5.2. Data Selection.ipynb

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    "# <font color='CC3D3D'> Data Selection\n",
    "    \n",
    "### 1. Pycaret\n",
    "- 통합한 <span style=\"color:green\">**Master_df.csv** </span>를 가지고 Pycaret을 가지고 Feature Importance를 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96f590f-9adc-4075-8a66-8e6f15caf398",
   "metadata": {},
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "06899787-196f-46c2-b749-a2befe998457",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pycaret.classification import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e138c95-a09c-4f63-bed9-cb733f20d65c",
   "metadata": {},
   "source": [
    "# Data Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "46378126-3512-4988-82bd-d32805eafd0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Master_df = pd.read_csv('../Data/Master_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccc28b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "Master_train = Master_df[Master_df.month < 6].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76f41658-4d5c-425d-a6b6-cadf6582e052",
   "metadata": {},
   "outputs": [],
   "source": [
    "Master_train.drop(['application_id','user_id'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f48653d-abfb-43cf-8b46-0e197c89f60e",
   "metadata": {},
   "source": [
    "# Pycaret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c2274ff-18db-45d3-9151-5c41bae120fe",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "IntProgress(value=0, description='Processing: ', max=3)"
      ]
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     "metadata": {},
     "output_type": "display_data"
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       "    <tr>\n",
       "      <th>Initiated</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>13:49:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>Preprocessing Data</td>\n",
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       "                                                                  \n",
       "                                                                  \n",
       "Initiated  . . . . . . . . . . . . . . . . . .            13:49:42\n",
       "Status     . . . . . . . . . . . . . . . . . .  Preprocessing Data"
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   "source": [
    "exp_clf = setup(data = Master_train, target = 'is_applied', session_id=123)"
   ]
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7b59e_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_7b59e_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th id=\"T_7b59e_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th id=\"T_7b59e_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th id=\"T_7b59e_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th id=\"T_7b59e_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th id=\"T_7b59e_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th id=\"T_7b59e_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th id=\"T_7b59e_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row0\" class=\"row_heading level0 row0\" >dt</th>\n",
       "      <td id=\"T_7b59e_row0_col0\" class=\"data row0 col0\" >Decision Tree Classifier</td>\n",
       "      <td id=\"T_7b59e_row0_col1\" class=\"data row0 col1\" >0.9176</td>\n",
       "      <td id=\"T_7b59e_row0_col2\" class=\"data row0 col2\" >0.6151</td>\n",
       "      <td id=\"T_7b59e_row0_col3\" class=\"data row0 col3\" >0.2744</td>\n",
       "      <td id=\"T_7b59e_row0_col4\" class=\"data row0 col4\" >0.2592</td>\n",
       "      <td id=\"T_7b59e_row0_col5\" class=\"data row0 col5\" >0.2666</td>\n",
       "      <td id=\"T_7b59e_row0_col6\" class=\"data row0 col6\" >0.2230</td>\n",
       "      <td id=\"T_7b59e_row0_col7\" class=\"data row0 col7\" >0.2231</td>\n",
       "      <td id=\"T_7b59e_row0_col8\" class=\"data row0 col8\" >123.3810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row1\" class=\"row_heading level0 row1\" >catboost</th>\n",
       "      <td id=\"T_7b59e_row1_col0\" class=\"data row1 col0\" >CatBoost Classifier</td>\n",
       "      <td id=\"T_7b59e_row1_col1\" class=\"data row1 col1\" >0.9469</td>\n",
       "      <td id=\"T_7b59e_row1_col2\" class=\"data row1 col2\" >0.8856</td>\n",
       "      <td id=\"T_7b59e_row1_col3\" class=\"data row1 col3\" >0.1011</td>\n",
       "      <td id=\"T_7b59e_row1_col4\" class=\"data row1 col4\" >0.5763</td>\n",
       "      <td id=\"T_7b59e_row1_col5\" class=\"data row1 col5\" >0.1721</td>\n",
       "      <td id=\"T_7b59e_row1_col6\" class=\"data row1 col6\" >0.1583</td>\n",
       "      <td id=\"T_7b59e_row1_col7\" class=\"data row1 col7\" >0.2258</td>\n",
       "      <td id=\"T_7b59e_row1_col8\" class=\"data row1 col8\" >432.6930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row2\" class=\"row_heading level0 row2\" >rf</th>\n",
       "      <td id=\"T_7b59e_row2_col0\" class=\"data row2 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_7b59e_row2_col1\" class=\"data row2 col1\" >0.9455</td>\n",
       "      <td id=\"T_7b59e_row2_col2\" class=\"data row2 col2\" >0.8971</td>\n",
       "      <td id=\"T_7b59e_row2_col3\" class=\"data row2 col3\" >0.0629</td>\n",
       "      <td id=\"T_7b59e_row2_col4\" class=\"data row2 col4\" >0.5099</td>\n",
       "      <td id=\"T_7b59e_row2_col5\" class=\"data row2 col5\" >0.1119</td>\n",
       "      <td id=\"T_7b59e_row2_col6\" class=\"data row2 col6\" >0.1012</td>\n",
       "      <td id=\"T_7b59e_row2_col7\" class=\"data row2 col7\" >0.1650</td>\n",
       "      <td id=\"T_7b59e_row2_col8\" class=\"data row2 col8\" >788.1160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row3\" class=\"row_heading level0 row3\" >ada</th>\n",
       "      <td id=\"T_7b59e_row3_col0\" class=\"data row3 col0\" >Ada Boost Classifier</td>\n",
       "      <td id=\"T_7b59e_row3_col1\" class=\"data row3 col1\" >0.9432</td>\n",
       "      <td id=\"T_7b59e_row3_col2\" class=\"data row3 col2\" >0.8278</td>\n",
       "      <td id=\"T_7b59e_row3_col3\" class=\"data row3 col3\" >0.0472</td>\n",
       "      <td id=\"T_7b59e_row3_col4\" class=\"data row3 col4\" >0.3520</td>\n",
       "      <td id=\"T_7b59e_row3_col5\" class=\"data row3 col5\" >0.0833</td>\n",
       "      <td id=\"T_7b59e_row3_col6\" class=\"data row3 col6\" >0.0713</td>\n",
       "      <td id=\"T_7b59e_row3_col7\" class=\"data row3 col7\" >0.1124</td>\n",
       "      <td id=\"T_7b59e_row3_col8\" class=\"data row3 col8\" >355.2560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row4\" class=\"row_heading level0 row4\" >xgboost</th>\n",
       "      <td id=\"T_7b59e_row4_col0\" class=\"data row4 col0\" >Extreme Gradient Boosting</td>\n",
       "      <td id=\"T_7b59e_row4_col1\" class=\"data row4 col1\" >0.5678</td>\n",
       "      <td id=\"T_7b59e_row4_col2\" class=\"data row4 col2\" >0.5252</td>\n",
       "      <td id=\"T_7b59e_row4_col3\" class=\"data row4 col3\" >0.0405</td>\n",
       "      <td id=\"T_7b59e_row4_col4\" class=\"data row4 col4\" >0.3440</td>\n",
       "      <td id=\"T_7b59e_row4_col5\" class=\"data row4 col5\" >0.0724</td>\n",
       "      <td id=\"T_7b59e_row4_col6\" class=\"data row4 col6\" >0.0663</td>\n",
       "      <td id=\"T_7b59e_row4_col7\" class=\"data row4 col7\" >0.1101</td>\n",
       "      <td id=\"T_7b59e_row4_col8\" class=\"data row4 col8\" >1121.6630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row5\" class=\"row_heading level0 row5\" >lightgbm</th>\n",
       "      <td id=\"T_7b59e_row5_col0\" class=\"data row5 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_7b59e_row5_col1\" class=\"data row5 col1\" >0.9460</td>\n",
       "      <td id=\"T_7b59e_row5_col2\" class=\"data row5 col2\" >0.8679</td>\n",
       "      <td id=\"T_7b59e_row5_col3\" class=\"data row5 col3\" >0.0379</td>\n",
       "      <td id=\"T_7b59e_row5_col4\" class=\"data row5 col4\" >0.5867</td>\n",
       "      <td id=\"T_7b59e_row5_col5\" class=\"data row5 col5\" >0.0711</td>\n",
       "      <td id=\"T_7b59e_row5_col6\" class=\"data row5 col6\" >0.0649</td>\n",
       "      <td id=\"T_7b59e_row5_col7\" class=\"data row5 col7\" >0.1392</td>\n",
       "      <td id=\"T_7b59e_row5_col8\" class=\"data row5 col8\" >37.6920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
       "      <td id=\"T_7b59e_row6_col0\" class=\"data row6 col0\" >K Neighbors Classifier</td>\n",
       "      <td id=\"T_7b59e_row6_col1\" class=\"data row6 col1\" >0.5653</td>\n",
       "      <td id=\"T_7b59e_row6_col2\" class=\"data row6 col2\" >0.3868</td>\n",
       "      <td id=\"T_7b59e_row6_col3\" class=\"data row6 col3\" >0.0283</td>\n",
       "      <td id=\"T_7b59e_row6_col4\" class=\"data row6 col4\" >0.1840</td>\n",
       "      <td id=\"T_7b59e_row6_col5\" class=\"data row6 col5\" >0.0490</td>\n",
       "      <td id=\"T_7b59e_row6_col6\" class=\"data row6 col6\" >0.0409</td>\n",
       "      <td id=\"T_7b59e_row6_col7\" class=\"data row6 col7\" >0.0612</td>\n",
       "      <td id=\"T_7b59e_row6_col8\" class=\"data row6 col8\" >587.7040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row7\" class=\"row_heading level0 row7\" >gbc</th>\n",
       "      <td id=\"T_7b59e_row7_col0\" class=\"data row7 col0\" >Gradient Boosting Classifier</td>\n",
       "      <td id=\"T_7b59e_row7_col1\" class=\"data row7 col1\" >0.9453</td>\n",
       "      <td id=\"T_7b59e_row7_col2\" class=\"data row7 col2\" >0.8451</td>\n",
       "      <td id=\"T_7b59e_row7_col3\" class=\"data row7 col3\" >0.0243</td>\n",
       "      <td id=\"T_7b59e_row7_col4\" class=\"data row7 col4\" >0.4878</td>\n",
       "      <td id=\"T_7b59e_row7_col5\" class=\"data row7 col5\" >0.0463</td>\n",
       "      <td id=\"T_7b59e_row7_col6\" class=\"data row7 col6\" >0.0414</td>\n",
       "      <td id=\"T_7b59e_row7_col7\" class=\"data row7 col7\" >0.0996</td>\n",
       "      <td id=\"T_7b59e_row7_col8\" class=\"data row7 col8\" >1777.7680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row8\" class=\"row_heading level0 row8\" >lda</th>\n",
       "      <td id=\"T_7b59e_row8_col0\" class=\"data row8 col0\" >Linear Discriminant Analysis</td>\n",
       "      <td id=\"T_7b59e_row8_col1\" class=\"data row8 col1\" >0.2794</td>\n",
       "      <td id=\"T_7b59e_row8_col2\" class=\"data row8 col2\" >0.2303</td>\n",
       "      <td id=\"T_7b59e_row8_col3\" class=\"data row8 col3\" >0.0303</td>\n",
       "      <td id=\"T_7b59e_row8_col4\" class=\"data row8 col4\" >0.0747</td>\n",
       "      <td id=\"T_7b59e_row8_col5\" class=\"data row8 col5\" >0.0385</td>\n",
       "      <td id=\"T_7b59e_row8_col6\" class=\"data row8 col6\" >0.0304</td>\n",
       "      <td id=\"T_7b59e_row8_col7\" class=\"data row8 col7\" >0.0357</td>\n",
       "      <td id=\"T_7b59e_row8_col8\" class=\"data row8 col8\" >207.9540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row9\" class=\"row_heading level0 row9\" >qda</th>\n",
       "      <td id=\"T_7b59e_row9_col0\" class=\"data row9 col0\" >Quadratic Discriminant Analysis</td>\n",
       "      <td id=\"T_7b59e_row9_col1\" class=\"data row9 col1\" >0.4681</td>\n",
       "      <td id=\"T_7b59e_row9_col2\" class=\"data row9 col2\" >0.1205</td>\n",
       "      <td id=\"T_7b59e_row9_col3\" class=\"data row9 col3\" >0.1237</td>\n",
       "      <td id=\"T_7b59e_row9_col4\" class=\"data row9 col4\" >0.0243</td>\n",
       "      <td id=\"T_7b59e_row9_col5\" class=\"data row9 col5\" >0.0313</td>\n",
       "      <td id=\"T_7b59e_row9_col6\" class=\"data row9 col6\" >0.0098</td>\n",
       "      <td id=\"T_7b59e_row9_col7\" class=\"data row9 col7\" >0.0110</td>\n",
       "      <td id=\"T_7b59e_row9_col8\" class=\"data row9 col8\" >41.0430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row10\" class=\"row_heading level0 row10\" >et</th>\n",
       "      <td id=\"T_7b59e_row10_col0\" class=\"data row10 col0\" >Extra Trees Classifier</td>\n",
       "      <td id=\"T_7b59e_row10_col1\" class=\"data row10 col1\" >0.1887</td>\n",
       "      <td id=\"T_7b59e_row10_col2\" class=\"data row10 col2\" >0.1732</td>\n",
       "      <td id=\"T_7b59e_row10_col3\" class=\"data row10 col3\" >0.0180</td>\n",
       "      <td id=\"T_7b59e_row10_col4\" class=\"data row10 col4\" >0.0829</td>\n",
       "      <td id=\"T_7b59e_row10_col5\" class=\"data row10 col5\" >0.0296</td>\n",
       "      <td id=\"T_7b59e_row10_col6\" class=\"data row10 col6\" >0.0262</td>\n",
       "      <td id=\"T_7b59e_row10_col7\" class=\"data row10 col7\" >0.0347</td>\n",
       "      <td id=\"T_7b59e_row10_col8\" class=\"data row10 col8\" >456.2260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row11\" class=\"row_heading level0 row11\" >nb</th>\n",
       "      <td id=\"T_7b59e_row11_col0\" class=\"data row11 col0\" >Naive Bayes</td>\n",
       "      <td id=\"T_7b59e_row11_col1\" class=\"data row11 col1\" >0.9391</td>\n",
       "      <td id=\"T_7b59e_row11_col2\" class=\"data row11 col2\" >0.5168</td>\n",
       "      <td id=\"T_7b59e_row11_col3\" class=\"data row11 col3\" >0.0084</td>\n",
       "      <td id=\"T_7b59e_row11_col4\" class=\"data row11 col4\" >0.0634</td>\n",
       "      <td id=\"T_7b59e_row11_col5\" class=\"data row11 col5\" >0.0148</td>\n",
       "      <td id=\"T_7b59e_row11_col6\" class=\"data row11 col6\" >0.0021</td>\n",
       "      <td id=\"T_7b59e_row11_col7\" class=\"data row11 col7\" >0.0033</td>\n",
       "      <td id=\"T_7b59e_row11_col8\" class=\"data row11 col8\" >27.5680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row12\" class=\"row_heading level0 row12\" >svm</th>\n",
       "      <td id=\"T_7b59e_row12_col0\" class=\"data row12 col0\" >SVM - Linear Kernel</td>\n",
       "      <td id=\"T_7b59e_row12_col1\" class=\"data row12 col1\" >0.4707</td>\n",
       "      <td id=\"T_7b59e_row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
       "      <td id=\"T_7b59e_row12_col3\" class=\"data row12 col3\" >0.0039</td>\n",
       "      <td id=\"T_7b59e_row12_col4\" class=\"data row12 col4\" >0.0959</td>\n",
       "      <td id=\"T_7b59e_row12_col5\" class=\"data row12 col5\" >0.0055</td>\n",
       "      <td id=\"T_7b59e_row12_col6\" class=\"data row12 col6\" >0.0021</td>\n",
       "      <td id=\"T_7b59e_row12_col7\" class=\"data row12 col7\" >0.0047</td>\n",
       "      <td id=\"T_7b59e_row12_col8\" class=\"data row12 col8\" >355.2510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row13\" class=\"row_heading level0 row13\" >ridge</th>\n",
       "      <td id=\"T_7b59e_row13_col0\" class=\"data row13 col0\" >Ridge Classifier</td>\n",
       "      <td id=\"T_7b59e_row13_col1\" class=\"data row13 col1\" >0.4727</td>\n",
       "      <td id=\"T_7b59e_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
       "      <td id=\"T_7b59e_row13_col3\" class=\"data row13 col3\" >0.0001</td>\n",
       "      <td id=\"T_7b59e_row13_col4\" class=\"data row13 col4\" >0.0602</td>\n",
       "      <td id=\"T_7b59e_row13_col5\" class=\"data row13 col5\" >0.0002</td>\n",
       "      <td id=\"T_7b59e_row13_col6\" class=\"data row13 col6\" >0.0001</td>\n",
       "      <td id=\"T_7b59e_row13_col7\" class=\"data row13 col7\" >0.0012</td>\n",
       "      <td id=\"T_7b59e_row13_col8\" class=\"data row13 col8\" >42.3830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row14\" class=\"row_heading level0 row14\" >lr</th>\n",
       "      <td id=\"T_7b59e_row14_col0\" class=\"data row14 col0\" >Logistic Regression</td>\n",
       "      <td id=\"T_7b59e_row14_col1\" class=\"data row14 col1\" >0.2836</td>\n",
       "      <td id=\"T_7b59e_row14_col2\" class=\"data row14 col2\" >0.1908</td>\n",
       "      <td id=\"T_7b59e_row14_col3\" class=\"data row14 col3\" >0.0000</td>\n",
       "      <td id=\"T_7b59e_row14_col4\" class=\"data row14 col4\" >0.0000</td>\n",
       "      <td id=\"T_7b59e_row14_col5\" class=\"data row14 col5\" >0.0000</td>\n",
       "      <td id=\"T_7b59e_row14_col6\" class=\"data row14 col6\" >0.0000</td>\n",
       "      <td id=\"T_7b59e_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
       "      <td id=\"T_7b59e_row14_col8\" class=\"data row14 col8\" >22.8070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7b59e_level0_row15\" class=\"row_heading level0 row15\" >dummy</th>\n",
       "      <td id=\"T_7b59e_row15_col0\" class=\"data row15 col0\" >Dummy Classifier</td>\n",
       "      <td id=\"T_7b59e_row15_col1\" class=\"data row15 col1\" >0.9454</td>\n",
       "      <td id=\"T_7b59e_row15_col2\" class=\"data row15 col2\" >0.5000</td>\n",
       "      <td id=\"T_7b59e_row15_col3\" class=\"data row15 col3\" >0.0000</td>\n",
       "      <td id=\"T_7b59e_row15_col4\" class=\"data row15 col4\" >0.0000</td>\n",
       "      <td id=\"T_7b59e_row15_col5\" class=\"data row15 col5\" >0.0000</td>\n",
       "      <td id=\"T_7b59e_row15_col6\" class=\"data row15 col6\" >0.0000</td>\n",
       "      <td id=\"T_7b59e_row15_col7\" class=\"data row15 col7\" >0.0000</td>\n",
       "      <td id=\"T_7b59e_row15_col8\" class=\"data row15 col8\" >1.7960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x282509ffaf0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = compare_models(sort = \"F1\",n_select=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29c046cf-a4aa-4f49-bc11-02c41bb46bb2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ViIyMREFBAdzd3REQEABjY2MAQGRkJEJCQpCcnAxdXV106dIFQUFBqF27NkJCQpCWlgZDQ0McPXoUurq6GDt2LL7++muF5nZ0dMSkSZOwZ88euLq6YtOmTQgLC8O2bduQkZGB2rVro0+fPggICMC1a9fg7+8v3S4yMhKWlpbYuHEj9u7di/z8fLRu3RqBgYGoX7++wscvCALy8vIqHpwSJBIJorQsEJVeAKCgSuasUZidapif8pid8pidapif8pidav5/fqkPM3HnSa7coUn/5OB0/F10tDGvlFIEQVCoIWHTUMkmT54MiUQivexn/vz58PPzQ2hoKNLT0/Hdd98hMDAQHTp0QFpaGmbMmIEDBw5gzJgxAICTJ09i5MiRiIiIwKlTp7By5Ur07NkTtra2Cs1/9uxZ7N27F6WlpYiKisKiRYuwcuVKNGnSBLdu3cLMmTPRvn17dOvWDbNnz8b27dtx8OBBmJqa4pdffsHRo0exevVqmJubY/v27Rg7diyOHj0KbW1theYvKipCfHy8cuEpQ8u66uYiIiIiUkGOJF+hcddvJ8FUklVpdejo6Lx1DJuGSpSbm4uoqCj8/vvv0hf5K1euRJ8+fZCSkgItLS0EBARIbzy2srJChw4dkJiYKN2HiYkJfH19IRaLMW7cOGzZsgW3bt1SuGkYPnw4GjZsCAC4desWFi9ejN69e0vn++mnn5CYmIjevXvDyMgIYrEYdevWBQBs3boV8+bNQ9u2bQEACxcuRKdOnXDhwgW4u7srNL+2tjbs7e0VGqsqiUSCNmn3YWFhAV1d3SqZs6YoKChAZmYms1MS81Mes1Mes1MN81Mes1PN6/nZwAj7L759m1aN7eFUSWcakpKSFBrHpqESnT9/HnXq1JF5gW9nZwdjY2OkpKSgZ8+e0NHRQWhoKBITE5GYmIikpCQMHDhQOt7KygpisVj6fe3atVFcXKxwDZaWltKvmzVrBj09PQQHByMpKQkJCQm4c+cOOnXqVGa7Fy9e4MGDB5g2bRq0tP7vfvn8/HykpaUpPL9IJIKBgYHC41VljEJYmxhU6Zw1QV6eGHmZzE5ZzE95zE55zE41zE95zE41r+fX2MIMgSdiytwE/Tp7cyP0dKq8exoU3S+bhkr0pu67pKQEJSUluH37NkaMGAF3d3e0bt0a3t7e2LFjh8zY8i4DqsgNy6/XcOHCBfj4+MDDwwOdO3eGj48PFixY8MYaAeCHH34oc1bj1f0YRERERKQ8kUiE5f1bYtiO8+XeDK0lEmFZv5Yavwka4CNXK1WnTp3w/PlzpKSkSJclJSUhNzcXtra2OHz4MNzc3LB69WqMHDkSLi4uuHPnTqU9xSgsLAyDBw/GwoULMXToUNjZ2SE9PV063+s/kHXq1IGZmRmysrJgbW0Na2trWFhYYOXKlUhNTa2U+oiIiIjeN57OH+PA6C6wNzeSWW5vboQDo7tUm89p4JmGSvTqaUi+vr6YO3cuAGDBggVwc3ODg4MDTExMkJCQgJs3b8LIyAj79+9HbGwsGjRoUCn1mJiYIDo6GgkJCdDS0sLmzZuRlZWFwsJCAIC+vj6ys7ORlpYGKysreHt7Y926dTAzM0PDhg2xceNG3LhxA4sXL66U+oiIiIjeR57OH8OjWQNcSHmEzOcS1DfWRydbfiL0e2X58uVYtGgRvL29IRaL0aNHD+mjTb28vBAXFwdvb2/o6urCzc0NPj4+OH78eKXUMnnyZPj7+2P48OEwNDRE165dMWLECOnTjdq1awdra2v0798fe/bswVdffYUXL14gMDAQubm5aNasGbZt28bLk4iIiIjUTCQSoYvdh5ou441EQk34RC+qlmJjYwEAzs7OVTJfXl4e4uPj4eTkxBuzKojZqYb5KY/ZKY/ZqYb5KY/Zqaa65afo6zXe00BERERERHLx8qR31KBBg+TekLxlyxa0bt26CisiIiIiopqKTcM7av369SgqKnrj+g8/rL7XxBERERHRu4VNwzuqfv36mi6BiIiIiN4TvKeBiIiIiIjkYtNARERERERysWkgIiIiIiK52DQQEREREZFcbBqIiIiIiEguNg1ERERERCQXmwYiIiIiIpKLTQMREREREcnFpoGIiIiIiORi00BERERERHKxaSAiIiIiIrnYNBARERERkVxsGoiIiIiISC42DUREREREJBebBiIiIiIikotNAxERERERycWmgYiIiIiI5GLTQEREREREcrFpICIiIiIiudg0EBERERGRXGwaiIiIiIhILjYNREREREQkF5uGKnL16lU4OjoCADIyMuDo6IiMjAwAwN27d/Hnn39qsjwiIiIilQiCgPPJD7EvOhXnkx9CEARNl0RqVEvTBbyPLCwscPHiRZiamgIAZs+ejTZt2qBr164aroyIiIio4iJi0+F79AaSH+dIl9mZGWF5/5bwdP5Yg5WRuvBMgwaIxWLUrVsXYrFY06UQERERqSQiNh3DdpyXaRgAIPlxDobtOI+I2HQNVUbqxDMN/3Lnzh0sXLgQN27cgLGxMcaOHQtHR0f4+/ujc+fOOHbsGCZMmIDx48dj3759+PHHH/H06VM0a9YMAQEB0kuQcnNzERgYiLNnz6JevXoYOnSodI6MjAz06NEDkZGRWL9+PaKioqR/du3aJbe+oqIiLFiwAKdOnUJhYSHatm2LBQsW4MMPPwQAHD58GKGhocjMzISTkxMCAwPRpEkTAEB4eDi2bNmCe/fuwd7eHv7+/nBzcwMAuLu747PPPsPhw4dhbm6OiIgIJCYmIigoCDExMbCwsMCXX36JUaNGVUbsapMNHTyQlEBPKNJ0Ke+U/PwSZqcC5qc8Zqc8Zqca5qe817PTLS3E9CPXUfqGS5FKBQF+x27Ao1kDiESiKq6U1IlNw2sKCgowduxYNG3aFAcOHMDdu3cxffp0rFmzBvfu3UNhYSHCw8Ohra2NM2fOYP369QgKCoKtrS0OHTqEL7/8En/88QeMjY0xb948pKSk4JdffsGTJ0/g5+dX7pxz5sxBWloaXF1dMWHChLfWuHv3bvz111/Yvn079PT0MH/+fCxZsgQ//PADLly4gDlz5mDOnDno0KEDdu3ahQkTJiAyMhLHjh1DUFAQ5s2bBxcXF4SHh2P8+PH4/fffpQ3H0aNHsW3bNgiCgIKCAnz99dfw9PREUFAQUlJSMHfuXNSuXRseHh4KZyoIAvLy8hQerwqJRIIoLQtEpRcAKKiSOWsUZqca5qc8Zqc8Zqca5qe8/59d6sNM3HmSK3do0j85OB1/Fx1tzKuouOpNIpHI/FfTBEFQqKFj0/Caixcv4smTJ1iyZAkMDQ3RqFEjBAQEQEvr5VVc48aNg7W1NQBgxowZmDBhArp37w4AmDp1Ks6fP48jR47Aw8MDJ06cwM6dO9G0aVMAwKRJk7Bw4cIycxoZGUFbWxsGBgYwMTF5a40ZGRnQ1dWFpaUlTExMsGzZMjx79gwAsH//fvTr1w8jRowAAMyaNQva2trIzs7Grl274OXlJX3BP2PGDPz111/45ZdfMH36dADAgAEDpGdKwsLCYGZmhqlTpwIAbGxscO/ePezcubNCTUNRURHi4+MVHq8yLeuqm4uIiOg9lyPJV2jc9dtJMJVkVXI175a0tDRNlyClo6Pz1jFsGl6TmpoKW1tbGBoaSpcNHjwYV69eBQBYWVlJlycnJ2PlypVYs2aNdFlBQQHS0tKQmpqKkpISNG7cWLrO2dlZLTUOHz4cx48fR6dOndCmTRv07NkTgwYNktb/+eefS8fq6OjA19dXWq+Pj4/Mvlq0aIHk5GTp95aWltKvU1JScPv2bbi6ukqXlZSUVPg+DG1tbdjb21doG2VJJBK0SbsPCwsL6OrqVsmcNUVBQQEyMzOZnZKYn/KYnfKYnWqYn/Jez84GRth/8e3btGpsDyeeaQDw8vVKWloabGxsoK+vr+lykJSUpNA4Ng2vqVVLfhyv/1IpKSnB7Nmz0b59e5kxhoaGuHfvXpltFengFNGoUSOcOXMG586dw7lz57BmzRocO3YMu3fvllt/eb8QS0pKUFpaWu6Y4uJitG/fHoGBgSrVKxKJYGBgoNI+KsIYhbA2MajSOWuCvDwx8jKZnbKYn/KYnfKYnWqYn/Jez66xhRkCT8SUuQn6dfbmRujpxHsa/k1fX79a/Owp+v+FT096jY2NDe7cuSNzjdny5cuxaNGiMmNtbW3x4MEDWFtbS/9s2rQJ//3vf9GwYUNoa2sjNjZWOj4uLk4tNR46dAhnz57FZ599huXLl2Pr1q24fv06Hj9+DGtra9y+fVs6tqSkBO7u7rh+/TpsbW0RExMjs6+YmBjY2tqWO4+trS1SU1NhZWUlPb7//ve/b71Rm4iIiN4fIpEIy/u3hNYbXnhqiURY1q8lG4YagE3Dazp16gRzc3MEBgYiOTkZkZGR2Ldvn/Sa/9eNGTMGO3bswKFDh5Ceno6VK1fixIkTsLOzg6GhIQYOHCh98tDVq1exfv36N85rYGCAtLQ0PH78+K015uTkYPHixbh8+TLu3r2Lo0eP4qOPPsIHH3wALy8vHDlyBBEREbhz5w6WLl0KQRDQtGlTeHt745dffsGhQ4eQmpqKVatW4fbt2xgyZEi58wwYMAD5+fnSLP78808sXrwYZmZmigdKRERENZ6n88c4MLoL7M2NZJbbmxvhwOgu/JyGGoKXJ72mVq1a2LhxIxYuXAhPT0+Ym5tj1qxZ5V5v1qdPH/zzzz8IDg7GP//8A3t7e4SGhsLGxgYAMHfuXAQFBWHMmDEwNjaGl5cXli9fXu68Q4cOxezZszFu3DhERETIrXHUqFF48OABZs6ciezsbDRr1gyhoaEQi8Vwc3PDvHnzsGHDBmRlZaFZs2bYtGkT9PT0ZOrNysqCk5MTtm/fDjs7u3LnMTQ0xJYtW7BkyRJ4eHjAxMQEo0aNUugJT0RERPR+8XT+GB7NGuBCyiNkPpegvrE+OtnW4xmGGkQk8DO+qZK8ujxLXTeBv01eXh7i4+Ph5ORULa4RfJcwO9UwP+UxO+UxO9UwP+UxO9VUt/wUfb3Gy5OIiIiIiEguXp5Ujdy8eROjR49+4/r69evj+PHjVVgRERERERGbhmqlcePGOHTo0BvXv+2RsERERERElYGvQqsRHR0d6SdOExERERFVF7yngYiIiIiI5GLTQEREREREcrFpICIiIiIiudg0EBERERGRXGwaiIiIiIhILjYNREREREQkF5sGIiIiIiKSi00DERERERHJxaaBiIiIiIjkYtNARERERERysWkgIiIiIiK52DQQEREREZFcbBqIiIiIiEguNg1ERERERCQXmwYiIiIiIpKLTQMREREREcnFpoGIiIiIiORi00BERERERHKxaSAiIiIiIrnYNBARERERkVxsGoiIiIiISC42DUREREREJBebBiIiIiIikotNg5plZGTA0dERGRkZGq3D0dERV69eBQC4u7sjPDy8wvu4evUqHB0dpd/Hx8fjxo0baquRiIiI3k4QBJxPfoh90ak4n/wQgiBouiR6D9XSdAFU+Q4ePAgDA4MKb+fq6oqLFy9Kv/fx8cHkyZPRsmVLdZZHREREbxARmw7fozeQ/DhHuszOzAjL+7eEp/PHGqyM3jc80/AeMDU1hZ6eXoW309HRQd26dSuhIiIiInqbiNh0DNtxXqZhAIDkxzkYtuM8ImLTNVQZvY94pqESZWdnY9WqVYiMjERBQQHc3d0REBAAY2NjAEBkZCRCQkKQnJwMXV1ddOnSBUFBQahduzZCQkKQlpYGQ0NDHD16FLq6uhg7diy+/vrrCtfh7u6OyZMnY9CgQfDy8kK3bt1w8eJFXL9+HQ4ODli9ejW2bt2KY8eOoW7duli0aBHatGmDq1ev4ssvv0RCQgK8vLxw7949+Pv7IyoqCsuWLVN3XGqRDR08kJRATyjSdCnvlPz8EmanAuanPGanPGanmuqenyAImH7kOkrfcClSqSDA79gNeDRrAJFIVMXV0fuITUMlmjx5MiQSCTZt2gQAmD9/Pvz8/BAaGor09HR89913CAwMRIcOHZCWloYZM2bgwIEDGDNmDADg5MmTGDlyJCIiInDq1CmsXLkSPXv2hK2trUp1bdiwAYsXL8acOXPg4+ODIUOG4IsvvsDBgwexZs0aLFq0CEeOHJHZJiQkBAMHDsTYsWMxaNAghecSBAF5eXkq1asoiUSCKC0LRKUXACiokjlrFGanGuanPGanPGanmmqcX+rDJ7jzJFfumKR/cnA6/i462phXUVUvSSQSmf9SxVS3/ARBUKjxZNNQSXJzcxEVFYXff/9d+iJ/5cqV6NOnD1JSUqClpYWAgAAMGzYMAGBlZYUOHTogMTFRug8TExP4+vpCLBZj3Lhx2LJlC27duqVy09C9e3d89tlnAICePXvit99+w5QpUyASiTBs2DD4+PiU2cbExARisRhGRkYwMjJSeK6ioiLEx8erVG+FaFlX3VxERESVJEeSr9C467eTYCrJquRqypeWlqaReWuK6pSfjo7OW8ewaagk58+fR506dWRe4NvZ2cHY2BgpKSno2bMndHR0EBoaisTERCQmJiIpKQkDBw6UjreysoJYLJZ+X7t2bRQXF6tcm5WVlfRrPT091K9fX9ph6unpoahIfadptbW1YW9vr7b9ySORSNAm7T4sLCygq6tbJXPWFAUFBcjMzGR2SmJ+ymN2ymN2qqnu+dnACPsvvn1cq8b2cNLAmYa0tDTY2NhAX1+/SueuCapbfklJSQqNY9NQSd70C6ikpAQlJSW4ffs2RowYAXd3d7Ru3Rre3t7YsWOHzFhtbe0y26vjMWu1asn+b9fSqrz74UUikVJPblKWMQphbWJQpXPWBHl5YuRlMjtlMT/lMTvlMTvVVPf8bE0NEXgipsxN0K+zNzdCTyfN3dOgr69fLbN7V1SX/BT9+eHTkypJp06d8Pz5c6SkpEiXJSUlITc3F7a2tjh8+DDc3NywevVqjBw5Ei4uLrhz5w6fvUxEREQQiURY3r8ltN7wgk5LJMKyfi15EzRVGTYNleTV05B8fX1x8+ZN3Lx5E76+vnBzc4ODgwNMTEyQkJCAmzdvIjU1FcuWLUNsbCwKCws1XfobGRgYICUlBc+ePdN0KURERDWep/PHODC6C+zNZe8ltDc3woHRXfg5DVSleHlSJVq+fDkWLVoEb29viMVi9OjRA/7+/gAALy8vxMXFwdvbG7q6unBzc4OPjw+OHz+u4arfbMSIEVi1ahXS0tKwfv16TZdDRERU43k6fwyPZg1wIeURMp9LUN9YH51s6/EMA1U5kcDrYaiSxMbGAgCcnZ2rZL68vDzEx8fDycmpWlwj+C5hdqphfspjdspjdqphfspjdqqpbvkp+nqNlycREREREZFcvDzpHTRo0CCkpqa+cf2WLVvQunXrKqyIiIiIiGoyNg3voPXr18v9LIUPP/ywCqshIiIiopqOTcM7qH79+pougYiIiIjeI7yngYiIiIiI5GLTQEREREREcrFpICIiIiIiudg0EBERERGRXGwaiIiIiIhILjYNREREREQkF5sGIiIiIiKSi00DERERERHJxaaBiIiIiIjkYtNARERERERysWkgIiIiIiK52DQQEREREZFcbBqIiIiIiEguNg1ERERERCQXmwYiIiIiIpKLTQMREREREcnFpoGIiIiIiORi00BERERERHKxaSAiIiIiIrnYNBARERERkVxsGoiIiIiISC42DUREREREJBebBiIiIiIikotNQzUVEhICLy8vlfeTm5uLQ4cOKTQ2IyMDjo6OyMjIKHd9eHg43N3dVa6JiIiophIEAeeTH2JfdCrOJz+EIAiaLolILWppugCqXD///DOuXr0KDw+Pt461sLDAxYsXYWpqWvmFERER1TARsenwPXoDyY9zpMvszIywvH9LeDp/rMHKiFTHMw01XEXe4RCLxahbty7EYnElVkRERFTzRMSmY9iO8zINAwAkP87BsB3nERGbrqHKiNSDZxqUlJGRgR49emDVqlVYsWIFJBIJPDw84Ofnh9DQUMTHxyM7OxuJiYlYv349mjdvjuDgYBw7dgzZ2dlo164d5s2bBwsLCwBAUlIS5s6di7i4ODRv3hz29vbSucLDw7F+/XqcOXNGuszLywtt2rTBt99+CwD46aefsGvXLjx9+hQtW7bE/Pnz8ddff2H9+vUAAEdHRyQkJCh0TJGRkbCyssLDhw8xZ84cXLt2Dba2tujatau6Y1S7bOjggaQEekKRpkt5p+TnlzA7FTA/5TE75TE71agzP0EQMP3IdZS+4Y26UkGA37Eb8GjWACKRSKW5iDSFTYOK1q9fj7Vr16K4uBizZs1C7dq1UatWLURGRmL+/Plo0aIFbG1tMW/ePNy4cQPLly+HiYkJVq1ahUmTJuHXX39FcXExxo8fj9atW2PRokW4cuUKlixZgpYtWypUw759+7B+/XoEBQWhSZMmWLNmDb777jvs2bMHiYmJiI6ORkhISIWP7bvvvoOBgQHCwsKQmJiIOXPm4IMPPqjQPgRBQF5eXoXnVoZEIkGUlgWi0gsAFFTJnDUKs1MN81Mes1Mes1ONmvJLffgEd57kyh2T9E8OTsffRUcbc5Xmqg4kEonMf6liqlt+giAo1MyyaVDRzJkz0bp1awAvX2SvWrUKI0aMgLm5OUaMGAEAyM7OxuHDh7Flyxa0a9cOALBq1Sp069YNly5dQklJCZ49e4b58+fDwMAAdnZ2iIqKwpMnTxSqYf/+/fD29kafPn0AAIGBgdi2bRsAwMDAANra2qhbt26FjutVs3H27FnUr18fjRo1wq1bt/D7779XaD9FRUWIj4+v0DYq0bKuurmIiIgA5EjyFRp3/XYSTCVZlVxN1UlLS9N0Ce+06pSfjo7OW8ewaVDR62cDmjVrhidPnuDp06ewtLSULk9LS0NpaSmaN28uXWZiYgJbW1skJyejuLgYNjY2MDAwkK53dnbGn3/+qVANqampaNq0qfR7c3Nz+Pr6qnJYSEpKgomJCerXry9TU0WbBm1tbZlLrSqTRCJBm7T7sLCwgK6ubpXMWVMUFBQgMzOT2SmJ+SmP2SmP2alGnfnZwAj7L759XKvG9nCqIWca0tLSYGNjA319fU2X886pbvklJSUpNI5Ng4q0tbWlX5eWlgIAtLS0ZH4BvemXUUlJiXSbf9+w/Pp+yztlVFxcLP26Vq3K+d8oryZFiUQimWaoshmjENYmBlU6Z02QlydGXiazUxbzUx6zUx6zU40687M1NUTgiZgyN0G/zt7cCD2datY9Dfr6+vzZU0F1yU/Rn0k+PUlFr196c+vWLdSrVw8mJiYyYxo0aIBatWrhv//9r3TZ06dPcefOHdja2qJRo0ZIS0tDTs7//bJ5fb/a2tp48eKF9HtBEGQ+S8Ha2hq3b9+W2Xe7du2QkZGh9C8nBwcHZGdn486dO+XWRERERC+JRCIs798SWm/4N1dLJMKyfi1rVMNA7x82DSpavHgxYmNj8Z///Ac//PADRo0aVWZM7dq1MXToUAQFBeHq1au4ffs2Zs6ciY8++ggdO3ZEhw4dYGFhgTlz5iA5ORnh4eH47bffpNs3a9YMz549w65du3D37l0sXboU2dnZ0vVeXl7YsWMHTp8+jdTUVMybNw9WVlawsrKCvr4+Hj169MYPbHsTOzs7tG/fHrNnz8bt27dx+vRp/PLLL8oHRUREVIN5On+MA6O7wN7cSGa5vbkRDozuws9poHcemwYV9enTBxMmTMD333+PoUOHYvz48eWO8/X1RYcOHTBlyhSMGDECurq6+Pnnn6GjowNtbW1s3rwZ2dnZ8PT0xN69e2WaDxsbG/j6+iI0NBQeHh4QBAGffPKJdP3AgQMxduxYLFiwAIMGDUJBQQGCg4MBAL169UJpaSn69u2Lx48fV+jY1q5diw8++ACff/451qxZo5ZPqCYiIqqpPJ0/xm2/gTg7qTf2fNEZ53x647bfQDYMVCOIBH6+uVL+/ZkGVFZsbCyAlzdQV4W8vDzEx8fDycmpWlwj+C5hdqphfspjdspjdqphfspjdqqpbvkp+nqNZxqIiIiIiEguPj3pPdK2bVsUFha+cf3x48dlHrFKRERERASwaVCalZUVEhISNF1GhRw8eFD6iNfy1KtXrwqrISIiIqJ3BZuG90iDBg00XQIRERERvYN4TwMREREREcnFpoGIiIiIiORi00BERERERHKxaSAiIiIiIrnYNBARERERkVxsGoiIiIiISC42DUREREREJBebBiIiIiIikotNAxERERERycWmgYiIiIiI5GLTQEREREREcrFpICIiIiIiudg0EBERERGRXGwaiIiIiIhILjYNREREREQkF5sGIiIiIiKSi00DERERERHJxaaBiIiIiIjkYtNARERERERysWkgIiIiIiK52DQQEREREZFcSjcN9+/fR25uLgDgypUrWLhwIY4dO6a2woiIiIiIqHpQqmk4deoUevfujZiYGKSnp2PcuHG4fPkyAgICsHv3bnXXSEREREREGqRU07Bx40Z89dVXaN++PY4ePYr69evj+PHjWLJkCX755Rd11ygjPDwc7u7uCo09ceIEHj9+DAAICQmBl5dXZZYGAMjIyICjoyMyMjIqfa6qlJubi0OHDmm6DCIionIJgoDzyQ+xLzoV55MfQhAETZdEVKPUUmaj5ORkrF+/HlpaWrh06RK6du0KLS0ttGjRAvfu3VN3jTL69OmDbt26vXXcvXv3MHXqVERGRgIAxo4dWyVNQ031888/4+rVq/Dw8NB0KURERDIiYtPhe/QGkh/nSJfZmRlhef+W8HT+WIOVEdUcSp1pqFOnDnJycpCTk4ObN2+iQ4cOAID09HSYmJios74y9PT0YGpq+tZx/36HoXbt2pVeW03Gd2yIiKg6iohNx7Ad52UaBgBIfpyDYTvOIyI2XUOVEdUsSp1p6Nq1KwIDA1G7dm0YGRmhY8eO+M9//oP58+crdBbglczMTCxYsACXL1+GmZkZBg0ahIkTJ8LPzw+xsbE4evQotLW18euvv2LJkiU4duwYLl++jPXr1+PMmTMAgDVr1iA8PBzPnz9H8+bNERgYiEaNGqFHjx4AgB49emDp0qW4d+8eoqKisGvXLoSHhyMiIgJubm7YvXs3SkpKMHjwYPj5+UEkEgF4+c76tm3b8OLFCwwaNAgJCQnw9PTEoEGDKpRVdnY2Vq1ahcjISBQUFMDd3R0BAQEwNjYGAERGRiIkJATJycnQ1dVFly5dEBQUhNq1ayMkJARpaWkwNDTE0aNHoauri7Fjx+Lrr79WaO6kpCQsXboUN27cQHFxMZydnREUFAQ7OztcvXoV/v7+mDRpEtauXYvCwkKMHz8eLVq0wNy5c/Hw4UP06tULy5Ytw6FDh7B+/XoAgKOjIxISEiqUQVXKhg4eSEqgJxRpupR3Sn5+CbNTAfNTHrNTHrN7+YbW9CPXUfqGN7ZKBQF+x27Ao1kD6b/vRKQcpZqGuXPnYt26dbh79y5CQ0Oho6OD69evo0WLFvD19VVoH4IgYPLkyWjcuDEiIiKQlZWFwMBAiEQi+Pv747PPPsOuXbswcOBArFixArNmzYKFhYXMPk6dOoX9+/djw4YNqFevHtauXQt/f38cPHgQYWFhGDp0KMLCwuDg4IAtW7bIbBsdHQ1zc3Ps3bsXsbGx8PPzQ5cuXdCxY0ccOXIEwcHBWLx4Mezt7bF69Wr89ddf8PT0rHBWkydPhkQiwaZNmwAA8+fPh5+fH0JDQ5Geno7vvvsOgYGB6NChA9LS0jBjxgwcOHAAY8aMAQCcPHkSI0eOREREBE6dOoWVK1eiZ8+esLW1lTtvaWkpvvnmG3To0AHz5s1DTk4OFi5ciJUrV0prefToEU6fPo1du3bh7NmzWLVqFRo3boxly5bh6dOn+Pbbb9GrVy/06dMHiYmJiI6ORkhISIWOXxAE5OXlVTg3ZUgkEkRpWSAqvQBAQZXMWaMwO9UwP+UxO+W959mlPnyCO09y5Y5J+icHp+PvoqONucxyiUQi819SHLNTTXXLTxAEhZpqpZoGPT09+Pn5ySz79ttvK7SPK1eu4P79+wgLC4OWlhYaNmwIX19f+Pv7w8fHB/7+/li0aBGuXr0KJycnDB8+vMw+7t27B21tbdSvXx/169fH3LlzkZKSAgDSS5hMTU2hp6dXZtuSkhIEBQXB0NAQDRs2xM8//4zY2Fh07NgRe/bswejRo/HZZ58BAJYvX46uXbtW6PgA4Pbt24iKisLvv/8ufZG/cuVK9OnTBykpKdDS0kJAQACGDRsGALCyskKHDh2QmJgo3YeJiQl8fX0hFosxbtw4bNmyBbdu3Xpr05Cfn4/PP/8cI0eOhIGBAQDA09MTW7dulY4pKiqCr68vbG1tUb9+faxYsQKjRo1CixYtAABOTk5ISUlBr169YGBgAG1tbdStW7dCGRQVFSE+Pr5C26hEy7rq5iIiIo3KkeQrNO767SSYSrLKXZeWlqbGit4vzE411Sk/HR2dt45RqmkAXr4g3rFjB1JTU/HDDz/g9OnTaNSoEdq0aaPQ9snJyXj27BlatWolXVZaWor8/Hw8ffoUHh4e+PXXX3HhwgWcPHmy3H307dsXv/zyC3r06IEWLVqgZ8+eGDJkiELzm5mZwdDQUPq9oaEhiouLAQAJCQkYP368dJ2xsfFbX6SXJyUlBXXq1JHZ1s7ODsbGxkhJSUHPnj2ho6OD0NBQJCYmIjExEUlJSRg4cKB0vJWVFcRisfT72rVrS+uUx8DAACNGjMChQ4dw69YtpKSkIC4uDubmsu+0NGjQAACkjZWlpaV0nZ6eHgoLCyt83K/T1taGvb29SvtQlEQiQZu0+7CwsICurm6VzFlTFBQUIDMzk9kpifkpj9kpj9kBNjDC/otvH9eqsT2cyjnTkJaWBhsbG+jr61dShTUTs1NNdcsvKSlJoXFKNQ23bt3CiBEj0KJFC9y6dQuFhYWIj4/H0qVLsWHDBoXelS8uLkbDhg2xcePGMuuMjIzw4sUL3L17FwBw7do16Yvb19WtWxcnTpzApUuXcPbsWWzbtg0HDhxQ6NGg5XVUr272FYvFZW78VeZG4Dd1bSUlJSgpKcHt27cxYsQIuLu7o3Xr1vD29saOHTtkxmpra7+xTnlevHiBIUOG4IMPPoC7uzv69euHlJQUbN++XWZcrVqyPwJaWur9kHCRSCQ901EVjFEIaxODKp2zJsjLEyMvk9kpi/kpj9kpj9kBtqaGCDwRU+Ym6NfZmxuhp9Ob72nQ19d/b/NTFbNTTXXJT9H7fZR6hbhq1SqMHTsWu3btkr6oXbRoEUaNGqXwNe+2tra4f/8+TE1NYW1tDWtra2RkZCA4OBgikQjr1q2DiYkJAgICsGzZMjx58qTMPs6dO4ewsDB069YNCxYswOHDh5GWloa///5bpRue7O3t8b///U/6fW5uLu7cuVPh/dja2uL58+fSS6aAl91cbm4ubG1tcfjwYbi5uWH16tUYOXIkXFxccOfOHbU8qSgqKgqPHj3Czp07MW7cOHTo0AH3799Xet+8gYyIiKobkUiE5f1bQusN/0ZpiURY1q8l/w0jUgOlmoZbt26V+7z+UaNGITk5WaF9dOrUCZaWlpg5cyYSEhJw7do1zJ07F/r6+oiLi8OePXsQGBiIzz//HFZWVliyZEmZfZSWlmLFihU4deoUMjIyEB4eDn19fZnTPbdv38aLFy8qdHxeXl7YuXMn/vjjDyQnJ2P27NnIy8ur8C8dOzs7dOnSBb6+vrh58yZu3rwJX19fuLm5wcHBASYmJkhISMDNmzeRmpqKZcuWITY2VuVLgoCX90Lk5eXh9OnTyMjIQFhYGHbv3q30vvX19fHo0aMa96F1RET0bvN0/hgHRneBvbmRzHJ7cyMcGN2Fn9NApCZKXZ6kra2N3NyyTyvIzMxU+NossViM0NBQBAUFYdiwYTAwMMCnn36K6dOn44svvkD//v3RsmVLAMC8efMwfPjwMo2Ku7s7pkyZgqVLlyIrK0t6udOrx5kOGDAAU6dOxYwZMyp0fH379sWdO3cwb948FBQUYPjw4bC0tCz3UqG3Wb58ORYtWgRvb2+IxWL06NED/v7+AF42J3FxcfD29oauri7c3Nzg4+OD48ePV3ief3N1dYWPjw8WLFiAgoICODo6IjAwEHPmzMHDhw8rvL9evXph37596Nu3L86cOQMzMzOVayQiIlIHT+eP4dGsAS6kPELmcwnqG+ujk209nmEgUiORoMT1KnPnzsX9+/exdu1adO3aFUeOHEFhYSGmTZuGZs2alXtW4F0SFRWFBg0aSB/xWlxcjHbt2mHDhg1o27athqt7d8TGxgIAnJ2dq2S+vLw8xMfHw8nJqVpcI/guYXaqYX7KY3bKY3aqYX7KY3aqqW75Kfp6TanLk3x9ffHixQu0a9cOEokEgwYNQr9+/SAWizFr1ixldlmtnD59GlOmTEFcXBzu3LmDpUuXwtDQUPooUiIiIiKi94lSlyeJRCLs27cPly9fRlxcHEpLS+Hg4IDOnTur/ek7mjBlyhQsXLgQY8aMQUFBAVxdXbF161bo6uqibdu2cu8LOH78OOrXr1/pNQ4aNAipqalvXL9lyxa0bt260usgIiIioppPqabBw8MD69atQ/v27dG+fXt116RxhoaGWLFiRbnrDh48iNLS0jduW69evcoqS8b69etRVFT0xvUffvhhldRBRERERDWfUk2DRCIp91OW3wflfV6EJlTF2QwiIiIiIkDJpuHLL7/Et99+i1GjRuHjjz8u00C4ubmppTgiIiIiItI8pZqGNWvWAACCgoLKrBOJRIiPj1etKiIiIiIiqjaUahoiIyPVXQcREREREVVTSjUNlpaW6q6DiIiIiIiqKaXvaZBn586dShVDRERERETVj1rONBQXF+POnTv4+++/MXr0aLUURkRERERE1YNSTcPSpUvLXb5hwwY8ePBApYKIiIiIiKh6UevHNw8cOBAnTpxQ5y6JiIiIiEjD1No0REdHQywWq3OXRERERESkYWq7ETo3NxcJCQkYOXKkykUREREREVH1oVTTUL9+fYhEIpll2tra+OKLLzBgwAC1FEZERERERNWDUk3DlClT8NFHH0FLS/bqpuLiYsTFxcHFxUUtxRERERERkeYpdU9Djx498OzZszLLMzIy4OXlpWpNRERERERUjSh8pmH37t3Yvn07AEAQBAwePLjMmYbnz5+jfv366q2QiIiIiIg0SuGmYdCgQXj69CkEQcCGDRvw6aefonbt2jJjateujd69e6u9SCIiIiIi0hyFmwZ9fX1MnjwZACASifDVV19BX1+/0gojIiIiIqLqQakboSdPnozi4mI8fPgQJSUlAF5eslRYWIjY2Fg+QYmIiIiIqAZRqmm4ePEifH198eTJkzLr9PT02DQQEREREdUgSj09ac2aNWjSpAk2b94MPT09rF+/HrNnz4ahoSFWrlyp7hqJiIiIiEiDlDrTkJSUhCVLlqBx48ZwcnKCgYEBvLy8YGBggG3btqFnz57qrpOIiIiIiDREqTMNYrEYRkZGAABra2v8/fffAIB27dohOTlZfdUREREREZHGKdU0NGrUCGfOnAEANGzYENevXwcAPHjwQH2VERERERFRtaDU5Unjx4/HlClToK2tjX79+iEkJATjx49HQkIC2rVrp+4aiYiIiIhIg5Q609CzZ0+EhYWhRYsWsLCwwNatWyEWi9GjRw8sXLhQ3TXKFRISAi8vL5X24eXlhZCQEDVVBPj5+cHPz09t+6suLl++zMvPiIjeQ4Ig4HzyQ+yLTsX55IcQBEHTJRFRFVPqTAMANG3aFABQWFiINm3aoE2bNmorqqqFhIRAW1tb02VUe97e3ti5cyfs7Ow0XQoREVWRiNh0+B69geTHOdJldmZGWN6/JTydP9ZgZURUlZQ60wAAe/fuhbu7O1q0aIG7d+9i/vz52LhxozprqzImJiaoXbu2pssgIiKqViJi0zFsx3mZhgEAkh/nYNiO84iITddQZURU1ZQ603D06FGsXr0ao0ePxtatWwG8vCF61apV0NPTw9ixY9Va5OuSkpIwd+5cxMXFoXnz5rC3t5euu3btGpYsWYKkpCRYW1tj8uTJ+OSTTwAA9+/fR0BAAKKjo6Gnp4c+ffrAz88P2tra8PLyQps2bfDtt99KLyuKi4tDVlYW9u7dC1NTUwQFBSEyMhIGBgb45JNPMHPmTOjp6UnnXbRoEVJSUtC9e3cAgL6+vkLHk5ubi8WLF+PcuXPIycmBlZUVZsyYIX1sraOjI9atW4fg4GDcv38fPXr0wLRp0zBnzhzExMSgadOmWLt2LT788EMAwNmzZxEcHIzk5GRYWVlh6tSp6N27NwDIHCcAZGRkoEePHoiMjISVlRUcHR2xYsUKbNmyBWlpaXBxccHy5cvRoEEDuLu7AwC+/PJLTJ48WbqP6iYbOnggKYGeUKTpUt4p+fklzE4FzE95zE55lZ2dIAiYfuQ6St9wKVKpIMDv2A14NGsAkUik9vmJqHpRqmnYvn075syZA09PT2zfvh3AyxeTBgYG2LJlS6U1DYWFhRg/fjxat26NRYsW4cqVK1iyZAlatmyJrKwsTJgwAdOmTUPnzp3x3//+F35+fjAzM0Pr1q0RFBQEAwMDHDp0CI8fP8aUKVPQsGFDjBo1qsw8hw8fxoYNG2Bubg4bGxt8++23KCoqwt69e1FQUIBFixZh4cKFWLJkCZ48eYIJEyZg+PDhWLNmDY4fP47169fD09NToWNavHgxUlNTsX37dujr62Pr1q2YM2cOunTpAh0dHQBAcHAwli1bBolEgnHjxiEqKgpz5syBv78/pkyZgi1btiAgIACXL1/Gt99+ixkzZqBr1644d+4cpk2bhv3796NZs2YK1RMSEoKgoCCYmZnhu+++w7p167B69WocPHgQ7du3R0hICDp27Kjw/zNBEJCXl6fweFVIJBJEaVkgKr0AQEGVzFmjMDvVMD/lMTvlVWJ2qQ+f4M6TXLljkv7Jwen4u+hoY672+SubRCKR+S8pjtmpprrlJwiCQo2/Uk1DamoqWrduXWZ527ZtK/VG6P/85z949uwZ5s+fDwMDA9jZ2SEqKgpPnjzB7t270aFDB3zxxRcAXn5+RHx8PHbs2IHWrVvj3r17aNq0KerXrw9ra2v8+OOPqFOnTrnzODs7S99ZT09Px+nTpxEVFSX9bIqgoCB4eHjA398fJ06cgKmpKWbOnAmRSIRvv/0Wf/75p8LH5ObmhjFjxsDBwQEAMHbsWISFheHx48ewsLAA8PJegubNmwMAnJycYGtri88++wwA0Lt3b9y+fRsAsHv3bnzyySfw9vYGANja2uLmzZvYvn071qxZo1A9Y8aMQfv27QEAI0aMwO7duwEApqamAABjY+MKXcpVVFSE+Ph4hcerTMu66uYiIqrBciT5Co27fjsJppKsSq6m8qSlpWm6hHcWs1NNdcrv1RvV8ijVNJibmyM1NRUNGjSQWR4dHY169eops0uFJCUlwcbGBgYGBtJlzs7O+PPPP5GSkoKzZ8/C1dVVuq6oqAi2trYAgHHjxmH27Nk4deoUunTpgj59+qBJkyblzmNpaSn9Ojk5GaWlpejSpYvMmNLSUty5cwdJSUlo3LixTIfm7OyscPfo4eGB06dP48CBA0hJScH//vc/AEBJSYl0zOs56+npydSnp6eHwsJCaa2ff/65zP5dXV3x66+/KlQL8LLZesXQ0BBFRaqd8tbW1pa5hKwySSQStEm7DwsLC+jq6lbJnDVFQUEBMjMzmZ2SmJ/ymJ3yKjs7Gxhh/8W3j2vV2B5O7+iZhrS0NNjY2Ch8STG9xOxUU93yS0pKUmicUk3D8OHDsXDhQvj7+wMAUlJScPHiRaxbtw6jR49WZpcK+/dj3l499ai4uBj9+/fHN998I7O+Vq2XhzhgwAC0b98ep0+fxrlz5zBlyhR8/fXXmDZtWpk5Xv/lW1JSAiMjo3JfeL+6j6C8mhRtGmbNmoXo6GgMHDgQI0aMQN26dTF8+HCZMWKxWOZ7La3y718v7x+N0tJSlJaWljv+9cbk9drVSSQSyTR5lc0YhbA2MajSOWuCvDwx8jKZnbKYn/KYnfIqOztbU0MEnogpcxP06+zNjdDT6d2+p0FfX58/e0pidqqpLvkp+vdXqacnff311+jTpw++//57SCQSTJgwAYsXLy73Rbs6NWrUCGlpacjJ+b9fYK8ufbG1tcWdO3dgbW0t/RMZGYmjR48CANauXYvHjx9jxIgR2Lx5M6ZOnYo//vjjrXPa2toiJycHIpFIut/8/HysWLEChYWFaNSoEeLi4mRegCt6OU5ubi6OHTuGtWvXYsqUKejVqxeys7MBlG1EFGFra4uYmBiZZdHR0dKzLTo6Onjx4oV03d27dys8BxERvR9EIhGW928JrTe8oNASibCsX8t3umEgIsUp3DSsWLFC5obW77//HleuXEFYWBgOHDiAK1euICAg4I3vgqtDhw4dYGFhgTlz5iA5ORnh4eH47bffAAAjR47ErVu3sHbtWqSlpeHo0aNYs2YN6tevD+Dl2ZCFCxfi9u3bSExMxJ9//vnGy5NeZ2dnh86dO2PGjBm4efMm/ve//8Hf3x95eXmoU6cO+vbtC4lEgsWLFyMlJQVbt27F9evXFToeHR0d6Ovr448//kBGRgYuXLggvSfk1SVHFeHt7Y2TJ09ix44dSEtLw88//4xTp05hxIgRAIBmzZrhxIkTuHnzJm7evIng4OAK7d/AwACJiYkyTRsREdVcns4f48DoLrA3N5JZbm9uhAOju/BzGojeIwq/wv/pp5/KXHLz3Xff4cMPP4SLiwsMDQ3VXty/aWtrY/PmzcjOzoanpyf27t0rffqRpaUlNm3ahAsXLqBfv35Yt24d/Pz8MGDAAADA/PnzYW5uDi8vLwwbNgz16tXDnDlzFJp3xYoVsLKygre3N8aMGQNbW1vpjcXGxsbYunUrYmNjMXDgQPznP//BwIEDFdqvjo4OVq5ciZMnT6Jv375YtmwZJk6ciLp16yp183Dz5s2xYsUK7N27F/369cOvv/6KdevWSW9sHjNmDJo0aYIvvvgC06dPx6RJkyq0fy8vL6xYsUKtn55NRETVm6fzx7jtNxBnJ/XGni8645xPb9z2G8iGgeg9IxIUvA6mcePGuHTpEszMzKTLXF1dceTIkTI3RBMBQGxsLICXN4ZXhby8PMTHx8PJyalaXCP4LmF2qmF+ymN2ymN2qmF+ymN2qqlu+Sn6eq3yriUiIiIiIqIaQamnJ5FiFi9ejIMHD75x/YQJEyr1xnEiIiIiInWoUNPAJyRUzMSJE6UfNlceY2PjKqyGiIiIiEg5FWoaFi1aJPNZAEVFRVi5cmWZTwheunSpeqp7x5mamko/SZmIiIiI6F2lcNPg5uaGrCzZj4l3dXXF06dP8fTpU7UXRkRERERE1YPCTcOuXbsqsw4iIiIiIqqm+PQkIiIiIiKSi00DERERERHJxaaBiIiIiIjkYtNARERERERysWkgIiIiIiK52DQQEREREZFcbBqIiIiIiEguNg1ERERERCQXmwYiIiIiIpKLTQMREREREcnFpoGIiIiIiORi00BERERERHKxaSAiIiIiIrnYNBARERERkVxsGoiIiIiISC42DUREREREJBebBiIiIiIikotNAxERERERycWmgYiIiIiI5GLTQEREREREcrFpICIiIiIiudg0aEhGRgYcHR2RkZGh9n27u7sjPDy8wttdvXoVjo6Ob1wfEhICLy8vVUojIqJKJAgCzic/xL7oVJxPfghBEDRdEhHVELU0XQBVH66urrh48aKmyyAiIiVExKbD9+gNJD/OkS6zMzPC8v4t4en8sQYrI6KagGcaSEpHRwd169bVdBlERFRBEbHpGLbjvEzDAADJj3MwbMd5RMSma6gyIqopeKZBw37//Xfs3LkTubm56NevHwICAqCjo4OwsDBs27YNGRkZqF27Nvr06YOAgACIxWL4+fnB2NgYDx8+xNmzZ2FiYoJp06bBw8OjzP5jYmLg7e2NOXPmYMiQIXJruXr1Kr788kskJCQAAJKSkjB37lzExcWhefPmsLe3r4wI1CobOnggKYGeUKTpUt4p+fklzE4FzE95zE55r7LLzCvG9CPXUfqGS5FKBQF+x27Ao1kDiESiKq6SiGoKNg0aduDAAaxduxYlJSWYNWsWNm/ejLZt22LRokVYuXIlmjRpglu3bmHmzJlo3749evfuDQDYvXs3vvvuO0yfPh07d+7EvHnz0KNHDxgZGUn3nZqaigkTJuDbb799a8Pwb4WFhRg/fjxat26NRYsW4cqVK1iyZAlatmxZof0IgoC8vLwKbaMsiUSCKC0LRKUXACiokjlrFGanGuanPGanPC0L7L/2AHee5ModlvRPDk7H30VHG/MqKqz6k0gkMv8lxTE71VS3/ARBUOgNBTYNGjZ79my0atUKAPDdd99h1apV6N69OxYvXixtEKysrPDTTz8hMTFRuszR0RFff/21dLudO3ciMTFR+qL+n3/+wbhx4zBs2DCMHTu2wnX95z//wbNnzzB//nwYGBjAzs4OUVFRePLkSYX2U1RUhPj4+ArPrzQt66qbi4ioGsiR5Cs07vrtJJhKsiq5mndPWlqapkt4ZzE71VSn/HR0dN46hk2Dhrm4uEi/btKkCf755x80aNAAenp6CA4ORlJSEhISEnDnzh106tRJOtbGxkb6taGhIQCguLhYuiw4OBjFxcX46KOPlKorKSkJNjY2MDAwkC5zdnbGn3/+WaH9aGtrV9llTRKJBG3S7sPCwgK6urpVMmdNUVBQgMzMTGanJOanPGanvFfZWVrqYb8C41s1tocTzzRISSQSpKWlwcbGBvr6+pou553C7FRT3fJLSkpSaBybBg3T0vq/e9FfPRrvr7/+wvfffw8PDw907twZPj4+WLBggcx22traZfb1+qP1unXrhjZt2mDdunX49NNPYWpqWuHa/v2ovvLmfBuRSCTTeFQ2YxTC2sSgSuesCfLyxMjLZHbKYn7KY3bKe5Vd26YNEWRmVOYm6NfZmxuhpxPvaSiPvr4+f/aUxOxUU13yU/T3Ap+epGF///239OubN2/io48+wpEjRzB48GAsXLgQQ4cOhZ2dHdLT0yv0vG13d3eMGjUKH374IVauXFnhuho1aoS0tDTk5PzfP0JVepkREREpRCQSYXn/ltB6wz/8WiIRlvVryYaBiFTCpkHDgoKCEBMTg0uXLiE4OBje3t4wMTFBdHQ0EhISkJiYCD8/P2RlZaGwsLBC+xaLxQgICEBERASio6MrtG2HDh1gYWGBOXPmIDk5GeHh4fjtt98qtA8iIqoans4f48DoLrA3N5JZbm9uhAOju/BzGohIZbw8ScNGjBiBiRMnoqioCMOGDcPo0aPxzz//wN/fH8OHD4ehoSG6du2KESNGKPVOf9u2bdG7d28sXLgQBw8ehFgsVmg7bW1tbN68GQEBAfD09ISjoyNGjRqFW7duVbgGIiKqfJ7OH8OjWQNcSHmEzOcS1DfWRyfbejzDQERqwaZBQ6ysrKSfhzBy5EiZdfXq1cO2bdveuO2yZcvKLHu1LwA4c+aMzLrg4GCFamrbtq3Mfho0aIAdO3YotC0REWmeSCRCF7sPNV0GEdVAvDyJiIiIiIjk4pmG98Tjx4/Rs2dPuWMqet8DEREREb0f2DS8J0xMTHDo0CFNl0FERERE7yA2De8JsVgMa2t+WjIRERERVRzvaSAiIiIiIrnYNBARERERkVxsGoiIiIiISC42DUREREREJBebBiIiIiIikotNAxERERERycWmgYiIiIiI5GLTQEREREREcrFpICIiIiIiudg0EBERERGRXGwaiIiIiIhILjYNREREREQkF5sGIiIiIiKSi00DERERERHJxaaBiIiIiIjkYtNARERERERysWkgIiIiIiK52DQQEREREZFcbBqIiIiIiEguNg1ERERERCQXmwYiIiIiIpKLTQMREREREclVS9MFEBERvY0gCLiQ8gj3n+ehfh0DdG5YDyKRSNNlERG9N9g0qMDR0RE7d+5E27Ztld5HRkYGevTogcjISFhZWamxOiKimiEiNh2+R28g+XGOdJmdmRGW928JT+ePNVgZEdH7g5cnaZiFhQUuXrwICwsLTZdCRFTtRMSmY9iO8zINAwAkP87BsB3nERGbrqHKiIjeLzzToGFisRh169bVdBk1RjZ08EBSAj2hSNOlvFPy80uYnQqYn/LkZScIAqYfuY5SQSh321JBgN+xG/Bo1oCXKhERVbIa1zQEBATgn3/+waZNm6TLgoKC8Pz5c3z//fdYsGABLl++DDMzMwwaNAgTJ06EWCwGAISFhWHbtm3IyMhA7dq10adPHwQEBEAsFsPPzw8AEBcXh6ysLOzdu1e6/yNHjmDx4sW4dOkSatV6GenJkyexdOlSnD17Vu4/Zv++PMnR0RErVqzAli1bkJaWBhcXFyxfvhwNGjQAANy8eRNLly5FXFwcPvroI0yZMgV9+/YFAERHR2PFihWIj4+Hqakpvv76a4wYMQIA4OfnBzMzM9y7dw9nz56FpaUlVq1ahZMnT2L37t0wMDCAv78/PvvsMwBAZmam3KwUJQgC8vLyKrSNsiQSCaK0LBCVXgCgoErmrFGYnWqYn/LekF3qwye48yRX7qZJ/+TgdPxddLQxr8QCqyeJRCLzX6oY5qc8Zqea6pafIAgKvfFS45qGvn37Yvz48cjNzYWhoSFKS0tx8uRJLFq0CJMnT0bjxo0RERGBrKwsBAYGQiQSwcfHB1FRUVi0aBFWrlyJJk2a4NatW5g5cybat2+P3r17AwAOHz6MDRs2wNzcHDY2NtI5e/Togblz5+LKlSvo1KkTAODEiRP47LPPlHr3KyQkBEFBQTAzM8N3332HdevWYfXq1Xj8+DHGjh2LAQMGYPHixfjvf/8LX19f2NnZQVtbG6NHj4a3tzcWL16MmJgYLFiwAObm5ujVqxcAYMeOHZg9ezamTZsGf39/jB49Gp988gn279+PXbt2Yd68efjkk08gEonkZlURRUVFiI+Pr3AGStOyrrq5iKhS5UjyFRp3/XYSTCVZlVxN9ZWWlqbpEt5pzE95zE411Sk/HR2dt46pcU1D27ZtYWxsjDNnzmDAgAG4du0aioqKIBaLcf/+fYSFhUFLSwsNGzaEr68v/P394ePjAwMDAyxevFjaIFhZWeGnn35CYmKidJmzszPc3d3LzFm7dm10794dv//+Ozp16gSJRII///wTu3btUuoYxowZg/bt2wMARowYgd27dwMAjh8/DmNjYwQEBEiPITs7G/n5+YiIiECTJk3w/fffAwAaNmyI5ORkbN26Vdo0NGvWDCNHjgQA9OvXD0uWLEFAQAD09PTg5eWFvXv34p9//kFycrLcrCpCW1sb9vb2SuVQURKJBG3S7sPCwgK6urpVMmdNUVBQgMzMTGanJOanPHnZ2cAI+y++fR+tGtvD6T0905CWlgYbGxvo6+trupx3DvNTHrNTTXXLLykpSaFxNa5p0NLSwmeffYbff/8dAwYMwIkTJ9CrVy/cuXMHz549Q6tWraRjS0tLkZ+fj6dPn6JZs2bQ09NDcHAwkpKSkJCQgDt37kjPHACApaXlG+ft168fAgICMH/+fJw7dw716tVDs2bNlDoGa+v/e7fc0NAQRUUvr/NNTU1FkyZNoKX1f/evjxkzBgCwfv16uLi4yOzH1dUV+/btk37/+tOZ9PT0YG5uDj09PQCQ/mNdWFiI5ORkuVl98MEHCh+LSCSCgYGBwuNVZYxCWJsYVOmcNUFenhh5mcxOWcxPefKyszU1ROCJmDI3Qb/O3twIPZ3e73sa9PX1+XOnAuanPGanmuqSn6K/P2tc0wC8fAHv5eWF3NxcnDp1CitXrkRCQgIaNmyIjRs3lhlvZGSECxcuwMfHBx4eHujcuTN8fHywYMECmXHy3kHs0qULSkpK8Ndff+HkyZPSewOUoa2tXe7yV/dLlKe82kpLS1FSUvLG7V9vPl5XXFwsNysioqogEomwvH9LDNtxvtybobVEIizr1/K9bhiIiKpKjXzkavPmzfHhhx9iy5YtEAQBbdq0ga2tLe7fvw9TU1NYW1vD2toaGRkZCA4OhkgkQlhYGAYPHoyFCxdi6NChsLOzQ3p6OoQ3PLXj33R0dNCrVy+cOnUKly5dkt6crE42NjZISEiQqWnq1KnYunUrbG1tERMTIzM+Ojoatra2FZ7nbVkREVUVT+ePcWB0F9iby75hYW9uhAOju/BzGoiIqkiNbBoAoE+fPvjpp5/w6aefQiwWo1OnTrC0tMTMmTORkJCAa9euYe7cudDX14dYLIaJiQmio6ORkJCAxMRE+Pn5ISsrC4WFhQrP2a9fPxw8eBAfffQRGjVqpPZj6t+/P549e4YVK1YgLS0N4eHhiIyMRMeOHTFy5EjEx8djzZo1SE1NRUREBPbs2YNRo0ZVeJ63ZUVEVJU8nT/Gbb+BODupN/Z80RnnfHrjtt9ANgxERFWoRjcNBQUF6NOnD4CXn4cQGhqK0tJSDBs2DN9++y26du2KgIAAAMDkyZNhZmaG4cOHY8yYMdDV1cWIESMq9OSftm3bSh/VWhnq1KmDzZs349q1a+jXrx+2bNmC1atXw8nJCfXr18fmzZtx4cIF9O/fH6GhofDz88PgwYMrPM/bsiIiqmoikQhd7D7EcFcbdG74Ic96EhFVMZGg6PU375hLly5h7ty5iIyMrLJ/XHJzc9GxY0ccO3ZM+rkK77PY2FgAL586VRXy8vIQHx8PJyenanFj0buE2amG+SmP2SmP2amG+SmP2ammuuWn6Ou1Gncj9KNHj3D9+nVs3rwZQ4YMqZKGQRAEnDx5En/88QdcXV3ZMBARERFRjVLjmoacnBzMnj0bLVq0kD6OtLKJRCKsXLlSelnP69q2bSv3vojjx4+jfv36lV0iEREREZHSalzTYGdnh+jo6CqfNzIystzlBw8eRGlp6Ru3q1evXmWVRERERESkFjWuaahueKkSEREREb3rauzTk4iIiIiISD3YNBARERERkVxsGoiIiIiISC42DUREREREJBebBiIiIiIikotNAxERERERycWmgYiIiIiI5GLTQEREREREcrFpICIiIiIiudg0EBERERGRXGwaiIiIiIhILjYNREREREQkF5sGIiIiIiKSi00DERERERHJxaaBiIiIiIjkYtNARERERERysWkgIiIiIiK52DQQEREREZFcbBqIiIiIiEguNg1ERERERCQXmwYiIiIiIpKLTQMREREREclVS9MFEBFRzScIAi6kPML953moX8cAnRvWg0gk0nRZRESkoBpxpiE8PBzu7u4KjT1x4gQeP34MAAgJCYGXl1dllgYAyMjIgKOjIzIyMip9rsr0enZERIqKiE2H49LD6L7xD4z65SK6b/wDjksPIyI2XdOlERGRgmpE09CnTx8cPHjwrePu3buHqVOnQiKRAADGjh2LkJCQyi6vRvh3dkREioiITcewHeeR/DhHZnny4xwM23EeR+LuaagyIiKqiBpxeZKenh709PTeOk4QBJnva9euXVkl1Tj/zq66yoYOHkhKoCcUabqUd0p+fgmzUwHzK58gCJh+5DpK3/D7o1QQEHgyFns++biKKyMiooqqdk1DZmYmFixYgMuXL8PMzAyDBg3CxIkT4efnh9jYWBw9ehTa2tr49ddfsWTJEhw7dgyXL1/G+vXrcebMGQDAmjVrEB4ejufPn6N58+YIDAxEo0aN0KNHDwBAjx49sHTpUty7dw9RUVHYtWsXwsPDERERATc3N+zevRslJSUYPHgw/Pz8pNfd/vzzz9i2bRtevHiBQYMGISEhAZ6enhg0aFCFjjE7OxurVq1CZGQkCgoK4O7ujoCAABgbGwMAIiMjERISguTkZOjq6qJLly4ICgpC7dq1ERISgrS0NBgaGuLo0aPQ1dXF2LFj8fXXXys09+PHjxEUFITz589DX18fgwcPxrRp03Dv3j306NEDISEhWLFiBR4+fIgOHTpg+fLlMDExKZOdoscsCALy8vIqlI+yJBIJorQsEJVeAKCgSuasUZidaphfGakPn+DOk1y5Y5KfvMB/s/Jgy7OYFfbqzC/PACuH+SmP2ammuuUnCIJC95hVq6ZBEARMnjwZjRs3RkREBLKyshAYGAiRSAR/f3989tln2LVrFwYOHIgVK1Zg1qxZsLCwkNnHqVOnsH//fmzYsAH16tXD2rVr4e/vj4MHDyIsLAxDhw5FWFgYHBwcsGXLFplto6OjYW5ujr179yI2NhZ+fn7o0qULOnbsiCNHjiA4OBiLFy+Gvb09Vq9ejb/++guenp4VPs7JkydDIpFg06ZNAID58+fDz88PoaGhSE9Px3fffYfAwEB06NABaWlpmDFjBg4cOIAxY8YAAE6ePImRI0ciIiICp06dwsqVK9GzZ0/Y2tq+dW4fHx+IxWL88ssvePHiBaZNm4Z69eqhW7duAIBNmzZhzZo1EAQBEydOxE8//YRp06aVyU5RRUVFiI+Pr3BGStOyrrq5iEiuHEm+QuP+kRQjLS2tcoupwZidapif8pidaqpTfjo6Om8dU62ahitXruD+/fsICwuDlpYWGjZsCF9fX/j7+8PHxwf+/v5YtGgRrl69CicnJwwfPrzMPu7duwdtbW3Ur18f9evXx9y5c5GSkgIAMDU1lf63vMuZSkpKEBQUBENDQzRs2BA///wzYmNj0bFjR+zZswejR4/GZ599BgBYvnw5unbtWuFjvH37NqKiovD7779LX+SvXLkSffr0QUpKCrS0tBAQEIBhw4YBAKysrNChQwckJiZK92FiYgJfX1+IxWKMGzcOW7Zswa1bt97aNNy+fRvR0dE4ffo0GjRoAOBlw/L6mYApU6bAxcUFANC/f3/ExsYqlN2baGtrw97eXuHxqpBIJGiTdh8WFhbQ1dWtkjlrioKCAmRmZjI7JTG/8tnACPsvvn2cuX4t2NjYQF9fv/KLqkEkEgnS0tKYnZKYn/KYnWqqW35JSUkKjatWTUNycjKePXuGVq1aSZeVlpYiPz8fT58+hYeHB3799VdcuHABJ0+eLHcfffv2xS+//IIePXqgRYsW6NmzJ4YMGaLQ/GZmZjA0NJR+b2hoiOLiYgBAQkICxo8fL11nbGys0Dv7/5aSkoI6derIbGtnZwdjY2OkpKSgZ8+e0NHRQWhoKBITE5GYmIikpCQMHDhQOt7KygpisVj6fe3ataV1ypOamgoTExNpwwAAPXv2BADpk52srf/vnXpDQ0MUFal2fbZIJIKBgYFK+6gIYxTC2sSgSuesCfLyxMjLZHbKYn7lszU1ROCJmDI3Qb/OzrQ2WtQ1gL6+PrNTErNTDfNTHrNTTXXJT9HHX1erpqG4uBgNGzbExo0by6wzMjLCixcvcPfuXQDAtWvXZF78vlK3bl2cOHECly5dwtmzZ7Ft2zYcOHAAhw4deuv85Z2aeXUDsFgsLnMzsDI3B7/p9E9JSQlKSkpw+/ZtjBgxAu7u7mjdujW8vb2xY8cOmbHa2tpvrFOe8rZTZgwRkSJEIhGW92+JYTvOl3sztJZIhIWfOEMkeq6B6oiIqCKq1SNXbW1tcf/+fZiamsLa2hrW1tbIyMhAcHAwRCIR1q1bBxMTEwQEBGDZsmV48uRJmX2cO3cOYWFh6NatGxYsWIDDhw8jLS0Nf//9t0ofJGRvb4///e9/0u9zc3Nx584dpY7x+fPn0kumgJenhXJzc2Fra4vDhw/Dzc0Nq1evxsiRI+Hi4oI7d+6o5elF1tbWePbsGTIzM6XLdu7ciUmTJr11W34IExEpw9P5YxwY3QX25kYyy+3NjXBgdBcMaGKpocqIiKgiqtWZhk6dOsHS0hIzZ87EtGnTkJOTg7lz56JDhw6Ii4vDnj17sGvXLrRo0UL69KRVq1bJ7KO0tBQrVqxA3bp14eTkhOPHj0NfXx82NjYoKSkB8PLa/g8++KBCtXl5eWHevHlo3Lgx7Ozs8MMPPyAvL6/CL6bt7OzQpUsX+Pr6Yu7cuQCABQsWwM3NDQ4ODjAxMUFCQgJu3rwJIyMj7N+/H7GxseWeVamoRo0aoV27dpgzZw58fX3x7Nkz/Pjjj5g4ceJbt311zd2r7Pi4WiJSlKfzx/Bo1gAXUh4h87kE9Y310cn25SdCV9XT1YiISDXVqmkQi8UIDQ1FUFAQhg0bBgMDA3z66aeYPn06vvjiC/Tv3x8tW7YEAMybNw/Dhw+Hh4eHzD7c3d0xZcoULF26FFlZWdLLnV49znTAgAGYOnUqZsyYUaHa+vbtizt37mDevHkoKCjA8OHDYWlpqdTlPMuXL8eiRYvg7e0NsViMHj16wN/fH8DL5iQuLg7e3t7Q1dWFm5sbfHx8cPz48QrPU56VK1diwYIFGD58OAwNDTF8+HCMHDkS9+7J/4AlU1NTmey8vb3VUg8RvR9EIhG62H2o6TKIiEhJIuFd+dQuDYuKikKDBg2kj3gtLi5Gu3btsGHDBrRt21bD1VVPr5685OzsXCXz5eXlIT4+Hk5OTtXixqJ3CbNTDfNTHrNTHrNTDfNTHrNTTXXLT9HXa9XqTEN1dvr0aURHR2PBggWoXbs2du7cCUNDQ7Ro0ULTpRERERERVSo2DQqaMmUKFi5ciDFjxqCgoACurq7YunUrdHV10bZtWxQWFr5x2+PHj6N+/fqVXuOgQYOQmpr6xvVbtmxB69atK70OIiIiIqpZ2DQoyNDQECtWrCh33cGDB1FaWvrGbevVq1dZZclYv3693M9V+PBDXk9MRERERBXHpkEN1PFkI3WoirMZRERERPT+qVaf00BERERERNUPmwYiIiIiIpKLTQMREREREcnFpoGIiIiIiORi00BERERERHKxaSAiIiIiIrnYNBARERERkVxsGoiIiIiISC42DUREREREJBebBiIiIiIikotNAxERERERycWmgYiIiIiI5GLTQEREREREcrFpICIiIiIiudg0EBERERGRXGwaiIiIiIhILjYNREREREQkF5sGIiIiIiKSi00DERERERHJxaaBiIiIiIjkYtNARERERERysWkgIiIiIiK5amm6ACIi0hxBEHAh5RHuP89D/ToG6NywHkQikabLIiKiaoZnGqpQYWEhDhw4UGn79/Pzg5+f3xvXu7u7Izw8/I3r4+PjcePGDQDA1atX4ejoqPYaiaj6iIhNh+PSw+i+8Q+M+uUium/8A45LDyMiNl3TpRERUTXDpqEKHT9+HJs2bdLY/AcPHkSfPn3euN7HxwdpaWlVVxARaUxEbDqG7TiP5Mc5MsuTH+dg2I7zbByIiEgGL0+qQoIgaHR+U1NTjc5fFbKhgweSEugJRZou5Z2Sn1/C7FTwruUnCAKmH7mO0jf8TioVBPgduwGPZg14qRIREQFg06CUzMxMLFiwAJcvX4aZmRkGDRqEiRMn4vDhw4iIiICbmxt2796NkpISDB48GH5+foiKioK/vz8AwNHREZGRkbC0tMTGjRuxd+9e5Ofno3Xr1ggMDET9+vWl4yZNmoQ9e/bA1dVVobMUubm5mDRpEi5cuAAbGxvMmTMH7dq1A/Dy8qTJkydj0KBB8PLygoODA86dO4eSkhKYmpri3r178Pf3R1RUFDw9PQEAe/fuxcaNG5Gbm4tPP/0UCxYsgI6OjsJZCYKAvLy8ikasFIlEgigtC0SlFwAoqJI5axRmp5p3KL/Uh09w50mu3DFJ/+TgdPxddLQxr9RaJBKJzH9JccxONcxPecxONdUtP0EQFHqDiE1DBQmCgMmTJ6Nx48aIiIhAVlYWAgMDIRKJYGFhgejoaJibm2Pv3r2IjY2Fn58funTpAjc3N8yePRvbt2/HwYMHYWpqil9++QVHjx7F6tWrYW5uju3bt2Ps2LE4evQotLW1AQBnz57F3r17UVpaqlB9p06dwsSJEzF9+nSEhYVh8uTJOHv2LIyMjMqMDQ8Px7Zt26CjowMrKysMHDgQY8eOxaBBgxAXFwcAOHnyJLZt24asrCxMnjwZLi4uGDFihMJ5FRUVIT4+XuHxKtOyrrq5iN5ROZJ8hcZdv50EU0lWJVfzEi+NVB6zUw3zUx6zU011yk+RN4TZNFTQlStXcP/+fYSFhUFLSwsNGzaEr68v/P394evri5KSEgQFBcHQ0BANGzbEzz//jNjYWHTs2BFGRkYQi8WoW7cuAGDr1q2YN28e2rZtCwBYuHAhOnXqhAsXLsDd3R0AMHz4cDRs2FDh+po1a4apU6cCAGbNmoXTp0/j2LFj5b7Q79atG1q2bCn9XiwWw8jISKbBmDdvHmxtbeHg4IAOHTrg9u3bFcpLW1sb9vb2FdpGWRKJBG3S7sPCwgK6urpVMmdNUVBQgMzMTGanpHctPxsYYf/Ft49r1dgeTlVwpiEtLQ02NjbQ19ev1LlqGmanGuanPGanmuqWX1JSkkLj2DRUUHJyMp49e4ZWrVpJl5WWliI/Px/Pnj2DmZkZDA0NpesMDQ1RXFxcZj8vXrzAgwcPMG3aNGhp/d/96Pn5+TKdp6WlZYXqc3FxkX6tpaUFJycnJCcnlztWkX1//PHH0q+NjIxQWFhYoXpEIhEMDAwqtI0qjFEIaxODKp2zJsjLEyMvk9kp613Lz9bUEIEnYsrcBP06e3Mj9HSqunsa9PX134nsqiNmpxrmpzxmp5rqkp+iv+fZNFRQcXExGjZsiI0bN5ZZFxUVVe7pnfJugC4pKQEA/PDDD7C1tZVZZ2xsLP26ou9aisVime9LS0ullzr9myL7/vf+NH0zNxGpTiQSYXn/lhi243y5N0NriURY1q8lb4ImIiIpPnK1gmxtbXH//n2YmprC2toa1tbWyMjIQHBw8Fu3ff0f4Dp16sDMzAxZWVnS/VhYWGDlypVITU1Vur6EhATp18XFxYiLi6vQ5U1E9H7wdP4YB0Z3gb257P1O9uZGODC6CzydP37DlkRE9D7imYYK6tSpEywtLTFz5kxMmzYNOTk5mDt3Ljp06FDmXfl/09fXR3Z2NtLS0mBlZQVvb2+sW7cOZmZm0rMXN27cwOLFi5Wu79q1awgNDUXv3r2xa9cuFBUVoV+/fgpta2BggJSUFDx79kzp+Yno3eHp/DE8mjXAhZRHyHwuQX1jfXSy5SdCExFRWWwaKkgsFiM0NBRBQUEYNmwYDAwM8Omnn8LX1xe//fab3G3btWsHa2tr9O/fH3v27MFXX32FFy9eIDAwELm5uWjWrBm2bdsmc3lSRXl4eODatWvYsGEDHBwcsHnzZoVvshkxYgRWrVqFtLQ0eHl5KV0DEb07RCIRuth9qOkyiIiomhMJvEidKklsbCwAwNnZuUrmy8vLQ3x8PJycnKrFjUXvEmanGuanPGanPGanGuanPGanmuqWn6Kv13hPAxERERERycXLk94RP/30k9ybrfv374+FCxdWYUVERERE9L5g0/COGDx4sPQD38rz+mdDEBERERGpE5uGd0SdOnVQp04dTZdBRERERO8h3tNARERERERysWkgIiIiIiK52DQQEREREZFcbBqIiIiIiEguNg1ERERERCQXPxGaKs2NGzcgCAJ0dHSqZD5BEFBUVARtbW2IRKIqmbOmYHaqYX7KY3bKY3aqYX7KY3aqqW75FRYWQiQSoWXLlnLH8ZGrVGmq+i+CSCSqsgalpmF2qmF+ymN2ymN2qmF+ymN2qqlu+YlEIoVes/FMAxERERERycV7GoiIiIiISC42DUREREREJBebBiIiIiIikotNAxERERERycWmgYiIiIiI5GLTQEREREREcrFpICIiIiIiudg0EBERERGRXGwaqForKCjA7Nmz0bp1a3Tq1Anbt29/49i4uDgMHToUzZs3x+DBg3Hr1i2Z9ceOHUPPnj3RvHlz+Pj44MmTJ5VdvkapM7tXQkND4efnV1klVyvqyk8QBPz4449wd3dHy5YtMXr0aCQlJVXFIWiMurIrKSnBqlWr0LFjR7i6uuK7777DP//8UxWHoFGV8Xf3xIkTcHR0rKySqw11Zte6dWs4OjrK/Hnx4kVlH4LGqDO733//HZ988glatGiBsWPH4t69e5VdvsapK79//8y9+nPo0KEqOIq3EIiqsYULFwr9+/cXbt26Jfzxxx+Cq6urcOLEiTLjXrx4IXTs2FFYtmyZkJSUJAQFBQkdOnQQXrx4IQiCIMTExAguLi5CRESEEB8fL3zxxRfC+PHjq/pwqpS6snvl6NGjgpOTk+Dr61tVh6BR6spvz549Qtu2bYUzZ84IKSkpwuzZs4Vu3boJeXl5VX1IVUZd2W3cuFHo3r27EBUVJSQmJgqjR48WxowZU9WHU+XU/Xc3Oztb6Nixo+Dg4FBVh6Ax6sruwYMHgoODg5Ceni48evRI+qe0tLSqD6nKqCu769evC02aNBH27t0rJCcnCxMmTBCGDRtW1YdT5dSV3+s/b48ePRJWrFghdO/eXXj+/HlVH1IZbBqo2nrx4oXg7OwsXLlyRbpsw4YNwhdffFFmbFhYmODu7i79hV5aWir06tVL+PXXXwVBEISZM2fKvNi9f/++4OjoKKSnp1fyUWiGOrMrKioSAgMDBWdnZ6F3797vRdOgzvyGDh0qbN68WTq+sLBQaNGihXDx4sVKPgrNUGd2ISEhwh9//CEdf/r0acHFxaWSj0Cz1JnfK3PmzBE+//zzGt80qDO7S5cuCR07dqyawqsBdWbn4+Mj+Pn5Scenp6cL3bt3Fx4/flzJR6E5lfH3VhBeZufs7CxcunSp8oqvAF6eRNXW7du3UVxcDFdXV+myVq1aISYmBqWlpTJjY2Ji0KpVK4hEIgCASCRCy5Yt8d///le6vnXr1tLxFhYWqF+/PmJiYir/QDRAndnl5eUhISEBBw4ckNlfTabO/GbNmoUBAwZIx4tEIgiCgJycnMo/EA1QZ3aTJ09Gr169AACPHz9GWFgY2rRpUzUHoiHqzA8AoqKiEBUVhW+++aZK6tckdWaXlJQEW1vbKqtd09SZXVRUlPTvLQA0aNAAZ86cgampaeUfiIao++/tK8HBwWjfvj06dOhQqfUrik0DVVtZWVn44IMPoKOjI11mbm6OgoICPHv2rMzYevXqySwzMzPDgwcPAACPHj2Su76mUWd2derUwb59+9C4ceNKr7u6UGd+rVu3xkcffSRdFxYWhuLiYrRq1aryDkCD1JndK8HBwejQoQNu3LhR4++pUWd+hYWFmDt3LgIDA6Gnp1fptWuaOrNLTk6GRCKBl5cXOnXqhK+//hqpqamVfgyaoq7snj9/juzsbJSUlOCrr75Cx44dMXHiRDx8+LAqDkNjKuP33v3793Hs2DFMmjSp0uquKDYNVG1JJBKZv4AApN8XFhYqNPbVuPz8fLnraxp1Zvc+qqz8YmJisHz5cnz11VeoW7eumquuHioju4EDB+LgwYNo3749xo4di9zc3EqovHpQZ34bNmxA06ZN0alTp0qsuPpQZ3YpKSnIzs7GxIkTsXHjRujp6cHb27vG/uypK7u8vDwAwKJFi9C/f3+EhoaisLAQEyZMKPOOe01SGb/3Dh48iGbNmqF58+aVULFy2DRQtaWrq1vmL9Gr7//9rtmbxr4a96b1+vr66i67WlBndu+jysgvOjoaX331Fbp06YLvvvuuEqquHiojO2trazg7O2PFihXIz8/HH3/8UQmVVw/qyu/vv//GgQMHMHv27MotuBpR58/etm3bcOjQIXTo0AEuLi5YtWoVCgoKcPbs2Uo8As1RV3ZisRgAMHToUHh4eEiz+/vvv8u9/KamqIzfeydPnpS5tLU6YNNA1daHH36Ip0+fori4WLosKysLenp6qFOnTpmx/34U4z///CM9Bfim9TX13V51Zvc+Und+V69exdixY9GuXTusXr0aWlo191evOrM7e/aszGUNurq6aNCgAZ4+fVqJR6BZ6srvjz/+QHZ2Nnr16gVXV1d8/fXXAABXV1ccOXKk8g9EA9T5s6ejo4PatWtL1+nq6sLKyqrGXmajruw++OADaGtro2HDhtJ1H3zwAUxMTGrs5cCA+v/NyMzMRFJSEnr06FG5hVdQzf2Xi955Tk5OqFWrlsy7E9evX4ezs3OZF13NmzdHdHQ0BEEA8PLZ+Ddu3JCe1mvevDmuX78uHZ+ZmYnMzMxqddpPndSZ3ftInfn9/fffmDhxIjp37ox169ZBW1u7yo5DE9SZ3fLly2WeTZ6bm4u0tDTY2dlV+nFoirry++KLL3DixAkcOnQIhw4dwqJFiwAAhw4dgru7e5UdT1VSV3aCIKBnz54IDw+Xjs/Ly8OdO3dkXgzXJOrKrlatWmjatClu374tHf/kyRM8ffoUlpaWVXIsmqDuf3NjYmKkD2ypVqr6cU1EFTF37lyhb9++QkxMjHDq1CmhZcuWwsmTJwVBePksY4lEIgiCIOTk5Ajt2rUTgoKChMTERCEoKEjo2LGj9LnHN27cEJo2bSocOHBA+jkNEyZM0NhxVQV1Zfc6X1/f9+KRq4KgvvyGDx8u9OnTR7h//77Ms7dfbV8TqSu7nTt3Cm5ubsK5c+eEv//+W/jmm28ET09PoaSkRGPHVhUq4+/ulStXavwjVwVBfdkFBQUJ3bp1E65cuSL8/fffgo+Pj9CvXz+huLhYY8dW2dSV3YkTJ4QWLVoIv/32m5CUlCRMmDBB8PT0rNGfcSEI6v17GxwcLIwdO1YjxyEPmwaq1vLy8oRZs2YJLVq0EDp16iT89NNP0nUODg4yzzWOiYkRPDw8BGdnZ2HIkCHC//73P5l9/frrr0LXrl2FFi1aCD4+PsKTJ0+q6jA0Qp3ZvfI+NQ3qyO/Ro0eCg4NDuX/KeyZ3TaGun72SkhJh8+bNQrdu3QQXFxdh4sSJwoMHD6ryUDSiMv7uvi9Ng7qyy8/PF5YuXSp07NhRaN68uTBhwgTh/v37VXkoVU6dP3f79+8XunfvLri4uAjjxo0TMjMzq+owNEad+QUGBgrTpk2rqtIVJhKE/39+hIiIiIiIqBy8p4GIiIiIiORi00BERERERHKxaSAiIiIiIrnYNBARERERkVxsGoiIiIiISC42DUREREREJBebBiIiIiIikotNAxERERERycWmgYiI5PLy8sKgQYPeuD4gIACffPKJUvsODw+Ho6OjwuNDQkLg7u4ud4yjoyPCw8OVqkeZmqrS06dPERYWpukyiOg9xKaBiIjkGjJkCP73v/8hOTm5zLqCggL8/vvvGDJkiFL77tOnDy5evKhqie+NFStW4MiRI5oug4jeQ2waiIhIrk8++QRGRkY4evRomXWnT5+GRCKBh4eHUvvW09ND3bp1Vazw/SEIgqZLIKL3FJsGIiKSS09PD3379sWxY8fKrIuIiEDXrl1Rt25d/P3335gwYQLc3NzQrFkz9OjRA9u3b5eODQkJwRdffIFp06ahZcuWCAoKKnMp0Nv28cqGDRvQtm1btGzZEjNmzMCzZ8/eWP/Zs2cxaNAguLi4oFevXli3bh0KCwsVPn4/Pz/MmjULixYtQuvWrdGmTRsEBwcjOTkZI0eOhIuLC/r374+YmBjpNo6Ojti9ezeGDRsGZ2dn9O/fH5GRkTL7PXfuHIYNGwZXV1d06tQJS5cuRX5+vsw+goOD0b17d3Tq1AnTp09HREQEoqKipJllZ2cjICAAnTt3RtOmTdG+fXsEBARAIpEAAK5evYomTZrgzz//RL9+/dCsWTN8+umnOH36tHQeQRCwY8cOfPLJJ3BxcSnz//rhw4eYNm0aWrdujbZt2+Kbb75BWlqawvkRUc3ApoGIiN5q8ODBuHv3LqKjo6XLsrKy8J///AdDhw6FRCLB2LFjYWJign379uHYsWP49NNPsXz5csTHx0u3+euvv2Bubo7Dhw/Dy8tLZg5F93Hv3j1cuXIFP/30EzZt2oTY2Fj4+/uXW/f58+cxdepUDBs2DMeOHcO8efNw4sQJzJw5s0LH/9tvv0EsFiM8PBze3t7YsGEDvvnmG3z11VcICwuDrq4uFixYILPNqlWrMHDgQBw+fBhdu3bF5MmTcePGDQDAqVOnMHHiRHTr1g3h4eFYsGABfvvtN3z//fcy+9izZw+Cg4Oxfv16zJ8/H5999hlcXV2ll3T5+fkhLi4O69evx8mTJ+Hv749Dhw5h//790n2UlJRg5cqVmDNnDo4dOwYHBwf4+vrixYsXAICtW7di7dq1GDduHI4dO4bPP/8cs2bNwpUrV5CXlyf9//TLL79g165d+OCDDzBs2DA8fPiwQhkS0TtOICIiUkC/fv2EBQsWSL/funWr0KlTJ6G4uFh4/PixsHnzZiE3N1e6Pj8/X3BwcBAiIiIEQRCE4OBgwcHBQXj+/Ll0zK+//io4ODgIgiAovA9nZ2chKytLOubixYuCg4ODkJaWJgiCIDg4OAi//vqrIAiCMGLECGHRokUyx3H58mXBwcFBuHv3brnH+XpNgiAIvr6+QseOHYWSkhJBEAThxYsXgoODg7BmzRrpmN27dwtNmzaVfu/g4CAsXLhQZr9Dhw4Vpk2bJgiCIAwZMkT49ttvZdafOnVKcHBwEBITE6X7WLJkicwYX19f4YsvvpB+v2vXLuH27dtl5vH39xcEQRCuXLkiODg4CKdOnZKuj4+PFxwcHIQbN24IpaWlQseOHYXVq1fL7OPHH38ULly4IBw4cEBo27atUFRUJF1XUlIidO/eXQgODi43PyKqmWppumkhIqJ3w+DBg7F582bMnj0btWrVwqFDh+Dp6QmxWAxTU1OMHDkSx44dQ1xcHNLT03H79m0AQGlpqXQfZmZmMDIyKnf/iu7D2toa5ubm0u+bN28OAEhMTIS1tbXMPuPi4nDz5k0cPHhQukz4//cFJCcnw8rKSqFjt7KygpbWy5PzBgYGAIAGDRpI1+vp6aGoqEhmm7Zt28p87+rqikuXLgF4eRlW3759Zda3adNGus7e3l56rPKMHDkSZ86cQUREBNLS0pCUlISMjAw0bNhQZtzr3xsaGgIAioqK8PTpU2RlZUkzfOXrr78GACxYsADZ2dlwc3OTWV9QUFDujfFEVHOxaSAiIoUMGDAAq1atwqVLl1C3bl0kJiZi/fr1AF5eqjR8+HCYmprC3d0dnTp1grOzM7p27SqzDz09vTfuX9F9iMVime9LSkoAANra2mX2WVpainHjxsHT07PMuorcgF3evl81EW9Sq5bsP7ElJSXSbYRybmh+1Ri9vp28vEpLSzFhwgQkJiaiX79+6NOnD5o2bYq5c+eWGaujo1NmmSAI5R7Xv+ewtbVFaGhomXWvmiciej+waSAiIoW8ejH/22+/wdzcHG5ubtJ3wo8dO4Znz57h5MmT0heiCQkJABR/4o+i+0hLS0Nubq70HfPr169DJBJJ351/XaNGjZCamirzjv3Vq1exc+dOzJ8/v1Jf+MbGxsp8pkR0dDSaNm0K4OVNzjdu3IC3t7d0/bVr1wAAdnZ2b9ynSCSSfh0fH4/z58/jwIED0jMFRUVFSE9PlzkLIo+RkRHq1auH2NhY9OjRQ7p8ypQpsLCwgIODAw4fPgwjIyOYmppK55g+fTo+/fRT9OnTR6F5iOjdxxuhiYhIYUOGDMHZs2dx8uRJmc9m+OijjyCRSPD777/j/v37uHjxovSmXkWfVKToPgoKCjB16lTExcXh0qVLCAoKgoeHBywtLcvs8+uvv8bJkyexfv16pKam4vLly/D390dOTk6lP+p1x44dOHr0KFJTU7F8+XIkJCRg9OjRAIBx48bhjz/+wMaNG5GamoqzZ88iKCgI3bt3l9s0GBgY4NGjR7h79y7Mzc1Rq1YtnDhxAnfv3kVsbCymTp2KrKysCj0davz48dixYwcOHz6M9PR07Ny5E5GRkejRowcGDBgAY2NjTJkyBTExMUhOToafnx/Onz9fbT8Aj4gqB880EBGRwjp16gQDAwM8e/ZM5lOgP/30U/zvf//DsmXLkJubC0tLSwwdOhSRkZGIjY3FiBEj3rpvRffRrFkzODk54csvv4RIJEKfPn3g5+f3xn2uXbsWmzdvxqZNm2BiYgJ3d3fMmDFDPYHI8fnnn+Pnn3/G33//jcaNG2Pbtm1o3LgxgJeffbFmzRqEhoZi48aNMDU1Rb9+/TBlyhS5+/Tw8MCpU6fQr18//PHHH1i2bBlCQkKwe/du1K1bF926dYO3tzfOnDmjcJ1ffPEF8vPz8cMPPyArKws2NjZYu3at9B6LX375BStWrMBXX32FkpISNG3aFNu3b5fb3BBRzSMSFD1vTERERApxdHTE0qVLMWjQIE2XQkSkFrw8iYiIiIiI5GLTQEREREREcvHyJCIiIiIikotnGoiIiIiISC42DUREREREJBebBiIiIiIikotNAxERERERycWmgYiIiIiI5GLTQEREREREcrFpICIiIiIiudg0EBERERGRXP8PwaM4dXE5NUwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(estimator = best_model, plot = 'feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf8dec13-eebf-453f-b970-f51a8027fc8d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}