZOwpnB9RSsFwsbsJzmQMd380MbfGaro4Cyh97Ahf9xQPGmptzKOuUuPi3/e6fQvpT4AL89Ysq2RST8G4nWrSA+npCtEQNqALh2qdOippXjckO+2pFODdBBUDNVzPUm7nuJziaodqgkI45EmxNU1Ap8IQibxOiqFVO2QjUfYgCo9SYhmUGKlAIK4kxKzc2wm8QqcDhgXeCwDdr2gL+DiUtKvA2uNYGwli0VLOW+jrfVYM311gM8l/YVncCmjeb7AJYKWgNltqB+11PcazI6hfljT3q4IxwZ/XuNeGSC/MOZw5nBVYL1XUL1TcXI+IX0+UH+nYv7A8Pym43ipaYJk42LO4cOa4EDNK0KWYIsMaR2H+YQXjqdsH+yzIwVGKfSyG5M4i5M6hjOe5hvFi6sNgRNvWFc/XvbGiu+yIePvn+YlhadyjRbL2MJtDMK5bpQYVWhSCdIijkrkQFLmp36VUZIYotJIn1j00mEmitCpObCByRsLsLC8mOMHCtnEUZVaerJOmVRdSOk9aPBa4VqBnGZkIWYV4Tkl65ayT98enwpFp8kWT4qbVMmEoHPuDD/JXAzI3JJN+5j15iG6U9RYJKVe4+7wle634+BMBksiPIYUFzRKQD+Dso1jISlgkMGkB4saZt1Kd9KH3/is4OUrqwTSs3rncfxcb50XzwQ6t1TG/7p/hT9oDvl2exJHObbkiir4QrbBZV1wQeV8PtugDo6HtmLXNxx00QAnzjALlqk3TL3h2Bv2fUMdPLLraEohSKxkIDRDodlSsSvzlpkDUUZ/TmWckxnnVc5NPeDVRMc8nRAog2MWLDNveGJr3ncRTD28lLE8f4721g0O7txj5/gEuSjJjmcki5L06gajsiF/fIA8OkHvHUYzuyxDGsmju+/CozuI9TW4dhl39SJufUTpLWFZMdw/Rr5xzJM/OOTwHBS3JOkVicg2SW+us/Z/vQQjoC+RtkbsV9gHDfZug3nSxDHL3GH3LTRdKrMQyCISWtPnM9xcY27XuGMbW2wQRzv/+JDm9ZLhr60jJ5r+z46Z/fOjCD48hBpm/yKSj/OX8nhx7MlOwRgVfWpNx27QigxrQRQSMokax5cSOprUiZFkttajbzXnjlqklxivOCn6LHXK8KRh5AJJlvD2xW2WQeMPW/zjGuZxERTzphwKj8oFaiSRYw09hVMJbadcDELgTeSruYY4IkpkzE6S8ehITEtiW1LTkrUVWVORNDVJO0W3NcI5vBfI0ClMQ1zwCqnwuSYUOoKMIoINOYqARI00sqdQPRnN/KQ4UybJqG48NTPtVFsrTxrRCTdc1i2UszgaCBC7TwuPSDsxgQsxQqLraKmejJwdERh/d06x09BuJJTXCtbSF/7c4/Qvqz4AL89Y8qQlO0pOvxfdrFa40BmHBYSNrUphA6r2uJ5ieaNgKXrYQmJGCq8lxV5L8aRFL21kt6cQUnWqHlrNhEPHrgqFoF3XZzPWFWCC2NLsLotexdFS0JIgISiBLRTthsZlkTS6SARPlGTcwIVjKOqAbAsS66m8pWkcjQzUyrPMPMU7C/JeQg9J3oL2IBvJ7GrO0Uf7HO8bym8swQjKfzejHis2Xsw4P5b0ax9dLq9pvvV2Rf6zY9zM075b8WjXc3Pdsmdz6kXg0s0+D95eIhsL3sf8l17K1CmKkzkXjvZ5lG/j5xV6URGkQFmHSTyld+ju0twQ2JApu67hwDc/Nng5/D7wsooc8E/liGTHC4T36Dp2QlY8KKEkQsSRkwDUWkKzehudYAgHQkNy0iJswI6T0/aaPjH079YQAosXe4REIGuPrsNp5668mOETSbZf46UmTBOysERiCRJk6zruryT1DbqbnbciZ6/3PG+s/zWulm+gROA7/Z9FBkfhl7T5Js/f/xOUrajUGKty9ornOEkvEqGLheCjfF8rliaNf2MH6I2JJ9JBBlujOCY66bzXBjn84svw5Q+rHzAQPFoE9mfRn+Pm+WfvliVC8tfzLT6ZTvjT5ojXzZwHruL/Wz7ksir4QrbOFd0jF4qbyYCbnK2Oq07+vN9lJT1yFbfNkmPfRu8X37LseCxHK+6BiQTinpD0hGYiEiYyYSwTchm9hvpSnwKabZUxlJoLHbD5stgCwIXAjqt5ZzTn7Ssvcfd4j8O7d5jeu09bl1TAkfckty6TlQ3F0YzscErxaJf00V7kO0iFqGvk/gHyW99FrI0Z3HoO++GbVM9fj2aNrUUeHCGf7KF+7wn98YzeNYd+DEE4SBvEWELWR14cMPhkYKhdJGhOLe7A0O447KHD7hmE8uTXUtRA4o4tYWap36xY/vsT/DwCj2ACdt8w+0cHDH95DTVSjH9jg9k/O8Q+sRGc5BGolN8sSW/GDs0KjPjS470gOZ+TXEpRY4ld61E1CctFwnKaYjeHtBsDzGhAkxZ4ES/0SsPVO7tcf/sxuXGkXlJuD+gtGhIp+LSYcufFi9y5dR2CxNeBcNxgFtEbK3agOOt8J13nYahIepCrlr5syUVNJhrytiKvluTzkqyuSLzpwhm70UwkKoJUBDEgJMOYBJ0IWAENFfk3px2XrqMfVt2b7tg6fV8dYIk7EpFLaTtFGFEoEE2UBK5z/fUiRKKxj7NcAafdsFDHPLZAfA/2YRsl7CeW5HrstkoF2Z5h+G7MHpu/kHHyyTXCUw5gf9X1AXh5xioeCV78v90FzvBFUCJ6c6Sy4ywobCGxPQUqdk9sHjsn/aklO7LIxhM0EZR0NtJBdjLEFSN9lS/hu69PS5yNj546wFaavdiJOXvM9BS2J/EPIqjRlcelknoroV5PuTtUrCnJhpTooNjwimYUSFOYrUtaF+Bbc4r/5wOaRLPY7jM5l5Olks01RRhosusZaiKpf3/K5L7g+E5FdafiSV8y2dKce2VAlks+vKl4649mDH55wsmeYXlkeTQNDPuGhhSVSrJC0tSeZLokCPB5ilsafG4YViV+PSN4j5qXOClJnKUEGjwrz9sGzzmZseuan4i0+/3gZdklStf408M1my0gBHTdxu3UkXV1JxtPM4lKBWIkI99lVStzOh/I5xZCiDJpGTfc5M0leuGwQlA+l+O1jOZTzpMfGvCB5dUihvNNHfP1PsWhJfUNIolKIvWU2kp3792j2C+u8bXNX2dgp8jgeK//GWrdJw0Nquhxbv9PGdW7SAKVzFnqCfeGn+j2sU4ijidJFQtzBuZ7aRfCKKBIYGscuS73DsAayDP4wovwq69qEvWD4OSdJ3H7Xd0U9LJnBy+rGsuEXyy2+Wy2zlebY75rZjx0Ff+wfMRlVfD5bJ0rqvge0FQIxRXd4wq908ciUbvlyBsOXcMDV/GOXfCuWbLra6beYENgHhxzHI+pu8VDR74Wgr5QDFAMZcJAagqhyIQ85cSMZQQ8Q5nQE4rP5Ot84cIG0+3nOXy14c6br/P+m69xMDvudrSU9sIW9tol5q+8SGhbsrIhOZ6hD6ckJzPyeUlxdMLgD79O8sffIhkNya9dpbj1PMmtG4SPfZq6rlg+2aF8/z3a9x6i+0ck5w1yaXGiBd1Svu2pBCRbkuySIns+J31eYmswM09zLJnuAsvA6KYnnQTSLwQG/6vztI8Miz9dQOPRKfgTQ3PoyScJYigZ/90tln8wo327IohIWBce2uNAeVuS/dI2/sqINi+obMriWGOLIf7aGqKvkVl0AHelxzYCi8YahfUKdIIUoFOPu6k4vjHg5psPuXh3n8I12ERSLGMo5q3bj9ieTnn9M89TnS9gu0dKSxYastCQuprMNaSmJrMVmW/JXIMK5jSeRWRxjEa37REQfA42O+PqdIsXEWJ35RR/rOY/3VgpdLSDsOqQrMIxfYgLuUjYOevcSggr0xgJKPBpd/0IXQYS4pR1vJpAQYh5U8bDIi6ycV1OH0QQ5KPDsblXk70yQDhi6rUSTF6bk++2tGsJR58cI/Mekh9vcfjTqA/AyzNWL3j6jxpE4AxwKIFLJLYfAUvoe1SlkFNLEKDbQDqz0RzJRlt3aeJsGQ+6tOipO0XVTgnavsb0FXagsEONGyl8T53yakSI2R56YVG1j+6pKj5BNCMDl8eOi555khkkC0dxYEhKhy59NECTUG8kVOcSmo0UUSToIiHva/qpYvNtxwIHiWQ4lDSlY6epePLYsDXUiBPJ+sM5xUhix5rJpYTLvzJm8fUlb7+25OTYcrBreVgJbn6iYLCpeX5W8/jA0P/SiMVvnzBdOtpg6I8SQDBcT6iftKR7J1QXNnBFRnK0ZHqhIKlbKLJIhKsdMnDqstv4yFcAaII7BRwHPyZ4CSGcyaRPx0ZnuUarSmdLhAsxFgBYJfFKATIRaC3J+hqTKqrVMR7iikdKgWg9svWEVEalUXxxRu+UqMbTJAqzkUT2f+tRC4NeWoKW1OdSkrnDZxorMvL9BSkGJ3UkXXYKhIBE4vBIatXn4eBl9nvXuHrwL/BIXl/7ufi3CINpPS8d/R4aQ6N7EGCebvJw8NLpm9d4siyhcYpVRl+RQvOUf8vGAM6N4Jt3o6+LUvDJG/A3X9U/oCACaG3gzt5/mKj7rDWWCb9QnOOz2RpfbY95rY0g5h91IOZT6RrP6d6PHEtJIdhQGRsq41Yy4HOrv7zjy+y5mm+aE77VTrlvKw58w9I7DAEXPC5Ex+djWvD1mQokBDQSJWL8gBKCFEkiJBmiiyboEpRvbKCf/2sM9g5xf/BV7O17NAqWowFm2IvzuX4G4wKuxk5OF2yDsJZkUaKrFuUqkve/jX7nmyQqITm3QXblMtnPfx516QKhbnAPH8OT98iqu+T6CUkxI2BongQWBxIfAgwkYi1yRPxmgrgkMa1id+Fgz7K2VVNsOdQ1wfDzW1SlZvpYESqPUoFFG+gPLbK1+I97lruaJzsj6nRAW/SwaZQCeyHRY0V2TkEqIZG0tWKxqyhGnrVzNYNeS5a3hMYRKge1w0yhngrKmYYgKLVATAyPr/VYXtzi0t1DNh+c4AeCfNZy6V5NO9e8OHuPk08MqG4lKC26jgcdT4Sz5Ga3Alrp2WJydQ3QcfyysgN4aoOzsrzoMMgpJfJ7XXv5wa/hzNL8dKXM9z1w9lhkLYSzc4sX0VywMxZdOaUTwHcZd6HxeONxs8gtsnOHFCGmaI8UxZdGYCDMLep8QjK3rH99jnCBxfMF8xd7rHEJJc4WMH/V9QF4ecaqFnnkJHhOgUuQcfWczh3J3JELCCsPgETgk9iVsanAbCaYkcYMdFQNEb1AVOPp7TQUjxuKJzW9Y4M4MmfZFoCdaKpLGfXFjHYrxfYE7SRuOukC+V5Ltt/iE8HxxwbYfkKQoCpHttdGE6JLaeRZ2EDvXkX+pKE4NuS3wfYk7XqCmyQk/eh8mTlIfaDWAXm5x9qdErVnmE0Ci8riEslknKDGiqM1jbnRoygSNl5xfPFPTrj3b3d4vOdY7NTc20m5eStKKK98Z8HBr2/SvFZi7takRmAbh840xUjD45ZktmT5/EVskZE2xxwUW2yWFZn0CBOBoehcdgNRLr3KIWqCZ6MbFa1M5v6iNQsWG2J68UTGg3MZLCGcGdQRAul8ibQGXTVnqxsZXZF7mylCgO5JQtIpjUTcpsFHZ2R9aBE2xK5dFnvD+ril2G2h9tQXM9wggppk7iieGIQJ1NspQQuyvYZyY4CqLMP9GQiJTTRpWyFPqVDxXydS7g4/wU7vJtfn30EC745epdRjZHAEpbly8lU2zA5tMkQ2SxCCd0efw8rYNpIEdKIJQtLGmCfSLpdJduq6cQ8urMFbj7tODPDyVfiFlxVXN384YLi9F0mW4x5sj3+sTfYDNZIJfyM/x2fT7wUxD6uKkdR8PBnz0WRE7xldmIUQDIRmIAfcSAb8L3uXAbDBs+ca7tiSu3bJ+3bJQ1cx6+IEojNzwImACwFLwASPDDGdfBUl0b3KWUNVQFhLCb/+M2A+S/7G+4y+9jrD199DljW2l2GGA3yW4Am4NKEZD7DjHmFzHImezkW5btVGTpYIcPAQ/vARIU2w4yF2bYS5egmz8VHcsKBo52y3Dzhn77Hm91Ftg8DH6YeQtG3Gss5Z2h6tSkjWPA+Mx+0LyKPLa5umVFdzGpfQtioqdRRsZ0dcTPcI1jOsNXvlFRamF+NJGhdNHytDfdKwMZizOZqT9VqSrGW+Izn+pqR/bcnWC46iZxA9T73weOvQuQVvsY9q7J0S/84CXVUktzLmH+9TF5KNY0Nx0lAdGwZ3DAMhKP5Mc3S9YP/lIX6iUQPVmex1o5xu+6x4ap0eo/NbCaeJyzaASEXsyGTylP/GirbjnnoOTjnBZ2DmNO0xnL4OHXiK+U8hgomnQi6DO1NmhRDAxv8PbQQuvvVgAt4EMPHn7JHFPjb4mSMsLWIg6f/8GsWHeygFalMjzqVQe/yBiXlOuWL9949Jjw12pNn72XWkzOhzsdO9/sepD8DLM1ZdrCH3HE6HyB/RK3WIQDc+EnVrH0c3TyWUOqBSCttLaXoJdT/QDjVmqHFbGpEE5NUCfcOTiEBx0NJ73DC4X5HNbbwI1YH0/Zrk/RqfCNrzKe3FjPZCRptLqhsF7RcmuJ4iOzH0nzRsfGNB736NHUjqDU090tRbGc2GZnm9YPKdOZNvLaKZ3sKjFg12ajDDFn+pR54naAe9QlLnCtN4+gtLPjOME8m88rRKkD0WjM+lPCgE1YZCILj24pCXhglX/uwJr323Zf6k4f7ljAtXcvS7Jdu/d0z9pQEHc0e9b2DRMso0SSojec04gpLYIkc6zwkZF6dz+tbQeo9DgXFoGx1XLIElnbNs8Kx3q4Fjb3AhxAC1v0CtRkZrMkE+Rbz0BEx3oRHGolqDbC2y7a7SnXGXsDDcSkEI1EASUrDdysyvToAS0sMG2XjsSBG6WIDRO2VMGDZgr2VxH/MhAtFDgzQhqo+A/EnL8eUR/aM52bLFywwVHLIDcqs2c0AxSzY5zK9SJmPOV7dpZcZb4y8RgJ5qET5wc/ENevaEqd5kzIxKjXh3ctp7QCuJ1pKy6QCL6LrW3d+2PogAZHcKJ8v4MzfOwc+8KHnl+g/fBiGEU6LuCxfFTxym+f01lAk/n5/jM+kaX29PeN3MmHnLHzSH/FFzxIvJgI8nYy6q/Md6bS0kF3XBRV3wRaJEygbPgW/ZdQ17rolKIx9l1U3wtMFTB9fdPE3n3mwJ+OBxxGvXqRYgS+ETH8N+/KOY4ynhyR7y/mOS4xPywymqaRCNQx/MEcsSJwSmX2B70ZOkHQ2wWRIvcMYQXLQ1CHWN3LekRyckdx4S0oTQ77E7GrM7+gJ6lDIpjlgTjxmJAxJqcJ4kwJpqMMIzs32OzICly8EG0uBQCaAgTw29tMFbsFYyrXrY9hzP9R4zGMzZzF5j2vRYzlPUskVVluy8IBk4UmFITc0oXZCJFjGwsNlS/emM8Cclw1+coDc0YiSwzmKfxHgBrgAXFeFTQ8zjjObtiun/7xA1UNSfGrJ3pcf6wGDSiuJxS/9uzcX7NcOvTLnTSzjIFSKRUYWVSdSaQq4n6JFCDGXsCEEc/XZGuaJLkhbdGEdEdwJkJhC5Ot2Q3kfQ4RYuZjjNHb6JuUy4uCAL3SgtrEDMarykYghvCKtxD08JNei4MMSDsorydV9FZVZoPLQQZCDdSsg/1iO9nEaBx4UMepJkK4mEaQ+h9tgDi/vKCcnfv4BaWja/MkUAR68Oac7njLnMBT6F+gnz436S+gC8PGOVNz7Ju393N/JchIguuq0jWTp0GeWqwnqkjd2NIEK0nrYe4UDZwMQBxiKmDequxdWeZj2lPp/SXspwI0W7ntKup5x8dIgqHfmTluHDkmJpwQmCAfXAkj+wyM2W5asDzK0Cl0WNvic6KS7XFGJfkMwsxcxSBBBhwfTDfaYfGXD88hAzUGx8Y94RjYlOrcuWKsDJh/oMz/fJE80AQRgX8NYUEkliHGPAHNfo2jP8E8d2CnuvCo4HivxawuUqkH/+EjfCHu+8UWLeKzn4uTHDi47BTsONpaHc1FS1pykttvHoTJL1JW1tcELh8gwkVCcVamwZ1iUHWuGEAGPRT7nslqcX7OiymwiJCTE6YENlP2qz/tBadWxWHZy6c2z1BGzXz1VNi7QO4QOqk0kLJZCpQBjIht2KpK9oM3G21AorDl4g34kdG9s7UxoNblcRvPhAdSXHJxLZenTl0HVMyKwuZggfcE6jraG3XyKIgFq69ixVGonCU8sBu/2bWFUwNocgJA96H2GebqLweN3j+cM/YtLu0Kg+whmc0OwV1znJLnSfSkzWbdsYj7Ii6SoVb+MiOnZmGt7dicBlvQ8/86LgZ1+SP3JMszuN0mql4Ma5ny5webqGMuHL+RZfzDZ42yz4tpmy42reNHPeNHM2Zcor6YQPJYNT5dqPW1pIzquc8520fFWr0dPUG6bBMPNnSqflKZhxpxODH1ahOIe/eIvmwyXz3V3mO09oF0tMXWPrKnIW0iTKfJsG1bSkhBhnIgUi6yPznOA9vmnwiwbbtngbxQPIzhVWSYTWiCxjUeQ0g+fobTiGm1P6oyN6nCDCCefVDlL5mLnTRrfdMHckxtDvG9IRiASEDvgqEBYWUUGSOVQiQEFYFzAMhCbgpwFdpqhCIzKgF6AXIriXGeNPbhJswO5YSED2Ywikv5FjD2JHQegYO5Bcyyk+PgTj8W3AHlp8gJOP5cz8kMl+w+b7C0Z3KyZLxyvLlrlT3N7KOVnrOjBaImqPXbhTr5kgOYv4CCAUyFQiM4FMZBzZrDosPkTF0FgjNjSiUMhcRVBzjlMpULBEzokLT90TOyY2IJo4CoaY0YQN0V/Kxe67DCFGxzjQPYFaSxC5QGUSJQIyk7RjjUklVgusFNR1QFxIkf2OEdwGaD3m3Yry//WY0f94E4Rg+3eOSJYOM9TsfXmDjAmX+Cw35C8iPwAv/+mXviixhUQvHfnSIWuPXBknSUFIo7TZdbPQ7zFoWa22VwSqENBLh57H5+qXDfKNiiCgnSQ0awntegIphGsJs2tj2scNa6/P6U8Ni+s5Jx/q045jh6F4VJN/tSbfb6mfy2nXU5aXcsqLGflOw+DdJfmRBwnjd0vyY0uzHn0DZrf6FE8adOORxuM9JIeWUHv264bBoqG/PSDJU4rPbuGXljBraZsWt9dQvleTDlNGb7WcvOhYbiQ8WHfosWAzJIw/vcW55R7styzeLjn6VB9rA2tPWq5O4M6JxFrJcmkYJSmDScLywCDmVYxrz1PC/gy7NaBfLdlPdTSPW1YoF0GDCZ5ZsKcS6ZbApkx54moOfgzwcvx9fJeYKB0pwavOiy6bGMTo3KkkGRlPXsKD1pLgQU0U9VMvH7oLvzSBZOlwhcKOdfzdhSHfi2oih6C+EMdDau7p368JUmDGGlco8t2WZlyQmobiUY0gIIRHeofw4LsRREAxSzfZL55DCs/QHVGFgrcnXwQEg0LgjOX6/BsMm1320yucX74FBN7qfgYg7cBV6zi1sNASsgTyzslzcwRvPIwhcFkKP/Mh+PJHFHn6o0HJux1R98Y5Qar/8sDLqhIh+Wg64qPpiB1X8+12ylt2zoFv+bf1Hr/fHPCCHjCQmhR5SrRNRYw1SJ/6Xp2Kz8/qjEN/9n8r4HY2etJcovih7y+EQIOn6roydedBU4f42Gr05NIJbnwBe/Nlpnu7HN69y8nuE6z3mLbFNTVK91H9AaYsscslZrmIvJiOH6FCoMh7DPoDsiIjLEqa/X2ag0P8dEaYL6Bp0Y1BtwbdtDHCI1GIdYF6QaA/pElfStA3opxXDAQMukmYD1HB0ymB5EDCMHaSg5dRuBDOOg/mcYPbtczfP4qdiEQi+gp9PiN5sSC5nqAKEYHJ5bQLUgwgAxqFGml8Z+QohTzloOEDwgu86UzwagFpoNEjHptzzG+XnPvjKYP7SzaMZ70yzBfwcDvhZD0n9ARpT6KGEjFS0dOmI8cGOMueKzuvGiliVpENUW6cSYISMe3Ze8g60m8kQHUK0UgzQK24N52iaHUt6ci8ovaoqUMeGeTUImcOMY/hvswcYWZhGXC1xWmwF3PCh3uEQULoKcJIx5FkLlH9bg9uAnZqsF+ZU/+/92kbz+h/vI7qKeTcsPGnMwD2vjQh9HtMuM6HxH+DFj98H/6rqg/AyzNWcv99tnaPYyppl0S6UgOFU5UQp0StM/DSnc46RVH0aXEoE1lcwndtxm7clC0sw/vRdtxlCteTmF7cTPMX+iwk6MoxfNgg7teoqUUeu07HD/njNgaw5V1sgPUIL3CFRJUeUQbyqiE9MsyfKzAbCe35lOE7JbKOzyMscYV/IaNMBL29kt6FPiMjWTea3nZKvzHMrWB61WMe1TBIOffHFfc3E5pNzZ3zBvnYsraWcvHVCfN/vU97rya7nDJ9dYT7wxM2ZGA9hUMXuyfGeHqTBPmkpbd/zOLqeVwvRy0q5v1t8nkJ+QDKmnRRxdUekfMyDZYMhcVSd6TdFXh58S+4rb9faXSWaxRzcwB0VUeLbhN9IlZkP5UIUtFJEhHIrYTqKfVMcPH8FEMT44rS5QoI9O9H59wQgM2EdqMzuVs48p02uidfz+N+stPiJgly6ujttXgUXgdUE2f0UW8QMLLg/ugTGN1nrd1hqjd40rvJcX6RRAmETrmx/zukZh53WG9R3mBlxuuTL3fvOhrqteasU61VNJPLkiiLTjXc3o1Bt1LCp27Az76k2Br9aEBStYF7Bz8dou6PU+dVzvki52fDJm+YGd9upxx7w3fN7Kf6OgOhmciEtU5lFG8pY6l/oMsjhCBHdU7Rz1jPXYTnPkG7XLLzne+w+/rrzHbn0I1SQwhkw3Pk127SLpYc373LwXvvYsrye55Gak0+HpOP1tDrF3FZwmI6xZ6c4I6nNNMpZrnEe0849Jg/MNR/WOGTltCz6OsJyc2M7OWc9GaOWtfxHASENo7EThUyWkQ+RhuQhUDkguzDBXYzjnqrP13gZhbhBYQZ4Z94fBXIPpyx/g+2ozvwCuw2gSBDHK1kEiy4pnP+tZGEKrI41pFjSZiACDLuzEqw2Bqw/Eyf4l7D+X97zOjdJZMGxm9VlBc8h58YMrvUw6QSufBkpYPM4yYanwuC1qcZRgTROQJ3hEUTECaKNZJFBB5q16Nbg3SO4ImdECVx3b3pbl4K0CAGIt6kiB0ZLwk6JaQJLnWEiYM1T5AC2ReIgUT2ZOTgrHiaPhoQykEn/nBEc8ulY/5vTpj+Twfg4+cz+vvnSF/ogYeL/+oQ1XrasWb/S+v0mPAy/4BCrv94B8NPsT4AL89argte0zLulEnnq/K0SWgHZk45L+Ep2bPtvGB8IGQSm0FQuvNy4dShN6ziSkXXmlQd8bdD7xDzQGTjSWY2WsqfFxH8LDzy0CIWHo6iLE4MBfQlIhfUG/H31dLHDtKhofzwEDPSNDf6bHx7Tv4wioHH71eU1wuazYR6rJn1AnPj8FVgUnryXgovrOFKw2LPkOyVuEwy+cqSw58fUV1OuX9oGTzy5Od7XHg+x75fcfJGxeBWwfELPexbJYOBZKmhnDlcrumPVExXXVaRlNjLyY/mHKUF+WIBaxNYdmTZjjzbduGMl1TOMnSkXfnjkXZDCD8AXurgMR0cWI2NkkV015WmS93tzqNKC/JM4W3cR5KJ5ijvtmfXgZMCkqnFK4EvulgAIejfr5Gtxwew2ylmpBEukBw16MphC8XiRi8+tmcJVyXZcYteeqxSEDyy86HpoBMLvca94Sts1vfJzZSD5AJvjb+IEIJhT5Mvdrh28jVG7R776VV67QFeKB71XqRJxoCIXlUdTzDQAZc0johGeXx8WkYzOoDrm/CFF+SfC0je63KMtkaC9cFfPXhZVSEUn0zXeDWZcN9V3LclDZGb0naclPap71v8qbrtWWoRLAtneeiqH/i/vlAdsEnZUhnbMmNTpT/W6Crt97n6+c9z9fOfp55O2X/rLfbfeovj+/doF3PaRTTW27x1ixf/5q+S5DnzvT0evfUmO++/x+LkmOPllPpgB+MsJBoxGsbb+WvIfo/MgzyZoU/mUNcksyV+scCbBvt2Rf1eRfXvSkLmUVuK/NWC7CM9kkspsqeipf0qEkISibErRY8UqDVN79N90isJdsfh9izuwOIOWuy+ofl2xZP//R2Kzw/IX+2jL6RRvqxADqNKSSjiyKWOC4F2t8LutWDjmAoHJAK9rqOHy0ghc8liO+H9v3eO/GHDxX93xOB+Re/BkvRJzWQtY34uZbaZMSs03kB20pBlPppzbuuYXq3F6fGHJ8amzB3uxEPlog267yTLuvNW8QERXMxU8mfk3RDEqaIvSIEoBKInT+/lhkRfUohExVFZ8tQxtOrWrIzoQlwkBxNwCxe7RccGe2QJy8DgF9ZRm5L+3xiRXssQCJL9hvVvLADY+WvrJMmQS3yObfXxv/C++ZdRH4CXZyyrJ3GlkIlI1k2iLl86ohZ+dQFT0dk2dOQp4QOyi3cPKoIRVyhcEQGJyyQulYRU4LKzx0IaAyBXoOaM4Q50nBt8QDeO9MQhy+jw6rUkVIHKSEqpY+hiJklGkI8FmfPoKrq6Zo8baC0iaORAs/fpMWvDhNHtCtk4hu9WNKOE5UaGkIH95zJqUXP1zZK1tE8+zhm9soWZPcY8bMl2SvoDxfzdBvOhnNmNjP1Fy6VKUtwaMZoF2gND880F/S9PONkx9Iwn6Uk4stjK4Uwg7UnSeYnwgWbYp+d2KQ8qRpsJIk8IPpDMlhEYeo9Rnpk33NDRr+N7FEd/Qbn0MjiaEGXGa53SKHZeImhZuesmixLhPKptT5VGQkYQ2VvXp/4LeqgoO5n0qhGnfCC/X+ILhSsUPpcIG+g9aZA2nmDa7RSXKZQJ9B61BC2wgzgySk5iIJsqLYO7FdYn2FyTmbI7ccbVrhUJ90cvo0PLVn2PuV7jILvKQfEceaLoJYaXdn4TXPyralGwZacAvDP5AitEliXROTcQuyp5Eh9bH0TctqwjQTcEGPXg8y/A516Q/0ECrA/h1NvlhYv/8YDL0yWE4JrucU33/tyfdR0H6owzeQZmwvf93CxYTrzhuIsJWN2q4FgGx9I5Hrl61SgBYCITtmTGOZV19ykDoZ+ZVJyPx1z57Ge58tnP0i6X7L/9Fg/efJ3Ht9/n0c4Dqid3KYOjzjTh6kXkZ38V0S8IswXi3kP0+3dhtiBpWmzdYJ/s4q3DZSmkCawPEcMLiETHTl/TQlURlhVhPoXFIe7wiPZfVoh/W6LXBekLKdmtAn0uQQ4UejNBTlQcj7QQVEAkEjVQqBcKsudCjAIwgdDE4yK0Ab+Mzr/uicHcb5ADhdrQkUs20ah1HcGQJzoEuwBeYB42mIfNGQoPsQMRWo/oSdR6gr6gSa8WzL48Yfy4YPtbc3qHLf3Hc/SBJB1oJl6wlIITLXlsBeYPHRwaZArqcobaTmJnKJOxwyTi4tTXLgKxQ4Pbs6eZUCsWvwginjZEDHVVPRnB1USjRgopNCLXoJ4yeXRxmbIK/BUJHY9GIBAILxHEDCQUuNpT/skx+kJK+nyOGidIqUifz0iupXGcJQDnufhbB0jrqTcSjj+3zpgLfFT8d8+0//1V1Afg5RkrG/eZXhvE8czZYDu29pToAEucgwoXHXZV40/PZEF0mUSZigBHxra+CJF0RRkVJYh4IQlK4HQEMHIBydwiRARPcymZjnLY0shBQtLz5C52UnwW58qph8QGah+Y9SOvokygVAE1TklVILlRoMpItIs8BsFiI2H4oQFbTyz9qePICfTM4ZQgyQzVRsJeUZH82T7pX7tEtl6Q/cIF3G8+wh+29G4v2Ewk7bam3kjY/bBg49uGdJRy/VMjlr93zPyNitEn+piXetT3atILCeJJS1s7nPUM1xKqwxJZt5h+EZVH+wv0+hiRaUIAPSvxUqKdw0jH8qlwxiY4zqt4ATrpkoW1kDxLrfxdxjI5/Z2Fi0nBnsDK5SWZlUhjUY053ReEBhwM1hO8ADFQCCWxT2UaCWL3rXjcYDZTTC4IiaB4WEcDQyWQtWNxNYtSzcbTf1ARhKDaTqM1+5GJo/ETiz5xtCJFSoM08Y2slCqlHvHa2i9xuXydkdnjUfECb0++iJSCfia49vj36DUHCBwnepu+OUQGS62GMUGaeO596k8k7Ui5VzdiDMDeFKo2nkOzBF6+Aj//sR9uRPd0PTyEsom/c23zmTbNf1KlhDjlvPwHS0Cf6LD7/VUHdwpkDjuX3z3fMPf29PF37eL05wuhGElNTyh6It4XQtGTqnss3iSiy1lqOXQthzQc3JrQ3Pw8oX4Vd/se7p338XcfEuoa8c5tsnfv0ROK0XjC+eee5+Lf/hLr/RHVwQHThw85un+PZbmkxVN7T9PWVHWFUwIz6OO3CkSiEUoS6pZQloSqJtgSqn1CtYvdOcHerRBJhT4n0BcT1LZGbyao7SRa4qc+BgBmEtKA6snIkbGrEyngNOlz2YpQFDvX3eIxKn5ianSwAbmmcUcGMXfIYY/0Zo6539Dergll5MeIoY5hhzOLmVnaN+tIrr6ccfTSgO1jw+RJQ760jBYOpwVJTzGSgatZoLyYMpv0mbpAe9/gv13C3CMzUBsafSlDXs1iZ+allNBTiJRuUdqplDqXXSWInB1AOo/yKweGyPQ/jdDzkbDrpcTmipDEc5VXRDKaVCA0EgVKg5R4aTn5p/cRowiKRBKfp/f5AXKs4uKrI/L03y8ZvVsSBOz+9XVyvcV1fp6e/A+Ejv0V1wfg5RlrNPYsbvWiMsd0mUJPn7tCdLcVHeL3mYw8CRcdEWljJpFeujjr7Np4sg3I1seMHONQTQwYE21Adj1DrwQBFccMqWCUSJxc0jpJuZlgLme4TFCPEpLSRsfFQczaSAJsuhZxWOMd2H5sraqFRSTgehLf1yACPldUlwRzG5jerzn/h1PW78D+h/so57EPPe1LBdOXB/Qf1HDnhM2XNpmMCua/cQn7zx7TOzaM3ptT/nPPwd9ZoxlLdm8kXG7BVpbzzxe0b8w5+ZMFk1+acHw1Q397gcolvgw4ExispRw+ntM7OKFcG2GLDI6WiHQdLQXOOeSywklF4iw2SVh6S9Ix9OrgGQhNJiRN8Bx7w9Yzkna/f2QEPyIaYDZHGotuzjovKpNIA72NlMXSwUhhk65/C3GsKATSOvTUYDZTXD+Sdft3K4QJtGNF77GgfK6AAMlxS3ricImkvBYfK3ZrfArFkwZfqpi/IyKTNvgIij2Ko/wKO8VzfPz4t6nkkMP8Mo97L1AkiuvhDuf2v4bzcJxdZFI/IvMlKjh288scZ5eA2Gkx7pQeQKrh+lYk6D4+juZ0JobdcnkDfunjks3hn39RX3Vdnj8vUPIZQMB/hpULxXmlfkCVVHnHnm+6YMfmNL26Co7KuR/xbH9+SWDSG7LxsVfZ/MTnWEeT7x7h7j1keucOJw/uE0oLr7/N49ff5jHQ3zrH+Mplbv383yDt9WjLktnDh0wfPKA86tLgHTD3QAtCUIwn9J+/RW9jg2w0JCl6SK0p6z2m1T2OF7c5qW+zmO9RLg5xDyv8SYPajiGDYRkQGuSa7mzrA6H1+KVFpCra6ouO/B4CQsgz/svKL8WB6ikYCPR6cuaPEoBPDaMBnek6O64LLswUIhVPadTjOOvYB8q5Y/O7C/IDg7SeYm7xOo58dSYYWctlAfUncxZ/a0K1lRJSFc1J23hNCIqomtKxe/80XeCUXLw6FMLK3C4udugiaFQbhRXSx98XNpDMo7W1tB5ZetTSoarozRNEdHH3EuaHLepeRfrza2TPZyAE6c2EkHZsYIigqHFs/+4JqvGUF3JmH9tgm5tcE3/9x973/jLqA/DyjDW7fJOs+HpkhXfZECsZ9KmNf8dZCSKyx10mCKuE6A6sREn1KmgxgqDvCVcUT911w0rhO1Bko1MvrgNKIaA9oBz2fI7JJHYribwJHR13BfF3ZSaR1pNVDn3YRP+QRSB94DEDhV1LCBMBicLlitkLfZZXc5Jl19p0oI4tbRCY8xmPfmUD+Y93yG6NGYqEi2nO+7+8xfw39xmWlnN3a5a/PaX8lQnllsDctri+5tpHhxw8ali8U2FfKhh/dsj8z+akmcQsLMY4+msaEaCYzSjPb+DyNPJFhCTDURsLlYz8C2upiIBl1Ult8Agh2JApjzvS7l8UvKw/DV78mYfMis6UHi+QrUE2Zx4vKpekLnq9AMiRpP2+BbfwkbAtiONFM4oub70HNdJ42qGmOZdiBwppPf17NQgwQ0WzlaJKR7pnsGsatXSIppNIty5282wcGlWqzxtrP8d6+5ix2eMgvcI74y8gpGSiK27e/+c0Hu4OP8bF8m36dobyFi8Ut4ev4jvzNts1D6WIXJcLa9FI7o1H0YTOuHgRWRvA51+QvHztz+9wzarAk+OYY/TCXyDH6L+UKqTimvze8ZUJnkPfsvSOKljK4E5vy+AofTTEqzqp9VgmbMqUDZmyqTI2ZcraU93E07o6hqvPwZe+hDOGk/v3Ob5zm6Pbd5jv7rDc32O5vwd8AwCVpowuXGTrpQ9RTNYAKI8OWezsstjfo10sqE6OqU6Ov+dlhJT0Njbpra9zafJ5rm//ddyLDe1oxnxynzLbZcETlmYP5yp8NDvpfhkYQTAae+zwh4bQQrCeUHnMnsEf2BjaOFHInkbmUfmUXsuiCmql7OlkzCJEEKEHMo6jTMc1ccSMoVwgkjPFktuE3ecKsr2WyXcXFE9iOKxsPWQSlwp0GZ3Mi4N4TrB9Rb2dUm2n1NsZPpedxJmuc9L1ScMZzeB0DBk6zmPolGtS4JJAm8gINByoOpqjJguLXjjUwnUL4WhuF6dHsWMjBZgm0P+75xDbcaSlGh95QU1cuAYRr0XrX5/Te1TjtWT/C2uMsxtsi1foi/M/lf37p1UfgJdnLOcPMevJmZKI1SyT05nmmT1mV91cdRXiGEMuiOmefY3rdeTe0IGYLtNilVG0+l6G7kW1wOkOJEEcV+mYkaRciOTRBlTjyXda9CKeyEIKvlD4VCKbQPGoIVlYQjcrtfNA2yhaJH4kcX+tj9vQ8UBoo5rKDhW+SEiPDEYq6ksFj3/lHObr+9y6uEEhJTcqxf75FPFeRXYCV9+VvHloaXuKaixQlcYNJdc/ucbsnz9i9u2SzV8c0/+ZIe2/jgoP23q8g7SQpIsK7Qwuz9DziqlISESgdfHv0nWL6r5ugj81Km26k94KvPxFeC9H7gfBy7zrvLRPkTSz6RzVtKdybaFAFpLCSbyPI6ZsIz1z1u1+TwRI91p8L0YC2J4iPTYkyxgdEXxgebMXpdMmjpeCFDTnU3wmKA4synnkoQEnoAaVtgjb7YNEh6xFssE3N3+FL+39f8AHDrIrPBx8mFQJPj39bVy5YKbXmabnuD7/JrmbI4Njlpxjr/c80C1Aw9luPelFt9w3HsK86tKjgTSDWxfgv/r4j/Zzebre34mfxoU1wbD4ALw8SyWdd8yfZ2gaQsARnnlM+nSpJGHj+efZeD5u/7YsObl3j9mjh8wePWb25DGubTm+d5fje3dPfy8bjhhdvMjFV14hHQwBcNZSHR2y3Ntnub+HbZqngND3v2dP6KVkk0uo0YhmPMNOStqNOWGjRvQ7lVImUQMNV7Lv+33AdZwYGzr5dSBUAffI4qRHX9bILKYjhybgbWwn+iYauYGIrrQLj1+6GAegQF3oRlp5BD/1JGHnZyZkRy2TN0p6j2qE9WgrqDZTzFBFt/UDg5pb+nPL4L2o6jIjTXUupd5KadY0QdHFxnTGc53qNOKZ2FnxiTyVUq/EHKGjK8RriKbe7j6P1SWou+YIH6cEvvaE2sfAxo0EUcgY9Hpsu867itxMB3puWfvOgmThmN0oaF48xzl5nQviMz91A8mftD4AL89YGyczytJ9T+ZE11B56sokOP22Q7wQLyrCh04OHQ3hVp4vqumk198HdKJ8mlOWp3DRqExWHtn4LsVYnLoqBgUulXFnl+CLaEYkPIjGkx5bXCYxY007TBl/bUlW29N2oWgCbhlojGS6U9N8tk/iPIO7JX6gmb/Uo95I0Y0nvV9RXs6prhUcl46dd2dc2hxRZAmTy0OOG0+yZ8kOGrZfK9n7zIB2U7J+5FlUnnMv9Bhd6TPdb5m9U7P2qSHVVxYsDqFdOkztGKwnVGVN2hpckZIez7jbn9ALgZAm4D1pVaF89F+pOw8MiJwX6EzmDBy65pm384rzsvFUGvXcR41wu2K8OIduWlTTnnq8CCmQSpD3VFQJAOlYU3ez7dWOoUIgf1BjBwrXV4RM0ns3sl1tIVEGZldiFIVeWvL9FnygOp8RBGS7Uaydzi2hVgQgCTa2pImrNCsS7g9eZpae5+LiLaxIeG3j50EkPF+/xvrJmxw7yTfO/yofO/pX6BA7LoWbsV9cYz+/Dt3ut3rreQqvXIeDOezP47iol0DjYifmb31KPRMQ8SHwfpdjdHP7P62T4X8OJYQ4TVj/SSvt9Tj30kuceylmW3nvKff3mT56xKy7Lfb3aOYz9t+esf/297wRemvr9Le2mFz7FEnR6xR3AbNcUp9MqY6PqacnmKpCVBIqjXycoxjRMkcyoApHNMxo12eoTwvSGwmyp75ntLNKcpfF968eIbn8Q7J3hqBIEETH2lNLftulLNuY/RMWEGYO/46FAci+OuWmNDZn57mc9ELD5O6C/pOa/ElDviMoL2TsfnKEF5AfGIqDluzYoo8twxPD8J0yJrAPFc1aSjvR2KGiLeLfpdqYIC+dJ1TuNGF6JfhYpUaH1eL1FNh0P+cCdmFxRy4qizpQpM+naAJSC1wucRfT0wgDfIy62fjqjHyvxQw1s4+N6Q9vMBHPM+b6T2Wf+mnWB+DlGcsNfp7z//Nv41NFm6eYLMWpSLBEEt0o8SBjdyVoCKnAp3Qp0t2O1ymRfKc6akddurRY7XwRWgcAL7p0UYkIAaksQnQRBC4g64BcBvSJRZ4E5IEHZwjbkjCQhKFC1gHRU7CW4FKBnWia7YAvYOu3j2GoosnT0pHkjsHUkt1d8PgjOb6QcOxJFw0j5/GfGke1yyxKd+vNlJNPjKi3anr3PBtWk53rUeSCpT2iN/VsfWvO/GrCySTlYgLV1CM2M174/BqPfusJy3sNxYWEwYsZxw8Nrgk4GxiuJxzcbkkWJa6XIZ3H7C8pX7hMyA8R1pIuK2SI4MUSaDjLNwLY7DJ5nrXzslJ/AKzLs5PePESLOutX7romjn8a85RBHZGcOU6wrSdISMaK49UisQO7MgR690rq6z3sIKqSevdrhAsxjbwJmPXIgymeNDH7KJW0awmq9qQzQ7p0SANWKJRwSO8JiUDUcWRUqiHf2fxFbk3/GOVb5skG740/z9Cf8Ors31C2gTcnXwQC680uQ3NEI3s0sscsPcc03T79kzyQSHh+Ozrm/u6bULcxObqxMYTxc7ckH3pGxdCjI6iaCIYu/6fD/fugnqGklAy2txlsb3Pp1Ujotm3L/PFjFrtxbLTcP6A82MdUFeXRYeTFvP29z5MOBhSTNfpbW2y+cAvdcWIE4ExLPZ1ST2fx/uSEsjykPZ6z/O0dZvo+4iVD8qJCDCQiJwKO1sS1YkcUDz5EMOKJaiZF7Lx0uUVCQWBlFd0tQ/XTzXON2FZEK8Ju3GQtAtdZHnTqIDFgfnONcmkYvn5McX/J4NAwODTYvmLxfJ/DT04IiSbfa8l3GoqdmvTYkpWQlhYencnMXB6P9XaiMCON60t8pkCsaAMxmFfUHuFWPmExx6i+b1i+V1M/qFk16wGyixn554aIiykiFagDg4SYs6eiuCQ5Mqx/Y0bvYYNeOGYv9jDXz7OZ3mJLvIz8CV2n/zLqA/DyjOXFBabntnAuiX27zuQnmDoaAAlA53GW6gNCe6T0KOlBOEhiAJ/N4ugnpAExAJHLDthA0B1BV8T2JjLEdqn2MZTLx5A/IpEcm4KYBJpVTsUS/FLjaodac4g0unuqRVxDhyxgRlG+V13KOX51TP5uSzFrMWsKtfT0ntSMvrOg/6ihvJrjNhImfzbF3W3xiaJ8vkCXns0/mXLw6RG2r/E9zZ1eTf++pRikeCdRl/q44yl56dh6fcnjn0moClhrJPXUMr5ccH6ouePg5I2KtSsZurekrT3WBIYbCaItUWVDuzlGtYbswT57536GQf6YMDeki/I05dV2DqTwNHiJ3ZMTb55JcbQaGQ2/zzxs6d1p7AIBdN2cGdR1ry9VVAiMt1Lmjcdp0COF0U9144ht3OzQUN4U1GONqh35fos0gXpdYUfyFAgVXYp5O4gp43rh6T2qkW3AC0WoJYlYRhWalCjvCGgOiyu8O/kCv3Tv/45HcXv0KYLUfObgn6Ncw666xLvjz/GJg38R/8DgyP2CaXaeg/xanJFztrBdH8DHr8EfvR2Bi5KxVd/PIgD59U8+eybRe93I6Ma5/3KJuv85lU5T1q5fZ+369dPHQgi0iwXL/X2W+/s/AGraxYJ2sWD68MEPPJ/UmmKyRjYaMdjePgM3q4wxv8+95e8wv/+Ipj8lFBYnGnxmCMERhIeU6HkiBEJ1I6RlwFUmcmYaR7LddR0CUY2kJWqjcwlOZVQ+ZdEYbiU9jmqfeGQIQEgfs4aMBSE5eWWN+Usj+reX9O4u0UvL5DtTxq/NqC5kLG71OPpED1QfWXvSIxNvx5b0yJBMLWrpKBae4h7RoM8JvHM4IbCJwqmURnpaAtaDsR7bCqq9Fr8K3xxq0s2E3nMZ+Y2csKVxhcQl8doUMhktbypLdmDov1cyvFuTHRpU7VlcL6ivDsnXr9MX26z/hW0+/2rqA/DyjDXZOGBZNmjT4Oss3owiND0oF4Qu5ybIAu8lwWkaclpyGnrUPiFUJcLPkaJB+SXFmiO9kKHXBMmoRaiOdCo9QoQzg7vOAyC0Ej9PUcEhVNxRnUsiSNIGpCfY+HPNmx7ZD+hCopREVAoaQeg3+KEnZJHL4geK5bCIF8mRpLw8YO9zawTjCUuYDRvcY0uvtmSvG6rNDJdLekvPxjfnTF/q06wnTM8nvJfArQeObJhS9ySLdU3/a3PyA4M4NhyuK54vHdVRizOGF6/mPF46jIAmUaSFpAwO21pCyEgUqKpFZ5qQKNL9KUZrbD8nmS1JF9UpSd4ET9XlG9UdiOkJRS4kdfAcecO5P4e0e/RDyLoQE6UdAd8BFVU1COcQ5imZtJIkQZD2FL52WAVyGDsoqxI+kvqEEPiubTx8cNZ18Zmi7atIjVpasi51ur6QEZQkOanI9lrsKKFay+m/WaOFO20fg6SVObeHnyRxFZvNfYKQvD35Ei/Mv8q2ecDcSv7s/K+R+JIr5duM2l1KPWatecxRdon94jpwOsWkSOK46M5eNKKzPhrTNTaSdP/25xR5+mz8imUTeHTUjYw+IOr+Z1tCCLLhkGw4ZP3Gje/5P1NVVMfHZ7eTY+rjkzhCmk3x1rI82Gd5sP8jnz8Zjxm84hiMtqHwCKnwwuGoaVlgy4q2nGHyGlKPSAMUAcYKdS5yS4IJiCyOX9RY4fYtzXdKXGmRhUIOJGpdI3vxa9lTpxyZUHt86bE7Lf7E4qYOTOzwCETky/jAoPFMZi292gIzBgFyJTiZJMwu5piBRiZxASsKhRxossqRLR35wpLVjrR0UaTBaWg9P2QQBhnRx2CsEOMUehKvHeFJhd/vuv5SoCoXo2lKd0ocLp4YsuOWIGB5tYcbKOzVbdZ6H2ZNvIAWf7F4lb+q+gC8PGM9LGvWZwKJQ/mKmKCj8FrjkgJXNYQmIJ1BiT7OZ2RekbqSvjki2KfMMoREyAS5C8m8JJtodNIipUPKMx7KmUwp0tFDkECNdwmiI8qE4DBtgZSBJK0Q0oKHgfXUdwICSTZM0WlKaNaRtUYmT/DpHFQ363UCNAQhESLFC4PXgXIrZbGZ8ebfukC2NAwfG7LQx4499qMKZT1mrGnWEqqLGe2+IZ/A9fueZKToTRLMC578sGHysGHnWp9LIpBmkmCht665rDwP6sDikUGNFEIamoWjLR3DNc2yiwEI/Rw9L0kPT2jHA/LHh6SzBaueRkug6ghJTXcvhGBTZjx0FQe++XPByw+TSZsQVzm+y5QB0GUdPV5ad7qZVC7JEolKZKTMDlUcu3UVQpwpp8cGN9C4gQYt6T9owEO7rrGFQhLwmaTYiXyaAFSXcoQN9O/XnSJBYPqarGyQmccnAiwEJKUe852NX+Lq/DUyt2SebrHQG3zp+B8TsHxj8quUyYRbJ3+C8oaenTJNzlHqMUEo9vPrp+MiKaIsepDDa/ehtdBPoTKwNYTP3pS8ePHZiaG3d6Oj7rmxYNz7ALz8l1hJUZAUBaOLF3/g/7xzp6OiejajmcfRUTOb03Tfm6rCTwPqD8c0t/YxWwsCAdkkCC8QRUJCgm4HhIcaK0vc80v8xRomjlB03Ylw5g3DFiRXM/KP9/Clxy+7ROa5I/iAnzt8HRBJ9I9RY41ag+RiGk3zGo87jsnW7sBiT+I5+EjA0SghLRRrS8ekNGjj2dhp2NhpKDPJrNDMC0WrY2enGkhkXyEHCXIjQxQSpSAlkDhIXCCxnsQGktajm3gTSsT3B4TWg/E/nPn01DgpCEE6c6jG0U5STl6dUF8ekIoh6bmrpHrCJh/+y9sZfsL6ALw8Y53nBtOvbdMfH5FkFVI1eK/At0D0X7HO4rrwGi1LgjF4AV5JPBKfZIg8RxaRaW7rBa3xzE2OyNfQWY1MW7yPHi1okNqBDghlEcrS2j4hpNTlFvlgjkpLjEtYzDeRwjIYPiFJKxJd4uoWswgEKxmupWRFi12O4fjXCdmfIMUTVOOQTcd6Fw6wceXQBvqPavqPGzYaSR3AXks5HnrC2phSVwx0i1ASbTxtKmnWNLszg7wsuXzPUo8F6dU+6dIyetKyf6PHkYZLPY1pHeK8YNtbdrWgnjr0RKMSQdsETOsZbWXsPDKIZYPv5+j5MYO37nH83BVwHjU/Gxu13dgoF+p0bASxi/LQVRy49kcsWc7qh4GXlbuuJ2BW0QDLCtlakqo5c9dNBflTyd56rGmeUhqtSNrpToOZaJr1BOEC+eP4HD6TmIEkKR0hkfQeNTE9VkI7TtClZfheiVeCeiMnOTGR/yTApgJ94DAyZ5ZusVs8x8cO/zUAT3ov8vL0d9Gh5V56i7uDjyOC57nFtxi1Oyz02qnKqNRjymQCnKmLbp6H23sRsIjYdWZUwPZE8L/49LMDkBDC6cjog67LB/XDSipFb32d3vqPzs2xbUszi6CmXS44Eu9w0HsNZ1vCUqD3BlS9PcxgiXvekFdDkncuo36/jysqyu0DzMv7uO0SkcZzKx0ZFg0qV6gJEdysxAyCjof4lGq0e/xsmRl1zaGLOsB1nBsD2IAJgn0f6O80DO9XFActhYDNjjNpJgnllYzqSk6zeTbSItBxHwXBCFovOGXwrU4qIcSQWBuQwUWn4RAiN8Z0PjNtpDm4kYidlb5mcLth8toMu54x+9RF3Lkxug4kZcGg/yFGXCETo5/+hv4p1Qfg5YKmAUoAAL5ISURBVBnr5q1tfvfeJY6SG+jJAj2u0IVH6RyhAyH12FDT2kVs46MIJCA1Ra9A5QWtVzib4k2Bsz32d4fc+8oh5WGKkp9GyFexlWaw9hbD9e8ilUVqx3ByQjE6AmFI8xlNPcTUY1x7mSw1pMURLljqap3d+jr94ROClwwmj2kPptQzQ1ooBtsJg6FDlJZm5/9IWfzPJOtvEpagporBsiZMLK5Q6Lolf7+KHgJHATUNZLcrhkmFkFPsJKF+PsNf0xRzi0tjzMHRhqQXQFxRrO9YyoGmt90jPSkZ7jXsTlLWdxypkqTGk1zsk05b6tITCoXOBKb2OOMZb2aIOzViUePW+uSPDijefcTByy+A8+h5VBvhPa30lN6RKxX9WEJACsFmpzg6egbS7g/zePlRuUbSWmQTf14ogcgkRRZ5Ml5AOtHUK7DUSdKkDxQPa9qRol5PKHYbpPExFkKCU6AKhWgD2WGLdAHTjwm22Z7psqwU9WbC6JslUnl8KvBpDFszIuOtyZfYrB8wNMc0ahBjDuweS1Hw9c1fhv8/e38aI1l6n/eCv3c5W+wZkftSe1VXV3dVr2SzuTY3kaKkq6sryzZEw3cMjwR/MHA/CBhYGAwGAw9gwDO4X/xhZgzjemBbgq41vvK1FtOiJO6kSIrsZu/VVdVVlVm5b7HH2d73nQ8nMqu7yW42u9lkk4oHCFRlZUTUycyTcZ74/59FCOaGL1PJDmgkW9yqPEjXn6aaHRyvjKAIozs5W/x947Bopoi84gW7FsGvv7eYNL1ZbLWL7iNf/2wm6k7w7oD2ffT0NOXp4iRa4AFG7qPcdl8gpYdAMs+vIlDs8BRJ3sXmBpELqqMVqoMVGCm6e6ts1L9OL7xDJgbkIsZ4MdbLCqunE4hEQHokdLPF1VIBCJxwhd7lVREZAqEZ678Kgn5M0x1gIZn3SB6oonqG0npMaT0m3EoL3Uvb0Him6IgbLYWFo3MhKEwhQoB/9ERwnAtx9IcnwQOj1CtssONjOnJVCXt8rLWrMc1nBkgXkN1zEh2GuCwjSqqU5i6iy02mxf3v0E/xx4MJeXmTqNTr6If+AVr1GdkePf86QfUOSmX4MhinPHYRxmKyBCElWhfrodxKcmdwOCyWXGbg95lZ2aU6ldDZSBgcbKHqG/h+DZzG5C1yc5Ny7QBrPbq7ZwhLhwgcvj8kCPp09k6xv7tEWKpSa97GD/t4/ghrApQe0u8sUG4q8rhH57bBkiF0F888C6UOsn+ZXt8Rzb9EPiWIK7OEvYwgOCRr+OT3KqK1Iflpj8Fuhc6TbU7OJVTiDN1OqX0voVtuYJZCotTS98BEkt2GRQuFMI5S32IbPqX2iKk7Cev3eOwZx2KokJlh+jAj8qFH0ZvmlRWjjiVLDEJKfGlJ4pR8poZyltrqNlmtBM4RHHaR1iKtJZXmWLALxeooQh2Ldvd+CHlJnaVnizyX75+8FK8Ex9UAnT4ySZH5+F8USB+ikipcl7KYvAx8OO6TcONOo42Ew0frmFBRfrGHcJA0NU4Jgp7FNBTBXoIwDhNIkrqHzCy1qwOckiQzISiBt5ehlMX4CufACE0uA77b+jRn+08Smi6piqhlOySiwd/M/jqxqgKS093vUk+KqcvAm8KNw0OOLNICmCrD2Vl4dq1oihYUbyanK/Dwacm9yz9ajsjR1OXUrED/kOqACSb4URCJac7z37HmvkqX22zyLeriNOfdr9LxbrGvnyehy6iyw2hmlxonWBQPc859mgOussaXid0+Cd3xrU1OggssGp+QFhKNISFnhCHHkWNciiHFuBQrcjh6/Xnl1p9X/P2oBgAwLUWvWaZ3uYzILNFGQulOQrQRoxJDZXVAZXWAk4K05ZHMeCTTRUaMKb8J54+4ewDuOHOj+LfqzYzZb8f4eR071yKdqpGFfVRQY7b135F6AyKmKTP3dn807ygm5OVNwrkhrfpTGAc1lxMbj16vhiptYWWCJ0IYzKFFCyVjnBOY2GGtwlDs+q1V2NzHGk1uQQV9tFbUWymVWh8r/hLrncYmDWxeRboycbdHdX4boXYI5TTWTCPVbaSOmVl5mtr0bQ63z3H7pfczs/Q8YamDHf+m+FEPi8KfreN3M+L9BC8aUpkeIpINhGxjD6boxFMEzQFeacCoVSPV0wReF91IiUcBQSejWutTqirWNzS8p8KcSJm6NqDyVJfOXIAWAiUEeaAYWEEvswg0njC4zBKVAqIsQSY53VAynQp8JVFCMC0se0DuQJeK6O9kYEiGhnpZ0E8y4sDHSYG/3yWrlIo3Q2mGSjO0NRg0A2fQiMI27SyRUMdEpGszMmfxXsdxdDSZKQlFJO++OIycIce+qhrA7w3QowRp72a8eF6R8ZKkFju2Sfc1gLjbJj0ySOdIahpyS+lOAtYVlkhfEm6nDOY8KnfiIkRTC4azIcF+SrCbYoWkvxQirEUd5LgArC+QHYERHvvhCqmuUEn3aGQ7ZCbiIDzBS9X3slU6DwjK2QELo2tMJeusVq5wvfYeLh/+JQB74QkAIh/uXYL9PnTHYXS+LrQvMzXBrz76o5GPOHOs7k9WRhO8c1Ai4CQfY5/n2XTfouNuErPPCT5KS9xLjzvsuefos0GX23TdbSJaTIv7uOR+k135NIfuJawzxBzSZ5Mh2+QkxBxQZYlp7kXgUcxiLU4YDBmZGJDRIxE9UnrkjMhJsGRYDK6okn7dY3e+ZHgqYngqAmsJdrNiKnMnwevmBHspwV4KDADIS4pk2i8IzYxP2vSK6cwxBEXzliymLuNWNoejumaZ/1pCoz1DMh0wWGkyqA9RjTma/hUyUTSfT4v73nWhdK/FhLy8SWS9A9xLf4GXW0QmKGWCVirJkgRFH5k7dOahkimOK9uEHecEKIS0IIoUWOvAGkmvXyHOfJwMcWYDoQ2ytI8olVBK4ihjsoDudw6JljvIynWmZmr4nTIHcsiwllCqdmksr3Ni7uvkW3Vog3I5amTRaxZ1YoCbSmk0PA5vRvi1YnpYCrsYERO1erQ3I1KTYjVEc12MqZGqMoSQLSryUkK4n+GdEcx/KUd9+YDscgnqCno5wXM94gdr1PpwWAMTKvZrhcisZxVTGeimQW8nNPczupWAvXXDbKDQkaYhHJ4WGK/oIVG3U7LYkIwMUzOajb0MMUrIoxCdpOg0I6tEkI7wRzHSWnIKoqHG5KVwHHmUxkV2Q2fYt+n39cgc4QetjABGNid3bqx7KRB0+qg4uRtQJ8D3FVFF04kNVhaCXV5RUS8d6IHBeYK46REeZuiRxUlBFsoi40cBDvyDtOhBsg5T0TRe6iIc5GVd6KkODYHNsH7RQK5Gmlz6XG08zone91gevIA2CUPd4HrjMb7X/OSRuZMzvSdpJJskqkw7WGCkawjn6HtNEl1BCZhvQL0MN3cEg9ghJXgK6hF85kFJOfzRpi4vbzushVZV0Kq8u18QJ/jZhRCCae6jxAy33RdI6HLd/Qnz4mGmuY+aXCF2h+zxPIfuOiP2WXNfRhPR5B5O8gkOxEsIJKFrkrBEnw0SugzZI6FDjZOUmX9V7knEFADSaSQ+xawjI6FHwiEjDknpYUjJGJIzICUeZ5gehSi4oipAKNJZTTITcPgg6H5hZy4ITIZ/mKGHBr06orxaEA0nBVndw4UBMqojS1VMxSMrQRKlxKUUF3hUthTzX9iltTqFa9ZpX6rQX5aI+jxSeGhCHJa6OE2D0z/ZH95bwIS8vEl4lMifqSF14biIPE0gPSQe7UFGprYRDkplzUKwQiYGpHQ4ftuNQlNGoDDEWDJajT6doWMwVHR3lxmsHSA9S21ZIX2QYohmSDP2aX9pnmprj0R3WD7T5GT5fnbUJluNXfqnY0QYI+cNrh+ANohM4JoOseHwZYZfSdBzA0axjwrA62lawx5ZqCkt9RluWnIZE76Uo2uHZJQw8xKmDCbS9GYlsq4JYoH8aofwxRG9j5Zofi+lfH1IejLCVj0qI0EndMShJO5bZFlQHTmysqIkNdVOzn6k6WlBUyl0I6S8mxLWJUnqEA2F8iVZYslTS2spRGwY9CAhjwL0YUJlbYukXsFtD/GGMcpaUopk3aNL42tFu8OxaPf1yMsPqgWAuwF1Rw4mrMVv9/GGybFYWGhB6EnCimZnK8OOHQlCjN9vOcCAd5CR1n2yuqb5fA8cJDMewoGKLXlV4+9lCAdZTSEPM7xBsQ+3QpDWdDF12TdIZcm1JtMeUkj6fos70UUePfgzWskdYl3lRuMxnmp+Cis0IFA241T3u0zFd9gs3cPVxgdoJpsA7IfLQDFduW8Z4lSweeiwDnwF9RJcXJI8ePpHIx+vFur+SA+dYIK3hJKY5Ty/ypr7Cj3W2HTfps3LLPEBSmKaZT7API9wwFX23QtkDNlxTxWPZZYWFxmyixAC31Xpsc6IXUAyZBeHZd49QlnMk4oesTskpYMVOcXbqAIeER4RFRaxzpCLATkJuRuRMsAQY8iK1RMpUIRbChSF71Ag6gJXzshXUlKTQ5Li7w8JthOC3RT/oGijj3ogXI5Tuzh2j2LesV5RcqQIqQzrVDcreKUZ1j7cIj4X4cIER0rIDFJopsQ5lvngOM/m3Y0JeXmTeLm/xB/X/2dMFhHKkOmyZL4umGsIqiHsDNoMan+OFwxYL0d8cO5xKmKGQ3GDQ3eNlN7xc4VMUWIGi8W6NUaDmN6eZe3ayxxujBjsLVE/9Ql0uUd96joZCSSW3Rua1sxtbvs9fvGBT/Jg6SH2Rpt8Y+9LDKrfxoY9ElUjH5YptbZRwjGoNHEdj3LzNmLR4PowigW5dniuSklkCO3h3SvpbSiSvQFKZGhi3E6EkCBLFvKcYGuE9gXujE++EaP3LaOlgPJWTPVbHTqfbFFOC43bKJDs1QWLXehXFF4gCZsh3n6fijXYmk9nx6B8KAc+0VRG98BglMQrSZK+IU8tSgl8acmTjDzyEfuO+u1N2vUKdnv3OKjuqN/oLnm5q3+ZVoXj6I1Euz+oFgA41sGk43dIMstReY6O4+IOonAaRZFCh5I0t7iShKmxY2y8axLWEewkjGZ8hIDKnQQEjOb8YqVkHXkoKW0kYBxJM8DLBZXbMSJ3jJrh8SQn3EuwocQGEkYeFse+v8j97S9RyQ4xQrNZucgzzY9hhOJo931i9Bwzo5exQrNVOst6+T4+0PsPAOwHKygBFxagVhI8dcsxKkqCKY/XRb/4oHhT3UWvxG6vyIdRCk7NTKYuE/xkoEXIKT7BIS+x6f6GEftcd3/MNJeY4yG0CJnlAaa5ny63OXAvjVdFOwzZASHxqJDSo8YKEdN0uEnGgCE73OLzVFnmlPskK+JDACR0iTkkpVtMWNxoPGkZkokRPrVCcyvAuJScGENMTkxGTEafnBGWHEM2Xk85pFZIAjyqqMhHNBT5WcjzjH6WoNpDZGeIHKaoQYrq50SHAjWyuDQh6Asaew2Ulchak+GvP0Fy4YBUbJIyQOFRZoGWuMgij7/r10VHmJCXN4mT04r3rExzbdNxOISNA+gOHdudotulGtWJ7/wS7cbnORwcsDv4Kx5crnM6vMJ5/ntGYpcDXqLjbhNzSMwhAFJopiuKKBoRVUrcinYZzl/F+V1UcIWdvYtUGuvIUpv6vTm93RmsM3zu+uf4pYdbLM8/zCdb5/jjOw320m/gvIz97Yvsti8zPX+NsNTGtgS7yWWqlZcRjREuNqROsDEtqZVm8ILiwl2bnWXjRpue6BGVMuKtJmF/RFDpQkmRzILLLDVtyCuC/FZG/yNlou2EYDchuDrAnKww14G1GUfuCwYlh7TQDIvsAu9Q0jjI2A0lHoLIKRpln6ox7FpHbhy6XKjrk35W6F4qglGckQdFknBldYvthy8XU4H+6JiwxONGXXj15KV1LNp9/Y6jH2STBui4vBDEHlUDxCnSWNTobm6P9CVBoMYV90BZkZbvtolDQU6C3YzOmTL+fobXzrCRIp4OqKyNigAsLfDbOcJCHkoCC34nLwKmPImpF86jaG+EDSRpxUPegpGuEZkeXpJhUKxX7iPx6myFZzmuxXWO04ffoplssB8uc7XxISSGRrIFwEG4wkwNLiwKdruOzeL0JPRgqgTvvyBYmPrR340dC3WnBb7+2XhRnODnA0IImtxDjRNs8E3a7mX23HN0uMUSj1MTJ5BC0eAMDXGGzA1pc4NDd52YQzL6AGRiiHEj6pzCkNPlFil92u5lnuHfUuMkZ/gFWuISkXiFzfsVp7tzdkxSjsjM8HjiYkkLsuIyMoYkdEjpkbguGf1X6FeK1e/xn7qI0xCRxFus0OAUTS5SdSfIzAHX8j/C5SOmDs5T2wtxoz7y3odYbf5X+m6DnGLtVGaeOfkQC7z7yhffCBPy8iYx35D89scFN7bhm9cMdw5gt+MQwpHk4KWSU60y3dEvcrP7HLb5At/OO2y0vspy/UmW/ftZ4gMsisdpc4MD9xIxB8djxpIqcb4V4J3LWd28DuELiPIO9eUyaRKRJWV85dCBjxtp9vt9/tv3fp9fvOKzWLmfT8/9Xf5wbUAcPU1z7jq3n/4NejuXOX3fnxNWtvGDEWlSIQgNzuQIZXA2YZAPiaRCeyl9Npk6FdFuCwwKsdhne+cUzXyDsNrBhYo2msNKlcpel9rVDqNU0T1fZupqn9r3ehxWFc7zWdhR3JkXxL7ADyGuKEQ/J6r5xN0EUbaYqkc+hFgKZkoet8ZNgLrhIVVGOjLEQ0OjLtnezTFh0eodre+RPlHBCEHQ7R8LZzPcuDxREL+CvPywjqPcWdq2ICOv7DSCopTRcjegTiUpIs/RcXIs6PcCiZaQGwdC4DU0A49iZTgOR1Gpxe9kJE1F7cYQBCQNDXLcKAt4Q1vElUeyWE91M5yA/lJItGXIp4tcfq9vyJqa3PfwMomUxdecy4A75YugPEa6zkBP3f0epOuc7n0X4Sx3yvexU72X+mgN6QyxrpD6De5bhkDDtc2xw0gWtujTc5L3XfjRiUuaO27tToS6E/x0oUXECZ5ginOsu2+Q0uOW+wtq7iRL4n14ogyAJ0rMcJkZcZmR2y+IDDeKbiNKjDggpUNIg5AmA7YKkuOGdLlNnTOccZ+mKS58HwkQQuJRwqP0+gf6Y/oVcc5yS3wLdECds8xWP4k4VTz5Hfs19t1VMoYYEgIanBQf/ZkjLjAhLz8ShBCcm4flluK7Nx0vrlt2u7DXBdVwdEcwWwtZbj7M169dYiO8hnTP0xn22ax8k8X6U8zrS0xziWl5CetyUnp3b6JPff4U7JS4eWcVqjnekiAotfGjNhZLqW5wRqHFAckw5/Mb/08eXf5lZqLzvG/xEl9pv4hf3eLC+/4/DHYvY0fziGiAX15D6iFCWvyaIx85BIZ0ZEl7C9RaW0gvI1c9ooomcynaxGjRp3fQwo8GKJ3iTRk6owbxOUH3ZED55ZzOmYhoIybqZZSuD8lnLHM7lv0PVEnLgmEk2J2WrOwK/HqIPoypJ4bU13QPDGhBo6IJqpKs71BNhfIE+cCSxYbWkgc7IJIc4ymC7UNsOSCXirDTRY5rATJryV3hRuy7u7vno1VQ1+akzuK/Zp/bthkO8IWkIl79K9F3ZuwXGKfrjmJkZlDpePIiQYcSz5PEWbGz1lVF5ou7KVYO9MjgXFGyWN4sknLjWR+vkyOArKSIdtJilTTvE+3m6JHBlBRxyyfcGOGA4CDFBhITaWSn+C8SVSKXAbcrV+gEcyyaO9wMzxbsgyLv6lz7G0wlG3T9aZ5vfRwjBK246JbZD1Zo1QTvPSf4i2ccB4PieUsBLE7Bhy4KysGP/sJ2a9dhTKGXmXn3Zl1N8LcEVbHMBX6NbZ5izz1Dl9v03QYLPEqTi6+6eEeiRUSLeR6lzwaH4jrKrWJci4w+Q3ZReEhqJHRI6BC7Lm1u0OQCJ93HaIp7fiqFhrs8y5A9FD7L4gPHX1dqB1x3f0pCG4shoM4cD3FCPvETP8YfBybk5S0g9ATvvyA4Oyf45jXLXtex24NbQ0gyS+gJFhsBw+Q+tl+8l9mFG7jp5zgcdNipPslc7Rlm1EWmuUQopgi5+w4ZAQv3fJj/+LX/xHanS7KwiLrHx9WeQng9jO4XdmhTQ1d2ScwB3937Q87OvodKucmp9Dyr+SHK71GeeRaG56lk95McnCQJr+JVbqO9GHAIZdD0cN4Gg16VUsUhvRF4CTKXWOdondqis90kHZaIaile5PCCIf39kMZiTvcBH32Qs39flYVvt4nWYwahxO7n3PO/JTz33zcxJcVBQzDT0pT2DUHJJ4pTdv0M39cIKWFoqDQ9BqO0CKsLJUm/KGn0A4WWlsxYTBjgDUYIX2OUImz30KYgLykWg0Wh6Nq7Ta2RUJSFYjB2HC28RrT7ypXRa9999O24UXq8kNLDBJWkyKwgR0IJ/EgR+JIkL2zSuqIwr3jNkhbU0GJCjd/NCA5ysoZmuBhQ3ijqAWwo8A8zcJC2fKb+pofTgv6JCC91CFE0mPv7GTaQOAfBTk4qIrr+NNvRWVIVIYQkJWB3bHsGCN2ASwd/hXSG1coVdsoXsBaa8R0AuqVl3nsOnrx1N5DOUzBXK0S69y69tXdk14uNFOfm33xx4wQTvJOQQrPAozQ4zbr7GkP2WHff4JBrzPIQVZZfda4KIamyTJVlLDkDsU2PO/TdBgO3xZA9JJqULjFtYvYZuG22eJKmO8cJ8VHmxUOon1A/UOwO2XZPArAg3ns8VQJ40f0hfdYxpIRMUWOFc/KXfyLH9U5gQl7eBubqgl96WPLCuuPpVUecOPb6IIVjpga9WFANFXJ4nvLheQbiNtvxM+z195mtPct09VlKoklVLFNliRKzSKEJo4AnfvHD/Nkf/jc6t3YJy79AW3+IJHoWqs9hRUqtuYqnawR2HStTbt5+nkcW/w7vbZ5k++lL7Id/jo4OSPshdXWG+8PHeeHwKxwMvgj+FlFlj7TTIagasCPSpA7SJ6r2EdIipEQ4iRI5nm9IR5qwqhDKUJlLSYeapC8Iqpa4FLAbQfl0Sv32kHAtJnGCxp7l7H/aZfvvzOB8xeG0Ro4s/myIXs3QNkc2A9Jdi+lb6k2fnfWUTAj8smR0IIh7he6lWpEc9AyZ1oSDAQQeudb47T46H4tqnSFzDl8UWpVXoqUCBvmQfZO8IXl5LQYuxzgwR5OXwaggL/lRxgv4oSQKFZ3UYRWomgR1N91bOPC6OYNZn6lnB+AceVmRNzRiNcZ6AhlbVOLIA1FoeTo5yaxP51KFqSf72KhI8tTdIjFOjRx5FtIJZujrJiOvzkb5Hi7GTzF0/nFmiwRO7X+zcCCpKk/N/jJOCIS1NJN1APLmCpWwEOl2R8XkqhwUgXLvOy/fUvvzQd+x3yts1mdmJ8RlgncXItHiLL/MPi+y5b7DkD1uuc8TMc0sD1DjxPcRbik0VZaosgQCMjegxzpdt8ouz9J36wzZJ+GQARsM2GDD/TWRa7HAY5zkY9TliXeMyDhnueO+isNQZZkpzuOco88G6/YbrPIFMgZjMrbItLiPsnh3B9G9ESbk5W1CScH9K4KT045v33B42hGnjv0ezNfh5i60B4Jq6Hj87Bmeu3OKbmeDzcEzHPS2ODN/QKwO2eUZJJqKW6Qqlpg7vcSlBy/y/FMv0r32NZbe+yts9B6m271AVvoOWEWpso9spDhzQM8O+ObXv8HH73mA37x0L/+PrwlofJ2ousFe/BznpgynzAM4dZWePGDYn8XFZZzbxhmNyTXD9klM5lNpriNVDkJgEx+/lNHZaOKFI8pTA7zI0VgZ0d/W2DwjrDs6tz1WL9S5uJ/i93KSmkK1YypW0786JLunQrcC9bJCiQBvrU81NgxUhq8VdaeIPIcwDjKHqitYz0iHRVhds6E5bBuslDhjUXlO7mm8fhedZuAcMfY4Dbf3iskLFMRkleEPTNp9vYwX6xwjZ16lefH7w+NWaQCkIAglYVWz20mxSiAa+lUrI5E7vP2UvKqoPNfDeZLRvH+UQg5joa6TkEz71K8OMKFkcCICCd5eRjpb/KoGBznCOJwT7IYn2A5PM5XvkcmQnj+HSTQjWTnWu2hheWTvvyCcZbVymZ3SOayFWrqDZxOcDmgtz3Bt07E3NsRpBWfm4NKSZKn5VqcuxfdrpSUI/Ql5meDdByEk01yizin2eJZ99yIj9rjt/pKQKWZ5kDonX9c27IkyTS7QFMWaaMguuzzLnnuOQ16izzYZA3ps0OePeJk/o2znmeMRpsRZQlHHo4pPGY8yPlWkeOuX5GJdtAtOUuc0L/On7Lhn6XOHAbs4DBHT1DhBKBrMiCtv+f96N2BCXn5MqEaCj94Ht/cE33jJEngOi+D0rOPapuO7NwWj1PGbHxKs7a3w1O0lRvsxd7obXLmwSazWyRnRZZWuWwWg9bEy1fwAcadD/+Wvc+LiR1k7qLCz/xHWt++ltfxtTF6iMf0SsrbOSNziT//0j3j8vU/wa+ffy/96YxtjNFF5nZfamodWYu7cvEDkbZGbPrJkGR1WKLeG2HbCwTffz0ujZa78yv+bxuJ1hHSoMCGQDqnOcOepJmce/x5emOKXLeWZHJMVV+hwytFra7YfqLH47UPUYUZ/3qdya0Tp2pDe2TLaCZKSLHRpSyX8O306wxRbLePFEGYGrQXGCnRTozSksSUeGKZPK26sFiF/WEd1Y4e0XELu76DSDOEcmbAkFOF0sbMkzhCMd85HNQE/SLT7ehkv8XiSY8YB2wBed4g3iI/vI7VAe5KgrMh2LS4ck5dXvN6p3OJ1c4QC3bckM4rRYoCMi1WQDRWl2zFOCUxJ0nguxgaKw/ur+AcZOs1JPA+ZW7xejok0A1nlMFxBCEmsKhwGi9RUTJJJ9sonxm1ysND5HvPDGxjh8ddzvwFFFNax3mVUWyY1kkEMo7RYFzXLcHJa8siZt0Y6cuO4uTMR6k7wswFPlFjgvcxwhT2eZc+9QMwhq+4LBNSZ5QEanHnD7BMhJGUxR5k5TvFxrDV0uM2m+xbrfI0uaxgSOtymyyqBa1BzJygzh0cJiYcQAuUCfKoFmREVPEpjt5Ea+4zkK5xH8vjjvtvkhvszEjooNLt8D+Mycoak9NFEVFligfcQi0NCpqiy/JP7Jr8DmJCXHyOEEJyagVIg+atnLVkO1ZLg0TPwzeuO5+44/vVfCD77QcenH5B87nshvf0zXHv+LB+/HzJ5SI91eu4OQ7YxasDpDwZcffoaytzCRHdYPvUA3tYCw9VpVp//DNOzNyGvEJxpo6YHdK9s8Zd/+QxX7t/nQvMDvDjqY61iM28zH0vmZypsDGbJowP6/RqEYJJVZKnL/CPfJfjiDN/93/8nHvv7/3cqU4VoQXkprZMvIuQFdq7NsnBpHakcOrBkA4FXyinPCPqbkv1WSH0hpLyZMPShXNeU1kb0+jmy5KFdEZev5yJGO0OifkaiHcZXBImkVFMkHQNTGh1I0pEhSwyRdig5HmZYmHr5DjvlGqn28IcjpLVkUjJwhlBIYmfp2pwZVZCXY7u0eTV5sc5xOJ7SvDbjpagGKJpkj8iL3x2gB3ft2coXeL7CaUGeWlxVETfU3bI2ByqxyKGlvh6DKhKIR3MB4W6KGlnyssLv5lhPEOxmCOsYzoVkDU20VST5SuvwD4r7xFM+h3tnOAznWB5e5dBfYLX2MKfjZ3HAfrgCFELd9+78JwSWtcoVdkrnsWPrdiu5g5QwrC4Tp3A4AC1BSbi0ApdPCKrRWyMeq/uQ5kU+zELjLT3FBBP8xKFFyDyPMs1l9nmePfccCR3W3JfZ5ilmuUyDs29qOiKlYoozTHGGe93fo+82uOk+zzpfY8QeGQP2eZ5DruFTIaCO76poors3F7ypsDjnHHs8x4hdHI6ABpLCNRnRYpr7meF+lsUHucZ/BmBGXP6Z16G9+2P0fgYxWxN88rLE96A3gsATfOYhiVaC27uOf/tFy/UtxxOXBJ6GnY7ja1choMmsuMJZ+Rkuic9yUnycE7VHWFo4jVQWl34PWfsmCxf/lAfe/x85dc+XSaxh/8Zn6Ow8CEoydd9Vtud9vvxsTPbMk+j2JbKkSm8Q8uJ6Tr3WQ/sjSqKOVhKhqsSDBjrMcMELzD1iUH2f61/9NbKkWnxBApQ3ojJ9QFCuk6cezoH0wAlN3BUI4aifsAx2BbsP1IopgoKkqvFSh7eRYnFkClTmIFToqodfUsSjhDgQeLJoZpZCYLXAK0mkgHhgMMOcqFycrsZX1F5eJy+HJJ5P0BsircUAiTWUxtOWrv1+x1Hf5ePqgAJtm2FwaCGoveZFaeSKNZRl3GvkHEG7i+4Pj78vOpT4gcAwjnSRgqwujycfOIceFULe8kaC1YJ4wcdqiRoaVGLx+qaYwHiCaDvBCcHh5SrSWtQwH7fKgtfOiRsBo3LIoVpBuwxtU7r+LHu1C5QHGwDsj/UuzeEtTvWeAuAbc38f+4pjasV3UAIOwmUGCRgDUhaNz/MNyX3Lb/2F7drmeOoyNxHqTvCzBy0C5sRDXBR/l3nxCIqAlC533Nd4wf2vrLuvM3DbRVPzm4AQgqpc4or6P/Ap+f/iMf5PzPEQES0UPhkj+mzRZ5OU3rhcoM+ALYZul6HbZeB2GLgt+m6Tnive4HbdKl13mx2eYsA2As0Ml1nkvUyLe5njIabFJc7IT3FKfpKuWCUnxqNM/Wcg/v+HYUJe3iG0qgWBCTw46EMvhl9/DOqlInb9yy9YvvGS46GTAilhdc/xNzfc8S+EEj51cZJl8QE+fvp/onbng+Q3FhC3B2A96pWElRM3WT73Naqn/5h8sIDLS6Az7vnM5xiENZ45mGb4nX2ywymyNGT7MOLGpsdcuYTQI6qVPi4r4bwlsr7Crw7plf43mg3F5vXH2L39AM74xRBBWYLSDmFVM9htASAV6CBn5xkPk0tKzaJBeeA0h/dVcaJoaxaRwtsc4VJHrsGGEj0y+PMllBTowDFqSrQnCP2ijdoY0FWJkIJkUIh2K+Ui58RoTWl1G1OOSHyfUqeHHHcPxVjC8WnddXd1L6FQxzbo/VdMX46Sdaek/33psfG4lDEbW7FFbtCjhKAfH2e86FDhe5LEjJ1GVQm+OM5sKJxGBr+TI3MHUjBaCECBGhlsIPH30yIWPHfIzDFcDInnfWRiCQ4z8qrGKgE55BWPdBixWTpHMy7Iyo3m+ynFuyibkagyPa/4+Xx0439B4NgsnedO5W69fSU/JDR9kIpeuMggKXQugYZzC/DoGfmWm5+7Q8dOxyEEnJ2sjCb4GYYSPrPiAS6Kv8uCeA8eJQwJ++5Fbrg/5ar7T2y7J0lc74c/2RhSaJbU43xI/d/4mPifuSz+EbM8QIlpBJIBW+zxDF0K6UBIk5KYoSxmKYt5KmKBqliiKpYps3D0toqIJqfEJzghniiCM9H4oswZ8RmmxX2AY9c9A8CMuP+nYuH+cWNCXt5BNCuCX7giCX047MPmYaGLOTkj2GrD+oHjOzcdi2On9NUNx7Nr38/mlVJ8/Bc+jdybp//tKapXH4TtzzBlHiCwM+AEIzOkt3sfUqXo2h0e+uwfUFs5JA5arH9rjnwEhoQXb85R8Wr4okwQpIRhjHNVkngBKXPCxk1KZ57G5j6rz3yIYWeFo9NE+TFa9kkH78HmxWokajhKLcPetRBrJaUZQdyBwwsl0rrHsKGQA4Nu57hBTqbAeRJnHVHgoZXEHxo6TTBNTTXSRWdQ7tAtjVSQx5Z4kNOojycvnibY62DKIanWlDtd1DioLrYGb/yL2bOvdRx9v+7l4A2cRiNnyJ27Ww2QZqg4QSXjpF5ZBNSFoSI1DitBVRVCivGkhiKgbphTuVlMa6wSDFcKt4HXMZhAEBwWqboydjgl6J4ugyqKHMP1hLyhySONE8Xz7g+W6XrTlEyHRJW5OfUY9eE4syVcASE41/kWp7rfxSH48sL/iBsTMymglayBgF60wNDoYyJ2zyKcmFastH7Ymf36uLZdfK8Wm28tG2aCCd5tUMJjRlzmovi7nBafZkqcO7ZHb7snuer+kBv2TzlwVzHu9VO8X4tINjkjP8X75f+Z94v/C2fFL1LjBAENQNBjnV2epufWsc5QcnPMu0c56T5Bg7PHAtwpLnCBXxsLj5/DklNmnmU+xIh9Vt0XeNH9ISk9FAFTXHjHvlc/SUw0L+8wGmXBp65IPv+MpTME4wTn56ESOLY64GnHnX2BFJAax1O3oBQ4zs69+oV/qtXg8Y++l698/uvsPv8kKx/4FXa7j7BkH+KLLyQQbpDOrVGqbVBuvQS16zz4q3/OnasL1FZr7O40aJ7YIvfXePmFD7N8T8ytZI9a6w6j1YvknCLp7RDUUspL/4XmS1fYuPkIS5e+TVRuo/xDnLCEU7fIhifYX72H2TPPIxU0z+a88CdlmqclXhmGu5YsEexfrrL41YzMEygJ9HPSisZKQRZJVCqIWhHZRh+9PqKzWKay7eMHgjwF2fKQPrgEkqGhvCSQWmKdRAyHEHoYqSh1BscpuzEWb3yh7rhXO46mpc9thq8iL29kk+7ZjAx73JOkkhSVGVQ6zniR4EWSIJIkqcVq8Bv6uB0axk6jdk60n5FVNWlTk5U1MrWo2GIqCpk5RObAWPqnI/JIoVKDGlm8oaHvK6yUiAwyX7GX30sz3QQEW5V76FLnUly8U9sPV/DMiI+Mpy7Xa+/ldu2h4vhFsRpqjdYQ4u56ScmiRXqpJXjPmbe+6jHW8fL23ZXRBBP8PKHIfFmkyiKLPE6X2xy66/TZZMA2A7fNOn9Nza1QEYtUWMCn9kN/n4QQVMUi9/B3OG//Bw65yjp/zb57npz4uG9pl6cxpOQMkc7Hp0qNZVb4KF2xSs+tktInosWQPW6K/3b8OgQgkCyKx1DCe/2D+RnChLz8BFArFROYzz9j6Y+KELBaqbhoWFesGKyD7gg85fjGS5bQ+36b6qWHLnL7xhqrL6/Ref7LTD3wyxwOJWdaAU/fOc2wfZrs4EHu+ej/lfLUKrHa5uIDPo0yqPVD/MYhShvWhnCh/SuUwi2G9GjO3WR38xLx6AJB5WkqU7s0L/wlB9/9Fa49+Uka02vUpvsIl4A0lOe/yejWR8izq3iBwa/A4gN9ulsBjeUU5QnSnmM4HWCVYNBU+E4gOzk0LdZJXKSwmSGYCglW+9idhN6FKvO+xBeSkXZYX+FHirSXE/dzSqkhLElGHYExDg+LEwI1GN2dvDjD0Sq6+5rJy/Rxx9H3k5fX1gIA7NoU6xzZUcZLkiGzDPkKm7QfKUpVTW+Q4ZRANBTubp0QKnOEOykycdhpSTwXYH1BsD8mRENb/Pw90Lmge6GE7Fmslehu8ZxZWSPSokZgaCJuNd7De3b/BIDrjfeDM8eZLXvhCd6780e04jVSVeKLS//o6FCphDBIoJXeQYqiSVrJYmV0YQHuX5bUSm+ddKwfQJxC6MNS84fff4IJflahhMcU55gS58jcgENu0HY3iDmkwy067hYAHiXKLFBhnjIL+FTfkMxIKWlxLy3uxdiUfV6ky23a7hb7vEDMAQ6HoJjwtLlFh/9vUe6KosZJlPBx5BSFBDPHLqiImZ8b4gIT8vITQzW6O4HpjUApgaNw0Ky0iuZd5+DOAfRjxxeft3zqAcl09ZVpj4KPfPqD/OG//SMO9w6YO3yKUfVhFprw0jYkGfT6Nbav/hpL9/8hIuowyipcvtCk6ft8ZVXTWrmJDL7Fl3d7PL7yGNeyLfxoi9rUbdp7p4m7t4mmOkyf+QK9l97Py+v3sbN5jmr9AOl1cKRIb0Rr5dukSQXldZAKGicNe9cLO1A0o0m6GbmDwZSPNI5oL8OqEnpgSH1BiCSTjjBQ6KkA0UmQhynJoof/jEBmFqMFuiwREuJ+DnFGGElGncJxVOr2CloxTFBHQXXWYkRBZF6b9dI8WhuNNS/OOQ6OnEY/YPKybwrj9VFXtRrGqDhB5GMNzLgaICxrsnaK0wIzpY7XMDjQsSHYTsE6TCgZzQdYLdG9DOsL/HYGxuGkYLjo45SE0IJ1lNcS0ppPHnlE+0OEcewMFkiiMuW8TaZCXio/SiPZQtuUVEWEWZfL+38OwNPNT3IQnkAAkVdMXbysRyk9BCXolZZQoqgAWJwS3H/i7U1LjrJdzs6JtxRsN8EEP4vwRJlZrjDDZWL26bJG3xUN1RlD2u4GbW4U96VMhQXKLBBSR+KjCZD436dDUdJn2t2HQBLTZoFHsS7Hp4FPicGxyLeLwmeKe6jLE5SYpcwcIVNvyq30s4oJefkJohzencB0h5DmhUtnbU/woYuCrY7AYbm1W+hfrLX86qOvfjdcrpb48Kc+wJ//57/k6pPP8P5fXubpfJZaZDm0kFvYW32M+sJ38Oeu0xbrlIfT3Hf6DN/6m0+wPvqvLJ37Gwie5ys3YOlUA9ghqu6T5yGD/SuE9S8TVLusXPk6219vce3JT9Oav8b03BBnisu59DtoGWBzkBp0AI2VEXHPwy8XDYs2cwzmQyp7CXEkUZ7A5JYkclRGjiSS+CPwmyHqIEa3MwbLZcoln/3+EOtANRRiDfLYYOOcKAo4dGAtNDZ26QgBSYaXFYQktoZ07AceOEPmLN74F/iIoAxczmj8ucxZJND4AeRlzxYOqaOuaj2K0b1XZrxI/EAifUGWWJwPWUPjHal1ncPfTQl2UpyWOC0YLvlIC/5hjvUkfidBWrAK+idDZGIxkUSklvLLIzr31sgCTb2TkXmK1d69LHAD4Sx7wQl6/jTnut8EoO3P8/ju/49atstBsMjfzPxqcdwaWlXY7cJUfAchoBvMgh9SCuDktODh0wrvLYp0AQaJY+NwsjKa4G8vhBBETBMxzZx4COtyhuzQZ4uB22TILhkDDt11Drn+fY+XTqPwUQTHfyZ0SWgDUGKGRfk4JTF9/BjnHDmFnu6VVQB/G/DzS8vepSgFBYFplCH0YBAXoV7fvOG4vCJ44pLi/Hxx32fXLP/xry2j9NUi3jP3nOKeyxdwzvH0l7/ClaWM+XrxOS0hSQMObj1BPGyAVWwO73Anfpn3f+Aqa1f/HlsvP8TwsM4w3mP9Vol4VMMLOpTra5Sn9+jvNhDSEJz8Kx6ovEh75ww7dy6RJiFKBmAkQhiUynGvyMGX0uBFOVKBVyoIxmgxwGSQ+xDspyR+sfIRucP5CiMgiDQBAnWYkZUktUulwnljQc35SC2wOaT9HH+c1mq0pHZ7HRv65LnDHxakYkjO0OXHhOWVot1AKKqy4OsHJj1eGU1JH/WaUW7qLF2XY8YhdQBef4Q/GN7NeAkEYaSxWpAkRSmjPMp4AaSDcC8j3E+wgSCraPKah7AO/zBDxQYnICsrTEmBJ5GZBeeIdjP00JLWgqJMcz+nR5nN4Aor/ecBuFZ/DIRgeqx3qaW7LPWfJ5MhT7c+RTeYQ0uYqRZTvSSHVnwHQdFnFPmw3IKVacmZ2bd8SgPw8rbDOZiti7e1eppggp8XSKGpiEXmxcOclb/EJfFZTotPMSuuUGYOnwqKu2+aLDkZQ2IOGbBNl1US2igClsUHOCt++VXEBQrC5Iny3zriApPJy08FkS/4+P2Sz33PYh1stWFOOL7youOTVwS/+qii9pzlG9fgxXXLv/2i4//4MYmv73LND3z8MTbXNum2e+w+/03OzH2Q1X3LMIOTLdjfvI/6wlPoxWfQTnK7fcip5g1OnPPZ3fwI09UeaXeX609e4AwZzXODosyxsclISnAWL+xSfe9zNP/qFDee/gXmTz5DfSpBCg/jMoTIEKoInTMZOAPKcwhlUYEGm5OWFYmv0S5HjgzGk4VlWji83DHyHV6oiMo+WTtl5CycClFCYJTA1RReKMhGjiyxhUZDg/U05bUd8pOnMf0+wWAEzpGIgnRMSZ99l9J1Gc1XvEBMS5+ezdmzKZkrJipTP2Dq0rYZuXMI7vYaeb0BXndwTF50KPFDSW5cUUsgJbKucaLoMyJzqKHB6xjSlkfS8rC+QBiHGhikBRBkVYXVAqcE0hTHVHl5SB5qRuUSQXuATB3bwxYiqlMeHjLUDdYqlxHO0ozvEOUdauk2lWyf1cplXph6ojjXPKiGsNkudFWteA0poFteoRLAQkPwyNsQ6ULx7u/a1iRRd4IJ3ghKeK/qRjqCcxZDhiHFkLzqT3DUOY3+CRU7/ixhMnn5KaEUCD5xvyTyBa1qoXXZ7lieuuUoh4LPPCz55YcL7cDL247f+6qlPbg7gfEDn4/+0ocRQvDSs9c5H96mGhYXzc4Qyr5PZ/M95IN5cgbkuebOQcaZM89jcp9ecp5T5xeYrb3E7c89ws71e8niKs5ZwprBWoHyc8TCVzk/dY3B7iI7q5fJUh8ndTF9kcXFHQfKh6QPeQI4gV+yFMMPwWgpwBhIfNCZIfUgdQ6dONJQYjyBXw8Q1qF6OVlTo3WxUrOhLMLqJKRDg8gNni/JPQ9vr4MNfZyQBMOCvGQ4ujanNp6wfL9ot3gR2LPJXZu0+n7y0rEZGQ4lxHG6btAZ4HcHxQcCvFASRoo4KzJeZFkhPIEY6z10L8frG2TqsKEimfWxWqBiizAWr1dE2+WhBCUwkcQhUENDsJuRVEJMSeB3MgySF/fOsZytEpgB7WCOneg09XQb34yYjW9RTfdo+/O81Hg/PX8aLYuOojSHYQraxNSzHYQEO7XMUlNwbl4yV397hGOrXUwQPV2E3E0wwQRvHkJItAgIRJWSmKYqFmmI07TEPbTExQlxeR1MyMtPEbVSMYEpB4JGCdb24bk1y9q+QwrB4xfUOJkXXtqAP/6uOe6MAVhYnufBx4pyrRe+8XVOTY3wvcLx4WnI2hfpHZ5ACUVsEhJjCD3H0tm/Zu3lhyiXK5z7UJNmtYe93qC7tUhvb5EsiXBECJUT1A+ov/9vqHLIzac/RjyqY61A4BWlg6IgKVIXt90XdeH2EYUORkhIlgPSIQhbZJckgSRVDp05tBVkCmSk8UJFuJUifEl0XwUSBwZUregKykY5Ls3RvsRKiWwPIfAwUuINC8eRAXoupzZ2D3Vf0y591F+0b9I3zHhp24zEGSTjagDnjqsBAJDglzRhWZGOV0Ziauw0GnOBoJ2hDwtBsAklg+UAREFOdOzAOWxQBPZZT+IEOA3lWzEig+5UE+Vl+J2cXlBmz9zDbHIbKxSrlctkqkQrXmM6vo20OaEdsF06w9XGBxFAs1KcBwf9IkG3mawjcCTBFFGjylwDHj799iclR0Ld07PiLYfbTTDBBBP8KJiQl58yWlXBE5ckUxWBVrBxCF+7aujHxQXhgxcFj5wpknrX9+GrL1r++prFjEWpj37wIabnWiRxQnbjK0xXHVoX4kznNKL7AGnvJEF5h95QIXVCVOpTnrrN6upZphuaC78YEsVXkJ0ArYYcbK5gaZL0FFIayit/zen7v0h/Z4H21j2Y3McJhbOyKE8Whd07qALW0N8JwUmkLsjLaN7HGoEeGoyxGAGjoNB3+KljEBapu4Gn8LdS0ILKI1XM0EBiUTMaKSFPLTbJ8QKJlQJGKZ4EKwRylB6n7A5sToWjyctrsl5eEVT3RhkvhzYldva4C0gYix4M8ZLiMUKAFwqisiJLi4JFGncDXoR1+F1DuH1X75JO++DAO0gRBpwSjOZ8wu2UvKKOMxlKGwly4BjMVlB5jurkbIymKUdlKtkhPW+azVIRNHWy9xTNZJ3ADNiKznG7+iADr4lWcHmlCEccpsVTTyeFRTquLbMwJbhvRb7l/qIjxJljdX+yMppgggl+spiQl3cBFqYEH7ggWZwSDFO4sw9fedFhrENJwQfvkZyZA6VglBUt1Z97ytIduSJ991eeQGtNsr9JrfscUhSBZLmB3c170KaFylsEpUPiVKCVYWruRV5eXaYe+njlfWYfmGcxfC8iLuHrIaN+jcHBNHkMUg9ZeOSrXLj/v7L6zPvJ0yogcU6DK/pNhQAdgV82dNZCklGIySRSO5wvSeZ9sgScAIwl9gWZLaYvVglMpPADDb0MnVuChgdlXZygMx7KFzjjMCOD9gAnsMZRymIsAjFKkdbigMRZ9FjD8VrycjR5GTpD7AoT9NQPyHhp24zYGdLx0kimWZHzchRQpwRepPACRZpanCewU2r8BQLG4bUzwt0MEynShsaECuGg9tKwEOYqQdrw0D2DKamC8LSLGoGRriCjBB0bxACudi+wzDalvE3bn2OrdAFlUu47/AKejRnqGj1/hqv1DwCw3ATfg81OkSsERbKupyFvrrA4BZdX3j7ZuLXjsBamKtCqTMjLBBNM8JPBhLy8S3BqVvDec4KT07DbdVxdtzx5q3hHuzAlOD0jma8LqqHA146DPvzZk5bVPcdUq8EHPvE+yqFg87nvMiX2iPwi9C7JJO3NB6nrOfyojbUeypUAR2X2u2ys3l+E4dW/Q55+gNnKRQKdkvQqBE2f9h2PPAEv2mH2/JPU5q4ybM/jrEI4VaTdinEgmw9e5MAZ0mFAMvBwRiAVZKcC4rZADcxYmyIYBQI/cwSpIytJdKhxOCrbGcqTeCcDnANX1XhhkSFjMouWhUjUOSi3e8dBdUcpu0NrjlN2X7s28oWk/gqyUpPesTPplTi0KYmzJONeI5llqFGMzF+RrluS6ECS5BanQDTu5jTIfCzW7WZYX5LVNSYUqJGhtDpCGlfoX1KDAPKyQlgorcaQw35jCaUTdN8QByH73fPUbJ9c+Bz6C/S8aR7c/zOivItvR6yXLrFaucLIa+BruHKyWDWm2fhnY3Omkk2kgMaJFR48JfH12yMbE6HuBBNM8NPChLy8i3BxUfLIGclKS7B+CN94qSAnAI+cKRqocwP3LktmaoIshy89b/n2Dcv5+89z7/2nkMIxfPHLRDol0IX+Zf3OGfr9GtNhi1p9t0h0zQOE3+HGdsJ0WKNRi3Gll3H9y0TiCi4PSYdVECFxW+LyBFUa0phaI40lxhYdPsIJrNUIUUxfqguQjVLSgYdJNaP2OGRuJcCkrliReGCUpRNKwqFDZ5BrcJFCh4pgK0VrQbgYYAU4CbosCyKTGyQO6Ry5gOigjbAWrzdEjnc8fZczjoqjbwu78yvxyjXRD1oZ5c6yP854iY/IS5rjdweFi4gi4yUsaYTm2CYtqhonHMKBTGwhQB4VouPRvI/TgmgrwevkmECSTHuEmyl5WRVrJ+fwOzkGjQkkwrPonmHXazCjBRXToePPsl65RC3d4b6Dv8KzMV09Q6pLXG28H4CzczBVhvVDjtdeU/kmvsyRUZmlE60fC9nY70N7UCRFn56ZkJcJJpjgJ4cJeXmX4YGTgkfPSqarsLoHf/49Q2/kKAWCK+ME1BfWHR+6Fy4tFx+/uO74/DPw3o9+gFazQjrs4W18i3JYpKoOE8mtaw9RllNUqh1m6jHDzhLWQVZ6noPt8yxOga4/T9ZfwVMeWi3Q2TmLqkyTDRzZCKTYR3g5o3YdYzRC5GO3keDoVApqoERCOtCoQNLf8XAO8pZGtBRZ1yEMWO0YhQKbO7SFTDpspAh9RdbPqIwcleWw0LbEFlEvpho2c0hr0RqMHxDsdZDWooYJOi30KH1bBPoriiLD3mumL68kLD+oFqBrc0bOIhDHayOdpASduzZp5QvCssIKiIcGJwWyqYscfge6nxO0M4RxZGXNaCFAjSzBbgpa4jxJ3PKItlNGC0HRgdQx4ASdaguvPEIKg+w5bptzzHBI2bTp+rNsl87z0P6fUc7bKJdzEC5zq/oQsa4RarhvCb76YkF0cQW5mB4VFmk1u8IjZ9X3NWi/FRwJdU9MCwJvQl4mmGCCnxwm5OVdBiGK9dF7zgkiH65tweeeKgS6F5cEjXKxCnj6NjxyRvKRSxJfw17X8VdXPR792IcQQtBdv0HUv4mniovY1uYJ7uxUmS83aTS6TDX36OyfJM4tz+7fYEotMt+0ULlGMpinFAgGh4+SDBdI4womMWjVRUUjkrjK/u2LWCuRyiKwGKNBgFAwdToj6WryLMAvO7J+oRFJTwWM9iHYyzCeIA8cAwl+6pAO8pLE8zV2kFGOHbVZHzyBs6AWfISEPLGQGbQvyLVC73dBSUSa443bngfO0HuFXfq1NQHT6o0nL21X6F08xHGjtB7GeJ3e3YyXoLBJp7kltw4kqIYu8lKcQw9sUQsgIZnxyOqacDvFO8iOM13yiiLYThktBuAcXjfDeIrDbBFZTUCBGyrag3vxhSUXPiNdoZ5s0og3qWV7pDKi7ze5Vn8fACdmirj/tX0wrugtAphO7+BrmDu7wuLU2ycamXHHzrfJymiCCSb4SeMdJS8HBwd89rOfpVar0Wg0+Mf/+B/T7/ff8DFPPPEEQohX3f7JP/kn7+RhvusgheAj90oePVuknX3nZtF1JIXgsXPFj+z6lmOn6zgxLfjMQ0Vi7yiBTTNP/cwVshziG9+gKvvUIuiOJC/fPINNWsxM9Zib7hJ3F0mSgJ3RAUlcplWRVKbWCN0UIKg11znc/BAiuJd0qMhjQym6QXV+ldXnHyPuz4AQCGVwThy7ZWrLYHNDnih0BMO2BAHJiYAsFoRrMSaUZNrRK4OXF/oVMxbA2twiYkM9l+i6xnkCZnyULkS7LrN4gcJohTroQ6ARaYYeO4FiV4TQVcdTlY59g8nLD8h4KcS6heg3O5q89EcE7fG5K8CLFGFZE8fjZN0pjVACoYqWTa+fE+6m2ECS1jVOFxbpaCMhr2rykkLmDt3OyWrFxMZvZ/RWKpS6IwgNIod2rUF9J6NsOnT8OXbCM9zT+Tq+HSKcxUiP5xsfIdEVfA2XTwj+61OQmcKxrRUoiiC7kg8Pv+fkj+Ucvb1bkOJqBHP1H8tTTjDBBBO8abyj5OWzn/0szz33HJ///Of5kz/5E7785S/z27/92z/0cb/1W7/F5ubm8e1f/st/+U4e5rsSSgo+/YDkyokiwfXzTzueWbXM1gVnx+90v3XdYp0rSh8fkCy3BIF25DNXSMNZIpUxuPolPGkIPNhcP8vNbYknAy4u5Swsv8TGzfeQZPCN9evUWGFhClRpHWEDgmiISesk6TmS3hIuU7h0RHX6Nov3foPn//rXMHlhY5Yyx7kiGl/5UJ8/YLAXIpQGJM5AuhIUtQEbOc5C5gn6dYkeOnQOxhvrXjyJ7WTUUwinfZwnsaFE+wJrwabF5MUhUJ0BSkmcdaikmLAkOPZsQl2MJy/u+x1HnpAoxOtmvMTOoBHkx9UAQ/xe0SGCKMS6YUkVehctENMalzuEcAgLamAI9nJMpEhmfFRSTJdUP8eUCg1MsJOSTmnQhdVcDiwHMzOUsh7Ct6iepePPEmY5kenR85pUsn08mxDlXSySvp7i2ljrMluDeuRYOyhEuuE4P7uS7lKSCdOtkKml+R/L+Xlj+24J49tJ551gggkmeCt4x8jLCy+8wOc+9zn+zb/5Nzz22GN88IMf5F/9q3/FH/zBH7CxsfGGjy2VSszPzx/farXaO3WY72r4WvB3HpOcmxfkxvEHX7es7VkePiXwvSLD46UNd3zfJy4JrpyQlCNFeP5D7A898s4ucvtpZqrQ7da5vT6LTVqUyl1mpw9QLqK9v8TagWFv2KXqh9TrPUo6Qgnwon3SuIaKHsbGZUwmIO3RXLhKWN2kvXMeEEhlsLZwBAkJ9aUheSqxRiEUZImEksSc8BgdQLSRkkeSQVmQj3I8A5mk0L0EmrSX4ktJo64hd1gFKhRY6zCpwfMK8iIGMcJXYB1Bv6jmTrEcmOxuUN1rJi+ekPxGaYnfKC0RvKbJFaA9zniRryAvfm+IHhX9Sa9yGsUGpwW2pcfqWIGwxWpMxYa8rIhnPVRSNEXrocVpSTzrU7k5YrhSpGeK3DGajXAHIaqWgnYw0MT9JuAwoqh7bKabAAR2CEJwrf4+UlVCCbh8otC6JFkhng68gsQ04zuEHqxcWEbIt/8r3x44djoOIQryMsEEE0zwk8Y7Rl6+8Y1v0Gg0ePTRR4//7ROf+ARSSr75zW++4WN/7/d+j+npae6//35+93d/l+Fw+Lr3TZKEbrf7qtvPEwJP8A8/UkS4J5nj337RMkwdD50qLhpP3XYMk3EwmhA8dFry2HlBUK4SnH2cYQq7Lz2NN9yiGsL2+jlubCkkipkanD77DJs3HyeOPf7mzgHa1pmtQxB2kNJRrm8x6i5iKJP0l1G2QtwBqRNOXvoiO6v3Y7IIKUBIc3zcXuSoz3XJUw+lBFm/mC5kZwKyEXi3Y2woSTT0AoGfFqujvCTxfYXpZ9hQMB/6kDlsPk7aBVxuKSiHw+UOj8LZ44/7jSywY2IqY2LSeY3mBWBBhSzp6Ad+z9umSNc1uONqAL/bR8fZ+PsMfkmhQ8kosaAoxLrjYxKZxe9kiMyRTGlMWSEyi4wtQoBVotDAbGbkFV2IakeGzukpSjdTzIzF+IpYRvibjlLWpqebVLN9jPAYqBrVdA8rNM80PwFAOSyyfdb2x8erIfSLlvHZdI16STB7ZuUtnYOvxdHUZakpKAUT8jLBBBP85PGOkZetrS1mZ19dVau1ptlssrW19bqP+83f/E3+w3/4D3zhC1/gd3/3d/n3//7f8w/+wT943fv/i3/xL6jX68e3lZUfzwv0uwklX/JbHyvSULsjx//yBctc3dGqFnbp79x8tRX4wZOSs3MQzZ9h/tx5DgeO9Se/zHI9obN/ktVdRZKUaNZjalNbVCtDdlYfZrvteG5nl6ofUK9AeVypIWVOmikIT6JsgHAh/Z0SUmUsnP8Sw94MIJHSHh+D1FBuDTGZAiFxTuCMJD0VoANQNxOcEyShpDeliomEAFPWeJ7CxgabWRbLHtoXCAFq3isyZXKHHBOWHIpeI2vxBiPkmLz0XI4/Pr1f6zZ6I1jn2LUphqInyQKY8XOP3UxCCoKyRnmCeGRwSqCaRSEjFuTI4h8UTqN43scJgcgpbNChJJ7zkblD9DOyZvF54ylSv0Ttdhc7ZXFCEudV7NBDuQzfxlihyIVHLd1Fu4zt6DS70RmkgJUWvLQJ8fhLrUVFi7QSjrl8jfkGhAsn3srp9yoY63h5ItSdYIIJfsr4kcnLP/tn/+z7BLWvvb344otv+YB++7d/m0996lNcvnyZz372s/y7f/fv+KM/+iNu3LjxA+//u7/7u3Q6nePb2traW/6/381oVCT/6COS0BNstR1/8HXH5ZViCnBrx7HVvktgpmtQDgUnW/DA+99HbarO7v6Qwxe+Rtn3aO+e4ulbPq2yRgrByqlnGB5epHM4x63dnJ2OZbYOUZigZE7UWGXUPgFyimTQwtOSwW6FUa+O1jHO5dhcHTtxEMVqxS8lSC9HKIfyLFkiyWc99Iwi3XWEOwm2JOk0JV47QxrIfYGNFFpKbDdFVhVlKEjBgg8CTG5xmcVXDuN7+N3+OL5/hHBFym7f5cdht12b416T9fJ66LmcocsRCOz4MTLLUMMEMY6qlVoQja3bydDgBMiWvlvIOMiINlOsL4lnApwncBL8vRTnS0bzAV47QwiBKUtQkFQ8xBB0luE02FRD1yPPQlyeU8sPSGXE0KvTyLZJVYmXGh/ASo2WUApgbY+ifkDBTBWyHCqmTUv3qVc1anrx7Z2EFOnPcQpRAEvNt/10E0wwwQRvCT8yefmd3/kdXnjhhTe8nTlzhvn5eXZ2dl712DzPOTg4YH7+zYsGH3vsMQCuX7/+Az8fBAG1Wu1Vt59XnJiR/A/vLTI1rm85vvSiO27x/eb1u31HnhK0qgJPC66c8fmlX/sIQkpuvLhKK36J3v459nuOrUNBJbTUW2vMttrsrb2fQax4biNBCMtUKaJS6SNlislDciPI5TLaKcq1DmvPPMCgMw1Y8jwoHEeCY9eRkI4gShAKrBHYDJCC9JRf1A68nGB9ybAiyawjTBypAltShJ4i7aW4kqQlFMI4XEUWjiMLZEVBo/E8vIM+wlp0b4gcE47uOJyuKFZ09N/k9OXIaRQKSTx+jExzgoO7NmkVSKKKR5wURZB4omiUHifWhtsZXicnLyuyWkFy8pIk2koxkSKe9Qk3UwiKvBcrBbkK8TYtop5ifAWJRPYsxIa62SNREW1/jkq6TyU7YDs6c9xvVA5gp3136rI4BWkRZMxMukYtgvrSAkJ/f6bNj4rrR0LdWfFjyYqZYIIJJngr+JHJy8zMDBcvXnzDm+/7PP7447Tbbb7zne8cP/av/uqvsNYeE5I3g6eeegqAhYWFH/VQfy7x0GnJh+4VeErw/Jpl49ChJHSH8NLm3enC7Ni+ut+DX3tilvd9+FEQcPM7f43ZSkniKtc2JWVVAmB68RmmvDo7d64QZ/DiumG27ihHBqlS/PIWw34Lp6YhmcLzLVGwxuHmWZJBjSwOwY51H+O0XSkdQTkDCkGtw+GsJDsTIDXoG8UaJgkEvZbCTyxWOExJ4QcK08nIA8nc6TKym5MbgYoE1hSOI8+XGKXQnT4Sh98borLiCt6zOQNnqMijgsY3R1469m7Gy2CcrqvSjGh37xUZL4KopIhjg1Mgp3URqiKLQsbSZoIeGZKWh/MkTgl0bFEjU3QclRXR7YSsoTG+xJQVxnlUbw3IViDXPowUxviI/oAAQ6wq5NLHtzG59Bl4U2xHZ1ECQg822wVn9HQh3B3ExccrrtC7VJbf/spoEDs2D8fkZbIymmCCCX6KeMc0L/feey+f/vSn+a3f+i2+9a1v8bWvfY1/+k//KX//7/99FheL8fX6+joXL17kW9/6FgA3btzgn//zf853vvMdbt26xX/5L/+Ff/gP/yEf/vCHuXLlyjt1qD9TEELwsfskD5wEEFzdgEHiyI3j2TVHmhcXl9lacXHZ6TqEEPyPf/cypy6cIU4d8dUvcXizwTCF/b7GOYgat4iiHrZzH0lcZq8rGcSWVtCgWt9Fh4fEg1mMk+ScQFhFbWqD7vYCo94MeVLC5Bprxu4dAUKD9IuYV69ckKw80yQrAUFdwJ0MOcgxvqDbUOhB0QqdVxSep7H9DGctM2dLqHaO9QReWYFzmNSiA4GVAt3ug5KoJMUfO4IGLqdtU2pi7Dj6EScvSkiGR+QlSYl2O8UdZDF5CSJFMi5kpKVxmRtnvECwnSAzRzrlYUoKEyrC7RQsjBYDZO4o3xySTPuYQJIHGgyUXx6RLGgyFSA3JXmimE03iL06u+Epatk+pbzDTnSGw2CRRFeKss60yHWRAi7MQz+BQRHky4q7Q6MsULNvXwt2Y7sQVc81BLW32UY9wQQTTPB28I7mvPze7/0eFy9e5OMf/zif+cxn+OAHP8i//tf/+vjzWZZx9erVYzeR7/v8xV/8Bb/wC7/AxYsX+Z3f+R1+/dd/nT/+4z9+Jw/zZw6+FnzyiuL8PIyywrq6fgj92HF144i8FNOP7hBGqWO2Lvn4L3+YE+dOIJxh+yu3iQcDEtej120BlpnlZwm1ZrjzMA7BtS3DdA1C3yJ1jFAjstQn1y3EaAo/yqnXv8fhzjmS0RRYgbPFRfdodaSkxS9lSA0Oh83ARhqz6GFSkDcSrC/oNCSqmyONwwQSQokWAtfL0TVN5BcFkKqucAJcZlFK4JCozhACD5lkBGPykjjDhhm9bsru6+FwPHkRcLw2UklGdNA5bs/2IoUKJfHIgBa46cLOLSTI3BJtp2Ad6bSHCSV5RVFaT7ChIJ7xUbEh2EvJKpK8rDBodD9HmQwTaOTAYYYhUbuHbxMG/hRyfCxD3SCTIVvReY5KGbqj4tgjHz50L6zvF2SmQp9pcUizKtAzy2/rnHPOHa+MJkLdCSaY4KcN/cPv8tbRbDb5/d///df9/KlTp14lpFxZWeFLX/rSO3lIPzdoVgQfuldhrOHGjqAeOW5sgZaO8wuO0BPUS0Vx3k4HTs7ApRXN3hNP8OyX/pI7N9c5fG4XdUVg0hPE+T4zreuotQfJu2ewyXMMOGCnmzMdTjNsbJKnZXqHpwmD64jkHHiHVJurdFffw6B9nlprFa1TrJM4Z46Fu0oXYl2pLbgiDyY9FaCvpcgXE8yVEqNAYIQjiiEtgykrQq1Jein+XJnWdEAnc8hGUQJpc0cgHOCQgxgXeEVFwJi8pM5yOx9x1qsC35+y+3o4NEmxNhKS5ChddzjCGxQMQQgIyhIdCEaJAcVdpxHgHWT4PYMTguF8QFbVOOvw9zNsWATWRVsppuHhPIX1BM4qyptDshVJ6kd464Y8VQSDLonfpOvN4ruUVIYUSTKwVTqPlAVJcRT9RfcuFYLabgyehBXu0KoIoulZRPCDbeFvFpvtYhXlazjReltPNcEEE0zwtjHpNvoZxrl5waUVyZlZGCSF7OLFDct3Xi4uunP1u6sjgBPTUIo0933449x37wL923XirZcxbo1ee4ZeYphZfB7jBNHwEQBW9xytisb3MnTQxRhFbkHU68TbsyhlmF38a3r7Jxgc3FNoXZQlT+9qd4V0SAlKg1AWmymSEwFeWeDfTjHGkfqCXlMRxpZMFOTF1xLTScl9weyJCJlYKBdt1ja3CFfYpV1qkFKg4gSZFhOWDMu6iamNE2G67odPXpxz7LuMHEfq7DF58QYx3rDoTUJAUNHoUBGPLE4VhYxCFfUI0UaCGhpMJLGRIqsWKyORW+JpH+tLgo14PJURWE+DEUS3U4anfVIZoLcdtYMDjPXYL50mMAMAtkrnEMDAa9D3WlhbiHKlhFYVTs4IdrpFTYSScNqt0qyAnnv7K6MjPdXpWYFWk8nLBBNM8NPFhLz8jOO9ZwULU4KTM0UWSpzBf33SsdUuqgQAtjvFhUdJwT0LAqU1Vz76ceZLZ3Gpw3WeYW+jRj+GxtxVhEwZdpfwsmXy3GNt31FV01Qam+jyOqPePBbB6OB+TOwRResgRhzuXsDZACEAJ7EWjkYF0rMgHHK8UUqmA2RdImKLvJNhfUG3JlEDgxGu0L34Gtspep2bFyqIvsFFEpTDOYdNDL60ZFrhxzHOQjgoVpAZjrZN8WVBXnpvYvIyHBc6Hl2ajx7h9YfIeJyuKwSlhg8aklFhk1Z1hfDH5GUzwevmhRg3EJiyorSVIjNLPF/ku4QbKfGsTx4qcl+j8pxgOyOtBejYoPYdXhJjbECiqzihOAwWceNf153SeZwoGrMdUPJhpgZp7tjvHU1iHGfcDWolUItn39Y5Nkgcd/aLc+jCwoS4TDDBBD99TMjLzzi0EjxxSVIvCc7MgLUQZ47f+5ol9IoLTnvAsZD3/IJASugkPr/29z6DjldwJsdtf43ufolBmlJqvcBBX3A+egSQ7HdhKgrxvJiousGg1yAzUD4dcPDsMs5kzCx9nf7BSZJu4WoRr7BMFx8XcfJCOpTKMFaTLvsoXyBfSrFa0KtKvG6OtGBCiQgkOgM3zAhnfMIcqChUIMEJTFzUBBjfJ+j0sEIQdYvyRAP0nTleS3Zc9kOzXo46jXwhyZw9bpT2eiNkXoh3pRaUphTJoAinE5FE6OIGULqToGJLPOOTTnkgBOFOUsT/zweIzBLup4WNuuFhhcBLMhyO1A8JN2KibswoCtjT52lkO2Qy5Nmpj7EQF1lHG9G542MuB0UgXTUUJGkh3pUClr0DFv0uQmr03NsrY7yxVQh1Z+uCRnlCXiaYYIKfPibk5ecAlVDwkUuSUiA5M1ckq24dwuefsShZrBZ2x60JkS84NVNcgDLhc/+9v0GpHDK1uMPql/bo9DLC1gtkJmNvv0lTnMVan81DiWeblBubOK9DlpTRpRzTuUzS9lFil1JjbTx90QjlijwWV/z/AM4VxEl6DpMr4pUAr+TwricYJRiVBC53RLEj8QR5WRFpTdrLsIGkWtU4X+KFxRdlU4MOCru0aherlaDbh3FQ3cDmx/wpd46RM7wRjsS6AYr+0WOdI9w9QJhxYJ0viKoe8dAe26RdahEK1DCntJEgLIwWAtKmhxzl+O0cpyXJtIcajlOIBeQljXACb9swOFfGoKleH6JSS79UxwqfTIasVq/ghMDPB2QyZD8s1kCeKmoByiGUfMfhYNwmLeCcuMnUeGUkvO8vn3yzsM5xbWsydZlgggneXZiQl58TzNYEj1+QNEqSZrmIcV/dhTsHjmHqjldHABcXi4vQ2j6cnV1m+eTDRGUP8iH71+6A62NKL3Fty/HY7ENIoemPNKEs4XkjarPP0z84gbXQuixZ+8ISZDGthe+we+cBTFYdkxZxLDAFgTOi0GgIUF7OYD5ChxB0MszAkGqIS4JoYEk0mIrCl5K8W+heplo+zjm8arEKsqktGqilLLJerEX3RkjrcMDIGfZtSnncLv3D7NIdN7ZJU1itAUSaUVnbKb4GAdqX+JEiSYpCRndsk4ZwN8M/yLAC4kUfU1KE2xnkjmTawwUKv5NhIomTgqzkIa0hvJMzWKgQjEYEW5Y4CumW5qjmbVJV4vmpJ1gYXkcA26UzOKHQAhrlgsAoCZWomLokeZH7ctm7gacEaunc63/BbwLrBzBMipLHE9Nv66kmmGCCCX5smJCXnyOcnhVcOSlYbBb6l14MOMfL247rW3fJS6sqmKkJrC00HKG9j9nTZ5m7MGTj6RqDjRtUpr/L2kHGzY0yZ0uXwGl6Q43Lq1Tqm/QzQ557eLMZ2eFJ+mshLusztfA0g4NTWCNftToSwuGcwIz5g+fnZL4ma3lo36Fv5hgt6EwpvH5ehNVVNF6g4CDFCZhaCJHGoRoKBDhj0aogKro3KsoZ273joLrMWW7lg2O7dPeH2KWP1kZSCAZjjYyKU8p3tgubtAQdSnQkGY0M0hMwrce6HkG0FuN1c2woSasezhOEOynCFOQFQPdy8to4nC5UCBzywJJ7msb1Dsb4tBtTlNsxCMHzU08Q6yoLo2sgOLZI+x5EXuH+KfmQ5oIkL6ZcrShjxd0p/r/FM2/rnLq2eTeUTsnJ5GWCCSZ4d2BCXn7OcOWE4NKSZKpcdPMYB8YW5Y1re3eLE4+mL6t7sBScpV4Nmb08R9ZrErct7vB5gvoz/LenHQ9MXSZUIVlaAlNG6ZTW8t/Q2TtDmsPSYxG3/3QWEw+pT79Me+c+rNEcqXXdWFmqfEs29MYOJJDSkpyM8ELwbsUYLek0JF7XgC0i9fEkOinEueWFEJ04qBSOI2ccCodDoHojhBToQYyXFM6gzFlu58NXkJc3nrwcBdThCvEuFOm65d0D4G7Giw6KNukjpxHjWoDGs32kccStIt/FKoi2E1RsiBcCRO7w2hmmpIgbQWElzx2DsI7OMyovj8g9n7gcIUeCoary/NRHKWeHNMweBsl2dAYB1KPCZXSkRbG2yHtREu4L1ih7ObJcQ9bf+rikN3JsjBN1z0+yXSaYYIJ3ESbk5ecMQggevyC4tCxwTtAdFuFlxjj+7El77BpZmS7K/OIUakEZmSxTKgWsfOwM21dXCBhSUf87h70B//mbHvdWH0SgyJMyznpUplbpD0OsE7Tug/wgYOfpKuQJ5albjLo1nJMU+6NjGoPyDDYfFxgGjtFCgPKhvJNiHAxLEmcK3UvqFW6dSEiyQYa/FBAmDqoa5FhTk1skDjlMcKFGxin+sMhkSTDcMfGbTtndMzEZlgxLUrQW4Q1G+N3hsQPJL8sioG5osBJUXSM8gUgM1RsjhHH0T4XYUKIHBt03CAvxTCHW9foWJwRpNUBai+pY+ifqVLe6uI5PXA8pjwaYOOClynuwUnMyvYZzsB+eIFchlahY4wiKyctU2dGNHcMEKgE8VLqJoFgZibfRP3R9LNRdmJok6k4wwQTvLkzIy88htBL84oOShalCwJlkUI2Kd+ZfesGyfuBQUhwLMLc7UHfnaJShvrSNafwGIq3giw5y+Pu8vGXYXr9AxauCKZOnJZTMmVr6Ft29U+RCM/dwmfU/bZL2EoLSIaPuNM4U6ysoyhStBeU78qRYtUgFg7oHkcQzBrFviH3ItSAaWmIP8qrCp9C9iKqi6iuoSJQnQTjyYY4vLM7YYhSR5kTjQLkUx45NqIpx1ssbrI1GztAeT2bs+LEAfm+ANyxs0ggIygo0ZJkFCbIsEb6g8ewAPcgBQbwQkNc04W6KyBxJy8eGEjUyOAnWk+QlBThkRyC8nPrtLmlSZlApIyzsuyVu1x5CAIvxNYwtgukEMFWC0Ic0L5KUmxXJdrs4xKkKnDKFK+ntrIyMvZuoOxHqTjDBBO82TMjLzykiX/D33ifRStBPChIT+QWB+NLzRaHj+XmBkrDfcyxGJwhkSBgMqSxmKP/XiQLJzNKLrD7552wdOnTvIUBhkikQjsbcS/R7deIc5t8X4cUJa19tIVxOUElJxisipOMoglYIh5C2yIARFHkpyyFSgb+ekHuCQUXgd3IyBaai0YHGHaQ4IajPBEWfUVTkquRxjq8cRmu8OMYIQbndBoqclq7N8UVxmr/R5OWokNFHkjlznPHit/uo4VG6rqDS8skSi1UCWStCa4SWNJ7po2KL8QU2VORlRbCdFQWNc0U4nX+QYwNJHiqcb8EJRqZOffeAYCdj5FcRwmETj03vBIelU1TkiOqg0K9sRedoVoqVW5oX05eVacFu19EbFR9fabZRgzZIiZ4/9ZbPn7X9YioXBbDUfMtPM8EEE0zwjmBCXn6OsdSSPHFJ4OvCNZLmjqVWoYH54nOW9hBOzRakYhBLguwsoQ/11nUOBw+yvPIwfuioTz/J9774Fbz4FCKdxqZ1sjRCCENz+W/otWeRtQqtc47NL08TH0ikUsWExR5NXyimL0YglQUkONA+9BcjhILyVkIuJe2mImgX6lNTVghP4PUMNreUZ32EKAoahRDYzKIDSa40fmeIRVDeL0oUHZA6Q39csPhGk5cjvUsgJF1rji3W4X7nWAAslKA6440zXkBMF04jL84pr44QuSMPBHlFYbWgtJmAdQznPKwS+O0MJwRxLQCPoqjRr9JY7WCHPnGlhJCOHe8UfVHGKp+F5GWssXT9GWK/QbNSkNBBUuS7PHa+ENWOMqiG8EjpJgBqdhnhB2/53DkS6p6bmwh1J5hggncfJuTl5xxPXJKcni0EnTd3oFmC5ZbAWPir5yytSnG/tX04EZ6jHEBlao1+nCLSJ1g8c5qZ8/t0d67xwje+xlT2CM4JkmEThKU2c41eu4URHrOPKLzRITc+Pw/WR2mLdYJC7SLASZw7ch4VYlOhYLjgI7UgaGcQW3plgRUQjRxpKDCRIkKS91PC2QDPgK4fOY4cWguskqj+AGEc5YPD468/dZZDmwKQOEv8OlkvR04jT0j6r6gSiLb2EHnR06S0IKh6xEOD8MY2aeOo3Bzh9YrQunjGx4YKNTDobo5TgmQmQOYWFRerLeNrUAKXKcqjLuHhiGxQIq36SJezKu+l7c+hJDQ714FiZdQsF4LcNINAw9l5yWFfsN8vhlizdVjJC/Ki34ZFujN0bLWLUMGJUHeCCSZ4N2JCXn7OEfqCj9wrmKoU1ukvPG+5uASLTYEx8N2bjsgviIQyLSJaSGUot27Q3l8iCs7SOnmC1j07rF69zuH3blNjhWQ4jTUSpROqs1cZDOqUVqpMzfU5eGaawXYDk/mFgMSBVA7ritZpa7ibvuvARoJ03kcI8NdTRqHAaEE4sIyOdC9OkvcygnmfwBSOI3CF40iCEwLdjxHWEu13j7/+kTVsmphIvHFNQMdmxFgkMDBjguMc1ZsbSFtcyFUgUCXJcFCQF1qFWLfx/ACVWKwvSWuKrCSLsDoHScvDRgrdK57TeArnFWE3qSsxc20LGTt6zCJLGe2ZFnbfslG7gicMzUGhX9kpnWO6WvycenEhtv7YfYLv3nT0xx/fu2Bh5zbw9vQuR6F0S01BOZyQlwkmmODdhwl5+VuAS8uSM3MCLWHzEL78vOOBEzA/JcgN7PUcg8Rxew9WwnvwFFRmnqUzssjuQ9Sm6iy9r0KqMm4+/wL6JU2oFHG/BcJQn7lGt9vCBQ3mH3aUGXHjv82TjUKMKYS1QoIzxc0acdx5BIUFebQSAhDtJSRakHgQHuakGkxV42kF7RR/yiNEIuqKgo84yIs1jxrESGcJ9trHX3uK4bYZUR3bpTuvszo6mrwA9Bnfx1hqd7aPj1GHCh0qRuOAOt3QeNYR7GaIzGI9sFWvcEhtJAjjiOcDrF/kvVhfkIYeeA4rBcZ61Na6iL7ksDKLJOfO1Fn2kwY2mqI+WMOzCbGq4KYWj4PotIJTM4KZGlzddKQ5TJXhofIqLs8QpSqyMfuWzpXcOG5MhLoTTDDBuxwT8vK3AL4WPHpGUi9DZwSj1PLFFxyPnob5hqDkF5qYg75jTp8nkGWkHuLKLyHSBdLhAo2ZOqc+MUVnCLe+e4vynmDUn8NaiRf0UKV1RqbBzHlNOdpi/6WInWuLmKSYeAiK6YVD4IzEHE1fREEMBotBkVK7n2FxdGuSqJ1jKXQv+BKvkyElVOseoqSQXhGEZ1ODdA41GCGtRQ7iYtUDpFg2zOjYLt17HdHunklJnSVzjnjcJq2SlOCgd5yu64cSFQqSYeEaEhVFMDB4/bwQ7lrIKxqcI9jPipTfOR+nBF47wyFJmx5IgXWK6n4HaRx5GpFXNYdz06SdiJule5HaZ3ZwDYCd8llm64I4K4o3Sz58+KLkm9cc/VFhslqaEszHtwDQS2ffskX69l6xliqHsDD1lp5iggkmmOAdx4S8/C3Bo2cg8gTloLBMjxL40guOx87DXEPSKMHLO44X1v//7f1pjGXXedj9/tdaezrzOTVXdXX1zG52N5tszpRpayBtyVMsx/GLe98gURLfGyCWA8sOEMRAbCOBAxnOF8eBEedFcDNcwLGvDUgeXjuJLMu0ZJMSRYni3GSP1d01D2c+e1zrfljV1WJMS01aZLO71w8oFOvUqapde5/ifnqtZ1AcCE/ZGUSNlzBG4/VPAzB9bEjznhN0hobucyPUoE0yagBQG79Apz+J8QNah/rUQrj0+Tk6S7XdLSIVGjsiQAMatBa7gUFeVxQNzzZy287pNiQFEMWGpCLQkSREkQ8yyuOBbUgX2q8v4oJAGUReIIyxybvDa9OlYV0nVL9FuXRmNFvaNrYrMKQ7j3uDEf7A9ngRAvyyAikoDKAEoiwobab4O1tCeaBImwpvoFHpzjZS00fkBiMEIhcYha040iH1q21kbljPFghLPdb2zNHdqFLU5zHaMDO0wUuvcYSJOmz27GLV/Ljg+Dx89bxmmNreLgdnwFv/m5dIX0vUPTIjkH+DHjGO4zjvJhe83CECT3JsXiAFaCMIfVte+8VXDY8fhbvmBEYLvnZBMxseQekKqAED73XqcoZRZx6E5tCjMWN3PcDKcoRZHDDcqqK1JCxvQdAmFXWmT0KzvE3vksf6602y2AMBUmmUr9FaonMwhbRxjQCkYLRgt46i9ZR+YChCQdgviH1bwRMgKXoZ4aSPEgK/bF++RVIQSIPJNQZBphTlTg+wKTcDXWB2IqjOW+S8dHYqjTwEqbnehTjo9K/3eMH2eMm1RiuQYx4ig8pygkw1xhPkZTCBwusXGClIpnzwBH4vt0GWUuAZtC/xupqglyFSw6p3F919DbQSnG8fQoYVatk65byDkR7+nv30Y8i1XRF54IBgvQeLm7b0faYJR2pddGfTlkjPHnhHr5GtvmG9a1fIDrtEXcdx3sdc8HIHOTFvE3j7Mcy1BFEAW334i9fhyXsECxM2B+YLL0lqxT1goB+8yFgtR/VOozV4tfPMndrH5IHD9M5NU6xvkec+YAiqSwyZJap5TO7vUanXOP9UnbgbXF99CWyFUZ5ItLa5L9dWX4Z7bL+X0lrGSAoyDypbmW1cV1f4QqA7KdGETyAkXlWBEOjc4HkGLUAVOQWS2sYmYH9sYordfJa32jZq64yYgkgoOjrdfby0vo23E7wIKahOeGSxDVTEuIcXF4RbGQjQSmB8iZEQdDKMgHgiQIeScC3FSEFa90FC4Skq2328UcGQOkGYsD4/QzoStM0RUMHuqku7doC5CZ/Vtg1UFsbh6JzkmTc0wwSUgrkWzCU7JdITc4ggekevj2uJugsTglLgghfHcd6/XPByB5ltSmYaMEwMixvw6BGB78Fq2/DlN+DHHrE9RDZ7gt76EZQpgxqwlr/BWDhB0tvHIDVUJ7/GwfsfIh+OUaz7jNZ9dOETVTcYCFsOPXn3gFo1Zf2FMluLNTvfCEAalNJIz+xOmb6WuBtP+ehQImKNzAz9kqC8XpBL0BWF9BSql1Ma9wmFQOxUHGFsx2AtPbz+EKMNjfXr5dKxKdje2S56q22j7Z2Vl0BI2sX1z9cWV1FpjpCAEJTHA4a9HOnb4MVPDUE732nEx87qy85sJiCZDDAS/O3MTpFuKTQS2Rf4SYpKCzb6B9iYm0YrxerWFDKsgVTMDM8iBBTTh1nvQVpAJYR9U5JG2fC1C4asgEYZppuCSntny+gdlkinuR3gCS5R13Gc9z8XvNxBxmv2ZucrwSg1XN2yfWCUhMubhgvrwgY0ytAfKYab96ANDIIXmWsVpJv3URSCNLhIY2LAvnsfpHd+iuHSAHSOlDmEPQZFlaBWMLfQJ6zUWHy6hc6ljVEMtlkJttOu0dc67wJKEO+xAwujdka7JjAYopEmrii0EoSFXb3xA4mq2QGNNlnWzgzyRhloTePK8u7vPdD5bvAyNAXZN20NAXSMrTRSCNrXerxoTX1xebfHi/QFUTOwZdKBxLQUUS/H7+Q26biwU7CNFIjCYJQgbXgIDQIBGZjAjgYIOwmyKCiMYhDP0F4YozCCjdEBhBcQ5X1ayRJKQrT/MOtde9r2TQoOTgmev2hY79qYb+8Y7G0ZipW/WYn0pXXIC9v4brrxjr6F4zjOe8YFL3cQXwnGa5LZpu3QenbF9nh5/Jit2jm3YvA9wf5JQZJDb/0IaVoio8+mOctUeYy0d4D2EPrB17nn9BEq3kGy7RLDtYIit6svbX+cwmjm70tpjVe48owkHdhSZSkN4KMLRZHsHNi11RcNwz0hQkJpJaVTERRlRdgvSCJBUZYEQlGMCspjAaqsUD4gDGQ2eFFJgtCa6vLm7u8do1kt4t0xAf97r5fdadIYhjsDGUWWU76ytpusq3xB0FTEQ41R4JUlQS9HpQaRG2SsbbCyM0E7nvRtOXXPjgTQQqGlRPUMXpYjDKTDKlerJ/DGhgyNx2gwC9JjanQOKaBozrGVVskL8BXsHbcl0V85p3fHA8w0YV5fwWQpolRBjs287deFMYbXl21Ad2RW/I2GOTqO47wXXPByh5lqQCUSu43pvn7RsDAhePSIfSmstu1Ncf8kZLlHe/kecg1x6UUmGwXxxr0kmSRWi5Rr6xx99LvoXZhmuKIRpo/nx2TKpyurTB9UjM8M6C2X2Vqs2AMQBukVpMOQpK/QxTe9BAWM9kQgBX43J5WCNBCUN3byXioKXwr0yG4deb5EBQIJ6KxAYgg6fUShYZAgUruKkmFY1QmlnZd7x7x562irSEmMJjfszjTyBiMqyxu7fyB+KFElSToqMAK8QBBuZWhfoBJtg5FxHzXSoA3JTIjxJNFahkGQNny0JylftasuOpDEK5NcHTsC5YKhDhj2ppDKY250FimB2cOsdOw4h30TUC8JLqzZFTPfg4kalEJJq3/eHvPcOyuR3uzb3Ccl4eC0C1wcx3n/c8HLHWaqbm9OjbJdUbi8YStMDs8I7j9gPxdntiKpXobh5l0kcYlR0SMLztMMm6TdQ2wP4GrxNU4fbzHefIx4u0K8GZOlAWFli1hFbAwLFk4XKL/G0nOl3UBFqQyvpBi1Q/KRnXEkhB04WISSeDrASDsuoFuG+lJG4kFRUXhIzCAjGvfxlcQrSVtqnRSEyuD3hujQJ/M8oi074+haxdG10t9vXnkpjGFDJxgMqbk+06iytEG0bTv12gZ10vacESBCSSgM0XYGElSsyUuSrOYRbmUIA6OpACQEGylaCbKGh98pCLqp7e3iKZY79xC0+sQiIh62KBKNUpLp+AKRD8ulI+SFDSoOTAmqkWBx3c6k8qXdMpptCszy36xE+vWd8uh9k4LId8GL4zjvfy54ucNM1e37JBPsHbf//bULBmMMJ/ZKjs8LyoFgENsAx1MeW8snGaWQVZ6nXspJt+4lThVds8TAO8O9j91PZ3GBwXqA0ut44ZBRVqeTj5g92aA1W3DlWY9spGyQIjRBpUD6hu7VgDwVOzOP7O7RaD7EAKX1lI1pD6ENQapJyxIjIBwaSmMeypd40U6jutzgU0CSoaMALSSNpTXABi99k1PslktfX3npmoyhKZAI+t9UiVRbXCHs9nc/9iNJURjMTpm0lxiCrWy31LsoK4pQIjNNsTOcUeY7o7MLQVHyqCzGCM9+XrQ9zjQfRzZjRgQMe9MIBDPJJXyRIyoNrhaTGGCibgOYrb5mcRNKPkQBNCuC+XIP3V4HIVDvoER6lBouru30dnGJuo7j3CJc8HKHiQK7ogK2u66SsNaxWxEA9x8QHJwWTDfsVoKvoLd+FHSJdtonap7Hp8pw/TSdIVzKv8zRQx3Gpz7GcL1CPhiRJxKv0icHspJg6kjB1oU6/TW1W13khwlCBWSJx6jtY3J2c1+G8xEICLcz+iFkZUXYKxg2JFoJogz8hkIoiSorG0Bog0KTex5Rrw9a01pc2v29ezqn2Cl56n5TkHIt3yUSko18JwlHaxrnrhD0R7uVRlHVIysAXyBbNjDxEjtssQjsqovIDaKwVUZaSbxeQVFSFEoiCkN5KcEEkJU9krUmsdegmDCYIqe/UUGWqsyPXiNQsFU/QpLbvjz7J2xQMUxgo2+3jMaqIKVgZmS3jNTEHDIqv+3Xw5klQ6Fhoi6YrL3tL3ccx7kpXPByB7q2ddRP4Oge+99fv6jRxiCErTjaPyWZrEE/Bl95DNZPIIGu/wIlv6Bon2DY3s8g0SyaL3DPvbP0108w2CihimW8YMRo2KQQm0yeqOEFHu1LHmZn60jIgqAqbVO5oSBP2C2nLhoeeWsnwXcroz2tqKxnxCVJEUl8ITFaE9YVfk2hbPNcpDFoqSivbWG0obl4veKop+0Ky7X/vubaTKMIyabJ7BZWoakurSHjZGelCKJxRbwzkFHWFUE7wyhhG9IJm+8S9AvYmWeEgnA9o4gUSSOgdmGAzAvSpo8OPNaXTlB4EtPIMEbTXwupKM3e0at4Ci6VjgN2FECjbBimsN41KAFJBjMNm5+i1nfyXfYcetuvg6wwnNnZMjox7xJ1Hce5dbjg5Q401bA3qfUOnJwXBB60B3DB7rKgpOB77hbcPS8JPBvAJO2jFHkJ5feQ1fMgBL2lD9Dt1emmA+aOfJGo9UP0N+oIHZOPEogysjRj6u4GzfmMjdd80oEEBEIYolqCFwqyWNrgZWcCtTE7gxqFIOzmbE561C4npD4UVYWHwAxzSi2foKyQaqcMu9AYKamsb6M1RFs9RGZXWQYmZ1unGGPe1GX32sqLJyRD7PKPN4wJN9q2zFmAUIJSK2DYz5G+RFYk5TUbvMjEllLHUz5+2359PB0CEKynGCHIqz7lyyNkUZA1fEzssZLdzWisDOTksSLLWhxNXyQQBZ3yPFvhHkLPbhlt9gWhMmz0QBs7DqBREZxe0BRL15N1366zK4Y0s+XR8+Pv6KXkOI5zU7jg5Q50Le9ls29QEk7utcHMNy5pCm3/Je4rwUdPSfbvDCe+suETxicIfZDNF0lzjTQBw6sfpjf02MyXuef+i3TbjzHcLuGZZZAZ/XaVSqPD+N0NNs8HJF12ersY/GgEgU+ReeQjiS52Vl+MLZlGQGkjpT2hEBooDEkkEMYmyZbHfPySZ5vGCSDXGAPV9S3SUoiRgqBj81YSNCOtyTAMzPUtpM7OyovWmryw5cLR+jaVtW37xyFAeoJozCceFrZMuiQIOjZQEUZQBIKipJCFoSgpsoZCxTby0VoQbSf4o4IiFGQlj7wfsZ3Pk08ITJEx2KwSRREH+s8jBCyOPUKhwfNgsg5pDt3R9WTqmSY8eFDhd5ZsiXRUQo7Pvq3XQKENr1615+D4vHRzjBzHuaW44OUOVI2gFNp28xs9ODonKIcwiG0OxDVRIPh7j9vVl1zD+XNHqfglmvUeYf08oxT6/Sbblz9AnEB96mXq0yfob43jBRlFvEkqBeiCyRMV4rZH3BG7KyxSaspNgzGKpC8odjrzG2xr/SKSqESjhwXbewPCfsGoIjFSEA00YctDhBIvsMGQzjRCalRnRBHYWvDShu20m2PHBCRGY7g+JuBamXSvt8FOvzyqV9eIdjr0CiGQvsCve7ZMGtvjxYsLVGzQPhShQsa2RDqeCTFC4LdzipIiL/vULg0QuWY0F6GVYtibYov9RI02JosZdsY4rC/g65iBP8Zm7QiVEAJlr0m9ZBimhu2B7YB8eEZyaBrypWtVRm+/RPrShv3eUQAHp9/Z68hxHOdmccHLHUgIsZv3st41eEpw7z77UnjxsiHNrwcwe8YVpw8KfAkr2z7Ll08w04LphRcYpppBDMXgAGtXT5Brw76jX6MfP0rcDfHlJmlcMGw3GN/XpzJbZ7guyAYCbSQITVQdokIfnSuyAbszkBCC4Z4INERrKduzPqWNlFFTohVECXjjChFI1M7Kiy4MkTQUShImMaLQ1K++ueLo2g/o6gxjDGs6ocgSOqMuYLv11i4sEfUGu+fACyQykhghkGVJYAwqNqi4IK376LJdaRGFIZ6xc5yCdkZe9TAGSus2EThuhZhc0evNsh3soVxbBZ0z6k9xOH4JIeDqxMMgBZ6Eehn6ia3ESnK7AtMsw4dP2PyU4uo7K5E2xvDKFbvKdGxOoKRbdXEc59bigpc71LWtozXbyoSD07Y0Os3g5cvmTc998IBgrmXzLc6ePUqRheyZ6jEzd4GsgKtbkG3cT9ybJfRTxvfmDAczBOWUYrREZyjwRZm5R2r0VgRJFzD2hqm8jKCmKDKfLLZbRxgbYoz27JRMLyV0xxTBVk5cU+hA4glhg5ZI4e+USxtt8HVO6geU17bQGhpXVnd/j67OdmOjrs7pmZx+kWI6m3SV3OnyW1C/tIwaJfZjAVFFoTEYD7yGR7i5MxIgN2RVRTLm4Q9sMvBoOkAYQ7CZk/uKytoIkWvimQCtFLoTsq4PQUnjyw5ow8RWh7IekKoKG2MnKfn25xbaBiuFEay27arLffslrapED3sUW6s7JdJvL3hZbsN2Hzzl5hg5jnNrcsHLHWqycX3lRRuDFIL79tuXw6tLhmFyPYCZbUn2TwlqJRiMfM6+cYJqBEeOfgPlxWBgvSe5evZ7kKZMuTJEVPaSpwHlxhZbiynk00zcFTPYCChSKFIDRiBVTlCOUVFIHivy0c6cI2A4Z/NW/G5OJgxxXZEDaSiQGvxEE7U8wrpC7gQvstBkgUd18SqFNlSWN+x8ImC9SMh3y6UzOjpjuH6FME3YDjwQAi9OqVxeRebadrkVEFQVqTa20qglCdqZjWtyA8oOjRTGdgDO6h5qaH+eKQTVq0PQMJoMKEKPtNNkzRyhUl+HIiMZtDg8eg2AK837CUIfT9otvayAkm/ojWy1UaMMHzpuT06+vFMiPTaDLFXe1rV/+Yo9B4dnBKFrSuc4zi3IBS93qFbF9gvJcltpBHZ2zmRdUBR2bMA1Mw2olWC8CpUILpw/ylanRLXa4/Qj/zciaNOLYRSX6Fz+EAKJF3gYv0VYTSnSS3S3KmRxg9bhGkUGRQzGSBAGL8wJagatQ9K+rToSgPGlLTsuDP56Rnt/iB9rRpG94fqjgtKYT1D1EJ5AIBBaUwhF6/IqWRigigJ/ZwtoU6ckaIwxdHXOxuoFhr1NlDEMwwCAcKtDeX3bBidSoDyBX1YkqUZ4AtlQBN0cYQzaE6hYY6TAAMPZ0FZIbWfkJQ81KpCpJq97aM+jyEI6xQyDbJJyfQ1hMoI1RUu30dJjdeJ+mhUodk59ORAoBasdiHybWDu5s91XXH1nJdKbfcPKtkEIuHuPC1wcx7k1ueDlDiWF2L0RrnXs3VIIwYOHbP7I+VXDSts+HgWCVsUObAw9MDpg88L3MhpWqVb7HL3vjyjXr9IbwcraNPHaQ5giRPgNVCAY37fJpWcF2WCM5qGItGfIRoY8tSXOnp/jhRpQpLGHvpaaYmC4ECFjTXQlZlhTGDSjukQLKMXgj3t4VQ/P22nPm2s0gmiY2gHWRbE7JmBEgcAOauwkA9Ze+UsSKdHVJtc6v5RXN6nsJOtKCSqURC2feFDYqqaKJOgXyNSQNj2MkjZZF4hnQzDgb+XkVUVlLUYUhsHeCFJJ3iuzXtpPMqpTrq6DzplZXALps9w4RaVeJsttQCkl1MuGlY7YrTC6d5/NdTFFcX3l5W2WSL+ysyW4f9KOG3Acx7kVueDlDva/570ATNQEh2fsTe0rZ6+XTs80BZVQMF4VVCPY2m7B6g9RjKaoV1L2HvsTotargGF96Ri9zYOM+rMEdY9SKybpvcJwY5p4tAe/JtEZFIldYpEqtyswkSbPymTD68cznA8x0ua9ZMKQepJhS6GVIEwNXlMhygrp7ZRLa4PB2ETZbh9TaEprNhjJgKK7QW9tkc3Xn6VdpCRhSFZrogGMoba4hr8zFkAI8CNFadIn7he29FqCSgpEqhntCclLdvUFIJ4JELnG6xVoXxJux2hPkLR8chPRTycZeU2ytEStukZpNKKxuYlQiivjDzHTtIm5w9SutISeYBAbpIB9E4J9O512s3MvYJIYUa6iJuZu+Hr3RoZLG9fKo13g4jjOrcsFL3ew3ZWXrp1tdM3p/YLQh84QXrt6PXgBmGnZm3qagzAhauOjFL0jlALD3MEvU55+Bo1m+cJjDLtzJOkklfGUvY9cYfGLEf32BEE1tEMYE40uJEIYZFDglwvAs71gdo4lr3okUyEi1vjLMYUvGIz5FBhUDqWKxAQSpQQC+3soDNpAfX0TUxjqV9bAGLTWjNau0G+v0Un6bAcBWWuSkQ1doCioLS7hxTs120LglyXRdECeapBQKTRSg/btqpEuKYyys4zymocaakQBaqCROQz3hpAoRt0WHd/2YlFegiJh6vIGmjKb9aMEzTHyQpBmEHg2v2Vp25BkdqL0RF3QrIApCtKX/xKA8MRjCHnjf8KvXrVB3WxLMFZ1wYvjOLcuF7zcwSZqdntilNguuteEvuCBg/al8Y1FwyA2TDds0BLuzNXJCyi0IPAUUe+7iNcfRAlBc/o1Fo59HmM0l177MO3Vo8jAZ+bEOpXm11l9dS9pPoUX2dyWbABCGHxPIz2D8guyNEBnXN862hchtCG6nJCFAiQknv1cyQi8mocf2jlN18qlcwT1pXVMVlBZ20QUBQjBdq1O2hhHzh1g68QDaC9ge6fBjEoy6m9cQeQaaacTUG56qKaHkaBqirBfIAwUZYnqFSAFKMFop79L0MkoyopwO0Fow2guxOQ+/Y15+v44WVaiUW8jh0Mal7cQKuDS2MMsTMD2wNBLbFWRkvYcBz7Mj9tVFyEE2cWX0L02IirjHzl9w9c6zgxnV6+PAnAcx7mVueDlDuYpuw0Eb946Ajg4ZccIFAU8e94QePZf61IIDkzaQGGlbSuRfE9Sik+yeenDSONRbixx9L4/sl1cn/l7DPqzSF9x5GOvozuvsb1+EKEkUhmyoY1QhNIYLfHCgjSp2p4v16qO5ksAlFYSTFyQY4jLAoOhPDQEUz5RVdk2LRq8IicPfEpX19FK4g+G+ANbxrTSaMH4NN7kXpIwIkfvTpMOOn3qV1cRgFQCqcCv+6hI2uCl5RG2bXZMVpbI3CByg5E7yboGws0MrQReWhBP+mSlgGHaIhnVGHoN4mGTRnONqaUr5KOAXmUeMzaPr2CU2j/IUmCTqA2wp2XP+f5JgdGa9EW76hKceBTh+Td8rV9fMhSFDTxnmu/o5eI4jvO+4YKXO9xkw76/lrR7jRCCRw7bPJLLG4arW2b3plcKBbMtQWcIlzc0R+egWRHI0QIrr/8AWVJhfKzD4Xv/CBV1OPf83yLLm1Smh0wtnCfrGTQRygchNEUKQmqkL23irgwYtdmdQB1P+eQ1hRoWBMsJutB2wrQUlBKDN+HhNzyEAoxBFAWZ5yGSgiBJUVlO0LlecVRgy8Njo9HYRF4wlDbbRBuda+1dUKGkMhMQxzuVRjVB2M4RBpK6hxEgU4MREM+FiMLgb+UgBWqk6R8okSch8eYE3foERkiywQRReJmp5ctkaYUrUx9gbswGj6PMdryV0vbUaZRhqmHfN8qQX3oF3d1ChBHBkftv+BrnheG1pWurLtINYHQc55bngpc73NQ35b3875oVsVtO+5WzmomafTzNbbJv5MNmH9Y6dsLxgSno98dYe/0HMckEpTDhwPHPkSV1RsMJ8qxGeTIlb2+ACG1A4EM6AKm0TbrFEJVG5GnEbkNcIRjujTAGwqUYlRi6EzbvxUsNYdPDq3soacul0Zos8DEelPsdRFYQbbQBGJAjgZEpiI1t959iwGgqSxsEO8m6CAhKktq+iFEvQ3oCX4HMDdoX6JqHzMH4gqzmkddtabQwgLRd9uKxkDwuk14aZzRpV49MUWZq/Q1UmtFjjtHkMSaqsD2wVUaeuv5+/4T9fRZ2EnWTa6sudz+CCMIbvsbnVm3uTCWChcm38+pwHMd5f3LByx3uWsVRdwhx+lcDmFP77Nyjfmwb2kkJcQqHpgXz47DetY9XI8HcmLK5G70yi698DDE6gJSayfmXAYGIInLjM1jVDDtVlC/wAtuwzhQaz7erFn4pIyvq16uOBAwWSrbb7nICiSZuKfK0QGSaSk0hqx5K2U67AsijEKOh7vWRaUp5ZQOAHENmDENTEBtNbjQFoIqc2qUl1CixKzgIyi2faMpn0CuQviQ0GjQkDQ9vp4mf9sXuFGm/nYMSqFQz2B+R+wGmH5FvVEkaAUqCMDnTly+SJxGLjQcYq0nWuvacJvbL0Rr2jovdbn37JwX54mvo9joiCAmOPXjD11cbwytXrlcYuQGMjuPcDlzwcocLfVvFAn817wXsdOkHd5J3X1uCys4/+BsV2/tlsm4DmJevGE4twJEZQcmH/shj4+L3sHHxu22+R2eWqNxn7t4O7atV1l8rUeQBMoCgBvnIIFQBwkMou180bEuMscHIcKFMXvOQiab+jS6FEqTCYIC6JzChRCqQwoA2qCRl2GzYCdHeiOrqJiIvKIDYFAx33jJjgAKVZDTPXNrprCsQEsKmT2kyYDjIMArKuUZgyGsKv5uCso30hnN2nlG0nlKEAplo+vtKFFlAvDzBdm0GIxVZUmMmfoVw2GdQNOnOnKZZgXOr0BtBJbAJuodnBAemBILrW0bpi38BQHDsIUQQ3fD1XdywgWfo24DTcRznduCCF+dNJdNvZWEC5sYEWsNW35Yjr3fggYOSZsVuI8Wp4flLhu8+Jjk2Z1cRklww2D7EuW98nKuvfYx4WMcLJff86CJ5XDDYquyMCMDOChI50lMIIIhi8qyGsZ320aGgfU+V3JeUFkdEF4aM7NxGGrFATPr4gUBKgdGGME0YTLYQZ7aQCmpJm3Bgt4R6RcZA5/R0RmYKQlK8fkz9/FWb7yLA8wSVPQGpMRSZHQNQGxS24qckEYlBZDbfZTQfITNNuJFSlJXdRgp9ZMcjv9KkO91CSRj1Jji4/TRozYXgJF61yblVe64A5sfhrlm7Xad2/jL3TQryK29QbK0i/IDg7odv+Lqab1p1uWtW4CsXvDiOc3twwYvDVONap923/rwQgocO2Rtqktv+Lysdw/yYYbYpmW3BRs8O+1vtwPfdp5iqQ7LTtjZQivb6cc4893HSpEp1TuNFBYKcPPbBYEuTdYFfikEKwmpGnofkyU7PFwnxVET3UBmRahov9hlEoIUhiA1qOiAs29UXUxg8NEYbYr+KijOieEBtuE1ITM8UbJuMoSno6QxFTtAdUFndsgVOAvyyon6wTHuQoXb6vXipHQlgfIE30phQktZsfxc5KpCZwXiC4VxA4XuY7ZBiuUo8Gdky89WURryEVpI3xAPkxqM3sltFB6Zs0BJ6ksMz9nwCLIxfX3Xxjz6ACEs3fF1XO7DZMyhpp0c7juPcLlzw4uzmvWz1DVnx1qsv9ZLgxF5BOYCVDgxiQ3ckePCgIPCu5cUYXrpsmKjCE/cIPGUnI+favu8tPcSwN0GWjzPsNMlTgTHCvi9AF6BUjF/SKL9AiJyk79tZRwKymmIwH5GHEhkXeGsJeZIjUk1lwido+Uhht1sUmmi7x2B2ArXYQYxyou0uNdWnoi4Sm5wMTc4AjSRa3SLo9BE7w6XLYx7hpE93I0OFkiAQiMKQtTzUzqqL9qUdCQCEaylFSVFEkqQRQKyIlybYNnspjQ1s2fPay6BzVup7ybwFAEo+lENbVRR6dt5UNbSrXM0K1DoXKDaWEJ5PcPcjN3xNtTF89bxtvnd4RhAFLnhxHOf24YIXh2okqES2NHmz99c/7+ReQaMsCDz7r/qVtqFVteMEmmVBnIPWhr983fDkPZLDM7ZCZ6c/HJ6ZpL1+AJCUZsa4+sI0Sc9D4KEzyGMoEmP7q0QFldaQNK5idhrgmpJEFYb2sYqd6Jxoiu0UkWsqdQ+/bjvLqUDgCYM3GJEHPqKbUHQL/NUuAqh7KxwPzzImPTw5QOdQP38VL0l3J0mXxwOicY/RVmrnG6ERGpKmh8psIo72BcP5EKEN1UsxeVWR1j2M8lBbgmxxnI2JPSg/prLVYaJ3AXTB2dIJStUykQ/lwAYpw8RO7b57j2B5ZwVs3zgkL3wJAP+u029revQby4btvu3We2rBBS6O49xeXPDiANfzXlY7b73yAqCk4KFDkmpktzXOrdjn3rfPBjTNMgxSu6304mX4xAcl5RA8afNiBgl0Lt+L1h6N2T5pNs6oXWa4FqI16AyyIRjkTmv+nLAao3fyXkwgMUKQNXyG0wE6FJiNGFMYxnwJdYXA4Pu2V4qXZ3j9EcNqDb8b46/3KDIYFB4ltcih8Iu01BYqzWmduYjItZ0k7QsqcyFJKGFkQEJUGIQxpA0Pr5uDEBglGM1GqJEmWLcrL2nLbhmJbY9iuUqyp0XU73Hga6/g+0M2JibpZ7O0mhU8hc3HiWxjusgXHJyC5W17Xuf1IsX6FVAewfFHb/haxqnh+Z2p4Pfud6sujuPcflzw4gBvPaTxrewZExydExhj+NpFQ6E1USA4tU/gSftWaMOrV2xX3u87JQl8u/qS5pD1jhL3WwTRgOreMskoIO1K0lEF5QMY8mFMlpQQCKQsMEaAASMhLyuysgJjSMcD0kIjRgWtAkzVQ3kCJQADgWcIt7ukjSoyy/F7Q/RAs1E0KYwilFvkSGQ/pnnu8u4fg19S1PZF9JMCctsBuLQTQeUVhdfP0aEgbXgUVYXfzTGBIBn30Z5C9AX5Sp0hEwTlDkde+Cp66JOMK64cPkQxmkLL0E7UlhBn0KrYUua17vUto/CsXXUJjtyLLNdu+Fo+f8mQ5tCq2kRdx3Gc240LXhzgerO6ja7tPvutfPC4zXPpDA1/+rJ97K5ZQb1sG9dJKTAGnj6j+cH7BXvHbS5HWkBv2KK3dABjBNXxAUZW0QSM1jyQAqPtwEYvKshiRZ5AkQW2ZFpA3lJ2dSQxDBZKJBVJMcgIt3K86YBKU9nmdgL8QBD2BkTbHfIoINjsQz8jw+NCfJxL6R7aWR3ZT6leXtsdR1AZs/1dOksjMCASTRBK8pqHN9KokUb7ktFsCBrKF0ckMxHpmI9W4G1rRhem6FX3cd+l3yNIYrreBBdOHsVIj1E8Q+RDKbTnJfAEjYoNCi+s2XN/QFyhWFkEqQhOPHbD13GjZzi7syL28CHp+ro4jnNbcsGLA9h/6QeeHQa43f/Wz61Fku8+Zm+Kf/6KZqOnUfJ6PxhjQEhDdwQvXYa/9z12+0hru3XU27ybZFjFC2L8RgkRKvI25GkAQti8kyLFLxnyxCfuBOidvBddUXYitS9R/Zz2iQrFIEf2c8oNRbnhIQHl7STuphnBdo+4WiHY6iI7CQZBIrqs53WGRUSw2SXc7iOVDZDK4z5q0ifbyCEQ+IXB8yCeDvC3coQ26EAwmg1Qiaa8OCJp+RShhxYKfxvySy0m+69Qy9dIw4hXjz+O8gv6Wy2CMELt/Kwst6suJ+YFaW7ziABmV3YqjA6dQlYaN3QNjTF85aydHH1gSuxWkTmO49xuXPDiALYcerLx7fNervnISTvUcZQa/uA5Q14Y9owJ9owJpICSb7/XmSXDZF3y0EG7ypBp2Ni4i6Q7hlIZQSlFliPSpMxoU+KX7FyfIsmQHpSaKTrPyWLPbh0FAi0hGfMIlxOGCyViNBSGiRy8miKKbFm3KWz+S2m9jRoleHlOsN4h0T6xNvQN5LmkcX4JlaQIAdITVPaEjCJBKTXgCUIFsrAzlvxRjvEkxhPEMyFqUCATQzIZoAXQ9zDLAdP9K0RskfseLx37bhrzmxids3F5jkarTqNkm8cpZQclHp0TnF+1gce8WMbfuABSEp688VWXc6u2NNpTcP8BF7g4jnP7csGLs+tG814AQl/y5Cnb+Ozcqua5nbLcBw7aYY7DxOZcGANPv675O49ImmXAwDCp0Fk/QhaXQWhUKSCTVUYrHkLt5LfkgNFIzxDVMrLYxxgwUpBVJLqsCLcyjIReTWAktCKJklCqSSgMQgq8QNqto40ORkqijTb9UURLDYiKISbRjL1yDqE1IPBLisZCRLebIUt2UnUgbHCUtny8boEJJGnDpygp/EFOXvNIWj5GSbzNgvpzKcJoZCnlzMkHGEQtWuVNksSjvz7NeKtiV480NMuCU/sknrQziAAObO6suhw4iay1bujaJZnh6xftNbAjHVzw4jjO7csFL86ua3kv613bRffbObUgOTBl5/I8e85Onm6UBcd2hjlmOUSBoR/DWk/wgbsEvoTCCDbW72bYHUN5GVLlBJNj9NYqJB1FULUJvqYwCCkRyuAHGbqwfWGysYC8pEAb/G5O3FSkoaAaKURhCEsKXxjUTpWNyguqS+sYIQi7A7zukKvxQa5ut/CGMa0zF5HYbZxS0yOcDhguxQwGBaYw1FrSrrZIgd/NKUKb7yJzQ7iS0D9cAiExWjL59TalxYyOP8Ebd99Hv96iUhqRjwZsXp2lVqsxUYeVNvjXVl1mBasduxJTHq3Q7LwBQhDc84EbvnYvLBri1I4ScA3pHMe53bngxdk1XgO1M3ixN/r2zw99wcOHJRM1wWrH8BdnNHFqOLUgCH17M55u7MxFumr48AkYqwEG2lsLDPsT6EIBUB5PiIsZRtseQdmWTRutwQhAIKRBG3tT1hVFVpIUZUWwnpKVFCkav+ahmj6hNIxHYqdrr0EFUFleRyYpfm+IP4hZLXKSAoL+iOriKmK3v4tPMe7hjwy5NihjqI57xOM+QTtD6J1hjDM+MtH43YJkPKCQitqLQ5ov9SiKEk9P/R+M9gZIWTDZ3KQ7Kti+Osfxu/eiDYwS25zusbsEnhKcXTGIPOGuq3+MFAJ//3FUffyGrtt233BmyQabDx2SKOmCF8dxbm8ueHF2KSkYr13Le7mxrzm2x1YTGQRrXcNfvmHwFZzeb19ay9uGhXG7fXRmWfDQIZsYXOiAztZ+Bu0phMgpVbfwZg6xfSWkyCFqQJFphCwoMkWRCUxhm8MZJUibHslEQPlybIOJrMD4gmg+wniC6YYgAKQSKF/iDRPC7S5Bb4BKErJ2n8JTBO0+4XYPKexz63tCBoFBDzQmENRaEmEgng0IV1OMZ/u7xFMhamSnWietkNLlmKmn2zDweK323VyePE4QDqhUOySdmFGvgl9Mc3RfmSubNv9npik4OitJc8Piesb0y7/LRLGCiMqE933whs6/MYavnLO5MgsTgtmWC1wcx7n9ueDFeZPZpn1/dvXGto4iX3D3vGRh3E6XvrKpeWPFcGjG5rykuW2VEvqG7hD2TwlaZZsgvL1+iH5nBs9LEUJTaiX0uwdJeoqwbsj6YLTACwxxxwdjS7ARgnzMJ/dB7nS7jQvbk6V8T52k5ZMLQcM3BIEEY+cHzb1xgXKvR9AbIvojtFLUF5fx0syWVoeSxsES/c2UeKQxUtCoCkQO8WyI6hXoUJK2PIpA4vVz8rKidCVj8ksd1LCgwwzPjf8Qldo6QkCjMqTd1Wwv7eGDD0+z1Tesd21J+eNH7arLpVXN+Eu/R3NwiUo1pPzE/+OGc10ursNax6AUPHjQBS6O49wZXPDivMmRWVups9G1N9kbcXyPoFoSNEp2u+mr5w29ke0zIgQsbdkgxxjbsv7QrM336HfnSJMG6agKRtAYu0JRuZf+WgAIghpgCqSnUZ5htB2gC2Fb81cU3bsqJBM+ItbkWqNzzcTRMsMDZbqhRzUwyMC2b1E+lDfafGj9HB/5+lfY09/GS1PGXjqH1BohBGFd4U0GZGs5xhfIpqRe2AGP2ZiPpw3ahyKUjD3XZeqpbZqvDWk900dkkiwIWQ2Pslo+TLm6QRAkFGkPg6Ro7+PBkxMsbtituYm64L79EmMMa3/2R1Q2ztCqe5Q/9OOo8dkbOu9pbnjugk3SvWevoBK54MVxnDuDC16cNykFggNT9ib4ytVvv/ICEAWCu2YFEzXbLTYvDF96TTNeg+8+ZgOYzhAKbVdOGiVBqwIYxfbGfrrbswijKVW2qYz36G7NoQuImoZsuDMXqZwz2AiJ2yU760gIsgmfzUdbiMJOe07ygqqG2vEKuu4hPA8ZKvAFQkmKHPK1PvvWV3jiG8/yD//oMzzwlWeRwv6M2piP9DXeUoIwhsqUh8wNWgoqV0bUXxlQOxdTOz+i+eqAaC0lD3zSekBnoUa7NsWqd4hUlSnX1mk0NuluC3obEzz5wBznVw1Xt+0ogEePCJQUbD39Bcz550EIZj76cbzZ/Td8rV68bBglUCvZ7ryO4zh3Che8OH/F3TvVQlc2Dd3RjQUwx/fYLZBWGdJcsNWHb1wy7JsUfOCoDWDAsNGHcmhoVcFTks76AUaDSYpcIFVGo3WV7vB+spFECIH0NMaAUoaonjHYCjFaYAAdKrSyOTBFKEmKgnArZ+ZElXjCI5OCamDwIwnGkGu4tJpyodQg8yRBnqJX2kgJQgpa0wHRlQHiwojo0pDDL3eonx9R2kwZ/0qXsJsDhmTMZ7A3pHO0whs/eITFj+1lOFPCbIUsqnsBTa2xjh+sksZliu0FvuuBWV5fhqyARlnwXXcJkpefYePZvwRAPfgDVA/dfcPXqDO0IxjAJek6jnPnccGL81c0K7bZnDG2SuhGlEPBkVmB7wnKgX3slSuGlbbh4JTgkSMST9lS6fZAUI3sROVBZ5JBb4rRoAEIyrUtvHLEYNs2nYnqkCd2UGNYy9CZsQ3rEBgF/kaKNyjQShAXBSYpmGn45AdLyKZHKdd4ZQnSVh/J7T6XTMgfnH6EPzh6mtHAjrxWvqB5qMTqdoGuSMIxj+qgwAhIpgLEqGA0G7B1us7ij03RPValc6RK1vDRuaJ0MSdPKlxSp4kqbSrVNkU2JBuVuX/+MBfXJVe2bK7L6QMCufgCo6/+CdsDw9bBjzD/0Okbvj7fnKQ7P26vleM4zp3EBS/OW7q2DXF21RBnN7j6Mm9b+xfall0bA3/5uibNDUdmBA8dFjQqdmtJChu8IAVbq4cZDcbRWYDnx7QmL7G9cdgm50qAAmMkQhqCSkHcD9BGYDxJOh2ihppwPSFPC7QxVNYz6o/XyasKKQVBZIMTqYSdIP2Ns9DPkVc7iDRDSAirimJvyHrgMTpcge+uM2j5bJyscuEn9lCUFPF0yOBACWkEfjdj0GygVIrqa8yVMtveHCO/Ra2+Tqm0SjqKMJ15Hr1vjmfOagaJHcPwWOV14qf/iH4Mm7OPkBx6lLkby8/FGMOz5wwr2wYlbVNAx3GcO40LXpy3NN2w1UJFAW8s31jwUgkFh2fszVQKm4sxiOGZN2zl0rE5yQMHJHvGbC8Z3wNPKjaXDzEajJMmAcqLKVW2SdIFdKYQAoKK3s1zCaIYk4HOJQhIx32GCyGl5RSZG/JRhr+VMb+3TDrtI2selcB2zgWQBqpvLNN66Twzf/48aufYyxM+PWXQqQZfMNlPKQpD+8E6EvAHGq0Eo9kQkWnC1ZRkvITWHmpJodtlliK77VNrLeN7a+gsYtK/i0vbEcvb9vd9qHaZ2jd+D4xhuXGKrYMf4eCUuOFtnxcXbU8XIeADd0nqJRe8OI5z53nXgpd/82/+DR/4wAcol8s0m80b+hpjDL/wC7/A7OwspVKJJ598kjfeeOPdOkTnWxBCcHx+p8HckqHQNxbAnNxZfVnv2k6vQsCldcMbO5OOT+yVPHBQsmdcoA0oJSjSiI3lo6RxSJGHeH5CpbnNsGe3jpRnMBry2EcokDJFF6CRFJEkmQzw2xloQy/PMVIws6apna4hagp/VBA0JUKCVBC1e8z/+VeZ+8tvIIVBCNvfpbuZIkuK0oSivJWTlhTDu6sEq4n9S5EQTwWoWJP7CjxDkfpErwgMgkv+vQBMzb6CMTkMxrl74TB/8bphmMLR0ir3L/8uFAV69ghn5r4fhODQ9I0FIGeWNN+4ZM/jg4cE+6dc4OI4zp3pXQte0jTlx3/8x/kn/+Sf3PDX/Mqv/Aq/9mu/xm/8xm/w5S9/mUqlwkc/+lHiOH63DtP5FvZN2C6wcQoX1m7sayrR9ZvxlS2b3wHw1XOGrb698Z5aEDx+VDBZx06QVh6bVw8RD8fIM4X0YsJSl15nwX5TaadU60Jh8BBKU+R2NUZ7kng6pCh7yMwQ5zkbJUNpOWH/qTpFKJGeJKx6KF8ilEAmGdFGD38QI6RAeoLm4RLxWg6eoOUZTKzpPdwAJai/OqAoK5LxAO0JwrWU3v4GGMhXa4Rbhn6tyVp6N76fUK+9jlISvX2cq4MGwwTqus2jK7/NWJiiZha4euRHMEgm6oJm5dsHIRfX7XYR2NlFx+bcoqnjOHeud+3/gP/qX/0rfuZnfoZ77rnnhp5vjOFXf/VX+Zf/8l/yIz/yI5w6dYr/9t/+G0tLS3z2s599tw7T+RaUFLtzcl69qm+oaR3AiXm74rK8bZiswZ5xQaHhi6/a/BchBPcfkHz/fRJPgZQeg94cnc15slghZY5SKdqE6EIiAC/IUUFBFrcQSiIwFJnt7JvVFJ2TVYKNFJEZ1oKCLNVUI4/G/hJBQxFqjV9TCEBoQ7Syicoyuy1VViSB/VMQvmCsn5ErwfD+GiopqL7epwgl8XSAzA3RcsJwokyelShdAhLB5dZx0qTGxNQ5PNVB6oAgO83VLUMeJ3xo+3eZDEYEEzOUPvh3OLfhAXD4BlZdlrbt6AVj7PTpUwtuxcVxnDvb++afbxcuXGBlZYUnn3xy97FGo8EjjzzC008//dd+XZIkdLvdN7053zlHZgWegvYAlrZv7GtqJcHBnS2Nl67AB+4SlEPojuDLZ23+ixCC77lbsnfcjgsQXsjKhfvIsyqmMCg/xfcL0qQMgBcahNRkgwwjW5hcIjzQBnSk6B8uEaymiMIQdHPOzknkZsrYow28UCEHBeVJDyQohR0TYAwCqLQ8unGOCiWNOnixZnC8gi7Z2UletjMSYDrA6xdkkQJPknUrlC4UZMrnvHoEgLm5p1GeYrg1C9Ee+rHm3tXfZ1xvMDFVo/Th/4PNJKIztMexb/Jbn8v1ruGpVzRaw/5JwUOHBEK44MVxnDvb+yZ4WVlZAWB6evpNj09PT+9+7q18+tOfptFo7L7t3bv3XT3OO03gXU/CffUGy6YBTu61qy9XNw2D+HqzuotrhrOr9jlC2O2jcgielGxtHmM4mETn2FIlAWlq816ExEYqomC4GWFUhABQAiNgOBOiywpvWBBsZwzGBasUlI9X8KqKak3hlRReKJES/EGMFAaEoDrjM+gVCCUYR1Now/ChOt6ooHqmjw5tqXU84eP1coZzFbK0hNqShO2U9ak52u39eKJgbPwlpID+xgmSXHJk/Snm03PUqj5j3/fjyEqdc6v2PC6MCwLvrw9E2gPDn76syQuYbQk+cNQFLo7jOPA2g5d/8S/+BUKIb/n22muvvVvH+pZ+7ud+jk6ns/t2+fLl9/Tn3wnu3nN9G+ha3sq3Uy8L9k/aG+3nXtQsbRtO7JRfP3tWs73zfcZrkgOT0KiAMSVWF0+RF2U8bwAUFGkFszNN2i8XKKXRyZA8qWGwKyJaCLKmT/dEhWg5QaYaESlW64KNQNO8v4ZfUpQzQ9T0QAikASltcnE4HYIvCIWmYjTxfEg8FRCupZRXUoqKnWdkBHjDguFEjWxUpXy5AF2wNHmIQXeKqfE3KNd6ZLlHyTxIY/1FZlafoRzA5Id/EDUxR14YLq7b3/1aUPhWBrHh8y9p0gzGa4IPHr/xiiTHcZzbnfd2nvzP/tk/4x/8g3/wLZ9z8ODBd3QgMzMzAKyurjI7e322y+rqKvfdd99f+3VhGBKG4Tv6mc6NqUaChQnBpXXDq1cN33X0xm6ip/cLtgeG9sCW+CppyHLIJfz5a5ofOC2JfDg0LSgFhq9dECxfepgDx79AVO5Q5Ipclm05ssrwogJVEvTXA4QoCKsBUiWYQKDLkt6RCpNfbDPcm+P1c/SEz1qnYPJ76/DFNqHWVCZ9+qspQtrVHD+SJAJUIGhVBMSa3kMNVGIoXxpilABp811EajCZIfMiVFsTXPLpNsZYS+9GCcmh+S9RSMj6kzQHsP/qH5MpgT72Xey57yQAi5uQ5VCNbDn6W4kzw5+8pG2ibxk+clLgKxe4OI7jXPO2gpfJyUkmJ7/NJv07dODAAWZmZvj85z+/G6x0u12+/OUvv62KJefdcXzeBi8X1w2n9xvK4be/mVYiwQ/dL7m8CS8sarb7AiEMZ1egXjbUS5pm2W6dfOSEwGjN02enaG8eoNJYw/eH5FkFnUcolSGlwQtzjArJ4hFpP8QbS0AJO9tozCeeCojOxYTHEuIHQ4QxbNcDagdC9Ksjqp5iK5KYogAgqCkGWUGYCZolKHzF8ECJ8lpC4QmEASNgNBmgMkNajcjSMuVeDuuG1XsO0t2aZ6bZpdR6A63BG+zjrgufpZdo9J67mHv8g7vbPed2SsYPTb/1FlBWGP70JTuBuxLBkyclke8CF8dxnG/2ruW8LC4u8vzzz7O4uEhRFDz//PM8//zz9Pv93eccO3aMz3zmM4DNf/jUpz7FL/3SL/H7v//7vPjii/z9v//3mZub4+Mf//i7dZjODZqoCaYaAq1t35cbJYRdtfnB05IPnZBMNyXzY7DRhT9+XvPsOU2WGzSC/9cTisk6XD3/CEUeUqpuM+y1SEZ1bGot+GFMdTpmsF1huOVhtMRIgVaCZMKne7xCeTXBe3GESDVBIBmNCrIn6+ALiAsa0+Fu4BBWFUVDUZWGwED/vhrCgIw18VwJmdncm2TCRyWa4VgdEoW3GCLKhs3xWeLeHu7Z+yxFkGGyEsdWVyEe0i9Nkdz3tzg0Y//MeiM7LkEIOPgWVUZrXcMffV2z2TOEPjxxUrpJ0Y7jOG/hba28vB2/8Au/wH/9r/919+PTp+3sli984Qt86EMfAuDMmTN0Op3d5/zzf/7PGQwG/ON//I9pt9s8/vjj/I//8T+IoujdOkznbbh7j2CtY3hj2XDPgnlbWxlCCPaOw/wYLG0r/uh5zZmrhhcvG3wJmTYcnJL86EPw//mz08TDFmGphxcMGY1qVBoCIwxeVOAHPQhm6K+lNOd7qEiDEOR1j97hMuNPd6iaAv1in60H6/ieIDlVwZvwCK6kRA2PoKzAGLyGh1SCVg2MhO49FYQxpC2f5jd6IAVp08f4ApEYsmZEtJWRnS2zPT1FfzDDoUmfbvoc1TBhYjultjliTU+xee/f4bE94W5S7rVE3ZmmoPpNQUlWGJ6/aDvnGgNRAB8+IWmUXeDiOI7zVt61lZf/8l/+C8aYv/J2LXAB29vlm3NohBD863/9r1lZWSGOY/7kT/6Eu+666906ROdtmh+3Lf/T/Pr2x9slhB0k+BMfEjx6RFIKYHsAixuaP31J0xlKmmWPlcv3obVHtbFGb3OeIg8QCDuHqJJSaazS3Z4m6QcYwCgwQNby6d9VpjwsqL8+QA0LgtwwGvOJ7ilhPNsYb/xASHMhoihLolFBZVoxurtCXg8QBYRrCZNf3LaVTAsh5AatFUURINsafytnbWYvaW8vM9FrmMqIsXiDKVMQb5Z5ce+PUR9vcHSnT44xhvNr17eMrlneNvzhc5rXrtrA5dC04G89IJmoucDFcRznr/O+KZV23v+kENy953rZtL7BpnVv+b2k5AfvlxzfI6iV7DDHWgmUhCOzcPXs4xR5SFTpMOw3SZMKBptk6wWa2lQHKQ3r5ycwWoAQGE+QtHy276kiU4OfGZov9UCAUpLiAzX8mkehC+rTIZXxgGLKo1YCGUi6xyvIuMDfSJn9H1sYX6JDRfeuCjIzJPUSYijJF2vE0xFxuYLpT9Juf56x4TqNXoJKA86N/xBqcp59k9dXTy5v2jlPgQd7xyHNDU+/rvmTFzX92Oa3fOSk5ANHJaHLcXEcx/mWXPDivC2HpgWhD/0YLm/8zb5XKRA8fszerLWBe/dJfvRhyf/zA5IoX6C3PYcAmuOL9NuzGG2XV6QvKNUyouoGaxf3k6ceSFs2bRSkkyGbx8oUoSLcLjBSIALB8EiJ8HBAUBV0M0Nfa7wA6jMeRUUxnA7whgVjL3QJN1KSyYDOsTI6kKjUkJYivGGOuBiyPjtPHpdh6WuMNS+xv3uRkl+lI05yrnQvEzXetOryjUsasE3/Vtrw+89pzu6sXt01ZxOb94y5oMVxHOdGuODFeVs8Jbhr1t5kX75iSPN3vvoCsDAhmKqD1vD0G9du8Ir/9xOK5fOPYRDUWstsr8+Q5yEGkLJABjBxcBtZbNNdb2KMQAcSUQDGMNobkikoLSeUL41QiSabDCidKqM8ST4lye6JqPQLqi3F4FAZhZ1bVH+5T1FXduzA8QrkBlLQ2kd0FQwU260x+ssRR47+OXdfvkTQqkJljqXgewh9mGkI5lr2d7y0YTsUC2HoDAxfeFkzSuxK0/eekjxyWH7LZnWO4zjOm7ngxXnbjs4JlITNnuH3vqp5Y+WdbyH5CuZaUA4Fo8TwF2fs93rsLonqPkKWlfHCBOKE3sY8GIkxoLyCykRKuTVi6YUJdA7GE2QND7+T03ixj0gMeSSZ/7/XqZ7pI2NNcrRMfcqj1MkJBppWU4ES9I6U8Po5Y1/pIIQkGQ/oHSmjI4XMDZkfIoeCYrlCe7KO0SmTlUWOp6vUVYBoTdNuHWVzc9quuuyxpdDaGL5+oWCjZ9js2WGVQtjS8x+6XzLTdEGL4zjO2+WCF+dtKwWCD5+Q1Ep24vQzr2v+6OualfbbD2CEEESBYGECELaa6eXLBiUFf+veCbqbCwgBjekNtpdnSOKanRwgNX5QMH6ox2ArJB34AHb6c8sjG/Px4gIyg0FQXk6IVlOG+yNqh0PKqaHaLajOBwwOldCBpHJ+RLiVYhTkVY/O3RVEqhG5IQ98ZB/0OcXmbJ0oGvBA/TxjnQHGzCMPHmGY+sTDMSbqdraTNoYvvmp49hysdAyNMjQr8LH7JA8clHiu8ZzjOM474oIX5x2ZbQl++AHJAwcFgQfbffjcC5o/e1nTHb29IKYUCELPllIDfOOSYaNneOyIR//q4xgjKDf7JJsZvfUpjPHAgFAF4/u7RLWMzkoN9E5DufmI4d4SyUxA+UpMXpY0Xhngb2bkVYU8EFKd92nVJeGYZHCoTLCdUXt9AFLaiqV9EUXNQ+QgEwNaUmx7dEdVdDVgT2WTmUtrkE7in36MnqzT67YYKwccmYbVDvzBc5r/+Y2CNDcsjAs+cFTyA6ddJZHjOM7flAtenHdMScHxecmPPCQ5OmfnH13eNPzBc5rnzusbzoc5spNDs9E1zDTtTMYvvaZRCo5VT5MlVTw/RVab9BYrZElEUSiE0PhBwtRJzfZyA0YGpKAoK/IABgdKjPaGRJdGRFcTKldjgo2c4aEyE49UmHykTDIdkjU8KueGGClQI01eUrTvqSITjcwNOQFiJChWYHtyiomZVe46t4IeeGTRg1Tv20t3aOh1J4gCuLoFf/ay5sKaodCCfZOCf/RhyV2z0s0nchzH+Q5wwYvzNxb5gocPS37ofsncmO3C+8oVw2ef1ZxZ0rQH5lu+TdYM9TLkhe3VUg6hN4KvnjM8fniC4fZ+EIbG7BZaPspwa8Z2lDN23MDU/otkZgIG0vZ8kZKs5oGGzskauu6jYs2eP1intJIwmAvRCrSE3qESwXqCygzRRkbW9BjuDcmaHmQghgZjJKYjSa42CE7Dvs2L+MsxOj3M1JMf5+pohd7IMOhOst2HXmx7yUgBx+bge++RlAL3p+Y4jvOd8q512HXuPM2K4ImTgqtbhufOazpD+MpZgw1JvrU4szOP3lgxHJqC9T5sDwzffUygtx/ATL9Kpb7G0quzVPun0MUSCI3ycjw/Ye7EJmk3IKqPKHzBaKYMamSDlYMlMIbauRETf7HNaO80/YNlVC8nmQ6ov9JHFAZvUJBMBWw+0ECONFJD0Q+gLsk3fbq1Q8xGF5l4bhPik8THfoR4bI031tbZ7CmSwSz7Jm3pc+gZXlwUlMPrK0uO4zjOd4YLXpzvuD1jgtmW5PVlw2tXDVlx/XP/e1HStQ+lFEzUDKsdw5kVaJRgsQ2//TQU3v1UD3yWIOpQmzzD9spRymMvURs/CwgQUG2tUAwriOEQJGRCIZsBQtvOtaXlhHjSZ+Iv22w83mTrdB1vUOD1cvK6z9jX++RVRe9AiaLmIQcF6jIkEyEyLTCjBt7xEQvfuIAZNVgPH2c0vcAry3/IKIPu8oMsNKv8n49LKqHgM8/a3+yeBeG2ihzHcb7DXPDivCukEBybExybu7Hna2PoDg2/96xmsw9jNfAUbPQM8WCSYXsv/nSf6tgFOlmd3to9VFqLoBOkMHhBQl5RqBRknqMGkDbKyJpBZobhnsg2sROCqT/ZYrSnRBFJgk5GsJ3j93Li6YDtB+rIuEDFhkRGFKmCUUBKjf2br+K3U7Y6p3jt2EcZLz1FbjKS/iyHK3fzxD2SRlny8mVNnEI1evMoAMdxHOc7w23EO+8LUgiaFclH71NM1gWeFPzthyWn9kn2TUjE4AR53KJcW8eoLnEaMOrO2qUbYRAIVDVFxICRyC6orkbjoT1J/2CE9gTD/XZS9NgzbYL1FB0p6q8OyCuS/qGyrUZKDKxJ4rEaQW4wm2W8aMTYhVVGg3Fenfo/mT1yHhGuU1IBzeRxZlqSQ9OCNDe8fMWuupza5xJ0Hcdx3g0ueHHeV2aagoPTAmNsB9/7DwgqIWxfeZhABSjhMb33BbJC0+vMoAsfYwRCaExgMFIgE1AqJ+oPMQKymoeRknTMTofu3VMDT+APCvzNlHArI6/bmUiqVyAySKtl5LCMHoQoL2Hy0gp5Wma59d3c/egYE3u+QehDZ+kRxkpVHtzp2/LaVUOSQb0MB6Zu9tl0HMe5PbngxXnfeeCgnZ/UHkCeGw7PCLLBHFtv/ChTtZAwyJjY8xLJqE4a1+0XCY1QGh0KTCZRfYHXMwTdDC0V8WRA2vCQqSZcSxjsK1GUFI0zQ9KmR39fiaKiEDkUo4BENgk3eogeyKwg7KaMwlkq930/e458kc6oYGttgXF1iD1jkr3jkGSGV67aVZd7FyRSuFUXx3Gcd4MLXpz3ncgXPHDQvjRfuAyn9wsqkWDj0uP4Wz9AI6hRCnPqY1dIRmPoQgECKTV5yYOhz2CsgegqSq8U0FMYJL1DJYwQeP2CaDlGDXOCrQwdKTp3V1D9AiMEmSxTWusjjEBEhvI2mKLO5ukHOHCww2JnmziOSDceoxpJHjpkRwG8csWQ5baL7r7Jm3sOHcdxbmcueHHelw5O2S2kooBvLMKpBfv4hZc/zLx4HFWM4XsFSoDRHmAQQqMDQTzto7ZChgermEgy9cVtRCoxyqN/sISKNdWLMfUzQ3RJMtwTUlQkCIHJfbxtg+/HiFBQijW80WL10HFKUzP05KvEKaxe/AAztRIn5gX1kiBODa8t7ay67JMIt+riOI7zrnHBi/O+JITgkcMCKWFpy1AJBdMNwShVXDnzJHv9k+jRLFr76KyKMTZYMGVDWvEpwhSA+IhHNiOZ/sImsjCMZgKKSOL3CqKVFB1IuodLoEF7CjqKMBiiUoGfFkRfP0SvMkP74AKz0+usdQ3t9cPUWaBeFpzca3/uS1cMeQHjtetjDhzHcZx3hwtenPetellwz05wsLhpGK8ZfAV5VmK49AQttUDeX2DYn8ZoDwEIYdBVQe9glcx4xGGFweESyYRi/EvbICTJuA/GYEJBPO6TTIa2W28KfpbhjzJ0ElF9/ShXRw+w9tghJqZW6A4VaVKhc/VhmhV46JBN0h0khtd3V12EW3VxHMd5l7ngxXlfO7FXUC+DL2GjJ5isw1oXBr1J6smj+Hqc0eZdJP0pWzUtQHkpIz3OWu0AsSwznKrQfrBOVlPUzg4YzYXIWCO0oXO0AgJMIPDXNL5OyPo1orWDbJof5sKpR5DVEdXqkN4wYPn848w0AubHJfNj9hhfuGQoNEw1BHOtm3q6HMdx7ggueHHe15QUPHrErnDUIkhzQbMEy9vQXruLGe8YylTZXr6PIosAkF5GfeoCzX3nYN+QvGUY7g3Z/GALkWu8gaZ7vMrWPXXivRECkF2NV+ToTgkTH+b14T/hhdoHKbUu0Zq4QjysM9i4Gz+bpRIKHjxoV1hevqI5u+JWXRzHcd5LLnhx3vemG4LDM4JqJEAY4lxQCgwrbRitPkLDm0bmY3TX78IYu/rieSlSpUi/oIgE2leMZkNWPjaFNpr+gTL9u8p2kKMShGs5OgkYcISvrP8zLvvHKLRmz6EvE0UZWX+OlUsPMNXYWQ0qCc6uGL523gYupw8IZpoucHEcx3kvuODFuSXcf0DQrEDJFxTa0B4Istyw3pUE7Q/jU0En08T9CYy2pdEYBUaiswqJrlJIn7TpsfHhcVRckO1UGPnbBdoLaKv9fHXrJ1Hjhun9f8njH/2/mJpYJ0t91s5+jMmaR70kODEvWNwwPPOGBuD4XsHJve5PyXEc573iZhs5t4TQF/zAacnLlw0vXoaiMKx0YGtgULJEhY/QU12S/iyj7l50Mk518iWkFyNVQp7XGI3GafqXkWj6h0sIBBooCkm3OclmfIQ9pa8yNbHJ9NQGQmZs9wXp5oPEg2mmJwQPHZKsd+GLr2mMgcMzgvv3uxUXx3Gc95ILXpxbhpKCU/sECxN21SPXsNq2s4SOzk1C9kGoLRKEXbTaIh6OUa5fRcqcqLJJEHnEaYWS1hhfoAOByGHYbJDoBjUJk3tfoVkKkUWT9uYEydIDXD1/H3MtmB+3nX//5EWN1rAwIXjkiMtzcRzHea+54MW55TQrgo/eKzkwpfnss1BsG164ZNg/eYRAPkF9z5dQXoqQPkl3P6ayShC1USpFlnLyXOFpu+WTEVAEIeV4moWpOvVgjl5nkquXjlP0DtAZKuqR3a46MgN/+rImL2C2JXj8mHAjABzHcW4CF7w4tyQhBEfnFD/5fZr/759rXr5iuLAOzeEHofMg1XKKMZrtYUEpGlKZ+yLVqecQlYsYCnThISnIaFDO93NX6268dJ6Lrxynuz0LCMaqgAAMHJwWPHPWkGYwURd88LhwE6Mdx3FuEhe8OLe0Wknyj58U/P+e1nzpjKYzAF9VyJMKm33ox9AdAS8vsPfwYfYceI7m3NcxIkN5MVVV557oe9m89CgXl5oABJ5hbkyw2jZgIApgaUszSmzS8EdOCHzlAhfHcZybxQUvzi3PU4K/86gkCgxPvWzY6sFmDwIfSoEtnY4zyeblR6n4JRbGIoLma/hK0xp+Py++8FHSzH6vPeOQZoKLa7YEuhRClhviQlCN4ImTktB3gYvjOM7N5IIX57YQeIKP3asoioJzq7DZtysozYrg7jlBKTR88VVBf+M+Xn2xxPGTAVpKzl18FHJolA3NiuTypiHNDXkhWJiAvIBRIogCeOIeSTl0gYvjOM7NJowx5mYfxHdSt9ul0WjQ6XSo1+s3+3Cc99ggMVxaN1QiWFw3XFy3jwsBsy14aVFzZQuC0jbTDYPIx6lGsNGD7tCQ5lAOBXta7K6wBB583ylJq+oCF8dxnHfL27l/u5UX57ZSCQXH522QsW8CTuw1fP2iYWnLsLQFYzWJUoZ2v0W7DQbDpZ0Ax1eC+TFolKEUCmqRoFaCu/cIF7g4juO8j7jgxbmtjVUFT5wUrLRtELPRNVQjGCaCYQJKwmQDjswITu8XtCo2t8VzCbmO4zjvWy54ce4IM03Bx+6Fy5uCr1/UXItNxmu20dy4W1lxHMe5ZbjgxbljCGGTcOfHJZfWwQD7J3GN5hzHcW4xLnhx7jhSCA5M3eyjcBzHcd4pNwrXcRzHcZxbigteHMdxHMe5pbjgxXEcx3GcW4oLXhzHcRzHuaW44MVxHMdxnFuKC14cx3Ecx7mluODFcRzHcZxbigteHMdxHMe5pbjgxXEcx3GcW4oLXhzHcRzHuaW44MVxHMdxnFuKC14cx3Ecx7mluODFcRzHcZxbym03VdoYA0C3273JR+I4juM4zo26dt++dh//Vm674KXX6wGwd+/em3wkjuM4juO8Xb1ej0aj8S2fI8yNhDi3EK01S0tL1Go1hBDf0e/d7XbZu3cvly9fpl6vf0e/t/PtufN/c7nzf3O5839zufP/7jPG0Ov1mJubQ8pvndVy2628SCmZn59/V39GvV53L96byJ3/m8ud/5vLnf+by53/d9e3W3G5xiXsOo7jOI5zS3HBi+M4juM4txQXvLwNYRjyi7/4i4RheLMP5Y7kzv/N5c7/zeXO/83lzv/7y22XsOs4juM4zu3Nrbw4juM4jnNLccGL4ziO4zi3FBe8OI7jOI5zS3HBi+M4juM4txQXvNygX//1X2f//v1EUcQjjzzCV77ylZt9SLetP//zP+eHf/iHmZubQwjBZz/72Td93hjDL/zCLzA7O0upVOLJJ5/kjTfeuDkHe5v59Kc/zUMPPUStVmNqaoqPf/zjnDlz5k3PieOYT37yk4yPj1OtVvmxH/sxVldXb9IR317+w3/4D5w6dWq3Edpjjz3GH//xH+9+3p3799Yv//IvI4TgU5/61O5j7hq8P7jg5Qb89m//Nj/7sz/LL/7iL/K1r32Ne++9l49+9KOsra3d7EO7LQ0GA+69915+/dd//S0//yu/8iv82q/9Gr/xG7/Bl7/8ZSqVCh/96EeJ4/g9PtLbz1NPPcUnP/lJnnnmGT73uc+RZRnf933fx2Aw2H3Oz/zMz/AHf/AH/M7v/A5PPfUUS0tL/O2//bdv4lHfPubn5/nlX/5lnnvuOb761a/ykY98hB/5kR/h5ZdfBty5fy89++yz/Mf/+B85derUmx531+B9wjjf1sMPP2w++clP7n5cFIWZm5szn/70p2/iUd0ZAPOZz3xm92OttZmZmTH/9t/+293H2u22CcPQ/Pf//t9vwhHe3tbW1gxgnnrqKWOMPde+75vf+Z3f2X3Oq6++agDz9NNP36zDvK21Wi3zn/7Tf3Ln/j3U6/XMkSNHzOc+9znzwQ9+0Pz0T/+0Mca9/t9P3MrLt5GmKc899xxPPvnk7mNSSp588kmefvrpm3hkd6YLFy6wsrLypuvRaDR45JFH3PV4F3Q6HQDGxsYAeO6558iy7E3n/9ixYywsLLjz/x1WFAW/9Vu/xWAw4LHHHnPn/j30yU9+kh/8wR9807kG9/p/P7ntBjN+p21sbFAUBdPT0296fHp6mtdee+0mHdWda2VlBeAtr8e1zznfGVprPvWpT/Fd3/VdnDx5ErDnPwgCms3mm57rzv93zosvvshjjz1GHMdUq1U+85nPcPz4cZ5//nl37t8Dv/Vbv8XXvvY1nn322b/yOff6f/9wwYvjOG/pk5/8JC+99BJf+tKXbvah3FGOHj3K888/T6fT4Xd/93f5xCc+wVNPPXWzD+uOcPnyZX76p3+az33uc0RRdLMPx/kW3LbRtzExMYFS6q9kk6+urjIzM3OTjurOde2cu+vx7vqpn/op/vAP/5AvfOELzM/P7z4+MzNDmqa02+03Pd+d/++cIAg4fPgwDzzwAJ/+9Ke59957+Xf/7t+5c/8eeO6551hbW+P+++/H8zw8z+Opp57i137t1/A8j+npaXcN3idc8PJtBEHAAw88wOc///ndx7TWfP7zn+exxx67iUd2Zzpw4AAzMzNvuh7dbpcvf/nL7np8Bxhj+Kmf+ik+85nP8Kd/+qccOHDgTZ9/4IEH8H3/Tef/zJkzLC4uuvP/LtFakySJO/fvgSeeeIIXX3yR559/fvftwQcf5O/+3b+7+9/uGrw/uG2jG/CzP/uzfOITn+DBBx/k4Ycf5ld/9VcZDAb8w3/4D2/2od2W+v0+Z8+e3f34woULPP/884yNjbGwsMCnPvUpfumXfokjR45w4MABfv7nf565uTk+/vGP37yDvk188pOf5Dd/8zf5vd/7PWq12u4+fqPRoFQq0Wg0+Imf+Al+9md/lrGxMer1Ov/0n/5THnvsMR599NGbfPS3vp/7uZ/j+7//+1lYWKDX6/Gbv/mb/Nmf/Rn/83/+T3fu3wO1Wm03v+uaSqXC+Pj47uPuGrxP3Oxyp1vFv//3/94sLCyYIAjMww8/bJ555pmbfUi3rS984QsG+Ctvn/jEJ4wxtlz653/+58309LQJw9A88cQT5syZMzf3oG8Tb3XeAfOf//N/3n3OaDQyP/mTP2larZYpl8vmR3/0R83y8vLNO+jbyD/6R//I7Nu3zwRBYCYnJ80TTzxh/tf/+l+7n3fn/r33zaXSxrhr8H4hjDHmJsVNjuM4juM4b5vLeXEcx3Ec55bighfHcRzHcW4pLnhxHMdxHOeW4oIXx3Ecx3FuKS54cRzHcRznluKCF8dxHMdxbikueHEcx3Ec55bighfHcRzHcW4pLnhxHMdxHOeW4oIXx3Ecx3FuKS54cRzHcRznluKCF8dxHMdxbin/f9TcDeMp0k6cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,arr = plt.subplots(1)\n",
    "for i,row in enumerate(umap_df['waveform'].tolist()):\n",
    "    arr.plot(umap_df['waveform'].tolist()[i],c=umap_df['cluster_color'].tolist()[i],alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40018130-22c1-4e04-bfea-c47e44c1adc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 450x700 with 101 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wavemap_paper.helper_functions import plot_inverse_mapping\n",
    "plot_inverse_mapping(reducer,umap_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9c51539c-1087-4c92-a4b0-955176701a3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 135 candidates, totalling 675 fits\n",
      "[CV 5/5; 1/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 1/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 5/5; 2/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 2/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.954 total time=   0.4s\n",
      "[CV 2/5; 4/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 4/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.920 total time=   1.0s\n",
      "[CV 5/5; 5/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 5/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.966 total time=   1.7s\n",
      "[CV 5/5; 9/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 5/5; 9/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   1.1s\n",
      "[CV 3/5; 11/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 11/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.897 total time=   0.2s\n",
      "[CV 1/5; 12/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 12/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.886 total time=   0.4s\n",
      "[CV 1/5; 13/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 13/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.875 total time=   0.6s\n",
      "[CV 3/5; 14/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 14/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.931 total time=   0.8s\n",
      "[CV 1/5; 16/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 16/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 4/5; 16/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 16/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.885 total time=   0.3s\n",
      "[CV 2/5; 17/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 17/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.932 total time=   0.6s\n",
      "[CV 4/5; 18/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 18/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.931 total time=   0.9s\n",
      "[CV 2/5; 20/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 20/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.920 total time=   1.8s\n",
      "[CV 1/5; 23/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 1/5; 23/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.875 total time=   0.8s\n",
      "[CV 4/5; 24/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 4/5; 24/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   1.0s\n",
      "[CV 2/5; 26/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 26/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.886 total time=   0.2s\n",
      "[CV 5/5; 26/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 26/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 3/5; 27/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 27/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.920 total time=   0.4s\n",
      "[CV 3/5; 28/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 28/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.931 total time=   0.7s\n",
      "[CV 1/5; 30/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 30/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.864 total time=   1.2s\n",
      "[CV 4/5; 32/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 32/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.908 total time=   0.6s\n",
      "[CV 1/5; 34/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 34/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.909 total time=   1.3s\n",
      "[CV 4/5; 35/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 35/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.943 total time=   1.8s\n",
      "[CV 3/5; 39/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 3/5; 39/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   1.1s\n",
      "[CV 1/5; 41/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 41/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.875 total time=   0.2s\n",
      "[CV 2/5; 41/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 41/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.886 total time=   0.2s\n",
      "[CV 5/5; 41/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 41/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 3/5; 42/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 42/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.908 total time=   0.5s\n",
      "[CV 3/5; 43/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 43/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.931 total time=   0.7s\n",
      "[CV 1/5; 45/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 45/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.864 total time=   1.3s\n",
      "[CV 4/5; 47/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 47/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 5/5; 48/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 48/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.954 total time=   0.5s\n",
      "[CV 3/5; 50/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 50/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.954 total time=   1.1s\n",
      "[CV 2/5; 53/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 2/5; 53/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.909 total time=   0.4s[CV 1/5; 1/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 1/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 4/5; 2/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 2/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.920 total time=   0.4s\n",
      "[CV 3/5; 4/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 4/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.943 total time=   1.1s\n",
      "[CV 1/5; 6/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 1/5; 6/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 2/5; 6/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 2/5; 6/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.864 total time=   0.2s\n",
      "[CV 5/5; 6/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 5/5; 6/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.897 total time=   0.2s\n",
      "[CV 3/5; 7/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 3/5; 7/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.920 total time=   0.4s\n",
      "[CV 2/5; 8/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 2/5; 8/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.909 total time=   0.7s\n",
      "[CV 4/5; 9/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 4/5; 9/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   1.0s\n",
      "[CV 2/5; 11/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 11/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 5/5; 11/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 11/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.931 total time=   0.2s\n",
      "[CV 3/5; 12/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 12/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.897 total time=   0.4s\n",
      "[CV 3/5; 13/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 13/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.920 total time=   0.6s\n",
      "[CV 1/5; 15/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 15/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.864 total time=   1.6s\n",
      "[CV 5/5; 17/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 17/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 3/5; 19/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 19/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.943 total time=   1.3s\n",
      "[CV 1/5; 21/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 1/5; 21/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.886 total time=   0.3s\n",
      "[CV 2/5; 21/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 2/5; 21/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.886 total time=   0.2s\n",
      "[CV 4/5; 21/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 4/5; 21/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.897 total time=   0.2s\n",
      "[CV 2/5; 22/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 2/5; 22/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.909 total time=   0.5s\n",
      "[CV 2/5; 23/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 2/5; 23/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.909 total time=   0.8s\n",
      "[CV 5/5; 24/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 5/5; 24/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   1.0s\n",
      "[CV 3/5; 26/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 26/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 1/5; 27/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 27/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.898 total time=   0.4s\n",
      "[CV 1/5; 28/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 28/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.875 total time=   0.6s\n",
      "[CV 4/5; 29/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 29/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.9s\n",
      "[CV 2/5; 31/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 31/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 5/5; 31/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 31/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.897 total time=   0.2s\n",
      "[CV 3/5; 32/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 32/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.943 total time=   0.6s\n",
      "[CV 5/5; 33/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 33/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.954 total time=   0.9s\n",
      "[CV 3/5; 35/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 35/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.943 total time=   1.8s\n",
      "[CV 3/5; 38/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 3/5; 38/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.920 total time=   0.8s\n",
      "[CV 1/5; 40/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 1/5; 40/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.864 total time=   1.5s\n",
      "[CV 4/5; 42/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 42/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.908 total time=   0.5s\n",
      "[CV 4/5; 43/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 43/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.920 total time=   0.7s[CV 3/5; 2/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 2/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.897 total time=   0.4s\n",
      "[CV 1/5; 4/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 4/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.909 total time=   1.1s\n",
      "[CV 4/5; 5/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 5/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.931 total time=   1.7s\n",
      "[CV 2/5; 9/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 2/5; 9/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.898 total time=   1.0s\n",
      "[CV 5/5; 10/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 5/5; 10/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   1.5s\n",
      "[CV 5/5; 14/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 14/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.8s\n",
      "[CV 3/5; 16/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 16/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 1/5; 17/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 17/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.898 total time=   0.8s\n",
      "[CV 2/5; 18/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 18/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.920 total time=   0.9s\n",
      "[CV 5/5; 19/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 19/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.954 total time=   1.3s\n",
      "[CV 5/5; 21/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 5/5; 21/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 3/5; 22/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 3/5; 22/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.920 total time=   0.5s\n",
      "[CV 3/5; 23/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 3/5; 23/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.931 total time=   0.8s\n",
      "[CV 1/5; 25/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 1/5; 25/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.875 total time=   1.4s\n",
      "[CV 5/5; 27/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 27/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.931 total time=   0.4s\n",
      "[CV 1/5; 29/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 29/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.875 total time=   0.9s\n",
      "[CV 4/5; 30/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 30/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.954 total time=   1.3s\n",
      "[CV 3/5; 33/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 33/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.931 total time=   1.0s\n",
      "[CV 2/5; 35/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 35/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.920 total time=   1.7s\n",
      "[CV 5/5; 37/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 5/5; 37/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 2/5; 39/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 2/5; 39/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.909 total time=   1.1s\n",
      "[CV 5/5; 40/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 5/5; 40/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   1.6s\n",
      "[CV 4/5; 44/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 44/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.9s\n",
      "[CV 1/5; 46/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 46/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 4/5; 46/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 46/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 2/5; 47/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 47/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.909 total time=   0.3s\n",
      "[CV 1/5; 48/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 48/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.920 total time=   0.5s\n",
      "[CV 3/5; 49/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 49/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.954 total time=   0.7s\n",
      "[CV 1/5; 51/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 1/5; 51/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 4/5; 51/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 4/5; 51/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 2/5; 52/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 2/5; 52/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.898 total time=   0.3s\n",
      "[CV 3/5; 53/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 3/5; 53/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.954 total time=   0.4s\n",
      "[CV 1/5; 55/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 1/5; 55/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.886 total time=   0.9s\n",
      "[CV 4/5; 57/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 57/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 5/5; 58/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 58/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.4s[CV 1/5; 2/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 2/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.898 total time=   0.4s\n",
      "[CV 4/5; 3/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 3/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 3/5; 5/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 5/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.943 total time=   1.7s\n",
      "[CV 4/5; 8/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 4/5; 8/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 2/5; 10/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 2/5; 10/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.920 total time=   1.4s\n",
      "[CV 4/5; 12/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 12/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.908 total time=   0.4s\n",
      "[CV 5/5; 13/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 13/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.954 total time=   0.6s\n",
      "[CV 3/5; 15/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 15/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.931 total time=   1.5s\n",
      "[CV 1/5; 18/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 18/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.898 total time=   0.9s\n",
      "[CV 4/5; 19/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 19/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.943 total time=   1.3s\n",
      "[CV 3/5; 21/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 3/5; 21/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 1/5; 22/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 1/5; 22/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.875 total time=   0.5s\n",
      "[CV 4/5; 22/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 4/5; 22/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.908 total time=   0.5s\n",
      "[CV 1/5; 24/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 1/5; 24/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.875 total time=   1.0s\n",
      "[CV 4/5; 25/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 4/5; 25/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   1.4s\n",
      "[CV 2/5; 29/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 29/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.909 total time=   0.9s\n",
      "[CV 5/5; 30/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 30/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   1.4s\n",
      "[CV 3/5; 34/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 34/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.931 total time=   1.3s\n",
      "[CV 1/5; 36/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 1/5; 36/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.886 total time=   0.2s\n",
      "[CV 2/5; 36/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 2/5; 36/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.886 total time=   0.2s\n",
      "[CV 4/5; 36/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 4/5; 36/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 2/5; 37/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 2/5; 37/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.909 total time=   0.5s\n",
      "[CV 2/5; 38/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 2/5; 38/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.909 total time=   0.8s\n",
      "[CV 5/5; 39/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 5/5; 39/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   1.0s\n",
      "[CV 3/5; 41/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 41/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 1/5; 42/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 42/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.886 total time=   0.5s\n",
      "[CV 1/5; 43/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 43/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.875 total time=   0.7s\n",
      "[CV 2/5; 44/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 44/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.909 total time=   0.9s\n",
      "[CV 5/5; 45/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 45/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   1.2s\n",
      "[CV 5/5; 49/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 49/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.954 total time=   0.7s\n",
      "[CV 3/5; 51/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 3/5; 51/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.931 total time=   0.2s\n",
      "[CV 1/5; 52/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 1/5; 52/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.886 total time=   0.3s\n",
      "[CV 1/5; 53/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 1/5; 53/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.875 total time=   0.4s\n",
      "[CV 3/5; 54/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 3/5; 54/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.954 total time=   0.7s\n",
      "[CV 3/5; 56/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 56/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.920 total time=   0.1s[CV 2/5; 1/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 1/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 2/5; 3/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 3/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.920 total time=   0.7s\n",
      "[CV 5/5; 4/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 4/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.954 total time=   1.1s\n",
      "[CV 4/5; 6/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 4/5; 6/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.885 total time=   0.2s\n",
      "[CV 2/5; 7/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 2/5; 7/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.886 total time=   0.4s\n",
      "[CV 1/5; 8/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 1/5; 8/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.875 total time=   0.7s\n",
      "[CV 3/5; 9/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 3/5; 9/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   1.1s\n",
      "[CV 1/5; 11/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 11/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.886 total time=   0.2s\n",
      "[CV 4/5; 11/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 11/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.874 total time=   0.2s\n",
      "[CV 2/5; 12/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 12/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.886 total time=   0.4s\n",
      "[CV 2/5; 13/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 13/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.909 total time=   0.6s\n",
      "[CV 4/5; 14/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 14/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.954 total time=   0.8s\n",
      "[CV 2/5; 16/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 16/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 5/5; 16/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 16/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.885 total time=   0.5s\n",
      "[CV 3/5; 17/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 17/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.931 total time=   0.6s\n",
      "[CV 5/5; 18/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 18/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.943 total time=   0.9s\n",
      "[CV 3/5; 20/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 20/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.943 total time=   1.8s\n",
      "[CV 4/5; 23/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 4/5; 23/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.931 total time=   0.8s\n",
      "[CV 2/5; 25/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 2/5; 25/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.920 total time=   1.4s\n",
      "[CV 4/5; 27/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 27/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.908 total time=   0.4s\n",
      "[CV 5/5; 28/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 28/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 3/5; 30/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 30/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.931 total time=   1.3s\n",
      "[CV 1/5; 33/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 33/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.909 total time=   0.9s\n",
      "[CV 4/5; 34/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 34/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.931 total time=   1.3s\n",
      "[CV 3/5; 36/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 3/5; 36/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 1/5; 37/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 1/5; 37/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.875 total time=   0.5s\n",
      "[CV 1/5; 38/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 1/5; 38/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.886 total time=   0.7s\n",
      "[CV 4/5; 39/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 4/5; 39/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.931 total time=   1.1s\n",
      "[CV 4/5; 41/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 41/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.862 total time=   0.2s\n",
      "[CV 2/5; 42/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 42/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.909 total time=   0.5s\n",
      "[CV 2/5; 43/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 43/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.909 total time=   0.7s\n",
      "[CV 5/5; 44/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 44/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.9s\n",
      "[CV 3/5; 46/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 46/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 1/5; 47/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 47/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.920 total time=   0.3s\n",
      "[CV 5/5; 47/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 47/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.954 total time=   0.3s[CV 4/5; 1/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 1/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 3/5; 3/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 3/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.931 total time=   0.7s\n",
      "[CV 1/5; 5/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 5/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.909 total time=   1.6s\n",
      "[CV 4/5; 7/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 4/5; 7/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.908 total time=   0.5s\n",
      "[CV 5/5; 8/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 5/5; 8/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 3/5; 10/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 3/5; 10/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.954 total time=   1.5s\n",
      "[CV 4/5; 13/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 13/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.954 total time=   0.6s\n",
      "[CV 2/5; 15/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 15/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.909 total time=   1.5s\n",
      "[CV 4/5; 17/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 17/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.908 total time=   0.5s\n",
      "[CV 1/5; 19/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 19/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.920 total time=   1.2s\n",
      "[CV 4/5; 20/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 20/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.943 total time=   1.9s\n",
      "[CV 2/5; 24/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 2/5; 24/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.909 total time=   1.0s\n",
      "[CV 5/5; 25/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 5/5; 25/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   1.4s\n",
      "[CV 3/5; 29/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 29/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.920 total time=   0.9s\n",
      "[CV 1/5; 31/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 31/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.886 total time=   0.2s\n",
      "[CV 4/5; 31/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 31/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.897 total time=   0.2s\n",
      "[CV 2/5; 32/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 32/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.909 total time=   0.6s\n",
      "[CV 4/5; 33/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 33/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.931 total time=   0.9s\n",
      "[CV 1/5; 35/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 35/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.909 total time=   1.8s\n",
      "[CV 4/5; 37/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 4/5; 37/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.920 total time=   0.5s\n",
      "[CV 5/5; 38/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 5/5; 38/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.8s\n",
      "[CV 3/5; 40/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 3/5; 40/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.954 total time=   1.6s\n",
      "[CV 5/5; 43/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 43/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 3/5; 45/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 45/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   1.3s\n",
      "[CV 3/5; 48/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 48/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.954 total time=   0.5s\n",
      "[CV 1/5; 50/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 50/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.909 total time=   1.0s\n",
      "[CV 4/5; 52/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 4/5; 52/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 5/5; 53/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 5/5; 53/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 3/5; 55/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 3/5; 55/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.966 total time=   1.0s\n",
      "[CV 2/5; 58/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 58/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.909 total time=   0.3s\n",
      "[CV 5/5; 59/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 59/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.931 total time=   0.5s\n",
      "[CV 3/5; 61/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 61/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.931 total time=   0.2s\n",
      "[CV 1/5; 62/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 62/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.898 total time=   0.4s\n",
      "[CV 2/5; 63/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 63/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.920 total time=   0.5s\n",
      "[CV 5/5; 64/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 64/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.954 total time=   0.7s[CV 2/5; 2/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 2/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.909 total time=   0.4s\n",
      "[CV 5/5; 3/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 3/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.966 total time=   0.7s\n",
      "[CV 2/5; 5/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 5/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.932 total time=   1.6s\n",
      "[CV 3/5; 8/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 3/5; 8/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.966 total time=   0.7s\n",
      "[CV 1/5; 10/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 1/5; 10/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.875 total time=   1.5s\n",
      "[CV 5/5; 12/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 12/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.954 total time=   0.4s\n",
      "[CV 1/5; 14/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 14/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.864 total time=   0.8s\n",
      "[CV 4/5; 15/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 15/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.954 total time=   1.5s\n",
      "[CV 3/5; 18/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 18/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.931 total time=   0.9s\n",
      "[CV 1/5; 20/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 20/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.920 total time=   1.8s\n",
      "[CV 5/5; 22/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 5/5; 22/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 5/5; 23/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 5/5; 23/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.8s\n",
      "[CV 3/5; 25/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 3/5; 25/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.954 total time=   1.4s\n",
      "[CV 4/5; 28/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 28/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.920 total time=   0.7s\n",
      "[CV 2/5; 30/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 30/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.898 total time=   1.2s\n",
      "[CV 5/5; 32/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 32/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.966 total time=   0.5s\n",
      "[CV 2/5; 34/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 34/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.920 total time=   1.3s\n",
      "[CV 5/5; 35/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 35/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.954 total time=   1.7s\n",
      "[CV 1/5; 39/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 1/5; 39/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.875 total time=   1.1s\n",
      "[CV 4/5; 40/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 4/5; 40/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   1.6s\n",
      "[CV 3/5; 44/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 44/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   1.0s\n",
      "[CV 2/5; 46/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 46/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 5/5; 46/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 46/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 3/5; 47/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 47/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.954 total time=   0.3s\n",
      "[CV 2/5; 48/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 48/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.920 total time=   0.5s\n",
      "[CV 4/5; 49/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 49/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.931 total time=   0.7s\n",
      "[CV 2/5; 51/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 2/5; 51/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 5/5; 51/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 5/5; 51/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 3/5; 52/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 3/5; 52/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 4/5; 53/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 4/5; 53/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 2/5; 55/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 2/5; 55/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.920 total time=   0.9s\n",
      "[CV 1/5; 58/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 58/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.875 total time=   0.4s\n",
      "[CV 3/5; 59/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 59/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 1/5; 61/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 61/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.886 total time=   0.2s\n",
      "[CV 4/5; 61/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 61/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.920 total time=   0.2s[CV 3/5; 1/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 1/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 1/5; 3/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 3/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.909 total time=   0.7s\n",
      "[CV 4/5; 4/135] START learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 4/135] END learning_rate=0.1, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.931 total time=   1.1s\n",
      "[CV 3/5; 6/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 3/5; 6/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 1/5; 7/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 1/5; 7/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.886 total time=   0.4s\n",
      "[CV 5/5; 7/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 5/5; 7/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 1/5; 9/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 1/5; 9/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.875 total time=   1.0s\n",
      "[CV 4/5; 10/135] START learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 4/5; 10/135] END learning_rate=0.1, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   1.5s\n",
      "[CV 2/5; 14/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 14/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.909 total time=   0.8s\n",
      "[CV 5/5; 15/135] START learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 15/135] END learning_rate=0.1, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   1.5s\n",
      "[CV 2/5; 19/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 19/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.920 total time=   1.2s\n",
      "[CV 5/5; 20/135] START learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 20/135] END learning_rate=0.1, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.954 total time=   2.0s\n",
      "[CV 3/5; 24/135] START learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 3/5; 24/135] END learning_rate=0.1, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.954 total time=   1.1s\n",
      "[CV 1/5; 26/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 26/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.875 total time=   0.2s\n",
      "[CV 4/5; 26/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 26/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.862 total time=   0.2s\n",
      "[CV 2/5; 27/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 27/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.920 total time=   0.4s\n",
      "[CV 2/5; 28/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 28/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.909 total time=   0.6s\n",
      "[CV 5/5; 29/135] START learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 29/135] END learning_rate=0.1, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.9s\n",
      "[CV 3/5; 31/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 31/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 1/5; 32/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 32/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.898 total time=   0.6s\n",
      "[CV 2/5; 33/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 33/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.920 total time=   0.9s\n",
      "[CV 5/5; 34/135] START learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 34/135] END learning_rate=0.1, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.954 total time=   1.3s\n",
      "[CV 5/5; 36/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 5/5; 36/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.931 total time=   0.2s\n",
      "[CV 3/5; 37/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 3/5; 37/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.920 total time=   0.5s\n",
      "[CV 4/5; 38/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 4/5; 38/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.8s\n",
      "[CV 2/5; 40/135] START learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 2/5; 40/135] END learning_rate=0.1, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.920 total time=   1.5s\n",
      "[CV 5/5; 42/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 42/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.931 total time=   0.5s\n",
      "[CV 1/5; 44/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 44/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.875 total time=   0.9s\n",
      "[CV 4/5; 45/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 45/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.954 total time=   1.3s\n",
      "[CV 2/5; 49/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 49/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.920 total time=   0.7s\n",
      "[CV 5/5; 50/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 50/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.954 total time=   1.1s\n",
      "[CV 4/5; 54/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 4/5; 54/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   0.6s\n",
      "[CV 1/5; 56/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 56/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.864 total time=   0.1s\n",
      "[CV 4/5; 56/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 56/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.897 total time=   0.2s\n",
      "[CV 2/5; 57/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 57/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.898 total time=   0.3s"
     ]
    }
   ],
   "source": [
    "from wavemap_paper.helper_functions import train_gridsearch_classifier\n",
    "\n",
    "UMAP_model, conf_mat = train_gridsearch_classifier(umap_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a19bc578-e924-42a7-8001-fa9beabb02b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wavemap_paper.helper_functions import plot_confusion_matrix\n",
    "plot_confusion_matrix(conf_mat,umap_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "24f47f5d-3065-4b95-b65a-35d5aa4c528d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pass `objective` as keyword args.\n"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "import xgboost as xgb\n",
    "\n",
    "xgbModel = xgb.XGBClassifier(UMAP_model.best_params_)\n",
    "xgbModel.fit(umap_df['waveform'].tolist(),umap_df['cluster_id'].tolist())\n",
    "explainer = shap.TreeExplainer(xgbModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea03a5fd-7e58-48c0-ad10-0d98327fc29e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ntree_limit is deprecated, use `iteration_range` or model slicing instead.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAOsCAYAAADX7yC0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAD1QklEQVR4nOz9fVxV553v/7834W4XLcqNgXIjRJvxJsYmsxkFEuqp+DDurTMlZbYG23EQQxg9tsPgcZwbPNY2cWobVBhQ0MJkgmDQ3wPFqDmODceTSkwAp4qJJI5jM1FhVBIQFTSQ/fsjX3bd2WCABWKS1/Px2I/odV3rWp+1889+e61rLZPD4XAIAAAAAAzwGOkCAAAAAHz5ESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsvoKKior0ySefjHQZAAAA+BohWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCTw+FwjHQRGFqPBT7Rr3HjsqqH5HynlxwZknkGYmPIC/f8nAAAAANhf73b0PHrrG9Lkp7/pGsoyhl2rFgAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADPMcyOC6ujplZGT02V9SUqJp06YZLqovZWVlGj16tBYsWDBs5xgKjY2Neu2111RbW6tLly5JkiIiIrRgwQIlJSXJ09P1az9+/Lhef/11NTY26j/+4z90+/Ztbdu2TRaLZSTKBwAAAAZsQMGix9y5cxUfH+/WHhERYbiguykvL1doaOh9Hyxeeuklvf3225o1a5aSkpLU3d2t3/72t/rFL36ho0ePKi8vTyaTyTn+tdde02uvvaYJEyYoKipK77///ghWDwAAAAzcoILFpEmTZLVah7qWEdXV1aXu7m75+PgYnmvhwoVat26dy1wLFy5Udna2Dh06pN/+9rd68sknnX3Lly/X3//938vb21svv/wywQIAAABfOsO2x+Lw4cNKS0tTQkKC4uPjtWTJEh05cqTXcZmZmbLZbIqNjdXs2bOVlZWls2fPuoyzWCxqamrSiRMnZLFYnJ+eW40sFovWrVvnNv/+/ftlsVhUV1fnbCssLJTFYtG5c+eUk5Mjq9WquLg4NTQ0SJJu376t4uJi2e12xcXFadasWcrMzFRjY2O/rv073/lOrwFlzpw5kqRz5865tI8bN07e3t79mhsAAAC4Hw1qxaKzs1Otra0ubV5eXvLz85MkFRQUqLi4WHFxccrIyJCHh4eqq6u1Zs0arV69Wna73XlcRUWF/P39lZSUpKCgIF24cEGVlZVKS0tTaWmpIiMjJUnr169XTk6OxowZo6VLlzqPHzt27GAuQZKUnZ0tHx8fLV68WCaTSUFBQerq6tLKlSt16tQpWa1W2e12Xb9+3VnT9u3bNWXKlEGd7/Lly5KkgICAQdcMAAAA3I8GFSwKCwtVWFjo0jZnzhxt2LBBjY2NKi4uVmpqqlasWOHsX7RokbKyspSfny+bzeYMIXl5eTKbzS5z2Ww2paSkqKysTGvWrJEkWa1Wbd26VQEBAUN2G9aoUaNUUFDgspl6586dqq+vV15enmJjY53tycnJWrhwoTZv3qyioqIBn+vmzZt6+eWXNWrUKH33u98dkvoBAACA+8WggkVSUpISExNd2gIDAyVJhw4dkslkks1mc1vVSEhI0NGjR9XQ0KCZM2dKkjNUOBwO3bhxQ11dXRo7dqzGjx+v06dPD6a8fktJSXF7QtOhQ4cUFRWlyZMnu9U/Y8YMHThwQJ2dnfL19e33ebq7u5Wdna2LFy/q5z//ufz9/YeifAAAAOC+MahgERkZqRkzZvTad/78eTkcDiUnJ/d5fEtLi/PPjY2N2rZtm+rr69XR0eEyLiwsbDDl9VvPbVZ3On/+vG7duuUWnO7U2tqqkJCQfp3j008/1fr163X06FEtX75cTz311KDrBQAAAO5XgwoWX8RkMik3N1ceHr3vDZ8wYYIkqbm5Wenp6fLz81NaWpqioqLk6+srk8mkF1980S1oDEZ3d3effX2tOkycOFGZmZl9HtfffR2ffvqpfvazn+nAgQN69tlnXfaGAAAAAF8lQx4sIiIiVFNTo5CQEEVHR991bHV1tW7evKmcnBy3l8G1tbW5PSnpznc/fJ6/v7/a2trc2i9evDiA6j+r/+OPP1ZMTEyfwag/ekLF/v37lZaWpueee27QcwEAAAD3uyF/3GzPxur8/PxeVwvuvA2q54e7w+FwGVNZWekyrofZbNa1a9d6PW9kZKQaGhrU2dnpbLt27ZqqqqoGVL/NZlNLS4t27tzZa39vdX2ew+HQz3/+c+3fv1+pqan6q7/6qwHVAAAAAHzZDPmKxdSpU5Wenq6ioiKlpKQoMTFRwcHBunr1qs6cOaNjx47p+PHjkqT4+Hjl5eVp7dq1stvtGj16tE6ePKmamhqFh4e7BZNp06Zp37592rp1q6Kjo2UymZSQkCCz2Sy73a7s7GxlZGTIarWqvb1de/fuVWhoaL/CQI9nnnlGb731lrZs2aLa2lrFxMTIz89Pzc3Nqq2tlbe3t9sTsT5vy5Ytqqqq0sMPP6zo6GgdPHjQpT88PFyPPvqo8+9nz57V0aNHJUmnTp2SJB08eFC/+93vJH32RK1Ro0b1+xoAAACAe21Y9likp6drypQp2rVrl8rLy9XR0aGAgABNmDBBq1atco4LDw9Xbm6u8vPzVVJSIg8PD02fPl2FhYXauHGjmpqaXOZdvny52tratHv3brW3t8vhcKiqqkpms1nz5s3TlStXVFFRoU2bNiksLEzLli2Th4fHgJ4u5enpqc2bN2vPnj06ePCgM0QEBwdr6tSpmj9//hfO8e6770qS3n//fa1du9atf/78+S7BomcD+53uXGmxWq0ECwAAANzXTI7P34eEL73HAp/o17hxWdVDcr7TS9zfqD7cNoa8cM/PCQAAMBD21/t+iFB/rLO+LUl6/pOuoShn2A35HgsAAAAAXz8ECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIbxuNmvoKKiIqWmpsrLy2ukSwEAAMDXBCsWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwk8PhcIx0ERhajwU+MdIlAOiHcVnVI10CvgZOLzky0iWMmN+/+7ORLgHo1eVfnnRrCzt8YwQqGVqsWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAM8xzI4Lq6OmVkZPTZX1JSomnTphkuqi9lZWUaPXq0FixYMGznGAo3b95UaWmpzpw5o/fee0+XL1/W448/rqKioj6P6erq0p49e7R//3598MEHeuCBBxQeHq6nn35aP/jBD+5h9QAAAMDADShY9Jg7d67i4+Pd2iMiIgwXdDfl5eUKDQ2974NFa2urioqKFBgYqEmTJqmlpeWu4z/55BP9zd/8jerq6vTUU0/pBz/4gbq7u/Vf//Vfam5uvkdVAwAAAIM3qGAxadIkWa3Woa5lRHV1dam7u1s+Pj6G5woKCtKBAwf04IMPSpKefPLJu47fsWOH3n77beXn58tisRg+PwAAAHCvDdsei8OHDystLU0JCQmKj4/XkiVLdOTIkV7HZWZmymazKTY2VrNnz1ZWVpbOnj3rMs5isaipqUknTpyQxWJxfi5duuTsX7dundv8+/fvl8ViUV1dnbOtsLBQFotF586dU05OjqxWq+Li4tTQ0CBJun37toqLi2W32xUXF6dZs2YpMzNTjY2N/bp2b29vZ6j4Ih0dHdq1a5cSEhJksVjkcDh048aNfh0LAAAA3C8GtWLR2dmp1tZWlzYvLy/5+flJkgoKClRcXKy4uDhlZGTIw8ND1dXVWrNmjVavXi273e48rqKiQv7+/kpKSlJQUJAuXLigyspKpaWlqbS0VJGRkZKk9evXKycnR2PGjNHSpUudx48dO3YwlyBJys7Olo+PjxYvXiyTyaSgoCB1dXVp5cqVOnXqlKxWq+x2u65fv+6safv27ZoyZcqgz/l5//7v/64bN25o8uTJ+tWvfqWqqirdvHlTY8aMUVJSkp577jl5eg7qfxMAAABwzwzqF2thYaEKCwtd2ubMmaMNGzaosbFRxcXFSk1N1YoVK5z9ixYtUlZWlvLz82Wz2ZwhJC8vT2az2WUum82mlJQUlZWVac2aNZIkq9WqrVu3KiAgYMhuwxo1apQKCgpcfrjv3LlT9fX1ysvLU2xsrLM9OTlZCxcu1ObNm++6CXugPvjgA0mf7R/x8vLSj3/8Y/n7++vQoUMqKSnR5cuX9dOf/nTIzgcAAAAMh0EFi6SkJCUmJrq0BQYGSpIOHTokk8kkm83mtqqRkJCgo0ePqqGhQTNnzpQkZ6jouQWoq6tLY8eO1fjx43X69OnBlNdvKSkpbqsBhw4dUlRUlCZPnuxW/4wZM3TgwAF1dnbK19d3SGroue3p2rVreuWVVxQVFSXps6D23HPP6cCBA/rLv/xLRUdHD8n5AAAAgOEwqGARGRmpGTNm9Np3/vx5ORwOJScn93n8nU9Jamxs1LZt21RfX6+Ojg6XcWFhYYMpr996brO60/nz53Xr1i234HSn1tZWhYSEDEkNPQHlkUcecYaKHjabTfX19aqvrydYAAAA4L42LDfvm0wm5ebmysOj973hEyZMkCQ1NzcrPT1dfn5+SktLU1RUlHx9fWUymfTiiy+6BY3B6O7u7rOvr1WHiRMnKjMzs8/jjOzr+Lxx48ZJ+sOKz52CgoIkfbaaAQAAANzPhjxYREREqKamRiEhIV/4r+zV1dW6efOmcnJy3B6z2tbWJm9vb5c2k8nU51z+/v5qa2tza7948eIAqv+s/o8//lgxMTF9BqOhNHXqVEnS5cuX3fp62gICAoa9DgAAAMCIIf/l3LOxOj8/v9fVgjtvg+r54e5wOFzGVFZW9vpSObPZ3Oe/3kdGRqqhoUGdnZ3OtmvXrqmqqmpA9dtsNrW0tGjnzp299n/Ry+4GKiwsTNOnT9c777zj8jjb7u5uVVZW6oEHHnDuRwEAAADuV0O+YjF16lSlp6erqKhIKSkpSkxMVHBwsK5evaozZ87o2LFjOn78uCQpPj5eeXl5Wrt2rex2u0aPHq2TJ0+qpqZG4eHhbsFk2rRp2rdvn7Zu3aro6GiZTCYlJCTIbDbLbrcrOztbGRkZslqtam9v1969exUaGjqgMPDMM8/orbfe0pYtW1RbW6uYmBj5+fmpublZtbW18vb2dnsiVm9eeeUVtbe3S/rs5XvNzc3asWOHJOnhhx9WQkKCc+z/+l//S88++6yWL1+uhQsXyt/fX//2b/+md955R88+++yQ7ecAAAAAhsuw7LFIT0/XlClTtGvXLpWXl6ujo0MBAQGaMGGCVq1a5RwXHh6u3Nxc5efnq6SkRB4eHpo+fboKCwu1ceNGNTU1ucy7fPlytbW1affu3Wpvb5fD4VBVVZXMZrPmzZunK1euqKKiQps2bVJYWJiWLVsmDw+PAT1dytPTU5s3b9aePXt08OBBZ4gIDg7W1KlTNX/+/H7NU1pa6lL/pUuXtG3bNknS/PnzXYLFpEmTVFxcrIKCApWXl+v27duKiorS//7f/1sLFizod+0AAADASDE5Pn8fEr70Hgt8YqRLANAP47KqR7oEfA2cXnJkpEsYMb9/92cjXQLQq8u/POnWFnb4xghUMrSGf3cyAAAAgK88ggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADOM9Fl9BRUVFSk1NlZeX10iXAgAAgK8JViwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGGeI10Aht7/72qq/Dy+J3X33j9r3ol7WxAAALgvhB2+MdIl4CuMFQsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQN6QV5dXZ0yMjL67C8pKdG0adMMF9WXsrIyjR49WgsWLBi2cwyFhoYGvfzyy3r//ff10UcfSZJCQkKUmJiolJQUjRo1yu2YY8eOqbS0VP/5n/+pGzduaNy4cUpISNCPfvQjBQYG3utLAAAAAAZkUG/enjt3ruLj493aIyIiDBd0N+Xl5QoNDb3vg8UHH3ygzs5OzZs3T0FBQXI4HHrnnXdUXFys3/zmN3rppZfk6+vrHF9ZWannn39ekydP1l/8xV/IbDbr3XffVXl5uaqrq7Vr1y6ZzeYRvCIAAADg7gYVLCZNmiSr1TrUtYyorq4udXd3y8fHx/Bc8+fP1/z5813akpOTFR0drdzcXL3xxhuaM2eOs+/ll19WUFCQduzY4Tz/008/rYCAABUXF+utt97SrFmzDNcFAAAADJdh22Nx+PBhpaWlKSEhQfHx8VqyZImOHDnS67jMzEzZbDbFxsZq9uzZysrK0tmzZ13GWSwWNTU16cSJE7JYLM7PpUuXnP3r1q1zm3///v2yWCyqq6tzthUWFspisejcuXPKycmR1WpVXFycGhoaJEm3b99WcXGx7Ha74uLiNGvWLGVmZqqxsdHQdxIaGipJunbtmkv7jRs39M1vftMt1AQHB0uSy+oGAAAAcD8a1IpFZ2enWltbXdq8vLzk5+cnSSooKFBxcbHi4uKUkZEhDw8PVVdXa82aNVq9erXsdrvzuIqKCvn7+yspKUlBQUG6cOGCKisrlZaWptLSUkVGRkqS1q9fr5ycHI0ZM0ZLly51Hj927NjBXIIkKTs7Wz4+Plq8eLFMJpOCgoLU1dWllStX6tSpU7JarbLb7bp+/bqzpu3bt2vKlCn9/p56PmfOnFFeXp68vLw0Y8YMl3GxsbF69dVXtWnTJn3/+9933gq1Y8cOPf7444qJiRn0NQIAAAD3wqCCRWFhoQoLC13a5syZow0bNqixsVHFxcVKTU3VihUrnP2LFi1SVlaW8vPzZbPZnCEkLy/Pbf+AzWZTSkqKysrKtGbNGkmS1WrV1q1bFRAQMGS3YY0aNUoFBQXy9PzD17Bz507V19crLy9PsbGxzvbk5GQtXLhQmzdvVlFRUb/m37Ztm0pLS51/f+ihh7Rp0yaFh4e7jFu1apU6Ozu1a9cu7dy509m+YMEC/cM//IMeeOCBwV4iAAAAcE8MKlgkJSUpMTHRpa3nyUWHDh2SyWSSzWZzW9VISEjQ0aNH1dDQoJkzZ0qSM1Q4HA7duHFDXV1dGjt2rMaPH6/Tp08Pprx+S0lJcQkVPfVHRUVp8uTJbvXPmDFDBw4cUGdnZ79uT3r66acVGxur9vZ2NTQ0qL6+3m1OSfL09FRISIhmzZqlJ598Ur6+vjp+/Liqqqr0wAMP6B//8R+NXCYAAAAw7AYVLCIjI91u5+lx/vx5ORwOJScn93l8S0uL88+NjY3atm2b6uvr1dHR4TIuLCxsMOX1W89tVnc6f/68bt265Rac7tTa2qqQkJB+zd9zjsTERL355ptauXKlJOmpp56SJH366adauXKluru79etf/1omk8k53t/fXy+99JLmzJnT5/cNAAAA3A8GFSy+iMlkUm5urjw8et8bPmHCBElSc3Oz0tPT5efnp7S0NEVFRcnX11cmk0kvvviiW9AYjO7u7j77+lp1mDhxojIzM/s8brD7OmJjYxUYGKg9e/Y4g8Xvfvc7/fu//7v++q//2hkqeiQmJuqll17SiRMnCBYAAAC4rw15sIiIiFBNTY1CQkIUHR1917HV1dW6efOmcnJyZLFYXPra2trk7e3t0vb5H9538vf3V1tbm1v7xYsXB1D9Z/V//PHHiomJ6TMYGXHr1i2Xp0JdvnxZUu8BqKftbuEIAAAAuB8M+S/nno3V+fn5vf4gvvM2qJ4f7g6Hw2VMZWWly7geZrPZ7VGtPSIjI9XQ0KDOzk5n27Vr11RVVTWg+m02m1paWlw2UfdVf1+uXr3aa/urr76q69ev65FHHnG2PfTQQ5Kk1157TV1dXS7j9+/fL0n9fgoVAAAAMFKGfMVi6tSpSk9PV1FRkVJSUpSYmKjg4GBdvXpVZ86c0bFjx3T8+HFJUnx8vPLy8rR27VrZ7XaNHj1aJ0+eVE1NjcLDw92CybRp07Rv3z5t3bpV0dHRMplMSkhIkNlslt1uV3Z2tjIyMmS1WtXe3q69e/cqNDS0X2GgxzPPPKO33npLW7ZsUW1trWJiYuTn56fm5mbV1tbK29vb7YlYn/eTn/xE/v7+evTRRxUSEqLr16/rd7/7nY4ePaoHH3xQ6enpzrEPP/ywvve97+n111/Xj370I82bN0++vr5688039cYbb2jatGn67ne/O4D/AwAAAMC9Nyx7LNLT0zVlyhTt2rVL5eXl6ujoUEBAgCZMmKBVq1Y5x4WHhys3N1f5+fkqKSmRh4eHpk+frsLCQm3cuFFNTU0u8y5fvlxtbW3avXu32tvb5XA4VFVVJbPZrHnz5unKlSuqqKjQpk2bFBYWpmXLlsnDw2NAT5fy9PTU5s2btWfPHh08eNAZIoKDgzV16lS3N2r3JikpSa+//rr27t2r1tZWeXp6Kjw8XEuWLNEPf/hDjRkzxmX8888/r7KyMr322msqLCzUp59+qtDQUKWmpmrp0qU8bhYAAAD3PZPj8/ch4Utv7guf6C/+9nt99s+ad+IeVgMAAO4XYYdvjHQJ+Aob+t3JAAAAAL52CBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMG5b3WGBk/SCoRPZPX5eXl1fvAw7f23oAAADw1ceKBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCTw+FwjHQRGFqPBT4x0iUAANAv47KqR7oEnV5y5K79v3/3Z/ekjnXWt/X8J1335FzAcGDFAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGOY5kMF1dXXKyMjos7+kpETTpk0zXFRfysrKNHr0aC1YsGDYzjEUbt68qdLSUp05c0bvvfeeLl++rMcff1xFRUVuY7/oO5WkHTt26Dvf+c4wVQsAAAAYN6Bg0WPu3LmKj493a4+IiDBc0N2Ul5crNDT0vg8Wra2tKioqUmBgoCZNmqSWlpY+x0ZHR2v9+vVu7bdv39YLL7ygMWPG6JFHHhnOcgEAAADDBhUsJk2aJKvVOtS1jKiuri51d3fLx8fH8FxBQUE6cOCAHnzwQUnSk08+2efYwMDAXr/L1157TZ9++qlsNps8PQf1vwkAAAC4Z4Ztj8Xhw4eVlpamhIQExcfHa8mSJTpy5Eiv4zIzM2Wz2RQbG6vZs2crKytLZ8+edRlnsVjU1NSkEydOyGKxOD+XLl1y9q9bt85t/v3798tisaiurs7ZVlhYKIvFonPnziknJ0dWq1VxcXFqaGiQ9NlqQXFxsex2u+Li4jRr1ixlZmaqsbGxX9fu7e3tDBWDtW/fPknSn/3ZnxmaBwAAALgXBvVP4Z2dnWptbXVp8/Lykp+fnySpoKBAxcXFiouLU0ZGhjw8PFRdXa01a9Zo9erVstvtzuMqKirk7++vpKQkBQUF6cKFC6qsrFRaWppKS0sVGRkpSVq/fr1ycnI0ZswYLV261Hn82LFjB3MJkqTs7Gz5+Pho8eLFMplMCgoKUldXl1auXKlTp07JarXKbrfr+vXrzpq2b9+uKVOmDPqc/XHx4kXV1dXpO9/5jqKioob1XAAAAMBQGFSwKCwsVGFhoUvbnDlztGHDBjU2Nqq4uFipqalasWKFs3/RokXKyspSfn6+bDabM4Tk5eXJbDa7zGWz2ZSSkqKysjKtWbNGkmS1WrV161YFBAQM2W1Yo0aNUkFBgcutRjt37lR9fb3y8vIUGxvrbE9OTtbChQu1efPmXjdhD6Wqqio5HA59//vfH9bzAAAAAENlUMEiKSlJiYmJLm2BgYGSpEOHDslkMslms7mtaiQkJOjo0aNqaGjQzJkzJckZKhwOh27cuKGuri6NHTtW48eP1+nTpwdTXr+lpKS47V84dOiQoqKiNHnyZLf6Z8yYoQMHDqizs1O+vr7DUlN3d7deffVV+fn5uX3HAAAAwP1qUMEiMjJSM2bM6LXv/PnzcjgcSk5O7vP4O5+S1NjYqG3btqm+vl4dHR0u48LCwgZTXr/13GZ1p/Pnz+vWrVt3/VHf2tqqkJCQYanpzTff1H//93/r6aefHrbwAgAAAAy1YXnckMlkUm5urjw8et8bPmHCBElSc3Oz0tPT5efnp7S0NEVFRcnX11cmk0kvvviiW9AYjO7u7j77+vrhPnHiRGVmZvZ5nJF9HV+kZ9M2t0EBAADgy2TIg0VERIRqamoUEhKi6Ojou46trq7WzZs3lZOTI4vF4tLX1tYmb29vlzaTydTnXP7+/mpra3Nrv3jx4gCq/6z+jz/+WDExMX0Go+Hy0Ucf6Y033tDDDz887BvEAQAAgKE05L+cezZW5+fn97pacOdtUD0/3B0Oh8uYysrKXl8qZzabde3atV7PGxkZqYaGBnV2djrbrl27pqqqqgHVb7PZ1NLSop07d/baf7eX3Rl14MABdXV16U//9E+H7RwAAADAcBjyFYupU6cqPT1dRUVFSklJUWJiooKDg3X16lWdOXNGx44d0/HjxyVJ8fHxysvL09q1a2W32zV69GidPHlSNTU1Cg8Pdwsm06ZN0759+7R161ZFR0fLZDIpISFBZrNZdrtd2dnZysjIkNVqVXt7u/bu3avQ0NABhYFnnnlGb731lrZs2aLa2lrFxMTIz89Pzc3Nqq2tlbe3t9sTsXrzyiuvqL29XdJnL99rbm7Wjh07JEkPP/ywEhIS3I7Zt2+ffHx8vnIvHwQAAMBX37DssUhPT9eUKVO0a9culZeXq6OjQwEBAZowYYJWrVrlHBceHq7c3Fzl5+erpKREHh4emj59ugoLC7Vx40Y1NTW5zLt8+XK1tbVp9+7dam9vl8PhUFVVlcxms+bNm6crV66ooqJCmzZtUlhYmJYtWyYPD48BPV3K09NTmzdv1p49e3Tw4EFniAgODtbUqVM1f/78fs1TWlrqUv+lS5e0bds2SdL8+fPdgsXJkyf1+9//Xk899ZS++c1v9rteAAAA4H5gcnz+PiR86T0W+MRIlwAAQL+My6oe6RJ0esmRu/b//t2f3ZM61lnf1vOfdN2TcwHD4d7uTgYAAADwlUSwAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhvMfiK6ioqEipqany8vIa6VIAAADwNcGKBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwzORwOBwjXQSG1mOBT4x0CcCXyris6pEu4QudXnJkpEv42tgY8sJIl/CF7K93j3QJkqTLvzzZa3tB9a17XIkxz3/SNdIlAF8JrFgAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADPMcyOC6ujplZGT02V9SUqJp06YZLqovZWVlGj16tBYsWDBs5xgKN2/eVGlpqc6cOaP33ntPly9f1uOPP66ioqI+j/ntb3+r4uJivf/++/L29lZMTIx+/OMfKyws7B5WDgAAAAzOgIJFj7lz5yo+Pt6tPSIiwnBBd1NeXq7Q0ND7Pli0traqqKhIgYGBmjRpklpaWu46/vXXX9ff/u3f6tvf/rZ+8pOf6Pr16yovL1daWppefvllBQcH36PKAQAAgMEZVLCYNGmSrFbrUNcyorq6utTd3S0fHx/DcwUFBenAgQN68MEHJUlPPvnkXc/7y1/+Ug8++KB27Nihb3zjG5KkuLg4/ehHP1JRUZH+4R/+wXBNAAAAwHAatj0Whw8fVlpamhISEhQfH68lS5boyJEjvY7LzMyUzWZTbGysZs+eraysLJ09e9ZlnMViUVNTk06cOCGLxeL8XLp0ydm/bt06t/n3798vi8Wiuro6Z1thYaEsFovOnTunnJwcWa1WxcXFqaGhQZJ0+/ZtFRcXy263Ky4uTrNmzVJmZqYaGxv7de3e3t7OUPFF6uvrdeXKFX3/+993hgpJ+qM/+iP98R//sQ4fPqyurq5+zQUAAACMlEGtWHR2dqq1tdWlzcvLS35+fpKkgoICFRcXKy4uThkZGfLw8FB1dbXWrFmj1atXy263O4+rqKiQv7+/kpKSFBQUpAsXLqiyslJpaWkqLS1VZGSkJGn9+vXKycnRmDFjtHTpUufxY8eOHcwlSJKys7Pl4+OjxYsXy2QyKSgoSF1dXVq5cqVOnTolq9Uqu92u69evO2vavn27pkyZMuhzft67774rSb3uTXnkkUdUW1urDz74QBMmTBiycwIAAABDbVDBorCwUIWFhS5tc+bM0YYNG9TY2Kji4mKlpqZqxYoVzv5FixYpKytL+fn5stlszhCSl5cns9nsMpfNZlNKSorKysq0Zs0aSZLVatXWrVsVEBAwZLdhjRo1SgUFBfL0/MPXsHPnTtXX1ysvL0+xsbHO9uTkZC1cuFCbN2++6ybsgbpy5Yokady4cW59PW1XrlwhWAAAAOC+NqhgkZSUpMTERJe2wMBASdKhQ4dkMplks9ncVjUSEhJ09OhRNTQ0aObMmZLkDBUOh0M3btxQV1eXxo4dq/Hjx+v06dODKa/fUlJSXEJFT/1RUVGaPHmyW/0zZszQgQMH1NnZKV9f3yGpobOzU9JnKz6f5+3t7TIGAAAAuF8NKlhERkZqxowZvfadP39eDodDycnJfR5/51OSGhsbtW3bNtXX16ujo8Nl3HA/arXnNqs7nT9/Xrdu3XILTndqbW1VSEjIkNTQE1A++eQTt77bt2+7jAEAAADuV4MKFl/EZDIpNzdXHh697w3vua2nublZ6enp8vPzU1pamqKiouTr6yuTyaQXX3zRLWgMRnd3d599ff1gnzhxojIzM/s8zsi+js/reZTs5cuXFR0d7dJ3+fJllzEAAADA/WrIg0VERIRqamoUEhLi9kP586qrq3Xz5k3l5OTIYrG49LW1tTlvBephMpn6nMvf319tbW1u7RcvXhxA9Z/V//HHHysmJqbPYDSUejaCNzQ0uK0CnT59Wn5+fho/fvyw1wEAAAAYMeS/nHs2Vufn5/e6WnDnbVA9P9wdDofLmMrKyl5fKmc2m3Xt2rVezxsZGamGhgaX/QjXrl1TVVXVgOq32WxqaWnRzp07e+3/opfdDdQf//EfKygoSHv37tXNmzed7e+//77q6+uVmJjotg8EAAAAuN8M+S/WqVOnKj09XUVFRUpJSVFiYqKCg4N19epVnTlzRseOHdPx48clSfHx8crLy9PatWtlt9s1evRonTx5UjU1NQoPD3cLJtOmTdO+ffu0detWRUdHy2QyKSEhQWazWXa7XdnZ2crIyJDValV7e7v27t2r0NDQAYWBZ555Rm+99Za2bNmi2tpaxcTEyM/PT83NzaqtrZW3t7fbE7F688orr6i9vV3SZy/Ba25u1o4dOyRJDz/8sBISEiRJnp6eWrVqlf7u7/5Oy5YtU1JSkm7cuKGysjKNHTtWzz33XL9rBwAAAEbKsPxTeHp6uqZMmaJdu3apvLxcHR0dCggI0IQJE7Rq1SrnuPDwcOXm5io/P18lJSXy8PDQ9OnTVVhYqI0bN6qpqcll3uXLl6utrU27d+9We3u7HA6HqqqqZDabNW/ePF25ckUVFRXatGmTwsLCtGzZMnl4eAzo6VKenp7avHmz9uzZo4MHDzpDRHBwsKZOnar58+f3a57S0lKX+i9duqRt27ZJkubPn+8MFpKUmJgoHx8f/frXv9bmzZvl7e2tmJgY/fjHP+71MbQAAADA/cbk+Px9SPjSeyzwiZEuAfhSGZdVPdIlfKHTS46MdAlfGxtDXhjpEr6Q/fW+H0xyL13+5cle2wuqb93jSox5/pOukS4B+EoY/t3JAAAAAL7yCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAM43GzX0FFRUVKTU2Vl5fXSJcCAACArwlWLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIaZHA6HY6SLwNB6LPCJAR8zLqt6GCoBcDevxnx3pEvo0+VfnhzpEjDEwg7fGOkSAHzFsWIBAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAM8xzI4Lq6OmVkZPTZX1JSomnTphkuqi9lZWUaPXq0FixYMGznGAo3b95UaWmpzpw5o/fee0+XL1/W448/rqKiol7Hp6en68SJE732/eu//qumTJkynOUCAAAAhg0oWPSYO3eu4uPj3dojIiIMF3Q35eXlCg0Nve+DRWtrq4qKihQYGKhJkyappaXlC48ZM2aM/uZv/satPSwsbDhKBAAAAIbUoILFpEmTZLVah7qWEdXV1aXu7m75+PgYnisoKEgHDhzQgw8+KEl68sknv/AYs9n8lftOAQAA8PUxbHssDh8+rLS0NCUkJCg+Pl5LlizRkSNHeh2XmZkpm82m2NhYzZ49W1lZWTp79qzLOIvFoqamJp04cUIWi8X5uXTpkrN/3bp1bvPv379fFotFdXV1zrbCwkJZLBadO3dOOTk5slqtiouLU0NDgyTp9u3bKi4ult1uV1xcnGbNmqXMzEw1Njb269q9vb2doWIgPv30U12/fl0Oh2PAxwIAAAAjaVArFp2dnWptbXVp8/Lykp+fnySpoKBAxcXFiouLU0ZGhjw8PFRdXa01a9Zo9erVstvtzuMqKirk7++vpKQkBQUF6cKFC6qsrFRaWppKS0sVGRkpSVq/fr1ycnI0ZswYLV261Hn82LFjB3MJkqTs7Gz5+Pho8eLFMplMCgoKUldXl1auXKlTp07JarXKbrfr+vXrzpq2b98+LHseLl++rCeffFK3bt2Sr6+vYmNjtWLFCkVFRQ35uQAAAIChNqhgUVhYqMLCQpe2OXPmaMOGDWpsbFRxcbFSU1O1YsUKZ/+iRYuUlZWl/Px82Ww2ZwjJy8uT2Wx2mctmsyklJUVlZWVas2aNJMlqtWrr1q0KCAgYsluGRo0apYKCAnl6/uFr2Llzp+rr65WXl6fY2Fhne3JyshYuXKjNmzf3uQl7sMLCwjR9+nR9+9vfloeHh9555x1VVFTo7bff1q9//WtNnDhxSM8HAAAADLVBBYukpCQlJia6tAUGBkqSDh06JJPJJJvN5raqkZCQoKNHj6qhoUEzZ86UJGeocDgcunHjhrq6ujR27FiNHz9ep0+fHkx5/ZaSkuISKnrqj4qK0uTJk93qnzFjhg4cOKDOzk75+voOWR3/+3//b5e/JyYmKiEhQc8995xycnJUUFAwZOcCAAAAhsOggkVkZKRmzJjRa9/58+flcDiUnJzc5/F3PiWpsbFR27ZtU319vTo6OlzGDfcTkXpus7rT+fPndevWLbfgdKfW1laFhIQMZ2l67LHH9Nhjj6m+vn7IgwwAAAAw1AYVLL6IyWRSbm6uPDx63xs+YcIESVJzc7PS09Pl5+entLQ0RUVFydfXVyaTSS+++KJb0BiM7u7uPvv6+rE+ceJEZWZm9nmckX0dA/Gtb31L9fX1am9vJ1gAAADgvjbkwSIiIkI1NTUKCQlRdHT0XcdWV1fr5s2bysnJkcVicelra2uTt7e3S5vJZOpzLn9/f7W1tbm1X7x4cQDVf1b/xx9/rJiYmD6D0b3yX//1X3rggQf0zW9+c0TrAAAAAL7IkP9y7tlYnZ+f3+tqwZ23QfX8cP/841UrKyt7famc2WzWtWvXej1vZGSkGhoa1NnZ6Wy7du2aqqqqBlS/zWZTS0uLdu7c2Wt/f152NxDXr1/v9Xv67W9/q5MnT2rGjBlD8m4NAAAAYDgN+YrF1KlTlZ6erqKiIqWkpCgxMVHBwcG6evWqzpw5o2PHjun48eOSpPj4eOXl5Wnt2rWy2+0aPXq0Tp48qZqaGoWHh7v94J42bZr27dunrVu3Kjo6WiaTSQkJCTKbzbLb7crOzlZGRoasVqva29u1d+9ehYaGDigMPPPMM3rrrbe0ZcsW1dbWKiYmRn5+fmpublZtba28vb3dnojVm1deeUXt7e2SPnv5XnNzs3bs2CFJevjhh5WQkCBJqqur06ZNm/Tkk08qLCxMDzzwgN555x0dOnRIY8aMUVZWVr9rBwAAAEbKsOyxSE9P15QpU7Rr1y6Vl5ero6NDAQEBmjBhglatWuUcFx4ertzcXOXn56ukpEQeHh6aPn26CgsLtXHjRjU1NbnMu3z5crW1tWn37t1qb2+Xw+FQVVWVzGaz5s2bpytXrqiiokKbNm1SWFiYli1bJg8PjwE9XcrT01ObN2/Wnj17dPDgQWeICA4O1tSpUzV//vx+zVNaWupS/6VLl7Rt2zZJ0vz5853BYvz48Zo8ebLeeOMNffTRR+rq6tK4ceP0gx/8QKmpqRo3bly/awcAAABGisnBa56/ch4LfGLAx4zLqh6GSgDczasx3x3pEvp0+ZcnR7oEDLGwwzdGugQAX3EjuzsZAAAAwFcCwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhvEei6+goqIipaamysvLa6RLAQAAwNcEKxYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCTw+FwjHQRGFqPBT4x0iVA0ris6n6PPb3kyDBWAtw/Noa84Pyz/fXuLxx/+Zcnh7McSVJB9a0hm+v5T7qGbC4A+LJhxQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgmOdABtfV1SkjI6PP/pKSEk2bNs1wUX0pKyvT6NGjtWDBgmE7x1C4efOmSktLdebMGb333nu6fPmyHn/8cRUVFfU6/t/+7d9UU1OjxsZG/ed//qe6u7tVVVWlb33rW/e4cgAAAGBwBhQsesydO1fx8fFu7REREYYLupvy8nKFhobe98GitbVVRUVFCgwM1KRJk9TS0nLX8bt379Y777yjb3/72woPD9cHH3xwjyoFAAAAhsaggsWkSZNktVqHupYR1dXVpe7ubvn4+BieKygoSAcOHNCDDz4oSXryySfvOn79+vUKCgqSp6enfvGLXxAsAAAA8KUzbHssDh8+rLS0NCUkJCg+Pl5LlizRkSNHeh2XmZkpm82m2NhYzZ49W1lZWTp79qzLOIvFoqamJp04cUIWi8X5uXTpkrN/3bp1bvPv379fFotFdXV1zrbCwkJZLBadO3dOOTk5slqtiouLU0NDgyTp9u3bKi4ult1uV1xcnGbNmqXMzEw1Njb269q9vb2doaI/QkJC5Ok5qIwHAAAA3BcG9Wu2s7NTra2tLm1eXl7y8/OTJBUUFKi4uFhxcXHKyMiQh4eHqqurtWbNGq1evVp2u915XEVFhfz9/ZWUlKSgoCBduHBBlZWVSktLU2lpqSIjIyV99q/6OTk5GjNmjJYuXeo8fuzYsYO5BElSdna2fHx8tHjxYplMJgUFBamrq0srV67UqVOnZLVaZbfbdf36dWdN27dv15QpUwZ9TgAAAOCraFDBorCwUIWFhS5tc+bM0YYNG9TY2Kji4mKlpqZqxYoVzv5FixYpKytL+fn5stlszhCSl5cns9nsMpfNZlNKSorKysq0Zs0aSZLVatXWrVsVEBAwZLdhjRo1SgUFBS6rBTt37lR9fb3y8vIUGxvrbE9OTtbChQu1efPmPjdhAwAAAF9XgwoWSUlJSkxMdGkLDAyUJB06dEgmk0k2m81tVSMhIUFHjx5VQ0ODZs6cKUnOUOFwOHTjxg11dXVp7NixGj9+vE6fPj2Y8votJSXF7RakQ4cOKSoqSpMnT3arf8aMGTpw4IA6Ozvl6+s7rLUBAAAAXyaDChaRkZGaMWNGr33nz5+Xw+FQcnJyn8ff+ZSkxsZGbdu2TfX19ero6HAZFxYWNpjy+q3nNqs7nT9/Xrdu3XILTndqbW1VSEjIcJYGAAAAfKkMy45hk8mk3NxceXj0vjd8woQJkqTm5malp6fLz89PaWlpioqKkq+vr0wmk1588UW3oDEY3d3dffb1teowceJEZWZm9nmckX0dAAAAwFfRkAeLiIgI1dTUKCQkRNHR0XcdW11drZs3byonJ0cWi8Wlr62tTd7e3i5tJpOpz7n8/f3V1tbm1n7x4sUBVP9Z/R9//LFiYmL6DEYAAAAAXA35L+eejdX5+fm9rhbceRtUzw93h8PhMqaysrLXl8qZzWZdu3at1/NGRkaqoaFBnZ2dzrZr166pqqpqQPXbbDa1tLRo586dvfZ/0cvuAAAAgK+jIV+xmDp1qtLT01VUVKSUlBQlJiYqODhYV69e1ZkzZ3Ts2DEdP35ckhQfH6+8vDytXbtWdrtdo0eP1smTJ1VTU6Pw8HC3YDJt2jTt27dPW7duVXR0tEwmkxISEmQ2m2W325Wdna2MjAxZrVa1t7dr7969Cg0NHVAYeOaZZ/TWW29py5Ytqq2tVUxMjPz8/NTc3Kza2lp5e3u7PRGrN6+88ora29slffbyvebmZu3YsUOS9PDDDyshIcE59sSJEzpx4oQk6cyZM5I+ewzvqFGjJEnLli3rd/0AAADASBiWPRbp6emaMmWKdu3apfLycnV0dCggIEATJkzQqlWrnOPCw8OVm5ur/Px8lZSUyMPDQ9OnT1dhYaE2btyopqYml3mXL1+utrY27d69W+3t7XI4HKqqqpLZbNa8efN05coVVVRUaNOmTQoLC9OyZcvk4eExoKdLeXp6avPmzdqzZ48OHjzoDBHBwcGaOnWq5s+f3695SktLXeq/dOmStm3bJkmaP3++S7Cora3V9u3b3Y7vQbAAAADA/c7k+Px9SPjSeyzwiZEuAZLGZVX3e+zpJe5vpQe+ijaGvOD8s/31vh+u0ePyL08OZzmSpILqW0M21/OfdA3ZXADwZcPuZAAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYxuNmv4KKioqUmpoqLy+vkS4FAAAAXxOsWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwzORwOx0gXgaE194VPRroE4Evt9JIjI13CfWVjyAu9tttf7x7S81z+5ckhne/zwg7fGNb5AeDrjhULAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgmOdABtfV1SkjI6PP/pKSEk2bNs1wUX0pKyvT6NGjtWDBgmE7x1BoaGjQyy+/rPfff18fffSRJCkkJESJiYlKSUnRqFGj3I45ffq0CgoKdPr0aZlMJj366KP6n//zf+qP/uiP7nX5AAAAwIANKFj0mDt3ruLj493aIyIiDBd0N+Xl5QoNDb3vg8UHH3ygzs5OzZs3T0FBQXI4HHrnnXdUXFys3/zmN3rppZfk6+vrHN/Q0KDnnntOwcHBeu655yRJFRUVevbZZ1VcXKyJEyeO1KUAAAAA/TKoYDFp0iRZrdahrmVEdXV1qbu7Wz4+Pobnmj9/vubPn+/SlpycrOjoaOXm5uqNN97QnDlznH2//OUv5eXlpe3bt2vcuHGSpDlz5ujP//zPtWnTJuXn5xuuCQAAABhOw7bH4vDhw0pLS1NCQoLi4+O1ZMkSHTlypNdxmZmZstlsio2N1ezZs5WVlaWzZ8+6jLNYLGpqatKJEydksVicn0uXLjn7161b5zb//v37ZbFYVFdX52wrLCyUxWLRuXPnlJOTI6vVqri4ODU0NEiSbt++reLiYtntdsXFxWnWrFnKzMxUY2Ojoe8kNDRUknTt2jVn24cffqh3331Xs2fPdoYKSRo3bpxmz56tt99+W1evXjV0XgAAAGC4DWrForOzU62trS5tXl5e8vPzkyQVFBSouLhYcXFxysjIkIeHh6qrq7VmzRqtXr1adrvdeVxFRYX8/f2VlJSkoKAgXbhwQZWVlUpLS1NpaakiIyMlSevXr1dOTo7GjBmjpUuXOo8fO3bsYC5BkpSdnS0fHx8tXrxYJpNJQUFB6urq0sqVK3Xq1ClZrVbZ7XZdv37dWdP27ds1ZcqUfn9PPZ8zZ84oLy9PXl5emjFjhnPMO++8I0l69NFH3Y6fNm2aqqqq1NjYqCeeeGLQ1wkAAAAMt0EFi8LCQhUWFrq0zZkzRxs2bFBjY6OKi4uVmpqqFStWOPsXLVqkrKws5efny2azOUNIXl6ezGazy1w2m00pKSkqKyvTmjVrJElWq1Vbt25VQEDAkN2GNWrUKBUUFMjT8w9fw86dO1VfX6+8vDzFxsY625OTk7Vw4UJt3rxZRUVF/Zp/27ZtKi0tdf79oYce0qZNmxQeHu5s61mNCA4Odju+p+3y5csDuzAAAADgHhtUsEhKSlJiYqJLW2BgoCTp0KFDMplMstlsbqsaCQkJOnr0qBoaGjRz5kxJcoYKh8OhGzduqKurS2PHjtX48eN1+vTpwZTXbykpKS6hoqf+qKgoTZ482a3+GTNm6MCBA+rs7HTZfN2Xp59+WrGxsWpvb1dDQ4Pq6+vd5uzs7JQkeXt7ux3fs9+jZwwAAABwvxpUsIiMjHS5nedO58+fl8PhUHJycp/Ht7S0OP/c2Niobdu2qb6+Xh0dHS7jwsLCBlNev/XcZnWn8+fP69atW27B6U6tra0KCQnp1/w950hMTNSbb76plStXSpKeeuopSXIGlNu3b7sdf+vWLZcxAAAAwP1qUMHii5hMJuXm5srDo/e94RMmTJAkNTc3Kz09XX5+fkpLS1NUVJR8fX1lMpn04osvugWNweju7u6zr68f7BMnTlRmZmafxw12X0dsbKwCAwO1Z88eZ7AICgqSJF25csVtfE/bnZu6AQAAgPvRkAeLiIgI1dTUKCQkRNHR0XcdW11drZs3byonJ0cWi8Wlr62tze32IJPJ1Odc/v7+amtrc2u/ePHiAKr/rP6PP/5YMTExfQYjI27duuXyVKipU6dKkk6dOqXvf//7LmMbGhpkMpk0adKkIa8DAAAAGEpD/su5Z2N1fn5+r6sFd94G1fPD3eFwuIyprKx0GdfDbDa7/Ci/U2RkpBoaGlz2I1y7dk1VVVUDqt9ms6mlpUU7d+7stb+3uj6vr8fDvvrqq7p+/boeeeQRZ1tERISmTJmi3/zmNy6rFleuXNFvfvMbxcTEOFc1AAAAgPvVkK9YTJ06Venp6SoqKlJKSooSExMVHBysq1ev6syZMzp27JiOHz8uSYqPj1deXp7Wrl0ru92u0aNH6+TJk6qpqVF4eLhbMJk2bZr27dunrVu3Kjo6WiaTSQkJCTKbzbLb7crOzlZGRoasVqva29u1d+9ehYaG9isM9HjmmWf01ltvacuWLaqtrVVMTIz8/PzU3Nys2tpaeXt7uz0R6/N+8pOfyN/fX48++qhCQkJ0/fp1/e53v9PRo0f14IMPKj093WV8VlaWMjIytGzZMi1cuFCS9Morr+jTTz/VX//1X/e7dgAAAGCkDMsei/T0dE2ZMkW7du1SeXm5Ojo6FBAQoAkTJmjVqlXOceHh4crNzVV+fr5KSkrk4eGh6dOnq7CwUBs3blRTU5PLvMuXL1dbW5t2796t9vZ2ORwOVVVVyWw2a968ebpy5YoqKiq0adMmhYWFadmyZfLw8BjQ06U8PT21efNm7dmzRwcPHnSGiODgYE2dOtXtjdq9SUpK0uuvv669e/eqtbVVnp6eCg8P15IlS/TDH/5QY8aMcRnfc81bt27V1q1bZTKZ9Oijj+oXv/iFHn744X7XDgAAAIwUk+Pz9yHhS2/uC5+MdAnAl9rpJUdGuoT7ysaQF3ptt7/e98MxBuPyL08O6XyfF3b4xrDODwBfd0O/OxkAAADA1w7BAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACG8R6Lr6CioiKlpqbKy8trpEsBAADA1wQrFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMJPD4XCMdBEYWnNf+GSkSxi000uODOq4jSEv9Nlnf7271/Z11rcHdS4AQ+/5T7pGugQAgEGsWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAM8xzI4Lq6OmVkZPTZX1JSomnTphkuqi9lZWUaPXq0FixYMGznGAqNjY167bXXVFtbq0uXLkmSIiIitGDBAiUlJcnT8+5fe25urv71X/9VZrNZb7zxxr0oGQAAADBkQMGix9y5cxUfH+/WHhERYbiguykvL1doaOh9Hyxeeuklvf3225o1a5aSkpLU3d2t3/72t/rFL36ho0ePKi8vTyaTqddj33vvPe3cuVPf+MY3xEvRAQAA8GUxqGAxadIkWa3Woa5lRHV1dam7u1s+Pj6G51q4cKHWrVvnMtfChQuVnZ2tQ4cO6be//a2efPJJt+O6u7v1/PPPKy4uTjdu3NCZM2cM1wIAAADcC8O2x+Lw4cNKS0tTQkKC4uPjtWTJEh05cqTXcZmZmbLZbIqNjdXs2bOVlZWls2fPuoyzWCxqamrSiRMnZLFYnJ+eW40sFovWrVvnNv/+/ftlsVhUV1fnbCssLJTFYtG5c+eUk5Mjq9WquLg4NTQ0SJJu376t4uJi2e12xcXFadasWcrMzFRjY2O/rv073/lOrwFlzpw5kqRz5871etyuXbv0n//5n1q9enW/zgMAAADcLwa1YtHZ2anW1laXNi8vL/n5+UmSCgoKVFxcrLi4OGVkZMjDw0PV1dVas2aNVq9eLbvd7jyuoqJC/v7+SkpKUlBQkC5cuKDKykqlpaWptLRUkZGRkqT169crJydHY8aM0dKlS53Hjx07djCXIEnKzs6Wj4+PFi9eLJPJpKCgIHV1dWnlypU6deqUrFar7Ha7rl+/7qxp+/btmjJlyqDOd/nyZUlSQECAW19TU5O2bdumZ599VqGhoYO+JgAAAGAkDCpYFBYWqrCw0KVtzpw52rBhgxobG1VcXKzU1FStWLHC2b9o0SJlZWUpPz9fNpvNGULy8vJkNptd5rLZbEpJSVFZWZnWrFkjSbJardq6dasCAgKG7DasUaNGqaCgwGUz9c6dO1VfX6+8vDzFxsY625OTk7Vw4UJt3rxZRUVFAz7XzZs39fLLL2vUqFH67ne/69a/YcMGhYWFafHixYO7GAAAAGAEDSpYJCUlKTEx0aUtMDBQknTo0CGZTCbZbDa3VY2EhAQdPXpUDQ0NmjlzpiQ5Q4XD4dCNGzfU1dWlsWPHavz48Tp9+vRgyuu3lJQUtyc0HTp0SFFRUZo8ebJb/TNmzNCBAwfU2dkpX1/ffp+nu7tb2dnZunjxon7+85/L39/fpf+1117Tm2++qR07dnzhE6MAAACA+9GgfsVGRkZqxowZvfadP39eDodDycnJfR7f0tLi/HNjY6O2bdum+vp6dXR0uIwLCwsbTHn91nOb1Z3Onz+vW7duuQWnO7W2tiokJKRf5/j000+1fv16HT16VMuXL9dTTz3l0t/W1qacnBz92Z/9maZPnz6wCwAAAADuE8Pyz+Mmk0m5ubny8Oh9b/iECRMkSc3NzUpPT5efn5/S0tIUFRUlX19fmUwmvfjii25BYzC6u7v77Otr1WHixInKzMzs87j+7uv49NNP9bOf/UwHDhzQs88+67I3pMf27dvV0dGh73//+/rwww+d7bdu3ZLD4dCHH34oLy+vfgcZAAAAYCQMebCIiIhQTU2NQkJCFB0dfdex1dXVunnzpnJycmSxWFz62tra5O3t7dLW17sfJMnf319tbW1u7RcvXhxA9Z/V//HHHysmJqbPYNQfPaFi//79SktL03PPPdfruKamJnV0dOgv//Ive+1PSkrSQw89pIqKikHXAgAAAAy3IQ8WVqtVr7zyivLz8/WLX/xCDzzwgEt/S0uLcz9Gzw/3z78IrrKyUi0tLW5PRzKbzbp27Vqv542MjFRDQ4PL/odr166pqqpqQPXbbDZt2bJFO3fu1I9+9CO3/jvr74vD4dDPf/5z7d+/X6mpqfqrv/qrPscuWbJE8+bNc2svKirSxYsX9dOf/lSjRo0a0DUAAAAA99qQB4upU6cqPT1dRUVFSklJUWJiooKDg3X16lWdOXNGx44d0/HjxyVJ8fHxysvL09q1a2W32zV69GidPHlSNTU1Cg8Pd7uNadq0adq3b5+2bt2q6OhomUwmJSQkyGw2y263Kzs7WxkZGbJarWpvb9fevXsVGhrqsqfjizzzzDN66623tGXLFtXW1iomJkZ+fn5qbm5WbW2tvL293Z6I9XlbtmxRVVWVHn74YUVHR+vgwYMu/eHh4Xr00Uclyfnfz6uoqFBTU9Nd93oAAAAA94th2WORnp6uKVOmaNeuXSovL1dHR4cCAgI0YcIErVq1yjkuPDxcubm5ys/PV0lJiTw8PDR9+nQVFhZq48aNampqcpl3+fLlamtr0+7du9Xe3i6Hw6GqqiqZzWbNmzdPV65cUUVFhTZt2qSwsDAtW7ZMHh4eA3q6lKenpzZv3qw9e/bo4MGDzhARHBysqVOnav78+V84x7vvvitJev/997V27Vq3/vnz5/cZKAAAAIAvI5Pj8/ch4Utv7gufjHQJg3Z6ifvb2ftjY8gLffbZX+99A/8669uDOheAoff8J10jXQIAwKDB704GAAAAgP8PwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhPG72K6ioqEipqany8vIa6VIAAADwNcGKBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCTw+FwjHQRGFqPBT4x0iWMqHFZ1YM67vSSI0NcyZfTxpAXRroE9MH+evewzOs1p2ZY5gUAfL2wYgEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAzzHMjguro6ZWRk9NlfUlKiadOmGS6qL2VlZRo9erQWLFgwbOcYCjdv3lRpaanOnDmj9957T5cvX9bjjz+uoqKiXsd3dXXpX//1X3Xw4EFdvHhR3/jGN/T4449rxYoVioqKurfFAwAAAIMwoGDRY+7cuYqPj3drj4iIMFzQ3ZSXlys0NPS+Dxatra0qKipSYGCgJk2apJaWlj7HOhwO/c3f/I1qamo0a9YsLVy4UB9//LH27Nmj1NRU/frXv9ZDDz10D6sHAAAABm5QwWLSpEmyWq1DXcuI6urqUnd3t3x8fAzPFRQUpAMHDujBBx+UJD355JN9jj169KhqamqUlJSkf/iHf3C2W61WLVy4UL/61a9UUFBguCYAAABgOA3bHovDhw8rLS1NCQkJio+P15IlS3TkyJFex2VmZspmsyk2NlazZ89WVlaWzp496zLOYrGoqalJJ06ckMVicX4uXbrk7F+3bp3b/Pv375fFYlFdXZ2zrbCwUBaLRefOnVNOTo6sVqvi4uLU0NAgSbp9+7aKi4tlt9sVFxenWbNmKTMzU42Njf26dm9vb2eo+CI9df3pn/6pS3t4eLgee+wxvf3222pubu7XXAAAAMBIGdSKRWdnp1pbW13avLy85OfnJ0kqKChQcXGx4uLilJGRIQ8PD1VXV2vNmjVavXq17Ha787iKigr5+/srKSlJQUFBunDhgiorK5WWlqbS0lJFRkZKktavX6+cnByNGTNGS5cudR4/duzYwVyCJCk7O1s+Pj5avHixTCaTgoKC1NXVpZUrV+rUqVOyWq2y2+26fv26s6bt27drypQpgz7n592+fVuS5Ovr69bX03b69GmFhIQM2TkBAACAoTaoYFFYWKjCwkKXtjlz5mjDhg1qbGxUcXGxUlNTtWLFCmf/okWLlJWVpfz8fNlsNmcIycvLk9lsdpnLZrMpJSVFZWVlWrNmjaTPbg3aunWrAgIChuw2rFGjRqmgoECenn/4Gnbu3Kn6+nrl5eUpNjbW2Z6cnKyFCxdq8+bNfW7CHoye/RO1tbX69re/7Wzv7OzU6dOnJYkVCwAAANz3BhUskpKSlJiY6NIWGBgoSTp06JBMJpNsNpvbqkZCQoKOHj2qhoYGzZw5U5KcocLhcOjGjRvq6urS2LFjNX78eOcP6+GSkpLiEip66o+KitLkyZPd6p8xY4YOHDigzs7OXlcYBsNqtaq4uFiFhYUym836kz/5E7W2tqqwsNB5/s7OziE5FwAAADBcBhUsIiMjNWPGjF77zp8/L4fDoeTk5D6Pv/MpSY2Njdq2bZvq6+vV0dHhMi4sLGww5fVbz21Wdzp//rxu3brlFpzu1NraOmS3Jn3zm99UQUGB1q5dq+eff97Z/vjjj2vJkiX69a9/rVGjRg3JuQAAAIDhMqhg8UVMJpNyc3Pl4dH73vAJEyZI+uwWn/T0dPn5+SktLU1RUVHy9fWVyWTSiy++6BY0BqO7u7vPvr5WHSZOnKjMzMw+jzOyr6Ov85WVlenDDz/UlStXFBwcrIiICG3ZskWSeJcFAAAA7ntDHiwiIiJUU1OjkJAQRUdH33VsdXW1bt68qZycHFksFpe+trY2eXt7u7SZTKY+5/L391dbW5tb+8WLFwdQ/Wf1f/zxx4qJiekzGA2XiIgIl3eB1NTUyM/PT9OnT7+ndQAAAAADNeS/nHs2Vufn5/e6WnDnbVA9P9wdDofLmMrKyl5fKmc2m3Xt2rVezxsZGamGhgaX/QjXrl1TVVXVgOq32WxqaWnRzp07e+2/28vuhtKuXbt07tw5paSkuG1uBwAAAO43Q75iMXXqVKWnp6uoqEgpKSlKTExUcHCwrl69qjNnzujYsWM6fvy4JCk+Pl55eXlau3at7Ha7Ro8erZMnT6qmpkbh4eFuwWTatGnat2+ftm7dqujoaJlMJiUkJMhsNstutys7O1sZGRmyWq1qb2/X3r17FRoaOqAw8Mwzz+itt97Sli1bVFtbq5iYGPn5+am5uVm1tbXy9vZ2eyJWb1555RW1t7dL+uzle83NzdqxY4ck6eGHH1ZCQoJz7I9//GOFhYXpoYcekslk0vHjx/V//+//1RNPPKG0tLR+1w4AAACMlGHZY5Genq4pU6Zo165dKi8vV0dHhwICAjRhwgStWrXKOS48PFy5ubnKz89XSUmJPDw8NH36dBUWFmrjxo1qampymXf58uVqa2vT7t271d7eLofDoaqqKpnNZs2bN09XrlxRRUWFNm3apLCwMC1btkweHh4DerqUp6enNm/erD179ujgwYPOEBEcHKypU6dq/vz5/ZqntLTUpf5Lly5p27ZtkqT58+e7BItHH31Uhw8f1quvvipJio6O1t/+7d/q6aef1gMPPNDv2gEAAICRYnJ8/j4kfOk9FvjESJcwosZlVQ/quNNL3N8M/3W0MeSFkS4BfbC/3vfDKIzwmlMzLPMCAL5e7u3uZAAAAABfSQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABjGeyy+goqKipSamiovL6+RLgUAAABfE6xYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDTA6HwzHSRWBozX3hk5Eu4b5xesmRkS7B6ffv/mykS8AIuPzLk8Myb0H1LZe/P/9J17CcBwCA/mLFAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGCY50AG19XVKSMjo8/+kpISTZs2zXBRfSkrK9Po0aO1YMGCYTvHULh586ZKS0t15swZvffee7p8+bIef/xxFRUV9ev4v/u7v9O//du/6aGHHlJFRcUwVwsAAAAYN6Bg0WPu3LmKj493a4+IiDBc0N2Ul5crNDT0vg8Wra2tKioqUmBgoCZNmqSWlpZ+H/vGG2/oN7/5jXx8fIaxQgAAAGBoDSpYTJo0SVardahrGVFdXV3q7u4ekh/0QUFBOnDggB588EFJ0pNPPtmv427evKl/+qd/0p//+Z/r//2//2e4DgAAAOBeGbY9FocPH1ZaWpoSEhIUHx+vJUuW6MiRI72Oy8zMlM1mU2xsrGbPnq2srCydPXvWZZzFYlFTU5NOnDghi8Xi/Fy6dMnZv27dOrf59+/fL4vForq6OmdbYWGhLBaLzp07p5ycHFmtVsXFxamhoUGSdPv2bRUXF8tutysuLk6zZs1SZmamGhsb+3Xt3t7ezlAxEAUFBfr000/1V3/1VwM+FgAAABhJg1qx6OzsVGtrq0ubl5eX/Pz8JH32A7m4uFhxcXHKyMiQh4eHqqurtWbNGq1evVp2u915XEVFhfz9/ZWUlKSgoCBduHBBlZWVSktLU2lpqSIjIyVJ69evV05OjsaMGaOlS5c6jx87duxgLkGSlJ2dLR8fHy1evFgmk0lBQUHq6urSypUrderUKVmtVtntdl2/ft1Z0/bt2zVlypRBn7Mvp0+fVkVFhZ5//nmNGjVqyOcHAAAAhtOggkVhYaEKCwtd2ubMmaMNGzaosbFRxcXFSk1N1YoVK5z9ixYtUlZWlvLz82Wz2ZwhJC8vT2az2WUum82mlJQUlZWVac2aNZIkq9WqrVu3KiAgYMhuwxo1apQKCgrk6fmHr2Hnzp2qr69XXl6eYmNjne3JyclauHChNm/e3O9N2P3V1dWln//855o5c6bmzJkzpHMDAAAA98KggkVSUpISExNd2gIDAyVJhw4dkslkks1mc1vVSEhI0NGjR9XQ0KCZM2dKkjNUOBwO3bhxQ11dXRo7dqzGjx+v06dPD6a8fktJSXEJFT31R0VFafLkyW71z5gxQwcOHFBnZ6d8fX2HrI6XX35ZH374oX71q18N2ZwAAADAvTSoYBEZGakZM2b02nf+/Hk5HA4lJyf3efydT0lqbGzUtm3bVF9fr46ODpdxYWFhgymv33pus7rT+fPndevWLbfgdKfW1laFhIQMSQ0ffvihduzYoaVLlyo8PHxI5gQAAADutUEFiy9iMpmUm5srD4/e94ZPmDBBktTc3Kz09HT5+fkpLS1NUVFR8vX1lclk0osvvugWNAaju7u7z76+Vh0mTpyozMzMPo8zsq/j8zZt2qRvfvOb+h//43/oww8/dLZ3d3erq6tLH374ocxms4KCgobsnAAAAMBQG/JgERERoZqaGoWEhCg6OvquY6urq3Xz5k3l5OTIYrG49LW1tcnb29ulzWQy9TmXv7+/2tra3NovXrw4gOo/q//jjz9WTExMn8FoKDU3N+vKlSsuG9rvlJSUpCeeeEKbN28e9loAAACAwRryYGG1WvXKK68oPz9fv/jFL/TAAw+49Le0tDj3Y/T8cHc4HC5jKisr1dLSotDQUJd2s9msa9eu9XreyMhINTQ0uOx/uHbtmqqqqgZUv81m05YtW7Rz50796Ec/cuu/s/6h8JOf/ETt7e1u7b/4xS/k7e2tzMxMVisAAABw3xvyYDF16lSlp6erqKhIKSkpSkxMVHBwsK5evaozZ87o2LFjOn78uCQpPj5eeXl5Wrt2rex2u0aPHq2TJ0+qpqZG4eHhbrcxTZs2Tfv27dPWrVsVHR0tk8mkhIQEmc1m2e12ZWdnKyMjQ1arVe3t7dq7d69CQ0MH9ObrZ555Rm+99Za2bNmi2tpaxcTEyM/PT83NzaqtrZW3t7fbE7F688orrzgDQ1dXl5qbm7Vjxw5J0sMPP6yEhARJ6nOvypYtW2Q2m++61wMAAAC4XwzLHov09HRNmTJFu3btUnl5uTo6OhQQEKAJEyZo1apVznHh4eHKzc1Vfn6+SkpK5OHhoenTp6uwsFAbN25UU1OTy7zLly9XW1ubdu/erfb2djkcDlVVVclsNmvevHm6cuWKKioqtGnTJoWFhWnZsmXy8PAY0NOlPD09tXnzZu3Zs0cHDx50hojg4GBNnTpV8+fP79c8paWlLvVfunRJ27ZtkyTNnz/fGSwAAACArwKT4/P3IeFLb+4Ln4x0CfeN00vc3/Y+Un7/7s9GugSMgMu/PDks8xZU33L5+/OfdA3LeQAA6K/h350MAAAA4CuPYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwHjf7FVRUVKTU1FR5eXmNdCkAAAD4mmDFAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYJjJ4XA4RroIDK25L3xieI5XY747BJXgfnP5lydHugQYFHb4xkiXAABAr1ixAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhnkOZHBdXZ0yMjL67C8pKdG0adMMF9WXsrIyjR49WgsWLBi2cwyFhoYGvfzyy3r//ff10UcfSZJCQkKUmJiolJQUjRo1ymV8Xl6e/v3f/10ffvihrl+/roCAAH3729/WD3/4Q1kslpG4BAAAAGBABhQsesydO1fx8fFu7REREYYLupvy8nKFhobe98Higw8+UGdnp+bNm6egoCA5HA698847Ki4u1m9+8xu99NJL8vX1dY5vaGjQxIkT9b3vfU+jR49WS0uLDh06pIyMDP30pz+VzWYbwasBAAAAvtiggsWkSZNktVqHupYR1dXVpe7ubvn4+Biea/78+Zo/f75LW3JysqKjo5Wbm6s33nhDc+bMcfYVFRW5zbFo0SJ9//vf17/8y78QLAAAAHDfG7Y9FocPH1ZaWpoSEhIUHx+vJUuW6MiRI72Oy8zMlM1mU2xsrGbPnq2srCydPXvWZZzFYlFTU5NOnDghi8Xi/Fy6dMnZv27dOrf59+/fL4vForq6OmdbYWGhLBaLzp07p5ycHFmtVsXFxamhoUGSdPv2bRUXF8tutysuLk6zZs1SZmamGhsbDX0noaGhkqRr16594dhvfOMb8vf379dYAAAAYKQNasWis7NTra2tLm1eXl7y8/OTJBUUFKi4uFhxcXHKyMiQh4eHqqurtWbNGq1evVp2u915XEVFhfz9/ZWUlKSgoCBduHBBlZWVSktLU2lpqSIjIyVJ69evV05OjsaMGaOlS5c6jx87duxgLkGSlJ2dLR8fHy1evFgmk0lBQUHq6urSypUrderUKVmtVtntdl2/ft1Z0/bt2zVlypR+f089nzNnzigvL09eXl6aMWNGr+NbW1v16aef6urVq6qsrNT58+f1p3/6p4O+PgAAAOBeGVSwKCwsVGFhoUvbnDlztGHDBjU2Nqq4uFipqalasWKFs3/RokXKyspSfn6+bDabM4Tk5eXJbDa7zGWz2ZSSkqKysjKtWbNGkmS1WrV161YFBAQM2W1Yo0aNUkFBgTw9//A17Ny5U/X19crLy1NsbKyzPTk5WQsXLtTmzZt7vXWpN9u2bVNpaanz7w899JA2bdqk8PBwt7E3b95UYmKi8+8+Pj5KSkrS3/zN3wzm0gAAAIB7alDBIikpyeVHsCQFBgZKkg4dOiSTySSbzea2qpGQkKCjR4+qoaFBM2fOlCRnqHA4HLpx44a6uro0duxYjR8/XqdPnx5Mef2WkpLiEip66o+KitLkyZPd6p8xY4YOHDigzs5Ol83XfXn66acVGxur9vZ2NTQ0qL6+3m3OHj4+PsrPz1d3d7eampr02muvqaOjQ52dnW7BCwAAALjfDCpYREZG9nk7z/nz5+VwOJScnNzn8S0tLc4/NzY2atu2baqvr1dHR4fLuLCwsMGU1289t1nd6fz587p165ZbcLpTa2urQkJC+jV/zzkSExP15ptvauXKlZKkp556ymXsAw884PKdfv/739dzzz2njIwM7dy50y0AAQAAAPeTYfm1ajKZlJubKw+P3veGT5gwQZLU3Nys9PR0+fn5KS0tTVFRUfL19ZXJZNKLL77oFjQGo7u7u8++vlYdJk6cqMzMzD6PG+y+jtjYWAUGBmrPnj1uweLzHnjgAT311FP6p3/6J504cUJ/8id/MqhzAgAAAPfCkAeLiIgI1dTUKCQkRNHR0XcdW11drZs3byonJ8ftRXBtbW3y9vZ2aTOZTH3O5e/vr7a2Nrf2ixcvDqD6z+r/+OOPFRMT02cwMuLWrVv9ftLTrVu3JPXvKVIAAADASBryX849G6t79gt83p23QfX8cHc4HC5jKisrXcb1MJvNff7IjoyMVENDgzo7O51t165dU1VV1YDqt9lsamlp0c6dO3vt762uz7t69Wqv7a+++qquX7+uRx55xKXGTz75xG1sR0eH9u3bJw8PD02dOrWf1QMAAAAjY8hXLKZOnar09HQVFRUpJSVFiYmJCg4O1tWrV3XmzBkdO3ZMx48flyTFx8crLy9Pa9euld1u1+jRo3Xy5EnV1NQoPDzcLZhMmzZN+/bt09atWxUdHS2TyaSEhASZzWbZ7XZlZ2crIyNDVqtV7e3t2rt3r0JDQ/sVBno888wzeuutt7RlyxbV1tYqJiZGfn5+am5uVm1trby9vd2eiPV5P/nJT+Tv769HH31UISEhun79un73u9/p6NGjevDBB5Wenu4ce+LECb3wwgv63ve+p/DwcPn5+enSpUs6ePCg/vu//1vPPvus8/0XAAAAwP1qWPZYpKena8qUKdq1a5fKy8vV0dGhgIAATZgwQatWrXKOCw8PV25urvLz81VSUiIPDw9Nnz5dhYWF2rhxo5qamlzmXb58udra2rR79261t7fL4XCoqqpKZrNZ8+bN05UrV1RRUaFNmzYpLCxMy5Ytk4eHx4CeLuXp6anNmzdrz549OnjwoDNEBAcHa+rUqW5v1O5NUlKSXn/9de3du1etra3y9PRUeHi4lixZoh/+8IcaM2aMc+zEiRP15JNPqr6+XocOHVJnZ6fGjBmjKVOm6O/+7u/0xBNP9Lt2AAAAYKSYHJ+/DwlfenNfcL+1aqBejfnuEFSC+83lX54c6RJgUNjhGyNdAgAAvRr63ckAAAAAvnYIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjPdYfAUVFRUpNTVVXl5eI10KAAAAviZYsQAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhpkcDodjpIvA0Hos8AmNy6oe6TLwJfAXf/s9tzb7690jUMlXz+VfnuyzL+zwjXtYCQAA9wYrFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDPAcyuK6uThkZGX32l5SUaNq0aYaL6ktZWZlGjx6tBQsWDNs5hkJDQ4Nefvllvf/++/roo48kSSEhIUpMTFRKSopGjRrlMv748eN6/fXX1djYqP/4j//Q7du3tW3bNlkslpEoHwAAABiwAQWLHnPnzlV8fLxbe0REhOGC7qa8vFyhoaH3fbD44IMP1NnZqXnz5ikoKEgOh0PvvPOOiouL9Zvf/EYvvfSSfH19neNfe+01vfbaa5owYYKioqL0/vvvj2D1AAAAwMANKlhMmjRJVqt1qGsZUV1dXeru7paPj4/huebPn6/58+e7tCUnJys6Olq5ubl64403NGfOHGff8uXL9fd///fy9vZ2rnQAAAAAXybDtsfi8OHDSktLU0JCguLj47VkyRIdOXKk13GZmZmy2WyKjY3V7NmzlZWVpbNnz7qMs1gsampq0okTJ2SxWJyfS5cuOfvXrVvnNv/+/ftlsVhUV1fnbCssLJTFYtG5c+eUk5Mjq9WquLg4NTQ0SJJu376t4uJi2e12xcXFadasWcrMzFRjY6Oh7yQ0NFSSdO3aNZf2cePGydvb29DcAAAAwEga1IpFZ2enWltbXdq8vLzk5+cnSSooKFBxcbHi4uKUkZEhDw8PVVdXa82aNVq9erXsdrvzuIqKCvn7+yspKUlBQUG6cOGCKisrlZaWptLSUkVGRkqS1q9fr5ycHI0ZM0ZLly51Hj927NjBXIIkKTs7Wz4+Plq8eLFMJpOCgoLU1dWllStX6tSpU7JarbLb7bp+/bqzpu3bt2vKlCn9/p56PmfOnFFeXp68vLw0Y8aMQdcMAAAA3I8GFSwKCwtVWFjo0jZnzhxt2LBBjY2NKi4uVmpqqlasWOHsX7RokbKyspSfny+bzeYMIXl5eTKbzS5z2Ww2paSkqKysTGvWrJEkWa1Wbd26VQEBAUN2G9aoUaNUUFAgT88/fA07d+5UfX298vLyFBsb62xPTk7WwoULtXnzZhUVFfVr/m3btqm0tNT594ceekibNm1SeHj4kNQPAAAA3C8GFSySkpKUmJjo0hYYGChJOnTokEwmk2w2m9uqRkJCgo4ePaqGhgbNnDlTkpyhwuFw6MaNG+rq6tLYsWM1fvx4nT59ejDl9VtKSopLqOipPyoqSpMnT3arf8aMGTpw4IA6OztdNl/35emnn1ZsbKza29vV0NCg+vp6tzkBAACAr4JBBYvIyMg+b+c5f/68HA6HkpOT+zy+paXF+efGxkZt27ZN9fX16ujocBkXFhY2mPL6rec2qzudP39et27dcgtOd2ptbVVISEi/5u85R2Jiot58802tXLlSkvTUU08NsmoAAADg/jOoYPFFTCaTcnNz5eHR+97wCRMmSJKam5uVnp4uPz8/paWlKSoqSr6+vjKZTHrxxRfdgsZgdHd399nX16rDxIkTlZmZ2edxg93XERsbq8DAQO3Zs4dgAQAAgK+UIQ8WERERqqmpUUhIiKKjo+86trq6Wjdv3lROTo7by+Da2trcnpRkMpn6nMvf319tbW1u7RcvXhxA9Z/V//HHHysmJqbPYGTErVu33J4KBQAAAHzZDfkv556N1fn5+b2uFtx5G1TPD3eHw+EyprKy0mVcD7PZ3OeP8sjISDU0NKizs9PZdu3aNVVVVQ2ofpvNppaWFu3cubPX/t7q+ryrV6/22v7qq6/q+vXreuSRRwZUEwAAAHC/G/IVi6lTpyo9PV1FRUVKSUlRYmKigoODdfXqVZ05c0bHjh3T8ePHJUnx8fHKy8vT2rVrZbfbNXr0aJ08eVI1NTUKDw93CybTpk3Tvn37tHXrVkVHR8tkMikhIUFms1l2u13Z2dnKyMiQ1WpVe3u79u7dq9DQ0H6FgR7PPPOM3nrrLW3ZskW1tbWKiYmRn5+fmpubVVtbK29vb7cnYn3eT37yE/n7++vRRx9VSEiIrl+/rt/97nc6evSoHnzwQaWnp7uMP3v2rI4ePSpJOnXqlCTp4MGD+t3vfifpsydqjRo1qt/XAAAAANxrw7LHIj09XVOmTNGuXbtUXl6ujo4OBQQEaMKECVq1apVzXHh4uHJzc5Wfn6+SkhJ5eHho+vTpKiws1MaNG9XU1OQy7/Lly9XW1qbdu3ervb1dDodDVVVVMpvNmjdvnq5cuaKKigpt2rRJYWFhWrZsmTw8PAb0dClPT09t3rxZe/bs0cGDB50hIjg4WFOnTnV7o3ZvkpKS9Prrr2vv3r1qbW2Vp6enwsPDtWTJEv3whz/UmDFjXMb3bGC/050rLVarlWABAACA+5rJ8fn7kPCl91jgExqXVT3SZeBL4C/+9ntubfbX+37gAfrv8i9P9tkXdvjGPawEAIB7Y+h3JwMAAAD42iFYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIzHzX4FFRUVKTU1VV5eXiNdCgAAAL4mWLEAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYZnI4HI6RLgJD67HAJ0a6BAyxcVnVw36O00uODPs5hsvGkBfc2uyvd/fr2HXWt4e6HDfPf9I17OcAAGCksWIBAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAM8xzOyevq6pSRkdFnf0lJiaZNmzZs5y8rK9Po0aO1YMGCYTvHULlw4YK2bdumt99+W+3t7XrwwQc1b948/eVf/qV8fHxGujwAAADgroY1WPSYO3eu4uPj3dojIiKG9bzl5eUKDQ2974PF73//e6Wmpqq7u1t//ud/rm9961tqaGjQjh07dPr0aeXm5spkMo10mQAAAECf7kmwmDRpkqxW67041T3T1dWl7u7uIVlNyMvL0/Xr17Vjxw5Nnz5dkvSDH/xA48ePV35+vg4dOvSV+/4AAADw1XLf7LE4fPiw0tLSlJCQoPj4eC1ZskRHjhzpdVxmZqZsNptiY2M1e/ZsZWVl6ezZsy7jLBaLmpqadOLECVksFufn0qVLzv5169a5zb9//35ZLBbV1dU52woLC2WxWHTu3Dnl5OTIarUqLi5ODQ0NkqTbt2+ruLhYdrtdcXFxmjVrljIzM9XY2Niva6+rq1NkZKQzVPToWWnZv39/v+YBAAAARso9WbHo7OxUa2urS5uXl5f8/PwkSQUFBSouLlZcXJwyMjLk4eGh6upqrVmzRqtXr5bdbnceV1FRIX9/fyUlJSkoKEgXLlxQZWWl0tLSVFpaqsjISEnS+vXrlZOTozFjxmjp0qXO48eOHTvo68jOzpaPj48WL14sk8mkoKAgdXV1aeXKlTp16pSsVqvsdruuX7/urGn79u2aMmXKXef95JNP5Ovr69be0/bOO+/I4XBwOxQAAADuW/ckWBQWFqqwsNClbc6cOdqwYYMaGxtVXFys1NRUrVixwtm/aNEiZWVlKT8/XzabzRlC8vLyZDabXeay2WxKSUlRWVmZ1qxZI0myWq3aunWrAgIChuw2olGjRqmgoECenn/42nbu3Kn6+nrl5eUpNjbW2Z6cnKyFCxdq8+bNKioquuu8Dz30kM6fP6+rV68qKCjI2d6zanLz5k1du3ZN/v7+Q3IdAAAAwFC7J8EiKSlJiYmJLm2BgYGSpEOHDslkMslms7mtaiQkJOjo0aNqaGjQzJkzJckZKhwOh27cuKGuri6NHTtW48eP1+nTp4f1OlJSUlxCRU/9UVFRmjx5slv9M2bM0IEDB9TZ2dnrikSPH/7wh/rHf/xHZWVl6cc//rG+9a1v6fTp0/rVr34lT09PdXV1qbOzk2ABAACA+9Y9CRaRkZGaMWNGr33nz5+Xw+FQcnJyn8e3tLQ4/9zY2Kht27apvr5eHR0dLuPCwsKGpuA+9Nxmdafz58/r1q1bbsHpTq2trQoJCemz/6mnnlJra6u2bdum5557TtJnt4qlpqbqt7/9rd59913nig0AAABwP7onweKLmEwm5ebmysOj973kEyZMkCQ1NzcrPT1dfn5+SktLU1RUlHx9fWUymfTiiy+6BY3B6O7u7rOvr1WHiRMnKjMzs8/j+rOvY9GiRXr66af1H//xH7p9+7YmTJig0aNHa/fu3QoKCtKoUaO+uHgAAABghIx4sIiIiFBNTY1CQkIUHR1917HV1dW6efOmcnJyZLFYXPra2trk7e3t0na3zc7+/v5qa2tza7948eIAqv+s/o8//lgxMTF9BqP+8vb2dtno/e677+rjjz/Wn/3ZnxmaFwAAABhuI/642Z6N1fn5+b2uFtx5G1TPD3eHw+EyprKy0mVcD7PZrGvXrvV63sjISDU0NKizs9PZdu3aNVVVVQ2ofpvNppaWFu3cubPX/t7q6o9bt27pxRdflLe3t370ox8Nag4AAADgXhnxFYupU6cqPT1dRUVFSklJUWJiooKDg3X16lWdOXNGx44d0/HjxyVJ8fHxysvL09q1a2W32zV69GidPHlSNTU1Cg8Pdwsm06ZN0759+7R161ZFR0fLZDIpISFBZrNZdrtd2dnZysjIkNVqVXt7u/bu3avQ0NABhYFnnnlGb731lrZs2aLa2lrFxMTIz89Pzc3Nqq2tlbe3t9sTsT7v3Llz+ulPf6onnnhC48aN00cffaRXX31VFy9e1Nq1axUVFTXg7xUAAAC4l0Y8WEhSenq6pkyZol27dqm8vFwdHR0KCAjQhAkTtGrVKue48PBw5ebmKj8/XyUlJfLw8ND06dNVWFiojRs3qqmpyWXe5cuXq62tTbt371Z7e7scDoeqqqpkNps1b948XblyRRUVFdq0aZPCwsK0bNkyeXh4DOjpUp6entq8ebP27NmjgwcPOkNEcHCwpk6dqvnz53/hHGPGjNG4ceO0d+9effTRRxo1apQee+wxrV+/Xo888ki/awEAAABGisnx+fuK8KX3WOATI10Chti4rOphP8fpJe5vuv+y2Bjyglub/fW+H8Rwp3XWt4e6HDfPf9I17OcAAGCkjfgeCwAAAABffgQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABjGeyy+goqKipSamiovL6+RLgUAAABfE6xYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDTA6HwzHSRWBoPRb4xEiX8LUwLqu61/bTS47c40runY0hL8j+evdIl3Hf8JpTM9IlAABw32DFAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGCY50AG19XVKSMjo8/+kpISTZs2zXBRfSkrK9Po0aO1YMGCYTvHULh586ZKS0t15swZvffee7p8+bIef/xxFRUV9Tre4XDo//yf/6OKigp98MEH+uSTTxQSEqI5c+bomWee0ahRo+7xFQAAAAADM6Bg0WPu3LmKj493a4+IiDBc0N2Ul5crNDT0vg8Wra2tKioqUmBgoCZNmqSWlpa7ji8oKFBJSYliYmL07LPPytPTU/X19SosLNSxY8dUUlIik8l0j6oHAAAABm5QwWLSpEmyWq1DXcuI6urqUnd3t3x8fAzPFRQUpAMHDujBBx+UJD355JN3PW95ebkmTZqk/Px8eXh8dndacnKyPD09dejQIb3//vv6oz/6I8N1AQAAAMNl2PZYHD58WGlpaUpISFB8fLyWLFmiI0eO9DouMzNTNptNsbGxmj17trKysnT27FmXcRaLRU1NTTpx4oQsFovzc+nSJWf/unXr3Obfv3+/LBaL6urqnG2FhYWyWCw6d+6ccnJyZLVaFRcXp4aGBknS7du3VVxcLLvdrri4OM2aNUuZmZlqbGzs17V7e3s7Q8UX6erq0q1btxQYGOgMFT2CgoIkSWazuV9zAQAAACNlUCsWnZ2dam1tdWnz8vKSn5+fpM9u7SkuLlZcXJwyMjLk4eGh6upqrVmzRqtXr5bdbnceV1FRIX9/fyUlJSkoKEgXLlxQZWWl0tLSVFpaqsjISEnS+vXrlZOTozFjxmjp0qXO48eOHTuYS5AkZWdny8fHR4sXL5bJZFJQUJC6urq0cuVKnTp1SlarVXa7XdevX3fWtH37dk2ZMmXQ5/w8X19fPfbYY3rzzTf1L//yL5o9e7YeeOAB1dfXa8+ePZo3b57zOwAAAADuV4MKFoWFhSosLHRpmzNnjjZs2KDGxkYVFxcrNTVVK1ascPYvWrRIWVlZys/Pl81mc4aQvLw8t3+Rt9lsSklJUVlZmdasWSNJslqt2rp1qwICAobsNqxRo0apoKBAnp5/+Bp27typ+vp65eXlKTY21tmenJyshQsXavPmzX1uwh6sn//851q3bp3++Z//Wf/8z/8sSTKZTFq6dOldN8sDAAAA94tBBYukpCQlJia6tAUGBkqSDh06JJPJJJvN5raqkZCQoKNHj6qhoUEzZ86U9IfbfBwOh27cuKGuri6NHTtW48eP1+nTpwdTXr+lpKS4hIqe+qOiojR58mS3+mfMmKEDBw6os7NTvr6+Q1aHt7e3wsLCZLPZFBcXJ0l6/fXX9etf/1re3t5KS0sbsnMBAAAAw2FQweL/397dx9V4/38Af51Kp3QrRaVSyk0ljeWuCJvbCmMpiyGRxNwz22TGGFmR3JURo9xkbhdGXzffXxoLo1BjlW3IXXRH912/Pzw6X8c5pc6JTK/n4+GhPtfn+lzv61zXdTrvc30+n8vCwgJdu3aVuywzMxOCIMDT07PK9V+cJSktLQ0bN27ExYsXUVhYKFWvRYsWioRXY/K6GGVmZqK4uFgmcXpRTk4OjI2N6ySGoqIijB8/Hm3btsV3330nKR8wYAC++OILRERE4MMPP4SlpWWdbI+IiIiI6HVQKLF4FZFIhDVr1sgMRq5kbW0NALh37x78/f2hpaUFPz8/WFpaQkNDAyKRCCEhITKJhiLKy8urXFbVXQcbGxvMnDmzyvWUGdfxsvj4ePz9999S3cYq9e3bFydOnMDly5eZWBARERHRW63OEwtzc3MkJibC2NgYVlZW1dY9deoUnj17htDQUDg5OUkty83Nhbq6ulRZdc9y0NPTQ25urkz5nTt3ahH98/ifPHmCzp07V5kY1aWHDx8CACoqKmSWVSZF1SVHRERERERvgzr/5Fw5sHrdunVyPxC/2A2q8oO7IAhSdfbv3y/3oXKamprIy8uTu10LCwukpKSgqKhIUpaXl4dDhw7VKn53d3dkZ2cjOjpa7vJXPeyutiqTr59//llmWWWZvb19nW6TiIiIiKiu1fkdC3t7e/j7+yMyMhI+Pj7o27cvjIyM8OjRI6SmpuLs2bM4d+4cAMDFxQXh4eFYuHAhvLy8oKOjgytXriAxMRFmZmYyiYmDgwMOHjyIDRs2wMrKCiKRCK6urtDU1ISXlxeCgoIQEBAANzc35Ofn48CBAzAxMalVMvDJJ5/g/PnzCAsLQ1JSEjp37gwtLS3cu3cPSUlJUFdXl5kRS57du3cjPz8fwPNnVdy7dw8//PADAKBNmzZwdXUF8Pzhefb29jh79iwmTpyIPn36AHh+N+f3339H37590a5duxrHT0RERERUH17LGAt/f3/Y2dlh165d2LlzJwoLC2FgYABra2vMmTNHUs/MzAxr1qzBunXrEBUVBRUVFTg6OiIiIgLBwcHIysqSajcwMBC5ubmIjY1Ffn4+BEHAoUOHoKmpiUGDBuHhw4fYs2cPVq1ahRYtWmDChAlQUVGp1exSampqWL16Nfbu3YsjR45IkggjIyPY29vDw8OjRu3s2LFDKv67d+9i48aNAAAPDw9JYqGqqor169dj69atOHnyJMLDwyESiWBubo7PPvsMo0aNqnHsRERERET1RSS83A+J/vU6Nu1R3yE0CM1mn5JbfnWs7BPm3xXBxsvgdZJjfio16pdY3yEQERG9NV7/6GQiIiIiInrnMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlcbrZd1BkZCR8fX3RqFGj+g6FiIiIiBoI3rEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKliQRBEOo7CKpbHZv2kCmb80BUD5HU3nWNX+s7BCK5lpaW1XcIREREbzXesSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqWp1abyhQsXEBAQUOXyqKgoODg4KB1UVWJiYqCjo4PBgwe/tm3UhbS0NBw7dgxJSUm4e/cuAMDc3ByDBw/GsGHDoKYm/bIvWrQIP//8s9y2li9fjr59+772mImIiIiIlFGrxKLSgAED4OLiIlNubm6udEDV2blzJ0xMTN76xGLbtm347bff0Lt3bwwbNgzl5eVISEjAihUrcObMGYSHh0Mkkn1g3eLFi2XK2rdv/yZCJiIiIiJSikKJRbt27eDm5lbXsdSrsrIylJeXQywWK92Wt7c3Fi1aJNWWt7c3goKCcPToUSQkJKBnz54y671rrykRERERNRyvbYzF8ePH4efnB1dXV7i4uGDs2LGIj4+XW2/mzJlwd3dH9+7d8eGHH2L27Nm4efOmVD0nJydkZWXh0qVLcHJykvyr7Grk5OSERYsWybR/+PBhODk54cKFC5KyiIgIODk5IT09HaGhoXBzc4OzszNSUlIAACUlJdiyZQu8vLzg7OyM3r17Y+bMmUhLS6vRvr/33ntyE5R+/foBANLT0+WuJwgCCgoKUFFRUaPtEBERERG9LRS6Y1FUVIScnBypskaNGkFLSwsAsH79emzZsgXOzs4ICAiAiooKTp06hfnz52PevHnw8vKSrLdnzx7o6elh2LBhMDQ0xO3bt7F//374+flhx44dsLCwAPC8m1BoaCj09fUxfvx4yfpNmjRRZBcAAEFBQRCLxRg1ahREIhEMDQ1RVlaGzz77DMnJyXBzc4OXlxcKCgokMW3atAl2dnYKbe/BgwcAAAMDA7nLe/fujadPn6JRo0bo2LEjAgMD2RWKiIiIiP4VFEosIiIiEBERIVXWr18/fPfdd0hLS8OWLVvg6+uLKVOmSJaPHDkSs2fPxrp16+Du7i5JQsLDw6GpqSnVlru7O3x8fBATE4P58+cDeN5NaMOGDTAwMKizLkPa2tpYv3691GDq6OhoXLx4EeHh4ejevbuk3NPTE97e3li9ejUiIyNrva1nz55h+/bt0NbWRq9evaSWNW3aFD4+PrC1tYWmpiZu3LiBnTt3YsKECQgLC0PXrl0V30kiIiIiojdAocRi2LBhMjMVNW3aFABw9OhRiEQiuLu7y9zVcHV1xZkzZ5CSkoJu3boBgCSpEAQBT58+RVlZGZo0aYKWLVvi6tWrioRXYz4+PjIzNB09ehSWlpawtbWVib9r166Ii4tDUVERNDQ0aryd8vJyBAUF4c6dO/j222+hp6cntfyzzz6T+r13794YOHAgfHx8sHz5cuzfv792O0ZERERE9IYplFhYWFhU+S16ZmYmBEGAp6dnletnZ2dLfk5LS8PGjRtx8eJFFBYWStVr0aKFIuHVWGU3qxdlZmaiuLi42ilec3JyYGxsXKNtVFRUYPHixThz5gwCAwMxcODAGsfWr18/HD58GH/99RdatmxZo/WIiIiIiOqDQonFq4hEIqxZswYqKvLHhltbWwMA7t27B39/f2hpacHPzw+WlpbQ0NCASCRCSEiITKKhiPLy8iqXVXXXwcbGBjNnzqxyvZqO66ioqMCSJUsQFxeHiRMnSo0NqQkTExMAzxMZJhZERERE9Dar88TC3NwciYmJMDY2hpWVVbV1T506hWfPniE0NBROTk5Sy3Jzc6Guri5VJu/ZD5X09PSQm5srU37nzp1aRP88/idPnqBz585VJkY1UZlUHD58GH5+fpg0aVKt2/jnn38A/K+bGRERERHR26rOp5utHFi9bt06uXcLXuwGVfnBXRAEqTr79++XqldJU1MTeXl5crdrYWGBlJQUFBUVScry8vJw6NChWsXv7u6O7OxsREdHy10uL66XCYKAb7/9FocPH4avry8mT55cZd3CwkIUFxfLlKelpSE+Ph5WVlYwMzOr+Q4QEREREdWDOr9jYW9vD39/f0RGRsLHxwd9+/aFkZERHj16hNTUVJw9exbnzp0DALi4uCA8PBwLFy6El5cXdHR0cOXKFSQmJsLMzEwmMXFwcMDBgwexYcMGWFlZQSQSwdXVFZqamvDy8kJQUBACAgLg5uaG/Px8HDhwACYmJjVKBip98sknOH/+PMLCwpCUlITOnTtDS0sL9+7dQ1JSEtTV1WVmxHpZWFgYDh06hDZt2sDKygpHjhyRWm5mZoYOHToAAP7++29MmzYNvXv3hrm5OTQ1NXHz5k0cOnQIKioq+Oqrr2ocOxERERFRfXktYyz8/f1hZ2eHXbt2YefOnSgsLISBgQGsra0xZ84cST0zMzOsWbMG69atQ1RUFFRUVODo6IiIiAgEBwcjKytLqt3AwEDk5uYiNjYW+fn5EAQBhw4dgqamJgYNGoSHDx9iz549WLVqFVq0aIEJEyZARUWlVrNLqampYfXq1di7dy+OHDkiSSKMjIxgb28PDw+PV7Zx/fp1AMCNGzewcOFCmeUeHh6SxKJp06bo0qULLly4gGPHjqGoqAiGhobo168ffH19YWlpWePYiYiIiIjqi0h4uR8S/et1bNpDpmzOg6rHp7xNrmv8Wt8hEMm1tLSsvkMgIiJ6q9X5GAsiIiIiImp4mFgQEREREZHSmFgQEREREZHSmFgQEREREZHSmFgQEREREZHSmFgQEREREZHSmFgQEREREZHS+ByLd1BkZCR8fX3RqFGj+g6FiIiIiBoI3rEgIiIiIiKlMbEgIiIiIqoBS0tLjBs3rr7DeGsxsSAiIiKiBi09PR2TJk1Cq1atoKGhAV1dXbi4uCAsLAyFhYX1HV61Tp8+DZFIJPffuXPn3mgsam90a0RERET0TunYtEd9h4DfsxMUXjcuLg4jRoyAWCzGmDFj0L59e5SUlCAhIQFz587FtWvXEBkZWYfRvh7Tpk1D586dpcpsbGzeaAxMLIiIiIioQcrMzMTIkSPRsmVLnDx5EiYmJpJlU6ZMwZ9//om4uLh6jLDmevbsCU9Pz3qNgV2hiIiIiKhBCg4ORkFBATZv3iyVVFSysbHB9OnTq1z/8ePHmDNnDhwcHKCtrQ1dXV0MGjQIV65ckakbHh4Oe3t7NG7cGE2aNIGTkxNiYmIky/Pz8zFjxgxYWlpCLBajWbNm6NevHy5dulTj/cnPz0dZWVmN69c13rEgIiIiogbp8OHDaNWqFZydnRVaPyMjAwcOHMCIESNgZWWF+/fvIyIiAr169cL169dhamoKANi0aROmTZsGT09PTJ8+HUVFRUhOTsb58+fh4+MDAAgICMDevXsxdepU2NnZITs7GwkJCUhNTUWnTp1eGYuvry8KCgqgqqqKnj17YuXKlXByclJovxTFxIKIiIiIGpy8vDzcuXMHQ4cOVbgNBwcH3LhxAyoq/+sE9Omnn6Jdu3bYvHkzgoKCADwfx2Fvb4/Y2Ngq24qLi8PEiRMREhIiKZs3b94rY1BXV8fHH38MNzc3GBoa4vr16/j+++/Rs2dPJCYmomPHjgrvX20xsSAiIiKiBicvLw8AoKOjo3AbYrFY8nN5eTlycnKgra2Ntm3bSnVh0tfXx+3bt5GUlCQzwPrFOufPn8fdu3cldzpqwtnZWeqOy5AhQ+Dp6YkOHTrgiy++wLFjxxTYM8VwjAURERERNTi6uroAno9LUFRFRQVWrVqF1q1bQywWw9DQEEZGRkhOTkZubq6k3ueffw5tbW106dIFrVu3xpQpU3D27FmptoKDg3H16lWYm5ujS5cuWLRoETIyMhSKy8bGBkOHDsWpU6dQXl6u8P7VFhMLIiIiImpwdHV1YWpqiqtXryrcxrJlyzBr1iy4urpix44d+OWXX3DixAnY29ujoqJCUs/W1hZ//PEHdu3ahR49euCnn35Cjx498PXXX0vqeHl5ISMjA+Hh4TA1NcXKlSthb2+Po0ePKhSbubk5SkpK8PTpU4X3r7ZEgiAIb2xr9Ea8OJ90s9mnarze1bHxdR7LretL6rS9RW6/1Wl79G5aWlp/M2IQETU0/+bnWEyaNAmRkZFITExE9+7dX1nf0tISvXv3xtatWwEA7733HgwMDHDy5EmpemZmZrCxscHp06fltlNSUoLhw4fj2LFjKCgogIaGhkydBw8eoFOnTrC0tERCQu33z9PTE3FxcXj69KnUGJDXiXcsiIiIiKhBmjdvHrS0tDBhwgTcv39fZnl6ejrCwsKqXF9VVRUvf0cfGxuLO3fuSJVlZ2dL/a6urg47OzsIgoDS0lKUl5dLdZ0CgGbNmsHU1BTFxcXV7sPDhw9lyq5cuYJDhw6hf//+byypADh4m4iIiIgaKGtra8TExMDb2xu2trZST95OTExEbGwsxo0bV+X6Hh4eWLx4MXx9feHs7IyUlBRER0ejVatWUvX69+8PY2NjuLi4oHnz5khNTcXatWvh7u4OHR0d5OTkwMzMDJ6ennB0dIS2tjbi4+ORlJQkNUuUPN7e3tDU1ISzszOaNWuG69evIzIyEo0bN8by5cvr4mWqMSYWRERERNRgDRkyBMnJyVi5ciUOHjyIDRs2QCwWo0OHDggJCcHEiROrXPfLL7/E06dPERMTg927d6NTp06Ii4vD/PnzpepNmjQJ0dHRCA0NRUFBAczMzDBt2jQsWLAAANC4cWMEBgbi+PHj2LdvHyoqKmBjY4P169dj8uTJ1cb/0UcfSdrOy8uDkZERhg8fjq+//ho2NjbKv0C1wDEW7yCOsaCGjmMsiIiI3jyOsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqXValaoCxcuICAgoMrlUVFRcHBwUDqoqsTExEBHRweDBw9+bduoC2lpaTh27BiSkpJw9+5dAM+ffjh48GAMGzYMamr/e9nv3r2LIUOGVNvekiVLMGjQoNcaMxERERGRMhSabnbAgAFwcXGRKTc3N1c6oOrs3LkTJiYmb31isW3bNvz222/o3bs3hg0bhvLyciQkJGDFihU4c+YMwsPDIRKJAABNmjTB4sWL5bYTHByM4uLiGj0JkoiIiIioPimUWLRr1w5ubm51HUu9KisrQ3l5OcRisdJteXt7Y9GiRVJteXt7IygoCEePHkVCQgJ69uwJANDU1JT7WiYnJ6OgoAAffvgh9PX1lY6JiIiIiOh1em1jLI4fPw4/Pz+4urrCxcUFY8eORXy87HMSjh8/jpkzZ8Ld3R3du3fHhx9+iNmzZ+PmzZtS9ZycnJCVlYVLly7ByclJ8q+yq5GTkxMWLVok0/7hw4fh5OSECxcuSMoiIiLg5OSE9PR0hIaGws3NTfK0RAAoKSnBli1b4OXlBWdnZ/Tu3RszZ85EWlpajfb9vffek5ug9OvXD8Dzx8O/yoEDBwA8f+gJEREREdHbTqE7FkVFRcjJyZEqa9SoEbS0tAAA69evx5YtW+Ds7IyAgACoqKjg1KlTmD9/PubNmwcvLy/Jenv27IGenh6GDRsGQ0ND3L59G/v374efnx927NgBCwsLAMDixYsRGhoKfX19jB8/XrJ+kyZNFNkFAEBQUBDEYjFGjRoFkUgEQ0NDlJWV4bPPPkNycjLc3Nzg5eWFgoICSUybNm2CnZ2dQtt78OABAMDAwKDaes+ePUN8fDxMTEzQtWtXhbZFRERERPQmKZRYREREICIiQqqsX79++O6775CWloYtW7bA19cXU6ZMkSwfOXIkZs+ejXXr1sHd3V2ShISHh0NTU1OqLXd3d/j4+CAmJkbySHQ3Nzds2LABBgYGddYNS1tbG+vXr5caTB0dHY2LFy8iPDxcamyDp6cnvL29sXr1akRGRtZ6W8+ePcP27duhra2NXr16VVv3+PHjePbsGUaPHg0VFU7cRURERERvP4USi2HDhqFv375SZU2bNgUAHD16FCKRCO7u7jJ3NVxdXXHmzBmkpKSgW7duACBJKgRBwNOnT1FWVoYmTZqgZcuWuHr1qiLh1ZiPj49UUlEZv6WlJWxtbWXi79q1K+Li4lBUVAQNDY0ab6e8vBxBQUG4c+cOvv32W+jp6VVb/+DBg1BRUXnlbFFERERERG8LhRILCwuLKrvoZGZmQhAEeHp6Vrl+dna25Oe0tDRs3LgRFy9eRGFhoVS9Fi1aKBJejVV2s3pRZmYmiouLZRKnF+Xk5MDY2LhG26ioqMDixYtx5swZBAYGYuDAgdXWz8jIQEpKCrp3717jbRARERHR62dpaYnevXtj69at9R3KW0mhxOJVRCIR1qxZU2U3HmtrawDAvXv34O/vDy0tLfj5+cHS0hIaGhoQiUQICQmRSTQUUV5eXuWyqu462NjYYObMmVWuV9NxHRUVFViyZAni4uIwceJEqbEhVTl48CAAYOjQoTXaBhEREREpJz09HcHBwThx4gTu3r0LdXV1ODg4wMvLC/7+/jLd9t9Gly5dwqJFi5CQkICioiK0atUK/v7+mDZt2huLoc4TC3NzcyQmJsLY2BhWVlbV1j116hSePXuG0NBQODk5SS3Lzc2Furq6VFnlsx/k0dPTQ25urkz5nTt3ahH98/ifPHmCzp07KzW+oTKpOHz4MPz8/DBp0qRXrlNaWoojR46gSZMm6N27t8LbJiIiInpTBiwrre8Q8MuXjRReNy4uDiNGjIBYLMaYMWPQvn17lJSUICEhAXPnzsW1a9cUGl/7Jh0/fhyDBw9Gx44dERQUBG1tbaSnp+P27dtvNI46Tyzc3Nywe/durFu3DitWrICqqqrU8uzsbMl4jMoP7oIgSNXZv38/srOzYWJiIlWuqamJvLw8udu1sLBASkqK1PiHvLw8HDp0qFbxu7u7IywsDNHR0fj0009llr8Yf1UEQcC3336Lw4cPw9fXF5MnT67Rts+cOYMnT55g1KhRMmM/iIiIiKhuZWZmYuTIkWjZsiVOnjwp9dlzypQp+PPPPxEXF1ePEb5aXl4exowZA3d3d+zdu7deJ/6p80+v9vb28Pf3R2RkJHx8fNC3b18YGRnh0aNHSE1NxdmzZ3Hu3DkAgIuLC8LDw7Fw4UJ4eXlBR0cHV65cQWJiIszMzGS6MTk4OODgwYPYsGEDrKysIBKJ4OrqCk1NTXh5eSEoKAgBAQFwc3NDfn4+Dhw4ABMTE6kxHa/yySef4Pz58wgLC0NSUhI6d+4MLS0t3Lt3D0lJSVBXV5eZEetlYWFhOHToENq0aQMrKyscOXJEarmZmRk6dOggs15lEsRnVxARERG9fsHBwSgoKMDmzZtlvtAGnnePnz59epXrP378GMuWLcMvv/yCzMxMqKiowMXFBcuXL4ejo6NU3fDwcGzcuBGZmZkQi8WwtrbGrFmz4OPjAwDIz89HUFAQDhw4gKysLOjp6cHR0RErVqxAp06dqowhJiYG9+/fx9KlS6GiooKnT59CU1OzXhKM1/K1uL+/P+zs7LBr1y7s3LkThYWFMDAwgLW1NebMmSOpZ2ZmhjVr1mDdunWIioqCiooKHB0dERERgeDgYGRlZUm1GxgYiNzcXMTGxiI/Px+CIODQoUPQ1NTEoEGD8PDhQ+zZswerVq1CixYtMGHCBKioqNRqdik1NTWsXr0ae/fuxZEjRyRJhJGREezt7eHh4fHKNq5fvw4AuHHjBhYuXCiz3MPDQyaxuHfvHs6dO4cOHTq8sgsZERERESnv8OHDaNWqFZydnRVaPyMjAwcOHMCIESNgZWWF+/fvIyIiAr169cL169dhamoKANi0aROmTZsGT09PTJ8+HUVFRUhOTsb58+cliUVAQAD27t2LqVOnws7ODtnZ2UhISEBqamq1iUV8fDx0dXVx584dfPTRR7hx4wa0tLTw6aefYtWqVbWayVRZIuHlfkj0r9exaQ/Jz81mn6rxelfHyj4ZXVm3ri+p0/YWuf1Wp+3Ru2lpaVl9h0BE1GD8W8dY5OXlQU9PD0OHDsWBAwdqtM7Ls0IVFxejUaNGUncHbt26hXbt2uGrr75CUFAQgOe9Uf78889qv+zW19fH6NGjsXbt2lrth6OjI/78808AgJ+fH3r37o3Tp08jPDwcI0eOxM6dO2vVnjL49DUiIiIianAqx+3q6Ogo3IZYLJYkFeXl5cjOzoa2tjbatm2LS5cuSerp6+vj9u3bSEpKqrItfX19nD9/Hnfv3q1VDAUFBXj27BnGjBmDNWvWYPjw4VizZg0mTZqEXbt24ebNm4rtnAKYWBARERFRg6Orqwvg+dgGRVVUVGDVqlVo3bo1xGIxDA0NYWRkhOTkZKnZSj///HNoa2ujS5cuaN26NaZMmYKzZ89KtRUcHIyrV6/C3NwcXbp0waJFi5CRkfHKGCqnwv3kk0+kyiu7WP36668K719tMbEgIiIiogZHV1cXpqamtRqL+7Jly5Zh1qxZcHV1xY4dO/DLL7/gxIkTsLe3R0VFhaSera0t/vjjD+zatQs9evTATz/9hB49euDrr7+W1PHy8kJGRgbCw8NhamqKlStXwt7eHkePHq02hspxHM2bN5cqb9asGQDgyZMnCu9fbTGxICIiIqIGycPDA+np6Qp/q79371706dMHmzdvxsiRI9G/f3/07dsXOTk5MnW1tLTg7e2NqKgo/P3333B3d8fSpUtRVFQkqWNiYoLAwEAcOHAAmZmZaNq0KZYuXVptDO+//z4A2We3VXapMjIyUmjfFMHEgoiIiIgapHnz5kFLSwsTJkzA/fv3ZZanp6cjLCysyvVVVVVlnscWGxsr8yH/5UcfqKurw87ODoIgoLS0FOXl5TIPem7WrBlMTU1RXFxc7T54eXkBADZv3ixV/sMPP0BNTe2NPnSZT2EjIiIiogbJ2toaMTEx8Pb2hq2trdSTtxMTExEbG4tx48ZVub6HhwcWL14MX19fODs7IyUlBdHR0WjVqpVUvf79+8PY2BguLi5o3rw5UlNTsXbtWri7u0NHRwc5OTkwMzODp6cnHB0doa2tjfj4eCQlJSEkJKTafejYsSPGjx+PLVu2oKysDL169cLp06cRGxuLL774QtJV6k3gdLPvoMjISPj6+qJRI8Ufb09ERERUE//W6WZfdPPmTaxcuRInTpzA3bt3IRaL0aFDB4wcORITJ06EWCwGIH+62a+++goxMTHIyclBp06d8P3332P+/PkAgNOnTwN4/tksOjoa165dQ0FBAczMzDB8+HAsWLAAurq6KCkpwYIFC3D8+HFkZGSgoqICNjY2mDRpEiZPnvzK+EtLS7Fs2TJERUXh7t27aNmyJaZMmYIZM2Yo9brUFhOLdxATCyIiIiJ60zjGgoiIiIiIlMbEgoiIiIiIlMbEgoiIiIiIlMbEgoiIiIiIlMbEgoiIiIiIlMbEgoiIiIiIlMbEgoiIiIiIlMbEgoiIiIiIlMbEgoiIiIiIlMbEgoiIiIiIlMbEgoiIiIiIlCYSBEGo7yCobnVs2kNuebPZp95wJDV3dWy8wusGGy+rw0jenOsav9Z3CO+kpaVl9R0CERG9oywtLdG7d29s3bq1vkN5K/GOBRERERE1aOnp6Zg0aRJatWoFDQ0N6OrqwsXFBWFhYSgsLKzv8F6puLgYn3/+OUxNTaGpqYmuXbvixIkTbzwOtTe+RSIiIiKit0RcXBxGjBgBsViMMWPGoH379igpKUFCQgLmzp2La9euITIysr7DrNa4ceOwd+9ezJgxA61bt8bWrVvh5uaGU6dOoUcP+T1ZXgcmFkRERESksBZ3jtZ3CLjTYpBC62VmZmLkyJFo2bIlTp48CRMTE8myKVOm4M8//0RcXFxdhfla/Pbbb9i1axdWrlyJOXPmAIAkQZo3bx4SExPfWCzsCkVEREREDVJwcDAKCgqwefNmqaSiko2NDaZPn17l+o8fP8acOXPg4OAAbW1t6OrqYtCgQbhy5YpM3fDwcNjb26Nx48Zo0qQJnJycEBMTI1men5+PGTNmwNLSEmKxGM2aNUO/fv1w6dKlavdh7969UFVVhb+/v6RMQ0MDfn5++PXXX/HPP//U5KWoE7xjQUREREQN0uHDh9GqVSs4OzsrtH5GRgYOHDiAESNGwMrKCvfv30dERAR69eqF69evw9TUFACwadMmTJs2DZ6enpg+fTqKioqQnJyM8+fPw8fHBwAQEBCAvXv3YurUqbCzs0N2djYSEhKQmpqKTp06VRnD77//jjZt2kBXV1eqvEuXLgCAy5cvw9zcXKH9qy0mFkRERETU4OTl5eHOnTsYOnSowm04ODjgxo0bUFH5XyegTz/9FO3atcPmzZsRFBQE4Pk4Dnt7e8TGxlbZVlxcHCZOnIiQkBBJ2bx5814ZQ1ZWlty7LZVld+/erfH+KItdoYiIiIiowcnLywMA6OjoKNyGWCyWJBXl5eXIzs6GtrY22rZtK9WFSV9fH7dv30ZSUlKVbenr6+P8+fO1TgQKCwshFotlyjU0NCTL35Ra3bG4cOECAgICqlweFRUFBwcHpYOqSkxMDHR0dDB48ODXto26kJaWhmPHjiEpKUlycpibm2Pw4MEYNmwY1NSkX/bw8HD8/vvv+Oeff1BQUAADAwO0bt0ao0ePhpOTU33sAhEREdE7rbLrUH5+vsJtVFRUICwsDOvXr0dmZibKy8sly5o2bSr5+fPPP0d8fDy6dOkCGxsb9O/fHz4+PnBxcZHUCQ4OxtixY2Fubo73338fbm5uGDNmDFq1alVtDJqamiguLpYpLyoqkix/UxTqCjVgwACpF6LS6+6/tXPnTpiYmLz1icW2bdvw22+/oXfv3hg2bBjKy8uRkJCAFStW4MyZMwgPD4dIJJLUT0lJgY2NDT744APo6OggOzsbR48eRUBAAL755hu4u7vX494QERERvXt0dXVhamqKq1evKtzGsmXLEBQUhPHjx2PJkiUwMDCAiooKZsyYgYqKCkk9W1tb/PHHH/j5559x7Ngx/PTTT1i/fj0WLlyIb775BgDg5eWFnj17Yv/+/Th+/DhWrlyJFStWYN++fRg0qOpZr0xMTHDnzh2Z8qysLACQjPN4ExRKLNq1awc3N7e6jqVelZWVoby8XO6tpNry9vbGokWLpNry9vZGUFAQjh49ioSEBPTs2VOyTN7cyCNHjsRHH32ErVu3MrEgIiIieg08PDwQGRmJX3/9Fd27d6/1+nv37kWfPn2wefNmqfKcnBwYGhpKlWlpacHb2xve3t4oKSnB8OHDsXTpUnzxxReSbksmJiYIDAxEYGAgHjx4gE6dOmHp0qXVJhbvvfceTp06hby8PKkB3OfPn5csf1Ne2xiL48ePw8/PD66urnBxccHYsWMRHx8vt97MmTPh7u6O7t2748MPP8Ts2bNx8+ZNqXpOTk7IysrCpUuX4OTkJPlX2dXIyckJixYtkmn/8OHDcHJywoULFyRlERERcHJyQnp6OkJDQ+Hm5gZnZ2ekpKQAAEpKSrBlyxZ4eXnB2dkZvXv3xsyZM5GWllajfX/vvffkJij9+vUD8Pzpjq/SuHFj6OnpSfr/EREREVHdmjdvHrS0tDBhwgTcv39fZnl6ejrCwsKqXF9VVRWCIEiVxcbGytxByM7OlvpdXV0ddnZ2EAQBpaWlKC8vR25urlSdZs2awdTUVG43pxd5enqivLxc6ovq4uJiREVFoWvXrm9sRihAwTsWRUVFyMnJkSpr1KgRtLS0AADr16/Hli1b4OzsjICAAKioqODUqVOYP38+5s2bBy8vL8l6e/bsgZ6eHoYNGwZDQ0Pcvn0b+/fvh5+fH3bs2AELCwsAwOLFixEaGgp9fX2MHz9esn6TJk0U2QUAQFBQEMRiMUaNGgWRSARDQ0OUlZXhs88+Q3JyMtzc3ODl5YWCggJJTJs2bYKdnZ1C23vw4AEAwMDAQO7ynJwcVFRU4NGjR9i/fz8yMzMxZMgQhfePiIiIiKpmbW2NmJgYeHt7w9bWVurJ24mJiYiNjcW4ceOqXN/DwwOLFy+Gr6+v5Evq6OhomXER/fv3h7GxMVxcXNC8eXOkpqZi7dq1cHd3h46ODnJycmBmZgZPT084OjpCW1sb8fHxSEpKkpolSp6uXbtixIgR+OKLL/DgwQPY2Nhg27ZtuHXrlsydlNdNocQiIiICERERUmX9+vXDd999h7S0NGzZsgW+vr6YMmWKZPnIkSMxe/ZsrFu3Du7u7pIkJDw8XGZQibu7O3x8fBATE4P58+cDANzc3LBhwwYYGBjUWTcsbW1trF+/XmowdXR0NC5evIjw8HCpW2Kenp7w9vbG6tWrFXqs+7Nnz7B9+3Zoa2ujV69ecpf37dtX8rtYLMawYcMwa9asWm+LiIiIiGpmyJAhSE5OxsqVK3Hw4EFs2LABYrEYHTp0QEhICCZOnFjlul9++SWePn2KmJgY7N69G506dUJcXJzk82ulSZMmITo6GqGhoSgoKICZmRmmTZuGBQsWAHjeUyUwMBDHjx/Hvn37UFFRARsbG6xfvx6TJ09+5T78+OOPCAoKwvbt2/HkyRN06NABP//8M1xdXZV7cWpJocRi2LBhUh+Cgf+NfD969ChEIhHc3d1l7mq4urrizJkzSElJQbdu3QD8b6S6IAh4+vQpysrK0KRJE7Rs2VKpwTQ14ePjIzND09GjR2FpaQlbW1uZ+Lt27Yq4uDgUFRVJ+sLVRHl5OYKCgnDnzh18++230NPTk6kjFouxbt06lJeXIysrC8eOHUNhYSGKiore6Gh+IiIiotq406Lq/v//Fq1bt67RF8e3bt2S+l0sFuP777/H999/L1V++vRpqd/9/f2lnoz9MnV1dQQHByM4OLjGMb9IQ0MDK1euxMqVKxVav64olFhYWFiga9eucpdlZmZCEAR4enpWuf6L/czS0tKwceNGXLx4UWae3RYtWigSXo1VdrN6UWZmJoqLi2USpxfl5OTA2Ni4RtuoqKjA4sWLcebMGQQGBmLgwIFy66mqqkq9ph999BEmTZqEgIAAREdHyyRARERERERvk9fyaVUkEmHNmjVSTyF8kbW1NQDg3r178Pf3h5aWFvz8/GBpaQkNDQ2IRCKEhITUyQM9XpxP+GVV3XWwsbHBzJkzq1yvpuM6KioqsGTJEsmTFF8cG/IqqqqqGDhwIJYvX45Lly5JHstORERERPQ2qvPEwtzcHImJiTA2NoaVlVW1dU+dOoVnz54hNDRU5kFwubm5UFdXlyp78dkPL9PT05MZTQ9A7ry+1TE3N8eTJ0/QuXPnKhOjmqhMKg4fPgw/Pz9MmjSp1m1UzgLAmaGIiIiI6G1X59PNVg6srhwv8LIXu0FVfnB/eZqu/fv3y0zLBTwfj1HVh2wLCwukpKRInjIIPP9AfujQoVrF7+7ujuzsbERHR8tdLi+ulwmCgG+//RaHDx+Gr69vtYNu8vLyUFpaKlNeWFiIgwcPQkVFBfb29jXfASIiIiKielDndyzs7e3h7++PyMhI+Pj4oG/fvjAyMsKjR4+QmpqKs2fP4ty5cwAAFxcXhIeHY+HChfDy8oKOjg6uXLmCxMREmJmZySQmDg4OktH6VlZWEIlEcHV1haamJry8vBAUFISAgAC4ubkhPz8fBw4cgImJSY2SgUqffPIJzp8/j7CwMCQlJaFz587Q0tLCvXv3kJSUBHV1dZkZsV4WFhaGQ4cOoU2bNrCyssKRI0eklpuZmaFDhw4AgEuXLmHZsmX44IMPYGZmBi0tLdy9exdHjhzB/fv3MXHiRJiYmNQ4fiIiIiKi+vBaxlj4+/vDzs4Ou3btws6dO1FYWAgDAwNYW1tjzpw5knpmZmZYs2YN1q1bh6ioKKioqMDR0REREREIDg6WPIq8UmBgIHJzcxEbG4v8/HwIgoBDhw5BU1MTgwYNwsOHD7Fnzx6sWrUKLVq0wIQJE6CiolKr2aXU1NSwevVq7N27F0eOHJEkEUZGRrC3t4eHh8cr27h+/ToA4MaNG1i4cKHMcg8PD0liYWNjg549e+LixYs4evQoioqKoK+vDzs7O3zxxRfo0aNHjWMnIiIiIqovIuHlfkj0r9exqfxkpNnsU284kpq7Olb2qew1FWy8rA4jeXOua/xa3yG8k5aWltV3CERERA1SnY+xICIiIiKihoeJBRERERERKY2JBRERERERKY2JBRERERERKY2JBRERERERKY2JBRERERFRDVhaWmLcuHH1HcZbi4kFERERETVo6enpmDRpElq1agUNDQ3o6urCxcUFYWFhKCwsrO/wqnXt2jWMGDECrVq1QuPGjWFoaAhXV1ccPnz4jcfyWh6QR/Vr8ndj4Ovri0aNGtV3KLUwqJ7WrUel9R0AERGR8qLLe9Z3CBil+n8KrxsXF4cRI0ZALBZjzJgxaN++PUpKSpCQkIC5c+fi2rVriIyMrMNo69Zff/2F/Px8jB07Fqampnj27Bl++uknDBkyBBEREfD3939jsTCxICIiIqIGKTMzEyNHjkTLli1x8uRJmJiYSJZNmTIFf/75J+Li4uoxwldzc3ODm5ubVNnUqVPx/vvvIzQ09I0mFuwKRUREREQNUnBwMAoKCrB582appKKSjY0Npk+fXuX6jx8/xpw5c+Dg4ABtbW3o6upi0KBBuHLlikzd8PBw2Nvbo3HjxmjSpAmcnJwQExMjWZ6fn48ZM2bA0tISYrEYzZo1Q79+/XDp0qVa75eqqirMzc2Rk5NT63WVwTsWRERERNQgHT58GK1atYKzs7NC62dkZODAgQMYMWIErKyscP/+fURERKBXr164fv06TE1NAQCbNm3CtGnT4OnpienTp6OoqAjJyck4f/48fHx8AAABAQHYu3cvpk6dCjs7O2RnZyMhIQGpqano1KnTK2N5+vQpCgsLkZubi0OHDuHo0aPw9vZWaL8UxcSCiIiIiBqcvLw83LlzB0OHDlW4DQcHB9y4cQMqKv/rBPTpp5+iXbt22Lx5M4KCggA8H8dhb2+P2NjYKtuKi4vDxIkTERISIimbN29ejWOZPXs2IiIiAAAqKioYPnw41q5dW9tdUgq7QhERERFRg5OXlwcA0NHRUbgNsVgsSSrKy8uRnZ0NbW1ttG3bVqoLk76+Pm7fvo2kpKQq29LX18f58+dx9+5dhWKZMWMGTpw4gW3btmHQoEEoLy9HSUmJQm0piokFERERETU4urq6AJ6PbVBURUUFVq1ahdatW0MsFsPQ0BBGRkZITk5Gbm6upN7nn38ObW1tdOnSBa1bt8aUKVNw9uxZqbaCg4Nx9epVmJubo0uXLli0aBEyMjJqHEu7du3Qt29fjBkzBj///DMKCgowePBgCIKg8P7VFhMLIiIiImpwdHV1YWpqiqtXryrcxrJlyzBr1iy4urpix44d+OWXX3DixAnY29ujoqJCUs/W1hZ//PEHdu3ahR49euCnn35Cjx498PXXX0vqeHl5ISMjA+Hh4TA1NcXKlSthb2+Po0ePKhSbp6cnkpKScOPGDYX3r7aYWBARERFRg+Th4YH09HT8+uuvCq2/d+9e9OnTB5s3b8bIkSPRv39/9O3bV+5sTFpaWvD29kZUVBT+/vtvuLu7Y+nSpSgqKpLUMTExQWBgIA4cOIDMzEw0bdoUS5cuVSi2ygf7vXjn5HVjYkFEREREDdK8efOgpaWFCRMm4P79+zLL09PTERYWVuX6qqqqMl2NYmNjcefOHamy7Oxsqd/V1dVhZ2cHQRBQWlqK8vJymQSgWbNmMDU1RXFxcbX78ODBA5my0tJS/Pjjj9DU1ISdnV2169clzgpFRERERA2StbU1YmJi4O3tDVtbW6knbycmJiI2Nhbjxo2rcn0PDw8sXrwYvr6+cHZ2RkpKCqKjo9GqVSupev3794exsTFcXFzQvHlzpKamYu3atXB3d4eOjg5ycnJgZmYGT09PODo6QltbG/Hx8UhKSpKaJUqeSZMmIS8vD66urmjRogXu3buH6OhopKWlISQkBNra2nXxUtUIEwsiIiIiarCGDBmC5ORkrFy5EgcPHsSGDRsgFovRoUMHhISEYOLEiVWu++WXX+Lp06eIiYnB7t270alTJ8TFxWH+/PlS9SZNmoTo6GiEhoaioKAAZmZmmDZtGhYsWAAAaNy4MQIDA3H8+HHs27cPFRUVsLGxwfr16zF58uRq4/f29sbmzZuxYcMGZGdnQ0dHB++//z5WrFiBIUOGKP8C1YJIeJNDxemNGLCsVKn1r46Nf2WdYONlkp+9TpYrtT36d1vk9tsb29bS0rI3ti0iIiKqHY6xICIiIiIipTGxICIiIiIipTGxICIiIiIipTGxICIiIiIipTGxICIiIiIipTGxICIiIiIipTGxICIiIiIipTGxICIiIiIipdXqydsXLlxAQEBAlcujoqLg4OCgdFBViYmJgY6ODgYPHvzatlEXnj17hh07diA1NRV//PEHHjx4gE6dOiEyMlKmbllZGYKDg3H9+nVkZWXh2bNnMDIygr29PcaOHYt27drVwx4QEREREdVOrRKLSgMGDICLi4tMubm5udIBVWfnzp0wMTF56xOLnJwcREZGomnTpmjXrh2ys7OrrFtaWorU1FQ4OjrCzc0NjRs3xv3793Ho0CGMGzcO4eHh6Ny58xuMnoiIiIio9hRKLNq1awc3N7e6jqVelZWVoby8HGKxWOm2DA0NERcXh+bNmwMAevbsWWVdTU1NbN++Xab8448/hru7O7Zv387EgoiIiIjeeq9tjMXx48fh5+cHV1dXuLi4YOzYsYiPj5dbb+bMmXB3d0f37t3x4YcfYvbs2bh586ZUPScnJ2RlZeHSpUtwcnKS/Lt7965k+aJFi2TaP3z4MJycnHDhwgVJWUREBJycnJCeno7Q0FC4ubnB2dkZKSkpAICSkhJs2bIFXl5ecHZ2Ru/evTFz5kykpaXVaN/V1dUlSYWimjRpArFYjPz8fKXaISIiIqK6YWlpiXHjxtV3GG8the5YFBUVIScnR6qsUaNG0NLSAgCsX78eW7ZsgbOzMwICAqCiooJTp05h/vz5mDdvHry8vCTr7dmzB3p6ehg2bBgMDQ1x+/Zt7N+/H35+ftixYwcsLCwAAIsXL0ZoaCj09fUxfvx4yfpNmjRRZBcAAEFBQRCLxRg1ahREIhEMDQ1RVlaGzz77DMnJyXBzc4OXlxcKCgokMW3atAl2dnYKb7Mq5eXlyM/PR1lZGe7fv48dO3bg2bNncrucEREREVHdSU9PR3BwME6cOIG7d+9CXV0dDg4O8PLygr+/PzQ1Nes7xBpbunQpFixYAHt7e1y9evWNbluhxCIiIgIRERFSZf369cN3332HtLQ0bNmyBb6+vpgyZYpk+ciRIzF79mysW7cO7u7ukiQkPDxc5mC5u7vDx8cHMTExmD9/PgDAzc0NGzZsgIGBQZ11w9LW1sb69euhpva/lyE6OhoXL15EeHg4unfvLin39PSEt7c3Vq9eLXcQtrIyMzMxcuRIqdh8fX2ZFRMREdFbrfSEc32HgEb9EhVeNy4uDiNGjIBYLMaYMWPQvn17lJSUICEhAXPnzsW1a9dey2e/1+H27dtYtmyZ5HP2m6ZQYjFs2DD07dtXqqxp06YAgKNHj0IkEsHd3V3mroarqyvOnDmDlJQUdOvWDQAkSYUgCHj69CnKysrQpEkTtGzZ8rVnWT4+PlJJRWX8lpaWsLW1lYm/a9euiIuLQ1FRETQ0NOo0lhYtWmDdunUoLS3F7du3ceTIERQUFKC0tFQmRiIiIiJSXuUXuy1btsTJkydhYmIiWTZlyhT8+eefiIuLq8cIa2fOnDno1q0bysvL8ejRoze+fYU+sVpYWKBr165yl2VmZkIQBHh6ela5/ouzJKWlpWHjxo24ePEiCgsLpeq1aNFCkfBqrLKb1YsyMzNRXFwskzi9KCcnB8bGxnUai6amptRrOmTIEIwePRrz5s1DeHh4nW6LiIiIiIDg4GAUFBRg8+bNUklFJRsbG0yfPr3K9R8/foxly5bhl19+QWZmJlRUVODi4oLly5fD0dFRqm54eDg2btyIzMxMiMViWFtbY9asWfDx8QEA5OfnIygoCAcOHEBWVhb09PTg6OiIFStWoFOnTq/cl//+97/Yu3cvfv/9d3z22We1fCXqxmv5KlwkEmHNmjVQUZE/Ntza2hoAcO/ePfj7+0NLSwt+fn6wtLSEhoYGRCIRQkJCZBINRZSXl1e5rKq7DjY2Npg5c2aV6ykzrqOmGjdujD59+mDbtm24ffs2zMzMXvs2iYiIiBqSw4cPo1WrVnB2Vqw7V0ZGBg4cOIARI0bAysoK9+/fR0REBHr16oXr16/D1NQUALBp0yZMmzYNnp6emD59OoqKipCcnIzz589LEouAgADs3bsXU6dOhZ2dHbKzs5GQkIDU1NRXJhbl5eX47LPPMGHChNf6TLlXqfPEwtzcHImJiTA2NoaVlVW1dU+dOoVnz54hNDQUTk5OUstyc3Ohrq4uVSYSiapsS09PD7m5uTLld+7cqUX0z+N/8uQJOnfuXGVi9KYUFxcDeP5aMLEgIiIiqjt5eXm4c+cOhg4dqnAbDg4OuHHjhtRnxk8//RTt2rXD5s2bERQUBOD5OA57e3vExsZW2VZcXBwmTpyIkJAQSdm8efNqFMfGjRvx119/yZ2B9U2q80/OlQOr161bJ/duwYvdoCoPgiAIUnX2798v96FympqayMvLk7tdCwsLpKSkoKioSFKWl5eHQ4cO1Sp+d3d3ZGdnIzo6Wu7y6h52p4gnT56goqJCpvzRo0eIj49H48aNJXd4iIiIiKhuVH6m1NHRUbgNsVgs+TxbXl6O7OxsaGtro23btrh06ZKknr6+Pm7fvo2kpKQq29LX18f58+clj1KoqezsbCxcuBBBQUEwMjJSbEfqSJ3fsbC3t4e/vz8iIyPh4+ODvn37wsjICI8ePUJqairOnj2Lc+fOAQBcXFwQHh6OhQsXwsvLCzo6Orhy5QoSExNhZmYmk5g4ODjg4MGD2LBhA6ysrCASieDq6gpNTU14eXkhKCgIAQEBcHNzQ35+Pg4cOAATE5NaJQOffPIJzp8/j7CwMCQlJaFz587Q0tLCvXv3kJSUBHV1dZkZseTZvXu35BkUZWVluHfvHn744QcAQJs2beDq6grg+WDxnTt3onfv3mjRogXU1NTw999/Iy4uDnl5eViwYEGdDxQnIiIiauh0dXUBQKlnhlVUVCAsLAzr169HZmam1GfXyomNAODzzz9HfHw8unTpAhsbG/Tv3x8+Pj5SjxUIDg7G2LFjYW5ujvfffx9ubm4YM2YMWrVqVW0MCxYsgIGBQb2Nq3jRaxlj4e/vDzs7O+zatQs7d+5EYWEhDAwMYG1tjTlz5kjqmZmZYc2aNVi3bh2ioqKgoqICR0dHREREIDg4GFlZWVLtBgYGIjc3F7GxscjPz4cgCDh06BA0NTUxaNAgPHz4EHv27MGqVavQokULTJgwASoqKrWaXUpNTQ2rV6/G3r17ceTIEUkSYWRkBHt7e3h4eNSonR07dkjFf/fuXWzcuBEA4OHhIUksOnbsiNTUVCQkJODRo0coLS1F06ZN0aVLF4wcOVJm4A8RERERKU9XVxempqZKzUK6bNkyBAUFYfz48ViyZAkMDAygoqKCGTNmSPVIsbW1xR9//IGff/4Zx44dw08//YT169dj4cKF+OabbwAAXl5e6NmzJ/bv34/jx49j5cqVWLFiBfbt24dBgwbJ3f7NmzcRGRmJ1atXS93pKCoqQmlpKW7dugVdXV0YGBgovI+1IRJe7odE/3oDlpUqtf7Vsa/unxdsvEzys9fJqgfI07tvkdtvb2xbS0vL3ti2iIioZv7Nz7GYNGkSIiMjkZiYKPX8sqpYWlqid+/e2Lp1KwDgvffeg4GBAU6ePClVz8zMDDY2Njh9+rTcdkpKSjB8+HAcO3YMBQUFcnunPHjwAJ06dYKlpSUSEhLktnP69Gn06dOn2pinT5+O1atXv3Lf6kL9jk4mIiIiIqon8+bNg5aWFiZMmID79+/LLE9PT0dYWFiV66uqqsqMFY6NjZWZPOjlbvnq6uqws7ODIAgoLS1FeXm5zCREzZo1g6mpqWQyH3nat2+P/fv3y/yzt7eHhYUF9u/fDz8/vyrXr2t88hoRERERNUjW1taIiYmBt7c3bG1tpZ68nZiYiNjYWIwbN67K9T08PLB48WL4+vrC2dkZKSkpiI6OlhkX0b9/fxgbG8PFxQXNmzdHamoq1q5dC3d3d+jo6CAnJwdmZmbw9PSEo6MjtLW1ER8fj6SkJKlZol5maGiIjz76SKa88g6FvGWvExMLIiIiImqwhgwZguTkZKxcuVIySZBYLEaHDh0QEhKCiRMnVrnul19+iadPnyImJga7d+9Gp06dEBcXh/nz50vVmzRpEqKjoxEaGoqCggKYmZlh2rRpWLBgAYDnzy8LDAzE8ePHsW/fPlRUVMDGxgbr16/H5MmTX+v+1yWOsXgHcYwFvUkcY0FEREQAx1gQEREREVEdYGJBRERERERKY1eod1BkZCR8fX3RqFGj+g6FiIiIiBoI3rEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIiKlMbEgIiIiIqoBS0tLjBs3rr7DeGsxsSAiIiKiBi09PR2TJk1Cq1atoKGhAV1dXbi4uCAsLAyFhYX1HV61CgoK8PXXX2PgwIEwMDCASCTC1q1b6yUWtXrZKhERERHRWyAuLg4jRoyAWCzGmDFj0L59e5SUlCAhIQFz587FtWvXEBkZWd9hVunRo0dYvHgxLCws4OjoiNOnT9dbLEwsiIiIiEhhd/pr1XcIaHH8qULrZWZmYuTIkWjZsiVOnjwJExMTybIpU6bgzz//RFxcXF2F+VqYmJggKysLxsbGuHDhAjp37lxvsTCxeAf99MgXP60EgNL6DgVXx8bXdwj0gmDjZfUdQpW8TpZLfl7k9pvk56WlZfURDhERNQDBwcEoKCjA5s2bpZKKSjY2Npg+fXqV6z9+/BjLli3DL7/8gszMTKioqMDFxQXLly+Ho6OjVN3w8HBs3LgRmZmZEIvFsLa2xqxZs+Dj4wMAyM/PR1BQEA4cOICsrCzo6enB0dERK1asQKdOnaqMQSwWw9jYWMFXoG4xsSAiIiKiBunw4cNo1aoVnJ2dFVo/IyMDBw4cwIgRI2BlZYX79+8jIiICvXr1wvXr12FqagoA2LRpE6ZNmwZPT09Mnz4dRUVFSE5Oxvnz5yWJRUBAAPbu3YupU6fCzs4O2dnZSEhIQGpqarWJxduEiQURERERNTh5eXm4c+cOhg4dqnAbDg4OuHHjBlRU/jcf0qeffop27dph8+bNCAoKAvB8HIe9vT1iY2OrbCsuLg4TJ05ESEiIpGzevHkKx1YfOCsUERERETU4eXl5AAAdHR2F2xCLxZKkory8HNnZ2dDW1kbbtm1x6dIlST19fX3cvn0bSUlJVbalr6+P8+fP4+7duwrHU9+YWBARERFRg6Orqwvg+dgGRVVUVGDVqlVo3bo1xGIxDA0NYWRkhOTkZOTm5krqff7559DW1kaXLl3QunVrTJkyBWfPnpVqKzg4GFevXoW5uTm6dOmCRYsWISMjQ+HY6gMTCyIiIiJqcHR1dWFqaoqrV68q3MayZcswa9YsuLq6YseOHfjll19w4sQJ2Nvbo6KiQlLP1tYWf/zxB3bt2oUePXrgp59+Qo8ePfD1119L6nh5eSEjIwPh4eEwNTXFypUrYW9vj6NHjyq1n28SEwsiIiIiapA8PDyQnp6OX3/9VaH19+7diz59+mDz5s0YOXIk+vfvj759+yInJ0emrpaWFry9vREVFYW///4b7u7uWLp0KYqKiiR1TExMEBgYiAMHDiAzMxNNmzbF0qVLFd29N46JBRERERE1SPPmzYOWlhYmTJiA+/fvyyxPT09HWFhYleurqqpCEASpstjYWNy5c0eqLDs7W+p3dXV12NnZQRAElJaWory8XKrrFAA0a9YMpqamKC4uru1u1ZtazQp14cIFBAQEVLk8KioKDg4OSgdVlZiYGOjo6GDw4MGvbRt1ISUlBdu3b8eNGzfw+PFjAICxsTH69u0LHx8faGtrS+revXsXQ4YMqba9JUuWYNCgQa81ZiIiIqKGxtraGjExMfD29oatra3Uk7cTExMRGxuLcePGVbm+h4cHFi9eDF9fXzg7OyMlJQXR0dFo1aqVVL3+/fvD2NgYLi4uaN68OVJTU7F27Vq4u7tDR0cHOTk5MDMzg6enJxwdHaGtrY34+HgkJSVJzRJVlbVr1yInJ0cy8Pvw4cO4ffs2AOCzzz6Dnp6e4i9SLSg03eyAAQPg4uIiU25ubq50QNXZuXMnTExM3vrE4q+//kJRUREGDRoEQ0NDCIKAa9euYcuWLfjPf/6Dbdu2QUNDAwDQpEkTLF68WG47wcHBKC4uRvfu3d9k+EREREQNxpAhQ5CcnIyVK1fi4MGD2LBhA8RiMTp06ICQkBBMnDixynW//PJLPH36FDExMdi9ezc6deqEuLg4zJ8/X6repEmTEB0djdDQUBQUFMDMzAzTpk3DggULAACNGzdGYGAgjh8/jn379qGiogI2NjZYv349Jk+e/Mp9+P777/HXX39Jft+3bx/27dsHABg9evQbSyxEwsv3b6pRecdi+vTp+PTTT19nXHINHjwYJiYmiIyMrPO2y8rKUF5eDrFYXOdtV/rxxx+xZs0afPfdd+jXr1+1dZOTkzF+/Hh8+OGHWLFiRa22M2BZ/T9xuxKfvP124ZO3iYiI6HV5bWMsjh8/Dj8/P7i6usLFxQVjx45FfLzsh8zjx49j5syZcHd3R/fu3fHhhx9i9uzZuHnzplQ9JycnZGVl4dKlS3BycpL8q7zl4+TkhEWLFsm0f/jwYTg5OeHChQuSsoiICDg5OSE9PR2hoaFwc3OT3L4CgJKSEmzZsgVeXl5wdnZG7969MXPmTKSlpSn1mlQ+Kr5y3uTqHDhwAADw0UcfKbVNIiIiIqI3QaGuUEVFRTKj3Rs1agQtLS0AwPr167FlyxY4OzsjICAAKioqOHXqFObPn4958+bBy8tLst6ePXugp6eHYcOGwdDQELdv38b+/fvh5+eHHTt2wMLCAgCwePFihIaGQl9fH+PHj5es36RJE0V2AQAQFBQEsViMUaNGQSQSwdDQEGVlZfjss8+QnJwMNzc3eHl5oaCgQBLTpk2bYGdnV+PXqfJfamoqwsPD0ahRI3Tt2rXa9Z49e4b4+HiYmJi8si4RERER0dtAocQiIiICERERUmX9+vXDd999h7S0NGzZsgW+vr6YMmWKZPnIkSMxe/ZsrFu3Du7u7pIkJDw8HJqamlJtubu7w8fHBzExMZI+am5ubtiwYQMMDAzg5uamSNgytLW1sX79eqip/e9liI6OxsWLFxEeHi41tsHT0xPe3t5YvXp1jbtibdy4ETt27JD83qpVK6xatQpmZmbVrnf8+HE8e/YMo0ePlnpEPBERERHR20qhxGLYsGHo27evVFnTpk0BAEePHoVIJIK7u7vMXQ1XV1ecOXMGKSkp6NatGwBIkgpBEPD06VOUlZWhSZMmaNmypVIPLKkJHx8fqaSiMn5LS0vY2trKxN+1a1fExcWhqKhIMvi6OsOHD0f37t2Rn5+PlJQUXLx4Ue68xi87ePAgVFRUXjlbFBERERHR20KhxMLCwqLKLjqZmZkQBAGenp5Vrv/iXL5paWnYuHEjLl68iMLCQql6LVq0UCS8GqvsZvWizMxMFBcXyyROL8rJyYGxsXGN2q/cRt++ffHrr7/is88+AwAMHDhQ7joZGRlISUlB9+7da7QNIiIiIqK3gUKJxauIRCKsWbOmym481tbWAIB79+7B398fWlpa8PPzg6WlJTQ0NCASiRASEiKTaCiivLy8ymVV3XWwsbHBzJkzq1xP0XEd3bt3R9OmTbF3794qE4uDBw8CAIYOHarQNoiIiIiI6kOdJxbm5uZITEyEsbExrKysqq176tQpPHv2DKGhoXBycpJalpubC3V1dakykUhUZVt6enoyTywEIPPkw1cxNzfHkydP0Llz59cyvqG4uLjKWaFKS0tx5MgRNGnSBL17967zbRMRERERvS51/sm5cmD1unXr5N4teLEbVOUH95cfpbF//36ZR58Dz8djVPWh3MLCAikpKSgqKpKU5eXl4dChQ7WK393dHdnZ2YiOjpa7XF5cL3v06JHc8p9//hkFBQVo37693OVnzpzBkydP4ObmJjP2g4iIiIjobVbnn17t7e3h7++PyMhI+Pj4oG/fvjAyMsKjR4+QmpqKs2fP4ty5cwAAFxcXhIeHY+HChfDy8oKOjg6uXLmCxMREmJmZySQmDg4OkiciWllZQSQSwdXVFZqamvDy8kJQUBACAgLg5uaG/Px8HDhwACYmJjVKBip98sknOH/+PMLCwpCUlITOnTtDS0sL9+7dQ1JSEtTV1WVmxHrZ9OnToaenhw4dOsDY2BgFBQW4fPkyzpw5g+bNm8Pf31/uepVJEJ9dQURERET/Nq/la3F/f3/Y2dlh165d2LlzJwoLC2FgYABra2vMmTNHUs/MzAxr1qzBunXrEBUVBRUVFTg6OiIiIgLBwcHIysqSajcwMBC5ubmIjY1Ffn4+BEHAoUOHoKmpiUGDBuHhw4fYs2cPVq1ahRYtWmDChAlQUVGp1exSampqWL16Nfbu3YsjR45IkggjIyPY29vDw8PjlW0MGzYMJ0+exIEDB5CTkwM1NTWYmZlh7NixGD16NPT19WXWuXfvHs6dO4cOHTq8sgsZEREREdHbRiS83A+J/vUGLCut7xAkro6Vfdo61Z9g42X1HUKVvE7+7w7lIrffJD8vLS2rj3CIiIiolvj0NSIiIiIiUhoTCyIiIiKiGrC0tMS4cePqO4y3FhMLIiIiImrQ0tPTMWnSJLRq1QoaGhrQ1dWFi4sLwsLC6uS5aq9TUlISpk6dCnt7e2hpacHCwgJeXl64cePGG4+Fc5oSERERkcK+alT/HyeVGY8XFxeHESNGQCwWY8yYMWjfvj1KSkqQkJCAuXPn4tq1a4iMjKzDaOvWihUrcPbsWYwYMQIdOnTAvXv3sHbtWnTq1Annzp2r8jEHr0P9nwlERERERPUgMzMTI0eORMuWLXHy5EmYmJhIlk2ZMgV//vkn4uLi6jHCV5s1axZiYmKkHizt7e0NBwcHLF++HDt27HhjsbArFBERERE1SMHBwSgoKMDmzZulkopKNjY2mD59epXrP378GHPmzIGDgwO0tbWhq6uLQYMG4cqVKzJ1w8PDYW9vj8aNG6NJkyZwcnJCTEyMZHl+fj5mzJgBS0tLiMViNGvWDP369cOlS5eq3QdnZ2eppAIAWrduDXt7e6Smpr7qJahTvGNBRERERA3S4cOH0apVKzg7Oyu0fkZGBg4cOIARI0bAysoK9+/fR0REBHr16oXr16/D1NQUALBp0yZMmzYNnp6emD59OoqKipCcnIzz58/Dx8cHABAQEIC9e/di6tSpsLOzQ3Z2NhISEpCamopOnTrVKi5BEHD//n3Y29srtF+KYmLxDvrYMAq+vr5o1KhRfYcCYFB9B0BS3uLj0e9/Py59ex7FQkRE76i8vDzcuXMHQ4cOVbgNBwcH3LhxAyoq/+sE9Omnn6Jdu3bYvHkzgoKCADwfx2Fvb4/Y2Ngq24qLi8PEiRMREhIiKZs3b55CcUVHR+POnTtYvHixQusril2hiIiIiKjBycvLAwDo6Ogo3IZYLJYkFeXl5cjOzoa2tjbatm0r1YVJX18ft2/fRlJSUpVt6evr4/z587h7967C8QBAWloapkyZgu7du2Ps2LFKtVVbTCyIiIiIqMHR1dUF8Hxsg6IqKiqwatUqtG7dGmKxGIaGhjAyMkJycjJyc3Ml9T7//HNoa2ujS5cuaN26NaZMmYKzZ89KtRUcHIyrV6/C3NwcXbp0waJFi5CRkVGreO7duwd3d3fo6elh7969UFVVVXjfFMHEgoiIiIgaHF1dXZiamuLq1asKt7Fs2TLMmjULrq6u2LFjB3755RecOHEC9vb2qKiokNSztbXFH3/8gV27dqFHjx746aef0KNHD3z99deSOl5eXsjIyEB4eDhMTU2xcuVK2Nvb4+jRozWKJTc3F4MGDUJOTg6OHTsmGd/xJokEQRDe+FbptYqMjHyLxlgQERHRu+zf/ByLSZMmITIyEomJiejevfsr61taWqJ3797YunUrAOC9996DgYEBTp48KVXPzMwMNjY2OH36tNx2SkpKMHz4cBw7dgwFBQXQ0NCQqfPgwQN06tQJlpaWSEhIqDauoqIi9O/fHxcvXkR8fHyN9uV14B0LIiIiImqQ5s2bBy0tLUyYMAH379+XWZ6eno6wsLAq11dVVcXL39HHxsbizp07UmXZ2dlSv6urq8POzg6CIKC0tBTl5eVSXacAoFmzZjA1NUVxcXG1+1BeXg5vb2/8+uuviI2NrbekAuCsUERERETUQFlbWyMmJgbe3t6wtbWVevJ2YmIiYmNjMW7cuCrX9/DwwOLFi+Hr6wtnZ2ekpKQgOjoarVq1kqrXv39/GBsbw8XFBc2bN0dqairWrl0Ld3d36OjoICcnB2ZmZvD09ISjoyO0tbURHx+PpKQkqVmi5Jk9ezYOHTqEwYMH4/HjxzIPxBs9erTCr09tMbEgIiIiogZryJAhSE5OxsqVK3Hw4EFs2LABYrEYHTp0QEhICCZOnFjlul9++SWePn2KmJgY7N69G506dUJcXBzmz58vVW/SpEmIjo5GaGgoCgoKYGZmhmnTpmHBggUAgMaNGyMwMBDHjx/Hvn37UFFRARsbG6xfvx6TJ0+uNv7Lly8DeP5MjsOHD8ssf5OJBcdYvIM4xoKIiIiI3jSOsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqUxsSAiIiIiIqWp1XcAVLcEQUBhYSHy8vLQqFGj+g6HiIiIiN4BOjo6EIlE1dYRCYIgvKF46A149OgRjIyM6jsMIiIiInqH5ObmQldXt9o6vGPxjhGLxXjvvfcQFxcHbW3t+g6H3rCCggK4u7vz+DdQPP4NG49/w8bj37C9ieOvo6PzyjpMLN4xIpEIqqqq0NXV5RtLA6SiosLj34Dx+DdsPP4NG49/w/a2HH8O3iYiIiIiIqUxsSAiIiIiIqUxsXjHqKurY+LEiVBXV6/vUKge8Pg3bDz+DRuPf8PG49+wvS3Hn7NCERERERGR0njHgoiIiIiIlMbEgoiIiIiIlMbpZv9Fbt26heDgYCQnJ0NLSwtubm4IDAx85RO2BUHAtm3bEBsbi5ycHLRp0wazZs2Cg4PDG4qc6oKix3/w4MHIysqSKT979izEYvHrCpfq0D///IPt27fj6tWrSE9PR8uWLbFnz55Xrsdr/92g6PHntf9uiI+Px5EjR5CWloa8vDxYWFjA29sbQ4YMqfYpyLz+3w2KHv/6uv6ZWPxL5OXlISAgABYWFli5ciUePHiAVatWoaioCJ9//nm1627btg0RERGYOnUqWrdujdjYWEydOhXR0dEwMzN7Q3tAylDm+APAhx9+iNGjR0uV1fcAL6q59PR0nD17Fvb29qioqEBFRUWN1uO1/25Q9PgDvPbfBdHR0TAxMcGMGTPQpEkTnD9/HkuXLsX9+/fh7+9f5Xq8/t8Nih5/oJ6uf4H+FbZs2SL06NFDyMnJkZT99NNPQpcuXYQHDx5UuV5RUZHg6uoqrF27VlJWUlIieHh4CN99991rjZnqjqLHXxAEwcPDQ1i+fPnrDpFeo/LycsnPX3/9tTBixIhXrsNr/92hyPEXBF7774onT57IlH377beCq6ur1LnxIl7/7w5Fjr8g1N/1zzEW/xKJiYno0qUL9PT0JGX9+vVDRUUFzp07V+V6ycnJePr0Kfr27Sspa9SoEfr06YOzZ8++1pip7ih6/OndoKJS+7dqXvvvDkWOP7079PX1Zcratm2Lp0+forCwUO46vP7fHYoc//rEd6t/iVu3bsHS0lKqTEdHB4aGhrh161a16wGQWdfKygr37t1DUVFR3QZKr4Wix7/SsWPH0L17d/Ts2RPTpk3Dn3/++XoCpbcGr30CeO2/qy5fvoxmzZpBS0tL7nJe/++2Vx3/SvVx/XOMxb9EXl4edHR0ZMp1dHSQl5dX7Xrq6uoyA3V0dHQgCALy8/OhoaFR5/FS3VL0+AOAq6sr2rdvD2NjY9y5cwdbtmyBn58f+9m+43jtE6/9d9Ply5dx/PhxzJgxo8o6vP7fXTU5/kD9Xf+8Y0H0jps7dy4GDRqEjh07wsPDA5GRkQCAHTt21HNkRPQ68dp/99y/fx9ffPEFnJycMHLkyPoOh96w2hz/+rr+mVj8S+jq6qKgoECmPD8/H7q6utWuV1JSguLiYpn1RCKR3G/B6e2j6PGXx9DQEO+99x5SU1PrKjx6C/Hap5fx2v93y8/Px7Rp06Cnp4fg4OBqx97w+n/31Ob4y/Omrn8mFv8SlpaWMn3pCwoK8OjRI5k+lC+vBwB//fWXVPmtW7dgbGzMW6H/Eooef2q4eO0TvTuKioowY8YMFBQUYM2aNdDW1q62Pq//d0ttj399YmLxL+Hs7IzffvsN+fn5krL4+HioqKigW7duVa7XoUMHaGlpIT4+XlJWVlaGU6dOwcXF5bXGTHVH0eMvz8OHD3H58mXY2dnVdZj0FuG1Ty/jtf/vVFZWhi+++AK3bt1CeHg4mjVr9sp1eP2/OxQ5/vK8qeufg7f/JT7++GPs3r0bs2fPxvjx4/HgwQOEhYVh+PDhMDIyktSbPHkysrKycODAAQCAWCyGr68vIiMj0aRJE9jY2CA2Nha5ubkyD02ht5eix//YsWNISEiAi4sLjIyMcPv2bWzduhWqqqo8/v8iRUVFSEhIAABkZWXh6dOnkg8M77//Ppo0acJr/x2myPHntf/uWLFiBf7v//4PM2bMwNOnT5GSkiJZ1rZtW6irq/P6f4cpcvzr8/pnYvEvoauriw0bNmDlypWYPXs2tLS08NFHHyEwMFCqXnl5OcrLy6XKxo4dC0EQsGPHDjx58gRt2rRBeHg4ZwX5F1H0+Ldo0QIPHz5ESEgI8vPzoaOjg86dO2PSpElo0aLFm94NUtDjx48xf/58qbLK3zdu3AgnJyde++8wRY4/r/13R+WzilavXi2z7NChQzA1NeX1/w5T5PjX5/UvEgRBeK1bICIiIiKidx7HWBARERERkdKYWBARERERkdKYWBARERERkdKYWBARERERkdKYWBARERERkdKYWBARERERkdKYWBARERERkdKYWBARERERkdKYWBDVwIMHD6Cnp4dNmzZJlY8bNw6Wlpb1E9Q7YtGiRRCJRLh169Yb2d7WrVtltldYWAhTU1N88803tW6vqnODFFd5jE6fPl3foVA9U/b9gedSw3Xr1i2IRCIsWrTojW739OnTEIlE2Lp1q0LrX758GSoqKjhz5kzdBvaGMLEgqoEFCxbAyMgIvr6+Nap/7949zJkzB+3bt4eOjg50dXXRunVrjBw5Evv27ZOq27t3b2hra1fZVuUf1gsXLshd/uTJE2hqakIkEmH79u1VtmNpaQmRSCT5p66uDktLS0yYMAH//PNPjfbrXaWpqYn58+dj5cqVyMrKqtW6tT03qGG7fPkyFi1a9MYSaap/t27dwqJFi3D58uU3ul2ea7JycnKwaNGitzrRfO+99/DRRx9h9uzZEAShvsOpNSYWRK9w+/ZtbNmyBZ999hnU1NReWf+vv/6Co6Mj1q1bh27dumH58uX47rvv4OHhgbS0NERFRdVpfNHR0SguLoaVlRW2bNlSbV0zMzNs374d27dvR1hYGLp27YotW7aga9euePToUZ3G9W/j5+cHkUiE0NDQGq9T23ODaubTTz9FYWEhXF1d6zuUOnf58mV88803/LDXgNy6dQvffPNNvSQWDflca9myJQoLC7FgwQJJWU5ODr755pu3OrEAgBkzZuDixYs4cuRIfYdSa/xLSPQKEREREIlE+OSTT2pU//vvv8eDBw9w4MABDB06VGb5vXv36jS+zZs3o0+fPhg6dChmzJiBjIwMtGrVSm5dPT09jB49WvL75MmT0axZM6xduxZRUVGYO3duncb2b6KlpYXhw4dj69at+PbbbyEWi1+5Tm3PjfpWXl6O4uJiNG7cuL5DqZaqqipUVVXrOwwi+hcTiUTQ0NCo7zAU0rNnT1haWmLjxo1wd3ev73BqhXcsqM5V9mn9z3/+g8WLF6Nly5bQ1NRE165dce7cOQDAmTNn0KNHD2hpacHExARLliyR29aFCxcwbNgwGBoaQiwWo23btli6dCnKysqk6v32228YN24c2rRpg8aNG0NHRwcuLi7Yv3+/TJvjxo2DSCRCbm6u5IO1hoYGXFxccP78eZn6sbGxcHJyQrNmzWq0/zdv3gQAfPjhh3KXGxsb16idmrh06RIuX76MsWPHwsfHB2pqaq+8a/GyAQMGAAD+/PPPKuscPXoUIpEIa9askbu8e/fuMDIyQmlpKYDaHQ95Ko+RPCKRCOPGjZMp3717N3r06AEdHR00btwYXbt2xd69e2u0vUqDBg3Co0ePcOrUqRrVr+rcqKiowNKlS+Hq6gpjY2Ooq6vDwsICkydPRnZ2tqReTk4ONDQ0MHz4cLntf/HFFxCJRFLfdObm5uLzzz+HjY0NxGIxjIyM8MknnyAjI0Nq3crrMD4+HkuWLIG1tTU0NDSwZ88eAMDx48fh7e2NVq1aQVNTE/r6+ujfv3+V/Xp/+uknODo6QkNDAxYWFvjmm28QHx8vty9xcXExli1bBnt7e2hoaEBfXx+DBw/G77//XqPXVV6/+Lp6X7G0tETv3r1x6dIlfPDBB9DW1oaBgQHGjh2LBw8eSNXNz8/HggUL0LVrV8l7kI2NDebPn49nz57JtC0IAjZt2oSuXbtCW1sb2tracHBwwMKFCwE879ZY2WWuT58+km6J8s7nlyUnJ2PYsGFo2rQpNDQ0YGdnh+DgYJSXl0vVq+37mzyV3S+vX7+OGTNmwMTEBI0bN8aHH36IP/74AwCwb98+dOrUCZqamrC0tERkZKTctn744QdJPT09PfTv3x8JCQky9SoqKvDdd9/BysoKGhoaaN++PaKjo6uMMSsrC5MnT4aFhQXU1dVhamoKf39/mWNYWzV9nXv37i13fN3L/fq3bt2KPn36AAB8fX0lx7x3794ApPvjh4eHo02bNtDQ0ECbNm0QHh4u037l+fuyl/v1K3quVZ4/2dnZGDduHAwNDaGjo4OPPvpI8qVYZGQkbG1toaGhgXbt2uHgwYMy7axfvx79+/dHixYtoK6uDhMTE4wePVru3ZPy8nIsWbIELVu2hIaGBjp06IDdu3fLHV9Tm/P75WNx+vRpWFlZAQC++eYbyWtSeRyrGxtR1d+kgwcPomPHjtDQ0IC5uTmCgoIkfwdfVpv3RZFIhAEDBuDYsWMoKCiQ297bincs6LWZP38+ysvLMX36dJSUlCAkJAT9+/fHjz/+CD8/P/j7+2PUqFHYs2cPFi5cCCsrK6lv0+Pi4jB8+HDY2Nhg9uzZMDAwwK+//oqFCxfi8uXLiI2NldTdv38/0tLS4OXlhZYtWyI7Oxvbtm3D8OHDER0dDR8fH5n4BgwYACMjIyxcuBDZ2dkIDQ2Fu7s7MjMzoaOjAwC4f/8+/vjjD0ybNq3G+21tbQ0A2LRpE2bMmFHlB+SXVdUVSd4HmEqbN2+GtrY2Pv74Y2hpacHDwwPbtm3D4sWLoaJSs+8NKhMhQ0PDKuv0798fxsbG+PHHH2Vei5s3b+LcuXOYNm0aGjVqBECx46GMBQsWYOnSpRg4cCCWLFkCFRUV7N+/HyNGjMDatWsxZcqUGrXTvXt3AM//wAwcOLDautWdGyUlJVi5ciU+/vhjDB06FFpaWkhKSsLmzZuRkJCAixcvQl1dHfr6+hgyZAgOHjyIx48fw8DAQNJGRUUFoqOj0aFDB7z33nsAnicVzs7O+PvvvzF+/HjY29sjKysL69evR9euXXHhwgW0bNlSKpY5c+agtLQUEydOhK6uLtq2bQvg+Qeex48fY8yYMTAzM8OdO3fwww8/4MMPP8SpU6fQs2dPSRu7d+/GJ598Amtra3z99ddQU1PDtm3bcPjwYZl9Ly0txcCBA5GYmIhPP/0UU6dORW5uLjZt2gQXFxf897//hZOTU42OhzzKvq8Az7uwffjhh/j444/h6emJS5cuYcuWLbhw4QKSkpIkd3QqX5OPP/5YkrifOXMGwcHB+P333/HLL79Itfvpp58iOjoaXbt2xVdffQV9fX2kpaVh7969WLx4MYYPH46srCxERkbiyy+/hK2tLYD/vWdU5cKFC+jVqxcaNWqEKVOmwNjYGIcPH8bnn3+OK1euyP0AXpP3t1cZO3YstLW18eWXX+Lhw4cICQnBgAEDsGTJEsybNw+TJ0/G+PHjsXnzZkyaNAl2dnbo0aOHZP3PP/8cwcHB6NKlC5YtW4b8/HxERkaiT58+OHjwINzc3CR1Z82ahbCwMLi6umLmzJl48OABpkyZIvfu699//43u3bujpKQEfn5+sLa2xp9//okNGzbg1KlTuHDhAvT09Gq0j8q+zq/i6uqKL7/8EsuWLYO/v7/kumrevLlUvfDwcNy7dw+TJk2Cjo4Odu7ciWnTpuHx48f4+uuva71dRc+1SgMHDoSZmRkWL16MP//8E2vWrMGwYcMwfPhwREZGws/PDxoaGlizZg08PT1x48YNyYd24Pmd+27dumHatGkwMDDA1atX8cMPP+DkyZNISUlB06ZNJXWnTp2KjRs3ok+fPpgzZw4ePnyIwMBAqfZepsj5bWtri1WrVmHmzJmSfQFQ7RjH6uzfvx8ff/wxLC0tsXDhQqipqSEqKgpxcXEydRV5X+zevTsiIiKQkJDwyr9HbxWBqI5FRUUJAISOHTsKxcXFkvKDBw8KAAQ1NTUhKSlJUl5cXCwYGxsL3bp1k5QVFhYKzZs3F3r27CmUlpZKtR8aGioAEE6dOiUpKygokInj6dOnQps2bQRbW1up8rFjxwoAhMmTJ0uV79mzRwAgbNy4UVJ28uRJAYAQFhYmd1/Hjh0rtGzZUqosPT1d0NXVFQAI5ubmgo+Pj7Bq1SrhwoULctvo1auXAOCV/158zSpfI319fWHs2LGSsgMHDggAhCNHjshsp2XLlkK7du2Ehw8fCg8fPhQyMjKELVu2CHp6eoKampqQkpIiN75Kc+bMEQAI165dkypfsGCBAEC4ePGipKw2x+Prr78WAAiZmZmSsspjJA8AqX2+ePGiAED44osvZOoOHTpU0NHREfLy8iRllefni9t7kZqamuDh4SF32YuqOzcqKiqEZ8+eyZT/8MMPAgBh9+7dkrKff/5ZACCsW7dOqm58fLwAQAgJCZGUTZs2TdDQ0BAuX74sVffWrVuCjo6O1OtSuZ9t2rQRnj59KhOLvGN07949oWnTpsKgQYMkZaWlpYKpqanQrFkz4fHjx5Ly/Px8wcrKSgAgREVFScorr89jx45JtZ2bmyuYm5sLvXr1ktnuyypjf/Ear4v3FUF4fh0AEFatWiVVXhn3d999J9VGSUmJTHyV5/z58+clZbt37xYACKNHjxbKy8ul6r/4u7x9exVnZ2dBVVVVuHLliqSsoqJCGDFihABAiI+Pl5TX5v2tKpXXpIeHh1BRUSEpDwsLEwAIOjo6wt9//y0pf/DggSAWi4WRI0dKytLS0gSRSCS4uLhIHa87d+4Ienp6QsuWLYWysjKpuh988IGkTBCeX9sikUjmeh0yZIhgZGQk/PPPP1JxJyUlCaqqqsLXX38tKavN612b17lXr14y7/2CIAiZmZkCAKkYTp06JXOdvLxMW1tban+Ki4uFzp07C2pqalLlLVu2lHsNyduGIuda5fkTGBgoVT5z5kzJ37Tc3FxJ+ZUrVwQAwvz586Xqy3t/qXxPW7FihaTs6tWrAgBhwIABUtdJcnKyoKKiUuXfhpqc3/KOhbyyStUdp5f/JpWVlQnm5uZC06ZNhYcPH0rKc3JyBAsLizp5X/y///s/AYDw/fffyyx7m7ErFL02kydPhrq6uuT3ym9qunbtKpWZq6uro0uXLpJvzgHgxIkTuH//Pnx9fZGTk4NHjx5J/lV+y3X8+HFJfS0tLcnPz549Q3Z2Np49e4YPPvgAqampyMvLk4lv5syZUr9/8MEHACAVx8OHDwFA6pvkV2nVqhWuXLki+ZY8JiYGM2fOhJOTEzp06ICLFy/KrKOhoYETJ07I/ffpp5/K3c6+ffuQk5ODsWPHSsrc3NxgZGRUZXeotLQ0GBkZwcjICK1atcL48eNhaGiIgwcPon379tXuV+V2fvzxR0mZIAjYsWMH2rdvj06dOknKFTkeioqOjoZIJMLYsWOlzpNHjx5hyJAhyM/Px6+//lrj9gwMDGrUnaK6c0MkEkFTUxPA89v8ledw5Tn24i37AQMGoHnz5lKvK/D8dVZTU8OoUaMAPH+to6Oj4erqihYtWkjtp5aWFrp16yZ1TVSaPHmy3DEVLx6jgoICZGdnQ1VVFV27dpWK7+LFi7h79y7GjRuHJk2aSMq1tbUREBAg0+6OHTvQrl07vP/++1IxlpSUoF+/fkhISEBhYaGcV7RmlHlfqaSrq4vAwECpssDAQOjq6kp111NXV5fchSsrK8OTJ0/w6NEj9O3bF4D0caz8Nvv777+XuVtY07uH8jx48ACJiYkYMmQIOnToICkXiUT46quvAEBuF8OavL+9yrRp06TuuFa+1kOGDIG5ubmk3MjICG3btpVq++DBgxAEAfPmzZM6XqampvD19cVff/0l6QJSWXfWrFlSY2s6deqEfv36ScWUm5uLn3/+GUOGDIGGhobUOWZpaQkbGxu518GrKPo615VRo0bBzMxM8ru6ujpmzpyJsrIyuXcGX7cZM2ZI/V557MeMGQNdXV1JeYcOHaCrqytzXlW+v1RUVCA3NxePHj2Co6Mj9PT0pK6bn3/+GQAwffp0qevEwcFB0k1Xnro4v5Vx8eJF/PPPP/D19ZW626+np1dn74uVd3WU7d73prErFL02L9/CrvxQIu/2ZpMmTaT6nqempgIAxo8fX2X79+/fl/z84MEDLFiwAAcPHpR7Eebk5Ei9GcqLr/IifjGOyj+qQi2nfLO0tMTatWuxdu1aZGVlISEhAdu3b8fhw4fh4eGBa9euSX0gVVVVlXxYeZm8/sjA825QRkZGMDMzkxof0b9/f8TGxuLRo0cy3ZssLS0lz1uo7JdsY2NTo32qTB6io6OxbNkyqKio4L///S9u3bqF4OBgqbqKHA9FpaamQhAEtGvXrso6L54rryIIQo26r73q3NizZw9CQkLw+++/y/S5ffLkieTnyuQhNDQUN27cQJs2bfD06VPs27cP/fv3l3SZePjwIbKzs3H8+HEYGRnJ3aa8D7Bt2rSRWzc9PR1fffUVfvnlF+Tk5MjdNwDIzMwEAEkXqhfJK0tNTUVhYWGVMQLPu/29+MG0NpR5X3mxjRc/7AKAWCxGq1atZMaqrF+/Hhs3bsS1a9dQUVEhtezF43jz5k2YmJjIdHFRVuXrb29vL7PM1tYWKioqMjEDNXt/e5XavtZ//fVXjeKuLMvIyICTk5MkfnnXsJ2dnVSi8Mcff6CiogKbN2/G5s2baxR3TSj6OteVyq5KL7KzswOA17rdqih7nZ08eRKLFy/G+fPnUVRUJLXsxevmVe8vR48erVF8ipzfynjVOfsyRd4XK/+21LQ79duCiQW9NlXN6lKT2V4qL6iVK1dK+pe/zNTUVFK3f//+SE1NxfTp0+Hk5AQ9PT2oqqoiKioKMTExMh8IqovjxQ+KlW8Cjx8/fmXMVTExMcGIESMwYsQIjBo1CjExMThy5IhMv+/ayMzMxKlTpyAIQpUfHHfs2CHzrZOWllaVCUxNjBkzBjNmzMDJkyfRt29f/Pjjj1BVVZXaF0WPx4uqeiN9edB+5fZEIhGOHj1a5TGV92GhKk+ePKn2zb9SdefGvn374O3tjS5duiAsLAzm5ubQ0NBAeXk5Bg4cKLP/Y8aMQWhoKH788Ud8++232LdvHwoKCqTuRlWel3379sXnn39e4/2Rd7eioKAArq6uePr0KWbMmAEHBwfo6OhARUUF3333HU6ePFnj9l8mCAIcHByqnba3Jq9vVZR5X6mt0NBQzJ49G/3798e0adNgamoKdXV13LlzB+PGjXvleVyfavL+pmgbddG2oiq3MXr0aKnr40WVdwtfp9q8R/0bt6vMsU9KSkL//v1hY2OD5cuXw8rKSvKspZEjR9bJdfM6zsHqPsAr+/oq8r5Y+bdFmffL+sDEgt5KrVu3BlCzD8LJycm4cuUKFi5cKPPk5B9++EGpOCo/kNbV7dVu3bohJiYGd+7cUaqdqKgoyQw0+vr6MssXLFiALVu2yCQWyvLx8cHcuXPx448/wsXFBXv37kW/fv1gYmIiqVMXx6Pybs7LA5rlfXPXunVrHDt2DBYWFnK/9auNW7duoays7JXdwoDqz43t27dDQ0MDp06dkvpgn5aWJrctR0dHODo6YseOHViyZAl+/PFHycDuSkZGRtDX10deXp5SySEA/Oc//8Hdu3exZcsWmQf7vTjnOwDJjCmVswG9SF5Z69at8fDhQ3zwwQdKdQF6nTIyMlBSUiJ116K4uBgZGRlS30Bu374dlpaWOHr0qNS+HDt2TKbNNm3a4ODBg7h//361dy1q++1j5TfE165dk1mWlpaGiooKhb6hf90qY7p27ZrMgOHr169L1an8Py0trcq6lWxsbCASiVBSUqL0dfCi2r7OBgYGcru1ynuPqskxr7xL/6KXX6fK7cr7MkPR7b4OMTExKC8vx9GjR6XucDx9+lTqbgUg/f7y8nks7/1FWdW9Ji/+3XnZy6/vi+fsy14+ZwHF3hcreyLU5O/R2+TtfNenBm/AgAFo1qwZli9fLvciLywsRH5+PoD/fXPx8jcVV69eVbpPrJGREezt7SXTWdbE6dOn5fYhr6iokPSVlXertKYqKiqwdetWODg4YMKECfD09JT598knnyAlJQVJSUkKb0ceIyMjDBo0CPv27UN0dDTy8vJkvjWsi+NReRcmPj5eqjwkJESmbuUYlC+//FJmSkigdt2gKo9zr169Xlm3unNDVVUVIpFI6ps5QRDw7bffVtne2LFj8ddffyEmJgYnT56Et7e31BzsKioqGDVqFH777bcqp9GtaV/cqo7R8ePHZaZsdHJygomJCbZu3Sr1oaCgoAAbN26UaXvMmDG4d+9eld/M1eZ4vC55eXlYv369VNn69euRl5eHjz76SFJWeRxffJ3KysqwfPlymTYrx8LMmzdP5hvZF9evnIGmpndBmzVrBmdnZxw+fBhXr16VavO7774DAAwbNqxGbb1JQ4YMgUgkwsqVK6W6AmZlZSEqKgotW7ZEx44dpeqGhoZKXcOXLl2SeQ9o2rQp3NzcsG/fPrnXniAIkvFPtVHb17lNmzbIz8/Hb7/9JimrqKjAqlWrZNquyTGPjo7G7du3Jb+XlJRg1apVUFVVhYeHh9R209LSpL6cKi4uxrp16xTa7utQ1fvLsmXLZK6NwYMHAwDCwsKklqWkpMjMulYXqntNrKysoKamJnPOJSYmypxr77//PszMzBAVFSU1o2NeXl6dvS+eO3cOampqcHFxefWOvUV4x4LeSlpaWvjxxx/x0UcfoW3bthg/fjxsbGyQk5ODtLQ07Nu3D/v370fv3r1ha2sLe3t7BAcH49mzZ2jbti1u3LiBiIgIODg4yP1WqTZGjBiBJUuWICsrS+qb+ap8//33OHv2LAYPHoxOnTpBT08P9+7dw08//YSLFy+iT58+Sj3w5vjx4/jnn3/g5+dXZZ2PP/4YixYtwubNm9G5c2eFtyXP2LFjcejQIcyePRt6enpSH8QA1Mnx+OSTT/Dll1/C398faWlpMDAwwLFjx+ROydu5c2csWrQIixYtwnvvvYcRI0bA1NQUWVlZkieXlpSU1Gjfjhw5AkNDQ8m8869S1bnh6emJn376CR988AHGjBmD0tJSHDhwoNqpg0eNGoV58+YhMDAQFRUVcrt5LF26FGfPnoWXlxe8vLzQrVs3qKur46+//sKRI0fw/vvvy52D/WU9evSAsbExZs+ejVu3bsHMzAyXL1/G9u3b4eDggJSUFEldNTU1fP/99xg1ahS6dOkCPz8/qKmpYevWrWjatCkyMzOlvgWcPn06Tpw4gblz5+LkyZP44IMPoKuri7///hv/+c9/JHdy6pO1tTW++eYbXL16Fe+//z4uXryILVu2oF27dlLTB3t6euKLL77AoEGDMHz4cOTl5SEmJkYyoPtFI0aMgLe3N3788UfcvHkTQ4YMQZMmTXDjxg388ssvkg+rnTt3hoqKCpYuXYonT55AS0sLVlZW6Nq1a5XxhoWFoVevXujZs6dkGtSff/4Zv/zyC3x8fKp8Zk59atu2LebOnYvg4GC4urrC29tbMt1sQUEBoqOjJR9A27VrhylTpmDt2rX44IMP8PHHH+PBgwdYu3YtHB0dZeb537BhA3r06AFXV1eMGTMGHTt2REVFBTIyMnDw4EGMGTNG8uyC2qjN6+zv74+QkBAMGzYM06dPh7q6Ovbu3Su3y4ydnR10dHSwfv16NG7cGPr6+mjWrJlkwDHwPGHo2rUrAgICoKOjg5iYGCQlJSEoKEiq3/3UqVOxa9cu9O3bFwEBASgpKcH27dvldnlU5FyrC8OGDcOqVavg5uYGf39/qKur48SJE0hOTpYZ92dvbw9/f39ERkaib9++GDZsGB4+fIh169ahY8eOuHjxYp3eeWnatClsbGywa9cuWFtbo3nz5tDS0sLgwYOhra2NcePG4YcffsAnn3yC3r174+bNm4iKikKHDh1w5coVSTuqqqpYtWoVvLy80KVLF0ycOFHyHKmmTZvi77//ltpubd8XBUHAsWPHMHDgQIWnw603r3nWKWqAqpviDi9NFVqpqulFU1JShFGjRgmmpqZCo0aNhGbNmgndu3cXFi9eLGRnZ0vq3bp1S/D09BQMDQ0FTU1NoXPnzsK+ffuUnspUEJ5Pj6impiZ3yjd5083++uuvwqxZswQnJyehWbNmgpqamqCnpyd069ZNCAkJEYqKiqTq9+rVS9DS0pIbjyD8b+rHyqk0PT09BQBCcnJylesIgiC0adNG0NPTk0x72rJlS8He3r7adWqiuLhYMDAwEAAIEyZMkFunNsdDXpkgCMK5c+cEZ2dnQSwWC02bNhUmTpwoPHnypMpz6Oeffxb69+8vNGnSRFBXVxfMzMyEgQMHChs2bJCqV9V0swUFBYKWlpYwZ86cGr8W1Z0bkZGRgq2trSAWiwVjY2Nh4sSJQnZ2dpXxC4IgeHh4CACE1q1bV7nNp0+fCosXLxbat28vaGhoCNra2kK7du2ECRMmCOfOnZPZz6qmmrxy5YowYMAAQV9fX9DW1hZ69eol/Pe//63y+tizZ4/g4OAgqKurC+bm5sKiRYuEffv2yUyfKwjPp6gNCwsTnJychMaNGwuNGzcWbGxsBB8fH+GXX36pct+qi72u3lcqp+u8ePGi0KdPH6Fx48aCvr6+MHr0aOHevXtSdcvKyoRly5YJ1tbWgrq6umBhYSHMnTtXuH79utwpK8vLy4W1a9cKHTt2FDQ1NQVtbW3BwcFBWLRokVS9rVu3Cra2tkKjRo2qPR9edPnyZWHo0KGS87tdu3bCihUrpKZnrWqfX/U6vayqa7K6qTqrmn41MjJSeO+99wSxWCzo6OgIffv2Ff773//K1CsvLxe+/fZbwcLCQlBXVxfs7e2FHTt2VBnLw4cPhTlz5gitW7cWxGKxoKenJ7Rv316YNm2a1JTYtZ1ytaavsyAIQlxcnODo6Cioq6sLJiYmwrx584S0tDS5r1FcXJzQsWNHQSwWCwAk04u+OMVpWFiYYGNjI6irqws2NjbC6tWr5ca4detWoU2bNkKjRo0ES0tLYcWKFcJ//vMfuVOl1vZcq+r8qW4qVnlT4O7fv1/o1KmT0LhxY6Fp06aCt7e38Ndff8mtW1ZWJixatEgwNzcX1NXVBQcHB2H37t3C7NmzBQDC/fv3XxmfIMie31Wdr+fPnxecnZ2Fxo0bCwCkztv8/HzBz89PMDAwEDQ1NYUePXoIZ8+erXK7P/30k+QcMDMzExYsWCAcP35c7mtVm/fF06dPCwCEn3/+We6+vs1EgvAGRlsR/csFBATg+PHj+OOPP6S+rRw3bhxOnz4t92mi9HbaunUrfH19kZmZKfXk3LCwMHz11VeS2X1qqqpzoyEICQnBnDlz8Ouvv6Jbt271HU6NWFpawtLSUuqp3kT15fTp0+jTpw+ioqJq9AT2hmTw4ME4efIk8vLyXsvkDG+zYcOG4Z9//kFSUtK/blYojrEgqoHFixcjOzsbUVFR9R0KvQaFhYVYvnw55s6dW6ukAmgY50ZJSYnM+JWCggKsW7cOTZs2lXqGCRFRbcgbk5icnIyjR4/igw8+aHBJxe+//46DBw8iJCTkX5dUABxjQVQjzZo1Q25ubn2HQa+JpqYmsrKyFFq3IZwbGRkZGDRoEEaOHAkrKytkZWVh27ZtyMzMxIYNG2SeCUFEVFPbtm3Djz/+CHd3dxgZGSEtLQ2RkZFQV1fH4sWL6zu8N65yzNC/FRMLIiKqlpGREbp164bo6Gg8ePAAampqcHBwwPLly+Hl5VXf4RHRv1inTp2wf/9+rFmzBo8fP4aOjg4++OADfP3115KZw+jfg2MsiIiIiIhIaRxjQURERERESmNiQURERERESmNiQURERERESmNiQURERERESmNiQURERERESmNiQURERERESmNiQURERERESmNiQURERERESmNiQURERERESvt/GapiKkzora0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x950 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "umap_cmap = mpl.colors.ListedColormap(colors, name='umap_cmap')\n",
    "shap_values = explainer.shap_values(umap_df['waveform'].tolist())\n",
    "shap.summary_plot(shap_values, color = umap_cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9165be98-38b6-4aa8-8f79-614158214ed2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV 5/5; 54/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 5/5; 54/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   0.6s\n",
      "[CV 2/5; 56/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 56/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.898 total time=   0.1s\n",
      "[CV 5/5; 56/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 56/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 3/5; 57/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 57/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 4/5; 58/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 58/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 2/5; 60/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 60/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.920 total time=   0.8s\n",
      "[CV 5/5; 62/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 62/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.931 total time=   0.4s\n",
      "[CV 3/5; 64/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 64/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.954 total time=   0.7s\n",
      "[CV 1/5; 66/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 1/5; 66/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 2/5; 66/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 2/5; 66/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 5/5; 66/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 5/5; 66/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.966 total time=   0.2s\n",
      "[CV 3/5; 67/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 3/5; 67/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.954 total time=   0.3s\n",
      "[CV 4/5; 68/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 4/5; 68/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.931 total time=   0.4s\n",
      "[CV 2/5; 70/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 2/5; 70/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.920 total time=   1.0s\n",
      "[CV 1/5; 73/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 73/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.864 total time=   0.4s\n",
      "[CV 4/5; 74/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 74/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.954 total time=   0.6s\n",
      "[CV 2/5; 76/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 76/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 5/5; 76/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 76/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.954 total time=   0.2s\n",
      "[CV 3/5; 77/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 77/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.954 total time=   0.4s\n",
      "[CV 2/5; 79/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 79/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.920 total time=   0.7s\n",
      "[CV 5/5; 80/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 80/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.943 total time=   1.0s\n",
      "[CV 2/5; 84/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 2/5; 84/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.920 total time=   0.6s\n",
      "[CV 1/5; 86/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 86/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.898 total time=   0.1s\n",
      "[CV 4/5; 86/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 86/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 2/5; 87/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 87/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.909 total time=   0.3s\n",
      "[CV 1/5; 88/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 88/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.875 total time=   0.4s\n",
      "[CV 4/5; 89/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 89/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 1/5; 91/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 91/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.909 total time=   0.2s\n",
      "[CV 4/5; 91/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 91/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 2/5; 92/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 92/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.898 total time=   0.3s\n",
      "[CV 3/5; 93/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 93/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 1/5; 95/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 95/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.920 total time=   1.1s\n",
      "[CV 4/5; 97/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 4/5; 97/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 5/5; 98/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 5/5; 98/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.931 total time=   0.4s\n",
      "[CV 3/5; 100/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 3/5; 100/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.954 total time=   1.0s\n",
      "[CV 4/5; 103/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 103/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.954 total time=   0.4s\n",
      "[CV 2/5; 105/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 105/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.932 total time=   1.2s\n",
      "[CV 3/5; 107/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 107/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 5/5; 108/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 108/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.943 total time=   0.6s\n",
      "[CV 3/5; 110/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 110/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.954 total time=   1.2s\n",
      "[CV 4/5; 113/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 4/5; 113/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.954 total time=   0.5s\n",
      "[CV 2/5; 115/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 2/5; 115/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.898 total time=   1.1s\n",
      "[CV 1/5; 118/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 118/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.886 total time=   0.4s\n",
      "[CV 2/5; 119/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 119/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.909 total time=   0.7s\n",
      "[CV 5/5; 120/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 120/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   1.1s\n",
      "[CV 2/5; 124/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 124/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.920 total time=   0.7s\n",
      "[CV 5/5; 125/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 125/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.943 total time=   1.0s\n",
      "[CV 3/5; 129/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 3/5; 129/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 1/5; 131/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 131/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.875 total time=   0.1s\n",
      "[CV 4/5; 131/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 131/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 2/5; 132/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 132/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 2/5; 133/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 133/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.932 total time=   0.3s\n",
      "[CV 5/5; 134/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 134/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.5s\n",
      "\n",
      "[CV 4/5; 66/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 4/5; 66/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 2/5; 67/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 2/5; 67/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.909 total time=   0.3s\n",
      "[CV 2/5; 68/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 2/5; 68/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.909 total time=   0.4s\n",
      "[CV 5/5; 69/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 5/5; 69/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 4/5; 71/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 71/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 2/5; 72/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 72/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.909 total time=   0.3s\n",
      "[CV 2/5; 73/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 73/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.920 total time=   0.4s\n",
      "[CV 5/5; 74/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 74/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.6s\n",
      "[CV 3/5; 76/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 76/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 1/5; 77/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 77/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.909 total time=   0.4s\n",
      "[CV 2/5; 78/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 78/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.920 total time=   0.5s\n",
      "[CV 5/5; 79/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 79/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.954 total time=   0.7s\n",
      "[CV 4/5; 81/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 4/5; 81/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 2/5; 82/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 2/5; 82/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.898 total time=   0.3s\n",
      "[CV 2/5; 83/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 2/5; 83/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.920 total time=   0.4s\n",
      "[CV 5/5; 84/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 5/5; 84/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.954 total time=   0.6s\n",
      "[CV 3/5; 86/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 86/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 1/5; 87/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 87/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.864 total time=   0.3s\n",
      "[CV 5/5; 87/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 87/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 1/5; 89/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 89/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.886 total time=   0.5s\n",
      "[CV 4/5; 90/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 90/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   1.0s\n",
      "[CV 2/5; 94/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 94/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.909 total time=   0.7s\n",
      "[CV 4/5; 95/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 95/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.931 total time=   1.1s\n",
      "[CV 3/5; 99/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 3/5; 99/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.954 total time=   0.6s\n",
      "[CV 1/5; 101/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 101/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.875 total time=   0.1s\n",
      "[CV 4/5; 101/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 101/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.931 total time=   0.2s\n",
      "[CV 2/5; 102/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 102/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.909 total time=   0.3s\n",
      "[CV 2/5; 103/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 103/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.920 total time=   0.4s\n",
      "[CV 3/5; 104/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 104/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.931 total time=   0.8s\n",
      "[CV 2/5; 106/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 106/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.909 total time=   0.3s\n",
      "[CV 5/5; 106/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 106/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.908 total time=   0.3s\n",
      "[CV 5/5; 107/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 107/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 1/5; 109/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 109/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.909 total time=   0.8s\n",
      "[CV 5/5; 110/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 110/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.943 total time=   1.2s\n",
      "[CV 2/5; 114/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 2/5; 114/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.898 total time=   0.7s\n",
      "[CV 5/5; 115/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 5/5; 115/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   1.1s\n",
      "[CV 5/5; 119/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 119/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 3/5; 121/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 121/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.954 total time=   0.3s\n",
      "[CV 1/5; 122/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 122/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.898 total time=   0.4s\n",
      "[CV 2/5; 123/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 123/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.920 total time=   0.5s\n",
      "[CV 5/5; 124/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 124/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 3/5; 126/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 3/5; 126/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.954 total time=   0.2s\n",
      "[CV 2/5; 127/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 2/5; 127/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.898 total time=   0.3s\n",
      "[CV 5/5; 127/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 5/5; 127/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 1/5; 129/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 1/5; 129/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.886 total time=   0.5s\n",
      "[CV 4/5; 130/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 4/5; 130/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   0.9s\n",
      "[CV 3/5; 134/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 134/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.954 total time=   0.6s\n",
      "\n",
      "[CV 1/5; 49/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 49/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.920 total time=   0.7s\n",
      "[CV 4/5; 50/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 50/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.931 total time=   1.1s\n",
      "[CV 2/5; 54/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 2/5; 54/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.909 total time=   0.6s\n",
      "[CV 5/5; 55/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 5/5; 55/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   0.9s\n",
      "[CV 4/5; 59/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 59/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.954 total time=   0.5s\n",
      "[CV 2/5; 61/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 61/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 5/5; 61/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 61/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 4/5; 62/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 62/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.931 total time=   0.4s\n",
      "[CV 2/5; 64/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 64/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.920 total time=   0.7s\n",
      "[CV 4/5; 65/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 65/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.954 total time=   1.0s\n",
      "[CV 5/5; 68/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 5/5; 68/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.966 total time=   0.4s\n",
      "[CV 3/5; 70/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 3/5; 70/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   1.0s\n",
      "[CV 3/5; 73/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 73/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 1/5; 75/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 75/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.875 total time=   0.9s\n",
      "[CV 2/5; 77/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 77/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.920 total time=   0.4s\n",
      "[CV 5/5; 78/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 78/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.954 total time=   0.5s\n",
      "[CV 2/5; 80/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 80/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.920 total time=   1.0s\n",
      "[CV 1/5; 83/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 1/5; 83/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.875 total time=   0.4s\n",
      "[CV 4/5; 84/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 4/5; 84/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   0.6s\n",
      "[CV 2/5; 86/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 86/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.909 total time=   0.2s\n",
      "[CV 5/5; 86/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 86/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 3/5; 87/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 87/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 3/5; 88/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 88/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 1/5; 90/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 90/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.886 total time=   0.9s\n",
      "[CV 4/5; 92/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 92/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 5/5; 93/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 93/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 3/5; 95/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 95/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.954 total time=   1.1s\n",
      "[CV 3/5; 98/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 3/5; 98/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 1/5; 100/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 1/5; 100/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.909 total time=   1.0s\n",
      "[CV 5/5; 102/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 102/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 1/5; 104/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 104/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.875 total time=   0.7s\n",
      "[CV 4/5; 105/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 105/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.954 total time=   1.2s\n",
      "[CV 4/5; 108/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 108/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.931 total time=   0.6s\n",
      "[CV 2/5; 110/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 110/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.920 total time=   1.2s\n",
      "[CV 1/5; 113/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 1/5; 113/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.898 total time=   0.4s\n",
      "[CV 4/5; 114/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 4/5; 114/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.954 total time=   0.7s\n",
      "[CV 2/5; 116/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 116/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 5/5; 116/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 116/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 3/5; 117/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 117/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.954 total time=   0.3s\n",
      "[CV 3/5; 118/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 118/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.954 total time=   0.4s\n",
      "[CV 1/5; 120/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 120/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.886 total time=   1.1s\n",
      "[CV 3/5; 122/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 122/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 1/5; 124/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 124/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.898 total time=   0.7s\n",
      "[CV 4/5; 125/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 125/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.931 total time=   1.0s\n",
      "[CV 2/5; 129/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 2/5; 129/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.909 total time=   0.5s\n",
      "[CV 5/5; 130/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 5/5; 130/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   0.8s\n",
      "[CV 4/5; 134/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 134/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.5s\n",
      "\n",
      "[CV 3/5; 58/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 58/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 1/5; 60/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 60/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.886 total time=   0.8s\n",
      "[CV 2/5; 62/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 62/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.920 total time=   0.4s\n",
      "[CV 4/5; 63/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 63/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.931 total time=   0.5s\n",
      "[CV 3/5; 65/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 65/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.954 total time=   1.1s\n",
      "[CV 3/5; 68/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 3/5; 68/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.966 total time=   0.4s\n",
      "[CV 1/5; 70/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 1/5; 70/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.898 total time=   1.0s\n",
      "[CV 4/5; 72/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 72/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 5/5; 73/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 73/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 3/5; 75/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 75/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   0.9s\n",
      "[CV 1/5; 78/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 78/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.920 total time=   0.5s\n",
      "[CV 4/5; 79/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 79/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 2/5; 81/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 2/5; 81/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.886 total time=   0.2s\n",
      "[CV 5/5; 81/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 5/5; 81/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.954 total time=   0.2s\n",
      "[CV 3/5; 82/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 3/5; 82/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 4/5; 83/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 4/5; 83/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 2/5; 85/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 2/5; 85/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.932 total time=   0.9s\n",
      "[CV 2/5; 88/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 88/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.909 total time=   0.4s\n",
      "[CV 5/5; 89/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 89/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.6s\n",
      "[CV 3/5; 91/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 91/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 1/5; 92/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 92/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.898 total time=   0.3s\n",
      "[CV 5/5; 92/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 92/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 1/5; 94/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 94/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.920 total time=   0.7s\n",
      "[CV 5/5; 95/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 95/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.943 total time=   1.1s\n",
      "[CV 2/5; 99/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 2/5; 99/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.898 total time=   0.6s\n",
      "[CV 5/5; 100/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 5/5; 100/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.931 total time=   1.1s\n",
      "[CV 1/5; 105/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 105/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.875 total time=   1.1s\n",
      "[CV 2/5; 107/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 107/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.920 total time=   0.5s\n",
      "[CV 3/5; 108/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 108/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.943 total time=   0.6s\n",
      "[CV 1/5; 110/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 110/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.886 total time=   1.2s\n",
      "[CV 4/5; 112/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 4/5; 112/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 5/5; 113/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 5/5; 113/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 3/5; 115/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 3/5; 115/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.931 total time=   1.1s\n",
      "[CV 4/5; 118/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 118/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.954 total time=   0.4s\n",
      "[CV 2/5; 120/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 120/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.909 total time=   1.1s\n",
      "[CV 5/5; 122/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 122/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 3/5; 124/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 124/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.954 total time=   0.7s\n",
      "[CV 1/5; 126/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 1/5; 126/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.909 total time=   0.2s\n",
      "[CV 4/5; 126/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 4/5; 126/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 1/5; 127/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 1/5; 127/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.898 total time=   0.3s\n",
      "[CV 4/5; 127/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 4/5; 127/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 5/5; 128/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 5/5; 128/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 3/5; 130/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 3/5; 130/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   0.9s\n",
      "[CV 4/5; 133/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 133/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 2/5; 135/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 135/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.932 total time=   0.7s\n",
      "\n",
      "[CV 1/5; 57/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 57/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.886 total time=   0.3s\n",
      "[CV 5/5; 57/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 57/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 1/5; 59/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 59/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.886 total time=   0.5s\n",
      "[CV 4/5; 60/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 60/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.954 total time=   0.9s\n",
      "[CV 3/5; 63/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 63/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.954 total time=   0.5s\n",
      "[CV 1/5; 65/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 65/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.898 total time=   1.0s\n",
      "[CV 4/5; 67/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 4/5; 67/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 1/5; 69/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 1/5; 69/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.886 total time=   0.6s\n",
      "[CV 4/5; 70/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 4/5; 70/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.931 total time=   1.0s\n",
      "[CV 3/5; 74/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 74/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.943 total time=   0.6s\n",
      "[CV 1/5; 76/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 76/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.886 total time=   0.2s\n",
      "[CV 4/5; 76/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 76/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 4/5; 77/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 77/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 1/5; 79/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 79/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.909 total time=   0.7s\n",
      "[CV 4/5; 80/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 80/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.954 total time=   1.0s\n",
      "[CV 1/5; 84/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 1/5; 84/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.875 total time=   0.6s\n",
      "[CV 4/5; 85/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 4/5; 85/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.954 total time=   1.0s\n",
      "[CV 3/5; 89/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 89/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.931 total time=   0.6s\n",
      "[CV 2/5; 91/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 91/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.909 total time=   0.2s\n",
      "[CV 5/5; 91/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 91/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.931 total time=   0.2s\n",
      "[CV 3/5; 92/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 92/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 4/5; 93/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 93/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.931 total time=   0.5s\n",
      "[CV 2/5; 95/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 95/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.909 total time=   1.1s\n",
      "[CV 1/5; 98/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 1/5; 98/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.909 total time=   0.4s\n",
      "[CV 4/5; 99/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 4/5; 99/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   0.6s\n",
      "[CV 2/5; 101/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 101/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.898 total time=   0.1s\n",
      "[CV 5/5; 101/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 101/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.931 total time=   0.2s\n",
      "[CV 3/5; 102/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 102/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 3/5; 103/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 103/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.931 total time=   0.4s\n",
      "[CV 5/5; 104/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 104/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.931 total time=   0.8s\n",
      "[CV 3/5; 106/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 106/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 1/5; 107/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 107/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.898 total time=   0.5s\n",
      "[CV 2/5; 108/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 108/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.920 total time=   0.6s\n",
      "[CV 5/5; 109/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 109/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.943 total time=   0.8s\n",
      "[CV 4/5; 111/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 4/5; 111/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.931 total time=   0.2s\n",
      "[CV 2/5; 112/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 2/5; 112/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.909 total time=   0.3s\n",
      "[CV 2/5; 113/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 2/5; 113/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.898 total time=   0.4s\n",
      "[CV 5/5; 114/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 5/5; 114/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 3/5; 116/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 116/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 1/5; 117/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 117/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.875 total time=   0.3s\n",
      "[CV 5/5; 117/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 117/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 1/5; 119/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 119/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.886 total time=   0.6s\n",
      "[CV 4/5; 120/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 120/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.954 total time=   1.1s\n",
      "[CV 3/5; 123/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 123/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 1/5; 125/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 125/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.898 total time=   1.0s\n",
      "[CV 1/5; 128/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 1/5; 128/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.898 total time=   0.4s\n",
      "[CV 4/5; 129/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 4/5; 129/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   0.6s\n",
      "[CV 2/5; 131/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 131/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.932 total time=   0.1s\n",
      "[CV 5/5; 131/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 131/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.943 total time=   0.1s\n",
      "[CV 3/5; 132/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 132/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.954 total time=   0.2s\n",
      "[CV 3/5; 133/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 133/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.954 total time=   0.3s\n",
      "[CV 1/5; 135/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 135/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.886 total time=   0.7s\n",
      "\n",
      "[CV 2/5; 45/135] START learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 45/135] END learning_rate=0.1, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.909 total time=   1.3s\n",
      "[CV 4/5; 48/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 48/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.931 total time=   0.5s\n",
      "[CV 2/5; 50/135] START learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 50/135] END learning_rate=0.3, max_depth=3, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.920 total time=   1.0s\n",
      "[CV 5/5; 52/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 5/5; 52/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 1/5; 54/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 1/5; 54/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.875 total time=   0.6s\n",
      "[CV 4/5; 55/135] START learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 4/5; 55/135] END learning_rate=0.3, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   1.0s\n",
      "[CV 2/5; 59/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 59/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.920 total time=   0.5s\n",
      "[CV 5/5; 60/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 60/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.931 total time=   0.9s\n",
      "[CV 5/5; 63/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 63/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 2/5; 65/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 65/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.920 total time=   1.0s\n",
      "[CV 5/5; 67/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 5/5; 67/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.954 total time=   0.3s\n",
      "[CV 2/5; 69/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 2/5; 69/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.909 total time=   0.6s\n",
      "[CV 5/5; 70/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 5/5; 70/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   1.0s\n",
      "[CV 2/5; 74/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 74/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.920 total time=   0.6s\n",
      "[CV 5/5; 75/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 75/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   0.9s\n",
      "[CV 4/5; 78/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 78/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 3/5; 80/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 80/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.943 total time=   1.1s\n",
      "[CV 3/5; 83/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 3/5; 83/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 1/5; 85/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 1/5; 85/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.898 total time=   0.9s\n",
      "[CV 4/5; 87/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 87/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 4/5; 88/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 88/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 2/5; 90/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 90/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.909 total time=   0.9s\n",
      "[CV 1/5; 93/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 93/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.909 total time=   0.4s\n",
      "[CV 3/5; 94/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 94/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.954 total time=   0.7s\n",
      "[CV 1/5; 96/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 1/5; 96/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 4/5; 96/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 4/5; 96/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 2/5; 97/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 2/5; 97/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.909 total time=   0.3s\n",
      "[CV 2/5; 98/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 2/5; 98/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.909 total time=   0.4s\n",
      "[CV 5/5; 99/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 5/5; 99/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.931 total time=   0.6s\n",
      "[CV 3/5; 101/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 101/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.931 total time=   0.1s\n",
      "[CV 1/5; 102/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 102/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.875 total time=   0.3s\n",
      "[CV 4/5; 102/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 102/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 5/5; 103/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 103/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.931 total time=   0.4s\n",
      "[CV 3/5; 105/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 105/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.931 total time=   1.2s\n",
      "[CV 1/5; 108/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 108/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.875 total time=   0.6s\n",
      "[CV 4/5; 109/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 109/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.931 total time=   0.8s\n",
      "[CV 2/5; 111/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 2/5; 111/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.886 total time=   0.2s\n",
      "[CV 5/5; 111/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 5/5; 111/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 3/5; 112/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 3/5; 112/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 3/5; 113/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 3/5; 113/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.920 total time=   0.5s\n",
      "[CV 1/5; 115/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 1/5; 115/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.898 total time=   1.1s\n",
      "[CV 4/5; 117/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 117/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 5/5; 118/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 118/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 3/5; 120/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 120/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.954 total time=   1.1s\n",
      "[CV 1/5; 123/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 123/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.898 total time=   0.5s\n",
      "[CV 4/5; 124/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 124/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.931 total time=   0.6s\n",
      "[CV 2/5; 126/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 2/5; 126/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.886 total time=   0.2s\n",
      "[CV 5/5; 126/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 5/5; 126/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 3/5; 127/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 3/5; 127/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 3/5; 128/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 3/5; 128/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.931 total time=   0.4s\n",
      "[CV 1/5; 130/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 1/5; 130/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.886 total time=   0.8s\n",
      "[CV 4/5; 132/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 132/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 5/5; 133/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 133/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 3/5; 135/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 135/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.954 total time=   0.7s\n",
      "\n",
      "[CV 3/5; 62/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 62/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 1/5; 64/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 64/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.898 total time=   0.7s\n",
      "[CV 5/5; 65/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 65/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.954 total time=   1.1s\n",
      "[CV 3/5; 69/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 3/5; 69/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.954 total time=   0.6s\n",
      "[CV 1/5; 71/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 71/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 3/5; 71/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 71/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 1/5; 72/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 72/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.875 total time=   0.3s\n",
      "[CV 5/5; 72/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 72/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 1/5; 74/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 74/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.875 total time=   0.6s\n",
      "[CV 4/5; 75/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 75/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.954 total time=   0.9s\n",
      "[CV 3/5; 78/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 3/5; 78/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.954 total time=   0.5s\n",
      "[CV 1/5; 80/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 1/5; 80/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.909 total time=   1.0s\n",
      "[CV 5/5; 82/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 5/5; 82/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 3/5; 84/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 3/5; 84/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   0.6s\n",
      "[CV 5/5; 85/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 5/5; 85/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.954 total time=   1.0s\n",
      "[CV 2/5; 89/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 89/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.909 total time=   0.5s\n",
      "[CV 5/5; 90/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 90/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   1.1s\n",
      "[CV 5/5; 94/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 5/5; 94/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 3/5; 96/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 3/5; 96/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.954 total time=   0.2s\n",
      "[CV 1/5; 97/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 1/5; 97/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.898 total time=   0.3s\n",
      "[CV 5/5; 97/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 5/5; 97/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 1/5; 99/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 1/5; 99/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.909 total time=   0.6s\n",
      "[CV 4/5; 100/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 4/5; 100/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.943 total time=   1.1s\n",
      "[CV 4/5; 104/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 104/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.954 total time=   0.8s\n",
      "[CV 1/5; 106/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 106/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.909 total time=   0.3s\n",
      "[CV 4/5; 106/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 106/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.908 total time=   0.3s\n",
      "[CV 4/5; 107/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 107/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.931 total time=   0.5s\n",
      "[CV 2/5; 109/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 109/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.920 total time=   0.8s\n",
      "[CV 4/5; 110/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 110/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.931 total time=   1.2s\n",
      "[CV 3/5; 114/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 3/5; 114/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.931 total time=   0.7s\n",
      "[CV 1/5; 116/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 116/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.875 total time=   0.2s\n",
      "[CV 4/5; 116/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 116/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 2/5; 117/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 117/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.886 total time=   0.3s\n",
      "[CV 2/5; 118/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 118/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.909 total time=   0.4s\n",
      "[CV 3/5; 119/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 119/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.954 total time=   0.7s\n",
      "[CV 2/5; 121/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 121/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.886 total time=   0.3s\n",
      "[CV 5/5; 121/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 121/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.931 total time=   0.3s\n",
      "[CV 4/5; 122/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 4/5; 122/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.931 total time=   0.4s\n",
      "[CV 5/5; 123/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 123/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 3/5; 125/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 125/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.954 total time=   1.0s\n",
      "[CV 4/5; 128/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 4/5; 128/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 2/5; 130/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 2/5; 130/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.909 total time=   0.9s\n",
      "[CV 5/5; 132/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 132/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 1/5; 134/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 1/5; 134/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.886 total time=   0.5s\n",
      "[CV 4/5; 135/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 4/5; 135/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   0.6s\n",
      "\n",
      "[CV 3/5; 60/135] START learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 60/135] END learning_rate=0.3, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   0.9s\n",
      "[CV 1/5; 63/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 63/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.898 total time=   0.5s\n",
      "[CV 4/5; 64/135] START learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 64/135] END learning_rate=0.3, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 3/5; 66/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 3/5; 66/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 1/5; 67/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 1/5; 67/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.898 total time=   0.3s\n",
      "[CV 1/5; 68/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 1/5; 68/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.864 total time=   0.4s\n",
      "[CV 4/5; 69/135] START learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 4/5; 69/135] END learning_rate=0.3, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.931 total time=   0.7s\n",
      "[CV 2/5; 71/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 2/5; 71/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.909 total time=   0.1s\n",
      "[CV 5/5; 71/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 5/5; 71/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 3/5; 72/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 3/5; 72/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 4/5; 73/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 73/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 2/5; 75/135] START learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 75/135] END learning_rate=0.3, max_depth=5, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.920 total time=   0.9s\n",
      "[CV 5/5; 77/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 5/5; 77/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.954 total time=   0.4s\n",
      "[CV 3/5; 79/135] START learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 79/135] END learning_rate=0.3, max_depth=10, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.943 total time=   0.7s\n",
      "[CV 1/5; 81/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 1/5; 81/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 3/5; 81/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 3/5; 81/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.920 total time=   0.2s\n",
      "[CV 1/5; 82/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 1/5; 82/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.886 total time=   0.3s\n",
      "[CV 4/5; 82/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 4/5; 82/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 5/5; 83/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 5/5; 83/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.954 total time=   0.5s\n",
      "[CV 3/5; 85/135] START learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 3/5; 85/135] END learning_rate=0.3, max_depth=10, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.954 total time=   1.0s\n",
      "[CV 5/5; 88/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 5/5; 88/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 3/5; 90/135] START learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 3/5; 90/135] END learning_rate=0.3, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   1.0s\n",
      "[CV 2/5; 93/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 2/5; 93/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.909 total time=   0.4s\n",
      "[CV 4/5; 94/135] START learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 94/135] END learning_rate=0.5, max_depth=3, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.931 total time=   0.7s\n",
      "[CV 2/5; 96/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 2/5; 96/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 5/5; 96/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 5/5; 96/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 3/5; 97/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 3/5; 97/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.954 total time=   0.3s\n",
      "[CV 4/5; 98/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 4/5; 98/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.943 total time=   0.4s\n",
      "[CV 2/5; 100/135] START learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 2/5; 100/135] END learning_rate=0.5, max_depth=3, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.898 total time=   1.0s\n",
      "[CV 1/5; 103/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 103/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.875 total time=   0.4s\n",
      "[CV 2/5; 104/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 104/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.932 total time=   0.7s\n",
      "[CV 5/5; 105/135] START learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 105/135] END learning_rate=0.5, max_depth=3, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.931 total time=   1.2s\n",
      "[CV 3/5; 109/135] START learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42\n",
      "[CV 3/5; 109/135] END learning_rate=0.5, max_depth=5, min_child_weight=1.0, n_estimators=100, seed=42;, score=0.954 total time=   0.8s\n",
      "[CV 1/5; 111/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 1/5; 111/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.898 total time=   0.2s\n",
      "[CV 3/5; 111/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42\n",
      "[CV 3/5; 111/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=10, seed=42;, score=0.943 total time=   0.2s\n",
      "[CV 1/5; 112/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 1/5; 112/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.909 total time=   0.3s\n",
      "[CV 5/5; 112/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42\n",
      "[CV 5/5; 112/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=25, seed=42;, score=0.943 total time=   0.3s\n",
      "[CV 1/5; 114/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 1/5; 114/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.898 total time=   0.7s\n",
      "[CV 4/5; 115/135] START learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42\n",
      "[CV 4/5; 115/135] END learning_rate=0.5, max_depth=5, min_child_weight=2.5, n_estimators=200, seed=42;, score=0.954 total time=   1.1s\n",
      "[CV 4/5; 119/135] START learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 4/5; 119/135] END learning_rate=0.5, max_depth=5, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.954 total time=   0.6s\n",
      "[CV 1/5; 121/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 1/5; 121/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.898 total time=   0.3s\n",
      "[CV 4/5; 121/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42\n",
      "[CV 4/5; 121/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=10, seed=42;, score=0.908 total time=   0.2s\n",
      "[CV 2/5; 122/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42\n",
      "[CV 2/5; 122/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=25, seed=42;, score=0.920 total time=   0.4s\n",
      "[CV 4/5; 123/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42\n",
      "[CV 4/5; 123/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=50, seed=42;, score=0.931 total time=   0.5s\n",
      "[CV 2/5; 125/135] START learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42\n",
      "[CV 2/5; 125/135] END learning_rate=0.5, max_depth=10, min_child_weight=1.0, n_estimators=200, seed=42;, score=0.920 total time=   1.0s\n",
      "[CV 2/5; 128/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42\n",
      "[CV 2/5; 128/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=50, seed=42;, score=0.909 total time=   0.4s\n",
      "[CV 5/5; 129/135] START learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42\n",
      "[CV 5/5; 129/135] END learning_rate=0.5, max_depth=10, min_child_weight=2.5, n_estimators=100, seed=42;, score=0.943 total time=   0.5s\n",
      "[CV 3/5; 131/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42\n",
      "[CV 3/5; 131/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=10, seed=42;, score=0.954 total time=   0.1s\n",
      "[CV 1/5; 132/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42\n",
      "[CV 1/5; 132/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=25, seed=42;, score=0.875 total time=   0.2s\n",
      "[CV 1/5; 133/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42\n",
      "[CV 1/5; 133/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=50, seed=42;, score=0.886 total time=   0.3s\n",
      "[CV 2/5; 134/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42\n",
      "[CV 2/5; 134/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=100, seed=42;, score=0.932 total time=   0.5s\n",
      "[CV 5/5; 135/135] START learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42\n",
      "[CV 5/5; 135/135] END learning_rate=0.5, max_depth=10, min_child_weight=5.0, n_estimators=200, seed=42;, score=0.943 total time=   0.5s\n"
     ]
    }
   ],
   "source": [
    "umap_df.to_csv('umap_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b9d1291-014a-4567-949c-927760f1543e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "STAR_Protocol_WaveMAP",
   "language": "python",
   "name": "star_protocol_wavemap"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}