attributed_networks_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "import random\n",
    "import math\n",
    "\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyperparameters\n",
    "\n",
    "seed = random.randint(1, 1000)         # random seed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiments on real data\n",
    "Here we use 'Email network' from 'Stacking models for nearly optimal link prediction in complex networks' paper by Amir Ghasemian et al:\n",
    " - https://github.com/Aghasemian/OptimalLinkPrediction\n",
    " - https://www.pnas.org/content/117/38/23393 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1\n",
    "### Graph and features imoprting, train-test splitting\n",
    "\n",
    "We import the desired network and its features from `OLP` class and split the data into train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Verbose output.\n",
      "INFO: Reading data ...\n",
      "INFO: Success!\n",
      "INFO: Train/test splitting the data...\n",
      "INFO: Success!\n",
      "INFO: Calculating topological features: ['RAI', 'JC', 'AAI', 'PA']\n",
      "INFO: Done calculating topological features\n",
      "INFO: Calculating topological features: ['RAI', 'JC', 'AAI', 'PA']\n",
      "INFO: Done calculating topological features\n",
      "INFO: Calculating topological features: ['RAI', 'JC', 'AAI', 'PA']\n",
      "INFO: Done calculating topological features\n"
     ]
    }
   ],
   "source": [
    "from lib.generators.AttributedNetwork import AttributedNetwork\n",
    "\n",
    "\n",
    "generator = AttributedNetwork(\n",
    "    './data/',\n",
    "    'cora_ml',\n",
    "    features_to_normalize=['RAI', 'JC', 'AAI', 'PA'],\n",
    "    with_attributes=True,\n",
    "    seed=seed,\n",
    "    vebrose=True\n",
    ")\n",
    "\n",
    "feature_names = generator.t_feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "link_prediction_train, link_prediction_test, classifier_test = generator.TRAIN1_DF, generator.TEST1_DF, generator.TEST2_DF\n",
    "\n",
    "link_prediction_train.dropna(inplace=True)\n",
    "link_prediction_test.dropna(inplace=True)\n",
    "classifier_test.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node1</th>\n",
       "      <th>node2</th>\n",
       "      <th>goal</th>\n",
       "      <th>node_1_attrs</th>\n",
       "      <th>node_2_attrs</th>\n",
       "      <th>RAI</th>\n",
       "      <th>JC</th>\n",
       "      <th>AAI</th>\n",
       "      <th>PA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7353</th>\n",
       "      <td>1607</td>\n",
       "      <td>2199</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.043530</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.087160</td>\n",
       "      <td>0.048286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6522</th>\n",
       "      <td>341</td>\n",
       "      <td>2826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2474</th>\n",
       "      <td>819</td>\n",
       "      <td>2041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.025313</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.061335</td>\n",
       "      <td>0.008384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2531</th>\n",
       "      <td>683</td>\n",
       "      <td>2409</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8849</th>\n",
       "      <td>2046</td>\n",
       "      <td>2099</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.011492</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.022694</td>\n",
       "      <td>0.004988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      node1  node2  goal                                       node_1_attrs  \\\n",
       "7353   1607   2199   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "6522    341   2826   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2474    819   2041   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2531    683   2409   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "8849   2046   2099   1.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                           node_2_attrs       RAI        JC  \\\n",
       "7353  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.043530  0.153846   \n",
       "6522  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.000000  0.000000   \n",
       "2474  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.025313  0.300000   \n",
       "2531  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.000000  0.000000   \n",
       "8849  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.011492  0.083333   \n",
       "\n",
       "           AAI        PA  \n",
       "7353  0.087160  0.048286  \n",
       "6522  0.000000  0.022817  \n",
       "2474  0.061335  0.008384  \n",
       "2531  0.000000  0.002229  \n",
       "8849  0.022694  0.004988  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "link_prediction_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2\n",
    "### Link prediction model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using plaidml.keras.backend backend.\n",
      "DEBUG: Falling back to TensorFlow client; we recommended you install the Cloud TPU client directly with pip install cloud-tpu-client.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"KERAS_BACKEND\"] = \"plaidml.keras.backend\"\n",
    "\n",
    "\n",
    "from keras import Input\n",
    "from keras import layers as l\n",
    "from keras.models import Model\n",
    "\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "\n",
    "def model(feature_names, attr_dim, attrs=True, embed_dim=4, hidden_dim=64):\n",
    "    \"\"\"\n",
    "    A simple DL model for classification\n",
    "    \"\"\"\n",
    "\n",
    "    # inputs to topological features\n",
    "    feature_input = Input(shape=(len(feature_names), ), name='Topological features input')\n",
    "\n",
    "    # node_attributes inputs\n",
    "    attr_input_1 = Input(shape=(attr_dim, ), name='Node 1 attributes') if attr_dim > 0 else None\n",
    "    attr_input_2 = Input(shape=(attr_dim, ), name='Node 2 attributes') if attr_dim > 0 else None\n",
    "\n",
    "    # dynamic representation the attributes\n",
    "    c = l.Concatenate()([attr_input_1, attr_input_2])\n",
    "    attrs_dyn = l.Dense(embed_dim, activation='relu', name='Dynamic representation of the the attributes')(c)\n",
    "\n",
    "    concat = l.Concatenate()([feature_input, attrs_dyn])\n",
    "\n",
    "    if attrs:\n",
    "        hidden = l.Dense(hidden_dim, activation='relu', name='Hidden layer 1')(concat)\n",
    "    else:\n",
    "        hidden = l.Dense(hidden_dim, activation='relu', name='Hidden layer 1')(feature_input)\n",
    "        \n",
    "    hidden = l.Dense(hidden_dim, activation='relu', name='Hidden layer 2')(hidden)\n",
    "\n",
    "    out = l.Dense(1, activation='sigmoid', name='output')(hidden)\n",
    "\n",
    "    if attrs:\n",
    "        model = Model([feature_input, attr_input_1, attr_input_2], out)\n",
    "    else:\n",
    "        model = Model(feature_input, out)\n",
    "\n",
    "    model.compile(optimizer='adam', loss='poisson', metrics=['accuracy'])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to rearange data for training\n",
    "\n",
    "def data_arrange(df, feature_names, train=True, attrs=True, goal='goal'):\n",
    "    x_features = df[feature_names].values\n",
    "\n",
    "    x_attrs_1 = np.array([np.array(item) for item in df['node_1_attrs'].values])\n",
    "    x_attrs_2 = np.array([np.array(item) for item in df['node_2_attrs'].values])\n",
    "\n",
    "    y = df[goal].values if train else None\n",
    "\n",
    "    if attrs:\n",
    "        return [x_features, x_attrs_1, x_attrs_2], y\n",
    "    else:\n",
    "        return x_features, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_EPOCH=15\n",
    "ATTRS = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:plaidml:Opening device \"opencl_amd_radeon_pro_5300m_compute_engine.0\"\n",
      "INFO: Opening device \"opencl_amd_radeon_pro_5300m_compute_engine.0\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "Node 1 attributes (InputLayer)  (None, 2879)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Node 2 attributes (InputLayer)  (None, 2879)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 5758)         0           Node 1 attributes[0][0]          \n",
      "                                                                 Node 2 attributes[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "Topological features input (Inp (None, 4)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Dynamic representation of the t (None, 4)            23036       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 8)            0           Topological features input[0][0] \n",
      "                                                                 Dynamic representation of the the\n",
      "__________________________________________________________________________________________________\n",
      "Hidden layer 1 (Dense)          (None, 64)           576         concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Hidden layer 2 (Dense)          (None, 64)           4160        Hidden layer 1[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "output (Dense)                  (None, 1)            65          Hidden layer 2[0][0]             \n",
      "==================================================================================================\n",
      "Total params: 27,837\n",
      "Trainable params: 27,837\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "attr_dim = len(link_prediction_train.head(1)['node_1_attrs'].values[0])\n",
    "\n",
    "link_prediction_model = model(feature_names, attr_dim, attrs=ATTRS)\n",
    "link_prediction_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABPEAAALhCAIAAACxOaZuAAAABmJLR0QA/wD/AP+gvaeTAAAgAElEQVR4nOzde1wU5eI/8GdhAUVkvaRJgSIXAVFQSRTJqJPiFSU1MTVMFDA53jheT0fjcNAyTcHQvCFqIomoCGIdNBBTIWlFUG6lgEBCCIrsIrLAPr8/5td89+yN3eWygJ/3H712np2deebZ7cM8zszzcCilBAAAAAAAAKAL0tF2BQAAAAAAAAA0hD4tAAAAAAAAdFXo0wIAAAAAAEBXhT4tAAAAAAAAdFVcyYW0tLQ9e/ZoqyoAAJ2fi4tLYGBgKzeyZ8+etLS0NqkPAEC3FBgY6OLiou1aAEDX8D/XaUtLS2NjY7VVFYB2kp6enp6eru1atKPY2NiysjJt1+KVkJ6e3iZ90bS0tO79m4RXU/fOorKyMpwjdZjY2NjS0lJt1wIAugyubNHZs2c7vh4A7efDDz8k3fqHzeFw1q1bN3/+fG1XpPtjfkttYvz48d34Nwmvpu6dRTExMV5eXvjftmNwOBxtVwEAuhI8TwsAAAAAAABdFfq0AAAAAAAA0FWhTwsAAAAAAABdFfq0AAAAAAAA0FWhTwsAAAAAAABdlZxxjwGgsLAwJCQkODjY1NRU23VpY01NTbdv354wYUJSUlJ1dTUhxNbWdvTo0ewKNTU1P/zwA7s4derUvn37dmQNBQLB6dOni4qKrKysFi5caGhoKPmuUCiMiYkpLi4eP3785MmT9fT0mPKMjIwHDx5IbWr8+PFDhw4lhFRXV1+8eLGkpMTBwcHd3d3IyIhZ4c6dO/379x8yZEj7HxYAyNHtw1YoFHbOpCVKw1ZR0hKNwhZJCwDtjko4c+aMVAlANzBv3rx58+ap9RFmtobLly+3U5XaFiHkzJkzqqxZU1OzY8eO2tpaSqlQKNy2bRshhMfjFRQUsOuIxWI+nz9y5Mjhw4enpKSIxeL2qrc8+fn5gwYNsra21tfXJ4RYWlqWl5dLvmtlZZWYmMicig0ePDg1NZWps6WlpWy+8fl8SmlmZuaIESPS0tLq6up27tzp4ODw+PFjZoONjY0rVqxgNqIKDX5L7bodgE5F9SxidaGwVesciQ3bzpm0VGnYKkpaqmnYqpu0VKPfEgC8ytCnhe5Ps/7DkydP2qMyUk6cONH6jaj4t7+srMzDw6OmpkaykDmbsbOzYzq6LObKSevrpq5p06ZlZWVRSisrK5cvX04I8fHxkXx32bJl7OKSJUsmTpxIKU1KSlq9enVRUVHDX5KSkszNzSmlzc3Njo6OGzduZD/l7Ow8efJkdrGpqWnatGnZ2dmqVA99WgAlNOuHdEDYtknSqn6OJBu2nS1pqdKwVZS0tBVhq1bSUvRpAUBNeJ4WQL7XXnutvXeRnJy8ZcuW9t4LKzAw8IMPPuDxeJKFVlZW7u7ueXl53t7elFK2vH///n369OmwujH4fP6iRYscHBwIIQMGDAgODtbR0bl16xa7Qnl5eU5ODrtoYGDQ0NBACDEyMtq7d6+5ubn+Xy5evDh37lxCSHp6elZWluQtf87OzleuXOHz+cyirq5uYGCgn59fxxwjAEhp77Dt4KQl8sK2UyUtaSlsFSUtaUXYImkBoF2hTwsgh1gsTklJycjIYEtKS0vDwsLEYvH9+/e3b9/+3XfficVi5q2ysrIDBw5QSq9du7Zly5bw8PD6+npCSEJCQmho6NGjRwkhAoFg//79oaGhzL/0E0JSUlI8PT2FQuGhQ4cSEhIIIVVVVV988cWff/7ZHkd0+/btxMTEefPmSZVzudzvv//e0tIyLi4uJCSELdfR0dHR+b98EAgEZ86cCQoKioiIKC0tVaVZGI8fPz527FhwcPBPP/3UYiXNzc0XLlzILpqYmDg5OUk+YzZnzpz09PRTp04RQoRC4YULF9auXUsIcXFxkaytWCw+f/78nDlzCCEFBQWEEMmTyLFjxxJCbty4wZZMmjRJIBCcP3++xRoCQNuSClvlkaJB2HZw0hIFYati0hJNw1atpCUtha2ipCWtC1skLQC0I8mLtrj3GLolde/zzMnJYU5Hvv32W6YkPj5+wIABhJC9e/cuXbp05syZhJAdO3ZQSk+dOtW3b9+ePXuuWLHCx8dn+vTphJCxY8eKRCJKqb29vampKbOR2tpaY2NjFxcXZjEzM9PV1XXAgAEpKSmZmZmU0iNHjhBC9u3bp+4BEhXu0Zo7d+6kSZNkyx0cHCil9+7dMzIy4nA4CQkJTPmhQ4fCw8OZ13fv3h05cuS5c+cqKyt3795tZGTE3MinpFkYycnJvr6+d+7ciYmJMTIyWrlypbqHNmjQIMkb8yoqKmxsbAgh69atc3d3P3/+vNxPXb9+/Y033mCeT4uOjiaEBAYGsu8yJ1iSJZRSPz+/0aNHt1gf3HsMoIQqWSRJKmyVR4pmYduGSaviOZLcsFUlaammYdv6pKX/G7YqJi1VP2xVTFqKe48BQE3o00L3p0H/ITs7W7JPSyndvHkzIeTq1avM4pgxY5ycnJjXixcv5nA49+/fZxa3bt1KCDl48CCza/Y0i/kU26ellHp6epqZmbGLQqHw9OnTUk9bqUKVv/3W1tbMPW9SmDMtSum5c+c4HA47igl7ptXQ0GBra7tt2zb2IwsXLtTX18/JyaFKm0UgEFhYWAiFQmZx2bJlhJC0tDTVjys1NdXU1FQgEEgWVlZWMiOUuLi4VFRUyP3gqlWrAgICmNclJSX6+vpOTk7sECyJiYmyZ7RhYWFcLrehoUF5ldCnBVBCg36IVNgqiRSqadi2VdKqeI4kN2xbTFqqadi2PmmpvLBVJWmp+mGrYtJS9GkBQE249xhADgMDA6mSnj17EkJsbW2ZxeHDh5eUlDCve/XqxeVy7e3tmcXNmzdzudzr16+rsiMOh8O+7tWr10cffdS7d+9WVl6WSCQqLCw0MTFRss6cOXM+++yz58+fe3p6CgQCtvzHH3/Mz88fP348WzJlyhSRSBQREUGUNkt0dHR9ff3GjRsDAgICAgLKy8stLS1lZ4BQpLm5edu2bfHx8ey8O4yIiAg3NzcfH5+0tLRx48axu2NRSs+dO8c830UIMTMzCwkJ4fP5S5cuvXz58tdff/35558TQhwdHSU/xePxmpqaVK8eALQJqbBVEimkFWHbMUlLVAhbRUlLNA3bViYtURC2LSYt0ShskbQA0E4wPy2AJnR1danEU0OSDA0NTU1Nnzx5osp2JM+02s/Tp0+bm5uZUyIlgoODs7KyEhISvL29p06dyhTm5uYSQiTPdSZOnEgIycvLk92CZLPk5OSYmJjs379fszqvX78+MDBQcrgRQkhkZOSZM2cyMjK4XK6rq6u/v39AQADzjBzr5s2bIpHonXfeYUs2bNjg7OyclJR048aNBQsWpKen//7771JbZg6wrKxs+PDhmlUYANqckqQl6oRtxyQtUS1s5SYt0TRsW5m0RF7YqpK0RKOwRdICQDtBnxagjTU0NFRUVEyZMkWVlTvmTGvQoEF9+vSRuiYgtzKnTp0aN25cXFxcQUFBQEAAIaRfv36EkLS0NObsihAyZMgQPT09yaGb5NLV1S0oKGhsbNTT01O3wocPHx49evSsWbOkyk+cODFt2jQul0sI8fHx+fXXXyMiImpqaiQHDo2NjZ09e7aurq7kB93c3Nzc3AghRUVF8fHxu3btkrpK8+zZM0KImZmZulUFAG1RPWw7rE+rStjKTVqiadi2JmmJgrBVJWmJRmGLpAWAdoJ7jwHaWHp6+suXL5kxPLhc7suXLxWtyeFwmpubO6ZW9vb2lZWVUoWU0hcvXkiWGBsbx8XF8Xg89srAuHHjCCGSd/fdv3+/sbHRxcVF+R4dHR3r6uoOHjzIltTU1Bw4cKDFql64cIFS6u3tzZakpqYyL7Kzs2tqatjy2bNni0QiyfFLKaWxsbHsvXBSRCKRl5eXjY3NypUrpd4qLy/ncDhDhw5tsXoA0EmoGLYdmbREXtiqkrRE07DVOGmJ4rBtMWmJpmGLpAWAdoI+LYAczFx8VVVVbEltbS0hRCQSMYtVVVXMKBfMYlNTE3tqEhsb6+bmxpxmubu7V1VVRUZG1tXVRUZGVldXFxYWMv9QTQgxMTGpqKgoLCx8+PBhXV0dn893dna+du1aexzRxIkT7927J1VYXl7+xx9/SJ0I2tjYREVFsbM1ODo6Llmy5Pr16+zzVDdu3LC2tmamGVTSLF5eXmZmZuvXr9+1a1deXl5MTIyfn9/HH3/MrOnn5zd9+nTZ6TSuXr26c+fOxsbG8PDw8PDwsLAwf39/ZhQZQoinp+eFCxfYGSzS09MdHBysra3Zj6elpQmFwvfff1+2Berq6nx9fYcOHXr16lXm+oOk4uJid3f3Hj16tNCOANCmpMJWedISjcK2I5OWyAtbVZKWaBq2ypOWaBS2LSYt0TRskbQA0F4kB4zCuMfQLak7xmx6ejozvcSIESMuXbpEKb127ZqFhQUhZPny5eXl5dHR0cbGxoSQoKCgxsZGf39/XV3dv//97xs2bFiwYIGHhwc7oqZAIGAG/LCzs2Mm8ZsyZcqRI0eYd1NSUrhcbp8+fZhhIZnxMNl3VUdUGB/y6dOnAwcOfPDgAVty9uxZ5jmoyZMnJycnS62/fft2djTO+vr6gIAAe3v748ePHz16dMaMGSUlJS02C6U0Nzd32LBhTNTY29vfuXOH3T4zoubu3bsld8rn83v16iWVUT169KiurmZWqKurW7Zs2YgRI0JDQ5cvXz5r1qzCwkLJLaxdu3bx4sVSx1JVVRURETFhwgRFM1I0NDT079//ypUrytuQYtxjAKVUySJJUmHbYqRoFrZtlbQqniNJha3qSUs1DVslSUs1CtsWk5ZqFLaqJy3FuMcAoCb0aaH7a+/+g7+/v56eHqW0pKTk+fPnsitUVlYyL+rr66XeqqmpkZxSQu7HW6Ti3/6DBw+yky6o4s8//5RcrKmpuXnzZmlpqbrVKy4ufvTokVThy5cvz5w5c/HiRXW3Rimtq6vLzc19+vSp7FuFhYVVVVVShRcuXHj48KGSDcbExMyePVuVXaNPC6BEe/dDNA7bNkla1c+R1ApbqaSlmoat3KSlrQhbJUlLNQpb1ZOWok8LAGrCvccAbcbMzIz5t3MpAwYMYF7I3nDF4/Ekx8+Q+/G24uvrW11dnZmZqeL6AwcOlFzk8XgTJkwwNTVVd79DhgwZPHiwVGFDQ0NaWtr06dPV3RohxNDQ0M7OTu64KUOHDu3fv79UoaenJ3OJQ678/PyoqKjo6GgNagIAWqFu2HZk0hI1w1YqaYmmYSs3aUkrwlZJ0hL1wxZJCwDtCn1agNZ68eJFU1OTUCjUdkVaoKOjc/z48W+//TYjI0PbdSG3b9/esWOH7HOtHezRo0dffPHFsWPHWpzoCAC0DmGrgc4QtkhaAGhvamdcUlJSdXU1IcTW1lZyzrGampoffviBXZw6dWqLU32whEJhSkrKjRs3du7cqW59CCEVFRX5+fnvvvuuBp9VV2FhYUhISHBwsKmpqVAo/Omnn+7evcvMKt4ZXL9+/Y8//mAX+/TpM23atA7YL/urYDg4ONjb23fAfjuDqKiopKQkSummTZt8fX1HjRql7RopY2BgcPjwYXYAEi2aNGmStqtACCH6+vrHjx/vsHk+1NLmYduapBUIBKdPny4qKrKyslq4cKGhoaG6W1AXwlbWq5y0BGGrqc4Qtp05aQGgm5C8EVmVZ0WEQuG2bdsIITwer6CggC0Xi8V8Pn/kyJHDhw9PSUkRi8Wq3wB99uxZc3PzwYMHq3HTNKWU0srKyn/84x89e/ZcvXq1up/VzNmzZwkhly9fppRGRka+9tprNjY2HbNrVTQ0NFy4cIH5Zvft2/fixYuO2W9lZeXq1asJIbq6usnJycxgjJ1Huz67WFNT8+wvHdbgUgieO+ooHfk8bZuHrcZJm5+fP2jQIGtra319fUKIpaVleXm5uhtRF8JWVidPWtrOWaT1sMWYIx0Jf9cAQC1q33vcq1evf//73/r6+s+fP/f09GQnFudwOGPGjPHy8lqwYMG7776r1r/GzZs3z9nZWYMbY4qLi729vevr69X9IOvkyZMtlkiaN2/ekydPmH+P/+STT95666023HXr6evrz549m5kVffHixe19kw97CAMGDGAmuBs1atR7773HnPi+Ing8Xp+/4K4qaENtHrYaJ+26dev++9///vbbb2VlZcuXL3/48OFnn32m7kakEq/FAGyrsG2PpCUdG7ZIWgbCFgAAFNHweVorKyt3d/e8vDxvb28qMXFc//79mb/xatdDR0dyljYVjR071tbWVoPdMZKTk7ds2aK8RNZrr73GvtbV1dXsXhpVdqQZDofDjITB4/HaY/ssqUNgdio7NwAAtEbbhq0GScvn8xctWuTg4EAIGTBgQHBwsI6Ozq1bt9TaiFRcqBiArQ/b9kta0lFhi6QFAABokYZjBnC53O+//37s2LFxcXEhISFbt25lyqVOmAQCweXLl/Py8szMzNzd3c3MzCQ38vTp09jY2OLi4rfeeotSKnm+8vjx4x9//LGsrMzV1VXujN7q+u2339LT07Ozs11dXT/44ANCSEpKiqenJ4fDOXTo0BtvvOHh4SFb8uzZs+jo6JUrV/7www/Z2dn/+Mc/dHR0UlNTjYyMxo4dK7n9W7du/fe//3VwcJg7dy4hJCEh4eHDh0ZGRsuXLxcIBCdPnmxsbDQxMfHy8pK7a0WHTClNTU29e/eurq6ura3t5MmTmfKqqqojR474+Pi8/vrrqhx+aWnp+fPnV61alZube/HixcGDBy9atIj5psrKyuLj4z/99NPU1NT//ve/b7755rJly3r27KnBIWjwLfz000+lpaWEEAMDgzlz5hgYGNy+fTs3N7dv376zZ89mPiW3ZWS/Gq2PNgTQHloftkqSlqgQtubm5mPGjGEXTUxMnJyclPzv1mLYGhkZtZi0XC5XLBa3MmwVxZTqYduGSUs0ClvNklbut9Bi2Cr6JSBsAQCgC5C8EVn1Z0UcHBwopffu3TMyMuJwOAkJCUz5oUOH2NnD7969O3LkyHPnzlVWVu7evdvIyOjEiRPsFvLz88eOHXvr1q3GxsZDhw4ZGBgMGzaMeSs5OdnX1/fOnTsxMTFGRkYrV65UUpOGhgZCiPLnaffu3fvuu++KxeKioiJzc/MDBw5QSjMzM11dXQcMGJCSkpKZmSlbcvz4cUNDQy6X+8033zg6OhJCoqOjmdnhv/32W2bLM2bMGDp06MyZM2fMmGFnZ0cIYacgt7e3NzU1ZV7X1tYaGxu7uLgwi7K7VnTI//znP5l54TMyMpydndkjOnLkCCGEmT5eFnM629zczCzGx8cz0xvs3bt36dKlM2fOJITs2LGDUnrq1Km+ffv27NlzxYoVPj4+zFj/Y8eOFYlE6h5CQUEBIeSdd95R61uoq6tjxjiRnNHO1taWfXpQbsvIfjVZWVlKfgDdfi5QgueOOkrHz0/byrBVkrRUzbBlDRo0KDg4WO5bqoRti0mblZWVk5PT+rCVjSklhyw3bJUnLf3fsFWStFTTsNUgaRV9C8rDVlGzqBu23TuL8DxtR+revyUAaHOt6tNSSs+dO8fhcNghTNjTrIaGBltb223btrEfWbhwob6+fk5ODrM4bty4DRs2MK/FYrGFhQVzpiUQCCwsLIRCIfPWsmXLCCFpaWmKaqJKn9bKyoqd/dzT03P69OnsazMzM8k1pUoWLVpECDl//jylNC8vj1KanZ0tdZqlr6+fn5/PHAXzr93MoCbz5s1jz1EopWPGjGE7hFI7UnTIYrH4tddeS0lJYcpDQkLYjwuFwtOnT0vOIC9Jqk9LKd28eTMh5OrVq2xlnJycmNeLFy/mcDj3799nFpmrQAcPHlTrEKgKZ1qKvoX4+HhCCHM2SSl9/Pgxe66v5Mcg+9UogT4ttBVt9WmppmGrKGmp+mHLSE1NNTU1FQgEct9VMWxbTFraRmErtSN1w1Z50lKZsFWStFTTsFU3aan6Yav8l6BW2HbvLEKftiN1798SALS51s5PO2fOnM8++0xqCBNCyI8//pifnz9+/Hi2ZMqUKSKRKCIighCSnJz8yy+/vPfee8xbHA5n7NixzB1x0dHR9fX1GzduDAgICAgIKC8vt7S0fPDgQWsqee3atZCQEEJIbm5uaWnp77//zr4l+4CWZMkbb7xBCGFOnpgHdw0MDKTWt7e3t7GxYT746aefEkISExNVqRW7I0WHzOFwbGxsvLy8Ll68SAhZv349+9levXp99NFHkjPIK8cMp8E+ezx8+HB2goFevXpxuVx2QojNmzdzudzr16+rdQiqUPQtzJw5087Obs+ePZRSQsjp06eZQVCI0h+D7FejXGxsLKf7IoR4eXlpuxavhNjYWNV/821Lg7BVkrREo7Btbm7etm1bfHy8kZGR3BVUD1uO0qQlbRe2kjtSN2zbMGlJK8KWo+aDxOqGrfJfgrph242ziHn0Rtu1eFWo9ZsHAGiDp2KCg4OzsrISEhK8vb2nTp3KFObm5hJCJM97Jk6cSAjJy8sjhGRlZRFCRowYwb7L5ldOTo6Jicn+/ftbXzHWm2++mZSUdOnSJTc3N0tLSz6fL7tfuSXMc1CqD6kyfvx4HR2dx48fq7KyKoccHh7+4Ycfenp6vv/++1FRUSo+09UiXV1dKjHYjCRDQ0NTU9MnT56osh21/uoo+hY4HM6GDRt8fHwuX748Y8aMq1evrlmzhnlLScto8NWsW7dO9dp2LV5eXmvXrnVxcdF2Rbq/vXv3anHv6oatqakpUZC0RKOwXb9+fWBgoORkuVJUD9vWJC1RJ2xVPOT2CFslSUvUCVt1z+/VDVvlvwR1v51unEVpaWmhoaHM1Vpob8y/IAAAqKgN+rQcDufUqVPjxo2Li4srKCgICAgghPTr148QkpaWxpxdEUKGDBmip6fXt29fQkhtbS0h5JdffpEcyIT5s62rq1tQUNDY2Kinp9f6ujG2bt3KjMnRs2fPc+fOSVVe9nA03pGxsbGRkZGFhYUqK7M7UnLIo0aNunPnzubNmw8dOjRmzJh79+4xDdt+GhoaKioqpkyZosrKKrZVZWUlj8cLCQlR9C0sWrRo69atX3/9tbm5ub29PTsASRv+GExNTefPn9/KjXRaXl5eLi4u3fgAOw9m0lRtUTdslSQtUf//r8OHD48ePXrWrFlK1lE9bFt5HUb1sJXcURcNW7WS1sDAQMm3IDds2/bPbvfOotDQ0G58dJ0K+rQAoBZN7j2mlL548UKyxNjYOC4ujsfjMZdhCSHjxo0jhEjeVXX//v3Gxkbmn29HjhxJCElOTpbduKOjY11d3cGDB9mSmpqaAwcOaFBPRlFRUUhICDt/oFgsZt/icDjNzc2SK8uWqCUzM7O2tpaZUJHL5b58+VLRmpI7UnTIDQ0N3333Xe/evffv35+YmFheXn7+/HmN66ai9PT0ly9fMqObqH4Iyvn6+paUlCj6Fggh+vr6a9euTUlJ2bBhw9KlS9nyNv8xAHQtrQxbJUlL1Pz/68KFC5RS9rkAQkhqaqrUOqqHbSuTlqgctlI76ophq1bS6urqKvkWiIKwRdICAEBXp0mftry8/I8//pD6A2xjYxMVFcXenuTo6LhkyZLr16+zTxPduHHD2traz8+PEDJr1ixbW9vvvvuOOQ97/PhxampqWVlZdnb23LlzzczM1q9fv2vXrry8vJiYGD8/v48//lhRZZ49e0YIUdL1EgqFhJDo6Oja2tqff/75+vXrz549EwqFAoHAxMSkoqKisLDw4cOHdXV1hBCpEqawurqa3RozJFVVVZXk9tmThrNnz3p5eTGzILi7u1dVVUVGRtbV1UVGRlZXVxcWFjK1ldrRzJkz5R4ypZQZPoTZ2muvvcbO1sjn852dna9duyb3kJmLM8x/JUtEIhGzWFVV1dDQwN4U19TUxJ4fx8bGurm5MadZqh9CXV3do0ePJHfBePHixerVq9nTNbnfArOmv78/j8erqqpiHzYjhHh5eSn6Mch+NQDdTyvDVknSNjU1Kfn/S8rVq1d37tzZ2NgYHh4eHh4eFhbm7+/PjOEkSfWwbTFpSRuFrdSOFB2yorBVnrREJmyVJy3RKGzVSloul6vkW2BWlg1b5b8EhC0AAHQBkgNGqTKm39mzZ9955x1CyOTJk5OTk6Xe3b59Ozu9RH19fUBAgL29/fHjx48ePTpjxoySkhJ2zaKiImbWQQsLi4ULF3p4eLz99tvffvttfX19bm7usGHDmOrZ29vfuXNHUWUuX77M3J0ycODAI0eOlJeXy13Nx8eHy+VaWVkdPHgwNjZWX1//b3/7W3V1dUpKCpfL7dOnDztVg2TJ0aNH33zzTULI/Pnzf/nlF0ppeno6M73EiBEjLl26RClNSkoaPXr0pEmTgoKC/P39//WvfzU2NjKbEggEzKgtdnZ258+fnzNnzpQpU9gBJ6V2LfeQ6+vrTUxMFixYcPbs2d27d0uOa8qMgMpujXXlypXly5cz25kzZ865c+copdeuXWPu0Fu+fHl5eXl0dLSxsTEhJCgoqLGx0d/fX1dX9+9///uGDRsWLFjg4eHBDvKp+iFERUU5OzsTQjgczrhx495///0JEybY29szd7IdPnxYybfAVn7FihX79++XOiK5LSP71SiHcY+hrXTkuMdtErZKkpYq+P9LCp/P79Wrl9Qfjh49ekj+z8tSMWyVJy1tu7CVDXm1wlZR0lJ5Yas8aSmlmoWtukmr5FtgKy8btop+CeqGbffOIox73JG6928JANqchnP5KPHnn39KLtbU1Ny8ebO0tFTuypWVlcz8AbIzQxQXFz969KiVlWFJTsbw8uVLyepJzdMgW9KiFy9eSHbXJVVWVjIvmDNI5TuSPeTGxsaGhga57fD8+XO1KqmIv7+/np4epbSkpETuNtU6BOUUfQuMyZMnP3v2TPALRUQAACAASURBVO4HW/ljQJ8W2krHz+WjhOphqyRpqZbCVoOkpRqFrdwdqR62bZW0tBVhq0FbaRa2rf8ldO8sQp+2I3Xv3xIAtLk2GCNKysCBAyUXeTzehAkTFK3MzFBP/nfQTsaQIUPasFaSkzFIThHB4/Gk1pQtaVHPnj0lh2CRxB5gjx49WtyR7CEzA3gMHjxYdsvMFYA21CaHoJyib4EQkpWVZWFh0adPH7kfbNsfA0D3oHrYKklaoqWw1SBpiUZhK3dHqodtmyctUT9sNWgrzcIWSQsAAF1U2/dpoWt58eJFU1OTUChUNOFku+Lz+Rs3bhw5cuS1a9fi4uI6vgKvoKamptu3b0+YMCEpKYl5Rs7W1lZydpaampoffviBXZw6dSozXHmHEQgEp0+fLioqsrKyWrhwoaGhoeS7QqEwJiamuLh4/PjxkydPZkdqzcjIkJ1bdfz48UOHDiWEVFdXX7x4saSkxMHBwd3dnf2137lzp3///jiVhw6AsH3VMGErFAo7Z9ISpWGrKGmJRmGLpAWAdid50Rb31bxqTp06xUzDuHLlyszMzI6vwO3bt3v37s3j8WJiYtpvL7j3mFVTU7Njxw7mvkShULht2zZCCI/HKygoYNcRi8V8Pn/kyJHDhw9PSUkRi8XtVW958vPzBw0aZG1tra+vTwixtLSUfE4+Pz/fysoqMTGRORUbPHhwamoqU2dLS0vZfOPz+ZTSzMzMESNGpKWl1dXV7dy508HB4fHjx8wGGxsbV6xYwWxEFZ3q3mPoQl6RsFU9i7oitc6R2LDtnElLlYatoqSlmoatuklLu/tvCQDaHPq0r7Samppnf3nx4oVW6tDY2Njc3Nyuu2jv/sOJEye0ux0V//aXlZV5eHjU1NRIFjJnM3Z2dlIP7IWEhAQHB2tWn9aYNm1aVlYWpbSyspIZg8fHx0fy3WXLlrGLS5YsmThxIqU0KSlp9erVRUVFDX9JSkoyNzenlDY3Nzs6Om7cuJH9lLOz8+TJk9nFpqamadOmZWdnq1I99GlBM69I2LZ3P0S7Yav6OZJs2Ha2pKVKw1ZR0tJWhK1aSUvRpwUANWkylw90Gzwer89fmMkMOx6Xy2VnJemKkpOTt2zZ0nm2o0RgYOAHH3wg9WyelZWVu7t7Xl6et7c3lZh0pH///ooeb24/fD5/0aJFDg4OhJABAwYEBwfr6OjcunWLXaG8vDwnJ4ddNDAwYGZ8MTIy2rt3r7m5uf5fLl68OHfuXEJIenp6VlaW5C1/zs7OV65c4fP5zKKurm5gYCAzzRhAO0HYtl6XDttOlbSkpbBVlLSkFWGLpAWAdtWF/7wBtDmBQHDmzJmgoKCIiIjS0lKmMCEhITQ09OjRo8wK+/fvDw0NZf7BPiUlxdPTUygUHjp0KCEhgVm/rKzswIEDlNJr165t2bIlPDy8vr5e3e1UVVV98cUXf/75Z1sd2u3btxMTE5n5USRxudzvv//e0tIyLi4uJCSELdfR0ZE8/ZXbMoSQ0tLSsLAwsVh8//797du3f/fdd+wMoozHjx8fO3YsODj4p59+arGS5ubmCxcuZBdNTEycnJwknzGbM2dOenr6qVOnCCFCofDChQtr164lhLi4uEjWViwWMxOiEEIKCgoIIZInkczcNjdu3GBLJk2aJBAIzp8/32INAaD1FOWJWiGpKGnV3U7HhK2KSaukcZSHrVpJS1oKW0VJS1oXtkhaAGhHkhdtce8xdEsq3ud59+7dkSNHnjt3rrKycvfu3UZGRuz9afb29qampszr2tpaY2NjFxcXSmlmZqarq+uAAQNSUlKYZ+ROnTrVt2/fnj17rlixwsfHZ/r06YSQsWPHikQitbZz5MgRQgg7qaZyRIV7tObOnTtp0iTZcgcHB0rpvXv3jIyMOBxOQkICU37o0CF2+lNFLRMfH8+M1Lp3796lS5fOnDmTELJjxw5248nJyb6+vnfu3ImJiTEyMlq5cqUqhyNp0KBBkjfmVVRU2NjYEELWrVvn7u5+/vx5uZ+6fv36G2+8wTyfFh0dTQgJDAxk32VOsCRLKKV+fn6jR49usT649xhACVWySEnSUpVDUnnSqr4dqk7YqniOJDdsVUlaJY2jPGxbn7T0f8NWxaSl6oetiklLce8xAKgJfVro/lTpPzQ0NNja2m7bto0tWbhwob6+fk5ODrMF9vSIUjpmzBjm9IhS6unpaWZmJrmpxYsXczic+/fvM4tbt24lhBw8eFCt7QiFwtOnT6s4KaUqf/utra2Ze96kMGdalNJz585xOBx2FBP2TEt5y2zevJkQcvXqVfaInJycmNcCgcDCwoKZFpVSumzZMkJIWlqaKkfESE1NNTU1lZpStbKykhmhxMXFpaKiQu4HV61aFRAQwLwuKSnR19d3cnJih2BJTEyUPYUNCwvjcrkNDQ3Kq4Q+LYASLWaR8jyh6oSkkqRVazuqh62K50hyw7bFpKWahm3rk5bKC1tVkpaqH7YqJi1FnxYA1IR7jwEIIeTHH3/Mz88fP348WzJlyhSRSBQREdHiZzkcjuRir169uFyuvb09s7h582Yul3v9+nW1ttOrV6+PPvpIcpLJ1hCJRIWFhSYmJkrWmTNnzmefffb8+XNPT0+BQMCWK28Z5slAW1tb5q3hw4eXlJQwr6Ojo+vr6zdu3BgQEBAQEFBeXm5paSk7A4Qizc3N27Zti4+Pl5r4JCIiws3NzcfHJy0tbdy4cezuWJTSc+fOMc93EULMzMxCQkL4fP7SpUsvX7789ddff/7554QQR0dHyU/xeLympibVqwcAGmhN0hKZkNQsaWW305FhqyhpiaZh28qkJQrCtsWkJRqFLZIWANoJ5qcFIISQ3NxcQojkX/SJEycSQvLy8lr8rFSfVoqhoaGpqemTJ09auZ3WePr0aXNzc4sj0wQHB2dlZSUkJHh7e0+dOpUpVKtldHV16V8PU+Xk5JiYmOzfv1+zOq9fvz4wMFByuBFCSGRk5JkzZzIyMrhcrqurq7+/f0BAAPskM+PmzZsikeidd95hSzZs2ODs7JyUlHTjxo0FCxakp6f//vvvUltmDrCsrGz48OGaVRgAWtSapCVKQ1L1pFW+nVZSJWzlJi3RNGxbmbREXtiqkrREo7BF0gJAO8F1WgBCCOnXrx8hJC0tjS0ZMmSInp6e5ABFiig/PWpoaKioqLCwsGjldlpj0KBBffr0kbomILcCp06dsrW1jYuLCwsLYwo1bhldXd2CgoLGxkYNKnz48OHRo0fPmjVLqvzEiRPTpk3jcrmEEB8fH19f36SkpJqaGsl1YmNjZ8+eraurK1no5ua2ffv2HTt29O7dOz4+Pjg4WOqyzLNnzwghZmZmGtQWAFTUmqQlSkNS9aRVvp1WUiVs5SYt0bRxWpO0REHYqpK0RKOwRdICQDtBnxaAEELGjRtHCJG8b+3+/fuNjY0uLi6EEC6X+/LlS7kf5HA4zc3NSracnp7+8uVLZkiP1mynlezt7SsrK6UKKaUvXryQLDE2No6Li+PxeOyVAeUto4Sjo2NdXd3BgwfZkpqamgMHDrRY1QsXLlBKvb292ZLU1FTmRXZ2tuR51ezZs0UikeSApZTS2NhY9l44KSKRyMvLy8bGZuXKlVJvlZeXczicoUOHtlg9ANBYi3micUhKJm1rttN6smGrStISTcNW46QlisO2xaQlmoYtkhYA2gn6tACEEOLo6LhkyZLr16+zTw3duHHD2tqamUzP3d29qqoqMjKyrq4uMjKyurq6sLCQ+fdmExOTioqKwsLChw8f1tXVMZ9tampiz1RiY2Pd3NyYMy3Vt8Pn852dna9du9ZWBzhx4sR79+5JFZaXl//xxx9SZ342NjZRUVHsbA3KW6a2tpYQIhKJmLeqqqqYwT8IIV5eXmZmZuvXr9+1a1deXl5MTIyfn9/HH3/MrOnn5zd9+nTZ+TOuXr26c+fOxsbG8PDw8PDwsLAwf3//7Oxs5l1PT88LFy6wM1ikp6c7ODhYW1uzH09LSxMKhe+//75sC9TV1fn6+g4dOvTq1avM9QdJxcXF7u7uPXr0aKEdAaAVlOcJUTNsFSWtWtvpgLBVJWlbbBxFYas8aYlGYdti0hJNwxZJCwDtRXLAKIx7DN2SimPM1tfXBwQE2NvbHz9+/OjRozNmzCgpKWHeEggEzLgddnZ2zFx8U6ZMOXLkCKU0JSWFy+X26dOHHdrR399fV1f373//+4YNGxYsWODh4cGOqKn6dpixMZm3WkRUGB/y6dOnAwcOfPDgAVty9uxZ5jmoyZMnJycnS62/fft2djRORS1z7do15k6/5cuXl5eXR0dHGxsbE0KCgoIaGxsppbm5ucOGDWOixt7e/s6dO+z2mRE1d+/eLblTPp/fq1cvqYzq0aNHdXU1s0JdXd2yZctGjBgRGhq6fPnyWbNmFRYWSm5h7dq1ixcvljqWqqqqiIiICRMmKJqRoqGhoX///leuXFHehhTjHgMopUoWKUlaqk5IKklatbajetiqeI4kFbaqJ62SxlEetkqSlmoUti0mLdUobFVPWopxjwFATejTQvenVv+hpqbm5s2bpaWlsm9VVlYyL+rr66U+Inku5e/vr6enRyktKSl5/vy5xtuR+1m5VPzbf/DgQXbSBVX8+eefUtVT1DLKFRcXP3r0SKrw5cuXZ86cuXjxorpbo5TW1dXl5uY+ffpU9q3CwsKqqiqpwgsXLjx8+FDJBmNiYmbPnq3KrtGnBVBC9X6I8jxRJSRbTFoVt0NVDlvVz5HUCluppKWahq3cpKWtCFslSUs1ClvVk5aiTwsAasK4xwD/g8fjTZgwQe5bzJT3hBCp+6Z4PJ7c9RUNg6Hidph/hm9Dvr6+ixYtyszMlBryV5GBAwdKLippGeWGDBkiW9jQ0JCWlrZr1y4NNmhoaGhnZyf3LbmPaXl6eirZWn5+flRUVHR0tAY1AQDNKM8TtcJWyYBDXSJspZKWaBq2cpOWtCJslSQtUT9skbQA0K7wPC1AG3vx4kVTU5NQKNR2RaTp6OgcP37822+/zcjI0HZdyO3bt3fs2CH7XGsHe/To0RdffHHs2LEWJzoCgE6l0yYtQdjKQNICQHtDnxagLUVFRSUlJVFKN23adPfuXW1XR5qBgcHhw4dff/11bVeETJo0qTOc3Ojr6x8/fpyZQgMAuopOnrQEYfu/kLQA0N5w7zFAW5o5c+aMGTOY1wYGBtqtjCKDBw/WdhU6CxMTE21XAQDU1iWSliBs/4KkBYD2hj4tQFtS9GwtAAC0FSQtAABIwr3HAAAAAAAA0FWhTwsAAAAAAABdFfq0AAAAAAAA0FXJeZ42Jiam4+sB0H7KysqINn7YlFJCCIfD6YB9paWldcBeoKyszNTUtK02hbDtTsRisY4O/pm4O2cRc2j43xYAoBPiMKfdjJiYGC8vLy3WBgCgk5s3b97Zs2dbuZEPP/wwNja2TeoDANAtnTlzZv78+dquBQB0Df/TpwWANvT777/v2LEjKirq9ddfX7dunZ+fn5GRkbYrBQBtJjc3d9euXadPnx4wYMC6detWrlyp9YlAAQAAXkHo0wK0r4qKioMHD4aGhlJKP/nkk82bN2OmPoCujs/nh4WFnT592tzcfNWqVf7+/j169NB2pQAAAF5R6NMCdITa2trIyMgvv/zy2bNn8+fP37p1q7W1tbYrBQBqu3Hjxs6dOy9dujRmzJg1a9YsWrRIV1dX25UCAAB4pWFAC4COYGxsvGbNmuLi4n379qWmptra2np4ePD5fG3XCwBUIhaLExIS3nrrrYkTJz579iw+Pp7P53t7e6NDCwAAoHXo0wJ0HAMDAz8/vwcPHkRGRhYVFb311luTJ0++efOmtusFAArV1dWFhYUNHTrU09PTxMQkIyPjxo0bHh4e2q4XAAAA/H/o0wJ0ND09PW9v73v37sXHx7948eLtt99+++23ExIS8CAAQKdSVVUVFBQ0ZMiQTZs2ubm55efnM5dqtV0vAAAA+B/o0wJoB4fD8fDwuHnz5s8//9y3b9/Zs2c7ODicPHmysbFR21UDeNU9evRozZo15ubmoaGhixYtKioqOnnyJJ6BBwAA6JwwRhRAp3Dv3r1du3ZFR0e/+eab69at8/X1NTQ01HalAF45zP+J33//ff/+/f39/detW8fj8bRdKQAAAFAGfVqATqS4uHjv3r1Hjx41NDQMCAhYvXp1v379tF0pgFcCM6BxYmKivb39hg0bPvroIz09PW1XCgAAAFqGPi1Ap/PkyZP9+/d/8803IpHIx8dn/fr1ZmZm2q4UQPdEKb106dKXX35569YtV1fXTZs2zZw5k8PhaLteAAAAoCo8TwvQ6QwYMCAoKOjRo0chISHnz5+3srLy9vbOy8vTdr0AuhWRSHTy5MkRI0bMnj27X79+N27cYAY0RocWAACga0GfFqCTMjIyWrNmzcOHD48cOfLrr7+OGDHCw8MjPT1d2/UC6PKEQmFYWJiVldXy5cudnJzu3buXkJDg6uqq7XoBAACAJnDvMUAXIBaLExMTt2/f/ssvv+D2SACN4cZ+AACA7gd9WoCuhB3GxsHBITAwcOHChVwuV9uVAugCioqKQkNDjx492qtXr5UrV2IANgAAgG4DfVqArufu3bt79uw5ffr04MGD16xZ4+fn17NnT21XCqCTysrK+vrrr6Ojo01NTdeuXYuJsgAAALoZ9GkBuqrCwsKwsLAjR4707t37008/XbNmTd++fbVdKYBOhL2vYeTIkf/4xz9wXwMAAEC3hD4tQNdWWVl54MCBsLCw5ubmpUuXbty48c0339R2pQC0iXn+fMeOHenp6Xj+HAAAoNvDuMcAXdvAgQODgoJKSkr+85//xMbGWlhYeHt7FxQUaLteAFrATM9jb2/v6en52muvpaWlYXoeAACAbg99WoDuoHfv3mvWrCksLDxy5Mgvv/wyfPhwDw+PjIwMbdcLoIMIBIKwsDALCwtfX9+xY8fev38/ISFh/Pjx2q4XAAAAtDvcewzQ3TA3Xv7nP//JyMhgbrz08PDQdqUA2gtz+/2+ffuampqWLl26YcMGU1NTbVcKAAAAOg6u0wJ0Nzo6Oh4eHrdv3/7555/79u07a9asMWPGnDx5srm5WdtVA2hLhYWFa9asMTc3//bbb1evXv3o0aOwsDB0aAEAAF41uE4L0M3duXMnNDT09OnT5ubmq1at8vf379Gjh7YrBdAqmZmZe/fuPX369JAhQ1avXo3prAAAAF5l6NMCvBIePHjwzTffHDp0qG/fvv7+/uvWrePxeNquFIDamOl5Ll26NGrUqHXr1mF6HgAAAECfFuAVUlFRcfDgwb179xJCPvnkky1btgwaNEjblQJomexT4pieBwAAABh4nhbgFTJo0KCgoKCHDx+uW7cuKirK3Nzc39+/pKRE2/UCUKihoeHkyZN2dnaenp6vv/76L7/8gul5AAAAQBKu0wK8ourq6o4ePbpnz57y8vIFCxZs2rTJ3t5e25UC+D+1tbWRkZFfffVVdXX1/PnzP/vsMxsbG21XCgAAADod9GkBXmmNjY3R0dE7d+7My8ubMWPGli1bJkyYoO1KwauOuUk+NDSUUvrJJ59s2rTpjTfe0HalAAAAoJPCvccArzQ9PT1vb+/79+9fvHjx6dOnrq6ub7/9dkJCAv61C7TiwYMHa9asGTp06MGDB9euXctMz4MOLQAAACiBPi0AEA6H4+HhcfPmTWZK29mzZzs6Op48ebKpqUnbVYNXBZ/P9/b2trW1TUxM/PLLL4uLi4OCgvr06aPtegEAAEBnhz4tAPwf5iLt3bt3R40atWzZMmtr67CwsBcvXmi7XtCdMWM+vfXWWzk5OceOHSsoKFizZg1mUQYAAAAVoU8LANIcHBxOnjz5+++/z5o165///Ke5uXlQUNDTp0+1XS/oVsRicUJCwltvvTVx4sRnz57Fx8czl2p1dXW1XTUAAADoSjBGFAAo8+TJk/379+/bt6+xsdHHx2fDhg2mpqbarhR0beyY22VlZdOnT//888/feustbVcKAAAAuir0aQGgZUKhMCIiYvfu3ZWVlV5eXlu2bLGzs9N2paDrqaqqCg8PDw8PFwqF8+fP37p1q7W1tbYrBQAAAF0b+rQAoCqRSPT9999/8cUXv/322/Tp0z/77LPx48dru1LQNTx69GjPnj0RERFcLnfJkiWbN282MTHRdqUAAACgO8DztACgKn19fW9v75ycnLi4uCdPnri4uGDiH2jRvXv3vL29ra2tY2Ji1q9fz0zPgw4tAAAAtBX0aQFAPTo6Oh4eHunp6czEP7NmzRozZszJkyebm5u1XTXoXJgBjR0dHTMzM48ePVpSUhIUFMTj8bRdLwAAAOhW0KcFAA0xF2kzMzNHjhzp4+MzbNiwsLCw+vp6bdcLtIxSmpCQ4OrqygxofPHixezsbG9vbz09PW1XDQAAALoh9GkBoFVGjRp18uTJ3377bebMmZs3b2Ym/qmpqVHykYaGhoKCgg6rIbSVkpKSuro6JSuIRKKTJ0+OGDFi9uzZ/fr1u3HjBnOplsPhdFglAQAA4FWDPi0AtAELC4uwsLDi4uJPP/00LCxs8ODBa9asefz4sdyVT506NXHixPv373dwJaE1CgsLXV1dIyIi5L4rFArDwsKsrKyWL1/u5OR079495lJtB1cSAAAAXkEY9xgA2lhtbW1kZOTOnTufPn06f/78f/3rX8OGDWPfFYvF1tbWRUVFPB4vJSVl1KhRWqwqqOi333575513KisrTUxMiouLJe8iZmYw/uabb0QikY+Pz/r1683MzLRYVQAAAHjV4DotALQxY2PjNWvWFBUVHT58OD093c7OzsPDIyMjg3n3/PnzRUVFlFKBQDBx4sTbt29rt7bQovz8/Lfffru6uppSWlFRcebMGaa8qKhozZo15ubmBw4cWLVqFTOgMTq0AAAA0MFwnRYA2pFYLE5MTAwODv71119dXV03bdr073//++7du8wgybq6uj169Lh69Srmue208vLy3nnnnZqamqamJkKIjo7OsGHDoqOj9+zZEx0dbWpqunbtWl9fX0NDQ23XFAAAAF5R6NMCQLujlF6+fHnnzp0///yz1Fu6urp6eno//PDDu+++q42qgTJ379597733hEIh06GVNGrUqI0bN3744YdcLlcrdQMAAABgoE8LAB1nzJgx9+7dk+og6ejo6OvrJyYm/u1vf9NWxUAWn89///336+rqpL4vXV1dGxubnJwcbVUMAAAAQBKepwWADnL37t27d+/KXvETi8UikWj69OlXr17VSsVA1q1bt9zc3OReoW1ubs7Nzb1165ZWKgYAAAAgBX1aAOggISEhiu5TFYvFjY2N06dPT0hI6OBagayff/550qRJL1++ZB57lqWnp/fll192cK0AAAAA5MK9xwDQEX7//XdbW1uxWKxkHQ6Hw+VyL1y4MGPGjA6rGEj56aefZsyY0dTUpKhDy+BwODk5OXZ2dh1WMQAAAAC5cJ0WADrCrVu3Ro0aNWDAAF1dXbaQy+UaGBiwk51SSpuamjw9PS9evKilar7qfvjhh2nTpolEIrZDK/UdMfr27WtjY5OamqqNOgIAAAD8D1ynBYViYmK8vLy0XQsAAOj+cDYCAAAawxwM0IIzZ85ouwrwChGLxc+fP3/69Gltba2Dg4PkRV1V7N27lxCybt269qmd9nl5ea1du9bFxaXNtywWi7Ozs42MjPr168fj8dRteQCNpaWlhYaGarsWAADQhaFPCy2YP3++tqsAoKqzZ8+Sbv2j9fLycnFxaacDXLBgQXtsFqBF6NMCAEBr4HlaAAAAAAAA6KrQpwUAAAAAAICuCn1aAAAAAAAA6KrQpwUAAAAAAICuCn1aAAAAAAAA6Kow7jEAvNIKCwtDQkKCg4NNTU21XZe2UVxcnJaWxrweNmyYk5OT5LtNTU23b98WCoXV1dWEEFtb29GjR7Pv1tTU/PDDD+zi1KlT+/bt2yG1/j8CgeD06dNFRUVWVlYLFy40NDRk3xIKhTExMcXFxePHj588ebKenh5TnpGR8eDBA6ntjB8/fujQoczr6urqixcvlpSUODg4uLu7GxkZEULu3LnTv3//IUOGqFtDpg0nTJiQlJTUOZtRSRsSTZtRbhsSec1YWFj4yy+/MK9tbGzGjBnTTocJAADw/1EABZiZabVdCwA1zJs3b968eWp9hJn+5/Lly+1UpbZFCDlz5ozydU6dOkUIiY6OLi8vr62tlXyrpqZmx44dtbW1QqFw27ZthBAej1dQUMCuIBaL+Xz+yJEjhw8fnpKSIhaL2+UwFMvPzx80aJC1tbW+vj4hxNLSsry8nH3LysoqMTGR6bANHjw4NTWVqbOlpaXsXzc+n898MDMzc8SIEWlpaXV1dTt37nRwcHj8+DGltLGxccWKFcxGVMe2IaW0czajkjakmjajojak8ppRKBQWFxf//PPPenp669ata7HC+FsDAACthL8ioBDOM6DL0aBPSyl98uRJe1RGyokTJ1q/EdX7tDU1NVLlZWVlHh4ekuVMn8fOzk6q68tcuG59bTUwbdq0rKwsSmllZeXy5csJIT4+Puxby5YtY9dcsmTJxIkTKaVJSUmrV68uKipq+EtSUpK5uTmzWnNzs6Oj48aNG9kPOjs7T548mXnd1NQ0bdq07OxsFasn24a08zWjkjakGjWj8jakipvR3NwcfVoAAOgAeJ4WAF51r732WnvvIjk5ecuWLe29F+UCAwM/+OADHo/HllhZWbm7u+fl5Xl7e1NK2fL+/fv36dOn42vI5/MXLVrk4OBACBkwYEBwcLCOjs6tW7eYd8vLy3NyctiVDQwMGhoaCCFGRkZ79+41NzfX/8vFixfnzp3LrJaenp6VlSV5Y7Czs/OVK1f4fD4hRFdXNzAw0M/PT8UayrYhr+SVPAAAIABJREFU6WTNqLwNiUbNqLwNifrNCAAA0LbQpwWAV5pYLE5JScnIyGBLSktLw8LCxGLx/fv3t2/f/t1334nFYuatsrKyAwcOUEqvXbu2ZcuW8PDw+vp6QkhCQkJoaOjRo0cJIQKBYP/+/aGhoczVJ0JISkqKp6enUCg8dOhQQkICIaSqquqLL774888/O+wwb9++nZiYOG/ePMlCLpf7/fffW1paxsXFhYSEsOU6Ojo6Ov/310EgEJw5cyYoKCgiIqK0tFRyC0raihDy+PHjY8eOBQcH//TTT6pU0tzcfOHCheyiiYmJk5MT+yTqnDlz0tPTmavQQqHwwoULa9euJYS4uLhI1lYsFp8/f37OnDnMYkFBASFEsqs5duxYQsiNGzeYxUmTJgkEgvPnz7dYPbltSFrdjMrbkKjZjMrbkGjUjC22IVGnGQEAANoc+rQA8OrKzc318vL629/+xl5xSkhIcHJyWrt27b59+/bs2ZOenu7t7b1z505CSFRUlIODw/r161euXPndd99lZ2evWrXKzc2tsbHRw8Pj6NGj//73vwkhvXv39vb2/vzzz8PCwpht9u3b18HBwcDAwMbGxszMjBASFxf3z3/+MyYmpsOO9KuvvnJxcendu7dUed++fePi4oyMjD7//PNLly7JfjArK8vV1VVPTy8gIKCmpmb48OEnT55k3lLSVoSQlJSUoKCg0aNH29nZeXp6BgQEtFjJ/v37czgcyZLS0tJp06Yxr/38/GxsbD7++OPAwMC5c+ceOnToo48+kt3IzZs3ORyOi4sLs9izZ09CyK+//squwDw1WlJSwpa4urpK9kUVUdSGpBXNqLwNifrNqLwNiUbNqEobEpWbEQAAoO1p9c5n6NTwjBN0ORo8T5udnU0I+fbbb9mSzZs3E0KuXr3KLI4ZM8bJyYl5vXjxYg6Hc//+fWZx69athJCDBw8yuzY1NWU3MmbMGBcXF3bR09PTzMyMXRQKhadPn5Z6/FIVRNPnaa2trZk7YyU5ODgwL86dO8fhcNiBjg4dOhQeHk4pbWhosLW13bZtG/uRhQsX6uvr5+TkMIuK2kogEFhYWAiFQqZ82bJlhJC0tDS1DjY1NdXU1FQgELAllZWVTG/KxcWloqJC7qdWrVoVEBDALpaUlOjr6zs5ObEDNSUmJhJC9u3bx64TFhbG5XIbGhqU10duG9JWN6OS31vrm1G2Dan6zahKG1J5zYjnaQEAoGPgOi0AvNIMDAykSpirUra2tszi8OHD2etRvXr14nK59vb2zOLmzZu5XO7169dV2ZHk1bNevXp99NFHcq/4tQeRSFRYWGhiYqJohTlz5nz22WfPnz/39PQUCARs+Y8//pifnz9+/Hi2ZMqUKSKRKCIigllU1FbR0dH19fUbN24MCAgICAgoLy+3tLSUnSdGiebm5m3btsXHx7NzxhBCIiIi3NzcfHx80tLSxo0bJ3WdkBBCKT137hz7MC0hxMzMLCQkhM/nL1269PLly19//fXnn39OCHF0dGTX4fF4TU1NyqvXYhsSTZtRye+tlc0otw2J+s2oShsS1ZoRAACgPWB+WgAAZXR1danEk4SSDA0NTU1Nnzx5osp2pO4I7UhPnz5tbm5m+k6KBAcHZ2VlJSQkeHt7T506lSnMzc0lhEj2iCZOnEgIycvLk7sRtq1ycnJMTEz279+vcZ3Xr18fGBgoOS5RZGTkmTNnMjIyuFyuq6urv79/QEAA83wy6+bNmyKR6J133pEs3LBhg7Ozc1JS0o0bNxYsWJCenv77779Lbpk5wLKysuHDhyuqjyptSNqiGSV/b61sRtk2JJo2Y4ttSFRrRgAAgPaAPi0AgIYaGhoqKiqmTJmiyspa7NMOGjSoT58+klcOZXE4nFOnTo0bNy4uLq6goIB5brNfv36EkLS0NKYPRggZMmSInp6e5JhDcunq6hYUFDQ2Nurp6WlQ4cOHD48ePXrWrFmShSdOnJg2bRqXyyWE+Pj4/PrrrxERETU1NZJjC8fGxs6ePVtXV1dqg25ubm5uboSQoqKi+Pj4Xbt2SV4kf/bsGSGEedRZEVXakHSmZpTbhqQVzai8DYlqzQgAANAecO8xAICG0tPTX758OXPmTEIIl8t9+fKlojU5HE5zc3MHVk2avb19ZWWlZAml9MWLF5IlxsbGcXFxPB6PvX44btw4QojkzdX3799vbGxkR2BSxNHRsa6u7uDBg2xJTU3NgQMHVKnqhQsXKKXe3t5sSWpqKiEkOzu7pqaGLZw9e7ZIJJIcO5pSGhsbK3njsRSRSOTl5WVjY7Ny5UrJ8vLycg6HM3ToUOUVk21D0lmbUVEbklY3o6I2JCo3IwAAQJtDnxYAXmnM5JxVVVVsSW1tLSFEJBIxi1VVVcywN8xiU1MT21eJjY11c3Nj+rTu7u5VVVWRkZF1dXWRkZHV1dWFhYXMlStCiImJSUVFRWFh4cOHD+vq6vh8vrOz87Vr1zroIAmZOHHivXv3JEvKy8v/+OMPqX64jY1NVFQUO6eLo6PjkiVLrl+/zj5yeePGDWtra3YmUkVt5eXlZWZmtn79+l27duXl5cXExPj5+X388cfMan5+ftOnT5c7ldHVq1d37tzZ2NgYHh4eHh4eFhbm7+/PjOPl6el54cIFdp6b9PR0BwcHa2tr9rNpaWlCofD999+X2wJ1dXW+vr5Dhw69evUqc5WSVVxc7O7u3qNHD+XVk23DNmlGJb83zZpRSRu2shmVtKFsMwIAAHQcbQxMBV0DxqKELkfdcY/T09OZ6UZHjBhx6dIlSum1a9csLCwIIcuXLy8vL4+OjjY2NiaEBAUFNTY2+vv76+rq/v3vf9+wYcOCBQs8PDzYsYsFAgEzCJCdnR0zseeUKVOOHDnCvJuSksLlcvv06cMMFcsMkMu+qzqi6bjHT58+HThw4IMHD5jFs2fPMk9LTp48OTk5WWoL27dvZwbspZTW19cHBATY29sfP3786NGjM2bMKCkpYd5S3la5ubnDhg1j/tDY29vfuXOH3T4z6O7u3bul9svn83v16iX1R6pHjx7V1dWU0rq6umXLlo0YMSI0NHT58uWzZs0qLCyU/PjatWsXL14s2yBVVVURERETJkw4f/687LsNDQ39+/e/cuVKi9WTasM2aUblbUgpVbcZlbehxs2ovA3lNiPFuMcAANBROFTB2CcAMTExXl5e+IVAF/Lhhx8SQs6ePdtO21+xYsWxY8dEIlFpaSmPx2O6H5KePHkyYMAAQsjLly+lLlg9f/5cR0eHfQSxtrZW9uMt4nA4Z86cmT9/vpJ1oqKiFi9eXFNTw+PxJMsPHTp079698PBwVXZUWVk5cOBAycrn5OQMHjzY1NRUrQo/evSIw+EMHjxYsrChoeHixYs9evSQfdqzRS9evHj06NGgQYNkH0YtKioyNjbu37+/VHlcXJyDgwPTdZR19uzZqKiouLg4VaqnVhuSbtSMytuQyGtGQsjQoUM/+OCDPXv2KK8M/tYAAEAr4d5jAAC1mZmZye2RMh1aQojsHZg8Hk9yTB0NOrRqYe6pluTr61tdXZ2ZmanKxyV7YoQQHo83YcIEdXtihJAhQ4ZI9cSYuqWlpU2fPl3drRFCDA0N7ezs5I6uNHToUNkOLSHE09NTUWcsPz8/KioqOjpaxeqp1YakGzWjkjYkCpqREKLdZ8gBAODVgXGPATpORUVFfn7+u+++q8FnS0tL79y5k52draOjY21tPXbsWA6HU1ZW9vbbb7d1NUGhFy9eNDU1CYVCqQk/OxU9PT1jY+Ply5e7uLiMHTt20qRJTLmOjs7x48dXrVrl6+s7duxYLdbw9u3bO3bskH0gs4M9evToiy++OHbsmNQMPUqq13nakHTiZrx///6PP/5YUlJSW1uLx2sBAKAD4DotQEd48uTJ+vXrLSwsLly4oO5nRSLRhg0bhg0bdvPmzTFjxkyYMKGwsNDJycnCwuL27dvtUVuQKyoqKikpiVK6adOmu3fvars6Cs2fP//58+fx8fFbtmxhO7QMAwODw4cPv/7669qqG2PSpEktTvTaAfT19Y8fP85MtCNJefU6SRuSTtyMI0aMWL9+/b59+2pqanbs2KHFugEAwCsCfVroJk6ePNmZN15cXOzt7V1fX6/uB1++fOni4nLkyJErV6589dVXM2bMeO+99zZv3pyRkWFqaio1iUgH6OTt3K5mzpyZn5//7Nmz7du329jYaLs6mpO9i/XVZGJiovGkwWhDVmuaEQAAoE2gTwvdQXJy8pYtWzrzxseOHWtra6vBB0NCQu7cubNhwwape4wtLS23bt1aV1fXyoqppfO3c7vi8Xh9/tIZro8BAAAAAMHztNAmhEJhXFxcQUHByJEjp0yZwo62KhAILl++nJeXZ2Zm5u7ubmZmxn6ktLT0/Pnzq1atys3NvXjx4uDBgxctWsRO56hog7/99lt6enp2drarq+sHH3zAFKakpHh6enI4nEOHDr3xxhseHh6EkMePH//4449lZWWurq7sRIvKd9rKjWusqqrqyJEjPj4+snczVlRUfPXVV4aGhqtXr5b94JIlS+Lj45U3tfJDVqup1WoKdZta7sbbvKkBAAAAoBvS7lRC0JmpOGdgXl7e9OnTs7KyGhsbP/roo/79+z98+JBSevfu3ZEjR547d66ysnL37t1GRkYnTpxgPhIfH88MD7t3796lS5fOnDmTELJjxw7lG9y7d++7774rFouLiorMzc0PHDjArJ+Zmenq6jpgwICUlJTMzExKaXJysq+v7507d2JiYoyMjFauXNniTlu5cVUw49CuXr1aqvzIkSOEEGbaUimXL18mhIwYMUL5lhU1tfJDVrepVW8KDZpaduOaNbW689N2OUSF+WkBuhbMTwsAAK2EvyKgkCrnGU1NTaNGjTp8+DCzyOfz/x97dx7XxNX2DfwMRFBAg7UuVHBfUKpU+FhBa12qqKBCLYigoKKildpHW7X61Pb15rZ6d1VbtYgiKAJlFcWlWgW0CxYLuICIVZRFoSyyhSUsmfePuTtPmpBhskAI/L5/kcnMmStnJueaQ2bOMTAwSEhIEIvFlpaWn376Kbump6engYFBVlYW83LHjh2EkKtXrzIvbWxsbG1tOQqkaXrUqFF+fn7MchcXF0dHR7ZwFxcXCwsL5u+ampoRI0aIRCLm5Zo1awghKSkpHDvVSOFtUtSnFYlE4eHh1dXV8pt88cUXhJBFixZxF8tR1RwfWYWq5l8VKlS1dOFtlq8I+rQAOgd9WgAAUBPuPQa1XLx48fbt205OTsxLGxubmpoaAwODc+fOPXjwwM7Ojl1z3rx54eHhQUFBX3/9NSGEeRyRfcR0/Pjxly9f5iiQEJKcnGxsbEwIuX//fkFBQXV1tXQk7CAlERER9fX127dvZ14WFRWNHDny0aNHdnZ2inaqkcJVrkNjY2MPD49W32Jm6eCe4/HHH3/kqGqOj6xaVfOsCtWqWnqkGZWrurCwMCoqinsdnZaSkqLtEAA0Cac0AACoCX1aUMudO3eMjY2ZG00ZTKfo/v37hBDpOTynT59OCMnOzm61HH19fZqmOQokhAwePPjKlSvnz5+fMWPGyJEj09LSpEtgu0NZWVlmZmaHDx9uM3h2p+1RuEZYWVkRQv7880+OdZSqaumPrFpVq1YVPKtauk+rclXfvHnT3d1d2a10yIEDBw4cOKDtKAAAAAA6C/RpQS0SiaS2tjYpKcnBwUF6OTNXYUpKCtO/IoQMHTq0R48effv2Va1AQsgnn3xy/fr1y5cv9+rVKzY2VuZdtjukr6+fk5PT1NTUo0cP/h+kXQtXma2trYmJSW5u7uPHj0eOHNnqOh1c1epXBZ/C1Snf1dU1OjpaqU10CEVRkZGRS5cu1XYgABoTFRXVtf8PBQAA7Q1z+YBaJkyYQAgJDw9nl5SXl585c2bKlCmEkBs3brDLMzMzm5qa7O3tVSvwyZMne/bsWbFiBXNTq0Qikd6Koij2Bl1ra+va2tqAgAD23crKyiNHjnDstF0LV0e/fv3+9a9/tbS0sLfgysjIyOjIqla/KngWrnL5AAAAANDd4HdaUMvixYsnTZp08uTJnj17urm53b17Nzk5OSoqytDQcOXKlXFxcfn5+UOGDCGE/PLLL6NHj/b19WU2ZJ6ibGxsZF6WlZWJxWKaphUV+PDhQ0JIRETEsmXL7ty5c+PGDbFYzAwg1Lt3bzMzs+Li4tzcXJqmFy5caGFhsXXr1oaGhoULF967dy8mJiYoKIhjpyKRSP3C21RRUUEIaWhokFmelpb27rvvfvHFFzNnzpTf6v333//999+joqLWrVv37bffstOi5uXlffbZZ15eXtOnT+eoakUfmaIoFaqaf1WoUNXShQ8aNMjd3X3Xrl2qVTUAAAAAdCMdPywV6AqeY1EWFhbOnTuXoiiKombOnFlYWMgsr6+v9/Pzs7KyCgkJOX78uJOTU35+PvNWcnLyiBEjCCFr164tKiqKiIjo06cPIWT37t1NTU2KCvTx8REIBKNGjQoICIiJiTEwMJg9e3Z5eTlN00lJSQKBwNTUlJkR5/79+2PGjGHOcCsrq/T09DZ3qmbhbbp48SJzc92AAQOOHTtWVFTEvhUbG0tR1LFjxzg2Dw0NHTJkyMCBAxcvXuzj4zNmzJilS5c+ePCAu6q5PzLHsVNUGzyrQrWqlilctarGuMcAOgfjHgMAgJoo+u+BWwBkMM848TxDKisrJRIJ82yntKqqqqysrCFDhpibmyu191YLrKmp6d27N/O3WCw2NDSU3pGenh77LiEkLy+Poijmp0s+2rVwbtXV1Uyvj1tFRUVmZmaPHj3GjBmjrarWSFXwL1zZ8t3c3AgheJ4WQIcolWsAAADk4d5j0AxTU9NWlwuFwqlTp2qqQOnejnSfk9mRzMpDhw5Vao/qFL5x40ZFxfr6+r722mvcu+bToSWE9O3blx0ISl7HVLX69axU4aqVDwAAAADdB/q0ABowa9YsRW9JT5YDAAAAAACahT4tgAYwt7wCgPqam5tTU1NFIlF5eTkhxNLSctKkSey7lZWVly5dYl/Onz+/zWmr2sOFCxeYgdAIIQUFBe+9956RkdGtW7cePXoks6adnd3w4cO5NySEiESiqKiop0+f2tnZzZ07l5nCKj09vV+/frhVAQAAgBv6tAAA0FlUVVUdOXLkvffe09PT++KLL/z9/YVCYWpqKjtamFAoHDt27KpVq1paWg4fPqzoqYd29eDBg0WLFrHPfy5btszIyIimaQ8Pj8ePH8usnJaWxvZpW92QEJKTk7Nw4cKDBw8uXbo0ISFh1KhRoaGhb7755sSJEzdt2uTh4fHmm2921IcDAADQPZifFgCAr1OnTnWqcrqYZ8+eeXl5bdy4sXfv3sbGxv/6178MDAyqqqpcXFxqamqYdSiKsrGxcXd3X7Zs2cyZMymK6vg4v/nmm8TExPy/BQcHE0KuXr3q5OT05MkT8d+uXLkybNgwGxsb7g0JIVu2bJkxY4ajo6OJiYmHh8esWbN27dpFCBEIBIcOHfrPf/5z7969jv+YAAAAugJ9WgAAXhITE3fu3Nl5yul6Pvjgg7ffflt6qLBRo0Y5ODhkZ2d7e3tLj4vbr18/rfxCSwgpLi6+e/fuqFGjLP7Ws2dPQoiJicn+/fuHDRtm8LezZ8++8847bW5ICCkqKsrKymLXNDQ0FIvFzN/6+voffPABO7M3AAAAyEOfFgC6o5qamsjIyN27dwcFBRUUFDALExISDhw4cPz4cWaFw4cPHzhwgJk8MykpycXFRSQSHT16NCEhgVm/sLDwyJEjNE0nJyfv3Lnz0KFD9fX1ypZTVla2b9++v/76SxvV0ImkpqZeuHDB1dVVeqFAIPjhhx9GjhwZHx+/Z88edrmenp6e3j/yV6sHlBBSUFBw8OBBiUSSmZn52WefhYaGSiQS9t3nz5+fOHHC39//2rVrPOP87rvvfv/9dwsLixEjRoSEhLA9bXt7e+mQJBJJXFzckiVL2tyQELJkyZKbN2+ePn2aECISic6cObN582b23Tlz5tTU1MTFxfGMEAAAoNvR1sS40Pkxl+DajgJACa6urq6urm2udvv27QkTJsTGxpaUlHz11VcmJiYnT55k3rKysjI3N2f+ZuYNtre3p2k6IyNj2rRp/fv3T0pKysjIoGn69OnTffv27dWr14YNG3x8fBwdHQkhkydPbmxsVKqcY8eOEUK+/fZbPh+QEBIZGalsteiEd955Z86cOTILJ06cSNP0vXv3TExMKIpKSEhglh89evTQoUPsaooO6Llz55iBx/fv37969eqFCxcSQvbu3ctslZiYuG7duvT09KioKBMTk40bN/KJ8/Lly9u2bXvjjTeYYZzmzJnT3Nwsv9qNGzdeeeUViUTCZ8Pi4uKxY8cSQrZs2eLg4BAXFydTmq+v76RJk/iEp4uQawAAQE3IIqAQrjNA5/Dp04rFYktLy08//ZRd4unpaWBgkJWVxZTA9kVpmraxsWH6ojRNu7i4WFhYSBe1YsUKiqIyMzOZl5988gkhJCAgQKlyRCJReHh4dXU1nw/Yhfu0o0ePZm4wlsb0aWmajo2NpShKKBTm5OTQ/+zTch/QHTt2EEKuXr3KvGVjY2Nra0vTdE1NzYgRI0QiEbN8zZo1hJCUlBT+Ad++fdvS0pIQsm/fPvl3N23a5Ofnx3/DkpKSkSNHEkLs7e2Li4tlNjl48KBAIBCLxfzD0yHINQAAoCbcewwA3cuPP/744MEDOzs7dsm8efMaGxuDgoLa3FZmRCJjY2OBQGBlZcW83LFjh0AguHHjhlLlGBsbe3h49O7dm+8H6IoaGxtzc3PNzMwUrbBkyZKPP/5YZrwoBvcB7dWrFyGE6UMSQsaPH5+fn08IiYiIqK+v3759u5+fn5+fX1FR0ciRI+Vn4uFgbW2dlpZmbm4eEREh8xZN07GxsdIP07a5YVBQ0IwZM3x8fFJSUqZMmcIEyRIKhc3NzUqFBwAA0H1gLh8A6F7u379PCDExMWGXTJ8+nRCSnZ3d5rbco+waGRmZm5uXlpaqWU439OLFi5aWFqb/qYi/v/+dO3cSEhK8vb3nz5/PLlfqgOrr69M0TQjJysoyMzM7fPiwOmEbGRk5OzufOHFCZvmvv/7a2NjIMQGPzIbBwcGRkZG3bt0SCATTpk1bv369n58f+9g2+fvTFRYWjh8/Xp2AAQAAuiT8TgsA3ctLL71ECElJSWGXDB06tEePHn379m1zW+6+qFgsLi4uHjFihJrldEODBg0yNTWV+QFWBkVRp0+ftrS0jI+PP3jwILtctQOqr6+fk5PT1NSkZuSWlpbs3LmsmJgYZ2dnfX19nhuePHlywYIFAoGAEOLj47Nu3borV65UVlayK1dUVBBCLCws1IwWAACgS0KfFgC6lylTphBCpO8QzszMbGpqsre3J4QIBIKGhoZWN6QoqqWlhaPkmzdvNjQ0MAMRqVNO92RlZVVSUiK9hKbpuro66SV9+vSJj48XCoXSv8FyH1BFrK2ta2trAwIC2CWVlZVHjhxRNuwzZ844OzvLhB0TE6PoxuNWN7x79650D9bZ2bmxsVF6KOyioiKKooYPH65seAAAAN0B+rQA0L1YW1uvXLnyxo0b7COLv/zyy+jRo5kpQB0cHMrKyoKDg2tra4ODg8vLy3Nzc5lfyczMzIqLi3Nzcx8/flxbW8ts29zczPavYmJiZsyYwfRp+ZeTlpb2+uuvJycnd3hNdC7Tp0+/d++e9JKioqJnz57J/Gtg7NixYWFh0rPmcB/Q6upqQkhjYyPzVllZGTPSkru7u4WFxdatW7/88svs7OyoqChfX18vLy+2WF9fX0dHR5k5lh4+fLh58+aMjAzmZVZWVm1t7a5du6TXSUlJEYlEb731Fv8NXVxczpw5w04ydPPmzYkTJ44ePZrd/OnTpw4ODux8tgAAACANfVoA6HYCAgK8vb0dHR1PnjwZFBR08eLFa9euGRgYEELc3Nzs7Ox8fHwmT55sampqa2v72muvxcbGMm/RNG1ra3vx4kVjY2OmKD09vSNHjmzfvt3DwyMvL499BpJ/OXl5eX/88QeG/9m+ffvz588fP37MvIyJifHw8Kivr1+8eHFSUpL0mk5OTv/+97+llyg6oNevXz9z5gwhZO/evcXFxT/88MPPP/9cU1Pj7++vr69/+fLlYcOGbd++ffz48f7+/jt37pQeqSsxMfHSpUvMnLEskUgUEhJiY2Mze/bsHTt2XLhwISkpiZmbhxUdHb1o0SLmdOK54aFDh5ycnKytrQ8ePMhMLxQfH8/22xsbG8+ePbt161Z1qhcAAKALo2ipad8BpEVFRbm7u+MMAR3i5uZGCImOjuazclVVVVZW1pAhQ8zNzWXeKi0tZeY1bWhokP5xrKqqSk9Pj+35bNiw4cSJE42NjQUFBUKhsE+fPqqVw0xgyydmiqIiIyOXLl3KZ2Wdc/To0Xv37h06dIjPyiUlJQMGDJBewnFAOeTl5VEUNWTIEJnlYrH47NmzPXv2XLx4sczy/Px8IyOjwYMHt1rgkydP+vTp069fP/kCuTesq6vLy8sbNGiQzJPA0dHRYWFh8fHx/D+UbkGuAQAANWHcYwDopoRC4dSpU1t9i+mIEkJk7vYUCoWtrq9o8B6e5fDs0HZ569atW758eUZGxqRJk9pcWaZDSzgPKIehQ4e2ulwsFqekpHz55Zcyyw0NDaXvCpan6KnXNjc0MjIaN26czMIHDx6EhYXJTxcEAAAALNx7DACgorq6uubmZpFIpO1Augg9Pb2QkJDvv//+1q1b2o6FpKam7t27lxmLWFvy8vL27dt34sQJ7lmOAAAAujn0aQEAVBEWFnblyhWapj/66KPbt29rO5wuwtDQMDAwcODAgdoOhMyZM0frPUkDA4OQkBBmsiLCQ5p0AAAgAElEQVQAAABQBPceAwCoYuHChU5OTszfhoaG2g2mi5F/urV7MjMz03YIAAAAOgB9WgAAVSh6thYAAAAAOhLuPQYAAAAAAABdhT4tAAAAAAAA6Cr0aQEAAAAAAEBX4XlaaIObm5u2QwDg6+bNm6Srn7T79++Pjo7WdhQAGlNYWKjtEAAAQLdRNE1rOwbopFJSUr755httRwGgS/7666/MzMy33npL24EA6Bj8pwYAAFSGPi0AgMZERUW5u7ujXQUAAADoMHieFgAAAAAAAHQV+rQAAAAAAACgq9CnBQAAAAAAAF2FPi0AAAAAAADoKvRpAQAAAAAAQFehTwsAAAAAAAC6Cn1aAAAAAAAA0FXo0wIAAAAAAICuQp8WAAAAAAAAdBX6tAAAAAAAAKCr0KcFAAAAAAAAXYU+LQAAAAAAAOgq9GkBAAAAAABAV6FPCwAAAAAAALoKfVoAAAAAAADQVejTAgAAAAAAgK5CnxYAAAAAAAB0Ffq0AAAAAAAAoKvQpwUAAAAAAABdhT4tAAAAAAAA6Cr0aQEAAAAAAEBXoU8LAAAAAAAAugp9WgAAAAAAANBV6NMCAAAAAACArkKfFgAAAAAAAHQV+rQAAAAAAACgq9CnBQAAAAAAAF2FPi0AAAAAAADoKvRpAQAAAAAAQFehTwsAAAAAAAC6Cn1aAAAAAAAA0FXo0wIAAAAAAICuQp8WAAAAAAAAdBVF07S2YwAA0FXPnz9fuHBhU1MT87Kurq68vNzCwoJdYdKkSadOndJSdAAAAABdn0DbAQAA6LBXXnmlsbExKytLemFVVRX797Jlyzo8KAAAAIBuBPceAwCoxdvbWyBo/f+DFEV5enp2cDwAAAAA3QruPQYAUEtBQcHQoUPl21KKomxtbW/duqWVqAAAAAC6CfxOCwCgFgsLCzs7Oz092eZUX1/f29tbKyEBAAAAdB/o0wIAqMvLy4uiKJmFEolk6dKlWokHAAAAoPtAnxYAQF1ubm4yS/T19WfOnDlw4ECtxAMAAADQfaBPCwCgrpdffvmtt97S19eXXujl5aWteAAAAAC6D/RpAQA0YMWKFdLDROnp6b399ttajAcAAACgm0CfFgBAA1xcXHr06MH8LRAInJychEKhdkMCAAAA6A7QpwUA0IDevXsvWrSI6da2tLSsWLFC2xEBAAAAdAvo0wIAaMby5cubm5sJIb169XJ0dNR2OAAAAADdAvq0AACasWDBAmNjY0KIq6trr169tB0OAAAAQLcg0HYAoDEpKSkFBQXajgKgW5s8eXJSUpKFhUVUVJS2YwHo1qZOnWpubq5mIYWFhb/99ptG4gEAAA2ysLCwt7f/v9c0dBWurq7aO68AAAA6kcjISPUTa2RkpLY/BwAAtMLV1VW6ucbvtF2Kq6trdHS0tqMA6L4kEsnnn3++c+dObQeikyiKioyMXLp0qbYDaRdRUVHu7u601IRP0H4oitJgaThqAJ2Wm5sbIaQLX/127cyoMua4S8PztAAAGqOnp7dt2zZtRwEAAADQjaBPCwCgSQIB7n8BAAAA6Djo0wIAAAAAAICuQp8WAAAAAAAAdBX6tAAAAAAAAKCr0KcFAAAAAAAAXYWxTAAAQFfl5ubu2bPH39/f3Nxc27FoWHNzc2pqqkgkKi8vJ4RYWlpOmjSJfbeysvLSpUvsy/nz5/ft27fjg7xw4UJ1dTXzd0FBwXvvvWdkZHTr1q1Hjx7JrGlnZzd8+HDuDQkhIpEoKirq6dOndnZ2c+fO7dGjByEkPT29X79+Q4cO7YiPBABdXZdPHFOnTr1y5Upnzh2EkPLy8sDAQHbuQ/XbefRpAQBAV6WnpwcHB7u5uXWxS5OqqqojR4689957enp6X3zxhb+/v1AoTE1NHTNmDLOCUCgcO3bsqlWrWlpaDh8+bGpq2vFBPnjwYNGiRezcrcuWLTMyMqJp2sPD4/HjxzIrp6WlsX3aVjckhOTk5CxcuPDgwYNLly5NSEgYNWpUaGjom2++OXHixE2bNnl4eLz55psd9eEAoMvq8omDEDJt2rROmzsYa9euTUlJYfu06rfzuPcYAAB0laura2lp6YIFC9p1L6dOnWrX8mU8e/bMy8tr48aNvXv3NjY2/te//mVgYFBVVeXi4lJTU8OsQ1GUjY2Nu7v7smXLZs6cSVFUR0bI+OabbxITE/P/FhwcTAi5evWqk5PTkydPxH+7cuXKsGHDbGxsuDckhGzZsmXGjBmOjo4mJiYeHh6zZs3atWsXIUQgEBw6dOg///nPvXv3Ov5jAkAX0zGJg3Rs7pBOHISQzpw7CCHHjh3LysqSXqJ+O48+LQAA6LCXX365XctPTExk/5HcMT744IO3335bKBSyS0aNGuXg4JCdne3t7c3+vEkI6devn7b+y15cXHz37t1Ro0ZZ/K1nz56EEBMTk/379w8bNszgb2fPnn3nnXfa3JAQUlRUJH2VY2hoKBaLmb/19fU/+OADX1/fDvyIANBltXfiIB2eO+QTB+mUuYMQ8vDhw4yMjIULF8osV7OdR58WAAB0lUQiSUpKunXrFvOyoKDg4MGDEokkMzPzs88+Cw0NlUgk7MqFhYVHjhyhaTo5OXnnzp2HDh2qr68nhCQkJBw4cOD48eOEkJqamsOHDx84cCAyMpIQkpSU5OLiIhKJjh49mpCQQAgpKyvbt2/fX3/91U6fKDU19cKFC66urtILBQLBDz/8MHLkyPj4+D179rDL9fT09PT+kcdramoiIyN3794dFBRUUFDALueumefPn584ccLf3//atWs84/zuu+9+//13CwuLESNGhISEsFdL9vb20iFJJJK4uLglS5a0uSEhZMmSJTdv3jx9+jQhRCQSnTlzZvPmzey7c+bMqampiYuL4xkhAECrZBIH4WwhVUgcpMNzR6uJg/DOHaolDqJS7mhqatq1a9fnn3/e6rtqtfM0dBWurq6urq7ajgIAQEWEkMjISP7rZ2VlMSn8+++/p2n63Llz/fv3J4Ts379/9erVzP+A9+7dy6x8+vTpvn379urVa8OGDT4+Po6OjoSQyZMnNzY20jRtZWVlbm7OrFldXd2nTx97e3uapjMyMqZNm9a/f/+kpKSMjAyapo8dO0YI+fbbb5X9dMy1TpurvfPOO3PmzJFZOHHiRJqm7927Z2JiQlFUQkICs/zo0aOHDh1iV7t9+/aECRNiY2NLSkq++uorExOTkydPtlkziYmJ69atS09Pj4qKMjEx2bhxI5+Pc/ny5W3btr3xxhvMME5z5sxpbm6WX+3GjRuvvPKKRCLhs2FxcfHYsWMJIVu2bHFwcIiLi5MpzdfXd9KkSXzCU/ZcUoTnUQMAbVH26lcmcdCcLaRqiYPWaO7g05q1mjhofrlDtcRBq5o7du3a9euvv9I0vWXLloEDB8qvwLOdlz/uaKm7DvRpAUCnqdAPuXv3rvSlyY4dOwghV69eZV7a2NjY2tqyK69YsYKiqMzMTOblJ598QggJCAigadrV1ZW9NGE2ZC9NXFxcLCws2LdEIlF4eHh1dbWyn45n72j06NHMTWLSmOsSmqZjY2MpihIKhTk5OfQ/r0vEYrGlpeWnn37KbuXp6WlgYJCVlUUrrpmampoRI0aIRCJm+Zo1awghKSkp/D/X7du3LS0tCSH79u2Tf3fTpk1+fn78NywpKRk5ciQhxN7evri4WGaTgwcPCgQCsVjcZlTo0wJ0Eypc/cokDpozd6iWOGjN5Q4+rVmriYPmkTtUSxy0qrkjOTl59+7dzN+K+rQ823n54457jwEAQFcZGhpKv+zVqxchhOkpEULGjx+fn5/PvmtsbCwQCKysrJiXO3bsEAgEN27caHMv0qNoGBsbe3h4MINwaFxjY2Nubq6ZmZmiFZYsWfLxxx/LjPnB+PHHHx88eGBnZ8cumTdvXmNjY1BQEFFcMxEREfX19du3b/fz8/Pz8ysqKho5cqT8TDwcrK2t09LSzM3NIyIiZN5iLqSkH6Ztc8OgoKAZM2b4+PikpKRMmTJF+vARQoRCYXNzs1LhAQDIkEkchDN3qJw4SEfljjYTB1GcO1RLHESl3FFZWXno0KGPP/6Y++Oo3M5jLh8AAOia9PX1aanHNWUYGRmZm5uXlpa2WU6HjQz54sWLlpYW5jJCEX9//zt37iQkJHh7e8+fP59dfv/+fUKIiYkJu2T69OmEkOzsbPlC2JrJysoyMzM7fPiwOmEbGRk5OzufOHFCZvmvv/7a2NjIMTGDzIbBwcGRkZG3bt0SCATTpk1bv369n58f8ygag/l0hYWF48ePVydgAAAOHLmDf+IgHZU7+CQOoiB3qJY4iEq5Y8uWLZMnTz537hzz8s8//2xoaIiLizM1NZ09eza7msrtPPq0AADQHYnF4uLi4nnz5rW5Zof1aQcNGmRqairzA6x8MKdPn54yZUp8fHxOTo6fnx+z/KWXXiKEpKSkMFckhJChQ4f26NGjb9++HKXp6+vn5OQ0NTUxT7eqzNLSkp3/kBUTE+Ps7Kyvr89zw5MnTy5YsEAgEBBCfHx8/vjjj6CgoMrKSnZ8zoqKCkKIhYWFOqECAKiMf+IgHZU7+CQOoiB3qJY4iEq5o7S09KeffmJfVlVV1dXVvf/++1ZWVtJ9WpXbedx7DAAA3dHNmzcbGhqYcS8EAkFDQ0Orq1EU1dLS0mFRWVlZlZSUSC+habqurk56SZ8+feLj44VCofS/0qdMmUIIkb4jLjMzs6mpyd7enmN31tbWtbW1AQEB7JLKysojR44oG/aZM2ecnZ1lwo6JiVF043GrG969e7eyspJ9y9nZubGxUXqY0KKiIoqihg8frmx4AAAawTNxkI7NHfKJg/DLHaolDqJS7jh//nyhlHfffbd///6FhYWXL1+WXk3ldh59WgAA0FXM/KVlZWXMy+rqakJIY2Mj87KsrIwZZ4Jdv7m5mc3lMTExM2bMYC5NHBwcysrKgoODa2trg4ODy8vLc3Nzmf8Wm5mZFRcX5+bmPn78uLa2Ni0t7fXXX09OTm6nTzR9+nSZGeeLioqePXsmc+U0duzYsLAw6ckYrK2tV65ceePGDfZ5p19++WX06NHMXH+Kasbd3d3CwmLr1q1ffvlldnZ2VFSUr6+vl5cXW6yvr6+jo6PM/BMPHz7cvHlzRkYG8zIrK6u2tnbXrl3S66SkpIhEorfeeov/hi4uLmfOnGHnirh58+bEiRNHjx7Nbv706VMHBwd2PlsAABXIJA7SVu5QIXGQjs0d8omD8MsdqiUOQgh37mg1cfCkejvPPagU6BCMewwAOo0oOVbtzZs3mSkZXn311fPnzycnJ48YMYIQsnbt2qKiooiIiD59+hBCdu/e3dTURNP0+vXr9fX133vvvW3bti1btmzRokXsEJQ1NTXMIBnjxo1j5lOdN2/esWPHaJpOSkoSCASmpqbMHAzM6JHMW0rhOYLuixcvBgwY8OjRI+ZldHQ08zzq3LlzExMTZVb+7LPPpOfyqa+v9/Pzs7KyCgkJOX78uJOTU35+Pk3T3DVz//599u5fKyur9PR06V0woxB/9dVX0gvT0tKEQiEhZNasWR999NHnn39eV1cnE9vmzZtXrFghs5B7w9ra2jVr1rz66qsHDhxYu3bt4sWLc3Nz2XfFYnG/fv1++umnNuuQxrjHAN2Gsle/MomDbquFVC1x0JrLHXxaM5nEQSuTO1RLHDRNc+SOVhOHjG3btsmPe8y/nZc/7hStePwM0C1ubm6EkOjoaG0HAgCgCoqiIiMjly5d2k7lb9iw4cSJE42NjQUFBUKhkEnP0kpLS5np+BoaGqT/SVxVVaWnp8eOV8nMQ6js3qOiotzd3fnk3KNHj967d+/QoUN8ii0pKRkwYID0kqqqqqysrCFDhpibm/MPLy8vj6KoIUOGyCwXi8Vnz57t2bPn4sWLZZbn5+cbGRkNHjy41QKfPHnSp0+ffv36yRfIvWFdXV1eXt6gQYNkHuiKjo4OCwuLj4/n83E0dS7xP2oAoBXtffWrcuIgGsodPFszpRIHkcsdqiUOoiB3KEocbeLfzssfd9x7DAAA3YuFhUWrFxbMdQkhROa6RCgUSk/AoMJFiVLWrVtXXl7O3qDLTaZDSwgRCoVTp05V9rpk6NCh8h1aQohYLE5JSXF0dJRZbmhoOHr0aEX9UkLI8OHD5Tu0fDY0MjIaN26cTIf2wYMHYWFh8tMFAQB0DGUTB+nY3KFU4iByuUO1xEEU5A5FiYObmu08xj3uXm7cuPHs2TP2ZY8ePfr37//KK69IP7DUGeTm5u7Zs8ff31+Fb1d3o5W6+vnnn69fv/7gwQM3NzeZgWFk1NXVXbt2LSUlZe/evRrZtcYLVJN0/YtEomvXrt2+ffv//b//p+24/o+ufOs7QF1dXXNzs0gkkp63oBPS09MLCQnZtGnTunXrJk+erN1gUlNT9+7dy4xFrC15eXn79u07ceJEm5NVaEVKSsrTp08VvWtoaLhkyRLN7rGxsfHnn38+f/783Llzlb1qlKFmBuEZCf+U0al0tksRmcbc1NR0wYIF7b3TK1eulJeXsy8nTpzIztTaTSBxqECFxKF+O4/fabuXiRMnPn782NPTc9WqVdXV1aWlpQkJCe7u7sOHD9+1a1dTU5O2A/yv9PT04OBg+efdQV7H11VaWtqXX365ffv2MWPGuLu7y4yqJ+Py5cubNm0KDQ3V1N41XqCapOs/JiZm7dq1ne2nJF351re3sLCwK1eu0DT90Ucf3b59W9vhtMHQ0DAwMHDgwIHaDoTMmTNH6z1JAwODkJAQZs6JTmj//v1bt25NT08vLi6+fv26p6dnYGBgaWlpTk7O/v3716xZo/E9ZmZmRkVFHThw4Pnz52oWpWYG4ROJUimjU+lslyJ2dna9evXy9PT09PQsKyubOXNmB+x00qRJN2/e9PT09PLyGjRoUHf7ZygSh2pUSBwaaOfbfAYXdAXPp+QLCgoIIePGjWOXSCSS6OjoPn36zJ07l33wXetKS0u1HUJHO3nypGqrdXBdLViw4N///jdN0xKJ5NmzZ/IryETo5eVlbm6u5k6ly9RIgTz3Jf9SnnT9z58/f+zYsRrZrwbpyreeaGhcn1ZVVlZW/E1+QKMOgNGGOpKmziU+R23x4sX3799n/j5//jwhZPPmzczLhoYG6e+dBt25c4cQosJ4M/LUzCBtRtJmymhT+7WNbeqA9KrUp5NIJMx0zS9evGi/kOh/RvXHH38QQmxtbdt1jypr1xFStZ446HbOjLpL/rjjd9puR/5ufoqiXF1dAwMDf/rpp+nTp7NjdmvXyy+/rO0QOlRiYuLOnTtVW62D6yorK0tfX58QQlHUK6+8IvOufITMyuqQKVP9Avnvi89xka5/fX191eZY53kCqEZXvvXtSigUmv5N6z88QlfyxhtvjBs3rtW3DA0NfXx82mOnzE19qrU2MtTMIG1Gwp0y2tSubWOb2ju9KvvpKIpins9kxg9vJzJRMXs0NjZuvz12WkgcOgTP08J/ubu7nzp16uLFi6mpqWKxmPlhh3kQyNDQMDU19f79+3379nV2di4oKIiLi9u0adP9+/fPnj07ZMiQ5cuXS0+T+PDhw5s3b969e3fatGlvv/02s7C+vv7s2bOLFy8uKSm5ePHiK6+8smjRIn19/b/++uvcuXN6enpubm7slbdEIrl+/bqJiQn7SIBIJIqPj8/JyZkwYcK8efNabc0rKioiIiI2btx46dKlu3fvfvjhhwKB4Pnz5z/++GNhYeG0adPYmRILCwvPnTv37rvvXr9+/fLly4MHD16zZg3bWvEvh6bp69ev3759W19f39LScu7cuczyVlcmhCiquqSkJBcXF4qijh49ytRMq9XY6mrydVVTU3Px4sXs7GwLCwsHBwcLCwvuvbd6PrRayPXr17OysgoKClJTU48ePWpmZiYzol2rEbJ1lZqaevny5ZEjR3p6erIXQIrqqs0yVS6Q0epZKrMvExMTmV3Lnxt6enoy9c/47bffLl++PHHixHfeeYdZkpCQ8PjxYxMTk7Vr19bU1Jw6daqpqcnMzMzd3b3Vz6jUKVdWVnbs2DEfHx+lbjqS/ta/8cYbinbKfeYo+y0A6GK2bdvG8e7WrVuJ4maZOxlxbCiPe02RSBQaGpqfnz969OjXX3993LhxTD+Tf7Zttc3kpihlKGoc+GQ9joaUaCJ9S5OpHO6WUNGhVLblV7Yx54hKhZBarXM+YbR6ely7do3jYlLRmdDqQeQTA3R3Hf9jMbQTnndfVFVVkX/ehcjy9/cnhOzdu7e2tpYZA+Dx48fsu5aWljk5OefOnWNGeNu/f//q1auZWaf37t3LrrZ///6ZM2dKJJInT54MGzbsyJEjNE0nJyczz2B8/fXXvr6+27dvNzIyeuedd44dO7Z8+fJly5ZRFLVo0SKmhKysLGbqsO+//55Zkp2d7ejoeOfOnaamJg8Pj379+kkHxggJCTEyMhIIBN999521tTUh5M6dO4mJievWrUtPT4+KijIxMdm4cSNN06dPn+7bt2+vXr02bNjg4+PDDGsxefLkxsZGpcqhafp///d/mRuubt269frrrzMLFa3MUXUZGRnTpk3r379/UlJSRkaGomqUX02+rm7fvj1hwoTY2NiSkpKvvvrKxMSEuYOozQMnTVEhT548SUpKIoSsX7/+1q1b2dnZMhvKR0jT9KpVq8zMzPz8/NasWePs7ExR1J49e7jrirtMNQtUVL3y+5J5KX9uREREyNS/k5PT8OHDFy5c6OTkxPx0Iz1Fp5WVFXvXNDOmv729faufUalTjqbpY8eOEUKYSfDk8fnWK9ppm2eOUt8CbqRL32GFe487kqbOJWWPmsy9xwxFLSp3MuLYkKbprKwsQsjx48fbXJOm6RcvXowZM+bGjRsikYjpb0yePHnz5s38s62iNlM+EmmtpgxFjQPPrEcrbkjVT9/SZCqHuyXkPpT8W366rcacpmnmvxUtLS3cUakWUqtR5eTkEELefPNNRSEpOj04LiYVnQmtHkRF+2W0673HnUHXzowqkz/uyK9dh/p92ri4OELIggULaJo+d+4ckXpC5vnz52zhO3bsIIRcvXqVeWljYyP9lMWoUaP8/PyYv11cXBwdHZm/v/nmG0JIdHS0dCGxsbHMy48//tjQ0JBpo2mavnv3LptImpubX3vttcDAQOattLQ0AwODhIQE+fiXL19OCImLi6NpOjs7u6amZsSIESKRiHmXGagjJSWFpukVK1ZQFJWZmcm89cknnxBCAgIClCpHIpG8/PLLSUlJzHKmW8WxU+6qc3FxsbCwaLMaZVaTqSuxWGxpafnpp5+y73p6ehoYGGRlZbV54FjchTDnj7+/v/yGiiJctWqVoaEhk8Nomra1tWX2y11XHGWqX6Ci6pXfl8xLmXOD/mf90zTt5ORkYGDw4MEDmqYlEgnzr+iLFy8y77q6uko/CWxjY8NeRkjvSKlTjiESicLDwxU9GcvnW89RexxnjgrfAg5dO3OjT9uROk+flrtF5UhG3BtK9yS516RpeufOnUOHDmX+TktLY7pAzEue2ZajzeTo09JyKYOjceCf9TgaUnXStzyZ5p07h3IcSp4tP4O7Maf/2afljkq1kOSjarNPy3F6KLqY5DgT5PMsN/Rpuyf5445f8+H/iEQi8vcjEwsXLhw3btw333yzZs0aiqLCw8O9vb2Z1ZjboiwtLZmX48ePv3z5MltIcnIyU8L9+/cLCgqqq6uZ5cz9SxMmTGBejh07lhDC/BOOKU0sFj9//pwZMd/Q0JAt8OLFi7dv33ZycmJe2tjY1NTUGBgYyMfPPKjD9CIsLS2PHTtWX1+/fft25t2ioqKRI0c+evTIzs7O2NhYIBCw49Hv2LFj3759N27cWL9+vVLljB071t3dPTAw0NnZmbnBLCIiQtHKbVad9PNIiqqRyD22JF1XP/7444MHD5h9MebNmxceHh4UFPT1119z751nIfLry5N/sKpXr15jxoxh/n711VfPnj1L2qor7jLVLJCjeuX3Jf1S5twg/6x/hpWVFXN6UxT17rvvnj179sKFC3xmXGB3xPFB5E85hrGxsYeHR5u7kMd+6zl2ynHmUBSl7LeA2/79+6WnUO9KCgsLyd/TxEP3wd2iciQj/k1xm2s+fvy4tLS0sbHRwMDA2tra2NiYuSOU8M623G0mfxyNA/+sx0Gd9C1PpnnnzqHc1xUcZD6dso05R1QqhyQfFTeOY6foYpLjTJDPs226efNm125au3BmVNnNmzdlLirQp4X/k56eTgiZMmUKIYSiqG3btvn4+Fy8eNHJyenq1av/8z//0+pW+vr6NE2zLwcPHnzlypXz58/PmDFj5MiRzL+E5cnMTN2jRw9CSG1trfyad+7cMTY2Zqe0JoS02qElhDAPkLAPt2RlZZmZmR0+fFjhB/6bkZGRubl5aWmpsuUcOnTIzc3NxcXlrbfeCgsLGzhwIP+dErmqk04hHNXIkWnu379PCJGeQm369OmEkOzs7Db3rlohreLOhQKBoKWlhShzgLjLVKFA7rOUo08rc260yc7OTk9Pj+d8G+yOlDrleEaiCPut5197MmeOmt8CgK5NqRZVOhnx37DNNWfNmhUVFfXLL7/Mnj27oqKisbGx1cdHObItz8zeJo7GQbWsJ0Od9K3sZ1GUQxky1xUcNDLQF5+o+IekbFTcx67Vi0mOQ6NsngVgoE8L/0XT9M8//6yvr8+muuXLl3/yySdff/31sGHDrKyseD6j/8knnzCjEfTq1Ss2Nlb9wCQSSW1tbVJSkoODg1Ib6uvr5+TkNDU1MR1mDmKxuLi4eN68ecqW89prr6Wnp+/YsePo0aM2Njb37t3jv1N50imEoxo5Mg0zr1dKSgpzQUMIGTp0aI8ePfr27cs/DPUL4ZkLlaorPmXyL5D7LOXo0wQAaeYAACAASURBVCqrT58+JiYmI0aM4LMyuyOlTjl1JnOT/tafOnVKtVNXs9+CLVu2LF26VNmtdEJUVJS7uzv+194xNNtPUIdSLap0MuK/YZtrrl279tGjRxs2bPjss8+SkpL27ds3f/58+b1zZFtNZXaOxkG1rKfy7jTblsrjvq6Q1mHnKv+QCO+oSkpKhELhnj17OE6PVi8m1UkT8uzs7Lpw00pRVBfOjCqT/2Ue/wWB/9qyZQszMTp7P7CBgcHmzZuTkpK2bdu2evVqPoU8efJkz549K1asYG6GkUgk6gfG3K4cHh7OLikvLz9z5kybG1pbW9fW1gYEBLBLKisrjxw5Ir/mzZs3GxoamMEV+JcjFotDQ0N79+59+PDhCxcuFBUVxcXF8d+pDIqimB8bCWc1Sq8mj/mN/caNG+ySzMzMpqYme3v7NgPQVCHcEUrjX1c8y+RZIPdZKrMv/h+nVRkZGdXV1eyNxwKBoKGhodU1pXek1Cmncmzkn9961U5dzX4LAHSd/E9kSrWo0smI/4ZtrikQCMzMzIKDgydOnLh///4PP/yw1eAVZVsNZnZFjYNSWY+jIeW5O423pfKkDyXPlr+98QxJqajWrVuXn5/PfXq0ejGJNAEahz5tt/P06VNCSH19vfQSPz+/b7/9dtOmTVu2bJFeef369UKhsKysjH0egxDCPCnBTmhZVlYmFouZRM48mxcREVFdXf3zzz/fuHGjoqJCJBLV1NTU1NQQQsRiMbMVs+aLFy+Yl8xdx+y7zB9lZWWEkMWLF0+aNOnkyZMbNmy4du3a/v372RH8ZDCFlJeXMy/d3d0tLCy2bt365ZdfZmdnR0VF+fr6enl5Me82Nzez92XFxMTMmDGD7dPyLIemaWasBUKIg4PDyy+//PLLL3PvlKPqzMzMiouLc3NzHz9+/NdffymqRunVpCuNqStra+uVK1feuHEjPz+f2cUvv/wyevRoX19f7r1L4y7k2bNnhJDi4mL5+mfIR1heXi4SidiD++LFi7q6uoaGBu664ihTzQI5zlL5fcm8lDk3ZOqfLZ9N6tHR0e7u7uwUBQ4ODmVlZcHBwbW1tcHBweXl5bm5uRUVFTL7XbhwIf9Tjik5LS3t9ddfT05ObvWg8PnWc9Qex5mjwrcAoAurrKwkhDADIzG4W1SiOBlxb8jsgmnN2tzF999/HxMT09TU1NjYmJ+fz7R1DD7ZlrvNlI5EnkzKUNQ4cOxCPqdwNKTqpG/54GWa9zZzqKJDybPlZ4LnbszZMNhnVrmjUiEk+ajy8vKkd8Goq6t7//332b6xotODIX8xyZEm5PMsAC/tPi4VdBQ+I7+dO3du5syZzKG3t7efO3euk5OTs7Pzhx9+eOvWrVY32bBhw+HDh9mXycnJzI2Ua9euLSoqioiIYCaV3b17d1NTE03TPj4+AoFg1KhRAQEBMTExBgYGs2fPPn/+PPPz78qVK3Nzc5OSkmxsbAghTk5OWVlZv/32G/Oc99KlS5kpzpgB9F999dXz58/TNF1YWDh37lyKoiiKmjlzZmFhoXycx48fHzx4MFPI77//ziy8f/8+O5KQlZVVeno6s3z9+vX6+vrvvffetm3bli1btmjRInaMQf7l1NfXm5mZLVu2LDo6+quvvmLHnFS0U+6qS0pKEggEpqamzAj+rVZjeXm5zGrydVVfX+/n52dlZRUSEnL8+HEnJ6f8/Hw+B06aokJSUlKYeSAsLCxOnjxZWVkpfyBkIoyIiGBu6Prwww+rq6vDwsL69etHCNm6datYLFZUVxxlaqRARdUrH7/0S/lzQ77+r1y5MmnSpDlz5uzevXv9+vW7du2Srt6amhrmVB83blxcXNySJUvmzZvHDAgps1+lTjmapmNjYymKYseWlMb/W9/qTrnPHGW/BdxIlx7dEeMedyRNnUv8j1pjY+N33303fvx4QghzNyY7f4miFpXmTEYcG/7+++/MHaSTJk1ihlXn2AVN02fOnGGG8GHNmTOnqKiIf7ZV1GbKRyKt1ZShqHHgmfVoxQ2p+ulbmkzltJlDOQ4l/5af5mzMf/rpp7Vr1zKfZcmSJbGxsdxRqRaSTFRhYWGvv/46IYSiqClTprz11ltTp061srJi7hlmRsnmSKksmYtJRYem1YPIDeMed0+Yy6cra6dv9dy5cysqKpTaRDolNzQ0aCqSiooKmSaSp6dPn+bl5UkvWb9+fY8ePWiazs/Pr6qqUrmcpqYmsVgss1DRym2qrKyUrjpF1SizmqKifv3114KCAqUC0FQhfCKUxqeulCqTT4EcZ6nMvpT9ODRN19XVSV9QyigpKWH+qK+v59gvreQpx/9M5qbsqavBb0HXztzo03akju/TtqnVFpVPMuLfFCta88qVK8HBwX/++efPP/98+fLluLg4T0/Pffv2KSqn1Wyr2czeauOgVNZT1JDy3B1Hw6WaNg8l/5ZfU425yiG1GhW3Nk8PRReTKlwsyUCftnvCXD6gnDt37owYMcLU1FSprXr37s3+LT/TicqUDYM1dOhQRW8x87ypXA4z1MGQIUOU2qkizHRHLEXVKLOaoqKmTp2qbACaKoRPhNL41JVSZfIpkOMsldmXsh+HENKrVy+OU4sdVlRm9G/5HSl1yjH/nlefsqeuZr8FAF0Vd4vK0WLwb4pbXTMtLW3VqlX5+fn6+vqjRo1iFjIjISsqp9Vsq9nM3mrjoFTWU9SQ8twdR8OlJkWHkn/Lr6nGXOWQWo2KG/fpwXExiTQBmoI+LbQiLS1t+/btEyZMSE5Ojo+P13Y4mldXV9fc3CwSiaQnPwAA0EXNzc2pqakikYh5As3S0nLSpEnsu5WVlZcuXWJfzp8/X6mB0DWrvLw8MDBw586dhJD09PR+/fp18yvaDkhGd+/eLSoqOn78+Jw5c4YOHfr06dPU1NS7d+8yRwE0pRNeV2g9pC5/MdkJMelg6tSpV65c6eQZQTodEE1kBIwRBa2QSCS3bt0KCQn5+OOPhw0bpu1wNCwsLOzKlSs0TX/00Ue3b9/WdjgAAKqrqqr68ssvJ0yYMG3atAcPHnh6es6aNevhw4fsCkKhcOzYsfv27duzZ4+ZmZnKN7xoxNq1aw8ePMj8PXHixP/85z/So/V2Nx2TjFatWvXVV1/98MMPVlZWpqamzIBM/v7+KtyBAop0wuuKzhBS176Y7ITYdEAI6fwZQTodEI1kBG3cAg3tQrNPFDQ1NbW0tGiqtE6lsrKy4m91dXXaDgcA/ou081NDJ0+e1GI57fE8bWFh4aJFi6RHazMwMCCEjBs3TuZZuD179vj7+2t278oKDAwcPXr0wIED2SXNzc0LFiy4e/euxvelqXOpXZ+C7uBk1NjY2N676LY64XVFJwmpAy4m2/t5Wu0mDpp3ayafDuhOnBHk0wGtZEaQP+74nRZaJxAI9PS65ukhFApN/8ZMpwYAXV5iYqJGbrbUVDka8cEHH7z99tvSP7iNGjXKwcEhOzvb29ublpplpF+/ftr9hfbhw4cZGRky04Dr6+t/8MEH7JQz3U0HJyNmoFpoD53wuqKThKTrF5M6lDjk0wHprBmh1XRA1M4IOnyeAQBAN1RTUxMZGbl79+6goKCCggJ2eUJCwoEDB44fP86sc/jw4QMHDjC/syUlJbm4uIhEoqNHjyYkJBBCCgsLjxw5QtN0cnLyzp07Dx06xM7fq1Q5ZWVl+/btY+aU7mCpqakXLlxgphthCQSCH374YeTIkfHx8Xv27GGX6+npyVxZKqrGgoKCgwcPSiSSzMzMzz77LDQ0lJ1vmRDy/PnzEydO+Pv7X7t2jX+oTU1Nu3bt+vzzz+XfmjNnTk1NTVxcHP/SAABU0Gqjp1SDTxTnDu0mjlbTAeGdEVRLB0SljMCRDoiaGUFTPyKD1nX50cwBoGsjPO6wun379oQJE2JjY0tKSr766isTExPpG7qsrKzMzc2Zv6urq/v06WNvb0/TdEZGxrRp0/r375+UlJSRkXH69Om+ffv26tVrw4YNPj4+jo6OhJDJkyezN2fyLIem6WPHjhFC2LklOWj8LtZ33nlnzpw5MgsnTpxI0/S9e/dMTEwoikpISGCWHz169NChQ+xqiqrx3LlzzFCo+/fvX716NfN/9L179zJbJSYmrlu3Lj09PSoqysTEZOPGjTxD3bVr16+//krT9JYtW2RuNqNp2tfXd9KkSUp/fk58ziU+MAMTQCfH8+qXI3fwb/C5c0d7JA6aX2vWajqg+WUE1dIBrWpG4E4HNO+MgPlpuzL0aQFAp7WZucVisaWl5aeffsou8fT0NDAwyMrKYl66urqylxQ0TdvY2DCXFDRNu7i4WFhYsG+tWLGCoqjMzEzm5SeffEIICQgIULYckUgUHh7OZyJHjfeORo8ezdxOJo25gqFpOjY2lqIooVCYk5ND//MKhrsad+zYQQi5evUq85aNjY2trS1N0zU1NSNGjBCJRMzyNWvWEEJSUlLajDM5OXn37t3M361exBw8eFAgEIjFYiU+fFvQpwXoJvhc/XI3evwbfJozd7RH4qD5tWatpgOaR0ZQLR3QqmaENtMBzTsj4HlaAADQVT/++OODBw/s7OzYJfPmzWtsbAwKCuKzOUVR7N/GxsYCgcDKyop5uWPHDoFAwHPERZlyPDw8pOdm7BiNjY25ublmZmaKVliyZMnHH39cVVXl4uJSU1Mj/RZ3NTKP3llaWjJvjR8/Pj8/nxASERFRX1+/fft2Pz8/Pz+/oqKikSNHPnr0iDvOysrKQ4cOffzxxxzrCIXC5ubmNosCAFCNOrlDusEnauSO9kscbaYDojgjqJYOiEoZgU86IGpkBMxPCwAAuuH+/fuEEOm5FqdPn04Iyc7O5rO5zKWJNCMjI3Nz89LSUjXL6TAvXrxoaWnhHvrF39//zp07CQkJ3t7e8+fPZ5crVY36+vo0TRNCsrKyzMzMDh8+rFScW7ZsmTx58rlz55iXf/75Z0NDQ1xcnKmp6ezZs5mFTCSFhYXjx49XqnAAAD7UyR3cDT7/3NF+iYNPOiAKMoJq6YColBH4pAOiRkZAnxYAAHTDSy+9RAhJSUlhki4hZOjQoT169OA5ZTzHJYVYLC4uLp43b56a5XSYQYMGmZqayvwAK4OiqNOnT0+ZMiU+Pj4nJ8fPz49Zrlo16uvr5+TkNDU1KTV8bmlp6U8//cS+rKqqqqure//9962srNiLmIqKCkKIhYUF/2IBAPhTJ3dwN/j8c0f7JQ4+6YAoyAgq14wKGYFPOiBqZATcewwAALphypQphBDpu7wyMzObmprs7e2ZlwKBoKGhodVtKYpqaWlRVPLNmzcbGhrYqQVULqcjWVlZlZSUSC+habqurk56SZ8+feLj44VCofQ/3dusxlZZW1vX1tYGBASwSyorK48cOcId5Pnz5wulvPvuu/379y8sLLx8+TK7TlFREUVRw4cP5y4KAEA13I2eOg2+dO7QYuKQTweEX0ZQLR0QlTICn3RA1MgI6NMCAIBusLa2Xrly5Y0bN9hHen755ZfRo0ez09k5ODiUlZUFBwfX1tYGBweXl5fn5uYy//Q1MzMrLi7Ozc19/PhxbW0tIaS5uZnN6zExMTNmzGD7tPzLSUtLe/3115OTkzu2JgghZPr06ffu3ZNeUlRU9OzZM5mLqrFjx4aFhUlP28BdjdXV1YSQxsZG5q2ysjJmrA53d3cLC4utW7d++eWX2dnZUVFRvr6+Xl5ebLG+vr6Ojo4qzE7x9OlTBweHnj17KrshAAAf3I2eUomDKM4dWkwc8umA8MsIqqUDQgh3RlA5HRA1MgL6tAAAoDMCAgK8vb0dHR1PnjwZFBR08eLFa9euGRgYMO+6ubnZ2dn5+PhMnjzZ1NTU1tb2tddei42NZd6iadrW1vbixYvGxsaEED09vSNHjmzfvt3DwyMvL4+dflCpcvLy8v744w+tjG+0ffv258+fP378mHkZExPj4eFRX1+/ePHipKQk6TWdnJz+/e9/Sy9RVI3Xr18/c+YMIWTv3r3FxcU//PDDzz//XFNT4+/vr6+vf/ny5WHDhm3fvn38+PH+/v47d+6UHuMkMTHx0qVLp0+fVupTNDY2nj17duvWrSrWAgAADxy5Q6nEQRTnDi0mDpl0QJTJCCqkg+bmZkNDQ46MoFo6IOplBIp92Bd0nZubGyEkOjpa24EAAKiCoqjIyMilS5e2uWZVVVVWVtaQIUPMzc3l3y0tLWVm1WtoaJD+X29VVZWenh6TdDds2HDixInGxsaCggKhUNinTx/VyiGEMPMQthlzVFSUu7u7ZnPu0aNH7927d+jQIT4rl5SUDBgwQHoJdzUqkpeXR1HUkCFDZJaLxeKzZ8/27Nlz8eLF/EuLjo4OCwuLj4/nvwkf/M8lbu1x1ABAg5S6+uVo9Hg2+G3mDs0mDsK7NVMqHRC5jKBaOiAKMoJq6YAokxHkjzt+pwUAAB0jFAqnTp2qKPUy1xOEEJmbl4RCofzcCRYWFoouLHiWw/O6pD2sW7euvLw8IyODz8oyHVrSVjUqMnToUPkOLSFELBanpKQ4OjryL+rBgwdhYWERERFKBQAAoBqORk+pxEEU5w5tJQ6l0gGRywiqpQOiICOokA6I2hkBfVoAAOh26urqmpubRSKRtgNRi56eXkhIyPfff3/r1i1tx0JSU1P37t0rEPCdTyEvL2/fvn0nTpxocwoKAIBOotPmDp1OB0QTGQF9WgAA6F7CwsKuXLlC0/RHH310+/ZtbYejFkNDw8DAwIEDB2o7EDJnzhylrkUMDAxCQkKYmSQAADq/Tp47dDcdEE1kBMxPCwAA3cvChQudnJyYvw0NDbUbjEa0ejNwJ2dmZqbtEAAAlKATuUMX0wHRREZAnxYAALoXoVCo7RAAAEDHIHd0Zrj3GAAAAAAAAHQV+rQAAAAAAACgq9CnBQAAAAAAAF2FPi0AAAAAAADoKowR1aXExMRQFKXtKAAAVOTu7u7u7q7tKNoRmmhdhKMG0Ml17S9pl8+MqnF1dZV+SdE0ra1QQLNSUlIKCgq0HQUAdH00TUdGRiYnJ1dWVo4bN27WrFlTpkzptBMbQPc0depUc3NzNQspLCz87bffNBIPdCpisfj3339PSkrKzs42NTWdOXOmu7t71+4XAXQxFhYW9vb27Ev0aQEAQBUSiSQxMTEwMPDs2bM9e/Z0dnb29vZ+6623cF0IAJ1WWlraqVOnTp8+XVVVNWvWLF9fXxcXlx49emg7LgBQC/q0AACgloqKiujo6ICAgIyMjLFjx65evXrlypWDBg3SdlwAAP/14sWLmJiYI0eO3LlzZ9y4cStXrly9evWAAQO0HRcAaAb6tAAAoBlZWVmhoaFBQUEVFRX4AQQAtK6lpSUpKSkwMDA+Pt7IyMjd3d3Ly+uNN97QdlwAoGHo0wIAgCaJxeJz586dOnXq0qVLQqHQ1dV148aN1tbW2o4LALqRBw8ehISEhISElJaW2tvbe3t7L1++3NjYWNtxAUC7QJ8WAADaxbNnz06fPn3s2LHHjx/b2tp6eXl5eXm99NJL2o4LALqs6urq+Pj40NDQa9euDR48ePny5b6+viNGjNB2XADQvtCnBQCAdiSRSH777bfQ0NCwsLCWlpZFixb5+vpiKCkA0Ky0tLTAwMDw8PCmpqbFixd7eXk5Ojrq6+trOy4A6Ajo0wIAQEdgfz+5evWqhYWFp6fn+vXrhw8fru24AECHMfeDHD9+/NGjR8z9ICtWrOjXr5+24wKADoU+LQAAdCj559xWrFhhZGSk7bgAQGewz+3/+OOPvXv3dnNze/fdd1977TVtxwUA2oE+LQAAaAHGIwUAFbDjq7948WL27Nm+vr7Ozs4GBgbajgsAtAl9WgAA0Kbi4uLIyMgTJ07cvXt3/Pjx3t7ePj4+/fv313ZcANCJVFZWRkVFHT16ND09fezYscuWLfPx8RkyZIi24wKATgF9WgAA6BSYIV4iIiLEYrGDg4O3t/fbb78tEAi0HRcAaI1EIklMTDx16lRMTAwhZOHChRhkDgDkoU8LAACdSENDQ0JCQmBg4LVr18zMzLy8vNasWTN69GhtxwUAHaqgoCA8PDwgIODp06e2tra+vr6enp4mJibajgsAOiP0aQEAoDPCFS1AN4T/agGACtCnBQCAzgt3HgJ0E3j6AABUhj4tAADoAOkRYsaMGePh4YERYgC6AIwSBwDqQ58WAAB0CTOTx4kTJ8rLyzGTB4COkp7Ny9jYeOnSpZjNCwBUhj4tAADoHrFYfOXKldDQ0DNnzvTu3dvNze3dd9997bXXtB0XALQhOzv75MmTISEhpaWls2fP9vLycnV1NTIy0nZcAKDD0KcFAAAd9vz589DQ0OPHjz969MjW1tbLy2vFihX9+vXTdlwA8A9VVVVnz54NDQ29evWqhYWFp6fn+vXrhw8fru24AKArQJ8WAAC6AmaAmfDw8KampsWLF3t5eTk6Ourr62s7LoBuTSKR/Pbbb6GhoWFhYS0tLYsWLcIwbwCgcejTAgBA11FdXR0fHx8aGnrt2rXBgwcvX77c19d3xIgR2o4LoNt59uzZ6dOnjx079vjxY+YeCi8vr5deeknbcQFAF4Q+LQAAdEE5OTnBwcEnT54sKSmxt7f39vZevny5sbGxtuMC6OLEYvG5c+dOnTp16dIloVDo6uq6ceNGa2trbccFAF0Z+rQAANBlSY+tamRk5O7ujrFVAdoJMyZ5UFBQRUXFrFmzfH19XVxcevTooe24AKDrQ58WAAC6vhcvXsTExBw5cuTOnTvjxo1buXLl6tWrBwwYoO24AHReRUVFdHR0QEBARkaGpaXlqlWrVq1aNXDgQG3HBQDdCPq0AADQjaSlpZ06der06dNVVVX4KQlAZRKJJDExMTAw8OzZsz179nR2dvb29sbgTwCgFejTAgBAt9PQ0JCQkBAYGHjt2rVBgwa5ubmtXbt2woQJ2o4LQAc8fPgwPDw8JCQkLy/P1tbW19fX09PTxMRE23EBQPeFPi0AAHRfBQUF4eHhR48effLkCXN17uHh0bt3b23HBdDp1NfXnz9/nvlP0CuvvLJixYq1a9eOGjVK23EBAKBPCwAA3R47hebp06clEgmm0ASQJj/584IFCwQCgbbjAgD4L/RpAQAA/quqqioyMvLUqVO//vrr6NGjPT09V61aNWzYMG3HBaAFRUVFUVFRx48fz8zMHD9+vLe395o1a15++WVtxwUAIAt9WgAAAFn3798/derUiRMnysvLZ8+e7eXl5ebm1qtXL23HBdDuGhsbL1++HBoaeubMGRMTk6VLl65fv97GxkbbcQEAKIQ+LQAAQOtwcQ/dCvOvnODg4LKyMvwrBwB0CPq0AAAAbXj+/HloaGhQUNCff/6JmzChi5G+5X7IkCEeHh4bNmzALfcAoEPQpwUAAOALg+VAl4Gh0QCgy0CfFgAAQDnyk5qsW7du5MiR2o4LgJfCwsKwsDBMYQUAXQb6tAAAACp6+PBheHh4SEhIXl4e0zdYvny5sbGxtuMCaEVDQ0NCQgLzv5hBgwa5ubmtWbNm4sSJ2o4LAEBd6NMCAACoRSKRJCYmBgYGnj17tmfPns7Ozt7e3riHEzqPtLS0U6dOhYWFVVZWzpo1y9fX18XFpUePHtqOCwBAM9CnBQAA0IyKioro6OiAgICMjAxLS8tVq1atWrVq4MCB2o4LuinmhPz+++9v3749bty4lStXrl69esCAAdqOCwBAw9CnBQAA0LCsrKzQ0NDjx4/jZzHoeC0tLUlJSYGBgfHx8UZGRu7u7l5eXm+88Ya24wIAaC/o0wIAALQLsVh87ty5U6dOXbp0qX///kuXLvXx8bG2tuazbU1NDcbsAQb/kyEnJyciIiI4OLiwsNDe3t7b2xsPeANAd6Cn7QAAAAC6JkNDQzc3t//P3p3HNXXs/QOfhLAoYKCKioKooGyCLFVA6kW9FhUU0QIRFbQq2ErtRUup1rYqpa6tog9SN8ANkUWNRXG5Koj2glCwgixaoVIQqIACCQJhye+P83vOk8sSAgpJ8PP+o6+cOXPmfGfyUvPtmTOTkJDw7Nkzf3//y5cvW1hYvP/++wcOHHj58qWYC+/fvz916tTCwsJ+CxVkVmFh4dSpU+/fvy+mTl1d3alTpz788ENjY+Pjx497enr+8ccf9+7d8/X1RUILAO8C5LQAAAB9S0dH56uvvvrjjz/u3r1rbW29ZcuW0aNHe3h43Lx5s9PZUuHh4QUFBdbW1vfu3ev/aEF23Lt37/333y8oKAgPD++0QmZm5tq1a0ePHu3r66upqXnp0qXi4uJdu3aNHz++n0MFAJAizD0GAADoV7W1tTExMadOnfr11191dXWXLl26du3acePGUWcbGhqGDx/O5/OZTCaTyTx+/PiKFSukGzBIRUxMjJeXV1tbW2tr6+DBgysrKwcPHkydKisro17Yfvr0qYmJibe39+rVq4cNGybdgAEApAU5LQAAgHTk5+efPHkyMjKyqqpq1qxZXl5ebm5u8fHxH3/8cVtbG13t888/379/P5OJqVXvCqFQuG3btu+//576TAhhMpknTpzw8PC4cePG6dOnL168qK6u7u7u/sknn1haWko7XgAAKUNOCwAAIE0CgeDy5cuRkZFXr15ls9mqqqplZWWtra10BQUFhQULFkRFRdGP6WAAa2xs/Pjjj2NjY0X/v4aCgsLo0aP5fH5tbe28efM+/vjj+fPnKykpSTFOAADZgZwWAABAJpSVlR04cGDv3r0d/2lWVFQ0NDRMTEzU1dWVSmzQP8rLy52dnXNyclpaWjqe/fLLL/39/UeNGtX/gQEAyDJMZAIAAJAJo0aNUlZWZrFYHU81NzdTq0ZlZmb2f2DQP3Jyct5///1Hjx51mtAqKioOGjQICS0AQEd4TgsAACAT2tradHR0ysvLu6qgoKCgqKh4jzItkgAAIABJREFU7ty5hQsX9mdg0A+uXbv20UcfCQSCThNayogRI54/f66goNCfgQEAyD48pwUAAJAJN2/eFJPQEkJaW1ubmpoWL168b9++fosK+sG+ffucnZ0bGxvFJLSEkL///vvWrVv9FhUAgLzAc1oAAHm1b9++1NRUaUcBb839+/dLSkroQwaD0e4DhVo6aNy4cZaWllgMWd61tbU9ePDgzz//JIS0+zbpX2iiP9V0dXVtbGz6M0LoU3Z2dhs3bpR2FAByr5OXdgAAQC6kpqampaXZ2tpKOxB4OwwNDaldapubm4VCYVtbG/XUrrm5mRDS0tLS1tZG7VZKCBEIBM+ePRs/frx0Y+6p+Ph4W1tbHR0daQfSJ0pLS9PS0tzc3CS/5NmzZ83NzdT3qKCgQG1KTL1TraioSAhhsVhMJpPBYFCHWOt4IElLS5N2CAADBHJaAAA5ZmtrGxcXJ+0oACTFYDA2bNjg4eEh7UD6RGxsLIfDwR9JkJC7u7u0QwAYIDBnCQAAAAAAAOQVcloAAAAAAACQV8hpAQAAAAAAQF4hpwUAAAAAAAB5hZwWAAAAAAAA5BXWPQYAAADZVVRUFBwcHBQUNGB2AHr27Bm9s/TEiROtra3pUy0tLenp6Xw+v7q6mhBiZGRkaWlJn62pqbl69Sp9OHfuXE1Nzf6Kur3q6uqjR49u3ryZOszKyho6dKienl5P25HTLnfa36Kiovv371OfDQ0NrayspBAlwDsJz2kBAABAdmVlZUVGRubk5Eg7kLfm119/Xbp0KYPBmDlz5sSJE+ny2travXv3mpmZ2dvbFxQULF26dObMmU+ePKErsNlsQ0PDnTt3BgcHa2tra2hoSCP8/2/NmjUHDhygD83NzXft2pWSktKjRuS3y532d8SIEdOmTdPV1V2xYsWZM2ekESPAOwo5LQAAAMguNze3ysrKefPm9eldTp061aftdzRv3ryRI0eqq6tTh8+fP/fy8lq3bp26urqqqur27duVlJRqa2tdXV15PB5Vh8FgWFlZcTicJUuWzJgxg8Fg9HPMtGPHjuXm5oqWsFis0NDQXbt2Sf5/H+S6y532V1VVVU9P74MPPhg9erQ0YgR4dyGnBQAAAJk2bNiwPm3/9u3b9Bxaadm4ceOiRYvYbDZdYmBg4OjomJ+f7+3tLRQK6fKhQ4dK93HlkydPHjx4MH/+/HblCgoKGzdu9PX1lbAdee9yT/sLAH0HOS0AAADIrra2tqSkpIyMDOqwpKTkwIEDbW1tjx49+uGHH06fPt3W1kZXLi0tDQsLEwqFycnJmzdvDg0NbWhoIIQkJCSEhIQcP36cEMLj8Q4dOhQSEhITE0MISUpKcnV15fP5R44cSUhIIIRUVVXt3Lnz77//7rc+pqenX7lyxc3NTbSQxWKdO3dOX1+fy+UGBwfT5Uwmk8n8r99vPB4vJiZm27Zt4eHhJSUldLn4sSKElJWVRUREBAUF3bp1S8JQm5ubv/nmm927d3d6dvbs2Twe78KFC922I5Uu96K/RGyXJe8vAPQp5LQAAAAgo/Ly8jgczqxZszIzMwkhCQkJ1tbW/v7+Bw8e3LdvX1pamre3N51sREVFmZubBwQErFu37vTp09nZ2evXr3dwcGhubl6wYMHx48e3b99OCFFXV/f29t66dSv1bqSmpqa5ubmysrKhoaGuri4hhMvlfv3117Gxsf3WzT179tjZ2dHzkGmamppcLldNTW3r1q2XL1/u9NqHDx/a29srKir6+fnV1NSYmJhQ86jFjxUhJCkpadu2bZaWlsbGxq6urn5+fpKEGhQU5O/v3zFUmr29vWg6Kjtd7l1/u+2yhP0FgD6FnBYAAABklImJyXfffUcfLliwYPXq1YQQMzOziIiIhIQEKyur8+fPU2eXLVvm7Ozc2Nj42WefhYeHX7ly5dtvv83IyIiIiCCEGBsb0+2oq6sbGBhQny0sLLS0tFRUVGbMmGFhYUEI8fT0PHv27MqVK/urlyQ7O3vUqFGdnpo0adLJkycJIcuXLxddPIkiEAiWLFmyaNGixYsXa2lpffHFFy4uLj4+Pnl5eeLHis/nr1mzZv/+/ZaWlu7u7hwOJywsLC0tTXycd+7cYbFY06ZNE1PH1NQ0JydHIBDIVJd7119JuixhfwGgTyGnBQAAANmlrKwsejho0CBCiJGREXVoYmLy119/0WdVVVVZLJapqSl1uGnTJhaLJclivKKLD6mqqnp6eop5FPl2CQSCoqIibW3triosXrx4y5Yt7RZPoly7dq2goMDW1pYumTNnjkAgCA8PJ2LHKjo6uqGhITAw0M/Pz8/Pr7y8XF9f/+nTp2LirKmpCQ0N3bJli/jusNnslpYW8U31f5d70V8JuyxJfwGgr2F/WgAAAJBXCgoKoosJtTN48GAdHZ3Kyspu25HigrovX75sbW2lkrGuBAUFPXz4MCEhwdvbe+7cuXR5Xl4eIURNTY0umT59OiEkPz+/YyOiY5Wbm6utrX3o0CHJ49ywYcOUKVN++eUX6vCPP/5obGy8cOGChobGrFmz6GpUMKWlpSYmJl011f9d7kV/iWRdlqS/ANDXkNMCAADAwNTU1FRRUTFnzpxua0oxpx05cqSGhka7p5HtMBiMM2fO2NjYcLncx48f0++Cvvfee4SQ1NRUKq8jhOjp6SkqKmpqaoq/qYKCwuPHj5ubmxUVFSWMs7Ky8t///jd9WFtb+/r1688//9zU1FQ0p3316hUhhHozuSv93+Ve9JdI1mVJ+gsAfQ1zjwEAAGBgSktLa2xspLZgYbFYjY2NnVZjMBitra39G9p/MTU1ffHihWiJUCh8/fq1aMmQIUO4XC6bzRZ9IGljY0MIEZ1c/ejRo+bmZjs7O/F3nDx5cn19/eHDh+mSmpqasLAwMZdcvny5VMSnn36qpaVVWlp6/fp10Wrl5eUMBmPcuHHiA+jnLveiv0SyLkvYXwDoU8hpAQAAQHY1NTURQqqqqqjDuro6Qgi9JE9VVVVTU5Po9OOWlhY6BYqPj3dwcKByWkdHx6qqqsjIyPr6+sjIyOrq6qKiIuohm7a2dkVFRVFRUWFhYX19fWZm5tSpU5OTk/utj9OnT8/JyREtKS8vf/78ebsk3NDQMCoqSnRXm8mTJ69YsSIlJYV+UfbevXsTJkygNk0VM1YcDkdXVzcgIGDv3r35+fmxsbG+vr5eXl5UTV9fXycnp97tZvTs2TNHR0cVFRXxTfVzl8X390263K6/ACAVyGkBAABARt2/fz8oKIgQEhMTc+XKlTt37ly8eJEQsmPHjoqKinPnzt29e5fH4wUFBbW0tFCXMJnMsLCwwMBAT0/P4uJiastZQoi7u7utre2qVaumTJmioaFhbW1tYWFBLYrr7u4uFAqtra0TExNVVVWLi4t/++23/lz1JzAwsKysrLCwkDqMj4/39PRsaGhwcXFJSkoSrens7Pz999+Llhw+fNjb29vJyenkyZPh4eGJiYm3bt1SUlISP1bKysrXr18fO3ZsYGCgiYlJUFDQ5s2b6WWxbt++ffXq1TNnzvS0IwKB4NKlSwEBAXRJV031c5cVFBTE9LfXXe7YXwCQCoaYlRUAAECWubu7E0Li4uKkHQiApBgMRkxMjIeHRx+1/8knn0RERAgEgpKSEjabPWTIkHYVKisrtbS0CCGNjY2iz9Zqa2uZTCad5NTV1XW8tluxsbEcDqfbX1ZRUVHLly+vqalhs9l04ZEjR3JyckJDQyW50YsXL4YPHy5aUltbm5ubO2bMGB0dnR7FXFxczGAwxowZI1rY1NR06dIlFRUVFxeXHrUWFxcXFRXF5XIlaUoqXe60v+LjFKNjfynjxo1btGjRvn37xF+Ov8MB3hY8pwUAAICBRldXt9OklEpoCSHtJouy2WzRp3a9SGh7ippTTfPx8amurn7w4IEk17bL7gghbDZ72rRpPU1oCSF6enqdJnipqalOTk49aqqgoCAqKio6OlrCpqTS5U77Kz7OrnTaX4p0X88GeAchpwUAAIAB4vXr1y0tLXw+X9qBiKOoqDhkyJA1a9bs3Lnz5s2bVCGTyTxx4sTPP/+ckZEh3fAIIenp6Tt27GCxerA7RnFx8c6dOyMiItrt0COmKbnucqf9ffTo0Y8//vj555/X1dXhDVuA/oS5xwAA8grz1t4iHo939uzZP//808DAYOnSpYMHD+7R5SUlJVlZWdnZ2Uwmc8KECVOmTGEwGKWlpR988EEfBSyn+nTucVRU1BdffPH333+vW7fOx8fHwsKiL+4ihoRzj8X766+/On2QKOPKy8tHjhzZuy2R5LHLb9JfGv4OB3hb8JwWAADedY8fP544ceJPP/20f/9+Hx8fc3PziooKCa8VCARffvnlxIkTf/31Vysrq2nTphUVFVlbW48fPz49Pb1Pw4Z25s+fX1BQ8OrVqx9++MHQ0FDa4fSS3GV3FG1t7V4nePLY5TfpLwC8dchpAQCgz506dUqWG9+wYcP169efPHlSWlq6Zs2awsLCLVu2SHJhY2OjnZ3dsWPH/v3vf+/Zs8fZ2XnmzJmbNm3KyMjQ0dFpt9lm/5Dxoe5TbDZb43+1mwELAAADGHJaAADoW7dv3968ebPMNp6Zmbls2TJzc3NCiJaWVlBQEJPJ/M9//iPJtcHBwVlZWV9++WW7Ocb6+vrffvttfX39mwTWCzI+1AAAAH1BYdu2bdKOAQAAeoN6C0uSVxP5fH5sbGxcXFxVVZWOjg69eAmPx+NyufHx8YWFhVpaWvTOIiUlJSdOnJg6dWpubu6xY8eKi4vNzMxEJ9p11eCTJ0+uXLly+vTp+vp6Y2NjQkhSUpKrq2tzc/N7771XXl5OzQgtKyuLi4tLSEhoaWkZP3483az4+75h411RVlaeOnUqfRd1dfUrV66oqqquXr2aEFJVVXXgwIGJEyeqqam1u7CiosLNzU1FRSUuLk5ZWbndWTMzMx6PZ2Ji8iZD3dU4v+FodPv99tFQE0K2b9/u7u5uamoqSWW5k5ubGx8fj19WICHJ/w4HgG4IAQBAPrm5ubm5uXVbLT8/38nJ6eHDh83NzZ6enkOHDi0sLBQKhb///ruZmdn58+dfvHjx448/qqmpnTx5UigU/vLLL9R+J/v37//444/nz59PCNmxY0e3De7fv3/GjBltbW1//vnn2LFjw8LChELhgwcP7O3ttbS0kpKSHjx4IBQKb9++7ePjk5WVFRsbq6amtm7dOqpZ8fd9w8Z7ZOTIkUFBQdTnY8eOEUIOHjzYsVpiYiIhZNKkSd022Luh7mqc33A0uv1++3SoCSExMTGS1JRHMTEx+GUFkpPw73AA6Bb+5gUAkFeS/B5qaWmxsLA4evQodZiZmamkpJSQkNDU1GRkZPTdd9/RNZcuXaqkpJSbmysUCjdt2kQIuXnzJnXKysrK2tpafINCodDAwMDPz48qd3V1dXJyoj/r6upSn3k83vjx4/l8PnVIPQtNTU2lDsXc980bl9CdO3d0dHR4PB51yOfzz549W1dX17Hmnj17CCELFiwQ32DvhlrMOL/5aIgZ5zdvXDzktAA05LQAb0sPdh4DAAC5k5iY+Pvvvzs7O1OHVlZWPB5PSUnpl19+KSgosLW1pWvOmTPn7Nmz4eHhP/30E7W+jpGREXXKxMTk+vXr4hskhCQnJ6uqqhJC8vLySkpK6urq6Mbpea3R0dENDQ2BgYHUYXl5ub6+/tOnT6lIxNz3zRuXRGtr63fffffLL7/QM41VVVU9PT07rUxtZdna2iq+zWvXrvViqMWM85uPhphxfvPGxY8GIYTD4XA4nG6ryS8shwuSc3Nzk3YIAAMBcloAgIHs4cOHqqqq1FxTCpUX5eXlEUJE3xGdPn06ISQ/P79jIwoKCsL/3XKzqwYJIaNHj75x48bly5cdHBz09fUzMzPpOvSv/NzcXG1t7UOHDkkSvOh933rjnQoICNi4caOlpaUklamXQv/44w/x1Xo31GLGmbzt0RAd57feeEf+/v52dna9uFD2paamhoSEUE9rAbq1f/9+aYcAMEAgpwUAGMja2trq6+uTkpIcHR1Fy9977z1CSGpqKpVfEUL09PQUFRU1NTV71yAh5Ntvv71z587169cHDRp0/vx50VN0LqSgoPD48ePm5mZFRcUedaRPG6ccPXrU0tLSxcVFwvrW1tZqampFRUWFhYX6+vpdVevdUIsZZyLnQ21nZzeAF8UJCQkZwL2Dt4taIwoA3hz28gEAGMjMzMwIIWfPnqVLqqurL168aGNjQwhJSUmhyx89etTc3NztA7SuGvzzzz+Dg4OXL19OzWtta2ujKzAYDHqC7uTJk+vr6w8fPkyframpCQsLE3/TPm2ccvHiRaFQ6O3tTZfcuXNH/CVDhw7dvn17a2srPQW3nQcPHhBCejfUXY0z6ePR6IehBgAAeLvwnBYAYCBzcXGxtLQ8efKkioqKu7t7dnZ2cnJybGyssrLyihUrLly48Ndff40ZM4YQcu/evQkTJvj6+hJCqFcoBQIB1UhVVVVTU5NQKGQwGF01+OTJE0JIdHT0kiVLHj58mJKS0tTURK0hpK2tXVFRUVRUJBQK58+fr6urGxAQ0NjYOH/+/JycnPj4+PDwcOpGXd2Xz+e/eeNi3Lx5c/fu3cuXLw8NDSWEtLa25uXlTZo0ycHBITMz89NPP92zZ8+MGTM6Xvj555/fv38/NjbWx8fn4MGDVB5ICCkuLv7hhx+8vLwIIZMnT+7FUHc1zoSQNx8NMd9vXw81AADA29fvq1IBAMDbIeGamaWlpR9++CGDwWAwGDNmzCgtLaXKGxoa/Pz8TE1NT5w4cfz4cWdn57/++ksoFCYnJ1N7ja5Zs6a8vDw6OnrIkCGEkG3btjU3N4tpcNWqVSwWy8DA4PDhw/Hx8UpKSrNmzaqurk5KSmKxWBoaGtSOOHl5eRMnTqT+DTI1Nc3KyqIuF3/fN2xcjMzMTGpJJFEqKirV1dVCofD8+fMMBuPYsWNiWjh9+vSYMWNGjBjh4uKyatWqiRMnenh4FBQU0BV6N9RdjfMbDnW332/fDbUQ6x4DiMC6xwBvC0MosiwEAADIEXd3dyLxG1k1NTVtbW3Uu52iamtrc3Nzx4wZo6Oj06O7d9ogj8dTV1enPjc1NSkrK9N3YTKZ9ClCSHFxMYPBoJ5bSqhPGxejrq6OyvrEe/Xq1aNHjxQVFSdOnNhxnElvh7qrL05Oh5rBYMTExAzUN05jY2M5HA5+WYGEevR3OACIgbnHAADvBA0NjU7L2Wz2tGnT3laDotkOnQhRd2lXU09Pr6d3fJPG161b11Wzvr6+FhYWYu4rSUJLCNHU1KRXgepU74a6qy9OZocaAACgnyGnBQCAgW/mzJldnRLdLwfgndXS0pKens7n86urqwkhRkZGopta1dTUXL16lT6cO3dut2ukv3V8Pj82NvbZs2e2trYffvghteZ2VlbW0KFD8f9WAN5xyGkBAGDgo+b4AUCnamtrw8LCPvvsMyaTuWfPnqCgIDabnZ6eTr8yzWazDQ0NV65c2draeujQoa6mD/Sdx48fz58//8CBAx4eHgkJCQYGBqdPn/7HP/5hbm6+fv16T0/Pf/zjH/0cEgDIDuzlAwAAAAPEqVOnZKodufD8+XMvL69169apq6urqqpu375dSUmptrbW1dWVx+NRdRgMhpWVFYfDWbJkyYwZM+idivvNhg0bHBwcnJyc1NTUPD09Z86c+c033xBCWCxWaGjorl27cnJy+jkkAJAdyGkBAABgILh9+/bmzZtlpx15sXHjxkWLFom+LG1gYODo6Jifn+/t7S265NXQoUP7/wktpby8PDc3lz5UVlZuamqiPisoKGzcuJHaHAsA3k3IaQEAAEC28Hi8mJiYbdu2hYeHl5SU0OUJCQkhISHHjx+n6hw6dCgkJITaQScpKcnV1ZXP5x85ciQhIYEQUlpaGhYWJhQKk5OTN2/eHBoa2tDQ0It2qqqqdu7c+ffff/f7MPSH9PT0K1euuLm5iRayWKxz587p6+tzudzg4GC6nMlkMpn/99Oxq6+JEFJSUnLgwIG2trZHjx798MMPp0+fbmtro8+WlZVFREQEBQXdunVLwjgXL16clpZ25swZQgifz7948aK/vz99dvbs2Twe78KFCz3pOgAMHMhpAQAAQIY8fPjQ3t5eUVHRz8+vpqbGxMSEngm8YMGC48ePb9++nRCirq7u7e29devWAwcOEEI0NTXNzc2VlZUNDQ11dXWjoqLMzc0DAgLWrVt3+vTp7Ozs9evXOzg4NDc396gdQgiXy/36669jY2OlNSB9as+ePXZ2dqIrXVM0NTW5XK6amtrWrVsvX77c8UIxX1NCQoK1tbW/v//Bgwf37duXlpbm7e29e/du6mxSUtK2bdssLS2NjY1dXV39/PwkidPX19fQ0NDLy2vjxo0fffTRkSNHPD09RSvY29uLpt8A8E5BTgsAAACyQiAQLFmyZNGiRYsXL9bS0vriiy9cXFx8fHzy8vKoCsbGxnRldXV1AwMD6rOFhYWWlpaKisqMGTMsLCyWLVvm7Ozc2Nj42WefhYeHX7ly5dtvv83IyIiIiOhRO4QQT0/Ps2fPrly5su97LwXZ2dmjRo3q9NSkSZNOnjxJCFm+fPmTJ09ET4n/mhYsWLB69WpCiJmZWUREREJCgpWV1fnz5wkhfD5/zZo1+/fvt7S0dHd353A4YWFhaWlp3cY5YsSIu3fv6uvr79+/n8fjddwWy9TUNCcnRyAQ9GoYAEC+IacFAAAAWXHt2rWCggJbW1u6ZM6cOQKBIDw8XJLLRdcuUlVVZbFYpqam1OGmTZtYLFZKSkov2vH09Oz4JHMAEAgERUVF2traXVVYvHjxli1b2q0XRST4mgYNGkQIMTIyog5NTEz++usvQkh0dHRDQ0NgYKCfn5+fn195ebm+vv7Tp08liTY8PNzBwWHVqlWpqak2NjZUgzQ2m93S0iJhUwAwwGAvHwAAAJAV1IM+NTU1umT69OmEkPz8fEkuF7Me7+DBg3V0dCorK9+wnYHk5cuXra2tVP7ZlaCgoIcPHyYkJHh7e8+dO5cq7OnXpKCgQK01lZubq62tfejQoZ6GGhkZGRMTk5GRwWKx7O3t165d6+fnR73wTKGCKS0tNTEx6WnjACDv8JwWAAAAZMV7771HCElNTaVL9PT0FBUVNTU1JblcTC7a1NRUUVExfvz4N2xnIBk5cqSGhoboA9iOGAzGmTNnjIyMuFwu9coxeYOvSUFB4fHjx9RbzT1y8uTJefPmsVgsQsiqVat8fHxu3LhRU1NDV3j16hUhhHoFGgDeNchpAQAAQFbY2NgQQkRnCD969Ki5udnOzo46ZLFYjY2NnV7LYDBaW1u7ajktLa2xsXH+/Plv2M4AY2pq+uLFC9ESoVD4+vVr0ZIhQ4ZwuVw2m00/hu32a+rK5MmT6+vrDx8+TJfU1NSEhYV1G2d2drZoBrtw4UKBQCC6GHV5eTmDwRg3bly3TQHAwIOcFgAAAGTF5MmTV6xYkZKSQr8tee/evQkTJtC7jzo6OlZVVUVGRtbX10dGRlZXVxcVFVHP6LS1tSsqKoqKigoLC+vr6wkhLS0tdBoWHx/v4OBA57SSt5OZmTl16tTk5OT+HYl+Mn369JycHNGS8vLy58+ft0v4DQ0No6Ki6I18uv2a6urqCCH0ik1VVVVNTU1CoZDD4ejq6gYEBOzduzc/Pz82NtbX19fLy4uq5uvr6+Tk1Om2Sa6urhcvXqQ3BEpLSzM3N58wYQJd4dmzZ46OjioqKm82HgAgl5DTAgAAgAw5fPiwt7e3k5PTyZMnw8PDExMTb926paSkRJ11d3e3tbVdtWrVlClTNDQ0rK2tLSwsqDV13d3dhUKhtbV1YmKiqqoqIYTJZIaFhQUGBnp6ehYXF4u+fil5O8XFxb/99ttAXXwoMDCwrKyssLCQOoyPj/f09GxoaHBxcUlKShKt6ezs/P3339OHYr6mO3fuXLx4kRCyY8eOioqKc+fO3b17l8fjBQUFKSgoXL9+fezYsYGBgSYmJkFBQZs3b6bX37p9+/bVq1epTWjbCQ0NdXZ2njx58oEDB3x8fLKysrhcLp1jCwSCS5cuBQQE9MEIAYAcYFCv7AMAgNxxd3cnhMTFxUk7EABJMRiMmJgYDw+PbmvW1tbm5uaOGTNGR0en49nKykotLS1CSGNjo+ijudraWiaTSeVIn3zySUREhEAgKCkpYbPZQ4YM6V07hJC6urpOL28nNjaWw+HI3S+rI0eO5OTkhIaGSlL5xYsXw4cPpw/Ff01iFBcXMxiMMWPGiBY2NTVdunRJRUXFxcWl06tev35dXFw8cuTIdi/uxsXFRUVFcbncHsUgdfg7HOBtwXNaAAAAkDlsNnvatGldZUpUIkoIaTfXlM1md9x0R1dXt6uMVMJ2JElo5ZePj091dfWDBw8kqSya0JLuviYx9PT02iW0hJCmpqbU1FQnJ6eurho8eLCxsXG7hLagoCAqKio6OrqnMQDAgIGcFgAAAAag169ft7S08Pl8aQci65hM5okTJ37++eeMjAzpRpKenr5jxw5qcWMJFRcX79y5MyIiQvyORAAwsCGnBQAAgIEmKirqxo0bQqHwq6+++v3336UdjqxTVlY+evToiBEjpBvG7Nmze5qaKikpnThxgtpbCADeWT34P2EAAAAAcmH+/PnOzs7UZ2VlZekGIy86TgaWfdra2tIOAQCkDzktAAAADDRsNlvaIQAAQD/B3GMAAAAAAACQV8hpAQAAAAAAQF4hpwUAAAAAAAB5hfdpAQDkWGlpaWxsrLSjAOiB1NRUaYfQV6iu4Y8kSKi0tLQXW/sCQEcMoVAo7RgAAKA33N3d4+PjpR0FAAD0kpubW1xcnLSjAJB7yGkBAABAFnl4eBA89gQAgO7gfVrLxpNHAAAgAElEQVQAAAAAAACQV8hpAQAAAAAAQF4hpwUAAAAAAAB5hZwWAAAAAAAA5BVyWgAAAAAAAJBXyGkBAAAAAABAXiGnBQAAAAAAAHmFnBYAAAAAAADkFXJaAAAAAAAAkFfIaQEAAAAAAEBeIacFAAAAAAAAeYWcFgAAAAAAAOQVcloAAAAAAACQV8hpAQAAAAAAQF4hpwUAAAAAAAB5hZwWAAAAAAAA5BVyWgAAAAAAAJBXyGkBAAAAAABAXiGnBQAAAAAAAHmFnBYAAAAAAADkFXJaAAAAAAAAkFfIaQEAAAAAAEBeIacFAAAAAAAAeYWcFgAAAAAAAOQVcloAAAAAAACQV8hpAQAAAAAAQF4hpwUAAAAAAAB5hZwWAAAAAAAA5BVyWgAAAAAAAJBXyGkBAAAAAABAXiGnBQAAAAAAAHmFnBYAAAAAAADkFXJaAAAAAAAAkFcsaQcAAAAAQAghKSkpqamp9GFBQQEhZPfu3XSJnZ3dP/7xDylEBgAAMowhFAqlHQMAAAAAuXXr1uzZsxUVFZnM9vPI2trampubb968+c9//lMqsQEAgMxCTgsAAAAyoa2tbeTIkZWVlZ2eHTZsWEVFhYKCQj9HBQAAMg7v0wIAAIBMYDKZy5YtU1JS6nhKSUlp+fLlSGgBAKAj5LQAAAAgKzw9PQUCQcdygUDg6enZ//EAAIDsw9xjAAAAkCFjx44tLi5uV6irq1tcXMxgMKQSEgAAyDI8pwUAAAAZ4uXlpaioKFqiqKi4cuVKJLQAANApPKcFAAAAGVJQUGBsbNyu8NGjR6amplKJBwAAZBye0wIAAIAMMTIyMjU1FX0qa2JigoQWAAC6gpwWAAAAZIu3tze9xLGiouKKFSukGw8AAMgyzD0GAAAA2VJSUqKnp0f9RGEwGEVFRWPHjpV2UAAAIKPwnBYAAABki66uro2NDZPJZDKZNjY2SGgBAEAM5LQAAAAgc7y8vBgMBpPJ9PLyknYsAAAg0zD3GAAAAGROVVXVyJEjCSFlZWXDhw+XdjgAACC7kNMCAIAUYK9RAHhb8GsW4B3HknYAAADwjvL397ezs5N2FCC7UlJSGAzG9OnT6ZL9+/cTQjZs2CC9oPoWh8PBn4seSU1NDQkJkXYUACBleE4LAABSwGAwYmJiPDw8pB0IyC4ej0cIUVdXp0vc3d0JIXFxcVKLqY/hz0VPxcbGcjgc/JoFeMfhOS0AAADIItFsFgAAoCtY9xgAAAAAAADkFXJaAAAAAAAAkFfIaQEAAAAAAEBeIacFAAAAAAAAeYU1ogAAAGDAKioqCg4ODgoK0tHRkXYsfaulpSU9PZ3P51dXVxNCjIyMLC0t6bM1NTVXr16lD+fOnaupqdnPEfL5/NjY2GfPntna2n744YeKioqEkKysrKFDh+rp6fVzMAAwkOA5LQAAAAxYWVlZkZGROTk50g6kb9XW1u7du9fMzMze3r6goGDp0qUzZ8588uQJXYHNZhsaGu7cuTM4OFhbW1tDQ6OfI3z8+LGlpeXIkSMDAwNra2sNDAxSUlIIIebm5rt27aI+AwD0DnJaAAAAGLDc3NwqKyvnzZvX1zc6depUX9+iK8+fP/fy8lq3bp26urqqqur27duVlJRqa2tdXV2pPX4JIQwGw8rKisPhLFmyZMaMGQwGo5+D3LBhg4ODg5OTk5qamqen58yZM7/55htCCIvFCg0N3bVr14D//w4A0HeQ0wIAAMBANmzYsL6+xe3btzdv3tzXd+nKxo0bFy1axGaz6RIDAwNHR8f8/Hxvb2+hUEiXDx06tP+f0FLKy8tzc3PpQ2Vl5aamJuqzgoLCxo0bfX19pRIYAAwAyGkBAABgwGpra0tKSsrIyKBLSkpKDhw40NbW9ujRox9++OH06dNtbW3UqdLS0rCwMKFQmJycvHnz5tDQ0IaGBkJIQkJCSEjI8ePHCSE8Hu/QoUMhISExMTHUVUlJSa6urnw+/8iRIwkJCYSQqqqqnTt3/v333/3QwfT09CtXrri5uYkWslisc+fO6evrc7nc4OBgupzJZDKZ//Xbj8fjxcTEbNu2LTw8vKSkhC4XM0qEkLKysoiIiKCgoFu3bkkY5+LFi9PS0s6cOUMI4fP5Fy9e9Pf3p8/Onj2bx+NduHChJ10HAPj/kNMCAADAwJSXl8fhcGbNmpWZmUmVJCQkWFtb+/v7Hzx4cN++fWlpad7e3rt37yaEREVFmZubBwQErFu37vTp09nZ2evXr3dwcGhubl6wYMHx48e3b99OCFFXV/f29t66deuBAweoNjU1Nc3NzZWVlQ0NDXV1dQkhXC7366+/jo2N7Yc+7tmzx87OTl1dvV25pqYml8tVU1PbunXr5cuXO7324cOH9vb2ioqKfn5+NTU1JiYm1AxqMaNECElKStq2bZulpaWxsbGrq6ufn58kcfr6+hoaGnp5eW3cuPGjjz46cuSIp6enaAV7e3vR9BsAQHLIaQEAAGBgMjEx+e6770RLFixYsHr1akKImZlZREREQkKClZXV+fPnCSHLli1zdnZubGz87LPPwsPDr1y58u2332ZkZERERBBCjI2N6UbU1dUNDAzoQwsLCy0tLRUVlRkzZlhYWBBCPD09z549u3Llyn7oY3Z29qhRozo9NWnSpJMnTxJCli9fLrpeFEUgECxZsmTRokWLFy/W0tL64osvXFxcfHx88vLyxIwSn89fs2bN/v37LS0t3d3dORxOWFhYWlpat3GOGDHi7t27+vr6+/fv5/F406ZNa1fB1NQ0JydHIBD0YhAA4B2HnBYAAAAGLGVl5XYlgwYNIoQYGRlRhyYmJn/99Rf1WVVVlcVimZqaUoebNm1isVgSLskruuqSqqqqp6dnx2enb51AICgqKtLW1u6qwuLFi7ds2dJuvSjKtWvXCgoKbG1t6ZI5c+YIBILw8HDS9ShFR0c3NDQEBgb6+fn5+fmVl5fr6+s/ffpUkmjDw8MdHBxWrVqVmppqY2NDDzuFzWa3tLRI2BQAgCjsTwsAAADvLgUFBdFVlEQNHjxYR0ensrJSknb6fyVhQsjLly9bW1up/LMrQUFBDx8+TEhI8Pb2njt3Ll2el5dHCFFTU6NLpk+fTgjJz8/v2Ag9Srm5udra2ocOHeppqJGRkTExMRkZGSwWy97efu3atX5+ftTrxxQqktLSUhMTk542DgDvODynBQAAAOhEU1NTRUXF+PHjJakslZx25MiRGhoa7R7AtsNgMM6cOWNkZMTlcul3gAkh7733HiEkNTWVLtHT01NUVNTU1BTTmoKCwuPHj5ubm3sa6smTJ+fNm8disQghq1at8vHxuXHjRk1NDV3h1atXhBDqhWQAgB5BTgsAAADQibS0tMbGxvnz5xNCWCxWY2NjVzUZDEZra2s/hvZ/TE1NX7x4IVoiFApfv34tWjJkyBAul8tms0WfwdrY2BBCRGdWP3r0qLm52c7OTsztJk+eXF9ff/jwYbqkpqYmLCys2zizs7NFM9iFCxcKBALRpaHLy8sZDMa4ceO6bQoAoB3ktAAAADBgUZugVlVV0SV1dXWEEHotoqqqqqamJnr6cUtLC534xcfHOzg4UDmto6NjVVVVZGRkfX19ZGRkdXV1UVER9WiREKKtrV1RUVFUVFRYWFhfX5+ZmTl16tTk5OR+6OD06dNzcnJES8rLy58/f94uAzc0NIyKihLdyGfy5MkrVqxISUmh32u9d+/ehAkTqH1iuxolDoejq6sbEBCwd+/e/Pz82NhYX19fLy8vullfX18nJ6eO+xi5urpevHiR3hAoLS3N3Nx8woQJdIVnz545OjqqqKi82XgAwLsIOS0AAAAMTPfv3w8KCiKExMTEXLlyhRBy586dixcvEkJ27NhRUVFx7ty5u3fv8ni8oKCglpYWQgiTyQwLCwsMDPT09CwuLqZf+HR3d7e1tV21atWUKVM0NDSsra0tLCyopYCps0Kh0NraOjExUVVVtbi4+Lfffuuf5Y4CAwPLysoKCwupw/j4eE9Pz4aGBhcXl6SkJNGazs7O33//vWjJ4cOHvb29nZycTp48GR4enpiYeOvWLSUlJTGjpKCgcP369bFjxwYGBpqYmAQFBW3evFl0Nazbt29fvXqV2odWVGhoqLOz8+TJkw8cOODj45OVlcXlcukcWyAQXLp0KSAg4K2PDwC8CxhdrYsAAADQdxgMRkxMjIeHh7QDAXni7u5OCImLi+uj9j/55JOIiAiBQFBSUsJms4cMGdKuQmVlpZaWFiGksbGx3RPF2tpaJpNJZ3d1dXUdL+9W7/5cHDlyJCcnJzQ0VJLKL168GD58uGhJbW1tbm7umDFjdHR0JL9pcXExg8EYM2ZMu/KmpqZLly6pqKi4uLh0vOr169fFxcUjR45s99ZuXFxcVFQUl8uVPABKbGwsh8PBr1mAdxye0wIAAAD8F11d3U4zUiqhJYR0nCLLZrNFH1f2IqHtNR8fn+rq6gcPHkhSuV1CSwhhs9nTpk3rUUJLCNHT0+uY0BJCmpqaUlNTnZycOr1q8ODBxsbG7RLagoKCqKio6OjoHgUAAEDDXj4AACCLUlJSnj9/Th+am5ubmpry+XzRzT8IIR4eHgoKCkVFRcHBwUFBQR1/l/P5/KSkpHv37u3evVvyU70jEAju3r17+fLlDz/8sKvf9P2joqKioKBgxowZklRuN9SKiopaWlqjRo0SfdfxHfH69euWlhY+ny+6w43sYzKZJ06cWL9+vY+Pz5QpU6QbTHp6+o4dO6j1jSVRXFy8c+fOiIgI8TsSAQCIgee0AAAgi8zNzf/444+lS5cuXbp00KBB+vr6hBA1NTUzM7MdO3YsXbr08OHD77//voKCAiEkKysrMjKy3Uo5lGvXrn3++efnzp3r0aneefToUWxsbEhISFlZ2dtqs6cqKysDAgLGjx9PvQ8pCXNz88LCwqVLl65cubKurq6ysjIhIYHD4YwbN+6bb77pxa4tcioqKurGjRtCofCrr776/fffpR1OzygrKx89enTEiBHSDoTMnj27R9mpkpLSiRMnqI2FAAB6BzktAADIIg0NjVWrVhFCRo8e7erqSk/1nDRpkqOjIyFkwYIF9INENze3ysrKefPmdWzHzc1t6tSpnT41EnOqd6ysrPz8/N5Wa73z7Nkzb2/vhoYGyS/R0NBYuXIlIURfX3/t2rWffvrpjz/+mJmZuXfv3v/5n/9xdnYWv/3pgDF//vyCgoJXr1798MMPhoaG0g6nNzqdDCzjtLW1pbK1LwAMJJh7DAAAMop6I7Hje4nUW4ui7y4SQoYNG9ZVO0wmU3QLEwlP9Q6VIUvxN/qUKVPo/Vck13GQGQyGm5tba2vrkiVLpk+fnp6erqSk9JZilFFsNlvaIQAAQG8gpwUAALnX1tZ2584dNTU1+mXCly9fxsfHP3v27P333xcKhaJJpphThJCysrJr166Vlpba29v/85//pApLSkouXLiwfv36vLy8S5cujRkzZtmyZRImw0+ePElLS8vOzra3t1+0aBEh5NatWyUlJYQQZWXlxYsXKysrp6en5+XlaWpqLly4UEwYr169io6OXrdu3dWrV7Ozs7/44gvJHzJXVVUdO3Zs1apVPZqeyuFwTp06lZiYmJ6e/sEHH4iJTcwQCYXCO3fu/P777woKCkZGRh9++KGYdgAAAHoKc48BAEC+5eXlcTicWbNmZWZmUiWPHz+eO3eumZlZUFBQVVUVl8ulE1cxpwghSUlJ27Zts7S0NDY2dnV1pSYSJyQkWFtb+/v7Hzx4cN++fWlpad7e3hIuKxUSErJ27VovL6/PPvts48aNP//8MyHEzs7uxx9//Pjjj21sbJSVlQkhU6dO3b17t7GxsZgwTp48qaOj869//Ss0NHTz5s2bNm3Ky8uTfJS4XO7XX38dGxsr+SUUW1tbQsjdu3fFxCZ+iL755punT5/6+/vb2dl98803YtoBAADoDSEAAEC/I4TExMSIr1NbW0sIUVNTW/jfqHcdDx8+TNfMzs4mhPz888/UoY2NzZdffkl9bmtrGz9+/MSJE7s9xePxxo8fz+fzqcPVq1cTQlJTU4VC4aZNmwghN2/epE5ZWVlZW1t3GnNubi4h5Pjx49ShgYGBn58f9dnV1dXJyYn6/MsvvxBCjh07Rh2WlZW5ubl1G8ayZcsIIRcuXBAKhfn5+V2NW1NTEyHk888/Fy3k8/lnz56tq6vr9BJqqI2NjTueunDhAiFk3rx5vRuitra2YcOGJSUlUeXBwcHi2xHPzc2NHqgBSZI/FyAqJiYGv2YBAHOPAQBApo0cOXL//v2iJSEhIY8fPxYtoZ52Um7fvn3//v2tW7dShwwGY8qUKdQytmJOEUKio6MbGhoCAwOpw/Lycn19/adPn9ra2lLruBoZGVGnTExMrl+/LknwycnJqqqqhJC8vLySkpK6ujqqfP78+cbGxvv27Vu9ejWDwTh79qy3t3e3YYwaNYoQQs1PpoORkKqqqqenZ48uofD5fOpy8bF1NUQMBsPQ0JDD4Rw9enThwoUBAQHi2+k2ntLS0l48bZYjqamp0g5BnmC4AIDgfVoAAJBxioqK48aNEy3R1NQUU//hw4eEkEmTJtEl9OxiMacIIbm5udra2ocOHeo2JAUFBaFQKEnwo0ePvnHjxuXLlx0cHPT19enZ0QwG48svv1y1alViYqKzs/PNmzf/9a9/dRsG9Xrq213UqltZWVmEEBsbG/GxtSM6RKGhoe7u7q6urv/85z+joqJGjBgheTsdpaWlcTicXlwoL0JCQkJCQqQdBQCAPMH7tAAAMKBQz0Lv378vWkjlrmJOEUIUFBQeP378drdj/fbbb4ODg3fv3v3RRx9RW+nSli1bNnr06J9++ik3N9fU1JRe7akvwug1oVB49+5dBQUFamGn3sVmYWGRlZW1bt265ORkKyurly9fvkkfMfcYRFFzjwHgHYecFgAAZJSwi2ehVHlX++WYmZkRQm7fvt2jU4SQyZMn19fXHz58mC6pqakJCwvrYdT/588//wwODl6+fDk1L7etrU30rJKSkr+/f1JS0pdffvnxxx/3XRhvYsOGDdRGtZMnT+5dbE1NTadPn1ZXVz906NCVK1fKy8svXLggU30EAAB5h5wWAABkVE1NDSGktLS0tbVVtJxa0OjZs2d0CbUqUlVVFSHExcXFyMjo9OnTKSkphJCysrI7d+6UlpZmZ2c7OTl1daqlpYXD4ejq6gYEBOzduzc/Pz82NtbX19fLy4v87wNeet/XqqqqpqamTlNuKjbqHVTqv9HR0XV1dXfv3k1JSXn16hWfz+fxeFTltWvXstnsqqoqU1NTugUxYdTX1xNCqqurxY/bq1evCCGNjY2ihZmZmVOnTk1OTu70EmowGxoaREv8/PwOHjy4fv36DRs2dBtbV0MkFAqp1bwIIY6OjsOGDRs2bJiYdgAAAHpMerNFAADg3UW6m2N57do1Z2dn6p+qZcuWZWZmCoVCHo+3Z8+e4cOHE0JGjRoVFBTU0tKSlpbm5uZGCJk0adLly5eFQuGff/5JbVQ7fvz4pUuXLliw4IMPPvj5558bGhrEnBIKhXl5eRMnTqRuampqmpWVJRQKk5OTx48fTwhZs2ZNeXl5dHT0kCFDCCHbtm1rbm4Wjfn+/ftz5swhhFhaWiYmJgqFwlWrVrFYLAMDg8OHD8fHxyspKc2aNau6upq+5JNPPjl06FC7vncaxvHjx0ePHk0I8fDwuH//flfjlpiYSL1uOnz48GPHjpWXl1Pl58+fZzAY9ErLon755ZcZM2ZQt7Ozs/vwww+dnZ0XLlz4xRdfZGRkSBKbmCHi8Xja2tpLliyJi4v78ccfv/vuOzHtdAvrHkM7WPcYAIRCIUMo2SoXAAAAbxGDwYiJifHw8Oi7W1RWVg4ePFhVVZXP56upqUl4ihBSXFzMYDDGjBnzVsLg8Xjq6urU56amJtElmgkhjo6OsbGxGhoaHS98u2EQQurq6qhU8831KLaWlpa2traKioqO9XvaR3d3d0JIXFxcj6KVI/3w52KAiY2N5XA4+DUL8I7DuscAADAwaWlpUR86Zq1iThFC9PT03mIYdEJL/nvPIULIw4cPx48f32lC+9bDIIS8rYSW9DA2avmrThPXt95HAAB4ByGnBQAA6FeZmZmBgYFmZmbJyclcLlfa4QAAAMg35LQAAAD9qq2tLSMjIzMz89ixY2PHjpV2OCCvWlpa0tPTp02bduPGDWrlMCMjI0tLS7pCTU3N1atX6cO5c+eK39u57zx8+DAlJUVJScnZ2VlHR6fd2erq6qNHj27evJkQkpWVNXToUDzAB4AewbrHAAAA/WrKlCkvX758+fIl9XYoQC/U1tbu3buX2p7K3t6+oKBg6dKlM2fOfPLkCV2HzWYbGhru3LkzODhYW1u7q1nufaqqqmrNmjWbN29euHDh2rVrOya0hJA1a9YcOHCA+mxubr5r1y5qZXIAAAkhpwUAAOhvLBaLycQ/wTLn1KlTMtVOV54/f+7l5bVu3TrqbW1VVdXt27crKSnV1ta6urrSm0UxGAwrKysOh7NkyZIZM2Z0taVz33n27JmxsXFTU1NiYmJXK4EdO3YsNzeXPmSxWKGhobt27crJyemvMAFA7uEfVAAAAABy+/ZtavqrjLQjxsaNGxctWsRms0ULDQwMHB0d8/Pzvb29RdcBHjp0qFSe0AoEAg8Pj/fee+/w4cNd1Xny5MmDBw/mz58vWqigoLBx40ZfX9++jxEABgjktAAAADDQ8Hi8mJiYbdu2hYeHl5SUUIUJCQkhISHHjx+nKhw6dCgkJITa4DQpKcnV1ZXP5x85ciQhIYGqX1paGhYWJhQKk5OTN2/eHBoa2tDQ0NN2qqqqdu7c+ffff7+trqWnp1+5coXak1kUi8U6d+6cvr4+l8sNDg6my5lMpuikgE5HhhBSUlJy4MCBtra2R48e/fDDD6dPn25raxNtv6ysLCIiIigo6NatW5LEuWXLloyMjMDAQFVV1U4rNDc3f/PNN7t37+54avbs2Twe78KFC5LcCAAAOS0AAAAMKA8fPrS3t1dUVPTz86upqTExMaEmAy9YsOD48ePbt28nhKirq3t7e2/dupV6k1NTU9Pc3FxZWdnQ0FBXV5cQEhUVZW5uHhAQsG7dutOnT2dnZ69fv97BwaG5ublH7XC53K+//jo2NvZt9W7Pnj12dnaie0TRNDU1uVyumpra1q1bL1++LPnIJCQkWFtb+/v7Hzx4cN++fWlpad7e3qLZZlJS0rZt2ywtLY2NjV1dXf38/LqNMzo6msVi5eTkzJo1S01N7R//+EdWVpZohaCgIH9//047Qgixt7cXzcwBAMRATgsAAAADh0AgWLJkyaJFixYvXqylpfXFF1+4uLj4+Pjk5eURQoyNjema6urqBgYG1GcLCwstLS0VFZUZM2ZYWFgQQpYtW+bs7NzY2PjZZ5+Fh4dfuXLl22+/zcjIiIiI6FE7np6eZ8+eXbly5dvqYHZ29qhRo7o6O2nSpJMnTxJCli9fLrpeFBE7MgsWLFi9ejUhxMzMLCIiIiEhwcrK6vz589SFfD5/zZo1+/fvt7S0dHd353A4YWFhaWlpYoJ8/vz58+fPJ02a9N13392+fTsrK+vp06cODg7Pnz+nKty5c4fFYk2bNq2rFkxNTXNycgQCgcQDAwDvLuS0AAAAMHBcu3atoKDA1taWLpkzZ45AIAgPD+/22narKKmqqrJYLFNTU+pw06ZNLBZLkiV5RdtRVVX19PTs6mlkTwkEgqKiIm1tbTF1Fi9evGXLlnbrRZHuRmbQoEGEECMjI+qUiYnJX3/9RX2Ojo5uaGgIDAz08/Pz8/MrLy/X19d/+vSpmBioR7Kurq7vvfceIWTixIn79u3j8/lhYWGEkJqamtDQ0C1btohpgc1mt7S0iL8LAAAF+9MCAADAwEE9j1VTU6NLpk+fTgjJz8/v9lrxKwMPHjxYR0ensrLyDdt5Ey9fvmxtbaXyTzGCgoIePnyYkJDg7e09d+5cqrBHI6OgoEAvNJWbm6utrX3o0CHJ46TWrxo2bBhdYmdnRwh5/PgxIWTDhg1Tpkz55ZdfqFN//PFHY2PjhQsXNDQ0Zs2aRRVScZaWlpqYmEh+XwB4NyGnBQAAgIGDejCYmppKJWyEED09PUVFRU1NzW6vFZ+LNjU1VVRUzJkz5w3beRMjR47U0NAQffraVQBnzpyxsbHhcrmPHz+mXn/t9cgoKCg8fvy4ublZUVFRwjgnTpxICMnMzKRLxowZo6ioSD2vrqys/Pe//02fqq2tff369eeff25qakrntK9evSKEUO8kAwCIh7nHAAAAMHDY2NgQQkRnCD969Ki5uZl6TshisRobGzu9kMFgtLa2imk5LS2tsbGR2njmTdp5Q6ampi9evGhXKBQKX79+LVoyZMgQLpfLZrPpx7DiR0aMyZMn19fXi27JU1NTQ80i7srIkSPnzJkj+s7tH3/80dzcbG9vTwi5fPlyqYhPP/1US0urtLT0+vXrdP3y8nIGgzFu3DjxsQEAEOS0AAAAMJBMnjx5xYoVKSkp9Oug9+7dmzBhArXfqaOjY1VVVWRkZH19fWRkZHV1dVFREfVIUFtbu6KioqioqLCwsL6+nrq2paWFzgnj4+MdHByonFbydjIzM6dOnZqcnPy2Ojh9+vScnJx2heXl5c+fP2+XZhsaGkZFRdEb+Ygfmbq6OkIIvSZTVVVVU1MTNf2Yw+Ho6uoGBATs3bs3Pz8/NjbW19fXy8uLqunr6+vk5NRxs6KffvqppKTkP//5D3WYlJRkbGws+VpZz549c3R0VFFRkbA+ALzLkNMCAADAgHL48GFvb28nJ6eTJ0+Gh4cnJibeunVLSUmJEOLu7m5ra7tq1aopU6ZoaGhYW1tbWFhQC/y6u7sLhUJra1O10m4AACAASURBVOvExER6S1UmkxkWFhYYGOjp6VlcXExvXSt5O8XFxb/99ttbXOsoMDCwrKyssLCQLomPj/f09GxoaHBxcUlKShKt7Ozs/P3333c7Mnfu3Ll48SIhZMeOHRUVFefOnbt79y6PxwsKCmppaVFWVr5+/frYsWMDAwNNTEyCgoI2b95Mr3p1+/btq1evnjlzpl2cpqamv/7663fffbd169YdO3Zcvnz51q1bLJZEb70JBIJLly4FBAT0bogA4F3DoBcAAAAA6DcMBiMmJsbDw0PagYA8cXd3J4TExcVJUrm2tjY3N3fMmDE6OjrtTlVWVmppaRFCGhsbRZ8E1tbWMplMOlv75JNPIiIiBAJBSUkJm80eMmRI79qpq6vreG2nJPxzceTIkZycnNDQUEnaJIS8ePFi+PDhouF1NTLiFRcXMxiMMWPGiBY2NTVdunRJRUXFxcWl06vKysoGDRokyfvMtLi4uKioKC6X223N2NhYDoeDX7MA7zg8pwUAAIABiM1mT5s2rdO0jUpECSHtpray2exON93R1dXtNCmVsB0JE1rJ+fj4VFdXP3jwQML6ogktETsy4unp6bVLaAkhTU1NqampTk5OXV01atSoHiW0BQUFUVFR0dHRPQ0PAN5ZyGkBAAAAOvH69euWlhY+ny/tQNpjMpknTpz4+eefMzIypB0LSU9P37Fjh4STirtVXFy8c+fOiIiIbvcrAgCgIacFAAAAaC8qKurGjRtCofCrr776/fffpR1Oe8rKykePHh0xYoS0AyGzZ89+i/mnkpLSiRMnqG2HAAAkhP1pAQAAANqbP3++s7Mz9VlZWVm6wXSl40xgeaetrS3tEABA/iCnBQAAAGiPzWZLOwQAAJAI5h4DAAAAAACAvEJOCwAAAAAAAPIKOS0AAAAAAADIKwZ2qQYAgP7HYDBsbW17sUMmvMvS0tIIIba2ttIOpK/Ex8fjz0WPlJaWpqWl4dcswDsOOS0AAEiBu7u7tEMAWZeTk0MIMTMzk3YgIOvi4uKkHQIASBNyWgAAAJBFHh4ehJDY2FhpBwIAADIN79MCAAAAAACAvEJOCwAAAAAAAPIKOS0AAAAAAADIK+S0AAAAAAAAIK+Q0wIAAAAAAIC8Qk4LAAAAAAAA8go5LQAAAAAAAMgr5LQAAAAAAAAgr5DTAgAAAAAAgLxCTgsAAAAAAADyCjktAAAAAAAAyCvktAAAAAAAACCvkNMCAAAAAACAvEJOCwAAAAAAAPIKOS0AAAAAAADIK+S0AAAAAAAAIK+Q0wIAAAAAAIC8Qk4LAAAAAAAA8go5LQAAAAAAAMgr5LQAAAAAAAAgr5DTAgAAAAAAgLxCTgsAAAAAAADyCjktAAAAAAAAyCvktAAAAAAAACCvkNMCAAAAAACAvEJOCwAAAAAAAPIKOS0AAAAAAADIK+S0AAAAAAAA/6+9O49r6sr/x/8OhEUBAypVLIt1Q0BZxwUZi7UWLQhFH2IEFTuKWqW2LgyVuqfUpbZuH6RugBsiiDaK4tIRBDsjhYJFZHOhUtZKUFaBBMjvj/ubfDMsISgQgq/nH33knnvuyfue6kPenA2UFXJaAAAAAAAAUFbIaQEAAAAAAEBZIacFAAAAAAAAZYWcFgAAAAAAAJQVcloAAAAAAABQViyxWKzoGAAAAADo9OnT+/bta2pqYi4FAgERDR48mLlUVVVdv369t7e3wuIDAIBeCTktAAAA9AqPHj0yNTWVUSE3N3fMmDE9Fg8AACgFzD0GAACAXmHMmDFWVlYsFqv1LRaLZWVlhYQWAABaQ04LAAAAvYW3t7eqqmrrcjabvWTJkp6PBwAAej/MPQYAAIDeori42MjIqLm5uUU5i8UqKCh49913FRIVAAD0ZhinBQAAgN5i2LBhU6ZMUVH5n59PVFRUHBwckNACAECbkNMCAABAL7J48eIWJSwWC9sdAwBAezD3GAAAAHqRly9fDhkyRCQSSUrYbHZpaemgQYMUGBUAAPRaGKcFAACAXkRPT++jjz6S7BSlqqo6c+ZMJLQAANAe5LQAAADQuyxatEiyTZRYLF60aJFi4wEAgN4Mc48BAACgd3n16tWgQYPq6+uJSFNTUyAQaGlpKTooAADopTBOCwAAAL1L//7958yZo6ampqamNmfOHCS0AAAgA3JaAAAA6HW8vLxEIpFIJPLy8lJ0LAAA0KuxFR0AAAC8jaKiohQdAvRqTU1N/fv3F4vFVVVV+NMCss2fP1/RIQCAImE9LQAAKACLxVJ0CADQR+CnWYC3HMZpAQBAMSIjIzG6AjIkJCSwWKz3339fUuLh4UFEFy5cUFxQ3YvFYuHvRadERUVxuVxFRwEACoacFgAAAHqjqVOnKjoEAABQAshpAQAAoDdSUcFOlgAA0DH8awEAAAAAAADKCjktAAAAAAAAKCvktAAAAAAAAKCskNMCAAAAAACAssIeUQAAANBn5eXlBQYG8ng8Q0NDRcfSxRobG5OTk6dMmXLr1q3y8nIiGjt2rI2NjaRCRUXF9evXJZezZs3S09NTQKBE6enpiYmJ6urqLi4urf9HlJeXHzt2LCAggIjS0tIGDRpkYmKiiDABQFlhnBYAAAD6rLS0tLCwsIyMDEUH0sUqKyv37t07fvx4InJwcMjJyfHy8vrggw8ePXokqcPhcExNTXft2hUYGGhgYKCrq9vzcQoEAh8fn4CAgE8++WTlypVt/mbBx8fn4MGDzGdLS8vdu3cnJib2bJgAoNyQ0wIAAECfNW/evLKyso8//ri7v+j06dPd/RUSRUVFixcvXr16tY6ODhFpaWnt2LFDXV29srLS3d29urqaqcZisWxtbblc7oIFC6ZNm8ZisXosQsazZ8/MzMwaGhpiY2ONjY3brHP8+PHMzEzJJZvNDgoK2r17d9/7NQQAdB/ktAAAANCXDR48uLu/Ii4ujpk62zPWr18/Z84cDocjXThq1CgnJ6fs7Gxvb2+xWCwpHzRokEJGaIVC4fz58wcOHHjkyJH26jx69Oj+/fuzZ8+WLlRVVV2/fv2KFSu6P0YA6COQ0wIAAECf1dzcHB8fn5KSIikpKCg4ePBgc3Pzw4cPv/322zNnzjQ3NzO3CgsLg4ODxWLxnTt3AgICgoKC6urqiCgmJubAgQMnTpwgourq6sOHDx84cCAyMpJ5Kj4+3t3dvaam5ujRozExMUQkEAh27dr1119/dccbJScnX7t2bd68eS3K2Wz2+fPnR44cyefzAwMDJeUqKioqKv/v573q6urIyMjt27eHhIQUFBTI0y2M4uLi0NBQHo93+/ZteeLctGlTSkqKv7+/lpZWmxVEItHmzZv37NnT+taMGTOqq6svXbokzxcBACCnBQAAgL4pKyuLy+VOnz49NTWVKYmJibGzs1u7du2hQ4f27duXlJTk7e3NpFXh4eGWlpZ+fn6rV68+c+bMgwcP1qxZ4+joKBKJXF1dT5w4sWPHDiLS0dHx9vbetm2bZAmonp6epaWlhoaGqampkZEREfH5/K+//joqKqo7Xuq7776zt7dnZh23oKenx+fztbW1t23bdvXq1dYV0tPTHRwc1NTUfH19KyoqzM3NmSnTMrqFER8fv337dhsbGzMzM3d3d19f3w7jjIiIYLPZGRkZ06dP19bWfv/999PS0qQr8Hi8tWvXtvkiROTg4CCdmQMAyICcFgAAAPomc3PzrVu3Spe4urouW7aMiMaPHx8aGhoTE2Nra3vx4kUiWrhwoYuLS319/eeffx4SEnLt2rUtW7akpKSEhoYSkZmZmaQRHR2dUaNGSS6tra319fU1NTWnTZtmbW1NRJ6enufOnfv000+746UePHgwbNiw9u6OGzfu1KlTRLRo0SLp/aKISCgULliwYM6cOXPnztXX19+wYYObm9vy5cuzsrJkdAsR1dTU+Pj47N+/38bGxsPDg8vlBgcHJyUlyQiyqKioqKho3LhxW7dujYuLS0tLe/LkiaOjY1FREVMhISGBzWZPmTKlvRYsLCwyMjKEQqHcHQMAby/ktAAAANBnaWhotCjp168fEY0dO5a5NDc3//PPP5nPWlpabDbbwsKCudy4cSObzZZzD17pHZi0tLQ8PT3bG4F8E0KhMC8vz8DAQEaduXPnbtq0qcV+UUR048aNnJycyZMnS0pmzpwpFApDQkJIZrdERETU1dX5+/v7+vr6+vqWlJSMHDnyyZMnMmJghmTd3d0HDhxIRGPGjNm3b19NTU1wcDARVVRUBAUFbdq0SUYLHA6nsbFR9rcAADBwPi0AAAC8vVRVVaV3VJLWv39/Q0PDsrIyedrpmV2FX7x40dTUxOSfMvB4vPT09JiYGG9v71mzZjGFWVlZRKStrS2pNnXqVCLKzs5u3YJ0t2RmZhoYGBw+fFj+OJn9q6R357K3tyei3NxcIlq3bt2ECROuXLnC3Hr8+HF9ff2lS5d0dXWnT5/OFDJxFhYWmpuby/+9APB2Qk4LAAAA0IaGhobS0tKZM2fKU7lnctqhQ4fq6upKj762F8zZs2cnTZrE5/Nzc3OZ5a/MkOm9e/eYVJaITExM1NTU9PT0ZLemqqqam5srEonU1NTkjHPMmDFEJFnGTETGxsZqamrM2HVZWdnPP/8suVVZWfnq1asvvvjCwsJCktO+fPmSiJj1yQAAsmHuMQAAAEAbkpKS6uvrmZNm2Gx2fX19ezVZLFZTU1PPRGVhYfH8+fMWhWKx+NWrV9IlAwYM4PP5HA5HMgw7adIkIpKeSv3w4UORSMSMoMpgZWVVW1srfSRPRUUFM4u4PUOHDp05c6b0mtvHjx+LRCIHBwciunr1aqGUVatW6evrFxYW3rx5U1K/pKSExWK99957smMDACDktAAAANCHNTQ0EJFAIJCUVFVVEZFk8yGBQNDQ0CCZZ9vY2ChJAqOjox0dHZmc1snJSSAQhIWF1dbWhoWFlZeX5+XlMWOJRGRgYFBaWpqXl/f06dPa2trU1NSJEyfeuXOnO95o6tSpGRkZLQpLSkqKiopaZN2mpqbh4eGSg3ysrKyWLFmSmJgoWSj7yy+/jB49mjkJVka3cLlcIyMjPz+/vXv3ZmdnR0VFrVixYvHixUzNFStWODs7tz646IcffigoKPjPf/7DXMbHx5uZmcm/b9azZ8+cnJw0NTXlrA8AbzPktAAAANA3/frrrzwej4giIyOvXbtGRAkJCT/99BMR7dy5s7S09Pz583fv3q2urubxeI2NjUSkoqISHBzs7+/v6emZn5/PnDdLRB4eHpMnT166dOmECRN0dXXt7Oysra0lOwN7eHiIxWI7O7vY2FgtLa38/Pzffvutm/Y38vf3Ly4ufvr0qaQkOjra09Ozrq7Ozc0tPj5eurKLi8s333wjuTxy5Ii3t7ezs/OpU6dCQkJiY2Nv376trq4uu1s0NDRu3rw5fPhwf39/c3NzHo8XEBAg2QErLi7u+vXrZ8+ebRGnhYXFv//9761bt27btm3nzp1Xr169ffs2my3XqjehUHj58mU/P7/X6yIAeNuw2tsXAQAAoPuwWKzIyMj58+crOhBQJh4eHkR04cKFbmr/s88+Cw0NFQqFBQUFHA5nwIABLSqUlZXp6+sTUX19fYshxMrKShUVFUmmV1VV1frxDsn59+Lo0aMZGRlBQUFyNvv8+fN33nlHOtTMzExjY2NDQ8NOhZefn89isYyNjaULGxoaLl++rKmp6ebm1uZTxcXF/fr163DVrrQLFy6Eh4fz+fwOa0ZFRXG5XPw0C/CWwzgtAAAAwP8wMjJqMyNlEloiaj0nlsPhSB/e8xoJrfyWL19eXl5+//59OetLJ7RExOFwpkyZ0tmElohMTExaJLRE1NDQcO/ePWdn5/aeGjZsWKcS2pycnPDw8IiIiM6GBwBvLex7DAAAvVFiYmJRUZHk0tLS0sLCoqamRjIXlDF//nxVVdW8vLzAwEAej9f6x/Sampr4+Phffvllz5498t96PUKh8O7du1evXv3oo49k/Ijfraqrq8+dO/fHH3+MGjXKy8urf//+HT7SoqvV1NT09fWHDRs2evTo7oy0N3r16lVjY2NNTY30gTe9kIqKysmTJ9esWbN8+fIJEyYoNpjk5OSdO3fKOam4Q/n5+bt27QoNDe3wvCIAAAmM0wIAQG9kaWn5+PFjLy8vLy+vfv36jRw5koi0tbXHjx+/c+dOLy+vI0eO/O1vf1NVVSWitLS0sLCw1hvnENGNGze++OKL8+fPd+rW63n48GFUVNSBAweKi4u7qs1Oyc3NHTNmzA8//LB///7ly5dbWlqWlpZ2+JSlpeXTp0+9vLw+/fTTqqqqsrKymJgYLpf73nvvbd68WSQS9UDkvUF4ePitW7fEYvFXX331+++/KzqcDmhoaBw7dmzIkCGKDoRmzJjRhfmnurr6yZMnmWOHAADkhJwWAAB6I11d3aVLlxLRu+++6+7uLpnqOW7cOCcnJyJydXWVDCTOmzevrKzs448/bt3OvHnzJk6c2OYgkoxbr8fW1pY5CFRR1q1bd/PmzUePHhUWFvr4+Dx9+nTTpk0dPqWrq8vsRjty5MiVK1euWrXq+++/T01N3bt37//93/+5uLh0eBpq3zB79uycnJyXL19+++23pqamig5HLq1nAis7AwODnjnpFwD6EuS0AADQSzErEluvS2RWLUqvXSSiwYMHt9eOioqK5DgT+W+9HiZDVsgP5ampqQsXLrS0tCQifX19Ho+noqIiOUlFttadzGKx5s2bd+zYsZ9//nnq1KmSI176MA6Ho/tfmPgKAKBEsJ4WAACUXnNzc0JCgra2tmRt4YsXL6Kjo589e/a3v/1NLBZLJ5kybhFRcXHxjRs3CgsLHRwcPvzwQ6awoKDg0qVLa9asycrKunz5srGx8cKFC+VMhh89epSUlPTgwQMHB4c5c+YQ0e3btwsKCohIQ0Nj7ty5GhoaycnJWVlZenp6n3zyiYwwXr58GRERsXr16uvXrz948GDDhg3Sg8zDhw+3tbWVXBoYGNjZ2UkqCASC48ePL126tFOzVblc7unTp2NjY5OTk//+97+/XheJxeKEhITff/9dVVV17NixH330kYx2AAAAOgvjtAAAoNyysrK4XO706dNTU1OZktzc3FmzZo0fP57H4wkEAj6fL0lcZdwiovj4+O3bt9vY2JiZmbm7uzMTiWNiYuzs7NauXXvo0KF9+/YlJSV5e3vLua3UgQMHVq5cuXjx4s8//3z9+vU//vgjEdnb23///ff/+Mc/Jk2apKGhQUQTJ07cs2ePmZmZjDBOnTplaGj45ZdfBgUFBQQEbNy4MSsrS/q7Bg0a1CI/LygokMzH5vP5X3/9dVRUVGe7d/LkyUR09+7d1+6izZs3P3nyZO3atfb29ps3b5bRDgAAwOsQAwAA9DgiioyMlF2nsrKSiLS1tT/5X8xaxyNHjkhqPnjwgIh+/PFH5nLSpEn//Oc/mc/Nzc0jRowYM2ZMh7eqq6tHjBhRU1PDXC5btoyI7t27JxaLN27cSET/+te/mFu2trZ2dnZtxpyZmUlEJ06cYC5HjRrl6+vLfHZ3d3d2dmY+X7lyhYiOHz/OXBYXF8+bN6/DMBYuXEhEly5dEovF2dnZsnsvISHB0NCwurqauaypqTl37lxVVVWblZmuNjMza33r0qVLRPTxxx+/Xhc1NzcPHjw4Pj6eKQ8MDJTdjmzz5s2TdFSfJM/fC5AWGRmJn2YBAHOPAQCgVxs6dOj+/fulSw4cOJCbmytdwox2MuLi4n799ddt27YxlywWa8KECcw2tjJuEVFERERdXZ2/vz9zWVJSMnLkyCdPnkyePJlZXTl27Fjmlrm5+c2bN+UJ/s6dO1paWkSUlZVVUFBQVVXFlM+ePdvMzGzfvn3Lli1jsVjnzp3z9vbuMIxhw4YRETM/WRJMm5qamrZu3XrlyhXJmTRaWlqenp7yxNxCTU0N87js2NrrIhaLZWpqyuVyjx079sknn/j5+clup8N4kpKSPDw8XuNFlMX+/fsvXLig6CiURmFhoaJDAADFQ04LAAC9mpqa2nvvvSddoqenJ6N+eno6EY0bN05SIpmRK+MWEWVmZhoYGBw+fLjDkFRVVcVisTzBv/vuu7du3bp69aqjo+PIkSMls6NZLNY///nPpUuXxsbGuri4/Otf//ryyy87DINZnirPOl4/P7/169fb2NjIE6RsaWlpRDRp0iTZsbUg3UVBQUEeHh7u7u4ffvhheHj4kCFD5G8HAACgQ8hpAQCgT2HGQn/99VcjIyNJIZO7yrhFRKqqqrm5uSKRSE1NrauC2bJlS0JCws2bN/v163fx4kXpWwsXLtyyZcsPP/wwfPhwCwsLyWZObx7GsWPHbGxs3Nzc3jR6IrFYfPfuXVVVVWZjp9eLzdraOi0tbePGjUePHrW1tc3IyHiTd5w8eXIfHsZksVjr1q2bP3++ogNRGlFRUVwuV9FRAICCYY8oAADopdobC2XK2zsvZ/z48UQUFxfXqVtEZGVlVVtbe+TIEUlJRUVFcHBwJ6P+f/7444/AwMBFixYx83Kbm5ul76qrq69duzY+Pv6f//znP/7xj64K46effhKLxZKZzESUkJDw2q+wbt065qBaKyur14utoaHhzJkzOjo6hw8fvnbtWklJyaVLl7q8qwEA4G2GnBYAAHqpiooKIiosLGxqapIuZzY0evbsmaSkoaGBiAQCARG5ubmNHTv2zJkziYmJRFRcXJyQkFBYWPjgwQNnZ+f2bjU2NnK5XCMjIz8/v71792ZnZ0dFRa1YsWLx4sX03wFeyRmtAoGgoaGhzZSbiY1Zg8r8NyIioqqq6u7du4mJiS9fvqypqamurmYqr1y5ksPhCAQCCwsLSQsywqitrSWi8vLy9nrsX//61549e0QiUVBQUFBQ0MGDB1euXMlsoJWamjpx4sQ7d+60+SDTmXV1ddIlvr6+hw4dWrNmzbp16zqMrb0uEovFzG5eROTk5DR48ODBgwfLaAcAAKDTFLM1FQAAvN2oo/1db9y44eLiwvxTtXDhwtTUVLFYXF1d/d13373zzjtENGzYMB6P19jYmJSUNG/ePCIaN27c1atXxWLxH3/8wRxUO2LECC8vL1dX17///e8//vhjXV2djFtisTgrK2vMmDHMl1pYWKSlpYnF4jt37owYMYKIfHx8SkpKIiIiBgwYQETbt28XiUTSMf/6668zZ84kIhsbm9jYWLFYvHTpUjabPWrUqCNHjkRHR6urq0+fPr28vFzyyGeffXb48OEW795mGCdOnHj33XeJaP78+b/++mvrHktNTWV2cpKmqanJfN3FixdZLJZkp2VpV65cmTZtGlPf3t7+o48+cnFx+eSTTzZs2JCSkiJPbDK6qLq62sDAYMGCBRcuXPj++++3bt0qo50OYd9jaAH7HgOAWCxmieXb5QIAAKALsVisyMjIbl03WFZW1r9/fy0trZqaGsn2vx3eIqL8/HwWi2VsbNwlYVRXV+vo6DCfGxoapLdoJiInJ6eoqChdXd3WD3ZtGERUVVXFpJpvrlOxNTY2Njc3l5aWtq7f2Xdkdjzu2+tpu/vvRR/DrKfFT7MAbznsEQUAAH2Tvr4+86F11irjFhGZmJh0YRiShJb+98whIkpPTx8xYkSbCW2Xh0FEXZXQUidjY7a/ajNx7fJ3BACAtxByWgAAgB6Vmprq7+8/fvz4O3fu8Pl8RYcDyqqxsTE5OXnKlCm3bt1iVlmPHTtW+gCnioqK69evSy5nzZol+xys7pOenp6YmKiuru7i4mJoaNjibnl5+bFjxwICAogoLS1t0KBB+GUHAHQK9ogCAADoUc3NzSkpKSdPnty0adPw4cMVHQ4opcrKyr179zJbeTs4OOTk5Hh5eX3wwQePHj2S1OFwOKamprt27QoMDDQwMGhvRkC3EggEPj4+AQEBn3zyycqVK1sntETk4+Nz8OBB5rOlpeXu3buZXdwAAOSEnBYAAKBHTZgw4cWLFy9evGBWh0Lvcfr06V7VTnuKiooWL168evVqZma7lpbWjh071NXVKysr3d3dJRtrs1gsW1tbLpe7YMGCadOmtXf8Vfd59uyZmZlZQ0NDbGxse6umjx8/npmZKblks9lBQUG7d+/OyMjoqTABQOkhpwUAAOhpbDZbRQX/BPcucXFxzPTXXtKODOvXr58zZw6Hw5EuHDVqlJOTU3Z2tre3t/SeSYMGDVLICK1QKJw/f/7AgQOlDyJu4dGjR/fv3589e7Z0oaqq6vr161esWNH9MQJAH4F/UAEAAKCvqa6ujoyM3L59e0hISEFBAVMYExNz4MCBEydOMBUOHz584MAB5jCY+Ph4d3f3mpqao0ePxsTEMPULCwuDg4PFYvGdO3cCAgKCgoKYI3w71Y5AINi1a9dff/3VVa+WnJx87do15vwqaWw2+/z58yNHjuTz+YGBgZJyFRUV6V+gtNkzRFRQUHDw4MHm5uaHDx9+++23Z86caW5ulm6/uLg4NDSUx+Pdvn1bnjg3bdqUkpLi7+/f+ogphkgk2rx58549e1rfmjFjRnV19aVLl+T5IgAA5LQAAADQp6Snpzs4OKipqfn6+lZUVJibmzOTgV1dXU+cOLFjxw4i0tHR8fb23rZtG7OSU09Pz9LSUkNDw9TU1MjIiIjCw8MtLS39/PxWr1595syZBw8erFmzxtHRUSQSdaodPp//9ddfR0VFddXbfffdd/b29tL7aUvo6enx+Xxtbe1t27ZdvXpV/p6JiYmxs7Nbu3btoUOH9u3bl5SU5O3tLZ1txsfHb9++3cbGxszMzN3d3dfXt8M4IyIi2Gx2RkbG9OnTtbW133///bS0NOkKPB5v7dq1bb4IETk4OEhn5gAAMiCnBQAAgL5DKBQuWLBgzpw5c+fO1dfX37Bhg5ub2/Lly7OysojIzMxMUlNHR2fUqFHMiYyixwAAIABJREFUZ2tra319fU1NzWnTpllbWxPRwoULXVxc6uvrP//885CQkGvXrm3ZsiUlJSU0NLRT7Xh6ep47d+7TTz/tqhd88ODBsGHD2rs7bty4U6dOEdGiRYuk94simT3j6uq6bNkyIho/fnxoaGhMTIytre3FixeZB2tqanx8fPbv329jY+Ph4cHlcoODg5OSkmQEWVRUVFRUNG7cuK1bt8bFxaWlpT158sTR0bGoqIipkJCQwGazp0yZ0l4LFhYWGRkZQqFQ7o4BgLcXcloAAADoO27cuJGTkzN58mRJycyZM4VCYUhISIfPtthFSUtLi81mW1hYMJcbN25ks9nybMkr3Y6Wlpanp2d7o5GdJRQK8/LyDAwMZNSZO3fupk2bWuwXRR31TL9+/Yho7NixzC1zc/M///yT+RwREVFXV+fv7+/r6+vr61tSUjJy5MgnT57IiIEZknV3dx84cCARjRkzZt++fTU1NcHBwURUUVERFBS0adMmGS1wOJzGxkbZ3wIAwMD5tAAAANB3MOOx2trakpKpU6cSUXZ2dofPyt4ZuH///oaGhmVlZW/Yzpt48eJFU1MTk3/KwOPx0tPTY2JivL29Z82axRR2qmdUVVUlG01lZmYaGBgcPnxY/jiZ/asGDx4sKbG3tyei3NxcIlq3bt2ECROuXLnC3Hr8+HF9ff2lS5d0dXWnT5/OFDJxFhYWmpuby/+9APB2Qk4LAAAAfQczMHjv3j0mYSMiExMTNTU1PT29Dp+VnYs2NDSUlpbOnDnzDdt5E0OHDtXV1ZUefW0vgLNnz06aNInP5+fm5jLLX1+7Z1RVVXNzc0UikZqampxxjhkzhohSU1MlJcbGxmpqasx4dVlZ2c8//yy5VVlZ+erVqy+++MLCwkKS0758+ZKImDXJAACyYe4xAAAA9B2TJk0iIukZwg8fPhSJRMw4IZvNrq+vb/NBFovV1NQko+WkpKT6+nrm4Jk3aecNWVhYPH/+vEWhWCx+9eqVdMmAAQP4fD6Hw5EMw8ruGRmsrKxqa2ulj+SpqKhgZhG3Z+jQoTNnzpRec/v48WORSOTg4EBEV69eLZSyatUqfX39wsLCmzdvSuqXlJSwWKz33ntPdmwAAIScFgAAAPoSKyurJUuWJCYmSpaD/vLLL6NHj2bOO3VychIIBGFhYbW1tWFhYeXl5Xl5ecyQoIGBQWlpaV5e3tOnT2tra5lnGxsbJTlhdHS0o6Mjk9PK305qaurEiRPv3LnTVS84derUjIyMFoUlJSVFRUUt0mxTU9Pw8HDJQT6ye6aqqoqIJHsyCQSChoYGZvoxl8s1MjLy8/Pbu3dvdnZ2VFTUihUrFi9ezNRcsWKFs7Nz68OKfvjhh4KCgv/85z/MZXx8vJmZmfx7ZT179szJyUlTU1PO+gDwNkNOCwAAAH3KkSNHvL29nZ2dT506FRISEhsbe/v2bXV1dSLy8PCYPHny0qVLJ0yYoKura2dnZ21tzWzw6+HhIRaL7ezsYmNjJUeqqqioBAcH+/v7e3p65ufnS46ulb+d/Pz83377rQv3OvL39y8uLn769KmkJDo62tPTs66uzs3NLT4+Xrqyi4vLN99802HPJCQk/PTTT0S0c+fO0tLS8+fP3717t7q6msfjNTY2amho3Lx5c/jw4f7+/ubm5jweLyAgQLLrVVxc3PXr18+ePdsiTgsLi3//+99bt27dtm3bzp07r169evv2bTZbrlVvQqHw8uXLfn5+r9dFAPC2YUk2AAAAAOgxLBYrMjJy/vz5ig4ElImHhwcRXbhwQZ7KlZWVmZmZxsbGhoaGLW6VlZXp6+sTUX19vfRIYGVlpYqKiiRb++yzz0JDQ4VCYUFBAYfDGTBgwOu1U1VV1frZNsn59+Lo0aMZGRlBQUHytElEz58/f+edd6TDa69nZMvPz2exWMbGxtKFDQ0Nly9f1tTUdHNza/Op4uLifv36ybOeWeLChQvh4eF8Pr/DmlFRUVwuFz/NArzlME4LAAAAfRCHw5kyZUqbaRuTiBJRi6mtHA6nzUN3jIyM2kxK5WxHzoRWfsuXLy8vL79//76c9aUTWpLZM7KZmJi0SGiJqKGh4d69e87Ozu09NWzYsE4ltDk5OeHh4REREZ0NDwDeWshpAQAAANrw6tWrxsbGmpoaRQfSkoqKysmTJ3/88ceUlBRFx0LJyck7d+6Uc1Jxh/Lz83ft2hUaGtrheUUAABLIaQEAAABaCg8Pv3Xrllgs/uqrr37//XdFh9OShobGsWPHhgwZouhAaMaMGV2Yf6qrq588eZI5dggAQE44nxYAAACgpdmzZ7u4uDCfNTQ0FBtMe1rPBFZ2BgYGig4BAJQPcloAAACAljgcjqJDAAAAuWDuMQAAAAAAACgr5LQAAAAAAACgrJDTAgAAAAAAgLJCTgsAAAAAAADKiiUWixUdAwAAvHVYLJaiQwCAPgI/zQK85bDvMQAAKEBkZKSiQ4Debv/+/US0bt06RQcCAAC9GsZpAQAAoDeaP38+EUVFRSk6EAAA6NWwnhYAAAAAAACUFXJaAAAAAAAAUFbIaQEAAAAAAEBZIacFAAAAAAAAZYWcFgAAAAAAAJQVcloAAAAAAABQVshpAQAAAAAAQFkhpwUAAAAAAABlhZwWAAAAAAAAlBVyWgAAAAAAAFBWyGkBAAAAAABAWSGnBQAAAAAAAGWFnBYAAAAAAACUFXJaAAAAAAAAUFbIaQEAAAAAAEBZIacFAAAAAAAAZYWcFgAAAAAAAJQVcloAAAAAAABQVshpAQAAAAAAQFkhpwUAAAAAAABlhZwWAAAAAAAAlBVyWgAAAAAAAFBWyGkBAAAAAABAWSGnBQAAAAAAAGWFnBYAAAAAAACUFXJaAAAAAAAAUFbIaQEAAAAAAEBZIacFAAAAAAAAZYWcFgAAAAAAAJQVcloAAAAAAABQVshpAQAAAAAAQFkhpwUAAAAAAABlxVZ0AAAAAABERAKBoKqqSnJZW1tLRHl5eZKSAQMGDB48WAGRAQBAL8YSi8WKjgEAAACAwsLCli5dKqNCaGjoP/7xjx6LBwAAlAJyWgAAAOgVKisr9fX1RSJRm3fV1NTKyso4HE4PRwUAAL0c1tMCAABAr8DhcJydndnsNhZGsdlsFxcXJLQAANAacloAAADoLRYtWtTU1NS6vLm5edGiRT0fDwAA9H6YewwAAAC9RX19/eDBg5ndoaT1799fIBD069dPIVEBAEBvhnFaAAAA6C00NTXnzp2rpqYmXaimpjZv3jwktAAA0CbktAAAANCLeHl5tdgmSiQSeXl5KSoeAADo5TD3GAAAAHqRxsbGIUOGvHjxQlKiq6tbVlbW5t5RAAAAGKcFAACAXoTNZnt6ekqmH6upqS1atAgJLQAAtAc5LQAAAPQunp6ekunHIpHI09NTsfEAAEBvhrnHAAAA0LuIxWIjI6OioiIiMjAwKCoqYrFYig4KAAB6KYzTAgAAQO/CYrEWL16srq6urq6+ZMkSJLQAACADxmkBAACg13nw4IGVlRXzYfz48YoOBwAAei/suAAAAH3Nvn377t27p+go4E1pa2sTEY/HU3Qg8Kbs7e3Xr1+v6CgAoM/C3GMAAOhr7t27l5SUpOgo3lLR0dGFhYVd0pSJicnw4cO7pKmuUlhYGB0dregolExSUhJ+xwQA3QrjtAAA0AdNnjz5woULio7ibcRisdatWzd//vw3byovL4+IRowY8eZNdZWoqCgul4s/Wp3i4eGh6BAAoI9DTgsAAAC9Ua/KZgEAoNfC3GMAAAAAAABQVshpAQAAAAAAQFkhpwUAAAAAAABlhZwWAAAAAAAAlBX2iAIAAABFysvLCwwM5PF4hoaGio6lizU2NiYnJ9fU1JSXlxPR2LFjbWxsJHcrKiquX78uuZw1a5aenp4CoiQiovT09MTERHV1dRcXlxb/I8rLy48dOxYQEMBcpqWlDRo0yMTERBFhAgC0AeO0AAAAoEhpaWlhYWEZGRmKDqSLVVZW7t27d/z48Q4ODjk5OV5eXh988MGjR48kFTgcjqmp6a5duwIDAw0MDHR1dRUSp0Ag8PHxCQgI+OSTT1auXNn6Nws+Pj4HDx6UXFpaWu7evTsxMbFnwwQAaBdyWgAAAFCkefPmlZWVffzxx936LadPn+7W9lsoKipavHjx6tWrdXR0tLS0duzYoa6uXllZ6e7uXl1dzdRhsVi2trZcLnfBggXTpk1jsVg9GSHj2bNnZmZmDQ0NsbGxxsbGrSscP348MzNTuoTNZgcFBe3evbvv/RoCAJQUcloAAABQsMGDB3dr+3FxcZKpsz1j/fr1c+bM4XA4kpJRo0Y5OTllZ2d7e3uLxWJJ+aBBgxQ1QisUCufPnz9w4MAjR460WeHRo0f379+fPXt2i3JVVdX169evWLGi+2MEAOgYcloAAABQpObm5vj4+JSUFOayoKDg4MGDzc3NDx8+/Pbbb8+cOdPc3CypXFhYGBwcLBaL79y5ExAQEBQUVFdXR0QxMTEHDhw4ceIEEVVXVx8+fPjAgQORkZFEFB8f7+7uXlNTc/To0ZiYGCISCAS7du3666+/uumNkpOTr127Nm/ePOlCNpt9/vz5kSNH8vn8wMBASbmKioqKyv/8PFZdXR0ZGbl9+/aQkJCCggJJueyeKS4uDg0N5fF4t2/fljPOTZs2paSk+Pv7a2lptb4rEok2b968Z8+eNp+dMWNGdXX1pUuX5PwuAIDug5wWAAAAFCYrK4vL5U6fPj01NZWIYmJi7Ozs1q5de+jQoX379iUlJXl7e0vSqvDwcEtLSz8/v9WrV585c+bBgwdr1qxxdHQUiUSurq4nTpzYsWMHEeno6Hh7e2/bto1ZBaqnp2dpaamhoWFqampkZEREfD7/66+/joqK6qaX+u677+zt7XV0dFqU6+np8fl8bW3tbdu2Xb16tc1n09PTHRwc1NTUfH19KyoqzM3NmVnTsnsmPj5++/btNjY2ZmZm7u7uvr6+8sQZERHBZrMzMjKmT5+ura39/vvvp6WlSe7yeLy1a9e2fgsJBwcH6eQcAEBRkNMCAACAwpibm2/dulVy6erqumzZMiIaP358aGhoTEyMra3txYsXmbsLFy50cXGpr6///PPPQ0JCrl27tmXLlpSUlNDQUCIyMzOTtKOjozNq1Cjms7W1tb6+vqam5rRp06ytrYnI09Pz3Llzn376aTe91IMHD4YNG9bmrXHjxp06dYqIFi1aJL1fFEMoFC5YsGDOnDlz587V19ffsGGDm5vb8uXLs7KyZPRMTU2Nj4/P/v37bWxsPDw8uFxucHBwUlKS7CCLioqKiorGjRu3devWuLi4tLS0J0+eODo6FhUVEVFCQgKbzZ4yZYqMFiwsLDIyMoRCodwdAwDQLZDTAgAAgCJpaGhIX/br14+Ixo4dy1yam5v/+eefkrtaWlpsNtvCwoK53LhxI5vNlmcPXukdmLS0tDw9PWWMQL4JoVCYl5dnYGDQXoW5c+du2rSpxX5RjBs3buTk5EyePFlSMnPmTKFQGBISQu33TERERF1dnb+/v6+vr6+vb0lJyciRI588eSI7TmZI1t3dfeDAgUQ0ZsyYffv21dTUBAcHV1RUBAUFbdq0SXYLHA6nsbGxwy8CAOhuOJ8WAAAAei9VVVXpHZVa6N+/v6GhYVlZWYft9Niuwi9evGhqamLyz/bweLz09PSYmBhvb+9Zs2ZJyrOysohIW1tbUjJ16lQiys7Obt2IpGcyMzMNDAwOHz7cqTiZ/aukd+eyt7cnotzc3HXr1k2YMOHKlStM+ePHj+vr6y9duqSrqzt9+nRJfSbOwsJCc3PzTn01AEDXQk4LAAAAyqqhoaG0tHTmzJkd1uyxnHbo0KG6urotBmBbB3P27NlJkybx+fzc3FzJ8ldmyPTevXtMKktEJiYmampqenp6MlpTVVXNzc0ViURqamryxzlmzBgiYpYxM4yNjdXU1HR0dMrKyn7++WdJeWVl5atXr7744gsLCwvpnPbly5dExCxRBgBQIMw9BgAAAGWVlJRUX1/PHDbDZrPr6+vbrMZisZqamnosKgsLi+fPn0uXiMXiV69eSZcMGDCAz+dzOBzpMdhJkyYRkfRU6ocPH4pEImYEtT1WVla1tbXS5/FUVFQEBwfLDnLo0KEzZ86UXnb7+PFjkUjk4OBw9erVQimrVq3S19cvLCy8efOmdAslJSUsFuu9996T/UUAAN0NOS0AAAAoUkNDAxEJBALmsqqqiogkOw8JBIKGhgbp6ceNjY2SPDA6OtrR0ZHJaZ2cnAQCQVhYWG1tbVhYWHl5eV5eHjOWaGBgUFpampeX9/Tp09ra2tTU1IkTJ965c6eb3mjq1KkZGRnSJSUlJUVFRS1SblNT0/DwcOmDfKysrJYsWZKYmChZQvzLL7+MHj2aOQm2vZ7hcrlGRkZ+fn579+7Nzs6OiopasWLF4sWLJc2uWLHC2dm59dlFP/zwQ0FBwX/+8x/mMj4+3szMTP6ts549e+bk5KSpqSlnfQCAboKcFgAAABTm119/5fF4RBQZGXnt2rWEhISffvqJiHbu3FlaWnr+/Pm7d+9WV1fzeLzGxkbmERUVleDgYH9/f09Pz/z8fObIWSLy8PCYPHny0qVLJ0yYoKura2dnZ21tzewM7OHhIRaL7ezsYmNjtbS08vPzf/vtt+7b3Mjf37+4uPjp06fMZXR0tKenZ11dnZubW3x8vHRNFxeXb775RrrkyJEj3t7ezs7Op06dCgkJiY2NvX37trq6uoyeUVVVvXnz5vDhw/39/c3NzXk8XkBAgPQOWHFxcdevXz979myLOC0sLP79739v3bp127ZtO3fuvHr16u3bt9lsuRamCYXCy5cv+/n5vUb/AAB0LZaMfRcAAACUkYeHBxFduHBB0YG8jVgsVmRk5Pz587up/c8++yw0NFQoFBYUFHA4nAEDBrSoUFZWpq+vT0T19fXSQ4iVlZUqKiqSTK+qqqr1sx2Kioricrny/Ox09OjRjIyMoKAgeZp9/vz5O++8I11SWVmZmZlpbGxsaGgof3j5+fksFsvY2LhFeUNDw+XLlzU1Nd3c3Np8sLi4uF+/frJX7bZw4cKF8PBwPp/fYU38fQSA7oZxWgAAAFA+RkZGbSalTEJLRC3mxHI4HOmhy9dIaDtl+fLl5eXl9+/fl6dyi4SWiDgczpQpUzqV0BKRiYlJ64SWiBoaGu7du+fs7Nzeg8OGDetUQpuTkxMeHh4REdGp8AAAuglyWgAAAFAar169amxsrKmpUXQgHVBRUTl58uSPP/6YkpKi6FgoOTl5586dck4q7lB+fv6uXbtCQ0Nln1cEANBjcJYPAABAr5aYmFhUVCS5VFNT09fXHzZs2OjRoxUYlUKEh4ffunVLLBZ/9dVXy5cvt7a2VnREsmhoaBw7dkyy25MCzZgxowtbU1dXP3nyZI+djQQA0CHktAAAAL2apaVlYmLili1b1NXVDx061NzcnJSUFBcX9/Lly4ULF27btq1Tp5IqtdmzZ7u4uDCfNTQ0FBuMnNqcDKzUDAwMFB0CAMD/wNxjAACAzjl9+nRPtqOrq8scrzJy5MiVK1euWrXq+++/T01N3bt37//93/+5uLhUV1d3STy9H4fD0f0vTHwFAAAGcloAAIBOiIuLCwgI6OF2Wm9oxGKx5s2bd+zYsZ9//nnq1KmSM0sBAADeNph7DAAAb6/q6urY2Njs7GwjIyMnJycjIyMiiomJefr0qba2to+PT3V19enTp0UikYGBAZfLjY+Pd3d3Z7FYR48eHTZsmKurKxEVFhZeuXJl1apVCQkJN2/efPfdd5ctW9avX79OtSMQCI4fP7506dIhQ4bIHz+Xyz19+nRsbGxycvLf//53prC4uPjGjRuFhYUODg4ffvghU1hQUHDp0qU1a9ZkZWVdvnzZ2Nh44cKFKioqRCQWixMSEn7//XdVVdWxY8d+9NFHkvbbbAoAAKBXwTgtAAC8pdLT0x0cHNTU1Hx9fSsqKszNzZnJwK6uridOnNixYwcR6ejoeHt7b9u27eDBg0Skp6dnaWmpoaFhamrKJMDh4eGWlpZ+fn6rV68+c+bMgwcP1qxZ4+joKBKJOtUOn8//+uuvo6KiOvsWkydPJqK7d+8yl/Hx8du3b7exsTEzM3N3d/f19SWimJgYOzu7tWvXHjp0aN++fUlJSd7e3nv27GEe2bx585MnT9auXWtvb79582ZJy202BQAA0NsgpwUAgLeRUChcsGDBnDlz5s6dq6+vv2HDBjc3t+XLl2dlZRGRmZmZpKaOjs6oUaOYz9bW1vr6+pqamtOmTWM23V24cKGLi0t9ff3nn38eEhJy7dq1LVu2pKSkhIaGdqodT0/Pc+fOMetmO2XcuHH035y2pqbGx8dn//79NjY2Hh4eXC43ODg4KSnJ1dV12bJlRDR+/PjQ0NCYmBhbW9uLFy8SkVgsPnbsGBPY3/72Nzc3N6bZ9prqbHgAAADdDTktAAC8jW7cuJGTk8MMcjJmzpwpFApDQkI6fLbFKSZaWlpsNtvCwoK53LhxI5vNTkxM7FQ7Wlpanp6eOjo68r7AfzEntWppaRFRREREXV2dv7+/r6+vr69vSUnJyJEjnzx5QkTMjkpjx45lnjI3N2fOmGGxWKamplwu9/Lly0Tk5+fHVJDRVIe4XC6rj+JyuUyngfyio6M7+6caAKBTsJ4WAADeRsx4rLa2tqRk6tSpRJSdnd3hsyyZJ3P279/f0NCwrKzsDduRU1paGhFNmjSJiDIzMw0MDA4fPtzhU6qqqmKxmPkcFBTk4eHh7u7+4YcfhoeHMwt65W+qNWYa82s82Pvdu3fvwIEDkZGRig5Emezfv1/RIQBAH4ecFgAA3kYDBw4konv37jGpLBGZmJioqanp6el1+KzsXLShoaG0tHTmzJlv2I48xGLx3bt3VVVVmY2dVFVVc3NzRSJRp06stba2TktL27hx49GjR21tbTMyMgYOHPh6TTHs7e3nz5/f2aeUxYEDB/rw23WHCxcuKDoEAOjjMPcYAADeRszApvQM4YcPH4pEImaAkc1m19fXt/kgi8VqamqS0XJSUlJ9ff3s2bPfsB15rFu3jjmo1srKioisrKxqa2uPHDkiqVBRUREcHCyjhYaGhjNnzujo6Bw+fPjatWslJSWXLl16vaYAAAAUAjktAAC8jaysrJYsWZKYmMgsKyWiX375ZfTo0StWrCAiJycngUAQFhZWW1sbFhZWXl6el5f38uVLIjIwMCgtLc3Ly3v69GltbS3zbGNjo2TScnR0tKOjI5PTyt9OamrqxIkT79y502a0z549I6K6ujrpEl9f30OHDq1Zs2bdunVMIZfLNTIy8vPz27t3b3Z2dlRU1IoVKxYvXkxEVVVVRCQ5xlYgEDQ0NIjFYrFYfOTIEWYespOT0+DBgwcPHiy7KQAAgF4FOS0AALyljhw54u3t7ezsfOrUqZCQkNjY2Nu3b6urqxORh4fH5MmTly5dOmHCBF1dXTs7O2tra2ajYA8PD7FYbGdnFxsby+zMREQqKirBwcH+/v6enp75+fkxMTFMufzt5Ofn//bbb21uwhQTE/Pll18S0bNnz6ZMmeLk5DR79uy1a9f269cvOTn50KFDkpoaGho3b94cPny4v7+/ubk5j8cLCAjQ0dFJSEj46aefiGjnzp2lpaXnz5+/e/dudXU1j8drbGz8448/vLy8oqOj9+3bt2rVKnd3dxlNdev/EQAAgNfAkmwRAQAA0Dd4eHiQ3Kv4KisrMzMzjY2NDQ0NW9wqKyvT19cnovr6ek1NTelHVFRUJAneZ599FhoaKhQKCwoKOBzOgAEDXq+dqqqq1s++nvz8fBaLZWxsLE/lxsbG5ubm0tLSNut3qikiYrFYkZGRfXXFaVRUFJfLxc9OndKpv48AAK8Be0QBAMBbjcPhTJkypc1bTCJKRNKJKPNIm/WNjIzepJ2uSmiJyMTERP7KbDabiNrLWjvVFAAAQM9DTgsAAPBGXr161djYWFNTI30yEEBrjY2NycnJNTU15eXlRDR27FgbGxvJ3YqKiuvXr0suZ82aJc8u3N2htLQ0Jydn2rRpzGVaWtqgQYPw2w0A6LWwnhYAAOD1hYeH37p1SywWf/XVV7///ruiw4Heq7Kycu/evePHj3dwcMjJyfHy8vrggw8ePXokqcDhcExNTXft2hUYGGhgYKCrq9vzQZaVlfn5+Y0YMYJZgM2wtLTcvXu39CbhAAC9CnJaAACA1zd79uycnJyXL19+++23pqamig6n7zt9+nSvakdORUVFixcvXr16tY6OjpaW1o4dO9TV1SsrK93d3aurq5k6LBbL1taWy+UuWLBg2rRpb3588Wt49uyZt7e39A7bRMRms4OCgnbv3p2RkdHzIQEAdAg5LQAAwOvjcDi6/9WvXz9Fh9PHxcXFBQQE9J525Ld+/fo5c+ZIr6AeNWqUk5NTdna2t7e39KZTgwYNUsgILWPChAljx45tXa6qqrp+/XrmpCsAgN4G62kBAACgp1VXV8fGxmZnZxsZGTk5OUm214qJiXn69Km2traPj091dfXp06dFIpGBgQGXy42Pj3d3d2exWEePHh02bJirq2thYeGVK1dWrVqVkJBw8+bNd999d9myZcxvFjrVjkAgOH78+NKlS4cMGdIdL5ucnHzt2rUTJ05IF7LZ7PPnz0+YMIHP5wcGBm7ZsoUpV1FRUVH5f0MO7XUUERUUFFy6dGnNmjVZWVmXL182NjZeuHCh5Nni4uIbN24UFhY6ODh8+OGHb/4WM2bMWLt27aVLl+bOnfvmrQEAdCGM0wIAAECPSk9Pd3BwUFNT8/X1raioMDc3l8wEdnV1PXHixI4dO4hIR0fH29t727ZtBw8eJCI9PT1LS0sNDQ1TU1MjI6Pw8HBLS0uqjELZAAAHUElEQVQ/P7/Vq1efOXPmwYMHa9ascXR0FIlEnWqHiPh8/tdffx0VFdVN7/vdd9/Z29u3Pt1XT0+Pz+dra2tv27bt6tWrneqomJgYOzu7tWvXHjp0aN++fUlJSd7e3nv27GHuxsfHb9++3cbGxszMzN3d3dfXt0texMHBITAwsEuaAgDoQshpAQAAoOcIhcIFCxbMmTNn7ty5+vr6GzZscHNzW758eVZWFlPBzMxMUllHR2fUqFHMZ2tra319fU1NzWnTpllbWy9cuNDFxaW+vv7zzz8PCQm5du3ali1bUlJSQkNDO9UOEXl6ep47d+7TTz/tpld+8ODBsGHD2rw1bty4U6dOEdGiRYuk94uijjrK1dV12bJlRDR+/PjQ0NCYmBhbW9uLFy8SUU1NjY+Pz/79+21sbDw8PLhcbnBwcFJS0pu/iIWFRUZGhlAofPOmAAC6EHJaAAAA6Dk3btzIycmZPHmypGTmzJlCoTAkJESex6V3TtLS0mKz2RYWFszlxo0b2Wy2nNvztmjH09Oz9ThqlxAKhXl5eQYGBu1VmDt37qZNm1rsF0VydBQzy1qy/NXc3PzPP/8kooiIiLq6On9/f19fX19f35KSkpEjRz558uTN34XD4TQ2NnZJUwAAXQjraQEAAKDnMMOM0mf5Tp06lYiys7PleVzGbsD9+/c3NDQsKyt7w3a61osXL5qammTvH8bj8dLT02NiYry9vWfNmsUUdrajVFVVmb2mMjMzDQwMDh8+3FWvIMEEU1hYaG5u3uWNAwC8NozTAgAAQM8ZOHAgEd27d09SYmJioqampqenJ8/jMnLRhoaG0tLSESNGvGE7XWvo0KG6urrSA7BtBnP27NmxY8fy+Xxm0S+9QUepqqrm5uYy64q71suXL4lIep8qAIDeADktAAAA9JxJkyYRkfQM4YcPH4pEInt7e+aSzWbX19e3+SyLxWpqamqv5aSkpPr6+tmzZ79hO13OwsLi+fPn0iVisfjVq1fSJQMGDODz+RwORzIM22FHtcfKyqq2tvbIkSOSkoqKiuDg4Dd8CyIqKSlhsVjvvffemzcFANCFkNMCAABAz7GyslqyZEliYiKz+JOIfvnll9GjR0vOPnVychIIBGFhYbW1tWFhYeXl5Xl5ecwIoYGBQWlpaV5e3tOnT2tra4mosbFRkgRGR0c7OjpKclr520lNTZ04ceKdO3e66ZWnTp2akZEhXVJSUlJUVNQi5TY1NQ0PD5ccxtNhR1VVVRGRZMcmgUDQ0NAgFou5XK6RkZGfn9/evXuzs7OjoqJWrFixePFiptqKFSucnZ3/+uuv9qJluqjNXwc8e/bMyclJU1Oz830AANCNkNMCAABAjzpy5Ii3t7ezs/OpU6dCQkJiY2Nv376trq7O3PXw8Jg8efLSpUsnTJigq6trZ2dnbW3N7Ojr4eEhFovt7OxiY2O1tLSISEVFJTg42N/f39PTMz8/PyYmRvIt8reTn5//22+/dd/WR/7+/sXFxU+fPmUuo6OjPT096+rq3Nzc4uPjpWu6uLh888038nRUQkLCTz/9REQ7d+4sLS09f/783bt3q6ureTyeqqrqzZs3hw8f7u/vb25uzuPxAgICJDtgxcXFXb9+/ezZs22Gev369S+//JKI+Hz+iRMnSktLJbeEQuHly5f9/Py6smsAALoCi9lOAAAAoM/w8PAgogsXLig6kLcRi8WKjIycP39+hzUrKyszMzONjY0NDQ1b3y0rK9PX1yei+vp66YHByspKFRUVJkP77LPPQkNDhUJhQUEBh8MZMGDA67VDRFVVVW0+3kJUVBSXy32Nn52OHj2akZERFBQkT+Xnz5+/88470qHK6CgZ8vPzWSyWsbGxdGFDQ8Ply5c1NTXd3Nw61dqFCxfCw8P5fH6nniL8fQSA7odxWgAAAFAADoczZcqU9vI0JhElohYzXTkcTutDd4yMjNrLSOVsR56E9k0sX768vLz8/v378lSWTmipo46SwcTEpEVCS0QNDQ337t1zdnbuVFM5OTnh4eERERGdjQEAoAcgpwUAAACl9OrVq8bGxpqaGkUH0jEVFZWTJ0/++OOPKSkpio0kOTl5586dbHYnTnPMz8/ftWtXaGio7BOJAAAUBTktAAAAKJ/w8PBbt26JxeKvvvrq999/V3Q4HdPQ0Dh27NiQIUMUG8aMGTM6m5qqq6ufPHmSOVsIAKAX6sRv6QAAAAB6idmzZ7u4uDCfNTQ0FBuM/FpPBu79DAwMFB0CAIAsyGkBAABA+XA4HEWHAAAAvQLmHgMAAAAAAICyQk4LAAAAAAAAygo5LQAAAAAAACgrrKcFAIA+qLCwMCoqStFRvKXu3bun6BC6C/Nq+KPVKYWFha9xuC4AgPxYYrFY0TEAAAB0JQ8Pj+joaEVHAQD/v3nz5l24cEHRUQBAn4WcFgAAAAAAAJQV1tMCAAAAAACAskJOCwAAAAAAAMoKOS0AAAAAAAAoK+S0AAAAAAAAoKz+P47j2EKC+OVLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(link_prediction_model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "8011/8011 [==============================] - 7s 874us/step - loss: 0.6328 - acc: 0.7169\n",
      "Epoch 2/15\n",
      "8011/8011 [==============================] - 3s 385us/step - loss: 0.5482 - acc: 0.7907\n",
      "Epoch 3/15\n",
      "8011/8011 [==============================] - 2s 286us/step - loss: 0.4853 - acc: 0.8346\n",
      "Epoch 4/15\n",
      "8011/8011 [==============================] - 2s 262us/step - loss: 0.4405 - acc: 0.8644\n",
      "Epoch 5/15\n",
      "8011/8011 [==============================] - 2s 253us/step - loss: 0.4085 - acc: 0.8849\n",
      "Epoch 6/15\n",
      "8011/8011 [==============================] - 2s 262us/step - loss: 0.3862 - acc: 0.9049\n",
      "Epoch 7/15\n",
      "8011/8011 [==============================] - 2s 267us/step - loss: 0.3711 - acc: 0.9185\n",
      "Epoch 8/15\n",
      "8011/8011 [==============================] - 2s 266us/step - loss: 0.3604 - acc: 0.9290\n",
      "Epoch 9/15\n",
      "8011/8011 [==============================] - 2s 273us/step - loss: 0.3543 - acc: 0.9360\n",
      "Epoch 10/15\n",
      "8011/8011 [==============================] - 2s 263us/step - loss: 0.3508 - acc: 0.9371\n",
      "Epoch 11/15\n",
      "8011/8011 [==============================] - 2s 265us/step - loss: 0.3437 - acc: 0.9440\n",
      "Epoch 12/15\n",
      "8011/8011 [==============================] - 2s 265us/step - loss: 0.3382 - acc: 0.9512\n",
      "Epoch 13/15\n",
      "8011/8011 [==============================] - 2s 265us/step - loss: 0.3336 - acc: 0.9542\n",
      "Epoch 14/15\n",
      "8011/8011 [==============================] - 2s 299us/step - loss: 0.3312 - acc: 0.9576\n",
      "Epoch 15/15\n",
      "8011/8011 [==============================] - 2s 276us/step - loss: 0.3266 - acc: 0.9592\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fc48c34e8b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = data_arrange(link_prediction_train, feature_names, attrs=ATTRS)\n",
    "link_prediction_model.fit(x, y, epochs=N_EPOCH) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node1</th>\n",
       "      <th>node2</th>\n",
       "      <th>goal</th>\n",
       "      <th>node_1_attrs</th>\n",
       "      <th>node_2_attrs</th>\n",
       "      <th>RAI</th>\n",
       "      <th>JC</th>\n",
       "      <th>AAI</th>\n",
       "      <th>PA</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>590</td>\n",
       "      <td>1016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.015471</td>\n",
       "      <td>0.031111</td>\n",
       "      <td>0.030359</td>\n",
       "      <td>0.067153</td>\n",
       "      <td>0.898570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2526</td>\n",
       "      <td>2717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021450</td>\n",
       "      <td>0.916677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>736</td>\n",
       "      <td>926</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.185652</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.139193</td>\n",
       "      <td>0.000701</td>\n",
       "      <td>0.940292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>2486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2375</td>\n",
       "      <td>2868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.061884</td>\n",
       "      <td>0.005556</td>\n",
       "      <td>0.053847</td>\n",
       "      <td>0.241273</td>\n",
       "      <td>0.550134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node1  node2  goal                                       node_1_attrs  \\\n",
       "0    590   1016   1.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "1   2526   2717   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2    736    926   1.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "3   2017   2486   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "4   2375   2868   1.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                        node_2_attrs       RAI        JC  \\\n",
       "0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.015471  0.031111   \n",
       "1  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.000000  0.000000   \n",
       "2  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.185652  0.350000   \n",
       "3  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.000000  0.000000   \n",
       "4  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.061884  0.005556   \n",
       "\n",
       "        AAI        PA      prob  \n",
       "0  0.030359  0.067153  0.898570  \n",
       "1  0.000000  0.021450  0.916677  \n",
       "2  0.139193  0.000701  0.940292  \n",
       "3  0.000000  0.000140  0.000294  \n",
       "4  0.053847  0.241273  0.550134  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predicting the links\n",
    "\n",
    "x, _ = data_arrange(link_prediction_test, feature_names, train=False, attrs=ATTRS)\n",
    "\n",
    "prob = link_prediction_model.predict(x).squeeze()\n",
    "prob = pd.Series(prob, name='prob')\n",
    "\n",
    "link_proba = link_prediction_test.join(prob)\n",
    "link_proba.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "\n",
    "def plot_auc(df, x='goal', y='prob'):\n",
    "    fig, ax = plt.subplots(1)\n",
    "\n",
    "    fpr, tpr, _ = roc_curve(df[x], df[y])\n",
    "    sn.lineplot(x=fpr, y=tpr, ax=ax)\n",
    "\n",
    "    ax.legend([f'ROC curve, AUC = {auc(fpr, tpr):.4f}'])\n",
    "    ax.set_xlabel('False positive rate')\n",
    "    ax.set_ylabel('True positive rate')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG: findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
      "DEBUG: findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
      "DEBUG: findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
      "DEBUG: findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
      "DEBUG: findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
      "DEBUG: findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
      "DEBUG: findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
      "DEBUG: findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
      "DEBUG: findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
      "DEBUG: findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font '.SF Compact Text' (SFCompactText.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF NS Display Condensed' (SFNSDisplayCondensed-Medium.otf) normal normal 600 condensed>) = 10.44\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Buhid' (NotoSansBuhid-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXIntegralsSm' (STIXIntSmReg.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF NS Display Condensed' (SFNSDisplayCondensed-Thin.otf) normal normal 400 condensed>) = 10.25\n",
      "DEBUG: findfont: score(<Font 'Bradley Hand' (Bradley Hand Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Damascus' (Damascus.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Lao Sangam MN' (Lao Sangam MN.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Superclarendon' (SuperClarendon.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Palatino' (Palatino.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Zapfino' (Zapfino.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Al Nile' (Al Nile.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Gill Sans' (GillSans.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Thonburi' (Thonburi.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF NS Display Condensed' (SFNSDisplayCondensed-Ultralight.otf) normal normal 400 condensed>) = 10.25\n",
      "DEBUG: findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.otf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Baskerville' (Baskerville.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Andale Mono' (Andale Mono.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Wingdings' (Wingdings.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Comic Sans MS' (Comic Sans MS.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Kharoshthi' (NotoSansKharoshthi-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.Helvetica Neue DeskInterface' (HelveticaNeueDeskInterface.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Charter' (Charter.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Menlo' (Menlo.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Lydian' (NotoSansLydian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Apple Braille' (Apple Braille Outline 8 Dot.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'New Peninim MT' (NewPeninimMT.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Phoenician' (NotoSansPhoenician-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Krungthep' (Krungthep.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Mukta Mahee' (MuktaMahee.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Cypriot' (NotoSansCypriot-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Coptic' (NotoSansCoptic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Arial Narrow' (Arial Narrow.ttf) normal normal 400 condensed>) = 10.25\n",
      "DEBUG: findfont: score(<Font 'AppleMyungjo' (AppleMyungjo.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Chalkduster' (Chalkduster.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Tahoma' (Tahoma.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Yi' (NotoSansYi-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Kaithi' (NotoSansKaithi-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Carian' (NotoSansCarian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hiragino Sans' (ヒラギノ角ゴシック W1.ttc) normal normal 200 normal>) = 10.24\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Armenian' (NotoSansArmenian.ttc) normal normal 900 normal>) = 10.525\n",
      "DEBUG: findfont: score(<Font 'Farisi' (Farisi.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXIntegralsUp' (STIXIntUpBol.otf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Times New Roman' (Times New Roman Italic.ttf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hiragino Sans GB' (Hiragino Sans GB.ttc) normal normal 300 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font 'Diwan Thuluth' (Diwan Thuluth.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Kohinoor Telugu' (KohinoorTelugu.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Sinhala Sangam MN' (Sinhala Sangam MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Syriac' (NotoSansSyriac-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Apple Braille' (Apple Braille Pinpoint 8 Dot.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Buginese' (NotoSansBuginese-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF NS Display Condensed' (SFNSDisplayCondensed-Semibold.otf) normal normal 700 condensed>) = 10.535\n",
      "DEBUG: findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.otf) italic normal 700 normal>) = 11.335\n",
      "DEBUG: findfont: score(<Font '.SF NS Text Condensed' (SFNSTextCondensed-Regular.otf) normal normal 400 condensed>) = 10.25\n",
      "DEBUG: findfont: score(<Font 'Times New Roman' (Times New Roman Bold Italic.ttf) italic normal 700 normal>) = 11.335\n",
      "DEBUG: findfont: score(<Font '.SF NS Display Condensed' (SFNSDisplayCondensed-Light.otf) normal normal 500 condensed>) = 10.344999999999999\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Myanmar' (NotoSansMyanmar.ttc) normal normal 900 normal>) = 10.525\n",
      "DEBUG: findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.otf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font '.SF NS Rounded' (SFNSRounded.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'DIN Alternate' (DIN Alternate Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Devanagari Sangam MN' (Devanagari Sangam MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Gujarati Sangam MN' (Gujarati Sangam MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Muna' (Muna.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.New York' (NewYorkItalic.ttf) italic normal 425 normal>) = 11.07375\n",
      "DEBUG: findfont: score(<Font 'Courier New' (Courier New Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Times New Roman' (Times New Roman Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Avenir Next' (Avenir Next.ttc) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.otf) italic normal 700 normal>) = 11.335\n",
      "DEBUG: findfont: score(<Font 'Heiti TC' (STHeiti Medium.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Myanmar MN' (Myanmar MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Chakma' (NotoSansChakma-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.otf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Tamil MN' (Tamil MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Courier New' (Courier New Bold Italic.ttf) italic normal 700 normal>) = 11.335\n",
      "DEBUG: findfont: score(<Font 'Avenir' (Avenir.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Geeza Pro' (GeezaPro.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF Compact Display' (SFCompactDisplay.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Tamil Sangam MN' (Tamil Sangam MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Arial' (Arial Italic.ttf) italic normal 400 normal>) = 7.413636363636363\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Glagolitic' (NotoSansGlagolitic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'PT Serif' (PTSerif.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Malayalam Sangam MN' (Malayalam Sangam MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Ol Chiki' (NotoSansOlChiki-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.otf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Brush Script MT' (Brush Script.ttf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'Georgia' (Georgia Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'DecoType Naskh' (DecoTypeNaskh.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noteworthy' (Noteworthy.ttc) normal normal 300 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font 'Apple Symbols' (Apple Symbols.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF Compact Rounded' (SFCompactRounded.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Bodoni Ornaments' (Bodoni Ornaments.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Imperial Aramaic' (NotoSansImperialAramaic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Kohinoor Bangla' (KohinoorBangla.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Meetei Mayek' (NotoSansMeeteiMayek-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'ITF Devanagari' (ITFDevanagari.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hiragino Sans' (ヒラギノ角ゴシック W0.ttc) normal normal 100 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Avestan' (NotoSansAvestan-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Sinhala MN' (Sinhala MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Papyrus' (Papyrus.ttc) normal normal 400 condensed>) = 10.25\n",
      "DEBUG: findfont: score(<Font '.SF NS Text Condensed' (SFNSTextCondensed-Medium.otf) normal normal 500 condensed>) = 10.344999999999999\n",
      "DEBUG: findfont: score(<Font 'Seravek' (Seravek.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Tagalog' (NotoSansTagalog-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.otf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Sukhumvit Set' (SukhumvitSet.ttc) normal normal 250 normal>) = 10.1925\n",
      "DEBUG: findfont: score(<Font 'Khmer Sangam MN' (Khmer Sangam MN.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Chalkboard SE' (ChalkboardSE.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Saurashtra' (NotoSansSaurashtra-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Arial Narrow' (Arial Narrow Bold Italic.ttf) italic normal 700 condensed>) = 11.535\n",
      "DEBUG: findfont: score(<Font 'Arial' (Arial Bold Italic.ttf) italic normal 700 normal>) = 7.698636363636363\n",
      "DEBUG: findfont: score(<Font '.SF NS Mono' (SFNSMonoItalic.ttf) italic normal 300 normal>) = 11.145\n",
      "DEBUG: findfont: score(<Font 'Skia' (Skia.ttf) normal normal 5 normal>) = 10.42525\n",
      "DEBUG: findfont: score(<Font 'Kannada MN' (Kannada MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Bamum' (NotoSansBamum-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.otf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'Silom' (Silom.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Bodoni 72 Smallcaps' (Bodoni 72 Smallcaps Book.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Georgia' (Georgia Bold Italic.ttf) italic normal 700 normal>) = 11.335\n",
      "DEBUG: findfont: score(<Font 'Euphemia UCAS' (EuphemiaCAS.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Verdana' (Verdana Italic.ttf) italic normal 400 normal>) = 4.6863636363636365\n",
      "DEBUG: findfont: score(<Font 'Oriya MN' (Oriya MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF NS Mono' (SFNSMono.ttf) normal normal 300 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Tai Tham' (NotoSansTaiTham-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Serif Balinese' (NotoSerifBalinese-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Webdings' (Webdings.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.Keyboard' (Keyboard.ttf) normal normal 100 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Gujarati MT' (GujaratiMT.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Kailasa' (Kailasa.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Verdana' (Verdana.ttf) normal normal 400 normal>) = 3.6863636363636365\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Oriya' (NotoSansOriya.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF NS Text Condensed' (SFNSTextCondensed-Heavy.otf) normal normal 800 condensed>) = 10.629999999999999\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Lepcha' (NotoSansLepcha-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Apple Braille' (Apple Braille Pinpoint 6 Dot.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hiragino Sans' (ヒラギノ角ゴシック W3.ttc) normal normal 300 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Kannada' (NotoSansKannada.ttc) normal normal 900 normal>) = 10.525\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Old Persian' (NotoSansOldPersian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF NS Display Condensed' (SFNSDisplayCondensed-Regular.otf) normal normal 600 condensed>) = 10.44\n",
      "DEBUG: findfont: score(<Font 'Trebuchet MS' (Trebuchet MS Italic.ttf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'Chalkboard' (Chalkboard.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Brahmi' (NotoSansBrahmi-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Waseem' (Waseem.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Athelas' (Athelas.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'System Font' (SFNS.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hiragino Sans' (ヒラギノ角ゴシック W9.ttc) normal normal 900 normal>) = 10.525\n",
      "DEBUG: findfont: score(<Font '.Arabic UI Display' (ArabicUIDisplay.ttc) normal normal 900 normal>) = 10.525\n",
      "DEBUG: findfont: score(<Font 'Gurmukhi Sangam MN' (Gurmukhi Sangam MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Cham' (NotoSansCham-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hiragino Maru Gothic Pro' (ヒラギノ丸ゴ ProN W4.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Javanese' (NotoSansJavanese-Regular.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Phosphate' (Phosphate.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXIntegralsUp' (STIXIntUpReg.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.otf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Limbu' (NotoSansLimbu-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Zapf Dingbats' (ZapfDingbats.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'PingFang HK' (PingFang.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Arial Unicode MS' (Arial Unicode.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Lucida Grande' (LucidaGrande.ttc) normal normal 500 normal>) = 2.872272727272727\n",
      "DEBUG: findfont: score(<Font 'Symbol' (Symbol.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXVariants' (STIXVar.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Thaana' (NotoSansThaana-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Kokonor' (Kokonor.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Plantagenet Cherokee' (PlantagenetCherokee.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Telugu Sangam MN' (Telugu Sangam MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Ogham' (NotoSansOgham-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Kefa' (Kefa.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Farah' (Farah.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'PT Mono' (PTMono.ttc) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Microsoft Sans Serif' (Microsoft Sans Serif.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Avenir Next Condensed' (Avenir Next Condensed.ttc) normal normal 700 condensed>) = 10.535\n",
      "DEBUG: findfont: score(<Font 'Noto Nastaliq Urdu' (NotoNastaliq.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Marion' (Marion.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Inscriptional Parthian' (NotoSansInscriptionalParthian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Mshtakan' (Mshtakan.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Sathu' (Sathu.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Copperplate' (Copperplate.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Georgia' (Georgia.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hiragino Sans' (ヒラギノ角ゴシック W4.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Mishafi Gold' (Mishafi Gold.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hiragino Sans' (ヒラギノ角ゴシック W5.ttc) normal normal 500 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font 'Galvji' (Galvji.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Lycian' (NotoSansLycian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Kannada Sangam MN' (Kannada Sangam MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXIntegralsUpD' (STIXIntUpDReg.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hoefler Text' (Hoefler Text.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans New Tai Lue' (NotoSansNewTaiLue-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Vai' (NotoSansVai-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Raanana' (Raanana.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Diwan Kufi' (Diwan Kufi.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Kohinoor Devanagari' (Kohinoor.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hiragino Sans' (ヒラギノ角ゴシック W2.ttc) normal normal 250 normal>) = 10.1925\n",
      "DEBUG: findfont: score(<Font 'Hiragino Sans' (ヒラギノ角ゴシック W8.ttc) normal normal 800 normal>) = 10.43\n",
      "DEBUG: findfont: score(<Font 'Shree Devanagari 714' (Shree714.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Old Turkic' (NotoSansOldTurkic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Arial Hebrew' (ArialHB.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hoefler Text' (Hoefler Text Ornaments.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hiragino Mincho ProN' (ヒラギノ明朝 ProN.ttc) normal normal 300 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font 'SignPainter' (SignPainter.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXIntegralsSm' (STIXIntSmBol.otf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Tahoma' (Tahoma Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Khmer MN' (Khmer MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Kayah Li' (NotoSansKayahLi-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Mongolian' (NotoSansMongolian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Ayuthaya' (Ayuthaya.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Corsiva Hebrew' (Corsiva.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Linear B' (NotoSansLinearB-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Trebuchet MS' (Trebuchet MS Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'American Typewriter' (AmericanTypewriter.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF NS Text Condensed' (SFNSTextCondensed-Semibold.otf) normal normal 600 condensed>) = 10.44\n",
      "DEBUG: findfont: score(<Font 'Noto Sans NKo' (NotoSansNKo-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hiragino Sans' (ヒラギノ角ゴシック W6.ttc) normal normal 600 normal>) = 10.24\n",
      "DEBUG: findfont: score(<Font 'System Font' (SFNSItalic.ttf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Old Italic' (NotoSansOldItalic-Regular.ttf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'STIXIntegralsD' (STIXIntDReg.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Gurmukhi MT' (Gurmukhi.ttf) normal normal 500 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Cuneiform' (NotoSansCuneiform-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'InaiMathi' (InaiMathi-MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXGeneral' (STIXGeneral.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Apple Braille' (Apple Braille.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Futura' (Futura.ttc) normal normal 500 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font 'Courier New' (Courier New Italic.ttf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'Arial' (Arial.ttf) normal normal 400 normal>) = 6.413636363636363\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Rejang' (NotoSansRejang-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Apple Chancery' (Apple Chancery.ttf) normal normal 0 normal>) = 10.43\n",
      "DEBUG: findfont: score(<Font 'Wingdings 2' (Wingdings 2.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Ugaritic' (NotoSansUgaritic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Courier New' (Courier New.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Inscriptional Pahlavi' (NotoSansInscriptionalPahlavi-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Shavian' (NotoSansShavian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Trebuchet MS' (Trebuchet MS.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Samaritan' (NotoSansSamaritan-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Marker Felt' (MarkerFelt.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Arial Rounded MT Bold' (Arial Rounded Bold.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF NS Text Condensed' (SFNSTextCondensed-Bold.otf) normal normal 700 condensed>) = 10.535\n",
      "DEBUG: findfont: score(<Font 'Snell Roundhand' (SnellRoundhand.ttc) normal normal 500 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font 'Herculanum' (Herculanum.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Savoye LET' (Savoye LET.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Al Bayan' (AlBayan.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Wingdings 3' (Wingdings 3.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXIntegralsD' (STIXIntDBol.otf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Cochin' (Cochin.ttc) normal normal 500 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font 'Myanmar Sangam MN' (Myanmar Sangam MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Rockwell' (Rockwell.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Tagbanwa' (NotoSansTagbanwa-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Apple SD Gothic Neo' (AppleSDGothicNeo.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Helvetica' (Helvetica.ttc) normal normal 400 normal>) = 7.322727272727273\n",
      "DEBUG: findfont: score(<Font 'Beirut' (Beirut.ttc) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Gurmukhi MN' (Gurmukhi MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Tai Le' (NotoSansTaiLe-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Arial Narrow' (Arial Narrow Bold.ttf) normal normal 700 condensed>) = 10.535\n",
      "DEBUG: findfont: score(<Font 'Helvetica Neue' (HelveticaNeue.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Malayalam MN' (Malayalam MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXIntegralsUpSm' (STIXIntUpSmBol.otf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Al Tarikh' (Al Tarikh.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Luminari' (Luminari.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Sundanese' (NotoSansSundanese-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'PT Serif Caption' (PTSerifCaption.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXIntegralsUpD' (STIXIntUpDBol.otf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Arial' (Arial Bold.ttf) normal normal 700 normal>) = 6.698636363636363\n",
      "DEBUG: findfont: score(<Font 'Arial Black' (Arial Black.ttf) normal normal 900 normal>) = 10.525\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Hanunoo' (NotoSansHanunoo-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Oriya Sangam MN' (Oriya Sangam MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Mandaic' (NotoSansMandaic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Old South Arabian' (NotoSansOldSouthArabian-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Bangla MN' (Bangla MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Bodoni 72' (Bodoni 72.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Big Caslon' (BigCaslon.ttf) normal normal 500 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font '.Aqua Kana' (AquaKana.ttc) normal normal 300 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font 'Trebuchet MS' (Trebuchet MS Bold Italic.ttf) italic normal 700 normal>) = 11.335\n",
      "DEBUG: findfont: score(<Font 'Lao MN' (Lao MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.New York' (NewYork.ttf) normal normal 425 normal>) = 10.07375\n",
      "DEBUG: findfont: score(<Font 'Devanagari MT' (DevanagariMT.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Iowan Old Style' (Iowan Old Style.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Comic Sans MS' (Comic Sans MS Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'KufiStandardGK' (KufiStandardGK.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF Compact Text' (SFCompactTextItalic.ttf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Lisu' (NotoSansLisu-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.Arabic UI Text' (ArabicUIText.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.otf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'AppleGothic' (AppleGothic.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'DIN Condensed' (DIN Condensed Bold.ttf) normal normal 700 condensed>) = 10.535\n",
      "DEBUG: findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Verdana' (Verdana Bold.ttf) normal normal 700 normal>) = 3.9713636363636367\n",
      "DEBUG: findfont: score(<Font 'Kohinoor Gujarati' (KohinoorGujarati.ttc) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font '.SF NS Display Condensed' (SFNSDisplayCondensed-Black.otf) normal normal 900 condensed>) = 10.725\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Batak' (NotoSansBatak-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF NS Display Condensed' (SFNSDisplayCondensed-Heavy.otf) normal normal 800 condensed>) = 10.629999999999999\n",
      "DEBUG: findfont: score(<Font 'Arial Unicode MS' (Arial Unicode.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF NS Text Condensed' (SFNSTextCondensed-Light.otf) normal normal 300 condensed>) = 10.344999999999999\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Syloti Nagri' (NotoSansSylotiNagri-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Tifinagh' (NotoSansTifinagh-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Impact' (Impact.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Telugu MN' (Telugu MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Osmanya' (NotoSansOsmanya-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Verdana' (Verdana Bold Italic.ttf) italic normal 700 normal>) = 4.971363636363637\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Gothic' (NotoSansGothic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Nadeem' (Nadeem.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Sana' (Sana.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Serif Myanmar' (NotoSerifMyanmar.ttc) normal normal 900 normal>) = 10.525\n",
      "DEBUG: findfont: score(<Font 'Bodoni 72 Oldstyle' (Bodoni 72 OS.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Mishafi' (Mishafi.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Bangla Sangam MN' (Bangla Sangam MN.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Songti SC' (Songti.ttc) normal normal 900 normal>) = 10.525\n",
      "DEBUG: findfont: score(<Font 'Arial Narrow' (Arial Narrow Italic.ttf) italic normal 400 condensed>) = 11.25\n",
      "DEBUG: findfont: score(<Font 'STIXIntegralsUpSm' (STIXIntUpSmReg.otf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'STIXVariants' (STIXVarBol.otf) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Noto Sans PhagsPa' (NotoSansPhagsPa-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Optima' (Optima.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Trattatello' (Trattatello.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Baghdad' (Baghdad.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Times' (Times.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Georgia' (Georgia Italic.ttf) italic normal 400 normal>) = 11.05\n",
      "DEBUG: findfont: score(<Font 'Heiti TC' (STHeiti Light.ttc) normal normal 300 normal>) = 10.145\n",
      "DEBUG: findfont: score(<Font 'Apple Braille' (Apple Braille Outline 6 Dot.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'PT Sans' (PTSans.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Didot' (Didot.ttc) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font '.SF NS Display Condensed' (SFNSDisplayCondensed-Bold.otf) normal normal 800 condensed>) = 10.629999999999999\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Egyptian Hieroglyphs' (NotoSansEgyptianHieroglyphs-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Times New Roman' (Times New Roman.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Tai Viet' (NotoSansTaiViet-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: score(<Font 'Hiragino Sans' (ヒラギノ角ゴシック W7.ttc) normal normal 700 normal>) = 10.335\n",
      "DEBUG: findfont: score(<Font 'Noto Sans Runic' (NotoSansRunic-Regular.ttf) normal normal 400 normal>) = 10.05\n",
      "DEBUG: findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/Users/andrejantonov/opt/anaconda3/lib/python3.8/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x625 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['figure.figsize'] = [8, 5]\n",
    "mpl.rcParams['figure.dpi'] = 125\n",
    "\n",
    "\n",
    "# ROC-AUC for the link prediction model\n",
    "plot_auc(link_proba, x='goal', y='prob')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3\n",
    "Classifing error of prediction into \"predictible\" and \"non-predictable\" classes using median error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node1</th>\n",
       "      <th>node2</th>\n",
       "      <th>goal</th>\n",
       "      <th>node_1_attrs</th>\n",
       "      <th>node_2_attrs</th>\n",
       "      <th>RAI</th>\n",
       "      <th>JC</th>\n",
       "      <th>AAI</th>\n",
       "      <th>PA</th>\n",
       "      <th>prob</th>\n",
       "      <th>abs_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>590</td>\n",
       "      <td>1016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.015471</td>\n",
       "      <td>0.031111</td>\n",
       "      <td>0.030359</td>\n",
       "      <td>0.067153</td>\n",
       "      <td>0.898570</td>\n",
       "      <td>0.101430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2526</td>\n",
       "      <td>2717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021450</td>\n",
       "      <td>0.916677</td>\n",
       "      <td>0.916677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>736</td>\n",
       "      <td>926</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.185652</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.139193</td>\n",
       "      <td>0.000701</td>\n",
       "      <td>0.940292</td>\n",
       "      <td>0.059708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>2486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2375</td>\n",
       "      <td>2868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.061884</td>\n",
       "      <td>0.005556</td>\n",
       "      <td>0.053847</td>\n",
       "      <td>0.241273</td>\n",
       "      <td>0.550134</td>\n",
       "      <td>0.449866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node1  node2  goal                                       node_1_attrs  \\\n",
       "0    590   1016   1.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "1   2526   2717   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2    736    926   1.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "3   2017   2486   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "4   2375   2868   1.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                        node_2_attrs       RAI        JC  \\\n",
       "0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.015471  0.031111   \n",
       "1  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.000000  0.000000   \n",
       "2  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.185652  0.350000   \n",
       "3  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.000000  0.000000   \n",
       "4  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.061884  0.005556   \n",
       "\n",
       "        AAI        PA      prob  abs_error  \n",
       "0  0.030359  0.067153  0.898570   0.101430  \n",
       "1  0.000000  0.021450  0.916677   0.916677  \n",
       "2  0.139193  0.000701  0.940292   0.059708  \n",
       "3  0.000000  0.000140  0.000294   0.000294  \n",
       "4  0.053847  0.241273  0.550134   0.449866  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# absolute eroor of prediction\n",
    "link_proba['abs_error'] = link_proba.apply(lambda row: math.fabs(row['goal']-row['prob']), axis=1)\n",
    "link_proba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07996255159378052"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_median_error = link_proba['abs_error'].median()\n",
    "train_median_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node1</th>\n",
       "      <th>node2</th>\n",
       "      <th>goal</th>\n",
       "      <th>node_1_attrs</th>\n",
       "      <th>node_2_attrs</th>\n",
       "      <th>RAI</th>\n",
       "      <th>JC</th>\n",
       "      <th>AAI</th>\n",
       "      <th>PA</th>\n",
       "      <th>prob</th>\n",
       "      <th>abs_error</th>\n",
       "      <th>quality_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>590</td>\n",
       "      <td>1016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.015471</td>\n",
       "      <td>0.031111</td>\n",
       "      <td>0.030359</td>\n",
       "      <td>0.067153</td>\n",
       "      <td>0.898570</td>\n",
       "      <td>0.101430</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2526</td>\n",
       "      <td>2717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021450</td>\n",
       "      <td>0.916677</td>\n",
       "      <td>0.916677</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>736</td>\n",
       "      <td>926</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.185652</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.139193</td>\n",
       "      <td>0.000701</td>\n",
       "      <td>0.940292</td>\n",
       "      <td>0.059708</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>2486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2375</td>\n",
       "      <td>2868</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.061884</td>\n",
       "      <td>0.005556</td>\n",
       "      <td>0.053847</td>\n",
       "      <td>0.241273</td>\n",
       "      <td>0.550134</td>\n",
       "      <td>0.449866</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node1  node2  goal                                       node_1_attrs  \\\n",
       "0    590   1016   1.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "1   2526   2717   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2    736    926   1.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "3   2017   2486   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "4   2375   2868   1.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                        node_2_attrs       RAI        JC  \\\n",
       "0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.015471  0.031111   \n",
       "1  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.000000  0.000000   \n",
       "2  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.185652  0.350000   \n",
       "3  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.000000  0.000000   \n",
       "4  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.061884  0.005556   \n",
       "\n",
       "        AAI        PA      prob  abs_error  quality_label  \n",
       "0  0.030359  0.067153  0.898570   0.101430              0  \n",
       "1  0.000000  0.021450  0.916677   0.916677              0  \n",
       "2  0.139193  0.000701  0.940292   0.059708              1  \n",
       "3  0.000000  0.000140  0.000294   0.000294              1  \n",
       "4  0.053847  0.241273  0.550134   0.449866              0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "link_proba['quality_label'] = link_proba.apply(lambda row: 1 if row['abs_error'] <= train_median_error else 0, axis=1)\n",
    "link_proba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "Node 1 attributes (InputLayer)  (None, 2879)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Node 2 attributes (InputLayer)  (None, 2879)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 5758)         0           Node 1 attributes[0][0]          \n",
      "                                                                 Node 2 attributes[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "Topological features input (Inp (None, 4)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Dynamic representation of the t (None, 4)            23036       concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 8)            0           Topological features input[0][0] \n",
      "                                                                 Dynamic representation of the the\n",
      "__________________________________________________________________________________________________\n",
      "Hidden layer 1 (Dense)          (None, 64)           576         concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Hidden layer 2 (Dense)          (None, 64)           4160        Hidden layer 1[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "output (Dense)                  (None, 1)            65          Hidden layer 2[0][0]             \n",
      "==================================================================================================\n",
      "Total params: 27,837\n",
      "Trainable params: 27,837\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "classification_model = model(feature_names, attr_dim, attrs=ATTRS)\n",
    "classification_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(classification_model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "2447/2447 [==============================] - 3s 1ms/step - loss: 0.8353 - acc: 0.6253\n",
      "Epoch 2/15\n",
      "2447/2447 [==============================] - 1s 273us/step - loss: 0.8048 - acc: 0.6878\n",
      "Epoch 3/15\n",
      "2447/2447 [==============================] - 1s 264us/step - loss: 0.7261 - acc: 0.8202\n",
      "Epoch 4/15\n",
      "2447/2447 [==============================] - 1s 265us/step - loss: 0.6376 - acc: 0.8954\n",
      "Epoch 5/15\n",
      "2447/2447 [==============================] - 1s 272us/step - loss: 0.5781 - acc: 0.9493\n",
      "Epoch 6/15\n",
      "2447/2447 [==============================] - 1s 262us/step - loss: 0.5491 - acc: 0.9689\n",
      "Epoch 7/15\n",
      "2447/2447 [==============================] - 1s 265us/step - loss: 0.5360 - acc: 0.9796\n",
      "Epoch 8/15\n",
      "2447/2447 [==============================] - 1s 263us/step - loss: 0.5289 - acc: 0.9849\n",
      "Epoch 9/15\n",
      "2447/2447 [==============================] - 1s 264us/step - loss: 0.5239 - acc: 0.9865\n",
      "Epoch 10/15\n",
      "2447/2447 [==============================] - 1s 266us/step - loss: 0.5210 - acc: 0.9886\n",
      "Epoch 11/15\n",
      "2447/2447 [==============================] - 1s 259us/step - loss: 0.5173 - acc: 0.9898\n",
      "Epoch 12/15\n",
      "2447/2447 [==============================] - 1s 262us/step - loss: 0.5142 - acc: 0.9922\n",
      "Epoch 13/15\n",
      "2447/2447 [==============================] - 1s 296us/step - loss: 0.5132 - acc: 0.9918\n",
      "Epoch 14/15\n",
      "2447/2447 [==============================] - 1s 262us/step - loss: 0.5122 - acc: 0.9935\n",
      "Epoch 15/15\n",
      "2447/2447 [==============================] - 1s 272us/step - loss: 0.5107 - acc: 0.9943\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fc48cac0100>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = data_arrange(link_proba, feature_names, goal='quality_label', attrs=ATTRS)\n",
    "\n",
    "classification_model.fit(x, y, epochs=N_EPOCH) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# testing the classifier\n",
    "\n",
    "T = 0.5\n",
    "\n",
    "x, _ = data_arrange(classifier_test, feature_names, train=False, attrs=ATTRS)\n",
    "\n",
    "quality_probability = classification_model.predict(x).squeeze()\n",
    "link_probability = link_prediction_model.predict(x).squeeze()\n",
    "\n",
    "quality_label = [1 if i > T else 0 for i in quality_probability]\n",
    "\n",
    "classifier_test = classifier_test.join(pd.Series(quality_probability, name='predicted_quality_prob'))\n",
    "classifier_test = classifier_test.join(pd.Series(link_probability, name='predicted_link_probability'))\n",
    "classifier_test = classifier_test.join(pd.Series(quality_label, name='predicted_quality_label'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08859878778457642"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_test['true_abs_error'] = classifier_test.apply(lambda row: math.fabs(row['goal']-row['predicted_link_probability']), axis=1)\n",
    "test_median_error = classifier_test['true_abs_error'].median()\n",
    "test_median_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node1</th>\n",
       "      <th>node2</th>\n",
       "      <th>goal</th>\n",
       "      <th>node_1_attrs</th>\n",
       "      <th>node_2_attrs</th>\n",
       "      <th>RAI</th>\n",
       "      <th>JC</th>\n",
       "      <th>AAI</th>\n",
       "      <th>PA</th>\n",
       "      <th>predicted_quality_prob</th>\n",
       "      <th>predicted_link_probability</th>\n",
       "      <th>predicted_quality_label</th>\n",
       "      <th>true_abs_error</th>\n",
       "      <th>true_quality_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1328</td>\n",
       "      <td>2258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.16268195837426763, 0.0, 0.0,...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.681386</td>\n",
       "      <td>6.641684e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>6.641684e-02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>1448</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>0.994837</td>\n",
       "      <td>3.275313e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3.275313e-02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>218</td>\n",
       "      <td>2676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.372436</td>\n",
       "      <td>1.707638e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>1.707638e-03</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>720</td>\n",
       "      <td>2639</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003571</td>\n",
       "      <td>0.001814</td>\n",
       "      <td>2.478519e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2.478519e-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>490</td>\n",
       "      <td>1294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.081...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.999756</td>\n",
       "      <td>1.817624e-09</td>\n",
       "      <td>1</td>\n",
       "      <td>1.817624e-09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node1  node2  goal                                       node_1_attrs  \\\n",
       "0   1328   2258   0.0  (0.0, 0.0, 0.0, 0.16268195837426763, 0.0, 0.0,...   \n",
       "1     69   1448   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2    218   2676   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "3    720   2639   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "4    490   1294   0.0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                        node_2_attrs  RAI   JC  AAI        PA  \\\n",
       "0  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.0  0.0  0.0  0.000494   \n",
       "1  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.0  0.0  0.0  0.000275   \n",
       "2  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.0  0.0  0.0  0.000494   \n",
       "3  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  0.0  0.0  0.0  0.003571   \n",
       "4  (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.081...  0.0  0.0  0.0  0.000110   \n",
       "\n",
       "   predicted_quality_prob  predicted_link_probability  \\\n",
       "0                0.681386                6.641684e-02   \n",
       "1                0.994837                3.275313e-02   \n",
       "2                0.372436                1.707638e-03   \n",
       "3                0.001814                2.478519e-01   \n",
       "4                0.999756                1.817624e-09   \n",
       "\n",
       "   predicted_quality_label  true_abs_error  true_quality_label  \n",
       "0                        1    6.641684e-02                   1  \n",
       "1                        1    3.275313e-02                   1  \n",
       "2                        0    1.707638e-03                   1  \n",
       "3                        0    2.478519e-01                   0  \n",
       "4                        1    1.817624e-09                   1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_test['true_quality_label'] = classifier_test.apply(lambda row: 1 if row['true_abs_error'] <= test_median_error else 0, axis=1)\n",
    "classifier_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true_quality_label\n",
       "0                     1224\n",
       "1                     1224\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_test[['true_quality_label']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predicted_quality_label\n",
       "0                          1431\n",
       "1                          1017\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_test[['predicted_quality_label']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metric</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sample length</td>\n",
       "      <td>2448.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Precision</td>\n",
       "      <td>0.683382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Recall</td>\n",
       "      <td>0.567810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F1 score</td>\n",
       "      <td>0.620259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Metric        Score\n",
       "0  Sample length  2448.000000\n",
       "1      Precision     0.683382\n",
       "2         Recall     0.567810\n",
       "3       F1 score     0.620259"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "classifier_test.dropna(inplace=True)\n",
    "y_true, y_pred = classifier_test['true_quality_label'], classifier_test['predicted_quality_label']\n",
    "\n",
    "scores_df = pd.DataFrame()\n",
    "scores_df['Metric'] = pd.Series(['Sample length', 'Precision', 'Recall', 'F1 score'])\n",
    "\n",
    "scores_df['Score'] = pd.Series([\n",
    "    len(classifier_test),\n",
    "    metrics.precision_score(y_true, y_pred),\n",
    "    metrics.recall_score(y_true, y_pred),\n",
    "    metrics.f1_score(y_true, y_pred)\n",
    "])\n",
    "\n",
    "scores_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x625 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_auc(classifier_test, 'true_quality_label', 'predicted_quality_prob')"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "4ef45355e186006b0a343647c814adce907f8a3ddeb3456c78680fb6c2f6cfb7"
  },
  "kernelspec": {
   "display_name": "Python 3.8.5 64-bit ('base': conda)",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}