psvi/notebooks/full-covariance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a70cd2d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dionm/.local/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from functools import partial\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.utils.data as data\n",
    "from sklearn.datasets import make_moons\n",
    "import pickle \n",
    "from collections import defaultdict\n",
    "import seaborn as sns \n",
    "import numpy as np \n",
    "\n",
    "def rec_dd():\n",
    "    return defaultdict(rec_dd)\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"../psvi\")\n",
    "\n",
    "C1 = sns.xkcd_rgb[\"green\"]\n",
    "C2 = sns.xkcd_rgb[\"orange\"]\n",
    "C3 = sns.xkcd_rgb[\"blue\"]\n",
    "C4 = sns.xkcd_rgb[\"red\"]\n",
    "COLS = np.array([C1, C2, C3, C4])\n",
    "UC1 = sns.xkcd_rgb[\"green\"]\n",
    "UC2 = sns.xkcd_rgb[\"orange\"]\n",
    "UC3 = sns.xkcd_rgb[\"blue\"]\n",
    "UC4 = sns.xkcd_rgb[\"red\"]\n",
    "UCOLS = np.array([UC1, UC2, UC3, UC4])\n",
    "title_font = {'size':'28'}\n",
    "EXTENT = (-3, 4, -2, 3)\n",
    "dnm = 'halfmoon'\n",
    "n_test_per_dim = 250\n",
    "def make_grid(n_test_per_dim=n_test_per_dim):\n",
    "    _x0_test = torch.linspace(-3, 4, n_test_per_dim)\n",
    "    _x1_test = torch.linspace(-2, 3, n_test_per_dim)\n",
    "    return torch.stack(torch.meshgrid(_x0_test, _x1_test), dim=-1)\n",
    "\n",
    "coreset_sizes = 50\n",
    "N = 1000\n",
    "(X, Y) = make_moons(n_samples=N, noise=0.1, random_state=42)\n",
    "X, Y = torch.from_numpy(X.astype(np.float32)), torch.from_numpy(\n",
    "                Y[...,None].astype(np.float32)\n",
    "            )\n",
    "    \n",
    "def predictive_entropy(g):\n",
    "    p = g[0,...]\n",
    "    return -p*np.log(p) - (1-p)*np.log(1-p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a37e6b56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading/Generating the dataset HALFMOON\n",
      "\n",
      "Bayesian neural network experiment.\n",
      "Inference via MFVI on halfmoon data over 1 trial.\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Running mfvi\n",
      "\n",
      "Trial #0\n",
      "Unconstrained data access\n",
      "outer architecture : fn2\n",
      "  0%|                                                  | 0/5002 [00:00<?, ?it/s]/home/dionm/miniconda3/lib/python3.9/site-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2895.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
      "predictive accuracy: 50.50%\n",
      "  4%|█▌                                      | 199/5002 [00:11<04:00, 19.97it/s]predictive accuracy: 49.50%\n",
      "  8%|███▏                                    | 400/5002 [00:22<04:02, 18.96it/s]predictive accuracy: 88.50%\n",
      " 12%|████▊                                   | 599/5002 [00:33<03:45, 19.51it/s]predictive accuracy: 88.50%\n",
      " 16%|██████▍                                 | 799/5002 [00:44<05:13, 13.43it/s]predictive accuracy: 88.50%\n",
      " 20%|███████▉                                | 999/5002 [00:56<03:30, 19.05it/s]predictive accuracy: 87.50%\n",
      " 24%|█████████▎                             | 1199/5002 [01:07<03:20, 18.99it/s]predictive accuracy: 87.00%\n",
      " 28%|██████████▉                            | 1399/5002 [01:19<03:20, 17.94it/s]predictive accuracy: 86.50%\n",
      " 32%|████████████▍                          | 1599/5002 [01:29<03:07, 18.11it/s]predictive accuracy: 86.50%\n",
      " 36%|██████████████                         | 1799/5002 [01:44<03:08, 16.97it/s]predictive accuracy: 86.50%\n",
      " 40%|███████████████▌                       | 1999/5002 [01:55<03:02, 16.42it/s]predictive accuracy: 86.50%\n",
      " 44%|█████████████████▏                     | 2199/5002 [02:09<03:29, 13.37it/s]predictive accuracy: 86.50%\n",
      " 48%|██████████████████▋                    | 2399/5002 [02:21<02:27, 17.59it/s]predictive accuracy: 87.00%\n",
      " 52%|████████████████████▎                  | 2599/5002 [02:33<02:08, 18.70it/s]predictive accuracy: 88.00%\n",
      " 56%|█████████████████████▊                 | 2799/5002 [02:44<02:34, 14.22it/s]predictive accuracy: 90.00%\n",
      " 60%|███████████████████████▍               | 2999/5002 [02:56<01:50, 18.18it/s]predictive accuracy: 95.00%\n",
      " 64%|████████████████████████▉              | 3200/5002 [03:10<01:43, 17.36it/s]predictive accuracy: 98.00%\n",
      " 68%|██████████████████████████▌            | 3400/5002 [03:22<01:33, 17.16it/s]predictive accuracy: 99.00%\n",
      " 72%|████████████████████████████           | 3600/5002 [03:34<01:21, 17.12it/s]predictive accuracy: 99.00%\n",
      " 76%|█████████████████████████████▋         | 3800/5002 [03:46<01:10, 17.01it/s]predictive accuracy: 99.00%\n",
      " 80%|███████████████████████████████▏       | 4000/5002 [03:58<00:58, 17.21it/s]predictive accuracy: 99.00%\n",
      " 84%|████████████████████████████████▋      | 4200/5002 [04:10<00:46, 17.07it/s]predictive accuracy: 99.00%\n",
      " 88%|██████████████████████████████████▎    | 4400/5002 [04:21<00:34, 17.42it/s]predictive accuracy: 99.00%\n",
      " 92%|███████████████████████████████████▊   | 4600/5002 [04:33<00:23, 17.36it/s]predictive accuracy: 99.50%\n",
      " 96%|█████████████████████████████████████▍ | 4800/5002 [04:45<00:11, 16.91it/s]predictive accuracy: 99.50%\n",
      "100%|██████████████████████████████████████▉| 5000/5002 [04:57<00:00, 17.09it/s]predictive accuracy: 99.50%\n",
      "predictive accuracy: 99.50%\n",
      "100%|███████████████████████████████████████| 5002/5002 [04:57<00:00, 16.81it/s]\n",
      "Trial completed!\n",
      "\n",
      "Storing results in ../results/fullcov.pk\n"
     ]
    }
   ],
   "source": [
    "! \\\n",
    "python3 ../experiments/flow_psvi.py \\\n",
    "--num_epochs 2501 \\\n",
    "--log_every 200 \\\n",
    "--lr0net 2e-4 \\\n",
    "--n_hidden 50 \\\n",
    "--datasets halfmoon \\\n",
    "--architecture fn2 \\\n",
    "--methods mfvi \\\n",
    "--num_trials 1 \\\n",
    "--init_sd 1e-10 \\\n",
    "--data_minibatch 512\\\n",
    "--log_pseudodata \\\n",
    "--mc_samples 64\\\n",
    "--fnm fullcov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "145c17d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14942/876338518.py:55: RuntimeWarning: divide by zero encountered in log\n",
      "  return -p*np.log(p) - (1-p)*np.log(1-p)\n",
      "/tmp/ipykernel_14942/876338518.py:55: RuntimeWarning: invalid value encountered in multiply\n",
      "  return -p*np.log(p) - (1-p)*np.log(1-p)\n"
     ]
    },
    {
     "data": {
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bPhKlh0RpSbqngcbMPsFc2wAiEWjK/stcN/inC4TRrnfMsnunYu+emF7/Ocr5gn5iuYBkV54CKMfGPSVYTu3PnKCjPpj2u/QAqG9ybVdEy/HhH1IkDj4Q9vuG75g/du78U+sPzjW9erB//66MtcmHapZPImM7OKf9lJSSDqKMXeP9l6nhNJJWy6SjUUoAvb6JVlrtUtECXkt/1+Zetr7MLuVd3K/dP39YsaYZjiM+LL9+rvwQwBLODZjP/o57YnkqoBwaO0f7PAUsJ/dvyKR6QNMcehg10YfR+gGwGtyFHqCG71Nv6ON9mIdA8fh5HzP3Mpauf7J7O08B6rnB1o75KdsOhyZ42A6MaputVhmu0T1wVNELnMQg2gXN2Pwq0tMkPVlBHPQTE6ULoINWqVpfZpxSsk9Y0DXJDptpZQA4w7e/l3rtPzR5emq8z11OuZLOqVEO9T8lSvaUAJ+hflO/BwF0wHfZdImAT3rLzAOh/vh23v3DHVs+vI19bfGQH/SY7Z36rBrSMgf7yROw95zarx/oE/s5x/pNyaGX0P6LXVDReubYmKSgZfZpGX1CRKzW7e/gawzBPq2W2YJkGgf9SBvs0yUt6FcY2dcwG/Ckqz12AoOgB6C90zFyyn4o2iU8FTXeD0HOrU1OzTkXKIf6HqNVxu1zQf0cPsy+77IPOAM37BCQjgHqGMjGwDYGmt0+7ntO/uWp0tcuH2NLjwGQZ9/GqbmVj8E1G+VfuuX9a64b7NM1w/ZZfPr8sSG4J1FRKkkPAPvBPsGf2f721yrtNRv8n2E5AKg/hNGL64cEiIdkPmBegHJYHkOjnJr3VK2y3+cUsBzqN7WdIRNt3GcPONVooM/+JvbXj2txMvowOGYbx/gYH7qtx5Y5gT9PAaTQ1t4c73BMLd4HAGRfg2zaoojY3jXbeTETOt8xUIVpG5Ms7XIcFBQI1kM+ZiArCCDY5lr2g33622vbIML3SLt0x0DPBDusXY6esh8YmJ6fGu+HIk8NlGPjj9Uq++tP0SynTLFD48ZSS0bb9lNJRjVGiX6z/3tv14/8ux3qPqR5xtsa/POMaLEPkbHjOgR4IodqKgtgH81/Obl/fbL3Q/yxxwYFBZnyWfZ/9wA1vib3/Zatdtlqm5HpVbVaYhsJ2/VZNv5Naf2XIm3qSFOVhK7WKLR+ypgVSGivyynfZXvY/tgGTskPUR4nSvZzlecCkmNjjgHK/vpJLfIACE6tm+3DjAJ9ojnGNMlBgIxAq/PGvzfB/pt/DGJTmuUhmfxTjqwb01znmmMPvSB0WHp4GPPP6LxyaOYjZUpTfMySXkH6AAm9F7kWmRq/ZQyUkU8yBsk+WMblvUKwT0y0Hnhk+2bYpmKJtJR6TSBPdE13iAqkNcn2g4AaQGT/Oh0Dyx+adgkXwDwsHwMkp+aYA5T9fudMJRlbt/dQGXg7D7+nTLGdecNi1+zV9pt30w5pp2P9+mP662KtcQ4AHrduYt9OmC/uc07T6lOZaoFpBp+Y0GCI9ecUYB0LPOsE/nQDfvrA2YJnaBtn9+lHx/Y/SdRnKNCn9Wv2afBi7bELog1AHgDAHyIgHpILYI7J5wCU/T5z1k36MCeiYfvLU6Da7xPvT/NQCt26GmP4PQZug8Aab+IBD4Hu7g9rhcPr902zz+1ZNF+zfERPzTHzPkowz8Q91lyXXWtGX6vsgqd0fJRxSomSfY1TS0SBFwGq0GqEDWjGRAW011hXw6Q1zUKkKbqGGDjH3AQPeVH7HOUCmH35FIByqP8UGPbXHxobt805trjfIWA/ZIrt7IJ0vod+T/kup7TKMf9i/xAOPRjiCNlzPkPm5F6e+tASHDYdU1B6uM8jgefQnMdu6OiCniMvbzB8vTIAmNEnpsITwVcnacExgGbL9OM1Q09EoAjEBT4QiNYEK9JGxDYAGfant1+dVJKob9j/2HcZX03799Jxp/NzlQtgBjn2ipgNJI8MlEN9HyOV5JD22F+e0jqbPiMBQJ033u4bfdPWB7UpkIu0gf76+EEyODY6rCkZG3eMGfgYOXbcOQDwoz00G7PrgaCeY6nx+tdk09a7L2KTrG+LLRnDoEmjVXbSSSLQ7PgvRSLNsuWPbYBVumbYfuFokS5ZQfiEHQn702iU9ABy6PSMnrYfLnr+sAHzuYHk2NhTomOnQPQYAB3THKfAMp5jDERjv+WEKfbQ74Om2b5M/Gk6hx+BaOfwDv1pZ1xTg7sVtT1F7uWUDAHnqX5LEcEeYWs9yPLjOh3uc4wMXrvSu2aFGDj3wLIBRRWZWmOygq75NdFt9GtLUBCV6VIxWUEEqs1yHMzTEhbExAUCHe21r4UC3d8jL40/YHzckx8mYD43oBwbdwpQ9vscq23OiYSdWp6ap+kzAKpR25A/Mn57HyIyGNM+h7TVzlwHpA+i7fjWHDs3WOKUoIrBN/8Tn2BjoDcEkFPt4/OP9J/Q3kdlTKscK+l11uolYz57adwHrem1tYjEtHitWZZWu4yiY/ttIvjo2D413rxgn2bXJPJZBnAMhyVtDmajafbAknj90Kn5gaPnDwswHwMo58z5MYByav2c9lPAcmjsmEl2yG/ZB0bf1pcxLfKQP3NIRMJnfOyUjP4JB7TTY+e4yIRMEh3MIF3fu1f613U/QjZe7l6nw/5LmpeqxiwLDcg1ka+D0bEx/V0Avx5xwYCGCe21q6LruuO7jEEyHHrnNFy0yyn5YQDmxwDKY82uY2PmAGW/30N8mJNRsjOWh0y4nfbIFBuvmwGMsfY4ZZrtt88Fw74Zds6YY/sd6nIo2Odcb/jC4cCfsL1jTbFB0zxWQ52Up869jL/D7869QXM9dsEy5o/dB9KO71IF8HTapQg+2KcbTRubZTum1T0Ns10mAkq/u/QjY6Ovmafngp6fL2A+BkjOmfcpgXKuRtlfN9p+IljOBs7Dfssx0+sQWPbNr/0HWPdUSG87TIpE2463OdV/v+00rbUZc2z/gQHnAKxDoHkMqM7qO8YbeyrTz/QOjVz30XfvWu9fnx2zrNAByth/2eGNjfIrE9X9boN+9otFtzmZXQ2zA44SmWVpr+UAtA1wNvdRGNfTPC/4uCefH2A+BlA+FUgO9X+SqNgZQDnUNgcshzTLEb/lIU0yXt4HwCHEmgaq9kHTD/AZBrr4wTRW/3KuSWtovx6iXY5vp1+m6xD4PQxk3T4eP8Egn+whgHQDh9fN0Uin/OzhO/4oAU9KEDP7IEQVSoY/HbL1vdxLz+yj5UCgT8vmo6LlPcICaQN+Qt8GBOPDZz5YXrRLJ58HYJ7yx3xOQDn45D0jUI6tO2iePULLDMtzNctm3QBYNrvUBcehfjIy11zNM5YAiEPtU7L/cJnuf2rfU+eYA4KnAuUYAPeB8xTT7qCM+SdPqZvZl8FgtFjTVP7n8PW4Z36No2Ml0i4DADZaZVsoOmiZbaBPlze2Bc3uC1zry+xS4TW3QnRtx9rlRY6TTxswL0B5eP0YiD42WA6Ccv+BNAJi8c09AIxD68Z8mEMyBoxzxs7fxsMeSP3RU8fyOUkDqnPNsOeW/gsd7IHlFEhKdE0H02xfu2wjYQNQtpywrUZJR4uMSQsarVEGUkWa337Xab8laoj7HDwln9tF9gD5NAHzApSH148BZbw8BZRDbefSLCOwbHd3whQrvT69tv4cg/My1n/iTyfDv4f7jmuvo76ij/QcCtpeVwds293vwyQGXVPvA3ZojiL4WHx8sSbZv3b3ImRH8i/DR0bo8Gj9mkGzDMAXkxTosCsEUGyjZaUHoEGbjL+FlqwgAGLH2jJ0b0XLTbcLPo7KpwOYnzpIjvU/BIKH+sz2X87ROk/QKvvLY5plDyzbTZ5gih1p656GaeDsS1db3T8l8bqx/MuHSOy/nDvrMZs/BIDdvufzc57lHPVzMqcI2efK1D3aB9Gmree/HIiC7X4ccUGiVUNWEDTMVAtpBJQh6CdVUcUSUSgJyyranTgPs/1ufZb+WoUo8KcFxSHtcgwsL9plV543YJ76x/oYQPkQkBzqd7aI2BOAcqhtanlv3TzNcg8se0B4yG95aFzTpweAY5rlMebVPpAOt8f7MT7XKcE+H+s5ppRgTNBCx/2XD/JXHqLCa/qZ06qUjN2TQy97YVkNX48SAaN7mRpm9wlk690amHHtS+iTE/SDfZqSYAMa5h5IRtplt31Yu7zIPHmegHnKH3EOSM6Z+3MHynjd0NhjwfNIsGyn7Gl80msbA5he+9D6oXVjGuZ4+7yHyeCf+YwPofPONYxBh3Ix4305Jr2kMf3KcfR4k/KQFJK+NCrbwH2zZ45V7cuX//TBMyYscODZpoAEs2uf8aebUkJHW+wH+7S7HG2zfRPraKAd7ZJ2f8Mc807PBUz78rwA81MCyrExHxso+8tToDjWd6xtCjjDtxqaowXLQfNrD5yGNMm+xtkfO2pyHQC+ITDsnJIRYD2njF5SZ93Kw+QUTfEhp6mzLTNTy2wHH9d/SiIQagC1B459i8ZYoWihT0RABKJu+kRoiNaHg326Pkw3JxEDUNcM63ZdBi+muCk208aHfZFx+fiAeepf6bGB8hiQHOv/UP/kofVjGuVYv0NAOdR3lknWtx3QLAdNqz2NcBQE+6cleiCMmWb3NcxhYO732dtOdJqm8i+7oLvf51xyzrmFeTE3MqKdjq2bu48BJPvf3U5m3/x6bmnVs3Y5iHKg6QAx+m5AUkXXRjDHtm26p1UOF4/u0uF1zbMx6LYvfDEIN9rkwHKH4ad/2NHhd0/HBT2H5OMA5nMFybGxj2127fc5Khr2CB/lWPsxvsvQ1vf3KOmOaX7vA+IQwIX1g23NZmf4M6O+8Zj+uvi52AfS/e2yJ0PgOibttvYHTAX7jJmKD0k850PqXT5beRizQu+7/3I39tm/PkWCD3P4kyhFov1HKRLtA4CkrXOZRuDZJ1wPy+Fai3M5W5Ntuz9x2smY73LqWrzIYXk6wPzYIHloro8BlA9Z/1RAOdT2ALBs59wHwzH/45xI2TGtNH5QzNUC+8/UKRk0MQ/8Hpr/3DKv6PShdJG+H3Jeeok71iPLeB3qPkhGMBAhe6rmOXU/9T4BHJtv1Qb87JtiVddXGQX+JIoGCFvSgn0KvETtg2PYHbeL0rmmm98w23c5drVcwHRcHh8wP0WgHBvzqQBlvG4uUMbtY/2GgLI/7gBYDplSx8DykHl2yFw72TYgfU1yX9Psfs+RwctkoHEK4I7VLsfmmhvQo0Soj9DcHlVDHSvh1e8zt2LJIRm6fkO7//Rf2OKPioBT6zb1KFzqIlFNSumaYQNABrBrfJb4dlpQ7JT2IqpxSXu5x/dX5xDj3yPX+NC6i3TlcQDzISf9KYByauxzAcpjA3n66x7LJHvIXwkHwXJIs4yXx0yy8fLQ95SZtttvf92ch0iccN6X0feu/p98rN9w8yw5pFnOBc0xGQNHkeN4asdk1pjHqHcJw29EDVB6AO29tPXBUryG1yErEDq+yoY/1vcNWqZWoWxX7MOUXrDPkA+zBeJ2f/aBMwbTzinoHf5F5sn5APPUMz8XIOdu41za5FD/c/gn+30OgugZTK9x+7FtfaCEfRNsp98+ePVNpFOaZWxibQ9jvil2uG3/MNt52ZMxsO72GQfzQ2MPybHa5VNKAIS6fjwCd2CYYP1gXuaRWmX/wmhAUto21buuIrNsv5RXh2xdurmXgQqvISVQXSq8fgpJnI4Sa5gS9RW/n80uQwckhfhaDof6TC6kT1QeDpiPDZQfGyTH+h4EugesP1abjNedAp6TGmYPKPvjI6B0TSMa4pFgOdU+ZpodNvdGu9o8XPq+zHhbDEpg9xmSU26BU5h9Ds0zVwKg9TXCGOiGtMUxIIz7unPZ0usdI7P8mjDPtzlX+td5HOijEhpzbAScXdCMQFILWrfsPuGjQ9CPEh/40ycuiNarbvCPA0g62mb4bj+R6ZbufQADj5qxU3EB04NyGmA+NkjO2cahuR4DKE/p8zGAcqz9nEDZWe6BVjNlF8DOoVl25h74PrQuli5o9tcNjz28PLipB8koiB+xsccyyx4ed/ImT1NTD/k2B19+B675Zt2YKTYO9lGdgtExQUEDbiHoR3pkBdKmgfSZflqAHPBhBnBsdrMLmvR+h+XeoY2uu8iwHAeYP1SgHOr3FEA5te7cptdO20RQT2c5NHeBL24bbB8DxYG2IYA6xhQ79b2/n+36zuGOfB8S2Ts/c8Z8/AeXjGiSw33nlfc6KHFXc2Qwz/ydbb9FRl4Ypf1E+Zd9oIzJCuLrpo2IdVN0qe3aqftUeC7AJzbRttfymA+zBXMacyz+++NfRZ+nzAPMxwbKWU+SE8yuU+M+BlDO9U9OrTsXUMbtY0A5NMeEVjlmGp2lWUZz7gFlv30AqPtyDDjFADpXhkB+6PeQHKMZjnU9xRQ7pmUeCW3Nfp2jWslBrXVsfUgneSiZQYM+4bfqfWg+4lXBoXSSQFYQ88X2SQviTxICg2Rfs+yaYiMwjl8Gid5n/X9hV92hBI2zd7jRYV/keJkGzFPO6g8BJA/1OQiiZw7kidvntsXtYxrl0ByHgHKgzxAgdtb1wHUSRAe2P9ivmW/8e2+OSOJnaP+0jcng5RWB8uCYgb7PRaRRW+wgOJ5qpp0le/mWj8Dy078XOr5594m1yvA7NsHuBfsEQPR+S6XayiSJdKuUpKrVLru+y24aSt+H2YBos6td3+UQKHZNtf3T8Lyuu+cs54mS/dRMrkP954DpQSCcAtEn0ibj9oNtxwGla94HwhjcBtcNAGK8bgxc98b0HgD9OQ6ZYvsyBazx+ric1/7DZmzuvSY3V7Ti0GNqzhwPlWNBb6q/yCOVrZxbhWSO7F3XsTYZgLJt6wNl1zw78JEu/V2iVBPgk+iIBk+1OZjhE3ybfR9m0Fa7RaPxYNoL+Il+t4c8fr1cwPI4eRhgfo7a5FC/Tw0oD/U90vTqmmXyey5Ytru6D4BDffo39J4Wuge4+4fdjh0Cxf1+h+RjPoDOBZbHgls4rjngeiwIT/LIDg+YPXe0U9HvqWdNDKQDL2RqPzq2Y5oNoNeswwOf1xSJgDICx0BWEHyfYUxzvfpD6IBjozIefzoucpqcGCX7GQDlUN/HAsp+33ObZJ8AKOPfY4A4tG6ofaotbHsIkIfMtf1925+3C6RDMgSyfe1yf8z0nMfK/ovBQ+Zqfx+LLccC6aljRuVULfIUiryOVindZbX/Yta9BtsXMB0Vho6BMuRRthR4XYafvoapo+t2KEo2mGebaxo6H+L2/qMpfF/A9UEyHzCfs9l1bMynrFE+FDzPaHptu+6DWFgeA7jB9T1g6oPgGICO7dOYKXa4b/+hNwTe3VPTn2dIunP1z9fk0LPJnO3Mr305DwT7/dwxPyKp+0PMsofuzQY03Ucpp2W2fsv4000nCSbTDrtPbzmuShITrnf6RRG2fR+m8kgYALFzL3Vvq+gwL77Lc8qBoJ9nAJJjY48B1VNA8lCfjx3Ic6hvvG4sPaTTFlbtA0j8fajfGIhOAemQ6XZSsxxoj8Fy7xB7QDq67yMSzzlkIp4rY/7LudrlQ0gKjhvTFn524LdfGFopOmw/U0A5do4OR8hOgGPItzxWswzA6HYsAkjZ+3bHGfktB8yxQbvsR8cG0oJUueCeVAuJOKafONgnrkoStMmG6UfFbTSRsX3tcp+4YPq8H1p3kXF5oA9zxkn/WEE8Q31P6XNw/SMCZdw+u+2RgHKk7xSQDmmVg0A6BJYjABrW9cEyljkm2T6w7oPq/inrz9+fa3gf9tedIofAcmr1HNAMD95D2mc81ylgHKQPlp3lIfL1c/s3OxGxvd8qgCY9oBwmK2io8GLtUjkqvEBeEKeTBAKDuKRXnK/Z+EWlp1lGgBjfB53D6l/n887GRWbK4zH9PIVvcqz/YwPlKf7JeN1TA2U8pgeUbtUBsDwAqlOm2YPgx/C6/nzxXIfNrd3v/W10T8mYxP7LIcAdeksfm/NcVHhDciwon8L647bx8DSSB40/NVp2zrOj/7sHSuETE6yPpZQE0AygF/PCtt/7rD7NdUt0C/u2ub7L9pDHgfSiXZ4u52f6ec7RrkP9HgMox9Y9BlDGv+cE88BBADwFKMP3HDBt1o8A6VhO5ZB/cwgk907V4Lh4n7tj+zLHdHvqQ2iuOfYxROSR0kAOybHbPMdOdgJ8YrPsQHsDjirSKuMUkyj6dcAc21QrkSjoR1r/5VCwTwuesYYZaZW0L1rxvTPmu6TT/+Gn7yJODgPmcwHJsf5zgPIQSPb7TJldp/qe2n5S22ka5RhodNadAJTh+xitc45pdhpEx8ByCAyHx/QlrItzL4fWD0l/+4N9RteMy3TdzHlzzAXHUBfTHYOdGfjT+jxbEnaaOU6SKYafc0kDkj0/pmoDfkScORZhz385qF1KS1yQ6jbnMm0I11XHdxn8lQ0AB9BslodLeAm9e5l5199FHianM/08d5Ac6vfY/smxdWcFzxnaZKctrNoHgMcGyv66IfBr2ocAtDfflGYZH9P4un3AmwLQ9gE0/hA6tA7GAW+OdnkOsDwkMdCFeeOqJUNMP/HD+dHYfqakH+xzVOBPBJTNctvWapKq8V32o2O1ViSJ6pTyagN1Wv9lqlzgj/bBPkHbjH2ZwUwbxjfbD9e8dAG1kz7C8Evc2KVxAdOHyfE+zI9lcp3b95Q+j2V23Zt7Lih+OkDZfB/ZZ0h7HFwXtbW/DwOf7O3PMLD2RaQtADwlU5rmc5UYCE+fowXIQ3Uxn0SOjpCduoelyb/c9112g3waAItNsj1Ns1P3Uu0z+8Sm2BYku6W7hPY77GnzOwLO9hDGX8IuYPlwOU8e5rmAcqz/pwCUc9pP0T7PFPU62HYOoOz1m+oztE+jmmW0bggs42MaAtDuPswDzqHx/QfOHCAdmusUeQrtsp3PoekY/A1pmo+5P0AX3afKds0pMN2/VwLidUyyXQ0z1jKRaIjQkK03jD29SNkGUKVrqh0L9tk3vboVDViG9viQDpy+Cz6eXx6Wh3kM6E2NeS5m137/hwDlQ/ueKZBnsG1G3yGAOwZQ54DpMCBOresD3CEAHdZC+7/jfRpi9hnSePf77DW5+eJxvTnnjH9s8Tg5u33OfKeOHZShPMvjOP2G779+W+indAco9zXMfeL12JwagDHV0vFhNqApdApFj2mYAt10Et8WR+/6xf1H3Pyzc5Ej5QST7MSf4zHMrp8SSI61H+o7ZXqdEcjjVh0At6j/OTXK8H1Mn0M5lsPr9gFyCkD39/3wuDbQZxgUh+QQkDbrJufYb3ts7XKq1Fe7HWGIuOBR5SFkBoek/YP77yjQR1SDVK1GKZEPsx/oM1zKK9HSRMcGv2WoVOKI2JUDS1GNRhoICmINM/xuHgP+PyH67L0Qthro3iPwY72RfWZyhEl25IR/DkE8p647ycQ61PYws2v8ewy45vQ5t0a59z0ComNa7RCotccyBaDHaZb9vkPjhuYZGjO03JeHPLzmDI3nPwXoDuVphnN2dhAN0z1KKa/evRYDpfvD0g/4adJKZkbHhmXH6tPzZ6pWuwyRsCKtKVZH7Q1wQuvDDMsSgXl8eJMvWBewPJecP0r2ApRH9j1eo3xOQNnvN2eOMaAdbG+Wh4Cxe6rGxvX79H9Dn6Rgnu/y0IPo1Moi5yzfNUcc+M3ptw+UAWsOMgkde0yHQHPKpzm6ExFoDvox/ctWFPiDsJeD2SFbj4CyrWXptU1x6SSxz3I/71Kav3dzDRPtWlhHF0Qv8nHkfFGy5wbKU/qcAyiPNcl+hkDZ/D4BUOds6xDQDmmWhzTEcU1wf1wfRMdMsWMP+YfgWX/OY+aa0/cQMMXg1tckRY6rdTl/f86giVozDKJzAn36f/BOny6IxlaPlrigjYYN10ifbD0GzeC3THTr09yPiiWaLyrlRXQPNLvc3otzfJf9l72LnE8eHiU7B9ge0u+5+yfH2gfbnj7idU6fc2qec+Y4rHWOA900mE6nnAz9PtxnGkBl7zjd8rmp8E4FyykQ7M8fdwsEBsfIEKg+SAJI9ifdCwKa0DYH7y9pANKhlXbpJKrVLuPfU6bYflpJql3uZaeslwTtUkXpJl2aPAeaLWFBC5zRMm175xA74Hraqb7IPDktSvZjg2S/zzGgOgf0xtadS5uM2wb7h1UHAGtG36MCgw4AxakaZb/vtNY5DIrx7/2++2OnADf8jjXL9ljZkyGQHT4/A2P3mya3dao5dkqT6INmDGxT/ko3pTCH8WeOJjm4j09Jk9cHymZZOtGxU/7LJHGkBZ1AH6VIVGD2cSAZSArCuqQB0G56iUTz9KNlHYh2fZdAR7s89He/yHnl4Vyyn4I2+VAA/YSBcgr4ZmmKc/vN2fdJMKXzewigxsAvXu6D3xzNcmgf+m2xTAEkzGf2mT9ucthZH4zi0fQQLMWge/xGThx3qgSQHGxvQbTxVQ6kk/Q1zb4pVqkQHUuUUhIF+0TaZcw5G7RKiDAcf+0x/PsiH0/mAeZcAJzb96mAcmrd2DaeCig7bWHVaeA2tf4hcx2zzWPm2O8zDmZTmuI4eE4DZ1+znNSmIznns+pTfO6FBzoPSDPpa7swM9p2zyx7ZB1MiABSonsvaJw0nz5oxmAZrq3GhxmWhU6pLq0icJSIOi/qr6Lrsq9dhnMV+y4b4Ow/usJ355r/BC+wT0COj5KdC5QfM9q1v/6pNUp4NoTop8z1kG0e16e/7rjfY+viefvA2f89vI39/Rvqf0jiLqc+wA4Ne8i8x+X/7wOdA5JQx/lMZb86haFtj+3nhKjYFuV766T99MCx+cxJJwmpJFFKSTDLthR5qvVlDphemzQT6PouaT+DhycX3+VTy0wN85lqk/31c0BvbN3JwT0DINlZPzR/WPVwcDsW/Prrjt6e73MK2M4Fyrb/2O/DANk8dKK+Y5plF0iH+8Qy1X+OD3Koyym+y2PAMvQdArbgx4xBVDg18Ce+Ds4UATTF9DNXy2wAUg1++n7LIcKCkE6SKOfDTLQ4EnYf5BODZhppm4nygUABICNTrIqu04551v8XtMwWTMPhjP/tL9rl48nxQT9zQHKo31OkhEyte0r/5NCYEcAa+o77nwsoH0ObnNtvCijbPvvrxrS/KVAc+z2UOrK/zcP7GcsUWB7zyDrVd/lQiQFyqg8PMMEeJXPA7xiA3GuLfZbS+d0AUwOUw+w+Wvf8l9JGx3aYfaJgn36+ZqtVBvNsHN3drm9AkmEQHLo8LmD5uPIwpp8fMlDC2ejrOr8/I6Dsfs8DpGngnDa3zgHLvhwLlmPgOSXdv/PsYbPnPIfE0bIiDwPIh44/WiZzMSNNslmWts2pfDQ+ws6nvX4asoII+BpfpYpqXqq4Mgmtlikx3V2ULhK1d64tOXxtNod4wcgnk9OCfj5noBzt+zyAsr9uLnCNzXksAPa3Owco+8unAefhSNqh30Nm2KG5p/ZnSgZBuLP+tKfZYz0ED4GZyERKY3Pu7OA8jwqUfeKCOUw/e8+KvTcskF79S2lzMPuBPuGa24+ObQkLmkLREkBTkYiKgn/8dSmtNonEUbB0fZf7t39n+QKWTyvHBf08F6B8DAA9BJRHRry61WMgEnea7jsWGHS0ptjr+xQaZX/5GC1ujoY61D6nz9z97RzvRB94Whq8h2qXMbBNAWR3zPzqWVNts6QhLDD7QBnWT8kQUDZ/vL7/sguWfRq82DSrtWrMq0HTTOI0EgnBPt4cK5FJVsW+yjZSttUw/X0N7f0ZgeccObfV4SL7cljDfC4gObXuY/snYRRABttGQHCqz2MD3EPzKOPTEtrmAOPY8jytc789/D63z3KoLcipJbxO8V0+5UPx0JZiHDqbmEPVSo6NlO0DpbQfpaGnVQ75MJOkBcsk5FdqaQkJPGBmiTSEBVo8CXsU4NP4L+NIWfogOuy77FzjvUODp70ufshyXNDPxwbKcwHoYNuBaNd43JnNrnP6fFSg9P2O0SYftjwNtod+h7f5/vhD+z21b2P9Ypl6ZH1qz7OghSoRzAGmH5FD2ucjHvwUJV7/3haJgNN9GrCUiDtW7WuXAeRCdOxeZRKJmH2krXXpUkr6xaM9Xktb+xLoapfxoUy8rF3kaeU8PszHBsr+us/EPzmnz3MAyn77KYAzT+McB9S5wNmvOrI/fh8Ix/a9u24YLE9l9RmTqWHnfGAGQPwo0t9syOOc2p8H52RK97dHrHA9N9e1MBAh29a+3PNfRhpmAM0AlM53STR/ey2qZnvddqELnP2/uAwczkWeTh7mw7wA5VEgONh/LnCN9H1KoOx+zwfK/vIhoBzvNw2iY5plPOfUvh1q67c/pAzXsWMfU7sQcfhzuA5mOH7xYwYKUJ9jN/vEBUMsPzAvxSSYYePffQ1TnJYZwLIFsPaa6uRjSjDH0miTAThVBJohijbOuWy5YfG5rj0zbLj+jjhdF83z6eQ04oIxADq67wzQi9edCp6HQPLQNmYA3yFgO7bPp6BRzgWeUzTKQ8vx73NFwp4Klv3HVf8BNvd5ds7nnurNdcg12N+P/VJe59dIrbXHzzkXJGH/Xu74MX3Aj+qaZN2381mGdBKtVZtCEgJ/vK8y0V7DVM406z4q8l/GROsxCEvHHKs8gvr3ERptM76mGTisC1g+qcz3YY6B2LF95657ymjXeNwMbXKwbQaoHgW8jwCUD9Emz7t8vnkPBfeMjZ2af6xfkCntcA5YPrZ22QfL0NYHzRgEA0ge0jL7492uWb+PZ9A4j+WHHZP2jxuBpLTLyn0PM/x0QVN5oGy4YrVqImMDq0+iW0Yf59fsmnVFiHyZtOkkElJLIrDcO4QLKD4XmREl+5kAJTw7s+sxfc+hUT53oBzrM9b/VLCcAs+pdjhOsxySYyNjzwGW8brjNE2HonMp8gLoTsnJWupYAemhnWh+D0X4txpmh2B9IDpW666/UoUoWfGMPrr9dqbZNt8yjo4NEbLN9ec//XSSzr065/AuQPrkMt+H+dim12cMlINtc4B1Bpg+BVDufc/ar9OBcbjPeUF4yl85ByxjOQSgcGq+5Hn7HZIpsHxMUUqo6z6JwQkTDQHiWB7mbIq8vt9Smu9wLzSgKV2ygjg6Ng74caklNGbaDtG6koaoIJ4XAkhG7bTgGS8PHsa8o73II8vxeZifMlAOzhNv5jMHSt9v+jjb5VPBbHj7p801pVX215+6zf1z0bYPAWW/ZehNfwgwHhIk9FgiMuybFBn2Y7pDsIPrHyKdfRgjLHAdD08WgaJb3gdLYqICFX8PkK4HX2QUKds3ycZE633tstUwY60yerETLr7LT0Tm+TDPAZJj6/p/+INBOweAciqQp9M/rJoBagP9T+lzDOgOAeHJ8xzcr/76hy7P63fM3IcqjRyjIcdtQ3PBPKDszzk099R8h+SYh+Jc7fJYs2y7L/MBcgyEO3LsPkxFy07vTPSJzLHSNcP2q5M05litmsCeQKieRr7KQLSe+k8oFB2X/mpp8CJNVqJ0EmhBk+HrtXtIF7D8WHI6ccE5tcl43THzHGt2hVHwGGybAUingt/ceaeAcuwY5mud/fUPXX58oBwb81ha5dSjaS5YTslY/8cAy+cqe9plZ+UMztgg3T8ujWYJHbBE6yYiFmEPLPfrXrYsP8lIsE/4NP5OiU2yccHobgkvoXcd9g9p4NAu8vHkeOKCU0D0WE3zCYAy/j3U9phkA8f0HVr/UMB9bKA8x9xdAoLjtn8McHe22W9nX6aAbGzVY5bvOgUsx7RMkeMiZd25eETyg7F9mFOdpN/WrHOgKRJpl9KNjo01zOC/3C8a7cysDcuPqGY5Bkb36REk0PLIQhc4h66JsZe8izy9nE5cMHfdyb7ImUB5aK7Bh/h8EBzsPwNMHwqqjzHPcwLKqT5ztcqH7Ht//kNgeQpQDs07R576oShyoILJ5Njz78+w+dV0v+eISAOQ+78hvHzG+ZfhW/VAsmuOjaJjm3WekF1ak2sLlAEQY2CcjoztL1/kecjxxAXHANzUuocC5cEx8erjgfIUjfKYvk8BlE+lUc4BwTngegpQ9tcds0/weEA5NPcxY+fIU5hiYzANmOPO0nk0yw5QjzlXh8DyYGmvAVOsN8fG+ZddwvVYu/QVR6LUkmCObX2aLXesVlHupUQMQQGUO990/ZYz/44X7fLjy8ygHxlu7687R/tDyAZgDzji32Mg89A+DwW3Q31O315//UOX5/U7ts9YQM/UPhyz/2NtAdCGHkNjD6c5z6xDWuXU6rkPxYeC5SGzbP9330zb10q75/4EMB0acoz/Mpb+c6GnYe5XJVGdIB/H7hPSRxSp9p9Is8y1ItNtAFAo5RUiYoMpNhSSjkEzgGTM7iN0z6H0DuUiz0POY5J9LKCEs/ooPzWgDN+fClBOAdQcoJyzD6dudwgoYR8shwBr7kNrjvn1HA/Ax9As+wB4HOPP+JxHg+cxPLF7G4xTSaT3Ub6cl25AMgT87KeTqAGfZatdxsE+2gNlolo2n4YOz7cRwLLZrYEczIn7pN9+kY8nj2+SfShQxu1Dcx0DlAP9zwGUx/R9EOhOgkgLDJ+aRnl43PztjrUFmVu7sr9/U3Ku/MrLQ9HLWP5l8xlYP/qciZ4d4kFSeuW8ZN932Q/4aepgqqhYtLR+y9A3gF+4XYNmifTICqJ7bO+lbe/QLtfFc5GHc8mOXajxunPlUXbawqozAeWMPh8NKHv95oDJw4Dz4wHl4LEfue2htnOSpZ8KkOd47p1Tuxwzy54qfS21u3K/KfS1U1rlyWZZNfgJL54BMFvg7OVfiida1226SNAs+8E+wRQbwDEmWw+aZeO7DNsfOScXed7y9CbZM5tepwDzGNPrLOA9ctsPBtyDYNpf/9Dl08ZN9YF5Psqh/Tg079y2uVrlGKA9VIOcM/xT1CKmdtkdzwPR+FizrAhdf6X/eFMskd+yMcXqHqtP479ULVmBVuTeHJtrIdPBb9n6L2O/ZbNZ6REVEMCzBU2he41LdCgXeX7yuCbZ0b5HAuXBh+rDgXLOfB9Dmxz+7q9/6PLD5hlqm8qjHG87HRTHQHgosGcOUJ7DzDp3irlg+RRRsUMSa45zgFAkDhyaCZxxJOxUWsmsHY6DfSIAVboBSq1bX2ZTwqsXHRu+G7BMhCyRDtuPbjTM/XzNNg/T7U4DpgSgvPguPzU5zSR7DFB22h85mGeg/yl9jga4Xt/PHSin+swFyqn9ONQ2p+9YBOzYHP1xp8hjPtseCyyHzLIx4HX7zqta0pep8zJJodf3WR5TraQDll2TbGO9CTjaN8eqNminCfiJPgEgHdF6W2DaaZLd67r9hO1J8713be4dygUsn5scV97rZAB9+jzKwf5nAq6j+k4B6YF5HqrpHQuUD9kWPJ6P8tA8nX2I25juG+QYkDz3M+y5PxTHwLNdL4BtKpY86HDmAOJYwE/M8CPSv1AI6SRxKknwXYoMU+IFLTPV7XJDsi5tKa9YmwygqQJ4hl1gHxA7hxDt6kWer8zzYZ4TKDvrzw+UJ4Hagb4nabITQDl3noeA1zBwfjygHH6R2e9/aJ6pvkNg2Qeksf59eYoH1xywfEozrMh40I6Dxbhv1+w6VvFkjnS5ZO3h8l2HqPHiHYgICxrtrpOHGQNlG+ST6JY/NlXOHOs4ZFU32Ed1/ZQxaCKxWXaopFd7/vbP3QU5n6Mcb5KdFQn7yIQDA/1P6XMuzbP5fbbt9dc/dHlev2PmhuMIB+Zud057/1lyrJ/yMbld58pTPhCngHBKjsnFdNuZH9A6a3+aMl8DPs2xHYAWJJtgn27+ZRz443yXiiTxwT4+yCeQFWTa+S2zhqggqlaiVJeoQO2DZvPCHB4PTVvY5eFr/SLPU06Lku08xX54zDxj4HwByvF9ONT/2L5wmp9yjwrviR9SxwDlQ7XLoXvqFOCcnv/0+fY0y+7KoQFTO9N+N5/Yh8moSbb1YUalvHSv5qXeJ1oPZbxi0JQILMdKeO1dr4zfBxd5XnJ6lOwphAOPoFF2fs8FwGP6HgLLifVPBZTDfc4LwvBwoDw0Zqxtbz/i/p0+nwdQwsPA8iEP3GO10SHQjDWogxIPbcyxR4Jw3wXUD/iJzKZNsM9eKolqo1v9J5AUxME+zUdCnuV+nctO4E/4SJdsnajtIp+OPMwke2Yf5WDbRP8xQDqm72MA5Vi/OVrX0PLHAMq47VQf5dx9Hpu7335KPuVgIeiRp9QP4c1+CBCPiZR1p2gIJM+6m07iKNkxn2Z/w0Ogqdr6lzG7z1jty6YySci3jAtINzmXgdnHbyL6CDTt4b4PeykDu9w9nM//GvyUZb5J9kyEA271eYFyqs9zBMp5wHcY3Ia3P9z3HEB5GPQOA+vYPKP70AdDhvv355oLlM/xAfWxtMuDc/e2E1cxOSbvsg/YdojubgwgR5mEBggLRBrgFGkDfYLvsku2rlqygoakQDVE68F36bRL1fgrA3hK7xOz/YR3jEMvlRd5/jIjreTxSdE7v+f0HwGtY/o+B6B8+PI04J6yDONAOQ5uxwHlHPA8lcpujun1OYJkkKcAyyEtc7o/wOExx867J02AT0xiYLrrx3fQ/+7R4UVkBQEsG3L1qEJJY2oNwT6J8sE+gdmnDfRJI9Nto6kKaOkSFQgRWIbleFf7y8/4uryIk5nlvc6rUZ4LKCfn/ohA2f8+BFb9tqcAyqG2x9YoHxMo+2OH1g/N8ZzkY2qWQ2bZjyrH1L4E98fumGPFRcdKa4rtBv14Vp+k1TaTAJyhbJePkA1mWi1qkMUnBPjgP07DhJigoH9fH7q2L/I8Zb4P8xn4KKf6nAqqU2A+KyfzIKD21z90+bRxU32m2HnOAZRz+p5qeh0cewRQzgWpxwaT52qGnUotechmnRm2/cZEqSPHRMju5V2GjwdNT4EXa5hxwE8T4NPkXao2MlZFROsSynl1Ta5KWoBsfJgxUDIMkBf5NOWwSXZuishjAGXUby5QHtP3HFrlcwXKOX2eA1DC6eToTwGUQ/3PDZ4PTR/5mHJ2rO5rlrO0y5jlp0+JJ819GjTN2CSr44okUb3LLPqdRkQFWuL0Efdx2uQQMYHQR8oYPB/xPecijyTTgNk3v3aefk9HYTc59wiwHNM3vrAfqpl+bKCcA2qPUY9yrH2s71zT6yGQHOrTn6Mdu9/vFOnPcwqAnmtfTtUuRR5AZjAQ1OP2wzYgMibzCQtsd3lIuhdfBJD+t9YQ51zqEPDTEhbowNwTiAm0YpEKeaI6wT5xVZL+R4k0/sugfTa7QQDPeffNRZ63zPRhjgPlJEDFv+cA64w+s7XEY/oOAODcfp8yUE4B3DHgd4yW+SkD5Zh8LA3xsR60AUxFurgVg2y8bgh85+6ajdNGDgHlWJ5Le4HSYfiJomODSdZVKWnLeDVaZaJIExfskyrVBvvoFigDFd6QOTZojlO+S5i+5i/y/OX4tJIJUBtrOwfpwDF9jwHKh87zVED5kLnPyfX6KQGlGz/YPDjunCw4jy1P+aCdW6VkCGBF9sftFY7e6xBFx86tVLJnlpU9Ory+7zKmwgsFoVPlAn0c4bpqfJeJKDelhMokNMvhOSDSM8vKsO/ygpGfrhyZVuK/DgDkIaA5ts9jAeVYn/nb668/fXkOID0XrfJQW2f7/fZOn+ExQ+P668fmaMcPNk+O+VTB81wyFSnbB8OHyt757aeUdDsP7xB0gbJZFho6vABiUTqJUkKS+FqYUTpJMhTs482u4Tsw+ewH/ES1LsOPzu5eImM/B5lpkvVfE2B2Lo1ysO3EvscCZb/vUwDlOeYZansqoBxrPxdQHgOSbvzoqqMeUKHvuYDTYrDUbm40gjowYnq/ziEiE9VJJtadJHb4XNqxwJ45AT8HImQDUHZzMGOygkBSoDrBPmmTe+lSSRJRPtgnosNT4TfNRbpXILrZreH75iKfnhwATP81B6gm+h/b57E0yoPbPLi9/vqHLp93XjiOdGAO8B5q29v+RwDKc4HkofGnAkhNgZFtp03bJYr05H15LDkmH9PtzyFWn6l1Q2W9ekB5EDRjkPT+SxFQSWOK1Vr7YB/dCfhJkqgqSaJZpIo8USySNtgn112igoZrVvDUeHHAjzsn8Xezm4zfPxf5dGQSMB8KlJ3fjwGUh/oeA/hR3wtQdtvm9P3cgPLQnHPB02IwskVICEY5i6WWDWLna5qP9YA9pEmKzCsgPVu8ptnxYw6aYQ/UxOzsRGyG9aCpW4afAJZ7VHhKNcE+Qats0kl8hKzugWRskhWJS3m1vkvoXf/zz85Fnrkc9GHOAbahfsdolYeA8pi+59ZOzwFqh+Z8yHbOWUHkVAB96jzKxwJKS43FICgEPdl3CmisBxBBIjNsfI4E62DzIGB+KprIg3cz9mEO8cr2z3UMkvFONMAphwN+mqCf1m/ZEK5LW8qr47dUXaICCcce7nPp3yf7J+ZT+ZteZF+O0jCPAcG5fc4NcIf8rU8JlI8xb9x2ilY5v/9+W7/9XJGvHx8oLTX3GGrELysyNAuG4xy726xtiaHAUAE1iDjQtQmgPBAnk3Od+5g+BWleOKZswHO0zAYku59wv3f5Y50ptomO9aAZImP7wT6O2acFy5bNJyJbh/3PHnCefp4u8nzkOJPsxIP4uQNl830BytlAOdb+WEAp4jQyQ4HFIiQoUvTE0+ZUUIlH1WyxUqOj28HYAoNGk03OYyip5R4lCsuOmhJlUzRLarmjpgAsQoFmgSbDYgBBBm6/TwEkx8gL+jJptrb9754Pc27QTwOW0n68OVZpR1bQZfZxoJkG/2WiyFNHUpBp58NsSdZVGx2rutGxAThj82wblRvtIuMvnxf59GQWYJ6ddCDqf26gPATOnxpQdoDqGfkp49u+/xAYGzO0Ph5vqKjlHhC0KCxbDDuUXXdMl0MPnTlRqP1RFkNNQcl7hNRrgoUDA9Eoa1DoSfNszQYl2s9nSWWBkYra3iNYxCYoMozsKO17rF2jJEHbVaNxti8KFRaDIkXNrO1+DhEZNi8PtQv7Hsux8UFailjb8V+2HWb6KtsNDlxEEWFBL/9SNUE+2mmXEVhmiSb3JOuLVDrMPh0fptDUv2zNssOsPuGcTL1MXuTTlFlRsvAw8Ov8nguUc+Y9A+geo3XFy3OB8tA8/f0YWv8xGHqmQG8MLPvPgzkaZSy1bBA02vulBIWhxFCiyQfHgAda7v2kjsxbsUSTjRpBLTUVdxhxBtmaDUYMmpyS91RsQFLW5kckXJOw2BtvvDYMEv02GAoqNmTyymkgVpHwAiMlIKRcd46jpqBm07RVtkCRkbAc2fvnK/v3/AEts1PKq8f6A4cijtgL+An5l1HAj4uU7aaUNIE9Te1L6dS7DCAp4q7jJiK2Y5aFRqtk/z6+yOcns4N+4t8naZUn9H1MoHTf/fUPXX7YPENtz9VPOaVV7o0beIj0xzvNyqKkqxkKGkuFSBew4nE194ioRqu0YrF2g0X7ABzbaJ1Oi7TUbEAETYIl8ZqtYsu3KBQKjcFQyj3WGjSp35eami2GiooNFe9J5RqAwr5Bs3TaoRhqtohVJKwQQKGx0PFlWiyGLUoiH6c4v6glOxh8dC6JtcQ5qSWHtMq+DBaNbhbGikUfC5ruo1Rb0suBZdckG/svQ+pIEwAkUZHoBijd8U76LiUmVZ++9i/y6cqjBv2cG+Bmg+5MoBxqm9I6x5ZPnWdq7mPo7OZqlcdomo/pp4RgQjUIghaNRTCC913G81uYAA1nhnUPVEOJuBmppMLYdz7iVZrIVVCApeIDmis02vsSFYYdO954zc75GSuxVCi0XZFyRcW9e4CiECCRJZW9Q8saJZnTFFmjWWIpEdJm/91LQT6w/10QDefN2Ar9RID5MaRDWtDRLmdWKGl+B9BUnfzLxo8ZB/skrf8yDvYJ+ZbBDBvzxrZaZVe77Pgu6d1T/eULcn4Wcvagn2NA9aEa5V7bTKA8BJzHapSH+n4OQDmlUQ75KfGmTgeACiXtpVazw8gOJRZjBYt2mpnNfYJ/ihJxOYzWkLLanz+Smm2zw84ia6ntFiU5CuW1OINgSbnGNP/uPJwKiV1ipUZ5QIXagTgGKxs29ufU3GCoyLh2xyWCZuV62w1aUsT6Y0FTU2ARLJXHANkjK3BgPiS29xd4/qKUUNfWXysjRxXqX3YbhzoObySAI9Axwwb/ZRTwo7VGRUAZExWEYJ9FolgkMhrso2PQ7Pku8cAZg2Osaba7/Gn9HS8yLmcL+vmoQDnS9xTNbj7gzQXVecuPXXLrGKCE9k9+LFCKeP+g3IMHFazFkqFZusAW2aGDCVK8D9Ju0SxxcFdSW+vjSFejZsmwdUuFksyZZAUMW7b2exbyFRUlFbcUvEFYsLJfk8gKS01hv6VmQ8qaih2l3VFL4dNL3NYrPrCz37FQitK+920bEhYoq0EStKRom1Ny7zRnqRGbkfGC2m4BhSJDkXizsPVpKDsQsNYFGgUwtRiw8qSBP08tjUl2z4c5wP4zJCLRpyVbjwN+Wnafltkn8588cRVJ8kR8hRIf6OPzMBttsgOUMaMPLVnByH1wkc9PZpCv+68TQWtszA/FTzmnz2OA5TGa5hytsv8AmDK91rJFSZT8L1DbAi0pUPh17QBFgpXSmThZYlngTKLdfkP7aCnRssLYwqdrGErusFRU3PlgIIOQUPILtiIs+QYALUuM3WFYYCmw1iACJd+jWKNJ2fG9T2vZUUiJYUfKCzJeomxCQoWxNVCztd9jxGnEO/lAajIWfE3KVWf/azbOXOu1bpGE2u6aZBMBNCscdJaMRf1+ctIPju2DZrNiJsvPXrHobsBPYPdxZOs+4EepJtjHgaYDziQUiRbVAGUc4KOFRrsUGPVdXnDy85aTuGRPAcBjgfLgXLOA/NzL550Xzp8qckjTjNsfw08ZQEt6psdEaQ9qFsPO9wMtGYq0WxbKQ0Zfwma6fk7ns0y48gBZgiQYqSj4gGBIWVNT4PI6MwrekPLC+yo1mgQlNyhyDDuEBMOWijtSrrBYtvyClCsEzY431NyR8BKsM+5W3IE4iC+5peaOUi2xJkGz9gE/7tgr7klk0ZhjFakzW5MjaGpKCr7HUvsUkxTFookUfi4SNK1jpZNaYgZ8mLM2HtXqjejw+uw+ce5lopw2mWhxxaJVW51Ei0tl0qrvs+zXvZz2XcL4fXeRT19ODvo5O1Ceca4fIlDGbUPz7m1/BCxPBcp2bLcxsPO4WFVvbmWDliUi1mt4BQndXMvOvP67DmZMr32GfMXK3lPJOyreg6QIhsTmFHKPZYtFeZ8lDlCpSViiWOFSV2o0uQvosQYlCRbtfaPKtaPRXKGosOwo2QCKFV+hvRm25B0gGO6BxOmH8h07uyTnC5e6QoGRHQUlUGERwGBFyO0XCBUVHxBlUSQYW2KowRakXB1kH3pMEZlyLQqxCjkGFHF0rI0DfrqdJnaiR1LQIVzXnWCf2BQbB/u4/EthkaqmSHQwxybS9Vk2nwhAL77LH648WpTs2TXKWdvsr3/o8vFAeKz5dQrkHmJqfXqgDHM6U6xI3fE9Glu7CFGpSWSNofRrLMZWjf/O5V0WLgDHJmhyH4Kzo2KLEpfeUXHvo2AzSr6nogCpwQf31OwQhJIaeIOLtfXpHdaRERipPQDjU0TeUkuBUAEa69NQwJJw7ee7w1CRssaljhRYW1L53E3DvdedayBFy5rS3iEkJJL546mopWBnvyeVKxQJtd1QhH0UFxzkiBx21HZHpq4ozXsspX+5eLro2R4WDq4PGDeGEZPFosN3HCE75b8E9quTjAf7pKkiSzVZohuygkXwYepesI/ua5k0JtqA0Rff5Q9Xjq5W8pz8lN22dvk8ANe2nwKMT61VDrXtbT/0ZbjvWP+hdUNj3RhIWVBxj7UlLqjFOhIB0RgRNAssGca64BrlvXcugX/b5GKW8oF7fo6yKYjB+KhbhSaRNVpyp11SENJPXP7kttm3gt+kZk3GCtDUpCiuqO0ODWSypmRLYX+BFcey44CvJucVFe+prSEXoeCWintHPIAiYUnCkq3cU1NT8Z6SLeJJDSB1mqxKMKZkyWsUORbFzn6HlrwxYaeywvgcT4Wi5NYTKDjavRIhly8QhNpuSVjvnfvHFCWCsYFUvguSQeaCRYiUnfRhtp33NzJkjvXpJDFothqmRntzbKoVeRPs49JHWtNs678MWmYgLGhMs83xT/suL9rl5ynziQsGNZfTgHBOn09Fqxxbjts+RjHnMeAbA8v+/X0qWLptaBJ75bQza1HiNTqqKFtCoVHeVFshKGruPCercYExUjmfn2zY8Fv4JA/ElqS8YskvUckH8HRyePituMV48oEFPyJsVLMgsQtyXoFYMm6wtkZTk8mX3NufYaVAsQQKLJDwghpQNsFKQkpGxpKK9yS8RKFISNhRUVNSc+d8oiw8AG4akoRaCs9Rq7CIIzYgJ2HtOGtlizW197++BVwQVI2htnfU3u9qKPfyVc8tY+QFIqcXlp6kxWv4Y6e0Sun+jgN+Ilaf1n/ZForOEvc7mGNjkAxFogP9XatRtr5LaIEy9l1O3UMX+fzkqLSSJ9UqD/Q9TWs8tHxeEA5AOdVn7Njngev+HP32Y8HylEoiqtcnmFUBtOSR+VBRU7LlO9dHSrRNSbj2xOc1QuJp4hQlG+/33EFDPWCoxVLzHTtuUTYhlRxh4Zl9aioqH0yzZMWPnVZGQco1SlKsdT5TRYoz4m49fAsFJQkZFSWWHQkpmbwms6+xtqSWO1z25e9Ck2GoMBaMFVJ17QkZdigULgXlDqHGkedtEVEU9gMWgyYh8YFGYKjszvHO4qjxak/mLihEFtT2Frju/SWHpctNmxDX4xyTY8GwvQ/t6PU0un9xPuZgHuYB4OwTr8f8sY2GKU3upVaeXN2bYTMfIZv4dJKGZF0i7lhpydWbdBL2z/7e8gU5P1t5sijZYzXK6b799Q9dftg8Q22fuq/yFKAE5wu0UjREBbW9x3heVMMOw46aLYV865h+JCfDYG1BRUnGGsQZPB3HjmLLO1y25jtAqNiScE3Br5PINRU5GVek3FCxRbAIKx+dmlBRUFOT+whUZV0VFBeo84ZS7l3ojRgqu0UELHfAAsjQQEKK5hoxX2BFOxASwdgaba9ZsGVLTcLaa5vvGzwwaFJJPZ3eDoPLOQ1ad8W9jyteornxzEE5zoT83vfbYdmxJSfj1ST4uZeGWxDxJlwXIex8uMc9zOeA6LFA26XFi8yyQ7mYexvrpZL43EvpVShp/Ze6ISnIEk3mTbHBf5kp3ZT00r1PMM2KtJGxIt0X9vYczD78i3zC8uhRsufSKN13f/1Dlx8fKKfmObxuvG2sfU6qyBRQDq2fMr/GYqmxUqKlLYkloqhtgQmpGrLFyL0nJBB2vGMr9yBfY6nZ2lvEZizkFQBlIFVHvPZZYjGOHJ0Ei6LgHZYKzYqEnBpNYtdYCkrZNUBdkGBMzZIfA4qd/Z5K3eGMwhsXdSuaglsUlsRzuSoA0RhryPkSa2scpZD1gUmQyVfs+BZByLkCVjiNusBwz5atIzJgg4gmtWu0Tbnn51gBQTkQFUHZHMd/m3jwK7w5Wqj5hedOSn06SvcPYakp+YCIaogPRDTGlt703U33OUaUCHXDN9v+bv/WgjqULhpVLtkjLdjrG+caSfvdA0uS1HPGhmAf3QT7JEkASh0x+6g22EdLpypJ0DLjVJJQE7MBy+h4x+6xi3yeMs8kS/ywnQbCOX2OB9Wh/emPGV8eBq/zgvBjm18PtcHT+yr3ROzgm7YIWFtTBc0HjfKVSBQpO96RcO/yHCVhZ791kaGs0ZJhuKbkZx68nJ8ScJGxuCAjF0xjsCRom6AlobQFUHq6uxdghYV8Q85rals4gEdIuCLlii3fefPtPcrT4EGBsPaaK+R8Rcqa0t5ibI0Rg7GVP8YcLQmuRFgwKe7Y8HMWfEnJBwLwu22n5PLaR9/uMFJyb38DIxUYoVJvfVCQomTrSd4LNvw61pbkvCLlhsCV6/ylOyrvR7XkUZUXB5p9ar79v9V5NMop3NgjXh+KjB2af5A/1n2PBfukntkn9Ww+ufdhNsE+URmvpkh0lEoiIhABpYRNT9xTF/l8ZRb31rEANy+q9TSwPHa5fxzHjJu7re42xvuMHdspbf32c5tgjwJK398ieHWpK1agCfEx/mFee23oPTW37Fw2IzlrMnmBsY5/tRZDbl+BWHb25xgpKbkn44aM1ygSFCENw6CsIZUvESypgOIFlb0nlxVKMrRNEXDBRdaV8womT0WGYFEsvN+1JtTMrNmy5BsK+8alf8gCKwYjW0puSViQy5qSWxRLMhZNYE7wSTof7RKh8hVVShwYuhqglsKXAHNUCAU7XFXQbwGNYYPwJVquQGoK887nZWrPHlShJcOyw7EHbf1LhssJfUgqinshaL9DW3u9Hzef7ZteO5GxM9JJJunwoo8KFUp8VKweDvZpwFKiVBLxoEkAyt7L5XGHfJHPQE4iX38YAM4EXc6n/T0GCPc1y3mAO77uUNvQ/sB5OGDPAZZu247CzVA15kDjI2AdSF6BdVVJKkoK3nLPtw7AqBDeeeq5pdPClKCNA6dr+5eQsmbLd9T210lkgSIjVASxJBjusZIg1BhbkMgVAi76FmcmLrnzgJigWXp2oK0jCfB6oVuv/L67YyvthsTeYpQitSsUsLNvsSggobYWzZUDUQrPT1uRsPIECZkHZPHMRjsKu8NwRy0u7cbdjg7YKrn10bfvcGXFnJ9zy2+S8xWK11hl2ZrvSFiwk+9BLMrmCCmaCuW1SkFjrTMxHyMhUvaQRhmDaHs9dAkKBiNk3cquaXZKAkJHQIlKOr5LnehOoWjHG6vdtw/6yaLcy0HfpbT+yg5ZAUP3Sv+4L/I5y0lRsvHvC1CO9zkWKIe2PdYGw1rlQ4ByaLwbt99vcFu4+NHabhtiAiEl8ew0KUuW/JiN/Sm1bJqgHmeadeQEJbcYrkjlBSmKSglb8x0iGYmkLO1L8Okalh0LfuQZV9+iWHjfqIsqLew9SqCWArjzqRsJxhYoq1jwmntTYqSi8pqiqzRiKSmp2ZKyojbGp5LsoHYmTouiRlGzI+caLCjJMVJ6hp/Sp7yUWB+hqlngiBpqxALK+RSFLYYNkFKzJeHGU+zV1NxjQ34qFs2Sgu/Z8hKxSxJZOjO1+BQUKbFWMDYBKrAu5SYEGZ1Lhi6JWOMMy33p5GB2CkX3TLF98ByqUCIKtPZmWN0ByzQNQOnAcpEoFmnrv3QRs74OplZdOjwlPkLWu3Pi37jrvn9oF7D8YcisKFk4HgCP6XsOAIv7HJr3ods6F1jOBcp++0f3VY70d/vjCiYbz5CjaAs7A+S8xNodivdspULzy9TcYSl9tRJnAnVGxMKNVTsK8y2p/E4y+ZqEV+zsd1TsnMlR1uBTSrAGLUsSUjbyG+zYkPPCk/J9cOW4bIYmQ+Ep68wOUYaEa2pbUsstiWgS+7Wft6SyWzBrSvke6tQbbMGqHRVLtF2QqgUKTcVLlN16q2HFji0175wZ2hqwBUv1u1AIhq2n4BMftLTy9H1g2Pk8zJqaBTUfyPkCSCi4I8U6E6+IZxCqnaYsFmUVWI0iJxS1Dvy9pxK5B8UxaJRKxFnbe2klR0ufIm+Ufy+ixYvKeTXapfdfhujYLFFkqdMyF6mPjE0UWcTsE+ddtpGxNBpmAMexF4SL/LBkNnHBEBCeqlUO9+23P3T5vCAMjxMBO9Rvqu9zAktLTU2Bi+jUDXmAA4ACEM/hk0YcqJZUXgApBR9ISKhJKPlAjdNME1Y4o+17nwqRoNUaBQgJCQnIFyhzSyY3lPYDIrDgK0SgsvckkoNVJLLyVHICaK+5ZIRCzom9QnEFpiS1xpt1a4x8QKzC2AJs7jyMxmKVsOUNiXkBQKEKrNkg5C6P0mYoZVByxYqVIy3gLZYllb0n45pEfY32wT+aG0AoeOfzJX1Eq+CqnrAj5TXwjoIdFTtnbkX78+RJ7Fl42r4tFffc2x2JZCT2FSlL8COk2W7w0w7/fW0TDdslMIgZf4b6H5LGNGtxE++B5UQ5r+Z3N/eyY5LVQpr6oJ9QJNr7LANhQZx7OVwgetx3OaRdXuSHIx+dS/YYres5geVhM+v+8Uz33z/ufvsQtd1U/3jM+L7MB8u4r6FypbME7+8rfe1HDRRoWbR97QYHdQu/9y74J/EUelVTgks86cD7JpDH0cTdeX+lbbQo98A3aLtAyxJrS0TcKCUVxpZk9sYFBUneaFWVbH3gj/PrGYs3V2ZUxpluhRTsNbVsEbMmMQv/bDfAGvG5nVoSVHGNUivE5mCFWmpq2SFSkdo1kJDI2u/Xgsy+9NpphWMo0qRcu/NmK6wt3bI1iFphEAq+pcaQ8dJzyX7pIoj9+XRlyVwg1cZ+Syn3aGXQfI1Iyda+R7FkyRf+PNbupYIr5j7+jwHFMZnFJzu88fY7TidR3eokDQ2eJ1gPnxAZ2+WNbTXKsbzLi+/yIn05O5fscX376x+6fNy4OX1OTReZq0F+LK2yP74dt99vqG9g4wlAZKip5D3GVh4sa4I5s5Ya7C3WP7AL+55CvnOmTu6ovO8w5ZqEKwwuMzLx4KRCbUhrsVJS+xqUCTc+mMgllFT2gycUSLG2RliQcUVp730AD4hVKLt00Gusz5PPMLakrhWYLxDZomuDZYPBUJoUV8YrvBw4Rlvs2ml5aUlNhk0qtNKoegVy5zRVa9CyBiofB7x1e2JrlKx9XmmFtTsy+wolCzQLlE2p7L0f+3PHxYsi4wVCSKm5x1rreISkorTvMGIQIOElLndz4+t5usowISrYMp2XeQggh9aLhJehfQl9Ww3TMOjD7P9uJo/J1lXHHKuT4L9sydZT7YAyFIpe+N+pZ/wZyruMmX2CehmD5ZB2eQHLH5bMzsN8jkB5aO5jNMihuZ+qBNfo9vugNzDHwTEPBMqh/tZDWjDpBe5XJTm1OP9bwa03ja5dfzHcm595ejhNLWDRaFIsazJe+gjSxGuV9z6yNCUjddG1tmTHB5AKZXM0C3bcUcl7D2E+lcLmZLxAs8DYstkHYyvEpGBTKhsA01LWws7eUdZbKgOVucdYhexeUKsPDnzrK6fJKQtSo+wKURmVVGTWFf9CSsRk6CSlMPegDAjszDtENJmsSGTt63SCstpVSbFbR0IgCa5Gp4C4yigJFTlfU/CGmi0VFktBiKbN1AusdRy1qT/OilsUzgjr6oCmKF/5JLXNH3W2xjhklq2tHdG8Ji6keHMNaPbMslN+THHnpfVfpq3vMtGO1adh9GmrkqwyxSoNgT6aTGlnrtXd6FgRaaqSBKL1/v0UduMiP0w5nIc5CQ49sJwJlENtj61VHjP3Q32Vp7T12+dolVNj+uvGxrtx+/2m+oc9CnmGNQWI42St7dZpmrgakc7HliAYamXY2W9ZyU/IUZTsSPkSzR2JL9TsalWuSO1LQKHF5VcaK0BNSo5LOanZ2e99Pqdp0liULDB2i/URpQZNbT0XrXGBL5W9RUxGVSusgW21pTaKosipqoqqvnJ+xNJSV1dYKRFxBmHSO8QsXAKIKhFdg1mSqBqra1AGEUXGK7cdVVGLJZcMZXJEIGWNxdEIprxkIZkvKyZUXhve8T1KMgreU3LvI13XJKRU7Cj5wFJ+4iNjLSlrxCOSM3gHPtoKoXYRyTbz5PBLsK509pT0tcgxrTPg2Jj0xwwSsM+qgRnnXyY+57Jl92m4YxNFnuqGaD3TTsvMPLNPplWXNzZwx6qInID9e/yCkxc5iUt2sG0mWJ4X4B4GukNtH5vaDp47WOI1wRxjtyAJDsq21HaDFYMzxRZU3KPJqPmA+Me5kS0Fv03CSyp21NxT8IGU92R8hSvx/NvkfMmSr7DWoGyNQmMFXxLLOjYbydnab13krY+qtSgScdqgtbXLv7SZB6ISaxW1sVTmlroW6rpmU7+lrjTFRjBVSlUZqrrGVCWUGZYEq5zPEbVEdE2lNiRJglYJyiRYbbGJQbTx5y0h5SWWLYlck7FuAkkSzyFrrCKVa7fftgRJqSkc+YFkngd3ScbOachUaNYY3HGlLKDJc80puXUvE2yp2FD4wtcZV4hNSXwJMWPek/LKm2aPE5FpbIul0y+mxINIu5wo7RU2CPSJCtC6IVoPwT7BJJt4soJAtJ4nUZHoUMJL2sokWtpr/ZDvsrtrFwj9ocnJ1HhjQDnct9/+0OVjQHXe8lNS243NDQ8P7BlaP3ZjH2bucbqK9ebX+B07EAFYu8NQU9p3JGqBYYPBYth4c2CGpfA+M7CUFLyj5D22STmpKNmg7G+j5IqUlxhKSvuBnNckrNjxjpYazQX/uNJfjmUn5aaJxBWbeBMuYF3f0u7ApNTWUtdQ1cLWfE+9W7AroSpqtuWOaqewO01tdphSMEUVnRT8Qzqh1mBSl/8ni5okUaASbLoF8SCLISEH8bkY6OYhbG2FFeVrb7rKLYYNlWwJGrlCkXHl6QR3uOoqtSdDyPy5U57bVrO1jrzAUPuUHItwTWqvSfkCZTUOuVRDkSeMeR1Pl5NwJPZnDk0Ya5j9dJIQ6OO5Y9OGrEBYpKpJJQlBP03eZUSHF8BThD2wnLr3LvLDkqPTSj51oBxqu5hg9/s70u974sepy+cLD1ohIceSISQUrJyWSYkrEH3l42XfIOBLTQmuDNeWksLznNakvCRlhYj2j/MEJdqF8ljHvaPJPBFB6n2m91ikMW/WbJzP0yYYa1FWY4wzIVbGFeuytaGuDXVl2NUbNlWF3dbcFQVVUVNtSsqqoLpPMWYL2xTK2/bkioIkbZLl09ygE40xhiRNgIykFqypyTKDTXK0XSDKuOhcsVilEF/GDB/EpCVFk7oXAO68Bp8R2IgSriipSHhBxhUuxGpHxQaxhooP7Mw7T3yeIhRkvGLlSR0SWaOs8Sk6ygf7POzJf0jTnOXLHNUy+0Wj96nwdBTwkzT+S0dUsPA5mKtUs0wVi+C71M4kO8zsE8gJZM8cC/va5kW7/GHKaWklI0D2GP7EU0F4bp/nGtgzNc/guDNolc4PWXru1Hu0ZA1AWgy1vUe49mTmYYjTRBPJfWKI06xyVmhSCvuBmjtEEl/bUbAssOzcOF758lkZeMOq45tdoCSjtls0CxKfx1j7gtIGqO2WTJw5trL3WLshsV+QmBSpV06btJbaQGlqbAVlZdjtagqzZbOrMLuS3Z2htHfsNjuo7uBeYGdgp8G4ItjuiZq4B3aSUeuEulyjkgQs1GkNQFolLEyOMhqVaRcrrBNE1hhVIGIRm5NIipG3TQ0Sd5IVS34JYzeIpBipEawLBDJCJi/QaGpbkbHCpZIYdrwBJVg0CSmKFySssFSAIrANueVwy8d/w3EtM/gt+4E/7fUkmJHR/UuwUwOzP1lTQHoALMNkSoNOmmCfJEn8p9Uu81SRpZpFqlhmgbBA+1JeTrOM6fBa/tj5FUku8sOVR0gr6a8/bnm4z8PmHmp7bK3y0LHBuFY5NaY/bmj9mBzSLCs2WEoQF4lZNsWLBUeqLihbNebYZl40QuoiZT2/a02BWEjsNZVYXDWRd97Ie0vKFQkLH8kZzIi151SN9znD+MLKOa8p7QcXEEOGliVY90BLzJcukKV+jbIpddAuK4MxQllBWe+oCmG3q9jWFbvdDrsRtncVVQFs78Ds4ANQes3HeDCLNRyduo+pMUnGTimqqkKUYI1tHrrgrjNrQSnt8z3dQ9oZuRcoSbDWmX0TT19XYhGbIjiauyU/QrN2uZmAy3V1KSH3/AapXKHIKXnrom55y5aU1Jcn03ZJJi9dyo9dkPN6T8OcAs39awXP+NMGAYmEwgZHGHjjSNlDG+zR4QVzbKh9GVJJUk9758p4CQvdMvvEIKlVm0YiDVh2K5KM784FQn+oMtskewgo3e9+n4cuHwOqw8tDbZ9LYE9//dD4duxgczPGUGEofFSqy6E0ssMlyq+xUnliAkHzRaNZuvSSxAfYWIyUuPy/Es0KuHXVR9igSHwgSo4r7pxiKEhw2zR2hxbnm1NkiHWmWKcZ4QkLcjK+QIlLP7FAZQuwKWJyxOTORFsbjLGUZU1VGYoipahLinLLrqgoNpZiA3W9obq7hWoHuzu4t1CYYXNhAE1dgSrcuiSjUglVlqG19oAp/hpL0NqNr2vB2rY0mfbmU0xFopbNJgwVKdeO6s46/7GrNIJLPcGlohhxvtlgYi24o+A7ajaU3HtSgxekXLt0H1uQ8ZWPJu5eDI6xaedzVZMmV3OudIHzcH87J9gnTNaApewTFvgyXuGTpZos1b5YtHQ4Y4dyL0VCDiYNUPaPK96Vi1zkJPL1h2p+88acf+5zkhAc2vZU+3MDS3APzeZ4Ay+rD/ipuHMPWlGU9i2hQkjImzTN8gJjP3jScY1FYdQWTUrKSxQran4GVKSe47T2YJvblygWrlqJXPs8y8wHHDlKN+OzDFPW1NaZZa1VWAuV3ZLV11TGYoylKCqMsWy3DjB3u5qi0BS7hGJTUxZQ3AvUt7B7A0UJmxIq22qV8Um2FqxvN7V7iJvamQgBygUbcVG34elrjEUpab61VihlA5sdWjmShdqWjcanUD54SfmIWWe2RQxiU1Ku3fmwYAQfWOWikp15N0N8wJX1HmGoyeQFyoIK5PNRNZmKW0Q0oaRZaZ0FIIBmxyzLvHqZhwgM3ELvpWQs8CcGS50iPu9Sa5d3GfyXgaxgkQir1OVgZt53maiWZD3WNEVa3yWRttlsevDYLvJDlZPSSh7Dn3iMn/CU7Z/iqzy0H4fa9vZhBCinxvTHDa3vj++OHWweGCNYW1OL1xRQXvNwFGqhDTGOfIDSB6pUnnXG+BSSlISXKDRGCrCJfxgrMm4QLFv7PYoFKWtSrtCsUORkXDkt1loQ5QDAgvaBPc2e2gTFktI47lljBDErykqoqpq6Nmy3FVVl2GxKB5ibHWVRUmwLis0WdhvYvIeqgN3OgV9dtQ9nnbQPaXD7ZGqoS/dtaii3UHkfW7nFArtqjTUWayx1lSLiuE2VEpLEU9hpB1dKxOV0qsxpkaI8kBkqthS8AaU8pSCuqor54NNVas+9e++BUbvUGxyvrPZEEIayiXO2za9ANlFQ8g4rlSeMWHgCBfZAc/+6askL2utp+Lc7fW15rz0Nc8h3CXTMsI3/OGkrk6SJC/TJXN7lMtfkqWadadaZYplollqTa09U0ANLLaCl9V0GsoJwb7S/B0/BRX6AcrQP83zm1u7yKWPn9nkoWB4DiufUKvvjhtYPzeHG7feb6u8KFr9HiUZIfAFnhWZFKmsEKNm6Gote16vZYeyOii1GKsQqElmSsfYP6hojBRUFCuMYexCUJOR84VIuEGq7Q/lUB/Eaq7WV55JNIGg6uJSRigqxGjErlIXK7KDMqaua3c5plNttSVkaNvdb6rJmu9lSFRX1ZgPbD1B6E2xdOtCEJqCHNIds5UAz8T7VunL9inv3XRUtcBYbD7ZO6yz8OTbGoLXGmIQkqTFGobVx2JAoRIK/U/vz7kggDBvHsas+IN7P68zUCbXsqOy9V2IFSIE7LAWKNeL5aR2FX4GhYIcz5yassbbGABVvqLmlVmXDZevKshUuZUdKsOI13hX7r3juWgzxUEP385B0NcwB4BzaiNLNtwzQ4aXeDJsnmmWmWaWKpWf2CabYTt1LaX2XjVbprQqidXustU8p0m77F+3yIieklfTbh/rOWx7ezulzndtX2V8/1Db2oHgoWM6l5DrHTVyzpeB7lDhqOcsWUBh7j5JlA2LYCisZJe/8sWgqKUg8EXotd4BQcEvC0qcxiCtXZR3oOX3ppaeSc2bOYB4MzEEu8MfneUb76TQTl4pSm5LS1BhrqcsUWwpFUVMUNWVZO82yrNjd79z3Zge7e9jeOoCrCqchBvOr0pAuIF9DvoLVDSpJfLoIVGWFqSq4f+/Gh08ATXDL/g+1U75oc5pgrSVNFdY6Nposc9dmJYZEKUcCj6Oer2WDkoRAC+jO39ZXJnFnvWZLJi7vNKPwwOmYk1ypNCh5R8KCGkNCSWnfsLM35AiIoeR7rDiDe82GnC9BoLJ3JHLlSe0TDBW13XjQPI/YvkY5ZYrt+S9b36XukBWk2pfxCrmXWpEp3ZbwinljVeS7NAYp7xBbuntTBEmX3pJQ+SBdBYvr9uXpIj9YOTKtpN8+1PfY5SkAmzfXUNtTRsHGMqUhngqWY5h4rGY5NMblW975KM7MpYLwATCk3GBtQcFb/8DOKLmn4oM31aWUvKcm9yFAFXDvKOHkS1xdzAplFeKzJbGCkrXzeVqnNblUlBIHj8MHECqLYJ35FVNBXWNroS4MZenAcrerKIqa7f2WqqzY3m+pyxLu3jmALDbuu668L1LcgzDJYHkDyxuS9RWL9YI0S505FUvh599mGdX9vdNSwZtli9ZEC4Tk+sKuHWCaAJygXbSP02pEYcSlvWgFVlxJNPfSkFJbhRUXWBX4e7G2IWLHkxsI2hHS4wB7x1tSrnyQ1BVChpWS0n4H3LvIY6wLLEKoArsQGYjLfQ0AGXzUDtLbfZ/DQ3vwZW4IKMO8A2DZcMdG2mWW+SLRPpUkRMeGnMssMsWqxm8ZRcUWd2BLSBxtIdbC+58hixtIl55I18DmHaxftyb6i/wgZSZgjredCyiPGTvV57ECe47t+9z8lWPjDA40xCoQqNl5QnTHz5rIS0eBh6LkHQXfoeWKkreUvAHcRZTxwpMTFChZYKkoeecKGZPgcg0FLStEFNjacZp635oL7GlzA/c0S4IfDEdtVwtlIdS1YbOpKMua7bZity2oiorN3YayKLG37xygBX9lADdocvpY3kC+Qr/4guXVktX1iuvrBddpzYu8xFhLuYN325S39xnbD8L9XcpOKecLvX/rALiuwG5aEKgrNlpTVzU60dR14tNMdBNwYi1IEjEYKRemI6LRXFHbD9TswJujU64bXyQNX68l5wtST5tXcodgyHhFU3qNO+7lHUv5McIHIPEvKxnKV3Ip+UBmrzyTUPtomIJGCQfRXF929AUv9mN2iNebzwAtXpNKkkKSeqAMvkv3vcicKXadada5ZploFtp9Ui0kOjLJCt4kK4g1iNkhSd7eo+UOtu+hLpF8DYkzz4tSTuscAkxjoNxAtXX7mi6dteIin50cAMz493HAON3nnHO1fc5Rs/IUTTNeN5eIYG/fB54y5wLLQ2/6odxTTUWoN+lSDFwFEJEVxlZARiZfgF/rSms5CraEJRUFFZYUhbAm44Za7mjLSInLObQKY3F+MhRiLdoHFsEwWNbGVRcxxml7dW3Zbivnp9yUFIUzuxbbgmJXUN3eOo3v/p170AXzqbVtUM/iyj3YXn5Nvsy5eXXD9c2CFy8WfLGGL9ISnV8DsN1W/Mrdb7G523CbFezWt7zJDD+7W3NnK+qycOZea1rTbF1idcKuWqG0IqsylFIYn3oSsEYpp69rSbG2AHHUfqmsnB/X7sgicnpDQWnv0LLE2MJroYXX+l0wVsW9r3aS+/JpC0R21NyiWWHZsENY8AJXUnuBNktS1iiJwbJumZciAgOPjf7aE6y43lMvop2/aQc4D5Tz0ql/udGNdumiY1UDlsssYZ0prkKwT6L3Ukl0T8NUtKw+IoCpkd1790KnU2d1qHbuWHQ67GM1Brbv3MlIMre8++CutXw9dLtd5BOWw9VKOL9WeczYuX2OAcvDQLq/7VgOmWWbdSeMmerzOGCpqO0OIzU1dy7lQHIfnRpAzOX8JSqh9P5N7c2tW7aIz7HULEhZIqRew7zFsKWytyzkG/dAF4OxJQpXcFk3vrpQKmxfjH++GmOpjCMjCKkiZVmz3exQxR3Z5gNqW2C3FfXmHlsWPiCn7GqV2dI93K6/hMWKF69fkK9yvvhi7cDyOueXFltW+ZJl5n2Ym3fUacKHJOVNqvnwQWHse5K05rcrzff3K6q6ctuKtc3trQsEyjKstejU+3KVNNes1i5H04FS6mgDlXIUtAgLvsBSOd+yJCSsqdhS2XtHYC93DY+uQZPzJRU/9YFXqSekAOEa60kPXJxs6Tl8M6wVEpY+H3aDkgAQofD30HXnImXnSKtZhk8PKDv5rtJ+i3iTrIuOTZKkKeWVZS4qNktcoM8qVSwT57tsg31A11u0qVE2QakVSnlOX6UQlbiAH8FZCooPUHxAFt5nm2Te1K5g0asdai0Udy4QLFuGtx9QC6dxpks8X+FFPhM5yiR7CuA9B1/l0H4c2pdD8/Tbj9EsY3lMzXKOGE9XJwgpN5S8ozLvyeVrajbUlO5BQ0ppoZYdJVt2vGHHLyh561l8QMhZ8w0LuwTJcGWO76nFMf8otPeTWcSKzykcNsNC67c0XrusjKWuHViWpaEonL9Sb98jxT23mwq73bAo7kjKez4UOBNseBjr1EXALm8gX5O9eEm+zHnx+or1OuPrL1a8vkr44nrBL+eW9SJjkSkwhkpZdrJgRYkk3jxY3HOVVFQ7zSI1/KLUbAppA4FCBK211JtVEwQENMn27rdBa4XRFsUSbQ3WGB+Y4ogGDDtcZRgPsuSIKP8yUfoqMS4zNuXGBfH4XE5IUb72qCMeVFi2zgtqczL7Co1GSYYgbj5rSVkB7TYPSXxP1vU+kO6X9RqhwwuTBQ3Tp5PEZOshOjZLFItUs0hdsE+IjM2UQoslKT6QJI6eUNcFbAtk+RLRqbtJ82vYvIHNO6S+d8e6fAmbD2DFWSDqAhYv3HVUFS7FBZwmuXnr/OmmdO2LmxboPQ/yRT4fObtJdrjP8wLLU0Hxsf2VQ336c3THDzZPjonFevNe2hRYrjFUIDqEdwA7rE+mr+wdVgochcAdBe8AYclXgKbijnfm/4dWv5uca6zj/iHlBrBUbLHco2yG8sn57b709i0CS2MtlXHE6SG4pywNu11FXexItrfcbi32/ha2t5TFlqy6Ja00pfEnNV04LSBfw82XDii/eMFimfLNN1e8Wgq/46bmqzV8td7yOhfyVJHmC6grbK2o7T0f6h15uuRKKr57lfL2XpPtVpRpyXJjqHVNEQKKQiCQqSHNMaZilwQN05WkAqdhpqlF62AyVGgVpTeI9UE6btlQUbP15BIVijW1vUVJMMlqMr6m4hc+LUVR+2onQuWjmnMUN2gUFouW6wYYHTOQY/7B//UFzVCwjwhtMRYOW0+aHMwx8OxP7vMvpZd7meeaPHNE68tMsc40y8SRrC983mVqt2gFKslRWhDl6CCkvEPSl07L1AmSrRBbugjscud8l6aG8h505vbDVLB77+PSBEQ7Iots5TTMJHVsUeW21TblApafmxyRVjK8PN3ndKCc6vNYpbgOzTPU/zHzK4fmcWOH+x4rIY0DQFDeoJeg5QplHUVaTUXNLQU1Fe9dxCU7KmoSX1GjZkvGtdNclFDywZGE+5QHV/w4977RgoQrn4M5bIINYGmt91t6TtiQNrLdOs1yt9lhdxvMZofd7ODuTRO1ak2Bsgkk6zYCdvWC5OqK65fXLNcLvv56zXqd8bteaX6sP/Cjl1fcXK14sVxwIzULSnSmsCaB+4JymZHlS5JNxRdyS05Gss55Yy16U/OmXLC8vaOoxaWwxBG0PuioTFKsaYNk6toBpTG2pXnz5BFa4dNNXF/bcB3de3B01VqU1VjJXUSyN80m3KF45c/oFZaC2m5QsvRk9iuw1lkOZOMCtBr2H4NhSyprFAtqaz0ZQt5cfwMKZEecktVeqIF4vfFfBpPskA8z1ix9bmzju8wSZ4rNE5dzmSdc5ZrrXLNKE1ZJQqrFVSWpK3SSdqnwVIoyvvSZKHffWQPZGhEFfGiDe1TioruVdpaJcDzGwO0v4Oor11ZuoSpBZQ5klYJsfTHHfoZykkn2GJB6iAZ5zsCe48bs79Pefhxhgj0FLM8ZCTslIWUAcBqgbFFW48pwmcYUqElI5BpDiSIlJceQ4qqZWEpum9JeChBfJjnhK0eeLi5lRbNErAPRKbDsapY2ShtxgFkWJbvNjnpXcnV/69h6AkDVFaI1RhYufy5bwYsvWa6XXL244tXrNdfXGT/+Ys3rvOYvX9/zMtN8tRbWcseVrUnTnEQ0drVGlfeYVz8mKQrU7S9Iyzt2i5rSaHarl3xZ7zB3iqtdyi4R3lVpq10EDdObZrl/T2UsOyWOe9baJj0iTVVLo+fPh7PaClpctRYjO2ftwziiCJYgyuVPcoXxwCgoEnVNxg3h6ix45zRNmwI1ibjUk9K+p5ZbFC9xPE6/cNea9y2LLKjt1gNqm1riEDBcl+Pk64MpKH1zbN+H2QT8JG10bMPs43yXy8yB5VWmWafKRcb6nMxEK7S4l49Q59JxxhpEaee/DNtTCdQbt73FNdQ7KAskySC7BlN0b9K6gO1bd6z5NeRXrXaJhezqEiX7mcosk+zxmuE8sOxu63CfIRPs9L4eBuWx45tqn2uCnRo3tP6QnEuzBBraO0vtwW7ntRYX5ypsndbpU0AETWpXFHJPzRbF0vs/E5czyIa6YfPR3txbkpC54s4292Tp1lPt7Yfnx5qlsS4itq5t5LOsKQoHluXOfapdyXZXsajuKKodWEuqDJVeUaorWL+C5bXTKq+WvHy54ssvV7xYZfzyq4wf6Xt+fLVgzY5XqWFlSjJbodMFiS3A7rB5Tp2k6OQWMa+waDTCCzF8qZb8olxwnSle31d8Ky9Jdluq2I8ZAoHYws5z8iZJY54tMhdM4jhRnU+TxD3Qm7JTNiGVK7bWpZgk5P5lRXzVF4WyC59X64pqW+vo70Rc4WhDgTFbKnEALL6mZiW3WK5xZVpcSJcSZ9i1tsKy8xy0NXFpN/z+xeW9mojToWsupJX0uWMH619KS4c3EOyzSB1orjLH6pMn3RJeSkBla1Tx3qUsiXMAiKkcwMWbSxdItXFEBaJaM2x25b6Lou28u2sDesCBqykhv3H7m60vYPkZy/xqJczRFudrf8domucwwR4aM7f9VH/lkByjWc6RY8ZW3COiSFi7JBEBaytMIODmLUYsmb3ylUUSNGufxn7lcy9fUnJLhaai9kE9K7QH09oalK1JJEWhMNQom/n8y670cy1NJ8jH+Swb/+WudKkj2w3sNtzvSurKsLA1SmBrc7bJS8ivSG5e+ZSRa66vc774Ysk3L5e8Wuf8jmv4xhZ8Y+5Y2Q1XdzU6SUlJEXOFThegBasg2X6gUhauX1LnN9Srr7h681Nepde8Lmu03nL3cs17hPVO867yfsxAuxe+i00DBEXibr8kc2czy1wkr+OcBRGFstYRreNo6lK5xlL28iSNN3tnDixl5Rh+Zcc9v4nCunxLgVReUPGOmnvu7IZcXpHzhdMs7c/QssYlXCSe3zbB2mCC1950333ZGfJtTooZyLnsTuhA0+fJBrB0GqbTxhceNFepi5ANRaJjgnWVZIi6Qcq3qM0daJDFa0Sn3swNYq3TGOsKbt85TdMCyxeQrpyZNdaGq60DVHzqSb1z4xEXGHQBy89aHmSS7bYdD4RzAPYpomDn9IXHD+4Zkzma5TFgaX0FSuUJCsSniVRyj7LGmf+4pbLvQWpym3hSAoO1isRqaslZy++gZEPNBzQpGd+QsyQhp+TW+c1Y+DqZa4QQ8Vk5arywP/5hazwpgbU0YOlA0gX4bLcVxbZgu9lS7krYeJq7YuvqPddZy9qzegWrG25e37BcL/jmmzUvrxd883LJj1/kfLVO+OV0w5ebO25WL0jrhOX9T1E2J600Uq2QxQLSBGsqjHIvBAAkFmWE3Ytf4bXK+cZUqMUVV/WWL9ItZQ3GGG5FsOW25ay1xiXGe3OtSTJ21pJmaUSfRxMMJCJ7/kwtOZUtHXevD6oKeazGl0ED54esuCVl6UnWN2gyErZU7LBssWLY8S2GrdNSZUXi8z1d9ZMSwVJxi2KJlYSaCmWXQDfFIgDQxEXX+Vu3fsxeEFDHHJtCmjVgmWUJi0XCIk8a3+VV7gJ+Fjoh9+w+iY6Yfax1aSTrL1HavQzK9j2yeun2affegWB5j+Drny5eOAag73/N8cguXjlzqzXOV5ko5xPXCdRLB5jJ0plzL/JZyxlMsqdrlVN94OG5lYfGzOkL5/VXDvUZmqcdP9g8a+zEiCbYx1UbuUOhHf+oXFGxc22SU7Khkt9AvAZjTIlijWKNJuOKlC3fUXNPxg2mecAqFGuW6gunkVhFyhortikI7VIYWjNs0Cxr63yWrupI3bL4eGKC3WYH2/uWRD0mT/eRsMmL1yzWC15/ec3NTcbv+Oaa11c5P3mR8eObjJtU80295YoFeVqR1vfkWeYiURdrZPUaoULufwurUurdW7Q4Oj/0Anv1ipusJrWKHyWKbFlyZxxFmzHO9KiU4l1ZwH3mczO9aTY8fJXG1iXbNMHUhiRJfPBPe02LOE4edx1Zn7u69nmZVaNZis+vtIIjUJealBsMWxQpNbeU3FGiqbinZsOSrwgl1Qo2ZPwI6+uYGhS13fgXq5yMGxQJVqx7AbIaETUYNRtfk12uddvNwZwEy6TxXyZZQpqn5LkDzFWecL1IuFlobvKEVeI0zTRR3RJeWHR1j0qXiCmR3R1ia2d2TVKfHmKQugKdO+BMFi5VRCsHjjp119fiJSxfu33NVm1Aj04IdVEBn39rWg35Ip+VPCBK9jzm1sfSKg+Nmdt+qr+yP3asz9A87fjB5lljJ8d4k5urjLH1RaMrLJqKHSVvSFh53+SOmi0pGZqcROUYcJGZtsJQomUB1qAkIyHDsvQmvdqnJCTUER+pM8Y5bRVasAy5lsbYvVzL3XZHsSvYbXewufOcsB4sA3tPkrm0kdULlldLllcLXr7IeXmz4Ecvl3y1TvjJTcbrRc51IqyrhLwsWWzekFY7dJJDfY/ykZViXNkvm+QuLcSCTtZkRrDKUmsLeskLXIHnH13XGAvb0gUnWQvbzZad0t73tQNz5017vvIJUG7XTsPMW1+mi+isfZqJRYlFK8FYUKIc8YOPWpXm76rBWqzULlWEFNvUxRSvMYaeORu+Y8GXaK5IcHmzYq8QUlJZ+qhaIefLxgzrXrQsIjVY5U3HXWDcC/5pXojsOFi2g5sIVZKcxBPgu1SShGWWDNDgOe2yU5FECUpc+TG5/x61+RaUIMnCRbnWFZIvEFvD3Rsk0e5vki6R4oMzrVogW4BKnanWbF2OZnELNnWgWZceHDNHqVcV7QlJcucvfYCb5SLPS04mLmj7HNP38cDyIaA4V7McOpahcWNjnwNYBtG4yMdaagd+lM7EJ7XTrKgouHXFhREsS19FxGLYoSRHW0c8UNoNYjUJN1jcA0Oz8BG4/qHlg0WaeozWPXoDi09IHzGNKbZLUFAVlQ/y8VVGQpmtQKAeGHwWVySrJV+sLa/XBT9ZbrheaL5eCV9dpbxaZLzIU5ZKyMt7MmqS9Sv07g2Yyh1XcYuq7hy4FPfY8g6s1zh4j81fkm/fUa9+jEkzrjyL0BfrirK23O9ybrcldW24v3WMMbtbT5NW+nqfsX9ze0cFzswMpFnSMAEFf6Z21ZsRCf5Mu/f3dzUtU8f9KzWuBibUOFpDS+XNtgaD81TXnsCgJGHB1yz4xv9d3PWgbI4LGoquOx8HfbKMgmVEhxdqXzbm2KjuZUSDl2v36QJl+5H775H77xBlne9SSsRWcPsBvn3rWHls6WntfDrJdgP61lEn1mundYp2wT7Ka5zVO1C5A9B87dbVhUs/CVLuvFn54tf8XOQE4oJjzKTz+8DDUkaG2sbA+xAYw8fVLOfIcT5LQ6h24VJFArtPS1CQeq2w4B2WmoJbAtEaCCXvyHhJwgqxW6zgcyxdAreLqCwdp6h/sKfceFB2QREWsLZ2D2GkGxHrcy6rqiUocP5LB5bFznHEso20y9oBTKxdyuqKL6+Er9bw4krx1RJe5BU/Trdc59e8yDJWqSaTiizRpMu1i4atS3R968JaVl8ju1v3sDM1srjBmgpd7aC4Q6dLyNakYsFuWPqIyZd5RX0Fm9Jwv1tgLdzeFogIu9uXDgxCaTFjW+14dw/WssudWa/IygYw0zSwATlWdmu99hb9/Z3+GHzTACnGCMgWLSsSCkp2ZLwGtmx5hwAZjqzAVTrZIeZ7lErc38e6gDBLiUE3/tuQguQMnt3rcSytBGINcyhKtpdS4k2yobxa8F/mecI6bzXMVepICmLAVOJpBwXE7FC2RLSCJEF05rTHu++QdAF1DbvvPMmAuL/19gPkCxfwU+8cXd6P/wqXTVMVnoP4GuTG//38C1sTDBSJTn1E7QUwPxc5uh6m+91fN7w81Sdum4qCHZ5/Xv9T2h/irxwaP9bvqTTLUJDYaRp3IPi0DkVp32EoycXl30FNad9yy89JZOEfqNqTVKcYCmCFsAJjfGjIO2+KfQkejGs2YFNnViSjNq3PUtmlIzCIci1d9RHjU0jaIJ+yNOw2O8qdy7lkc+e4WXd3XVNs6vMtr15xc7PgqxcFX78QVtcZX13nfLW2fK025No6RphMk5uCbHVDUoIqb1H2BdosEGsRlToTnuRw/Qq7/R5twWzfYvNX2DRzEbjJClVuqXSOEs1rkzl+VWPZeUKC+/uCJFFs77dskgRbbJx27DlmHWDegampNkuwoH26iYgDykBqYLXTmhrTbKRlGhwXsBXQZKSsKe0bDDuMrdAqJeWlN6ku2fBz8KT44jXWSt1xy79NwpqFvPY8vzm1vQNfWQYgYU14AZq6NgNItmBJq1mGY4+1zIiogNSXV8tTFouU5TJhvUhZLxJeLp3/cpVoVklC1uReis+/9KBZ7JDFtVNciztECtybnsb5LnegFFIXcPudfwnbQnkNqwKufuIO+f3PnP8yyR2QhmP05OyYiov8MORB9TCH2uaA5Tm0yqcwwR4DlmPm248Jli7XcuP5SGtnShPB2J0r7iwJlh0b+x2V3FLzlo18iwLP++OqbgjXrhqJuUPJgtSu0Fw1OXkBDEH7DMUEIXUPfzLPGas70bBxrmUAy9L7/hztXU1ZlE31Ebu5c8DSaGh1GxyyuILlNfnVipubnK/WJVc3V7y8WvDlTc7LheY6MeTaNA9XrVK0SlGyQNf3SL5Gpa+QzRskzRD9FXz4Lfj2HaIFIwmqqrDJBlvew/UvkYiirhWZsqAU16nzQVbGsK0MCnh/X5Ikmru7HTrRfNi+hnvtgkPiOpqmhvvckRokzocpShxQWu0jaF1Qy5BptmYH4jT+QGmXqy/AKhK1pCBlwxs0jpBgxU+oufPBW7nTMP1LkYjrU9tbX+or9X/brAFYG8HlIRL2DmjWNaMsP0G7TDJIM5IsIcszFouE5dKB5Y0P9rmKWH20aiNjG7AUX5EkXSK2cmQFOgH7Fqkr5P57oHJ1L8WCcRHBrL5wJliVOfBMV47WyJgu20+70z7oZ+GDhiIt01Ru/EU+G5kFmO53t+2h4HmsVnlo3jntU6D3UM1ySD4mWAI+aV0as5uIy6EzvoyXN5KCGJ/WHogIVJNq4ijtFmh7hcaZ6ZSPyKwwrsIJBYrUA6cCsQ1he5A+WMYRsUGzdLmW3hxblA4wd4XzWxabFiyDZpIkTrvM16jVFcv1kvVqwc31lutVzotlwk2mWKcJWVK3GogStM7R5Ralr1DlLbLdIGWNaI2ka8TeOjBWCVYp/3LhCm2Trlypqd0HZHtLktSQ31AlmrVNKDLDF6ua2sC3VzlK4O3NEhFhc3dFZU0bKWuNJzXAHZ8odtsMUQ4w0zRBBIpCk2VQ1YZEq8Y061JNwErVBOaEKNmaGisVpb3DinjLgkG8T1OTApnTQimBHQbjU0nuQFLEvkOJRtka8dfMMRR5DY42tHhmWLsMYOkBU9LMaZg+lWSZadZ5wrWnwcu1IvdRseFvqkQasFQCoheuEkl6hdgP7pxv3jpf4/oLZ9LfvnUVSkKlEe3TkrKlSymhdmkkWrcFwjsH6CNi06UDyMoRS7jrM7uYYz8zmVneK3zvA9acPg8Fy3OkjIztLzzMX9kfP9XvIT7LU6XlitUukCOcJ1xhYcT6Je2IqVk6MyobKnakXFPxnoRrElkBJZWUGJyWqkhBtGP6sYXze1maIB/ogqVb9vUtPVjWjTk2IicoSirvv2QXgWXIZxRpH0jLK/JlznK9ZHm9IF1UvMotL3NhnQpXVGTZgiTNXSSlWLQIavkSVdyishVsv0ckQwTElJ5m7x7SlXuxMAUiKegcSRLU9h3KAosVicqR4gOZrDBJytok3GQp9Qq+vHIax4sXDmTuPyy5qw0mLjtm6jbdRBRsc0pPNl7s3N8sy1zwT5JYwDSmWRF3fl3pNJdBWXHvg7gSCm4xsiFhQc4VmgUlGwrzDktFre4Bw4635Lxq/KDO4H5HLRptX6Jk4aKd7baJsnYXkqG2JbU46orwSImLRXe+97hkvf8wzr30VHhplja8sSsfIbvOVEuDp3UTGRvA0hWFdqkjki2BGjEFqBuwK0d39+In8O43QVm4NbC9RVavIF+53F4lsPHA9+pX4eprB4jF1kXCZp4pqC6dmVb7F8Pli+j61Je0ks9QzkaNN9R2jijYof2ZM2ZqrinNstvveLAcm24KLB8rIlaRUtkCLdoTn+M1CRflKFb5HL0NRgrc5VADloxrKk+5ruwCTUYtG1cvkYLKbljwFRk3FPYNVlw6Q20KX52kS6EWNEtoI2IDWAZi9RgsA5tPG+TT8sSitDvR2QryFfl6yfJqyfV1ztXNiuQq53ot3CzgKk1I0wUqX6LSjKS6J6l2ThPRyvmxsKhyi2zeuzqI2QpZvYbN91i9wFXLSFHZFaYqEFMgNkclK0iWaOtM3WmxxaQ5K5twUztT49fXFVoJb+8WKCXc3V1hreW2fBkF/dx50+zOnawko1YJOyVNObAs041pFjRG245pVsgc1aFUKE8w4FJLQHzwjviAnQQNrMnlJ9zzc/9i5EqwWYSae0oc8XjNyl0rXntV4l68tE8bquQO4y9NIwWIwvYsC9ZajDHOh2nqrpbZXuBN0WaV5Q4s89Rplz7Y52bhtMtF4j7BbxlICnR5h6637u8qIHXufJjWuGO3uLSRqoD775GicCBpvH9SL+H1SygLeP/bsFg7YoPizu3b9VfeGuBBP79y42IJ4Gm9GfdCwP5Zyckm2aG2Q4D2qfgr++P6Y4fGT8lDzLBT4w+JSzPIqL1mqWxGbXfeFFf7NBCAN1Teh1XyDo3CJziQ2ms0V5TywQcAObOuFcPW/gJNjhbnpzG2JJUbxDqjnpA2xATgnyEDEbGhCsluV7HbtkE+dVG44Jhy63MYoyLQSQbLa1hds75ec32z4uVLV/z56jrn6oXlJq1Z5kvybEGyviGzOxJKdLZwJrvNL1Dbt6irr5AXv+zA88NvO40kWcLVV+7cG4NdvkLSHNl+QFZfo1UC1Q4p32PJEZ1jFFgtWKu4zlKUCD++rsm18HazJNWK+3v3tzC14b6unWbVFJ32Ub+b94Ar87xNNNa4OprGOKJ2Zz0U0kRRG5djLyQksmJrv/M+6MxHRCusGM/E9MoTVCSOJo8XZGxQuDqQW75z1xsLLBWaJdpqT8IeX4POb1rZbRMUZrAon4zkADRpra7BZ21Mq1HH0bJBuwzBPnlKtshYrVLW64ybVcqLVcqrVcJ1nnCVJh4wvf9SCdqWqHrreGF9cJTUO6TcIIt1ex8VH6DeIq9+xQHhK5A3P3UkGKsbWH4J73/TpZqYEtBw973jiDWVr6f6cvyms9Zfr/fuwEU7YNXp+JiLfDJykkl2qG2OCfY4DXEeWM5JGem3PwZYnqJZzpGHjk9YYskwtvaxri+ih5/xNS4XbG0NUqPIfcSsxT0qX6JYUNjfRssCxQoLaBRGhMK+8/UwnRlWkYBAbQrER8NCWwTaWhfk48gJWr9lUdQUu6rRLOvd1pliG82yDCfEmWHTBayuWa6XrK4WXF1l3KwzbpYZX6xTrtYLsjQhXS9caSgtJGWBzhbu5cGUqPIOSXKXTqAVZC8QzxMqIo5NqK6wyxu4+23X78WvoJRgyjskWaIsKF/dItFLTKIxGHJjgYSbLEGAr69dYNT7V6U79uCntcZFzZY7p0mHICCdgAhFnoOFNE+xNiVNnaangxYVsQApm5ByDWJI0d7DmVBx72EtcYlFdueqxqCd9UAcz6+QsvB/b0UC1iKSU9sNXj3E2BrNtUtVkhqxaScAyAV3lVjbNc1aE7TMHmiGv2kEmFmekS9yx+qzSLheptwsNC+iyNjU+6Mbn3S5QenUVyTx97TOkGoLto1sFZW685vmLg2p2sHrX3H78+Zn8It/w5nil69c+a506V7MrIH7d/Dyl9ubyxiXVoK05tdy64A4CXU0DWzfOaagi4n2k5cHF5A+1KYG1Kip/MljNcWxtuesWbp5Hjb+GBEfvdouB9FeCxSshMqVL9EswHPNOn0yAdFYttSegFuxwNjKm2grsLYJ+gm5ekFCRKy1rd/SAWbrtwyBPlXp/ZbFtgXLOIUkpJHkK/JlTr7MWa2yVhNZJk1txGWaoIPZTvBlngSptqjiA7J5C2mOhEATW0GSOrduces+6QrZvYPVl9jVC6TcQLFwkbS2BtGIaAe+61eNxpNowVhh7U2qX66cxvXmLqNoqP5W3BlLtfGkBlXRBgF5zlm73VDQkhoUhau8kaa1A0xxTEAtC1BGbe+x4gK3EtYuAlSShms2szeAYSO/zZY3lHxL3QT8JJ4eb4mSFaV9Q2lBq9yVZZMFYhfeX0kHLP1fm2COH0wracAyukYa32UGWebNsS4yduXNse5v2s27TOK8Sx8k1ZQZExBxEcXg76VyB3ffIrv3cF+CJHDzFdz8kvNNJjls3rXkEsV7qDZQZk4LXmStplhsHDAGSTJI106zDGAJziRrlQsGugDmJy8zNcx9ba9d1+0Ttz0Vxd3Y/vbHPBfN8inBckocoUFBIlfkvKZEodBUbEhZIWSOOo81Yl1ZKs3Cm+GcX0isovY1Ml0ayw5rhIRVJyLWWqh9GknQLqvKtIDpo2LLXelMsbHfMphigxaSLWGxZrFckC9zrq5Srlcp1wunidwsNEutu9UrtEIZjZT3TiNMF65ocLXzQSFr93At3sP99+7BqbR7YK5fQ3WL1CtsmkN5hyyuXKULU6GVol6+QCcpVglWC6lxoLH2FUleLRMqC6/WOWXl8kw3myV1XXN764NIio0Dy8ACJAK7OyxQ7JyvrMhdYFKSqAYotBLE4j4kaFli7LYxk+Z85f3H1v/tUu74GTv5BQXfY6nAMwAVvGPFj/3f2dkkEslJWCKiwbr7wNgdYhPvE0/99eRCjpTNnY0i/P1N3xwbUeMF7VInkC0cDZ7njV3mXsP0kbHLyHfZZfYBlSyQ6ha0bjRMqUsX5CMCdYncf0coFk26cCZ4Yxzw1TtnPt3dw/Z710+njt1p+97NkV05jbKy/mUqioCtCkd7CANv7+qSq/mZyIPTSoba5kbBdsceP6bfPrXuU9Isn0osDoicITWjRjkmH1/PsuQNIKS+zNfO/AKjKufHtBm5XThQtXdYqTEYarshkSXWOJNeTHvXapZ1Q3lXloZiF9e33LWBPn1TbADL5RXpasViveD6esH1OufFOuPVKuH1KuE6S5w2kqjGdKe1Qqkr1PtfRyXOvyjZ2pnsENi9cyY6o6DcIjffuICP7R12e4csrrHVFuzCmfG275D0ylW2QNDlDqs1VgSsUGsBHJWbiPAirxARbm8cz2tRGTYb9xDd3m+pVNLyzTam2Z1rs5bdZul8mVkC5CRJq2G6+601zWqboiQlZGpK72ovuaXgu+ZFR3HtX4zaiiyGEmtLV+FEVp4WEawYjC29XzNBWee3tP6fMiuwgrV1o1UaY7DGOvAJEcGx/9LTx6ksJ1/kTrNcpd5i4P6uN953mccEBVGErKQ5ypaounCcseAsB9naaeHlFmyN5AuoVPt3r3xhb5U4Vh6duLi3u992L2z5Day8STZZwuYN6IXrH0uSeapD2Q/2MZXTPi/yycsMpp/492Gz7LlrVz60HZ4ubWRovu48o6tmjT+/uHdxi3V+K5tTy5bS3lFxD2JJ5ZqSDySsWcovY42jy3P5mUssO7TcABZjaxIUta1dCotNG7B05AQhyKeNjC0KV7Jrt905YvWtJygot/7BartgubgiW69ZrpdcXy+4ucl4dZXxcuV8l+HButADmojKUcsbf+QWufkGuf4Gig9IuUWWX8L9B6dtvPsNb5rbIZJjbYXwEvIMa2ukvEWZwgFkssAkGSpJSSxghVQrRCyZsSiBF5ljAfrRTe0qutSWbenI1bf3V2wSzW57A+quTTep6gY87f2SXb10gGldCbAuYNJjAYI+Byy43NwdbzDKaZQWQ80tioySOzJuXI6uzcCWaFl6TdBVtqm5p6YmYUHCKxRLEpthrCGxfkZTNnm2xhiMMdR1KKRdtv5LEQdQ6QKypTOxr3KurnKur3NerjNerxNeLx1RgcunVWSJ6qSThChZWd4gpnLWj1CRRPzrgjVNgpXkK2d+NZXLycxu3MuRKaG8A22dNvnhF25/0yUsUxADH34T8peuMHlfQuRs8cGZYYNmKXo/mvYin6ScNa3k3CkjD22H48BySJ4SLJ9aBE1NieEOvAezxpCwQEtOTY0r/LxlJ1uW9sdoclIfben0kB0KwVrn2TTWnWcHmEkTERtSSAIxgQNMT6rucy7ZbVtTbDBNQtcUu7xufJdXVynrVcb1MuXlMuE6VyyTYIqN6iIGYAGXHmJLX6JJu7+lVi7XrriHzVtk59MIdOJMs8V7ZPdr2Ne/itzsIL3CSoJYA7e/BV/+Zah05cnOrTOTepdd6jXNZaIxWF7kCbuV5b4wvL/PMMby/v0KkR7fbGACCqQGuzuwhmLrHryJ98/GfLNjLEAu0xbPJLz1IKKpeI+hoKYk44UjbkdT21sy+xIhBV+OreID+NJg7trYArdoDOKtEuL5iiEmpzCY2jiGn/A3DX/XoF0mGSzWZIuMxTJntUp9oE/iAn3SxP9dI5KC8BH8i4O//wJIhucUOItBtYHNB2dS9X1QiSvrlWQgV3D7m4RapWjlTPEWVxg6W3utUrugoGrtrscgdeU15dyb8n1Ud7p2YHlJL/ksZDaXrPvdbXsIWD61ZtmXQ6B9aPyc+dp5Hj7H44jxfqrAFrv2gTxbH8DjSj4Z/8itZYe1FZZ3nqxdA7aTTmetS2awNkUFc6z/hAokwW9ZlRVV6QCzLnwVkmrXPlh9zcim4kO2JMlz/2BNHV1a7lIN1plilWgW2iW1x5qlVoKyFWzfI2bneEWL98jipfN5WaB4A29+DX7xrziTnKmBEmzitAQf4EOy9NGV91hxAVBS3mLLW0QyVLLAqsyBJq7KiLWQJooVTkt6uTDcrRLerDNqY3lzlWGt5W61pLDGpTjExAaBGQgX/KOUotgV6ER7wNQghl1dOmIDm6FJUD0NM5jgQyRMyY6aO8TT5mW8IrU52ubOjOnpDSGntO+wGBJZk7BGywJjDUjlyfzTjr86aJiN/7Ku9wN+msjYnCRrI2OvPA3eC++/XCU9f7SnwNPhRchrkfEH/P209RYDnYKtkM33nhPWF36++sYRDnzwL2k690C6BPXBgautnbkWHA1jcu2uhUA0Ya37XnhfdKjleZHPTo4q7zXUdi5/5Tnb4XjNcg5YPqZm+VRgGW/FUCGSkPqoWADFijv7M7SkFLyl5BYcJwwVJSv5ArjHoBErGFuBWKwNHKOuDiQm8ea5QE7QC/Ip66YKidMu71u/ZRzoo6M6lwsXGbtcucjY62XapBzcLHSjXaaRj0t5zVJt3qCq+9ZUZ2qkuHPh/ps38PbXXA5maeDuW6dtponjCM1v4Oolki3cq0O5xe7eumCQV38JanePEVD2Dps5jl2d3YA44AanaVorXKcplbF8sTa83WRYC9+/WCAi3L13GkuxuXYP4XAeLO7cWAvbO3biNUwPmElaUustYlJsrRG1waDArlEYEOM1QKFkw4731LwnxT3grfdlJnZJKi+opXDXBhaxjn1WyQprC3JeRZSHBmfcdjm3QWwT3GUiDbNnjo0jnhvtMm3+ri+WGS+XoUB0W++yMcXGGibu/glaZvMcqh1VnWS5C/pZ3jhQ2753IJmuHBju7l3u6/bW/a3Ta9j8zF0Du5qmBE9dO+q8m5WvWCItkYZOxx8QF/ls5OS0Evg4fLCH1j00uGdojrF+Q/N15xld9QxFeX5ZfLRrTcl7Cj6A93SV9h2JLEhtjkKhWAEKY7TTNKzFGIUyS2orGGOicl2tObbYOa1yt905sNx8aLliQ9kuEffW7vPlWN2wXC9ZrBdcXeXc3OS8WGXcrDJXvcI/WJdJVxNRSlDVFtl864i4lUKS3KWEbD8Ab2HzrTPZlfdOwxALu1uQFZCBLmGhYfsBufsO++InCDUka6ypnD/z6mtMVbiiV1pjq3vIbki0QomlTixiDbUy1InCLDI2Nwat4P39giRR3N1dobSi2L1yfxJTO1NsMM9a6x7s1rDVGgm+y2xHKpDpzJEE6IRKSqz+zvualX+VqRtWp4RrhALBkrAAFiQSKBE3DiwRKtmRWPcSUItlx1sHrqx9mI8z/gbpgGVtqCtnSegwNYUI1DSHfEW2XPgArpwXL3JeX+V8eZXwapl2fJdp4gN+Yjq8BiiDtun2Q0RckE+wfFnjbki9dBpktnbX2vYN8MaV8dIJ3P8CTOHWZUsHklnugFVryL12qRLPPXshJPghyclpJXNYe7pjz69V7u3TkWA5JB8DLJ8mhSSY45TXBJ1GaW2FFeUepM4WiaDY2u8wsvMP28zBqdxT8I5Mfgw2xbDFkJKQo8zKWaYMPiLWdIpBd6NiiyaFxBS7LrF6rIHoxIFlviJdLlisFqzWjqTgymuXL5e68V0uA11abIrFoDbfOb+kuBxM6hTJ1lB+gCR3aSXp0pEW7H7m6NJWL8Ds+P+3955NkiPLltgJDZGqRPfM3Jkrn+AauUaj8f9/4I9Y49qS9pZP3Tt3RIsSKSAigh88AohEZWaJFtNVHcesOxUSmQmgcODux4/D93QAOEsn2diK4Hqy0NMzMmd3PRinHlQuFPWRMkrJsr6F3r2FaLZwToA7DjCBTUl/fm/nZJt3vSRThXbXYu2SqCyOwwIGg3YrDZpgXsBLB/gCjbI0JlpyQPTw6IjsAql0WENAQXoFy7aB6HggPYfO30CwEiUu4NGhR48O13DM0ngzxgbDPCB6EBdU+5ykYp3zsL2F7S183yf9tCEdKzXVkYP5xGxeYrEwOKtNEPpIzANZKsH29uuewTpDMjB6/HtiABjniK5EQ+qUDnqKHte/0v7dbehYKM/oezVroOuB+jUJfJQM6dqCfoOZ55rkV4pHt5Uciir333P3/V8iWX7KyPIx+NRkSbHhNqTYWIggOUVIoCkmjX8LwQsIr+AhYNgZduxv6GAppYcW1GsnAWwBT6TbooXDTVAgduC+hPN8qFtGshxqlr0b6pZ918N1HRFLbB+JxtXAaH+XpOx0oanloJCoC4laC8w09eYZwaH4/iBhMimgaInLgtoMhACzHUWQAJgqqU7p+xCBhM9sd0Dr6aReLgG7AZwG5t+BSQG/XYOJJbxtaeKFLMCECuvzQxO96G6BzTuIdg3PFXTfwPMKtbNYcA9fSpzX9Gf4ZmHgnMf6tkDf9Wja2dhEH0eA9S1dTDRrdEqDCw7R0p7UHbWaSOkA1kA5Bc8wqmY5h/cODBo9rgD0kCipdxYbMC9RsBkYJCw2aIOpQY81JJtDo4RHC+8FBAwYFIQ3wFDL9kOESSIvIky0u3H/AiEVa/aUsWQ8QeYTq1JiWVJrUCkmnrEJOe5FmMnfEwPoooaJQI5ryhyA0dxLLqmdBIyeFw2AmtpedEUXRt5TOn7+Ooh4NnT815c5qvyK8ShrvA9JwaavP+X5Y9/p4Gt3lv18kSWt696Pe9B6PgYsdvDMBv9Yqlt2/gqKzcEhoVDCYwfmBXgQ8rS4QePXAGvBUcOih8V10F0uwMDR4ho9a0ERa0HnJb8GXAXv2NBCEs3ViSxHg4Khbtk1d+uWjI3qSVNDDlZpClUlB/eXWnOUisMIAc1Hb1GekCa3O4om0VIju6e0M2tvADULszQXwHUgJqEBMKBagS1eAeUFYFv46gfAd/S6UDTFol0HI+8reOvBzv9AUWrfgMkKzFtwuwW4hzc19WdCQLcb9GqBud2hVwbnwQVoWWlY63AbCLNrZ3DNZiRK70ZDA6GAxqATHHqr0fEtuo56PpWyYKKDchUc92EgDaO3sw3AejLmR4se78BRQ0IDkLC+gWNkk8fgoLCCxAIIpv09OigYaJyDQ8N5PyRk96JLO14c7e3f6BurKzKfqArUtSFrw1JhVSqswrzLUlAqdph1GfctA3jfgLU3NOtSV2BmDqYUkXJzC9ZvyGDd+VGAYxuKEm1LfrFSAWZJj82M0t0dA2R0k5rTycT1ADgwu6Tnc3T51eLBNcxTKtj0+U9Blh87DTt96rcgy88BcvLpyRc0wKEH5wze90DwFuWhKT2qXi0aGLbADj+HIdLRAM2De4MtfobFBgwFlFuhxwbMlbCeUpXeirt1y9A+EslycPPp2zF6SsUgugR0CVGWRJYVtRuUpcKsUKgLFRxgOClj5d3ocohAhAJjc6pTeprZAqlIJdvvyDOUCaC9opOms2BmQYIg7yiAWnxDJ9VmTYRoG9oqXACrH6ihZHdN79M1mCrB+xaeccC1EL0DmIQUAuAcmlvUUsBKhcu6h2AMbxfUMrJed0M0vm6XQSXbjCbtfUspRUETaFp9BoChUS08c5C9g2Iz9HBhHBhtD8960IBpA4YdOCQYFlCYw+AMLX8P7114HuAoweHg0MBiAwEPBgfHdrC+CcdONdgexuiSLpCodjmk3G1P/2SYN1nMYOoK9ZyGfp/NDS7mBq9mCktDtctC8WHgtxRj/ZJ3a/DmPQ2CFhx8+xbYvgUWvwPrN2CuB7bXJPpxIX0c+ylVCbQOqF9RlMllGPwsqVapl6EcoIFiEez8Wjo+bAtUZ5/hLzfjS8WDIszfqmXkvte+tDTsl1S3JDlGix47COhwEqSaTrqlWBgA5UJN06GDZzZULhewCCOn4ODB0OEtGC7IzSWoZ+E6wAvAcfS2B7MsMSegumU0Ve+aDnYbei0HN59+PxUrVKhdUgRS1AXqWlMUElWUQRlbhckVh9oOOAOgKvB+C2Zm5Bvre3Lq4QrgHKyjKJcJDqx+TyddxoHtGzpJMg4UZ2DMw/cdpRJtR9GKWVGLSbGAFwbYvqO0r+0osmEMvLkCcz28awEIaOcBrqC4gy4rzJnCWd9DMob3CzI32Lb9SD5dj4bxcXtFEmi3dGIH0GwrOKcgpYLvJDQrwbQAZz127BYSHJ5JgCsYrNDjBh1bQ2AGCTMcKxwGzlMk61kwFoCiNCwsHDbgqCFQwLMe1ltILIeK5t6otqZDu2tHI4qYjg3mE2a5RL2scXZW4vKsxDerEr9bKFzWCiujUMvEpCAR+gh4sG5N0bsuxnNT34Bd/5XGstmGIn8bnHfWb4DNr4CoSbgjBPVZyoqyBsUKuP2Vaph6BihFzzEP3PxMbShVqG/GUV/8rilExsvHScJ8aFSZPp/J8n58DrK02JJHKLM0x9IzCNTgUOjcFoLRrqcuTAPvN8HezsJ6apOQqCFRwcNBoEcPCxtaDag3U1BTu9dgngNOw/d8TMHGaSTBVL1rKMKk8UdJ7dJZ2vBDzyUpY0VJNa7YzF4V1J83MwK15iikgOZj7ZIHkqTByvSPq2IgSapvcYoeOHmXQpekoLz5GTj/IwBPEYcuKGVczKmW1aypnUSWYVyTBKvO4fuQ3lMarG3gmaKsY7OFsz0RaLGi7WwtYHeQroWbn6HjlL6eKWrJuah6OA/c7AzanqLzZlsBHmg2cyLvqJYd/GY5sL1B52u0O1LJtjqIt1QHJmg7CO4Bx+CFh/AVODx61qBDC4cdFM6gwkBwgQICCsyXaNkvYFCQOAOC04/DEs43UDgDqaQdbOizjaKutmnht4nFIUAXQ8UMqFaoFzUWywrnZyUu5gVezzUua4WlVpgplZAl37sQYt5CuB6c871zDuOC2oRcTx6//Zp6Zr0Dtu/pOGvfkM2dXAYrvB0gV0C9omXrV3TsxR7cdk37n4eWkegO1G7HnsuMrwqP6q79nPXKl0qWnwMepJDkTEFhht5v4JlD76/BfT2k0cB6wCNQ5mUw6KYfI1gJEvsYIFihOXcFxc8BT5FQ57bwvAOsg3IS3NawPYO1ibl6Yk7Qdz1804yEGckSGGtbQejDjIHU0YhbkBm3Fii1RKU4KsVhBL8j9olkOTjAcE41Sp8YITRX+8KNSNZ9S8uqkvrxrn+kk6Op6QTJOUUtxRlw+0swQuioHafbwFsL3P5MEQzjYOt3YEoD0oDLggYscwXGDXi5grQArEMlBTw8VoUgI4NKY9tYNPMet7cFnHVoynCCHtpMgnMOdoNytmuon7NpO0C1EL2G6jTABHreopfvYP01OONQbAUHS6pnLMkVCAqKFWDeQHga+dV4D88dABoaLfEaMUXPQGbsHhhSsUN/7a4de2ttR/tVKKBaQc1qzBYllssitJBovKoVFlphrkMLSTrrMrXBY2IQ+bCQemcAXfSoityGetoecF3oX3Uk7nEtIGd0QeRU2NcVDYauzumfc8Dbf6Hjc/c+XFjNALYI9UsZIteMrxEf1IeZyfLxZPk5hD4OHaUVQSc1GRx8rG8RhwF7UEM230vXEtlqdgYGji1+gkNHsxRdDQ4D5gQsduCugPQCO3cFjw1M90fAavR9D2sTV5+pKnYvukxNuNmer6hOhD5FoYbJFZUWKDVHqTk059AijS5Tstx3gIGQ4eQaanqcj8dOJEzXj/vHO4ooikCgzQ2gDdi2oXSl98Cbf6MDwHtAklUaa6/hqzMS/wgNttsBtQMrF+DgZCHYk0uPggNAkZSHxEzTMxc7iW2r0XQWN7MWznlsbisa6r29BpBMcUlcgNqGjMa7tgHTFlIoSOHgRAOhrsEdgxUWLa4hvYLADAU0FKsBhFmVYLBsC/gCAgZz9ke0fo2O3QR3J2pBAoLHeHBzijXrtrXYbXfodg31ucaIOLSR6PkM1bzC2VmJs2WBy0WB13OFi0phoeUQXcZWkpQ0ebgAYsUS7PZvYIKOW+Yo3ctMRVHhbg3c/EpzKDdvgqmApMxAH0h8cw28/lOoXc8oHSsERaPbK2or8jUdPe0t/QY9Gy/sMr5KPMkaL33+Ywp47iO7D3Xvma7j1HLH1vkh+HxuPjy0kYyPBTQcOji0EEwSaXobXFrCJIqQzBUwsI6j4K/g0MB5AQcL6RdwbBMe38B6T/qZ5hV66+ET67u2JZKMTj5dNFZPzQkOju2aASXVLk1IxdY19VzOjBpGPUW7NM3FpD8PA2mGHz9EIWEnUErWdvtRptAUkThL38e2Y+9l39D7VEUmCzc/UQ9fvwOqC0rZOQ/WXMMLCcY4zRdVBmz3Frj5EVwaeD2nRLauAM5gw5fUktKLS63AALyeWXSWPHjXuw6cM2zXFXaco9nM6LtEwnR+nG6yuUHjAakZvHSQXIMLBibeo+wVmOTgTEEwBS4suC/BWB1ajfphe3HPwBknF6cwyYZhhji+DV5B+wt4MPRurFtuNi12mwbNzS2wviLnHIAuhKoVMDvD8mKJxbLEt5c1LhcF/nRu8O1M47IwWGgFo8Rgrq4SdSxlDsJFUbUA4wxs/QtdAHGVDIauAN0D/m9B1BPahMDI97f4C5086hUgqtCbyUYS3F0RcVpL7+23lKbdXQH4no6bcvXR/lYznheebI139/6xZe5//r7X7osqD+FzkOWXloqN4FCkYmSjC4sFpQ4lqxDpgzEO6ztSzkIOhgbWN+BsBuZ7OF+gxRbCKcAbWN6De0nvsz1YX4P1C3RdD99ZtC2lYru2g+3sIPbZm0IybSGJ/ZamBsp66M0rSxoMXZcKs1C7nBuBWvHBjFuL/XRsFPwMkSZi+m4kTWZmdAIMdU3Ak3hHleQZ2jeUeitoCgu2bzG4u7RrsO0V/O6aejldD1ZfwHMDbN6BVSt4HqZUtGswvSBCcj3Y7h28OYfXJQVdcaKJ9GDMYxaGTTelRW89rAM2Damab29rCCHQbC/G9PG0JWdHkVCjJCAttmwHMAmpOuhOgTEHyRh6sQX3HhIMBvQbrR/t8wBOXsJMovNXRLLQdEHlPQQM4A1a18JZh+22w2bTYX29weZ2A1z/OqaOw0WQOP8G9aLGN98ucLYq8KdXNV7PFH5YGJwbjaVRKLXYm0ayP8IrZhDCvq3I5o65Dli/pWkjzQ0RHUC9s8Uc6G7J1P/2Z3rt/V8pmqzPyaCiPCdC7LYUhV79GEzY2XgxZN04daSY5z7MrxhPGu+V3n8o8T0l4gQ+XdvIoeVOrXOKLzEVO3wWBCQqWL8N6ldKQ8ahwNPv5b0FtZhY9L5Fx64hUMJ7DoEaxpdgXqL3HbRdofM9mDOQVsD1Etb2sL0hguws1S/bsXaJ9sDJPQ4Pjv2WB1OxlIatjUSpJWoT+i6lgE7IMu27JFeX1FeUDQfNsAs4BytXwSjBUgQUrc4kRdvwnmqY7/6DvrvvafRXLI5Gq7X6HICjp6Sik3Z3AeY9vNJgUsOrJdjsNbztwJob8C2DlyUE10D47gAZtNeQ2PYKbenQWB/8Zj2uFlSf3NwW2GE1essOhuZ+MAPvmwZsrcFlB6E9fL9Dozh8p8H4DoIVsPwWHg2k78B8cChiAszTkHA6jjgEKAojk3YTxEAanetgrUDXWKzXLW5vG6yv1+hvryltHFtIygWwfIXF+QLLsxrfvqpxOTf4w8rgopI4N3ovslRiv37JJ2S5l2USgi5autAeokoa4nz1Iz0/u6T95HoS9WhD203XIQWrw3HI6fv++v+SPaIKkef6Z6p5Vivg9X8BZhef4s814xnh0TXM6f1jzz00sjz2HuDjkOVDP+vUOqf4kskyIg74jabqAFmjAVGyEVJR3gdDAk/WaYx8Qh0cPFr06CDdGTq/g/MdgALebSCshu2o7tc7C9eJwZwgFfr4pqGewWnd0oeG8r10bAVdaCgT6palRBnIstIctRaoVKhdJqnYYYgwG5WyQ2SZ/NsDYyTIOQbbU+1KGNpWV28B7gCmgH4H1q3hOalnwQX9psX3ABNkl9duaROrGdjyG+oN7Nbg3sNzBtFv4P0OUPM7Bu0zJdE6h/PS411FM0XncyLO25oGSTebenTPiaYGXWgB2q3RcQEpFXquwfgZet6CmQ7KlvAM4K6A8SuAMyjMwBiH8zsItr9NGACBGvAy2Cky9LaDtQx9I7DZdCNZXr8DNlejerecA/NL1OcrXLxa4OKixPfnFV7PFb6ba8wU9VxqyWESVawSPFE7Y9g+kTRp97FwkXAbjiNOx9nNjyTWefcflE61LtgbesByGtVlW0qpS0VE2jdUr25vgeX31ILS7wC+Avrb4PjzzcP/+DJeLB5Zw/x40ePHjiwfmoI9tuyhdR7Cl5qGPQQWTncRHCKQpguk6ULLwCLUqzw40xSJ+mCk7fvQpE59hHAavPewvoN1El3H4XYV+i4IfQJRtk1LLi9tcPOJqtjojRqjy2CRhqKGKUwyE1HS+K5ggzcPrSSlEigEpWOHKIRhcIDZMyxImHLvRHtoW02fb27DdAoFiBkpKbsNpfScoxOtMDRjynlaVpV0cgVFLUwX8GZB9zsiX886cCbgXAO+uwH6LaScg4kSWlL6vHICfZgl+npGdndX6wJCcNzetmCMUdtGjC6dHQ0NACJxxtAIDiElGApYyQF2i1428MygxBKC1+De0jbzCj5MKeEgcuz9Le1z1lCrBhi81YAV6BuPzbrF+/c7XL+9xu79NXD9C302QOnOs+8wvzzH5Tcr/PC7Ob5dlfjnVyUuKolXZYFayb00rAnmE1KEfZhaHKb7EKDjqAt1canoQqbdUKq9WYNS6e+JSLuGnIXMnFpHbn8BfvkfwOoPwMU/klDo9ldAabr4mb+mfd23QGfI2ELmNGzGg2uYxyPET12vBD4eWZ6Obl9GZHkKDJLSr8wHMlXgkOj9Dg4dLFuD+woMBhbvYH2HDi3gJYx/RWk438Bbgd5Z4r62RN9walaPE0i6Hv2uIQ/RZkNX8F1z2M1HFUC1gK5oEkkVTArqWmNWKMyL4C1axFFPZLJeyNFjdGpYEIlyPNE+PlOB5pZOwLqkd3SbYLruAK0Bp8Buf4G3jtoRygVYfU6tJaoAM32oZVpg/S6khjtwWcK1N+DBq5RxDuUacG/hRBi3FTzCqXXHoVAc69ah1BJN0+O9FrDW4oYxOMboM9KUd3Arct5jyxicdeCihHQO2teAlHClQScduHehp5HSrw4dHOtgfQ9ABAVtMCZwHZq+hW0M3r7d4vpqi3e/vEP/5idSpXZBHLX6Dlhe4vUPr/H69Rx//G6Of/xmhu+XGn9clpgrssBTgqPQAjrsw1TkEwmTtkOyn3yYJGMbimKFIAKsz4n4wAGEfcIN8P5f6c3zV2FfegAW0AsiQrsFmKELnuYNpWuVIUWsBrB5S0SbkYGPoJI99fxjXgN+m3rlsfXur/Pky09a528BhxaKzYDg+MPAYNFhx/4OBo7eb8GwhmU9pFtA+ALObclgz3dAbwDn0Lh36F2LriuB1qBt7aiGbajfcvCJ7SYtEMDo5qNpGLOMBgVVgarSpIotyAJvUci9eZcjWbLEkHscJrwXYWIkTeDhpQEixo7SdAipY2GA2/8E6jPAMWDzV3IIml3A24Z+7+Yt1SxNBTAJJsnqzm/fA70Fq1+FaHNDU1K6BpxLcGGAfgfFLBm7+1h1BlqjoTjHzdKiUhzbtodSZD3IGMOt4Oi9pxaOzVVIUzZhdAjNn9l0MzDGIDoP7hso5QHPobWHUhJccDjmIDgHYwacFXBYAwzoHQ2F7qxD1wPXt2vsbjq8+ekK6+s1/K9/pc/uGkrBFnPM/vAnzFdz/OUvZ/j+vMZfLkv8+bzAyih8UxXQkqMyNIXERHOCA0rnvagy3rEdpUsV1VlRX1KK9d1fgZu/h77ZBdU0GYDVH4GupWjSOaC/oSzA5TcUNRZzis5nc+CXdzQrkwU7wnZLEWm5+KC/u4yXg0dPK3no8495DfhtUrCH1vsx8CWSJQCq4yD2HzI479GzNRx6KH8GyRQ6v0bvtwAkOCpwX8P5FtZ3VCbzO7iugncarmOwboOul7DB0adrO/gu8Ykd2h8mbj5D3TJJxQZz9aKgiSSVianYQJSC/qW1SzE50Q6GBfQT90664an74W04IfuxRhjHOomCalyrPwArRifv3fXY63fzC9jNT/B6CSy/A8wcjCt4MJq96Dx9n86DcwGnNHgwRxfhy1rB4Dyl0xdOQTCGb2c9jGC43pXgnKHrbMhsM7zvLbyQY1o2pr+bDUVhtsOac6hGwjMPVe7goKGbCoUq4EwPITik5IOwpmekNrWWTAm22w5ta/Hu/RrN9Q63P/9KkezmPe1TUwGXf4CZVfjd7y9xfl7in79b4oeVxh9WBV6XBnOtBqIsVGgdkWl2IBFrJftt7+/JRl/XjvYmF8Did5TFWP8cSvOc6pjbq2BGcU4KYiFJxCODIEnoMHP0Bug4MDujfe4cEe7ZHyk9+6X+PWd8dnyASna63MdJwR46ND9FCvbQeu+u8/T7n7LO3xLMK3jWhdQsADj0voFkmqzbvAbzHQQzsK6HdAWcU4D36P0trOvgHEff9zSRotPoug6tvYVt1SjyOWR9B2Bo+lZmGNuljBrs78oymqtLzIwKbSR8mHdZSHL2maZhx5rlGFnukaVz1H7gqCbJpEI80g7vLzY603SbMES4CybtCoAlJSUAbBxYOYe/fQds3gHtNS2jamo5KRbwRU0tCtsrsO3PVGObfwNfLsEZhw8k4QX5+ioRTCeYh3WC/GV7CyU4blsHKRj1vXoPzhlsb7FVEl3fjj2ZzobRYG647SoGL+fodhJ9Q6LRRgg0xkFKDqU4RLDSI9/gNWwn0HUW63WHXbPGzS9r2BsLvP3P0de2WgHVEuffvcJiVeEf/rDC62WB//XbEq8qjcvSDOKeUoshshShdWTPzhBHiDLC9tTmw8Opi4ehzrIEytdA0QYSlyTikSVQLcI4r4Lcfs7+SMtYR6IeZ4M13jf0Rx/bTvJUkowJnqiSPfz8Y14Dvuyo8qWRJQAIGPS+g0UXzA264PhTBFu8QKqeFLbOczjnAavgvETnGnjLYTsG2xl0TY++s7Bti3bn0bft2GuZTtcAaIfEGZeGXHFMVdKMy6m5eqGwLCkVuyypbllJiUrK0V90GmGGky5ChBTTsXCWIo1ASui3QK+ox/LY/hKSohgGOnl6R9+724U0Xpho0a4pvdc58pntAlk2P4OJCp4zGjL9+/8T8D284MD5H8FufwVkAe56+G4DziW8KkmgEy5kGKMeTe89BGe49AZVL2BXHnMjoDhDoQXqSoc+zRLvGEO7CbZw7SbUj0NLj90A1qHfrdBrgd2igjAcSpRQuISUClJJMgRgDN57dG6Nvt+ibztsbzbAdgf8+o7mRtqOMgTLb2C+/T0W5wv84z9d4vWyxP/x+wUua4k/zCvMtQqWhiJElgKCs2DWAMhjCtiIKBADKPILrTMQmvaPs2SuzjiwuAS276i+aVuKPG0T/GE97bvtmrIEr/4X4Nf/hwhUl0B1SVGy70GuR3684IvTSzKBfvX4KDXMx7wGfLx2kUyWjwMDh/A1HHpy+PE1NACLNQAF5z2c43AMgAV66+H6Hr3r4NoariPru64hr9h216Lvd2g2Peyu3zcniCO7gH2Rj66AegVpDMoZ1S7nc5qHuJobnNU0jeSipnTsXEmUUqBWcmg9mPbpxfaDmE7cc/dp12CCg4WIhDEEEdKOrNSOoViE2lwgIFWGFoREyFRUoBReC8CDmRWpj50Hui3Yzd/hzQy4/oms+aSBFz1gZmCyCCOvOrDqnKz0vAfraRIKcx6MazAlIIUHA2AkB2cMC91jbjjOKomf5wa1kbjatPixVNhsWlzVFZrNFrh5O9YX2RrYWaD7EVTY1LBCwRpgZ/8OdHVIKwdScDakozdEOs2OarcxQ3Dxe6izC5y9PsMPPyzxzXmN//rDHK9nCn9Z1qiVxLJUMJLDKD7UKpVg4K6HaDdgsOCyoHpuIOoBlowf4Dq6eFFl+E4NAEFpVOfH0VvleYg8qdUJQgGL18D1G0rHMk5mBmdzoLsht57ZNwCzNJlECNqnYQA4rb+jSLV3ANZ0oSTuPWVmvGB81Brm9LVDr2ey/O3gQ8+lgIbzYQamF7Cw6P0GDnQuZf0MzJbwzsJaD9cVcC2DbT16bNF2Fn3r0HVbdF0Lu2HAbhts71KRT2wfSSJLU0OVBbTRKOsSZSkxm2nMKrK/m5cKq1JgVYohsiyDUYGcuPpwPraPjFElkt5LD2bDSK4UXIUT7wnCjKYGHhRhmopOppt3NBsz/s7NX2laSe8A1lME6xhQlAAzYM0a/uYNWFnTwOq+gXek4mQMgHXgylB6td2OSk/naQgyr8CZhOcWkgsACqWg7VFIgfOKzOjfbXqUWuJ22+GX2mC9brC+nmN7u4XbboDuZ1KE8lugZ/Tb+iakJVuguwZc0oMZBVoAkY1ZkAhq+QqqqnD+zTnOzip8/+0Mf7ic4YeVxj+cl1hohVeVGdKvd8wIXEvmDUpRFN2v6QKhWo2f1zXBA1aH6NBR32S3ox0rNLDZ0s6uVhRNNsG3lkkAoabqemoVqS+J/PSMLnK8p/mXr/+ZPgfB9MHThQ66lt5fnSXRrSV1bvo9M746POhy6VOQ5VNrldP13LfsofUeXue9izx53V8CYsoVoAvz4TnPofw5uG/R+w7MCcCJ4C/g4FoHOwwEBrreoNvdwtkG3c6h3ymKLFNFrO33ey2FCn1wo/WdLvQwELoOREktJAKLQlKPXqKK3ZuJuFe7xAHSDOm9IKaJ32XcVR7RnP4gbEcnYGlG558uTN1gngizb4DbN8DFP1FUc/V34OYdmBDwXFBLDesAzsgSL0amjJEISFFfICtCy4J3QL8BVwVFmh6wnQc2f4PQSzAHWMcAUUEKCcEZFOeoZQ8tGK53FqXmuN72qAqJ202Hq6sSNzcNmm2Dze0MHa7gmrdEmO0WQAd0DtRr6YEuOR0wHi50DM0RnZ9BG43VqxVmM4Pvv5vjclngTxcV/nCmcVFofFuVKBTHrJCQgo/CntQgv9mA6SJkADxF/rYbzQOaNZFYGNVFdnRndFxxDoCFYdAquPhE03hPGQzPAP8jHZO3P4f9YClSNDUgw7pcT2RczEm0FdPu8Zjt2xBxBjUuDxGoczk1+0JRyPs1gQ+uYdLjh5Hl5xT2HFv22Ho/Jp47WXoPuHALr8C9hLOOBD12NFLvgkcsGao72K1A32n06z70WqZ1y35fERvVsKYCq2akhq0KFCXVLMtSYVEpLCqNs0piFdpIZorM1ccWkkNiHzIsOJiKBeg7yAKs342uPt6T606xPL7Bul1oS0j2r9QUYYiwnuiTqirgpqB0X3MF3L4DgwuEWAHlKxL/bH6Fbwuwiz9TivCX/w4/+wa4/GcwSYOtHRtrsN55oN+ACQUnJRgkrHPg3QadXkIJCSU45lqiUgLbyuKskrhtLL5ZaLzb9LjatHh702Cz63F1tcC2O8NNZ+A6oN+1VIveCVjfAa0E7Cik4YJDKglTGiijsDqrUVUa338zw7LS+PNFicuZxLe1wVmhMVNkYahCZCk4CylkDPsLztIYMd8D2zVYvw0pTk9RYnkeRm+FoeQizJ/c/hraRWaUSt28oZOItUDzd0qfxws129E6raQB3+v/j6wZ6zB5ZHsdLgKCib2Z0/4uglew0EC3Hq0GI2EC4wVYxovDQ8gSeHCE+XGIEnhaCvYYUR5a9r51T/GSU7DA/WTpHHn+9NaRzqEnwowm6kSUFl3Xo9mSKcFus4PtOhqR1AXCjK0X8fOEGgcGmxpstkQ1q6hmuShRlgqrRYFZIXE+MzirJC5qhYtKolI05qkQItS+2Eiak8b2tOcyTcsCgTx1SMH1u7AVGKXmZJJ+vLPR3FjLi2CMnovmBbtrUtC2O6C9IdEIF0TEzRWY7QGu4bvbcCImKz7veoo6zTkAC3RrqnuqkiaFMRpA5n0Hxjy4EPCCgzMO4QDRtVDuGj0z0EbDgkQ11nm8Kgs01uKPyw7b3uKm7fF+Z7FuLN5sejSdw5vtJTbdLZqWoesZ+r6Dsx6wFeCDFV1oMTFKoC4USiPwam4wMwK/X2nUSuKyMKiUQK0lChWcehS1/OhAlFLwYX9wxmg2abcGa0MrB5djvytjRIRmPqZW4/a++iuRmFkEQiWLPmxvAFOM+6NvgO0tMFuG/eeAy78Am58A/orS0M4SKcbeynhhJ4rxwJHF2MsaEeubz+hvP+Pj40k1zOlrh17/0muVtO4HLfakdf/WOJqCBc0vBKi/zgbStNYPQ4AjUfa9Q9P0aBvqr4yEaTeBICNRpi4+8QQ0uPgswcoa1axCNa9QVhqrVYGikDifG1RG4nJGbj4XlaRePUkp2bRPb6qMpWHCo9hnqFumZEl3wMoZ4CrAOzAu7k+pSUMpQZ6Q6jAOjIV62nY0GQcj0YiehzaHJfDz/w3MXoPpEr7ZUqS0vga7/QkoL2j8FwRQ3ARvXQff3IKJjkgeHr7fwjEZImfAdzcQbgfHNSQ6KN/AqQWMUnDOo9QO1kmsrIZ1Hr112PUOrbPYdBa9s2i2Em2r8L5v0XiFnpWAN/Dgw+6j7QwUMpjdh0hWc4GlUZCcodQCUpCYR4XpIqnylXsP1m0pumcMTJdgQtIfXdsSWUoN9MHkon5F/a2uJDXrriF18+YNtYeoipbfrYHNL0DX0e3s2zCCbR2sFotg4BCGR9eXY/+lZ9TeM3u9L96RBRFxvIgSio4BZ4PdoKdoV5+oeWc8Szw0soz4oBrm9LWIL50sv9Z6pR/u0+OULK11sJaI0tokwmw6dIEwu6aDbROibLejPywQzrahfzG0joh6Tn2WdYF6ZlBVGvNaozISi5KcfM7LmIaVg8hnGlEOIp+kdsmSFOxQt0RClkiOFc7B2AMH/0oz2vnxoMjsd0SEthsjnChmYgDWv1CUIqpRTMMNYFZgagH/7t+A9XtgtgLqCzDXANbC2x7s9u/A2R8o6tm+h78hAQtr3kNUFzRKzNF0F641nC7gwSFsD+c2kMUKzgPKcXjvSd3saZ7mzPlhwLPfvoeTCpZ9i9ZawLbohEEvKzhPpvwM47aVjENxumCJoh0dLmDSEVxS0MWLiB6w8GDtLZjvqefV02M4T9G9czSOy7ZhaPgMREoFXVi4fmwNWf9CKlZZB2FOmFfq18DiO3rcrcn6bnMF7H4m27vdllLitqH1cUZErKu7IjBV0mfF/e0dPWfCeDfGc93yheGxRBnx5Brmodc/da3y2PLH1v0x8dzJkh4nqVg/RpVpvXIvwuz6YZ5l13Sw26CCnbr4pJGl1PSvmAGmHAdB1wVmM4O6pnrlrJA4q6h1ZFUKVCr0WyoRzAniCTlGkxj+jUKffWeYU3jU/mOM6mK2C4IQUAsC52GcFCjiODunE//2HUWXuqSTbf0n6lUswgnbWrB+BzAPL2Lk0gCqBmuuyJTdMzI4LxbA238PYpdz+JufKE3aW/jZK/jFdyQsAsC5gu92pHMRHK5v4bsdHAAnDTyXcM5T90XfAOCwzMAzBtf38FBA38GWBn6Sgo7ROmNjjytn4/5QYmzpiZG+YADgwV0PuI4GaMftLgQRGjdU7wVGkU2M4iRZBOL9z8CbfwHa98EjtwfsXylNe/YDfbvqPMy7bKh1plhS9KkWQH1BP6BZA+/+nQwn4Gg/Xv3n3ckjnFNtNF78iVDLfCZ/8xkPx1OJMuLBTUVfcgr22PrvrvveRZ687i8BDxH3+BB5RKKkf34vBdv3Ds2uge0sdtsduqaD2+3Gnr5mPfbpTWuWMbKcLaCMQjkrUVYGi0X4Vyqcz6gedlErcvLRClXotYwin9SI+1gryclULD7wfBejZS7oBB8jDC7pd3Y3AJvTibq6BELECFXRgTYLUzHgQnP8AuACrNsC7/8DWLyG71sy9159R9NMGKdU8O6aohvmQ0tETyPFfEeG7mEaigfghYDnALpbUuJymnKC/gpelvCqgmccHhaetYDfwjdrUvLyAo53JI5Joi6P5O93qA+P5MkZG0wiIlmi3YL35CjEXA8mxbgDnKMUdrsO9d5zSp02t+NkEQ+K6oQBmvckoGKctuH2Grj8MxkU3PxMrTvnf6ZU6vaa3JW27wG7Ay7+Qtur+4mO1/lrupghlRBFqn0TjNgP7O9Tte2MZ4sPJcqIB9cwx+f2H39qojy2/Kn13133vYs8ed1fAu5LwVrn76RgqV5paSRXUrPcbVvY3mK32aHverTbHV25t9v9mmWcackFEUisWc4uwIsC9aKGLjRWZzXqWmE5NzifG8xLhW8WCnMj8LpWKKXAUisUoX0kpvpiK4IUnCLLaFIQxD50Ej8g8kG8Pz73YRvX7de7hAYQUnQ2qcXxirxiqxVtl6u/UvrPOaATANsSuZZzipiEBOuC2YN9DS+D6KS9pe9+9e/A4jsw3sL3NrRL7MCu/gYUM3gWPIFFAb97Q4Rh5kPtjcj4Gr5YArKAt2uAdQBzgKIIFf6WRDfX/xOYfxtSn0ciq3hxkmxzxhiYs8D6PVh7AxQzMGWA1hKxzV9THbi5oW3lHSldt+/o+W5LKVVR0x/p7j2lsnc3wPySvku7pVrlr/8CMA2wd0BRk+DHO4o657+jOuhuQ5+jCkrX6oq2t5CU7u3DKDDbfNgxkfHV4hER5v7jp6RfD63n0LpOLXvf+j8WngNZnkq/plHlNAXb9/t1yq6zaBoL21s022a8jUrYNknFRp9S4M7UEZgKej6DMgr1okZRSCyXBrNK42ymcT4zWBQCF5VCrTlmSqIQYrC8SyNLKdid9pEHiXxw/3HzKMiCTrAizEPkHDBlcJ7pQ21zTif+xe/pS93+SqlVLkO0OKdBx/0O8JzIrVuTAGXxO3IFiu40zRZ+/YaiqJD6Zr4lYjVzYH0NrL6hXk7bk7BISPosb4HdFdUihQJgAKng+2CRJzWRkqqIxGIqmXtg95aMDarzu7aBtg/16pbWqyswrkKd9QrYXRFRphFj1wDv/0bbarehaFkZ+o6yCMboPSA4YNfAu1/CxVhLtdz2Jhx3a/Ll7W6By38AZuf0OeG3on5NpHj7llKxzXWwNrS0L9pbSrfC0QWBb8NQ8IyXjo8VVaZ4tOgHeFpUeWg9h9Z1atmHfMb+uu9d5Mnr/q3x0KjSh/tRAeucQ9tS60jXJT2WYeBzsyPCHOqVzWZiSOAwtFjEFJapgXIBUZYoa/KHnc3ImGA5o6hyGfosl4XAqhAwwcWnSOzu1MTNJ9YuB1N1lkaVh71HTwnUnoQoCGk2GFzdhQKqkKalPDeR5y6QkGsppSpkiAwDWdz+SK0oxYwERGc/0PvaBli/IxKwLVh1TqOqfvofgPCArIC2ha8vKIItlhTx3bwFdm/Ism93M0SXTBdAYeAtbRASO1kintse2P0YIj5B34Vzei/no9gm9h86SyTEaX8zuEC6NbB7DwZGrwlJv/P9f5CIR5UAHM3JvPoRuPgTRXkyTAi5+omEOlIRyUpD6en1L0D9PV0AtDtK1ZqKtrvSRH68oPfNX9O+uP0lWC6eB9WyJ0cm52h5EeZb9htyeTKzDz8uMr5YfAqijHiU6Af47VOwxz7j7vrvXeTJ6/6t8eBaZUjBOrcfWXYd3TZNj66jwc/trh1ufdfSCTi2jkQT9TSy5IJOqroEygVkTT2W5axEUUgsFoYizJrM1C9qhfMyjOtSEkaQwEdzvmdMMHXyGetmR8wJgIOR5Ufbj/HiwHYAXBA2Jf14MeTlGphd0AlaVxQFOQvglmznIOjkL0vg/HsS9cS2hfZmPInrilKVw8QUFnoT31Cjv/6BoiRmqI7qe6Dg5KPqWsB6wMzgmw0RbzQG2FoiDKPJtm93E9o4HMBBxOMdbWjbADoQZr+jvlEZImyEVPT1f46/e3dDx0e3CWl60Dra8Fgbeu2mDYIcSYQ2u6S6plC0ruqS2km6W4pIGejC5PZt8CEO1oS6BKo5/a540SY0fR4YpWxX35HwSi8pmvaetu35n7Mf7AvFpyTKiAcfOc8pBftSyfIhKdhoRDAV9pAZAd0fhT1ju0izox5LrK/pJD6cBHf7SljGiSilppNZUcPMZijqAkVVYLksUFUKF8uCeiznlIZ9VSusClLDLrSCFjSyS4qxVWEw52ZJhJk4+aSqTeATR5YR7YbqfFXiDNQ1NPEkdYGJEMG8gInQEjGj5WGB1Q+Ujm1C9K5LIjnXh0Z+AExRunLxHYlYtu9DOjREoQuy14Nbk4ft/DWt23bAzU/A7RWlbNtriljLRUgnh5pzsaRIrt9R1NXdAEUYpty3GEawxW3oewyjtAAi2HYbTAdCtkEUZA1YVCFK7se8ed/ShcLumn7n3/4bebR274H3/0bp2foCwxnlm38gorv5G7D8HX1fc06p276hyH3+bTg2QwrZOVqfCxkQaQA1B1hDSlpd0j6sgzrW9pk0XxA+B1FG3HvUfA6iPLb8qfUfw0sjy5QkgcPp12NEGW+7zg0RpbUOu22Hvh+de9pdC78LJ/Hd7dhjmdYro4pQKDoJ6xJ8vqKWkXmFekYjus7OSswKiVfLEpUW+N1CY2Y4VoXCTAkUQmKmyGtUDy4+fKhbphNIpl6xU7L8pJFlRL8d7fAihCLCOUSYAEUyfRdsAUPDPRP0T82A27/TSVwoirC8C43zBTCbA2sd+geDAEYF4/ebn4H1/wVc/Jn6PbvNqFhuwn3eEEE1G6D7F/oeq+/Ido4zItb6FaV5o+hGhb7T+DtVcM+xLUXKtqcoV+nQJ2np+25vgM3PtANu39AoNAZat1QAOH2XzTta97t/Aza3dJzBAf1/AFD0WAhK8579QL919jq4KllqBdGGLjR8G2z0Lui7N5tQ0+yJiJubIDIywNnvydRg836codlwStHWl3eVshkZ9+AkYX4JUeVD8VSipO/w5ZHlKaKk28PpVxsGC0/7KvveYbfrB0HPQJhtP6pgo7AnNSMAxhSsqYfIkhUlqvlodTeb0Yiu8xm595xXpIQ9q2jqyExRKrYUMokoDwh8Uru7I2nY4zXyj7wf4z5goQ5p23GqhVBA4Y8oShkJUWxohC9W4eQeDMXr17Qtt+8BcIqSolmC60Pj/dsxsm82wPot0LwN3rU/BoERI6K5+jspdPuOiMq1ZKruekC/A5o5IAOhmJq+Y7Uajc67lj5fGEoNM06ft31D62xuAPErRXZROMQV4Heh/zGoYF0LnP2ZCHgdRESbN0SA2+vQw3oFtCURmW3pdxYzEkABJASSAFbfU6p6e02KWYZQKz8DwEKE2dFnzi/pQs921NrDWqpnFnMi9V//hS5cGII/bUfbK7YNZTxbfM7oEniMSnb6+CMIe+79zM9AZF8aWR4jyvhaJEoAd6LKNLKM7SJdZ9F10bVnFPb0XY9+19DJuB0VmQdHcwk1inuUgZzNoI1GNatQlArzOc20XJQKy1pjZmjsVK3JUi0qYSNZyrRlZC+aTKPKUdiTtjJETB9/EjA2zsCMZClkaLQHEU5xREDifRj11RG5cEHqUxEiqvaaIh7OaN1Rbbu9BtAH0vqF+hE9iHzUnJa/+pnWqarQf9gSYUhN9UEX+mN374HmjEjl9oYIefX96GYjFHD5TyFlGxxtGCODgZufiPCloUis74j8qksiwG5D6WOl6bsvvg+tG1tg8S1Fhz/9jWzwypASbTbA8jsiLWUAtQIciLznr0JEe02kHftCt9djfbU6o21mLaXKdQHsdmH5YIl39vsQ+TPazu/+nbZZOQ+p76BabjfBASgT5nPE5ybKiIepZB+yzJGFPnVkSZ/xtPd9SWR5H1HS7XFRT4ws29YO5unR6o6mjPRDy8hus6MT4PZ237nHdoede6K4p16Ca4NqVkEXGoslzbNcLQssK41FpXBZK9Ra4KIiBWxsHamVDDMtx9aRQ/XKtGaJA2R5yNXnk+5HFYdG39CJ3trQRjMjcrDF3XqYc9TyoGqqR9odpWP7juqY7YbaHcyMUpXrX+h97ZaI4fIfgfXP1JMIEPGVKyKdfgMwS2lOviYFKROUkox/zpv39BwcKU17F5yLEqUzl2TIENtlUmzfA+jpAimqTuFI4MMFnRDe/itFzY4REdevABVaW7ZvgCb0Wtr3wNV1SP92Y3oaIMKKrjoIx5uZ00XCphhPPOUZfQfb0jpcT7+7vgTAibyFoIHRwgRlcz+SqirG32m7UFcN9oYZzwq/FVFGnFbJHnrugSlY4PP0Vz53snxIREnPH1e/pqbpVK90ewrYrukGwnR9T72VgwJ2IuwBxnplMvQZ5QzlvIY2GouzGZmnn5eoC3LuWVQKi0Lgm5lCqalmaQQngQ+nemXqQ8rZWK+UnNSwsecyjSynAp/PElmm4JzEMbYNak4xEiRj2Bu0HOHCydrbkCIN7jzRbs2E0VK2HWt3ugyiGxAx6ho4+44Uo+uf6Ln1e1p3uaSU424djMvPKUpjofa5W1NvJSTNjdy+I9KevSJSqs/3v6/t6bsyThHh7S/U5O88HRcqkNf6Z3I0co5Mz9//K313rkI0vQG2a7q4aBtanwsj35oNffbNG/ptJQsXIZL6VlVNy3dbAJxacGavR6MDWVBq24VZqwhzKetzYBsN9RltJ1aRAnf7ntZ/E9XAQdDUboJ7Uxb+ZDwOjzpivpSWEfqMBy32QZ/xqTAlSeBwjZKe3ydK77En6CHj9P1WkTT9Gr1g+45mEB5UwNp+/CKxrmMqOlGWC4iKhj7XixpFSdNGylLhckFK2POZxllJPZbnFRFljCxJCcsPmqkLwQdTglTgE2dCTvFZI8sUnI8TLKY49B1cUHCaWfjSioK0dh3s8wzVA9t1EN3UFCXZbiTQON6qWIRWiiooWEGtI92OyGj+u5FstQbWHfm17t4GUg2N/Lv3lDItz8bv6T3VFWPt9ObvpKDdXZOyt22B0gA3YRqNCMYD1TeAC8cQWwMIpCcNtXQ4S59frkjkVAX/2F//55gKLs7CMbgmAu225PHqHUXf+pvQClLS8Xj7C0XgqqC6KRejGUO5DGb5G6r/lit6nQuAlZQ2j4YK8T3zb5EN1Z8PfuvIMuLR8zDH5w4vm8nyOD6UKA/VKUcv2EiUPfqelK+RMG2zC4Oew0k5KmBjeg4YWwmUCSc0kuPL2YwGPlcF5osCRaGwCi0jy1qjNhIXIbpcFCH1yjm59wgezAjGlhHBEyVsiCJFQpJDzfJAZEmPP9XeOQEuicDiyRag+1wdTmkO8Bi/uU9MHxjVHxmn552lCDH2uqqCUqyOUaR3/heKsjwD5AKQYdjx+V/CZ1jg/HfA+1+J6M6/B3YL2s+7K1LXViuKjuuLkLJlo2VduQJu3o7G4/Ures/tvwLuLBgZzCkdevUTGREUSyLzpgHcltZbXFK6WGhAN0T09cU4N7RYUQToQmp4/i1tE6Uodep6WieCYCk1hOeCUq3FIhy7DW3brhlr7fqSUrrxIImORtV5SPXeAJpRu8p0YknGF4kvhSgj7iXMx6VHPz1Zfgh+K7J8KlF6jC0j9ylgm4YUsO2u3SNMv0sEPe2GTlRx0HPaMhKVsLENol5C6iDsqQpUtQ5mBAqrWqPUEmeVwkzTtJGFGSeOaMFRTJWwExN1xqZG6vs1yxRTsvzs+5ExOlG3G4pkgNPzEbkMrSVtiGI88ZquR3cgoYhI4EeXHF2HWZy39Pr7t/Q5r/4JuP4rNfBzD4jQkmJbIidVk3PQAgB3lB0wNXD9Btj8PfwGQfXUd/9JKd7zP47GCT/+9yDoWVBUCUcpXKlJlFOcByvAHfD+r8DZ96MQyihgwyhlqkNE168B5sJYtI4Innlg9Tv6HlwFoc9rIsVY7y0XwTbvHWD4mPreXoXRWypEusGMgYeygQ+GElzun2hUGEzdrkP69pK2ce7BfBb40sgS+Ajm6xE5stzHKZJMX0+JEthXvvo7Tj1uL/06KmCJKG1y2++2dFUf20VSBWyagk3N06WhBn1TDvXKelGhrsnqbjk3mIWaZa0FXs3IE3ZV0LSRUo4R5p165aTHchpZxttDNUt6vP/8ZwfnlNrz9f4XOrisJIJR5XhRwkWImBjV1ryldTgLMEPrjidyeQb0oe1CCoruok/qzS/UU1gsicBNRRETPPVCqhrwnEQ3khMB8ZAS5oJSoHIWejrfUiq0uQ5GCsGRx4WezGJBLSewAHq637eAZZRy9T0tx8Wo9mUgkmeKPheM6pln31ONtduSYrVc0meZMPGlDSnTaMun66D0vaUpJUoT6W7fj2Ip39NvOYU8geTZ4UskyohH1jDvPvchREnLvAyyPESQwGmSpNf3I8phsoiNRHm3Ttm2Pd0GMU/f9WibFrYLRNm1yWSRHRFkqoAF9oU9Ug8pWL1YQGmFelmjKBSWSzIkWFRq6LF8PVOoNMdlrVAIjjoMfi6EQKFGopy2joiQhk0jzCji+U1MCZ6Ch3wHHmqPu5sQUYLI0swDMWC/HtrtQAul6wi1zOKM3uNaev9uTbXJ+hWw/pW+z+2vYbwaA1zwjb3+hQhQhvQueqpXCkX3t2tKpfow6Hr7N0o7cwQnno6+Iwv+srEHt15QH6g0QH1GNdFuF7IT82CWPg+q3C2ZGFz9SKYDdU0Cqph+dX2w1rsiAjULItzZN/S7djcUbauKjk8ZIvF2DRhGLT8ZGZ8RTzJf/xjv+RxK2E+NYyQJPCyaPEaUdOtCVBkjSze2ijQ9rLXoGrK2i7VKRA/YKKmP6depYw8XdOJUIa2oS6BehIiyhjIKy2VJ5ulhJNei0jirFGrDcV5JlIpjHlpFyJBAQIWaZSRKzrAXVco7NncYx3QdiCC/OLJ8DIQi4kkjTO+ohjcVD/FQH03rodGirn4FgFHdsbumDXb2eyIn21JLSrcd3XH6Fvj7fwstL9H/15P5ebcF0AM37wLhnVMDv20oXbn+O8DLMC5rF4hP0Oe0Lb13exPaXUJNVhgi8jLUOm3oVV39gUQ8LrSn9C157XoblMahZ7Ra0jFYreiaQchA1OEAicKc5pb+qHiIyvtuX8CU8ezxJUeWEU9O5n9oZPlccYokgacTpXP7CthU0EORJRGms26YKNK1Hfq2pzFcu/VoaWc7qvnEIc8Rg7An9A5Gn82yHqaMVME8fbksUBYSZzODupBYlApnlUSlOBZmnDaiBUcpxF33nkCO4xzLUCpNI0vcFfh8sZHlUxBrwxEnD53Ji4wFp5qroAwNU0XEikho8yvtz9m3ZDknVHDlWQPf/hegehVmmF6R8nV7FQZftxTttbcUYZo5wAqyzjMhBavmwA9/ppri+/8gsrZvQr/lt0G401C7BtPA9/97yFScE2kXs+Bt+zPVQGdBEdyEVhtVUGS5uCTyLELfpOsp+7u7BsCDY9GGvpeZEeH3of2lPMumAy8Ez4EoIx49reQ+fMw0LPBlRJePIcl0+VNE6RKSjMpX74ko077KNLLsmo7qlE073KJtE/OBZiTK2Jw9mKanwp5ybBkpy2HKiClI2FOWEsuZJiVspTEvBJaFxKoUqLXALPRWxmkj0b1HJpGl4KknLJLpIy88sjwFHgQvU/Nv198dORX3obN0MTT7loQ27Zr26eYNRXFcU38klyENvwupUgvUK2Bjxpmeag7MzwERCHT9PoiGquBk1ITa4Y7qjMoEdyJDTjxXfws9le8AeUn3VRF+jxiHZ7e3oWc0Cs0sCYe4p9+6/AOli+OotFi/VIb6JZv39H2qC7oIiGnZahUuAPk4TSXjWeM5kSXwhAjzcyphfyuyvI8ggftJkpbZJ0qbCHsOGQ+kkeVoRNBTFDklyj6JKtNaZUqUQCBKNRKl1BQ5mBKmLlGUBUxlMJ9Tb+VyaVAbifOZQaklLmc0kmtuBBaGxnIttITi/E6PZereI/dcfLCniB1nWsav+MIiy1Mws7HPMf48Ve2nY50lcUtMSaowbaS+pBpg855qgwCRyfxbisS4pKhxex3UoJwI1fHg7FNhmC/5/f9GRKVKEtOs3wGLb0hA5BlFpaqkOqHrwxBoAUgGdEFIJAPB+Q5Y/OPYquEs0P2Vfl/9Kvi3hhos40Tkcd/GLEh8r21ByuFg9F4uwmDqK9oWsqBtkPGs8dyIMuJRhPmlt40cw0MI8CE4RpJ0Py5zOJqMkeS0Ttn3PhClHW6t9eha6qe0HRFkTMXC2iT9mtQpbXe3VSTWKqOwp1wAqoCakQK2mlcoSo261pjPNUojcbEoUCiBy5lGraleOTPRQD3ULOU4bSSSZBT1pLXL6N6T1i2/arIEaJ+Uq9GxJjbYp+h244UOQGTHBZFiuQxRXTA8YILqgJv3RMRyRkQpFHm6NtcUecbor7sFykuqK779TyJcaGB2Dpz9kSJRYcj4vN1RK4irgPZXWlYWIdJrga4n4/P6FYmFgKBsvabv2G2plhoNCJqbUdCDZPkUzo5CKe8oEq9WQGsAsyK1bEbGb4Qnz8NM8SnI8ktIxU4JMuJjEGVUvo6RZaxT9nDWDWKePokwsQvDnKMBQer/mnrAAkE1KUcfWFUANSlgq3kFbTRmIaqczTQWwYRgVWlUgShrzbEsJEopBgN1fYQoh17LGE2GOmVqdffVk2VESoaHYNvRti2aJngHxJmWDBRtRuMDIGQPCooQb34E4qBnboDv/itFk906XEhJWtfyG+D2Z2pH4QDe/gs58yy/Hedj3vydiFkp4NYD7ZuxflgWRICmJpKPE1e6W6q/Ch1MC8KUFlXc7V2N7TbRuk7IQLKMhl0D4Tdz5P7J54/nGllGPOgI/NoEPvcR5am0a0qUaR/lVNAz1inHnkpn3RBNpoTpm4ZORO12VMDGfspUAQuMJyBlxnmMhv6VdUmEOatgjAj1SkU+sCXVLM+CAnZVCpRyVMAayVFIAclpfiXn+1FlbBcZWkYSkkQmy8eByzE1CdA+7DZjP600wHxFJNQHr1Y9D56vjFKpoqQ2DKGJaNZXZFiwfkP1Tz0PadKKUpw8pG0lD+nYMPEjHluqBC7+QO49Zkbp1TjTM0bKAEWVekbCIF2EtG34nvXlXTs6xuj3bd7S7+WKomepMQypjt662cru2eK5E2XEo+ZhTvGxBT7j5z76LR+MD4kmARwlyvRfSpRpndJau1enjITpuo7SYvFEOa1TpgYEMQUbhzwPwp4ZZFlCG41yRrfLZYGiEFjOg8VdpbCsxikjleZYGgktBOaK6pZGcKiJEnaPOJPIMhoQHBvPlcnyHjAevFkV3Y/+q8sfRvcagOqTy99RHyMQ0ryOaqLR4B0IzkJBdWvDMeU78qQtZhRB7q7osxgn4RCXRFAsiHJ2N7RuXdNttSTFK+f0uboeP0uVQNMHIwMBwI/EOEW3o1Qvl+Mc1vl3FEX3Da3fLLL5wDPFSyHKiCfnOD4VWX4OHCNH4G698xBJxuWmJJn6vToXDdLHtOsdouwt+q4fWkQGQY8Nk+SPCXqmRMl4IEo5WtuVCzBjYEqDoipoHNeihDGCWka0GIwIViX9I9ceSrvOlYIWHNUDhT2xViliZDmkZO/3hf1Sj5PfBM4RYc0ug+tO6FsEDwQ62VZCjj2QztEy03Rv39CxEx1yTEXEdPuG6toiiIraYHoh9Gjt1zckCooWdDpMD1m/D61JC3ouEpo09P2LZTB17yjdW57frdU6G2wANYmJUNJvsLvg/1p/kk2ckfFUPIkwn9v57RRBAo8jSQAH65PTtCvVKN1E0BMIM5BkJMqYivVR0BMnicQ6ZWo+kKpfY10nmhDIYLGmC5i6gjIKRVWgrELqdWFgjBgUsOdB2LMsBFYFza+cawnNx/mV6ZQReaBmmY7l4l9Tj+Wngg/7OKbUY106ThQ51HvIOcBPtFn0u5FEhaJoznsMw5g9iPSKFfV31mGs1s2PJOCpVtRm4hnVJ6tzAAxY/I5ILU2VqnKMFIUcDRimbTNAaBFh+wcE5/QH6/p7jO0zvnS8tOgSeAJhfo7zWzT0+NB1nMIxkqT3Ho8mAQzkOCVKMkYfJ4pE0hwIsyWHHttZ9D3VJ+N9NLtxkkhKmOlUkZQspwrYYF3G6zmkkijqgpSwtUFd66G3slACq1qj0gLnIaqcG4GZFjCJsKcYhD38KFFOx3J91T2WHxPpZhm2EZu88NgVJseOWQTFqwnHFQBoOt6qV2FiCIDVn8YpJVzRavRrSvfb/i5ZAvS4XI7HbjxOj+7rj6Ngz/hy8BKJMuKLlZ09ljTvI0jgcHvJhxJlTL2mRJmmX0nQMypf421Mx/qupTRYTLumkeWhqBIYFbBSU50y1CqZ0ijrElJLmjJSyME4PY7jKrXEeaUGYU+t+ThlhBNhRmGPOBBVToU9ez2WOBxZYnh+3NaZLI+AS5DLjRvJyLnktSdAlVSjjNEp50SWF3+hSLbdjipWVe7vKKnGmZoxWnRRlHNEhMPYw2qOIpjCp+puZwHwp//WjN8ML5koIz7YfP34sh++7R5CgqdwrP/yPpIE7op4punXdORWajzQ92n61Q7EmBLmUKeMgp50mHO3oy8T+yojYlTJBZ3YpKYrfV0CpoKpDKSSqOc1lJZhFJfEvNaYl2oY9FwqjotaBYs7iULQ7MoyjOXSsU4pqZ9yqohNzdOnPZaHIsuchn0kGAvG7ddU/4tbsFg8XSUqg5tPvx0DupiVYAwoD6Q+vQ+CnDX9Y5zSqlE1Wyz3l41pWB58Yh/yXbkIMzVvxnYZBOP6fIw8G3wNRBnxoi7jHkOQ6fMxkoyvpyR5KJqMPZSHIsq+d3BJnTKmXl2wthtUirE2GXspY4tI9H6dpl8jUUZhjzJAOYcyCtpoFHUBpRUWiwJai8GxZ1Fq1AWZD1zWCoVkOK/UYEBgBLWLGHF3HFcaUaauPWOEib3brIT9SBAyGLcHcdd0zuNTYOowmNoBYPf3NDY3dCxKDYhzEiD1DQmMpBkJ0ftxEDXjQO8BrIlQH9I3KTXAz6h2C3yc35qR8YnwYML8Uo/hDyFJYLzgti5Nux7qoxzTrmONcqxT7hGltXvCnr7rSdDTJMboU+OBQ3XKmAJLFbBCAtUCTGmY0sAUBrrQqGcGWksslwZaj8KeZaWwKKhGeVYJFIJjpsjaLt4qMaZgDzn28IQw04gy9limZJmVsB8J95kbPAVcAHiAYXlUZEe7OsYouuyb4EecRI+23V8WCOrXNdUyH/S9OEaroIzngq8psoz4JBHmpz45PoQkgfuJMkaTwP1Emc6oTMly6KPsenjnh7RrSphoE0FPTLtGkpxa2gH7op5Yq4wKWKUhSzIgiO0i5NZDKdhFqFOuahL4nJcSs0JgpqMCllKwSjAUUgwinmHKSGqentQn4+M9154DkSVwlywznhm8O7LzWIhQE3TN3UiSi6DqdU9PI2d80fgayRL4RPMwvfcflTRPecE+liTjMofSrgBOEmWsT45RJRFiJMi0Ttl3YUK97e7WKfcEPW7/R8T0ayRKoQYFLKtmpICtCiijUFZkbVeWEvO5GXorCy0SYY/E3JBReh0iykoJSDZa26VDnvfGcg23+0rYQ5ElcDgNmyPLZ4holnAHnl5LwTnV4w8smq+YXh6+VqKM+GQ1zJTkHnPSvM8o/dDLTyXJfUGPg/dA37u9fsp9QU+4DWnXQ0Rpuw7o+9GdJ1W83keUUV0o1CjsCbXK2CoilUQ1K2EMKWCrSqEqKKIstcRZpVBqjvOSHHtmOnrBhsiScxhJQp6pF2yqgD00lmuPMDGqmLMS9oVBSPKl7ZqxJcR2wWZvkiaWhuzw4vELUHQ5VdtmZLwAfBbRz4dOCzlFkulrp0gS2G8L2Rf0HE+7TonSWTeIeVLCtG1Qvh4yHEgdek4R5SDsUYP/q9BUq5RKkrWdpmiyKARmlUZdKMwKiUWlUSmOi5oMCFZFHPIsSAHLBYog6DmmgE0FPUOkmaZeGZLWEfr6WdzzQmFmJMDpd/QHpargLzuBUOQr267DE56mnRyywct4tvjaI8uIL1Ile4xfHxpJxtfviyZjenVKlPE2EqXt7WA44NxYnxxSr1H5eijtGkU9x+qUewpYlcysrKG0gi6IMJVWmM9JAbtYUNp1WVF/ZWUkziuBSgmsyiDsCUOe46SR6NiTCnnSIc/RC1YcmTJyzEA9/Tnj/fy39ezB2Ni2dB9UNFm343Gd8SKQiXIfXwxhPoYk6f7DiXKsTz6MKAdbO7sv6ImpWNsRgfq+J6JMhTypoOe+9GsU9USiFGowIDClgTaaRD0VKV/nc00tI0HQs6gUakM1ylVBKdhR2EN9lYUQQdTDB6LcG+486a08NGUkTxvJuBfsAa0qGc8KmSzv4jc7wk9lad3kxQ+JJtO0K5FkapDu76Zde0vk2FMUeZAouxbou8OGA6fqlLFVJJJknFcp1KCA1WUxCHt0oQe3nmhCUCiBs5lBoQSWpcDcCNRaYFFQZBmFPXXSMnKoTrlXr0zUsFNxzyElbCbLjIyXi0yUx/Egwpye8x+Lx5BjuvzeaK3J8veR5L7P691RW1OitD2lW4dUa7+fdnXOAW273z9pw5y/oU7p7yfKNP2qTBjDZaACUQ7WdpVGUShUlURdaZQhBZsqYJcFkWWpBGZK7BGlDvVJuddbOdYn+ZQkD4h7KDWbp41kZGRkAE+IMD9QvwPg45EkkKZbxz7KlBij4jVVvsa0a0qU07RrFPdYa4koXU+qwb20azcKeQ4JelKijEOdowJWGUAXEKaA1BRRSiVR1VSnnM1CdFkozEsFowTOaoVCjsKehREoQo2yCoRZSnHQqWfPA/YeBWyqhD1ElpkfMzJeHnJkeT8+S0r2EEECp2uS8X0pkR4jzH37utNEOfZRjqQ4JcwYYVLaNa1PhhFLqY1dFPQcc+lJFbDRqcfUgCaiNIWB1BRZKkVEaYxAXWvUQdAzLxVNGSkkSs330q9GkPq1FHKoVXKGPQOCQynYab1yGlkeEvdM/5pyZJmR8fyRifLh+GSE+RiSBI5Hk+n9+9Ku+w49d4mS0q8k5omCnph2HZSwvSUbuy5pC0mVr949LP0azQfiCK7YKiINRFlCKgldkKhHaYW61kHYY2C0wKJUKDUR5rKUqBTHWSVRKIaFltBC7Al7VDLceUi7Ju0inN1Nvx5y7smRZUbG14FMlI/HRyPMhxAkPb4/kozvO0aSh9KuqbAnNUlPhTwxqowR5BBhxojShigyWtZNDQdSG7tTRJk69chQpxQSrKwhlRzaRKKtHfVWaii1r4CttMDMCCwLQTVLI6EExzzUKQspoPn+vMp9a7tRBZumYo8592QlbEbG14FMlk/DownzGDFGHCNI4P50a3r/FElOCZJup+lXIkh/RNAzRJl9IMIuIcc4qihNvXp/d5IIcJcoo1G6NFSnlBqsrCGkGAwIiqqA1hJlSaIeYyTm1UiUhRJYheHOUQFrEtN0cuzZN0xPW0XYEcIUwZpn6LXk+6bpmSwzMjIyjuMkYd5HjsBhEdBDSTK+/xhhTv1d79YpR6IkS7tHEqWzYx/lEE02d/so74soDzn16BLQBbhSMKWBkGIQ9sT0a1UpVKVCqQUWpYZWHKuSrO1WIQ0702PqNVrbFZIfFPSkhCmTEVwpMe7VKYdbdtDmLv7M8SdnsszIeM7IkeWH4Ukp2ftIEjhck4zLPSSaPBZVTokyjShjfXIq6En7KZ1zZDjQd8fTrntE6fcdeoDRzWTaKsLF6NRTVFBaDSlYUsCaQdijtUAVbO1KLYMSloiy1BxLE8Q8UqCSElrQ/VPCnjTtmpLlQXEP7pLl8POSnzn+5Px3lpHxXJGJ8uPg0X2Y+88/nCTj4+M1yvF+NBiIz0VyJMIcCZI8YENkGchxKug5SpRpnTIKeaZEeV+dckjBqiH9iqIGl3IY7ExjuIgoKbLkZESgBGaFQl1IMiOoggdsSUQ512G4syDzdMX3hzunTj2pB2yabj1kQpB7LDMyvh5kovy4OEmYp+qRwN10a3yPP0CYh8nyMEnerVPenSBirRsceeAxqFwPRZhDH+UQRfb7dcq0jzKSZYpD6VehRqIUCihqMEnKV6nkMNiZUq9jq4iWHMtKwyiBWSExN2IYwzVVwBaCUrFK3DVLn/ZRTltGYr0yi3syMjIyPg7uIcyHEWS67P2p12n0eLefcr+Pcur36gbF66E6ZaqEJSFPfzrtmhJl+gOA43VKGaJJqQFTAlLBFGYQ9iitYAqFopCDCYFW1CoSb0stMDcci4JqlXNN5JgKe9LBzofqlFNruzGyPJKCZXdTsJksMzJeHnJk+Wlwb0p2mo19bLoVINPz6fNp3yQts29dd4goXUqYKVEmhOmsC6nWbj+ijIKeqHZN65OHUq/A8TpldOpRBtIYCCVQlEUQ9lD6NQ521lpgXmkYybGoKMI8qyQqRYRZa0rBkgnBKOxR97SKpO0h1Et5IgWLfSXs+DPzwOeMjJeGTJafDqcjzHD7UJKMr50S8BxLu05Jcki7hvrkcJv0U6Z1ykH1Gvsop0YD0z7KQ0QJjFElY5RqjXXKQQErybFHU31SG50Ieyj9GhWwxohB0FMogWWlUEg2KGBrzVGpOF1EQgmGQop7Z1UOZJkIfEQSPbJ7IstTStiMjIyMjMO4t63kvpTrIfJMo0ZgjCbvkud+fXK/TjlGlFOijGTpnYftAgm2E3/XNPWa9lEeEvMA+1FlKuiZtoooDRlIUmk1KmArBSkFZjMyIqiD+rXUgghTRqeefQVsGVpGRmu7uynYO8KewIJTkoxECkaDn3FA3DP8XOQUbEbGS0KOLD89ThOmuz/dOo0kgWld8i5JTtOvx+qTKWHupV2j4tXau9NC4uPpv1NEmdYp06gyinsmRKmNhtR0S3VKibpWUEpgXiloSYKeyshx9Jbiw3DnWo1tIppHYQ+7o4BNDQiGmiVCvfJQChbIZgQZGRkZnwgfQJh3CfQUQdJjd8QYfYwmYwR5KMIc7OuOEWVan3wIUQJjnVLqfQWs1KFWqSEM1SejAlYXGsYoaE21SmME6iDoienXWTE69SyL0CpioqBnf7jzoTplJMxpzXJan0xTsIfI8RRZZqLMyHj+yJHl58O9bSUPjSantckpUabp1mNEuZd2nRKlc0Dfj4Q4Vbweqk8eUr4Cx/sp0zqlKgBTQIZWkaiAFTKSpBxrlYEoowK2UDRRpNYClaIxXOTUM47gImEPn6Rf7wp70hQsT26nKdhTZgSZLDMyXh4yUX5+3BthPjSanFrapRHl3X+HifKQQTo87raHxD7K2D85Jcn70q+MJSbp6i5RBku76NQTCbOsDJTiQ1RpwugtLQNhyuDUozjmRqA2RI401JkNClgTiDLWKw+1ikxVsKnF3XCbkiUbyTIrYTMyXi4yUf52OEmY1sZ2kH2SBPYHNh+KLKf1Se/HmuS0j3Lq1DMoXvsj0eQhIU+MJqc2dsBhQU9KlNHSTijwgvxep0QpJQ8KWCLMKOipi2hpp6CDAraQDHM9DneOEeUhBewxA4KUKI/VK+P9bEaQkfF1IJPlb4t7I8xTkeQhIc+dCSKRHANhHky7Ji0jQw/lXn0yRJiHhDzH2kOA44KeOKcyCnoCUQophjYRGu5MI7dIAUuWdtGhx+jQMlLIoVWkkHyYLFJJASP2a5VT79dD9cm7k0QOk2dao5zeB7LFXUZGRsbHxj0R5uF2EOC04nUvigxkCI89opzWKfeI8r62kJQkHyLoEXIU9gg1pl6FBEwFIQWUUVSnLAyRpdmvUyrFh/RrNEyvNKVeK01EGZ16dKhTasHDvMpglh4niYTHe+KdhCD3iBJ3U7DHyHL4+dPHmSwzMp41cmT5ZeBBKdlDUWZqiJ4Obj4WUR6LLMkMPSXH/ulECdw1HojK12hpFwc76wKQZDwQFbBCChQlOfUYIwenniqkXWelgpGjArZSsWWEYa73FbBG0HDnWKscjNHZ6O96TNizJ+jBfi/lviL2eGQ5bo78d5aR8VyRifLLwgNEP/vin0NtIUfrk5Yiy77vh77K+BoZDBxw40n/PZUo0zolY/uWdoEspZIQakzB6pB+LUsVbiWKQsIEQY8SfBjBtYyCnsQDNgp6KkX1ytguMv0H7Ncsp60h900XQUKkqXNPTsFmZGRkfFqcJMw4Oms6j/JQ2jVGk3fSrYmgZ+ihjJNCpunXqHKd1iePtYcAp5WvMbIMdcrYJiK13FPASjnWKasq9FdqMh4olAgjuDhW4XZmOCpFIp7YKlJJCcHZoIA9ZZZ+qiY5nTIyFfQcs7jLKdiMjIyMT4sHE+Yhoox1yT13ngOEeSfteogop+O1nhJRRrI8UKfkgkMZNVjaUWQpUBRyEPYoJVCF+ZSlIcI0oVUkCnoKxQajdCPGnspCCHDOIDk7qYAFRmHPQH5stLlLo8lpCpYnRJhVsBkZLxc5Fftl4hGEeZooDxEmiXgmLjxD32S/H01OfV5PESUwCnoGopT7o7eE3KtTcsGHEVwqCHpiCja2ihgphoHOpZaYGTGYEETCjHVKE+qUhRBDnTJa28V6ZVqrnAp6iAATQjzRMjK850C6NZNlRsbLQibLLxf3EGaSbj1Qp4z1yakCdi/16iaCnilRHjIcAJ6efo11SqkgjQEXfHTqKWiiSFS/RgWslBzzghx76lC3nBlBwp6ghDVyJMpCUISpxeFaZYwcpynYPWEPjqRlD9Qr05RrjiwzMl4mMlF++ThNmF1/sD55Z+RWWGYQ8qQEObjxHOmjTCNK4HHp1xhRckHzKYUaXHpi6jX1gI3pV2PkONy5kIOgR0ka6FwojpkWmBkeZlWOdUol2DBZJFXAxpaRqaBnqF1iP5pMVbCn6pWHyDITZUbGy0EmyueD04TZ9qFl5G669Wg0OW0HuUOS7mGpV+Cu8cCxOiUXgCnB5SjmkUoOvZVlSaO3YpuIMeTSQ4IeIsplIaElw7Iga7tKcZRqNCGITj2SsXtnVU6deabCnjuqWOxPGQH2R3LlqDIjIyPjt8eDIsyD9cljaddDBBkjzadElNF4IE2/xmkiQdADIcZoMjr1KAlTULqVWkXoVkuOykiUQdBTFzTUeVlI6qcM6ddS0iguzcnaLipg0xFcqbhnKuyZ+sAeS8WmpDk1JQByy0hGRkbGl4IHp2TJSD0QpXdkiJ6an0fCPKR4nUaWwMMFPYyF0VuJU0+cVZnUKZVRkJKiSqWJIItibBWRkqMuJLQUqI1EaQS0HEdvLQoBIxlqNdrZGSEGNeyhEVyprV1qQnAqmgQen4IFMllmZLw05FTs88Npp5+kTkmkOGkDuePGc6SP8j6iBE4bD0zrlEqDKT1ElFHYI6WEHgQ95NQjJR+ceiozEmalOQrFsQyWdlMFbHTriSYEsWWEHSDMhwp7HtIykuuVGRkvG5kony/uqWG2+1ND9lx4oqDnwBzKh9YogeN1yiOCHiYltYakgh45pl+NkTCGFLClkdBqFPbMAnHWmkQ9NIaLB4ceGsEV+yr3PGCTyDJtGbmjcsXxFCxjgMhRZUZGRsazxUnCRLO9O3My7aE81T/5VKKMgp5IlFzcqVNKJQcP2EiUxVCvpAizUAKVkVCSY2aisIfSr3PDUWqOQoxEWQhSwMbJIsecevb7Ku9a250S9kyjymOuPXHzpM9nZGQ8f+To8nnjNGH2kzplSpTHBjY/JvWaThJJhT2xVqkKQEpIrffqlNHaTgWnnkiUSgkUWqJICVNwLMuogKX0a61J8RoVsIKRyEfyoIBlOEqYx2qUx6LKY16w90WV8fmMjIznj0yULwMPJMwDadenECVwV9AzNR5IBD1cB0HPHQUstYZQZCkHBawSHLWhWZWFolqlFqR8TRWwRahPGhFbRUjYc8ja7tDMyqMzKSfCnkMRJoA7ZDndRNPnMzIyMjJ+e5wmzG5Ht6emhtxHlMDh9OtU0BPbRKQBtIYQYUalGGdVaqOhFIfWVKdMibIyEkYJlFoMt/NgPBCHOtea6pMmqGDj/YcoYBnSuZQYo8kDKdhjwp5cr8zI+LqQI8uXhYdFmJEYY1QJPDz1CtwlyvR2UqeMPZSpAtYUehD0kFMPRZhKUkSpJAl6lOCYGYFSk/FArUez9FTQUwhx0NZu6gG7l1I9oIB9iLDnWAo2vJSjyoyMjIxngtOEaTu6TcdrPZUop4Ke1CBdqIEkpaIa5ZCCTQQ9RSGglBjGb5lgQqAkx7yQ0IJ8XwfCVJyiz6ROqfjoAfvQVpEpKU6jSiC1v8tRZUbG144cWb5MnCbMxxIlcL+gJ7W0kwpcqaFOOVjahZqlMQJSiqFOaULaNfZUxlYRLRkWQdAT65WlFIEgSQErQ4QpGDn1PGVW5TFhz0CUODBhBHfJMkeVGRkvE5koXzZOE+Zja5TTWmWafhVyXwEb6pRSy0H5GglT61HQE63tpOQoNbnzFJoUsNFwgAhTwgg2KGA1H40HHqqABe5a2x1TvsafPXjBJoqetIYJ3G0ZGTdb/tvKyHgpyGT58vGwCPMYThFlKuiJdcpE0CO1vCPsUWpMuaYtIzHtGgc6G0Xjt8wg6GGYGYowy0CWOhH2qGRW5aG+yrRWuSfWiT8xC3syMjKOIBPl14PThHkMh4gy/TcV9KR1SiXuEKUM6ddImPF+oQV0MEgfWkZUFPTwYEIgoAVDpSQEo7SrFnxw6+GcQR0Z6nyf9+sposTkftpOQpsoC3syMjIyXhIeR5j3CXpS44FhmLOEUKNDT7yd1iljm4jW1Eepk8iyDvXKuSGnnlKPhFmpUflKI7j44P/6GAXsfbVK4Lil3UOiSiCTZUbGS0KOLL8+PIwwHyvoCcYDTO2nX2OdkohSQQiGoqDbWKc0aWSZEGYhGWotUCkyTT+lgE2JcuoBy4AhugTupmBPCntA/z2WLDNRZmRkZDx/nCbMQ0R5SNCTOvQEQQ/npHzlgg/9lKSEHeuU0dJOCI7CSGjJUShqGRlqloKF0Vscs2BEYMRIkHFWZSE+ggIWp4U9h+qVnKWbK5NlRsZLR44sv17cQ5jJuK1prTK1sot1Sqn2BT2BMGOdMop5UgVsEdKtMf1aBlu7oT4p2XBbD0bpNKsytopwxgZhj+BjfTJVvj60ReShRJlTsBkZGRlfF04TZky7PlTQI8VQo4yRpZBjNElzKkdhj+RxRuV4W8a0a5gqoiWN4tobvRW8YCWjVhHG7o7gSmuVaavIofTrIWHPIZHPVNgD3CXP+FxGRsbLQ44uv27cQ5jqdD9lYjyQWtrFyDLWKaNRelS/6uD1uh9ZUp1ypsmpp5Ak7NGhTin5OILLcLHXKvIYBWwaVR6rVR6dKoL9qDI+lyKTZUbGy0MmygzgIRHmoToll3f6KfcJU4aIUg6WdjElq4OwJyVMLfcVsBRhsv06JXucAvYh1nbTFOwhspyKfIDcMpKR8bUgE2VGitOEqYrE9zWoYNUo4JneCjkKeoTgQ6uICXXKIkwSmbaKxDplITlKRU49X3U/MgAABZ9JREFUMQUr2SjsMYIPdcpDQ51TggT220foMY4rYKck+ghhT3w+IyPjZSATZcYhnCZMU4d6pR4FPaE+OVXApoIeuk9EKcXo+1ooAaNJ8VprMQx1Vnx06tkbvSX5wVmVx1pFUsK7r1UE6bLIwp6MjIyMjNO4hzBLgIvBeCAV9EzrlFqTQXokShGIUnKGKkwSianYYlC+krBHcXLqie48RUKU4kD6dSruOZl+feD9h5imA5ksMzJeMnJkmXEKJwmTawPOOYQSkFLuufUoLUPadRT0SMlJARvSrqUWg6DHhHRrlVraSYZKUi2zFKMCVgsOycahzqcUsA/1gAVOC3v2rO5wmiwzUWZkvDxkssy4DycJUxkFzvn+rEolh/rkSJhjhGkkEWZsFZGCkQesIKceUsDS/WGSCBsFPlOz9FTQcyyqfIgCFsDe8imhHlTEZmFPRsZXgUyUGQ/FScI0hRlaRqKgJ61TUmQZlLBqVMDGCDPWJQfjAb3v1BNbRWQwI5B8X9ATI8u0n3IgSH6XKIGHKWAPpWCzsCcjIyMj4xROE2ZJhBlJMhLmWKdkKDVFkaWm9hCtBCpNE0TmJgp8iCgLyQdhT3TqiQYEadp1am13NP2Kw60i6fNAFvZkZGTcRY4sMx6LeyJMBc7Z0E9J00TEYJIe65MqiTCjU08kTC0Z5qHnkgY6M2guBkFPbBU5ZkAQo8lD6dfp/UNR5WOEPRGZLDMyXi4yUWY8FacJ08hAmBRVDn6wgohRin1hTyEZCkVWdgNhChb6KimyTGdVPlUBe6o+GV4+KezJkWVGxteHTJQZH4p7CTO1tospVyU4ylCfpFQsiXhSpx4tGGpFfrGxPhn7K2OtMvZWxvrk1Pv1mAfssRQsLXtguQOp2YjsBZuR8fKRyTLjY+AkYdY1pWRN8H81ktNw5wlRasFQG+qvnA51jkQpOIPmZEBwaFbl1AcWeDxRxvdMhT2nTNOBTJYZGRkZGffjJGGWwanHJArYIipgNR9Hb4nRKF0LjiKkXVNhzzFBz6HeymPp13trlThAqifaRXDg+YyMjOePHFFmfAqcJMzKSAjOBmGPTiztai1gAmFKzlApAc33iTJGltHSLhX0pIQJHEi/Yl8BS6/tR5X3tY5k0/SMjK8LmSgzPiXujzA5Q6EFtBTD+K2UMCn9SlGkiYIeQdZ2DxnBdd+MyjSqPJSCjWKeLOzJyPh6kYky43PgJGHOCklDnjVHEYY6V4ru14pDBKee2CqiBBsUsKmgZ2pAMG0ZeQg5ThWwh4Q9h4gSJ57LyMh4vsgkmfG5cZIwV6WCEuTQowNZlorvWdpN069TQc8pA4JjZul7ESZOE2IW9mRkZGRkfA6cJMxFQWQ4WNoFWzvJySx9cOoJ0eQhQc+hWZVTUc99EeZ99clTUSWS5zNRZmQ8f+TIMuO3wumUrOZknq4T44Eo7AkjuPSBiSJT79eDCtiJkOcQWaZR5J365COFPRkZGc8bmSgzfmucJkwTBD3BqUcH0ozCHhEEPYeiyilhAqF2OY0kTyhgU0I8JuzJKdiMjJeLTJIZXxJOEuZcS4hQp1QJWR5qFXmKUw+9hmEZ4DBR3hdV4sRzGRkZGRkZHwOn+zAltZVE/9dIlqkJwaOHOgP31iofOrMSyFFlRsZLQo4oM75knO7DlAKCsSH9OihgJ5ElcNcw/aCQJyG3VAEbHx+MKifvzWSZkfGykEky47ngJGEWwdJuOqtyakJwqlUEeJgC9jEp2GP2dulrGRkZXyYyQWY8V5wkTJUIejjfr1XeMR7g9xukH3p8qnZ5rLcyvJyJMiMjIyPjs4F573/r75CRkZGRkfHFg//WXyAjIyMjI+M5IBNmRkZGRkbGA5AJMyMjIyMj4wHIhJmRkZGRkfEAZMLMyMjIyMh4ADJhZmRkZGRkPAD/P2UZiISzU+iaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "meth = 'mfvi'\n",
    "coreset_sizes = -1\n",
    "with open('../results/fullcov.pk', 'rb') as f:\n",
    "       results = pickle.load(f)\n",
    "gpreds = (\n",
    "        results[dnm][meth][-1][0]['grid_preds'],\n",
    ")\n",
    "def plot_full(idx=-1, a0=0.05, a1=0.9, ax=None):\n",
    "    if ax is None:\n",
    "        f, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "    ax.scatter(X[:, 0], X[:, 1], c=COLS[Y.squeeze().int().numpy()], alpha=a0)\n",
    "    ents = predictive_entropy(gpreds[idx][-1]).reshape((250, 250)).T\n",
    "    ax.imshow(ents, extent=EXTENT, origin=\"lower\", cmap='Blues')\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    ax.axis('off')\n",
    "    plt.title('VI on learned coreset')\n",
    "    plt.show()\n",
    "plot_full()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "62e2e1b8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading/Generating the dataset HALFMOON\n",
      "\n",
      "Bayesian neural network experiment.\n",
      "Inference via PSVI_LEARN_V on halfmoon data over 1 trial.\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Running psvi_learn_v\n",
      "\n",
      "Trial #0\n",
      "Coreset/Subset with 64 datapoints\n",
      "SETTING UP THE MODEL \n",
      "\n",
      "\n",
      "architecture : fn2\n",
      "  0%|                                                  | 0/5001 [00:00<?, ?it/s]second term tensor([1520.3517, 1516.7902, 1529.3739, 1521.3885, 1523.3922, 1509.3047,\n",
      "        1514.3756, 1510.2081, 1508.7117, 1510.4327, 1496.7621, 1523.0945,\n",
      "        1499.1127, 1523.7996, 1529.9670, 1520.3245, 1516.6361, 1522.1854,\n",
      "        1509.5485, 1505.9147, 1523.7471, 1523.1002, 1519.2290, 1520.2125,\n",
      "        1516.7062, 1504.1310, 1512.0505, 1523.5677, 1516.2742, 1518.5651,\n",
      "        1497.9524, 1517.7395, 1507.3685, 1509.7526, 1502.2972, 1517.0465,\n",
      "        1506.6536, 1516.9247, 1506.3148, 1518.1228, 1513.1124, 1494.5435,\n",
      "        1519.0763, 1507.9503, 1512.1107, 1508.4919, 1521.3171, 1511.4072,\n",
      "        1508.7190, 1523.7622, 1505.0776, 1530.9536, 1489.0725, 1520.6040,\n",
      "        1511.9758, 1521.1169, 1515.4257, 1510.1997, 1495.9468, 1525.4619,\n",
      "        1520.0999, 1521.2288, 1522.9418, 1513.6531], device='cuda:0') | tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0.], device='cuda:0', requires_grad=True) tensor([[1.0000e-05, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 1.0000e-05, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 1.0000e-05,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        ...,\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e-05, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 1.0000e-05,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         1.0000e-05]], device='cuda:0') \n",
      "second term tensor([25738.6641, 25733.6777, 25766.5586, 25701.2988, 25705.1348, 25719.7559,\n",
      "        25660.1875, 25790.5684, 25704.0215, 25794.9141, 25734.7383, 25786.0078,\n",
      "        25727.3457, 25773.5488, 25698.8789, 25731.2812, 25674.1191, 25748.2520,\n",
      "        25677.2891, 25719.0820, 25755.3457, 25764.2324, 25736.8828, 25781.0820,\n",
      "        25766.4316, 25741.9160, 25779.2598, 25768.3770, 25749.8320, 25772.3281,\n",
      "        25731.9766, 25686.5938, 25700.2480, 25744.0078, 25783.7012, 25703.9199,\n",
      "        25751.0586, 25755.5762, 25695.6875, 25757.2598, 25768.5117, 25753.3984,\n",
      "        25699.6250, 25808.6094, 25751.2930, 25715.5098, 25746.5254, 25739.0254,\n",
      "        25687.3242, 25787.2461, 25746.1074, 25769.6719, 25674.5488, 25784.9629,\n",
      "        25723.1211, 25779.1309, 25771.5508, 25746.6855, 25674.9062, 25740.1914,\n",
      "        25732.6797, 25760.6719, 25773.4844, 25766.4277], device='cuda:0') | tensor([0., 0., 0.,  ..., 0., 0., 0.], device='cuda:0', requires_grad=True) tensor([[1.0000e-05, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 1.0000e-05, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 1.0000e-05,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        ...,\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e-05, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 1.0000e-05,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         1.0000e-05]], device='cuda:0') \n",
      "second term tensor([1029.8108, 1028.5878, 1040.9204, 1024.7126, 1032.1205, 1028.7209,\n",
      "        1024.3196, 1014.9034, 1019.8400, 1030.7258, 1022.5677, 1026.0422,\n",
      "        1021.9056, 1033.2253, 1028.7998, 1031.1011, 1028.2882, 1027.7400,\n",
      "        1028.4683, 1030.2975, 1022.5580, 1034.7191, 1016.6711, 1028.5150,\n",
      "        1013.0582, 1030.8650, 1039.0144, 1032.0884, 1023.5239, 1030.9222,\n",
      "        1032.7622, 1027.8301, 1022.0432, 1029.4794, 1036.1141, 1019.4852,\n",
      "        1030.7300, 1024.9199, 1037.5896, 1025.1542, 1031.5083, 1036.5352,\n",
      "        1030.0897, 1027.0247, 1040.3494, 1037.0055, 1033.7302, 1022.5200,\n",
      "        1033.0731, 1024.8149, 1034.1686, 1029.3934, 1030.8496, 1026.8320,\n",
      "        1049.8640, 1025.8147, 1027.0217, 1032.2122, 1031.5951, 1035.4843,\n",
      "        1024.8900, 1036.2460, 1027.0089, 1026.9875], device='cuda:0') | tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0.], device='cuda:0', requires_grad=True) tensor([[1.0000e-05, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 1.0000e-05, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 1.0000e-05,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        ...,\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e-05, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 1.0000e-05,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         1.0000e-05]], device='cuda:0') \n",
      "second term tensor([1510.8486, 1520.5878, 1527.9703, 1515.6902, 1517.9789, 1520.8251,\n",
      "        1499.1984, 1521.0432, 1508.1167, 1500.0306, 1516.1952, 1519.1407,\n",
      "        1515.3816, 1506.3335, 1513.9882, 1515.6926, 1511.7296, 1521.6704,\n",
      "        1521.1505, 1509.5090, 1523.7527, 1500.2312, 1518.8939, 1525.0172,\n",
      "        1525.8115, 1527.0049, 1499.9365, 1507.4832, 1522.4697, 1500.6063,\n",
      "        1521.4725, 1518.3313, 1527.5969, 1521.1831, 1508.5146, 1508.5212,\n",
      "        1499.5464, 1484.2600, 1510.0383, 1517.2664, 1501.6113, 1516.3789,\n",
      "        1508.4517, 1519.5327, 1512.0963, 1524.5938, 1505.0824, 1514.7551,\n",
      "        1512.8022, 1515.9873, 1510.5016, 1516.4220, 1510.3901, 1514.0406,\n",
      "        1512.9354, 1519.0288, 1504.4067, 1517.1407, 1527.6243, 1513.1841,\n",
      "        1505.4757, 1515.2012, 1519.3835, 1497.4017], device='cuda:0') | tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0.], device='cuda:0', requires_grad=True) tensor([[1.0000e-05, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 1.0000e-05, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 1.0000e-05,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        ...,\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e-05, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 1.0000e-05,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         1.0000e-05]], device='cuda:0') \n",
      "second term tensor([25744.2168, 25767.7383, 25663.8633, 25758.0039, 25801.4062, 25664.4844,\n",
      "        25721.3262, 25719.4980, 25778.8320, 25734.3711, 25728.8711, 25694.8906,\n",
      "        25679.9531, 25760.3965, 25719.5820, 25744.1309, 25822.8047, 25789.1914,\n",
      "        25776.2773, 25726.3164, 25692.1289, 25688.6152, 25777.8320, 25756.9180,\n",
      "        25699.0508, 25785.4297, 25671.8027, 25772.3555, 25684.3867, 25755.1270,\n",
      "        25733.3398, 25759.6719, 25682.9648, 25817.2617, 25738.7246, 25791.0059,\n",
      "        25726.5312, 25718.3418, 25661.3262, 25695.4824, 25780.3535, 25727.3262,\n",
      "        25749.2129, 25766.2422, 25735.6758, 25694.7754, 25733.0703, 25740.4883,\n",
      "        25760.3086, 25752.2656, 25725.3164, 25737.2969, 25707.7383, 25764.7383,\n",
      "        25696.4609, 25743.9258, 25739.5547, 25830.5078, 25710.5742, 25768.4980,\n",
      "        25804.1934, 25758.7188, 25695.5938, 25775.6113], device='cuda:0') | tensor([0., 0., 0.,  ..., 0., 0., 0.], device='cuda:0', requires_grad=True) tensor([[1.0000e-05, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 1.0000e-05, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 1.0000e-05,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        ...,\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e-05, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 1.0000e-05,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         1.0000e-05]], device='cuda:0') \n",
      "second term tensor([1027.7617, 1029.5288, 1032.4281, 1037.0927, 1028.4982, 1035.3843,\n",
      "        1021.2310, 1037.2842, 1042.4768, 1021.3887, 1027.1526, 1029.1815,\n",
      "        1023.4792, 1027.7057, 1033.0338, 1027.6868, 1025.4641, 1025.2307,\n",
      "        1032.0862, 1043.7634, 1018.2267, 1031.4620, 1023.9630, 1034.0469,\n",
      "        1030.1238, 1021.8857, 1028.7316, 1027.4221, 1018.4808, 1031.4778,\n",
      "        1026.4233, 1023.4656, 1036.3331, 1026.6151, 1029.4291, 1037.0144,\n",
      "        1036.3466, 1034.0599, 1030.5668, 1030.4709, 1022.5621, 1023.4731,\n",
      "        1039.2039, 1032.5585, 1040.8489, 1023.6508, 1029.1216, 1026.8545,\n",
      "        1035.5436, 1030.5444, 1028.4083, 1016.2490, 1025.9878, 1027.1254,\n",
      "        1034.7327, 1036.1206, 1030.8499, 1022.5452, 1029.1431, 1028.5231,\n",
      "        1030.1371, 1021.2095, 1032.5927, 1025.5649], device='cuda:0') | tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0.], device='cuda:0', requires_grad=True) tensor([[1.0000e-05, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 1.0000e-05, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 1.0000e-05,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        ...,\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e-05, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 1.0000e-05,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         1.0000e-05]], device='cuda:0') \n",
      "\n",
      "logging predictive grid at 0\n",
      "/home/dionm/miniconda3/lib/python3.9/site-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2895.)\n",
      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "second term tensor([1522.2908, 1518.7656, 1520.1853, 1519.9290, 1516.8962, 1506.2457,\n",
      "        1520.9893, 1512.9304, 1515.1772, 1510.0779, 1505.3984, 1497.9460,\n",
      "        1509.5498, 1513.4694, 1513.3159, 1512.4830, 1523.8838, 1517.7013,\n",
      "        1513.0914, 1509.3528, 1517.9293, 1515.8015, 1502.7399, 1521.0867,\n",
      "        1490.5118, 1518.4014, 1512.6592, 1516.8094, 1522.8262, 1500.3674,\n",
      "        1503.9807, 1525.2200, 1517.6083, 1509.7971, 1527.4795, 1527.0215,\n",
      "        1504.9911, 1517.7235, 1516.7994, 1527.4539, 1517.0286, 1515.0162,\n",
      "        1504.1080, 1511.6951, 1510.2581, 1532.4288, 1508.8363, 1507.4357,\n",
      "        1516.9069, 1513.4413, 1518.0389, 1515.2933, 1517.0404, 1525.5774,\n",
      "        1509.7448, 1511.2524, 1515.5546, 1523.1482, 1514.6639, 1506.3895,\n",
      "        1522.6570, 1505.3098, 1521.5498, 1518.7788], device='cuda:0') | tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0.], device='cuda:0', requires_grad=True) tensor([[1.0000e-05, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 1.0000e-05, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 1.0000e-05,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        ...,\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e-05, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 1.0000e-05,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         1.0000e-05]], device='cuda:0') \n",
      "second term tensor([25671.9336, 25762.1953, 25733.7539, 25733.2051, 25739.4473, 25740.2168,\n",
      "        25725.6582, 25741.0820, 25736.6055, 25746.5762, 25715.2422, 25741.3047,\n",
      "        25720.5820, 25749.3418, 25745.7109, 25707.1055, 25764.6738, 25678.5234,\n",
      "        25775.0117, 25771.8027, 25721.3086, 25704.8945, 25809.5312, 25741.1914,\n",
      "        25774.4648, 25735.3848, 25803.4883, 25721.6777, 25686.1211, 25724.0430,\n",
      "        25745.0508, 25714.8301, 25765.0137, 25735.8945, 25745.6426, 25742.5195,\n",
      "        25738.2324, 25728.9883, 25729.2520, 25700.4102, 25706.2852, 25749.1914,\n",
      "        25726.9688, 25754.0781, 25735.5762, 25739.7930, 25733.7695, 25727.1289,\n",
      "        25716.6562, 25781.4902, 25628.5547, 25749.0664, 25725.9551, 25695.5781,\n",
      "        25786.6582, 25722.2090, 25730.8906, 25818.2520, 25748.0195, 25718.3125,\n",
      "        25764.8223, 25725.7539, 25714.1094, 25821.8711], device='cuda:0') | tensor([0., 0., 0.,  ..., 0., 0., 0.], device='cuda:0', requires_grad=True) tensor([[1.0000e-05, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 1.0000e-05, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 1.0000e-05,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        ...,\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e-05, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 1.0000e-05,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         1.0000e-05]], device='cuda:0') \n",
      "second term tensor([1027.2655, 1020.2507, 1028.7552, 1021.8365, 1033.4919, 1034.4358,\n",
      "        1031.4347, 1020.7148, 1024.0624, 1034.7563, 1034.6094, 1033.1122,\n",
      "        1020.9921, 1025.4159, 1031.2816, 1032.5798, 1013.9269, 1026.6836,\n",
      "        1040.3594, 1043.3102, 1034.1434, 1039.0502, 1009.2964, 1029.3801,\n",
      "        1032.5020, 1020.6460, 1023.0878, 1036.1454, 1031.7051, 1027.4902,\n",
      "        1036.3612, 1039.8046, 1017.4390, 1035.4381, 1009.7300, 1029.1145,\n",
      "        1029.9441, 1028.1528, 1021.0115, 1032.1611, 1039.3809, 1030.4585,\n",
      "        1015.9369, 1016.0220, 1025.6163, 1038.3843, 1038.0566, 1039.6130,\n",
      "        1036.7645, 1037.1927, 1022.5012, 1028.8203, 1030.8788, 1027.0286,\n",
      "        1034.8038, 1027.4448, 1035.1825, 1035.6104, 1039.6990, 1029.1323,\n",
      "        1034.3580, 1026.6434, 1035.5696, 1023.0374], device='cuda:0') | tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 0., 0.], device='cuda:0', requires_grad=True) tensor([[1.0000e-05, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 1.0000e-05, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 1.0000e-05,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        ...,\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 1.0000e-05, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 1.0000e-05,\n",
      "         0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         1.0000e-05]], device='cuda:0') \n",
      "\n",
      "predictive accuracy: 54.50%\n",
      "/home/dionm/miniconda3/lib/python3.9/site-packages/torch/autograd/__init__.py:276: UserWarning: Error detected in PowBackward0. Traceback of forward call that caused the error:\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/camera_ready/../experiments/flow_psvi.py\", line 555, in <module>\n",
      "    (experiment_driver(\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/camera_ready/../experiments/flow_psvi.py\", line 391, in experiment_driver\n",
      "    results[dnm][nm_alg][ps][t] = inf_alg(\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/camera_ready/../experiments/flow_psvi.py\", line 307, in <lambda>\n",
      "    lambda *args, **kwargs: PSVILearnV(*args, **kwargs).run_psvi(*args, **kwargs)\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/inference/psvi_classes.py\", line 734, in run_psvi\n",
      "    psvi_step(xbatch, ybatch)\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/inference/psvi_classes.py\", line 462, in hyper_step\n",
      "    params_history = inner_loop(\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/inference/psvi_classes.py\", line 439, in inner_loop\n",
      "    optim(params_history[-1], hparams, create_graph=create_graph)\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/hypergrad/diff_optimizers.py\", line 40, in __call__\n",
      "    return self.step(params, hparams, create_graph)\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/hypergrad/diff_optimizers.py\", line 139, in step\n",
      "    loss = self.get_loss(p, hparams)\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/hypergrad/diff_optimizers.py\", line 47, in get_loss\n",
      "    self.curr_loss = self.loss_f(params, hparams)\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/inference/psvi_classes.py\", line 451, in inner_loss_function\n",
      "    return self.inner_elbo(model=fmodel, params=p, hyperopt=hyperopt)\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/inference/psvi_classes.py\", line 318, in inner_elbo\n",
      "    kl = sum(\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/inference/psvi_classes.py\", line 319, in <genexpr>\n",
      "    m.kl()\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/models/neural_net.py\", line 419, in kl\n",
      "    return dist.kl_divergence(self.param_dist, self.prior_dist)\n",
      "  File \"/home/dionm/miniconda3/lib/python3.9/site-packages/torch/distributions/kl.py\", line 171, in kl_divergence\n",
      "    return fun(p, q)\n",
      "  File \"/home/dionm/miniconda3/lib/python3.9/site-packages/torch/distributions/kl.py\", line 401, in _kl_multivariatenormal_multivariatenormal\n",
      "    term2 = _batch_trace_XXT(torch.linalg.solve_triangular(q_scale_tril, p_scale_tril, upper=False))\n",
      "  File \"/home/dionm/miniconda3/lib/python3.9/site-packages/torch/distributions/kl.py\", line 140, in _batch_trace_XXT\n",
      "    flat_trace = bmat.reshape(-1, m * n).pow(2).sum(-1)\n",
      " (Triggered internally at  ../torch/csrc/autograd/python_anomaly_mode.cpp:104.)\n",
      "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
      "  0%|                                                  | 0/5001 [00:11<?, ?it/s]\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/camera_ready/../experiments/flow_psvi.py\", line 555, in <module>\n",
      "    (experiment_driver(\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/camera_ready/../experiments/flow_psvi.py\", line 391, in experiment_driver\n",
      "    results[dnm][nm_alg][ps][t] = inf_alg(\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/camera_ready/../experiments/flow_psvi.py\", line 307, in <lambda>\n",
      "    lambda *args, **kwargs: PSVILearnV(*args, **kwargs).run_psvi(*args, **kwargs)\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/inference/psvi_classes.py\", line 734, in run_psvi\n",
      "    psvi_step(xbatch, ybatch)\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/inference/psvi_classes.py\", line 462, in hyper_step\n",
      "    params_history = inner_loop(\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/inference/psvi_classes.py\", line 439, in inner_loop\n",
      "    optim(params_history[-1], hparams, create_graph=create_graph)\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/hypergrad/diff_optimizers.py\", line 40, in __call__\n",
      "    return self.step(params, hparams, create_graph)\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/hypergrad/diff_optimizers.py\", line 141, in step\n",
      "    p_new, m_new, u_new = adam_step(\n",
      "  File \"/home/dionm/Blackbox-Coresets-VI/psvi/hypergrad/diff_optimizers.py\", line 197, in adam_step\n",
      "    grads = torch.autograd.grad(loss, params, create_graph=create_graph)\n",
      "  File \"/home/dionm/miniconda3/lib/python3.9/site-packages/torch/autograd/__init__.py\", line 276, in grad\n",
      "    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
      "RuntimeError: CUDA out of memory. Tried to allocate 26.00 MiB (GPU 0; 3.82 GiB total capacity; 2.88 GiB already allocated; 13.56 MiB free; 2.96 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF\n"
     ]
    }
   ],
   "source": [
    "torch.cuda.empty_cache()\n",
    "\n",
    "! \\\n",
    "python3 ../experiments/flow_psvi.py \\\n",
    "--num_epochs 5001 \\\n",
    "--inner_it 100 \\\n",
    "--log_every 200 \\\n",
    "--lr0u 1e-7 \\\n",
    "--lr0v 1e-7 \\\n",
    "--lr0net 1e-3 \\\n",
    "--n_hidden 50 \\\n",
    "--datasets halfmoon\\\n",
    "--architecture fn2 \\\n",
    "--methods psvi_learn_v\\\n",
    "--num_trials 1 \\\n",
    "--init_at subsample \\\n",
    "--init_sd 1e-5 \\\n",
    "--data_minibatch 128\\\n",
    "--log_pseudodata \\\n",
    "--coreset_sizes 64\\\n",
    "--mc_samples 64\\\n",
    "--fnm fullcov\\\n",
    "--trainer hyper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9f08ef9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "48abe1a1",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'slice'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m<cell line: 25>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m     plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mVI on learned coreset\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     23\u001b[0m     plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[0;32m---> 25\u001b[0m \u001b[43mplot_full\u001b[49m\u001b[43m(\u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36mplot_full\u001b[0;34m(idx, a0, a1, ax)\u001b[0m\n\u001b[1;32m     14\u001b[0m     f, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m     15\u001b[0m ax\u001b[38;5;241m.\u001b[39mscatter(X[:, \u001b[38;5;241m0\u001b[39m], X[:, \u001b[38;5;241m1\u001b[39m], c\u001b[38;5;241m=\u001b[39mCOLS[Y\u001b[38;5;241m.\u001b[39msqueeze()\u001b[38;5;241m.\u001b[39mint()\u001b[38;5;241m.\u001b[39mnumpy()], alpha\u001b[38;5;241m=\u001b[39ma0)\n\u001b[0;32m---> 16\u001b[0m ax\u001b[38;5;241m.\u001b[39mscatter(\u001b[43mus\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m, us[idx][:, \u001b[38;5;241m1\u001b[39m], c\u001b[38;5;241m=\u001b[39mUCOLS[torch\u001b[38;5;241m.\u001b[39mtensor(zs[idx])\u001b[38;5;241m.\u001b[39msqueeze()\u001b[38;5;241m.\u001b[39mint()\u001b[38;5;241m.\u001b[39mnumpy()], alpha\u001b[38;5;241m=\u001b[39ma1, marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m, s\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e3\u001b[39m\u001b[38;5;241m*\u001b[39mvs[idx])\n\u001b[1;32m     17\u001b[0m ents \u001b[38;5;241m=\u001b[39m predictive_entropy(gpreds[idx])\u001b[38;5;241m.\u001b[39mreshape((\u001b[38;5;241m250\u001b[39m, \u001b[38;5;241m250\u001b[39m))\u001b[38;5;241m.\u001b[39mT\n\u001b[1;32m     18\u001b[0m ax\u001b[38;5;241m.\u001b[39mimshow(ents, extent\u001b[38;5;241m=\u001b[39mEXTENT, origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlower\u001b[39m\u001b[38;5;124m\"\u001b[39m, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBlues\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'slice'"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coreset_sizes=10\n",
    "meth = 'psvi_alpha_v'\n",
    "with open('../results/fullcov.pk', 'rb') as f:\n",
    "       results = pickle.load(f)\n",
    "us, zs, vs, gpreds = (\n",
    "        results[dnm][meth][coreset_sizes][0]['us'],\n",
    "        results[dnm][meth][coreset_sizes][0]['zs'],\n",
    "        results[dnm][meth][coreset_sizes][0]['vs'],\n",
    "        results[dnm][meth][coreset_sizes][0]['grid_preds'],\n",
    ")\n",
    "\n",
    "def plot_full(idx=-1, a0=0.05, a1=0.9, ax=None):\n",
    "    if ax is None:\n",
    "        f, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "    ax.scatter(X[:, 0], X[:, 1], c=COLS[Y.squeeze().int().numpy()], alpha=a0)\n",
    "    ax.scatter(us[idx][:, 0], us[idx][:, 1], c=UCOLS[torch.tensor(zs[idx]).squeeze().int().numpy()], alpha=a1, marker='x', s=1e3*vs[idx])\n",
    "    ents = predictive_entropy(gpreds[idx]).reshape((250, 250)).T\n",
    "    ax.imshow(ents, extent=EXTENT, origin=\"lower\", cmap='Blues')e \n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    ax.axis('off')\n",
    "    plt.title('VI on learned coreset')\n",
    "    plt.show()\n",
    "\n",
    "plot_full(idx=-1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}