examples/bnn_classification/demo_bnn_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install aif360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "from aif360.sklearn.datasets import fetch_adult\n",
    "from aif360.sklearn.metrics import disparate_impact_ratio, average_odds_error, generalized_fpr, statistical_parity_difference, equal_opportunity_difference\n",
    "from aif360.sklearn.metrics import generalized_fnr, difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "import torch.utils.data as data_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "from IPython import display\n",
    "import os\n",
    "from torch.utils.data.dataset import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from uq360.algorithms.variational_bayesian_neural_networks.bnn import BnnClassification\n",
    "from uq360.metrics.classification_metrics import entropy_based_uncertainty_decomposition, plot_risk_vs_rejection_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Non-white</th>\n",
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       "      <td>Some-college</td>\n",
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       "      <td>Never-married</td>\n",
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      "text/plain": [
       "                   age  workclass     education  education-num  \\\n",
       "  race      sex                                                  \n",
       "0 Non-white Male  25.0    Private          11th            7.0   \n",
       "1 White     Male  38.0    Private       HS-grad            9.0   \n",
       "2 White     Male  28.0  Local-gov    Assoc-acdm           12.0   \n",
       "3 Non-white Male  44.0    Private  Some-college           10.0   \n",
       "5 White     Male  34.0    Private          10th            6.0   \n",
       "\n",
       "                      marital-status         occupation   relationship  \\\n",
       "  race      sex                                                          \n",
       "0 Non-white Male       Never-married  Machine-op-inspct      Own-child   \n",
       "1 White     Male  Married-civ-spouse    Farming-fishing        Husband   \n",
       "2 White     Male  Married-civ-spouse    Protective-serv        Husband   \n",
       "3 Non-white Male  Married-civ-spouse  Machine-op-inspct        Husband   \n",
       "5 White     Male       Never-married      Other-service  Not-in-family   \n",
       "\n",
       "                       race   sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "  race      sex                                                                 \n",
       "0 Non-white Male  Non-white  Male           0.0           0.0            40.0   \n",
       "1 White     Male      White  Male           0.0           0.0            50.0   \n",
       "2 White     Male      White  Male           0.0           0.0            40.0   \n",
       "3 Non-white Male  Non-white  Male        7688.0           0.0            40.0   \n",
       "5 White     Male      White  Male           0.0           0.0            30.0   \n",
       "\n",
       "                 native-country  \n",
       "  race      sex                  \n",
       "0 Non-white Male  United-States  \n",
       "1 White     Male  United-States  \n",
       "2 White     Male  United-States  \n",
       "3 Non-white Male  United-States  \n",
       "5 White     Male  United-States  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y, sample_weight = fetch_adult()\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.index = pd.MultiIndex.from_arrays(X.index.codes, names=X.index.names)\n",
    "y.index = pd.MultiIndex.from_arrays(y.index.codes, names=y.index.names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pd.Series(y.factorize(sort=True)[0], index=y.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, X_test,\n",
    " y_train, y_test) = train_test_split(X, y, train_size=0.7, random_state=1234567)\n",
    "(X_train, X_calib,\n",
    " y_train, y_calib) = train_test_split(X_train, y_train, train_size=0.7, random_state=1234567)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       race  sex\n",
       "32587  0     0      1\n",
       "8287   1     0      0\n",
       "35740  1     1      1\n",
       "31610  1     0      0\n",
       "8317   1     1      1\n",
       "                   ..\n",
       "3582   1     1      0\n",
       "26929  1     1      0\n",
       "33290  1     1      0\n",
       "4366   1     0      0\n",
       "11868  1     1      1\n",
       "Length: 22158, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       race  sex\n",
       "32587  0     0      False\n",
       "8287   1     0      False\n",
       "35740  1     1       True\n",
       "31610  1     0      False\n",
       "8317   1     1       True\n",
       "                    ...  \n",
       "3582   1     1       True\n",
       "26929  1     1       True\n",
       "33290  1     1       True\n",
       "4366   1     0      False\n",
       "11868  1     1       True\n",
       "Name: sex, Length: 22158, dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[\"sex\"]==\"Male\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train = X_train[X_train[\"sex\"]==\"Male\"]\n",
    "#y_train = y_train[y_train.sex==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>89</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>32587</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1887.0</td>\n",
       "      <td>40.0</td>\n",
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       "    <tr>\n",
       "      <th>8287</th>\n",
       "      <th>1</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
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       "    <tr>\n",
       "      <th>35740</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1887.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31610</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8317</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0    1    2    3    4    5    6    7    8    9   ...   89  \\\n",
       "      race sex                                                    ...        \n",
       "32587 0    0    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n",
       "8287  1    0    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n",
       "35740 1    1    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n",
       "31610 1    0    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n",
       "8317  1    1    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n",
       "\n",
       "                 90   91   92   93    94   95   96      97    98  \n",
       "      race sex                                                    \n",
       "32587 0    0    0.0  1.0  0.0  0.0  27.0  9.0  0.0  1887.0  40.0  \n",
       "8287  1    0    0.0  1.0  0.0  0.0  54.0  9.0  0.0     0.0  80.0  \n",
       "35740 1    1    0.0  1.0  0.0  0.0  55.0  9.0  0.0  1887.0  40.0  \n",
       "31610 1    0    0.0  1.0  0.0  0.0  18.0  9.0  0.0     0.0  30.0  \n",
       "8317  1    1    0.0  0.0  0.0  0.0  38.0  9.0  0.0     0.0  41.0  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = make_column_transformer(\n",
    "        (OneHotEncoder(sparse=False, handle_unknown = 'ignore'), X_train.dtypes == 'category'),\n",
    "        remainder='passthrough')\n",
    "X_train  = pd.DataFrame(ohe.fit_transform(X_train), index=X_train.index)\n",
    "X_test = pd.DataFrame(ohe.transform(X_test), index=X_test.index)\n",
    "X_calib = pd.DataFrame(ohe.transform(X_calib), index=X_calib.index)\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 100 columns</p>\n",
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      "text/plain": [
       "             age  education-num  capital-gain  capital-loss  hours-per-week  \\\n",
       "  race sex                                                                    \n",
       "0 0    1    25.0            7.0           0.0           0.0            40.0   \n",
       "1 1    1    38.0            9.0           0.0           0.0            50.0   \n",
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       "5 1    1    34.0            6.0           0.0           0.0            30.0   \n",
       "\n",
       "            workclass_Federal-gov  workclass_Local-gov  workclass_Private  \\\n",
       "  race sex                                                                  \n",
       "0 0    1                        0                    0                  1   \n",
       "1 1    1                        0                    0                  1   \n",
       "2 1    1                        0                    1                  0   \n",
       "3 0    1                        0                    0                  1   \n",
       "5 1    1                        0                    0                  1   \n",
       "\n",
       "            workclass_Self-emp-inc  workclass_Self-emp-not-inc  ...  \\\n",
       "  race sex                                                      ...   \n",
       "0 0    1                         0                           0  ...   \n",
       "1 1    1                         0                           0  ...   \n",
       "2 1    1                         0                           0  ...   \n",
       "3 0    1                         0                           0  ...   \n",
       "5 1    1                         0                           0  ...   \n",
       "\n",
       "            native-country_Portugal  native-country_Puerto-Rico  \\\n",
       "  race sex                                                        \n",
       "0 0    1                          0                           0   \n",
       "1 1    1                          0                           0   \n",
       "2 1    1                          0                           0   \n",
       "3 0    1                          0                           0   \n",
       "5 1    1                          0                           0   \n",
       "\n",
       "            native-country_Scotland  native-country_South  \\\n",
       "  race sex                                                  \n",
       "0 0    1                          0                     0   \n",
       "1 1    1                          0                     0   \n",
       "2 1    1                          0                     0   \n",
       "3 0    1                          0                     0   \n",
       "5 1    1                          0                     0   \n",
       "\n",
       "            native-country_Taiwan  native-country_Thailand  \\\n",
       "  race sex                                                   \n",
       "0 0    1                        0                        0   \n",
       "1 1    1                        0                        0   \n",
       "2 1    1                        0                        0   \n",
       "3 0    1                        0                        0   \n",
       "5 1    1                        0                        0   \n",
       "\n",
       "            native-country_Trinadad&Tobago  native-country_United-States  \\\n",
       "  race sex                                                                 \n",
       "0 0    1                                 0                             1   \n",
       "1 1    1                                 0                             1   \n",
       "2 1    1                                 0                             1   \n",
       "3 0    1                                 0                             1   \n",
       "5 1    1                                 0                             1   \n",
       "\n",
       "            native-country_Vietnam  native-country_Yugoslavia  \n",
       "  race sex                                                     \n",
       "0 0    1                         0                          0  \n",
       "1 1    1                         0                          0  \n",
       "2 1    1                         0                          0  \n",
       "3 0    1                         0                          0  \n",
       "5 1    1                         0                          0  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.workclass.cat.remove_unused_categories(inplace=True)\n",
    "pd.get_dummies(X).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32587</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1887.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8287</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35740</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1887.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31610</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8317</th>\n",
       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0    1    2    3    4    5    6    7    8    9   ...   89  \\\n",
       "      race sex                                                    ...        \n",
       "32587 0    0    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n",
       "8287  1    0    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n",
       "35740 1    1    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n",
       "31610 1    0    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n",
       "8317  1    1    0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0   \n",
       "\n",
       "                 90   91   92   93    94   95   96      97    98  \n",
       "      race sex                                                    \n",
       "32587 0    0    0.0  1.0  0.0  0.0  27.0  9.0  0.0  1887.0  40.0  \n",
       "8287  1    0    0.0  1.0  0.0  0.0  54.0  9.0  0.0     0.0  80.0  \n",
       "35740 1    1    0.0  1.0  0.0  0.0  55.0  9.0  0.0  1887.0  40.0  \n",
       "31610 1    0    0.0  1.0  0.0  0.0  18.0  9.0  0.0     0.0  30.0  \n",
       "8317  1    1    0.0  0.0  0.0  0.0  38.0  9.0  0.0     0.0  41.0  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize the features\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "norm_X_train = scaler.transform(X_train)\n",
    "norm_X_test = scaler.transform(X_test)\n",
    "norm_X_calib = scaler.transform(X_calib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train = data_utils.TensorDataset(torch.Tensor(norm_X_train), torch.Tensor(y_train.values).long())\n",
    "#train_loader = data_utils.DataLoader(train, batch_size=50, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = data_utils.TensorDataset(torch.Tensor(norm_X_test), torch.Tensor(y_test.values).long())\n",
    "test_loader = data_utils.DataLoader(test, batch_size=50, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {\"ip_dim\": X_train.values.shape[1], \"op_dim\": 2, \"num_nodes\": 128, \"num_layers\": 1,\n",
    "          \"num_epochs\": 10, \"step_size\": 0.001}\n",
    "config['hshoe_scale'] = 1e-1\n",
    "config['use_reg_hshoe']=True\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[10/10], neg elbo: 1.621017\n"
     ]
    }
   ],
   "source": [
    "bnn = BnnClassification(config=config, device=device, prior=\"RegHshoe\")\n",
    "\n",
    "#bnn = bnn.fit(train_loader=train_loader)\n",
    "bnn = bnn.fit(X=norm_X_train, y=y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def get_test_results(test_loader, bnn):\n",
    "    correct = 0\n",
    "    total = 0\n",
    "\n",
    "    all_mean = []\n",
    "    all_var = []\n",
    "    all_true_labels = []\n",
    "    all_pred_labels = []\n",
    "\n",
    "    all_total = []\n",
    "    all_epistemic = []\n",
    "    all_aleotoric = []\n",
    "\n",
    "    for test_batch_x, test_batch_y in test_loader:\n",
    "\n",
    "        with torch.no_grad():\n",
    "            predicted, pred_mean, pred_var, y_prob_samples = bnn.predict(test_batch_x)\n",
    "            pred_total_uq, pred_aleo, pred_epi = entropy_based_uncertainty_decomposition(y_prob_samples)\n",
    "\n",
    "            all_mean.append(pred_mean)\n",
    "            all_var.append(pred_var)\n",
    "            all_true_labels.append(test_batch_y)\n",
    "            all_pred_labels.append(predicted)\n",
    "\n",
    "            all_total.append(pred_total_uq)\n",
    "            all_epistemic.append(pred_epi)\n",
    "            all_aleotoric.append(pred_aleo)\n",
    "\n",
    "            total += test_batch_y.size(0)\n",
    "            correct += (predicted == test_batch_y.numpy()).sum()\n",
    "    print(\"accuracy: %d %%\" % (100 * correct / total))\n",
    "    \n",
    "    all_mean_mat = np.concatenate(all_mean, axis=0)\n",
    "    all_true_labels_mat = np.concatenate(all_true_labels, axis=0)\n",
    "    all_pred_labels_mat = np.concatenate(all_pred_labels, axis=0)\n",
    "\n",
    "    all_total_mat = np.concatenate(all_total, axis=0)\n",
    "    all_epi_mat = np.concatenate(all_epistemic, axis=0)\n",
    "    all_aleo_mat = np.concatenate(all_aleotoric, axis=0)\n",
    "\n",
    "    return all_mean_mat, all_true_labels_mat, all_pred_labels_mat, all_total_mat, all_epi_mat, all_aleo_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 85 %\n"
     ]
    }
   ],
   "source": [
    "all_mean_mat, all_true_labels_mat, all_pred_labels_mat, all_total_mat, all_epi_mat, all_aleo_mat = get_test_results(test_loader, bnn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/9ttvKV++PEopQkJCEpffunWLzp074+fnR506dThy5Ei6r0EIIYTICKlgC5EBBQsW5ODBgxw8eJChQ4fy+uuvJz53cHBItm16K9ip+eWXX6hXr16ySnJ63M+1cuVKhgwZQmxsbOK6L774goMHD/L1118zdOjQZPstWLCAf/75h5deeonRo0cDcOTIEV555RXmz5/PsWPH2Lt3L+XLl09XjtDQUD744AN2797N33//zQcffPBQ5Rlg/PjxNG/enFOnTtG8eXPGjx+fuC4+Pp7333+fVq1aJdsnT548ia/9b7/9lri8QYMGbNy4kVKlSiXb/tNPP8Xf359Dhw4xd+5cXnvttXRdgxBCCJFRub6CffToDv7+N2OVFyGS2rRpE9WrV8fX15cBAwYQHR3NlClTuHz5Mk2bNqVp06YADBs2jFq1alG1alXef//9dB174cKFfPnll1y6dIng4OAMZ6tQoQLOzs4pVmrr16/PpUuXUtwv6brPP/+c//3vf1SuXBkwtRwPGzYsXedfv349LVu2xN3dnQIFCtCyZUvWrVv30HYrV66kb9++APTt25dff/01cd0333xDx44dKVy4cLrOWb16dVKaevvYsWM0a9YMgMqVKxMUFMS1a9fSdUxhPS7cvMt32/ew7myg0VGEEA+KuQt//QA3/gWtjU5jqFxfwf72z//xi8MOgn4ZDLFy05HImKioKPr168fixYs5fPgwcXFx/PDDDwwfPpzixYuzZcsWtmzZAsAnn3zC3r17OXToEFu3buXQoUOpHvvixYtcuXKFOnXq8Pzzz7N48eIM59u/fz8VKlRIsXK6bt06nnnmmRT3S7ruyJEj1KxZM8XtFixYkNhNI+mja9euAFy6dIkSJUokbu/l5ZVipf7atWsUK1YMgKJFiyZWfC9dusSKFSsYOHDgQ/tERUVRq1Yt6tWrl6xC/ijVqlVj+fLlAPz999+cP3/+sf5oEcY6diWMybvn8+b214iNj017ByFE1rm0D9aNge/qwKSqsPJlOLIcu9gwo5NluVw/TN+gtj/y58bnmHf1D977qRU8PxcKlDY6lkiHD34/yrHLKf+njY+Px9bWNsPH9C6ej/c7VE339vHx8ZQpU4aKFSsCptbX7777jhEjRjy07ZIlS5g2bRpxcXFcuXKFY8eO4efn98hjL168mOeffx6A7t27M2DAgFT7Pie9cW7SpEnMmjWLkydP8vvvvyfbbvTo0bzzzjsEBweza9euZOt69epFTEwMERERyfpgP0qvXr3o1atXmttlhFIq8VpGjBjBhAkTsLF5uC3g/PnzeHp6cvbsWZo1a4avry/lypV75HHHjBnDa6+9hr+/P76+vlSvXv2x3iPCWAWc7UmI8UCTQHBEMGXcZKhDIaxGmUbw2iE4uwXObIbjv8OB+TRAwbnqUK4ZlG8OXrXBNmfPA5DrW7BreFWmUFwDlrjm4+ydC/BjYzixxuhYIoc5d+4cEydOZNOmTRw6dIj27dunOUzbwoULmT17NqVLl6Zjx44cOnSIU6dOAab+x0lvqgwNDcXDwyPx+euvv87Ro0dZtmwZL774YrJzffHFF5w8eZIJEyYwYMCAZOdcsGABZ8+epW/fvrz66qsAVK1alX379qWYMa0WbE9PTy5evJi4fXBwMJ6eng8dp0iRIly5cgWAK1euJLa47927l+7du+Pj48PSpUt56aWXElur7x+nbNmyBAQEcODAgVRfz3z58jFr1iwOHjzI3LlzuXHjBmXLlk11H2F93F0cSIgxvdcvhF0wOI0Q4iEFSkHNfqYGyzfPwYsbCSrdHWwdYMckmNUWJpSBhT3g7+lw80yO7E6S61uwAfoVb8P4a3t4uchTrI0KhkU9oMFr0Gws2MpLZK1Sa2nOqolJbG1tCQoK4vTp05QvX5558+bRpEkTAFxdXQkPD8fDw4OwsDBcXFxwc3Pj2rVrrF27loCAgEce9+TJk0RERCTrTvH++++zcOFCxo4dS5MmTZg/fz7PPfcc9+7dY8mSJXz++ecPHadjx47MnDmTOXPmMGTIkGTrXnnlFX766SfWr19P69atE5crpfjoo48oV64cJ06cYPTo0Tz77LM0bNiQihUrkpCQwLRp0xg6dGiaLditW7fmnXfeSewD/scff/DZZ5+lmHPOnDmMGTOGOXPm0KlTJ8D0hwmYfp+vvvoqTz/9NM888wy3bt3C2dkZR0dHQkJC2LlzJ2+++eYjcwDcvn0bZ2dnHBwcmDFjBo0bNyZfvnyp7iOsT35nB3RsQQAuhEsFWwirZmMLJWpzvnQkZQICIOoOnNtmat0+vQn+NTdo5i/1X+t2mcbg5GZo7MyQ61uwAbyc8+ObtyPBHGZt40+g1gDYORnmdICwK0bHE1bMycmJWbNm8dxzz+Hr64uNjU3iyByDBw+mTZs2NG3alGrVqlG9enUqV65Mz549adCgQarHXbhwIZ07d062rEuXLomjiUyePJnly5fToEED6tWrx3PPPUfjxo1TPNbYsWP56quvSEhISLZcKcW7776bYsU8T548vPHGG3zxxRf4+fnx9ddf06NHD6pUqYKPjw9nz55N1+vj7u7Oe++9R+3atalduzZjx47F3d0dgIEDByYOTzhmzBg2bNhAhQoV2LhxI2PGjEn1uMePH6dWrVpUq1aNpk2bMmbMGLy9vQGYMmUKXl5eBAcH4+fnl9h/+/jx4/j4+FCpUiXWrl3L5MmT03UNwrrkd7ZHxzvjoFw4H3be6DhCiIxwcoMqHeDpSTDiELy6H9pNhCI+cHgpLH7B1Lo9sxUEjoeLeyA+zujUj0XpHNQsX6tWLf2o8YRTExgYSLlqfrRd0Y4CdiXZ1nsx6vAv8Ptr4OACXWZC2SYWSJy1AgMDU201zQ6OHz9OlSpV0twuJ02tnRq5zuwrpfeyUmqf1rqWQZEMkZFy+34Z5jtuPQXKf0/lwkWY1mqahROmn7WXsdacz5qzgeR7UunKFx8LwXtMrdtnNsOl/YA2VcrLNDG1bpdrBvlLGpMvBamV2dL/waxEAXdq5+/G3vCfWHJ0I938noeifrCkN8x7Bpq+Aw3fgBRuthJCCJF13F0csNeFpYuIEDmJrT2Uesr0aPYu3A2Fs4H/VbiPm+c7KFjeVNEu1xxKNwTHvKke1ihSW0xiQsvB6Fh3Ju37mgSdAIUrw6AtUPVZ2Pwx/Py86RcuhBDCMPmdHSDOgyuRV2SoPiFyKmd38HkWOn0Lrx+Fl/+GNuPBvSwcmA8Lu8GE0jCrPWybCJcPwANdIY0kFewkCru60KRQbyK5wPT9y0wLHfNClxnQ/ks4t9U0ykhwxruhCCGEyBzuzvbER3uQoE1D9QkhcjiloFAlqDcMev0CbwVBn9+g/ssQHQabP4JpAfBFOVg6wFQBD7tsaGSpYD/g4xa9Ibo40498T0y8eRg0paD2QBiw3vTzT21g9485clgZIYSwdgWcHbgbWQCQofqEyJXsHE33xrX8AIZuh1Gn4dnpULE1BO0wTXDzVRX4rh6sewdObzTNMpmFpIL9gAIujrQpPpBoQpj899zkKz1rwJBtpo72a9+EX/pBdLghOYUQIrcq4OJARLhpGC/phy2EIG8h8HseOk+FN/6FYX9Cy4/AtSjsmQHzu5i6k8ztZBol7rblyw2pYKfg3ead4F55fv53JhExEclX5ikA3RdCi3GmDvc/d5cp1oUQIgsVcLYnMsoRVwdXGapPCJGcUlCkKjQYDn1+hTHn4YVlUGcQRFyHDWNhcW+Lx5AKdgrc8jjwbOnBxKkIJuz68eENbGyg4eumryPO74AVQ6yqY72wLFtb22SzFo4fPz7V7adOncrcuXMfuT4wMJA///wz0/INHDiQY8eOZWifESNG4OnpmWys7HHjxjFx4sRk25UuXZqQkBDgv9fBx8eHDh06cPv2bQCCgoLIkycP/v7+eHt706dPH2JjTTeiBQYG4ubmhr+/P5UrV2bUqFGJx46IiGDIkCGUK1eOmjVrEhAQwO7du9OVPzQ0lJYtW1KhQgVatmyZOLHNg+bMmUOFChWoUKECc+bMSVweEBBApUqVEn+n169fB+DChQs0bdqU6tWr4+fnx5o1pkkRHpzB0sbGJnFq+X379uHr60v58uUZPnw4OWkoVGtRwMUBUBRz9pIuIkKI1NnngfItoPUn8NIuqP4ChF+1+GktXsFWSrVRSv2rlDqtlHpo9gilVGOl1H6lVJxSqmsK6/MppYKVUt9aOmtSo5u2REX689u5hYTcC0l5I9+u0OpjOPYrrH9H+mTnEnny5OHgwYOJj7QmRRk6dCh9+vR55PrMrmDPmDEjcdKV9EhISGDFihWUKFGCrVu3pnu/+6/DkSNHcHd357vvvktcV65cOQ4ePMjhw4cJDg5myZIliesaNWrEwYMHOXDgAKtWrWLnzp2A6Q8Dd3d3Tp06xb59+5g1a1ZiZT4t48ePp3nz5pw6dYrmzZun+EdPaGgoH3zwAbt37+bvv//mgw8+SFYRX7BgQeLv9P5U7R9//DHPP/88Bw4cYNGiRbz00ksA9OrVK3HbefPmUaZMGfz9/QEYNmwY06dP59SpU5w6dYp169al+zW1BtmhzC7g7ABAIScv6SIihMgYl0Jw96bF62wWrWArpWyB74C2gDfQQyn14Cf/BaAf8PMjDvMRsM1SGR8lr6MdvSoOJp5YPtox5dEbPvUq1HsZdv8Af6ayncjxSpcuzZtvvomvry916tTh9OnTQPKW4ClTpuDt7Y2fnx/du3cnKCiIqVOnMmnSJPz9/dm+fTs3btygS5cuibMf3q+Ajhs3jr59+9KoUSNKlSrF8uXLee+99/D19aVNmzaJrcQBAQGJMySuW7eOGjVqUK1aNZo3b55i7sDAQKpWrcqwYcMSZ4rMqPr16yeb1v0+W1tb6tSpk+K6+63cly5d4syZM+zevZuPP/4YG/NY82XKlKF9+/bpOv/KlSvp27cvAH379uXXX399aJv169fTsmVL3N3dKVCgAC1btkyz8quUIiwsDIA7d+5QvHjxh7ZZuHAh3bt3B+DKlSuEhYVRr149lFL06dMnxSzWKruU2fcr2Pnti8pQfUKIjHH2gIRY0+gjFmTpiWbqAKe11mcBlFKLgE5A4vfXWusg87qH+lgopWoCRYB1QJbPbvZq4/r8PL0emy/9xoWwgZTM94jZg1p9DOFXTP168haFat2yNqjIUvfu3UtsrQR4++236dbN9Dt3c3Pj8OHDzJ07lxEjRrBq1apk+44fP55z587h6OjI7du3yZ8/P0OHDiVv3ryJ3SV69uzJ66+/TsOGDblw4QKtW7fm+PHjAJw5c4YtW7Zw7Ngx6tevz7x58/j666/p3Lkzq1ev5plnnkk8140bNxg0aBDbtm2jTJkyhIamPIb7woUL6dGjB506deKdd94hNjYWe3v7dL8e8fHxbNq0iRdffPGhdVFRUezevTvFaclv3brFqVOnaNy4MX///Tf+/v7Y2tqmeI7WrVtz9+7Dd4BPnDiRFi1acO3aNYoVKwZA0aJFuXbt2kPbXrp0iRIlSiQ+9/LySlbx79+/P7a2tnTp0oV3330XpRTjxo2jVatWfPPNN0RGRrJx48aHjrt48WJWrlyZeA4vL69HniMbyBZldgEX0/vTxaZY4lB9ZdzKWOp0QoicxLmg6d+7N02zRFqIpSvYnsDFJM+Dgbrp2VEpZQN8CbwAtMj8aGlzdrBjgPdgpgftYdz2ifzU/hEt1DY2pjtXI2/AypdMd7OWa5a1YXOjtWPg6uEUV+WJjwPbx3h7F/WFtqn3qb7fNSIlPXr0SPz39ddff2i9n58fvXr14plnnklWGU5q48aNyfpQh4WFERFhutm2bdu22Nvb4+vrS3x8PC1btgTA19eXoKCgZMf566+/aNy4MWXKmCoe7u7uD50rJiaGNWvW8NVXX+Hq6krdunVZv349Tz/9NEqpFPPdX37/D41Lly5RpUqVxCxg+kPA39+fc+fO0b59e/z8/BLXbd++nWrVqnHq1ClGjBhB0aJFUzxPUuvXr0/3VOlKqUdmf5QFCxbg6elJeHg4Xbp0Yd68efTp04eFCxfSr18/3njjDXbt2kXv3r05cuRIYiv77t27cXZ2xsfHJ0Pns2LZosx2N7dgO2hTV54LYRekgi2ESJ/ECnaoadIaC7HmqdJfAtZorYNT+7BUSg0GBgMUKVKEwMDADJ8oIiLikftVSdDY3GnEHpvNzFk/h1KOpR55HFuvYVS/cRGnn3tw0P8zIlwt94t7HKldZ3bh5uZGeLhpaETH2Bhs4uNS3lBD3KPWpSIhNobo8LSHXgxPYRutNZGRkYSHhyd21wgPDyc6Ohp7e3vCw8NZtGgRO3fuZO3atXz00Uf89ddfydaDqUV4w4YNODk5JTv2g9vZ29uTkJCQeL77546PjycyMpJ79+4RGxubYtb71q5dy+3btxMriHfv3sXOzo4mTZrg4uLCtWvXku0fFhaGra0t4eHh5MmTh+3bt3P37l06d+7Ml19+ybBhw4iIiKBMmTJs376dmzdv0rJlSxYvXky7du24e/cu9evX55dffiEoKIjmzZvTvn17SpUqxcGDB7l9+3aKrditWrUiMjLyoeUff/wxTZs2pVChQpw6dYqiRYty9epVPDw8Hrpud3d3duzYkbj83LlzNGzYkPDwcPLly5e4/Nlnn2XHjh107tyZ6dOns3z5csLDw/Hx8eHevXsEBQVRqFAhAObOncuzzz6buK+bmxsXLlxIfH7q1CkKFy6c4u8gKioq2/9/fEC6ymx4/HL7fhkWl2DqO3nxbCTYweYDm9FnjL8HxtrLWGvOZ83ZQPI9KWvK5xoWRE3g0O5AQk+bGq8skk9rbbEHUB9Yn+T528Dbj9h2NtA1yfMFmPr6BQEhQBgwPrXz1axZUz+OLVu2pLr+x21HtfeMOrrrir5pH+zOZa2/qqr15+W1Dj33WHksJa3rzA6OHTuWru3CwsIslsHFxSXF5aVKldKfffaZ1lrrefPm6aefflprrfX777+vv/jiCx0fH6/PnTuntdY6JiZGFytWTN+6dUtPnDhRjx07NvE4PXr00J9//nni8wMHDiQ7TtIc968z6bomTZroPXv26OvXr2svLy999uxZrbXWN2/efChzjx499M8//5z4PCIiQhcqVEhHRkbqf/75R/v4+CSeY9myZbpp06Ypvg779+/XJUuW1LGxsfrcuXO6atWqieuWL1+u69Wrp7U2vQfbt2+fuO6rr77S3bt311pr/dxzz+n//e9/OiEhQWut9blz5/SqVau01mn/PkeNGpX42n/22Wd69OjRD21z8+ZNXbp0aR0aGqpDQ0N16dKl9c2bN3VsbKy+ceOG1tr0e+nSpYv+4YcftNZat2nTRs+aNUtrbXrvFStWLDFffHy8Ll68uD5z5kyy89SuXVvv2rVLJyQk6DZt2ujVq1enmDml9zKwV1uwTE7rkdVlts5guZ20DKs6dp0et/KIrv9zff3Rro/SfQxLsvYy1przWXM2rSXfk7KqfDfPav1+Pq0PLEhc9Lj5UiuzLT2KyB6gglKqjFLKAegO/JaeHbXWvbTWJbXWpYFRwFytderDNVhIn3qVcIpsxYk7+/jzUhqjPeQrZhpvMT4G5j0LkTezJqTIMve7Rtx/JB1F5NatW/j5+TF58mQmTZqUbL/4+HheeOEFfH19qV69OsOHDyd//vx06NCBFStWJN7kOGXKFPbu3Yufnx/e3t5MnTr1sXIWKlSIadOm8eyzz1KtWrXEfuL33b17l3Xr1iW7kdDFxYWGDRvy+++/4+fnxyuvvELDhg3x9/dn6tSpzJgxI8Vz3R/GLqWbJJ955hnu3r3L9u3bH1o3dOhQtm3bRlBQEDNmzODatWuUL18eHx8f+vXrlziaR1rGjBnDhg0bqFChAhs3bkz8nezdu5eBAwcCphbs9957L/Hm0bFjx+Lu7k50dDStW7fGz88Pf39/PD09GTRoEABffvkl06dPp1q1avTo0YPZs2cndj/Ztm0bJUqUoGzZ5N9Uff/99wwcOJDy5ctTrlw52rZtm65rsBLZpszO72zP7XuxlHQtKUP1CSHSL2kfbEt6VM07sx5AO+AkcAb4n3nZh0BH88+1MfXziwRuAkdTOEY/4Nu0zmWpFmyttZ6185T2ntZQt/2ls45PiE/7oOd3af1RYa2nNdM6OvKxcmU2q/oL8jFZQwv2o5QqVSqxJTSrGHGdRsiJ12mNLdg6i8ts/QQt2B2+2a77/rRbj946Wrde2jrdx7Akay9jrTmfNWfTWvI9KavKl5Cg9YceWv/x3zfH2bEFG631Gq11Ra11Oa31J+ZlY7XWv5l/3qO19tJau2itC2qtq6ZwjNla61csnTU1PeuUxfnu01yMPMW6c+kY17ZkPegyEy7vh6X94TH6AwshRFbLLmV2fmcHbkXGUNK1pAzVJ4RIP6VMrdgWbsGWmRzTycHOhpH1uhEfVZQv/p6cvsK8ytPQ7gs4uQ5Wvy4T0eRwQUFBeHh4GB1DiFzB3dmeW3djKZWvVOJQfUIIkS7OBU2jiFiQVLAzoGutkrjde4aQ6MssPbk0fTvVHgiNRsH+ubB1gmUDCiFELpHYgm2en0D6YQsh0s3ZXVqwrYm9rQ0jG3YkLrIMU/Z/z93Yhye+SFGzd8H/BQj8DPbNtmhGIYTIDdxdHAiPjqOYs2liH5kyXQiRbtJFxPp0ru5JwZhniYi7zeyjc9K3k1LQ4Wso3xJWvQ7/rrVoRiGEyOkKOJtmc9Txzrg6uHI+7LzBiYQQ2Yazh1SwrY2drQ2jmrQkNsyHmYdncfNeOn9Btvbw3GwoVg1+6Q8X91g0pxBC5GQFXEyzOd65FydD9QkhMsa5INy7BQnxFjuFVLAfQ4dqxSka35mY+Gh+/Gda+nd0zAs9fzGNlf3z8xB6znIhhUX9+uuvKKU4ceJE4rKgoKDHnjL79u3bfP/994+171NPPZXhffz9/enevXuyZQEBAezduzfxedLrCQwMxM3NDX9/fypXrsyoUaMSt5s9ezaFChVKXJd0/O9x48bh6emJv78/3t7eycbJPnnyJO3ataNChQrUqFGD559/nmvXrqUr/759+/D19aV8+fIMHz78/tBwyWitGT58OOXLl8fPz4/9+/cnWx8WFoaXlxevvPLfYBcBAQFUqlQpcYzz69evJ9tn2bJlKKUSX6ebN2/StGlT8ubNm+w4wvIKmKdLD42MoVS+UgSFBRkbSAiRfTgXBLSpkm0hUsF+DLY2ilHNGhJzuxZLTi7hYtjF9O+ctxD0Wgo6ARb1gpiHp38W1m/hwoU0bNgwxYlVHsfjVLDj4kxDP/75ZxqTHz3g+PHjxMfHs3379hSnH3+URo0acfDgQQ4cOMCqVavYuXNn4rpu3bpx8OBBdu7cySeffMLFi//9n3j99dc5ePAgK1euZMiQIcTGxhIVFUX79u0ZNmwYp06dYv/+/bz00kvcuHEjXVmGDRvG9OnTOXXqFKdOnWLduoeHzly7dm3i+mnTpjFs2LBk69977z0aN2780H4LFizg4MGDHDx4MNlEN+Hh4UyePJm6desmLnNycuKjjz5i4sSJ6cotMs/9CvatyBi8C3pzJfJK+r9RFELkbg4upn8tWAeTCvZjaudTjFI2zxCfoJi8f0rGdi5YDrrOhBvH4deXZPi+bCYiIoIdO3Ywc+ZMFi1alOI28fHxjB49mtq1a+Pn58ePP/6YuG/z5s2pUaMGvr6+rFy5EjDNRHjmzBn8/f0ZPXo0WmtGjx6Nj48Pvr6+LF68GDC1JDdq1IiOHTvi7e0NQN68eRPPO2HCBHx9falWrVqyGSaTWrhwIb1796ZVq1aJ58+IPHny4O/vz6VLlx5aV7BgQcqXL8+VK1ceWlehQgWcnZ25desWP//8M/Xr16dDhw6J6wMCAtL1DcCVK1cICwujXr16KKXo06cPv/7660PbrVy5kj59+qCUol69ety+fTsx1759+7h27RqtWrVK93W/9957vPXWWzg5OSUuuz/zZdJlImt4FsgDwJkbEVQtaBqK++jNo0ZGEkJkF3aOpn/joi13CosdOYezsVGMbVufAb81ZL1ax4Cb/fEu6J3+A5RvAS3GwYaxsGMSNBppsawic61cuZI2bdpQsWJFChYsyL59+6hZs2aybWbOnImbmxt79uwhOjqaBg0a0KpVK0qUKMGKFSvIly8fISEh1KtXj44dOzJ+/HiOHDnCwYMHAVNXhIMHD/LPP/8QEhJC7dq1E1tb9+/fz5EjRyhTpgzh4eGJ51y7di0rV65k9+7dODs7Exqa8hifixcvZsOGDZw4cYJvvvmGnj17Zuj6b926xalTp1Js/b1w4QJRUVH4+fk9tG7//v1UqFCBwoULc+TIkYdes/v+/fffh6Z1T0hIwMbGhsDAQC5duoSXl1fiOi8vrxQr+5cuXaJEiRIPbVekSBHeeOMN5s+fz8aNGx/ar3///tja2tKlSxfeffddlFLs37+fixcv0r59e7744otHvzgiy7jlsaeMhwuHgu8woJE3NsqGwyGHaez18PtSCCGSsTM3isRFWe4UFjtyLtCwggd13Z/lYPxuvvj7K2a1nZGxAzw1HK4cgk0fQlFfqNDSMkFzqAl/T+BE6IkU18XHx2Nra5vhY1Z2r8xbdd5KdZuFCxfy2muvAdC9e3cWLlz4UGXxjz/+4NChQyxdahov/c6dO5w6dQovLy/eeecdtm3bho2NDZcuXUqx3/GOHTvo0aMHtra2FClShCZNmrBnzx7y5ctHnTp1KFOmzEP7bNy4kf79++Ps7AyAu7v7Q9vs3bsXDw8PSpYsiaenJwMGDCA0NBR3d3eUUg9tn3TZ9u3bqVatGqdOnWLEiBEULVo0cd3ixYvZtm0bJ06c4Ntvv03Wojtp0iRmzZrFyZMn+f3331N9bQEqVaqU+IfGfeHh4bi6uqa5b3p8//33tGvXLlkl/b4FCxbg6elJeHg4Xbp0Yd68ebzwwguMHDmS2bNnZ8r5Rebx83Jj99lQnO2dKZe/HIdDDhsdSQiRHSRWsKUF22q927YmHeY2Y6/tKv68/CdPFc/ADWdKQcdvIORfWPoiDNoMHuUtF1Y8sdDQUDZv3szhw4dRShEfH49S6qFWTa0133zzDa1bt062fPbs2dy4cYN9+/Zhb29P6dKliYrK2F/QLi4uj51/4cKFnDhxgtKlSwOmG/2WLVvGoEGDKFiwILdu/XfDR2hoaLKZKRs1asSqVas4d+4c9erV4/nnn8ff3x8w9cH+9ttv2bt3L61ataJjx46JFfDXX3+dUaNG8dtvv/Hiiy9y5swZqlatytatW1PMmFYLtqenJ8HB/83aFxwcjKen50PH8fT0TNYX/P52u3btYvv27Xz//fdEREQQExND3rx5GT9+fOJxXF1d6dmzJ3///TedOnXiyJEjBAQEAHD16lU6duzIb7/9Rq1atdL5ygtL8PPKz8qDl7keFoWvhy+bL2xGa53iH4tCCJEosYuItGBbrUpFXXmmXFdW39rJhN1fsuKZetioDHRtd3CG7j/DtABY1BMGbgSnfBbLm5Ok1tKcmS2eSS1dupTevXsn9qkGaNKkCdu3b6dkyZKJy1q3bs0PP/xAs2bNsLe35+TJk3h6enLnzh0KFy6Mvb09W7Zs4fx509i9rq6uybp7NGrUiB9//JG+ffsSGhrKtm3b+OKLL5KNWvKgli1b8uGHH9KrV6/ELiJJW7ETEhJYsmQJhw8fpnjx4gBs2bKFjz76iEGDBhEQEMD8+fNp0aIFSinmzJlD06ZNHzpPmTJlGDNmDBMmTHjoJs9atWrRu3dvJk+ezGeffZZsXceOHZk5cyZz5syhT58+fPbZZ6xevZr27dsDsG3bNtzd3fHx8Um1BTt//vzky5ePv/76i7p16zJ37lxeffXVh3J27NiRb7/9lu7du7N7927c3NwoVqwYCxYsSNxm9uzZ7N27l/HjxxMXF8ft27fx8PAgNjaWVatW0aJFC9zc3AgJCUncJyAggIkTJ0rl2gr4ebkBcCj4Dj4ePiw/tZzgiGBKuJZIY08hRK6WBS3YcpNjJhjVsiqEtuZs2EnWnXt4NIM05S9pGiP75mlYMRQSEjI9o8gcCxcupHPnzsmWdenS5aGK5sCBA/H29qZGjRr4+PgwZMgQ4uLi6NWrF3v37sXX15e5c+dSuXJlwHRzYIMGDfDx8WH06NF07twZPz8/qlWrRrNmzfj888+TdclISZs2bejYsSO1atXC39//oZEttm/fjqenZ2LlGqBx48YcO3aMK1euMHjwYFxdXalWrRrVqlUjIiIi2XB8SQ0dOpRt27YRFBT00Lq33nqLWbNmJfuD4b6xY8fy1Vdf4ejoyKpVq/jmm2+oUKEC3t7efP/99xQqVCjVa7zv+++/Z+DAgZQvX55y5crRtm1bAKZOncrUqVMBaNeuHWXLlqV8+fIMGjQozVFaoqOjad26NX5+fvj7++Pp6cmgQYPSzFK6dOnELiReXl4cO3YsXdcgnlzV4vmwUXAo+Da+Hr4AHAk5YnAqIYTVy4IWbLTWOeZRs2ZN/Ti2bNnyWPslNemPE7rK1Ja66aKWOiYu5vEOsusHrd/Pp/WWz544T0oy4zqNduzYsXRtFxYWZuEk1kGuM/tK6b0M7NVWUJZm5SMj5XZKZVjrSVt1n5m7dUx8jK41r5ae8PeEdB8vs1l7GWvN+aw5m9aS70lZXb7r/5rqW4d+0Vo/fr7Uymxpwc4kg5uUwzmiIzeirrDk3yWPd5C6Q8C/FwR+BsdXZW5AIYTIgXw93Th86Q52yo4qBatIC7YQIm1ZMEyfVLAzibODHaMbdyQushzfHPiBiJiIjB9EKWj/FRSvASuGwPVH97cVQggBfiXyExoZQ/Cte/h4+HD85nFiE2KNjiWEsGZZMEyfVLAzUdeaJSgW34XIuDvMPDzr8Q5i7wTd5oO9MyzqAfduZ2pGIYTISaolvdGxoA9R8VGcuX3G4FRCCKsmLdjZi62N4oM2bYgN82P20TmE3AtJe6eUuHlCt3lw+yIsGwgJ8ZkbNJvTMvOlyObkPZx5KhV1xcHWJtmNjjIethAiVdKCnf00qlCIai7diUuIZfK+7x7/QCXrQbvP4fQG2PxR5gXM5pycnLh586ZUUES2pbXm5s2bMr16JnG0s6VyMVcOBd/By9ULN0c36YcthEidTJWePX3YLoBOi+qy8sxyXvTtS2m30o93oFoDTDM97pgERf3A59lMzZkdeXl5ERwczI0bN1LdLioqKldUYOQ6sycnJ6cUZ5IUj8fPy42VBy6jNfh4+EgLthAidUqBraNMNJPdVCrqSlvP3myI2Mf43V8ztdXXj3+wtp/D9eOw8mXwqGCaUj0Xs7e3T3Ga8AcFBgZSvXr1LEhkLLlOIUwzOs7/6wJnQyLx9fBl2qFp3I29i7O9s9HRhBDWys5J+mBnR2+3ro2+3YSdVzZx+MYTtKbYOcDzc8HJzTTT493QzAsphBA5wP0ZHQ9fMvXDTtAJHLspE/4IIVJhZ9kWbKlgW0jhfE70rdqXhLi8fLDz8yfrM+xaBLotgPBr8EtfiI/LvKBCCJHNlS+Ulzz2tvxz8Q5VC1YF4OjNowanEkJYNTtHacHOrl4JqIpjeGv+vXOQ7Ze2P9nBvGrC05Pg3DZY/bpMpy6EEGZ2tjb4eObjUPBtCuYpiGdeT+mHLYRInZ0jxN2z2OEtXsFWSrVRSv2rlDqtlBqTwvrGSqn9Sqk4pVTXJMv9lVK7lFJHlVKHlFLdLJ01szk72DHqqd4kxBTko50TiX/S4faq94JGo2D/XFg+COJlMgUhRObKrmW2r2d+jl4OIy4+AR8PHxlJRAiROltHi9ajLFrBVkrZAt8BbQFvoIdSyvuBzS4A/YCfH1h+F+ijta4KtAG+Vkrlt2ReS+hWqywFYzpxNeocK09nwvTnzd+D5u/DkaWwqBfEWu6vLyFE7pKdy+xqJdyIjkvg5LUIfAr6cCniEjfv3cyq0wshshs7B4iPsdjhLd2CXQc4rbU+q7WOARYBnZJuoLUO0lofAhIeWH5Sa33K/PNl4DpQyMJ5M52tjeLDFj2Jv+fJxD2TiY7PhP4+jUaaplQ/9QfM7wJRYU9+TCGEyMZltp9XfgAOBd/Gx8MHkH7YQohU2Dpk6z7YnsDFJM+DzcsyRClVB3AAsuX8twGVilDJoTvhcTeYdXhB5hy09ovQZQZc3A1zOkCktNQIIZ5Yti2zSxd0xtXJjn+C7+Bd0BsbZSP9sIUQj2brYNEuIlY/DrZSqhgwD+irtX7ozj6l1GBgMECRIkUIDAzM8DkiIiIea7+MeLZQST69UJEfD/5IydCiONtkxvisHrhXfZuqRycQ9V1jDvl9QLSTxyO3zorrtBa55VrlOoW1SavMNm/zWOV2Wu+DEi4J/HkimL/db1LUrihb/91K1dtVM3gFj8/a36fWnM+as4Hke1LWmM/vTgR2cZHsDwy0TD6ttcUeQH1gfZLnbwNvP2Lb2UDXB5blA/Y/uPxRj5o1a+rHsWXLlsfaL6OGLlqpfWb76A93fJG5Bz63Q+tPPLX+ykfrkNOP3CyrrtMa5JZrlevMWYC92oJlclqPrC6zdQbL7bTeB+PXHtfl3l6t78XE6bE7x+oGCxvohISEdB//SVn7+9Sa81lzNq0l35OyynwLumn9QwOt9ePnS63MtnQXkT1ABaVUGaWUA9Ad+C09O5q3XwHM1VovtWDGLDOubSsSwquz9PRCrkVey7wDl24A/X6H2Ej4qQ1clbvnhRCPJVuX2dW83IhL0By/EoaPhw93ou8QHB5sRBQhhLWzs2wXEYtWsLXWccArwHrgOLBEa31UKfWhUqojgFKqtlIqGHgO+FEpdf+ulOeBxkA/pdRB88PfknktrUg+J7qVG0S8jufDnV9n7sGLV4f+68DWHma3g4t/Z+7xhRA5XnYvs33NNzoevnQHXw9f08/SD1sIkZJsfpMjWus1WuuKWutyWutPzMvGaq1/M/+8R2vtpbV20VoX1KYhntBaz9da22ut/ZM8Dlo6r6W90aw+dhEN2HZ5NWdvn83cgxeqCAPWgXNBmNsJzmzO3OMLIXK87FxmF3dzwiOvA/9cvEO5/OXIY5eH/df3Z2UEIUR2YeGbHGUmxyzm4mjHKzWGoLUd47ZPzvwT5C8JA9aDezn4uRscS9e3u0IIke0ppahZqgA7T4dgix31i9Un8GLg/f7hQgjxH9vsPQ62SEG/uj44RzXhwM0t/Bt6KvNPkLcw9Ftl6jbyS184kElDAwohhJVr61OMq2FRHLh4i+almnPt7jUZD1sI8TBbB8iMuUkeQSrYBrCztWFU3aHoBHvGbp1kmZPkyQ+9V0DZAFj5Euz63jLnEUIIK9K8SmEc7GxYdegKTbyaYKts2Xh+o9GxhBDWJjvf5Cgerat/RfLHNedY2HaO3DhumZM4uECPReDdCda/TelzP4N8VSqEyMFcnewJqFiINYev4Gqfj1pFa7HpwiajYwkhrE12v8lRpMzGRvG/hkPR8U68t/Ury53IzhG6zoLqvSl9fjHstEC/byGEsCLt/YpxLSyafRdu0aJkC4LCgjL/pnIhRPZm6wA6HhJSnA/riUkF20BtqpShcEIrTkf+xb6rhyx3Ihtb6DCF64Uawsb34egKy51LCCEM1rxKERztbFh96ApNSzQFYOMF6SYihEjCxtb0b0KcZQ5vkaOKdFFK8WHTYej4PIy1ZCs2gI0NJyq/BiXqwvIhMk62ECLHyutoR9NKhVlz+AoeeQrj5+En3USEEMnZ2Jn+1fGWObxFjirSrWE5L0rYtuNC1D52Xtxn0XMl2DpA94WQrzgs7A6h5yx6PiGEMEp7v2JcD49mb1AozUo249jNY1yJuGJ0LCGEtbhfwZYW7Jzr0+ZD0XEufLDDQiOKJOVSEHotBZ0AC56Du6GWP6cQQmSxZpUL42Rvw+rDV2hesjkAmy/K5FtCCDOpYOd81b2KUsGxA1di/mHT2b8sf0KP8tD9Z7h9Hhb3tuhdtEIIYQQXRzuaVS7MmsNXKeFaivL5y8twfUKI/yRWsKWLSI42vtVQdFxePv4zC1qxAUo9BZ2+g/M74LfhMnyfECLHae9bnJCIaP4+Z+omsv/6fkKj5Fs7IQRyk2NuUalwQXzzdiYk/hi//7sta07q9zw0/R8cWgRbJ2TNOYUQIos0rVyIPPa2rD58mRYlW5CgE9h6cavRsYQQ1kBJBTvX+KLVEHRcPibsnozOqhblxqOhWk8I/Az+WZw15xRCiCzg7GBHsyqFWXfkKuXdKlLcpbiMJiKEMJE+2LmHVwE36uR/njv6JEuOZlFfQaWgw2Qo3QhWvgxBO7LmvEIIkQXa+xYjJCKGPUG3aFayGX9e/pPI2EijYwkhjCZ9sHOXCa0GQlx+Ju37Nutase0coNs8cC8Di3pByKmsOa8QQlhY00qFyWNvyyrzaCKxCbFsv7Td6FhCCKMl9sGWCnauUMjVhcaFexDJWX46sCbrTpynAPT6xfQX3YKuEBmSdecWQggLyeNgS3NzNxHfgtVwd3Jn83kZrk+IXE+6iOQ+n7XsD3HuTP3nexISErLuxAVKQ8/FEH4VFvaA2HtZd24hhLCQp/2KERoZw56gOzQt0ZRtl7YREx9jdCwhhJGkgp375HPKQ1vP3kTZXOCb3b9m7cm9asGz0yB4D6wYCllZwRdCCAsIqFQYZwfTaCLNSjYjMjaSv65kwZwDQgjrZU0VbKWUs0VSiIeMa9YHm7hCzDk+jbh4y/QPeiTvTtDyQzj2K2z+MGvPLYTINFJmmzjZ29KiShHWHblKrcJ1cLF3YfMF6SYiRK5mDX2wlVJPKaWOASfMz6sppb63SCIBgLODA51L9yPW9hJf7Pgl6wM89SrU7A87JsG+2Vl/fiHEY5My+2Ht/Ypx624s+86H09izMVsubiHeQh+sQohs4H4FWxt7k+MkoDVwE0Br/Q/Q2CKJRKJ3mvTELr4oi0/PJDo2NmtPrhS0mwjlW8CqkXBaxo4VIhuRMvsBTSoWwsXBltWHrtCsVDNCo0I5cP2A0bGEEEaxli4iWuuLDyySP/0tzMHOjh4VBhJvd5WPAhdmfQBbO+g6CwpXgSV94PLBrM8ghHgsUmYn52RvS0vvIqw7epV6RRvgYOMgk84IkZtZyUyOF5VSTwFaKWWvlBoFHLdIIpHMGw264pDgyW8XZhMRHZ31AZzyQa+lpmH8FnSF0LNZn0EIkVFSZqegvV9xbt+N5Z8LUdQvXp/NFzZn3XwDQgjrYg19sIGhwMuAJ3AJ8Dc/FxZma2PLAO8haLsbjN00z5gQ+YrBC8tNf+XN7wIRN4zJIYRILymzU9CoggeujnasPnSZ5iWbcznyMidCTxgdSwhhhPst2Noyo6WlWcFWStkCk7XWvbTWRbTWhbXWL2itb6bnBEqpNkqpf5VSp5VSY1JY31gptV8pFaeU6vrAur5KqVPmR990X1UO81KdZ8ijS7Lh8nxu3b1rTIhCFaHnLxB2xdSSHR1uTA4hRKqkzH60+91E1h+9xlPFGmGjbNh4YaPRsYQQRrAxV4GNasHWWscDpZRSDhk9uLmg/w5oC3gDPZRS3g9sdgHoB/z8wL7uwPtAXaAO8L5SqkBGM+QESile8n8J7G/y9h+zjQtSojY8PweuHjb1yY6TiRqEsDZSZqeuvV8x7tyL5dilBGoUriHD9QmRWynrGEXkLLBTKfWeUmrk/Uc69qsDnNZan9VaxwCLgE5JN9BaB2mtDwEPttG3BjZorUO11reADUCbdObNcfpWa0deyrIjZCFXwyKMC1KxNXScAmc2w8qXZSIaIayTlNmP0LCCB65Odqw+dIUWpVpw+vZpgu4EGR1LCJHVrKQP9hlglXl71ySPtHgCSe9kDzYvS48n2TfHUUoxstYrKPvbvPXHDGPDVH8Bmr0Hh5fAhveMzSKESImU2Y/gaGdLK++irD96lQbFmgBINxEhciMLt2DbpWcjrfUHAEqpvObnBjahJqeUGgwMBihSpAiBgYEZPkZERMRj7ZfVPLQdLnFl2HdnMT+vrkRxlzwZ2j9Tr1PXpLxne7x2fcvp65EEl3gmc46bSbLL7/RJyXWKlFhzmQ2PX25n1vugol08y6LimPXbcco6lmXeP/MoE1IG2/sfuI/J2t+n1pzPmrOB5HtS1pjPOfIidYBjRw4T4VIj0/Olq4KtlPIB5gHu5uchQB+t9dE0dr0ElEjy3Mu8LD0uAQEP7Bv44EZa62nANIBatWrpgICABzdJU2BgII+znxE+KunMyB0DWRr2F8vbf5ChfTP9Ops0hqUDKH9sFuWrPQV+z2fesZ9QdvqdPgm5TpESay6z4fHL7cx6HzTRmtWXd7L9ehzvPf86w7e8yt2Sd+lQrsMTHdfa36fWnM+as4Hke1JWmS/kNOwB7yqVuR6aN9PzpbeLyDRgpNa6lNa6FPAGMD0d++0BKiilyphvuOkO/JbOc64HWimlCphvlGllXpartSxXF0/7upyMWsX+4PPGhrGxhc4/QulG8Oswme1RCOshZXYqlFIMbFSWsyGRxIZXpmKBisw4PIMECw3XJYSwQkaPImLmorXecv+J1joQcElrJ611HPAKpkL2OLBEa31UKfWhUqojgFKqtlIqGHgO+FEpddS8byjwEaYCfw/woXlZrvdZ07dAxfP2li+NjgL2TtB9ARSqAot7w6X9RicSQkiZnaZ2PkXxzJ+HGTvOMdB3IGfvnGXLhS1p7yiEyBmsZRQR893opc2PdzHdpZ4mrfUarXVFrXU5rfUn5mVjtda/mX/eo7X20lq7aK0Laq2rJtn3J611efNjVkYvLqeqXqwCVZxbcSk+kA2n/jE6Dji5wQtLwbkgLHgObp4xOpEQuZ2U2Wmws7Whf4PS/H0ulGJ2dSjpWpLph6fLzI5C5BZWMorIAKAQsBxYBniYlwmDTGw5CqUd+fDPL4yOYuJaFHovN82INP9ZCL9mdCIhcjMps9OhW+0SuDraMXPHBQb4DODozaPsurLL6FhCiKxgDS3YWutbWuvhWusaWuuaWusR5nFOhUFKFShM3QJduc0/LPjHSiZK8KgAvX6BiOum2R6jwoxOJESuJGV2+rg62dOjbknWHL5CdffmFHYuzIzDBg+DKoTIGtbQgq2U2qCUyp/keQGllFXevJKbTGj5MsTlZ/L+r6zn5hyvWvD8XLh2FBa/ILM9CmEAKbPTr99TpVHAgr8u069qP/Zc3cPB6weNjiWEsLTEFmzL1J/S20XEQ2t9+/4Tc0tIYYskEulW0CUvbYr3457Neab89YvRcf5ToSV0+hbObTWNLiKzPQqR1aTMTqfi+fPQ3q8Yi/dcpFXJTuR3zC+t2ELkBlYyikiCUqrk/SdKqVKA3AliBT5s0Qeb2OLMOfED0XHRRsf5j39PaDEOjiyFbVbST1yI3EPK7AwY1KgsEdFxrNx/gxeqvMDW4K38G/qv0bGEEJZkDX2wgf8BO5RS85RS84FtwNsWSSQyJI+9PT3KvUSczU0+2DrN6DjJNRgBft1g63g4t83oNELkJlJmZ4CPpxv1yroza2cQXSt0w8XehZmHZxodSwhhSTZW0EVEa70OqAEsBhYBNbXW0p/PSoxq3AH7mCqsujCP0Hu3jY7zH6Wg/VfgXg6WDTTd/CiEsDgpszNucOOyXLkTxY6TkXSr1I3159dzPszgybyEEJajzFVgIyvYSqkGwD2t9SogP/CO+StHYQXsbG0Y6jucBBXFO5u/NjpOco554fk5EHXHVMm2UF8nIcR/pMzOuICKhSlXyIXp28/Su0pv7G3smXXEqofyFkI8CWuoYAM/AHeVUtWAkcAZYK5FEonHMrBOA1xi6rHzxq8E3blgdJzkilSFdl+YbnrcNtHoNELkBlJmZ5CNjWn69COXwjh5BTqX78zKMyu5GnnV6GhCCEuwkgp2nDZNb9UJ+E5r/R3gapFE4rHY2CjeqjscrW14a/PnRsd5WPXepv7YgZ/B2a1GpxEip5My+zF0ru5JQRcHZmw/R3+f/qBhztE5RscSQljC/Qq2hUY6S28FO1wp9TbwArBaKWUD2FskkXhsnf2qUjC+BcfCtrL/6iGj4yR3vz+2RwVTVxGZ6VEIS5Iy+zE42dvSu34pNp+4zt27rrQr246lJ5cSGhVqdDQhRGazkhbsbkA08KLW+irgBcjYa1ZGKcW4xq+QEOfC/7Z+hqkBy4o45oXn5kB0OCwfJP2xhbAcKbMfU+96pXC0s2HmjnO86Psi0fHRzD823+hYQojMphSgDB9F5KrW+iut9Xbz8wta68T+fEqpXRZJJzKsacWSlFCdCI46wvpzVjKFelJFvKU/thAWJmX24yuY15EuNb1Ytv8S+Ww9aVGqBYtOLCI8JtzoaEKIzKZsDG/BTotTJh1HZIJPWwwiIdqDT3ZNJC4hzug4D6v+Avh1l/7YQhhHyuxUvNiwDDFxCczddZ6BvgMJjw1n8b+LjY4lhMhsysbwiWbSYmV9EXK3GiU98Hbqwe24YBYcXWp0nIcpBe2/lP7YQhhHyuxUlCuUlxZVCjP/r/OUzVeJBp4NmHdsHvfi7hkdTQiRmbJBC7awMp+06k783VJ8e/A77sbeNTrOw5L1x5bxsYUQ1mVgo7KERsawbH8wg3wHERoVyopTK4yOJYTITDa2Vl/BVpl0HJFJKhbNR738fYlKuM13+610yt/E/tjbYJvcfyVEFpIyOw11y7jj6+nGzO3nqF6oBjUK12DW0VnExscaHU0IkVmUDVhoQIj0zuT4qlKqQCqb9M6kPCITfdC6PfHhviw4MYeQeyFGx0lZ9RegWg8IHC/9sYXIJFJmPzmlFIMal+VsSCSbT1xnoO9ArkZeZfW51UZHE0JkFivoIlIE2KOUWqKUaqOUStb6obU+kvnRxJMq4e5M62IDiNNxTPhrstFxUpbYH7ui9McWIvNImZ0J2vkUxTN/HqZvP0tDz4ZUdq/MzMMziZcubULkDEpZrItqeofpexeoAMwE+gGnlFKfKqXKWSSVyDRvt2xIwp16rLuwkquxVjrlr4MLPDdb+mMLkUmkzM4cdrY29G9Qmt3nQjl86Q4DfQcSFBbEpgubjI4mhMgMygr6YJun3b1qfsQBBYClSikrnJdb3Fc4nxPPleuPjndg8Y2VRsd5tCLe0H6i9McWIpNImZ05utUugaujHdO3n6NFyRaUzleaGYdnWN9EXkKIjDO6i4hS6jWl1D7gc2An4Ku1HgbUBLpYJJnINCOaVofbzTkde4Rdl614fgn/XtIfW4hMIGV25nF1sqd7nRKsOXyFq2ExDPAZwPHQ42y+aIUTeQkhMsboCjbgDjyrtW6ttf5Fax0LoLVOAJ62SDKRaQq4ONDftw8JMQUZu+Nj670LXvpjC5FZpMzORP0blEEBUwPP0KFcB8q5lePLvV8SEx9jdDQhxJOwggr2WiA0MY9S+ZRSdQG01sctEUxkrqGNK2Ef2pGr9y4w5+jctHcwioMLPJ90fGzLvPGFyOGkzM5ExfPnoXudEvz89wXO3rjHm7Xf5GL4ReYfn290NCHEk7CCmRx/ACKSPI8wL0uT+Q72f5VSp5VSY1JY76iUWmxev1spVdq83F4pNUcpdVgpdVwp9XY6s4oUuDja0bOkH7Hh3nx/8AeuRFwxOtKjFa4C7T439cf+6zuj0wiRHUmZnclGtqyEi4MtH606Rv3i9Wni1YRph6ZZ7xCoQoi0GT0ONqB0kjs6zF8z2qW5k1K2wHdAW8Ab6KGU8n5gsxeBW1rr8sAkYIJ5+XOAo9baF1O/wSH3C3LxeGoXtcXHsQ+x8Ql8/Nd4o+OkrnpvqPw0bPoQrsqIYkJkkJTZmczdxYHXWlRk+6kQNp+4zqhao4iOj+abA98YHU0I8bisoIJ9Vik13NxCYa+Ueg04m4796gCntdZntdYxwCKg0wPbdALmmH9eCjQ3j9mqARellB2QB4gBwtKZV6RAKcVnHRsTe7MZ2y5tZuelnUZHejSloMNkcMoPywdDbJTRiYTITqTMtoA+9UtRtpALH68+TnGXkvSs3JMVp1Zw/Kb0uhEiW1LK8D7YQ4GngEtAMFAXGJyO/TyBi0meB5uXpbiN1joOuAMUxFRwRwJXgAvARK11KOKJVCjiSs/KvUmI9mDczo+t+yYdFw/o9B1cPwqbPzI6jRDZiZTZFmBva8N7T3tzLiSSOX8GMaTaEPI75mf83+Nl2D4hsiML3uSY5leGAFrr60B3iyR4tDpAPFAc0/it25VSG7XWyVphlFKDMX9wFClShMDAwAyfKCIi4rH2y27uX2dtJ82y0E5cdZzJ2N/H0iZ/G6OjpcKBCsXb4rnrWw7eLcLtAn7p2iu3/U5zutxynZnFmstsePxy2xreBwrw87Dlqz+OU+SeM61cWrH4+mK+XvM1FXQFw/Olxhpev0ex5mwg+Z6UtearExVN+LWrRDhmfr50VbCVUk6Y+t1VBZzuL9daD0hj10tAiSTPvczLUtom2PzVohtwE+gJrDMPL3VdKbUTqMUDX3NqracB0wBq1aqlAwIC0nNJyQQGBvI4+2U3Sa8zxqMib23/m/VqI6+2eBXPvA82UlmRp+rAj43xPzcV2uyEPAXS3CU3/k5zstxynZnFmstsc47HKret5X3g5R1Bm6+3sSvSg4+feZsDqw6w9t5aqrpXtYp8j2Itr19KrDkbSL4nZbX5DrvgXMiDvHnzZnq+9HYRmQcUBVoDWzEVuuHp2G8PUEEpVUYp5YCpReW3B7b5Dehr/rkrsNl8c84FoBmAUsoFqAecSGdekYaO1Yrj7dCLuHjNx7s+MzpO6hyc4dlpEHENVo8yOo0Q2YGU2RZUvnBeetcvxeI9F/j3aiRv1X6LK5FX2Bwmk88Ika1YQR/s8lrr94BIrfUcoD2mPn2pMvfPewVYDxwHlmitjyqlPlRKdTRvNhMoqJQ6DYwE7g8L9R2QVyl1FFOhP0trfSi9FyZSp5Tis06NibnZnB2Xt7IteJvRkVLnWQOajIEjS+HQL0anEcLaSZltYSOaV8Qtjz0frjpK7aK1aVGyBRvCNnAtUibIEiLbUDaY7s/OfOnqIgLcn/rvtlLKB7gKFE7PjlrrNcCaB5aNTfJzFKbhnR7cLyKl5SLzVCrqSo+KL/DL1X188OcnrH62Dk52TmnvaJSGr8PpDbD6DShZD/KXSHsfIXInKbMtzM3ZnpEtK/LeyqOsP3qVkbVGEnghkMn7J/Npo0+NjieESA8rmMlxmlKqAPAupq8Hj/Hf2KciGxvZqgqOd7py/d5lfjr8k9FxUmdrB51/NM269OswmeVRiEeTMjsL9KhTkkpFXPlkzXEKORWnab6m/H72dw7dyLEN90LkLEaOg62UsgHCtNa3tNbbtNZltdaFtdY/WiSRyFL5nOx5t3kHYu9UY9rhGVwMv5j2TkZyLwNtxkPQdpnlUYgUSJmddexsbRjbwZuLofeYueMcrdxa4ZHHgwl7JsiwfUJkB0b2wTbPAPamRc4urELn6p5UduhBfLwNH+/61Po/GKq/ILM8CvEIUmZnrQblPWjpXYTvtpwmKsaB4dWHc+jGIVafW210NCFEWqygi8hGpdQopVQJpZT7/YdFEoksp5Ti046NiL7Rgj+v7CDwYqDRkVKnFHSYYhqub/kgmeVRiIdJmZ2F/teuCrHxCSw9FUun8p3wLujNpH2TuBt71+hoQohUGT+KSDfgZWAbsM/82GuRRMIQ3sXz0b1SDxKii/Dhrk+5F3fP6EipcylonuXxmMzyKMTDpMzOQqU9XBjQoAw7LsVx5FIYb9V+i+t3r/PTESu/r0WI3M7IPtgAWusyKTzKWiSRMMwbrbxxuN2VkKirTD80w+g4aavQEmoPhF3fwtlAo9MIYTWkzM56rzQrTz4H+PD3Y1QvXJ02pdsw++hsLkdcNjqaEOJRjO4iopTqk9LDIomEYdzy2PN2s/bE3qnOT0d+4nzYeaMjpa3lR1CwAqwYBvduGZ1GCKsgZXbWc3Wyp0sFB/aev8Xvh64wsuZIACbtm2RwMiHEIxldwQZqJ3k0AsYBHVPbQWRPXWt4UcG+O/Hxdny46xPrv+Hx/iyPkddN42MLIUDKbEM08rKjavF8jF9znPwOhenv0591QevYf22/0dGEECkxuoKttX41yWMQUAPIa5FEwlA2NorPOjYg+kZL/r66i00XNhkdKW2eNSBgDBxZJrM8CoGU2UaxUYr3O1Tl8p0oftx2hv5V+1PYuTDj/x5PgoU+xIUQT8DoCnYKIoEymRlEWA8fTze6Vnie+KiifPzX+OxxJ3yD16FEXVMr9m0rH8tbiKwnZXYWqVPGnfa+xZi69Qy3IxWv13yd46HHWXl6pdHRhBAPUsrYmxyVUr8rpX4zP1YB/wIrLJJIWIU3W5tueLwZdY1ph6YZHSdtD87yKK1FIheTMttYY9pWJkHDhHUnaF+mPX6F/Ji8fzIRMRFGRxNCJKVsAAMr2MBE4Evz4zOgsdZ6jEUSCauQ39mBMU3bEnu7JrOPzuHsnbNGR0pbklkeS1z81eg0QhhJymwDlXB3ZnCjsqw8eJn9F24zpvYYbkbdZPrh6UZHE0IkpRQkxFvk0OmtYF8Admutt2qtdwI3lVKlLZJIWI3na5WgvH034uPteXf7WGLiY4yOlLbqL0CVDpQ5twCu/GN0GiGMImW2wYYFlKOwqyMf/n6UqgV96FC2A/OOzWN78Hajowkh7rOCFuxfgKTfucebl4kczMZG8WnH+ty70pnDN/9h7J9jrX9UEfMsj7H2rrBsEMRkg/7jQmQ+KbMN5uJox1ttKvNP8B3m7ApiVO1RlM9fnlc3v8qvp381Op4QArCGmRzttNaJzZfmnx0skkhYFT+v/PTx7UT09dasPruaH/75wehIaXN250TlERDyL2wYa3QaIYwgZbYV6FzdkyYVC/HB78eYEXidGS1nUrtobd7b+R7TD023/gYLIXI6o2dyBG4opRLHUFVKdQJCLJJIWJ3/ta9CQJFuxNyuxQ///MBvZ34zOlKabrn7Q72XYc90OPmH0XGEyGpSZlsBGxvFjL616FGnBN8HnmH0L/8ysdEU2pVpx5QDU/hk9yfEW6j/pxAiHaxgmL6hwDtKqQtKqQvAW8AQiyQSVsfWRjGlRw2qOQ0gPrI8Y3e+z56re4yOlbbmY6FwVVj5EkTcMDqNEFlJymwrYW9rw6edfRn7tDcbjl2jx7S9vOo7ln5V+7H438WM2jqK6Phoo2MKkTspg7uIaK3PaK3rAd6At9b6Ka31aYskElbJyd6WGX3rUSJuKHHR7ryyabj1jyxi7wRdZkBUGPz2isW+BhLC2kiZbV2UUgxoWIaZ/WpzIfQunb/fRfMiL/Jm7TfZeGEjg/8YzJ3oO0bHFCL3MfomR6XUp0qp/FrrCK11hFKqgFLqY4skElYrn5M98/o3we3OMO5Gw+D1LxEaFWp0rNQV8YaWH8DJdbD3J6PTCJElpMy2Tk0rFWb5S0/hZG9Dtx934RbbnC8af8HhkMP0W9ePq5FXjY4oRO5iBV1E2mqtb99/orW+BbSzSCJh1Qq7OvFz/7bYh7zItbvXGbbhFev/erPOECjXDNb/D26cNDqNEFlBymwrVbGIKytfbkg1r/wMX3iAY6fL8kOLH7gaeZVea3px6tYpoyMKkXtYwU2Otkopx8Q8SuUBHFPZXuRgpQq6MLfXc+jrPTgWepg3t75DgjXPnGhjA8/8APZ5YPlAiMsG43kL8WSkzLZi7i4OzBtYh641vZiy6RRzt9gzrcVPaK3pu7Zv9rjHRYicwuAW7AXAJqXUi0qpF4ENwByLJBLZgo+nG9O69CfuRjs2X/yDSXunGB0pda5FoeM3pslntnxidBohLE3KbCvnaGfLF139eLttZdYcvsK7S27ydeOZeDh7MGTDEP4IktGPhLA4o1uwtdYTgE+AKubHR1rrzy2SSGQbT5Xz4MtWrxF7qw6zj83kl3+XGR0pdVWehhp9YedkOCezqYmcS8rs7EEpxZAm5ZjWuxanrkcw+KezvF3tW6oWrMqoraP4+fjPRkcUImezgj7YaK3Xaq1HmR/rLZJGZDvt/IrzTr13iIuowEd/fcSuy7uMjpS6Np+Be1lYMRTu3TI6jRAWI2V29tHSuwjLhj2FrY2i/09Hec7zIwJKBPDZ35/x9b6vZUIaISzF6GH6lFL1lFJ7lFIRSqkYpVS8Uiosnfu2UUr9q5Q6rZQak8J6R6XUYvP63Uqp0knW+SmldimljiqlDiulnNJ9ZSLL9KlXjgEVxhIX5cHLG0dw5vYZoyM9moMLdJkOEVdh9RsydJ/IkaTMzn6qFMvHry83wLtYPoYvPEp5/RLPVXyOmUdm8u7Od4lNiDU6ohA5j9HD9AHfAj2AU0AeYCDwXVo7KaVszdu1xTQeaw+llPcDm70I3NJalwcmARPM+9oB84GhWuuqQAAgJYyVGtWyGu0Kv0t0rC19Vg8m5J4VTxrnWRMCxsCRZXBoidFphLAEKbOzoUKujvw8qB7P+Bfnqw1nuBn0NEP9Xua3M7/x6qZXuRt71+iIQuQsVtJF5DRgq7WO11rPAtqkY7c6wGmt9VmtdQywCOj0wDad+O/mm6VAc6WUAloBh7TW/5jPf1NrLXPKWimlFBM6NaGm0xvcibnFC78P5V7cPaNjPVrDkVCyPqwZBbfOG51GiEwnZXb25GRvy6Ru/oxuXYmV/1xh0y5fRtV4l7+u/EX/9f2tu/FCiGzH4C4iwF2llANwUCn1uVLq9XTu6wlcTPI82LwsxW201nHAHaAgUBHQSqn1Sqn9Sqk305lVGMTWRjG92zOUThhM8N2TDFrzhvUO32djC51/NP28YggkSD1A5ChSZmdjSileblqeH3rV4NiVMKatLsRo//GcvX2WPmv7cCHsgtERhcgZLDiKiErPzRNKqVLANcABeB1wA75Pa+pdpVRXoI3WeqD5eW+grtb6lSTbHDFvE2x+fgaoC/QDXgZqA3eBTcC7WutND5xjMDAYoEiRIjUXLVqU9lU/ICIigrx582Z4v+wmq67zXpxm3NEN3M3/OzUdmtGvWGeLn/NB6b3WIlcDqXJiEmfL9OJCqeezIFnmkvduztK0adN9WutaT3ocay6zzfs8Vrlt7e8DS+QLuhPP5P3R3I3TPOt7lS2x01EohhYeSinHUobnyyzWnA0k35Oy1nyVj08m/+0jbPSd9Fj5Ui2ztdYWewD1gfVJnr8NvP3ANuuB+uaf7YAQQAHdgTlJtnsPGJ3a+WrWrKkfx5YtWx5rv+wmK6/zetg9XeuHYdpnto+eceCXLDvvfem+1oQErX8ZoPW4Alpf3GvRTJYg792cBdirLVgmp/XI6jJbZ7Dctvb3gaXyXb1zT3f4ZrsuPWaV/mLTdt16aWtde35tvevyLqvIlxmsOZvWku9JWW2+FS9p/aX3Y+dLrcxOdx/sx7QHqKCUKmP+urI78NsD2/wG9DX/3BXYbA69HvBVSjmbb55pAhyzcF6RSQq5OvFL1/GoqLJMPjie06FBRkdKmVLQ/kvIV9w0y2N0hNGJhDCSlNlWqEg+JxYPrk+bqkX59o87NHL5AK+8Xry88WUCLwYaHU+I7EtheB/sx6JN/fNewVTwHgeWaK2PKqU+VEp1NG82EyiolDoNjATGmPe9BXyFqcA/COzXWq+2ZF6RuUp75OP9+h+TkGBD/9WvERtvpQMK5MkPnadC6DlY/7bRaYQwjJTZ1iuPgy3f9qzB87W8mB4YgrcaQ0X3iozYMoI1Z9cYHU+I7MmCw/TZZXQHpVRRrfXV9G6vtV4DrHlg2dgkP0cBzz1i3/mYhn0S2VQXP182nXmJ7WFf8vof4/m27XtGR0pZ6YbQYLhplsca/cCrptGJhMgUUmbnHLY2ivHP+uHsYMfsP4PoWmsEjoWnMmb7GCLjInmuYoq/FiHEIxk/ikhS8qeyyJDJHfuQL7YRgdd+YeWJLUbHebTGo8HZAza+LxPQiJxEyuwcxMZG8X4Hb4Y3K8/SvSHkuTmEp4o35MNdHzLn6Jy0DyCE+I8FRxF5nAq2yvQUIkezt7VhTqePUbGFeX/Xu1yNuGF0pJQ5ukKTNyFoO5y14j8EhMgYKbNzGKUUI1tV4u22lVl7+CZRl3rTomRLJu6dyPcHv5ep1YVIL6OnSn/A9ExPIXK88oXcecP/Q+K4S+/f3rDeD4Ca/SB/Sdj4ASRY6RjeQmSMlNk51JAm5fiksw9b/w3l8qkuPF2mEz/88wNf7P3CestYIayKwuip0hNprb+3RBCR8/Wr/RTV877A1dgDjN3yo9FxUmbnCE3/B1cOwrFfjU4jxBOTMjtn61W3FF9382dv0B2OHm5N1/LdmXdsHh/s+oB4mUBLiNQpZVVdRIR4bDOeGUGeOF9+vTCVrecOGh0nZb7PQWFv2PwxWOvIJ0IIYdbJ35MfetXgxOUI/tzTkBcqv8iyU8sYs30MsQlShgnxaFbUgi3Ek3C0t2Vmu4kQ78LrgaMJi440OtLDbGyh+VgIPQMHZEAEIYT1a1W1KD/1q82Fm/dYt92fF71fZV3QOl7f8jpRcVFGxxPCOhl9k6NSqohSqob5UcQiSUSu4VusOIMq/48YdY0+v75rdJyUVWwDJerC1gkQc9foNEJkiJTZuVPDCh7MH1iHkIholmwsx0s+b7EteBsvb3qZyFgrbMwQwmhGdRFRSvkrpf4CAoHPzY+tSqm/lFI1LJJI5ArDG7SjolMHzkRt5Kudvxgd52FKQYtxEH4F/p5mdBoh0kXKbFGzlDsLB9UjKi6BmWuL8orve+y7to/BfwwmMl4q2UIkZ1wXkdnAa1rrKlrrFuZHZWAEMMsiiUSuMbfze9jHlWbWv1/wz5VzRsd5WKmnoEIr2PEV3LtldBoh0mM2Umbnej6ebiwZUh87G8V3v7vxStUPOR56nCnXphByL8ToeEJYDwNvcnTRWu9+cKHW+i/AxSKJRK6R19GJb5p/gVYJDFo7kqjYGKMjPaz5WIgKg51TjE4iRHpImS0AKF84L78MrY9bHnsmrXTg5SqfERIXQv91/bkSccXoeEJYCeNasNcqpVYrpboppZ4yP7oppVYD6yySSOQqDUpX5rnSw7lne5qBv00wOs7DivqaRhX56wcIT/ds00IYRcpskaiEuzO/DK1P8fx5+PzXBFrZDyHkXgh91/XlfNh5o+MJYTyjWrC11sOBb4GmwNvmR1PgO631KxZJJHKd95v2wdOuIQfDf2HWvs1Gx3lY03cgIdZ0w6MQVkzKbPGgIvmcWDykPhWLuLLkkCf9ykwgKi6Kvmv7cvLWSaPjCWEsI2dy1Fqv1VoP1Vp3MD+Gaq3XWCSNyLXmPzMBu4SCfHXwfc6EXDc6TnLuZaBmf9g3B26eMTqNEKmSMls8yN3FgZ8H1aVcfhvG/xZON69PsbWxpf+6/hy+cdjoeEIYyArHwVZKDc7MICJ383DJx6cNx6Ntw+jz25vExVvZNOWNR5tmedzyidFJhHgsUmbnbq5O9rxRy4nGFQrxxao7tPf4GFcHVwb+MZA9V/cYHU8IY1jpTI4q01IIAbSrWJcWRfsSZruPV1dNNTpOcq5FoN5LcGQZXPnH6DRCPA4ps3M5R1vFtD41aetTlCnrQ2mcdxxFXYoybOMwtgVvMzqeEAawwhZsrfWPmRlECIAvW72Gu01VtofOYPnh/UbHSa7BcMhTADZ9aHQSITJMymwB4Ghnyzc9qtOlhhfTttykmu3blHUry2ubX2N90Hqj4wmRtYyeyTElSqn+mRlECABbG1vmdpiEDY68/9eb7DwXZHSk/zi5QcORcHojnNtudBohMkTKbHGfna0NX3T1o2/9UszdeZN7FwZTyrUyb257k8/3fM6d6DtGRxQiaxh5k2MqPsi0FEIkUSp/MT6uPwHsQxm68UX++NeK7nSvMwjyecKmDyz2V68QFiJltkhkY6MY17Eqn3fxIyRMcfDv5ykQ34D5x+bTbnk75h2bR2x8rNExhbAwg7qIKKUOPeJxGChikURCAB0rN2Ziw29QdncYuW0wyw9ZyZ3u9nkgYAwE74F/ZWAGYV2kzBYZoZTi+dol2DIqgP+1rcbti88QcXY4NrEl+HzP5zyz8hk2nd+ElsYEkVNZ8CZHuzTWFwFaAw/OE62APy2SSAiz1uUbkMd+Kq9seYn3/n6ZyOiv6V27htGxoFpP08yOmz6Eim3AxtboRELcJ2W2yDAne1sGNS5LtzolmL7tLDO2exLndJzQkn8wInAENYvUZHSt0VT1qGp0VCEymXE3Oa4C8mqtzz/wCAICLZJIiCQal6rF9JbTsbOLYfw/r/LDjl1GRwJbO2j+Htw4Af8sMjqNEElJmS0eWz4ne95oVYltbzajp08rQk68TNz1zhy5foruq7vz9va3uRopM9qKHESZq8EWaMVOaybHF7XWOx6xrmempxEiBXU9qzGv7Swc7DTf/juSCZsCjY4EVTpC8eoQ+BnERhmdRghAymyROQq5OvJBJx82jWxOm5LPcvPfkXC7GWvPrufpFU8zZf8UImMjjY4pxJNT90cvzeIKthDWwq9IFRZ1mIuTnR1zg8bwvzXrje0XqBS0GAd3LsLen4zLIYQQFlKyoDOTuvmz5pWW1HF7gTunRhIbXpXph6fTfnl7fjn5C3EJcUbHFOIJSAVbCCoVLM/STvNxtndi5dV3GfHr7yQkGFjJLhtgemyfCFFhxuUQQggLqlIsHzP71eaXge2oqIYQee5l7oS58eGuD+n6W1d2XtppdEQhHo+5BVtZoCohFWyRrZTJX4rlz8wnr30+Nt36kEFLlho7rXrzsXD3Juz6zrgMQgiRBWqVdmfJkPrM7P4shSNHci+4F0GhYQzdOJQhG4Zw6tYpoyMKkUHZuAVbKdVGKfWvUuq0UmpMCusdlVKLzet3K6VKP7C+pFIqQik1ytJZRfZQIp8XKzrPJ79jQXbfG0/vBT8THRdvTBjPmuDdCXZ9CxE3jMkgRCaSMlukRilF08qFWTu8MV+2741ryBiirrXnr0sH6fJbV8b9OY6QeyFGxxQifbJrH2yllC3wHdAW8AZ6KKW8H9jsReCW1ro8MAmY8MD6r4C1lswpsp9ieYvx67ML8HAsyuH4L3l+7mzuxhjUF7DZexB7D7Z9bsz5hcgkUmaL9LKxUXTy92TTyBaMbTQUu8vvEB1an+WnfqXtsvZMOzSNqDi5AVxYucQuItmsgg3UAU5rrc9qrWOARUCnB7bpBMwx/7wUaK6U6YqVUs8A54CjFs4psiGPPB6seHY+xZxLckZ9Q+fZ07lzz4CZxzwqQI0+ppsdQ05n/fmFyDxSZosMcbCzoXf90mwf1Z7h/qPQF0cRcbs03xz4hrbL2vP7md9JsNBU1EI8Ocu1YCtLjsSglOoKtNFaDzQ/7w3U1Vq/kmSbI+Ztgs3PzwB1gShgA9ASGAVEaK0npnCOwcBggCJFitRctCjj4xJHRESQN2/eDO+X3eTU67wbf5fPL31HSEIweUN78k7VOtjERmbptdrH3Kbu7iHcKlCNoz7vZNl5c+rv9EG55TqbNm26T2tdy6jzZ0WZbd7nscpta38fSD6IiNGsPhfLpuv/YldoNbZ5LlHcrgTPFXyW8k7lDc32JCTfk7HWfCUuLKPc2bmsqf4Tzm4FM7x/amV2WjM5GmkcMElrHaES+8g8TGs9DZgGUKtWLR0QEJDhEwUGBvI4+2U3Ofk6G8c0pteqQZxhAeNP2jCqQs2sv1an0xTa/BEBpe2gdMMsOWVO/p0mlVuuM5sbRzrKbHj8ctva3weSz+Rp4MqdRkze2Ijlp1ZxqdA6Jl+bTIBXM0bVHkmpfKUMy/a4JN+Tsdp8Ow7CWcib14XGmZzP0l1ELgElkjz3Mi9LcRullB3gBtzE1CLyuVIqCBgBvKOUegUhUpDXIS8LO8ykSgF/wl3nMeHYbiKjs7hPdr2XIJ8n/PEuJMhXoiJbkjJbZIpibnkY38WfdS+OoKHjF0Rfb8XWizvp9Gsnvj3wLbEJBnTnE+JB5pkcs2Mf7D1ABaVUGaWUA9Ad+O2BbX4D+pp/7gps1iaNtNaltdalga+BT7XW31o4r8jGnO2dmdd+GuXz+RJZYAkDFy3P2nGyHZxNw/ZdPgBHlmbdeYXIPFJmi0xVtlBepr5Qnx87voXDlXeIuVONHw/9SN+1fbkYdtHoeCK3y66jiGit44BXgPXAcWCJ1vqoUupDpVRH82YzgYJKqdPASOChYaGESC8nOyd+avctLiofh2K/5qN1f2ZtAN/noVg12PiBaWQRIbIRKbOFpTStVJg/hj9NkwLDuRfck6M3ztDlt66sPL3S2Fl5RS5nuQq2xftga63XAGseWDY2yc9RwHNpHGOcRcKJHKmAUwFeKzaEz69OYvGFj6iy7zuer1kua05uYwOtPoE5T8Nf30OjN7LmvEJkEimzhaW4uzjwfa8arDhQhPdXleFu4YW8u/NddlzaQdP4pkbHE7lRNh6mTwhDFHcszsQmn2PrdJlxu8ay73xo1p28TCOo1A62T5LJZ4QQIgmlFM/W8GLtqx3xVm8Sfb0164M28Onl8ey9utfoeCLXSf2G7CchFWyRY7Uo3ZRhfsOxdT3Eiys/5dLtLOyy0eIDiL0LgZ9l3TmFECKb8CrgzKJBTzG67ktEn3+JO1E2DFg/gCn7p8gNkCLrqPvVYGnBFiJDhlV/kaae7Yh3W0+vn6dl3WyPhSpCrQGwbzbc+DdrzimEENmIjY1iUOOyrBzcHbcbI4i+XZPph6fzwurenA87b3Q8kRtIFxEhHo9SiolNP6JM3qqE5JnDoEW/Zt3IIgFjwMEFNoxNe1shhMilKhfNx/v13OhX8U3uXerF8ZBzdFn5HCtOrZAbIIWFZdNRRISwBg62Dsxq9x35HPJzMGYSH6/bnTUndvGARiPh5Do4uzVrzimEENmQvY3i7bZVWNB9EHlD3uRueDHG/jmWkYFvcCf6jtHxRE6VXYfpE8JaFMxTkJ/afI+9fTSLLnzIL/vOZs2J6w4Dt5Lwx/8gIT5rzimEENlU3bIFWf9qR9p5jCP6ehs2nt9Ep1+fZc/VPUZHEyJDpIItco3KBSszofF4bPNc5P0/32d/VowsYu8ELd6Hq4fh0GLLn08IIbI5Vyd7Jj5fnSlt38Du6muEhCUwYP2LfLV3ErHxcgOkyETSB1uIzNG6TAsG+7yMbb6DDPh1ApezYmSRqs9C8Rqw6SOIuWv58wkhRA7Qxqco61/uSU37j4i5VYtZR3+i26qeBN0JMjqayClkFBEhMs8rNYbQuFgr4vOvoefPMyw/soiNDbT+FMIvwy6ZOVoIIdKrsKsTs/s24P3644i/0odTNy/w7G9dWXZymdwAKTKB9MEWItMopfiq+aeUcqlMSJ7ZDFn0u+VHFilVH6p0gB1fQ/g1y55LCCFyEKUUPeuWZM3Alygd/R73wkswbtc4Xt00gttRt42OJ7Iz6SIiROZytHVkVrvvcbV3ZX/Ml3y6/m/Ln7TFBxAfDVs+sfy5hBAihynt4cKKwe0YVnE8sdfbsTV4Kx1WdGb3lSwaGUrkQNKCLUSmK+RciBltvsPO/h4/B33Isv3nLHvCguWg9iA4MA+uHbPsuYQQIgeys7VheItK/NLjHQrcfoOb4YqBfwzi878nyg2QIuOUTJUuhEVU9ajKZ40+xdb5Au/t/MDyI4s0eRMcXWHDe5Y9jxBC5GC+Xm788XJPni0ykZhbdZh3fA5dVnbn7J0sGoJV5Axyk6MQltOubGv6ew/BNt8+Bvz6BcevhFnuZM7u0Hg0nN4IpzdZ7jxCCJHDOdnb8nGnGsxo/xkONwdw9lYwz/76HDMOzide5h0Q6SJ9sIWwqNdrvUyDYs2Jz7+K5xd/wJ5zNy13sjqDoUBp+OM9mXxGCCGeUKMKhdg09GVa5/+CmMhSTP5nAo1/bsfCY8uJS7DwKFEie5OZHIWwLKUU3zT/ghYl2kOBP+i3aiR/HAu2zMnsHKHFOLh+FA4usMw5hBAiF8nv7MCXXRqz+vk5+Nm/yq1Izad73qfRz62Zf3Sx9M8WjyAVbCEszt7Wnq+afsYA76HY5NvPiK0v8fOeE5Y5mfcz4FUHNn8M0RGWOYcQQuQyZTxcWdBzMCs6LaWKeo07EQ5M2PsxjRe2Yu6RBUTHRxsdUViTxGH6Mv/QUsEWIgmlFK/Xfplx9T7FLs9FPjkwjK+27LTEiaD1JxBxDf6ckvnHF0KIXKxS0Xws6TOQRe0XUj7+dW6Hu/DFvvE0WdiSnw7P5m6szKorkJschchqXSp1YFrL6Tg4RjHz7EjeWrUy82cNK1EHqnaGnVPgxsnMPbYQQgj8SuRnxYABzG49h5LRb3AnzJ1J+7+k6eKW/PjPdCJi5BvE3O1+F5GETD+yXaYfUYgcop5nLZZ1WkjP3waz+sb7XP3lKj91HYytTSaOm9nqYwjaAQu7wcBNplFGhMjlYmNjCQ4OJioqKtlyNzc3jh8/blCqtFljPicnJ7y8vLC3tzc6iqHqli3IqkF92XaqPZ9sXMNF/TvfHpzCzMM/0a9qb3p598LN0c3omCKrWbCLiFSwhUhF2fylWff8ErosH8z+e9/SacFllnUfi6O9beacwM0Lui2AOU/Dkj7wwnKwc8icYwuRTQUHB+Pq6krp0qVRSSaCCA8Px9XV1cBkqbO2fFprbt68SXBwMGXKlDE6juGUUjSpWIjGFfqw/mhrxm/+g6s2q/nh0A/8dGQOL3j3oG/VvhRwKmB0VJFVEruIZH4LtnQRESIN+Z3ys7bbfCrnDeB8wnJazn+J23fvZd4JStaFjt9A0HZYMwosMB6nENlJVFQUBQsWTFa5FhmnlKJgwYIPfROQ2ymlaONTlE2v9GZ8g6/Id/MtIm6XZ+bhn2jxSysm7plIyL0Qo2OKrGCuYMs42EIYxMHWgSXPTqFJoRe4ZfMnLRe+wPlbNzLvBNW6Q8ORsH8O/PVD5h1XiGxKKteZQ17HR7O1UTxT3ZPA13ryft3PcL7xFpG3qjDn6FxaLW3Np7s/5WrkVaNjCkuSmxyFMJ5Sim/bvUXPMmO4Z3OGTit6sO/yqcw7QbP3oPLT8Mf/4OQfmXdcIUS63bx5E39/f/z9/SlatCienp6Jz2NiYpJt+/XXX3P3btqjUQQEBLB3715LRRZPyN7Whh51SrJ1RHdG1/gA+ytjiAz1Y9GJJbRZ1pYPdn1ASKy0aOdIiS3Y2bCLiFKqjVLqX6XUaaXUmBTWOyqlFpvX71ZKlTYvb6mU2qeUOmz+t5mlswqRHm837sWb/pOII5z+63qz+uSfmXNgGxt4dhoU8YGlA+Dascw5rhAZkNvL7IIFC3Lw4EEOHjzI0KFDef311xOfOzgkvz8ivRVskT042dvyYsMybB/5PMP9/oe++Bb3Qmux7OSvfHj5I/63438E3QkyOqbITNm1BVspZQt8B7QFvIEeSinvBzZ7EbiltS4PTAImmJeHAB201r5AX2CeJbMKkRF9qjdlUqOZkODMmD9f5se9yzLnwA4u0GMRODibRhaJlFYTkXWkzE7Zpk2bqF69Or6+vgwYMIDo6GimTJnC5cuXadq0KU2bNgXg9ddfp1atWlStWpX333/f4NTicbk42vFy0/LsGNmVgVXeIC5oDNE367PqzDo6/dqJN7e9yalbmfjtpTCOBVuwLT2KSB3gtNb6LIBSahHQCUjaNNcJGGf+eSnwrVJKaa0PJNnmKJBHKeWotZZpmIRVaFnBh3ku8+m7+iW+PTqOfdcO8GnTEXg4ezzZgd08oftCmN0OFr8AfVaaplcXwvKsrsz+4PejHLscBkB8fDy2tk8+go938Xy836FquraNioqiX79+bNq0iYoVK9KnTx9++OEHRowYwVdffcWWLVvw8DD9n3/vvfcoVaoU8fHxNG/enEOHDuHn5/fEeYUx3JztGd26Mv0blOGdefkJPNsc2/zb+IMtrD23lnrF6tOsZFMaezXGM6+n0XHF48iuLdiAJ3AxyfNg87IUt9FaxwF3gIIPbNMF2C+Va2FtqhUvzu9d55I3ugl/3vidZkvaMHrTBMJiwp7swF414Znv4cIu+H2EjCwisoqU2Q+Ij4+nTJkyVKxYEYC+ffuybdu2FLddsWIFNWrUoHr16hw9epRjx6SbV07gkdeRnlUcCXz9aZ4pNZi7Z94i+kZzdp0/xae7P6XNsjZ0WNGJSfsmsf/afuIS4oyOLNLLgqOIWP042Eqpqpi+gmz1iPWDgcEARYoUITAwMMPniIiIeKz9spvccp2Q9df6cfkurL9Un/Vha1kXPJ/1C5bRwLkFnQsF4GDzuONaF6RU6R6U+ednzoTbc7Hksw9tkVt+p7nlOnOCtMps8zaplttubm6Eh4cDMDKgZOLyzGrBBhKPn5ro6GiUUsTHxyduf/fuXeLi4ggPD0drTUREBI6OjgQFBTFlyhQCAwMpUKAAQ4cO5fbt24SHhxMfH09kZGS6zmkJUVFRBAYGWvX/I2vOBqZ8Jw/uprU7BDQuyLHQpzl4vS0Hr13lrsNxTkeeIOjObH468hNOyhmfPN74OPtQxakKzrbOWZLP2l8/a8xXIPQw1YB79yIzPZ+lK9iXgBJJnnuZl6W0TbBSyg5wA24CKKW8gBVAH631mZROoLWeBkwDqFWrlg4ICMhwyMDAQB5nv+wmt1wnGHOtzYGP4rozaWsgP5/6kR02v7P74nb6eQ9kWI2e2Ns+xkxqugksjabc0bmUq9MaKrdPtjq3/E5zy3VaAYuX2ZB2uX38+PEUJ2zJ6olcHB0dsbOz4+LFi1y7do3y5cuzbNkymjdvjqurK/ny5UNrjaurKwkJCbi4uODl5cWNGzfYuHEjLVu2xNXVFVtbW1xcXAybhMbJyYnq1atb9f8ja84GD+drbf5Xa83Ry2FsPnGdDSfOc/zOHmJdTrA37jh77+7FBhv8C1enaYkAGns1poxbGYsMnZjdXj+rcUbDIXB2cqJ6JuezdAV7D1BBKVUGU6HcHej5wDa/YbohZhfQFdistdZKqfzAamCM1nqnhXMKkSkc7WwZ07w5rzRswiebVrPqwkymH5vI/ONzedn/JV7weQZbmwy0wCll6ipyKwiWDYIB66CY9OkUFiNl9gOcnJyYNWsWzz33HHFxcdSuXZuhQ4cCMHjwYNq0aUPx4sXZsmULfn5+VK5cmRIlStCgQQODk4usoJTCx9MNH083hjevwI3wRgT+e53NJ66y/cIB7jkeYW/0CfZf/5Iv931JUWdPmpcyVbZrFamFg63M3GsoC/bBtmgFW2sdp5R6BVgP2AI/aa2PKqU+BPZqrX8DZgLzlFKngVBMBTrAK0B5YKxSaqx5WSut9XVLZhYiM+R1tOOzdp0YHdGGsRuWseX6HCYeGMe0f2Yyus5rdKrYKv2tGPZ5oMdCmNYUFvaAQZvBtYhlL0DkSlJmJzdu3LjEnw8cOPDQ+ldffZVXX3018fnUqVNTbKW2xq/GhWUUcnXkuVoleK5WCWLiarI3KJRNJ66z4eS/XInZT3DeEyyIXMKC4wtwtM1Dg+JPEVCiCY28GuGR5wlvkBcZl41HEUFrvQZY88CysUl+jgKeS2G/j4GPLZ1PCEtyz+vIt517cul2J95Zv4C9YQt5769RTNlXkfcavEHTUk+l70CuRaHnIvipDSzqCf1Wg72TZcOLXEnKbCEyh4OdDU+V9+Cp8h68hzdnb7Rk84nrbDxxkf3X9xLjcpzN0XvZfHETAFUKVKVpqSY08WpCFfcqMgtnVsiuLdhCCBPP/C7M6TaYU9eeZ8yGnzhxdznDA4fg5VSNjxuPpmaxamkfpFg100Q0i1+AlS9DlxmWDy6EECJTlC2Ul7KF8jKwUVnCop5ix6kQNh67xpZzB4m0PcyReyc4HvoD3x/8HndHD5qWNFW26xWvRx67PEbHz5mycwu2EOI/FYrkZ9kLI9l/4QX+t2kaFyJW0e+PF6iQty4fNXmDqh5VUj9AlQ7QfCxs+hAKVQZqZ0luIYQQmSefkz3tfIvRzrcYCQnV+Cf4tulGyX/PcDpiL9fynmDZvdUsO7UMBxtHGng2oEWp5jTxaoKbo5vR8XMOacEWImepUbIwa/u/y9ZT/Xg/8AdO3vmD7qufxzd/Iz5o/DoVClR49M4NR8KNk7DlYwpXeQMIyKrYQgghMpmNjaJ6yQJUL1mAN1pV4sqdpmw5cYP1x4L56+JeYlyOEBi7jy0XN2OjbKldpDYtSjWnWclmFHYubHT87C03j4MtRE7WpIIXW8p/zO+HB/DZn1M5FL+FZ1d2oXahZoxtOILSbqUf3kkp6DgFbp+nyvGv4K9CUG9YlmcXQgiR+Yq55aFn3ZL0rFuSsKjabD5+ndWHLrH9wgESnI/wd8xRdl/9i092f4Kvhx8tSjUnb2xeo2NnT4kt2NJFRIgcRylFR79ytPf5nPl/H2PK3un8rbfRYcVmGhdvw5j6r1LCtUTynewc4YXlhEx7hkLrxkDoOWjzGWRkCEAhhBBWLZ+TPc9U9+SZ6p5ERtcg8N8brDl8mS1njxDndIjD0cc4HDIJgJ9/XUiLUs1pUaoFlQpUkpsk08Pmfgt2fKYfWirYQlgJWxtF33pV6VbzS6bt/IcZh39i66UNbF/+By1LPM2oOi9TLG+x/3ZwcOZo1bcIiN4Ef30Hty9A15ng4GLcRQiRQ9ja2uLr65v4vHv37owZM+aR20+dOhWlFEOGDElxfWBgIA4ODjz1VDpHDkrDwIEDGTlyJN7e3plyPGH9XBztaO9XjPZ+xYiK9WfbyRusO3KVDaeOE2V/iNP3jnHmzjR+PPQjxVyK06JUc5qXbI5/If+Mzb+Qm9iYqsFyk6MQuYCTvS3DA2rQv54vk7fsZeGpOaxPWMWGi6voUKYzw2sOpYiLeRxsZQttPgX3MrD2TZjVFnouMQ3rJ4R4bHny5OHgwYPp3n7o0KGpToUeGBhI3rx5M62CPWOGjCKUmznZ29KqalFaVS1KTJwfU1cU4pLqwfoTp4m0PcQlt6PMj1zIvGPzKODoTrOSTWlesjl1i9WVyW2SUqY/PCxRwbZJexMhhBFcnex5t219AvtPpk3+ycTersXKs8tptbQtH/35GSH3Qv7buM4g6L4QQk7D9OZw7ahxwYXIwUqXLs2bb76Jr68vderU4fTp04BpUpopU6YAMGXKFLy9vfHz86N79+4EBQUxdepUJk2ahL+/P9u3b+fGjRt06dKF2rVrU7t2bXbu3Jl4nL59+9KoUSNKlSrF8uXLE8/Xpk0bYmNjAQgICGDv3r0ArFu3jho1alCtWjWaN29uwKsijORgZ4NfITsmdPVj79vPMPu5V+hcfCwOlz7iXnAPQkK8WHFqFS9teonGi5rw5rY3WR+0nruxd42Objyb+xVs6SIiRK5TyNWRiZ2bMPxmbT7+Ywfbrv/MkpOLWH56Kb5OfgQfC6aqR1UqlW2Mc/818HM304Q0z82G8vJhK7K5tWPg6uH/t3fn8VWU96PHP9+TnGxkXwhLIAn7FogGkX1xA/oDQaUq4ta6XCu9tv6utnSxl/rTFvC2v1tprXpr61IFWlxQEWWNEQEJYICwExIgrCF7JCQkee4fM4QkJJCQk5MT8n2/XvM6szwz833OOXnmmznPzADgX1kBXi44bHVKgMnzLluktLSUxMTE6ulf/OIX3HPPPQCEhISwc+dO3n77bX7605/y6aef1lp33rx5ZGZm4uvrS0FBAaGhoTzxxBMEBgbyzDPPAHDffffx9NNPM3r0aI4cOcLEiRPZs2cPABkZGaxbt47du3czYsQI3n//fRYsWMAdd9zB8uXLmT59evW+cnJyeOyxx0hJSSE+Pp68vLzmvz+qzfL2cjCqVySjekXy29sHsfXwSFakn2BF+lFyKndREZTOqvNfsSJzBU6HDzdED6VnWE/iguOIDY4lNjiW6IDo9tN/W7uIKKW6RwTw+szb2HX8Rp7/PIXthe+ztcM+vk21zmIJDqL9u5OQcBtJhzcwcOks+t78Av43PNrKkSvV9lyui8jMmTOrX59++ulLlg8ePJhZs2Yxffr0WslwTatXr2b37t3V00VFRZSUlAAwefJknE4nCQkJVFZWMmnSJAASEhLIysqqtZ1NmzYxduxY4uPjAQgPD29KNdU1zMshDIsPZ1h8OM/9xwC2Z9/I5+kn+Sz9GMfP7cE7KJ2vSzPZeDwVI+er1/Nx+NGlQzd6hsbRIzSOuBAr+Y4Ljrv27sGtdxFRSl0wsEsIS344lS1Zo/jn6i0UirAnbw95FYfI9jvG8eItrPIth85ROHb9X7rufoO4TqMZ3jWRwR0H0je8rz4VTLUdNc40lxYXExQU1IrBWGqe3avvTN/y5ctJSUnhk08+4cUXX2Tnzp2XlKmqqmLTpk34+fldsszX1xcAh8OB0+ms3ofD4aCiosJV1VDtSM17bc+Z3I/dJ25gc2YeR/NKOZxXwuGCExz/7gjnHacp98nhYNEZDuVuw+FcA3Ix+QzwCqZLh+70CI2jX3gP4kKt5Lt7UHf8vC/9Lns8R8v1wdYEW6k2amhcOCW9fBg/fjxwK8XnzrPvZDG7jxfy7Ykj7MnZQY9zS8H3GNuyV/LVyc/tNR1EOLuTGJXElN7jGNF1GB2ceucRpRpryZIlzJkzhyVLljBixIhay6qqqjh69CgTJkxg9OjRLF68mJKSEoKCgigqKqoud9ttt7Fw4UKeffZZANLS0mp1SWms4cOH8+STT5KZmVndRUTPYqvLEREGdglhYJfaZ6ONMeR+V86RvLMczTvLkdyzHM4rIqPgCMdKjlJQcYxy5xmKis6wPzeFlUdqd40K8o6ib1h/JsaPZXTMKGKCYtxZratT3UVE+2ArpRoQ5OdkaFw4Q+PCeZB4YBxVlbMpWPMHwja8wPaQBF4KmcHes0c5JZmsLlvGmuPvg3EQ7duXYdE3Mq3veJI6D8HboU2Dat/q9sGeNGkS8+ZZZ9Pz8/MZPHgwvr6+LFq0qNZ6lZWV3H///RQWFmKM4amnniI0NJSpU6cyY8YMli1bxsKFC3n55ZeZPXs2gwcPpqKigrFjx/Lqq682Oc6oqChef/117rzzTqqqqujYsSOrVq1qVt1V+yQiRAb6Ehnoy/Xdw2osSQKgvKKK4wWlHMk7y5G8sxw6k8vBgiyOFh/mTNkx8hyn2Fy6gy05KbAZQry7MKzTcKb0Gs/5qvLWqdSVtOBdRPQoqtQ1zOHlIPy2Z6FrLxI/+B+86/UWzFpKvl83vj50ks8PbiItZzPHz6XzcdlbfHL0TRzGjy5+CQzvPJy7+k9gYFSv9nPBi1K2ysqGz2g9++yzzJ8/v9a8uXPnUlxcjNPpZP369Zes06dPH3bs2FFr3pIlSy4pN3fu3FrTF/pl112WnJxcPT558mQmT57cYLxKuYKPt4O4yA7ERV74xTMWuL56+emic2w6lMvqgztJPbWJXNJZWf4Jq7I/AOPFfy9+i1FdRzK93wQGRvbHIR5wIzvtIqKUapaBd0BwV1h0L/ztZsLuXcSUhBFMSegO3E3RufOkHMxi+YH17MhN5UjxLrLLUlmatRCvqjC6+Q9hVJcR3D3wJnpE6D22lVJK1dYx2I/bE7tye2JXYBKni8+xIeMkKw5uJPXE1xwp28exstf416HX8DbB9Ai8jvHdx3D3wJuI7hDVOkHrbfqUUs3WbRg8uhrevRvemgKRfSC8B4T3IDiiJ1PCezLl5okQ9DBnz1eycv9uPjvwJen5W8g8+w1Zmcm8m/l7vCqiCXN2p3tgTwZF9WFEt0EMi+mNj7c2J+raV/cuHkqp+nUM8mN6YhzTE+NITu7MwKQRrN5/gM8zvmR3QSp7C7ewf8+XvL7nBfxMDPGBgxjV9Qam9R9NXGgX9wQpmmArpVwhvAc8shI2vAw5++DMATiwEipr9I/z9icgvAfTI3owPbwnxN7M2eAfsiKviOXH0jlYtIe885nkFG1hW7Hh7UNgqpz4ms5E+sQSH9KTIdH9GdN9EAM6xuBweMDPgEoppVpVVJAvM5MGMTNpEAA5xaV8tHszq7O+IqN4O7uL17Jn/+f8bT94V0XQ1X8gN3RKYkqfUVzfuU/LdFX0sbq7eFWec/mmNcFWqr0JCIdb5l6crqqEwmzIy4DcDMg7ZA2n98K+z6HqPAHAXcBdzg4QPRB63EJ+1xtJLvMh9dQB9uUd4MTZTI6Xb+d43ld8nQev7AEq/fEnhmj/WHqF9qJLUCTRgSF0Cgqhc3AIoX5BdHB2IMA7AF8vX+3rrZRS7URUkD+P3TiOx24cB0De2VI+3bOFdYe/YV/BDrK+28rhwyksPfzfSFUQHZ39GBw5hMHRvUjoFEPX4Ggi/CNwOpxXH4TDC3yD8a4ouXLZJtIEW6n2zuEFYbHW0POm2ssqK6DwqJ18H7Jes7fAV/+HMFPFHT6B3BE7CnpOgB4PQ1RfMvNzSMnaybcn93Kw4ACnz2WRVbqerPLVcLrhMAQvvMUPX4c//t4BBDg7EOTTgVDfQM4VlrBuwzp8HD74eNmDwwenlxNfL9/q+U4vJz4OH3y9fKuX9Q/vT4AzoEXfQqWUUs0THuDPg0ljeDBpDABl5ytZdXAnKzM2sjM3jVNle1l1KpVVp4Aa1wv7OoIJ84kgukMU3UI6EuobSohvSPVriG8IQ6KGNHw7WmcAjqoyl9dHE2ylVMO8vCE83hp61Zhfmg9Z6yFjHRxaBwe+sOYHdSa+x3jie0zgoTEzICgasO4NvPv0MbLyczhZXMjpkiLOnC0it7SIgtISistLKCn/jrMVZzlrSsl3lCGOcsRxGhxHEcd5thUfwOFVAVJBlamgisb1mQvwDmBy/GTu7H0nCZEJepZcKaXaAF+nF1P6JzKlfyIAVVWGb48fYWt2JntzjpGZf5LjJacpLM+lxLuYY/kn2H7qIN7OUipMaa1t9QrtxZIpS/Dx8rl0R94+OKrOXzq/mTTBVko1nX8Y9J9qDQD5h+FQspVs7/8Cttv3Bu44AHpMwNFzAoNiRzKoU7crbvpseQVnisvJKSkjp7iMMyVlbNy+jwq/cA7lfEdW7necrzRAFUgl4R0cxEb60C3ch64R3nQOcdIp1El4B+Fs5XesObKGzzI/4/0D79MrtBcz+sxgSo8p194jf5XLffTRR9xxxx3s2bOHfv36kZWVxZQpU0hPT2/ytgoKCnjvvfd48sknm7zuyJEj2bBhQ5PXU+pa4nAISTGxJMXE1pp/7nwlB0+XsPdkMWv3nmLlrlNUVFWQGOvDbYODCA8/zoubn+fV7a/y1PVPXbphL19NsJVSHiosFpIesoaqKji53Uq4M9ZB6t9g01+sJ2aFxFi3CwzuYg9dIajzxXmBHQnw8aZ7hDfdIy5264g5l8n48UMBqKisIju/lENnSsg4/V3164b9JZwpuXjWwuklxEZ0YFCX6Twedw/fObey6fRnzNs8jz9u+SM3x97MXb3v4oZON3jG/ViVx1m0aBGjR49m0aJF/Pa3v23WtgoKCnjllVealGBXVFTg7e2tybVSl+Hn9GJQ1xAGdQ1hRlIMZ0rK+GBbNotTj7LgkwICfYPplzCBv6f/nVtib2FAxIDaG/D2xVFR4fK4NMFWSrmWwwFdrrOG0U9D+Vk4usnqUpJ/GIqOw9HNUHyi9t1LwLplUlDn2gl4cBcic4ohtxuExePtdfFhBzf1q7164dnzZJwp4VDOd2TklHDwdAkbD+XyUVoZEI6/8yH6di/GOySV5CNfsSJzBd2CunFn7zuZ1nMaUQGtdC9W5XFKSkpYv34969atY+rUqZck2JWVlcyZM4fk5GTKysqYPXs29913HyUlJUybNo38/HzOnz/PCy+8wLRp05gzZw4ZGRkkJiZy6623smDBAn72s5+xYsUKRIRf//rX3HPPPSQnJ/Pcc88RFhbG3r172b9/P4GBgdUPnJk/fz7//Oc/cTgcTJ48ufrpkkopS2SgL4+P7cljY3qw5XA+r6ccYtW2MUT0/ZZfr3+OJVMW4/SqcWGkMwBHufbBVkq1NT4B1sWTdS+grKqCs7lQdMxKuouOWUn3hfFT6VZ3k4pSBgHsmgfODhA9AKIHWXcz6ZRgdUPxCwYgJMDJ9d3Daj3m1xjDsYJSth0pYNvhfLYdySctbTwVZiTeQemcjNrGn4r/xMvbFpIUNZIHBt7N2G5j9HHxHmL+5vnszdsLWEmtl5dXs7fZL7wfPx/288uWWbZsGZMmTaJPnz5ERESwdetWIiIiqpe/8cYbhISEkJqaSllZGaNGjWLkyJH079+fDz/8kODgYM6cOcPw4cO5/fbbmTdvHunp6aSlpQHw/vvvk5aWxvbt2zlz5gw33HADY8eOBWDbtm2kp6cTHx9fK6YVK1awbNkyvvnmGwICAsjLy2v2e6HUtUpEuCEunKGxYSxcG8KfNt5OOW+zcNtr/OcNP75Y0OmPV2Why/ff4kcQEZkE/AnwAv5mjJlXZ7kv8DbWw+5zgXuMMVn2sl8AjwCVwFPGmC9aOl6llJs4HBAYZQ1dEusvYwycK2DrqqUkxfjCyXQr8d71AWz9x8VyobFWsh098GLyHRYPDgciQkxYADFhAdw+xHp4QWl5JTuPFbLtSAJbD09i6/H9lDg3sLliK1ty1uNtQon3H46Pw+8yFTB4V5XjrCrFWVWGd+U5nFXW0DO0Pz+6c/5l1vVc2mZbFi1axE9+8hMA7r33XhYtWsSPf3zxoLxy5Up27NjB0qVLASgsLCQjI4N+/frxy1/+kpSUFBwOB8eOHePUqVOXbH/9+vXMnDkTLy8voqOjGTduHKmpqQQHBzNs2LBLkmuA1atX84Mf/ICAAKv7VHh4eEtUXalriojw1M29iYucxZyUnfxj1/9jU9ZxHFj/rL+X+SUtcUVOiybYIuIF/AW4FcgGUkXkY2PM7hrFHgHyjTG9ROReYD5wj4gMAO4FBgJdgNUi0seYFnjcjlLKM4mAfxjFwb3h+vEX5xtj3bv71C44tdNOvHfB3uWAscr4BELH/hAQCd4+4OVrvXr74e/lyzBvH4Z5+UK8D6aXL/llAzhY0JOV+Zl8VbGPDLMSxNqWdd8RU+e1AQ4oOZPv8rfCHTyxza55prm4uJigoKDmbK5R8vLyWLt2LTt37kREqKysRESYPXt2dRljDAsXLmTixIm14nv33XfJyclh69atOJ1O4uLiOHeuaQ+x6NChgduJKaWu2u1DuhDS4XmeSn6c3SWfVc83LbS/lj6DPQw4aIw5BCAii4FpQM3Gehow1x5fCvxZrPtoTQMWG2PKgEwROWhvb2MLx6yU8nQiENrNGvpOuji//Czk7LmYcJ/eDUXZUFFmDZXltV/tK8cFCMdqYIbVtz+/UOgQBR0irSEgsuFp/3Dr9oZtk7bZwNKlS3nggQd47bXXqueNGzeOo0ePVk9PnDiRv/71r9x00004nU72799PcHAwhYWFdOzYEafTybp16zh8+DAAQUFBFBcXV68/ZswYXnvtNR566CHy8vJISUnhpZdeYu/evQ3Gdeutt/L8888za9as6i4iehZbqcYb1yuO7b1W1p65MAlyD7p8Xy19FOgKHK0xnQ3c2FAZY0yFiBQCEfb8TXXW7dpyoSql2jyfAOiaZA2NUVUFlQ0k3xgIiLAGr2Y8Kaxt0TYbq3vIz39eu4/2XXfdxe9///vq6UcffZSsrCyuv/56jDFERUXxzjvvMGvWLKZOnUpCQgJDhw6lXz/rStyIiAhGjRrFoEGDmDx5MgsWLGDjxo0MGTIEEWHBggV06tTpsgn2pEmTSEtLY+jQofj4+PC9732P3/3udy3zJijVXjzxNV99uZYxLt6sGNNSJ8dBRGYAk4wxj9rTDwA3GmN+XKNMul0m257OwGrQ5wKbjDH/tOe/Aawwxiyts4/HgccBoqOjkxYvXtzkOEtKSggMDGx6BduY9lJPaD911XpeWyZMmLDVGDO0tfbvjjbbXnbZdjskJIRevXrVXc1lFzm2FE+N7+DBgxQWFnr035EnxwYaX3Ndq/Fdrs1u6TPYx4CaT5aIsefVVyZbRLyBEKwLZxqzLsaY14HXAYYOHWrGjx/f5CCTk5O5mvXamvZST2g/ddV6Khdr8TYbrtxu79mzp96+1u7qg321PDU+Pz8/rrvuOo/+O/Lk2EDja672GF9LP10hFegtIvEi4oN1AczHdcp8DDxkj88A1hrrtPrHwL0i4isi8UBvYHMLx6uUUu2ZttlKKeUCLXoG2+6f92PgC6xbPv3dGLNLRJ4HthhjPgbeAN6xL4jJw2rQscv9C+vimgpgtt5BRCmlWo622Uop5Rotfqm7MeYz4LM6835TY/wc8P0G1n0ReLFFA1RKKVXNU9psYwzWzUlUc7TkdVZKqYa1dBcRpZRSqkn8/PzIzc3V5LCZjDHk5ubi53e5ByYppVpCm71Zq1JKqWtTTEwM2dnZ5OTk1Jp/7tw5j04WPTE+Pz8/YmJiWjsMpdodTbCVUkp5FKfTWe+jwpOTk7nuuutaIaLG8fT4lFLuo11ElFJKKaWUciFNsJVSSimllHIhTbCVUkoppZRyoRZ9VLq7iUgOcPgqVo0Ezrg4HE/UXuoJ7aeuWs9rS6wxJqq1g3CnJrbbnv490PiunifHBhpfc12r8TXYZl9TCfbVEpEtDT1L/lrSXuoJ7aeuWk/Vnnj690Dju3qeHBtofM3VHuPTLiJKKaWUUkq5kCbYSimllFJKuZAm2JbXWzsAN2kv9YT2U1etp2pPPP17oPFdPU+ODTS+5mp38WkfbKWUUkoppVxIz2ArpZRSSinlQu0qwRaRSSKyT0QOisicepb7isgSe/k3IhLXCmE2WyPq+Z8isltEdojIGhGJbY04m+tK9axR7i4RMSLisVcwX0lj6ioid9uf6y4Rec/dMbpCI7673UVknYh8a39/v9cacSr3EJFwEVklIgfs17B6ykwQkbQawzkRme4p8dnluovIShHZY/+NxnlYfJU13r+P3RFbU+KzywaLSLaI/NlTYhORWBHZZr9vu0TkCXfE1oT4EkVkox3bDhG5x5Pis8t9LiIFIvKpm+JyXx5ojGkXA+AFZAA9AB9gOzCgTpkngVft8XuBJa0ddwvVcwIQYI//6Fqtp10uCEgBNgFDWzvuFvxMewPfAmH2dMfWjruF6vk68CN7fACQ1dpx69Ci34kFwBx7fA4w/wrlw4G8C+2bp8QHJAO32uOBHhhfiad/vsCfgPeAP3tKbHY75Vvjc80CunhQfH2A3vZ4F+AEEOop8dnLbgamAp+6ISa35oHt6Qz2MOCgMeaQMaYcWAxMq1NmGvCWPb4UuFlExI0xusIV62mMWWeMOWtPbgJi3ByjKzTm8wT4L2A+cM6dwblYY+r6GPAXY0w+gDHmtJtjdIXG1NMAwfZ4CHDcjfEp96vZJr8FTL9C+RnAihrtW0u7YnwiMgDwNsasAjDGlHhSfK2sUfGJSBIQDax0T1hAI2IzxpQbY8rsSV/c2yugMfHtN8YcsMePA6cBdz3IqlGfrTFmDVDsppjcmge2pwS7K3C0xnS2Pa/eMsaYCqAQiHBLdK7TmHrW9AiwokUjahlXrKeIXA90M8Ysd2dgLaAxn2kfoI+IfC0im0Rkktuic53G1HMucL+IZAOfAf/TPaGpVhJtjDlhj5/ESrIu515gUcuGVEtj4usDFIjIB3bXppdExMuD4gPwE5Etdtsx3T2hAY2IT0QcwB+AZ9wYFzTyvRORbiKyA6vtmm8nsh4T3wUiMgzrrG1GSwdma+rfrju4NQ/0vpqV1LVBRO4HhgLjWjsWV7Mb5T8CD7dyKO7ijdVNZDzWLxIpIpJgjClozaBawEzgTWPMH0RkBPCOiAwyxlS1dmDq6ojIaqBTPYt+VXPCGGNEpMHbXolIZyAB+MLD4vMGxgDXAUeAJVjt0hseEh9Yj3s+JiI9gLUistMY45JEzAXxPQl8ZozJdvUPyq5474wxR4HBItIF+EhElhpjTnlKfPZ2OgPvAA+5sq10VXzXqvaUYB8DutWYjrHn1VcmW0S8sX6CznVPeC7TmHoiIrdg/RGMq/ETV1typXoGAYOAZLtR7gR8LCK3G2O2uC1K12jMZ5oNfGOMOQ9kish+rIQ71T0hukRj6vkIMAnAGLNRRPyASKyfPlUbZIy5paFlInJKRDobY07YScLlPue7gQ/tvwFPii8bSDPGHLLX+QgYjosSbFe8f8aYY/brIRFJxvpnwCUJtgviGwGMEZEnsfo5+4hIiTGmwQvb3RhbzW0dF5F0rH+mljY3NlfFJyLBwHLgV8aYTa6Iy5XxuZlb88D21EUkFegtIvEi4oP1U2Ldq6U/Bh6yx2cAa43d070NuWI9ReQ64DXg9jbaVxeuUE9jTKExJtIYE2eMicPqa94Wk2to3Hf3I6yz14hIJNbP0ofcGKMrNKaeR7AuikFE+gN+QI5bo1TuVLNNfghYdpmyM3Fv9xBoXHypQKiIXOj7ehOw2w2xQSPiE5EwEfG1xyOBUZ4UnzFmljGmu92OPwO87Yrk2hWxiUiMiPjb42HAaGCfG2JrbHw+wIdY75lLkv4maMrfrru4Nw+82qsj2+IAfA/Yj/Wf+a/sec9jJV5gHaz/DRwENgM9WjvmFqrnauAUkGYPH7d2zC1Rzzplk2mjdxFp5GcqWF1idgM7gXtbO+YWqucA4Gusq7/TgNtaO2YdWvT7EAGsAQ7Y7Va4PX8o8Lca5eKwzjw5PDS+W4Ed9t/mm4CPp8QHjLTj2m6/PuJp71+N8g/jvruINOa9u/C5brdfH/ek9w64HzjPxWN9GpDoKfHZ019hnSQpxfq1Z2ILx+W2PFCf5KiUUkoppZQLtacuIkoppZRSSrU4TbCVUkoppZRyIU2wlVJKKaWUciFNsJVSSimllHIhTbCVUkoppZRyIU2wlccTkUoRSRORdBH5RERCr1D+CRF58Cr2E2o/zODCdBcRccm9Q0UkWUT2ich2EUkVkcSmxKKUUq1BRH4lIrtEZIfdDt94hfJvisiMq9hPnIjcV2N6qIi8fDUx19nuN3bcR0Qkxx5Ps/dX0tzt17O/h0Xkz01cJ8u+B3nd+XNFxN2PiFcuogm2agtKjTGJxphBQB4w+3KFjTGvGmPevor9hGI9lvfCdo4bY5p8oLiMWcaYIcArwEtNiUUppdxNREYAU4DrjTGDgVuAoy20uzigOsE2xmwxxjzV3I0aY240xiQCvwGW2MeSRGNMVmPWt5/mp1STaYKt2pqNQFcAEekpIp+LyFYR+UpE+tnzq//rv0yZaBH50D6jvF1ERgLzgJ722Y2X7DMc6XZ5PxH5h4jsFJFvRWSCPf9hEfnA3scBEVnQxDoEisgaEdlmb3uaXaZWLHbZZ+2z3ztE5Lcuej+VUqohnYEzxpgyAGPMGWPMcQARSRKRL+229Qv7cdi1NFRGRHqJyGq77d0mIj2x2rwxdpv3tIiMF5FP7fLhIvKR3fZtEpHB9vy5IvJ3+xfCQyLS5IRcRF6049gkItH2vDdF5FUR+QZYcJnjyPfF+mV1u4ik1Nhsl/qOCSIy027n00VkfgPx/EpE9ovIeqBvU+ujPIi7njqkgw5XOwAl9qsX1hOWJtnTa4De9viNWI80BZgLPHOFMkuAn9bYbgjWGZT0Gvutngb+F/B3e7wf1iO7/bCeLHbIXt8POAx0q6cOydhPkgR+CvzOHvcGgu3xSKynR0k9sdwGvG4vcwCfAmNb+7PRQQcdrt0BCMR6+t9+rF/extnzncAGIMqevqdG+/gm1iOmL1fmG+AOe9wPCADGA5/W2Hf1NLAQ+N/2+E1Amj0+196Hr91+5gLOBuryMHWeAgkYYKo9vgD4dY06fAp42dMNHUd2Al3t8dAa+7nkmAB0sY8bUXa7vxaYbq+TZcefZG8zAAi2jwfPtPb3QIerG/SnD9UW+ItIGtZZ3z3AKhEJxHrE779F5EI535orXaHMTcCDAMaYSqBQRMIuE8NorEYeY8xeETkM9LGXrTHGFNr73A3EUv/PqO+KiA/WQSvxQpjA70RkLFBl1zG6nnVvs4dv7elAoDeQUk9ZpZRqNmNMiYgkAWOACcASEZkDbAEGYbXFYJ2kOFFn9b71lRGRIKyk9EN7H+cAarTR9RkN3GWXXysiESISbC9bbqwz7GUichqr/cxuZBXLsRJpgK1Yjz6/4N/GmMorHEe+Bt4UkX8BH9RYt75jQgSQbIzJsee/C4wFPqqx3hjgQ2PMWbvMx42sh/JAmmCrtqDUGJMoIgHAF1h9sN8ECozVt64hjkaUcYWyGuOVNPx3NQurEX8JK1m/054XBSQZY86LSBbWWY+6BPi9MeY1VwWtlFJXYp+ASAaSRWQn8BBWO7bLGDPiMqtKfWXsBNuVGtv+1ue8MdYp5HrW/c5+bfA4Yox5QqyLPv8D2Gr/M9LcmNQ1QvtgqzbD/q/+KazuGmeBTBH5PoBYhtQpX3SZMmuAH9nzvUQkBCgGGmr8v8JKhhGRPkB3YN9V1MEAzwHD7X58IcBpO7megHWmg3pi+QL4oX02BRHpKiIdm7p/pZRqLBHpKyK9a8xKxOrysA+IEusiSETEKSID66xebxljTDGQLSLT7fm+9smTxra/47H6hRc1v4ZXdrnjiIj0NMZ8Y4z5DZCD1RWkIZuBcSISKSJewEzgyzplUoDpIuJv/yMy1dX1Ue6jCbZqU4wx3wI7sBqnWcAjIrId2AVMq1nUfm2ozE+ACfYZma3AAGNMLvC1fQFK3bt8vAI47PJLgIftnyWvpg6lwB+AZ4F3gaH2dh8E9tplasVijFkJvAdstMsupeGDkVJKuUIg8JaI7BaRHcAAYK4xphyrn/V8u21Nw+pGUe0KZR4AnrK3uQHohNWuV9oXDD5dJ465QJJdfh7WWXR3aug48tKFixax6rG9oQ0YY04Ac4B1drmtxphldcpswzq+bAdWAKmurohyH7n464hS1wYRWQhsM8b8o7VjUUoppVT7o2ew1TVFRP4L6ypvvThEKaWUUq1Cz2ArpZRSSinlQnoGWymllFJKKRfSBFsppZRSSikX0gRbKaWUUkopF9IEWymllFJKKRfSBFsppZRSSikX0gRbKaWUUkopF/r/Ms4guvWTYBAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plot_risk_vs_rejection_rate(\n",
    "    y_true=[all_true_labels_mat, all_true_labels_mat, all_true_labels_mat],\n",
    "    y_prob=[None, None, None],\n",
    "    y_pred=[all_pred_labels_mat, all_pred_labels_mat, all_pred_labels_mat],\n",
    "    selection_scores=[-all_total_mat, -all_epi_mat, -all_aleo_mat],\n",
    "    plot_label=[\"Total\", \"Epistemic\", \"Aleatoric\"],\n",
    "    risk_func=accuracy_score,\n",
    "    subgroup_ids=[None, None, None],\n",
    "    num_bins=20\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us now to how to use `ClassificationCalibration` to calibrate the probability scores.\n",
    "\n",
    "#### BNN scores are typically well calibrated. We will artificially make the scores overconfident to show the effect of post-hoc calibration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from uq360.algorithms.classification_calibration.classification_calibration import ClassificationCalibration\n",
    "from uq360.metrics.classification_metrics import plot_reliability_diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.special import softmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "calib_from_features = ClassificationCalibration(\n",
    "    num_classes=2,\n",
    "    fit_mode=\"features\",\n",
    "    base_model_prediction_func=lambda x: softmax(10*bnn.predict(x).y_prob, axis=1)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "calib_from_features = calib_from_features.fit(X=norm_X_calib, y=y_calib.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob_calibrated_from_features = calib_from_features.predict(norm_X_test).y_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yLpdZ72ykpMIOQG5+CbPe2QjA1JE9mnXubdu28eKLL3Laaadx/fXX8/TTT/Puu+/y3nvv0blzZ9566y3+/Oc/89JLL3HrrbfSpk0b7r77btauXcvLL7/MqlWrMMYwduxYzjrrLNq3b8/27dt55ZVXGDduHJ988gnbt2/n22+/xRjD5MmT+eKLLzjzzDOb/XMJAz2A3W73c5zbqq0iKyI3Y/VQ07VrV9LT06udJDExkaNHj9b7RHa7naNHj/Lhpn08+OF2Siut3tzc/BJmLs6ktLSEC4Z0bVIjCgsL2bZtG08++STPPvssv/nNb/jHP/7Bbbfdxm9+8xvefPNNOnXqxOLFi7nnnnv417/+xXXXXUebNm244447+OKLL3jxxRereoTPOeccRo8eTVJSEtu3b+df//oXTz/9NGlpaWzZsqVqv8svv5yPP/6YcePGVWu/MYazzjoLm80GwPTp07n66qs5evQonTt39vizstvtTJ8+nYSEBADOPvtsHn744Wr7fPHFF8yaNavWsQkJCaxYsaLath9++IFu3bpVPVeHDh3Izs6u8/dUVlZGTExMrccLCwsxxjT4+y0tLa31uggHhYWFYRm3tyKxfa+/3ouysr44HEJZmYOXXsqirGxXs8+rCbRSyuceen8zW/YcqfPxdbvyKbdX/3i7pMLOPYsyWfCt5wvboO7teODCwQ0+d8+ePTnttNMA+NWvfsVf//pXNm3axIQJEwAr8ejWrXan6VdffcVFF11E69atAbj44ov58ssvmTx5Mr1792bcuHEAfPLJJ3zyySeMHDkSsN5wtm/f3lISaK8YY54DngMYPXq0SU1Nrfb41q1bq8qc1fVasdvt2Gw2j6+V0koHsz/YzruZBzw+f0OvlTZt2tCzZ8+q18R1113H/PnzmTJlClu3buWiiy6qiqFbt260bduWuLg44uLiaNu2LevWreOSSy7huOOOA2DatGl89913Va+V8ePHA9ZrauXKlVWvjcLCQnJzc7HZbNXKvIkIn3/+OZ06daraduSI9TOpqxyczWZjwYIFjB49us52XnDBBVxwwQV1Pu6udevW1eJq1aoV0dHRdT6/+8/DXZs2bRCRBsv0xcfHV/0fCifp6enUfD1HkrBtX+ZCSJsDBTmQmAzjZ8OwywCIi4PXX4eyMgdxcVFcf30/UlL6NfspNYFWSgVczYSooe2NUXOGf9u2bRk8eDAZzfjczpVUg9VbOGvWLG655ZYmny+M5QI93e4nO7f5jb9eKzVfJyKCMSYgr5WGemfBGq7Rpk0bfvzxR/r1a9qb/cqVK/njH/9Ya3urVq34+uuvq23r0aMHOTk5VfdzcnLo0aN5nwYpFRCZC+H9O6CixLpfsNu6DzDsMlJSIC0NXnopy5k8++ZpNYFWSvlcQz3Fpz36Gbn5JbW290hK4K1bmnd127VrFxkZGaSkpPDGG28wbtw4nn/++aptFRUV/PDDDwweXD3GM844g2uvvZaZM2dijOHdd9/ltddeq3X+iRMncv/993PVVVfRpk0bcnNziYmJoUuXLs2KO0wsBW4XkTeBsUCBMebnBo6pV12vFddiHP56rdR8nZx++ukMGDCAAwcO+P214hp20ZBZs2bx29/+lrfeeot27dpRWFjIO++8w9VXX+3V8WeffTbr16/3at9u3brRrl07vvnmG8aOHcurr77K7373O6+OVSqo0uZUJc8Zu8eQnnUGqX2+JCVtTlUvdEoKlJXt8knPs4sm0EqpgJsxcUC1MdAACTE2Zkwc0OxzDxgwgKeffprrr7+eQYMG8bvf/Y6JEydyxx13UFBQQGVlJX/4wx9qJUUnn3wy1157LaeccgoAN954IyNHjiQrK6vafueddx5bt24lxdmN0aZNG/773/9GRAItIguAVKCTiOQADwAxAMaYZ4FlwC+AHUAxcJ2/Y/LXa6Xm6+S2224jNjaWRYsW+f214imBPvvss6vGQA8bNoxXX32V2267jcLCwqpJezExMdx1111Vx1x11VVV5+rUqVOtcc2N9a9//Ytrr72WkpISzj//fM4//3zAqgYCcOutt7J3715Gjx7NkSNHiIqK4oknnmDLli20a9eO6dOnk56ezsGDB0lOTuahhx7ihhtuaFZMSjWowPrkJGP3GMa/upRyeyyxtnLSrp6CjzqbPRJPdR5D2ejRo82aNWuCHUbAhO14pGZqie0O9zZv3bqVgQMHer3/knW5PPbRVvYeKaN7UgIzJg5o9gTCUOLp5yEia40xdQ9ajUCertnevFbcl4Nesi6Xecu3sSe/JCJeKy15KW+Xxl4vQkW4X6cbEnbtK8mHeSeAo4JHvryT+1f+GbuJxiYVzD3/aWZ9+IeqXZvatrqu29oDrZQKiqkjezD+hHYRnUgo35g6skdYJ8xKKT+oKIEF08FhB1scqX2+JNZWTrndEBtdSeoVQ/369JpAK6WUUkqp8GGvhMU3wq4MmPYiOOykpM0h7eoppO+9gNQrhpLy6/F+DUETaKWUUkopFR6MgQ//SMan+0iPW07q0bFWZY1hl5ECfh337E4TaKWUUkopFR5W/oWM97Yy/vWPKK+MIfY/Vpk6X5Wn85Yu5a2UUkoppULfqn/DF/NIr7iD8spo7HYoL4dgLJ6oCbRSSimllAptmxbDR/fCgAtIvfkXxMYKNhvExkIwCodoAq2Uinhvv/02AwcO5OyzzyY9Pb3WKmxKufj6tZKVlUVCQgIjRoyo+nrjjTcAa2nvW265heOPP55Ro0aRmprKqlWrAGupbvdjHn300Wa37ZVXXqF///7079+fV155xeM+hw4dYsKECfTv358JEyZw+PBhAL7//ntSUlKIi4vj8ccfb3YsSjXKzpXwzi3QKwWmvUjK6dGkpcHcucEZvgE6Blop1QK8+OKLPP/885x++uk8+OCDtGnThlNPPdXr4ysrK4mO1stlS+CP18rxxx9fbUVA11LeN954I3379mX79u1ERUXx008/sWXLFgASEhK8XkXQG4cOHeKhhx5izZo1iAijRo1i8uTJtG/fvtp+jz76KOPHj2fmzJk8+uijPProozz22GN06NCB+fPns2TJEp/FpJQ3MpZuI/3pb0gdejEp0/8GMdbiQSkpwUmcXfQdQSkVHJkLaf3pg3B0DyQmw/jZVcuuNsfUqVPZvXs3paWl/P73v2fv3r189dVX3HDDDQwbNowvv/wSm83Gf//7X5588klOOukkbr31Vnbt2gXAE088wWmnncaDDz7Izp07+fHHH+nVqxcLFixodmyqiTIXWsv1FuT47LVS83Vy8803M2fOnIC9Vnbu3MmqVat4/fXXiYqyPgzu27cvffv2bVa76rJ8+XImTJhAhw4dAJgwYQIff/wx06dPr7bfe++9R7pzQOk111xDamoqjz32GF26dKFLly58+OGHfolPKU8yPs5h/LRelFfOIPbLKNIuiQpq0uxOE2ilVOBlLoT37yCqosS6X7Ab3r/Dut3MxOill16iQ4cOlJSUMGbMGD7//HM+++wzHn/8cUaPHl3Vq3j33XcDcOWVV/LHP/6R008/nV27djFx4kS2bt0KwJYtW/jqq688Lr2sAsT5WsHHr5War5NLLrmE2bNn++W1snPnTkaMGFF1/7HHHqOsrIwRI0ZULd9dU0lJSbVjZs2axeWXX15tn3nz5vH666/XOvbMM89k/vz51bbl5ubSs2fPqvvJycnk5ubWOnbfvn1069YNgOOOO459+/Z5jE8pvzu6l/SnFlNe+VvsJrpqsqAm0EqpyPXRTNi7se7Hc1aDvaz6tooSeO92WOt5bCbHDYXzGx4HOn/+fN59910Adu/ezfbt2+vdf8WKFVUfmwMcOXKEwsJCACZPnqzJs7/V8VpJsFeCLdpvrxVPr5OOHTvWe0xTXyuehnCsXLmy3ufyZgjHjBkzmDFjRr37NIeIICJ+O79Stbh/2hQVTWq3McTG/Y7yiuBNFqyLJtBKqcCrmRA1tN1L6enprFixgoyMDFq1akVqaiqlpaX1HuNwOPjmm2+Ij4+v9Vjr1q2bFY/yAT+8VpryOgHfvlYGDx7Mhg0bsNvtdfZCN6QxPdA9evSoGpoBkJOTQ6qHbKRr1678/PPPdOvWjZ9//pkuXbo0KTalGq3mp02OClJ6rSXtuZWk54wnNTV0ep9BE2illD801FP8jyHWR/E1JfaE65o+xrKgoID27dvTqlUrvv/+e7755pta+7Rt25YjR45U3T/vvPN48sknq3ry1q9fX+2jc+VndbxWSo4epW3btn55rXjzOgH/vlaOP/54Ro8ezQMPPMDcuXMREbKysti8eTMXXHCBV+doTA/0xIkT+dOf/lRVVeOTTz7hkUceqbXf5MmTeeWVV5g5cyavvPIKU6ZM8b5RSjVH2pxjybOLvYyUg78nZdam4MRUDy1jp5QKvPGzq2ZSV4lJsLY3w6RJk6isrGTgwIHMnDmTcePG1drnwgsv5N1332XEiBF8+eWXzJ8/nzVr1jBs2DAGDRrEs88+26wYlI/54bXizesEfPdacY2Bdn0988wzALzwwgvs27ePE044gSFDhnDttddW9fi6xkC7vmbOnNnk9gJ06NCB+++/nzFjxjBmzBhmz55dNaHwxhtvZM2aNQDMnDmTTz/9lP79+7NixYqq5927dy/Jycn8/e9/5+GHHyY5ObnaHxdKNVfGpm488uWdZOweU/2BgpzgBNQAMcYEO4ZGGT16tHH9R28J0tPTPX7MFulaYrvDvc1bt25l4MCB3h+QuRDHpw8S5eMqHKHC089DRNYaY0YHKaSg8HTN9ua1ctTVAw1+qcIRTNXaFoG8aV+jrxchItyv0w0JVvsyMmD8WSXW0ty2ctKunkxKz9XWg4k94Y/N74Fuatvqum7rEA6lVHAMu4yivudHdCKhfGTYZWGdMCul6pf+mZ3yyhir2obdkJ51hpVA++CTSX/RBFoppZRSSgVNartXiLVdQblDiLVVktrnK6vn2ctPm5asy2Xe8m3syS+he1ICMyYOYOrIHn6NWRNopZRSSikVHFlfkZJ3J2kPHybdcRepqQmkpHzq9eFL1uUy652NlFTYAcjNL2HWO1ZpTH8m0ZpAK6V8xhijdWOxfg5KKaU8y8iwFkVJHXeElHU3Q8fjSbn5FlLiGn+uecu3VSXPLiUVduYt36YJtFIq9MXHx5OXl0fHjh1bdBJtjCEvL89jrWCllGrpMjJg/HgoLzfE2uJIu7oXKQ89AnFtmnS+PfkljdruK5pAK6V8Ijk5mZycHA4cOOD1MaWlpRGZaMbHx5OcnBzsMJRSKuSkp0N5OdjtQrkjinTbbFK6j2zy+bonJZDrIVnunuTfVWQ1gVZK+URMTAx9+/Zt1DHp6emMHNn0C2cwBGOyivI9m83G0KFDq+pBv/LKK7Rq1crr42fMmMGyZcv4xS9+wbx58xr13I8//jgPPfSQx8cKCwu56667WLFiBUlJSbRt25bHHnuMsWPH1nm+Pn36sGbNGjp16sSpp57K119/TXp6Oo8//jgffPBBo2Jz95///IfzzjuP7t27A1a96DvvvJNBgwY1+ZxKpaZCbIyDcoed2BgHqb/2XIfdWzMmDmDGog1U2I8NnUuIsTFj4oBmRlo/XUhFKaW85JqskptfguHYZJUl63KDHZpqpISEBNavX8+mTZuIjY31elGUyspKAJ577jkyMzMbnTwD/N///V+dj91444106NCB7du3s3btWl5++WUOHjzo9bm//vrrRsVit9vrfOw///kPe/bsqbr/wgsvaPKsmi1lTBlpv/sNcyc+QdqHhaSc1rxUdOrIHpzSpwMCCNAjKYFHLh7q944NTaCVUspL9U1WUeHrjDPOYMeOHRQVFXH99ddzyimnMHLkSN577z3ASiQnT57MOeecw/jx45k8eTKFhYWMGjWKt956iwMHDnDJJZdUrfL3v//9D7B6k6+77jqGDh3KsGHDWLx4MTNnzqxaZfCqq66qFsfOnTtZtWoVDz/8MFFR1ttz3759q5b2njp1KqNGjWLw4ME899xzHtvSps2xcaRHjhzhggsuYMCAAdx66604HI6qfe666y6GDx9ORkYGc+bMYcyYMQwZMoSbb74ZYwyLFi1izZo1XHXVVYwYMYKSkhJSU1OrVixcsGABQ4cOZciQIdx7773Vnn/OnDkMHz6ccePGsW/fPl/8ilSYysiARx6xvldJm0NKqwXMemIIKed29MnzFJZVMrZfB3569AL+N/OcgHwqqEM4lFLKS8GarKLcZu2nQkqK785bWVnJRx99xKRJk/jLX/7COeecw0svvUR+fj6nnHIK5557LgDfffcdmZmZVctft2nThvXr1wNw5ZVX8sc//pHTTz+dXbt2MXHiRLZu3crcuXNJTExk40arpNbhw4e55JJLeOqpp6qOdbd582ZGjBiBzWbzGOtLL71Ehw4dKCkpYcyYMVxyySV07Fh3AvLtt9+yZcsWevfuzaRJk3jnnXeYNm0aRUVFjB07tqonfNCgQcyebS1W8etf/5oPPviAadOm8dRTT/H4448zenT1Rdj27NnDvffey9q1a2nfvj3nnXceS5YsYerUqRQVFTFmzBjmzZvHPffcw/PPP899993n/S9ERYxjkwUhNhbS0iClcxpkPAVjboIB5/vkecorHWz9+SjXndbHJ+fzlibQSinlpWBNVmnpPL4RNzOJdvUCg9UDfcMNN3DqqaeydOlSHn/8ccCa5Lpr1y4AJkyYUJU817RixQq2bNlSdf/IkSMUFhayYsUK3nzzzart7du3b1bM8+fP59133wVg9+7dbN++vd4E+pRTTqFfv34ATJ8+na+++opp06Zhs9m45JJLqvZbuXIlf/vb3yguLubQoUMMHjyYCy+8sM7zrl69mtTUVDp37gzAVVddxRdffMHUqVOJjY1l0qRJAIwaNYpPP/W+nq+KLMcmC1rf05cXktL6Vug8EM6b67Pn2bb3KOV2B0OTE312Tm9oAq2UUl6aMXEAd7+9gUpHYCertHS13ojTm59Au8ZAuzPGsHjxYgYMqP77XLVqFa1bt67zXA6Hg2+++abZFWUGDx7Mhg0bsNvttXqh09PTWbFiBRkZGbRq1YrU1FRKS0vrPV/NcpKu+/Hx8VXnLy0t5Te/+Q1r1qyhZ8+ePPjggw2etz4xMTFVz2Oz2arGjKuWJzXV+oPX+sPXkMojUFoAVy+xluj2kczcfACGJyf57Jze0DHQSinlpakjezCkeztsURLQySotneuN2Gazvqem+ud5Jk6cyJNPPlm1EM66deu8Ou68887jySefrLrvSswnTJjA008/XbX98OHDgJVkVlRU1DrP8ccfz+jRo3nggQeqYsjKyuLDDz+koKCA9u3b06pVK77//nu++eabBuP69ttv+emnn3A4HLz11lucfvrptfZxJcudOnWisLCQRYsWVT3Wtm1bjh49WuuYU045hc8//5yDBw9it9tZsGABZ511VoPxqJYlJcX6tGjuXEib/x4pPAXnPQxdB/v0eTJ3F9C+VQzJ7QP7SaAm0Eop1QgVDsMZ/TsFdLJKS1ftjdgHwzfqcv/991NRUcGwYcMYPHgw999/v1fHzZ8/nzVr1jBs2DAGDRpUVdHjvvvu4/DhwwwZMoThw4ezcuVKAK699lqGDRtWaxIhWJUu9u3bxwknnMCQIUO49tpr6dKlC5MmTaoquTdz5kzGjWu49NeYMWO4/fbbGThwIH379uWiiy6qtU9SUhI33XQTQ4YMYeLEiYwZM6bqsWuvvZZbb721ahKhS7du3Xj00Uc5++yzGT58OKNGjWLKlCle/axUZPI4WRDr/+qs6zeT8vPN0H8inHKTz587M7eAoclJAV/AS8JtydnRo0cb1yzgliA9PZ1Uf3W3hLCW2G5tc+gzxjD0wU+YNiqZByc3vhdFRNYaY0Y3vGfk8HTN3rp1KwMHDqz3uKNHj9K2bVt/hhY0kdw28K593rwGQlG4XbMaqynt8zhHofVCSJsDBTkQZYPoBLhjHbTp7NN4S8rtDHlwOb9JPZ67zqt/KF1Tf3d1Xbe1B1oppbyUV1ROYVklvTt6v+CGUkpFslpzFN7MhPfvgILdgAFHJdjL4ceVPn/uLT8fwe4wDO0R2AmEoAm0Ukp5LTuvCIA+HeueUKaUUhEjcyH8Ywg8mGR9z1xYa5fUswyxsQabzRAbYye1/AGoqFGtyF5m9Uj7OrycfACG90zy+bkbolU4lFLKS9l5xQDaA62UinyZC62eZFcyXLAb3vsNbH4PEhKt4RkFOaQc2UPa9CGkZ51Bap8vSem62vP5CnJ8HuLGnAK6tI2ja7vmVcBpCr8m0CIyCfgnYANeMMY8WuPxXsArQJJzn5nGmGX+jEkppZoqK6+YKIHk9ppAN5cxJuCTflRoCLe5Vy1W2hwPPckVZKzYR/rPo0gdbiPl5A4w4HxSzu1JSmIPaHchvHUVHNlT+3yJyT4PcUNOPsMCXL7OxW8JtIjYgKeBCUAOsFpElhpjtrjtdh+w0BjzjIgMApYBffwVk1JKNUd2XhE92icQG62j35ojPj6evLw8OnbsqEl0C2OMIS8vr9k1s1UAeOgxztg9hvGvLqXctCI2vY6qOOc+VL3nGqy6z+Nn+zS8o6UV/HiwiCkjglMJyZ890KcAO4wxPwKIyJvAFMA9gTZAO+ftRMDDnyxKKRUasvKKdfyzDyQnJ5OTk8OBAwfq3Ke0tDRik6xIbhs03L74+HiSk33fG6l8rF13OJJbbVN61hmU22Oxm3oWNRp2mfXdVYUjMdlKnl3bfWRT7hGMgWEBXoHQxZ8JdA9gt9v9HGBsjX0eBD4Rkd8BrYFzPZ1IRG4Gbgbo2rUr6enpvo41ZBUWFrao9rq0xHZrm0Pfjr1FjD0uOqxiDkUxMTH07du33n3S09MZOXJkgCIKrEhuG0R++1qMpD61EujUE74l9n9QXtnAokbDLvN5wlyTawJhxA3h8NJ04D/GmP8TkRTgNREZYoxxuO9kjHkOeA6smqKRXIOxpkivOVmXlthubXNoyy8up+jjTzl1WH9Sz+gX7HCUUsp/Ni6CXf+DARfA3syqnuSUi68h7dpo0tOt5Nlfixp5IzO3gOT2CXRoHRuU5/dnAp0L9HS7n+zc5u4GYBKAMSZDROKBTsB+P8allFKNdqwChw7hUEpFsEM/wft/IKP8WtJ//Dup59iqJcopBDdxdsnMyQ/a8A3wbx3o1UB/EekrIrHAFcDSGvvsAsYDiMhAIB6oe1CcUkoFSVZVDWitwKGUikziqITFN5CxezTjn/gH9z9gY/z42kt0B9vhonJ2HyoJ2vAN8GMCbYypBG4HlgNbsaptbBaROSIy2bnbXcBNIrIBWABca7S+jVIqBLl6oHt2iNwEWkQmicg2EdkhIjM9PN5LRFaKyDoRyRSRXwQjTqWUf/T96XXIXUt63F8oL486trpgerAjqy4ztwCAYUFYgdDFr2OgnTWdl9XYNtvt9hbgNH/GoJRSvpCVV0S3xHjiY2zBDsUvtPSoUmEic6F/Klzs/Ixeu9+Bk68htfNgYp+zkud6JwsGyUbnBMIhQRzCEexJhEopFRay84ojfQVCLT2qVKjztDrg+3dYt5uTRBcegHdvpahVMq0nPUpKrFXjORQmC3qyIaeAfp1a0y4+JmgxaAKtlFJeyM4r4tyBXYMdhj/5rPSoUspPPK0OWFFibW9iAp3xPwfp//yE1MQTiJ5yGWNirY6ClJTQS5xdNuYUMK5fh6DGoAm0Uko14GhpBQcLy7UCh5elR31Ruz/caoQ3RiS3DSK7fcFu21kFOXhau9MU5PB5E+LavLkdd985hIqKy4mJuZS5A76lqE3jzxNI+aUO9h4pJaH0YKN+F77+3WkCrZRSDXBNIIzwChw+Kz3qi9r94VQjvLEiuW0Q2e0LetvWJVvDNmqQ2NakpoyGuDaNOl3G8j1UVERhN9FgN2zb1p277w7tOvcrtuwD1nBJ6ihG9/G+F9rXvzt/lrFTSqmI0EJqQGvpUaVC3Rl31d4WZYPyQnh6LHz/offnKjtKaumfiI2uwGYzxMYKI0bk+yxUf8nMySdKYFD3dg3v7EeaQCulVANcNaAjeRKhlh5VKgw4Kq3vbboCAok9YeqzcMOnEJ8Ib14Jb15lVehoyLJ7SElcQtqbPzB3rpCWBoMHH/Fr+L6QmVvAiV3b0io2uIModAiHUko1IDuviM5t42gdF9mXTC09qlSI++5VOG4o3PpV7cdu+Rwynob0R63e6LP/DKfcDLZj162MDGdljeQ0Una+AWfeQ8o5w3HNFQz1oevGGDJzChh/Updgh6I90Eop1ZCsvOJIH/+slAp1P2+AvZkw8mrPj9ti4PQ/wG+/gd6nwvJZ8PzZkPsdYCXP48fD/fcbxt9wKhnl18FZ9wYufh/IzS/hUFE5w3omBTsUTaCVUqoh2XlFkT7+WSkV6r57DWxxMOzS+vdr3weuXAiXvgKF++GF8fDRvaS/vpby0krsdqG8Mpr0w1dU650OB5k5wV+B0EUTaKWUqkdJuZ19R8q0B1opFTwVJbBxIQyaDAntG95fBAZPhdu/hdE3wKpnSS25l1hbOTapINZWQapjrrUwSxjJzCkgxiac1K1tsEPRMdBKKVWfXYdaRAUOpVQo2/oBlBbAyF837rj4RLjgcdj6Hik9V5N29WTSs84gtc+XpHRbDWnZvlkGPEAyc/I56bh2xEXbgh2KJtBKKVUfVwWOPppAK6WCZd2rkNQb+pzh1e5VkwVTnasJFlrVJlN6rial5+pjO3pTrSNEOByGjbkFTB7ePdihAJpAK6VUvbKdCXQvHcKhlAqGQz/BT1/A2fdBVMMjb12TBcvLITYW0tIgJdHzAiwkJvshYP/IyiviaGklw5KDP/4ZdAy0UkrVKyuvmA6tY0lMiAl2KEqplmj96yBRMOJKr3ZPT7eSZ7vd+p6eDoyfDTEJ1XeMSbC2h4mNuc4JhMlJwQ3ESRNopZSqh1WBQ3uflVJB4LDD+jfg+PGQ2MOrQ1JTrZ5nm836npqKNc75wvnWwiuuBVgunB9W45837C4gPiaK/l0at1y5v+gQDqWUqkfWwWJO6dsh2GEopVqinZ/BkVyY9IjXh6SkWMM2qo2BBitZDqOEuaaNufkM7p5ItC00+n41gVZKqTqUVdrZU1CiPdBKqeD47lVo1QlOPN/jw7UmCzqlpFS/H+4q7Q425R7h8jE9gx1KFU2glVKqDrsPlWAMmkArpQKv8ABsWwZjb4Xo2FoPe5wsGEFJs7udB4ooqbAzvGdoTCAEHQOtlFJ1clXg0BrQSqmAy3wTHJV11n72OFkwQm3IyQdgaI+koMbhThNopZSqQ1aetYiK1oBWSgWUMdbS3cljoMtJHnfxOFkwQm3MKaBtXDT9OoXOtViHcCilVB2y84poGx9N+1Zawk4pFUA5q+HgNqtSRh3qnCwYgTJz8hnSI5GoKAl2KFU0gVZKqTpk5RXTp2NrRELnoq2UagG+exViWsOQi6s2eZowGGmTBT0pr3Sw9eejXHdan2CHUo0m0EopVYfsvCKG9gidSStKqRagrBA2vwuDL4K4tkDLmjBY07a9Rym3O0JmARUXHQOtlFIeVNgd5Bwu0fHPSqnA2vwulBfCyccmD7akCYM1uSYQhsoS3i6aQCullAe5h0uwO4yWsFNKBda616Bjf+g5tmpTS5owWNPGnALat4ohuX1CwzsHkA7hUEopD7KcJez6hNCsb6VUhDuwDXavgglzwW3uRUuaMFjThpx8hiYnhdxcFE2glVLKg2xnCTvtgVZKBcy618jIHUf6p9eTGhXZqwt6o6Tczvb9hUwY1DXYodSiCbRSSnmQlVdEq1gbndvEBTsUpVRLYK8gY+k2xr+6lHJ7HLF/a1mTBT3Z8nMBdocJycncDY6BFpFLRaSt8/Z9IvKOiJzs/9CUUip4svOK6a0l7JRSgfLDx6RvHUJ5ZUyLnCzoSWZOAQDDeyYFNxAPvJlEeL8x5qiInA6cC7wIPOPfsJRSKriy84roo8M3lFKB8t1rpA7aSmyctMjJgp5k5hTQpW0cXdvFBzuUWrxJoO3O7xcAzxljPgRi/ReSUkoFl91h2H2ohN5awk4pFQhH9sCOT0mZOoi0NGHuXB2+AdYKhKFW/9nFmzHQuSLyb2AC8JiIxKHl75RSEeznghLK7Q7tgVZK+U21lQUrXgfjgJG/IqWDJs4AR0sr+PFgEVNG9Ah2KB55k0BfBkwCHjfG5ItIN2CGf8NSSqngOVaBQ3uglVK+V31lQUPazZmkjDgDOvQLdmghY1PuEYwJvQVUXBrsSTbGFAP7gdOdmyqB7f4MSimlgulYDWjtgVZK+V71lQUN6RtPgJG/bvC4liSzagXCpKDGURdvqnA8ANwLzHJuigH+68+glFIqmLLziomLjqJr29CbuKKUCn/VVha0VZJ64joYNDnYYYWUzNwCktsn0KF1aE6782YIx0XASOA7AGPMHldZO6WUikRZB4vo3bEVUVFawk4p5XspKZD27zTS38wk9bgPSem5Bba+D8MuC3ZoIcOaQBiawzfAuwS63BhjRMQAiIgOClRKRTRXDWillPKFahMGU4DMhaTsuoOUMSXWDuXA+3dYtzWJ5nBRObsPlXDV2N7BDqVO3iTQC51VOJJE5CbgeuB5/4allFLB4XAYsg8VcUb/TsEORSkVAapPGHSWp/tmDlSUVN+xogTS5mgCjTV8A2BYCK5A6NJgAm2MeVxEJgBHgAHAbGPMp36PTCmlgmD/0TJKKxz07qQ90Eqp5qs+YdC6n1KW43nngjq2tzAbnRMIh4T5EA6cCbMmzUqpiFdVgUNrQCulfMA1YdDVA53a+0v4wXjeOTE5oLGFqg05BfTr1Jp28THBDqVOdSbQInIU8PQbFsAYY9r5LSqllAqS7KoEWnuglVLNl5JiDdtIX2lI7bSIlB9ugvZ94ejPUFl6bMeYBBg/O3iBhpCNOQWM69ch2GHUq84ydsaYtsaYdh6+2mryrJSKVFl5xcTYhG6JWsJOKdU4GRnwyCPWd3cpp1Qya9idpOy5EQZNgd9kwOQnIbEnINb3C+e3+PHPS9blMu6vaew9UsrKbftZsi432CHVyashHCJyMtZCKgb4yhizzq9RKaVUkGTnFdGzfSuibQ2WyVdKqSoeJwumAKVHYNF1sGMFnPYHGP8AREVZyXILT5jdLVmXy6x3NlJSYQegoKSSWe9sBGDqyNBbztubhVRmA68AHYFOwH9E5D5/B6aUUsGQdbCY3jr+WSnVSJ4mC1KQAy+fDztXwoX/hAkPWcmzqmXe8m1VybNLSYWdecu3BSmi+nnTA30VMNwYUwogIo8C64GH/RiXUkoFnDGG7LwiTukb2mPvlFKhp9ZkwSHb4YULoawQrnobThgf1PiWrMtl3vJt7MkvoXtSAjMmDgipnt09+SWN2h5s3vwZtAdwHwwYB3g1KEVEJonINhHZISIz69jnMhHZIiKbReQNb86rlFL+cLCwnKJyu1bgUEo1mmuy4Ny5kPbSN6RkngVigxuWh0TyPOudjeTml2CA3PwSZr2zMaTGGNc176R7UkKAI/GONz3QBcBmEfkUawz0BOBbEZkPYIy5w9NBImIDnnbunwOsFpGlxpgtbvv0B2YBpxljDotIl2a1RimlmsFVgUNrQCulGlJrdUGs7ylRz8HH98Jxw+DKt6DtccEME6h/eESo9EIf36UNewpKq21LiLExY+KAIEVUP28S6HedXy7pXp77FGCHMeZHABF5E5gCbHHb5ybgaWPMYQBjzH4vz62UUj6XlVcMtNwSdiIyCfgnYANeMMY86mGfy4AHsTpUNhhjrgxokEqFgGoTBqMrSbvlGlLafwixraG8EAb8Ai55wbofAkJ9eMS763L4cvtBUk/sxPb9RSE7zMSdNysRvtLEc/cAdrvdzwHG1tjnRAAR+R/WBftBY8zHTXw+pZRqluy8ImxRQo8Q/cjQn/RTQ6W8V23CoMOQvulEUs74wEqeo6KtUnUhkjwXlFQQbRMq7LWX9giF4RFb9hxh1jsbGdu3Ay9cMyZsKiA1mECLyC+BuUBv5/6+XEglGugPpALJwBciMtQYk18jhpuBmwG6du1Kenq6D546PBQWFrao9rq0xHZrm4Nv9dZSOsTB1199EexQgkE/NVTKS1UTBksribVVkNrny2MPOirhs4dh+BVBi8+lsKySa1/+FrvDEGuLotzuqHosOkqCPjwiv7icW/67hqSEWJ668uSwSZ7BuyEcTwAXAxuNMXWsPelRLtDT7X4ytScf5gCrjDEVwE8i8gNWQr3afSdjzHPAcwCjR482qampjQgjvKWnp9OS2uvSEtutbQ6+f2z6ipOSY0hNrflhWYugnxoq5aWq1QXv+wupfb4kpefq6jsU5AQnMDfF5ZVc//JqMnMK+NdVoyh1jnnek19CfEwUJRUOOreNC1p8DofhD2+tZ29BKW/dkhLUWJrCmwR6N7CpkckzWElwfxHpi5U4XwHUHCu3BJgOvCwinbAuzj828nmUUsonsvKKmTy8e7DDCGUB+9Qw1D6d8KVIbhtEXvs2b27H+vVJjBiRT+/ebm0zhhln/YtoR2mtY0rjOvFNEH8G5XbDP78rZUueg1uGxxF/8Hvigb+MiwJaU1JpmJtRwq2vrOLBUxPomGD1/Abyd/fu9nLSd1Zw9aBYjvy4gXQ/Z3++bps3CfQ9wDIR+Rwoc200xvy9voOMMZUicjuwHKun4iVjzGYRmQOsMcYsdT52nohsAezADGNMXhPbopRSTZZfXE5BSUVLXkQlpD41DLVPJ3wpktsGkdW+jAyYMeNYbed58+C3vz3ZevCzh8FRao15dlQeOygmgfgL/krqsNSgxFxe6eCW19awOa+Yxy8dzrRRyR73O3F4IVOe+h+v7ozlrVtSiI+xBex3t2LLPt77eA2XjkrmoWnDEBG/P6ev2+bNYJO/AMVYtaDbun01yBizzBhzojHmeGPMX5zbZjuTZ4zlTmPMIGPMUGPMm01rhlJKNU9Lr8CB26eGIhKL9anh0hr7LMHqfUY/NVQtQc3VBdevT7IeWPUcfDEPTr4apj4DiT0Bsb5fOD9oS3RX2B38bsF3rNx2gL9cNKTO5Bng+M5tePzS4WzIKeCh9zcHLMasg0X8ceF6hvRox9ypQwKSPPuDNz3Q3Y0xQ/weiVJKBZGrBnSfTi2zB1o/NVSqtpqrC44YkQ+b3oGP7oEBF8AF/wBbdNASZnd2h+HOhRtYvnkfs385iKvG9m7wmElDjuO21ON5Jn0nI3om0dXPMRaXV3LLa2uJjhKe/dUo4mNsfn5G//EmgV4mIucZYz7xezRKKRUkWQeLEYHk9uGfQIvIhcCHxhhHgzu7McYsA5bV2Dbb7bYB7nR+KRXxqiYLplvJdMLeL+GdudBrHEx70UqeQ4DDYbhnUSbvb9jDvZNO4vrT+3p97N3nDWBjTgH3v7eZWWNirY+Y/MAYw72LN7J9/1Feuf6UsL/WejOE4zbgYxEpEZEjInJURI74OzCllAqk7LwiuicmhHWPiJvLge0i8jcROSnYwSgVDjIy4JFHrO/uUlJg1ixI6b2eIZsegU79YfoCiAl+DWWwEtP73tvE4u9y+MO5/bkt9fhGHW+LEuZPH0nnNnE8ta6MQ0Xlfonzpf9l8f6GPdx13gDO6N/ZL88RSA0m0MaYtsaYKGNMgjGmnfO+L2pAK6VUyMjKK6JXh/DuEXExxvwKGAnsBP4jIhkicrOIeDV/RamWxrWy4P33W99rJtEc+hFen0ZldBv41WJIaB+UOGsyxjDngy28sWoXt551PL8f379J5+nQOpZnfzWKgnLDHQvWYXc0tvBa/b75MY+/LtvKxMFd+U0jE/xQ5dVnDyLSHmumdbxrmzGmRa40oJSKTNl5xZw32N8jAAPHGHNERBYBCcAfgIuAGSIy3xjzZFCDUyrE1JwsmJ5u9TwDULgfXrsIHHYyh83llHbBLXW5ZF1uVT3n1nHRFJZVct1pfbh30oBmTcgbmpzI1YNieWnTQR7/ZBv3TvLNh1d7C0q5/Y3v6N2xFY9fOjxsJw3W5M1KhDcCv8cqabQeGAdkAOf4NTKllAqQI6UV5BWV0ztCKnCIyGTgOuAE4FXgFGPMfhFphbWyoCbQSrmpOVmwqtpZ6RH47yVWEn3N+xTvKAxilFbyPOudjZRU2AFrpUFblDCsR6JPEtMzk2MoaXUcz6TvZHhyEpOGHNfkOF1JfrRNEGDBTeNoGx/T7BhDhTdjoH8PjAGyjTFnY30smO/PoJRSKpB2VZWwi4whHMAlwD+c5UHnuZbcNsYUAzcENzSlQk9KCsz+1wF6nLuTxEv+x92ff8bSNT/BW1fB/i1w2WuQPDrYYTJv+fdVybOL3WF4/JMffPYcD04exPCeSdz99gZ27G/8HwyuJD83vwQDVNit4SCb90TW9DlvEuhSY0wpgIjEGWO+B4K7eLpSSvlQlrOEXaT0QAMPAt+67ohIgoj0ATDGpAUpJqVCRs0Jg0vW5fLKzrXIiO+J65HPz/lFxCy9FX76AqY8Df3PDUqcFXYHG3bn8+JXP/Hb178jN7/2qocAe/JLfPaccdE2nrnqZOKio7j1v2spLKts+CCnfUdKmfPBllpJfrndMG/5Np/FGAq8GQOdIyJJWAX0PxWRw0C2P4NSSqlAynb2QEfQKoRvA6e63bc7t40JTjhKhQ7XhEHXcI20NJj3+TYm2D/nntiFdJeDFBNPGynlyehr+d3wK/wSh/swh+5JCcyYOIDUAZ35btdh1mYfZk3WYTbk5FNaYVWj7JGUQEKMrVZyCtA9ybcVQbonJfDk9JFc+cIqRj/8KWUVjqoYp47sgTGGvUdK2ZhTwKbcAjbmFrBpzxEOHC2r85y+TPJDQYMJtDHmIufNB0VkJZAIfOzXqJRSKoCyDhbRpW0crWJDo6arD0QbY6pqURljyp2rCyrV4nmaMDj66Kc8EvMCrcT6b9OGUiqMjR3F/vmjuuZY5tz8Ev741npctS9sUcLg7u24YkwvRvdpz6je7emWmFDrOICEGBszJvp+YMD+o2VER0lVAp+bX8Ldb2/g35/v5EBhGQcLrZ9VlMAJXdpwRv9ODO2RyNMrd1Q95s7XSX6w1flu4ZxsUmGMqXDeHwCcjDUW2j9FApVSKgiy84ojbQnvAyIy2bl6ICIyBTgY5JiUCgm1JgyeUUn/FW/QiuqpTYzYuSdmIe+tv50LhnYj2ubNqNeGVdodHoc5GKBdfDT//vVohvdM9PgH/dSRPQBq9Vy7tvvSvOXbqKxRzq7SYdi+v5CpI3swtEciQ3okMqhbOxJij9XPb98qNmBJfjDV193yMdZkk+0icgJW5Y3XgV+KyCnGmJmBCFAppfwtK6+Is04M/8L+bm4FXheRpwABdgNXBzckpQIkcyGkzYGCHEhMhvGzqy21nTLyCGmvbCN9eSGpx33IuPT/IngeXtBN8vj9m+v5+6c/cNtZx9OpGfWR9+SX8Obq3SxcvbvOxUqOllaScnzHes8zdWQPvyTMNdU15MLuMDx+6fA6jwtkkh9M9SXQ7Y0x2523rwEWGGN+5/wYcC2gCbRSKuwVl1ey/2gZfTpFTg+0MWYnME5E2jjvB7f2llKBkrkQ3r8DKqzkL2PTcaQv207qpKdIGbgLdmXAvk2kGAcpPaMo7zyExYfGc475nA5ytNbpJDGZZ6eN4umVO5j5zkY6xAt3xP3EFaf08mrVUrvDkL5tP2+s2sXKbfsxwJn9O1Nud3hMokNpmEP3pARyPSTR3sQYqCQ/mOpLoN3/zDoHmAdVY+kcfo1KKaUCJAInEAIgIhcAg4F4V31YY8ycoAallL+lzTmWPO8ew/hXl1JujyU2vZy06y8j5dT2cOY90Gscu1oNYvormzlaWcGY1Al0+Hpm1bEAxCQg42czachxTBzclc9/OMBfl6zlwfe38NTKHdx4Rj9+Na43K7bsq9XbOq5fR95cvYu3Vu/m54JSOreN47bU47liTC96dmgV0LHMTTVj4oCQjzGY6kugM0XkcSAXqxj/JwDOihxKKRURsp0l7CJpDLSIPAu0As4GXgCm4VbWTqmIVZBTdTM96wzK7bHYTTTldkN6r/dIucbqNf7xQCFXPr+Ksko7b9w0jt49EqFjqzqHfogIqQO6wNgEEnoN5amVO3j0o+/554ofqLCbqrHCufkl3LlwPa6RHmf078QDFw5i/MCuxLiNoQ6HYQ7hEGMw1ZdA34S1iEof4DxnAX6AQcDjfo5LKaUCwtUD3SuyeqBPNcYME5FMY8xDIvJ/wEfBDkopv0tMhoLdAKT2+ZJYWznldkNsdCWp51hDD7bvO8qVL6zCGMOCm8dx0nHtrGOHXVZtrHRdxvbryNh+HdmwO5/L/51Ra6Kdw0CbuGiW3XFGvdeVcBjmEA4xBkudCbQxpgR41MP2r4Gv/RmUUkoFSlZeMR1bx9IugpaYBVyrLRSLSHcgD+gWxHiUCozxs6vGQKf0XE3a1ZNJ3302qb8eR0rKeLb+fIRfvbCKqCjhzZvHcUKXtk1+quE9kyir9DyitaisMtL+KFc1REzRU6WUaorsvKKIG/8MvO8cbjcP+A5rTsvzQY1IKT/KyLDqOaemXkbKhVQNxUgZspeU3/eHYePZlFvAr15cRXy0jTduGku/zm2a/bzNmWinwpsm0EqpFi07r5ixfTsEOwyfEZEoIM0Ykw8sFpEPgHhjTEFwI1PKP2qvLHgZKX+sPhRj/e58rn5xFW3jY1hw0zif9Q7rRLuWq86q4CLymvP77wMXjlJKBU5phZ09BSX0jqAJhMYYB/C02/0yTZ5VJPO0sqC7NVmH+NULq0hqFctbt/gueQZrjPAjFw+lR1ICgrXc9iMXD9Vxwy1AfT3Qo5xj564XkVexivFXMcYc8mtkSinlZzmHizEG+nSKuCEcaSJyCfCOMabpKz8oFQZqriwo3Q5w2qMb2ZNfQsc2sRSUVNCzfSveuGkcxyXG+/z5daJdy1RfAv0skAb0w1o4xT2BNs7tSikVtrIOumpAR04PtNMtwJ1ApYiUYl2/jTGmXXDDUsr3UlIgLc3qeZZuB3hl59qqIRUHC8sR4NrTevsleVYtV51DOIwx840xA4GXjDH9jDF93b40eVZKhb0sZw3o3h0iqwfaGNPWGBNljIk1xrRz3tfkWUWEjAx45BHru0tKCsyaBe/vrT4eGawev39//lNgg1QRr8FJhMaY20RkOHCGc9MXxphM/4allFL+l51XTLv4aJJaRVQJO0TkTE/bjTFfBDoWpXyp9oRBK3l22eOhIkZ925VqqgYTaBG5A7gZeMe56XURec4Y86RfI1NKKT/LyiuiT6fWuJa6jiAz3G7HA6dgDcU7JzjhKOUbniYMuifQWlZOBUqdQzjc3AiMNcbMNsbMBsZhrVKolFJhLTuvOBLHP2OMudDtawIwBDgc7LiUai7XhEGbzfqemlr98RkTB5AQY6u2TcvKKX/wpg60AO4DiuzUqMihlFLhprzSQc7hYqaM6B7sUAIhBxgY7CCUai73CYOpqdV7n4Gqahjzlm9jT34J3ZMSmDFxgFbJUD7nTQL9MrBKRN513p8KvOi3iJRSKgBy80twmIiswIGIPIk1dwqsTxpHYK1IqFTYOLa6YPVEOSWlduLsTsvKqUDwZhLh30UkHTjduek6Y8w6v0allFJ+5qrA0SfylvEGWON2uxJYYIz5X7CCUaqxGposqFSwebWUtzHmO7T3QikVQbIPOkvYRWAPNLAIKDXG2AFExCYirYwxxUGOSymvNDRZUKlg82YSoVJKRZysvGJax9ro1CY22KH4QxrgXnYgAVgRpFiUarSGJgsqFWxe9UArpVSkyc4ronfHiCxhBxBvjCl03THGFIpIRI5VUZGpocmCSgWbJtBKqRYpO6+Yk7q1DXYY/lIkIic7h98hIqMAXUlChZWGJgsqFUzeLKRyMfAY0AWrfJ0ARpeFVUqFK7vDsPtwMROHHBfsUPzlD8DbIrIH65p9HHB5UCNSqg51VdtQKpR50wP9N+BCY8xWfwejlFKBsCe/hAq7idQKHBhjVovISYBr9YhtxpiKYMaklCdabUOFK28mEe7T5FkpFUmy86xiFBFagQMR+S3Q2hizyRizCWgjIr8JdlxK1eSp2oZS4cCbBHqNiLwlItNF5GLXl98jU0opPzlWAzoyE2jgJmNMvuuOMeYwcFPwwlHKM622ocKVN0M42gHFwHlu2wzwjl8iUkopP8vOKyI+JooubeOCHYq/2EREjDEGrDrQQETW61PhTattqHDlzUqE1wUiEKWUCpSsvGJ6d2hNVFQTSthlLoS0OVCQA4nJMH42DLvM90E2z8fAWyLyb+f9W4CPghiPUoDnCYNabUOFI2+qcCQDTwKnOTd9CfzeGJPjz8CUUspfsvOKmjZ8I3MhvH8HVDgrwhXstu5DqCXR9wI3A7c672diVeJQKmh0wqCKJN6MgX4ZWAp0d36979ymlFJhx+EwZOcV06dTExLotDnHkmeXihJrewgxxjiAVUAWcApwDqCTwVVQ6YRBFUm8GQPd2RjjnjD/R0T+4Kd4lFLKr/YdLaWs0kHvppSwK6jjg7e6tgeYiJwITHd+HQTeAjDGnB3MuJSCYxMGXT3QOmFQhTNvEug8EfkVsMB5fzqQ57+QlFLKf7IOOkvYdWhCD3RisjVsw9P20PA91jC7XxpjdgCIyB+DG5JSFp0wqCKJN0M4rgcuA/YCPwPTAJ1YqJQKS9nOEnZN6oE+7fe1t8UkWBMJQ8PFWNfplSLyvIiMx1qJ0CsiMklEtonIDhGZWc9+l4iIEZHRPohZRaCMDHjkEeu7u5QUmDVLk2cV/rypwpENTA5ALEop5XdZecXE2ITuSQmNP9hht763OQ4K94VcFQ5jzBJgiYi0BqZgLendRUSeAd41xnxS17HOUndPAxOAHGC1iCw1xmypsV9b4PdYY6yVqmXz5nbMmKGTBVVkqzOBFpF7jDF/E5Enseo+V2OMuaOhk4vIJOCfgA14wRjzaB37XQIsAsYYY9Z4G7xSSjVWdl4RPTu0wtaUEnabFkPXoXDbV74PzIeMMUXAG8AbItIeuBSrMkedCTTWZMMdxpgfAUTkTawkfEuN/eYCjwEzfB23igzr1yfVmiyoCbSKNPUN4XDN2F4DrPXwVS+33ozzgUHAdBEZ5GE/7c1QSgVMVl5x00rYHc6GnG9hSHgtxGqMOWyMec4YM76BXXsA7gO8c5zbqojIyUBPY8yHPg5TRZARI/J1dUEV8ersgTbGvO+8WWyMedv9MRG51Itza2+GUiqkGGPIzitiXL8OjT94s3Px1TBLoH1FRKKAvwPXerHvzVh1qOnatSvpTahXVlhY2KTjwkEktw2gd+9C5s2zeqJHjMinrOxIxJSsi/TfXSS3z9dt86YKxyzgbS+21eSpN2Os+w7uvRkiogm0UsqvDhSWUVxub1oP9KbFkDwG2vfxeVwhIhfo6XY/2bnNpS0wBEgXEbAWZlkqIpNrDr0zxjwHPAcwevRok9qELsj09HSaclw4iKS2eVpZMD09nd/+9uRghuU3kfS78ySS2+frttU3Bvp84BdADxGZ7/ZQO6CyuU8c6N6McBXJfw3WpyW2W9vsfz8ctiYBFuTuID09y+vjWhXlcMrejWw/4UZyI/d3tBroLyJ9sRLnK4ArXQ8aYwqATq77IpIO3K3zVlouXVlQtWT19UDvwRr/PJnqY56PAt7UFQ2p3oxwFcl/DdanJbZb2+x/B9bshlWZTD47pXErEa58BBD6T76b/u26+S2+YDLGVIrI7cByrInfLxljNovIHGCNMWZpcCNUocbTyoKaQKuWor4x0BuADSLyLlBkjLFD1eTAOC/Orb0ZSqmQkp1XjC1K6NG+ESXsjLGGb/Q5HSI0eXYxxiwDltXY5rHItTEmNRAxqdClKwuqlsybhVQ+AdzfbRKAFQ0dZIypBFy9GVuBha7eDBHRutJKqYDLyisiuX0CMTZvLn1OezdC3nYYcon/AlMqDLlWFpw7V4dvqJbHm0mE8caYQtcdY0yhiHi1hJf2ZiilQkl2XjG9GzuBcNNiiIqGgfp3v2rZPE0YTEnRxFm1TN4k0EUicrIx5jsAERkFlPg3LKWU8i1jDFl5RYzsldSYg2DTO9DvbGjd0W+xKRXqdMKgUtV5k0D/AXhbRPYAgjXZ73J/BqWUUr6WX1zB0dLKxvVA56yGgl1w9p/8F5hSYUAnDCpVXYMJtDFmtYicBAxwbtpmjKnwb1hKKeVbWXlFAPTp6NUINMumxWCLg5Mu8FNUSoUHnTCoVHXe9ECDlTwPAuKBk0UEY8yr/gtLKaV8KzuvGMD7HmiHHTa/CyeeB/Ht/BiZUqHPNWGw5hhopVqqBhNoEXkASMVKoJcB5wNfAZpAK6XCRlZeESLQs4OXJeyy/weF+7T6hmpxPE0WBJ0wqJQ7b3qgpwHDgXXGmOtEpCvwX/+GpZRSvpWdV0z3xATiom3eHbBxEcS0hv4T/RuYUiFEJwsq5R1viqGWGGMcQKWItAP2U32FQaWUCnlZeUX06eTl+OfKcti6FE76BcQ2Ysy0UmHO02RBpVRt3iTQa0QkCXgea0nv74AMfwallFK+lp1XTK8OXo5//jEdSg7DkGl+jUmpUOOaLGiz6WRBpepT7xAOERHgEWNMPvCsiHwMtDPGZAYiOKWU8oWCkgoOFZV7X4Fj02KIT4Tjz/FvYEqFGJ0sqJR36k2gjTFGRJYBQ533swIRlFJK+dKuxlTgqCiB7z+AwRdBdKyfI1MqeHSyoFJN580kwu9EZIwxZrXfo1FKKT+oqgHtzRjo7Z9AeaFW31ARTScLKtU83oyBHgt8IyI7RSRTRDaKiA7hUEqFjWxnAt2rgxcJ9KbF0Loz9DnDz1EpFTw6WVCp5qmzB1pEehljdgFaw0kpFday8orp2i6OVrENfOhWdhR+WA4nXw02b9eZUir86MqCSjVPfe8QS4CTjTHZIrLYGKOfZyqlwlJ2XpF345+3fQSVpTp8Q0U8nSyoVPPUl0CL2+1+/g5EKaX8JSuvmLMHdG54x42LoF0yJJ/i/6CUCiBPEwZ1sqBSTVdfAm3quK2UUmGjqKySA0fLGu6BLj4EO9Ng3G0Q5c30EKXCg04YVMr36nuXGC4iR0TkKDDMefuIiBwVkSOBClAppZoj21nCrk9DCfTW98FRqYunqIijEwaV8r06e6CNMbZABqKUUv7gqsDRu6FFVDYthg7HQ7fhAYhKqcDRCYNK+Z5OM1dKRbSsqkVU6kmgj+6DrC/hjLtBpO79lApDOmFQKd/TBFopFdF2HSqiU5tY2sbH1L3TliVgHFp9Q4U9XV1QqcDQBFopFdGyDhY3PIFw02LoMhi6nBSYoJTyA50sqFTg6FRzpVREs2pA1zN8I38X7F4FQ7X3WYU3nSyoVOBoAq2UililFXb2FJTWX4Fj87vW98EXByYopfzENVnQZtPJgkr5mw7hUEpFrN2HvJhAuHER9BgFHfoGKCql/EMnCyoVOJpAK6UiVlZDNaAPboe9mTDxrwGMSinf0NUFlQoeTaCVUhHLVQO6zgR60zuAwOCLAheUUj6gEwaVCi4dA62UilhZeUUktYohsZWHEnbGwKZF0Ps0aNc98MEp1Qw6YVCp4NIEWikVsbLziundoY7xz/s2wcEfYIhOHlThRycMKhVcOoRDKRWxsvKKGNmzvecHNy0GscGgKYENSikf0AmDSgWXJtBKqYhUXukg93AJF43oUftBY6wEul8qtO4U8NiUagxdXVCp0KMJtFIqIuUcLsZh8LwKYe5aawGV1FmBD0ypRtDJgkqFJh0DrZSKSNmuEnadPIyB3rgIbLFw0gUBjkqpxtHJgkqFJk2glVIRKctZwq5WD7TDbq0+2P88iE8MQmRKeU8nCyoVmnQIh1IqImXnFdMmLpqOrWNrPPA1FO7V6hsqLOhkQaVCkybQSqmIlJVXRO+OrRCR6g9sWgwxreHEScEJTKk66GRBpcKHJtBKqYiUnVfMoG7tqm+0V8CW92DA+RBbx+qESgWBThZUKrzoGGilVMSptDvYfaiY3h1rTCD8MR1KDsGQS4ISl1J10cmCSoUXTaCVUhFnT34plQ5Dn5oTCDcthrhEOGF8cAJTqg46WVCp8KIJtFIq4hyrwOHWA11RCls/gIEXQnRckCILbSIySUS2icgOEZnp4fE7RWSLiGSKSJqI9A5GnJHINVlw7lwdvqFUONAx0EqpiJN9yFUD2q0HesenUH4UhurwDU9ExAY8DUwAcoDVIrLUGLPFbbd1wGhjTLGI3Ab8Dbg88NGGP08TBnWyoFLhQxNopVTEyT5YRHxMFF3auvU0b1wErTpBnzODF1hoOwXYYYz5EUBE3gSmAFUJtDFmpdv+3wC/CmiEEWLz5nbMmKETBpUKZzqEQykVcbLyiunTsfWxEnZlR+GH5TB4Kti036AOPYDdbvdznNvqcgPwkV8jilDr1yfphEGlwpy+kyilIk52XhH9OrsN39j2MVSWaPUNHxGRXwGjgbPqePxm4GaArl27kt6EDLGwsLBJx4WDAQOiiY7ujTFCdLShXbsNpKcfCXZYPhPJv7tIbhtEdvt83TZNoJVSEcXhMGQfKuack7oc27hpMbTrAT3HBS+w0JcL9HS7n+zcVo2InAv8GTjLGFPm6UTGmOeA5wBGjx5tUptQUiI9PZ2mHBce0lm50uY2BvrkYAfkU5H8u4vktkFkt8/XbdMEWikVUfYeKaW80kFvVwm74kOwYwWMvQWidNRaPVYD/UWkL1bifAVwpfsOIjIS+DcwyRizP/Ahhh9dXVCpyKQJtFIqorhK2PVxlbD7/gNwVOjwjQYYYypF5HZgOWADXjLGbBaROcAaY8xSYB7QBnjbOb58lzFmctCCDnG6uqBSkcuvCbSITAL+iXUxfsEY82iNx+8EbgQqgQPA9caYbH/GpJSKbNl5Vgm73q4SdpsWQ/u+0H1kEKMKD8aYZcCyGttmu90+N+BBhTFPqwtqAq1UZPDb55luNUXPBwYB00VkUI3dXDVFhwGLsGqKKqVUk2XlFRFri+K4dvFQuB9++sLqfXZV5FAqQHR1QaUilz97oLWmqFIq4LIPFtOzQwK2KIEt74FxwNBpwQ5LtUCu1QU9jYFWSoU3fybQnmqKjq1n/zprivqiJFK4iuSSMvVpie3WNvvG5l0ldIgX0tPTGfndi9ha92bNln2wZZ9Pn0cpdzpZUKmWJSQmETZUU9QXJZHCVSSXlKlPS2y3trn5jDEcTFvOhGG9SB3RFtK3wjn3kXqm755DqZp0sqBSLY8/azo1tqbo5LpqiiqllDcOHC2jpMJOn06tYPO71sbBFwc3KBXxPE0WVEpFNn8m0FU1RUUkFqum6FL3Hdxqik7WmqJKqebKclXg6Njaqr7RfSR0PD7IUalIp5MFlWp5/DaEQ2uKKqUCzVUD+gTbXvh5PZz3l+AGpFoEnSyoVMvj1zHQWlNUKRVI2XlFREcJx+1yXnYGXxTcgFRE8jRhUCcLKtWyhMQkQqWU8oWsvGKSk+KxbV4MvU6FxB7BDklFGJ0wqJQC/46BVkqpgMrOK+K0dgfg4DYYopMHle/phEGlFGgCrZSKEMYYsg8WM9F8CWKDQVODHZKKQDphUCkFOoRDKRUhDhdXcLSsgpFHPoN+Z0GbzsEOSUUgnTColAJNoJVSESIrr4jhspO2Jbkw5E/BDkdFAF1dUClVF02glVIRITuviAttGZioGOSkXwY7HBXmdLKgUqo+OgZaKRURsg4U8kvbNzhOOBcSkoIdjgpzOllQKVUfTaCVUhEhOieD4+QwtqHTgh2KigA6WVApVR8dwqGUiggn7P+EUokjfsD5wQ5FRQCdLKiUqo8m0Eqp8GevYFzpl/yQdDrDYlsHOxoVZnSyoFKqsTSBVkqFvaLvP6M9R1nd8wKGBTsYFVZ0sqBSqil0DHQkylwI/xgCDyZZ3zMXBjsipfyqbP1CjphW0H9CsENRYUYnCyqlmkJ7oCNN5kJ4/w6oKLHuF+y27gMMuyx4cSnlLxWltP3pY5bYRzOsS/tgR6PCjGuyoKsHWicLKqW8oT3QkSZtzrHk2aWixNquVCTasYKYykLed6TQq0OrYEejwoxrsuDcuTp8QynlPe2BjjQFOY3brlS427SYQlsiO1uPIiHWFuxoVIjzNGFQJwsqpRpLE+hIUlEKMQlQUVz7sbi2YAyIBD4upfylrBC2fcT/Ys8mOaldsKNRIU4nDCqlfEWHcESKwgPwyoVW8hwVU/0xsUHZEVh8Q+3hHUqFsx8+hsoSFpWPo09HLV+n6qcTBpVSvqIJdCTYtwWePwf2boRLX4Gp/4LEnoBY3y96Fs59EDa9Ay//Ao78HOyIlfKNTYtxtDmOFUX96N1Jxz+r+unqgkopX9EhHOHuh09g0fUQ2xquWwY9Tra2e6q40elEWHyTlWxPfwO6jwxsrEr5SuZCWPEgHMnFxLThwqiv6dNxdLCjUiFOVxdUSvmK9kCHK2Pgm2dgweXQoS/c9Nmx5LkuJ10ANyyHKBu8dD5sfjcwsSrlS65SjUdyAbBVFPJozAsMPbQ8yIGpUJKRAY88Yn13l5ICs2Zp8qyUah5NoMORvQI+vBM+ngkDfgHXfwyJPbw79rihVrJ93FB4+1pIf8xKxpUKF5/OrjWWv5WU02PtvCAFpEKNa7Lg/fdb32sm0Uop1Vw6hCPclByGhdfAT5/D6XfCOfdDVCP/DmrTBa55H97/PaT/FQ5ugylPWxU8lAol9krYtxF2rYLd38Dub+Go5zH8Uc4eaaU8TRbUHmellC9pAh1O8nbCG5fD4SyY+gyMuLLp54qJtyYXdjkJVjwEh36CK96Adt18Fq5SjVaSDzlrrGR51zeQu/ZYWcZ2ydBrHOxIg9L82scmJgcyUhXCdHVBpZS/aQIdLn76Ehb+GhC4Zin0PrX55xSB0/9YY3LhAug+ovnnVqohxhBf8jOsXwC7V1lf+7cCxiq9eNxQGPlr6DUWeo49liDXXK4eKJc4YsfPDk47VMjRyYJKKX/TBDocfPcqfPBH6HA8XPkmdOjn2/O7JhcumA4vTbJ6pgdP9e1zKFVZDj9vONa7vPtbxhXttx6LS4SeY2DwRVay3GMUxLXxfB5XhZm0OZiCHHIdHdk08PdM8lR5RkU8TysLgq4uqJTyL02gQ5nDTr+dL8PuJXD8OXDpfyA+0T/P5Zpc+OZV8PY1cPDPcOYMXblQ1S1zIaTNsZaJT0yG8bOrl08syoOcb53J8irI/Q7sZdZj7fvA8eewrbQ9A8b/GjoPbNxY/mGXwbDL2L7vKOf94wvmD9KSjC2RriyolAoWTaBDVVkhvHMTvXYvgzE3waRHwebnX5f75MKVf4ED3+vkQuVZzWEUBbth6e8g6yswdmvSX95267GoGOg2HE65yepd7jkW2nYF4Of0dAZ0HdzkMLIOFgHQp6MuotIS6WRBpVSwaAIdigpy4I0rYP9mtp9wM/0vCGB5Ltfkws4DrN5FnVzonYZ6YyOFvdKqv7z8T7WXha8she9egYT2VpI84kpr0l/3kX77Iyw7z5pg2LuDLuPdEulkQaVUsGgCHWpy1sKb063k5Mq3yc2Npn+gYxCBM+60kmidXNgwT72x799h3Q63JNoYKDoAh7MhP9uq+OL6fjjb+gPB2Os5gcA9PwVs6E9WXhHtW8WQ2ComIM+nQotOFlRKBYsm0KFk0zuw5DZo0xWuXmqVmMtND148OrmwYWWF1oI2NXtjK0pg6R3w0xfQqqPbV4fqt+MSG1/H211Ter7LjlZPkKtuO7+7ysa5tO4MSb0heQwMnWbd/myulWjXlJgc0HHz2XnF9O6ovc8thacJgzpZUCkVDJpAhwJj4It51rjjnuPgitehdadgR2XRyYW1leTDDx/DlqWwM80auuBJZQnsWAFFB8FR4XkfsVUl1SPKo2BvvxoJt/tXe+t7XDvr519Xz7ej0hpCUdV7XCNZLjlUPYbYttC+t1Xd5fizrQl+Sb2tbUm9INZDghqTUKuUHDEJVgIfQFl5RYzu3T6gz6mCQycMKqVCiSbQwVZRCktvh41vw/DpcOE/ITou2FFVp5MLrST4+w9h61L48XMrIW7bHU6+Bja/U0dvbE/44ybrD6TyQijOc34dqvHd+bXnR2uxnJzV1n1HpedYoqKtRNrTPhUl1qcY1faPsRLh9r2h2whnYuxMkNv3tcYsN/YPIrdScsEa911WaWdPfgm9TtYFVFoCnTColAolmkAHU+F+q2c351sr+Tj9ztDt2fU0uXD6Amh7XLAj858je2DrB1bSnP0/MA6rd3bcbTBoCnQ/2Rp+kTy6/t5YEYhra32171Pn061PTyfVNQvKGCg7Un/C/d0rdcc+9ZljSXLbbhBla+5PozZnKblgyTlcgsNoBY6WQicMKqVCiSbQwbJvs7Usd9FBuOxVKyELda7JhZ1OhHduhufOjrzJhYezrYR5y1LrDxuATgPgjLtg4GRrSEvNP3L80RsrYtX8jk+se+GcnZ9ZwzZqSuzZvGXew0R2nlXCTsdA+46ITAL+CdiAF4wxj9Z4PA54FRgF5AGXG2OyAhGbThhUSoUSTaCD4YflsOh6q0fy+o+sMl/hZOAvrcmFb1xhTS68+N/h8QdAXQ5uhy3vWYnzzxusbccNhbPvg0GTrV73hgSjN3b87JAYhxwsWQetyY7aA+0bImIDngYmADnAahFZaozZ4rbbDcBhY8wJInIF8Bhwua9jyciA11/vRVycri6olApNmkAHkjHwzTPwyZ+tBG36m9Cue7CjaprjhsLNK60hKAuvhrPDaHKhMbBvk9XLvHWpNaYbrCoTE+bCwAuhQ9/gxuiNEBiHHCxL1uXyf59sA2DyU18xY+JJTB3ZI8hRhb1TgB3GmB8BRORNYArgnkBPAR503l4EPCUiYowxvgrCNVmwrKwvr7+ukwWVUqFJE+hAsVfAshmw9mU46Zdw8XOeqxuEk3CaXGiMtZT01vdg6/tw6EeQKOh1Kpz/N+t3khiGCViQxyEHw5J1ucx6ZyMlFVY96tz8Uma9sxFAk+jm6QG4jwnKAcbWtY8xplJECoCOwEH3nUTkZuBmgK5du5Kenu51EK+/3ouysr44HEJZmYOXXsqirGxXY9sS0goLCxv1Mwk3kdy+SG4bRHb7fN22yE+gg7lCnPtz22LBXmZNFDzn/ubV/g0lNScX5qyx/lg4+nNwf96JyXDOfVb1iS1LraT5SI5VwaLvmXDqHVbS3KZzYGJTTVZaYWfXoWJ+OlhE1sEi/pm2vSp5dimpsDNv+TZNoEOEMeY54DmA0aNHm9RGzPiLi4PXX4eyMgdxcVFcf30/UlLqmAcQptLdJwxHoEhuXyS3DSK7fb5uW2Qn0MFcIa7mc9vLrCS6y8DISZ5dXJMLiw7AN/86tr1gN7x3Oxz+CfqfZ/X4ItZ3ibKOc91G3O4LcaX7IX+3V/siUbB5idXDX+n2u373Fuu2LQ6OPwfO+TMMON8q26ZCSlmlnd2HivnpYDFZB4v4Ka+I7Lwisg4Ws6egBG8GCOzJL2l4J1WfXKCn2/1k5zZP++SISDSQiDWZ0GdckwVfeinLmTz78uxKKeUbkZ1Ap83xvELc+7+H7Z+Aw24tS+ywV79d9d1h1dmttc1uba+5zdiP7V9aANR417eXWzFF6kfuW9+vvc1eBiv/an01QgrANz6IqVVH+P0Ga8Km8qkl63KZt3wbe/JL6J6UwIyJA+rtAS6vdLD7sDNBPlhEVl4R67aXcN+qz9iTb5Wkc0lqFUOfjq05pW8H+nRsTZ9OrZzfW/OLf35JrodkuXtSCA4dCi+rgf4i0hcrUb4CqFnOZSlwDZABTAM+8+X4Z5eUFCgr2xVxPc9KqcgR2Ql0QY7n7RXF1lCDKJu1ElxUtNUrLDa3bc7t0fHVt1U91sC2b59rXEyRoM62iVXuzjisscjGYX1hamw79tj332/lpAEnejiGY7fdH/v0fs9PXXwoopPnxiaxvnze6uOQS5j1zkbsDgcn9+5QLUnOyrOS5pzDxdWS5Hbx0XSMg1F923Pxycn0dSbJfTu1JqlVbJ3PPWPigGrPDZAQY2PGRC+qpag6Occ03w4sxypj95IxZrOIzAHWGGOWAi8Cr4nIDuAQVpKtlFItTmQn0InJddfJ/f16/z73to/qeO4IXjWtzp93sjV0ohH2FqRz0smp3h/w7XNB/XkHI5GtK4mFxk+mszsMZZV2SiscVd9LK+yUVtgpq3Tdth4rq3Dwl2VbPY5DvuvtzGrb2sRF06dTK4YlJzJlRPeqXuS+nVrTvlUMn3/+OampjSvj6GpbMP5wiHTGmGXAshrbZrvdLgUuDXRcSikVaiI7gQ5mndyWWKM3yD/vyvd+R7S9tGpTpS2e6AA8t68SWWOsJLas0kGZK1mtebvSQZkzqX3w/c0ek9g/L9nIqp/yaiXDxxJhVzJsnau00k6F3Xefws+bNoy+naxEuWPrWMQPpQ2njuyhCbNSSqmgiewEethlrM46TM/v5tHFHGS/dGL30BmMCcQY5CDW6A3Wx/rB/HkvsZ/GVxU38gfepLvkscd05AnHFZxuP42pTThfpd1BqTPhLCm3U1Zpp6TcQWmldb+0wk5JRf29sX96dyNp3++vSngbSojLKh2w/ONm/yyKyuykbd1PXEwU8dE24mNsxEVHkRBjo32rGOKibdZjzu3xMTbiXduio4iLsRHvPDau6rvbvjFRTHsmg71HSms9d4+kBC4d3dNDVEoppVTkiOgEesm6XGat7k1JxT+rtiWstvFIz9zAjBO1n8a8svnsKS2he3wCM+wDmpTMNeo5vewNNcbgMOAwxvpyeL5tNwbj3M/uqH7bYazz2J3HpH2/j6e+7UVZ5bGfd+y3UdyasI3TT+iMMQaDdTzWP2sIM8fOawAMbNhfiX3rvmrbjfNB5+HWdufth97fzOHyU1nEqdV+Hsvf28T3e4+69bxaiW9phcOZAFe/79rHFz2yxeV2NucWEOtMSuOio2gTF03H1lZiGhcdZSWz0VHExUSxN2c3J57Qz3nfud21T43942OiuOqFVew7UlbreXskJfC/mec0O/76zDz/JB2HrJRSqsXyawItIpOAf2JNSHnBGPNojcfjgFeBUVilkC43xmT56vnnLd/msWfwwaWbqbA7iBLBFiWIUHU7SkBEsIlY8wpdt8V6LCqq9m2b1D7HZ9/v5++f/mD1KmIlsvcuzmTngULG9etIubPHscLuoLzSQbn92G337TuzylhZsIlyu2u7obzSTnnVbQdldgcVznP8dLAIu8PUavMf3lrPPYsyrWTXmXgGQnmlg/lpO5iftqPxB3+3ptnPf7S0kpe++snqUY2xkRBr9bbGx9qIj46ifetYukU7t8e4elhtJDh7WhOc949ts5EQayWy1jE2pj3zNT8XeO6N/ezuVK9jTU/fR2rqCV7vP+v8gUFLYnUcslJKqZbMbwm0iNiAp4EJWCtarRaRpcYY92VhbwAOG2NOEJErgMeAy30VQ111YfNLKpixKNPjY/5UVungyc928ORn3iWT0VGCTQzx+/YQGx1FrC2q2vcYmxAbHUVibAyxzts79hfWeb4bzuhLlOBM+J3Jv/OPBNftKOdjNucfCMf+gLAei4qq47YIt/53bZ3P/d8bxlolmwUEcX63ntv64wNw277uu+8YNWqUMx7rHO7HuraLc/uVz69i/9HavbHdk+L5euZ4r37eTXXvpOD0xgY7idVxyEoppVoqf/ZAnwLsMMb8CCAibwJTAPcEegrwoPP2IuApERFf1RXtnpTgsV5s13ZxLLr1VOcwBedwBcex29WGKhhTNdzBGrZgPea6XWv4g/O4299YV2dcb908zkqE3ZPi6Chi3BNkWxRRUdLolXNOe/Qzj23ukZTAvZNO8vo8TdGjjp93j6QETu/fqVHnKvjRxvCeSV7v/6dfeO6NvWeif9sMwU1kNYlVSimlAs+fCXQPwL2uWA4wtq59nDVIC4COwEFfBFBXvdhZ5w+kZ4dWvniKOj2y7Ps6k8mx/Tr67XmDWSM3mM+tvbFKKaWUChTxwyJS1olFpgGTjDE3Ou//GhhrjLndbZ9Nzn1ynPd3Ovc5WONcNwM3O+8OALZ5G0dUQrsOtjYdeogtOtbYK8vthYdyHSVHDjWrcV4+b3S7zr0RObZutzGOyiMHshv5/J1o5B8UwWqzj5+70e2OANrmyNfbGNM52EEEkogcALKbcGgkvzYiuW0Q2e2L5LZBZLevqW3zeN32Zw90LuBezyrZuc3TPjkiEg0kYk0mrMYY8xxQx9J+kU1E1hhjRgc7jkBrie3WNqtI1NQ/GCL5tRHJbYPIbl8ktw0iu32+bltUw7s02Wqgv4j0FZFYrCVfl9bYZylwjfP2NOAzX41/VkoppZRSyh/81gPtHNN8O7Acq4zdS8aYzSIyB1hjjFkKvAi8JiI7gENYSbZSSimllFIhy691oI0xy4BlNbbNdrtdClzqzxgiQIscukLLbLe2WaljIvm1Ecltg8huXyS3DSK7fT5tm98mESqllFJKKRWJ/DkGWimllFJKqYijCXSIEJFJIrJNRHaIyMw69rlMRLaIyGYReSPQMfpaQ20WkX+IyHrn1w8ikh+EMH3Oi3b3EpGVIrJORDJF5BfBiNOXvGhzbxFJc7Y3XUSSgxGnCjwvXhtxIvKW8/FVItInCGE2iRdtu9N5Tc90vv57ByPOpvLmfcu53yUiYkQkbKo7RPJ7ciS/B4nISyKy31km2dPjIiLznW3PFJGTm/xkxrnSnn4F7wtrkuVOoB8QC2wABtXYpz+wDmjvvN8l2HH7u8019v8d1kTUoMcegN/1c8BtztuDgKxgxx2ANr8NXOO8fQ7wWrDj1q+QeW38BnjWefsK4K1gx+3Dtp0NtHLevi1c2uZt+5z7tQW+AL4BRgc7bh/+7sLyPTnS34OAM4GTgU11PP4L4CNAgHHAqqY+l/ZAh4aqZc+NMeWAa9lzdzcBTxtjDgMYY/YHOEZf86bN7qYDCwISmX95024DtHPeTgT2BDA+f/CmzYOAz5y3V3p4XEUmb14bU4BXnLcXAeNFRAIYY1M12DZjzEpjTLHz7jdY6yWEC2+v4XOBx4DSQAbXTJH8nhzR70HGmC+wqrrVZQrwqrF8AySJSLemPJcm0KHB07LnNdeFPhE4UUT+JyLfiMikgEXnH960GbA+3gf6cizBCmfetPtB4FcikoNVxeZ3gQnNb7xp8wbgYufti4C2IuK/Ne9VqPDmtVG1jzGmEigAwuG14fU1zukGrJ6xcNFg+5wfj/c0xnwYyMB8IJLfk1vie5C7xv6/rJMm0OEjGusjo1Ss3tjnRSQpmAEF0BXAImOMPdiBBMh04D/GmGSsj5teE/cl4SPT3cBZIrIOOAtrldKW8vtWLZyI/AoYDcwLdiy+4rxm/R24K9ix+Ekkvye3xPegRtMfSGjwZtnzHGCpMabCGPMT8APWf95w5U2bXa4gMoZvgHftvgFYCGCMyQDigU4Bic4/GmyzMWaPMeZiY8xI4M/ObfkBi1AFizf/H6r2EZForI+U8wISXfN4dY0TkXOxXvOTjTFlAYrNFxpqX1tgCJAuIllY402XhslEwkh+T26J70HuGpN71EsT6NDgzbLnS7D+0kVEOmF9fPRjAGP0NW/ajIicBLQHMgIcn7940+5dwHgAERmIdfE6ENAofavBNotIJ7cejlnASwGOUQWHN/8flgLXOG9PAz4zztlAIc6b1/1I4N9YyXO4jKF1qbd9xpgCY0wnY0wfY0wfrDHek40xa4ITbqNE8ntyS3wPcrcUuNpZjWMcUGCM+bkpJ9IEOgQ4x/W5lj3fCiw0zmXPRWSyc7flQJ6IbMGaZDXDGBMOvTAeedlmsP5zvxkmb5gN8rLddwE3icgGrJ73a8O5/V62ORXYJiI/AF2BvwQlWBVQXr42XgQ6isgO4E6gznJpocTLts0D2gBvi1Wus1YnQqhqxDU87ETye3KkvweJyAKsDrcBIpIjIjeIyK0icqtzl2VYf+jsAJ7HqvLTtOcKk5+JUkoppZRSIUF7oJVSSimllGoETaCVUkoppZRqBE2glVJKKaWUagRNoJVSSimllGoETaCVUkoppZRqBE2gVVgQkeNE5E0R2Skia0VkmYic2ITznCEim50lo3qIyKI69ksPk4L/SikVcvSarSKdJtAq5ImIAO8C6caY440xo7AW2+jahNNdBTxijBlhjMk1xkzzZaxKKdXS6TVbtQSaQKtwcDZQYYx51rXBGLMB+EpE5onIJhHZKCKXA4hIqrM3YpGIfC8irztXHboRuAyY69zWR0Q2OY9JcPaWbBWRd4EE13OJyHkikiEi34nI2yLSxrk9S0Qecm7f6Fw1ERFpIyIvO7dlisgl9Z1HKaUijF6zVcTTBFqFgyHAWg/bLwZGAMOBc4F5ItLN+dhI4A/AIKAfcJox5gWsZTxnGGOuqnGu24BiY8xA4AFgFFQt0XofcK4x5mRgDdZqaC4HndufAe52brsfa3nQocaYYcBnXpxHKaUihV6zVcSLDnYASjXD6cACY4wd2CcinwNjgCPAt8aYHAARWQ/0Ab6q51xnAvMBjDGZIpLp3D4O64L+P+tTSWKxlgl1ecf5fS3WmwNYbwxXuHYwxhwWkV82cB6llIp0es1WEUMTaBUONgONHfdW5nbbTtNf6wJ8aoyZ3sDzNPQcDZ1HKaUihV6zVcTTIRwqHHwGxInIza4NIjIMyAcuFxGbiHTG6pH4tonP8QVwpfPcQ4Bhzu3fAKeJyAnOx1p7MZP8U+C3brG2b+J5lFIqHOk1W0U8TaBVyDPGGOAi4FyxSiJtBh4B3gAygQ1YF+x7jDF7m/g0zwBtRGQrMAfn+D1jzAHgWmCB8yPCDOCkBs71MNDeOVFmA3B2E8+jlFJhR6/ZqiUQ63WulFJKKaWU8ob2QCullFJKKdUImkArpZRSSinVCJpAK6WUUkop1QiaQCullFJKKdUImkArpZRSSinVCJpAK6WUUkop1QiaQCullFJKKdUImkArpZRSSinVCP8PpCTteXquy6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plot_reliability_diagram([y_test, y_test],\n",
    "                         [softmax(10*bnn.predict(norm_X_test).y_prob, axis=1), y_prob_calibrated_from_features],\n",
    "                             [None, None],\n",
    "                         plot_label = [\"before\", \"after\"],\n",
    "                            num_bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### When base model precomputed probability scores are available or computing them again is expensive, `fit_mode` can be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob_calib = bnn.predict(norm_X_calib).y_prob\n",
    "y_prob_test = bnn.predict(norm_X_test).y_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "calib_from_probs = ClassificationCalibration(num_classes=2, fit_mode=\"probs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "calib_from_probs = calib_from_probs.fit(X=softmax(10*y_prob_calib, axis=1), y=y_calib.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob_calibrated_from_probs = calib_from_probs.predict(softmax(10*y_prob_test, axis=1)).y_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plot_reliability_diagram([y_test, y_test],\n",
    "                         [softmax(10*y_prob_test, axis=1), y_prob_calibrated_from_probs],\n",
    "                             [None, None],\n",
    "                         plot_label = [\"before\", \"after\"],\n",
    "                            num_bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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