33_downsample_size_loop_rand_hier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8816b366-5be4-4748-b769-4dc6f7d1735f",
   "metadata": {},
   "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": "a945de0c-f042-46b2-9d06-ab064af6c05b",
   "metadata": {},
   "source": [
    "## Loop downsample size - fixed randomization, NAICS base\n",
    "Randomize training before model, loop downsampling training data.  Loop the downsample fraction.  (Fixed random injection at 10%)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e6fba8f-d564-466c-8e50-7773b095157f",
   "metadata": {},
   "source": [
    "Base NAICS only, and data with no missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47a7ec9-7d7b-47df-a902-b2f1e54f1135",
   "metadata": {},
   "source": [
    "*This script takes about 4 hours on my MacBook Air*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb42f9b4-99a8-4753-aacf-8722469be4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf51170a-4487-4d10-b63d-c88f41b529f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import importlib, pickle\n",
    "from sklearn import model_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edd1164e-3ceb-4e3a-ad25-79bb9cf19a2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "09dd973b-60a0-434b-9193-8c5fcec27dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import re\n",
    "\n",
    "import keras\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e9d62b12-8bbf-4f4f-90ca-9318dccadab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5dedd82d-1901-4327-8b7f-f00026a84f18",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f1e4f2e-0e45-4e28-8e9c-212e0bb51199",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sba_nn.sba_nn import sbnn_metrics, sbnn_model, sbnn_plot\n",
    "from sba_nn.sba_nn.sbnn_model import CatInjectGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2a50c5c6-be2c-43a5-9795-d9eabb8a5f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sbnn_plot.plot_defaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d333dac-3cc2-4d62-8e70-c4663a79199d",
   "metadata": {},
   "source": [
    "## Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e014cd31-0f8d-42e3-82f9-b529eec8a3cc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sba_loans = pd.read_parquet(Path(setup.temp_path).joinpath('01_DATA_transformed_nomiss.parquet'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8e65cb7c-792c-46ff-a530-0d5c64240d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(Path(setup.temp_path).joinpath('01_DATA_features.pkl'), 'rb') as fin:\n",
    "    imputer_features = pickle.load(fin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6f0ce28e-a80c-415b-b068-5c4d49370da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NAICS': 1170,\n",
       " 'NAICS_5': 764,\n",
       " 'NAICS_4': 345,\n",
       " 'NAICS_3': 107,\n",
       " 'NAICS_sector': 21}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(Path(setup.temp_path).joinpath('01_DATA_naics_max_encodings.pkl'), 'rb') as fin:\n",
    "    naics_max_levels= pickle.load(fin)\n",
    "naics_max_levels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e5fd3f-ee27-482b-a0ae-eb90d023d62b",
   "metadata": {},
   "source": [
    "##### Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f98c6e41-38d0-4d5f-b821-e3837b8c2068",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NoEmp', 'CreateJob', 'LowDoc', 'DisbursementGross', 'new_business', 'urban_flag', 'franchise_flag', 'missingindicator_LowDoc', 'missingindicator_new_business', 'missingindicator_urban_flag']\n"
     ]
    }
   ],
   "source": [
    "# Numeric features\n",
    "features_numeric = [f for f in imputer_features if 'NAICS' not in f]\n",
    "features_numeric_len = len(features_numeric)\n",
    "print(features_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a74fa386-4011-45ee-bdb5-d671db9ffa91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NAICS feature - just base\n",
    "features_naics = ['NAICS', 'NAICS_4', 'NAICS_3', 'NAICS_sector']\n",
    "features_naics_max_levels  = [naics_max_levels[n] for n in features_naics]\n",
    "features_naics_emb_dim = [setup.nn_naics_embed_size_dict[n] for n in features_naics]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "867031ee-8b6e-424d-b497-8e19409ba1d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1170, 345, 107, 21]\n",
      "[8, 4, 4, 2]\n"
     ]
    }
   ],
   "source": [
    "print(features_naics_max_levels)\n",
    "print(features_naics_emb_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b208e70-4f1f-4397-9fd9-f409a8b35bfd",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Data Adjustment\n",
    "Inject \"1\" values into train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9813c1ba-0668-40f0-82fa-f04d3b514e3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, n in enumerate(features_naics):\n",
    "    adj_index, _ = train_test_split(sba_loans[sba_loans['dset'] == 'train'].index, \n",
    "                                    train_size=0.10, random_state=645 + i)\n",
    "    sba_loans.loc[adj_index, n] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "61e7b587-cdf0-4074-810b-8462bb12d822",
   "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>NAICS</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>95552</td>\n",
       "      <td>57496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train</th>\n",
       "      <td>402164</td>\n",
       "      <td>44684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val</th>\n",
       "      <td>95593</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NAICS   False  True \n",
       "dset                \n",
       "test    95552  57496\n",
       "train  402164  44684\n",
       "val     95593     11"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show unseen counts - expect some in train now!\n",
    "pd.crosstab(sba_loans['dset'], sba_loans['NAICS'] == 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f892e5-64c1-4ce4-a6d5-09136eb19bfd",
   "metadata": {},
   "source": [
    "##### Datasets for train, validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "43bca011-a59d-4496-9f03-87f4e350a253",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sba_loans[['dset', 'LoanNr_ChkDgt'] + features_numeric].set_index('LoanNr_ChkDgt').sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa162d01-ed73-4ae3-832e-4aeea09b70f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training X: (446848, 10), y:(446848,)\n"
     ]
    }
   ],
   "source": [
    "X_train = X[X['dset'] == 'train'].drop(columns='dset')\n",
    "y_train = sba_loans[sba_loans['dset'] == 'train'].set_index('LoanNr_ChkDgt').sort_index()['target']\n",
    "print(f'training X: {X_train.shape}, y:{y_train.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b32b5fd3-6f6e-4aec-8e20-0e2b71a4814c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val X: (95604, 10), y:(95604,)\n"
     ]
    }
   ],
   "source": [
    "X_val = X[X['dset'] == 'val'].drop(columns='dset')\n",
    "y_val = sba_loans[sba_loans['dset'] == 'val'].set_index('LoanNr_ChkDgt').sort_index()['target']\n",
    "print(f'val X: {X_val.shape}, y:{y_val.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "01b8ff96-159b-4283-b5fb-c4b5dbd277d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.20230592953308507\n"
     ]
    }
   ],
   "source": [
    "base_thresh = y_train.mean()\n",
    "print(base_thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1b8ce4c9-6907-4b70-982a-b92a52b3159d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_naics = [sba_loans[['dset', 'LoanNr_ChkDgt', n]].set_index('LoanNr_ChkDgt').sort_index() \\\n",
    "           for n in features_naics]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a6f129e0-a4d0-4d93-9886-153c9803d403",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_naics_train = [Xn[Xn['dset'] == 'train'].drop(columns='dset') for Xn in X_naics]\n",
    "X_naics_val =  [Xn[Xn['dset'] == 'val'].drop(columns='dset') for Xn in X_naics]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53be11f2-9c6d-47d7-b5b6-55ca8ed1a4fe",
   "metadata": {},
   "source": [
    "## Loop for different fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2a9a48fe-9d07-4fec-af0f-3f08e691cff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_fit_loop(rate = 0.1):\n",
    "    this_model = sbnn_model.create_emb_model(features_numeric_len,\n",
    "                                         features_naics_max_levels,\n",
    "                                         features_naics_emb_dim,\n",
    "                                         naics_embedding_names = features_naics,\n",
    "                                         hidden_size = setup.nn_layer_sizes,\n",
    "                                         activation='tanh',\n",
    "                                         lr=setup.nn_learning_rate,\n",
    "                                         opt_func = setup.nn_optimizer,\n",
    "                                         dropout = setup.nn_dropout)\n",
    "    \n",
    "    # Downsample\n",
    "    train_samp, _ = train_test_split(X_train.index,\n",
    "                                train_size=rate,\n",
    "                                random_state=343)\n",
    "    X_train_ds = X_train.loc[train_samp, :]\n",
    "    X_naics_train_ds = [xn.loc[train_samp, :] for xn in X_naics_train]\n",
    "    y_train_ds = y_train.loc[train_samp]\n",
    "    \n",
    "    callback = keras.callbacks.EarlyStopping(patience=3)\n",
    "    \n",
    "    # Fit\n",
    "    this_history = this_model.fit([X_train_ds] + X_naics_train_ds, y_train_ds,\n",
    "                              validation_data=([X_val] + X_naics_val, y_val),\n",
    "                              batch_size=setup.nn_batch_size,\n",
    "                              epochs=setup.nn_epochs,\n",
    "                              callbacks=[callback],\n",
    "                                 shuffle=True)\n",
    "    \n",
    "    this_history_df = sbnn_model.process_history(this_history)\n",
    "    this_history_df['rate'] = rate\n",
    "    \n",
    "    # Predictions then metrics\n",
    "    this_predict = this_model.predict([X.drop(columns='dset')] +\n",
    "                                     [Xn.drop(columns='dset') for Xn in X_naics])\n",
    "    this_predict_df = pd.DataFrame(this_predict, index=X.index) \\\n",
    "        .set_axis(['predict_prob'], axis=1) \\\n",
    "        .reset_index() \\\n",
    "        .merge(sba_loans[['target', 'LoanNr_ChkDgt', 'dset', 'dset_naics_holdout', 'NAICS']], \n",
    "               on='LoanNr_ChkDgt')\n",
    "    \n",
    "    # Threshold tune\n",
    "    this_pred_train = this_predict_df[this_predict_df['dset'] == 'train']\n",
    "    thresh_tune_data = sbnn_metrics.get_f1_frame(this_pred_train['target'], \n",
    "                                        this_pred_train['predict_prob'])\n",
    "    thresh_tune_data.sort_values('f1', ascending=False, inplace=True)\n",
    "    best_thresh = thresh_tune_data['thresh'].iloc[0]\n",
    "    this_predict_df['predict_bin'] = sbnn_metrics.get_binary_predictions(this_predict_df['predict_prob'], \n",
    "                                                                            best_thresh)\n",
    "    \n",
    "    this_metrics_dset = this_predict_df.groupby('dset') \\\n",
    "        .apply(lambda x: sbnn_metrics.dset_metrics(x.target, x.predict_bin, x.predict_prob)) \\\n",
    "        .reset_index()\n",
    "    this_metrics_test = this_predict_df[this_predict_df['dset'] == 'test'] \\\n",
    "        .groupby(['dset', 'dset_naics_holdout']) \\\n",
    "        .apply(lambda x: sbnn_metrics.dset_metrics(x.target, x.predict_bin, x.predict_prob)) \\\n",
    "        .reset_index()\n",
    "    this_metrics = pd.concat([this_metrics_dset, this_metrics_test])\n",
    "    this_metrics['rate'] = rate\n",
    "    this_metrics['thresh'] = best_thresh\n",
    "    \n",
    "    return this_history_df, this_metrics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "375ed31d-be98-458c-97ba-dc86cddacc18",
   "metadata": {},
   "source": [
    "## Run Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f2773b95-7041-41cc-ac49-4a5310138fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_rate_vals = [0.01, 0.05, 0.1, 0.2, 0.4, 0.7, 0.9, 0.95, 0.99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "74f979e5-8aaa-4d0a-adea-70eac817d042",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = pd.DataFrame()\n",
    "metrics = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9bdc1866-2381-4cde-a0ef-a1cd77133cba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-25 21:03:09.903987: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1\n",
      "2024-05-25 21:03:09.904026: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n",
      "2024-05-25 21:03:09.904036: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n",
      "2024-05-25 21:03:09.904090: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:303] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2024-05-25 21:03:09.904115: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:269] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
      "2024-05-25 21:03:11.664731: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:03:17.763314: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:07:11.170463: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:09:06.457173: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:09:24.625581: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:13:07.229480: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:14:42.156529: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:15:17.173134: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:19:08.526058: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:20:45.090412: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:21:54.651651: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:30:48.048554: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:32:24.868910: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 21:34:44.510025: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 22:00:12.961254: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 22:01:52.561844: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 22:05:58.497877: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 22:37:46.948177: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 22:38:55.774568: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 22:42:12.036628: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 23:07:08.009655: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 23:08:46.374481: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-25 23:14:09.933062: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:12:21.503307: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:13:57.298008: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:19:12.136672: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2024-05-26 00:53:41.850752: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "for r in sample_rate_vals:\n",
    "    this_hist, this_met = gen_fit_loop(r)\n",
    "    history = pd.concat([history, this_hist])\n",
    "    metrics = pd.concat([metrics, this_met])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ad4783f2-8d0b-4850-a45a-3a4be8e63318",
   "metadata": {},
   "outputs": [],
   "source": [
    "history['epoch'] = history.groupby('rate').cumcount()\n",
    "history.to_csv(Path(setup.temp_path).joinpath('33_REPORT_loop_history.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b1854de9-c6e9-41d4-813a-4c62d43b2bc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics.to_csv(Path(setup.temp_path).joinpath('33_REPORT_loop_metrics.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d934b1b9-a617-4330-8f1e-f3b7c89b1e0f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.446012</td>\n",
       "      <td>0.324138</td>\n",
       "      <td>0.714757</td>\n",
       "      <td>0.376498</td>\n",
       "      <td>0.724300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.22</td>\n",
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       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>0.649058</td>\n",
       "      <td>0.445712</td>\n",
       "      <td>0.328165</td>\n",
       "      <td>0.694463</td>\n",
       "      <td>0.377031</td>\n",
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       "      <td>0.444791</td>\n",
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       "      <td>0.650470</td>\n",
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       "      <td>0.329937</td>\n",
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       "      <td>0.378626</td>\n",
       "      <td>0.726469</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.99</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   dset  accuracy_score  f1_score  precision_score  recall_score  \\\n",
       "0  test        0.570612  0.405391         0.282053      0.720422   \n",
       "0  test        0.608642  0.421198         0.301066      0.700850   \n",
       "0  test        0.638237  0.431103         0.316759      0.674633   \n",
       "0  test        0.635568  0.439390         0.319579      0.702910   \n",
       "0  test        0.646400  0.442711         0.325626      0.691270   \n",
       "0  test        0.639241  0.446012         0.324138      0.714757   \n",
       "0  test        0.649058  0.445712         0.328165      0.694463   \n",
       "0  test        0.653442  0.444791         0.329721      0.683235   \n",
       "0  test        0.650470  0.448226         0.329937      0.698738   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  rate  thresh  \n",
       "0                 0.319149       0.672997                 0.0  0.01    0.16  \n",
       "0                 0.334310       0.692044                 0.0  0.05    0.20  \n",
       "0                 0.346837       0.704644                 0.0  0.10    0.24  \n",
       "0                 0.363880       0.714775                 0.0  0.20    0.18  \n",
       "0                 0.371701       0.719841                 0.0  0.40    0.22  \n",
       "0                 0.376498       0.724300                 0.0  0.70    0.22  \n",
       "0                 0.377031       0.724737                 0.0  0.90    0.24  \n",
       "0                 0.378425       0.725134                 0.0  0.95    0.18  \n",
       "0                 0.378626       0.726469                 0.0  0.99    0.24  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics[metrics['dset_naics_holdout'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "73e89efd-75a9-4b27-81a8-13e12d5482be",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.0</td>\n",
       "      <td>0.20</td>\n",
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       "      <td>0.644710</td>\n",
       "      <td>0.448929</td>\n",
       "      <td>0.324886</td>\n",
       "      <td>0.726194</td>\n",
       "      <td>0.379335</td>\n",
       "      <td>0.735910</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.657112</td>\n",
       "      <td>0.456388</td>\n",
       "      <td>0.333589</td>\n",
       "      <td>0.722266</td>\n",
       "      <td>0.382931</td>\n",
       "      <td>0.738976</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.660695</td>\n",
       "      <td>0.454761</td>\n",
       "      <td>0.334498</td>\n",
       "      <td>0.710046</td>\n",
       "      <td>0.382847</td>\n",
       "      <td>0.737933</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test</td>\n",
       "      <td>0.658973</td>\n",
       "      <td>0.454176</td>\n",
       "      <td>0.333442</td>\n",
       "      <td>0.711966</td>\n",
       "      <td>0.380355</td>\n",
       "      <td>0.738236</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dset  accuracy_score  f1_score  precision_score  recall_score  \\\n",
       "1  test        0.541894  0.424164         0.282964      0.846644   \n",
       "1  test        0.589762  0.435698         0.300119      0.794711   \n",
       "1  test        0.648884  0.442376         0.323607      0.698874   \n",
       "1  test        0.653024  0.448005         0.327985      0.706555   \n",
       "1  test        0.641022  0.446821         0.322425      0.727503   \n",
       "1  test        0.644710  0.448929         0.324886      0.726194   \n",
       "1  test        0.657112  0.456388         0.333589      0.722266   \n",
       "1  test        0.660695  0.454761         0.334498      0.710046   \n",
       "1  test        0.658973  0.454176         0.333442      0.711966   \n",
       "\n",
       "   average_precision_score  roc_auc_score  dset_naics_holdout  rate  thresh  \n",
       "1                 0.316332       0.702268                 1.0  0.01    0.16  \n",
       "1                 0.345442       0.718133                 1.0  0.05    0.20  \n",
       "1                 0.349815       0.721211                 1.0  0.10    0.24  \n",
       "1                 0.368928       0.731093                 1.0  0.20    0.18  \n",
       "1                 0.375402       0.733959                 1.0  0.40    0.22  \n",
       "1                 0.379335       0.735910                 1.0  0.70    0.22  \n",
       "1                 0.382931       0.738976                 1.0  0.90    0.24  \n",
       "1                 0.382847       0.737933                 1.0  0.95    0.18  \n",
       "1                 0.380355       0.738236                 1.0  0.99    0.24  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics[metrics['dset_naics_holdout'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0dae1f5-a6e2-46c3-bba7-f0966f93d3ed",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ae6c4e12-c72e-4f71-aaca-62f0704dca6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#metrics = pd.read_csv(Path(setup.temp_path).joinpath('33_REPORT_loop_metrics.csv'))\n",
    "#history = pd.read_csv(Path(setup.temp_path).joinpath('33_REPORT_loop_history.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b14d8c25-2623-40be-a84c-43c752f60fa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'sba_nn.sba_nn.sbnn_plot' from '/Users/valeriecarey/Documents/projects/2024_05_blog_nn_rand/code/sba_nn/sba_nn/sbnn_plot.py'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(sbnn_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7fba165-3d44-4a97-8227-03057298768b",
   "metadata": {},
   "source": [
    "##### Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5126ef4e-45a3-4a1c-9dba-37c59b8f75ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u3Q6cLhgO6SVfAk1u4e+1cbwznHf3kJ6zpwRR4oFdT8m+3UsVz76oG+Tk3ekZre5ow+P+tQhp5klnPw+c7SScuJs8ESGEKE2aPO2w3Y27JpXfjNYO82mKSFxsbX9XAJWvR8qltgRRwqQUReH0tTTCTsez9VQ8J66kGJz3qelI92aehDT15OEGNbC2kqSvRZEASwghHpQmTzuBXBc45e+NuhVbdBBlZZcviGpkmCvKpY5kLhdGKYqCKldDTp4GlVr7NSdXgyo37/bXgv+tKuR4Tq6GtGw1e6NucCU5S38NCwsIqled7s286N6sFr4ezlV2PtX9kABLCCGKQ6PR5oPKnx9K1yN1Kwbycgp/r5WtdpuX/Ek2dUN61bwliKpA8jQKOfmClKKClpw8YwGP7l/B4zlGjxVSNq946Q9Kyt7GkuDGHnRv6knXJrXwcLErletUBRJgCSGEjkYDadcM50Lp9s+7FQO52YW/19IGavgYJtnUTSx3rSsbEj+g3DzjgUdRAY4+aMnXy1P4e7Vl777G3fXmaswzs5GttSV2VpbY2Vhia2WJnY3V7a+Whl+trbRlrS2xvf3PztoKO2tLAr1d6dTYHXsb+Vk1BQmwhBBVi6JogyhjyTZvxkBuVuHvtbTWBlFudw3l1fQF13oSRN0nVW4eUQnpnL2extnraZy5nkZUQjrpqlx9cGOOcY2FBdpAxWhAY3VXwGMY4OQPcu4Oeuz0/+4+biw40tYrQ3fmRwIsIUTloyiQHm8k2ebtrWDUmYW/18IKajS4a9+82z1SrvXBSn5t3i+NRiHuViZnbgdSZ+O1X2OSMsgrQQRlZWlhEIzog49CenDsrO4V0BQMcPL37NxdTlfG2tJCAhtRKPlNIYSomBQF0hOMJNu8PaSnzij8vRaWUL2+YY4oXTBVvT5YVYytOMzZjXSVvjfq7PU0zsSncT4+jcwc4/m7qtlb08SrGv5eLvp/NRxtCwQ9tlaWsnpNVAgSYAkhzJeiQEZSwS1fbtzujcpJK/y9FpbaYbu70xu4+WqDKGvZV9QUsnLyOBd/Z2jvbHwqZ6+nkZRufNK/rZUlD9Vypkm+QKqJVzU8q9lJb5CoVCTAEkKUL0WBzBsFt3zRDempUot4s8XtIKpRwSG9Gg3AWlZAmUpunobYG5n5hva0gdTFm5kY29HWwgLquzni75k/kHLBp6aT9ECJKkECLCFE2clOgfNbIemc4ZCeKqWIN1loV+G5NSw4pFe9AdjYl1nzqwJFUUhIU90e2kvVD/GdT0gnJ9d4aoCaTrYGQZS/VzX8PJ1xtJWPGFF1yU+/EKJ05arg/BaIXAPn/oE8lfFy1byNZyyv4QM2srdZaUjLVnMuPt88qetpnItPIznTeGJUBxsr/DydbwdT1Wji5YKfp4vkShLCCAmwhBCmp9HAxb1wYg2c2qDtudJx94f67QyH9Go0BFvH8mtvJZeTq+FC0p00CLpgKn/W7vwsLaChu5N+0rmfp7Znqr6bI5aWMk9KiOKQAEsIYRqKAtdPaIOqE79C2tU751zqQGB/CBwIXi1kD71SoigKV5KzDFbvnb2exoWkdNR5xtMgeFaz0/dG6eZLPVTLWZJNCvGAJMASQjyYWxfhxFrtv8Qzd47buUKzp6HFIGjQUZJwmlhyZo7B0N7Z66mci9cm5zTGxc4av/zzpG4HU9UdZTWlEKVBAiwhRMll3IBT6yFyLcTtv3Pcyhb8ekLgIGjcQyagm0C2Ol+W8/g7wVR8qvG5bDZWFvh6OBsM7fl7ueBd3UHSIAhRhiTAEkIUT04GnP1LO1k9ehtodD0lFtAwWBtUNe0DDtXLs5UVlkajcOlm/izn2hV8sUkZhW4T413doUA+qYbuTthaSxoEIcqbBFhCiMLl5cKFndp5Vaf/NMyO7tVCO/zXvD9Uq1NuTayIEtN0Wc61uaTOxadxLj6dLLXxLOfVHW3w97yTAkHbO+WMi71knBfCXEmAJYQwpChw+ZA2qPrvN8hMunOuegNtUBU4EDz8y6+NFUSGKpfzCekG+aTOXk/jRobxLOd21pY09nTON7SnnXxey0WynAtR0UiAJYTQSjqvHf47sRZuxdw57lgTAp7VBlZ128gKQCO0Wc4z7pp0nkbcrcKznDdwczTIJ+V/O8u5laRBEKJSkABLiKos9Rr896s2qLp27M5xG0do8pS2p8q3q2x+fJuiKFxPzdYHUOduB1NRiYVnOXd3tjOYJ+Xv6UJjyXIuRKUn/4cLUdVkp8DpP7S9VbH/gnI7MLCwgoe6aSerN+kNtk7l285ylpqt1gdQ+gSd8WmkZBnPcu5oa0VjTxeaeObfMsaFms6S5VyIqkgCLCGqglyVdg/AE2vg7N+G29XUbasd/gvoB07u5dfGcpKTqyE6Md0gn9TZ62lcTck2Wt7K0oKG7k7aIMrzzuq9ujUcJMu5EEJPAiwhKqt7bVfTYiA0H6DdRLkK0Gi0Wc51++3pgqkLiRnkFpIHobarvcHQnr+XC74ekuVcCHFvEmAJUZkoCsT/px3+++9XSL1y55xLbW1KhRaDKv12Nbcycu70Rt0Ops5dTyMjx3gaBBd7a30ApU+F4OmCq6PMPRNC3B8JsISoDG5dhP/WaTOrJ56+c9zOFZr10c6r8ulU6baryVbncT4+XZ9P6my8dq5UQlrRWc6b3LV6r7arvaRBEEKYlARYQlRUVWi7mjyNwsUbGfmG9rT/Ym8UnuW8npsD/p7VDFbwNXR3wsZKspwLIUqfBFhCVCQ5mXB2szatQlSY4XY1Pp20w39Nn66w29UoikJiukofQOmCqfMJaWSrjadBqOFoo59orguk/DxdcLaTX29CiPIjv4GEMHeVdLuaDFWufkgvfxqEm0VkOfe7KwWCv5cLHs6S5VwIYX4kwBLCHCkKXDmsnax+8jfISLxzrnoDbQLQFoMq1HY1Go3CjrMJrDt8mf+uphB3M8toOUsL8KnpVGD1XgPJci6EqEAkwBLCnCSd1w7/Ra6pNNvVpGSpWXsojp/2X+TijUyDcx4ut7Oc58sn1dhT0iAIISo+sw+wIiIiWLBgAadPnyY7Oxt/f39CQ0Pp3bt3seu4dOkS8+bNY+/evSQnJ+Ph4UFwcDBjxoyhdu3a+nKXL1+mW7duxapz27Zt1K1bt8T3I0QBade1KRUi1xjZruZJ7WT1CrhdTVRCGsv2XeTXI5fJvJ0eoZq9Nc+1rU9X/1r4e7ng5mRbzq0UQojSYdYB1saNG5k8eTLW1ta0a9cOKysrwsPDeeutt4iKimL8+PH3rCM2NpZBgwaRkpJCo0aNaNWqFTExMaxevZrNmzezYsUKmjRpAoCjoyN9+vQptK6zZ89y7tw56tSpQ40aNUx2n6IKyk7Rzqc6sQZidhvZrmYg+PcGO+fybWcJ6YYBl+6L5d/zSfrj/p4uvNTBh2eC6sgefEKIKsFCUYzt9V7+kpKS6NatG5aWlqxYsYKAgAAAoqOjCQ0N5caNG/z666/644UZOHAgkZGRjBs3jrFjx+onw86dO5c5c+bQokUL1q5de8/2JCYm0rdvX9LS0lizZg1NmzYtUEbX+7Vt27aS3q6oCirxdjWp2WrWHrrM8vBY/TCgpQWENPVkWEcf2jeqKRPRhRBmqzQ+v832T8mVK1eSnZ3NqFGjDIIoX19fJkyYwLvvvsuyZcv44osvCq0jNjaWyMhIvL29DYIrgNdee43FixcTGRlJcnIy1atXL7I9U6ZM4caNG0ydOtVocCWEURoNXNqnHf47tQGyk++cc/fTDv8FVtztaqIS0lm2L9boMODQRxtQz82xnFsohBDlw2wDrF27dgEQEhJS4FxISAjTpk1j586dRdbh4+NDeHg4qampBf56VqvVqNVqAKysip5Q+/fff7Nnzx6aN29OaGhoCe5CVEm67WpOrIUTv0Lq5TvndNvVBA6E2i0r1GR1HY1GYee5BH7cazgM6OfpzLAODWUYUAghMNMAS1EUoqKiAGjcuHGB866urri7u5OYmEh8fDyenp6F1uXm5oabm5vBsaysLD766CPUajUhISG4uLgU+v6cnBx9L9m0adOwtJQs0KIQyZdurwC8e7uaatDs6Qq/XY2xYUALC+je1JNhHXxo7yvDgEIIoWOWAVZKSgoqlQonJyccHY0PMdSqVYvExESSkpKKDLDyCwsLY9WqVURGRpKamkrXrl2ZMWNGke/59ddfuXLlCp06daJ169YlvhdRyWXehJPrtYHVpfA7x61stdvUtBgEjXtW6O1qohLSWR4ey7rDMgwohBDFZZYBVlaWNgGhg4NDoWXs7OwAyMzMLLTM3cLDw9mzZ4/BdWJjYwkMDDRaXlEUli9fDsDo0aOLfR1RyVXy7Wqg6GHAlzr40C/IW4YBhRCiCGb5G1I3DFec4QaNxvj+ZMaMHj2ad955h/j4eFasWMHSpUsJDQ1l3bp1+Pr6Fii/d+9eLly4QMuWLWnTpk3xb0BUPnm5ELNTO/x35k/ISb9zzitQO/zXvD+4epdbE00hNVvNutvDgLH5hgFDmnoyXIYBhRCi2MwywHJycgIgOzu70DIqlXaJe2FDiMZ4eHgAUK9ePaZOnUpWVharV69m0aJFRocK//rrLwCeeeaZYl9DVCK67WpOrNUmAjXYrqb+7RWAA6FWk/Jro4nohgF/PXyZDBkGFEKIB2a2AZaTkxNpaWlkZ2djb19w/kpCQgKgnYt1v/r27cvq1as5efJkgXMajYYdO3ZgbW1Nr1697vsaogJKitLmqjqxFm5euHPcsaY2T1XgIKjXtkKuAMxPo1HYdS6RH/fFsvvcneCxcS1nhnWUYUAhhHgQZvnb08LCgsaNG3Ps2DGio6MLJBNNTk4mKSkJV1fXIie4Hzp0iA0bNtCiRQsGDhxY4LytrXabjtzc3ALn/vvvP27cuEG7du0KrEIUlVDadfjvN21gdfXoneP67WoGgu/jFW67GmOKGgYc1sGHDjIMKIQQD8wsAyyA4OBgjh07RlhYWIEAKywsDEVR6Ny5c5F1JCcns2bNGg4ePEj//v0LpFjYvXs3gNFs8JGRkQAEBQU9yG0Ic5eRBL+P0U5Wz79dje/j2snqFXC7msJEJ6azfJ92NaBuGNDF3prn2tRj6KM+1K8pw4BCCGEqZhtgDRgwgB9++IGlS5cSHBysT5Fw4cIFZs+eDcDIkSP15RMSEkhLS8PFxUU/bNi5c2e8vb2JiYlh5syZvP322/oga/v27SxYsAArKyuGDx9e4Pr//fcfAM2aNSvN2xTlbduHcH6L9nXdttqeqoB+4OxRvu0yERkGFEKI8mG2v1m9vLyYNm0a06dPZ8iQIbRr1w5bW1vCw8NRqVRMnDhRv0kzwFdffcX69evp16+ffsK6ra0ts2bNYsSIESxevJitW7fi7+9PXFwcZ86cwdramg8++MBoD9bly9rs2zVr1iybGxZlL+UKHPtZ+3ro7+DbtVybY0qFDQN2a+LJ8I4yDCiEEKXNbAMs0G7U7OXlxcKFCzl27BhWVlY0a9aMl19+mR49ehSrjqCgIDZs2MD8+fPZs2cPO3bswNXVlV69ejFy5MhCc2DdvHkTgGrVqpnsfoSZCZ8LGjU06FRpgisZBhRCCPNgoSiKUt6NqCxKYzduUUoykmB2IKgzYchv8FC38m7RfZNhQCGEeDCl8fktv3VF1XRggTa4qt1KO6G9AkrLVrPu8GWW7ZNhQCGEMDcSYImqJzsVDizUvg6eWOHyWckwoBBCmD8JsETVc2gxqFLA3Q+aPFXerSkW3TDg0n2x7Mo3DPhQLWeG3d4b0MlO/ncWQghzIb+RRdWizoLwedrXnSbAXbnRzI1uGHB5+EVikjIAGQYUQoiKQAIsUbUc+Um7p2D1+hA4oLxbU6gLieksD7/I2kNxBsOAgx+pR2h7GQYUQghzJwGWqDpyc2DvN9rXHd8wu21vNBqFXecTWbpXhgGFEKKik9/Wouo4sRZSL4NTLWg1pLxbo1f4MGAthnVoSMeHZBhQCCEqGgmwRNWgyYM9X2tfd3gdbOzLtz3IMKAQQlRmEmCJquH0H3DjPNi7wiMvl1szdMOAy/bFsvOs4TDgSx18eFaGAYUQolKQ3+Si8lMU+HeW9nW70WDnUuZNSMtW8+vhyyyTYUAhhKgSJMASlV9UGFyPBBsnbYBVhnTDgOsOXyZdlQtohwEHPVKP0PYNaFDTqUzbI4QQomxIgCUqP13v1SPDwdGt1C+n0SjsPq9NCpp/GNDXw4lhHRvKMKAQQlQB8lteVG4X98GlcLCyhfavl+qldMOAy8MvcuGuYcCXOvjQ6SF3GQYUQogqQgIsUbnpeq9avQjVapfKJWKSMlh2e29A/TCgnTWD2sgwoBBCVFUSYInK6+ox7fwrC0ttYlETKnIYsIMPz7auK8OAQghRhckngKi89nyl/dp8ALg1NEmVhQ0DPu5fi2EdZRhQCCGElgRYonJKPAenNmpfd3rrgauTYUAhhBAlIQGWqJz2fA0o0OQp8Gx2X1XIMKAQQoj7JZ8OovK5dREiV2tfd5pQ4renq3K1SUH3xRYYBtStBrS0lGFAIYQQhZMAS1Q+++aAkgeNukDdh4v9tsKGAQfeTgrq4y7DgEIIIYpHAixRuaTFw5Hl2tfBE4v1lj3nk1i85wI78g0DNvJwYngHH/q1rouzDAMKIYQoIfnkEJXL/vmQp4K6bcAn+J7Ffz18mYlrjwPaYcCu/rUYJsOAQgghHpAEWKLyyLoFBxdrXwdP1EZMRchW5/HlP2cB6BfkzRvdGsswoBBCCJOQAEtUHhE/QE4a1AqAxj3vWXzpvliup2bjXd2Bz54NxN7GqgwaKYQQoiqwLO8GCGESqnTt8CBA8ASwLPpHOyVTzfwdUQC81d1PgishhBAmJQGWqByOLIOsm+DWCAL63bP4d7uiSc3Oxd/ThX5B3mXQQCGEEFWJBFii4stVaVMzAHR8EyyL7o26npLNj3tjAJjU0x8rmcwuhBDCxCTAEhXf8VWQdg1c6kDL5+5Z/Jtt51DlanikQQ26Na1VBg0UQghR1UiAJSq2vFzYM1v7usM4sLYrsnh0YjprDl0GYMoTTWRjZiGEEKVCAixRsZ36HW7FgGNNePilexaf+c9Z8jQKIU1r8YiPW+m3TwghRJUkAZaouDQa+HeW9vWjY8C26BxWx+KS+eu/61hYwKSeTcqggUIIIaoqCbBExXXub0g4BbYu0OaVIosqisLnf50B4Nmguvh7uZRFC4UQQlRREmCJiklR4N+Z2tdtR4JD9SKL7z6fRPiFG9haWfJW98al3z4hhBBVmllnco+IiGDBggWcPn2a7Oxs/P39CQ0NpXfv3sWu49KlS8ybN4+9e/eSnJyMh4cHwcHBjBkzhtq1axt9T3R0NN999x379+8nOTmZmjVr0qVLF8aPH0/NmjVNdXviQcTshiuHwdoeHn2tyKIazZ3eq6HtG1C3hmNZtFAIIUQVZrY9WBs3biQ0NJSIiAiaNWtGmzZtOHnyJG+99RbffvttseqIjY1lwIAB/P7777i4uNClSxccHR1ZvXo1ffr04cyZMwXe8++//9KvXz/++OMPatWqRefOndFoNPzyyy+88MILpKWlmfpWxf3Qzb1qHQrORada+CPyKqeupeJiZ83Yrg+VQeOEEEJUdWYZYCUlJTF9+nQcHBxYvXo1ixcvZuHChfz++++4u7szf/58Tp48ec96Jk2aREpKCuPGjWPz5s3MnTuXTZs2MW7cONLS0pg+fbpB+Vu3bjFp0iTy8vL44osv+O2335g/fz5bt24lJCSE2NjYYgd3ohRdPgQxu8DSWpuaoQg5uRpmbTkHwKjOjXBzsi2LFgohhKjizDLAWrlyJdnZ2QwZMoSAgAD9cV9fXyZMmICiKCxbtqzIOmJjY4mMjMTb25uxY8ca5Dt67bXXcHR0JDIykuTkZP3xX375hVu3bhEaGkrfvn31x+3t7Zk6dSoeHh7ExMSY7kbF/fn3K+3XFoOhev0ii/5y8BKXbmbi7mzHiOCGZdA4IYQQwkznYO3atQuAkJCQAudCQkKYNm0aO3fuLLIOHx8fwsPDSU1NLZBMUq1Wo1arAbCyurOtyj///APAsGHDCtRXt25d9uzZU5LbEKUh/hSc3QRYQKe3iiyaocrl223nAXij20M42prlj7sQQohKyOw+cRRFISoqCoDGjQuu9nJ1dcXd3Z3ExETi4+Px9PQstC43Nzfc3AyTSWZlZfHRRx+hVqsJCQnBxUW7XD8nJ4fz58/j6emJp6cnly5dYtOmTVy+fJmaNWvSs2dPg940UU723O69atYX3IteDbh4TwxJ6Tk0qOnIc22L7ukSQgghTMnsAqyUlBRUKhVOTk44Ohpf7VWrVi0SExNJSkoqMsDKLywsjFWrVhEZGUlqaipdu3ZlxowZ+vNXrlwhNzcXDw8PVq5cyWeffabv5QJYuHAho0aNYsKECQ92g+L+3bwA//2qfR1c9PfhRrqKhbsvADCxhz82VmY5Gi6EEKKSMrtPnaysLAAcHBwKLWNnp91vLjMzs9j1hoeHs2fPHlJTU/XXiY2N1Z/XrQ6MjY3l448/ZtCgQWzdupUDBw7wxRdf4OLiwvfff8/q1atLekvCVPZ+A4oGHuoOtVsWWXTejmjSVbkE1KnGU4HG03EIIYQQpcXsAixLS22TirMJr0ajKXa9o0eP5sSJE4SFhTFs2DD2799PaGgo0dHRgHaIECA9PZ2+ffvy3nvvUb9+fapXr07fvn355JNPAJg7dy6KopT0tsSDSr0Kx37Wvg6eWGTRy7cyWbH/IgDv9GqCpaVs6CyEEKJsmV2A5eSk3U8uOzu70DIqlQqg0CFEYzw8PLC1taVevXpMnTqVwYMHk5mZyaJFiwDDHrMXX3yxwPt79OhBzZo1SUhI4MKFC8W+rjCR8HmQlwP1O0CD9kUW/WrrOXLyNHTwrUlwY/cyaqAQQghxh1kGWE5OTqSlpRUaZCUkJADauVj3S5eGQZdPK/9k+Lp16xp9j7e3N6DNlyXKUOZNOLRE+7pz0b1XZ66nsv7oFUDbe1WcnlAhhBDC1MwuwLKwsNCvHtQN3+WXnJxMUlISrq6uRU5wP3ToENOnT2ft2rVGz9vaahNO5ubmAlC7dm2qVasGQHx8vNH3JCUlARRYmShK2YEFoM7Uzrvy7VZk0S//PouiQO9AL1rWq1427RNCCCHuYnYBFkBwcDCgXfl3t7CwMBRFoXPnzkXWkZyczJo1a1i8eLHRuVq7d+8GMEi9oKtz8+bNBcqfO3eOa9eu4eHhQYMGDYp/M+LBZKdqAyzQzr0qokfqYOxNtp1JwMrSgrd7+JdRA4UQQoiCzDLAGjBgAA4ODixdupQjR47oj1+4cIHZs2cDMHLkSP3xhIQEoqOj9UOHoA2WvL29iYmJYebMmQZB1vbt21mwYAFWVlYMHz5cf3zYsGFYWVnx448/6pOdAty8eZP/+7//Q1EUhg4dapCcVJSyQ0sgOwXc/aBJn0KLKYrCjNsbOg96pB6NPJzLqoVCCCFEARaKmS6JW7t2LdOnT8fS0pJ27dpha2tLeHg4KpWKiRMnMmrUKH3ZKVOmsH79evr162eQ2+ro0aOMGDGCjIwM6tevj7+/P3FxcZw5cwZra2vef/99Bg4caHDdn376iU8++QRFUWjZsiU1atTg6NGjpKSk0LFjRxYuXIi1tfH0Yd26aYevtm3bVgpPpApSZ8HsFpCRAM98B61eKLTo1lPxvLL8EPY2luya1BXPavZl2FAhhBAVWWl8fptdolGdgQMH4uXlxcKFCzl27BhWVlY0a9aMl19+mR49ehSrjqCgIDZs2MD8+fPZs2cPO3bswNXVlV69ejFy5EgCAwMLvGfo0KE0bdqUH374gaNHj3L27Fnq16/P6NGjCQ0NLTS4EqXg6AptcOVaDwIHFlosT6Pw5T/a3qvhHRtKcCWEEKLcmW0PVkUkPVgmlKeGb1tDyiXoPRPavlJo0bWH4pi0LhJXBxt2T+6Kq4NNGTZUCCFERVcan99mOQdLCE6s0wZXTrUgaEihxbLVeXy99RwAr3XxleBKCCGEWZAAS5gfjebOps7tx4JN4dsmrdh/kasp2dR2teelDj5l0z4hhBDiHiTAEubnzB+QdA7sXeGRlwstlpqtZu6OKADeDGmMvY2s7hRCCGEeJMAS5kVR4N9Z2tdtXwX7aoUWXbjrAsmZanw9nOjf2nj2fSGEEKI8PFCAlZycbPR4YmIiYWFh+izpQhRb9Da4dhxsHKHd6EKLJaRms3hPDACTejbB2kr+VhBCCGE+7utTae/evfTr14+ZM2caPb97927GjRtHz5499RnThSiWf2/PvXp4ODjVLLTYt9vPk6XOI6h+dXoGFL5lkhBCCFEeShxg/fXXX7z66qucPn2aU6dOGS2jUqmwt7fnypUrjB49mg0bNjxwQ0UVcDEcLu4FK1vo8HqhxWKTMvglIg6QDZ2FEEKYpxIFWBcvXmTy5Mnk5eUxevRoli1bZrTcCy+8wL59+3jppZfQaDS8//77XLlyxSQNFpWYbuVgqxegWp1Ci83ccpZcjUIXfw8ebVR4L5cQQghRXkoUYC1btgy1Ws0777zDm2++iYuLS6FlHRwcmDp1KmPHjiUrK4uffvrpgRsrKrFrx+H8FrCwhI5vFFrsxOUU/oy8hoUFTO7ZpAwbKIQQQhRfiQKsffv2UatWLYYOHVrs97z66qu4urqyZ8+eEjdOVCG6uVfN+4Nbo0KLfXF7S5y+LevQrE7hKwyFEEKI8lSiAOvatWv4+/tjZVX8fEO2trYEBQVx+fLlEjdOVBFJ5+HU7Xl6nd4qtNjeqCT+PZ+EjZUFE3v4l1HjhBBCiJIrUYBlY2ODjU3JtyKxsbEhLy+vxO8TVcSe2YAC/r3BM8BoEUVR+Pxvbe/Vi+0aUM/NsezaJ4QQQpRQiQKs2rVrc+nSpRJfJDY2lpo1ZTKyMCI5DiJ/0b4Onlhosc0nrhN5OQUnWytef/yhMmqcEEIIcX9KFGC1adOG6Oho/vvvv2K/58yZM5w/f56AAOM9E6KK2zcHNLnQ8DGo+4jRIuo8DTO3nAVgZHAj3J3tyrKFQgghRImVKMAaMGAAiqLwv//9j/T09HuWz87O5t1338XCwoInn3zyvhspKqn0RDhyO9VHEb1Xaw7FEZOUQU0nW17pXPgEeCGEEMJclCjAatasGc899xwnT57k+eefZ8eOHSiKUqBcXl4eO3fupH///pw6dYrg4GB69+5tskaLSmL/PMjNBu9HoGFno0WycvL4Juw8AK8//hDOdtZl2UIhhBDivpT40+rdd9/l6tWr7N69m9deew0nJycCAgJwd3cnNzeXmzdvcurUKTIzM1EUhXbt2vHtt9+WRttFRZaVDBE/aF8HT4RCsrEv2RtDQpqKujUceKFd/bJrnxBCCPEAShxg2drasnDhQlauXMnSpUuJi4vjwIEDBco1btyYESNG8Mwzz5iinaKyObgIctKgVjPw62W0SHJmDgt2RQMwsYcfdtbFTw8ihBBClKf7Hm958cUXefHFF/nvv/+4cOECiYmJWFtb4+HhQfPmzalfX3obRCFyMmD/d9rXnSaApfGR6vk7o0nLzqWJlwt9W3qXYQOFEEKIB/PAE1qaN29O8+bNTdEWUVUcWQ6ZN6BGQwjoZ7TI1eQslu6LBbQbOltayobOQgghKo4STXIX4oHl5sDe23PyOr0JVsZj/Nlh58jJ1dC2oRtd/D3Krn1CCCGECZSoB2vq1Kn3LGNnZ0e1atWoU6cObdq0wdfX974bJyqhyF8g7Sq41IaWzxstcj4+jXWHtVsrTXmiCRaFTIAXQgghzFWJAqz169djYWFhNDVDfvk/ELt168ZHH31EjRo17q+FovLIy4U9X2tfdxgH1sYThn75z1k0CvRo5knr+vJzI4QQouIpUYD1+uuv37NMXl4eGRkZXLx4kYiICMLCwrh58ybLli27r30MRSVy6ne4eQEc3KD1S0aLHL54iy2n4rG0gMm9ZENnIYQQFZPJA6z80tPTefvtt9m1axebNm2SlA1VmaLAv19pXz86BuycjRS5s6HzgIfr8lAtl7JsoRBCCGEypTrJ3dnZmRkzZmBnZ8fvv/9empcS5u7cP5BwEmxdoO0rRovsPJtIRMxNbK0teTPEr4wbKIQQQphOqa8irF69OoGBgVy8eLG0LyXMlaLAvzO1r9uMAIeC86o0mju9V8M6+FCnukNZtlAIIYQwqTJJ0+Dh4cGNGzfK4lLCHMXugcsHwdoe2o81WmTD8SucuZ6Gi701r3WRladCCCEqtjIJsNLS0nBxkfk0Vda/s7Rfg4aCc60Cp1W5eczacg6A0Y/5Ut3RtixbJ4QQQphcqQdYOTk5REZGUrt27dK+lDBHVw7DhR1gaQ0dxxst8vOBS1y+lUUtFzte7tiwjBsohBBCmF6pB1jffvstqampdO3atbQvJcyRbuVg4CCoXnB/ynRVLnO3RwHwRkhjHGxlQ2chhBAVX4nSNBw8ePCeZRRFISsri8uXL/PPP/9w8OBBatSoweDBg++7kaKCSjgNZ/4ELKDTW0aLLNp9gRsZOTR0d2LQI/XKtn1CCCFEKSlRgDV06NASbVuiKAru7u7Mnj0bd3f3EjdOVHC6rO3NngaPgmkXktJV/PDvBQDe7uGPjZVsjSmEEKJyKFGAVadOnXuWsbKywsnJiXr16vHwww/Tv39/meBeFd2MgRPrtK87TTBaZO72KDJy8mhR15XegV5l2DghhBCidJUowNq+fXtptaNQERERLFiwgNOnT5OdnY2/vz+hoaH07t272HVcunSJefPmsXfvXpKTk/Hw8CA4OJgxY8YYnXy/dOlSPvvss0Lre+WVV3j77bfv636qjH3fgpIHD4VAnVYFTl+6kcnKA9rcaO/0kg2dhRBCVC4lCrAeRHR0NL6+JctvtHHjRiZPnoy1tTXt2rXDysqK8PBw3nrrLaKiohg/3viqtPxiY2MZNGgQKSkpNGrUiFatWhETE8Pq1avZvHkzK1asoEmTJgbvOXnyJABdunQx2vvWrFmzEt1HlZN2HY6u0L4Onmi0yFdbz6LOUwhu7E7Hh2T4WAghROVSqgGWWq3mn3/+4ZdffuHIkSOcOnWq2O9NSkpi+vTpODg4sGLFCgICAgBtoBYaGsr8+fPp1q2b/nhhJk2aREpKCuPGjWPs2LH6npK5c+cyZ84cpk+fztq1aw3ec/r0aSwsLJg1axbOzgX3zBP3sG8O5OVA/fbQoEOB06euprLh+FVA23slhBBCVDalMqs4Li6OmTNn8thjjzFp0iQOHTqEoiglqmPlypVkZ2czZMgQgyDK19eXCRMmoCgKy5YtK7KO2NhYIiMj8fb2NgiuAF577TUcHR2JjIwkOTlZfzw7O5sLFy7g4+MjwdX9yLwJh37Uvi6k9+qLf86gKPBUi9o093Ytw8YJIYQQZcNkPVgajYbt27ezatUqwsPDURQFRVFwdXXliSeeoF+/fiWqb9euXQCEhIQUOBcSEsK0adPYuXNnkXX4+PgQHh5OampqgTk+arUatVoNaCfm65w9e5a8vLx79oyJQhz4HtQZ4NVCO//qLvsv3GDn2USsLS14u4d/OTRQCCGEKH0PHGDFx8ezdu1a1q5dS0JCgr6nqkuXLjzzzDM8/vjj2NqWbOsTRVGIitImn2zcuHGB866urri7u5OYmEh8fDyenp6F1uXm5oabm5vBsaysLD766CPUajUhISEG86x0869cXV1577332Lt3LwkJCXh7e9OnTx9GjhyJnZ1die6nylClwYEF2tfBE+GuoFZRFGb8pd3Q+bm29fBxdyrrFgohhBBl4r4DrD179rBq1Sp27dpFXl4eiqLg5+dHcnIyiYmJLFiw4L4blZKSgkqlwsnJCUdHR6NlatWqRWJiIklJSUUGWPmFhYWxatUqIiMj9dnlZ8yYYVBGN09s5cqVuLm5ERQUhKenJydPnuTbb79l9+7d/Pjjj4W2q0o79CNkJ0PNxtC0T4HT/5yM51hcMg42VozvVjBwFkIIISqLEgVYN2/e5Ndff2XNmjVcvnwZRVFwdnbmySefZMCAAQQGBjJ06FASExMfqFFZWVkAODg4FFpG14uUmZlZ7HrDw8PZs2ePwXViY2MJDAzUH9MFWP379+f999/X977FxcXx+uuvc+zYMWbNmsX06dOLf0NVgTobwudqX3d6CywNt7zJzdPw5T/a3qsRnRpSy8W+rFsohBBClJkSBVhdunRBrVZjZWVFp06dePrpp+nevTv29qb9sLS01M69L05uJI1GU+x6R48ezTvvvEN8fDwrVqxg6dKlhIaGsm7dOn0KiZUrV3L58mUaNWpkMDerXr16zJgxg379+rF27VomT54sQ4U6mjzY/Dakx4NrPWgxqECRX49cJjoxgxqONox6rFE5NFIIIYQoOyVaRZiTk4OdnR3Dhw9n4sSJ9OnTx+TBFYCTk3ZuTnZ2dqFlVCoVQImG6jw8PLC1taVevXpMnTqVwYMHk5mZyaJFi/RlHBwcaNy4sUFwpdO0aVO8vLxQqVScP3++2Net1PJyYf2rcPQnsLCEnp+AlY1BkWx1HrPDtM9rbNeHqGZvY6wmIYQQotIoUYD17LPPYmlpyQ8//EC/fv3o1asXCxYsID4+3qSNcnJywsnJibS0tEKDrISEBEA7F+t+9e3bF7gzsb04dHsqFhX8VRm5ObBuGJxYC5bWMGAJNOtboNjy8FiupWRTx9WeIY82KPt2CiGEEGWsRAHWp59+yt69e/n4449p3bo1sbGxfPPNNzz++OOMHDmSzZs361MfPAgLCwv96sHo6OgC55OTk0lKSsLV1bXICe6HDh0ymkhURze/Kjc3F9CuiJw2bRqTJk0qtM64uDiAYk+sr7TUWfDLC3D6D7CyhcErIKBgKo6ULDXzdmi/h29198PepmDPoBBCCFHZlDjRqIODA/3792flypVs2bKFV155BXd3d/bs2cPEiRM5fvw4APv27StxctH8goODAe3Kv7uFhYWhKAqdO3cuso7k5GTWrFnD4sWLjc7V2r17N4A+55WzszMbN25k48aNnD59ukD5nTt3kpycjI+PD/Xq1SvxPVUaqnRYORCitoK1A7ywGvyfMFr0+13RpGSp8fN05tnWdcu4oUIIIUT5eKBM7vXr12fChAns2LGD77//nu7du2NtbY2iKIwYMYLg4GA++eQTjh07VuK6BwwYgIODA0uXLuXIkSP64xcuXGD27NkAjBw5Un88ISGB6Oho/dAhQOfOnfH29iYmJoaZM2caBFnbt29nwYIFWFlZMXz4cEA7NNmnjza9wP/93/9x8+ZNffnY2Fg+/PBDAMaOHVvi+6k0slNgxbMQ+y/YOsPQ38D3caNF41OzWbI3BoBJPZtgZSkbOgshhKgaLJQH6WYyIjk5mQ0bNvDbb79x9uxZ/UrAunXrsnXr1hLVtXbtWqZPn46lpSXt2rXD1taW8PBwVCoVEydOZNSoUfqyU6ZMYf369fTr188gt9XRo0cZMWIEGRkZ1K9fH39/f+Li4jhz5gzW1ta8//77DBw4UF8+JSWFoUOHcvbsWVxcXGjdujV5eXlERESQk5PDsGHDmDp1qtH2duvWDYBt27aV6D4rjMyb8FM/uHYM7F1hyHqo+3Chxd9df4KfD1zikQY1WDu6fbFWhQohhBBlrTQ+v02+2XP16tV56aWXeOmllzh16hTr1q3jzz//5PLlyyWua+DAgXh5ebFw4UKOHTuGlZUVzZo14+WXX6ZHjx7FqiMoKIgNGzYwf/589uzZw44dO3B1daVXr16MHDnSIAcWaDO4//LLLyxZsoTNmzcTHh6OnZ0drVq1IjQ0lO7du5f4PiqF9ARY3hcSToFjTRj6O9RuUWjxC4nprD6ona/2zhNNJLgSQghRpZi8B8uYnJwcwsLC6N27d2lfqlxV2h6slCuw/Gm4EQXOXvDSRvAoeh/BsSuPsOnENbo1qcXiYW3KqKFCCCFEyZXG5/cDzcEyZsGCBYSGhhocs7W1rfTBVaV1KxZ+fEIbXLnWg+Gb7xlcRV5OZtOJa1hYwKResqGzEEKIqsfkQ4TR0dEcPHjQ1NWK8pB0HpY9DWlXwa0RhG6E6vdePfn539otcfoFedPEq1ppt1IIIYQwOyYPsEQlEX9SO+cqIxE8mkDoBnDxuufb/j2fyN6oG9haWTKhu18ZNFQIIYQwPxJgiYKuHNGmYsi6BV6B2gntTu73fJtGo+h7r4Y82oC6NYq/jZEQQghRmUiAJQxd2q9NIqpKBe9HYMg6cKhRrLduOnGN/66k4mxnzeuPP1TKDRVCCCHMl8kDrDJYlChKy4VdsOo5UGdCg07wwi9g51Kst6rzNMzachaAUZ0b4eZkW5otFUIIIcyayQOsMWPGMGjQIFNXK0rbuS2wegjkqbSZ2QevBNviD/H9fvQKsTcycXe2Y0SnhqXYUCGEEML8mTxNg6+vL23bti1wfOPGjaa+lDCVUxu0GzfnqcD/SXj+lxIFVwDrDmsTyQ7v6IOTnYw8CyGEqNruK8DKzs4mPDyczZs3ExUVVWTZ6Ohohg4dyjvvvHNfDRSlLHINrB0OGjUEPAuDloG1XYmquHwrkwMxN7Gw0KZmEEIIIaq6Enc1/Pnnn3z88cekpKToj3Xv3p3PP/8cBwcH/TGVSsW8efP48ccfUavVslWKOTq8FP54E1Cg1Yvw9BywtCpxNRuOXQXg0YY1qVPd4R6lhRBCiMqvRD1YBw8e5O233yY5OZmaNWsSEBCAjY0NW7du5cMPP9SXO3LkCE899RSLFi1CrVbTqFEjli1bZvLGiwew/zv44w1AgTYj4em59xVcKYrCb0e0w4PPtpbeKyGEEAJKGGAtXrwYgGHDhvHvv/+ybt06tmzZQsOGDdmwYQPx8fFs2LCB0NBQ4uLisLOz46233mLDhg1G52WJcvLvLPh7ivZ1h3HQeyZY3t90vBNXUohOzMDexpInAmubsJFCCCFExVWiT9XTp0/j4eHB22+/rR/y8/LyYuLEiWg0GpYsWcK0adPIzc2lQ4cO/PHHH7z66qvY2NiUSuNFCSkKbP8Ytt3ubXxsCnT/CB5g+Pa3I1cA6NHMC2eZ3C6EEEIAJZyDdfPmTdq1a4e1teHbWrduDcDy5cuxsrJi2rRpDB061HStFKZx9i/Y/aX2dcgH0OnNB6pOnafhj+Pa+Vf9ZHhQCCGE0CtRgKVWq3Fzcytw3NXVVf969uzZhISEPHjLhOldOaT92uK5Bw6uAHafS+RGRg7uznYEP3TvrXSEEEKIqsIkebAsb8/fCQgIkODKnCVf0n71bGaS6n47qh0efLplHaytTJ5STQghhKiwTPqp6OPjY8rqhKnduqj9Wr3+A1eVkqVm66l4QFYPCiGEEHczaYBleZ8r0UQZ0fVgmSDA+uvENXJyNfh5OhNQp9oD1yeEEEJUJhIRVRXqbEi/rn1d3eeBq9MND/YLqitJZIUQQoi7lHhd/b59+wgNDS3xOQsLC0k2Wp5StMlAsXECx4ILFUoi7mYmEbe3xnkmqI4JGieEEEJULiUOsJKSkkhKSirxOenlKGfJ+eZfPeD3YsMxbe9V+0Y1qe0qW+MIIYQQdytRgPXZZ5+VVjtEaTPR/CtFUfIND8rkdiGEEMKYEgVY/fr1K612iNJmogDr+OUULsjWOEIIIUSRSjxEmJeXx4oVK9i6dSs3btygTp069OnTh2eeeaYUmidMxkQB1vrbGzv3DJCtcYQQQojClOgTMicnhxEjRnDo0CEURQEgJiaGffv2sWPHDr755ptSaaQwARMEWOo8DX9EXgNkeFAIIYQoSokCrJ9//pmDBw/i6enJyJEjqV+/PufOneOHH35gy5YtbNiwgb59+5ZWW8WDMEGAtetsIjdvb43TSbbGEUIIIQpVogDrn3/+wd7enlWrVlGnjnZ5/mOPPUZwcDD9+vVj06ZNEmCZI4McWA3uu5r1tye3920lW+MIIYQQRSnRp+SFCxd45JFH9MGVTpMmTQgICODMmTMmbZwwERPkwErJUrP1tHZrHBkeFEIIIYpWogArIyMDV1dXo+fq1q1LcnKyKdokTM0EObA2394ax9/TRbbGEUIIIe6hRAFWbm4uVlZWRs/Z2NigVqtN0ihhYskPvsnz+iO3c1+19paksUIIIcQ9yESaqkA3wb3G/c2/iruZSUSsdmucvq1kaxwhhBDiXiTAqgoecAXh77cnt3fwla1xhBBCiOKQAKsqeIAAy3BrnLqmbJUQQghRaZU4FXdYWBjdunUrcPzWrVsARs+BdrPnsLCwEl0rIiKCBQsWcPr0abKzs/H39yc0NJTevXsXu45Lly4xb9489u7dS3JyMh4eHgQHBzNmzBhq1773Vi+3bt2iT58+JCYmcvLkSaytK2D28gcIsI7FJROTpN0ap1dzLxM3TAghhKicShwtZGZmkpmZWej5K1euGD1e0onRGzduZPLkyVhbW9OuXTusrKwIDw/nrbfeIioqivHjx9+zjtjYWAYNGkRKSgqNGjWiVatWxMTEsHr1ajZv3syKFSto0qRJkXW89957JCYmlqjtZkWdBena9Ar3kwNLl/uql2yNI4QQQhRbiT4xly9fXlrtMJCUlMT06dNxcHBgxYoVBAQEABAdHU1oaCjz58+nW7du+uOFmTRpEikpKYwbN46xY8fqg7y5c+cyZ84cpk+fztq1awt9/2+//caWLVtMd2PlQZcDy9YZHGqU6K05uRr+OH4VgH6tZXhQCCGEKK4SBVht27YtrXYYWLlyJdnZ2YwaNcogiPL19WXChAm8++67LFu2jC+++KLQOmJjY4mMjMTb29sguAJ47bXXWLx4MZGRkSQnJ1O9evUC7798+TIff/wxbdq04eDBgya9vzL1ADmwdp1L5FamGg8XOzr61iyFxgkhhBCVk1lOct+1axcAISEhBc6FhIRgYWHBzp07i6zDx8eH8PBwlixZUmB4Uq1W63N2GcvrpdFomDx5MgAzZsy4n1swHw8w/+q3I9rer74tZWscIYQQoiTM7lNTURSioqIAaNy4cYHzrq6uuLu7k5KSQnx8fJF1ubm54ePjY3AsKyuLDz74ALVaTUhICC4uLgXet2jRIg4fPszUqVOpW7eCD43dZ4CVkqlm2+kEQJtcVAghhBDFZ3azllNSUlCpVDg5OeHo6Gi0TK1atUhMTCQpKQlPT89i1RsWFsaqVauIjIwkNTWVrl27Gu2dOnnyJHPmzKFr164MHDjwge7FLNxngLXpxDVy8rRb4zSrLVvjCCGEECVhdj1YWVlZADg4FJ7Q0s7ODqDI1Yx3Cw8PZ8+ePaSmpuqvExsba1AmOzubSZMm4ezszMcff1zClpup+wyw1h/VDg8+K1vjCCGEECVmdgGWpaW2ScX5UNdoNMWud/To0Zw4cYKwsDCGDRvG/v37CQ0NJTo6Wl/myy+/JDo6mg8++AB3d/eSN94c3UeAdelGJgdjb93eGkeGB4UQQoiSMrsAy8nJCdD2JhVGpVIBFDqEaIyHhwe2trbUq1ePqVOnMnjwYDIzM1m0aBEA//77LytXruTpp5+mZ8+eD3AHZuQ+c2Dpcl919HXHy9W+NFomhBBCVGpmNwfLyckJJycn0tLSyM7Oxt6+4Ad8QoJ28nWtWrXu+zp9+/Zl9erVnDx5EoDPP/8cRVFIS0vj7bffNvqed955BwsLC959913c3Nzu+9plJjlO+9XWpdg5sBRF0Q8P9guS3ishhBDifphdgGVhYUHjxo05duwY0dHRBZKJJicnk5SUhKura5ET3A8dOsSGDRto0aKF0cnqtra2AOTm5gJ35nPt2LGj0Dr//PNPAN58880KEmDlGx4s5jyqo3HJxN7IxMHGSrbGEUIIIe6T2QVYAMHBwRw7doywsLACAVZYWBiKotC5c+ci60hOTmbNmjUcPHiQ/v376+d26ezevRtAX//27dsLrcvf3x+g4u1FmD/JaDGtP6IdHuwZ4ImTbI0jhBBC3Bezm4MFMGDAABwcHFi6dClHjhzRH79w4QKzZ88GYOTIkfrjCQkJREdH64cOATp37oy3tzcxMTHMnDnTYEL89u3bWbBgAVZWVgwfPrz0b6i8lHCCe06uhj8itVvjPCtb4wghhBD3zSy7KLy8vJg2bRrTp09nyJAhtGvXDltbW8LDw1GpVEycONFgk+avvvqK9evX069fP31uK1tbW2bNmsWIESNYvHgxW7duxd/fn7i4OM6cOYO1tTUffPDBPfczrNBKGGDtOJtAcqaaWi52dHyokqyiFEIIIcqBWQZYAAMHDsTLy4uFCxdy7NgxrKysaNasGS+//DI9evQoVh1BQUFs2LCB+fPns2fPHnbs2IGrqyu9evVi5MiRBAYGlvJdlLMSBli64cG+repgZSm5r4QQQoj7ZaEoilLejagsunXrBsC2bdvKuSW3fdkYMhJg1C6o06rIoimZatp8EkZOnobN44NpVkeytwshhKgaSuPz2yznYAkTUGdpgysoVg/WnyeukpOnoYmXiwRXQgghxAOSAKuyKmEOLN3w4LOysbMQQgjxwCTAqqxKkAPr4o0MDl28haVsjSOEEEKYhARYlVUJcmDpt8Z5yB3ParI1jhBCCPGgJMCqrHQ9WDWK3oNQuzWONsCSrXGEEEII05AAq7IqZg/WkUvJXLy9NU7PANkaRwghhDAFCbAqq2LmwNJt7NyruZdsjSOEEEKYiARYlVUxAixVbh5/HL8GyOpBIYQQwpQkwKqMcjIhI1H7uogAa8eZRFKy1HhWs6ODr2yNI4QQQpiKBFiVUcrtHFh21cC+eqHFdMODfVt5y9Y4QgghhAlJgFUZFSMHVnJmDtvPaDO9y+pBIYQQwrQkwKqMirGC8M/Ia6jzFJp4udC0tmyNI4QQQpiSBFiVUTEmuP92RDs8KJPbhRBCCNOTAKsyukeAFZuUwZFLybI1jhBCCFFKJMCqjO4RYMnWOEIIIUTpkgCrMioiwFIUhd+PaQMsGR4UQgghSocEWJXNPXJgHbl0i4s3MnG0la1xhBBCiNIiAVZlo8+B5QoONQqc/u2ItveqV4AXjrayNY4QQghRGiTAqmxuFZ6iQZWbx5+R2q1x+snwoBBCCFFqJMCqbIrIgbXzrGyNI4QQQpQFCbAqmyImuO+NSgK0w4OyNY4QQghReiTAqmyKCLAiYm4C8GijmmXZIiGEEKLKkQCrsikkwErOzOHM9TQAHvFxK+tWCSGEEFWKBFiVTSEB1qHYWwA08nDCw8WurFslhBBCVCkSYFUmORmQqZ1ndXeAFRGrHR5s11B6r4QQQojSJgFWZZKcPwdWdYNTuvlXbWR4UAghhCh1EmBVJoUMD2aocvnvSgoAbaUHSwghhCh1EmBVJoXkwDp6KZlcjUIdV3vq1nAsh4YJIYQQVYsEWJVJIT1YuvlX0nslhBBClA0JsCoTXYBVo4HB4YiYGwC0bSj5r4QQQoiyIAFWZWKkBysnV8PRS8kAtG1YcPNnIYQQQpieBFiViZE5WCeuJKPK1eDmZIuvh3M5NUwIIYSoWiTAqixU6ZCpHQrEtZ7+8AF9eoYaWFjI/oNCCCFEWZAAq7JIuZ0Dy94wB9bBGN0Ed5l/JYQQQpQV6/JuQFEiIiJYsGABp0+fJjs7G39/f0JDQ+ndu3ex67h06RLz5s1j7969JCcn4+HhQXBwMGPGjKF27doFyufk5LBs2TI2bNjAxYsXcXR0JCAggNDQULp06WLCuzMxI/Ov8jSKfoscyeAuhBBClB2z7cHauHEjoaGhRERE0KxZM9q0acPJkyd56623+Pbbb4tVR2xsLAMGDOD333/HxcWFLl264OjoyOrVq+nTpw9nzpwxKK/RaHjjjTeYOXMm8fHxdOjQgaZNmxIREcGrr77K3LlzS+NWTUMfYN1ZQXjmeippqlyc7axpWrtaOTVMCCGEqHrMsgcrKSmJ6dOn4+DgwIoVKwgICAAgOjqa0NBQ5s+fT7du3fTHCzNp0iRSUlIYN24cY8eO1c9Bmjt3LnPmzGH69OmsXbtWX/6XX35h+/btBAQEsGTJEqpXrw7AmTNnePHFF5k7dy69evXioYceKp0bfxBGJrjrtsd5uEENrCxl/pUQQghRVsyyB2vlypVkZ2czZMgQgyDK19eXCRMmoCgKy5YtK7KO2NhYIiMj8fb2NgiuAF577TUcHR2JjIwkOTlZf3zjxo0AvPPOO/rgCqBJkyb06dMHRVH4999/TXOTpmZkiDAiRhKMCiGEEOXBLHuwdu3aBUBISEiBcyEhIUybNo2dO3cWWYePjw/h4eGkpqYWWD2nVqtRq9UAWFlZ6Y8vW7aMmJgYGjduXKC+zMzMAuXNyl0BlqIoHJQM7kIIIUS5MLseLEVRiIqKAjAa6Li6uuLu7k5KSgrx8fFF1uXm5oaPj4/BsaysLD744APUajUhISG4uLjoz9nZ2dGkSZMCQVRYWBh///03jo6O9OzZ8z7vrJTdFWBdSMogKT0HW2tLWtR1LceGCSGEEFWP2fVgpaSkoFKpcHJywtHR+MbEtWrVIjExkaSkJDw9PYtVb1hYGKtWrSIyMpLU1FS6du3KjBkzimzH//3f/xEVFcWFCxeoU6cOM2bMKPb1ylT+HFi3AyxdeoZW9apjZ22mvW5CCCFEJWV2PVhZWVkAODg4FFrGzs4OuDNsVxzh4eHs2bOH1NRU/XViY2MLLR8XF8eWLVu4cOGC/tjZs2eLfb0ypc+BVV2bB4s7868kPYMQQghR9swuwLK01DapOFnHNRpNsesdPXo0J06cICwsjGHDhrF//35CQ0OJjo42Wr5hw4ZEREQQERHB119/TU5ODp988gnfffddsa9ZZoxMcL+TwV0CLCGEEKKsmV2A5eTkBEB2dnahZVQqFUChQ4jGeHh4YGtrS7169Zg6dSqDBw8mMzOTRYsWFdoOV1dXXF1d6d27N3PmzMHCwoKFCxeSkZFRgjsqA3cFWFeSs7iSnIWVpQWtG8gGz0IIIURZM8sAy8nJibS0tEKDrISEBEA7F+t+9e3bF4CTJ08Wq3zr1q2pX78+mZmZRQ4tlotbsdqvt5OM6uZfNa9TDWc7s5tmJ4QQQlR6ZhdgWVhY6FcPGhu+S05OJikpCVdX1yInnB86dKhAItH8bG1tAcjNzQUgIyODGTNmMHny5ELrvPs9ZuOuHqyIWBkeFEIIIcqTWXZvBAcHc+zYMcLCwgpkaw8LC0NRFDp37lxkHcnJyaxZs4aDBw/Sv39//dwund27dwPo63dwcOC3334jJSWFwYMH8/DDDxuUj4uLIyYmBltbW/PL5H53gCUJRoUQFYiiKKjV6hLNqxXCGEtLS2xsbIo1j7u0mWWANWDAAH744QeWLl1KcHAwrVu3BuDChQvMnj0bgJEjR+rLJyQkkJaWhouLi37YsHPnznh7exMTE8PMmTN5++239UHW9u3bWbBgAVZWVgwfPhzQflMGDRrEokWL+N///sePP/6Ih4cHANevX2fChAnk5uby0ksv6eeJmY18AdaNdBVRCemA9GAJIcxbXl4eSUlJpKWl6ZM/C/GgbGxscHFxwd3dvVyTg5tlgOXl5cW0adOYPn06Q4YMoV27dtja2hIeHo5KpWLixIk0adJEX/6rr75i/fr19OvXT5/bytbWllmzZjFixAgWL17M1q1b8ff3Jy4ujjNnzmBtbc0HH3xg0EP2+uuvc+zYMQ4ePEjPnj1p3bo1arWayMhIMjMz6dSpE2+//XaZP48iqdIgS9tjRfV6HIy6BYCfpzM1nGzLsWFCCFG4vLw84uLiUKlUuLq64uzsjJWVlVn0PIiKSVEU8vLySE9PJzk5maysLOrVq1duQZZZBlgAAwcOxMvLi4ULF3Ls2DGsrKxo1qwZL7/8Mj169ChWHUFBQWzYsIH58+ezZ88eduzYgaurK7169WLkyJEEBgYalLe3t+fHH39k+fLlbNiwgQMHDmBtbU3jxo159tlnGThwoPltlZNsmAMrIuYKIMODQgjzlpSUhEqlon79+kXmPRSipJydnXF1deXSpUslSkhuahaKoijlcuVKqFu3bgBs27at7C569m9YNRi8WsDof+kzZw8nrqTwzXOt6NvKu+zaIYQQxaQoCtHR0Tg7O+Pl5VXezRGV1LVr18jIyMDX1/eePaOl8fltdqsIRQnlm3+Vlq3m5NUUQHqwhBDmS61Wo1arcXZ2Lu+miErMxcVF/7NWHiTAquiSL2q/Vm/A4Yu30ChQz82B2q7S5S6EME+61YJmN+VCVCq6n6/yWp0qAVZFp+vBqtGAg7fzX7X1qVmODRJCiOKRCe2iNJX3z5cEWBVdviFC2eBZCCGEMA8SYFV0twMslXNdjsdp51+1kQBLCCEqjfJYiybr3x6cBFgVWb4cWCfSXMjJ0+DhYodPzeJvgi2EEMJ8/fnnn0yYMKHMrqdSqZg7dy7ff/99mV2zspIAqyLTDQ861CD8inaVRFsft3IfdxZCCPHgDh48yMSJE7l+/XqZXXPRokXMmTOHrKysMrtmZSUBVkWWf/5VrOw/KIQQlUl5rH6ToUHTkQCrIrsdYGlc63PkonaLHAmwhBBCiPInAVZFdjvAumHtSUZOHtXsrfH3dCnnRgkhhHhQU6ZMITQ0FIAjR47g7+/P0KFD9edjY2OZMmUKnTt3pnnz5nTu3Jlp06Zx5cqVAnWpVCrmzZtHv379aN26NUFBQfTr148FCxYYDAU+/vjjzJ07F4AFCxbg7+/PnDlzSvlOKy8JsCqy20lGz+doe63a+LhhaSnzr4QQoqILCgqiQ4cOALi5udGnTx/9f4eHh9OvXz/Wr1+Pq6srjz/+OK6urqxbt45nn32W//77T1+PoiiMGTOGb7/9lhs3btCuXTvatWtHXFwcX3/9NaNGjdIPC4aEhODn5weAn58fffr0wd/fv4zvvPIw282eRTHc7sE6nKLttZL0DEIIUTkMHjwYHx8f9u3bh4+PDzNnzgTg1q1bvPnmm6hUKmbPns0TTzyhf8/q1at57733ePPNN9m8eTO2trYcPnyYvXv30rZtW5YsWYKNjQ0AN2/eZNCgQURERBAREUG7du149913mTNnDufOnePxxx/nrbfeKpd7rywkwKrIbgdYuxO02+LI/CshRGWhKApZ6rzybkaJOdhYlepK7rVr15KcnMzQoUMNgivQBmU7duxgx44dbN26lSeffJKEhAQAPDw89MEVaHvFPvroI65du0a9evVKrb1VmQRYFVV2KmRpJ7afzqqOg40Vzeu4lnOjhBDiwSmKwoAF4Ry+vXinInmkQQ3Wjm5fakHWgQMHAGjXrp3R88HBwezYsYMDBw7w5JNPEhQUhI2NDZs2bSI9PZ3u3bsTHByMl5cX7du3L5U2Ci0JsCqqlDgAVDaupGc70qF+dWytZUqdEKJykNmkxl27dg2A119/vchyutxZtWvX5osvvmD69Ons2rWLXbt2AdC4cWO6d+/Oc889h6enZ+k2uoqSAKuiuj08GG+p/R9DhgeFEJWFhYUFa0e3lyFCI/LytM+ka9euODs7F1ruoYce0r/u3bs3wcHBbNu2jd27d3PgwAHOnz/P+fPnWbp0KT/++COtWrUqtTZXVRJgVVS3A6yo2ysI2/pIgCWEqDwsLCxwtJWPqLvVqlWL2NhYQkND9asKi8PFxYVnnnmGZ555BoCTJ0/y1VdfsWfPHmbPns3SpUtLp8FVmIwpVVS6AEtdE2tLC4Lq1yjnBgkhhDAlYz1hbdq0AdAP9d3tiy++4JlnnmHNmjUALF68mK5du/L7778blAsICGDSpEnAnWHHwq4p7o8EWBXV7RxYlxV3Auu64mBrVc4NEkIIYUp2dnYApKen648NHjwYR0dHVqxYwaZNmwzKb9++neXLl3PmzBkCAwMBaNCgAVevXuW7774jMTFRX1ZRFDZu3AhAixYt9MdtbW0LXFPcH+l/rahu6QIsD5l/JYQQlVDdunWxsrLi3LlzvPTSS/j7+/Puu+/y+eefM2HCBCZMmMC8efNo1KgR165d0ycYfffdd2natCkA3bp1o3v37mzdupXu3bvTunVrnJycOHfuHLGxsbi7u/PGG2/or+nj4wNo00Fcv36dLl26MHDgwDK/98pAerAqqttDhJcVD9pJgCWEEJVOzZo1+eSTT6hbty6HDx9mx44dAPTo0YNff/2Vp59+mrS0NHbu3ElSUhJdu3Zl+fLlvPTSS/o6LCws+Oqrr5g4cSI+Pj4cOXKEnTt3otFoGDp0KL///jt169bVlw8JCWHYsGE4Ojqye/duDh8+XOb3XVlYKLJ1tsl069YNgG3btpXuhbJTYEZ9AJqrFrP3vb64Otjc401CCGEesrOziYmJoWHDhtjb25d3c0QlVZKfs9L4/JYerIooWZsD66biTD0vTwmuhBBCCDMjAVZFlG94sK2PrB4UQgghzI0EWBVR/gCrYc1ybowQQggh7iYBVgWkSooBtAFWm4bSgyWEEEKYGwmwKqCUa9EAZDl6U8tFJogKIYQQ5kYCrApIczsHVrXavuXcEiGEEEIYIwFWBeSUdRUA74ZNyrklQgghhDBGAqwKJiv1Fi6KdguDZk0Cyrk1QgghhDBGAqwK5uzZkwDcohrenu7l3BohhBBCGCMBVgVzMfo0AKn2tWXXcyGEEMJMSYBVwaRci9K+cK1fvg0RQgghRKGsy7sB9xIREcGCBQs4ffo02dnZ+Pv7ExoaSu/evYtdx6VLl5g3bx579+4lOTkZDw8PgoODGTNmDLVr1y5QPicnh6VLl7Jp0yYuXryIoijUr1+f3r178/LLL2NnZ2fKWyy2nFyNdgWhJbh4NSqXNgghhBDi3sy6B2vjxo2EhoYSERFBs2bNaNOmDSdPnuStt97i22+/LVYdsbGxDBgwgN9//x0XFxe6dOmCo6Mjq1evpk+fPpw5c8agfFZWFqGhocyaNYsrV67QunVrHnnkEa5evcrs2bMZOnQomZmZpXG79/Tf1RRqK4kAVK/zULm0QQghhBD3ZrY9WElJSUyfPh0HBwdWrFhBQIB2xVx0dDShoaHMnz+fbt266Y8XZtKkSaSkpDBu3DjGjh2rn7c0d+5c5syZw/Tp01m7dq2+/Pfff8/Ro0dp3bo18+bNw83NDYAbN24wZswYjh8/zty5c5k8eXIp3XnhImJuEmyhDbAsazQo8+sLIYQQonjMtgdr5cqVZGdnM2TIEIMgytfXlwkTJqAoCsuWLSuyjtjYWCIjI/H29jYIrgBee+01HB0diYyMJDk5WX/8t99+A+DDDz/UB1cANWvW5L333gNg06ZNprjFEouIuUnd2wEW1WUOlhBCiKpDUZTybkKJmG2AtWvXLgBCQkIKnAsJCcHCwoKdO3cWWYePjw/h4eEsWbKkwIo7tVqNWq0GwMrKCoCMjAzq169PkyZNaNy4sdH6ABITE0t6Ow8sT6NwJjYOV4vbw5Ou9cq8DUIIIcrOnDlz8Pf35+uvvy6V+h9//HH8/f25ePFiscp//fXX+Pv7M2fOnFJpT2Hy8vJYuXIln3zySZle90GZ5RChoihERWlXyxkLdFxdXXF3dycxMZH4+Hg8PT0LrcvNzc2gJwq086w++ugj1Go1ISEhuLi4AODk5MSKFSsKrevEiRMARV6vtJy9noar6jrYgeJYEws75zJvgxBCCFHW/vjjDz788EP69OlT3k0pEbMMsFJSUlCpVDg5OeHo6Gi0TK1atUhMTCQpKanYAU9YWBirVq0iMjKS1NRUunbtyowZM4r1Xo1Gw+zZswHo2bNnsd5jSgdj7wwPWlSX+VdCCCGqBo1GU95NuC9mOUSYlZUFgIODQ6FldKkSSrKiLzw8nD179pCamqq/TmxsbLHe+9lnn3Hs2DHc3d0ZNWpUsa9pKjL/SgghhKg4zDLAsrTUNqs4mcpLEtmOHj2aEydOEBYWxrBhw9i/fz+hoaFER0cX+h5FUfjss89Yvnw5dnZ2zJ49u8CQY2lTFIUDMTepa5GkPSABlhBCVCm7du3ixRdfJCgoiDZt2jBixAiOHz9eoFxUVBSTJ08mODiY5s2b06lTJyZNmqSfdlMcCQkJfPDBB3Tt2pUWLVrw7LPPEhYWds/2jRgxgrZt2xIYGEjPnj358ssvDRaRAVy+fBl/f386d+5stJ6hQ4fi7+/Pvn379P89depUQDtU6O/vz5QpU4p9L+XJLAMsJycnALKzswsto1KpAAodQjTGw8MDW1tb6tWrx9SpUxk8eDCZmZksWrTIaPmcnBzefvttli5dioODA/Pnz6dNmzYluBPTiL2RSVK6ivqWEmAJIURV888///Dqq69y48YNOnbsSPXq1dmzZw8vvvgip06d0pfbvn07zz77LBs2bKBGjRp069YNNzc3Nm7cSP/+/dmxY8c9r3X58mUGDRrEzz//jK2tLV26dEGlUjF27NhCg6yvv/6aUaNGER4ejr+/P127diUrK4sffviBZ599lri4uPu+9w4dOhAUFARA3bp16dOnj/6/zZ1ZzsFycnLCycmJtLQ0srOzsbe3L1AmISEB0M7Ful99+/Zl9erVnDx5ssC5W7duMXbsWA4fPkz16tVZsGBBuX1TI2JuAPCQ7Q3IBWQOlhCislMUUJdPUucHYuMIJt4nNiYmhokTJ/LKK69gYWGBWq3m9ddfZ+fOnfz000989tlnJCYmMnHiRHJycpgxYwb9+vXTv3/dunX83//9HxMnTuSvv/4qct7yxx9/zLVr13jhhRf4v//7P6ysrFAUhW+//Zb58+cXKL99+3YWLFiAm5sbixYtonnz5oC2g+LDDz9k7dq1vPHGG/z666/3tX/umDFj8PT05OjRowQFBTFz5swS11FezDLAsrCwoHHjxhw7dozo6OgCyUSTk5NJSkrC1dW1yB+UQ4cOsWHDBlq0aMHAgQMLnLe1tQUgNzfX4Hh8fDyhoaHExsZSv359Fi1apE/RUB4iYm4B6LO4Sw+WEKJSUxRY0hPiDpR3S0qu3qPw8t8mDbKaNGliMPfXxsaG0NBQdu7cydmzZwFYvXo1mZmZPPvsswbBFcCAAQM4fPgwv/32G6tWreLNN980ep3r16+zY8cO3N3dmTp1qj6FkYWFBePHj2fbtm366+ksXboUgMmTJ+uDK9B+vr7//vscPHiQkydPsn//ftq3b/+gj6JCMcshQoDg4GAAo12SYWFhKIpS6BiuTnJyMmvWrGHx4sVG52rt3r0bwCCAS01N5aWXXiI2NpbAwEBWr15drsEVwOVbmVQjA/u8NO2B6pIDSwhR2Zm2F6giMzZ64uXlBaBftHXw4EGg8FXuuv17IyIiCr2O7tyjjz6q74DQsbCwoFu3bgbHcnNzOXLkCJaWlnTv3r1AfdbW1vTo0QOAAwcqYLD8gMyyBwu0EfcPP/zA0qVLCQ4OpnXr1gBcuHBBny5h5MiR+vIJCQmkpaXh4uKiHzbs3Lkz3t7exMTEMHPmTN5++239BHpdt6aVlRXDhw/X1/PBBx8QExPDQw89xNKlS3F2Lv98U//rE8CVM+mwG3B0B1un8m6SEEKUHgsLbS+QDBECUK1atQLHrK21H9+6zgPdtBlvb2+jddStWxcoOlG2ro7CRoZ0degkJyejVqupUaNGoZ+VxbluZWW2AZaXlxfTpk1j+vTpDBkyhHbt2mFra0t4eDgqlYqJEyfSpEkTffmvvvqK9evX069fP31uK1tbW2bNmsWIESNYvHgxW7duxd/fn7i4OM6cOYO1tTUffPCBvgcrKipKvw1OtWrVeP/99wtt3xdffKEP1kpbszrVaJaindQvw4NCiCrBwkL+mLytOHOX7rWNjC4Qu7tnqiTX0Q0ZFveaoM3Cfq/rGitfGZhtgAUwcOBAvLy8WLhwIceOHcPKyopmzZrx8ssv67sd7yUoKIgNGzYwf/589uzZw44dO3B1daVXr16MHDmSwMBAfdl///1X/wNz5MgRjhw5Umi9M2bMKLMAC4DkS9qvEmAJIYS4S61atYiJieHKlStGd0DRreSrWbNmoXXoeq6uXr1q9Lyuh0unevXq2NjYkJKSQnp6utFerLuvq/vcLCzFUlpaWqHtq2jMOsAC7Vws3XysosyYMaPQrOz16tXjs88+u2cdw4cPNxguNCsSYAkhhChEmzZtOHDgAP/88w9dunQpcP6vv/4CoG3btoXW8eijj2Jpacm+ffvIyMjQp0zS0e0RrGNjY0NQUBARERFs3bq1wOT63Nxc/Tzqdu3aAXdSK6WkpKBWq7GxsdGXT01N5cKFCwXadT+rD82B2U5yF3eRAEsIIUQhBg0ahKOjI+vXr2f9+vUG53799Vc2bNiAo6NjgSAoP3d3d5588klSUlKYNm0aOTk5+nNLly7l0KFDBd7z0ksvAdppM/lTHqnVaj744AMuXbpEkyZNePjhhwFtr5eXlxc5OTn88ssv+vIqlYr33nuvwKp+uLNzS3p6enEehdkw+x4scZsuwKrhU67NEEIIYX48PT35/PPPmTBhAlOmTGHp0qU0bNiQmJgYzpw5g4ODA1988UWhk+B13n33Xc6ePctff/3F0aNHadmyJXFxcZw6dYqgoCCOHj1qUD4kJISXX36ZJUuWMHDgQB5++GFq1KjB8ePHuX79Ot7e3nz99dcGU2pGjhzJxx9/zMcff8ymTZtwd3fnyJEjqNVqHn/8cbZv325wjQYNtLkfd+7cyauvvkpQUBCjR4820ZMrPdKDVVFID5YQQogi9OjRg3Xr1vHUU09x48YNwsLCSE1NZcCAAfz6669GUynczc3NjZ9//plRo0ZhY2PDjh07yMnJ4ZNPPmHQoEFG3/POO+8wf/582rVrx5kzZ9i5cydOTk6MGTOG9evX06hRI4PyQ4cO5fPPP6d58+acOnWKgwcP0rZtW3777Td8fX0L1B8QEMDEiRPx8PBg7969+m10zJ2FUpxlAKJYdDlCtm3bZtqKs5Lh89vZ29+9BrbF3x5ICCHMTXZ2NjExMTRs2NDoTh1CmEJJfs5K4/NberAqAl3vlZOHBFdCCCFEBSABVkUgw4NCCCFEhSIBVkUgAZYQQghRoUiAVREkX9R+lQBLCCGEqBAkwKoIpAdLCCGEqFAkwKoIMm5vklndp1ybIYQQQojikUSjFUGHcXBhJzR6rLxbIoQQQohikACrImjWV/tPCCEqEUnDKEpTef98yRChEEKIMmVtrf3bXqVSlXNLRGWm+/nS/byVNQmwhBBClClra2ucnJy4efMmeXl55d0cUQnl5eVx8+ZNnJycyi3AkiFCIYQQZc7d3Z24uDhiYmJwdXXFwcEBKysrLCwsyrtpooJSFIW8vDyysrJISUlBo9FQu3btcmuPBFhCCCHKnKOjIw0bNiQhIYFbt26RlJRU3k0SlYSVlRWOjo7UqlULW1vbcmuHBFhCCCHKha2tLXXr1kVRFNRqNRqNprybJCo4S0tLbGxszKInVAIsIYQQ5crCwqJcexqEKA0yyV0IIYQQwsQkwBJCCCGEMDEJsIQQQgghTEwCLCGEEEIIE5MASwghhBDCxCTAEkIIIYQwMQmwhBBCCCFMTPJgmVBCQgJ5eXl069atvJsihBBCiGK6du0aVlZWJq1TerBMyM7Ortw2lRRCCCHE/bG2tsbOzs6kdVooiqKYtEYhhBBCiCpOerCEEEIIIUxMAiwhhBBCCBOTAEsIIYQQwsQkwCoDERERvPzyy7Rv356goCCee+45Nm/eXKDcjRs3eOONN2jdujUdOnRg4sSJJCYmGq3z66+/JigoiKSkpNJuvlnSaDSsXr2awYMH07p1awIDA+nZsydffvklqampBcrHxcUxefJkunbtSosWLXjiiSf4/vvvUavVBcrm5eUxc+ZMOnbsSOvWrRk6dCgnTpww2o69e/fi7+/Prl27TH6P5iwnJ4c+ffrg7+/PxYsXC5xPSkriww8/pHv37rRo0YJu3brx5Zdfkp6ebrS+JUuW0LVrV1q1asXAgQPZs2eP0XLR0dE0a9aMlStXmvR+zNH169d577336Nq1K82bN6djx468/fbbXLp0qUBZed4PbvPmzTz33HMEBQURGBjIk08+ydy5c1GpVAXKyvO+PxERETRp0oS1a9caPZ+ens7XX39Nr169aNGiBZ07d+Z///sfN27cKLTOrVu38vzzz9OmTRseeeQRhg8fTnh4uNGyFy9eZMSIEbRs2ZLHHnuMDz74oNDv2cSJE+nSpYvR739xyST3UrZx40YmT56MtbU17dq1w8rKivDwcHJychg7dizjx4/Xlx0+fDj79u2jVatW2Nvbs3//fpo0acKvv/5qsDoxPj6enj17MmLECMaNG1cet1WuNBoN48ePZ+vWrTg4OBAYGIijoyORkZHcvHmTBg0a8PPPP+Pu7g7A+fPnefHFF0lJSaFly5Z4enpy6NAhbt68Sfv27fnhhx8Mnu93333H7NmzqV+/Pg899BD79u3DwsKCTZs24e3trS+nKArPPvsszs7O/PTTT2X+HMrTjBkz+PHHHwHYsmULDRo00J9LSEjgueee48qVK/j5+dGwYUNOnDjB1atX8fPzY9WqVTg7O+vLb9y4kUmTJlGrVi0CAwM5ePAg6enp/PLLL7Rs2dLguq+99hpRUVFs3ry5Uq/YPXXqFMOHDyc5ORlfX18aNWrEmTNniIuLo0aNGvz666/6n0V53g9u1qxZLFy4EBsbGx555BHs7e05fPgwqamptGzZkuXLl2Nvbw/I875fFy5cIDQ0lMTERD7++GMGDhxocD49PZ3Q0FBOnjxJ/fr1adq0KefOnSMmJgZPT0/WrFmDl5eXwXsWLFjA119/jaOjI+3atSM7O5uIiAg0Gg0ff/wxAwYM0JfNzc2lT58+XLhwgfbt25OZmcnx48fp0qUL33//vUG9J0+epH///nz66ac8++yz93/Tiig1iYmJSosWLZRWrVop//33n/54VFSU0qFDB8Xf319//MyZM4qfn5/yxhtv6Mt9/vnnip+fn7Jjxw6DeqdNm6a0b99eSU9PL4vbMDtr1qxR/Pz8lJ49eypxcXH642lpacqrr76q+Pn5KePHj9cf79evn+Ln56esXr3aoOyLL76o+Pn5KT/++KNB/W3btlW6dOmiZGdnK4qiKDt37lT8/PyUGTNmGJTbsGGD4ufnpxw7dqwU7tJ87du3T/H391f8/PwUPz8/JTY21uD82LFjFT8/P+Wrr77SH1OpVMobb7yh+Pn5KR999JFB+X79+imtWrVSbty4oSiKopw7d07x9/dXxo0bZ1Du0KFDip+fn7Jp06ZSujPzoFKplF69eil+fn7K999/rz+em5urvP/++4qfn58yZswY/XF53g/mzJkzir+/v9K2bVvl3Llz+uO3bt1S+vbtq/j5+SkLFy7UH5fnXXL79u1T2rdvr/+dsWbNmgJlPv30U8XPz0+ZPHmyolarFUVRlLy8PP3x0aNHG5Q/ffq04ufnp3Tq1Mngc+DgwYNKq1atlBYtWijXr1/XH9+2bZvi5+enfPHFF/pj48ePV/z8/Ay+74qiKMOGDVOeeuopJS8v74HuW4YIS9HKlSvJzs5myJAhBAQE6I/7+voyYcIEFEVh2bJlAMTExADQpk0bfbl27doZnANtF/Jvv/3Ga6+9hpOTU1nchtn57bffAJgyZQp169bVH3d2dubTTz/FwsKCbdu2kZ2dTXh4OCdPniQoKIhBgwYZLbts2TKU2x25N2/eJDk5mRYtWuhzorRt2xYw/D7k5OQwe/ZsevbsWeCv0MosNTWVqVOn0qBBAzw8PAqcv3jxImFhYdSuXdugd9XW1paPPvoIZ2dn1q5dS0ZGhv5cTEwMvr6+uLm5AdC4cWNq1Khh8LwBvvzyS5o3b84TTzxRSndnHv7++28uXLhAz549GTVqlP64lZUVkydPpk6dOly5coW8vDx53iawb98+FEXhiSeeoHHjxvrj1atXZ+TIkQAcPHgQkJ/vkrpx4wbvv/8+L7/8MikpKdSpU8doufT0dNasWYODgwPvvvuuvvfO0tKSyZMnU69ePbZv324wPL5kyRIAxo4da/A5oBsmzM7O5pdfftEf1z1v3e/z/K/zfy/27NnDvn37mDBhApaWDxYiSYBVinTzckJCQgqcCwkJwcLCgp07dwJQrVo1AIPxXt3/pPkDqZkzZ+Lt7c3gwYNLq9lmz9XVlUaNGhkNbNzc3HB1dUWtVnPr1i399+Dxxx8vULZ+/fr4+/tz9epVzp49C2gDL0tLS4PvQ2ZmJmD4ffj555+Jj4/nrbfeMum9mbsPPviAhIQEvvjiC2xtbQuc3717N4qi8NhjjxUY4nBxcdF34+/fv9/geP7nrSgKWVlZBs9769atHD16lEmTJmFhYVEKd2Y+/vnnHwCGDRtW4JyDgwM7duxgw4YNWFlZyfM2Ad39Xb9+vcC5mzdvAtrfOSA/3yW1YMECVq1aRf369Vm2bJm+0+BuERERZGZm8sgjj+iftY6VlRVdu3YF0H9eQtGfr927dy9QXvcZm52drT929+92RVGYOXMmbdq00V/zQUiAVUoURSEqKgrA4K8iHVdXV9zd3UlJSSE+Ph5/f3/s7OxYu3YtcXFxJCYm8tNPP2FhYaEPJA4dOsT27dt56623sLGxKdP7MScLFizgr7/+okaNGgXOXbp0ieTkZGxsbHBzc+P8+fMA+Pn5Ga3roYceAuDcuXOA9i/RJk2asG/fPg4cOEB6ejrfffcdAK1btwYgLS2N7777jgEDBtCwYUOT35+5+vPPP/nzzz8ZNWpUob12JX3eAC1atODcuXNs3ryZzMxMfvjhB7KysggKCgK0cydmzZpFcHAwjz76qClvySydPHkSS0tLmjdvTkJCAkuWLGH69Ol8/vnnREREGJSV5/3ggoODsbCwYMeOHXzzzTckJiaSnp7O5s2b+fbbb7G1tWXo0KGAPO+SqlevHv/73//4888/eeSRRwotV9RnJRR8romJiSQnJ1OjRg39XNv8fH19sbCwICoqiry8PAACAwOxsLDgp59+IjExkUuXLrFu3Trs7e1p2rQpoJ0vd/r0aSZNmnT/N51P5ZpFZ0ZSUlJQqVQ4OTnh6OhotEytWrVITEwkKSmJgIAAxo0bx8yZMw0i8pdffhl/f39A23tV2bqQTe2rr74CoGvXrtjZ2ZGQkABgdDgr//H8q1SmTp3KK6+8QmhoqP5Y/iHGhQsXkpOTw+uvv14q92COrl27xgcffEBAQABjx44ttNz9PO8JEyYQERFh0Bvo4+PDmDFjAFi3bh0XL15k9uzZD3obZi8nJ4dr165Ro0YNdu3axTvvvGMw3LRkyRL69evHxx9/jLW1tTxvE/D19eXjjz/mk08+Yf78+cyfP19/rnHjxnz66ae0aNECkJ/vksr/O7Qouudaq1Yto+d1z1W3av5e3wdbW1tcXV1JTk4mLS2N6tWr06RJE55//nl+/vlnOnXqBGh7L6dNm4abmxs5OTl888039OjRw2TTPiTAKiVZWVmAtku/MLo5PrpuyldeeYUWLVqwc+dONBoNHTt2pHPnzoB2pdbRo0dZunSpvgtZrVaTlZWl7/qs6pYuXcpff/2Fg4MDb775JnDn+6BbAXQ33fcg/4dY27Zt2bBhA3/88Qc3btwgICCAfv36YW1tTXx8PMuXL2f48OH6/7k1Gg1paWm4uLg88Ji9OVIUhXfeeYfs7Gw+//zzIntP7/W8dcfzP+9GjRqxadMmfv31V65evUrjxo0ZMGAADg4OZGZmMnfuXJ566imaNGmib09qaipOTk6VbqWVbsl4ZmYmEyZMoGvXrowfPx4vLy8OHjzI//73P9avX0+tWrWYMGGCPG8Tefjhh+nUqRO7d++mRYsW2NraEhkZSVRUFMuWLeOzzz7D1tZWnncp0X0G3uu56sqV9PO1evXqAPzvf/+jY8eO7N+/Hzs7O0JCQvQ9iStXriQ+Pp4ffvhBX4dKpUKtVhusCi2JqvHdKwe6D9rijKdrNBr963bt2hUYp9Z1IXfq1In27dujVqv56KOPWL9+PTk5OTRo0IDp06cTHBxs2puoQJYuXcpnn32GhYUFn3zyCb6+vgD63dHv9X1Q7spW4uPjYzQFxjfffIOjo6N+8uvKlSv59ttvSU5OxtXVlVdffZURI0aY4pbMxo8//siBAwd45513Cu3C17nf5+3h4cHo0aONXjslJUUfMG/ZsoWPP/6Y+Ph4HB0def7555k4caL+uhVdTk4OoP3F3rZtW7799lv9ua5du+Lu7s7AgQNZunQpI0eOlOdtApGRkQwfPhx3d3c2bNiAj48PALdu3eLtt9/mzz//xMrKii+++EKedykp6XO9389X0M7ZunveVmpqKgsWLKB///40atSI9PR03n33XcLCwsjLy6NZs2Z8+OGHBAYGFvueQOZglRrdpLn8E+ruppv4WNgQos7atWu5ePEib7/9NqAdotIl2dTlABk3bhxXr141UesrDkVR+OKLL/jss8+wsrLis88+48knn9Sf1z3bwpLFFfd7ANr5F7///jtjxozB2dmZQ4cO8eGHHxIQEMDs2bPp0KEDX3zxBX///bcJ7sw8nD17lq+//po2bdoYnXR9t3s9b93/D8V53jdv3mTx4sW88MILeHt7ExcXx4QJE3B3d+frr7+mX79+LF68mKVLlxb7fsxd/r/gX3zxxQLnAwMDCQwMRKVScfToUXneJvDpp5+Snp7ORx99pA+uAGrUqMGXX36Js7Mzf/zxB1euXJHnXUp0z6uwz8u7n+u9ykPJfrcvXLgQlUql/6P6008/ZevWrYwZM4YvvviClJQUxowZU2hS0sJID1YpcXJywsnJibS0NLKzs412fd5r3Bm03Zvz5s3jqaee0k/EW7FiBX5+fvzf//0foF0N179/f1atWsXEiRNL4W7MU3Z2NpMmTWLLli3Y29sza9asAn+ZeHp6curUKRITE/XPLz9dpvzCxvLzmzVrFrVr1+a5554D4KeffsLS0pKZM2fi5uZGt27dCA8P58cff6RXr14muMPy99VXX5GTk4OFhQWTJ082OHfr1i0APv/8cxwdHRkzZgyenp4Ahe5AUJLnPW/ePCwtLfV/+a9atUrfexsQEEDv3r05ePAgP/74Y6XpNXRxccHGxga1Wm2w9Dw/b29vIiMjuXXrljzvB5Sdnc2xY8ewt7c3Ognbzc2NwMBAwsPDOXPmjDzvUqJ7roXtTHL3c71X+ZycHFJSUrCxsdEPDxbm+vXr/PTTTwwbNgwPDw/S09PZsGEDjz32mD7gsre3Z/z48fz555/63//FIT1YpcTCwkI/nBIdHV3gfHJyMklJSbi6uup/WIxZsmQJycnJ+i7kW7ducfPmTf0QGNxZeXF3XpXKLD09nWHDhrFlyxbc3NxYvny50eW6RX0P4N6rgnQOHjzIjh07ePPNN/XpCS5cuECNGjX0+W1sbW1p0KBBpfo+6OY8RERE8Mcffxj8053btm0bf/zxB0lJSSZ73pcuXWL16tWMHDlSv1r0woULAAY/+w899JB+1VdlYGVlpb+/+Ph4o2V0Hyo1a9aU5/2A0tLSUBQFS0vLQudP6oav1Gq1PO9SonuuutWEd7v7udaoUQMPDw+SkpL0f+jlFxUVhaIoNG7c+J7zYr/55hscHBx45ZVXAIiNjSU3N7fA9wFK/hkrAVYp0s2JCgsLK3AuLCwMRVH0k9iNuXHjBkuWLOGFF17Q/zWrG4POv4eebsJfZZxgbYxarWbUqFEcPXqU+vXrs3r16kJXfeier7HvwaVLlzh37hy1a9fWr9QszJdffklAQABPPfWU/phGoymwl2FWVlal+j789NNPnD171ug/3VYtW7Zs4ezZs7Rr185gybtuebROWloaBw4cwMHBwSDZnzFfffUVNWrU4KWXXtIf082lMPazX5lyBz322GMAhe5XevLkSWxtbWnZsqU87wdUs2ZNqlevTmZmpj6ZaH5paWn8999/ADRt2lSedyl55JFHcHR0JCIigrS0NINzeXl57NixAwsLC4N5xrrX27ZtK1Df1q1bAYr8fAVt2of80z7A+PdBNxRZ0t/tleeTwAzpVoosXbqUI0eO6I9fuHBBvyRXN1namHnz5mFhYaFfzgvaLmt3d3cOHz6s/6tm9+7dQOE5RCqbuXPncvjwYTw8PFixYgX169cvtOwjjzxC06ZNOXToEMuXL9cf101iVBSFl19+uchfYH///TfHjx9n4sSJBuUaN25MamoqR48eBeDy5cvExMRUme+DMd7e3nTt2pXLly/z5Zdf6v8gyMnJ4b333iMjI4PBgwfj4uJSaB2RkZH8/fffjBs3zmCVkO656hIMpqenc/ToUby9vSvVrgbPPfccjo6O/Pnnnwab4mZmZjJt2jQyMzPp168f1apVk+f9gCwtLfXpV9577z2uXLmiP5eens7UqVNJTk7mscceo0GDBvK8S4mDgwP9+/cnIyOD9957T7/YQ1EUvvzySy5fvkxISIhB3sEXXngBS0tLZs+ere/9Azhy5AhLly7Fzs5On7+sMLNmzaJOnTo8//zz+mONGjXCxsaGffv26YOs+/2Mlc2eS9natWuZPn06lpaWtGvXDltbW8LDw1GpVEycONFgK4z8Ll68yJNPPsnYsWMNAiyA77//nq+++gpvb2/8/PzYu3cv9vb2/PPPP/rhqsrq1q1bdO3alaysLJo0aVLkD/yUKVNwd3fn9OnTDBkyhPT0dAICAqhbty6HDh3ixo0bdOnShXnz5hW6HDo3N5cnn3ySOnXq6Dc31jl06BBDhw7F0dGRtm3bcvz4cW7cuMGPP/5Ihw4dTHrf5ujxxx/nypUrBTZ7vnbtGoMHDyY+Pp5GjRrRuHFj/Wa4AQEB/PTTT0V+YAwdOpSkpCT96i2dixcv0rdvX/Ly8ujYsSPnzp3jypUrRjeOrei2bNnChAkTUKvV+Pn5UbduXU6cOEFiYiJNmjRhxYoV+g9xed4PJicnh1GjRhEeHo6NjQ1t27bF2tpaP8+tUaNG/PTTT/qElvK879+UKVNYv359oZs9P//885w7dw5vb2+aN2/O+fPnuXDhAt7e3vzyyy8F5it/8803zJ8/Hzs7O9q3b09OTg4HDhxAo9Hw5Zdf0qdPn0LbEhERwdChQ/nyyy95+umnDc699957rF69Gl9fX2rXrs3evXupV68emzZtMrqDRaEeaCdDUSy7d+9WhgwZorRq1Up5+OGHlcGDByv//PNPke8ZP3680rFjRyUzM7PAOY1Go8yfP1/p1KmTEhAQoDz//PPKiRMnSqv5ZuWff/7Rbxh6r3/5NyG+cOGC8uabbyrt2rVTWrRooTz11FPKDz/8oKhUqiKvt2LFCoNNue+2detWpXfv3kpAQIDSs2dPZePGjSa9X3PWtWtXo5s9K4qiXL9+XZk2bZrSsWNHpXnz5kqPHj2Ur776SklLSyuyTt3G2lu2bDF6/uDBg0r//v2VgIAApUuXLgU26q5Mzpw5o7z55ptK+/bt9c/wm2++UTIyMgqUlef9YNRqtbJixQplwIABSqtWrZTmzZsrTzzxhDJ79myjz1Ce9/155513Ct3sWVEUJSUlRZkxY4bStWtXpXnz5kq3bt2U999/X0lISCi0zo0bNyoDBgxQWrZsqbRr104ZNmyYsn///nu2ZcCAAUrfvn0VjUZT4JxKpVI+/fRTpW3btkqLFi2UV155Rbl48WLxb/Q26cESQgghhDAxmYMlhBBCCGFiEmAJIYQQQpiYBFhCCCGEECYmAZYQQgghhIlJgCWEEEIIYWISYAkhhBBCmJgEWEIIIYQQJiYBlhBCCCGEiUmAJYQQ4r5JrmohjJMAS4gqat++ffj7+99zQ1RRdvz9/fH39yc3N7e8m1Isf/75JxMmTCjvZghhliTAEkIIUWIHDx5k4sSJXL9+vbybIoRZkgBLCCFEiWk0mvJughBmTQIsIYQQQggTkwBLiEouJyeHhQsX0rt3b1q2bElISAgLFy4kLy+v0PdERUUxefJkgoODad68OZ06dWLSpElERUXpy6SlpREQEEBQUBBqtdrg/Zs3b8bf35/27dsXqHvNmjX4+/szb948AKZMmYK/vz+nT59mw4YN9O/fn1atWtG2bVvGjRvH+fPnC9QRHR3NxIkT6d69O82bN+fRRx9l1KhR7Nq1y+j9/P3334waNYqOHTvSvHlzHn74YQYNGsTKlSsL9MQMHToUf39/0tLS+PHHH+nVqxeBgYF07dqVb775htzcXFQqFbNmzaJLly4EBQXRr18//vnnH4N6Dhw4gL+/Px9++CGnT59m+PDhBAUF0b59e1577TVOnjxZ6PO/m0qlYtGiRTz99NO0bNmShx9+mNDQULZv317sOn777Tf8/f1ZunQpX3/9NW3atCEoKMhgDtV///3HpEmTePzxxwkMDKRVq1Y88cQTzJw5k9TUVH25KVOmEBoaCsCRI0eMzuWLjY1lypQpdO7cmebNm9O5c2emTZvGlStXit1mISoy6/JugBCi9KjVal555RX2799P9erV6dy5Mzdv3uSrr77C19fX6Hu2b9/Om2++iUqlwt/fn9atWxMTE8PGjRvZsmULs2fPpmvXrri4uNC6dWsiIiI4fvw4jzzyiL6O/fv3A3Dz5k3Onz9P48aN9ed2794NwOOPP25w3Xnz5rF161aaNGlCcHAwkZGRbNmyhX379vH7779Tr149QBtcDRw4kIyMDAIDA2natCkJCQns2rWLXbt28emnn9K/f399vR9//DE//fQTDg4OtG7dGmdnZy5evMjx48c5fvw4ly5dYurUqQWew6RJk9i1axdt2rShbt267N+/n/nz55OSksL58+c5efIkrVu3JiMjgyNHjjB+/Hjmz59Pt27dDOqJiorihRdewNbWlk6dOnH16lW2bdvGnj17mDNnDo899liR38P09HSGDx9OZGQkbm5uPProo+Tk5HDw4EEOHDjA66+/zrhx44qsI79Vq1YRFxdHx44dSUtLw8fHB4C//vqLiRMnotFoaNWqFc2bN+fGjRscO3aMRYsWER4eztq1a7G0tCQoKIj4+Hj27duHm5sbHTt2NPh5Cg8P57XXXiMzMxM/Pz9atWpFTEwM69atIywsjMWLF9O8efNit1mICkkRQlRaixcvVvz8/JSBAwcqKSkp+uM7d+5UAgICFD8/P2XIkCH64wkJCUqrVq0Uf39/5bfffjOoa+3atYq/v78SFBSkXL9+XVEURfnhhx8UPz8/5dtvvzUoGxISojRt2lTx8/NTVqxYoT+ek5OjBAUFKY899pj+2DvvvKP4+fkpTZs2VTZt2qQ/np2drTz33HOKn5+fMmPGDP3xqVOnKn5+fsovv/xicM0tW7Yofn5+SteuXfXHTpw4ofj5+SmPP/64kpiYaFD+jz/+UPz8/JSWLVsqOTk5+uNDhgzRHz927FiB+nXXuHr1qv7cl19+qfj5+SmjRo3SH9u/f7++/ODBg5Xk5GT9uVWrVil+fn5Kx44dlfT0dP1xXXm1Wl3g+UyYMEHJyMjQH4+JiVG6du2q+Pn5KXv37lXu5ddff9XXv3nzZv3xvLw8RaVSKe3atVMCAgKUgwcPGrwvKipKad26teLn56ccOnSowP0999xzBuVv3ryptG3bVmnatKnBdRRFUX755RfF7//bu/+Yquo/juPPyy/jjkwlLv64hsQtwsZt3YY3zRKiLmloECxcmWLrj2i1ktZiuNyMbIuRbW791dTwuuUPkDCcmyEgaAaXe81qTW+U3YAUL3cacUGZP75/8D1HDveCprftK9/34z/O55wPn3Muymufz/t8uP/+q5mZmVcvXrx43TELcTuTJUIhJrAdO3YA8MEHHzB58mT1+KJFi1i+fHnA+Tt37mRgYIDc3Fxyc3M1bfn5+eTm5uL3+/nyyy8BSE9PB4a3fFB0d3fzxx9/sHjxYgDa2trUNpfLhd/vV68b6cknn2TJkiXq15MmTaKgoABAs0zo9XoBmDlzpub6p59+mnXr1vHee++py359fX1kZWVRXFzM3XffrTk/OzubyZMnMzg4iM/nCxhPXl4eDz30kKb/6OhoAF577TVmzJihttlsNgA8Hk9APxEREWzcuJG77rpLPbZ8+XIWLVqE1+vlm2++CbhG0dPTw969e4mLi6OsrAy9Xq+2zZkzh5KSEgA2b948Zh+jxcXFqZ8NQFhYGL29vSxcuJDVq1drZiIBkpKSePTRRwHo6uq6bv+7d+/m/PnzvPjii5rvA1BQUEBGRgadnZ3j3rcQE4EELCEmqJ6eHjweDwaDgQceeCCgffRSFgy/eg+QlZUVtE8lACmhKSkpidmzZ/PDDz/g9/uB4eUhgJycHOLj49U+AbVGKiMjI6DvkWFGYTAYABgcHFSPpaWlAfD222/z4Ycf0tzczIULFwB46aWXyMrKIixs+L+2BQsWsGnTJp599ln1+qGhIX755ReqqqrUIDY0NHRD45k6dSpAwPNUwmuwfh5++OGAMAjDgQ2Ga7XG0t7ezuXLl0lNTdWEK8Vjjz1GWFgYTqdz3Jq6kZKTkwOOzZw5k4qKCt555x312NWrV+nq6uLAgQNqsAp2f6Mp92O1WoO2P/7445rzhJiopAZLiAmqp6cHgPj4+KDtRqMx4NjZs2cBmDVr1rjXKLNIMDyLZbfbcTgcpKenc/ToUSIiInjkkUdIS0ujrq6Ojo4OTCYTLS0t6PX6oMXvI2d4FOHh4YB2S4DVq1fjdrv5+uuvsdvt2O12oqKisFqtZGdns3TpUvU6GC4Q/+qrr6ivr6ejo4MzZ86o/el0OiD4buTBxqOcP7pNOR5MQkJC0OPTp08Hrj3zYP78809guC4uWDBSDA4O8tdffzFt2rQxz1FMmTJlzLZDhw5RU1PDyZMn6ezsVF9eGO/+Rjt9+jQAb7zxxrjnyf5ZYqKTgCXEBHW9X4oREYH//IMFjZGUYBIVFaUeUwLWt99+S3p6Oq2treqMi9Vqpa6ujtbWVmJiYnC73Tz11FOa6290vIrIyEgqKiooKipSi+CPHTtGS0sLLS0t7Ny5k8rKSqKiojh79iwrVqzA4/EQExOD2WwmMzOT5ORk5s2bR2FhoRpign2fUBgZ9kZSnvVY7XDteZtMJlJSUkIynmDP+cqVK7z++us0NjYSGRnJ3LlzycnJwWQyYbFYsNvt7N2794b6V2bSMjIyiImJGfM8k8l0czcgxG1CApYQE5QyczVWgAg2c2IwGDh16hTd3d2aN/8UnZ2dAMTGxqrH5s2bh16v5+jRo/z66694vV6ef/554NoyUVtbmxpYgi0P3oykpCSKioooKipicHCQpqYm1q9fj8vlYv/+/Tz33HN8+umneDweli5dyoYNG5g0aZKmj5FbD/xblJnE0ZTPRZnJCiYuLg6AlJQUKioqQj+4/6qtraWxsZHk5GQ+//zzgFnP/v7+G+7LYDDw+++/s3LlShYsWBDqoQpx25AaLCEmKIPBgMlkwufz4XQ6A9qbmpoCjin1TaP3dFLs378fGA5ViqioKObPn4/b7VbblWCVkJDA9OnTcTgcNDc3o9Ppgha436jLly/z8ssvs3DhQrXuCiA6OprFixezbNky4Nry07FjxwB49dVXA8LV8ePH1eDwb+5K7nA4ggYUpcj7iSeeGPNa5fNwOByaOjTFjz/+iM1m480337ylP7qsPKe8vLyAcOX3+9X2kc9prBlHZcxj7UlWXl5OTk4Ou3btuunxCnE7kIAlxARWWFgIwPvvv6+ZSWlvb2fr1q0B57/wwgvo9XpqamqoqanRtFVXV1NbW4terw94w1CZlaqsrCQyMhKLxaK2Wa1WfD4fjY2NmM3mgLf5/onw8HDuvPNOvF4vGzdu1BR2nz9/Xt1jy2w2A6g1SQcPHtT043a7effdd9WvL168eNNjuh6/38+6des0BeLbt2/n8OHDJCYmjhuwZs+eTWZmJmfOnGHt2rWaoObz+Vi7di0ej4cZM2b8ozqp0ZTn1NzcrPlD0+fOnWPNmjWcO3cO0D4nJbCODo8FBQXo9Xq2b9/Ovn37NG0NDQ1s27aNEydOkJqaetPjFeJ2IEuEQkxg+fn5fPfdd9TV1fHMM88wf/58BgYGaGtrw2w2qzMTivj4eD7++GOKi4spKSnhiy++IDExkVOnTnHixAmio6MpLy8PKIJXQkJfXx8Wi0XdzgCGA1ZtbS2XLl0KyfJgSUkJTqeTyspK6uvrSUlJYWhoCJfLRX9/P0uWLFGL6AsLC3E6nWzatImDBw9iNBrp6enh+PHj3HHHHRiNRrq6uujt7b3lcY1l2rRpHDhwAJfLhdlsprOzk59//pkpU6ZQXl4etB5tpLKyMjweD/v27ePIkSOkpqai0+lob29nYGAAi8XCmjVrbmmM+fn52O12Dh8+jM1m48EHH6S/vx+Xy8WFCxcwmUx0dHRonpPRaCQ8PBy3282qVatITk6mtLRU8zNUXFzMZ599xr333svp06f56aefACgtLQ1ZTZkQ/6tkBkuICUyn01FRUcH69etJSEjgyJEj/Pbbb7zyyit89NFHQa+x2WxUVVWRnZ2Nz+ejvr6evr4+8vPzqa6uVrcXGCk+Pp65c+cC2uVD0L6uH4qAdc8997Bjxw5ycnK4cuUKTU1NuFwu7rvvPsrKyvjkk08097J582bS0tLo7u6moaEBr9dLbm4ue/bsYcWKFQA0Njbe8rjGMmfOHCorK5k1axaHDh2it7eXvLw8qqqq1Jm28cTGxrJr1y7eeustDAYDDoeD77//nsTEREpLS9m6dasm0N4Mo9HI7t27ycrK4tKlSzQ0NHDy5EmsVitbtmyhvLwc0D6n2NhYNmzYgNFoxOl0atpsNhvV1dUsW7aMv//+m6amJnp7e8nIyGDbtm2sWrXqlsYrxO1Ad/VWFu6FEEIE1draysqVK7FYLOrGrEKI/x8ygyWEEEIIEWISsIQQQgghQkwClhBCCCFEiEkNlhBCCCFEiMkMlhBCCCFEiEnAEkIIIYQIMQlYQgghhBAhJgFLCCGEECLEJGAJIYQQQoSYBCwhhBBCiBCTgCWEEEIIEWISsIQQQgghQkwClhBCCCFEiP0H7Fgj5wBMuEAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sbnn_plot.plot_metric_dependence(metrics, xformatter = ticker.PercentFormatter(1),\n",
    "                                      ylabel='PR-AUC',\n",
    "                            xlabel='downsample rate')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('33_PLOT_average_precision.png'),\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e38578-60a3-4986-bd7f-fd76b992f135",
   "metadata": {},
   "source": [
    "##### History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ffc7e367-f428-4f76-a517-4faa77cf7095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'sba_nn.sba_nn.sbnn_plot' from '/Users/valeriecarey/Documents/projects/2024_05_blog_nn_rand/code/sba_nn/sba_nn/sbnn_plot.py'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(sbnn_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2342a31f-4891-4834-851a-b90b8b729c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sbnn_plot.plot_history_group(history,\n",
    "                                  col_format_func = lambda x:f'{float(x):.1%}',\n",
    "                                  ylabel = 'PR-AUC (validation)',\n",
    "                                  leg_title='down rate')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('33_PLOT_hostory_val_auc.png'),\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9e92ea2f-378d-47a1-b067-2ec715935c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S+Yrm5cdl/ZjWeS6bfnOnfH17HOuU0XRYQogvwNvLhzO7L6ClpaLrpA6aDkdkMYcOHWL8+PHJjhClxM/PjwULFlCzZk2WL1+uXpG+VKlSjB49mg0bNtCnTx8Adu3aRZ48eXB1dUVbWxsdHR0mT55MYGAgFhYWAKxevRp9fX26dOmS/g/4BclIUyZSp2N1mvSoi6IozOg6n7dBIZoOSQjxBaz9OX6UqX7XWhQpVVDD0YispG/fvgwaNIg8efLQvHnzVPfbu3cv0dHRuLi4JNrCp2XLllhaWuLu7q4+5ufnR6FChdQbSltZxY+UPn/+HIDAwEBWr17NsGHD0NPTS4/H+mokacpkBs7vSeGSBQh89orZvZegKIqmQxJCpKNrJ2/xz8GraOto03Vie02HI7KYBw8eMHz4cHbu3EnRokVT3e/q1asAlCtXLtFxlUqFg4MDDx48ICQk/i/yFhYWiUaxgoODATA3Nwdg4cKFWFlZ0aJFi894Es2Q13OZjKGxAT9tGsaQqj9xZvcF9iw5SMuBjTUdlhAiHSiKwtqJfwLQpGc98hez1HBE2YuiKESERWo6jGQZGOmny8Km+/fvT9PozosXLzAyMiJHjhxJzuXLlw+AZ8+eUapUKapVq8bOnTtxd3enfPny/PHHHxQvXpwCBQrw8OFDtm3bxtKlSzPlQq2SNGVCxctb03uGM0uGr2XpiHXY1yyFtb0UigqR2V06fI3rnrfR1dfl+wnfaTqcbEVRFIbVnMitM3c0HUqyylQvyZyTUz470Ujr67CQkBCMjY2TPWdgYABAWFgYAM2bN+fUqVPq4u98+fKxcOFCtLS0mDNnDpUqVaJmzZppikPT5PVcJtVmaFOqNC1PdGQ0v3WZl2H/diSESJ33R5la9G9EnkK5NRxR9pMJBz4yhIQykYRaJy0tLVxdXTl69Cg7duzg0KFD2Nvbc+XKFQ4ePMjIkSMBcHd3p3nz5tSoUYNRo0YluyxBRiMjTZmUSqVi5Oof6Oc4gkc3n7JsxDqGLumr6bCEEGl0bu8/eHvdw8BIn05jW2s6nGxHpVIx5+SUDPsX0PR6PZdWxsbGBAYGJnsuYbkBU1PTRMcLFixIwYL/TmRwdXXl22+/xd7eHi8vL8aNG8fo0aMpV64cv/zyC6NGjWLVqlVf7iHSgSRNmViuvGaMWT+YsY2nsnfZISo2cqRGGydNhyWE+ERxcXGs/Tl+lKn14G/JZZlTswFlUyqVCkNjA02HkSEVKlSImzdv8u7duyTrKr148QItLS0sLVOuwTty5AjXrl1j//79QPyyBIULF6ZXr14A9O7dm9GjRxMQEEDevHm/3IN8Jnk9l8lVbOhIh1GtAHDrvYSAp8n/TUAIkXF57jjPg6uPMcphSPtRLTUdjhBJODg4AHD9+vVExxVF4dq1a5QoUSLFRSpjY2OZPXs2HTt2pEiRIgAEBASo12yCf2fWvXjx4kuEn24kacoCuk/pSMnKNoQEhzLDef4nrfAqhNCs2NhY9erf7X5sQQ5z04/0EOLr+/bbb9HV1WXVqlWJlrrZvXs3AQEBtG3bNsW+7u7uvHjxgoEDB6qP5cuXj2fPnhEXFwfAkydPAD44WpURSNKUBejq6fLTpmEYmhhw3fM2m6a5f7yTECJDOLrxFE+9n2FqbkLbYU01HY4QPH36lN27d3P58mX1sYIFC9K/f388PT3p2bMnW7duZebMmUyYMAF7e3s6deqU7LXCw8OZP38+PXv2JHfufyc3tGjRgoCAAEaNGsX69etZvHgx1atXl6RJfB0FbPIxZHH8EvZ/TN7GjdPeGo5ICPExMdExrP91KwAdRrXC2Cz5Kd1CfE0XLlxg9OjRbNmyJdHxQYMG8csvvxAQEMDkyZM5cOAAHTt2ZNWqVeplB/5r3bp1xMXF0aNHj0THnZycmDp1KlevXmX+/PlUrlyZ33///Ys9U3pRKbKkdLqpX78+EF/wpikzuy3g8IaT5C1iwdLLv2OaK/NshChEdrNv+SHm9l9OzrxmrL+/UIqQhdCQ1H5/y0hTFjN4YW8K2FgS8CSQOX2XyjYrQmRQURFRbJy6A4DO49pIwiREJiBJUxZjZGrIT5uGoa2jjeeO8xxYqblRLyFEyvYtP8xL3yDyFMpN834NNR2OECIVJGnKgkpWLk7PaZ0BWDxsDY9v+2o4IiHE+8JDI9g8PX7CxvcTvkPPIHPt9C5EdiVJUxbVbkQLKjR0IDI8it86zyUqIkrTIQkh/s9j0d8E+78hfzFLGveoq+lwhBCpJElTFqWlpcWYdYPImScHD649ZsXoPzQdkhACCH0bxhbXXQB0/bk9OrqyMYMQmYUkTVmYeb5cjFzzAwC7Fh7g7J6LGo5ICOE+dx8hr95RuFRB6n1fQ9PhCCE+gSRNWZxT0wp8N6wZALN6LibwecbfRVqIrOrtqxC2u+0BoNsvHdDW1tZwREKITyFJUzbQc/r3FC9vzdugEGZ2lW1WhNCUbb97EPY2nGIOVtRs942mwxFCfCJJmrIBPX1dfto0FAMjfa4cu8lWVw9NhyREthPs/5pdCw4A0G1yR7S05I9fITIb+V2bTRQuWZAfFvQCYO3Pf3Lr3F0NRyRE9vLnjF1EhEVSqkpxqraopOlwhBBpIElTNtK4ex3qdKpOXGwc07+fR+ibUE2HJES28NI3iD1LDwLQfUonVCqVhiMSQqSFJE3ZiEqlYtiSPuQrmocXDwOYN3CFbLMixFewadoOoiOjsa9VmgoNHDQdjhAijTJ80uTl5UXPnj2pWrUq5cuXp1OnTuzfv/+zrrlnzx5KlizJyJEjkz0fFxfHxo0badOmDY6Ojjg6OtKmTRs2bNiQ6Yuojc2MGbdxKFraWhzbfJpD609oOiQhsjS/h/4cWHUUgO6TZZRJiMwsQydNHh4euLi44OXlhZ2dHZUrV+bmzZv8+OOPzJ8/P03X9PPzY/LkyR9sM2rUKCZPnsyjR4+oXLkylSpV4uHDh0ydOpXBgwdn+tEZu6ol6fZrRwAWDFqJ793nGo5IiKzrjynbiY2JpWIjRxxq2Wk6HCEYN24cJUuWTPaXu7v7B/vGxsaydu1avv32WxwcHKhXrx5z5swhIiIiUTtFUZgzZw5OTk5UrVqVSZMmERYWluR6rq6uNG3aNNMMSGTYpWgDAwOZOHEihoaG/PHHH5QpUwaA+/fv4+LiwuLFi6lfv776eGooisKYMWN4+/Ztim2OHz/O3r17KVy4MBs3bsTS0hKA58+f06VLF44cOcKBAwdo2rTp5z2ghnUc04rLR65x5dhNpnWey7wz09DT19V0WEJkKU/vPOPw/0dzu0/uqOFohIjn7e1N4cKFGTx4cJJzFSpU+GDfX3/9lS1bttC4cWNcXFy4desWy5Yt48aNG6xcuVI9krpnzx6WLl1K165dyZs3L0uWLEFbW5uff/5ZfS0/Pz/++OMP3NzcMs2aZRl2pGnjxo1ERETg7OycKDGysbFh+PDhKIrCunXrPumaa9as4fz581SuXDnFNqdOnQKgY8eO6oQJoECBAnTuHL8J7oULFz7pvhmRtrY2Y9YPJkduU+5dfsjqnzZpOiQhspz1v24jLk6hastKlKpSQtPhCEFMTAz37t3D0dGRVq1aJflVuHDhFPtevXqVLVu20KFDB+bPn0/nzp2ZMmUKQ4YM4dSpUxw4cEDddteuXdjb2zNhwgT69u1L586dcXd3T/SmZt68edjZ2dGgQYMv+szpKcMmTSdOxP/tLLkfZoMGDVCpVBw/fjzV17tz5w5z5syhbt26tG3bNsV2CVmyv79/knOvXsWvpm1mZpbq+2ZkFgVzM2LVAAB2zNnLhb8uazgiIbKOB9cec/zP0wDq1+FCaNrDhw+JiorC1tb2k/vu3LkTgO7duyc63r17d3R1dRO92vPz88PKykr92crKivDwcPX36J07d9i9ezejRo1Kw1NoToZMmhRF4d69ewCUKJH0b2dmZmZYWFjw5s2bZJOb/4qKimLkyJEYGxszderUD7atVasWAJs3b+aPP/4gODiYN2/esHnzZjZu3IiZmRnt2rVLw1NlTNVaVqbVD00AcO2+iFcvgjUckRBZw7pJWwCo3aEqNo5FNRuMEP/n7e0N/PvdGh4enup6oqtXr2JqaoqNjU2i40ZGRpQoUYJr166pjyV8RycIDg5GR0dHPegwa9Ys6tSpQ8WKFT/reb62DJk0vXnzhsjISIyNjTEyMkq2Td68eYH42qePcXNz4+7du/z6669YWFh8sG3NmjUZMmQIKpWKKVOm8M0331ClShV++eUXHBwc2LJlC4UKFfr0h8rA+v7eFWv7IrwOeINr90XExcVpOiQhMrU7F+5xZvcFtLRUdJ3UQdPhiFRSFIWIsKgM+Su9JiAlJE0nTpygbt26lCtXDkdHRwYOHMiTJ08+2PfFixfkz58/2XOWlpa8efOGkJAQAKpVq8a5c+c4ceIE3t7euLu7U7VqVXR0dDh37hynT59mxIgR6fJMX1OGLAQPDw8HwNDQMMU2+vr6AMlW47/v7NmzrF27lpYtW9K4ceNU3b927dqcO3eOW7du4ejoSHR0NDdu3ODq1ats3ryZcePGZalpw3oGeozfPIwfKo/ln4NX2eG2l/YjW2o6LCEyrbU//wlAfedaWJXOWn/JyqoURWFEtxXcuvLhxEFT7MoXYfbaPp/93XPnzh0Arly5wsCBA8mZMyeXLl1iw4YNXLp0iW3btqVY1xQSEkKRIkWSPZfwfR0WFoapqSndunXj4sWL9O3bF4DixYszadIkFEXh999/p3Xr1hQvXvyznkUTMmTSlLAnU2r+5/jQqMjbt28ZN24clpaWTJw4MVX3Pnr0KEOGDKFs2bL89ddf5MmTB4ifPffDDz+wbt06jI2NGTp0aKqul1lY2RVmwJzuzO2/nNXjN+FQpwwlK9l8vKMQIpHrnre5+PdVtHW06fpze02HIz5F1vm7cIqaN2+Og4MD/fv3R09PD4CGDRtSvnx5Bg8ezOzZs5k7d+4nXzdhJCzh+9vIyIiVK1fy6NEjIiMjKV68ODo6Ouzbt4979+6xaNEiFEVhxYoVbN++ncjISBo2bMiIESM+OGCiaRkyaTI2NgZIsu7D+yIjIwFSfH0H8VMjX7x4werVq8mRI8dH7xsTE8PkyZOJi4vj999/VydMED97btasWTRv3py1a9fSt2/fDP0fNi2a9mnAP4eu4rnjPL91mcuSf1wxMs1azyjEl6QoCmsmbgagSc965C9m+ZEeIqNQqVTMXtuHyPBoTYeSLH1D3XR5w9G6detkjzdq1Ij8+fOrZ5Anx9jYWP0m6L8Svq/f/65VqVRYW1urP0dFRTFnzhy6du1Kvnz52LlzJ3PnzuW3337D0tKSMWPGEBUV9dG1FDUpwyZNxsbGhISEEBERgYGBQZI2AQEBwL+1Tf91/fp19u7dS86cOXF3d09U1e/r6wvA5cuXGTlyJDY2NgwYMIDHjx/j5+eHtbV1ssOTNjY2FCpUiCdPnvD48WNKlSqVHo+bYahUKn5c3h9vr3s8v/eChYNXMXrtIE2HJUSmcfnIda6fvI2uvi7fT/hO0+GIT6RSqTAw0tN0GBqTO3du9XdrcgoWLMizZ8+SPffixQty5cqlLp1Jzp9//klISIj6ld2uXbuoXLmyOpHr1KkTS5cu5ddff82wJTAZshBcpVKpK/vv37+f5Pzr168JDAzEzMws0VpK70uodXr9+jV79uxJ9Ovy5fip9b6+vuzZs4czZ84AqAvYdHRSziUTFuCKjs6Yfxv5XKa5TBj3x1C0tFQcWn+CIxs9NR2SEJlC/ChTfC1T834NyVMot4YjEiKxwMBAWrRowZAhQ5Kci46O5vHjxx9cp8nR0ZHXr18nKRgPDQ3l3r17lC9fPsW+7969Y/HixfTr1089GhUQEJBocpa5uTmRkZHqZQkyogyZNEH8LDaAw4cPJzl3+PBhFEVRLw+QHCcnJ+7cuZPsr+nTpwPQokUL7ty5w4YNGwAoWrQo2tra3L9/Xz0a9T5fX1+ePHmCnp5ekimXWYl9zdI4T4yvxZg/cAXP77/QcERCZHzn913C+7wPBkb6dBrbWtPhCJFE7ty5iYqK4ujRo9y+fTvRuWXLlhESEsJ336U8QtqiRQsAVq5cmej42rVriY6O/uAaiCtWrMDQ0BBnZ2f1sXz58vH06VP154Tv11y5cn3Sc31NGTZpateuHYaGhqxdu5ZLly6pjz948EBdpNa7d2/18YCAAO7fv//BocWPyZkzJ02aNCEuLo5Ro0YRHPzvmkWBgYGMGjWK2NhY2rVr98Faqqygy/i22NcsTVhIOL91mUt0VNYcWRMiPcTFxalnzLUa1ATzfBn3D32RfalUKn755RcAXFxcmDNnDhs3bmTw4MEsWLAAJycn9cKV3t7e7N69W71EAcRvsdK2bVu2bNnC0KFD2bZtGxMnTmTBggXUrVs3xZW9AwICWLduHUOGDFEXn0N8Enb16lWmTp3KihUr2Lx5M61bt1YXk2dEGbKmCeIz0PHjxzNx4kScnZ1xcnJCT0+Ps2fPEhkZyYgRIxLVFLm5ubFz507atGnDjBkz0nzfn3/+mXv37nHp0iXq1atHlSpViI6O5urVq7x7944KFSowevTo9HjEDE1bR5uxGwbTr9wo7ly4z7qft9B7hvPHOwqRDZ1yP8/9K48wMjWkw6hWmg5HiBRVrVqVzZs3s2jRIjZt2kR4eDiFCxdm2LBh9OrVS53UHDp0iIULFzJo0KBE37VTpkyhSJEi7NixgyNHjpAvXz4GDBhAv379UqxDWrBgAYULF6ZVq8S/N9q0aYO/vz9//vknkZGRtGjRgjFjxny5h08HKiW9Vsz6Qjw9PVm+fDk3btxAW1ub4sWL07NnTxo1apSo3dixY1OdNLm7uzNu3DhatGjBrFmzkpwPDw9n3bp17N+/n8ePHwNQrFgxWrRogbOzc6JM+X3169cH4MiRI2l51AzJc8c5JrefjUqlYsbfE6jQwEHTIQmRocTGxtLXYQRPbj+j68/tcflFFrMUIrNJ7fd3hk+aMpOsmDQBzO2/nH3LD2GeLyfLrs4iZ56ssfeeEOnh0IYTuHZbiGkuYzY8WISxmbGmQxJCfKLUfn9n3BeHIsPo79YNK7tCvHrxmlk9F6fbcv5CZHYx0TFs+HUbAB1GtZKESYgsTpIm8VEGRvr8tGkYuvq6nN93iV0LDmg6JCEyhINrj+P3wJ+cec1oNfhbTYcjhPjCJGkSqVLMwYp+s1wAWDF6A/euPNRwREJoVlREFH9M2Q5A53FtMDROugivECJrkaRJpFrLgY2p2rIS0VEx/NZ5LuGhKW9zI0RWt3/FEV76BmFR0Jzm/RpqOhwhxFcgSZNINZVKxYiVA8hdIBdP7zxn8dA1mg5JCI2ICItk0287APh+Qjv0DLLv1htCZCeSNIlPYmaRg7EbhqBSqfhr9VFObD2j6ZCE+Oo8Fv1FsP8b8lnnpXGPOpoORwjxlUjSJD5Zubpl6TyuDQBz+i3jxaO0r8IuRGYT+jaMLa67Aej6c3t09XQ1HJEQ4muRpEmkSddJ7bGrakvomzCmfz+P2JhYTYckxFexc95+3gaFULhkAep/X1PT4QghviJJmkSa6OjqMG7jUIxyGHLr7F31WjVCZGVvX4WwbbYHAC6/dEBbR1vDEQkhviZJmkSa5Sualx+X9QNg02/uXD1+U8MRCfFlbZu1h7C34VjbF6FW+6qaDkcI8ZVJ0iQ+S52O1WnSoy6KojCj63zeBoVoOiQhvojggDfsmr8fgO6TO2XondiFEF+G/K4Xn23g/J4Uss1P4LNXzO69RLZZEVnSlhk7iQiLpGRlG6q2rKTpcIQQGiBJk/hshsYGjN/8I7p6OpzZfYG9Sw9qOiQh0tVL3yA8lsT/f919SmdUKpWGIxLi8129ehU7OzvOnEm6dMzz588ZPXo0NWrUwNHRkQ4dOnzSZvTh4eHMmzePhg0b4uDgQOPGjVm1ahWxsYknDUVFRTFx4kQqVKhArVq1mDt3LjExMUmuN3ToUHr27PnpD5nOJGkS6aJ4eWt6z3AGYMnwdTy8/ljDEQmRfpaPWk90ZDT2tUpTsaGDpsMR4rM9evSIH374IUkSA/Dy5UucnZ05fPgw3333HWPGjCEmJoaBAweyZ8+ej147Li6OIUOGsGTJEqpUqcL48eMpWbIkrq6u/PLLL4narlq1Cnd3d1xcXGjTpg3Lly9n3bp1idpcu3aNgwcPMnLkyM965vSg8zmdIyMjuXTpEn5+fgQFBRETE4OZmRnW1tY4OjpiZGSUXnGKTKDN0Kb8c/gqXvsv81uXeSw4Px0DI31NhyXEZ7l05DrHt5xBS0vFwLk9ZJRJZHqHDh1i/PjxvHnzJtnzCxcu5NmzZ2zatImKFSsC0LZtW9q3b8+0adOoX7/+B7/f//rrL06ePMmPP/5I//79AejYsSPjx49n69attGvXDkdHRwB27dpFgwYNGDZsGBCfzG3fvp1evXqpr/f777/TtGlT7Ozs0uPxP8snJ02xsbEcOHCAHTt28M8//xAdHZ1sO21tbZycnOjQoQONGzf+7EBFxqdSqRi5+gf6OY7g0c2nLBuxjqFL+mo6LCHSLDoqmoWDVgLQcmATipez1nBEQnyevn37cuLECYoXL07NmjXZu3dvovOxsbF4eHjg6OioTpgADAwM6Nq1KxMnTuT48eM0bdo0xXvs3LkTXV1dnJ2dEx3v06cP27dvx93dXZ00+fn5JcoRrKysOH78uPrz8ePHuXz5MgcOHPicx043n5Q07d+/nzlz5uDr64uiKFhYWFCqVCkKFSqEqakpcXFxvHr1ioCAAK5du8bp06c5c+YMxYsXZ/DgwTRq1OhLPYfIIHLlNWPM+sGMbTyVvcsO4VinDHU6Vtd0WEKkyY45+3h65zk585rRbXJHTYcjxGd78OABw4cPp0ePHixbtizJeR8fH8LCwtRJzfscHOJfTV+9evWDSdO1a9ewsbHBxMQk0fGiRYtiZmbGtWvX1McsLCwSjXgFBweTO3duIP413+zZs+nUqROFCxf+tAf9QlKVNAUGBjJu3Dg8PT2xtrZm1KhR1KtXj6JFi6bYR1EUvL29+euvv3B3d2fo0KHUqVOHadOmYW5unl7xiwyoYkNHOo5uxRbX3fzeYxH5i1lSsnJxTYclxCcJeBrIxinbAejr2hWTnMYajkh8aYqiEBGZ/NsTTTPQ102XV8P79+9HTy/lDab9/f0ByJ8/f5Jz+fLlA8DX1zfF/uHh4bx+/Zry5csne97S0jJR/2rVqvHXX3/RqlUrAA4ePEiTJk2A+BGrZ8+eMXDgwI881deTqqSpRYsW5MyZk4ULF9KgQYNUXVilUlG6dGlKly7NkCFDOHDgAHPnzqVFixacPn36s4IWGV+PaZ15eOMJXvsv83NrVxaen06eQrk1HZYQqbZ0xDoiwiIpW6MUDbrW0nQ44gtTFIWB4zdz/c5zTYeSLPtSBVk8tdNnJ04fSpgAQkLi19pLrmbJ0NAQiE+M0tI/4Rrv9x86dCi3bt2ic+fOAFSoUIFhw4YRGRnJ/Pnz6dWrV4YaaElV0tSnTx9cXFzQ0Ulb3bi2tjbNmzenUaNGSariRdakra3NT5uGMaz6BB7dfMqk1jOZfWIyhsYGmg5NiI+6ePAqntvPoaWtxeCFvaX4O7uQ/84fXGcv4dyHfj98bJ0+RVES9c+TJw/btm3j/v37aGtrU6xYMVQqFcuXLyc2NpYePXoQHR2Nm5sbf/31F9ra2rRu3Zr+/funOSf5HKm6Y3qtjaCnp0efPn3S5Voi4zPOYcRkjzEMdhqHz6WH/N59IRO2DJeVlEWGFhUZzaIhqwBo9UMTijlYaTgi8TWoVCoWT+2U5V/PfYyxcfxr6IiIiCTnEkaIcuTI8dH+KY1GRUREJOmvra2Nra2t+nNwcDDLly9nxIgRGBkZsXDhQrZu3YqrqyuKojB69Gh0dXXp16/fpz1cOvj6aZrIVvJbW/KL+yhGN5iM547zrPt5Cz2mdtZ0WEKkaIfbXnzv+pHL0oxuv3bQdDjiK1KpVBgafPj1VVZXqFAhAF68eJHkXEK9U0JtU3JMTEzIlSuXuu1/vXjxgiJFinwwhqVLl5I7d27at28PxC9L0LhxY+rXrw/At99+i7u7e+ZLmu7fv8/du3cJDw8nLi4uxXbt2rX7nNuITK5sjdIMW9aP33ssYtNv7hQpXYj639fUdFhCJBHw5CUbp/6/+Pt3F4zNpPhbZC/FihXD1NQ00Qy3BFevXgXi644+xMHBgbNnzxIeHq6ugwJ4+PAhb9++TbFIHOKLzDdt2sSsWbPUr98CAgKwsLBQtzE3N082qfsa0pQ0RUREMHz4cI4dO5aq9pI0iUbd6vDkti9bXHczu/cS8hfLi13VkpoOS4hElgxfR2R4FPY1S0tiL7IlHR0dmjZtytatW7l06ZI6QYqIiOCPP/7AwsKCWrU+PDGiZcuWnDhxgvXr1ycaDVqxYgUQv1BmSubOnUvp0qUTrd2UL18+nj59qv785MmTD452fUlpSpqWLVvG0aNH0dHRoWLFiuTNm1cjBVkic+n5Wxd87z7n9K4LTGrzOwvPT8fSKo+mwxICgAt/XeaU+/n/F3/3kuJvkW0NHjyYo0eP0qdPH3r06IG5uTnbt2/Hx8cHNzc39PX/3enh8uXLPHnyhAoVKqjXUmrWrBnbtm1jzpw5PHv2DHt7ezw9Pfn777/5/vvvU1zZ+9atW+zdu5f169cnOt6iRQuWLl3KvHnzADh8+DCDBw/+Qk//YWnKdPbu3Yuenh5btmyhdOnS6R2TyKK0tLQYs34ww2pO5MHVx/zcaiZzPKdgZGr48c5CfEFRkdEsHLIagDaDv8XaXoq/RfaVJ08eNm/ezOzZs1m/fj3R0dGULFmSZcuWUbt27URtt2zZws6dO5k+fbo6aVKpVCxZsoQFCxawf/9+du7cSaFChRg3bhwuLi4p3nfWrFnUqlWLKlWqJDrer18/QkJC2LJlC1paWvTq1SvRNitfk0r52PzAZNjb21OtWrVkVxPNzhKK1D5lJ+jsKODJSwY5jSPY/w1VW1Zi0o6RaGtrazoskY1tnLaDtRP/xDxfTlZ7z8M4h+ybKUR2ktrv7zTN/bawsEhxzzkhPiZvkTz8snM0uvq6nPW4yOqfNms6JJGNvXgUwObf3AHoN8tFEiYhRIrSlDQ1bNiQy5cva6x6XWR+dt/YMnJ1/NL4W3/fzd9rUzepQIj0tnT4WiLDo3CobUfdzjU0HY4QIgNLU9I0ZMgQihQpQp8+fTh+/DivXr0iNjaWuLi4ZH8JkZx6nWvw/YTvAJjbbxnXPW9rOCKR3Zzff4nTuy6graMtK38LIT4qTYXgXbt2JSIigidPnjBgwIAPtlWpVNy6dStNwYmsz+WXDjzxfobn9nP80jZ+Rl3+YpaaDktkA1ERUSweGl/83XZoU4qWyRi7qAshMq40jTTdvn2bx48foyjKR3/JSJP4EC0tLUavHUSJisV4GxTCxJYzCH0TqumwRDaw9XcPnt/3J3eBXDj/3F7T4QghMoE0jTR5e3undxwiGzMw0mfyrtEMchrH41u+TOs8lykeY9HWkRl14svwe+jP5unxxd/9Z3eTZS+EEKkiO6eKDMGiYG5+3TUGfUM9Lvx1heWjNmg6JJGFLflxLVER0ZSrV5baHappOhwhRCbx2ct4nzt3Di8vL16+fImenh65c+emSpUqVKpUKT3iE9lIyUo2jF43iCkd3HCft48ipQvSrG9DTYclsphze//hrMdFtHW0GbRAVv4WQqRempMmX19fhg0bxs2bNwFIWCMz4Q+g0qVL4+bmRtGiRT8/SpFt1GpXle6TO7H25z9ZMGgVBUvkp1zdspoOS2QRkeGRLPp/8fd3PzbHqnQhDUckhMhM0pQ0vXnzBhcXF54/f46VlRWNGzemUKFCxMbG8vTpUw4fPsytW7fo3bs3O3fuxNTUNL3jFllYl/FteXz7Kcc2n2Zyu1nMPzedQiXyazoskQVsdfXgxcMALAqa4zzxO02HI4TIZNJU07RixQqeP39Ohw4d2L9/P8OHD6dDhw507tyZ0aNHc+DAAdq3b4+vry9r165N55BFVqdSqRixcgClnEoQEhzKxBbTCQl+p+mwRCbn98CfzTN2AtDfrTuGJlL8LYT4NGlKmg4dOkS+fPn4+eefk90zTFtbm0mTJpEvXz7+/vvvzw5SZD/6hvr8unMUeQrnxveuH1M7uhETHaPpsEQmtnjYGqIjo6nQwJ5a7b7RdDhCiEwoTUmTn58fjo6O6Oik/HZPR0cHR0dHfH190xycyN7M8+ViisdYDIz1uXT4OouHrdV0SCKTOuNxgXN7/0FHV5sf5kvxt8je7ty5w5AhQ/jmm28oW7YsdevWZerUqbx9+zZROx8fHwYOHEjVqlUpX7483bp1459//kn1fYKDg5kyZQp169bFwcGBli1bsn379iTt3r59y5AhQyhXrhz169dn/fr1yV6vffv2TJw48dMeNp2lKWkyNDQkODj4o+2Cg4PR19dPyy2EAMDGsShjNwxBpVKxZ8nf7F70l6ZDEplMRFgki4euAaDd8BYUKVVQwxEJoTkPHjygU6dOnD17lo4dOzJhwgScnJzYuHEjXbp0ITQ0fnHh+/fv06VLF65du0bXrl0ZNmwYfn5+dOvWDS8vr4/eJywsjJ49e7JlyxYaNmzITz/9RK5cuRg/fjxLly5N1NbV1RVPT08GDhxI7dq1mTZtGn/9lfjP+gMHDuDj48OgQYPS74eRFkoa9OzZUylTpoxy69atFNvcvHlTsbOzU3r27JmWW2RK9erVU+rVq6fpMLKkzTN2Kg1U7ZRGOh2UC39f0XQ4IhNZM3Gz0kDVTulcpJ8S9i5c0+EIoVE9evRQypQpo9y7dy/R8XXr1im2trbK0qVLFUVRlF69ein29vbKkydP1G2CgoKUatWqKU2bNv3ofZYtW6bY2toqHh4e6mOxsbHq+z9//lx9zN7eXpk5c6a63Xfffaf06tVL/TkqKkpp2LChMnv27LQ9dCqk9vs7TSNNLi4uxMTE0Lt3b3bv3k1YWJj6XFhYGLt27aJPnz7ExcXh7OycbgmeyL46jm5Fw261iYuNY2pHN554P9N0SCITeHbPj62uuwEY4NYdQ2MDDUckhOZERkZy4cIFKlWqhI2NTaJzrVu3BsDLy4vAwEA8PT2pX78+hQv/uyejubk57dq14969e1y9evWD99q1axd58uShefPm6mNaWlr06tWL6Oho9uzZA8CrV6+IjIzEyspK3c7Kyornz5+rP2/dupU3b97Qp0+fND97eknTkgO1a9emX79+LFu2jLFjx/LTTz9hZmYGxC9HEBcXh6Io9OrVi7p166ZrwCJ7UqlUDFvaj+f3XnDz9B0mtpjOgnPTyZFblrMQyVMUhUVD1xAdFUPFRo7UaOuk6ZCE0ChdXV3279+f7J6wgYGBQPxEroSEyNHRMUk7BwcHAK5evZrseYCQkBAePHhAvXr1ktQPJvS5du0aADlz5kRbW5s3b96o2wQHB5M7d24AQkNDWbRoEQMGDMgQyxeleRuVH3/8kcWLF1O5cmW0tLR49eoVr169QktLi0qVKrFw4UJGjRqVnrGKbE5PX5df3EeRr2gent/359d2s4iOitZ0WCKDOrP7AhcOXEZHV5tB83tK8bfI9rS0tChcuHCiUZ0EK1asAMDJyYkXL14AkD9/0vXx8uXLB/DBSV7+/v4oipJsfxMTE4yNjdX9dXR0cHJyYvv27Xh7e3PixAm8vLyoWbMmAKtWrcLAwIAuXbp84tN+GZ+1jUq9evWoV68esbGxvH79GkVRyJkz5wdn1QnxOXLmMWOyx1iGVZ/AtRO3WPDDKn5c3k++EEUiEWGRLB4WX/zdfmRLCtkW0HBEIjNQFIXwqIy5tImhns4X+3PO3d0dd3d38ufPT8eOHdm4cSMARkZGSdoaGMS/4g4PD0/xeiEhISn2h/jJZO/3nzBhAv3796dVq1YANGjQgO7du/Py5UvWrFnDpEmT0NPTS9vDpbN0yW60tbXVQ2lCfGnWZYvw0+Zh/NxyBgdWHcHKrhDf/dj84x1FtrH5N3cCngSSt4gFnX9qq+lwRCagKAouC7dw5ZGfpkNJVvmiBVg3qEO6J07bt2/n559/xsjIiPnz52NiYqLeFu1DPhTHx/oripKov42NDfv378fHxwcTExOKFCkCwIIFCyhSpAgtW7YkNDSUadOm4enpiYmJCc7Oznz//fepfMr0k6qkafTo0ahUKkaNGoWFhQWjR49O9Q1UKhUzZ85Mc4BCJMepaQX6/u7C0hHrWD5qPYVs8+PUrKKmwxIZgO/d52yb5QHAgDlS/C1ST0X2GrGeP38+ixYtwsTEhGXLlqnrlYyNjQGIiIhI0idhhChHjhwpXjehf0qjUeHh4RQqlHjfR11dXezs7NSfHzx4wI4dO1iyZAlaWlpMnTqVM2fO4Orqip+fH+PHj8fMzCxRofnXkKqkycPDA5VKRf/+/bGwsMDDwyPVN5CkSXwpbYc148ltX/avPMJvXeYx9/RUrMsW0XRYQoMURWHhkNVER8VQuUk5qreuoumQRCahUqlYN6hDtng9Fx0dzYQJE9i1axd58+ZlxYoVlCpVSn0+IaFJqG1634fqnRIULFgQlUqFv79/knMhISGEhYWpa6NS4ubmRsWKFalVqxaxsbHs2bOHAQMGULVqVSA+L9m5c2fGTJqmT58OQJ48eRJ9FkKTVCoVgxb24tm9F1w9fpOfW85g/rnp5MprpunQhIac2unFPwevoqunw8B5UvwtPo1KpcJIX1fTYXxRsbGxDB8+nIMHD2Jra8vy5cuTJED29vZoaWmpZ7i9L+FY+fLlU7yHiYkJNjY2XL9+Pcm5hJl5FSpUSLH/5cuXOXz4MNu2bQPiZ9NFR0djYWGhbmNubo63t/cHnvTLSFXS1KZNmw9+FkJTdPV0+XnbCAZ/My5+Rt13v+N6eBJ6WfwPPpFUeGgES36ML/7uMKoVhUqk/DdhIbKruXPncvDgQRwcHFi1alWyr9ksLCyoVq0aBw8eZMiQIeq1ml69esWOHTsoVapUoldpyWnZsiVubm7s3btXPRoUFxfH6tWr0dPTo1mzZin2dXV1pUmTJtjb2wPxCZKenh5Pnz5Vt3ny5MlHR6u+hDQVgo8bN44KFSrQvn37D7ZbtmwZ586dY82aNWkKTojUyJHblCl7xjGk6k/cPH2Huf2WMWrNDzLKkM1smubOy6dBWFrlodM4+YudEP/19OlTVq9ejUqlomHDhhw7dixJm9y5c1OjRg3GjBlDx44d6dy5M927d0dPT4+NGzfy9u1b5s2bl6jP6dOnCQwMpHr16urRoG7duuHh4cHYsWO5efMm1tbW7N+/n7NnzzJ69Gj1m6v/Onz4MNevX2fGjBnqY1paWjRr1oyNGzdiZmaGv78/169fZ9asWen400mdNCVNO3fuJDY29qNJ0+XLlz9pcz8h0qpIqYJM3Dqcn5r+xqH1JyhSuhCdxrTWdFjiK3l65xnbZ8fXWg6c2wMDI9nzUoj/8vT0JCYmvmZr9uzZybapUKECNWrUwNbWlk2bNuHm5sbixYvR0tKibNmyzJw5k3LlyiXqs3TpUry8vFi/fr06aTIwMGDDhg24ubmxe/duQkNDsba2ZubMmerVx/8rNjYWNzc32rdvn2QtqfHjx6MoCitWrMDExIRx48bRokWLz/uBpIFKScXcwv/ufuzh4UHhwoU/+E4zJCSEEydOkCdPHk6cOJE+0WZw9evXB+DIkSMajiT72r3oLxYOXoVKpWLSjpFSCJwNKIrC2CZTuXToGlWalmfqnnEyyiiE+CSp/f5O1UiTtbU1U6ZMUX9WqVQ8ffo00fvFlHTt2jU1txAiXbT6oQmPb/myZ8nfzOg6nzmeUyhezlrTYYkvyHPHOS4duoauvi4/SPG3EOILSlXS1LlzZ4yNjVEUBUVR+OmnnyhfvjwdOnRItr1KpUJPT4+iRYt+tFhMiPT2w7wePPN5zqXD1/m51UwWnp+Oeb5cmg5LfAHh78JZOnwdEL+pcwGbr18YKoTIPlKVNGlpaSV6B7lz505q1Kghs+hEhqSto82ELcMZWm08T+88Z1Kb35l1dBL6hlLnktVsnLqDl75B5Cuah05jW2s6HCFEFpemDXs3bNhAv3790jsWIdKNaS4TJnuMxTSXMd7nfZjde0mqtgYQmcfj275sd9sLwMB5PSUpFkJ8cZ+195y/vz/Pnj0jKioq0ReSoihERkYSGBjI0aNHWbJkyWcHKsSnKlQiPz9vH8nYxlM5tvk0RUoVwnliO02HJdKBoigsGrKa2JhYvmlekaotKmk6JCFENpCmpCkqKoqRI0dy6NCh9I5HiHRVrm5ZhizqzZx+y1g3aQuFSxWkdvuqmg5LfKaT285y+ch1dPV1GTi3h6bDEUJkE2l6Pbd69WoOHjyItrY2pUqVUi/BXqlSJWxtbdHW1kZRFKytrZk/f366BizEp2rapwFth8avPvt794XcuXhfwxGJzxEWEs6S4WsB6Dy2DfmLWWo2ICFEtpGmpOmvv/5CS0uLdevWsXPnTn788UcAJk6cyO7duzly5Aj29vY8efLkg5v6CfG19J3VlcrflicyPIpJrWcS+CxI0yGJNNo4ZTtBz4PJX8ySDqNbajocIUQ2kqak6cmTJzg4OFCxYkUAHBwcUBSFS5cuAWBpaaleZn316tXpFKoQaaetrc34zcOwsitE0PNgfm7tSkRYpKbDEp/o8a2n7Ji7D4hfWkKKv4UQX1Oakqbo6OhEG+UVLlwYHR0d7t69qz5WoEABypcvL9uoiAzDOIcRUzzGYmZhis8/D3DtvpC4uDhNhyVSSVEUFg5eRWxMLFVbVsKpWUVNhySEyGbSlDSZm5sTHBz870W0tChQoAA+Pj5J2r169erzIhQiHeUvZsmkHaPQ0dXGc/s51v+yVdMhiVQ69udprhy7iZ6BFH8LITQjTUmTo6Mjly5d4vHjx+pjxYsX58aNG7x79059zMfHBzMzs8+PUoh0ZF+zNMOWxa8ztnHqDo5u8tRwROJjQt+GsWzkegC6/PQd+Yrm1XBEQojsKE1JU+fOnYmOjqZDhw5s2LABgGbNmhEeHs7QoUM5evQoEydO5OHDh7KNisiQGnevS4dRrQCY1WsJt87d/UgPoUkbft3GK79gChTPR/uRX39ncyGEgDQmTVWrVmXMmDGEhYVx5coVAL799lvs7e05ffo0P/zwA9u2bUNbW5shQ4akZ7xCpJuev3WmastKREdG80sbVwKevNR0SCIZD288Yef8/QAMmt8TPQM9DUckRNYQGxtLly5dKFmyJDExMYnOPX/+nNGjR1OjRg0cHR3p0KEDR44cSfW1w8PDmTdvHg0bNsTBwYHGjRuzatUqYmNjE7WLiopi4sSJVKhQgVq1ajF37twksQAMHTqUnj17pu1B01GaVwTv0aMHzZs3JzAwEIiva1q/fj2rVq3iypUrmJub4+zsTNmyZdMtWCHSk7a2NuP+GMKwGhN5cO0xE1vNZK7nFAxNDDUdmvg/RVFYMGglcbFxVG9ThcpNyms6JCGyjKVLlyY7Wevly5c4Ozvz+vVrunbtiqWlJdu3b2fgwIHMmjWLFi0+PNobFxfHkCFD8PT05LvvvsPBwYHTp0/j6urKo0ePmDJlirrtqlWrcHd3p0+fPiiKwvLlyzE1NaVXr17qNteuXePgwYPs2LEj/R4+rRSRburVq6fUq1dP02GIT+T/OEBpZ9lLaaBqp/zceqYSGxur6ZDE/x3+46TSQNVOaWbURXnxKEDT4QiRZVy9elWxs7NTypYtq9ja2irR0dHqcz///LNia2urXLx4UX0sPDxcad68ueLk5KSEhoZ+8Nr79u1TbG1tlSVLliQ6/tNPPym2trbKlStX1McaNWqkDBkyRP15yJAhSpMmTRL1c3Z2VoYPH56m50yt1H5/p+n1nBBZSd4iefh15yh09XU5s/sC476dxvP7LzQdVrYX+iaU5aP+X/w9/jssrfJoOCIhsobQ0FBGjhxJzZo1KVeuXKJzsbGxeHh44OjoqF6LEcDAwICuXbsSHBzM8ePHP3j9nTt3oquri7Ozc6Ljffr0AcDd3V19zM/PDysrK/VnKysrnj9/rv58/PhxLl++zLBhwz7xKb+MVL2eq1+/fppvoFKpOHz4cJr7C/E12FUtyZh1g5jZbSGXDl2jj/1wuoz/jg6jWqKrp6vp8LKl9b9s49WL1xQskZ92I6T4W4j0Mm3aNEJCQpg6dap6R48EPj4+hIWF4ejomKSfg4MDAFevXqVp06YpXv/atWvY2NhgYmKS6HjRokUxMzPj2rVr6mMWFha8efNG/Tk4OJjcuXMD8a/5Zs+eTadOnShcuPCnP+gXkKqk6dmzZ6m6mLa2dpIir8/l5eXF0qVLuX37NhEREZQsWRIXF5cP/gf7mD179jBy5EhatGjBrFmz1Md9fX1TnSAeOXKEQoUKpTkGkfHU7lANm/LWLPhhBZcOX2ftxD85usmTYUv7YV+ztKbDy1YeXHvMroUHAPhhfk/09CVxFV+eoiiEJ1OEnBEY6uigUqk++zoJtUGLFi3CwsIiyXl/f3+AZLdAS1jU2tfXN8Xrh4eH8/r1a8qXT77+0NLSMlH/atWq8ddff9GqVSt1fE2aNAHiR6yePXvGwIEDU/l0X16qkqaDBw8m+hwREcHw4cMJCAhg4MCB1K9fn0KFCqFSqQgKCuLYsWPMnTuXwoULs2LFijQH5+HhwejRo9HR0cHJyQltbW3Onj3Ljz/+yL1799I0M8/Pz4/Jkycne87IyOiDBW537tzh7t27FChQgFy5cn3yvUXGV6hEfmb8PZGjm06xdMQ6ntx+xvDaP9OkZz36zHQmR25TTYeY5SnvFX/X/M6Jyo3LaTokkQ0oikKH7X/yj9/zjzfWgIr5C7C1XafPSpz8/f2ZOHEi7dq1o0GDBsm2CQkJAeK/D//L0DB+kkx4eHiK9/hQ/4RrvN9/6NCh3Lp1i86dOwNQoUIFhg0bRmRkJPPnz6dXr16Ym5un4um+jlQlTUWKFEn0efbs2Tx69IitW7cmWYcpd+7ctGvXjvLly9OmTRtmzZrFL7/88smBBQYGMnHiRAwNDfnjjz8oU6YMAPfv38fFxYXFixdTv3599fHUUBSFMWPG8Pbt22TPm5ubJxp5et/Lly9p1aoVenp6LF68GGNj409+JpE5qFQq6n9fk8rflmPV2I3sX3mEv1Yf5azHBfrN6kaDrrXS5W98InmH/zjJjVPeGBjp0392N02HI0SWkPD9Z2pqyk8//fTBdh8796E//z7UP+H8+/3z5MnDtm3buH//Ptra2hQrVgyVSsXy5cuJjY2lR48eREdH4+bmxl9//YW2tjatW7emf//+6OikeQGANEvTHT08PHBycvrgwpU2NjZ88803HDp0KE1J08aNG4mIiKBv376JEiMbGxuGDx/OTz/9xLp163B1dU31NdesWcP58+epXLkyFy5c+KR4xo4dS1BQEOPGjaN0aXlVkx3kMDflx+X9aditDvP6L+fRzae4dl/IwfXHGbq4D4VsC2g6xCzn3etQlo+KXzD3+wnfkbeIFH+Lr0OlUrG1Xacs+3puzZo1nDt3jkWLFhEZGUlkZPyG5dHR0QC8fv0aXV1d9YBAREREkmskjBDlyJEjxfsk9E9pNCoiIiJJf21tbWxtbdWfg4ODWb58OSNGjMDIyIiFCxeydetWXF1dURSF0aNHo6urS79+/VL7+OkmTUnT27dvMTAwSFXb5H7wqXHixAmAZIcQGzRowPjx4z9awf++O3fuMGfOHOrWrUujRo0+KWn666+/OHXqFGXLlsXFxSXV/UTWULZ6KRb/M5Mdbnv5Y8p2rhy9QV+HEXQe15aOY1tLvU06WjdpC68D3lC4ZAG+G95c0+GIbEalUmGkmzV/Px87dgxFUVKsD6pevToFCxZk6dKlALx4kXQGcUK9U0JtU3JMTEzIlSuXuu1/vXjxIsnbq/9aunQpuXPnpn379gDs2rWLxo0bq2uOv/32W9zd3TNP0lS0aFHOnTtHQEAAefMmvwfU/fv3OXfu3Ce9PkugKAr37t0DoESJEknOm5mZYWFhwcuXL/H398fS0vKD14uKimLkyJEYGxszdepUTp48mepYoqKi1KNZ48ePR0tLVmnIjnT1dOk0tg21O1RjwaCVXPjrCut/3crRzZ4MXdKXcnVlEdfPdf/qIzwW/QXEF3/LrEUh0k9KpSkzZszgzp07rFq1CkNDQ4oVK4apqWmiGW4Jrl69CsTXHX2Ig4MDZ8+eJTw8XF0HBfDw4UPevn2bYpE4xBeZb9q0iVmzZqlfvwUEBCQqWjc3N082qfsa0pQBdOzYkdDQULp27crx48eJiopSnwsPD2fv3r10796d6Ohounfv/snXf/PmDZGRkRgbG6dYTJaQrCWsSP4hbm5u3L17l19//TXZ2QIfsmPHDp49e0aNGjU++j+KyPryF7Nk2r6fmPDnj5jny4nvXT9G1f8V1+4Lef3yzccvIJIVFxcXX/wdp1CrfVUqNkw63VkIkXZly5alWrVqSX6ZmZkB8M0331CxYkV0dHRo2rQply5d4tKlS+r+ERER/PHHH1hYWFCrVq0P3qtly5ZERUWxfv36RMcTJoa1bds2xb5z586ldOnSNG7cWH0sX758PH36VP35yZMnHxzt+pLSNNLUqVMnLl26hIeHBwMGDEBbW1v9g3/9+jVxcXEoikK/fv0SPXhqJbwLfT9D/S99fX0AwsLCPnits2fPsnbtWlq2bPnJsSiKov6P3r9//0/qK7IulUpF7Q7VqNjIkdXjN7N36UEOrT/Bub3/0Ne1K4171JVC8U90eMNJbp6+g4GxFH8LoWmDBw/m6NGj9OnThx49emBubs727dvx8fHBzc1N/f0LcPnyZZ48eUKFChXUayk1a9aMbdu2MWfOHJ49e4a9vT2enp78/ffffP/99ynWQ9+6dYu9e/cmSbZatGjB0qVLmTdvHgCHDx9m8ODBX+jpPyzNpeeurq7Ur1+fzZs3888//xAUFATEJzPffPMN3bp1o1q1amm6dsIrsNR88cTFxaV47u3bt4wbNw5LS0smTpz4yXGcPn2aBw8e4OjoSOXKlT+5v8jaTHIaM2RRbxq61GZuv2U8uPaY2b2XxBeKL+mLVWlZxys13r0OZcXo+OJv54ntyVMot4YjEiJ7y5MnD5s3b2b27NmsX7+e6OhoSpYsybJly6hdu3aitlu2bGHnzp1Mnz5dnTSpVCqWLFnCggUL2L9/Pzt37qRQoUKMGzfug3XBs2bNolatWlSpUiXR8X79+hESEsKWLVvQ0tKiV69eifam+5pUysfmB6aCoigEBwejUqnSZf2id+/eUbFiRUxNTbl48WKybdq2bcvNmzfZvn079vb2ybYZMWIE+/btY/Xq1YkSOHd3d8aNG5dkccv/Gj9+PNu3b2fSpEl06dLlo3EnFKl9yk7QImuIiY5h57z9rP9lKxFhkejoatNhVCu6jG+LvqH+xy+QjS0cvIrdi/6icKmCLLvyu9QyCSG+utR+f6fLIgcqlSpdF58yNjbG2NiYkJAQIiIikp2pFxAQAJBiIfr169fZu3cvOXPmxN3dPdFeNwmrkV6+fJmRI0diY2PDgAEDEvWPi4vj2LFj6OjoqFcnFSIlOro6tB/Zklrtq7JwyCrO7fmHTb+5c3zLaYYs7iM1Oim4d/khe5b8DcCgBb0kYRJCZGipSppGjx6NSqVi1KhRWFhYMHr06FTfQKVSMXPmzE8KSqVSUaJECa5cucL9+/eTzMB7/fo1gYGBmJmZpThzLqHW6fXr1+zZsyfZNr6+vvj6+lKlSpUkSdONGzcICgrCyckpQ61GKjI2S6s8TN41htO7vFg0ZDXP7/sztvFU6nWpQf/Z3chlmVPTIWYY7xd/1+lYjQr1kx8xFkKIjCJVSZOHhwcqlYr+/ftjYWGBh4dHqm+QlqQJoGbNmly5coXDhw8nSZoOHz6MoigfrOB3cnLizp07yZ5Lzeu5hOmWH5oaKURyVCoVNdo4UaGBA2sn/snuhQc4uukUXvsv03vG93zbu74sXQEcXHeCW2fvYmCsT79Zsv6ZECLjS1XSNH36dCC+OOz9z19Su3btWLlyJWvXrqVmzZrq6f4PHjxg7ty5APTu3VvdPiAggJCQEExNTVN8Zfcpbty4AfDBVc+F+BAjU0MGzu1Bg661mNtvGT6XHjK3/3IOrj/BsCV9sLa30nSIGhMS/I6VY+KLv10mdcCioBR/CyEyvlQlTW3atPng5y8hX758jB8/nokTJ+Ls7IyTkxN6enqcPXuWyMhIRowYQalSpdTt3dzc2LlzJ23atGHGjBmfff+EuqfcueUPc/F5bCvasODcdHYv+ou1E//k1pk7DKg4hnbDm+P8c3sMjLJfofiaCX/yJjAEK7tCtBnaVNPhCCFEqnz93e4+Qfv27cmXLx/Lly/nypUraGtrY2dnR8+ePWnUqNEXvferV6+AD++xI0Rqaeto03ZoM2p+9w2Lhq7m9E4vtrju5sTWMwxa2Bunpll74dTYmFjuXX7Idc/bXPe8zVmP+Fmxgxb0Qkc3Q/8xJIQQaqlaciBhQak03UClYsiQIWnun5nIkgMitc7uucjCwasIeBK/on2t9lUZMKc7FgWyxqSDyPBIvL3uccPTm2uet7h99i7h7xLvQ9m0d31+XC6LxgohNC+139+pSppKlSqFSqXiU5Z0SmivUqm4fft2qvtlZpI0iU8R/i6c9b9sw33ePuJi4zDKYUjPaV1o3r8h2tramg7vk4S+DePWmTtcO3mbG6duc8frHtFRiXeLN8lpTNkapShbozT2tUpT2qmErJwuhMgQ0nWdpkGDBn1+REKIRAxNDOk3y4X6zjWZ13853l73WDh4FYfWH2fYsn4UL2et6RBT9PrlG657enPD8zbXPW9x/8oj4uIS/6XKPF9OytYsjX3N0jjUsqNo2cIya1AIkamly4rgIp6MNIm0io2NZd+yw6z6aSNhb8PR0taizZCmdPu1A4YmKe/B+LUEPHnJdU9vrp+8xTXP2zz1fpakTT7rvNjXKo1DTTvK1ixNweL5ZCRJCJEpfNUVwT8kNjY2071qEOJr09bWpuXAxlRrXZmlw9dyYutZdszZy8ntZxm0oBfVWn69vQ8VRcH37nOun7zNNc9b3PD0xv/xyyTtipYpjH3N0urRJNkzTgiR1aU5aQoLC+Po0aP4+voSFRWVqN4pLi6OqKgoXr58yenTpzl79my6BCtEVmdRwJwJfw6nUbfLLPhhBS8evWRSa1eqt67MwHk9yVvYIt3vGRsby8NrT7h28hY3Tt3muqc3rwPeJGqjpa1FiQrWlK0R/6qtbI1S5Mhtmu6xCCFERpampCkgIIBOnTrh5+eX6HhC4XdKn4UQqVPl2/KsuDGHPyZvY7vbXk7vusClw9fpPrkTrQY1QVsn7aO3UZHR3L14nxuet7nmeZubp70JexueqI2uvi6lnIrjUNMuvmj7G1uMTDX/mlAIITQpTUnT0qVLef78OQUKFKBhw4Z4e3tz4cIF+vbtS1hYGOfOncPHx4cSJUqwYcOG9I5ZiGzBwEif3jOcqf99TeYOWMGtM3dYMnwth/84wdCl/ShZySZV1wl/F87tcz7/H0ny5va5u0RFRCdqY2RqSJnqJbGvaYd9zVLYVi6Onr5snitEVuLr66uu3UlJwYIFOXr0KAA+Pj7MmTOHy5cvExERgYODA0OGDKFixYqpul9wcDALFy7k6NGjBAUFUbRoUVxcXGjXrl2idm/fvmXChAmcPHmS3Llz061bN1xckm6t1L59e0qVKsWUKVNS+cTpL01Jk6enJ4aGhmzbto3cuXNz/PhxvLy8qFmzJpUqVSIuLo4JEyawc+dOzp07R5MmTdI7biGyDWt7K+acnMyBlUdYOXYjPpceMuSbcbT8oQndp3TCOIdRovZvX4Vw45Q31/8//f/uPw+Ii41L1MbMwhT7WnbY/3/6fzEHq88avRJCZHzm5ua4urome27Pnj14enqqF46+f/8+Xbp0QV9fn65du2JsbMzGjRvp1q0bq1evpkqVKh+8V1hYGD179sTHx4cuXbpQrFgxDhw4wPjx4wkMDKR//3/XaHN1dcXT05OBAwfy4sULpk2bRt68eRPlDgcOHMDHx4eFCxemw08i7dI0e87R0ZEKFSqwZs0aAPz9/alduzajRo2iV69eAERERFCzZk3KlCnD2rVr0zXojEpmz4kvLdj/NUtHrOPoplMA5C6Qi76/u6ClpeLayfjp/49uPE3SL0/h3DjUslMXbhcpVVBenQshAPD29qZ9+/Y4ODiwbt06dHR06N27N15eXuzbt4/ChQsD8TtltGjRgpw5c7Jv374PXnP58uXMnj2bWbNm0aJFCyC+3jnhuocOHSJ//vzExcVRrlw5nJ2dGT16NBC/92zOnDlZuXIlANHR0TRr1owmTZowfPjwL/Iz+KKz57S0tDAzM1N/trS0xMDAgHv37qmPGRgYUL58ea5du5aWW4j3+Nx6zrnjt2ntXA3THFJXkp3lsszJuD+G0qhbHeYPXMHz+/5M/z7piv2FSxbAvmbp+NGkmqWxtMqjgWiFEBldXFwc48aNA+C3335DR0eHwMBAPD09adq0qTphgviRqnbt2rF06VKuXr2Ko6NjitfdtWsXefLkoXnz5upjWlpa9OrVi9OnT7Nnzx769u3Lq1eviIyMxMrq3w3MraysEi2KvXXrVt68eUOfPn3S89HTJE1JU968edUb2iYoUqQId+7cSXRMT0+P0NDQtEcnANj1xxmO7L2Cl+ddflvWXRInQcWGjiy/NpvNv+1k34rDWBQ0j0+SapambI1S5LLMqekQhRCZgLu7O7du3WLAgAHqxOXq1asAySZFDg4O6jYpJU0hISE8ePCAevXqJRnRTuiTMKCSM2dOtLW1efPm3xm7wcHB5M4dv4RJaGgoixYtYsCAAZiaan7GbpqSJicnJ7Zt28bBgwfV7z/t7OzYs2cPDx48oFixYkRERHDlyhUsLS3TNeDsqH3Pmlw8fRefm8/4qd9aSZwEAPqG+nSf0onuUzppOhQhsgRFUQiPjf54Qw0w1NZN91fq0dHRLFiwgJw5cyYaxXnx4gUA+fPnT9InX758AEkGTt7n7++PoijJ9jcxMcHY2FjdX0dHBycnJ7Zv306tWrXw9/fHy8tLvWftqlWrMDAwoEuXLml/0HSUpqSpZ8+eeHh4MHToUNq1a8eUKVPo0KEDu3btonfv3jRq1IizZ88SFBTEd999l94xZztFi1syY0VPxvZZHZ849V3Db8t7SOIkhBDpRFEUvj+5hsuvktYEZgQVzAvzR60e6Zo4/fXXX7x48YLBgwdjbGysPv7u3TsAjIyMkvQxMDAAIDw8PMm5BCEhISn2BzA0NEzUf8KECfTv359WrVoB0KBBA7p3787Lly9Zs2YNkyZNQk9P7xOf7stI00ZQRYsWZeXKlRQvXpzY2FgAKlSoQOfOnXn+/Dlr167lzp07WFpaqrNF8XmsbfMxY0VPzHIZ4XPrOT/1XUPI25T/pxVCCPFpstvUiI0bN6Kvr4+zs3Oi46mZH/ah5O1j/f+7hqONjQ379+9n586dHDp0iEWLFqGnp8eCBQsoUqQILVu2JDQ0lJ9++omaNWvy7bffsnHjxo/G+CWkeUXwSpUqsWfPHsLCwtTHJk2aRNOmTbl8+TLm5uY0adIEExOTdAlU/Js4je2zWp04yYiTEEJ8PpVKxR+1emSb13P+/v5cuXKFBg0akDNnzkTnEkadIiIikvRLGCHKkSNHitdO6J/SaFR4eDiFChVKdExXVxc7Ozv15wcPHrBjxw6WLFmClpYWU6dO5cyZM7i6uuLn58f48eMxMzNLVGj+NaQpaTp+/Dg1a9ZEW1s7yfBb5cqVqVz56+2Tld1Y2+Zj5spejOm9ShInkelERkRz54Yvj+75803tUuTNn1PTIQmhplKpMNLJGK+BvrTDhw+jKApNmzZNci4hoUmobXrfh+qdEhQsGL+kib+/f5JzISEhhIWFqWujUuLm5kbFihWpVasWsbGx7NmzhwEDBlC1alUAPDw82LlzZ+ZImvr370/u3Llp2rQpLVu2xN7ePr3jEh9QtISlJE4iU3gTHMqtK0+4cekxNy8/5t6t58TExL/Sv3/bjx9/baPhCIXInry8vFCpVNSsWTPJOXt7e7S0tJJdMijhWPny5VO8tomJCTY2Nly/fj3JuYSZeRUqVEix/+XLlzl8+DDbtm0D4mfTRUdHY2Hx796b5ubmeHt7p3iNLyVNNU1169bl7du3bNiwgQ4dOtC0aVOWLVvG8+fP0zs+kYKExMkslzE+t54zro/UOAnNUhQFP99XHPa4zLxfd9G39Tw61p7Or0M3smPdKbyvPSUmJpZcFibUbFSWNs7VNB2yENnWjRs3KFq0aLLT+C0sLKhWrRoHDx7k6dN/C+NfvXrFjh07KFWqVKJXaclp2bIlfn5+7N27V30sLi6O1atXo6enR7NmzVLs6+rqSpMmTdQDMubm5ujp6SWK5cmTJx8drfoS0jTStGTJEt6+fcvff//Nnj17uHjxInPmzGHevHlUrFiRVq1aST3TVxCfOPVkTO/V3LsdnzhNXyEjTuLriI2J5cFdf25efqz+9eplSJJ2RYrloUx5q/hfFazIVzCXrEYuhAZFRUXh6+tLjRo1UmwzZswYOnbsSOfOnenevTt6enps3LiRt2/fMm9e4gV1T58+TWBgINWrV1ePBnXr1g0PDw/Gjh3LzZs3sba2Zv/+/Zw9e5bRo0eTJ0/yC+4ePnyY69evM2PGDPUxLS0tmjVrxsaNGzEzM8Pf35/r168za9asdPhpfJo0baPyXy9fvmTfvn3s3buXGzduAKCvr0/dunVp3bo1derU+dxbZAqa2kblkY8/Y3qv5k1wKMVLF2D68u6YmiU/1VOItIoIi8L7+lN1gnT76lPCw6IStdHR0aa4XQHKVohPkuzKFcEsl3EKVxRCaIK/vz+1atWiadOmzJkzJ8V2t2/fxs3NjX/++QctLS3Kli3LsGHDKFeuXKJ2Xbt2xcvLi/Xr1+Pk5KQ+/urVK9zc3Dh69CihoaFYW1vTvXt3Wrdunez9YmNjadGiBU5OTkyaNCnRuZCQEKZOncqxY8cwMTHBxcWF7t27p/VHkERqv7/TJWl639OnTzlw4ACbNm3C398flUrFrVu30vMWGZYm956TxEmkt9dB77h55Ul8knTpMfe8nxMbk3jjXyMTfewci6hHkWzLFMTAMHsU0gohso4vuvdcSq5fv86BAwc4duxYqirsRfopWsKSmat6MqbX/1/V9V0riZNINUVReP4kiJuXH3Pj8mNuXnrCs8eBSdpZ5M1BmQrxCVLZ8lZYFbdEWztNpZFCCJHpfHbS5OPjw759+9i/fz9Pnz5FURSMjY1p27YtrVq1SjRUJ76sosUTJ05j+6xhxooekjiJJGKiY7l/x089inTz8mNev0q6T2TR4pb/H0WKH03Kmz+n1CMJIbKtNCVNT58+Zd++fezbt4979+6hKAra2trUqFGDVq1a0bBhQ/T19dM7VpEK7ydO9739JHESAISHRXL72lN1guR9zZeI8MT1SLq62tiWLRQ/klTeCjvHwvL/jRBCvCdNSVPDhg1RqVQoikKpUqVo1aoVLVq0SLSGgtCchMRpbG9JnLKroJch3HpvVtv9Oy+Ii01cj2RiaoBdeSvKlI8fRbItUxA9fV0NRSyEEBlfmpImCwsLWrRoQevWrSlZsmR6xyTSQdHilsxYKYlTdqAoCr6PAtULSN68/Bi/p6+StMtbICdlysfXIpUpb0URmzxoaUk9khBCpFaakqaTJ0/KH7aZgCROWVdUZDSXzt7nzNFbnD9xhzfBieuRVCoV1raW/66PVL4IefLl1EywQgiRRaQqaVq1ahXdu3dHW1sbIM0JU1RUFGvWrKFfv35p6i8+Xfyrul6M6bVKEqdMLiw0kguedzl95CYXPO8mWiNJT1+HkmULqZOk0o6FMZFFToUQIl2lap2m6tWrY2ZmxsiRI6lXr94n3yQmJob9+/czb948IiMjOXXqVJqCzeg0uU7Txzy+H8CYXqt4/SoUm1L5mb68BzlySuKU0b0JDuXccW9OH7nF5bP3iI6OVZ+zyJuDag3sqF7PjtLliqCnl64riAghRLaRrotbvnz5kvHjx3Py5Emsra1p37499erVo2jRoin2URSFW7du8ffff+Pu7k5QUBC1a9fmt99+w9zc/NOeJpPIyEkTSOKUWbx88YYzR29x5sgtrv/ziLi4f3+LFrTKTfX6dlSvX4YSZQrIa3IhhEgHX2RF8AMHDuDm5sbTp09RqVTkzp2bkiVLUrBgQUxNTYmLiyM4OFi9L0xoaCiKolC8eHEGDRpEkyZNPu+pMriMnjRBfOI0tvdqgoPeSeKUgfg+CuTM0VucPnyLOzd8E52zKZWfavXtqF7fDiubvLJOkhBCpLMvto1KbGwsBw4cYOfOnVy8eJHIyMhk2+no6FClShXat29PkyZNssUf9JkhaQJJnDICRVF4cOcFp4/c5PThWzy+H6A+p1KpKF2uMNXrl6F6vdLkK5Q1R2aFECKj+Cp7z0VGRnLlyhX8/PwICgoiJiYGMzMzihYtioODA0ZG2euLOLMkTZA4cSpWMh8zVvSUxOkLi4uL4/bVp5w+Ev/q7cWzYPU5bR0tHKsUo3p9O6rWLY25hakGIxVCiOzlq+w9p6+vL9ukZFJWNnnVK4c/uPOCsX1WS+L0BcREx3L1wgPOHLnNmWO3CA58pz6nb6BLxWrFqVbfDqfapTCV2W5CCJGhyXSbbKxIMUmcvoSI8Cgunb3H6cO3OH/Cm3chEepzRib6ONUuRfX6dlSqVgIDIz0NRiqEyI58fHyYM2cOXl5eKIqCnZ0dvXv3pnbt2sm2u3z5MhERETg4ODBkyBAqVqyYqvsEBwezcOFCjh49SlBQEEWLFsXFxYV27dolavf27VsmTJjAyZMnyZ07N926dcPFxSXJ9dq3b0+pUqWYMmVK2h/+M0nSlM0llzhNX94Ds1zGmg4tU3n3NhwvzzucPnyLi6d9iIyIVp/LaW5M1XqlqV6/DI5VrNHVld92QgjNuH79Oi4uLkRHR9OpUyesra05evQoffv25aeffqJbt24A3L9/ny5duqCvr0/Xrl0xNjZm48aNdOvWjdWrV1OlSpUP3icsLIyePXvi4+NDly5dKFasGAcOHGD8+PEEBgbSv39/dVtXV1c8PT0ZOHAgL168YNq0aeTNmzfR5LEDBw7g4+PDwoULv8wPJpU+q6ZJJJaZapr+68mDAMb0+rfGSRKnjwsOesfZY7c5c+QWV84/ICbm3zWU8hbIGb80wP/XUNLWlqUBhBCa17ZtW27evMnSpUupW7cuED8x5ccff+Tw4cPs2bMHa2trevfujZeXF/v27aNw4cIAvHr1ihYtWpAzZ0727dv3wfssX76c2bNnM2vWLFq0aAHE13UmXPfQoUPkz5+fuLg4ypUrh7OzM6NHjwagXbt25MyZk5UrVwIQHR1Ns2bNaNKkCcOHD/8iP5fUfn/Ln+QCiB9xcl3di1y5TXhw5wXj+q5JsjWHAP/nwezccIaR3VfQpd5M5k/ezcXTPsTExFKkWB469anNgj8Hsu7ACPqNakrZikUlYRJCZAh+fn7cvHmTKlWqqBMmiJ+x279/f6Kjo3F3dycwMBBPT0/q16+vTpgAzM3NadeuHffu3ePq1asfvNeuXbvIkycPzZs3Vx/T0tKiV69eREdHs2fPHiA+EYuMjMTKykrdzsrKiufPn6s/b926lTdv3tCnT5/P/hl8LnlPINQKW+fBdXUvRvdc9f9XdfFbrmT3EacnDwI4ffgWp4/c4t7t54nOlShT8P+LTdpR2DqPhiIUQoiP8/PzA6BkyZJJziUkLTdu3FAnRI6OjknaOTg4AHD16tVkzwOEhITw4MED6tWrl2S5oYQ+165dAyBnzpxoa2vz5s0bdZvg4GBy584NQGhoKIsWLWLAgAGYmmp+VrEkTSKR9xOnh3ezZ+KkKAo+t55z+vBNzhy9zdOHL9XntLRUlKlgRfX6ZahWrzR58+fUXKBCiHSlKApRcVEfb6gBelp6n73eYcIyQKGhSd8iBAfHL4Hy8uVLXrx4AUD+/PmTtMuXLx8Avr6+Sc4l8Pf3R1GUZPubmJhgbGys7q+jo4OTkxPbt2+nVq1a+Pv74+XlxZAhQ4D4vW8NDAzo0qXLpzzqF5PuSVN0dDRv3rzBwsIivS8tvhJ14tQr+yROsTGx3Lj0OL5G6egtAvz+/VuPjo425avaUK2eHVXrlCJnbhMNRiqE+BIURWHq7Rnce3dP06Ekq4RJccaXHvtZiVPx4sUxMzPjxIkThISEJBq5OXDgAAARERG8exe/NEpyay0aGBgAEB4enuJ9QkJCUuwPYGhomKj/hAkT6N+/P61atQKgQYMGdO/enZcvX7JmzRomTZqEnl7GmGmc5qQpKCiIzZs3U69ePezs7AD4448/cHNzIzw8nAIFCvDzzz8nmcIoMofC1nlwXZW1E6ew0Ej+Oe3D2eO38Tp5l3dv//1NbGCoR6UaJahe344qNUtibGqgwUiFEF9DVt+3QkdHh4EDBzJ9+nR69uzJ6NGjyZcvH8ePH2fRokWYmJigo6NDauaHfSh5+1h/RVES9bexsWH//v34+PhgYmJCkSJFAFiwYAFFihShZcuWhIaGMm3aNDw9PTExMcHZ2Znvv/8+lU+eftKUNL148YJ27doRFBREnjx5sLOz4+bNm0ybNg1FUciRIwfPnj3jhx9+YNu2bZQuXTq94xZfwX8TpzG9VzNzZc9MnTgFvQzh3PHbnDvmzZXz94mO/nfGW46cRlSpVZLq9e2oULU4+ga6GoxUCPE1qVQqxpcem6VfzwF0796dyMhIFi9ejLOzMwB58+bl999/x9XVlZw5c2JsHP9nfERERJL+CSNEOXLkSPEeCf1TGo0KDw+nUKFCiY7p6uqqB2AAHjx4wI4dO1iyZAlaWlpMnTqVM2fO4Orqip+fH+PHj8fMzCxRofnXkKakafny5QQGBtKoUSOqVq0KwJYtW1AUhR49ejBmzBiOHz9O//79WblyJbNnz07XoMXX837i9MjHP9MlToqi8OTBS84eu825Y7fxvp74PXz+wuZUrVuaqnVKYVeuCNo62hqKVAihaSqVCn1tfU2H8cX169cPZ2dn7ty5g4GBASVLliQ2NpahQ4fi6OioTmgSapve96F6pwQFCxZEpVLh7++f5FxISAhhYWHq2qiUuLm5UbFiRWrVqkVsbCx79uxhwIAB6pzDw8ODnTt3Zo6k6dSpUxQsWJC5c+eipRU/nfrYsWOoVCr1wlh16tShXLlyXLhwIf2iFRqRXOI0Y0VPcppnzMQpNjaO21eecPbYbc4e9+b5k6BE50uWLUTVuqX4pm5prGzyZovNpIUQAuJrl3R1dWnQoAEVKlRQHz916hTR0dFUqVIFe3t7tLS01DPc3pdwrHz58inew8TEBBsbG65fv57kXMLMvPfv/V+XL1/m8OHDbNu2DYgvUo+Ojk5UK21ubo63t/dHnjb9pSlp8vf3p3bt2uqE6e7du7x8+RJra+tE2aOlpSU3btxIn0iFRv03cUrYciWjJE4RYVFcOnePs8du43XyDm+Cw9TndHW1Kedkwzd1S/FN7VLkzpvysLIQQmRlmzZtwtvbm0qVKpEzZ04A3r17x/z588mbNy/NmzfHwMCAatWqcfDgQYYMGZJoccsdO3ZQqlSpRK/SktOyZUvc3NzYu3evejQoLi6O1atXo6enR7NmzVLs6+rqSpMmTbC3twfiEyQ9PT2ePn2qbvPkyZOPjlZ9CWlKmkxMTBK96zx58iSAetgsQUBAgPrdpsj8EhKnMb1XZ4jE6XXQO86d8ObsMW8un7tHVGSM+pxJDkOq1LKlap3SVKxeAiPjrD/kLoQQHzNw4EB69epF165d6dChA4qisG3bNh49esTixYvVs+PGjBlDx44d6dy5M927d0dPT4+NGzfy9u1b5s2bl+iap0+fJjAwkOrVq6tHg7p164aHhwdjx47l5s2bWFtbs3//fs6ePcvo0aPJkyf5de0OHz7M9evXmTFjhvqYlpYWzZo1Y+PGjZiZmeHv78/169eZNWvWF/oppSxNSZO1tTUXL14kKCiInDlzsmfPHlQqVaIVRi9dusSVK1c+uj+NyFwKW+dh5sqeGkucnj58yblj3pw9fpvbV58mmqVhWSBnfH1S3dKUKW+Fjq7UJwkhxPuqVq3KypUrWbx4MfPmzUNHR4dy5coxbdo09cKVALa2tmzatAk3NzcWL16MlpYWZcuWZebMmZQrVy7RNZcuXYqXlxfr169XJ00GBgZs2LABNzc3du/eTWhoKNbW1sycOZPWrVsnG1tsbCxubm60b98+0QrhAOPHj0dRFFasWIGJiQnjxo1Tb8/yNaVp7zkPDw9Gjx5Nrly5MDQ05Pnz51hZWXHgwAG0tLT4+eef8fDwIDIyEjc3N7799tsvEXuGk5n3nvtUTx++ZEzv1bx6GULR4pbMWPllEqe4uDi8r/nG1ycdu43vo8BE50vYFeCb/ydK1iUspT5JCCHEJ0vt93eaRppatmyJr68vK1asIDg4mGLFiiUqCr948SIxMTGMGzcu2yRM2U2iEad7/oztvTrdEqfIiGgun7vP2WO3OX/Cm9ev/l29VkdHG4cq1lStU5pv6pQiTz6zz76fEEIIkRppGmlKEBUVxbt37zA3N090/Ny5c9ja2iY5ntVlp5GmBL6PAhnda9Vnjzi9CQ7l/Mk7nDt2m3/O3CMyIlp9ztjUgMo1bKlatzSVqpeQhSaFEEKkq9R+f39W0iQSy45JEySTOK3okaqtRp4/CVK/drt15Qlxcf/+r5gnnxlV65bmm7qlsK9YFF1d2SZRCCHEl/FFX89B/CjT/v37cXR0xNraGoivev/999/x8/PD0dGRcePGfXRaosj8ChW1+P+sulXxr+r+v+XKfxOnuLg4fG4+j9/f7dhtntwPSHTeplR+vqlTiqp1S2NTKr/UJwkhhMhQ0jTS9Pr1a7p06cLDhw+ZMmUK7dq149GjRzRv3pyYmH+nfRsbG7Nr1y71Gg9ZXXYdaUrg+yiQMb1XERTw74iTkYk+V70ecvb4bc4d9+bVyxB1ey1tLRwqFuWbuvH1SfkK5tJg9EIIIbKrLzrStGrVKh48eEC5cuXU+8r9+eefxMTE0KpVKyZOnMju3buZMmUKy5cvZ8qUKWm5jchkChW1YObKf0ecBnVcTOi7CMLD/t3LydBIj0o1bKlatxSVa5bENIehBiMWQgghUi9NSdOxY8ewsLBg/fr16OnpAfHZmUqlon///piYmPD999+zbds2Tp8+na4Bi4zt/cQpMOAtALnzmvJN7dJUrVsKhyrF0NOT+iQhhBCZT5q+vXx9falRo4Y6YXr8+DFPnz6lQIEC6vomACsrK44ePZo+kYpMo1BRC9zW9+X0kVuUKW9FCbsC6uUohBBCiMwqTUmTvr5+otolT09PAL755ptE7YKDg9WJlcheLAvkom3X6poOQwghhEg3afrrf9GiRbl69Srh4eEA7N+/H5VKRZ06ddRtHj58yKVLlyhRokS6BCqEEEIIoUlpSpqaN29OcHAwbdu2pXPnzly6dAkLCwt10rR06VK+//57YmNjadOmTXrGK4QQQgihEWl6Pefs7Iy3tzc7duwAwMzMjFmzZqlfxbm7u/Pq1Su6detGx44d0y9aIYQQQggNSVPSpFKpmDZtGoMGDeLly5fY2tpiYPDv1haDBw+mRIkSlCpVKt0CFUIIIcTn8/HxYc6cOXh5eaEoCnZ2dvTu3ZvatWsnalevXj2ePXuW7DWOHDlCoUKFPnif4OBgFi5cyNGjRwkKCqJo0aK4uLjQrl27RO3evn3LhAkTOHnyJLlz56Zbt264uLgkuV779u0pVaqURpcx+qy53/nz5yd//vxJjrdo0eJzLiuEEEKIL+D69eu4uLgQHR1Np06dsLa25ujRo/Tt25effvqJbt26ARASEsKzZ8+oU6cOTZs2TXKdj+0tGxYWRs+ePfHx8aFLly4UK1aMAwcOMH78eAIDA+nfv7+6raurK56engwcOJAXL14wbdo08ubNS5MmTdRtDhw4gI+PDwsXLkynn0TafFbS9PLlSzZv3oyXlxcvX75ET0+P3Llz4+TkROvWrZNNqIQQQgihGZMmTSIsLIylS5dSt25dALp06cKPP/7I77//Tq1atbC2tubOnTtA/ErZrVq1+uT7/PHHH9y6dYtZs2apB1I6dOhA7969WbhwIa1atSJ//vzExcXh4eGBs7Mzffv2BeDatWts375dnTRFR0czZ84cXFxcsLS0TI8fQ5qlOWk6ceIEI0aMIDQ0lPd3YvHx8eH8+fOsXLkSV1dX9dLkQnxNMTGxhEVEEx4RRXhENGHh8f8Mj4iKPx4eRdj/z4X//1xYRBSRkTHo6mqjr6eDgb4u+no66OvrxP9TTxeDhH///zkD9fl/2xr8/991dLRk/zwhRIbh5+fHzZs3qVKlijphAtQLUx84cAB3d3dGjBihTprSOgN+165d5MmTh+bNm6uPaWlp0atXL06fPs2ePXvo27cvr169IjIyEisrK3U7Kysrbt++rf68detW3rx5Q58+fdIUS3pKU9J0//59hg4dSmRkJK1bt6ZZs2YUKlSI2NhYfH192b9/Px4eHowcORJ3d/dEC14K8V8xsXH/SW6iCA+PVic97yc87ydAYf/tExGtbhcVHavpx0JbS4Xee8mXgb4Oenr//ntCIhafaCV8fi8hS9RP5z/HEidpurramn5cIUQG5+fnB0DJkiWTnEtIWm7cuAGAt7c38G/SFBoaipGRUar+IhgSEsKDBw+oV69ekvaOjo5A/GgSQM6cOdHW1ubNmzfqNsHBweTOnVt930WLFjFgwABMTU1T/7BfSJqSpmXLlhEZGcm0adNo27ZtonM2NjbUrl2bKlWqMH78eNasWcPkyZPTJViRMSiKQmRUDO/CIgkLiyI0PJLQsChCw6MIC4skNDxKfS7sP8lMcgnQl0xwdHW0MTTQxdBAFyNDPQwN9OL/3UAPQ8P//9NAF0PD+H/q6+kQExNHRGQ0kVEx6n9GRcUQERmjPhYVFUNEVAyRkTFERMV/TjiXMPAaG6eon/1L0tHR4qdB39KoZukveh8hsjpFUYhVIjQdRrK0VQafPXJtZGQExCci/xUcHAzEl91AfNJkamqKq6srBw4c4O3bt+TIkYNWrVoxfPhw9bWS4+/vj6IoyZbomJiYYGxsjK+vLwA6Ojo4OTmxfft2atWqhb+/P15eXgwZMgSI3+vWwMCALl26fNazp5c0JU1nz56lZMmSSRKm93333Xds2LCBU6dOpTk4kb4URVEnKqH/T25CwxL+PT7xSf7cv8lQwr/Hxikfv+Enej/BMXwvqUlIcNQJz/8THEMDPYz+kwypjxvqYaj/9UdgFEUhOiaWiMiERCs+6Xo/AYuM/M/nqBgiE5K0/37+f9vEx/7tFxenoMQpREXFfDw4IUSKFEXhxPMevIq4qulQkpXboBy1Cqz+rMSpePHimJmZceLECUJCQhKN3Bw4cACAiIgI4uLi8PHxITw8nMDAQCZPnkxsbCyHDh1iw4YNXL9+nQ0bNqS440dISAhAiomVoaGhenFsgAkTJtC/f3917VSDBg3o3r07L1++ZM2aNUyaNCnD7C6SpqQpODiYSpUqfbSdtbU1R44cScstxH+8n8z8d4QncaKTMOqT0Oa9xCc8irh0THa0tFQYGephbKiHsZH+v/800sPIUB8jw38TGiPD+ATn3+RGDyPDfxMfQwO9LPGKSaVSoaerg57ul9+UWFEUYmLiiIuLQ19f94vfT4isTkXWrkHU0dFh4MCBTJ8+nZ49ezJ69Gjy5cvH8ePHWbRoESYmJujo6BAVFcWgQYMwNjamc+fO6v7Nmzdn6tSpbNiwge3bt6c4+vN+nXNK599P/mxsbNi/fz8+Pj6YmJhQpEgRABYsWECRIkVo2bIloaGhTJs2DU9PT0xMTHB2dub7779Ph5/Kp0nTn+zm5uY8fPjwo+0ePnyImZlZWm4h3rNxlxdLNpxMt+tpa6kwSkhyDPUwMtLH5P+JjrHRv0mQkaEeJu8lQe+fMzbSw0BfVwqdNUilUv0/0cz8yaYQmqZSqahVYHWWfj0H0L17dyIjI1m8eDHOzs4A5M2bl99//x1XV1dy5syJgYEBvXv3TrZ/t27d1G+RUkqajI2NARKNJr0vPDw8yRpPurq62NnZqT8/ePCAHTt2sGTJErS0tJg6dSpnzpzB1dUVPz8/xo8fj5mZWaJC868hTUnTN998w549e9i9e3eKUxF37dqFt7f3V3+grCg2Ng6VCrRUqkQjOYmTGP3/J0B6GBv+PyEyThj9iT9mZBSfBOnr6UiyI4QQ/6FSqdBRGWo6jC+uX79+ODs7c+fOHQwMDChZsiSxsbEMHTpUXaidkvcLtFNSsGBBVCoV/v7+Sc6FhIQQFhZGvnz5PngfNzc3KlasSK1atYiNjWXPnj0MGDCAqlWrAuDh4cHOnTszR9LUr18//v77b8aNG4eXlxeNGjVSZ42+vr78/fff7Nq1Cz09PfW6CyLtXL77hs4tK8sUdiGEEJ/lwIED6Orq0qBBAypUqKA+furUKaKjo6lSpQonTpxg+vTpdOjQgZ49eybqf+/ePYBESwT8l4mJCTY2Nly/fj3JuatX42vG3r/3f12+fJnDhw+zbds2IL4kKDo6GgsLC3Ubc3Nz9Qy/rylNG/ba2NgwZ84c9PX12bFjB/3796d58+Y0b96c/v374+7ujr6+PrNnz8bW1vazAvTy8qJnz55UrVqV8uXL06lTJ/bv3/9Z19yzZw8lS5Zk5MiRKba5f/8+I0eOpEaNGpQtW5batWszadIkgoKCPuveaaWrqy0JkxBCiM+yadMmxo0bx+vXr9XH3r17x/z588mbNy/NmzenePHiPHnyhI0bN/Lu3Tt1u5iYGObNmwdAmzZtPnifli1b4ufnx969e9XH4uLiWL16NXp6ejRr1izFvq6urjRp0gR7e3sgPkHS09Pj6dOn6jZPnjz56GjVl5DmatV69epx6NAh/vzzTy5evEhAQACKopA3b14qV65M+/btP3vlTg8PD0aPHq2ekqitrc3Zs2f58ccfuXfvnnpK4qfw8/P76BIInp6e/PDDD0RGRlKmTBkcHBy4fv06f/75J+fOnWP79u0ZYr0IIYQQ4lMMHDiQXr160bVrVzp06ICiKGzbto1Hjx6xePFiDAwMKFiwID/88APz58+nXbt2dOjQAZVKxZ49e7h58yb9+/enfPny6muePn2awMBAqlevrh4N6tatGx4eHowdO5abN29ibW3N/v37OXv2LKNHjyZPnjzJxnf48GGuX7/OjBkz1Me0tLRo1qwZGzduxMzMDH9/f65fv86sWbO+7A8rGSrlY2XuGhIYGEj9+vXR0tLijz/+oEyZMkD8CJCLiwtBQUHs2LFDfTw1FEWhW7dunD9/HojfI++/P/Tg4GC+/fZbQkJC+O2339Q1WxEREYwYMYLDhw/j4uLC+PHjk1w/YfVzmTEohBAiozpz5gyLFy/G29sbHR0dypUrx8CBA3FwcEjUbv/+/axbtw5vb2+0tLSwtbXFxcUlyShR165d8fLyYv369Tg5OamPv3r1Cjc3N44ePUpoaCjW1tZ0796d1q1bJxtXbGwsLVq0wMnJiUmTJiU6FxISwtSpUzl27BgmJia4uLjQvXv3dPl5QOq/v7/ovOgFCxYQGhrK2LFjP7nvxo0biYiIoG/fvokSIxsbG4YPH85PP/3EunXrcHV1TfU116xZw/nz56lcuTIXLlxIts2ff/5JcHAwPXv2TFTkbmBgwLhx47h69WqqZg4KIYQQGVG1atWoVq3aR9s1bdo02c16/2vDhg3JHjc3N2fq1KmpjktbWzvF8htTU1NmzpyZ6mt9KWmqaUqtLVu2sG7dujT1PXHiBBC/yNV/NWjQAJVKxfHjx1N9vTt37jBnzhzq1q37wUU5//77b4BkM9hChQpx6tQpVq5cmer7CiGEECJr+PIr8KWBoijqCv3kNgs0MzPDwsKCly9f4u/v/9HaqaioKEaOHImxsTFTp07l5Mnk1zyKiorCx8cHS0tLLC0tefLkCfv27cPX15fcuXPTuHHjT3odmF6eBr7G0/sR1UtaYZUn11e/vxBCCCEyaNL05s0bIiMjMTY2TnEZ9rx58/Ly5UsCAwM/mjS5ublx9+5d5s+fn2jK4n89e/aMmJgY8uTJw8aNG5k+fTrR0f/uG7Z8+XL69u3L8OHD0/ZgabTgrzMcuBy/43S5ovlpWcmOxuVsyWFo8FXjEEIIIbKzL/p6Lq0SVhE1NEx5kTF9fX0AwsLCPnits2fPsnbtWlq2bEnjxo0/2DZhv5xHjx4xdepUOnTowKFDhzh//jyurq6YmpqybNkytmzZ8imP89n6NqhCjVJF0VKpuPLIj8nbj1D3l+WM2rAPz9sPiYmN+6rxCCGEENlRhhxp0tKKz+VSsy5RXFzKCcPbt28ZN24clpaWTJw48aPXioqKAuLXrGjTpg0///yz+lyrVq0wNDRk8ODBLFy4UD0F82sons+CJX3aEPDmHfsueeNx8Rb3XgTx15W7/HXlLhamRjSvWJqWlewokT/lkTQhhBBCpF2GTJoS9q2JiEh5D6DIyEgg5V2UAX799VdevHjB6tWryZEjx0fv+/7IVnIbATZq1IjcuXMTEBDAgwcPsLGx+eg101NeMxN61K1E9zoVueUbgMfFW+y/5E1gSBhrj//D2uP/ULpQXlpVsqNp+VLkMsn62wEIIYQQX0uGTZqMjY0JCQkhIiICA4OktTsBAQFAfG1Tcq5fv87evXvJmTMn7u7uuLu7q8/5+voC8Uu1jxw5EhsbGwYMGIC5ubm6zX83E0xQsGBBgoKCCA4OTvPzfS6VSkWZwpaUKWzJyBa1OHn7IR4Xb3Hy1kNu+wZw2zeAWXtOUqu0NS0r2VGrtDW6OrKpqxBCCPE5UpU0ubi4pOni7y/T/ilUKhUlSpTgypUr3L9/P8mMtdevXxMYGIiZmVmKReAJtU6vX79mz549ybbx9fXF19eXKlWqMGDAAPLnz0+OHDl4+/Yt/v7+5MqVdKZaYGAgQKIES5N0dbSpb1+c+vbFefUujAOX77D74i1u+wZw9MZ9jt64Ty5jQ74tX5KWleywK5RXtmMRQggh0iBVSZOXl1eab5DWL+iaNWty5coVDh8+nCRpOnz4MIqiUKtWrRT7Ozk5cefOnWTPubu7M27cuGRXBK9VqxZ79+5l//79lCpVKtG5u3fv4ufnR548eT64WaGmmJsY8X3N8nxfszw+foF4XLzF3n9uExgSxqZTV9h06grF8+WmVSU7mlUsRZ4cJpoOWYgMz//NO64/9sPHL5BadsUoU/jztocSQmReqUqapk+f/qXjSKJdu3asXLmStWvXUrNmTfWOyA8ePGDu3LkA9O7dW90+ICCAkJAQTE1NU3xllxrdu3fnwIEDrFmzhooVK1K7dm0gfjn4CRMmoCgKXbt2RVs7Y7/uKpHfghEtajG0aQ3O3n2Mx8VbHL1xn3svgpi915M5+05RraQVrSrZUbesDfq6GfJNrRBfVXhUNLd9A7j22I9rT15w7bEf/m/+3bD0+lN/FvdurbkAhRAalapvyo/tZvwl5MuXj/HjxzNx4kScnZ1xcnJCT0+Ps2fPEhkZyYgRIxKNBLm5ubFz507atGmTaKO/T2Vvb8+4ceOYNm0affv2xdHRkVy5cnH58mXevHlD9erV6dWrV3o8Yqo9fB2M5+NHfFe6DMZ6ep/UV0dbi5qlralZ2pq34RH8feUuuy/c4upjP055P+KU9yNMDfRpXM6WVpXtcLTKL6/vRLYQF6fwODCY6/9Pjq49ecHd5y+JjUu8HaeWSkWJ/BbYF8nH9zXLaSZYIUSGkKGHF9q3b0++fPlYvnw5V65cQVtbGzs7O3r27EmjRo2+2H27du1K6dKlWblyJZcvX+bOnTsUKVKE/v374+Ligo7O1/2xzT9/lt13brPpxjWWNGuJdc60rQqew9CA9lUdaF/VgUcvg/G4eIs9F2/z4nUI289dZ/u561hZ5KRlZTtaVCxN/lwfn3EoRGbxJiyCa4/91EnS9ScveBsemaRdnhzGOFjlx6FIPuyt8lOmUF6M9D/tLytCiKxJpSiK8vFmIjVSu0vyp7oR4E8vj528DAvFVE8ft8bfUt86fZY7iItTuHD/KR4Xb3Homg/hUTEAqFRQxaYwLSvb0cC+BEb6uulyPyG+hujYWO4+D0yUJD0OfJ2knb6ONnaFLHGwyo+9VT4ci+THMqeJjLYKkc2k9vtbkqZ09KWSJgD/d+8YdGAP//g9B2BIlaoMcaqKVjr+4R4aEcWhaz54XLzFhfu+6uOGero0dChB68p2VCxWCC0t+ULJKBRF4XnwW/R1dMhlYoi2VoZc5P+LUhQF/9fvuPrEj+uP4xOkW77+RMbEJmlbNE8u7Ivki0+SiuTDtoAFuhm8PlGI9Obj48OcOXPw8vJCURTs7Ozo3bu3uoY3wdGjR1m2bBne3t4YG/+vvTuPj6o6+D/+uXNnzUz2nSXsYYewhbqAstQNUbFirQtai1b0eeyv1uJSl2qtu9b6tOrjY11abVWqiCJURRRckAASCGFfk0CWyT6T2WfO7487mSQkgQAJk8Tz7otmcu6dO2cGmfnOWa3k5uZy2223tbknbFtqamr4y1/+wurVq6mqqmLgwIEsWLCAK664osV59fX13Hfffaxdu5bk5GSuv/76Nmftz58/nxEjRvCHP/zh5J98O2RoioKuDE0AvmCQP371Jf/Ymg/AjIGD+dP5FxJn6vw96A5X1/HRph18uGE7xVV1kfI+iXHMnaytPp6VktDpjyt1jBCCr3Yc4OVVeWw5VApoY2+SbBZS42ykxFlJjbVqP8N/mspiMJ7mLubO5PL6KSwpb9GKZK9vaHVenMXE2HA3W2NIio+R+zVKP2wFBQUsWLAAv9/PVVddxaBBg1i9ejVff/019957L9dffz0A//rXv/j9739PcnIyV199NTExMSxZsoTy8nJefvllJk+efMzHcblcXHPNNezZs4err76awYMHs3LlSr777jt+/etfc8stt0TOve+++/j4449ZtGgRZWVlvPXWW/z5z3/mggsuiJyzcuVK7rnnHj755JPj7jd7MmRoioKuDk2N3ttRyH2rV+ENBhgQn8CLcy5hREpqlzyWEIL8g0dYtmE7n2zZjdPjixybMLAPl0wZxfnjs4m1mLrk8aWWQiHBqoI9vPL5BnYc1hZ4VXUKQkDoBP4px8eYtSAVDlFamLI1hatwwLKaozuWJxQSHKiojsxkKygqY09pZavnquoUsvuktghIA1ISZauoJB3l8ssvp7CwkJdeeokZM2YA2vv8r3/9a1atWsVHH31EfHw8M2bMwGQysWzZMjIzMwFoaGjg0ksvRVVVli9fjsHQ/rCNl19+mWeeeYann36auXPnAtq2ZwsXLiQvL4/PPvuMzMxMQqEQOTk5XHvttSxevBjQZs8nJCTwyiuvAOD3+5kzZw4XXHABd9xxR5e8Lh39/O65Xzd/wH4ycjTDk1NY9PGHHKqr5Sfv/pPHZ5/P3OwRx7/zCVIUhQmD+jJhUF/unjeD1QV7+XDjdtbtLmLzwSNsPniEx5d+wcwxQ7lkyijOyM76QXYRdbVAMMTK/F288nke+8urAa3b9Kozx7HgnEkkWC3UNLiw1zdQWd+Avb4Bu6PpdmXkdxf+YJA6l4c6l4e9ZVXHfFyL0dCilap5qEqJbSpPsJo7ZRxQtdMVbj3SQlJhcTkOT+vB2unxtshg7XEDMhnZLw2LUY67k6RjKS0tpbCwkNzc3EhgAu19/pZbbmHlypW8//77DBkyBI/Hw8KFCyOBCbTdOq677joeffRRvvvuO6ZNm9buY33wwQekpqZy8cUXR8p0Oh2/+MUv+Oabb/joo4+4+eabqa6uxuv1tlj7cMCAAezYsSPy+7vvvktdXR033XRTZ70UJ02Gph5qTFo6y666hl/952O+KS7iV//5mK3lZdx11nT0XRRazAY9F00cwUUTR1BR52T5ph18uHE7+8qrWZm/i5X5u0iLszJn0kgunTyKIRnJXVKPHxJfIMCyDdt59YuNlIS7SWMtJq45O4drpk0gwdq0v2BqnO24C5YKIah3e7HXO48bsFxeP26fn6LKWoraGETdnF7VkRLbFKZatFg1K0uyxaBXtf8+/YEgO4/Yten+h8ooKCpt0RXcyGLURwZrN85oS4+XC7NK0okqLdW68ocPH97qWGNo2bZtW2T/16MXeAYYOHBg5Lz2QpPD4WD//v3MnDmz1Zep8ePHA7B161YAEhISUFWVurqmf/s1NTUkJ2ufHw0NDfz1r39l0aJFxMbGdvi5dhUZmnqwJEsMr1/6E55e9zX/u2kDf9u8icKKCp6/8GJSjrGRcWdIi7dx48wp/HzGZLaXlLNsww5WbN5JRX0Dr32xkde+2Mjo/unMyx3NhROGE2eRY0lOhNvn573vCnjty01UhBdXTLJZuG76RK46azw288l1hyqKQnyMmfgYM0MzUo55rsvr0wJVi5aqBiodDS3Ka10eAsEQZbUOymodx3l8SLTGkGA1U1JVh6+NwdqD0pIYNyAjEpKGZqREgpYkdSUhBAh3tKvRNsVyyq25jRvcNzS0HgPYuJ+q3W6PnOd0Oo95XnvKy8sRQrRopWpks9mwWq2RPWD1ej1Tp07l3//+N9OnT6e8vJy8vDxuv/12AP72t79hNpu5+uqrT+SpdhkZmno4VafjrrOmMy49g8Wf/YfvDhdz6dv/4IWLLmF8Ruv/YDubtnlwBqP7Z3DnJdNYu/0AyzZu5+sdByksLqewuJynlq1h9rhhzMsdzZQh/eU4k2Nwery8/c0W/rH2e6qd2pt3WpyVn8+YzE9+NPa0dkHFmIwMSDUyIPXY64L5AgGqHK6mIOVoaLMVq8rhIiQE1U4X1U5tb8iEGLM2WHtABuOyMhmTlS4DthQVQghE9c/A/320q9I2w0RI+tcpBaehQ4cSHx/PmjVrIjtoNFq5ciUAHo+HSZMmAbB8+fJWi1s3P689Dof25SmmnS/vFosFt7spnN53333ccsstXHrppQDMnj2bG264AbvdzmuvvcaDDz6I8QQXdu4qMjT1EhcOzWZYUjK3fLyM/TU1/PTf7/DQuTP56Zhxp60ORr2e2eOGMXvcMKocLj7+fidL87axt6yKj7/fycff76RvUhyX5Y7m0smj5OKZzdQ2uHnrq8289XU+jvCCi32T4vjFzClcOmVUt57tZtTryUyMO+7fZzAUoqbBTWV9A9VON/2S4+mfHC/XRJKk00Sv13Prrbfy2GOPceONN7J48WIyMjL48ssv+etf/4rNZkOv1zN69GhmzpzJ6tWrueeee7j++uvR6/X885//JD8/P3Kt9hxvfpkQosW/+yFDhrBixQr27NmDzWYjKysLgP/5n/8hKyuLSy65hIaGBv74xz/y1VdfYbPZuPbaa7nmmmtO/UU5Qd33nVg6YUOTkll65TXc+dlKPtu/j3tWf8aW8jIePGcmptP8oZscG8OCcyZy3fQJFBaX837eNlZu3sXh6nr++p91vPDJOs7MHsBluaOZOWZItw4FXamyvoG/r9nE299uxe3zA1r31E2zpnDhhBG9qltK1WnjnlJirdGuiiS1oigKJP2rV3fPgba/qtfr5YUXXuDaa68FIC0tjaeeeoonn3yShIQEAJ5++mkeeOABli5dyvvvvw9o24w999xz3HDDDZHz2tI4Jqp5a1Jzbrebfv36tSgzGAyMGjUq8vv+/ft57733ePHFF9HpdDzyyCN8++23PPnkk5SWlvK73/2O+Pj4FgPNT4cf5idVLxZrMvHinEt5cWMez677mrcLC9hRaeeFiy4hMwqD6BRFYUxWBmOyMvjtJeewqmAvS9dvY8O+Er7ZdYhvdh0iPsbMnIkjuHzqGIb36ZqlE7qb0pp6XvtiE++vL4gswjiiTyo3zc5l9thhsgtTkqJAURRQunY8aHfwy1/+kmuvvZZdu3ZhNpsZPnw4wWCQX/3qV5GB2larlWeeeYa77rqLoqIiUlJSGDhwIOvXrweItAa1pW/fviiKQnl5eatjDocDl8tFRkbGMev47LPPMmnSJKZPn04wGOSjjz5i0aJFnHHGGQB8+OGHLF26VIYm6dTpFIXbpkxlbFo6v/rPx2wpL+OSt//B/1w4lx/16x+1elmMBuZOGsncSSMprqzlgw3bWbahkPI6J//8Op9/fp3PyH5pXJ47hgsnDO+VCxEestfwt9Ub+GjjDgKhEADjB2Ry8+xcpo0cJLuqJEnqUitXrsRgMDB79mwmTpwYKf/666/x+/3k5ubicDj47LPPGDp0KOPGjSMtLS1y3po1awDIzc1t9zFsNhtDhgyhoKCg1bEtW7YAtHjso23evJlVq1axZMkSQBt87vf7SUlpmrySlJTEzp07O/isO0/vafuXWpk+YCDLrrqGkSmpVLndXLd0CX/bvOm4/c2nQ/+UBP77wjP55L5f8OJN8zhv/DD0qo4dJRX88f3VzHzoZRa/uYLvdhcRCkW/vqdqT2kli99cwSVPvMHSvEICoRBTh/bnb4uu4B///VOmjxosA5MkSV3un//8J/fccw+1tbWRMqfTyfPPP09aWhoXX3wxBoOBhx56iCeffLLF58W+fft49913mT179jFbmgAuueQSSktLWb58eaQsFArx6quvYjQamTNnTrv3ffLJJ7ngggsYO3YsoAUko9FIcXFx5JyioqLjtlZ1BdnS1MtlxSfw7/k/497Vn7Fs1w7++NWXbC0v47FZ5xFzjNVcTxdVp+PsEQM5e8RAapxuPv5+B+/nFbKntJKVm3excvMu+ibFcemU0Vw2pecNHi8sLuPlVXms3rYvUjZ95CBump1LzsA+UayZJEk/RLfeeiu/+MUvuO6667jyyisRQrBkyRIOHjzICy+8gNmstfAvXLiQv/zlLyxatIgZM2ZQWVnJ3//+d2JjY7n33ntbXPObb76hsrKSs846K9IadP311/Phhx9y9913U1hYyKBBg1ixYgXr1q1j8eLFpKa2PRRj1apVFBQU8Pjjj0fKdDodc+bM4a233iI+Pp7y8nIKCgp4+umnu+hVap/cRqUTna5tVE6GEIK/b93MH79aQyAUYnhyCi/NuZQBxxjMFy1CCLaXlLM0r5AV3++KrAitKPCjYVlcnjuGGWOGYDJ038z//f7DvLxqPd/sOgRodZ89dhg3zcplZL+049xbkiSp63z77be88MIL7Ny5E71eT05ODrfeeivjxjXNtg6FQrz99tv861//ori4mISEBKZPn85tt93Wau+36667jry8PP7+978zderUSHl1dTXPPvssq1evpqGhgUGDBnHDDTdw2WWXtVmvYDDI3LlzmTp1Kg8++GCLYw6Hg0ceeYQvvvgCm83GggULuOGGGzrtNZF7z0VBdw5NjfIOl/BfKz+i0uUizmTiT+dfxIyBg6NdrXa5fX4+L9jLB3mFrN/b1DQbZzExZ+JILp86mhF9u0cIEUKwbncRL69az6b9hwFtT7SLJoxg4awpDE6XK6RLkiR1RzI0RUFPCE0A5U4nt634kO/LSlGA//ejM7ltyo/QdfMxNcVVtSzbsJ0P8rTB441G9k3jstzRzJk4IiqDx0MhwZrt+3l51Xq2FWuzRQyqyqVTRnHjzMn0T0447XWSJEmSOk6GpijoKaEJwBcM8oe1X/BWgTaTYdagwTxz3kXEmU5ue47TKRgK8d3uIpbmFbJ62z78QW3KvlGvMmvMUOZNHc3UoVldPm0/GArx6ZY9/N/neewprQS0/fmuOGMsN5w7We6PJkmS1EPI0BQFPSk0NVqyfRv3f7EKXzDIwIREXppzCdnJx96TrDupbXDz8fc7eX/9NnaHgwtAn8Q4Lp0yisumjKZPUucOHvcHgyzftJNXV2/goF3bh8lqMvKzs8dz7bSJJMf2/nVeJEmSehMZmqKgJ4YmgIKKchZ9vIwjDgcxBgNPzDqfOdmtd8HuzrTB4xXhweM7Wwwenzo0i8unjmHmKQ4e9/oDLM0r5LUvNnKkph6A+Bgz106fwM/OyumV60pJkiT9EMjQFAU9NTQBVLlc/OqTj/m2uAiAmyZO5rdnTkOv63lLeXn8AT4v2MvSvG2s39M0eDzWYtJWHs8dc0Iz2FxeH0vWFfD6lxupdGgbzSbHxnDDOZO48sxxxJi6x0aSkiRJ0smRoSkKenJoAgiEQjy97mte3rQBgDP6ZfH8BXNIbmen6p6gpKqOZRsK+WDDdspqHZHyEX1SuSx3NBdPGtluC1G928O/vt7Cm2u/p9al7eidmRjLz8+dzLypYzB34yUPJEmSpI6ToSkKenpoarRizy4Wr/oEl99Ppi2WF+dcwrj007/yamcKhkKs31PM0rxtfF7QNHjcoKrMGjuEeblj+NEwbfB4tdPFm2s3869v8nF6fABkpSSwcOYULp40EoNejeZTkSRJkjqZDE1R0FtCE8DuqkoWffwhB2prMKoqD587iytHj412tTpFncvD8k07WJpXyK4j9kh5ZmIskwb35fOCvbh9AQCGZiRz06xczhufjV7teV2VkiRJ0vHJ0BQFvSk0AdR7vdz56UpWHdC2APnZmHE8MH0GJn3v6ZZqXHn84+934nB7I+Wj+6dz06xcZowe0uVLF0iSJEnRJUNTFPS20AQQEoIXNqznT999gwAmZGTy14vmkmGLjXbVOpXXH2D1tn1sPVTK2SMGcubwAXIDXUmSpB+Ijn5+y/4G6Zh0isJ/5f6Iv11yOXEmE5vLSpn7rzfJO1wS7ap1KpNBz4UThnPXZedy1oiBMjBJkiRJrcjQJHXIuQMHseyn1zIiJZUqt4trly7h9fzvkQ2VkiRJ0g+FDE1Shw1ISODf83/GJcNHEAiFeHjtF9zx6Urcfn+0qyZJkiRJXU6GJumExBgM/Om8i7h/+gxURWHZrh1cseRfFNXVRrtqkiRJktSlZGiSTpiiKPw8ZyJvzptPsiWGHZV2Ln37LdYcPBDtqkmSJElSl5GhSTppU/v158OrriUnPZM6r4cbP3yfv274jpAc5yRJkiT1QjI09QBbqkt4fOsnfFOxj6AIRbs6LWTGxvKvn1zJz8aMQwDPrPuGRR8vo97rPe59JUmSJKknkaGpB3hrfx5v7PuOhd+8yaz/PMefCj/ngKMy2tWKMOn1/HHmj3l81nkYVZXP9u9j3jtvsaeqKtpVkyRJkqROIxe37ERdtbhlhdvBS7vWsqJkG3V+T6R8fGI/5g0Yz4V9xxBnbHvT2dNtS3kZt378IaVOBxa9nty+/RiTlq79SU2nT2ysXANJkiRJOiHeYA359kep9+3jnL6vYVTjO/X6ckXwKOjqFcF9wQBflO1m6aF8vq7YSzD8V2fUqczKHMFlWeM5M20Iel10GxCrXC5u/8/HrCspanUsyWxhdFoaY9LSGZ2azti0dPrFxckgJUmSJLWp0v09eeX34AlWoCpmzsv6EIs+tVMfQ4amKDid26jYPU6WF29ladEW9tRXRMpTzTbm9h/HZVnjGRaX1uX1aE9ICArKy9haUc62inIKK8rZXV1FINR6TFa8yRwJUmNT0xmdlsaA+AQZpCRJkn7AhAixq/Y1dlS/iCCIzTCQ3PTHSTAN7/THkqEpCqKx95wQgu11ZXxwKJ/lJQXU+tyRY2MT+3Bp1njm9BtLgtFy2urUHm8gwM6qSgrDQWpbRTm7qirxtxGkYo0mRqemMSYcpsakpTMwIRFdFwapkoYa9jkqyUnqR3w3eL0kSZJ+qDyBKjZW3EeF+zsAsmxzyEm9F70upkseT4amKIj2hr2+UJA1Zbv54NAW1pbvIRCeaWfQqczIyOayrBzOTh+CQadGpX5t8QWD7K6q1EKUvYJtFeXsrLTjCwZbnWszGBmV2hiitJ+DEhJRT7I7ssxdz3r7AfLsB/mu8gBHXHVAU3fn5QNyOCNtMKoi50tIkiSdLhWu9WyouA9vsBJVMZOTcg9ZsXO7tPdBhqYoiHZoaq7K28Dy4gKWFW1hR11ZpDzZZGVu/7FclpXD8Pj0KNawff5gkL3VVRQ0du3ZK9hut+MNBlqdG2MwMDIllbHh1qjRaekMSUxqc1xXlbehKSTZD3CoobrFcb2iI90Sx2FXbaQswxLHpVnjmZeVwwBbUqc/V0mSJEkjRJAdNS+zs+b/AEGcYQi5GU8QZxzS5Y8tQ1MUdKfQ1NzOujI+OLSFj4q3Uu1zRcpHxmcwb0AOF/cbS6Kpa5o8O0sgFGJvdRWF4daobRXlbLdX4A60DlJmvZ5RKakMTUnCYlWoFw3scpaxz2FvcZ4OhdGJmUxNGcTU1IFMSM4iRjWwva6MpYc2s7y4oMVsxcnJA7h8QA7n9R2FVW/s8ucsSZL0Q+EOVLCh/F4qPZsAGBg7j3Epv0WvOz1DJWRoioKuCk3fVa3ni4o1jE8Yx9kpZxFniD2p6/hDQb4u38vSoi18WboLf7j7Tq/oOCcjm3lZ45mWMQxjN+q+O5ZgKMT+mhoK7eUUVJSzpeIIO+rL8KledOYgiiHE0a258TorI+MyODdzGBcPHEOy2dru9b3BAKtLd7G0KJ+vy/fS+A8lRm/kgr6juDxrAhOT+8sB65IkSaegzPUNG8vvwxeqRa/EMCH1PvrHXnha6yBDUxR0VWj6+8G3+LxiNQCqojIxIYdz0qYzOm4UupMcb1PjdbGiZBtLi/IprC2NlCcaY7i4/1guyxrPyPiMbh0I3AE/31cXsd5+gPX2gxTWHoksw9DILIz43Apel0LIq0Ko6fkYdSrZKSmMTUsPDzpPZ3hyCia9vtVjlbnrWVa0hfcP5VPUrFtvgDWJywfkcGnWeNItcV33ZCVJknqZkPCzvfoFdte+DkC8cThT05/AZhxw2usiQ1MUdFVo8oX8fFP5LWvsX3GgoWlT3BRjMtNTpzEt9SySjCc/3mZ3fQXLDuXzYXEBlV5npHx4XDqXZY3n4v5jSTHbTuk5dAZfMEB+TUlkTNLW6pJIa1mjLGsiU1MHMTVlIFNSB5JmjiUkBEV1tZFuvcYB521t9aLX6chOSmZ0syA1IiWVGIMB0GYrbqoqYumhfP5zuBBX0A9oXX1npQ/h8gE5zMwYjlFtHbwkSZIkjctfSl753VR7twIwOO6njE3+NarOFJX6yNAUBadjTFORq5gvK9ayrmodrqC2vICCwviEcZyTOo3xCeNQlZPrXguEQnxTsY8PivL5vHQX/pA2g01VFKalD2Ne1njOzcg+bYHAHwqyreYIeZVaSNpcVYw31HIMU6YljtyUQfwodSC5qYPoE9OxVWKFEBTX17GtoqJZmCqn1uNpda5OURiSmMTo1DRGp6UzJjWNkalpqKrCp4e3896hzWyqalrIM95gYW7/scwbkMOohMxTexEkSZJ6mSMNX7Kp4kH8oXoMOhsTUx+kr212VOskQ1MUnM6B4L6Qjw3VG1lj/4pdjt2R8gRDPGennMX01Gmkm09+cctan5uVJdv4oGgLW2sOR8rjDRbm9B/DvKwcRidkdmr3XVCE2Flbxnf2A6yvPMimqiJcAV+Lc1JMVnLDLUlTUweRZU3stDoIITjicFBoLw8POK+g0F5ORUNDm+cPiE9gTFoao1LTSI21sMdTyidHCin3OCLnjIhPZ15WDnP7j+v2g+0lSZK6Ukj42Vb1Z/bWvQVAomk0uelPYDX0jXLNZGiKiq4KTcK/C7yfg3kuir5/q+Ol7jLW2NfydeW3OAJNH9ij4kZyTuo0JiVOxKAznPTj73dU8kFRPsuKtlLRLBAMiU1lXtZ45maNI8184oPTQ0Kwt76C7+wHyKs8yIbKQ9T7W7b0xBss5KYOZGrqQKamDGJIbMppH2dV0eCk0F5BYUUF2+zlFFZUcNhR3+a5GTYbfVMsuA0uDnntTWtlKTpmZA5n3oAczk4bGvWtbiRJkk6nBn8J68vvota7HYCh8dcyJvl2dMrJfzZ1JhmaoqCrQlOo9jfg+QjQg+UyFOstKPqsVucFQgE21+azxv4V2+oKEeH5Xja9jbOSz+CctOn0tfQ56XoERYh1Ffv5oGgLq47sjHSVNY7nmZeVw8zM4Zja6b4TQnDQWRUJSevtB6lptgSCVlcTk1MGRELS8Pj0Ll0FvGX9QhA8DGq/4wazGrdbC1LhELXNXsHB2pqWJ+kEuhg/ptgQIX1Tt2KS0crlA8Zz+YAJDIpN6YqnIkmS1G2UOD/je/vDBEJOjLp4JqU9RKb1nGhXqwUZmqKgy1qaAkWI+ofA91W4RAXLvHbDE4DdW8lX9q9Za/+aGn/Th/lQ21DOSZ3G1KQpmNSTH3Dn8HtYWVLIB0Vb2FxdHCmPM5i5sN9oLsvKYXxiXw67aiMh6Tv7AeweZ4vrWFQDE5OzIiFpVELmaW+FEcIH7uWIhv+D4D4wnYMS9ziKmnxC13F4veyotEcW5CysKGdvTTUhIVAMQXTWAGqMn+ZDzlL1cUxPy+aqwZMYlXL6AqIkSVJXC4a8bK16hgP1SwBINucwJf0xYvQZUa5ZazI0RUFXj2kSvs0I519OKDyFRIitdQWssX9Ffs0WQmjdRRbVwo+Sp3JO6jQGWQeeUr0OOKpYVryFD4u2UOpu6raKNZhw+FvOUDPqVHKS+kdC0tikvlFbF0qEGsC9BNHwKoTKWh7UpaDEP4FimnZKj+H2+9lZaQ+3SlWwraKM3Q1liBiftpaU0lgX0HmNDDKmk5sygDHpGYxOTWNoUrLsypMkqcdx+A6RV76YOp825jY74UZGJd3SbbrjjiZDUxR0VWiqdG+myLGceNNwUi2TsIl6aDg6PF2GYl3UbngCqPXV8lXlN6yxf4Xd27Q69oCYLM5Jnc4ZyVOJ0Z/8YOWQEKy3H+CDoi18emQ7nmAAvaJjbGJfLSSlDiInqR9mNbr/aESoGtHwJrjeBFGrFepSUaw/B8NERP39ENijlcfcgBJ7J4rSeSuA+4JB9lRVsq70EJ8e2c4u9xF8ir+pfgGFYIOBYIMeE0ZGpKREZu2NTksnOym5zbWkJEmSuoMixwo22x8hKNyYdIlMTn+E9Jgzo12tY5KhKQq6KjRtKL+PYufHkd9NahIp5sn0M6WRGtqE3r8xfKRj4SkkQux07GJNxVdsrNlEQGjjbYw6I7lJkzkndTrDbENPacC10+9ln8PO0Li0brPliAgeQTS8Bu53QWjLNaAOQLEu1F43ReuuFMKDcDwBLm2GB/oRKAnPouiHdk29hGBTVTH/2LOeNRW7m5ZVEBDyqgQb9ITcehDa38fRa0mNSk0jwWzGpOox6/WY9ComVY9RVbv14qTdRUgIvIEAnvAfd8Af+d3drNwXDNAnNo6xaelYDN3z27IkRVMg5GZL5RMcciwDINU8hcnpj2DRn/xM7tNFhqYo6KrQ5A3WsL9uCZWeTVR5thASLbu80vSxjDYJ4ikPl6hguTQcno69sqrT7+SbqnV8aV/LEfeRSHkfcybnpE7jzJQzT3rblu5C+Pdo45U8y4FwINGPRrHdDKbzUNpZ10p4ViPq7g63RplR4u4Fy0+7NIi4Aj4+O7KDpYfyWV95MFJuUPQkiFjqa0LUOwNAx+pgUptClFmvx6SqmPTabeMxjjW/n6mtY6oaDmhNx0zNjp1qYAuEQu0EGL8WYvwBPMFwud+PNxjA7Q8HnGAAj9/fLAQFml1DK/c2C0RtbQR9LKqiMCIllfEZmUzIyCQnPYNBiUlyPJr0g1bv28f6ssU4/PsBhZGJv2RE4sJ231+7GxmaouB0rNMUFD5qPAXY3RupdG+iyruFkNDWMkrUBRlu8JOh1xalFOgIGGdhiP0tOsPAY15XCME+5z7W2L/iu+o8fCHtmqqiMilxIuemTmdk3IiT3rYlGoRvM6LhZW25hkbGM1CsN4PxzA59qItgOaLuLvB9qxWYfowS/wiKLrGLat2kuKGGD4ryWXpoC6Xuukj5QGsy4+OysAVs7K+uZXdVJQ0+LTh4AwG6wz9oBZpClF7FfFT4Mqp6/KFgswDTFHI8gQCBUOi4j9EVDDodZr0BczggWsJh0KzXY1BV9lVXU97gbHW/OJOJ8ekZ5GRkkpORyfj0DJIscl0uqfcTQnDIsYwtlU8QFB7MagpT0h8l1TIl2lU7ITI0RcHpXNyyUTDkpdpbQKV7E3b3Bqo9W4nTeRjRLDyFBFQrA/Ca55NovQCr/tibzLqDbr6rymONfS0HGg5GylNNKdq2LSlnkWjs+tBwMoQQ4FuDcP4f+DeESxWtRcl2E4ph3ElcMwSu1xCOZwE/6NJR4p9CMf2oU+venpAQfGc/wNJD+Xx2ZEek+07baHkYl2XlMDI+g2SzFZNOjz/cSuMNBvAFgnjDLTLeQABvMKi1ugSbfm9+zNfWuY23G+/T6jpNj9EVGsOL1rJliNzW/hia3dY3CzuGSEtYe+dbwr83P0ftwKD7UoeDzWWlbCkvZXNZKdsqytt87gPiE8IhKoOcjD6MTEnFqPaMb92ni9PnY191FXtrqtlbXcXe6ir21dTg8fsxGwzE6PVYDNrfWYzBgFlvIMag/TdgMRiw6A3hn03HI8fC99euY8Bi0EK87LLuPP5QA/n2Ryl2rgAgzXIGk9Mewaw/+W29okWGpiiIRmg6WjDkodq7Dbt7Ax73F2SIbWSE1wgSAooDeg6GMrCazyTVMokUy2Ss+vbXJTrUUMQa+1dtbttybup0xiWMPeltWzqTEAHwrNRalgK7wqWGcDflQhT94FN/DH8hovYOCB4AFLDejGK7HeU0zgap93lYcXgbSw/lt1ipvZFVbyTZZCPFZCXZfNRPk41kk5UUs/YzppPHmgkh8AWDkYDlCwTxBPyRgBUJaOHwZQh38bUXcCx6Q48Yl+UPBtldVRkOUmVsLjvC/pqaVucZVZXRqWmR1qgJGZn0jY3r9s+vM1S7XeytDgejmmotKFVXUeps3WrXlRRoFrb0LUJXY3lMi5CmD4e0lrfbuo+qUwgKQSgkCIoQwfDPkBAtbwtBKBQiKMJlIdHqdkiEj4eabkeuK0T4mqHI44SEaHW71eM2u1ZjHbKTU7ghZ+JJvZa13l3kld+F038IBZVRSbeSnXADSg/qjWhOhqYo6A6h6WjBkIe6hqXoXa9jE9pmv43haZffQIPQYdFnkGrWAlSqZQox+j6t3si9QS8bazbxpX0tux17IuUJhgSmpZ7FOanTSDWlntbnBtqgbdzvIRr+BsESrVCxamOPrDegqJ27HogIuRCOP4JbW3cEw1iU+GePO3asK+ypr2DpoXxWle6k3F2PL7xXYEfFqIZwgNJCVLLZSkrjbZONFHPjT1u3GcwfLUIIXIHDGHXxGNTjj/Gr83jYWl7G5rJS8stLyS8rbXNfw2RLTKQlKicjg3FpGcSaorNh6akSQlDe4GRPdRX7qqu11qOqKvbVVFHldrd7v9QYK4OTEkmJNWEwg1vxIpQQZp0Bg6LHoKioQkVFBaGghBRESCEYEgT8IdxBbYyby+/HHfDj9vsj499cfq3r1xc8sX8bPySbb76NeLO5w+cLIThQv4StVc8QEj4sajq56Y+TbMnpukqeBjI0RUF3DE3NCf9WQo7nUXxrtd8FlAQM7PTraRBN3w60EKUFqBTLZKyGlquIH3GXstb+VattW0bHjeKc1OlMTMw5pW1bOvRcQnXg+ifC9QaEqrVCXRJKzPUQczWKrmMb957043v+g6i7D0Q9KFaUuPvBPC9qrQZCCBx+L1VeJ5XeBqq8Tqo8DVR6nVR5GyK3K8PlR298fDwW1XBUmAq3XJmtpJpskdspJi1g9ZbWk2DIQ7HzE/bXv0Otdwc6xUhmzDlkxc4hPebMDq85I4TgUF0t+WVl5JcdIb+8jB32CvxHjd1SgGFJyZFB5uMzMslOSu5Qt+HpEgyFKKmvZ2+NFo6aQlIVTp+v3fv1jY1jaFIygxITiIvRE9D7qQ442eOoYFd9eWSD8BOhQyHWYCbOaNZ+hv9EbofLraoJs86AUdcyhAWDzUJXJHD5w5MK/OEgFogEMfdRwazxtsvvj4wl1Ot06BQFVVFQFR06nXZbp+hQm99WFHQ6Xfi8ptu68P1UXdNt3VH3067TeE5jWdPjtrjuUddqfnt4cgo/HtLxWcH+oIPv7Q9zuGEVABkx05mU9hAmNeGE/+66GxmaoqC7h6ZGwl+gLZLp/UL7HYU6ZTh7AhaOePYjaPmBGqPPJMUyhVTzJFItk4kJh6jGbVu+rFhLYf32yLYtsXobZ6WcyTmp0+hzCtu2tFn3YDmi4XVwvw0ivJGu2g8l5kaI+QmKYunUxzt2XUoRtXc2jZ0yz0GJewhFF3fa6nAyhBA0BHyRANUYtCo9Ti1seRuoDJdXeRtwB/3Hv2gzJp3+qFYrrUswJRystO5DG1m2RNRu2pTv9BdzoG4JBx3L8IcaF2zVAU0hx6RLpF/s+WTZLibBNOqEg6I3EKDQXkF+mdYSlV9eSkl96z0NYwwGxqVlMD4jQ5utl5FJmtV2Cs+uY3zBIIdqa9lbUxUeb6R1r+2vqWl3xqGqKGTFJzAsKZkhSUkMSkzEYAJHyMVeRwWFtaXtBqQ4g5lRCZmMTsgk3mjB4fdQ7/fg8Htx+D3U+dw4/B7ttt9zUiHraAad2iJkHSt4NZVZImWNC/MKob379eYZlNWeQvLK78IVOIyCnjHJv2Jo/DW95guSDE1R0FNCUyMtPP0VvKvDJTqE6UJq9NMo8x2m0r2RGu/2NkJUX1Itk0mxTAp352W0u21Lf0s/JidNYnLiRPpa+p70PzAROIBoeAXcHwDhD3H9cG0mnPlCFCU6iz0KEYSGlxHO54Eg6PqiJDyNYpwUlfp0hYaAjyqPFqDszYNVJHQ1BS9XoP2WhqMlGWM4NyObmZnDOTNtCBZ9lBc9FUHKXN+yv/4dyl3fQvhLQIw+k0Fx8xkYdxnuQDlFjuUUO1fiDVZH7htrGERW7Bz62y4ixpB50nWwuxrYUlZKfpnWtVdQXobT3/o1zbTFRgJUTkYmY9LSMJ/k6+f2+9lfU82e6mr21VRFWo4O1dW2O4vRqKoMTkxiaGISQ5OSGZqUxICEBPyKj92OCrbXlnY4II1O6MOYxD70i0k4ofcHT9CvhSqfFq7qw4Eq8tPnjoQu7bi7xfFgJ3z0mVV9JEidnTaEu8edf8rX7G6EEOyr+ycFVc8hCBCj70Nu+hMkmcdEu2qdSoamKOhpoamR8G8Ltzw1hSfMF6PYbiWoy6DKk4/dvRG7eyO13u0IWr4JWvX9IgEqyTyBvQ121lSsZUtdAUHRdG6GOZ3JiZOYnDSJgTEDOjbl31+gzYTzfkLjhxiGydoaS8Zzus23HOHLR9TdER5XpUOx3QbWRVELc9HiCvjCLVXOFmGqKvK7FrzsHkeLFiyzqufM1CHMzBzOuZnZJJusp63O3mAth+o/YH/9v3EFmgbXp1vOZHD8lWTEnN1qrZmQCFDhWkeR82OONHzZbO00hVTzZLJiL6aPbRYG3ak9j2AoxN6aavLLStlSps3W21NdReiot229TseI5JRIiMrJyGRQQmKLfx/1Xk/TYOxmM9YO19e3u0yF1WBgSFKy1nKUmBRpQcqw2djnrKSw5sgJBaQxiX0YnXDiAamzCSFwBf3U+1oGqbrmweuoMKYdb2zt8ra6ZrzBzNcX/bZXbXvkC9axqeJBSl1rAOhjncXE1AcxdmBcX08jQ1MU9NTQ1EgLT39ttq5RU3hqnH3mDzVQ5cmnMhKidrQOUYb+pJonYzOO4rDHz5b6w2yr24NfNLVYpRiTmZQ4kclJkxhqG9Ji/Sdt2YBvtZlwvnVNFzbNRLHe1G1bcUTIqW2s7NFWw8UwESXhGRS1b3Qr1g35Q0E2VRWxunQnn5fu4oiraR0qBchJ6s/MzOHMyhzOoNiULqlDtaeQ/fXvUOL8JLLWmUEXy4DYSxkcPx+bof1V9ZvzBx0cbvicIsfHVHo2RspVxUwf6wz6x84hzTIVXScFaKfPR0F5GfnlpWwJt0jZXQ2tzos3mRmfnoE/FGJfTRUVDa3PaZRoNodbjFqGo0xbLH4RYk99xQkFpDEJfRidmNktAlJXCIoQDX4f9X53JFgNtCWTYeneXfMnosqTT175PbgDZegwMDblTgbHze91f5eNZGiKgp4emhq1H54WoeiHtDjXH3JS5d6M3bMp3J23g+bjPhoZdYmgJFEbUCn2uKkP6HEGTTQETcQakrQAlZjDcGMRSsMrENgWvqeqPbb1JhRDdpc+784i3B8i6h/Uxlwpsdo4J8vF0a5WtyWEYFd9OZ+X7uKL0l0U1pa2OD7IlszMzOHMzBzO+KR+pzQOKhjyUuL8hP3171LjLYyUxxtHMCT+SvrZLkCvO/lxcS7/EYqcKyhyfIzTfzBSblJT6G+7gKzYi0kwDT/p67dFCMERpyPSEpVfVsq2ioo2xx1lWG0MSUqKBKTG7rXkGG0hTl8o2OGAFG8wMyqhj9bF1osD0g+NECF2177B9uq/IghiM2SRm/4ECaYR0a5al5KhKQp6S2hqJPyF4W67xuejNGt5GtLmffxBB5XhlqhqbwFO/6EWYz/a4g3p6aOGGGNyk6hqb/QhDAjzFehjb+6RLTUiUISouxP8+VqB5XKU2PtQdF0/gLenK3PXs7p0F6tLd5FnP4BfNIXwJGMM52ZmMytzBGekDu7wOKgG/2H21y/hUP0yfKFaAHQY6Gs7j8Hx80kyjevUD3shBDXeQoocyylxfhJ5TIA44zCyYi+mv+2CLtuTyx8MsrOqkoLyMgyqyrCkZAYnJhHXbDmDowPSttoj7K6vkAHpB8wTqGZjxf1UuLUdEPrbLiQn9Xen3M3cE8jQFAW9LTQ1Opnw1Jw/5MTpL8LpK8LpL6LBr/30+A/RR61kiD6ARaf9Z+gTsN9vYL/fgFcohBQbMfr+pFtGEG8chM2Qhc2QhdXQr8PTvaNFCD/C+QI0vAiEtM2BE545qVXJf6gcfg9fle/li9LdrCnf3WIsyfHGQQkRotz1Lfvr36XM9TWNY+Is+gwGx13BgNh5p2Xl4pDwU+b6hiLHcsoa1hJqnMiAjjTLVG38k3XGKbVwHY8MSNLx2N0b2VB+D55gJTrFRE7KXQyIvewH8/cuQ1MU9NbQ1Ej4t4fD06pwiaJNs7fdiqLv+FofIlipra/k+icIbZ2ngBJPlTKenT6VSu8hhKhCrxxrLSEdVn0frIYsbIb+kTBlM2YRo+/TaeNHOoPwbdCWJgiVAnoU26/A2nM2suwu/KEgGysPsbp0F5+X7mqxH58CTAiPg5qenokQX3OgbgkNgZLIOWmWMxgcP5/MmOlRe+19wXpKnJ9S5FxOtWdLpFyvxNDHNoss2xxSLZNPun5CCMo9DvbV29nrsLO3voIddWUdDkhjEvrQVwakDhMiRIX7Ow7Uv48rcAS9YkWvi8Ggs6HXxaDXWTHorOFyK4ZwmT5cpv2unRut9ywhguyseYUdNS8DIWINg8hNf5J4U8ff03sDGZqioLeHpkZth6eLUGy3HTM8iUARouFVcP8bCE+jVgdrywZYLkZRmladDoaC7HHkk1/zJYecmxGiEqvqxap6sale9Er7G7oq6LEa+jZrlcqK3I7Rp0flA1OE6hB194P3P1qB8Uco8U92+orlPxRCCHbWlbO6TOvG215bSh9LLT9K3s+4hBIMOu2/Dx1WBsZdypD4K4k1nv5V24/F6S+iyLGCYsfHLcKdRU2nf+yFZMVeTJyx7ZbckBCUuurY67Czz2GPhKR9DjsN7Sz70DwgabPYMmVAOkneYC2HHB9yoO7fNASKO+WaOsXULGDFaLd11nD4smFQmgWuxmCmNAtmzco72grvDtjZWHEfdnceAANiL2V8yl1d2urZXcnQFAU/lNDUSAtPfwXvZ+GStsOT8O9ANPwfeFYQGSRuGIdi/SWYZh13ryIhBIdcRWys3sTGmk2UekoxKQFsqodY1UeWxUqaScWoOHEHjjSb/t2aTjFi1fdr0TLVGKwsamqX7pskhNC2fHH8AYQblASU+D+imH/cZY/Z2wVDXg43fMbOmn/i9O+IlJe64/iuajBbavoRZ4znnIxhJzwO6nQRQlDt2UKRczklzk/xh5pW2Y83jiDWNJP6wHgOOHyRYLTfUdnuoqN6RccAWxJDYlMZEpvK8Ph0GZA6gTZOrYD9de9S0vBZsxmXNrJi55JmmUpQePCHGgiEGgiEXARCDdrvQvvdH3JGygOhBvzCdcz3q5OlwxAJWEe3bjUGMlUxc9CxFG+wGlWxMCH1XrJif7gTVmRoioIfWmhq1HZ4uhDFfCHCtQTC27YAYJymtSwZc0/6Dfyw+0gkQBW5mr7lKSgMsw1hYsIQhlkT0VGrjaXyF9HgL8bpL261UGdzqmLGauiHSZeIQY0N7zMWh1EXh0EXh1GNx6iLxaDGY9Rp5Xqd9YSDlggcQNT+pmmGoOUqlLh7Tutq5j1dg/8IB+qXcLD+g8ggawU9/Ww/JsN6GVtrLHxRtpu15XtajYM6K00bB3VORvvrQQkR1IKtaADhCv90g9oHdK33ZjxV/lCQ4oYa9tQd5nDDGkKhb0gw7EUXblENCoU9jjTya/qzoz6TgFAx6FQG2ZK1cBSXytBwSMqyJUVWqpZOXSDkoti5kv11S6jz7YqUxxtHMDh+Pv1tF55Sy0xI+NsJWE3hyx9yEhAuAuHQ1SKYiYbI70HRen/D44k3DiM3/QlijYNO+jn0BjI0RcEPNTQ1Ev4d4fD06VFHdGC+AMV6M4phVKc+Zrmngk0137OxehP7Gva3ODbIOjCymGaGOR0hgrgCZTj9hyJhqvGPy3+k1XpTHaPTQpUaGw5X8RgbA5cuDqMaF/nZdDwOo2JBcb0IDa9ol1EHoyQ82+mvT2/SOH5kX907lLm+IjKwW01jcNylDLDNxqQzhQOOFnYCQScHHUXsqztIsfMIwZATq+onRg0QowbINKukm/QkGsGoeMIByaUFpPbo0sCQg2KcAIYcMIxGUTq24akvGOBgQzX76sPdag47e+vtHHJWtZglCBCjehmXcJiJicX0jalpdsRCsvlcRiRcTlrMxB67q3x3V+/bx/66JRQ5PyYQcgJaF1o/23kMjptPomlMt2u5C4kAgZA7HLzCrVnNWraaB6xAyIVFn87Q+KtRdR3fsLe3kqEpCn7ooamR8O/UwpN/s9b9Zv0Fir5jCwWeiipvtRagajax27EnshceQD9LPyYnTWRy4iT6tbGdS0j4cfmP0BA4jC9Yhy9Ujz9UH77twB8pc+AL1uEPOU7qW11zOsVIpt7AOEM1JiVASCgcVsZTp07RWrjUcPCKhC8tkBl0cd1qoPvJEKF6CNU1a8lxNYWdkHZbhMtDoRpcvr24A4dQhBs9oFcERsWAXlHQ4YN217Q+NUGh4BN6PELFL3SkqJ5I608TAxhGgGECiiEHDDl4SeOAs1oLReExR/scdooaqtvdviNGNTAkLjXSrTYkNpWhcan0iYnH5T9EkeNjipwf4w6UNd1Hn0n/2Dlk2S7udmO2eqKQ8HOkYTX765ZQ6dkUKbca+jM47gqyYi/pFZvTSq3J0BQFMjR1H3X+Or6vyWdj9SZ2OHa22M4l3ZTO5KRJTEmcxEBrx7ZzaUsw5G0WrurxherwB8PBKlSHLxg+Fj7eGML8IUeLVi0jggkmL5l6raw8oPK914SX9uulV6yRLkSjGo9JTcKsJmNWUzHrkzGrKZjUFMz6FIy6+OhuWRFygn8b+AsQ/gKtWzJYcvw7nqCQAK9Q8YRUPEJteTukwytU3CGt3BtScYujboda3scTUgmgQLO/h5mpZ7Cg73jw5SP8+QjfZhRR2aoudp+ZfEcqW5wpbHGkUNiQhDekBV2b3sTQcDgaGu5aGxKbSoYl7rgbvgoRotLzPUWO5Rx2riIgmlb5TjSNJSv2YvrZzpMf7CfI5S/lQP374TE+VeFSHZnWcxgcdyVpllzZotfLydAUBTI0dU/OgJP8mi1srPmebXXbOrydS1cRQhAQrnDrVbjlKliH0fcZif7l6Ajgx8wBcrCHLPiDWgjzh+rxh7sJToSCHrOajFFNxqgmYVATMSgJqGoCqhKPTolD0cUCNoJoMxf9wk8gFMAvAgRFAH8oQECE/4QC4eNBAs3OC4gAIuQhSVdGqq6MdLWCTNVOir6+zXp5wkGmKdioeIUOhRBGnQ9VFyAgFAKAM2ig1BdHqS8OlzDgaQw44ft5hIpP6OCooKmgYFbNmHRGTDoTJtWk/dSZMKsmjOEyHSqlrgYOOmvZV1+FKxAkJHSEhIKqGBiX2J8fpQwlRo1nv7OSfY5K9tbbKXXX0sfUQI6tkvGx2p8RMdUYdC3fVkNCxaUMRjVOxGLORTHmgNrvlMJsIOSmtGENRc7lVLi+iwRxBT0Z1mlk2eaQYZ2G2mxWqtREiBDl7nUcqFtCqesrGiepmNQUBsVdzsC4y4nRp0e3ktJpI0NTFMjQ1P25g2621hawoWYTW2sL8IaaBgnHG+LJSRhHjBqDQCBEYwefaPG7IKT9FILGksbbrcoRCBG5FbkOkXNDTWcKQZKuigtjPiVV1VZR3+Qdwxp3LkHU8CatIRR86PCG//hR8aDDjao0oOJBr3gwKh4Mig+j7lhrXbXmC6l4Qga8IQMeoW+63azMGzLgFyoqgn4GF4NMDgYanQwyOuhraEBtIwdUBkwc8MVywGvjgC+WQz4bbtHUxRinCgaZK+ljLMOo02aFCaHgEgPxKONQdQMwqeaWwUc1YdaZWpWZdEbMOjNG1YRB0Z9wMPGFgmyoPMgX4VXJS91th75GKSZrZDD2kNhUhtniGGa1Eyd2aCvC+/MhZG99R12KNjbKMAGMOWAYc9KTATyBSoqd/6HI8TF1vp2RcoMujoyYaSSbc0g25xBnHPyDbzHxBms4VP8hB+rfa7FcQKp5iraOl/Xcbr9wrtT5ek1oysvL46WXXmLHjh14PB6GDx/OggULuOiii076mh999BF33nknc+fO5emnn251/PXXX+exxx5r9/433XQTd955Z6tyGZp6Fl/Ix7a6QjZUbyK/Nh9X8BiDf08jPSGuTDzAj2OPAFDks/K/VcM54j/xrQwUQph0Acw6P2bFj0nnx6wLYNEFMOuCmHV+TDofRsWHTjnWW4EgVhEkqEESdSESdIJ4XbDNgOQVMTQo/XHphuHTjSagH4WiJqNX9OgVPQadHr1iwKDocfoLKHa8Hx7YrX3TN6tpDIr7CYPiLses75rNejtKCMGOujJWl+7im4p9WFQjQ+NSGBqbxpDYVAbHppBoijnuNQgdAd9mhD9fG+vn3wGtZnLqQT8CjDmRsVGo/U849NV591Dk/Jhixwo8wZZhzaCLDQeo8SSbJ5BoGo2qM7Vzpd5DCEG1dysH6pa0sVzAJQyOu+IHP3vsh66jn9/dejTphx9+yOLFi9Hr9UydOhVVVVm3bh2//vWv2bt3L7fffvsJX7O0tJSHH374mOcUFmobeZ577rnExsa2Oj5qlJzh1BsYdUYmJk5gYuIEAqEA2+t3sNOxGyFCoGhdO6Cg035BafY/FNChfWNXlBZHIt/kFZRmx7QrtbiO0lge/v8W5yrsEFsYGvobWUYHf8gsoEh3NXbdbBSdLnxWuHYKzQKJAVVRMegMkYCiKnoMih69Th857+gPYiEE/lA9nmAlHr8df2AXSmA7+sA+TOIIMaIGVWk9u9AnoDakoyaoaj9DOjwCoCT850tMugRM+hTMaoo27kqfgk4xUeL8D07/oci1tG/6V4a/6XePtyZFURiVkMmohEz+a+S5J30N1L5g6RvZuFkID/gLITwuSmuNqtDGewW2IXhTu7MuGdGiNWrscVuj4k3DGGv6f4xJ+m/s7k1UejZS5dlCtWcr/pCDMtdX4ZCqdeUlmkaRbJkQDlI5mNTEk3qe3VF7ywUkGEcyOH7+KW/QLP3wdNuWpsrKSmbNmoVOp+PNN99k9OjRAOzbt48FCxZQVVXFe++9FynvCCEE119/PevXrwdot6Xp4osvZu/evWzcuBGbreMbrMqWJqmziaAdUXc3+LQPOUyztAUxdZ23Z5oIloUHaWuDtfFvA1Hb+kQlBvSjEPpR+NUBeJQ0GkJ6vKEqLWwFwj+DlXgClXiD1cdcFwu0Ae1ZsRczOP5K4oyDO+059TRaa1RpG61RRy9gqR7VGjWhw61RIeGnzrubKk8+lZ7NVHm24A22HsRuMwwk2ZxDilkLUlZDVrebWn88x14u4EoSTaN73HOSulaPb2l666238Hg83HzzzS2C0ZAhQ7jjjju49957eeONN3jyySc7fM3XXnuN9evXM2XKFDZs2NDmOR6Ph/379zNw4MATCkyS1BUUNRUS/w9cf0c4ngLv54jKAoh/EsV05glfT4SqW85k82/TWjhaaZxGPxZFPw4MY0A/JLIFjQqYgYRjPZYI4QvV4glU4glW4QnYtZ/BSnzBOpLN4+gfO+cHsYP68WitUX3A0gfFMgcAIbzh1qjNCF++FqRCFRAohEAhgre0O0dao3LC60aNRdG17jLUKQYSzaNJNI9mKNcghKAhUEKVJ58qdz5Vns04/Adw+g/i9B/kkOMDAExqUmRMVLJ5Agmm4d1yzE/TcgHvUun5PlKuLRcwnwGxl2BU46NYQ6k36Lahac2aNQDMnj271bHZs2fzu9/9ji+//LLD19u1axd/+tOfmDFjBuedd167oWnXrl0Eg8ETasGSpK6kKDqw3gDGXETtHRDcj6j5OcK6EMX2qxZ79jWnTfUvhEBjQCpoZ6q/Cvqh2oetYSwYxoI+u93rnki9TWoSJjUJ+VF14hTFBMaJYJyIYm1sjSo7qjVqO4SqtDDtbfyGrCL0w5tao0yzwrMjj76+Et7suj8DYucC2p5q1Z4tWpDy5FPjKcQbrOZIw2qONKzWrq6YSTSN0UKUJYdk0zgMauvrny7acgHvcdDxQYvlAvpYz2VQ3Hy5XIDUqbplaBJCsHfvXgCGDRvW6nh8fDwpKSnY7XbKy8tJTz/2tFCfz8edd96J1WrlkUceYe3ate2e2zieKT4+ngceeIBvvvmGiooK+vbty9y5c1m4cCEmU+8fOCl1P4phFKQsRdQ/Bu63oeH/EN51kPAMqJlad46/eUA6QJuLPqqDwgFpjBaQDKPkFi49gNYalQmWTBSLNhFGa43aflRrVDkEtkNgO4J/gnEqStI/OvQYJjWBTOs5ZFrPAbS1yGq9O8LdeflUe7bgC9VR6dlIpWcj1AIoxBuHkmyeEGmRijFkdslr0KhxuYD9de9S5vqapkkEKQyUywVIXahbhqa6ujq8Xi9Wq5WYmLZnpqSlpWG326msrDxuaHr22WfZvXs3zz//PCkpx56Ns337dkDrHkxKSmLChAmkp6dTWFjI888/z9q1a3nttdfarZckdSVFsaDEP4wwnY2ou08bNFw5F+1Do43xQ7o+zVqQxmjT2nVxp7vaUhfRWqMmgHECSriXUxujtjk8wHw7imnGSV9f1Zm01iRLjnZtEcLhPxju0tPGRTUEiqnz7aHOt4f99e8CYFHTtfuFg1S8cWika/dUaMsFLAsvF9DUappqyWVw3BVyuQCpy3XL0OR2a1O/LZb2v/02tva4XK5jXmvdunW8/vrrXHLJJZx//vnHfezG0PSTn/yE3//+9xiNWhdFcXEx//Vf/0V+fj7PPPMM999/f4eeiyR1BcV8HhjGIep+Cz5tYgO65KO62MaiqMnRrah02ilqBqjahtmdfm1FR5xxMHHGwQyKuxzQ1ohq7M6r8uRT692JO1hOifMTSpyfANqA/yTzOG2AuWUCiaYxHZ61pi0XsIX9df/msFwuQIqybhmadLqmqdzHEwodvQ9Uk/r6eu655x7S09M7HHLeeustSkpKGDx4MKra9M2of//+PP7448ybN48lS5awePFi2U0nRZWiZkDi6xDYBbpE0GXIGUHSaWfWp9DXNpu+Nm38aSDkptpTEAlR1Z6tBEQDFe51VLjXQQ0oqCSYRjQbYJ7Tak2uQMhFkWMFB+qXUOfbHSlPMI1kcJxcLkCKjm4ZmqxWrZ3Z42l/Q1SvV1vJ+VjdZA899BBlZWW8+uqrxMV1rEvCYrG0OY4KYOTIkWRkZFBaWsqePXsYM2ZMh64pSV1FUVQwyHXDpO5Dr7OQFpNLWkwuAEIEqfPtpSo8LqrKnY87WE6Nt5AabyF767RZgFZ9/3CX3nhtgU7H8sjeetpyAeczOO5Kksxyko4UPd02NFmtVhwOBx6PB7PZ3OqcigptmnRaWlqb1ygoKGD58uUkJCTw/vvv8/7770eOlZRofeGbN2/mzjvvZMiQISxatKhDdUtJSaG0tPSYgU6SJEnSKIpKgmk4CabhDIm/CtBmvDWuF1XtyafOt5eGQDENjmKKHB9F7mszZDEobj4DYufK5QKkbqFbhiZFURg2bBj5+fns27ev1fT/2tpaKisriY+Pb3cQeONYp9raWj766KM2zykpKaGkpITc3FwWLVpEeXk5zz//PD6fj6eeeqrN+xQXa3sVHW/wuSRJktS2GEMmMYZM+sdq4658QQfV3q1UufOp9m7FqEtgUNw8UuVyAVI30y1DE8C0adPIz89n1apVrULTqlWrEEIwffr0du8/depUdu3a1eax999/n3vuuafViuA2m40PP/wQn8/HjTfeyMiRI1vc78svv6S2tpaBAwfSv3//U3h2kiRJUiOjGktGzFlkxJwV7apI0jF12wh/xRVXYLFYeP311/n++6bVXffv389zzz0HwMKFCyPlFRUV7Nu3L9JtdzKsVitz52qLvN13331UV1dHjh08eDCyZ91tt9120o8hSZIkSVLP1G1bmjIyMvjd737H/fffz7XXXsvUqVMxGo2sW7cOr9fLb37zG0aMGBE5/9lnn2Xp0qXMmzePxx9//KQf96677mLbtm1s27aN8847j4kTJxIMBsnLy8Pn83HDDTdwySWXdMZTlCRJkiSpB+m2oQlg/vz5ZGRk8PLLL5Ofn4+qqowaNYobb7yR8847r0seMz4+nrfffptXX32VFStWsG7dOkwmEzk5OSxYsIAf//jHXfK4kiRJkiR1b4oQoo19FqST0dFdkiVJkiRJ6j46+vndbcc0SZIkSZIkdScyNEmSJEmSJHWADE2SJEmSJEkdIEOTJEmSJElSB8jQJEmSJEmS1AEyNEmSJEmSJHWADE2SJEmSJEkdIEOTJEmSJElSB8jQJEmSJEmS1AEyNEmSJEmSJHVAt957rqepqKggGAxGlmOXJEmSJKn7Ky0tRVXV454nW5o6kclkQq+XOVSSJEmSehK9Xo/JZDrueXLDXkmSJEmSpA6QLU2SJEmSJEkdIEOTJEmSJElSB8jQ1M3l5eVx4403csYZZzBhwgSuuuoqVqxYEe1q9SqhUIh33nmHn/70p0ycOJGxY8dy/vnn89RTT1FfXx/t6vVaPp+PuXPnMnz4cA4dOhTt6vQ6ZWVlPPDAA8yYMYMxY8Zw1llnceedd1JUVBTtqvUqK1as4KqrrmLChAmMHTuWOXPm8Je//AWv1xvtqvVoeXl5jBgxgiVLlrR53Ol08qc//YkLLriAcePGMX36dB588EGqqqq6tF5yTFM39uGHH7J48WL0ej1Tp05FVVXWrVuHz+fjtttu4/bbb492FXu8UCjE7bffzmeffYbFYmHs2LHExMSwdetWqqurGTBgAP/85z9JSUmJdlV7nccff5zXXnsNgE8//ZQBAwZEuUa9x/bt2/n5z39ObW0tQ4YMYfDgwezcuZPi4mISExN577336Nu3b7Sr2eM988wzvPzyyxgMBiZPnozZbGbTpk3U19czfvx4/v73v2M2m6NdzR5n//79LFiwALvdziOPPML8+fNbHHc6nSxYsIDCwkKysrIYOXIku3fv5sCBA6Snp/Puu++SkZHRNZUTUrdkt9vFuHHjRE5Ojti2bVukfO/eveLMM88Uw4cPb1EunZx3331XZGdni/PPP18UFxdHyh0Oh/jlL38psrOzxe233x7FGvZO3377rRg+fLjIzs4W2dnZ4uDBg9GuUq/h9XrFBRdcILKzs8X//u//RsoDgYD4/e9/L7Kzs8WiRYuiWMPeYefOnWL48OEiNzdX7N69O1JeU1MjLr30UpGdnS1efvnlKNawZ/r222/FGWecEXlvePfdd1ud8+ijj4rs7GyxePFi4ff7hRBCBIPBSPktt9zSZfWT3XPd1FtvvYXH4+Haa69l9OjRkfIhQ4Zwxx13IITgjTfeiGINe4f3338fgLvvvpt+/fpFym02G48++iiKovD555/j8XiiVcVep76+nnvuuYcBAwaQmpoa7er0Ov/5z3/Yv38/559/PjfffHOkXFVVFi9eTJ8+fTh8+DDBYDCKtez5vv32W4QQXHjhhQwbNixSnpCQwMKFCwHYsGFDtKrX41RVVfH73/+eG2+8kbq6Ovr06dPmeU6nk3fffReLxcK9994bWeZHp9OxePFi+vfvz+rVq7usG1qGpm5qzZo1AMyePbvVsdmzZ6MoCl9++eVprlXvEx8fz+DBgxk/fnyrY0lJScTHx+P3+6mpqYlC7Xqnhx56iIqKCp588kmMRmO0q9PrfPLJJwDccMMNrY5ZLBa++OILli1b1qGF/KT2KYoCaGPHjlZdXQ1o7y9Sx7z00kv861//IisrizfeeIOpU6e2eV5eXh4ul4vJkye3en1VVWXGjBkAXfb5KENTNySEYO/evQAtvsE0io+PJyUlhbq6OsrLy0939XqVl156iZUrV5KYmNjqWFFREbW1tRgMBpKSkqJQu95n+fLlLF++nJtvvrnNoCqdusLCQnQ6HWPGjKGiooJXX32V+++/nyeeeIK8vLxoV6/XmDZtGoqi8MUXX/DnP/8Zu92O0+lkxYoVPP/88xiNRq677rpoV7PH6N+/Pw8++CDLly9n8uTJ7Z53rM9GgKFDhwKwe/fuzq8kchuVbqmurg6v14vVaiUmJqbNc9LS0rDb7VRWVpKenn6aa/jD8OyzzwIwY8aMDq0UKx1baWkpDz30EKNHj+a2226LdnV6JZ/PR2lpKYmJiaxZs4a77rqLhoaGyPFXX32VefPm8cgjj8jdC07RkCFDeOSRR/jjH//ICy+8wAsvvBA5NmzYMB599FHGjRsXxRr2LAsWLOjQeRUVFYD2GdiWxi7/ysrKzqnYUWRLUzfkdrsBrSm9PY0f4i6X67TU6Yfm9ddfZ+XKlVgsFv7f//t/0a5OjyeE4K677sLj8fDEE09gMBiiXaVeyel0Atr7wh133MHZZ5/Nxx9/zKZNm3jppZdIT09n6dKlPP/881Guae8wadIkzj77bMxmM7m5uZx99tnExcWxd+9e3njjDXw+X7Sr2Os0fua1NyuxsbyrPhvlV41uSKfTsmxjn/mxhEKhrq7OD87rr7/OY489hqIo/PGPf2TIkCHRrlKP99prr7F+/XruuuuudpvVpVPX+CHt9XrJzc1tEY5mzJhBSkoK8+fP5/XXX2fhwoXExcVFq6o93tatW/n5z39OSkoKy5YtY+DAgQDU1NRw5513snz5clRV5cknn4xuRXuZxrF4x/t8FF20mpJsaeqGrFYrwDFnbDUunNZe95104oQQPPnkkzz22GOoqspjjz3GnDlzol2tHm/Xrl386U9/YsqUKW0OTpY6T/Nv39dcc02r42PHjmXs2LF4vV42b958OqvW6zz66KM4nU7+8Ic/RAITQGJiIk899RQ2m42PPvqIw4cPR6+SvVDjZ157n4+N5V312Shbmrohq9WK1WrF4XDg8XjabIY8Xr+udGI8Hg+//e1v+fTTTzGbzTzzzDNtzlyUTtyzzz6Lz+dDURQWL17c4ljjrMQnnniCmJgYFi1aJFv2TkFsbCwGgwG/399iCY3m+vbty9atW+WM0FPg8XjIz8/HbDa3OWg5KSmJsWPHsm7dOnbu3CkXEu1EjWN42xuzZLfbAbpsORMZmrohRVEYNmwY+fn57Nu3r8U6TQC1tbVUVlYSHx8vB4F3AqfTycKFC9m8eTNJSUm89NJLcmZXJ2ocW3CsmVuff/45APPnz5eh6RSoqsqQIUPYuXMn5eXljBkzptU5jR82ycnJp7t6vYbD4UAIgU6niwynOFpjN5Lf7z+dVev1Grv3G2fRHW3Pnj0AZGdnd8njy+65bmratGkArFq1qtWxVatWIYRg+vTpp7tavY7f7+fmm29m8+bNZGVl8c4778jA1Mn+8Y9/sGvXrjb/NH4D//TTT9m1a1e7a7NIHXfOOecAtLlHZVVVFYWFhRiNRvnf+SlITk4mISEBl8vV5gKWDoeDbdu2ATBy5MjTXb1ebfLkycTExJCXl4fD4WhxLBgM8sUXX6AoSuQztLPJ0NRNXXHFFVgsFl5//XW+//77SPn+/ft57rnnACKrzkon7y9/+QubNm0iNTWVN998k6ysrGhXSZJOyVVXXUVMTAzLly9vsdmpy+Xid7/7HS6Xi3nz5slB4KdAp9Nx5ZVXAvDAAw+0GLfkdDq55557qK2t5ZxzzpF7KnYyi8XCT37yExoaGnjggQcikx+EEDz11FOUlJQwe/ZsBg0a1CWPLzfs7caWLFnC/fffj06nY+rUqRiNRtatW4fX6+U3v/lNiy0SpBNXU1PDjBkzcLvdjBgx4pizuu6++265aW8XmDlzJocPH5Yb9nayTz/9lDvuuAO/3092djb9+vWjoKAAu93OiBEjePPNN4mNjY12NXs0n8/HzTffzLp16zAYDOTm5qLX6yPjxQYPHsw//vEP+b5xku6++26WLl3a7oa9P/vZz9i9ezd9+/ZlzJgx7Nmzh/3799O3b1/efvvtLhvvK8c0dWPz588nIyODl19+mfz8fFRVZdSoUdx4442cd9550a5ej7dhw4bImlg7d+5k586d7Z773//93/LNT+oxzjvvPN577z1eeukl1q9fz8GDB+nTpw9XXnklCxculLNuO4HRaOSVV17hnXfe4YMPPmDz5s0EAgH69+/Pz372M37xi19gs9miXc1eyWaz8dZbb/Hiiy/yySef8MUXX5Cens7VV1/Nrbfe2qV7WsqWJkmSJEmSpA6QY5okSZIkSZI6QIYmSZIkSZKkDpChSZIkSZIkqQNkaJIkSZIkSeoAGZokSZIkSZI6QIYmSZIkSZKkDpChSZIkSZIkqQNkaJIkSZIkSeoAGZokSZIkSZI6QIYmSZKk02z9+vUMHz6cn/3sZ9GuiiRJJ0CGJkmSJEmSpA6QoUmSJEmSJKkDZGiSJEmSJEnqABmaJEnq0b799ltuuukmpk6dytixY7nwwgv5n//5H1wuV+SckpIShg8fzi9/+UtKSkq47bbbmDx5MlOmTOGGG25g3bp1bV7b4/Hw4osvMnfuXMaNG8fEiRO5+uqr+fDDD9utz/r167n11ls5++yzmTBhAnPnzuXFF19sUZ/m9u/fz+23387UqVMZP348l19+OcuWLTu1F0WSpC6hCCFEtCshSZJ0Ml5++WWeeeYZDAYDY8aMITU1lfz8fCoqKhg5ciRvvPEG8fHxlJSUMGvWLEaPHo3dbsfpdPKjH/0Ih8PBxo0bURSFhx9+mPnz50euXVNTw4IFC9i9ezcJCQlMnjwZj8dDXl4ePp+Pyy67jMcffxxFUSL3eeWVV3j66adRFIWJEyeSmJhIfn4+drudSZMm8frrr2M0Glm/fj0LFiwgLS0Nl8uFyWRiwoQJlJeXU1BQAMADDzzANddcc9pfU0mSjkFIkiT1QOvWrRPDhw8X06dPFzt27IiUe71ecffdd4vs7Gzxm9/8RgghRHFxscjOzhbZ2dnivPPOE6WlpZHzV69eLUaNGiXGjx8vjhw5Ein/r//6L5GdnS1uueUW4XQ6I+UHDx4Us2bNEtnZ2eLvf/97pHzr1q1ixIgRYuLEiWLjxo2RcpfLJa699lqRnZ0tXn31VSGEEN99912kPrfddptwu92R81966SWRnZ0tZs6c2YmvliRJnUF2z0mS1CO98sorCCH43e9+x4gRIyLlRqORBx98kJSUFFasWEF5eXmL+z322GNkZGREfp8xYwZXXnklbreb999/H4DDhw/z6aefEhsby1NPPYXVao2cP2DAAB599NFIHRq98847hEIhbrnlFiZNmhQpt1gs3H333WRlZWG321vURVVV/vCHP2A2myNlN9xwAzqdjpKSEpxO56m8RJIkdTIZmiRJ6nGCwSAbN24EYOrUqa2Om81mJk+e3OI8gD59+jBx4sRW5//4xz8G4LvvvgNgw4YNAJx55pnYbLZW5+fm5pKamkpZWRlFRUUA5OXlATBz5sxW548ePZrPPvuMxYsXtygfOHAgiYmJLcpMJhNJSUkAOByOtp6+JElRoo92BSRJkk5UbW0tbrcb0ALMsZSWljJ+/HhAayVqS2PLU0VFRYufffv2bfe6/fr1w263U1FR0aIVqU+fPh1+HnFxcW2Wq6oKaOFQkqTuQ4YmSZJ6nMYwYTQaOf/88495bvOg1BhGjibC82H0+o6/JTavA0AgEOjwfRs1H0QuSVL3J0OTJEk9TkJCAgaDgUAgwKOPPhoJLu0pKSkBaDW+qdGRI0eAphantLQ0QBvbdLxrpqSkAJCamsrhw4cpKytj0KBBrc5/++23SUtLa7P7TpKknkGOaZIkqccxGo3k5OQQCoX46quvWh0XQnDddddx1VVXsXXr1kj53r17KS4ubnX+Z599BsC0adMAmDRpEoqi8M0337Q5GPu7776jurqafv36RbrjGsdKrVmzptX5+/bt48EHH+TPf/7zSTxbSZK6CxmaJEnqkW644QYA/vCHP7B9+/ZIeSgU4rnnniMvL4+SkpIWM+uEENx7770tgtCqVav497//TUJCApdeeikA/fv3Z9asWTidTn7729/S0NAQOb+4uJj77rsPgGuvvTZSfs0116AoCi+88EKL+jQ0NPDwww8DcMkll3TiKyBJ0ukmu+ckSeqRZs+ezY033sirr77K/PnzGT16NGlpaezcuZPi4mIsFgvPP/98i667mJgYdu7cyY9//GOmTJmC3W7n+++/x2w288QTT7SYyfbwww9z8OBBVq9ezaxZs5g8eTJutzuyuOXFF1/M9ddfHzl/woQJ/OpXv+K5555j/vz5TJ48GavVSn5+PlVVVZx11ln8/Oc/P62vkSRJnUuGJkmSeqy77rqLKVOm8NZbb1FQUMCOHTvIzMzkiiuu4Oabb241Wy42NpZXX32VJ554gq+++gqLxcIFF1zAokWLWrRIASQnJ/POO+/w+uuvs3LlStauXYvFYmHChAlcddVVXHTRRa3qs2jRIkaNGsUbb7xBQUEBbrebfv36ce2117Jw4UJ0Otm4L0k9mdxGRZKkXq9xG5X09HTWrl0b7epIktRDya89kiRJkiRJHSBDkyRJkiRJUgfI0CRJkiRJktQBckyTJEmSJElSB8iWJkmSJEmSpA6QoUmSJEmSJKkDZGiSJEmSJEnqABmaJEmSJEmSOkCGJkmSJEmSpA6QoUmSJEmSJKkDZGiSJEmSJEnqABmaJEmSJEmSOkCGJkmSJEmSpA74/5Qu7QJPYEiCAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sbnn_plot.plot_history_group(history,'val_loss',\n",
    "                                  col_format_func = lambda x:f'{float(x):.1%}',\n",
    "                                  ylabel = 'Loss (validation)',\n",
    "                                  leg_title='down rate')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('33_PLOT_hostory_val_loss.png'),\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0c6acda4-3326-4ece-9ae9-8fc784e9475f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sbnn_plot.plot_epoch_agg(history,\n",
    "                               xformatter = ticker.PercentFormatter(1),\n",
    "                               ylabel='# Epochs',\n",
    "                               xlabel='downsample rate')\n",
    "fig.savefig(Path(setup.temp_path).joinpath('33_PLOT_hostory_epochs.png'),\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5334471-c99c-44c5-9e2b-4c1b6e602b4a",
   "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
}