classification.ipynb

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      "text/plain": [
       "       Diabetes_binary  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
       "0                  0.0     1.0       0.0        1.0  26.0     0.0     0.0   \n",
       "1                  0.0     1.0       1.0        1.0  26.0     1.0     1.0   \n",
       "2                  0.0     0.0       0.0        1.0  26.0     0.0     0.0   \n",
       "3                  0.0     1.0       1.0        1.0  28.0     1.0     0.0   \n",
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       "...                ...     ...       ...        ...   ...     ...     ...   \n",
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       "70691              1.0     1.0       1.0        1.0  25.0     0.0     0.0   \n",
       "\n",
       "       HeartDiseaseorAttack  PhysActivity  Fruits  ...  AnyHealthcare  \\\n",
       "0                       0.0           1.0     0.0  ...            1.0   \n",
       "1                       0.0           0.0     1.0  ...            1.0   \n",
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       "70691                   1.0           1.0     1.0  ...            1.0   \n",
       "\n",
       "       NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex   Age  \\\n",
       "0              0.0      3.0       5.0      30.0       0.0  1.0   4.0   \n",
       "1              0.0      3.0       0.0       0.0       0.0  1.0  12.0   \n",
       "2              0.0      1.0       0.0      10.0       0.0  1.0  13.0   \n",
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       "...            ...      ...       ...       ...       ...  ...   ...   \n",
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       "\n",
       "       Education  Income  \n",
       "0            6.0     8.0  \n",
       "1            6.0     8.0  \n",
       "2            6.0     8.0  \n",
       "3            6.0     8.0  \n",
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       "...          ...     ...  \n",
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       "\n",
       "[70692 rows x 22 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "diabetes = pd.read_csv('diabetes_binary_5050split_health_indicators_BRFSS2015.csv')\n",
    "diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Diabetes_binary', 'HighBP', 'HighChol', 'CholCheck', 'BMI', 'Smoker',\n",
       "       'Stroke', 'HeartDiseaseorAttack', 'PhysActivity', 'Fruits', 'Veggies',\n",
       "       'HvyAlcoholConsump', 'AnyHealthcare', 'NoDocbcCost', 'GenHlth',\n",
       "       'MentHlth', 'PhysHlth', 'DiffWalk', 'Sex', 'Age', 'Education',\n",
       "       'Income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>70687</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70688</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70689</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70690</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70691</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70692 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Diabetes_binary  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
       "0                  0.0     1.0       0.0        1.0  26.0     0.0     0.0   \n",
       "1                  0.0     1.0       1.0        1.0  26.0     1.0     1.0   \n",
       "2                  0.0     0.0       0.0        1.0  26.0     0.0     0.0   \n",
       "3                  0.0     1.0       1.0        1.0  28.0     1.0     0.0   \n",
       "4                  0.0     0.0       0.0        1.0  29.0     1.0     0.0   \n",
       "...                ...     ...       ...        ...   ...     ...     ...   \n",
       "70687              1.0     0.0       1.0        1.0  37.0     0.0     0.0   \n",
       "70688              1.0     0.0       1.0        1.0  29.0     1.0     0.0   \n",
       "70689              1.0     1.0       1.0        1.0  25.0     0.0     0.0   \n",
       "70690              1.0     1.0       1.0        1.0  18.0     0.0     0.0   \n",
       "70691              1.0     1.0       1.0        1.0  25.0     0.0     0.0   \n",
       "\n",
       "       HeartDiseaseorAttack  PhysActivity  Fruits  Veggies  HvyAlcoholConsump  \\\n",
       "0                       0.0           1.0     0.0      1.0                0.0   \n",
       "1                       0.0           0.0     1.0      0.0                0.0   \n",
       "2                       0.0           1.0     1.0      1.0                0.0   \n",
       "3                       0.0           1.0     1.0      1.0                0.0   \n",
       "4                       0.0           1.0     1.0      1.0                0.0   \n",
       "...                     ...           ...     ...      ...                ...   \n",
       "70687                   0.0           0.0     0.0      1.0                0.0   \n",
       "70688                   1.0           0.0     1.0      1.0                0.0   \n",
       "70689                   1.0           0.0     1.0      0.0                0.0   \n",
       "70690                   0.0           0.0     0.0      0.0                0.0   \n",
       "70691                   1.0           1.0     1.0      0.0                0.0   \n",
       "\n",
       "       AnyHealthcare  NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex  \\\n",
       "0                1.0          0.0      3.0       5.0      30.0       0.0  1.0   \n",
       "1                1.0          0.0      3.0       0.0       0.0       0.0  1.0   \n",
       "2                1.0          0.0      1.0       0.0      10.0       0.0  1.0   \n",
       "3                1.0          0.0      3.0       0.0       3.0       0.0  1.0   \n",
       "4                1.0          0.0      2.0       0.0       0.0       0.0  0.0   \n",
       "...              ...          ...      ...       ...       ...       ...  ...   \n",
       "70687            1.0          0.0      4.0       0.0       0.0       0.0  0.0   \n",
       "70688            1.0          0.0      2.0       0.0       0.0       1.0  1.0   \n",
       "70689            1.0          0.0      5.0      15.0       0.0       1.0  0.0   \n",
       "70690            1.0          0.0      4.0       0.0       0.0       1.0  0.0   \n",
       "70691            1.0          0.0      2.0       0.0       0.0       0.0  0.0   \n",
       "\n",
       "        Age  Education  Income  \n",
       "0       4.0        6.0     8.0  \n",
       "1      12.0        6.0     8.0  \n",
       "2      13.0        6.0     8.0  \n",
       "3      11.0        6.0     8.0  \n",
       "4       8.0        5.0     8.0  \n",
       "...     ...        ...     ...  \n",
       "70687   6.0        4.0     1.0  \n",
       "70688  10.0        3.0     6.0  \n",
       "70689  13.0        6.0     4.0  \n",
       "70690  11.0        2.0     4.0  \n",
       "70691   9.0        6.0     2.0  \n",
       "\n",
       "[70692 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = ['HighBP', 'HighChol', 'CholCheck', 'BMI', 'Smoker',\n",
    "       'Stroke', 'HeartDiseaseorAttack', 'PhysActivity', 'Fruits', 'Veggies',\n",
    "       'HvyAlcoholConsump', 'AnyHealthcare', 'NoDocbcCost', 'GenHlth',\n",
    "       'MentHlth', 'PhysHlth', 'DiffWalk', 'Sex', 'Age', 'Education',\n",
    "       'Income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7120893204046674 0.002661814803689377\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import numpy as np\n",
    "\n",
    "from scipy.stats import sem\n",
    "\n",
    "feature_cols = [ col for col in feature_cols if col != 'Diabetes_binary' ]\n",
    "diabetes_tree = DecisionTreeClassifier(random_state=0, criterion='entropy', max_depth=3, min_samples_leaf=50, class_weight='balanced')\n",
    "cv_score = cross_val_score(diabetes_tree, \n",
    "                           diabetes.loc[:, feature_cols], \n",
    "                           diabetes['Diabetes_binary'], cv=10,\n",
    "                           scoring='accuracy')\n",
    "\n",
    "print(np.mean(cv_score), sem(cv_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, criterion=&#x27;entropy&#x27;,\n",
       "                       max_depth=3, min_samples_leaf=50, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, criterion=&#x27;entropy&#x27;,\n",
       "                       max_depth=3, min_samples_leaf=50, random_state=0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(class_weight='balanced', criterion='entropy',\n",
       "                       max_depth=3, min_samples_leaf=50, random_state=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_tree.fit(diabetes.loc[:, feature_cols], diabetes['Diabetes_binary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tree depth: 3\n",
      "Number of leaves: 8\n"
     ]
    }
   ],
   "source": [
    "print(f\"Tree depth: {diabetes_tree.get_depth()}\")\n",
    "print(f\"Number of leaves: {diabetes_tree.get_n_leaves()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vq1i8eDEREREZjr99+zatW7dm4cKFtGzZkn79+hEeHo6fnx+7du16jpmKiIiIiIi8/HRnk4iIiIiIPBWj0UiOHFnv99bOnTvHF198wcGDB/njjz+e6tmRI0cSFhZGSEgIPj4+AAwePJiqVavSvXt3oqKinkfKIiIiIiIiWULW+xOiiIiIiMgr6vr16/Tv359KlSpRrFgx2rZtS0hIiEVM7969GTBgABcuXKBjx46ULl2acuXK0bNnT/766y8A+vTpw5w5cwDo2bMngwYNAmDQoEH06tWLc+fO8cEHH+Ds7Gwe99SpU/j6+uLs7Ey+fPmoXbs2a9eutZg7ICCA8ePH89tvvxEQEICzszOenp5MmjTJfMfR6NGjadiwYbrFmW7dutGyZUvu3buXeS/tATdv3uT06dPY29tTu3btp3p28eLFVKlSxVxoAihatCgtW7bkzJkz7N+/P7PTFRERERERyTJUbBIRERERyQLOnTtH9erVWbp0KY0aNaJHjx5ER0fj5+fH9OnTzXFHjhxh06ZNvP7668TExPDee+9RqlQpFi1aRNeuXQFwc3OjePHi5n92dXUF4OjRo+zZs4e33nqL2bNn89prrwHw66+/Urt2bU6dOsX777/PZ599ho2NDe+++y5jx441z71t2zYWLlyIr68vd+/epU+fPuTNm5fhw4fTp08fANzd3fn1119ZvXq1xfrOnj3L0qVLcXR0xNb2+RzA4OHhwS+//MIvv/zC8uXLM/zc1atXiY+Px9vbO02fm5sbAIcOHcq0PEVERERERLIaHaMnIiIiIpIFDB8+nOjoaPbt24eXlxcAX3zxBT4+PgwbNoyuXbvi5OQEQHR0NMOGDWPChAkYDAaMRiO1a9dm27ZtAPzzn/8kJSWFvXv3Mnz4cKpVq2aeJywsjJYtW7Jy5Urc3d0xmUwMHjyYXLlysWfPHkqUKAHAJ598wptvvsm4ceMICAgwF10iIyP5+uuv+fDDDwEYO3Ys3t7eLFy4kH79+tGmTRvy58/PmjVrGDZsmHne1F1SnTt3Tnf9V69eZfbs2U98T+3atcPT0/Op3u2ThIWFAZgLdA+qUKECAFeuXMnUOUVERERERLISFZtERERERF5y165dY/ny5dSuXdtcaAKws7Ojd+/ebN++nXXr1hEYGAhAnjx5+PzzzzEYDADkyJGD+vXrc/jwYc6dO0fJkiUfO9/YsWNxd3cH4PDhwxw+fJh3333XXGgCyJkzJ927d2f79u1s3brVXGxycHBgyJAh5rgcOXIwYsQIduzYwZYtW6hZsybvvPMOy5YtIzo6mjJlygCwevVqChcuTMuWLdPNKTY2ljFjxjzxXbm5uWV6sSkiIgLAXMx7UOnSpYH7RxyKiIiIiIi8qlRsEhERERF5yYWFhWEymUhMTCQgIMCiLyEhAbi/oyhVkSJFyJ07t0Wco6MjAImJiY+dy9nZ2eI+o/DwcAAaN26cJrZGjRoAnD592txWvnx5c5ErVWrxJzXHzp07s2zZMtasWcM///lPYmJi2L9/P/379ydnzpzp5uXu7s6tW7cemzvcL8Bltly5cgH3i34PS70HK/X9ioiIiIiIvIp0Z5OIiIiIyEsuLi4OuF/0yJkzp8WnUKFCdOrUyWI3T548eR45lslkeuxcqYWVVFevXgUw70B6UFJSEgA2NjbmtvSOmsuXLx+AuQDWrFkzihUrxpo1awBYs2YNJpOJTp06PTIvg8FAnjx5nvh5MJfMUqxYMQCioqLS9KUWoJydnTN9XhERERERkaxCO5tERERERF5yZcuWBe7vGgoODrboS0lJ4ebNm+TNm/e5zP2Pf/wDgN27d9OqVSuLvr1791rkB///yLkHRUdHA///fiMbGxvee+89ZsyYQUxMDKtXr6ZcuXLUrVv3kXlcunSJsWPHPjHfnj17UrNmzSfGPQ03NzcMBkO6xaY//vgDwOJ4QxERERERkVeNik0iIiIiIi85V1dXnJ2d2bx5M8nJyRZHzU2YMIFRo0axe/duGjRokOlzV69eHTs7O7Zu3cqkSZMs+nbu3ImNjY3FPUunT58mPDyc8uXLm9sWLVoEQLVq1cxtnTt3Zvr06UyfPp19+/Y98T6m69evs2DBgifm27hx40wvNpUoUYJGjRqxa9cuIiMjKVeuHADJycksX74cFxeXTJ9TREREREQkK9ExeiIiIiIiLzk7OzsmTJhAQkICnTt35vDhw0RERDB16lTGjRtH8+bNqV+//lONWbp0aQDmzZvHwYMHHxlXokQJBgwYQGhoKP379+f48eOEhYUxZswY1qxZQ+fOnS0KSykpKbz99tv8+OOPnDhxgrFjxzJz5kz8/f1p2LChOa5mzZq4u7szffp0ALp27frYfN3d3UlKSnrix9/f/6neQ3rmzZuHra0tX375pbltxIgRJCcn4+/vz7p169ixYwd+fn5ERUUxf/78NPdUiYiIiIiIvEq0s0lEREREJAvo1asXt27d4pNPPmHVqlUA2NraEhgYSFBQ0FMXO5o3b06dOnX417/+xalTp9ixY8cjYydMmEBKSgozZszgX//6l7m9b9++zJgxwyK2WbNmuLi48O6772I0GgFo0qQJs2fPTjNu586d+eyzz2jRooX5uL6XgclkIiUlxeJ+qxYtWrBs2TICAwNp164dAA4ODnz99df4+PhYK1UREREREZGXgsH0pBuCRURERESyKIPBwMqVKzNlt8vL4ubNm4SGhpKYmEjlypUpVarU3xrvwoULFChQgAIFCjwx9sqVKxw5coRcuXJRpUoVHB0dLfoLFy5M7dq1+fnnn4mPj+fQoUO4uLhQsWLFdMf78ccfadu2LWvXrqVt27Z/ax0vyr179zh06BBGoxEvLy9sbGysnVKmSv1ZSS1oioiIiIiIZMBq7WwSEREREclCChQoQKNGjTJtvBIlSmQ4tkiRIrRo0SJDsY6OjjRv3vyxMd999x0uLi60bt06wzlYm62tLXXq1LF2GiIiIiIiIi8VFZtEREREROSFCgoK4vz584SEhDBz5kxsbfXHEhERERERkaxMf6oTEREREZFMUbx4cQoXLvzEuHnz5pGYmEhgYCB9+vR5AZmJiIiIiIjI86Rik4iIiIiIZIpjx45lKO7s2bPPORMRERERERF5kXJYOwERERERERERERERERHJulRsEhERERGRNEJCQvjhhx+sncZzZzQanxhjMpmIj49/Yty9e/cwmUwZmjcxMTFDcSIiIiIiIlmBik0iIiIiIpLG5MmTGTp0qLXTyJBt27bh6en52E/Xrl3N8adPn2bw4MGUKVOGQoUK0apVK7Zt25Zm3Pj4eHr37k2+fPlwcnKiYMGCBAQEcPXqVYu4kJAQatSoQf78+SlatCjvvfceu3btSjPe4cOHadmyJU5OThQoUIBixYrx/vvvk5CQkPkvRURERERE5AXSnU0iIiIiIpKlGQwGcubMmW7fnTt3CAsLo3z58gDcvn2b1q1bc/78eTp27EihQoVYu3Ytfn5+/Oc//6FRo0YA3L17F19fX/bv30/Pnj2pW7cuBw4cYN68eZw7d449e/YAsGLFCjp16kSZMmUYOnQo58+fZ9WqVfz8888cOHCAChUqAHDo0CG8vb2xtbWlY8eOODk5sXLlSubNm0doaCj79u0jRw79LqCIiIiIiGRNKjaJiIiIiEiW1rRpU44cOZJu38CBA0lISGDOnDkAjBw5krCwMEJCQvDx8QFg8ODBVK1ale7duxMVFQXAkiVL2LdvH1999RUff/wxAL169cJgMDB37lwOHTpElSpVGDp0KPny5ePw4cM4ODgAMHHiREqWLMl7771HaGgoAN988w23b99m//79VKtWDYAvv/wSb29vtm3bxtq1a2nfvv3ze0kiIiIiIiLPkX51TkRERETkObtz5w5jxoyhXLly5MqVi/Lly/P+++9z8+ZNi7idO3fywQcf4ObmRqlSpejQoQNz5swhJSXFHNO7d2+6detGREQEgYGBlCpViqZNmxIcHAzA119/Tc2aNSlSpAg+Pj6Eh4dbzBEQEMD48eP57bffCAgIwNnZGU9PTyZNmvTE+4uuX79O//79qVSpEsWKFaNt27aEhIQ801pfhP/85z/Mnj2b4OBgihUrBsDixYupUqWKudAEULRoUVq2bMmZM2fYv38/AMHBwRQpUoSBAwdajDlixAiWLl2Ks7MzJ0+e5Pz58/j6+poLTQBFihShRYsWHDlyhBs3bgDw22+/Ua1aNXOhKVWPHj0AOHDgQOa/ABERERERkRdExSYRERERkeesf//+BAUF0ahRI6ZMmYKvry/Lli2jZcuW5pgdO3bg7e3NDz/8QMuWLQkMDCQmJoZ+/frx6aefmuOOHDnCli1baNy4MXv37uWNN95gz549dO3aFV9fX4YNG0bJkiVp0KAB27dvx9vb26KItG3bNhYuXIivry93796lT58+5M2bl+HDh9OnT59HruHcuXNUr16dpUuX0qhRI3r06EF0dDR+fn5Mnz79qdb6IsTFxdGzZ08CAgJo2rQpAFevXiU+Ph5vb+808W5ubsD94+4AwsPD8fHxwc7OjqioKP7973/z+++/U6JECbp06ULp0qW5cOECAK+//nqa8VLbTpw4QXJyMi1btmTAgAFp4mJiYgBwcnLKhFWLiIiIiIhYh47RExERERF5jpKSkggODuatt95i0aJF5vZy5coxePBgTp8+jZubGytWrMDW1pbIyEjzLplhw4ZRtmxZNmzYwOTJk83PXrp0iXHjxjFy5EgAOnTogK+vLzt37uTEiRPmwkn37t1ZsmQJERER5jaAyMhIvv76az788EMAxo4di7e3NwsXLqRfv37UrFkzzTqGDx9OdHQ0+/btw8vLC4AvvvgCHx8fhg0bRteuXcmXL1+G1vqwq1evMnv27Ce+y3bt2uHp6fnEOIAPPviA69evM3HiRHNbWFgYAMWLF08Tn3q30pUrV0hMTOTixYsULVoUPz8/Nm7caI5zd3dn0aJF1KlTh3LlygGwfft281F7qU6ePAncLzbVq1ePWbNmpZnzypUrfPvtt+TMmZNWrVplaF0iIiIiIiIvIxWbRERERESeo9Qj8Hbu3EloaCjVq1cHYMCAAfTq1YvcuXMD8NFHHzFw4ECL49ju3r2Lg4OD+Si2VDY2NgwdOtT8vWrVqsD9u4seLOY0adKEJUuWcPLkSYt2BwcHhgwZYv6eI0cORowYwY4dO9iyZUuaYtO1a9dYvnw5tWvXNheaAOzs7Ojduzfbt29n3bp1dOzYMUNrfVhsbCxjxox57HuE+7uPMlJsOnHiBKtWrWLEiBG89tpr5vaIiAgg/V1EpUuXBu4fFZgaN2PGDFxdXZk1axb16tVjz549DBs2jDZt2nDs2DHKly9PrVq12LZtGwsWLCAgIACj0UhwcDCrV68GsDgC8UEbN26kV69exMbGMn36dCpXrvzEdYmIiIiIiLysVGwSEREREXmO8ubNy5gxY/jss8+oUaMGHh4evPHGG/j6+tKyZUtsbGyA+ztm4uLimDp1Knv37iU6Oprw8HASEhIoUaKExZglSpTAzs7O/D21iPNwXOrYd+/etWgvX748BoPBoi21iBMZGZlmDWFhYZhMJhITEwkICLDoS0hIMD+X0bU+zN3dnVu3bqXb96AH1/w4kydPxs7Ojo8++siiPVeuXMD94tnD/vrrLwAcHR3N/UlJSaxZswZ3d3cAatSoweXLlwkKCuKHH35g0KBBLFy4ED8/P3r37s3gwYMxGo0YjUZ69+7N3Llz0xTHIiMj+fDDD9mwYQOurq58//336R7rJyIiIiIikpXoziYRERERkeds5MiRREREMGrUKPLmzcucOXNo1aoVnp6eXLp0CYApU6ZQsmRJxo4dS3JyMt7e3ixevJj69eunGS9fvnzpzpMjR8b+9z69Y+RSx0xv91FcXBxwv1iTM2dOi0+hQoXo1KmTuaiSkbU+zGAwkCdPnid+HlWsetCff/7J8sz63QIAACAASURBVOXLefvtt9PsYCpWrBgAUVFRaZ5LLTA5Ozvj4uICQJ06dcyFplR+fn4AnDp1CoDKlStz7NgxFixYQN++fRk7diwHDhzAw8MDwKLYFBwcTLVq1di5cyeTJ0/m+PHjKjSJiIiIiEi2oJ1NIiIiIiLP0d27d7l16xZlypThyy+/5Msvv+TSpUsEBQXxzTffMGvWLIYMGcLw4cNxdnYmPDycAgUKmJ8PCgrK9JxSj4l7UHR0NPD/7y56UNmyZYH7O6KCg4Mt+lJSUrh58yZ58+bN0FrTW8+lS5cYO3bsE/Pu2bNnuvdJPWjevHncu3ePXr16pelzc3PDYDCkW2z6448/APDy8jIfvZecnJwm7vbt2wDY29tz9+5dzpw5Q+HChdPMN3HiRIoXL24ueG3cuJGuXbtSt25dVqxYYXG8n4iIiIiISFannU0iIiIiIs/R9u3bcXR0ZMWKFea2YsWKme9cio+P5+zZsxiNRtq2bWtRaIqJieHIkSOZntPp06cJDw+3aFu0aBEA1apVSxPv6uqKs7MzmzdvTlOAmTBhAo6Ojhw4cCBDa03P9evXWbBgwRM/6R3x97AtW7bg5OREs2bN0vSVKFGCRo0asWvXLouxkpOTWb58OS4uLtSsWZM8efLQtGlTfv/99zTvaf369QDUq1ePW7du4e7uzsCBAy1izp07x9q1a2ndurW5bcSIEdjb27NmzRoVmkREREREJNvRziYRERERkeeofv36FClShC+//JKSJUtSvXp1IiIizDt83nrrLSpUqED+/PlZuXIlPj4+uLu7s2fPHkaNGkXBggVJTEwkLCws3V1HzyIlJYW3336bcePG4ebmxrp165g5cyb+/v40bNgwTbydnR0TJkwgMDCQzp07M2zYMAoWLMhPP/3EuHHjaN68OfXr1ycxMfGJa02Pu7s7SUlJf3td8fHx/P777/j5+T3ySMERI0bw1ltv4e/vz8iRI3F0dGTSpElERUWxceNG811WEydOxMvLC39/f8aPH0+pUqXYvn07c+fOpUGDBuZCUtOmTVmzZg3e3t688847RERE0KdPH0qWLMmUKVPMeR0/fpzq1aszderUdPNq0qQJrVq1+tvvQERERERExBpUbBIREREReY4KFCjA999/T7du3XjjjTfM7blz5yYoKMhcgFm4cCE9e/Y0FzGcnJyYNm0a+fLlo1u3blSqVCndY92eRbNmzXBxceHdd9/FaDQC94sds2fPfuQzvXr14tatW3zyySesWrUKAFtbWwIDAwkKCsJgMGR4rc/Ljh07MBqN1K1b95ExLVq0YNmyZQQGBtKuXTsAHBwc+Prrr/Hx8THH1a5dm02bNtGjRw98fX3N7a1btzbvAoP7/946dOhAr169zEfp1ahRg+XLl5t3qe3ZsweTycThw4c5fPhwunkZDAYVm0REREREJMsymEwmk7WTEBERERF5HgwGAytXrsTf39/aqXDr1i2OHj3Kn3/+SeHChalUqRJFihSxiImLiyM0NJTixYtTsWJF8y6buLg44uPjcXV1/dt5FC5cmNq1a/Pzzz8THx/PoUOHcHFxoWLFihl6/ubNm4SGhpKYmEjlypUpVarUM63V2u7du8ehQ4cwGo14eXlhY2OTblxycjLHjx8nNjaWypUrU7x48TQxJpOJY8eOERUVRY0aNbL0MXmpPyupBUUREREREZEMWK1ik4iIiIhkWy9Tsell8WCxSeRhKjaJiIiIiMgzWJ3+QeYiIiIiIiIiIiIiIiIiGaBik4iIiIjIK6R48eIULlzY2mmIiIiIiIhINmJr7QREREREROTFOXbsmLVTEBERERERkWxGO5tERERERERERERERETkmWlnk4iIiIjIcxASEkJCQgLvvfeetVPJsHnz5hEbGwuAh4cHbdu2tei/d+8eBoMBGxubTJnvace7efMmd+/epVChQun2m0wmrl+/jqOjo1Xyy+zxnnW9W7Zs4eDBgwDkyZOHjz766O8lLiIiIiIi8gTa2SQiIiIi8hxMnjyZoUOHWjuNpzJjxgzGjx/P4sWL2blzp7n9+++/p169ehQoUIDcuXPj7u7ON998g9FoNMcYjUaqVq2Kp6dnms9bb71lMU9GxntYXFwc7u7uNGzYME1ffHw8vXv3Jl++fDg5OVGwYEECAgK4evXqM72HrL7e/fv3s3jxYiZNmkRQUNAzvQMREREREZGnoZ1NIiIiIiJi1qhRI37++Wfz96VLl9K9e3cqVKjA4MGDuX37NmvXrmXgwIFcv36dzz77DIBz585x9OhRKleujJOTk8WYD37P6HgP69WrFxcuXMDe3t6i/e7du/j6+rJ//3569uxJ3bp1OXDgAPPmzePcuXPs2bPnqdafHdY7atQoRo0aRbdu3di4ceNTrV9ERERERORZGEwmk8naSYiIiIiIPA8Gg4GVK1fi7+//wudu0qQJkZGRxMTEvPC5n5WnpyevvfaaRbGpatWq3Llzh4MHD1KwYEEALly4wD/+8Q+cnJy4ePEiANu3b6dZs2YcOXKEqlWrPnKOjI73oH/96198+OGH5MuXj6JFi3Ly5Elz3/z58+nTpw9fffUVH3/8sbm9b9++zJ07l4MHD1KrVq0Mv4PstN7UYlNcXFyG15/6s7Jq1aoMPyMiIiIiIq+81TpGT0REREQEGDhwIA0bNkz3L//79OlDixYtuHv3LgA7d+7kgw8+wM3NjVKlStGhQwfmzJlDSkrKI8fv2rUrnTt3TtM+ceJEGjZsyL1798xt169fp3///lSqVIlixYrRtm1bQkJCMmGVT+fGjRscP34cHx8fc6EEoESJEjRt2pRr166RnJwMQHh4OAaDATc3t0wZL9WJEyf4+OOPmTRpEsWLF08zZnBwMEWKFGHgwIEW7SNGjGDp0qU4OztrvSIiIiIiIs+ZjtETEREREQFcXV355ptvWLduHR988IG5/cKFC3z33Xe0b98eOzs7duzYQfPmzbG3t6djx44ULlyYrVu30q9fP6Kiopg8eXK64//+++/p3tETHh7Or7/+au47d+4cDRs2JDY2lq5du2Jvb8/mzZvx8/Nj6tSpDBky5Pm8gHTY2tqya9cuypYta9F+48YNjh49SosWLciZMycAERERvPbaayQmJrJt2zYuX76Mh4cHXl5e2NjYPPV4AHfu3KFDhw40bNiQQYMGMX/+/DQ5hoeH4+Pjg52dHVFRURw/fhwXFxeqVq1Kly5dtF4REREREZEXQMUmERERERGgY8eO/POf/2TNmjUWxaZVq1ZhNBrp0aMHACtWrMDW1pbIyEgcHBwAGDZsGGXLlmXDhg2PLDZl1PDhw4mOjmbfvn14eXkB8MUXX+Dj48OwYcPo2rVrmjuCAK5evcrs2bOfOH67du3w9PTMUC758uWjfv365u/Tp0/n7NmzbNq0iZSUFD799FNzX0REBAkJCZQpU4Zbt26Z22vWrMmyZcvw8PB4qvEAhg4dyoULF9i8eTMGgyFNfomJiVy8eJGiRYvi5+dncT+Ru7s7ixYtok6dOhla66u4XhERERERkcyiYpOIiIiICODs7IyPjw8hISFcuXKFIkWKAPDDDz/g4uJC8+bNAfjoo48YOHCgudAEcPfuXRwcHLhx48bfyuHatWssX76c2rVrmwtNAHZ2dvTu3Zvt27ezbt06AgMD0zwbGxvLmDFjnjiHm5tbhotNDxs5cqS5sOLp6UmePHnMfREREdy8eZOgoCDeeecdYmNjWbJkCd999x1t2rQhNDSUfPnyZXi8jRs3mneapXecXOqcADNmzMDV1ZVZs2ZRr1499uzZw7Bhw2jTpg3Hjh0z/7vUekVERERERJ4PFZtERERERP5Pt27d2LBhAz/++CPvv/8+0dHR7N+/n08//ZQcOe5fd+ru7k5cXBxTp05l7969REdHEx4eTkJCAiVKlPhb84eFhWEymUhMTCQgIMCiLyEhAYDIyMh0n3V3d7fYYfModnZ2z5zfX3/9ZT72b8SIEXh5efHnn39SrFgxFi9eTK5cuahUqRIA5cuXp169etjb2zNlyhTWrVuX5pi3R41nMpno0aMHgYGBvPPOO4/M59q1awAkJSWxZs0a3N3dAahRowaXL18mKCiIH374gUGDBmm9IiIiIiIiz5GKTSIiIiIi/6dVq1Y4OjqyZs0a3n//fVauXAlA9+7dzTFTpkxh9OjR5MqVi8aNG+Pt7c3IkSOZOnUqZ86ceeo5UwsIAHFxcQDkypXL4i4fgEKFCtGpU6dH7koyGAwWO2Uyg8lkwmQymQttcL+oUr58eXLkyEH37t0JCQmhZ8+e1KxZM90xfH19mTJlCsePH8/weNHR0Vy9epUbN26Yjy8EOH/+vLkw4+bmRtu2bQGoU6eOufCSys/Pj6CgIE6dOqX1ioiIiIiIPGcqNomIiIiI/J9cuXIREBDAggULiIuL44cffqBevXq4ubkB94+qGz58OM7OzoSHh1OgQAHzs0FBQY8d22AwYDQa07SHhYWZ/7ls2bLA/YJEcHCwRVxKSgo3b94kb9686Y5/6dIlxo4d+8Q1Pq5Q8rCJEycyYsQINm3ahK+vr0Vf4cKFAYiJiSEmJoYDBw5Qu3ZtXnvtNYu4qKgoAIoUKZLh8ZydnalWrRrh4eEWMUlJSRiNRo4cOUKOHDnMcyUnJ6fJ/fbt2wDY29tnaK2v4npFREREREQyi4pNIiIiIiIP6NatG3PmzGHSpEkcOXKE+fPnm/vOnj2L0Wikbdu2FoWmmJgYjhw5QtGiRR85bpkyZdi6dSvJycnmXUsnTpww38MD4OrqirOzM5s3b7aIA5gwYQKjRo1i9+7dNGjQIM34169fZ8GCBU9cX+PGjTNcbKpcuTIAW7duTVMsSX0vVatW5dq1a7z77rv06dOHuXPnWsSl7g5r2LAhV65cydB4b7/9NgMHDkyTT82aNbl9+zahoaHmtqZNm7J9+3bCw8MpX768uX39+vUA1KtXL0NrfRXXKyIiIiIikllUbBIREREReUCdOnVwc3Pj66+/Jm/evPj7+5v7KlSoQP78+Vm5ciU+Pj64u7uzZ88eRo0aRcGCBUlMTCQsLIwKFSqkGdfLy4tNmzbRvXt3evfuTUREBJMmTcLe3p6rV68C9+9TmjBhAoGBgXTu3Jlhw4ZRsGBBfvrpJ8aNG0fz5s2pX79+unm7u7uTlJSUqe/C19eXypUrM2vWLBwcHGjZsiXnz59n9erVbNiwgdq1a9OqVStsbGyoW7cu8+fPp1ChQrRt2xaj0UhwcDBbtmyhXbt2vP766xiNxgyN9zQmTpyIl5cX/v7+jB8/nlKlSrF9+3bmzp1LgwYNaN26NQDTpk1j6NChjB49mtGjR2f79YqIiIiIiLxQJhERERGRbAowrVy58qmfGzt2rAkwde7cOU3fqlWrTPnz5zcBJsDk5ORkWrJkiWnNmjWmfPnymWxtbU0mk8nUuHFjU8mSJc3P/fXXXyYfHx/zcy4uLqbhw4ebhg8fbgJMSUlJ5tiZM2eacufObY61tbU19e3b1xQXF/cMbyHjKlasaHrzzTct2iIjI01169Y155L6adu2rencuXPmuMuXL5v8/PzSxPXr189069atpx4vPTVq1DB5eHikaQ8JCTEVLVrUYrzWrVtbvK+vvvrKBJg+//zzx86RXdZrMplMXbt2NTk5OT12joe1b9/e1L59+6d6RkREREREXnmrDCaTyfT8SlkiIiIiItZjMBhYuXKlxe6kzBAXF0doaCjFixenYsWKGAwGc3t8fDyurq6PfDY2Npbz589TtWpV83PpuXnzJqGhoSQmJlK5cmVKlSqVqWtIj6enJ6+99ho///yzRbvRaOTMmTP873//I0+ePFSoUAEXF5d0xzh79ixhYWE4ODjg4eFhcdzgs4yXUcnJyRw/fpzY2FgqV65M8eLF08QEBQVRtmxZOnTo8Nixsst6u3XrxsaNG4mLi8vwuKk/K6tWrfpb+YmIiIiIyCtltYpNIiIiIpJtPa9iU3b1qGJTdhAREUHLli3ZuXPnCyncvQxUbBIRERERkRdkdQ5rZyAiIiIiIi+PI0eO4O/vz7Rp06ydSqaKjIxkw4YNr0ShadGiRfj7+/PLL79YOxUREREREXlF2Fo7AREREREReTm0aNGCmJgYjEYj2e0AhJYtW1o7hRfGZDJhNBqpVatWusf6iYiIiIiIZDYVm0REREREBCDb7WZ6VfXs2ZOePXtaOw0REREREXmF6Bg9EREREREREREREREReWba2SQiIiIikg2FhISQkJDAe++9Z+1UMtXJkydZv349ycnJjBkzJsPPZdf3ISIiIiIi8jJQsUlEREREJBuaPHkykZGR2aq4cuXKFWrVqsXt27fx9PR8qmJTdnwfIiIiIiIiLwsdoyciIiIiIlnCnj17uH37NhMmTOD48ePWTkdERERERET+j4pNIiIiIiJZmMlkwmQyWTuNFyI+Ph6AGjVqWDkTEREREREReZCKTSIiIiIiWdAff/yBt7c3Dg4O5M2bFy8vL37++efHPrNz504++OAD3NzcKFWqFB06dGDOnDmkpKSYY+7cucOYMWMoV64cuXLlonz58rz//vvcvHnzqWIy2/Dhw5k2bRoAn332Gd27d3+qdT0so2u4fv06/fv3p1KlShQrVoy2bdsSEhLyXNYoIiIiIiKSVenOJhERERGRLGbnzp34+PhQqFAhevXqxY0bN1i7di2tW7fml19+oV69emme2bFjB82bN8fe3p6OHTtSuHBhtm7dSr9+/YiKimLy5MkA9O/fn6VLl9KlSxeqV69OZGQk8+fP59ixY/z2228ZjslsJUqUoEiRIgCUKlWK0qVLP9W6HpaRNZw7d46GDRsSGxtL165dsbe3Z/Pmzfj5+TF16lSGDBnyXNYqIiIiIiKS1RhMr8qZGyIiIiLyysmTJw/z5s2jS5cu1k4l0xiNRmrUqEF0dDSHDh3C1dUVgP/9739UrFiRjh07EhwcTJMmTYiMjCQmJgaAPn36sHTpUi5duoSDgwNwf3dP2bJlsbe359SpUyQlJVGgQAF8fHz46aefzHPOnDmTwYMHExYWRunSpZ8Y4+bmlibvq1evMnv27Ceur127dnh6eqbbt3DhQnr16sXu3btp0KBBhtcFWLyPjKzTzc2Nzp078/3337Nv3z68vLwAuHv3Lj4+Pvz6669cvHgRJyenJ64pK/Hz88PJyYklS5ZYOxUREREREck6Vmtnk4iIiIhkW05OTly9etXaaWSq0NBQ/vjjD7p162YuNAG4u7szc+ZMjEZjus999NFHDBw40FyQgfuFEwcHB27cuAFgPnZu586dhIaGUr16dQAGDBhAr169yJ07N0lJSU+MSU9sbCxjxox54vrc3NweWWx61nU9LCPrvHbtGsuXL6d27drmQhOAnZ0dvXv3Zvv27axbt47AwMAM55oVXL161eK/KxERERERkYxQsUlEREREsi0PDw+OHz9u7TQyVUREBACVK1dO0zdgwIBHPufu7k5cXBxTp05l7969REdHEx4eTkJCAiVKlAAgb968jBkzhs8++4waNWrg4eHBG2+8ga+vLy1btsTGxiZDMY+a/9atW09cn52dXUZew1Ot62EZWUNYWBgmk4nExEQCAgIsnk9ISAAgMjLyqXJ92ZlMJk6ePGlxH5aIiIiIiEhG5LB2AiIiIiIiz0vdunXZvn27tdPIVLGxsQC4uLg81XNTpkyhZMmSjB07luTkZLy9vVm8eDH169e3iBs5ciQRERGMGjWKvHnzMmfOHFq1aoWnpyeXLl3KcMzDDAYDefLkeeLnUcWqv7uuhz1pDXFxcQDkypWLnDlzWnwKFSpEp06dnmoHVlZw6NAhEhISqFu3rrVTERERERGRLEZ3NomIiIhItrV//37q1KnDwYMHqVWrlrXTyRQbN27Ez8+PIUOGMG3aNIu+pUuXYjQa6d69u8UdRbGxsRQrVgxnZ2fCw8MpUKCA+ZlatWpx8eJFzp8/z927d7l16xYFChQwF30uXbpEUFAQ33zzDSNGjGDMmDFPjAkKCkqT96VLlxg7duwT19ezZ09q1qyZbt/DdzZldF1geWdTRtaZWkxq3749q1atssgjJSWFmzdvkjdv3qfeifUyGzZsGCtXriQ6OtraqYiIiIiISNayWjubRERERCTb8vLywtPTk2+//dbaqWSa2rVrkydPnjQ7tlKPP/vll1/SPHP27FmMRiNt27a1KMjExMRw5MgR8/ft27fj6OjIihUrzG3FihVj6NChAMTHx2coJj3Xr19nwYIFT/w8zdF0GV3XwzKyBldXV5ydndm8eTPJyckWz0+YMAFHR0cOHDiQ4Vxfdrdu3WLRokX06NHD2qmIiIiIiEgWpGKTiIiIiGRrw4cPZ9myZY8tPmQlRYsWZciQIRw9epS+ffty6NAhli5dSocOHbC1taVv375pnqlQoQL58+dn5cqVbNiwgfDwcBYvXky9evUoWLAgiYmJhIWFUb9+fYoUKcKXX37Jzp07uXHjBr///jtDhgwB4K233spQTHrc3d1JSkp64sff3z/D7yKj63pYRtZgZ2fHhAkTSEhIoHPnzhw+fJiIiAimTp3KuHHjaN68+ROP6stKJk6cyN27d+nXr5+1UxERERERkSxIx+iJiIiISLZmMplo3LgxRqOR3bt3YzAYrJ3S35aSksJnn33GlClTSElJAaB48eLMmDGD9u3bA5bHxgGsXr2anj17kpiYCICTkxPTpk0jX758dOvWjaSkJJKTk/nvf/9Lt27duHDhgnm+3LlzM2rUKEaMGAGQoZjn4eFj9J5mXQ+/j4yuYdasWXzyySfcuXMHAFtbWwIDAwkKCsLJyem5rfVF+vPPP/Hw8GDcuHF8+OGH1k5HRERERESyntUqNomIiIhItvf777/z+uuvM23aNAYNGmTtdDLNX3/9xdGjRylYsCDly5d/4v1BcXFxhIaGUrx4cSpWrGguvMXFxZmPjoP7R6odPXqUP//8k8KFC1OpUiWKFCliMVZGYl6UjK7rYRldw82bNwkNDSUxMZHKlStTqlSp57qeFyk5OZk333yTCxcucPToUXLmzGntlEREREREJOtRsUlEREREXg0TJkxg1KhRrF+/nlatWlk7HZGXQr9+/QgODubXX3+latWq1k5HRERERESyptW6s0lEREREXgmffvopXbp0oVOnThw4cMDa6YhY3bhx45g3bx7Lly9XoUlERERERP4WFZtERERE5JUxd+5cGjRowBtvvMGaNWusnY6IVSQnJ9OnTx8+//xzvvnmG/z8/KydkoiIiIiIZHEqNomIiIjIK8POzo5///vf9OrVC39/f0aPHs2dO3esnZbICxMdHc2bb77JihUr+PHHH+nXr5+1UxIRERERkWxAxSYREREReaXY2Ngwc+ZMZs+ezbRp0/D09OTHH3+0dloiz9WtW7cYPXo0FStW5MKFC/z666/a0SQiIiIiIpnGYDKZTNZOQkRERETEGi5cuMCwYcP4/vvvqVatGoGBgbRu3ZqSJUtaOzWRv81kMnHo0CHWrFnDokWLuHv3LqNHj2bgwIHkzJnT2umJiIiIiEj2sVrFJhERERF55R06dIiZM2eybt06/vrrL0qVKoWrqytOTk7kyKHDACRruXPnDrGxsZw8eZKEhARKly5Njx496NevH0WKFLF2eiIiIiIikv2o2CQiIiIikurOnTv8+uuvHD58mDNnzhAfH4/RaHymsa5du8bJkyepU6cOtra2mZypvKouX75MVFQUNWrUIFeuXOnG5M6dG0dHRypWrEjdunWpUqXKC85SREREREReMSo2iYiIiIhktjlz5jBkyBCaNGnC8uXLcXJysnZKkk388ssvdPl/7N13XJXl/8fx14HDEERFAXGgKO6NijNnbtPMPXFrVq76pvbN3FZaVo7MlXtr5kjT3OZWVHAPHKg4cbBkn98f5PnGD3AlnNT38/Hg8ZD7uq77ft/04Bw6n/u6rk6diIuL4+eff6Zhw4aWjiQiIiIiIrJCa4KIiIiIiLwkUVFR9OjRgw8++IABAwawfv16FZrkpapRowbHjx+nSZMmNGrUCF9fX8LDwy0dS0RERERE3nCa2SQiIiIi8hJcuHCBFi1acOXKFebOnUuzZs0sHUlecytWrKBPnz5kypSJBQsWULVqVUtHEhERERGRN5NmNomIiIiI/FPr1q3Dx8cHa2trjhw5okKTpItWrVpx4sQJihUrRs2aNRkyZAixsbGWjiUiIiIiIm8gFZtERERERF5QfHw8I0aMoFmzZjRp0oTdu3eTP39+S8eSN4i7uzvr1q3jxx9/ZMqUKVStWpWzZ89aOpaIiIiIiLxhVGwSEREREXkBd+7coWHDhowbN45p06Yxf/58HBwcLB1L3kAGg4FevXpx6NAhEhIS8Pb2ZuLEiWjFdBERERERSS8qNomIiIiIPKdDhw7h4+PD2bNn2bVrFz179rR0JBGKFi3K/v37GTRoEJ988gkNGjQgODjY0rFEREREROQNYDDpcTcREREReU6bN28mKCjomfq2aNGCLFmypHGi9DNjxgz69u1L3bp1WbBgAc7OzpaOJJLM/v378fX15c6dO0ydOpV27dql27XPnDnDnj17ntjH0dGRtm3bPvM5N2zYQGho6HONERERERGRdLNCxSYREREReW5NmzZl3bp1z9T35MmTFCtWLI0Tpb3w8HB69OjBypUrGTp0KMOGDcPKSgsFyL9XZGQkn332GZMmTaJVq1ZMnz49XYqj06dP5/33339iHw8Pj2cuWAPUrFmTwMBArl69+k/jiYiIiIjIy7fCaOkEIiIiIvLq+e677xg2bJj5+3PnztGhQwfq1avH2LFjk/TNnz9/esd76c6dO0eLFi24efMmGzZsoF69epaOJPJUDg4OTJw4kYYNG9KtWze8vb2ZCFXbAQAAIABJREFUN28eNWrUSJfrDxw4kKZNm6bYZm9vny4ZREREREQkfajYJCIiIiLPrUCBAkm+t7GxASBr1qyUL1/eEpHSzOrVq+nSpQsFChTg0KFDeHp6WjqSyHNp0KABx44do1evXtSqVYu+ffsyfvx47Ozs0vS6hQoVombNmml6DRERERER+XfQuh8iIiIikub69etH9+7duXbtGh9++CGurq4A+Pr60rFjx2T9v/76a6pVq0ZcXJz52IMHD/jggw8oUaIE7u7uNG/enA0bNqRZ5ri4OIYMGULz5s1p06YNe/fuVaFJXllubm6sXr2auXPnMnv2bMqXL8+xY8csHQuAHTt28OGHH1KoUCE8PDxo164d06ZNIz4+PtUxUVFRDB8+HC8vL+zs7ChYsCC9e/cmLCwsSb/0ft0QEREREXlTqdgkIiIiImkuICCAPXv20LhxY6ZOnUqePHkA8PPzw8/PL1n/8+fPs3v3bhISEgC4du0a3t7ezJ8/n+rVq9O1a1cuX75MkyZN+OGHH1563uDgYGrWrMmkSZP4+eefmT59Ora2ti/9OiLpzdfXl+PHj+Ps7EzlypUZN26c+ffMErZv306dOnVYunQp9evXp0ePHly9epU+ffrw2WefpTrugw8+YOzYsVSvXp1vvvmGRo0asWDBAurXr2/uk96vGyIiIiIibzItoyciIiIi6eLs2bPUr1+fZcuWUaRIkecaO2TIEC5fvsz+/fupWLEiACNHjqRhw4YMHjwYX19fsmbN+lJy7tq1i7Zt25IxY0YOHDhAyZIlX8p5Rf4tPD092b59O99++y3Dhg1j3bp1zJ8//6XvrzZt2jQ2bdqU7LjRaGTFihUALFmyBKPRSGBgIFmyZAFg8ODB5M+fn3Xr1jF+/Phk46Ojo1m4cCGNGzdmzpw55uNeXl7079+fc+fOUahQoXR93RARERERedNpZpOIiIiIpJvRo0c/d6Hp3r17LF68GB8fH/MHxgC2trb07NmTmJgYVq1a9Y+zmUwmJk6cSJ06dfDx8eHgwYMqNMlry9ramsGDB3P48GHCwsLw9vZmxowZL/UaFy9eZO/evcm+9u3bZ+7z8ccfc+jQIXOhCSAmJoYsWbIQGhqa4nkfL6+3Y8cOjh49aj7+0UcfER4ejpeXV7q9boiIiIiISCLNbBIRERGRdOHq6oqPj89zjzt79iwmk4nw8HDatGmTpO3xh9GBgYH/KFtYWBjdunVj9erVjBkzhkGDBmEwGP7ROUVeBSVLluTAgQOMGDGCPn36sHnzZqZNm0a2bNn+8bnHjx/P+++//8Q+RYoUISQkhAkTJrBv3z4uX77M+fPnCQ0NJWfOnCmOcXBwYPjw4QwdOpSyZctStGhRatWqRaNGjahfvz7W1tbp8rohIiIiIiL/o5lNIiIiIpIu7OzsnrnvvXv3zP8OCQkxj7exsUnylS1bNjp06EDx4sVfONeZM2eoWLEiO3fuZOPGjQwePFiFJnmj2Nvb8/XXX7Np0yb2799PiRIlWL9+fbpc+5tvviF37tyMHj2a2NhY6tSpw9y5c6lateoTx33++edcuHCBL774AgcHB6ZNm8Y777xD8eLFuXnzZpq/boiIiIiISFKa2SQiIiIiFmMwGEhISEh2/OzZs+Z/P95HpmDBgixcuDBJv/j4eMLCwnBwcHih6y9atIjevXvj7e3Nli1bUp1JIfImqFOnDsePH6dv3740adKEnj178t133+Ho6Jgm17tz5w5DhgzB1dWV8+fP4+TkZG4bO3ZsquNiYmKIjIzE09OTUaNGMWrUKG7evMnYsWOZMmUKkydPpkOHDkDavG6IiIiIiEhymtkkIiIiIhbj6enJ5cuXiY2NNR87efIkFy5cMH9foEABXF1d2bRpU5J+AF999RXOzs4cPHjwua4bHR1N//796dixIx06dGDbtm0qNIkAWbJkYcGCBSxbtoyVK1fi4+ODn59fmlzrypUrJCQk0Lx58ySFpqtXr3Ls2LFUx23btg1nZ2eWLFliPubu7s6nn34KwP3799PkdUNERERERFKnYpOIiIiIWEzFihWJiYmhS5cu7Nixg1mzZtGsWTMyZ85s7mNra8tXX31FaGgoHTt25MiRI1y4cIEJEyYwZswY6tat+9Qlt/7u2rVr1KxZkzlz5rBs2TKmT5+OjY1NWtyeyCurVatWHD16FHd3dypVqsSIESOIj49/qdcoXLgwGTNmZNmyZaxbt47z588zd+5cqlSpQqZMmQgPD08yy/GxqlWr4ubmxqhRo9ixYwcPHz7Ez8+PAQMGANC4ceOX/rohIiIiIiJPpmX0RERERMRiPvnkE/bt28fixYtZvHgxuXLlolOnTgB8/fXX5n7du3cnMjKSQYMGsXz5cgCMRiM9evRg7Nixz7zH0vbt22nXrh3Ozs7s27dPe7aIPEGePHnYunUrM2fOZODAgWzcuJEFCxZQsGDBl3J+JycnZs+eTbdu3WjatCkAWbNm5fvvv8fR0ZHOnTtTokSJZDOTnJycWLRoEZ07d6ZWrVrm4/b29owdO5bGjRsDL+91Q0REREREns5gMplMlg4hIiIiIm+2O3fucP36dUqXLv3ED4DDwsI4evQo4eHhlCxZEg8Pj2c6v8lkYvz48Xz++ee0bt2amTNnptk+NCKvo1OnTtGxY0fOnj3Ll19+Sf/+/V/auUNCQjh69Cg5cuSgWLFi5teAkJAQ85J4KYmMjCQgIICgoCBcXFwoUaIEbm5uyfq96OuGiIiIiIg8sxUqNomIiIjIay00NJQuXbqwbt06xowZw+DBgy0dSeSVFBsby9ixY83L0M2ePZscOXJYOpaIiIiIiFieik0iIiIi8vo6evQoLVu2JDo6muXLl1OlShVLRxJ55e3bt49OnToRGhrKzJkzeffddy0dSURERERELGuFlaUTiIiIiIikhfnz5/PWW2/h4eHB4cOHVWgSeUkqV67MkSNHeO+992jWrBm+vr6Eh4dbOpaIiIiIiFiQik0iIiIi8lqJioqiV69edOnShR49erBlyxbc3d0tHUvktZIpUyamT5/OL7/8wu+//07JkiXZtWuXpWOJiIiIiIiFqNgkIiIiIq+NoKAgatSowbJly1ixYgUTJ07EaDRaOpbIa6t58+acOHGCkiVLUrt2bYYMGUJMTIylY4mIiIiISDpTsUlEREREXgvr16+nTJkyxMTEcOTIEVq0aGHpSCJvhOzZs7N27Vpmz57Njz/+SPny5QkICLB0LBERERERSUcqNomIiIjIK81kMjFu3DiaNm1K48aN2bNnD15eXpaOJfLG8fX1xd/fn0yZMlGxYkXGjRtHQkKCpWOJiIiIiEg6MJhMJpOlQ4iIiIiIvIi7d+/SsWNHduzYwbhx4+jfv7+lI4m88eLi4pgwYQJffPEFNWrUYM6cOeTOndvSsUREREREJO2s0MwmEREREXkl+fn54ePjw6lTp9i5c6cKTSL/EkajkcGDB7N7926uXr1KiRIlWLhwoaVjiYiIiIhIGlKxSUREREReOTNmzKBKlSrkz5+fw4cPU7FiRUtHEpH/p0KFChw9epTOnTvj6+tL69atuXfvnqVjiYiIiIhIGlCxSURERET+VWbOnEl0dHSKbVFRUXTv3p3333+fgQMHsnnzZtzc3NI5oYg8qwwZMjBx4kQ2btzInj178Pb2Zvv27U8ck5CQQFhYWDolFBERERGRl0HFJhERERH519i7dy+9e/dOcUm88+fPU6FCBdasWcOGDRv4+uuvsbLSn7Mir4J69erh7+9PuXLlePvtt+nduzeRkZEp9v3uu+/o0aNHOicUEREREZF/wmAymUyWDiEiIiIiEh0dTYkSJbh48SIJCQnMmzcPX19fANauXUvnzp3Jnz8/K1euJF++fBZOKyIvasWKFfTu3ZscOXKwcOFCvL29zW0BAQGUL1+e2NhYZs+eTdeuXS2YVEREREREntEKPQoqIiIiIv8Ko0eP5tKlSyQkJADQo0cPDh8+zIgRI2jWrBlNmjRh9+7dKjSJvOJatWrF0aNHcXV1pWLFiowYMYL4+Hiio6Np27Ytj5+H/OCDDzh79qyF04qIiIiIyLPQzCYRERERsbiAgADKli1LfHy8+ZjRaMTe3p74+HimTZtmnuUkIq8Hk8nEpEmTGDx4MGXLlqVo0aLMmzfP/DpgY2NDkSJFOHz4MLa2thZOKyIiIiIiT7BCxSYRERERsaj4+HjKly/PyZMniY2NTdJmNBqpVKkSu3btwmAwWCihiKSlY8eO0bx5cy5dupSszWg0MmDAAL755hsLJBMRERERkWekZfRERERExLImTJhAQEBAskITQFxcHHv37mX8+PEWSCYi6SFfvnxERUVhZZX8f0/j4uKYMGEC69evt0AyERERERF5VprZJCIiIiIWc/78eUqUKEFMTMwT+xkMBjZt2kTdunXTKZmIpJdOnTqxdOlS4uLiUmy3srIic+bMnDp1Cnd393ROJyIiIiIiz0Azm0RERETEMkwmE926deNZn31q3749169fT+NUIpKeVq5cycKFC1MtNAEkJCQQHh6Or6/vM79eiIiIiIhI+lKxSUREREQsYsaMGezduzfF5fMgcTaTtbU1BoOBypUrM2LECOzs7NI5pYikldu3b9O7d2/z7/qTxMbGsnXrViZOnJhO6URERERE5HloGT0RERERSXfBwcEULlyY8PDwJMcNBgNWVlYkJCTg4+ND+/btadWqFTlz5rRQUhFJSxcvXmTLli1s2rSJTZs2ERERgZ2dHdHR0Sn2t7Gx4cCBA3h7e6dzUhEREREReYIVKjaJiIiISLpr3Lgxf/zxB3FxcUkKTJUqVaJ9+/a0aNGCHDlyWDqmiKSjuLg4/P39WbduHb/++ivHjx8HwNra2rzMntFoJFeuXBw/fhwnJydLxhURERERkf9RsUlERERE0tfSpUtp164dkDiTqUKFCnTs2FEFJhFJ4ubNm2zatImNGzeyceNGHjx4gLW1NfHx8XTr1o2ff/7Z0hFFRERERCSRik0iIiIikn7u3r1LyZIlyZcvH+3ataNFixZaIk9EniohIQE/Pz82bdrEb7/9hp+fH0uWLKFly5aWjiYiIiIiIio2iYiIyK1bt9ixYwf+/v7cunWLsLAwS0eS11hYWBhGo5EMGTI8U38nJyeyZ89O6dKlqVmzJtmzZ0/jhCJiKVFRUezevRs/Pz8uXbrEgwcPSEhISLFvbGws9+/fx83NLZ1TijyZvb09zs7OFCtWjEqVKlG6dGlLRxIRERFJDyo2iYiIvIni4uJYunQp036ayr79B7C2MlAojzs5sjqR0d7G0vFEzMKjYrlxL4xzQTeJTzBRuVJF3u/zAW3btsVoNFo6noi8BIcOHWLS5Mms+mUVkZERuOTIjatHfhycnDFYWVk6nshziYuJIvLhPa5dOE1keCi5PfLQo3s3+vTpo+KoiIiIvM5UbBIREXnT7Nixg359P+LMmbM0rlSMNrXKUKN0ATLYqcgk/16PomPZ6X+BZduPsX7/KYoUKcykyVOoWbOmpaOJyAsKDg5m0ODBLF60iLxFSlO1mS+lqzfEOXsuS0cT+cdMJhNXTh/Db8tq9q1bSEJsDCOGD6Nv377Y2OhvLhEREXntqNgkIiLypggPD6dXz54sWbqUBhWKMaZ7Q7xyulg6lshzCwy+y9Cff2fjwVO0a9uWGTNnkjFjRkvHEpHnMG3aNP7zn0/J6OxKi4Fj8K7VxNKRRNJMTNQjfp/zHZsXTCJv3rwsX7ZUy+uJiIjI60bFJhERkTfB1atXadrkHa4HXebHfs2pW76wpSOJ/GObD5/lw0mryOWRl7W/rcfDw8PSkUTkKeLj4xk4cCBTpkyhcY9BNOr+CTa29paOJZIuQoKDmDfqQ4JO+rFkyWKaNFGRVURERF4bKjaJiIi87k6ePEndOm/jbG/Nki86ksfN2dKRRF6aoNv3aTd6AfejEti8ZSvFixe3dCQRSUVMTAzNmr3Hth076DpyGuXqNLN0JJF0Fx8Xy+KvP2H3mgVMmTyZPn36WDqSiIiIyMugYpOIiMjr7Pbt21T08cHdycjyYb44OdhZOpLISxcWGU3rUfO4GRbHgUOHtQG7yL9Ul65dWbHyFwZMXUu+EuUsHUfEotbPGs/aaV+yevVqzXASERGR18EKK0snEBERkbQRFRVFs3ebYoqNZMFn7VVokteWk4Mdiz/viHV8NI0bNSQyMtLSkUTk//nqq69YsGAB3cf+rEKTCNC4xyCqN+9Ku3bt8ff3t3QcERERkX9MxSYREZHX1MiRIzl14jgrhvniktnR0nFE0pSzkwNLhnbk/NkzjB492tJxRORv/Pz8GDp0KK0//opS1RpYOo7Iv0bbQePJU7wcrdu0JTY21tJxRERERP4RFZtEREReQ4GBgXz/3XcM7ViHgrldLR1HJF0UzO3K5x3q8N2ECZw7d87ScUQEMJlM9B8wEK/SFajdtrel44j8q1gbbegy8ieuBAUxefJkS8cRERER+Ue0Z5OIiMhrqGmTJgSeOMLOHz7EaP1qPFty5dZ9tvqd5filGzwMj6JcYQ8qFMlDKa+c2NkY0/z6R85f4+Tlm9T2Lkgul8wp9tnid5Yb98JoVaM09rY2bD58lrDIKJpXL53qeVft8ic2PoE2tbzTKvoziYiKwdHe1qIZ0kNcfAI1BvyIVwlv1q77zdJxRN54CxcupHOXLgxduBOPwqUsHeeF3b1+hRN7N3P17HEiwx6Sv2R58peqQN4ipTHapv0ytZdPHuHa+RMUq1ybrNlzp9jnxJ7NPLhzg4oNW2Fjl4Hju/8gKiIUn/otUz3voU0riYuLo3LjtmkV/ZlER0Zg5/DmzsJe89NYdi6bxoXz57TvoIiIiLyqVqT9JzciIiKSrk6ePMm6335j+fAur0yhaf4fh/hs5m9ERsVgZ2PEztbIr7sDAMjtmoVVo7ql+QytdXtP8MPKnSwd1jnVYtOUX/9kp38g9csXxt7Whom/7OTSzXvmYtPB01fYFRCIb/0KuGXJmDhm9W7CH0VbpNjkHxjMqHkbOXL+Gg/CH+GWJSONKhVjVNdGT9zDK8Fkonq/ScTFJyRry5PdmeXDu6Rh6n/GaG3FiM71aD1yLidPnqR48eKWjiTyRvvyq6+p3LjtK11o+vPXeSz7djDRjyIx2tphY2vH4c2rAMjqnpuBU1fj7lkoTTP4bV3Dxrnf03fi8lSLTX8smMzpgzsoWa0+me0ysHHeD9y5dslcbAr0P8CZQzup9l4XMmVzM4+JioywSLEp6Iw/qyYP59LJI0SGPiBTNjfK1GhMy4FjyODolOo4U0ICo9q9RUJ8XLK2bDnz0G/SyrSMnSYadv2YP1fN5qeffmL48OGWjiMiIiLyQl6NT6BERETkmc2ePZv8udyoUy5tP/h6WSb+sov+k1fh7uzEui97cnX5CC4tGca27z5kSPs6XLvzgPqDpnEjJNTSUZ9q78nLjF24mVv3wiwdhaPnr9H085kcu3CdljXK8Gnb2mRytGfuxoM0GzqLhCdMbg+++5CTl29ibW2FS2bHJF/OTg7peBcvpk65QuTL6cqcOXMsHUXkjXbgwAFOnzpJrTav7vJ5G+f9wPzRfcns4s5/Zqxnyu5gJu4I4vOFO2na+zPu3bzG113r8uB2sKWjPtX5o3tZPXUMD+/etHQULp86yre9GnPl1DEqNmjNOz0HkyFjJnatmsN37zfBlJD8YYfH7t8O5tr5ExisrMno7JLkyzGTczrexctja5+Byk068vNsvW+JiIjIq0szm0RERF4zv61dQ9PKRTEYDJaO8lQ3QkIZv3QrebM7s3NiXzJm+N9sG++CufEumJugW/dZvNWPJVv9+Lh1LQumfbXMXL+PqOhYtkz4kJL5cwDw3w51aTZ0Fjv9A1m75wTN3iqZ4tiLN0IAmP5xa0rky5FumV8Wg8FA08pFWbdmNd9++62l44i8sX777TfccnuSt2gZS0d5IQ9uB/PbjK9xyZWXLxbvxt4xo7nNs5g3nsW8uXsjiL1rF7F33WIadf+PBdO+WrYvm05sdBSfzt9mnvX2bp/P+e79ppw+uAO/rWsoX/e9FMfevhoIQPcxM/AolPL72Kuo3NvvsnHu9wQEBFCq1Ks7E1BERETeXCo2iYiIvEZCQkI4dyGQLztWt3SUZ/LNsm1ERsXwRd/mSQpNfzemeyOiYuMIj4pJ1vYwIopR8zex98Ql7oVGUKFoXnzr+VC3fGFzn/6TV2FrY+ST1jUZ+vMGDpy+gtHaiqol8jG+d1McXtI+RgOmrGL70QsAfDRxJZWKezKuVxNz+5mgW3y/cie7/AOxs7Gmeikvxr/fFHtbmxe63u0H4azdc5yyhTwoWzD5kkoHTwdRMn9Oc6HpsQ51yrPTP5Aj566mWmwKDL6LwWCgQC6XF8r2b1CtpBcTf9nFvXv3yJo1q6XjiLyR9uzdR8Gyb1k6xgv7bdY3RD+KxPfD4UkKTX/XeuCXxEZHE/0oIllbZNhDfp0yknNH9hD+IASvUhWp9l5nSr5Vz9xn/ui+GG3saNT9E1Z8/zkXju3H2tpIoXJv0W7wt9hleDmzSeeP6cep/dsAmDvyQwqUqUS7Qd+Y24MDT/P7nO84c2gXRltbivjUoP3gb7Cxy/BC1wsNuY3f1tXkK14ez+Jlk7Vf8D+AR+GSyZZXrNq0I6cP7uDySb9Ui023ggIxGAy45y3wQtn+rfIW88YhYyb27dunYpOIiIi8klRsEhEReY2cPn0agKJ5s1s4ybM5duE6VgYDTauUSLWPs5MDP3+afC+J4LsPaTh4OndDI2hbuyyZHOzZduQcbUfNY0z3xvR5tyoAxy/dIORhBOv3nyRv9qy0qF6Kw2evsmiLH6GRUcz/rONLuRevXK6cunKLoNv3KZDLhfw5spnbQh5G0OS/M8nsmIHWNctw/OINFmw+TERUDD8PavfM1wgJjWDt3hOs/vM4e05cIj4hgfmfdUxWbIqNi6d22YKUK+SR7BzX7z4AeOJyeJeCQ8jtmpnwRzHs9A/k9oNwCud2pVxhD6ytXo1VmB//Dpw5c4YqVapYOI3Im+n06dNUb/e2pWO8sCunjmCwsqJcnXdT7eOY2ZleX81Odvz+reuM616f8Pt3qfxOezJkzMTJfVuZPKA1rT/+kjrtPwDg6tkAwh6EcHTHb7jkzEuF+i25eOIwe9Yu5FF4KH2+XfhS7iV7ngIEXzhFSHAQ2fMUwM3Dy9wWfv8u3/ZqTAanzFRs1JqrZwPYvXo+0ZER9Pr62Zd1C38Qgt/WNRz+41fO+e0mISGePt8uTFZsio+LpUTlt/EsUT7ZOe7dugaAwxOWw7tz9SJZ3XMTFRnB6YM7CQ25TY58hclXsjxWVtbPnPffxmAwkDN/Yc6cOWPpKCIiIiIvRMUmERGR10hISOLyZ9kyO1o4ybO5cP0OOV0yY2NM+uHQ1dsPiIxOPpMpf45s5r4j5m0k6PZ9Nn/7AeULJxZVPutQh1bD5zBi7u+0re1tLqgE3b7PgJY1GOZbH4PBQILJRO2BU9jpH5jsGv2nrMLRLuXZTsFP2Deq73vViI9P4NCZIAa0rJlkRtG9sEiGtK/D4L996Frnk6lsO3o+1fM99iD8Eev2nWT1nwHsCggkLj6BEvly8EnrWjSqVIzSXjmTjbExWjO+d9Nkx+88DGfm+v3YGK2p71Mk1WtevBFCWGQ0pbqP41F0rPl4mQK5mP5xawp5uD01t6U9/h24e/euhZOIvLnu3QvBKaurpWO8sFtXLuDslhNrY9IZqCE3rqY4kyl7Hi9z318mDyckOIj/zt9Gvr+KKk3f/y8TP2rBLxOHUblxOxwzJxZUQoKDaNBlIM37jsBgMGBKSGBMxxqcPrgj2TXmj+6X6myn+7dvpHov9X37YUqIJzDgIA27Dkwyoyj84T2a9v6MJr0/Mx/70rcWJ/dvTfV8j0WGPuDItrUc3ryK0wd3kRAfh0ehkjTu8SllajYmT5HSycZYG21oNzj5Eqdh9+6wfflMrI02lK7eINVr3r56kUcRYQxpXJyYqEfm43mLlqH7mJnkyFc41bH/dhmdXcx/y4mIiIi8alRsEhEReY1ER0cDYGfz73+LD42IIiwymgK5kn8Q+eHEFfwZcDHZ8aMzP8XTPSv3wyJZudOfsgVzmwtNALZGazrX92FXQCDr9p3Et54PAPa2NgxpV8e8j5WVwUDFYp74BwYTfPchOV0ym8/hkskRl1SKdffCIomKiU2x7UmsDAYGtKyR5FjpArnwO3c12fUfu/0gnI8mrmTHsQuYTCaqFM/HmO6NaVSxGB5uWZ47w6ZDZ+g78RfuhkbwVc93KObpnmrfizdCCH8UzdBO9XmncjFCHkawZNsRFvxxmPZjFrBrYt+XtvxgWnn8OxAVFWXhJCJvrpjoaIw2L7ZUqKU9Cg/lUUQY2VNYqm3uiD6cObQr2fEv1wXgmsuTiIf3Ofj7CjyLlzUXmgCMNrZUb96ZM4d2cmTbWqq91xkAG7sMNH3/M/N7lMHKigJlKhF0xp/7t67jnD2X+RxOzi44Oae8xGn4w3vERj9Kse1JDFZWNOz6cZJjeYt6c+mEX7LrPxYacpu5Iz/g1P7tmEwmCpWtSuuPv6RMzcZky5F8Vu3TBPy5kbkjPyT8/l3a/GccuQoUT7Xv7asXiYoI572PhlG2VhPC7t9l77rF7F49nykD2zJsyZ6XtvxgejPa2ut9S0RERF5Z//5PokREROS1lMnRHicHO27eSz5baFDbt+nasKL5+3kbDyaZhXTh+l1MJhPhUTF0G7ckydjQR4kf0ly6cc98zDWLI3a2Sf/syeKYuA9FxP/bC+oL3/qpzvppNnRWirOhniZHtkzJCoCZHe1TvP5jdx+Es/nwWYzWVvR8pzId6pSn+BMKRKm5dCOE/85az8aDp8mfIxseRLngAAAgAElEQVQz/tOGmmWevM/F1AGtsLMxmpei88rpQoWiecnkYM+kVbtYt+8kbWp5P3cWEZFXRYaMmcjg6MSDu7eStb3TczA1WnYzf7/rl7lJZiHdvHIek8lEdGQE0wd3STI2KiLxPe/OtUvmY5myumBja5+kn+Nfy8j9/xlU7300jFLVUp718937TVOcDfU0zq45Mdom3TfRwSlzitd/LOz+HY7v/gMrayO12/Si6rsdyV0w9SVxU3Pn2iWWfTsE/12/4+aRn55jZ1G0Yq0njuk6chpGG1tyFSgGgFseL7xKVySDUyY2zZvIkW1rqdw4+fK7IiIiIpK2VGwSERERiymYy5VjF64T/iiajBn+90HXWyXzJ+k3f9OhJN/fC4sEwM7GGqMx6R5CWZ0caFWzDEXz/G+pN3vb1J+sN5lML5z/WT1pFlBq1y/k4cby4V34dXcAi7b48dOaPeTN7kzjSsVpXKkYFYvlfer+Scu3H+XjqasxGAyM7NqQ3k2qPNOstzIFkj/FDlC3fGEmrdrF6Ss3n3oOEZFXXXbPglw5fYyoiHDsHTOajxcuXy1Jv12r5iX5PuJh4sMORls7rP/fa65jlqxUbNSanF5Fzcds7DKkmiE93qNsnzALKLXr58hXmH6TVnJ48yr2rF3IlsVTccmVF++a71Cm1jsUKFPpqfsn7d+wjIVfDsBgMNCy/2jebvd+sqJXSvIWLZPi8ZJV67Fp3kSCL5x66jlERERE5OVTsUlEREQsxrtgbo6cv8a8TYf4sNlbKfYJjYjCPzA4yTFP96xA4oybGZ+0SdIWn5BA+KMYMti9mks3PWa0tqJu+cLULV+YmLh4tvqd49fdAcz/4xBT1+wmWyZH6vsU4dO2tc0/j7/bdOgM73+/Ap8iefj507bkdn22pfeu332I37mrlC2YO9mYyzcTP0B1yZwxpaEiIq8Vz+LluHzyCH/+Ope6HT9Ksc+j8FCCzhxLcswllyeQuIdTjzGzkrQlJMQTFRGOrX3qBaZXgZW1kZJv1aPkW/XoNDSGk3u3cGjzKv5cPY/Ni34kY5ZslKregHd6Dsb1r5/H3wX8uZHZX/Qif6kK9PpqDlndcz/Tde/dusblE354Fi+XbMyd65cBXul9wkREREReZU9+HFZEREQkDQ1qW5tMjvZM+mUnJy4l39g8PiGBwTPWcf+vmUyP5cuRDZfMjmw9co7YuPgkbd+v2IFn25EcOXc1TbOnJ1ujNQ0rFmXGJ224sHAo8z/rSI3SXqzec5yAi8Epjhk1fxOZHOyYP6TDMxeaAO6HRdL5q0VMWL49WduvfwYAULm45wvdh4jIq+SdnoPIkDETG+f9wNVzx5O1JyTEs2T8p0Q8vJ/kuJtHfpycXTi5dwvxcUn3+ft99gT61/Dg8km/NM2enow2tpSu0YgeY2bx3daL9Pl2IUUr1MRv869cPeOf4phVk0eSIWMm+nyz4JkLTQARD+/z06edWD/rm2Rth//4BYCC3lVe7EZERERE5B/RzCYRERGxGDdnJ0Z0acDHP66m/qBp9HqnMqW8cuKSKSMXrt9h9u8HCAy+y7tVS7BmzwnzOFujNcM6N6DfpF/oPWE5/VvWwMnBjg37T/Htsu3UKlOQikXzpvv95HFLLOrM3XSQDnXKUbbgs3+A9v/dfRjBws2HU20vmT8nXrlcyJE1U7K2B+GPOH3lFqXy52DK6j9THP9WyfzU9ynC3I0H+c9PaxjUrjaD2r5NcU93fIrkYd6mQzg7OdCkcnESTCaW7zjGtqPnaVqlBOUKPf/m7yIir5rM2bLTov8oFo4dwLiudanVpjd5i5YmYxYXbl05z86Vs7kVdIFydZrht2W1eZzRxpbmfUcwb9RHzPq8Jw27DsTe0YljO9bz26xvKFapFl6lK6X7/WTLkfjavWvVXKo27Yhn8bIvfK6w+3fZs2ZBqu15ipQie94CZHbNkawtMvQBwYGn8Chcij8WTE5xfOHy1ShVrQG7Vs1h0Zcf806vwTTpNYTcBUvgVaoCf/46l4xZsuJduymmhAT2b1jKyX3bKPv2u+QrUe6F70tEREREXpyKTSIiImJRXRtUJH8OF/7z02p+WLnTfNzO1kgZr1ys/6oXGTPYJSk2AXSqW55HUTEMm/s7v+5OnHFjtLaiUz0fvuhUD4PBkK73AVDTuyDlC+dh9ob9nLt6m3Vf9nzhc92+H8bIeRuf2q9EvuQf5B04fQWTyYR/YHCyJQgfMwD1fYpgwkR8QgKPt+UwGAwsGtqJfpNW8f2KHXy/Yod5TLdGlRjbvdGL3I6IyCupRotuuHnkZ9FXH7Nx7vfm4za29uQtVoZPZ23E3sExSbEJ4K1mvsREPWLlD19wePMqIHHpuWrvdea9D4dZ5D2qaKXa5C/pw44Vs7hx6Sz/mbH+hc8VGnKLXyYNf2o/j0Ilkh274L8fk8lE0Bl/glKZ+WQwGChVrQEmk4mEhHgev0kZDAY+/G4J80Z9xIbZE9gwe4J5TM1WPWj98dgXvCMRERER+acMpvTYcVRERETSxfLly2nTpg33131l6Sgv5NqdB5wJuoWzkwMl8+fE1vjkzcUBwh9FExAYTERUDMU83cnlkjkdkj7ZzXuhZMxgR8YMT9/o/N/s6u0HnL9+h8yO9hT2cHvl7se5yWcsW7aM1q1bWzqKyBvJYDDQe9xcytdtbukoL8W9m9cIDjyNY+aseBQuidHG9qljoiLCCTrrT3RkBLkKFiNr9hef8fqyPLhzA3sHJ+wdX+3990JuXOXm5fM4OGUmR77Cr/z9AEwf3Jl8WYwsX77c0lFEREREntcKzWwSERGRf43crlmea38hgIwZ7KhSIl8aJXox7iksbfcq8nDLgofb8/33EBF5XWV1z/1c+wsB2DtmpFDZqmmU6MVkSWFpu1dRthwe5qUBRURERMTyrCwdQERERERERERERERERF5dKjaJiIiIiIiIiIiIiIjIC1OxSURERERERERERERERF6Yik0iIiIiIiIiIiIiIiLywlRsEhEREXkNJJhMLzQuLj6BqJjYp/YzmUw8CH/0QtcQEZE3hykh4YXGJcTHYXrB9zIRERERsTyjpQOIiIiI/FMHT19hV0AgvvUr4JYlo6XjpJsL1+8ya/0+Nhw4RWhEFBWLefLBu29Ro7TXU8duO3qekXM3cjroFnHxCXi4ZuGj96rRvXElrAwGc78H4Y8YPud3lu84RlRMLBkz2FG3XGG+6dOUbJkc0/L2REReOYH+BzhzaCfV3utCpmxulo6Tbm5ducD25TM4tmM9j8JDKVCmEnU6fEjRCjWfOvb47j9YPXU0wRfPkMHRiSI+NajZugeFylZNdczn75ahcPlq+H4x2Xzs9MEdLB0/6InXylu0DN1Gz3jm+xIRERGRZ6eZTSIiIvLK23vyMmMXbubWvTBLR0k3UTGxtB89n4WbD/N22UJ0a1SJi8F3aTtqHntPXHri2J3+gbQcPoeg2/fpUKcc3RtVIiomlkHT1zJu8VZzv5i4eFqNmMuCzYdpVaM0k/q1oGWN0vy6O4D2Yxak9S2KiLxyzh/dy+qpY3h496alo6Sb2OhHTBnYht1rFlC8ch1qturBraBAJvdvw7kje5449uDGlUzu34rIsAfU9+1PqWoNCPjzd6b0b83Ny+dTHLN37SJuX72Y7LjBYMDaaJPiV0JCPMEXz/Ao4s35O0FEREQkvWlmk4iIiLzREkymJDN5XhWj5//B+et3WDGiC3XKFQbg/aZVqdZ3Ih/8sIJjs1J/uvubpVsxmUxs++5D8uXIBsDwzvUp1uUrpqz+k0HtamNtZcWSrUc4fDaI0d0a8dF71QDoVLc8BgzM2XiAo+ev4V0wd9rfrIjIG8CUkIDB6tV7HvTXKaO4efk8/Sf/QomqdQF4u10fRratwpzh7/PVuuMpjouLjWHFD0OxzeDAF4t34+CUGYAW/UbyaYMizPisK8OW7Abg/q3rrJvxNZdPHuHquZTPV8SnBsOWplzcWjLuPzwKD6Pj5z/809sVERERkVSo2CQiIiIW8TAiilHzN7H3xCXuhUZQoWhefOv5ULd8YXOf/pNXYWtj5JPWNRn68wYOnL6C0dqKqiXyMb53UxzsbRkwZRXbj14A4KOJK6lU3JNxvZoweMY6IqNi+Kx9Hb5bsYPVu49zYdFQAM5dvc3nP2/g6PlrhEdFUyyvOwNa1qBplRLma3cbt4QS+dx5q2R+pq3dy66AQFwyO9K2dln6taiOlcHAl4s282fARX4a2ApP96xJ7q/P9yu4fT+MZcO7YLR++R8eLt7qR3FPd3OhCcAtS0Zqly3E0m1HOHz2KuULe6Q49vrdh+R0yWwuNAFkzGBHuUIe7D1xieiYOBzsbVm+4yiumTPSq0mVJOM/bl2TisXy4pL5zVmyUEReX5FhD/l1ykjOHdlD+IMQvEpVpNp7nSn5Vj1zn/mj+2K0saNR909Y8f3nXDi2H2trI4XKvUW7wd9il8GB+WP6cWr/NgDmjvyQAmUq0W7QNywZ/ykxjyJp+v5/2TDnOw7/sYrvtyXOQL1x6SzLv/svl08eIToyglwFitKw68eUfftd87WnD+6CR6ESFC5fjS2Lf+LMoZ1kyupK5XfaUd+3PwYrK9b8NJYzh3bRbfR0XHN5Jrm/2cN6Expyi36TVmJl/fI/AtizbhG5C5YwF5oAMmVzo3jlt9n32xIunThMvhLlk427cfEMD24HU75uc3OhCcApqyvFK9cm4M9NPAoPJUPGTERFhnPrygUyZMyEZ/GyXD555Jnzndi7he0rZvHxT2vInC37P7tZEREREUnVq/fYlIiIiLzygu8+pHq/SSzddoQqJfLRvk55gm7dp+2oefy05n9PJR+/dIM/Dp2h9sc/cv3uQ1pUL0Uul8ws2uLH+98vB8ArlyvZszoBUCCXC/n/KqCcvHSDA6eu0HrkXH7esJ/crokfZO0/dZlaH//Iuau36dKgAp+2qY21lYHOXy3im6XbzNfe6X+BhVv8aDVyLjFxcXRuUAEHe1tGztvIgMmrACiU25X9py6zenfSp6yv3n7A0m1HyJIxQ5oUmkJCI3gQ/oiaZQokayuQywWAYxeupTr+ncrFCb77kM2Hz5qPnb9+hz8DLlKtlBcO9rYABAbfpU75Qtgarbl88x6/HzjNsQvXcc+aiTa1vPFwy/KS70xEJH3dv3WdUe2qsu+3xRQqW5WqTTsSciOIyQNas2XxVHO/q2cDCNi9kbGdanHv5nUq1G+Js3tu9qxdyOwvegGQPU8Bsri4m//t5pG4f9618ye54L+fSf1asWP5TLK6J84IvXBsH2M71uDGpbPUaNGNxj0/xWBlzU+fduK3mePM1z5zcAe71yxgYt8WxMfFUL1FV2ztM/DLpOHMH9MPAHfPQlw4tg+/zb8mub+QG1fZ99sSHJyc06TQFP4ghMjQBxStWDNZW/a8ie9Rl0+lXBh6cCdxqcF8Jcola/MsnngsOPA0ADnyFebTWb/z6azf6fnl7GfP9/Aec0d8gE+95hTxqfHM40RERETk+Wlmk4iIiKS7EfM2EnT7Ppu//cA8++azDnVoNXwOI+b+Ttva3jg7OQAQdPs+A1rWYJhvfQwGAwkmE7UHTmGnfyAAfd+rRnx8AofOBDGgZU1K5s9hvs7563d4u2wh5gxuT8HcrphMJobMWIedjZFN37yPe9ZMAPRrXp2Ww+fw7bJtvFetlLlgc+lGCGN7NOaDd98C4POOdWk2dBYLt/jRrVElGlUshqO9LWv3nmBAy/99iLV27wkAWtfyTvH+Q0Ij+HnD/qf+nJpWKUGRPMmfwr5w/S4A2f/K/3cFcrkCcOdBRKrn7fVOFXb6X6DNqHlUKJIHe1sjfwZcxD1bJoZ2SnySPyIqhlv3wnDLkpG2o+ax6dAZ8/iCuV2ZOqAl5Qvneeo9iIj8m/0yeTghwUH8d/428+ybpu//l4kfteCXicOo3LgdjpmdAQgJDqJBl4E07zsCg8GAKSGBMR1rcPrgDgDq+/bDlBBPYMBBGnYdiEfhUubr3Lx8nuKV36b3uLm4exbCZDKxZPwgjLZ2DJmzmSyuie9dDToP4IcPm7N+1jf41GthLtjcuXaJ1p98Rd0OHwLQrM9QvuvTlD1rFlCzVQ/K1GyMnYMjfltW06DLQPN1j2xdA0Clxm1SvP/wByFsXz7zqT+ncm+/S06vosmOP95XKfNfRba/c89bEICwe3dTPKdr7nwAnDm0k3qd+iZpu3Ex8WGI4MDTeJWu+NR8qVn81SdEhj2kRb9RL3wOEREREXk2KjaJiIhIurofFsnKnf6ULZg7yTJvtkZrOtf3YVdAIOv2ncS3ng8A9rY2DGlXB8Nf+ypZGQxULOaJf2AwwX8tB/ck/+1Yl4K5Ewsw/oHB+AcG827VEuZCE4CN0Zr2dcqxKyCQHcfOm4tNmR3t6dO0qrmflcHAx61q8WfARbYfPU+ZArl4p3Jxlm0/StDt++RxS/xAcvXu42TL5Eht74IpZgp5GMFXi7Y89WdVIKdLisWmi8EhADhnzJCs7fFso4cRj1I9b2ZHezxcnTl+8QZHz1/DaLQmwWTCaGVF+KPoJNeYtnYv+XJkY3zvplQompcDpy4zfO5G2o9ewJ4f++OqpfRE5BUV8fA+B39fgWfxskmWeTPa2FK9eWfOHNrJkW1rqfZeZwBs7DLQ9P3PzO9HBisrCpSpRNAZf+7fuo5z9lxPvF6zD77A3bMQAEFn/Ak640+5Os3MhSYAa6MNVZp24MyhnZw6sM1cbHJwykyd9h+Y+xmsrGjU7RPOHNrFyX1byVu0DGVrNWHf+qWEBAeRLWfiwwCHN/9KxizZKF757RQzhd2/y9ppXz71Z+Wet0CKxabbVy8CmAtyf5ctR2KGyLAHKZ4zex4vPIt5c/rgTv78dR4+9VpgMiWwf8NSDm9JnKGVkBD/1GypCQ48zeHNq2jU7T/m2WQiIiIiknZUbBIREZF0deH6XUwmE+FRMXQbtyRJW+ijKAAu3bhnPuaaxRE726R/smRxTCyyRETFPPFaLpkdKVvwfx8wXQxOfLq6aon8yfqW9sppzveYV04X84eKjxX9q/hz6UZiMaZ1LW+WbT/Kmj0n6PteNa7ffYjfuat0b1QJG6N1irkK5nblxi9Pf8o6tfF2NonH74cnLyhF/vUzyZJCIeqxhkOmc+ryTSb0eZfm1Utjb2tk8+Fz9J+yitYj57J/6kDuh0cCEB0bx/zPOpgLdqW9cnL7QTgTlm9n1a4Aev+//ZxERF4VN6+cx2QyER0ZwfTBXZK0RUWEAokzih7LlNUFG1v7JP0cMyUWWaIfpT6bFMDJ2QXP4mXN398OStxrsFC5t5L1zVukNAC3rlwwH3PL45Xs/ehx8edxxoqN2rJv/VIOb1lNfd9+3Lt1jUsnDlOzVU+sjTYp5nL3LMTUfbeemB3A2sY2xeM2tonHIx7eT9b2+GfikCl5IQoSC2ZdRkxlcv/WzB/dl6XfDMKUYMJkSqD6e13Y+cvsFAtcz2rjvB+wtrGlbsePXvgcIiIiIvLstGeTiIiIpKt7YYlFDDsba4xGqyRfWZ0caFWzDEXzuJn729um/AEZgMlkeuK1bG2SFqlCQhOvnSd78g++omMTn562tvrfn0eP94L6u8f7Gdn9latGaS/cnJ1Yuydx36Y1e45jMploXbNMqrkMBgP2tjZP/fp7lr9zc07MdfnmvWRt98MSC1AumR1THHvu6m1OXb7JWyXz061RJbJkzIC9rQ1NqhSn/dtleRQdy7q9J8mRLXHmV/nCecyFpscaVEj88O/s1dup3qOIyL9dxMPE11CjrR3WNsYkX45ZslKxUeskxQ4bu9SL+E97PzLa2iX5PuxB4rVdciZfjjQuNvGhASur/z1wkNIydbYZEl/nHxfAilasQeZs2fHbshoAvy1rMJlMVGzUOtVcBoMBG7sMT/36e5a/y5Qt8QGMu9cvJ2uLCE0sQDk5Z0v1+rkKFGfE8gN0HjaFGi278+4HQ/nvgu3kyFcYgJz5X6zYdO/mNQ78vgLvmu+kOOtKRERERF4+zWwSERGRdOXpnhVInDU045Oke0jEJyQQ/iiGDHapF5j+ibx/FZn2nbxEfZ8iSdoOnbmSJB/8bym5vwu6nfjhWcG/ltqztrKiRfVSTFu7l+t3H7J693Hy5ciGT5HU9zO6fT+M8Uu3PTVvx7rlKVMg+bJMBXIlzri6kkKx6cSlGwCUK+SRrA3gxOXEDdmrlsiXrK1WmYL8uHo3D8If4eGauBxfXHzyJYyiYmKBxOX4REReVS65PIHE5dx6jJmVpC0hIZ6oiHBs7VMvMP2ja+fMC8D5I3spVa1BkrbAgAOJfXL/73X6zl/L1f1dSHDi+5a7Z+KSrVZW1vjUb8HWJT9x79Y1/Db/imvufHiVqpBqjocht/ht5vin5n3r3U7kLZr8IYrseQtgMBiSzAB77Nq5xP0L85XwSfGccbEx3L1+hYzO2XirmW+Stt/nfE9mF/cXLhTt+mUOCfFxyc4rIiIiImlHxSYRERFJV/lyZMMlsyNbj5wjNi4+yVJx36/YwdiFm/l9XG8qFfN86dcu5ZUTW6M1249eYESXpG27j1/E2sqK2mX/t89SYPBdAoPv4pXTxXxs0ZbDAJTMn9N8rHVNb35as4ef1uzm8NmrDG6X8t4Yjz2MiGLBH4eemrdqyXwpFpvcs2aiSnFP9py4xKUbIeTLkfjUeGxcPCt3HiNHtkwpjgMo4pE4a2zNnhMMaV8nSduvuwMAKOaZHXtbG6qX8mJXQGCyn8H6/acAqFAk71PvQUTk38rNIz9Ozi6c3LuF+LjYJEvN/T57AqunjmHw7E0UKFP5pV87T5FSGG1sOXVgOy3+X9vZw7uxsrKmxN/2Wbp55QK3gwJxy+NlPrZnzUIAPAqXNB+r1LgtWxZPZcuiqVw8fogmvYY8McejsIfs/nXeU/MWLlc1xWJTFtccFCxblXNH9nLn2iVc/yqQxcfFcuD35WRxy5niOICYqEd80bwcFRq0pOeXs83H79+6zpGta3jr3U5PzZWak/u34pjZmaIVarzwOURERETk+ajYJCIiIunK1mjNsM4N6DfpF3pPWE7/ljVwcrBjw/5TfLtsO7XKFKRi0ecrYuRxS5yFM3fTQTrUKZdkn6a/c8+aiZ7vVObH1bv55Kc19GhUCaPRipU7/Fmz5wTt3i6bpKgSn2Ciw5gFDO1UjwK5XFi39yTT1+7lvbdKUbm4p7lfmQK5KJjblZ/W7AGgbe2y///SSRTM7cqtX8c81z3+fx+3rkWbkXPpOm4xn7SuTZaMGZj4y04u37zHsuGdzXt7zN14kP/8tIZB7WozqO3bFMmbndreBdl29Dwth8+hdc0y5MnuzG/7TrJylz9F8mSncaXiAAzv0oA6n0yl67jFDPOtTy6XLOwKCGTOxgNUKuZJw4ovvpeGiIilGW1sad53BPNGfcSsz3vSsOtA7B2dOLZjPb/N+oZilWrhVbrSc50zW47EWaW7Vs2latOOSfZp+rssrjmo1aYXmxdOYdFXH1OzVQ+sjTb8H3v3HVZ19Qdw/H3hsvceIoIsB4gi7k2ucqU5M2fmyNIsR8td+jPLkWZZ5qxMxJHmVtw5cIF7oaKg7CHIvvf3B3oLL8olUSs/r+fxeeJ7Pt9zPuf7FNfu53vOObp1Ncd3rqdhh9eLFZbUqkK+eb8Xrw4fj5O7NyfCN7Dr1+8IbtUFn1p/np1XqWpNnD182fnLAgAatH/9sfk6e/jy7ZGkx8aU5pWBo/l6RFe+G9uXdoPGYGphzdals0mMvc6Iuas1n0f71i7h52nv037wODoM/hBTCyuq1GnG8Z2/UbXeCoJadCDhZjTLPxuBjZMrXd/7e5+T9zLSuHH+FIFNX0bxiO1ohRBCCCFE+ZNikxBCCCGeuT6tgsnOyWPC0i2a1TRKfT36tK7D+D6ttQ5BL03zWj4E+7mzePNhLt1MYOO0tx4ZO6FfWwpVar7bcJDFmw9rrg94uR7/G9yhWGyzQC9c7CzpN/1nVPfP42gcUJkv3+6k1W+PFrX4bMV2Qmr5aLbre5pCavmw8P3uvDtvLX2nF73dbmVmzOeD2tGytp8mTo2aQpWKB8eJ6CkULBrTk7ELN7JmXyS7TlzSxDb092T+iNcwvL/aLMjHjdCJ/Rg+J4xuk5Zq4l6uV5VvRnZ96nMUQoinrfGrfcnLySZszniO7VgLgJ6+kiad+9F5+IQyfx5VrR9C5YA67Fm9iNvXLjL6+02PjO3y7iRUqkJ2/fIte1b/uY1fs65v0nPMjGKxVeo2x8bRhW/H9EGtUgHgF9yE3h/P0uq3frserP9mKtUbhGBf4emvQK3eIIRBn33Psinv8O3oNwAwtbCix/vT8W/UShOnVqtRqQrhL+db9Z+0gB8+GsCyycNZNnk4AO5VAnlr2mKMzcz/Vj4Xju1DrVI9dvtAIYQQQghR/hTq0k4yFUIIIcS/RmhoKD169CB14/TnnYpOMrNziboaR1ZOHtU8nKlgb/VE/d1JycDcxAhzE6NSYxPTMzkdfRsjAyXVPZyxNi9+LofX61Op5etG2KQBpGVmc/LKLVztrPC7vw3dw34/dJY+035i+Udv0KFh9SeaR1kUFKo4deUWKpWa2n4V0S/DW9xxSemcj4knJ68AXzcHzVlQD8svKOR8TDxJ6VlUr+SMk61FeU7hqbHp8BGrVq2ie/fuzzsVIV5ICoWCITOWEtyqy/NOpVQ5WZnEXIwk914WFXyqYetU8gpZXaUl3sbY1EKngsndlERiLp7GwNAQNx9/TC2ti7WPauGBR/UgRs5fy72MNK6fO4m1owuulauU2N/J3RtZ8EFvhn35E0EhHZ9oHmWhKizg+rmTqFUqPAOC0dPTL/0miopQsVfOknjrOpWq1sTW+cme/b/ZwnH98LRWEhoa+rxTEUIIIXLNpEoAACAASURBVIQoq9WyskkIIYQQz425iREN/T1LD9SRs62lzrEOVuaE1PIpPRCwNjehRc3Hx67YcQwXO8tnvrWcUl+PYD/3v3Wvq70VrjoU+AyU+tT4yxlVQgjxX2NsZo5vUKNy68/awUXnWAtbB6o3CNEp1tTSmmr1Wzw25sD65Vg7ulKz2Ss651Ae9PSVVA6oU+b7FAoFbj7+uPn4P4WshBBCCCHEsyLFJiGEEEKIJ/BV6G7ikjPYcewiMwZ3QKkv50MIIYR49jb9OJO0hNucPrCdnmNnoqcv/7svhBBCCCGeHfnbpxBCCCFECZxsLbCzNCs1bunWo2Tl5NG3dTD92sr5EEIIIcqXlYMz5tZ2pcbtW7OE3OwsGnfuR9Mu/Z9+YkIIIYQQQvyFFJuEEEIIIUrwx/z3dIo7vXjcU85ECCHEi2xS6GGd4mZsPveUMxFCCCGEEOLRZJ8XIYQQQgghhBBCCCGEEEII8bdJsUkIIYQQL6Qdxy6ydl/k807jbykoVFGoUukUq1KrdeovJy+/1Di1Wk1aZna5jSuEEKJkpw9sJ2Jb2PNO429RFRagUhXqFKfW4bNCVVhAfm7pnz26jguQey9LpzghhBBCCKE72UZPCCGEEC+kuWv2cu1OCl2aBj7vVHS2es8pfth0iKjoOAoLVXg62/FW+wa82a4+egqFJu5KbBKLNh1i85FzZGTlUK+aB293akyzQK9i/YWfvMzkpVs5HxNPQaGKig7WvNO5iVZ/aZnZTFyyhdA9p8jJy8fcxIhWtf2YOaxjsXOtdB1XCCHE421dNofEW9eo06br805FZ0c2h7I79HtiLkRRWFiAY0VPWvQYQotug1Do/fme6+kD21m/YCpx0RcwMbOgSp1mNO8+CN+gRsX6O3sonLXzJhJ75RyqwgJsXSrSus8Irf50HTfmQiRr503k2tkT3MtIw9LOkZrN2tF11GeYmFk8/QckhBBCCPEfJ8UmIYQQQoh/gV/DT/D2nDC8K9gzrGMjsnPz2fDHGcYu3EB6Vjaje4QAkJOXz+tTlxOXnE635jWxsTBl4x9n6DllGWsmD6ChvycAeyOv0nXiEqzMjOndsjZKfX02HDzN2IUbSErP4qPeLQHIKyik26SlHL90kzda1qZO1UqcuHSTpVuPEpuczrYvhpZpXCGEEP89h35fyZKJQ3Gq5MNLrw8jPzeb47s2sHLGaLLvptFu0FgAjm4NY9Enb2Ln6k6bviNJS4jj2I61nDm4nY9X7MHZwweA80f3MPedzphYWNGo4xvoKw04vms9K2eMJjM1iY5DPy7TuNfPnWTW0A7o6yup17Y7ZlY2RGxfw761S4i5GMnHy8KLFaaEEEIIIUTZSbFJCCGEEOJfYP66/Xi52rHrq+FYmBoB8F7XZgS++QU/bDqsKTZNXb6dy7GJrJ7Un5a1/QAY2rERTd6dy9tzVnNqUdEXbzN/3YVarSZ81nA8XewAmNivDdX6T2f++v2M7RWCvp4eK3ed4NjFGKYOfIV3OjcBoE+rYBQoWLL1CCcv36KWj5vO4wohhPjv2b5iHo7uXny8YrdmlVDb/qP4qH0Au1f9QLtBYynIz2P1nE8xNDFl/C8HMLWwAuC1EZMZ07YK3380gAkrDwDw+w9foFar+fSnvTi4Fb2s0OXdSYxt68f2FV/TfvA49PT0dRoXYPeqheTn5jBmeTgV/WoA0GnYJ8wa2pHzR/dwfNdvBLfq/EyfmRBCCCHEf40Um4QQQgjxxHLzCpi1eg+he04Sl5SOm4M1TQO9mDrwFcxNjDRxB05Hs/7AafacukJ2Xj71q3nQ2N+Tvm3qoH//jeKR89aSV1DImJ4hzFm9h10nL+PlascbLYPp3qIW36w/wOo9J4lNSifQuwIzBnfAy9VeM8bAGSvx93SmcUBlvtvwB/uirmJvZUbPkCBGvNa02PZwD0vPymHK8m38ceYaKRlZ1K1aib6t69Aq2K/Mcy1PGVk5nL8Rz+AODTWFJgBnW0uaBHqxP/Iq+QWFGCj1+WXXcap7OGsKPgCO1uaEBPnya/gJjl28SbBfRWKT0nG1t9IUmgDMTYyo7VuRP85cIzevAFNjQ0L3nMTBypzBHRoWy+n97s2pV60S9lbmADqPK4QQz1p+Xg5bFs/i8KZfSU2Iw9bZjSp1mtFt1OcYm5lr4i4e28+xnes5dzic/NwcvGs2wK92I5p06Y+enj4Ay6e+S0F+Hu3fGseWJbM4e2gXTu5eNOrUh/qv9GDHT/M5vHkVqfGxVKpak15jZ+Lo/udWogvH9aeirz9+wU3Y+cu3XIjYi6WtAw3a96JN35GPXV1z72466+ZP5tKJg2SmJeNVox5NOvcjoHHrMs+1PGVnZhB79RwhPYcU247O2sGFKnWaciFiH4UF+dyOvkBaQhzBrbpoCk0AFrYOVG8QQtT+bWRnZmBibknqnVvYOFXQFJoAjM3M8fAP5vKJg+Tn5qIqLNBpXH2lAVcij1DRL0BTaHqgUcc3OH90D9fPHpdikxBCCCHEE5JikxBCCCGe2AffrufX8JP0aFGLGl6uXLudzLJtEZy9foftM4cBsD8qms7jf8TSzJiuzQKxszRj98nLvL9gPdfvpDB5wMsAnL52m9ikdPacuoKVmTFNAiqzbn8UB05fY/XeSPacukyrYD8qOtqwPeICr376I5E/jtUUkfZGXuHU1Vjmrt1Hk4DK9Gtbl90nLzN52Vai45L4esRrJc4hLimdl8ctJCkji54hQViaGhN+4hI9pyzjszfbMaxTI53nWt6U+npsnjEEDyfbYtczsnI4e+0OLWr5YKDUJzkji7TMbHq3rK3Vh3eFooLcqSu3CParSPsG1Zm/bj87jl3UFNMuxyayPyqa5jW9MTU2BOBqXBItg30xVOpz/U4K52/E42Jnib+nCz1a1AIo07hCCPGs/TztfQ79vpL67Xvi7hdI4q1o9q1dRuyVs3y4dCcAFyL2MXtYJ0wsLKnXthvm1nacO7Kbn6aNIjH2Ol1HTgXg5sUoUuPjOH9kD6YWVlQJbkLE9rVcPLafI1tCOXd4NwGNW2Pn4s7pA9v4amgH/vf7GU0R6cLRPdw4f5Kty+ZQpU5Tmr42gHOHdrHm64nEx1yl34T5Jc4hNT6WGW+2ITM1iQbtX8fE3JKzh3Yx773udH9/Gi1ff1vnuZY3PX0lYxdtxcHNo9j17MwMbl0+S7X6IegrDUhLvAOAp7/2Z4VH9dpE7d9G3NXzeAXWo1ZIB7avmMfpA9s1xbQ71y9zMWIfVes1x8jElNzsezqNW1iQj3+Dl/DwD9YaNyX+FgCmljbl8CSEEEIIIV5sUmwSQgghxBPJzS8gdM8pWtfx45v3/jzI3NPFjg+/38iV2CS8K9izZt8plPp6nPxhDFZmxgC891ozAt/6gi1Hz2uKTQAJqXf5tE9rPujeAoCuzQLpNmkpB05Hc+ibUZoCxttzVrNy1wmi45I11wCu3U7m80HteLtTYwA+eaMVr366iJ92HmfgK/Wp6V1Bax6Tlm0lJiGVHV++rSmKfNS7Jd0mLmHS0i30DKl1f6VP6XN9WHJGFj9uPlzqs+zY0J8q7k5a102NDalXtZLm529/O8jNxFS2R1xEpVLxfrfmAFyJTQLAydZSqw/vCg4AJKZlATC4fUP2Rl6hx5Rl1K3ijrGhkv1R0TjbWfJpn6Iv9rJy8ohPuYujtTk9pyxjW8QFTX8+bg4seK8rwX7uZRpXCCGepYK8XA5vXkVAkzYMmPSt5rqDW2V+nTmW+BtXcKrkzdFtYegplUzbEKVZddN2wCg+al+DyL1bNMUmgPTkeF4dPp52b44BoG7bbsx99zUuHjvAlLCjOFXyBmDJxKH8sfEXEm5Ga64BJN66RvcPptOq93AAXh32KbOGdeTgbyto3m0QlarW1JrHmnkTSY6L4ePl4XjeL5p0HPoxc995jTVzJ9CgXS+MTEx1muvDMtOS2R36Q6nPsvZLnXD1qqp13cjEFO+a9TU/7/xlAclxMUQd2IZKVcgrAz+4n0fRKqULEXtp3efdYn3cjr4IoCk2hfQcyvmje5k3shtegfUwMDTmwrF9WDu40PmdiWUaV19pQK9xX2rlfTclkd2hP6CvNCCwadtS5y+EEEIIIR5Pik1CCCGEeCIqlRoo2iIvKjqOGpVdAXirfQP6tArGyLDorxvDX23C4PYNNYUmgLyCQqzMTMjIyinWp76eHu92aar52d/TBYCmgV7FijmNAyqzctcJLt5MKHbdysyYYR0baX7WUyh4v1sL9kdFs/vkZa1iU+rde4TtjSTIx63Y6htDpT792tRhX9RVNh46S7dmNXWa68OS07OY/nPpb5R7u9qXWGx62NQV28jOzQegirsTxkYGAETHJQNgY26idU9FR2sA0rOygaJnVNHBhtPRtzl5+RZKpT4qtRqlnh6Z2bnF+vtuwx94utjxxZCO1K1aiSPnrjNx6VZen7qCg9+MLNO4QgjxLKlUKgAuHdtPzIVI3KsEAhDSYzCNX+2LgVHR1qSt33iHl3oOKba9W0F+PqYWVmRnZhTrU09PnzZ9R2p+dvP1B6Bq3abFijl+tZvwx8ZfiIu+UOy6qYWVZiUSgEJPj1cGfsCFiH2cPbRLq9iUlZ7K0S2r8agepCk0ASgNDGnapR8XIvZyInwD9V7urtNcH3Y3NYkN30177HMEcK7kXWKx6WHr5k8mL6fod76rV1UMjIo+953cvfCoVovzR/eyf90y6rR+DbVaxeHNv3Js5zoAVKpCzTOyc6nIzYtRXD97An2lErVKhZ6+PjlZd8s0bkmi9m9l6eThZKYm0WP0DCp4Vy91XkIIIYQQ4vGk2CSEEEKIJ2JiZMC4Xi/x2YrtNBs5D9+KjjQJqEzrYD9Cgnw1ZzH5uDmQcvce89ftJ+JCDDEJqVyNS+LuvVycH1oR42xrgaFSX/OzkUHRX1lcbC2KxT3oO6+goNh1L1d7FA+dzVT1fhHn2u1krTlciU1CrVaTmZPHwBkri7VlZOfcvy9F57k+zMfNgdtrppTY9lcGf5nz48SFTeFqXBKHz91gyvJttPxgAWcWj8PIoOj+1Eztws69nDwArO8XhF7+cCHnrt/hq2Gd6NI0EGNDJTuOXWLk/LV0n7yUwwtGkZp5Dyhavbb8o974uBWtUgr0ciUhLZOvQnezdl8UDlZmOo8rhBDPkqGxCR2GfMj6b6Yy9fUmuHj64VenKQGNWuPf8CXNWUzOHr5kpqewfcU8rkYdJTnuBgkxV8nOuou1g0uxPq0dXFAaGGp+NjAsKmpYPRSn0C/qu7Agr9h1R3cvrc+oB0WcxFvXtOZw58Zl1Go1ufeyWDiuf7G2nKwMzX26zvVhzh6+LDgUX2LbX+n/Zc6P880f8STEXOXyqUOsmzeZaX1DmLHlHFZ2TvSftIB5I7uzfOq7/DpzLGqVGrVaRdPO/dm7ZrHmOcwY2IbYK2fp/dEs6rbpioGREacP7mD51Hf5ekRXpoRFYOfqrvO4DyTeusaqLz8kct8WHCtW5q3PF1G1Xgud5iWEEEIIIR7v0aePCiGEEELo6IPuLTjx/WjG9AzB1MiAJVuO0GPKMhoMn01CatEbyF+v3Ue1/tOZuSqc/MJCmtf0ZsF73YptD/fAg/OCHqan0O2vLk4PFaX+2qeRoYFWW8rdoqKKkYE+SqVesT+2FqZ0a16Tqu6OOs/1YQqFAmNDg1L/PKpYpVarUanVxa55udrTu2VtJvVrS35BIduPXcTRpmje1++kaPWRereoEGRvZcalmwmcu36HxgGVGfhKfazNTTA2NKBDw+q8/lIQ2bn5bPzjLC52RUXAYD93TaHpgbZ1i74QvHgzQedxhRDieWj35himbYik/VvjMDQ2YW/Yj8wb2Y0JXeuSnlxUZNm2bC5j21Th9x9mUFiQT9V6LRgw5bti27Q9YGhiWuI4un5GWdk7l9Bn0e/IB4Wrv8pKL/rdqjQ0Qt9AWeyPmbUt9V7prinS6DLXhykUCgyMTEr986hilVqtRn1/BdkDju5eNOr4Bl1GTKawIJ/TB7YDUMG7OpNCj9BvwnyadX2TTm9/yscrduPiWXR2oGvlqty+dpHYK2fxC25C826DMLW0xsDIhKCQjjTs0Ju8nGxOhG8o07gAhzevYnLPhlw8vp+uI6cyefURKTQJIYQQQpQjWdkkhBBCiCeSV1BIdm4+7k42fNy7FR/3bkVC6l2+DN3ND78fYuHvhxjWsRGTl27F3sqM49+Pxtzkz618vgrdXe45PdjW7a9iElIB8CnhTCUPZ1ugqIDz/Qc9irUVqlRkZudhYmSg01zH3z/v6K8SUu/yxa/hpeb9RqvgEs+TmhO2lynLtxE6sT+tgv2KtdlZFn3pGZuUTsvavigUCm6UUPQ5c+02ALV9K3LmetEh7Y38PbXiWtT04Zv1B0jLzKaiQ9EWeAWFhVpxOXlF2/hZmRnjXcFep3GFEOJZK8jPIy8nGztXdzoN+4ROwz4hPTmezYtmEr7qe8J/XUjL199mzbyJWNjY8/n6UxibmWvu37RoZrnnlHgzWutactwNAJw9fLTa7Ct4AEXb0A36bFGxNpWqkJysTAyNTXSaa+fhE7T6T0+O5/cfvig178ad+pR4ntSWJbNYN38yI74OI6Bx8c9Ac2s7AFLv3KIgP4+k2BuY29jR+NW+D/UxGyt7Z8ysbDh3pOjz0rd2Y62xqtUPYcdP88nKSNN5XCjaNm/x+MFUrlGXwdOXYOvsVup8hRBCCCFE2cjKJiGEEEI8kf2RV/HoOZk1eyM11xxtLBhx/8yltMxsbiakolKrad/Qv1ihKTYpndPRt8s9p6txSVyNSyp27eedxwAIuH/O0l95uthhb2XGrhOXyC8oXliZvXoPHj0nc+LSTZ3mWpL0rBxWbI8o9c+1O9pFMoBqHkVvwe8+dVmrbdm2CKDoXCtnW0saVvfg4JlrxbYLzC8oJGzvKVzsLKnpXYEqFYtWaf128IxWf+sORN0f0wljQwOa1vDi1JVYree56fA5AOpWqaTzuEII8axdiNjHyGYVObo1THPNys6JNv3eA+BeRhrJt2+iVqkICulYrNCUEn+LmxdPl3tOd25cISHmarFrB3/7CYCKfgFa8Y4VK2NhY8/ZP3ZSWJBfrG3L4q8Y2awi188e12muJcm+m86BdctK/ZN4S7tIBuDmU3Te0bnD2i9V7F+3tCjGN4C8nGzGd6nNyhmji8WkxsdyYtdv1Gz2CgCulasAcHzneq3+jm1fe3/MajqPC7B23mRMzC0ZNnOFFJqEEEIIIZ4SWdkkhBBCiCdSr1olHKzM+eLXcFztrahR2ZXo28maFUttgv3wcXPAzNiQdfujaFXbFx83B46cv8HnP+3AwtSIrJxcLscm4lPBoZTRdFOoUtP7sxV82qc13hXs2fjHWRZu+IPOjWvQoLqHVryhUp8J/doy4us1DPkqlJFdm2FhasTmw+f4ctVuWtT0oV7VSmTl5JU615L4uDkQv+6zvz2fVsF+VPNw5vuNh7AyM+GlIB/ikjP47cBpth49T5CPG23qFH059373FvSYvJQBM37hg+4hWJubMHfNXq7fSWHVxH4oFAqqVHIipJYP4Scv03XiEro3r4m7kw2/HzpL2L5Iqrg70a5+0Zd4E/u3peUHCxgw4xcm9G1DBXtr9kVdZcnWI9Sv5sHL9arqPK4QQjxr3oH1sbB1YOP3/8PG0RX3KoEk3IzWrFgKaNwG50o+GJmaEbF9Df6NWuHi4cuVU4dZ/+1nGJtbkHsvkzvXL5e46ujvUKsK+eb9Xrw6fDxO7t6cCN/Arl+/I7hVF3xqNdSKVxoY0uXdSSyb8g6LPnmLlweMwtjMglN7NvH7oplUq98Cr8D65N7LKnWuJXH28OXbI0kltukioFFrKnhXJ/zXhZhaWFG9YUvSEuI4tmM9kfu24FE9iMCmbdHTV1KlTjOO7/yNqvVWENSiAwk3o1n+2QhsnFzp+l7R56Rr5apUbxDC2UPhzBnemfrtemLv6s6J8I0c3RqGq1dVajZvj76+Uqdx72WkEXf1HBX9arB9xbwS5+AX3IQaTdr+7WcghBBCCCGk2CSEEEKIJ2RuYsT3o3swbPZqOnz8g+a6kaGS8X1a0/p+EWT+yK68MzeMXlOXA2BjYcq0Qe0wNTZk2OzVNBw+h8T1n5dLTs0CvXCxs6Tf9J81Zx01DqjMl293euQ9fVoFk52Tx4SlWzSre5T6evRpXYfxfVqjUCh0nmt501Mo+PmTPgz+ahX/+2Un//tlp6atQ8PqzBjcEaV+0YL1kFo+LHy/O+/OW0vf6UVvyluZGfP5oHa0rO2n6W/RmJ6MXbiRNfsi2XXikqa/hv6ezB/xGobKorM5gnzcCJ3Yj+Fzwug2aakm7uV6VflmZFfNz7qMK4QQz5qxmTlvfb6IxROG8uXgdprrBobGdB4+gRpNigow/ScuYOnkt5n/XtFWqmZWNvT44H8YmZiyeMIQJnarx8II7a1C/44qdZtj4+jCt2P6aM4c8gtuQu+PZz3ynsav9iUvJ5uwOeM5tqNodY+evpImnfvRefiEorMBdZxreVPo6TF89kp+/GQQGxZOZ8PC6Zq2oJCO9Br7BXr6RV899J+0gB8+GsCyycNZNnk4AO5VAnlr2mLNqjKFnh5vTVvCyi9Gc3RrGGcP7dL05xvUiP6TFqA0KDqHUZdxr0QeRq1WE3MhkpgLf65MLjYHhUKKTUIIIYQQT0ihVj902rQQQggh/rVCQ0Pp0aMHqRunlx5czrJz8zl7/TY3E9OwszSjaiUnHKzMi8Wk3L1H1NU4nG0t8KvoqFntknL3HmmZ2VR2sXviPLxen0otXzfCJg0gLTObk1du4Wpnhd/9reNKk5mdS9TVOLJy8qjm4UwFe6u/NdenQaVWc+NOCpdvJWJsZIBPBQdc7CxLjC0oVHHqyi1UKjW1/Sqir1fy7slxSemcj4knJ68AXzcHzflLD8svKOR8TDxJ6VlUr+SMk63FE437LNh0+IhVq1bRvXv355aDEC8yhULBkBlLCW7V5XmnQl5ONrcunyHlzk3Mre2o4FUNC9viq2kz01O4eSESK3tnXCpX0fwuzExP4V5GGo4VKz9xHqNaeOBRPYiR89dyLyON6+dOYu3ootk6rjQ5WZnEXIwk914WFXyqYeukvSWcLnN9GtQqFUlxN7h97RKGxsY4V/LB2lF761q1Wk3slbMk3rpOpao1H7utXWp8LHHRF8jLycbF0xenSj5an1G6jvtvsHBcPzytlYSGhj7vVIQQQgghymq1rGwSQgghRLkwMTIg2M+dYD/3R8bYWpjSvKZ3iddtLUzLPSdrcxNa1CzbtkfmJkY09Pd8bIwuc30a9BQKPF3s8NShKKfU19MpP1d7K1xLKKg9zECpT40Szrv6u+MKIcSzZGhsQuWAOlQOqPPIGHMrW6rWa1HidXMr23LPydTSmmr1tcd7HGMzc3yDGj02Rpe5Pg0KPT0c3DxxcHv8Z6hCocDNxx83H/9S+7RxqoCN0+PP/NN1XCGEEEII8XQ9v1dNhRBCCCGEEEIIIYQQQgghxL+eFJuEEEII8Z/iZGuBnaXZ805DCCGE0GLl4Iy59ZNvGSuEEEIIIcQ/jWyjJ4QQQoj/lD/mv/e8UxBCCCFKNCn08PNOQQghhBBCiKdCVjYJIYQQQgghhBBCCCGEEEKIv01WNgkhhBCi3Ow4dpG793Lo0jTweaeis6Vbj5KckQWAr5sjHRpW14rJysnDzNiw1L50jStPBYUqCgoLMTY0+Ef290Bmdi55BYXYWpg+Mqak5xd+8jInL98CwNjQgOGvNi7XvIQQ/02nD2wnJyuDOm26Pu9UdLZv7RLupiYB4OLpR1BIx7/Vj6qwABQK9PT0S43NvZeFkWnpW8/qEqcqLKCwIB8DIxOdcy2PcdVqNdl30zG1tC6XOAC1SoVCT7d3c3OyMikoyMPcyhaAs4fCuX7uOACGRia0euMdnfoRQgghhPi3k2KTEEIIIcrN3DV7uXYn5V9VbPpuw0FiElJxsbWkZbCfptgUeTWOKcu2cuLyLdIys3G0NueV+tWYMuAVLEyNNPfrGvdXtQd/SeOAysx9t8sj8yotJvzkZSYv3cr5mHgKClVUdLDmnc5NeLNdffQUijI/h9L62xt5lXELNzy2j5reFfju/e5a11Pu3qPRO3OxNDPmyIJRxdpKe37HL97k1/ATJKRlYqDUl2KTEEInW5fNIfHWtX9VsWnXL9+SFHcDawcX/Bu1KrHY9EmnmvgFN6Hv+HlabUc2h7I79HtiLkRRWFiAY0VPWvQYQotug4oVTmIuRLJ23kSunT3BvYw0LO0cqdmsHV1HfYaJmUWZ484eCmftvInEXjmHqrAAW5eKtO4zQmtcXek67r2MNMLmjufw5lDyc7MxNjPHv2Fren/0VbFzsXSNi79xhd2h33NqzyayMzPwrlmflr2HU7Vu80fmmpmewuTuDTAxt2TKmggArp2J4NDvK8lISUBfaSDFJiGEEEK8MKTYJIQQQogXXkN/T8ImDdD8fPLyLV4d/yNKPT26NquJjYUJ6/ZHsXTrUaKuxrHjq7fRUyh0jvurX3YdJ/p2Mo0DKj8yn9Ji9kZepevEJViZGdO7ZW2U+vpsOHiasQs3kJSexUe9W5Zp/rr0p1CAgbLkt+Rz8wq4HJuIl6t9ie3vzl3DnZQMLM2Mi13X5fmN6RnCmJ4hDJu9mm0RF8o0LyGE+LfxDWrEyPlrS2z7Y8PPJNyMxi+4iVbbod9XsmTiUJwq+fDS68PIz83m+K4NrJwxmuy7abQbNBaA6+dOMmtoB/T1ldRr2x0zKxsitq9h39olxFyM5ONl4Sj09HSOO390D3Pf6YyJhRWNOr6BvtKA47vWs3LGaDJTk+g49OMyzV/XcQvy85j77mtcO3OMRp364FWjLtfOHGff2iWkJsTyz9HzwQAAIABJREFU4ZIdADrH5edmM39UD1IT4qjXtjvm1rYc3/Ub80b24L1v1uIb1KjEfJdNHk5a4m1MzC0119q/NY72b41j8YQhRO3fWqb5CyGEEEL8m0mxSQghhBDiIT9sOkRObj47vxpOQGUXAD7u3YpXP13E3sirbDh4hlcbB+gcF5eUzoyVuzhx+RZnrt0ucUxdYh6Y+esu1Go14bOG4+lS9Fb2xH5tqNZ/OvPX72dsrxD0y/A2uS79Na3hxf6vR5R4/9iFG8jIzmH28Fe12hZvPsyuE5ewKWH7PF2fnxBCvKhS42PZ+P3/uH72BDcvnX5k3PYV83B09+LjFbs1q3/a9h/FR+0D2L3qB02xafeqheTn5jBmeTgV/WoA0GnYJ8wa2pHzR/dwfNdvBLfqrHPc7z98gVqt5tOf9uLg5glAl3cnMbatH9tXfE37weN02s7vAV3HPbTxF6JPR9Bt1Oe07vMuAI1f7YtCoWDvmsVcP3cSj2q1dI5bN38Kd65fZuS8Nfg3agXAS72GMblnQ5ZMHMr0jdrPfs/qRZz5YydmVjY6z08IIYQQ4r+s7GvahRBCCPGfMXbhBl4et5D4lLtabe/NX0uX8YvJKygE4MDpaEZ/+xvBQ76i+oD/8ebMX1my5QiFKtUj+x86K5TBX63Suj4nbA8vj1tIQeGf96Zn5fDBt7/RYPgc/Pp8Tp9pP7Hj2MVymGXZHT0fQ0BlV00B5IHeLYMBOHHpZpniMrNzuRKXhKWZMUE+biWOqUvMA7FJ6bjaW2kKQwDmJkbU9q1IQUEhuXkFZZjtk/W36/glftx0mO8/6IGjjUWxtgsx8Xzy42Ym9W+L00NtoPvzE0L8962cMZoZA9uQnnRHq235ZyOY/XYnCvLzALh4bD8//+8DPnm1FmNfrsr3Hw1kb9iPqFSFj+x/8fjBLPp0kNb1LUtmMWNgm6Jzju67dzedn6e/z8Ru9figlTcLPujN6QPby2GWZZdzL5P4G1cwMbfEo3pQiTHZmRnEXj2Hf6NWxbaZs3ZwoUqdpmRlpFJYkA/AlcgjVPQL0BRyHmjU8Q0Arp89Xqa41Du3sHGqoCk0ARibmePhH0xhQQH5ubllmq+u4x7evAoLWwdCeg4pFvfKm6MZOPV7LGzsyxR3cOPPuPn4awpNAJZ2jlRv8BJJsTe4duZYsfvjrp4ndNYndB0xBSt75zLNUQghhBDiv0qKTUIIIcQLrLKLHYfPXWfjoTPFrt9JyWDF9mPYWJhgqNRnf1Q0r376I2v3RxES5EPf1nWITUzj/QXrmbJs2yP7P3UlllNXYrWuX41L5vC566jUaqBoVU/TEV/za/gJGvp78nrLYGLiU+k5ZRnf/nawfCddivyCQkKCfHirfQOtttikNABsLEx1jgPwrejIpumD2TR9MD+M6VniuLrEPNC+QXXiktKLFeMuxyayPyqaJjW8MDU21G2yT9hfyt17DP86jM5NatC0hlextty8At6c+SsNq3swpENDrXvL8vyEEP99jhW9uHLqECfCi58Nl5Z4mwPrl2NmaYvSwJALEfuYNbQjEdvC8G/wEk1e7Utq/C1+mjaKtfMmPbL/G+dPcePcKa3rCTFXuXLqEKr7L06kxscypVcjDv3+C75BjWjU8Q2Sb8cw773u7PxlQbnOWRcunn6MWbSFMYu28Na0xSXG6OkrGbtoKy/3L34mXnZmBrcun6Va/RD0lQYUFuTj3+AlWvQYotVHSvwtAEwtbXSOA6gV0oHU+Nhixbg71y9zMWIffsFNMDLR/fd4WcaNj7lKQKNWKA0MSYy9TuTezdw4fwprB2catOuJnUtFneMy05K5l5FG1XrNtcZ1quQNwPVzJzTX8vNy+P6jgfgGNSCk11Cd5yeEEEII8V8n2+gJIYQQL7CuzWoyfvFmfjt4hkHt/vzSf93+06jUal5vWRuANftOodTX4+QPY7C6f+7Oe681I/CtL9hy9DyTB7z8RHlMWraVmIRUdnz5NsF+RV8QfdS7Jd0mLmHS0i30DKlVYuEhOSOLHzcfLrX/jg39qeLupFMuBkp9vhiifSh7YnomP2w6jIFSnzZ1qugc9zQMbt+QvZFX6DFlGXWruGNsqGR/VDTOdpZ82qf1M+tvzLe/kZ6Zw6T+bbXaxi/ZzJ3kDNZOHojioXOrQPfnLIR4MdR9uRuhsz/h+M7faNF9sOb6sR3rUKtUNOpUtLLl6LYw9JRKpm2IwtTCCoC2A0bxUfsaRO7dQteRU58ojzXzJpIcF8PHy8Px9C9aZdlx6MfMfec11sydQIN2vUrcNi0zLZndoT+U2n/tlzrh6lX1iXJ8mJGJKd4162t+3vnLApLjYog6sA2VqpBXBn4AgL7SgF7jvtS6/25KIrtDf0BfaUBg07Y6xwGE9BzK+aN7mTeyG16B9TAwNObCsX1YO7jQ+Z2JZZqHruPm3ssiPekOlraOzBvZvdi5SM4evgyY/C2VA+roHHfn+mWAElcoOVfyuZ9DkuZa2OxPSU+8zagF60r8fBNCCCGEeFFJsUkIIYR4gdlbmdGyth87jl0kMT0TBytzANbuj8TFzpIWtYq+ZBn+ahMGt2+oKTQB5BUUYmVmQkZWzhPlkHr3HmF7IwnycdMUmgAMlfr0a1OHfVFX2XjoLH1b19G6Nzk9i+k/7yx1DG9Xe52LTSXZFnGBd+euISkji+lvtaeaR8lb5uga96SszIyp6GDD6ejbnLx8C6VSH5VajVJPj8zssm1Z9Hf7uxATz7oDp3m/W3PcHKyLtW2LuMAPvx9ixcdv4GSrvX3eozyr5yeE+OexsLEnoFErTh/Yzt2URCxsHQA4ujUMa0dXqtVrAUDrN97hpZ5DNIUmgIL8fEwtrMjOzHiiHLLSUzm6ZTUe1YM0hSYApYEhTbv040LEXk6Eb6BJ535a995NTWLDd9NKHcO5kne5F5setm7+ZPJysgFw9aqKgZHxI2Oj9m9l6eThZKYm0WP0DCp4Vy9TnKmFFXYuFbl5MYrrZ0+gr1SiVqnQ09cnJ0t7i96yKmncmxejANi58lscK1am17gv8Q6sx5VThwmbO4H5o3oyOfQwaYm3dYpLuBkNUGIR0c7FHYB7d9M0+YSv+p63v/pZts8TQgghhHiIFJuEEEKIF1yvl4LYevQ8vx86y4C29YhJSOXYxZuM6tYcvftv7Pq4OZBy9x7z1+0n4kIMMQmpXI1L4u69XJxtLZ9o/CuxSajVajJz8hg4Y2WxtozsokLWtdspJd7r4+bA7TVTSh3DQKn74eR/de12Mh8v2sTWo+ep7GLH96N70Lym99+OKy8vf7iQc9fv8NWwTnRpGoixoZIdxy4xcv5auk9eyuEFo3B31P3A8r/T39w1+zBU6jP81cbFrsen3GX4nDD6tq5D+wYlf2n5sGf9/IQQ/0wNO7xO5L4tnNi9kWavDSQ5LoZrZ47xysAPUOgV7QDv7OFLZnoK21fM42rUUZLjbpAQc5XsrLtYO7iUMsLj3blxGbVaTe69LBaO61+sLSerqJCVeOtaifc6e/iy4FB8qWPoG5Rtm9O/45s/4kmIucrlU4dYN28y0/qGMGPLOazs/nzpIvHWNVZ9+SGR+7bgWLEyb32+iKr3C3p/VVrcjIFtiL1ylt4fzaJum64YGBlx+uAOlk99l69HdGVKWAR2ru5lnsPjxs3KSAWgIC+XYTNX4OzhC4B7lUAykhPY9ONMjm5bQwXvqjrFWdoWnduUlZ6qlUdudhZQtH1fetIdlkwcRpPO/ajVokOZ5ySEEEII8V8nxSYhhBDiBdemThWszU3YcPAMA9rWY+2+ojeGX3+ptibm67X7mPbzDowMlDTy96R5TW8+6N6C+ev2cyNe+8uZ0qTevaf555T7/2xkoI9SWfw4SVsLU7o1r0lVd8cS+1EoFBgbGpR5fF2E7j7J+wvWo1AomDzgZYZ0aIiRgfZfnXSNKy+XbiZw7vodGgdUZuArf26b1KFhdY6cv8436w+w8Y+zWkWg8uzvVmIaYXtP0bGhv9b2hj9uOUxyRhYZ93IYPidMc/12cjpqYPicMLwr2DOqW3Pg2T8/IcQ/V40mbTG1tOb4zt9o9tpAIravAaBhh96amG3L5vLbt5+jNDTEt3ZjqtZrQbtBY9i+Yh5JsTfKPOaDwgVAVnrRiw1KQyP0H/o9ZGZtS71Xuj9yVZJCocDAyKTM45cHtVoNarWmIAfg6O6Fo7sXCoUeSyYO5fSB7TTu1AeAw5tX8dO091AoFHQdOZWXeg1FaWik1W9pcbevXST2yln8gpvQvNsgzfWgkI5cOXWYHT/N50T4Blq98U6Z5lPauNYOrgBUDqijKSA9ENjsZTb9OJPb1y5SvcFLOsW5+RS9GJEUe10rlwf/fljY2LFn9Y9kpiWTnZnBkknDNDFpCXGo1WqWTBqGk7u3ZttCIYQQQogXjfyfvBBCCPGCMzJQ0rlJDVZsjyDl7j3W7o+kbtVKeFcoetM3KT2LyUu3Ym9lxvHvR2Nu8ucXPl+F7n5s3wqFAvX9Q9f/6nLsn2cfeDjbAuDlas/3H/QoFleoUpGZnYeJUckFpYTUu3zxa3ipc3yjVTA1vSuUGvfAtogLDJ29mjpV3PlxTE+tbeLKGleezly/A0Ajf0+tthY1ffhm/QHSMrOfan9Ltx6loFDFG62Dte6xtzQjoLILV+OSil3PzS9ErVZz+locenpFK+aex/MTQvxzKQ2NqNP6NfavW0ZmegpHt63BK7AeTpWKVjreTU1izbyJWNjY8/n6UxibmWvu3bRo5uM7VyhQq7U/jx6c1wNgX8EDACd3LwZ9tqhYnEpVSE5WJobGJReU0pPj+f2HL0qdY+NOfahUtWapcWWxZcks1s2fzIivwwhoXPycPXNrOwBS79wCiraBWzx+MJVr1GXw9CXYOruV2KcucbcunwHAt7b2yw3V6oew46f5ZGWklWkuuoxr51J0rbAgX6vtwfaBJuaWOsc5VfJGoVCUuGrt1qWiOXr61yE66igV/WoQH3O1WEx+Xh5qtYqbF0+jp9DT6kMIIYQQ4kUhxSYhhBBC8PpLQSzZcoS5YXs5HX2bue920bTdTEhFpVbTvqF/sUJTbFI6p6Nv42BtXlKXALg72bDn5GXyCwo1W9ldiInn2u1kTYynix32VmbsOnGpWBzA7NV7+PynHWyZMYT61Ty0+k/PymHF9ohS59cowLNMxaYpy7dhaWrE8g97P/bMIV3jylOVikWrvH47eIYPX29ZrG3dgaJVadU8dD+f6u/0t/vkZWwsTGkWqL3V3eAODRncoaHW9ebvzSM7r4B9c0dorj2P5yeE+Gdr2OF19ob9yNals7l5MYq+4+dp2pJv30StUhEU0rFYoSkl/hY3L57G0q7kVbAA9q7unDu8m8KCfPSVRS8wxF09rzmvB8CxYmUsbOw5+8fOYnEAWxZ/xfoFnzFu8Ta8azbQ6j/7bjoH1i0rdX5+tRuVe7Hpwcqcc4fDtYpN+9ctLYrxDQBg7bzJmJhbMmzmiseeOaRLnGvlKgAc37mejkM+KtZ2bPva+7lVK9NcdBnXwMiEKnWacSFiLwkxV3F099K0ndzzOwDegfV0jrN2cMEnqBGXTvxB4q1rOLgVvXxRWJDPkS2hWDu6UqlqTTyq1SKk5xCtfKa+3oT83BwmrDxQprkKIYQQQvzXSLFJCCGEEAT7ueNdwZ5v1h/AxMiAzo1raNp83BwwMzZk3f4oWtX2xcfNgSPnb/D5TzuwMDUiKyeXy7GJ+FRw0O7XtyLbIy7w9pww+rWpQ/TtZOaG7cXS1JjkjKJzEAyV+kzo15YRX69hyFehjOzaDAtTIzYfPseXq3bToqYP9apWKjFvHzcH4td9Vq7PIi0zm/M34qlR2YX56/eXGNM4oDL1qlbSKa5NnSrlml+VSk6E1PIh/ORluk5cQvfmNXF3suH3Q2cJ2xdJFXcn2tUv+uJxwW8HmLB4C2N7hTC250tP3B8UPZ9TV2JpW7eq5kyvv0PX51zez08I8c9WOaAOTpW82bFiPobGJgS3/vPlB+dKPhiZmhGxfQ3+jVrh4uHLlVOHWf/tZxibW5B7L5M71y/j7OGj1a+nfzBR+7exZOJQmnTuT8LNaLYunY2JuSWZaUUvQCgNDOny7iSWTXmHRZ+8xcsDRmFsZsGpPZv4fdFMqtVvgVdgfa2+oejMpm+PJJXY9rQFNGpNBe/qhP+6EFMLK6o3bElaQhzHdqwnct8WPKoHEdi0Lfcy0oi7eo6KfjXYvmJeiX35BTfBO7C+TnEBjVpTvUEIZw+FM2d4Z+q364m9qzsnwjdydGsYrl5Vqdm8PQA7fv6GsNmf0n7wODoM/rDEPnXNr0aTtrw2YjLT+rbgu3H96PLORGycKnAhYh/7wpbgXbMBgc1eAdA57pWBo/l6RFe+G9uXdoPGYGphzdals0mMvc6IuatRPMHnnRBCCCHEi0KKTUIIIYQAoEeLWnz+0w46NAzEwvTPFUzmJkbMH9mVd+aG0WvqcgBsLEyZNqgdpsaGDJu9mobD55C4/nOtPt/p3ISjF2II23uKsL2ncLGzpEeLIADmhO3RxPVpFUx2Th4Tlm7RrKZR6uvRp3Udxvdp/Uy/5Dly/gZqtZrIq3FEXo0rMeZBNrrElXexRE+hYNGYnoxduJE1+yLZdeKSpq2hvyfzR7yG4f3VYSqVmkKVCrW6fPoD2B8VjUqtpk6Vsh/4/le6PmcpNgnx4mnQrifrF3xGUEhHTMz+XPVobGZO/4kLWDr5bea/V7TtqpmVDT0++B9GJqYsnjCEid3qsTAiRavP1n1GcDXqKEe2rObIltVYO7rSoF1PoGgbugcav9qXvJxswuaM59iOotU5evpKmnTuR+fhE/6RRQeFnh7DZ6/kx08GsWHhdDYsnK5pCwrpSK+xX6Cnr+RK5GHUajUxFyKJuRBZcl/356dLXI0mbXlr2hJWfjGao1vDOHtol6bdN6gR/SctQGlgWNSfSoVKVcjjPpB0za9Gk7Z4VA9ixNdhLJk0jLnvvqZpD2z2CgMmfav5Wde46g1CGPTZ9yyb8g7fjn4DAFMLK3q8Px3/Rq0embMQQgghhPiTQq1+3NcPQgghhPg3CQ0NpUePHqRunF56cBml3L1H1NU4nG0t8KvoqPlCKuXuPdIys6nsYvfIe5PSs7idnIG/p/Njv6jLzM4l6mocWTl5VPNwpoK9VbnP42H1356Nm6M1YZMGPPWxyltcUjrnY+LJySvA180B7wr2Ws/3q9DdeDjb8lrTwHLp73kZNns12yIuEP3LeJ3vsenwEatWraJ79+5PMTMhxKMoFAqGzFhKcKsupQeXQWZ6CjcvRGJl74xL5Sqa31OZ6Sncy0jDsWLlR957NzWJtIQ43HwDHvv7LScrk5iLkeTey6KCTzVsnUo+26g8TexaF1tnN0bOX/u37lerVCTF3eD2tUsYGhvjXMkHa0fXcs6yZKnxscRFXyAvJxsXT1+cKvloPd9NP87EoYInddt2LbdxCwvyib1yjrupybj5VHvk1nu6xqkKC7h+7iRqlQrPgGD09PRLjNPF4glDiNq/lTm7b+h8z8Jx/fC0VhIaGvq3xxVCCCGEeE5Wy8omIYQQQujE1sKU5jW1z+ixtTDF1sL0sffaW5lhb2VW6hjmJkY09Pf82zm+aFztrXB9TEEu+nYyP+04xu/TB5dLf0II8U9gbmVL1XotSrxubmX72HstbOyxsLEvdQxjM3N8gxr97RyfB4WeHg5unpozh54lG6cK2Dg9+mzEhJvRHFy/gtGLNpfruPpKA9yrlP4yha5xevpKKgfUKY/UhBBCCCFeOFJsEkIIIcQL73T0bQbM+IU6Vdx5u1Pj551Oubl2O5lfJ/R7JivEnpafdx5n5/GLHL9083mnIoQQT93Ni6dZOK4flWvUpVXv4c87nXKTeOsa78wNfSYrxJ63gxt+4szBHVw7c+x5pyKEEEII8UxJsUkIIYQQL7SQIB9iE9NRqdSPPdvo3+ilIN/nncITU6vVqFRqanm7YW5iVPoNQgjxL1Wtfggp8bGoVKrHnm30b1S9wUvPO4VnpuhzS0WlakEYm5k/73SEEEIIIZ4ZKTYJIYQQ4oU2bVD7552CeIw3WgXzRqvg552GEEI8dT1G/+95pyDKQeNOfWjcqc/zTkMIIYQQ4pnTe94JCCGEEEIIIYQQQgghhBBCiH8vKTYJIYQQ4l9px7GLrN0X+bzTKHcXbyYwK3Q3M1buKtN9/9XnIYQQ/3anD2wnYlvY805DCCGEEEKIp0q20RNCCCHEv9LcNXu5dieFLk0Dn3cq5SYxPZPm780nJy+fKu5OjOul+xkX/8XnIYQQ/wVbl80h8dY16rTp+rxTKTdXI49wIWIvTTr3x9LO8XmnI4QQQggh/gFkZZMQQgghxD/EkXM3yMnLZ2K/Nhz65r3nnY4QQghRossn/2D9gs9IT7rzvFMRQgghhBD/EFJsEkIIIcQ/mlqtRq1WP+80nom0zGwAanhVeM6ZCCGEKKsX6fNKCCGEEEKIh8k2ekIIIYT4Rzpz7Taf/riJE5dvkVdQiL+HCx++/hIta/s98p4Dp6NZf+A0e05dITsvn/rVPGjs70nfNnXQ1yt6xyY3r4BZq/cQuuckcUnpuDlY0zTQi6kDX8HcxEjnmPI2edlWtkVcAODzFdsJ23uKBe9103leD9N1DulZOUxZvo0/zlwjJSOLulUr0bd1HVoFP/o5CyGE+NPNS6dZPesTrp89TkF+Pm6+1ek45GP8G7V65D0Xj+3n2M71nDscTn5uDt41G+BXuxFNuvRHT08fgPy8HLYsnsXhTb+SmhCHrbMbVeo0o9uozzE2M9c5prwt/2wE5w6HA7B08nC8a9an19iZrPxiDHnZ9+g49GM2L5nFse1rmR1+jcXjB6NSqxj02aJi/WxZMouo/dsY88Mm9PSLvpq4dzeddfMnc+nEQTLTkvGqUY8mnfsR0Lj1U5mLEEIIIYQoP1JsEkIIIf5DFAoFUPR29YN//jc6cDqarpOWYGthSp9Wdci4l8OGP87Qa+pyNk0fTN2qlbTu2R8VTefxP2JpZkzXZoHYWZqx++Rl3l+wnut3Upg84GUAPvh2Pb+Gn6RHi1rU8HLl2u1klm2L4Oz1O2yfOUznmPLmbGuJg5U554mngoMVFR1syjSvh+kyh7ikdF4et5CkjCx6hgRhaWpM+IlL9JyyjM/ebMewTo2eylyfhQerC/7N/x0I8W/3Ivz3d/HYfua+8xpm1rY0frUv2ZkZHN/1G/Pe68HYRVvwCqyndc+FiH3MHtYJEwtL6rXthrm1HeeO7OanaaNIjL1O15FTAfh52vsc+n0l9dv3xN0vkMRb0f9n777jsqr7P46/2C5Q3FsURQUVt2GamStNHJUrEleOrKSpZKVUVpp3dz+s7tLUjIY7zT1wYrl3iqgg7oHI3nBdvz+8pZtARQUP4Pv5eFyPh5zzPee8vzzwXHB9zvf7ZftvP3Lx9DF85wXmuk1eq1SzLpdOHyfy0jkq1axLxRrOAFw4dYzYyKvMGNePC6f+omaDm2sIng0+hMlkynaea+dCOX1oJyaTCUsriLp6kWkjuhEfdR2Pni9QvJQDx3Zu4qvX+9P/zU/p/MLYfOlPQVLYf38TERGRR5uKTSIiIkVIqVI3n2JOSkmjRDFbg9PcH5PZzLvfr8LOxppVn42iTpVyAIx79gnajP2S2Wt25VhsWrr9ENZWlhz8/h1KlywGwOvPdcB95Oes3RPMh8O6k5KWzqKth+jaqj7fvP73Qu21q5TDd9ZKTl+8To2KZe7apm618tmuHxmbwJw1u+7av15tG9GgZqVs20d7tqVkMVu2HwllbO92PObqlOt+/VNu+lm3Wnn8flzHuWtRbPzXWFrWrwHAu16d6Tf5B/zmrWXgU81wtC9x1z4VRPFJqQA4ODgYnETk0VWyZClSkhKNjpFvzCYTC//li7WtLe98v4aKNeoA0G2ID5Oea8WWxd/nWGzas34JltbWfLriCCXsSwPw9LA3eLdnEw5vW8vzPh+TnprCrjULady+G8P8vs08tkL1OiyYPp6rZ09TrkqNu7apVKtutuvHR0eyZdH3d+1fi069qercMNv2bt7jMJsyCD2yh+7D3qBG/SaZ+66En8LNoxOjp82jspPLXa/xv5Z+NZnIS+eYGLCZ2o1aAtBrzET8X32Opf6T8HhmECVLO97TOQub1MR47Gtl/x1HREREpDBQsUlERKQIqVKlCgAXr8dQr3oFg9PcnyOhl/jrzGUGdWqeWWgCqFe9AtNGeWK6zXoYr/Rpz6iebTMLMgCp6RmULlmc2IRkAEymm8fuOBrGkbBLNKlTFYCRPT0Y3KUldrbWpKZl3LVNTiJjEvjsl7s/RV63avkci023k5t+/VNu+hkVl8iSbYdpXq96ZqEJwNbaiiHdWrH9SCgrdx7Du2urXGctSC5HxgBQuXJlg5OIPLoqVanCjSsXjI6Rb86FHOH8yaO09Xwhs9AEUNnJhYHjp2POYTQPQNcXX6XTwNGZhSaA9LQ0StiXJik+FiBzJNDJfUGcO3E4c5TQUwNG0a6PNzZ2dqSnpt61TU7ioq6z4rtP79q/yrXq5lhsups+Yz+450JTQkwUe9YuxsmteWahCcDaxpYnnh3Cib3bOLB5Be37DrnnPIVJTMQlKnfMXqAUERERKQxUbBIRESlCGjZsiI2NNYdDLxbaYlPY5UgAXGtlLxKM7Olx2+PqVa/AjbhEvl4WxN4T5zh3LYrQS9eJS0yhctmbo1uK29kwYVAnpvy0gQ4+X+FSoyLtG9eha8v6PNXcBStLS4rbWd61ze2uf3npR3ftn421VW6+DffUr3/KTT9PX7yO2WwmPjmV4dPmZzk+NulmEevM5Rv3lLUgORx6CRsbaxo0aGB0FJFHlnuTxpw5cdjoGPnm2vlQAKrVdcu276m83JmuAAAgAElEQVQBo257XGUnF+JjbrDhp68IPbKHyEtnuXYulKSEOMpUuPnQiG2x4niO9mX5Nx/z8QvtqVK7PvVbPUHjx7vSqG0nLC2tctXmdtf/z86rd+2flc29j5C2dyyPk1vzez7uytlTmM1mUhITmDlhaJZ9yQk3C3ARF87c83kLk5SkRC6dOUXjxo2NjiIiIiJyX3L+tEREREQKJTs7O9p6eBB44JTRUe5bZEwCAFXL3dv0ZzN+247r0M+YvnAzaRkZPNm0Lv95vR9t/jHl3lv9O3Jg1tu8M/ApStjZ8MPa3Qz46Ec8XvmSa1FxuW7zTxYWFhSztbnr63bFqgft1z/drQ834m5ObWVnY4W1tWWWV1n7EvR7sikNa1a8p6wFSeCBk7T18MDuNk/2i0j+e6pjR07s3U56WqrRUfJFXNTNhyMcK1a5p+PW/+jP+G4NWPX9NDLS02jYpiPDPvqOuk0fy9LumRHv8OmKw/QcOQHbYsXZtmQOX/n0Y9LzrYmJvJrrNv9kYWGBjV3xu75uV6y6E2vb3N9zE2Kj/v53zI3M461srLO8SpYpS5se/e9rlFVhcmLPVkymDJ588kmjo4iIiIjcF41sEhERKWL6Pvsc7098l/ikFEoVL3wftNesdHM9hn0nz/PsE+5Z9i3YfACT2cwLnVpk2X49JoEP562jfOmS7J/1dpZ+f7FoS+a/U9MzSEpJo2YlRyZ6dWGiVxeuRcXxr0Vb+H7VTmau2smEQZ3u2uaDwV2z5b4WFcfnCzbftX8vdmlJ07rVcvW9yG2//ik3/ez/ZFMAnKuWZ9ZbA7Icn2EyEZ+USnE7m1zlLGjik1JYs/sEn3w21egoIo+03r174+Pjw6Gtq2jZ5Vmj4+S58lVrAhB2dB+tuj2fZd/OVfMxm0y07eWVZXtc1HWWfjUZe8fyfLL8EMVKlsrct3r29Mx/p6elkpqcRLmqNen98nv0fvk9YiKvsmb2dDYvnMXmBTPxHOV71zZ9X5mULXdM5FVWff/5XfvXrvdgajVsek/fkxxZWGA2Z59S8Er43w/GlK/mBEClms68NGV2lnYmUwbJCfHYFiv+4FkKsF2rF/CYR1sqVcr9VLsiIiIiBYlGNomIiBQx3t7emMwwd+1uo6Pcl2b1qlPM1obth0OzbA85f42x/7eEP45mn0bn/LUoTGYzPds2ylKQuXg9hqNhlzO/DjocitPAD1m67e9pnSo62jPu2ScAiI5PylWbnMQkJPPThr13fZ25Epnr70Vu+/VPuelD7SrlKF+6JJsOnCQtPSPL8V8u3orTwA85cPJ8rrMWJHPX7sZkhsGDBxsdReSRVr16dXr0eIbAn77CfJv19gozJ9fm2NgV58Te7Vm2Xwo7wQ+TxxByYEe2YyIvn8dsMtH8qV5ZCk03rl7gfMjRzK9P7N2OT4ca7Fm3JHNb6XKV6DbkdQASY6Nz1SYnSXEx7Fj2411fERfC7uO7kl35qjWJvHSOjPS0zG2XQoO5dv7v81esUQd7x/Ic+zMwSzuAtXO/wKdDDcKP7c+TPAXRtXOhHNq6mpfHjDY6ioiIiMh908gmERGRIsbR0ZF3xo9n+vTPGfBkMyqVtTc60j2pWKYUL/d+nC8Xb+XNb5YzuGtLQs5f4+tlQVhbWTKse/aFs+tVr0DJYrYsCzpClxYu1Ktegd3BZ/nk543Yl7AjITmFUxcjaONaiwqlS/H5gs1ULV+aJnWqEnY5MnOUULeW9XPVJif1qlfg6rIpefq9yG2/6lXLuj5Xbvpga23FpCFPM27GUkZ/sQif5ztgX8KONbuO86+FW+jYtN5dp+oriK5Fx/PF4m28M348jo6ORscReeR99tmnuDdtys5V82nr+YLRcfKUQ7mKdPEay5q5X/DzJ6/Tru8QLoedYMNPX2FpZc2Tz4/IdkzlWvWwK1GSvRuW0ujxLlRxcuH0oV0s/3YKxUrZk5IYz5XwU9R1fwz7shVYOWsqjhWrUrOBO9fOh2WOfmrcrluu2uSkspML3+6+/kB9L1elBgDbf5vH471evOM6TbUbteRI0Hp+mDyG9n2Hcu18GOvmfUnxUg7ER998AMPaxpZnX/Pjx49eZfZ7I+k+7A2KlbTn0NbVrJo9HdfHOuLs/thtr1HYLfrCl3ouLgwcONDoKCIiIiL3zcJcFB8xExERecQlJibSsEF92tevzNc+zxkd555lmExM+WkjX/22nQzTzal3KpW1Z+pIT/q0u7lwds93Z3Hmyg2O/eALwPIdR3nVfwkJyTfXBnG0L8GnLz1DiWK2vPzlYlLT0olY/glbD53m5S8Xc+VGbOb17GytGT/gKd7s3xEgV23yw88b9/HajKWsnTaax1yd7qlf//x+5LYPs1b+yaR5a0lJTQfA2sqSwV1b8cHgrjjal8i3vuaXV/2XEnTiCsEhIZQoUfjyixRFr7zyCvMX/8aHv+2neMnC9QDE3ZhMGSz/5mPW/+iPyXRzlGjp8pUZ+M40WnbpC8D0kT2IuHCGz9cGA7Bv4zLmfTiWlMSbaxSWLO3IgLemYle8BHMnjSYtNZWZe28QvHsLcyeNITri75GsNrbF6DlyPD1GvA2Qqzb5IT7mBl+N60fY0b3Ub9met2etztbPW1KTk/j2nRf564+NAJSpWBWPZ24WVdb+8G++3X0daxtbADYvmMmS//uAtNRkACytrGnfdwh9X5lEydJF8wGCozs2MGPc82zZskXrNYmIiEhhtljFJhERkSLqt99+4/nnn+cbn+cZ1On2TxwXZInJqRwLv4J9CTvqVC2PrfWdFyu/EZfIkdBLVC5rT/0aFbGwsMjcHh2fRJ0q5QBISknjWPhlzkdEU86hJA1rVaJC6VJZzpWbNg9Lbvv1T7ntQ3xSCkdCL5GQnIqrU2WqlS+dr/3JL/M3HeAV/yUsWbKEZ58teuvDiBRWkZGRNHR1o1rD5oz9Yj4WlkVvNveUpEQunPqL4iXtqVjTObN4cjvxMTc4f+IwpctXpkqdBpn39fiYGyTGRlOxRh3gZqHmwqm/uHHlPKXKlKOasyv2ZbOOZs1Nm/wSHXGZYiXss0wJeDtxUdeJvnaJ6i6NM/ubk+SEeM6FHCYlMYFq9VwpW6l6XkYuUCIvneOzIR3p3qUz8+f/CsDIkSNxcXFh9OjRODg4GJxQREREJNdUbBIRESnKJk6cyL+mf87SD4fTvkkdo+OI5Jtdx8Pp88Fc3nr7HT755BOj44jIP+zbt48nnuhA++dH0O8N/R8VSU6I5/PhXXAsYcMfO4IoVaoUJpOJCRMmMGvWLABGjx7NuHHjqF696BbcREREpMhQsUlERKQoM5lM9O/Xj82BG/hlohcebk5GRxLJczuPheP16S881bkrixYvxrIIjpoQKQrmz5+Pl5cXPUf54jnK946jW0SKsviYG3z75iBiLoezd89uatSokWV/XFwcc+fO5YsvvuDKlSv06dOH8ePH07JlS2MCi4iIiNzdYv0lLiIiUkRERkaya9euLNssLS356eefeapzV/p8MIf5mw4YlE4kf8zfdIA+H8zhqc5d+ennn1VoEinABg0axMyZM1k791/MeX9E5ro8Io+SK+EnmTbkKZJvXGZT4MZshSYAe3t7fHx8OH36NLNnzyY4OJhWrVrRrl07Vq5ciZ4ZFhERkYJIf42LiIgUctHR0UyaNInatWszduzYbPuLFy/O4iVLeOvtd3jFfwmv+i/lWnS8AUlF8s616Hhe9V/KK/43f7YXL1lC8eLFjY4lIncxcuRI1q1dy4mdm5g2tDOnD+00OpLIQ2HKSGfzwllMHdqJWlUrsXfPbtzc3O54jK2tLd7e3hw9epSgoCAcHR3p1asXzZs3JyAggPT09IeUXkREROTuNI2eiIhIIZWQkMDXX3/NtGnTsLCw4LXXXuPNN9+842LSy5Yt43WfcUTdiOTt/k8yvHsbShW3e4ipRR5MfFIKc9fu5l+LtuLoWI7/mzGDvn37Gh1LRO7R6dOnGTv2FQIDN9Lm6efxHD2RijWdjY4lkufMJhN//RnIshmTuHo+lDffeIPJkydTrFix+zrfoUOH+Pe//838+fOpXr06Y8aMYcyYMZQuXTqPk4uIiIjcE63ZJCIiUtgkJiby/fffM3XqVOLj43nllVfw9fWlTJkyuT7+888/Z/rnn2NpAT3aNKBzcxfcnatStXxpFZ+kQIlLTOFSZAxHQi8ReOAka3afwGSGd8aPZ/z48ZQoUcLoiCLyAFasWMEbb77FmbBQ6rdoR5MOPXBu0pqKNepQ0sERC02NKYVMWmoy8VGRXAwNJmTvdg5u/p2r58/g6dmLf//7C+rWrZsn1wkPD+fLL79kzpw5WFlZMXToUMaPH0+1atXy5PwiIiIi90jFJhERkcIiNTWVefPm8eGHHxITE8OIESN47733qFix4n2dLyoqioCAAJYv+40df/xBenpGHicWyTvW1la0e/xx+j77HIMHD8bR0dHoSCKSRzIyMlizZg2//Por69atJyY6yuhIInmibj0X+vTuxbBhw3B1dc2Xa8TExDBv3jw+//xzrl+/zoABA/D19c2364mIiIjchopNIiIiBV1aWhrz58/Hz8+PixcvMnToUPz8/KhSpUqeXSMlJYXjx49z9epV4uLi8uy8j6pffvkFAC8vL4OTFH729vZUqlQJV1dX7Ow06k6kqDObzYSHhxMWFkZ0dDQmk8noSEXOkSNH+PXXX5k6darRUYokOzs7HB0dcXNzo2zZsg/tuikpKSxcuJCpU6cSEhJCjx498PHxoXPnzg8tg4iIiDzSVGwSEREpqEwmE0uXLmXixImcPXuWYcOGMWnSJE2PUgj0798fgEWLFhmcREREJKtFixYxYMAA9FFA0WQymVi9ejXTpk3jjz/+oEWLFowbNw4vLy+srKyMjiciIiJF12JNgC0iIlLAmEwmFi9ejKurK15eXnh4eBAcHMzMmTNVaBIRERGR27K0tMTT05MdO3awb98+XF1dGT58OC4uLvj7+5OYmGh0RBERESmiVGwSEREpIMxmMytXrqRFixYMHDiQJk2acOzYMQICAnB2djY6noiIiIgUIi1atCAgIICQkBB69uzJu+++i5OTE35+fkRGRhodT0RERIoYFZtEREQKgMDAQFq1akXv3r2pXr06Bw4cYNGiRdSrV8/oaCIiIiJSiDk7O+Pv7094eDhjx47l66+/plq1anh7exMSEmJ0PBERESkiVGwSEREx0I4dO+jQoQNdunTB0dGRffv2sXLlStzd3Y2OJiIiIiJFSMWKFfHz8+Ps2bPMmDGD3bt34+rqiqenJ7t27TI6noiIiBRyKjaJiIgY4I8//qBTp060b98eW1tb9uzZw8aNG2nevLnR0URERESkCCtZsiSjRo0iODiY5cuXExERgYeHB+3atWPlypWYzWajI4qIiEghpGKTiIjIQ7Rr1y48PT1p164dKSkpbNmyhY0bN9KqVSujo4mIiIjII8TS0jJzVFNQUBCOjo707t2b+vXr4+/vT3JystERRUREpBBRsUlEROQhOHr0KP3796dt27ZERkYSGBjIjh07ePLJJ42OJiIiIiKPuFujmg4fPkzHjh3x9fXFyckJPz8/oqKijI4nIiIihYCKTSIiIvno+PHj9O/fH3d3d0JCQli4cCF//vknnTp1MjqaiIiIiEgWjRs3ZubMmZw5c4YxY8YwY8YMatasiY+PD+fOnTM6noiIiBRgKjaJiIjkgzNnzjB69GiaNGlCcHAwCxcu5NChQ/Tr18/oaCIiIiIid1S5cmX8/Pw4e/YsU6ZMYdmyZdStW5f+/fuzd+9eo+OJiIhIAaRik4iISB46e/Yso0ePxsXFhaCgIObOncvhw4fp168fFhYWRscTEREREck1e3t7fHx8CA0NZfbs2QQHB9O6devMaffMZrPREUVERKSAULFJREQkD5w/fx4fHx/q16/Phg0b+Oabbzh69Cje3t5YWurtVkREREQKLxsbG7y9vTl69ChBQUE4OjrSq1cvmjVrRkBAAGlpaUZHFBEREYPp0y8REZEHEBERga+vLy4uLixfvpwZM2Zw6tQpRo0ahZWVldHxRERERETy1K1RTQcPHqRJkyaMGDGCevXqMW3aNGJiYoyOJyIiIgZRsUlEROQ+XL9+HV9fX2rVqsUvv/zC1KlTCQkJYdSoUVhbWxsdT0REREQkXzVt2pSAgABOnTrFwIED+eyzz6hZsyY+Pj5cvHjR6HgiIiLykKnYJCIicg9u3LiBn58fzs7OzJkzh8mTJ3Py5El8fHwoVqyY0fFERERERB4qJycnpk6dyrlz5/joo49YunQpderUwdvbm+PHjxsdT0RERB4SFZtERERyIT4+nmnTpuHs7MzXX3/NG2+8QWhoKBMmTKB48eJGxxMRERERMZSDgwM+Pj6EhYXx/fffs2/fPho1aoSnpyeBgYFGxxMREZF8pmKTiIjIHSQkJODv74+zszOffPIJo0ePJjQ0FD8/PxwcHIyOJyIiIiJSoNja2uLt7c1ff/3F77//TnJyMl26dKFFixYEBASQkZFhdEQRERHJByo2iYiI5CA1NZVZs2ZRr1493nvvPYYNG8bZs2eZOnUqpUuXNjqeiIiIiEiBZmlpiaenJxs3bmTfvn24ubkxfPhwXFxc8Pf3JzEx0eiIIiIikodUbBIREfkfaWlpzJo1izp16vDGG2/Qr18/QkNDmTp1Ko6OjkbHExEREREpdG6NagoJCaFnz55MnDgRJycn/Pz8uH79utHxREREJA+o2CQiIsLNIlNAQAANGzbktdde45lnnuHUqVP4+/tTqVIlo+OJiIiIiBR6zs7O+Pv7Ex4eztixY/n666+pXr063t7ehISEGB1PREREHoCKTSIi8kgzmUwsXrwYNzc3XnrpJdq2bcuJEyeYOXMmVatWNTqeiIiIiEiRU6FCBfz8/Lh48SKzZs1i9+7duLq64unpyc6dO42OJyIiIvdBxSYREXkkmc1mVq5cSbNmzRg4cCBNmzbl+PHjBAQEULt2baPjiYiIiIgUeXZ2dnh7exMcHMzy5cu5fv06bdu2pV27dixevBiTyWR0RBEREcklFZtEROSRExgYSMuWLenTpw/169fn+PHjLFq0iLp16xodTURERETkkWNpaZk5qikoKAhHR0cGDBhA/fr18ff3Jzk52eiIIiIichcqNomIyCMjMDCQ1q1b07VrV6pWrcr+/ftZtGgR9evXNzqaiIiIiIgA7dq1Y+XKlYSEhNCjRw98fX1xcnLCz8+PGzduGB1PREREbkPFJhERKfJ27NhBx44d6dKlC6VLl2bv3r2sXLmSpk2bGh1NRERERERyUK9ePfz9/QkPD2fMmDHMmDGDWrVq4ePjw7lz54yOJyIiIv+gYpOIiBRZO3fupHPnzrRv3560tDS2bdvGxo0badGihdHRREREREQkFypVqoSfnx9nz55lypQpLF++nNq1a+Pp6cnevXuNjiciIiL/pWKTiIgUOUeOHKF///60bduW5ORkNm/ezI4dO3jiiSeMjiYiIiIiIvfB3t4eHx8fTp8+zYIFC7h69SqtW7fOnHbPbDYbHVFEROSRpmKTiIgUGX/99Rf9+/enadOmnD9/nhUrVmROoSciIiIiIoWfjY0N/fr1Y8+ePQQFBeHo6Ejv3r1p1qwZAQEBpKWlGR1RRETkkaRik4iIFHrBwcF4e3vj7u7OiRMnWLhwIX/++Seenp5GRxMRERERkXxya1TTwYMHadKkCSNGjKBWrVr4+fkRHR1tdDwREZFHiopNIiJSaIWHhzN69GgaN27M/v37WbBgAYcPH6Zfv35YWFgYHU9ERERERB4Cd3d3AgICOHXqFN7e3vzf//0ftWrVwsfHhwsXLhgdT0RE5JFgYdaktiIiUsicO3eOTz75hLlz51K3bl3effddvLy8sLKyMjqaPIJWr17NihUrsmwLDAwEoHPnzlm29+rVi2eeeeahZRMRkUfblStXmDx5cpZtYWFhBAYGMmrUqCzbq1WrxqRJkx5mPJF8Exsbyw8//MD06dOJiIhgwIABTJgwATc3N6OjiYiIFFWLVWwSEZFC48KFC0yfPp2ZM2dSuXJlJk6cyPDhw7G2tjY6mjzC9uzZQ5s2bbCyssLS8uagcZPJBJDl64yMDHbv3k3r1q0NyyoiIo8Ws9lMjRo1uHz5cuZDOWazGbPZnPkeZTabSU9P5+2332b69OlGxhXJc6mpqSxYsIBp06YRHBxMp06dGDdunKbbFhERyXuLNY2eiIgUeNevX8fX1xcXFxeWLVvGtGnTCAkJYdSoUSo0ieFat25NrVq1yMjIIC0tjbS0NDIyMrJ9XaNGDVq1amV0XBEReYRYWFjg7e2NlZVV5ntSenp6lveo9PR0AF544QWD04rkPVtbW7y9vfnrr7/4/fffgZsjzVu0aEFAQEDmz7+IiIg8OBWbRESkwIqMjMTPzw9nZ2fmzp3L5MmTOXnyJD4+PtjZ2RkdTyTT4MGDsbGxue1+Gxsbhg4dqrXERETkoRs0aBBpaWl3bFOnTh2aNWv2kBKJPHwWFhZ4enqyceNG9u/fj5ubG8OHD8fFxQV/f38SExONjigiIlLoqdgkIiIFTlxcHNOmTcPZ2ZlvvvmGiRMncvbsWSZMmECxYsWMjieSjZeX1x0/yEtLS2PAgAEPMZGIiMhNjRs3pn79+rfdb2try5AhQx5iIhFjNW/enICAAE6ePImnpycTJ06kVq1a+Pr6cvnyZaPjiYiIFFoqNomISIGRkJDAtGnTqFWrFp9//jmvv/46oaGhTJgwgeLFixsdT+S2GjRogJub221HLrm5uWlBahERMYy3t/dtR+CmpqYycODAh5xIxHh16tTB39+f8PBwXnnlFebMmUPt2rXx9vbmxIkTRscTEREpdFRsEhERwyUmJuLv74+zszNTpkxh1KhRhIaG4ufnh4ODg9HxRHLl1poY/2RjY6MnxkVExFBeXl45rk1jYWGBu7s7Li4uBqQSKRgqVKiAn58fFy5cYNasWezZswc3Nzc8PT35888/jY4nIiJSaKjYJCIihklNTWXWrFnUq1eP9957jwEDBhAaGsrUqVMpU6aM0fFE7smgQYPIyMjItj09PZ1+/foZkEhEROSmWrVq0bx582wjcK2srPRAhMh/2dnZ4e3tzfHjx1m+fDmRkZE8/vjjtGvXjsWLF+f4e56IiIj8TcUmERHJM6mpqbz55puYTKY7tktLSyMgIIAGDRrw2muv0bNnT06dOoW/vz8VK1Z8SGlF8laNGjVo06YNlpZ//3plaWlJmzZtcHJyMi6YiIgIOY/AzcjIoH///gYlEimYLC0tM0c1BQUF4ejoyIABA2jQoAH+/v4kJSUZHVFERKRAUrFJRETyxK3RG19++SULFy7MsY3JZGLx4sW4urry0ksv0aVLF8LCwpg5cyZVqlR5yIlF8t7gwYOzPDVuaWmJt7e3gYlERERuGjhwYJYHgiwtLWnXrh3VqlUzMJVIwdauXTtWrlxJSEgIPXr0wNfXFycnJ/z8/Lhx44bR8URERAoUFZtEROSBmUwmhgwZwurVq7GwsOC9997Lsi7ArSJTw4YN8fLywsPDg+DgYGbOnKkPOKRI+efT4Wazmeeee86gNCIiIn+rWLEiHTp0yBzdZGFhoQciRHKpXr16+Pv7Ex4ezssvv8xXX31FrVq18PHx4ezZs0bHExERKRBUbBIRkQdiNpsZO3YsCxYsICMjA7PZzNmzZ/n5558xm82sXLmSFi1aMHDgQNzd3Tl27BgBAQE4OzsbHV0kz5UvX55OnTphZWWFlZUVnTt31tSQIiJSYAwePBiz2QzcLDb17dvX4EQihUulSpXw8/Pj7NmzTJkyheXLl1OnTh08PT3Zs2eP0fFEREQMpWKTiIg8EF9fX2bNmpVlWhaz2czbb79Ns2bN6NOnD/Xr1+fYsWMsWrSIevXqGZhWJP+9+OKLmM1mzGYzXl5eRscRERHJ1LdvX6ysrLCwsKBbt26UK1fO6EgihVKpUqXw8fHh9OnTLFiwgKtXr9KmTZvMafduFXVFREQeJSo2iYjIffvwww+ZPn16tj+mzGYzN27cIDU1lb1797JgwQIaNGhgUEqRh6tPnz7Y2NhgbW1N7969jY4jIiKSqUyZMnTv3h2z2czgwYONjiNS6NnY2NCvXz/27NlDUFAQjo6O9O7dm6ZNmxIQEEBaWprREUVERB4aFZtEROS+zJgxAz8/vzs+tXf9+nUaNmz4EFOJGM/e3h5PT0969eqFg4OD0XFERESyePHFFylZsiQ9e/Y0OopIkXJrVNPBgwdxd3fnpZdeombNmvj5+REdHZ2rc0yaNImYmJh8TioiIpI/VGwSEZF79sMPP/D666/fsc2t0U2zZ89+SKlECg4vLy9efPFFo2OIiIhk4+npyZAhQyhZsqTRUUSKJHd3dwICAjh16hRDhgzB39+fmjVr4uPjw4ULF2573OHDh5kyZQpPPPEEkZGRDzGxiIhI3rAwayJZkQd24cIFVqxYweZNmzh08ADXIiKIi08wOpZIJvtSJalYoQJNmzXnqU6d6NWrF9WrV7+vc/3yyy9ZFpe+m/Lly3P27FlKlChxX9eThy/znrZ5M4cPH+Lq1WvExcUZHUseAfb29lSqVBF396Y89dRTD3SvEpGiKSkpibVr17J+/XoOHNhPWNgZoqOjs6wdKZIfihUrhqNjGdzc3HjsMQ969uxJmzZtjI4lhUBsbCw//PAD06dPJyIiggEDBjBhwgTc3NyytPPy8mLx4sUAODs7s3XrVipVqpSnWXbv3s2qVav448+dHDt+nJioKFJSkvP0GiL3y9LSEofSZahduw6tWjanW7dudO/eneLFixsdTURyZ7GKTSIP4MiRI0z64H1WrV5NcVsb2rlUpEkNR6qUKU6pYjZGxxPJFJ+cxqw5KvgAACAASURBVOXoJI6cj2LHyWskpabR85ln+OjjKTRp0iTX51m+fDnPPffcXT/QsbS0xMbGhvT0dDIyMpg+fTpvv/32g3ZD8tmRI0eYNGkSq1atokTx4nRs/zjN3BtRrUpl7O1LGR1PHgFxcfFcvHyFg4f/YkvQHyQmJdGzZ08++uije7pXiUjRExMTw2effcasWbOIjY2lVfNmeLRqjnOd2pR1LIOlpSbtkPyVnJzM9RtRHDt+gq07/uTM2XO4ubni6/suXl5eWFhYGB1RCrjU1FQWLFjAtGnTCA4OplOnTowbNw5PT08uXLiAk5MTGRkZwM21oGrUqMG2bdse+MEbs9nML7/8wiefTeXE8WOUq1qLGu7tqFjbleKly2JtWywvuifywMxmE8mxUdy4GMal4L1cCD6Avb0DY0aP4t1336V06dJGRxSRO1OxSeR+3Lhxgw8++ICZM7/DvVZ5Xn7Khe5NqmNjrT9ypeBLSzex9sgFvt18ksNnrzN69Bg+/vhjypYte8fjNmzYQM+ePTMXubW0tMTKyoqMjIzM4lPx4sVxcnLC1dWVunXrUrduXZydnWnQoAFVqlTJ977J/fn7njaT5u6NefPV0fTq3g1bWxXNxTipqWmsWLuef389kwOHjzJ69Ohc3atEpGgxmUz88MMPTJw4EbMpA5+XRzLsxYFUqlDB6GjyiDtw6AjfzP6BnxcupU2bNsyYMYMWLVoYHUsKAbPZzKZNm/D392fVqlU0a9aMihUrsnnz5sy/tQCsra2pWLEi27dvx9nZ+b6utX//fl59bRx79uymSZcBtOz7ElVcmuZVV0TyVUJUBIfW/Mzepd9iZ23J1M8+ZdiwYXrARKTgUrFJ5F7t3LmTvr17QXoy73k2on+bOughNimMzGZYtDuMT1b+BdbFWPb7Cjw8PHJsGxQUxNNPP01iYiKOjo7UqVOHhg0bZhaTnJ2dqVu3LhX0wU+hs3PnTvr27YulBUz5YAKDBzyvJ3OlQDGbzfy0cAnvfzwNkxmWLVt223uViBQt0dHR9O/fny1btvDyiCFMmvAWjmX0VLMULIePHuP1dyfxx649fPLJJ/j6+hodSQqRXbt28dlnn7FixYoc99vY2FCmTBm2bt2Kq6vrPZ176tSpvPfee9Rq/BidX/2MynUb50VkkYcuOS6abfOmsf/3OTzZsSNLFi+iTJkyRscSkexUbBK5F/Pnz2f4sKF0qF+Jb4Y+hr2mypMiIC45jVfm7WJbyFXm/jCPQYMGZWuzYcMGKlSogLOzMw4ODgaklPwwf/58hg8fTucn2/PjdzNw0FR5UoDFxsUzZMw4ArcGMXfu3BzvVSJSdISGhtKzZ0/iY2NZ9stcmrnrQ1IpuMxmM1/Pmsvb73/I4MGDmTlzJra2tkbHkkJi6tSpvP/++5lT6P2TtbU1Dg4ObNmyJVfTCqempjJq9Gh++uknuoydQuu+o9ATslIUXD51mCXve1HB0YG1q1fd94g/Eck3KjaJ5Nb333/P6NGjGf1UAyb1bYqVpX5Zk6Ijw2Tmo2WHmLn5BDNnzmTkyJFGR5J8duue5vPyS0z1ew8rKyujI4ncVUZGBr5+n+D/7Wzdq0SKsNDQUDw8PKhZvSrLfvmBqpUrGR1JJFfWBW7mhRFjade+PStWrNDvV3JXKSkp1KhRg4iIiDu2s7a2pnjx4gQGBtK6devbtsvIyKCnZy+2bQ+izwdzqNumc15HFjFUXOQVlrzvRdL1C+zetVMFJ5GCRcUmkdzYtGkT3Z9+Gp9uDXnnGT1VKUXX9NVH8V8fzNp16+jUqZPRcSSfbNq0ie7du+P7xqtMmvCm0XFE7tlH0/7N1C+/Zu3atbpXiRQx0dHReHh4ULK4HZtXLqFkiRJGRxK5J/sOHuIpz+cZMeIlZsyYYXQcKeDmzp3LyJEjM9fAvRMrKyuKFSvGhg0baNu2bY5txo0bx8zv5zD4y5VUbdAsr+OKFAhpyYn8/IYnxc1J7Nm1U1PqiRQcKjaJ3M3p06dp3bIFHV3K8Z+hHhp9LkWa2Qwvz/uTrSdvsGfffurWrWt0JMljp0+fpnXr1nR7qgMBM2dofSYplMxmM96jx7F+8zb27Nmje5VIEWEymejWrRsnjh/nz8BVGtEkhdbSFasZNHwM33zzDWPGjDE6jhRQZrMZNzc3QkJCsLa2Ji0tjbt9RGdlZYWtrS1r166lQ4cOWfZ99913jH3lFZ6bNJeGHXrlZ3QRw8VFXmHe2M40b+zKxg3rsbS0NDqSiKjYJHJ3T3frysUTB1nzVifsbDQNghR9KWkZ9PhiE9UaNGPd+g1Gx5E89vTTT3Pl0gV2rP+dYnZ2RscRuW/JKSm069abylWrs27dOqPjiEgemDNnDmPGjGHnxlVao0kKvcmffo7/d3MICQmhatWqRseRAig+Pp4FCxZw+fJlrl27xuXLl7l48SJXrlzh6tWrJCUlZWlvY2ODpaUlKSkp2NnZ8fvvv9OtWzcALl26RD2X+rR4dgxPDp9oRHdEHrrLpw7zw8tdmDnzO0aMGGF0HBFRsUnkzn7//Xf69u3Lsjc64VG3otFxRB6avWHX8fxiI8uXL6dXLz0VV1TcuqdtWrGY9m3bGB0n36zZsImr1yJo07I5rg1cjI6TJ+Li40lNTaNcWUejoxQoO/fso0OPZ3WvEikCYmNjqV+/PgP6evLFJ35Gx7kvQX/u4lTYmWzbHUuXxs21AXVrO2V58vp/23sP7Ie1tXWO5z0ecpJde/cD0Kt7N8qXKwtAdEwsv61cTe2aNen4xON53JucxSckUKpkyTu2SU9Px8rK6pEfPZ2SkkrTdp1o49GWn376yeg4UgglJiZy5coVrly5QkREBJcuXeLq1atcu3aNixcvEhsby/Tp02nZsiVeXi+yftsfjJq7E2vbovFA2bkjfxJ5ISzb9uL2palQ25Wy1WpjYWGZY3v3bgOxtMr5nhpxNoQLx/YCUP/x7pQoXQ6A5PgYgrevxLFKTZyaPZHX3cnGlJGOhYUFFpZ6qPlBbPh6IqHbf+PUyRBNpydiPBWbRG4nIyODBi71cC8P/xnqYXSchyo2KY3Gvr+RnJbB002q8+OY/P9FKz9sDb7MlOWHOHEphnSTieplS/Jyp4YM61APyzv88Wsym+n06VrSTdlvjzXLleSXsU/mY+qCY+y8nRy+DidOntLixkVARkYGDRrUp1XTxgTM/MroOPkmJjaOGg2bk5ScTK/uXVn68xyjIz2wyBtRNG/fBQcHe47u3HLHtiaTiZZPPk16enq2fU41a7BiwY/5FdMw3qNfY++ho5w4EaJ7lUgh5uvry9w5szm+JwjHMqWNjnNfRrz6Bj/+uui2+zs+8Tjz53yXWSz63/Zrl/5Kl44dcjxu0PAxLF6+EoBdm1bTsllT4GYRqolHR/r18WT+3O/ysitZHDx8lIkffca+g4eIio6hUoUK9OrRjWkfvY+DvX1mu7UbN/PBJ9MIDjmJg709Hds/zssjhtC+7WN3PL/JZKJFh65kpGdk21erZnVWLiy8hZrlq9bSb8hIdu/eTatWrYyOI0XU3r17adOmDc9/+CMN2vc0Ok6eWTHtVQ6v+/W2+52aPcFzk+dkFov+t73X9KXUadkxx+OWfjic41uXAzDi202Za1tFnA3hu6EeuD7Zh+cmz83LrmRxNHAx+5bP5sqpI5gyMnCs6kSrviNp2WdEluLZ//rmxZbUavo4Pd/2v+v5zWYT37/UAVNG9r8HSleuyaCpCx+4DwVJclw03w1pzdhRI5g6darRcUQedYs1oaXIbaxevZrQM+G880wjo6M8dL/vP0tyWgb2xWzYdPwSUQkpRke6Z0EhVxj49RbORyYw0KMOw55wITktg4mL9vGv1UfveOzlqESOX4zGysKC8qXssrzKlLB9SD0w3jvPNCL0TDhr1qwxOorkgdWrVxMaGsZk37eMjpKvFi9bQVJyMg72pVi3aQuRN6KMjvTARo17m0tXruaq7YVLlzl6LBgrKysqlC+X5VXWsWg+6TfZ9y1CQ8N0rxIpxJKSkpg1axY+L48stIWm/7Vx+SJCD+8m9PBuju8NYvXin+nftxdbtv+Br9+UbO2trKxY9NuKHM+VmJTE6vWBhhXT9x88TOfe/Thw+AiDnu/L+++8TmkHe77/8We69hmIyWQCYMHS5fQa6E1MTCxvv/Yyz3TrzOr1gfQeNJSQ06F3vMat9y5LK0vKly+X5VXWsXCP6O3TsztNmzTi66+/NjqKFGEzvvqKqi5NilSh6X8N/vdyxi04zLgFhxn7015emLYYt459CT+4nU0z/bK1t7C04tjm33I8V1pKEqd2rjdsNNGRDQtY/ukYkuOiaf3cGFr2HkFqUgLrZkxgx8//zvGYw+t+5cbF7CO8bic24hJXw45hYWVFiTLls7yKOxTue2pOitmXodVzLzNz1vfZpp4UkYcv5zGlIsL8X3+lXYMq1K5gf/fGRcyCXWGUty/Ga11dmbz0AL/vP8fQJ+oZHeue/HvNX5jNsH7C0zhVKAXAe73daTpxOd8GnuCtHo2xssx5dNOZiHgAvhnqgVv1ovfLWG7VrmDP4/UrM//XX/H09DQ6jjyg+fPn82T7tjjXdjI6Sr76cf5iKpYvzzs+Y3nng49YsnwVo4cPNjrWfZs59yfWb96a60JR6JlwAH781p8mjVzzMVnB4VzbiQ7tPJg/f77uVSKF1Nq1a4mNjWXYiwONjpInqlatTK0a1TO/dnGuQ7dOHdm6409Wrw/EZDJlmU6vQzsPlq9eyzdfTMXW1ibLuVavDyQxKYmOTzzOlu1/PLQ+3PLN7B9ISk5m58bVuDd2A8Dv3Xfo2ncAm7ft4LeVa+jVvRsTJn1MyRIl2Lt1PWVKOwDw6aSJ1GrUghdGvMz+bbdfBzQ0LByAgO++KpLvXSMGv8CEyVOYNWsWdlovU/JYcnIyS5f+RsdRHxodJd/Yl69K6Uo1Mr8uV90Z59adCD+4g5M712M2m7KMCHJq2o4TO1bT480vsLLO+rDoqZ3rSUtJwqnZE4Qf3P7Q+nDLroXfUK5aHYZ/G4hdiZufNbV9wYevBjZl3/LZtB/8NnCzYLT9x2lcOnGQq6F/3dM1ov5bmOoz8TsqOT8aD0837fEiW3/4lHXr1tG3b1+j44g80jSySSQHZrOZ9evW0tWtstFRHrrTV2PZF3advi1r8WwrJywtLFi8J/vc8wDHL0YzbFYQLT/4He/vtrNwVxjbT1zhpe93ZBkNFZOUyoQFe3ni49U08v2NYbOCCPzr0gPljIhNZu62kxw6G5nj/otRiVQtUyKz0ARQqpgNzZzKkWYykZLDNB23hF2Lw8ICnCs5PFDGoqBroyqsW7sGzbhauJnNZtavX0/Pbp2NjpKvTp4OZdfe/fR/thcDn++DpaUlPy9ammPbo8eC6ec9krrNPHjWazgBCxazaVsQA4eNyTIaKjomllffmYj7452o3qAZ/bxHsnbj5gfKeTXiOt/O+ZF9Bw/fsd3xEyd554OP+GzyRCpXyt26gadDz2BhYUG9unUeKGNh07NbZ9atW6d7lUghtX79elq1aEalChWMjpKvypYpQ2kH+yyFJoDnevUkKjqGwK3ZP/hcvGwllStWpL1H/qy1eDUigv/Mnse+g4dy3L9zzz7cG7llFppuGfrCAAD27j9IcMhJLl6+QvcuT2UWmgAqVihPl44dOHz0GDGxcbfNcCqsaL939Xy6CwkJCQQFBRkdRYqgoKAgkhITqNf2aaOjPHTFHcpQrKRDtqnnGnboRXJcNGH7tmY75viWZZQqW5GaTfJnqYSEqAj2LZ/NpRMHs+1LSYjl2plgnNt0ySw0AdiXq4xT8/YkxUZjSk8DIDUxnsjzodiVdMic5i+3blwIAwsLylWv+2CdKURKOlagRsMWrFu3zugoIo88FZtEchAWFkZUTCwt6xTtP3hzsmjXzcJSvza1qehQjLYuFdkXdp0zEVn/QNx5+ho9pm9g1+lrtHGuQJkStry7cB8TF+1j5cFzJKXeLOZcik6k06drWbTrDB71KjLIow7nI+MZ/O02Zm0+cU/ZbsSnEBB0muf8N+E+cRnvLtzHxajEHNv2aFqDS9GJWYpap6/G8sfJq7RzqUQJ29sP7DwTEUc1x5IkpKSz8ehFfv0zlL1h18nIYQ2noq5l7fJExcQSHh5udBR5AGFhYURFRfFYqxZGR8lXAfOXADB44PNUrliBDo97sGvv/szRPrcE/bmbdt16s2PXHto91hrHMmXwGf8BPhMmsXTFahL/O/3ChUuXaflkN35esJT2bdswxGsA4ecv0OeFYcz4bvY9ZbseeYPv592ccqiWW0vGjX+f8xdvX3RPTknBa+QrtPNozaujhuf6OqfPhFOzejXi4xNYvT6QH35ewM49+8jIuH2BvSh4rFULoqKidK8SKaT27duLR8vmRsfIV7+vXkfI6VAGPtcn274uHZ+gVMmSLFqWdSq9+IQE1m7cxLO9euRpMf165A1mzfuZLn36U9O1BePGv8f5C9nfk9LS0un61JO8MnJYtn233sMcHctkTvXaqkX2D0RbNb+57fiJkNvmCQ07k+W9a+7P84vUe1f1qlWoXq0qBw4cMDqKFEH79+/HsVJ1HCpUNTrKQxWyYzXXz5/GrdNz2fbVadUR2+IlOb5lWZbtqUkJnNq9kQZP9II8vKcmxkRyYOU8fnqzD18+78pa//HERlzI1s7SypohM1bz+CCfLNtTEmK5FnqMOq06Yml9c3Rr+VouDPFfxRD/VfR9//t7ynPjYhilK1YnNSmeUzvXc3DNz1w4tgezqWjcU2+ncsMW7NufvcgnIg+XptETycGZMzcLLrX/Z1TMo8BkNrNo9xlcqpTGvebNhYv7tqzFjpCrLNkTzjvPNM5s996i/dhZW7LR92mqly0JwMudG9BlatYnSaYsP8T5yATWju9Gc6ebC3eOf6YJA7/ZwkfLD9G/TR3KlLz9OkjRiamsOXSe3/efY0fIFdJNZtyqO/L602487V6dJjXK5njcS0+6EHTiCi9+u5VWdSpgZ23FHyevUrl0cd7t5X7H78OZiDjik9No8f7vJKX+vaime82yfDO0LfUqPzojnupUvPnE1ZkzZ6hdu7bBaeR+3bqnFeUp9EwmEz8vXELD+vVo7n7zXjXg2V5sCfqDXxb9xqQJb2a2e/3dSdjZ2bJr0+rMaY7eeGUUbTr1yHLO9z76jLPnLvDHhhW0/u8HaJMnvEXP/oN598NPeXHA83ec3i4qOoblq9ayePlKtgT9SXp6Ok0aufLum6/Rq0dXmjVpfNtjJ0yawuUrV1mz5BcsLHKe8jMnoWHhxMbFUbepR2bRDKC5e2N+/G4GDVyK5hOOdevcvD/pXiVSOIWHn2X4C/2NjpFnXnr1TUqWKAFASmoqJ06eIuJ6JL2feZp3fF7J1t7Ozg7P7l1ZsWY9KSmp2Nnd/N149fpAkpKT6d+3Fxu3bHugTFHRMSxbtYbFy1ayJeiPzPekiW+No1f3bjRzz/6eZGNjjf+07GtMXYu4zrez52FjY03Pbl2w+e/Uf1u27+DNV0ZnaRscchKAYydC8GjdMsdsp8/cfO9ydm+T9b2raRN+/G4GDV0K13TeOannXCfz9zGRvBQeHk7Z6kVzVOAtK6a9im3xm/fU9NRUrp87SWL0deq3eyZb4QbA2sYOl7bdCdmxhoy0FKxsbk5feWrnetJTknHr2JewfVseKFNyXDTBQas4vmUZ4QeDMGWkU8m5Ee1ffAuXdt2pUi/7Zw42xUpQo9Hfo1R3L/mWmKsXOLVrAyZTBu283nigTLfcuHiGlMQ4Zgx0Jy3l73tqFZem9Jn4HeVrueTJdQqactWc+XPTIqNjiDzyNLJJJAexsbEA2Be/fRGkKNoWfIXL0Yn0a/33B3XPNK2BjZUlS/5nKr2j56M4diEK7/Z1MwtNAA2rlqFPi1qZX0cnpPLb3nCa1iqXWWgCsLG2ZPDjdUlLN7H60Pkcs0TEJuP1n600mvAb7/y6h3STCb/nmrN/Sm82T+zO+J5NbltoAnAobkv1siUxm+Hg2Uj2n7mOyWzGysqChOS0O34fzkTEE5+cxtvPNGKnnyer3u7K4HZ1+etCFN7fbSPxfwpQRd2t/wPR0dEGJ5EHceueVtqh6K5BF7g1iIuXr/Bi/7+fbuzTszs2Ntb8svjvBYIPHT3Gkb+OM3Loi1nW02jk2oD+fXplfn0jKpr5S5bTspl7ZqEJwNbWhhHeL5CamsbyVWtzzHI14jq9Bg6hWoOmjH3rXdLTM5j+8QecPrST/dvWM9n3rTsWmlavD+Q/s+fx3f99TpVcTp93y+kz4cTFJ/DB+DcI3rud7WuX8dIQLw7/dZy+XsNJSMx5NGhhd+tnW/cqkcIpNjY2y/RrhZ3JZMJkvvmysrKkTGkHrK2t2bh5221Hxvbv24vYuDjWBf49VeuiZSuoWrkSbdu0uu8sVyMi8BwwmKr1mzD2TV/SM9KZ/vEkQg/v5sD2jUz2fTvHQtPtrF4fSNN2nbh4+QqffzSJRq4NqFenNi2aubN5+w7m/PQrcfHxxMTG8Z/Z81jy+yoAMjJMtz3n6bD/vndNeJPgvTsIWvc7I4e8yOGjx3jWa1iReO8q7WCv9yjJFzExMdiUKDr3z5yYzSbMJjNmkxlLKyuKlSqNpZU1YXs3s3vpdzke4/ZUX1IS4zi9OzBz27Ety7AvV5kaje9/WtKEqAjm+w7gi2frs+bfb2LKyKDL2I8Zt+Awo2Zvp8Mw3xwLTTnZMnsKu5d8y40LoZQoXQ5r22L3net/RV0MIzUxnieGTuCVn/Yy7Ot1NO85hCunj7LwfS/Skgv/PTUndqVKExcbY3QMkUeeRjaJ5CA9/WYxwdoy90+TFwULdt1cSPJiVAJfrv17Ecry9sUIj4hnb9h1WtUpT3hEPJDzmkb1q5TO/Pfpq7GYzZCQksaoOTuytItLulnwCb+e8/zt1+OTCfzrEtaWFgx/sj6DPOrgWu32Iwj+qdcXGwm+FM20ga3o07IWxWys2HTsEm/+spsX/rONoA+eoUa5kjkeO8P7MeysLWlQ9eb16lS0p1Wd8jgUt+GbjcGsPniefm0ejSfnb/0fuPV/QgqnzHuaddF92w+Yf/MptnMXL/LpFzMyt1csX56wM2fZuWcfHq1bZk6pV7+uc7ZzuDb4+ym/k6dDMZvNxCck8MKIsVnaxcbdvG+Fhp/NMUtExHXWbtyMtbU1Y18aytAX+tPYrWGu+nH56jVeeu0thg8eRJ9n7n3u/bnffImdrS1uDesDN0f8eLRuSWkHe7746juWrVqbpSBXVNz62da9Sv6fvbuOjiJZGzj8mzgh7kKAGIRgAYIFdydogODBF9cFloWPZbEFFltcA8GCu0twdyceSIAQIkQgMvP9MWRgmAkJtizces7JOXu7qrur+x7q7em3q0r4MWVmZqKpqfm9m/HVrFw4RyXOxL2Mx6dbL37/czrFirjSomkjpfIGdWphbGRI4PZdeDdpyKvkZA4eOU7v7p1V1nj6FLGxcYqY1L9Xd7r5tstzTHpfSFgEw3+bwJ4Dh3FxKszapf9Qp0Y1ADQ0NFgx/2+8O3Slz+CRDB09XpFw69nFl6WrAyjulvOX9KsWzEZXV4fixdwAcHV+F7tmzl/E9t376NSuzefdgP8ILU3Nn2ZaQOG/JSsrC42fqP9Ux3v0QswdlEfnpyW9ZPOEbhxf/icWBYvgVq2pUrlzhTro5jfizvHtFK3ahPTUZEIuHqFss+4qazx9ipT4WIIvHEZDU4vyLXtRupEv1k7Fc99RjdEHnvDycQiRt85zfPkkVvxSj8GbbmFg9mkfm32o+egFaGrrYuUo7+vNCjhToHgFdA2MOLdxPvdO7qZU/XZfdI7/Ig1NTbLEbwFB+O5+3rdOgiB8ksS0dPbfeIxEAmtOBauts/lCGOWdLEhMTQfALL+uSh3pe+saxae8AUBXSxMtTeUHOlMDXVpXKExRW/UJJFcbY9b9UpNdVyPZeC6EpcfuU9DcgEalC9DIowAVnCzRzCEZ+OhpEveiE/AqYk236u+m3Wji4cDFkFgWH73P3utR9K3jpnb/7CkEP1S3uB0LDt/jfrT4WkYQ/ksSEpPYufcgEomEZavXqa0TELiNyhU8SUiQ//tVN/1d1nvzmMe9jAfkUxtpays/LpmbmeLbtiXuRdW/OHMr4sKujf5s2bEH//WbmLd4OYULOeDduCHejRvgVdEzx5eqS1au4UXcS5KSXtFjwDDF9uiYp8hkMnoMGEYRFyd+HTJA7f5lc/g6vVG92syav5g793JeM0MQBEH4dszNTPm/0SOo1bQ1+w8fU0k26eho06JpI7bs2ENqWhq79x/i9Zs3+LRsnsMR88atiAu7N61l845drF63kbmLluFYqCDejRvg3aQhXhXL55roWxe4lf7DxyCRSJg2cRwDe/dQTPWXrYS7G9fPHGXzjt3cu/8QGxtr6tWszonTZwFwdyua4/HLepRSu71RvTrMnL9IxC5BEFTkMzKjZvfR+A9uSvCFwyrJJk0tHdyqNeXuiR1kvEnjwdn9ZKa/oXitll90XotCRegwbRN3j+/g+v51XNiyCBPbQhSt2hi3qk1wKFERiUYOfapMhgyZUrLLrIAzZgWckWhosGtaf4IvHMajUccvaqNtEQ+1210r1uPcxvnEht37ouMLgiB8jEg2CYIAwI7LEbzJyGJWxwp0qqL81VBGlpQSo7ex82oEk9uWU4wIuhgSS/2S9kp1b0XFK/67kIV8zSsnK0MWdvNSqpcllZH8JgN9bfXdkJaGhLol7Khbwo6MzAocuxvNzquRrDsbwpJj9zEz0KV+CXuGNS6hOE+2u0/kbfByVf0iqEYxWxYfvU/C24TZh6LjU7kaHkeZQmbYmymPfAp/IR/RZWGommQTBOH7Cdy2k9dv3rB4Gci0FgAAIABJREFU9nR6dPFVKsvIyKRAsTJs2bGb2VMmUqigAwBnL16macN6SnWv37yj+G+nwvIpQV2dHPFfPE+pXlZWFq+SU9DPl09te7S0tGhUrzaN6tVmUfp0Dh47wZYdu1m5dgNzFy3DwtyMxvXrMG7EEBwLF1Ta19LCnNIli/MoVHldhzdv0pHKpNy4fTfHL9yjnkRz6ep1PMuUpmAB5b45LDwSkI/0EgRBEL6PzEz5Rw1v0tU/h/q0bI7/+kD2HTpK4PZdONjbUdGz7Bed8/2YlD47g4NHj7N5x25WrN3AnLcxqUmDuvw2Yogi9r1v78EjdOs3mErly7Fu+UKV+AKQnp5BWGQkFmZm+HXqoFQ2fc4/2Fpb5bjGoTx2XcOzjIfKsUMj5COILS1F7BIEQZU0Sz6KJStDfZ9avFZLbhxYz6Nzh7h7fDtGVvYUcFe/dlxeaWhq4VKxHi4V69EkczYhF49y9/gOru1dy4XNi9A3Nse1cgOqdRmBqW1hpX3PrJ/DseWT6DBtEy4VlX+H6BvLlx1Iev74i9qX9PwJT+5fxc6tDMZWBZTK4mPkfWp+U8svOocgCMLHiDWbBEEAYOO5UHS0NGhWtqBKmbamBo1LO5CQks6RO9G42RmjpSEh6N5TpXoRL5I5ef/dNkdLQ8wNdDl+N4aMD+Zpn3fwDkWGb+FqRFyubdPW0qBBqQIs7ObF3emtWNm7GtWK2rDraqRScitbkbdT+e2+GqlStuuK/AGrmJ36H7zxKW/osewUsw/cUSnbeUV+vEouXzasXRCEr8t/w2Z0dXVo7d1UpUxbWwvvJg15GZ/A/iPHKF6sKFpaWhw5fkqpXlh4JEeD3m1zdiyMpYU5h46dICNDeTqG6XMWYOlUnEtXr+faNh0dbZo1rIf/4nlEP7xOoP9Salevwtade7l267ZK/f69unP5xAGVP7cirjgWKsjlEwdYNm+m2nPFxyfQrlsfpv49X6UscPsuAKpWrpBrmwVBEISvLyMjk0Ur/AGoUbWy2jp1alTD3MyUZasDOHTsBG1bNEMi+XrTeuvoaNOsUX3WLJlPzKObbPZfRu3qVdmyYw/X1cQkgN8mTZVP7+e/TG2iCSA1LY3iFaoz+NdxStsfR8ewbfdemjWqn2Ob4uMT8Onam6mz5qmUBW6Tx65qlT9/fRVBEH5O0swMLu9YAUAhj6pq6ziWq0E+IzOu7llNyMVjuNdsAV+xT9XU0qGIVyNa/LaE4dsf0fYPfxzLVufuiR08fXRLpb6VkzsAoZdPqJRd27MGAGvnEl/UprRX8WyZ0JXTa2eplN05Jl/HtmAp9TFIEAThaxAjmwRBAOBqeBxNyzhgnE9HbXmLcoVYfzaEzRfCaFS6AL1qu7HoyD0GrTmPd7mChD5/xaqgh0r7aGtpMK6FB0MDLtB/9VkG1HfHUE+bAzce8/f+29QoZkMFJ/Vf1cQlv2H92ZAc21vSwRRna0NsjFVHFhS1NaZmMVtO3Iuh/T/HaVPBEQfz/Oy7HsW2yxEUtTWmcWn5Vz5rTwfz68ZLDG9ckuGNS+Bub4qnkwUBZ4Ixy69LYw8HZDIZWy6GceJeDE3LOFCmsHleb6sgCN/Y/YfBXLxyjVbNGue4uLxPy+asCtjIuk1b8W7cgIF9/Ji9YCk9+g+jbctmBIeGsXC5v9I+OjraTP59NL0Hj6Rr30GMHNwPI0NDdu07xJRZc6lbsxpeFdV/GRn7Io7V6zbl2GaPkiVwdXbCzsb68y8cWO6/jgEjf2PcyCGMGzmEksWLUal8OVasWY+5qSktmzVCKpWyLnAbh4+fpFWzxpQvq35aDUEQBOHrmTF3AWYm7z5siouP59yFyzwMCaVh3dp0ad9W7X5aWlq0bNqY5WvkU8K2/cIp9EAek1at25hjuUepEhRxccLOxkalLD4hkTv3HuBRqgSz/1msdv8aVb1o0qAutapXYeuuPdSpWY0WTRoREhZGnyGjKGBnx/Q/flfUX+YfwIARYxk3cii/jxqqiF3L16zD3MyUlk0bIZXJWBe4lcPHg2jVrImIXYLwP+7shrnoGb7rU9OS4nl8+wJxj0NwqViX0g3aq91PQ1OLYtWbcvVtIudLp9ADSE14wfX96qftBrBxLYWZgwuG5qp9qkulelg5uXNx21L0DIxwLl+HpBcx3Duxg4dnD2DnVgbXyg0+qT1X9/izb/YIqncdSfUuo7B2Kk6B4uW5uncN+YzNKFatKTKZjJuHAwm9fJxi1Zth5/ZlI2YFQRA+RiSbBEFQaFPBMceyKkWtMTPQ5dDtJySkpjOuhQfG+bRZcuwBm86HYppflzYVCmOUT5tZ+25joKcNgK+XM6npWfyx/ZpiZJCWhoSOVVwY27x0jh8WPU9K488duY8aKG5vqrJNQyJhiV8VxgReZvvlcI7fjVGUVXaxYk7nSmhryQd2ymTyKf1kMvlaUxIJ+PepztCAC8w9eIe5B9+NcOpW3ZWJrcWDmSD8l/hvCATA16dVjnVqVq2MhbkZew8dIT4hkSnjx2BibMy8xctZs3Ez5mamdGjTEhNjI/6cMQcjQ0MAundqT2paGqMnTGbzjt2A/EWgX+cOTPptVI5fmz97HsvYP6bm2vZSJdw/9XKVyGQysrKy3uu/JGwNWE6fwaOYPucfps/5R1G3j19nZkwa/0XnEwRBEPLmww8OLMzNKF6sKL39ujC4b8+PjlZq18qb5WvW4Vio4FdJsjx7HsvYiVNyracuJp29cAmZTMa1G7e4dkP1K32Qx54mDeqyfP7fdOz5C70GDqfXwOEAlCldkoBlCzA0eDfltUyGSuzaFrCS3oNHMG32fKbNfjc6t69fF2b8OeGTrlcQhJ/Ph8kdfWNzLB2LUa65HxXb9P3oaKXitVtxdc8aTGwLfZUkS/LL5xxdOjHXeupGKEkkGvj8GcCOyX0IWj2doNXTFWVu1ZrRcNA0NDQ/8TWtTIZMmiXvXOUnwefPAPbMGMyZdbM5s262omo5bz/q9/vz044vCILwiSSy7Kc8QRAUAgMDadeuHc8W+uZeWSAhNR0TffmIqLGBlzl06wmXJ3kr1Ul+ncGtx/GkvM7E3d4EO1P9f6Vt0QmpPIhO5HVGFi7WRrhYG+V55PzjlykEP0vCOJ8OrjZGigTa/xrrX9azadMmfHx8vndThM+U3adlxEV976b858QnJGJqIp96c/Cvv7P30BGCr51TqvMqOZnrN++QnJJCCXc3HOztvkdTP0lE1GMeBodiYmyEWxEXpRd9PyttcwfRVwnCD0oikbBh5WLatmj2vZsifAGZTMbtu/cJDY+gTOmSOU67lxN57ArBxNj4p4td7bv3QaKTj8DAwO/dFOEn4+Pjw53YdFpPWPW9myLkkUwmJSEmgheRj9DW1cPcwRVDC9uvfp7EZ1HERQWjZ2CMRcEi6Oj/PH2qOndP7GDrRD/Ea25B+K42i5FNgiB8stcZWbSac5RyjhZMalNWkWhKTc/k+N0YShRQHW1koKdN5e+w1pGdiT52Jp+X2Cpglp8CZvm/cosEQfie0l6/pp53Oyp6lmXW5AmKRFNKaiqHjgXhUaK4yj6GBgZU8/qx1oso5FCAQg4Fcq8oCIIgCF+JRCKhZPFilCxe7LP2F7FLEIT/BRKJBqZ2jpja5TyzzNdgbO2AsbXDNz2HIAjCh0SySRCET6anrYlpfh1WnHjAq7R06pW0JzE1nQ3nQolJSOPvTj/WS1lBEP535NPTw8zUhAXLVpGYlESTBnVJSEhk9fpAomOesnTujO/dREEQBEEQBEEQBEEQhB+OSDYJgvBZFnWvwtwDdwi6H8PG86Ho62hRysGMgH41vssIJkEQhLxau/Qfps2ez5ETp1izYTP59fUpU7oEOzas+uFGMAmCIAiCIAiCIAiCIPwXiGSTIAifxSifNr+39OB3PEhKy8BATwuNvC6GJAiC8B0ZGxkydcJYpk6AxKRXGBrkR0ND43s3SxAEQRAEQRAEQRAE4Yclkk2CIHwxo3za37sJgiAIn8XYyPB7N0EQBEEQBEEQBEEQBOGHJz7jFQThf5ZUJvumx8+UyviUUyS/ziA+5c23a5AgCD8NqVT6WftlZmYiy0PHlNd6MpmM+ITEz2qLIAiC8HMSMUoQBOHrkck+r0+VZmWS+eZ1nuqmp6V81jkEQRA+JJJNgvA/6lLoC2bvv01sUt4ePn4WIc9fMW7zFcqN24nbiK10WniCUw+e5rqfVCaj1uR9VJu0V+Wv48ITSnWP3I6m7tT9OA7ZRPFft9J7xRnOBT//6PHjU95QZeIems06/CWXJwj/E85dvMyUWfN4FvviezflX/UoJJShYybg4lEZa5eSeHfoxrGTp/O07/7DxyhfqxEmBYti71aGjj1/4dTZC59dLz4hkT5DRmFcoAhWziUwK1QM3x6/8CLu5RdfpyAIwo/s3MXLTJ45l2exsd+7Kf+qhyGhDB0zHufSFbFyLk7z9l04FpT3GOVZswHGDq7YFS2Nb49+nDp7/rPrxSck0mfwSIzsXbB0cse0YFE6+PUVMUoQfkCP71zk1NqZpMT/b/WpcY9DODh/DPPal2Zmc2c2jmlP2NWgPO0bevk4y3rXZGpDe6Y2smd+Bw8ubV+mkrSKeXSDdSNbM6O5E9MbO/B3q6LsnTWUN6mvcjz2gk6e7Jk5+IuuTRCEn5tINgnC/6jzwc+Ztvsmz5LSvndT/jWvM7LosiiI9WdDqOVuS7fqroQ+f0WnhUG5JoNi4lO5+yQBTYkECwNdpT8TfR1Fve2XI+i06ASJqRn0r+dO/ZL2HL71hM4Lgwh+lpTj8YesvcDTxP+d/y8E4UucPneRCVNm8PTps+/dlH9N2uvXtPD1Y/W6TdSrXYM+3bvwKCSMFh26q00GvW/j1p14d+hGQmIiwwf0pXH9Ouw9eJQWvt14GBzyyfXS0zNo1q4LqwI20r5NC5bOnUG71t5s3rGbVp16fLN7IAiC8CM4de4CE6b8xdOnH3+2/JmkvX5Nyw7dWBWwkfq1a9LXryvBIWF4d+iqNhn0vo1bd9C8fRcSE5MYMbAfTRrUZe/BI3h36MYDpRiVt3rp6Rk09enEyoANdGjTkqXzZtK+dQs279hNy47dv9UtEAThG4m8eY4TK6fwKi73D0R/FplvXrNpbAeu7w/AuXxtynn78fJxCBvHdiDy5tmP7ht2NYh1o9qQ8DQSj4a+eHr3IONNGgfm/crJ1X8p6sU8uMbaod7EPLxBiTptqNZlJHr5jbi6x5+A4S3Ujqa6cWA9L5+EfvXrFQTh5yLWbBIE4ZNJZTI0JJLv3YxPNmXnDYKfJbG+f03qFLcDoFetotSaso9B/ue5NKl5jvuGxSYDsKBbZYoXMFVbJyNTysRt19DX0eLI2IYY55Mnoca18MBj7A76rDjD0bGNVPZbffIRx+5GY5JfR6VMEISvSyqVoqHx431r8/uff/EwOITdm9bQsG4tAAb27UG5avXwGzCUR1fV//BMT89g9IQ/ya+vz6XjBzAxNgJgyoQxFC5RHt+e/bl84kCe6wGs3biZC5ev8tcfvzO0f28Aundqj0QiYdnqAK5cv0k5j1Lf+pYIgiD8dH7YGDVpGg+CQ9gTuJaGdWsD8hhVtmo9/PoP5dG1c2r3S0/P4Nfxk+Sx58TBd7Fn/FgKlSiHb49+XAk6lOd6AGs2Bspj1KTxDOvfBwC/Th2QSGDp6gCuXLtBuTKlv/UtEQThP0AmkyKR/Hh96rEVk4iLCqbDtEBcKtYFoGLrvizpUZWd0/ozcP21HPc9tWYmyGT0XHwUUztHAOr0Gs+ctsU5F/gP1buORKKhyaXty8l88xq/RYexcSkJQM3uYwgY3pKwq0HcC9qNe01vkmKjOek/nej713gWcvvbX7wgCD+8H6/XFYT/YYlp6fy68RLVJ+2lxOhtdF96iiO3o5XqDFt3gTGbLvM0MY2+K89QdtxOKozfxZC150lNzwRg+LqL+J96BMCQtef5LfAyAL8FXmZowAWiE1IZvfESxUZtVRz30dMkfBecoNiorRQeEkiD6QfYcy1K6dy9V5xm7oE7XAp9Qe8Vpyk2aivVJu1l/qG7ivWRpu+5SfNZh4l4kaxyfQP9z9Fu/nEypd9mLaVN50NxtzdRJJoALI30qFXMlsi4ZK6Gx+W4b+jzV0gk4GxtlGOdB08TiUlIpW4JO0WiCcDCUI+axWy4/TiepLQM5X1iEpmw9SrjW5bB2ijfF1ydIPy3JSQmMWDkWEpXqUMBtzK07dKL/YePKdXpM2QUg0aNI/rpMzr3HoBz6UoUKVeFngOHk5KaCkDfob+yZPVaAHoOGsGQ0eMBGDJ6PL0GjeBxdAwDR/2GbZF3L5LuPwymWbsu2BYpjXGBIlSq04Rtu/cpndu3xy9Mmz2fcxcv49vjF2yLlKaUV21mzF2oWHvi/6bOpGaTVoSFR6pcn98vQ2ncpiOZmZlf76a9Z82GQEoWL6ZINAFYW1pQv3ZNwiOiuHhF/Y/Oew8e8iTmKY3q1Va8nAOwsrCgXq0a3Lh1h8SkV3muB7Bu8zasLCzo30v5C/HRQwewatEcLMzNvuKVC4IgfHsJiUkMGDGGUl61sS/qQZsuPVVj1OCRDBr1G9FPn9GpV3+cSlWgSFkveg4c9i5GDRnF0lXyGNVj4DCGjP4dgCGjf6fXwOHyGDVyLDauJRXHvffwEU19OmPjWhIjexcq1mnMtt17lc7dwa8vU/+ex7mLl+ng1xcb15KUqlyLv+YueC9GzaBG45aEhkeoXF/3XwbTqLXvN4tR/ooYVVuxzdrSkvp1ahAWEfnpMcoyjzHKUk2MCtyGlaUFA3r5KZ1r9LBBrF40FwsL86956YIgqPE6OZF9c0awuLsXf7cqyubxXQi+oDxd/J6Zg9k/dxSv4p6y/c9ezGtXin98y7L7r4FkvJb3qXtnDeHKrlUA7P5rIAfmjwbgwPzR7P5rIEmx0eyfM5JZLVwVx30R8ZANo32Y1cKVaQ3tWd63DvdO7lY699aJfpxe9zeP71xk60Q/ZrVwZXG3ypzdMFcxoufEqqmsHtSY+JhwlevbOfUX1o1sjTTr2/SpNw5swNqpuCLRBJDf1BLn8nVIiIngyb0rOe6b9PwJRpZ2ikQTgI6+AXbFyiLNzCQzXb5GdNSdi1i7lFAkmrKVbuQLQPR9+TnSU5OJiwpBN78Rdm5lvto1CoLw8xLJJkH4QUQnpFJnyn4Cz4dR2dWKDpWdiIpLpvOiIJYeu6+od+dxPIdvP6Hh9INEx6fSslwh7E312XAulAGr5V8VOlsbYm0sT2w4WRlR2NIQgLtPErgYEkvHBSdYdfIRBUzzA3AhJJb60w7w8GkiXaq6MKxRCTQkEnosO8Xf+9593XLy/jPWnwvBd8Fx0jOldK7qgr6OJn/uuM7wdRcBcLU24kJILLuuKr+sffwyhcALYRjr66Cl8fVHTb1MfkNCajrV3WxUyrITSNcjck42hcW+wt40PylvMjl86wnrz4ZwKfQFWe8lxp4myKfBK1NI9UdsmcIWgDy5lO1NRhZ9VpyhkosVPWsW/bwLE4QfwOPoGDxrNiBg41aqeVWka8d2hEc9poVvd+YtXq6od+PWHfYdPopX3aZEPYnGp1VzHOzt8F8fSPd+QwAo4uyErbX12/92xMWpMAC37t7j7IXLNG/flcUr1uBgbw/AmfOXqFSnCfcePqJX146MHTEYTU1N2nXrw+SZcxXnPnbyNKvWbaJZuy6kp6fTs0tH9PPlY+wfU+k79FcAirq6cOb8Jbbs3KN0fRFRj1m7aQumJsZoaX39QeMv4l4Sn5BInRpVVcpcXZwAuHL9ptp9o99ONVi+rIdKWfa2u/cf5LkeQHBIGA3r1kJHR5uw8Eh2HzjM1Ru3sLOxppNPawo5FPjUSxQEQfhuHkfH4FmjPms3bqG6V0W6dWxHeGQU3h26MnfRMkW967fvsO/QUSrXaUzUk2jatfKmgL0dq9dtols/+foVri5O2FhbAfJ45exYGICbd+5x9sIlmrXrzKIV/jgUyI5RF6lUuzH3Hz6id7dOjB0xBE0NDXy69ubPGXMU5z528jSr122kqU8n0jMy6NW1E/n08zF24hT6DhkFZMeoi+pj1MZvH6Pq1qimUubq7AzA5Ws31O6riD3lVF9gli8r36YUo3KpBxAcEqqIUaHhEezef4ir12/KY1S7NiJGCcI3lhQbzbJeNbh5cCMFS3vh0agjCU8j2TimAxe2LFLUexp8m+Dzh1jRtw6Jz59QvHYrjKzsub5/HTum9APArIALBuby537zAs6Y2csTKM9D7hB1+wIbR7fj8s4VGFvJ/11H3TrP8r61eRHxkLLNulGt8wg0NDTYMqErp9bMUJw77OpJru9bx/pffcjKzKBs065o6elzdOlE9s6U/+awKOhK1K3z3DuxU+n6Ep9FcfPQRvIZmqCh+fX71NTEOF6/SsCxXA2VMnMHeZ8a8yDnkU1FqzUhKTZaKbkXFxVM+LXTFC5TFW09faSZGTiXr035lr1U9k96/gSAfIby2VwsChWh69w9dJ27h5bjlqnUFwRB+JCYRk8QfhB/7rhOVFwK+0c1oGxheTJjVJNStF9wnD92XMenopNiGraouBQG1nfnN28PJBL5tHcNph3k1AP5PMe/1C1GllTG5dAXDGrgTon3poULfpZELXdblvaoiquNETIZjAu8go62BntG1MfmbZKqf71itP/nOH8fuI23ZyGcreQJq/DYZP5oU5Y+td0AGN2sFG3mHmPDuRC6VXelYekC5NfVYs+1SAbWd1ecN3uUVNsKhdVe/8vkN6w6+SjX+9S0jANFbY1Vtmevl5SdZHufs7W87S9evc7xuGGxr0h+nUG5cTtJS3/3BVPpgmYs6OaFq40RhS0NADj98Bn96hZT2j87yfQgJoHyTvLE08Tt13iamMamgbX4AWclFIQ8++2PqUREPubMoV1UePuiaMKvw2nq05kxE6fQqV0bzExNAIiIfMzIwb8w+ffRSCQSpFIpleo05dhJ+SLjwwb0ISsri/OXrjBqcH9KlyyuOM/D4BDq167BhhULKerqgkwmY+iYCejq6nBy/w7sbOQ/VkcM7EeTtp2YMmsuPi2b4eosT9iEhkUw88/xDO4n/+E1cewIGrTswOp1m+jr14XmjetjkD8/W3ftZeTgXxTn3b57PwC+bVupvf4XcS9ZvHJNrvepVbPGuLsVUdmevV5SdpLtfUXeJpuex75Qe0xnx0IAHD91RjHlXbZ7Dx4CcPf+Q6p5VcxTvZLFixHz7DlWVha08O3O3oNHFPWKurqw4p9ZVPQsm+u1CoIg/FeMnTiF8Mgozh7e8y5GjR5BE5+OjJk4mc7t2ypiVHhkFKMG92fy+DGKGFWxTmOOBZ0CYPiAvooY9euQAUox6kFwCPVr12TDysW4vY1RQ8aMl8eoAzsVMWrkoF9o3NaXKbPm4NOqOUXexqiQsAhmTv4/hihi1Ejqt2zHqnUb38aoBooYNWpwf8V5s0fydvRprfb6X8S9ZNEK/1zvU+vmjXF3U/04KjtG2dioxqiirvIXo7Ev1Mcop+wYdfK0Ysq7bNmx5879B1TzqpSnetkxytrSEu8OXZVilJurCysWzBYxShC+sWNLJ5LwNBK/hYexL1YOgBrdR7N+lA9Hl0ykVIP2ikRGwtNIvDoMpk6v8SCRIJNJWdGnDmFXgwCo3G4AMmkWj+9cwst3iNIonLioYJzL16bVhJVYFHQFmYyD88egpa1Lt38OYGgu/8jUq/0g1o1qy6m1s3Cv3QrzAvJ+KT46jPq/TKZiW3liq2aPsQQMb8m1/eso5+1H0SqN0cmXn7tBu/DqMFhx3uxRUiXr+ai9/tTEOC7vXJHrfSpWvTmWhd1UtsdFBQNgYK76kay5g3wEV0q8+j4VoEKr3oRfPcmGMe1xKF4BLR09wq+dwtDChlo9xwGgoaVNw0HTVfZNSYjl8o7laGhp41q5Qa7XIAiCoI5INgnCDyAhJZ1tl8LxKGSuSDQBaGtp0LmKC6cfPGPv9Sg6VpE/OOlpazKySUlFAkNDIqGCswU3o14SnZCKnYn+R883ulkpXG3ko31uRr3kZtRLmpUpqEg0AWhratC+khOnHzwj6F6MItlknE+H3rXePTRpSCQMaVicMw+fceJeDKULmtHYw4HNF8KIikvBwVw+emrX1UjMDHSp6W6rtk1xyW/4a4/6L/ff52xlqDbZFBYrn1rDRF91XSQHM3kbPpziTnn/ZJJfZzDGuzSNSzsQl/yGTedDWX82hC6Lgzg6thFOVoZ4FDLj5P2nrDsTgne5gkhlsOViGLvfjuTKHgl1+NYTVpx4yKre1dQmwAThZ/EyPoENW3bgWaa04iUegI6ONj26+HL81Bl27NmPX+cOAOTT02P8r8OQvO3ANDQ08KroybWbt3gcHUMBO/V9RLaJY0dS1NUFgGs3b3Pt5i1aN2+ieIkHoK2tRRffthw/dYYjJ04pkk0mxkYM6ttTUU9DQ4PRwwZw4vRZDh8PomzpkrRo0pCAwK1ERD6mUEH5V5Rbdu7BwtyM+rVrqm3Ti7iXTJw2K9d75erspDbZFBwWDqB42fm+7C+0ExKT1B7TxcmRch6lOHbyNCvXbqBty2ZIpTLWb97Glp3yaZqysrLyXC8kVN6W+UtW4OxYmLnTJ1G5QjnOXrjMmP+bQquOPbh25jBWFha5Xq8gCML3Jo9R2/Es46ESo3p26cjxk2fYvmcfPTrLpxXKp6fH+NHDlWJUlYrluXYjbzHqj99G4pYdo27c4toN9TGqawcfjp88w5HjJxXJJhNjIwZ/EKPGDBvEiVNnOXQ8iLIepWjRtBEBm7YQHhlF4YIOAGzZsfujMSo2Lo6J02bmeq+KuDipTTYFv40L6mOUfARXTjHK1cmRcmVKc+zkaVasXY9Py+ZIpTLWBW5VjNDKypLmuV52jJq3ZDlAdPhrAAAgAElEQVTOjo7Mnf4nXhU8OXPhEmP+bzItO3bn+umjWFmKGCUI30Laq3huHd2CnVsZRaIJQFNLh7JNuxB+7ST3T+6hTJPOAGjp6lGj22iyX1xIJBoUKFmRmEc3SIqNxsjSTu15stX0+02eaAJiHt0g5tENitVorkg0gTyxUrphB8KvnSTs8nFFsknPwJiKbfoq6kkkGlTtOIzwa6cIvXQc2yIeuFVrys1Dm0h4GomJTUEA7p7Ygb6xOc7la6NOakIcQaum5XqvzAu4qE02vXwSCkA+I9V1oo2t5f366+RElbJsegbGGFs78DT4FtH3r6GhpYVMJkVDU4v0VNWlDLI9OneQ3X8NIiXxBQ0GTMHKyT3HuoIgCB8jkk2C8AMIfpaETAYpbzLoveK0UtmrtwmS8BevFNssDPXQ1dZUqmf8NsmS8vrj8wqbG+ji8d40cGHP5cet7GqlUrdUQfm6HKHP353b0cpQZZROdvInPFb+cNOmQmE2Xwhj97VIfqlbjOj4VK6Gv6B79SJoa6qf3dPF2oiIue0+2nYgx/11teT3IyE1XaUs9Y38nhirSURlm9elErpaGrjZyX9IO1kZUt7JAqN82iw4fI+916JoW9GROZ0r0WlhEMPWXeC3zVeQymRIZTI6VXVmzalgitoa8ywxjUFrz9OxijONPRxyvSZB+JE9DA5BJpORnJKCb49flMqSXsn7jpD31pewsrRAT1dXqZ6pibwPSUlJ+ei5LC3M8Xxv0e9HIWEAVK9SSaVu2VIl37YvVLHNxclR8QIxW/aLtZAweRt9fVoRELiVrbv2MmxAH6KeRHPxyjX6+nVBW1v9Y1VRV2eSnuQ+MlNHW1vtdl0d+f14GZ+gUpa9Tkj2PfqQhoYGy+fPooVvd/oMGcXQMROQSqVIZVJ6dPFl2eoA3N2K5rneywR5G968SSdw9RJFYq9MqZI8ex7L1L/nE7htFwN6+6ltjyAIwn9JdoxKSUmhg19fpbKkV/Ln1tCwvMWo5DzFqHdTlT4KzY5RlVXqli0tj1GPQt6PUU45xqjsNnb0aUXApi1s3bWX4QP6KmJUvx5dc4xRbq4uvIoO+Wjb4SMxSlf+/Kw2RqXIp5j+WIxaMf9vvDt0pc/gkQwdPV4Re3p28WXp6gCKuxXJc73sNrx5k06g/1JFYq9M6ZI8j41lyqx5bNq2k4F9euR6vYIgfLq4yGCQyUhPS2HrROVnwTep8uf++Ogwxbb8JpZo6Sj3qfkM5L+309M+3qfqm1gorSH08rG8vyxUuopK3ey1XOOi3vV1ZgWc+PDFRXbyJ7uNJev5cPPQJu4F7aJyuwEkPX/Ck3tX8PTugYaW+j7RoqArYw5Eqy17n2YOfaqWtvx+pCXFq5RlvJbfEz1D1eR+ttWDGvM89C6Nh8ykeO1WaOnoEXzxMHtmDGHD6Hb0XX1OkTjLvtZDC37j4dkDmNk70XLcUrVT+AmCIOSVSDYJwg8gPkW+iKOuliZaHyRTTA10aV2hMEVt3z1w5NNRTjR9iuykTLa4ZPm5C74dgfS+N5lZgHz0UjZ1o3T0deVdjd7bBFi1ojZYGemx+6o82bT7WiQyGbQuXzjHdkkk7/b/HFZGegBEvFD9mif+bQLK3EBXpSxb6YLqF7yvW9yOBYfvcT9a/nVRMTsTgsY1ZtfVSB7EJGJtnI8abjacffQcADdbE5Ycv8/L5De8Sstg8JrzimM9TUhDhozBa87jZG3I4AbF1Z5TEH4kcS/lP5R0dXVVXnSZm5ni27Yl7kXfjebR09PL8VgyWY5F8nPoKCeM416+BFC7PsObt4vjamq+61dsrVWT6vn15SNBs18u1q5eBRsrS0WyaduuvchkMnzbtsyxXRKJhHwfua7c2FhbAhAaobroe/aLNcuPLHhewt2Na6cPs2XHHu4+eIittTV1a1Yj6Ix8Hb/s0VR5qfciTr62XUXPsopEU7amDesx9e/53HsY/NnXKgiC8G/KjlE6ujpof/DiTx6jWimNOP1YXy7LJUh9GKNexMljVPYIpPe9eSN/NtXUePfcb2ujLkbJn7v19LJjVFVsrKzYunMPwwf0ZasiRqmf5hW+QoyykrcrLFxdjJLfXwvzj8eo62eOsnnHbu7df4iNjTX1albnxOmzwLuEWl7qxb4Xo9xUYlR9psyax/2HuX/8IQjC50lLkvdrWtq6aH6QjNE3MqNk3bZKo3m0dT/S9+TSp2ppK/epqYnyf//vJ1KyZWbIn/sl771LMTBTnaZOO5/8uV9TR94ux7LVMTCz4l7QTiq3G8C9oF0gk1GybtucGyaRoPWx68qFgZm8T42PCVcpS3sl71Pzm6jvU19EPOR56F0KeVSlnPe7ZJ9btWZE3brA+c0LuX9qD5Xayj8AvHU4kH2zh4NEQt0+E6nQujea2jm/ExEEQcgLkWwShB9AIQv5WkBOVoYs7OalVJYllZH8JgP9HL5W/FIF3577fHAs9UraK5VdDn2h1D54N13d+6Li5F/gOFvLp+bT1JDQwrMwy47fJzo+lV1XIylsaYCnU85TWjxPes3f+27l2t4OXs5qE0NO1kZIJOqTTXceyx/ayhZWf375yKs4yhQyw95MOekW/vZ4Foa6ZGRKiYhLxtxAF18vZ6V68w7dxdo4Hyb5dTA30KNEAVOlEWEgT95JZTJuP45HQ0Ms4iT8HJwKy9djcHVyxH/xPKWyrKwsXiWnoJ/v20wlmf0C7/T5izRpUFep7Pylq2/b9+4HafZ0de+LiJSvJ5e97oSmpiY+rbyZv2QFUU+i2bJzL06OhahUvpzKvtmePo9l8sw5OZZn696xveJr9ve5Osu/Zg8Lj1Qpu3n7LoDS9E/vS0/PIDwyEnMzM7p3aq9U9tfcBdhaW2FmapLnevnyyX88Z2SoTjua9lq+7p2xkWGu1yoIgvBf4Pg2Brg6ObJmyXylsm8doxwLyc99+twFlRh17tKVt+0rpNiWPUXc+yIiHwNQxOVdjGrX2pt5i5fLY9SO3TjnGqOeM3lGHmOURymV7a4u8hgVqi5G3ZHHqIofiVFhkZFYmJnh16mDUtn0Of8oxai81FPEqEzVmRzS0rJjlFGu1yoIwucxsSsMyEcNtfhtiVKZTJrFm9RktPW+TZ9qYivvLyNvnlNZb+jJnUsAmNoWVmyLfxLKhxKeyvsxCwd5slqioUnx2q25sHUxSc+fcDdoB6Z2jhQoXj7HdiS/fM6pNTNyba9H447YFvFQ2W7m4AISCQnR4Splz4LvAChNUahUHiovVze6y8mzFuc3L+T1K/mHao/OHWTH1H4UcC9Pq/HLMbZS/ThPEAThc4hkkyD8ABwtDTE30OX43RgysqRKU8XNO3iHabtvsmt4PSo6W371c5d0MEVbS4Og+zH8jvLD0NmHz9HUkFDrvXWWQp8lEfr8FU5W7142bjgnf5ArUeDd6Ks2FQqz9Nh9lhy7z5WwF4xorPqC9X2JqekEnM19io/KrtZqk002xvmo7GLFuUfPCY9NprClPEGWkSVl26UIbE30cxy9FJ/yhh7LTtG5qgszfSsole28In8greRiRWpGJlUm7qGlZyEW+717wItOSGXvtSg6eMnn3O9Zswg9a6quy1Jv6gHSMjI5OrZRrtcpCD8KZ8fCWFqYc+jYCTIyMpVGN02fs4AJU2ZwYu82qlTK+Ufb5/IoVQIdHW2OnDjF1AnKZUGnz6GpqUn92u+miXgUEkZwaBguTo6KbavXBwJQusS7kYYdfVoxb/Fy5i1ezoXLV/l91NCPtiMxMZGVazfk2t7qXpXUJpvsbKyp5lWRU2cvEBoWoVhQPSMjkw1bdmBva6N2P4DUtDSKV6xJ+9berF36j2L74+gYtu3eR7eO7T+pXj49PWpVq8LxU2dU7tXOvQcBqFzBM9drFQRB+C9wcXR8G6OCVGLUtNn/MGHKXwTt206VShU+cpTPkx2jDp84yVR+UyoLOn32bYyqqdj2MCSURyFhuDq/H6M2yY9V8r0Y1bYVcxctY96iZW9j1LCPtiMhMYkVa9fn2t7qVSqrTTbJY1QlTp09T0hYBM5KMWq7PEap2Q/exp4K1WnfugUByxYotstjz166vx+j8lAvn54etapX4fjJMyr3aue+A4CIUYLwLZnZO6JvYkHIxWNIMzOUppo7vW42J1ZOodu8fTiUVJ3i+kvZuJZCU0uH0CsnqPNBWfj100g0NJXWWYp7HMLLxyGYFXj3keiN/fK+0Nrl3XN1yXptubBlERe2LOLx3cvU6Drqo+14nZzItb1rc21vIY8qapNNhuY2FCrlRcTNs8RHh2FqJ+/HpJkZ3D66BUMLW7X7AVgWko8EvRe0kxrdflUqu3tiB4BiLaZjyyehl9+IthP9MTC3RhAE4WsRySZB+AFoa2kwroUHQwMu0H/1WQbUd8dQT5sDNx7z9/7b1ChmQwWnT0s0ObwdobP2dDAdKjsprdP0PhvjfPSoUYTFR+/z68ZLdK/uipamBtsuhbP7WiTtKjkpJZayZDK6LjnJmGalcLY2Yu+1KJYff4B3uYJUcnk3/Ufpgma42hix9NgDAHwqOaqc+32uNkY8ntf+o3VyM7hhcTouOEGv5acZ0qg4Jvo6zD90l4gXyQT8UkMxZfPa08H8uvESwxuXZHjjErjbm+LpZEHAmWDM8uvS2MMBmUzGlothnLgXQ9MyDpQpLL9/VYtas+daFOvPhtDYw4Hw2FcMX3cRWxN9JrRU/1WnIPzMdHS0mfz7aHoPHknXvoMYObgfRoaG7Np3iCmz5lK3ZjW8Kn7ai5+Cb6fFW7ZmHd182ymt0/Q+OxtrfunZjTkLlzFg5Fj6+cnXrNiwZQdbd+2lS/u2SsmSrKwsWnXqwR+/jaKIsxPb9+znn6UraduiGVUrv3vRWLZ0SYq6ujBv8QoAOrdr89H2FnV1ISVG9evJTzF66ECat+9Ke79+jBk+EFMTY2bMXUhYRCQ7N6xWrOOx3H8dA0b+xriRQxg3cggmxkbUqlaFrbv2UqdGNbybNCQkLJy+Q3/F3s6W6RPlLzjzWg9gyoQxeNVrRnu/fvw57lcc7O04fuoMy1YHUKVSeZo1rPdF1yoIgvBv0dHRZvL4MfQeNIIufQYwanB/DA0N2bX/IFNmzaFuzep4Vfy0jyGyp25d5h9At47tlNZpep+djTX9e3Zn9sKlDBgxhn49uqKlrc3GLdvlMapDW6VkSVZWFq07+cljlIsT23bvZ/6SFW9jVEVFvbIepXBzdWHu4uUAdG7/8Rjl5upC6tPwT7rGD40ZNpBm7brQ3q8PY4cNwtTEhL/mLiA0PJJdG/0VMWqZfwADRoxl3Mih/D5qqDz2VK/C1l17qFOzGi2aNCIkLIw+Q0ZRwM6O6X/8DpDnegBTJ4ylct2mtPfrw+TfR+Ngb8exk2dYumotVSpVoFmj+l90rYIg5ExTS4favcazZ8Ygtk/ug5fvYHT1DXl4Zj+n1s7CybMmDiUq5n6g9xhby2cquLrHH4+GHZXWaXqfobkN5Vv25PzmheybMwJP7x5oamlz+8gW7gXtonSDDkqJJak0i8BxnajZ4zfMHVy4f3I3F7cuwb1mCwqWereWnm0RDywKunJhy2IAStX/+DsJi4KujD389JOu8UNVOw1jw+h2bP0/P6p2HoaegQlnN8wlPjqc9lM3KtaaurrHn32zR1C960iqdxmFpaMbTp61CL18nPWj2lCyng8mNgW5f2oPt49uwbKwG0WrNub1qwSeh93D1qUU5wL/UduGwh5VVUaICYIg5IVINgnCD8LXy5nU9Cz+2H5NMZpGS0NCxyoujG1e+sO1LXNVo5gN5RwtWH3yEQ+fJrF9yIff/7wzztuDLKmMZccfsPrku3nOu1ZzZXJb5SHc1YraYGuiT49lp5G+nWfZq4g109ur/lBvU8GRqbtuULOYLQXNDVTKv7aaxWxZ0M2LoQEX8Ft6CgDjfDr80aYsdYrbKerJZPLpCbPn3pdIwL9PdYYGXGDuwTvMPXhHUbdbdVcmti6r+N9zO1eiz8ozDA24wNCACwCUcjBjsZ8XBnrqFwEVhJ9d907tSU1LY/SEyWzesRsALS0t/Dp3YNJvo1QWPM9N3ZrVqOhZliUr13L/QTBHdgXmWHfy72PIypIyf8kKlqx895Vh7+6dmT1lolLd2tWrYm9rQ7tufZBKpQDUqFKZ+TMmqxy3o08rxk/+i3q1qlO4kOp6G19bvVrVWb1oLn0Gj8Sna29A/vJt5p/jaVi3lqKeTCYjKytLae2QZfNn0qnXAHoNGkGvQSMAKFOqJAFL/8HQwOCT63mWKc2ujf70HDCMZu26KLY3a1iP5f/8/W1ugCAIwjfi16kDqalpjJ7wp1KM6tG5A5PGjf6MGFWdip5lWbxyDfcePuLori051p08fixZUinzFi9n8co1iu19undm9tRJSnVrV6+KvZ0NPt16v4tRVSvzz8ypKsf19WnN+MnTqVerhmK6vm+pXq0a+C+eR+9BI2jbtReQHaMm0LDuu5EEMhkqMWr5/L/p2PMXeg0cTq+BwwEoU7okAcsWKMWevNbzLOPB7k1r6NF/GE19Oiu2N2tUnxX/zP42N0AQBIUyjTuR+TqVI0smKEbTaGhqUaZJZ2r1HMenvrhw8qyJvbsnV3au5EXEQ7rM3pVj3dq9xyOVSrm4dTFXdq5UbC/XvDsNBir3lY5lq2NkYceWCd2QyeR9aiGPqjQeOlPluCXr+XB8xWScPGsppuv7lpw8a9Fi7GJ2zxjE5vFdAdAzMKZ+/z9xqfjetKsyGTJplmJ9K4lEg1bjl3Ng7q/cPraVkEvHFFULlvKi+a/z5aO/bh8HmYyYRzeIeXRDbRskEolINgmC8FkkstxWMhWE/0GBgYG0a9eOZwt9v3dTVCS/zuDW43hSXmfibm+Cnan+Fx3vaWIaBrpaeUqEvHj1mtuP49HR0sTd3gQTfeVFOd1GbqVMITM2DKhFQmo6NyJeYmuSjyK2xmqPt+96FN2XnmJl72o08fj2L2uzZUpl3IiIQyqDsoXN0fyE9ZEev0wh+FkSxvl0cLUxUnvfZDK4F51AxItkSjmYqqzz9KOx/mU9mzZtwsfH53s3RfhM2X1aRlzUd23Hq+Rkrt+8Q3JKCiXc3XCwt8t9p4+IfvoMQ4P8Si+ZcvL8xQtu3LqLro4OJYsXw9REuV+ycS2FZ5nS7AlcS3xCIleu38Te1oZiRV3VHm/H3gO07dKLQP+ltGz67019mZmZyZXrN5FKpVQoVwZNTc087SeTybh99z6hEZGUKVWCggXsv6geyKdIunPvPrFxLynh7oatteri9f8mbXMH0VcJwg9KIpGwYeVi2rZo9t3aII9Rt0lOSf33Y1TsC27cvvM2RrmrxChrlxJ4lvFg7+aAtzHqBna2NrgXVZ2WGWDHnv206dKTzf7LaNms8Rddx6f44hgVHkGZ0iVzj1G51IP3Y1QcJdyLffcY1b57HyQ6+QgMzPkDGUH4HD4+PtyJTaf1hFXfuylK0lOTeRp8k/S0FKwc3TGyyvnfa168inuKbj4DdPRz71NTEmJ5FnwbTW0drJ2Ko2doolQ+09sFO7cy+E7fzOtXCUQ/uI6hpa1iGroP3T+1h83ju9D2D3/cqv17cUqalUnMg+vIZFLsi5VDopG3PhUgKTaa2PD7ZL5Jw7xgEfk6VJ/6hfIP5u6JHWyd6Id4zS0I39VmMbJJEH4wBnraVHb5ej+WbIzzvkCnhaEeNYvZ5l4RMNHXoUYxm4/WWX82BFsTfRqU+ncXo9TSkFDO0eKz9i1glp8CuSSPJBJwtzfB3d7ko/UE4X+NoYEB1bw+beqMj7Gzyfv84lYWFtSrVT1PdU1NjKlbs9pH66wK2Ii9rc2/PmWclpYWFT3L5l7xAxKJhJLFi1GyeLGvUg9AW1sLj1IlPrktgiAI/0XyGPX11hH5pBhlaUG9WjVyr0h2jPp4PFsZsEEeo/7lKeNEjBIEIZuOvgEFS3l9teMZmn/83cL78ptY4uRZK/eKgJ6hCU6eNT9a5/q+AAwtbCni9e+urayhqYW9++etM2dkaYeR5Zd9NCEIgvA5RLJJEIR/3ZwDd4hJSOXInWgmt/VE6xNGFgmCIHxPU/+ez5OYGPYfPsacaX+gpSUepQRBEIT/himz5hGtiFGTRIwSBEH4AqcDZpH0IoZHFw7TcOA0NDRFnyoIgpAb0VMKgvDVWBvnw8xAN9d6a04Hk/Img45eLnSp6vIvtEwQBOHjbKytsDA3y7Xecv91JKek4Ne5Az27dPwXWiYIgiD8r7Oxts5jjAogOSWFHp196dW107/QMkEQhB+Pobk1+sbmuda7utuf9LQUyjbpTNlmXf+FlgmCIPz4RLJJEISvJmhc3uaEv/qn9zduiSAIwqe5fvpInuqF3Dj/jVsiCIIgCMpunDmap3qhNy9+45YIgiD8+PqsPJOneoM23fzGLREEQfj5aHzvBgiCIAiCIAiCIAiCIAiCIAiCIAg/LpFsEgThixy5Hc2OyxHfuxn/CZlSGVlSWZ7qSmW518uUyshDNUEQPtP+w8fYtG3X927GN5WZmUlWVtb3boYgCILwieQxauf3bsY3J5VKc62TmZlJ2uvXeT7mq+Rk4l7Gf0mzBEH4CQVfOMydY9u+dzM+W3paSq51pFmZZL7Jvb+UZmWSl5cNeT2eIAhCNjGNniAIX2TB4buExybTwrPQ927KZ6k0YTdeRaz4u2NFxbZTD54yNvDKR/crXdCMf7pWBmDrxXBWnnzIrah4srKkFLY0xK9GEbrXcEVDIlHsE/L8FauCHrL/xmNepWVQwdmCPnXcqFbURunYR25HM233DR7EJGKop03VojZ0r+FKZRerr3jlgiDMnL+I0LAI2rVq/r2b8lncPKtRo2pllsz5S6Vs/ebtLFrhz/Wbt8nMysKpcCH69+pGX78uaGjIvzWSSqV41mxIZmamyv6FCzqwa6P/J9X7lPYJgiAIHzdj3kJCw8Jp1+rHnH7azbMKNap4sWTuDJWyhyGhLFq+ml37DpKYlIRXxfIM6deb2jWqKtU7fDyIMROncOfefTIzsyjkUICh/fvQr0dXRSz7UNzLeMpUrYOxkRG3zp9QbJdKpZSrUZ+sTNUPMAoVLMDuTWu/7IIFQfjPO7thHvHRYRSv3ep7NyXPYh7d4NjSP4h+cI3XrxLIb2pJ0SqNqdvvD3T1DRX1Qi8f5+jSiTwPu4c0KxMTawcq+fTHs0UPJJJ3/WXwhcMcXzGZ2PAH6OY3xLFMNTxb9KBgKS+l8+Z2vLCrQRycN/qjbbct6oH3mEVf94YIgvCfJ0Y2CYLwP2vj+VDCYl+pbJcgQVtTQ+2fVCrjYUwir9IyAAi8EEZ//7MkpqbTu1ZRutcoQsqbTMYGXmbugTuKY77OyKLLoiDWnw2hlrst3aq7Evr8FZ0WBnEu+Lmi3vbLEXRadILE1Az613Onfkl7Dt96QueFQQQ/S/r2N0UQhB/Cmg2bCQkLV1u2dtMWuvUbTHxCIgP79qCvXxdSUlIY/OvvTJv9j6Le4+gYbt25h6amJpYW5kp/ZqYmn1wvr+0TBEEQfm7+6wMJDg1XW5b2+jUtO3RjVcBG6teuSV+/rgSHhOHdoSunzr5bF/FY0Gkat+lIRGQU3Xzb069HV9LSXjP413FM+uvvHM/da9Bwop8+U9meHcs0NDWwsDBX+jMzNf3SSxYEQfjqYh5cY+1Qb2Ie3qBEnTZU6zISvfxGXN3jT8DwFshk8pGhYVeDWDeqDQlPI/Fo6Iundw8y3qRxYN6vnFz97qOv20e3smFMe14nJ+LVfiBFKjfg4bmDbBzTgbioYEW9vBxPIpGgoaWt9k8qzSI24gFvUlTftQiC8PMTI5sEQfifEp2Qysy9t7ge8ZI7j9VPr1G1qDXHxjZSWzY28DKvXmcww7cCAIuO3MPJ0pD9oxpgqKcNwID67nj+vpOVQQ8Z2qgEAFN23iD4WRLr+9ekTnE7AHrVKkqtKfsY5H+eS5Oak5EpZeK2a+jraHFkbEOM8+kAMK6FBx5jd9BnxRmO5tAuQRB+fo+jY5j012wuX7vBzdt3c6w3e8FSXJwcOXt4N0aGBgCMHPwLrmUqs2j5asYOHwSgSAb5L5pLqRLuOR4vr/Xy2j5BEATh5/M4OoZJ0//m0rXrH40Bv0+axoPgEPYErqVh3doADOzbg7JV6+HXf+j/s3fW4VEeXR++4y5EiGBxAgRIIFhwdwnFtQR3d14oUKhAW7RIkeIUl1AkWCC4JLhFCRHi7tn9/lhYumxCFkjf0u+d+7r26rUz88zMeWjmPPv8Zs7hReA1AL5dvgKpVMr1cydxtJdFUFgyfzaVqnny85oNzJs+GQ0NDYW+12/ZzulzF4rcCBHyRvzavn71B32ZQCAQfCncOryJgtwcfNb5Ye1UHYBmQ2azc6o3YXf9eeJ/nKrNunJ5+3KQShm2/hxlbO0BaDl8Pit6VuPavjU0GTwdiaSQs+vno62rz/CNF9E1NAGgxYj5rOjpxqFFQxn+mz+ASv3ZeTRhxKZLRc771KqZ5Gal02FK8RsDBALB/1/EySaB4AsiN7+QH33vU3f+McpP2Ev9BceZtvsmGTn5Cu2uPn/NrL23aPDNcTzmHGHklitsu/xCIV/QlF03GL/tGmHx6UzeeQOPOUfovuIcB26GA7D+3FNaf3eKqjMO0nfNBULjFHedjNgcwMpTj7gVmsCIzQFUmXGQxotPsPrM4xLzDaVm5zFz7y2aLD6B26xDDNl4mbMPoz/J1tImM6eA0NfpGOtq4V7J/KOuPf84hq3+L/j1ay/KGuuSlp3P0+hUWlSzlQtNANYmejR2sSI5K4/8Qtluoz+uh1K1nKlcaAKwNNaleRUbXiZmcDc8kWexqcSkZNHKzVYuNAFYGOnSrIo1D18lk5b9994fgeBTycnNZeH3P+FSuyEGNg64ejZm9JRZpPhYUKQAACAASURBVGdkKLTzv3KN8TPmUqVOE+yr12XA8LFs3LpDIa/QyEkz8BkzmZCwcEZMnI599bq07tqbXftkMdZX/PobdZt3wLayO516DSQ4NExhjH5Dx/D9L6u5dvM2/YaOwcalJjW8WrBs5a8l5oZISU1j3PQ51GzYkvKuHvQcNJyTfuc/ydbSJiMjkxchoZgYG+HpUbPINqlp6Tx68ox2rZrLhSYAW2srmjduSFJKCvn5snB4wSFhqKmp4ezk8MFxVW2nyvwEAoHgn0C2bi/HpZYX+tZ2uHo2ZPTkmco+KuAa46fPoUqdRti5edJ/2Bg2vO+jJk5nyJiJBIeGM2LCNOzcPGnVtSe79h0E4JdfN1KneTtsXGrQsecAXoQo+qi+PqP47udVXLt5m74+o7B2rk6NBs35ceVa1XzUtNnU8GpBucru9Bg0rBgfVbKtpU16RgbPQ0IxMTbG08O92Hbb9uyjerUqcqEJwMrSkjYtmxIW8ZKbdwIBeBUVTXlbG7nQBGBkaEidWu7kF+STk5ur0O/jp8+YPm8h3y2Yi7WVldK4L0JV82UCgUA1CvJy8d/6PWv61WJpa2vWDvDkxE+TyctSXGsiggI4uWI6awfWYWUvNw4tHsadY1uRSt6tq77LJ3L0uzEkRYXiu2wCK3u5sWNKVx747QPg+v5f2TSiOT95u7B7Zk+SXoUojHFwoQ8Bu37m1aObHFzow0/dnFn/dQOu7lkpP/1THDkZqfy5Yhrrh3jxc/fK7J8/iOAbfp9ka2kT+egmVk5ucqHpLTXb9wMg+qks9H9aXBTGlrZyYQhAW98Q2yq1kBQUUJCXS3z4M9ITYnCq11ouNAEYmFri6Nmc2OAH5GamqdxfcYTcPMftI5vxnrsBQzORBkAg+F9EnGwSCL4gZu69xb4bYfSsa49bhTJEJGSwIyCYJ9EpnJjWBoArz1/Tc9V5jPS0+KqOHWYGOvg/jWXGnltEJGQw39sDgEevkolOzuLS01iM9bVpWNmKI3ciuPriNYduheP/JIaWbuUob26A38Moeqw6z+3FXeQ5hi49fc29l0ms8XtMQxcrBjZywv9JDN8eCSI0Lp1fBtQr0obolCy6/ORHYnouverbY6ynxYXHMQxc58/CrzwY0cJVZVv/DpytjTkypRUAYfHp1F9wXKXrkjNzmbTjOl1rV6RRZdkPWE0NNY5OaUUlC0OFtmnZ+TyOSqFZFRu0NNRJysglJSuPPg2Uf9w6WhkDEBSRSEVzWT8eRYhgHnYW+D2M5llMKnUcLFQ3WCD4LzFu2hx2/nGQ/r2641HDjZCwCDZv383Dx0+5fOoIABcDrtKuez9MjI3o81U3LMzNOHvxMmOnzSE04iXffzMXgHsPHhEVHcM5/8uYmBjTrJEX+44cw//KNfYePMLZi5dp36o5lSqU40+/c7T17suLwKvy/A3nLwVw5959lq9aR7NGXgwb1B+/C/7MWfQdL0LD2FhE/giQ7cpu1rE7CQlJDOjzFSbGxpw570+3fkNYtvg/TBg1TGVb/w5cXZw4f/wAIDtt5OrZWKmNpqYGF3wPYG+nmEcvNS2dB4+e0Lp5U7S0ZI9/wWHhVCxfjoyMTM77BxAXn4CrixN1a3so7BZXtZ0q8xMIBIJ/gnHTZrNj7wEG9P4K9+puhIZHsGnbLh48fkLA6WMAXLx8lbbd+2BibETfHt6Ym5lx9uIlxk6dRVh4BN8vnAdA0MNHREXFcO7iZUxNTGjWuCH7Dh/FP+Aaew4c4ezFS7Rv3YJKFcrz55mztPXuTXDQdQUfdffefZav+pVmjRsyfPAAzlzwZ87CpQSHhLFx1fIibXgVHUOzDt7EJyQysE8PTIyNOX3+Il37DmbZ4vlMHD1cZVv/Dqq4OHPBVya4BYeG4+rZUKlNQmISySmpfN2vt1Kds6MjALcD71G3tgfdOrbj57UbOOl3nvatZcLUs+AQLgZcpWXTxhjo68uvzcnNpd+wMTRqUI/xI4eyaftupf5DQsMUfNnr+HiquDgr+TKBQKAaJ1dM497pvdRo0xtr5+okR4Vz98Q24sIeM2TNaQDCAy+zc1p3dA2McWvVA30TM0JvX+TPX6aSHBNOq5ELAYgNfkh6fBRhd2Qnbuw8GvPo/GHCgwJ4eO4Aobcv4lSvNSbWFXhx7Qw7p3ozfm+QPBdR2N1LxDy/x9U9q7DzaEytToMJeZNzKOlVCJ2mryrShrT4aLZN6EBmSgI12vZB18CYkFvn2Tu7L63HLKZej9Eq21raSArycazTAlvXWsrzjosCQM9IFgK0cuOOXN+3luAbfjjVaw1AYmQw4YEBONRuipauPhmJsQCUq1JbqT/bKrV4cf0M8eFPKV+trkr9FUV2WhLHfhxHtRbe2Hk0+fybIBAI/pUIsUkg+ELIK5Bw4GY4rdzKsXJQfXl5JQtD5u2/Q0hcOo5ljTh0KwINdTVuLuoiP/0yvk1V6sw/xpkHUXKxCSAuLYfZXWoyqV01ALw9K9Fv7UWuPH/NpfmdcCwrSyg5Yft1/rgeSlh8hrwMIDw+g0U9ajHyjUA0q3MNeqw8z55rIXzdxJmaFc2U7Pj2SBCRiZmcnNGWWnYy0WRGxxr0WXuBRUeC6FXPAX0dTZVsfZ+kjFy2XnpR4r3s5FGByjYmJbb7GGbuvU1qVh7/6fZup6a+tiZ1HS3l3zeef0pkUhZnH0ZRKJUysa3svr/NtWRloqfUr6OVzM6E9ByauFoDEPD8NaNbVVFo9ywm9c1/U4TYJPjiyM3NY/f+Q3Ro3YLNa96FS3C0r8Tk2Qt4ERKKs6MDew8eRVNTg2d3rmBqIhNap08cg4uHF76nzsrFJoDYuHgWzZ3B7CnjAej9VVc69x6Ef8A17l89h7OjTLwdOnYK2/fK8gO9LQMIDYtg+bfvXr4tnDONtt59+X3XH4zyGUStmoo7BAHmLvqOiJevuHLmGHVry9bSBTOn0qnXQGYvXMqA3j0w0NdXydb3SUhMYv2W7SXey+6dO1DV1aXEdsVhoK+PV7068u+r1m8iIjKKP8+co7CwkJmTxsrrQkLDSUtPx8m9AVnZ2fLyWjWrs239KlxdnD6qnUAgEHyJ5ObmsWvfQTq0acnmNb/Iyx3sKjF59nyeh4Ti4ujA3oNH0NTU4Pnda3IfNWPSWJzd6+N7yk8uNgHExsWxaO5MeVjSPl91pVOvgfgHXOX+tQu4vPEDPmMnsX3PfoLDwuVlACFhESxf8g2T5D5qOm28e7N1116Zj3KvoWTHnIVLCX8ZyVU/33c+atY0Ovbqz+yFSxjYpycG+voq2fo+CYlJrNu8rcR7+VWXDlR1rVxiu+J4Hiw7jWBtrXzyqLKzTGyKT0gAYOwIH85dCqBLn0E0qOuJrq4OFy9fxdbaim/nzVS4dub8xcTEvubkwT2ovdk49z7BYTJf5liznqIvc6/BtvWrqOLi/Ml2CQT/axTm53Lfbx/O9dvQZea7fKBlytlxevVsEl+FYF7ekUfnD6Kuocm43Xflp2m8+k5idT93Xlw9JRebADKS4mg+dC6NBkwFoFqLr9gzqxfhQQGM+v0a5uVla8Sx78dy7/QekqLC5GUAydFhtBmzhHo9ZQJRs6Fz2DnVm8CTu6jd1QcbF+UTl+c3LiQl9iU+v/rJRZimQ2axe0Yvzm1YSI22fdDW1VfJ1vfJSk3k9tHNJd7LKk26YGnnqlSurqlFuwk/KJVnpsRz+8gm1DW1cG7QFoC63UcQfvcSe2b3oUK1umhq6xIeeBkjC2uaD5P5rrenlMLuXqJ+r7EKfSaEPwMgLkwmNqnSX1GcXDGdnIxUWoxYUKLdAoHg/y9CbBIIvhAK34TNuPr8NQ8ik6leQbZLZWgzF/o3dERHU7bjblRLV4Y1d1EIs5ZXKMFYT4v090KsaairMfYvokW18rI+G1e2UhBzvFzK8sf1UJ7HpCqUm+hpM6L5uwcfdTU1JrWrxpXnr7n4JEZJbErJzOPQrXDcK5nLhSYALU11BjZ0IuDZa04ERdK9TiWVbH2fxIxcfvS9/8H7COBY1qhUxaZnMakcuxvBxLbVKGdmUGy7pcfuk50nC09V2cYEXS2ZHWHxshCFpvraStdUeNNfWnY+DmWNcK9kxqWnsey6EkLX2hWRSOHAzTCO330JoBAqUSD4Uih8EwbD/8o1gu4/xL2GLFfZmGFfM2RAH3R1dACYPGY444YPkb/EA8jLy8fExJi0NMVQnhoaGkwdN0r+/W1+heZNGiqIOU0bNWD73v08fvZCodzUxFh+EglAXV2dWVPGcTHgKn4X/JXEpqTkFPYcOIKnR035SzwAbW0thg7qx4XLVzjie5I+PbqpZOv7JCQmsfD7nz54HwGcHR0+S2x6n/98+6P8pVpVVxf0dHXldcFh4aRnZLJ47gy6dWpHfEIi2/ceYOvOvXj39+G2/ykM9PVVbicQCARfInIfFaC4bo8dPgSfgX3l6/aksSMYN8LnPR+Vh6mJCalpaQp9amhoMG38aPn3Gm6yDUbNmzRUEHOaNvRi+579PHn6XKHc1MSYie/5qNlTJnDx8lXOXPBXEptkPuownh7uSj5q2KD+XLh0hcO+f9K3h7dKtr5PfGIiC78v+kTVX3FxcvgssSn4Td6konIqVapQDpCFCgQwNTGhUoXy3HvwiNuBQWhpaiGRSNDU1CQ9I1N+3YnTZ1n721YObN+EjVXxIZuCQ9/4snkz6daxPQmJiWzfs58tO/fQvf8QbvufEb5MIFCRtyE/I4ICiH1xH2tn2ZpVx3s4Hh0GoqktW2vq9RxLHe8RCmHbCgvy0DU0ITdDcV1VU9egQZ/x8u9WTrL1y96jiYKYU8m9IfdO7yEh/KlCua6hCfV6vPvtoKamTqP+UwgPvEzorQtKYlN2ejIPzh3A1tVD4bSPhqY2tToNIjzwEk8v+eLWqodKtr5PVkoi/lu//+B9BDAv71Sk2FQUL66d5viPE8hMTaDtuKWUdagqt93EqgKxwQ+IfhqIuqYmUqkEdQ1Neag/s/IO2FT2IOzuJQJP7KBac2+kUgkP/Pbx+OJRAHloQ1X6e5/48Kc8uniERv2nYFK2vEr2CASC/58IsUkg+ELQ09ZkWsfqfHfsHq2+O4mztTGNXKxo6VaO5lVt0FCX7dJztjYmOTOXdWefcDssgcjETELj0knPycf6vZMzViZ6aGm+S82m+0bEsTJR/CGl8WYHYF6BYjxj+7JGvL858K2IEx6v/JAR/DoNqRQyc/MZsTlAoe6tEBaekK6yre/jZGVMxErlsBvvo6VRuuno1px5jJaGOqNbVvlgu/AVvQiNS+dGSDxLjwbR/sfT3F3STS6epWTlKV2TlSsTp0z0tVFXU2PFwPoM+NWfKbtuMHf/HSRSKRKplAGNHNl+ObjUT2wJBKWBvp4e/5kxhflLfqRO8/a4ujjRrLEX7Vu1oE2LpvLwNJWdnUhMSuaXtRu5fusO4S8jCQ4NIy09A9v3djnbWluhrf0uF9rbF2Q277VTf/P3npen+Pfl5GCvtLv57QuykLAIJRueB4cglUrJyMyk39AxCnVp6TIhLCQ8QmVb36eysyNpUSWfzNTW0iqxzceQ+uo5waFhXLl+i3mLv8erTWdC79/EuqwlW9b+go62NtWqyO6Lk4M9Dep6YmJsxE+r13PY9yQDen2lcjuBQCD4EpGt21OZv+QHPJu1pYqLs2zdbt1SYd12feOjfl67ges37xAeGUlwSBhp6ekq+yhba2uFdhpvfVS+4oYwJweHYn1U6Ad8VGZmJn19RinUpaVnyK9T1db3cXV2Ij06pMi6v/K5PkpHR7bxKik5RakuM1O2MaKMqexZt2mHbjx8/JQ1y7+jd/eu6OrqcMrvPCMnTadz74Hcv3YBHR0dho6bzNCB/ejWqf0Hx9669hd0dLSpVkX2UtfZ8Z0vW756HYeP/8mA3j0+yz6B4H8FLR09mg6ewYXNS/htRDMsKrlg594Yp/qtcazTAjV12VpjUdGZ7LQkru9by6tHN0mJjSTpVQi5WekYmSuul0YW1mhovtuc+VbEMbJQbPe278ICxXXVrLwD77+8eCviJEcr5s4DSHwZDFIpedmZHFzoo1CXm5Uuv05VW9/HoqIzs09FF1n3VzRUWFeTo8M4s3Yuz6+ewqycA97zNmJfu6m8/vcJHYgLfUyHScup1qI7mtq6BN/0w3fZJPbM6s2o369hal2RLjNWs3dOX3yXT+T06llIpRKkEikenQZx9/jv8vulan9/5eqeVWhoalO/l+LvKIFA8L9H6b6RFQgEn8WkdtW4sbALUzq4oa+tybbLwQz49SJNFvkSl5YDwFq/J9Scc4SfTz4kv1BKE1drVg2qrxDO7S0GOkXryeoq/uUXFfZN/02fb0/t/JXkTFmiSB1NDTQ11BU+ZQx1+KquHZVtTFW29X3U1GTjlvQpTqz6FKKSMjl0K5z2NStgaqB4MkkqBYlU8aSRQ1kj+jZwYF43d/ILJZx9GEVZY9lJgogEZYEu+Y0AZW4oe5iuYmuK/7wO/DKgHoMbOzGrcw1Oz2iLi7Xsh7erjfJOUIHgS2D2lPE8ux3A3GkT0dfTY+PWnXTt+zU1vFoSGxcPwE+r12PnVocly1eQn59Py2aN2bz2F4Wwb2/RL2Z3sbqKC1hRu5vf7lguamd3YlIyADo6OmhpaSp8zM3K0K+nN1Uru6hs6/uoqamhp6tb4udz80ZIpVKlBPNODvYM7teLJQtmk59fwKk3yeRr1awuF5D+ytvcGI+ePPuodgKBQPClMmfqBJ7ducq86ZPQ09djw9YddOkziBoNmhMbFwfA8tXrqFStNkuW/UJ+QT6tmjZmSzE+qrgTMOoqPoPaWBflo2TP3bq6xfsobR1ttLS0FD4yH9VdfipWFVvf57/lo6zLyuwOC1cW1JKSZTZamJvz5PkLHj5+StNGDRjlM4gypibo6eri3bkDg/v2Iis7m8O+J9mwZRsJiUmkpqXJRKc3n+iYGKJjYxk6bjLf/7IakIXLeys0/ZX2rVsCwpcJBB9LowFTGbfrDo0HTUdLR587x7ayd3Yf1n/dgIwk2Vpzbe9qVvSsxqXtyygsKMC+dlO6zFpLBTfl/M/axeQBQk21Z39DM2ulMi09WZ8a2rpKddlpSQBoaumgoaml8NE3NqN6q55y8UUVW5XnrYamjm6Jn+LEqrc88NvHxmFNCA8KoNXIhYzaekVBaEqIeE5c6GMquTeidlcfdI1M0dTRxbVxZ2q260t+bjZPL/sCUNahKqO2XKHT9FXU7jKEZj5zGbr+LJaVZP7D0t71o/p7S2rcKx6eO4Brow7yPFICgeB/F3GySSD4QsgvkJCVX0AFcwNmdqrBzE41iEvLYcWph2y++JxNF58xsoUr3x4JwtxIh+vfdMZQ990umBWnHpX6nN6Gf/srkYmysBWOVsZKdZUsDAGZ4PLr114KdYUSKRm5+ehraapk65wuNZX6j0vL4ec/H5Q4775ejkXmk/oUtgcEUyCR0t9LOQ7zqjOPWHr0HrvGNKOVm61CnZmh7IE2OjmLFtVsUVMrWmx69Er2w7qWnQX5BRIiEjMwN9Sh33vjrTrzGCsTPSXBSyD4EsjLyycrO5tKFcvzzexpfDN7GrFx8Xz30yp+3fQ7azduZcKoocxZ9B2W5mY8uX0ZI0ND+fXf/VR00t7PITgsXKks4mUk8C4vxF9xsJOF93R2sGfbesX5FBYWkp6Rib6enkq2Lp43Q6n/2Lh4lixfUeK8h/TvU2Q+KVX5ceVa5i3+gWN7t8nFoLdYmMnWxcioaCKjorl1NwhPj5pULF9OoV1YuCxsZ1kLC5XbCQQCwZfK23XbrmJ5vpk9nW9mTyc2Lo7vflrF2t+2vvFRw5izcCmW5uY8vROg4KOW/lz6PirkTTi5vxLx8hUALk7KPsreTraD3NnBnu0bVivUFeWjPmTr4vfyHYEsB9WSZSr6qCLySamKs5PsRFfoG//xV+4/egxAvdoePHj0BICmDRsotWvVvAm//LqRlJQULC0sqFm9Gi9CFU8t5ObmIZFKCHrwCHU19Te+LBBPD3clXxYaIRO+LC2FLxMIVKWwII/8nGxMrSvSbMhsmg2ZTUZSHAE7f+LW4d+4dWgj9XqM4tzGheibmjNu5x209d+tqwE7f/5A759GclSoUllKrGytsaignF/U1NYOkJ2I6jZ3g0KdVFJIblYGWrp6KtlaVB6jjKQ4Lm9fVuK83Tv0LzKfFMjC5h35bjTlq9ah+/xNRYanex0qew9UqWZDpToHz+Zc3/8rOekpFBbkkRLzEn0TMzw6DFBod2X3CgzNrdAzKkPo7Ysq9fdX7h7fhqSwAPcOA0u0VyAQ/P9HnGwSCL4QLj9/jcvUAxy+/W6nX1ljXXnOpdSsPCITM5FIpXR0r6AgNEUnZ/HwjWhRmoS+TiM0TlFw2nNN9hDnVl75hI29pRHmhjpceBxDfqHizvpVpx/hMvUAdyMSVbK1KFKz8th5NaTET1Eh/j6Vi09iMTXQprGrciLjqraye+D/NFapbmdAMCDLk2VtokcDp7JcexGnMLf8QgmHbkVgY6pPzYpmZOUX0HChL7P/uK3QV3RKFicCI2lbQ/HHsUDwpXDh8hUsHaqx9+BReZl1WUumjpeF+klOTeHlqygkEgndOrdXeIkXGRXNvYePS31OL0LCCH7v5dPvu/cBUPNNbo2/4mhvh6WFOWfOXyQ/v0Ch7ocVa7F0qMatu0Eq2VoUqampbNmxp8RPSBEi2cfg9mbX9tmLl5XqNm/fDcjyXyUnp9D765F89/NqpXb7Dh8DoFGDuiq3EwgEgi+VC5cDsLCvwt6DR+Rl1mXLMvVNzqXklBReRr5CIpHgXZSPelD6G7qeh4TyIuR9H/UHAO7VlX2Uk739Gx/lr+Sjvv9lDRb2Vbh1N1AlW4siJTWNzTt2l/gJKeJE0sdga21FY6/6XL56XSGkbX5+AXsOHKacjTW13GvITxIfOHpCqY/9R44D4Fa1CuNG+HDH/4zSx7WyMw6VKnHH/wy/rf6J5OQUeg0eUeTmln2HZL6scQPlkxYCgaBowu9eZllnex6eOygvMzQrK8+5lJ2RQurrSKRSCa5NOisITWlxUcQGl7yB9GNJfBVC0ivFcKD3Tsqefa2clDdymZWzR9/UgpCb55G8F5IvYNcvLOtsT/STuyrZWhQ5GakEnthR4ic5OrxYm85vWoyugTE9F24rNg+SZSVZ9IEn/keV6h5flPmCsg5Vyc/J5tdBdTm1SnHDQVp8NE8uHcfFq/1H9fdXQm+fR8+oDPa1mxRri0Ag+N9BnGwSCL4Q6jpYYGGky09/PsDWVA+3CmaExafLTyy1ciuHk5URBjqaHLkTQctqtjhZGXMzNJ4fjt3HSFeLzNwCgl+n4VTEqaNPoVAqZfCGS8zuXANHK2NOBEay6cIzutauSH0n5fAfWprqzOvmzuSdNxj7+1XGtamKka4Wp+694ueTD2laxZq6DpZk5uaXaGtROFsb82pVn1KxTRVSsvK4/zKJNtXLof5+8iqgpZstVWxN2XzxGSZ6WjSvakNMSjbH7r7kzIMo3CuZ07q6zJaJ7arRf+1Fhm8KYFL7apjqa7P6zGMiEjLYOaYpampgoqdNo8pW+AZGsvtqCB3cKxAen87UXTexMdVngbeH0hwEgi8Br3qelLWw4NtlKyhfzgb36m6EhIXLX+p0aN0SFycHDA0M2H/4OO1aNsfVxYmrN26zYOkyjI0MycjM5HlwSJE7uj+FwsJCug8YyqK5M3BxdOCw70nWbNxCz26dixRHtLW1WPKfWYyYOJ3BoyYwfeJojI2MOPbnGZb+tJJWzRrjVc+TjMzMEm0tisrOTmTGKO+4LG3at26BW1VX1v62FVMTY9q0aEpUTCwHj/rie/osnh416dimJRoaGtSvU5vN23djXqYM3p3bI5FI2LXvEH4XLtG9cwfq1HJHKpWq1E4gEAi+VLzq1aGspQXfLvuF8ra2uNdwIyQ0TH5iqUObVrg4O2JoYMC+w8do16oFlZ2duHrjFguW/vjGR2XxLDiEyqXoo74a4CPzUU4OHDp+ktUbNr/xUUWEl9LWYsn82YyYMI1BI8cxY+JYjIyMOHbyNEt/WkGrZk3wqldH5qNKsLUoXJ2dyIoNLxXbSmL2lPF07j2IPj4jmTNlAmVMTflx5VpCw19ybO821NTUqOrqQuvmTfG74E+HHv3p36s7dhUrcMT3JHsPHKGqa2W6dmin8pjVq1Whfp3abNq+C3OzMnh3ao9EKmXXvoP4XfCne+eOwpcJBB9BBbd6GJhacmn7MowtbbF2rkFSVKj8xJJz/TaYV3BGW8+Ax+cP41S3FRYVnYl8eIOLW5aiY2BEXnYmiZHBmBdx6uhTkEgK2TdvAM2GzsW8ghNPLx3n5sENVG3WjYo1lE9Jamhq02L4fHyXTeDwkpF49ZuIjr4Rz6+c5PKOn3DwbEYFt3rkZWeWaGtRWFR0Zo6f8qZUVclJTyEu7Ak2TjW4tm9NkW3s3BvhVL81Dp7NCb19gd0zelC9dS9MrSvy9LIvD88dwNLOlcqNOqChqY2dRxMe+x/DvnYzXBt1JCkqjBM/TcLY0pbWoxYBslB6qvT313nGPLuHi1c71FQMeSgQCP5/I8QmgeALwVBXi3VDvBi/7RreK87Jy3W0NJjTpSat34RpWzGwPpN2XGfgOn8ATA20WdyjNvramozfdo2mi08QtaZvqcypcWVrbEz1GfpbgDw3kZeLFT/0UY5d/5Z+Xo5k5RWy6HAgR+/Ijq1rqqvRv6ETc7rURE1NdVv/aa48f41EKsXToeiwGupqamwb1YQxv19l2YkHLDvxbodWR/cKLOlVG803sfubVbFh7ddeTN55A5+NshMHJnraLOpRi5bV3tm7cmB9Rm65wuSdN5i88wYANSqYsd7HS+E0m0DwJWFkaMj2javwGTOZegJ+fwAAIABJREFUVl16yct1dXRYPG8GHdrIBJjfVi9n+PhpePeXJeE1K2PK8iULMNDXx2fMZGo2bEX2a+UEvp9CiyaNKGdjTe+vR8pzGDVt2IDVy5YUe82QAX3Iys5m1oIl8p3Tmpqa+Azsy+K5M1BTU1PZ1n8KdXV1Du3YzKBRE1j0w88s+uFdmBLvTu355ftFaGrKHv8O7tzEyIkz+GHFGn5Y8e5H7EifgSxbPB+Q5fFQpZ1AIBB8qRgZGrJj4xqGjJ5Iyy495OWydXumfN3etOZnho2bQrd+XwMyH/XTkoUYGOgxZPQkanq1ICfu8072vKVFk0aUs7Wm19cj3vmoRg1Ys/y7Yq/xGdCXrKxsZi34VsFHDR3Yl8XzZsl9lCq2/pO0bt6UbetXMWLCNHoOHg6AqYkxy79dQLtWsvCv6urq7Nr0KxNnzmPvwSOcOX9Rfn1jr/psWv0z2tqqPxerqalxaOcWRkycxve/rJbncQIY5TOIZd8uKB3jBIL/EbT1DfGet5Gj341m++Qu8nJNbR2aD5snF2A6z1jD8R/H8cfcfgDoGZWhzbglaOkacPS70awf4sXcs8XkPPpI7Gs1wdjClgMLvkYqla2rldwb0WHy8mKv8egwgIKcLM5uWCA/taOuoYlHx4Gy0HhqairbWtpEPrwBUikxL+4R8+JekW3U1NRwbtCW7vM3cWrlTB6eP0jIrfPy+oo1vOgyczUamrJQ/F1mrubQ4mEc/3E8x3+Uncyyca5J93m/yU+fqampq9wfQHjQZaRSCeWrFf+OSCAQ/G+hJpW+l91eIBCwb98+evfuzetf+/3Xx87OK+BxVAqvkrIwN9TB1dYECyPFhJbJmbk8iEzGykQPF2sT3h66Sc7MJSUrD3tLo8+eh+v0g3hUMmPPuOakZOVxLyIJG1M9XGxMVLo+IyefB6+SycwpoGo5U2zLKCf8VMXWfwMSqZSXCZm8eJ2GnpYGjlZG2JgWneC0QCLlXkQiEinUsjNHo4hE0lIpPIlOISIhgxoVylDOzODvNuGDWI3ZzR9//EGvXr1Kbiz4Inm7puUnRv6t42RlZ/Pg0RNevorCwtyMalUqK+XzSUxKJujBQ2ysrKhS2Rm1NwtYYlIyKampONrbffY8rJ1r4OlRE999O0hOSeVO0H3K2VhTpbKzStenZ2QQdP8RGZmZuFV1pUI5ZQFcFVv/SSQSCeERkTx9EYyeni4uTo6Us1FOnAwQEfmK58GhmJoY4+ripBBC6lPa/VNomVcQa5VA8C9FTU2NPVvW07Nb579tDIV12+zNum1ZnI8qS5XKLgo+KjklFScHu8+eh5WTG54e7pzYv/ONj7qHrY21PHRcSch81EMyMrNU81HF2PpPU1BQwJ2g+0gkEurW9kBDQ6PIdq+iY3j89BnZ2TlUdnGispOj/N/lU5D5shBMTUz+q76sz5CRqGnrsW/fvv/KeIL/HXr16sWj+Dy+WrD1vz52fm42cSGPSI17hb6JGZb2VTAwtVRok52WROyLBxiaW8lCtL35+81OSyI7PQWzcg6fPY/lXZ2wdfWg3w/7yUlPIfpZEEaWNvKQcCWRl5VBbPB98rIzKWtfFeOyypFWVLH1nyYtPpr48KcU5GZjXtFFlqvq/fVSKuV12GNSosOxdqlZbHg+lfv7gnh88QgHF/ogXnMLBP8o+8XJJoHgC0NPW5Pa9hbUti++TRkDHZq4Kr80LGOgQxkDnVKfk6m+Nk2rFP2SsjgMdbVoUESovb+iiq3/BtTV1LCzNMTOsuQfq5rqatS2//CPfTU1qFrOlKrllPNiCQRfMvp6etTzrEU9z1rFtjE3K0PLpo2LLDc3K1PqcypjakKrZsrjfQgjQ0Mae304d4Mqtv6TqKur42BfCQf7SiW2rVShPJUqFP9D82PbCQQCwZfIl+ujPi7HhcxH1f9gmy/dR4HsVJYq8ytva0N5W5tSG1f4MoGg9NDS0aNcVU/KVfUsto2esRn2tZsWWa5nbFbqc9I1MsXBs9lHXaOtb0jFGl4fbKOKrf80xpa2GFuWECVGTQ0rh2pYOSjnB/yk/gQCgeA9REBNgUAgEAgEAoFAIBAIBAKBQCAQCAQCwScjxCaBQFAkViZ6mBmW/ikpgUAg+LuxtiqLhXnp75QUCAQCgeBzsbayEj5KIBAIShEjcyv0Tcz/6WkIBAKBABBh9AQCQZH4z+vwT09BIBAIPomggLP/9BQEAoFAICiSe1fO/dNTEAgEgv9XjNxy5Z+egkAgEAjeIE42CQQCgUAgEAgEAoFAIBAIBAKBQCAQCD4ZcbJJIPgCOfswmoycfLp5lpzY/UthR0AwiRm5ADhbG9PRvcJn9ZeRk09+oYQyBkWH8iuQSCkolKCrpVFiX5m5BRjoqLbclTTux1Ca436MvUVx8UkMQRFJAOhqaTCqpesn9SMQfAon/c6Tlp5B7+5d/umpqMymbbuIT5T9zbi6OOHdqf0n95WRmYmhgcEH2xQUFKChoYGamppKfaZnZJCXl//ZCeulUikpqWmUMTUpsa0qdnwufhcucTvwHgB6urpMGjP8bx1PIBAIZD4qnd7du/7TU1GZ37btJD5B5qOquDjh3fnTIhIUFBSQX1CAnq5uiW0lEgnq6h/eq/qxvqw0UXV+ampqaGh8+Hm6ON/od8GfW3ff+Cg9XSaPGfF5kxYI/uUE3/AjNzOdai26/9NTUZm7vtvISkkAwKKSC66NO39Wf3lZGRQW5KFn/HnhUSWFBaira0Aprp952Zlo6/29z+5KSKXkZKSia2T6wWaSwgIkBQVo6ij6n9DbF4h+ehcATR096vcc87dNVSAQ/D0IsUkg+AJZ6/eY8PiMf5XYtPHCMyITM7E20aNlNZsixab6C47j5VKWn/vX+2BfyZm5NPv2T4z0tAiY30mh7uKTGL49EsTT6FQKJBLKmxkwumUVhjR1Rv0vD2b3I5NYcuQeQRGJpGTlYWmsS7sa5VnQ3QMjXa2PHldVSntcVeyVSKW0XHqSAolUqe+K5gbsGtOMu2GJ7LsRRnx6DpoaakJsEvxXWb56HaFhEf8qsWnVhs1EvHyFjbUV7Vo1x8TYiEmz5n/wmto1a7B13QoAAu8/YO6i77kdeI/klFSsLC3o3KEtPyych7GRofyak37nmb90GU+ePcfYyIjmjb0Y5TOYxl7Fr5OJScnUatwaY2MjHly78En2JaekMuubJezZf5jsnByMDA1p16o5q378ViGXiCp2SCQSPJu1o6CgQGkcu4oVOLZ3G+cvBah0/5wc7dmx9wBx8QloaWkKsUkgEPztLFv1K6Fh4f8qsWn1+s2Ev4zE1saKdq1a4N25AxKJhNpN21BYUKjUvlLF8hz/Y4f8u98Ff2YvXMqjJ08pKCikUoXyTB47ktFDBysINs9DQlm36XeO/Xma1LQ0vOrVYdLoEbRo2kih/5N+5/nPkh/+4ssaMnroYBp71f9o2z7GDlXnt3v/IX7d9DtB9x9SUFiIo10lxgwfomRvckoqsxZ8y+79hxR84+plS7EwN+PG7UB2/rGf13EyHyXEJsH/Olf3rCI5OuxfJTbdPLCelNiXGFnY4FivFToGxpxeNeuD19hUdqfr7HVK5dlpSWzwaYSOoTGjf79e7PVrB3hSyb0hnaatVKoLvuHHhc1LiA9/ho6BEfYejfHsNpSKNbw+3jgg5sU9zm9cRPSzQHLSUzAoY0nlhh1oNXoROvpGHzU/qVTCb8OaIilUfsY3sa5I3+//kH/PSU/h7IYFPDi7n4LcHLT1DXGq24r2k5Yp5NMKvX2BcxsXEhf2BElhAaZWFajfayye3YaipqZO1OPb3D/zBxnJcWhoaAmxSSD4FyLEJoFAUGo0cLJkz7jmRdbtvR5KWHw6Xi5lS+xn0o4bxKZmY6SnKM5cfhZLnzUXMNHTpk8DB7Q01Dke+JI5+26TmJHDjE41AAiKSKLnqnNoqKvTvY4dpgbaHL0dwY6AYB5EJnNyRhsFYaqkcVWltMdV1d6Y5CweR6VQxdaUMgbaCn2Y6su+T+ngxpQObozfdo0zD6M+yT6B4H+Nxl718N0ne6l14fIVtLSKXhtycnJ5HhyCs4M9AHeC7tPWuw+ampr0+aobZmVM2Xf4OJu27SLo/kOunDmGuro6ew8eZdDI8VSqWJ6p40YRFRPLgSO+nDp7gWtnfXFxcixyvBETphEd+xpj46J/MJZEXl4+nXsP4uadQL7u35sGdWpz824Qm7bt4lV0DJdOHv4oO15Fx/Dg0RPcqrpiVkZxF+Pb72pqairdv7nTJjJ32kR8xkzmxBmRe0sgEAiKo7FXfU7s3yn/rrgWK556/ev38/4BdOjRH1MTY77u1wctLU0OHj3BxJnzSEhMZMGsaQBk5+Tg3fdromJi6dvDG3OzMhw6doKufQfz54FdciFp78EjDBwxDruKFZg2fjRRMbHsP3xc5svOnaByMb6sOFS1Q9X57dh7AJ+xk6js5MiEUcPIzsnh0DGZvSmpacydNhGQ+cZOvQZw804gQ/r3oX7d2ty6E8Rv23byKjqGy6eOMm/6JOZNn8SQMRM5cVr4KIHg30rFml70+2E/AOGBl1DXLPoZtSAvh8TIYMzKF72OHf9xAumJsegYGhc71r1Tu0mKCqWSe0OluofnDnJ4yQhMrSvi1Wc86QkxPLpwmOAbZxm6/hzmFZw+yq6YZ4HsmOqNuoYmbi17oGdchsfnD3HXdxuxwffx+dUPNTXFE6Afml9afDSvQx9R1qEqekaK67Ge8bvvhQV57J7Vi6gnd/Bo359y1eoS/eQOd323kRYfzZA1pwAIu+vPrhk90DU0wb1dP9Q1tXjif5RTq2aSlZJI0yGzaDxoOo0HTefod2N4ce30R9kvEAi+DITYJBAI/jaiU7JYfuIBQRFJPHqVrNI1v196wfnH0Zi+J5oA/PznQ6RSOD2zHXaWsh31c7vWxH3OEdadfcrUDtXRUFdji/9zsvMLOTWjFW7lZQ9BMzvVoMfK81x+FotvYCRdalVUeVxVKe1xVbU3LD4DgLVfN6Ba+c8LqSUQCIqmeeOG3PEv+gfPxJn/IT09nbU/fw/Ar7/9TnZODlfPHKdm9WoAfDN7Gm29+3L+UgCHjv9Jl/ZtmbXgWwz09bl14RSmJrIfqUsXzMbOrQ79ho3l9sVTSmNt2LKD0+cvKok6H8OOvfu5cfsuPy76D5PHynZlDxnQBzU1NX77fSd3gu5T272GSnb06NqJkLBwALatW0kNt6qfff8EAoFA8PGEhIYDsH396mLXYoBvl69AKpVy/dxJHO1lURSWzJ9NpWqe/LxmA/OmT0ZDQ4P/LP6eZ8Eh+O7bQbtWLQAYP2ootRq1xmfsZF4EXiMvL5+Z8xfLfNnF0+982fw5VHKrTb+ho7njf+ZvsUOV+QH8vHY9zg72XD3ri7GRbJPG9IljcXKvx7pNv8vFpu1798l84+L5TBk7EgCfAX1RU4ONv+/kTuA9anvU/ChbBALBl4+dRxNGbLpUZN2pVTPJzUqnw5SfleruHN1C8M1zSiIMyESaS9t+IPppIK9DHhbZd2FBHmfXz0dbV5/hGy+iaygL29lixHxW9HTj0KKhDP/N/6NsuXV4EwW5Ofis88PaqToAzYbMZudUb8Lu+vPE/zhVm3VVaX4AyVGhAHSbsx4rR7di2907tZeox7dpPXox9XuNBcCjwwBQU+Pu8d+JeRaITWUPLm9fDlIpw9afo4ytbJNey+HzWdGzGtf2raHJ4OmoqX9a2gCBQPDl8OGgxgKBQCXm7LtNl5/8eJ2arVQ3dddNeq0+T36BBICrz18za+8tGnxzHI85Rxi55QrbLr+gsIgQaG8Zt+0aY36/qlS+6vRjuvzkpxA+LTU7j5l7b9Fk8QncZh1iyMbLnH0YXQpWfjyZOQWEvk7HWFcL90rmJbZ/FpPKgoN3me/tgZWxnlJ9VHIWtqb6cuEFwFBXCw87c/IlEnLfhNu4FRqPW/kycsHnLX0bOAAQGJ74UeOqSmmPq6q9oXHpqKmBo1XxO6oEgo9h4sz/0Kxjd2JexynVjZo8k/Zf9SMvLx8A/yvXGD9jLlXqNMG+el0GDB/Lxq07KCxUDn/zliGjJzF41ASl8h9XrKVZx+4K4dhSUtMYN30ONRu2pLyrBz0HDeek3/lSsLJ0OH3uIuu3bGfbhlVYl7UE4OrN29R0qyYXaN4yuF8vAG7dDeLJs+dExcTSvnUL+cs5gLIWFrRu3pR7Dx6RmpaucP3jp8+Z/p9FfLdgDtZWJZ8SLY5d+w9R1sKCscOHKJTPmjyOretWyMPoqWIHQHBIGGpqajg7OXz0XIq6fwKBQPAhJs6cR9MO3kX7qEkzaNe97zsfFXCN8dPnUKVOI+zcPOk/bAwbSvBRX4+eyKCR45XKf1ixhqYdvJV91LTZ1PBqQbnK7vQYNOwf81EvQlVbi19FRVPe1kYuNAEYGRpSp5Y7+QX55OTKcrBu27OP6tWqyIUcACtLS9q0bEpYxEtu3gks3pdZFu/LSssOVeaXmpbOoyfPaNe6hVxoArC1tqJ540YkpSSTny/799y17xBlLS0YN9xHYZxZUybw+7qVWFiU/FtGIPjSObVqJr9P6EBG4muluhM/TWLX9O4UFuQBEBEUwMkV01k7sA4re7lxaPEw7hzbilRS/Pp59LvRHFkyUqn8yu4V/D6hg0I4tpyMVP5cMY31Q7z4uXtl9s8fRPANv1KwsnQIuXmO20c24z13A4Zmis/d8eFPObNuHq1GLsDQ3Erp2rysDBIjQ9AxMMbW1aPI/uPDn5GeEINTvdZyoQnAwNQSR8/mxAY/IDcz7aPmHPnoJlZObnKh6S012/cDIPrpHZXnB5D0KhTU1DAv/+ETVg/89mFgakmd7orhrxsNmELX2evQN7UAIC0uCmNLW7nQBKCtb4htlVpICgooyMtV3ViBQPDFIsQmgaAUsLc04kZIPCeCIhXKY1Oz2X01BFN9HbQ01bny/DU9Vp3n8J0Imle1oX9DR6KTs5ix5xZLjgYV2/+9l0ncf5mkVB4Wn86NkHikb8Sm6JQsWi49yb7rYTRwLkvfBg5EJmYwcJ0/G88/LV2jVcDZ2pgjU1pxZEor1vt8OOZwbn4hIzdfob5TWYY1q1xkmw7uFYhOyVIQz4Jfp3Hl+WsauVihr61JfqGE5lVtGNrURen6qOQsAIVTRKqMqwp/x7iq2Auy/w/KlTEgM7cAvwdR7L4awq3QhA8KmALBh3BysOPK9Vsc8T2pUB4d+5qtO/diVsYUbW0tLgZcpa13X/YdOkabFk3xGdiXyKgYxk6bw9zFxZ9SuXPvPneCHiiVvwgN48r1W0je/L/7KjoGz2Zt2bn3II296jG4f2/CI1/Rrd8QVq3fVLpGfwKJSckMHz+VXt6dad5YFnoiP7+ANi2aMmb410rtX0XJ/pbNypgSHSv7kV+nlrtSu7dlj58+k5fl5ObSf/hYGjWoy7gRPkrXfAzBIWG0a9UcbW0twsJfcvyUH3fvPcDW2ooBvb6iUoXyKtsBEBwWTsXy5cjIyOTE6bNs3bmXazdvf/BlLhR9/wQCgaAkHO3tuHL9JoeP/6lQHh37mi0797zzUZev0sa7N38cOkqbFs3wGdiPyKhoxk6dxdxF3xXb/92g+9wNuq9UHhwSxpXrNxV9VNM27Nh7gCZe9fi6f2/CX0bSte9gVq77rTRNVomQ0DCFtXjLzj1FrsXdOrbjVXSMgij2LDiEiwFXadaoIQb6+iQkJpGckkqrpo2VxnF2lIWTuh14750vq638srJOLVnZX31Zadmh6vw0NTW4eOIQ0yeOVWiTmpbOg0ePad28KVpasufp4JBQuW8MDY/g+Mkz3A26L/ONvXtQqUL5j7JDIPgSMStnT+SD6zy9fFyhPD0xlsATO9EzKoOGpjbhgZfZMdWbR+cP4VinBR4dB5IWF8Wfv0zl3G+Liu0/5lkQMc+V32skvQoh8sF1pFLZBty0+Gh+G96U+6f3UrGmF+7t+5MS+5K9s/ty44BybqT/NtlpSRz7cRzVWnhj59FEoa4gL5dDi4dRsXoD6nZXFtYALCq5MHilL4NX+uI9r2h/kJEYC0C5KrWV6myr1AJkopaqSArycazTgjreyvlO0+JkYfTfnsJSZX4ASVGhmJQtT152Bi+unSbwz528enRTSXBMigrBqV4rNDS1SY4J5/nVk8Q8D8LI3JoabXpjYiXL5125cUfS4qMVRMXEyGDCAwOw82iElq6+yvYKBIIvFxFGTyAoBbrXseObg3c5HhiJz1/EhmN3IpBIpfKTLYduRaChrsbNRV0w0ZMJD+PbVKXO/GOceRDFfO/id5WowrdHgohMzOTkjLbUspPtvpvRsQZ91l5g0ZEgetVzKDJcW1JGLlsvvSix/04eFahsY1Jiu09h4eFAYlOz+WN8c4pIawTAsGYuXH4ay4B1F6njYImOpgZXnr/G2kSP2V1kYS20NNRZ2stT6dqE9By2+j9HS0OdNtXLfdS4qvB3jKuKvSATmzJy8qk97yjZee92i9WsaMbar71wthYnngQfR5+vujFj/rccPHqC0UMHy8sPHDmORCJhcL/eAOw9eBRNTQ2e3bki39E8feIYXDy88D11lu+/mftZ85i76DsiXr7iyplj1H3zImvBzKl06jWQ2QuXMqB3jyLDySUkJrF+y/YS++/euQNVXZUFYlWZMGMuKalpLJk/W16mpaXJyh8WK7WNS0hg3eZtaGlp0rFNK7S1ZbHhL1y+Ig9l95Ynz54DspNMDerK1pWZ878lJvY1fx7YhdpnLFYZmZnEvI6jbFkLuvUbopBzorKzE5vX/EQ9z1oq2wGykEdp6ek4uTcgK/vdCd9aNauzbf0qXF2K3g1Z1P0TCASCkujbw5sZ8xdz8JgvY4Z9LS/ff/gYEomEr/u/9VFH0NTU4Pnda3IfNWPSWJzd6+N7yo/vF877rHnMWbiU8JeRXPXzfeejZk2jY6/+zF64hIF9ehbro9Zt3lZi/1916UBVV9U3QgWHydZix5r1FNdi9xpsW7+KKi7OAIwd4cO5SwF06TOIBnU90dXV4eLlq9haW/HtvJkAPA8OAcDaWnnHfmVnmZgTn5BAy2YysefCpQB56Lm3vPVlj54+k/uy0rJD1fkZ6OvjVa+OvG7lut94+SqKP8+cpbBQwqzJshNsb32jlaUlXfsOVvCNrs5ObF77C/U8a6lsg0DwpeLWsgd+6+bz2P8Ynt2GycsfXziMVCqhZvv+ADw6fxB1DU3G7b4rP3Xj1XcSq/u58+LqKVqNXPhZ8zi/cSEpsS/x+dVPLrY0HTKL3TN6cW7DQmq07VNkeLqs1ERuH91cYv9VmnTB0s71k+d3csV0cjJSaTFigVLd2fXzSU+Ipf+PB/mcFwhvT/eE3b0kDz33loRwmUgfF/aU8tXqqtSfuqYW7Sb8oFSemRLP7SObUNfUwrlB24+aY1JUGLlZ6azqU5P83HfrsY2LO93mrMeikgt52ZlkJL7GoIwle+f0VcizZFHRmS4z11KuqswH1O0+gvC7l9gzuw8VqtVFU1uX8MDLGFlY03zY5/lkgUDw5SDEJoGgFDA31KFFNVvOPYomIT0HCyNdAA7fjsDGVJ+mVawBGNXSlWHNXeRCE0BeoQRjPS3Ss/M/aw4pmXkcuhWOeyVzudAEoKWpzsCGTgQ8e82JoEj6N1RObpmYkcuPvso7ON/HsazR3yI2+T2IYvPF52wd0Rgrk+LD2BnraVPezICHr5IJjEhES10diVSKhoYamTnF3z+/B1FM2nmDxIwcFveoTRVb048a93Ps+pxxVbU3LD6DjJx8ZnetSYeaFUjMyOWP66HsvhrCoPX+nJvTXn4KSiBQBUsLc9q1as5Jv/PEJSRQ1kIW+uCPQ8coZ2NNqzcvlyaPGc644UMUQufk5eVjYmJM2keGzXmfpOQU9hw4gqdHTflLPABtbS2GDurHhctXOOJ7Ep+BfZWuTUhMYuH3P5U4hrOjwyeLTY+fPmf/EV9mTR5HxfLlPtj2xOmzjJgwnfjERH5e+g1uVV2RSCTUdq/B+UsBbNmxh57enZFIpOzef4gDR08AyHdxnzh9ll83/c7+7b9h8xnh8+BdLozVGzbjaG/Hyh8W06Buba7euM3sb5bSvf9QAq/4yf/NS7IDZC8G0zMyWTx3Bt06tSM+IZHtew+wdedevPv7cNv/FAb6ijsVP+b+CQQCwV9R8FHxCZS1fOujjr7xUbJd6JPGjmDcCJ/3fFQepiYmpKZ9XGii95H5qMN4ergr+ahhg/pz4dIVDvv+ydCB/ZSujU9MZOH3y0scw8XJ4ePEptA3a/G8mXTr2J6ExES279nPlp176N5/CLf9z2Cgr4+piQmVKpTn3oNH3A4MQktTC4lEgqamJukZmfK+gCLFskoVZGt2Smoazg721PaoyflLAWzesZte3l2QSKTs2neQA0d9ASgslKhsg6p2qDq/9/nPtz/IBayqrpXR05X9ZnvrG1dt2ISjvT0rf/gWr7qeXLlxi9nfLMG7/xCCAs7J/18TCP6t6Jta4FSvFS+u+5GZEo+BqSyE8aPzhzCysMHBsxkA9XqOpY73CIXwboUFeegampCb8XnrZ3Z6Mg/OHcDW1UPhVI+Gpja1Og0iPPASTy/54tFxoNK1WSmJ+G8tOceneXmnTxab4sOf8ujiERr1n4JJWcUTjS+unebW4d/ouWh7keHzPgaz8g7YVPYg7O4lAk/soFpzb6RSCQ/89vH44lGAD4YsVIUX105z/McJZKYm0HbcUso6FJ8HryiSo0L5P/buOiyqrA/g+JfuBkEFlRQb7Fi7c+3WNdbuWlvXde21xdq1de1CdO3uDsSiFERE6a6Z94/R8R1CBhd3QM/neXgelzn33t9v3pfzm7nnnnNSEuKo//M0XH9oSUJ0OA+O7+Tese3sntaDgX9eIOLDvk4zI7obAAAgAElEQVQ39q/DvKg9zUYuwLZsVYIf3eD0+l/ZPbUHgzZdxsDUCl1DE0ys7Qj1fUTI03uoa2oilUpQ19AkJSHuX+UqCEL+Ie4+CkIe6VLdgZOPXnPsfjC9azsRFB7P3cBwRjUtg/qHJ16cbYyJjE9mzekn3A54T1B4PP5hscQmpWLzLwc7fN/GIJVCfHIqAzdcVnjt40BW4Pusb/46WRvzcnmXHK+hpZH3K2++jU5k5Lbr9KjlSAs3u8+2bbP4FE9ColjQtQptKxdHV0uDM49DGLvjBt1XX+DS9JbYWRjI2we+i2P6vjucfPQaeysj1vStSR1Xm1xfN7fy6rrK5ruid3V0NNVx/TCY5VDIiCoOlhjraeFx6glH7wXRqZr9Z68lCBn16toRr+OnOOx1nAF9evLyVTA379xj4ujhqKvL+oKSzk6ER0Sy1GM912/dIfBVEL7+AcTExlEki6eNc+O5rx9SqZS4+Hi69x+q8FpMrKwv8wt8meWxJZ0diXmd82xNbS2tL47vjxVr0NbWYvTQgdm28Q94ybhps/A6fgpH+xJsXb+Chh+W/FFXV+evlYtp270vg0b/wpjJM5FIJEikEvr37s6fm7dT2rUkb96G8fOIcfTr1Y22LZt9cbwfRURFAZCcnMKezeso6SybdeRevhxvw94xb8lK9hzwVFiq73N5AGz0WIqOtjZlSsluijo52FOjamVMjI1YvHItB73+oWfnDrl+/wRBELLTu1snvI6f4tDR4wzs05PAV0HcvHOPSWNGyGuU64catcRjHddv3iEwKAhfvwBiYmPzrEbFx8fTrd9ghddiYmU3zPwDsq5Rrs5OxIb45XiN3NaoTR5L0dHRpkwp2Q1WZ8dPffEfK9dw8MgxenbpSN0WbfH2ecqqP+bRpf2P6OrqcPzUWQaNnkDrLr14eO0cOjqyB+MiIqMyXSc+XjZYY2Zqgrq6OhtWLuHHbj8xaNQExkyaIa9lP/fuzvrN2ymTy4c6lMlD2fgyinnty4sPyyFOmz2fGo1bEvDolvw8yckp7NmyHtePtbFCOcLevWPu4hXsPnCYEYP65yoXQciPyjftxvOrx3l26SgVW/chKvQVr5/coVaPMaipyfpPy2LOJMZEcH2PB8GPbxIVGkREsB/JCbEYWdj8q+uHv/IFqZSUxHj2z1JcGjo5QfYZPzIkIMtjLYs5M/l4zvtRa/yLz/hXd65AQ1Ob6p0Vv3/Ehb/Fc8Fw3Fv2wrV2qy8+/0dqauq0+WUlu6Z0w+uPUZxYOQmpVIJUIsW9VW/uHtn8xQNmkSEBnPSYyvOrxzEv6kC7aeuxr1Q31+dpM8kDDS0dCtmXAsDc1hHbMlXRMTTm2q6VPLl4BGOrwgCkpybTcdYWLIvJZtEWdq5AXOQ7Lm9fzOOzB6jafhCbR7YgzN+HFqP/oEyD9mhq6+J78xRei0azc1IXBm++hqlNsS/KWRCE/EMMNglCHmlSriim+tocufeK3rWdOHRH9gWzS41Pm9t6nHrCAq+H6GiqU8PZmjquNoxuVoY1Z57y6n3un+SIjE/O9G8dTQ00MwwKmRnq0KFqCUoWzvz0H8hmf+tqaeT6+nlh86UXRMQlE5uYyqit1+W/D41KRIqUUVuv42BtRIsKdjwJiaKmizV96jjL27V0s+Om3zvWnnnK0ftBDG4o+0C272Ygv+y8iZqaGjPauTOgfkm0NdVzfd1RTcvkKp+8um5u8q1QzDzLWBqVKYLHqSc8DYnOVQ6CANCySSPMTE3Y73mMAX16suegJwA/de8kb7N45Vp+nfcHOjra1KlZnYb1ajN53EiWeqwn8OWrXF8z8v9uGoVHRAKgo6Mj30/hIwtzM7p3akfpklnfwFJTU5M/rfw1vAp+zc79h2jfunmWT1WDbKPx4eOnoKamxvxfpzJ8YD/5zbGPypZ25d7lU+w75IXPs+cUtramUb3aXLhyDYDSri6sWPsX78MjiImJpf/wsfJjQ96EIpVK6T98LC5ODkwcPVyp2IsWlt0gqFa5onyg6aNWzRozb8lKnjz3zVUeFSsobkL8UfPGDVi8ci2Pnyju16HM+ycIgvA5LZs0ltWow14M/L8a1bt7Z3mbP1au4de5i2Q1qlYNGtWtzZSxI1nise6LatTHwXr4VKO0dbTRynBTU1aj2mc7c/Zr1aiKbuWz/H3zxg35Y+UaHj95xpPnL/D2eUrdH2owuF9veZt2rVtw9cYtlq5ez0Gvf6hYXtavB2TxUEdEpCx3SwvZSgplS7ty/8oZ9h46wpOnz7GxsaZxvTqcv3wVIFezs5TNo1mjBkrFJ5VKkUql8gFIkA1eOTvao66uTr9ho/nn1Fn5UnvVKleUDzR91KpZE+YuXsHT5zk/xCIIBYFLjSboGpnic+EwFVv3wefcQQAqNPs0E/ParpWc3zQXDS0dileohX2luvzQcyzX93gQ9SbrgfTPSYz91H8mxsj2otbU0kFDU7H/1Dc2p1yjTtkPsqipoanz9T7jR4cF431mH6XqtM60jN9tz40kRIeTHB+D54JPn7tj379BKpXiuWA4FraO1OoxRunrFXIozeCNV3h8/hDvA59iaGGDQ+V6vLwve3jYyj73g02PTu3h2NJxoKZGo0GzqNphIBpaOrk+D8iWy8uKc7XGXNu1kncBT+Sz04qWriwfaPrIpWYzLm9fzPuXz3n/8jlh/j4Ud/uBSj9+GmR0rd2aoEc3uL53NU8veVG9k+IgnyAIBY8YbBKEPKKtqc6PlYqz44ovkfHJHLr9kioOljgWMgJkS9X9fug+FkY6XP+1NYa6nz5YLTv++LPnVgPSP2xG/P98336awl7c0hCQzWpZ3aemQrt0iZS45FT0tbL+kw+LSWLJsUc55titpmO2AxtfysJQl7K2ZviHKc66Sk5LRyKV4h0cibq6Gj6vZV8cazpnXkKqbqnCrD3zlKiEFEC2TN3wLVepbG/Jun61KGpukOkYZa+bG3l5XWXzDYlM4G5gOO7FzTNdL/DDAKal0Zd9uBS+bzo62nRq14aN23YSHhHJ7oOe1KhaGWdH2QD6u/fhTPltHlYW5jy5fQkjQ0P5sfMWr/jsudXU1JBksSzEM99PT3o7lCgOgLODPVvWKp4vPT2d2Lh49PWynhEaGvaOOX8syzHHvj26ZjtQ8jl/bdlBWloafXtmXsIPZMvN9R06mupVKrH9z1VZLhOXkpJK4KtXWJib07dnV4XXFi73oLB1IczNTLGytKBCuTK88Fd8wjM5OQWJVMIDbx+Fm2g5sfsQS2pq5qVHE5OSADAxNlI6j6DXIdy6e5/K7hUyvR4QKLuZm3FJvpzeP0EQhJzo6GjTuV0bNnysUQcOU6NqZVz+v0bNmouVhQVP71xWqFFzl3y+RqGmhkSSeem35y8+1Sj7ErInr50d7Nm6bqVCu5xrVBhzFilZo7IZeMlI1hffo7K7W6a+2P+l7MawlZUljx4/AaBurRqZztGofh2Wrl5PVFQUzk4OqKmp4R+YeVDu4WMfAKpVciclJZWAV6+wNDenX4Y+fcGyVfJapixl81A2vgXLVjFt9nyO7N5G88YNFNpZWsi+0wQFh1Csw4famJZGRomJH2uj2ANV+DZoaOlQpn477h3dRmJMBI/PHsC2TFUsbGXL7SdEvefM+lnom1owfPsdtPU/9Z+Xty/5/MnV1JBmsXRmeNCnwVrTIiUA2TJybaeuU2gnlaSTnBCHlm7W/WdcRBiXti7KMUe3Fj2yHSj5nLtHtiBJT8OtReYl/AxMLLBxKkdEsL/C79NSk5FKpLz1fZSrfVXT01KIevMKfRNz3Fv0VHjtyt/LMLSwznLfqs95ce0Eh+YNwbZ0FdrP+CvTMoC5ERP2mtdP71LE1T3TeSI/DDgamFnJX5Nk0X+mfdjnScfAmLf+sntexSvUytTOoXJ9ru9dTVJs5tmqgiAUPGKwSRDyUJfq9my59IKVJ33wDo5kSY9q8teCwuORSKW0dLNTGGgKiUzAOzgSK6Psn9ApZmHA+aehpKZL5EvZPXsTTeC7T7Oh7K2MsDDU4ZzPG4V2ACtOPGb+kYd4jmtMNUerTOePTkhh+9Wcl/Oo4Wyd54NNP9dz4ed6mZ/8bDzvOImpaZyZ0hyAJyGyDx5H7r5iQkvFm8OeH2aRfdwTac7hBxjrarNhQPZ7Iil73dzIy+sqm29kfDL9/7xErx+c+KO74uahh+/IvoBXd/p3e7wI36/eXTuyftM2Fq1YzYNHj1m3bKH8tVfBr5FIJLRt3VzhJl7Q6xAeePtg/Zl9DUoUs+P0+YukpqbJZy35PH2OX0CgvI2jfQmsLC04efa8QjuABcs8mDl3EeePHqBW9SpkFB0dzcZtO3PMr07N6l802HTq3EXMzUxpUCfzlyWAab8vwMTYiN2b12W7x1JCYiJlqtWja4cf2bZ+lfz3wSFvOHDkGH16yAaghg3oy7ABfTMdX7V+CxKTErl9/niuYtfT1aV+7Vqcu3QFX/8AnBw+LbF5+KhsQ9+PG7krk0dkZBRd+gzi5596sGaJ4hr6H2ca/FBDsW/K6f0TBEFQRu9unVm3aRuLlnvIatTyTzcgXwUFI5FIaJdVjXr0OIcaZZtFjXqGb8CnQX8ne/sPNepCpho1f+kqZs5dyIVjB6lVPfPG7lHRMWzY9neO+dWpVUPpwabIyCg6/zSQAT/1ZM1SxQ3i9xyQ9cW1a1STz6jad/goMyaOU2i399ARAMqWLkURG2tq16zOpavX8Qt4iaO97AGQ1NQ0du47SNHCNlR0K090TCxlqtaha4e2bP/TQ34uWS07St8eig9T5FUeysYXGvYOgNPnL2YabPpr6w4AKpQtLauNdWpx7uIVXvgF4Oz4f7XxmKzOfqyNgvAtqNC0G3c8N3Fl53JCfR/Ravxy+WvRb4OQSiW41mmtMNAUE/aaUN9HGJplvp/wkalNMfxvn0eSlor6h1lL7wKfEvH6U/9pXtQefVNL/G6eVWgHcHnHUs5vnEufFcewK1c90/mT4qK5d3RbjvkVd6v1RYNN/rfPomdkhn2lOpleq9J+IFXaZ17++a+B9UlNSWTAnxdyda3UpERW965K2YYdaDftT/nvY96F8OTiEdya98h1/Gf/mo2ugTGdZm3513tKJcZGsm/mT1Rs9RMtxy1VeO3x2QMAFCtfA00dXUq41yHw3kUigv0wt/20R/jTy8cAsCtbFVMbWT/95MJh6vaZqHA+n/OHAHK9p5QgCPmTGGwShDxUyV42k2ntmafoaWvSptKn9WadrI0w0NHk0J2XNCxTBCdrY276v2OB50OMdLWIT07D920MTtaZn5qrWMKSU94hjNx6nV61HAl4F8fKkz4Y6WkRESdbPk9LU51pbd0Ys/0GwzZfZXiT0hjpanH8QTBL/vGmbikbqjpk/cHQ2caY4BW5+zL4XytZ2IR6pQpz/skbuq46R8eq9thZGHDsfhAHbr+kZGETWlSwJSohhadvoihna86aM0+zPFct50I0Lqf8hvTrzj5l1oF7jGtRjnEtymbZJq+vq2y+mhrqVHawZPsVX8wNdGjhZodUKmXfzQDOP3lDK3c73EtYKJ2rIPy/apUr4uzowLLVf6Kvp0fHtq3lr7k4OWBoYMDeg0do1rA+ri5OXL1xm5lzF2FsZEhcfDzPff1wcXLMdN6qldw5dvIM/YePoX/v7vj5B7Jw+WpMjI15Hy5bWkNbW4s50ycxcNQEfho8kgmjhmBsZITnsZPMXbycRvVqU7Na1jd+Sjo7Ef/GP8vX/q3IqGjuPnhEq6aNspxRFBkVzeMnz3ArV5alHuuyOIPsifKWTRtRv3Yt9nsepWHd2vzYshl+AYEMHjORokUKs2DW1FzHtnzNn0ycOYdpE0YzbcLobNvNnTmZmo1b07XfEH6fNhG7okU4d+kKf27eTq3qVWjdrLHSebRo0pDqVSqxYevfWJiZ0a51cyQSCTv2HODUuYu0b92CKhU/fdnP6f0TBEFQVrXKFXFxdGDp6vXo6+nR6f9rlLMjhgYG7DnoSbNGDSjp7MTVG7eYOXfhhxqVwDNfP0pmWaMqcuzkGfoNG83PvXvgGxDAouUemWvUjMkMHDme3oOG88uoYRgZGeH5zwnmLl5Go3p15EuzZeTq7ERCaGCevhflypSiepVK/LV1BxbmZrRr1RyJVMqOPfs5de4C7Vu3pEpFNyQSCY3r1+XUuQu06NiDHp3bU6KYHYe8/mHXvkOUdi3Jjy1k+wNOHjuC1l1607XfIKaMHYmZqSkLl3vgH/gKz11bUFNTw9TEmPp1arHf04uG9WrTtmVz/AICGDT6F2yLFGHBb9PlMS5b8ycTZ8xm2oQxTP8l62WmlM1D2fiaN25A2dKurFq/ERNjY5o2rMfrN2/Yd8gLr+OnqOzuRsumjQCYN3MKNRq1omu/QcyZPgm7okU4e/EK6zdto1b1qrRu3iRP/zcTBFUqWroyFraOXN+zGi0dPUrXbyt/zcLOGW09A3zOHsSpaiMsizkT5H2D8xvnomNgREpiPOFBvljYOWU+b6lKvLh+ksPzh1GxVW8iXgdwdedydA2MSYgOB0BDU5sGA2bgtWgkB+cMomb3UejoG/H8yj9c2rYYh8r1sCtbLdO5QbZn05RToV/lPUmKjeLNswe41Gwm37vqa9I1NKGEex18LnhiX6kerj+0JOJ1AEcXj8bYqgiNB/8mb3tj7xpOrZ1BnZ8mUKf3L9nGHxbwhMJO5bm2Z1WWbUq4/YBzjaZKxWftUAbbMlW4e3QreibmlKrdCqlUysNTe/C/fY5SdVpTxLUiAA0HzmTD0Ebsn9WP+j9Px7hQUQLvXeTukU3YlauOS83mSKUSHCrXx//2Of7+pSPlGnfG1KYYTy954X1mH1YlXCn5Q4tcvouCIORHYrBJEPJYp2r2zD/ykJZuthj93wwmQ10tlvWqzuht1+m1RvbUi6mBNrM7VkJfW5MRW65Rd/ZRXq/KvKzQkEau3A54z4FbgRy4FUhhU306VSsBwIoTPvJ23Ws6kpCSzm8H78lntWiqq9GjlhNT2lQgF7O68x11NTXW9avF5D23OXg7kHM+b+Sv1XAqxLJe1dHSVOfWk3dIpfAwKIKHQRFZnksNcjXYJJFISZfI1nzPzi2/vL2usvkCbBlUhzHbb7D8xGOWn/i0JGOfOs7M6lBRqesJQnZ6dunAzLmLaNe6OcZGn55uNDI05M+VfzBgxHja9ZCtu21uZsofc2ZioK9Pv6FjqFCrEYlvM2/wO2bYQK7fusPOfYfYue8QRQvb0KNLBwAWLvv0ZHTfnl1JSExk0sw58ieuNTU16derG7On/pKrpSryyvnLV5FIJFSvUinL16/euIVUKuXew0fce5j18qRqamq0bNqIP1f+Qc8BwxkwcjwDRo4HwL18ObavX6XwJL6yJBIp6enpn+2rACq7V8Bz1xZ+Hj6W1l0+7dnRullj/lq1JNd57N/+F4NG/cKCZatYsOzTl9tB/XqxaPYMhWNyev8EQRByo0eXjsycu5B2rVtgbGQk/72RoSF/rVrCz8PH0rZ7H0BWoxbPmYWBgR59h4ymQs0GJIVl3ntk7PBBH2rUQfksmZ5dOgIo9HH9enYjISGRSTN/V6hR/Xt1Y/a0Sf9pjVJTU+PA9o0MHDWe+UtXMn/pp6X9BvfrzaLfZwKgrq7Ojr9WM2riNHbtP8TJs+fl7WrXrM5fK5egrS37/tK4fl22rF3BwJHj6fTTAABMTYz54/eZ8j2TAP5auYQePw9lwIhxDBghmy3lXqEc2//0UKhlEokkxxqlbB7Kxqeurs6B7RvpPWgEvy1YzG8LFsuPb9e6Bcvmz0ZTU3ZLpLK7G0d2b6X/sLG06vxpCa3WzZuwYZXiU/2C8C0o16QL5zfOxbVOa3T0P/Wf2vqGtP5lFUcWDmf3VNk+TnpGZjQZPgctXQMOzxvC2r41mXo6LNM5q3cZTrDPLbzP7MP7zD6MLAtTvkkXQLY03EfuLXqSlpTA6XUz5bNa1DU0cW/Zi/o/T0MVNy4C719CKpVgWybrBwW+hjYTV3Jg9s8cWTiCIwtHAFDYuQLtp/2pMKtMKpUglaTDZ/rPIO8bIJXy5sUD3rx4kGUbNTU1pQebUFOj8+/b8Vo0iis7lnJlx6d+sNKP/Wgy5Hf5fxdxdafbvN14LhjGzkmf9k50qdmcNpNWfTidOu1n/MXx5RPxPrsfv1tn5e2Kla9Jm4kr0dBU3BdWEISCSU2a0x0JQfgO7dmzhy5duvB2dfecG+dSZHwyj4IisTbRw8XGRP45KjI+maiEFOytjLI9NjwumTdRCZQpavbZz19xSak8Co4kPimN0kVNKWKmn8dZZFZ79lFszfTZObz+V79WSFQCz0KiSUpNx8naGCdr46/+eXTZ8ccUtzSkXeXiX/dCWVA23+CIeHzfxmCip42zjbHCco0fjdhyjZPer3m2qKPS17ce+je7d++mc+fOOTcW8qWPfVpqeFCenzs8IpL7j7wpbG1NqZLO8ptr4RGRREVH42hfIttj370PJ+RNKOXLlv7sTbnYuDjuP3xMXHw8ZUu7Yle0SB5nkVn5mg0oZlsUrz05L9Xxb0ilUrx9nuL/8hXu5ctmuS9SbsxbshL74sXo2uHHHNumpqbx+MlT3oVHULa0a7ZL5SnrZVAwz339MTUxxtXF6YsGzD7qN3QMR0+e5q1vzvsJfqRlYSf6KkEooNTU1Ni5ca3C7KS88KlGFaJUSReFGhUZFY2TQ4lsj81djfImLj7hv6tRNepjZ1uUo3u3Z3pN1hf7YWpi8tm+ODjkDT5Pn5GYmERJFydKOjlmmWdaWhp37j9EIpFQtZI7GhoamdrIa1ngS9wrlMu2ls1dvAKHEsXo2qFtlq9/SR7KxCeRSAh4GcSzF77o6eri4uxI0cI2WZ7vU20Mp2zpUlnWxr5DR3H0xGnC/D6/7+7/69p3EGraeuzZs0fpYwRBGZ07d+bxuxQ6zNyUp+dNjIkg9MUjDC2ssSpeUj4AlBgTQWJsFOZFHbI9NiHqPTHv32DjWPazA0cpCXGE+j4kJTGeQvalMS707z4HK2NtnxoYW9vSfcHer34tpUilvA3wISokEBuXCtnus3R5+2JMC5egbMMO/3GAsqUVw4N80TU0wbKYi8JA2P+TpKUSFvCEhOhwCtmXznYpv5h3IbwLfEpaciIWxVywtHPK9P+Tw/OG8uLaCcZ75rzdw0c+5w+xf1a/HB+8EwThq9orZjYJwn/MzECHOq6Zv9yYGehgZqDz2WMtDHWwMPx8G5DNoqrxDe/TU8RUnyKmX38A7aOAd7H8fdWPQ2Ma/WfX/H/K5mtrboCtucF/EJEgfGJhbkbDurWz/L2F+ec3tbWytMDKMudlHo0MDaldM+vlNAo6NTU1ypUpRbkypf71ufwCAtm0YxdnPJX78qylpYlb+ayXBv0Sxe1sKW735RsRC4Ig5LX/rkZl3ltEVZTti22LFMa2SOEc22lqalKt8udnyitTy3z9A9m8YxdnjuzL8ZqgfB7KxKeuro6jfXH53k6fk9e1URAKKj1jc+wr1c3y93rGn9/HWd/UEn3T7PfG+0hb35Bi5Wt+cYzfBDU1rB3KYO1QJtsmEa/9uX9sB72XHfkPA/vExNoOE2u7HNupa2ph45zzXoPGVkUwtvr6D2YIgqAaYrF8QRDyjHdwJAP+usy6s1nvWVRQBb6LY/uQuv/JDLGvZec1fwb8dZmrLzIvdyAIQtYePHpMt35DWL7mz5wb5wN+AS85/Pfm/+Sp+q9ly9976NZvCBevXld1KIIgCPnag0eP6dp3EMsKSI3yDwzk0M6CXaM279hN176DuHhF1ChBKMje+j5i/6y+3Ni7RtWhKCUyJJAu83b+JzO/VO3+PzvYP6svLx9cUXUogiB8ITGzSRCEPFGvVGFCIuORSKWfW0q4QKpfOuenPvM7qVSKRCrFrbg5hjqZl9cTBEFR4/p1CX4dgkQiKTB9WpMGmZ8+LWikUikSiYRKbuUxNBQzNQVBELLSuEFdgoJDkOSwp2h+0qRBPVWH8K9JkSKRSKnkVgEjI1GjBKEgcqjSgJiwYKQSCVIKRv/pWKVBzo2+IVKJhCIl3dDWz36LCUEQ8i8x2CQIQp6Y3fHzy1cIqtW9piPdazqqOgxBKDAWz5mZcyMhz/Xp0YU+PbqoOgxBEIR8bfGcX1Udwnepb4+u9O3RVdVhCILwLzQZNkfVIQif4da8B27Ne6g6DEEQ/gWxjJ4gCIIgCIIgCIIgCIIgCIIgCILwxcTMJkH4zp32DiEuKZW2lXPeMLcgef4mmn8eBJOaLmF8y3JKH/etvh+C8C3659RZYmLj6NK+japDyVNPnr3g8LHjpKamMf2XMUof962+H4IgCAWRrE+OpUv7H1UdSp7yefacw0dlNWrGxLFKH/etvh+CIKiG741TJMfHUqZBe1WHIgiCIPwfMdgkCN85j1M+BL6L+6YGV97HJtF4/nGSUtMpWdgkV4NN3+L7IQjfqj9WrsE/4OU3NbgS9v491Rq0IDEpidKuLrkabPoW3w9BEISCatGK1fgHBH5Tgyth795TrX7zDzWqZK4Gm77F90MQBNW5unMFkSEB39RgU/DjmwTcvUjFVj9hYGal6nAEQRC+iFhGTxCEb85Nv3ckpaYz9Uc3Lk5vqepwBEEQlHb1xm0Sk5KYM30SD66cUXU4giAIgiB39cYtWY2aMZmHV8+qOhxBEIRvyquH1zi/cS6x4aGqDkUQBOGLicEmQfhOSKWyn+9BVEIKAOWLmak4EkEQ8oJUKkX6nXRgUVHRALhXUH5GpiAIgqA631ONioyW1aiKokYJgvBf+Z5uZAiCIHwDxDJ6gvCNexwcycz997j/MpyUdAmli5oyoWU5GpYpku0xV5+/xfPuKy48DSUpJZ2qTlbUdC5Ez1pOaKirAZCcms7yE4/ZdzOQkKgEbAzfWmQAACAASURBVM0M+KGkNb+2d8dQV0vpNnnt90P3OeUdAsA8z4ccuPWSFb2rK51XRsrmEJ2YwtzDD7j2IoyI+GSqOFjRo6Yjjcpm/z4LgvB5D719mDB9Nrfv3SclJZVyZUoxY+JYmjWqn+0xF65cY99hL06fu0RSUhK1qlehTs3q9O/dHQ0NDQCSkpNZsHQVO/Ye4HXIG+yKFqV+nVos/G0aRoaGSrfJa1N+m8exk7LZTDPmLGTXvkNs8FiidF4ZKZtDVHQM036fz6WrNwgPj6BG1cr069WN5o0bfJU8BUEQvgUPvX0YP30Wt+8+ICUlhXJlSjFz0jiaNcq+77xw+Rr7Dh/h9PmLJCYmUat6VerUqsHPmWrUSnbsOUBwSAjFbItSv/YPLJw9PUON+nybvDZl1lyOfqhR039fyM59B9nosUzpvDJSNoeo6BimzZ7HxY81qlpl+vfqLmqUIHzj3vp5c2r1dEKe3iU9LYVCDmWo22cSTtUaZXvMy/uX8Tl/GP8750lLTsSuXHWKV6hFxVa9UVOX9UVpKclc2bGUR6f2EPMuBBNrW0q416bxkNlo6xsq3SavHV08Gv/b5wE4snAEduWq02zEfI6vnERqYjx1+07myo6l+Jw/xLhDLzg8bwhSiYS2U9cpnOfK38t4cf0kvZd6oq4hu+WbFBfN2b9m8+rBVRKiw7ErWw33lr1wqtb4q+QiCML3TQw2CcI37Orzt3T1OI+5gQ7dazoSm5SK171X9F5zgUNjG1PFwTLTMVeev6XTirMY6WnRoUoJzA10uPA0lF923uLl+zhmtHMHYOKuW+y5EUCnqvaUtTPj5fs4tl325UlIFEfHN1G6TV6zNtHD0kgHgKJm+tia6+cqr4yUySEkKoE2i08RHptM5+r2GOtpcc7nDb3WXGBWB3cGNnD9KrkKwrfswpVrtOrUCwtzM/r27EZ0TAwHjxyjXY9+nD2ylxpVK2c65vzlqzRr3x0TYyO6dmiLpYU5p89fYtj4Kfi/fMX8X6cCMHz8FLbv3k+Pzu1xL18Wv4CXbNj6N94+T7l0/JDSbfJaERtrClla8phn2BUtQjG7ornKKyNlcggOeUO9lu15/z6Cnl07YGJszMmzF2jbvS+LZk9n5OCfv0qugiAIBdmFy9do2akHFuZm9OvZleiYWA4cOUrb7n0557U/6xp16SpN23fFxNiIbh3bYWFuzunzFxk2bhIBgS+ZP2saAMPHT2bbrn307NIBt3Jl8Q98yV9bdvDI5wmXT3gq3SavFbaxppCVBY+fgJ1tEYrb2eYqr4yUySE45A31WrTj3ftwenXtiImxMSfOnufHbj+xaPYMRg0Z8FVyFQRBtV7ev8zfEzuhZ2yOW8ueJMfF8OTiEXZP7c5Py72wLVM10zGB9y6xfXx7dA2MKduoI/om5vjfPs+xpeOIfBNIo0GzAPhn2XgenNhF+SZdsHEuR+TrQO4e3UJYgA99V51Quk1eM7d1IizgCVGhr7CwdcS8qD0AYX6PiYt4y65JXXjr/5jCzhUAePPsPlKpJNN5IoL9CHp0Xf5azLsQtoxsQXzUe8o37YqugTF+t86ya3I3Gg+dTbWOQ75KPoIgfL/EYJMgZEFTU/ankS6RZjvjJb+TSKVM23cXHU11Do5piL2VEQBDG5Wi9mwvNl18nuVg04FbL9FQV+Pmb20w0dMGYEST0lSZ4cnJR6+Z0c6dlDQJ+24G0qhsUZZ/mDUEUNzSkGl77+AXFouduUGObRwLGWW6fkRcMpsuvsgxv1budpQsbJLp9wPql8RAR5PLz94yqKEr1RytlM4rI2XydCxkxO+H7hMUHs8/vzSlYgkLAH5pWZ6uHuf47dB9OldzwNRAO8ec8qM0iWzJgo9/E0LBJO/T0tOzfcI4P5FIJIyd8is6OtqcObIXR/sSAIwfMZhyNRqwduPWLG/k7dp/GE1NDZ7duYKpiTEAE0YNxcW9Jl7HTzP/16kkJ6fw994DtGjcgA2rlsiPdbQvzpjJM3nh508xW9sc2zg7OmS6/vvwCNZu3Jpjfu1bt6C0q0um3w8f2A9DAwPOXbrCqCEDqFW9itJ5ZaRMns6ODkz9bR4vXwVz5aQnVSvJ+sGZE8fRqnMvJs+aS88uHTE3M80xJ1VLS0sDRF8lCAWVpqYm6enpqg5DKRKJhDFTZnyoUftxcigBwLiRQyhXvR5rNmzJpkYdQlNTg+d3r8n78l9GD8PZrTpex08xf9Y0kpNT2LFnPy2aNGTDqqXyYx1KFGfM5Bk89/OnuK1tjm1csqlRazZsyTG/Dm1aUNq1ZKbfjxjUH0NDA85dvMLoIQOoVb2q0nllpEyeLo4OTJk1l8BXQVw95fWpRk0aT8vOPZg8aw69unYqEDUKIC09Hd0C8BlMKHg0NDSQSjIPOhRUUqmEE6umoKGlQ+9lRzAvKuvPanQdyZo+1bl9aEOWg02Pz+5HXUOT4X/fRddQdp+gZrfRrOzuxourx2k0aBbpqck8PLUH5+pNaDNxlfxYs6IlOLFyMuHBfpha2+bYxsLWMdP1E6LDuX14Q475larTBqsSmR9GrdFlOFJJOsGPb1Gz+2hsnD4tVxoe5ItjlQa0n7kRy2LOOV7j/51dP4uo0Ff0W32KoqUqAVC37yT+/qUzZ9bNonzTrugZfRvbD0jS09EQ3wUEQeXEX6EgZMHERPbhJDYxtcAOEjwKiuRxcCRdqjvIB5oAnG2MmdOpMpJs1j0e3NCVn+u7yAdkAFLSJRjraRGbmApA+ocPs1efv+VRUCTl7GQfTvrXc6FHLUd0NDVISUvPsU1WwuOSWej1MMf8HAsZZTnYlB1l8spImTyj4lM4cCsQt+IW8oEmAC1NdXrVcuLys7ccvR9Ej1qZP5AWBLGJsv2vTE0Lxhd5IWsf+7TomNgCcVPm/qPHPPT2oXfXTvKBJoCSzk4sm/8bkmy+UI8ZOoDhA/rKb3YBpKSkYmJiTExMLADpElnfdOHKNe4/9MatfFkAhv7ch749u6Kro0NySkqObbLyPjyCWfMX55ifs6NDloNN2VEmr4yUyTMiMoqd+w5R2b2C/CYegLa2Fv17d+fcpSsc8vqHfr26KR2rqkR/eB9EXyUIBZOJibH87zi/u//QW1ajunWSDzQBuDo7sWz+7Gxr1OhhAxk+sF+GvjwFUxMTomNigP/ruy8r9t3DBvSlX69uijXqM22y8i48nFnz/8gxPxcnhywHm7KjTF4ZKZOnrEYdpLK7W6Ya9XPvHpy7eIWDXsfo36u70rGqUnRMLDa2xVUdhvANMjExIeXFG1WHkWdCXzzkrZ83FZp2kw80AVgWc6bZiPlZzuYBqNZpGFXaDZQPNAGkp6Wga2hCcpysL/rYP7+8f5nQFw+xcS4PQJV2A3Bv0QtNbR3SUlNybJOVhKhwLmyan2N+FrZOWQ425aRev6m5HmhKjI3k0Zl9FHF1lw80AWhoalOxVW8C713k6UUv3Fv2ynU8+VFyXDRGxsrfIxIE4esQg02CkAV7e9mUZb+wGCrZZ579UxAEvpN9YS9VJHOx7V8v+5uczjbGRMYns+b0E24HvCcoPB7/sFhik1KxMdEDQE9bk/EtyzHP8wGN5v2Ds40xP7hY07BsUeqXLoyGuppSbbLiZG3My+VdcsxPS0NdmbchV3llpEwOvm9jkEohPjmVgRsuKxz/cRAr8H3BuHmSFd+3stgdHDI/ISsUHB/7tBd+/lSrXFHF0eTM1z8QgLKlM38RG/pzn2yPK+nsRHhEJEs91nP91h0CXwXh6x9ATGwcRWysAdDX02P6L2OZMWchVeo3x9XFiXq1a9K8UQOaNKiLhoaGUm2yvr4jMa9znpmprZW7PeuUySsjZXJ47uuHVColLj6e7v2HKhwfEyv72/cLfJmrWFXlua8fIPoqQSio7O3tefHh7zi/8w0IBKBs6VKZXhs2oG+2x7l+6MuXeKzj+s07BAYF4esXQExsbIYaNY4ZcxZQuV5TSrk4y/ruxg0z1KjPt8nu+rEhOb/Hua1RyuSVkTI5fKxR8fHxdOs3WOH4mNg4APwDCkaNAlmdat6qjarDEL5B9vb2RBzyUnUYeSbidQAAhRxKZ3qtSrvsl860LOZMYkwE1/d4EPz4JlGhQUQE+5GcEIuRhQ0AWjp61P3pF85tmMOfA+thWdyFEm61careGMcqDVBT11CqTXbXn3w8JMf8NHLZxwLom1pSxDXrZf8/J/yVL0ilpCTGs39WP4XXkhNkn/UjQwJyfd78KjzYD0fHgvmQryB8S3J3t1YQvhP29vaYmRhzO+C9qkP5Yu9jkwEobKqfq+M8Tj2hwpRDLPnHm9R0KXVcbVjRuzpVPyxH99HoZmW4MasNY1uURV9bky2XfOm5+jx1fvMiLCZJ6TYZqamBrpZGjj+5Xd5Q2bwyyimHyHjZ+6yjqYGmhrrCj5mhDh2qlqBk4YL7pP3dwPeYmRhTvLh4ErMgs7e3x8zMjOu37qg6FKW8Dw8HoEhhm1wdt3jlWkqUrcKcP5aRmppKw3q12eCxlJrVqii0mzx2BM9uX2bq+FHo6+mxftN2fuzWh/I1GxIa9k7pNhmpqamhp6ub409ulzJUNq+McsohPCISAB0dHbS0NBV+LMzN6N6pHaVLKj8DS5Vu3rmHmZmZ6KsEoYCqWLES1+/cU3UYSnn3XlajiuayRv2xcg3Fy1RizqKlpKal0qhubTZm0ZdPGTeSZ3euMm3CaPT09Vi3aRttuvamfI36hIaFKd0mo69Vo5TNK6OccvhYo7R1tNHS0lL4kdWo9rmaJaxKwSFveB3yBnf33N8sFoScVKpUici3r4l5l/NAR0GQECW7B2NkWThXx13btZJlncpwcesi0tPSsK9UlzaTPLArW02h3Q89xzF8xx1q956Alo4+dzw3sWtyV9b2qUFcRJjSbTJRU0NTRzfHn+wGqz5HU0v51XYSY6M+/Tsm4sPxOmhoain86BubU65Rpy+aZZVfhT69QyV3N1WHIQjfPTGzSRCyoKamRtNmzTn54CKDGhTM4lvMwgCAO4HhtK2sePNtz40AJFIpXasrPgEeHpfM74fuY2Gkw/VfW2Oo++mpm2XHH8v/nZomISE1DTsLAya2Ks/EVuUJi0li2XFvNpx/zl/nnzGhRbkc20xpUyFT3GExSSw59ijH/LrVdKRCMXOl3gtl88pImTw7VikBgEMhI1b3qalwfLpESlxyKvpaBberPfHoDc2at0BNrWDuXSbIqKmp0bRpU7xOnC4Qm2l/3HT85p17dGmv+BTwtt37kEqk9O7WSeH3796HM+W3eVhZmPPk9iWMDA3lr81bvEL+75SUVBISEylezJZfJ4/n18njCQ17x7zFK1j912Y81m9i+i9jcmwze9ovmeIODXvHnD+W5Zhf3x5dqVihXI7tcpNXRsrk2b1TOwCcHezZslbxXOnp6cTGxaOvl/XMz/zmyPFTNGvWTPRVglBANW3alA0bNvD23TusrT7/IJCqlShmB3ysUT8qvLZt1z4kEgk/de+s8Pt378OZMmsuVhYWPL1zWaEvn7skc40qUcyWXydP4NfJEwgNC2Pe4hV4/LnpQ40am2Ob2dMmZoo7NCyMOYuUrFFu5ZV6L5TNKyNl8uz2fzVq67qVCscXuBr1z0kMDAyoXbu2qkMRvkE//PADevoGvLh6nEo/9sv5gHzO1KYYAK+f3KFMg/YKrz08uQupREKFZorLZyZEvefM+lnom1owfPsdtPU/9UWXt3/auzQ9LYXUpERMbYpRr+9k6vWdTFxEGJe3L+bWwT+5dWA9dfr8kmOb+j9n3osuLiKMS1sX5ZifW4seFHbJgwERNTWk6ZmXFAwP+rTKgmmREgCY2zrQduo6hXZSSTrJCXFo6RaMfjQncRFhvPK5TbPfJqk6FEH47omZTYKQjW7du3PlWSgB7wrmEmhuxS3Q1dLg8rNQhd8/fxPNyK3XuPY88xM5QeHxSKRSWrrZKQzIhEQm4B0cKf/vS8/f4jJuHwdvf1q6opCxLsMayZYTiU5IUapNVqITUth+1S/Hn8B3cUq/F8rmlZEyOdhbGWFhqMM5nzekZviwt+LEY1zG7ePuy3ClY81P/MNiufo8lG7dC8Za+MLndevWjQuXr+H3Yfmf/KxyRTf0dHU5d+mKwu+fPHtB/2FjuXDlWqZjXgW/RiKR0LZ1c4WbXUGvQ3jg7SP/73OXrmDlUIZd+w/Lf2dTyIpxI2RL9ERGRynVJivR0dFs3LYzx5/c/G+gbF4ZKZODo30JrCwtOHn2PKmpaQrHL1jmgZVDGW7dva90rKri6x/AxSvX6dYt/+8tJQhC1po3b46xsTGbtu9SdSg5qlyxAnq6upy9qLh8ss+z5/QbNpqLV7OoUUHBSCQS2mXVlz/69ODTuUuXsbQvxa79h+S/sylUiHEjhgAQGRWlVJusREXHsGHb3zn+5Gb5VGXzykiZHJzs7T/UqAuZatT8pauwtC/FrbsFYzbchm076dChAzrZ7KclCP+Grq4uHTq058E/21QdSp4o4loRTR1dAu9eVPj9u5fPODx/GC8fXM10TPTbIKRSCa51WisMNMWEvSbU99ODrIF3L7GotT3eZ/bLf2doXogaXUcAkBgXpVSbrCTFRXPv6LYcfyJDAnP/pmTB1KYYUaFBSNI+7T/9LvCpfBlCAPOi9uibWuJ386xCO4DLO5ayqLU9IU/u5kk8qvbgnx2YmJjSrFkzVYciCN+9gvu4vSB8ZS1btsTRvgSLjnqzuk8NVYeTa1bGugxq4MryE4+ZsPMmPWo68Tw0mjWnn6Cprs5PdTJvLulkbYSBjiaH7rykYZkiOFkbc9P/HQs8H2Kkq0V8chq+b2Oo6mCJpZEui489ooipHmXtzAl4FyufJdSobFGl2mTF2caY4BVd8/S9UDYvJ2tjheOUyUFLU51pbd0Ys/0GwzZfZXiT0hjpanH8QTBL/vGmbikbqjrk7yd0s/PHMW8c7UvQokULVYci5IGWLVvi6OjArPmLMz0hnN9YW1kycvDPLFi2iqHjJtO/Vzd8nj1nqcd6NDU1GNQ38ya2Lk4OGBoYsPfgEZo1rI+rixNXb9xm5txFGBsZEhcfz3NfP2pWq0whS0t+X7QM26KFcStXFr+AQPksoRaNGyrVJislnZ2If+Ofp++Fsnm5OCmuT65MDtraWsyZPomBoybw0+CRTBg1BGMjIzyPnWTu4uU0qlebmtUq52k+X8NvC5bg6Ogg+ipBKMD09PQYOHAgy9f8yaC+vTEzzb8bfFtbWTFqyADmL13J0LET6d+rOz7PXrDEY+2HGtU70zEuzo4YGhiw56AnzRo1oKSzE1dv3GLm3IUf+vIEnvn6UbNaFQpZWfL7oqXYFimCW/my+PkHyGcJtWjSSKk2WXF1diIhNDBP3wtl8yqZqUblnIO2thZzZkxm4Mjx9B40nF9GDcPIyAjPf04wd/EyGtWrk+NSffnBIa9/ePDIm/V/bVB1KMI3bOSIEeyoVo2nl7xwrd1K1eH8KwZmVlTrOIQrO5ZydMlYKrbsxbuXz7i+2wN1DU0qtcm8N56FnTPaegb4nD2IU9VGWBZzJsj7Buc3zkXHwIiUxHjCg3yxK1sNA1MrLm5dhLFVEWycyxPx2l8++8m5ehOl2mTFspgzU06FZvmaskysZTNn73ptwa1Zj8/u01S0VCVeXD/J4fnDqNiqNxGvA7i6czm6BsYkRMsedtXQ1KbBgBl4LRrJwTmDqNl9FDr6Rjy/8g+Xti3GoXK9TMsMFkRJsVHc2r+GoQMHoFdAZrwKwrdMTSqVSlUdhCDkV56enrRt25aDYxpSw6mQqsPJtXSJlPlHHuBx6gnpEtmfurWJHr93qkSbirLp6e2WnibwXRz35rYFwPPuK0Zvu058suwJQlMDbWZ3rIS+tiYjtlwjJS2d16u6cfFpKCO2XCM0OlF+PR0tDcY1L8uoZmUAlGrzNfx91Y8x22/gOa4x1T7syaRsXhnfD2Vz+Ov8c347eI/k1HQANNXV6FHLiSltKmBqoPway/nFLf/3tF58isOHD9O6dWtVhyPkkY992hnPvdSumb+/WKSnpzNj7iIWr1xLerrs76qwdSGWzJtFxx9lX6IbtumEf8BLAh7dBGDfYS8GjBhPXHw8AOZmpvwxZyYG+vr0GzqG5JQUEt8GcObCJfoNHUNI6Fv59XR1dJg6YRSTxsieWlSmzdewecduBowcz/mjB6hVvUqu8sr4fiibg8efm5g0cw5JybI96DQ1NenXqxuzp/6CuVn+3nPu2s3b1G3RXvRVgvANiI2NpWTJknT6sSVL5s5SdTiflZ6ezow5C/lj5RqFGrV0/mx5jWrQuiP+AYEEet8GZH35z8PHKvTli+fMwsBAj75DRpOckkJS2EvOXLhE3yGjsui7RzN57EgApdp8DZt27GLAiHFcOHaQWtWr5iqvjO+HsjmsWr+RSTN/V6hR/Xt1Y/a0Sfm+RiUnp+D2Q0Oq16zF1q1bVR2O8I3r2asXx89dZuDGa2hqF+xZdFJJOuc2zOHqrpVIJbI+1tDCmqbD51O6nmz50q2jWxMZEsCoPd4A+Jw/zJGFw0lJlPVFekZmNBk+By1dAw7PG0J6agpTT4cRcOcCh+cNITb808CQprYOtXtP4IceYwGUavM1JMZEsHNyV1773Ka42w/0XuqZKc+PUpMT2TfzJ3xvnAZke1yVb9IFgCt/L2PKqVA0NGX3IW4dWM/pdTNJS5H1o+oamri37EX9n6ehZ2T21fL5r5xYORn/SwfxffEcE5P8+7CKIHwn9orBJkHIQbOmTXj99B7HxjVERyv3mznmBwkpafgER2Gkp4WDlRFamp9fQTMyPplHQZFYm+jhYmPCxy0wIuOTifqwdBxAYkoaPq+jCI5IwMJQB9ciJlga6SqcS5k2/xVl88pI2RziklJ5FBxJfFIapYuaUsRM/2um89Ukp6bTYvEZirq6c/zESVWHI+SxZs2aERoSzOUTh9EtAMu5xCck8OjxE4yNjHBysEdbW+uz7cMjIrn/yJvC1taUKuks38MnPCKSqOhoHO1LAJCQmMijx094FfwaSwtzypQqSSFLS4VzKdPmv6JsXhkpm0NsXBz3Hz4mLj6esqVdsSta5CtmkzeSkpP5oemP2BSx5fjx46oORxCEPLBhwwYGDx7MtVNeuCu5t50qfapRhjg5OOSiRhWiVEkXhb48MioaJ4cSQIa+2/xD3231mRqVTZv/irJ5ZaRsDrIa5U1cfEKBqVEAM+cuZPnaDTx79owiRQpGzELBFRISgrNLSSq1H0y9flNUHU6eSE1K4K3fY3QMjDC3dZAPnmQnMSaC0BePMLSwxqp4ST5+4U+MiSAxNgrzorI9q1OTEwnze0x0WDD6JuZY2ZfCwFRxNRJl2nwtseGh6OgZKiwJmJ2EqPfEvH+DjWNZeb5ZSUmII9T3ISmJ8RSyL41xoaxXmilo3rx4wKYhjVm3bi39+/dXdTiCIIjBJkHIma+vL1UrV6K+iwWr+9T4XP0WhAJPKoUhm69y/nkEN2/fwcnJSdUhCXnM19eXqlWr0rRBXbauWyG/ISQIBYlUKqX3oJGcOHuBmzdvir5KEL4REomEpk2b8tTHh6unvShiY63qkAThi+z3PEq3foPx8PBg8ODBqg5H+E6sXbuWocOG0WHGRkrVbaPqcAThq4oND2Xz0EZULFeaUydPoK7++YeqBUH4T+wVf4mCkAMnJyf27j/AkXtB/HHsUc4HCEIB9sexR3jdC2bv/gPi5u03ysnJib1797Lf8yizFy5VdTiC8EVmL1zKfs+j7N27V/RVgvANUVdXZ+/evRgaG9OuR1/iExJUHZIg5Nrte/fpO3QUw4YNEwNNwn9q8ODBDB82DM/5Qwl5ek/V4QjCV5OalMC+aT2wMjNm/769YqBJEPIR8dcoCEpo2LAhHqtXs/iYNzP335PvfyQI34p0iZSZ+++x+Jg3HqtX07BhQ1WHJHxFDRs2xMPDg98XLWPC9N/k+00IQn6Xnp7OhOm/8fuiZXh4eIi+ShC+Qaampnh5efEqOIQGrTsq7OkjCPnd8dNnadK2K/Xq1WfpUvFQj/DfW7p0KQ3q1+Pv8W3l+/kIwrckNjyU7WNak/g+mH+OemFqmr/37xOE740YbBIEJQ0YMIAdO3aw+bIffddfJjYpVdUhCUKeiE1Kpe/6y2y+7MeOHTsYMGCAqkMS/gMf+7S1G7fRsfcAYmLjVB2SIHxWTGwcHXsPYO3GbaKvEoRvnKOjI9euXSM+MZmajVpx74FYXUDI36RSKSvXbeDHbn1o36EDBw8eREOjYO73KxRsGhoaHD50kM6dOrB7ajduHlgnWytdEL4Bb148YPPQRuhJE7lx/RqOjo6qDkkQhAzEnk2CkEvXrl2j3Y9tIC2Jqa3L0rmag9jHSSiQpFLYc8OfOUe8QVOXg4c9qVGjhqrDEv5j165do127dqirwe/TJ9KrS0exj5OQr0ilUrbt3se02QuQSOHgwYOirxKE70RUVBSdO3fm3LlzDOn/EzMmjsPM1ETVYQmCggePHjN68gyuXL/JnDlzmDRpkqpDEgQA5s+fz9SpUylerjqNhs/DxqmcqkMShC+SFBvFhc0LuHN4A/Xq12ff3j1iRpMg5E9izyZByK0aNWrg8/QZHbr1ZsyOm7RYfJrDd16RmiZRdWiCoJTUNAmH77yixeLTjNlxkw7deuPz9Jm4efudqlGjBj4+PrRr34GBIydQq0kb9h46QkqKmL0pqFZKSip7Dx2hVpM2DBw5gXbtO+Dj4yP6KkH4jpiamnL8+HHWrl3L7oNHKF21NvOXriQ0LEzVoQkCd+49oP/wMVRt0Jx01Ll586YYaBLylUmTJnHz5k2s9WHD4AYcWTCcN8/EXk5CwREXEcaVHUtZ+1NV/C8dZN26tZw8cVwMNAlCPiZmNgnCv/Dw4UNmZ0hVCgAAIABJREFUTJ+G19Gj6Glr8YNLIcrZmlLETB9DXS1VhycIcnFJqYREJvAoOIrLz8NITEmlVcuW/Db7d8qXL6/q8IR84uHDh8yYMQMvLy/09fSoX7sWbuXLYFukMEZGhqoOT/gOxMbGERzyhvsPH3Pu0hUSEhNp1aoVv/32m+irBOE7Fx0dzbx58/jzzz+JioqiSkV3alSpiJOjA2amJmLJMuGrS0xM4n1EBI+fPOX8pasEvgqibNkyTJw4iR49eoiZ4UK+JZVK2bFjB3Pmzeepz2MsChfDzq02hexLo2dijqa2rqpDFAQApJJ0EmMjiXjtzxufWwQ9uYuJiSmDBg5g8uTJmJiI2c2CkM/tFYNNgpAHgoOD8fT05OyZMzy4f4+wsDBi4uJVHZYgyBkZGlDIygo394o0aNiQH3/8kaJFi6o6LCGfkvdpZ8/y8OED3r4NIyYmRtVhCd8BIyMjrK0LUaGCGw0aNBB9lSAImSQmJnL8+HFOnDjBnTu3CQgIICoqmvT0dFWHJnzjdHV1MTMzpUyZMlSvXoPWrVtTtWpVVYclCLly8+ZNjhw5wtVr1/F+/JjoyEiSk5NUHZYgAKCmpoaOri6urqWpWqUSzZo1o3nz5ujqigFRQSggxGCTIPwXPD096d+/P2ZmZnh5eeHi4qLqkAqEzp07A7Bnzx4VRyIIQn41ZMgQ9u7di7e3NzY2NqoOp0CYPn06S5Ys4c6dO7i6uqo6HEEQhG9CcnIyP/zwA6mpqdy4cQMdHR1Vh1Tg3blzh1q1ajFlyhRmzJih6nAEQRCypaamxu7du+X3MIQvs2nTJoYPH46LiwubN2+mQoUKqg5JEITcEXs2CcLXlJSUxKhRo2jbti3Nmzfn7t27YqBJEAQhj5w6dYp169bh4eEhBppyYebMmZQvX54ePXqQkpKi6nAEQRC+CaNGjeLZs2f/Y++uw6LK/jiOv0k7sLtjFRFz7VbUtVEExcAO7G4x11Z2114VUFERuxXE7kJQxMLEBEQUiYH5/cFPdlmxmbkDfF/Pc5/Hnblzz2dYvtyZe+45h82bN0tHUxKpXLkyCxcuZNq0aRw6dEjpOEIIITSsR48e+Pr6YmJiQpUqVRg3bhyRkZFKxxJCfAfpbBJCQ27cuEG1atVwdnZmw4YNuLi4kDGjrHkihBBJ4c2bN/Tq1QsbGxusra2VjpOsGBoasmHDBu7cucPkyZOVjiOEEMnepk2bWLVqFevWrZMRo0nM3t4eW1tbOnfuzMOHD5WOI4QQQsOKFi2Kp6cnS5cuZenSpVSpUoVLly4pHUsI8Y2ks0mIJKZWq1m1ahVVq1Ylffr0XLlyhc6dOysdSwghUpSBAweiUqn466+/lI6SLBUvXpzFixezYMECPD09lY4jhBDJlr+/P/369WPEiBG0b99e6Tgp0vLly8mTJw/W1tYyIlcIIVIBPT09+vbty/Xr18mVKxc1atSQUU5CJBPS2SREEnr58iWtWrXC3t6eMWPGcOrUKYoVK6Z0LCGESFF27tzJ5s2b+fvvv8mWLZvScZKtXr16YWVlRffu3QkKClI6jhBCJDvv3r3D0tISU1NTZs+erXScFCtDhgzs2LEDPz8/xo4dq3QcIYQQWlK0aFE8PDxYunQpy5Yto3Llyly8eFHpWEKIL5DOJiGSyOHDh6lQoQI3btzg2LFjODg4YGBgoHQsIYRIUV6+fEm/fv3o27cvv/32m9Jxkr0VK1ZgYGBAnz59lI4ihBDJzoABA3j9+jXu7u4YGxsrHSdFK1WqFKtWrcLR0ZGtW7cqHUcIIYSW/HuUU548eahZs6aMchJCh0lnkxA/KSIigqFDh9KsWTNq167N1atXqVWrltKxhBAiRerfvz8ZMmRg/vz5SkdJEbJmzcr69evZvXs3a9asUTqOEEIkG3/88Qeurq5s2LCB/PnzKx0nVbC2tmbgwIH07NkTPz8/peMIIYTQoiJFinDkyJEEo5wuXLigdCwhxH9IZ5MQP+HmzZtUr14dJycnXFxccHNzI2vWrErHEkKIFGnNmjXs2rWLdevWkSlTJqXjpBh169Zl5MiRDB06FH9/f6XjCCGEzrtw4QKjR49m+vTpNGnSROk4qcrixYspX748HTt2JDw8XOk4QgghtOjjKCcfHx/y5s1LrVq1GDduHBEREUpHE0L8n3Q2CfED1Go1q1atomrVqqRLl44rV67QpUsXpWMJIUSK9eDBA0aMGMHIkSOpV6+e0nFSnJkzZ2Jqaoqtra0svi6EEF8QHByMtbU1jRs3Zvz48UrHSXWMjIzYvHkzz58/lylghRAilSpcuDCHDx9m6dKlLF++HDMzM06cOKF0LCEE0tkkxHd79eoVrVu3xt7ensGDB3PixAmKFy+udCwhhEixYmNj6dGjBwUKFGD69OlKx0mRjIyM2LhxI/7+/kybNk3pOEIIoZNiY2OxtbUlNjYWZ2dn9PXl67QSChYsyKZNm9iyZQurV69WOo4QQggFfBzl5OfnR5kyZWjQoAH9+vXj/fv3SkcTIlWTT8dCfIcjR45gbm6Oj48PXl5ezJkzByMjI6VjCSFEiubo6MipU6dwdnYmbdq0SsdJsUqUKMHChQuZM2cOR48eVTqOEELonOnTp+Pp6cnmzZvJkSOH0nFStY8jywYPHszly5eVjiOEEEIh+fLlY/fu3WzevBl3d3fMzc05fvy40rGESLWks0mIbxAREcG4ceNo1qwZtWvX5urVq9SuXVvpWEIIkeLdunWLiRMnMnXqVKpUqaJ0nBSvb9++tG/fnu7duxMcHKx0HCGE0BlHjx5l5syZODo6UqNGDaXjCGDatGnUq1eP9u3bExQUpHQcIYQQCrKyssLX1xdTU1MaNmwoo5yEUIh0NgnxFX5+ftSoUYPly5fj5OSEm5sbJiYmSscSQogUT6VS0b17d8qWLcvYsWOVjpNqrFy5Mn5aCiGEEPDkyRNsbGywsrJiwIABSscR/6evr8/69etRqVTY2dmhVquVjiSEEEJBefPmZdeuXfGjnMqXL8+xY8eUjiVEqiKdTUJ8gYuLC1WqVMHY2JgrV67QtWtXpSMJIUSqMXPmTK5fv46zs7NMWapFJiYmrFmzhh07duDs7Kx0HCGEUFR0dDQ2NjZky5aNVatWKR1H/EeuXLlwd3fn8OHDzJs3T+k4QgghdICVlRU3btzAzMwsfpTTu3fvlI4lRKognU1CJOLVq1e0bt2anj17MnjwYE6dOkXx4sWVjiWEEKnG1atXmT17NnPnzsXU1FTpOKlOkyZNGD58OPb29ty+fVvpOEIIoZhRo0bh7e3N9u3byZQpk9JxRCKqV6/O77//zsSJE/Hw8FA6jhBCCB2QJ08edu7cyZYtW9i2bRvm5uZ4eXkpHUuIFE86m4T4Dw8PDypUqIC3tzdeXl7MmTNH7qgXQggtioyMpFu3btSpU4dBgwYpHSfVmjVrFiVLlsTW1pbo6Gil4wghhNa5ubnxxx9/sGzZMsqWLat0HPEFw4cPp127dnTp0oXAwECl4wghhNARH9dyKl++PI0aNZJRTkJomHQ2CfF/kZGRjBs3jqZNm1KzZk2uXbtGnTp1lI4lhBCpzoQJE3j06BFr165FX18+qiglTZo0uLq6cvPmTWbMmKF0HCGE0Ko7d+7Qp08fBg0aJFNpJwN6enqsXbsWExMTOnfujEqlUjqSEEIIHZEnTx527NgRP8qpfPnyHD16VOlYQqRIcgVHCMDPz48aNWqwbNkyli9fztatWzExMVE6lhBCpDqnT5/G0dERR0dHChcurHScVK9MmTLMnz+fWbNmyeK6QohU48OHD3Ts2JFffvmFBQsWKB1HfKNMmTLh5ubGxYsXmTRpktJxhBBC6JiPazlVrFiRxo0byygnITRAOptEqufi4kLVqlUxMjLiypUr9O3bV+lIQgiRKr1//x47OztatGiBnZ2d0nHE/w0cOJCWLVvSrVs3QkJClI4jhBAaN2DAAB49esTmzZtJkyaN0nHEdzAzM2PVqlXMmzePHTt2KB1HCCGEjsmdOzfbtm1jy5YtbN++HTMzMzw9PZWOJUSKIZ1NItV6/fo1bdq0oUePHvTq1YtTp05RokQJpWMJIUSqNWzYMN68ecPKlSuVjiL+4++//0alUtGvXz+lowghhEatXLmS9evXs2HDBooWLap0HPEDbG1t6dWrFz179uTevXtKxxFCCKGDPq7lVLlyZZo0aUK/fv0ICwtTOpYQyZ50NolUydPTkwoVKnDt2jW8vLxwdHTEyMhI6VhCCJFqHT58mDVr1rBs2TLy5MmjdBzxHzlz5sTJyQl3d3c2bNigdBwhhNAIb29vhg8fzqRJk2jevLnSccRP+PPPPylWrBiWlpZ8+PBB6ThCCCF0UO7cuXF3d08wysnDw0PpWEIka9LZJFKV6OhoHBwcsLCwoHr16ly9epW6desqHUsIIVK1oKAg7Ozs6NKlC1ZWVkrHEZ9hYWHBkCFDGDBgAHfv3lU6jhBCJKk3b95gaWlJjRo1mDJlitJxxE9KmzYt27dv58mTJwwbNkzpOEIIIXTYx7WcqlatioWFhYxyEuInSGeTSDVu3bpF9erVWbRoEcuXL8fd3Z1s2bIpHUsIIVI9e3t79PX1cXR0VDqK+Iq5c+dSvHhx7OzsUKlUSscRQogkoVar6dmzJ+Hh4WzcuBEDAwOlI4kkULhwYZydnVm9ejVOTk5KxxFCCKHDcuXKxdatW9myZQs7duzAzMyMI0eOKB1LiGRHOptEquDi4kKVKlUwMDDg8uXL9O3bV+lIQgghgE2bNuHm5sbq1asxMTFROo74ijRp0uDq6sqVK1eYNWuW0nGEECJJzJkzh71797J161aZyjWFadmyJaNGjWLAgAFcvXpV6ThCCCF03MdRTnXr1sXCwoKOHTsSEhKidCwhkg3pbBIp2ps3b7CxscHOzo5evXpx6tQpSpYsqXQsIYQQQGBgIIMGDWLgwIGyNkYyUrZsWebMmcPMmTM5e/as0nGEEOKnHDt2jClTpjBv3jxq166tdByhAbNnz+bXX3/F2tqa0NBQpeMIIYTQcTlz5sTFxYU9e/Zw+vRpTE1N2b17t9KxhEgWpLNJpFhHjx6lXLlynDlzBi8vLxwdHTE2NlY6lhBCCOKmLOrTpw9Zs2Zlzpw5SscR32nw4ME0a9YMW1tb3r59q3QcIYT4IS9evKBz5860aNGCoUOHKh1HaIihoSFubm68e/eO7t27o1arlY4khBAiGWjZsiW+vr60atWKNm3a0LFjR4KDg5WOJYROk84mkeKoVCocHBxo0qQJ1apV49q1a9SrV0/pWEIIIf5l1apVHDx4ECcnJzJmzKh0HPGd9PT0WLt2LR8+fGDw4MFKxxFCiO+mUqno2LEjGTNmxNnZGT09PaUjCQ3KnTs3rq6u7Nu3T9aIFEII8c1MTExYuXIle/fu5cyZM5QrV45du3YpHUsInSWdTSJF8ff3p1q1asybN49Fixaxbds2smXLpnQsIYQQ/xIQEMDo0aMZO3YsderUUTqO+EE5c+Zk3bp1rF+/nk2bNikdRwghvsuECRO4cOECW7ZsIUuWLErHEVpQv359pk+fzpgxYzh16pTScYQQQiQjLVq0iB/l1LZtWzp27EhQUJDSsYTQOdLZJFIMFxcXqlSpgr6+PteuXZOpMIQQQgfFxsbSo0cPChYsyJQpU5SOI35Ss2bNGDhwIAMGDODBgwdKxxFCiG+yd+9eFixYwPLly6lYsaLScYQWjRs3jhYtWtCpUydevXqldBwhhBDJSNasWVm5ciX79u2LH+W0c+dOpWMJoVOks0kke6GhoXTu3Bk7Ozt69uzJ6dOnKVWqlNKxhBBCJGLhwoWcPXsWV1dX0qZNq3QckQQWLFhA4cKF6dq1KzExMUrHEUKIL3r48CHdu3end+/e2NnZKR1HaNnHaWCNjY2xsbGR85YQQojv9ttvv+Hr60vr1q1p166djHIS4l+ks0kka15eXpQrVw4vLy8OHDiAo6MjxsbGSscSPyA8PJyQkJAEW1RUFFFRUZ88Hh4ernRcIcQXvHjxItG7hW/evMmUKVOYNm0a5ubmCiQTmpA2bVpcXV25dOkSc+bMSXSfx48fazmVECI1i4yMTPTxiIgI2rdvT4ECBWTdnlTMxMSE7du3c/bsWaZPn57oPp/7HRJCiDdv3nxyjQLg/fv3nzyuUqkUTis05eMop/3793P27FlMTU3ZsWOH0rGEUJx0NolkSaVS4eDgQJMmTahatSq+vr40bdpU6VjiJ2zYsIFs2bIl2Hbt2sWuXbs+eXzDhg1KxxVCfIG7uztly5ZNsHCqSqWie/fuVKxYkdGjRyuYTmiCqakps2fPxsHBgXPnzsU/HhkZyciRI6lRowZqtVrBhEKI1MTBwYFhw4YRFRWV4PHBgwdz7949tm/fTrp06RRKJ3SBubk5ixcvZubMmRw4cCDBcw8ePKB27dpcuXJFoXRCCF1mZWX1yTUKgJ49eyZ4LFeuXPEdUSLlat68Ob6+vrRp0wZLS0s6duzI69evlY4lhGKks0kkOwEBAdSrV4958+axcOFCtm/fTvbs2ZWOJX5S+/btMTQ0/Op+hoaGtG/fXguJhBA/6tChQwQFBdG2bVu6d+9OaGgo06ZN4+bNmzg5OWFgYKB0RKEBw4YNw8LCAltbW8LCwrh58yZVqlRhyZIlPH36FG9vb6UjCiFSCVdXVxwdHalVq1b8yMqNGzeyZs0a1q5dS/HixRVOKHRBv3796Nq1K126dCEgIACAffv2YW5uzqVLl3B3d1c4oRBCF3Xq1Omr++jr69OwYUNy5syphURCaVmyZGHlypUcOHCAc+fOUa5cObZt26Z0LCEUIZ1NQqfMnj0bPz+/zz7v4uJC+fLliYyM5OrVqwwdOlSL6YQmZc+eHQsLiy9ehDYwMMDCwkI6F4XQYSqViqNHj8aPYtm0aRMlSpRgzpw5zJ8/X9bUS8H09PRYt24d79+/p0WLFlSuXBl/f39iY2MxMjL65M5xIYTQhKtXr/Lo0SMAvL29MTMzY8WKFfTt25cxY8bQrl07hRMKXbJ8+XIKFSqEtbU1kyZNolWrVrx79w6I67QUQoj/6tChwzct39C1a1ctpBG6pFmzZvj4+NCmTRusrKy+Osrp6dOnODs7azGhEJonnU1CZ5w+fZrJkydjZWX1yRzZoaGh2NraYmdnR8+ePTlz5gylS5dWKKnQlC5duhAbG/vZ59VqNV26dNFiIiHE9zp//jzv37+P/+/o6GiCg4NRqVRcvXo1wXMi5dHT06NgwYKcPHmSiIgIoqOjgbhOyH9PqyiEEJqybds2jIyMgLhzUFhYGAMGDCB79uxMmzZN4XRC16RLl45ly5Zx5coVZs+ejVqtjv8+8vDhQxmVK4T4RObMmWnWrNkXZ2YxNDSkdevWWkwldMV/RzmZmpp+dqRsr1696NOnD5cvX9ZySiE0RzqbhE54+/YtNjY26Onp4e/vz/jx4+OfO3v2LBUrVsTT05N9+/bh6Oj4TXeRiOSnTZs2pEmT5rPPGxsb06pVKy0mEkJ8ryNHjnzyN/rjRRsnJyfKlCnDyZMnlYgmNMzDw4Ny5colemFOrVZz8eJFgoKCFEgmhEhNNm3aFN/RDf+cgwIDA2nSpAkvXrxQKprQQadOnaJt27bo6el9sragsbGxTIMkhEhUly5diImJSfS5jx1NmTNn1nIqoUuaNm2Kr68vbdu2pWPHjnTs2JFXr17FP+/k5MThw4eJiYmhXbt2vHnzRsG0QiQd6WwSOmHw4MG8ePGCmJgYVCoVS5YsYc+ePTg4OFCnTh3Mzc25ceMGzZs3Vzqq0KD06dPTpk2b+LtR/83Q0JC2bduSMWNGBZIJIb7V/v37P1mQ/SOVSkVgYCD169dn0qRJn1zUEclTRERE/HpNr1+/TnCR978OHz6sxWRCiNTGx8eH+/fvJ/pcTEwM586dw9zcnDNnzmg5mdA1arUaR0dH6tevT1BQECqV6pN9oqKi2LBhgwLphBC6rlWrVqRPnz7R52JiYrC1tdVyIqGLMmfOzMqVKzl48CDnz5+nXLlyuLi4EBgYyJAhQ4C4m2KeP3+Ora2tfD8WKYJ0NgnF7dixAxcXlwQXp/T09LCysmLevHksXbqUHTt2yDo9qYStrW2iFyrlA5sQui80NJQrV658cR99fX0yZMhApUqV0NPT01IyoUlPnz7Fy8sLPT29L06Fqq+vz759+7SYTAiR2vx7Cr3EREdH8/LlS+rXr8/x48e1mEzompEjRzJs2DBiYmI+OzoBICAgAF9fXy0mE0IkB2nTpsXS0jLRc06GDBnkRmmRgIWFBT4+PtjY2NCjRw8qVarEhw8f4juXoqOjOXjwIEuWLFE4qRA/TzqbhKICAwPp0aPHJxccY2NjiYmJoWLFivTt21ehdEIJzZo1S3S4ecaMGbGwsFAgkRDiW3l5eX2xs8HAwAAzMzOuXbuGpaWlFpMJTSpevDiXLl1i0qRJ6OvrY2BgkOh+KpWKvXv3fvF3RAghfoarq+sXR1caGhqSOXNmnJycqFevnhaTCV0zY8YMhgwZgp6e3hfXXTEyMpKp9IQQiercufMn5xwjIyM6duz4xeUBROqUOXNmHB0dGTduHC9evPhkRG1sbCyjR4/m9OnTCiUUImlIZ5NQjFqtpkePHoSHhyc6VFSlUnH27FmWL1+uQDqhFCMjI2xsbBKs+WJkZESnTp1krS4hdNyRI0cSvbtPX18fPT097O3tOXfuHMWKFVMgndAkIyMjpk2bxsmTJ8mbN+9nL9yFhoZy8eJFLacTQqQG/v7+3LlzJ9Hn9PXjvva2adOG27dv07lzZ21GEzooQ4YMODo6cvr0aYoUKfLZ81Z0dDSurq5aTieESA4aN25MtmzZEjwWHR0t5xjxWc+ePeOvv/764gwfHTp04PXr11pMJUTSks4moZglS5bg4eHxxbsP1Wo1w4YN4/r161pMJpTWqVOnBGu+yAc2IZKHffv2fbJek5GREVmzZmX//v04Ojp+cXojkfzVrFkTX19frKysAD75ImVsbMz+/fuViCaESOG2bNmS6Dnm43nI3d0dd3d3cuXKpUA6oatq1KiBj48PEydOxMDAINFOp9u3b+Pn56dAOiGELjM0NKRTp04Jzj3Zs2enfv36yoUSOq1///4Jps/7r5iYGIKCgujcubPMBiGSLelsEorw8fFh7Nix3/THU6VSYWtrS0REhBaSCV1Qt25dcufOHf/fOXPmpE6dOgomEkJ8zcOHD3n48GGCx/T19alVqxa+vr40a9ZMoWRC27JkyYKrqytubm5kzJgxwRfwqKgodu7cqWA6IURKtWnTpgQ3sX0czdS6dWtu375N+/btlYomdFzatGlxcHDg8uXLlClT5pPpYGUqPSHE53Tq1Cn+3GNsbEzXrl0/O6W0SN02btzI7t27v3jDPcTdbO3p6cm8efO0lEyIpCWdTULrIiMjsba2/mxPPhB/R1maNGlo2rQp9vb20tmUiujr69OlSxeMjY0xNjamW7du8RcMhBC66fDhw/FfrAwMDNDX12fy5Ml4enqSN29ehdMJJVhZWeHj40PVqlUTfOn28fHh2bNnCiYTQqQ0t2/f5tatW/H/bWRkRI4cOdi5cyfu7u5kz55dwXQiuTA3N+fKlSvMmjULIyOj+JslZCo9IcTn1KxZk3z58gFxN1VZW1srnEjoqnPnzpEjRw4g7prnlzolY2NjmThxIidOnNBWPCGSjFy9FVo3ceJEbt++nWAxPD09vfgP8/ny5aNnz564ubnx+vVrDhw4QP/+/cmaNatSkYUCPk6lFxUVJVPoCZEMHD58mNjYWAwNDcmTJw9nzpzBwcFBOopTucKFC3PixAkcHBzipydSq9UcPHhQ6WhCiBRk27Zt6OvrY2BggJ6eHr169eLu3bu0adNG6WgimTE0NGTs2LFcvnwZU1PT+IuBfn5+n10TTAiReunp6dGtWzcAChYsSLVq1RROJHTVn3/+yatXr7h37x5Lly6lU6dO8VP7frxZ89/09PSwtLSUm/REsqOn/tLwEiGS2NGjR2ncuDFqtRojIyNUKhV6enpUr16dtm3b0qJFC8qWLat0TKEjihYtCkBAQIDCSYQQXxIbG4uJiQlv377F0tKStWvXkiVLFqVjCR1z4cIFrK2tefDgAZaWljIlkRAiyZQvXx4fHx8KFSqEs7OzrJchkoRKpWLRokVMnjyZqKgoZs+ezfjx45WOJYTQMd7e3lSoUIGJEycyc+ZMpeOIZEStVuPr64uXlxdHjx7Fy8uLt2/fYmxsjEqlIjY2lvr16+Pp6Sk3cYrkYqt0NgmtCQkJoVy5cgQGBmJiYkKrVq1o2bIlFhYWclFSJMrBwQE9PT2mTp2qdBQhxBdcunSJunXrsmTJEvr27at0HKHD3r9/z9ChQ3F3d+fVq1cJ1nMSQogfce/ePUqXLs3QoUOZMWMG6dOnVzqSSGFu375N9+7diYqK4vLly0rHEULoIDMzMzZv3oypqanSUUQyFhsby7Vr1/Dy8sLT05Pjx48THh7O1KlTcXBwUDqeEN8i6TqbIiMjuXHjBi9fviQsLCwpDilSmN27dxMREUGlSpUoXrw4enp6ie6XKVMmcufOTdmyZUmTJo2WUyY9qY0fFxgYCBA/B7L4NlJDQtsuXrxInjx5KFiwoNJRvkhqQ3ckl98ZkVBKqyG1Wk1AQAABAQGEhIR8cT1RobsuX75M5syZKVmypNJRvipNmjSYmJhgampKtmzZlI7z01JTDX2cArZmzZpyo2QqlNJqFyA4OJgbN24QEhJCZGSk0nGSvcuXL1O5cmWlYyR7+vr6ZM2alaJFi1K0aNHPXjdMbn603mJiYrh//z5+fn7Uq1dPzj8iSWmo3rai/gnBwcHqJUuWqOvVqa02NDBQA7LJlmSboYEiBVRTAAAgAElEQVSBul6d2uolS5aog4ODf+ZXVevia6NuHbWhodSGbMpshoYG6np16yTvGqpTW2pItiTfDA1TwPmlXj21oaGh4j9L2VLnZmhoqK5Xr16yrCGVSqXetWuX2sbGRm2SNaviP0vZUu9WqmQJ9ciRI9W+vr5Kl8V3+XcNZTUxUfznKJts2t5KliyVLGtXrVarfX191SNGjFCXKFlK8Z+jbLJ9bcuS1URtbWOj3r17t1qlUildPt/tY70VKyH1Jpvub5mTrt7cfmhkU3h4OPPmzWP+vLnoE0tz09w0KJ2T8gWykCdLWjKmMfzeQwoR712kiuehEVx/EoqX/ysO3HhBLPqMHjOWMWPG6PTUGAlqQw9+q1KCRuZFMC+am7wmmciYzljpiCIVePchimchYXgHvMDT+wH7L90lVk3yrKFKRWlkVojyRXKRN1sGMqaVGhI/7l1EFM+C33P9wUs8fR6x/0pA8quN+fMxMDCgdZu2WDRtSsWKlciXPz+ZMmVSOqJIBcLCwgh8+pSrV69w+NAhdu/aSUxMDKNHj9b5GoK4UfYjRwzn3v0A6lYtT/M6Valm/gvFC+bFJHMm9PVTxt2zQndFRkUT9OYtN+4+4PhFH3YfPce9R09p3aoVCxctokSJEkpH/KLdu3czYsRI7t+/R+WadandpAXlK1enQJHiZM5qIutJiBQrKiqS0OAg7vr5cunMcY4f2MmjgHu0at2aRQsX6nzt3r17lxEjRrJnz25yFypG+fqtKV2lDvlKlCVjluwYGif/0coiZVDHxvL+bQivHt8nwOcCvqcO4n/pJEWLFWfxooW0bt1a6YhfdffuXYaPGMnePbvJlr8YBau3IK9ZbbIVKUPazNkxMJJrGkI3qNWxRIa94e2z+7y4dYknFw/x9PppihQtzpLFP1xv3z+N3o4dOxg2ZDAhwa8Z3rA43WoWls4loVHvIlW4nHnIYs97mGTPzpI//qJdu3ZKx/pEXG0MISQkiJFtq9GjcQXpXBI64d2HKNZ5XGPhjnOYZMvOkj/+1OEaGkxIcBAjW1fBrlE56VwSGvUuIgonT18W7r6k+7UxbBhv3rxh/ISJ9OnXXzqXhE4ICwtj9coV/D57FlmzZmXJkiU6WUN3797FfuBAjnh4YNWsHpMGdKZ4wbxKxxICtVrNkTNXmOjoxL1HgQwfPoKpU6eSNm1apaMlcPfuXQba2+Nx5AhN21rTZ9RkChYprnQsIRSjVqs563WYv2ZN4FHAXUYMH66TtRsREcG0adNYtHgxuQoWp83g6ZSt0TjFTE0mUodXj++zb9VsLh5yp1HjJixftlQnO3jj623RYrLkL0bl7lMpWKkhSL2JZOTtswCuuM7l7vHtP1pv397ZpFarmThxInPmzMG6akEm/vYLOTPJ3Q9Ce16FRTJr/y22XHzMuHHjmDVrlk58SPp3bXSqZ8aUTnXImSWD0rGE+MSr0PdM33SSTcd9dLeG6pRlcsca5Myi23fHi5TlVWg4M9zOsunkTZ2tjW7d7Zg5aza5cudWOpYQn3j54gWTJk7AxdlJp2oIwNPTE6sOHSiYJzsLx/SlZsWySkcS4hOqmBj+3nqAGcs3UqZsOXbu2kWuXLmUjgXE1VAHKyty5y/EyBmLqfBrTaUjCaEzYlQqtq1fzar50yhbtgy7du7Umdp9+fIlrdu0xffmTVr0m0Td9j3RN5AbxUXyde/aWbYuGEPoi8dsc99Ko0aNlI4U72O9Xfe9SUXbcZRpbif1JpK15zfPc37VeCKDnnxvvX1bZ9OHDx/o2sWWPbt3s6CDGR2rymLOQjluFx8zyt2HVq1bs37DRtKlS6dYlrja6MKePbtZ3NsCm3rlFMsixLfafNyX4X8fplWrVjpVQ4t6NMCmThnFsgix+aQfI9Z56U5tdO3Knj17WLZiJV27dVcsixDfar2LMwP794urofXrFa0hgNWrV2NvP5B2jWux3GEIaY1ltKzQbbcfPKHD0JnE6Bmwd99+TE1NFc0TV0P2NGxhyeRFKzFOo1ujNoTQFQ/u+jPKzhK92Bj27dureO3euHGD31q0JFKtT7+FW8hTpJSieYRIKtFREWyYbs/Vo7tYtnQpffr0UToSN27coHmLlrxX6dNo0gayFiipdCQhkkRMVCQn/xhCwOk9LFv2zfX29c6m2NhYOlp14OjhA6zrXpnqxbIlTWIhfsK5+8H0cL5MQ4vmuG11V2R+8LjasOLokUOsH9mGGr8U0HoGIX7U2VtP6LpwFw2bNMVt61YFa6hDXA0N/Y3qpfNpPYMQ/3XOP5Cujvv/XxsKnl86dsTLy4ut23dQu3YdrWcQ4kedOnUSK8t2NGjQADc3N8XWcNm0aRO2traM72vDhH6ddGaklRBfExwahvXwWQQ8C+L8hQsULKjMjZ4fa6j38In0HjFRakiIrwgNCWZ0zw48fxzAhfPnFavdx48fU/XXamTOU4S+C1zJkEWu4YmURa1Ws3/1HPb/PZeNGzfSqVMnxbI8fvyYKr9WwzB7YRpNdCZtJqk3kcKo1VzeNJ+rmxd8a71tNXBwcHD40h4TJ05k3bq1uPSoQs3i2ZMsqxA/o4BJOqoWMWGW61EiIqMUGT77sTZcR7ejVlkZ7SeSl4I5MvNrqXzM+Hu74jW0cXhLapbJr/X2hUhMgRyZ+LVkHmau3a1wbaxjx67d1K1bT+vtC/EzChUqTI2aNZk6eTIRERGK1NClS5ewbNeOATYtmTa4m1wkF8lKurRpsGxcC7cDx3Bz30aXLl0x1vKovEuXLtHO0pKOPe0ZOH661JAQ3yBtunQ0amnJoZ1uuLttoUuXLlqv3fDwcJo1/433KhiydDfpM5totX0htEFPT49SlesQ8T6MP3+fSqNGjRTp3A0PD6dp898IiVDTfOY20mSUehMpkJ4e+cxqER0ext8LptH46/V284sjm7Zv306HDh1wtDaXqfOETnK7+JihW7xxd3fH0tJSa+1+rI2/+jeXqfNEsrb5uC+DVhxQrIb+7NNYps4TOmnzST8Gr/ZQrDZWr1krU+eJZG29izN9evXUeg0FBQVhWrYslcsUZcuiiejry0VykTw9DHxJvW6jaNykKa6bNmmt3aCgIMqamvJL+SrMW6vM6HchkrNnjx/So2VtLJo0ZpOrq1bb7tSpMwcOezDK6SjZ8xbSattCaJs6NpaVozvz7NYV/G7eIHt27Q6QsOnUmb2HPGi14BCZcsk1c5GyqdWxeMzqxruAa9z6cr19fhq98PBwypQuRc18BiyxNtdcWiF+0rAt3pwJVOHnf4f06dNrvL2PtVG7ZHb+7NdM4+0JoWmDVxzk1N0g/Pxva7eGimflj966s6inEP81ZLUnp+6/0W5tlClDg4aNWPX3Go23J4Sm9e3dC6+jnvj5+WmlhgDs7e3ZvnUL13YsI1MG7bQphKYcOnUJy8HT8PLyon79+lpp097enq3btuN2/DoZMmXWSptCpDSnjx5keNe2Wq3dY8eO0aBBAwYu2Uq5WhZaaVMIpUW8D2OGVRU6WVmydOlSrbX7sd6aTd1EwSqNtdauEEqKCg9j+8AadLFuz7LP19vWz96mNHfuXIKDXjG+eWnNJBQaEfvlJbi+6l2kipD3UZ99XhWrJib2622o1RD6IfqnsnyrSS1+4U1wEPPmzdNKe3PnziU4OIhJ1rW10p7QnJ+tl69RxcQSEaVKsv00ZWrnurwJCdZ6DU3sUF0r7Ymkoel6+ehdRBTB7yKSbL+fMcW6ptZrIyQkhOkzZmqlPZG8xcbGKh3hq2bN/p3Q0FCt1dCNGzdYtWoVM4Z0k46mZC72G75vJEYVE8NXliRW9Hjfq2ntKjSv+yuD7AeiUmn+s+LHGrKfMEs6mlKxHz2/xKhU31QvMSoVkREfvmm/2JiYH8qitFoNm1GnyW/Y2w/SSu3GxMQweMhQzOs2l46mFEj9gzUZG/NtNfm9bUSEv+N9aPAPZUpqaTNkotXAqaxcuRJvb2+ttBkTE8OgIUMpWq2pdDSlQGr1D37HUquJfPfmq7vFxqhQRWn2OoamGKfPRKWuk75ab4mObAoJCaFA/nyMalSMgQ2KazRoUrr4IJhTd4LoUr0QOTOlUTqO1tx79Z51pwM46PucsAgVVYtko1+9YtQpmeO7jhPyPooGC46TKZ0hJ8c0SPDctitPWXc6AJ8nocTEqimcIwO9ahXBrlYR9P81h3foh2im77nJtitPiYiOIWMaQxqWycUcSzOyZdDcfMXLvO6xwPM+T54GYmKiuXlSP9bGGMvqDG71q8ba0YYLt59y0vcR3RqVJ2eWDErH0Zp7z4L5+9BVDly6w9sPkVQrVYABLapQt1zhz77mhO9Dxjl5fPG45kXzsNy+BQBe1x8w3fU4fo9foYqNpWCOLNi3rEpPi4oJ6uVb99OGP/dcYN72c9qroTZVGNSiksbaSWoX7jzj5M0ndKtvSs4sqecC5r3nb1hzxJsDV+7zNjyKX0vlZUCzitQ1/fw0ASduPGb8+uNfPK55kVws6//pF+HgdxHUnbCRzOnScGZul8++/lv3Swp/7bvCvF2XtFMbBQowecpURowarbF2dMnZs2c4dvQovXr3IVfu3ErHUUTZX0pRr159lq9c9U3737l9m+XLl7Fn9y7ehoZSo2Ythg4bRoOGujtKdNGC+cyYPo0nT55otIYA2rRuTeCD2xxznp+s15g55+3H8QvX6WHZlFzZsyodR2vuPHzKqi372HvsPKHv3lOjQlkGd2lD/V+/PsvGoVOXmLZ0PbfuPyZThvTU/7U8fTr+Ru1K/0x3HRurpobNEFSJXLwunC832/+c+l3H05a7jwKp0t6etevW0aWLZs95rdu0IeDxM9bsOZGsa+hnXL90jkunvWjbuRfZcuZSOo7WPLp/h61OKzhxaA/v3r6lfNUadO47hKq1G3z1taePHmTFXAcCbvuRIVMmqtRqQIfu/ahYPeHNmeePe/DX7Encu3WDmBgVeQoUwrbfMDp075dgusaD2zex1WkF/r7exMSoKFC4GFY9Bnyyn657HHAX6wYVWbd2rcZrd8OGDfTo0ZNJbufJVTD5XMP7Fvevn8f/4glqtetO5myppyZfPrrL8a2r8T6+jw/v3lLcvDqNOttTuurX13P1PX2YPctn8CzAn7QZMlG6Sl3qWfWmRMVaP93G+9BgZnaqSbqMmZniduGn32dSUKvVLOzZCNOi+dm9e5fG29uwYQN2PXrSfukpsuQrpvH2tOmF30UCr5/kl6ZdSZc1p9JxtCb06T1u7lvDg/MHiXr/ljxlf6Vcm/7kN6/71ddGvnvDhXXTuHvMHVVUBEbpMlKwciNqDZhH2szZ4vd7ctWLC04zCHl0i9gYFZlyFsCs3UDKtuiJnp4+T71PcHblhC+2laOEOfVHaG8EX6LUavaMbkaVUgXYk3i9JT6yycXFBX1i6Vbz8xdfddH5+8HMPXiLF28jlY6iNRHRMXRbe4FNFx7ToHQuutcsQsDrd3Rdc4Fz94O+61jD3bx5/vbT3lW3S08Y5HqFN+HR9KlbDLtaRQiPVDFhhy+OHnfj94uOiaXz6vO4XniEZaX8LOpoTruK+dl9LZDuay/+9Hv9km41C6NPLOvXr9doOy4uLujrQY/GFTTajjacvfWE2W4neR7yXukoWhMRpaLz/O1sPOZDQ/Oi9GhSkXvPQ+g0bxtn/B5/9nV6emBkYJDoFhOrxv9JEGEf4kYEnvB9iNXvbjx6FUrn+mb0bFKJD1Eqxq7zYJ776fhjfut+2tKjcQX09dBaDdk1Sl5rnZ3zD+R397O8eJO66sV20R42nrhJA7PC9Ghkxv3nb+i8aA9nbz397Ov09PQwNNBPdIuJVeP/NDi+Xv5r6GqPb/qb9K37JQW7RuW0VhsGBgb06ddfo+3oktMnT+IwdQrPnj9TOooiXJyduHf37td3/L8PHz5g2a4NzuvWYmHRlL79B3D37h3atWnNyZMnNJj05/Tp1x8DAwON19CTJ0/Yt38/Q7u2TfYXyc9cucH0ZRt4/lo37hrWhg+RUVgNm4HLriM0rlmJPla/ce9RIB2GTOfUFd8vvtbt4AnaD5lOaNh7hnW3pHndqhw4cZEOQ2Zw58E/56unL1/je+cBBgYG5DTJkmAzyZLpu4+nLSUK5aNVg+qsXLFCo+08efKE/fv2Ydt/WLKvoZ9x7fwpVsybxuuXqefcFBnxgZF27dmz2Znq9ZvQvntfHgfcZUR3S66eO/XF1x7auYUR3doR9vYNXQaMoHbj3zjlsZ8RdpY8vHc7fr+Lp7wYYtuKwMcPaWXTjQ7d+xEZ8YEFk4bz96JZ8fvtd9/I1CE9CQt9g03vQXTo3o/w9+9YMGk4Tn/O1djPQBMKFi1B/WatWbFypcbbWrZ8Beb1W6S4jiaAu1fPsmfFTN6+fqF0FK2JjvzA8hE2nNm9nrLVG1G3fS9ePrrHsuHW3L365WsFlw65s3x4R8LDQmnSdQhmtZvhc+ogy4Zb8+LhnZ9uY/2MQYS+0q2/j3p6ejSwHcz+/ft48uSJxttbunwFRar/luI6mgCe3zjLpQ2/Ex6ceupNFRXBoRld8D/iSoFKDSj7Ww9CA+9zeLotz3zPfvG1saooDjp04taRjRSv1566Q5ZQvJ4l90/t4vDMf24yeOp9ggNTrXn38jGlGnei7G89UUVFcGbleK64zgfifo/1DQ0T3WJjYwh57E9UeJhGfxbfRE8P07YDv1hvhok9uGObO81Nc5MxTaJPp0ixarXWRxIkhd/33+Ley3e49qlGw1/i7vLoU6coDRceZ8ima1yY+G13ujqdecDRWy/Jmt7ok+dWHLtHsRwZODC0DpnSxv1ODGpYgqozPVl3OoDhTUoCsOXiEy4/DGFqq7IMqB/3IadztULo6YHL2Yd4P36DeUHN3J2ZMY0hzU1zs919K0OGDNFIGwA7tm/jtyolyJhOc6O0koPkWi8zN5/gbmAwW8Z1oHGFuA8G/ZtXps4YJwYt38+VP/ol+ro6poU5Ptcu0efGrvMgLDyKRb3jRmks2H4GtRo8ZnejaO643/cpnepSbuAylu69yOj2tTDQ1/vm/bQlYzpjfqtSgu3b3DVfQ5WKkjFt6qmh5Fovs7ae5e6zEDaPakNj87ibT/o1rUDdia4MWnWEy4vsEn1dnbIFOD6rc6LPjXM5TtiHKBb2bPjJc+s8ffC8/hCTjGm/mOtb90sqGdMa81ulopo/v+zYQes2bcmUKdPXdxbExsYmq7ubP3r65AkzZ0zn0sWLXL/+fVN9TJk8idv+/uzeu4+mzZoDMHjwECpXqkDvnj3wv3NPE5F/WqZMmWjdpi3bt2/XaA3t2rWLDOnS0rJ+6p6iNTZWjb4WPz8klWl/uXDnwVN2/OWARa3KANh3bk0168H0m7KEG3v/TvR1UdEqJi5eS4Z0aTizyZEsmeJG688YYkfJpnZ0GzePs5sdAbj/KO7i2JqZIzArVfSnj6dNNi3qYzNiNi9evCC3hkaC7tq1i3TpM1C3aSuNHD81SK7npuVzpvLw3m2WrN9FzYZNAbDpNYjOTaowbXhvdp69lejroqOj+GPGeNKlz8D6Q+fIlDnuO82gCbNoUbkYEwd0ZcPh8wCsWTIbtVqN84HTFCgc9z3MfvwMWlQpzsaVS+g9fAL6BgZsWLGEgkVLsG7vyfipHLvZj6JttdJsdVpBz6HjNf3jSFLNLDsxpldHjdbu8+fPOX/uLP0WuGrk+MmZOjYWvWRYk7uWzeDFwzvYO7pjWrMJAA06DWB2p1o4Owxgxq7rib5OFR3FdsfJGKdLz4QNJ0mXKQsAbQc5MKFFGdZM6MGEjad+uI0T7mu4edaDDJk1O1L9R5jXa4Fx2nTs3r2bgQMHaqyd58+fc+HcWRpPdNFYG8mVWh2Lnl7yq7dLLrMIfXqXZg6bKVg57hp6udZ92Ta4HseXDMLm78uffe1tzy289L9EtZ7TKN8u7veudBNb9NDD76Azr+5cI2fJClzdvBDUatouOkLmvEUA+LX7JFztynN95zIqdRpFvvJ1sPzjWKLtnFk5nugPYdS2X5Ck7/1HFaneHKM0n6+3T34LIiIiOHP2HA1Ka2e4XOiHaMZt86He/GOYORymp9NFPP1eJthnpJs347f78PxtBAM2XKHyTA+qzfZk2JZrhEfFTYMwaqs3zmcfAjB8yzUm7oi7A27iDl+Gb/HmWWgE47b7UHbKofjj3nnxjs6rz1N2yiGKjttP0yUn2Xs9YQ993/WXcfS8w8UHwfRdf5myUw5Rd94x/jp6N379inkH/Wnz12keBoV/8v6GbLqKzapzqH5w3vGv2XzxMWXzZo7vaALImSkNDUrn5FFwOFcehXz1GP7Pw3DYfZPJLcuQO3PCi3dvI6K59TyMhr/kju9oAsiTOS11SuYgJDya6Ji4+SzdLz8hR8Y09K6T8Mvb0EYl+bNzRbJn1OzUhvVL5+TM2XNERmpmZFtERARnzpylkXkRjRz/W4S+j2T0miPUGrWWX/otpdvCHRy5ej/BPsNWHWTMWg+eh7yj7597MB+0gspDVjF4xQHCI+PW0Rq++hBOR64BMGTFgfgp4sY5eTBk5QECg8MYs/YIpfr8GX/c20+DsJ7jTqk+f1Kg22IaTXBhz/nbCdru5bibxTvPceH2U3o57qZUnz+pOWoNjrvPx9fL726naOHgyoOXn85lOnDZPjrMdkMVo5l1KDYd98W0UM74jiaAnFky0NC8CA9fhnL57vfdoePpHcDaw1dZObgFubLGXYx4+jqMfNkyxXcgQVxHTqUSeVHFxBIZrfqu/bSpYfkinDlzVvM1ZFZII8f/r9DwSEY7eVF73EbKDPqb7o778PB+kGCf4Ws8Get8jOch7+m77CAVhq2j8khnhqz2+Kde1h7FydMHgMGrPeKniBu//jhD//YgMPgdY5yPUXrg6vjj3g4MxmbBLkoPXE3BXstoPGUzey4mHMHQ+68DLN59kQt3ntH7rwOUHriaWuM28Mfey/H1MmfbOVrOcOfhy9BP3p/9yiNYzdupuXo5eRPTgjniO5oAcmZJT0Ozwjx89ZbL955/1/E8rz9krcd1VgxoSq7/TEV462kQk11PMtWmFrm/MK3nt+6X1BqaFdLC+eUMFk2bauT4SenNmzcMGWRPxfJmFMyfl44d2nPwwP4E+wzo15ehQwbzLDCQbl1sKVGsCL+UKkHf3r14/z5uRNrA/v1YtSruTt++vXoxfNhQAIYPG0q/Pr15+uQJQwcPIn+efz7f3PLzo3XLFuTPkwuTzBmpWe1XdmzflqBt2042zP19NmfPnsG2kw358+SiQvlyLJg3N34NimkOU2lYry4B9xOePwF69bCjRfNmGltfISwsjDu3b5MlSxaqVKn6Xa9d7+yEmVn5+I4mgFy5c2Nh0ZQHAQFcuHA+qeMmmSYWFpw5c0ZjNQTg5XWUulXNMDZS5ma50LD3DJu9nCod7CnauCudRs7m0KlLCfaxn/4nI+as4NmrYHqMn88vzXtSrlUf+js48v5D3OwCg2b8xd/uBwDo7+DIqLlxUyyOmruKAdP+4OmL1wz/fTmFGtjGH9c/4DHtBjlQqIEtOWt0oI7tCHZ6nknQdrexc5m/xo1z3n50GzuXQg1sqdx+IIvWucevjzRj2Uaa9BxLwJNP/773mbyY1gOnJDoFXVJYv9uTciWLxHc0AeTKnpXGNSrx4OkLLvr4J/q6W/cfEfgyiKa1q8R3DAHkzJaFRjUqct3/Pm/fxf3dufsoED09PUoUzv/ZHN9zPG1qUK0CBvr6HDt2TGNteHl5UblmPYyMkt8NQWFv3zB3/BBsGlaiWYVCjOltzemjBxPsM2v0AOZPHMarF8+YbN+d1r+WpF3NMswY0ZcP4XH/T2ePGci29XGf52aM6MeCySMAWDB5BDNH9uPls6fMmzAUC7N/foce3LnFsK5tsDDLT90S2ej+Wy2O7tuRoO0J/buw7o+5XL90jgn9u2Bhlh/rBhVxWbog/ty0cv50+rZryNOHAZ+8P4ehvRjcuSUxGjo37XFbT4kyZvEdTQDZcuaier0mBD56gO/VxGcpCbjtx6vngdRs2Cy+ownAJEdOqtVrzO0b3rwLi/sM+yLwCbny5o/vaAJInzETphWqoIqOJjIygndhodz3v0HNhk0TrBmWM3deqtSuz9uQEFQq7awLnVR+rdMQfQMDjdbusWPH0Nc3oHTV+hpr43t9CAtl89wRzLCuzrimJVk1ugu+pw8n2GfjrCFsmTeK0FfPWDupF5NamTKlrTnrp9sT9SE8fp+T29YAsH76QNwWjAHAbcEYNswYxJuXgWyeO5IxTf75vXoe4M/SoR0Y06QYw+rkZW63+lw9ujtB22vG23Fw3ULuXz/PmvF2jGlSjBkdq3HYeUn82kV7V85iYZ9mvH764JP35+zQnz8HtyM2RjM1eW7vRvKXNI3vBALInC0XZWo0IijwIQ98LyX6uucB/rx5FUi5mhbxHU0AmbLlpEz1hjy57cOHd29/qI1n9/3YtmQi7QZPI3OOPEn5dpOEoZExparWw/PoUY22c+zYMfT0Db5pejVtiXofyunlY3C3r8OGrmU5MtuOx5cSLgNx8s/hnF4xjvDg5xyd349NPSuyuU8VjjsOQRURV28n/xqB3wEnAI47DuHMqrjO/TOrxnPij6G8fx3I6eVjWW9bOv64bx7f5qCDDettS7OuQyF2jmhCwJm9Cdr2nNuba25LeOF3Ec+5vVlvWxr3gbXxdv8jfn2kyxvnsGdsS8KeP/zk/R1bPIgDUzpqrN5ue24mW5Gy8R1NAOmy5qRApYaEvXjES//PdzbdPbaVdFlyUK5V7wSPV+g4jPojlpIuS3YA3r1+SoYc+eI7mgCM0mUkZ6lKxKpUxER9/jvS48tHublvLQ1GLie9iW5MJapvaEy+8nXw9Ey83j7pbPLz8yNapcIsfw91uC8AACAASURBVJbE9k9Sz0IjaLzoBG6XHlOjWHZsqhbkcfAHuq65wKoT/1wA8A18i4ffS5ovOUngmw+0rZCffFnTsfnCYwa7XgWgWM6M5M6c5v//zkDRHHFfDm4+e8uFB8HYrj6P0+kH5DdJB8D5gGCaLjnBnZfv6FajMMOblMJAT4/ezpdYdOSfC+gn77xm0/nH2K6+QLQqlq7VC5PO2ICZ+/wYtTWup79EroycDwhmj3dggvf3JOQDbpeekCWdEYYauMMw+H0UoR+iqVvq07WZiuXMCID3408vUP5bpCqW/huuUL1YNnrX/nQIqKG+PjvtazKoYcLh2G8jorkZ+Jb6pXNiZBD3axTw+j0Ny+TCyECfh0HhHLrxnOtPQsmdJS1WlQtQ4P8/e00pnz8L0SoVt24lfufVz/pYG+WLKLOuRGBwGPXHObH5hC81yxTEtr4Zj1+9pfO8bazY/88HAZ8HLzly9R6NJ7rwNCgMy5plyJ8jE67HfBiwdB8AxfOakNskQ/y/i+WJuzPl5qNXnPd/is0cd9Ycvkr+HHEf8s/dekKjCS7cfhpE98YVGGVZAwN9PewW72TB9n8uZpzwfchGr+tYz3EnShVDt0bmpDM2YrrrcYaviuvoLZk/G+duPWHXuYQXDR6/fsuWEzfImiEthgZJfzdEUNgH3ryPoJ5ZkU+eK5E3bh7Va/e/vbMpOOwDg5cfoF3NX6hj+s/F+Ba/liQwOCxBJ+DdwGBO3XhEbdNCpE9j9F37aZN50dxaqSGzIpq/mSEw+B0NJm5iy6lb1PglH53rluXRq7d0XriHFQevxe/n8/AVR649oMnULQQGvaNd9VLkz5YR1xM3Gbgi7gtRiTxZyf3/zsQSeU3iOwhvPHrN+dvP6LRgN2s9rpM/e9yIlHO3A2k8ZQu3n4bQvWE5Rrb5FQN9fXr8sZ8FO/+Z1/rEjcdsPH4TmwW7iFLF0q1BOdIZGzJ9y2lGrIk7aZfMa8K524HsupCwo+rx6zC2nPLTcL1EUq/cp2szFc8T9/6vBbz85LnPCX4XwZDVHrStXpI6ZQskeC4yOoa+Sw9So3Q++lp8forSb91PE8oXyan52oiOpkKFiho5flJ5+uQJv1apxIb1LtSpW5fudj14+OAB7dq05k/HJfH7eV+7xoH9+6hZoxpPHj+mo7UNBQsUxNlpHT3tugNQsmRJ8ubJG/fvUqUoUTzuc4bv9eucOXOaNq1asmL5MgoWjOucPn36FDWr/8qtW3707tuP8RMmYmBggE1HK2bPnBHfttdRT5yc1tGmZQuioqLo1acv6dOlZ+KE8QzsHzd6tXTp0pw+fYpt7lsTvL9HDx+yYb0LJiYmGBpqpsPilzJl8PA6hofXMVw2bPzm171+/ZqQkBAaNv50EeKSJeNGmF+5lPiFB11QsWIloqOjNVZDANe9vTEvrcx0Jk9fvKaGzRBc9x6ldqVydG3dmIeBL+gwdDp/bfxnLvPr/gEcPHmRul1G8OTFazo0q0uB3DlYv8uDPpMXA1CycH7y5MgW/+9iheLqxPdOAGev3cRy8DRWue2nYN64c+mZqzepYzsC/4DH9OrQjLF9rDEw0Md21O/MWb05vm2v89647PSg3aBpREWr6Nm+GenTpmXyH84MmhF3c1HpogU4c/UmO44knDbr0bOXuO49iknmjBgaGCT5zy/ozVvevH1Hw+qf/m0v+f+OoSs3E59y8tmruKkGq5Qr9clzHx+7ee8RAPceB1IwT07eh3/gwImLOO88wjlvP2L+tSj69xxPm9KnTUOpogXx8fHRWBve3tcpVe7r62PpmpfPntLFohr73TdSsVptWll359njh4zsbsmm1f+6ce7GdU55HsDut1q8CHxMkzYdyZ2vAHu2uOAwtBcAhYqVJEeuPPH/Llgk7tx0188H74tnGda1Le7OK8nzP/bOMjCK6+vDz27c3ZV4AgFCgECw4O7uWiq0QKFAaaGUQqG0pS2UGhQt7lKcoMElEByiECNEiUKS3ffDZDdZdiNIgP7ffb60zL07c2cyZ+7M+d1zjoMwN129cIbhnZoQc/8OPYe8x+iJn6MhFvP52IEs/2We/NgXw46yZ+NqJg7pTmHhM3oOGYOunj5L5s1g3lRhRbCrhxdXL5wh9F/FRRRJ8Q/Yt3UdxiZmaFTD3JSZnkZ2VgYNmylHnbu4C/PL7WuqHW2PHwnfTjUD6iu1ybZF370NQEiH7qQkJSiIgHFR97h05gSBTVqgp2+AhoYmf20PZdi4zxT2lZOdReSt6wS1aIOm5pv/RnoVdPX0cXX3qlbbjYiIwM7VE23d6vW3VJXMlETmDW7Kub0b8KwXTONug0lLiuOPSf05uuF3eb/4uxHcCDvIguGtyHiUQGC7PpjZOHJ2z1pWzRoLgI2LByYlwoa1swdWjsI8n3D/JlHXzvHbxD6c3Po3ZrbC90XU1bN8NzyE5Ji7NO01ko6jpyDS0GDZtKHs+/t7+bHvXDzB2d3/8NuEPhQVFdK05wi0dfXYuWQW6+ZNKDm2F1FXz3LlyE6F80tPesj5vRvQNzJFrPH6bTInM428J5n4NFSul2bj7AFA3O1wlb/NLElv51IzUKnNtWRbUvTtFz5G4bMCln85Go+AxoT0f3dTfjt61eZaRPXZGgj2ZuHkgabOu2FvuamJbB/fkntHN2FXqzHebQaR/egBB78ZzI1dpSk802Ju8PDiIXZOakduagLuzXtiaOnAvSMbOPbzOABMHNzRN7eV/7+xnWBv6TG3SL51gQOzB3Jr3woMrQR7S751nh2T2pL58B6+HYYT0H8yIrEGR+aP5MrGhfJjJ147xd0j6zgwewCSokJ8OgxDU1ePC6vncOpXYVGHiaMnybfOEx2mWAMoJ+Uh949uQtvQpFrsreBJOk9zMnGoq1ynzMRBeAdIjbyq1CYjKzEap/ptEGtqk50cR9z5A6RGXkPf3BbPlv0wtBb8KTUadyY3NVFBBMxKiCQxIgz72k3Q1FVdF7wgO52Ti8fj1qwH9rWbvcqpvnbM3fy5GqE6ylLpL5WUJDyc7E2rPz3N3H9v8TA9j30TmlLPWXB2T+3gzcCl55nz72361XeSp3V7mJ7Hx608+LKTLyKRkJao/S+nOHU/FYCPQtyRSKRcis3gk1ae1HIoXQkTlZJDS28rlg5riYe1IVIpzNh5A21NMXs+aYJtSTTPuJbuDFx6np8P36d7XQfcrQTnYmxaLrO71+T95oKhTevoTd8/z7HhwgNGBLvSoZYtBjqa7IlI4uNWHvLjyqKk+gQqOtZkpOc+Y+Xp2EqvU5fadnjbKqfViUzJAcDaWPlv5WEtiE2pORWvIJ295xbJTwrYODYIVVme9LU1aFijtKDZ0pPRxGfkc+TWI4qlUsa3Fs4392kRj54UYGWow9DlFzh8qzS/p4e1IYsG1CXQpXpDbe1K7tmkpCTq1Hn9H0oy25A5lN8036w/wYPHWRyaO5RAD8HxMK1vE/rN38rs9Sfo37yWPK3Ug8dZTOgWxMyBLeT20vqLNZy8IawS+LhLQ4olUi7eS2RC90b4u5aq45GJ6bSqU4PlE7vhaW+BVArTV4eio6XB/m8GY2sm3FufdAui77wtLNx+lp6NfXAvEWxiHmUyd1grPuwkfGB80b8ZPeduYt3xCEa1rUun+p4Y6Gqz+9xdJnQLkh93z3lBfOrbrKbK80/LzmfFIdUvVWXpGuSFj6OyABuZKDgPZCJbWTzshbE/zlKOTiyPqSsOk5VXwFcDFSel9zoEcvJGHAO/30pDLwd0tDQJu/kAWzNDZvRv9sL93iT25sK9Xe02ZF79NvTNptM8SH3Cwa/7EeguvDBN6xVE/x928c2m0/Rv6lNqL6lPGN8lkJn9msjtpc1Xmzh5S6jjNa5TPcFeIpOY0CWQWi6lYllkUgat/F34++OOeNqbIZXCF/+cREdLg31f9cW25H77pEsg/b7fyU+7LtKzkZdcsIlNyWLO4GZ82EEQGab3aUSv73aw7uRNRrb2p2OgGwa6Wuy+cJ/xXUo/GmRRUn2DS1cVlSUtO58VRyp/0e7a0B0fBwul7ZFJQuShTGQri4ed8CxPfVJ1e5m2+jhZeU/5qn8TpbZZG8JIzshly9QeKuehF+1XHdibC8+96rYNRydlce9d4ssvphMXG8upM2dp2FB4fn8162u6du7EF9M/Z/DQYZibC8/TuNhYPps6jbnfzkMkEiGRSAgOasixo6EAfDr5M4qLizl37ixTpk2jTp1SJ/O9u3dp26496zZsxNvHB6lUyuSJE9HR0eHEyTDs7O0BmDxlKl06dWT+vG/p268/nl6CIzg6KoofflzI+ImfAvD17G/o0K4tq1auYOwHH9C1W3cMDQ3Zvm0bn02dJj+uLEpq0ODSiJGypKam8tcfv6tsK0vP3r3x81M9l70s9+4Jc6SdrfJKUi9v4TmQ8vjxaz3m68TBUXgXri4bAkhMTMLRRnn+fxPMXLyauMQUjq/5kQb+wt9jxoeD6fHxLGYuWsXgLq3kNYHiElOYNLIP33wyrMQ2pDQb8inHzgtpFScM60lxcTHnI+4weWQfapcR0O7HJtAmuB7/fD8NL1dHpFIpU35Yira2FqGrfsDOSrC/T0f0ovu4WSxYtone7ZrJBZvo+CS+mzyGT4Z0B+Crj4bQ+YMZrNl1hPf6daJzSBCG+rrsOHKGSSP7yI+784iwsGhAJ2WnFAhi0dLN+1S2laVH62B83ZWjm++V1EGSiWxl8XQVxv44XTkiHsDNUXgnPn4hgvFDeyq03YkWRKHbUQ9oVMeX6IdJPMnNw7fTaPIKSr+RAnw9WP7tJLxrOL3Q/t409tYW8vmiOkhMSsTG/t2eh1SxZN4Mkh7GseLfU9QKECJGx342kwmDu7Hk2y/p3HcwxqbCvZX0MI5h4z5j3PQ58rlpRKcmXDx1DIAhH3yKpLiY65fPM/zjz/CqWfq8iou6R6OQtsz7cy2uHt5IpVJ++moyWto6/L3rOFY2wr0z9KPJjB/UlRW/fEfbbn1xdhMEm/i4aCZ+/T2D3hPSib4/ZRbj+ndkz8bV9Bk2lubtu6JvYEjo3h0KYsuxfUKUVMfeA1Wef2Z6GltXV17Pq1Wnnrh5+yltl9VVsrRRnl+c3YV5NSNV9fwii1K6FHacwe9PVGiLuScsLoi+e4va9RvRb9RHXAw7xqRhPaldvxHaOrpcPnMCSxs7Ppo2GwA9fQPqNGgs38eGZb+SnPCAsCP7KZYUM+KTqZWe57uItZ1DtdpuUlISJjblR2y+aXb+Oou0pAdMXRmKay3BJ9B57Bf8NqE3O3+dRVDngfI0bGlJD2g3fCLdx32NSCRCKpHw3fAQ7l4UMkm0GTIeSbGE6OsXaD9iEo5e/vLjPIq7j1/j1oyetwpbVy+kUimbF05DS0uHz5YfwsRKsMm2wyawZHwvDqz4gfrtemFdIqY8jo+hz6fzaDVIcLR3/WAGi8Z14+zuf2jeezR1WnRGR9+A8NCdtBteen+HHxWc4Q079ld5/jmZaZzcqjr1a1kCWnXDzk15LpHVVTKxVF7kbOMiPE+y01XbpJWjkGXo3qWTtBnyiUJbUvQd+X+lJVk0qnqMHYtmkvU4iU9+3f5O1/Qzs7YnuRptDQR707N4d+ztwuo5ZKc8pPuPB7D2FvwFgYOnsn/WAM6v+gbPVv3QMRLsLTvlIXX6jKfhsBkgEiGVStj5aVsSrwm1X2v3HIe0uJhHdy5St88ELNxK62xnJUTiWK8lraf9jamjJ0ilnF36BRpaOnT7YZ9cpKrT62P2z+pP+KafcG/WQy7YPEmKpdGYOfh3F8TK+kOms29Gb+4eWY9fp5G4BnVES9eAmDN7qNOnNO22LErKs2Vfledf8CSdW/tWVHqdagR3wczZR2l7VoLgV9E3V7YFUwfhWZGfmapyn4UFueSlP0LP1IqD3wzmwcXS6E1TR09aTFyMtbfwDKzZZQwJ105y4JtB2Pg0QFNbl8SIMPQtbGkw9Mtyx336j2k8y8mi4YiZlZ7jm8bA0p47yaqz3SiJTbL0Jvra1ZuCIjOvkO3hCdR1MpULTQBaGmKGNHImLDKVvdeTGBwkfJDoamkwpb233MEkFolo6GrO9fgskrIKsDOpWByb1tFHLsBcT8jienwWXWrbyYUm2bH7N3QiLDKVk/cey8UmEz0txjYr/dATi0RMaO3B6chUjt9NobajCZ1q2bLlcjwP0/NwMhcUyT3XEjE30CaknJSEaTnP+OGg6pQQZXG3MlApNsWmCn8rMxV1lmRRRFn55YeZH771iBVhMawY0UApfV55zN9/h/yS1IXetkboagmrG2NSBafjslPR1LA0YF7PWjRwNedCbDpz/r3N8BUXOT6lBZbVmErPoOSezc6unoJpctvQefOpJTJyCth6+hYB7nZyoQlAW1ODYa1rc+pmHP9euMfQVrUB0NXWZFrfJgr2EuTtQETMIxLTs+WiQnl80a8pnvaCAzoiJpmImEd0C/KWC00g2MugkFqcuhnH8euxcrHJxECHDzqWrm4Ti0RM6tGYsJsPOBYRSx03Wzo38GTzqZs8eJyFs5UQRbnr3F0sjPRoVUd1Dv20J3l8t6XiArUgRGqpEptikoWUkmYGyitQnEoiuLJyq5be5058KjvP3eHTHo1xtDRWaDPR18HR0oTrsSmERyWjqSFGIpWiqSEmp+DZC/d7kxiU1FGqfhuq3hWJGTkFbDt7lwA3G7nQBCX20rIWp27F8++lKIaGCM5gXW1NpvVqpGgvXnZExKaQmJ4jFxrKY3qfRnjaC/NYRGwKEbEpdGvoIReaQLCXgc39OHUrnuM3HsjFJhN9HT5oXxrNIhaJ+LRbA8JuxXPsxgPq1LCmc6A7m0/f4UHqE5xL7rfdF+5jYaRHy9ouqCItO58F289Veq087ExVik0xjwSnnqmB8tzgZCk8P6psLwlp7Dx/j0+7NcDxObH+UHgMfx++xuoJnVUKWy/ar7ow0HkztmFg8ObPraqkp6ezccN66tdvIBeaALS1tRk9ZgzHjx1l547tjBotpBDQ09Nj5lez5B+kYrGYxk2aEB5+hYT4eLn4UB5ff/MN3j7CB0F4+BXCw6/Qq3cfudAEoKWlxbDhwzl+7CihRw7LxSZTU1M+mVDqFBCLxXw+fTonjh/jyKFD1KsXSPcePVm39h/iYmNxcXUFYNvWrVhaWtK2nep0hqmPH/PN7K8rvVaeXl6vXWyKihQ+hMzMlZ3xzi7CcyAzU7Uz/l3A0FB4jlaXDQHk5eejr/dmarmVJSMrm837TxBY01MuNAFoa2kysld7TlyIYNfRs4zoKdR21NPR5ssPBpaxDRGN6/hx9XYUCY9ScahEMPvqoyF4uQr2c/VOFFdvR9GzTRO50ASgpanJ0G5tOHEhgqPnrsrFJhMjAz4e3E3eTywWMXV0X05ejCD0bDgBvh50bdmYDXuPEZeYgou9sBhpx+EwLEyNaROsOvoyNSOLuX9UHqnn4WKvUmyKfihkhzAzVp5vne2EMWRlq05d5+5sTz0/T45fuMaqHYfo064ZEqmEDXuPs70kQksWuRT1MImc3Hy+/ngoXVs1JjUji3V7jrJ6xyH6TZzLmY2LXmh/bxpDPR1ycnKqbf/5eXno6ateWfuu8iQznYM7NuJXt75caALQ0tKmx+BRXDp9nGP7dtF90EgAdHT1GDt5hsLcVLtBY+5cDyclKQFru4odiB9MmYWrh2Dnd6+Hc+d6OK0795ILTQCamlp06TeMS6ePc/5kqFxsMjI2ZeCYUuevWCxm5PhpXD5zgnMnjuBTux4hHbuxb+t6kh7GYeckPNuP7NmGqbkFjULaoorMtMcs/XGOyrayOLt5qhSb4mOFen8yQa4sdiURXNlPVM8vTjU88K0TyMWwY+xav5K23fogkUrYv22DPEJLIhF8B0YmJtg5OnPv5jVuXb2MppYWEokEDU1NcnNV39d/LJhFQUk6NTdvP3R03/wz/nWgp29Yrbabl5eHVjmr4t80uU8yuHhwCy5+9eRCEwgpzpr0GMHdiye5enQ3TXoIke5aOnp0HjtdbpMisRj3Oo14eOcamSmJmFrbqzyOjK4fzMDWVXj/e3jnGg/vXCOgdXe50ASgoalFoy6DuXvxJLfPH5OLTXpGJrQcWFprRCQW02HkZ9y7dIrb54/i7FuXuiFdOb9vI2lJD7CwE+zhSuhODE0t8Gusuj56TkYq//41T2VbWaydPVSKTY8fCplP9FXURTK3ExYE5OeozmBk7eSOi28Ady6e4PTO1QS2641UIuHC/k1cCRUitCTFxS90jOunDnB881LG/rBWHmX2rqKjb0heOc+T10VeXh4a70hU09PsDCJPbMPKM0AuNIGQ4syn/VASI04Rc3YvPu2GAKCprUvgwKnIHB8ikRgbv4akRkWQm5qIgWXF9lZ/yHRBaAJSoyJIjYqgRpOucqFJOLYWXm0GkBhxioSrx+Vik7aBCf7dSmuki0Ri6vadSGJEGPHhx7D0qINr487cP7aZ7JSHGJVEBEWH7UbX2BzHANULngqyUrm8bkGl18rE3l212JQo2IKOobItGFoL79vPclXb25NEIe3tjd1/YWznRvD787HxbcijWxe4sGo2h+YMpfdvp9AzsUTbwARDKyfSom/w+P5VxJqaSKUSxGINCvNV37MZD+4QHbaLgL4T5dFk7xJaugbl2puSoiRTuKtbrI5MyUEqhdxnRYz9RzEsO6dAyMMoE1MALA210dFUTBUki3rKfVpx3kYLQ23qOpXmEI5+LFyMYHdlB1vtkvSBUY9LL1gNSwOl6yETf2JL6jT1DnRky+V49kQk8VGIO4mZ+Vx5kMGIYFd5mrnn8bA2JPa7ThWOHSj399ol1yMjT1lQktWyMtVT7dR99KSACRuvMjjImU7+VZ8wYuZ3Ijo1lwvR6czbd5tOi8K4PLMNmfmCc/xZkYTlw+vLhT1/RxMeZz/llyP32RmeqFTP6XUi+xvJ7uHXzZuyDVVEJqYL9lLwjNGLFPMNZ+cJDt/YR6UfAVbG+ug8V7NA5jTOrUTIsDTWJ8C99OUsqkSkaeKnvNKxdg2bkvGV1gZzszVXukY+ToKtyRzY/ZrVZPOpm+w+f5ePuzQkIS2by5GJjGpbr9z73dPegoQ1kyocO4CWpur0LtolwmhGbr5Sm6w2j6lh1T5gFu8+j7amBh91Vq630fnr9dx68JgfRrelV7AvulqaHLkazcSlB+i/YBtnfhyFs5VJlfu9Sf5XbCgyOaPEXgoZs2S/Qlt2ybMqtkwNJCtjPXS0FO8bUwNBGM8tqDgvvIWRHgFupatgokvu8WAfZWdF7ZL0gVFJZe3FVNleHISP/dhHwhj7NvFh8+k77LkQybhO9QR7iUpmVOva5duLnTnxyysvjFqevciuR2ZugVKb3F5UCFGq+PXfK2hpaMijt2Q8yszlk2VHGBpSk8713cv5ddX7VSdvzjbe3ZWC9+7dRSqVkpObw+CBAxTanjwRcr9Hl6mBZGVtje5zTiEzM+ElvjKHi5WVlUI9o8j7wgrP5s2Vc6MH1KsnjK+kD4CHh6fStZSJP7IxDho8mHVr/2H7tq18Ovkz4h8+5MKF87z/4Udoaal+d/L28SGzHId3WbS1X/+iFB0d4ZmUkZ6u1JZXIlbKru+7iOzvUV02JNv327Che3EJgm3kFTBsmuLH7pMc4Z0j+mHpKlsrc1N0n7tHTI0FoVlWt6k8LM1MCKzpKf935ANBpGkaWEupb12fkvRfcQnybR7O9krXSCb+yMY4sHNLNuw9xs4jp5kwrCfxyalcvHGPsf06oVVOCi8vV0dSz21T2VaW8uppaWsLNpfxRPnZkFdyTUxVCFEgCGZ/fj2ePhPmMO6bX5ny/VIkEikSqYSRvdqzfOsBfN2Ec1z6zUR0tLTw8xCc+B7O9jSq44uxoT6/rN7O7qNnGdi5ZZX396YRiUT/kzb0KsRF3UcqlZKXm8MXHwxRaMstqUsSH1c6N5lbWqGtozg3GZsIz87KHJRmFpb41S11nj+IERYBBDRWzkjg4y9E6z6ILp2bnNw8lK6vTPyRjbFjr0Hs27qe0L3bGfLBpzxKjOdm+EX6DH+/3PRxLh7enIqqvE5zebW4tEqeR08yleeX/BKhR3aNnkcsFjPzp7+YPLwX3075kIVfTUYikSCVSOgxaBTb1/6Nm5dwjmN7tCbyzg2mzV9Mu+590dbR5czRg3w75SM+HdqDTcfC5QKbjJOR6TyMieTqhTP8/t1MRnZuxp6LkVhYv5209i/N/yPbfRQn2OTT/FyWTx+h0JafKyw4SU0orUtmZG6JlraiTeobCb67gryKbdLQzBIXv3ryf6c8FIRTz3pNlfo6+whRiikPSlOyWju5K103OzfBGf04Xhhjw479Ob9vI+GhO2kzZDwZjxKIvXGJ5n3GoFGOTdq4erEorPLathrl2KSmtvDOl/dE2a6fltik7Bo9j0gsZuhXv/H7pP6s+3Y8WxZOQyqRIpVKaNpjOKe2r8TOzUeebq+yY2SlJvPPNx/RpMdw6oZ0rfSc3jrVbGtQ8i77jthbZkIkSKUUFuQSukCxZlBhvmBv2Umx8m26plZoaCsGAOgYCPdSYUHF3zi6JhZYeZZ+z8tEGrtawUp9Ld2FRfBZCVHybSb2bkrXTSb+PCkZo0fLPtw/tpmY07up3XMcuakJpNy7jF+nUYjLsTdTR09GbntY4diBcu1VQ0u4Hk9zlG1BVstK21C1vcl+U1z4jDbTV8iFOEv32uRnphC++WeiTu6gVtf32PN5F9Jjb9Pkw+9xb94TDW1d4i8d4eSSSRyYPZA+v5+WC2wyrm1bgoamFv49Pqz0/N4KFdjb26mgC2TkCQ4/HU0xWs/VMzLT16J3PQeFaB5drfLzg1f2LNF+zhmXniscWxaBVJZnRcJKNY0yRqAq6kcW+SUTwJp5WmJtpMO/WBbs5AAAIABJREFU1wSxaU9EElIp9K5XvvooElV8XpUhS5/3IE05lZHs+lqUE0m0+kwc6bnPeFJQxISNpfknk7MKkEphwsaruFsZ8EkrT6RIEZe5Hm6WBrhZGiAWwfiNVwm9/YiGroJzNNDFTC40yWhX04Zfjtznfkr1rWb9Xye9xFmho6Wh5Fw2N9Kjb1M/vB1LxVNd7fIjRyq1l+ecz+nZwrGdVAgfzwoFUVOjjA2rijqQRYPJBLDmtVywNjVg9zlBbNp9/i5SKfRtqrzaToZIJESgvCyyccU9Ul6VkJEjODIsjStfoRKf+oRtp2/TtaGXPA2bjHsJadx68Jgmfs6Mals6EXdp6MX5uwn8vvci/164R5u6blXqp0rMUlM5GdnC31NbS0OpnpGZoS59gr3lgg5UfF9Jqdhgnhep0krsxfm5iDcoay+lY1JtL1oK+25e0wlrE312l4hNuy9GIpVCnyaqU+jBq9uLtYkwP5YV5WRklEQ0WVTFXtKy2Xb2Ll0beCjZy8rQ66Rl5/Mk7xmfLD0s356UkYMUKZ8sPYy7nRkFz4qq1G9iV+V6AWpeH+lpaQDoaOsoiTEWFhYMHDQYP7/SZ7ieXvn3R2Ufgdo6iu8uaalC6gJZBFJZnj4V7keNMnVkbO3slPrpl0SNyVZFt2zVGhtbW7Zv28ankz9j+7atSKVSBg4aVO64RCJRhedVndiWpM+LiVEuHJ9eIkBZWb6dFHL/30nPFN5vdbS1lGp9mZsa0b9TCH5lonl0K4iQr8w2dJ57v0vLEJzpLvbKjtenz4SFARpl5kFVaeoMSqLBZOMKCaqDjYUZO0rEph1HTiOVSunfKaTccYlEIvReIfLf1kJwZMfEKzvn0rOE62tpVv4CnJqerlzcsoRth8O4E/0AW0tzWjWqy8lLQjpZ2fUP8PVQ+fsOTevzy+rt3IyMe6H9qXn7ZGUIc5O2tg6az81NJmYWdOg1UC52gBDZVC6V2J/Wcw66zHTh2PbPCSQAz0oKfWuIS+cmWS2osujpCe9bOiUCWIOmLbGwtuHov4LYFLp3O1KplA69VKfQA8H+KjyvSrAoGVdCnPL88iRDmF9MLcqfXzx8arEh9DJH9mwj+t5tLG1sCWremstnhLRMbt6+xN6/Q+SdGwQ2bk7vYWPlv23ZqQfXLp5l/dJFHNu/k4HvjUcqlSIu867sVMMDpxoeiMViZk8cw+mjB+g2YPhLn6+a6iW3RLTU1NJWcu4ampjTsEM/hWge7YoiRCqxSc3nxJqcTMEmZRFIZSl6JvjHRGVsUlWUjo6e8L4os3fvhi0wtrDhSugu2gwZT3joTqRSKQ069it3XCKRCK1XiHwxthDm9NSEWKU2mThkaFa+Tdp71GTGxnNcObydpJi7mFja4BPUivuXTwFg5+6LpCRCt7JjnNq2nJzMNPJzslgzu3QhY+bjRJBKWTP7I2xcPGg/ovJFwWpeP0+zhb+Vhpa2khijY2SOR0gfzJxLfQaa2hUsFq3E3mSijIyCJ4K9Gdko21txobK9qUpTJ6tTpFEyLoc6zdE3sybm9B5q9xxH9Ok9IJXiEdK7/IGJRBWfVyXomwkR9NnJcUptsuurZ6IcqAJgYCF8c1p715cLTTKcG7YnfPPPZD68R+bDe6TH3sbOvwl+nUbK+7gGdyH59gWu7/yD2DP/KohKOY/jiTqxDdfgLvI0iP8l3prY5GIh3FQ1LA35bXA9hbZiiZScp0Xoa7/+ArQAziXHPhedTls/xRv+UlyGwvgAYlKVFd6HGYLAIxNWNMQiegQ4sOxUNImZ+ey5loirhQH1Xcu/KVKyn/LT4XuVjndQQ2dqOyp/YLlZCRFXcWnK47uVKHx81nNWrcBaGGpTy8GYmFTF1SJPiyRIpFJuJmYhFon49eh95u27w7oxQbT2tVboa24oTO4JmQU4lKTtKyxWTitRUChsM9L9bxXzfJdwtRb+jm62Zvz5cReFtmKJlJz8Z+jpVI85O1sL9965O/G0r6cYVXDxvrBa1tWm9D6LeaS8IuDBY8Fh7VlSG0lDLKJXsC9/7b9EQlo2u87doYaNKQ28yg/bTcnM5cftZyod7+AQf+q4Kb84utsJEVexKcppIG7EpQAQ6FFx2DDA6tBrFBVLGFKSsrAsNx8IeY1VRYGF1Hbl970XycwtqHI/NS+HS8k9625jyp8fKqbDKpZIySl4hl41pYp1KRFlz95NpF2AYiTnxcjkkvGVClExj5TvxwepwvNbVhtJQyyiV2Mv/jp4lYS0bHafj8TV2oQGHsoOdRkpWXn8uPNCpeMd3NyPOjWslba725oJ80vKE6U22f1bNkVheaw5doOiYgmDWygLyRZGetRysSLquWvwtKgYiUTK9QepiMUiajlbVamfmuqlRg0hnbCHpyer1vyj0FZcXEx2djb61ZR+ybWGYEthYWF06qw4B54/exYAtxql9hYVFcnzxMXFAuBdkmpPQ0ODfv0HsGTxIuIfPmTb1q24ubvTqFFjpd/KeJSczLxv51Y63uEjR1KvnnJh5lfB09MLkUikED0mI6KkMGuDoCClNjXVTw1H4Vno4WzPim8nK7QVSyTk5Oajp1s9aaRdHYTvmNPhN+nYXHGByvmIOyV9Sp/VUSXp6soSlyi8A8lqI2mIxfTt0Jzf1u8mPjmVHYfDcHO0I6i2cuoRGY/SMvhu6cZKxzusR1uVgo+HiwMikYiYBGWx6fo9wQHewN9L5T6fFRYRl/AICzNjhvdQTDO2cMVWbC3NMTMxIj45lUs37xFY0xMnW8UU5zKRy9rcpMr7U/Nu4OAsPPudanjwza8rFdokxcXk5majq1c9c5ODsysA4edP07SNYraS65fPA2DvUjo3PYyN4nmS4gXnlktJbSSxhgbtuvdj499LeJQYT+iebTi6uOEfWP7zPS3lEct/qTxlV7cBw/GpXU9pu7ObEA2c8EBZbLp/SxBYy6YoLEth4TMSH8Riam5Jt4EjFNpWL/kRS2tbjE3NOX9SqNdYr7FyhHJQ89asX7qIJ5mZrF7yA79/9xU//7OTJq06KPQzNRecfY8SK1/BrubtYenoCoC1szsj5ixTaJNIinmam4PWK4ijFR7bXhB+I6+exb+Z4v0TfV34LrJ0cJVvk6WSK0taolCbT1a3SCzWoH673hzb+AcZjxK4cmQnVo41cPNvWO44nqQ9Yt/f31c63uBuQ3H2rau03cZZiLhSJQTF3xNssmyKwrIUFT4jLTEOQ1MLgrsPU2g7tOonTCxtMTA2q/IxYiIu4OjlT8oDxedX0bNnSKUS4u9FKIjDat4sRrbCPW9i70bLyX8otEklxRTm56BZTSn/jGyEYyffPIdzg3YKbSl3LiqMD0pTzpUlJ0V4nstqI4nEGrg178mN3UvJTU0gJmw3xnau2PiUvwg7LyOF8I0LKx2vd9tBWHoo1401cXAHkYgnybFKbWkxNwGw8lL9XWdQktpOUqycDaf4WckCaANj0mNvAaqjwBzqhnB95x88zVH0ddw5sAZJcRE+7VTXEn7XeWtiUw1LAywMtTl+N4XCYolCtMbi0EgWHLjDro+bEFRDeQXeq1LLwQQtDTEn7z0GFHOkno5MRUMsIsSn1PkW/TiH6NRc3CxLV6BvvCAYRS37UhGoT6AjS09Gs/RkNJfjMpjcrvxV5wBP8gtZd+5BpeMNdrNQKTbZGuvSyM2Cs9HpxKbl4mohjK+wWML2KwnYmehS21G12DS6aQ1GN1VOadf255MUFBZzZFILQKjrBHDi3mMlsWltydhr2hujq6VBUw9LwiJTla7V/uvCB1yDCoQ3NRVTw9YUS2N9jl6LUbKXX3aeY97mU+z9ehCNfF5/Hs/arjZoa2pwPCKWWYNaKLSF3XyIhlikUGcpKimD6OQM3GxL/97rj5d8qLiW3kP9mvrx575L/LnvEpfuJzK1d5MKx5GVV8A/RyMqHW+wr5NKscnWzJBgXyfO3H5IzKNMapQIZIXFEraevo2duSF1alTuPD8WEYuZoS7NaymvYvQuqX2z+9xdpvVRPJ+dZwWnj5+TFV5V7Kfm5ahhY4KFkR5Hr8cp28ueS8zfepZ/Z/ahUQXi5svi72Il2MuNB8xC8W97+na8YC/+pfdOVHIm0cmZuNmWPqs3nLwl35eMvsE+/HngKn8dvMqlqCSm9KzYqZyV+5S1x29WOt5gHweVYpOtmQGNvR04czeB2JQsXEsEvMJiCVvP3MPOzJA6rsq/e55j1x8I9lJTWVh9r10d3mun/MLXauZGCp4VcXzuQIW+Vemnpvpw9/DAysqKw4cOUlhYqBDd9P138/l61lccPXGSJk2UU5e8KnXrBqCtrU3o4cMw/zuFthMnTqChoaFQZ+n+vXtE3r+Ph2fpCrPVq1YBULtu6Uf9oMGD+XXRLyxevIjz588xY+ZXFY4jMzOTFcsrL/jcrHnz1y422dnb06xZc8JOnSQ6Kgo3d2HxR2FhIRs3rMfeweG1H1NN1XBzssPSzITDZ65QWFSkkGrux+Vb+Ob3tRxesYDggPKjt1+WOj7uaGtpcvRcOEwYodB26tJ1NMRi2gSXOpcj4xKJfJCIh3Pp/PfPriMA1PYqrU87oHMIS9bt4rf1u7hw/S5fvF/xczYrO5dVOw5V2AegaaC/SrHJzsqcpvVqcvryDaLjk3BzFBZTFBYVsXn/CeytLcqNSsoveErdnh/Qr0NzVs6fIt+e8CiVnaFnGNajDQAZT7IZ/Nl8RvXuwK8zxinsY+shYbV3cEDNKu9PzbuBYw13zCwsOXfiMEVFhQqp5lYt+Z4/v5/N0h1HqdtQ2cHzqnjVqouWljYXTobCl98qtF05cxKxhgaNy9RZehB9n4cxkTjVKL2X92xaI+yrZul7TsdeA9mw7Fc2LPuVG1cuMGZS+YXDQaintGv9ygr7ANRr3Eyl2GRlY0dAo6aEnwsjPi4aRxfhWVBUVMjBHRuxsrVX+TuAgvw8+javTfse/Znz22r59pSkBI7u3SGPQKrhJfhcQvdu573JMxT2cWTPVgA8fGvKhcELJ0OVxKad64QC8F5+ygv+1Lw7WDm6YWhmya2zoRQXFSpENx1c+RN7/pzL5GUHcK9b/uKel8XJuzaaWtrcOX8UPpmt0Hbv8inEYg2FOkuPHkSS8jAKa6fSBbVn96wFwNHLX76tYcf+HN3wO0c3/E7MjYt0eu/zCseRl53FmV1rKh2vZ2BTlWKTiZUdHgFNuH/lDI/jY7ByFHwtxUWFXDy4BVMre5x9lH8HUFiQz+w+9anfvg+j5i6Xb89MSST86G6Cuw15oWO4+AYQ0v99pePMH9qcwqcFfLGu8praaqoPE7sa6JpYEH/lGJKiQoXopqtbFnFp7Xy6LvgXW7/XvyDN0t0fsaY28eHHaThC8Rsq8fppRGINHOu1km/LSowiKzFaSKdXwt0j6wGwcCtNB+0Z0pcbu/7i+q6/eHT3EoEDS9/FVPEsN4s7h9ZWOl47/2CVYpO+uS12NRuTfOMsT5JiMbZzBUBSVEjkiW0YWNhhpeJ3IESK2dduRmLEKaVziz23DwAbnwZyYS7m9B4CB01V2Ed0mFBLzdxV8TshPvw4OkZm2NdRXqTxX+CtiU1aGmK+7OTLpM3XGLc+nE9aemCoq8mBG8n8fOQeLbys5KnZqopjSXTN2nNxDGjopFCnqSy2xrqMburKnyei+XzbdUY0cUVLLGJ7eAL/RiTRr4GTglhSLJUyYsVFPu/og7uVAXuvJ/H3qRi61bUnyK10jLUdTfCwNmTpSUGx7Ve/Yse/h7UhD7/v/ELn+DwTWnsy5O/zvLfmMhPbeGKqp8WSo5HEpeWxdkxDeUrMf87F8fm260xq68XkdqpXBqqita81vnbGLA+LwVhPi5beViRlFbDnWiKHbj6irpOpPDpsRhdfOi46xdg1l/iiky/2pnqE3U9lzdlYgmqY077mu11M8F1GW1ODmQObM+GvA3yw5F8mdAvCSE+bfZciWbjjDCH+rgR5v5jQ5FSS5mtN6DUGhdRSqNNUFlszQ8a0r8fvey8yZflhRrULQEtDzNbTt9h9/i4DWtRSEJaKJRKG/LidL/s3x93OjH8v3GPp/sv0aOxD4zJiWB03WzztLfhz3yUA+jdXrjdQFk97C5LWTq6wT2V82qMxAxZsZdQvu5jcszGmBros2n2euEeZbJjWW24vq0OvMWX5Iab0bsKU3qUfp5m5BVyLTqZ9oIdCakkZPk6WtKztyrGIWPrO30Lfpn44W5mw9+J9tp2+jY+jJZ0aeKKpIa5SPzUvh7amBjP7BzPx71A+/OMg47sEYqSnzf7L0SzceYGQWs4Eeb6Y0ORkKaxiXn3sBoOa+ynUaSqLrZkBY9rW5vf94UxZdYxRbYS6StvO3mX3hUgGNPNVEJaKJRKG/vIvX/RpjLudKf9ejGLpwWv0CPKkkXfpGOvUsMbT3ow/DwhpT/s3VS4mWxZPezMSV46rsE9lfNqtAQMX7mbUr/uZ1L0Bpvo6LP73MnEpWayf3E1uL2uO3WDKqmN81iOIKT1LV/ll5j7lWkwK7QNqqLQXNf8ttLW1mfPtPD4Y+x4jhg3ls6lTMTYyZs/uXcyf9y2t27QlOLjiRQPP4+wivHgvX7aMYSNGKNRpKoudvT0fjvuYRT//xPiPx/H+hx+ipanFxo0b2L5tK0OHDVcQloqLi+nTuyezv5mDp5cXO7dv57dfF9Onbz+aNi2trVGvXiDePj78uugXAIYMHaZ07LJ4+/iQnfdmok7/XraU8R+P48sZM/myRASbNn063bt2YdCA/nz+xReYmZnxw/ffExMdzc7de96Zeg3/39DW0uSb8cP5aPZiRn+5kEkj+mBkqM/e4+dY8PcmWjWqS+O6FT+zn8fJXhDzV2w7yNDubRTqNJXFzsqcDwZ0YfE/O5k47w95XaVN+4+z48hpBndtrSAsFUsk9P90LrPGDcXTxYFdR8/w+4bd9G7XlCb1asr7Bfh64OXqyJJ1uwAY1KWV0rHL4uXqSMaFHS90js8zZXQ/en0ym6FTFzB1dD/MjA1ZuGobMQnJbFs8S35/r9h2gInz/mD6+wOZPnYAJkYGtGhYmx1HztAy6DDdWjUm6mESH89ZgoONBfMmjgKglqcrQbV9WLn9IOYmRnRv3RiJRMrGvccJPRtOj9bB1K8lfCNVZX9q3g20tLQZN30ucz/7gK8+HsmwcZMxMDLm5ME9rPjlO4Kat6ZOgxdzats6CimBdqxdTtf+wxTqNJXFysaOviM/ZP3SRSyYPl6oq6SlxcEdmwjdu53OfYcoCEuS4mI+G9WXD6d+jbObJ8f272TT8t9o07UPdYNK50+f2vVw9fBm49+/AtC5zxClY5fF1cOb07HKkegvwshPpjFxWA++eH8wI8dPw9jUjNW//UjCgxh+Wr1Dbn871i5nwRfjGfPpF4z59EuMjE2p3ySE0H+306BZK1p27MbDmCjmTR2Htb0D42fOB8DNy4+gFm04f+II4wd3pWOvQdg5uXB8/y4O7tyMm7cfLTp0Q0NDEw+fWmxa8TuGxiY0DmlHSlICof9u59ThvfjVra8URabm3UJTS5se42axdu4nrJr5Hu2Gf4qugRHXTuxl/4of8A1qiVudRi+0T3M7YdFa2I6VNO46RKFOU1lMrOxo0W8soeuWsHHBJHldpYsHthAeuotGXQYpCEtSSTF/TR5E149mYOPsQfjRPRzb9CeBbXriEVDqA3D2rYutqxdHN/wOQKPOFS/AsHX1YvGZxy90js/TYeRkfpvYl7+nD6fjqCnoG5lyaM3PpCbE8tHPm+U2GbZjFRu/m0Sn96bRacw09IxM8G7QnPDQXZxpGELdkK48jo9m3bfjMbW2p+eEuS98DDXvLmJNbRoOn8nJxRM5tvBD6vQZj7a+EbHn9hO+aSEOdUOw9S0/Ck8VhiV1g+4cXINXm4EKdZrKom9uS80uo7m+8w9O/zEVv04jEWtqEXl8GzGn9+DVeoCC+CKRFHN47jDqD52OiYM7MWf2cnP3Mtyadse2ZukzwdKjDqaOntzY9RcAnq36VzheU0dPRu9IqLBPZdTtN5GDswcRumA0Af0+RdvQlGvbFpOdHEf7WevltabuHFhD2B9TqTfwM+oN+AyAhiNmsnNye0IXjKbBsBkYWtqTEBHG7QOrsfULwiWoA1KpBMeAlsSHH2P/rH54hvTF0MaZ2LN7iTq5HTNnH1wbdZSP52lOJqmR13Bu2B6R6L8ZOfjWxCaAQUHO5BcW882eW+y+KqR10BSLGNzImekdfV+45loLLysCXcxYdSaWe4+y2f5R+SuYvuzsS7EElp2KZtWZWPn24cEuzO2h6PRu5mmFrYkuY1ZfQlKSxzLY3YIFvfx5nj6Bjny3/w4h3lY4q6gJ9boJ8bZiyaAAJm2+xuhVgtPeRE+L2d39aFUmOksqFdJHvWipPLFIxKqRDRi3/go/HrzLjwfvyts6+9sxt2ctNEvSF9V1MmXdmCAmbLzKoGXn5f3a17Rl0QDVKy/UVJ0hLWuT/7SIWeuOy6NfNDXEDG1Vmy/7N39hewnxd6W+pz0rDodzNyGN3V8NKLfvzIHNKZZI+Gv/ZVYcDpdvH9m2LvOGt1bo27yWC3bmRoz4aafcXpr4OfPDKMVUJAD9mvnx7aZTtKztKk99Vp20rO3KH+M6M+GvAwz/SVhBYGKgw9xhrWhTt3QilEqlgr08l7c27OYDJFJpuen+xCIRy8Z34/OVR9h25hZHr5WGCgf7OrH4g47ymlhV7afm5RjSoib5T4v4emMYO88LxZk1NcQMaVGTL/s2fvH5pZYz9T1sWRl6nXuJGez6ole5fWf0C6ZYIuWvg1dZGXpdvn1EK3/mDVVcmdLczwk7c0NGLt5Xai++jnw/oqXSfvsG+zBv61la+jvjYqVcE+p109LfmT8+aMeEv0MZsWgvACb6OswZ3Iw2dUqjs0rnl+fs5Xa8YC+e5af7U/PfYuSo0eTn5TH982ls3bIZAE1NTUaNHsPsOXNf+KO0dZu2BAU14q8//+D27dscDj1abt+5386juLiYJYsX8defpWkixr7/AQt//kWhb8tWrXFwcGBAv77ynPTNW4SweMlvSvsdNGgws76aSZu27eTp+t4FpFIpxcXFCvNQm7btWLl6DR+MfY/+ffsAYGpqyg8/LqR9h47l7UrNG2B4j7bkFRQw4+dVbDskrPLV1NBgRM92fP3x0Be3jUYBNPT3ZtmWfdyJeciBZeWnyJr9yXCKiyX8tn43y7bsk28f06cjP0wdq9A3pGEd7K0tGDxlPhKJcG81q+/PL198xPMM7NyS2b/9Q+vGAfJ0fdVJ68YB/P3tJMbN/pVBnwkOahMjA76bPIZ2TUqj9oQ5R6JgG39+PYERn//Ah7MX8+HsxQDU9XVn5fwpGBoIixFFIhGbfp7BR98s5scVW/hxxRb579/r24n5k0e/0P7UvDt0GziCgvw8Fs/9Qh4lo6GpSfeBI/no89kvbH9BzdtQq15Dtq1ZSuz9O/yxtfyovXFfzEEiKWbj30vYtmapfHuvoe8xeY5iWp8GTVtiZWvP52MHyuemwMbNmTZ/sdJ+O/YeyB8LviaoRRvsS9L1VSdBLdowe/EKvv3sQ6a9J3wTGhmbMnHW9wS3Ko0cliJF8tzcNPOnpcz4aChzJ7/P3MlCBISPfwBzf1uDvqGwWEssFvPt72v4YcYkDu3cxLnjpTU4Axo1ZeZPS9Eqqb/zw4otfPXJCJYtnMuyhaWO8ZadevDZnJ/Q0HyrLiw1VSC4+zCeFeSzY/FXXD4iLEQQa2jSpMcwun301QvbpG9QS2r4N+Dk1uUkx9xl4p97y+3bfdwsJMXFHNv4Bye3lkb2NOs9ir6TFyj09W7QAlNre5ZNG4a0xCa9Apsy4POflPbbsGN/dv8xB99GrbCwV85w8rrxbdSKEd8sZe3cj1k6VRCc9YxM6PPpPGoGl/pVpFIpEomiTQ6d+TsrvhzF2jkfs3bOxwA4+dRh1Nzl6OobvvAx1LzbeLcdTFFBPudXfU10mLBISKyhiXe7ITQY+gUv6vhwCGiBtXd9bu1bScbDe3SZt7Pcvg2Hz0AqKebG7qXc2lcaYevbcQTBYxUjfh3qNMfAwo4j80chlQr2ZuffhCYfKaec9GjZl0v/zMMxoKXKmlCvG8eAloRM+p2Tv07k8HyhppK2gQmNxszBKbDU3ylFilRSrFDfysozgA6zNnDil0848HWpT9UlqAMtJgjzu0gkptWUvzjz13QiT24n/soxeT+7Wo1pPn4xYs3SGnRJEWFIpRJsfP67NalF0ue8qZs3b6Z///4kL+z6xgaR87SIGwlZ5D4txtfOCHvTV3uJT35SgKGOJoZVqGGTmvOUGwlP0NEU42dvjImeYl0h368OEuBkyvr3gsjKL+Tqw0zsTHTxslGdq3vf9WRGrbrI8hH16ez/5hxsRRIp1x5mIpFKqedshsZrrl8hkUp5kJ5HZEoOuloauFsZYmeiughbYbGEO8nZpOU8w9fOCBvjly/W9qLYTt7Dpk2b6Nev/IKNL4vMNtI2Tq28czWSk/+MiNhH5BYU4udshYPFq+WNT87IwVBXG0O9ygs7pz7J43psCtqaGtR0scLUQPFv6/nerwS42bJ5el8ycwu4Gp2MnZkR3o6qC+rtvXifYQt3sHpSD7o0rHrE3atSVCzhanQyEqmUQA/7124vAInp2dx5mErBsyI8HczxsLNQOc9Xtd+bwmLA99VuQ6n/jH/t+y6PnIJnXI99TO7TQnwdLV6DveRiqKeFoW5V7CWf63GP0dHSwM/JElMDxZodXh8uJcDNhk1TupOZ+5SrMY+wMzPE20F1VO/eS1EMX7SXVRM606W+u8o+1UFRsYSrMSmCvbjbVou9/BewHLq42m3jaZFy3cN3kezsbK5eDSc3J4datfxxdFJOk/giJCUmYmhkhJFR5fYOtPF2AAAgAElEQVT5OCWFa9euoq2jg79/bczMFFP02ttYEVi/AXv27iMjI4Mrly9hb++Ar5/qFGa7du6gX5/ebNqylR49yxeR3yWKioq4fPkSEomEhg2D0ND4byxO0NEUV5sNgSAmrFkwjd7tXn8qx6qSk5vP1btR5OYVUNPDFUfb8gt4V4Wkx+kY6etVSeB4nJ5FxN1otLW18Pd0xdTYUKHdKWQQgTW92Pnb12Q+yeHKrUjsrS3wcVNtv7uPnmXg5Hms/3E63Vu//hRk5VFUXMyVW5FIJBIa+HujUcVaEFKplJuRccTEJ1PX112pLlNZHiSlcD82ARMjA3xqOKm8vi+yvzfF0KkL0DC1Y/PmzdWyf5FIxLw/19Kma59q2X91k5eTzd0b18jLy8HDpxY29q+WXvzxoyQMDAzlgklFZKQ+5t7Na2hp6+DpVwsjE8W5qW0te/zq1mfR2t1kZ2Vw69oVrG3t5enlnuf4/l1MHdOfBcs20rJTj1c6jxehuKiI2xFXkEgk1ApogLiK84tUKiXqzg0S4mLw9g/A1qH894KUpASi797iaUE+Lh7euLh7KYkPEomExIexxEXeRUdXDxd3L6xsX3/66zfF9PcHY64nrjbb7devH1EZhYyZv7ryzm+Qgrwc4u9eoyAvFwePmpjZOLzS/rIeJ6FjYKQgmJRHdvpj4u9dR1NbGwePWugbK2Y9mtKmBi5+9fh48TbynmQSdzscU2s77Gqork949fgelk4Zwtjv11K35ZvzlUqKi4i7FY5UKsG1Vn3E4qrbZGLkLVITYnDyqYu5bfnPw5c9xrvI5SM7WD59hNKi4ddJv379uJz0lNbTllfe+Q1SmJ9DatR1igpyMXf1xcDy1ewtLz0ZLT1DtPQqt7f8rFTSoq+joaWDuasfOoaK9vbPIG+svOrS4etNJVE7V9G3sMPMSXXpmdizezk8bwRtp6/ENbiLyj7VgaS4iNTIq0glUqy96yF6AVuQFBWS8eAO+VmpmLv4oW+ueqFWbmoiGQ/uUvQsH1NHT6Fe1X80ijA6bBehC8aosrct78SyEEMdTRq5qXZGvwy2LyBuWBrqEOJdtY8HEz0tWnhV3Hf9+QfYmei+8ZRxmmIRgS7VVxNJLBLhamEgrwtVEVoaYvwdqj9K5f8rhnraBPu+mmOvLLZmlU8eMiyN9WlZ27VKfU0NdAnxr7jv2qMR2Jkb0iFQdR7+6kJTQ0z9F0yj9qLYmxthb175x2lV+6l5OQx1tWns82ovWmWxNav8GSjD0liPlv5VW4ljaqBDSK2K+649cRM7M0M6BLzZ6AtNDTH1PdRpUNWUYmRkRLNmry9/tJ191Z/HVtbWtGnbrvKOgJmZGa3bVLwyc+WKFdg7ONCla7cqj+Fto6mpSVDQi6WgUfNmMDTQo2m9itMCvwh2VlVPKW5lbkLrxqpTnTyPqbEhrRpVnHVg9c7D2Ftb0Dnk9ef5rwhNDQ0a+ldc91YVIpGIWp6u1PJ0rbSvs501znYV1x18kf2peTfQNzQioNHrE5utbKq+cNTM0oqgFlWr52VkYkZQ89YV9tm1YRVWtvY0b/fmnGwgRITVqvdiKZdAsBcPX388fJUzvzyPtZ0D1nYVv5uLxWIcXdzk9aPU/DfR1TfEI+DFUixXhIlV1W3SyNwK30YVp4CVoW9sim+QclaJspzZ9Q+mVvbUbv5mI8nFGprU8FedZroiRCIRDp41cfCsWWnflz2GmncLLT1D7Gq9vlpo+uZV//7XM7HEMaBiG5KhY2iKQ92QCvvcPbwOAws7nIM6VNjvdSPW0MTa++WiicSaWli4VT4HGljaY2D53108UVXeCbHpf4FfjtwnOauA0DuP+LZHaWo5NWrUKPPTjrMkpedw+GoU341og6bGfzMPqRo1b4Kfdl0kKSOHI9dimT+0hdpe1Kh5DXw371sSExM5sH8fP/+yCE11Wh41agD4/u/NJKakcTDsEj9OHYvmfyRyTo2a/wVWLv6OlKREzhw9wGR1yjg1at46B1b8SObjRG6ePkTfz75HrKG2STVqqovwzT+Tl5bEg0tHCB47T21v/2HUf7lKsDHSwdyg8nRJ/5yLI/dpEYODnBnauPpzuKpR8y5iY2qAhXHltcpWh14jt+AZQ1vVYVjrOm9gZGrUvHvYmBpgblR5aqQ1x26Q+7SQISG1GNby9a2YV6PmfxFbWzssLStPXfb338vIzclh1OgxjH5vbKX91aj5r2NrZY6FWeX1/lZsO0BOfgEjerZjVO83u6JUjZr/VSxt7DA1rzyTy461y8nLzaX7oJH0HDK60v5q1Kh5OUwsbTE0rdwmw3as5GleLsHdh9G054jqH5gaNf+D6JnboGNcub3dObCGwoJcfNoNwbfDsDcwMjXVhVpsqoTjU0Kq1O/yjKqFzatR879M2A+jqtTv2pIPqnkkatS8+5yaP7hK/a7+MrKaR6JGzf8OV65FVKlfZHRs9Q5EjZp3jItbllSp3539K6p5JGrU/P9jQ+jlKvXbfeF+NY9EjRo1ADM2nq1Sv7l7blbzSNSo+d+nz5KTVeo3cEV4NY9EzZtCnYtHjRo1atSoUaNGjRo1atSoUaNGjRo1atSoUaNGzUujFptUEHo7hZ3hCW97GNWORCqttE+RREoVusnJeVpERu6zVxiVmv8FDodHs/3M7bc9jJdCKoXM3IJK+xUVSyiWVG4cVd2fjJz8Z6Rn51e5v5r/PkeuxbLj3L23PYyXJreg8LXurypzk4ycgmek51TdvtT8tziwfx+bN21828N4p8jOziYtLe219VPz/4+DYZfYcrBqK0zfNaRSKZlPcqrUNydPPTeoefc4ffQAh3ZtftvDeCXycrLJykh/5f0UFxXxtKDyb56q9lOjpircOH2IS4e2ve1hvFMU5OWQm1W+TUuKiyh8qrZBNS/Ow0tHiDq5420Po1qRFBchlRS/1n1KpZLX2u9toE6jp4IlxyKJTc2lR4DD2x7KS9F4/lGC3S1Y2E+5Fk7U41xWno7hwI1ksguKaOBqzvst3GjmqVjfIPR2Ct/tv8PdR9kY6WrS1MOSkU1caeRWfp7NjNxntPzxBEZ6mpya2lK+XSKV0mbhSYpUOOadzPVYNyboFc5WzbvIr3vOE5OcSa9g37c9lCqTmVvA1+uOsyXsFgXPijDU06ZNXTe+H9UWizJ1dbaE3WL5oStExKRQLJHgamPKe+3rMapdAGKR6IX3V5b07HyaTV2Jsb4OZxeq87T/f2Hxv5eJTcmiZyOvtz2UKhMRm8KczWcIj35EZu5TrEz06VjPjdkDm2Kkp7rOYYPP1tDU14GfR7dWaotKzmT54WvsvxLNk7xnNPSy48MOATSv6VTuGNJzCmj+xTqM9XQ4s2DIazs3Ne8OC3/4gajoKPr1H/C2h/JS+Pl40aJFCH/8tfSV+shIS0sjMKAOJiYmXLtefloXVf2OHQ3l04kTKtx/vXqBrFi1utJxqPlv8/OqbUTHJ9O3ffO3PZQqk/kkhy9/WcmmfcfJf/oMQwM92jcJ5OfpH2JhWlqH6urtKL76dTWXb94n80kO1hamdAlpxLxPR2JkINQUlUikNB4wnqJiZceAi70N23+dxfEL15i84K8KxxTg68Hfcye93hNV8z/PP78vJD42mnbd+73tobwUWRnpDGodiIGxCZuPX5Vvl0gkDGkXRHFxkdJv7B1d+PmfnfJ/nz9xhCXzZhB15ybFxUXYOjoz+P2J9Bn+PmKx+IX7qVHzIhxes4jH8dHUb9f7bQ/lpZjVKwCvwGYM/nKxwnapRMK8IU2RFCnPbRb2znz0yxaV+8vNSmfuwGD0DI35avMFhbbb546yc8ksEqNuIykuwtzWidZDPqFFnzGIxGLuXjzB5h+mVjheZ9+6DJ9d8Xyq5n+Xa9t+5UlyLO7Ne77tobwUm8Y2xN6/Cc0++VmpLfL4Vm7tXUFqdATS4mKMbF2p2WU0fp1HIRIJc5RUKmH7+JZIVbxzGto40WHWBvm/sxKiuPV/7J1nWFRX97dveu8dAekgihU7FqzYa+ya2Hs3MbbYo4nG2I1GjT3YNXZFqVYsYEFFegdB6tDL+2FgYJhRx8SU9/lzXxcfPHvNnrOOZ80+Z//2WvvSPqLvX6VIkI2pSwsa9JtCnUbiz+uy2v3b1I7S/2McD4ojKk0gta2guJQx+x/w+4M4PJyM+bKNNVFpuYze94B7kVWrX88+SWDUvvtk5Rcz3cOOri4m3AhNYfS+B0Skvn814dwTISRnS64iTMosIDQpGwV5OQw0lcX+9NSlT0rWUss/SVFJKUPXn+KIz1MGt3Vhy2RPBrWpx7m7rxi14YzI7rj/C6buuEhmbiFTejRjXNcmCAqKWPibNz+fvffJ/dVk1u6rJGfItmK3llr+LYKjUum/7gwhUakMau3Egv4t0FZT5pDPcwauPys1M+n3gJdEpWRK7a+gqISRmy5w1D8UD9e6jO3sSmRyJiM2XeDuq/dnGc/+1ZvkDOnjXS21/NscOniAiPDwv2xTnckTJ5CUmPin7OTk5FBSVJL6V1paysvQULKzs2U+l1pq+acoKi5hwIwVHDx3gyE9OrBz+SyGeHbg9PVAhsxZI7J7HPqGHpOW8CQ0nKE9OvDtxGHoaGqw//RVek1eSlnForeE1DSev4lGQUEBIz0dsT89HS0A5JBDSVFR6l9pWRmvIuPIEeT9K9ejllr+TdbMn8zblCSJ46lJCYS/fIaCvAJ6BoZif9p6eiK7oEAfZo3sQ2JcDH2GjWHwl5MpLMhn49K57N209pPtaqnl/xL3Lh7lbVyk1LaM1EQS3rxATkEBTT1DsT91bT2pnwE4vHoGWW8lY/p1kB/bZw0kPSmW1n1G0n7wBIoLCzix4Wsu/bq+wkoOBUVFqX9lZaUkRb0iX5DzOVyvpZZ/nLCbXmQnRUlte3PrOD6bplGYm0mDvpNx6TmWkgIBd3YvIvjEZpGdIC2Rd9GhyCnIo6pjIPanolUVlyVFBVxbPYrXN45h0dQDl55jyUqM5PqqkSQ9v/vJdv8FajOb/gdIyipg47XXBMdl8iLx/RMF6y6/IiI1l2MTW9LJ2RiAie1s6PSTH7N+D+bBks4Ul5ax6kIo6sqK3JjXHh01JQCW9qpHk1XeTD7yCO95HST6PnAnmluvUtFVV5Joi0oXTgZuH9GE+ubaEu211PJv4+X3nIdvElk1yoPpvZsDMMqjIXJychzwDiY4MpnGtqbsuPgAW1N9bqwdLcremN2vJU1m7mbv9cfMH9j6k/qrzv4bT7gVEomepuo/6HkttXw6e2+EUFBUyvUVg2hQ1wiAbwe1YuD6s/i/iONCUDj9WjiQ+C6XDWfv8zgyhRexae/tb+3Ju4QnZeC1oB9dGtUFYHL3xrRfcowZe27waNNXEp/57eYzbj6NqY2XWv5TJMTHs2b1Kh4GBfH0aciftpHG7l92cf3aVfT19f+UXUePTgQ9lr7p7pzZs8jOzmbHzl0yn08ttfxTHL1wkwfPXrNu3jhmjRaujP2yf1fk5GDfqas8Dn1DUxcHfvG6REFhIX6Hf6Khky0Ay6aNpNfkpfg+COHczdsM7OpOZKxwUm3fmnm4OtpI/c4OLRpy7/hWqW3zf9hNTm4+W5dO/xu8raWW/y6nD+3hrs91tHUlx6H46AgAVm7bj4NLw/f2sW/z95SXl3Pwym0s6grjdPqi1fRys+Po7s1MmLsYeQUFme1qqeV/nczURC79up6YF4+If/P8vXaVItRXq/Zg4dBApr79T+0j9K43GlLEqMt7f6S8vJyFB30xshCOlf1mrGBxT2e8j26j58SFODVvz+Jjt6X2fWLD1xQIchixSDIjpJZa/qsI0hJ5/PsG3r55QnrU+6tIPD27Ex1zW/r9dA1ldeFCpUaDZ/H7+GaEXtpHk6HCzPdKsarjvF0Y2NR/b38PD60lKyEczxVeWDYTVoFp0HcSp2d2wG/zDIbtffRJdv8F/naxqbCkjK0333DqUTyJmQVY6Knhbm/I8r4uaKpUff2diHT+CEnE//Vb8otLaWlrQGtbA0a1skJBXliaav6JEIpLy5jb1ZFtt8LxeZWKrZEmw1tYMriZBb/4RXL6cTyJmfk0stBlzYAG2BpqiL5j0uFH1DfXpo2dAb8GRBH4Jg1DTRWGuFkwzcNOrARWTbLyi1l3+RV3I9N5JyiiubUeI1vWpXM940/29XOTW1BCxFsBWqpKNLbUJThO+upxr6A4XMy0RUITgJGWCh5ORpx4GM/j2AyUFRRIyiqgb2NzkdAEYKipQgdHI7xfppBdUIy2alXb6+QcVvwRyrLe9ThyL1ZiVXvkWwFycmBnpEEtn05hcQk/n7vHyYBQEt/lUMdQm/b1rVg1ygPNauWqbofGcv7ea3yeRlNQVEIrZwva1LNkTOdGohias+cqRSWlfD2wDZvP3+dWSBS2ZnqM7OjKkHb12XkpiJOBoSSm59DIxoT1Y7tga1r18DF+yx80qGtMWxdLdl95RMDzGAx11BnavgEz+7T4cAwJClnj5c+dl3Gk5+TTwtGc0Z0a0bWJ7Sf7+rk5GRiKobY6Ez2bih2f278VLZ3qYKCtTnZeIS/j0pjo2VSsTJipnibt6lsR8CKW4tIylBTkZeqvOq/i01h22IflIzpy6GbIJ+1ZU4s4hcWlbL4QxInbr0l6l0sdAy3auViwaoQ7mqrV4uVlPOcfhOP7PJaCohJaOprTtl4dRndsIIqXuftuUlRSxoL+zdly4RG3nsVga6rLyPYufNHWmZ1XnnDqzmsS0nNobGPMutEdsDXVFX3HhO1XqG9lSNt6Fuy5FkxAaDyG2moMda/HjF5NPxwveYWsOXGHu68SSc/Np4WDGaM71qdLI+tP9vVz8+BNEg3qGoqEpkpGtHfB/0UcjyNS6NfCgdyCIsKTM9FWV6GJrQlPIlOk9vd7QCj1LQ1FQhOAkY46nVzrcjzwJY8ikmlmVyXOvkpIZ9mxAJYPa8thnxe18SIjBQUF/PjDeo4dPUJCfDyWVlZ4eHRi/Y8b0NLSEtn5+/ly+tQpvL1vkJ+fT9u27rRr357xEyaiUDGxM3XyJIqKili8ZCkbfvyB69evYW/vwFdjxzJi5Ci2/LyJ348dIz4+jiZNm/Hz5i3YOziIvmPk8GE0bNiQ9h07sn3rVnx9bmFkbMyoUaOZt+DrD5bHyczM5LulSwjw9yctPY3Wrdswbvx4PHv0/GRfPzc5OTm8CQtDR0cHN7fmPHwY9KdsahIa+oKFXy/g+3Xr2bd3L2Xvqc8tq111rl+7yu5dO7ly7TompqYfta9FnIKiIjbuO4nXZV8SUtKwNDWiQ4tGrJs7Dk2NqpK5AQ+fceZGILfuBZNfWESbxi64uzVg7MDuKFTc79NXbaOouIRvJw3jp/0n8b77BDsrc77s14VhvTzYevgcx6/4Ep+cRpN69mxcOAl7K3PRd4xZ+AOujja0c3Nl57E/8H3wFCN9HUb27sScLwchL/+BMSdHwPJthwh8/Jz0zGxaNarHVwO60d3d7ZN9/dx4XfLFSF+HKcP6iB3/evwQWjd2wVBPB4D7IS9p6GQrEpoqGdOvC74PQnj4PIyBXd0Jj01ETk4O+7qfXi79xp1H7DlxiYu71mBi8P6V4rX8MxQVFnBg2waunDlGamICpnUscXPvyOxl61HXrPqtf3TXn5sXTnPf/yaFBfk0btGWpq3b0X/EOJFgsfbrqRQXFTF+7mIObt/APd8bWNrY03fYV/QYNJxje7Zw9YwXKYlxODdsyoLVm7C0sRd9x+Ipo3BwcaVZmw547d3Ow9s+6Bka02vwSEZNnffBcS0nO5Od677jyf1AMt+l0dCtNf1GjKVtJ89P9vXvIvJ1KJtXLmTGkrWcO7afsjLx8SU2Khw5OTmsbB3e04OQlMR4jM3qiAQkAHVNLeo3duPJvUAKCwtQU9eQ2a6Wf5fiogKu/baJB1eOk5maiJ6pBU5u7Rk4Zy2q6poiu7BHgTz2Psur+z4UFeZj37g1Dk3daTvgS+TlhTF4dO0sSoqL6Dn+G64d/JnQu94YW9nRpu9oWvQYys2j23lw9QQZKQlYOTdmyNc/YmxpJ/qOfYu+oo6jK47N3PH5fRevH/qjpWdEy17D6Tp6FnIfiMH8nCzO71zJm8d3EGSmY9uwJW36j6FB226f7OvnpkCQQ0pMOKqaOtR1aUpM6GOpdqlxEcjJyWFiZSe1vSZJkS85vXkJA2auJPDsQYm9XzJS4tE1NhcJTQCq6ppY129G+JM7lBQWoqymXrNbAELveuN3ai+ztp9H28BERk9r+RilRYUEn9zMG9+TCNKS0DSqg3mjdrQatxIltap7MOnZbSID/yAh2JeSogJMXVpi1qANzt1HI1cRbwHb5lJaUkzTYfMJPrmF+Cc+6Jjb4tRlBPYeX/Ds3C7CfU+Rm5aAkX0jWk9ah4551e/xzR8mYGDTADPXtjz/YzeJTwNQ0zHCodMQGg6aISojJ40iQRZBh9aS9PwuBdnpmNRrgXO3UVi6dflkXz83xfm5ZCVGoKyhjZFDE96+kVyoVyTIJiPmFfX7TBAJTQDq+qbUaeROQkggZSXFyCsqkZUYCXJy6NT5cFyG3fRC39pFJCABqOkaYdG0E29uHSf19SOMnZrJbPdf4G8XmxaefsrJh/EMbmaBax0dotMFHLkXy8vkbC7OdAfgdngaQ3bfQ0tVkYFNLdDXUMY/7C0LTz8l5p2A73q7APA8MZukzHz8w9LQVlPC3d6Qc8GJ3IlI48zjBPzD3tK5ngmWemrcCE3li1/uErSks2hCL+BNGk/jsthxK4K29gaMblUX37C3rLn0ksg0AZuk7HEEwsyhvttvk55byBA3S7RUFfF9/ZbR+x6woq8Lk9rbyuzr34GDiSbnprcBICpNQOt1tyRs3gmKyMovZngLyf0vbI2EwRoSl4WVvnDAaGKpK2HXxEoX75cpvE7Oobm1cEVTYUkZU448ppWtPhPcbTlyL1bic9FpAuroqiEoLCXgTRpvcwpxMNGkqZWeaFK3lvezYN8Njvs/Z0i7+jS0MSEqOZNDt0IIjXvL1VXCfUoCX8QycO1xtNVVGNTWBQMtNXyfRbNg33ViUjNZMbIjAM+iU0l8l4Pfsxh0NFRwr2/FubuvuB0ay+nbL/F9Fk2XxrZYGmpz40kkA9Yc58m2yaIY8n8eQ0hkMlv/uI97fSvGdG6Ez9NoVh3zIzIpgy2TPaX6kPguh17Lj5GWncew9g3QVlfhVkgUI348zerRHkzp6Sazr38HEUkZdGlii7KiAtGpmbyKS8NMT5P6dY0Z0k64AiGvsJiLK0ZgbaIj9tnsvEJexL7Fo6E1SgryMvdXSWFxCRO3XqC1swWTPJtx6KbsK91rkeTrAz4cD3zJkLbOuFobE52SySHfF7yMS+fK8i8ACAyNZ9APZ4Xx0toJfS1V/J7HsuA3H6JTs1gxTPh7/SzmLYnvcvF7EYuOugru9Sw4d/8Nt1/Gc/rua3yfx9GlkTWWhlrcCI5m4PqzPP75q6p4eRFHcFQq2y49wr2eJWM8GuDzLIZVx28TmZzJ5gmSexcBJL7LpffqU6Tl5DPU3VkYL09jGPHTBVaNaMcUz8Yy+/q5KS4to5NrXZraSb44JKQLyyRUZhs5mutzYYmwHnpUSibNFxyS+Ex6Tj6ZgkJGtHeRaLOrEO6Co1JFYlNhcSmTdlyltZM5k7o15rDP+1cc1SLOrBnTOXL4ECNHjaZx48ZEREayf++vPH/2DL9A4apEP18fenTvho6ODkOHj8DQwICb3t7MnD6NqKgo1q3/AYCQ4GDiE+K5edMbXV1dOnb04OSJ4/j7+eL1++/c9L6BZ4+eWNWty5XLl/Ds3pWw8EjRZJvPrZs8fvyInzZuoENHD8ZPnIT39essWbyI8PBwftnzq1QfEuLj8ejYnrS3bxk1egzaOjrcuHaNAf368uOGjcycPUdmX/8OnOvVw9vHF4CI8HBcnCX3YZPFpjoFBQWMHjGCtu7tmD5zFvv27v1LdtVJT09n4oTxfDFkKB09On3UvhZJ5ny/i2MXbjG8tweNnO2IikvitzPXePEmmlsHNwDgH/SU3lOWoa2lwdAeHTDQ1ebWvSfMXruTqPhk1s4ZC8DT11EkpKThcz8YHS0N2ru5cvp6AAEPn3H8ih+37gXT3d0NKzNjrgYE0XvyUkIv7ROJSD73Q3gSGsGmA2fo0NyVcYM8uXn3Ccu2HiQ8NpGdy2dJ9SEhJY2u4xaSlpHNiN6d0NZUx/vuYwbPXsW6eeOZMbKfzL7+HYTHJtKtrRvKSopExScTGhGDubEBro42DO8l3CO2uKSELm2a4tZAMp7iU4SZtfoVJfIi4hKxNDVCkJeP7/0QUt9l4mRjQXNXJ5HwJ413WTlMWb6Vwd3a0aHF+zM3avnn+GHRLC6dOkrPQSNwatCY+JhIzh3dT/jL5+z7ww+AR3f8mD6sJ5paOngOGIqOvgEP/G+y/tuZJMREMXPp9wCEvXhKalI8QQG30NTRxa1tB26cP8Xju/5cO+fFff+btO3kiamFFbe9rzB9aA/O3XstGteCAm/x6uljDu/cRLO2HRgwagL3KvYcio0KZ+nGX6T6kJqUwMQBnchMT6Pn4JFoautwz/cG878cyOzvfmD4xJky+/p3UVRYwNJpY2jcsi1Dx0/n3LH9EjbxURGY1rEkTyAgKNCHd29TsXZwpkGT5mIZSB09+3F092Zu37oqEtNiIsJ4eMePFu06iQQkWe1q+XfxWj+f+5d+p0XPYVg6NSQtIYrAswdJDA9lwf4bAIQ9DGDr9H6oaWrT3PMLNHQNeHXfh9/XzyUtIZoBs1YBEP/6KRmpibx64Iu6pg5Obu15dOMMbx4FEnT1JC/v+9CgbTcMTK14fvsaW6f1ZfX5ZyIR6VWQH7GvgrlxaDOObu1xH/CVaM+h1LgIRi3dJtWHzNREfqZZgrUAACAASURBVJrQnZzMNFr1Go6apjahd2+ya95QBs1ZS6fh02T29e/A1MaJeXsuA8LspeUDm0i1exsXiZ6pBYV5Al498CPnXSqmNk5YN3ATCXqVFBcVsG/JeOybtKbj0CkEnpXcr7OxRx+8j2zj+e3rItEtJeYNYQ8DcG7Z8b1CkyDrHYdXTadZ14E4Nf9v7SHz/zuBu77mza0TOHh8gYFdQ3KSonh17TAZ0S/pu0F4jyQ+DeTyssEoa2hj32EQqtr6xD/xJXDn12Qnx9By7HIA0qOeI0hLJCHYDxUNHcxd3YkIOEfis9uE+50mIdgPS7cuaBpbEht0g8tLBzFs3yORiJQYEkBaRAghZ7Zh7uqOs+cYEp748uDgarISI2k/a7NUHwRpiVxY2Jv87HQcOw1FWV2buMe3uLZqJK3Gr6JBv8ky+/p3oGvpSO91fwDCrKTjk1pI2MgrKNLnhwtomdQVO14kyCY9KhSLJh2RVxQmZ2QnRqFpZEFJvoDEEH/yM9+ia+GIsVNTkfBXkP2OwtxMHLsMl/iuSpEqLTwYbTMbmez+T4hNRSVlnH6UQJd6JmwZ1lh03NpAg6XnnhPxVoCdkQZnnySgIC/H/cWdRdk0MzvZ02LtTa6/SBGJTQCpOYV828OZOV2Eq2YGNK3DiF/vcyciHb9vPETZM7O8gjkRFEdUWp5YRk10uoCV/eozuVIg6uHEF7/c4/cHsXzVxpqGFuITyQBrLoYS9y6Py7PdaWolXMX2jacTw/fcZ/XFlwxxs0RdWUEmX2vyTlDEb7ejP3otezc0w8n0z69aCq/Ya8lYW7LkkL2xUGxKyy2kvYMhAIHhaUztKK6+hqUIJxJfJ+eKxKaVF0JJzi7Aa1JL3rdIPypNQE5BCW5rvckvqtoYraGFDjtGNMXB5O9Tpv9/p7C4lJMBL+jaxI7tU6tWbduY6rLowE0ikt5hZ6bP6TsvUVSQ59GWyehoqACV5d32cPVRhEhsAkjNFLBkaDvmDRCWfBvUth5D158iMDSWOxvHYWcm/L+dvusyXn7PiUrOEB0D4aTxmjGdmFohEC0e2o4Ba45z1Pcp47o2ppGt5MroVcf8iH2bxfU1o2lmbwbAwi/aMmTdKVYe82No+waoqyjJ5GtN0nPy2X9demmg6vRp6YizhaHEcUFBESmZuRjraDDix9NcexwhanMwN2D71B64OZijrqJES6eqVbC/XH5IXFo21x9HUFpWzpz+rT6pv0q+O+JLckYupxZ/8d4YqkU2ikpKOXn7FV0b27BtUlfRcWsTXRYf9iMiORM7U11O3wtDQV6ehz99iY56Rbz0dqPpvANcexIlEpsAUrPyWDy4NfP6CcshDmztxLCN5wl8mcDt9aNEgsiMPTfwCnhJVEqW6BhAdGoWq0e2Y6qn8MVg0WBhubmj/i8Y29mVRjZVmaaVrDp+m9i0bK6tGCISWRYObMnQDedZdfw2Q92d0VBVksnXmqTn5LPf+9lHr2WfFnY41zGQOK6kIM/6MZKlVNOy89nn/RQlBXm6NZZelkga4UnCTFwTXcnx0d5Mr6Lvqn0xlv8eSHKGgJPf9K+Nl0+gsLCQY0eP0KNnL37dVzVJZGdry7y5c3gTFoaDoyPHvbxQVFTkZVg4urrC+2fBNwtxcrDj0sULIrEJICU5mZWrVvPt4iUADB02jL69e+Hv50vw0+c4OAonfSeMG8vhQweJCA8XHQOIjIhgw8afmDVnLgArVq7Cs1tXDvy2n0lTptC0qeSD8pLFi4iJjibgzl1atGgJwHfLV9CnV08WL/qWkaPHoKGhIZOvNUlLS2P3rp0fvZYDBg3CxeX9ZRA+N98u/IakpEQuXbmK3AduelntqjN75gyyMjNZ+/26z3W6/6coLCrG65Ivnu2as3vlHNFxG0szvv5xD29iEnCoW4cTV/1RVFTgxYVf0dES/tbNGzuI+r0mctnvgUhsAkhJz2D59NF8M2EIAEN6dGDAjBUEPHzGw9M7cKjIxpn03WaOXrhJRFyi6BhAZHwS6+dPYOYooUD03bRR9JqylEPnvZk4pCdN6lVlYlSybOtBYhJT8T20keauTgAsnTqS/jOWs2zLAUb27oS6mqpMvtYkPTObPSc+PinQv3Mb6tlZSRzPzSsgOe0dxga6DJ69iiv+VZmAjtYW7F41hxauTigpKvLTwskSn3/7Lovdxy+hpKhIj3bCcTwyLolsQR71eo4nr6BQZNuknj371s7DyUZyYR7A3HW7yMrJZdXsrz7qTy1/P0VFhVw5/TvunXvw3c9VCxQs6try03fziY18g5WtA9fOHUdRQZGzd0PR0haOa19OX0D/Vs4E3LgkEpsA0lNTmLpwBWNnfQtAt35DmTO6H4/u+HPc54koa2flnAlcOnmE+OgIsUye+JhI5qz4kREThcLu5K+XM31oDy54HWTwmEk4NxSveACw/fulJMXFsP9iAA2aCO/RSQuWMXtkX7avXUKvL0aiqq4hk681yXyXzqmD0kWu6nTqOQBbJ8lFP5VsXb2ItymJbP394nvHl7joCHJzcujX0pGC/KrnNueGTVm1dT/WDs4ADBk3jaBAH+aNGUBDt1Yoq6jy6I4fhiZmTFu4UvQ5We1q+fcoKSrkwZXjNHDvzpjlVc9PhnVsOPnTQlJjwzG2sifo2knkFRVZdS4ENS3hXFu3L+fwXb9GPAu4IhKbALLTU+g7dRme4xYA4NZ9MDtmDybsUSDfnbiPsZVwDDu0cir3Lh7jbXyk6BjA2/goBs/9nk4jhGVO+0xZypbpfbn7x2HaDxqPVb2qebpKzm1bTnpSLN/8dhPrBsL5jV6TFrNj9iDObVtOy17DUVFVl8nXmuRmpuN/6uOLf5p06ouZbb2P2n2It/GRFAhyWNq3AUUF+aLjVvUa89XKPZjaOImOnd2yjKy3Sczcdua9Md1xyGRePfBl19wh2DZsiaKyCmGPAtAxNKXv1O/eex5eP8wnLyeL/jNq4/RzUlpcRLjvKayad6XDnCrhVMvMhrt7FpOVEIFOHTsi/M8gr6DAsF+DUNYQxlujQbPwmtiM2AfXRGITQF5GKm6jF9NkiPBdzK7DQK6uGEbSs9sM3hEoEjD8Ns8U7l+UGCWWoZOdFE2rCatx7TcFALdRi7i8dBCvvY/h0nMshvaSyRwPDq4mJzWOfhuvikSRZiO/4cryYdw/sAqHTkNQVNWQydeaFGS/I/Sy5GKImti06Y2elfNH7d6Hoqo6JvWqRKjn53eT8zaOuKAblJeV0viLqufk7KQoivNy+H18E0oKq+LS0L4RHvN2omvpSFaCcP9edX3Jxby6dYS/K/mZaTLb/Vf4W8Wm0oqNWO9EpPEsIQvXOsKbfZy7NSNaWqGiKFRFp3SwY7y7jVjZtqKSMrTVFMnJLxHrU0FejukeVTeWS8UeQO72hmJiTls7A04ExRGWkiN2XEdNiUntqtL/5OXkmN3Zntvhafi+TpUQmzLzijnzJIHGlroioQmEE26jWlkRGJ7GpWdJDGxSRyZfa5KeW8SGa68/eB1BWILur4hN0WnCfZP0pOypZKEnLH2RlV+MjZEGjSx1CXiTxtH7sfRrbE5ZeTmnHyVwIURY47zy//VGaAr7A6PY/1VzTKSIWJVEpQkQFJawqKczPVzNSM8t5ERQPMcexPLl/gd4z++AunJtzWVpVJYouB0ay9PoFBpaC39YJnRvyiiPhqgoCUN4Wi83JnZvKhKaQBhDOhoqZOcVivWpIC/HjD5VP44N6gonu9vXrysm5ri7WOHl95zX8elix3U0VJjSo6qsirycHPP6tybwRSw+T6MlxKaM3AJO3Q6liZ2ZSGgCUFZUYEznhgS8iOHigzAGt60nk681Sc/OY/3JwA9eRwA7Mz2pYlNksnCye/eVh9iY6PHD2C60cKzD/dcJrDjmy8gNZ7i9cRyGNUrfrTkeQH5hMQDOFoaoKit+cn/XHkew99pjDs0fgIlurej6V6n8bbr9Mp5nMW9xrSjzNqFrQ0Z1cKmKF88mTOzaSCQ0gVCo0lFXITtfSrz0qpocaGAlvIfauViIiTlt61ngFfCS1wnpYsd11FWY0r1qBZq8nBxz+zYnMDQen+exEmJTRm4Bp+++pomtiVjpOGVFBcZ4NCAgNJ6LDyMY3MZJJl9rkp6Tzw9n7n3wOgLYm+lKFZukcf1JFLP2epOek8/aUR1wsZTtcyAUrwF0NSTHEEtD4ZiXJSgUfc/eGyEcnN1LqjhVy/spLRUu9PD38yU4+AmNGwvvyanTZ/DVuPGoqgqv/+w5c5k2Y4ZIaAIoKipCV1eXrKwssT4VFBSYt+Br0b8bNhS+UHT06CQm5rTv0IHDhw7y8mWo2HFdXV1RJhKAvLw83y5ahJ+vD97Xr0uITe/evcPr92O4uTUXCU0AysrKjJ8wAV+fW5w7e4Zhw0fI5GtN0t6+ZdXKFR+6jAA4ODr+Y2LT5UsX2bVjOydOncbUzOwv21UnNPQFp06eYOGixVhaSU7y1/JxSiue0fwfPiPkVSSNnIXvF1OG9ubL/l1RVRGWM501uj9Th/UWCU0AxcUl6GhpkJ0rEOtTQV6eOV8OFP27ck+hDi0aiYk57d1cOXrhJq8i48SO62hpMGNkX9G/5eXl+Gb8F/gHPeXm3ScSYlNGVg4nrvjRrL6DSGgCUFZSZOzA7vg9eMr5W3cZ0qODTL7WJC0jizW7jn7wOgLY1zWXKjZFxiUCsPPYH9hamvHTwsm0alSPuyGhLNt8gCFzVhN0cgdG+pKLBa/4BzF15RbSMrL58euJ1HewBiAiLolcQT4rZoymT6fWpGVkcfTCLQ6evc6QOWu447UFDTXx34mXEbGcvh7I1+O/wNLUSOK7avnnKasY1x7d9ef182CcGggnkb8YO5W+w79CRUX4fzhi0myGjJsmEpoAiouK0NTRRZAtPq7JKygwauo80b8d6gsz2NzadhQTc5q16cClk0eICnspdlxLW5fhE2ZW9Scvz9hZC3l0x497ft4SYlN25juunfXCpbGbSGgCUFJSpv/IcTy87YvP5fN0HzBUJl9rkpn+lj0bV3/wOgJY2Tq8V2wK9L7Mid928ePe4xgav7/Uanx0BHmCHKYtXEnHHv3ISE/j0snDnP/9APPHDubI9fuoqWugpaODmYUVYS9CCA1+hKKSEmVlZSgoKiIQ5Ir6k9Wuln+PynmKsEcBxL1+iqWTMF46DplE235jUFQRvmd1HjkDj6GTRUITQGlxMWpaOuTniu83Li+vQJfRVVm4lXsPOTVvLybmODZz597FYyRFvhI7rqalg0dFJhKAnLw8nmMXEPYwgJf3b0mITYLsDIKunaSuS1OR0ASgqKRM2/5f8TrIn+Bbf9Dcc4hMvtYkNyONi7u/l9pWHWMr+78uNsVFUiDIpd+0ZTTy6ENuRhr3Lh7jzvnD/DJ/OIuPBqKsps6zgKv4ntjDpA1H0DF8f0yraemgb2ZFfNgzYkIfI6+oSHlZGQoKihTkSY/BpMiXPPY+S/ex89E3tfhL/tQiTnmZcMxLenab9MhnGNi6AlC/93icu45EQVl4D7r2n0r93hNFQhNAaUkRyho6FAnE401OXoFGA6v2n6zcU8i8UTsxMcfMtS1hN73IiHstdlxZQwfXvlULfeTk5Gn8xRwSnwYS/8RHQmwqzMkg3O80Rg5NxLJv5BWVce4+msSnAUTdvYR9h0Ey+VqTgqw0Hh39QWpbdXTM7f6S2FSToMNrRUKSnpUzitXG5KykKIrzc3EbvRjr1r0oyEon7JYXr68f5fqa0Qzc4iMstQeoaEqWZ9Y0FsZRkSBLZrv/Cn+r2KSmrMD8bo6sv/KKrpv8cTDRpK29IV3qGdPRyVhUQs3eWJMMQRG7fCN4FJNB7Ls8UTaMaQ0Rw0RbVVSqCkBVUShSmOqI33CVJSWKS8Rrj9oYakishq4UcaLT86hJeGou5eUgKCph0mHxzbZyC4RCWHSaQGZfa2JvrEn0+p5S26pT3ec/g3KF2JWRVyzRlleRbaSrpoS8nBybhzZi9L4HzD8RwtJzzykrK6esHEa1suLQ3RicTbVIyS5gtlcwI1ta0dP1wzX+tw5vgrKiPM4V19nWUIPm1vpoqSmy0yeCS8+S+KJZ7WAkDTUVJb4Z3Ja1xwPw+PYgjnUMaFffii5NbOnU0EZ0XzmYG/AuJ58dF4MIepNA7NtsIpPekZNfhKmeuIhhqqeJsmKVuFc5KW2qL25X2XdRSanYcVtTfYkYcq6YXK6cOK5OeOI7YQwVFDF+yx9ibTkVQlh0SqbMvtbEwdyAhEPzpLZVR0lRuqCZKRAODIXFpRyY1w8Hc6EvDW1MSM0SsOnsXc7ceckkT/GJz/iDc4lMzuDeq3hWe/nTbclhnu6YKnN//Vo5MXPXZUZ3akiv5h+ub16LbKgpK/L1gJZ8f+ouHkt/x9FcH3cXC7o2ssbD1apavOjxLreAnZcfExSeTGxaNpHJmRXxIi5iSMaLguh4dariRXzMsTXVlYyXOkLxNjpF8mEgPDmjIl6KmbD9ilhbTn6R8HOpWTL7WhMHM33i902T2lad98VLdaJTs1hyxJ9rT6KwMdFl9zRPOtSXviL8fVRez0xBgURbXoWYq6uhSkqmgJm/ejO6Y316uclWi7yWKtTV1Vm67DuWf7eMlm7NcK5Xj44dPfDs0YOu3bqL9mJycnYmPT2dzZt+4t69e8RERxMe/obs7GzMzM3F+jQzN0dZuWqCWaVCxDGvYVfZd1FRkdhxe3sHidWUlSJOZGSkhA9hYa8pLy8nV5DLyOHDxNqys7NFn5PV15o4OTuTmSOQ2lad6j7/nSQnJTFx/DjGjZ9Av/4D/rJdTX7asAFlZWVmV2SW1fLpqKuqsHjycFbuOEyb4bNxsrGkQ3NXuru70aVNU1FJNkdrC95l5bD18Fnuh7wiJjGV8NhEcgR5mBmJZ2ybGeujXG2xgKqycJFYTTuFiveComLxRXn2VuYScVUp4kTGJUn4EBaTIIyrvALGLBR/Qc/OzRd9TlZfa+JobUHavdNS26qj/J4FEhlZwgmtwqJijm1chKO18H2hcT07UtMz+XHvCU5e82fa8Kr9nCLjk1i4cS+X/R5gZ2nGb98vwKNl1QTjnlVzUFFSwsVeWP7E3sqcVo3qoa2pzuaDZ/jj1l1Reb5KNh04jbKSIrNG9f+oL7X8M6iqqTNx/hJ2/bCC0d1bYe3gjFubDrTt7EmrDl1Fpdus7Z3IynjH0d2befboPolxMcRFhSPIycbIRFycNzIxQ0mp2rhWMYFsZCpuV1mSqrhYfFyztLWXiL9KESc+RnJci4l4Q3l5OXmCXBZPES8XLqiYhI+PiZTZ15rUtXciICJDalt1qvtcnbTUZFbNnUi/EWPp2KPfB/tY/vOvKCmrYOcsHMctbexp6NYKTS0dDu/ahM/l8/QcPIJJ/TsT/uo5C9dtpVu/L1BWUeXOrWus/Xoac0f357jPE8ws68psV8u/h7KqGr0mfssfu1azblQ7TG2ccGzWjgZtu+HSurMoTkytHRFkvcP7yDainj0gPSmW1NgICgQ56BiJx5aOkSmK1e7HyknbmqJIZfmpkhLxuS1jSzuJGDSzFU4qv42PkvAhJUYYg4X5AvYt+kqsLV8grOqTlhAls681MbF2ZEtgstS26ii8JwY/hTHLd6GorIy5nfA3x9jSDtuGLVHT1OHG4S088fkD5xYdObxqGm37f0njjn0+2N9PEz1JDH/BsIWbcOs2CCUVFV7c8ebompnsnDOYZSceYGAmvkjk+qEtKCgp03nkjL/sTy3iKKqo0XT41zw8/D1nZndC19IRc1d3LN26YNHUQxQTuhYOFOS84+nZnaS+CiInNY7sxEiK8nJQ1xePI3V9U+QVq+49BSUV0fHqVJaqLKsRbzrmttSc6KgUcbKToiV8yEwIh/JyigsE3Pxhglhbcb4w3nKSomX2tSa6Fg6MPR0nta06CoqSCRh/hbGnYslKjCQl9D5Bh9Zwbn53hu8PRl3PmI5ztqGgpIxeXaGYrGNui0m95iira/P0zHai7l4UXffCXMnxuqRAqFEoa+rKbPdf4W/fs2lOFwf6NzHnRFA83i9TOHQnhgO3o4Xl86a3xVhLhZ0+Efxw9RUqigq0tjOgvaMRc7o48otfBLE1BKD3ZcB8aKP16kjLwFGvyEiQln2UkVckalOqMXmnp67EoKZ1RGKVLL7WRE4OVJX+/qyeyvJ5Na8nVPlooCk8v3pm2vh+3ZE/ghMJS8nBWFuVDo5G3IkQpuQ5mWqxxz+Sd4IisgtKmO0VLOorOauA8nKY7RWMnZEGszo7SC1NCNClngk7fSJ4lZTzWX39X2PegNYMaFMPL7/n3AiO5Lcbwey7/gQ7M30uLh+Osa4G2y48YN2JQFSUFGhTz5KODeoyf0Brdlx8QEyq+IS2+ntWn8ocQ1IyCir7lJZN8a5iskJFSUFCNNXXUuMLdxecLAxk9rUmcnKIsor+DGZ6wvh1czAXCUOVeDazZ9PZu4QlpFNeDuWUi10nW1M9bE31kJeTY/quy9x4Eikqtfex/vZfDyY9J5/svEJm7KoqMZOUkUN5OczYdRl7M31Reb5aZGNev+YMbO2IV8BLboREc+DmM/Z7P8XOVJcLSwdjrKPO9kuPWXf6rjBenC3oUN+SeX2bs/PKY2Leiq/4UVeRfm/Jut2c9HgRPuCoSPntz8gRii7KSgoo1ogXPU1VBrdxEolVsvhak78aL5WcvP2KBQd8kEOOFcPcmdS9kZgoJyuV5xidKim8ZVRkNBloq/HbzWcV8VLEzD1VddGTMnIpp5yZe25gZ6bHnD5uEv3UIuTbxUsYMnQYhw8f4urly+zZ/Qu/7NqJg6MjN2/5YmJqyqaNG1i5YjkqKiq0a9+BTl268O3ixWzetImoaPGXdA0N6dllH9oEvTrSMnDUK/pUkZJ99C49XdimrIKSkvhLgoGBAcNHjMTFxUVmX2siJyeHmpqaTOf+T7B79y+kpaWRlZXFxPHjRMcTE4XiwMTx43BwcCC/oEAmu2++XSRqi4uNxev3YwwYOAh9fcnytLXIzjcThjDYsx1H/7jFtcCH7D11hT0nLuNQtw7X9q3DxECPzQfPsHrnUZSVlWjXrAEerRrzzYQhbD18luiEFLH+1N+TeScv46Bjaij5/1mZpSMt++hdpvAZXEVZCUVF8bFBX1eLoT074lIhVsnia03k5ORQe89zpyyYGwufo1q4OomEpkp6tm/Bj3tP8DqyamLB65IPs7/fCXJyrJ0zlqnD+6CiLP57Ia2UIICnuxubD57hRXiM2PG45LecuOJH/y5t0NP581Umavn8jJ31LV37DeHSiSPcvnWVM4d/5dTB3VjZOrD7tDcGxiYc3rWJ3RtWoaysQtPW7WjRrhPjZi3k6O7NJMZGi/X3vn2A5D+w0Xl1pGX+qFXsayIt+ygrQziuKSuroFhjXNPRM8Bz4HBsHV1k9rUmcnJyqKj++XHt9ME9ZL5LJzc7m1VzJ4qOv01KpLy8nFVzJ2Jl68BXM7+RWiIQoE1nTw7v2kTE6xdEv3lF+KvnNGvdnkFjJolsPHr2JyToLsf2bMHnyjnaeHSXyW7EpNl/2rdaPg+e4xbQrNsg7l86xvPb1wk4sx//U3sxtrJn3p7LaBuYcOPwFi7+8j2Kyso4NHXHuUVHPMct4OaR7aQliv/eqqj9tWdLaZk6lX0qScmGEGS+A4SZTDUnoDV19GnhOUSUcSSLrzWRk5NDSeWfebaUViIQoH7bbtw4vIXEiJe8jYsgNzOd/NwsDq2sWnyY+TYRyss5tHIaJnXtadShF4nhL3Bs5k77weNFdo09+hARco+bR7cTfOsPMVHpXXI8QVdP0qRTXzS0JZ8HavnrNBkyF7t2A3hzy4vYh968vHKA0Mv70aljR+91f6CuZ8zTM9t5eHQ9CkoqmDVoQ53GHWgyZC5Pz+4kJyVWrD8lVen7bsnJOOZJK+emWNGngrLkmFeYIxRJFJSURXsaVaKipY99x8HoWTnJ7KuUE0dRyvd+dsrLKadc7DrpmNuKxDe/zTOJe+iNU9cRUksJAli5deHpme1kxLzCsplwH++c5BgJu8prpqZjIPL5Y3b/Ff5Wsam4tIy8olIs9dT5xtOJbzydSM0pZLP3G/YHRrEvIIpJ7W1Zc+klBprK3F3UCc1qE3ubvcM++zlFpUmuWo3LEAowlXsXVaeugTBYbAw12TFS/CGqtKyc3MIS1JUVZPJ1UU/JVL3UnEI23fi4nyNaWL1XtJEFWyNhRldMuqT/oYnCydWmVroUl5YRm56HvoYyI1qKr1TYdiscE21VdNWVMNBUpkEdbaLSxFNoC0vKKCsv50ViFvJyciRm5vM4NpMmlrrU0RMfaCvPxVDzn1kl/P8jRSWl5BeWYGWkw6Ih7iwa4k5qpoCfzt5l77XH7Ln6iCk93Vh1zA8DbXUebp6IplrV9fzp7N3Pfk5RKZJKeuxb4USxg7nkJIe1sVBdtzXV45cZvcXaSsvKyc0vQk1FUSZflw6T3GQyNVPAxjN3PnreIzu6St1PysJQWIqzuLRMoq2gSLh6Q1tNhS3n77Hayx+vhYPp2sRWzE5fW3hvJ6RnY2FYT6b+DLXVcbU2JjJZ/HoWFpdSXl7O85hUmSeXahFSVFJKflEJlobafDuoFd8OakVqVh6bzgex90YIv14PYYpnY1Ydv42BthpBG8egqVoVL5v+CPpA738Oadl+sWnC39zKPYmqU9dY+DtvZ6LLL1O7i7WVlpWTW1CEmrKiTL4u+aK1RP+pWXlsPPfgo+c9sr2L1P2kQFjObtru6zS3N2PPdE8sDP785JudqZ5wbErNlmh7EfsWgGZ2pgS9SaJBXSMialzPwpJSysrKeRabVhsvH6CoqIi8vDzqWluzfMVKlq9YSUpyMuvWfc+uHdvZsWM7M2fNZsniRRgaRCmKhAAAIABJREFUGRH6Kgwtrar/1/Xff7wEyKcSEREucSwmJhoAJyl7KtnYCH937R0cOHDosFhbaWkpOTk5qKury+TrqtVrJPpPSU7m+7WSx2vy5dixUveT+twYGRrSqFFjwt+8ETteWFhIWVkZIcHByMvL06hRI5nsqrN376+UlJQwdtw4avnzFBWXkF9QSF1zE5ZNG8myaSNJSc/gx70n+MXrIrt+v8iMkX1ZtvUAhno6PDu/B02NqmfhH/ee+OznFFFRdq46MYmpADhYS+6pZGMhfC6ytzJn/9r5Ym2lZWXkCvJRU1WRydcVM0ZL9J+SnsH6PV4fPe8x/btKFYEszIQl64prZNkDFBQKF8tpV5QnvOIfxIRlP9OyoTMH1n8ttdxdfHIaD1+E0ay+g0R7VLxw9blxjZJ8+09fpaS0lC/7d/uoH7X8cxQXF1GQn4e5RV0mf/0dk7/+jvTUFH7bup4Tv+3ixG87GTZhJju+X4qugSFnAl+grlk1ru3f+vFSO59KXHSExLGkeOGkUF07yXGtjpWwTKaljT2rtv0m1lZWWopAkIOqmrpMvk6Vso9RemoK+zZ/fPzuO+xLqWKRnoEhjvUbERclPl4XFRVSXlZG2IunyMvLk5IYz4snQbg0dsO0jniGe0KMcKGKvqERb14K9wxt2lryfa5l+84c27OF7MxMme1q+XcpKS6iuCAfA3Mrek9eQu/JS8hOT+Hq/o34ntiD7/HddBo+jXPbV6Cla8iKs09QVa+ab7u6f+NnP6e3cZIZhOmJwgl2k7qSlUQMLawBMLay46vVv4q1lZWVUijIRUlVTSZf+06T3McoOz2Fy3t//Oh5t+k7+r1ikSxkpCQQ/eIRdV2aSpSvS0sQxqCWniGKSspYOLqSGiv+W1VSVER5eRnxYcKYTgh/AYBDU3dqUq+lBzePbicvRzwGA8/+RllpCW36jfnTftTyfspKiigpzEfLxJJmI7+l2chvyctIJfjEz7y4uJcXF3/Fte8UHhxYjaqOAUP3PEBJrSrenpz4+bOfU3aiZLZgbqpwAVDlHkLV0TIVZqPqmNviMX+XWFt5WSnF+bkoqqjJ5Gvz0Usk+s/LSOWJ108fPe8PiUCyEHxqC0GH1uK5/Hcs3bqItalqC8UeQVoCgrQEUsOeYOTQGE0j8bjMrhCMVHUMhaUJ5eTITo6W+K70KGEsGjk2Q8PAVCa7/wp/q9gU8CaNEb/eZ/uIJgyuKJNmrKXC9I527A+MIiu/mPiMPMrKy+nlaiYmNCVm5vMiMRsjTen1GP8skW9ziUwTYGtYtWrC64EwIBqYS4o5NoYaGGgq4/s6leLSMrHMjK03w/nh6ivOz2iLoLDko75KIzu/mKP3YqW2VaeNrcFfEptMtVVpZWvA3ch3RKcLsDYQ+l9cWsaZxwmY6ajS0EKX7IJi2v7gw4Amddg1quqhMymrgEtPkxjeQvgAOd7dhvHukpvAd/3Zn4LiUrznCWu7v0jMZsLBh4xuVZcNXzQUsz0fLHwhbmX731Ff/2sEvIhlyLqT7JreiyHthGUJjHU1mNmnBXuvPSZLUEh8WjZl5eX0aeEoJjQlpOfwPDoFI53Pu7dJRFIGkckZ2JpWTZQf8xW+FDSwlpyctjHVxVBbnVshURIxtPncPb4/EcClFSMQFBZ/1FdpZOUVcPjW04+ed5t6llLFJlVlRdrVr0vAixgJvy4FCSfvWjjVEaXk+z6LlhCbDt0Ufn+DusYy9+fZzJ6JnpIvdh6LDlJQVILv+q8+6lMt4gSExjN0w3l2TenGF22F4r6xjjozejVl740QMgUFxKXlUFZeTm83OzGhKSE9h+cxbzGSkg30V4hIziQyORPbavs4/e4fCiDaZ6k6NiY6GGipcetZjGS8XHjIulN3ubhsMIKC4o/6Ko0sQSFHfF989LzbONd5r9i05uQdtNVU+G1Wz7+8d5Kpngatnepw53UC0alZWFeIbcWlZZy6E4aZniaNrI1pbGPMxG6SD4WdlnkJ42XN8L90Hv/r+Prcok+vnvx28BAjRgpL9ZiYmjJ//gJ27dhOZkYGsbExlJWVMWDAQDGhKT4ujpCQYIxNJFev/RXehIUR/uYN9g5VL/8HDxwAoGFjyRduO3t7jIyMuHH9GsXFxWLZTT+uX8eK5d9xy88fQW7uR32VRmZmJvv3fXwT53bt2/8jYtO0GTOZNmOmxPFWzd3IL8jnwaPHYray2FXiff06+vr6eHTq/HlP+v8YfkEh9J++gn1r5jGsouyaiYEec74cyC9eF8nMziU2KZWysnL6dWojJjTFJ6fx9HUkxgaft9xFeEwi4bGJ2FtVlbM8fN4bgIaOthL2tpZmGOrpcOPOY4pLSlCqlt20cd9JVu08wo39PyDIz/+or9LIyhFw4Oz1j563ezNXqWKTmooyHVo0xO/BUwm/LvgI9x9s1Ug4Bi7ffghtTXWObvxWaoYXQEZ2DiMXrGPcIE+2LZ0u1nbqegAAbZqI78l28+4T9HS06Njiz09M1PL5eRjoy+xRfVm59Td6DBI+AxgYmzBq6jxO/LaL7MwMkuOF45pHj/5iQlNKYjxhL0IwMJT+nPNniY18Q1xUOJY2VffyheOHAHCsL3n/WNjYoWdgyD2/G5SUFKNYbaX3ge0/8suPK9lz9hb5gtyP+iqNnOxMzh/7TWpbdZq2bidVbBoybhpDxkmWXh7j2ZqCgnyOXL8PwJvQp3w7aTgDRo1n0Q87xGxv/HESgMYt24qyrG5eOsPE+UvF7LwvnALAvl59rB2cZbKr5d8l7KE/22cN4qtVe2jRQ7ivmLaBCV1Gz8b3xB7ycjJJT46jvKyMxp36iAlNGSkJxL9+hpbB543BlNhwUuMiMLasKrl998IRACwcXSXsjSxs0dQzJPTuTUpLisWym679tokLv6xh/q9XKcwXfNRXaeTlZHHn/KGPnrdDM/e/JDYJsjP4deFo3Ad8xYjFW8TaHt04A4B9kzZY129Gx6GTJT6/bnR7igsLWHxUuA92YoXY9PjmeXpNWiRm++jGWQDM7cVj8OW9W2ho6+HcvMOf9qOW95MQEsjVFUPxmLcTe48vAFDXM6bhwOm8uLiXotwsclLjKC8vw6ZNbzGhSZCWQHrkc9R0P++ek1mJEWQlRgozeip47X0MAAPbBhL2OmY2qOoYEP/Yh7KSYrHspuCTW3h4ZB19frhIcb7go75Ko0iQxavrRz563maubf6S2KRvXVEeN9hXQmx6dU24IFLfpj4FOZl4rxuLs+cY2k0XF8EiA84Jz6V+K9T1TTGr35rk53fJTopG28waEJYtDPc7jYaBGUb2jUBOTja7/wh/q9jUwkYfQ00VNl0Pw1xXjQZ1tIlOyxNlLHVxMcbOWBMNFUXOBSfQydkYexNNgqLesf7KKzRVFBEUlRCRmoudlKyjP0NpeTlf7Q/i2x7O2BlpcOlZEnsDoujb2JyWtpIvJkoK8izpWY95J0KYfuwJMz3s0VRV5OrzZH72DqODoxEtrPURFJV81Fdp2BtrEvdjr8/i28eY3dmBUXvvM/HQI+Z0cUBXTYntt8KJSc/jyIQWyMmBjpoS7vaGXHyaRPsHsfR0NSMqTcCCkyGY6ajyXR/pm4e+Dxczbdys9ThyPwY9DWV6uZpSVg6nHsXj+/otvRua0cTqv1NX8r9GS8c6GGqrs+H0HeoYaOFqbUJUcoYoY6lrU1vszfTRUFXm7N2XdG5sg2MdA+6/TuD74wFoqakgKCgiPPEd9lKyjv4MpWVljNp4hiVD22NnpsfFB2HsufKI/q2dae0sufeWsqICy4a3Z/buq0zZfpHZfVuipabM5Yfh/HT2Dh1drWnpZIGgoOijvkrDwdyApCPzpbbJyvIR7em69DDjNp9n6bD21DHQJuBFDAe8Q2jlbIFnM3vKystxsTLi16uP0FFXoVMjG5Le5XL+3iuuPQqniZ0Z3ZraydxfLZ+flo5mGGqrseHcA8z1NXGta0xUSqYoY6lbYxvszXTRUFXi3P03dGlkjYOZHvffJLHu1F201JQRFBQTnpQhNevoz1BaVsbozRdZPLg1dma6XAyKYM+1EPq3dKCVk7mEvbKiAsuGtmHO3ptM3XWNWb2boaWmzJVHkfx07gEdG1jR0sEcQWHRR32VhoO5Hom/TZfaJguZgkJexqfjWteYHVeeSLVxd65DtybSv18ac/s2Z/hPfzBu2xXm9WuOrroKWy8+IiY1i2Pz+0rseVXLp9O6TVuMjI1Zu2Y1FhYWNGrchIiIcFHGUo+ePXF0dEJTU5OTJ47T3dMTJydn7ty5zYrl36GtrY0gN5ew169xdHL6LOdUWlrK4EEDWLlqNQ6Ojpw7c4Yd27Yy+IshuLu3k7BXVlZm9drvmTJpIl+NGc2Cb75BW0ubC3+cZ933a+ncpStt2rQlNzf3o75Kw8nZmZw86SLt/xIZGRk8fvz/2DvrsCizLoD/gKG7BQEREcVCFAsDxY61u7s7185d13XdtVZXXbu7u7ATAQMMQOnuoWHm+2N0WHYGHRTX+Ob3PPP4eN/z3nvP5T1v3HPPud60bfeDwmlplMinnkslzE0MWbpxH6UtzXCp6EBQWJQ0YqlVQzecytigp6PFoQs3aFG/Jk5lbbjrG8CidbvQ19NBmJHFqzcRcqOOPoZ8kYgek5Ywf0w/ypcpzfErt1m39wRdWjSgfg3ZyVkNdQGLxg9g9MLVDJm9gskDu6Kvp8Npr7ss+3s/nnWrU6+6M+kZWR/UVR5O9jYk3T/6STotHj8Qj35T6Dd9GQvH9cfG0gyvB4/ZfOgc7q6VaOtRh+RUIf6BIbhUdGDVjmNy62nkVpVWDd2oU60iW4+cx8RQnw5N6yESidl32ovLd3zo2NQdtyoFESjJqUJ8AgJp06i2Mnr2K8OlVj2Mzcz5+4+fsLAuTYUqLoS/DpJGLDVo1poy5ZzQ0dXj4olDuHu2xN6xAn4P7vDXrwvQ1TMgIyOdkKCXcqOOPgZRfj5TB3dj1PQF2DmU5+rZY+zf/CfNfuhK9Tr1ZeTV1TUYM3MJS6aOZN7YQfQfMwVdfQOunz/JlpW/UKdRU1xq1SMzXfhBXeVh71iBW29kI8dLGkfnqlStWYdju7dgaGxKk9YdEIlFnD28l3vXLuHZthOVq9dCJBJRx6MZ965dYnyfH2jduTdWtmXwOnuc88cO4FChEh6t2qOmJlBITsmXxcGlLvom5pzZtAwji9LYVqhGXFiwNGKpSv2WWJYpj6aOLt4XjlDZvTmW9k4E+93lxPolaOnpk50hJCbkldyoo49BLMpnw5Te/DB6DpZ2jvhcOcnV/X9Rs1knHF3dZeQF6hp0HDOfXUvGsW3uMFoMmISWrj5+105zdstynOs0wcGlLtmZ6R/UVR6l7J1YfTuuRHR7H6UdK+NQtTa3jm1H19CE6p7tEYtE3D+7n4C7V3D1bI99ZcUXSlk5OONc15OAu1dYO74ztVv3wNTKDl+vUzw8fwgrB2dcPArmMDNSkwl97kvVhq2l+/soKVlKVaqNtqEZj/b9hq6ZNaYOVUmNei2NWLJ1a46RjSPqWroE3TiGbc2mGNqUJybgPg93LUVDR5+8rHRSIgIxlBN19DGIRPlcXNIft34zMSxdjte3T/PsxCYcGnSgVGXZrSBUBRrUHjCX66sncnXFKFy6jkdDR583d8/is38Fpas3ppRzbXKz0j+oqzyMbMoz5GhEiej2PmzdmmFi78yzk3+jqWuITY0mpCdEE3zzOKH3z2Ne3hW7Wi1QVVXDsmItnp/fiZa+Cfb12oJYxCuvg4T7XKWsezvMnSQLPap3n8j5hb25vGwIrt0noaFnhN/h1aRFh9By/h7p3liKyn0NfFZnk56mgHV9XBm/15fO6wrSXGkKVJnZpiLNnCWrZFf2cGHifj/6b5Gk9jHSUWdRhyroaKgxfq8PHsu9CF/eTm4bxaVheXNKGWoxdPtDRGIxAO7lTFnWWXalwzt617EjMzefRSf9OfE2GkegqkKfunbMbO2Mioriun5JGlcwZ21vVyYf8GPItoeAxLm0sEMlPCsWOMNW9qzOyF3eTNrvx6T9fgBUtTFkfd8ahaLPFEFFBbYNqsXkA36svvyK1ZcL0rwMdLdnQfviOa/+39DT1mDjuB8Yve407RcVpCHRVBcwp2cjWrhKnBtrRrZm3F9n6bNcsnLFWE+Ln/o3RUdTndHrTlN/2hZidk8tkT41qlIGKxN9Bv5+TGpD9SvZsXyw/Js+QN8m1cjMzmP+bi+O3XkOgEBNlX6e1Zjdo5HEhhTU9XPgWs6KfTO6Mnb9WXr8ckha3qqmI2tHSSYmVVVU2Dm1EyPXnmLZoVssO3RLKteuthO/DGwm3WNHkfqUlDx6WhpsGN2KMRsu0OHnI9JyTXU1ZnerR/Pq9gCsHtaM8Zsu0ef3k4DEXpb0aYiOpjpjNlykwczdRG8rmY1NG1WyxcpEj0GrzxTYi7MNvw5sUuQ5fT0qk5mdx4J9Nzl2T3LPFKip0tejMrO71ZPYi4K6ljT3X0YiFsPjN7E8fhMrV0YFiuVsalLVjvUjWzDh78sMXHUaAEMdTRb3aUgzF+XmzyWBvr4+O3buYvCggTRv6ikt19LSYtHiJbRuI/lg3Pj3ZoYPHULnjpKNwE1MTFi+4nd0dXUZMmggri5VSc/KkdtGcWni2ZTSpUvTs3s3RCJJ2tFGHo1ZvfbPIs8ZNHgImRkZzPxxBocOSia5BQIBg4cMZeHiJaioqCis6/8r17yuIhKJqFtXuR/gp6Knq83Wn6cybO4ftBo2S1qupaHBgrH9aNWwFgDrF0xg1IJVdJu4GABjQ31+nToUHW0ths/9A7euY0h5KN9BUlwa13bB2sKUPtOWIhJJnjkN3aqycpZsdMI7BnRsTkZWFnP+2MbhC5JVzQI1NQZ2asGCsf1QUVFRWNfPQc3K5TmyZj4j5q+k09gF0vK2HnXYsFCyZ8sd3wDEYjG+AUH4BsimMgPJN0nrRrXY/8ccRi9azW9bDvLbloPS48O6tWHplCGFzrn24DEikZg61WRToSv5sujo6bN47XYWThjCqK4FKQ41NLUYNWMh9ZtKHDBzf9/A4skjmDKwCwAGRiZMXrgcLW0dFk4cSk/PGtwJkR+ZV1xqNWiCeSlrfhzeS/pcq1mvETOWri7ynPa9BpKVmcHqJbOkUTtqAgEdeg1i9I8LUVFRUVjXL4WKigq/bTnEkqkj2bbmV7atKUgb1qX/cCbOlzjFVFVV+WndDpbPmcyFY/u561WwB6dr3QbM/X0j6uqSrAOKyin5cmjp6DFo8d9sXzCSlSML3q3UNbRoP3oeVRpIHDD95q5j5+LRrJ/cEwBdA2O6Tl6KhrYu2xeMYHGPuqy9m1AifapQywMjC2s2zeiP+K0NOtVsQM8ffy/yHPcO/cnJyuTo6nl4X5IsjlBVE1C/Y3/aj56HioqKwrp+KVRUVBjx2x52LRnH+W2/c35bgb6Nug6hy8SfilefqiqDf9rCgeXTeHj+EP53LkuPObrWp9+8PxH8wwZfet9ALBLhULX2pyujRC7q2no0mfoXXn+M5dSsjtJyNQ1NavWbjV0tyVxcowmrub5qPOcXS7I7aOobU2/oYgRaunj9MYZDYxoy5FhUifSptEsjdE2tuLR0MGKxxN6sqtan/uiiU0dWaN6HvKxM7m1bQPDN44DE3iq06EutfrNARUVhXb8UKiqqtJi9k6srRuG951e89xToa+/eDvfhP6OqJpk3bz5nB9dXT8T34Ep8D66UylVqM4g6QxZJ/2/j2oTGk9dxfc1ELi4dBICGriF1hy6W7ulUHLmvARWx+O3s11sOHDhAjx49iF7xQ4k1kpmTj39UKhFJmZjoaVCxlD5m/0qPl5Sew5OIFCwNtHCy1Jc65JLSc0jOzKWs2aenAnOedx5XWyP2DKtDSmYuvmHJWBlK2lMEYXYeTyNSSM/Ox9lKH2sj2c3+FNH1S5MnEuMXloxILKaGnTFqclbqicUQEJ1KSEIG1Uobyuy39DGEJ2USGCvEUFud8pZ6xXZcKUKpKSfZv38/3bt3L/G639lGwr7pJV73h8jMzuVZaBzh8amYGmjjbGuOmUHhdF+JaZk8eROLpbEuFUqbSW0oMS2TlPQsypb69EiN8sPW4OpQigMzu5GcnoVvcDRWxvpUsFEsFaIwM4fHb2JIz8qlkp05peXs9aKIrp+L3HwRAWFxJKRmUsnODEsj2YhKkVhMSGwKryIT0NYQ4GhlipWJ/MhLRer72jDt+etnt6H4neNLvO5/kpmTx7PQeMIT0jDV18bZxhQzg8L3sERhFk9C4rA00qGCtWmBvQizJPZi+ekRl06jNuLqYMn+aR1ITs/G93UMVsZ6VCitWJShMCuHJ2/iSM/OxdnGVL69KKDrt0Jevgjf17GIxGJqlisl99n0JTHrt/qz20Z2nuxebyVJRkYGT548Jiw0FDMzMypXroK5ReHI64SEBPx8fShlZYWzcyVpCtGEhASSk5Io5/jpq+GsLc2p6VaLk6fPSCJtvB9ibV0a50qKLUBJS0vD19eHdKGQKlWqYmNrKyOjiK5K/ls0BaqfzYZAMtmyY9kMurSQ3WPgc5KRlc3TV28Ii4rDzMiASo5lMP/Xvj+JKWn4PQ+ilJkJFR1spXaVmJJGUqqQcrZWn9wP28a9qVnZiWN/LiA5Vcgj/0CsLUyp6CBrH/IQpmfi+yKI9IwsKjvaY1PK7KN0/Vzk5uXhHxhKfFIKlcuXKTJVnqKERsXy6k0Ehvq6VCxrWyjN4ddKv+nLUDOy4sCBkt/vCyQ29PNfu2j2Q9fPUv/nICszg8CAp0RHhGJkYka5CpUxNiucKiglKZEXT30xsyhFWSdnqf2lJCWSmpKErf2nL2hrXsWaStXdWLXrBGkpSfj7PcKilDVlnZwVOj9DmMaLp35kZAhxrFgFS2vZbBGK6PqliQoPJTToJXqGhpR1rFgofeE/iY2KIPiFP9lZmZRxrECZck7Sv8vHyH3tzBzRBxNt1c9mu927dycoKZehS7d/lvrfR05WJhGvnpIYE46eoSnW5ZzRNyl8XaanJBL24jGGZpaUKltR+jdMT0kkIzUZc1v5GUyKw7RmZSlTqQZjVx8mIzWZkAAfjCyssCqr2GKBrAwh4S/8yMpIp7RjZYwtZSOOFdH1S5MYFUZMyCu09Q0pVbZCofSFH0NybCSRQQHkZmdiae+EZZnyX7UNel86yuaZA/nXNHeJ0r17d7yjsmk6Y/Nna6Mo8rIzSXzzDGFcBFoGJhiXcUbbsPD7WlZaIglBT9AxscTYtoI02iUrLZEcYQoGVoovCi2Knb0rYO5UnVYL9pMtTCY+0BcdUytJewqQmykkPugJeVnpmNg7o2sma2+K6PolEYtFpEWHkhz+CoGmFoalHdE1lf8+L4wNIzkiEE1dQ4xsnQqlOfwnovw84gN9EYvEWFSogYqq2ifJfW6Cbx7n8rKh8uzt4GeNbHqHtoYaNcsYU7NM0ZPdxroaNHKSvVEb62pgrFvyK1cMtdXxkNPe+9DTFHxwfyFFdP3SCFRVPtg/FRVJCrxKVgYl1q6NsTY2JeC0+n9EW1Mdt/LWuJWXTbv1DhN9bTyqykYBmOhrY6Jf8uNupKtF46r2xTpHT1sDd+f3T3ooouvnQl1NlWr2749CVFVRoaylkULOCEXqU1LyaGsIcHMshZuj7B5d7zDR08Kjsuy1aKKnhYmeVon3yUhXk8ZV7Ip1jp6WBvUqvj+1kiK6fisI1FS/Cz2+ZnR0dKhTpy516hQd1WJqaopn02Zyy01NS36PRWNjY5o2K94KNX19fRo2lN04/J8ooqsSJSWBjpYmtatWoHbVoj+wTQz1aVJHdj8GE0N9TAwVW/RWHIwM9PCsW7z9H/R0tWlQQzbH/j9RRNfPhbpAgEvFT5+QfIedlQV2VkoH9LeOlrYOVWrUpkqNolfUGxqbULuhp9xyQ+OSSTP+T/QNjanTqHgrjHX09HGt+35HuSK6fmmsbOywsvnw+66FVWksrD6cPlRROSVfDg0tbcpWrUXZqkVHuOoamlCxdmO55bqGJW+DOgZGONcpOouEPLR09HB0lU13+U8U0fVLY2Jli4mVYotMFMHIwhoji/9+XkaJfASa2lhUcMOigvz0xQBa+iaUri67d5aWvgla+iVvb5p6RpSu3rhY56hr62FVpd57ZRTR9UuioqKKgZW9dO+k96FnYYuexYftUlVNoJC+isp9SZQJNZUoUaJEiRIlSpQoUaJEiRIlSpQoUaJEiRIlSpR8NP9XziZLfU1MPkOUlBIl/y9YGuli+h+ltFOi5FvH0kj3s0QVKlHyrVOqlBVmZl9PGgQlSr4HSpmbYGpcchkJlChRojhmllYYmZR89K8SJUoUw9CsFHpGShtUouS/QNvEEk0Dpb0pKZr/JI3e14LXtMZfugtKlHzT3Fw++Et3QYmSb4YbS/t86S4oUfJV8sjv8ZfughIl3x0PDq790l1QouT/lr2Xvb90F5Qo+b9mzr47X7oLSpT839B17fUv3QUlXzn/V5FNSpQoUaJEiRIlSpQoUaJEiRIlSpQoUaJEiRIlSkqWry6y6XJALGlZuXR0/XY2gtx5N4QEYQ4A5S31aFvVqtDxPJEYNRUVVFTeX4+icmIxpGblYqit/sG+icRiVD9UoYIUR4+8fBFa6mof1Y7Xizh8w5IB0FJXY6RHyW0G/C1z0SeYtMxsOrs7f+muKMz2y34kpGYA4FTalHa1neTKCTNzyMnLLzLlWF6+SHJNabz/liUWQ0pGFka6Wgr170PtfkkUtV15clcfv8EnKAoALQ0Bo9t+vZuY/pdc8ntDWmYOnerKvw6/RnZcfUp8aiYATqVNaOdW7qPqkdhGNka6miXSr7x8EWqqqh98HhQXYVYOOXkiTPTeb8OKyv2bq09C8QmOAd7aRmvXj+7r98S5s2cDaUBOAAAgAElEQVRITU2le4+eX7orCvP3po3Ex8UBUNHZmY6dOn9UPWKxmOTkZIyNjd8rl5eXh4qKCmpqH/du87nJy8sjNzcXbe2Se54JhUL09PQKlV26eIGHDx4AoK2tzYRJk0usvW+Z8zcfkpqeQbeWjb50VxRmy+FzxCelAlChrA0dmroXOp6Xny+55lXfvzYxLz+f3Lx8tDVLLlW5SCRGVbXkHjDCjCz0dN7/vPiv9L18xwfvZ68A0NLUYHy/jh9Vz/fGrSvnSE9LpUWH7l+6KwpzdNdmkhMlzyF7x4o0afNxf8v8vDxU1dRQ+cBLVf7b55DqB55DitZXFCKRCNUP2EFxyEgXoqOr914ZsViMMDUZfcP3P4uldQrTyM3NxdBY/mb3n2MM7l27hL/fQwA0tbTpPXzCR9X9vfH01gWy0tNwa9HlS3dFYW4e3YYwKR6AUmUrUL3JD3LlsjKE5OfmoGso/zoT5eehoqr4daZIffl5uahr/vdzE2KRCJUSsntRfh6oqKCq+v57VVHjF3D3CiH+jwBQ19SiaZ+xJdKv74Gwh5fIyUijXKNOX7orCvP83A4yUxMAMLYpj717OxmZ3Kx01LV0FaovN1NIfl4OWvry7eh7QiwWoaLyYbtUZPxE+XkSm/yXvYX7XCXulS8AAg0tqnYc9fEd/gdfnbNp7dVA3sSnf1POpk3XXxOWlEEpAy08K1pInU2XA2L55exzXsSkoa8loIGjGYPq21PXoXBuS0XlUjJzWXTSn8OPIsjKzUdPU4CnswW/dK5aaC+qoLh0tt56zbmn0aRl5VHL3oQRHg40LP9x+yMo2j+vF3H8dDqA59Fp5IlE2BjrMMrDgYH17aWT4SKxmGYrrpMnEsu0Y2uize6hdXgUmsTBh+HEpWUjUFNROpvesubkPV5HJ39TzqYNZx8SGpdKKWM9mlUvK9fZlJiWScPpWzHQ0eTOiiGFjl19/IZFe64REBZHnkiErZkhY9rVYnAL10IOluT0LBbs9uLgTX+ycvLQ09agWXUHfh3cHNMiHEnva/djcJu4iQaVbFk5vFWhcpFYTOMZ28gTiWTOsTM3ZN+MrtL/B0Ul8vd5H84+fEVqZjZ1nGwY1daNRlXKFDrvQ3LegZHsu/6MuJR01NVUlc6mt6w+5c2b2JRvytm04bxvgQ25lJHrbKo1dQcNnEvzx5CmMseS07NZuO8mB2+/kNiGlgbNXMqwbEBjqW2IxGKazN4r9xq1NTNg39T20v9f8nvDTwfv8CIiEX1tDRpVtmVw06rUq/jpz+xEYRaNZu3GQFuT28v6FltOET28g6LZf/M5cSkZqAtUlc6mt6xYvpyg4KBvytm0ds1qQt68wcrampatWtOxU2dEIhG1a9YgLz9PRr5MGXuOnzwl/X9SUhKzfpzB3j27yczMRF9fn5atWrNqzdpC+0nt3bObv9atw9fXh7y8PBzKlWP06DGMGDVaOhFVnHYVoTj1Xbp4gdkzZ/Ls2VPy8vKwK1OGSZMmF+rfv6lU0QkPj8as37BR5piPzyPmzJqF98MHJCUlYWFpSfv2HVi67FcMDAy4f+8eu3btJDYmBnV1daWz6S1/bDtMcHj0N+Vs+nPPCUIjY7EyN6FFfTeps2n/GS827D+N3/Ng8vLzKWtbipE92jG8e9tCDqDLd3yYu3ob/oGh5OXnY2dlzvh+naRyXvf9mLJsw3v74OrsyN9LJNfQq5AINu4/zSmve6QI06lXvRLj+nagcW2Xj9LPNyCIeWu24/3sFcmpQixMjWjXuC4/TxqEvm7B/qP/tb6OZazZc/IKMYnJqAsESmfTW3auW0H4m+Bvytm0b/NaosJCMLe0op5nS7nOpi71K1PDvRGzl6+XOXbryjn+WraA1y8D0NXXx61+E7oOGIFr3QaF5M4d2cvBbX/x4qkf+fl52JRxoNugUXQdMKLQfV7R+uQRGvyKg9v+4vr5kwhTU6lWqx69h4+nVoMmHzEy8PyJD38unYu/rzdpKUmYmFvg0fIHxs9Ziq5+wd5yaSlJrF4yi3NH9pGdlYmOnj7uTVoy/edVRe6BlZKUSO+mNdE1MOSAl2+hY59zDJ763OfMwd0kxMciEKgrnU1vubhjFXHhwd+Us+nq3vUkRIVgaG5FZffmcp1N6SmJLOnljraeAfMO3C907OmtC5xcv5io1y/Q0tWnglsjPLoNxdG1fpFtvq++gLtXOLZ2PpFBAYjy8zApZUvTvuPw6Dr0kx1A8zu74lSzIX1mr5Y5FhsayLWDm/C7dppMYSrlXOrStPcYKtTykMqIRSJ+7tsAUV6+zPmm1naMXnlQ+v/75w5w/cAmwl48Jj8/D3Obsnh0Hy6jx4fG7/XTh9w7s5e0hFjUBOpKZ9M/8Du8htToN9+Us+npiY2kxYaiY1IKW7emUmdTfNBjHmxfQtwrH7KFyWgbmVOmbmvqDFqAho6+3Lqy0hI5PNYDDV0Duq27BUCE33XubJj13j6YObrQePKfMuX7h9fGump9Go774xO1VLy+D8mkRAThf3ozb+6dIyc9lVKValOlw0hKuxT+xlB0/MIeXuLBzp9JDnuBuo4+1tUaUqnNYKyq1AMg9sUjXl3ZT2ZyHKoC9RJzNinT6JUQ9RxMuTPTk586VQHgqE8EfTffIyUzlzFNytG8kiUX/WPot/k+QbFC6XmKyuXmi+i96R577ofSuUZpfu/uQifX0pzwjWTAlgdSuazcfPpvuc/e+2E0qWDBAHd7XscL6bf5PneDE4qtl6L9u/Eqnl6b7hKamEHPWrYMdLcnKzefWUefsuLCS6lcVHIW/lGpqKmqYKqnUehnrCNxmE1u7sSdmZ60qVqq2P1V8vXhXtGGhyuH8cvAZnKPj99wjugkoUz59achdFt6gNC4FHo3rsrg5jXIzMljxtZL/HrollQuJy+fHr8cYtfVx3StX4lVI1rRxd2ZY3ee03f5kSL7VVS7H8Pea095HZ0k91hkQhrPQuNQU1XFzECn0M9Yr8ARlpWTR+/lR9jt9QRPl7IMau5KUHQSvX49zO2AsGLJTe3szsOVw2hbq3yJ6Kfky+JesTQPfuvP0n4eMsf23gjgdUyy3PNy8vLp+dtxdl17Rtd6FVg5tCmd6zlx7N4r+v1RMFkdmSjkWVi85L6sr13oZ/yPyKEjd17Sa8UJUjOyGde2Bi1dy3Le5zW9fz9JYJT86784TNh0ieik9I+WU0SPqR1r8+C3/rT9yAgxJV8XDRo2wv/5S/5YuQqAiPBwnjx5jJqqGuZm5oV+JiYFq99ycnLo0K4tW7dspmev3vy1cRM9evbi0MEDdO1UMFm4a+cOBg3oT1JyEmPHT2DEqNEIhUImThjPsqU/S+UUbVdRFK3v6pXLtGvTmpCQNwwYOIgRo0aTmZnJxAnjWbJ4kdy6d2zfRlBgoNxj3t4PadmsKT6PvOnRqzez5szF0NCQvzdtpHWL5ohEImbNmYv/85d06PjtfOQqKZr6Narw+MRGfpsxHIA9p64wZM7vJKUKGd2nPcO7tyE9I4spyzawfPMB6Xle9/3oMGY+IZGx9OvQjOHd25CZncOUZRv4ecMeAFRQQV0gkPvLF4l4HhxGWrok+j0zO4duExez4/hFmrnXYFi3NgSFRtJ1/CJuPnpabL0e+b+i9fDZ+PgH0qO1Bz8O64mhni5bDp+j7Yg5iN4uevsS+v44rCePT2ykfZN6H/dHU/JV4Vq3AYdvPWPq4t9ljp06sJOwN0Fyzzt/bD+T+3ciLTWZvqMm06BZG25eOsPkgZ0JCSr4dj5zaDfzxw8mLSWZnkPH0nXACDLShfw2ZxLb1iwrdn3yyM7KZMrALpzct526jZvTZcBwwl4HMnlAZ3zu3iz2mAT4eTO6WyueP35Eq049GDJxFnr6hhzdtZkxPdsgersoKDc3hwl9O3Bi7zZadurBnN/+omXH7lw6eYipg4p2XCyZMoK4mCiZ8s89BkMmzuLwrWc0btX+vXUp+TZwdK3PwiM+dJ/6q9zjOxePJSVO9jp7eP4Q6yd1JyMtheb9xlO1QSue3DzHukk9iAl5VWR7RdX34sE11o7vTEJUKPV+6EOjrkPJzc7iwPJpnN70y8crCNw9tZu4sGC5x3KzM1k/uSe3T+ykUt2mNOoyhNjQINZN6kGgT8FcS1JsJBGvnqGipoaesVmhn45BQSTivdN72T5vOBlpyTTpNRKPbkPJzkjnwPJpnNu6QiqnyPi1GTqdhUd8cCki4kzJt4dVlXr02Hgf9+FLAYh75cvp2Z2ID/SjnEcXavScgoauAc/P7eDMnC6IxbKLRwGur5pIRmJ0oTIVFRVUBQK5P5Eon6SwF+RkpMnU9fLyPlKjXpeYjorU9yGZvJwszi/uy4uLe7Cp0YRKbQaREhnMhUV9iHpasC+douMXdP0I5xb1Jic9hWqdx2JXqwWhDy5wYXEfUiIk34M1ek6hx8b72NdrWwKjUMBXF9n0PZCbL2LRSX90NARcnNxImu5uTltnXBddYsQuby5N9lBYDmD/g3C8Q5KY/0MlRjWWTJL1rmOHigrsuBOCX1gyLrZGLD3znKBYIXuG1cGzogUAwxqWxXPFNcbv9eX+bNmV75+qB8DvF18iFsP5SQ2xN5WE781u64zroous9wpicnMn1FRVeJ0gmSBc29uVytYG8htW8n/Dlos+XPELLjSh/Y7fjtxGLIZLP/enrKURAPN6NaLK6HX8eeoB07rUR01VhX3XnvLwVSSL+jZhTDtJBE/fJtVQUVFh2yVffIOjqe5QSuF2FSUyMY1fD93CJyiapyGxRcq9jpFMwq8f05YqZSyKlFuy7zqBkYns/7ErzapLovlGtq5Jw+nbGLv+DI9WjyiWnJLvl8hEIcuP3uNRcAzPQuOLlNt/I4CHgdEs6tWA0W1qANDXozIqKrD9ylN8X8dSvawFr2NSAFg/siWV7eRHwObk5TN/7010NNW5sqQXhjqSdHzzetSn6vjNDP3zHF5Len20TlsvP+Hy45AP2uT75BTRQ8n3TVCQ5KV5644dVKtWdDTEzh3buXfvLst+Xc7EyVMAGDR4CCoqKmzauAFv74fUrOnGyt9/x7F8eW7evouBgeSdZdq06Tg5OrB+/Tpmzp5TrHZLWo+flyxBLBZz++59HMpJ3g2X/PQzDmVsWfn7CmbPmYuamhoR4eEsWbyIhw8e8PixX5H1rf/zTzIzM7l55y4uLtUBmL9gIa1aNOfqlcscPXKYLl27fbJ+Sr5eVu04iqOdNdd3rZBG/0we1JVKbYewYf9pZgzrAcAvG/chFou5sft3HGwkGR0WjRuAY8uBrN55jJkjeuFRuxp398uupAaYsmwDacJMVs8ZA8DCtTt49SaCo2sX0KJ+TQDG9G5PnR7jGDFvJc9O/V0sPf7ad5qs7Gyu7VxBtQqSd6W5o/vQdsQcvO77cezyLTo3b/DF9FXy/RIbFcGm35fg7+vNK//HcmVyc3NYvXgm2jq67Dx/F30DyXfO2Fk/0bamA7NH9WPXhXsA7PprJbZlHdl66oY0Iqj/mKl0rFOBg9v+YvCEmcWqTx7rf5lPSNBLVu48jrtnSwB6DhlL7+ZuLJw0lGN3nhdrDA5sXU92ViZbT9/AqbLkGTZi2jzG9GjNg5tXuXrmKE3bdeH0gV08fXSfCfN+oc+IiQC07zUQFVQ4sutvAvy8cXapWajuwzs2cufqBQyMCi/k+NrGQMm3zfVDm/G/cwldg8JpHfNycziyai4a2jrM2nUDbX1DADqOXcCsts5snjWIWbtlHbRF1Qdw5u9fEYvFzNjuhblNWQA6jF3ArDYVubR7DW2GzfhgOrp/khwbyelNvxDyzJvwV0Uv1ji+bjExIa8Ys+oQld2bA9Ck1yh+7lWf7QtGsfi45P71zlk1cNFGbMpXKbK+S7vXYG5bjunbrqClK4mqaNF/InM7VOPawU20HjLto8dPyfeH/+nN5GVn0XHFeUwdJNdVzT4/cmZOFyL8rvP61ikcGhR27Puf2Ur4o8to6he2I+tqDem82ktuO7c3zCQ3M40GY34DID0+kkd7lxP3yoeE188+WQ9F6itOmw93/ERKRCCtFuzDtqZk7r5K++EcHufBtZVj6fm3N6DY+NnXbcW9LQtQ19Sh86oraOhK7K32wHnsGViNy78Oo/Oqq588BkXxyZFNs44+pcPaW8SkZskcm3rQjx4b7pKbL/Gq3Q5K4McjT3BfegXXRRcZuesR22+HkC8npdo7xu3xYczuRzLla64E0mHtrULp2FIyc/nx8BM8lntRdcEFBm97wOWAoieBPxcvooVEpWTR1Nmi0L5KZnqaeDiZ8zQildSsXIXlAA55h2Omp8nQhmULtTWhaXnW9HbFVE8y8bfvQRiVrAykjiYAc31NmlQwJzQxg0ehiq8+L07/IpMzsTLUkjqaAPQ0BbjaGZObLyY7T3INBMelo6IC5cwVy8f5LTNj6yXaLthDTLJs9MykTefp8tMBct6GI9/yD2X6lovUmriJqqPXM2z1SbZe9H2vbYz68zQj18qm5ll5/C5tF+whL79gNUBKejbTNl+k/tQtVBzxJ/1XHOWij/xVLv8Vz8PjmbvzKvN7N8bSSDaPd0R8GtYm+lJHE4CetgY1HK3IyxeRnStJL3Twpj9mBjoMa1Wj0PmTOtZl/Zi2mBroFCr/ULuKIszMISgqCQMdTVzLWRUpFxSVhIoKOFq9f3X73mtPqWxnLnUgAZgb6uLpYk9IbAregVHFkvse+HHHNdotPkRMsmwUy6QtV+i67FiBDQWEM327F7Wn7aDahC0M+/Mc2648ea8Njf7rAiPXn5cpX3XyIe0WHypsQxnZTNt2lQY/7sZ57N8MWHWaS35vPl3Jj0CYlUNgdLLk2nOwLFLuwO0XmBloM7RF4cnqSe1rsW5kC2kaveDoZMl9uZSRvGoAeBmRSFSSkOYu9lJHE4CZgTaNq9rxNCSO1Iycj9LneUQCc/fcYH7P+lgaFv1s+JCcInp8L0ycMB5Pj0ZER8na++iRI2jTqiU5OZK/x/VrXkwYN5bKzhVwsLejX5/ebNzwF/n5sukw3jF44AAG9u8nU7582S94ejQiL68gvVtycjLjx47BtVpVbEtb0b1rF86dPVMCWhafV69eoaKiQvny70+VuXf3bswtLBg9dlyh8hk/zmTLtu2Ym5mTkpLCs2dPadmqtdTRBGBlbU3jJp4kJSaSm5tbrHZLWo+w8DBK29hIHU0A+vr6uNWqTW5uLllZknfztLQ0Xr18iaGhIW5uRadVvXPnNi4u1aWOpncMGDgQgAcPHsg569tkyrINNB88g+j4RJljYxev5YdRc8l5+55x4+ETJi1dj0uHETi1GsTAH5fz96Gz5MtJ2fmOoXN+Z/DsFTLlK7YeovngGeT9w/5S0tKZ+PN63LqOoWyzfvSa8jPnbz4sAS2LR6owHf+gEFrUr1kozZyVuQketVxISk0j963th8fEU9rSTOp4AdDT1catihO5eXlkZRf9PLh425uNB06z5acpWJpKJgx2nrhMlfL2UkcTgIWpEc3q1eBNRAwPnrwoli73/AKoVsFB6mh6R/8Okij7h09fflF9vwd+mzOJ4Z08iY+Nljn28/TRjOvVltxcybh437nOr7Mm0KVBFdq5lWPO6P4c2bkJ0XueQwsmDGbeuEEy5dvXLmd4J0/y//EcSktNZtnM8fT0rEGr6nZMH9qDW1fOlYCWxSdDmEZo8Cv0DAyoVN1NrszrlwHERUfi7tlK6hQBMDYzp45HM14+80OYloIwLYXgF89w92xZKPWcuaUVbg0ak5qURF5ersL1FcXJAztxdK4qdbIAmJhbUNejOZGhb3jqU7x7/+OHd3Gq7CJ1NL3jhx79AXjmI7m/nT2yB2Mzc7oPHl1IbuD4GSxYtQUjU/NC5cEv/Fm5cAZjZ/+EmWXhhYRf2xh8zRxYPo0Vw1qREi9ru7t/Gs+asR3Je2u7L71vsm/ZFBZ0rsGsts5smT2YG4e3IBIVbbvb549g29xhMuXnt/3BimGtJPv5vCUzLYV9yyazuEddfmxZno3T+vL01oUS0PLjiQoO4PDK2XQatxADs8LXWfTrFyTHRVLFvYXUUQKgb2KOc11Pwl8+IVOYqnB9AEkx4RhZWEsdTQBaOnrYV66JKC+PvOzsYvU/Kz2NmJBAtPQMKVOpRpFyd0/tpnT5ylJHE4CBiQXO9ZqSEBnCm6cSO40NC0JFRQVLu6KzQmQKU4kMCqCye3OpownA0NwKJ7dGZKQmkZ+X+1Hj961ze8NMTs5oR0ZijMyxG2snc2ZuN0R5EnuLenKLW+tncGBEHfYMcuHK8uEEnN2G+D325vX7GK6ukE175ntoFSdntCtkbznpKdxaP51DYxqyq18lLv48kLCHl0pAy+ITE3AfU4cqUkfJO5yaSRavxr0s7ANICn3Ovc3zqD1wPjrGRc9//JMw7yv4n95Ckynr0TGWzI/nZgpJiQxCQ9cA8/Kfnk5fkfqK0+bLy/swsa8kdTQBaBuZY1PDk7SYUGJfSJxNioxfUuhL0hOisHVrJnU0AWgbmmHj2piE4KfkpH8+e/tkZ1NZMx3uvU7kzJPCD6vo1Cz23AvDSEcddTVVbgXG0+2vOxzziaBxRQv61C1DZHImMw4/5qczAUXW7xeegl+47ItBcFw6914nIhZLJhKjUrJo9vt1DjwMo56DKT1r2RKWmEm/zffZeP2/nVR/53hztZWd8HK1k5S9iE5TWA7gdXw6ns4WqKupEpKQwfln0TwOT8HSUItuNW2wMdYmMT2HlMxcGjnJruh2MJdMqvuFFf2S9bF6ALSpakVUSlYh515QrJBbgfE0cDRFR0OyGuNNfDqljbRJz87non8Me+6F8uBN4nsnhL9VHEoZc/d5OKfuFw7bj04SsuvKY4z1tNAQqHHzWSidluznyO0APF3K0s+zGhEJqUzdfIHFe68VWb/f62h8g2UfWsHRSdx9Ho7orW1EJqbR+Mdt7Lv+FHdnW/o0rkpYXCq9fz3MX2f++8kMgOzcPIatPkm9ijYMb1VTrkzb2uWJTEwr5BQLjEzk5rNQGlS2Q0dT4gANikqimasDGgI13sQmc847EL/gaEoZ69G9YWVszQo+zhRpV1GcSptycn4vTs7vxaZxspscvuN1dDI2pgYIs3I4/yiIXVcfc/9lRKFrPiEtk+T0LDyq2suc/85J5RscpbDc90JZS0Puvozk1MPCKUiik9LZ7fWswIb8w+n8y1GO3n2JZ9Uy9G1cmcjENKZuvcriA7eKqB383sTi90Z2QUJwTDJ3X0b+w4aENJm9l/03n1OvojW9G1UiNC6V3itO8tc5X5nzPzdO1iacnN2Fk7O7sHF0yyLlgqOTaeZij4ZAjZDYFM49CsbvdSyljHTpXr8itmaSj4HgmGRsTPVJz87lgs9rdl17xv1XUYWu0ei3Dr8acpxbNd5GDj6PKH6q1uzcfIb/eY56FawZ3qL6J8kposf3gmO5cty6dZNjRwunCo2KjGTrls2YmJigoaHBNa+rtGrRnAP799G8RUsGDx5CeFgY48aMZs7sonNbP3rkzaNH3jLlgYGB3Lp1U5oCJyI8nNpuNdi1cwcNGzViwMBBhLx5Q6cO7VmzamXJKq0AQUFB2NrZIRQKOXP6FFu3bObOndsyjrVXga9o1ao1GhoavA4O5tTJEzx65I2VtTV9+vbDrkwZBAIBl72uMW3a9ELnpqSk8OTJY5o1b4G6unqx2i1pPTp07EREeHgh597LFy+45nUVj8ZN0NWVOGUrOjtz6aoXl656sWPXbrlt5ubm0rxFS0aNkY28CA+TpGg1Mf5+JsodbK247ePP8ct3CpVHxSWy/dgFTAz10VAXcP3BY9qOmMPB8zdo5l6DgZ1aEB4Tx4Sf1jFv9fYi6/cJCMTHXzZdYWBoJLd9/KVp3CJi4qnXczx7Tl2hQY0q9GvfjJDIGLpOWMTa3cdLVukPoKamxoXNy5j8r/RVqcJ0nr56TdN6rqgLJEkxfmhSj4iY+EJOsVdvIrj+4DGNalVDV1t+lGpiShoj56+ma4uGeNSuBkBCcirJqUI868re28uXkewH+EjOWBZFbl4ezdxrMLKn7LtZeIwkGtjEUP+L6fu9YGNfDt/7t7l65lih8riYKE7s3YaBkQnq6hp4377GmB6tuXD8IPUaN6dD70HERIbxy4/j+HPp3CLrD3jsw/PHsgtOw14H4nv/NqK3KWJioyLo26IOZw7txrVOA37oMYCosBCmDOjM3k1rSlZpBbAvX5ENhy+x4fAlFv8p/x7xLgVcZVdZZ9S7suAXAaipCdhw5DL9x0wtJCNMSyHQ/wl1PJohEKgrXJ88khMTSEtJonZDT5ljZcpJ0nEH+Mm+DxRFXl4u9Ro3p9sg2cnPmMhwAAyMJM+SsOBA3Ju0RF1dg4iQ11y/cIrnjx9hbmlFm669sbKxk56bk53FnNH9qV6nPj2GyD6nvqYx+Noxt3UgyPcOvldPFipPiYvi9omd6BgYI1DX4OXDG6we3R7vC4epVK8p9TsOICkmgr2/TOL42oVF1h8a4Evoc9nvo7iwIIJ870jfIZNjI/m5TwPunt5L+Rru1Gvfh4SoENZP7sGVvetKVmkFyc3JYvPsITi61qNxj5Eyx5PfpsErU1l2HsH+bVlUcMF19qH6AKo3+YHk2MhCTraYkFe8fHgDJ7eGaGjryD2vKEqVrcDkjWeYvPEMg5dslisjTE4gIzWZirVl92SztHMEICTAB5BENhmXsiE7I50nN85x+/gOgh/fK+RwVFMTMHnjWVoMmFiorkxhKhGBz3Cu64maQL3Y4/c9YGBVlmj/e7y5U3iReEZiNC8u7EZT3whVgQaRj29yek4Xgm4cxaaGJxVb9EUYF8HNddO4v31JkfXHB/oRHyibtSA1Mpho/3vw9lmZHh/JkfFNeHllP1ZV6lGhWW/SYj6jVHYAACAASURBVEI5v6gPT4+/f7/JkkaUl4tNDU8qt5PdN10YHwGApn7BHHR+TjZXlg+nVOV6VPlB1pEtj6y0RK6vHo9Dw45YV2soLTeydaLd0hO0W3oCz2mfrrci9SnaZlZqItnCZEpXl906wbC0xNkbH+ir8Pi9Szdo7iTrdH5XlhRavMVcxeGT0+h1drVh4Ql/TvpFMqi+vbT8hK9koq5nbclLwlGfCNRUVbg3q6k0SmacpyO1f7rMhWcxzGtX6ZP6seSUP2GJGZyZ0IAadpIXmOmtKtBr4z0Wnwqgu5stRjrqMuclpuew9dabD9bfrpoVFUrJ36Ts39ibSh4INwPjpSnv3vEyRuKceREtpJ6DiUJylawMiEnNwlxPk36b73PRv8DB4Gihx6qe1alZxpjAt3soWRjIfvA4WkicTfFCxVdGKKpHLXsThjQoy41XcfTdfI9a9iZoClS5FZiApYEmM9s4S897HZ9OWlYebj9dIjOn4AFVzcaQP3vXoLzlx0eafG10qe/MvF1XOX73JUNaFBj4sTvPEYnF9G5cFYDDtwMQqKnivWoEhrqSiIEJHergOm4j57yDWNCn8Sf1Y9Gea4TGpXBhST9qOkpWZM7oVp/uSw+xcM81ejSqIjclVUJaJlsu+Hyw/h/qOFHRpngpq+bt8iI6ScihWd1QUZEvM6xVTa4/DaHXr4eo7VQaTXUBN5+FUspYjzk9JA+M9KwcYpKFWBjq0vvXw5x/VOCUKG9tytpRrXErb12sdkua4Jgk0jJzqD5uA5nZudJyF4dS/DWmLU6lTQmMlKystjSWjdZwtJbcJ+JSMhSW+17o6l6B+XtvcuJ+IEOaFUzSHLv3CpFYTK9GknvL4bsvUVNV5eGKAdKomwnt3KgxeRvnfV6zoOeHNwV+H4v23yI0PpXzC7pTs5zEsTKjcx16LD/Oov236NGgYtE2dOnJB+v/oXY5KpaWvwnyx5KelUtMcjrmBjr0/v0kF3wKcgOXtzZmzbDmuDlKdHkdkyK5RiduJTOnYPWTS1kL1o9sgZO1CfYWktUw1/3DpSn53vHy7XX5IjyB2uWLjvKTx/y9N4lOSufg9I7vtUlF5BTR43uhR6/ezJg+jcOHDzNydMGky6GDBxCJRNJIlP379iEQCAh4GYiRkeSlfer0GVQoX47Tp06y9Jdl8qpXmNmzZhLy5g03bt+hdu06AMybv4Af2rZh1swf6dOvv9w9i+Lj49mw/sMTCZ26dKFSpcoK9ycoMJC01FScypUlI6PgXlijRk22bt9BRWdnhEIh0VFRWFha0qlDe86cLvj4q1CxIps2b6FOnbro6uri7l6w0fOaVSsJCQ3l7JnT5OfnM/3HH4vVbnFQtL4xY8Zy9fJlOrb/gXr13NHU0uKa11WsrK1ZtKToD1N5qKurs3KVbAqwuNhY1q9fh7q6Om3aFb2w4lujR2sPZv2+haOXbjGiR0GO8sMXbiASienXXhIBc+DcdQQCNZ6d3IShvuTZO3lQFyq3HcaZa/f5aaJs5EVxmLt6OyGRsXjt+I1aVSsAMGdUHzqOnc/cVdvo084TY0PZ74+E5FQ2HvhwBGHHpu44l7P7oByArrYW9aoXXKtrdx8nLCqOczcekC8SMXVwQQrFUb3a4XXfjy7jF1HXpSKaGhpcf/gYK3NTFoyVjYp8x6Sl60lJE7JowkBp2cs3kg/jUmay94ry9hJnU1yi/L0J5aEuELBihmxK4bjEFDbsP426QEDrhrW+mL7fCy079WTV4h+5cuoI3QYWTKJeOnEIkUgkjWI5f2w/AjUBR+/4SyNOBoyZSse6Fblx8TTj5vwst35FWfvzHKLCQthy6gZVXCWRm8OnzmVCn/as/Wk2bbv1kUm5BhIHw6Htf32wfs82nXCo8GnzFP/Gpowk4u7hTS9p+rh3vH4pSdcW/MKfam51calVsLfX3k1riI4I5eals+SL8hk4bnqx6/s37/Yy+nekEIBdOUl0bVJ8nMK6CQTqTF0iu+l5UnwcB7f9hUCgToPmbchIFxIfG42JuSWTB3Tm5qWC+5m9YwXm/bGJKjVqS8tWL55JXEwkq/eeQkXOi+DXNAZfO7VaduPwyjn4XD6GR7eCiVvvS0cRi0TU+6EvAA/OH0RVIGDRMT9pFEqLAROZ18GFJzfO0mm8/L0hFeXYmvkkRIUyfetl7KtIHIJth8/izwldOLZmPnXa9pKbdk6YnMD1Qx9Orerq2R4rh+K9fx1dNZeUuCjGrTki9zp7F3308uF1mvUtHB0fFfxc+q9DtToK1QfQuPsInt/3Yv2k7jhUq4NAQ5OX3jcwNCtF+1HzitV/RXm3N5KhmewiQssyEgdrWqLkmo8LDyYrPY057auQk5UplbNzrs7AhRspVbYCGto6lHMpsK0re9eRGBXG05vnEefn03LgZKD44/c94OjRhXtb5hN86ySV2hY4B4JvHEcsFlGhWW9AsreOqpoaPTc9kEahuHQZz75hNQm9f546g+Z/Uj/ub19MWmwYHX47h0UFiWOvZp/pnJ3fk3vbFlHes7tMejqQOED8z2z5YP1l3dthbFdRob6oCtRxH7FUpjwzJR7/01tQFahjV6tgQe29rQvISIih9cKDKDqJd2v9DHKEKdQeWPSilq+Nd3so6ZjI2qVRaYkTODM5XuHxU3u7KDLy8Q2qdSocQfzOyZQU+hxL56KzXnwKn+xsMtXTwNPZgssBscQLszF7m87tmE8kVoZaeLyNshnpUY4hDcoWSseWkyfCQFtAWmae3LoVJTkjlyM+EVS3NZI6mgDU1VTpW9eOm4HxnH4SRZ86sh9bCcIclp//sDevnLmuws6msua6uNgaceNVPLvvhdKhujUisZjD3hGc9JN48/NFYoXlXsdLJho23QimrJkuP3eqQi17E+6/SWTxqQAGbHmA1zQP3sRLVp4by3Gq2RhL0iWlZObKHPtUPQAMtAXYGOvwNCIVn9Bk1NVUEInFCNRUEWYX/H1fx6eTnp3HzDYVaV3VigRhNgcehLPnfigDttzn0hQPaRTUt46ZgQ7Nqjtw0SeI+NQMzN6mcztyOwArEz0aV7MHYHRbN4a1rCF1NIHENgx1NUnNKF7Y9L9JEmZx6JY/ruWspI4mAA2BGv2bVuPGsxBO3X9JP0/Z1ZYJqRn8cvDDOXPLWRkXy9l0/lEQf59/xI4pnd6bxs5QRxMbM0OevInFJygagZpqwTWVJQk1Do6WTD5sOPuQspbGLBvUjNpOpbn3IoIFe7zos/wIt34bjJmBjsLtljSvo5MQZuUwu0dD2tV2Ij41g33XnrLr6mP6LD/CtWUDeR0tSW9prKstc/67yKyU9GyF5b4XTPW1aVqtDJf83hCfmomZgUTvo3dfYmWsR+Mqknv66FauDGvuUii9W05ePoY6mqRmfroNHb7zAlcHS6mjCd7aUJMq3PAP59TDIPo1lp0QT0jLZNmRux9sw9HKqMSdTa9j3trGeV8cLI34pb8Htctbce9lFAv336LvHye5ubQvZgbavI5JRpiVy+xu9WhbsxzxaZnsuxHA7mvP6PvHKbyW9MKhlBHVy1pw/VkYO72e0alueUQiOHj7OcfvST5Y8sXFiyC64POavy/6sX1CWyyNik6fp6icInq8i4j81jE3N6dlq9acO3uGuNhYzC0kqQEO7N+PdenSNG0mSYkxYeIkRo8dK3U0AeTk5GBkZERKiuKRzvJITExk3949uLnVkjqaADQ0NBgydCheV69w7OgRBg8ZKnNufFwcixYu+GAb5Z2ciu9sSktj0eIldOjYibj4OHbt2MHWLZvp0rkj9x8+ku6HtHb1KsqVc2TlqtXUdXfnzq1bzJr5I106dcTH97F0TN8xb+4cqeOnUqXKaGtrF6vdd1FGJaWHrq4uhkZG2JUpg5+fLw8fPkBdXR2RSIRAIECYJrsRbnE5c/oUw4cNJT4ujhW//0GVKlU/uc6vBTNjQ1o0qMn5Gw+JS0zB3ETyYX/o/A2sLUzxrCtJczG+X0dG9WwndTQB5ObmYaivS6pQNsVrcUhKSePA2WvUrFxe6mgC0FAXMKhzS67df8zxK3cY2KmFzLnxSSksWS8/Su2fOJaxVtjZ9G8Wrt1JRpbkGepczg5tzYJnrJG+LnZW5jx+EYz3s1eoCwSIRG/f0dIz5dYXEBTK4Qs3mTakG7alCtJjBYdFAmBsIPtuZmclscOUtE8b67PXHzBq4Srik1L5ddowKpe3l5H5r/T9XjA2NcO9SUtuXTlHUnwcxmYSHS8eP4B5KWtqN5KkgOk9fALdB48ulNosNycHPUMj0lM/7TmUmpzI+aP7qFTdTepoAlBX16Bjn8E8vOXF1TPH6dBb1imcnBDHxt8Wf7ANO4fyJe5ssi3riLNLTR7cvMrxPVtp3r4rIrGIs4f3cvnUYQC5acrWL5tPVqbkOeRQoRKaWlqfVB9A+BvJQj15Djmr0pJ7R1qq4s5eedy8dIbFU0aQnBDP5IW/4VixCi+fSVbi7/97LTZlyzF1yR9Uc6vL4wd3WPPTbKYM6sK+y48wNjPn5qUzHNi6nl//3o+ZhaxD6FsYg68JPWMzKrs359mtC6QlxqFvIrHdhxcOY2RujXMdSbRL0z5jadJjRKF0Z/m5uWjrG35yqrP01CQenD9ImUo1pI4mAIG6BvU7DuTFg+v4XjlB/Y4DZM4VJsVzasOHndQWdo7FcjY9uXEOrwMbGb58F4Zy0t0BWNiWo4yzK88fXOPWse3UbNEFsUjE/bP7eXRZEuX5Lj2oIvUBaOsbYmJlR/jLJ4T4P0JVIEAsEqGmJiArQ3Y7hpLg3T5MOnKceSZWtgBkClOkslnpQjqMnotLkx8QJsVz99Qebh/fyV9TejFr902Z6KsT6xZJHVNWDs6oa0remYszft8LWoam2NRsStjDS2SmxKNtKJk3C7pxFF1TK0q7SqJYqnYcReV2wwqlO8vPy0FD1/CTU51lpyUReO0w5uVdpY4mAFWBBhVb9iPy8Q1e3zlNxRZ9Zc7NSonHe/eHFycaWpdT2Nkkj9AHF7i+agKZqQnUG/YTJvbO0vJnp/6m+axtcp0w8kgKfU7wzeO4dpuInrnNR/fpvyYlUmKXmnqydqlnIdEjJ13+e5O88ROLRZiXr06k33WeX9hFuYYdEYtEBHod5PXNEwDvTdH4qXyyswmgu5stF57FcOZJNP3rlSHs7d5A45uWR/Wt59HRQo+k9BzWewXhHZJEaGKGNMqllJxInOIQGCtELIb0nDyG7ywc4izMkjg63jli/s3/2DvvqCiuLoD/lt470hHpiCgqIqBiL7FrjL3XJMYSY5Ivxa6x9xpjb7FjjQ17Fxtil16V3ju73x+LEFyQRVGU7O+cnGN27rx57zF33sy9795rW0OD0Hkdy72Gorz0GQflBAKW9anHoI23+GGvP78feohQKEIogoEelmy7HoajsabUcslZYsN6br6QjUPciqKUXMy1iUvLYZnvCw7di8ZAQwmApExJh1JmYRSRjqr0hjZp+wfQbdU1nsSkMu9LF3rUN0NZQY6zT2P5Ya8/Azbc5NKPLbDQU2NFv/ooKcgVnWdtoE4jKz00VRVYcz6I4wExfNXw83kglEdf7zqcvBPIsVvPGdrGlfA4cU2did09inTDzlSfxLQsVh/zw+9FFOFxqQTHJJKWlYux7vs5RQKjE8W6kZ3LiOVHShxLK3Rkhb4q/eXZzlSfqG2Tyr2GooL0zsFXyemMW/sPg1rVpVMju7fKdpq+i8fhcSwc0ZaeXk6oKCrgez+YietP0mf+Aa4tGk5y4Qd+Tl4BWyZ1w85UbLCvW8uI2JQMlvhc5+C1J3TzcJD6upXN6m86oqSogJOF+MXC2lgXd3sztNSUWXn0FsduPUdJUTyHSaUYLDILo6F0NFSklqtO9G3mxKl7IRy/HcSQVnUIj0/lTtBLJnZx+5cO6ZKYns2af+7iF/iS8PhUgl8mF+rQ+9WHC3yZVKhDeYxcdaLEsbTCZ3NobOmLvp2JHpEbvy312L+piA5JS1Kh0zE3v4DN4ztiZyp+aalrVYO41EyWHPbj4I3njG5Xj1Wj26KkKI+TuVh/rI11cLczQUtNiVXH73LsdhC9mziyYlRbBiw5wvcbz/Lr9osIRSJEIhjU0pmt5x5WyGH2KjmDcX/5MqiFM53cys4BLq0cIPU4qguDBg/m+LGjHDrkw6jRYwgLDeXWrZv89L9fkJMTv7M4ODqSkJDAsiWLuXHjBmGhoQQGviA1NRUTU9NyrvB2nj9/hkgkIj0jnQH9+pY4lpoq/igKDi49jbGDoyPJUhiPlZSUKtSnDZs3o6ysjLOzOH+1rZ0dnp5eaGlrs2TRQg75HMTMTBwpkZOTw+69+3BwFN8T9es34FVsLPP+mMPePbsZO258ibaTUtMJfPGCq1evMOX332jq6UFQSBhGxsZSXXfAwLKjH95lHAMGDqJVc28ePgxgxarV9O7TFxUVFU6dPME3Y0bTrUtn7j94SE0rqwrNIUBwUBCTf5jE8WNHsbG1Zdv2HbRq3abC7XzqDOjSmn8u3uLIueuM6NWBsOhY/AKeMXn4V8jJidcXeytzElPSWLHdh5v+TwmLjiUwPJq0jExMDN8vWvJ5WJRYhzKzGfxzyQ/51HTxOh8cUXpqXHsrc+JvHCj3GkqK7/6pF3d9P4Hh0Vy/95hpK7fRfNAPPDu5CSN9XdoM/x+PXoSy7Ndv+Kq9N8rKSpy+cpuxs1bRc9wMbh9YQ03Tkg7bJVsOoKSowPiB3Uv2UUn8bZKUKmlUy8wSp/TWKcURJQ3BkTH8vGgD/1y8hY2FCZv/mEzLxqWnYv1Y461OdPpqIJfPHOf8ycP0HDiSmIgwHt7zY+i4n4rWIStbB1KSEtn55zIC7twkOiKMiJBAMtJSMTSqWDT0m4QFvUAkEpGZkc6vX5c0kmUUGsMjw0pfh2raOnA5qPxaxoqKFVuHpEFOTo4pS/7khyE9mfPjNyye+gNCoRCRUEj3/sM5uGMD1vaSDq5LgYlFaQTXzJvCsE7NOOoXiH4No3dqD0CxcJ1NTZasX5dV6NjS0n63FKqRYcEsnfYjl88cx8LKhlmrthalqktNFs99bm4O89b/jZWt2OHu6FKfhLhYNq+Yx+nDe2nd5Utmfj+Kbv2H0eKLbmVe613n9EPPwaeKR+f+BFw+wf0Lx2jWcxgJMeGEPrxN+6GTEBTqrrGVPRkpifjuWElIwC0SYsKJDQ8iOyMNbcP3091XYWLdzcnKYOMvQ0scy8oQb5aJjwop5UwwsrJn+RXJelNvIl8B3U2Jf8n2md/SpPsQXFt0KVNOICfHoKmrWTOpDzvnjGff4p8RCUWIREKadh/C5YObMbF2lLo9gMWjOhAd+Ii+Py/Brd2XKCor8+iaLztnj2PNxF5M2XsLfZN32zRSFgpK4s0UmamSz8CcwnterTCN2eBpa1FQUsLURqw/NSxssK7bGFUNbc5sX86980do3LHkd8Cyyy+JLUybeHj1TBYMbcWcY4/Q0jeSav6qG/at+xJ+6xSh14/j1GEIabERxD67g+tXExEIxPqmY25HdloiD3zWEPvUj7TYCFKjg8nNTENNr2xnpTQkRwWCSERedgZn55fcBJiXJda3tJjQUs/VMbdj2IGIcq8hr/BuGzpTY0K5vuF3wm+dQsukFi0nrytKI5eZ+IqLy8bh2G4gVp6d3t7Qv/A/sAp5BUVcukumc/2UkVcU62VOuqRe5meL9VJJo2SJm7fNn0Agh/f4FZyaNYDLK7/n+vpfEYmEiIQiHNsP4snJre/lICyPSnE2tatthLaqIscexDDYsyaH74t3qPVpZFEks+Z8EPNPPkVZQR5PG3287Q2Z2MaedReDCE+oeNqn5H8VIk8q/LeyghyKciXD6nTVFPmygVmZUUkCAagoVr6hz8lEiws/tuDI/Wiev0qjhpYKze0NuRYkzhX+uj/SyCUUpr5rWFO3yNH0mnbORizzfcGL2DRqm4oNKKXN5+s50tdQljj2vuN48SqdJzGpeNnoM9TLqujcTi4m+IUksu5iMMcDXvJ1c2vqmmuXep02TkasOR/E05j334n7KdGugQ066iocufmMoW1c8bkuDg/u37y4kNvKo7eYu/cKyoryeDlZ0KJOTX7o4cnqY7cIK8OI/TaS0rOL/p1YaKxQVpSXcJjqaaryVdPaOJiXbiAWCEBFqVIeEUVsOn2fhLQsUjNz+G5tcbqEmKQ0RCL4bu0/2Jro0bGRHY/D42hS25LhbYuL6HV2t+fmsyjWHPfj2K3ntKsvNj672ZkWOZpe06GhLUt8rvM8KkHq607sLpla4X2pZ136y0Hb+tasPHqLJxHxtHEVh5SHvZL8e7/+expoqRZFdZQnV51oX78WOurKHLn1giGt6nDohjiKpp938QfjquN3mXvguliHHM1p7mzBpK6NWHPiLmFxFd8JlPSvdKNJaeJ5VVKUR+ENHdLVUKGXlwOOZqUbHD+EDkmLSaGTzc3WuMjR9Jr29Wux5LAfz6PEH9X1atWQOB+gbT0rVh2/y5MIcS2m2hb6XJ47gEM3X/A8KhEjHXVa1LHkyhNxDn6HMuahNDafDSjUyVzGrT9T9HtMUjoiRIxbfwYbE12yc/OlkpvYxU3qcVQXOnbqjK6uLgcPHGDU6DHs27sHgMGDi3eBLlm0kBnTp6GsrEwz7+a0atOG//36K8uWLCEktPSP+LeRmFhsiElMEM+nspJyUe2i1+jr69Ov/wBq1y7dsCMQCEpEBlUWDRqUXovviy++YMmihTx69IhGjcRpeRo39ihyNL2mU+fOzPtjDk+fPkUkEiESiYoMpiB2+tja2SEnJ8fI4cM4ceIfhg4bLtV1K3scT5884eHDALybt2DM18UfU9179OTatWssX7qEQz4HmfB9+ZtG/s2unTsYN/ZbBAIBc+fNZ+y48SgrV+zd8XPhi2aN0NHSwMf3KiN6deDAqUsADOxaXJR32daDzFqzEyUlRZo1rENLD1d+GtmbFdt9CI2SrJtZHkkpxe+5icnifysrKaKgUHKt0NPRpE/HFtQuIypJIBCgqly5RnBR4QYCuX99S9lammJraYqcnIDRU5dx6vJtGtdz5NGLUJq5uTDqq+INe91ae3HD/wkrth/i8NlrjB9U7GSJeBnH3hMX6d7GSyItoLG+eI0KiZQ0HiYWzpeBbunfD29j9/HzTPhjDQgEzJk4jG/6dUFZqfhZVVXjrU40bdsRTW1dzh07SM+BIzlzZB8AnXsXO9e3r13CnwtnoqSkTAPPZrg3a8Xw8T+z889lRIeHVviaKcnFRpiUJPE6pKSkjMIb65C2rj4devYr08EgEAhQVqm6d2Zbxzr8ffYOvkcPEPz8CQZGxjT2bs2da+LnkLWDU6nrkEUtWyxq2SInJ8eMiSO5eu4kXfsOkaq90tAvjBSKCpN8J0hNEq/5OvoVS5cOcOLA38z/ZRwIBIz7/Q/6jBiLklLxWmJoLN7wUqeBe5Gj6TXN2nVi84p5hLx4yoGt60lOTCA9NZWZ3xenfIuLiUYkEjHz+1FYWtsxdNxPn9wcfMq4NG2PmpYO984eolnPYdw5La4B6tllQJHMme3LObbuDxSUlLBr0BRH9xZ0GD6ZsztWER8dVuFrZvzLsZFR6NhTUFSSMFJraOvh3qF3mVFJAoGgKFKmsrh8YCPpyQlkpaewbUbxRsHkuGgQidg241uMatrSfugkTG2d+X33De6eOUhMyDO0DYxwbNyKF3cuA2Bi48T5v9dI1V695p2IDnyEfcOmePcqTrHm2rILQf43OLtzFffPHaH1gO8qdbxa+uIIkfioUIljrx1QGrrie97SqfQNGs5N2nFm+3Kig54gEolAJCpyVILYKVXDwgaBQI5tM77h4dXTeHUdJNX8VTcsG7VDWUOHkKtHceowhOBLPoDYCfWaBwdXcXvnPOQVlTGp44WZa3Pq9/6eBz5rSHsVXuFr5qQl/+vf4r+pvKIScm/om7KmHrYteqFrWfI5XIRAgILSh9nMHHh+H1fW/AgCAY2HTcO5y+gSTuInJzaTnZpIbmYqF5cVp13MSIgBRFxcNg5tMxtcvypOnZoeF0nQxQNYeXUuNS3gp4yartiWkfZS8vn6+m+oql1s8yxv/gD0rJzoteoSwVcOkxT+DDU9I8xcWxATIK5rXubfvRKoFCuYkoIc3eubsvNGOEkZuRy6H0UjKz1sDMXGroT0XGYff4K+hhLXf2mFhnLxZZf5Pn9r2wIBiEop7h0YV7z7rWZhbaFaBhqsHlCylkSBUER6Tn6Zqdli03JYcubtfQDo725ZpqPkTfIKhIQnZKKnrkT/N1L3rTwXiJGWCjpqilLLqSjKFbX7Jtl54t80VRSxNlRHIICwBMndwo+jxQbXBpY6EsfedxwXn4vzuXrZSDotvO0NWXcxmJSsXKKTs7gbnkx9Cx3MdEu+ILzu8+vorOqCsqI8Pbwc2X7uAYlpWfhce4K7vRk2JmKjbHxqJjN3XURfS43by0ahoVo8/sU+18tqFgABgqLiuP/mdV0fAKsa4r+3tbEu674rWWuhQCgiPSsXVeXSHwOxyRksOnit3DEOaOFSpkPlTQy01HCxqkHwy5Le+py8AkQiEQ/DYpGTE/AoXHxPNaltIdFGi7pWrDnuR3JGNuaFqeNK1Y1ccaSPlqqy1NetbKIS0rgbGEN9G+Oivr4mtNBhZKClho2JHgIBhMZKRpk9DIsFoKGtKca6GlLJVSeUFOTp3tieHRcfkZiezcEbz3G3M8HGWHxvJ6RlMXPPVfS1VPFbNBgNlWIdWnLE761tCxAUFan9N4ExxfdJzcJaRTZGOqz7pn0JuQKhiPTsXFTLcCjFpmSy6NCtcsc4wLt2mY6Sd8VcX2zcKl03xBG/WmpK4ns0+BX1rY2KznlNaKx43TDUUiU3v4DwuFT0NFUZ2LxkWrNlR29jXKv2KgAAIABJREFUpKNeat2qstDXVKVOTUOC3oiszMkvQCgUERAej5ycgDqWhlLJSTuO6oSysjJf9e7Dpo0bSEhIYO+ePXh6emFnL64vEBcXx2+//oKBoSGPnz5HU7N4Xub98fb0I2XpxvPnxWmHa9US10ewtbNjy7btJeQKCgpIS0tDTa30osavXr7kjznl1xQaMmxYmY6XN4mMiMDP7xZubo2wsCz5zvI6wqqGoWHRsbw8ySjw7CzxBg0tbW0Wzp/HlN9/4/DRY3T4omQEvIGBQdE1pb2utEjbXkDAAwC8m0sWkW3Tpg3Lly4hKbliaX/+OX6M4UOH4OHhyfaduySuX91QVlKkV7tmbPE5TWJKGvtOXcajnhN2NcWbt+KTUpiyYgsGutoEHF6Pxr9S2C7YsPetbQsEAkSlvKO9rk8EUMtc/O5ka2nKpjk/lJArEApJz8hCVaV0R9+rhCTmrd9d7hgHd29LfSfbcuUAFm/ez7SV2zi4chrtm7qVOKavI36HiXwVj9rzUACaNazzZhO08nBlxfZDJKeVjFLadOAk+QUFDOkumRLQtqYZAoGAkChJZ1PAc7Hxt5GLvVRjeM2JS36MnLKUxnUd2TLvx1LT2FXVeKsTSkrKtO3ai8N/byYlKZHTh/dR180DS2txFoGkhHhW//E7OvoGHLzyCDWN4nVo04q3p+URCAQUlLIOva6vA2BmKd6sZVHLlpkrN5eQExYUkJGRhopq6etQQuwrNi4rPxVX175DcKwrWVz7fcjLyyU6PBQdPQO69hta4tjWVYswqGGMlo4eW1YuYM28qSzdfogmrTqUkNPRE393v4qOkLq90rC0tkMgEBAVLuloefFYXHf03ykKpeGK7z9MnzAcl4aNmb1mO8Zmkt9zr3/Lz5csp5BTmH5LQ0sbXX0D7J3rERESWEImNzcHkVDI80cPkJOT++Tm4FNHQUmZhm17cvXQNjJSErl9ej/WdRtTw1K8XqQnxXNo1XQ0dQyY7nMPFbXiDc8nNy16e+MCAcJSvj9e1wkCMDC3AqCGpQ1DZ/1VQk4oLCAnIx3FMpzBqQmv+GfDgnLH6NV1UJmOkjfR0DHA3N6F2PCgEr/n5+YiEgmJfC6+z/LzckmIDkNDRx+vboNLyJ7esgRtA2PUtXSlbi8qULwZya6BZH1hp8YtObtzFZlplZ/C0cjSBoFAUKqzKfK5+J63quNG0qsoQh/doWbtBugZl8w+9DryTFPXgNNbl3J49Qy+XbaPOk1KrnsaOuJnVdKrKKnnr7ohr6iEdbPuPDu9g+y0RIIu+2Dk1AhtM/HG6eyUBG5tmYWKtj591t9CUbVY3+7tlayBVwKBAFEpa2VyVPEzU9O4JgDapta0/GFtCTmRsIC8rHQUynDgZibFcm/34nLH6NC2Pwa29cqVe02432nOLx2LkaMbrX5cX2q6OxUtA/St6xSll3uNMC8HkUhIQsjDEg5OgKcntyEsyMex3QA+N7TNbEAgIPVlqMSxhBDxs8LQXvxdLM38CfNzSXsVjrKWHg5tS86H//7lqOkZfVCHXKVtue7tZsHWa2GsPB/Iw6hUFvcuvtEikzIRikR0cjEp4WiKTs7iUXQqhm+JtrHQVePi8zjyCoRFkRnPXqYRGl8cvVPLQB19DSUuPIstIQew4mwg808+5fB3TWhcS/IFIzUrj503yvcUe1nrS+1syswtoMn88/Sob8bagcUvpzEp2Rx/EEM/d4sKyakoytPU1oArgfEEx2dgbVCcFupEgPjDrJGVLsZaKnhY63M9OJHQhAys9MVyeQVCDt6NwkRbhbrm0jubpO2fvZH4YXj0QQyT25f0jB7xF0e5ORlrkZSZx8ittxnkUZOFX5WsEfQ6Gs7DunLrlnwK9PWuw+Yz91lx5CYBobEsG138sRAZn4pQJKKLu30JR1NUQhoPQ19hqF12CjBLQ20uBISWuOefRsYX1fUBqGWsg4GWGuf8QyR0Y9mhG/yx9zLHp/fHw1Hy4ZSSmc32cw/KHZ+Xk4XUzqZRHRowqoPkB1vLX7aSnZvPhXlDAXhc6Gw6cuMZP/dqUkL2UGF0WG0LQ1SUFGjmXJPLj8IIfpmEtXHxw/K4n/iF1t3BjA4NbaW6bmWTlJ7F0KWHGNK6HktGlXRU+Fx/AoCnoznGuhp4OVlw7UkEIa+SqWUk1tO8AiH7r4prfNWrZYxAgFRy1Y2+zZzYci6AFcdu8zAsjqUjinedR8SnIRSJ6OxmU8LRFJWQxsOwOAy1SzcyAFgYanLxYURJHYpKKKp3BFDLSBt9TVXOBYRJ6tDR28zdf51jU3rhYS/p5EvJyGHHhfIjGrwczSrd2aSipECz2uZcfhxJ8MtkrI2Ln/3/3BG/sLnbmZCckcOwFf8wuGUdlgxvVaKNQzfFBh0PBzOycvPx+Gk7PT3tWf9t8TMsOjGdY36B9G9esXoGo9rVY1Q7yRfSVlN2i3Vydr8SsuXJPQqPl2oc1Y2Bgwez/s91LFq4AH//+6z9c33RsfDwMIRCIT169CzhaIqMiMDf/z41jMrOe13TyoqzvmfIy8srilp6/PgRQYHFHy42trYYGhpy5vSpEnIAC+bNZfq0qZy7eIkmTSQ/oJOTk9m0sfzizs28vaV2NiUmJdK391eMHDWa1WtLFn3ft1fsGGjSrBmqqqq0aNmKC+fPEfjiBbZ2xalVDx8+DICnp2fRTnJfX18JZ9PGDeK+161XT+rrSktFxgFwcP9+pkwtWTR4/z5xdEGdOpLG8bcx5bff0NbWZvfefRibvF+KnM+FAV1bs2H/CRZv3s+DZ8Gsnlq8czI8JhahUES3Vl4lHE2RL+N58CyYGvplv1PXNDXi3I175OXno1gYtfQkKLxEWjxrCxMMdLU5c+1uCTmARRv3MXPNDs5smo9Xfcnna0paBlt8Tpc7vqYNXaR2NjnbWgFw7sZ9CefL5oOnAHCxr4V1oZPMx/cqv33dv4TcgdNXSrT1mrPX76GrrUkLd8nnuYmhHk0bOHP1zkOCI2OwNhffe3n5+ew9cRHTGvpSj+E101ZtQ0tDjZ2L/oexQekG5qoab3Wjc+9BHNz+F9tWL+L5I39+W1hsyHoZKV6HWn7RvYSj6VV0JM8f+aNvUPa7j4l5TW5dOkt+fh4KhTuxg589JjKk2HhrXssGXX0Dblw8U0IOYMuqBaxbMIP1PudwdfeSaD8tNZnDuzZL/P4mDTybVbqzKTsrk6+869K+ex9mrd5a9HtsTBTnjvvQta84QtnWSfwMv3XprISz6dBOccF2+9p1pW6vNAyNTKjv0ZR7N64QGRaMeU3xRpL8/DxO+ezG0Ni0wuNfM3cqGprazPtrd5k1lpRVVHFr0oLbVy8QERKIRa1iHb94Upz6va6bB97tOtN7uGRK6sEdPMnOzmLH6ZuA+O/5Kc3B54BHp/5cPrCJ01uXEfk8gAG/rSg6lvAyApFQiGurLiUcTUmvooh8FoCmftm6q29qydOb5ynIzyuKWooJflJUJwjA0NwaDV0DHl8/W0IO4NTmJRxdN5sf/jqJjaunRPuZaSlcO7yt3PHZNWwqtbOpRZ8xtOgzRuL3uYO8ycvJ5ted4md9VloKM3q54da+F8NnbyySS46N5t65I3h1HVih9qILnU13zx6m0+hfSsjeOSOOfjG1lb52qbRoG5pgW78JL+5eIy4yBENzseO+ID8Pv1P70DE0xdLRlajAR/z18yCa9hhK/1+Xv9E/cTScbX0v0hLFtpunN89LOJuuHBLro7ldHfKys6Sav+qIfes+PDmxBf/9K0kIfkizccVOpLTYCEQiIbW8OpdwNGXER5EQ/BBVnbI3rWkaWRB57yLC/LyiqKWk8KekxhQ7z7VNaqGirU/k3fMl5ADu71vO7R1z6TL/GMa1G0u0n5uRwtPTO8odn4mLV4WcTX7bZqOkpkWb/20usxaTc5eROHeRrP3rM7E1+bnZ9Fx+XuJY5L0LKGvqYlrPW+q+fCqo6Rlj4uzJy4fXSY0JRcvECgBhfh6BFw+grm+CYeEcSzN/+TlZ7P3aExvvnrT68c+i3zPiowm5dgyHNv1LPa+yqDRnU8OautgYqvPnxWBUleTpWq/Y8GZTQwN1ZQUO3Y+ilWMNbI008AtJZN6Jp2goK5CRm09QbDo2NSTzcTeoqYPvk1dM2H2fgR41CYnPYNW5QDRVFEjMEKeGU5SX47eOTkza68/YXfcY19IWDRUFTj58yVLf5zS3N8TdqvQPDdsaGkQskD7/ozRoqyrS1NaAYw9i8L4VTkcXE0LiM5i8zx8TbRWmdqldITmA3zs78cXyy4zedptfOzphqqPKlRfxbLseSuNaerR3Fr/ITWhtx8ANNxm17Q4T29iho6rIqnOBhCVksmOkO4UlTvjzUjAzjz5mUlt7fmhX+m5BafvnYKxJCwdDLjyLo9/6G3zZ0BwLPTVOBMTgczcKB2NNvnAxRkFODjcrXXbcDENXXYlOLsYIRbD/TiQXnsXRua4J9SsQefW54GZnio2JHmuO+6GqrEh3z+K0PbYmeqirKOFz/QmtXWthb6bPzWdR/LHnMpqqymRk5xIYnYitqeT929DOhNP3gvhuzT8Mbl2X4JfJLD9yEy01ZRLSxDvClBTkmdLPmwl/nuTrVceY0LUxmqpK/HM7kMU+12jhYkVjh9JrZNmZ6hOz44dSj31oHC0MaFnXivMPQvlq7j6+alobS0Ntjvu94MDVJziaG9CxsPbStP7etP19O8OXHeb3vt6Y6Wtx+VEYW3z98XA0p0PDihko1v5zm2k7zvPjl0348UvJj9OK4GxZg0b2pmw754+upipd3O0RCkXsvfKI8w9C6dLYnga2YqPK99096Tt/P8OXHeaHHp7oqKuw/MhNwl4l8/fPXxbprrRy1Qk3W2NsjHVYe+IeqkoKdG9cbBy2NdFBXUWRQzdf0KaeFXYmutx8EcPc/dfRVFUiIzuPwJgkbE0kd200tDHmzP1Qvlt/hsEtnAl+lcKKY3ckdaiPFxM3nOWbtacY37khmqpKnLgTzOJDt2hRx5LGdqVHk9mZ6hK9eeyHmRQpmNqnCe2m72H4qhP8/pUXZvoaXH4cyZZzAXjYm9KhgTUiETSyNWH7hYfoaajQuZEtQqGIfdeecj4gnC6NbGlgI36BaVbbnKO3Atnp/JhObjaEvErm+03nMNXTYEa/YofC2pP3mP73FSZ3b8yPPdw/ylhrWxhIPY7qROPGHtjZ27N86RLU1NTo9VXvomP29g5oaGiwb+8e2nfogIODI9euXWX6tKloaWmRkZ7O82fPsHeQDJ93d3fnxD/HGTl8GCNGjiQwMJBFCxegra1NfLw4ja6SkhKz5vzB16NHMXTwICb/9BNamlocPXKYuX/MoXWbtnh5NZFoG8Q1m9Iys0s99q64uNTFw8OTjRv+Qk9fn+49eiAUCtm1cye+Z07To+eXRSn05sydS1NPD/r37cOsOXMwt7DgwrlzbFj/J02aNKVzl64IhULq1HFhzaqV6Ghr07Z9e6Kjojiwfz/Hjx3Fza0RHTt1Rl5eXurrrli2lP/9/BO//T6F36ZMfa9xCIVC2rRth++Z03Tu+AX9BwygppUVhw8dYs/uv6ld25mu3aSvFZOUlMSjRw9xda3P0qVLSpVp3rw5HTt1LvXY54q7iwN2Nc1Yuf0QairKfNmu+FlmX9McDTUV9p++TLsmDbGvZc6N+0+YuWYHmhpqpGdm8yI0CjsrSUd2Ixd7Tl72Y/TUZQzv2Z6giBiWbN6PloYaCcniaEslRQVmjh/CtzNWMOK3xUwa2gtNDTWOX7jB/A17aOXhiqdr6elk7K3MSbrlU6lz0b6pG852Vqz9+yjamuq08WpAdGwCPmeu8M+lWzR0tuML70bICeRo7Vmfs9fv0W3sNPp1bIGlqRFHz19n34lLONlY0qVlcVri5NR07j0JpKO3e5lR5D+O6E3PcTMY9NN8fhrRG10tDRZvOUBI1EsOrJiGoPDlZuWOw/y2dBO/jOnHL6P7ltpWcmo6jwPDqOdozfJth0qV8XZzqdLxVifqNHDH0tqOneuXo6KqRpsuXxYdq2ljj5q6BmeO7MerVXusbB3w97vOugXTUdfQIjMzg7Cg59S0kfwWrdOgEVfPnmDGxFF0HzCcyJAgtq1ehIaWFsmJ4vR5iopKjP1lNrMnf83U74YxeOwPqGtqcenUUTYtm0dj79bUayRprAZxLamroe9XdP1d0dTSwa1JC84eO0ijZq1o+UVXIkKC+OOnsdQwNWP8lLkAeLXqgK1jHfZsWoOGljaeLdoRGxPF2WMHuXzmOLVd3WjapiPyCgpStQfgs2Mj838dz8jvf2Xk978BMGzcz0wc3J1fxwxg2Pif0dLRZevqRUSFh7Bkq0+R/u36awUrZv1S4tw3SUtJIujZIxzquLJz3bJSZRp6edO0TUe++3U2wzo345cxA/j2fzMxMjXn9tULHNy+AVd3L7zbSb/eSDun7zsH1YlaLo2oYWnL2Z2rUFJRpWHbnkXHjGraoaymzp3TB3H2aouRlT3B/jc4snY2Khqa5GSm8yrsBUY1JWshWzm78fDKKbbN+IYm3YcSFxHE6W3LUNXQIj1ZrLsKikp0HzuNHbPHsWXKKNoN+R4VdU38Lx7nxKaFODVuiXW90tPbG1vZs+Ja3IeZlHJQ1dTGoZE3984e5pp7C1xbdCEuMpidc8ajU8OUHhPKj9r/NybWTjh5tOLJjXOsGt8T9y/6oG9iyf0Lx7h9aj8m1k7Uay62VZ7btZqDy6fQcdTPdBz583uPpcOwH1g98Ss2/DKEL4b/iJqmDqe3LSU+KpRvl+5FIBBgZuuMtYs7Vw9tRV1bD9dWXREJhdw6sYcnN85Rv1VXrJwbIhIKMbV15sKeP1HV1Ka2R2uS42K46+tDwOUT1KzdAJdmHZCTV6jU+fucqOHghraZDQGH1qKgrIp10+IadDrmtiiqqBN0+RAWDVujbW7Hqye3uL1jLkpqmuRnZ5ASFYi2maRdy9C+IeF+Z8S1jdoPIiUmBP/9K1BS0yQ7VZzxSE5BCfchU7i0YiLnF39DvV7jUVLTJPTGCe7tWYyZawuMnUr/Ztcxt2OET1Spx96VnPRkEsOeYmDtQsChNaXKmLg0wbJRxaLDc9KTiQ/0x9K9fVEtrHcl4PA6bm6aToN+k2nQd/J7tVURXHtP5NSM/pydP4L6vb9HSUMH/wMrSHsZRvtpu0AgqND8mdZtRsi1ozw70xwrz46kxoRwedUk1PVNaDx8+gcdS6UWk+jV0IL5J5/SycUETZXipjWUFVjWpx4T9/gzeJM4pZCOmiIzu9VBTUme8X/fo/nCC0QulHyh+Ka5DbdDkzh4N6ooOqdXQ7FxfOW54h22/RtbkpVXwMyjjzlSGCWjICdggIclv3zh9NENsMv6uvL1jjt8v8ef7/f4A+Birs3agQ1KphGUUs7VQoedIxszYfd9+v91s+j39s7GLO9bvGOjhYMhq/rXZ9Jef0ZsuQ2InUYzutWmlWPxDhShUESBUIRkgsKKj0NOIGDdwIb86hOAz70ozj8rXvw9rPVZ1rdeUTTAlmGNmLTXnxVnX7DibHEo9VAvK6Z3rdjO+M+JPs2c+WPvZbq426P5rwgmDVUlVn79BePWnWDAQvHuEF0NFeYMbo2asiLfrjlOkx838Wqn5ANubKdG+D2PZv/Vx+y/+hgTPQ36NBPvgFt2+EaR3MCWdcnKyWfazgtFUUEK8nIMalWX3/p4f5LOCTmBgL/Gd+V/m305cO0x5/yLd2Z4OVmw4usvUFIQp8asb2PC7p978d3aE/SZt79IrkNDW1Z901Gi7fIo0g1RedpRPgIB7Jjckwl/nmTZoRssO1T8dxnetj6zBrUs+v+Wda1YO7YTE/48yZAlYuOItroyswe3oo2rdYXlqhu9mzoxd/91OjeyLalDKkqsGNWG8X/5MmDJUUCsQ7MHNENNWZGxf56h6S87eblFMtf12I4NuB0Yw4Frzzhw7Rkmuhr0bip2Bi8/ertIbmBzZ7Jy8pm++wqHboqfWwrycgxs7sxvX3l+kjoEUN/aiL9/6Ma4v87Qd9Hhot87NLBm5ag2gPge3f59ZyZs8GXZ0dss+9e4h7V2YVb/4oiMFaPaMnr1SSZs8GXCBl8A6lrV4M9vO5SIKhMVrS/vr0PSUpFxVDcGDBjI9GlT6d6jJ1paxek6NTU1Wb9hI6NHjqBnd/EHjZ6eHgsXL0FdXZ0Rw4ZSv54LGdm5Em1OnPQDN27cYPffu9j99y5MzcwYMFBcg2Ph/HlFcsOGjyArM5Nf/vcz+/eJo24UFBQYPmIkM2bN/qjGGYFAwP6DPowZPYoF8+ayYF6xYWnM198wf2Fx2hc3t0YcPnqMkSOG07Vz8Yajzl268tdG8W5xOTk59h/0YejgQcyaOYNZM2cUyXXv0ZOly5YX1dmR9rpCoZCCgoK3ri/SjkNOTo7tO3fx/YTx7Nn9N2dOnyqSa9bMm/UbNqKkJH1q4uvXriISibh37y737t0ts2/VzdkE0K9TS2au2UHX1l5oqhdHw2qoq7J2+gS+mb6crybOAkBXW5MFk0eipqrC6ClLces1lpTbkg6N8YN6cNP/KXtPXCyKzunXSbzmL95c/K4ypHtbMrOz+X3plqIoGQV5eYb2aMf07wZ9VB2SkxOwZ8lvjPhtMXPW7WLOul1Fx7q19mLRT6NRkBe/e22d+yOT5v/JvpOX8L1WfL80bVCHtTPGo6RY/A1z0e8BQqGIxnXLLkLc2rM+G+ZMYuyMlfSfLL7ntTXVmffDSNo1KY5uFImEFAiFb9Wh6/fF9SPuPwni/pOgUmUEAvjCu1GVjbe60bFXf9YtmEHLjt1Q1yxeh9Q0NJmy5E9mTRrDD0PFTigtHT0mzViIiqoaMyaOpG+rBlwPS5doc8CYiQTcuckpn91F0SUde4nTwGxdtbBIrmu/oWRnZbJi9q/4HhXrlryCAt36DePb/834ZJ0EU5as5/dvBzH7hzHM/kEcAeHoUp/Zq7cVRYHJycmxcNM+po4byl+LZ/PX4mJDbMuO3Zk8awnyheuQNO0BiBAhfGMdaty8DTNWbGLO5G/4eZTYiauppcPEaQvwalWcmUEkFEqc+yb+ftcRiUQ8DbjH04B7pcoIBAKatulIbVc3lm47xMxJo5g4qNjw6t2uM1OXri/13LfxMeagutG4Y1+OrpuNa8uuqKgXz5GKmgaDpqxh+6xvWTtJPB/qWrr0mjQXJVV1tk4fw6w+Hqy6IVkPtc3AcYQE3MLv5D78ToqjZBp36gPAqS3F0Rxe3QaTm52Fz4qp3PEVb56Qk1egSffBdP126ieru4OmrGHTb8PZMes7dswSf19aONZj+OyNJaLApEEgJ8fwOZvYu/BHbp/az+PrZ4uO2dZvwqCpq1EorMEiFAoRCt+ufxXByaMVQ2euZ8fs71j/kziiSFVTm17f/4GzV1tx/wQCxizaxY7Z4zi1ZQmnthRvRvLuNYIvJ84pGsfXi3axZcoojq+fy/H1xe+uri270PvHBcjJi59VlTl/nxt2LXtze8dcrLw6o6RWrG+Kqhp4T1jBpeXjOTVL/LdQ1tTFc+QsFFTUubB0LPvHNmPEoRiJNuv2+JbYp34EXjxQFP1i11K8+fD+/uJoNIe2A8jPzuLmlukEXxHbBeTkFXBoN5BGg37lYxo0Xj25BSIR8UEPiA8qK5OSoMLOppgHVxCJhBg5upUvXA4ioRCRsAAqSd+kxbx+S1pMWsOllRM5M3cYAErq2niMnIVFQ3F2n4rMX/MJyzm3cAyXVkzg0ooJABjY1KXVj3+WiKL7EAhEbzyt9u7dS58+fXi5uEulXywpI5eAqBSMtFSwN9Isup+TMnJJzsqjlkHZKcMS0nOJScnG2VTrrXqQnpPPw6gUMnIKcDLRxFTnw9do8F5wAXNdVXaNKhl2KBLBk5ephCVkUtdMW6JOUUXlQJwu6+nLNBLSc3Ey0cRIq/QaGflCEf4RyQhFIhpY6iJfys66Zb4vqKmvRo/6b08tVJH+xaRk8/RlGtl5BdjV0MDGUKPUv1dkUhaBseloqypiZ6RRwrH2mvF/3+P041c8ndVBsoEyMP7hKHv27KF3797lC1eQ17qRsPunSm87MS2LgNBYjHTVcTAzKJqzxLQsUjKyqWVcdi7N+NRMYhLTqVOzxtt1IyuXB6GvyMjOo7alIWb6H75gsdfkjZjra7H3l6/euY3oxDSeRsSTnZuPnZketib6pY4zr0DIk4g4ElKzqG1pgJHOuz88l/hcp2YNHb5sUnkFKiPiUwmMTkRbTRl7M/0SaRP/TX6BkPvBLxGKRDS0NS1Vd6WV+3bNcU7fDSJww3ip+6nfd8EH16H47dL3R1oS07MJCIvDSEcNB9PieyQxPVusQ0ZlR00mpGURk5iOs6Xh23UoO5eA0DgycvJwMtf/KDrU5H87MNfXZM+P3coXLoO8AiFPIxOIT82itoU+Rjqlr7UR8WkExiQV3qO6JRxIrxGJ4HFkPGGxKdS1qiFRH+k1Sw77YVVDm56eFauzURlIM46xf57h9P0QXqwdLXW7BoNWfHDdyMmXzLn9viQkJOB//x7GJiY4OdUu+nhPSEggOSkJG9uyoz/j4uKIjo6ibt16b/3oT0tL4/79e2Skp1OnjgvmFpL1GSob17p1sLCw5OjxfySOhYeF8fz5M7R1dHB0dCqRRvDf5OXl8ejRQ+Lj4qhTx6XU1HFCoZDQkBCePXuKqqoq9vYOmJqV/t4kzXXn/TGHWtbW9Onbr5QW3m0cUZGRPH78iKysLBwcHLF3cPjgRpoRw4byz/FjxMTGS32OsoLcB9MhEBtFts3/uUR0UmWQmJKG/9MgjA30cLS2KJrbxJQ0klLTsbEoO+VgfFIK0bEJuNjXeuvfJD0ji/vPgsjIzMbZ1gpz4w9fjL7hl99iYVyDQ6unl/ig3lG+AAAgAElEQVRdKBQRGv2S5yGRqCorY2dlhmmN0tNcR72K50lQOFk5uTjUMseusP7Su5JfUMDdx4EIhUIauTggLye5M3XBhr1YmRvTu0PlpEipqvGOmrKUE5f9iLywq3zhQgb9NB95HRP27n17zbB3RSAQ8Me6HbTp0qtS201JSuTZw/sY1DCmlr1T0ZylJCWSmpKEhZVNmecmJcQT9zIKu9p13zrXmelpPHvoT2ZmOraOdTAyLT1zQ2XSp2V9jM0sWL7jyDudLxKJCHr6kKiwEBxc6pda2wjE61B0RChhgc9QVlGlpo09hsaSUfXStlcWBfn5PHlwVxzZW78RcvKSNa83r5iHqWUt2nfvU6G230Z+fh5BTx+RnBCPjVOdMlPvScPHmIPpE0ZwxfcEvo+ipW73lzED0FOV+2C627t3b4KS8hg5d2v5whUgIyWRiGcP0DYwwriWY5EOZqQkkpmajKFF2Rsd05PiSY6Lwcyuzlt1Nzsznchn/mRnZmBm64yu0YdPez2rd2N0jc35bsWBdzpfJBIRHfiY+KgQLBxdJeoZvQvJsdFEBz0hLycLIyt7jGraSczbyU2LMDCzwq195T2jhQX5hD2+h0gkxKqOG3Jypde6T4yJ4FXYC1Q1tTGu5VCqY0gkFBIfHcar0OcoKqtiZGWLjmHpzypp5m/r9K95ePkkC8+GSj2eO74+bPxlaKU55Uqjd+/e3InJofXPG8sXrgDZaYkkBAWgpmeEroVDkQMoOy2R3PQUtExqlX1uSgIZCTHo13J+q+MoLyud+KAA8rMz0LNyQt3gw+vb/m+bolHDjA7T93zwa1Um9/YuRcu4JjbePcsXrmSEBfnEB95HJBRRw6EBgjL0UipEIhLDnpD6MhQDm7ql1ncCuLD0O8L9TjN41/NSj5dG8JXDnJ0/sjR921epkU3loauuhLe9ZL5JXXUldNXfvvtSX0MJfY3yd2hqKCt8MnV/BAKobaJFbROtSpEDccpAF7Pya0cpyAloWLNsB0VIfAZ/3wrH59vy04RVpH8m2iqYaJdfJN5cVxXztzit/mvoaarS3KVmqb/rab59ngy01DDQKrsuzWs0VJXwcvrwBsDKxlRPE1O98o36ivJy1LV6/zRZIS+T2Hk+gCPTyjcEVgQLAy0sDMrXIQV5OdzKSMv2LnL/FfQ0VGjuLHl/62mooKfx9meSvqYq+uXoGYijqDwdP7+6P4rycrjULDvX82ssDDSxMHi7rgkE4GxhgLNF2YbQkFfJ7Lz0mCO/fvwXM5BuHP8l9PX1adW6Tam/6+u//X3J0NAQQ8Py7x1NTU2aNft0cmNb1qyJZU3JNfVNFBUVcXWt/1YZOTk5rG1ssLYp2xgq7XWDAgPZsmUzvmclc4y/S3uvMTM3x8z8wxtW/6voaWvSsrFk3Qc9bU30tN/+rDHQ1cZAt/z3dg11VZo2qFiNrQ+FnJwAa3OTotpJb8PMyAAzo8pzjCnIy+PuIpna8zVBETFsO3yGk3/NLVOmolTleP8raOvq4d6sVam/a+uWnur+Nbr6Bujqlz/nahqa1PeoXEfzh0YgEGDr5IKtk8tb5eTk5DCvaV1US+h92ysLeQUF6jQoO/1xRGgQR3ZvZd3+M+/UflkoKCjiUEe62jrl8aHn4L+GurYeju4tSv1dXfvtuquha4CGbvm6q6KmgW390tMuf6oIBALM7Jwxs6u8eko6NUzRqVH2t31cRDDXjmzn+z8lN1m9D3LyCtRyaVSunJ6JBXomb7clCeTkMDSvVVQDqky5DzB/1QEVTT3MXJuX+ruK5tv1TUVbHxXt8u3giqoamNQpPb2sjGJSY0J4dmYnXeYeLl/4AyAnr0ANh/eP0AJAIEDPqjZ6Vh83k9hHdTZVZx5GpTBq2x3crHQZ4/3pp7IKTchk+wj3jxL5VVF234rg7NNY7oUnVXVXZFQCAWGxDF92mEb2ZnzTsZIemB+QkFfJ7Prpy48StfKh2HUhAN/7wdwNlAy1lvH5ERAWx4iVJ3CzM+abDm83jH8KhMSmsGtSl09Sh3ZdesxZ/zDuBr+s6q7IqAT8/e/Tv28fPDw8GD/x+6ruTrkEBwfhc+jIR4n8+lBs3bKZUydP4ud3q6q7IqMSePAsmEE/zce9riPjBr57BO3HIiQihv3Lp36UyK8PxfbDvpy+eofbD6XfOSrj0+X5I39+GTMAF7fG9B9V+ZH7nxJRocEs2XLwo0SNfYoc3bONa+dO8fi+X1V3RUYlEPk8gA2/DMHaxZ1W/auuxq60xEWG8M3SPR8l8ququX50B4+unSH00Z2q7oqMSiIh+CFn54+ghmMjXLp9XdXdKZfUmFDaT935USK/qprnvn8TcceX2Oelp759V2TOpkqghYMh0clZiESij53S8Z1p6VD+TuWqQoS4Xo6rhQ7qpaTXk/H50LJuLaISUhGKKqcG0segVb2378T5HBCJQCgS4WpjXGoKMRmfDy1dLIlKSC/UoarujXS0KiVK81OhSDdqGaGholjV3ZHxHrRp247IiAiE5dRv+ZRo2+7zr/sgEokQCoU0bOhWZlo/GZ8HbTwbEPky7rPSoTZeDaq6C+/Nax1qUNsWDfVPb9OfDOnxaN6GV9GRiETCj17XoSrwaNG2qrtQpYhtPUKc6jVETb1615ap7jh5tCLpVaS4Lstnoru1PVtXdRc+GiKRCJFQSE2n+iiryd41P3fMGrQgIy4KkfDzWSvNG7QsX6ia8FrfDG3rVWodJ5klvxKY2U0W/lmZ9HO3pJ+7ZVV3Q0YlMGewZMoMGR+eAS1dGNDy3dJHyPi0mD3g00lNVh0Y0Lw2A5p/3BByGR+GRYuXlC8ko9IZOmw4Q4cNr+puyKgE5k8eWdVd+E8yuHtbBnf/bxvtqwvfT19Y1V2Q8RHp2ncIXfsOqepuyKgEek2qvFSsMiofr66D8Oo6qKq7IaOS8Bw5u6q7IOMtOLTtj0Pb/pXermTVVRkyZMiQIUOGDBkyZMiQIUOGDBkyZMiQIUOGDBkypKRaOpvOPonl0L2oqu5GpfP8VRrLz75g0emK5fiurvMho3I4cy+Yg9eeVHU3Kp1nkQksPXSDBfuvVui86jofMioHX/9QfG5UvzoLz6ISWXrEjwU+Nyt0XnWdDxmVz8kT/7B3z+6q7oYMGZ8Vp67cZt+pS1XdjUrnaXAECzfu5Y8//67QedV1PmR8+lw9d5LTh/dWdTdkyJDxLx5ePc3t0wequhsyZPwniLjtS9Aln6ruhozPhGqZRm/V+UBC4zPoXr/6FPOKT8+h3dLLZOcV4GCsyeR29lKfWx3nQ0blsfLoTUJeJtPTy6mqu1JpxKdm0urXrWTn5uNobsBPvZpIfW51nA8ZlceKY3cIjU2hh4f0z+BPnfjULFpP3S3WFzN9furRWOpzq+N8yPgwLF64kKDgIHr36VvVXak0rl+/xoVz5xgxchQ1jIyqujsyqiFLtxwgOPIlX7WvPilV4xJTaNp/Ilk5uTjZWPLrmH5Sn1sd50PG58H2NYuJDA2mXbfeVd2VSuPB7Rvcvnqe7v1HoGdYo6q7I0NGhTmzbTlxkcG4tfuyqrtSaQQ/uMkzv0s06TEELT2ZXsr4dPA/sJLUl6HYePeo6q5UGq+e+BH94DKO7QehqmNY1d2pVlTLyKbqyK2QJLLzCvitkxMXf2xR1d2RIeOT5uazKLJz85nSz5uri2S1JWTIeBs3n0eL9aW3F1fmDajq7siQ8dlw9fJlpk+bSszLmKruigwZnw3X7z8mKyeXmeOHcHv/6qrujgwZ/1nu37zCugUziI+VrWEyZHwqBN67ztF1s0mNf1XVXZEho9rz8tF1bu+YS2aiTN8qm8/a2SQSif/7L5CSlQuAi5l2FfdExufKf0lfktOzAahXy7iKeyLjc+U/pS8ZOQDUtZLtnpPx/ohEIkT/FeWRIaOS+C/pTXJqOgCujjZV3BMZMiT5L+miDBmfCzK9lCHjI/JfMoTI+GB8lmn0HkWnMv3II+5HJJObL6S2qRY/tneglWPZhrJrQQkc8Y/m0rM4svIKaGytj6e1PgM9LJGXEwCQky9kxdkX7L8TSXRyNua6qjS1NWBa19poKCtILVPZzD7+BN/HYk/rvBNPOXgvihV9XaUe15tIO4aUrDzm/vOU68EJJGbk0shKlwGNa9LaSWaQ/Jx4GBbLlO3nuRcUQ26+EOeahvzcqwltXK3LPOfq43AO33jG+QehZOfm4+FojpeTBYNb1yvWl7x8lh66wb7Lj4lOTMPMQAtvZ0tmDmyJhqqS1DKVzcxdFzl9LwiAOXsus//qY1Z/01Hqcb2JtGNIychh9u5LXHsSQUJaFu72pgxqVY+29cueZxmfHo/C45my6zL3gl+Rm1+As6UBP/XwoE29mmWec/VJJIdvBXLhYTjZufk0tjeliZMZg1rU+Ze+FLDsqB97rz4jJjEdM31NmtU2Z2b/pmioKEktU9nM3HOVM/dDAfhj/3UOXH/GqtFtpR7Xm0g7hpTMHGbvvcb1p9EkpGfhbmfCoBbOtKln9UHGKePD8+CBPz9Nnsyd237k5ubi4lKXKdOm0b7DF2Wec+niBQ7s34+v7xmysrJo0qQpzby9GTFyFPLy8gBkZ2ezYP48du3cQVRkJBaWlrRs2Yp5CxaiqakptUxl8+3XY/D1PQPA6BEj8GralKXLlvP9xAlkZmQwddp0Fsyfx/59e4l6GcvwoUMQCoVs2ba9RDsL58/jxD//cPrsORQUxO9gycnJTP39Ny5fukR8Qjyenl4MHzGCDl90/CBjkVF1BDwP4X+LN3Ln0Qvy8vKpY2/Fb1/3p12ThmWec/l2AAfPXOHcjftk5eTi5Vqbpm51GNazPfJy4n2E2bm5LNq4j93/XCDqVTwWxoY0d6/H3O+Ho6GuKrVMZTNlxVZOXvYDYMbq7ew5cZH1MydKPa43kXYMKWkZTFu5jSt3H5KQnIpHPSeG9mhH+6ZuH2ScMj4/Xjx+wLIZP/PY/w55ubnYObkw6off8WrVvsxz7ly/xNmjB7h56Sw52Vm4ujehgWczuvcfjlzhGpabk82WlQs5cXAXsdFRGJtZ4Na0BROmzENNQ1Nqmcrmj5++5ealswDMmjSGeu5eTJ61hEVTJpGdmcHoyVPZsnIBvkf3czogiukThiMUipi5cnOJdrauWsjVsydYu+808oVrWFpqMmvmTuXezSskJ8ZT182Tbv2H0aRVhw8yFhnVl8gXDzmw9FfCHt+lIC8PMztnOo3+BWevtmWe8/zOFe76+vD05nlyc7KwdfXErkFTmvQYgpycWC/zcrM5tXkJt07sITk2Gl1jcxzcvOk5cQ4qahpSy1Q2O+eM5+nN8wBsn/ktNq6e9J68gL2LfiI3K5POY37l5ObF3PX1YcGZYLZOG4NIKGTorL9KtHNqy1IeXj3F9+uOIScv1sustBQOr5nBi7vXyEhOwLpuY7y6D6ZOk3YfZCwyPj8SQh5xc+NU4l7coyA/Fz0rZxr2/wmLhq3LPCcm4CrBV44Qdf8C+bnZGNdujEkdLxzbD0JQqG8FuTnc37eMFxf2kREfg4ahGab1muExfAaKqhpSy1Q2l1dNIureBQAuLh+PsXNjvEbP5dr6X8jPzqRh/5+5v285wVcOMWjnMy4sGYtIJKTlD2tLtHN//3Ii/M7Q6Y9DRfqWm5GC37Y5xDy8TnZqAkZO7ji2G4iFW5sPMpZPkc/O2XQtKIF+62+gp65Ef3dLUrPzOf4ghsEbb+Ez1otGVnoS51wNjKf3nzfQVFGgZwNz9NSVuPQ8jp8PPCAsMYOpnWsD8POBB+y7HUmvhua4mGkTmpDBjhvhPHmZyrFxTaWWqWyMtVQw0FAG0jDTUcVCV7VC43oTacYQk5JN11VXSUjPobebBZoqClx4FsegjbeY3rU2o71lBvTPgauPw/lq7n70NFUY2LIuqVk5HL35nAELD3J0Wj/c7SXreF15FE7POXvQUlPmyya10ddU5UJAKJM3niYsNpnpA1oAMHnjGfZcekjvZs7UrWVEyMtktp3z53FEHCdnDpRaprIx1tPAQEsdiMdMXxMLA60KjetNpBlDdGIanabtIj41k77eddBSU+acfwj9Fxxg1qCWfN1RZsz4HLj6JJLeCw+jp6HKwObOYn3xC2Tg0qMc+e1L3O1MJM658jiSL+f7iO8rTwf0NFW4+DCcyZvPExqbwvS+4mfqj1vOs+fKE3o3ccTFqgahr5LZduERTyISODHtK6llKhtjXQ0MtMRripmeBhYGmhUa15tIM4boxHQ6z9pPfFoWfZo6ivXlQRj9Fx9lZv9mfN3B9YOMVcaH49LFC3Tp1BE9fX2GDh9BakoKPgcP0LN7N3zPX8DT00vinIsXzvNF+3Zoa2vTp19/DPT1Oevry7ix3xISEsLcefMBGP/dWHZs38aAgYNwdXUlKDiYTRv+4mFAABevXJVaprKxs7Pj0cOHhIWGYmdvj62NOErj4YMHvHz1km5dOhMQ8ID69RsAcPfuHYRCoUQ7gYGBXL16pehYVGQkLVt4Ex8Xx8BBg9HS1ubMqVP06NaVBQsXMW7CxA8yHhkfn8u3A+g+djp6Opr/b++8w6K4ujj80pbemyCgUkWMPfbeo2CJxobGbuw9iZrYW9RYYzTWaBKjxoKx96jYuyI2Ooj03hfY/f5YhSwlu0QXMN+8zzPPs8ycmXvPMr+9M/fcey7DenciJT2TI+ev89mUxZzZsZymdYuvHXnlzmM8x87FyFCf/p+0wdzEiIs3HzBl6SZCXkWzdOpwAKYu28zvxy4y0LMddWs6ERIRxc+Hz+AfEMrF3auUtnnf2FiaYWkmy9JgV8USBxvLMvlVFGV8iIyJp9OIr4lPSmWQZ3uMDPQ4f+M+facsYvn0kUz07qkSXwU+HO7duMIU7x4Ym5rRc8Aw0tNSuHjiCDOG92HLofPUadS0+DnXLzNhQDcMDI3p2rs/xmbm3L5yge9mTSIyLIRJ3y4DYMXsyZw4uIdufQbhVrser8KCObJnJ4HPnrDj6GWlbd43Do4uBD33JyoiDAdHF+yry9qwwGd+JMTGMHVILwKf+VHzo/oAPHv8AGkJbVhESCAPb19HIpWgAcRGRTK6d3uSE+Lp1tcbAyNjbl46x4yhnzJl3goGjp6kEn8E/nu8vHeVHyf3Qd/YjOY9Pyc7PZUHF/9k8/QBTN96Esc6xdeYfXnXlw0TeqJrYMTHXT9D38Sc57f+Yu9304iPDKX35EUA7PtuBrdO7KVxtwHYu9UhPjKEqz67eR34lJk7zylt876xruZMVNAzEqLCsXJwxtJO1ucWGeBPakIMP07tS2SAP/Y16wIQ/uwhUmlxXcZFBBH08AYSiQR1DUiOfc3qUV1IS46nafeB6BoY8fTGBTZP70+fqUtpP3C8SvwR+HCI8rvGqQX90TE0w62TN+LMVEKuH+fs4sF4Lj+KtfvHxc55/fgqJ+f2RaRvhHObPugYmfHqwSWubvqS1OgwmgyfD8DVzV8ScPEPXNp9hrlTHdKiQnh+5leSQp/RY9VJpW3eN8ZVnUgKe05abATGVZ0wspHpLTHkKZlJsZxeOJDE0KdYONUBID7wUYl6S30dTPTTW/DmWEb8a4597UlWagKu7fsj0jMi4v5FzizypunIRdTu+YVK/KlsFAs26ejoACDOkyDSrFxZ9iRSKXOPPEFbU4PD45tTw0IfgAntnGi18i92XQstMdjk8yASDXU1bs3pgLGuFgCT2jvTeOkFzvrHMM+zFuI8CYfuRdLR3Zr1Awo7uqqb6/PtkScExWVgb6qr0MbJUr9Y+YkZYn6+FqrQP886NrhVKT56aVSrGuhra3A1MJ4xbRxpUsNMab+KooyfTpb6LDn+lIjETE5OaUkDB1MAvurqxsCtt1h8/Bn9Gtljoqel0KfyJDs3HwBdXdWMxnyrjZzcfLS1NFRSxvtEIpUyZ/dFtLU0ODZvIDWqyP6Pk7wa02zGDnaefVBisOnQ9Wdoaqhzb/0XGOtrAzClZxPqT9rK6XtBLPBuS05uPgd8/elU34mN4wpHWteoYsLsXRcIikrEzsJYoY2TTXG9JqRlsfPsA4X+eTVxpaadRbH9Y7o2RF9bhK9/GOO6NaJpTTul/SqKMn462Zix6PfLhMelcHbJEBo6ywISX3/Wgn7LD7Lw98v0b10bUwMdhT5VNNniPED1GhLn5SPSrFwakkilzPntCtpaGhz95lNqWJsAMLFbQ5rP+pWd5x+XGGw6dPMlGurq3F09FGO9N/eVZyMaTN/FmQchLBjQEnFePgeuPadTvRr8MKZwJF51axPm/HqZoOhk7C0MFdo4VTEpVn5CWhY7z/sp9M+rsRM1q5oX2z+mc130tbXwffqKsZ/Up6mrrdJ+FUUZP52qmLBo/zXC41M5s6AfDZ1kqS6//rQJ/Vf9yaL91+jfsmal00t5aSMnJwdtbW2VlKEqJBIJM6ZNQ1tbm/MX/sLJ2RmA6TNmUvcjD7Zs3lxisGn/vn1oamry7GUgJiaye3vmV1/j5uLEiePHWP7dCnJycvh9z2980q0723bsLDjXydGR6dOmEvDyJQ7Vqim0cXF1LVZ+fHw8WzZvUuhf7z59qFXLo9j+aTNmkp+fz82bN/jy66+pW7fwmerlixd06tyFPXv34VazpsIy/s43c2YTFhqK7/UbNG4s60iZN38BXt27MWf2LLyHfI6ZWfG2s7KTlZUFqE5DADo62ohzc1V2/feJRCLly1XbEIm0OL19OU72svZl2tBPafDpeLb+cbLEYNMfp6+gqamB/7FtGBvK3jmmD++DR/fRnLx8m6VTh5MjzmXfiUt0bfUxWxYWBidr2Nvw5cqtBIRF4mBjpdDGpVrxZ8SE5FS2/qH4pb9Xh+a4OzkU2z9+oBcGujpcvv2Yid49aV6/ltJ+FUUZP12qVWXuht2EvY7l0i/f8/FHbgB8O86bXhPnM3f9Lrw922NqrJrZI++brBwxlirUkLaODuIcscquXxmRSCSsmTcTkUibnw6dKwi6DB43nf5t63Fw95YSg01njuxHU0MTnxtPMTSStWFDJ8ykV9Oa+J47waRvlyEW53Dq0F5adviEeWsLZx7YVXNk9bwZhAcHUMXOQaGNg6NLsfKTExM4uPsnhf6179YbR7fifQKDx05Dkp+P371bDJ04E1ePugXHwoJe0rRtJ5b99BvVnd0UlvF3Ni77lqiIMHYe96V2fVnn5JiZc5ni3YONS7+h+2feGJl8eG3YuyLOyULXrPh76/tCR0eHfHGGyq5f3kglEg6u/hpNkYhpW05gaS/rBO44ZDKL+zXmyoHtJQab7pw5gLqmJouOPELXUDawofPQqczrWRc/31P0nryIPHEOt0/tp3bLLnw+v/A50KJqDQ6s/prY8EDMqtgrtLFycC5WfnpyAlcOblfoX/32PbBxLN7Gdxw8GUm+hGC/23QZNh07148KjsWEBVCrWQdGLttFlerFn2v/iSM/zCchKpyvfr5A9dqyQbDdx8zhxyl9OPLDfJp0H4i+kWmZrllZyc3OQkdHde0kyPQmyU1TaRnliVQq4ca2b9DQ0sZz+Z8Y2dQAoM6nEzkwvgVPT+4sMdgUdOUw6hoaDNh2B5G+TG91+0xm3+iGhN8+Q5Ph88nPFRN46SAOH3eizdQfCs41tKnBja1zSIkMwsDKXqGNcdXiqZezUxN5enJnsf1FqdHcE1OH4u9jdXpPQJqfT8zzO9TrOwVzx9oFx1IiA7Fr0I4OX2/HxK54G/xP3N69mLTYCHp+fxorN1mmgobeX3Fq/gBu7VqES/t+aBv+N/SWl5OFdil6KxZsMjeXdUYlZIixMa5cnT1PIlPxf51Kv4/tCwJNAM5WBiztVRtJKWklx7ZxYmTLGgUBGZAFXYx0NUnLknUg5b85+XpQPH6RKQVrI41oWZ1BTRzQ1lRHnCdRaFMSCeliVp15odA/J0v9EoNNpaGMX0VRxs/kzFwOP4iknr1JQaAJQEtDncFNHbgaGM8Jvyi8mxR/iaxIkjJlHQxv7+H3zdvrJqZlYWOmmqmc7xO/kBiehMUyoE3tgkATgIutOd8N64iklDys47s3YnSXBgUBGZDdV8b62qRmytZ2eTsC+9rTcB6HxlCnujUAo7o0YHC7OmhraSLOzVNoUxIJqZl8d+CqQv+cbExLDDaVhjJ+FUUZP5PSszl47Sn1nWwKAk0AIk0NPu9QB1//MI7ffsmQ9nWUrmtFkZgu6wxUtYYS0rKwMa1cGvILjcM/PJ4BrdwLAk0ALramLB/SptT2ZXzX+ozuVLcgIAOyoIuxnjapWbL76u3v7rVnr/ALi+OjarKR3KM61WFwm1oyveTlK7QpiYS0LFYcvqnQP2cbkxKDTaWhjF9FUcbPpPRsDt14QX1H64JAE7zRS7va+D59xfG7QQxpW7xzvyJJfLMOnMq1ER+PbdXiHbyVmYcPH/D48SOGfD60INAE4FazJmvXrS9xNg/AlKnTGD9xYkGgCUAsFmNiYkJKSgoA+fkyXVy5fImHDx9Qr55shPW4CRMZNmIkOjo65OTkKLQpifi4OBYtXKDQPxdX1xKDTYpYsGhRmQNNiYmJ7Nv7O40afVwQaAIQiUSMHDWKS39d5IjPYUaMHFXm+lQ0iQkJgOo0BGBmakp8UqrKrv8+efQiCL+XIXh7dSgINAG4Vrfj+6/GlPqMNnlIL8YN8CwIyADk5uZhbKhParqsozH/jeau3PXj0fNg6taUddKN7e/J0F6d0NEWkSPOVWhTEvFJKSzZvEehf87VbEsMNpWGMn4VRRk/k1LS+OPUZRp6uBQEmgBEWpoM/7QLl28/5s+LNxjW+8NII5SQkoabCoPNZqZmJCfFq+z6lZGXTx4S8PQx3T8bXBBoAqju7MaMxWtKnM0DMGjMFPqNGF8QaALIFYsxMDYhI1XWhknetGH3blzhxZOHuNWWDUr4bEmaK0cAACAASURBVPg4egwchra2DmJxjkKbkkhOiGPr94sV+ufg6FJisEkRY7+cX+ZAU2pyImd89lGrXqOCQBOAlpaIXt4juHvtEn+d/JOeg0qeqfhfJiUxATOPsgUIyoKZmRkZjxX3N30oRLx4zKuAJzT1HFQQaAKoUt2Vz2auLFWXHbwn0q7/FwWBJoD83Fx0DY3JSpc9H7x9Ln15z5eIF4+xd5O9o7ftN4YWPT9HU1ubPLFYoU1JpCfFc3zLMoX+WTk4lxhsUoTX2G/LHGjKSE3izpkDVKvVoCDQBKCpJaJFr2G8uHOFhxeP0qLX0DLXpzKSkZKIqYoHZZmZmSFOe67SMsqThCA/EkL8ce0woCDQBGBi50LzMctKXS/to17j8PAcXRBoAsjPEyPSN0acIdObVCJrB6P8rpEQ7Ie5oyyA6uE5kpqdvNEQaZOfK1ZoUxLZKfHc27NCoX/Gtk4lBpsU0Wjw7DIHmnLSkgi8fAhLl/oFgSYAdU0RNbsM4fVjX0JunKBmZ9VkeSpvctKSMDEtWW/Feq9qvnkxfhaVWumCTSHxspcN9xICMiNa1ii27y3OVgYkZYjZfCmIe2FJhCdmEhKfQVp2HlWMZD7qijSY0dmV7049p9OaK7hYG9DC2YKO7la0dbNCQ11NKZvSyg/9TnGefS2Nss0kU8avoijjQ2BsOlIpZIjzGPPrPbnz07NlAYTQ+Mo3cuZZlOwHrWYZO3eU5e11n0bEfRDBpuCYZABq2VsWOzaqS4NSz3OxNScxLYsfj9/hTkAk4XGpBEclkpYlpsqbAIGuthZf9W3B0v2+tJu1G9eq5rTycKBjfUfa16kh04sSNqWVH/nLdIX+aZVxZowyfhVFGR8CXyfK9JItZuT6o3Lnp70JYoW++V9Udp5FyDoYVK+hhEoXbCrQi13xjtBRneoW2/cWF1tTEtOz2XTyPncCowmPTyU4OvnNfSXrNNMVafJl7yYsO3iDdt/uxdXWjJa17OhUtzrtPnJ4074otimxfBszXu1QnPqg7HpR7FdRlPEhMDrpjV5yGbXxlNz5aVmyB83Q2JQy1bU8ePZK1lGuam08eeL3wQWbggIDAahdu3axY+MmTCz1PLeaNUlISGDdmtXcvHmTsNBQAgMDSE1NxcZWNsNOT0+Pb+fOY/68uTRp1JCa7u60bduOrp98QqfOXdDQ0FDKprTyk9MUP8uIRGVfL83S0pJGjYqPAFTEy5cvkEqlpGek4z1wgNyx1FTZM05wcHCZr1sZ8Pd/AqhOQwDu7rV4GhSmsuu/T4LCowCo7VJ8PcCxAzxLPc+1uh2JKWls+NWHW4+eE/Y6lsDw16RlZGJjKXvZ09PRZs4XA1n44680HzgFtxr2tPn4I7q0bETH5g3QUFdXyqa08uNvHlLon6iUARLv4ldRlPHhZVikTFOZ2Xz+tXyHROqbATbBEVFlqmtFIZVKeR4UzohxqtSQO8HP/VV2/cpIRKhsnVdn9+JtWL/h40o9r7qzGylJiezZsg6/e7d4HRFGREggGWmpWFrLAsg6unqMnvENm1csYEiXplR3qUmj5m1o0aErTdt0Ql1DQymbkqjm7IZvUJJC/7S0yt6GmZpbUKte2VOAhwUFIJVKycxIZ85Y+Y60jDcd/a/CPsw27F2QSqUEv3zG+NGqC7K5u7uzbcdOpFIpamolvzN8SMS9kt0ntk7FA6Vt+40p9bwq1V3JSEnk/G8/EOJ3m4SocGLDg8jOSMPYUqZLkY4u3UfP4ujmxSwf3IoqNdxwbdiK2i06U6tZB9TVNZSyKQnr6q6svxqt0D+Nf6FLA1MLqtUqvQ+nNGLCZLrMycpgx+xhcseyMmSzc+IjQ8p83crK66BnuLuXPZBXFtzd3UncvhOkUvgP6C01Svb/N61W/Hvz8Cx9gJuJnQvZaYk89tlE7PM7pMVGkPo6GHFmGnpmskGlmtq6NBj4JXd/XcbhKe0xsXfF9qOW2DfqiF2DdqipayhlU1r5ww9FKPRPQ7PsGbl0jM2xdKlf5vOSIwNBKiU3O4MLK+S/u9wsmd7SokLLfN3KSlLYM2rVKllvxd4kzM3NcXFy5FpggsorVlYS0mUdt2UNgm36K4h6i86x9lwAuflSWrtasn5AfRrXkH95mdrRhZtz2jO9kyu6Whr8cj2Mwdtv02blX8Sm5ShtUxQ1NdDR0lC4ldaZ+K5+FUWRD0mZsg4/bU11tNTV5DZTPS36NKhaphlY5cXVwHhcnBxVll7G3NwcV2cnrvqHq+T675uE1EyAMgfGfjh2m9rjN/P94evk5kloW7saP47vThM3+Q7Q6b2bcXf9GGZ+2hxdbS1+PveQgSsO0XzmTmKTM5S2KYqaGuiINBVuZdWLsn4VRZEPb2cDaWtpoKWhLreZGeryWctauJUQwKiM+D4Jw9XZSfUaevpKJdd/FxLSZP/Hsupl44n7fDR5B9//eZvcfAltPOzZOKYTTVzlU+5N7/kxd1cPZWavxuhqa7Lrgh8DVx+lxazfiE3JVNqmKKrSi7J+FUWRD0lpshlCIi0NNDXU5TZTAx36NnejZtXKl17F92mE6rXh6sqlS5dUcn1VEh8XB1DmINma71fhWM2eZUuXkJubS/uOHdm+82eaN28hZzdrzjc8exHAnG/noqerx9YtP9Grhxf16tQmJjpaaZuiqKmpoaurq3ArLVj1T4jKkAoxMTGx8POb2T/aIm20tLTkNnNzcwYO8qZWrbKPUK8M/PXXX7i6uqo0BWCz5s25fEdxWtHKQHySLKhuY1W254N1uw/j0nkYy7fuJzcvn3ZN67F10VSa1ZN/0ftqVD/8jm1l1ugB6Olos/3gKfpMXkSjPhOISUhS2qYoampq6GqLFG6lBave1a+iKPIhMVn2Yq8t0kJTU1NuMzMxpH+3ttQqwwysiuT+00BS0zNo1qyZyspo3rwZd69dUtn1KyNJCbKBVpZVbMt03q+b19C9oSM71i4nLzeXxq3aM3/tNup+LP//GT55FoevP2Xk1Dno6Opx+NdtTPu8N/3b1SchNkZpm6KoqamhraOrcCstWPVPaJUyirwkUpILfytSkmRtmEikjaaWltxmbGpO108H4uj6YbZh78KzR/dIT0tVqXabNm1KZnoa4c8Up6L/EEh7M8PSxKpsujz363rmdHPn1I6V5OflUbNxWz5fsBmnuvKpMLuOmMlCn4d0G/UVIh1dfA/vZNO0fizq14TUhBilbYqipqaGlrauwq20YNU/oVmGAFVGaqEuM5ITC87X0NSS2wyMzWjctd+/mmVVWQm870vzZsVTn75PmjZtSnZGGnGBD1VaTnmRlSLTm775P7/nF+Xx4Y38PqwOD/avRpKfR9V6bWgz9Qes3RvL2dXvN43+W+/QYMAMNLV1eXZqF2cWDeLghJZkJsUqbVMMNTU0RToKt9KCVf+Ehpby7WBOWvLfPie9OV+EuqaW3KZtaIZz276YOpRt1nBlJubJNVqUorcSh5159ezF4d+2821390oVqLU30wPgfngyverLd2r8cfcVUqmU/h/by+1PSBez5MQzzA1E3JjdHgPtQpfXnX9Z8Dk3X0KmOB97Uz2+6urGV13diE3LYd35AHZeDWGHbwgzu7gqtJndrfhos9i0HNace1lsf1EGNXagjp2xQruy+FUUZfzs01D23dawMOBHb/nRE/kSKek5eeiJKt96Kyf94+gzZLRKy/Hs0ROfvbuZN7BNpdJGSdhbyu6le4FRfNpc/gFi/xV/JFIpA9vIj+KLT81k0e+XMTfS4+660RjoFj7UrPa5UfBZnJdPVk4eDpbGzO7Xktn9WhKbnMFqnxtsP3Ofrafv8VXfFgptvh3Quli9Y5Mz+P7wdYX+ebf9iLqOVRTalcWvoijj52ctZemVHKuY8tNE+dHI+RIp6VlidLXLNsK3IpBIpRy/G8Sng4aptBzPHj3x+X0X8/q3qFQacrAwAuBeUDS9m8qnJ9h/9RkSKQxsJa+jhLQsFu2/hrmRLne+/xwDncL7as3ROwWfxXn5ZInzsLcwYlafpszq05TYlEzW/HmH7ecese3sI77s3VihzTefFX9JjU3J5PsjtxX65926FnVrWCn1XSjrV1GU8bNvc9nDlZO1CT+N6yJ3fr5ESnq2GF1R5dKLRCrlxL1QPvVWbdoXT09PfA4fYumy5R/UqNRq1asDcPv2bfr1l5+N89uvvyCRSPh86DC5/XFxcXwzZzYWlpY8ff4SQ8PCASzfLStMPyIWi8nMzKRa9erMX7CQ+QsWEhMdzfLly9j840Z+/HEj386dp9Bm0eIlxeodEx3NsqXF9xdl6PDhNGjQUKGdItRQKzGl4MuXhWlvatSQpYpxdnFh1y+/ytnl5+eTlpaGnp7eO9elvJFIJBzxOUzvXr1UWo6npydLlizh/tMAGtQqW9qL8qaarSwt712/l3zWRf5Z6PfjF5FIpAzu0UFuf3xSCnM37MLC1Bi/P7dioF+YI33l9j8KPotz88jKzqGarTVzx3szd7w3MQlJrNz+Bz/tO87mvceZ88VAhTYLJg4pVu+YhCS+27pPoX+f9+pEfffi61mUhLJ+FUUZPwd0awuAs4MtO5fOkDs/XyIhPSMLXZ0PY508n/PXqOZgT506qkvL/FZDzx7dw73uu//ufQjY2stmF/o/uEPnnv3kjp08uAeJRIJnP3ktJCXE8+OybzExt+DwVX/0DArbsJ0bCmfQ5eaKyc7KxNauGl98OY8vvpxHQmwMP2/4jj9+3swfP29i1PRvFNqM+3phsXonxMawY53idF09BgylZp2yz4YoipqaWkHqyr8TFlTY71DVQZZpxr6GM4t++FnOTpKfT0ZGGjq6H14b9q5cOOGDQ7VqKtVunTp1sLN34MHFP//V7JfKhrmNbBBA6JO7NOrcR+7YrRN7kUolNPX0ltufnhTPkY0LMDSxYIHPA3T0CgcQnt75fcHnvFwxudlZmNs64PnFN3h+8Q2pCTGc3vk9l/7YyqX9W+g2epZCmx7j5xWrd2pCDCe3r1ToX/MeQ3Bwr6fQTiFqakjyi+syJiyg4LOFXXUArBycGLZ4m5ydRJJPTkY6Wipe46i8CHt6n7jIMLy8vFRaTp06dahq50DI9eP/avZLZcPQWqa32Jf3cGrdW+5YwMX9SKVSXDvIv+NlpyRwe9didIzN6b/1Nlq6hXp78Mfags+SPDF5OVkYWtvT0HsWDb1nkZkUy8M/1uJ/fDv+x7fRcOCXCm0+HvJNsXpnJsXyYN9qhf65dRqEhXPpmWqURk2txBSeyZGBBZ8Nq8ieKYxtHWk3Y7OcnVSST25WOpra/w29xQU8IDk6vFS9lTjsbMSIEYTGpnLxeSkRxAqinr0JOloaXA2QzyX9MiaNKfsecD2o+GysV0mZSKRSun9kIxeQeZ2chf/rwrzuvgHxuH17Gp8HkQX7rAy1mdBWlrs5JStXKZuSSM3KZc/NcIVbWVLTKetXUZTxoYaFPuYGIi69iCW3SOO14UIgbt+e5n545UoL9tfzOEJjUxk+XLWdgSNGjCAkKoHzDyt/CoAGTlXQEWni+0Q+pcyLVwlM2HyCa0+Lz9B6FZ+KRCrFq7GrXEAmMiGNJ6GFI3h8/cNxHLmeQ9eeFuyzMtFnkpdsFENKRo5SNiWRkpnNrxcfK9xCypCaTlm/iqKMDzWqmGBhpMfFRyHF9LLuyE0cR67nfmDlT9Fy4WEIIVEJ5aOh6EQuPA5VaTllpb6jtUwv/vKzrl5EJjJx6zmuPys+GysiPg2JVIpnIye5gExkQhpPwuIK/vZ9+gqnL7Zw+EZhp7KVsR4Tu8teBpMzspWyKYmUjBx+u+SvcAspQ2o6Zf0qijI+1LA2xtxQl4t+YcX1cuwuTl9s4X5w6ZqsCC4+DiMkOrFctBEcFMSZ06cUG1ciGjX6GF1dXS5dvCi3/9nTp4waMRzfK1eKnRMeHoZEIqF370/lAk2vIiJ49KhwlOClvy5ibWHG/n17C/ZZV6nCjBkzAUhOSlLKpiSSk5PZuWO7wi04KOhffCvFqVa9OmGhoeTmFj4rPn3qX5CGEMDJ2RlLS0vOnT0jZwew8rvlWFuYceeO4uByZePsmdMEBwWpXENNmjTBo5Y7W/afVGk574OGHi7oaou4dPuR3P7nwRGMmbcO33vFZ2iFR8UikUjp2b65XEDmVXQ8j18UPpdevvMI29YDOHDqcsE+a3NTpg79FIDk1HSlbEoiJS2DXT5nFW7BEYrTCJXVr6Io44OjvQ0Wpsacu36f3Dz59Wy/33EA29YDuPtE8YDAiiYzO4dfj15g+IiRKi2nSZMm1KrlwYFdP6m0nMpErbqN0NbR5c7VS3L7Q14+Y+HUUdy/UbwNi34la8PafdJLLtAU8/oVL/0LNX336iU6uFfhzJHCoKm5lTWDx8nShacmJyllUxJpqcn8+fvPCrf3lbbOxq4aURFh5OUVtk3BL57yKqSwjbSr4YSpuQU3L5+TswPYtXElHdyr4P/g7nupz4dCdlYmJ/b/wggVt39qamqMHDGcW8f3IM7OUmlZ5UE1jwZoaevy4u5luf1RIc/5ZeE4Xt67VuychOgIpBIJ9dp7yQWakmIiefWisE19efcKM9o7cPfMwYJ9RubWdBwyBYDMtGSlbEoiMy2F63/+onCLe09p68xtHUiMCif/b3qLCn5GXESh7i3tHDEwteDpjQtydgBnfl7DjPYOhPnLL53xoXLl4HZqedSmcePGio3fATU1NUaNHE7Qhb3k5Xz4erN0qY+mSIfXj3zl9idFvODSuklE+RXXW1psBFKphBrNPeUCTRnxkSQEPyn4O/LRVXYPcCbo8uGCfXqmVtT5dAIA4vQUpWxKQpyRwvOzvyncUqNDy/6llIChtT1pMRFI/qajpPDnBWkIAYxtaqBjbM6r+3/J2QE8PLCe3QOciX3535iB+uzkTtxrla63EocPe3h44OXZnSWnrtPGzRLNMqbfURWWhtqMae3IhgsBfHXwMd5NHHgRk85Pl4LQVFdnaPPiec+drAzQ19bkyMNI2te0wtnagDshiXx36jkG2ppkiPMIik2ncQ0zLAy0WXP2JbYmutSuakRofGbBLKGOtayUsikJZysDIlZ2f6/fhbJ+OVnJp4RSxgctDXW+6ebO9D8eMeH3B0xq54yBjiann0Sz9vxL2rha0rh65UlzlCeRsuTUS7y6d8fDQ7WLyr/VxsJ9vrSrUx3NMq6zVZ5YGusztlsj1h25yYztZxnSvg4vXiXw4/HbaKqrM7xT8VEYzjZm6OuI8LnxjA71auBa1ZxbLyJZtt8XQ11tMrLFBL5OpIlrVSyM9Fh16DpVzQ35qLo1IdFJBbOEOjVwVMqmJFxszYn6bUaJx/4tyvrlbCt/Xyvjg0hTg7kDWzNly2nGbjzOlB5NMNQVcfJuIKt9rtP2o+o0cbN7r/68b/LyJSzc54uXZzlq6I+btK3tUGk0ZGmsx9gu9Vh37C4zfr7IkLa1eRGZwKaTD9BUV2dYh4+KneNsY4K+jhZHbgXQsW51XGxMuRUQxfKDNzDUFZGRnUtgVBJNXG2wMNJl1ZHb2JoZ8FE1K0JikgtmCXWuV0Mpm5JwsTXl9c8T3ut3oaxfzjamcucp44NIU4O5/ZszdfsFxm0+w2TPhhjqijh1L5jVR27TtrYDTVzKljJDleTlS1j4x83y04aXF3NmzaJjp85oalauGV6lYWVtzaQpU1n53XImjh/H8JEjefb0KevWrEFTU5PRX3xR7BxXVzcMDAw48Md+unTtiptbTa5fv8aC+fMwMjIiIz2dly9e0Kx5CyytrFi6ZDF2dnbUrVefoKDAgtlPn3TrppRNSbjVrElaZslBXGVxqCZ77tyxbRufDxv2j+s0NW7cmFMnTzBqxHBGjhpFYGAg369aibGxMfHxskFUIpGIxUuXMXbMaIZ9PoSZX32FkaERx47+yfJlS+nQsVOxNIOVnby8PObMmoWXl5fKNQQwa/Ychg0byoRBXtRxK/k5ozJgZW7CBO+efL/zAJOX/siw3p15HhzB+l980NTQYFTfT4qd41rNDgM9HQ6e9aVzi4a41rDj5sNnLNr0G4YGeqRnZhMQGkmzurWwNDNm+dZ9VLW2oG5NR4IiogpmCXVt1Ugpm5JwrW5H0m2f9/pdKOuXS3X5rBbK+CDS0mTR5KGMX7iBkd+sZvqwvhga6HHi0k1WbN9P+6b1FKbqqwys3nmQ3DwJ48aVvobQ+2L27FkMGzaMAaMm4urxHkYCV3LMLK0YOHoSu35YyXdfT6TnoOGEBDznt5/WoaGhyaefF18fppqTK3r6Bpw7epDm7btQ3dmNR3du8NPKBegbGJGZmUFY0EvqftwMUwtLtq9dipVtVdxq1+VVSFDB7KeWHT9RyqYkqju7cS209AGmylDFTjaa3ee3HXj1//wf12mq3eBjrl04xcKpo+nlPYJXIUH88uP3GBgZkZwoG/CrpSViwuwlLJk5lnkTh/P5hBnoGxpx5cwxdq77jiatOxRLM/hfZ/fG78nLE5eLdsePH8+ates4u3sNnl8UnwXwIWFkZkX7geM4s2sNe5dPpXmvoUQHP+f8nh9Q19CkdZ8Rxc6xruaCtp4+984exqN5J6yruxL86CZHNy9Bx8CQnMx0YsICcKzbFEMzS05uW4GJVVXs3eoQFxFcMPupdosuStmURJXqrmy4XvrAPGUws5Flarrq8zPNvAb/40y16h6NeHL1DL8sHEeLXsOIiwji7C/r0DUwIj1ZpktNLRG9JszntyWT2DV3NJ2HTkNH35BHl09waucq3Ju0w7GuatPOlQevXvpx6+Q+du/aVS7lvdXbo4Praeg9q1zKVBW6JpbU7jmWhwfWcfXHmbh1HkxyxAse+2xCXUMT90+GFTvHxM4ZLR19gnyPYN+wA8Z2LsQ8u83d35Yj0jMkLzuDlMhAqtRqjK6xBff3fY++hS3mjh+RGhVSMPvJvlEnpWxKwsTOhZE+kSUeUxYDK5nenp/5BdeOA/9xppqla0PC75zj8rpJ1OwyhJSoEB4d3IBIz5DsVFm6SnVNEY2HzuXKhqn8tXocdftORqRnSOjNUzzYv5qq9dpSxV21wdDyICH4CQEXD7B7965SbdSkUqm0pANBQUHU9qjFvG6ujGhZcidXRZAvkfLdqedsuhREvkRWdWsjHZb08sCrrqyDqvem64TGZ/BgnuymPPboNVP3PyIjRzaizURPi0U9a6Mn0mDy3geI8yS8WuXJlZdxTN77kOjUws4HbU11pnd2ZUoHWToOZWxUwd7b4Uzb/4g/J7agyZs1mZT1q+j3oawPO66GsOjYU3LyZKPPNdXV8G7qwOxP3DHRK/sia6pix9UQFh5/gd+TJ7i6uio+4R2RacODhYNaMapL5Z6mni+Rsmy/Lz8cu1WoFxMDlg/rQM+msnRWPRbtJSQ6Gb9NsofgP2++YNJPp8jIlq3dZWqgw9LPO6CnrcX4TScQ5+UTs2cml/3CGL/pBNFJhaNftbU0+bJPc6b1kj2wKGOjCvb85cfkLac4sWAQTWvalcmvot+Hsj5sO32f+XsukZMr06OmhjpD2tfhm/6tMTUo2zpz5c220/eZt+dyOWuoFgv6N2NUp8rTkZEvkbLs4A02nrj3N73os2xIa3o2lv029lh6iNDYFB6vl73k/Hk7gMnbzpORLRu5YmqgwxLvVuhpazFhyznEeflE75rIZf8IJmw5S3RS4QxWbS0NZvZqzLQesg5qZWxUwZ7LT5my/TzH5/alqattmfwq+n0o68O2s49YsO8qObn5gEwvg9t48M1nzSqVXradfcT8fdfLVxu1a/PdipWMmzBR5eW9L/Lz85k/by5rvl9Ffr7sf1rFxoY1a9fRp+9nAHRq346g4CCCQ2Wzag8dPMCYUSNJT5f9tpqZmbFq9Rr09fUZOXwYOTk5ZGSLuXjhPCOGDyPq9euC8nR0dJjzzbd8PXsOgFI2qiAhIYHePby4desmrdu05dyFi8X8fEtmZiYD+n1WMHPNtmpVvAfLUjOtWvEdaZnZiESymYSbNv7A7Flfk50te07T1NRkxMhRLFy8RKVrHqmCTRt/4OuvvsTPz69cNCSVSmnTujV5GUmc21G5U1LmSyQs3Pgr63YfLkhPVcXCjFVfjebTTi0B6DpqNsGvonl5WpaS6vC5q4xbsJ70N4FSU2NDVs4chZ6uDmPmriVHnEvK3SP8desho+euJSqucE0wHZGIWWP68+VIWaowZWxUwS9HzjFu4QbO7VxB8/q1yuRX0e9DWR827zvGt2t3kS2WPQNqamgwrHdnFkwcgqlx5VuH9u9ERMfR4NPxLFm6jGnTpqm8PKlUSus2bUjPzmeLz4VKraH3hSQ/n80rF/Dr5jVI3rRhFlZVmLF4NR08ZSm8xvbtxKvQYI7flc3kuXD8EIunf0FmhqwNMzIxY/rCVejo6rFw6ijE4hxuhKVz2/ciC6eMJC6mMMuBSFuHkVNnM3zy1wBK2aiClKREpn3eiyf3b9OwWWs2HzxbzM+3ZGdlMmvMQK5fPAPI1rjq1leWxmz3xlVcC01F682aMn/s3MSGJXMQ58j0rKGpSc+Bwxk/ayFGJh9WG/YuREdG0L9NXZYuXVIu2gVYs2YNs+d8w9w/bmNuW3wg9oeERJLPsc2LOffLBiQSmS6NLarw2YwVNOgoS8u79ovuxL0KZtmJZwDcP3+EXxePJydT9h6ib2RK3+nLEenqs3vBF+SJxWy8mcDz25fYvWAsKXGFmtMS6fDJqK/oOlw26FUZG1WQkZLIpmn9CPG7g2vDlkz96UQxP98izs5i29dD8L9+DgATS1uadO8PwJlda9lwPa5gradL+7fgs2EeuWKZLtU1NGnR63N6jJ+HvpH8AMIPkfVju2Oilc+N69fKrd1as2YNs2Z/Q59N1wpS0X2oSCX53P11OY8Ob0T6Rm96ZtY0G70MKEEKegAAD1RJREFUx5Y9ADg+uyep0aEM+lk2gzf46lGurJ9MbrZMb9qGpjQbtRhNHX0urZ2AJFfMyCNRRD68zKW1E8lMLJz1riHSpkH/mdTrNxVAKRtVkJ2WyJmF3sS+uIvNRy3wXHakmJ9vycvJ4vzy4UTcuwDI1rhyaSd71nx4cD0jfSJR15Tpzf/Ydm7tWkC+WJbNSV1DE7fOg/l4yBy0DT98vZ2c05Oq+lJulq63A6UGmwBmz57N5h/WcWJiM5ytyrZouqrJFOfz9HUqhjqaOFrqo6VgdHxShhi/yBSsjXRwtTYsWCskKUNM8pvUcQBZ4nyeRqUSmZSFmYGImlUMsTCQz+WtjE15oaxfRVHWh/ScPJ5EppCRk4+7jSG2JpUrv2RgbDrdN95g3KSpLF++vNzKnT17Nps3buDMooG42JZtceeKIDMnF/+wOAz1RDhWMUWk+c9rbiWmZeEXGou1qT5uVS0K7qvEtCxSMrKpUUX2A5mVk4t/eByv4lMxN9LF3d4SCyP5XNzK2JQXyvpVFGV9SM8S8zg0hozsXGo5WFLVvHJ3YAAEvE6gy7y9jJs4uQI0tJ7Tc/viYlu5GtzMnFz8I+Ix1BXhaG2iWC/p2fiFxWFtooebrXnhfZWeLbuvrE0AyBLn4R8ez6uENMwNdXG3M8fCSP43VRmb8kJZv4qirA/p2WL8QuPIyMnF3c680ukl4HUSXRcfZNzEKeWujZ9++okrV6/jVrP4OpCVmYyMDPz8HmNkZISzs0tB8KQ0EhISePTwAVVsbHB3r1XwsJqQkEByUhJOzrI1XzIzM/Hze0xEeDgWFhZ4eNTG0kp+NrkyNqoi6vVrDAwN5VIClkZcXByvX0dSp07df3wZTktL4+HDB2Skp1O79kfY2duXaltZefH8Oa1bNmfs2LHlqqF79+7RuHFjVswcxfiBqs3b/z7IyMrmSUAoRvp6ODnYItL651mNiSlpPHoeRBULM2o62hfcR4kpaSSlpuNkL1vgOTM7hycBoURExWFhYkQt52pYmsmvDauMTXmhrF9FUdaH9IwsHr4IIiMzGw/n6thVsVCpP++D3Lw8ek1YQHRKFo/9nqClVT6D/d5qaNqCVfQf+X5nT1dmsjIzCHz2BH0DQ+wdnQuCJ6WRkpTIiycPsbCqQg1X94J7NiUpkdSUJOyry9LUZ2dlEvjsCdGR4ZiYWeDk5oGphaXctZSxURVxMVHo6xvIpQQsjaSEeOKiI3GpVecf27DM9DRePHlEZmY6zjVrY21bubM8vG/y8nKZ4u1Fanw0fo8fl5t2c3Nzqf1RHdSNrJmw4RAampVngPC/RZyVyavAJ+jqG2Jp71QQPCmNjJREIl48xtjCmio1ahbcpxkpiWSmJmNpL5v1LM7OIjLgCYkxrzAwNsfWyR1DM3nNKWOjKlLiotDWN5RLCVga6UnxJMdFUdWl9j/qMjsznVcvHpGdmUFVZw9MrauWavsh8de+nzi0dja3b9+mYcPyW28wNzcXj9p1yNK1pMuC/aj/B/SWl51JQqg/Ij1DjG0dC4InpZGdlkhCkB96ZtaY2rvxtsMgOy0RcXoKRjayiSt5OVkkhvqTHheJjpEZptXc0TWWfw5TxkZVZCZGo6VrIJcSsDSyUxLISIjCvIYH/7QIeW5WOvFBfuRlZ2BW3R19i/+G3p4c28at7d8q0ts/B5uys7Np37YNkUHPODWpOeYG/3yjCQiUJ8mZuXTbeAOzqo5c9r1arotmZ2dn075dW16HBHB28aAKC54ICLwLSenZdJn3O6Y2Dly+4ltBGnrJ2fl9MTesXIFsgf9vktKz6broEKY21bjsWwHaaN+eqOhorl6/iaVl+bzUCgi8TxITE2nVohkmxsZcvny5XDUEsHz5cubO/Zb9a77lk9aqmxkqIKBKpizbxL6TV7h67Rp165bvTHCZhuayaucBWnYsOR2pgIBAyayYNYnTPnu5dvVquWvX39+fps2aU6ddTwbP3ViuZQsIlDdPb1xg87R+LFmymFmzyj+d3Vu92TX1pNXk9eVevoBAefLq/l+cXTRIGb0d+MfpQDo6Ohw5egw1PRNG/HKftOy8fzIXECg30rLzGLrrLvnahpw4dbrcOzF0dHQ48udR1LT1Gbr2KGlZ4nItX0DgXUnLEjN49RHyNXU5cfJUBWrIgKEbTgkaEqg0pGWJGbL+JPlaepw4VUHaOHIEpFL69+1Dauq7rckgIFDepKam0rd3L/Jyczlx4kS5awhkMwSHDPmcEd+s5u6Tl+VevoDAu7Ji2352HjrD73v3lntnNbzV0BDmTRyG/8M75V6+gMCHys71y/HZs4O9v/9eIdr18PDg9z2/cfP4Hk7tWFXu5QsIlBeh/vfYOWcog4cMrpBAExTq7cWFfTzYv6ZC6iAgUB7EvbzPXytH4j1YOb0pXJndysqKEydPEZ6uRo9NN4lIzHwvFRUQ+LdEJGbi9eMNwtPVOHHyNFbllCanKFZWVhw/eYqwpBw+WbCX8LiUCqmHgEBZCY9L4ZMFewlLzOb4yVOVQENiui05THi80KkuULGEx6fSbclhwpLEFa+N48cJDg6ibauWhIWGVkg9BATKSlhoKG1btSQ4OIjjx49XmIYAtmzZQstWrflkzDf4nL9WYfUQECgLuXl5TFy8kaVb9rJx40a8vCouFeSWLVto1aol4/t24cKJwxVWDwGBD4G8vFyWfTWebauXVLh2vby82LhxIye3Lef3ZZPJz8utsLoICKiCBxf+ZP04T1q3asXWLVsqtC5eXl78uHEj9/eu5OrG6UgEvQn8xwi5doyT3/SibetWbNuqnN4UBptAFq29decu2uZ2fPLDDS48i32nigoI/FsuPIvlkx9uoG1hx6079/Dw8KjQ+nh4eHDr9h20ja3pPPd3zj0IrtD6CAgo4tyDYDrP3YO2sRW37tytHBq6cxdtE2u6LDzI+UehFVofgf9fzj8KpcvCg2ibWFcebdy6hZaWJi2bN+X0qZMVWh8BAUWcPnWSls2boqWlya1btypcQyKRiKPHjjFy1GiGfLWCxZv2kC0WZtEKVF7CXsfSc8ICDpy5io+PD+PGjavQ+ohEIo4dPcqoUSOZ84U3W1YtQpyTXaF1EhCojERFhDHF24tzf/5RKbQLMG7cOHx8fHhw9hA/Tu5DQlR4RVdJQOCdyRVnc3zLUrbPHsqYUSM5fuyowjViy4Nx48ZxxMeHsKs+nFnQn7TYiIqukoDAO5MvzuHenu+4sGIkY0aPKpPelAo2Adjb2+N77Tqdunnhvf0WQ36+S3B8xr+utIBAWQiOz2DIz3fx3n6LTt28uHrtBvaVZLFsmTau0bFrdwasOMigVT4ERydVdLUEBOQIjk5i0CofBqw4SMeunvheu17JNHRdpqHvj+K99gTB0ckVXS2B/xOCo5PxXnuCAd8fpWPX7pVPG76+dOjQgZ5envTu2YPAgICKrpaAgByBAQH07tmDnl6edOjQAV9f30qjIQ0NDTZs2MCmTZvY+PsxGvWdyNGLNyq6WgICcmRm57B40x4a9hlPTEoWV69dq9BZEX/n7xrat30DA9rV59KpPyu6WgIClYLsrEy2rFpE/7b1SI2P5trVq5VGuyCbcXHt2lUkqTEs7teY41uWIs7OquhqCQj8Kx5eOsbS/k24vHcTmzZtYsOGDWhoaFR0tQrw8vLi+rWr6GbFcWh8C+7t+Y68HEFvAh8moTdO4DOxJc+PbWHTpk38UEa9qUmlUmlZC7106RKTJo7nxYuXdPWoQt+GtrR2sURXVHmELvDhkyXO50pAHAfvvea0fzRubq78sHETbdu2reiqlcqlS5eYPHECz1+84JNGLvRvVYs2tauhq61V0VUT+D8kKyeXy0/C2O/7lFN3A6jp5saGjT9+OBpq6ES/5q60qe2Arkizoqsm8B8iS5zH5Sfh/HH9JafuBX042pg8mefPn+PVoyfegwfTvkPHClkPR0AgMzOTixfOs+e33zh29E9q1qzJhg0bKrWGXr9+zddff8WePb9Tp6YTw3p1onubxlS1tqjoqgn8HyKVSrn/NBCf89f49egFcvMkzJs/n0mTJqGlVTnfG2Qa+po9e/bgVrsuPQYOp3VnT6xsqlZ01QQEyg2pVMqzR/e4cMKHE/t/IS9PzPx58yq1dnNzc/nhhx9YsHARappaNPEcTIMOPXFwr4+amlpFV09AoFSSY1/z+MpJbvy5m/AXfgzy9mblihXY2tpWdNVK5e96k6hr4dxhIDVaeGHpXA8EvQlUYjLiXxN2+zQB5/YQF+THoEHerFz5r/R24F8FmwDy8vLYt28fWzZv4vrNm2ioq+NsbUwVIxEGIqUnTAkIFCNdLCEqVUxQTAr5EgnNmzZl7PgJ9O/fH03Nyt/hLK+NW2ioq+Fc1RIbU30MdSp//QU+fNKy83idlE5QZDz5EinNmzb5D2jIAhsTPQy1K3/9BSovaTl5vE7KIOh1woetjS1buH79OhoaGri6umFbtSpGRkYVXT2B/wNSU1OJfPWKgICX5Ofn07x5c8aOHfvBaAjg7t27bNiwgcOHDpGRmYmdjRVO9jaYGhqgri50AgiolmxxLvHJqTwPCic1PYNqDvYMHzGScePGVegaZ2XhrYYOHT5MZkYGVaraYV/dCUMTM9TUhH4Agf8muTnZJCfGE/zyGelpqThUq8aI4cM/KO3GxsayefNmtu/YyauIcPQMDLF1dEfPxBxNkXZFV09AAABpfj5Z6SnEhQeREBOJnp4+ffr2YfKkSTRq1Kiiq6c0b/W2bftOIl+Fo6NviFm1mogMzVDXEvQmUDmQSiTkZSST+jqYlLjX6Orp0/fd9fbvg01/JyYmhkuXLvHo0SNiYmJIS0t710sK/B9jaGiItbU1devWpW3btlhbW1d0lf41gjYEKgJBQwICJSNoQ0Dg3fgvaSg7O5urV69y//59QkJCSEpKQiKRVHS1BP7j6OjoYGpqSq1atWjWrBl16tSp6Cr9awQNCfw/8V/SLsCjR4+4efMmT58+JSkpiexsYT02gcqBuro6JiYmODo60qBBA1q2bImOjk5FV+udEPQmUFlRkd7eT7BJQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBA4P+SA8I8dwEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAYF/jRBsEhAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEPjXCMEmAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBgX/N/wAFIPwyzh3eQgAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "import graphviz\n",
    "from IPython import display  \n",
    "\n",
    "dot_data = tree.export_graphviz(diabetes_tree, out_file=None,  \n",
    "                                feature_names=feature_cols,  \n",
    "                                class_names=[\"false\", \"true\"],  \n",
    "                                filled=True, rounded=True,  \n",
    "                                special_characters=True) \n",
    "\n",
    "graph = graphviz.Source(dot_data)  \n",
    "display.display_png(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7340012141383488"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "diabetes_rf = RandomForestClassifier(n_estimators=50, max_depth=None, min_samples_split=2, random_state=0)\n",
    "cv_score = cross_val_score(diabetes_rf, \n",
    "                           diabetes.loc[:, feature_cols], \n",
    "                           diabetes['Diabetes_binary'], cv=10,\n",
    "                           scoring='accuracy')\n",
    "cv_score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. feature 3 BMI (0.171603)\n",
      "2. feature 18 Age (0.127095)\n",
      "3. feature 13 GenHlth (0.108014)\n",
      "4. feature 20 Income (0.083186)\n",
      "5. feature 0 HighBP (0.073335)\n",
      "6. feature 15 PhysHlth (0.069337)\n",
      "7. feature 19 Education (0.056019)\n",
      "8. feature 14 MentHlth (0.052021)\n",
      "9. feature 1 HighChol (0.039205)\n",
      "10. feature 8 Fruits (0.028713)\n",
      "11. feature 4 Smoker (0.028314)\n",
      "12. feature 16 DiffWalk (0.027513)\n",
      "13. feature 17 Sex (0.025791)\n",
      "14. feature 7 PhysActivity (0.024005)\n",
      "15. feature 9 Veggies (0.022647)\n",
      "16. feature 6 HeartDiseaseorAttack (0.018281)\n",
      "17. feature 12 NoDocbcCost (0.011925)\n",
      "18. feature 5 Stroke (0.010092)\n",
      "19. feature 10 HvyAlcoholConsump (0.009523)\n",
      "20. feature 11 AnyHealthcare (0.007539)\n",
      "21. feature 2 CholCheck (0.005843)\n"
     ]
    }
   ],
   "source": [
    "X = diabetes.loc[:, feature_cols]\n",
    "y =  diabetes['Diabetes_binary']\n",
    "\n",
    "diabetes_rf.fit(X, y)\n",
    "importances = diabetes_rf.feature_importances_\n",
    "\n",
    "std = np.std([tree.feature_importances_ for tree in diabetes_rf.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "for f in range(X.shape[1]):\n",
    "    print(\"%d. feature %d %s (%f)\" % (f + 1, indices[f], feature_cols[indices[f]], importances[indices[f]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Select top 10 features\n",
    "top_n = 10\n",
    "indices_top = indices[:top_n]  # Get the indices of the top 10 features\n",
    "importances_top = importances[indices_top]  # Corresponding importances\n",
    "std_top = std[indices_top]  # Corresponding standard deviations\n",
    "features_top = [feature_cols[x] for x in indices_top]  # Names of top 10 features\n",
    "\n",
    "# Plot the feature importances of the forest (top 10)\n",
    "plt.figure(figsize=(9, 5))\n",
    "plt.title(\"Top 10 Feature Importances\")\n",
    "plt.bar(range(top_n), importances_top,\n",
    "        tick_label=features_top,\n",
    "        color=\"r\", yerr=std_top, align=\"center\")\n",
    "\n",
    "# Set x-axis limit and rotate labels for better visibility\n",
    "plt.xlim([-1, top_n])\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Assuming 'feature_cols' is a list of column names you want to use as features\n",
    "X_train, X_test, y_train, y_test = train_test_split(diabetes[feature_cols], diabetes['Diabetes_binary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "xgb_reg = xgb.XGBRegressor()\n",
    "xgb_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = xgb_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33169414247667794"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "mean_absolute_error(y_test, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.41702237 0.4137493  0.3374292  0.43259454 0.42423044]\n",
      "0.4050051738693735\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(xgb_reg, \n",
    "                         diabetes[feature_cols], diabetes['Diabetes_binary'], \n",
    "                         scoring=\"neg_mean_absolute_error\",\n",
    "                         cv=5)\n",
    "print(-scores)\n",
    "print(np.mean(-scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_reg = xgb.XGBRegressor()\n",
    "xgb_reg.fit(diabetes[feature_cols], diabetes['Diabetes_binary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 4))\n",
    "\n",
    "# Plot only the top 10 most important features\n",
    "_ = xgb.plot_importance(xgb_reg, ax=fig.gca(), max_num_features=10)\n",
    "\n",
    "# Show the plot\n",
    "plt.title(\"Top 10 Feature Importances\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "\n",
    "lgbm = lgb.LGBMClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 26483, number of negative: 26536\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003892 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 198\n",
      "[LightGBM] [Info] Number of data points in the train set: 53019, number of used features: 21\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499500 -> initscore=-0.001999\n",
      "[LightGBM] [Info] Start training from score -0.001999\n",
      "[LightGBM] [Info] Number of positive: 26483, number of negative: 26536\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003240 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 198\n",
      "[LightGBM] [Info] Number of data points in the train set: 53019, number of used features: 21\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499500 -> initscore=-0.001999\n",
      "[LightGBM] [Info] Start training from score -0.001999\n"
     ]
    }
   ],
   "source": [
    "# Train with the resampled data\n",
    "lgbm.fit(X_train, y_train)\n",
    "\n",
    "lgbm.fit(X_train, y_train, feature_name=feature_cols)\n",
    "y_pred = lgbm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7553329938323997\n",
      "Precision: 0.7372216995923487\n",
      "Recall: 0.7957802098612208\n",
      "F1 Score: 0.7653825284861638\n",
      "Confusion Matrix:\n",
      "[[6296 2514]\n",
      " [1810 7053]]\n"
     ]
    }
   ],
   "source": [
    "# Assuming y_pred contains predicted probabilities\n",
    "y_pred_prob = lgbm.predict(X_test)  # Get predicted probabilities\n",
    "\n",
    "# Convert probabilities to binary labels using 0.5 as the threshold\n",
    "y_pred = [1 if prob > 0.5 else 0 for prob in y_pred_prob]\n",
    "\n",
    "# Now evaluate using classification metrics\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')\n",
    "\n",
    "# Precision\n",
    "precision = precision_score(y_test, y_pred)\n",
    "print(f'Precision: {precision}')\n",
    "\n",
    "# Recall\n",
    "recall = recall_score(y_test, y_pred)\n",
    "print(f'Recall: {recall}')\n",
    "\n",
    "# F1 Score\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(f'F1 Score: {f1}')\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(f'Confusion Matrix:\\n{conf_matrix}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 28277, number of negative: 28276\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.008538 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 199\n",
      "[LightGBM] [Info] Number of data points in the train set: 56553, number of used features: 21\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500009 -> initscore=0.000035\n",
      "[LightGBM] [Info] Start training from score 0.000035\n",
      "[LightGBM] [Info] Number of positive: 28276, number of negative: 28277\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004085 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 198\n",
      "[LightGBM] [Info] Number of data points in the train set: 56553, number of used features: 21\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499991 -> initscore=-0.000035\n",
      "[LightGBM] [Info] Start training from score -0.000035\n",
      "[LightGBM] [Info] Number of positive: 28277, number of negative: 28277\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.010198 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 199\n",
      "[LightGBM] [Info] Number of data points in the train set: 56554, number of used features: 21\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n",
      "[LightGBM] [Info] Number of positive: 28277, number of negative: 28277\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005599 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 197\n",
      "[LightGBM] [Info] Number of data points in the train set: 56554, number of used features: 21\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n",
      "[LightGBM] [Info] Number of positive: 28277, number of negative: 28277\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.011104 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 199\n",
      "[LightGBM] [Info] Number of data points in the train set: 56554, number of used features: 21\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n"
     ]
    }
   ],
   "source": [
    "scores = -cross_val_score(lgbm, \n",
    "                          diabetes[feature_cols], diabetes['Diabetes_binary'],\n",
    "                          scoring=\"neg_mean_absolute_error\", cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 4))\n",
    "\n",
    "# Plot only the top 10 most important features\n",
    "_ = lgb.plot_importance(lgbm, ax=fig.gca(), max_num_features=10)\n",
    "\n",
    "# Show the plot\n",
    "plt.title(\"Top 10 Feature Importances\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create precision-recall curve\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "# Assuming y_pred_prob contains predicted probabilities\n",
    "average_precision = average_precision_score(y_test, y_pred_prob)\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_pred_prob)\n",
    "\n",
    "plt.figure()\n",
    "plt.step(recall, precision, color='b', alpha=0.2, where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2, color='b')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title(f'2-class Precision-Recall curve: AP={average_precision}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "\n",
    "Selected_features = ['GenHlth' ,'CholCheck' ,'HighBP' ,'AnyHealthcare' ,'PhysActivity' ,'BMI' ,\n",
    "'HighChol' ,'Age' ,'Fruits' ,'Income' ,'DiffWalk' ,'Education' ,'HeartDiseaseorAttack'\n",
    ",'PhysHlth' , 'Smoker']\n",
    "Target = 'Diabetes_binary'\n",
    "\n",
    "X = diabetes[Selected_features]\n",
    "y = diabetes[Target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train , X_test , y_train , y_test = train_test_split(X, y , test_size=0.2)\n",
    "cat  = CatBoostClassifier(verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train with the resampled data\n",
    "cat.fit(X_train, y_train)\n",
    "\n",
    "y_pred = cat.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7496286866115001\n",
      "Precision: 0.7338983050847457\n",
      "Recall: 0.7897025813692481\n",
      "F1 Score: 0.760778483578862\n",
      "Confusion Matrix:\n",
      "[[4970 2041]\n",
      " [1499 5629]]\n"
     ]
    }
   ],
   "source": [
    "# Assuming y_pred contains predicted probabilities\n",
    "y_pred_prob = cat.predict(X_test)  # Get predicted probabilities\n",
    "\n",
    "# Convert probabilities to binary labels using 0.5 as the threshold\n",
    "y_pred = [1 if prob > 0.5 else 0 for prob in y_pred_prob]\n",
    "\n",
    "# Now evaluate using classification metrics\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')\n",
    "\n",
    "# Precision\n",
    "precision = precision_score(y_test, y_pred)\n",
    "print(f'Precision: {precision}')\n",
    "\n",
    "# Recall\n",
    "recall = recall_score(y_test, y_pred)\n",
    "print(f'Recall: {recall}')\n",
    "\n",
    "# F1 Score\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(f'F1 Score: {f1}')\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(f'Confusion Matrix:\\n{conf_matrix}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Assuming 'cat' is your trained CatBoost model\n",
    "cat_feature_importances = cat.get_feature_importance()\n",
    "\n",
    "# Assuming 'Selected_features' is your list of feature names\n",
    "feature_names = np.array(Selected_features)\n",
    "\n",
    "# Sort the feature importances in descending order\n",
    "sorted_indices = np.argsort(cat_feature_importances)[::-1][:10]\n",
    "\n",
    "# Plot the top 10 features\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.barh(range(10), cat_feature_importances[sorted_indices], align='center')\n",
    "ax.set_yticks(range(10))\n",
    "ax.set_yticklabels(feature_names[sorted_indices])\n",
    "\n",
    "plt.title(\"CatBoost: Top 10 Feature Importances\")\n",
    "plt.gca().invert_yaxis()  # Invert the y-axis to display the most important feature at the top\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision-Recall AUC: 0.8148098498328389\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create precision-recall auc\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import auc\n",
    "\n",
    "# Assuming y_pred_prob contains predicted probabilities\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_pred_prob)\n",
    "\n",
    "# Calculate the area under the curve\n",
    "pr_auc = auc(recall, precision)\n",
    "print(f'Precision-Recall AUC: {pr_auc}')\n",
    "\n",
    "# Plot the precision-recall curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(recall, precision, marker='.')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "OPENBB40",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}