62_xgb_meanc_emb_nn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c65f557-651f-4958-a567-a1b6b5a82b2b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Modify the file 00_setup.py to define input/output file paths on your system\n",
    "# The information in 00_setup.py will be used across notebooks\n",
    "from importlib.machinery import SourceFileLoader\n",
    "setup = SourceFileLoader(\"setup\", \"./00_setup.py\").load_module()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b01d0fdb-d4bf-465a-8541-997addc50b0c",
   "metadata": {},
   "source": [
    "# XGBoost with Mean Encoding (Grouped, NN Embed)\n",
    "Mean Encode NAICS grouped by cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56161f89-1f54-47f9-860d-3f945a941c1f",
   "metadata": {},
   "source": [
    "*This script takes about 5 minutes on my MacBook Air*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec6b1778-7abc-4129-ab5f-ac2992bd8ca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "785af927-a634-4e34-84c4-7a532236f2ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9550b514-7063-4f3d-8a84-f36cf34c61a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import TargetEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f7051f7b-05fd-4ecb-8b9c-b8f3d896a684",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd09527b-32dd-47fa-b62f-f9307f89f553",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn as sk\n",
    "from sklearn.inspection import permutation_importance, partial_dependence\n",
    "from scikitplot.metrics import plot_lift_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0732ba40-8318-457b-b29f-f9b6de3b70aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sba_gnn.sba_gnn import sg_plot "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d14a5947-6599-4253-acf3-91ddeffdedd3",
   "metadata": {},
   "source": [
    "## Import Processed Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d31d6e-2d0c-4d95-b505-42173727446e",
   "metadata": {},
   "source": [
    "##### Get loans data, append clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7fa98cc-da79-4996-8b23-aa0f3533bde1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sba_loans = pd.read_parquet(Path(setup.temp_path).joinpath('01_DATA_transformed.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32a6a5dd-0d35-48a0-9339-9a1ca11bdf41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NAICS', 'NAICS_2', 'NAICS_sector', 'NAICS_sector_desc', 'NAICS_num',\n",
       "       'menc_NAICS', 'cenc_NAICS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sba_loans.filter(like='NAICS').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "63b6d02f-f147-42c9-92a8-e09e5978e029",
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_df = pd.read_parquet(Path(setup.temp_path).joinpath('60_DATA_embeddings_tsne_naics.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39563568-9060-4be4-ab5e-3bf9de340773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['emb_000', 'emb_001', 'emb_002', 'emb_003', 'emb_004', 'emb_005',\n",
       "       'emb_006', 'emb_007', 'NAICS', 'NAICS_2', 'NAICS_sector',\n",
       "       'NAICS_sector_sel', 'NAICS_sector_desc', 'NAICS_sector_comb',\n",
       "       'train_count', 'NAICS_sector_filt', 'tsne_0', 'tsne_1', 'NAICS_orig',\n",
       "       'all_target_mean', 'menc_NAICS', 'cluster', 'NC__0.0', 'NC__1.0',\n",
       "       'NC__2.0', 'NC__3.0', 'NC__4.0', 'NC__5.0', 'NC__6.0', 'NC__7.0',\n",
       "       'NC__8.0', 'NC__9.0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embed_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "684d093a-3977-4263-a53d-92ffc0fabfff",
   "metadata": {},
   "outputs": [],
   "source": [
    "sba_loans = sba_loans.drop(columns = 'cluster', errors='ignore') \\\n",
    "    .merge(embed_df[['NAICS_orig', 'cluster']].rename(columns={'NAICS_orig':'NAICS'}),\n",
    "           on='NAICS', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b692da0-bc3e-452e-91bf-cee0fb496f55",
   "metadata": {},
   "source": [
    "## Fit the Target Encoder(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a3c5eef8-3755-45a3-9a17-cd1b47a08444",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Per sector function\n",
    "def mean_enc_grp(data):\n",
    "    naics_grp_encoder = TargetEncoder(target_type='binary', random_state=3453)\n",
    "    naics_grp_encoder.set_output(transform='pandas')\n",
    "    \n",
    "    train_out = naics_grp_encoder.fit_transform(data[data['dset'] == 'train'][['NAICS']], \n",
    "                      data[data['dset'] == 'train']['target'])\n",
    "    other_out= naics_grp_encoder.transform(data[data['dset'] != 'train'][['NAICS']])\n",
    "    out_data = pd.concat([train_out, other_out], axis=0).sort_index()\n",
    "    out_data.columns = ['menc_clus_NAICS']\n",
    "    return out_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8326fe3e-1ec2-4556-8375-a3f5a0cd8d0d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "grouped = sba_loans.groupby('cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea203be6-471e-4725-b68e-1010d5bd9794",
   "metadata": {},
   "outputs": [],
   "source": [
    "sba_loans = pd.concat([sba_loans.drop(columns='menc_clus_NAICS', errors='ignore'),\n",
    "                       pd.concat([mean_enc_grp(g) for n, g in grouped])],\n",
    "                      axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a5f6c362-c1ab-48d8-812b-2b970b06586b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = sba_loans[sba_loans['dset'] == 'train'].copy()\n",
    "test_df = sba_loans[sba_loans['dset'] == 'test']\n",
    "val_df = sba_loans[sba_loans['dset'] == 'val']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d56e9533-8071-494a-a7da-4917a03fb701",
   "metadata": {},
   "source": [
    "#### Set the predictor features.  Inlcude the mean-encoded NAICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c07ddd4e-6987-454a-926a-3b59abf6caa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NoEmp', 'CreateJob', 'LowDoc', 'DisbursementGross', 'new_business', 'urban_flag', 'franchise_flag', 'menc_clus_NAICS']\n"
     ]
    }
   ],
   "source": [
    "predictor_features = setup.predictor_features + ['menc_clus_NAICS']\n",
    "print(predictor_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2a4f4a9e-f7e5-4143-b1e4-b5fbb5f7280d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NoEmp</th>\n",
       "      <th>CreateJob</th>\n",
       "      <th>LowDoc</th>\n",
       "      <th>DisbursementGross</th>\n",
       "      <th>new_business</th>\n",
       "      <th>urban_flag</th>\n",
       "      <th>franchise_flag</th>\n",
       "      <th>menc_clus_NAICS</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NoEmp</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062158</td>\n",
       "      <td>-0.013926</td>\n",
       "      <td>0.095658</td>\n",
       "      <td>-0.041427</td>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.006409</td>\n",
       "      <td>-0.026406</td>\n",
       "      <td>-0.027403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreateJob</th>\n",
       "      <td>0.062158</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.062740</td>\n",
       "      <td>0.134686</td>\n",
       "      <td>0.039302</td>\n",
       "      <td>0.009062</td>\n",
       "      <td>0.050517</td>\n",
       "      <td>0.005556</td>\n",
       "      <td>-0.010002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowDoc</th>\n",
       "      <td>-0.013926</td>\n",
       "      <td>-0.062740</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.133141</td>\n",
       "      <td>0.153034</td>\n",
       "      <td>-0.120001</td>\n",
       "      <td>0.040841</td>\n",
       "      <td>-0.082667</td>\n",
       "      <td>-0.079523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DisbursementGross</th>\n",
       "      <td>0.095658</td>\n",
       "      <td>0.134686</td>\n",
       "      <td>-0.133141</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.065443</td>\n",
       "      <td>-0.007016</td>\n",
       "      <td>0.085712</td>\n",
       "      <td>-0.187829</td>\n",
       "      <td>-0.120788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_business</th>\n",
       "      <td>-0.041427</td>\n",
       "      <td>0.039302</td>\n",
       "      <td>0.153034</td>\n",
       "      <td>-0.065443</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.047592</td>\n",
       "      <td>0.137325</td>\n",
       "      <td>0.027394</td>\n",
       "      <td>0.013878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>urban_flag</th>\n",
       "      <td>0.001610</td>\n",
       "      <td>0.009062</td>\n",
       "      <td>-0.120001</td>\n",
       "      <td>-0.007016</td>\n",
       "      <td>-0.047592</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006588</td>\n",
       "      <td>0.055582</td>\n",
       "      <td>0.053785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>franchise_flag</th>\n",
       "      <td>0.006409</td>\n",
       "      <td>0.050517</td>\n",
       "      <td>0.040841</td>\n",
       "      <td>0.085712</td>\n",
       "      <td>0.137325</td>\n",
       "      <td>-0.006588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028694</td>\n",
       "      <td>-0.012731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>menc_clus_NAICS</th>\n",
       "      <td>-0.026406</td>\n",
       "      <td>0.005556</td>\n",
       "      <td>-0.082667</td>\n",
       "      <td>-0.187829</td>\n",
       "      <td>0.027394</td>\n",
       "      <td>0.055582</td>\n",
       "      <td>0.028694</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.230826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.027403</td>\n",
       "      <td>-0.010002</td>\n",
       "      <td>-0.079523</td>\n",
       "      <td>-0.120788</td>\n",
       "      <td>0.013878</td>\n",
       "      <td>0.053785</td>\n",
       "      <td>-0.012731</td>\n",
       "      <td>0.230826</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      NoEmp  CreateJob    LowDoc  DisbursementGross  \\\n",
       "NoEmp              1.000000   0.062158 -0.013926           0.095658   \n",
       "CreateJob          0.062158   1.000000 -0.062740           0.134686   \n",
       "LowDoc            -0.013926  -0.062740  1.000000          -0.133141   \n",
       "DisbursementGross  0.095658   0.134686 -0.133141           1.000000   \n",
       "new_business      -0.041427   0.039302  0.153034          -0.065443   \n",
       "urban_flag         0.001610   0.009062 -0.120001          -0.007016   \n",
       "franchise_flag     0.006409   0.050517  0.040841           0.085712   \n",
       "menc_clus_NAICS   -0.026406   0.005556 -0.082667          -0.187829   \n",
       "target            -0.027403  -0.010002 -0.079523          -0.120788   \n",
       "\n",
       "                   new_business  urban_flag  franchise_flag  menc_clus_NAICS  \\\n",
       "NoEmp                 -0.041427    0.001610        0.006409        -0.026406   \n",
       "CreateJob              0.039302    0.009062        0.050517         0.005556   \n",
       "LowDoc                 0.153034   -0.120001        0.040841        -0.082667   \n",
       "DisbursementGross     -0.065443   -0.007016        0.085712        -0.187829   \n",
       "new_business           1.000000   -0.047592        0.137325         0.027394   \n",
       "urban_flag            -0.047592    1.000000       -0.006588         0.055582   \n",
       "franchise_flag         0.137325   -0.006588        1.000000         0.028694   \n",
       "menc_clus_NAICS        0.027394    0.055582        0.028694         1.000000   \n",
       "target                 0.013878    0.053785       -0.012731         0.230826   \n",
       "\n",
       "                     target  \n",
       "NoEmp             -0.027403  \n",
       "CreateJob         -0.010002  \n",
       "LowDoc            -0.079523  \n",
       "DisbursementGross -0.120788  \n",
       "new_business       0.013878  \n",
       "urban_flag         0.053785  \n",
       "franchise_flag    -0.012731  \n",
       "menc_clus_NAICS    0.230826  \n",
       "target             1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[predictor_features + ['target']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f8c475d-7ead-4f92-a19b-326343528f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('62_DATA_features_predict.pkl'), 'wb') as fout:\n",
    "    pickle.dump(predictor_features, fout)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b204a92-d109-4f6d-87a5-3d5dc05d2e5f",
   "metadata": {},
   "source": [
    "## Model Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "af48634b-fc39-4c29-9792-bcf1cdf241c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subsample': 0.8,\n",
       " 'scale_pos_weight': 1.0,\n",
       " 'reg_lambda': 0.01,\n",
       " 'reg_alpha': 0.001,\n",
       " 'min_child_weight': 50,\n",
       " 'max_depth': 8,\n",
       " 'learning_rate': 0.1,\n",
       " 'gamma': 2}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params_df = pd.read_csv(Path(setup.temp_path).joinpath('03_REPORT_fit_parameter_selected.csv'))\n",
    "best_params = best_params_df.to_dict(orient='records')[0]\n",
    "best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc698909-3663-440f-98ce-fcfe312faa82",
   "metadata": {},
   "source": [
    "##### Fit using pre-determined parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8eca4a6-66e8-4eb2-b68a-1271f59cdbec",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_params = dict(best_params, \n",
    "                   **{'objective':\"binary:logistic\", 'n_estimators':setup.xgb_n_estimators})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6d124de6-1d5d-4ff9-8071-4d05b0717f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb.XGBClassifier(objective=\"binary:logistic\",\n",
    "                             n_estimators =  setup.xgb_n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9eebd329-7e39-4e52-be94-d839ad559b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb_model.set_params(**xgb_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a7f80910-9719-486b-8eac-93889064708a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.50053\tvalidation_1-logloss:0.50399\n",
      "[1]\tvalidation_0-logloss:0.49313\tvalidation_1-logloss:0.49676\n",
      "[2]\tvalidation_0-logloss:0.48689\tvalidation_1-logloss:0.49067\n",
      "[3]\tvalidation_0-logloss:0.48167\tvalidation_1-logloss:0.48556\n",
      "[4]\tvalidation_0-logloss:0.47728\tvalidation_1-logloss:0.48131\n",
      "[5]\tvalidation_0-logloss:0.47344\tvalidation_1-logloss:0.47757\n",
      "[6]\tvalidation_0-logloss:0.47018\tvalidation_1-logloss:0.47443\n",
      "[7]\tvalidation_0-logloss:0.46741\tvalidation_1-logloss:0.47174\n",
      "[8]\tvalidation_0-logloss:0.46498\tvalidation_1-logloss:0.46941\n",
      "[9]\tvalidation_0-logloss:0.46292\tvalidation_1-logloss:0.46746\n",
      "[10]\tvalidation_0-logloss:0.46111\tvalidation_1-logloss:0.46574\n",
      "[11]\tvalidation_0-logloss:0.45956\tvalidation_1-logloss:0.46429\n",
      "[12]\tvalidation_0-logloss:0.45824\tvalidation_1-logloss:0.46309\n",
      "[13]\tvalidation_0-logloss:0.45714\tvalidation_1-logloss:0.46209\n",
      "[14]\tvalidation_0-logloss:0.45607\tvalidation_1-logloss:0.46111\n",
      "[15]\tvalidation_0-logloss:0.45516\tvalidation_1-logloss:0.46028\n",
      "[16]\tvalidation_0-logloss:0.45429\tvalidation_1-logloss:0.45955\n",
      "[17]\tvalidation_0-logloss:0.45357\tvalidation_1-logloss:0.45893\n",
      "[18]\tvalidation_0-logloss:0.45297\tvalidation_1-logloss:0.45842\n",
      "[19]\tvalidation_0-logloss:0.45245\tvalidation_1-logloss:0.45797\n",
      "[20]\tvalidation_0-logloss:0.45201\tvalidation_1-logloss:0.45761\n",
      "[21]\tvalidation_0-logloss:0.45150\tvalidation_1-logloss:0.45720\n",
      "[22]\tvalidation_0-logloss:0.45112\tvalidation_1-logloss:0.45688\n",
      "[23]\tvalidation_0-logloss:0.45078\tvalidation_1-logloss:0.45659\n",
      "[24]\tvalidation_0-logloss:0.45049\tvalidation_1-logloss:0.45638\n",
      "[25]\tvalidation_0-logloss:0.45023\tvalidation_1-logloss:0.45618\n",
      "[26]\tvalidation_0-logloss:0.44990\tvalidation_1-logloss:0.45595\n",
      "[27]\tvalidation_0-logloss:0.44965\tvalidation_1-logloss:0.45577\n",
      "[28]\tvalidation_0-logloss:0.44947\tvalidation_1-logloss:0.45566\n",
      "[29]\tvalidation_0-logloss:0.44927\tvalidation_1-logloss:0.45552\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=2, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=8, max_leaves=None,\n",
       "              min_child_weight=50, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=30, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=2, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=8, max_leaves=None,\n",
       "              min_child_weight=50, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=30, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=2, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=8, max_leaves=None,\n",
       "              min_child_weight=50, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=30, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model.fit(train_df[predictor_features], train_df['target'],\n",
    "              eval_set = [(train_df[predictor_features], train_df['target']),\n",
    "                         (val_df[predictor_features], val_df['target'])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9bbd8636-a5f1-4cbc-9498-329a78165472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    346866\n",
       "1     89254\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0913a102-a730-4b94-9977-e584df764d64",
   "metadata": {},
   "source": [
    "##### Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "af21dd7c-09d4-407f-9960-0748ba8f2247",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model.save_model(Path(setup.temp_path).joinpath('62_MODEL_xgboost.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8e5d3e7d-0bd5-4252-b287-c9cc65f7a132",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('62_MODEL_xgboost.pkl'), 'wb') as po:\n",
    "    pickle.dump(xgb_model, po)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f34ad059-70dc-4672-adff-0d961fc74b07",
   "metadata": {},
   "source": [
    "## Probability Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6a7c8509-0109-4f8e-a466-d6e21164012c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df = pd.concat([sba_loans[['LoanNr_ChkDgt', 'dset', 'target', 'dset_naics_holdout']].reset_index(),\n",
    "                     pd.DataFrame({'predict_prob':\n",
    "                                   xgb_model.predict_proba(sba_loans[predictor_features])[:,1]})],\n",
    "                    axis=1) \\\n",
    "    .set_index('index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7084012d-b68a-4ca4-b714-160f8d564500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</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>0</th>\n",
       "      <td>0.197687</td>\n",
       "      <td>0.126273</td>\n",
       "      <td>0.016573</td>\n",
       "      <td>0.636797</td>\n",
       "      <td>0.178655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.297074</td>\n",
       "      <td>0.118929</td>\n",
       "      <td>0.016638</td>\n",
       "      <td>0.644007</td>\n",
       "      <td>0.299842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean       std       min       max    median\n",
       "target                                                  \n",
       "0       0.197687  0.126273  0.016573  0.636797  0.178655\n",
       "1       0.297074  0.118929  0.016638  0.644007  0.299842"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df.groupby('target')['predict_prob'].agg(['mean', 'std', 'min', 'max', 'median'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d33cb4dc-6c89-4a37-98c7-d3a666abc4a5",
   "metadata": {},
   "source": [
    "## Decision Threshold\n",
    "Max f1, on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d7727498-825f-4f53-882f-cfcce730b1fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train_df = pred_df[pred_df['dset'] == 'train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7b553798-eead-48f8-b14b-5f036d9183d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_tune_data = sg_plot.get_f1_frame(pred_train_df['target'], \n",
    "                                        pred_train_df['predict_prob'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7a1cde01-a30b-475f-8fd7-409b13946c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh_tune_data.to_csv(Path(setup.temp_path).joinpath('62_REPORT_thresh_tune.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "947a487c-e71d-433c-bbf0-2ed7e57a908a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.22\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>thresh</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.452692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.452416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    thresh        f1\n",
       "11    0.22  0.452692\n",
       "12    0.24  0.452416"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresh_head = thresh_tune_data.sort_values('f1', ascending=False).head(2)\n",
    "best_thresh = thresh_head['thresh'].iloc[0]\n",
    "print(best_thresh)\n",
    "thresh_head"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ee5330e-c1a7-4310-afbe-be9bc0d26f43",
   "metadata": {},
   "source": [
    "##### Apply decision threshold\n",
    "All datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f0d4c1cf-6d5b-49e9-9123-a067697a9e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df['predict_bin'] = np.where(pred_df['predict_prob'] >= best_thresh, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8253c32c-0438-47b9-8570-7cde6f7f5590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predict_bin\n",
       "0    361696\n",
       "1    326385\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df['predict_bin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dbb46f4a-a5ef-4fd6-9da4-30de4a00334d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df.to_parquet(Path(setup.temp_path).joinpath('62_DATA_combined_predictions.parquet'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab860d0-eddc-4f63-b853-88da15d451f4",
   "metadata": {},
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f2801964-124e-425f-b4cb-9bcf1cf3f04a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dset</th>\n",
       "      <th>accuracy_score</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>precision_score</th>\n",
       "      <th>recall_score</th>\n",
       "      <th>average_precision_score</th>\n",
       "      <th>roc_auc_score</th>\n",
       "      <th>dset_naics_holdout</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.579911</td>\n",
       "      <td>0.423714</td>\n",
       "      <td>0.289689</td>\n",
       "      <td>0.788526</td>\n",
       "      <td>0.314081</td>\n",
       "      <td>0.697044</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train</td>\n",
       "      <td>0.638035</td>\n",
       "      <td>0.452692</td>\n",
       "      <td>0.327775</td>\n",
       "      <td>0.731452</td>\n",
       "      <td>0.390008</td>\n",
       "      <td>0.733126</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>val</td>\n",
       "      <td>0.635093</td>\n",
       "      <td>0.450836</td>\n",
       "      <td>0.327761</td>\n",
       "      <td>0.721919</td>\n",
       "      <td>0.385223</td>\n",
       "      <td>0.726640</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.634380</td>\n",
       "      <td>0.449642</td>\n",
       "      <td>0.325650</td>\n",
       "      <td>0.726109</td>\n",
       "      <td>0.383574</td>\n",
       "      <td>0.728576</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.501660</td>\n",
       "      <td>0.393603</td>\n",
       "      <td>0.252672</td>\n",
       "      <td>0.890026</td>\n",
       "      <td>0.313323</td>\n",
       "      <td>0.714015</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dset  accuracy_score  f1_score  precision_score  recall_score  \\\n",
       "0   test        0.579911  0.423714         0.289689      0.788526   \n",
       "1  train        0.638035  0.452692         0.327775      0.731452   \n",
       "2    val        0.635093  0.450836         0.327761      0.721919   \n",
       "0   test        0.634380  0.449642         0.325650      0.726109   \n",
       "1   test        0.501660  0.393603         0.252672      0.890026   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  \n",
       "0                 0.314081       0.697044                 NaN  \n",
       "1                 0.390008       0.733126                 NaN  \n",
       "2                 0.385223       0.726640                 NaN  \n",
       "0                 0.383574       0.728576                 0.0  \n",
       "1                 0.313323       0.714015                 1.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_dset_df = pred_df.groupby('dset') \\\n",
    "    .apply(lambda x: sg_plot.dset_metrics(x.target, x.predict_bin, x.predict_prob)) \\\n",
    "    .reset_index()\n",
    "metrics_test_df = pred_df[pred_df['dset'] == 'test'] \\\n",
    "    .groupby(['dset', 'dset_naics_holdout']) \\\n",
    "    .apply(lambda x: sg_plot.dset_metrics(x.target, x.predict_bin, x.predict_prob)) \\\n",
    "    .reset_index()\n",
    "metrics_df = pd.concat([metrics_dset_df, metrics_test_df])\n",
    "metrics_df.to_csv(Path(setup.temp_path).joinpath('62_REPORT_metrics.csv'), index=True)\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d906270-4264-4b0b-9f50-dcef8f75a5fd",
   "metadata": {},
   "source": [
    "## Lift\n",
    "Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "82d9c0ab-8989-450d-a2b1-7997ca636052",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred_test_df = pred_df[pred_df['dset'] == 'test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c01f5279-1151-4a8d-9b21-e12d4092f3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lift_curve(pred_test_df['target'], \n",
    "                pd.concat([1-pred_test_df['predict_prob'], pred_test_df['predict_prob']], axis=1), \n",
    "                title='Lift Curve', ax=None, figsize=(4,3), \n",
    "                title_fontsize='large', text_fontsize='medium')\n",
    "plt.gca().legend(loc='upper right')\n",
    "plt.gcf().savefig(Path(setup.temp_path).joinpath('62_PLOT_lift_curve.png'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04994f41-3c98-412a-bb2f-11e5ae22b432",
   "metadata": {},
   "source": [
    "## Feature Importance "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "926af7df-e407-484b-bd86-ae7d76a46392",
   "metadata": {},
   "source": [
    "##### XGBoost gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ec3f6e1f-2b29-4b46-ac12-7a8451a3a45b",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_impgain = pd.DataFrame(xgb_model.get_booster().get_score(importance_type='gain'),\n",
    "                                     index=['importance']).T \\\n",
    "    .sort_values('importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9586c637-7add-4401-b61c-37abd55b11b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_impgain.to_csv(Path(setup.temp_path).joinpath('62_REPORT_importance_gain.csv'), index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "311e111f-d8e5-4a52-8adc-6a7c5f9daa8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>urban_flag</th>\n",
       "      <td>310.565308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowDoc</th>\n",
       "      <td>118.237740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>menc_clus_NAICS</th>\n",
       "      <td>86.230820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DisbursementGross</th>\n",
       "      <td>49.666847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>franchise_flag</th>\n",
       "      <td>21.106710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   importance\n",
       "urban_flag         310.565308\n",
       "LowDoc             118.237740\n",
       "menc_clus_NAICS     86.230820\n",
       "DisbursementGross   49.666847\n",
       "franchise_flag      21.106710"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_impgain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9c88564a-7289-4c49-b2cd-b97338d1b042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_impgain.plot(kind='barh', legend=None)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel('gain importance')\n",
    "plt.gcf().savefig(Path(setup.temp_path).joinpath('62_PLOT_importance_gain.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e5fb90-f305-40ff-acaf-285d7d056c15",
   "metadata": {},
   "source": [
    "##### Permutation Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e2c1c123-20d0-423d-b684-585c0a5d6d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_importance = permutation_importance(xgb_model, \n",
    "                                         train_df[predictor_features],  train_df['target'],\n",
    "                                         n_repeats=30, scoring = 'average_precision')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ab0df42e-af4e-46b1-b0cf-630877137678",
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_importance_df = pd.DataFrame({'features':predictor_features,\n",
    "                                   'importance': perm_importance['importances_mean']}) \n",
    "perm_importance_df['abs_importance'] = np.abs(perm_importance_df['importance'])\n",
    "perm_importance_df = perm_importance_df.sort_values('abs_importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0cbc5d84-6775-404f-a685-44f5be5cd481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>importance</th>\n",
       "      <th>abs_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>menc_clus_NAICS</td>\n",
       "      <td>0.078143</td>\n",
       "      <td>0.078143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DisbursementGross</td>\n",
       "      <td>0.068165</td>\n",
       "      <td>0.068165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>urban_flag</td>\n",
       "      <td>0.035613</td>\n",
       "      <td>0.035613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CreateJob</td>\n",
       "      <td>0.021682</td>\n",
       "      <td>0.021682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LowDoc</td>\n",
       "      <td>0.014928</td>\n",
       "      <td>0.014928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            features  importance  abs_importance\n",
       "7    menc_clus_NAICS    0.078143        0.078143\n",
       "3  DisbursementGross    0.068165        0.068165\n",
       "5         urban_flag    0.035613        0.035613\n",
       "1          CreateJob    0.021682        0.021682\n",
       "2             LowDoc    0.014928        0.014928"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perm_importance_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "38059608-2f72-491f-86b3-0c1418b83531",
   "metadata": {},
   "outputs": [],
   "source": [
    "perm_importance_df \\\n",
    "    .to_csv(Path(setup.temp_path).joinpath('62_DATA_importance_perturb.csv'),\n",
    "            index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c27499c8-065b-4143-a7dc-bce301bf3edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perm_importance_df.head(10).plot(kind='barh', x='features', y='importance', legend=None)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel('permutation importance')\n",
    "plt.ylabel(None)\n",
    "plt.gcf().savefig(Path(setup.temp_path).joinpath('62_PLOT_importance_perturb.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd44a7c-bcf5-4284-8b9f-b575fa523ff3",
   "metadata": {},
   "source": [
    "## PDP Plot - NAICS mean encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0e9ee010-d1ce-4b1a-86ef-27d691583fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = partial_dependence(xgb_model, train_df[predictor_features], ['menc_clus_NAICS'])\n",
    "pdp_df = pd.DataFrame({'grid_values':results[\"grid_values\"][0],\n",
    "                       'pdp':results[\"average\"][0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "730411c5-81d3-4911-ad8f-8743dc1617e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3267866a-3453-443f-9015-5dbcd90e7594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 3))\n",
    "pdp_df.plot(x='grid_values', y='pdp', legend=None, ax=ax)\n",
    "ax.set_ylabel('effect')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('62_PLOT_pdp_naics_meanenc.png'),\n",
    "               bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a0ef4c8-fce6-43d5-b0c0-f354628157c0",
   "metadata": {},
   "source": [
    "## NAICS variation plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4686a4a5-0d81-4f86-a8da-f4f44a2a416a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sba_loans['cluster_desc'] = ''\n",
    "naics_info = sg_plot.naics_variance_data(sba_loans,\n",
    "                                        naics_sector_feat='cluster',\n",
    "                                        naics_sector_desc_feat='cluster_desc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "52c2da4e-6c97-4fef-8821-937ab0558287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot higher volume NAICS\n",
    "plt.close()\n",
    "fig = sg_plot.naics_variance_plot(naics_info[naics_info['count_naics'] >= 100],\n",
    "                                  naics_sector_feat='cluster',\n",
    "                                  naics_sector_desc_feat='cluster_desc',\n",
    "                                  num_sectors=5, xlabel='loan default rate')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('62_PLOT_naics_counts_rates_150.png'),\n",
    "           bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b90aacf8-442a-4e7d-bc56-b59c9b60cf54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sg_plot.naics_variance_plot(naics_info[naics_info['count_naics'] >= 100],\n",
    "                                  naics_sector_feat='cluster',\n",
    "                                  naics_sector_desc_feat='cluster_desc',\n",
    "                                  xlabel='loan default rate')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('62_PLOT_naics_countxs_rates_100_all.png'),\n",
    "           bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "334a928e-bd10-4a7c-9c4e-16e694bf4745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xp/ZhCwNuIuvS0tLw1FNPmWVs69ati27dumH79u0oLCxEeLjtzjgbN25E+/btjQEwADRt2hQJCQnYsGGDWRB85coVFBUVoWnTpqKPj3+5CsHuEETOc7XbhSeCJGvbTEuK93grLzHOTu9pDPa92etZrky4t7tg+MoFEJG3abVahIaGWiwPCwuDTqfDmTNn0LFjR6vrlpaW4tSpU3jppZcsHuvQoQN2794NtVqNiIgIAMCwYcOwf/9+OFPlyyBYIVgOQeQ9ngjOlFT6YK0jg9iZ5pjVJCKpNGnSBIcPH4Zer4dKVXZO0ul0yMjIAFB205stubm50Gq1qFu3rsVjhmXZ2dkO64rt4dlOIVgOQRWR2DpOb3+87evcyT5LmdW01ZpM6gsGe7MhEpF8XnvtNfzzn//Eyy+/jKSkJJSWlmLGjBm4ceMGAECj0dhc1/BYSEiIxWOVK1e2WD81NdXp4+NZQiFYDkH+zlF9qL1AV0lZVleZBmZqdSFSU1OhUqksZozzh5/VwFuBqKuzIRKRZ7366qu4evUqPvroI6xatQoA0K5dOyQlJWHmzJl264ENZRRardbiseLiYrMxruJZQiFYDkH+zlFwx9nMrM8+Z1D+IqH8hBQAXJr8ovw6UmfdrQWjjiavcCWr62+zIRL5i5kzZ2L8+PH45ZdfEBkZiZYtW+Ldd98FADRu3NjmejVq1EBISIgxa2zKsCw6OtqtY2MQrBAshyCyzRMzfrnz8bkr2VrLID/of//+NVmGaRDnzJTH5csfXCmH8NQNfNYCU2eea2Z1iXxf9erV0aVLF+P3e/bswf3332+3k0NgYCBatmyJY8eOWTyWkZGBBx54wHhTnKt4ZlEIlkOQv7MWyIoNRA2zhXmqD6xSAi3Tn9FURcuSm2Z1i3QlxslR2P+XyPetX78eR48exccff4zAwEDjcmstzhITEzFp0iQcO3YM7dq1AwBcuHABP/74I8aPH2+2XbZI82EshyB/507wUqQrcaoPrKc/PjdtOSYlOeuBHWXDi3QlVssnHHG3vELO/r+mGHwTOe/AgQOYPn06evTogaioKBw+fBgrV67EU089hTfffNNsrLUWZ6+99hqWL1+O3r17Y/z48QgKCsL8+fNRu3ZtvP322w7Xd4R/1QrBcgiqiEwDUUdTIZtylBn1dG1oRQyIyj/ncvQ/ljMjznpjIufVq1cPKpUKH330EdRqNRo2bIgZM2Zg3LhxqFTJ8Xk0IiICqampGDt2LGbMmIHS0lLEx8djwYIFqFWrltvHFyA4EzKTx3Tp0sVqJtjWfNxEcpO6PMGZINgRbwQs7mQlDd0hhIBAbPn5Co6X1MNPyb3MnkNHpSLWHvenzhJKYyjnqYgXQES2FBQUIDIy0mdjFf41KwTLIUgurgZzUn1MbQgqPNExwJPk7mnMbgjeZbjA4HNO5D8YBCsEyyFILlJkD935mNoQVPhLxwDxz6ehO0RDAPafQ1uPZc3ubZaRd/fiwFEAL6YmuKLdxEdEvkuZ7yIVELtDkLeUL2PwFcx8Wip/w6C7nAlg5agJFsuXfq+JSD4MghWC5RDkLrHBUPkyhmPJCaLWE1OTSt5VPuvMLGwZ/i4SkRg8UygEyyHIXdJM4GCbvSyskssTSHnKl1Lwd4eI5MAzj0KwHIJ8jRLKEzw1gYY72xXzUbyhO4RKpYJer0d8fDwiIsJdPlZ7GXkldowoX0oh9+8REVVMDIIVguUQ5C6xdZC+WMZgKOEoX/Kh0emN/79TqEVRsO2SEGd+TrkmaHD3tbCWkXenPlbM74ong2xPzhJIRMQzikKwHILc5UqA4CtlDGICLU/dqOW5OltDd4hA4GjZBbArGVFHGXmpXl+pfles1aDbutCQ8mLEF37Pici7OFmGQnCyDCLbpJxIQ8l8sSygfHBqmj1WEl98bomUjpNlkCRYDkFkqXwP3PLE9K01sJYJtJZdNN2mte0atuNssGd6/IXqu0hLS0OgSoVKcK8mWG7MsBKRr+LZSyFYDkFUxt5H4KYBV1hwJbPHo8JDnA7IXCmzMGQUna2FtQyYy8ohFnbUIyxYJWswKWXtrWl5hrslDL5Yv05EvoNnFIVgdwiiMraCy/JBZNbs3oroUKE0rgSeUk+Bbet7d7SbsdetAJ032RFReTwLKATLIYi8z1rnBLHZx/Lrmq6XlhRvzCBb24ZpizRr+3cnWHO3W4MUU2BbI+bmN2tMn0tHHUDE8qWOH0TkOfzrVAiWQxCVMQ0uPf1xuKPt2euIYC3raa0MwNo29MEqhKgAlQoo0gFFOj1UVtrAWQvWfDWokqKVmlQdQLx54x4/qSBSLt88m/ohlkMQlbEXdPpKAOhMmcbEo0HGFmnlWQvWHG3Xlb7A5bPYtm4wdHQjoj8F7UTk/3h2UgiWQxB5lxS9aZUY4Ll7TGKzrWLHGYLy8t09TAPq3W91tRt491iY5nCcPYZ1eJMdEZniWUAhWA5BFZm7AakrAY2Yj+cdfWwuxUfdc9rfM2uRJkdHBE/uQ8zzbAhypRpXXtbs3g5LVMrjjXRE/o9/1QrBcgiqyNwNSH257jJEBZst0rxZAlK+jMKT0yF7myEALl9vLmYdMWOlwkCbyLv4F6cQLIcg8i5btbNy96YVW08sdWBW/uc2LV8oP4EI4Hppguk2rZU5mC63Vnfsyk1tcnbMcIYvX8wR+SIGwQrBcgiqyOQISMVsS8k343kyUysm6HO1NMGZbUnVDYKIyBplnt0rIJZDUEWmpIBUygk4bNc666HVAyoAer15izTyPnf6RROR75L1r/qXX37B1KlTkZmZiZycHISFhaFZs2aYMGEC+vTpYxy3fPlyrF69GufPn0deXh6io6MRHx+PKVOmIDY21jguJSUFI0aMsLm/1atX44UXXgAAXLhwAZ9++ikyMjLw888/Q6vV4vLly2bbA4A7d+7giy++wLZt23Du3Dncu3cPTZs2xdixYzFo0CCzsYWFhfjoo4+QkZGBI0eO4M8//8TKlSvx4osvOnwuWA5BZMnXZ4Szn60N+t+/gTZbpPmitKR4l9YzLYMw2P1WV9QID5bgqMw5O7Odkj8RICLXyfpX/fvvv0OtVmP48OGIjo5GUVERvv32W/Tt2xfLli3DqFGjAADHjx9Hw4YN0bdvX1SvXh2XL1/G8uXLsX37dpw8eRLR0dEAgLi4OHz11VcW+1mwYAFOnjyJhIS/atzS09OxaNEiNGvWDA8//DBOnDhh9RjT09Px3nvv4e9//zuSk5NRqVIlfPvtt3j++edx9uxZTJs2zTj29u3bmD59OurXr49HHnkEqampop8LlkMQkT+QsoRBypILU2IurHz9AoyIHAsQBEGQ+yBM6fV6tG3bFsXFxTh//rzNcZmZmWjXrh0+/PBDTJo0yeY4jUaD2rVro2PHjti9e7dxeW5uLoKCghAREYGPP/4YEyZMsJoJvnz5MgIDA9GgQQPjMkEQ0L17dxw8eBB37txBlSpVAABarRZ//vkn6tSpg2PHjqF9+/aiM8FdunSxmgnOz89H1apVHa5PpASeaCslV6sqKW48s7WNQvVdpKWlIVClQqlej65duyL+k8Nu74/EYXBLJI2CggJERkb6bKyiuM93VCoVYmJicPToUbvjDMFqXl6e3XHbtm2DWq02lkEY1KhRQ9TxNGzY0GJZQEAAnn32Wfz444/47bff0LJlSwBASEgI6tSpI2q75bEcguTiTrBXvm7S3nZd3Y9Gp3d7G57qI+wek3KIn5URADsz25yvtVBzZSY9IvJvigiC7969C41Gg/z8fGzduhU7d+60qLcFyupz9Xo9rly5gunTpwOAWYmDNWvWrEFoaCj69esn6THn5OQAAGrWrCnJ9lgOQXIwzbK6q3xHAU+0lXJ1m8z8iePMxYLUQaXpBZWBlDeksaaXiMpTxFnh7bffxrJlywAAgYGB6NevHxYvXmwxrl69etBqtQCAqKgoLFq0CE8++aTN7ebm5uL777/Hs88+i4iICMmONzc3F59//jm6du2KunXrSrJNdocgOfhaNs+bnAnynO0koFYXIjU1FSqVCnq9HvHx8VCFVDYLApXekcCTx8YZ2ojIGxRxlnnrrbeQmJiI7OxsbNiwAXq9HjqdzmLczp07UVxcjHPnzmH16tW4e/eu3e1u3LgROp3OohTCHaWlpXjhhReQl5eHf/3rX5Jtl+UQRI65Gxi6W/phi2nJhun/7Y3X6QGhVMD2E9fx/tF92PZWgsUYU54KCpUSbPJGNCLyNkWc/Zo2bYqmTZsCAIYNG4YePXqgT58+yMjIMCsTeOKJJwAAvXr1wjPPPIMWLVogPDwco0ePtrrdNWvWoEaNGujVq5dkxzpmzBh8//33+PLLL/HII49Itl2WQ5Acyk8j60w2U44ssrutqrxxzOK7IxhqgmMBWHZC8NZEEQw8iaiiUkQQXF5iYiJeeeUVXLx4EU2aNLE65sEHH0Tr1q2xZs0aq0HwlStXkJaWhlGjRiEoKMjKFpw3bdo0LFmyBLNnz8Y//vEPSbZpwHIIskbqqXGldCw5wWw6XWtT3JqyNVZsUKuUjGVFJFeHDiIiT1LkmUyj0QAoaw/maJyhRri8r7/+GoIgSFYK8e9//xtTp07FW2+9hYkTJ0qyTVMshyBr5KrZdeUGNGcyl6ZjxWQii3QlklwQlO9gYcpRQG+vzMHaRA/eYMjke6rMw9rNat66MGOgTUSeJutZ5o8//sB9991ntuzevXv48ssvERoaimbNmqGkpARqtRrVq1c3G3fkyBGcPn0aQ4YMsbrttWvXon79+ujSpYvbx7l+/Xq88cYbeOGFFzB//ny3t2cNyyGIbPP2xYC3ShFmd9Bjy89XcLykHn5KLivbEluSYhqMeqIThy3e2hfLNIjI02QNgl955RUUFBQgLi4O9erVQ05ODtasWYPz589j3rx5CA8PR15eHmJiYjBo0CA0b94cVapUwenTp7Fy5UpERkZi8uTJFts9c+YMTp06hUmTJtkMLvPz8403thkysIsXL0a1atVQrVo1Y4nFkSNHMGzYMERFRSEhIQFr1qwx207nzp3xwAMPGL9fvHgx8vLykJ2dDaCsT/G1a9cAlNUTR0ZGWj0elkNUHM58tOzN3qbO1gST+0JUgCpAQAD+qncWG/z5e2cPsRln/p4SkatknTFu3bp1WLFiBU6fPo07d+4gIiICbdu2xZgxY9C3b18AgE6nQ1JSEvbt24esrCxoNBpER0eje/fuSE5OtpjhDQDeeecdzJ49G6dOnTJOZFFeVlaW1YkwAKBBgwbIysoCAKSkpGDEiBE2f4byM8LFxsbi999/tzrW2ox0BuPHj8e8efMslvvqLCz+TsoJJlx9Ew8LriRpraY36z6dff4cdWYQW5dsj7XteOo5MLRICwsua5GWkJDgdBvH2Ek7PHJsvoYZYyL5+PqMcYqbNrmimjBhAj7++GOL5b76i+XvlBCAnJ3e06PZW08GwUp4/sTwVIClVquxd+9eY59gV4Jgdy7E/CmLzCCYSD6+HgTzcySFYDkEOat8ICN1rSaDC9d4K6PuznbFltmYXmSVz7Lby8CbPmbr4ozlN0QkN551FILdIXyLO7W61u64r2ikrHX2djDlKANr+ritsUU6PbR6IMy1yg23OdOWznAxZC977+6NhO68fkW6EgbQROQSnjkUgt0hfItUb7pSzIDmi9k0T2ZHPf0cOFNKYP9iJwj/erzU/QOSmNSZbGcu+Fy9ODRcVPnK7z8RKQPPGArBcoiKw1PTw3ojAFQiTrfrutuFxWYXUYB5IGqayTbtsWy4KVMpn2gYgnb+HhCRMyreO6ZCsRyCXMEA0PsclXKIyc4bukMA0tdD2C7BsFxuGsRaC2htBbnebN1HROQpDIIVguUQRL7BmWy7rey8PliFEA/VA3uj84PYfdhrWWd685yY8abEvAac6pmIHOFZQSFYDkHkH5id/4uzN8yJHW8rE23rpkR32skxeCbyX/zrVgiWQ1BFw0ydZ1gLEIt0JdDo9GbLvDU1tCc4m+12p3aZFzRE/ovvOgrBcghSAjGBqTtZNVvbkWqbDKStPwe+NjmGaSDva8dORL6D7xgKwXIIcpVUAWT5bdnarieCEqm6DDBr5x9MA3lXb8Lz1faBROQ9PCsoBMshyFWeypQppf0VuU9MICkmaCx/YWQvsJQqCJUieK2o7QOJyD6eFRSicuXKch8CkUeVD8SYqfMeZ59bW0GjO4Gst19f3qBIRI4ECIIgyH0QBGi1Wmi1WuP3BQUFiImJQX5+PqpWrSrjkZHSSV0O4anAtPy2KvKNcWq1Gnv37oVKpYJer0dCQgIiIiLkPiwiIqcUFBQgMjLSZ2OVivOuo3AhISEICQmR+zDIB/nqFMTM1BERkZwYBBOREQNTIiKqKALlPgAiIiIiIm9jJlihDKXaBQUFMh8JEUlNrVajqKgIgiAgICAABQUF4O0ZRORrDDGKr56/GAQrlFqtBgDExMTIfCREREREtqnVakRGRsp9GE5jdwiFKi0tRXZ2NiIiIkTPJmfoKHH16lWfvEvTn/C1UA6+FsrB10I5+Foohy+/FoIgQK1WIzo6GoGBvldh63tHTERERETkLkFmFy9eFAYNGiTUq1dPCA0NFZo0aSJMmzZNuHv3rnHMrl27hJdeeklo3ry5EBgYKDRo0MDqts6dOydMmDBBeOSRR4Tw8HChTp06wt///nfh6NGjFmM3bdok9OjRQ6hbt64QHBws1KtXT+jfv79w+vRpi7FvvfWW0Lp1a6F69epCaGio0LRpU2HKlCmCWq02G6dWq4X3339f6Nmzp1C9enUBgLBy5UqXnperV68KAPjFL37xi1/84he/FP119epVl2IduclaE3z16lV06NABkZGRGD16NGrUqIH09HRMmTIFmZmZ2LJlCwBg7dq1WL9+Pdq0aYPo6Gib2/v888+xYsUK9O/fH6+99hry8/OxbNkydOzYEd9//z26d+9uHHv69GlUr14db775JmrWrImcnBx88cUX6NChA9LT0/HII48Yxx49ehRdu3bFiBEjULlyZRw/fhyzZ8/Gnj17cODAAeNHALdv38b06dNRv359PPLII0hNTXX5uTE0zvfFj0eIpKBWq7F//37jzWPdunXjhBJERApiKOXw1XOzrDXBs2bNwnvvvYczZ86gefPmxuXDhw/Hl19+idzcXFSvXh3Z2dmoVasWgoKC8PTTT+PMmTPIysqy2F5mZiaaNGmC8PBw47I7d+7g4YcfRuPGjfHTTz/ZPZ6bN2/i/vvvx8svv4xPP/3U7th58+Zh/PjxSE9PR8eOHQGUzfr2559/ok6dOjh27Bjat2+PlStX4sUXXxT/pPyPr8/CQuQuzqpGRKRsvh6ryFoTbGitUbt2bbPldevWRWBgIIKDgwEA0dHRCAoKcri9tm3bmgXAABAVFYWuXbvi3LlzDte/7777EBYWhry8PIdjY2NjAcBsbEhICOrUqeNwXSJfUaQrQeykHYidtEPS6ZmJiIjkJms5RHx8PObMmYOXX34Z06ZNQ1RUFA4dOoSlS5fijTfeQJUqVSTZT05ODmrWrGn1sby8PNy7dw85OTlYuHAhCgoKkJCQYDGupKQEeXl50Ol0OHPmDJKTkxEREYEOHTpIcoxE7rIXpBbpStBuxl4AwLHkBNHTIZtu0xNBsCenZSYiIrJH1negp556Ch988AFmzZqFrVu3Gpe/9957mDFjhiT7SEtLQ3p6OpKTk60+3rFjR1y4cAEAEB4ejuTkZLz88ssW444dO4ZOnToZv2/SpAm2bt2KGjVqSHKcWq0WWq3W+D0nySBnNXt/l6hxhmDYWa6uZw+naHZPka7E+Lqfnd5T8osKT2+fiEhOsp/RYmNjERcXh/79+yMqKgo7duzArFmzUKdOHYwePdqtbf/xxx8YMmQIGjZsiKSkJKtjVq5ciYKCAvz2229YuXIlNBoN9Hq9Rb+7Zs2a4YcffsDdu3dx6NAh7NmzB4WFhW4dn6kPPvgAM2fOlGx7pEwMKsgWVzLtpuvcKdSiKNi1bL2t30NPfxIg9jiIiDxB1jPOunXrMGrUKFy8eBH3338/AKBfv34oLS3FxIkTMXjwYERFRbm07bt37+Lpp5+GWq3GTz/9ZFErbGCa3X3++efx8MMPAwA+/vhjs3FVq1Y1dpd45plnsHbtWjzzzDP4+eefzTpJuMo0C0z+xxBA2Asq3A0Azk7vafy/2Kxw+fXKK19GAcClsgpyzJnXzJquc1OlORAbPPFJQHn8ZICIvEnWd7AlS5agdevWxgDYoG/fvkhJScHx48fN2pqJpdPp0K9fP5w6dQq7du1CixYtRK1XvXp1/O1vf8OaNWssguDy+vXrh3/84x9Yt26dJEFwenq629sg5bIW4JQPKtwNAEwD0vKBra2aYKky0wyGiYjI18j6znXz5k1Ur17dYvm9e/cAlN2M5qzS0lIMGzYMe/fuxYYNG9CtWzen1tdoNMjPz3c4TqvVorS0VNRYMTp27IiDBw9Ksi0iV4JSa5npsOBKxuA8dtIOs8dNg3hm8NxnLyNvYC8zn5YUj9BglUv7tlcOIWXmX+rtERG5Q9YzUOPGjbF7925cvHgRjRs3Ni7/+uuvERgYiFatWjm9zTFjxmD9+vVYtmwZ+vXrZ3PcH3/8gfvuu89sWVZWFvbu3Yt27doZl+Xl5aFKlSoWLdo+//xzADAb645Klfhm4M8MAY5cQYC9YNZA6sw0lTHNtnvyNQ8NVrm0bdNPBeTGDiRE5E2ynh0mTJiAnTt3omvXrhg9ejSioqKwfft27Ny5EyNHjjTODnfq1Clj94j//ve/yM/PN3aPeOSRR9CnTx8AwMKFC7FkyRJ06tQJYWFhWL16tdn+nnvuOWPbtZYtWyIhIQGPPvooqlevjkuXLmHFihW4d+8eZs+ebVwnNTUVb7zxBhITE9GoUSPodDqkpaVh06ZNaNeuHYYOHWq2j8WLFyMvLw/Z2dkAgG3btuHatWsAygL0yMhIq8+FXq9367kkZbP2RmzItPrKzXJnp/dkJs8KR4Gb6eNS1tWW35ar2zZc7DiqSZa6JtgbNcYAL+aIyDZZ38Hi4uJw6NAhTJ06FUuWLMGdO3fQsGFDzJw506ybw88//4zJkyebrWv4fvjw4cYg+MSJEwDK6mut1dhevnzZGAT/85//xI4dO/D9999DrVbjvvvuQ48ePfDuu++iZcuWxnVatmyJJ554Alu2bMGNGzcgCAIefPBBvP/++5gwYYJxQg+Djz/+GL///rvx+02bNmHTpk0AgKFDh9oMglkT7N9MAyHTj72LdCVO34Ev5Q10zgS15R8zBPEVnbs3tBERkTxknTaZ/jJ+/HjMmzfPYrmvTkVI5myVILhCysyWkrPQSpg22dbzY3qxInUQbK+2V6PTG7tA2BvnzOvozXIIOT5JUNLvNJG/8fVpk3l2UAjWBJMcTGuF/Y0UQZ2tPrye/ChfbKsze+Ps3WRnKyi0d3OcJy6U+EkCEcmNZyCFYE2wf3OmFy8DA2lInaH1dB9eKdn72cV0oTAl5YQZ/nzRRUS+h++2CsGaYP8mNrBldsySVg/o9UCRTg+VAjoY+Dp3Lg7czYAzACYiJeG7rUKwT3DFxeyYfeMPBwIIBI765t+HoZ+vgelFjumnAKbjbY1x9ZMC3rxHRGSJQbBCsCaYyFyRTg+tH1QJOeq4Ub71nCeUD8QdkToIJyJSIp7NFII1weRPpLgp7bGPDgIIcjhO6ZzNwtoLhuXureupch0ldykhIv/FM41CsCaY/Ikvf/zuzKQgvvxziuVOuY7YiyFXbr5joExE7uJZRCFYE0ykDEW6Emh01j+ZKR94OdtpwdXjUUo5grMZW1cuEsRmu1lHT0TuYhCsEAEBAXIfApFkpAgO1epCpKamGifL6Ph4F3RbmAHgr2DQE5lYe9MRZ83uXSE+ureVjZWyXZq7TPfvj68BEXkezxwKwXII8idSBCX6YBVCVIBKBegBs9nR5GolV37mP1cCQWeP21COEDtph9dqgsWQ+1hML4CYFSYiVzAIVgiWQxDZFxpkWZtqqN8Vw3TKYaBs2mHAvUkwXAkEGbARUUVx9OhRrFq1Cvv27UNWVhaioqLQsWNHzJgxA40bN3a4fl5eHpKSkrB582YUFRWhQ4cOmDdvHtq0aSPJ8TEIVgi2SCMST4qPwk0zy95kyCY7W98rdz0woKz6ZCJSvjlz5uDgwYMYMGAAWrVqhZycHCxevBht2rTB4cOH0aJFC5vrlpaWonfv3jh58iQmTJiAmjVrYsmSJYiPj0dmZiYaNWrk9vHxDKYQbJFGJJ4UtcByf5zvyv7L11qLCUI9VcPM2Q2JyJFx48Zh7dq1CA4ONi4bNGgQWrZsidmzZ2P16tU21924cSMOHTqEb775BomJiQCAgQMHonHjxpgyZQrWrl3r9vHxDKYQrAkmIkfKB/9ibkB09mY2R5N7sJyDiMTq3LmzxbJGjRqhefPmOHfunN11N27ciNq1a6Nfv37GZbVq1cLAgQOxevVqaLVahISEAABu3LiB/Px8PPjggwgKEt9fnkGwQrAmmMgxQxDn7AxoBqZ1weVrgtOS4i1KJExbpWl0evRYmObSfj1Fyok4DOQIcitCxw0iKiMIAm7evInmzZvbHXf8+HG0adMGgYGBZss7dOiAzz77DBcvXkTLli0BAO+88w5WrVqFy5cvIzY2VvSx8EyjEKwJJnJMypZo5W+Ic+UGOUeZ2PI1tABE19TaytqWv8FPau62PnMl2yxF6zUGzkS+Yc2aNbh+/TqmT59ud9yNGzcQFxdnsbxu3boAgOzsbGMQ7CqeNRSCNcFE/icsuJJZoOxMgCdXzbLcs+C5+nOzTINI+c6fP4/XX38dnTp1wvDhw+2O1Wg0xnIHU5UrVzY+bpCSkoKUlBSnj4dBsEKwJpjIOq0e0OuBIp3e6TIIZ8sf7DFkGk2DRHcCRrlvzCMi8qacnBz07t0bkZGR2LhxI1Qq++ff0NBQaLVai+XFxcXGx93FIFghWBNMZN34w4EAAoGj7v19uFv+4I0pkn1R+efF1XIItl4j8l/5+fno1asX8vLykJaWhujoaIfr1K1bFzdu3LBYblgmZhuO8EyjEKwJJlI2ucsEXCEmc14+W24rO24rMC2/3N0Alq3XiPxLcXEx+vTpg4sXL2LPnj1o1qyZqPUeffRRpKWlobS01OzmuIyMDISFhYmabMMRnmkUgjXBRNZ93LEUer0e8fHxiIgItzu2fBbSWoBXkcoQnP1Z7WXHPVlzy9ZrRP5Jr9dj0KBBSE9Px5YtW9CpUyer46y1OEtMTMTGjRuxadMmY5/g27dv45tvvkGfPn3M6oXZIs3HsSaYyLoQFVAk8hrRXtDnyY4KRERk6e2338bWrVvRp08f5ObmWkyOMXToUADWW5wlJiaiY8eOGDFiBM6ePWucMU6v12PatGlm22GLNB/HmmAi2yYeDXK7JljpjiUn2KyLFVMzy7paIlKaEydOAAC2bduGbdu2WTxuCIKtUalU+O677zBhwgQsWrQIGo0G7du3R0pKCpo0aSLJ8fEsqRCsCSbyf9ZqdA0TcpSfmMPaGGuPAeLrdYmIvCk1NVXUOFstzqpXr47PP/8cn3/+uUvrO8IzpEKwJpj8lTOzgZlPmqCHVg+oAHzQ5h66du0KVUhlp1ucOcqaepPYfdor3RBT1mHYD+tsiYhsYxCsEKwJJl8kph2WM7OBWXZgMNzgEAj8fNjsEbE1vo4CQWdLB5wpO/DFjhJERBUFg2CFYE0w+SJngzw5sq+2Am9HWWkxHLXzEtNb2HB89lqVOWpjxrIHIiLn8cypEKwJJvrL2ek9oVYX4rGP3L8wtBeoO1Mu4ExZh4EzY0yD9ajwEIsb42w9RkREruGZVCFYE0y+yFGm09VygLDgStA7MaWxq8SUc1gba289V7o0GDLKtoJy9tElIpIeg2CFYE0w+SJPZyTntL8HlUrlcLIMe4GnvUDc1SBdbFmH2HGGiwlmeImIvIdnXIVgTTD5o/KZYtNgNS0pHgAsal1NA8EQFaByYrIMX2UIxpntJSLyHgbBCsGaYBLLldpUudg7tqjwEIvvbY13ZrKMijQtMhERuU65754VDGuCyR5b9ajWalO9GRQ7G5Bbq2319+ynmA4RRETkfQyCFYI1wWSPrdpVa1lPMUGlMzeEid2Ou9s0LZUoHzjOaX/Pbk2wM9s2rRn2Rh9fJWfqiYgqMp6dFYI1weTNMgdPBH9SliEU6UqgN5kxzhZXnqN2M/Yan19bWVprAX1uoQ49FqYBAHa/1RU1woMlOR4iIpIHz9gKwZpg/yU2Q2ovq3osOcH4f9OJE5yd7cxX/BVQm8wYZ6Um2JlSg2PJCcbtOnpNrD2nmuC/SpZqhAejZnhl0fsmIiLl8b93Tx/FmmD/5UrWVWxW1dGMZbZIVadqrczAWoDpqZvVPN3izBp/r2EmIqooGAQrBGuCyRWuZoE9kT02BOSeKLUoXxPsjVpeIiLybwyCFYI1wf7LNOvqTvCmxC4Dcs1kZloeYotp2Uj5nsSGZaEuzEonprzFH0tUiIj8Dc/UCsGaYP8lVUCk5MDKNDB0NkDd/VZXhAaroNHpzW48M/wfcK5PsDWmwa+9ZVJx58LAl/pAExH5Mp5dFYI1wRWDvW4Etqb99QXuZLhNg117y3yJO+3ipGw7J5Ycv28M9olIbjzrKARrgisGMW/0rt7sRsohVc2yt2a/c5S59kQw7o1gn39HRGQPzxAKwZpg3+BK9krMOnLV1kpFTL1y+UBHo9NbfG9aDqHXFePQwUMIVKlQqtej8+OdER5eRfQxld9ejfBgizphZ2qCK/J0zJ6+EdFTz60v/00RkecxCFYI1gQrh72slOljdwq1KAq2PtY00HUn4+UrmSwxx+lMIPVXOYRJn+CTR50/MIvt/cWTNcGA6zcy+nppDBGRr5D17PrLL79g6tSpyMzMRE5ODsLCwtCsWTNMmDABffr0MY5bvnw5Vq9ejfPnzyMvLw/R0dGIj4/HlClTEBsbaxyXkpKCESNG2Nzf6tWr8cILLwAALly4gE8//RQZGRn4+eefodVqcfnyZbPtAcCdO3fwxRdfYNu2bTh37hzu3buHpk2bYuzYsRg0aJDZ2KNHj2LVqlXYt28fsrKyEBUVhY4dO2LGjBlo3Lix3eeCNcHKITZYcyWIcjbjpcRMlrfqVH2dFMGrUkpjPNGZhME+EclN1rPO77//DrVajeHDhyM6OhpFRUX49ttv0bdvXyxbtgyjRo0CABw/fhwNGzZE3759Ub16dVy+fBnLly/H9u3bcfLkSURHRwMA4uLi8NVXX1nsZ8GCBTh58iQSEv66az09PR2LFi1Cs2bN8PDDD+PEiRNWjzE9PR3vvfce/v73vyM5ORmVKlXCt99+i+effx5nz57FtGnTjGPnzJmDgwcPYsCAAWjVqhVycnKwePFitGnTBocPH0aLFi1sPhesCSZf4Qs9es9O7yl6Ig9rXCmbkLILiNIufjwdoCol2CeiiiVAEARB7oMwpdfr0bZtWxQXF+P8+fM2x2VmZqJdu3b48MMPMWnSJJvjNBoNateujY4dO2L37t3G5bm5uQgKCkJERAQ+/vhjTJgwwWom+PLlywgMDESDBg2MywRBQPfu3XHw4EHcuXMHVaqU1SkeOnQI7dq1Q3BwsHHspUuX0LJlSyQmJmL16tU2j3P8+PGYN2+exfL8/HxUrVrV5nokPUflEIbAyl5wVL4cwtWMlxIDg9hJO+Q+BIdsBcGeDOCVFriKwQ4NROSOgoICREZG+mysorgznkqlQkxMDI4etV//ZwhW8/Ly7I7btm0b1Gq1sQzCoEaNGqKOp2HDhhbLAgIC8Oyzz+LHH3/Eb7/9hpYtWwIAOnfubDG2UaNGaN68Oc6dO2d3P6wJVg6xgUBUeIjTQYM/ZLyc/Whcjsxx+X1646Y2WxdPzlwEGR7zVsmJHO3YAGVe3BFRxaOIM9Hdu3eh0WiQn5+PrVu3YufOnRb1tkBZfa5er8eVK1cwffp0ADArcbBmzZo1CA0NRb9+/SQ95pycHABAzZo17Y4TBAE3b95E8+bN7Y5jTbBvcOWjaiV+vO0OBjDWiQn2HQXjhgsMOS4cvNn9wp/+HojIdyni3eztt9/GsmXLAACBgYHo168fFi9ebDGuXr160Gq1AICoqCgsWrQITz75pM3t5ubm4vvvv8ezzz6LiIgIyY43NzcXn3/+Obp27Yq6devaHbtmzRpcv37dGLTbwppg8gaxH3+XzxC6cwOT6QxyYmttK2o7Ml+otyYi8heKCILfeustJCYmIjs7Gxs2bIBer4dOp7MYt3PnThQXF+PcuXNYvXo17t69a3e7GzduhE6nsyiFcEdpaSleeOEF5OXl4V//+pfdsefPn8frr7+OTp06Yfjw4XbHsk8wAdJ9JG1rO6a9eZu9v8tmIGorCJUyOFVaizJv1xC7Q6puDezQQEQVmSLOeE2bNkXTpk0BAMOGDUOPHj3Qp08fZGRkICAgwDjuiSeeAAD06tULzzzzDFq0aIHw8HCMHj3a6nbXrFmDGjVqoFevXpId65gxY/D999/jyy+/xCOPPGJzXE5ODnr37o3IyEhs3LgRKpX9u8tNf06quLwddHk6EJWTOwGdoXZbimDTE4GmJ4JVf6hXJyJyhiLPeImJiXjllVdw8eJFNGnSxOqYBx98EK1bt8aaNWusBsFXrlxBWloaRo0ahaCgICtbcN60adOwZMkSzJ49G//4xz9sjsvPz0evXr2Ql5eHtLQ0Yws3e1gOUbGw124Ze0GmWl2I1NRUqFQq6PV6xMfHIyIi3GPHYq12W+qgUGmBpr/VqxMROUM5Z2MTGo0GQFkw6WicoUa4vK+//hqCIEhWCvHvf/8bU6dOxVtvvYWJEyfaHFdcXIw+ffrg4sWL2LNnD5o1ayZq+yyHqFiU+jG7J6UlxQOAWU2wPWHBKoSoAJUK0OpLEf/RPmgQ7HOtvBhoEhEpk6zvJH/88Qfuu+8+s2X37t3Dl19+idDQUDRr1gwlJSVQq9WoXr262bgjR47g9OnTGDJkiNVtr127FvXr10eXLl3cPs7169fjjTfewAsvvID58+fbHKfX6zFo0CCkp6djy5Yt6NSpk+h9sEUa+bvypReOSjFS3+wItQ4IVAE6HVD6v+Xlp6sWGxD7UuBMRESeJ+u7wiuvvIKCggLExcWhXr16yMnJwZo1a3D+/HnMmzcP4eHhyMvLQ0xMDAYNGoTmzZujSpUqOH36NFauXInIyEhMnjzZYrtnzpzBqVOnMGnSJJu1tvn5+cYb2wwZ2MWLF6NatWqoVq2ascTiyJEjGDZsGKKiopCQkIA1a9aYbadz58544IEHAJR1udi6dSv69OmD3Nxci8kxhg4davO5YIs0/yC2zMG0Y4KBs7OUmY73R/GfHAZgKGUKMS539WdmNpaIiEzJOmPcunXrsGLFCpw+fRp37txBREQE2rZtizFjxqBv374AAJ1Oh6SkJOzbtw9ZWVnQaDSIjo5G9+7dkZycbDHDGwC88847mD17Nk6dOmWcyKK8rKwsqxNhAECDBg2QlZUFAEhJScGIESNs/gwrV67Eiy++CACIj4/H/v37bY6191R36dLFajmEr87C4k+cmVVLCbOpib2Zq3zA7u9tyXytjIKISOl8fcY4xU2bXFFx2mTvcfamNGfu7ldCra/YjKcSAnZvy5rd2+yi5lhygvG1ZZBMROQcXw+CecZXCNYEe487gaqr2VIx2Vn2bPW824XFZr2Scwv/6kdevtbYlOG14GtCROQ/eEZXCNYEe44vtyPzZNAl1YQLznAm0HelRZqjC5zyFzE9FqYZ/y+m1ph1xURE/oNBsEKwT7DnKKFEwdljMARrzgRdztQuA/JnNR31zNWbtEjTo6xlmrXx3rzIcbQvuZ9TIiISj2dshWCfYLLGmQDPdKwrgaE3AjhP9Mz15kWOo305+tmcvVAhIiLP4RlYIVgT7Dme+tjfGzW8rgZ4rtQuy/1Rf/nAvUinh1YPqADo9WXfqxRe2uLo4sPdCxVHGFQTEYnHM6ZCsCbYc7yV4azIAYizGU5rAaD1gN/QJzgQOKr8T0qcuWjxREs6uS9kiIh8ScV911YY1gT7Hm9Mh+tMFttTmWkxGUtnM5xKqNMmIqKKjUGwQrAmmKxxNZCVMjPt6k19FZ21Wf+cnRVQrIr8KQQRkat45lQI1gSTWLZKD7yRmZaSsy3SHuvcFeERVURvX+5ppR3tW8pj86XXnYhIKRh5KQRrgqk8W2UFYksPpMoOli/JkKqUod2MvXaDt/It0uI/OSzJfomIiAAGwYrBmmAqT0ywaa/0QKrsID9q9ww5JishIqK/8N1NIbp06cKaYPIJ9oI3T7aNy5jwuMMZ49whxbE7sw1eXBARyYtnYYWYNm0a3n33XeP3BQUFiImJkfGISG62gk1v9Ce2R+z+pG4bZ2vGOE9w9dh9rS6biKgiYxCsECEhIQgJCZH7MEhBxARhSutP7MtBoC8fOxEROU85755EJAqDNSIiIvcFyn0ARERERETexkywQgmCAKCsNpioIlKr1SgqKoIgCAgICEBBQYHx74KIiORniFF89dzMIFih1Go1APDmOCIiIlI0tVqNyMhIuQ/DaQGCr4bvfq60tBTZ2dmIiIhAQECAqHUMHSWuXr2KqlWrevgIyR6+FsrB10I5+FooB18L5fDl10IQBKjVakRHRyMw0PcqbH3viImIiIiI3MRMsEJdu3aNpRBERESkeFevXsX9998v92E4Tfaa4EuXLmHy5Mn46aefkJubi/r162PIkCEYP348wsLCAAC7d+/G+vXrkZGRgXPnziEmJgZZWVkW28rOzkZSUhKOHj2K7OxsqFQqNG7cGK+//jqGDRtmUVawbt06zJ07F2fPnkVERAT69u2LOXPmoGbNmmbjli5dih9//BEZGRm4evUqhg8fjpSUFKs/T15eHpKSkrB582YUFRWhQ4cOmDdvHtq0aePU8xIREQEAPvnxCBH5NrVajf3796NSpbK3iJKSEnTr1s14XiIiAv4q5fDVc4OsQfDVq1fRoUMHREZGYvTo0ahRowbS09MxZcoUZGZmYsuWLQCAtWvXYv369WjTpg2io6Ntbu/27du4du0aEhMTUb9+fdy7dw8//PADXnzxRVy4cAGzZs0yjl26dClee+01JCQkYP78+bh27Ro++eQTHDt2DBkZGahcubJx7Jw5c6BWq9GhQwfcuHHD5v5LS0vRu3dvnDx5EhMmTEDNmjWxZMkSxMfHIzMzE40aNRL93BgC9qpVqzIIJiKvCggIQFhYGEJDQwEAGo0GVatW9dk3OiLyLLH3LimOIKOZM2cKAIQzZ86YLR82bJgAQMjNzRUEQRCuX78u6HQ6QRAEoXfv3kKDBg2c2s/TTz8tVKlSRSgpKREEQRC0Wq1QrVo1IS4uTigtLTWO27ZtmwBAWLRokdn6WVlZxnFVqlQRhg8fbnU/69evFwAI33zzjXHZH3/8IVSrVk0YPHiwU8ecn58vABDy8/OdWo+IyJ672ntCg4nbhQYTtwt3tfesjikoKBA2b94sfP/998L3338vbN68WSgoKPDykRKR0vl6rCJrJtjQX6527dpmy+vWrYvAwEAEBwcDgN3srxixsbEoKiqCTqdDaGgozpw5g7y8PAwaNMjs6uXpp59GeHg41q1bhzFjxhiXN2jQQNR+Nm7ciNq1a6Nfv37GZbVq1cLAgQOxevVqaLVaTo1M5GOKdCVo9v4uAMDZ6T0lnaa6SFci2bZc2aet/Rfp9NDqgUB92S0jWn3ZMpUEx6ukab6JqGKT9WwUHx+POXPm4OWXX8a0adMQFRWFQ4cOYenSpXjjjTdQpUoVl7ar0Whw9+5dFBYWYv/+/Vi5ciU6depk/GhPq9UCgPF7U6GhoTh+/DhKS0udbvdx/PhxtGnTxmK9Dh064LPPPsPFixfRsmVLq+tqtVrjcQGcJIPIE1wJOsUEjY7YCvwMwbVc2s3Ya+fRIAD6v/5/9KAk++SU30SkFLIGwU899RQ++OADzJo1C1u3bjUuf++99zBjxgyXt/vJJ5/gnXfeMX6fkJCAlStXGr9v1KgRAgICcPDgQYwYMcK4/MKFC7h16xYA4M8//0RUVJRT+71x4wbi4uIsltetWxdA2Y17toLgDz74ADNnznRqf0TkHHeDTvtBo21np/d0a79ERCQ92T+Xio2NRVxcHPr374+oqCjs2LEDs2bNQp06dTB69GiXtjl48GC0a9cOt27dwvbt23Hz5k1oNBrj4zVr1sTAgQOxatUqPPzww3juuedw/fp1jBkzBkFBQbh3757ZeLE0Go3VcgfDTXb2tllS4v2PRYnIO+TM+JYPwIt0JcZg/lhygtUstVpdiNTUVISGGs5dxYiPj0dERLjnD5iIyEtkDYLXrVuHUaNG4eLFi8b+cv369UNpaSkmTpyIwYMHO52NBcpqeA11vIMHD8aoUaPQvXt3XLhwwVgCsWzZMmg0GowfPx7jx48HAAwdOhQPPvggNm3ahPBw50/2oaGhZiUNBsXFxcbHiUg+hoDQ00Hp2ek9ZS91MLBXgxsWXMni8SJdCeI/2oe2QdcwuHMjVAoMQKkKCAtWsZ6XiPyKrGe0JUuWoHXr1hYNlvv27YuUlBQcP34c3bt3d3s/iYmJWL58OQ4cOICePcveBCMjI7FlyxZcuXIFWVlZxsC5c+fOqFWrFqpVq+b0furWrWu1hZphmbs3+BGRe7wVxIUFV7JZAmEtOHYlWyu2PtnaONP9lX+8SFcCAYBeCIBWL0AvSHtjnIG3A2pP3uBIRL5J1rPAzZs3Ub16dYvl9+7dAyBdiYChDCE/P9/isfr166N+/foAyia6yMzMRP/+/V3az6OPPoq0tDSLm+oyMjIQFhaGxo0bu7RdIpKW2BpdMcGoLVKNtZatBTydzQ5GeklDpP9U+r/vpbsxzsDdG+SKdCVOvT6evMGRiHyTrH/RjRs3xu7du3Hx4kWzAPHrr79GYGAgWrVq5dT2bt26hVq1alksX7FiBQICAhzO2vbOO++gpKQEY8eOdWq/BomJidi4cSM2bdqExMREAGUTeHzzzTfo06cP26MRKYQrwYytYNRZZ6f3tAjerO3L37solA9KrQWz9oLc8hcBzty0KNUNjgyKiXybrH/BEyZMwM6dO9G1a1eMHj0aUVFR2L59O3bu3ImRI0caywdOnTpl7B7x3//+F/n5+cbuEY888gj69OkDAJg5cyYOHjyIp556CvXr10dubi6+/fZbHD16FGPGjMFDDz1k3Pfs2bNx5swZPPbYY6hUqRL+85//YPfu3ZgxYwbat29vdpzbtm3DyZMnAZRlqU+dOmXcf9++fY3BemJiIjp27IgRI0bg7Nmzxhnj9Ho9pk2b5sFnkog8wRPBaPnAydVAyp2OE/aCyyJdCbrM2InWla5jQMcHUSkwwOLGOGuZVI1Oj65zU0Ufg61Mtq0A1dXAVUrlj9nfL1SI/J2sQXBcXBwOHTqEqVOnYsmSJbhz5w4aNmyImTNnIikpyTju559/xuTJk83WNXw/fPhwYxDcu3dv/Prrr/jiiy9w69YtVK5cGa1atcLKlSsxfPhws/VbtmyJzZs3Y+vWrdDr9WjVqhU2bNiAAQMGWBznt99+i1WrVhm/P378OI4fPw4AuP/++41BsEqlwnfffYcJEyZg0aJF0Gg0aN++PVJSUtCkSRO7z4Ver7f7OBH5D3eDazkm2TClhICUiMhdAYIgCHIfBAFdunTBwYOWNXf5+fmoWrWqDEdEREoVO2mH3Ifg16yVqADm2e60pHhEhTsucWPJBPmzgoICREZG+myswr9OhejYsaPVIJiIyJekJcUjNFjl1Drlg0vD+raW21oPgNV9O9oOYJ7dFpPpFlv6wZIJIuViEKwQlSrxpSCqKNxt1+XuDHSmNcHWgsJC9V2kpaWhcmhZprNYo0XXrl0RHmF9KnvTIDPUzX7CttaPCg+xutw0yBSTIXembpmI/BsjLyLyC0roA+tK715n6nsNP5OUP5vtoDAIgEmLtJ8Pi9qeu/XC1tb3VjbV0cWFOy3ziEh5+BdMRIri6k1fUvSBNXA1uHGld68zQaNUwaC/Bm+2glixwauzvZ2dGa+EizQiMse/QiJSFCkmgXA3G1kR6jjtZT3V6kKkpqYiNLQySkoFfHP4VxwvqYefknvZnL1OCRlSMfu1NVW06b/22Jttzx4pL9KcwWCbyDb+dRARScQbM9GJ2bZUSkoFfJX+OyoHSLZJM96cxtrehY1nZ9+z5M0WcxXhgo7IVQyCiUhRXL3pSwnZSG/ORGcr2JUuoDPUBMcYl4gJ3pwJ8Gy91lK/dixFICJreCYgIkWRIkCRaopjT5FiJjpvZy89wdbPUP65cTe77agUwVZfYEecuehyNFW2gZh2bgZK/h0n8gX8C1IIzhhH5B5PTHFMyiBlwC9VKYKrgTNQ9rsq5mdy1M6Nv+9E7mEQrBDp6elyHwIR+RB3ewXbY7gxLigk5H81wSU4XlIPxQhma7D/cXZyDQMpXzdXM+R8/cibLl26hMmTJ+Onn35Cbm4u6tevjyFDhmD8+PEICwuzu+7169cxduxY7N69G6WlpXjiiSewYMECPPDAA5IcG/8SFIIzxhGRM+wFMt7sPiAFR0GZlJODOAriPV1mIuX2Xd0WM8jkLVevXkWHDh0QGRmJ0aNHo0aNGkhPT8eUKVOQmZmJLVu22Fy3sLAQTzzxBPLz8/Huu+8iKCgICxYsQLdu3XDixAlERUW5fXwMghWCM8YRkVSkCbQsb4wDPNPZwFFQJmXm0lG9uLWA2x/qr4nk8NVXXyEvLw8//fQTmjdvDgAYNWoUSktL8eWXX+LPP/9E9erVra67ZMkSXLp0CUeOHEH79u0BAL169UKLFi0wb948zJo1y+3jY+RFRER+zZl6cWsBcvnA2BeCYns1y1KXUbD7BtlSUFAAAKhdu7bZ8rp16yIwMBDBwcE21924cSPat29vDIABoGnTpkhISMCGDRvMguArV66gqKgITZs2der4+JtKRORn3C0fMJ0sAwDUhUX44OcAv6oJdiZw88Wf1xMZe3sz8ln7vzf44mtTkcTHx2POnDl4+eWXMW3aNERFReHQoUNYunQp3njjDVSpUsXqeqWlpTh16hReeukli8c6dOiA3bt3Q61WIyIiAgAwbNgw7N+/H4IgOHV8/O0hIvIz7gYG+mAVQlRAiKpslgydCjDMl+GN9nPeCKTcCdxc6Qxh+pz5QibZGjHH7c2JQADWNyvdU089hQ8++ACzZs3C1q1bjcvfe+89zJgxw+Z6ubm50Gq1qFu3rsVjhmXZ2dlo0qSJW8fHIJhI4fhRI8mtkioQR95LMGZdPM0fZ3AzDdbKT70s5qY9R4G6aX9hIiWJjY1FXFwc+vfvj6ioKOzYsQOzZs1CnTp1MHr0aKvraDQaAEBISIjFY5UrVzYbAwCpqakuHRvfTYm8wJ3MlpQfNTKAJm/iBZx1ps9DWHAlnJ3eE83e34V2M/bafJ6UmD02BPNKmK2RlGndunUYNWoULl68iPvvvx8A0K9fP5SWlmLixIkYPHiw1S4PoaGhAACtVmvxWHFxsdkYd/A3lcgLpHoDczdjxY8OyR5D0BoKHT5oX1Zbp9UL0OqBIp0eKicvwly9gLNWbuCJqZTFzOAm5f7EPOZL7e2svSZKn62RvGvJkiVo3bq1MQA26Nu3L1JSUnD8+HF0797dYr0aNWogJCQEN27csHjMsCw6Otrt4+NvqkJwxjiiiknKbKlU0wsLALR6IFgv4I0DegBBwFH3+pgr+QJO7Axu3uDtmlpTrt5QydkayZabN29abYF27949AEBJifVzVmBgIFq2bIljx45ZPJaRkYEHHnhAkvIsBsEKwRnj/Js7d+vzo0b5SZ2dM31N05LiJduPVIFcMYLx3s8A4L8X5wzcLPHcQlJr3Lgxdu/ejYsXL6Jx48bG5V9//TUCAwPRqlUrANZbnCUmJmLSpEk4duwY2rVrBwC4cOECfvzxR4wfP95sP662SAsQnO0nQR4xfvx4zJs3z2J5fn4+qlatKsMRkVKwrlJ+sZN2yH0IPulYcoJkF3DOrOvK34ycZQhKudDluYWcVVBQgMjISJuxyoEDB/C3v/0NUVFRGD16NKKiorB9+3bs3LkTI0eOxPLlywGUtVIr3+JMrVajdevWUKvVGD9+PIKCgjB//nzo9XqcOHECtWrVMo61tr4Y/I1XCM4YR7YwY0VScfSJhCEYqwwdJrcREBEeBgDQaIoRHx+PiIhwl/dtWivqSsDpzDrerrF1NIW1MwE5a2rJn8TFxeHQoUOYOnUqlixZgjt37qBhw4aYOXMmkpKS7K4bERGB1NRUjB07FjNmzEBpaSni4+OxYMECswDYHfxLIyJyQMkzhjlTaiM2uAoAzPoEl6qAsGCVS8GZtQs4bz5/3qixtfcaiA3Iy7dNs4UBMvmaDh064LvvvrM7xlaLs/vvvx/ffPONw32wRRoRkYeUDzzcnZGtfE2wob+rrY/C7X1cLmVQZPjUQa1WY+9e+W7Q8jVig3opAnJ+KkQkHQbBREROcjXwtJbhCw1WuX3jpDM8mUkU+9G/vZ/X2SyxtW1JWWOrpKw/EUmLQTARkZdYC6iczQ7K3WpMqn63UgXjnq6xlTLrz+4uRMrCv0YiIjtsZTd9aVIDKUn10b+tYLx80OlKECnlzaRSl5swCCZSDv41ElGFZi2YNV2m0f3VK/dOoRZFwWWPyTmpAeD6LGdKv+nK3jH4YhDJ7i5EyuVbZxM/xhnjiOThTM2n4QY2JfBEEJ41uzeKdCXoMHMP2gZdw+DOjVApMMBsjKNOCFJ/9M8gkog8hUGwQnDGOCKSW5GuBEW6EggA9EIAtHoBeqFsCuUinR6qcllkubO2nEiGiNzBM4ZCdOzYEQcPHpT7MIgqHFvdBQw0Or0xA5yWFI/QYJXDbZYPxjzRYcDVG7bsHctfjwUjvaQh0n8q/d/3QcBRy/NT+QyttayttUBVqnpqT0yKwUCaqOLgX7tCcMY4InlYC3pMl5kGV1HhIS4FSWJnajMQU0qghGBNTOBpOqbZ+7vMplKWklTbZOkFUcUh/1mUiEjBpKhJdSZgdfZjfWdLAsQE5F1m7ETrStcxoOODqBQYAI2mGBOPBlmMdSXDLfcNhUREBgyCiYhMyFFnKibQtpV1dbY3r5ifJwCAKkBAiCoAlQIDUOq4AsTjPD0pBhFVPDxjEJEFf7jhyNUaUanqTKV+zsRkXV3tzWsqLLgSjrzXHXv37jXrDJEx4XFERIS7dXyO2MtSe3pSDCKqeHjGIPJzrgRy7gaCSghGpAjK3PnoXmwJha9ccIQFq+wem60A1pl6Z2d/drZPIyJ3KPNsS0SScTcYdCUQZGBiyZ1yhrDgSqKCTDlLAuwFtmen9zT+HhrGORv4+8rFAhH5Dp5FiMgvudpCzFNBpTvlDFmze4ueLtiTwaE75SGmr0f5jhGA4/ZztmbuK6/8z89gmYhs4dlBIThjHHmKK8GgUrKL7pBqtjKl/+zeLAnwRL9jA2dm43NmLD+VICJblH12r0A4Yxx5irtBnC8EglLyVFCp9HIGf+VM9prPPVHFwr94heCMcaQkvOFIekooZ7DGWpBYpNNDqwcC9QKAv6ZNPpacYDFW6X1/ncle83eeqGJhEKwQnDGOqGLy5gWHtYDXdpAYBED/1/+tTJvsb4p0JcwGE1Ug/GsnIvJBrtyk5smaXn/Q7P1dzAYTVSAMgomIfBADWnmxZRuR7wuU+wDKmzlzJgICAtCiRQvjsqKiIvz73/9Gjx49ULduXURERKB169ZYunSpRVeF7OxsDB06FE2aNEFERASqVauGDh06YNWqVRAEwWJ/e/bswRNPPIGaNWsax3711VcW4wICAqx+zZ4922Ls9evXMXDgQFSrVg1Vq1bFM888g99++02CZ4eIiDzlWHICinQlor8MnFnHme16QpGuBLGTdiB20g6P74tI6RR16Xrt2jXMmjULVapUMVv+22+/YcyYMUhISMC4ceNQtWpV7Nq1C6+99hoOHz6MVatWGcfevn0b165dQ2JiIurXr4979+7hhx9+wIsvvogLFy5g1qxZxrFbt27Fs88+i06dOmHq1KkICAjAhg0bMGzYMNy+fRtjx441O44nn3wSw4YNM1vWunVrs+8LCwvxxBNPID8/H++++y6CgoKwYMECdOvWDSdOnEBUVJRUTxcRVWCu9kEWQ60uRGpqKkJDKwMANJpixMfHW0yb7Kizha9lq129yU/qmwOtlWRIFbBKNS24NcyGk68JEKylR2Xy/PPP49atW9Dr9bh9+zbOnDkDoCywvXnzJpo3b242/qWXXsLKlStx6dIlPPTQQ3a33adPH+zbtw/5+flQqcoasvfo0QO//PILfvvtN4SEhAAASkpK0LRpU1SpUgUnT540rh8QEIDXX38dixcvtrufuXPnYuLEiThy5Ajat28PADh//jxatGiBpKQksyDc1KRJkzBnzhyL5fn5+ahatardfRJRxeSpj+TVajX27t2L0NDQsu8L72Ly0QBoEGy2H0f7lzrIcqednC8F5NaC4NhJO2Q4EuewnrriKSgoQGRkpM/GKoq5bDtw4AA2btyI48ePY8yYMWaP1axZEzVr1rRY57nnnsPKlStx7tw5h0FwbGwsioqKoNPpjCf2goICVK9e3RgAA2VdGqzty0Cj0SAgIACVK1e2+vjGjRvRvn17YwAMAE2bNkVCQgI2bNhgMwgmIv/hrY+ZTfdjbxY1ZxUW6qDWAfdUpWXf64DScvsxBKC2ZoJzhyfaydnLnHsiQHZnxkJDwMtaYyLPUsRfl16vx5gxYzBy5Ei0bNlS9Ho5OTkAYDVo1Wg0uHv3LgoLC7F//36sXLkSnTp1MgbAABAfH485c+Zg8uTJGD58OAICArB27VocO3YMGzZssNhmSkoKlixZAkEQ8PDDDyM5ORlDhgwxPl5aWopTp07hpZdesli3Q4cO2L17N9RqNSIiIqw+B0TkH+TIOjozi5o4Qfgr9P0rUSD9fizZyii6007OF4NJ04sKaz2aXaHR6Y2voWGqal98boikoIjf/E8//RS///479uzZI3odnU6HhQsXomHDhmZZV4NPPvkE77zzjvH7hIQErFy50mzM5MmTcfnyZcycORMzZswAAISFheHbb7/FM888Yza2c+fOGDhwIBo2bIjs7Gz8+9//xgsvvID8/Hz885//BADk5uZCq9Wibt26FsdjWJadnY0mTZpYPM4Z44iI5OEoa2tahiGWFBdCnp6IxBAMs4yBKirZg+A7d+7g/fffx+TJk1GrVi3R640ePRpnz57Fjh07rE40MXjwYLRr1w63bt3C9u3bcfPmTWg0GrMxISEhaNy4MRITE9GvXz/o9Xp89tlnGDp0KH744Qd07NjROLb8bG4vvfQS2rZti3fffRcvvvgiQkNDjds3La8wMJRPlD8GA84YR+Q/PBFUedPO19rj0MFDqBwaDAAoLCzGvDMB0CEYu9/qitBgFUKDVU5vV6kZR0fHxS4KRP5J9jNScnIyatSoYVEHbM9HH32E5cuX44MPPsDf//53q2MaNGiABg0aACgLiEeNGoXu3bvjwoULxpKI0aNH4/Dhw/j5558RGFjWLW7gwIFo3rw53nzzTWRkZNg8huDgYIwePRqvvvoqMjMz0aVLF+N2tVqtxfji4mIAMCvHMMUZ44j8h6OgSuk3afVachRl5RCG+6b/urDvsTDN7e17o9bVU4GroYRACtaeA3du/iMi58j613Xp0iV89tlnWLhwIbKzs43Li4uLce/ePWRlZaFq1aqoUaOG8bGUlBRMnDgRr776KpKTk0XvKzExEcuXL8eBAwfQs2dP6HQ6rFixAklJScYAGACCgoLQq1cvLF68GDqdDsHBwTa3GRMTA6CsDAIAatSogZCQENy4ccNirGFZdHS06GMmIvJHUgSopsGitaDaUxcaUtZEOypDcPbmPyJyjqx/XdevX0dpaSneeOMNvPHGGxaPN2zYEG+++SYWLlwIANiyZQtGjhyJfv364d///rdT+zKUIeTn5wMoK8MoKSmxekPavXv3UFpa6vBmNcMEGIYyjsDAQLRs2RLHjh2zGJuRkYEHHnjA6k1xRFSxlC+XcDX758x6zow17RP8xgHpb9qVuhTEn8oV3Ln5j4icI2sQ3KJFC2zevNlieXJyMtRqNT755BM8+OCDAMpaqD3//POIi4vDmjVrzLK3pm7dumW1tnjFihUICAhAmzZtAAD33XcfqlWrhs2bN2P69OnGjG9hYSG2bduGpk2bGksXrG1TrVZj4cKFqFmzJtq2bWtcnpiYiEmTJuHYsWNo164dAODChQv48ccfMX78eGefIvJznHrV/3kqQDNkCV0JmBxlGPXBKoSogBBVgDuH6DXuBNViWpmxRIHIP8n6l1yzZk08++yzFssNmV/DY7///jv69u2LgIAAJCYm4ptvvjEb36pVK7Rq1QpA2bTLBw8exFNPPYX69esjNzcX3377LY4ePYoxY8YY+wmrVCqMHz8eycnJ6NixI4YNGwa9Xo8VK1bg2rVrWL16tXH7//73v/Gf//wHffr0Qf369XHjxg188cUXuHLlCr766iuzkonXXnsNy5cvR+/evTF+/HgEBQVh/vz5qF27Nt5++20Jnz1SAncDHKlnb+Kbs/I4+7G82IDO2eDX1YB5UZzK5oxxgPgA0dY4ueujnf2bYYkCkf/wib/ky5cvG8sYXn/9dYvHp0yZYgyCe/fujV9//RVffPEFbt26hcqVK6NVq1ZYuXIlhg8fbrbee++9h4YNG+KTTz7BtGnToNVq0apVK2zcuBH9+/c3jnv88cdx6NAhfP7557hz5w6qVKmCDh064IsvvsDf/vY3s21GREQgNTUVY8eOxYwZM1BaWor4+HgsWLDAqe4X5BukfAOX4iNifozqOl/LynuyBKBIp4dWDwTqPT+hqKv9b631uzUl5evHEgUi/6SoaZMrMk6b7JuUNpWpq7NUWSNHEChnbaenPvIW8zOVD+iiwi3bLJYndwZV6Ri0Enkep00mSXDGON8kNuj0VsAi5X7kCCKUEtjJ2cO369xUSS9miIjIOgbBCsEZ43yT0j8yJ9+klIsBZyn1pjFfK3UhIu/gmUAhOGOcfzs7vafPBjbeJGcGVO4OAP7w+yHVTWNFuhK3Xw9bN51KXXLDgJrId/GvVyE4Y5x/4xulOEp5nqTsACA2C+mpCwBXgsk/7uQjLS0NlUPLapOLNVp07doV4RFVbK5j2K4UQWb5CwIpy1OkLnVh7TGR71LGOw5RBSB1kCN35tJT5Pro2tkOAGKDPVezkJ74ucUG9/GfHEbZtMml/1sSBPx8WPLjISKSk3+8axL5AE8Gc57uXerNrg2e/OjaEWeeQ1fKF5zJQkqVYWR7L+nIUa7DemYiz+FfE5GP8mZwI1e9qre7NDBYLJMx4XHjtMkAoC4swgc/B6AYwV771EHKTzqk2pbUP7eYizwlfZJA5G/4V0JE5AKxWUG5ylasZRDl7MPszs/t7icdSs2Ge2o2QYAXdERiMAgmIoe8+TGwr9Q625se2NbH14ZgztkMoCusZRCdC7qCABj6l/81eYer2XlvTfNMRCSWMt9diEhR5ApEPV3rDEhfg1x+e2HBlcwuIkyDZG+Rc/IPsk3MxaWvXBQS+SL+NRGRong7A+jJgLSiBZ+c6c45zga03rgoJKpI+NekEOwTTESOOBNkupNBVKsLXboxjgGa9FgWQuQ5PGMpxOTJk5GUlGT8vqCgADExMTIeEVHF4Ev9m93pauDMuvpgFUJUQIgqAABQGhyIo+8lICIiwqX9ExEpEYNghQgJCUFISIjjgUQkKV/u3+xo38wgEhHZxiCYiEhCDD6JiHxDoNwHQERERETkbcwEK5QgCADKaoOJiLxJrVajqKgIOp0OAFBSUoKCggLjeYmICPgrRvHVcwODYIVSq9UAwJvjiIiISNHUajUiIyPlPgynMQhWqDp16uDIkSOoXbs2AgPFVa2o1Wo0a9YMZ8+e5V3cMuNroRx8LZSDr4Vy8LVQDl9+LUpLS3Hz5k3UqVNH7kNxSYDgqzlsP3ft2jVmgYmIiEjxrl69ivvvv1/uw3Ca7JngS5cuYfLkyfjpp5+Qm5uL+vXrY8iQIRg/fjzCwsIAALt378b69euRkZGBc+fOISYmBllZWRbbOn/+PL744gvs3r0bv/76K8LDw9GmTRtMmzYN7dq1sxi/bt06zJ0713j11bdvX8yZMwc1a9Y0G7d06VL8+OOPyMjIwNWrVzF8+HCkpKRY/Xny8vKQlJSEzZs3o6ioCB06dMC8efPQpk0bp54Xw9Xg1atXUbVqVafWJbJGrVZj//79EAQBAQEB6Natm89lHYiISDkMcxr46nuJrEHw1atX0aFDB0RGRmL06NGoUaMG0tPTMWXKFGRmZmLLli0AgLVr12L9+vVo06YNoqOjbW7v888/x4oVK9C/f3+89tpryM/Px7Jly9CxY0d8//336N69u3Hs0qVL8dprryEhIQHz58/HtWvX8Mknn+DYsWPIyMhA5cqVjWPnzJkDtVqNDh064MaNGzb3X1pait69e+PkyZOYMGECatasiSVLliA+Ph6ZmZlo1KiR6OcmIKCsSX3VqlUZBJMkAgICEBYWBpVKBb1ej6pVq/rsiYuIiJTDELP4HEFGM2fOFAAIZ86cMVs+bNgwAYCQm5srCIIgXL9+XdDpdIIgCELv3r2FBg0aWN3esWPHBLVabbbs9u3bQq1atYTHH3/cuEyr1QrVqlUT4uLihNLSUuPybdu2CQCERYsWmW0jKyvLOK5KlSrC8OHDre5//fr1AgDhm2++MS77448/hGrVqgmDBw+280xYys/PFwAI+fn5Tq1HZEtBQYGwefNmYevWrcLmzZuFgoICuQ+JiIh8mK/HKrL2CTa01qhdu7bZ8rp16yIwMBDBwcEAgOjoaAQFBTncXtu2bREeHm62LCoqCl27dsW5c+eMy86cOYO8vDwMGjTI7Orl6aefRnh4ONatW2e2jQYNGoi6ytm4cSNq166Nfv36GZfVqlULAwcOxJYtW6DVah1ug4g8q0hXgthJOxA7aQeKdCVyHw4REclE1iA4Pj4eAPDyyy/jxIkTuHr1KtavX4+lS5fijTfeQJUqVSTZT05OjlmdryEYDQ0NtRgbGhqK48ePo7S01On9HD9+HG3atLHo5tChQwcUFRXh4sWLTm+TyFcoIbgs0pWI+nJ2PINlIiL/I2tN8FNPPYUPPvgAs2bNwtatW43L33vvPcyYMUOSfaSlpSE9PR3JycnGZY0aNUJAQAAOHjyIESNGGJdfuHABt27dAgD8+eefiIqKcmpfN27cQFxcnMXyunXrAgCys7PRsmVLq+tqtVqzTDEnyfANRboSNHt/FwDg7PSeCAuW/V5Tl0gR5JUPLqXg7PNpeC3Eajdjr+ixnAqZiMi/yP6OHRsbi7i4OPTv3x9RUVHYsWMHZs2ahTp16mD06NFubfuPP/7AkCFD0LBhQyQlJRmX16xZEwMHDsSqVavw8MMP47nnnsP169cxZswYBAUF4d69e9BoNE7vT6PRICQkxGK54SY7e9v84IMPMHPmTKf3SZ4jJpBzNvBTapDsbPDoiDPBpT0MPImIyFNkfUdet24dRo0ahYsXLxr7y/Xr1w+lpaWYOHEiBg8e7HQ21uDu3bt4+umnoVar8dNPP1nUCi9btgwajQbjx4/H+PHjAQBDhw7Fgw8+iE2bNlmMFyM0NNRq3W9xcbHxcVtKSvhxq9J4IqsoRVDnL9lnTzg7vafDMUW6EuNrdSw5gc8fEVEFJevZf8mSJWjdurVFg+W+ffsiJSUFx48fN2trJpZOp0O/fv1w6tQp7Nq1Cy1atLAYExkZiS1btuDKlSvIyspCgwYN0KBBA3Tu3Bm1atVCtWrVnN5v3bp1rbZQMyyz196tUiW+EVck7pQLmK5rCIbFBHNFOj20ekAFQK8v+97QIE1M8CjmuOQOLp3dZ1hwJQbBREQVlKxn/5s3b6J69eoWy+/duwfAtexoaWkphg0bhr1792LDhg3o1q2b3fH169dH/fr1AZRNdJGZmYn+/fs7vV8AePTRR5GWlobS0lKzm+MyMjIQFhaGxo0b21w3KSkJr776qvF7wzSKJB9PZRVNM7lSEV9+YOiyEggcPWjMTEsdCCo5uAwLrsQyCyIikrc7ROPGjXH8+HGLrglff/01AgMD0apVK6e3OWbMGKxfvx5Lliwxa1UmxjvvvIOSkhKMHTvW6f0CQGJiIm7evIlNmzYZl92+fRvffPMN+vTpY7Ve2GDu3LmIiYkxfjEAlp8hkCv/5Q5/7jRgCC6zZvdWbABMRERkIOs71YQJE7Bz50507doVo0ePRlRUFLZv346dO3di5MiRxvKBU6dOGbtH/Pe//0V+fr6xe8QjjzyCPn36AAAWLlyIJUuWoFOnTggLC8Pq1avN9vfcc88Z267Nnj0bZ86cwWOPPYZKlSrhP//5D3bv3o0ZM2agffv2Zutt27YNJ0+eBFCWpT516pRx/3379jUG64mJiejYsSNGjBiBs2fPGmeM0+v1mDZtmt3ngjXBvsNWFleqm8HcZSuDrVYXIjU11ThjnKFFIRERUUUkaxAcFxeHQ4cOYerUqViyZAnu3LmDhg0bYubMmWbdHH7++WdMnjzZbF3D98OHDzcGwSdOnAAApKenIz093WJ/ly9fNgbBLVu2xObNm7F161bo9Xq0atUKGzZswIABAyzW+/bbb7Fq1Srj98ePH8fx48cBAPfff78xCFapVPjuu+8wYcIELFq0CBqNBu3bt0dKSgqaNGli97lgTTC5Iy0pHqHBKtHjQ1RAmBPjvYE3/BERkTcFCIIgyH0QVNYX2LQ3sKEmOD8/H1WrVpXxyKg8d8sZTOuI5bKw4z0kJCQgIiLC8WARpOoz7Ikb6xhMExF5RkFBASIjI302VuG7g0LMnTuXfYJ9hD8EVdr/dYdQSdTbWKl9hgH2GiYiIut8/93cT7AmuOIw7U5QPoMqdTBpy8SjQcDRg6LGMogkIiJ/xCBYIVgTXDH5ala5fPB+LDnB7W1qdHp0nZsKoKzGGYDZ945qnsuvbxjvbKmGr74mRETkHNYEKwRrggnwbG2tte4QERHiZkYsf8Na7KQdbh+nUjk7cQiDZiKqqFgTTJJgTTABnpu0QqoZ6vy1x7EpZ0tSWC5CROSbGAQrBGuCSSrWZkT7K7AznzHOWXJ3tSAiIpIKg2CFYE0wVRSGcgNHWWXTGl/Asi7YXg2xlC3WiIjIP7EmWCFYE0yeVKQrcbkm+E6h1izYjAq3Pf23GIbg1JN1xYYgmIEwEZHnsCaYJMGaYPKksOBK0AerEKICVCpAj7IZ46wFic7U/So1yDSUbbBel4iIbFHmO1gFxJpgUgp7N4aZlicA7gWZYrowyDm7HqdxJiJyT2FhIT766CNkZGTgyJEj+PPPP7Fy5Uq8+OKLotbPy8tDUlISNm/ejKKiInTo0AHz5s1DmzZtJDk+ntUVgjXB5OucDRqdDSrL9yK21Re4/DG5Sq6uGAy2ichf3L59G9OnT0f9+vXxyCOPIDU1VfS6paWl6N27N06ePIkJEyagZs2aWLJkCeLj45GZmYlGjRq5fXysCVYI1gSTp6nVauzdu9dYE5yQkICIiAiLAK/899aCTdNlhvpbW/2J3WW6HX/uT2zAEg4i8hWOaoK1Wi3+/PNP1KlTB8eOHUP79u1FZ4I3bNiAQYMG4ZtvvkFiYiIA4NatW2jcuDF69eqFtWvXun38TDkoBGuCyZOKdCUo0umh1QMqAHo9UKTTQ2WSvRWjfDkEYL1tmpQlDAwKiYh8U0hICOrUqePSuhs3bkTt2rXRr18/47JatWph4MCBWL16NbRaLUJCym7UvnHjBvLz8/Hggw8iKCjI1iYtMAhWCNYEkydJ1SdYbmen9/RYxrk8b+2HiIgsHT9+HG3atEFgYKDZ8g4dOuCzzz7DxYsX0bJlSwDAO++8g1WrVuHy5cuIjY0VvQ+e1RWCNcFE5sT2Ey7PE8Eq260REXnXjRs3EBcXZ7G8bt26AIDs7GxjEOwqntUVIikpCa+++qrxe0NNMHlWRekAcHZ6T5f7BBuYBqOO6oRt3ahmSux4e+Ua1souTLtOuPN6Wpt5j4iIvEOj0RjLHUxVrlzZ+LhBSkoKUlJSnN6Hf77j+yDWBEtPTAbRlQ4AvhgoO9Mn2BZbwai1OmFry+xxdrw9psfJIJaIyDeFhoZCq9VaLC8uLjY+7i7fezf3U6wJlp4zN3wB4m/mYmBFRETkWXXr1sWNGzcslhuWRUdHu70PBsEKwZpgUjrDTWmmnC2BELOuo206s09/zu4TEfmzRx99FGlpaSgtLTW7OS4jIwNhYWFo3Lix2/vgmV8hWBMsPWdnJFNqBwC56patBZD2suXulDTYWtfRNqUqo2B2n4hIPtZanCUmJmLjxo3YtGmTsU/w7du38c0336BPnz5m9cJskebjWBMsPWeDRaV1ADAEodbqlr1xnM6WkxAREZW3ePFi5OXlITs7GwCwbds2XLt2DQAwZswYREZGWm1xlpiYiI4dO2LEiBE4e/asccY4vV6PadOmme2DLdJ8HGuC5aHkDgDWglBDJlapx+yI3Nl5X8j8ExH5k48//hi///678ftNmzZh06ZNAIChQ4ciMjLS6noqlQrfffcdJkyYgEWLFkGj0aB9+/ZISUlBkyZNJDk2vgMoBGuCSQlMSy/EBInOBpWmjzvb/9eTvHksDLyJqCLJyspyOMZWi7Pq1avj888/x+eff+7S+o7wbKwQrAmm8kwnizAEmoYbwVwJ2mxNm2w+RlxbOdPjce4Y/tq+mHILKadf9ua27fHVLD4Rkb8JEARBkPsgCEhOTrZaE5yfn4+qVavKcESkFKbZWfJ9DIKJyF8UFBQgMjLSZ2MVZoIVgjXB3uVLM8UZ6pZjJ+2Q+1BkI6aWWAzWBBMRkQHfARSCNcHuc6ZEwJmZ4kwDJzkDZkeBoD9niz3xnCutGwgREXkXyyEUoqCgAAUFBcbvDTXBvvoRgxy8kSl19mYxufv6mlKrC5GamgqVSgW9Xo/4+HhERIRbbMNRplSObCqDVSIi5WE5BEmCfYJ9g7M3U3lzxjJH29AHqxCiAlQqQA8gLFhlsY6zLeOYTSUiIl/Fdy+FYE2w+5ypG/VW6YDYoNmXbpZScm9lIiIisRgEKwRrgl3nyk1ux5ITLNqO2ZJbqEOPhWnSHCwREREpAiMvhWCfYHOeusnNwDRQ7jo3VfS+nOWJmllf6mxBRESkVHz3VAjWBJtztVxBrgkQ3OXpoL9srOPJMgwYWBMRkb/jO51CsCbY+8TWELvaDcF0PU9xfvtB//s3EDh60OYo1vwSEZG/YxCsEKwJNufMTW6uBqmuZDutdUMon5l1dUphIiIi8h72CVYI9gl2nTdqZE33UT7QdrfThLOzobka9IvpE2zAcggiInKEfYJJEqwJdp2rLbtcrcP1RImDq0G8M316xfQJJiIiqij4DqgQrAn2PqVMM+zKjW6m2WPTdRjUEhERicN3TCKZmWaW3c0y84Y2IiIicRgEKwRvjPM+qW++U0JmuUhXorhsMPsaExGREvHdSCE4WYb3uRqM2arDdfYGNwMpg+dm7+/ySja4fNmGvYsEV/saAyzvICIiz+E7jELwxjhlE3PznRIDNvMAVPxkGY7YC9ztlXQ4W+5h78JCic83ERH5Dr6LKARvjKu4HGWQXW2JBlgLVsVNlqEU9oJt1j8TEZE7GAQrBGuCKy6pM5rOlhwQERFVRLJOlvHLL79g6tSpyMzMRE5ODsLCwtCsWTNMmDABffr0MY5bvnw5Vq9ejfPnzyMvLw/R0dGIj4/HlClTEBsbaxx39epVfPHFF9ixYwcuXboElUqFFi1aIDk5Gd27d7fYf2ZmJqZMmYJjx46hsLAQDzzwAEaOHInXX38dKpXKOG79+vXYtm0bMjIy8N///hfdunVDamqqxfaOHj2KVatWYd++fcjKykJUVBQ6duyIGTNmoHHjxnafC06WQWLETtoh9yE4lJYUj9Dgv/5+NDo9us5NtfqYLWIuDFgOQUQkL06W4Ybff/8darUaw4cPR3R0NIqKivDtt9+ib9++WLZsGUaNGgUAOH78OBo2bIi+ffuievXquHz5MpYvX47t27fj5MmTiI6OBgBs2bIFc+bMwbPPPovhw4ejpKQEX375JZ588kl88cUXGDFihHHfmZmZ6Ny5Mxo1aoSJEyciLCwMO3fuxJtvvolff/0Vn3zyiXHs0qVLkZmZifbt2+POnTs2f545c+bg4MGDGDBgAFq1aoWcnBwsXrwYbdq0weHDh9GiRQub67ImuOLyt8ytIeB19jFTLHUgIiJPU9y0yXq9Hm3btkVxcTHOnz9vc1xmZibatWuHDz/8EJMmTQJQllmuXbs2atasaRyn1Wrx6KOPorCwEFevXjUuHzVqFFatWoUbN26gRo0axuXdunXDiRMnkJ+fb1x29epV1KtXD4GBgWjRogVq1qxpNRN86NAhtGvXDsHBwcZlly5dQsuWLZGYmIjVq1fb/HkmTZqEOXPmWCz31asrMmevTZgvZHe9jUEwEZHyMRMsMZVKhZiYGBw9etTuOEMZRF5ennFZ8+bNLcaFhITg73//O+bPnw+1Wo2IiAgAZS9c5cqVUa1aNbPxdevWxYULF8yWxcTEiDr2zp07Wyxr1KgRmjdvjnPnztldlzXB8vNkRtadNmFysXXDnjs36hERESmFIt697t69C41Gg/z8fGzduhU7d+7EoEGDLMbduXMHer0eV65cwfTp0wEACQkJDrdvqDcOCwszLouPj8f69evxyiuvYNy4ccZyiE2bNuGjjz6S7GcTBAE3b960GqCbYp9g+Xlrsgt3Z4VzxFbdbaH6LtLS0hCoUqFUr0fXrl0RHlHF+Hj5YLb896bZbNMxDIKJiMgXKeLd6+2338ayZcsAAIGBgejXrx8WL15sMa5evXrQarUAgKioKCxatAhPPvmk3W3/97//xaZNmzBgwACzm93+7//+D7/88guWLVuGzz//HEBZFnrx4sVmwai71qxZg+vXrxuDdltYE0xSsV93a9Ii7efDZo+Uz/xamxDDwDQD7ExmmwEzEREphSLekd566y0kJiYiOzsbGzZsgF6vh06nsxi3c+dOFBcX49y5c1i9ejXu3r1rd7tFRUUYMGAAQkNDMXv2bLPHVCoVHnzwQfTs2RMDBgxA5cqV8fXXX2PMmDGoU6cOnn32Wbd/rvPnz+P1119Hp06dMHz4cLtj2SdYfq7O+CaGvRICJUy3DDh3HK5ms1nrS0RESqGIILhp06Zo2rQpAGDYsGHo0aMH+vTpg4yMDAQEBBjHPfHEEwCAXr164ZlnnkGLFi0QHh6O0aNHW2xTr9fj+eefx9mzZ7Fz505jBwmD2bNn45NPPsGlS5cQHh4OABg4cCCeeOIJvP7663j66afdqtPNyclB7969ERkZiY0bN5ploa1hTbD8yk/1a+tGNin2Y7o9Q/DtSq2wo/Zjpo9//3o7HE1Px+Sfg0BERFTRKa47BAB89tlneOWVV3D+/Hk0adLE5rjOnTtDEASkp6dbPPbSSy8hJSUFa9asweDBgy0er1+/Prp27Yo1a9aYLV+wYAHGjRuHS5cu4aGHHrJYz153CIP8/HzEx8fjypUrSEtLE1Xbyz7B0pDqpjNP3fxlbzu+2iXCmQw6yyGIiPwHu0N4gEajAQCzNmW2xhlqhE1NmDABK1euxMKFC60GwABw8+ZN6PV6i+X37t0D4Hp5QnFxMfr06YOLFy9iz549om9uY02wNDxRWiDljWym5QC+0iXCEQa2RETki2R99/rjjz9w3333mS27d+8evvzyS4SGhqJZs2YoKSmBWq1G9erVzcYdOXIEp0+fxpAhQ8yWf/TRR/j444/x7rvv4s0337S578aNG+OHH37AnTt3EBUVBaCshGLDhg2IiIjAgw8+6PTPo9frMWjQIKSnp2PLli3o1KmT6HVZE1zxyFUL/HHHUuj1esTHxyMiItzqGLZBIyIifyfrO9srr7yCgoICxMXFoV69esjJycGaNWtw/vx5zJs3D+Hh4cjLy0NMTAwGDRqE5s2bo0qVKjh9+jRWrlyJyMhITJ482bi9zZs3IykpCY0aNcLDDz9sMTnFk08+idq1awMom5xi6NCheOyxxzBq1CiEhobi66+/RmZmJmbMmIGgoL/qJg8cOIADBw4AAG7duoW7d+9ixowZAIC4uDjExcUBKOtysXXrVvTp0we5ubkW+x86dKjN54I1wdKQ6uY2BoF/0ej0dp8LQ0a7Ij9HRETke2StCV63bh1WrFiB06dP486dO4iIiEDbtm0xZswY9O3bFwCg0+mQlJSEffv2ISsrCxqNBtHR0ejevTuSk5ONk2YAwNSpUzFt2jSb+9u3bx/i4+ON3+/atQsffvghfvnlFxQUFKBJkyZ4/fXX8corr5itZ2+7U6ZMwdSpUwGU9R7ev3+/zf3be6pZE6wsnrwxzuB2YbHTgbaj4NxeiUWh+i7iPzls83F3uXMBwgCaiMj3+HpNsCJvjKuIkpOTrdYE++ovlr/w9CxyUgfBSmm35iy2TiMi8j2+HgQz/aIQrAlWJiXPIufpmeeIiIj8GYNghWBNsPfZyvKaZlv9xdnpPaFWFyI1NRUqlcqpG+PSkuKNvYYren00ERH5D76bKURSUpLZdM2GmmByj71yBl8pHZDiZr+w4ErQB6sQogJUKkAPICxYZTOgDQuuZCxRMH0Oy0/0QURE5Kv4bqYQ7BPsGb4S6Nojd9BpGhATERH5CwbBCsGaYDIlVas3IiIiso5BsEKwJtgzHAWTvpApFtOhQu5sMRERka9hizSFYJ9gaYltbVakK4FGpzfe+JWWFA8AZt+HBqsUf6OcmHIFtVqNvXv3Gm+MS0hIQEREhBeOjoiI/BFbpJEkWBMsLVczvIbg19b3RERE5B8YBCsEa4L9l2lJhjvTMXMqZyIiIunwXVQhWBMsLWduLPN0X2B7bchsPebsTHVFuhIGxURERE5gTbBCsCZYPkW6EmP5hKMMqyvZWFeC09hJO5xex1FdMGuCiYhISqwJJknMnz8f06ZNk/swKiRX++By4ggiIiLfxUywQmi1Wmi1WuP3BQUFiImJ8dmrK3KPtXIIR1loRwE5M8FERCQlZoJJEiEhIQgJCZH7MEghHAW0zEITERG5h++iRD6C0xcTERFJJ1DuAyAiIiIi8jZmghXKUKpt2jGCyB1qtRpFRUUQBAEBAQEoKCgAbwkgIiJXGWIUX30vYRCsUGq1GgAQExMj85EQERER2aZWqxEZGSn3YTiN3SEUqrS0FNnZ2YiIiEBAQICodQwdJa5eveqTd2n6E74WysHXQjn4WigHXwvl8OXXQhAEqNVqREdHIzDQ9ypsfe+IiYiIiIjcJcjs4sWLwqBBg4R69eoJoaGhQpMmTYRp06YJd+/eNY7ZtWuX8NJLLwnNmzcXAgMDhQYNGtjc3owZM4Q+ffoI9913nwBAmDJlitVx3377rTBw4EChYcOGQmhoqNC4cWNh3Lhxwp9//mkxdt26dcILL7wgPPTQQwIAoVu3bla3eeTIEeH1118XmjVrJoSFhQkxMTHCgAEDhAsXLjjxjJS5evWqAIBf/OIXv/jFL37xS9FfV69edTrOUQJZa4KvXr2KDh06IDIyEqNHj0aNGjWQnp6OKVOmIDMzE1u2bAEArF27FuvXr0ebNm0QHR1td5vJycmoU6cOWrdujV27dtkcN2rUKERHR2Po0KGoX78+Tp8+jcWLF+O7777Dzz//jNDQUOPYpUuXIjMzE+3bt8edO3dsbnPOnDk4ePAgBgwYgFatWiEnJweLFy9GmzZtcPjwYbRo0UL0c2OYxMAXPx4hIiLPU6vV2L9/PypVqoRKlSqhpKQEJSUl6NatGyfCIa8wlHL46u+brEHwV199hby8PPz0009o3rw5gLLgtLS0FF9++SX+/PNPVK9eHbNmzcLy5csRFBSEp59+GmfOnLG5zcuXLyM2Nha3b99GrVq1bI7buHEj4uPjzZa1bdsWw4cPx5o1azBy5Eiz46xXrx4CAwPtBrLjxo3D2rVrERwcbFw2aNAgtGzZErNnz8bq1asdPSVGhjrgqlWrMggmIiILAQEBCAsLQ2hoKEJCQqDVaqHRaFC1alWfDUrIN4m9d0lpZK0JNrTWqF27ttnyunXrIjAw0BhMRkdHIygoSNQ2Y2NjRY0rHwADwHPPPQcAOHfunNnymJgYUQXfnTt3NguAAaBRo0Zo3ry5xTaJiMh3FelKEDtpB2In7bA6zTkRKZ+smeD4+HjMmTMHL7/8MqZNm4aoqCgcOnQIS5cuxRtvvIEqVap49XhycnIAADVr1pRsm4Ig4ObNm8ZMNxERKYM7wavpulIEwZwGncj7ZP2re+qpp/DBBx9g1qxZ2Lp1q3H5e++9hxkzZnj9eObMmQOVSoXExETJtrlmzRpcv34d06dPtztOq9VCq9Uav+ckGURElop0JWj2ftn9Hmen93QreDRsx13tZux1exucEp3I+2S/9IyNjUVcXBz69++PqKgo7NixA7NmzUKdOnUwevRorx3H2rVrsWLFCiQlJaFRo0aSbPP8+fN4/fXX0alTJwwfPtzu2A8//BDTpk2TZL9ERL7OVnZVTAaWWVUiEkPWM8W6deswatQoXLx4Effffz8AoF+/figtLcXEiRMxePBgREVFefw40tLS8PLLL6Nnz56YOXOmJNvMyclB7969ERkZiY0bN0KlUtkdP27cOLOb8dRqNZo1aybJsRAR+RoxWVpbGdis2b1FZYzPTu/p8vEV6UqM+z+WnMDAm8gHyfpXu2TJErRu3doYABv07dsXKSkpOH78OLp37+7RYzh58iT69u2LFi1aYOPGjahUyf2nJD8/H7169UJeXh7S0tIctnUDgPnz5zMTTER+yds3jhXpSiSv2XUWg2Ii5ZP1r/TmzZuoXr26xfJ79+4BAEpKPHvi+vXXX/HUU0/hvvvuw3fffYfw8HC3t1lcXIw+ffrg4sWL2LNnj+hsLjPBROSvpKq9dXV/UtTs2mNr++7WLBORZ8n619m4cWPs3r0bFy9eROPGjY3Lv/76awQGBqJVq1Ye23dOTg569OiBwMBA7Nq1y25PYbH0ej0GDRqE9PR0bNmyBZ06dRK9LjPBRET+xdMZ6CKdHlo9EKgXgBIBWr0Arb5sucpk3wzEiawLEARBkGvnBw4cwN/+9jdERUVh9OjRiIqKwvbt27Fz506MHDkSy5cvBwCcOnXK2D1i9erVuHnzJt5++20AwCOPPII+ffoYt/nVV1/h999/R1FRET788EM88cQT+Nvf/gYA+Mc//oEGDRoAAB599FGcPHkSSUlJaNmypdlx1a5dG08++aTZcR44cAAA8K9//QthYWF4+eWXAQBxcXGIi4sDALz11lv45JNP0KdPHwwcONDi5x06dKjN56KgoMCsI4QhE5yfn8/JMojIp3mrHMHbGWdfwc4T5CkFBQWIjIz02VhF1iAYAI4cOYKpU6fi+PHjuHPnDho2bIjhw4cjKSnJWJ+bkpKCESNGWF1/+PDhSElJMX4fHx+P/fv3Wx27b98+4yQZ9mY36datG1JTU43fT5061WaWdsqUKZg6darDfQNlPYNtsbUPX/3FIiKSmqNgmkGwda4EwVK2oiP/xSCYJMFMMBGRfbGTdsh9CIq2KE4FjaYY8fHxiIgou8elSFfiUgArVfcLBs/+zdeDYP52KsTcuXMla89GREQVT4gqAKUqICxYZQw+pciOu3NjodJLMZjxrtj4ahMRkU9wp6+vlJTSI1itLkRqaipCQysjJDgE0OtkOQ6lcKX2XIpWegycfRdfOYVISkrCq6++avyeLdKIiMwpMdgIC64k23Hpg1UIUZVlgEMqBUCrtxzj6MLBVkCvlEDfGe5mvV3NeCs92022Kf+3uoJgizQiIt8QFlzJZwIf06BWiu0Q+RPeGKcQvDGOiIicoVarsXfvXoSGhiIkJARarRYajQYJCQmIiIgwG+vuTYW+EPS7Wg5RPuPtbBa8Il8g8MY4kgQzwURERK4TE4x6ome1qx04SH7MBCsEM8FEROQMa5lgdeFdTD4aAA2Czbod2Ar+xGY9/SXI81SbPV/IlHsCM8EkCWaCiYh8l7dmxTPfp/VpkwXj49Idkzvb8pcAmvwPM8EKwUwwEZHv4kQetkmVJZWip6+YYN6VzhgVNdD39UxwoNwHQGXmz5+PmJgY4xfboxERkT8o0pVI9uXuNsUwDWgNLfAcfZFtWq0WEydORHR0NEJDQ/HYY4/hhx9+ELXu9evXMXDgQFSrVg1Vq1bFM888g99++02yY2MmWCGYCSYi8l3eLIcwZCorQ4fJbQREhIchJDgEWp0W6sIifPBzAIoRLGt/XylmqpNLRa3vdYWYTPDgwYOxceNGvPXWW2jUqBFSUlJw9OhR7Nu3D126dLG57cLCQrRp0wb5+fl4++23ERQUhAULFkAQBJw4cQJRUVFuHz8vXxSCNcFERMojR62vr2NmlAyOHDmCdevW4aOPPsL48eMBAMOGDUOLFi2QlJSEQ4cO2Vx3yZIluHTpEo4cOYL27dsDAHr16oUWLVpg3rx5mDVrltvHx0ywQjATTESkPKz1dV7W7N4ea0XmjVnsGMSL5ygTnJSUhPnz5yM3N9fs8Q8//BDvvvsurly5gpiYGKvb7tChA4CyQNpUz5498euvv+K///2vcdmVK1dQVFSEpk2bOnX8fKUVgplgIiLyF54OJFmL6xuOHz+Oxo0bWwTIhgD3xIkTVoPg0tJSnDp1Ci+99JLFYx06dMDu3buhVquNk8IMGzYM+/fvh7N5Xf4GKcS4ceMwcuRI4/eGTDAREcnn7PSePl3fasvZ6T3lPgSneXO6aik6URBw48YN1K1b12K5YVl2drbV9XJzc6HVah2u26RJE7eOj6+qQjATTESkPP4a/PjrzwVIU8ddvhOFFPz5ObdFo9EgJCTEYnnlypWNj9taD4DodVNTU106vor3iigUM8FERMrk6aypaa2rgZiaV7W6EKmpqQgNrWzsDqHRFCM+Ph4REeGePGRFkzpzX/61cVVF7DoRGhoKrVZrsby4uNj4uK31ALi0rjMYBCsEM8FERMrkbAZPCR0lKmLWkZSnbt26uH79usXyGzduAACio6OtrlejRg2EhIQYxzmzrjP4V6IQzAQTESmTs/WhUmQixWcfgwDoART99f3RgxUy62ggRebeW50o/N2jjz6Kffv2oaCgwOzmuIyMDOPj1gQGBqJly5Y4duyYxWMZGRl44IEHjDfFuYMzxikEZ4wjIvI8b8xURvISO8ub2FngpN5eRZKYmAi9Xo/PPvvMuEyr1WLlypV47LHHjJ0hrly5gvPnz1use/ToUbNA+MKFC/jxxx8xYMAAs7HW1heDfYIVgn2CiYg8T66+v2Kyk87WBturCa4dVU2KwyayS8yMcQMHDsTmzZsxduxYPPTQQ1i1ahWOHDmCvXv3Ii4uDgAQHx9v0eJMrVajdevWUKvVGD9+PIKCgjB//nzo9XqcOHECtWrVMo61tr4YFfPSRIFYE0xERFLxVEa6omY0yXVffvklJk+ejK+++gp//vknWrVqhe3btxsDYFsiIiKQmpqKsWPHYsaMGSgtLUV8fDwWLFhgFgC7g5lghWAmmIjI86wFh/7YB9hTKnKtMVkSkwlWMl7SKQQzwUREnsdMpntcyTDzOSelYiZYIZgJJiKShzdvZquIWWdmj/0XM8EkCWaCiYjkwUwlUcXETLBCMBNMROT/vFWTbNqNQu6et7zI8F/MBJMkmAkmIk9xdrIH8hylPfdKOx4ib2ImWCGYCSYisZytYXUnE8ggyfNMX09v1wyzXpfcwUwwSWLu3LmYOXOm3IdBRD7AnUBJ/HS8ZRgkeZ6SLjT4qQFVJPztJiIiUggxM8sZqNWF2LP3R2w8+QdKoMKITvfjnlaL+Ph4RESEm4219elB+eXlp4gWi8Ey+SKWQygEyyGISCxfLodgplE6arUau3b/gHXHb6IEKvzf4/VxT1uMhIQEREREmI319HTR/MSgYmI5BEmCN8YRkVjuBI5hwZXsrm8vMygFVzONrvL3ILuSKhD/1/UBhISEQKvV4p7cB0TkQ5gJVghmgolICTydMfQ2JWQoPZX9VqvV2Lt3L0JDQ41BsEajsZoJFnvB4eqnBv5+sUHWMRNMkmAmmIjI94gJLpVQZ+vKthx9akDk65gJVghmgolICTxdouCNiRtM9+HpumNPZs4dZbGdyQQTeQIzwSQJZoKJSAm8mflzNtPoSoAudh1mPIkqHmaCFYKZYCIi++TMutoithzCE3W23soEs6MH2cJMMEmCmWAiIt/jbEDoqTpbbYkArV6AVg8U6fRQSVjWwo4e5K+YCVYIZoKJiOzzZIcDXwy8TDPBr+zzfHDqLc5m5Zmplg8zwSQJZoKJiOxjh4OKwZXJYFxd1xv4+6dczAQrBDPBRETkDNNMMFTB0Oq00GiKrU6b7I7ymXXD/0kcJfSq9hRmgkkSzAQTEZGrQioFAPoAlKqAsGCVx7KPzGqSP2EmWCGYCSYiIme40h1CivpZayUHhm1629npPb3Se9odSjseKfl6JjhQzp3/8ssvGDBgAB544AGEhYWhZs2aiIuLw7Zt28zGLV++HN26dUPt2rUREhKChg0bYsSIEcjKyjIbl5KSgoCAAJtfa9asMY7dtGkTBg0aZNx3kyZN8PbbbyMvL8/iONevX4+hQ4eiUaNGCAgIQHx8vNWf5+jRoxg9ejSaN2+OKlWqoH79+hg4cCAuXrzo8LmYP38+YmJijF/NmjVzuA4REfmPIl2Jk196aPUo6wpRrjuEvfVc35/5+kpQvubb8L2Svki5ZM0Ef/fdd1i0aBE6deqE6OhoFBUV4dtvv0VaWhqWLVuGUaNGAQBee+01FBUVoWXLlqhevTouX76M5cuXQ6/X4+TJk4iOjgYA/Pbbbzh06JDFfhYsWICTJ0/i2rVrqFOnDgCgZs2aiI6OxrPPPov69evj9OnT+PTTT/HAAw/g559/Lqux+p/4+HhkZmaiffv2OHHiBFq1aoXU1FSL/SQmJuLgwYMYMGAAWrVqhZycHCxevBiFhYU4fPgwWrRoYfO5YCaYiKhi82QfZLmcnd7T5mNSZHAZZMrL1zPBiiuH0Ov1aNu2LYqLi3H+/Hmb4zIzM9GuXTt8+OGHmDRpks1xGo0GtWvXRseOHbF7927j8tTUVIuM7pdffonhw4dj+fLlGDlypHH51atXUa9ePQQGBqJFixaoWbOm1SD40KFDaNeuHYKDg43LLl26hJYtWyIxMRGrV6+2eZxTp061WhPsq79YREQkjiG7KldJgSfZuymMrc18n68HwYr7jVOpVIiJicHRo0ftjouNjQUAq+ULprZt2wa1Wo0XXnjBbLm1kobnnnsOw4cPx7lz58yWx8TEODxuAOjcubPFskaNGqF58+YW2yxv3LhxZoG3IRNMRCQ1Bh/KopTg117W1hPCgiv5decEUj5FnPnu3r0LjUaD/Px8bN26FTt37sSgQYMsxt25cwd6vR5XrlzB9OnTAQAJCQl2t71mzRqEhoaiX79+Do8jJycHQFmphFQEQcDNmzfRvHlzu+PYHYKo4vJ2naWcfVUZcCsXXxuqaBTxG//2229j2bJlAIDAwED069cPixcvthhXr149aLVaAEBUVBQWLVqEJ5980uZ2c3Nz8f333+PZZ58VNY/6nDlzoFKpkJiY6OJPYmnNmjW4fv26MWi3hZlgoopLzkygt3u+MvNnydUMrFpdiD17f8TGk3+gBCqM6HQ/7mm16Ph4F3RbmAFAmd0SiJRCEX8Zb731FhITE5GdnY0NGzZAr9dDp9NZjNu5cyeKi4tx7tw5rF69Gnfv3rW73Y0bN0Kn01mUQlizdu1arFixAklJSWjUqJHLP4up8+fP4/XXX0enTp0wfPhwu2OZCSYiqpgcBaneytYb9sOgmSoKxd0YBwA9evRAXl4eMjIyEBAQYHXMr7/+ihYtWuCjjz7C6NGjrY7p1q0bzpw5g5ycHAQFBdncX1paGnr06IFu3bph+/btqFTJ9gnA3o1xpnJycvD444/j3r17OHz4sLGDhS3sDkFUcclRDiFXX1UGWM7zdtcIZutJLN4Y5wGJiYl45ZVXcPHiRTRp0sTqmAcffBCtW7fGmjVrrAbBV65cQVpaGkaNGmU3AD558iT69u2LFi1aYOPGjXYDYLHy8/PRq1cv5OXlIS0tzWEADDATTFSRyRkYspep8sjdi1fM/vk7Q/5Akb/FGo0GQFkw6WicoUa4vK+//hqCINgthfj111/x1FNP4b777sN3332H8HD351ovLi5Gnz59cPHiRezZs0d0XS9rgonIW3hXvrLJ3S1CzP75+0P+QNYZ4/744w+LZffu3cOXX36J0NBQNGvWDCUlJfjzzz8txh05cgSnT59Gu3btrG577dq1qF+/Prp06WL18ZycHPTo0QOBgYHYtWsXatWq5d4Pg7Iex4MGDUJ6ejq++eYbdOrUSfS6nDGOiIiIyHtkrQl+7rnnUFBQgLi4ONSrVw85OTlYs2YNzp8/j3nz5mHcuHHIy8vD/fffj0GDBhmnIz59+jRWrlyJypUr4/DhwxY3sp05cwYtW7bEpEmT8OGHH1rd96OPPoqTJ08iKSkJLVu2NHusdu3aZl0nDhw4gAMHDgAA/vWvfyEsLAwvv/wyACAuLg5xcXEAym7w++STT9CnTx8MHDjQYp9Dhw61+VywJpiIyDN8rS+y6fF6i7MdKpT+HJJ3sCbYDYMGDcKKFSuwdOlS3LlzBxEREWjbti3mzJmDvn37AgDCwsIwcuRI7Nu3Dxs3boRGo0F0dDQGDx6M5ORk46QZptasWQMAGDJkiM19nzx5EgAwd+5ci8e6detmFgT/+OOPFvW6kydPBgBMmTLFGASfOHECQNkEHdu2bbPYrr0gmDXBRETiOFszK0VfZE8EfbaCczkCTAa1VBEpsjtERcRMMBGRON7ulgB4ZjY1e106xATrheq7SEtLQ+XQEARWCsKqtF9ROaAE019NRM3qkQ7XN90fg2Byha9nghkEK0RycjJmzpxpsdxXf7GIiDxFjiDYH/HmNnKXrwfBvPQjIiKf4mxWVmxfZLm7MnibO63YmDkmf8BMsEKwHIKIyDPE3hgn56QlvoZZZAKYCSaJ8MY4IiLPENsXmdlN8UwvGPi8ka9iJlghmAkmIqp43Mk+q9WFSE1NRWhoZYQEh0Cr00KjKUbHx7ug28IMAOKnxXanFIRZYWn4Wis/gJlgkggzwUREFY87gY4+WIUQFRCiCkBIpQBAH4BSFRAarDLbvi8EU75MqjIaKVr5lcfX3j5mghWCmWAiInKGWq3G3r17ERoaipCQEGi1Wmg0GiQkJCAiIsKpzKKtoEvMTYUVPdBScrcST2fpmQkmSTATTERErtDqBaBEgFYvQKsHinR6qHQlojOLYrPFzCqTv2EmWCGYCSYiImcYMsFvHQ5yazu+UNOr5HpZKcshxLTyc4annydmgkkSzAQTEZE/8ESrOU/Uy5pyJ1j0RKDJrLt3MBOsEMwEExGRMwyZ4MBg8+4Q8fHxiIgIF51ZlDrYUnKNrC2+kA1XImaCSRLMBBMRkSvKd4cIC1ZZBLbMLBJZYiZYIbRaLbRarfH7goICxMTE+OzVFREReZaj7hBy8VQ5hNT1sqZ4geAaZoJJEiEhIQgJCZH7MIiIiNzi6YCSWW2SCn+LiIiISNHETn1N5IxAuQ+AiIiIiMjbmAlWKEOptmnHCCIiIgO1Wo2ioiLodDpUqlQJJSUlKCkpQUFBAXi7D3mDIUbx1d83BsEKpVarAQAxMTEyHwkRERGRbWq1GpGRkXIfhtPYHUKhSktLkZ2djYiICAQEBIhax9BR4urVqz55l6Y/4WuhHHwtlIOvhXLwtVAOX34tBEGAWq1GdHQ0AgN9r8LW946YiIiIiMhdgoyGDx8uALD5de3aNeHu3bvC4sWLhSeffFKoU6eOEB4eLjz66KPCkiVLhJKSEottzpgxQ+jTp49w3333CQCEKVOmWN33+fPnhbfeekvo1KmTEBISIgAQLl++bHWsWq0W3nzzTaFevXpCcHCw0LRpU2HJkiVWx+7evVt4/PHHhdDQUKFatWpC//79bW7XnqtXr9p9bvjFL37xi1/84he/lPB19epVp+McJZC1JviVV15B9+7dzZYJgoBXX30VsbGxqFevHs6cOYMxY8YgISEB48aNQ9WqVbFr1y689tprOHz4MFatWmW2fnJyMurUqYPWrVtj165dNvednp6ORYsWoVmzZnj44Ydx4sQJq+P0ej169uyJY8eO4fXXX0ejRo2M+//zzz/x7rvvGsdu374dzzzzDNq0aYPZs2ejoKAAn3zyCbp06YLjx4+jVq1aop8bQ6NzX/x4xJ+p1Wrs378flSpVMrsRpVu3brI2pyciIvI2QymHz77/yR2Fl5eWliYAEGbOnCkIgiDcunVLOHPmjMW4ESNGCACES5cumS03ZF1v3bolALYzwXfu3BEKCgoEQRCEjz76SACsZ4I3bNggABBWrFhhtrx///5C5cqVhZs3bxqXNWvWTHjooYcErVZrXHbixAkhMDBQGDdunMOf3VR+fr4AQMjPz3dqPfKsgoICYfPmzcL3338v7Nu3T/j++++FzZs3G3+XiIiIKgpfj1UUVxO8du1aBAQEYMiQIQCAmjVronnz5hbjnnvuOQDAuXPnzJbHxsaK2k+NGjVEXbmkpaUBAJ5//nmz5c8//zyKi4uxZcsWAEBubi7Onj2L5557DsHBwcZxjzzyCB5++GGsW7dO1HFRxVCkK0HspB2InbTDI1OMEhERkX2KapF27949bNiwAZ07d3YYzObk5AAoC5I9SavVQqVSmQW2ABAWFgYAyMzMxP/93/9Bq9UCAEJDQy22ERYWhl9++QU5OTmoU6eOR4+XpGMtOC3S6aHVA4F6ASgRoNUL0OrLlqucCGZNt+3JIJhTixIREVmnqHfIXbt24c6dO3jhhRfsjtPpdFi4cCEaNmyI9u3be/SYmjRpAr1ej8OHD6NLly7G5YYM8fXr1wEAtWvXRrVq1XDw4EGz9e/cuYOzZ88ax9oKgrVarTGQBjhJhhI0e99WTXkQAD2Aor++P3rQxljH2s3Y6/K6jnCaUSIiIusUVQ6xdu1aBAUFYeDAgXbHjR49GmfPnsXixYtRqZJn4/ghQ4YgMjISL730En744QdkZWXhs88+w5IlSwAAGo0GABAYGIhXXnkFe/fuxTvvvINLly4hMzMTAwcOhE6nMxtrzYcffojIyEjjFyfJcA3LDIiIiEgMxWSCCwsLsWXLFvTs2RNRUVE2x3300UdYvnw5PvjgA/z973/3+HHVqVMHW7duxT/+8Q/06NEDAFC1alX861//wvDhwxEeHm4cO336dNy+fRtz587F7NmzAQA9evTAyy+/jE8//dRsbHnjxo3DyJEjjd+r1Wo0a9bMQz+VfyrSlVgtM3C1JODs9J4Wy9TqQqSmpiI0tDJCgkOg1Wmh0RQjPj4eERHhdrLH0rJ2bERERCSeYoLg//znPygqKrJbCpGSkoKJEyfi1VdfRXJysteOLS4uDr/99htOnz6Nu3fv4pFHHkF2djYAoHHjxsZxwcHB+PzzzzFz5kxcvHgRtWvXRuPGjTFkyBAEBgbioYcesrmPuXPnYubMmR7/WfxFka7EGHCend4TYcGVLAJQQ5mBqyUB1oJnfbAKISogRBWAkEoBgD4ApSogLFjl1fpb1voSERG5RzHvpGvWrEF4eDj69u1r9fEtW7Zg5MiR6NevH/797397+egAlUqFRx991Pj9nj17AMCizzFQVh9cu3ZtAGV9hlNTU/HYY4/ZzQSTOUelDM7cWFakK/Fa0OhMhrZIV2IM1I8lJzCwJSIi8iJFvOveunULe/bsweDBg41dF0wdOHAAzz//POLi4rBmzRrZ56e+desW5syZg1atWlkNgk19/PHHuHHjBv71r3/ZHZeUlIRXX33V+H1FL4dwpqzA0Y1lzd7f5bUbxFwNZMOCK/lkEGwtI09EROQLFPGOtX79epSUlFgthfj999/Rt29fBAQEIDExEd98843Z461atUKrVq2M33/11Vf4/fffUVRUduf+gQMHMGPGDADAP/7xDzRo0AAAkJ+fbwxMDR0dFi9ejGrVqqFatWoYPXq0cZvdunVDp06d8NBDDyEnJwefffYZCgsLsX37drOAfPXq1fj2228RFxeH8PBw7NmzBxs2bMDIkSPRv39/u8/B/PnzMW3aNNHPGUlLjmAuLLiS17s3SH2zoKdbvTGoJiIiTwkQBEGQ+yA6deqE3377DdnZ2VCpVGaPpaam4oknnrC57pQpUzB16lTj9/Hx8di/f7/Vsfv27UN8fDwAICsrCw0bNrQ6rkGDBsjKyjJ+P27cOGzZsgXXr19H1apV8eSTT+KDDz7AAw88YLbekSNHMGHCBJw+fRoajQZNmjTBP//5T4waNQoBAQF2noGylmimbdEMmeD8/PwKOW2ymBIH01ICRxwFU2JLExzdGCd2f3KJnbRD7kNwClu8EREpV0FBASIjI302VlFEEExAcnKy1RvjfPUXy9PKZ2691ZVBLKUGbwyCiYhIKr4eBCszXUXkgBylBP7A2o177pQxaHR6dJ2bCgBIS4pHaLDK/gpOrsfZ9IiIyFOYCVYIlkO4x91gydPlEEq+gczXssNS4UUUEZF7mAkmSfDGOPdIGVTa69Rg6BPsSPmg3PT7Zu/vcqklmpICZyIiIl/HTLBCMBOsfEW6EmMmeOLRIK/v///bu/OwKMv1D+DfYYBhEMSdRVG03PcFTX8JKK6ZS4qZZmpp2iktcyEszV00cyuPaR5DTU0N9ViZqZm4ZSim5o6ZCwnuAsM2I8P7+4MzbwwzAzMww7zDfD/XNddp3vUZ3jpzc3M/91O4lMFaQbE9lpeWQo9k/lJBRFQ6zASTVXDFOOn7Z/Jd2QfA+vfPZ60/59s7GHTUHslEROTY+M1DZII9MqTOghMbiYjI3lgOIREsh5AeKU8Yk9rkutKS8sRBIiIyjuUQZBWcGFf2HDnwsvVYyzoLbuuV58zhSM+fiIhKj5lgiWAm2LrMCaSKm5xl7BpadY5ZLdLMvUdJ2Tpgk3IW3FZYnkFEZBlmgskqODHOuixdQU4XqBbn/EchUMgBhVwGhasM0MqQJwc8/7fYQ1GZZU4AIyIikg5+IxNZIEujhVoLuGgFIFeAWitArc3fLpcZ/5N+wvRwtJt3sNj+wFIKkI2tLGdLUmiZRkREtjV//nxMnz4dTZs2xYULF4o9/s6dO3j//fexf/9+5OXloUuXLli2bBnq1atnlfGwHEIiWA5hXdYoh7A0m1xazvzneEeuzyYiclaWlEP8/fffaNiwIWQyGYKCgooNgjMyMtCmTRukpaVh8uTJcHNzw7JlyyAIAs6ePYuqVauWevz8ppEIlkNYV0lWYysvgZcjBpRsmUZEVL5NmTIFzz33HLRaLR4+fFjs8atWrcK1a9dw8uRJBAcHAwB69+6NZs2aYcmSJViwYEGpx2TRt2Nubi60Wi0UCoW47d69e1i9ejUyMzPRr18/PP/886UeFFFZKC7wMlYSoFsxztjEOLnCQy+zXFydsSUlB5Z0TChppwVHCJaJiMjxHDlyBLGxsThz5gwmTJhg1jmxsbEIDg4WA2AAaNSoEcLDw7F9+3a9IPj27dvIyspCo0aNLBqXRd96b775Jtzd3bFmzRoA+X+yDw4ORk5ODvz9/bFs2TLs3r0bL7zwgkWDICAyMhJvvfWW+F5XDkH2Yywo1LrLTU6Mkxc43tPdVQxyjZVdWJqtLWlphrkT/gDnLscgIiLb0Gq1mDBhAsaMGYPmzZubdU5eXh7++OMPvPHGGwb72rdvj/3790OlUsHb2xsAMGLECBw+fBiWVvhaFAQfP34cK1euFN9v3LgRWq0W165dg4+PDz744AMsXryYQXAJsE+w9GVpcoucGOetKNmEMq5MR0RE5dXq1atx69Yt/Pzzz2af8/jxY6jVavj7+xvs021LTk5Gw4YNSzU2i4LgO3fuoH79+uL7gwcPYtCgQfDx8QEAjBw5EjExMaUakLOaNGkSxowZI75nJlh6/snGugHQAsj65/2p42Zdw1hm1pJsraWORoYBADp/Eie+V/6vnVthLJ0gIiJrevToET7++GPMmDED1atXN/u87OxsANArv9Xx8PDQOwYA4uLiSjQ+i77JPDw89G7622+/YfHixXr7MzIySjQQZ8eJcWQLuuDX1PuSYukEEREVZ/r06ahSpYrZdcA6SqUSAKBWqw325eTk6B1TGhYFwa1atcLXX3+N6OhoHD16FPfu3UPXrl3F/devX0dAQECpB0UkRZfm9BQnxrm4K8SJcTnZanTu3Ble3hUMzsnWaI1mYU1tB/SzrGXdpo2IiMgarl27hi+//BLLly9HcnKyuD0nJwdPnz7FzZs3UbFiRVSpUsXg3CpVqkChUCAlJcVgn26bNeJNi/oEHz58GL1794a/vz9SUlIwdOhQrFu3Ttz/9ttvIzMzExs2bCj1wJwN+wQ7BpVKhYMHD2Lib242u0fBLKs16oVtsRAFyyGIiKioPsFxcXHo0qVLkee/9957WL58udF9wcHBkMlkOHnypN72Hj164Pr167h+/Xqpxg5YmAkODQ1FQkICDhw4AD8/PwwePFhvf6tWrdC+fftSD8oZcWKcNBUOQnUT48rynjqFA8+S9AMuT/2QiYhIupo1a4Zdu3YZbJ8+fTpUKhVWrFiBZ555BoDxFmcRERGIiopCQkIC2rVrBwC4evUqfvnlF0yZMkXvmiVtkcYV4ySCmWBpCoraY+8hiAp3nmCGl4iI7MmSFeN0wsLC8PDhQ70V48LCwgxanKlUKrRu3RoqlQpTpkyBm5sbli5dCq1Wi7Nnz+pNtDN2vjks+sY7ffo0pkyZgt27dxt82LS0NAwYMADLly9Hy5YtLRoEcWIcFa+o+mBrdZjghDciIpICb29vxMXF4f3338e8efOQl5eHsLAwLFu2zKJOE0WxKAhesmQJunbtajTa9/HxQbdu3bB48WJs2rTJKoMjKmuFSxESpofrvc9QZeLo0aPwUOpPjAvu2BG9/p0A4J+JbkVNfrNlWzQiIiKpMtbOzFSLs1q1auHbb78t0TXNYVE5xDPPPINdu3ahRYsWRvefP38e/fv3x19//VWiwTgzlkNIg73LHyxdStma5RAFr2dujTERETmvkpRDSInFi2XolqgzxsvLy2g7CyoeJ8YRYFlNbsGsdbZGa9Wg1Rar2DGoJiIiKbHoW6l69eq4evUq6tata3T/lStXUK1aNasMzNlwxThpKC4Tq+sTrFR6iOUQ2dk5eO7/nkfo8ngApcvKljT4tNYiGDq2KNdgvTEREUmJRd/U3bp1w/z589GrVy+DfYIgYP78+ejWrZvVBudMODHOvsxtN6Z1l0MhBxRyGRSuMkArQ54cBotdlDQItnc5BhERkbOw6Jt6+vTpaNu2LTp06IDJkyejYcOGAPIzwEuWLEFiYiLWr19vi3ESmaWkmdSC5xV1DV2fYBetAOQKUGsFqLWAIMgcJtNpKttti5ZrREREUmVxn+CEhASMGjUKly5dgkwmA5CfBW7SpAliYmIQHBxsk4GWd5wYZx32zKRaIwi2JIgvadBq6riSLL5BRETOy6kmxgFAu3btcOHCBZw5cwZ//vknBEFAgwYN0KpVKxsMz3lwYpzjM3elt6KUNPC0xkpwnu6uDpPNJiIiKi2uGCcRzASXjOGyxrmS68HLwJKIiMojp8oET5o0yazjli5dWqLBODNOjCuZolZRIyIiIjLFoiD4zJkzthoHkd0UXhWuqJXeLFkxTscaPXdZn0tERGRdLIeQCJZDlEzRnRyMTxxzxOwxSyqIiEhqHL0cwsWaF7t8+TKmTJlizUs6jaVLlyIwMFB8caGMf2RpchEUtQdBUXsMgl7dhDBTL2PHEREREZU6E5yZmYmtW7di3bp1+O2339CkSRNcuHDBWuNzGs6eCS5JRteU4vYXV55g60xxcavSGcPgnYiIpMbRM8El/mY9fvw41q1bh+3btyM7Oxvvv/8+vvrqKzRq1Mia43Mazj4xztzA05zOD8WVDtg7oLT3/YmIiMjCIPj+/ftYv349vvrqK6SlpWHo0KGIi4tDx44d8cYbbzAAJodjLCtceKJcQQUnxrm4umHD0evwkOVizlsRqFbZRzyuqMl11pgop8OAmoiIqGQsKodQKpWIiIjA8OHD0b17d7i45JcUu7m54dy5c6xjLQVnLYfQBYRFBYZFBZTG9lkSGDriJLmCOGGOiIjsxanKIerUqYNjx46hdu3aqFOnDjO/VuSs5RCWBqG6gNfSfUREREQFWRQEX7lyRawFDg4ORoMGDTB8+HAAgEwms8kAicpKUWUQOqb6BHfu3Ble3hUA6GenAcPsdWmw/IGIiMg6StwdIiMjA9988w1iYmLw22+/ITQ0FMOGDcOAAQNQvXp1a4+z3HP2cojSnG9J5whTHKUs4tKcngyEiYhIEhy9HMIqi2VcunQJ69atw6ZNm/D48WM8ffrUGmNzKrNmzcLs2bMNtjvqv1hlIUuTqxe8liZADIraY61h2VRpAv2iMLAmIiJLMQguIDc3F9999x0GDhxo1vEXL17ErFmzcPr0ady9exeenp5o0qQJpk6dir59+4rHrV27Fps2bcKVK1eQmpqKgIAAhIWFYebMmQgKChKPy87Oxvjx4xEfH4+kpCRotVo888wzeOONN/D222/Dzc1N7/4HDhzA7Nmz8fvvv0OhUCA8PByffvqp3jUBYNu2bfj+++8RHx+PP//8E6GhoYiLizP4PBkZGVi8eDHi4+Nx8uRJPHnyBDExMRg1alSxPwtnzQQDJc8GWysL7OnuavYYVKoMxMXFQan0EMshsrNzEBYWBm9vL4fJKBfGCXZERGQpRw+CLYoaXFxciq39lclkyM01L6C4desWVCoVRo4ciYCAAGRlZWHHjh3o168f1qxZg7FjxwIAzpw5g7p166Jfv36oXLkybty4gbVr1+KHH37AuXPnEBAQACA/CL548SJeeOEFBAUFwcXFBb/++ivef/99xMfHY8uWLeK9f/jhB/Tv3x9t2rTBwoULkZ6ejhUrVuD555/HmTNn9Eo6vvjiC5w+fRrBwcF49OiRyc/z8OFDzJkzB7Vr10bLli2NBsqmOOvEOMA6pQjm9A825ebCPmYH0Fp3ORRyQCGXQeEqA7Qy5MkBTwu7UhAREZF9WZQJ3r17t8l9J06cwGeffQZBEJCdnV3iAWm1WrRt2xY5OTm4cuWKyeNOnz6Ndu3aITo6GlFRUUVec8KECVi5ciVSUlLg5+cHAGjatCk0Gg0uXrwId3d3AMC5c+fQpk0bTJw4EUuWLBHPT0pKQs2aNeHi4oJmzZqhWrVqRgNctVqNJ0+ewM/PDwkJCQgODjY7Ezx9+nSjQbCj/nZlCXuXIhSVBS2cIS4uE5ylyS2ypZs5CgfT1sp4W3JPIiKi4jhVJrh///4G265evYqoqCh8//33ePXVVzFnzpxSDUgulyMwMBCnTp0q8jhdyUJqamqx1yx4rJ+fHx4/foxLly5h6tSpYgAMAC1btkTjxo2xdetWvSA4MDDQrLErFAoxyLZUZGQk3nrrLfG9rhzCGZRkGWHA+sFhwRpjXX2x8Sy1GwAtgKx/3p86bvSaJWnbVlRQ7unuyoCViIjICkr8bZqcnIyZM2diw4YN6NmzJ86ePYtmzZqV6FqZmZnIzs5GWloavvvuO+zduxdDhgwxOO7Ro0fQarW4ffu2GGyHhxu2tdJoNEhPT0d2djYSEhLw6aefok6dOnj22WcB5GdsgfzFPwrz9PTExYsXcffu3RIHtCWxdOlSoxPjnIE1gjpdcFiS+uIsTa5eQK3bJhWe7q6s2SUiIrIyi6OPtLQ0LFiwAJ9//jlatWqFgwcPonPnzqUaxOTJk7FmzRoA+XXHAwcOxMqVKw2Oq1mzphjAVq1aFZ999hm6d+9ucNzOnTsxdOhQ8X27du3w1VdfwdU1/+P6+vqiUqVKOH5cP3v36NEjXLp0CQBw586dMg2CJ02ahDFjxojvnSkTXFLGgkNrTUwrTY1xwvRwm5cvEBERUelY9O38ySefYNGiRfDz88M333xjtDyiJCZOnIiIiAgkJydj+/bt0Gq10Gg0Bsft3bsXOTk5uHz5MjZt2oTMzEyj1+vSpQsOHDiA1NRUHDx4EOfOndM71sXFBePGjcOiRYswbdo0vPHGG0hPT0dkZKR439LUNZeEM0+MK28KBr0sXyAiIpImiybGubi4QKlUolu3bpDLTU/22blzZ6kG1aNHD6SmpiI+Pt5kN4rr16+jWbNmWLx4McaPH1/k9RYsWIDo6Ghcu3ZNzO5qNBq8/fbbiImJQV5ennjfevXqYfXq1Thz5gxatWplcK2iJsYVxIlx9mGstMHW5rZ5qrdinDlBLwNjIiJydI4+Mc6iIHjUqFFmLY8cExNTqkF9+eWXGDduHK5cuYKGDRuaPK5Tp04QBAEnTpwo8nqJiYlo2LAhVq9ejXHjxuntu3fvHhITE+Hr64sGDRpg2LBh2LZtG9LS0uDl5WVwLVsFwc7cJ9ja7N1twhys8SUiIkfn6EGwRemo9evX22gY+nSlCGlpacUep6sRLun1fH194evrCyC/PVtcXBw6dOhgNAC2JWedGGesI0NRxxIRERFZg13/Jnv//n3UqFFDb9vTp0+xceNGKJVKNGnSBLm5uVCpVKhcubLecSdPnsT58+cxbNgwcdvDhw9RtWpVg2z1f/7zHwD5E+SK8umnnyIlJQWff/55aT5WiZSniXGWBKsFjy3uPKmvxlbSVm9ERERU9uwaBI8bNw7p6ekICQlBzZo1cffuXWzevBlXrlzBkiVL4OXlhdTUVAQGBmLIkCFo2rQpKlSogPPnzyMmJgY+Pj6YMWOGeL1NmzZh9erVGDBgAOrVqweVSoV9+/bhwIED6Nu3L7p27ap37I4dOxASEgIvLy/8/PPP2L59O8aMGYNBgwbpjfPIkSM4cuQIAODBgwfIzMzEvHnzAAAhISEICQkRj125ciVSU1ORnJwMAPj+++/x999/A8hftMPHx8foz6I8TYwrabBalnW8tsA6XyIiIsdh12/tIUOGYN26dfjiiy/w6NEjeHt7o23btli0aBH69esHIL9v75gxY3Do0CHExsYiOzsbAQEBGDp0KKZPny4uhAEAzz//PH799Vd88803uHfvHlxdXdGwYUMsXboUEyZM0Lt3gwYN8PjxY8ydOxfZ2dlizbBuqeaCfvnlF4NSBV3wPXPmTL0g+NNPP8WtW7fE9zt37hQnCg4fPtxkEEyW0WVdS1IiUXBFt/0TO6PH8qMAzFvdLUOViaNHj8JDqRBXjMvJVpe6TSARERGVLYsmxpHtlKeJcZaWQxjrqVvcanC691KaBMfJbkRE5EycamIc2U55mhhXOGC1xYQ2R5okZ8nkPyIiIiobzARLRHnKBBdmr2ytORPVCmacS1sOUaOq8VKX4rLa1sLgmoiIyhIzwWQV5WlinFRYGhTq6oSL5wYgD0D2P+9//82sM205+Y/lGEREROZjEEw2Z27rsIIlDgUnrxWXoS14bMHjzS2ZuDSnp+TbrxEREZF1sRxCIspzOYS57DnJzdxAXaXKQFxcHJRKD7EcIjs7B2FhYfD2Nr7ACsshiIioPGI5BFkFyyHsq8nH+8yatKZ1l0MhBxRyGRSuMkArQ54c8HSXmxWEerq7MlglIiKSAH4bk2QUlY21drlCwvRwAPqlFEWVT5QmcPV0d2W9LhERkcSwHEIiWA5RtKIC1ILlBoDpkoPSBNK6IFalUuHgwYNQKpVQKBRQq9XIzs5GeHg4vL29S3x9IiIiR+Po5RAu9h4A5Vu6dCkCAwPFV5MmTew9JEnRlREYe1Xz8jC7ptcRZWlyERS1B0FRexyqPzIREZGUsRxCIiZNmoQxY8aI73WZYDJPwZIDa0+wK0l3C2sqeF1bB8GsVyYiImfBbzyJ4MQ46TI3MCyLNmu27DMMsNcwERE5DwbBVO4Ul7ktq5ZlREREJF389peIGTNmIDIyUnyfnp6OwMBAO47IcVkS1FqzZZmt6pIZtBMREVkfv00lQqFQQKFQ2HsYTsFWLcvKIjhln2EiIiLr4LcpkcSxzzAREZH1sUUaERERETkdBsFERERE5HRYDiFRuoX8Cq4iR/anUqmQlZUFjUYDV1dX5ObmIjc3F+np6eDii0RE5Ex0MYqjfv8xCJYolUoFAOwQQURERJKmUqng4+Nj72FYjEGwRPn5+eHkyZPw9fWFi4t5VSu6VeYuXboEb29vG4+QisJnIR18FtLBZyEdfBbS4cjPIi8vD/fu3YOfn5+9h1IiMsFRc9jl3N9//80sMBEREUleUlISatWqZe9hWMzumeBr165hxowZOHbsGB4/fozatWtj2LBhmDJlCjw9PQEA+/fvx7Zt2xAfH4/Lly8jMDAQN2/eNLhWcnIyIiMjcerUKSQnJ0Mul6NBgwZ45513MGLECMhkMr3jt27dik8++UT87atfv35YtGgRqlWrJh6TlJSEr776Cnv27MG1a9cgl8vRrFkzTJ8+Hd26dTMYQ2pqKiIjI7Fr1y5kZWWhffv2WLJkCdq0aWPRz0X322BSUhIqVqxo0bnkfFQqFQ4fPgxX1/z/pHNzcxEaGupwWQUiInIcuoW9HPW7xq5BcFJSEtq3bw8fHx+MHz8eVapUwYkTJzBz5kycPn0au3fvBgBs2bIF27ZtQ5s2bRAQEGDyeg8fPsTff/+NiIgI1K5dG0+fPsWBAwcwatQoXL16FQsWLBCP/eKLL/D2228jPDwcS5cuxd9//40VK1YgISEB8fHx8PDwAADs3r0bixYtwoABAzBy5Ejk5uZi48aN6N69O7766iu8/vrr4jXz8vLQp08fnDt3DlOnTkW1atWwatUqhIWF4fTp06hfv77ZPxtdwF6xYkUGwVQsmUwGT09PKJVKAEB2djYqVqzosP/HREREjqNwktFR2LUcYsGCBfjoo49w4cIFNG3aVNw+cuRIbNy4EY8fP0blypWRnJyM6tWrw83NDS+++CIuXLhgNBNsSt++fXHo0CGkpaVBLpdDo9HA19cXLVq0QFxcnPjwfvjhB/Tt2xefffYZJkyYAAC4ePEifH199bLDarUarVq1QkZGBpKSksTt27dvx5AhQ/Dtt98iIiICAPDgwQM0aNAAvXv3xpYtW8wec3p6Onx8fJCWlsYgmIqlUqlw8OBBvSA4PDzc7kFwliYXTT7eByB/WWmudkdEVH44eqxi128kXWsNX19fve3+/v5wcXGBu7s7ABSZ/TVHUFCQ2NZKqVTiwoULSE1NxZAhQ/R+e3nxxRfh5eWFrVu3ikFwweBcR6FQ4IUXXsDSpUuhUqnEQCM2Nha+vr4YOHCgeGz16tXx8ssvY9OmTVCr1VwamSQnS5NbJte25X2Aslm2moiIyg+7fmuEhYVh0aJFGD16NGbPno2qVavi119/xRdffIF3330XFSpUKNF1s7OzkZmZiYyMDBw+fBgxMTHo2LGjmCVTq9UAIL4vSKlU4syZM8jLyyuyK8Pdu3fh6ekp1i0DwJkzZ9CmTRuD89q3b48vv/wSiYmJaN68udHrqdVqcVwA+wOTbRjLzOre21q7eQdten0uLU1ERJaw64pxvXr1wty5c3HgwAG0bt0atWvXxiuvvIIJEyZg2bJlJb7uihUrUL16ddStWxejRo3Cc889h61bt4r769evD5lMhuPHj+udd/XqVTx48ADZ2dl48uSJyev/+eef2LlzJwYNGgS5XC5uT0lJgb+/v8Hxum3JyckmrxkdHQ0fHx/xxc4QVFpZGi2yNLkGr3/259o8O0tERCRVdv/7YVBQEEJCQjBo0CBUrVoVe/bswYIFC+Dn54fx48eX6JpDhw5Fu3bt8ODBA/zwww+4d+8esrOzxf3VqlXDyy+/jA0bNqBx48Z46aWXcOfOHUyYMAFubm54+vSp3vEFZWVlYfDgwVAqlVi4cKHevuzsbKPlDrpJdqauCQCTJk3CmDFjxPe6voFElsrNExB7OgkTfzte5HFFZWYvzelplbFkaXLF+yRMD2fJAhERSYZdv5G2bt2KsWPHIjExUewvN3DgQOTl5eGDDz7A0KFDUbVqVYuvW6dOHdSpUwdAfkA8duxYdOvWDVevXhVLINasWYPs7GxMmTIFU6ZMAQAMHz4czzzzDHbu3AkvLy+D62q1Wrzyyiu4dOkS9u7da1CrrFQq9UoadHJycsT9pixduhSzZ8+2+LOS8zGWvc3SaKHWAi5aAbl5ArRC6Wbq2iJY9XR3ZRBMRESSYddvpFWrVqF169YGDZb79euH9evX48yZM0Z78VoqIiICa9euxZEjR9CzZ36Gy8fHB7t378bt27dx8+ZNMXDu1KkTqlevjkqVKhlc580338QPP/yAzZs3o2vXrgb7/f39kZKSYrBdt62oCX7MBJO5TNfwugHQ/u+f61p0TWtlfgvzdHeVdK0uu1cQETkvu/4//r1791C5cmWD7U+fPgWQ3/DfGnRlCGlpaQb7ateujdq1awPIX+ji9OnTGDRokMFxU6dORUxMDJYvX46hQ4cavU+rVq1w9OhRg0l18fHx8PT0RIMGDUyOkZlgsidHC/6sVctsq+4VjvbzJCJyRnb9f+oGDRpg//79SExM1AsQv/nmG7i4uKBFixYWXe/BgweoXr26wfZ169ZBJpMVu2rbtGnTkJubi/fff19v++LFi/Hpp5/iww8/xHvvvWfy/IiICMTGxmLnzp1in+CHDx/i22+/Rd++fYtsj8ZMMBWlYICWMD0cAJCt0aLzJ3EAgJ/eaYdTJ07AQ6lAbp6AXSdv4Jw2AMen9y6XAZktOlpYs3uFlLPfRESUz67fjlOnTsXevXvRuXNnjB8/HlWrVsUPP/yAvXv3YsyYMWL5wB9//IHvvvsOQH5nhrS0NMybNw8A0LJlS/Tt2xcAMH/+fBw/fhy9evVC7dq18fjxY+zYsQOnTp3ChAkT8Oyzz4r3XrhwIS5cuIAOHTrA1dUV//3vf7F//37MmzcPwcHB4nG7du1CZGQk6tevj8aNG2PTpk16n6F79+5in+OIiAg899xzeP3113Hp0iVxxTitVltslveTTz7B/PnzS/kTpfKquKCv178TkF8Okfe/LUEAWIdLRERkil2/HUNCQvDrr79i1qxZWLVqFR49eoS6deti/vz5iIyMFI/7/fffMWPGDL1zde9HjhwpBsF9+vTB9evX8dVXX+HBgwfw8PBAixYtEBMTg5EjR+qd37x5c+zatQvfffcdtFotWrRoge3bt2Pw4MF6x507dw4AcO3aNbz22msGn+HQoUNiECyXy/Hjjz9i6tSp+Oyzz5CdnY3g4GCsX78eDRs2LOVPi8hyjhoAF1eaoMuGF6dgtvxoZBiU7nKL9pviqD9XIiL6h12XTaZ/pKen6y2QoSuHcNSlCMm6jHeE+Kf92OGJHfDb8WNQKnXt+HIQFhYG36qVjJ4n9clgQVF77D2EIrHcgYiIyyaTlXBiHBWlpIGqqeC5qP22HA8REZFUMBMsEcwEU0nYK2Nq60yopcG5qUU5bLVYB38JICJiJpishJlgIuspGPyag0EtEZHzYSZYIpgJppLQZUxVqgzExcUZ1AR7exuufGiN7Ghpg8biMr22aIFWlNIuFsIgmoicETPBZBVskUYloQu+tO5yKOSAQp6/XHKeHPB0lxcbnFmzhZolE+7KOsgtTmnHw4lyRESOh0EwkRPRZWALZj6lvvoaERGRLbAcQiJYDkGloVKpcPDgQSiVSgD5S4WHh4fD29tb7ziptx4zV+HyhUcZaoN+v7aYEGcKyyGIyBmxHIKsghPjiEqu4CIX5i54YS0MgImIHBMzwRLBTDCVhrmZ4KLKFEo7Ya7w+ZZ0Z3BkrAcmImfl6JlgF3sPgPItXboUgYGB4qtJkyb2HhKZIUuTi6CoPQiK2iOJOli1VvjfC8jSaJGlydV7ERERlbXff/8d/fr1Q5UqVeDp6YlmzZrhs88+K/a8O3fu4OWXX0alSpVQsWJF9O/fH3/99ZfVxsVMsEQwE+xYdAGlqexpWf+JXJcJnvibW5netyilbTtmCVstimEOlkMQkbMyJxO8f/9+9O3bF61bt8aQIUPg5eWF69evIy8vD5988onJa2dkZKBNmzZIS0vD5MmT4ebmhmXLlkEQBJw9exZVq1Yt9fj5/94SwRZpjsVYS62Cf/7nn8iLDw4taalm6X0ZmBIR2V96ejpGjBiBPn36IDY2Fi4u5hcgrFq1CteuXcPJkycRHBwMAOjduzeaNWuGJUuWYMGCBaUeH78piMqRz0LyJ4UVtViGKbbMphorxTC3pZo54/B0d+UvHkREErNlyxbcu3cP8+fPh4uLCzIzM6FUKs0KhmNjYxEcHCwGwADQqFEjhIeHY/v27XpB8O3bt5GVlYVGjRpZND4GwRIRGRmJt956S3yvK4cgadL9qd+ef4Y3xtLFMkyxNJtaXFa3uMUoippEx+CWiMgx/fzzz6hYsSLu3LmDAQMGIDExERUqVMBrr72GZcuWwcPDw+h5eXl5+OOPP/DGG28Y7Gvfvj32798PlUolTv4eMWIEDh8+DEsrfBkESwRbpDkWYwGiI/4ZvnAGtqSLaHChDCIiKuzatWvIzc1F//79MXr0aERHRyMuLg6ff/45UlNT8c033xg97/Hjx1Cr1fD39zfYp9uWnJyMhg0blmp8jvWNXY5NmjQJY8aMEd8zE+wYHP3P8LZYvthardHKcmIdERFZX0ZGBrKysvDWW2+J3SAGDhwIjUaDNWvWYM6cOahfv77BednZ2QAAhUJhsE+XPdYdAwBxcXElGh9bpEkEW6SRrRRuk+ZoLdOKGr+jfiYiImeg610/dOhQve3Dhg0DAJw4caLI89RqtcG+nJwcvWNKg5lgiWAmmIpSmk4Ktsj2loWSjtuRM/NEROVJQEAALl68CF9fX73tNWrUAAA8efLE6HlVqlSBQqFASkqKwT7dtoCAgFKPj0GwRLAmuPyyRnayuJrbLI0Wai3gos2fFKBbLEPOzCgREdlJ27ZtceDAAdy5c0evfjc5ORkAUL16daPnubi4oHnz5khISDDYFx8fj3r16hmsiFoSXCxDIrhYRvkVFLXH3kMwKmF6uMl92RotOn8SBwA4GhkGpbvc7OsWdW5ZTBx0tMmJRESOqrjFMs6cOYM2bdpg2LBh2Lx5s7h92LBh+Pbbb3Hr1i0EBAQYbXG2aNEiREVF4dSpU2jXrh0A4OrVq2jatCmmTJmChQsXiseWtEUag2CJmD59utHFMhgEOz6pBsH2wFIFIqLyw5wV40aPHo2vvvoKL7/8MkJDQxEXF4dvv/0W06ZNE3v9hoWFGbQ4U6lUaN26NVQqFaZMmQI3NzcsXboUWq0WZ8+e1csiGzvfHEyZENlYSbscFCx7KC4zm6HKxNGjRzHjd+ksm0xERLR69WrUrl0bMTEx2LVrF+rUqYNly5Zh4sSJRZ7n7e2NuLg4vP/++5g3bx7y8vIQFhaGZcuWmSyjsBQzwRLBcggqrLxlkK25NDIREdmfOZlgKeM3kkRwYpxzcOYWXgyAiYhISpgJlghmgp2DI2d3S7t4BYNgIqLyhZlgsgpmgqXFmTO2plgziC1N32MiIiJr4DePRHCxDGlx1AUmdBYFP0VYWBi8vb1KfI3CvwhY8xeD4voeWwuDayIiMoXfEBLxySefGG2RRlQSCjng6S4vVRBYVr8ItJt30GbXZks2IiIyhUEwkRGlrX81JUuTKwZ9CdPDrZapVKkyEBcXB6XSAwCQp3lq9njKsyxNLrPBRERkFL8dJCIyMhJvvfWW+J7lEPZVViubWes+Wnc5FHJAIZcBALLNPM/Ryz6K0+TjfcwGExGRUQyCJYIT45yDp7srgzIiIiIJYBAsEZwY5zzYGcE2bFXCQkRE5RO/fSWCmWDps1b9rC06I2RptFa5jiPjLxNERGQJLpYhEVwsQ/qkvtDF8ueeQqlUAgCys7MRHh4Ob2/vIs8pLggvTZCerdGi8ydxAICjkWEAoPde6S4v8bWNqeblYdXrERFR0bhYBlkFW6SRPRSXPbXWxDld8GvqvTWw1pqIiCzBIJgI5mU8E6aHm9xXOOtZVJazuGNL8md9lSoDJ47GWXweERGRs2I5hESwHMK+pFTqUJKMpkqlwsGDBy0uhyiOtVeJs0WPZB3WBBMRlS2WQ5BVcGIc6ZQk8MzSaKHWAi7a/N9p1VaaJ2erwNKaPZKJiIhKgplgiWAm2L7MCTwdbWGJ8x+FlDoTTEREZAozwWQVzARbl6W9eMtjVjJLo4W8mOC+PH5uIiIiczATLBHMBOsrbS2qtetPPd1dixyTo2WJddhRgYiISoqZYLIKtkjTZ82gUhcMl8bNhX2YNSUiIipH+K1OZIbiMtO69mnWDJQtyWarVBmIi4uDUpm/YER2dg7CwsLg7e1ltfEQERGVJwyCJSIyMhJvvfWW+F5XDuGsLs3paXS7vcoOzL2vrcoLiuumoHWXQyEHFHIZACBPDni6y4sNyi2tnSYiIiovXOx584sXL2Lw4MGoV68ePD09Ua1aNYSEhOD777/XO27t2rUIDQ2Fr68vFAoF6tati9dffx03b97UOy4pKQmzZ89G+/btUblyZVSrVg1hYWH4+eefDe595MgR9OvXD4GBgfDw8ICfnx969eqF48ePGxy7f/9+jB49Gs2aNYNcLkdQUJDRz3PlyhVERkaiVatW8Pb2hr+/P/r06YOEhIRifxZLly5FYGCg+HLmABj4J+gr/HImnu6uuLmwT4lLMbI0WmRpcot9/XN88cdas28wERGRPdl1YtyPP/6Izz77DB07dkRAQACysrKwY8cOHD16FGvWrMHYsWMBAG+//TaysrLQvHlzVK5cGTdu3MDatWuh1Wpx7tw5BAQEAABWrlyJyMhIDBgwAP/3f/+H3NxcbNy4Eb///ju++uorvP766+K9//Of/+CHH35AcHAw/Pz88OTJE2zatAnnz5/Hnj170KtXL/HYUaNGYdu2bWjTpg1u374NuVxuEIADwJQpU7Bu3ToMGjQI7du3R1paGtasWYObN2/ip59+Qrdu3Uz+LDgxzjzWCsJstXCDpdexVibW2GIZE39zK9G1isPJdEREBDj+xDjJdYfQarVo27YtcnJycOXKFZPHnT59Gu3atUN0dDSioqIA5GeWfX19Ua1aNfE4tVqNVq1aISMjA0lJSUXeOysrC/Xq1UOrVq3w008/iduTk5NRvXp1uLm54cUXX8SFCxeMBsGnT59Gw4YN4eX1Tx3mo0eP0LhxYzRo0ADHjh0zee9Zs2YZbZHmqP9iSV1xwaex/bbIglorGDdWE/zBKQbBRERkO44eBEvu78tyuRyBgYE4depUkcfpShJSU1PFbU2bNjU4TqFQ4IUXXsDSpUuhUqmKXDzA09MT1atX17smADHTXJy2bdsabKtatSo6d+6MuLi4Is+dNGkSxowZI7539ppgW/N0d9WrOy4c4BYuEwBsX49c+i4WbgC0Bf75H6ZqrG29lDEREZFUSeIbLzMzE9nZ2UhLS8N3332HvXv3YsiQIQbHPXr0CFqtFrdv38acOXMAAOHh4cVe/+7du/D09ISnp6fBvvT0dGg0Gjx8+BAbN27EhQsX8OGHH5b+QxW6f8HstDFskVb2zA1qrdFizd7MCW6dse6aiIiclyS+8SZPnow1a9YAAFxcXDBw4ECsXLnS4LiaNWtCrVYDyM+wfvbZZ+jevXuR1/7zzz+xc+dODB48GHK53GD/yy+/jH378oMhd3d3jBs3DjNmzCjtRxIdPXoUJ06cwPTp0612TTJPWUzi0rVGK0pRgaUtyyHMaZGmm3xHRETkbCQRBE+cOBERERFITk7G9u3bodVqodFoDI7bu3cvcnJycPnyZWzatAmZmZlFXjcrKwuDBw+GUqnEwoULjR6zcOFCTJ48GUlJSdiwYQM0Gg1yc60TPN2/fx/Dhg1D3bp1ERkZWeSxbJFmfWXRTs2cLLG5QWZpMrElbZFWENulERGRM5HEt1yjRo3QqFEjAMCIESPQo0cP9O3bF/Hx8ZDJZOJxXbp0AQD07t0b/fv3R7NmzeDl5YXx48cbXFOr1eKVV17BpUuXsHfvXpN1va1atRL/efjw4WjTpg1GjRqF2NjYUn2mzMxMvPjii1CpVDh27JjeZDljli5danRiHJVvtsjEqrUC1Nr8FmlyC7LhxuqgS4LBMxEROQLJdYcAgC+//BLjxo3DlStX0LBhQ5PHderUCYIg4MSJEwb73njjDaxfvx6bN2/G0KFDzb73hx9+iIULFyIzM1NsN1VQUd0hdDQaDfr27YvDhw9j3759CA0NLfa+bJFmfaUJ5Kw5YawsgsKCLdLGHbJvL1+WVxAROQd2h7CB7OxsAPntwYo7TlcjXNDUqVMRExOD5cuXWxQA664pCAJUKpXRILg4eXl5GDFiBA4ePIjt27ebFQADzATbgjV7/zK7ab6HGTl6v0BU8/Kw84iIiIgM2TUTfP/+fdSoUUNv29OnT/Hcc8/h8uXLuH//Pjw8PKBSqVC5cmW9406ePIlOnTph2LBh2Lhxo7h98eLFiIyMxIcfflhktwVj905NTUWLFi0AALdv3zZ6XnGZ4HfeeQerVq3SW+zDHMwEU2kUzASrtYLJiXH2WHbaVHs2c/CXDyIi6WImuBTGjRuH9PR0hISEoGbNmrh79y42b96MK1euYMmSJfDy8kJqaioCAwMxZMgQNG3aFBUqVMD58+cRExMDHx8fvU4Ou3btQmRkJOrXr4/GjRtj06ZNevfr3r07fH19AeTXFdeqVQsdOnRAjRo1cPv2bcTExCA5ORnbtm3TO++PP/7Ad999ByC/20RaWhrmzZsHAGjZsiX69u0LAFi+fDlWrVqFjh07wtPT0+D+L730EipUqGD0Z8EWadLjqBPFFHJZiSbG2UppAm+WVhARka3Y9RtyyJAhWLduHb744gs8evQI3t7eaNu2LRYtWoR+/foByF/AYsyYMTh06BBiY2ORnZ2NgIAADB06FNOnTxcXzQCAc+fOAQCuXbuG1157zeB+hw4dEoPgN954A1u3bsWyZcuQmpqKypUr47nnnsOWLVvQuXNnvfN+//13g7ZpuvcjR44Ug+CzZ88CAE6cOGG0TvnGjRsmg2AqPWu3RLPWRLGilHWQak5W1h7ZYiIiorImyYlxzojlEKUXFLXH3kOwmKlMp6VZ6ILlEEB+bXt4eHiRKySaYmz1PEsmCTrapEIiIioZlkOQVXBinHMylWG2NAudpdFCrQVctPm/06q1xZxQhMKBZ2nauHFSIRERSRUzwRLBTHDp2aIcwtKMpqlz7FFicP6jkBJlgomIiMzBTDBZBTPBpWfOn+lLOtFNl9Esi6WYiYiIyPaYCZYIZoJto3BZgbEsrSXBsLXrjk1NVLM0C61SZSAuLg5KZX5P3uzsHLzQo2Q1wUREROZgJpisgi3SbMNUGYIuwATs24bLnADcnLparbscCnl+ezQAyJOXbDyO2haOiIjIUvyGI7JAcS3GrNUZoajJaMZKMoxNjMvSaCG3sHyjLNrCFcQgm4iI7IXlEBLBcgjbsHY5hDn3s3Um1RFbwZnCxTCIiBwXyyHIKjgxzjZMBaG2at1VmnZizsjSbDMzx0REZC3MBEsEM8FUmKmssrHA0djEuLCwMHh7e1l8z4L10lLDXzCIiKSDmWCyCmaCnYt5C2BYVp9beGKcp7ucmVMiIiITmAmWCGaCnYst6nqXP/fUJssml4Q1l04uiEE9EZF0MBNMVsFMMEmFtQNNLp1MRERSxEywRKjVaqjVavF9eno6AgMDHfa3KyqaueUQ5mZTVaoMnDgaZ5VMMBERkTmYCSarUCgUUCgU9h4GlRFLM6PFZVO17iVcHYOIiMhJMQgmkii2WyMiIrIdF3sPgIiIiIiorDETLFG6Uu2CHSOITFGpVMjKyoJGowEA5ObmIj09HSz5JyIiW9HFKI76XcMgWKJUKhUAIDAw0M4jISIiIjJNpVLBx8fH3sOwGLtDSFReXh6Sk5Ph7e0NmUxm1jm6jhJJSUkOOUuzPOGzkA4+C+ngs5AOPgvpcORnIQgCVCoVAgIC4OLieBW2jjdiIiIiIqLSEuwsMTFRGDJkiFCzZk1BqVQKDRs2FGbPni1kZmaKx+zbt0944403hKZNmwouLi5CnTp1jF7r8uXLwtSpU4WWLVsKXl5egp+fn/DCCy8Ip06dMjh2x44dwssvvyzUrVtXUCqVQoMGDYRJkyYJT548MTh269atwquvvio8++yzAgAhNDTU5OfJyckRIiMjBX9/f8HDw0No3769sH//fkt/LEJSUpIAgC+++OKLL7744kvSr6SkJIvjHCmwazlEUlISWrRoAR8fH7z11luoUqUKTpw4gfXr16Nfv37YvXs3AGDUqFHYtm0b2rRpg9u3b0Mul+PmzZsG15syZQrWrVuHQYMGoX379khLS8OaNWtw8+ZN/PTTT+jWrZt4bLVq1RAQEIABAwagdu3aOH/+PFavXo169erh999/FxcdAICwsDCcPn0awcHBOHv2LFq0aIG4uDijn2no0KGIjY3FxIkTUb9+faxfvx6nTp3CoUOH8Pzzz5v9s0lLS0OlSpUc8s8jUqVSqXD48GEIggCZTIbQ0FAuJkFERFRCulKO1NRU1gRbasGCBfjoo49w4cIFNG3aVNw+cuRIbNy4EY8fP0blypWRnJyM6tWrw83NDS+++CIuXLhgNAg+ffo0GjZsCC8vL3Hbo0eP0LhxYzRo0ADHjh0Tt8fFxSEsLEzv/I0bN2LkyJFYu3YtxowZI25PSkpCzZo14eLigmbNmqFatWpGg+CTJ0+iQ4cOWLx4MaZMmQIAyMnJQbNmzVCjRg38+uuvZv9sHH0VFilSqVQ4ePAg5HI5tFotV1QjIiIqBUePVexaE6xrreHr66u33d/fHy4uLnB3dwcABAQEwM3NrdjrtW3bVi8ABoCqVauic+fOuHz5st72wgEwALz00ksAYHBsYGCgWQXfsbGxkMvlGDt2rLjNw8MDo0ePxokTJ5CUlFTsNahs5Grz0H7+zwiK2mPWEsZERERUvtg1CNYFoqNHj8bZs2eRlJSEbdu24YsvvsC7776LChUqWOU+d+/eRbVq1cw6DoBZxxpz5swZNGjQwOC3ofbt2wMAzp49W6LrkuWyNLlGXlqotRBfQpHH/vMiIiKi8seufYJ79eqFuXPnYsGCBfjuu+/E7R999BHmzZtnlXscPXoUJ06cwPTp04s9dtGiRZDL5YiIiCjRvVJSUuDv72+wXbctOTnZ5LlqtRpqtVp870yLZGRpctHk430AgEtzesLTvfT/WuquZ0j3FwWFuKXdvINFXotLFxMREZU/dl8sIygoCCEhIRg0aBCqVq2KPXv2YMGCBfDz88P48eNLde379+9j2LBhqFu3LiIjI4s8dsuWLVi3bh0iIyNRv379Et0vOzsbCoXCYLuHh4e435To6GjMnj27RPd1FKayqgW3F5V5tUZwTERERATYOQjeunUrxo4di8TERNSqVQsAMHDgQOTl5eGDDz7A0KFDUbVq1RJdOzMzEy+++CJUKhWOHTtmUCtc0NGjRzF69Gj07NkT8+fPL9H9AECpVOplc3VycnLE/aZMmjRJbzKeSqVCkyZNSjwWKTKdnf1HUVlZSzKyl+b0NNimUmUgLi4Ocrkcas1TzP1dhhy4I2F6OANsIiIiJ2PXmuBVq1ahdevWYgCs069fP2RlZeHMmTMluq5Go8HAgQPxxx9/YPfu3WjWrJnJY8+dO4d+/fqhWbNmiI2NhatryYMhf39/pKSkGGzXbQsICDB57tKlSxEYGCi+ylsAXNY83V2NvORQyCG+ZEUe+8+rKFmaXARF7eEEOyIiIgdj1/TXvXv3ULlyZYPtT58+BQDk5loeVOTl5WHEiBE4ePAgtm/fjtDQUJPHXr9+Hb169UKNGjXw448/FpktNkerVq1w6NAhpKen602Oi4+PF/ebMm3aNEyaNEl8r+u9V54Yy84C+YGkLgNcVllZLVwQNzUM3t5e4hhKwtxSDksxM01ERGRbdv2mbdCgAfbv34/ExEQ0aNBA3P7NN9/AxcUFLVq0sPiaEyZMwLZt27BmzRoMHDjQ5HF3795Fjx494OLign379qF69eol+gwFRURE4NNPP8WXX34p9glWq9WIiYlBhw4dyl1QaylzAjtzsq/W8MEpN+DUcates7gJdpbgZDwiIiLbsmsQPHXqVOzduxedO3fG+PHjUbVqVfzwww/Yu3cvxowZI5YP/PHHH2L3iD///BNpaWli94iWLVuib9++AIDly5dj1apV6NixIzw9PbFp0ya9+7300kti27VevXrhr7/+QmRkJI4dO6a3kIavry+6d+8uvj9y5AiOHDkCAHjw4AEyMzPF+4eEhCAkJAQA0KFDBwwePBjTpk3D/fv38eyzz2LDhg24efMm1q1bV+TPwhkmxpni6e7KoM9KbNFpg4iIqDyy64pxQP4qa7NmzcKZM2fw6NEj1K1bFyNHjkRkZKRYn7t+/Xq8/vrrRs8fOXIk1q9fDyB/eeUNGzaYvNeNGzcQFBQEAJDJZCaPCw0N1VsRbtasWSYD1JkzZ2LWrFni+5ycHMyYMQObNm3CkydP0KJFC8ydOxc9exovBdAx1iItMDDQYVdhkaKCK8ZlabQIC/unHKKkLC3lMLdkoqTBqz1KSxhoExE5J0dfMc7uQTDlYxBseyqVCvv2H8COMymQy/Kw9P3hqFHFsCbdEpZmXoOi9pTqflLELD4RkXNy9CCYKRyJcOZyCGsz3Y84f8U4rSADIEO2RlvqvsQs5SAiInJMzARLRHp6ut4qcbo+wY7625U9WSvbaovg1tZt1FgOQUREZYWZYLKKpUuXMhNczhgrlSjLgLGs70dERORImAmWCNYEW4+pbKtKlYGfD/6C3efuQi7Lw8Lxr6B6lUomr2NJAGnsnuZkZRmkEhGRo2ImmEhiTAWW2v+tGCeXCZDLBCjd5VYLQotbEtpUD2HWExMREdmHXZdNpn9ER0fDx8dHfDn7whqFWWt5Yle5C4Z1qI2ItoFQuvF3QCIiImfFKEAinGHZZEuZWpK44D9LpZzA2JLQ9pikRkRERObhtzJJlqkSg4KlBdYoJ7DGKmvFncNJakRERNLCb2WJYJ9g+8jS5BrNMlsjYGUPYSIiIulidwiJYJ9gQ4WDU2OlBZYEqwWXTdZqtQgPD0fz+UeMHsvglYiIqGjFdYe4ePEiZs2ahdOnT+Pu3bvw9PREkyZNMHXqVPTt27fY66empiIyMhK7du1CVlYW2rdvjyVLlqBNmzZWGT8zwRLBPsGGTAW4LC2wPWuUiBARkXO7desWVCoVRo4ciYCAAGRlZWHHjh3o168f1qxZg7Fjx5o8Ny8vD3369MG5c+cwdepUVKtWDatWrUJYWBhOnz6N+vXrl3p8zARLBPsE256xTLBcoTSaZXbkoM8aq9LZalKfI/9ciYhIX0n6BGu1WrRt2xY5OTm4cuWKyeO2b9+OIUOG4Ntvv0VERAQA4MGDB2jQoAF69+6NLVu2lHr8/EYip1Y4KHP0ABgovmexpUz1OC4JlpkQETk3uVyOwMBAnDp1qsjjYmNj4evri4EDB4rbqlevjpdffhmbNm2CWq2GQqEAAKSkpCAtLQ3PPPMM3NzczB4L+wRLBPsE249uAtvNhX0kEwBbqy8yERGRvWVmZuLhw4e4fv06li1bhr179yI8PLzIc86cOYM2bdrAxUU/VG3fvj2ysrKQmJgobps2bRoaN26MO3fuWDQuaXzjE/sEOwlzA1pTfZHNYaxnsaXY45iIiKxl8uTJWLNmDQDAxcUFAwcOxMqVK4s8JyUlBSEhIQbb/f39AQDJyclo3rx5qcbFbzYiKytqUllJShUsLUewdslBeSgRISIi+5k4cSIiIiKQnJyM7du3Q6vVQqPRFHlOdna2WO5QkIeHh7hfZ/369Vi/fr3F4+I3m0SwT3DZy9JoIbdBqUFpsrhSwR7HRERkLY0aNUKjRo0AACNGjECPHj3Qt29fxMfHQyaTGT1HqVTqNQzQycnJEfeXFoNgiZg0aRLGjBkjvtf1CSbb6bD4uM3vYc1JZYVZo+yBiIiorEVERGDcuHFITExEw4YNjR7j7++PlJQUg+26bQEBAaUeB4NgiWCfYMuwjy3bjRERkWPSlTKkpaWZPKZVq1Y4evQo8vLy9CbHxcfHw9PTEw0aNCj1OPgtKhGcGKevuDICS0sOjAWM8VP/D97eXpYPrhicVEZERATcv38fNWrU0Nv29OlTbNy4EUqlUvyLt7EWZxEREYiNjcXOnTvFPsEPHz7Et99+i759++rVC5e0RRq/nUmSLJlAZk7JgbH6Vk93uc0DVE4qIyIiZzVu3Dikp6cjJCQENWvWxN27d7F582ZcuXIFS5YsgZdXfiJq2rRp2LBhA27cuIGgoCAA+UHwc889h9dffx2XLl0SV4zTarUGfzk3dr45+O0sEc44Ma68ljRwUhkREREwZMgQrFu3Dl988QUePXoEb29vtG3bFosWLUK/fv2KPFcul+PHH3/E1KlT8dlnnyE7OxvBwcFYv369yTpiS3HZZIkoj8smm1PSYKpsoPC5hQNkS0sOPN1djS6b7O3tbfbnISIion+UZNlkKSkfqTeSJGuWNBSVWWXJgb7ymmEnIiKyJn47SoQzlkOURnktObBGX2Fr9ylmEE1EROURyyEkIj09Henp6eJ7XZ9gR/0TA1C6cojCrBGIOUI5RFDUHnsPwUB5/GWDiIhKj+UQZBXlsU+wJYGrvUsaWEJARETkXPhNLxHO2Ce4tCUNlv6pP0ujhVoLyAFotfrLJttyqWNLAmprrALHPsVERETF47cjOSxLJt79Q9dE2wU4ZXzZZGsvdWxJoG/tgNXeGXYiIiKp4rejRHBiHFlLeZ00SEREZE2cGCcR5bFPsK1ZWragUmUgLi4OuZAjT6tF586d4eVdAQCQrdGi8ydxAICjkWFQusvF84raZw5mY4mIqDzixDiiUijNhDRLA0utuxwKOfDBby4AXIDffzN6nC7gtXRfUZiZJSIikhYXew+A8kVHR8PHx0d8lfdJcVmaXPFlbJu1J6cRERERFcRyCIkoj32Ci2JOP1xrZ091fYJzkd8nOCwsDN7eXsWeZ41uCyyHICKi8oblEGQV5bFPsLVZq5evQg5oAXi6yy2+But7iYiIygd+m0uEs/UJ1vXDNZZlzTLSu7fwe1PlErYIUNltgYiIqPxhEEx2YSxY1WVZzen/a6qXL4NVIiIiMgeDYIlw1j7B5THLyiWYiYiIpI/fzhLhbOUQRTG1dLAtlwO2ZjcKqSzBTERERKbxG5Ukx5xAryQT1LKf5iL2dBK0ggsGtfbX21eyJZiLZ88lmImIiMg0BsES4azlEOYomE0tmCW2NMuardFCK8igFWRQa4EsjRZy9iMmIiJySuwTLBHO1ifYEub0FLa3wsG5rco2WA5BRERSwT7BZBXsE+zYTAWnpe0rXHiSHREREVkHM8ESoVaroVarxfe6iXGO+tuVNVlrctmDx6mIWrkVWsEF/Vv6oVt4V7NWjCtOlibXZhlaW2aVC2KGmYiILMVMMJGNWStAU7rLIZcJAAQo5CVbMc4YW02qK8zak+wK4oQ7IiJyNi72vPnFixcxePBg1KtXD56enqhWrRpCQkLw/fff6x138uRJvP3222jbti3c3Nwgk8mMXi8pKQmzZ89G+/btUblyZVSrVg1hYWH4+eefDY5NSUlBVFQUunTpAm9vb8hkMsTFxRm97oIFC/Dcc8+hevXq8PDwQP369TFx4kQ8ePDA4Ni8vDx88sknqFu3Ljw8PNCiRQt88803xf4soqOj4ePjI76ctT2aObI0uQiK2oOgqD0WZYmVbq6IaBuIYR1qw1Vu13/1iYiIyM7smgm+desWVCoVRo4ciYCAAGRlZWHHjh3o168f1qxZg7FjxwIAfvzxR/znP/9BixYtUK9ePSQmJhq93u7du7Fo0SIMGDAAI0eORG5uLjZu3Iju3bvjq6++wuuvvy4ee/XqVSxatAj169dH8+bNceLECZPjPH36NFq1aoVXXnkF3t7euHz5MtauXYs9e/bg7NmzqFChgnjsRx99hIULF+LNN99EcHAwdu/ejWHDhkEmk+GVV14xeQ/2Cc5nTlBryz68JWHLWt2yKocgIiJyNpKrCdZqtWjbti1ycnJw5coVAMC9e/dQsWJFKJVKjB8/Hv/+979hbNgXL16Er68vqlWrJm5Tq9Vo1aoVMjIykJSUJG5XqVR4+vQpqlSpgtjYWAwePBiHDh1CWFiYWePcsWMHIiIi8M0334jB7Z07d1C3bl2MHTsWK1euBAAIgoDQ0FDcuHEDN2/ehFwuN3o91gTns2UniPMfheDgwYOQy+XQarUIDw+HXKGU9OpuXH2OiIikytFrgiX3N2G5XI7AwECkpqaK23x9faFUKos9t2nTpnoBMAAoFAq88MIL+Pvvv6FSqcTt3t7eqFKlSonHGRQUBAB649y9ezeePn2Kt99+W9wmk8nwr3/9C3///XeR2WaWQ9helkYLtRbiK0ujNcgqW/qyNd2y0jcX9mEATEREZEWS+FbNzMxEdnY20tLS8N1332Hv3r0YMmSI1a5/9+5deHp6wtPTs8TXEAQBjx49Qm5uLq5du4aoqCjI5XK9zPGZM2dQoUIFNG7cWO/c9u3bi/uff/55o9efNGkSxowZI77X9Ql2NuaUFpgqEShuglqHxccBuP3vnQtw6rje/pJMPOOEMiIiIsckiSB48uTJWLNmDQDAxcUFAwcOFMsJSuvPP//Ezp07MXjwYJOlCOa4d+8e/P3/WWq3Vq1a2LJlCxo1aiRuS0lJga+vr8HEPd15ycnJJq/PPsH5LM12lrYPLxERETknSUQPEydOREREBJKTk7F9+3ZotVpoNJpSXzcrKwuDBw+GUqnEwoULS3WtKlWq4MCBA8jJycGZM2ewc+dOZGRk6B2TnZ0NhUJhcK6Hh4e43xROjDOfrkSgsEtzehZZopChysTRo0fhIpcjT6tF586dIVd4oPMncQCAo5FhULr/84tStkZrcp9OSUsiGLgTERHZlyS+iRs1aiRmVEeMGIEePXqgb9++iI+PN9kOrTharRavvPIKLl26hL179yIgIKBUY3R3d0e3bt0AAC+++CLCw8Pxf//3f6hRowZefPFFAIBSqdSb3KaTk5Mj7ifb8XR3NaNnb4FyiN9/09ujC3iNKWpfSbCMgoiIyL4kNzEOACIiInDq1CmTrdDM8eabb+KHH37A+vXr0bVrVyuOLl+nTp3g7++PzZs3i9v8/f1x9+5dg84VKSkpAFBkIM6JcURERERlRxKZ4MJ0ZQNpaWklOn/q1KmIiYnB8uXLMXToUGsOTU9OTo7eGFu1aoX//Oc/uHz5st6ktvj4eHG/KSyHsI6iJtapVBmIi4tDLv4ph/Dyzu/xbKz0oeA29uglIiIqX+z6rX7//n3UqFFDb9vTp0+xceNGKJXKEnVHWLx4MT799FN8+OGHeO+990o9xszMTMhkMoPOEjt27MCTJ0/Qrl07cVv//v3x/vvvY9WqVXp9glevXo2aNWuiU6dOpR5PeWDL3rdFXUvrLodCDnzwmwuMlUPoGCt94AQ8IiKi8sWu3+rjxo1Deno6QkJCULNmTdy9exebN2/GlStXsGTJEnh5eQHIX1nu66+/BgAkJCQAAObNmwcAqFOnDl577TUAwK5duxAZGYn69eujcePG2LRpk979unfvDl9fX/G97hoXL14EAHz99dc4duwYAGD69OkAgGvXrqFbt24YMmQIGjVqBBcXFyQkJGDTpk0ICgrSC7Rr1aqFiRMnYvHixXj69CmCg4Px3//+F0ePHsXmzZuL7E4RHR3t0N0hLJkgVtIV3+wZhDIAJiIiKl/sumLc1q1bsW7dOpw/fx6PHj2Ct7c32rZtiwkTJqBfv37icXFxcejSpYvRa4SGhiIuLg4AMGvWrCIDycIrwhU16U73Y3n48CE++ugjHDlyBElJSXj69Cnq1KmDPn364KOPPjJYnCMvLw+LFi3CmjVrkJKSgvr162PatGl49dVXi/xZpKenIz09XXyv6xPsKKuw2HKlN53STiZTqVQ4ePAgcpG/YlxYWBi8vfN/0SpueWIGwURERPocfcU4yS2b7KxMBfCO8i+WIwXBBZdN9vb2BmC6RIPLFhMRERnn6EEwv9ElwtEnxpmz0ptOcVlXeyn4GXRlGpaUbkjlcxAREVHx+K1NVlHSAFAqE86K7y9c/LLK7P1LRETkOOwffRAAx58YZwlTK74Zw3IEIiIisgVGFBLh6OUQ1mCs3MCccgRrBMamyjmkWrpBREREpcNvdJKM4koSTJUjWKMMwZzgViqlG0RERFR6/EaXCGcqhyjMkl7BxZ1v7SDVktINIiIichxskSYRjt4nuDSs2V6tqIC1qBZpREREZBm2SCOrWLp0qdNmgomIiIjKGjPBEqFWq6FWq8X3uolxjvrblSUeZuQAALI1WnT+JA4AcDQyDEp3ud42cyamFbWfmWAiIiLrYSaYqJSMTXjTBb4FcWIaERERWQsjColw5olxxeHENCIiIrI2lkNIhDOXQxRcothYT15rZX9ZDkFERGQ9LIcgq1AoFFAoFPYehl0YC3JZ+kBERES2xCiDJIM9eYmIiKisuNh7AEREREREZY2ZYInSlWoXXECDSkelUiErKwuCIEAmkyE9PR0siSciIioZXYziqN+lDIIlSqVSAQACAwPtPBIiIiIi01QqFXx8fOw9DIuxO4RE5eXlITk5Gd7e3pDJZGado+sokZSU5JCzNMsTPgvp4LOQDj4L6eCzkA5HfhaCIEClUiEgIAAuLo5XYet4IyYiIiIiKi1BAk6fPi307dtXqFy5sqBUKoWmTZsKK1asEARBEDIzM4WVK1cK3bt3F/z8/AQvLy+hVatWwqpVq4Tc3FyDa82bN0/o27evUKNGDQGAMHPmTKP33LFjh/Dyyy8LdevWFZRKpdCgQQNh0qRJwpMnTwyO3bp1q/Dqq68Kzz77rABACA0NNflZcnJyhMjISMHf31/w8PAQ2rdvL+zfv9/in0lSUpIAgC+++OKLL7744kvSr6SkJIvjHCmwe03w/v370bdvX7Ru3RozZsyAl5cXrl+/jr///hsA8Ndff2HChAkIDw/HpEmTULFiRezbtw9vv/02fvvtN2zYsEHvetOnT4efnx9at26Nffv2mbzv2LFjERAQgOHDh6N27do4f/48Vq5ciR9//BG///47lEqleOwXX3yB06dPIzg4GI8ePSry84waNQqxsbGYOHEi6tevj/Xr1+OFF17AoUOH8Pzzz5v9c9Et4uCIfx4xRqVS4fDhw+KktNDQUC5UQURE5MB0pRyO+n1u1yA4PT0dI0aMQJ8+fRAbG2u0nsTPzw/nz59H06ZNxW3jxo3DG2+8gZiYGMyYMQPPPvusuO/GjRsICgrCw4cPUb16dZP3jo2NRVhYmN62tm3bYuTIkdi8eTPGjBkjbv/6669Rs2ZNuLi4oFmzZiavefLkSWzduhWLFy/GlClTAAAjRoxAs2bNEBkZiV9//bXYn4mOrg64YsWK5SIIlslk8PT0FFdrq1ixosP+R0NERET/MHfuktTYtSZ4y5YtuHfvHubPnw8XFxdkZmYiLy9P75hq1arpBcA6L730EgDg8uXLetuDgoLMunfhALioawYGBppV8B0bGwu5XI6xY8eK2zw8PDB69GicOHECSUlJZo2tPMp+movY00nYEn8budq84k8gIiIisiG7BsE///wzKlasiDt37qBhw4bw8vJCxYoV8a9//Qs5OTlFnnv37l0A+UGytZT2mmfOnEGDBg0MMrft27cHAJw9e7ZU43Nk2RottIIMWkEGtdbeoyEiIiJnZ9dyiGvXriE3Nxf9+/fH6NGjER0djbi4OHz++edITU3FN998Y/Q8jUaD5cuXo27duggODrbaeBYtWgS5XI6IiIgSnZ+SkgJ/f3+D7bptycnJJs9Vq9VQq9Xi+/K2SEbo8ngAdQEAJ38H+vW273iIiIjIudk1CM7IyEBWVhbeeustfPbZZwCAgQMHQqPRYM2aNZgzZw7q169vcN748eNx6dIl7NmzB66u1vkIW7Zswbp16xAZGWn0nubIzs6GQqEw2O7h4SHuNyU6OhqzZ88u0X2JiIiIyDJ2LYfQdWAYOnSo3vZhw4YBAE6cOGFwzuLFi7F27VrMnTsXL7zwglXGcfToUYwePRo9e/bE/PnzS3wdpVKpl83V0ZV2FOw4Udi0adOQlpYmvspb/fDhiR3Q0fUG2stvYX4bw58RERERUVmyayY4ICAAFy9ehK+vr972GjVqAACePHmit339+vX44IMP8NZbb2H69OlWGcO5c+fQr18/NGvWDLGxsaXKLPv7++POnTsG21NSUgDkf15TFAqF0SxyeaF0l0Muy28pqJDbezRERETk7OyaCW7bti0AGASOutrZgi3Odu/ejTFjxmDgwIH497//bZX7X79+Hb169UKNGjXw448/wsvLq1TXa9WqFRITEw3qeePj48X95U2WJhdBUXsQFLUHWZpck8cp3VwR0TYQwzrUhqucCxUSERGRfdk1Gnn55ZcBAOvWrdPb/p///Aeurq5iG7MjR47glVdeQUhICDZv3myV9anv3r2LHj16wMXFBfv27Suyp7C5IiIioNVq8eWXX4rb1Go1YmJi0KFDBwQGBpb6HmUtS5Nb7Mu8Y9kSgoiIiKTDruUQrVu3xhtvvIGvvvoKubm5CA0NRVxcHL799ltMmzYNAQEBuHXrFvr16weZTIaIiAh8++23etdo0aIFWrRoIb7/+uuvcevWLWRlZQHID6DnzZsHAHjttddQp04dAECvXr3w119/ITIyEseOHcOxY8fEa/j6+qJ79+7i+yNHjuDIkSMAgAcPHiAzM1O8ZkhICEJCQgAAHTp0wODBgzFt2jTcv38fzz77LDZs2ICbN28aBPqFSbU7RJOPTa+6V1i7eQeL3L/8udKOhoiIiMg6ZIIgCPYcwNOnT7FgwQLExMQgOTkZderUwTvvvIOJEycCAOLi4tClSxeT58+cOROzZs0S34eFheHw4cNGjz106JCYXS5qdRNdMK4za9Ysk50bCt8/JycHM2bMwKZNm/DkyRO0aNECc+fORc+ePU3er6h7pKWl2XXFuKCoPVa71vLnnoorxoWHh3PFOCIiIgeWnp4OHx8fu8cqJWX3IJjyGcsEBwYG2v1frKLqfHX7dRnghOnh8HQ3/scFlSoDJ47GMQgmIiIqJxw9CLZrOQT9Q6rdIUwFtaaONXW81p0tIYiIiEg6OE2fSsXT3RWX5uSXejT5eF+xmWMiIiIiKWAmmEwyN6At3CHC+DFaqLWAHICWjSKIiIjIzhgES4QUu0NY0hlCp+gOEW7/+18XvNCjREMiIiIisgqWQ0hEdHQ0fHx8xJcj9hQmIiIichTsDiERUuwOYUk5RHEdIlSqDMTF/dMd4oUe7A5BRETkyNgdgqxCit0hLOkMUfAcY+dp3eVQyAG5HGBJMBEREdkbg2AqNU93V9xc2Ed8n6XJFeuJL83pWaJgmoiIiMiWGJ2Q2UrTLaJwd4gsjRbyQtdjsExERERlhTXBEiHFmuDCrLmEsjEFs8lEREQkbY5eE8zuEBLB7hBERERUnowaNQoymczk686dO0Wef+fOHbz88suoVKkSKlasiP79++Ovv/6y2viYCZYIR8gEl6ZbROHuEGFhYfD29tI7j+UQREREjqO4TPCJEydw/fp1vW2CIOCtt95CUFAQLl68aPLaGRkZaNOmDdLS0jB58mS4ublh2bJlEAQBZ8+eRdWqVUs9fkYdEiHF7hCFlaZbROHuEJ7ucga9RERE5VjHjh3RsWNHvW3Hjh1DVlYWXn311SLPXbVqFa5du4aTJ08iODgYANC7d280a9YMS5YswYIFC0o9PpZDkNmyNLkIitqDoKg9RWaFdd0ibi7sw0CXiIiIRFu2bIFMJsOwYcOKPC42NhbBwcFiAAwAjRo1Qnh4OLZv36537O3bt3HlyhWLx8IgmPRkaXKLfJl7nLmlE0REROQcnj59iu3bt6NTp04ICgoyeVxeXh7++OMPtGvXzmBf+/btcf36dahUKnHbiBEj0LhxY4vHwzSdRBirCbYHXX/f4uhqfovCbg9ERESks2/fPjx69KjYUojHjx9DrVbD39/fYJ9uW3JyMho2bFiq8TATLBHsDkFERETl2ZYtW+Dm5oaXX365yOOys7MBwOhcKQ8PD71jACAuLg4l6fPATLBETJs2DZMmTRLf67pDlLVLc3qa3Ges6wMRERFRcTIyMrB792707Nmz2M4OSqUSAPT+Qq6Tk5Ojd0xpMIqRCKl0hzA3sNV1fSAiIiIqzn//+1+zukIAQJUqVaBQKJCSkmKwT7ctICCg1GNiFENm03V9yNLkirXDl+b0ZDBMRERERdq8eTO8vLzQr1+/Yo91cXFB8+bNkZCQYLAvPj4e9erVg7e3d6nHxJpgMspaXSKIiIjIuT148AA///wzXnrpJXh6ehrsN9biLCIiAqdOndILhK9evYpffvkFgwcPLvZ8czCFJxFS6Q6hY60uEewQQURE5Ny2bduG3Nxck6UQI0aMwOHDh/Umt7399ttYu3Yt+vTpgylTpsDNzQ1Lly6Fr68vJk+eXOz55mAmWCLYHYKIiIjKo82bN6NGjRro1q2b2ed4e3sjLi4OISEhmDdvHmbMmIGWLVvi8OHDqF69ulXGJRNK0lOCrM5YJjgwMNDkety2VlQpgyVdInT7VCoVDh48CLlcDq1Wi/DwcKvU8xAREZF9pKenw8fHx26xSmmxHEIipNIdQoddIoiIiKg8Y/RCZincEYK1vkREROTIGAQ7OXM7OBTuCGEOZoiJiIhIqhilSIS9ukOY2wWioOI6QugwW0xERERSxe4QEsHuEERERERlh90hJMJe3SEsKYcwtyOETsFj2B2CiIiofGF3CLIKe3WHKEndLjtCEBERkaNjJENm8XR3LbbGt3AHCQbKREREJFWMUsgkc0sljB1f+NwsjRZqLSAHoNXmv5cXOoZBMxEREZUV1gRLhNRWjAOAoKg9ZXo/dpMgIiJyHI5eE8zuEBLB7hBEREREZYeZYImQYia4JOUQpjpIqFQZiIuLE7tDhIWFwdvbS+98lkMQERE5DkfPBDPqkAh7dYcoSmmC0sIdJLTucijkgFwOaAF4ussZ9BIREZHdMAohqzGng4Q1sAsFERERlRajBypTaq3p7hDmKqoLRUkxkCYiInIu/OaXCGM1weXRlN9cALgAp45b5Xq6GuTSYmcKIiIi58LuEBLB7hBEREREZYfdISRCit0hrEmlUuHgwYPIhenuEOYqqgtFSbEcgoiIyDLsDkFWIcXuELagKGV3iIIBMGDYhYKIiIjIHIweqMypSzExruBEuITp4QbbLMHgmYiIyHkxCpAIZ5kYBwAfnHKzysS40k6K42Q4IiIi58WJcRLBiXFEREREZceuE+MyMjKwePFixMfH4+TJk3jy5AliYmIwatQoveNOnjyJ9evXIz4+Hn/88Qdyc3NhzrCPHTuGzp07AwAePHiAatWqifuuXr2K1atXIz4+Hr///jvUajVu3LiBoKAgg+ts27YN33//PeLj4/Hnn38iNDQUcXFxJf48xjjLxDi5XI4sTcknxllzUhzLIYiIiErO0SfG2TUT/PDhQ8yZMweXL19Gy5YtTR73448/4j//+Q9kMhnq1atn1rXz8vIwYcIEVKhQwej+EydO4LPPPoNKpULjxo2LvNYXX3yB3bt3IzAwEJUrVy715zFGoVCgYsWKeq/ySiH/Z2JcSV46JT2fk+mIiIjIrkGwv78/UlJScOvWLSxevNjkcf/617+QlpaGhIQEdO/e3axrf/nll0hKSsKYMWOM7u/Xrx9SU1Nx/vx5vPrqq0Ve6+uvv0ZaWhp++eUXBAQElPrzlHdZmlwERe1BUNQeq63opqNbmvnmwj4MZImIiKjE7BpFKBQK+Pn5FXucr6+vRdd9/Pgxpk+fjjlz5uD+/ftGj6lSpYrZ1zO3Ptfcz+PIzAlqjS1rnKXRQq0F5DC+bDIDWiIiIipL5TLymDFjBvz8/DBu3DjMnTvX3sMxi6N0h2jy8T6Ljtfv4OD2v/81XDaZnRqIiIioLJW77hB//PEH1qxZg6VLl0Iul9t7OGZjdwgiIiKislPuMsHvvvsuevfujR49eth7KBaZNm0aJk2aJL7XdYeQmktzehZ7jLEODipVBuLi4iCXl37ZZCIiIqLSKldB8LZt2/Drr7/iwoUL9h6KxRxl2WRLa3d1nRi07nIo5IC8lMsmExEREVlDuYpCpk6disGDB8Pd3R03b94EAKSmpgIAkpKSoNFoiuzuQP/I0uSK9b+X5vS0KGDVdXAgIiIikqpyFQQnJSVhy5Yt2LJli8G+Nm3aoGXLljh79mzZD0zCTHV7MNbhoTBmcomIiMhRlasoZteuXQbbtm7dim3btmHjxo2oVauWHUZlHnt1hzCn24N+h4d/MNtLREREjsruQfDKlSuRmpqK5ORkAMD333+Pv//+GwAwYcIE+Pj44NatW/j6668BAAkJCQCAefPmAQDq1KmD1157DQAwYMAAg+vrMr+9e/fWWzY5LS0Nn3/+OQDg+PHj4lgqVaqESpUqYfz48eKxR44cwZEjRwDkL7+cmZkp3j8kJAQhISEWfR5joqOjMXv27OJ/YERERERUajJBEAR7DiAoKAi3bt0yuu/GjRsICgpCXFwcunTpYvSY0NBQxMXFmbz+rFmzMHv2bDx48EAvCL558ybq1q1r9Jw6deqINcUFr2HMzJkzMWvWLIs+jzHGMsGBgYE2X4+7qHKIwh0eCrOkHEKlUuHgwYNid4jw8HB4e3uXbNBERERkd+np6fDx8bF5rGIrdg+CyTh7/4tVmolxxjAIJiIiKl/sHauUlt3LIUiapNbhwdpBORERETk3RhJUZtRaQA5AqwWyNFrITZRiGGNOtwpLMIgmIiJybowEJMJe3SHK0gen3P73Ty7AqeMlvo6pbhWWkFKWm4iIiMqei70HQPmio6Ph4+MjvqS4ZDIRERFRecGJcRJhr+4QZUWlUuHH/f9MjAsLC4O3t5fZ55vTrcISLIcgIiIqHU6MI6tQKBRQKBT2HoZNKeSAXA5oAXi6y0sciHq6uzKIJSIiolJhJEFlLlebh/bzf0Y23M3u9CC1bhVERETk2BgEk1UU17EhS6MVu0OotYAgbres0wMzwERERGQNjCgkwtG7Q+h6+BZN1x3in7IPSzs9MBtMRERE1sDuEBLB7hBEREREZYfdISTC0btDFFfWoFJlIC4uDnK5HGrNU8z9XYYcuFvc6YHlEERERNLA7hBkFY7eHaK44FTrLhe7Q0AOyAqcx8CWiIiIyhqjDypzrnIXnPwoHN7e3vYeChERETkp1gQTERERkdNhJliidKXajtYlwhSVSoWsrCwIggCZTIb09HSwHJ2IiMhx6WIUR/0+ZxAsUSqVCgDYJYKIiIgkTaVSwcfHx97DsBi7Q0hUXl4ekpOT4e3tDZlMVvwJ+KejRFJSkkPO0ixP+Cykg89COvgspIPPQjoc+VkIggCVSoWAgAC4uDheha3jjZiIiIiIqJSYCZaov//+m6UQREREJHlJSUmoVauWvYdhMcnVBM+fPx/Tp09H06ZNceHCBXF7WFgYDh8+bHB8z5498dNPP1l8PQBYsGABvvvuO1y/fh0qlQqBgYHo06cPPvroI1SvXt3gOvHx8YiPj8f9+/cxc+ZMzJo1y+B+V69exerVqxEfH4/ff/8darUaN27cQFBQkEU/B137MEf884itqVQqHD58GK6urnB1dUVubi5yc3MRGhrKtmtERERlRFfK4ajfvZIKgv/++28sWLAAFSpUMLq/Vq1aiI6O1tsWEBBQ4uudPn0arVq1wiuvvAJvb29cvnwZa9euxZ49e3D27Fm986ZPnw4/Pz+0bt0a+/btM3nPEydO4LPPPkOTJk3QuHFjnD17tohPbJquDrhixYoMgguRyWTw9PSEUqmEQqGAWq1GdnY2Klas6LD/IRIRETkqc+cuSY2kguApU6bgueeeg1arxcOHDw32+/j4YPjw4Va7GThwHwAAKqNJREFU3o4dOwy2dezYEREREfj+++/xyiuviNt12dyHDx8aZIkL6tevH1JTU+Ht7Y1PP/20xEEwlV6WJhdNPs7/heXSnJ5cmY6IiIhEkpkYd+TIEcTGxmL58uVFHpebm4uMjAyrXa8wXdlCamqq0e3FqVKlCrORZUCtBdRaAepcIf9/tUCWRossTa7eS6fwdmMvIiIich6SSI1ptVpMmDABY8aMQfPmzU0el5iYiAoVKkCj0cDX1xdvvvkmPv74Y7i5uZXoekB+e49Hjx4hNzcX165dQ1RUFORyOcLCwqzx0cymVquhVqvF9+VlkQxb+eCUGwAtgKz/bXEDTh03eXy7eQeLvebNhX2sMjYiIiKSPkkEwatXr8atW7fw888/mzzmmWeeQZcuXdC8eXNkZmYiNjYW8+bNQ2JiIrZt22bx9XTu3bsHf39/8X2tWrWwZcsWNGrUqOQfqASio6Mxe/bsMr0nERERkbOyexD86NEjfPzxx5gxY0aRtbbr1q3Te//aa69h7NixWLt2Ld5//30899xzFl1Pp0qVKjhw4ABycnJw5swZ7Ny506xyC2ubNm0aJk2aJL7Xzbgk4xYFP4VS6QGFuwJqjRrZ2TkICwuDt7eXeEyWJlfMACdMD2dNMBEREYnsHhVMnz4dVapUwYQJEyw+d/LkyVi7di1+/vlnMQi29Hru7u7o1q0bAODFF19EeHg4/u///g81atTAiy++aPGYSkqhUEChUJTZ/RxNwUluJyM7QSEHFHIZFK4yQCtDnhzwdJebDHQ93V0ZBBMREZHIrlHBtWvX8OWXX2L58uVITk4Wt+fk5ODp06e4efMmKlasiCpVqhg9X5cpffz4sVWuBwCdOnWCv78/Nm/eXKZBMBmnm7BWcOJatkYLtRZw0Qoo6tcGT3dX1vkSERGRUXYNgu/cuYO8vDy8++67ePfddw32161bF++9957JDg9//fUXAIhlD6W9nk5OTg7S0tIs+zClxIlxxumyvwWFLo8HkD8xbn2vMh8SERERlQN2DYKbNWuGXbt2GWyfPn06VCoVVqxYgWeeeQbp6ekG5QKCIGDevHkA8leNs+R6AJCZmSkuulDQjh078OTJE7Rr185qn9McnBhHREREVHZkgiAI9h5EYWFhYXj48KG4zHFcXByGDh2KoUOH4tlnn0V2djZ27dqF48ePY+zYsVizZo1F1wOAs2fPolu3bhgyZAgaNWoEFxcXJCQkYNOmTahVqxYSEhJQtWpV8fivv/4at27dQlZWFqKjo9GlSxd07doVQP4kvTp16gAA0tLS8PnnnwMAjh8/jp9++gmTJ09GpUqVUKlSJYwfP97oGI1lggMDA5GWlubUK8YVLIfQTXI7PLEDfjt+DEqlByp6eogrxoWHh7NHMxERURlJT0+Hj4+Pw8YqDjFTqE6dOujcuTN27dqFu3fvwsXFBY0bN8bq1asxduzYEl2zVq1aGDRoEH755Rds2LABT58+RZ06dTB+/Hh89NFHegEwkN+d4vDhw+L7Q4cO4dChQwCA559/XgyCnzx5ghkzZuidu2TJEvFzmAqCOTHOOGOT2ZTucnFiHBEREVFJSDITTI7/25UtqVQqHDx4EEqlEgqFwiATzOWSiYiIbM/RYxVGByQ5xS1hnFWgOwQKLZssN7JccmkxiCYiIip/mAmWCNYE/yMoao+9h6CHbdaIiIgMOXom2MXeA6B80dHR8PHxEV9cLY6IiIjIdpgJlghmgv9RXAmDSpWBuLg4k8smW3u5ZJZDEBERGXL0TDC/3SWC3SH+UVzQqS3QHcLYssmFg2gGsURERFQYowNyGP/0DC56Ypyp88zBgJmIiMg5sBxCIlgOUbyymDDHSXBERETmcfRyCE6MkwhOjCMiIiIqO8wESwQzwcXTlTXYcmIcyyGIiIjMw0wwWYVCoUDFihX1Xo4qS5OLoKg9CIraY5XFKnQ83V3/99KfGKeQy6AoMDGuYCD7zznmvYiIiMg64uLiIJPJjL5+++23Ys+/c+cOXn75ZVSqVAkVK1ZE//798ddff1ltfPzWpxIpKri1ZMU2WwSenu6urO0lIiKSiHfffRfBwcF625599tkiz8nIyECXLl2QlpaGDz/8EG5ubli2bBlCQ0Nx9uxZVK1atdTjYhAsEcbKIaSsycf7zDpOV5ZgCoNVIiKi8q1z586IiIiw6JxVq1bh2rVrOHnypBhA9+7dG82aNcOSJUuwYMGCUo+L5RASwYlxREREVF6pVCrk5ppfIhkbG4vg4GC9DHKjRo0QHh6O7du36x17+/ZtXLlyxeIxMRMsEdOmTcOkSZPE97qJcVJ1aU5Pk/usvWIbEREROa7XX38dGRkZkMvl6Ny5MxYvXox27dqZPD4vLw9//PEH3njjDYN97du3x/79+6FSqeDt7Q0AGDFiBA4fPgxLez0wOpEIR1sxztzAVkoTzrI0uWIZx6U5PSUzLiIiovLI3d0dgwYNwgsvvIBq1arh0qVL+PTTT9G5c2f8+uuvaN26tdHzHj9+DLVaDX9/f4N9um3Jyclo2LBhqcbHKICszpYT07I0uRavGFfwXGP/bCkGz0RERMXr1KkTOnXqJL7v168fIiIi0KJFC0ybNg0//fST0fOys7MBwGhy0MPDQ+8YIL8LRUnw21wiHG1inL38MyHPDYAWQNY/708dN/s6xU3YKwon8xEREZXMs88+i/79+2Pnzp3QarWQy+UGxyiVSgDQi4t0cnJy9I4pDU6MkwhOjCMiIiJnEBgYCI1Gg8zMTKP7q1SpAoVCgZSUFIN9um0BAQGlHgdXjJMIrhhnnixNbrErxhV1rjUm7LEcgoiIqOQrxkVERGDPnj3IzMyEi4vxfGxwcDBkMhlOnjypt71Hjx64fv06rl+/XqqxAyyHkAxHmxhnD7qJbUpoMDdYEFeMg1aGvAIrxplDShP2iIiIyqMHDx6gevXqetvOnTuH7777Dr179xYD4Nu3byMrKwuNGjUSj4uIiEBUVBQSEhLEThJXr17FL7/8gilTpuhd09j55mAmWKIcfT1uc1kyQU2XyfWABjPaCPD28rQoE8ygl4iIyHqKi1W6du0KpVKJTp06oUaNGrh06RK+/PJLuLm54cSJE2jcuDEAICwszKDFmUqlQuvWraFSqTBlyhS4ublh6dKl0Gq1OHv2rF5wbex8czAqkAhnnRhn7spzBeXAHR/9Dlg6MY4T2oiIiMrOgAEDsHnzZixduhTp6emoXr06Bg4ciJkzZxa7bLK3tzfi4uLw/vvvY968ecjLy0NYWBiWLVtmkF0uKWaCJWLWrFmYPXu2wfbyngkOitpTZvdiEExERGQ9jv5XawbBEuGsE+NYDkFEROSYHD0IZlQgEc46Mc7T3dXildxkABRylGpiHBERETk3RgxUJorK+FqyktulOT2hVWfj4MGSL3ZBRERExCBYIsr7xDhzJ8CZs5Lb+Y9CSjscIiIicnJcMU4iuGIcERERUdnhxDiJKO8T44orh7BkJTddOYRSqYRCoYBarUZ2djbCw8Ph7e1t1XETERGRcZwYR1ZR3ifGmTsBzpyV3FTqIncTERERFYtBMFlNcZPaipoAd2lOT6P72O2BiIiIbIERhkSUh4lxlqz+Zs4EOIALXBAREZFtcGKcRHBiHBEREVHZ4cQ4iSgPE+PMKYewZAIcYLwcQqVScWIcERGRnXFiHFlFeZgYZ0n9rjkT4IiIiIhshVEIlRlPd1ejNb6WLptMREREVFqMNsimiiuRKHyMOccTERERlRaDYIkoD90hjLGkYwTAZZOJiIiobLA7hESwOwQRERFR2WF3CIkoD90hjDG3HILLJhMRETkWdocgq3D07hCmJrdZOsmNyyYTERFRWWAQTMWy1eS2goEyV4YjIiKismTXIPjixYuYNWsWTp8+jbt378LT0xNNmjTB1KlT0bdvX/G4tWvXYtOmTbhy5QpSU1MREBCAsLAwzJw5E0FBQeJxSUlJ+Oqrr7Bnzx5cu3YNcrkczZo1w/Tp09GtWzeD+58+fRozZ85EQkICMjIyUK9ePYwZMwbvvPMO5HK5eNy2bdvw/fffIz4+Hn/++SdCQ0MRFxdncL2MjAwsXrwY8fHxOHnyJJ48eYKYmBiMGjWq2J+FlCfG2WJyG8AlkYmIiMh+7Dox7tatW1CpVBg5ciRWrFiBGTNmAAD69euHL7/8UjzuzJkzqFu3LiIjI/HFF19g+PDh2Lt3L4KDg5GcnCwet3v3bixatAjPPvss5s2bhxkzZkClUqF79+6IiYnRu/fp06fRqVMn3Lx5Ex988AGWLFmCevXq4b333sOkSZP0jv3iiy+we/duBAYGonLlyiY/z8OHDzFnzhxcvnwZLVu2tOhnwYlxRERERGVHchPjtFot2rZti5ycHFy5csXkcadPn0a7du0QHR2NqKgoAPmZZV9fX1SrVk08Tq1Wo1WrVsjIyEBSUpK4fezYsdiwYQNSUlJQpUoVcXtoaCjOnj2LtLQ0cVtSUhJq1qwJFxcXNGvWDNWqVTOaCVar1Xjy5An8/PyQkJCA4ODgUmWCpTIxzhaT2wDL64V1uGwyERGR/XFinJXJ5XIEBgbi1KlTRR6nK4NITU0VtzVt2tTgOIVCgRdeeAFLly6FSqUSg6T09HR4eHigUqVKesf7+/vj6tWretvMzcoqFAr4+fmZdayxc6U6Mc4Wk9t0uFocERER2YMkIo7MzExkZ2cjLS0N3333Hfbu3YshQ4YYHPfo0SNotVrcvn0bc+bMAQCEh4cXe31dvbGnp6e4LSwsDNu2bcO4ceMwadIkeHp6Yu/evdi5cycWL15svQ/nBHSZ4ktzehpsM/dcy87RQq0FXLQCkCtArZXUHzOIiIjIAUgiCJ48eTLWrFkDAHBxccHAgQOxcuVKg+Nq1qwplgxUrVoVn332Gbp3717ktf/880/s3LkTgwcP1pvs9uabb+LixYtYs2YN/vOf/wDIz0KvXLkSb731lrU+mtmkPDGuOJZOnDPF3Al1+dwAaAFkAQCWP2eVIRAREZGTkEQQPHHiRERERCA5ORnbt2+HVquFRqMxOG7v3r3IycnB5cuXsWnTJmRmZhZ53aysLAwePBhKpRILFy7U2yeXy/HMM8+gZ8+eGDx4MDw8PPDNN99gwoQJ8PPzw4ABA6z5EYsVHR2N2bNnl+k9iYiIiJyV5CbGAUCPHj2QmpqK+Ph4yGQyo8dcv34dzZo1w+LFizF+/HiD/VqtFi+99BL27duHvXv3omvXrnr7Fy5ciBUrVuDatWvw8vISt3fp0gWJiYm4desWXF0Nf0coamJcQeVpYlxxzC1jMHWupRPqVKoMxMXFQan0gMJdAbVGjTxNDifGERERlSFHnxhn1xZppkRERODUqVNITEw0ecwzzzyD1q1bY/PmzUb3v/nmm/jhhx+wfv16gwAYAFatWoWuXbvqBcBAfnu25ORk3Lx5s1SfwVIKhQIVK1bUezkK3US4kr4sv44cCjmgkMugcJVBITf+i5K9ZGlyERS1B0FRe0r1CwIRERHZjiTKIQrLzs4GAL02ZaaOK5g91Zk6dSpiYmKwfPlyDB061Oi59+7dg1arNdj+9OlTAEBuLoOXsmDv1eJsEaSWZLKfJdhBg4iIqPTs+m16//591KhRQ2/b06dPsXHjRiiVSjRp0gS5ublQqVQGi1ScPHkS58+fx7Bhw/S2L168GJ9++ik+/PBDvPfeeybv3aBBAxw4cACPHj1C1apVAeSXUGzfvh3e3t545plnrPQpzePIE+McmbUm9Zli2WQ/83ClPSIiotKzaxA8btw4pKenIyQkBDVr1sTdu3exefNmXLlyBUuWLIGXlxdSU1MRGBiIIUOGoGnTpqhQoQLOnz+PmJgY+Pj4iKvMAcCuXbsQGRmJ+vXro3Hjxti0aZPe/bp37w5fX18AQFRUFIYPH44OHTpg7NixUCqV+Oabb3D69GnMmzcPbm5u4nlHjhzBkSNHAAAPHjxAZmYm5s2bBwAICQlBSEiIeOzKlSuRmpoqrmT3/fff4++//wYATJgwAT4+PkZ/FpwYR0RERFR27DoxbuvWrVi3bh3Onz+PR48ewdvbG23btsWECRPQr18/AIBGo0FkZCQOHTqEmzdvIjs7GwEBAejWrRumT58uLpoBALNmzSoykDx06BDCwsLE9/v27UN0dDQuXryI9PR0NGzYEO+88w7GjRund15R1505cyZmzZolvg8KCsKtW7eMHnvjxg298RbkyBPjypo1V4yzVTmEpZP9LMFyCCIikgJHnxgnye4Q5Pj/YllbwZXlTkZ2wq9HDkt22WSugkdERM7A0WMVfjtTmSltKzWdbCMrxqm1+SvJyc28hy0DU3tP9iMiIqLiMRMsEc5QDhEUtcfeQxAxSCUiIiodR88ES7JPsDOKjo6Gj4+P+AoMDLT3kIiIiIjKLWaCJcIZMsHWWlnu8MQO+O34Mb0V47KzcxAWFgZvb69irpSPdbpERESl4+iZYEYCEqFQKKBQKOw9DJsqKvC0ZDKZstCKcdDKkCcHPN3lDG6JiIjILIwYqEwUlwU2Z5W1S3N6AgBUqgyotYDSesMjIiIiJ8MgWCLK+4pxlqzMZt4qa25Y06Xk4yEiIiLnxolxEsGJcURERERlhxPjJKK8T4wzpxzC3FXWVKoMxMXFoZKXNBfLICIicgacGEdWUd4nxlkyYc3T3bXI47X/mxhHREREVFIMgslmLOn4wFXWiIiIqCwxCCaLmdvv15yOD4WxxRkRERGVBUYcEuFI3SEs6fSgY17HBy5nTERERGWD3SEkgt0hiIiIiMoOu0NIhCN1h7CkHMLcjg865hyjUqlw8OBBKJXsDkFERGQv7A5BVuFI3SFKUrdbXMcHIiIiorLEqIRshh0fiIiISKpYE0xEREREToeZYInSlWpLuUuEvahUKmRlZUGj0cDV1RW5ubnIzc1Feno6WOJORERUNnQxiqN+9zIIliiVSgUA7BJBREREkqZSqeDj42PvYViM3SEkKi8vD8nJyfD29oZMJjPrHF1HiaSkJIecpVme8FlIB5+FdPBZSAefhXQ48rMQBAEqlQoBAQFwcXG8CltmgiXKxcUFtWrVKtG5FStWdLj/kMorPgvp4LOQDj4L6eCzkA5HfRaOmAHWcbywnYiIiIiolBgEExEREZHTYRBcjigUCsycOdNhFt0oz/gspIPPQjr4LKSDz0I6+CzshxPjiIiIiMjpMBNMRERERE6HQTAREREROR0GwURERETkdBgEExEREZHTYRDsANRqNT744AMEBARAqVSiQ4cOOHDggN4xa9asQd26dVGlShW89tpr4nreOnl5eWjdujUWLFhQlkN3aBkZGZg5cyZ69eqFKlWqQCaTYf369UaPvXz5Mnr16gUvLy/xGTx48EDvmNTUVLz66quoXLky6tWrh3Xr1hlcJyEhAZ6enrhx44YtPpJDOnXqFMaPH4+mTZuiQoUKqF27Nl5++WUkJiYaHMvnYFsXL17E4MGDUa9ePXh6eqJatWoICQnB999/b3Asn0XZmz9/PmQyGZo1a2aw79dff8Xzzz8PT09P+Pn54d1330VGRobeMXfu3EGfPn1QsWJFNGnSxOhz3blzJ2rUqIG0tDSbfQ5HExcXB5lMZvT122+/6R3L5yAxAkneK6+8Iri6ugpTpkwR1qxZI3Ts2FFwdXUVjh49KgiCIBw9elSQyWTCe++9J6xYsULw8/MTxo4dq3eN1atXC3Xr1hVycnLs8REc0o0bNwQAQu3atYWwsDABgBATE2NwXFJSklCtWjXhmWeeEVasWCHMnz9fqFy5stCyZUtBrVaLx40ePVoICAgQVqxYIUyYMEGQyWTC8ePHxf15eXlCx44dhWnTppXFx3MYgwYNEvz8/IQJEyYIa9euFebOnSv4+voKFSpUEM6fPy8ex+dge3v27BF69uwpzJo1S/jyyy+F5cuXC507dxYACGvWrBGP47Moe0lJSYKnp6dQoUIFoWnTpnr7zpw5I3h4eAitW7cWvvjiC+Gjjz4SFAqF0KtXL73jwsPDhUaNGgmrVq0SXn31VUGhUAg3btwQ92dnZwt169bVe9YkCIcOHRIACO+++67w9ddf670ePHggHsfnID0MgiUuPj5eACAsXrxY3JadnS0888wzQseOHQVBEIQPPvhA6NKli7g/JiZG8PPzE98/efJEqFatmrBjx46yG3g5kJOTI6SkpAiCIAinTp0yGQT/61//EpRKpXDr1i1x24EDBwwCA19fX2HDhg3i+9DQUCEqKkp8//XXXwsBAQGCSqWywadxXMePH9cLnARBEBITEwWFQiG8+uqr4jY+B/vIzc0VWrZsKTRs2FDcxmdR9oYMGSJ07dpVCA0NNQiCe/fuLfj7+wtpaWnitrVr1woAhH379gmCIAhZWVmCTCYTDh8+LAhC/i8gdevWFVavXi2eM3fuXKFVq1aCVqstg0/kOHRB8LffflvkcXwO0sNyCImLjY2FXC7H2LFjxW0eHh4YPXo0Tpw4gaSkJGRnZ6Ny5cri/ipVqiArK0t8P2vWLDRv3hwDBw4s07E7OoVCAT8/v2KP27FjB1588UXUrl1b3NatWzc0aNAA27dvF7cV9ZwyMzMRFRWF6OhoeHl5WfFTOL5OnTrB3d1db1v9+vXRtGlTXL58WdzG52AfcrkcgYGBSE1NFbfxWZStI0eOIDY2FsuXLzfYl56ejgMHDmD48OGoWLGiuH3EiBHw8vISn0dOTg4EQRCfh0wmQ6VKlcTncefOHSxcuBArVqyAiwtDB1NUKhVyc3MNtvM5SBN/ghJ35swZNGjQQO8/GgBo3749AODs2bMIDg7GTz/9hP379+PatWtYsmSJuP/SpUtYvXq10f9zpNK7c+cO7t+/j3bt2hnsa9++Pc6cOSO+Dw4OxtKlS3Ht2jXs27cPP/30k/icFixYgJo1a+K1114rs7E7MkEQcO/ePVSrVg0An0NZy8zMxMOHD3H9+nUsW7YMe/fuRXh4OAA+i7Km1WoxYcIEjBkzBs2bNzfYf/78eeTm5ho8D3d3d7Rq1Up8HpUrV8YzzzyDBQsW4MaNG9i8eTPOnj0rPo/IyEj07t0bISEhtv9QDur1119HxYoV4eHhgS5duiAhIUHcx+cgUXbORFMxmjZtKnTt2tVg+8WLFwUAwurVq4Xc3Fxh4MCBAgABgBAYGCj88ccfgiAIQo8ePYS33nqrrIdd7pgqh9Bt37hxo8E5U6dOFQCIddh//PGHUKtWLfE5DRo0SNBqtcJff/0lKJVK4cSJE2XxUcqFr7/+WgAgrFu3ThAEPoeyNm7cOPHn5+LiIkRERAiPHz8WBIHPoqytXLlS8PHxEe7fvy8IgmBQDvHtt98KAIQjR44YnDt48GC90rmDBw8KlStXFp/HxIkTBUHIL0lSKpXCzZs3bfxpHNPx48eFQYMGCevWrRN2794tREdHC1WrVhU8PDyE33//XRAEPgepYiZY4rKzs42uJ+7h4SHul8vl2LFjB65du4aEhAQkJiaiefPm+O6773Dy5EnMnTsXd+7cQd++fREQEIC+ffsiOTm5rD9KuZSdnQ0AxT4jAGjevDmuXbuGU6dO4dq1a4iNjYWLiwsmT56MQYMG4bnnnsPOnTvRsmVL1K1bF3PmzIHAVc0NXLlyBe+88w46duyIkSNHAuBzKGsTJ07EgQMHsGHDBvTu3RtarRYajQYAn0VZevToET7++GPMmDED1atXN3pMcc9Dtx8Aunbtitu3b+O3337D7du3sWzZMuTl5eHdd9/F5MmTUadOHXzxxRdo1KgRGjZsiNWrV9vmgzmYTp06ITY2Fm+88Qb69euHqKgo/Pbbb5DJZJg2bRoAPgepcrX3AKhoSqUSarXaYHtOTo64X+fZZ58V/1mj0WDy5MmYOXMmqlWrhs6dO8Pf3x/ff/89Fi5ciGHDhiEuLs7m4y/vdD9/c5+Rh4eH3p/DfvnlF+zfvx9Xr17F1atX8corr2DNmjUICgrC0KFDERgYiNdff93Gn8Jx3L17F3369IGPj49YLw/wOZS1Ro0aoVGjRgDyaxp79OiBvn37Ij4+ns+iDE2fPh1VqlTBhAkTTB5T3PMo+CwAwMvLCx06dBDfx8TE4O7du4iKisLPP/+MqVOnYtOmTZDJZBg2bBgaNmyILl26WOkTlR/PPvss+vfvj507d0Kr1fI5SBQzwRLn7++PlJQUg+26bQEBAUbPW7ZsGVxdXTF+/HgkJSXh2LFj+OSTT9C2bVt88sknOHz4MP7++2+bjt0Z+Pv7A4DJZ1SlShWjv/kD+bV87733HqKiolCzZk1s374dnTp1wuuvv44uXbpg3Lhx2Lx5s03H70jS0tLQu3dvpKam4qefftL7d5/Pwb4iIiJw6tQpJCYm8lmUkWvXruHLL7/Eu+++i+TkZNy8eRM3b95ETk4Onj59ips3b+Lx48fFPg9T3yFA/mSujz76CAsXLkSFChXwzTffICIiAgMGDED//v0RERHB51GEwMBAaDQaZGZm8jlIFINgiWvVqhUSExMNFr+Ij48X9xeWkpKCefPmiYGwrvRB9x+Z7n/v3Lljw5E7h5o1a6J69ep6EyB0Tp48afT56HzxxRdQqVSYMmUKACA5OVnv/wgDAgL4jP4nJycHffv2RWJiIn744Qc0adJEbz+fg33p/pSblpbGZ1FG7ty5I/6JvG7duuIrPj4eiYmJYvlIs2bN4OrqavA8NBoNzp49W+TzmDNnDurWrYtXX30VAJ+Hpf766y94eHjAy8uLz0GiGARLXEREBLRaLb788ktxm1qtRkxMDDp06IDAwECDc6KiohASEoJevXoBAHx9fQHk11ICENtKmdP+i4o3aNAg/PDDD0hKShK3HTx4EImJiRg8eLDRcx4/foyZM2di8eLFYp2kr6+v+IyA/OfEZ5SfHRwyZAhOnDiBb7/9Fh07djR6HJ+D7d2/f99g29OnT7Fx40YolUrxlxM+C9tr1qwZdu3aZfBq2rQpateujV27dmH06NHw8fFBt27dsGnTJqhUKvH8r7/+GhkZGSafR2JiIlauXIkVK1ZAJpMB4PMwpfBKiABw7tw5fPfdd+jRowdcXFz4HKTKzhPzyAyDBw8WXF1dhalTpwpr1qwROnXqJLi6uorNtAuKj48XFAqFcPXqVb3t7dq1E1q3bi2sXLlSaNWqldChQ4eyGr5D+/zzz4W5c+cK//rXvwQAwsCBA4W5c+cKc+fOFVJTUwVBEITbt28LVatWFZ555hnhs88+ExYsWCBUrlxZaN68uckV+t5++20hNDRUb9sff/whyGQy4a233hKio6MFDw8PYdWqVbb+iJL33nvvCQCEvn37GqzG9PXXX4vH8TnY3oABA4SuXbsKs2bNElfva9SokQBAWLJkiXgcn4X9GFss4/Tp04JCodBbqczDw0Po0aOHyeu88MILwogRI/S2ff/994KLi4swbdo0Ydq0aYKLi4vw448/2uRzOJIuXboIL7zwgjBv3jzhyy+/FCZOnCh4enoKPj4+wqVLl8Tj+Bykh0GwA8jOzhamTJki+Pn5CQqFQggODhZ++ukng+Py8vKEDh06CJMmTTLY9+effwohISGCl5eXEBISIly/fr0shu7w6tSpI7apKfwquIzlhQsXhB49egienp5CpUqVhFdffVW4e/eu0Wv+8ccfgru7u3DmzBmDfevXrxeCgoKEqlWrCpMmTRJyc3Nt9MkcR2hoqMlnUPj3eD4H2/rmm2+Ebt26Cb6+voKrq6tQuXJloVu3bsLu3bsNjuWzsA9jQbAgCMLRo0eFTp06CR4eHkL16tWFd955R0hPTzd6jT179gheXl5CcnKywb7o6GghICBA8Pf3FxYtWmT18TuiFStWCO3btxeqVKkiuLq6Cv7+/sLw4cOFa9euGRzL5yAtMkFgvxkiIiIici6sCSYiIiIip8MgmIiIiIicDoNgIiIiInI6DIKJiIiIyOkwCCYiIiIip8MgmIiIiIicDoNgIiIiInI6DIKJiIiIyOkwCCYiAhAWFoaJEyfaexhGjRo1CgMGDLDonOPHj6N58+Zwc3Oz+NyiBAUFYfny5Va7HhGRvTAIJiIqhyZNmoRWrVrhxo0bWL9+vc3uI5PJ8N///tdm15fyLydE5NgYBBMRlUPXr19H165dUatWLVSqVMnewzHw9OlTew+BiJwcg2AiIiOePHmCESNGoHLlyvD09ETv3r1x7do1cf+jR48wdOhQ1KxZE56enmjevDm++eYbvWuEhYXh3XffRWRkJKpUqQI/Pz/MmjWryPtqtVpMmjQJlSpVQtWqVREZGQlBEPSOycvLQ3R0NOrWrQulUomWLVsiNjYWAHDz5k3IZDI8evQIb7zxBmQyGdavXw+tVovRo0eL5zRs2BArVqwwGG/hrOuAAQMwatQoo2MNCgoCALz00kuQyWTi+8J0Y9q2bRtCQ0Ph4eGBzZs3F/szHDVqFA4fPowVK1ZAJpNBJpPh5s2bAIALFy6gd+/e8PLygq+vL1577TU8fPiwyJ8tEVFBDIKJiIwYNWoUEhIS8N133+HEiRMQBAEvvPCCmMHMyclB27ZtsWfPHly4cAFjx47Fa6+9hpMnT+pdZ8OGDahQoQLi4+PxySefYM6cOThw4IDJ+y5ZsgTr16/HV199hWPHjuHx48fYtWuX3jHR0dHYuHEjVq9ejYsXL+L999/H8OHDcfjwYQQGBiIlJQUVK1bE8uXLkZKSgiFDhiAvLw+1atXCt99+i0uXLuHjjz/Ghx9+iO3bt5f4Z3Tq1CkAQExMDFJSUsT3pkRFReG9997D5cuX0bNnz2J/hitWrEDHjh3x5ptvIiUlBSkpKQgMDERqaiq6du2K1q1bIyEhAT/99BPu3buHl19+ucSfhYickEBEREJoaKjw3nvvCYIgCImJiQIA4fjx4+L+hw8fCkqlUti+fbvJa/Tp00eYPHmy3jWff/55vWOCg4OFDz74wOQ1/P39hU8++UR8//TpU6FWrVpC//79BUEQhJycHMHT01P49ddf9c4bPXq0MHToUPG9j4+PEBMTY/I+giAI77zzjjBo0CC98ep+Bjr9+/cXRo4cKb6vU6eOsGzZMvE9AGHXrl1F3ufGjRsCAGH58uVFHicIxn+Ghcc0d+5coUePHnrbkpKSBADC1atXi70HEZEgCIKrfUNwIiLpuXz5MlxdXdGhQwdxW9WqVdGwYUNcvnwZQH7ZwoIFC7B9+3bcuXMHGo0GarUanp6eetdq0aKF3nt/f3/cv3/f6H3T0tKQkpKid19XV1e0a9dOLIn4888/kZWVhe7du+udq9Fo0Lp16yI/17///W989dVXuH37NrKzs6HRaNCqVauifxhW1K5dO7335v4MCzt37hwOHToELy8vg33Xr19HgwYNrDpuIiqfGAQTEZXA4sWLsWLFCixfvhzNmzdHhQoVMHHiRGg0Gr3j3Nzc9N7LZDLk5eWV+L4ZGRkAgD179qBmzZp6+xQKhcnztm7diilTpmDJkiXo2LEjvL29sXjxYsTHx4vHuLi4GNQfW3MCW4UKFfTem/szLCwjIwN9+/bFokWLDPb5+/tbbbxEVL4xCCYiKqRx48bIzc1FfHw8OnXqBCB/ItzVq1fRpEkTAPl9ePv374/hw4cDyJ+slpiYKO4vCR8fH/j7+yM+Ph4hISEAgNzcXJw+fRpt2rQBADRp0gQKhQK3b99GaGio2dc+fvw4OnXqhLffflvcdv36db1jqlevjpSUFPG9VqvFhQsX0KVLF5PXdXNzg1arNXschcdU3M/Q3d3d4Ppt2rTBjh07EBQUBFdXfo0RUclwYhwRUSH169dH//798eabb+LYsWM4d+4chg8fjpo1a6J///7iMQcOHMCvv/6Ky5cvY9y4cbh3716p7/3ee+9h4cKF+O9//4srV67g7bffRmpqqrjf29sbU6ZMwfvvv48NGzbg+vXr+P333/H5559jw4YNRX6mhIQE7Nu3D4mJiZgxY4bBRLauXbtiz5492LNnD65cuYJ//etfevc2JigoCAcPHsTdu3fx5MkTiz6rOT/DoKAgxMfH4+bNm3j48CHy8vLwzjvv4PHjxxg6dChOnTqF69evY9++fXj99ddLHJATkfNhEExEZERMTAzatm2LF198ER07doQgCPjxxx/F8obp06ejTZs26NmzJ8LCwuDn52eVldkmT56M1157DSNHjhTLFl566SW9Y+bOnYsZM2YgOjoajRs3Rq9evbBnzx7UrVvX5HXHjRuHgQMHYsiQIejQoQMePXqklxUGgDfeeAMjR47EiBEjEBoainr16hWZBQbyu1kcOHAAgYGBxdYkF2bOz3DKlCmQy+Vo0qQJqlevjtu3byMgIADHjx+HVqtFjx490Lx5c0ycOBGVKlWCiwu/1ojIPDKhcAEYEREREVE5x1+ZiYiIiMjpMAgmIiIiIqfDIJiIiIiInA6DYCIiIiJyOgyCiYiIiMjpMAgmIiIiIqfDIJiIiIiInA6DYCIiIiJyOgyCiYiIiMjpMAgmIiIiIqfDIJiIiIiInA6DYCIiIiJyOv8PtMYGBq50/X4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sg_plot.naics_variance_plot(naics_info[naics_info['count_naics'] >= 150],\n",
    "                                  naics_sector_feat='cluster',\n",
    "                                  naics_sector_desc_feat='cluster_desc',\n",
    "                                  xlabel='loan default rate')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('62_PLOT_naics_countxs_rates_150_all.png'),\n",
    "           bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "44b608dd-ef8a-4c38-af12-1ed8c479a022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(cluster)</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4502.890936</td>\n",
       "      <td>500.321215</td>\n",
       "      <td>3209.517996</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>623002.0</td>\n",
       "      <td>97117.734831</td>\n",
       "      <td>0.155887</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  df        sum_sq     mean_sq            F  PR(>F)\n",
       "C(cluster)       9.0   4502.890936  500.321215  3209.517996     0.0\n",
       "Residual    623002.0  97117.734831    0.155887          NaN     NaN"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm_cluster = ols('target ~ C(cluster)',data=sba_loans).fit()\n",
    "res_cluster = sm.stats.anova_lm(lm_cluster, typ=1)\n",
    "res_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1211d4b1-fb14-4dfb-aaa0-d2196bb7e3e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C(cluster)    0.044311\n",
       "Residual      0.955689\n",
       "Name: sum_sq, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_cluster['sum_sq'].transform(lambda x: x/x.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c280c44-14dc-4c38-b4ad-caa01a2aabc2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf_p39",
   "language": "python",
   "name": "tf_p39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}