{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '..')\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from utils.plot_metrics import plot_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To train the model, follow the following steps:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Extract the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !7z x -y ../data/raw/DocLayNet_core.zip -o./DocLayNet_core"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Run the below script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output_dir = \"./output\"\n",
    "# device = \"cuda\"\n",
    "\n",
    "# !python ./layout-model-training/tools/train_net.py \\\n",
    "#     \"--dataset_name\"            DocLayNet \\\n",
    "#     \"--json_annotation_train\"   \"./DocLayNet_core/COCO/train.json\" \\\n",
    "#     \"--image_path_train\"        \"./DocLayNet_core/PNG\" \\\n",
    "#     \"--json_annotation_val\"     \"./DocLayNet_core/COCO/test.json\" \\\n",
    "#     \"--image_path_val\"          \"./DocLayNet_core/PNG\" \\\n",
    "#     \"--config-file\"             \"./layout-model-training/configs/prima/fast_rcnn_R_50_FPN_3x.yaml\" \\\n",
    "#     \"--resume\" \\\n",
    "#     \"OUTPUT_DIR\"                \"{output_dir}\" \\\n",
    "#     \"SOLVER.IMS_PER_BATCH\"      4 \\\n",
    "#     \"MODEL.DEVICE\"              \"{device}\" \\\n",
    "#     \"SOLVER.BASE_LR\"            0.01 \\\n",
    "#     \"SOLVER.MAX_ITER\"           80000 \\\n",
    "#     \"SOLVER.CHECKPOINT_PERIOD\"  300"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NB: the files at `./layout-model-training/tools/train_net.py` was included by `git subtree add --prefix model/layout-model-training https://github.com/Layout-Parser/layout-model-training.git master`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>data_time</th>\n",
       "      <td>0.008384</td>\n",
       "      <td>0.008775</td>\n",
       "      <td>0.008960</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.009425</td>\n",
       "      <td>0.008924</td>\n",
       "      <td>0.008733</td>\n",
       "      <td>0.008826</td>\n",
       "      <td>0.009223</td>\n",
       "      <td>0.008738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eta_seconds</th>\n",
       "      <td>30040.116253</td>\n",
       "      <td>30032.604346</td>\n",
       "      <td>30199.984691</td>\n",
       "      <td>30397.155697</td>\n",
       "      <td>30450.676296</td>\n",
       "      <td>30443.054099</td>\n",
       "      <td>30435.431902</td>\n",
       "      <td>30306.135462</td>\n",
       "      <td>30336.655440</td>\n",
       "      <td>30290.952027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fast_rcnn/cls_accuracy</th>\n",
       "      <td>0.971680</td>\n",
       "      <td>0.962891</td>\n",
       "      <td>0.930664</td>\n",
       "      <td>0.922119</td>\n",
       "      <td>0.927246</td>\n",
       "      <td>0.922607</td>\n",
       "      <td>0.918701</td>\n",
       "      <td>0.878906</td>\n",
       "      <td>0.879150</td>\n",
       "      <td>0.889648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fast_rcnn/false_negative</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.958960</td>\n",
       "      <td>0.995067</td>\n",
       "      <td>0.961984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fast_rcnn/fg_cls_accuracy</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024907</td>\n",
       "      <td>0.004933</td>\n",
       "      <td>0.031249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iteration</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loss_box_reg</th>\n",
       "      <td>0.005330</td>\n",
       "      <td>0.045536</td>\n",
       "      <td>0.182375</td>\n",
       "      <td>0.244092</td>\n",
       "      <td>0.200095</td>\n",
       "      <td>0.219698</td>\n",
       "      <td>0.240223</td>\n",
       "      <td>0.371495</td>\n",
       "      <td>0.394405</td>\n",
       "      <td>0.402448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loss_cls</th>\n",
       "      <td>0.242587</td>\n",
       "      <td>0.356886</td>\n",
       "      <td>0.420098</td>\n",
       "      <td>0.368185</td>\n",
       "      <td>0.321174</td>\n",
       "      <td>0.327423</td>\n",
       "      <td>0.343623</td>\n",
       "      <td>0.426194</td>\n",
       "      <td>0.434159</td>\n",
       "      <td>0.423370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loss_rpn_cls</th>\n",
       "      <td>0.700573</td>\n",
       "      <td>0.584717</td>\n",
       "      <td>0.443300</td>\n",
       "      <td>0.333261</td>\n",
       "      <td>0.271206</td>\n",
       "      <td>0.204834</td>\n",
       "      <td>0.148327</td>\n",
       "      <td>0.124927</td>\n",
       "      <td>0.118580</td>\n",
       "      <td>0.105196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loss_rpn_loc</th>\n",
       "      <td>0.336152</td>\n",
       "      <td>0.313502</td>\n",
       "      <td>0.241430</td>\n",
       "      <td>0.246864</td>\n",
       "      <td>0.228371</td>\n",
       "      <td>0.217888</td>\n",
       "      <td>0.214341</td>\n",
       "      <td>0.206750</td>\n",
       "      <td>0.202433</td>\n",
       "      <td>0.200037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>0.000799</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>0.001199</td>\n",
       "      <td>0.001399</td>\n",
       "      <td>0.001598</td>\n",
       "      <td>0.001798</td>\n",
       "      <td>0.001998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_data_time</th>\n",
       "      <td>0.008384</td>\n",
       "      <td>0.008775</td>\n",
       "      <td>0.008960</td>\n",
       "      <td>0.009312</td>\n",
       "      <td>0.009425</td>\n",
       "      <td>0.008924</td>\n",
       "      <td>0.008733</td>\n",
       "      <td>0.008826</td>\n",
       "      <td>0.009223</td>\n",
       "      <td>0.008738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roi_head/num_bg_samples</th>\n",
       "      <td>497.875000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>476.500000</td>\n",
       "      <td>472.125000</td>\n",
       "      <td>474.750000</td>\n",
       "      <td>471.500000</td>\n",
       "      <td>470.375000</td>\n",
       "      <td>452.250000</td>\n",
       "      <td>451.125000</td>\n",
       "      <td>452.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>roi_head/num_fg_samples</th>\n",
       "      <td>14.125000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>39.875000</td>\n",
       "      <td>37.250000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>41.625000</td>\n",
       "      <td>59.750000</td>\n",
       "      <td>60.875000</td>\n",
       "      <td>59.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rpn/num_neg_anchors</th>\n",
       "      <td>180.125000</td>\n",
       "      <td>186.250000</td>\n",
       "      <td>184.375000</td>\n",
       "      <td>190.125000</td>\n",
       "      <td>186.750000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>183.625000</td>\n",
       "      <td>185.250000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>192.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rpn/num_pos_anchors</th>\n",
       "      <td>75.875000</td>\n",
       "      <td>69.750000</td>\n",
       "      <td>71.625000</td>\n",
       "      <td>65.875000</td>\n",
       "      <td>69.250000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>72.375000</td>\n",
       "      <td>70.750000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>63.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>0.375595</td>\n",
       "      <td>0.377317</td>\n",
       "      <td>0.381449</td>\n",
       "      <td>0.387139</td>\n",
       "      <td>0.385061</td>\n",
       "      <td>0.380078</td>\n",
       "      <td>0.376928</td>\n",
       "      <td>0.347513</td>\n",
       "      <td>0.387798</td>\n",
       "      <td>0.360441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_loss</th>\n",
       "      <td>1.385000</td>\n",
       "      <td>1.235328</td>\n",
       "      <td>1.374799</td>\n",
       "      <td>1.180483</td>\n",
       "      <td>1.041189</td>\n",
       "      <td>1.030759</td>\n",
       "      <td>0.980173</td>\n",
       "      <td>1.161567</td>\n",
       "      <td>1.168232</td>\n",
       "      <td>1.147360</td>\n",
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       "    <tr>\n",
       "      <th>bbox/AP</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>bbox/AP-Caption</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/AP-Footnote</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/AP-Formula</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/AP-List-item</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/AP-Page-footer</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/AP-Page-header</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/AP-Picture</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/AP-Section-header</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/AP-Table</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/AP-Text</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/AP-Title</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/AP50</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/AP75</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/APl</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/APm</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bbox/APs</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      0             1             2  \\\n",
       "data_time                      0.008384      0.008775      0.008960   \n",
       "eta_seconds                30040.116253  30032.604346  30199.984691   \n",
       "fast_rcnn/cls_accuracy         0.971680      0.962891      0.930664   \n",
       "fast_rcnn/false_negative       1.000000      1.000000      1.000000   \n",
       "fast_rcnn/fg_cls_accuracy      0.000000      0.000000      0.000000   \n",
       "iteration                     19.000000     39.000000     59.000000   \n",
       "loss_box_reg                   0.005330      0.045536      0.182375   \n",
       "loss_cls                       0.242587      0.356886      0.420098   \n",
       "loss_rpn_cls                   0.700573      0.584717      0.443300   \n",
       "loss_rpn_loc                   0.336152      0.313502      0.241430   \n",
       "lr                             0.000200      0.000400      0.000599   \n",
       "rank_data_time                 0.008384      0.008775      0.008960   \n",
       "roi_head/num_bg_samples      497.875000    493.000000    476.500000   \n",
       "roi_head/num_fg_samples       14.125000     19.000000     35.500000   \n",
       "rpn/num_neg_anchors          180.125000    186.250000    184.375000   \n",
       "rpn/num_pos_anchors           75.875000     69.750000     71.625000   \n",
       "time                           0.375595      0.377317      0.381449   \n",
       "total_loss                     1.385000      1.235328      1.374799   \n",
       "bbox/AP                             NaN           NaN           NaN   \n",
       "bbox/AP-Caption                     NaN           NaN           NaN   \n",
       "bbox/AP-Footnote                    NaN           NaN           NaN   \n",
       "bbox/AP-Formula                     NaN           NaN           NaN   \n",
       "bbox/AP-List-item                   NaN           NaN           NaN   \n",
       "bbox/AP-Page-footer                 NaN           NaN           NaN   \n",
       "bbox/AP-Page-header                 NaN           NaN           NaN   \n",
       "bbox/AP-Picture                     NaN           NaN           NaN   \n",
       "bbox/AP-Section-header              NaN           NaN           NaN   \n",
       "bbox/AP-Table                       NaN           NaN           NaN   \n",
       "bbox/AP-Text                        NaN           NaN           NaN   \n",
       "bbox/AP-Title                       NaN           NaN           NaN   \n",
       "bbox/AP50                           NaN           NaN           NaN   \n",
       "bbox/AP75                           NaN           NaN           NaN   \n",
       "bbox/APl                            NaN           NaN           NaN   \n",
       "bbox/APm                            NaN           NaN           NaN   \n",
       "bbox/APs                            NaN           NaN           NaN   \n",
       "\n",
       "                                      3             4             5  \\\n",
       "data_time                      0.009312      0.009425      0.008924   \n",
       "eta_seconds                30397.155697  30450.676296  30443.054099   \n",
       "fast_rcnn/cls_accuracy         0.922119      0.927246      0.922607   \n",
       "fast_rcnn/false_negative       1.000000      1.000000      1.000000   \n",
       "fast_rcnn/fg_cls_accuracy      0.000000      0.000000      0.000000   \n",
       "iteration                     79.000000     99.000000    119.000000   \n",
       "loss_box_reg                   0.244092      0.200095      0.219698   \n",
       "loss_cls                       0.368185      0.321174      0.327423   \n",
       "loss_rpn_cls                   0.333261      0.271206      0.204834   \n",
       "loss_rpn_loc                   0.246864      0.228371      0.217888   \n",
       "lr                             0.000799      0.000999      0.001199   \n",
       "rank_data_time                 0.009312      0.009425      0.008924   \n",
       "roi_head/num_bg_samples      472.125000    474.750000    471.500000   \n",
       "roi_head/num_fg_samples       39.875000     37.250000     40.500000   \n",
       "rpn/num_neg_anchors          190.125000    186.750000    185.000000   \n",
       "rpn/num_pos_anchors           65.875000     69.250000     71.000000   \n",
       "time                           0.387139      0.385061      0.380078   \n",
       "total_loss                     1.180483      1.041189      1.030759   \n",
       "bbox/AP                             NaN           NaN           NaN   \n",
       "bbox/AP-Caption                     NaN           NaN           NaN   \n",
       "bbox/AP-Footnote                    NaN           NaN           NaN   \n",
       "bbox/AP-Formula                     NaN           NaN           NaN   \n",
       "bbox/AP-List-item                   NaN           NaN           NaN   \n",
       "bbox/AP-Page-footer                 NaN           NaN           NaN   \n",
       "bbox/AP-Page-header                 NaN           NaN           NaN   \n",
       "bbox/AP-Picture                     NaN           NaN           NaN   \n",
       "bbox/AP-Section-header              NaN           NaN           NaN   \n",
       "bbox/AP-Table                       NaN           NaN           NaN   \n",
       "bbox/AP-Text                        NaN           NaN           NaN   \n",
       "bbox/AP-Title                       NaN           NaN           NaN   \n",
       "bbox/AP50                           NaN           NaN           NaN   \n",
       "bbox/AP75                           NaN           NaN           NaN   \n",
       "bbox/APl                            NaN           NaN           NaN   \n",
       "bbox/APm                            NaN           NaN           NaN   \n",
       "bbox/APs                            NaN           NaN           NaN   \n",
       "\n",
       "                                      6             7             8  \\\n",
       "data_time                      0.008733      0.008826      0.009223   \n",
       "eta_seconds                30435.431902  30306.135462  30336.655440   \n",
       "fast_rcnn/cls_accuracy         0.918701      0.878906      0.879150   \n",
       "fast_rcnn/false_negative       1.000000      0.958960      0.995067   \n",
       "fast_rcnn/fg_cls_accuracy      0.000000      0.024907      0.004933   \n",
       "iteration                    139.000000    159.000000    179.000000   \n",
       "loss_box_reg                   0.240223      0.371495      0.394405   \n",
       "loss_cls                       0.343623      0.426194      0.434159   \n",
       "loss_rpn_cls                   0.148327      0.124927      0.118580   \n",
       "loss_rpn_loc                   0.214341      0.206750      0.202433   \n",
       "lr                             0.001399      0.001598      0.001798   \n",
       "rank_data_time                 0.008733      0.008826      0.009223   \n",
       "roi_head/num_bg_samples      470.375000    452.250000    451.125000   \n",
       "roi_head/num_fg_samples       41.625000     59.750000     60.875000   \n",
       "rpn/num_neg_anchors          183.625000    185.250000    187.000000   \n",
       "rpn/num_pos_anchors           72.375000     70.750000     69.000000   \n",
       "time                           0.376928      0.347513      0.387798   \n",
       "total_loss                     0.980173      1.161567      1.168232   \n",
       "bbox/AP                             NaN           NaN           NaN   \n",
       "bbox/AP-Caption                     NaN           NaN           NaN   \n",
       "bbox/AP-Footnote                    NaN           NaN           NaN   \n",
       "bbox/AP-Formula                     NaN           NaN           NaN   \n",
       "bbox/AP-List-item                   NaN           NaN           NaN   \n",
       "bbox/AP-Page-footer                 NaN           NaN           NaN   \n",
       "bbox/AP-Page-header                 NaN           NaN           NaN   \n",
       "bbox/AP-Picture                     NaN           NaN           NaN   \n",
       "bbox/AP-Section-header              NaN           NaN           NaN   \n",
       "bbox/AP-Table                       NaN           NaN           NaN   \n",
       "bbox/AP-Text                        NaN           NaN           NaN   \n",
       "bbox/AP-Title                       NaN           NaN           NaN   \n",
       "bbox/AP50                           NaN           NaN           NaN   \n",
       "bbox/AP75                           NaN           NaN           NaN   \n",
       "bbox/APl                            NaN           NaN           NaN   \n",
       "bbox/APm                            NaN           NaN           NaN   \n",
       "bbox/APs                            NaN           NaN           NaN   \n",
       "\n",
       "                                      9  \n",
       "data_time                      0.008738  \n",
       "eta_seconds                30290.952027  \n",
       "fast_rcnn/cls_accuracy         0.889648  \n",
       "fast_rcnn/false_negative       0.961984  \n",
       "fast_rcnn/fg_cls_accuracy      0.031249  \n",
       "iteration                    199.000000  \n",
       "loss_box_reg                   0.402448  \n",
       "loss_cls                       0.423370  \n",
       "loss_rpn_cls                   0.105196  \n",
       "loss_rpn_loc                   0.200037  \n",
       "lr                             0.001998  \n",
       "rank_data_time                 0.008738  \n",
       "roi_head/num_bg_samples      452.375000  \n",
       "roi_head/num_fg_samples       59.625000  \n",
       "rpn/num_neg_anchors          192.125000  \n",
       "rpn/num_pos_anchors           63.875000  \n",
       "time                           0.360441  \n",
       "total_loss                     1.147360  \n",
       "bbox/AP                             NaN  \n",
       "bbox/AP-Caption                     NaN  \n",
       "bbox/AP-Footnote                    NaN  \n",
       "bbox/AP-Formula                     NaN  \n",
       "bbox/AP-List-item                   NaN  \n",
       "bbox/AP-Page-footer                 NaN  \n",
       "bbox/AP-Page-header                 NaN  \n",
       "bbox/AP-Picture                     NaN  \n",
       "bbox/AP-Section-header              NaN  \n",
       "bbox/AP-Table                       NaN  \n",
       "bbox/AP-Text                        NaN  \n",
       "bbox/AP-Title                       NaN  \n",
       "bbox/AP50                           NaN  \n",
       "bbox/AP75                           NaN  \n",
       "bbox/APl                            NaN  \n",
       "bbox/APm                            NaN  \n",
       "bbox/APs                            NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_df = pd.read_json('./trained_model/metrics.json', orient=\"records\", lines=True)\n",
    "mdf = metrics_df.sort_values(\"iteration\")\n",
    "mdf.head(10).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Azwocg0dERERECczAgQMxZMgQlC9f3qEV7Y8//kCpUqVQrlw5HDt2DB07dkTmzJlRvXp1lCpVCp988omp8rt164YXXngBZcqUQdmyZTF//nyv6qkefwcAc+fOxYQJE1CmTBlUq1YNN27ccNher/56GjRogOjoaBQvXhyDBw+2ZejMmjUrpk2bhpYtW6Js2bJo06YNAOCzzz7DvXv3UKpUKZQtWxabN28GAIwcORJNmjRBtWrVkDNnTsP3YXS+XZ2n9u3bI2/evChevLiHZ82YkBJZq1OlSpWkffv2+bsajurVAzZsAKpWBXbutC9nyx4RERFRsnL8+HFLL9aTgwoVKmDPnj0ICgryd1XiXe/evVG+fHm8++67htvofaaEEPslSaqktz2TrFiBLXhERERERF45cOCAv6vgFxUrVkRoaCjGjh1rabkM8KzEljoiIiIiSuSOHj2KDh06OCwLDg7Gnj17/FQj1+7cuYM6deo4Ld+4cSMyZ87shxqZs3//fp+UywDPCuyKSURERERJROnSpd3Oh5eQZM6cOVHV19eYZMUKAc9PIwM8IiIiIiLyIwZ4VlBa8GJj/VsPIiIiIiJK1hjgWcFdF83t24F+/eKtOkRERERElDwxwLOCuyyaNWoA48fHT12IiIiIiCjZYoBnJY7BIyIiIiI/S5MmjU/Lr1WrFhLcvNRkwwDPCsyiSURERERkqZiYGH9XIVHiNAlWYIBHRERERBr9+gFWZ+8vVw4YN87ctpIkYeDAgVi9ejWEEPjss8/Qpk0bXL9+HW3atMHDhw8RHR2NKVOmoFq1anj33Xexb98+CCHQtWtXfPTRR4Zlz507F926dUN0dDRmzpyJKlWq4O7du+jatSvOnTuH1KlTY9q0aShRogSqVq2K0aNHo1atWhgyZAgCAgLw9ddf65abP39+tGnTBuvXr8fAgQORKVMmDB06FBEREXjxxRcxa9YspEmTBqtWrUL//v0RGhqK6tWr49y5c1ixYoXnJzQJYoBnBQZ4RERERJTALF68GIcOHcLhw4dx+/ZtVK5cGTVr1sT8+fNRv359fPrpp4iJicHTp09x6NAhXL16FceOHQMA3L9/32XZyj5bt25F165dcezYMQwdOhTly5fH0qVLsWnTJnTs2BGHDh3C7Nmz8eabb2LixIlYs2aN2wnTM2fOjAMHDuD27dto2bIlNmzYgNDQUHz33Xf4/vvvMXDgQPTs2RNbt25FgQIF0LZtW6tOWZLAAI+IiIiIyAfMtrT5yvbt29G2bVsEBgYie/bsePXVV7F3715UrlwZXbt2RVRUFJo3b45y5cqhYMGCOHfuHD788EM0btwYr7/+usuylaCqZs2aePjwIe7fv4/t27dj0aJFAIDXXnsNd+7cwcOHD1GyZEl06NABTZo0wa5du5AyZUqXZbdp0wYAsHv3bvz333+oXr06ACAyMhJVq1bFiRMnULBgQRQoUMBWl2nTpsXpXCUlHINHRERERJSM1KxZE1u3bkXu3LnRuXNnzJkzBxkzZsThw4dRq1YtTJ06Fd26dXNZhtBkkde+1jp69CgyZMiAW7duua1faGgoALmLab169XDo0CEcOnQI//33H37++We3+yd3DPCsYLaLJrtwEhEREVE8qVGjBn7//XfExMQgLCwMW7duRZUqVXDx4kVkz54d3bt3R7du3WzdIWNjY9GqVSt89dVXOHDggMuyf//9dwByK2H69OmRPn161KhRA/PmzQMAbNmyBVmyZEG6dOmwePFi3L17F1u3bsWHH37otvun4uWXX8aOHTtw5swZAMCTJ09w6tQpFC1aFOfOncOFCxcc6kIydtG0AsfgEREREVEC06JFC+zatQtly5aFEAKjRo1Cjhw58Msvv2D06NEICgpCmjRpMGfOHFy9ehVdunRBbGwsAODbb791WXZISAjKly+PqKgozJw5EwAwbNgwdO3aFWXKlEHq1Knxyy+/4Pbt2xg8eDA2btyIvHnzonfv3ujbty9++eUXt/XPmjUrZs+ejbZt2yIiIgIA8NVXX6FIkSKYPHkyGjRogNDQUFSuXDmOZyppEVIiC0oqVaokJbh5N1q2BJYsAUqVAo4etS/XNlXHxAABbDQlIiIiSqqOHz+O4sWL+7saSd7jx4+RJk0aSJKEDz74AIULF3aZ9TMx0/tMCSH2S5JUSW97RhtWYAseEREREVG8mT59OsqVK4eSJUviwYMH6Nmzp7+rlGCwi6YV3AwqtWEASERERESJxAcffIAdO3Y4LOvbty+6dOkS57JbtGiB8+fPOyz77rvvUL9+fVP7f/TRR0m2xS6uGOBZiQEcERERUbInSZLbrJKJwaRJk3xW9pIlS3xWdlLizXA6dtG0ArNoEhERERHk5CN37tzx6sKcSE2SJNy5cwchISEe7ccWPCvoBXhRUf6pCxERERH5TZ48eXDlyhWEhYX5uyqUBISEhCBPnjwe7cMAzwraAO/PP4HWrZ23450cIiIioiQtKCgIBQoU8Hc1KBljF00rqPtY37ghB3hERERERETxjC14Vnr2DMiZ03g9W/CIiIiIiMiH2IJnpfBwf9eAiIiIiIiSMQZ4VmAWTSIiIiIiSgAY4FnBbIBHRERERETkQwzwrGB2IksGgERERERE5EMM8KxgNsAjIiIiIiLyIQZ4VuAYPCIiIiIiSgAY4FlBCfBiY11vxwCPiIiIiIh8iAGeFcwGeEOH+r4uRERERESUbDHAs4LZLppjxvi+LkRERERElGwxwLOSuxY8IiIiIiIiH2KAZyWOsSMiIiIiIj9igGcltuAREREREZEfMcCzgtJyxwCPiIiIiIj8iAGeldhFk4iIiIiI/IgBnpXYgkdERERERH7EAM9KDPCIiIiIiMiPGOBZiV00iYiIiIjIjxjgWYkBHhERERER+REDPCu8/LL8WLCgf+tBRERERETJGgM8K/TqJT8qgR4REREREZEfMMCzghBAqlRMskJERERERH7FAM9KUVH+rgERERERESVjDPCsIgSwaJG/a0FERERERMkYAzyrCOHvGhARERERUTLHAM8qDPCIiIiIiMjPGOBZhQEeERERERH5GQM8qzDAIyIiIiIiP2OAF9/WrgVee41TKhARERERkeV8FuAJIWYKIW4JIY4ZrG8vhDgihDgqhNgphCjrq7rEC7MteK1bA5s3A48e+bY+RERERESU7PiyBW82gAYu1p8H8KokSaUBjAAwzYd18T2zAZ4k+bYeRERERESUbKXwVcGSJG0VQuR3sX6n6uVuAHl8VZd4YTbAY8sdERERERH5SEIZg/cugNVGK4UQPYQQ+4QQ+8LCwuKxWh5gkhUiIiIiIvIzvwd4QojakAO8QUbbSJI0TZKkSpIkVcqaNWv8Vc4TDPCIiIiIiMjPfNZF0wwhRBkAMwA0lCTpjj/rEu84Fo+IiIiIiCzmtxY8IcQLABYD6CBJ0il/1cMynrbgMcAjIiIiIiKL+awFTwixAEAtAFmEEFcADAUQBACSJE0F8AWAzAAmCzk4ipYkqZKv6uNzDPCIiIiIiMjPfJlFs62b9d0AdPPV8eOdpwEeJzonIiIiIiKL+T3JSpLBFjwiIiIiIvIzBnj+whY8IiIiIiKyGAM8q7AFj4iIiIiI/IwBnlUY4BERERERkZ8xwLMKk6wQEREREZGfMcDzF7bgERERERGRxRjgWeXhQ8+2f/LEN/UgIiIiIqJkiwGeVR4/9mz7KlV8Uw8iIiIiIkq2GOD5i6ctfkRERERERG4wwCMiIiIiIkoiGOARERERERElEQzwiIiIiIiIkggGeEREREREREkEAzwiIiIiIqIkggGeP0VF+bsGRERERESUhDDAs1r58ua3HTzYd/UgIiIiIqJkhwGe1YYPN7/tkSO+qwcRERERESU7DPD8KTbW3zUgIiIiIqIkhAGe1YQwv60k+a4eRERERESU7DDAs5o6wPvpJ9fbMsAjIiIiIiILMcCzmjrA69HD9bYM8IiIiIiIyEIM8PzpwAE5INy1y981ISIiIiKiJIABntU8GYP36JH8+NdfvqkLERERERElKwzwrOZJgKdgV00iIiIiIrIAAzyrMcAjIiIiIiI/YYCXEDDAIyIiIiIiCzDAs5o3LXjr1wPLlllfFyIiIiIiSlYY4FnNmwDv8GGgWTPr60JERERERMkKAzyreRPgERERERERWYABHhERERERURKRwt8VSHKEAD7+GLh+3d81ISIiIiKiZIYBntWEAMaM8XctiIiIiIgoGWIXTatxDB4REREREfkJAzwiIiIiIqIkggGe1diCR0REREREfsIAz2oM8IiIiIiIyE8Y4FmNAR4REREREfkJAzwiIiIiIqIkggGe1diCR0REREREfsIAz2oM8IiIiIiIyE8Y4FktrgHe7dvW1IOIiIiIiJIdBngJycaNQNaswMqV/q4JERERERElQgzwrKZtwVuzxvy+u3fLjzt3WlcfIiIiIiJKNhjgWU0b4NWv7596EBERERFRssMAz2pMskJERERERH7CAC8hkiR/14CIiIiIiBIhBnhWi0sLHlv/iIiIiIgoDhjgWS0uQdrnn1tXDyIiIiIiSnYY4FktLgFebKx19SAiIiIiomSHAR4REREREVESwQDPahxHR0REREREfsIAz2oM8IiIiIiIyE8Y4FklRQr50YoAj9MkEBERERGRFxjgWSVtWuN11avHXz2IiIiIiCjZYoBnlTRp5Ee9Frzt2+O3LkRERERElCwxwLNKUJD8yDF4RERERETkJwzwrKIEdgzwiIiIiIjITxjgWSWAp5KIiIiIiPyLUYlVAgPlR7bgERERERGRnzDAs4rSgscAj4iIiIiI/IQBnlWsDOw4Dx4REREREXmBAZ5VlBa82Fj/1oOIiIiIiJItBnhWUcbgMcAjIiIiIiI/8VmAJ4SYKYS4JYQ4ZrBeCCEmCCHOCCGOCCEq+Kou8ULposkAj4iIiIiI/MSXLXizATRwsb4hgMLP//UAMMWHdfE9pYumu/Fzw4YBly/7vDpERERERJT8pPBVwZIkbRVC5HexSTMAcyRJkgDsFkJkEELklCTpuq/q5FNKgBcT43q7oUN9XxciIiIiIkqW/DkGLzcAdVPWlefLnAgheggh9gkh9oWFhcVL5TzGJCtERERERORniSLJiiRJ0yRJqiRJUqWsWbP6uzr6rAzwOE0CERERERF5wWddNE24CiCv6nWe58sSJ3dj8MqVM1/W/fvA3bvA2LFArlzABx/EtXZERERERJQM+DPAWwagtxDiNwAvAXiQaMffAe7H4B08aL6sadPkfwoGeEREREREZILPAjwhxAIAtQBkEUJcATAUQBAASJI0FcAqAI0AnAHwFEAXX9UlXnAMHhERERER+Zkvs2i2dbNeApB0mqbMTpNARERERETkI4kiyUqiEBoqPyoTnrszf77v6kJERERERMkSAzyrzJgBfPop8Oqr5rZvq2rg/PRTc/ucPcsWQiIiIiIiMsQAzyrZswNffWXvqumJOnXcb7N1K1CoEDBzpuflExERERFRssAAzwI/7fsJladX9r4Ao8ybasePy4979nh/HCIiIiIiStIY4Fng7rO72HdtH8Kjw70rwEyAZ3ZsHxERERERJVsM8CyQPiQ9AOBB+APPdjx5EtiwQR6316SJD2pGRERERETJiT8nOk8y0gc/D/AiHiB7muzmdyxSRP4HAMuXu26lO31afmSSFSIiIqIEKTImEkEBQRDseUV+xBY8C6RJmQYA8CTyie8OMmaM/vKJE4ErV3x3XCIiIiIvzD86H8duHfN3NeLNs6hnCP4qGMO2DPN3VWxiYmMwaP0gXHt0zZLy9l7da1lZevZc2YP+a/tj37V9kCQJo3eMxt1nd23r2y5qi/lHvZ9qbND6Qdh6casVVU3QGOBZIEWA3BAaHRvt+4OpW/AuXwb69AHeeMP3xyUiMmH2odn4fNPn/q4GJSMxsSbGsQMIjw7Hrsu7fFwbuxa/t4AY7n0rzpPIJ/jn6j9e7Xvg+gGsOr0KYrjAgesH3G5/++ltj69homKi8Me/f0By0bOo/eL2KD2ltEflqt18fBNfbf3KdoyY2BjESrEeldFuUTt0WtoJf534C0dvHkXHJR1x8/FNh23uPruLG49vmC4zOjYavVf1xuUHl23LTtw+gdTfpAYAjN8z3m0ZkiQhIjoCZaeWxarTqwAAsVIsNp7baHuP2y9tx98X/nb5nq8+vAoxXKDqz1VRZGIRnL17FgCw8dxGFP2xKNaeXYtRO0eh54qeTvueuXsGEdERAID91/Y7DDU6cvMIYqVYvL/yfYjhAmK4wOrTq1FlRhWUmFQCAHDn6R2H/3/Pop7h3L1zAOTP76cbP8WF+xdw7t45iOHC8PMcFRNle/7yzy/jh90/oPL0yuizug8GbhiIbsu62db/duw3tF/cHp9t+gxPo546lHPv2T3sv7bf8FxJkoRRO0fh1dmvYvXp1Th04xDSfpsWY3aOwa0ntyCGC8w+NNt2/q4+vGr6+yWhYYBngXgN8NSinx/v7l3X2xERxVGnpZ3QcUlHt9t1+asLvtr2VTzUKP49CH+AAesGeJ9QS2XT+U2YeVB/2pt7z+7ZLtK8tf7sepy6c8rr/bdc2IIB6wag45KOuuU8jXpqu+hW91659uiaU0Cx5PgS3Hpyy2HZxnMbIYYLXLh/AY8jH+PO0zte1XPbxW1IMSIFdl7e6bTug5UfQAwXtgvOD1Z+gGozq+HC/QtOdZm8dzIWH1+MWCkWjyMf43jYcdN1uP30NgauH+h0DbD0xFJT+284twHPop4hOjYazX5rhu2XtgMA2ixsg5dmvIRHEY8ctr/x+Ibtgrvg+IK2z1F4dDgiYyJRfWZ1VJxWEY3nNwYAVJxWEZ2XdtY9dq6xuSCGC2QdnRWdlnZyWCdJEi49uITALwMx4u8R+OPfPzB+tz1wGbVjFNosbGN7nzsv78RLM17ChnMbbPvriZViMfvQbKw9sxan78jDT6JiojD70GycvXsWYrjApvOb8CjiEXKMzYHPN3+OLKOzYMelHUgxIgXeXfauQ3mPIh5h47mNeO2X1zB572T8cugXXHl4BR2WdMB/Yf9hwbEFmHN4Dpr/3hxlppbB3CNzUXhiYYcL98yjMiPn2Jy4cP8CTt05hcEbBuP1ua9jxoEZ2HFph227j9Z8BDFcoPvy7pi0dxJ6ruhp+/wXn1Tctt2DiAd4HPlY9/0fu3UM4dHhaLe4HUK+DsGRm0fQeH5jrD69Gp2XdkbduXUx4u8RmHt4LmrMqoFav9RC8UnF8f2u7237j9w+EmFPwlD156rI80MeAMDuK7tx+u5pzDw4ExHREag7ty5O3TllC1hWnFrh8P/sfvh9FJ5YGNVmVsOzqGeoNL0SMnyXAc1/aw4xXKDs1LII/DIQU/ZNse3TaH4j2/u79+wesozOghQjUtiCv7aL2uLFCS+i14peeG/le/hm+zcoML4AXpzwIgDg5RkvO52PIRuGIOVXKTFu9zhUmV7FYd2Pe38EACw5sQSPIh7h8I3DtnVfb/sabRe1xaL/FiHsSRjuh99H0R+LotL0SnhvxXt4GPEQ9X+tj85LO+OXQ78gIjrCoeWu0fxGKP9TeTyOfIxP1n+C7GPkIVbT9k/D/fD7qDu3LvL8kAcpRqTA2J1jdf+WCZlwdeclIapUqZK0b98+f1fDwcZzG1F3bl1s7bwVNfLV8L4go/7akmRf17Ur8PPP8vMLF4ACBYAXXgAuXvT+uESU4MRKsfh227foWaknsqTO4u/q2FoipKGufzPU20XHRqPy9MoYUXsEmhTRTyQ178g81C1YF7uu7ELDQg0RnCLYsOwFRxfgxO0TGF57uNv6Po58jM3nN6Np0aZutzUjIjoC/df2x+R9k5ErbS4c6XUEmVNnNtxekiTESDG2G4Baynk68cEJfLrpU8xtMRepglKh4byGWHNmDQAg9otY2ziel2a8hMq5KuPHRj8aHnPekXlYeHwh5jSfg3Qj08n1/iwCKQNTYt6ReSietTgKZyqMtMFpbfs8jnyM++H3kTlVZsw6NAs9K/ZE2NMw5Byb07ZNqhSp8GjII4zcPhLvVngXISlCkPG7jPiy1pd4/cXX8fLPL2Nlu5VoVLgRUn+dGs+in9k+J3+d+AvNf2+Oyrkq45/u/+BRxCPcfXYXX2z5AnMOz8GHVT7EXyf/wqUHlxD7RSxipVgcunEIlaZXQq+KvfAk6gmGvjoUL2Z6Ufc9D94wGN/t+A5f1f4Kn9b8VPccA8DEhhMx48AMHL55GDu67kC1vNWw/9p+3H56Gw3mNbBt1618N1x9dBWrz6xG5GeR2HR+ExrMa4C6Bevii5pf4MjNIzhz9wweRDzApzU+Reqg1OixogdWnFqBpW2WolmxZlh/dj3Co8Pxxm9y7xrlXNx8fBN3n91F8axyIPD2wrfx+7+/AwB6VOiBwa8MRsEJBW1/+5CvQxAZE4l5Ledh95XdGNdgHACg6YKmthYfxZr2a9Dyj5YonKkwDt88DD37uu9DxVwVba8lSULAl473+aWhEqJionD45mHD6Z9uDriJQBGIzzd/7nDxry3n7N2zKDSxkO11p6WdsP3SdlvArehRoQemHZgGAKhXsB7Wn1sPACicqTBO3z2tW/7V/lfRc0VPrDi1Qne9WUffO4q2i9q67Ub6d+e/8fG6j7Hvmv71Z6VclZzWDag6AKmDUqNktpIokKEAKueujGuPriH397kRKAIRI7luGcqSOgtuP73tsOydMu/g1yO/mnhnxmK/iMXOyzvxyqxX4lROsSzFcOL2CY/329BhA248voGHEQ/RqkQrW2BltVdeeMV2s8RTGztuRJ05jnNUu/vt8wchxH5JkirprmOAF3dbL27Fq7NfxaaOm1C7QG3vC/I0wLt4EcifH8ibF7h0yfvjEiUQkiTh6qOryJMuj6XlPo16in3X9qFmvpqWlutLf1/4G7V+qYVMqTIh7JMwRMZEIiRFiOH2UTFR2HR+E+oXqu+0Ljo2GrFSLFIGpgQAHL5xGBP2TMCXtb9E7nS5ERkTiUARiMCAQMPyvQnwbj25ZfvxrpK7CvZ024NlJ5chV9pcqJSrEmYdnIWuy7ra9u1duTcmNproUR3WnV2Hu8/u4u1Sbzts23RBU6w4tQKr269G+uD0qJK7CvZd24cquasg7GkY0gWnQ0iKENsF15r2a1A5d2U8iXyCQzcOIWfanKiUy/67WfXnqth9ZbftddqUaXF/8H3su7YPlXNVxv3w++i7pi/GNRiHdMHpEDQiCIB8MRUeHY5Vp1dh+anlmNlsJq49uoa8P+QFAASIAMRKsdjZdScuPriItova2o7Rp0ofjG843u35j4mNQas/WuGvk38BACY1moQPVn0AAHgw+AHCo8Ntf4cKOStgX/d98t3vUm3ReH5jnLxzEqPrjcYn6z/BlMZTEBUThT5r+jgcY32H9ag3tx7alW6Hoa8ORdEfizqsV+qq1HNc/XE4ffc0Ju2dZNumQaEGtuBVT5uSbfD7v78jW2g2pxa/s33O4sL9C4iMicT+a/tx+OZhnLt3Dvuv27tjhX0Shlxjc2HIK0Pw5dYvHfbXXlD/+/6/KDm5pG49ggODERETgTH1xmDA+gGG9dWa0ngKmhRpYvvbKoa8MgTf1PnGdm42dNiAUtlKIcfYHKbLtsqg6oNQIEMBTN0/FYduHHJaLw2VTHcrbVm8JRYfX2x4nO92fGd77ep8q4UGheJJlA/zGcQj7WducqPJeH/V+36skaxd6XZxGsOWXDHA87GEGODtvLwT1WdWx9p31uL1F1/3viBPA7xLl4B8+YA8eeTxeESJ3IKjC9BucTts67INr7xg/u7iw4iHSB2U2m1ryYW+F5AvQz5L6qr10oyX8Gq+VzGq3igAcjeq0KBQh9YSRUxsDB5FPkKGkAzYdnEbUgeltt1Z//vC3zh26xiKZSmGunPrArBfLD379JlhkNdtWTf8fPBnHHvvGEpms19IbTi3AfXm1gMg/0BdenAJ+cbZz0HzYs2x9MRS1CtYD+s6rIMkSdh1ZRdSBKTAw4iHiIqJQo40OVBhWgUAQM+KPXHl4RUUzlQY39f/HgdvHESACMDUfVMxqPogWwvEiNoj8FaJt1BsUjHbsZ787wlCvwkFAOzsuhPVZlZzeA9KHfSoW7bO9z2P/BnyIyomCim/Sml7b2rai9QimYvg1J1T+KnJT+i5oifqFqyL9R3WY/ah2ejyVxeUyV4GR24ecdjn9ie3kWV0FsxtMRcdlnRwqpNy1/6zGp8ZdkvVXgQvaLXAIYhTrGq3ytb9SW1Ptz04deeU7vEB4OU8L6NKriqY8M8E3fW3BtzC+fvn8dKMl2zLPq76McbuGosSWUvgv7D/dPdzZe07a1H/V+cbCdObTkf35d09Ls8qTYs0xfJTy/12fFeyh2bHzSc33W/oZ7nT5sbVR1f9XQ2iBOWzGp9hxGsj/F0NJwzwfOyfq//gpRkv2bqoeM1MgNelCzDz+biNy5fl7pkM8Ehl3O5xKJq5KBoWbmh52RP3TET+DPm97va28tRK5E6XG+VylNNd339tf/yw+weMqjsKn1T/RHebKw+v4OTtk6hTUO4+oXQzalOyDea2mIsRW0dg3O5xeDjkoW0f5WL/YM+Dhse+/OAyOi7tiEWtFyFTqkyIjIlEgJC7L6UISIFbT25h/7X9uuf16sOrtnEQGztuRM18NW0tOHp3/YZsGIKRO0ZiU8dNeG3Oaw7bKXXd3Gkzav8i9wjIGJIR98Lv4eaAm8gWmg2APA4jIjoCFXNVxPjd49FvbT8AwP4e+yEgsPnCZny87mOH417rfw0N5zU07MLVqHAjtCnZxmksjlnuAoZ176zD67+6vgkW8VkE/vz3T7Qt3RYBIgDh0eE4fOMwXv7ZeeyG2uRGk9GhbAeEBoVi1elVaLLA/dyiSouRkXH1x6Hf2n6o8UINbLu0zW15cZErbS4Uy1IMm85vsrTci/0uOgT0RERm1X+xPtaeXeuwLD6+D+NiSuMpeG/le5aWeaTXEZTO7n2yIF9hgOdjB64fQMVpFfHX23/hjaJxyGhpFOAdPAiULy8/Z4BHbpjtSufrsmccmIFa+WuhUKZCTvtHfhaJp1FPkT4kPW48voG0KdMiNGUovvz7SwzdMtThGJ9v+hw18tVA7fy1cf3xdVT4qQLuPLtjWx8eHY5UX6dyOr66jspxlbE35+6dQ9Efi+JAjwOIjo3Gtkvb8F/Yf/hp/08YU28MCmQsgFZ/tAIAZEqVCXcG3rGVEf5pOO48u4MH4Q9sY2m0rUXq7kn3B91HmpRp8CjyEerOqevQpUzt5oCbSB2UGmm/lVv88qbLi8sPHf9fpwtOhznN5+CF9C/YWtS0quWtpptwwqzXX3wd687qt6L5Q840OXH98XVT26rH7xAlRdlDs6NiropOY/ASqtRBqZ0yHaqpx9+506xoM1x7dA17r+31uB7n+pxD9ZnVcf3xdXQq2wm/HP7Fti5DSAbcD79vuO8P9X/AR2s/sr2unrc6dlzeYbi9GQUzFsTF+xcRI8Xg1xa/Ys3ZNdh+abtTAiAA2PXuLsw/Oh/ZQrPh883mMhRv7bwVaYPTYt6ReRizyz7NllE36QFVB9i2U85H7fy1sanTJqffN3c36vZ134fTd0+jUKZCqJizotM4z4HVBmLUzlGm3gcgjwF+Fv1Md53eOM2YL2IQ+KXzcIMdXXcgd9rcSBmYErm+z4W3S72N34795rTdt3W+RcrAlLYbpLve3YWX87i+wegvDPB87MjNIyg7tSwWtV6ElsVbel+QmUkxO3cGZs2SnysBXu7cnAvPQw8jHiJlYEqXY5r8ITo2GoEiME4TpKqDsJWnVmLA+gE40usIggKDsPfqXhTNUhTpgtPhysMrmLJ3Cj6p/gkyhGTwuGxJkrDp/Ca8mv9V7Li0A6/mf9XhfQSNCEK20Gy4OeAmPt34KWYcnGEbV9OyeEusObMGV/tfRcbvMtrKrPBTBRy8cRCA3A3vSeQTlJpSSrcusV/EYvWZ1bZMcVp3Bt5BplSZHOoNAItaL7IFbyEpQmwZEesUqION5ze6PQdL2ixBi99buN1Ozd0FBJEVGhZqiNVnVvu7GqbFJQmCVf59/19IkmT4PaNnyCtD8O32bwEAWzptQa1fanl9/IkNJ+LD1R/aXp/sfdI2vjEoIAhRsVEO21/qdwl50+fFzcc3420M3/Baw2033rSU8YpGFrVehIPXDxp2YT794WkUnljY5fHTpkyLte+sRdW8VfEg/AEyfJfBYf3UxlPRa2UvdCnXBXUK1ME7S95xKkN9w+/Kwyu2cZLdynfD9Dem234jUqVIhWsfX8PTqKfI/X1uAPI41vQj09v21waIgNw7YuI/E22fC2VMq6J63uqomLOirSv10FeHYlitYYiKiUJQoNzb4+jNoygztYxDubc/ue2QzEmp5/WPr+PYrWPYfH4zbj25ZWtRK5+zPEpmLYnPan4GAA6Jbk71PoWMqTIi6+isAIC7A++i8MTCuPPsDu4Nuoe0KdPi7rO7SBucFsGBwbbrEHX3+IVvLUTL4i0xZd8UfPn3l7j55Cbal26PeUfnGbZyaQPEfd33YfDGwdhwbgNyp82NABGApkWaYvK+yQCA2c1m4174PdTKXwt50uVBplSZUGt2LaQLTodhtYZh0IZB2HR+E258fANZQ7Ni+cnlaP57cwDy+OP3K8vjHH879hsCRADCnoShQ9kOSBeczqlumUdlxt1nd/F4yGOEpgzFrSe3bL1klHrfH3Qf6UPSO+2bELgK8PQHrJBHlHE/6nk84oUSBCSyID0hSD8yPYplKYbjHzimwr704BLypsvrMsB6GvUUISlCbN33AGDz+c3InS43imQugiM3jyB9cHqnsV7Hw47jwPUDaF+mvcPyWCkWq0+vRsPCDRE0Igj9X+6Pr177Cn/+9ydq5quJnGlyuswsqKa+YbP4+GK89edbiJVicfvpbWRMlRFVZlTBawVew8aOG9FxSUdsvrAZ32z/BuPqj0Pfl/vincXvYN7ReehTpQ9uP7uN6nmrI3+G/Gg8vzHeLW9PTX3h/gUcDzvuMGZoS6ctWHd2HdqXaY+86eQfz1tPbiH0m1CnO7jKmKRG8+z7T9wz0RbcAUCB8QVcvtfo2GjDNPMAUG5qOZzofQJn7p5xWK4EdwAc0t2bCe4AeBzcAWBwl0yNrDMSgzcOtr0ul6OcU2KL9yu9j9zpcuPTTZ/CrCyps6Bs9rIOn9kcaXJgYeuF+P3Y7w6Ja8xY+NZCPIx4iLP3zuLrbV87rDMbhK1ou8JUt1i1bV22YejmobaEKGZvsqjlSZcHi1svRpUZcnr1Pd32YMelHZi0dxJq5KthSxFfK38tlMlWBqWylUKPFT0AyC0RJbLK83nt674PZXOURaAIRM3ZNR3e86aOm1AoUyEU/bEopjWdhnfKvIMB1QZAQCBjqoy69drTbY9t3OPT/z21zY+m1btKb9TKXwulp5RGz4o9USRzEduNp8f/e4zgrxy/+/Oml79b1Tcn25Rsg9VnVuNhxEOkD06PbhW6oXZ+uXv33CNzdbshNyvaDFsubMGDiAdO65T1StKe3lV62wK8T2t8avuMVM1TFTObzcTF+xcdMpEqRtQegZbFW9pu7Cm/uZ5eON8eeNuWIEpv+24VuqFnJfscb1subMGMgzNsr2vlr+WwfZqUaWzPq+atCkD++2cIyYDc6XIjJEWIw03P0KBQWxAJABLsv7OPhzzGk6gnyBaaDd/U+QZV81TFxH8mos9LfWwBnvI+913bZwvwlCBCCe4AOARH9wfdx7VH1wwz9eZIkwM50uRA3YJ1ddcrlOuUF9K/gMKZHQPpdMHp8Hfnv7Hwv4VIH5weQghkDc3qVMaSNktw7dE1nLl7xpZn4v3K76Nj2Y5IGZgSKQNT4teWxpk9t3beimUnl+HzVz+3BVmr2q1CVGwUUgfJ/y++2fYNAKB96fboVM55iMDWLvbpDZa3XY6HEQ+RPY2cOKpZsWb4860/8STyicO+2sRbeg70OIBjt44hNKU8Nlz5uwD2XjTKusSGAZ4FggLk/6DxNg/eo0fA9etA6uc/GMk8wLv5+CaCAoNsrTWKf67+gzLZyxi20p24fQKxUizSfJMGP9T/ATXy1UDJySUx9vWx6FCmA7KNyYZZzWahQ5kOWHRcbp2VJAmh34SieJbi+POtP/Ff2H94GvUUnf/qDEC+S1h2alkAcqtN+9Lt8WOjH/Eg/AFKTJYvJLQB3tR9U/HBqg8woYH8xT9532RIkPDD7h8AyK1d2UOzY/Arg/Ew4iGKZi6K9ovb4/Oanzv8IGy5sMUhZbE6kImIibB1Y9x1eRfKTS3nMA6r39p+tjFcAGw/QupMWz8f/Nn2vOXvLR2CMQBYeXolRu8cjW+2f4PLH9m7FrrqnrPrin3SYW3WPnfWn1uPRccXGa6//PCyLaEHJQ5GCUgUSnr2+Ufno1WJVrY77O60Ltkaf/z7h8ttlFZWvSDMnUKZCqFUtlJoVKgReqzogZJZS+Lvzn9j5emVDttt6LABWUY7TnkxvuF4nLh9whbgaYOcYa8OwxevfoGN5zfakuVc+egKglMEe9wd+8MqH2LiPxORO21uVH+huu2cCCHQpXwXAPKcbsduHbN1QVNPsLyx40aEPQnD24vkCydlnOj4BuPRuEhjzG0xF7FSLJoWaYoMIRlsXbM+rvox1p5di2O3jtm6AH9STb74bVOqDb7c+iUCRADWvrMWKUbYL0vcjZEE5O5T6qy7FXJWQJXcVfBRVblL3axms5z2UQK8ei/Wsy1TTx+wrcs23Ht2D/OPzsf7ld+33fB7+qn9u0z9e3P0vaOIiI7AouOL8O32b9G1XFfkTKOaZiIoFaI+j0LbRW2x8L+FtuVXPpJ73pTKVsrhbxj2SRgkSULKwJSGQW/6kPQYXW80GhRqgFLZSmHd2XWYum8qFrRa4HBD8OKDi/j939+xp9seFMpUCBvPbUTrha3RrnQ7p3L3dt+LO0/v2DLx1p1TFxvPb0S64HT4rMZnKJCxADqU6WAL8Ga8MQPFshRD7rT2/4exX8SiyYImWHV6lS0ngRKE/NLcsdVLG6yte2cdSmUrhVzf57Ita1uqrS24U0xtPBVZUmfBnWd3MHXfVKfsv5MbT3YI8D6t4XjzJENIBjQu3BgrT6+03RRV//21AgMC0bNST6QKSoVOSzs5zJ8XmjLUIQBoWrSpbYz672/+jnI5ytneZ6VclXC1/1VsubAFbUq20T3WZzU+Q970eZE+JL0lrUbK5/Tjqvbx2Gvar8GvR39FYEAgSmYr6ZCUS09IihAUzFgQBTMWdFiuDpRdqZGvhtMUYkGBQQ7B7Ttl3sH4PePxxatfuC0vdVBqW2CoeLPEm6bqopUvQz7DxGvbumzD9kvbDZO3JXTsommB8/fOo+CEgpj5xkzbj6RXzHTL69QJ+PdfYN8+OYvmCy8AOXLIAV8yUOGnCni3/Lv4oIqcAvzUnVMo+mNRpA5KjSf/s6dWVjIFdinXBTObObbyKF1q1UKDQrGw9UI0nNcQr7/4Ooa+OhTVZ1bHy3lexv9e+R/e+O0NtC3VFu9Xfh81ZhnPdRj7RazuvEJd/upiu5O88K2FOHTjEGrlr4Ua+Wog/cj0CI8ON3URCgD/dPsHVWZUQZbUWTC63mi0L90elx9etk0kmhzlSpsL1x5d83c1EhQlrX1cvJrvVfx98W+P9un/cn98v/t7l9sMrzUce67uwd6rexH2NMy2/Gyfsy4/x9pAJv+4/Lj44CKWt12Oif9MdBg7qM6Kqc6uqNciNbHhRPSo2AM3H99EzrQ5ETQiCCkCUqBE1hJoV6qdQyucVs40OXHtY/3P3pLjS9Dyj5YonqU4VrdfjTzp8tgCmKmNp6Jzuc62i/GTt0+iUKZCWHZyGVr+Ye/qr37Pk/dOxnc7vsPFfvK8p2K4QINCDbC6vb1b5syDM/HusnfRolgLTG0y1WGOqdgvYnHu3jm8mOlF9FjeA9MPTAcALG69GC2KO7ZMj9k5BmfvnkXxrMXRd01f2/5CCNOBpdL96fYnt5EhJAP2XtuLVClSodxP5bCz605b68nTqKdIEZACKQNTOnTnUlq+lHFcSvCdMjAljvQ6gqjYKJTKJnetrPZzNey6sstUsOurccqjd4zGwA0DMaL2CHxc9WOk/iY1QlKE4Nmn8vihZ1HPMGrHKAz7exiaFmmKZW2XuS3zUcQjnL9/HumC08ljh9LmcruPmiRJtr+51ifrPsGYXWMwp/kcpA5KjVYlWjmsfxz5GGfvnkXZHI6/l08in2DbpW1oUEhutVO65H/z2jcYUmMIrj+6jvlH56N/1f66vWG05//YrWMYu2sspjedjhQBKdBkfhPkTJMT09+Y7tF7VYuIjkCMFOMUCChO3D6B5r81x7Yu23RbrQBgz5U92H5pOz6uJgdHypQq6gzA8Z0635dj7CnxYRdNH1OanD9c/WHcAjwzJEkO7pTn6sckLiomCgdvHETv1b1tAZ4yVkHbSvQgXO52okw8eurOKaQLTofsodl1g6j0IeltdwnXnV2H9yrJGZh2X9ltm6x2wbEFWHBsgcs6aoM7QE6Jf+mBfZ7CN/+U7zRpxySYbTVQEk7cfnobXf7qgi5/dUGBDK67MyZ1fV/qi0EbBllerpk5mVwlEXj26TNby+myt5fZPksvZnwRZ++d9apOh3sdxvqz613OzzWyzkiHrjvbumyz3ZgokbUE3qv0nm3cz/K2y5EyMKVu2vtpTachdVBq3A+/j9JTHMdWjKg9wjbgf0y9MahfqD5SBqbE1H1T3b6HWCkWK9utxGebPrO1BmQIyeDQ6qGl7jqjWN52OWYcmIFGhRvh3rN7tgBvR9cdKJWtFD5a8xH+vvi3w0TxtfPXdgjwKuWqhE5lOyFlYEpb97d176xD0SxF8UL6F7Dk+BKn417tfxWPIx+j6I9FXWZOThUk/+3zps/rcJe4UeFGDl3KAKBoFvm7rEXxFpCGSjh5+6TTJMfvV37fNr4EkDOOBgr9uQvTBadzOGefVPsEQgjbhf6HVT60BXjq86MYUE3+fEmShAHrBiAqNsp2sV4zX01cvH/R8H0r7gy84/BaSVSgvTjVuwjf32M/UgWlwph6Y9CwcENkD82OlIEpkW5kOpTOVtp2vhRr31nrNHeekaVtlqJARuu/M3tX6Y3bT2+jf9X+SBWUCpMbTXZouUgVlApDaw3F0Fr649n0pA1OizLZy7jf0ID6b641qt4ofPXaV4ZDANKkTOMU3AFyq5US3AHyMBX13zRn2py2oMiMUtlKObS0rmgXt8nLAbgd1lAsSzGc6O16ku6X8ryEl/LYpxfJlTaX7X2OrDPSMCMzUULAAM8CSoAXL5NzqoO5RBbYzT86H0O3DMWp3qc8TiIy/+h8t11gH0U8QrvF7TCq7ihb+WfvnXW4I5wjTQ60K9XOad/gwGCHMXXejLMy8sGqD3D32V232526c8pUec1+a+a07Pz98x7Xy5/ypMuDZkWbOUyCbEb70u1xL/yeLYNc21JtMb7BeFvraN+X+mLM62NsUxQA9ikGvNG1fFdM/EeeeFuvdRaQ00gvOeEcBACO42SaFGmCVsVbISImAsvbLkfqr1PbMoM9+d8TfL31a/wb9i/+OvkXWhVvZdj9tEz2MiiTvYwtwFNnRbvY7yL2Xt3rlM1XPafgkV5HEBgQiN5VeiMmNkZ3cvORdUYibXBaFMlcBABsA93vPrtr69rUvUJ3XH90XR7TVLGHrRyjXiGtirdC6qDUmHtkrq2LU4+KPfDbsd+wseNG5E6X23ah+CzqGZ5GPcVLM15Cu9LtUP/F+rrdaEpnL22bCFydZa1K7ipIEZACPzeTuxU/jHiIWYfkC8jPa36OCjkrYO2ZtZi6fyr+evsvp7kK1V33imWR5/H7/vXvse7cOhy9edTWinKuzznkTmfcTVTpvh8ZE2lb9mDwA6RK4Zz1VatolqIoiqIut9F2XVMvUz5781rOQ1BAEN4q+ZbDdqWzl0b4p+FYc2aNU/cpNSEELn902WEc6aaOm+KUCMqVzZ02I11wOlTIKWeJ1QYKGztu1A140gan1Z1zUk+zYs7foVZIFZQK39WzT+79XmVrU7VbTQhheny3lf7u/DdCgxJ39/lBr1h/Q9GM0x+eTvTnjuIHAzwLqPsRS5Lksx8+J7Gx7rfxgwfhD/DXyb/QsWxHh+Udl3REjBSDiJgIjNoxCi2KtcDyU8tRIWcFNCjUAIM3DMZ3O75DmpRpcPmjy0gfnB6bzm+yTfastvHcRoeBzgAwYN0ArDi1AitOrcAHleUWPm3Lyo3HN3S7jwkhMHiDcTesuPhp/08+KdcKLYu3xB9v/uEw7sWVVe1WYdnJZZi6330rjSuTGk1C0yJNbQHehb4XkH98fpf7/O+V/+HrOl/jWdQzfLv9W3Qp18V2F14ZC5A2ZVqkCEiBeS3nYfP5zRjfcDweRTzC8dvHUfuX2mhfuj1+bfkrRu0YhWO3jmHukbkY9uowtCvdDk+inqD8T/J0JItbL0b2NNnx9wV790QhBIa9OgzD/h4GAJjWZBp6rOiB3lV6GwZ4akIILGxtH3/z9NOnOHD9ACRJQuqg1Pi6zteIionCp5s+xeBXBtsCvHkt56H9YnncZvNizZ3KVXfPA+TB9IqFby20fT89+/QZggKCHAI69fPIzyJx/v55XHl4Ba8VeM3pOAd7HsTxsOOomrcqDt04hOxpsmNSY+cAvUmRJhi3ZxzSB6fHg4gHmNBgAirkrIDqL1TH11vl1jplXNcL6V/AmT5nnMpIFZQKqYJS6a4z8lJu+U77mvZrnMZMqLOnBQUGoXmx5mhUuBHer/y+2y5vxbMWx6V+l5AnXR7buC6Fu1Yg5dyrE3DpZXKzUuuSrXHk5hEMeWUIAKBdaecbWorgFMGmgp3sabLbEhoA0L0pYBVtQgwtvc8mJS4189X0dxUSLfW0Q0SucAyeRd78400sOr4INz6+4fBD6BEzgWGHDsDcufLzs2eBF18EsmUDbt707pgeiImNgRDCoaULAO49u4fWC1tjyCtDcD/8vi25x5FeR5ArbS6M3zMeFXNWtKWxvfLRFduk0AppqOSUStdTSguDNzKnyow7z+643zCRqpyrMirkrOAUbP7S/Bd0LNvR8NzfGXgH/1z9Bw3nyZN7X+1/FYduHDKcmsCM39/8Ha1LtgYgd4HNFpoN2UOzI8239gHbaVKmwePIxw5z3MR8EeP02VNEREfgq61fYdArgwwHfp+9exZ50uWx3bGOiY3BouOL8FaJt2w3ZZTzoIw1io6Nxs7LOx0uSNTbnLh9wjYXnnqdQhoqYcnxJdh1ZRdG1TM/7w8AtFvUDvfD72NFuxVY+J+cmlo9hcb3u75H+RzlUbtAbY/KjQ8xsTH45+o/6LCkAw70PGALau48vYN3lryD2c1me/896ea4RsHHyO0jkT44fby2qtx6cgvZx2TH7GazdTPDEREReYvz4MWDX4/8ig5LOuBU71NOqWhNMxPgvfMO8OvzdLRnzgCFCgFZswK3zI09iAsxXKB63urY3tUxQUGrP1rZ0t6ru5YtfGsh5h6Za0u1rNjXfR8qTXf8PFbNU9Uho2JiMqPpDHRb3s2jfb6s9SWeRj3FyB0jXW6nnWBVzWxiEb0Jvy/1u4QMIRlsXZqMAjxl39JTSuPYrWOI+SIGt57cQs6xOVE9b3V8Uu0TNP+9OYpkLoLhtYajSZEmCA4Mxvpz621BYNtSbW1jF5U5nLRipVgEfhmId8q8g54Ve+KVF17B1otbUTpbaWQalcnpffjKF5u/wIitI1wey9Ug9wPX5YnTX5rxEoIDgxH+WbjTNkRERERxxSQr8UAZ6B4jxbjZ0kIWJVlZemIpSmYtaRiYrj692jbf2Y7LO7Dp/CaUzlYaETERyJkmpy24A+SxLgolmYiWNrgDEC/BXZHMRUyPc3NleK3hiI6NxoitIwAArUq0wq9Hf8WWC1tMl9G0aFNbUpU6BepgYsOJtmkUhr46FKfunMKcFnOQIiCFboB3qvcpFMhYAIM3DMbYXWMd1qnf5+Fehx3Wne1zFn/8+wfypMvj0JX45oCb2HR+E/JnyI886fIg7w95beOvAHlczOUHlxEgApAjTQ6H4EYv0FEnnpjfaj56VeqFFAEpdIM7QJ6rR1uO0mpW44Uatklcfe3L2l/iy9pfutxmb/e9OHzjsO46ZdzQ1f5XDafnICIiIvIlBngWUcZ8+HwuPC+SrGy7uA0N5zXExX4XkS44HSRIDoPzW/zeAgLyQPrc6XLj1pNbSBecDiEpQjDvyDy8s+Qdh/LqzKlje64kEVCsP7feizcVP/5860/b9AhdynWxJV3QypwqMz6r+Rk2nNuAladXokeFHmherDlmHZqFvi/1RfUXqgOALcDLEJIB75Z/VzfAe7/S+6iSuwp+OfwLNl/YbFueNXVWCMgBVs60OR26+Q2rNcyhjCO9juDus7sonb00tlzYgltPbtmCce34oaf/e4oAEYCQr+XgQpuMoGDGghj8ivNYw2yh2RwmBT3+wXFkD7V3ocuSOotupj1Xjn9wHFceyvM8xWXMxdp31nrd9dYXKuWqhEq5dG+Y2XiaypyIiIjIKgzwLKKM+1BPgOkT8+bZnytJVlSBXvixQ7iZLTX23zmGCjkrIH+G/Phm+zd4EvUE/1z9B/3W9sOpO6dwc8BNhxTaEiTk+SEPbg64aZs3KfaLWKfgTisqNsrl+oQgbcq0aF2ytUOwM7PZTPSq1Aut/2yN2gVq27IwAsCtT24hQASgZ8We+OvkX2hTsg2EEGhYuKHhMdqXbo/a+Wvbxha+WeJN27ipOgXroFO5Tth4biNezvMyLj64iNzpcttSLNd/UU5P/9fbf9mCPjX1ZOYti7d0WNe7Sm8AwMfr5ExzSlr2mwNuxumzqGQOjItiWYpZUk6qoFTIHWRuQmsiIiKi5E4/YwF5LN5a8NS0LXi3bqH9iPLIP6UoWv3RChWnVQTgOL+a0nWv01J5wL86uxsgTziq0EsJHx86l+sMAE5zzLz+4uu25znS5ECdAnWgtaHDBrQq3gqr269GzjQ5seztZXg45CFmvDEDAFA+R3lUyytPUFoldxVc6HfBln0PALpX6G5L5JEqKBXeLvW2YVbUnGlyIm3K52PYhEDudLlxd+BdnOp9Cr+/+Tt2dN2BOgXtdaxTsA5CU4aiRFa5K2bZHGVxd+BdvFNGDqLfKPoGmhZtavo8AXJK9P5V+zstzxaaDTnTGs8pRkRERERJE1vwLOKPMXiLL6/DvzWB1cUf4NGU0jha+08sLmFfr3Rru/H4BgDYxtEBwJoza1Brdi38ffFvhzLV3S/jS7+X+mHcnnG21zOazsCEBhOQNjgtwqPDbRNF50ufDwd6HMCaM2vwSfVPkCIgBRrPb4xVp1fhq9pfoXT20qhTsI4tqLr2sXMCkgM9DzgtU7cyTWs6zXS9L/Zznug3Y6qMyJgqIwDYAklXlG2JiIiIiKzAAM8iSgue1V00v6gNjHgViPoSSBEL3AsBMilDqHb2BV4DgGjg1jHERkY47X/+nvEE2Nrgzh+mNJ6C9qXb2wK831r9hsCAQFt2x5AUIfix4Y/ovbo3UgSkQPmc5VE+Z3nb/j81+QmjdozCoFcGOc19ZZYS4PWo0MOj/dTzH/pbpVyVEtQ4NSIiIiLyDwZ4FlHG4FndRXP080agrs2AOUuAQzmMt11y2TnBScEJBS2tT1wpkx8r8qbLi9CUoWhQqAH6vdQP9QvVd9onNGUoADiMGVTkSZcHExpOiFOdcqTJgSO9jiTqCUT3dt/r7yoQERERUQLAAM8ithY8K7ponjgBFCuGGAGEP28kmltWDvBe62y825v7Pon7sU3oXqE7nkU/Q5ZUWRy6Vqop3S4HVx+MH/f+iB1dd2Dd2XX4uOrHiJVi8SjyEfZc2WML6Fa3X214vA5lOuBRxCP0qOhZC5sn1IlMiIiIiIgSKyZZsYgyBs+KedYQKJc1pG7ci9LTolgLr/YbXW80AKBhoYaY22IufmjwAwB53jVpqIRtXexzlY2tPxYPBz/Et3W/xaMhj1AmexkMqDYAQggEBgQiQ0gG3dY6PYEBgfjwpQ8RnCLYq3oTERERESUXDPAsonTR7Lmip9dlzCoHLCsKIIXcGvir4xRmEMO8LtrB1699jbkt5gIABlYbiNnNZmNgtYG41t+elEQJ5poXa25b1r9qf6zvsN5h2YW+F/BPt38AAK+88AqWt12OdqXbIUAE2MbRERERERFR/GAXTYuoE3xIkmSYWt+Vrs2f758iBUZXA65bGB+9VuA1bDq/CQBQPGtxFM9a3JaeX8+AagMwoNoAAMCYnWPwyguvIEAEoG5Bx2bFfBnyObxuUqQJmhRpYl3FiYiIiIjINLbgWUTpogkA5+/bM1c+iXyCg9cPutw37EkYxHB7QFhuaX0MfN3FDi60Paq/XGmxc6dcjnJIH5zeYdmAagPwcp6XvasQERERERHFG7bgWUTdgvcw4qHtebvF7bDs5DI8GvIIaVKm0d1315VdDq8P3/nP63pke+K8rGjmosiVNpep/Q/2PAhJO4E6ERERERElCmzBs4i6q+K43eMwfvd4AMD2S9sBAEduHsF7K95D6DehTvtGRDvPX+fK0C3AlbHAb3/al90cDcxeAnR53ljYMKt9ku1uFbp5VL433UuJiIiIiMj/2IJnkQwhGXCk1xGUmVoGvxz+Bb8c/gXfbv/WNvl09ZnVbdtGxkRiz5U9+C/sP/So2AN91/R1WfaAHcCY57u/dAUYtkV+XuqW/FgsTG6563RYfr30TGXU/XontuUDyl8HssU+BApcR8+KPbHz8k4r3zYRERERESUgDPAslDl1ZofXN5/c1N0u+Ct7uv+ImAhcf3zdsMxL3wM5HgPNTwCdWgBzF9vX5XwsP75x0nGfZg9zAlFAgzPPF3w5Ati1G1PXrTP9XoiIiIiIKPFhgGehzKkyu99Iw1Xr3cmJQN7nw/mqXwbOTHBcn+mZ3DUz81PNjgE6PW+fPfO4bkRERERElLhwDJ6FrJ6Iu8gd99tkewIEanOi6I2hczeuTpKAv/+WH4mIiIiIKFFigJcAfVJ1AO6NjOeDLl4M1KoFTJ0azwcmIiIiIiKrMMCz2KRGk+JcRmjKNMgQHocC/v3XeZm7FrwLF+THM2dcbkZERERERAkXAzyLKVkzzXi71NsOr4e+OhQAECvFxq0Sp045L9MGeLGxwK+/AtHRcTsWERERERElGAzwLNaiWAvT2zYr2gz/vm9vbQsQ8p8jzgGeGfPmAR06AGPH+v5YREREREQULxjgWaxktpJ49ukzpA9O73bbWvlroUTWErbXdQvWBQDUKVhHTnZSqJDP6omwMPnxpv5UDkRERERElPgwwPOBkBQhCPskzGl5/gz5bc8vf3QZOdLkcFhfLW81RH4WiVr5a8kLVq50LrxnT+8qpe2iyWyZRERERERJDgM8H0kR4DzF4MGeB5EmZRoAQMrAlLblncp2QuPCjQEAQYFB9h2KFHEuOCTEuwoZJVnRLn/0CLh3z7tjEBERERGRX3Gicx8ROgGVOugLCrAHcrObzzZfcFCQ+23M0LbgKa+nT5f/sYWPiIiIiCjRYQtePAoUgUgXnA6APaGKx3Ll8m4/dy14DOiIiIiIiBI9tuDFoxQBKbC502YsOb4E6UPcJ2HRVbWq9xX4+2/g0iU5eyanRyAiIiIiSnIY4MWjwIBAFMlcBINeGeRdAY0aAeXLe1+BWrXkxw4dgCFD5OfuJkAnIiIiIqJEg100fWhr560Or73ulqlYuRIIDvZu302b4nZsIiIiIiJK8NiC50M18tWANFTCurPrMPfIXH9Xx0493o4teERERERESQYDvHjw+ouv4/UXX/d3NexiYvxdAyIiIiIi8gF20UyOYmPtz9mCR0RERESUZDDAS47ULXgM8IiIiIiIkgwGeMlRRIT5bWvXBr77znd1ISIiIiIiyzDAS46ePrU/nzPHscum1pYtwODBPq8SERERERHFHQO85OjZM/vz69eBWbP8VxciIiIiIrIMA7yETm+MXNq0cStTHeABQFhY3MojIiIiIqIEwacBnhCigRDipBDijBDCqZ+fEOIFIcRmIcRBIcQRIUQjX9YnUdIL8Jo1i1uZ6i6aAKdNICIiIiJKInwW4AkhAgFMAtAQQAkAbYUQJTSbfQbgD0mSygN4G8BkX9Un0dIL8NQTlXtD24LnagyeokkTZtwkIiIiIkrgfNmCVwXAGUmSzkmSFAngNwDapicJQLrnz9MDuObD+iROAc//RKlS2ZepA7wpUzwvUy/Acxc0rlzp+XGIiIiIiChe+TLAyw3gsur1lefL1IYBeEcIcQXAKgAf6hUkhOghhNgnhNgXltzGiymtZidO2Jcpwdi8eUDTpp6X+cknjq/ZRZOIiIiIKEnwd5KVtgBmS5KUB0AjAHOFEE51kiRpmiRJlSRJqpQ1a9Z4r6RfKQGe+n0rXSqFAHJrY2YTjh1zfM0Aj4iIiIgoSfBlgHcVQF7V6zzPl6m9C+APAJAkaReAEABZfFinxGfWLKBgQSA42HmdVWPizIzBIyIiIiKiBM+XAd5eAIWFEAWEECkhJ1FZptnmEoA6ACCEKA45wEtmfTDdaNsWOHvWPhYPiHuSFS0GeERERERESUIKXxUsSVK0EKI3gLUAAgHMlCTpXyHElwD2SZK0DMDHAKYLIT6CnHClsyRZHb0kQcopUlrw0qQBHj/2vrxz5+K2PxERERERJQg+C/AAQJKkVZCTp6iXfaF6/h+A6r6sQ5KkDfDCwuQAzdvxiQsXxq0+y5cDpUsD+fPHrRwiIiIiIooTnwZ45CNKgKd02wwJ8W83yzfekFsRHz3yXx2IiIiIiMjvWTTJG9oWPO1zf2AXTyIiIiIiv2OAlxjpDVP0RYCXLZv1ZRIRERERkc8wwEuM9FrwfJGbxmhS+du3gbFjfXNMIiIiIiLyGgO8xEgvwEth8XBKo8nP58wBOnUCBgwA/vmHQR4RERERUQLCAC8xSplSfgwMtC8LCpKnO7DKN9/oL+/UCVj1PDFqVBSwYIFn5V67BmzcGLe6ERERERGRLmbRTIwmTQLy5QMaNXJcXqCAdcdYu9bcdr//7lm5FSsCN27ILX/XrgF79gAtWnhePyIiIiIicsIALzHKmhUYPdq3x8ic2f02QgDLlnlW7o0b9uevvgqcOQNERzu2RhIRERERkVfYRZP0Zczo+2OcPSs/chwfEREREZElGOCRvuBg99vEdWoGJbBjgEdEREREZAkGeEmV0RQHZj16ZE09zGCAR0RERERkCQZ4SVVQUNz2NxPghYfH7RiK2FhryiEiIiIiSuYY4CVVAXH80z5+7H6bZ8/idgxFwYLAoUPWlEVERERElIwxwEuq3AV4adO6Xm8mwHv/ffP10VJ3y7x+HfjyS+/LIiIiIiIiAAzwki6jaQe++05+dDfu7eJF98e4dMl43a1bwNy5xut37nR8zW6aRERERERxxgAvqTJqwTM7Ni8uSVqGDgWyZwc6dgSuXNHf5pVXHF8z0QoRERERUZwxwEuqjAI8ZbkvAqqnT4EFCxy7W0ZGmttXkoDt24Hly62vFxERERFRMpHC3xUgHzGaoy6uyVdcCQ11XiZJ8r/Zs4H27Y33jY0FatSw70NERERERB5jgJdUGY3B82ULnh5JApYsAbp2BU6fdr0dERERERHFCbtoJmUbNgDTpwOZM9uXxXeA9+uv9vnyzp833s6bJCvr1sXvhOxERERERAkcW/CSsjp15MfFi4HVq+XnvuyiqWf4cODFF+XnT54Yb+dpwHnpElC/PtC8udxCSERERERE5lvwhBCpfVkRiifx3YIHAGfPyo+uEqisXWt/bqZuyjx9J054Xy8iIiIioiTGbYAnhKgmhPgPwInnr8sKISb7vGbkG/4I8DzlSd2MkskQERERESVDZlrwfgBQH8AdAJAk6TCAmr6sFPlQfHfR9IaZAC8hB6hERERERH5i6mpfkqTLmkUxPqgLxYek0oKnbMMWPCIiIiIiGzNJVi4LIaoBkIQQQQD6Ajju22qR11avBvLkMV6fN2/81cVbCTn4JCIiIiJKwMy04PUC8AGA3ACuAij3/DUlRA0aAKVK6a9btgzIlk1+npCDqDNnHOuXOTPwww+O27AFj4iIiIjIidsAT5Kk25IktZckKbskSdkkSXpHkqQ78VE5slhAgGMXzVWrgNatXe/z77++r5dWiRLAuHH213fvAv37629rFOBJEvDPP5ZXzbSnT+X34M38fkREREREXjKTRXOWEGKm9l98VI4sJoRjkpWGDYHff3e9T4kSvq2TkZ07nZdt2GB/7qoFcvZsoGlT4KWX5FZLf/jf/4CPPpLnICQiIiIiiidmxuCtUD0PAdACwDXfVId8LjEkWVFT13PlSqBuXcf1ei14XbrYnytz8MW3u3flR1eTuxMRERERWcxMF81Fqn/zALQGUMn3VSOf0JsmYWYCbZDdsUPuRqpQB3tmA1QzY/QkCRgyxNpJ0zk2kIiIiIj8wJtJ0QoDyGZ1RSie6LXgqVu81F0jmzc3V2amTHGulhMhgFdeAZo0sb5srTNngJEjgbfe8v2xiIiIiIh8yMwYvEdCiIfKI4DlAAb5vmrkE0ZdNKtWdXwEgCVLzJUZGhr3ennr7l1g1Ki4dTkNC5MfU6e2pk5A4ukCS0RERERJitsxeJIkpY2PilA8SfH8T64NQLZsASIj7a/TevBnT5MmztVyok6oolDqPG4cMHGi/PzqVWDQIODll4GaNZ33MdNV8sED+TFDBm9q6hq7ahIRERFRPDIM8IQQFVztKEnSAeurQz6XMqXxcmXdhg1AkSLmy/RFC969e/rLJUnOTqnVpg3Qpw8weLDjcjMBVlSU/Gh0boiIiIiIEglXLXhjXayTALxmcV3IV9StdWaCmDp1PCs/hZlkrBZRgjGtGzfkqQkGDnRcbibAi46WH33xPhYsADp1Ah4+9KxVlIiIiIjIC4ZXtJIk1Y7PilA8EMI3rVTxFeBNmAC85ua+gjcTiytBY1CQ5/u6s2aN/Hj+PFCmjPXlExERERGpmMqiKYQoJYRoLYToqPzzdcXIRxJzgAe4z+ypDfD69HG9/ddfA2+/LT/3RYBnxuXLjuMfiYiIiIi8ZCaL5lAAE5//qw1gFIA3fFwvslLnzvJj6dJAYKD35fTvr788PgM8dzxtwfvsM/tzXwZ4Rlk1nzwBXngB6NHDd8cmIiIiomTDTAvemwDqALghSVIXAGUBpPdprchabdrIAUaePHHL6vjuu/rLE3qAZzbo8zTAW7kSWLrU3LZGAd7Tp/ayiIiIiIjiyMyVebgkSbFCiGghRDoAtwDk9XG9KCEyav1LSAHemTPOy44fB0qWdL9vSIhnx1ImYdcL3jgPnjy/4L17nmVkJSIiIqI4cTVNwiQACwD8I4TIAGA6gP0AHgPYFS+1I99o1Ah4803P9wswaPBNSAFe48bOy0qVMhdw+XLCdnfHT4oBYaFCcvbQpPjeiIiIiBIoV1fmpwCMBpALwBPIwV49AOkkSToSD3UjX/G2O6DSnVArLuP6rHb1qvttbt6Uk81kzOi43JsMnEa0XWGTQ5ATEwPcuQNkyya/fvjQv/UhIiIiSoYMx+BJkjRekqSqAGoCuANgJoA1AFoIIQrHU/0oIUlvMPQyIbXgGdm3T37cuBHIkQPInNl5m0OHgEmT4rVaNnEZG5lQDB4MZM8uB3lERERE5Bduk6xIknRRkqTvJEkqD6AtgOYATvi6YpQA5c+vvzwxBHiVK8uPdevKj3otauvXA717W3M8q1vsDh2SJ3NPyJSEMwzwiIiIiPzGzDQJKYQQTYUQ8wCsBnASQEuf14wSpkyZgPffd1ym7qJ5/DhQtGj81slqel01d+4EOnXyPnDzZL+zZ4FbtxyXlS8vj2lLyJRWyOTQHZWIiIgogXKVZKUe5Ba7RgD+AfAbgB6SJD2Jp7pRQqS0zkyebF+mbsErVgwoWxY4eTJ+62XGwoXmtouIAFKlclxWvbr8WLo0MGCAtfVSTJ0qP773nhw0R0c7rn/yBFiyBDhyBBg61Dd1iAsGeERERER+56oFbwiAnQCKS5L0hiRJ8xnckS5XLXoJyVtvmdtuwwbjdQsWeHfsa9fkAKhRI/nx8WPnbd57T/4HyAlL9LRsCQwb5l0dfI0BHhEREZHfuUqy8pokSTMkSboXnxWiROann4CKFR2XGU2noNixw3f1scIbb8hdTfUYZdrcvh3o2tU4uFFaNFevlh8vXpQfk1IwxACPiIiIyO8SQXYM8rtPPjFe16OH8zKj1idFypRxq098MEoUYhTg1asHhIcD48cDadPK4+jUtEGvNhhKCkERAzwiIiIiv2OAR+6NGuXZ9o8euV4fFOR9XeKLUSBntDxTJrkbZliYHODt2uW4XhvgKa+TUjDEAI+IiIjI79xm0STymNGE6IqEEuAdO2a8zqgV0miftGnlx/v39ddr57lTXisBY1KYB48BHhEREZHfMcAjaxQsCDRoID+fNk0ej2YkoXTRrFfPeJ1RS5277V9+WT+BijaAK1ZMzuqZlIIhBnhEREREfscAj6xx9qw9gUihQsDPPzuuVwdTZlrw1q61rm5GXAUip0/LwVrLlsAHH7gvSwnwoqKAbduc1+u10L31VtIKhhjgEREREfkdx+CR7737rjxvXnCw/DqFiY9djhy+rRMA3LxpvE6ZrsAsdVCjF8w9e+Z+PyssXAhkzgzUrm1tuWb4M8B79Ajo1g348Ucga9b4Pz4RERFRAsEAj7yTPbvrAAkAfvsNyJdPbgkD5MQisbHmumjqbRMaKk/2nRC5C/AGDXK93+3b1tRDmevPH0GWPwO8GTOAP/4AcuUCfvgh/o9PRERElEAwwCPvnDgBPHjgeps2bRxfFywInDljrgUvoU6WDsgBjDaI83TMnrqspIJdNImIiIj8jmPwyDsZMsitc57YsgX4809761xIiPG2eq1g7iZQjy81ajgvUwc1W7eaKydtWmDgQGvqFFcrVgB//RW3MswGeL5shWVwSURERMlcArlipmQhd27gzTfNTQmgvlAfOVJOdlK6tO/q5okdO5yXqVvwvvnGXDkpU8rdCo1ERXlWr7ho2hRo3tz7/YUADh6Un2uDLPXrv/4C0qQB9u3z/lhGxyciIiIiBnjkB0r3yxde0F9fsqTj6wYNgEWL3I/di0uAElfetBxFR7ter81EqvbKK8Ddu54fMz64CvDWrZMf9+6Nn2MTERERJTMM8Cj+BQfLLVebNtmXqYO3d96RM0EqlGkV3LXSxGcXzlOn7M8LFACuXvW8jMhI1+uPHgWWL9dft2OHnMQGAC5fNn/M6Ghg925g/nzz+/zzD7B5s/nttUGW2fGJJUsCrVubP46/LF8ODBjg71oQERER6WKAR/7x1ltyl03FpUvAG2/IzwMDgUyZ7MlYlEd3AVx8BnjqgO7CBe/KMJo6QTF5sv2c6AkIAFatkltCjQJBrb59gapVgfbtzQdeL70EvPaauW0B7wO8//6Tx2gq+5QpI78+fhz45Rfzx/e1N94Axo71dy2IiIiIdDGLJiUM2bPLWTYBexfOTJmAW7fsLXjuArj4HIcVn+PjjAQEAPv3y88//BC4ccP9PupAMDbW+6D41i15vjkhXHfJ1HtttEwtIkJuwezQQX4OAJ06GW/PMXhEREREAHzcgieEaCCEOCmEOCOEGGywTWshxH9CiH+FEB70G6MkoUoV+5i7mBj5UQnwlNdWtuCNHu15HfUklABPCWwuXgR69HC/jxIsA95P7XD1qhyQf/utfjna1//9Z39uNhBTAkBP68gxeERERJTM+SzAE0IEApgEoCGAEgDaCiFKaLYpDGAIgOqSJJUE0M9X9aEEas8e4Ngx+bk2oFNem23BMxPgVa7seR29PZavBQYCn3/u2T7qOQi9DfAePZIff/wRePgQ6NPHcb0kARs32l9XqOD5MZS/vdmAjS14RERERAB824JXBcAZSZLOSZIUCeA3AM0023QHMEmSpHsAIEnSLR/WhxI6Jauk0oKnvFaCkqpVXe9v5iLfzCTrZng77s4bYWH6y80GmatW2cewWdGCp+wXGwtMnCiPFdSuP3/eeT9JAnbtMncMJcAzW0e23BEREREB8G2AlxuAOr3flefL1IoAKCKE2CGE2C2EaKBXkBCihxBinxBiX5jRxS4lfvnzy4958siP2ha9Dz5wvb+ZgMeqAO/9960px4xs2fSXmw3wGjcGOneWJ1ZXWt8A4NdfgfBwz+uj/F0CAoB06ZzXx8bqB1x//AEcOOBc1sqVztsbteCNGiUHrElFVFTC6O5LRERESYa/+5mlAFAYQC0AbQFMF0Jk0G4kSdI0SZIqSZJUKWvWrPFbQ4o/AwfKF++NG8uvtV00tS102uDCTMCjbsHydN+ExlWdT54E/v3Xcdnjx3K2UkXPnsBgnaGx16+7Pq7SsiqEHDRq6QV4T544Ti2hrB8zBmjSRJ4AXe8Y2nIGDbJ/PtQSaxfNbNmAjBn9XQsiIiJKQnx5VXsVQF7V6zzPl6ldAbBMkqQoSZLOAzgFOeCj5CgwEGjY0H6x7i7Jivai3kyQljq1Z8sTMlfvt1gxoFQp92Vo5++rWhXIlQvYsMF4H+XvIoT+5PN6AZ5RF0qlK6e2m6dyDE8pxzlyxPsuqPHp/n05+HVFkoBt29gNlYiIiEzxZYC3F0BhIUQBIURKAG8DWKbZZink1jsIIbJA7rJ5zod1osREG+BpAzp3r/UYtZaEhnpWt4QgIEAOxly5e9ezMnfvlh+1XSkBecJzwLEFT6/Lq1EXTfXfR3muBKnr1ztu62mApy57506gbFlg3DjPyrDSvXvuAzd33noLGDZMPkc1awI//2xJ1YiIiChp81mAJ0lSNIDeANYCOA7gD0mS/hVCfCmEUGZvXgvgjhDiPwCbAXwiSdIdX9WJEpkpU4D06e1JV7QBnDaI0AYNeowCvMTagucuqM2c2brjvfSS/KhuwdNrJdML8E6e1C9Tr/537+oHeGZasCQJOHNGfn7woPvtfSVTJqBAgbiVsXAhMHy4/bW6iysRERGRAZ9OdC5J0ioAqzTLvlA9lwD0f/6PyFGvXvI/hdlxVoGBxt3z9LoUAol3DJ6vuu1dviyf702bnNcpLXgBAfqBWGysfXJyxdtv2wMvAFi3Tk5Uo3feT5wArl1zXm72vSp/e23ZkiQHSUWLmivHHUly/Zm0OiFUYuhySkRERH6XCK9qKdnSXrC/9Zb77QoXdg4MCusM80yMSTp8GeD9+KP8qDd5urqLpl6At2AB8NFHjsu0AZ+SVEXvvFevrv+3XbrUZZVtjAK8efPksYlr1pgrx+xx4ouvx+BFR9u76BIREVGixQCPEg91MHDrltyFU025AFa6dGr30W5nVHZi0a+f98lIzFK3uinU0yToHX+Zdqgt5BZBPZ6c91atzG2nBF7qzwFgH1eozS7qLV+fey1fB3hDh8pJdvbt8+1xiIiIyKcY4FHioQ4GsmY1ntNO3XJjNoBIjAHepUtyoBvf3LXgBQebL8uq864uRx2A3rhhnxpC+VxoW94GDPCszoqk1oJ35Ij8qNc9loiIiBINn47BI7KUq3Fy1aubS7JiJDEGeFbw5n2rk6zoBXghIebKGT9envw8rqZNAz78UH4uSY4teDlz2pefPSs/1wZmY8d6d9ykFuAp/2+0xzlxQh5PWKOGb49PCce9e/Icl0Y30YiIKEFjCx4lHq6CkbVrzXfRJEd63TBdUbfgqZPgKMy2hvXr535SdSN//w106ADcuSNP2K5mNAZPGcPnLjDzNJnLwoXABx+Y2ycu4ivA056f4sXlaRq0wsI8SyTzzz/2z45ZFy5w/r/4Fh0tZ4HV/r8iIqJEgwEeJR6ugjX1PHbuumjqXTAGBAAjR3pft8TMk/fdpw8QFSU/N/p7xEfLVq1awK+/Ok4joD2++nPw+ef2564ChmrVgGzZzNVBOc5bbzlP1G6Gch7N8leAZyRbNvPn6tAheZqNzz4zX5/t2+WpJmbPNr8PxZ3yuZw3z7/1ICIirzHAo8TDbNdLT5KsNGli365WLa+rlqh50g1r4kTg8GH5uT8DPIW2DpMn21sF1Z+Xr76yP3dVv127gNu3zR17/Xrn41+9ah/zBwDff298vC+/NC5bbx9vA7xnz4D//U9+dMXTAM8TN27Ij4cOmd9HSYaza5fl1Yk3kgSMG2f9lBm+xF4P1pAkuVvzkiX+rgkRJUMM8Cjx8GQePO0+rVsDhQrJz9UXsOqWHWXbrFmdy6xb13w9fW38eGvL8zRwCA+XH40mLz9/Pm71iSulxUebRVNhFMCcO2d/vnq1++MsWOC8LE8eIF8+++uPPwZ++01/f1fnyWh+QW+MHw98+y3www+ut/NlgKfw5LOm/H9MzF00DxyQpwzp1Cn+jx0d7Tw9iScS83lPCGJi5FboN9/0d02IKBligEeJh7sAT7kg0eui+fvvwOnTzuWoE7Mo++kdp3Ztz+vrTsaM3u338svW1sPTC3olwEsI9C5ClWX37+vvY/R+X3zR/rxRIyAyUn5+8qT+hbLZaRKePNFf7mp/vbFq3l5wK3V3d7GvfO59EeB5E6wlhQBPaTV98CD+j12unPmER2qJ+XwnJMp5ZIsoEfkBAzxK3Nq0cV6mvlvu7sdVWS+E8bYrVwKDBnlXP1fU4wY9ERRkbT0Sc4DnqiujEqAZrY+JARYvNi67aFG5y2WxYkDfvo77Gh3b1fEAOdhr3FhuvXMV4Omt81cWTStYHeDFxsrdbrVB/K+/Ahs3elVFnzBK+BMfvJ3vUTnfDPTiJr6z7BIRqTDAo8Rt/nznZBV58sitFXXqADNmuN4/VSr58dkzx2CvQwf7NrVqGXf3UzPqimckdWr58bXXPNvP6tTlRYt6tr27sVzxyVVLl9EFlrL8hx9cT55+4YKcLh4A/vxTzgKpvujVTug+Zox+Oep9VqwAVq2Sbxi4ugA00zo4fTqwe7f77Z4+lR/nzAEOHjTezpddNJX/Wxs2mA8cXAV4q1bJ3av79HFc3qFDwupOrderIKFjYGcNnkci8qNE9KtDpCMgwB7wKIkcoqOBlCnli8kqVZz3adxYfvz3X/t4u3v3HAO8bt0cj2GGp904lXrnyOHZfmaCTVfKlXN8nSmTZ/sbdTn0B1dp940Clf/+kx/VyVCMKK2Ad+/KWSAvXDDe9pNP9JerL/TUQYurQMpMF80ePYCqVY3LAOQxYKNGyc8vXAAqVADmztXf1qoA7+5d5wBV3TpuJihV76N3oaz8XR4/ti8z6pLrT/5swfMWW/CswRY8IvKjRPSrQ+TGmjXy44EDrrf7/nu5i1yJEvbgpkUL4+6aZgMqTy/ilGMogV758ub2i+uF1/z5jnUwO5ZMoW258qeff3ZeppxXo/e1eLE8f97Eie7L17YOHz/uWf2M6iZJrs+7XvfS2Fj53+TJ7ltRt24F2rcH9u51Xtexo/4+VgR4T58CmTPbu7TqMTs9hJlunep1v/5qrtz4pJzLxDQOi4GdNRLj356IkgwGeJR0jB0rP7q7QAkKAvLnl5+nSCFf7E+ZYpxkxWzgpt3upZfMba999DXtGD5PA7yE7uZN+dFV616uXObK0gYjSndHTxi14Lk670prtLacZcvkSdX/9z/HdWPGOE5MXb++HMgbjUME5AnuGze2vyd1gHfqlHep/ZUWtT/+cFyu/j/laRdNV+v0zm1Ckhhb8NjyZA2eRyLyo0T0q0PkRu7c8qOnF+GZMsmBnl52TcD7AG/SJP2L2cOH5TnUtAGl2eNoy0yTxtx+Cu2FcFIL8BSuAjxXgY+r7Vq39r4+gPkumnrBlSTZu8cqQazik0+AadOMj6enf395LNuGDfLrW7fkx9hYeVym2UnM1WMP1Z9NSbJ3m/QmwHO1vV6AFx9B1KVLchdUsxJjgMcWPGuwBY+I/CgR/eokPjExci/ApUv9XZNkQp0wxRtGXTT1fqDHj5fnOFLTXsQZde0sU0ZOBGF2ey31Bdj06fJYLMD8Bbn2Ai6pBnjqee28ZbY7oStGrUyeZtFUJ0lx1VX0zh17plOji8s7d+zrlIBl5Urn+pq52FfGHv7zj/xPOe64cfJUIBcveneRqw7ihHCfHVe9TBsAWyVfPsd5Dt1JzElW/BnoXbliT3CkeP11YMIE432WLXNMjuVvbMEjIj9KRL86ic+9e/J12Lvv+rsmyUTZsvJjy5be7a8O8FzZsQP48EP78RRKgKlwF7Bp09J72oJXqpScDOarr+SMkG+/bbyPum7KhO+A3JXuo4/k56+8Yu74iYWSTCUuzLb0ueJNkhW9i+uoKHs35EOHjPfNksV9nerVs3/eunQBrl+3r1O3yKnrePcucOSIcZkvvQS88Yb8XAj7FBQDBsithZ7SttLNmeO8TVxa8O7dk+fH9JQ6sYs7bMHzTt68QMGCjsvWr3c9trNZs4Q1DpMteETkR4noVyfxWLlS/k7Xm1ebfChfPvmCvHNn7/Y3G+BVqyZvo71oCwpyHBvlbjoDbVILdwGhMm5QO4FuqlRAv36uLyKXLHFdNgDMm+d+m+TGiha8zZvtgZkSIC1dau8aCcgJgqZNk/+mP/3k3Vg/PUYX64cPO37Or161Pz91yv5c3ZL48svyTY2wMOfPk6ugYOFCx4A0LtMkTJggt05a0UWzfXv5psiZM+63PXfOu8AnMV7kJ4QWPMA4K2qpUp5PSeMPbMEjIj9igOcDSpJCJRt4YvptT/SCgrw/4coPcnCwuTL0ArKvv3Zer75gVvM0wFu1Sr7w1wZ4ZijBoStxmUDdKDNjYmdFC96iRXIrlhDAsGH62zRsaE+S0qsXMHiwd8fSXpTfuaO/XWysuYBIHeApd6xatHBuJS9TxvG1q8+mXgIZPXpBXN++wJdfOm/7+DFw8qTr8i5elFvfFco0GUp3ViMnTwIvvuj4f9sstuBZ799/494Vs3Vr4McframPEQZ4RORHiehXJ/FQrksePnR8TQmcMnavShX9P9pLL8l3jxXuAjJlfeHCzt2NAPtFn3IR7e4iMHVqubXOKMBzdWFmJniLS4DnybikxMSKcXzeuHzZ/LYvvmh/rp4CAzAOKAHPAzyFXt2OHXN8LYTx57FdO+OgSpLkMX3HjxtPk3D/vr1lVVlXooTxRPOK/Pn1uyGry4+KsiebUdy+LT+uWOG6fD2JOcBz9X3SuLHnyZ3ii5kA9c8/5W72vsQAj4j8KBH96iQeym+5q2srioPgYN+UW6mSPK+a0lUOcJzqYPdu4OhR+2t1gPfOO87lqbtoKuWpWxC0AZ67gFEJwIwCPFd3EswEb+66lKqlS+f4OmNG8/smJp9+6p/jetJyqA5Cr10zv5+ZoEPvIlU71tSIqyQyRsHYlSvyugYN7J9n7bkQAnjzTfm5JMldYLVBp5m7anoB5HvvAdmzOwag6dPLj8odO0/ENcCrWtV88iSrmAmQVq2SM7qeOeM43nLBAuOxr5Kk3x02IkL+sfQ2OZZWQgmsvOlpER8kSR6rqGTkJaIkiQGeD3hy3U0eWr/efVcsbwkBdO0KhIba707nzet6e4Ve0KkXsKkvjpX9zXbRdBfgpU5tvK+Z4M1sFk+98jzZl3wnIsL8tmamLtAL0sz8ra9ft/dR16MdYxgR4Zh4Rn2xv2iR47baer/2mnP52vejl0xF74t54UL5UWm1A+zBmXqcollxDfB27/ZuPkIj9+7JwZTyd33vPeebU9oWPEkyHhO6b5/8qJzfdu2AkiX1t/3xR7k3g5JpVTF5MjB8ODB6tHG9XX2WtBJKgJdQ6qG1Y4fcxbVPH3/XhIh8iAGeDzCg86G6deOnO2CpUvJFy88/m9teL4BSXwjrJXDRjsFTXwTOn+/crVM5RvHiQJ48wKhRjuvTpjVXVyOeXITWr29//tFHnrX+ke+4G0+m5m0XTSuyk6r/H9y6BYSEyJlgFWFhxi2A6n2NMlpqL65/+cW4LnqJWvLmlcd6qcvypgVPezMmPFxOVrNnj+dlWaFvXzmYUqbEmDrVObmSNjgeO1a+6aU39YQ6A7C7lr9du+RHbSueEjy6+uxWrer42tWPbEKZ9iWhJthRPsfqzLmJ0ePH8udy1Sp/14QoQWKAZ7F9+5yvJazqeULxrHVr566IRtQBjxLY6QU96h/7AQPkxxIl5Ef1BXfbtvJk02pKK2Hq1HKXtLp1HdfnymVcPzPdrjxJrqC+GO/Rw/sWPKO7/eQdT1rw1J+3Bw/0t/H1xfLx43J6ewD4+GPHz6DRRPXq/0NG3cxy5bLPzwc4trBLEnDihPM0JdqylYynRv8vIiOB3Lnt3RPVFi+WAxbtzZt//5WDu5dfloNIvZaxLVvk7w6jBDlxoZwvV9lhte9XyVipN/ZSHcS461ZsNKbS6q6MCS3A03P5sjw9iRVJnDyVULuOeurkSfn/z2ef+bsmRAkSAzyLLVjgvIwBXhImSXLShxYt7MuUi0l10KNceIeE2Jc1bSrvnymT8/aAnC7/o4/kO+fr1rnuggnojwNU11OtfHnnbTxJsqLetmhR7wM89bxVroLppk2dl338sXfHTMq87aL5+ef62/j6YrlFC8fud+rPqZkWPFcXyOpxfur/dxMmyK3ghw87H1Md9CoX6EYX6mFh8phHbbKO7duBVq3kGzjaAE/9Gb9yRQ40tUaOlN+7tiujFby50aPtSq63bUCA+6k9zAZ46ulDvGHlZ/b+ff0A3gxXLXjvvQfMng2sXettzbyX0LOkmpXYA1QiH2OAZzG938CEckORfERJwqBImVJ+VP+QKhc/epnnlA+Isp8ib17g++/lJAv16rmvR0CA8Zxe2h917bEAOTC9d8/9cQDHYFMIIHNmc/tpqS+o//rLeDullUdt+HD35Tdu7HmdEjO9bnRG1OfeKFDy1ZfX+vXy58bVlAlmWvDMzlOoDvC047mMWvCU924U4Bm1hCjdRk+fdj8GT1kfHS2X88039psl/vrh0H5X6LV0KtRBjPId5+5mj1GAoZxHM991ri7u3Y198yTAad9eDtbPnze/j9l6AP4JUqxowRPCcc5XIkpwGOBZTO83OancMCOTlPSp6mBOufgJDXXeXvnQxGWaAoU6Zb6atvXP6FgZMjgvy5JFfpw5075MfcEMAM2bA9Ony60PgJyswmj+P7XAQKBQIfm5p0kJzIz7e/ttz8pM7P74w/y26gs8o4tyXyWK2LtXftR2DVUHderWXTV1vc0mXFJ30dS+J0mSW1IuXNBvwZs0Sb9Mowtl5VgREfb3o82Yqz2G8v3w7bfeBXj379vn9Ysrb1rwhLC3HuvdPFKX42kXTU/fl7vz5slnWslQq9cNZ9Eiuc7qhDx6x9F7XwnhokCp16NH8o1ET/+vf/ut9XXyRkI4l0QJEAM8iyXUxFkUj/r2lX901BeVygWCrwM8PdWr27uBKjw5llI/vW6dCiGAbt3kcU9r18oTYRcu7L7sgADghRfk567+81Sr5rzMzHvwJHFMUp3qwYi6e6BRsBzfrUh//ml/rp5SRG3cOM/LVb8/bRAsSfK0DAUKOH5enj2Tp8kwSrRkdGF5/Lj8GBlpP39GQZI2Y6UQzsmXAPlvJYQ9MNYqVSpuyafU5RoFeHqUbaOj7a1cRv/nvAnwYmM9f1/uPrNGLcN6jOoMAOPHy4/qpEM3b9p7Qbj6PvPnODjtsfv3l7u7J7ZkJdpzt3QpMGWKX6pClBAxwLMYAzzStXYt8NZb+kGJcsFhdOc7rvQSmXiS9VK5YDIz/2BAAPD66+YvXAICjLuAKeOVwsPlMVN6+7ojhOvAVC25TfWg7qqYUAI8X807aCZIARwzC86fL3eZ1DNsmD2tv7bsDz6QH9UBnlELnl6Ap3wO1eMplayX330nd/3U8mYKBzV1AhUlQFUonxO9IEdZtmCBPemT0bn2NsBzVZYed59ZV916N21yDABdHUcvEM+Rw9wNKzPl+4r2fN+/Lz+6G0Op3d9TP/zgPOWJK0+fAl995b4btlKfFi2A99/3rm5WkiTOMUgJAgM8ixll0uZ44GTu9deNu8/5ugVPj3aKBVeMxghaISDA/p9De2F28aKciCIuE9sLof+f7/XXnZcltwBPbds2/eWeXJAlZN6M2dLrrqwYPlye1w2QxxG+957zOMbISPNdNNXLlW2PHXNetmgRUKSIcb3MMpqsdf9+x4zASqZfQO4JoL3Y1jt36hsvd+/aA1VvAjxvggl3gZVRC96mTUCdOua7HuoFeIB9DKaruvuzW6G7MZbh4a7rZ3R+N2yQPyNG57d/f+DNN83X86uv5ORP6qEBaq5aV/1p+HB5eIbZ8ezkuf375SleyCUGeBbTTilE5JYvAyhA/8KpXDn7c73EL2qetOBpDR3qOgFLYKDxhVKGDHIaeoWZVscRIxxfp02r//7bt3de5u1k1EnZkCH+roHvGbX4mL3hEhUlX2xou7jpteBdu+a4jfKZV7qjPnxo3/brr+3baT/Dvuoqom0dHDvW8bW2lUfv4lpd18yZ5R4EV69a24IXEWE8B6K3XTSVVky9FlK996n93tJuY2YevITQgqd+HzdvAqlSOU6Do2V0fjt0AHbulOe1tILy9zWaHzGh3jVXxg7fuSN3Odf7PFHcVKok31Qjl3hFQ+RvygWH1S142mBHz+uvyxedd+/al7VrB9SoAZQtC8yaZf9BT5XK8zoMG+b6rkdAgL3lzN2dWHXaeyPVqzu+LlvW+UKgWzd5nsH9+53rQsmPUaDg6Rxl2hZgvSQr2qyuShCgzhKrl5Zf+xk2mz3UlUePnJe5K9eoBVJNW9ezZ4E8eawN8AC5O686CDaqo5bR94xyLPXf0VUrkfI3vXFD/jtr6+rpGLwFC1xnEraaXoCnBLnz5xvv5276Enff40I43+gwU08jCa0FT12f1q2BYsX8VxdK1nhFY7GKFY3XJbTvIUogfNVFU2k5M/qBvHpVvqBIm9Yxwci8ecDWrcChQ0DnzvaLVHfz8BlxdaGj7qKp3q5OHedt+/Z1fyztRXaePM6B26hR8rnWdsFLzl00k7OGDfWXe5oaX/v50UuyoqX3f0OvhUm7v1ErlN6PzI4dcsKjx4/t67dvd5yTT1nuLqjVHtdV4KM1a5brOgshTwCv5uq7Y8IE/Umuvc2iqW1tVepkRNmuQwegTx/nc2Mmi6Z6Xbt2cjZiX1qyBNizx/HYej0oXF2sxDXAA+T5Xbdscb2NmUAxIVLPDQkkzcQMkgRMnqx/k4gSDAZ4FnP1f9lM1nhKhpRALGtWa8vNm1d+NMpmmSuX83QHepQfLG0A2qKFPaGEK0rLX4cOzusCAoCCBeXn6oDrrbdcl6mXLW3IEP0gzehCoGBBYMUKoEoV+bXevm3byt1BKHFSgjRXSUiMvrTV2RHN0AvwlIv+Eyf0fwC8veu3erX8GB3tGFjExMjvRz21w8CB8vyY6jHAmzbpl6tO7KLHKEmMmtGYWqN91MGOEnwovLk4jmuAp/c94C6QXbTIsfVz4kTXd3vjkyTZ58ds2VJO1APoB3hmgiZ3iW8kSf7nKmnLH38AtWu7P5a6XF+4dg348EPPMqu6Y6Zrrjf++CPhBFQbN8q//WZuupLfMMCzmKtGmGLFgJ9+ir+6UCLx8cfyHHJdu1pbbpMm8hdxv37WlKe98Fm82J5owpVXX5UvOPW2DQqSsxEuWiR3C1UYtQJkzy4/9urluPzff+XuWnr7aX9o1V1NGze2T12ht6+2RYESl19+kT9bet0eraYNItUteNu2AUWLOu9jNoDRfjaVGyDFiwPZstmXR0XJXf1697YvU7JivvuucXdUhZUBnjIfqJZRgKfHaPyVK95OdB6XAO/WLbkFTtGnj/252RY8K0VHAx07AnPmyNN85MgBHD6sv62nLXiVK+svV7+XUaPk79WwMM/qreaqDr/9Bqxf7347d3r2lH+XlLISqsOHgTZtgB49/F0TmZIl1GgOSEoQGOBZzN1UWhwXSk6CguRxYZ5MXWDWa6/FfWzZwoVykObtxYgQcvpqdZcwRVCQnLylZUvH5UYJZ86f17+LWaKEfBx3SR/69nVutVT20buwM3tX21tt23q/b1zmPUsuhg/3LHNfXLz7ruPriAjrJt02+gyeOeOYrS8yUs4+q6Zev2GD8TH++8/e0mPEzBi8gAD5OF99pV+GJ2Pw1MGrWXrn/MAB+Xtm6lTji3m9MXhKJlMzXVGXL3dft0eP5K6JngR4Dx7IFxabN7veLjZWDuzef1+eqmHuXKBTJ3trr7YFWTm2uou8mW6W6nLUnzX1vsoYPuWmhzctsa7OUdu2jhle9fYzQ/msWDl+RinLyq6Zyo0Z7f9tf0moGUytdv263MsnkWKAZzF3v+chIXIW5rVr46c+lIgk1CQfrVq5Hy/hiTx57M+NmryNxsOlSuU666e7CzG9LqnaMRNq7n6k4zqNgKtU/O4klB97slN/XtRdNI2Y7Rpm9mIxKkoOCNzR/j+5e1fOdql03zNipgXv/n3XQbUkyeMC27XzzfikmBg5sFe32k6YIP893ntPHldstB+gPy+n3g+72e/rR4+Affvk5+3by10T3QXSavv3y+f0yy9db9e4sfx9OmWK43yOeoEr4NxFU5I8v5mlfh+uLvrdje1Ml84xs7Oap0lWdu8237KklK1NfhQXSn30/l7nzxtP3eOqPCuSKpGdJAG//y5//+7fD3z/vf52deoATZta24U3HiXQK8rEy0yA97//AQ0axE99KBFJqAGelW7edBzbpA3wlPGC3l74qX/slTGI6h9TV5M1e9OCp0zu7C13U1RQ4jJxov25JNkv7I2YvXDQy2qo91mOijJ3V137mTY7MbOZFrzISNdB5n//AY0ayV1JHz50nuw9rmJi5O6hrVrZy92+3dx+gP17YNw4x3Xh4UD37vZpADz5vla6NSotgu66yrqqFyCfd+25X7PG9f7a+moDvOho+6Tnyt/i2DHX2Zj1Lnj0/o7uuv4+emTvQrptG5Azp+Okwk2bGn8P//uv4+tq1YCqVV0fT+GL3hnK+z9wwHmdN11Bhw61j1f0d4vZ06fyOTMKiOLqwgXflKu1aBHw9ttya0ulSvIwGVf1cTWeNAFLBleU8cvd77WZnBaUTPk6wFuxwnUXrfiQLZuctVOhDfCqVZMfvQ3w1Ptps8UBjsdWKGPw9IKtmBj9i4Bvv5Uz3umt82TcnnJsShpOnnR8rZ0bT8tsgLd7t/MyvYtrs1M7aMdjmb3Q1R5TSavviQkTHFuNlIvWzz/3vCzAMRAAnL875syRp2rQc+mS/bny3pRgV901KzZWHvc1YwYweLC8zNPv62++sSf9UR6NzvuxY/a/kV6AlzevnCTLDKXrv3Y8ozbAGzJE7tIP2P8mVasCX3whB2j//efca0D9+dVrCfzsM3lfs5/Lc+fkz8GNG/ZpbISw/y3MBjhnzrjfRpKcp8qxgquMtN4EaD//HLf9raS0jP79t/xoZX3WrwcKFJBb1lxRjy/2lvI+XCXfAuzj9c3eAEtgGOBZzF0LntWZ8CkJ8XWA17ix/vQDnjhxwv2Xoie071m5iHH3H0mtaVP95Tlzyo8vvGBf9sknztvNni3fpV6zxjE5AmAcaA4eLKcc1/ubeTJhvTZZTFJidVbYxMDT/8Nx6fqTO7fzMm+7ci1YYG67pUvlMbkKM3NT6lGP+4prC542k5/2u0MbdKvly2dPQqPsN326PJ5OHXzFxDhPeeHp3/rTT52X6QV4EycCpUvLXRZ/+skenKoDvGvX5N4QYWHuP0PK95FRK5ryPpTWO7Vnz+z1LFkSyJ/fcX1MjNwa+9NP+tPdrFwpJ9hy14KnUFqIAP0ELZ5OGh4dLU84roiMBNatk59PmCAHklYzO+WIke3bgaNHjdd36aI//+PEiY6tzmoDBljTWunLKR8OHpQf3QXd6gzBceXuO4cBHqm5uy5V3zBUtGljv3FGyVhCnddHrWhR83eOzdC+51q15McSJcztL0nAsmWOr7WmTrU/Dw52Xp8tm3ynOUMGYPx4x3WxsY7JTAICHC+G9S7yMmeWL4bMULKCemLGDMcxNlpGWe7im7uMU0mRNwGet+Nbla6CapGR3n2P6LUQ6hkyxP0UJmYodfzf/+QsunFx9678qJx7M+ME1ZSgQX3xumGDcYCnHMeKG3I//SR3Mdy1y75MfZOpVy97i4VeF/Js2dxnblNaRLSBYEwMUKaM/pQz2kQhrqaWWL1arqfSKqnt9RAZaT7YiY21n1ellUVdltENDKP6DRsGZMliD14HDwbq15c/71u3mquT1vXrrj9TcW3Bq1FD/rscOqS//+zZ+vM/9ukjzy+oZ+xY98f1hpUteEZdic0ef9064IcfzO1nNkmM0uWOAR4B3t2Q/eMP9wmyKBlQvnQ6dvRvPfypQwe525fZMRRaehe3cRnnFhsrB1S//SZ3sTp9Wu677+p4M2aYa4E0unvsbq6jF1+U054bKVvW/bHjQ4UK/q5B/PM0uIqJMT8fmBlKi0tCpwRl06ZZV6bR/HvqLKJ6lNYldZAQFeXYnV095m3GDLkXg7vxlWb8+SdQqpS9a7orgYHy9486GFTKMEN7cfLkieuWIsB9a5TeGGSjcZl6duxw3tcowRYg/130WmSNepXMnCk/XrkiPyr7njnjfuqUH35wHDP39dfyZyxXLsduk1p650wJfj0JiMqXlx/V3ynKGE5vxTUg82UXUeXz6ck4RXV96tcH+vc3t58ypk69v97fVPksMskKAZ71LCNy8vCh6x+P5ECdZdNTr7wS9+Pv2WPvXhgbK4/ba9NGbr1QJmVX6N1tzJTJXFcWo9Y7dwGpux/Z9u2BkSPdH9/XkuPneMIEz7Y3k/HSE5Uq6d/5T8piYuQgTa+LYFSU+8lnlQBP/eOtbdWqXduxpaxIEeNxfb4SGCgfVxsMmu02p71IdXUTTfsdo3RrNEMvMZVRF03t93VsrHOiDXVZ0dH6d8P1bqyEh9t7bCjjD5Xt/vpLvz5q/fsDr79uf61uNXPV+qcX4Cm/G54GSFeuOAavnib7ePbMsfVJ7wL1v//krLPuhIUBAwe63ubOHbkbtzeUz/GBA87jao14E3DOmqUfCHbr5r5uiQwDPIsl0kCfEoq0aX0zH15C07y5b8q1ottUlSr2blHuvti1FxZKdzu9/az6uyoXS6VL66+3ulXIW6lT+7sGCZ8VCQO0Nm60vkwrvfiiteWtXOmYvUx9EWuma2BEhNyNZvJk88f0R1Y9o5at8HBz46K1nzUz3QwVnnTLjYlx3H/cONddytWOH3c9zi462vx4vrZt7edM2Uf5vlaPI/WGq1ZGo1ZP7Xkxo1Ahz7bXSp3a8YahXoBXo4bcldVdN8Rs2ZzPm/b9tGoFtGjhPA3I0aPup65Q102vng8fOtfR08DrvfeArl0928eoPokAAzwLxcbGvQWdKFlYtMj7QejxoUgR+dHdD6w2oHz1VflR74fHqlY15WJFSSKjFRNjHOjGdbxTcpMYJ5NP6HebzV7se0qvi6aZ7tkREXILvd6YxoTEKKiIivKu18OwYXGqjiHt5+/mTXnCdTP0srJqW/DMjm1butTeyqrNIBpXKVLILVp6gb7y/aytpzdTAJgNZs3audN5mdKF2ZsgRpLkoFx5r8r51r73MmXsXU6N6GVkVUuf3jmxlCffddevO47HB9wH3Hq9AhIRBngWUoY/FCjg33pQMrd3b/zNJ+OtgADfpZTVZnrzRps28viQd95xvZ1RIKX3Y/nRR3IacFeUH8EnT4zH1riat085ttH4N71pIjzVpUvcy7DCjBm+P0Zi/DL3dyp1d3zV+qUk4PA0k2hCP18KJaNffPj3X/s4SU81buzcxU6dyTIujAI8d7RdNI08fer8+Vy+3Hm7wEC5RatSJfuye/fkIETpdq0NCoSw9rN27Bhw5Ihn+6iz+S1e7FgnbzLwrlkjJ0TTS16mpYyDVJ4rwfwXXwA9ejieL6O/k7ZLuyfn0+ycjWb3TQQY4FlI+Xx+8IH5ff74wzd1oWSsUqXE2fJglb177SmXvSWEPNbF3QWB0Xq9H4SAAPcBg5LYIXVq/R/NFi2AJk3k50YBXnS0ceDZubP+ck+6AsVHYKWl90OcPr3vj+vJlBdx1b59/B0rKWvc2LPtE0uAN2tW/B5PmevMU1evOt/IcnWOT52yPx8yxHm9utXJKMBz95307JkcqLn7W2fM6Dw36RtvOG+nfPceP27/zt692/XYsRMnHIModV0iIuTpOTxpKSpdWj+h1tOn8ntYssT1/trMmq7GF5ltSTT7fylvXvv0RSNGyO9d/Ztptpw6dYBNm8xtq/ebyBY8MsubLK+uxnUSkReyZJHnkPInb34QwsLkBC2ujB1rH8vnqgUPcE4IA8hB4+XLznehate2dy91x9fzNRrRXrgrAV7q1L67U+aLAK9mTf3l/jqvRL7m6kK6aFHX+86ZY39uFOB9+aXrMiZNkgM19eT1epSy3X1/q79769WTH0+ccL1P48aOyT3UxxgxQm7FsuJ77Kef5CBPL1hW07bYXb4s10OvO2urVp7VQQjPu8W6G4OnZ+dOx6zW7uqkZfamCVvwSPkMuBp/q1CupcxsS0SJQPfu9ufKfH6eyJLF8bXeD5K6VU/95REebr/IUcbj7N7tnIZcWf/jj44/boGBCT+5j/biTJ2F1Fd190W56m5dagzwKCFJiPOyRkd7d7Ht6QTp7ror6l24mU3Rr1BazA4csE9cbkVWXaUeRl2hDx2Sp7fYu9dxeeXKcnfJefOc91m50tyxlUD+5Em5S/Hcueb2Axz/rpkzuw+YjSjTl0gS8N133k1RoWALHimUz4CZoE1J+sUAjyiJUM/ppZ4i4IUX9CcT9oZ63KL6Aiw4GPj0U+DwYXsAkTWr6/m11N01X3nFN2MitZOd9+plf/7SS3ErO29e+bFSJceLg4oV41aumi+CrsyZ9ZfH5ccgOfyQ6M25Rr5jZUpwq7rBDhniXaIoT1uTtMHFmTOOr7U3fswGQGrKd5a61c7KoNqou2j58q4z7T57Jv+9ZsxwPyermvo37vBh+XHxYtfBkTaBjto//8hJW77/3vVxJcnx81W5svx49ao8sb0ypMFsJth9++Sbo2pswSNPumjGxspjmK0ae0xECYg6bfuZM46BjVl6P/auWpQCAuRsZUZczUvXvj0wcaL5upmlfQ/q8SxxuZhZtUoOHnfuBJYtc+xemi6d/j5KimNPppCIzwAvLscyKtNb331nbXlW8FULpycXscmJJ1MjxBdv05SbGUOmvojXdvEvXNjxtfaGym+/eV6nBw/k1jv1/7WePT0vZ8AA/SDK21anDz+Ug87u3YGPPzaf1Ob994Fr1+Tn6q6u2uDIqDxti+OtW/LY8I8/tmf61CNJ+gGYkvVQCe71MojqqVzZPkckW/BI4UkXzdhYz6bdIaIEzFWCDL1AJkcO92Wq9/v+e3kOnwwZPK6ajbv5fwoVsn7yZu1FuVWtAg0byo9Vq8pj8bJmlX/Qd+40zqKaJYs8F9OkSdbUwSztuEqjcZZxuUts9bySyriihMRXWST/9z/flEsJh5kgpU0b8+VpL/LMTJyutXat+aDDlbFj9Vs149JqqkxrMX26uQyZWsr4v9hYx+98vdYxxezZjq8/+cT+3NXvxoMH+gGg8jf3pv6AnKFUOYdswSNPumhu3Gg++Q8RJWDPngG//GK8XhvghYcDFy/aX7dt637C6woV5DtCvh4XU7Ag8Nln9tdZs8qP2vnEfv/dXHnK/gqzqbjXrwc6dDC3rSIkxH7nVU9AgNzSZSZxysqV8uB9pRtoXGjfs1Frm7cTlL/7rjx2xkq+msIkLrTZDSnxiM9stHrMBHiLFpkvT9uTwptW4Dx57OPD4mrwYOdlcQlKrJp/b8UKx+CscmXvWuJdBXjR0fKUFVrK39zbz556Wge24JG6i+bSpe6z1G7e7Pg6KgoYM8b6uS2JyIdCQlzf1dEGZcHBjj868+frd49U7+fqbuwPP5irp1nKsb780rhFr3VrOUW1WqNG+tupqbuuaqm7WdatK2fPq1TJ/VyEWkbnSrmwSJ3afRmNGgELFtj/rnHpsqi9+DMK8PSCXzNdLwsWtL77or8vyPXMn+/vGpC3EuLnKS7cZe00o1499+PL4iKhtDppgzO9yezd0U5wbobS5TMwENi2zfP9ly6Vp8EAEs659BADPAupu2g2a+Z5r5mqVeVW6TFjLK8aEcU3JTDxdaubkjXTKkqApK63XgA7ebL8RVelCtCnj/yDqM0C98UXwP379tdK10q98tevdz7G3r2eZWIDgBo19JcrQVDOnPrZRfV40u/eiDYQNuqiqXeXukUL9+ULEbeuu3ry5wdefNHaMuNKm7AnMVJPNJ2cJPQMvUmRlUly4mLXLsfX3mTH9LS76eXL9h4RBw4YT02jpT5n06fbn7MFjzzpoqln/375MSzMmvoQkR/NnClnUfI2wFPvp9fqpKy3eqJm5cewWjW5a+bgwcDWrc7bFSkiB3V79gDjx8vd+rQJTgIC5DFye/YAW7YYn4vQUHn/kyfl7eKiSxfg0iXnY6lfu8ouqp7GQHk/RolbzNCOCdR2d1W6Q+pdRJj5MQkIkMenGV2E/PSTcUuB0bijlCkdJ6BOCKZOtT9fs8b1uRk1yvf18Ua+fEDx4v6uRfxLKMFGcpJQghLt/KVWtH6689Zb3rUa//ij/nK24JE3E53rYRdNoiQgKMj9xOVmValiTTlaR48Cf/7puKx+fbnV7bXX5KDo229dZ+c0o0oVxy6Y2rFtypdmkSLmJ1w3IoT+2DkzX8yPHjnecR48WA6OunRx3rZpU3sigAIFjMscM8ZxzGLKlI5p25X5qLz94lcCV3UAqyRJAORWVr1WzV9/dU5sANgTAMXlh8zVWEhvqS/Y6td3fdGlniMxIYmMTJgJbHzN02kKiOLiwQPvbioYzR3oSQKeBIQBnoWU5EBxDfb5XUhEtgv2uF4QumqtKlUKePNN5+Xp08ftmK7cvg3895/jMl90Y9UmCtEGLOr5pxTBwY7dyUJCgI8+0u9ipp5/6f335WQ7U6Y4T3IfGuo4FjFlSsfMbkoXW6NEEO7ueOudO/UNgYAA/dau9u31M8wpqc7jworsgFpKy6desK2VUOcFjI5OOC0r8YkteBSfIiK8u2HmKhuq+qZZIsEAz0LK705cEyOxBY+ILAt6/v47Yd01ypzZuZuiL2i7wmkDPL15vjwJDCTJfrEuhBwM9url3GqrLVMbLCqJZ/QCPCGAzz93XDZ+PDBvnuM2WurATQjj96Xdt3Bh348ZNePWLeeEPHXqyMmIxo1zv39CDPC6dZNbc/3d3atoUf8en8jXIiLMz9+n9s8/xuvmzPG+Pn7CAM8H4powigEeEdkYjbFTusG56h4IyAGFt3MBxRdfBBUrVwJff21/babLoafdEpW/jXq/KVPkyXkVrsYCAq7/NnrnpU8foF07uWXRaBt1cKTXgmc00DuhjLvLmtU5wBNCnk7E3XjI/ft9Nym6t1askJM25Mnj/7olxOCXyEretuAlMQnsWzBpMDvVkxF+LonINrGzUfbAjz6Sk5Kok4IkVr4I8HLndpzE2uoLa3UXTXX9s2VzTIXs7r0pLXp163p+fKPy1UFjQIDze8+SxbNj+cKRI67Xm23l1c4BWKFCwgti1IkmRowAPvjAmnJXr/Z8n9hYoGVLa45PlBBFRso9HZI5Bng+oG4Z9mbu2oTUm4qI/OSVV+Q57n76SX+9EHJSksRKnQgjProFWn2M8eNdB1meOH/ecfyHMqhfKXfdOud9OneWH994w3mdtoumt8Gt3gTCn35qT8SiNWeOPXHL7duuy9YrY9o0ezA0fbo8TtDIoUOOfwM1X7SSnTljTTkZMzpn6zt2DKhe3XnbevVc/x8PDfU8i25srGeTeidm06cD5cv7uxYU3548sb7MhNB13UMM8HxAHeC99pq5uWrV7t71rvswESUhQgD9+iWc+b++/BIYNsy68mbOBIYPl5/Hx49nXC/61X3vf/gBKFTIPgYvrmXnzy9PhTFggPz6lVfkR3Winfnz5fGUirJl5Yv7QoWcy3OXYMYspRVZcfSo/Dl491397Tt0sCcjcPfDpzeWoXt3uTsjADRoIGf6NFK2rNxdVY+ZLsnlyrnfRs3MvIDeTs8QGiqPO9Rq2lRupTfizd/Vqs+sL7VqZU05b7zBMYfJkS8SGcV17JUf+PR/uBCigRDipBDijBBisIvtWgkhJCFEou5rpNyQ1vZ+qF/fs3IOHtTvrZMck28RWSIwUE4gQd77/HNg6FDrysuYEejRQ36eGAK8ixeBSZPk58rk5XFpwdObnHz0aMeun2pt25qfsFc9b6IQruvn6mJa3cp66JCcdTUgQO5m+PChfd3mze67XGr58oJJr+VR68ABz8v98EPX67XjBs0KDASaNHFe7u5H35PPnTIdh1XzOXnKTPZThVV3uAMDrftumTXLmnIocdLeNEsEfPY/XAgRCGASgIYASgBoK4QoobNdWgB9AezxVV3iS548QKNGzlNWaX+rq1Vzn5Bn2zbH10eOyN9VK1fGvZ5EyU54OHD8uL9rQUZ8GeDlzGnNMXLkkKdDkCSgdGl5mScB3nffOQZT584Bly/rbxvXrp8lND+16ov5zZsd1y1cCGzaJHeP1FIniylb1v5cCMfWvVq17OfELF8GeFmzut/Gm3ObO7fr9Z50l3z4EKhdW36eKpXj+VW4q6Mnx1PGXSoBnplzZKV33jG/rVXTKgQGWhfIGnVL9jdvxmEWK2Z9PZI6BngOqgA4I0nSOUmSIgH8BqCZznYjAHwHINGPPHvwwNz0Udu2ed5tfscO+bF7d7bkEXksRYqEl3iBrBvD5squXcBvv7nextukF8pFs15rnNbAgXIwpciY0T4HnhFvz4s6Fb+6BS9/fud5+gA50Oje3Xm5ev4+rbj+f7Lq/6PeOXJ3MeZqbkhAf25I9bGMWj1z5nRftiJtWmDpUjngzpJF7t6qpXS/HTLEvixdOvt4Pb0Lieho4OefnZcr51u5gIiPzLrqv7EnF8hKgOeqG62Z/xsZMrjfTm8Mq159EuoYLG03ajMScvfchIpdNB3kBqC+PXnl+TIbIUQFAHklSXLZLiWE6CGE2CeE2BdmlN45AXAX4E2YAJw+7fh/64UXjLcfNgx4/Fh+rnyPX79uH8NORJSoxUeAly+fPWmJkR9/9PyuGyBfeP/0k9x90kre1EVNPRefOsCLa7lqVv7NKlQwXte5s+cD2fUmpgfk+QPDw4GtW53XqbujqucZ/OsvYPJkx20LFrQ/DwlxzPjpSWa1dOnsAXdAgL3baNascpDWsKH8+ptvgHv3nPfX+3sGBtrHhag/B8qFhxL8+zpgqVFDvmDRHv//7d13mBRF+gfwby1LEBCQpCIKqxIPFQVUjIiK4ElSzAEDZsyRM8dTznQmxIRZQTAgpyInKIoiGUSSBJUMyklG2N36/fFO/bq6p3umZ7ZnZ3b4fp5nnunu6emu6Un9dlW9FUZ5BqFduiR+/Kyzom3qGbV0LpQwwEsda/DCU0oVAHgCwE3J1tVav6i1bq+1bt+gvJsVpGDDBv8AzyRxOuSQ+P7wxx4bvL377pOLfG+84a61W7Wq7GUlIso684N5+eXZ2f+zzybvV5VI1arSjzBTJ0zpnlRWrgy8+aY0K9tlF2c75d38Y9Kk+GEMbJdfDnzyiawX1O9qyJDkGTm9/E56t2yR8QOrVo1//MEH3f0i7Kv1PXoAV14p034B1datkhDGqFZNhskwCYRSYQLHq64CLr44+P1P9rkwfQHtY2pekwn6rr8+9fKlYu+93c1A/T573hOiVq3k3tTglWVQeNM02/5u2hcKTD/VZE1HzfPLeyxR+yJC1HI1WM1lDPBclgPY25pvHFtm7AqgDYCvlFK/ADgcwMiKmmhFaxm/zq+2/Kab5MKcXxbkMO64w/2/kutjFhMRhVKjhpzMJQoCbP/6l7t2payuvlqaVuSLhQuBn36S6fPOkxoUux9SujV46SYP6dABuO46Z96bSOSFF2RZpUrRnkD51eD5/Tm/+650bL/jDqnpDct7gmyaV5oxKW+6Kfxn2la7tqR4T5bMKFmNrDlJsAO8Bg3keJs+W9de6//8oUMT7zNddrBWUiJXrr39ok3GS7Ou3RcvlYsoWgMrVsi0fQxWrJBMtAcfLN+V777zvypfo4YzbV73McdIcqHyonW4ZoHpBMFNm8p9UFPkfBNUo58KBngukwE0U0oVKaWqADgLwEjzoNZ6vda6vta6qda6KYCJAHporadksEwZYwY39/s+FhTED8WSyv/sypXubNBR9T8mIorcxRenlnAjleZPN98stTD5Lt1AbL/94hOsAM7JcTonOm+9lXqGTL99A1JbFzW/5p1hX+dZZ0lmNCD+M7jPPlILZevVS+69n8EePeQ9KyoKt99Eqlf3/z7Yy5LVyPrV4AFSY+p9TV5nnAF07Bi/PFGA9cILTqIAw/sZtgORggIJir3vk7cZqf2coUOBefOCtx8UoNu1hJUqSaA2bZrU8Pm9TkAuIpmTLlMmpeRCQJBU06Uns2SJu4mteU8fe8y9XrIA74Yb4peZWst0+x5XNImGGgkrV4YrSkHGAjytdTGA/gBGA5gLYJjW+iel1P1KqRC9WisW8zsath+m+a7uumvydb3f37K0WiAiyqhXXilbQEDR901s1Ai4807g889Tf+6555ZtiBFzgpypK+A9ewKLFgG33eakmfY2wQyTdt97rH/9FfjtN/eyFi3kvfHLeJlpu+4qyXCGDk3+uSgslOP91FPR7T9RDWeTJsmTy4S5aGE+K+Yqtn2hqFo19+fQvurdsGHwa73rrvjtJ1OjhpPgxS+wtu29twRMZvxGW6I+OH4SjdlnahXtvqJA8hPCffcFPv3UveyMM6Qv6jHHxI/rVd68FwYyIdUm9I0aOWORGnZLhAoio33wtNafaq2ba63301o/FFt2t9Z6pM+6nSpq7R3g/H+E/Q/r00ea/T/ySOoX/ZSS38rbbks8lM/PPwNPPJF69wUiIsqiU0+Vk/R+/aLZnlLSvCzqQZ9fe03Gx0u2byCzTZz23Vf+TE1tnLdmKMy+M9Ev6dVXo6slKSiQ4Sy6dg2XNGf79uhOSocMkaaNQf76K36Zt2xmSIhEvAFeo0ZOopmCAveJuh3QTZ8efBJvX3UP+x4nOq7eDKpt2kh/18JC92usXx8YNgy44IJw+wTi+wObvp8A8Mcfcl+pEjB6tLM8WYBXWOj/uqtWlWM2YkT48mVC2Kyz6ahVS+7tCz4LFgSvP3as3Ddr5k7EVFwcfSKtcsBUOhFJtQavUiWp7d91V+D++1PbV2mptAgZOBBo1066Ofj9HjVvLl0BGjSQ/uVERFQBNGki7f7btMl2SRLr2zd5bZZpRlieTZzSySyYaoBnmmsmctFFksgnaqlkRU2n/6S93f33l0ym9pAe77wj9x06yP02n1GuvGULek/sk6YrrpB7c2z32MP5/HjfH3u+UaOyBejeTJpaO/0Yva/NHurErGt07+5+rGFDqTm3he2bOXSoJHAC3E29KlWS8v7wg/Sl69hRhtqw359XX3Wma9TIbFIVO0lNsmFfjMGDw29/zZrUymMz740d/JvkO37svsr2MaugQywxwItIqgGeLdXa42+/dbcGmDLF6QNozJnjnr/qqtTLRUREVCa1a0vtmneA9UxK54Q21ecMH+5fc1WevEHUYYfFr7N2LfDnn9HtA5DaDK2drJd+AV5Y69c708cdJ9u9805JwnL99f4n6X7JQUwtbaKxp4KMHi37sZuAmqQ8qbw2u4zm82SCg732kn2YWrlEJ34tWkgzSr/kSCZR0aGHSj+92rVlqA277HaSGJMtNBN++cV5nbfcIs2aw9SWmzE3TQ1bIg0apJ8kxRw3O0BLFKxlK9twhkSQWoaA8g3w7N9Dw1tL/7e/uecXLUptH0RERJG47bbs7TtZRkoj1QCvUqXsXdn3lnXIEGmm6NccyB6jL6z69Z3pRCe7DzwgNSy9e8c/FjZRULVq0jd05kxnmUnCYu/fvObNm/1TiZu+aWXpk9K0qfRt0doJ8LZuTfwc+3X6ncx5gzS7T6r3AoF5jZ07B28vKGCzPxP282rXTh6wDB8eHzT36RNfW+nVpImz39atZb9hgiOlgI0bw5/8LlggzcF//FGyBCdqZunH3k+ifWZivNAsYg1eRFLtg2ezP29160om8ET8ftuqVwdeekk+n++/H/84hz0hIipHI0dKO3pye/BBYNCg8tvfvfeW377Ki/dE9MILgX//2z/lfzpeew248Ub3Pvzss48Mu+BXE+MXiIwf75+M5KSTgFtv9d+HNzCqXt0JrNescQZTb9hQ7svSH8WMx3nAAc6QF8n6MSYL8LwXAfwy2u6xhxPQ+q0LSG2mqfnyY5/k2TV4nTu7H/NrKu3tVwjEN/uyM5j67dcch1tukftHHgkuKyAXHsxnZOnSxOsWFcm4kh98EFwOP3YN3tNPS59I+/0YMcJ5n/MQa/AiYvoEp1OTbL7Dp50mF0w2b3a+I36++sp/uWmufcYZqZeBiIgi1L17fJ8cSpxqPhdEMWZWprVpA3z5pXvg7ijVqycJP554Ir3mavfc4x8YHX106tsK6oMHuAdST6em0uu009wBW6o1OX41ut6gz7wO+3NmgtTnn/d/rtbAk08m3rd9fOxjUVDgfmzy5MTbMbx9N5s3d6bPPluGrLH3a47VP/8pNwC4/fZw+wrTVNNIp7aioMBJYGO/p0cdJbXHpsbaNH0zFzcquArwS1YxlCWrtfkOm98xDmRORERUzp56Cjj++GyXIrmBA2V4CO8Au1EqS7OfKGtN/frg+bFrrcqTHTAkOmZmPXOil+hCgrfvWJhA09530PhbnTvLWJlheANme/smyY693K+ML77o1DwkkqkMu36fHft1NGzo7p9at27eNM8E2EQzMlEGeBXhAiIREVHOev55YOLE1J5z3XW5n7kUkM7+YYYdKAsTXEQROHnHbktFoho8W3meOL3wgtO3s1EjZ7lfEOott93c6+qrEweufklWgpiml8cdF5/Z9sAD5T7MkB3vvCPNyMK+74kCvLDjRUbx3iXq25Sor2yqSTAqEIYSEYkywMuEsOXauFESttSpk7myEBERZZQ9hhgl16WLDIlgNG4MPPpoan0+/vgjflD5KVNkMPB07bef9N0LO8xGOpnuUmX66u2/v3u4jDBJd+rVk4D32WclkUmiYTRSOaF84QXJrHnjjfHPa9gwfM2UGe8tbFOyRAGeOR7160uWz++/999GFAGeCZyNKlXCnZhX0CEQwsjf0LWcRRHgJRuvsixMuTZsSLzeHnuU73BFRERElGWjRwPPPededuutklkyrLp15STC1q6dkwAlHc8+C3zySbjaoHHjgPnz099Xqs47z92UMVGtlzlJrFIFWLXKf6gHMwSCt1lj2Bq8m26KLqNelAHerrsC330X/Dkw2/A2j37zzXBlAJz+naa554cfBp+Y160rFy/sMuYhBngRKUuAZz6Pmf6cDRsmSbbGjw9ehwOiExERUU6oXt0JfJLp1Cm1gDRqffpItkubCZSKipI/f5995GTS9K00NVumiWUmrVoFrF7tzIetCTU1An5ZU70ntfPnA8uX+2/n998lI6sdKLZuHa4MAHDkkTKkhRm0vqREkqgA8TWEf/zhZG3N4yaa+fvKyllZArzjjwduuAEYPNhZ1q9fNOUylALOPFOmJ0yQ7ML33598iBciIiIizJ4tNTHkr1Kl+MyR9epJev+RI1PfXpUqUiv52WepP3fjRrmFtfvu7hq2sDV4994r/d9M006bCZ7MiXGdOu4+i7Z69eKTrYQNvkxSmWrVnKCypERq8aZPTxysJqtZCSpvBcA+eBEpS4BXWCjZiG0vvSTf66gGKJ80yZkuLXX6PO/YIWOVEhER0U6mbVupvQnDpJGvaKpXL//mSfbJoN9A8GF16pTe88o6bIRfUDRpUnyGzurVnSETvFLJApro+YlMn+5uFnzJJRJMH3ywHIO2bRM/P1EQuXRpNMNvZAkDvIiUJcBLts0o2L9tO3Y405s2yX1pqTvz7aJF0n941iwZ8/PLL+V73LFjdGUiIiKiLJo+PdslyLylS1OrzSI5md26FdhlF2dZhw6pbaOs/Y7C1OB5A7gePVI7eU500t64cfjt5CA20YxIJgI8b1bNli2lZq+s/JplvvUWcP75zvxHH8m9GXD9hBOAI44o+76JiIiIyk3dukCTJtkuRcXjHew8VeaEON0T46AAb8CA9La3k2GAF5FMBHh2jRoAPPxw8trmMEytHeCUd80a9zqmKfTo0e71iYiIiMjHbrtJv5uBA7Ndkmh8/TUwd256z02UYTOMoBrAu+5Kb3tBKlcGHnoo2m3mADbRjEgmAjy/5pD29+Tll6WmbdSo1Lb7/PPu+XHjgLFj3cvsZpzeJtdERERE5FGlivsEqqI75pjs7TuoBi/qzJfesRvzBGvwIpKJAM/rmGOAQw5x5hs1Aq65pmzbfPJJoHPn+CRNps+sydZrW7kSuPvuzA7MTkREREQVXNRNNDN5op1HGOBFJNMB3urVkkXWW2PtzSobNb8grl8/ybz5r3/J61VKhl4AgGnTgL/+ymyZiIiIiCiPBQ3VkMdj10WJRykimQ7w/Lardfz4jVGbOTN+mQng3n/fWfbAA5J5s1074KabMlsmIiIiIspjQQEea/BCYYAXkUwHePYFi+rVnX3aw3+Uh8GDnVpDe3iQ4mJpugmEy7q8dSvwv/9FXz4iIiIiyjKThTPdDKYM8MqEAV5EMhXgme3ZAZ497mWzZkDz5jL98sv+22jVKrryXHGFM/5lw4bO8h07nBq9atWkz+qCBcDTT/tvp107yVy8fn10ZSMiIiKiHLDXXnJiOHx4es9PFuC1aJHedncSzKIZkfKswfPus00bCabq1Ilfp21bYPfd089y62fKFLn/4w9n2fjxcgMkI6f9vezXz6l1NEx56tQBSkrYpJqIiIgor/Tpk/5zg5JMKCVjeB10UPrb3gnwtDoimQrwTOBjb9e7j8MOk/v99ot/fpjmkqlasULuvUMrBNm+XWrqLrzQv8YunzIKExEREVEZJTqh7tJFai8oEAO8iJRnE03vPm++GZg/P/kg6LVrR1q00D75RGrqXn8deOKJ+MfzdAgSIiIiIkrFPvtkuwR5gQFeRMozwPPuo6DA6Ydn82azfPfdaMsW1gUXONPFxfGPswaPiIiIiDB5styoTBjgRSSbffBsNWrI/fbtwGOPxT/+5pvRlitVfgHe0KHAL78kft7EiVJLSURERER5qmFDoH37bJeiwmOAFxEzIHjUyUL8AkazzC/A+/574MEHg/umnnqqM71sWfzjdeumXsZUDBwY/5quugro1i3x8zp2BFq2zFy5iIiIiIjyAQO8iGSqBq9nT7mvVMlZduihcr/33vHrH3AAcMcd7mV2mezslnvt5W4+CbgzY5andevk9tRT0ldw4ULnsR9/9H9OcTGbdxIRERER2RjgRSRTAd6bbwK//uqukfvHP4CZM4FDDkl9e3agmEuUAk46CbjhBmDDBkmQZJKvHHigs97Gjc50y5b+w6QoBdx5Z7j9DhwIHHxw8vVmzQLuuy/cNomIiIiIsoUBXkQyFeBVrRqfUKigwB30lIVfM8+jj45m26lYvdoZXw8AliwBnnkmvnw//QSsWiXTixbFP26ayj70ULj93nYbMGNG8vUOPxy4996y1xhOmAAceSQzhxIRERFRZjDAi0imk6xE7bzz5N4vwOvc2f85l10GHHVU5srkVVoqg6DbOnYE9twTWLvW/zl2EpeSEuCWW4CVK8telq1b5d7veKXi0kuB774Dfv657GUiIiIiIvJigBeRXA7wTPNOU7bSUieb5vHHx69/993+2xk8WMa0Ky+1avln3QQkyZIfe/3TT5dMoo0apbf/n38G5s51LzM1hOnK5c8JEREREVV8DPAikssn7i++CNx4I3DiiTJvl7FvX2nyeM89wNNPyzI7E6i3xqpWLWd64MDomor62bAh9SaRdoD34Yfhn/fLL5Lkxda8OdC6tXuZt0YxXbn4OSEiIiKiio8BXkRyOcDbYw/g8cf9E6woBey+u/Qvu+YaZ3mHDv7bKiiQZpqvvirNH2fOdD8eJmFJWLfe6vS3S+TOO4H33pP7pUuD1ystBR55JD6QA4CiIqkVHD068b6iqsEjIiIiIsqEwmwXIF/kcoCXjm++CU4E8s037vkzzgCGDZPpNm2A6dOjK0fz5snXsROqJEquMn48MGCAJHMZPjz+8ZISoGtXYPlyYORI/21s3Ajsuqv/Y927A716AZdckrzMqSouluC6tFQ+Y7maDZWIiIiIsos1eBHJtwCvatXgQMZr6FBnOpUarjp1UipSmZn3yCRdeekl//X+/nfgyiv9H+vXz3/5jh3AqFHBj3vLsHy5s+zjj4OTxhiVK8uYiJUrAwcdlHjdRDZtAiZOTP/5RERERJTbGOBFJN8CvHQlCvAGDADOOQcYN07my/NYlZYCf/0l0999J/eXXea/rnfYBHvQ9Vmz/AeDDxojb+tW4NtvnXnzOTnxRODLL6WfYa9eElQmM2qU3P/0U/J1g1xwgWQizdaA9kRERESUWQzwIrKzB3gffwx8/bUT4B1zDHD11c7jgwYBDz8MvP020KSJLDv77PIrX6VKQLdu6T23WTNnevlyoH59qQWcOhWYNAnYsiW4aejll8u4gr/+Gv/YlCnO8Zo/P7UymWBvxQrgxx/DP2/aNLm3B4zPFfPmybiE7KdIRERElD4GeBHZ2QO8Hj0kqDNZJq++Gnj2WRmqAADq1XPWLSqSoMg0ZzzwwPI/qV+ypGzP/+oroH174LDDJMjy+vZb+Sx89JHMN20KLFjgfp0lJc7xSjVbaPfuct+4sRy/X34B1q8PXn/NGtl3lSoyb2ozM2nSJOCdd8Kvf8opkpn1t98yVyYiIiKifMcALyI7e4BntGsn902byr0JYLxJQXbZBWjbVrJajhlTXqVz7Ltv2Z5/zjnOdIHnW1Ra6gw5YdeUjRrlDvCWLXOGcigulqaX77wjtWxffhmuHGZ7RUUSbPpZtkwypT78sPStBIBt28JtvywOOww491yZfu89YMSIxOubz0pZM5USERER7cyYRTMiDPDErbdKJsq2bWX+7LOBDz7wHz5BKWmS56d+feD332W6a1fg888zUtxImGDOWLhQaii9KlcGFi1y5gcNcqaLiyUDqW3xYmm62bWr/36/+so9H9TM0ww18eGHTg2eX/nSsWmTBI5vviljJAb1DzTNcYuL/TOADhoktZAAA7woTZwIFBZKbTMRERHtHFiDFxEGeKKgwAnuAKBPHzk2RUXJn2sPyzBhgjNtB0I2UxsVherV03/uv//tnl+/Hli9On69O+8M3oZfE9VmzaTf4KWX+j/HL5jybkdrYPZsmf7rr9QDvKVLg7ONAsCjjwL//KfUEs6Zk3x7P/zgTP/xhySZ0Rq46ipneUmJLOvf370+pa5jx+AxLYmIiCg/McCLCAO8sqtc2Zm2x7+rXTs+iAKSD+NgD9yeTJgANCwz1p7Xhg2pbcc0WXz5Zf/H/YLIu+5yzw8dClx0kUzPnu309du8OVwZTjxRso3++af/40FjJQY58kjn2NSvL30It251r2Mynj73nPTrLE+LF0tz0hde8B8rkYiIiCjXMcCLCAO8zCksBM47L355osQsd90l/fvCatAg9XIFCdt/rqweeCB+2fPPu+d//tk9P3Wq3NsJWdasAS6+WAItrSU5imGCyKBx+uygHJCawaIi9zEww1IYHTpIjR8gfRS9GT2Li53gtrjYf79+JkxIL1nP6tVO0HnwwdKc9MornQRB27Y55SEiIiLKdQzwIsIALxpjxwK33+5eVlgI1K0bXwOWqK/W/ff7N7t85RX/9fv3T62cuep//5PspT/9JJlN777bf70LLnA+s7fcAgwZIrV1BQWSHKVjR2D0aKfmzq5R3bIF6NtX+hp6A7wFC6Qv3RVXOMuOPDJ+/5984kx7A7wdO5zALmx/vJEjgaOOAgYPdi/XWoLeP/+UANZvewce6DRj9Ktl3WUX4MILw5UjVe+8A7z1Vma2TURERDsnBngRyecAb+RIZ9y1TDvuOOnTZSuMpQLyNsncYw//bfToEbz9iy+OX3bGGcBpp4UvY6675hpJ2GIHUX5MrZQZMsHu9zhxYnxylxYt5H7kSOCNN6QG0fTpM0yTTXtw+GS8zTy3b3fX3D30UPB7bZhhL7z9AL/9VobsuOQSCfgHDIh/7po1wds13+u33nLGMly5Epg8OXF5Ro2SY5jMuecC55+ffL3yMG+efP/CNt8lIiKi3MQALyL5HOB17w78/e/lv9/ateXeL+viq68CX3zh/zz7BPXTT4Hrrw/ex4oV/jUo778fupgVlgmswvajW7BA7k3wtWJF/HODhmpIxDTXNOwaPECS05imokuXSm3cn39KUP799xJsms+IqaH7/HPZhmmK+v33cv/qq852f//dfwB6mz1eYNOmkhjmgAOAQw+V73xQ1tDu3aUWtKy0Bl5/vXzGLbz1VsnMWl5NjImIiCgzGOBFJJ8DvGz54QdJtOEdZw6QxCGNGzvzo0c708cd50x36wY8+aT/9rt3l/HhvM0MAWmWl+86dpQgKJVEKcuXyzh9APDf/wL33lv2cpx0knt+69b4xCuAfMf22UdqJ08/XYbfOOIIaS5qvnelpVKubt2khtG8tpUr5d4MvfHcc9Lv0ozXGMSbbXTDBgnyAMku2qaNNCs23n5bmrt6LV7s7tsIAP/5T+J9m3UuvDBxBtaomJryVPo9EhERUe5hgBcR04+IAV50WrRwp88HgCZNgAcfjF+3Sxdn+sYbw21/5Eh38NitmzNtD8GQLFun4TecwS23+GcAzQWzZkmA9vXX4Z/TuHFwwByVLl38s5qa92r5cgnibOZ799FH8jgg/TBHjvTfh1+fS78ELYmGiDBDONiJbM47z78Z8H77xddunnKKM92zpzvQnjVLXpNp5vnYY8HNQhs0iCbbKAM8IiKi/MAALyImqQQDvMz65Rfgjjv8H3vhBemr5Vf79tFHUmuTiF2jYgd469aFK9uzzzpZKo2BA4Frr412GAYgPhFNur78UgYrL29mUPOomOBs5UoZ5sB4/fX4dYOa9s6cGb/Me5ztINBuCqx1/HtvjBvnnv/wQ+C339zLRo4ETj7Zmf/4Y7l/911n2bBh/tv//Xfgm2/8H0uFqck2Q2lk06xZiftGTpggF15MjSwRERE5GOBFjAFe9lx+udMUz6tnz/jaQC/z3l17rWRWBCRRianZAKS25Oab/ZOyVKkCHHKI/7btbUShbt1otpOtz+vAgdFuzw7eg4Z0MIJqwg4+OPl+/AK8G26QsQrbt/d/TufO7uefeqp//zy771vDhnJvX1yIYqiGjh3jm8QaUdbgzZjh9HtMx0EHyS3Io4/KhQk7MRAgzWVnzpTPdSaS12zcKMNmUMU2bVp8318ionzCAC9ifv25KHPSCXTM+GZ+tJYmlbVry7RpRldSIv271qwB/vUvJwGM8cYbifdpJ4oxCWvM8AHeYQTq15caLjsw8PJmrwSAY49NXAY/qQ5UnqvmznWmk/WfLEt/tlatnGlz7LZulSDPz5lnuudN7eGKFcH70NppEWAP65AswFu3Tmq1lApOijRxYnANph3g/fFHak13lyyRLJyA1GQefLD0jyyLVauCHzOff+/n9/jjgbZtZTrZ8BPr1qU+REWtWkC7dqk9p6IpKZHfq0GDsl2SzGnXTvrzEhHlKwZ4EfM78abM+fFHYPz41J4zbFjqA2IXFATXdvXs6V9bYKfstwO8jz6SoMD0KatZ0/28khLpa1ijhswPHQo89ZRzYnnNNTIQ9wsvyLh+1arJ8osuSu01AcHNClNlhlDIBd9+m7lt280X7emgoQW8zSrN0BN+iYMA4MUXpe+gn9JSSfpy5pmSTXTOHHdtZb16TnPbTz8FWrcOfh3G4MFOJlAT4H3zjdRGduoU/nuy774S/G7c6F+TuXRp/DKtpebVZGc1vIHs4sUSLJrkPoBzIa0stY3nnCPf21SG9ADih+JIx+rVwBNPpP47VB62bZPP2k03pf5cpWRYkoogF489EVFUGOBFzO67RZnXqBFw9NHlv99atZzpoCv6dm2PCdbGjZMT6WrVnJN8c5Jqxu8ztTZmeY0awHXXSdPCtWuBp5+WCwmXXy4JPSZMkGQuUaTlT8fNNwN77pmdfWdTWfqqBdX0X365OzOpPfB6SYmMrzdsmCQS+tvfEjcrtWs1lyxx12y2by8XR664QjKB2mV6/XWnj+Rjj7lPhEeOlMeOOkoS3cyY4a5l/N//3GXYsUMCmX32cdcIai0BZP/+Tk1fcbEEsHZinD//lL6x338vFzMAuShhvhvbtklzX6VSP2E3TfT8MrZmklLSV/imm9wJesrDnDlAhw7uz5WXeT/NBa1Zs+S3KWxt//PPl62MRERUdhH3DCIGeDuHBx+U/nidO8sJk+3ll+MDngcflOyQdk2X6WdlavfMCbipwTAnsebEWylpvul1yCFy847p9uGHQO/eqb2udFSpEp/son9/STqTz8pSe1SlSupj2w0a5GQINcfbzCfz9dfuvmNTpzr9TAEJ2vz6id56K7D33nIRY9gwd/PWE0+MX79JE/e83aLhuedk/MDXXpN5U/P+559y37u3BLC23XZzz69Z464hXLgQePhhmfYmrjG0BmbPlvEL58+XZqyNGjkBjNYyVmbXrtKH9/LLJdBctEhqHk84QS6snHuu//bLIt0WH5s2SdDr9x4kcuedwJQpEpyfeqr/Oub3xxyfiy+Wz8vMmfG/dcl89pkcXzuBEBERZR5r8CLGAG/nUKMG8I9/AIcfHj8Q+yWXxJ/QnHiinOjYgd/gwdLfz6TPr15d7k3gYO7DJmjxfvZ69ZJAoE8f//W9/cYefjh8un070CwtlRNoQIYJAMIlLPFKNSDMdlMwM4RBOsywKqkytVtffZXa84KSDxkzZwYP53H22UDz5mUfi+/99+UzftVV7oRH5vvjDe68CgriLyTY4xQGjWk4eLAEs4cfDrRsCey1l7M9wOl3262b1Krt2CFBZ8uWTgB1333AmDFhXmVi3lrGdBPn9O0rF4yCglpAajzvusu9zNTOBTURBpya6c2bgdtuc9b11s56+dWgnnxycH/QVM2YIU1bc9nKlalfuCEiygQGeBHznuwTBdltN8nYaU66TIBnTvpMEOXtoxfE7+JCvXru5qSA9AEEZABtY//9gQEDwifW8PZDMmMFDhokJ8NhMhg2auSe99Z6/vOfiZ/P/q7h/eMfiR9/4IHyKYef7dultjmZGTOkWaot0VAKgAQdV14p02bcQkCG0jA1VH5NTD/5xL2dN98M3seWLcFNLQcPBurUcYIfb7NeM19cLOV59NHg/djM98/0uZw7V2rN7dfSr1/8mKHe5pd+7JrpgQOd/zS/7KujRkmNp/d5ZbVli3wutJY+zp9/LheNvO+/raQkuxlOtZbftEzU9FJ4Z5wBfPBBtktBlH0M8CLGYRIoVSag8wZ4L74oJ4hhm0UF1R57a+V69pSrzAce6NSI2INzm5qUTp2cWhu7f1/nztI81dBa+gcOHy7B6N13h8smO2yYe4w4u/w9e0othW3bNmeIgyOPdNdC9O0rTc+8XnwxcdZUElEl20lXUHNBm1/ynHfeSfycoAQqZ5/tjHvoV4vmHazer8/aH3/I/QMPSA2nqV0aNszJqnvFFcD69U6tjmmOapgAz9RE3n67UzurtcwvWhS/b7t5KSD9kJ97LnHmUXv9deuA7t3dNaJr18ox9l54sS9aemueu3eX7xjg7qNnLiKFsX49cMEF7iFBatSQlg3FxVJrbWoBzTH307dv8gy6gDsIjkJpKTB6tBPgjhgRvO4XX5R/v8udzfvv+w9jZIwZkxtjfRJlGgM8oizz1uCZ+Tp1gMsuC3/RICjAu+CC+CyGpvarXj1pimUP6G3KYZflpJMkmcoPP7jHazPlbNEi8Z+q7fnn5SToyCMliPTuCwCGDJEaveHDpdnc2LHy+tq3l6uzo0e7A7yjjpJ+YqY/lnHppcED3B91VLjyevuVUcURJtV/sgsofk0PZ8+WGvZXXnGCOZPR9MwzJdgwg9UD8h3p3Dn+YoMJCuwTzvHjJaCcN09q9Hr1kuXz50tN4vLl7gQ6WicOfLZskRr1MWOc79jzz8uFnKeflvkNG6RPsF/CKvt7lqhpsR3g9e7tH5j6efppeV2HHuo09QakxtYE32GCsrfflvtkyXaiPrl/+mnpv/n++4nX++wz+R1t3jx5c1dK7D//iR8DM4xvv5WmzXffHX2ZjLfekv/soMzKROWFAR5RlpmTGHP12SRfSVWlSv4nYEoBjRsHP6969eAg0pwsVa4s4/8demj8OjfemFo5r7zSXWNj+tLZCSPMOIOnnSaZHI87znmsd2+5wr/33s4yU36/jKZ2baNx7bVSM/Df/8rjicZsW7Ik8euh3PXkk2XfhulXajvgALl/7TWnhstbE3jddc70X39JbbV3SBcTbNhNyu65Rz7/ZtgTE/S0bCkXa+zvstZOkAZIX0JvOa69Vk46u3RxhsQwwehTT8n606fHv0bDrsH74w//WsLS0vjAaezY+PVOPlmytALSguDuu51mlYsWOcfV8BsCZ9WqxBk9t2+XC1Hnnuvf9zRRgPftt+7jl+i3EZBaR9OXOdnvhH2Rbfx4OWZTpsgFgv32i37YhnXrEmdLDatmTfkM/fJL2ftAjholx3P+/LJt55RT3BfoBg6UVh/JmM9uJmtRTRbkFSvcF2Ly0dy58n7m6+vctEnGVTUthyoaBnhEWWauThcWygnj99+nv62aNYFJk6ILSsL02QnqC2f3q0p0ov3ss3JyU62aBH6PP544CYRx1VXx/RPD9IFdtUpOhHfbTQbGXrNGrgb/+KP/+uk0u/YLhP3UqZP6tql8JWoG+u23zjALI0fKxQgjzPALCxfKibjpJ2iz+wwG9Y3V2j1Y+8CB0mTSbq5sjx9oMu2a+40b5fl2TbqX/V08+mipWa9aVQJGY/r0+KDr1lud6UmT5P6zz5y+vz/+KM1bE/Wb8+v3t+eekjDnnHP8x+obO1Yef+cdp7nppEnyPV62zB3gHXaYU9Mybpy8vsJC5/0w72FQQNC9uzNtZzG2X9P8+RLg278jvXrJb1WHDlLbu3hxuJrFyZPdNcN+xo2Tz1W9enKhYMUK57ENG+Sk9frrneUjRkjZFi2S4zNvnnt7mzcDzzwDFBXJ8B6bNknrC28/7O3b4zM5e733ntzbn21APhPeWrXSUmmuHCbwve02+f4lG67HbMu8Fxs3StZrs3zIEKfJcbrMf+aTT8pYpNlu/p5J5v0cOjTz++rWTfoVl6fvvpOm/AMGlO9+I6O1rlC3du3a6VwkPxHZLgVVRHPnal2rlta//prZ/YT9jI4dK+t16qT1rbfK9D//md72Vq3SevFimX7jDa1vvDH1cidy4YVShldekfktW7Q+/PD4spl5QOu1a4O399Zbznrz52s9ebIsb9nSvQ1zq1JF65Ur45cvXx6/bNdd45fVqOG/3Shuhx6auW3zlvymVHb2cfPNqW2jfftoyvLVV4kfLy52pu3vZP/+7vW839dkt23btP7mm+DHtdb6/PNl+rXXtF692v34hAmyzosvupeXljrTs2e7fydKS5OX035+795an3RS4vU3bpRtfved1p995uzr44+1njnTvb9Ego6B97EePbT+/nutDz5Y5ocO9d++d1uffCL3J52kda9eWv/wg6zXr58s//NPrTdv1vrrr+PLdu65ss4bb/jvY+tW+Q3XWuvnnpNlH3wQ/BrDvGbbsGHyWJ8+Mm/+P0xZwxzfZJo0kW20aSP35r8pKpMmab1+fbTbTNfdd8trvOceZ9nmzc73I0r2e/Pf/2p9113R78Nr+HDZZ69emd9XugBM0do/XspoDZ5SqqtSar5SaqFS6nafx29USs1RSs1SSn2plGqSyfJkitbZLgFVZC1bSqKBffbJ7H6aNQs3GLr9eTbTfjVqu++efHu77y5XfgHpB/T44+HKGpb3u7fLLvF9BL0S1Q7aGfCaN3fGXJs7150l0PQ3bNNGrmrbHnkkPlEF4J/MwxybRMIkjvAqLZWr5Ham1ETCJMXJNd7mfLmmPP4X/PZh1yKG4ZecKB3JhuIIqtF89VX3fKrHrVo1/76Dxn77Of3zFi4EFixwP26+X96aRHt4B2+ZCgqkr2Ui9u/Mhx9Kv+FETCKeI46Q2gpAah579gQOOsj/OY895k4kdHvcWZZYsSL+NRQXy++3aZ5r/z4leg9q1JD7H3+UZDrnnCPzn30m9+vXS3KhY4+Vmknjiy/ctcl++yoqcvqAm/fp5Zfd66fSwmXtWnezUrMvU4NnmmyabLRBJkyQ1xWGOY6mZviSS+R+yBD3GJ7p+OsvaR1SHuPbhuFN9vTnn/L58Gbv3bhRWiFEUZu5Y4eMTWpnfl62DPjmm3DPX7RIat7tIXaCmC4vJkt4RZOxAE8pVQnAcwC6AWgN4GylVGvPatMBtNdaHwhgOICBmSpPJkWdlYsoExYskCYHqTBNDdu2jX9s1arUt5cpdvOnZM07kzW5DAq0K1WSP6kvvnAStxx+ePx6ZlgKO+lNjx7+AV6yxAyA9CsMM+yEcfbZzmscMiTcczp3ds8nSkefK8yJZqL+pVR+ko1LGZRZ03uiFaZ5dioWL3b+ox98MD4YNIGVNwA1AQsg4w1efLEkfrn5Zlk2bFi05axfP/4kNWj4kEGDJAPqLbc4Y64+/3zwMBt77RUf+HuPswnUALlIFcQExKaJp2kWby4SFRc7zd3trLEnneT01dJa5vv1c69jAq5LLnHG5fz0UwnyBgyQ59lBYt26kmk6SMOG7gtw5j32/gcsXx7/3CVLpPn05s3S369HD2mG6hcs28xnzU6yorV8fqZOlfE6g/qsrVsnx6NrV/f/kNbS/NqMe+mXUbhLF3dfddtnn8nwEV47djiv5ccfZRvbtknZjzvOaYa7YwdwzTXu5r5AfIBn+pm++640bTTHolYtaQZe1gAX8O8S0qqVky1848bE789NN0lf0C++SL4vE+B5h5qqMIKq9sp6A9ARwGhrfgCAAQnWPxjAhGTbzcUmmjt2RFO1T5QLFi+Wz/Kjj8p8ppuOlkXfvlLWIUOcZX/9lbgJz7p1ibf5++/xTbL8zJsnzcO01nrqVK07d5btDxoky2bMcDfXqlzZmf/0U6f5p7dp0YAB7vljjpGmS2b+0kvdj3ub5K1Z4y5nUJOwkSOd6eeek2Y2rVpp/dhjzjbN8U3n9uij6T83zO2EEzK7fd52jtu4cfI9uffe7Jeld29nWmv3b8EHH7jXNfO1a2vduHHybdtN1wGtu3dPvP6cOVpPmRK//Pvv45d17OhMz5un9SGHyLT5jfP+DtnNVSdODH98ZszQ+vHHw61r71Nr6R5g5s84w33e5l1/8+bE237ppfj/g5ISud9jD1nHbn7vPfZXXOF+7ooV/r+1xqRJMn/QQXJfqVL8/s1zliwJfmzHDmeZaULcv7/MH3mkzI8f7zTDPflkeezTT53Pp818Z0xzSe9nw3Tt8L6mKVO03r49vpwjRjjnHUGvwb55m0qb5td+XUqMXr1knREjZP7XX7UeNSrxurffHry9bEOWmmjuBcBOzr4stizIJQA+83tAKXWZUmqKUmrKWpP+K4ewBo/ySVGRNGu55RaZz3TT0bLQOn6ZX6KVjz+W5kvHHpv8aly9euFqsFq0cGrpDjnEqc0ziVPsZlVKOTV4r74qTbC8VzNXrpRMdQ8/7GQRBeRKe7VqkkGxc2e5Uv/MM5LR8Ikn3FfttfbPGmo780ypNbETRFx5pWR/mzNHrnDef7/sY/BgqSl77TVn3dGjZT/JMqcla37qN/yEyUAXxm67yX3YhDa2++9P/TlBw5CkatasaLZD0di2TWovUvnsZYpdY/f66+6aJu9YkWZ+/XppopaMtxlisprS1q39a1z8BrS3m01u2eJse80aqfnxNn+1m6umkhBs1SqnxjWZNWvc+3jiCWd+x47EzdIPPDDxtp95xukK8MMPUuNYqZJkwzW1kHYN3sSJ7ue/8IL7dRx/vJNd1qtrV+c3zozdac45hw6Vz4jdzLqoSFqGmHVt118vz33qKaeG+tln3dv0a+FiEiiZ91FrSbDifV+9n7EBA5xxdY3p0+VzdfLJMjzK9OnSlLZSJen6cNttksTHO2aoH29iJ9Mc9623nDDQy3w2TztNavSbNJGsrKNGxdfim1YHFbWJpm/UF8UNQB8AL1vz5wN4NmDd8wBMBFA12XZzsQZv2zb5KD30ULZLQrRzWbBArhbbtXJ2coPytG2b1oMHO1dytZar2T/9JNNVq8ZfRdXav6zTpmndurUsP+645PtO9HqDrgprLZ3VE13t9NvOpk3OMr8EM+a2fXviq+p33ul+zE68YNd2Bt1MAqDbbku+rvf222+JH9999/hl06Zp/eST7mXNmqW+b2+SD96ye7v//uyXoTxu++3nnm/QIHP7atXKPX/99cHrPvRQ+O2+/LLWZ5+denm8tYTHHhu/zuTJqW/X7/c17G3uXKcFSJUqqT9/6tTEvz9Nmmj9wgtav/OOe/n48Ym3+803UsMIODV45rPSpYvMjxsX/7y//U2S1yQrd/368cs6dYpf9sYbkrCnf//ENaomcY99239/rU85xXmP3n5bkqYtW+bUgvrdGjd2/oNmz3aWB9Uq5gIkqMHzXRjFDSGbaAI4AcBcAA3DbDcXAzzTfCrsiRIRZZb9B5wrpk1zZxszgso6Zows79w5+bYTvV7z2HPPyQlSuoL28fLL/n+WWmt9330yfdNNzvp162p9+umSVdFkKQPkT9y7r0S3Vau0vuQSyYpqln3/vdYdOvifgHifm+jxq6+OX/brr/EBXocO/s//+WdnuqjI/diOHcEB5tChklHXzF98ceJy2s1seeMtV2/dugU/ZprBZfJ20UXu+aDvbaq3KDLQ9u0rTS7TeW716uHWKyhwpk2W7KDbt98603vtpfV//pP592effeKXnX++XNwEtH7ggdS2t/fezrTpKlFYGO653gshgNYPP5z+/2amZSvAKwSwGEARgCoAZgL4m2edgwEsAtAs7HZzMcAzVxceeSTbJSEireX7ePfd2S5FOOZPxGvdOq132UX+kJMZMsTpSxS0fbtmMR1B5dRarvR6/xSN1avjay0N+yqp374S/TEXF7vXb9YseBvmZoYVWLdOjtmbbzq1hfbJ2vbtkj5/5kxn2YYNchHPzPfurfUBB7i3366d3Nv9QP/9b//j4lc+w6RaN6nng26jR6d24hP2Nneu1osWJV7H21eUN954y/1bsotCp54av6xLl8yWKerhgjJRO3388cn+IbMjUYCXsT54WutiAP0BjI7V0A3TWv+klLpfKdUjttq/ANQE8L5SaoZSamSmypNJWst9OgMiE1H0tAbuuy/bpSib3XaT/ixBmdFsF14YPFi1ybIXdXZC27vvBj/WsKEMHu3HLN9/f/dye/gNO815167AdddJdjO7r+W8efGDJzdtGr8/kw22sFCO2XnnST8OrWUQ6d13l8crV5Z09QceKP0rAaBmTXefk5ISyXpntGgBjBkjmVbtTG/XXAPMng1cdZWTZS4ZM7BuQYH06zGv1ds31H5P7b59dv/KRI46ypkeNw7417+k/C1bJu9Dec897vnTT3emox5249JL3fPz58e/v/feGzxMQLZ5y0+ULT16JH78gw/il/n1a4+S3V8xCmH7aaYi2fBLuSij4+BprT/VWjfXWu+ntX4otuxurfXI2PQJWuvdtdZtY7ckH73cZAK8TJ5AEVF+GjEi+RhZZfHJJ5JUIJNq1ZIO6nZigzBMgOcdQuLzz53punWd6c8+kwQBNWu612/Rwkm6YviN7/b555KAIKjT/MKFkn7eNmaMjEemlLsTfkmJBIhGUZGUwaTrNpSSwOy558IP6WBfNOzdG+jf3ymLSZ7w1lsS1JvA8YADgLPOksc6dHC2ZZbZqlcH7rgDGDvWWdapkwwBYMqf6ILldddJ0pk2bWR8TcB9TL3JD8IoLY0fow6QlP2DB8tYZPffL+NTNm8en5yjRg1JFpSLXnzRmb7++qwVI2Patct2CSiTwiQ8ySXp/P7kI4YkEUiUfYiIKJFTT5XxhzKloCC4Bi1K1aolz+DpZa4MewO8WrVk7C5j1Cj/K8uJ1KsnAy4Dkmn099+lNrFPn+Dn1Kwpz7PVresETGbsPQB4/HHJeGoCjeuuS618hglq7CyJ5j/FXDQ0A1rvuafUKq5fD5x7rryvduBoas7s2q0HHgC++sq9zzZtJINcOjVtN94oQTYg2SdfekmmvWMpGvPmOdPNm7sfM+OlKSW3Ro2cx8aOlWNzyy3y2BFHAHfdJbV3foqL5f0FZIy1ZO68M/k6XkrJZ8B8rrxatpSTYb8g7vPPJQhu1Sr5fsJkxQTc2XazyW+MNcofqQwunwu82T13VgzwIsAmmkRE4tRTJRAJIyjAA6Smygy0+/e/S01WqszQBpUrxwdu6bj1VuDJJyWYaNFCArCmTeU/oGvX1Lb13XeS5rxZM0kTbjdz9QZ4hYXAfvs5jwcN9fHww1Kr2KePM8Bz1aoyPIj5nwIkGDduuCFcGvCWLeXe2zTy2GNleI9zz3Uvf+89qUU1+6pd212DedttTsBnymber7p1pWlys2aJW8b89JMTsBcXy77mzJFaca+iIhlaxEjUhNuuNTZ27JDbpk0y0LixZInTJPbTT+V1Pvlk/PNPOgnYsCHcRZC99pLtPvMMMHJk/ODshjm2YYPVPfaQ92rtWvn8BjXrTlVQjUmYQDubXn9dLpYkYl/UIapIGOBFgE00iSjfrVvn7nMWZMQIYMWKcNvcc08ZP3DIkPjH6tULV9uRiPlNjmqs0mrVpHYmij4pHTs6NR/167v77aXbKqRxYxlbapddnPHK/Mbvs5c98YQEHn7sJqnTp8v7dNVV8euZMQ3HjHH6wZ15pjTd22UX57XYAcUVVzg1iL16yX1hoQQ1332X6FU6Wrd2+reZcShbtfI/KW/e3B24FRRIraDtoIPkNS5aJE1CjVatpGx+73vTphKEaZ283yIgQc/JJ0utyL77Os1K7XEzzXb795fgcfx4d9PhO+6QmtO335ZA+N57/YNaryVL5L2qX19qVseNcz9uxvAMY8YM4JVXZDqoz1Oqte5eicaeTPd8y4zvetZZMrZosnFRq1dPbz+Z8vTT7qbV5j2Imrmgk6qgmnwqfwxJIsAmmkSU73bbLb6fW1kVFsqJ7oknRrtdw5wE2rVXFUEUFw1Nv0u/Zphhk37YNX3VqklimkTB7QknAP/8p3uZWb+gAPjHP5zlpaXyn7l0qbv2sn9/qV0Kq0sXqZk74QT38qlTnekRI/wTAU2YIMf67bdl/tJL5TXWqQMcdpiznjcQSsX557vnq1cH/vMfCUgXLXL6cZqg1G9wccD9WXjgAaBfPxkge+xYOcannpq8nPb76XXPPTKw84IFUjNqajtPOy1+3datJSC9+GLgiy8kKZAZFNq47z55rUEBlElelIj9HqbjnXekhtqoX18+n0uWJE4MZTvvPPkOjR7tXIjIplq1JKg3rSSSBajpWL48/UAt3cCQoscALwKswSMiyj3moltUNXipMBk502GaYx5wQPrbMLVT9kn9oYfKCa6d8TKZxo2dPnfpqFtXMomOGSOByNKlwMCBTm1X48aJA4+w+/Cyj92pp7ovTnjfm3POkcysdu2k/X+eznt55plA377AG28kXs+cEF93ndS6erPBGqYfbYsWwReTO3Vy9+UEgMsukxpGE8QGufdeaW7brJkEcHXrAosXS4IbL7tP1oknSiDXs6eTVP7LL93BPAB8+6173q4tNEGwyer6738Dr74anzXWFnTRZsQIpxxnny19Ro1ff5XPoF+G3SAtW0oT1C5dgA8/DE5YNXSoe75ePfe+vezkRytWSNPi+++X+RNPlGDdry+ZaU5tLgQUFvonUkrXpk3SF/aii8Ktb/rgGmEy2b7zjjMddEEj19h9wisKhiQRYA0eEVHuMf2zstEXaP788MkyvE45RYZtuOSS9Pc/bJg0ozNNJAEJHlIdSmDp0vQTyADyv/j0006NTePGTuKUTApKLLRmjX+2zpo13WVSSmq1/LKxAlJzdccdwft/7z3gtdeSl7NuXQlGzjhDgo+gC8W77irbGzMm8fa8zx88WJp5nnOO//qJsnoWFfnXECWrNerc2Tn+JhCrVUuaml5+ucwfeKCz/htvyHrmJLpBAyfACAryTCBpe/TR+ADX7veZrLnl6NHSPNgk6wEkIZGtsFD6Mb71lsy3bSsBqd/wA48/LuWcM0cy8drfo9deAyZOBJ5/XmrjWrVysgO3bi3BetWqkvV32jSnj6+5+GP6LRcWAnvvLdPmfVm82NmPnbjI+PRTuQBkmgV//LE09Vy+3KlJDts01e7bPHeulKVVKwngTXNYL/t7Nngw0K1buH0dcUR8f+FUazDtzMdB/H4jx49PbT85IWiAvFy95eJA56tXy/WiZ5/NdkmIiIhIa2eQ4p3JGWfIaz7ySK17945mm95Bn1PRqJE8Z+VKmS8t1XrQIK03b9a6TRuta9Z01r3gAll36FBnWefOsuyJJ7S+6y6nDKecIo+fdJLWRUWy7L77/MswY4bWixYlf31Gz54yP2VK8HNGj5Z1TjjBWfbbb1pPnSrL69ePf8727YmP4YsvymM33RT/2NKlWr/2mhw/rbX+4gtZd/VqrS+7zDkHXb7c/Zp23z3+/Zs7V9bZsUPrbdv8y7Jwof+A3x995Ex37qz15MkyfcEFznNLS7UuKdF62jT/bbz7rjM9bZqsn2yg8bPOks/M3/7mXl5SEvwcpeKXlZRoffjhMv3UU/LZMI/NmKH1FVdIeX7/3Vn+1FPBn4NsQ4KBzrMesKV6y8UAb9UqOZLPP5/tkhAREZHWcvJvBws7gyFD5Hxk1qzotvnMM87J7pgxqT33u++0fugh/8dKS52ARWut167V+vrrJRAyTLA1YYI8Dmj90ktab9nirLNsmdYHHST36fAGXZs2JQ7utNZ661atu3d3giVj+3YJ7t59N/45JpDp189/m3/9pfWAAVqvX59a+Zcs0frYY7Vet85Z9uijWn/zjdb16jmBWeXKTpCTzNKlznEZNcp9jKZM0Xr6dDkGWstr/fPP+G1s3OgOrnr2lPfNDvBWrJB1X3lF69NPd6//3nsSME+dKsGo1lofeKB7Ha2DA7yCAnf5zzxT1j/qKJn/5ht3GW2bN/svzzWJAjwlj1cc7du311OC2kxkycqVUg0+aFDw+DhEREREmbZunX+/xLIwzerK+5Tx99+lCecNN2SuWW95vrZNm6TZdBSZeMN4913g2muln18q417+/rs0la1SRbKkpnuM1qyRppudOklW1d69geHDpR/w8ccD//2vs+7tt0szW2P6dGkCazv6aKc/5/nny2dj8mSnia/dJLdSJWnK+vvv7qakRx4pTXG/+04y/ZpsrfZrKy113qNcDpOUUlO11r49GdkHLwLmzWcfPCIiIsqmqIO7bKpfX5KV5Mv5Vc2a5RfcAZJoZu3a1II7wAl6TI4J75AiYTVs6IzDacYy7dlTgk5vJtOTT3bP20PHGCZZ0muvSSIeAOjQQRJI7bUX8NVXzrrmM+M93ma+UqXg45IPSRMDuiFTKkyAlw8fCCIiIiLb4sXusfjySfXqwJYt2S5FbjEBnknm8uWXwObN0Wy7cmVJTON1zDFyPl21qmQu9Qu+nnlGHj/jDP9ESsceK8liCgqAffaRZd713nxT9t++vRMEVpRsnqlggBcBZtEkIiKifFVUFG4g94ro119lmAxyVK4smU5NRslq1co+nElY27fLvTdjJgA0aSKZRRMxmUODavCaNAGeeMKZnz3b3bQzXzDAiwCbaBIRERFVPPXry40cSgEzZ2a3DGVtarzvvsC8ecnPzYOG4vjii4od+DHAiwCbaBIRERERlU2bNlKr5tcHLxVjxwITJqRf83jiiWXbf7YxwIsAm2gSEREREZXN119Ldvqy2nNPoE+fsm+nomKAFwHW4BERERERlU3duvmVCTZbGJJEgDV4RERERESUCxjgRYBJVoiIiIiIKBcwwIsAm2gSEREREVEuYEgSATbRJCIiIiKiXMAALwJsoklERERERLmAAV4E2ESTiIiIiIhyAUOSCLCJJhERERER5QIGeBFgE00iIiIiIsoFDPAiwCaaRERERESUCxiSRIBNNImIiIiIKBcwwIsAa/CIiIiIiCgXMCSJAGvwiIiIiIgoFzDAiwCTrBARERERUS5ggBcBNtEkIiIiIqJcwJAkAmyiSUREREREuYABXgTYRJOIiIiIiHIBA7wIsIkmERERERHlAoYkEWATTSIiIiIiygUM8CLAJppERERERJQLGOBFgE00iYiIiIgoFzAkiQCbaBIRERERUS5ggBcB1uAREREREVEuYEgSAdbgERERERFRLmCAFwEmWSEiIiIiolzAAC8CbKJJRERERES5gCFJBNhEk4iIiIiIcgEDvAiwiSYREREREeUCBngRYBNNIiIiIiLKBQxJIsAmmkRERERElAsY4EWATTSJiIiIiCgXMMCLAJtoEhERERFRLmBIEgE20SQiIiIiolzAAC8CrMEjIiIiIqJcwJAkAqzBIyIiIiKiXMAALwIlJXJfqVJ2y0FERERERDs3BngRYIBHRERERES5gAFeBEyAV1iY3XIQEREREdHOjQFeBIqL5Z41eERERERElE0M8CLAGjwiIiIiIsoFDPAiwBo8IiIiIiLKBQzwIsAaPCIiIiIiygUM8CLAGjwiIiIiIsoFDPAiwGESiIiIiIgoFzDAiwCbaBIRERERUS5ggBcBNtEkIiIiIqJcwAAvAqzBIyIiIiKiXMAALwKswSMiIiIiolyQ0QBPKdVVKTVfKbVQKXW7z+NVlVJDY4//oJRqmsnyZAqTrBARERERUS7IWICnlKoE4DkA3QC0BnC2Uqq1Z7VLAPxPa70/gCcBPJqp8mRS3bpAq1aAUtkuCRERERER7cwyWYN3KICFWuvFWuvtAN4D0NOzTk8Ar8emhwM4XqmKFyZdey0wZ062S0FERERERDu7TAZ4ewFYas0viy3zXUdrXQxgPYB63g0ppS5TSk1RSk1Zu3ZthopLRERERERUsVWIJCta6xe11u211u0bNGiQ7eIQERERERHlpEwGeMsB7G3NN44t811HKVUIoDaAPzJYJiIiIiIioryVyQBvMoBmSqkipVQVAGcBGOlZZySAvrHpPgDGaq11BstERERERESUtzI2NLfWulgp1R/AaACVALyqtf5JKXU/gCla65EAXgHwplJqIYB1kCCQiIiIiIiI0pCxAA8AtNafAvjUs+xua3obgNMzWQYiIiIiIqKdRYVIskJERERERETJMcAjIiIiIiLKEwzwiIiIiIiI8gQDPCIiIiIiojzBAI+IiIiIiChPMMAjIiIiIiLKEwzwiIiIiIiI8gQDPCIiIiIiojzBAI+IiIiIiChPMMAjIiIiIiLKEwzwiIiIiIiI8gQDPCIiIiIiojzBAI+IiIiIiChPMMAjIiIiIiLKEwzwiIiIiIiI8gQDPCIiIiIiojzBAI+IiIiIiChPKK11tsuQEqXUWgC/ZrscPuoD+D3bhdhJ8dhnD4999vDYZxePf/bw2GcPj3328NhnT64e+yZa6wZ+D1S4AC9XKaWmaK3bZ7scOyMe++zhsc8eHvvs4vHPHh777OGxzx4e++ypiMeeTTSJiIiIiIjyBAM8IiIiIiKiPMEALzovZrsAOzEe++zhsc8eHvvs4vHPHh777OGxzx4e++ypcMeeffCIiIiIiIjyBGvwiIiIiIiI8gQDPCIiIiIiojzBAC8CSqmuSqn5SqmFSqnbs12eikop9apSao1Sara1rK5SaoxS6ufY/W6x5Uop9XTsmM9SSh1iPadvbP2flVJ9reXtlFI/xp7ztFJKle8rzE1Kqb2VUuOUUnOUUj8ppa6LLeexLwdKqWpKqUlKqZmx439fbHmRUuqH2DEbqpSqElteNTa/MPZ4U2tbA2LL5yulTrKW8zcqgFKqklJqulJqVGyex72cKKV+if0uzFBKTYkt4+9OOVBK1VFKDVdKzVNKzVVKdeSxzzylVIvY593cNiilruexLx9KqRuU/M/OVkq9q+T/Nz9/87XWvJXhBqASgEUA9gVQBcBMAK2zXa6KeANwDIBDAMy2lg0EcHts+nYAj8amTwbwGQAF4HAAP8SW1wWwOHa/W2x6t9hjk2Lrqthzu2X7NefCDcCeAA6JTe8KYAGA1jz25Xb8FYCasenKAH6IHathAM6KLX8BwJWx6asAvBCbPgvA0Nh069jvT1UARbHfpUr8jUp6/G8E8A6AUbF5HvfyO/a/AKjvWcbfnfI59q8D6BebrgKgDo99ub8HlQCsAtCEx75cjvdeAJYA2CU2PwzAhfn6m88avLI7FMBCrfVirfV2AO8B6JnlMlVIWuvxANZ5FveE/BEhdt/LWv6GFhMB1FFK7QngJABjtNbrtNb/AzAGQNfYY7W01hO1fEPfsLa1U9Nar9RaT4tNbwQwF/JDyGNfDmLHcVNstnLspgF0BjA8ttx7/M37MhzA8bErtD0BvKe1/ktrvQTAQsjvE3+jAiilGgP4O4CXY/MKPO7Zxt+dDFNK1YZcUH0FALTW27XWf4LHvrwdD2CR1vpX8NiXl0IAuyilCgFUB7ASefqbzwCv7PYCsNSaXxZbRtHYXWu9Mja9CsDusemg455o+TKf5WSJNUE4GFKLxGNfTpQ0E5wBYA3kj3oRgD+11sWxVexj9v/HOfb4egD1kPr7QsBTAG4FUBqbrwce9/KkAXyhlJqqlLostoy/O5lXBGAtgCFKmie/rJSqAR778nYWgHdj0zz2Gaa1Xg7gMQC/QQK79QCmIk9/8xngUYURuxrFcT0yRClVE8AIANdrrTfYj/HYZ5bWukRr3RZAY8hVwJbZLVH+U0qdAmCN1npqtsuyEztKa30IgG4ArlZKHWM/yN+djCmEdIcYpLU+GMBmSLPA/8djn1mxfl49ALzvfYzHPjNi/Rp7Qi5wNAJQA0DXrBYqgxjgld1yAHtb841jyygaq2NNDhC7XxNbHnTcEy1v7LOcACilKkOCu7e11h/EFvPYl7NYM6lxADpCmuIUxh6yj9n/H+fY47UB/IHU35ed3ZEAeiilfoE0pekM4N/gcS83sSvq0FqvAfAh5OIGf3cybxmAZVrrH2LzwyEBH499+ekGYJrWenVsnsc+804AsERrvVZrvQPAB5D/gbz8zWeAV3aTATSLZeGpAqlyH5nlMuWTkQBMdqi+AD62ll8QyzB1OID1seYNowF0UUrtFrta0wXA6NhjG5RSh8faUF9gbWunFjserwCYq7V+wnqIx74cKKUaKKXqxKZ3AXAipB/kOAB9Yqt5j795X/oAGBu74jsSwFmxzF9FAJpBOtvzN8qH1nqA1rqx1rop5JiM1VqfCx73cqGUqqGU2tVMQ34vZoO/OxmntV4FYKlSqkVs0fEA5oDHvjydDad5JsBjXx5+A3C4Uqp67NiYz31+/ubrHMhsU9FvkCxHCyD9Zu7Idnkq6g3yY7cSwA7IFcZLIO2dvwTwM4D/AqgbW1cBeC52zH8E0N7azsWQTq8LAVxkLW8POYFYBOBZACrbrzkXbgCOgjQHmQVgRux2Mo99uR3/AwFMjx3/2QDuji3fF/KnsRDSjKdqbHm12PzC2OP7Wtu6I3aM58PKnMbfqKTvQSc4WTR53MvnmO8LyTI3E8BP5vjwd6fcjn9bAFNivzsfQTIx8tiXz7GvAakJqm0t47Evn2N/H4B5sePzJiQTZl7+5qtYgYiIiIiIiKiCYxNNIiIiIiKiPMEAj4iIiIiIKE8wwCMiIiIiIsoTDPCIiIiIiIjyBAM8IiIiIiKiPMEAj4iI8p5SalPsvqlS6pyIt/0Pz/x3UW6fiIgoFQzwiIhoZ9IUQEoBnlKqMMkqrgBPa31EimUiIiKKDAM8IiLamTwC4Gil1Ayl1A1KqUpKqX8ppSYrpWYppS4HAKVUJ6XUN0qpkQDmxJZ9pJSaqpT6SSl1WWzZIwB2iW3v7dgyU1uoYtuerZT6USl1prXtr5RSw5VS85RSbyulVBaOBRER5aFkVyWJiIjyye0AbtZanwIAsUBtvda6g1KqKoAJSqkvYuseAqCN1npJbP5irfU6pdQuACYrpUZorW9XSvXXWrf12depANoCOAhA/dhzxsceOxjA3wCsADABwJEAvo36xRIR0c6HNXhERLQz6wLgAqXUDAA/AKgHoFnssUlWcAcA1yqlZgKYCGBva70gRwF4V2tdorVeDeBrAB2sbS/TWpcCmAFpOkpERFRmrMEjIqKdmQJwjdZ6tGuhUp0AbPbMnwCgo9Z6i1LqKwDVyrDfv6zpEvD/mIiIIsIaPCIi2plsBLCrNT8awJVKqcoAoJRqrpSq4fO82gD+FwvuWgI43Hpsh3m+xzcAzoz182sA4BgAkyJ5FURERAF4xZCIiHYmswCUxJpavgbg35DmkdNiiU7WAujl87zPAVyhlJoLYD6kmabxIoBZSqlpWutzreUfAugIYCYADeBWrfWqWIBIRESUEUprne0yEBERERERUQTYRJOIiIiIiChPMMAjIiIiIiLKEwzwiIiIiIiI8gQDPCIiIiIiojzBAI+IiIiIiChPMMAjIiIiIiLKEwzwiIiIiIiI8sT/AexH5OKU00RNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics([\n",
    "    [\n",
    "        (mdf[\"iteration\"], mdf[\"total_loss\"], \"total_loss\", 'red'),\n",
    "        (mdf[\"iteration\"], mdf[\"fast_rcnn/cls_accuracy\"], \"fast_rcnn/cls_accuracy\", 'green'),\n",
    "        (mdf[\"iteration\"], mdf[\"loss_box_reg\"], 'loss_box_reg', 'blue')\n",
    "    ]\n",
    "], \n",
    "remove_na=True,\n",
    "title = \"Metrics\",\n",
    "xlabel=\"Iteration\",\n",
    "ylabel=\"Value\",\n",
    "figsize=(15, 10))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dss-env",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}