embed_model_fakeddit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4dd2b23c",
   "metadata": {},
   "source": [
    "### Fakeddit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b46a53",
   "metadata": {},
   "source": [
    "### Setup Environment:\n",
    "* [Paper](https://arxiv.org/pdf/1911.03854.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73d24b2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "from src.classifiers import preprocess_data, process_labels,split_data\n",
    "\n",
    "from src.classifiers import VQADataset\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from src.classifiers_cpu_metrics import train_early_fusion, train_late_fusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7611e182",
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = 'Embeddings/fakeddit_subset/'\n",
    "COLUMN = 'embeddings'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c247eebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_path = 'text_embeddings.csv'\n",
    "images_path = 'Embeddings_dinov2_base.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9137c3dc",
   "metadata": {},
   "source": [
    "## Get data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd8783c",
   "metadata": {},
   "source": [
    "### Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e00f0085",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>clean_title</th>\n",
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       "      <td>michaelconfoy</td>\n",
       "      <td>loose talk got there first keep it under your ...</td>\n",
       "      <td>1.429942e+09</td>\n",
       "      <td>i.imgur.com</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>30</td>\n",
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       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>[1.095641016960144, -0.3601342439651489, 0.657...</td>\n",
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       "      <th>2</th>\n",
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       "      <td>fdr addressing the nation</td>\n",
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       "      <td>i.redd.it</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>fakehistoryporn</td>\n",
       "      <td>FDR addressing the nation (1932)</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "      <td>[0.9112146496772766, 1.1340166330337524, 0.273...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YOLO2THEMAX</td>\n",
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       "      <td>1.448948e+09</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>http://i.imgur.com/nLdvOJ5.jpg</td>\n",
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       "      <td>4</td>\n",
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       "      <th>4</th>\n",
       "      <td>FarMojo</td>\n",
       "      <td>a japanese kamikaze pilot of the sixth air arm...</td>\n",
       "      <td>1.559169e+09</td>\n",
       "      <td>i.redd.it</td>\n",
       "      <td>True</td>\n",
       "      <td>bul96p.jpg</td>\n",
       "      <td>https://preview.redd.it/3ro69vkv88131.jpg?widt...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
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       "      <td>A Japanese kamikaze pilot of the sixth air arm...</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>Anazron</td>\n",
       "      <td>giant flesheating spiders could soon be moving...</td>\n",
       "      <td>1.472864e+09</td>\n",
       "      <td>grimsbytelegraph.co.uk</td>\n",
       "      <td>True</td>\n",
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       "      <td>CaptainBubblesMcGee</td>\n",
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       "      <td>i.redd.it</td>\n",
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       "      <td>https://preview.redd.it/pbzko489ys501.jpg?widt...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>fakealbumcovers</td>\n",
       "      <td>Birdie - Fjord</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>val</td>\n",
       "      <td>[-0.03460288047790527, 0.24736623466014862, -1...</td>\n",
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       "    <tr>\n",
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       "      <td>prohitman</td>\n",
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       "      <td>1.456299e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
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       "      <td>http://i.imgur.com/aABgTcA.jpg</td>\n",
       "      <td>479m43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>psbattle_artwork</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>[2.6579291820526123, -3.2106573581695557, 0.60...</td>\n",
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       "      <td>lux514</td>\n",
       "      <td>supreme court lets full trump travel ban take ...</td>\n",
       "      <td>1.512429e+09</td>\n",
       "      <td>amp.cnn.com</td>\n",
       "      <td>True</td>\n",
       "      <td>7hlb60.jpg</td>\n",
       "      <td>https://external-preview.redd.it/Cqtqrno3dAQVv...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>neutralnews</td>\n",
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       "      <td>1</td>\n",
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       "      <td>todayilearned_SS</td>\n",
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       "      <td>1.496293e+09</td>\n",
       "      <td>vibe.com</td>\n",
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       "      <td>https://external-preview.redd.it/KdCCqVLfe9ass...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18</td>\n",
       "      <td>subredditsimulator</td>\n",
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       "      <td>0.95</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>val</td>\n",
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      "text/plain": [
       "                    author                                        clean_title  \\\n",
       "0            michaelconfoy  loose talk got there first keep it under your ...   \n",
       "1          SaltMineForeman  this tree at my campsite naturally grew a hear...   \n",
       "2                Shawn_666                          fdr addressing the nation   \n",
       "3              YOLO2THEMAX                                         face morph   \n",
       "4                  FarMojo  a japanese kamikaze pilot of the sixth air arm...   \n",
       "...                    ...                                                ...   \n",
       "68073              Anazron  giant flesheating spiders could soon be moving...   \n",
       "68074  CaptainBubblesMcGee                                       birdie fjord   \n",
       "68075            prohitman                     spittin hot fire and hairballs   \n",
       "68076               lux514  supreme court lets full trump travel ban take ...   \n",
       "68077     todayilearned_SS  til on apr st oliver brickar pranked the entir...   \n",
       "\n",
       "        created_utc                  domain  hasImage           id  \\\n",
       "0      1.429942e+09             i.imgur.com      True   33snxf.jpg   \n",
       "1      1.554421e+09             i.imgur.com      True   b9k2ta.jpg   \n",
       "2      1.522982e+09               i.redd.it      True   8a5wy9.jpg   \n",
       "3      1.448948e+09                     NaN      True  cxiu2qb.jpg   \n",
       "4      1.559169e+09               i.redd.it      True   bul96p.jpg   \n",
       "...             ...                     ...       ...          ...   \n",
       "68073  1.472864e+09  grimsbytelegraph.co.uk      True   50whzb.jpg   \n",
       "68074  1.514092e+09               i.redd.it      True   7lte89.jpg   \n",
       "68075  1.456299e+09                     NaN      True  d0bptg9.jpg   \n",
       "68076  1.512429e+09             amp.cnn.com      True   7hlb60.jpg   \n",
       "68077  1.496293e+09                vibe.com      True   6eklc3.jpg   \n",
       "\n",
       "                                               image_url linked_submission_id  \\\n",
       "0      https://external-preview.redd.it/TfwZkzBSsqgq0...                  NaN   \n",
       "1      https://external-preview.redd.it/ulTUeOqBi2y7F...                  NaN   \n",
       "2      https://preview.redd.it/fyop34q987q01.png?widt...                  NaN   \n",
       "3                         http://i.imgur.com/nLdvOJ5.jpg               3urf4a   \n",
       "4      https://preview.redd.it/3ro69vkv88131.jpg?widt...                  NaN   \n",
       "...                                                  ...                  ...   \n",
       "68073  https://external-preview.redd.it/HEa-SZzApziHx...                  NaN   \n",
       "68074  https://preview.redd.it/pbzko489ys501.jpg?widt...                  NaN   \n",
       "68075                     http://i.imgur.com/aABgTcA.jpg               479m43   \n",
       "68076  https://external-preview.redd.it/Cqtqrno3dAQVv...                  NaN   \n",
       "68077  https://external-preview.redd.it/KdCCqVLfe9ass...                  NaN   \n",
       "\n",
       "       num_comments  score           subreddit  \\\n",
       "0               6.0     70   propagandaposters   \n",
       "1               2.0     30   mildlyinteresting   \n",
       "2               1.0     12     fakehistoryporn   \n",
       "3               NaN      2    psbattle_artwork   \n",
       "4               0.0      8     fakehistoryporn   \n",
       "...             ...    ...                 ...   \n",
       "68073          33.0   1587      savedyouaclick   \n",
       "68074           1.0     25     fakealbumcovers   \n",
       "68075           NaN     99    psbattle_artwork   \n",
       "68076           1.0      3         neutralnews   \n",
       "68077          22.0     18  subredditsimulator   \n",
       "\n",
       "                                                   title  upvote_ratio  \\\n",
       "0      \"Loose talk got there first! Keep it under you...          0.98   \n",
       "1      This tree at my campsite naturally grew a hear...          0.89   \n",
       "2                       FDR addressing the nation (1932)          0.80   \n",
       "3                                             Face morph           NaN   \n",
       "4      A Japanese kamikaze pilot of the sixth air arm...          0.75   \n",
       "...                                                  ...           ...   \n",
       "68073  Giant 'flesh-eating' spiders could soon be mov...          0.96   \n",
       "68074                                     Birdie - Fjord          1.00   \n",
       "68075                 Spittin' hot fire... and hairballs           NaN   \n",
       "68076  Supreme Court lets full Trump travel ban take ...          1.00   \n",
       "68077  TIL on Apr 1st 1974 Oliver Brickar pranked the...          0.95   \n",
       "\n",
       "       2_way_label  3_way_label  6_way_label  split  \\\n",
       "0                0            1            5  train   \n",
       "1                1            0            0  train   \n",
       "2                0            2            2  train   \n",
       "3                0            2            4  train   \n",
       "4                0            2            2  train   \n",
       "...            ...          ...          ...    ...   \n",
       "68073            0            2            5    val   \n",
       "68074            0            2            1    val   \n",
       "68075            0            2            4    val   \n",
       "68076            1            0            0    val   \n",
       "68077            0            2            3    val   \n",
       "\n",
       "                                              embeddings  \n",
       "0      [-0.970077633857727, 2.124223470687866, 2.7805...  \n",
       "1      [1.095641016960144, -0.3601342439651489, 0.657...  \n",
       "2      [0.9112146496772766, 1.1340166330337524, 0.273...  \n",
       "3      [-0.42378801107406616, -2.792628526687622, 3.3...  \n",
       "4      [1.358902931213379, 1.4641591310501099, 3.4799...  \n",
       "...                                                  ...  \n",
       "68073  [2.9539952278137207, -1.3575800657272339, 0.64...  \n",
       "68074  [-0.03460288047790527, 0.24736623466014862, -1...  \n",
       "68075  [2.6579291820526123, -3.2106573581695557, 0.60...  \n",
       "68076  [5.113027095794678, -0.20547792315483093, 1.69...  \n",
       "68077  [2.610384702682495, -4.375671863555908, 0.8735...  \n",
       "\n",
       "[68078 rows x 18 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = pd.read_csv(os.path.join(PATH, text_path))\n",
    "text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4b6a694",
   "metadata": {},
   "source": [
    "### Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "457536c6",
   "metadata": {},
   "outputs": [
    {
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       "      <th>760</th>\n",
       "      <th>761</th>\n",
       "      <th>762</th>\n",
       "      <th>763</th>\n",
       "      <th>764</th>\n",
       "      <th>765</th>\n",
       "      <th>766</th>\n",
       "      <th>767</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33snxf.jpg</td>\n",
       "      <td>0.726765</td>\n",
       "      <td>-2.655783</td>\n",
       "      <td>-0.286309</td>\n",
       "      <td>2.284077</td>\n",
       "      <td>0.620112</td>\n",
       "      <td>-0.513821</td>\n",
       "      <td>-0.157407</td>\n",
       "      <td>-3.692554</td>\n",
       "      <td>-0.593593</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.377306</td>\n",
       "      <td>-1.257390</td>\n",
       "      <td>1.434935</td>\n",
       "      <td>2.678490</td>\n",
       "      <td>-0.813287</td>\n",
       "      <td>-0.247679</td>\n",
       "      <td>3.506964</td>\n",
       "      <td>2.777536</td>\n",
       "      <td>-0.494098</td>\n",
       "      <td>-0.088294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b9k2ta.jpg</td>\n",
       "      <td>-0.082641</td>\n",
       "      <td>2.290078</td>\n",
       "      <td>0.258090</td>\n",
       "      <td>1.336584</td>\n",
       "      <td>-1.426128</td>\n",
       "      <td>0.364081</td>\n",
       "      <td>-1.149799</td>\n",
       "      <td>-0.933333</td>\n",
       "      <td>-1.592730</td>\n",
       "      <td>...</td>\n",
       "      <td>3.144165</td>\n",
       "      <td>-3.026214</td>\n",
       "      <td>1.481284</td>\n",
       "      <td>-1.304235</td>\n",
       "      <td>-1.012776</td>\n",
       "      <td>-1.283712</td>\n",
       "      <td>-3.979227</td>\n",
       "      <td>-0.434095</td>\n",
       "      <td>-0.187055</td>\n",
       "      <td>-0.657386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8a5wy9.jpg</td>\n",
       "      <td>0.442696</td>\n",
       "      <td>0.138868</td>\n",
       "      <td>1.574348</td>\n",
       "      <td>-2.774248</td>\n",
       "      <td>0.554317</td>\n",
       "      <td>0.693501</td>\n",
       "      <td>3.486655</td>\n",
       "      <td>-1.426318</td>\n",
       "      <td>0.438649</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.711389</td>\n",
       "      <td>-2.506644</td>\n",
       "      <td>0.409061</td>\n",
       "      <td>-2.343080</td>\n",
       "      <td>-4.166866</td>\n",
       "      <td>2.738598</td>\n",
       "      <td>-0.623580</td>\n",
       "      <td>2.672139</td>\n",
       "      <td>-0.628692</td>\n",
       "      <td>0.444250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cxiu2qb.jpg</td>\n",
       "      <td>-2.166476</td>\n",
       "      <td>-0.665304</td>\n",
       "      <td>1.496656</td>\n",
       "      <td>0.308857</td>\n",
       "      <td>1.179391</td>\n",
       "      <td>2.050377</td>\n",
       "      <td>0.852495</td>\n",
       "      <td>0.368653</td>\n",
       "      <td>0.921106</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.844824</td>\n",
       "      <td>-1.604552</td>\n",
       "      <td>-2.791408</td>\n",
       "      <td>2.023891</td>\n",
       "      <td>0.001955</td>\n",
       "      <td>1.305366</td>\n",
       "      <td>-2.714160</td>\n",
       "      <td>2.073888</td>\n",
       "      <td>-0.763779</td>\n",
       "      <td>-1.020749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bul96p.jpg</td>\n",
       "      <td>0.618357</td>\n",
       "      <td>0.724958</td>\n",
       "      <td>-0.999924</td>\n",
       "      <td>0.586965</td>\n",
       "      <td>0.384611</td>\n",
       "      <td>0.061411</td>\n",
       "      <td>-1.794428</td>\n",
       "      <td>-1.519707</td>\n",
       "      <td>-2.022282</td>\n",
       "      <td>...</td>\n",
       "      <td>2.398909</td>\n",
       "      <td>0.064441</td>\n",
       "      <td>-0.306176</td>\n",
       "      <td>0.836847</td>\n",
       "      <td>2.341117</td>\n",
       "      <td>3.536660</td>\n",
       "      <td>1.313194</td>\n",
       "      <td>1.197324</td>\n",
       "      <td>-0.442961</td>\n",
       "      <td>-0.541935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 769 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ImageName         0         1         2         3         4         5  \\\n",
       "0   33snxf.jpg  0.726765 -2.655783 -0.286309  2.284077  0.620112 -0.513821   \n",
       "1   b9k2ta.jpg -0.082641  2.290078  0.258090  1.336584 -1.426128  0.364081   \n",
       "2   8a5wy9.jpg  0.442696  0.138868  1.574348 -2.774248  0.554317  0.693501   \n",
       "3  cxiu2qb.jpg -2.166476 -0.665304  1.496656  0.308857  1.179391  2.050377   \n",
       "4   bul96p.jpg  0.618357  0.724958 -0.999924  0.586965  0.384611  0.061411   \n",
       "\n",
       "          6         7         8  ...       758       759       760       761  \\\n",
       "0 -0.157407 -3.692554 -0.593593  ... -1.377306 -1.257390  1.434935  2.678490   \n",
       "1 -1.149799 -0.933333 -1.592730  ...  3.144165 -3.026214  1.481284 -1.304235   \n",
       "2  3.486655 -1.426318  0.438649  ... -2.711389 -2.506644  0.409061 -2.343080   \n",
       "3  0.852495  0.368653  0.921106  ... -1.844824 -1.604552 -2.791408  2.023891   \n",
       "4 -1.794428 -1.519707 -2.022282  ...  2.398909  0.064441 -0.306176  0.836847   \n",
       "\n",
       "        762       763       764       765       766       767  \n",
       "0 -0.813287 -0.247679  3.506964  2.777536 -0.494098 -0.088294  \n",
       "1 -1.012776 -1.283712 -3.979227 -0.434095 -0.187055 -0.657386  \n",
       "2 -4.166866  2.738598 -0.623580  2.672139 -0.628692  0.444250  \n",
       "3  0.001955  1.305366 -2.714160  2.073888 -0.763779 -1.020749  \n",
       "4  2.341117  3.536660  1.313194  1.197324 -0.442961 -0.541935  \n",
       "\n",
       "[5 rows x 769 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images = pd.read_csv(os.path.join(PATH, images_path))\n",
    "images.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921bd2ee",
   "metadata": {},
   "source": [
    "### Merge and preprocess the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29e6ebe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>author</th>\n",
       "      <th>clean_title</th>\n",
       "      <th>created_utc</th>\n",
       "      <th>domain</th>\n",
       "      <th>hasImage</th>\n",
       "      <th>linked_submission_id</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>score</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>title</th>\n",
       "      <th>...</th>\n",
       "      <th>image_758</th>\n",
       "      <th>image_759</th>\n",
       "      <th>image_760</th>\n",
       "      <th>image_761</th>\n",
       "      <th>image_762</th>\n",
       "      <th>image_763</th>\n",
       "      <th>image_764</th>\n",
       "      <th>image_765</th>\n",
       "      <th>image_766</th>\n",
       "      <th>image_767</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>michaelconfoy</td>\n",
       "      <td>loose talk got there first keep it under your ...</td>\n",
       "      <td>1.429942e+09</td>\n",
       "      <td>i.imgur.com</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>70</td>\n",
       "      <td>propagandaposters</td>\n",
       "      <td>\"Loose talk got there first! Keep it under you...</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.377306</td>\n",
       "      <td>-1.257390</td>\n",
       "      <td>1.434935</td>\n",
       "      <td>2.678490</td>\n",
       "      <td>-0.813287</td>\n",
       "      <td>-0.247679</td>\n",
       "      <td>3.506964</td>\n",
       "      <td>2.777536</td>\n",
       "      <td>-0.494098</td>\n",
       "      <td>-0.088294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SaltMineForeman</td>\n",
       "      <td>this tree at my campsite naturally grew a hear...</td>\n",
       "      <td>1.554421e+09</td>\n",
       "      <td>i.imgur.com</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30</td>\n",
       "      <td>mildlyinteresting</td>\n",
       "      <td>This tree at my campsite naturally grew a hear...</td>\n",
       "      <td>...</td>\n",
       "      <td>3.144165</td>\n",
       "      <td>-3.026214</td>\n",
       "      <td>1.481284</td>\n",
       "      <td>-1.304235</td>\n",
       "      <td>-1.012776</td>\n",
       "      <td>-1.283712</td>\n",
       "      <td>-3.979227</td>\n",
       "      <td>-0.434095</td>\n",
       "      <td>-0.187055</td>\n",
       "      <td>-0.657386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shawn_666</td>\n",
       "      <td>fdr addressing the nation</td>\n",
       "      <td>1.522982e+09</td>\n",
       "      <td>i.redd.it</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>fakehistoryporn</td>\n",
       "      <td>FDR addressing the nation (1932)</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.711389</td>\n",
       "      <td>-2.506644</td>\n",
       "      <td>0.409061</td>\n",
       "      <td>-2.343080</td>\n",
       "      <td>-4.166866</td>\n",
       "      <td>2.738598</td>\n",
       "      <td>-0.623580</td>\n",
       "      <td>2.672139</td>\n",
       "      <td>-0.628692</td>\n",
       "      <td>0.444250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YOLO2THEMAX</td>\n",
       "      <td>face morph</td>\n",
       "      <td>1.448948e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>3urf4a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>psbattle_artwork</td>\n",
       "      <td>Face morph</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.844824</td>\n",
       "      <td>-1.604552</td>\n",
       "      <td>-2.791408</td>\n",
       "      <td>2.023891</td>\n",
       "      <td>0.001955</td>\n",
       "      <td>1.305366</td>\n",
       "      <td>-2.714160</td>\n",
       "      <td>2.073888</td>\n",
       "      <td>-0.763779</td>\n",
       "      <td>-1.020749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FarMojo</td>\n",
       "      <td>a japanese kamikaze pilot of the sixth air arm...</td>\n",
       "      <td>1.559169e+09</td>\n",
       "      <td>i.redd.it</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>fakehistoryporn</td>\n",
       "      <td>A Japanese kamikaze pilot of the sixth air arm...</td>\n",
       "      <td>...</td>\n",
       "      <td>2.398909</td>\n",
       "      <td>0.064441</td>\n",
       "      <td>-0.306176</td>\n",
       "      <td>0.836847</td>\n",
       "      <td>2.341117</td>\n",
       "      <td>3.536660</td>\n",
       "      <td>1.313194</td>\n",
       "      <td>1.197324</td>\n",
       "      <td>-0.442961</td>\n",
       "      <td>-0.541935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4879 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            author                                        clean_title  \\\n",
       "0    michaelconfoy  loose talk got there first keep it under your ...   \n",
       "1  SaltMineForeman  this tree at my campsite naturally grew a hear...   \n",
       "2        Shawn_666                          fdr addressing the nation   \n",
       "3      YOLO2THEMAX                                         face morph   \n",
       "4          FarMojo  a japanese kamikaze pilot of the sixth air arm...   \n",
       "\n",
       "    created_utc       domain  hasImage linked_submission_id  num_comments  \\\n",
       "0  1.429942e+09  i.imgur.com      True                  NaN           6.0   \n",
       "1  1.554421e+09  i.imgur.com      True                  NaN           2.0   \n",
       "2  1.522982e+09    i.redd.it      True                  NaN           1.0   \n",
       "3  1.448948e+09          NaN      True               3urf4a           NaN   \n",
       "4  1.559169e+09    i.redd.it      True                  NaN           0.0   \n",
       "\n",
       "   score          subreddit  \\\n",
       "0     70  propagandaposters   \n",
       "1     30  mildlyinteresting   \n",
       "2     12    fakehistoryporn   \n",
       "3      2   psbattle_artwork   \n",
       "4      8    fakehistoryporn   \n",
       "\n",
       "                                               title  ...  image_758  \\\n",
       "0  \"Loose talk got there first! Keep it under you...  ...  -1.377306   \n",
       "1  This tree at my campsite naturally grew a hear...  ...   3.144165   \n",
       "2                   FDR addressing the nation (1932)  ...  -2.711389   \n",
       "3                                         Face morph  ...  -1.844824   \n",
       "4  A Japanese kamikaze pilot of the sixth air arm...  ...   2.398909   \n",
       "\n",
       "   image_759  image_760  image_761 image_762  image_763  image_764  image_765  \\\n",
       "0  -1.257390   1.434935   2.678490 -0.813287  -0.247679   3.506964   2.777536   \n",
       "1  -3.026214   1.481284  -1.304235 -1.012776  -1.283712  -3.979227  -0.434095   \n",
       "2  -2.506644   0.409061  -2.343080 -4.166866   2.738598  -0.623580   2.672139   \n",
       "3  -1.604552  -2.791408   2.023891  0.001955   1.305366  -2.714160   2.073888   \n",
       "4   0.064441  -0.306176   0.836847  2.341117   3.536660   1.313194   1.197324   \n",
       "\n",
       "   image_766  image_767  \n",
       "0  -0.494098  -0.088294  \n",
       "1  -0.187055  -0.657386  \n",
       "2  -0.628692   0.444250  \n",
       "3  -0.763779  -1.020749  \n",
       "4  -0.442961  -0.541935  \n",
       "\n",
       "[5 rows x 4879 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = preprocess_data(text, images, \"id\", \"ImageName\")\n",
    "df.drop(columns='image_url', inplace=True)\n",
    "df.to_csv(os.path.join(PATH, 'embeddings.csv'), index=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d70fe5b8",
   "metadata": {},
   "source": [
    "## Data Perparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1231caed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape: (56214, 4879)\n",
      "Test Shape: (5911, 4879)\n"
     ]
    }
   ],
   "source": [
    "# Split the data\n",
    "train_df, test_df = split_data(df)\n",
    "\n",
    "# Select features and labels vectors\n",
    "text_columns = [column for column in df.columns if 'text' in column] #[f'text_{i}']\n",
    "image_columns = [column for column in df.columns if 'image' in column] #[f'image_{i}']\n",
    "label_columns = '2_way_label'\n",
    "\n",
    "\n",
    "# Process and one-hot encode labels for training set\n",
    "train_labels, mlb, train_columns = process_labels(train_df, col=label_columns)\n",
    "test_labels = process_labels(test_df, col=label_columns, train_columns=train_columns)\n",
    "\n",
    "\n",
    "train_dataset = VQADataset(train_df, text_columns, image_columns, label_columns, mlb, train_columns)\n",
    "test_dataset = VQADataset(test_df, text_columns, image_columns, label_columns, mlb, train_columns)\n",
    "\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61060909",
   "metadata": {},
   "source": [
    "### Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43148c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_input_size = len(text_columns)\n",
    "image_input_size = len(image_columns)\n",
    "if label_columns == '2_way_label':\n",
    "    output_size = 1\n",
    "else:\n",
    "    output_size = len(pd.unique(train_df[label_columns]))\n",
    "multilabel = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca5a81fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Early Fusion Model:\n",
      "Model Memory Usage: 2.38 MB\n",
      "The number of parameters of the model are: 623105\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "STAGE:2024-02-10 18:33:44 4334:4334 ActivityProfilerController.cpp:312] Completed Stage: Warm Up\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15 - Test Accuracy: 0.9000, macro-f1: 0.8949\n",
      "Epoch 1/15 - Training Time: 7.83 seconds | Inference Time: 0.39 seconds\n",
      "Epoch 2/15 - Test Accuracy: 0.9075, macro-f1: 0.9023\n",
      "Epoch 2/15 - Training Time: 7.86 seconds | Inference Time: 0.42 seconds\n",
      "Epoch 3/15 - Test Accuracy: 0.9076, macro-f1: 0.9030\n",
      "Epoch 3/15 - Training Time: 7.84 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 4/15 - Test Accuracy: 0.9107, macro-f1: 0.9059\n",
      "Epoch 4/15 - Training Time: 7.87 seconds | Inference Time: 0.46 seconds\n",
      "Epoch 5/15 - Test Accuracy: 0.9100, macro-f1: 0.9049\n",
      "Epoch 5/15 - Training Time: 7.91 seconds | Inference Time: 0.38 seconds\n",
      "Epoch 6/15 - Test Accuracy: 0.9147, macro-f1: 0.9106\n",
      "Epoch 6/15 - Training Time: 7.82 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 7/15 - Test Accuracy: 0.9107, macro-f1: 0.9062\n",
      "Epoch 7/15 - Training Time: 7.83 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 8/15 - Test Accuracy: 0.9081, macro-f1: 0.9035\n",
      "Epoch 8/15 - Training Time: 7.83 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 9/15 - Test Accuracy: 0.9115, macro-f1: 0.9065\n",
      "Epoch 9/15 - Training Time: 7.85 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 10/15 - Test Accuracy: 0.9031, macro-f1: 0.8987\n",
      "Epoch 10/15 - Training Time: 7.86 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 11/15 - Test Accuracy: 0.9066, macro-f1: 0.9016\n",
      "Epoch 11/15 - Training Time: 7.85 seconds | Inference Time: 0.42 seconds\n",
      "Epoch 12/15 - Test Accuracy: 0.9080, macro-f1: 0.9042\n",
      "Epoch 12/15 - Training Time: 7.86 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 13/15 - Test Accuracy: 0.9059, macro-f1: 0.9010\n",
      "Epoch 13/15 - Training Time: 7.82 seconds | Inference Time: 0.39 seconds\n",
      "Epoch 14/15 - Test Accuracy: 0.9092, macro-f1: 0.9043\n",
      "Epoch 14/15 - Training Time: 7.85 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 15/15 - Test Accuracy: 0.9102, macro-f1: 0.9061\n",
      "Epoch 15/15 - Training Time: 7.88 seconds | Inference Time: 0.38 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "STAGE:2024-02-10 18:36:47 4334:4334 ActivityProfilerController.cpp:318] Completed Stage: Collection\n",
      "STAGE:2024-02-10 18:36:49 4334:4334 ActivityProfilerController.cpp:322] Completed Stage: Post Processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg       CPU Mem  Self CPU Mem    # of Calls  \n",
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "                                         training_batch         5.97%        7.323s        50.73%       62.271s       2.363ms     -15.27 Gb     -12.95 Gb         26355  \n",
      "enumerate(DataLoader)#_SingleProcessDataLoaderIter._...        10.37%       12.726s        48.23%       59.205s       2.030ms      16.89 Gb     -19.90 Gb         29160  \n",
      "                                               aten::to         5.72%        7.028s        31.93%       39.196s      10.429us      14.22 Gb     334.19 Mb       3758314  \n",
      "                                         aten::_to_copy         8.61%       10.575s        30.09%       36.939s      10.078us      16.89 Gb       1.40 Gb       3665340  \n",
      "                                            aten::copy_        22.53%       27.652s        22.53%       27.652s       7.426us       5.90 Gb       5.89 Gb       3723600  \n",
      "                              Optimizer.step#AdamW.step         6.19%        7.605s        20.74%       25.463s     966.173us       4.75 Mb    -118.40 Gb         26355  \n",
      "                                   DataParallel.forward         2.54%        3.120s        13.24%       16.256s     558.046us      16.92 Gb      -2.17 Gb         29130  \n",
      "    autograd::engine::evaluate_function: AddmmBackward0         0.40%     492.417ms         6.35%        7.798s     147.949us      45.47 Gb     -16.07 Gb         52710  \n",
      "                                         AddmmBackward0         0.28%     347.310ms         5.61%        6.892s     130.750us      61.54 Gb           0 b         52710  \n",
      "                                           aten::linear         0.21%     256.438ms         5.36%        6.582s     112.971us     458.57 Mb       2.08 Mb         58260  \n",
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "Self CPU time total: 122.761s\n",
      "\n",
      "Average Training Time per Epoch: 7.85 seconds\n",
      "Total Training Time per Epoch: 117.75 seconds\n",
      "Average Inference Time per Epoch: 0.40 seconds\n",
      "Total Inference Time per Epoch: 6.04 seconds\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.93      0.93      3571\n",
      "         1.0       0.89      0.89      0.89      2340\n",
      "\n",
      "    accuracy                           0.91      5911\n",
      "   macro avg       0.91      0.91      0.91      5911\n",
      "weighted avg       0.91      0.91      0.91      5911\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train early fusion model\n",
    "print(\"Training Early Fusion Model:\")\n",
    "train_early_fusion(train_loader, test_loader, text_input_size, image_input_size, output_size, num_epochs=15, multilabel=multilabel, report=True, p=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a24a5985",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Late Fusion Model:\n",
      "Model Memory Usage: 1.19 MB\n",
      "The number of parameters of the model are: 311809\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "STAGE:2024-02-10 19:17:41 4334:4334 ActivityProfilerController.cpp:312] Completed Stage: Warm Up\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15 - Test Accuracy: 0.9007, macro-f1: 0.8962\n",
      "Epoch 1/15 - Training Time: 10.75 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 2/15 - Test Accuracy: 0.9066, macro-f1: 0.9025\n",
      "Epoch 2/15 - Training Time: 8.90 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 3/15 - Test Accuracy: 0.9026, macro-f1: 0.8977\n",
      "Epoch 3/15 - Training Time: 8.89 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 4/15 - Test Accuracy: 0.9085, macro-f1: 0.9041\n",
      "Epoch 4/15 - Training Time: 8.86 seconds | Inference Time: 0.41 seconds\n",
      "Epoch 5/15 - Test Accuracy: 0.9092, macro-f1: 0.9045\n",
      "Epoch 5/15 - Training Time: 9.01 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 6/15 - Test Accuracy: 0.9063, macro-f1: 0.9020\n",
      "Epoch 6/15 - Training Time: 8.72 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 7/15 - Test Accuracy: 0.9044, macro-f1: 0.8990\n",
      "Epoch 7/15 - Training Time: 10.26 seconds | Inference Time: 0.61 seconds\n",
      "Epoch 8/15 - Test Accuracy: 0.9061, macro-f1: 0.9007\n",
      "Epoch 8/15 - Training Time: 11.48 seconds | Inference Time: 0.59 seconds\n",
      "Epoch 9/15 - Test Accuracy: 0.9037, macro-f1: 0.8984\n",
      "Epoch 9/15 - Training Time: 9.07 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 10/15 - Test Accuracy: 0.9070, macro-f1: 0.9016\n",
      "Epoch 10/15 - Training Time: 8.78 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 11/15 - Test Accuracy: 0.9051, macro-f1: 0.9002\n",
      "Epoch 11/15 - Training Time: 8.74 seconds | Inference Time: 0.40 seconds\n",
      "Epoch 12/15 - Test Accuracy: 0.9058, macro-f1: 0.9018\n",
      "Epoch 12/15 - Training Time: 8.72 seconds | Inference Time: 0.39 seconds\n",
      "Epoch 13/15 - Test Accuracy: 0.9108, macro-f1: 0.9061\n",
      "Epoch 13/15 - Training Time: 8.82 seconds | Inference Time: 0.42 seconds\n",
      "Epoch 14/15 - Test Accuracy: 0.9085, macro-f1: 0.9037\n",
      "Epoch 14/15 - Training Time: 8.87 seconds | Inference Time: 0.42 seconds\n",
      "Epoch 15/15 - Test Accuracy: 0.9061, macro-f1: 0.9020\n",
      "Epoch 15/15 - Training Time: 9.18 seconds | Inference Time: 0.43 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "STAGE:2024-02-10 19:21:20 4334:4334 ActivityProfilerController.cpp:318] Completed Stage: Collection\n",
      "STAGE:2024-02-10 19:21:22 4334:4334 ActivityProfilerController.cpp:322] Completed Stage: Post Processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg       CPU Mem  Self CPU Mem    # of Calls  \n",
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "                                         training_batch         6.35%        9.171s        57.15%       82.564s       2.834ms     -16.88 Gb     -15.22 Gb         29130  \n",
      "enumerate(DataLoader)#_SingleProcessDataLoaderIter._...         9.51%       13.746s        42.85%       61.907s       2.123ms      16.89 Gb     -19.86 Gb         29160  \n",
      "                                               aten::to         5.16%        7.461s        29.63%       42.801s       9.927us      15.15 Gb       1.21 Gb       4311774  \n",
      "                                         aten::_to_copy         8.92%       12.880s        27.90%       40.310s       9.555us      16.89 Gb    -197.61 Mb       4218795  \n",
      "                              Optimizer.step#AdamW.step         7.90%       11.412s        23.31%       33.671s       1.278ms       2.38 Mb     -59.98 Gb         26355  \n",
      "                                            aten::copy_        19.44%       28.092s        19.44%       28.092s       6.524us       3.98 Gb       3.98 Gb       4306185  \n",
      "                                   DataParallel.forward         3.38%        4.886s        15.21%       21.970s     754.194us       1.64 Gb      -1.01 Gb         29130  \n",
      "                                             aten::mul_         4.61%        6.662s         5.87%        8.477s      13.403us      -2.67 Mb      -5.21 Mb        632520  \n",
      "                                           aten::linear         0.24%     351.976ms         5.50%        7.952s      90.993us     458.57 Mb       5.54 Mb         87390  \n",
      "    autograd::engine::evaluate_function: AddmmBackward0         0.41%     592.198ms         5.32%        7.683s      97.170us      30.18 Gb    -801.12 Mb         79065  \n",
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "Self CPU time total: 144.471s\n",
      "\n",
      "Average Training Time per Epoch: 9.27 seconds\n",
      "Total Training Time per Epoch: 139.05 seconds\n",
      "Average Inference Time per Epoch: 0.43 seconds\n",
      "Total Inference Time per Epoch: 6.46 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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ABzuXQMrGRocnHGC6bdq0aUyaNInevXvTt29f3njjDQoLC5k8eTKgTq9ERkZaalw2bdrEyZMn6dGjBydPnuS5557DZDJZPtEDFBQUcPjwYcufjx49yvbt22nevDmtW7dGp9Px8MMP85///If27dvTpk0bnn32WSIiIrjmmmts80LdfdRRHXtz92nU0wsKCgBYsGABCQkJNb5nnjrr1asXR48eZdWqVfzyyy/ceOONDB8+nG+++abO1zEajXzyySekp6fj5uZW4/6FCxdy+eWX4+3tfcFzXOz7er0e5Zx9js6tKwLw9a058rZhwwZuvfVWnn/+eUaOHGkZrZo7d26drz127Fg8PT357rvv8PDwoLy8nOuvv/6CzxFCuDhzPVJjptqqi4yHnrdB0mJY+Rjc8wfoL1ziIC5M8yRpwoQJZGVlMXPmTNLT0+nRowerV6+2FFWnpKSgr5Zhl5SUMGPGDJKTk/Hz82P06NEsXryYoKAgy2O2bt3KZZddZvnztGnTAJg0aRIff/wxAE888QSFhYXcc8895OTkMGjQIFavXv2PlXNWo9M1etpLC6GhoURERJCcnMytt9563scFBAQwYcIEJkyYwPXXX8+oUaM4c+YMzZs3x93dHaPReMHrrFy5kvz8fJKSkmrULe3evZvJkyeTk5NDt27dMJlM/PHHHzVWJpp1796dTz75hPLy8lpHk0JCQmoU+BuNRnbv3l3jZ6U2f//9N9HR0TzzTFUfkuPHazZu6969O2vXrrUk9+dyc3Nj0qRJLFq0CA8PD2666aaLJlZCCBdWkAVpO9RjayVJoNYm7VsB6btg28fQ5y7rnbspsn1HAtd0oT4Lzt7jZtGiRUpgYKDlzwsWLFC8vb2VN998Uzlw4ICyc+dOZeHChcrcuXMVRVGUuXPnKl988YWyb98+5cCBA8pdd92lhIWFKUajUVEURWnfvr1y//33K2lpacqZM2dqvea4ceOUCRMm/ON+o9GohIWFWXpj3XHHHUpUVJTy3XffKcnJycpvv/2mLF26VFEURcnOzlZatGihXHvttcqWLVuUgwcPKp9++qmyf/9+RVEU5b333lN8fHyU//3vf8q+ffuUKVOmKAEBAcqkSZMs14uOjlZef/31GjF8//33ipubm/Lll18qhw8fVt58802lefPmNf6OfvvtN0Wv1yszZ85U9u7dq+zcuVN5+eWXa5zn4MGDisFgUAwGg7Jx48YL/hs4+8+QEOIidnyl9jb670Drn3vje+q5X45WlMLT1j+/k3OaPknCOdx99918+OGHLFq0iG7dujFkyBA+/vhj2rRpA6grv1555RV69+5Nnz59OHbsGCtXrrSMAM6dO5c1a9YQFRVFz549/3H+jIwMfvzxR6677rp/fE+v1zN+/HjLMv///ve/XH/99fzrX/+iU6dOTJkyhcLCQgBatGjBr7/+SkFBAUOGDCE+Pp4FCxZYRpXuvPNOJk2axMSJEy1F0xcbRQK4+uqreeSRR5g6dSo9evTg77///sfS/aFDh/L111+zYsUKevTowbBhw/6xvU779u0ZMGAAnTp1+sfUpRCiibH2VFt1ve+Cll2g+Cz8+oL1z9+E6BTlnCINUSd5eXkEBgaSm5tLQEBAje+VlJRw9OhR2rRpY7vpO+F0FEWhffv2/Otf/7JMAZ+P/AwJ4cIUBeZ2hIIMmLgCYmtfjNIox9bDx2MAHdzzO0T0sP41nNSF3r/PJSNJQthBVlYW77zzDunp6eetWxJCNBEZe9QEyd1H7bRtCzGDoOt1gAKrnlATM1FvmhduC9EUtGzZkuDgYD744AOaNWumdThCCC2Zp9piBoFbI5oUX8wVL8CB1Wo/pp1LIe4m213LRUmSJIQdyKy2EMLCvF9b20ZsRVIXgZEw+DFY+zz8/Cx0HA1eF55eEjXJdJsQQghhL2VFkFK5kXpj9murq/4PQPO2UJgJf/yf7a/nYiRJsiEZPRANJT87Qrio43+BsQwCo6BFO9tfz80TrqxMjja9B1kHbH9NFyJJkg2Yl5wXFWmwoa1wCeafncZusSKEcDCHqy39b+Tm43XW/grocCWYKqSIu56kJskGDAYDQUFBZGZmAuDj44POXv8ZhFNTFIWioiIyMzMJCgqq0X1cCOECzPu12WOqrbpRL6nXTv5d7cjdue77nTZlkiTZiHnHe3OiJER9BAUFWX6GhBAuIvcEZB8AnR7aDLbvtZvHwsCHYN2r8NMz0O4K8Gjc3p5NgSRJNqLT6QgPD6dly5a1bqIqxPm46xQMRRn2G4oXQtiHeaotsjd4a9AKZNA02LEEclPhrzfgsqftH4OTkSTJxgwGg0yZiLopL4bExfDXm5B3Aq75L/S4ReuohBDWotVUm5mHD4z4D3w9Cda/AXE3Q/M22sTiJKRwWwitlearidEb3WHV42qCBPDbHDDKKKQQLsFkVOuBwDb7tdVV53HqVJ+xVJ12ExckSZIQWik+C7//H7zRDdbMVPuYBLaG0a+Bb0vITVGHxoUQzu9kIpTkgFcgRPTSLg6dDq58FfRucOBHOPSLdrE4AUmShLC3wmz45Xl4vRv8/pKaLLVoB+PehYcSoe8UGPCg+tj188BYoW28QojGM0+1xQ4Fg8aVLi07Qd971eNVT0BFqbbxODBJkoSwl7xTsHo6vN5VTX7K8qFlF7h+ITywGXreCobKvki97wTv5nAmGfYs0zZuIUTjHanWH8kRDH1SHbE+cwQ2vqt1NA5LkiQhbO3sMfjhYXgzTv1lVFGsDrff9CXct17dqVt/TnG/p5+6nQDAutfAZLJ31EIIaynOgRNb1WNb79dWV16BcMXz6vEfr6of4sQ/SJIkhK1kHYTv7oO3esG2RepWBK0HwG3LYMqv0Gk06C/wX7DvFPUXWfYB2Pe9/eIWQljX0XWgGCG4AwRFaR1Nle43Qau+UF6o1kWKf5AkSQhrS98FX98B8/vCji/VX45th8EdK+HOVery37r0QPIKhIT71ON1r8lWAkI4K0ebajPT62H0q4AOdn0Nx/7SOiKHI0mSENZyYit8cRO8Nwj2fAco0HGMOmp0+3cQM7D+50y4Dzz8IGM3HFhl9ZCFEDamKHC4smjbUabaqovoAfGT1ONVT8hCkXNIkiREYygKHFsPn46DDy+Hg6sAHXS5Fu77C27+AiLjG35+n+bqtBvAuldkNEkIZ3P6iNrOw+DRsA9K9jBsJngFqR/Gti7UOhqHIkmSEA2hKGp/kUVXwsdj1CZxOgP0uBWmboEbFkFYV+tcq98D4OYNp5Kqhu2FEM7B/H+2dT/w8NU2lvPxbQHDZqjHv/1HbVMiAEmShKgfkwn2/Q8+GAqfXwcpG9RPiL3vgoeS4Jp3Ibi9da/pF6K2BAB1FYqMJgnhPMz7tTniVFt1ve+EsG5Qkgtrn9c6GochSZIQdWEywq5v4L8DYOmtkLYd3H3UUZ5/74Sr5kGzaNtdf8CDYPCE1I1w7E/bXUcIYT0VpVX/Xx2taPtceoPaiRvUPSRPJmobj4OQJEmIC6koU39hvNMHvr0LsvaBZwBc+ig8vAtGvQQB4baPIyAcet2uHq971fbXE0I0XuomKC9SmzaGWmn63Zai+0P3CYACKx+X/mxIkiRE7cpLYPMCeLsXrJiqdqX1bgaXzVCTo8tngm+wfWMa+DDo3dWeKymb7HttIUT9Ha629P9CPdEcyRWz1RW1J7fCji+0jkZzTvKvJoQdHfwZ3uwOKx+D3FT1U+AVL8DDu2HI4+AdpE1cQVHQ42b1eN0r2sQghKg7835tjj7VVp1/GAx5Qj3+5Tm1W3gTJkmSENUpCqx4EAoyIKAVjH4NHt4JAx9StwrR2qBp6iq6w7/AyW1aRyOEOJ+CTEjfqR47U5IEkHA/tGgPhVnw+8taR6MpSZKEqO5MMhSkqyvWpm5WexS5e2sdVZXmbaDbDerxurnaxiKEOL8jv6lfw7qrK1SdiZsHXPl/6vHmDyBjr7bxaEiSJCGqS9mgfo3o5bg9TS59FNDBgR8hfbfW0QghauOMU23VtbscOl2lbqu06okm23pEkiQhqjtemSRF99c2jgsJ6QBdxqvHstJNCMdjMlUlSe0cvD/ShYx8Cdy81DYGe77TOhpNSJIkRHXmkaTWDpwkAQx+TP2693vIOqBtLEKImjJ2Q2EmuPtCVILW0TRcs2gY9Ih6/PMMKCvUNh4NSJIkhFl+hrrUHx1E9dU6mgsL7aIOhaPAn1KbJIRDMY8ixQwCN09tY2msgf+GoNaQd7JJ/q6RJEkIs9SN6teWndWeSI7OPJq062t1E00hhGMw79fmzFNtZu7e6rQbwN9vN7nfNZIkCWHmDPVI1UX0hHZXgGKC9a9rHY0QAtQpqZTKD1yOvl9bXXW6Si1AN5bBT09rHY1dOUSSNH/+fGJiYvDy8iIhIYHNmzef97Hl5eXMnj2btm3b4uXlRVxcHKtXr673OdPT07n99tsJCwvD19eXXr168e2331r9tQkn4iz1SNWZm77t+BJyUrSNRQgBx/5Sk4nA1tCirdbRWIdOB6P+D/RucHA1HPxJ64jsRvMkaenSpUybNo1Zs2aRmJhIXFwcI0eOJDMzs9bHz5gxg/fff5+3336bvXv3ct999zF+/HiSkpLqdc6JEydy4MABVqxYwa5du7j22mu58cYba5xHNCGl+VWN35wpSYrqC22GgKkC1r+hdTRCCMtU2zA1uXAVIR2g37/U41VPqls3NQWKxvr27as88MADlj8bjUYlIiJCmTNnTq2PDw8PV955550a91177bXKrbfeWq9z+vr6Kp9++mmN8zRv3lxZsGBBrdctKSlRcnNzLbfU1FQFUHJzc+v+YoXjOvSLoswKUJTXu2odSf0d/VONfXawouSe1DoaIZq2t+LV/497vtc6EusryVOUVzuor++PV7WOpsFyc3Pr/P6t6UhSWVkZ27ZtY/jw4Zb79Ho9w4cPZ8OGDbU+p7S0FC8vrxr3eXt7s379+nqdc8CAASxdupQzZ85gMplYsmQJJSUlDB06tNbrzpkzh8DAQMstKiqqoS9bOCJzDYEzjSKZRQ9U4zaWqYWVQght5KTA6UPq1kFtBmsdjfV5+qsb4IK60i33hLbx2IGmSVJ2djZGo5HQ0NAa94eGhpKenl7rc0aOHMm8efM4dOgQJpOJNWvWsGzZMtLS0up1zq+++ory8nJatGiBp6cn9957L9999x3t2rWr9brTp08nNzfXcktNTW3MSxeOxhnrkcx0Ohj8uHq8dREUZGkbjxBNlXnpf6ve2m2EbWvdb1R/T5YXwc/Pah2NzWlek1Rfb775Ju3bt6dTp054eHgwdepUJk+ejF5fv5fy7LPPkpOTwy+//MLWrVuZNm0aN954I7t27ar18Z6engQEBNS4CRdRUQYntqrHzpgkgbryJDIeKophg4wmCaGJw5X1SK6yqq02Oh1c+Qro9LBnGRxdp3VENqVpkhQcHIzBYCAjI6PG/RkZGYSFhdX6nJCQEJYvX05hYSHHjx9n//79+Pn5ERsbW+dzHjlyhHfeeYeFCxdy+eWXExcXx6xZs+jduzfz58+3wSsVDi1th5pceDeHkI5aR9Mw1UeTtnwERWe0jUeIpsZYAUf/UI+ddb+2ugrvDr3vVI9XPQnGcm3jsSFNkyQPDw/i4+NZu3at5T6TycTatWvp3//Cn+i9vLyIjIykoqKCb7/9lnHjxtX5nEVFRQD/GH0yGAyYTCarvDbhRKpPtTnzapQOoyCsG5QVwMb/ah2NEE3LqUQoyQWvIIjspXU0tnfZM+oHy8y9sOVDraOxGc2n26ZNm8aCBQv45JNP2LdvH/fffz+FhYVMnjwZUJfqT58+3fL4TZs2sWzZMpKTk/nzzz8ZNWoUJpOJJ554os7n7NSpE+3atePee+9l8+bNHDlyhLlz57JmzRquueYau75+4QAsSVI/beNorOqjSZveV39hCyHswzzVFjsU9AZNQ7ELn+Zw+Uz1+LeXoKD2tj3Ozk3rACZMmEBWVhYzZ84kPT2dHj16sHr1akvhdUpKSo0Rn5KSEmbMmEFycjJ+fn6MHj2axYsXExQUVOdzuru7s3LlSp566inGjh1LQUEB7dq145NPPmH06NF2ff1CYyZTVZIUPUDbWKyh01gI6QRZ+2HzB1VJkxDCtsxF264+1VZdr4mwbZFasvDL83CN65Wr6BRFUbQOwhnl5eURGBhIbm6uFHE7s8z98G4CuHnDUyng5qF1RI2382tYdrc6FP7wLvD00zoiIVxb8Vl4JVbdIuiRPRDYSuuI7Cd1M3x0hXp891p1ZZ+Dq8/7t+bTbUJoKuVv9Wur3q6RIAF0vRaat4XiM7D1I62jEcL1Jf+hJkjBHZtWggRq1/+4W9TjlY+po/MuRJIk0bQ5cxPJ89Eb4NJH1eO/34ayIm3jEcLVNcWptuqGPwce/nAqCZIWax2NVUmSJJq24+Z6JBdKkkBt+BbUGgqzIPFTraMRwnUpSlWS1M6F+yNdiH8oXFa5wGrt8+r0o4uQJEk0XbknIDdF3UKgVR+to7EugzsMekQ9/utNqCjVNh4hXFX2IchNBYOHukVQU9X3HnXRSNFpdbWbi5AkSTRd5qm2sG7qnkSupset4B8B+acg6TOtoxHCNZlHkVr3Bw8fbWPRksEdrvw/9XjLh5C+W9t4rESSJNF0Ha8s2naFpf+1cfOEgf9Wj9e/4dJdcYXQzJHK/khNdaqtutih0HmcWsS+6gl1KtLJSZIkmi5L0baTN5G8kPhJ4NtSnVbcuVTraIRwLRWlcGy9euzK+7XVx4gX1ZYqx/+C3d9qHU2jSZIkmqbis2o7fXCtlW3ncveGAQ+qx3/OVfeXEkJYR8pGKC8Cv1AI7aJ1NI4hKKpqde3PM6C0QNt4GkmSJNE0pW4GFLWfkF9LraOxrd53qo0lzyTDnu+0jkYI12Geams7zLn3fbS2AQ9CsxjIT4N1r2odTaNIkiSaJks9kguPIpl5+kH/f6nHf77mcs3ehNDMYXN/JJlqq8HdC0a9rB5vmA/Zh7WNpxEkSRJNkys2kbyQvveAV6C6p9u+FVpHI4Tzy8+AjF3qcexQTUNxSB1GQbsrwFQOq5902iJuSZJE01NeAqcS1eOmkiR5BULCferxutec9heWEA4j+Tf1a3gc+IVoG4sj0unU0SS9Oxz+BQ6s0jqiBpEkSTQ9J7eBsUwttmweq3U09pNwH3j4qZ9+D67WOhohnNsRmWq7qOB2MGCqerz6KfUDqpORJEk0PSmVW5G07te0ii19mkOfu9XjP16R0SQhGspkkv3a6urSx9SmtjnH4e+3tI6m3iRJEk2PJUly0SaSF9J/qtrD5FRi1cocIUT9ZOxS90X08IOoBK2jcWyefjDiBfX4z3mQk6JtPPUkSZJoWkzGyuX/uHYTyfPxC4Hek9XjP16V0SRRu6wDsPJxyEnVOhLHZB5FirkU3Dy0jcUZdL0OogdBRbHaO8mJSJIkmpaMPVCaBx7+ENpV62i0MeAhMHhC6saqbsFCVPfjo7D5A1hys1PWkdjc4Wr9kcTF6XTqvm46A+z9HpJ/1zqiOpMkSTQt5qX/UX3A4KZtLFoJCIdet6vH617RNhbheNJ2wrE/1eP0XfDTdG3jcTSlBVW/R2S/troL61pVE7nyCafZS1KSJNG0pFQ2kWyK9UjVDfw36N3g6DpI2aR1NMKRbHpP/RraFdDB1oWw6xtNQ3Iox/9Se/8ERTet1bHWcNl08GkB2Qdg0/taR1MnkiSJpkNR4Hhl0XZT6LR9IUGtIe5m9djJtw0QVlSQBbu+Vo+veqNqD64f/g3ZhzQLy6Eclq1IGsy7GQx/Tj3+/WW1IaeDkyRJNB1nj0FButrcLDJe62i0d+k0tUbg8Bo4mah1NMIRbF2o9hCL7K1OSQ+drhbclhXA13dAebHWEWrPvCpUptoapsdtENELyvLhl+e0juaiJEkSTYd56X9ET3D31jYWR9A8FrrdoB6ve03bWIT2Kkph60fqcb/71a8GN7juQ/AJhozdsOpJ7eJzBGePw+nD6oeLNoO1jsY56fUwuvL3zY4vHH66X5Ik0XRUbyIpVJc+CujgwI+QvlvraISW9nwHBRngHw6dx1XdHxCuJkroIPET2PmVZiFqzrz0v1Ufdasf0TCt4qHnberxqsfV1iwOSpIkUVNJLiweDxve1ToS67PUIzXxou3qQjpAl2vU4z9lNKnJUhTYWPl/vu8UMLjX/H7by2DIE+rxDw9D1kG7hucwZKrNei5/DjwDIW2Hmnw7KEmSRE37/qd+Wvr1BSgr1Doa6ynMhtOVhafSIbemSx9Tv+5ZrjYRFE1Pykb1zcrNC+In1/6YIU+qzRPLC+HrSVBWZN8YtWasgOR16rHs19Z4fiFw2dPq8doXoOiMtvGchyRJoibzEvnyIqfdtblW5qm2kEvUPcxElbCu0HEMoKjbBoimxzyK1H3C+f9/6A1w3Ufg2xIy96rTJE3JyW1Qmquu0IrooXU0rqHP3dCyMxSfgd9e1DqaWkmSJGoyN0kD2L1MuzisTZb+X9iQyje8XV/DmWRtYxH2lZMC+/+nHifcd+HH+oeq9Uk6PSR9Btu/tH18jsI81RY7VE0YReMZ3ODKyoa2WxeqjUwdjCRJokpBprpyw+zwGijO0Swcq7IUbUuSVKuIntDuClCMMprU1Gz+ABST+uYf2vnij48dAkOeUo9/nAaZ+20ansMwF23LVJt1tbkUulyr/gyufNzh9pOUJElUMY8iteys3oxlVZ8wnVlpgVpvAZIkXcjgytGkHV/KxqZNRWkBJH6qHifcX/fnDX5MTarKiyrrk1yofrE2xWfV6TaQ/dpsYcR/wN1H3U/SwVZPSpIkqlRfIt/1WvV497faxWMtJ7eqIyQBrSAoSutoHFfrBLX3i6kC/npD62iEPez4Ul3R2jwW2o+o+/P0Brh2AfiFQtZ++PEx28XoCJJ/V0c6QjpBYKTW0biewEg18QZYMxNK87WNpxpJkkQVS5I0QB3+BEj+Q92qwJlJPVLdDa5c5p24GPLStI1F2JbJVLV/VsJ9apO/+vBrCdcvVOuTdnwBSZ9bP0ZHIVNtttd/qpqsF6TDH46z8bYkSUJVWlBVNBfdH1q0VVvHK0bYu1zT0BpNmkjWXcwgdUrSWOqwq02ElRxZq7bF8AyAHrc07Bwxg6qWcf/4KGTstV58jkJR4LA5SZKpNptx84RR/6ceb3zXYXpxSZIkVCc2qwlRYBQEtlLv63qd+tWZV7kZy+HEFvW4tTSRvCidDobNAHSQtNg1pltF7czL/ntNBE//hp9n0KNq8lBRrNYnlRZYJz5HkX0Q8k6AwVMa0dpahxHQYZQ65b/qCYco4pYkSajMRdvVC5u7jAd0au+k3BOahNVoaTvV4lKvILWeQFxczKCq3d9XPASnj2gbj7C+zP3qFJJOr3bYbgy9Xq1P8g9XE4ofpznEm5vVmKfaogeAh4+2sTQFo+aAwQOSf3OIhUOSJAnV8comktWnpAIjqz457fnO/jFZQ/WptvrWXDRl1Xd//2pS09v9vfgsFJ7WOgrb2fSe+rXjaGgW0/jz+QZX1icZYOdSdRTSVRyu7I8kU2320TwWBjykHq9+WvPfPQ7xrjF//nxiYmLw8vIiISGBzZs3n/ex5eXlzJ49m7Zt2+Ll5UVcXByrV69u0Dk3bNjAsGHD8PX1JSAggMGDB1Nc3MTeDKBySmqrenzucLKzr3KT/kgNU2P3912w+imtI7KfM0fh7d7wTrxrFq8XnYEdS9TjfvVY9n8x0QMqp2pR+924wobJ5SVwbL16bOf92owmhdIKx9341aYunaauRs5NgfVvaBqK5knS0qVLmTZtGrNmzSIxMZG4uDhGjhxJZmZmrY+fMWMG77//Pm+//TZ79+7lvvvuY/z48SQlJdXrnBs2bGDUqFGMGDGCzZs3s2XLFqZOnYq+KY42pO1Q6wm8giC4Y83vdb5G/XR4Ksn5pl0URZKkxqi++/u2j2Hn11pHZHslefDlTVCUrY4m/TJL64isL/ET9f97WDeIHmjdcw98WG1KWlFSWZ/kOEu5GyR1o/p35Rem9o6zE5NJ4Zr5fzH01d/Jyi+123UdhocvjPyPuqjAL0TTUDTPCObNm8eUKVOYPHkynTt35r333sPHx4eFCxfW+vjFixfz9NNPM3r0aGJjY7n//vsZPXo0c+fOrdc5H3nkER566CGeeuopunTpQseOHbnxxhvx9PSs9bqlpaXk5eXVuLmM6onEuUmib7DaNA6cr4A7+xAUnVY37ZS9lhqmxu7v/1b/Tl2VyQjf3q32/fEJBnTq1JG5hYQrMJbD5gXqccL9aqG+Nen1MP59CIhUu/f/8LBz1ydVn2qz9t/VBSSlnmXXyVzSckt4eVUT6Wh+rs7XwL93qPu7aUjTJKmsrIxt27YxfPhwy316vZ7hw4ezYUPtv5hKS0vx8vKqcZ+3tzfr16+v8zkzMzPZtGkTLVu2ZMCAAYSGhjJkyBDLOWozZ84cAgMDLbeoKBdqSnixPkKWVW5ONuVmTv4i49XlpaJhqu/+/pUL7/7+y3Nw6Cc1qb71K3XVF6gbuZpcZNpj3w+QdxJ8Q6r+X1ubb4uq+qTd36ijkM7KXLRt56m2H3emW46/TTzB1mNn7Hp9h6DTOcRm5JomSdnZ2RiNRkJDQ2vcHxoaSnp6eq3PGTlyJPPmzePQoUOYTCbWrFnDsmXLSEtLq/M5k5PVDTyfe+45pkyZwurVq+nVqxeXX345hw7V/kl5+vTp5ObmWm6pqS6ybUNdpqQ6jVFXG2Ttg4w99outsWSqzTpq7P6+R12a62q2fwF/v6Uej5uvJtaXzwSvQEjfBdsWaRuftWz8r/q1913g7nXhxzZG634wvHKqctWTDrlx6UXlp0PGbkBXNZpuByaTwqrd6vtZ2xBfAJ79fg8VRpPdYhBVNJ9uq68333yT9u3b06lTJzw8PJg6dSqTJ0+uVy2RyaT+sN17771MnjyZnj178vrrr9OxY8fzTvN5enoSEBBQ4+YSsg9C8Rn103N4j9of4x1UtWWBM40mmVfsSaftxjPv/m7un2Qu/HUFKZvUqURQ96/rdr167BsMl1UWIq99wflXu53YpvZD07tD7zttf73+D0L7kWpj0q/vUOu9nMmR39Sv4XHqz4KdJKXmkJZbgp+nG5/dnUCgtzv70vL4fFOK3WIQVTRNkoKDgzEYDGRkZNS4PyMjg7CwsFqfExISwvLlyyksLOT48ePs378fPz8/YmNj63zO8PBwADp3rlmId8kll5CS0sR+EM2JRGRvcPM4/+Oqr3JzhhqDvFOQc1ztA9Oqr9bRuIbYITC0cpXb/x6BrAPaxmMNOamw9FZ1M+dOV8HQp2t+v/edENoVSnLg1xc0CdFqNlWOInW7Xk16bU2vh/HvqauUzhxRE1Fn+N1hdqSyHsnOU20rd6mjSJdf0pLwQG8eG6kupnnt5wNkFzTBIm6NaZokeXh4EB8fz9q1ay33mUwm1q5dS//+F/707+XlRWRkJBUVFXz77beMGzeuzueMiYkhIiKCAwdq/pI/ePAg0dHR1np5zsHcRPJioy0dRqm7NJ89BicTbR5Wo5mn2kK7gpeLjPo5gsGPV+3+/pWT7/5eWgBf3gyFWRDaTS04PndE2uAGV1buI7XtYzi13d5RWkdeWlWvs4T77Hddn+Zww8egd4M9y2DrR/a7dmMYy6tGkuzYH8lkUlhVmSSN7qZ+mL+lb2u6RgaQX1LB/zXVIm4NaT7dNm3aNBYsWMAnn3zCvn37uP/++yksLGTy5MkATJw4kenTp1sev2nTJpYtW0ZycjJ//vkno0aNwmQy8cQTT9T5nDqdjscff5y33nqLb775hsOHD/Pss8+yf/9+7rrrLvv+BWgtpZYmkrXx8FUbz4FzTLnV1kFcNF6N3d/3qf1wnJHJBMvvU3tA+YbAzV+Ap1/tj40ZCN1uABT19ZqcsDZky4fqVg+tB9h/pWdUHxj+vHq8erpjJ5rlJerqv7d6qm0gPPztOhK9/UQOp3JL8PUwMKSDuvTdoNfx/NVdAfh62wm2HT9rt3iEAyRJEyZM4LXXXmPmzJn06NGD7du3s3r1akvhdUpKiqUoG6CkpIQZM2bQuXNnxo8fT2RkJOvXrycoKKjO5wR4+OGHmT59Oo888ghxcXGsXbuWNWvW0LZtW7u9ds3lnoSclLpPSZlXw+xZ5vhvFBdbsScazq+lWsit08P2z51z9/ff56grvQweMOFzCGp94cdfMRvcfdWanp1L7ROjtZQXw9bKWst+dhxFqq7/A+qHLGNZZX1SrjZxnE9pAfz9NrzZHVY+Brmp6geB8f+9cBmClf240zzVFoqXu8Fyf3x0M26IV/fUnPn9bowmJ5q2dHI6RXGmSWLHkZeXR2BgILm5uc5bxL3rG/j2LrUw8d51F398RSm81l79BXfHj+oeX46oJBdejgYUePQA+Nde3yYa6Y9X4bf/gJs33PMbtLxE64jqZve38E1l4fK4d6HnrXV73vrX1TYBvi3hwa3qyjdnkPgprHgQAlvDQ0nqFKIWis/Ce4PVLsqdx8ENn9i191DtMeXAlgWw4V11AQuom3wP/Df0vN22KwDPYTIpDPq/XzmVW8J7t8UzqmvN31vZBaUMe+138koqeGFcF27vH2O32FxNfd6/NR9JEhqq75SUmydcMlY9duQpt9TNgALN2kiCZEuXVtv9/Ssn2f39ZCIs/5d6PODBuidIAP3+BS3aQWEm/PGKbeKzNkWpWvafcI92CRKAd7PK+iR32Pt9VVNLLRSeVlcsvtENfv2PmiA1j4Wr34EHE9VNf+2YIEHNqbahHf/ZZTrYz5NHR6hF3K/+dIDTUsRtF5IkNWUN6SNknnLb+71a3OiILEv/B1z4caJx9HoY/0Hl7u8H4MdHHXv1Ul4aLLlF3TKj/YiqOpm6cvOEUf+nHm96DzKdoIj26DrI3KtOFfa8XetooFW8OnUJ8PMz9l8Ekp8OPz0Db3SFP1+D0jwIuUSdPn5gC/S63a7Ta9WtPM9UW3W3JrTmkvAA8koqeGW1C6wudQKSJDVVxTlVjSHrkyTFDFYLXYtOQ/IfNgmt0SwjZBcpRheN5xdSVZ+0cwkkfaZ1RLUrL1YTpPw0COmkxqyv/Y3ogtoPh45j1CLoVU84dlIIVaNIPW5R+505gn73q+0WzPVJxTm2v2ZOiprEv9EdNryjrs4M7wETPoP7/1bbImg4yqYoCqt2q82OzavaauNm0PPCuC4ALN2aSlKKFHHbmiRJTZV5Sqp5bP16phjc1D11wDGn3CpK4eQ29bi1jCTZRczAaru/P+Z4XdkVBb6fCqcS1Smfm79sXFuIkS+CwROO/gH7VlgvTms7fQQOrlaPE+7VNpbqdDq1q3lQtNrL7PsHbJdsnj4Cyx9QV6tt+VBtbBnVD279Fu75XS0fcIBNzben5nAyp/i8U23V9Y5pznW9zEXce6SI28a0/+kQ2rAs/W/A6i/zlNv+/6lLZh3JqST1F6FvCLRoQisVtTbwkard379ysN3f/5yr7iGmd4MbP1U/GDRG8zYw6GH1+KdnHHcvu80fAIo6tRjcXutoavIOqqpP2v8/2PS+dc+fsRe+uQve6Q3bP1NH/mKHqgtO7lytjghqXTRejbmB5LALTLVV99SVnfD3cmPXyVyWbGliDZDtTJKkpqoxfYSiEtRdvkvz4PAa68bVWMer9X1yoF+CLs+8+7t/BJw+pHbkdoSpqH0/VHXKHv0qtBlsnfMOfFhdBZWbqq56czQluVVTn/ZsHlkfkb3UUTmAn2eo26Y01slEWHIr/Le/mhgrJuhwJdy9FiZ+r67IdbDfC4qisHKXOtU2plvdFpqE+Hsy7YoOALyy+gBnCstsFl9TJ0lSU1ReUjUl1ZDiZr2+5jYljkSaSGrHtwXcsEjd/X3X15D4ibbxpO+CZZXTTH3vse5+ZR4+VW/wf70JZ45a79zWkPQ5lBVAcEe7doyut773qO0ATOWV9UkNrLE5vgEWXwsLLlNHptCpZQH3/gm3LIFWva0YtHXtOJHLyZxifDwMDO3Yss7Pu71fNJ3C/MktLufVn5xgEYGTkiSpKTqVpBZN+oY0fOrBPOV2YLXjLP02mSBVkiRNte4Hl89Uj1c+oSYqWijIUrccKS9Up1lGzrH+NS65Wj23sRR+evqiD7cbk1FdfQdqkbSDjZzUoNPB1W+r7TpyU9T6obqOQCoKHPkVFo2BRaPUvdZ0Boi7GR7YDDd+AuHdbRu/FVim2jq1rNNUm5mbQc/scWon7iVbUtmRmmOL8Jo8SZKaour1SA39BRreQ02wKorhwCqrhdYomXvVaQZ3Xwhz/F+OLmvAQ1W7v2tRn1RRCktvU6fCmrdVa19ssXJJp1P3ddO7wYGVcMhBpp4PrlYLor2bQfcJWkdzcV6Blf9GHnDgR9j47oUfryjq75wPL4fF4+H4evW58ZPhwW3qprohHewSemMpimLpsj3mAqvazqdvm+aM7xmJoqiduE1SxG11kiQ1Rccb0B/pXDoddL1ePXaUKTdz36eoPto2zWvqtNz9XVHgf9PUEUXPQLh5iZos2EpIx6qan1VPqgma1szL/uPvUKcFnUFEDxj5knq8ZiakbvnnY0xG2L0M3hsEX96klgy4eUPC/fDQdhj7hlpU70R2Vk61ebvXb6qtuulXdsLP040dJ3JZujXVyhEKSZKaGpOxcvk/jd/XzDzldviXhtcSWJOlOaYs/decT3O1PknvpibR5r3DbG3DfHU1k04PNyy0z4jCkCfVfb7OHLn4KIitpe+CY3+q00597tY2lvrqczd0uVZdifbNZCiq3CbEWA7bv4D5Cer9GbvVjWcHPQIP74IrX4bASG1jb6CqVW0t8fZoQN8uoGWAF49Yirj3k1MkRdzWJElSU5O5F0pzwcMPQrs17lwtO0HLLmrR5b4frBNfQylKtREyaSLpEKL6wvDn1OPV0yFth22vd/BnWPOsejzyJWg33LbXM/MKqOoi/cerkHfKPtetzcbKWqTO4yCwlXZxNIROB2PfVKdIc1Nh+f1qcv12L/X49CHwCoKhT8Mju9SfLb8L9xRyZIqi8OOuhk+1VTepfzQdQ/05W1TOqz9JJ25rkiSpqTGv/mplpSmpbpWjSVpPueWkQP4pdeTCgVeyNDn9p6pLsM31SSV5trlO5n51s2bFBL0m2n/Ze/cJamuM8kL4+Vn7XtusIAt2faUe97tfmxgayyugsj7JU62t+t8j6v9t3xB1G5lHdsPQJ207hWonu07mcuKsOtV2WQOn2szUIm61E/cXm1PYeSLHChEKkCSp6bH2vmZdKlsBHF0HBZnWOWdDmKfawuPAw1e7OERNOh1c8666A/3Zo+pu9NauTyo6o9aolOapU62j59p/RZe5iBud2p/n2F/2vT7AtkXqqtXIePVDkLMK7672tAK1H9uVr6jTaoMeBk9/TUOzJnPB9rBODZ9qqy4htgXjekRUFnHvkSJuK5EkqSlRlGp1O1aakmreRv2lrJjUTW+10pDNeoV9WOqT3GHvcnV7CGsxlsNXE9UELKg1TFis2QalRPSA3pPV45WPg7HCfteuKKv6e01w8GX/dRE/CR7ZqxZkJ9wL7t5aR2RVNabaujduqq26p0dfgq+Hge2pOXy9TYq4rUGSpKYk57i6wafeDSKtOCVlXuW26xvrnbO+rLFiT9hOq95VdTs/Pa326rKG1U+phcoefupKNt9g65y3oYY9q04FZe6xX7E6wJ7voCAD/MPVeiRXEBipXcJrY9acaqsuNMCLh4erRdz/t/qAFHFbgSRJTYm5Him8h3WXBne5BtCpy65zNPj0UngasiuLFSVJclzn7v5ektu4821eUDl6ooNrF0BoF2tE2Tg+zdVECeC3/0Bhtu2vqSiwcb563Odul00sXMmPu6w71VbdHQNjaN/SjzOFZcz9+aBVz90USZLUlFjqkaycSAREQPRA9XjPd9Y9d12Yu2wHd1S3xhCOSaeDce+o02JnjzVu9/fkP9S+RADDZ0Gn0VYLs9Hi71CbmZbkwtrnbX+9lI3qykE3L7WhonBo6l5tapI0upGr2mrjbtDzfGUR9+ebjrP7ZCM/jDRxkiQ1Jbas27Hs5abBlJu166yE7Xg3q9r9fd8PDdv9/fQRtQ5JMaqrygY+bO0oG0dvqCo8TlxctU+irWyqbB7Z/Ub5kOAEdp/MI/VMMV7uei7rZJsWBgPaBjM2LgKTAs9KJ+5GkSSpqSjMhuzKoVdbJEmdr1Eb2KXtgOzD1j//hZjrkay1Yk/YVmQ8jPiPevzzjPolESW56kq2khy1rm7sW45ZpNy6H3S/CVDUIm6TyTbXyUmp6lFm77YHokGqT7X5eNhuZ4BnKou4k1Jy+CbxhM2u4+okSWoqzPVIIZ3Uuglr820BbS9Tj/css/75z6esENK2q8cykuQ8Eu5VN4itz+7vJiN8c6ea7AdEwk1fgLuXzUNtsCueVztDn9wGO76wzTU2L1BXlrYZ4hg1WeKCbD3VVl1YoBcPXd4egP9btZ/conKbXs9V1TtJiomJYfbs2aSkpNgiHmEr9piSMm9Tsusb++3VdXKbuo2BfwQERdvnmqLxzPVJzWLU0ZC67P6+Zqa6BY6bt5og+YfaJdQG8w9TGx8CrJkFxTnWPX9ZISR+oh47a/PIJmbPqTxSzhTh5a5nWCfrrWo7n8kD29A2xJfThWXMWyOduBui3knSww8/zLJly4iNjeWKK65gyZIllJY6wKaO4sLssa9ZpzFqp9zsA5Cxx3bXqa76ViSOOO0izu8fu7//9/yPTVwMG95Rj8f/V+1J5Az63gvBHaAoG35/2brn3vGlOv3YrA20H2ndcwubME+1XdbRtlNtZh5uemaP6wrA4o3H2XNKirjrq0FJ0vbt29m8eTOXXHIJDz74IOHh4UydOpXExERbxCgaq6ywat8sW44keQVC+yvUY3ttU5Ii9UhOLaJntd3fn4UTW//5mOMb1O0pAIZOhy7j7RdfY7l5wJX/px5v/gAy9lrnvCZT1T5t/e4HvVROODp7TrVVN7BdMGO6hWNSYNb3e1DsNcrvIhr8P6tXr1689dZbnDp1ilmzZvHhhx/Sp08fevTowcKFC+UfwpGc2KpOSQVEqsuvbalrtb3cbP0zYKyAE1vUYxfrj9SkVqP0uVst/DdVqPVJ5t3fAc4eh6W3qbVLna+BwU9oFGQjtB0Gl4xVV+OtesI6/y+OrFU3fPUMgB63NP58wub2nMrj+OkiPN3sM9VW3TNjLsHb3cDW42dZlnjSrtd2dg1OksrLy/nqq6+4+uqrefTRR+nduzcffvgh1113HU8//TS33nqrNeMUjZFixympDqPA3Vft7m3rpc8Zu6CsADwDoeUltr2WjRWUVrBmbwbPLt/NkFd/o8OMVfy6P0PrsOxDp4Or31anjXJTYfm/1ESitAC+vFmdqgqPg2v+67wjJiNeVPsYHfvTOr3EzFOTPW93qf3MXNnKalNtvp62n2qrLiLI21LEPWfVPnKLpYi7rur9L5WYmMiiRYv48ssv0ev1TJw4kddff51OnTpZHjN+/Hj69HHiDRZdjT33NfPwURv77fpaHU1qZcXtT85lqUdKUHvTOBGTSWHPqTzWHcrij4NZJB4/S8U5o0ezVuxhYLtgPN2c67U1iFcA3PgJfHgFHFwFf78FKZvU7T38QuGmL63bJd7emkXDoGnw+0tq24MOIxu+EXPWAXUkCR30nWLVMIVt1Jhqs+JebfVx16A2fL0tleSsQl5fc5DnrpbVkHVR749lffr04dChQ/z3v//l5MmTvPbaazUSJIA2bdpw0003WS1I0QjGCkitnJKyV92OZcptmbps21ZSKjuIO8nS/4y8Er7ZdoKHvkyi94u/MPad9bz60wE2Hz1DhUkhuoUPt/eL5r3betHS35PUM8V88vcxrcO2n/A4GDVHPV4zUy3mNniqK9kCI7WNzRoGPqROd+edhD/nNvw8myprkTqNUTeYFg5vz6k8jlVOtV1u56k2Mw83Pc9XJkafbjjGvrQ8TeJwNvUeSUpOTiY6+sJLrX19fVm0aFGDgxJWlL4TygvVouoQO01Jtb1cvV5BuroVSptLrX8NRanq/WTLFXuNUFJuZMuxM/x5KJt1B7PYn55f4/t+nm70b9uCwR1CGNw+mOgWVSML+SUVPP7NTt5ee5jrerWihZ+nvcPXRu874fhfVYX/496x7WikPbl7w6iXYckt8Pfb0ONWaNG2fucoOgPbv1SPpXmk09Byqq26S9uHcGXXMFbtTmfm97v56t7+6GRV8AXV+18rMzOT9PR0EhISaty/adMmDAYDvXu7yC80V2GeaovqZ796DjcPtVFg0mL1zc4WSdLpI1CYpS4fj+hp/fM3gKIoHM4sYF1lUrTp6GlKyqs6Let00C0ykMHtQxjcIYSerYNwN9T+b3Jdr1Ys+usYe9PyeHPtIcsyXpen08HYN9UkO7yHutWGK+k4Wv0QcWQtrJ4Ot35Vv+cnfgoVxRDaDWIG2SZGYVWOMNVW3YyrOvP7gSy2HDvL8u0nGd+zldYhObR6v2s+8MADpKb+c6f3kydP8sADD1glKGFFWu1rZp5y2/s9GG1QJGh+XZHxmnZdzikq4387T/HkNzsZ8PKvXPH6Ol74317+OJhFSbmJ0ABPro9vxVs392TbjCtYMXUQj43sSN82zc+bIAHo9TpmjFFH/j7flMLhzPzzPtblePrDVa9D/CStI7E+nU5tCaB3h0M/wYHVdX+usVxtIwDqsn8ZAXAKe9O0n2qrLjLIm6nD2gHw0sr95JVIEfeF1Hskae/evfTq1esf9/fs2ZO9e63UA0RYh6Jot69Zm8Hg2xIKMyH596r+SdZiz2L0aiqMJran5rDuYBbrDmWz80QO1eutPdz0JLRpbhkt6hDq1+Dh7AHtghl+SSi/7MvgpZX7WXiHLIZwCcHtof+/4K83YfVTEDu0bon+vh/Ueiaf4KoPIcLhmUeRhnYM0XSqrbq7L23DN9tOcDS7kDfWHGLm2M5ah+Sw6v0v5unpSUZGBrGxsTXuT0tLw83NMX4ARKXTR9Tl0wZP+09J6Q3Q5Rr1k+/ub506SUo9U8S6Q1n8eTCbv45kk19SUeP7HUL9uLQyKUpo0xwvd+utRps+uhO/H8jk1/2ZrD+UzaD2wVY7t9DQ4Mdhx1I4e1TtJD74sYs/x1yw3ecux96zTlioU23pgH0bSF6Mp5uBWWM7c8eiLXyy4Rg39mlFp7AArcNySPXOakaMGMH06dP5/vvvCQwMBCAnJ4enn36aK66w8huhaBzz6q/IeHDToPC363VqkrTvf3BVsVq4ag35GXAmGdBBVF/rnLOa0goj6yvritYdyuZodmGN7wf5uDOoXTCD24dwaYdgwgOt9Lpq0TbEj9v6RfPx38f4z497+fGhSzHoZZrF6Xn6w4j/wLK71ZVucTdB4AVqQ05ug9RN6jRd77vsF6dolH1p+RzNLsTDTc/llzjWXoNDO7ZkZJdQftqTwczv97D0nn5SxF2LeidJr732GoMHDyY6OpqePdXRie3btxMaGsrixYutHqBohOMa1SOZteoLgVFqg8BDa6Dz1dY5rzn5C+0C3kHWOWc19y7exu8Hsix/Nuh19GodVJkUhdAtMtCuicq/L2/PssQT7E/P55ttqUzoY+Ou6cI+ul0PWxeqP88/z1D3sTsf8xYkXa9z/I19hYVlqq1DCH4OMtVW3bNXdeaPg1lsPnqGFTtOMa6HC7TasLJ6F25HRkayc+dOXnnlFTp37kx8fDxvvvkmu3btIioqqkFBzJ8/n5iYGLy8vEhISGDz5s3nfWx5eTmzZ8+mbdu2eHl5ERcXx+rV/yx+rOs5FUXhyiuvRKfTsXz58gbF77C03tdMr6/aZ8uae7lZlv5bf6qtpNzIX4ezAbipTxTv3RZP0swr+Pq+ATx4eXt6RAXZfSSnma+HpVvuaz8fpKC04iLPEE5Bp4PRr4BOr3bhPrqu9sflpcGeZepxP1n27yyqr2ob4wCr2mrTqpkPDwxVi7hf/HEf+VLE/Q8NWhPu6+vLPffcw/z583nttdeYOHEi7u7uDQpg6dKlTJs2jVmzZpGYmEhcXBwjR44kMzOz1sfPmDGD999/n7fffpu9e/dy3333MX78eJKSkhp0zjfeeMM1hxjz09V6BxtNSdWZucD04GootdIKreO2ayK551Qe5UaFYD8P5lzbjVFdwwjwatjPtjVN7B9DTAsfsvJLef+PI1qHI6wlrFvV9NnKJ2pfCbr1I3Vfu9b9Habdhbi4/en5JDvoVFt1UwbHEt3Ch8z8Ut5ae0jrcBxOgxvn7N27l9WrV7NixYoat/qaN28eU6ZMYfLkyXTu3Jn33nsPHx8fFi5cWOvjFy9ezNNPP83o0aOJjY3l/vvvZ/To0cydO7fe59y+fTtz584977WqKy0tJS8vr8bNoZlHkUK7qj1ntBIeBy3aQUUJHFjV+POV5EHGbvXYBiNk21NzAOgRFeRQybOHm56nrlQ723+wLplTOcUaRySs5rKnwacFZO2DLR/W/F55sTolB+qyf+E0zKNIQxx0qs3My93Ac2PVTtyL/jrGwYwm1G6kDuqdJCUnJxMXF0fXrl0ZM2YM11xzDddccw3jx49n/Pjx9TpXWVkZ27ZtY/jw4VUB6fUMHz6cDRs21Pqc0tJSvLxqruzw9vZm/fr19TpnUVERt9xyC/PnzycsLOyisc6ZM4fAwEDLraFTi3ZjWfpv3yXy/6DTVdumxApTbic2g2KCoGgIiGj8+c5RPUlyNCO7hNE3pjmlFSZe/emA1uEIa/FpDpfPVI9/ewkKqo147/oaik6rtX0dx2gTn6g3RVH40TzV5kCr2s7nsk4tGX5JKBUmhZnf70ZRlIs/qYmod5L073//mzZt2pCZmYmPjw979uxh3bp19O7dm99//71e58rOzsZoNBIaWnMoMjQ0lPT09FqfM3LkSObNm8ehQ4cwmUysWbOGZcuWkZaWVq9zPvLIIwwYMIBx48bVKdbp06eTm5trudXWUNOhaNVEsjbmJOnwWnVbhcawYT0SQFLKWQB6tm5mk/M3hk6nY8ZVaoPJ75JOsvNEjrYBCevpebvaYbw0D355Xr1PUaoKtvveAwbHHY0QNR3IyCc5yzzVpn0DybqYNbYznm56Niaf4YedaVqH4zDqnSRt2LCB2bNnExwcjF6vR6/XM2jQIObMmcNDDz1kixhrePPNN2nfvj2dOnXCw8ODqVOnMnnyZPT12HJjxYoV/Prrr7zxxht1fo6npycBAQE1bg6r+pSUnZst1iqko7qNgqlcbYjXGDYcIcsuKOXE2WJ0OujeSsMpygvo3iqIa3uqK1D+87998onPVegNMPo19Xj7Z3Biq1rInbkH3H2g1+3axifqZWVlkjG4fQj+DlDTWBdRzX34l6WIe68sEKlU7yTJaDTi7+8PQHBwMKdOnQIgOjqaAwfqNwUQHByMwWAgIyOjxv0ZGRnnnQILCQlh+fLlFBYWcvz4cfbv34+fn5+luWVdzvnrr79y5MgRgoKCcHNzszTBvO666xg6dGi9XoNDSrXtlFSDdL1W/dqYKbeKUji5VT22QfK3PSUHgHYhfg79i+2xkR3xctez+dgZftpT+4ircEJRfdRNbwFWPgYb31WPe9wC3o43silqV32q7SoHXdV2PvcOiaV1cx8y8kp5W4q4gQYkSV27dmXHjh0AJCQk8Morr/DXX38xe/bsf3ThvhgPDw/i4+NZu3at5T6TycTatWvp3//Cb4JeXl5ERkZSUVHBt99+a5k2q8s5n3rqKXbu3Mn27dstN4DXX3+dRYsW1es1OCStl/7XxpwkHftTbQbZEGk71AJw7+YQ3MF6sVVKSlWn2hyxHqm6iCBv7rlU/b82Z9V+SiuMGkckrGb4c+AZAKeS1BWhAAmy7N+ZHMjI54iTTbWZebmrnbgBPlp/tGntGXke9U6SZsyYgcmk7mw+e/Zsjh49yqWXXsrKlSt566236h3AtGnTWLBgAZ988gn79u3j/vvvp7CwkMmTJwMwceJEpk+fbnn8pk2bWLZsGcnJyfz555+MGjUKk8nEE088UedzhoWF0bVr1xo3gNatW9OmTZt6vwaHo9G+ZhfULAZa9VFHuPYub9g5LEv/+9tkc09z0bYj1iOd694hbQnx9+T46SIWbziudTjCWvxawtCq33e0u0Ld6004DWecaqvu8ktCubxTSypMCrNW7GnyU/r1rgQcOXKk5bhdu3bs37+fM2fO0KxZswYtmZ4wYQJZWVnMnDmT9PR0evTowerVqy2F1ykpKTXqjUpKSpgxYwbJycn4+fkxevRoFi9eTFBQUJ3P6dIqStUtDMCxkiRQC7hPbFGn3BLurf/zzUXbNqhHMpkUdqbmAo4/kgTg6+nGYyM68OS3u3hz7SGu7dWK5r4eWoclrKHvFNj+uVpXOOBBraMR9VBjVVv3i6+adlSzxnbhz8PZ/HX4NIs3Hmdi/xitQ9KMTqlHmlheXo63tzfbt2+3jL40VXl5eQQGBpKbm+tYRdwpm2DhCLXvyuNHbDLi0mB5aTDvEkCBh3dBUD221zCZ4JU2UJIDd/8KreKtGtqhjHyueH0d3u4Gdj03AjdDg1uI2Y3RpDDmrT/Zn57PHQNieO7qLlqHJKyl6Iy6nU94nNaRiHo4kJ7PyDfW4WHQs/XZ4Q7RiLahPlh3hJdW7seg1/HpnX0Z2M51Nteuz/t3vd4J3N3dad26NUaj1EA4rOpTbY6UIAEEhEPMIPV497L6PTf7gJoguftAeHerh5ZUWbTdrVWgUyRIoO4pN2OMWj/w2cbjHMkq0DgiYTU+zSVBckLmUaTBHYKdOkECmHJpLON7RmI0Kfzr88R/bPTdVNT73eCZZ57h6aef5syZRva7EbbhiPVI1TW0saS5HqlVbzBY/5dPkqUeKcjq57alQe2DLfUDc1bu1zocIZo0c5ft0U7QQPJidDodc67tRo+oIHKLy7nrky3kFje9vd3qnSS98847rFu3joiICDp27EivXr1q3ISGTCabN1tstM7jQO8G6Tshux5LTG2c/FmKtp2gHulc00dfgkGv45d9GfxduTmvEMK+DmbkczizAA+DnuGdXaP+1cvdwAcT4wkP9CI5q5AHv0yiwmjSOiy7qnfh9jXXXGODMIRVZO236ZSUVfg0h7bD4NDP6mjS0Kfq9jwbJn9FZRUcSFf34usR5fgr287VrqUftyW05pMNx/nPj/v44cFBGPQONtUqhIv7sXJV26XtnX+qrbqW/l4smNib69/7m3UHs3hp5X5mVrYJaArqnSTNmjXLFnEIa0gxT0n1scmUlNV0va4qSRry5MVrp3JS1SJWnUF9bVa280QuJgXCArwIC/S6+BMc0L+Hd2BZ0kn2puXxbeIJbuzt4HsLCuFiXGmq7VxdIwOZd2MP/vV5Igv/OkqHUD9u6luPhTdOzDkqVEXdOPpUm1nH0WDwhOyDkL7r4o83v67w7uDpZ/VwtjtpPVJ1zX09eHCYuqXAaz8doFC2FBDCbg5l5HMoswB3g85lptrONbpbOI8MV5v4zli+m43JpzWOyD7qnSTp9XoMBsN5b0JDNtzXzKq8AqDDCPW4LgXc5hGy1rbpIG7ejsQZ+iNdyKQBMUQ19yYzv5T31yVrHY4QTYZ5Vdul7UMI9HbgUfxGeujydlzVPZwKk8L9n20j9UyR1iHZXL2TpO+++45ly5ZZbkuXLuWpp54iPDycDz74wBYxirrISYW8E+qUVGRvraO5uK7Xq193L1N3O78QywhZP5uEYh5JcvYkydPNwPQrLwHUHifpuSUaRyRE0+DKU23V6XQ6Xr0+jm6RgZwtUle85Ze49oq3etckmfdIq+7666+nS5cuLF26lLvuussqgYl6Mq/+Co+zyZSU1bUfAR5+kJuiduGO6lv744rOQOZe9dgG04hpucWk55Vg0Ovo1irQ6ue3tyu7htE7uhlbj5/l1Z8OMPdG6bUjhC0dzsznYIY61XaFi061VeftYWDBxN5c/c56DmYU8PCS7XwwsbfLLhaxWk1Sv379amwqK+zM0fsjncvDR61NggtPuaVuUr+2aAd+IVYPwzzV1jHUHx+Pen9mcDg6nY4ZV6krT75NPMGuE7kaRySEa/txZzrg+lNt1YUFevHBxN54uulZuz+TV1a7bo82qyRJxcXFvPXWW0RGRlrjdKIhnKUeqbpulVNue74D03m6uNupP1IPJy7aPlePqCCu6REBwH9+3NvkN6gUwpaaylTbuXpEBfHK9WqrmffXJfPNthMaR2Qb9f7ofO5GtoqikJ+fj4+PD5999plVgxN1VHQGsvapx84ykgQQexl4BUFBBhxbD7FD/vmY47ZNkpJcpB7pXI+P6sSq3elsOnqGn/dmMLKL8262KYSjOpyZz4GM/CYz1XaucT0iOZRRwDu/HebpZbtoE+xDfHRzrcOyqnonSa+//nqNJEmv1xMSEkJCQgLNmjlfIz6XYJmSag++TrQJoZsHdL4aEj9Vp9zOTZLKi+FUknpsgxGyCqPJMh3Vy4VGkgAig7yZcmks7/x2mDkr93FZx5Z4uEnHDyGsyTzVNqhdcJOZajvXtCs6cCgzn5/2ZHDv4m0sf2AgrZr5aB2W1dQ7SbrjjjtsEIZoFPO+Zs401WbW9Xo1Sdq3Aka/piZOZie3gakc/EKhWRurX/pARj7F5Ub8vdyIDXaCYvd6um9oW5ZsSeXY6SI+23icOwdZ/+9QiKasqU61VafX65h3Yw+uf28D+9LymPLpNr65rz++ns5f4wkNqElatGgRX3/99T/u//rrr/nkk0+sEpSoJ2dpIlmbmEHg2xKKz0LybzW/V70e6WJduRvAXI8U1yoIvQuuzPDzdOPREWrztzfXHiKnqEzjiIRwHYczCyxTbSM6N+3pbF9PNz6c1JtgPw/2peUx7avtmEyuUQtZ7yRpzpw5BAf/c0qnZcuWvPTSS1YJStRD9SkpZ0yS9AboMl49PneVm6UYXZpINtSNvaPoFOZPbnE5b609rHU4QrgM8yjSwHbBBPo0zam26iKDvHn/9ng8DHp+2pPBvDUHtQ7JKuqdJKWkpNCmzT+H7aOjo0lJSbFKUKIeLFNSYdAsRutoGsa8ym3/j2rSB+pqt9TN6rGNmkgmucB2JBdj0Ot4ZozaYPLTDcdIzirQOCIhXINMtf1TfHRzXrq2GwDv/HaY77ef1Diixqt3ktSyZUt27tz5j/t37NhBixYtrBKUqIfqS/9tMCVlF636QGBrKCuAgz+p92XshrJ88PCH0K5Wv2ReSTlHKhMGVx5JArV/y2UdQ6gwKby8ynX7mQhhL0eyCtifno+bXseIJriq7UKuj2/FvYNjAXj8m52WsgZnVe8k6eabb+ahhx7it99+w2g0YjQa+fXXX/n3v//NTTfdZIsYxYXYeF8zu9DpoOs5U27m5C+qrzolZ2U7U3NRFIhq7k0LP0+rn9/RPD36Egx6HT/vzWDDkaaxMaUQtrJyZ9VUW5CPx0Ue3fQ8MaoTl3dqSVmFiXs+3erUWyTVO0l64YUXSEhI4PLLL8fb2xtvb29GjBjBsGHDpCbJ3owVNp+SshvzXm6HfoaSvKqibRut2EtKOQtAj6im0baifag/t/RtDcCLK/e6TFGlEFowb2g7RqbaamXQ63jjph50CPUjM7+UKZ9upbjsPA2DHVy9kyQPDw+WLl3KgQMH+Pzzz1m2bBlHjhxh4cKFeHhIRm1XGbvVKSrPAAjtonU0jRPWTe3zVFECB1bardN2Txefaqvu4eHt8fd0Y/fJPL5Lcv5aASG0kFx9qq2LTLWdj7+XOx9O7EMzH3d2nczlsW92OGX3/wZ3l2vfvj033HADV111FdHR0daMSdSVeem/jaak7Eqng67XqcfrX1e7cOvdITLe6pdSFMUltyO5mBZ+njwwrB0Ar/50gKKyCo0jEsL5mAu2B8hU20W1buHDe7fF46bX8ePONKdcYVvvJOm6667j//7v//5x/yuvvMINN9xglaBEHVnqkZxw6X9tzElSVmVxcWQvcPe2+mVOnC3mdGEZ7gYdncMDrH5+R3bHgBhaNfMmPa+EBeuOah2OEE7nx11ql+0x3Zp2b6S6SohtwX+uURffvP7LQUuS6SzqnSStW7eO0aNH/+P+K6+8knXr1lklKFEHiuLcTSRrE9JBnXYzs1GdVWJlPVLn8AC83J18BK6evNwNPHVlJwDe++MIGXnOW1AphL0dzS5kX1pe5ao2SZLq6qa+rZk8MAaAaV9tZ/fJXG0Dqod6J0kFBQW11h65u7uTl5dnlaBEHZxJVqekDB42mZLSjHk0CWy2Ys9Sj9S6aRRtn2tMt3B6tQ6iuNzIaz8d0DocIZxG9am2Zr4y1VYfz4y+hMEdQigpNzHl061kOskHtHonSd26dWPp0qX/uH/JkiV07tzZKkGJOjCPIkX0AncvbWOxpq7XATq1Himqr00uYalHakJF29XpdDpmXKX+X/0m8QR7TjnPpzohtPTjTvOqNhlFqi83g563b+5JbIgvabkl3LN4GyXljr/ird470D377LNce+21HDlyhGHDhgGwdu1avvjiC7755hurByjOw1KPZP0pqdyicrw89Hi6aTAVFdQablkKOgP4NLf66UsrjOw5pY54NtUkCaBX62ZcHRfBih2nePHHfXx+dwI6Z21GKoQdHM0uZG9aHgaZamuwQG93PprUh2vm/8X21Bye+nYnr0/o4dC/e+o9kjR27FiWL1/O4cOH+de//sWjjz7KyZMn+fXXX2nXrp0tYhS1sdG+ZkezC+n70i88vGS7Vc9bLx1GQvvhNjn1vrR8yipMNPNxJ7qFj02u4SyeGNURDzc9fx85zdp9mVqHI4RDs0y1tW0hU22N0CbYl3dv7YVBr2P59lP8948jWod0QQ1qATBmzBj++usvCgsLSU5O5sYbb+Sxxx4jLi7O2vGJ2hRkwpkjgM7qU1K/7c+ktMLET3vSXXLX+O2WJpJBDv3pxR5aNfPhrkHqPowvrdxHudGkcURCOK6qqTZpINlYA9sF89xYdcr/1Z8O8POedI0jOr8G90lat24dkyZNIiIigrlz5zJs2DA2btxozdjE+ZgbLbbsDN7WLT7eVplEmBT442CWVc/tCKrqkZpm0fa5/jW0LS18PUjOLuTzjce1DkcIh3Ss+lRbF5lqs4bb+8dwW7/WKAo8vHQ7+9Icc+FXvZKk9PR0Xn75ZUsjyYCAAEpLS1m+fDkvv/wyffr0sVWcojrL0n/r1yMlHj9rOf5tv+tNwTTFJpIX4u/lzrQRHQB4Y+0hcovKNY5ICMfzY7WptuYy1WY1s8Z2YUDbFhSVGbn7k61kF5RqHdI/1DlJGjt2LB07dmTnzp288cYbnDp1irffftuWsYnzOV5ZtG3leqRTOcWkVduI8I+DWRhdaI+vM4VlHDtdBECPVkHaBuNAJvSOokOoHzlF5bz96yGtwxHC4ZjrkUbLVJtVuRv0vHtrL6Jb+HAyp5j7P9tGaYVjrXirc5K0atUq7rrrLp5//nnGjBmDwdC0mvA5jNJ8SN+pHlu5iaS5yWKnMH/8vdw4W1RuGXlxBTsqX0tsiC+BPu7aBuNA3Ax6nhmj1gd8suEYx7ILNY5ICMdx/HQhe06pU20jZarN6oJ8PPhoUm/8Pd3YcuwsM77b7VB7vNU5SVq/fj35+fnEx8eTkJDAO++8Q3Z2ti1jE7U5sQUUEwS2hsBIq5468XgOAAltmjO4QwjgWlNuSU28P9KFDOkQwpAOIZQbFf5v9X6twxHCYZin2vrHylSbrbRr6c/bt/REr4Ovt53go/WOs2VSnZOkfv36sWDBAtLS0rj33ntZsmQJERERmEwm1qxZQ35+vi3jFGaWpf/W34rEXLTdK7oZwzq2BOBXF0qSLJ22JUmq1TNjLkGvg1W709l89IzW4QjhEGSqzT6GdmxpGdF+aeU+h/mAXu/Vbb6+vtx5552sX7+eXbt28eijj/Lyyy/TsmVLrr766gYFMX/+fGJiYvDy8iIhIYHNmzef97Hl5eXMnj2btm3b4uXlRVxcHKtXr67XOc+cOcODDz5Ix44d8fb2pnXr1jz00EPk5jpB52HzyjYrF22XlBvZU7mfTq/WzRjaMQSdDvam5ZGe6xzt4y/EZFKqLf+XlW216RDqz019WwPwnx/3YnKhejQhGiLldBG7T5qn2kK1Dsfl3Tkwhpv6RGFS4KEvkziUof3gS4NbAAB07NiRV155hRMnTvDll1826BxLly5l2rRpzJo1i8TEROLi4hg5ciSZmbVnkTNmzOD999/n7bffZu/evdx3332MHz+epKSkOp/z1KlTnDp1itdee43du3fz8ccfs3r1au66664GvQa7qSiDE1vVYyvva7brZC4VJoWW/p60auZNCz9P4iqLm3874BgZfWMcPV1IXkkFnm56OoX7ax2Ow3pkeAf8PN3YeSKX73ec1DocITRVfaqthZ+nxtG4Pp1Ox+xxXekb05z80gru+mQrZwu17dfXqCTJzGAwcM0117BixYp6P3fevHlMmTKFyZMn07lzZ9577z18fHxYuHBhrY9fvHgxTz/9NKNHjyY2Npb777+f0aNHM3fu3Dqfs2vXrnz77beMHTuWtm3bMmzYMF588UV++OEHKioqar1uaWkpeXl5NW52l74TKorV3kjBHax66m2VS//jo5tZmiwO66ROuTnKsGdjbE/JAaBbZCDuBqv82LukEH9P7h/aFoC3fz0so0miSZOpNvvzcNPz39t60aqZNylninji252axqPpu0VZWRnbtm1j+PCqLSj0ej3Dhw9nw4YNtT6ntLQUL6+aG7p6e3uzfv36Bp8TIDc3l4CAANzcat/Obs6cOQQGBlpuUVFRdX6dVmNe+t+6P+it+09nTpJ6ta6airqssi5p/eFsh1uWWV9NfVPb+pg0IAZ/LzeSswpdqiZNiPpIOV3ErpO5MtWmgRZ+nnw0qQ9dIwN4clQnTWPRNEnKzs7GaDQSGlrzBzA0NJT09NrblI8cOZJ58+Zx6NAhS9H4smXLSEtLa/A5s7OzeeGFF7jnnnvOG+v06dPJzc213FJTU+vzUq3D0kTSukXbiqKQVK1o26xLRAAh/p4UlRmdvpA3KbWyHkmaSF6Un6cbtyZEA/DBumSNoxFCGyt3q+8p/WKby1SbBjqG+fPD1EG0a+mnaRxON+/w5ptv0r59ezp16oSHhwdTp05l8uTJ6Bs4spKXl8eYMWPo3Lkzzz333Hkf5+npSUBAQI2bXZlM1Yq2rZskpZwpIrugDA+Dnq6RVa9Lr9dxWUe1FYAzjyiUlBvZn6YWAPZsLUXbdXHHgBjcDTo2HztjSaCFaErMe7XJVJt2HGF/TU2TpODgYAwGAxkZGTXuz8jIICys9qZdISEhLF++nMLCQo4fP87+/fvx8/MjNja23ufMz89n1KhR+Pv789133+Hu7sANBk8fguIz4OYN4dbdSNg81dY1MgBPt5pNQl2hLml3ZVF6iL8nEYFeF3+CICzQi6vj1D5cH/7pOD1LhLCHv49ks+tkLnod0kCyidM0SfLw8CA+Pp61a9da7jOZTKxdu5b+/S88WuLl5UVkZCQVFRV8++23jBs3rl7nzMvLY8SIEXh4eLBixYp/1Dk5HHM9Uqve4GbdhmbmTtvx0f8cZRnYLhh3g45jp4s46qSdmJMqi7Z7RAU5xCcTZ3HPYPWDx6rdaaRUbucihKs7nJnPfYu3ATC+ZyuCZaqtSdN8um3atGksWLCATz75hH379nH//fdTWFjI5MmTAZg4cSLTp0+3PH7Tpk0sW7aM5ORk/vzzT0aNGoXJZOKJJ56o8znNCVJhYSEfffQReXl5pKenk56ejtHooAXKNppqA9hW2Wm7Vy1TUf5e7vSJaQ4475SbFG03TMcwf4Z0CMGkwMK/ZDRJuL7sglImf7yFvJIKerUO4sXxXbUOSWis9qVcdjRhwgSysrKYOXMm6enp9OjRg9WrV1sKr1NSUmrUG5WUlDBjxgySk5Px8/Nj9OjRLF68mKCgoDqfMzExkU2bNgHQrl27GvEcPXqUmJgY277ohrBRE8mC0goOpKvtDHrVMpIE6pTb30dO89v+TO4a1Maq17cHS6dtKdqut3sGx/LHwSyWbknl35e3p5lsyyBcVEm5uhN96pliWjf3YcHE3ni5yx6lTZ1OcaSd5JxIXl4egYGBltYBNpV7El7vDDo9PJUCntZrhvjX4Wxu/XATrZp5s/7JYbU+5khWAZfP/QN3g46kmSPw89Q8t66zzLwS+r60Fp0Odj030qlidwSKojDmrfXsTcvjsREdmDqsvdYhCWF1JpPCA18ksmp3OoHe7iz71wDahmi7qkrYTn3evzWfbhN1YB5FCutu1QQJau+PdK7YYF+iW/hQblRYf8i5NjU2b2rboaW/JEgNoNPpLLVJH/99nJJyB52OFlaVkVfCgnXJnNG427G9vLx6P6t2p+Nh0PPB7fGSIAkLSZKcgQ3rkS5UtG2m0+ksjSV/d7ItSqQeqfHGdA8nItCL7IJSvt8uW5U0BTOW7+bFlfuY8P4GsvJLtQ7Hpj7beNzSD+yV67uTENtC44iEI5EkyRmYm0hGWzdJMpkUEo9fPEkCuMzcCuBAJs40Q2vejkTqkRrO3aDnzspatA/WJctWJS4u5XQRv+xTW6gcyizgpg82kJnn/Jtc1+a3A5nM/H43ANOu6MA1PSM1jkg4GkmSHF1xDmTsUY+tPJJ0JKuAvJIKvN0NdAq78DReQpvmeLsbyMgrZc8pDfatawCjSWHniRxAOm031oQ+Ufh7unEkq9AlNjwW5/fJhmMoijr6GhHoxZGsQiZ8sJH0XNdKlPaeymPq54mYFLg+vhUPDmt38SeJJkeSJEeXuglQoHlb8Gtp1VObp9riogJxu8imr17uBga2Cwacp7Hkocx8CsuM+HoYaN/SurVcTY2/lzu3JLQGZKsSV1ZQWsFXW9Qtl/49vD1L7+1PZJA3R7MLmfDBBk7mFGscoXWk5RZz58dbKCwzMqBtC14a3016qIlaSZLk6GzaH+niRdvVDas25eYMzFNt3VoFYtDLL8DGumNgDG56HZuOnmFHZa2XcC3LEk+QX1pBbLAvQ9qHENXch6X39qN1cx+Ony5iwvsbSD3j3I1FC0oruPPjraTnldCupR//vS0eDzd5KxS1k58MR3e8Mkmycj0SQGJlEnGxeiSzyzqp+7glpeY4xaqXqv5Isl+bNYQHenN1jwgAFvwpo0muxmRS+PivY4CaEOsrP1i0aubDknv6EdPChxNni7npg41O24G9wmhi6heJ7EvLI9jPg0V39CHQ24G3oxKakyTJkZWXwKlE9djKI0k5RWUcziwA6p5EhAd60ynMH0WBPw46/miSrGyzvimXqu0AVu5Kc/oRBVHTH4eySM4uxN/TjWt7tarxvYggb5bc05/YYF9O5hQz4YMNTrdNkaIoPPfDHn4/kIWXu54PJ/UhqrmP1mEJBydJkiM7lQjGMvBtCc1jrXpq835mscG+NK9HF2XzlNuv+7OsGo+1FZRWcCAjH4CekiRZzSXhAVzaPhiTAh+tl61KXIl5FOnGPlG19hQLC/Riyb39aNfSj7TcEia8v4EjWQV2jrLhPvzzKJ9tTEGngzcm9JQPT6JOJElyZCnVptqsXFRoLto+31Yk52NOkv44kEmF0WTVmKxp54kcFAUiAr1oGeDgmxc7GXNzya+2ppJT5PjTruLiDmcW8MfBLHQ6mNQ/5ryPa+nvxZJ7+tEx1J/M/FImvL+RQ5UfRhzZql1pvLRqHwDPjL6EUV3DNI5IOAtJkhzZcdsXbde1HsmsZ+tmBPm4k1dSYelm7YikHsl2BrUL5pLwAIrKjHy+KUXrcIQVfPL3MQAu7xRK6xYXnoIK9vPkiykJdArzJ7uglJs+2MiBdMdNlJJSzvLw0u0oCtzeL9op958U2pEkyVGZjJXL/7F6klRhNFmSiLqubDMz6HUMbq8WcP/qwK0AzCvbZEjd+tStStQ3mo//PkZphWxV4sxyi8v5NvEEAHcOjKnTc1r4efLllH50iQjgdGEZN32wgb0O2D8t9UwRd3+yldIKE5d1DGHW2M6y1F/UiyRJjipzL5TmgYcfhHa16qkPZORTVGbE39ON9i3rv0eRpRWAgyZJiqJYRrmkiaRtXNU9grAAL7LyS/k+6ZTW4YhG+HprKkVlRjqG+tO/bd235Gjm68EXd/eje6tAzhaVc8uHG9l9MteGkdZPblE5dyzazOnCMjqHB/DOLb0u2g9OiHPJT4yjMk+1RfUFg3U3ZjVvRdKjdZBlmW99DOkQgl4H+9PzHbK53KncErLySzHodXSNCNQ6HJekblUSA8AHf8pWJc7KaFL4uHKqbfLAmHqPsgT6uLP4rgR6RAWRU1TOLQs2OkQPrbIKE/d9to0jWYWEBXix8I4++MoG16IBJElyVHZoIlnfeiSzZr4ellofRxxNMk+1XRLuj7eHQdtgXNhNfVvj5+lmKfoVzueXfRmcOFtMkI8743o0bN+yQG93Ft/Vl/joZuSVVHDbh5ssC0O0oCgKTy3byYbk0/h6GFh4Rx/CAmXxhmgYSZIckaLYNEmqbxPJ2pin3H53wO7b21MrR8qkHsmmAmSrEqdnXvZ/c9/WjfpA4e/lzid39qVvTHPySyuY+NFmth47Y6Uo6+ettYdZlngSg17H/Ft70TkiQJM4hGuQJMkR5RyH/DTQu0NkvFVPnZVfSsqZInS6xiURQzuqxdt/HT5NSbljFe4mWYq2ZWWbrd0xQN2qZEPyaXadcJx6FHFx+9Ly2JB8GoNex+39oht9Pj9PNz6+sw/9Y1tQUFrBxIWb2ZR82gqR1t13SSd4/ZeDAMwe14WhHa2736VoeiRJckTmeqSIHuBh3Y6w5mHwjqH++Hs1vB1/5/AAwgK8KC43stHOvwgvpNxoYldl8aiMJNleRJA3Y+PUrUo+kK1KnIp5FGlU1zAigrytck4fDzcW3tGHQe2CKSozcseiLfx9JNsq576YjcmneeKbnQDcOziWWxMan/gJIUmSI7LlVNvxhjWRPJdOp7Ps5eZIdUkH0vMprTAR4OVGbLCv1uE0CXdfqrYDkK1KnMeZwjKWbz8JwOQBMVY9t7eHgQ8n9WZIhxCKy43c+fEW1h+ybaJ0JKuAexdvo9yoMLpbGE+O6mTT64mmQ5IkR2SHou369keqzWWVQ9m/HshEURxjdVNS5UhZXFTDVu6J+usSEcigdsEYTQqLKkcnhGP7cnMKpRUmukUGNqo28Xy83A28f3s8wzq1pKTcxJ2fbLFZ/eLpglImL9pCbnE5PVsHMe/GHvJ/X1iNJEmOpjAbstU5dVr3s+qpyypM7KycirLGL8aB7YLxMOhJPVPMkSzH2OzS3B9J9muzL/NWJUu2pJBbVK5xNOJCyo0mFm84DjRs2X9debkb+O9tvbiicyhlFSbu+XQbv+7PsOo1SsqN3P3pVlLOFBHV3JsFE3vj5S4rWoX1SJLkaFI2ql9DOoFPc6uees+pXMoqTDT39SDmIlsP1IWvpxsJsWqMjjLltl2aSGri0vbBdArzV7cq2Xxc63AaxWRSmLNyH7N/2OuS/Z9W704nPa+EYD9PxnQPt+m1PN0MzL+lF6O6hFFmNHHv4m38vCfdKuc2mRSmfbWdpJQcAr3dWXRHX4L9PK1ybiHMJElyNHaZaguy2qdHy5SbAyRJuUXlJFeOaMnKNvvS6XRMuVQdTfr4L+fequTNtYd4f10yC/86yjfbTmgdjtWZm0femtAaTzfbj7p4uOl5+5aejOkeTrlR4V+fJ7JqV1qjz/t/P+1n5a503A063r89nnYN2D1AiIuRJMnRDHgQrl8IPW+3+qnNS+MbW7Rdnblf0pZjZ8gr0XaaZfuJHACiW/jQ3NdD01iaorFxEYQGeJKZX8qK7c65Vcnq3em8ufaQ5c8vr97vUtOHO0/ksO34WdwNOm7t19pu13U36HlzQg/G9YigwqQw9csk/rez4T8jX2xK4f0/1NWUr1zfnX6xdd9ORYj6kCTJ0fiHQdfroJV1+yOBdYu2zWKCfYkN9qXCpNh8BcvFyKa22vJw0zN5oLrSbcGfyQ5TzF9XBzPyefSr7QBM7B9N+5Z+nCks47WfD2gbmBWZC+vHdo+gpb99u1C7GfTMu7EH1/aMxGhSeOjLJL6vXGFXH78fyOTZ73cD8PDw9ozv2craoQphIUlSE3Eqp5j0vBIMeh1xrYKseu7LHGTDW+m0rb1bEtStSg5mONdWJblF5dzz6VYKy4z0j23Bs1d15vlxXQD4fNNxh9q4taEy80osozd3DIzRJAaDXserN8RxQ3wrTAo8snQ739ZjSnNfWh5Tv0jCaFK4tlck/768vQ2jFUKSpCbDPIrUJSLA6vuZmeuSfjuQpVmhq6IolqLtnlYcKRP1E+Dlzk19ogDn2arEaFJ4aEkSx04XERnkzTu39MTdoGdA22DGxkVgUuDZ73c7fRH355tSKDcqxEc3o7uVPyjVh0Gv4/+u687NfVtjUuCxb3bw1ZbUiz4vI6+EOz/eQkFpBf1im/Pytd1ttjJPCDNJkpoIW0y1mfVt0xxfDwPZBaXsPqXNJ+7jp4s4W1SOh0HPJeH+msQgVJMHtcGg1/H3kdNOMQLz6k8H+ONgFl7uej6YGE+Laiuknhl9Cb4eBpJScvgm0XmLuEsrjHy+qWrZv9b0eh0vXtOV2/tFoyjwxLc7+WJTynkfX1hawZ0fbyEtt4S2Ib68f1tvPNzk7UvYnvyUNRHmJovWLNo283DTM6h9MKDdKjfzKFLniAC7rNgR5xcZ5M1VlUvLFzj4ViU/7DjFe38cAeCV6+PoEhFY4/thgV78e7g6pfPyKuct4v7fjjSyC8oIC/BiZJcwrcMB1ERp9rgu3FHZ8fvp73axeMOxfzyuwmjiwS+T2HMqjxa+Hiy6oy+BPg3fUkmI+pAkqQkoLjOy51QeoC7/twXzKrffDmhTh1I11RakyfVFTeZ2AP/bmcbJnGKNo6nd3lN5PP7NDgDuHRLL1ZV70J1r8sA2liLuuWucr4hbURTLsv/b+0fjbnCcX/s6nY5ZYzszpXJrm2e/38Oiv45avq8oCrP/t5df92fi6abnw0m9aW2FHm9C1JXj/G8RNrPzRA4VJoXQAE8irbSR5bnMu23vPJFDdkGpTa5xIeZO21K07Ri6RgYysF0LjCaFheuPXvwJdnamsIx7Fm+lpNzEpe2DeWLk+ff6cjfoLUXcn210viLubcfPsutkLp5uem7pa79l/3Wl0+l4evQl3DekLQDP/7CXBZX1bB+tP8qnG46j08EbE3pIvaGwO0mSmoDEyqXx8dHNbFboGBrgRZeIABQFfrfzaFJJuZG9lbVQPaWJpMMwjyYt2ZxCbrHjTFNVGE1M/SKRE2eLiW7hwzs398Jwkb2+qhdxz3SyIm7zsv/xPSNp5qD9w3Q6HU+O6siDw9oB8OLKffx7SRIvrtwHwPQrO3FlN9t2BxeiNpIkNQG2LNqubphGrQD2puVRblRo7utBVHPbjJSJ+hvSIYSOof4Ulhn5cvP5i3Lt7aWV+/n7yGl8PAwsmNi7zvUt5iLuRCcq4j6VU8zqym1AtFr2X1c6nY5HR3Tk4coasO+3n0JR1M7g5oRbCHuTJMnFKYpCog2Ltqsz90tadzCLcqPJpteqztxEsmeU9bZbEY2n0+m4u7LWZNFfRymrsN/PxPl8u+0ECytrXubdGEeH0LqvhKxexP1/TlLEvXjjcYwmhf6xLegUFqB1OHXy8PAOPD6yIzodDL+kJc9f3UX+XwvNSJLk4o6fLuJMYRkebnq6RNj2l2RcqyCa+3qQX1phGb2yh+1Sj+SwxvWIJDTAk4y8Un7Yoe1WJTtP5DD9u10APDSsHaO61n/6ZvLANrRr6cdpJyjiLq42gucIy/7r44HL2rH1meEsmNgbNwcqNBdNj0P89M2fP5+YmBi8vLxISEhg8+bN531seXk5s2fPpm3btnh5eREXF8fq1avrfc6SkhIeeOABWrRogZ+fH9dddx0ZGRlWf21aMycr3SIDbb403qDXMbRDCGDfKbckc6dtWdnmcDzc9NwxQPutSrLyS7l38TbKKkwMv6QlDw/v0KDzuBv0zL7aOYq4l28/SU5ROVHNvbn8klCtw6m3Fn6eMoIkNKd5krR06VKmTZvGrFmzSExMJC4ujpEjR5KZWfub7IwZM3j//fd5++232bt3L/fddx/jx48nKSmpXud85JFH+OGHH/j666/5448/OHXqFNdee63NX6+9mafa4m081WY2tHLKzV79kk4XlJJ6Rl1irmUXYXF+tyS0xtfDwP70fNZpsL9fWYWJf32+zdKI8PUJPdBfpFD7Qga0C+aq7uEOXcStKAofVxZsT+ofc9HCdCFE7TRPkubNm8eUKVOYPHkynTt35r333sPHx4eFCxfW+vjFixfz9NNPM3r0aGJjY7n//vsZPXo0c+fOrfM5c3Nz+eijj5g3bx7Dhg0jPj6eRYsW8ffff7Nx48Zar1taWkpeXl6NmzOwV9G22ZD2IRj0Og5lFpB6psjm1zNPtbVr6UegtzSYc0SB3u5M6KMuPV+gwVYls/+3hy3HzuLv6cYHE3vj79X4n5MZYzrjU1nE/a0DFnFvOHKaAxn5+HgYuKF3lNbhCOG0NE2SysrK2LZtG8OHD7fcp9frGT58OBs2bKj1OaWlpXh51dy92tvbm/Xr19f5nNu2baO8vLzGYzp16kTr1q3Pe905c+YQGBhouUVFOf4vnvyScg5k5APQKzrILtcM9HEnvjIh++2A7UeTpB7JOdw5SB3NWH84mz123Lrmy80pfLYxBZ0O3ry5B21D/Kxy3rBAL8vmqo7YiXth5SjS9fGt5MODEI2gaZKUnZ2N0WgkNLTmfHloaCjp6em1PmfkyJHMmzePQ4cOYTKZWLNmDcuWLSMtLa3O50xPT8fDw4OgoKA6X3f69Onk5uZabqmpF9+QUWs7UnNRFIhq7k1Lf6+LP8FKLrNjK4CkypVtkiQ5tlbNfBhT2efmwz/t01xy2/EzzPx+NwCPXtGBYZ2sW5dTvYh7ngMVcR8/Xcja/Wp95aTKLT+EEA2j+XRbfb355pu0b9+eTp064eHhwdSpU5k8eTJ6vW1fiqenJwEBATVujs7eU21m5n5Jfx85TXGZ0WbXMZkUdshIktMw97r5YccpTtl4q5L03BLu+yyRcqPC6G5hPHBZO6tfw8Otqoh78cbjdh0hu5BPNxxHUdQ+VdYaOROiqdI0SQoODsZgMPxjVVlGRgZhYbVvwhgSEsLy5cspLCzk+PHj7N+/Hz8/P2JjY+t8zrCwMMrKysjJyanzdZ3RNjsXbZt1CPUjItCL0goTG5JtV6ibnF1AfmkFXu56OoXVvd+N0Ea3VoH0j21BhUmpsT+XtZWUG7n3s21k5ZfSMdSfV6+Ps9kqqZpF3Hs0L+IuKK3gqy3qKLezLfsXwhFpmiR5eHgQHx/P2rVrLfeZTCbWrl1L//79L/hcLy8vIiMjqaio4Ntvv2XcuHF1Pmd8fDzu7u41HnPgwAFSUlIuel1nYTIpJKVoM5Kk0+ksU262XOVm3m6le2SQ9FJxEvcMVj/MfLk5lbwS69fxKIrCzO93syM1h0Bvdz6YGI+vp5vVr1PdM2MuwcfDwLbjZzUv4v522wnySyuIDfZlcPsQTWMRwhVo/s4ybdo0FixYwCeffMK+ffu4//77KSwsZPLkyQBMnDiR6dOnWx6/adMmli1bRnJyMn/++SejRo3CZDLxxBNP1PmcgYGB3HXXXUybNo3ffvuNbdu2MXnyZPr370+/fv3s+xdgI4ezCsgvqcDHw6DJKEvVFiVZNuuNYynalv5ITmNIhxDat/SjoLSCJTbYqmTxxuN8tfUEeh28c0tPolv4Wv0a5woP9K5ZxK3RPnUmk8Infx8D1C1IGtPmQAihsu1HrDqYMGECWVlZzJw5k/T0dHr06MHq1asthdcpKSk16o1KSkqYMWMGycnJ+Pn5MXr0aBYvXlyjCPti5wR4/fXX0ev1XHfddZSWljJy5Ejeffddu71uW0usrEeKa6XNKMuAtsF4uuk5mVPMocyCem3/UFfbpWjb6ej1OqYMjuWJb3aycP0x7hjQBg836/x8bkw+zewf9gIw/cpLuNSOIymTB7bhq62pHMkqZN7PB3h+XFe7Xdvsj0NZJGcX4u/lxnW9Wtn9+kK4Ip2iVQtcJ5eXl0dgYCC5ubkOWcT9+Nc7+HrbCaZe1o7HRnbUJIY7Fm3m9wNZPHVlJ+4b0taq5y4qq6Dbcz9jNClsmD6M8EDZ2NZZlFYYGfR/v5GVX8q8G+O41gpv6Cdzihn79nrOFJYxrkcEb0zoYfduzX8dzubWDzeh18EPDw6iS0SgXa8/ceFm1h3M4u5BbZhxVWe7XlsIZ1Kf92/Np9uEbWyzbGobpFkMl3W0XV3SrhO5GE0KoQGekiA5GU83A3dULk3/YF3jtyopLjNyz6dbOVNYRpeIAF6+trsm21kMbBfMGI2KuA9nFrDuYBY6HUzsH2O36wrh6iRJckFnC8tIzioEoGeUfYu2qzPXJW07ftbqzfakiaRzuzWhNT6VW5WsP9zwFZCKojB92U72nMqjua8H798ej7eHbfcovJAZ1Yq4lyWdtNt1zbVIwy8JpXULH7tdVwhXJ0mSCzJv+Bob4kszXw/N4ohq7kO7ln4YTQp/Hs6y6rmrkiTtkkDRcEE+HtxYuV3GB43YquTDP4+yfPspDHod797ai1bNtE0QwgO9eaiyiHvOyn12KeLOLS63rKqTZf9CWJckSS7I3EQy3s5L/2szzEatAMxJUk9Z2ea07hrUBr0O/jyUzd5T9d8L8c9DWcxZtQ+AmVd1pl9sC2uH2CB3DmxD2xBfTheW8fqagza/3ldbUikqM9Ix1J/+DvJ3IISrkCTJBSUezwHs30SyNkM7qiuM/jiQZbUajfTcEtJyS9DroFukfYtjhfVENfdhtGWrkvqNJqWcLmLqF0mYFLghvhUT+0fbIsQG8XDTM7tyddunG47ZtBO30aTwyYZjgDqKpEUtlhCuTJIkF1NhNFlGWXo5QJLUJ6Y5/p5unC4sY8eJHKucc3vldGKHUH+bNwoUtmVuLrlixynScuu2VUlhaQX3LN5KbnE5cVFBvHBNV4dLDqoXcc+yYRH3L/syOHG2mGY+7lzTM9Im1xCiKZMkycXsT8+nuNyIv5cb7Rxg3yZ3g55LOwQD1tvwNkmm2lxG91ZBJLRpToVJ4ePKnesvRFEUHv9mB/vT8wnx9+T92+LxcteuUPtCzEXcW21YxG3e3uXmvq0d9u9BCGcmSZKLSay2FYmjdNw1twL47YB1irfNTSS1XLknrOfeIepo0hebUsi/yFYl7/5+hJW70nE36Hjvtl6EBXrZI8QGqV7E/fIq6xdx70vLY2PyGQx6Hbf1c5zpRiFciSRJLsZctG3v/douZGhlkrTrZC6ZeSWNOleF0cTOE2qNh2xH4hqGdmhJu5Z+5JdWsGRz6nkf9+v+DF77+QAAs8d1JT66ub1CbDBzEXd2gfWLuM0jb6O6hhERJL3ChLAFSZJcjHkkyRGKts1C/D3p3kotsP69kaNJBzMKKC434ufpRlsHmE4UjafX65hyaRsAFv51lHKj6R+POZJVwL+/3I6iqD2Wbu7b2t5hNoiHm57nr64q4m7IKr7anCksY/l2dQrvTln2L4TNSJLkQjLzSkg9U4xOB3FRjrXqy1rdt81F6d1bBWJwkOlE0XjjekQS7OdJWm4JP+5Mq/G9/JJy7vl0K/mlFfSJacassV00irJhBrUPZkw3cyfu3VbZ8PnLzSmUVpjoFhnoUKPGQrgaSZJciHkUqWOoP/5e7hpHU5O5X9L6w9mUVfxzpKCuzCvbpGjbtXi5G7hjgFpXU32rEpNJ4ZGl2zmSVUhYgBfv3hpvtQ1x7WnGVdWKuBMbV8RdbjSxeMNxQJb9C2FrzvfbRpxXYmVBsyNNtZl1iwwk2M+TgtIKth470+DzJFW+Rum07Xpu6xeNt7uBvWl5/HX4NABvrD3EL/sy8XDT8/7t8YT4e2ocZcOEB3rz4LDKTtyNLOJevTud9LwSgv08GdM93FohCiFqIUmSC3HEom0zvV5naSzZ0Cm3/JJyDmcVALJnmysK8vFgQp/KrUr+TGb17nTeWnsIgDnjuxHn5P/mdw1qQ6wVirjNy/5v69caTzdZ9i+ELUmS5CJKK4zsqlz15YgjSVCtLulAw5KknSdyURSIDPJ22hEFcWF3DlS3Kll3MItHlm633HddfCttA7MCDzc9sxtZxL0jNYfElBzcDTpuSXCO4nUhnJkkSS5iz6k8yowmWvh6EO2gu4Bf2iEYN72O5KxCjp8urPfzZb8219e6hQ9XdlWnkIrLjQxo24KnR3fSOCrraWwR98d/HwNgbPcIWvo7bo8oIVyFJEkuIvG4uaC5mcMWcgZ4udM7Rh3lasiUW1JlYbpMtbm2+4a0xaDX0aqZN+/c0gs3g2v9mnpmzCV4u9e/iDszr4T/7TwFwOSBbWwVnhCiGtf67dOEOWJ/pNqYV7nVt/u2oigyktREdGsVyE8PD+bHBy+lua+H1uFYXURQVSfu+hRxf7YphXKjQnx0M7q1cqwWH0K4KkmSXICiKNWKtoO0DeYizEnSxuTTFJVV1Pl5J84Wk11QhpteR5cIeYNwde1a+hHo41htLKypvkXcpRVGvthUtexfCGEfkiS5gJM5xWTkleKm19G9VZDW4VxQ2xA/WjXzpqzCZFnmXRfmUaTOEQGykadwemonbrUpZl2KuP+3I43sgjLCA70Y2SXMHiEKIZAkySWY+yN1iQjA28OxEwidTmcZTapPXVJVf6QgG0QlhP1d2j6E0d3CMCkwa8X5i7gVRWHR3+qy/9v7R+PuYjVaQjgy+d/mAqoXbTuDyyqTpN8PZNZ5dY+507YkScKVzBjTGW93A1uOneW7pNqLuLceP8vuk3l4uum5uY8s+xfCniRJcgHmeiRHL9o26x/bAi93PWm5JexPz7/o48sqTOyunI6QJEm4koggbx68vB0AL63cT17JP4u4P/7rGADje0bSzAUL2YVwZJIkObmisgr2pqkJhLMkSV7uBga2DQbqNuW2Ly2PsgoTgd7utAn2tXV4QtjV3YNiK4u4S/9RxH0qp5jVe9IBuEMKtoWwO0mSnNzOE7kYTQphAV5EBHlrHU6dDTW3AqhDkmQu2u4RFeSwPaCEaKiaRdzH2ZdWVcT96YbjGE0KA9q2oFNYgFYhCtFkSZLk5JylP9K5zMXbiSlnOVtYdsHHVk+ShHBF5iJuo0mxdOIuLjPy5eYUAO4YEKNtgEI0UZIkOTlz0XYvJ0uSIoO86Rjqj0mBdYcu3FjSkiQ5eA8oIRqjehH38u0nWb79JLnF5UQ19+byS0K1Dk+IJkmSJCemKIpl+b+jN5GszWV1mHI7W1jG0Wx1n7ceDt4DSojGqF7E/eKP+/nwz2QAJvWPwaCXaWYhtCBJkhM7drqIM4VleLjpnbILtXnK7Y+DWRhNtbcC2H4iB4A2wb6yske4vLsHxRIbrBZxH8kqxMfDwA29o7QOS4gmS5IkJ2Ze+t89MhAPN+f7p+zVOogALzfOFpVb+iCda7s0kRRNiIebnucqi7gBro9vRaC3627PIoSjc753VmHhbP2RzuVm0DO4Qwhw/lYAUrQtmprBHUK4NaE1YQFeTLk0VutwhGjSJElyYkkpzlm0Xd0wS13SP4u3FUWxJEk9nbDmSoiGenF8NzY+fTlRzX20DkWIJk2SJCeVV1LOgQy1W3UvJ9mOpDZDOoSg08HetDzSc0tqfO9odiG5xeV4uOmlR4wQQgi7kyTJSW1PyUFRoHVzH0L8PbUOp8Fa+HlaptJ+O1Bzys08itQ1IsApa66EEEI4N3nncVLO2kSyNpd1VKfczq1LqqpHcv7XKIQQwvloniTNnz+fmJgYvLy8SEhIYPPmzRd8/BtvvEHHjh3x9vYmKiqKRx55hJKSqmma/Px8Hn74YaKjo/H29mbAgAFs2bKlxjkKCgqYOnUqrVq1wtvbm86dO/Pee+/Z5PXZirlo2xn7I53LXJf01+FsSiuMlvuTKle2ST2SEEIILWiaJC1dupRp06Yxa9YsEhMTiYuLY+TIkWRm1r7S6YsvvuCpp55i1qxZ7Nu3j48++oilS5fy9NNPWx5z9913s2bNGhYvXsyuXbsYMWIEw4cP5+TJk5bHTJs2jdWrV/PZZ5+xb98+Hn74YaZOncqKFSts/pqtwWRSLEvjnblo26xLRAAt/T0pKjOy+egZAErKjZY9rGRlmxBCCC1omiTNmzePKVOmMHnyZMtojo+PDwsXLqz18X///TcDBw7klltuISYmhhEjRnDzzTdbRp+Ki4v59ttveeWVVxg8eDDt2rXjueeeo127dvz3v/+tcZ5JkyYxdOhQYmJiuOeee4iLi7vgKFZpaSl5eXk1blo5lFlAfmkFPh4GOob6axaHteh0un9Mue05lUuFSSHYz4NWzZxn414hhBCuQ7MkqaysjG3btjF8+PCqYPR6hg8fzoYNG2p9zoABA9i2bZslmUlOTmblypWMHj0agIqKCoxGI15eXjWe5+3tzfr162ucZ8WKFZw8eRJFUfjtt984ePAgI0aMOG+8c+bMITAw0HKLitKuC655qq1HVBBuBs1nTK3i3C1Kkqo1kdTpZEsGIYQQ9qfZO2x2djZGo5HQ0JobN4aGhpKenl7rc2655RZmz57NoEGDcHd3p23btgwdOtQy3ebv70///v154YUXOHXqFEajkc8++4wNGzaQlpZmOc/bb79N586dadWqFR4eHowaNYr58+czePDg88Y7ffp0cnNzLbfU1FQr/C00jCsVbZsNah+Mu0HHsdNFJGcVkGTpj+Q6r1EIIYRzcaphiN9//52XXnqJd999l8TERJYtW8aPP/7ICy+8YHnM4sWLURSFyMhIPD09eeutt7j55pvR66te6ttvv83GjRtZsWIF27ZtY+7cuTzwwAP88ssv5722p6cnAQEBNW5aSbQUbbtOAuHn6UbfNs0BdcpNtiMRQgihNTetLhwcHIzBYCAjI6PG/RkZGYSFhdX6nGeffZbbb7+du+++G4Bu3bpRWFjIPffcwzPPPINer6dt27b88ccfFBYWkpeXR3h4OBMmTCA2Vm3vX1xczNNPP813333HmDFjAOjevTvbt2/ntddeqzH954jOFJaRnF0IuN6qr8s6tuSvw6f5ZtsJTuYUo9NB91bOt3GvEEII16DZSJKHhwfx8fGsXbvWcp/JZGLt2rX079+/1ucUFRXVGBECMBgMgLqFRXW+vr6Eh4dz9uxZfvrpJ8aNGwdAeXk55eXltZ7HZDI1+nXZmnkrknYt/Qjy8dA4GusytwLYn652Em8X4oe/l2zuKYQQQhuajSSBuhR/0qRJ9O7dm759+/LGG29QWFjI5MmTAZg4cSKRkZHMmTMHgLFjxzJv3jx69uxJQkIChw8f5tlnn2Xs2LGWZOmnn35CURQ6duzI4cOHefzxx+nUqZPlnAEBAQwZMoTHH38cb29voqOj+eOPP/j000+ZN2+eNn8R9eBK/ZHO1SbYl+gWPhw/XQS43kiZEEII56JpkjRhwgSysrKYOXMm6enp9OjRg9WrV1uKuVNSUmqM+MyYMQOdTseMGTM4efIkISEhjB07lhdffNHymNzcXKZPn86JEydo3rw51113HS+++CLu7lUjEkuWLGH69OnceuutnDlzhujoaF588UXuu+8++734BnLFom0zcyuAj/8+BkinbSGEENrSKefOU4k6ycvLIzAwkNzcXLsVcZcbTXR/7meKy42seWQw7V2gR9K51h3MYuJCtcXDyocupXOEbGwrhBDCeurz/q3pSJKon/1p+RSXGwnwcqNtiJ/W4dhEQmxz2ob44qbX0yHUNV+jEEII5yBJkhMxT7X1im6GXu+aDRY93Qysfngwep0Og4u+RiGEEM5BkiQnss0F+yPVxt1FuogLIYRwbvJu5ETMSZIrFm0LIYQQjkaSJCeRkVfCyZxi9DqIky7UQgghhM1JkuQkzFuRdAwLwM9TZkmFEEIIW5MkyUlU9UcK0jYQIYQQoomQJMlJNJWibSGEEMJRSJLkBEorjOw+mQdI0bYQQghhL5IkOYHdJ/MoM5oI9vOgdXMfrcMRQgghmgRJkpyAuWi7Z+tm6HTSYFEIIYSwB0mSnID0RxJCCCHsT5IkB6coCttSJEkSQggh7E2SJAd34mwxWfmluOl1dIsM1DocIYQQosmQJMnBmfsjdYkMxMvdoHE0QgghRNMhSZKDMxdtx0t/JCGEEMKuJElycOZ6pF7SaVsIIYSwK0mSHFhRWQX70vIBKdoWQggh7E2SJAe2IzUXo0khPNCL8EBvrcMRQgghmhRJkhxYomWqTUaRhBBCCHuTJMmBSdG2EEIIoR1JkhxU9SaSMpIkhBBC2J8kSQ4qObuQnKJyPN30dA4P0DocIYQQosmRJMlBmafa4loF4eEm/0xCCCGEvcm7r4MyF233lP5IQgghhCYkSXJQicdzACnaFkIIIbQiSZIDyi0u52Cm2kRSiraFEEIIbUiS5IC2p+agKBDdwodgP0+twxFCCCGaJEmSHJD0RxJCCCG0J0mSA6oq2pYkSQghhNCKJEkOxmhSSErJAWQkSQghhNCSJEkO5lBmPgWlFfh6GOgY5q91OEIIIUSTJUmSg9lWWY/Uo3UQBr1O42iEEEKIpkuSJAeTU1SOl7teptqEEEIIjekURVG0DsIZ5eXlERgYSG5uLgEB1t1brdxoorTChJ+nm1XPK4QQQjR19Xn/1nwkaf78+cTExODl5UVCQgKbN2++4OPfeOMNOnbsiLe3N1FRUTzyyCOUlJRYvp+fn8/DDz9MdHQ03t7eDBgwgC1btvzjPPv27ePqq68mMDAQX19f+vTpQ0pKitVfX0O4G/SSIAkhhBAa0zRJWrp0KdOmTWPWrFkkJiYSFxfHyJEjyczMrPXxX3zxBU899RSzZs1i3759fPTRRyxdupSnn37a8pi7776bNWvWsHjxYnbt2sWIESMYPnw4J0+etDzmyJEjDBo0iE7/3969B0VZ/X8Afy8X13VnJcCAXQVFM0DES0IMwlQmE6KR17xESDqNoyGCGgOmq5YiYpOaWRBN+Yd5mSzxmhoxQOF4IcjbRKij440AnYyFNcl2z/cPx/39VtbL1y88R9n3a+aZYc/zLPs+Z5b143nOPk9wMEpLS3HixAkYjUZ07ty53ftMRERETwapp9siIyMRERGB9evXAwCsViv8/f2RmpqKrKysVsfPnj0b1dXVKC4utrXNnz8fR44cQXl5Of7++2/odDrs3LkTo0aNsh0zZMgQxMfHY/ny5QCAyZMnw93dHRs3bnzorC0tLWhpabE9NplM8Pf3b5fTbURERNQ+nojTbf/88w8qKysRGxv7f2FcXBAbG4tDhw45fM7QoUNRWVlpOyV37tw5fP/99xg5ciQA4N9//4XFYmk1I6TRaFBeXg7gdiG2d+9ePPvss4iLi4OPjw8iIyOxY8eO++bNycmBh4eHbfP393/UrhMREdETQFqRdO3aNVgsFvj6+tq1+/r6oq6uzuFz3njjDXzwwQeIiYmBu7s7+vTpg5deesl2uk2n0yEqKgrLli1DbW0tLBYLvv76axw6dAh//PEHAKChoQHNzc1YuXIlRowYgR9++AFjx47FuHHjUFZWds+8CxYsQGNjo227dOlSG40EERERPY6kL9z+b5SWlmLFihX47LPPUFVVhe3bt2Pv3r1YtmyZ7ZiNGzdCCIHu3btDrVZj3bp1mDJlClxcbnfVarUCAEaPHo25c+di0KBByMrKwquvvor8/Px7vrZarUbXrl3tNiIiIuq4pH2Fqlu3bnB1dUV9fb1de319Pfz8/Bw+x2g0IikpCW+//TYAICwsDGazGTNmzMDChQvh4uKCPn36oKysDGazGSaTCXq9HpMmTULv3r1tr+vm5oZ+/frZ/e6QkBDbKTkiIiIiaTNJnTp1wpAhQ+wWYVutVhQXFyMqKsrhc27cuGGbEbrD1dUVAHD3+nOtVgu9Xo/r16/jwIEDGD16tO11IyIiUFNTY3f86dOn0bNnz/+5X0RERNQxSL0Yz7x585CcnIzw8HA8//zzWLt2LcxmM6ZNmwYAmDp1Krp3746cnBwAQEJCAlavXo3BgwcjMjISZ8+ehdFoREJCgq1YOnDgAIQQCAoKwtmzZ5GRkYHg4GDb7wSAjIwMTJo0CS+88AKGDRuG/fv3Y/fu3SgtLVV8DIiIiOjxJLVImjRpEq5evYrFixejrq4OgwYNwv79+22LuS9evGg3c7Ro0SKoVCosWrQIV65cwdNPP42EhARkZ2fbjmlsbMSCBQtw+fJleHl5Yfz48cjOzoa7u7vtmLFjxyI/Px85OTmYM2cOgoKC8N133yEmJka5zhMREdFjjbcleUTteVsSIiIiah9PxHWSiIiIiB5nLJKIiIiIHGCRREREROQAbzX/iO4s5TKZTJKTEBER0cO68+/2wyzJZpH0iJqamgCA93AjIiJ6AjU1NcHDw+O+x/DbbY/IarWitrYWOp0OKpVKdpw2ZTKZ4O/vj0uXLjnlN/ecvf8Ax4D9d+7+AxyDjtx/IQSamppgMBhaXaD6bpxJekQuLi7o0aOH7BjtytnvUefs/Qc4Buy/c/cf4Bh01P4/aAbpDi7cJiIiInKARRIRERGRAyySqBW1Wo0lS5ZArVbLjiKFs/cf4Biw/87df4Bj4Oz9v4MLt4mIiIgc4EwSERERkQMskoiIiIgcYJFERERE5ACLJCIiIiIHWCQRACAnJwcRERHQ6XTw8fHBmDFjUFNTIzuWVCtXroRKpUJ6errsKIq5cuUK3nzzTXh7e0Oj0SAsLAy//PKL7FiKsVgsMBqNCAwMhEajQZ8+fbBs2bKHusfTk+inn35CQkICDAYDVCoVduzYYbdfCIHFixdDr9dDo9EgNjYWZ86ckRO2ndxvDG7duoXMzEyEhYVBq9XCYDBg6tSpqK2tlRe4jT3oPfD/zZw5EyqVCmvXrlUsn2wskggAUFZWhpSUFBw+fBhFRUW4desWXnnlFZjNZtnRpKioqMDnn3+OAQMGyI6imOvXryM6Ohru7u7Yt28ffvvtN3z00Ufw9PSUHU0xubm5yMvLw/r161FdXY3c3FysWrUKn3zyiexo7cJsNmPgwIH49NNPHe5ftWoV1q1bh/z8fBw5cgRarRZxcXG4efOmwknbz/3G4MaNG6iqqoLRaERVVRW2b9+OmpoavPbaaxKSto8HvQfuKCwsxOHDh2EwGBRK9pgQRA40NDQIAKKsrEx2FMU1NTWJvn37iqKiIvHiiy+KtLQ02ZEUkZmZKWJiYmTHkGrUqFFi+vTpdm3jxo0TiYmJkhIpB4AoLCy0PbZarcLPz098+OGHtra//vpLqNVqsWXLFgkJ29/dY+DI0aNHBQBx4cIFZUIp6F79v3z5sujevbs4deqU6Nmzp1izZo3i2WThTBI51NjYCADw8vKSnER5KSkpGDVqFGJjY2VHUdSuXbsQHh6O119/HT4+Phg8eDC++OIL2bEUNXToUBQXF+P06dMAgOPHj6O8vBzx8fGSkynv/PnzqKurs/s78PDwQGRkJA4dOiQxmVyNjY1QqVR46qmnZEdRhNVqRVJSEjIyMhAaGio7juJ4g1tqxWq1Ij09HdHR0ejfv7/sOIraunUrqqqqUFFRITuK4s6dO4e8vDzMmzcP7733HioqKjBnzhx06tQJycnJsuMpIisrCyaTCcHBwXB1dYXFYkF2djYSExNlR1NcXV0dAMDX19eu3dfX17bP2dy8eROZmZmYMmVKh7zpqyO5ublwc3PDnDlzZEeRgkUStZKSkoJTp06hvLxcdhRFXbp0CWlpaSgqKkLnzp1lx1Gc1WpFeHg4VqxYAQAYPHgwTp06hfz8fKcpkr755hts2rQJmzdvRmhoKI4dO4b09HQYDAanGQNy7NatW5g4cSKEEMjLy5MdRxGVlZX4+OOPUVVVBZVKJTuOFDzdRnZmz56NPXv2oKSkBD169JAdR1GVlZVoaGjAc889Bzc3N7i5uaGsrAzr1q2Dm5sbLBaL7IjtSq/Xo1+/fnZtISEhuHjxoqREysvIyEBWVhYmT56MsLAwJCUlYe7cucjJyZEdTXF+fn4AgPr6erv2+vp62z5ncadAunDhAoqKipxmFunnn39GQ0MDAgICbJ+JFy5cwPz589GrVy/Z8RTBmSQCcPurvqmpqSgsLERpaSkCAwNlR1Lc8OHDcfLkSbu2adOmITg4GJmZmXB1dZWUTBnR0dGtLvtw+vRp9OzZU1Ii5d24cQMuLvb/d3R1dYXVapWUSJ7AwED4+fmhuLgYgwYNAgCYTCYcOXIEs2bNkhtOQXcKpDNnzqCkpATe3t6yIykmKSmp1drMuLg4JCUlYdq0aZJSKYtFEgG4fYpt8+bN2LlzJ3Q6nW3NgYeHBzQajeR0ytDpdK3WYGm1Wnh7ezvF2qy5c+di6NChWLFiBSZOnIijR4+ioKAABQUFsqMpJiEhAdnZ2QgICEBoaCh+/fVXrF69GtOnT5cdrV00Nzfj7Nmztsfnz5/HsWPH4OXlhYCAAKSnp2P58uXo27cvAgMDYTQaYTAYMGbMGHmh29j9xkCv12PChAmoqqrCnj17YLFYbJ+NXl5e6NSpk6zYbeZB74G7i0J3d3f4+fkhKChI6ahyyP56HT0eADjcNmzYIDuaVM50CQAhhNi9e7fo37+/UKvVIjg4WBQUFMiOpCiTySTS0tJEQECA6Ny5s+jdu7dYuHChaGlpkR2tXZSUlDj8u09OThZC3L4MgNFoFL6+vkKtVovhw4eLmpoauaHb2P3G4Pz58/f8bCwpKZEdvU086D1wN2e7BIBKiA56KVkiIiKi/wEXbhMRERE5wCKJiIiIyAEWSUREREQOsEgiIiIicoBFEhEREZEDLJKIiIiIHGCRREREROQAiyQiIiIiB1gkERG1EZVKhR07dsiOQURthEUSEXUIb731FlQqVattxIgRsqMR0ROKN7glog5jxIgR2LBhg12bWq2WlIaInnScSSKiDkOtVsPPz89u8/T0BHD7VFheXh7i4+Oh0WjQu3dvfPvtt3bPP3nyJF5++WVoNBp4e3tjxowZaG5utjvmq6++QmhoKNRqNfR6PWbPnm23/9q1axg7diy6dOmCvn37YteuXe3baSJqNyySiMhpGI1GjB8/HsePH0diYiImT56M6upqAIDZbEZcXBw8PT1RUVGBbdu24ccff7QrgvLy8pCSkoIZM2bg5MmT2LVrF5555hm713j//fcxceJEnDhxAiNHjkRiYiL+/PNPRftJRG1EEBF1AMnJycLV1VVotVq7LTs7WwghBAAxc+ZMu+dERkaKWbNmCSGEKCgoEJ6enqK5udm2f+/evcLFxUXU1dUJIYQwGAxi4cKF98wAQCxatMj2uLm5WQAQ+/bta7N+EpFyuCaJiDqMYcOGIS8vz67Ny8vL9nNUVJTdvqioKBw7dgwAUF1djYEDB0Kr1dr2R0dHw2q1oqamBiqVCrW1tRg+fPh9MwwYMMD2s1arRdeuXdHQ0PCoXSIiiVgkEVGHodVqW53+aisajeahjnN3d7d7rFKpYLVa2yMSEbUzrkkiIqdx+PDhVo9DQkIAACEhITh+/DjMZrNt/8GDB+Hi4oKgoCDodDr06tULxcXFimYmInk4k0REHUZLSwvq6urs2tzc3NCtWzcAwLZt2xAeHo6YmBhs2rQJR48exZdffgkASExMxJIlS5CcnIylS5fi6tWrSE1NRVJSEnx9fQEAS5cuxcyZM+Hj44P4+Hg0NTXh4MGDSE1NVbajRKQIFklE1GHs378fer3eri0oKAi///47gNvfPNu6dSveeecd6PV6bNmyBf369QMAdOnSBQcOHEBaWhoiIiLQpUsXjB8/HqtXr7b9ruTkZNy8eRNr1qzBu+++i27dumHChAnKdZCIFKUSQgjZIYiI2ptKpUJhYSHGjBkjOwoRPSG4JomIiIjIARZJRERERA5wTRIROQWuLCCi/xZnkoiIiIgcYJFERERE5ACLJCIiIiIHWCQREREROcAiiYiIiMgBFklEREREDrBIIiIiInKARRIRERGRA/8B2zMjy0bS2e0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gSjQAAArQGeU8RcZmSwAAANeaMGGCnnjiCVmWpaefflpjxoyhQMMVo0QDAMCF7Jsts5sic2azZU5TZBFisyUAAEBe7N69W926dZNlWerevbtGjx5NgYarQokGAEAe2Tdb5jRFtkvSsTy8T06bLe3XbLYEAAC4epUqVdKkSZO0cuVKffDBB7LZ+A4LV8dmWVZup0a8SnJyssLCwpSUlKTQ0FDTcQAAboTNlgAAAJ4vJSVFhQpxB1jkXV67IibRAAA+I0Pn7jm2U9kfuXR2s2VOJRmbLQEAAMz47LPPNHz4cC1fvlzXXnut6TjwMpRoAACvkdNmS/s1my0BAAC816effqqnnnpKkjRp0iT17dvXcCJ4G0o0AIDHyLrZMrsb9zuz2fLim/Wz2RIAAMBzjR07Vt27d5ckPf/883r11VcNJ4I3okQDALiN7DZbZr2+2s2W10kqJzZbAgAAeJOPP/5YPXr0kCS98MILGj58OEsEkC8o0QAABcKVmy1zKskixGZLAAAAXzJmzBj17NlTkvTiiy/q3XffpUBDvqFEAwC4hH2z5eVKspN5eJ+SynmKLEJstgQAAMA5KSkpGjNmjCTp5Zdf1ttvv02BhnxFiQYAyJPLbba0f1ztZstKOjdpBgAAAOSmUKFCiouL05QpU9SnTx8KNOQ7m2VZud2D2askJycrLCxMSUlJCg0NNR0HANxGms5tr9wp12y2zKkkY7MlAAAArsbmzZt10003mY4BL5LXrohJNADwETlttrRfX+lmy6zXbLYEAABAfhoxYoReeuklffnll+rQoYPpOPAxlGgA4CVcudny4pv1s9kSAAAApr333nt66aWXJElbt241nAa+iBINADxA1s2WOZVkV7vZ8jpJpcVN+wEAAOB+hg8frpdfflmS1L9/fw0YMMBsIPgkSjQAcANstgQAAACy98477+jVV1+VJA0YMEBvvvmm4UTwVZRoAFAA7JstL1eS5WWzZRnlXJJdJzZbAgAAwLsMGzZMr732miRp4MCB6t+/v+FE8GWUaADgAhdvtry4INsjNlsCAAAAzrAsS4mJiZKkwYMHq1+/foYTwdfZLMvKbRmbV8nr2lIAyMoVmy0DdK4IY7MlAAAAkDeWZSkuLk4xMTGmo8CL5bUrYhINAHThZsvsSrIr3WyZ9ZrNlgAAAEDuJk+erBYtWqhQoUKy2WwUaHAblGgAvJ6rN1tmLcYixGZLAAAAwFUGDBiggQMHqlGjRpo7d678/fkxNNwHJRoAj5d1s2XWYmynnN9smdMU2XWSSoiSDAAAAMgPlmVpwIABGjRokCSpbt26FGhwO5RoANxebpstd0tKycP7sNkSAAAAcD+WZenNN9/U4MGDJUnvvvuuXnrpJcOpgEtRogEwzr7ZMqcpsrxutiyvnEuySpIKuzg3AAAAgKtjWZb69++vIUOGSJLee+89vfDCC4ZTAdmjRAOQ787o3LTYTmV/X7J9cm6zZXZHLtlsCQAAAHieIUOGOAq0ESNGqE+fPoYTATmjRANw1U4o54Jsp9hsCQAAACB7MTExGj58uAYNGqTevXubjgNcFiUagMuydG5z5U7lfOP+vGy2LKrsN1rar9lsCQAAAPie2rVra+vWrSpbtqzpKECuKNEAH5fTZsus12y2BAAAAOAKlmVp0KBBatq0qe644w5JokCDx6BEA7ycKzdbXq4kY7MlAAAAgMuxLEsvv/yy3nvvPX344Yf6+++/VbJkSdOxgDyjRAM8XHabLbNes9kSAAAAgGmWZenFF1/UiBEjJEmDBw+mQIPHoUQD3JyrNltWVM73JGOzJQAAAID8YlmWXnjhBY0cOVKS9Mknn+jpp582nApwHiUaYNgJ5TxFtkvn7leWm6ybLbMrydhsCQAAAMAEy7LUp08fjRo1SpI0duxYPfXUU4ZTAVeGEg3IR5fbbGm/ZrMlAAAAAG/1ySefOAq0Tz/9VE8++aThRMCVo0QDroIl6ZD+V4zZP1/pZsucSjI2WwIAAADwRJ06ddKsWbPUrl07de3a1XQc4KpQogGXkXWzZXYlGZstAQAAAOBClnXurs02m01FihTR0qVL5efnZzgVcPUo0YBsZEpqImmpct9saZNUQRcWY/bPbLYEAAAA4EsyMzPVs2dPhYeHa+DAgbLZbBRo8BqUaEA2dkhadP764s2WF5dk10oKKvCEAAAAAOBeMjMz1aNHD40dO1Y2m00tWrTQbbfdZjoW4DKUaEA27BsxK0v6W2y2BAAAAIDLyczMVPfu3fXpp5/KZrNpwoQJFGjwOpRoQDYOnP9cThRoAAAAAHA5mZmZeuqppzRu3Dj5+fnpyy+/VIcOHUzHAlyOEg3Ihn0SrazRFAAAAADg3jIzM9WtWzeNHz9efn5+mjhxoh577DHTsYB8QYkGZMM+iVbGaAoAAAAAcG8rVqxwFGiTJk1S+/btTUcC8g0lGpANe4nGJBoAAAAA5CwyMlJjx45VaGio2rVrZzoOkK8o0YBscJwTAAAAALKXkZGhEydOqHjx4pKkp556ymwgoID4mQ4AuCOOcwIAAADApTIyMtSlSxfVrVtXR44cMR0HKFCUaEA2mEQDAAAAgAtlZGSoU6dOmjRpkv766y/98ssvpiMBBYoSDbiIJSbRAAAAACCr9PR0dezYUV9//bUCAgI0bdo0NWzY0HQsoEBxTzTgIkmSUs9fU6IBAAAA8HX2Am3KlCkKCAjQ9OnT1bx5c9OxgAJHiQZcxH6UM0xSYZNBAAAAAMCw9PR0Pf7445o6daoCAgI0Y8YMNWvWzHQswAiOcwIX4SgnAAAAAJxz6NAhrVy5UoGBgZo5cyYFGnwak2jARVgqAAAAAADnlC9fXvHx8dqyZQv3QIPPYxINuAiTaAAAAAB82dmzZ7V69WrH19dffz0FGiBKNOAS9hKNSTQAAAAAvubs2bNq27at6tSpo3nz5pmOA7gVSjTgIhznBAAAAOCL0tLS1KZNG82ePVs2m03+/v6mIwFuhXuiARfhOCcAAAAAX5OWlqbWrVvr22+/VXBwsObMmaMGDRqYjgW4FUo04CJMogEAAADwJampqWrVqpXmzZun4OBgffvtt4qNjTUdC3A7lGjARZhEAwAAAOAr0tLS9Oijj2r+/PkqVKiQvv32W9WvX990LMAtUaIBWWSKSTQAAAAAviMgIEDh4eEqVKiQ5s6dq4ceesh0JMBt2SzLskyHKEjJyckKCwtTUlKSQkNDTceBmzkiKfz8daqkIINZAAAAAKAgZGRk6K+//tKtt95qOgpgRF67IrZzAlnYj3KWFAUaAAAAAO+UkpKi9957T+np6ZIkf39/CjQgDzjOCWTBUU4AAAAA3iwlJUXNmzfX4sWLtXnzZo0bN850JMBjUKIBWbBUAAAAAIC3OnPmjJo1a6bvvvtOISEh6tChg+lIgEehRAOysJdoTKIBAAAA8CZnzpzRI488oqVLlyokJEQLFy5U3bp1TccCPAr3RAOy4DgnAAAAAG9z+vRpPfzww1q6dKmKFCmiRYsWUaABV4BJNCALjnMCAAAA8CaWZalNmzZatmyZo0CrU6eO6ViAR2ISDciCSTQAAAAA3sRms6lnz54KDw/X4sWLKdCAq8AkGpAFk2gAAAAAvE1sbKz++ecfFS1a1HQUwKMxiQZkwWIBAAAAAJ7u1KlTatu2rbZs2eJ4jAINuHpMogHnZUg6fP6aEg0AAACAJzp58qQaN26sFStWaP369frzzz/l7+9vOhbgFSjRgPMSJWVKskkKN5wFAAAAAJx18uRJNWrUSD/88INCQ0M1YcIECjTAhTjOCZxnXypQSrTLAAAAADzLiRMn1LBhQ/3www8KCwvT0qVLdc8995iOBXgVugLgPJYKAAAAAPBEycnJatiwoVauXOko0P7v//7PdCzA6zCJBpzHUgEAAAAAnuiVV17RypUrVbx4cS1btowCDcgnlGjAefbjnJRoAAAAADzJ0KFD9dBDD2nZsmW66667TMcBvBbHOYHzOM4JAAAAwFOcPXtWgYGBkqQSJUrou+++M5wI8H5MogHnMYkGAAAAwBMcP35cderU0ahRo0xHAXwKJRpwHpNoAAAAANzd8ePHVb9+fa1evVqDBw/W0aNHTUcCfAYlGnAeiwUAAAAAuLNjx47poYce0q+//qprrrlGcXFxKlmypOlYgM/gnmjAeRznBAAAAOCu7AXamjVrFB4erri4ON16662mYwE+hRINkHRWUuL5a45zAgAAAHAnR48e1UMPPaS1a9cqPDxcy5cvV82aNU3HAnwOxzkBSYfPf/aXdI3JIAAAAABwkW+//VZr165VqVKlFB8fT4EGGMIkGqD/3Q+ttGiWAQAAALiXLl26KCkpSTExMapRo4bpOIDPokQDxFIBAAAAAO4lMTFRwcHBKlasmCSpd+/eZgMBYOgGkFgqAAAAAMB9HD58WNHR0WrYsKFOnDhhOg6A8yjRAP1vEo2lAgAAAABMOnz4sOrVq6cNGzZo+/btOnjwYO4vAlAgKNEAMYkGAAAAwLxDhw4pOjpaGzZsULly5ZSQkKAbbrjBdCwA53FPNEBMogEAAAAw6+DBg4qOjtbGjRtVvnx5xcfHq1q1aqZjAciCSTRALBYAAAAAYE7WAq1ChQpKSEigQAPcECUaoP8d52QSDQAAAEBBO3LkiA4dOqRrr71WCQkJqlq1qulIALLBcU5ATKIBAAAAMKd69epavny5ChcuzD3QADdGiQaflyrp+PlrSjQAAAAABWH//v3asWOH7r//fklSzZo1DScCkBuOc8Ln2Y9yBkkqbjAHAAAAAN/w77//KjIyUvXr19ePP/5oOg6APKJEg8/LupnTZjIIAAAAAK+3b98+RUZGauvWrSpVqpQqVKhgOhKAPOI4J3weSwUAAAAAFIS9e/cqKipK27Zt03XXXaeEhARFRESYjgUgj5hEg89jqQAAAACA/LZnzx5FRkZq27ZtioiI0Pfff0+BBngYJtHg8+yTaJRoAAAAAPLDgQMHFBkZqR07dqhy5cpKSEhQpUqVTMcC4CRKNPi8rPdEAwAAAABXK1mypGP7Znx8PAUa4KEo0eDzOM4JAAAAID8FBQVp+vTpOnLkiMqVK2c6DoArxD3R4PNYLAAAAADA1Xbu3KkBAwbIsixJ54o0CjTAszGJBp/HJBoAAAAAV9q5c6ciIyO1a9cuBQQEqF+/fqYjAXABJtHg81gsAAAAAMBV/vnnH9WtW1e7du1S1apV1aVLF9ORALgIJRp82ilJJ85fc5wTAAAAwNXYsWOH6tatq927d6tatWpKSEhQhQoVTMcC4CKUaPBp9im0wpKKmQwCAAAAwKNt375ddevW1Z49e3TjjTcqISFB5cuXNx0LgAtRosGnZV0qYDMZBAAAAIDHSklJUUxMjPbu3aubbrpJCQkJLBEAvBAlGnwaSwUAAAAAXK1ChQrpnXfeUc2aNRUfH6+yZfkvDMAbUaLBp7FUAAAAAMCVsizLcd26dWutXbuWAg3wYpRo8Gn2STSWCgAAAABwxpYtW1S3bl3t3bvX8VhAQIDBRADyGyUafBrHOQEAAAA4a/PmzYqKitIPP/ygnj17mo4DoIBQk8OnZV0sAAAAAAC5sRdoBw4cUM2aNfXZZ5+ZjgSggDCJBp/GJBoAAACAvNq0aZMiIyN14MAB3XrrrYqLi1OpUqVMxwJQQCjR4NNYLAAAAAAgLzZu3KjIyEgdPHhQtWrVokADfBAlGnyWJRYLAAAAAMibZ555RocOHdJtt92muLg4hYeHm44EoIBRosFnnZR05vw1JRoAAACAy5kyZYratGmjZcuW6ZprrjEdB4ABLBaAz7JPoRWVVMRkEAAAAABu6cSJEypWrJgkqVy5cpo6darhRABMYhINPoulAgAAAABy8scff6hq1aqaNGmS6SgA3AQlGnwWSwUAAAAAZGf9+vWKjo7WwYMH9fHHHysjI8N0JABugBINPoulAgAAAAAutm7dOtWrV09HjhzR3XffrUWLFsnf3990LABugBINPotJNAAAAABZ/f77744C7Z577tF3332n4sWLm44FwE1QosFnMYkGAAAAwG7t2rWqV6+ejh49qnvvvVdLlixRWFiY6VgA3AjbOeGzWCwAAAAAwO7bb7/VsWPHHAVaaGio6UgA3AwlGnwWxzkBAAAA2A0YMEClSpVSx44dKdAAZIvjnPBZHOcEAAAAfNtff/2llJQUSZLNZlPPnj0p0ADkiBINPskSk2gAAACAL1u9erXuu+8+tWjRwlGkAcDlUKLBJx2XlHb+urTBHAAAAAAK3s8//6z69esrOTlZJ0+eVHp6uulIADyA8RJtzJgxioiIUKFChXTPPffol19+uezzP/jgA914440qXLiwKlasqD59+vBTAzjNfpSzuKRCBnMAAAAAKFirVq1yFGh169bVwoULVbRoUdOxAHgAoyXatGnT9Pzzz+vNN9/U2rVrVatWLcXGxurQoUPZPn/y5Ml69dVX9eabb2rTpk0aP368pk2bptdee62Ak8PTcZQTAAAA8D0rV65UbGysTpw4ocjISC1YsIACDUCeGS3RRowYoSeffFJdunRR9erVNXbsWIWEhOjzzz/P9vkrV67U/fffr/bt2ysiIkL169dXu3btcp1eAy7GUgEAAADAt/z000+OAi0qKkrz589XkSJFTMcC4EGMlWhpaWlas2aNYmJi/hfGz08xMTFatWpVtq+57777tGbNGkdptmPHDi1cuFCNGjXK8fdJTU1VcnLyBR8Ak2gAAACAb/Hz85PNZlN0dDQFGoArEmDqN05MTFRGRobKlLlwFqhMmTLavHlztq9p3769EhMT9cADD8iyLKWnp+vpp5++7HHOYcOGaeDAgS7NDs/HJBoAAADgW2rXrq0ffvhBVatWVUhIiOk4ADyQ8cUCzkhISNDQoUP18ccfa+3atZo9e7YWLFigwYMH5/iavn37KikpyfGxZ8+eAkwMd2Uv0ZhEAwAAALzXihUrtHbtWsfXtWrVokADcMWMTaKFh4fL399fBw8evODxgwcPqmzZ7KuNN954Q48//ri6du0qSapZs6ZOnTqlbt266fXXX5ef36WdYHBwsIKDg13/B4BH4zgnAAAA4N0SEhLUuHFjBQcH66efftLNN99sOhIAD2dsEi0oKEh33nmn4uLiHI9lZmYqLi5OtWvXzvY1p0+fvqQo8/f3lyRZlpV/YeF1OM4JAAAAeK/4+Hg1btxYp0+f1t13362IiAjTkQB4AWOTaJL0/PPPq1OnTrrrrrt0991364MPPtCpU6fUpUsXSVLHjh1VoUIFDRs2TJLUtGlTjRgxQrfffrvuuecebdu2TW+88YaaNm3qKNOAvGASDQAAAPBOy5cvV5MmTXTmzBk1aNBA33zzjQoVKmQ6FgAvYLREa9OmjQ4fPqz+/fvrwIEDuu2227R48WLHsoHdu3dfMHnWr18/2Ww29evXT/v27VOpUqXUtGlTvfXWW6b+CPBAmfpficYkGgAAAOA94uLi1KRJE6WkpKhRo0aaNWsWBRoAl7FZPnYOMjk5WWFhYUpKSlJoaKjpODDgsKTS56/TJAUazAIAAADANVavXq3IyEilpKSocePGmjVrFvfHBpAnee2KjE6iASbYp9DCRYEGAAAAeIuaNWuqdu3aKlKkiGbOnEmBBsDlKNHgc1gqAAAAAHifkJAQzZ8/X/7+/hRoAPKFse2cgCksFQAAAAC8w5IlSzRo0CDZ71IUEhJCgQYg3zCJBp/DJBoAAADg+RYtWqTmzZsrNTVVVatWVbt27UxHAuDlmESDz7GXaEyiAQAAAJ5p4cKFatasmVJTU9W8eXO1bNnSdCQAPoASDT6H45wAAACA55o/f76aN2+utLQ0tWjRQtOmTVNQUJDpWAB8ACUafA7HOQEAAADPNG/ePLVo0UJpaWlq2bKlpk6dqsDAQNOxAPgISjT4HCbRAAAAAM+zZ88etWrVSmfPnlWrVq00ZcoUCjQABYrFAvA5TKIBAAAAnqdixYr68MMPFR8fr0mTJikggP+cBVCwbJZ9F7CPSE5OVlhYmJKSkhQaGmo6DgpYuqQgSZbOlWkUaQAAAIB7y8jIkL+/v+Nry7Jks9kMJgLgbfLaFXGcEz4lUecKND9J4YazAAAAALi82bNn65577lFiYqLjMQo0AKZQosGn2I9ylpLkf7knAgAAADBq1qxZatOmjdasWaMPP/zQdBwAoESDb2GpAAAAAOD+ZsyYoTZt2ig9PV0dOnTQm2++aToSAFCiwbewVAAAAABwb9OnT1e7du2UkZGhxx9/XBMmTLjgnmgAYAolGnyKvURjEg0AAABwP9OmTVP79u2VkZGhTp066YsvvqBAA+A2KNHgUzjOCQAAALin1NRUvfbaa8rIyFDnzp01fvx4CjQAboUSDT6F45wAAACAewoODtbSpUv1yiuvUKABcEuUaPApTKIBAAAA7mX//v2O6+uvv15vv/22/Pz4T1UA7oe/meBTmEQDAAAA3MekSZN0/fXXa968eaajAECuKNHgU1gsAAAAALiHL7/8Up06dVJKSoq+++4703EAIFeUaPAZaZKOnr+mRAMAAADMmTBhgrp06SLLstS9e3eNGjXKdCQAyBUlGnzGofOfAySVMBkEAAAA8GFffPGFnnjiCVmWpWeeeUZjxozhHmgAPAJ/U8Fn2JcKlBH/4AMAAAAmfP755/rPf/4jy7LUo0cPjR49WjabzXQsAMgTugT4DJYKAAAAAGatXLlSlmXp2Wef1UcffUSBBsCjBJgOABQUlgoAAAAAZn366aeqV6+e2rZtS4EGwOMwiQafkfU4JwAAAICCsXTpUqWnp0uS/Pz81K5dOwo0AB6JEg0+g0k0AAAAoGB98sknql+/vjp27KiMjAzTcQDgqlCiwWfYJ9Eo0QAAAID8N2bMGD3zzDOSpPLly7OBE4DH428x+AwWCwAAAAAFY/To0erZs6ck6cUXX9Tw4cM5wgnA41GiwWdwnBMAAADIfx9++KGeffZZSdLLL7+sd999lwINgFegRIPPYLEAAAAAkL8++ugjPffcc5KkV199VW+//TYFGgCvEWA6AFAQUiQlnb9mEg0AAADIH9WqVVNwcLCef/55vfXWWxRoALwKJRp8gn0KLVhSmMkgAAAAgBeLjY3VH3/8oapVq1KgAfA6HOeET8i6VIB/lQMAAACu88knn2jLli2Or6tVq0aBBsArUaLBJ7BUAAAAAHC94cOH65lnnlFUVJQSExNNxwGAfEWJBp/AUgEAAADAtd599129/PLLkqSnnnpK4eHhhhMBQP6iRINPYBINAAAAcJ23335br7zyiiRp4MCBevPNNw0nAoD8R4kGn2CfRKNEAwAAAK7O0KFD1bdvX0nSoEGD1L9/f8OJAKBgsJ0TPiHrYgEAAAAAV+bzzz/X66+/LkkaMmSI4xoAfAGTaPAJHOcEAAAArl7z5s115513aujQoRRoAHwOk2jwCSwWAAAAAK5eiRIl9NNPPyk4ONh0FAAocEyiwScwiQYAAABcmYEDB+qDDz5wfE2BBsBXMYkGr3dS0qnz15RoAAAAQN5YlqUBAwZo0KBBkqQHH3xQd9xxh+FUAGAOJRq8nv0oZ4ikoiaDAAAAAB7Csiz1799fQ4YMkSS99957FGgAfB4lGrweRzkBAACAvLMsS2+88YbeeustSdKIESPUp08fw6kAwDxKNHg9lgoAAAAAeWNZll5//XUNGzZMkjRy5Ej17t3bbCgAcBOUaPB6TKIBAAAAebNixQpHgTZq1Cj16tXLcCIAcB+UaPB69kk0SjQAAADg8urWrauhQ4eqaNGievbZZ03HAQC3QokGr2efROM4JwAAAHApy7KUkpKiwoULS5L69u1rOBEAuCc/0wGA/MZxTgAAACB7lmXpxRdfVExMjE6cOGE6DgC4NUo0eD0WCwAAAACXsixLL7zwgkaMGKGVK1dq6dKlpiMBgFvjOCe8HpNoAAAAwIUsy1KfPn00atQoSdLYsWPVokULw6kAwL1RosGrWWKxAAAAAJCVZVnq3bu3PvzwQ0nSf//7X3Xr1s1wKgBwf5Ro8GrJklLOX3OcEwAAAL7Osiz16tVLo0ePliR99tln6tq1q+FUAOAZKNHg1exHOUMlFTYZBAAAAHAD//77r6ZNmyabzaZx48bpiSeeMB0JADwGJRq8GksFAAAAgP+pUKGCli9frnXr1qlDhw6m4wCAR6FEg1djqQAAAAB8XWZmprZu3aqbbrpJklSjRg3VqFHDcCoA8Dx+pgMA+YmlAgAAAPBlmZmZ6t69u+688059//33puMAgEdjEg1ezT6JxnFOAAAA+JrMzEw99dRTGjdunPz8/LR3717TkQDAo1GiwasxiQYAAABflJmZqW7dumn8+PHy8/PTxIkT9dhjj5mOBQAejRINXo1JNAAAAPiazMxMde3aVV988YX8/Pw0adIktW/f3nQsAPB4lGjwaiwWAAAAgC/JyMhQ165dNWHCBPn5+enrr79W27ZtTccCAK/AYgF4NY5zAgAAwJdkZmYqKSlJ/v7+mjx5MgUaALgQk2jwWpb+V6JxnBMAAAC+IDAwUFOnTtXPP/+sBx980HQcAPAqTKLBax2TdPb8dWmTQQAAAIB8lJGRoS+//FKWZUmSgoKCKNAAIB9QosFr2e+HVkJSsMkgAAAAQD5JT09Xx44d1blzZ/Xu3dt0HADwahznhNdiqQAAAAC8mb1AmzJligICAhQZGWk6EgB4NUo0eC2WCgAAAMBbpaen6/HHH9fUqVMVEBCgGTNmqFmzZqZjAYBXo0SD17JPorFUAAAAAN4kPT1djz32mKZPn67AwEDNmDFDjzzyiOlYAOD1KNHgtZhEAwAAgDfq2LGjo0CbNWuWmjZtajoSAPgEFgvAazGJBgAAAG/UrFkzhYSEaPbs2RRoAFCAmESD12KxAAAAALxR69atFRkZqdKlS5uOAgA+hUk0eC2OcwIAAMAbpKWlqVevXtqzZ4/jMQo0ACh4lGjwWhznBAAAgKdLS0tT69at9dFHH6lhw4ZKT083HQkAfBbHOeGVMiQdPn/NJBoAAAA8UVpamlq1aqW5c+cqODhY77//vgIC+E84ADCFSTR4pSM6V6TZJJUynAUAAABwVmpqqh599FHNnTtXhQoV0ty5cxUbG2s6FgD4NH6MAa9kP8oZLv4hBwAAgGdJTU1Vy5YttWDBAkeB9tBDD5mOBQA+j34BXomlAgAAAPBUL730khYsWKDChQtr3rx5qlevnulIAABxnBNeiqUCAAAA8FSvvfaa7rjjDs2fP58CDQDcCJNo8EpMogEAAMCTWJYlm80mSSpbtqx+/fVX+fkx8wAA7oS/leGVmEQDAACApzhz5owaN26siRMnOh6jQAMA98PfzPBK9hKNSTQAAAC4szNnzuiRRx7RokWL1LNnTyUmJpqOBADIASUavBLHOQEAAODuTp8+rYcfflhLly5VkSJFNH/+fIWHh5uOBQDIAfdEg1fiOCcAAADc2enTp9W0aVMtX75cRYoU0aJFi1SnTh3TsQAAl8EkGrwSk2gAAABwV6dOnVKTJk20fPlyFS1aVIsXL6ZAAwAPwCQavM5ZSfY7STCJBgAAAHfz9ddfKz4+3lGg3X///aYjAQDygBINXuewJEuSv6RrDGcBAAAALvbkk09q9+7datSoke677z7TcQAAeUSJBq9jP8pZWueKNAAAAMC0U6dOyd/fX4UKFZLNZtOQIUNMRwIAOIl7osHrsFQAAAAA7uTkyZNq2LChmjdvrpSUFNNxAABXiBINXoelAgAAAHAXJ06cUMOGDfXDDz9o1apV2r59u+lIAIArxHFOeB0m0QAAAOAOkpOT1bBhQ61cuVJhYWFaunSpbrnlFtOxAABXiBINXsdeojGJBgAAAFOSk5PVoEEDrVq1SsWLF9fSpUt11113mY4FALgKHOeE1+E4JwAAAExKSkpSbGysVq1apRIlSmjZsmUUaADgBZhEg9fhOCcAAABM2rFjh/766y9HgXbHHXeYjgQAcAFKNHgdJtEAAABg0u23367vvvtOwcHBuv32203HAQC4CCUavA6TaAAAAChox48f1+7du3XrrbdKku69917DiQAArsY90eBVUiUdO3/NJBoAAAAKwrFjx/TQQw8pMjJSa9euNR0HAJBPKNHgVQ6d/xwoqYTJIAAAAPAJR48eVUxMjH777Tf5+/srMDDQdCQAQD7hOCe8StajnDaTQQAAAOD17AXa77//rlKlSmn58uWqUaOG6VgAgHzCJBq8CksFAAAAUBCOHDmievXqUaABgA+hRINXYakAAAAA8tuRI0cUExOjdevWqXTp0oqPj6dAAwAfQIkGr2Iv0ZhEAwAAQH4pXLiwSpQooTJlyig+Pl633HKL6UgAgALAPdHgVezHOZlEAwAAQH4JCQnRvHnztH//ft1www2m4wAACgiTaPAqTKIBAAAgPxw+fFijR4+WZVmSpCJFilCgAYCPYRINXoXFAgAAAHC1Q4cOKTo6Wn/99ZfOnDmjl156yXQkAIABTKLBq7BYAAAAAK508OBBRUVF6a+//lL58uX1yCOPmI4EADCEEg1eheOcAAAAcJUDBw4oKipKGzduVIUKFZSQkKBq1aqZjgUAMITjnPAapyWdOH/NJBoAAACuhr1A27x5s6NA4x5oAODbmESD17DfD62QpFCTQQAAAODRUlNTFR0drc2bN+vaa6+lQAMASKJEgxfJulTAZjIIAAAAPFpwcLB69+6tSpUqUaABABwo0eA1WCoAAAAAV+nWrZs2btyoKlWqmI4CAHATlGjwGiwVAAAAwJXat2+fWrRoocOHDzseK1KkiMFEAAB3w2IBeA37cU4m0QAAAOCMvXv3KioqStu2bVN6errmzp1rOhIAwA0xiQavwSQaAAAAnLVnzx5FRkZq27ZtioiI0Icffmg6EgDATVGiwWtkXSwAAAAA5Gb37t2KjIzU9u3bVblyZSUkJCgiIsJ0LACAm6JEg9dgsQAAAADyateuXYqMjNSOHTt0/fXXKyEhQdddd53pWAAAN8Y90eA1OM4JAACAvOrcubP++ecfR4FWsWJF05EAAG6OSTR4BUssFgAAAEDeff7554qOjtb3339PgQYAyBMm0eAVTko6ff6aEg0AAADZSUtLU1BQkCSpcuXKiouLM5wIAOBJmESDV7BPoRU9/wEAAABk9c8//+iWW27R3LlzTUcBAHgoSjR4BZYKAAAAICc7duxQZGSktm3bpn79+ik9Pd10JACAB6JEg1dgqQAAAACys337dkVGRmr37t268cYbtWTJEgUEcFcbAIDzKNHgFVgqAAAAgItt27ZNkZGR2rNnj2666SbFx8erXLlypmMBADwUP4KBV2ASDQAAAFnZC7R9+/bp5ptv1vLly1W2LN8tAgCuHJNo8Ar2STS+LQIAAIAkffbZZ9q3b5+qV6+u+Ph4CjQAwFVjEg1egcUCAAAAyGrYsGEqXLiwunfvrjJl+C4RAHD1mESDV+A4JwAAAHbv3u3YvOnn56cBAwZQoAEAXIYSDV6BxQIAAAC+bdOmTbrnnnv0+OOPO4o0AABciRINHs8Sk2gAAAC+bOPGjYqKitKBAwe0ceNGnThxwnQkAIAXokSDx0uSlHb+mkk0AAAA32Iv0A4ePKhatWpp+fLlKlGihOlYAAAvRIkGj2efQguTVMhkEAAAABSov/76S1FRUTp06JBuu+02xcXF6ZprrjEdCwDgpSjR4PE4ygkAAOB7/vzzT0eBdvvtt1OgAQDyHSUaPB5LBQAAAHzPgQMHlJycrDvuuEPLli1TyZIlTUcCAHi5ANMBgKvFJBoAAIDviYmJ0dKlS1WjRg3ugQYAKBCUaPB49kk0SjQAAADvtn79ehUqVEg33nijJKlOnTqGEwEAfAnHOeHx7JNoHOcEAADwXuvWrVN0dLSioqK0bds203EAAD6IEg0ej+OcAAAA3m3t2rWKjo7W0aNHValSJZUqVcp0JACAD6JEg8djsQAAAID3Wrt2rWJiYnTs2DHde++9WrJkicLCwkzHAgD4IEo0eDwm0QAAALzTmjVrVK9ePR07dky1a9emQAMAGEWJBo+WKenQ+WtKNAAAAO+xfv16xcTE6Pjx47rvvvu0ePFihYaGmo4FAPBhbOeERzsqKf38dWmTQQAAAOBS1113nW644QYFBwdr0aJFKlasmOlIAAAfR4kGj2a/H9o1kgJNBgEAAIBLFS9eXN99950CAgIo0AAAboHjnPBo9vuhsVQAAADA861evVpjxoxxfF2iRAkKNACA22ASDR6NpQIAAADeYdWqVYqNjdWJEydUunRptWrVynQkAAAuwCQaPJr9OCclGgAAgOdauXKlo0CLjIxUo0aNTEcCAOASlGjwaBznBAAA8Gw//fSTo0CLiorS/PnzVaRIEdOxAAC4BCUaPBqTaAAAAJ7rxx9/VIMGDXTy5ElFR0dToAEA3BolGjwak2gAAACead++fY4CrV69epo3b55CQkJMxwIAIEcsFoBHY7EAAACAZ6pQoYL69eunuLg4zZ07V4ULFzYdCQCAy7JZlmWZDlGQkpOTFRYWpqSkJIWGhpqOg6tUVueOdK6TVMtsFAAAAOSBZVmy2WyOr9PT0xUQwM/2AQDm5LUr4jgnPFaGpMPnrznOCQAA4P4SEhIcSwTsKNAAAJ6CEg0eK1FSps79Q1zKcBYAAABc3vLly9WoUSMtXbpUb731luk4AAA4jRINHst+P7RwSf4mgwAAAOCy4uLi1KRJE505c0YNGzbUgAEDTEcCAMBplGjwWCwVAAAAcH/Lli1zFGiNGzfWN998o0KFCpmOBQCA0yjR4LEOnv9MiQYAAOCeli5dqqZNmyolJUVNmjTRrFmzFBwcbDoWAABXhBINHss+icZSAQAAAPeTmpqqrl27KiUlRU2bNtXMmTMp0AAAHo0SDR6LSTQAAAD3FRwcrAULFuiJJ56gQAMAeAVKNHgsJtEAAADcT3JysuO6Ro0aGj9+vIKCggwmAgDANSjR4LFYLAAAAOBeFi5cqIiICCUkJJiOAgCAy1GiwWNxnBMAAMB9zJ8/X82bN9exY8f0+eefm44DAIDLUaLBY3GcEwAAwD3MmzdPLVq0UFpamh599FGNHz/edCQAAFyOEg0e6aykI+evmUQDAAAwZ+7cuWrZsqXOnj2rVq1aafLkyQoMDDQdCwAAl6NEg0c6dP6zv6SSJoMAAAD4sG+//VaPPvqozp49qzZt2lCgAQC8GiUaPFLWo5z8QwwAAGDGlClTdPbsWbVt21ZfffWVAgICTEcCACDf8G85eCSWCgAAAJg3ceJE3XfffXrmmWco0AAAXo8hHngklgoAAACYsWbNGmVmZkqSgoKC1KtXLwo0AIBPoESDR2ISDQAAoODNmDFD99xzj3r27CnLskzHAQCgQFGiwSMxiQYAAFCwpk2bpnbt2ikjI0MnT550TKMBAOArKNHgkewlGpNoAAAA+W/q1Kl67LHHlJGRoU6dOumLL76Qv7+/6VgAABQoSjR4JI5zAgAAFIwpU6Y4CrQuXbpo/PjxFGgAAJ9EiQaPxHFOAACA/Dd58mR16NBBmZmZeuKJJzRu3DgKNACAz6JEg0diEg0AACD/BQYGymazqWvXrvrss8/k58d/PgAAfBe7qOFxUiQdP3/NJBoAAED+adWqla677jrdddddFGgAAJ/HvwnhcexTaEGSihvMAQAA4I1mzJihPXv2OL6+++67KdAAABAlGjyQvUQrI8lmMggAAICXmTBhgtq0aaPIyEglJiaajgMAgFuhRIPHsS8V4H5oAAAArvPFF1/oiSeekGVZio2N1TXXXGM6EgAAboUSDR6HpQIAAACuNX78eP3nP/+RZVnq0aOHxowZI5uNmX8AALKiRIPHsU+isVQAAADg6o0bN05du3aVZVl69tln9dFHH1GgAQCQDUo0eByOcwIAALjG1KlT9eSTT0qSevXqpVGjRlGgAQCQgwDTAQBnZV0sAAAAgCsXGRmpm266SbGxsRo5ciQFGgAAl0GJBo/DJBoAAIBrlC1bVqtWrVJYWBgFGgAAueA4JzwOiwUAAACu3Mcff6wvv/zS8XXx4sUp0AAAyAMm0eBxWCwAAABwZUaPHq1nn31WNptNt9xyi+666y7TkQAA8BhMosGjnJJ08vw1k2gAAAB599FHH+nZZ5+VJL300ku68847DScCAMCzUKLBo9iPchaWVNRkEAAAAA8yatQo9erVS5L06quv6u233+YIJwAATqJEg0fJulSAb/sAAAByN3LkSPXu3VuS1LdvXw0dOpQCDQCAK0CJBo/CUgEAAIC8W7FihZ5//nlJ0uuvv6633nqLAg0AgCvEYgF4FJYKAAAA5F2dOnX0wgsvKCQkRAMHDqRAAwDgKlCiwaNkPc4JAACA7GVkZMjf3182m03Dhw+XJAo0AACuEsc54VHsxzmZRAMAAMjeO++8oyZNmiglJUXSufKMAg0AgKtHiQaPwiQaAABAzt5++229+uqrWrx4sWbPnm06DgAAXoUSDR6FxQIAAADZGzp0qPr27StJGjRokNq3b284EQAA3oUSDR6FxQIAAACXGjJkiF5//XVJ0uDBg/XGG28YTgQAgPdhsQA8hiWOcwIAAFxs8ODB6t+/vyTprbfe0muvvWY4EQAA3okSDR7jhKSU89dMogEAAEj79u3Te++9J0kaNmyYXn31VcOJAADwXpRo8Bj2KbRikkJMBgEAAHATFSpU0JIlS/Tzzz+rd+/epuMAAODVKNHgMVgqAAAAIFmWpf3796t8+fKSpHvvvVf33nuv4VQAAHg/FgvAY7BUAAAA+DrLsvTmm2/qlltu0dq1a03HAQDAp1CiwWOwVAAAAPgyy7L0xhtvaPDgwTp+/LhWrVplOhIAAD7FeIk2ZswYRUREqFChQrrnnnv0yy+/XPb5x48fV48ePVSuXDkFBwerWrVqWrhwYQGlhUn245xMogEAAF9jWZZef/11vfXWW5KkkSNHqkePHoZTAQDgW4zeE23atGl6/vnnNXbsWN1zzz364IMPFBsbqy1btqh06dKXPD8tLU0PPfSQSpcurZkzZ6pChQratWuXihcvXvDhUeCYRAMAAL7Isiz17dtX77zzjiRp1KhR6tWrl+FUAAD4HqMl2ogRI/Tkk0+qS5cukqSxY8dqwYIF+vzzz7Ndz/3555/r6NGjWrlypQIDAyVJERERBRkZBrFYAAAA+BrLsvTqq6/q3XfflSR9+OGHevbZZw2nAgDANxk7zpmWlqY1a9YoJibmf2H8/BQTE5Pj/R3mzp2r2rVrq0ePHipTpoxq1KihoUOHKiMjI8ffJzU1VcnJyRd8wDOxWAAAAPias2fPas2aNZKk0aNHU6ABAGCQsRItMTFRGRkZKlPmwkqkTJkyOnDgQLav2bFjh2bOnKmMjAwtXLhQb7zxht5//30NGTIkx99n2LBhCgsLc3xUrFjRpX8OFByOcwIAAF8TFBSkuXPnavbs2dwDDQAAw4wvFnBGZmamSpcurU8//VR33nmn2rRpo9dff11jx47N8TV9+/ZVUlKS42PPnj0FmBiuYonFAgAAwDdYlqWFCxfKsixJUkhIiJo3b244FQAAMFaihYeHy9/fXwcPHrzg8YMHD6ps2exnjcqVK6dq1arJ39/f8djNN9+sAwcOKC0tLdvXBAcHKzQ09IIPeJ5jks6ev6ZEAwAA3sqyLPXp00eNGzfWm2++aToOAADIwliJFhQUpDvvvFNxcXGOxzIzMxUXF6fatWtn+5r7779f27ZtU2ZmpuOxrVu3qly5cgoKCsr3zDDHXrWWkBRsMggAAEA+sSxLzz33nEaNGiVJ3IYEAAA3Y/Q45/PPP6/PPvtMX375pTZt2qTu3bvr1KlTjm2dHTt2VN++fR3P7969u44eParnnntOW7du1YIFCzR06FDuD+EDWCoAAAC8mWVZ6tWrlz766CPZbDaNGzdOTz75pOlYAAAgiwCTv3mbNm10+PBh9e/fXwcOHNBtt92mxYsXO5YN7N69W35+/+v5KlasqCVLlqhPnz669dZbVaFCBT333HN65ZVXTP0RUEBYKgAAALyVZVnq2bOnPv74Y0eB9sQTT5iOBQAALmKz7Hcs9RHJyckKCwtTUlIS90fzIB9I6iOpjaSpZqMAAAC4VM+ePTVmzBjZbDZ9/vnn6ty5s+lIAAD4lLx2RR61nRO+i0k0AADgrW699Vb5+/vriy++oEADAMCNGT3OCeSVfbEAJRoAAPA23bp1U3R0tG644QbTUQAAwGUwiQaPwGIBAADgLTIzMzV48GAdPnzY8RgFGgAA7o8SDR6B45wAAMAbZGZmqlu3burfv79iY2OVnp5uOhIAAMgjjnPCI9iPczKJBgAAPFVmZqa6du2qL774Qn5+fnrppZcUEMC34wAAeAr+rQ23lynp0PlrJtEAAIAnysjIUNeuXTVhwgT5+fnp66+/Vtu2bU3HAgAATqBEg9s7IilDkk1SKcNZAAAAnJWRkaEnnnhCEydOlL+/v77++mu1adPGdCwAAOAkSjS4Pfv90K6RFGgyCAAAwBXo27evo0CbMmWKWrVqZToSAAC4AiwWgNtjqQAAAPBk3bt3V5UqVTR16lQKNAAAPBiTaHB7LBUAAACerHLlyvrrr78UHBxsOgoAALgKTKLB7TGJBgAAPEl6ero6deqkb7/91vEYBRoAAJ6PEg1uzz6JRokGAADcXXp6ujp06KCJEyeqffv2Onz4sOlIAADARTjOCbdnn0TjOCcAAHBn6enpeuyxxzR9+nQFBgZq8uTJKlWK3eIAAHgLSjS4PSbRAACAuzt79qwee+wxzZgxQ4GBgZo5c6Yefvhh07EAAIALUaLB7TGJBgAA3NnZs2fVrl07zZo1S4GBgZo1a5aaNm1qOhYAAHAx7okGt8diAQAA4M7Gjx+vWbNmKSgoSLNnz6ZAAwDASzGJBreWLinx/DUlGgAAcEfdunXT+vXr1bRpUzVq1Mh0HAAAkE8o0eDWDkuydG5k8hrDWQAAAOzS0tLk5+engIAA+fn56ZNPPjEdCQAA5DOOc8Kt2ZcKlJbkbzIIAADAeWlpaWrVqpU6dOig9PR003EAAEABYRINbo2lAgAAwJ2kpqaqVatWmjdvngoVKqQNGzbo9ttvNx0LAAAUAEo0uDWWCgAAAHeRmpqqli1basGCBSpUqJDmzp1LgQYAgA+hRINbsx/npEQDAAAmpaSkqGXLllq4cKEKFSqkefPmKSYmxnQsAABQgCjR4NY4zgkAAExLSUlRixYttGjRIhUuXFjz5s1TvXr1TMcCAAAFjBINbo1JNAAAYNqGDRsUHx+vwoULa/78+YqOjjYdCQAAGECJBrfGJBoAADDt//7v//Ttt98qMDBQUVFRpuMAAABDKNHg1lgsAAAATDhz5oz279+v66+/XpJUv359w4kAAIBpfqYDAJfDcU4AAFDQTp8+rYcfflj333+/tmzZYjoOAABwE5RocFtpko6ev+Y4JwAAKAinT59W06ZNtWzZMp08eVKJiYmmIwEAADdBiQa3dej850BJJUwGAQAAPuHUqVNq0qSJli9frqJFi2rx4sW6//77TccCAABugnuiwW3Z74dWWrS9AAAgf9kLtISEBBUrVkyLFy/WfffdZzoWAABwI5RocFssFQAAAAXh4gJtyZIlql27tulYAADAzTDgA7fFUgEAAFAQ0tPTderUKYWGhuq7776jQAMAANliEg1uyz6JxlIBAACQn8LCwvTdd99px44duuOOO0zHAQAAbopJNLgtJtEAAEB+OXHihKZOner4unjx4hRoAADgsphEg9tiEg0AAOSH5ORkNWzYUCtXrtTRo0f1zDPPmI4EAAA8ACUa3BaLBQAAgKslJyerQYMGWrVqlYoXL667777bdCQAAOAhOM4Jt2U/zskkGgAAcIWkpCTFxsZq1apVKlGihJYtW6a77rrLdCwAAOAhmESD22ISDQAAuIq9QFu9erWjQOMeaAAAwBlXNYmWkpLiqhzABc5ISj5/TYkGAACuRlpamurXr6/Vq1erZMmSiouLo0ADAABOc7pEy8zM1ODBg1WhQgUVLVpUO3bskCS98cYbGj9+vMsDwjfZj3IGSwo1GQQAAHi8oKAgNW/eXNdcc42WL1+u22+/3XQkAADggZwu0YYMGaIJEybo3XffVVBQkOPxGjVqaNy4cS4NB9+V9SinzWQQAADgFV599VVt2rRJtWrVMh0FAAB4KKdLtIkTJ+rTTz/VY489Jn9/f8fjtWrV0ubNm10aDr6LpQIAAOBqHD16VE899ZSSk5Mdj5UqVcpgIgAA4OmcXiywb98+3XDDDZc8npmZqbNnz7okFMBSAQAAcKWOHj2qmJgY/f777zp48KDmzJljOhIAAPACTk+iVa9eXT/88MMlj8+cOZP7S8Bl7JNolGgAAMAZR44cUb169fT777+rVKlSGjJkiOlIAADASzg9ida/f3916tRJ+/btU2ZmpmbPnq0tW7Zo4sSJmj9/fn5khA+yT6JxnBMAAORVYmKiYmJitH79epUuXVrLly/XLbfcYjoWAADwEk5Poj3yyCOaN2+eli1bpiJFiqh///7atGmT5s2bp4ceeig/MsIHcZwTAAA4IzExUfXq1dP69etVpkwZxcfHU6ABAACXcnoSTZLq1KmjpUuXujoL4MBiAQAA4Ix27drpjz/+cBRoN998s+lIAADAyzg9iXb99dfryJEjlzx+/PhxXX/99S4JBTCJBgAAnDFy5EjdeuutSkhIoEADAAD5wulJtJ07dyojI+OSx1NTU7Vv3z6XhAJYLAAAAHJjWZZsNpskqUaNGvr999/l5+f0z4gBAADyJM8l2ty5cx3XS5YsUVhYmOPrjIwMxcXFKSIiwqXh4JtOSjp1/prjnAAAIDuHDh1S06ZN9c477ygyMlKSKNAAAEC+slmWZeXlifZvSmw2my5+SWBgoCIiIvT++++rSZMmrk/pQsnJyQoLC1NSUpJCQ0NNx0E2tkmqKqmIzhVqAAAAWR08eFDR0dHauHGjqlatqo0bNyog4Ipu9QsAAJDnrijP321kZmZKkipXrqxff/1V4eHhV58SyAZLBQAAQE4OHDig6Ohobdq0SRUqVNCCBQso0AAAQIFw+juOf/75Jz9yAA4sFQAAANnZv3+/oqOjtXnzZl177bWKj4/XDTfcYDoWAADwEVf0Y7tTp07p+++/1+7du5WWlnbBr/Xq1cslweC7WCoAAAAutn//fkVFRWnLli2qWLGi4uPjVaVKFdOxAACAD3G6RPv999/VqFEjnT59WqdOnVLJkiWVmJiokJAQlS5dmhINV80+icZxTgAAYDd8+HBt2bJFlSpVUnx8vK6//nrTkQAAgI9xeoVRnz591LRpUx07dkyFCxfWzz//rF27dunOO+/Ue++9lx8Z4WM4zgkAAC72zjvvqEePHkpISKBAAwAARjhdoq1bt04vvPCC/Pz85O/vr9TUVFWsWFHvvvuuXnvttfzICB/DYgEAACBJR44ccSy3CgwM1OjRo1W5cmXDqQAAgK9yukQLDAyUn9+5l5UuXVq7d++WJIWFhWnPnj2uTQefxCQaAADYs2eP7rnnHvXo0cNRpAEAAJjk9D3Rbr/9dv3666+qWrWq6tatq/79+ysxMVGTJk1SjRo18iMjfAyLBQAA8G179uxRZGSkduzYoczMTB05ckSlSpUyHQsAAPg4pyfRhg4dqnLlykmS3nrrLZUoUULdu3fX4cOH9d///tflAeFbLLFYAAAAX7Z7925HgXb99dcrISGBAg0AALgFm2VZlukQBSk5OVlhYWFKSkpSaGio6Ti4yHFJJc5fn5ZU2FwUAABQwHbt2qWoqCj9888/jgKtYsWKpmMBAAAvl9euyOlJtJysXbtWTZo0cdXbwUfZj3KGigINAABfsnPnTkVGRuqff/5RlSpV9P3331OgAQAAt+JUibZkyRK9+OKLeu2117Rjxw5J0ubNm9WsWTP93//9Hzd9xVVjqQAAAL5pw4YN2rNnj2644QZ9//33uvbaa01HAgAAuECeFwuMHz9eTz75pEqWLKljx45p3LhxGjFihJ599lm1adNGf/75p26++eb8zAofwFIBAAB8U9OmTfXNN9/ojjvuUIUKFUzHAQAAuESeJ9FGjRqld955R4mJiZo+fboSExP18ccfa8OGDRo7diwFGlyCpQIAAPiOHTt2aPfu3Y6vmzZtSoEGAADcVp5LtO3bt6tVq1aSpBYtWiggIEDDhw9n1B4uxXFOAAB8w/bt21W3bl1FRUVpz549puMAAADkKs8l2pkzZxQSEiJJstlsCg4OVrly5fItGHyT/Tgnk2gAAHivbdu2qW7dutq7d6+CgoIUGBhoOhIAAECu8nxPNEkaN26cihYtKklKT0/XhAkTFB4efsFzevXq5bp08DlMogEA4N3+/vtvRUVFad++fbr55pu1fPlylS3Lv/kBAID7s1mWZeXliREREbLZbJd/M5vNsbXTXSUnJyssLExJSUkKDQ01HQcXuUvSGknzJTU2nAUAALjW1q1bFRUVpX///VfVq1fX8uXLVaYM8+cAAMCsvHZFeZ5E27lzpytyAZfFYgEAALzT33//rcjISO3fv1+33HKLli9frtKlS5uOBQAAkGdOHecE8lOm/ndPNA51AADgXUJDQ1W8eHGFh4crLi5OpUqVMh0JAADAKZRocBvHJKWfv+bn0gAAeJcyZcpo+fLl8vf3p0ADAAAeKc/bOYH8Zj/KWVJSkMkgAADAJTZt2qTJkyc7vi5btiwFGgAA8FhMosFtcJQTAADvsXHjRkVFRenw4cMKCQlRs2bNTEcCAAC4KkyiwW2wVAAAAO/w119/KTIyUocOHVKtWrVUp04d05EAAACu2hWVaNu3b1e/fv3Url07HTp0SJK0aNEi/fXXXy4NB99iL9GYRAMAwHP9+eefjgm022+/XXFxcbrmmmtMxwIAALhqTpdo33//vWrWrKnVq1dr9uzZOnnypCRp/fr1evPNN10eEL7DfpyTSTQAADzThg0bHAXaHXfcoWXLlqlkyZKmYwEAALiE0yXaq6++qiFDhmjp0qUKCvrf7d+jo6P1888/uzQcfAuTaAAAeK5///1XUVFRSkxM1J133kmBBgAAvI7TJdqGDRvUvHnzSx4vXbq0EhMTXRIKvonFAgAAeK5y5cqpS5cuuuuuu7Rs2TKVKFHCdCQAAACXcno7Z/HixbV//35Vrlz5gsd///13VahQwWXB4HtYLAAAgOey2Wx69913debMGYWEhJiOAwAA4HJOT6K1bdtWr7zyig4cOCCbzabMzEz99NNPevHFF9WxY8f8yAgfwXFOAAA8y++//6727dsrJSVF0rkijQINAAB4K6cn0YYOHaoePXqoYsWKysjIUPXq1ZWRkaH27durX79++ZERPiBD0uHz10yiAQDg/tauXauYmBgdO3ZM1157rd59913TkQAAAPKVzbIs60peuHv3bv355586efKkbr/9dlWtWtXV2fJFcnKywsLClJSUpNDQUNNxcN5BnZtAs0lK0xW0uwAAoMCsWbNGMTExOn78uGrXrq3FixfzfRUAAPBYee2KnO4qfvzxRz3wwAOqVKmSKlWqdFUhATv7UoFSokADAMCd/fbbb3rooYd0/Phx3XfffVq0aBEFGgAA8AlO3xMtOjpalStX1muvvaaNGzfmRyb4IJYKAADg/n799VfHBNr999/PBBoAAPApTpdo//77r1544QV9//33qlGjhm677TYNHz5ce/fuzY988BEsFQAAwL2lpaXp0UcfVVJSkh544AEtWrRIxYoVMx0LAACgwDhdooWHh6tnz5766aeftH37drVq1UpffvmlIiIiFB0dnR8Z4QPsxzmZRAMAwD0FBQVp2rRpatKkCQUaAADwSVe8WMAuIyNDixYt0htvvKE//vhDGRkZrsqWL1gs4J5ekDRC0ouShhvOAgAA/ictLU1BQUGmYwAAAOSbvHZFTk+i2f3000965plnVK5cObVv3141atTQggULrvTt4OPsk2gc5wQAwH2sXLlSVatW1Zo1a0xHAQAAMM7pEq1v376qXLmyoqOjtXv3bo0aNUoHDhzQpEmT1KBBg/zICB/AYgEAANzLTz/9pNjYWO3evVtDhw41HQcAAMC4AGdfsGLFCr300ktq3bq1wsPD8yMTfBCLBQAAcB8//vijGjZsqJMnTyo6OlqTJk0yHQkAAMA4p0u0n376KT9ywMexWAAAAPfwww8/qGHDhjp16pTq1aunuXPnKiQkxHQsAAAA4/JUos2dO1cNGzZUYGCg5s6de9nnPvzwwy4JBt9xVlLi+Wsm0QAAMGfFihVq1KiRTp06pYceekjffvutChcubDoWAACAW8hTidasWTMdOHBApUuXVrNmzXJ8ns1mc/vtnHA/h89/9pd0jckgAAD4uBEjRujUqVOqX7++5syZQ4EGAACQRZ4WC2RmZqp06dKO65w+KNBwJez3Qyutq1gXCwAArtrkyZPVr18/CjQAAIBsON1ZTJw4UampqZc8npaWpokTJ7okFHwLSwUAADBn+/btsixLkhQSEqLBgwdToAEAAGTD6RKtS5cuSkpKuuTxEydOqEuXLi4JBd/CUgEAAMyIi4tTzZo19cYbbziKNAAAAGTP6RLNsizZbLZLHt+7d6/CwsJcEgq+hUk0AAAK3rJly9SkSROdOXNG69at47YcAAAAucjTYgFJuv3222Wz2WSz2VSvXj0FBPzvpRkZGfrnn3/UoEGDfAkJ72afRKNEAwCgYCxdulQPP/ywUlJS1KRJE82cOfOC7+0AAABwqTx/t2Tfyrlu3TrFxsaqaNGijl8LCgpSRESEWrZs6fKA8H72STSOcwIAkP+WLFmiRx55RKmpqWratKlmzJih4OBg07EAAADcXp5LtDfffFOSFBERoTZt2qhQoUL5Fgq+hUk0AAAKxuLFi9WsWTOlpqbq4Ycf1owZMxQUFGQ6FgAAgEdwem6/U6dO+ZEDPoxJNAAACsa+ffuUmpqqRx55RNOnT6dAAwAAcEKeSrSSJUtq69atCg8PV4kSJbJdLGB39OhRl4WDb2CxAAAABeM///mPKlSooOjoaAo0AAAAJ+WpRBs5cqSKFSvmuL5ciQY4I1XS8fPXlGgAALheXFycbr31VpUqVUqSWAQFAABwhWyWZVmmQxSk5ORkhYWFKSkpSaGhoabj+Lzdkq6TFCQpRRL1LAAArjNv3jy1bNlSN910kxISElSyZEnTkQAAANxOXrsiP2ffeO3atdqwYYPj62+//VbNmjXTa6+9prS0tCtLC59lXypQRhRoAAC40ty5c9WyZUudPXtWN910k+NUAQAAAK6M0yXaU089pa1bt0qSduzYoTZt2igkJEQzZszQyy+/7PKA8G4sFQAAwPXmzJmjRx99VGfPnlWbNm00efJkBQYGmo4FAADg0Zwu0bZu3arbbrtNkjRjxgzVrVtXkydP1oQJEzRr1ixX54OXY6kAAACu9c0336hVq1Y6e/as2rZtq6+++koBAU4vZAcAAMBFnC7RLMtSZmamJGnZsmVq1KiRJKlixYpKTEx0bTp4PftxTko0AACu3rx589S6dWulp6erffv2mjRpEgUaAACAizj9XdVdd92lIUOGKCYmRt9//70++eQTSdI///yjMmU4lAfncJwTAADXqVGjhsqXL686depowoQJFGgAAAAu5PR3Vh988IEee+wxzZkzR6+//rpuuOEGSdLMmTN13333uTwgvBuTaAAAuE7lypX1888/q3Tp0vL39zcdBwAAwKvYLMuyXPFGKSkp8vf3d/ub1uZ1bSkKRh1JP0qaLqmV4SwAAHii6dOnKzg4WI888ojpKAAAAB4pr13RFc/4r1mzRps2bZIkVa9eXXfccceVvhV8GIsFAAC4ctOmTdNjjz0mPz8/rVy5UnfddZfpSAAAAF7L6RLt0KFDatOmjb7//nsVL15cknT8+HFFRUVp6tSpKlWqlKszwovZj3NyTzQAAJwzZcoUdejQQZmZmerYsaNuv/1205EAAAC8mtPbOZ999lmdPHlSf/31l44ePaqjR4/qzz//VHJysnr16pUfGeGlTks6cf6aSTQAAPJu8uTJjgLtiSee0Lhx47gHGgAAQD5zehJt8eLFWrZsmW6++WbHY9WrV9eYMWNUv359l4aDd7NPoRWWVMxkEAAAPMhXX32lTp06KTMzU127dtV///tf+fk5/XNRAAAAOMnp77gyMzOzXR4QGBiozMxMl4SCb7DfD62MJJvJIAAAeIiVK1c6CrQnn3ySAg0AAKAAOT2JFh0dreeee05TpkxR+fLlJUn79u1Tnz59VK9ePZcHhPdiqQAAAM6599571blzZwUGBurjjz+mQAMAAChATpdoo0eP1sMPP6yIiAhVrFhRkrRnzx7VqFFDX331lcsDwnuxVAAAgLyxLEs2m01+fn767LPPJIkCDQAAoIA5XaJVrFhRa9euVVxcnDZt2iRJuvnmmxUTE+PycPBuTKIBAJC7zz//XMuWLdPEiRMVEBBAeQYAAGCIUyXatGnTNHfuXKWlpalevXp69tln8ysXfIB9Eo0SDQCA7I0fP15PPvmkLMtS/fr11blzZ9ORAAAAfFaeS7RPPvlEPXr0UNWqVVW4cGHNnj1b27dv1/Dhw/MzH7xY1sUCAADgQp999pm6desmSerZs6c6depkOBEAAIBvy/N5gNGjR+vNN9/Uli1btG7dOn355Zf6+OOP8zMbvBzHOQEAyN6nn37qKNB69eqlDz/8UDYbu6wBAABMynOJtmPHjgt+Atq+fXulp6dr//79+RIM3o/FAgAAXOq///2vnnrqKUnSc889pw8++IACDQAAwA3kuURLTU1VkSJF/vdCPz8FBQXpzJkz+RIM3s0Sk2gAAFxs//796tOnjySpT58+GjlyJAUaAACAm3BqscAbb7yhkJAQx9dpaWl66623FBYW5nhsxIgRrksHr3VSkr1+ZRINAIBzypUrp2+//Vbx8fF66623KNAAAADciM2yLCsvT4yMjMz1Gzmbzably5e7JFh+SU5OVlhYmJKSkhQaGmo6js/6W1I1SUUlnTCcBQAA05KSki74oSQAAAAKTl67ojxPoiUkJLgiFyCJo5wAANh9+OGHGjZsmOLj43XTTTeZjgMAAIAc5PmeaIArsVQAAABp1KhReu6553TgwAHNnj3bdBwAAABcBiUajGASDQDg60aOHKnevXtLkvr27au+ffuaDQQAAIDLokSDEfZJNEo0AIAvGjFihJ5//nlJ0muvvcYSAQAAAA9AiQYj7JNoHOcEAPia9957Ty+88IIkqV+/fhoyZAgFGgAAgAegRIMRHOcEAPiitLQ0zZgxQ5LUv39/DRo0iAINAADAQ1xRifbDDz+oQ4cOql27tvbt2ydJmjRpkn788UeXhoP3YrEAAMAXBQUFacmSJfr00081cOBACjQAAAAP4nSJNmvWLMXGxqpw4cL6/ffflZqaKklKSkrS0KFDXR4Q3olJNACAL/n1118d18WLF9eTTz5pMA0AAACuhNMl2pAhQzR27Fh99tlnCgwMdDx+//33a+3atS4NB+9kicUCAADfMWzYMN19990aOXKk6SgAAAC4Ck6XaFu2bNGDDz54yeNhYWE6fvy4KzLByx2XlHb+urTBHAAA5Le33npLr732miTpzJkzhtMAAADgajhdopUtW1bbtm275PEff/xR119/vUtCwbvZj3IWl1TIYA4AAPLT4MGD1a9fP0nS0KFDHWUaAAAAPJPTJdqTTz6p5557TqtXr5bNZtO///6rr7/+Wi+++KK6d++eHxnhZVgqAADwdoMGDVL//v0lnTvO2bdvX8OJAAAAcLUCnH3Bq6++qszMTNWrV0+nT5/Wgw8+qODgYL344ot69tln8yMjvAxLBQAA3mzgwIEaMGCAJOmdd97Ryy+/bDYQAAAAXMLpEs1ms+n111/XSy+9pG3btunkyZOqXr26ihYtmh/54IVYKgAA8GaFCp27WcG7776rl156yXAaAAAAuIrTJZpdUFCQqlev7sos8BH2STSOcwIAvNErr7yiqKgo3X333aajAAAAwIWcLtGioqJks9ly/PXly5dfVSB4P45zAgC8iWVZ+vTTT9WuXTuFhoZKEgUaAACAF3J6scBtt92mWrVqOT6qV6+utLQ0rV27VjVr1syPjPAyLBYAAHgLy7L02muv6emnn1bDhg2Vnp5uOhIAAADyidOTaCNHjsz28QEDBujkyZNXHQjej0k0AIA3sCxLffv21TvvvCNJatOmjQICrvhOGQAAAHBzTk+i5aRDhw76/PPPXfV28GIsFgAAeDrLsvTKK684CrSPPvpIvXr1MpwKAAAA+cllPy5dtWqVYxsVkJNMcZwTAODZLMvSSy+9pPfff1+SNHr0aPXo0cNwKgAAAOQ3p0u0Fi1aXPC1ZVnav3+/fvvtN73xxhsuCwbvdERSxvnr0iaDAABwhQYNGuQo0D7++GN1797dcCIAAAAUBKePc4aFhV3wUbJkSUVGRmrhwoV688038yMjvIh9Cu0aSYEmgwAAcIUeffRRlS5dWp988gkFGgAAgA9xahItIyNDXbp0Uc2aNVWiRIn8ygQvxlIBAICnu+WWW7R161aFhYWZjgIAAIAC5NQkmr+/v+rXr6/jx4/nUxx4O5YKAAA8jWVZevnllxUfH+94jAINAADA9zh9nLNGjRrasWNHfmSBD7BPorFUAADgCSzL0rPPPqvhw4eradOmOnTokOlIAAAAMMTpEm3IkCF68cUXNX/+fO3fv1/JyckXfACXw3FOAICnsCxLPXv21JgxY2Sz2fThhx+qdGnW4gAAAPiqPN8TbdCgQXrhhRfUqFEjSdLDDz8sm83m+HXLsmSz2ZSRkZHTWwCO45xMogEA3FlmZqZ69uypTz75RDabTePHj1eXLl1MxwIAAIBBeS7RBg4cqKeffvqC+4EAzmISDQDg7jIzM/XMM8/ov//9r2w2m7744gt16tTJdCwAAAAYlucSzbIsSVLdunXzLQy8H4sFAADubvz48Y4C7csvv9Tjjz9uOhIAAADcQJ5LNEkXHN8ErgSLBQAA7q5z585aunSpHn74YXXo0MF0HAAAALgJp0q0atWq5VqkHT169KoCwXulSzp8/ppJNACAO8nMzJQk+fn5KTAwUNOmTeOHhwAAALiAUyXawIEDFRYWll9Z4OUSJVk6txI23HAWAADsMjMz1bVrVwUHB2vMmDHy8/OjQAMAAMAlnCrR2rZty2p3XDH7Uc5SkvxNBgEA4LyMjAx17dpVEyZMkJ+fn5544gn93//9n+lYAAAAcEN+eX0iP5HF1WKpAADAnWRkZOg///mPJkyYIH9/f02ePJkCDQAAADlyejsncKVYKgAAcBcZGRnq0qWLJk2a5CjQWrdubToWAAAA3FieSzT7DXeBK2Uv0ZhEAwCYlJGRoU6dOunrr7+Wv7+/pk6dqkcffdR0LAAAALi5PB/nBK6W/Tgnk2gAAJN+/fVXTZ06VQEBAZo2bRoFGgAAAPLEqcUCwNVgEg0A4A7uvfdeTZkyRf7+/mrRooXpOAAAAPAQlGgoMCwWAACYkp6ersTERJUte+7fQq1atTKcCAAAAJ6G45woMCwWAACYkJ6erscee0z33Xefdu/ebToOAAAAPBQlGgoMxzkBAAXt7Nmzat++vaZPn669e/dq48aNpiMBAADAQ1GioUCkSTp6/ppJNABAQbAXaDNmzFBQUJBmz56tBg0amI4FAAAAD8U90VAgDp3/HCCppMkgAACfcPbsWbVt21azZ892FGiNGzc2HQsAAAAejBINBcK+VKCMGH8EAOSvtLQ0tW3bVt98842CgoL0zTffqFGjRqZjAQAAwMNRoqFAsFQAAFBQkpOTtXnzZgUHB2vOnDkc4QQAAIBLUKKhQLBUAABQUMLDw7V8+XJt2rRJUVFRpuMAAADAS3CyDgUi63FOAABcLTU1Vd99953j67Jly1KgAQAAwKUo0VAgmEQDAOSX1NRUtWzZUg0aNNDEiRNNxwEAAICXokRDgbBPolGiAQBcKSUlRS1atNCCBQtUqFAhVahQwXQkAAAAeCnuiYYCwWIBAICr2Qu0RYsWqXDhwpo/f76io6NNxwIAAICXokRDgeA4JwDAlVJSUtS8eXMtXrxYISEhWrBggSIjI03HAgAAgBejREOBYLEAAMBVzp49q2bNmmnJkiUKCQnRwoULVbduXdOxAAAA4OW4JxryXYqkpPPXTKIBAK5WQECAbr31VhUpUkSLFi2iQAMAAECBsFmWZZkOUZCSk5MVFhampKQkhYaGmo7jE3ZJipAULOmMJJvRNAAAb2BZlnbs2KEqVaqYjgIAAAAPl9euiEk05LusSwUo0AAAV+L06dPq16+fUlJSJEk2m40CDQAAAAWKe6Ih39nvh8ZRTgDAlTh16pSaNm2q+Ph4bd26VdOnTzcdCQAAAD6IEg35LuskGgAAzjh16pSaNGmihIQEFStWTL179zYdCQAAAD6KEg35zl6iMYkGAHDGyZMn1bhxY61YsULFihXTkiVLVLt2bdOxAAAA4KMo0ZDv7Mc5mUQD8P/t3XmcjfX/xvHrzD6WGWQZW6isIdmGhBmGQYUo+5IUlSjLV0LGUqnsRWQpEhFlSRjrCEmyF0a2KMZuxj7LuX9/MOdnMmP2uc+c83o+HvPozD33Oec66v7yvbw/9wdIqWvXrqlZs2bavHmzfHx8FBoaqlq1apkdCwAAAE6MEg2Zjkk0AEBqtW/f3lagrVmzRv7+/mZHAgAAgJNjd05kOjYWAACk1tChQ/Xwww9r7dq1FGgAAACwC0yiIdOxsQAAILX8/f31119/ycPDw+woAAAAgCQm0ZAFWM4JAEhOVFSUnnnmGf3++++2YxRoAAAAsCdMoiFTXZN0/e5jJtEAAImJjIxUkyZN9Ouvv+rQoUM6dOiQ3N3dzY4FAAAAJECJhkwVfz+0HJJymRkEAGCXIiMjFRwcrO3btytv3rxatGgRBRoAAADsEss5kanu3VTAYmYQAIDduXLliho3bqzt27crX758Wr9+vapWrWp2LAAAACBRTKIhU7GpAAAgMfEF2o4dO2wFWpUqVcyOBQAAACSJSTRkKjYVAAAkZsSIEdqxY4ceeughbdiwgQINAAAAdo9JNGSq+OWcTKIBAO71wQcf6PTp0xoyZIgqV65sdhwAAAAgWZRoyFRMogEA4t24cUPe3t6yWCzKkSOHFi5caHYkAAAAIMVYzolMde/GAgAA53Xp0iU9/fTTGjp0qAzDMDsOAAAAkGqUaMhUbCwAALh48aIaNmyo3bt3a+bMmTp37pzZkQAAAIBUs4sSbcqUKSpZsqS8vLzk7++v3377LUXPW7BggSwWi1q2bJm5AZFmLOcEAOd24cIFNWzYUHv27FGhQoW0ceNGFSrEX60AAAAg+zG9RFu4cKH69eunkJAQ7dq1S0888YSCg4OT/VvqEydOaMCAAapbt24WJUVqGWJjAQBwZvEF2t69e20FWoUKFcyOBQAAAKSJ6SXa+PHj9eqrr6pbt26qUKGCpk2bphw5cujLL79M8jlxcXHq2LGjRowYoUceeSQL0yI1oiTduvuYEg0AnMv58+fVoEED7du3T35+fgoLC1P58uXNjgUAAACkmaklWnR0tHbu3KmgoCDbMRcXFwUFBWnbtm1JPm/kyJEqWLCgunfvnux73L59W1FRUQm+kDXip9B8JOUwMwgAIMuFhYVp//79Kly4sMLCwlSuXDmzIwEAAADpYmqJduHCBcXFxd13b5RChQopIiIi0eds2bJFs2bN0owZM1L0HqNHj5avr6/tq3jx4unOjZRhUwEAcF4vvvii5syZo40bN6ps2bJmxwEAAADSzfTlnKlx9epVde7cWTNmzFD+/PlT9Jx3331XkZGRtq9Tp05lckrEY1MBAHAuZ8+eTXBP0y5dulCgAQAAwGG4mfnm+fPnl6urq86ePZvg+NmzZ+Xnd3/1cvToUZ04cULPPfec7ZjVapUkubm5KTw8XI8++miC53h6esrT0zMT0iM5bCoAAM4jIiJCDRo0kJubm9avX68CBQqYHQkAAADIUKZOonl4eKhatWpav3697ZjVatX69etVu3bt+84vV66c9u/frz179ti+mjdvrsDAQO3Zs4elmnaGSTQAcA5nzpxRYGCgDh48qMuXL3P/UQAAADgkUyfRJKlfv37q2rWrqlevrpo1a2rixIm6fv26unXrJunOUpCiRYtq9OjR8vLyUsWKFRM8P0+ePJJ033GYL34SjRINABxXfIEWHh6u4sWLa+PGjfdNhQMAAACOwPQSrW3btjp//ryGDRumiIgIValSRatXr7ZtNnDy5Em5uGSrW7fhLjYWAADHdvr0aQUGBurw4cN6+OGHtXHjRj3yyCNmxwIAAAAyhcUwDMPsEFkpKipKvr6+ioyMlI+Pj9lxHFp1STsl/SjpWZOzAAAy1r///qvAwED99ddfKlGihDZu3KhSpUqZHQsAAABItZR2RaZPosFxsbEAADiu2NhYRUdHq0SJEgoLC1PJkiXNjgQAAABkKko0ZApD3BMNABxZfHkmiQINAAAAToGbjSFTXJYUc/dxQTODAAAyzKlTp7Rq1Srb9yVLlqRAAwAAgNOgREOmiN9UIK8kTzODAAAyxMmTJ1W/fn01b948QZEGAAAAOAtKNGSK+BKNpZwAkP39/fffCggI0PHjx1WiRAlVrFjR7EgAAABAluOeaMgUbCoAAI7hxIkTCgwM1IkTJ/Too48qLCxMxYoVMzsWAAAAkOWYREOmYBINALK/EydOKCAgQCdOnFDp0qW1adMmCjQAAAA4LSbRkCnYmRMAsrezZ8+qfv36OnnypEqXLq2NGzeqaNGiZscCAAAATMMkGjJF/CQayzkBIHsqUKCAGjVqpDJlyigsLIwCDQAAAE6PSTRkCpZzAkD25uLiounTp+vy5ct66KGHzI4DAAAAmI5JNGQKNhYAgOznyJEj6tOnj2JjYyXdKdIo0AAAAIA7mERDpmASDQCyl7/++kuBgYH6999/lSNHDn300UdmRwIAAADsCpNoyHBxks7ffUyJBgD27/DhwwoICNC///6rChUqqG/fvmZHAgAAAOwOJRoy3EXdKdIskgqYnAUA8GDh4eEKCAjQ6dOn9fjjj2vjxo0qVIjF+AAAAMB/UaIhw8Uv5cwv1gsDgD07dOiQAgMDdebMGVWsWFEbNmxQwYIFzY4FAAAA2CVKNGQ4NhUAAPsXHR2tZs2a6cyZM6pUqRIFGgAAAJAMSjRkODYVAAD75+HhoS+++EL+/v7asGGDChRgAT4AAADwIKy2Q4aLn0SjRAMA+2O1WuXicufv0Bo1aqSgoCBZLBaTUwEAAAD2j0k0ZLj4STSWcwKAffnzzz/15JNP6tChQ7ZjFGgAAABAylCiIcOxnBMA7M8ff/yhwMBA7du3T/379zc7DgAAAJDtUKIhw7GxAADYl/379yswMFDnz59X1apVNXfuXLMjAQAAANkOJRoyHJNoAGA/9u3bp8DAQF24cEHVqlXTunXrlC9fPrNjAQAAANkOJRoyHBsLAIB92Lt3rxo0aKCLFy+qevXqWrdunfLmzWt2LAAAACBbokRDhoqRdOHuY5ZzAoC5Bg8erIsXL6pGjRpau3at8uTJY3YkAAAAINuiREOGOi/JkOQq6SGTswCAs5s3b5569uxJgQYAAABkAEo0ZKj4pZwFdKdIAwBkrfPnz9se58mTR9OmTZOvr6+JiQAAAADHQImGDMWmAgBgnp07d6ps2bIaP3682VEAAAAAh0OJhgzFpgIAYI7ff/9dQUFBunz5sr7//nvFxMSYHQkAAABwKJRoyFDxk2hsKgAAWWfHjh0KCgrSlStX9NRTT2n16tVyd3c3OxYAAADgUCjRkKFYzgkAWWv79u0KCgpSZGSk6tSpo9WrVyt37txmxwIAAAAcDiUaMlT8ck4m0QAg8/36669q3LixoqKiVLduXa1atYoCDQAAAMgklGjIUEyiAUDW+fXXXxUVFaV69epp5cqVFGgAAABAJnIzOwAcCxsLAEDWefvtt1WwYEE1b95cuXLlMjsOAAAA4NCYREOGYmMBAMhcO3fuVFRUlO37Dh06UKABAAAAWYASDRnmtqTLdx8ziQYAGW/r1q0KCAhQkyZNEhRpAAAAADIfJRoyzLm7/3SXlNfMIADggDZv3qzg4GBdu3ZNOXPmlJsbd2QAAAAAshIlGjLMvUs5LWYGAQAH8/PPP6tp06a6fv26goKCtHz5cuXIkcPsWAAAAIBToURDhmFTAQDIeJs2bbIVaI0aNdLy5cvl7e1tdiwAAADA6VCiIcOwqQAAZKxNmzapWbNmunHjhoKDg7Vs2TIKNAAAAMAk3FAFGSa+RGMSDQAyRoECBZQzZ07Vq1dPS5YskZeXl9mRAAAAAKdFiYYME7+ck0k0AMgYFSpU0C+//KJixYpRoAEAAAAmo0RDhmESDQDSb926dXJxcVGDBg0kSY899pjJiQAAAABIlGjIQGwsAADps2bNGrVo0UIWi0VbtmxR1apVzY4EAAAA4C42FkCGYWMBAEi70NBQNW/eXLdu3VKjRo30+OOPmx0JAAAAwD0o0ZBhWM4JAGmzevVqtWjRQrdv31aLFi20aNEieXp6mh0LAAAAwD0o0ZAhbki6evcxk2gAkHKrVq2yFWgtW7bUd999Jw8PD7NjAQAAAPgPSjRkiPj7oXlJ8jEzCABkIzt27FDLli0VHR2tVq1aUaABAAAAdoyNBZAh4ku0QpIsZgYBgGykSpUqevbZZ2WxWPTtt9/K3d3d7EgAAAAAkkCJhgzB/dAAIPXc3d21YMEC22MAAAAA9ovlnMgQlGgAkDLLly9X7969ZbVaJd0pzyjQAAAAAPvHJBoyxL3LOQEAiVu6dKnatGmjmJgYPfnkk3r55ZfNjgQAAAAghZhEQ4ZgEg0AHmzJkiV68cUXFRMTo3bt2qlLly5mRwIAAACQCpRoyBBMogFA0n744Qe1adNGsbGxat++vebOnSs3N4bBAQAAgOyEEg0Zgkk0AEjc4sWLbQVax44d9fXXX1OgAQAAANkQJRoyRPwkGiUaAPy/iIgIde7cWXFxcercubPmzJlDgQYAAABkU/xJHulm6P8n0VjOCQD/z8/PT998841Wrlyp6dOny9XV1exIAAAAANLIYhiGYXaIrBQVFSVfX19FRkbKx8fH7DgO4aokn3se5zIxCwDYg9u3b8vT09PsGAAAAABSIKVdEcs5kW7xSzlzigINAL799ltVrFhRJ0+eNDsKAAAAgAxEiYZ0Y1MBALhj/vz56tSpk44cOaIvvvjC7DgAAAAAMhAlGtKNTQUAQPrmm2/UuXNnWa1Wde/eXaNGjTI7EgAAAIAMRImGdGNTAQDObu7cuerSpYusVqteeeUVTZ8+XS4u/BYLAAAAOBL+hI90YzknAGc2Z84cde3aVYZhqEePHvriiy8o0AAAAAAHxJ/ykW7xyzmZRAPgbGJiYjR27FgZhqHXXntNU6dOpUADAAAAHJSb2QGQ/TGJBsBZubu7a926dZo5c6beffddCjQAAADAgfGnfaQbGwsAcDZHjhyxPS5UqJCGDBlCgQYAAAA4OP7Ej3RjYwEAzmTmzJkqV66cZs+ebXYUAAAAAFmI5ZxIF0Ms5wTgPGbMmKEePXpIkvbs2WNuGAAAAABZikk0pEukpOi7j5lEA+DIpk+fbivQ3nrrLU2YMMHkRAAAAACyEiUa0iV+Cs1XkpeZQQAgE02bNk09e/aUJPXt21cTJkyQxWIxORUAAACArESJhnRhUwEAju7zzz/X66+/Lknq16+fxo0bR4EGAAAAOCFKNKQLmwoAcHQnT56UJA0YMEBjx46lQAMAAACcFBsLIF3YVACAoxs9erTq1aunpk2bUqABAAAAToxJNKRL/HJOJtEAOJIffvhBN2/elCRZLBY1a9aMAg0AAABwcpRoSBcm0QA4mgkTJqh169Z6/vnnFRMTY3YcAAAAAHaCEg3pwsYCABzJ+PHj1a9fP0lSjRo15ObGXQ8AAAAA3EGJhnRhYwEAjmLcuHHq37+/JOm9997TyJEjWcIJAAAAwIYSDenCck4AjmDMmDEaMGCAJCkkJIQCDQAAAMB9WKeCNLNKOnf3MZNoALKriRMnauDAgZKk4cOHKyQkxOREAAAAAOwRJRrS7JKk2LuPC5oZBADSoXbt2vLx8VH//v01bNgws+MAAAAAsFOUaEiz+E0FHpLkYWYQAEgHf39/HThwQEWLFjU7CgAAAAA7xj3RkGZsKgAguxo7dqx+//132/cUaAAAAACSwyQa0oxNBQBkRyNHjlRISIjy5MmjgwcPys+P/xUDAAAAkDwm0ZBm8cs5mUQDkF2MGDHCtnHAO++8Q4EGAAAAIMWYREOaMYkGILswDEPDhw/XyJEjJUkff/yxbUdOAAAAAEgJSjSkWfwkGiUaAHtmGIZCQkI0atQoSdKYMWM0YMAAk1MBAAAAyG4o0ZBmbCwAIDv4+uuvbQXauHHj1K9fP5MTAQAAAMiOKNGQZiznBJAdvPjii/rmm2/UrFkz9e3b1+w4AAAAALIpSjSkGRsLALBXhmFIkiwWi3LkyKHVq1fL1dXV5FQAAAAAsjN250SaxEk6f/cxk2gA7IlhGHr33Xc1dOhQW5lGgQYAAAAgvZhEQ5pckGTVnRa2gMlZACCeYRgaNGiQPvnkE0nSc889p1q1apmcCgAAAIAjoERDmsTfDy2/JOY7ANgDwzA0cOBAjR07VpI0efJkCjQAAAAAGYYSDWnCpgIA7IlhGBowYIDGjx8vSZoyZYreeOMNk1MBAAAAcCSUaEgTNhUAYC8Mw1C/fv00ceJESdLUqVP12muvmRsKAAAAgMOhREOaMIkGwF5s377dVqBNmzZNPXv2NDcQAAAAAIdEiYY0iZ9Eo0QDYLZatWpp+vTpMgxDPXr0MDsOAAAAAAdFiYY0iZ9EYzknADMYhqGoqCj5+vpKkl599VWTEwEAAABwdC5mB0D2xHJOAGYxDENvvvmmnn76aZ0/f97sOAAAAACcBCUa0oSNBQCYwWq1qlevXvr888/1559/6ueffzY7EgAAAAAnwXJOpAmTaACyWnyBNm3aNFksFn311Vdq3bq12bEAAAAAOAlKNKRajKSLdx9TogHIClarVa+//rqmT58ui8Wi2bNnq0uXLmbHAgAAAOBEKNGQaufu/tNVUj4zgwBwClarVT179tTMmTPl4uKiOXPmqFOnTmbHAgAAAOBkKNGQavfuzMlN9QBktgsXLmjt2rVycXHR119/rY4dO5odCQAAAIATokRDqrGpAICsVLBgQYWFhWnnzp3cAw0AAACAaSjRkGpsKgAgs8XFxWnXrl2qUaOGJKlkyZIqWbKkuaEAAAAAODVW4yHV4ifRKNEAZIa4uDh1795dTz31lJYsWWJ2HAAAAACQRImGNLj3nmgAkJHi4uLUrVs3zZkzR4ZhKCYmxuxIAAAAACCJ5ZxIA5ZzAsgMcXFxeumll/TNN9/I1dVVCxYs0AsvvGB2LAAAAACQRImGNGBjAQAZLTY2Vl27dtX8+fPl5uamBQsWsIkAAAAAALtCiYZUYxINQEaKjY1Vly5d9O2338rNzU0LFy5Uq1atzI4FAAAAAAlQoiHV2FgAQEZycXFRzpw55ebmpkWLFqlly5ZmRwIAAACA+1gMwzDMDpGVoqKi5Ovrq8jISPn4+JgdJ9u5Jcn77uNLkvKamAWA47Bardq9e7eqVatmdhQAAAAATialXRG7cyJV4qfQPCTlMTEHgOwtJiZGkyZNUmxsrKQ702gUaAAAAADsGSUaUuXeTQUsZgYBkG3FxMSoffv2evvtt/Xyyy+bHQcAAAAAUoR7oiFV2FQAQHrExMSoXbt2+uGHH+Th4aG2bduaHQkAAAAAUoQSDanCpgIA0io6Olrt2rXTkiVL5OHhoSVLlqhZs2ZmxwIAAACAFKFEQ6rET6IVMjUFgOwmOjpabdq00bJly+Tp6amlS5eqSZMmZscCAAAAgBSjREOqsJwTQFp07drVVqAtW7ZMwcHBZkcCAAAAgFRhYwGkyr0bCwBASnXv3l158+bV8uXLKdAAAAAAZEtMoiFVmEQDkBZBQUE6fvy4fH19zY4CAAAAAGnCJBpShY0FAKTErVu31K1bNx06dMh2jAINAAAAQHZGiYZUYWMBAMm5deuWWrVqpdmzZ+uZZ55RTEyM2ZEAAAAAIN1YzokUuy7p2t3HTKIBSMytW7fUsmVLhYaGytvbWzNnzpS7u7vZsQAAAAAg3ZhEQ4rFL+X0lpTLzCAA7NLNmzfVokULhYaGKkeOHFq5cqUCAwPNjgUAAAAAGYJJNKTYvZsKWMwMAsDu3LhxQy1atNC6detsBVr9+vXNjgUAAAAAGYZJNKRY/CQa90MD8F/vvfee1q1bp5w5c2rVqlUUaAAAAAAcDiUaUuzeSTQAuNewYcPUoEEDrVq1SvXq1TM7DgAAAABkOJZzIsUo0QDcKzY2Vm5ud34b8fX11bp162SxsNgbAAAAgGNiEg0pxnJOAPGuX7+uxo0ba/z48bZjFGgAAAAAHBklGlKMSTQA0p0C7ZlnntHGjRs1YsQIRUREJP8kAAAAAMjmKNGQYkyiAbh27ZqaNWumTZs2ycfHR6GhofLzo1oHAAAA4Pi4JxpSjEk0wLnFF2ibN2+Wj4+P1qxZI39/f7NjAQAAAECWYBINKWKIEg1wZlevXlXTpk21efNm+fr6au3atRRoAAAAAJwKk2hIkauSbt19zHJOwPksX75cW7ZssRVoNWrUMDsSAAAAAGQpSjSkSPwUWm5JOcwMAsAUHTt21IULF1SnTh1Vr17d7DgAAAAAkOUo0ZAibCoAOJ/IyEhJkq+vryTprbfeMjMOAAAAAJiKe6IhRbgfGuBcIiMjFRwcrCZNmigqKsrsOAAAAABgOko0pEj8JBolGuD4rly5osaNG2v79u06fPiwTp48aXYkAAAAADAdyzmRIvGTaCznBBxbfIG2Y8cO5cuXT+vXr1fFihXNjgUAAAAApmMSDSnCck7A8V2+fFmNGjXSjh079NBDD2nDhg2qUqWK2bEAAAAAwC4wiYYUYWMBwLHFF2g7d+5U/vz5tX79elWuXNnsWAAAAABgNyjRkCJMogGO7dy5czp16pTy58+vDRs2qFKlSmZHAgAAAAC7QomGFGFjAcCxlS1bVhs2bJBhGNwDDQAAAAASQYmGZBliYwHAEV28eFGHDh1SnTp1JEmPP/64yYkAAAAAwH6xsQCSdVlSzN3HlGiAY7hw4YIaNmyoRo0aKSwszOw4AAAAAGD3KNGQrPilnHkkeZqYA0DGOH/+vBo0aKC9e/fKx8dHhQpRjwMAAABAcijRkCw2FQAcx/nz59WwYUPt379ffn5+CgsLU/ny5c2OBQAAAAB2jxINyWJTAcAxnDt3Tg0aNND+/ftVuHBhhYWFqVy5cmbHAgAAAIBsgY0FkCw2FQCyv4sXL6pBgwb6888/VbhwYW3cuFFly5Y1OxYAAAAAZBuUaEgWyzmB7M/Hx0flypXT5cuXtXHjRpUpU8bsSAAAAACQrVCiIVnxyzmZRAOyL3d3d3377bc6c+aMHn74YbPjAAAAAEC2wz3RkCwm0YDs6cyZMxo1apSsVqukO0UaBRoAAAAApA2TaEgWGwsA2c+ZM2cUGBio8PBwRUdHa9SoUWZHAgAAAIBsjUk0JIuNBYDs5fTp0woICFB4eLgefvhhdevWzexIAAAAAJDtUaLhgaySzt19zCQaYP/+/fdfBQQE6PDhwypRooTCwsL0yCOPmB0LAAAAALI9lnPigS5Kirv7uICZQQAk659//lFgYKCOHDliK9BKlixpdiwAAAAAcAhMouGB4pdy5pfkbmYQAA8UExOjRo0a6ciRIypZsqQ2bdpEgQYAAAAAGYgSDQ/EpgJA9uDu7q73339fZcqU0aZNm1SiRAmzIwEAAACAQ6FEwwOxqQCQfbRu3Vr79+/Xww8/bHYUAAAAAHA4lGh4oPgSjUk0wP78/fffatiwoU6ePGk75uHhYWIiAAAAAHBclGh4oPjlnEyiAfblxIkTCggI0IYNG/Tqq6+aHQcAAAAAHB67c+KBmEQD7E98gfb333+rdOnS+vLLL82OBAAAAAAOj0k0PBAbCwD25fjx46pfv77+/vtvlSlTRhs3blTRokXNjgUAAAAADo9JNDwQGwsA9uPYsWMKCAjQqVOnbAVakSJFzI4FAAAAAE6BSTQ8EMs5AfvRq1cvnTp1SmXLllVYWBgFGgAAAABkIUo0JClW0oW7j5lEA8w3e/ZstWrVSmFhYSpcuLDZcQAAAADAqbCcE0k6L8nQnaY1v8lZAGd1/fp15cyZU5JUqFAhff/99yYnAgAAAADnxCQakhS/qUBBSa5mBgGc1OHDh1WuXDl99dVXZkcBAAAAAKdHiYYksakAYJ7w8HAFBATon3/+0YQJExQTE2N2JAAAAABwapRoSBKbCgDmOHTokAIDA3XmzBlVrFhR69atk7u7u9mxAAAAAMCpcU80JCl+OSeTaEDWiS/QIiIiVKlSJa1fv14FChQwOxYAAAAAOD0m0ZAkJtGArHXw4EEFBAQoIiJClStX1oYNGyjQAAAAAMBOUKIhSfGTaJRoQNb44YcfdPbsWT3xxBNav3698udnX1wAAAAAsBcs50SS2FgAyFqDBw9W7ty51bFjRz300ENmxwEAAAAA3INJNCSJ5ZxA5vvrr7908+ZNSZLFYlGfPn0o0AAAAADADlGiIUlsLABkrv3796tOnTpq2bKlrUgDAAAAANgnSjQkKlrSpbuPmUQDMt6+ffvUoEEDnT9/XhcvXtStW7fMjgQAAAAAeABKNCTq3N1/ukvKa2YQwAHt3btXDRo00IULF1S9enWtXbtWefNypQEAAACAPaNEQ6Li74dWUPxHAmSkPXv2qEGDBrp48aJq1KhBgQYAAAAA2QT9CBLFpgJAxtu9e7caNGigS5cuqWbNmlqzZo3y5MljdiwAAAAAQApQoiFRbCoAZLzY2FjFxcXJ39+fAg0AAAAAshk3swPAPjGJBmS8GjVqaNOmTSpVqpR8fX3NjgMAAAAASAVKNCQqfhKNEg1In99//12GYahGjRqSpCpVqpgbCAAAAACQJpRoSFT8JBrLOYG027Fjhxo1aiRJ2rx5sypVqmRyIgAAAABAWnFPNCSK5ZxA+vz2228KCgpSZGSkKlWqpJIlS5odCQAAAACQDpRoSBQbCwBpt337djVq1EhRUVGqW7euVq1apdy5c5sdCwAAAACQDpRoSBSTaEDa/Prrr7YCrV69elq5cqVy5cpldiwAAAAAQDpRouE+NyVF3X1MiQak3L59+9S4cWNdvXpVAQEBFGgAAAAA4EDYWAD3iV/K6SnJx8wgQDZTunRp+fv7Ky4uTj/++KNy5sxpdiQAAAAAQAahRMN97l3KaTEzCJDNeHt7a9myZTIMgwINAAAAABwMyzlxHzYVAFJu8+bNGj58uAzDkCTlyJGDAg0AAAAAHBCTaLgPmwoAKbNp0yY988wzun79ukqUKKFu3bqZHQkAAAAAkEmYRMN94ifRKNGApIWFhalZs2a6fv26GjdurHbt2pkdCQAAAACQiSjRcJ/4STSWcwKJ27hxo5555hnduHFDwcHBWrZsmby9vc2OBQAAAADIRJRouA/LOYGkbdiwwVagNWnSREuXLpWXl5fZsQAAAAAAmYwSDfdhYwEgcWfPnlXz5s118+ZNNWvWTEuWLKFAAwAAAAAnQYmG+zCJBiSuUKFCmjRpkp577jn98MMPFGgAAAAA4EQshmEYZofISlFRUfL19VVkZKR8fHzMjmOXckm6LukvSY+ZnAWwB1arVS4u//93DoZhyGKxmJgIAAAAAJBRUtoVMYmGBK7pToEmMYkGSFJoaKj8/f11/vx52zEKNAAAAABwPpRoSCB+KWdO3ZlIA5zZ6tWr1aJFC/3+++/66KOPzI4DAAAAADCRXZRoU6ZMUcmSJeXl5SV/f3/99ttvSZ47Y8YM1a1bV3nz5lXevHkVFBT0wPOROmwqANyxcuVKtWjRQrdv31aLFi00evRosyMBAAAAAExkeom2cOFC9evXTyEhIdq1a5eeeOIJBQcH69y5c4meHxYWpvbt22vjxo3atm2bihcvrsaNG+vff//N4uSOiU0FAOmnn37S888/r+joaD3//PP67rvv5OHhYXYsAAAAAICJTN9YwN/fXzVq1NDkyZMl3bmBd/HixdW7d28NGjQo2efHxcUpb968mjx5srp06ZLs+Wws8GCfS+ol6XlJP5icBTDDihUr1Lp1a0VHR6tVq1ZasGCB3N3dzY4FAAAAAMgk2WJjgejoaO3cuVNBQUG2Yy4uLgoKCtK2bdtS9Bo3btxQTEyM8uXLl+jPb9++raioqARfSBqTaHBmMTEx6t+/v6Kjo9W6dWsKNAAAAACAjakl2oULFxQXF6dChRLegatQoUKKiIhI4lkJvfPOOypSpEiCIu5eo0ePlq+vr+2rePHi6c7tyCjR4Mzc3d0VGhqqPn366Ntvv6VAAwAAAADYmH5PtPT46KOPtGDBAi1ZskReXl6JnvPuu+8qMjLS9nXq1KksTpm9sLEAnNG992AsWbKkJk2aRIEGAAAAAEjA1BItf/78cnV11dmzZxMcP3v2rPz8HjwLNXbsWH300Udas2aNKleunOR5np6e8vHxSfCFpDGJBmezZMkSlSpVSkuWLDE7CgAAAADAjplaonl4eKhatWpav3697ZjVatX69etVu3btJJ/3ySefaNSoUVq9erWqV6+eFVGdBpNocCY//PCD2rRpoxs3bmjp0qVmxwEAAAAA2DE3swP069dPXbt2VfXq1VWzZk1NnDhR169fV7du3SRJXbp0UdGiRTV69GhJ0scff6xhw4Zp/vz5KlmypO3eably5VKuXLlM+xyOwBCTaHAe33//vdq1a6fY2Fh17NhRs2bNMjsSAAAAAMCOmV6itW3bVufPn9ewYcMUERGhKlWqaPXq1bbNBk6ePCkXl/8fmJs6daqio6P1wgsvJHidkJAQDR8+PCujO5xISbfvPmYSDY5s0aJFat++veLi4tSpUyfNnj1brq6uZscCAAAAANgxi2EYhtkhslJUVJR8fX0VGRnJ/dH+I1xSOUk+ulOoAY7ou+++U4cOHRQXF6fOnTvrq6++okADAAAAACeW0q4oW+/OiYzFUk44g40bNyouLk5du3alQAMAAAAApJjpyzlhP9hUAM5gypQpqlWrljp16kSBBgAAAABIMSbRYMMkGhzVzz//rJiYGEmSi4uLunbtSoEGAAAAAEgVSjTYUKLBEc2bN0+BgYHq2LGjYmNjzY4DAAAAAMimKNFgw3JOOJq5c+eqS5cuslqtypMnT4KdfgEAAAAASA3+HyVsmESDI/n666/VtWtXWa1W9ejRQ9OmTaNEAwAAAACkGf+PEjZMosFRzJkzRy+99JIMw9Brr72mqVOnUqABAAAAANKF/1cJGybR4AjmzJmjbt26yTAMvf766/r8888p0AAAAAAA6eZmdgDYB6ukc3cfU6IhOytWrJg8PT3VvXt3ffbZZ7JYLGZHAgAAAAA4AEo0SJIuS4q5+7igmUGAdGrYsKF27dqlcuXKUaABAAAAADIMa5wg6f+XcuaT5GFmECAN5syZo4MHD9q+L1++PAUaAAAAACBDUaJBEpsKIPuaNm2aXnrpJQUGBioiIiL5JwAAAAAAkAaUaJDEpgLInqZOnarXX39dktShQwcVKkQNDAAAAADIHJRokPT/k2iUaMgupkyZojfeeEOS1L9/f40bN44lnAAAAACATEOJBkn/P4nGHA+ygylTpujNN9+UJP3vf//TmDFjKNAAAAAAAJmKEg2SWM6J7GPhwoW2Am3gwIH6+OOPKdAAAAAAAJnOzewAsA9sLIDsokmTJqpZs6YaNGigDz/8kAINAAAAAJAlKNEgiUk0ZB++vr4KCwuTl5cXBRoAAAAAIMuwnBOS2FgA9m3ChAkaN26c7Xtvb28KNAAAAABAlmISDYqTdO7uY5Zzwt6MGzdOAwYMkCTVrl1bTz31lMmJAAAAAADOiEk06IIkqySLpAImZwHuNWbMGFuBNmzYMNWuXdvkRAAAAAAAZ0WJBttSzvxiNBH24+OPP9bAgQMlScOHD9eIESNYwgkAAAAAMA0lGthUAHZn9OjRGjRokCRpxIgRCgkJMTkRAAAAAMDZMXgENhWAXdm+fbsGDx4sSRo5cqTee+89kxMBAAAAAECJBv3/JBqbCsAe+Pv76+OPP1ZMTIyGDBlidhwAAAAAACRRokEs54R9uH37tjw9PSXJdi80AAAAAADsBfdEg205J5NoMMuIESMUEBCgqKgos6MAAAAAAJAoSjQwiQZTDR8+XMOHD9evv/6q5cuXmx0HAAAAAIBEUaKBjQVgCsMwFBISohEjRkiSxowZo06dOpmcCgAAAACAxHFPNLCxALKcYRgaNmyY3n//fUnS2LFj1b9/f5NTAQAAAACQNEo0Jxcj6cLdx0yiISsYhqH33ntPH3zwgSRp/Pjx6tu3r8mpAAAAAAB4MEo0J3f+7j9dJT1kZhA4jfPnz2vGjBmSpAkTJujtt982NxAAAAAAAClAiebk4pdyFhQ3yEPWKFiwoDZu3KjNmzerZ8+eZscBAAAAACBFKNGcHJsKICsYhqEjR46odOnSkqQKFSqoQoUKJqcCAAAAACDlGD5ycmwqgMxmGIYGDhyoJ554Qhs2bDA7DgAAAAAAaUKJ5uTiSzQm0ZAZDMPQgAEDNHbsWN28eVN//fWX2ZEAAAAAAEgTlnM6ufjlnEyiIaMZhqH+/ftrwoQJkqSpU6dyDzQAAAA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      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.92      0.92      3571\n",
      "         1.0       0.88      0.89      0.88      2340\n",
      "\n",
      "    accuracy                           0.91      5911\n",
      "   macro avg       0.90      0.90      0.90      5911\n",
      "weighted avg       0.91      0.91      0.91      5911\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train late fusion model\n",
    "print(\"Training Late Fusion Model:\")\n",
    "train_late_fusion(train_loader, test_loader, text_input_size, image_input_size, output_size, num_epochs=15, multilabel=multilabel, report=True, p=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8210e63b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:retina_embeddings_v0_0_1]",
   "language": "python",
   "name": "conda-env-retina_embeddings_v0_0_1-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}