Linear_Autoencoder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>EJWY</th>\n",
       "      <th>VALM</th>\n",
       "      <th>EGXO</th>\n",
       "      <th>HTGR</th>\n",
       "      <th>SKRF</th>\n",
       "      <th>NNSZ</th>\n",
       "      <th>NYLC</th>\n",
       "      <th>GWID</th>\n",
       "      <th>TVUT</th>\n",
       "      <th>CJHI</th>\n",
       "      <th>...</th>\n",
       "      <th>LKKS</th>\n",
       "      <th>UOBF</th>\n",
       "      <th>VBHE</th>\n",
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       "      <th>NDYZ</th>\n",
       "      <th>QSBO</th>\n",
       "      <th>JDUB</th>\n",
       "      <th>TEVK</th>\n",
       "      <th>EZTM</th>\n",
       "      <th>Label</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.032145</td>\n",
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       "      <td>-9.658715</td>\n",
       "      <td>-6.210495</td>\n",
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       "      <td>7.457850</td>\n",
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       "      <td>8.508296</td>\n",
       "      <td>3.959194</td>\n",
       "      <td>-5.246654</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.209663</td>\n",
       "      <td>-10.340123</td>\n",
       "      <td>-7.697555</td>\n",
       "      <td>-5.932752</td>\n",
       "      <td>10.872688</td>\n",
       "      <td>0.081321</td>\n",
       "      <td>1.276316</td>\n",
       "      <td>5.281225</td>\n",
       "      <td>-0.516447</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.306217</td>\n",
       "      <td>6.649376</td>\n",
       "      <td>-0.960333</td>\n",
       "      <td>-4.094799</td>\n",
       "      <td>8.738965</td>\n",
       "      <td>-3.458797</td>\n",
       "      <td>7.016800</td>\n",
       "      <td>6.692765</td>\n",
       "      <td>0.898264</td>\n",
       "      <td>9.337643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.851793</td>\n",
       "      <td>-9.678324</td>\n",
       "      <td>-6.071795</td>\n",
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       "      <td>-8.082792</td>\n",
       "      <td>-0.557089</td>\n",
       "      <td>-7.817282</td>\n",
       "      <td>-8.686722</td>\n",
       "      <td>-6.953100</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.570842</td>\n",
       "      <td>6.985462</td>\n",
       "      <td>-1.842621</td>\n",
       "      <td>-1.569599</td>\n",
       "      <td>10.039339</td>\n",
       "      <td>-3.623026</td>\n",
       "      <td>8.957619</td>\n",
       "      <td>7.577283</td>\n",
       "      <td>1.541255</td>\n",
       "      <td>7.161509</td>\n",
       "      <td>...</td>\n",
       "      <td>1.376085</td>\n",
       "      <td>-8.971164</td>\n",
       "      <td>-5.302191</td>\n",
       "      <td>2.898965</td>\n",
       "      <td>-8.746597</td>\n",
       "      <td>-0.520888</td>\n",
       "      <td>-7.350999</td>\n",
       "      <td>-8.925501</td>\n",
       "      <td>-7.051179</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.139972</td>\n",
       "      <td>0.579422</td>\n",
       "      <td>-9.526530</td>\n",
       "      <td>-5.744928</td>\n",
       "      <td>4.834355</td>\n",
       "      <td>5.907235</td>\n",
       "      <td>-4.804137</td>\n",
       "      <td>6.798810</td>\n",
       "      <td>5.403670</td>\n",
       "      <td>-7.642857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270571</td>\n",
       "      <td>-8.640988</td>\n",
       "      <td>-8.105419</td>\n",
       "      <td>-5.079015</td>\n",
       "      <td>9.351282</td>\n",
       "      <td>0.641759</td>\n",
       "      <td>1.898083</td>\n",
       "      <td>3.904671</td>\n",
       "      <td>1.453499</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.738104</td>\n",
       "      <td>0.234729</td>\n",
       "      <td>-11.558768</td>\n",
       "      <td>-7.181332</td>\n",
       "      <td>4.189626</td>\n",
       "      <td>7.765274</td>\n",
       "      <td>-2.189083</td>\n",
       "      <td>7.239925</td>\n",
       "      <td>3.135602</td>\n",
       "      <td>-6.211390</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013973</td>\n",
       "      <td>-9.437110</td>\n",
       "      <td>-6.475267</td>\n",
       "      <td>-5.708377</td>\n",
       "      <td>9.623080</td>\n",
       "      <td>1.802899</td>\n",
       "      <td>1.903705</td>\n",
       "      <td>4.188442</td>\n",
       "      <td>1.522362</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       EJWY      VALM       EGXO      HTGR       SKRF      NNSZ      NYLC  \\\n",
       "0 -2.032145  1.019576  -9.658715 -6.210495   3.156823  7.457850 -5.313357   \n",
       "1  8.306217  6.649376  -0.960333 -4.094799   8.738965 -3.458797  7.016800   \n",
       "2  6.570842  6.985462  -1.842621 -1.569599  10.039339 -3.623026  8.957619   \n",
       "3 -1.139972  0.579422  -9.526530 -5.744928   4.834355  5.907235 -4.804137   \n",
       "4 -1.738104  0.234729 -11.558768 -7.181332   4.189626  7.765274 -2.189083   \n",
       "\n",
       "       GWID      TVUT      CJHI  ...        LKKS       UOBF      VBHE  \\\n",
       "0  8.508296  3.959194 -5.246654  ...   -2.209663 -10.340123 -7.697555   \n",
       "1  6.692765  0.898264  9.337643  ...    0.851793  -9.678324 -6.071795   \n",
       "2  7.577283  1.541255  7.161509  ...    1.376085  -8.971164 -5.302191   \n",
       "3  6.798810  5.403670 -7.642857  ...    0.270571  -8.640988 -8.105419   \n",
       "4  7.239925  3.135602 -6.211390  ...   -0.013973  -9.437110 -6.475267   \n",
       "\n",
       "       FRWU       NDYZ      QSBO      JDUB      TEVK      EZTM  Label  \n",
       "0 -5.932752  10.872688  0.081321  1.276316  5.281225 -0.516447    0.0  \n",
       "1  1.428194  -8.082792 -0.557089 -7.817282 -8.686722 -6.953100    1.0  \n",
       "2  2.898965  -8.746597 -0.520888 -7.350999 -8.925501 -7.051179    1.0  \n",
       "3 -5.079015   9.351282  0.641759  1.898083  3.904671  1.453499    0.0  \n",
       "4 -5.708377   9.623080  1.802899  1.903705  4.188442  1.522362    0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = pd.read_csv('./anonymized_data.csv')\n",
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_x = ds.iloc[:, :-1]\n",
    "data_y = ds.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>EJWY</th>\n",
       "      <th>VALM</th>\n",
       "      <th>EGXO</th>\n",
       "      <th>HTGR</th>\n",
       "      <th>SKRF</th>\n",
       "      <th>NNSZ</th>\n",
       "      <th>NYLC</th>\n",
       "      <th>GWID</th>\n",
       "      <th>TVUT</th>\n",
       "      <th>CJHI</th>\n",
       "      <th>...</th>\n",
       "      <th>WDRT</th>\n",
       "      <th>LKKS</th>\n",
       "      <th>UOBF</th>\n",
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       "      <th>0</th>\n",
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       "      <td>-9.658715</td>\n",
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       "      <td>-10.340123</td>\n",
       "      <td>-7.697555</td>\n",
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       "      <td>1.276316</td>\n",
       "      <td>5.281225</td>\n",
       "      <td>-0.516447</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.306217</td>\n",
       "      <td>6.649376</td>\n",
       "      <td>-0.960333</td>\n",
       "      <td>-4.094799</td>\n",
       "      <td>8.738965</td>\n",
       "      <td>-3.458797</td>\n",
       "      <td>7.016800</td>\n",
       "      <td>6.692765</td>\n",
       "      <td>0.898264</td>\n",
       "      <td>9.337643</td>\n",
       "      <td>...</td>\n",
       "      <td>4.341376</td>\n",
       "      <td>0.851793</td>\n",
       "      <td>-9.678324</td>\n",
       "      <td>-6.071795</td>\n",
       "      <td>1.428194</td>\n",
       "      <td>-8.082792</td>\n",
       "      <td>-0.557089</td>\n",
       "      <td>-7.817282</td>\n",
       "      <td>-8.686722</td>\n",
       "      <td>-6.953100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.570842</td>\n",
       "      <td>6.985462</td>\n",
       "      <td>-1.842621</td>\n",
       "      <td>-1.569599</td>\n",
       "      <td>10.039339</td>\n",
       "      <td>-3.623026</td>\n",
       "      <td>8.957619</td>\n",
       "      <td>7.577283</td>\n",
       "      <td>1.541255</td>\n",
       "      <td>7.161509</td>\n",
       "      <td>...</td>\n",
       "      <td>4.028944</td>\n",
       "      <td>1.376085</td>\n",
       "      <td>-8.971164</td>\n",
       "      <td>-5.302191</td>\n",
       "      <td>2.898965</td>\n",
       "      <td>-8.746597</td>\n",
       "      <td>-0.520888</td>\n",
       "      <td>-7.350999</td>\n",
       "      <td>-8.925501</td>\n",
       "      <td>-7.051179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.139972</td>\n",
       "      <td>0.579422</td>\n",
       "      <td>-9.526530</td>\n",
       "      <td>-5.744928</td>\n",
       "      <td>4.834355</td>\n",
       "      <td>5.907235</td>\n",
       "      <td>-4.804137</td>\n",
       "      <td>6.798810</td>\n",
       "      <td>5.403670</td>\n",
       "      <td>-7.642857</td>\n",
       "      <td>...</td>\n",
       "      <td>3.974559</td>\n",
       "      <td>0.270571</td>\n",
       "      <td>-8.640988</td>\n",
       "      <td>-8.105419</td>\n",
       "      <td>-5.079015</td>\n",
       "      <td>9.351282</td>\n",
       "      <td>0.641759</td>\n",
       "      <td>1.898083</td>\n",
       "      <td>3.904671</td>\n",
       "      <td>1.453499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.738104</td>\n",
       "      <td>0.234729</td>\n",
       "      <td>-11.558768</td>\n",
       "      <td>-7.181332</td>\n",
       "      <td>4.189626</td>\n",
       "      <td>7.765274</td>\n",
       "      <td>-2.189083</td>\n",
       "      <td>7.239925</td>\n",
       "      <td>3.135602</td>\n",
       "      <td>-6.211390</td>\n",
       "      <td>...</td>\n",
       "      <td>3.799633</td>\n",
       "      <td>-0.013973</td>\n",
       "      <td>-9.437110</td>\n",
       "      <td>-6.475267</td>\n",
       "      <td>-5.708377</td>\n",
       "      <td>9.623080</td>\n",
       "      <td>1.802899</td>\n",
       "      <td>1.903705</td>\n",
       "      <td>4.188442</td>\n",
       "      <td>1.522362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       EJWY      VALM       EGXO      HTGR       SKRF      NNSZ      NYLC  \\\n",
       "0 -2.032145  1.019576  -9.658715 -6.210495   3.156823  7.457850 -5.313357   \n",
       "1  8.306217  6.649376  -0.960333 -4.094799   8.738965 -3.458797  7.016800   \n",
       "2  6.570842  6.985462  -1.842621 -1.569599  10.039339 -3.623026  8.957619   \n",
       "3 -1.139972  0.579422  -9.526530 -5.744928   4.834355  5.907235 -4.804137   \n",
       "4 -1.738104  0.234729 -11.558768 -7.181332   4.189626  7.765274 -2.189083   \n",
       "\n",
       "       GWID      TVUT      CJHI    ...         WDRT      LKKS       UOBF  \\\n",
       "0  8.508296  3.959194 -5.246654    ...     4.048589 -2.209663 -10.340123   \n",
       "1  6.692765  0.898264  9.337643    ...     4.341376  0.851793  -9.678324   \n",
       "2  7.577283  1.541255  7.161509    ...     4.028944  1.376085  -8.971164   \n",
       "3  6.798810  5.403670 -7.642857    ...     3.974559  0.270571  -8.640988   \n",
       "4  7.239925  3.135602 -6.211390    ...     3.799633 -0.013973  -9.437110   \n",
       "\n",
       "       VBHE      FRWU       NDYZ      QSBO      JDUB      TEVK      EZTM  \n",
       "0 -7.697555 -5.932752  10.872688  0.081321  1.276316  5.281225 -0.516447  \n",
       "1 -6.071795  1.428194  -8.082792 -0.557089 -7.817282 -8.686722 -6.953100  \n",
       "2 -5.302191  2.898965  -8.746597 -0.520888 -7.350999 -8.925501 -7.051179  \n",
       "3 -8.105419 -5.079015   9.351282  0.641759  1.898083  3.904671  1.453499  \n",
       "4 -6.475267 -5.708377   9.623080  1.802899  1.903705  4.188442  1.522362  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    1.0\n",
       "2    1.0\n",
       "3    0.0\n",
       "4    0.0\n",
       "Name: Label, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_x = scaler.fit_transform(data_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.26590249, 0.19337622, 0.35079626, 0.174354  ,\n",
       "       0.85015373, 0.08343097, 0.69542425, 0.68843639, 0.17652479,\n",
       "       0.76120555, 0.68149417, 0.17246126, 0.32567559, 0.88534597,\n",
       "       0.61979791, 0.15089949, 0.91084242, 0.22432691, 0.18339152,\n",
       "       0.62518272, 0.12589593, 0.25006804, 0.26934915, 0.24438403,\n",
       "       0.92087766, 0.4204996 , 0.76146479, 0.89187148, 0.69449859])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 30)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_inputs = scaled_x.shape[1]\n",
    "hidden_inputs = 2\n",
    "num_outputs = num_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(num_inputs, activation=None))\n",
    "model.add(Dense(hidden_inputs, activation=None))\n",
    "model.add(Dense(num_outputs, activation=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='MSE',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 500 samples, validate on 500 samples\n",
      "Epoch 1/10\n",
      "500/500 [==============================] - 2s 4ms/step - loss: 0.4143 - acc: 0.0280 - val_loss: 0.3256 - val_acc: 0.1040\n",
      "Epoch 2/10\n",
      "500/500 [==============================] - 0s 160us/step - loss: 0.3054 - acc: 0.1160 - val_loss: 0.2786 - val_acc: 0.1120\n",
      "Epoch 3/10\n",
      "500/500 [==============================] - 0s 190us/step - loss: 0.2625 - acc: 0.1060 - val_loss: 0.2462 - val_acc: 0.1040\n",
      "Epoch 4/10\n",
      "500/500 [==============================] - 0s 170us/step - loss: 0.2338 - acc: 0.1040 - val_loss: 0.2189 - val_acc: 0.1040\n",
      "Epoch 5/10\n",
      "500/500 [==============================] - 0s 180us/step - loss: 0.2067 - acc: 0.1040 - val_loss: 0.1925 - val_acc: 0.1040\n",
      "Epoch 6/10\n",
      "500/500 [==============================] - 0s 160us/step - loss: 0.1811 - acc: 0.1040 - val_loss: 0.1679 - val_acc: 0.1040\n",
      "Epoch 7/10\n",
      "500/500 [==============================] - 0s 210us/step - loss: 0.1577 - acc: 0.1040 - val_loss: 0.1461 - val_acc: 0.1040\n",
      "Epoch 8/10\n",
      "500/500 [==============================] - 0s 200us/step - loss: 0.1375 - acc: 0.1020 - val_loss: 0.1279 - val_acc: 0.1000\n",
      "Epoch 9/10\n",
      "500/500 [==============================] - 0s 210us/step - loss: 0.1208 - acc: 0.0800 - val_loss: 0.1131 - val_acc: 0.0720\n",
      "Epoch 10/10\n",
      "500/500 [==============================] - 0s 170us/step - loss: 0.1076 - acc: 0.0680 - val_loss: 0.1016 - val_acc: 0.0480\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2dbfd4dbdd8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(scaled_x,scaled_x, batch_size=32, epochs=10, validation_data=[scaled_x,scaled_x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 30)                930       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 62        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 30)                90        \n",
      "=================================================================\n",
      "Total params: 1,082\n",
      "Trainable params: 1,082\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.keras.backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = K.function([model.layers[0].input], [model.layers[1].output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_output = outputs([scaled_x])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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5dfgMPo/69jJt6Ew2L93B8D/fzNV9Nb6zHvf0ac+s0X9ishgBgdVuYeicN3TJ5WzQnfulRqYAocWbAVASL6sp1yNCCAwGgRK6vonBeOlfXIuUKqwZ+mmYTY54ypk0FEXBmerky5ensvD7FSh+Pw1v/x/Pju5KsTJFQq6ZPuxnZgyfFXg7Obb3BAu+Xcq4jR9mu+FIURRevnUwJ/afwp+xCLx73T5eajGIr/d8FiJJkHQqBbfGIqbH5SXx1Pm1pEXfr+DsieSAYwfwOD1sXrqd3ev35WrGLYTgmRGPc+9zd7B5yXbiCsdQr01tjKYwf1c6gB6WufSYaoLU8CzYwNrusptzLZByNpXZX85j2tCf2bpiJ9ntshZC0Oz+xoHdpucwW0zc1umWPLUrPTkdl0baoBYGg4HSlUponrPaLQgheOW2t1kwbSluhxuv28e/v6/juUav40wLVm88tvcEX7/9Y8CxA3jdPpJOJfNKy8G8//ho1s3fqPk5/ffPFs6eSAw49nP4PD7mTV0U0r5Wi2ohKZUAFpuZ2i2qB37fsnwHLs1Fa8nONRcmUFasTBFad2lOw9v/pzv2CNCd+yVGGKIg7g3ABpx7hbSBsQQi6tF8tOzqZPPS7XQp14dxr07l68HfM6D9uwy+fyRbV+xk3fyNmrK1fcd2p2TF4thjbVhsZuwxNspWL03PDx7PE5t2rdtLjzqv8ECxbtxf8CkG3TeClDOpOV5XpVElzeN+v8K8qYs4tucEXk/wLlhnmpOF351fqzm29wS96r0W4pwBfF4/B7YcZsG0pbzd8QO+eGlKSJvj+05qpjS6nR4O7zwWcrxW82rUal49oLED6kJx7VurU/OWqoFjpSoVx2Izh1xvMBq5odzl38R1PaKHZS4DhqhHkKYqSMc3oJwGayuE/SHV8etEjM/n550HhgVJ8rrS3aycvZa1czditprwe/10H/kYd/dqh9Gozu7iCscyYfNHbFiwmcM7j1GuRhnqtKwRiNc60134vf4cKyq5nW4Wfb+CbSt3UapSCdo/1RKvx8ert70dpAy55s8NvNbmHb5c/0G2MeGsC5DnsNjMbFm2Q9Nhu9Ld7PnvvLLj5y9OjkiV0pXu5o8Jf3Nnz7bcWK104HiFuuU0bbRFW6nWOPThI4Tg3d/689ekf/hr8kKEENzetRXtn2oZ1E/7p25j+nszgzTwDUYDBQrHUq9NrRzt1bl4dOd+mRCWughL3fw246pm15LX8LjSCVnDkKoWyjnFw7HPT2L8q1/ToXsbenz4BBarGYPBQP22dajftk7gssRTyXzYdSzr529CAjdWK81rk5+l4v/Kh4ydfDqF5xq9TlJCMq50Nxa7hWnv/cRtjzbD580S0vD6ObbnJNtX7c42PbBC3fKsm7cpRKnR7/NTrUmljB2Yweds0VZuyljoBFj/92bNBU4tFEVhzZ8bgpx7lYYVqdKoIttX7go4YqPZSFzhWFo+2kyzH6PJyJ092nJnj7aZ7tnHgmlLWfrzSmILxnBnz7Z8uPBtRjwxhmN7TyAlVG9amQHf9A08dHUuLbpz17kqkN7t4F4GlM6xLagx5z8nLSApIZm3vns5tD8pefW2wRzdcwJ/hnPet+kgr7QczJRdowObjc4xZdB3nD56JuDIPU4PHqe6cKgloysMcGLfyWyd+9292jHr0zlB16tqjJW4vWsrZo3+gyO7jgcWJQ0GgS3aSqvO59cKLDaz5vhaGE1GouLOvy0qioLf52fYnDf49r2fmDt5IV63j2b3N6Lr0M7YoqxIKTm6+zhej48bq5fGYAiN5Pq8Pl5r/Q57NuxX4/4C/p6+lAZt6/DkO49QtXFFbNE2YgvGRGSnTt6QY8xdCDFJCHFKCLElzPkCQojZQoiNQoitQojQXR06OheLZwWVaqdjsUa+G9Lj9LLyt7WcPnY25NymJdtIOHwm4NjP4fP6+POrBSHtl/28KmSGDuBMc2nqsig+hQp1y2VrX+ESBfl0+VBqNa+GMAgsNjNtn7yVd2cPwGAwMGrRO7R4qCkmiwmD0UD9dnUZ8+9womLtgT7aP3WbZnERLaSE5g805tjeEwy8Zzh3RT9GB1tnutd8ieLlijH90JfMPD2ZVyb0pmCxAhzcfoSu1V6kV73X6Hvzm3Qq3ZONi7aG9Lvo+xXnHTuABF/GAvDIp8fyQrO3NPVpcoPf7+f4/pOkJqZdVD/XEznquQshWgBpwNdSypoa598ACkgp+wshigI7geJSymxFH3Q9d53cIB0/IlPeY8sqA28+Vh6pgMdtQCpwfqE6lOgCUbz3++vUbFY16PjcKQv57PmvgjJMztHm8Rb0n/p80LFOZXpy+mjoQ8JoNhJXMIaUs6n4feqDx2K3UL9tbYb80j/i+8u6MzQz5/5Gtc65nW46lelF6lltp2e1WzCajUhFMvDHV1j52xrmjJsfkvcPEFMwmjdnvESDdnXwerx0LtuL5ISUIG0ZW7SVyTtHU6RkocCxwfePZMWva8Lem/pgqsOwOW+EbZMZv9+PK91NVKw9oEz5aZ8JuNLdKH4/DdrXpf/U54gukP0aybVKnhXIllIuAUK/1ZmaALFC/ebFZLTVFtbQuaqQ0oV0/ISS9CJK6kik71D+GWNrD0JQs3E6367ZTs+3j/HEaye468lELPbQrIxzeN1eylQpGXK84v/Kawpi2aKtmqGU27u1CpmhG00GareozufrR9Kqc3NiC0ZTuGQhOg24j4E/hIaCssNgMIRdfA3n9AGsdivNH2gctt+7erXljWkv8OPJiSSeSGLe1EWajh0gLTGdtzt+wMmDCaz+YwMepzfkM/L7FOZPXYSUEk9GOCi2YHS2C8eKX2Hd/I34wujtBPr2+/nq9WncX/ApHijSlc5lezF9+ExGPDGG5ISUQEro2r/+450HR2Xbl07exNw/A34DjgGxwCNSSs13ZyFED6AHQNmyZfNgaJ1LhVTSkGceBP9xwAmYkOlTkdHPIGL6IERoKOJSIgxxED8OmfQssQUV7nzCA3gQ8e/R8qmi/DDyV9bO2xiU1meNstD2iZaauzQr1ClH7RbV2LR4W0DL3GgyElMwmjaPtwhp3+n1jmxdvpPt/+5CSonBaCC+aAH6TX2OIiUL0W/Kc3l+zzvX7OG7Eb9waPsRouKiiCscQ41mVenQvTXxRQsA6ow/8UR4Ibq18zbRa9RTAPwy5k9NAa7MuJ1upg+bScX/ldfM1vG6vSyZ+S/fvPMjXo8PIQQ31iiN2WoKyoy5ECa89g2/j58fsPH00bNMHfR9SKqm1+Nj6/IdnDhwiuLlil3UmNcyEZXZE0KUA34PE5Z5EGgGvAxUAOYDdaSUKdn1qYdlrmyU1DGQPh7QipUWRBSZhjBVvNxmIaUXPOsAP1gaBj1k9m8+yBcvT2Xbip1Ex0fT8YUOPPTqPZqLgABej5cZ7//CnxP+xuPycvO9Deg6tHPIYmpmdq7dy87Vu/F6fNRoVoXK9SuE7f9iWDVnHe8+8lHG7Pn836jZYsIaZeXTFUMpW7VUtuElUKV7vz86nrjCsTxZ+XmO7TmR49gGk4F3ZvXjvYc/0izioYXZYgIBil+GPBQMRgP/a1WT9+cODHu92+nmgSJdIx4vuoCdIb8OwGA04HF6qH5zFWxR14eIWKRhmbxw7nOA96WUSzN+/wcYIKVcnV2funO/slESOoA/GzlVY1lEkfnXnbbHv7+v4/3HR6uCcIokpmA07/42gAp1yuXZGFJKupTrTcLhM5rnhYBaLaozauE7PNtoALvWht/xaTQbmXl6MlGxdiYO+JaZn8wJ2hil3b+g3ZO34nF5WTl7bdgHR1bufe52KjeowKQ3ZuBIcaiLzTYzPq8fk9lI6col6Ta8i2Z5vpMHE+he46WId/iaLCZi4qNxO90IIVD8Ci9P6M1tYdI3ryXyLOYeAYeA1hmD3gBUAfblQb86+UlOG6z8CeC/sG3kVysnDpzivUc/Jj3ZgSPFiTPNRcLhM/RrMwS3y8OmJduYOOBbZgyfyalDCRc8TsqZVBJPhg+1SAmbFm+jW/UXOXsiZ32ic2GNh/vdS6GSBbFGZR9Sk1Kyc+1e6retQ1yROMzWyKK3W5ftoN0TLfl2/1hendSHWzo2QlEUFL+Cx+Vl36aDDHnwQ1bNWRdybaES8RFr/VijLBiMgqRTyThTXThSnLjS3Yzq9jmHdx6NqI/rgUhSIWcAK4EqQogjQohuQoheQoheGU3eBW4WQmwGFgD9pZTa1Xh1rhpE1GMg7Nk0MED2CVHXHHMnL8TvC531+jw++rcdwpt3DuP7kb/yzTs/8nS1F1ny08oLGsceY4vojejQjqMknkzO1ikaTQbmf7MYUDXoRy18m7t6ts1Wm0UIgdvp4bO+X3HqYEKQJn12HM0I+ZjMJlo82JRda/fh8wSHaNxOD+Ne+ybwu8ftZfLAGXS5sTc+ry9HB28wGrijW2tMptAHjs/nZ+7khRHZej2Q4yNZStkph/PHgHxRwJJKMkgvwhiqkqdzkdjuVWPbzp8ALeEzK5iqXG6r8pXEk0khzgrU2P3ONXsDm43OhT1GPjWWBrfX5czRs7gdHsrXKhuR4JXFZuG2Ts1Y9N3yHBcp/V4/wiAwmo0hOfug5vrv33yIvyb9w4QB3+JIcWjeQ1YSMkn1RorX42P/lkOUr1kWv9/PqUPac7zMcf/B941k05JtgQLcwiAwGA0YjAK/T0Fmyeyx2i143T78Gno4fq+fpIRsl/quK65K4TDpP4Fypgvy1M3IhJYoCbcjvZtzvjCv7VBSkO5/kb5rLzwhhMBQ4F0oMhsMZYBzi1UWwIaIH4UQ19c28gbt62LWEMPye/2ajtBgEHSv/iK96/fj5ZaDeKh4d82QhBZ9x3anUYd6mK3mHGezVruFu3q009zMZIu2YhCCz/pOIuV0akSOPaqAHYs1fHppOCw2cyCcZDQaiS9WQLNdoeLqgvX+zQfZvPS8Ywe1upLFaqZOy5ohjh1UaWG30625KcoWY6PJXfVzbfe1ylUnPyClH3m2c0aKXsYX1b8PefYJKPI3wnhhZdMiGltJRDq+A896VafduxWEVS2bZ6qMKDguovGl4gDPKhBmsDS67GmFucFgqogsOhfcC5Du5WC8Acw3I13fI5PfAGNRRPQzCFv7/Db1knNg62FNJ160bBFOHgiNsTvTXSGiXu8+8hHjN46iZIXi2Y5ltVsZ/NOrnDmeSMLh03hdXj7qOY4jGkqNil/h6aGPsmPNbvZtPBAIoxiMBuwxNras2BHxDlGT2UjdVjVZ88eGkHPCIDBbwqc8+jw+KtU7r8vz2MAHmNB/WsjYiaeS2LhoK6cOn9bMNHI53HhdXuwxtpDPz2Qxhq1UVaH2jTS9O8d1xuuGq2/m7lmZUeQiywxE+pDOny7ZsNJ/HJlwO6R9Dp7F4N0AeECmAi7wbUMmPZ9TNyjOP5CnmiKTX0Em9VX/35NtYlG+I4QJYWuPocAQhP1+SHoGnL+Cchy8m5DJ/VDSvspvMy8p6SkOZgybFTKbNJqMVG1UUbuWp0Yimt/r548Jf0c8buESBanaqBK1WlTn+THdQhZDrVEW2j3Zkui4KEbOH8TtT7ciKtaOxWbm5nsa8tnq9zV31mpxLs//7LFEzTi72Wpi3H8fMuvsFIqUKqTWUw1ca8DvU3i0VA+GPDyKM8cTuafP7ZozaZ/Hz1t3Dyc1MU1zI5nFZqZuq5qUqVoqSDbYardQpFQhtJYjTBYTrTrfouu8Z+Lqc+7+o6C5R8oN/oOXbFiZ+iHIZLTzvgF84N2M9IfPI5a+w5A8AHCqtVRlGshUZGIPpHJ5NTOkkop0L0N6N2db7CLkurRxai3YzA9X6YS00UgZqqV+rbB/8yHNrBG/z8+KX9dQrUklzDYzFpsZW7QNs9WkqWfu8/pJOKKd4pgT9drU5uWJvSlYPB6z1Yw1ykKrzs3p9Pr9AETF2un7+TP8mvw1cxzTGfzzqxQrU4QKtctl26/RZCC6QBSPDriPTq93ZP/mQ5rfCY/Ty4u3vMXYFybRbXgXHnjxTsrVKIM1yoLM+Cw8Li8rflnN801ex+v2cjrMvbrS3Xw1YBqK3x9SSMVkMXFXhqrkI/3vo0SFGyhVqTid33qA1l0FAgvaAAAgAElEQVRaaL45+L1+Us7oujOZuerCMphroTklElEI8yV8JXMvIbQOalYbTKAkg1H7lVs6f0NzcVIC7gVgv/dirYwIJX0KpI5Clc5VwFAMCk1CmCLYNexZjaa6hDCCbz+Yq4eeywVSSUGmfQauPwAT2B9AxPRAiPzdoFK4RMGw+eFet4/tK3fT9vEWlKtRlqg4OxXrladvk1AtFVu0lQbtLlz6udWjt9Dy4ZvZt+kgo/tMZP7Xi/n7myUUL1+UAd/0pXL9CiHXdBvemX5thwTtTrXYLNRrW4uipQvT6I56NLyjLkajkdfavJNtXnvy6VT+/mYJi39YSXScnSeHPML4ft+g+M7/bfh9CmmJ6Sz9eRWGbETNPC4vRrORQsXjSTyRhJSSm2rfqD7AMjaSPTH4YZ4Y/HDgmi3LtvPDB7+G2GiNslCrRbWcP8DriKvOuQtzdaSlsRqz5lw8zgyGwmDvcAkHjsqYuWeHAUzZ1IaUKWTV51bxg5Jz5Z68QHpWQ+pHBL2BKIeQZx6GYitzTsEzltDOb5ceMFxchR0pPcgzD6lvZ2Q4ovQJSM8aKDQ1XzdMlbjpBirXv4kdq3drLkq6HG7+/mYJ3x+bECj60e6p2/j7m8UBR2SxWShx0w3c+sjFFUWXimTQvSM4cywxkMN+eMcxXmv9Dl/v+Swgt5CWlM7Uwd+z+MeV2KKsxBWOJT3FwQ1li/L44Idp3jFUk0arhJ4WXreXpAQvk96cEbQgeg5nmot9mw5wR7fW7F63L+wDw+/1k3QqmV+Sv0bxK0FFxLWo0awqtW+tzsZF2wKxfGuUlZq3VA0q86dzNYZlAFFwLMT0AUNpddYZ9Sii8M8Ikf0XQwupJCI9a5H+49k3jOqCWipP0yLADrGDESJ8loGwtlQfElpY87aeZzhk+jecfyhmPnEW6Zie4/Uiuiehn4MFrLcgjBdZPs01D/wnCTh29SB4N4L3v4vrOw9455d+1G5RI6wIpcli4siu8wuefcd256Xxvah5S1Uq1C3H44Mf4tMVQy8oEyUza+f+R3qSI0Rzxef1M3fKIkBNz+zb9A1+HzefxBNJJJ9OJTkhhXI1yjLuvw81HTtAtSbapf/C4Xa4MVlC54j2GBs3Vi9D6y7NaXxnvWw3Tvl9CharOUfHDmoW15Bf+tP7oyep2rgSVRtVpNeoJ3n3twHX3W7pnLjqZu4AQlgQMb0gplfOjcMgpUSmDgPHdyAsID1IazNE/McIjc07Iror0rcdXH+rWS7SB4YC6sPFVBYR9RTCUkdjpExYmoClWUbRiYz4tLCD/VGEqVyIfXhWIF0LwBCDsN+PMIVWCMo12awJ4PwRortke7mwNkbGDYHUoYBX/RystyEKvH/Rpknvf4BD44wfvJvBErpt/XISVyiWEfMG8uZdw1itkU3i8/goUvp8tpQQgladbqHVRRbiVhSFDQs2c3DbEcpULcWJgwmaol4ep4fj+9R/3xW/rOH00bNB2T3ndoluXro97Cx3/teLc2WbyWwiOj6Ks96koDx7n9ev7jo1GHjru5fZtW4vI5/8jEPbjwQtohoMgvrt6uTKMWtVgtIJ5ap07nmBdHwLjh8AN8iMV0b3cmTKO5qOSggTIv5jVfbWtxOMpRC5jC8LISB+DLjnq/F3YUHYHwRL8Gu64k+CpL4Zs1UXqiLjFGTc2xiiOl7YDZ/D0gB8G7XPSS3HGooh6j6k/S41fGIogDCEF9rKFcayqG8FWd4shBmMpfJmjDzgicEPs3Hh1iCRK5PFRIP2dYN0zvOC1MQ0Xr51ECcPJODz+DBZTcQVjtV8e7DF2KjZTI0771izR7O2qt/jY8/6/ZrO3eVwc3zfqVzZ5/cr3NWjLRsWbeG/Befr+XjdXt7u+CE9Rj7GPX1up3L9Cgz74w2ea/w6zjQXrnQ3tmgrtigrz3/WLVdj6kTGdevccUwmMHsO4Abn78i4IWFzz4WpLESy8BgGIQxga6+ZFy6lH5nyHjh/IDg271N/UgYjbW0RhtgLHp/oHuCYROiitABz5KJLQpjAdGPODXOBsN+DTPsUZGanZAARC9Zb83SsnNizYT9zJswn5Uwaze5tqFZEMqt/LlUaVuSt719mxJNjSEtUi1xLRWHP+v0c2nGUslXz7kH05ctTg0rteT0+vC6v6uAlgQeMyWKiSMmCNH+wCQAlKxTHFmUNEeIyWc3cUC40fHZs7wlGdf8iJNQTggCkOnv2+/xIRWHa0J/xef0YjIag690ON+P7fUvbJ1tij7ZRrGxRpu4ew4Jpy9i7cT831bqR1o+1CKospZN3XJUx9zxBCbc4KtXUvnxApn8JzploL7oCmNQ8/4vAYCwIMQOArHFfCa4fUJL7I5X0ixrjQhGGeEShbzNkDczqj7kuovAM9WFymfhj4t+82Pwt/hj/N0t+XMnHPcfx6m1v4/Wc/3cpWDwej+v8zN3vU0g4cpr+7YagKJGXAsyJxT+uDNk45fP6ST6TSpeBD1DiphsoUroQ9z53O2P+HR6I57fq1AyT1RQ0wzcYDVjtFk4cTOCf6UtxpqsPUUeqk75N32Tz0u3Z2mIwGbDYLBQuWTBwjx6XF4/Li+JXNB8MRpOBvf8dCPxuj7FzV8+29PzwSc6eTKJHnVfoUq43k9+aEbBHC5/XR3qKI1dpu5ea4/tPMmf8fP6ZsQxn2pWXBnz9ztwtjcD9DyEzWGNxEKHFHS4L6VMJfZsIaoB0LQJLY4RBe2t3OKRvD9LxE8gUhLUVMv5zSP8MvJs4/xl4wTkH6T8G9gcBI1hvvbg3hexs8p9AOn8FJQlhvQUsTRHmaogis5HKWcCY6/u8WNJTHIx9YXJQBogr3c3ejQdYOGM57Z5sCcDsL+aGbPSREtKTnWxdvpNazfMmLU/xh5ELkJJH+99PpwHaYTqz1Uz5WmWDHLbBZCA92cGk16dhNBkZ/exERswbyN7/DuByuDS3+wfZ4lPw+DyciVBzHdTc97jC6vfn+P6T/Pb5XA7vOMq+zQdJOpkc+Ax/+mg2a+b+x5h/h2E0ns9797i9fPnylAzRNoWipQvT9/PuNLw9f9dfJr05nZ8//h1hUHVwPukF781+/YrK2LlunbuI7Yf0rMqIt3tRX2IsiLh38m/VPfv6Jiqu2Uj3Aij8M8JUOqJuFcfPkPIO6n36kc4/1MVJU1XwbiE4994DnlVIz0Y1d136kQVGqWmg6WNVqV9TBURsf4T1wlP6pHsRMrEv6t4BD9I5A8wNoOCX6vqGIW9j11p4PV5OHzlLfLE47DFqaGDr8p2YzEY8WZ6xrnQ3i35YEXDuyQkpms5QCILqmUopL+r71LhDfVb8tiZoVmwwGmjQvm62/U4YMI2dq/cEzV18QQ8j9S1k4L0jiC0UnWOFpgvBYDRQpkopylYtxZZl23n9jqH4PD7NQuMel5cjO4+xdu5GGneoFzj+YdfPWf7L6sDGpRMHTvHOgx8yatEQqjQIzem/HGxctJVZo/8I2Uw16N4R/HBi4kVnQ+UV121YRpjKI4r8DvZHwVQDbB0Qhb9XZ5D5hSmS2Z4bZDIydXhEXUolLcOxuzjvxB2qfIJnDZqbqkBtL9PV/ya/AClDzuef+7YjE3up+ecXgJQeZNLLGTZlOBXpUO1xzbmgPnPLz5/8zoNFu9Gjzis8WKwbn/aZgM/rU2UEtPbICQL56wA339tQU3LA5/FR85aqLJi+lMcrPks748PcX+gpvn3vpwsK1zw7+mkK3lAAe4yaJmiLthITH0XVRpVYNmtVUKgoM399tSCisncpp1M5ujOHNOAIMZoNCMP5B47VbuG5Md2QUvJh189xpbs1Hfs5nGkudq05v4ci8VQyy2auCsmjdzs8DLx7OKePBu9+lVLiTHdd8tDNX5P/0dTqkVKyceEWjSvyh+vWuQMIY0kMBQZiKDILQ/xHCHP+7nATcQMJn0ufGQXcSyPrNJwDlw41jTEk9q6Fj9DceJcqyXAheELTCFWcSOesC+szFyz8bjmT3/oOR6pa5MHj8jJ/6iLGv/YNNZpVwarhtC12K3f1PJ9617pLc0pXLok1U2k3a5SVB166i5dbDub9x0ZzIiPzJC0pne/e/4XxmXTMI6VIqcJM2TWGZ0d35f4XOlCqUglcDjczhs9i5FOf0bls75ACFVLKiMvV+X1+Tfnc3GKNsmCLtgUpWLocbgbd+z7H9p7g1OGcSzzYoq0ULXtevjvh8OmwhUISTybT9+Y38XrUMoS/jv2TB4t14/6CT/LQDd2Y/eXci76ncHicHk1NHCBi7fvLwXXt3K84hB0sTdXskBzbRraTUCpnCLtAayoNwkbYXTk5caFSx8KE5vQYiOxhc3FMe++nkJmX2+lhzoS/UfwK7//1FvFF44iKtQdEuLq89UBQPNVis/DJsnfp+eET1G1Vk+YPNOHd3/qzas56Dm49HDKm2+Fm1ug/WP7rmlzP4G1RVto/dRulKhbnyK7jeJxePC4PzlQXyQkpDHloVFB7IQTVm1681r7ZaqJo2cKaUsKZKVmxOF2HdcbvU4Jy3aUicTu9LJu1OqwzPG+zuk5w60NNAsdKVSyeraZ8WlI6K39by5wJfzOh/zRSzqTi9ykkn05l3Kvf8NfkfyK70VzS8tFbNN/a/F4/dW6rcUnGvBB0536FIF0LkWceAc/CDKXJc2g5XivYHwjfl3cbSmIflFO3Qdq48IMayiAK/wzW1iCiQRQiV8swxgtMCTXX5bw+fCZEFCLqoQvrMxecOa5dmk4qCo4UJzfVvpHvjo5n4I+v8PLE3nx74As6Dbg/pL3VbuXuXu344O/BDPrxFQoUiePY3vCbxBS/wrAun/DWXe9rbkLKiTnj/w55KEkpObb3JCcOBOen9x3bHXusHZNFXZw0mo1qtkwOJfYy43X7qHVLNZre3SBbB9+8Y2NsditS46Hldrg5svMY9dvVCREIE0JgMIqMxd8b+XjJkMDaB0B0gWjue/6OIPXJ4L49bFm2gy9emhL6sHa4+XrwDxHfa25odl9D6retHXDwJrMRq93Ci+N7Eh2XQ3nKy8h1u6B6JSGlgkx5C01ZAOxqPrkvU1laSz1E7EvafXk2Is8+jqodk910yYAw34QwlUMU/DxwVEkepMr54kJ9sFjAXAd8m7KkiNoQsS8Ej+3doYZqvJsydN6fRWTR+5G+vcjUkRl9CdSvoEH9f9tdYL30uw6rNKzIunmhG7li4qOJLRQDqHncDdrlsOM4C2dPJOUoOetxeNi8dBuLvl9B6y7Nc9V/uHi1ECJkhntT7RuZuOUjZo3+g70b9lOx3k3c+9zt/DN9GVMHfYffF9nbw/q/N5OenJ5t+1mj/6Dv58+g+EO/b7ZoK5Xq3UTLR25mQPv3OLzzKAajAa/HR/OOjenxwRMYDCIgFJaV7u8/RsrZNOZO+idk9q/4FWaN/iOsXaePRSZ1nFsMBgODf36NDQs2s+K3tUQXsNP2iZaUrlTikox3oYj8yhtt0KCBXLt2bb6MfaWhasW3IXx+e2zGOQWQENUZEftGULZEQK4gaQDIkxGMakUUnYcwBn8hpZTgXaNm1AgTwn4PmGoh0ydC+nj1rcJQHGIHYLDffv467y7k2YcznPa575QdYl/CEP1Uxn0eQ56+K2Oh9lwbM5hrI+KGIMy50zW5UPZs2M9LLQbidngCi2/WKAsvT+hFq065c7iZSTmTSqcyPSNayGzQvg7D/3wrV/1PG/oT04fNCllgNFlM1GlZg3t6t6fpPQ2yzaJ558EPWDYz8voBRrMBgch2IRRUJ+5yuEPmEwWKxjF19xii46KQUrJ7/T5OHjxNxbrlKHHTDRHZ4PP66FbjJU4dTMjRjsyUqlyCKTtGR9z+akEIsU5KmaMErj5zvxIQMYTPWgHIohjp/EHN07eps1wpPcjEZ1SBrWwlBGxqcFMqEPd2iGOHDIkESyOEpVEWE59BRncHvJq7d2Xa6CyOHcAJaZ8iozojhAWZ9lXG7tPMbbxqOuYlrKCVlYr/K8+ny4cyZdB37Fq7jxLli9Fl4IO5nqln5p/pS5ky6LuwssBZMVtyv7bQ8cW7WDZzNUd3H1elBTJ2i/o8PtbN28jW5Tu4s2dben34ZNg+UhNzt0HN71WCMmDCoaX6aDAaeOb9LoFQhRCCyvUraMoSn0NKyao56/nti79wpLho+cjNdOjemjErhzG+3zfMm7IoomwYq91CjxGP59juWkZ37lcAwhCLNFUF37bILpBOpGM64pxzd/yQkYESfocfIgqiXwP/TlV50bsW6amAsESuLa7OCMPEbL0byTptO3XEzJxvCnL48PvUav4/7um0AqOWFjxCXZy1XFhuu/SsQ6aOAt9uVfMn5gWE7bZsr7mp9o0M+aX/BY2XlblTFjLmua8iLmVni7ZyR/fWuBxuJr81g3lTF+F1e6nVojp3dG1FnZY1ArK9mbFH2/hs1XBW/LqGOeP/5r+FW4Ji9650N7+Nncv9z3fghhtDJQbOHE9k/+ZDub6/nDY3hUPxK2z4ZzPtn24V8TUTX5/Gb2P/Cjws9mzYx7ypi/h0+Xvc0a0VcycvzLEPYRC89f3L1309VX1B9Uqh4AS0M0XCzJoyz9Cds8jWsWMBWxfwzAfXb+qirXMm8uwTKGkTL9zmzBiDN1RtWxvFMy2r8NO4giz/ZTOTB37H8t/PhsmacCENJS9oWOlZgzz7NHjXqhutfNuQSS+gOGdfUH+5Hl9KJr0xPaxjt2WkSgoh1OpMdgttn7iVJnfV5627hjP7y3mkJabjdnhY+9d/vPvIR3Qq05PPX5ysmVVjNBlp/kATzFaj5qKs1+3lycrP80nv8UG6MnOnLqRLud6knL48dQPOsfTnVThSI9uan3DkDLM+/SPoLcDt8HB4x1F+HDWb/u3ey7EPs9VE4zvrUyxTSuX1iu7crxAMxqKIovPB3JDAAiM2tBdF1aLcysmGKKdaghJanDkYv5p+6P0v00NBAi5I+wTpv7Cyb5kRMc9yLkdfShj1UhlcDiM+j/oVczs8zPikKD6v9sNKGC5MPEqmjkQrB5/UEZdFh8Tn9ZF4KnwRF6PZSJ/RT9P/6+fo+eGTfLFuJH3HPsPe/w6wY/UevFnj81LNUvlj4gJmfzkvbL8n9odXb/R7/cyfuoghD6r7EDYv3c6YZycGpSleLoRBsOj7FRG13bJsRyC7JzOudDe/fzEvSMsnKyaLEQQofsnGhZvpe/MbvH7HUDzunNc/rlV0534FIYwlMRSeBsU2gIhHu16rDfCpIRyZDMoxUM6glswLhx8cX2vH44X5osXIAHVnb9y7YChE8tkoTh4ODd/s22ZneG/t9EmpRCC9kPUaKcG7Q/ukcjZiCeOLwWQ2BbRTtEhPdjDu5al81ncSv479kx2rdiOlZP/mQxiyiWW7HW4mvT6dFb+uwa+lL5ODpIHH5WXj4m0c2X2cH0f9dknkBSLB7fCEraOaldhCMQiNN1WjyYAjzRk2PHQuldJoVN9mnGlu3A4Pm5ZsY8awmRdl/9VMjs5dCDFJCHFKCBF2X60QoqUQ4j8hxFYhRO7U/q9hpJKGdM5Cpk9CerdGfJ3wLEHdlp/1y2wAYxnUpZLMr+zejLbZ1RlNR/ufWyClVw1vXOQM3hB1L6LoCiwlZyPDbEbas0V7hi5kdmGlUKR3C/J0G4KrNmXu0KpuCrvECCF4YvBDQTtVs3KupujhHccY89xEpg+bSanKJXJ8s3CkOnn/8dH0azMEnzd4raL2reErQp3DbDGxdfl2dqzeE/H9REIkC6znsMfYqNKoYkRt/9eqJtZoa8hzy2Q2Ubl+hbBZQF63lz3r94eEqTxOD39MXBCxrdcakczcpwC3hzsphIgHPgfukVLWAC79LpSrAOnZiExogUx+B5k6CnmmM0rSS0gZQX6xcgak1iu0AjIN7Rm9APt9GZk3WljRXAyVLkh5HXn2cWRCU5TTHVEuop6rEAZiCpbhf7cWwWQKdl5Wu5+7nwqzDd0UuQiUVFKRZ58A/2G0w1YWiO6qaudfBu7u3Z6eHz4e0QYhV7oqHVC+VlnKVC2lWaIuM840F7vW7mXe1MWsm7+REU+O4f0nxlC1YcWwW/PP4XZ6GN3nK5KyCRudI75YHBabtv1mmxmDUWCxWzCZjcTER+X04gCAxWambLVSNLw9skV7o8nIBwsGc0O5YthjbETF2bHH2nh1Uh+eGfEYFnv4DKNwD0pvNqGca50cs2WklEuEEOWyadIZmCmlPJTRPnelXK5BpFSQSX0yHPE5vKrEsOt3sN8Teo3/DLjmIJXEkMXJACJK1Tr3JEBI1okfnL+o8gWeVQRLB9sgqpO6ozT1fTUUA2pR68CsPwPfFjh9B7LowmzrwWaHdP3Jqx8tZ8DDpTh+0IIwgN8naNwmjY7PaDl3c+7Gcs0h+9RRP1gjK5YupQekB2EI91DMGSEEd/dqT4mbivN2x5E5hkAMRgMnDyQwcv4gRj87kSU/rsh2k5Ar3c3Xb39PerIjsNi4fNYqqjaqxJalOzQdmzAIhCDbOLUwCOwxNrq89QD3Pns7L94ykCO7juFKd2MwGjCajLR5vAWtOt1C9WaVGd55NGv+2kDqWe10SpPFRIHCMYF0y7hCsVRrWpmzJ5IirlB1Y7XSfL3nM/b+dwBnmosqDSsEHjpDf3+D19q8ox2eyUgLzYzRZKTpPQ0Dv3s9Xlb8upZda/dQskJxWj7a7IraUZrXRLSJKcO5/y6lrKlx7hPUNI8aqLttPpVSfp1Tn9fyJibp3YI80wVNbXZzIwyFvw1u716BTOqt5p/jVsMJIhaU1Ex92MBUCQp8CGfu0+4bUKUJHgTnTxm1Xr1guxPs9yOUBKSxvJp26F4M7vAZJSL+U4Ttjtzdd8Z3SZ6+E/x7kBJ2rI/i5BELFWs6KF3Bj/qymHmRywT2+zEUGBr5OGljkGljsmlhguinMMT2C9+H4kCmvgPOjAeFsSyiwHsIS8Ow12Qm+XQKXreXwiULBcIFiqLwRodhbF2+QzPv+xxmq5nvjo4jrpAaq/f7/ezdcIDJA2ewbv4mbSlhgwg5brFb8Lq9mu3tsXYURcGtYYc1ysJrk5+lWpPKFCoeH6gw5XF7WfTdclbOXkuh4vHc1bMt5Wup1bZ2rN7Na63f0bwvW7RV3bgl1PTHzE7WaDZij7ExdvX7lKxQPOxnEimj+0zgr8kL8WZaKDVbTdRrW5vNi7fj8/rwuLxYo1T1zM/XjqBQ8YKkJqbR9+Y3OXP0LM40F7ZoK2armU+WvReonOX3+zmx/xTRBaKIL3p56wjkhkg3MeWFc/8MaAC0BuzASuBOKeUujbY9gB4AZcuWrX/w4MEcx74aUTwb4OyjaIYMTHUwFPkx8KuUXuSpphpa7jZ1hu/boc6wbfciorsghA3pWYs82w1NBy+iEAU+Ujc5+Q8hsUDSc6CcACkAnxqPlk7C74gFLG0Q1vpqAXBbW4QIr1YplURkyhBwzeP8WoDWzNoEporgP6Sm1AgBxpsQhb4OmjlL7w61iId0qeUILY2Dd+O6lyGTnst+wdTeEUM2RbuVs13Bs5rgmL0dUWQmwlRBfVD5toOSAuaaAfsSjpxhWOdP2LlmD0IICpcqxICvnw8Idfl9fhZMW8r8bxbjTHOx978DQdIAVruF5g80of/Xz4fapCh0LtuLM8eCtW9MZpNa6ShLamTWsnaZMVvUUIqWOmSlejfR5vEWLJu5irjCMdzz7B3Ua11Ls5+zJxKZ9/Vilv70L7vW7Q0jhyxAhM+HNxgEzTo2ZtAPr2ieP3X4NONe/Zo1f27AbDNz+9OtePKdhzXDRM40J/3bv8f+Ted9R9lqpRg5fxAel5c/v1rAoe1Hqda0Mm0fv5WoWDs7Vu9mWOdPOL7/VJD9QgiqNKzAmH+Hs/yX1Xzccxxuhxu/T6F2i2q8Mf1F4grH4nF5+OGDX/lr8kIUv6RV52Z0fuMBHCkOzhxLpEzVUpe1VODldO4DAJuU8u2M378C/pJS/pi1bWau5Zm74loEST20T1pvx1Dw/JZo6VmLTOyRJYSTgeVmDIWmaI+R3F8Nw2j9tdmfwFBA3dqunOkC3vVkH8bQwpzRt0kt5F1ohqY8gJT+jJn6IUJDRVkwFIYiyxC+Derbg6kimOsFOW4lfQqkfkRgQVnYwdoOUWBEoJ2UCvJsl4xCIxozZBGltteoUwsgfYdUm0OuNYL9QUR0T2Rid1COq8ekD2L7g70TT1Xuy8mDCUFO1R5jY9KOTzVDD0t//pexfSeRcjYNIaDtk7fS55OuYQs67F6/j/5th6gKi34FqShUbliRXWv2hMgaWGxmjCajZiHscjXLIqUMUag0GAXxxeJJT0oPOH5rlJXHBz3II/3uC2q7ack23rxzGIpfiUhSITtiC8Uw8/TkkOPpyek8Vv5Z0pLOh3rMVhO1W1Tn/bkDNfuSUrJj9R4ObjtC2aolqdakcuC7cepQAr989hf7Nh2kauNK3FC2KGP7fhVWAtlgNPDhgsG83mFoUDjNZDZSqX4FPl3+Hq/c9jY71+wJyD6YLCbMVhM+jx+z1YTf66fTGx3p8mZ4Mb+85HLKD/wKfCbUIpcWoDHwcR70e9UilBMZ2SIafxAhcr4Gwgt8hV8QFFFdkM45aGaLOKehWOqq8XfvRnLv2OG87T6QLuSZB6DYotAKSZ7loJwkR8eOHWL6YTAYwdJA/cmC9J+C1A8JuifpAPc88HQEqyoHK4QBCk1Bpn+boXeTROAzFHYwVVeVLgEp3eBeri4cW5siDAXVhVhhQa3ClRk/+HYjE7tmPKwyzYpTR7B7o4WkhOSQ2bLH5WXO+Pk8+fYjIffU/IEm3NKxMcmnUzLkg8Mvuno9Xg5uO0Ld1rVwpbup1rgit3VqTnSBKB4r3yf0AiF4fPBDTB38Q9AmKqvdQvf3u1CkVCF61+8XNKNW/JLEk0lBx9wON1+//QN3dKG4fBMAACAASURBVG8dCBUpisLQTp9kG17KDRa7BUVRMBiCv9Nvd/wwyLGDmue/ZflO9m48QIU65UL6EkJQrXElqjUOnmzsXr+PV1oOxuvx4vP42bR4Gz6PN1u5YcWvMH34zJD9Bj6vn30bD7Bg2lJ2r9sbpOfj8/jOFyzPCA99N3wWpSoWp+UjkReZv9REkgo5AzXUUkUIcUQI0U0I0UsI0QtASrkd+AvYBKwGJkopr5xyJPmBuQbaz027GuoIalsb7S39dkQ2sr7CXBss4crc+SG5PyT2jMzeAAbC58u7kGe7hx727Vfj+prdFVfz9U3VEfEfY4gKlc0Nwr1MLe2XFelEuv4KOiSEFUNMN0SxfxEFx6vO3NIUETsQUWgKQpjU9M5TTZHJryJT3kSeao6SPk1du5BaMzmzmmqqnCI41RTATYxtlmbowe/zM/OTOSSf1s7VF0IQX7RAto7d4/Lw4i0D+bT3eJb+9C/r5m3k+5G/smvdXgoWK8Bb372ELdpKVJydqDg71igrr3/bl4deuYcXvniG4uWKYjQZKVutFG99/zKNO9Rj5+o9WO0aOkAa92CymNj+7+7A74e2H8WRkndFn5NOJtPlxt5sWX5+X8LB7UfYtCSM5IaU7NuUu7Dtxz3H4Uxz4fOokxmvO3vHfo59mw6ihPlMtizbEZKGqoXL4eb7D37Nlb2XmkiyZTpF0OYD4IM8segKRM2ocIAoEFE9TGGuhbTUA886zu+eNIOxiLq4mbmtMEHBsWoYQII6azWDrQ3ktKBprgmeJYQ6IgAv+HahpkDmlA5mUJ1wocmQ9DL4wxTh8G1D+g4hTJk2IpkqZizcZhlDREHMc2BtBc5fkWkfq4ug9ocRUQ+p950JKf1I94oMYbGsiLA560IItYi39dYs/Tkzwl1ZMjtSR4ClPtjvzlhMPefAhFq4xHoruLVyoyUFi3pRFG0H7Up389nzX/HmDG0p5pz4c9I/HNx2JDADV/wKboeHT3qMo9m9DWl6dwN+ODGRDQs2g4T/ta4Z0D5v+/ittH381pA+d6zZE/HMWyoyIHcMalgiXNquPcaGy+HOleaM3+fn9NGzvH7HUP7P3nlHSVFtXfx3qzr35EDOAgKCiWBAUDFi1mfErKgYEEwgShDFh/hQDIiKYlYwIZhARRQEDKCoKIigIALCDEyezl33++P2hJ6u7ukZhuQ3ey3Xku6qW7d6us+9dc4+e7/6x1QyctP57qMf4h4fCoVp2TH5AmwoGGL9yg1JH18dBf8Umb4e9AfpckRHFs78qnLBSISi7bVTTvckGjtUE0BKH0bxPcjthyPz+iLzj0P6v0zqXJH5LKRcD1pz0HLAeREi+x2EiG12EbZeiNyvEGmjEam3I7JfR8t4pFaettJKT8St9hGfVVMBK1g6I7LfRLN2TfA0AGBEumGrwXYU6C2I1sXRVcG0ZDzkHw1lkyC0FkK/QulDyKJbY0aWxaPA/wnxeOvCeXYt91ED/sVx3gggvbMRaQ+oxUdrpnoD7AMQ2e+qRcL0ScSBI+t0+gw8zHRUI2xEHIfqJ3mw6M1l5r6chmRtxFfU6XZw9Fm9Ofrs3lGmFvHQpmtL0517TQghSM9Ni0pztOzUnNxW2TF8drvLzsWjzsWV6oyy1LO77DRrHytWVhNGpOBccU48nn9Gbjpdj+xc63gV0HQtxgxkV+Bw2znn1tMYMKgfDpej1sYtTdc4bIB5UXpvoTG4J4AsGgHeD1A73yAY/yALb0UGV9V6rhA2tJRb0JosQmuyDC19rMr3xjteS0W4zke4ByOsMXVr83MsHaGGYUYsanmkdF6ClvM+wqIobyqllACWaPs2ITRE1hvgPAMljWADkY3K2ZsFSS8EliKDP1e+IsNbwDcP8+YsHVJvA72J6gFIFjHywxVQjWBC6Ggp16E1WYzW9Ae0zKeVcYmWCql3oIhfFXCA3hzhvIDRs26LCmpRI++CF2m8Dlefx18v1yaAk688DqvdGhWgNU0jo2k6dpcdd5oTh9tOi45NmfTpmKinUiEE9703grScNJyRWoHdZaPPaYfRtG0urQ9sQWpWCmnZqXTv15Vx79zBHc+b1AVqIOALsjNiotHvP0eYHqPpgonz70nqKbn6fQ0Y1A9rjUK1zWGlefsm2BzWWpu+ABDQqnNzhj9zA4MnXorNbuWxJQ/Q8fAOcU/RLRquNCdXjL8w6fnuCTRK/saBDO8A/xfEBhw/suxZRObUvTGtGAj78cjSR0lIa4wLC8JSQ43R3j/B8W7T9IjQ0hHpkyB9klJjLDanvFVChiHwQ6TegNKHMS1wAnoXKHsRWapojVJrCZnPo1k7qJx62bMQ3gy23gj3DQhLpAHM1lexXGIm60I4Tq6aSmA5svRxCP8Jlk6IlGFo7quQ1m7I8leURo39RITrIoTmRtfg6LN7s2zu8qhgrmmC3qceWqeAVB0tOzVjhZmns4CNv/5tuivctjGPlZ+vwpXm4ojTD69UoKxAWlYqjy4ez50DxlepQWogwwaPLhqPt9SHO93FAYe2M513266teGPTM3z74fcUbCui+zFdmPPkPB674dnKdI/dZcMIhTl0QHeGHHZXQnomREw9ygO8NuEduvTpyMhXbuHhK6ei6zpSSsIhg9ufH0L77m0Tfl4+jx9PiYeUTDcfTV/A/BkLCQZC5LbKZufWAixWnWAgRJ+Bh3PPzGEUbitiR4Tj/uWspXz/+SryN8U21DncDi6++9wol6wWBzTj/OGnM6XafVdAaII+p/XklievIS07lZULV+FwOziw9wExxeM9jcbgHg/GVszFO6QqIu5mSMOjmC6aGyw94geN0F8R3np9gnsIaT8t6i41PQfDdiwEzCSCgkjPawh3rAmClCFk8BcoHpXEdSUSa9V19VZx5q9DeDVRO3BjM+w8E8M9DMqfoLKe4N2E9H0M2e8hLG0Qei4y5VYomxo5xgBcKo1kUwuY9H+FLLyZyrpIYAeyYCVkPoOwHx1jWFKBm5+4ht++XUd5sUc1xKQ4cKU4uPUpk4JzMp+GlHz9fhxasISivBIMw6Aor5iUDDc2h40Z97zO7Mc+QmgaQgOBYOL80Rx0dPST1fofNkYxPYyQQcnOUiZe+jgvrHkcIQTlJR62bcijSZscUjOjO3WNsMGvy37j05cXEfQH8fsCyHB1pk2ADas28cZ/Z5O/eWetTy++cj8fPvtpZb7e6rBy69RrcbgdSAm9Tz2UlAx33PP9Xj9P3jKDhW8sAaQqmAoI+dVCbnfaaNW5OZeNu4AOPdpWNk41aZNLkzYqbdTzpEN459EPeGnsrNhuYilp2y22Q3z9jxtM6xcWi84hx3bj58WreXzIdDRdQ0pJSrqbBz8aVdkEtjfQGNzjQAbWYq6RrkEdDC7qA8MzW+WrhQ4YkWLn8yoNUxOWjnGYH8nAjpAFQHThSmROR24/AqhZaApA2ZNI12XR3HTPe1D6YO2NUZUIKkleWx+EtRPCeiDS2gWCv5qcb5ZaCUL55BqvhUCWIcueQGSo97SU6zGs3aD0EbW719uA8/LKWoYseRAzuWBZOhFhj9+9m9Mii5d+f4LFb3/DHz//RfvurTn2wqNjds7J4udFqynKM2fa2BxWQsEQFzYbHHFfEvQ86WB+WPBzDPf8tv5jeHTR/XTv26XytQ+e+TQmKEkJW//YztuTP2DH1p189OxnWGwWgv4QJ11xLLc+NRjdonbSo06dwO8r/kjIc/eW+fhx4S9JSwpXL8QGfUEeGfwMLTs244I7z8KZGr9ZDmDyNdNYNnd5VIdqdfi9Abb+sR2b3ZawI/aUq49n5sTZBHxVHb5Wu4X2PdpwYO/o39mvy9ay5pt1aBYNo4ZMhMVuwWKz8Nj1z0Zx6b2lPkacdD+zNk+v1Vd3d6Ex524CKWVkV2j6LsJdV4phHa4dXA0l9wFe1dgkPSrXX3AV0kRMTPoXY85jF9QqGygsMQuDNApUV2w88w9ZSnWjbOn/FkrGRTps6/L04IsqrIrM58B+PKowa0EpWcTfwcWZHAS+qfqXUaieJELrUEYeq6DoRozy19XfOPyn+TCh9fi9fnZsLTCX2wXsTjsnXXEsQyZfwSlXHV/vwA6w5pt1BAPmn5073cXcqfMo3lFKwBck4A3w7Uc/mOrXSEMy8uQHooyh42nLGGGDF+59nTlPzCPgC+Ip8RL0B/n89cW8NHYWAL99t571KzfU2sBktVlo0bHZLgWxLeu38dSwF3jggkfjHlO8o4Slc5bXOh9vmY9VS9YkPCY1M4Unv5lIr5MOQbdo2F3q7/nQJ9G1h1fGv8XIk+/nlyW/xQR23aKRnp3Gpt82m9orBrxBfvi89vrc7kJjcDdFIIEBhiWaCtjAkJ43iaUuSkXpC0QbG0v/t4raVzO4i1TInAGWrigqpAXzQK8j0TGK7sDYcQ5G3onIvH7IgkGYFzcBkYbEhgxvUZLG5c+S2AUqAcJ/ICPpGKGlo2VOBfctKK59OVB3jXdVzFWQ5S+DUUj05+mFsojBh8gwHaK81MF52VdzZaehPHHtWZT92RdjWw+MHWcrumYD4qvZ3zLrYXP+vMWmY3fbYwJ5otRHwBdgyGF38fzdr1G8o4QBg/rFZcyEQ0YMu8fvCTB36nyklPz508akeOKaReOKcRfQsnPz5IqWcRD0h1g65ztmTXrP9P0dWwqSGt/utJHbMtaTt7y4nNmPf8TEyx5n1kPv4Upz8t959zI/8CYflr3Gbc8OiZIR2P5XPm9OmhNXCK7DIe14bOkESneWmf5NJJLSApPO8z2ExuBuCptJJ2kEEVNpaRQijbqZDScFYwfmvHXU7rP6P8umYhpYZRCht0LLmaPcnbI/AOvhinsOqN2xA9zXQsGlSmUxtBqMTUAwwg03+1U7VbNQfl9k/kBk3pEQXFnfO1WoVr+Q4W1QPg21sNSTeeKqpjjt/xJzjr8OwbXgvo5oVgwEAxZen5JNwBfkpAu2csPY9bhc+WpOoTXIwiFI/zcmY1ZBSknBtkJKCxP/sH9dtpZJVzxBeZG5Ro7D5SBQV5MNCcX5Jcx+/COGHHYXx1/SlzbdWiVFiayAt9xHOBSm+QFNzQ1FhBIEc6U6Sc1MYdw7d9K0bRMmL7yPk688HleqE7vTRrejD0yocx8PL46eydoVsb0WLTo2S6ieWQHdonPcxdGU3rxN+Vx14K28cO8bLHxjCS+MnslFLa5nxr1vRNkRVsf3n/6UkALpK/eT3TyTI8/shcMde5/hYJiDj+1W63x3FxqDuwmEEJAyhJo/fHCA/RyM/IHIvGOQeX0wCq5TqYyGurbjhGpBuBpkEKyqZV8aRRg7L4Lgd7HHARCsNAcRejM06wGIrNcQ6ZPBOQjcN0LOfPC+jVocEv1grKgmp6Zg6Q6+d0AWRs4LRKVoYlHbLksHTRlBS+lFlk6hfqyfqusJ1/lV/9Ti+GjKEGhZCPdgcF8NOEE4kTh566lc3n0mAyEkV47YhsNVc5HzIcseiTuD1V+v5eouw7is/c1c2Pw67jpxPDv/Madwzpw4O+6usHWXljy6+H66HNHJtJgej45ZgaA/RPHOUubPWMgTyx7k2omDaj2nAgLB2RlXMvKkBwiHwuiWqvOEgJQMNw8vGMukz8bw9vbn6X2q4v6701wMf+Z65ha/woflrzNl8f0cfmKPOgd4IywZdvQ9TLzsiajuUKfbwSV3nxMTSIUQWG0WNE1D0zWatMlh9bJo3cJn7niZkh2llZ+3NCRG2ODNSXO45YhRpgHe4bYn7FvY8vtWdmzZSf/zj6SJiSF5VvNMMnJjjc73FBqDexwI1zWqyUWkosSzMtW/vc9HOjgjPO7AUmTBFbvk1ymNAmTgB2Q4HxxngN6B6IXFCSlDELoKVrLozohoVrxrhqF4PNKo2jkKoSMcJ6Kl34eWegtCc0H4n9onp6WjNftN6bqEvjc5oGJhqP5VckLKSGUX6Lwq/tjWQxF6M2Q4H5l/itK6j7vQ6JA6MeIxG2fRsHSLUq8UFYE7+oZU05alDUIItNThiKbfIXI+Js/7IW9Naw0I3GlhXClx5hIy7+DN37yTu0+ZwJZ1/xD0BwkFQqxavJo7B9xn+v3Y+sd203GcKQ5GvXYr7bu34aoHLsbuskVx1e0uOzdMvoKuR3VGs8T/CQd9QVZ88iMWq4WBg09MykwE1JNHBcMm4AtiGBJNV4Gz65GdeWzJBA7u140ufTpF5dnDoTDvPPoBV3YeysWtruepYS9y0cizEaLKvUmzCLQILzwRwiGDpe99y0tj34x6fdC9/2HY09fT9qDWpOekcsy5RzB+zgg0i45EBeyNv/7NhIun8PHzCyrPWz7vR1OJAWlItv25nU9eXBj1+vqVG5g7bT4Bb/zNhmFIru4yjNXL1lK0PbbLtSivmE9f3nvGdI3BPQ6EEGgp1yGaLEc0+Q6y56h2/hjKXkgxMYI/1vkaUoYxikcj8/ojC69D5g9QnZpZrygVQusRimOdOQ0t5RZ1jlEYKRrWtsP1I73xGR+y/BVqFxTTwNobGf4HfPOJH3hdYDtWdeNaD0Zk/A/hPB12nAjel0yOtyl6Z4bqFZDFYyLiY4nuKQyl90FwBXEbs0K/YJTPVnIRgLD3jaReasJfY+GzI/SW5LZqXhmsPKU6AV+cR3K9penLHz23IEaHJBwy2LmlgFVfxRb4Dup7oOlu2ggbtOqs0n/tu7fhsSUT6D3wMNKyU2nfow13vXgz5w07nUe/HM+1Dw4ip1VsflndF+S2zsbn8XPrUffU20dVGhIENG2by5i376BJ62zWLl9Pfg1v1AcHPcZLY2exdf02dm4t5OPpnzHihPvxewKVNQUjJDFCBq27tGTESzcnvK7fG+DDGibhQghOvKw/z696lHfyXmDcu3fy6UtfEvAGYsTQpo94le/m/cBX736T0PHK7w2wZHbVU/DHzy9g2DH3xuz+zeAr9/PgJY+ZFlR95X4+ffmLWsfYXWikQtaKELJkbDWtcrPAIiC8BTBvTY8HWf4seN9HpTciPzzfZ6DloqXdDe5BsScZZSQ2w66AF0Lm3pkyvAXKn6vlfF01LKUMRXpmknghMBAZjyK0KnaLUXBFJGCbwH4KWqZKbRiedyGw0Py4GNSmk2JA6d3I0vHIrPfQbB0icsd6tfkbENqALHsMkTY66myL1cLVEy7m+ZGvKyGop5pwya15ON3VFzUHIsVcP2brun8I+s0Xnu1/xRboB406j8Vvf4231Fe5s3e47YoSWE1e4IBD2vHgh/fEnG+xWrjwrrO58K6zubnP3fzx48aoblab0855w89g/gsL2frHtl3qoDVCBtv/yue2fmMo3FaEbtUJBUL06NeVMW/dzo6thXz70Q/R6onBMMShR/6+4g+Ov+QYNvyyiTlT58eoMlbAU1q7eNnqr9eaPhmVF3m477zJgCQUDKPpAiMce5wQkN5EpU/KSzxMG/Ziwh17TZQWlqHr5r/JZFNhuwONO/daIEufAN8CVGEuzo5RhsDate6Dl7+CGc8a76z4aR69pWpsqg3ChbDFkRLwf0V8mqQbtLbgPBeyZkPZI1D+EvFlDDRwDYoK7NIog0ACrf7AEmR4G0bwT8Xnb3B4oWAgRuk0CCwjdmEKRBbVWJx980BGvHwLBxzajvmzOvLVvMMJGymABlpTSP8vwjHA9Nwe/bqaUiKNsEHnXrEesc07NGXqtxPpe24f0nNSadutFUOnDubysXW3IZ7w4Si6HNERm8OKM1X5jw57+jq6HdmZJbO/TWrXnijFU3Ef2zbk4fcG8JR4CfiC/Lx4NQ9d/iTrvv8TvQ6BzGK1oOka1z98Bf9bMI6MJubORzU556DSP3Ofmsf1h9zBNd2GJyx6Bv1Bgv5QJMcuTb/2Nqedc25WNtG/Ll2Lbq3bnldKTPn5DredgdeeUKexGhKNO/fa4J1JYqqfA+z9EHUweK6EjGNELb2op4TY3YAQGqQ9iCwaRlXnZU1YQKSBI46PqLBjvq5bwH0lwn2t4sAHViADX5P4/i3geR0j8K1iEgV/BpEVZ14V91eEzI9VMWxYSCh/nLh1CelDGgWx+vQoDfZ+/zmy6lApUSkja0J5gRMv78+sSXMI/RNSu1ZUe37vUw+jbVdzX9zWB7Zk3Dt3JntTcZHZJJ3HvppA3qZ8SgrKaNutFVab0lmprvZoBotV57iL+pLVPIMNq/7mp0W/Ru3AEyHoD7Hi058YODj5IGZ1WDnx0v6V7fkHHX0gEz4cxZ3H30fQHyAcMtAtGla7jVuevDbm/PsvfITvP/25UmjNYtMRQiRV92rZsRnlxR48pT50XcPn8RMKBBl7zsOces0Aep1ySJ3qZ5qu0fGw9tz8+NWMPPkBpCEJ+oPoVgt9Bh4eJWOwp5GUE9PuwP7ixGRs60LcQKW1ANclCPc19TKTNnYOiuSQa8DSBS3HfGdZARlcg/S8BKG/Fc3RKFCmFoQUXdF9sxIDk2VIz6vg+xL0XITrKrB2Reb1I1Yx0qL0zMMRHW2RhjLCqA9MHIvrPEQGWLpEUisN7WKvK4epjGkqN58EpDQgsEw1jmkZCOc5CD1am6cov5hXx7/Nkve+w+GycfoNJ/Of4afvtS5FgB+/+IV7z5gYN2Af1LcLj331AKAMQ14d/zbvPPph3C5QM5x9y6ms+PQntv25vVa6YpcjOvHwgrE43dG73c3r/uHtyXNZv3IjHQ9rxwV3nk2rTs2jjlm/cgPD+42OeRKx2tWTgBCCoD8Ydw6uNCdzCl/mhwU/M/78yfjKfJVcfpvDSo/+Xdmw6m8KTBhOQoBusyDDEotNR9M1UrNSePTL+2naNhdPqZcls7+lOL+Eg487iANNntYaAg1qs7c7sN8E950XmXO5rYeiZb+1S2PL4K/KLk76UakDDbAjsl5A2HrWcrY5jPLXoewx1dmKXX0jZYCqfLUTUkeAlgLFd+3S/HcrtHbgvgbhOj9iqfcz9XOUqgUiDdHk61oXZylDyMIhajGWHhRFVEdkTFH01X0cb/5vLi/e+0Z00BOq4efhBePoVkNet2RnKTf3vpui/GJ85X5sDivhUBgpzZuo7C47Z914Mut+2MCvS39DCuVYVHN9F5rg2AuP5t43htdp/nl/7+DF0TP56t1vTaWRAc666RTOv+NMtm3MZ8QJ5um+rOaZdD2iE8vmLjfdodudNka+eitP3PQcPo+foC8Qs1BYrBZcaU5uf34IR57RM26+fXdhT9rs/ash0sZGAnBFzj3iKZo2dtfHth4E2XOUsmFwuaJdOs+tUkusIwzPbCh9mKodedBk8+xVxzjOJrrQuDvgAls/CHxG7U1JOmqykeOMjVA6ERlYhMh8HlkyBnyfNPx8ZQky/yRkym1orgSa8b6PIbCcqM+WILL4TrB/Y6rTvztQXuJh/ozP+fGLX2neoSln3XxqzO7WW+7jnUc+4PPXv8Ji1el33hF079eV6T89wpL3vmXR219TuL2Yjoe256oHLqJzz9gdZlp2Ks/98ihfzFzCz1+toVWnZvQ65TBGnTrBtOvS7/Hz0fQFzC54kfJiD3/89BfjzpmEtzQ6pScNye/LzQv98VC8o4Sbeo6kZGdJ3I5Zm8NK03a5NG/flGbtmpCanULpzuh5CiHwe/188+GKuKkX3aIKxZM+Hc2tR91rSp8MBUP4PX62rt+2xwN7XdAY3GuBCsDvI8tnQHA1WLupNEyF/vmuQstRJhbGTpBboWwKsvwFyH4ToTet21jlT1K7OQdKkCzwFbs3sINyg/qepLpNLUdC6OsaL3rBvwwZXI9I+y8ybZJKPRXfS70lD8xgbIWSMRjGVoTjTNCbIEQ0J1x652D+2QolX2w/quHmEwfFO0q4secISnaqZhzdovPx858z/r276HnSIYAqNt7efyyb1myu1GD5a/VmdIuGpuuccGk/pq2YlFRQcrhUQbB6UXD6z49wefubKmsK1RHwB/GV+UjLSqXjoe3iuhe1qLEY1YYPnv6E8hJPQikEzaJz0hXHASqIP/zpWG7rP4aAP4gRMrDaLaTlplGcV5IwbWQYBi07NeO5ka8T8AXiXtPvDfDdxyu54I6z6nQvexKNbJkkICxt0NLHo+W8jZY+vuECOyBLH1FNMbIcpWxYDsZ2ZPG9dR8snJfkRUOgJ29hVj/Ylaa6EadoXBOhHzFfBHxQeDkyryfsOAF8X9Cggb36dcqmIHecpjqPy56K3t2JeA1AUhWf9wBef/BdCrcXV+abw6Ewfo+f/10zrXKuy95fwZZ1/8SIa4VDBkF/kC9mLeHNh+vv9ZnTIsuU+QNK5MwZ0WZJzUxhwKBjYmQP7C4bl94b7Q0spWThzCXc1Hskl3W4icdvnB4lfvbz4jWVhtRmSMtJ5aH5o8msxrjpeFh7Zm2ZzrBp13PJqHPJaZVNcV5JwnGsNgvte7Slc88D+GXJmsSLiSbIbWPeX7CvoDG47234PiC2eSesOl+TkPKVRikytFEda4nvFlMFHSwHIFKGo0TFdgcEOE6G9AkkpxEjiB+wJaqYGlbG1f55DTXJOPCpnHrZdKTn9aoZOi+I4+NqA2vd+hvqi2Vzl5sGp7LCMrZtUAv7L0vWKGngOPB7Asx9ctc+w8EPXRbT7Wp32Rn80KVRBhXDnr6OgYNPwO60YbHqNGmdwz1vDI+SJAb431VPMekKRafcvjGfj5//nBsPu4vCPKWl1PrAFnGZu1anhVueuCZGxx6UHMJpg0/AMAx2bC5IGNh1i8axFx3NxPlqU+VON5EAqX5du5VzbqnF43gvozG473XUr6AtpR+jaAQy7yjkznOQeUeAtSfK6q46LIBV+YTiAEsXROazCPsRYD9pF+deAVVcjL6eE6HlKMNpamMSSZJPESV7nBXlL1vfXbUXyp+t+qd9ADjOQy2IdhBuECnqs9xDO/d4vqlG2MCRov7uTdvm1ioSVl5iLlSWLHr068rEeaPpdlRnkPQIJQAAIABJREFUXGlO2h7UmpGvDOXUq6P5/1ablZsfv4Y5RS/z9vYZvLZxGkef1TvqmJkPvcdnry6KKtIaYYOyYg9znvgYgHNvPa2S1lkTQgp6naL8FdYuX8+sh97jo+mfRYm2LXxjSVzmj27RadYul/dLX2Xky0Nxp7kqr2km12Cx6rjTXdz+/BDTWsW+hMac+96G42TwziW6SUgDW6+YvG91yJLxEd/Rat2t3vdUu31gkUr16K1VN6WtD4TWgJYZzcdvkKDkAGv3avILkfvwvo+0dEakT0IW3QyBH1FBPF6XaUMWd22Q8TjCdgTS90mkUcofub6VpMXJqpmBCyEQ6eOQ7svBvwy0dLCfoDR69hDOHTqQabe9FMUW0S0aXY/oXJmSOOHSfpV67GYQAnocU4+Guxro0a8rjy99MKljLVYLKRmx37Xy4nJeGfemyRmKafPD56u4esIltOrcggc/GsXYcx7GV/FUItTueehTg3Gnu/jvpY+xbO4KQoEQFpuFZ+54mQkfjOKQ4w5K2Jtw1Fm9uHXaddjs0b+18+84k3/+3M6nryzCZrcS9Afp0b8rl4+9gM69Doi72OxLaKRC7mVIowC580Il9Ss9gAuEA5H9VlzdeGmUK7lds0BpOQgtx1wPuyaMsueh7AnMUyKpkfEtqKAbxDzFIlAPgCaBWW+Nlvu5mnNoEzK0GYquNT82hhcfcaEyfbLR4szFqV5PG43muqjyVcPzFpTcH7muHrmXJL73lm5oOXPivi1lQNVIREa9vVPrAsMweHzIdBa8thiLzYIRNmjargkPfzaGrGZV5uu/fbeOBy95jJ1bC6LkECxWHZvDxhNfP0jbbq13+3wTwe/1M6zvaP74cWPcY+xOGwjodtSB3DD5Ctr3aMM3H37Pys9XkZqVwgmX9qNlx+YsemsZk6+dFuM4lZqVwlv/PMdrE97l7f/NjapDCE3Q6fAOPPXdQwnnWZhXzN+/baF5h6bkxtHw2dNo5LnvR5AyAL7PkKE1CL09OAYm3BHK8FZk/kBM2RsiC5E2UskG2/sjInliGfgO6Z0NMoRwnqGEvmQRMv+kSKdsxffAooyis+cAPvB9DkYR0tYTioZBeGPyNyYy0Zp+W+0+vcjth2EemG3KMjAUEWuyHaUUIMufrnGfDsiYAmVPKZYREnCD4xyEvTfYj0ZoVSYc0ihC5h8XWTjrAgekDAX/AqUbZD0EkTIMYe2MlAFkyUTwvguEQcuA1NFozj2Tg83blM/aFX+S2yqLA3t3NF1YpJRs25DH9r/y+fyNr9j4y9906dOR828/k6Ym8rS7G+FQmI+fW8DHz3+OETZwpTlZ8+26pK35nCkOpn3/cAztE+Ce0x5k+fxY4T5XmpMJH4yic68O3HXi/WxctYmAL4jNYcXusjPlqwdMx9vX0chz348ghA2cpyM4PbkTtCao9ELN4C5AFqqUTUUFKvNZpO9L8LyO2qFLpH+BUptM/x8ie5ZSZQyuBHRwnIRIGx8JGE5wnlExMobWtA7BXQP7MTVei+SqzWQX9OZoOXMiao2RnXlwBVJeqTjmRj5YOyNSRyBsvZEiBVlYofjoB9+7SEoRjlOjx/XNo3Y7IRtqN29BpZl6qCJp2ZNUPtX4FyADSyFrFtLzCng/rHrPyIfikUgtS9UydjOqmz3HgxCC5h2a0rxDUw49vvtun1MiSCm57z+TWfn5qrgNSLXB7w3w5kPvcceMm0zGj39dKSV2p53Hl0zgxy9+Ye3yP2jaNpe+5/TGarfy/tOf8OakORTnl9Dx8A5KSvmITvWa476GWoO7EOIF4AwgT0oZ91sihOgNfANcJKV8p+Gm2IgYyHLQXGDUtKGLfMur7VJl4XUgw0S170uPUp90fY+w9UJkz4zY3WkIoQqj0ihGemYpeWG9NcJ9BSQtsWBTwmWpt0e9KoSGdF8HZTV3405wD1XHaCkRg/D7IjUBGUlTvY6wqq+flEFk0S1Ep5NC6p4cn0L1AG8UkZg6KdS5VOjHWEDLAu8bNc6TIL3I0ocjdoc1mUw+ZPlTeyS4726s++FP3nhwNhtX/02nw9oz6N7/0O6gqjTOhlV/8fK4t1i7fD3ND2jKZWMu4PATesQdb+3y9XUK7BabJYbZYoQNfovT/NRn4KGs+CR25+73BOh6pArUQggOG9CDwwZUzfPl+97knUc+qEzn/Lr0N+46YTyPL53AAYe0S2qu+zKSYcu8BJya6AChIsIk4JMGmFMjqkH6PsPIPwVjWzeM/BMwvB8iSyZEFfsSDxAmHn9c+r9UhwRXIwsHI/N6qWuUzkDuOB3KpkJgKXjfRO44G/T2xJpf1IC1F7gHI3LmIUx0z4X7Bki5Qe3gsSn9mNSRaK6zInP5PWIQ7kMZhJeDsRNZcHUVNTT4I+Z5ey/S8270S7Y+JDYKr9YVGxkD/4JI34HJscGf4y9yoU0JrrN/YOXCVdzWfwxL53zH5rVbWfTWMoYeMYq1kcC6/scN3Hr0vSybu5wdWwpYtXgNY89+iIWzlsQd89elazFCyRfLzSiLQhO07WYuvpb3907T1JRu1ePm9H0eP29P/iAmTx/wBnjlvl2TFdlXUOvOXUq5WAjRrpbDhgLvAr1rOa4RdYD0LUAW3UHlDjL8NxTfg9phJvtjCRFfOdKNDK1XhtgVu/1wOZQrDeyq8yK7Wu+rYOkZx5FJjSmyXkrI8hFCgH0A0nIg6G0RlvZVTwsyjCx/HnORsDD4l4LjeBIXQ9WcZWiT0sv3/1DL8WYIxD9Hb67+DjHQ6i0bsS9h6tAZUaJchiHxefyMPXsSZ910Ct9/9nNMQPR7Aky57hmm3/kKoWCYfucdwVUPXEx6jtJIz2yWgcVuMTW0SBY2h5WL7z436rUNq/7imTte4ceFv5jKCVitFrZvzKdLn9g0S/7fO0z9YaWUrF+5Ieb1/RG7nHMXQrQEzgUGUEtwF0JcD1wP0KaNOROkEVWQpZMx1XuvE+I1ERlgG4AsfRJkzTETLByhVWDtB8GvYt+z9UlM3zQKkAWDFU1T6CADSNdFkDoaAl8hi0dG0igmc5ZGVa7eeiimD53CiXCepxasnRdE7qs+9Mo4n5lwIlJuQYb+gvKniPKPFQ5EytB6XGvfQSgY4u/ftpq+V7CtiNcnvBs3QPvK/ZVBf/4LC1nxyU88/+uj2J12+p7Tm6lDZ8Sco+larQYiQkDLzi249anBdDy0feXrW//YxrC+oxM2bIWCobjdtNktsqKMTaqjdRdzp639DQ3RxPQYMFJKWeuvSEo5XUrZS0rZKzd3z1fs9zuEEz3m19x1aKDlVjPXTuJPWzJCBeukukirXUfuMH9LGhhFt2FsPxRjey+M4geQRlV6QxbdBqHfAK9KuRAAzyyVcim8IZJqivc1CoFN5bOFsCEyHo90jDpQn4UTbP3BMVBJOkhPgrEqbqU1sU1f8WCDlDsQjlMQ7usgdbzyuhVpYOuHyJqJsEbvEGVgOUbBNRh5AzAKhyGD65K81t6BbtFjzKerI9mddygYpmhHCYveUlpBdqedR764jxYdm2F32XC47eS0yqJ999o3eJpVp9PhHeh+THRX66xJcwj44ndw2102jjnvSJp3MNdncqU6OfXaATHm3XanjcvGnG96zv6GhmDL9AJmRXJeOcBpQoiQlDI+QbgRyUFvYR7gRSaKGeNFFSadyp0p600IrYXyF1SrfngT8VMSYaXbrh8AbK7bvCroijUR/AaC1Tjo3jeRoVVqXrIQAt8T6+gUhOCyWi5oA+cllUJqUkql9eK6XOnZW1oj7APAepgybQgsJ/59O9R7zv8g0u6FwHcR4xMjUp+II7ymtURzXwFEGppc54DrnLgzNryfQXG1lJp/KzKwCLLeQFi71XK/ewdCCM666RTmPDkPf5JmHfHgK/Ox+uvfOfnK4wBo36MtL619gi3r/iEcNmjTpSXvT5vPcyNfS+gSFQ6EWTbnO15qmcV1D19e+fra79abCoAJAdktszn/9jM4Z2hiaupNU67GlerkvSc+xu8NYHfaOW3wCXTumYyMx76PXd65SynbSynbSSnbAe8ANzUG9gZCynBid5ZOSLkNkbtAmWg7L4HUUYiczxBaJpQ9qvjf4b+oNdcsg5EUR81r2EEzL16pMRN9bar/4AIQXKtolpUUx7oi0iTlnaWeCgwfsmgIsnAwlD8P/i8jNE+qimpaZpyxrJAzF9HkW7T0+xDCirD3VXrumc9CxiQULdIEWmI3o+qQUkLpA0Sn0AyQHsW22Ydx1QMXM+DSfljt1lpt9xLB7rRVmnxXQAhBq84taNu1FUIITr1mAG26tMTqSMzC8nsDfPBstFF2226tTO31LHYrT303kf8MP6NW5UvdotOmayukIZUcsMfPvBmfM/Soe/DVk7K5L6HWv54QYibwNXCgEGKzEOJaIcQQIcSQ3T+9/9/QnGdA2gPKuxOUPHDqKDT3xQgtBc09SKlVui9GaG6k52XF2Ei6YSeoeOsZjyhXqQqet+sSRO4n4ByE4tOLyHt2SL1TSSbEPPTFY6R4FaVSb1WnAFkFiQqSftVQVXwH+L+hyorQC7IcWXSLckoCcA0mltVjB+fZaJb2MQ1iQlgRtt5ojlPB0pnYn4UT4Va7RmmUYpS/hlE8Aemdi5QmQUCWxmczBX+uy83vcVisFm6fPoRZW57l2v8OMtVXSQa6Va/ctceD3Wnn8WUPctuzN9DlyE4xKZLq8JX5o4qmF408B5sjem42p42+Z/eJ6tZNBJ/HzxM3PUfAF0RGdNt95X62rPuHeTM+T2qMfRmNHar7AKRRqpptjAJF3YukF6KOkeFKVkk8GPmnQ9gsr6uhgqTZ39qhqIspQyOt9BW7+CBCOBVN0vcpYEU4ByIsHapJJuxUQVY4AWsk4JrteKyInHkQ/hNZeCtVOi/1gQVTs27hRmS9irB2V80rpY+A52VFW5RBsB+PyPhfraYaMrwVWXBFVXCWIXCei0gbr+a/82KUno9X1Te0LET2O1FerFIGkdt7Ylr81tui5X5Wz3vfs5BS8tiN01nw6mIEIHSBvzwQw0zRLTqZTdMpiqg4tj2oNXe9eHOdueJSSoYeeU8l7bI6OvXswLTlkyr/XWEHOHfaJ3hKPFitFtp0a4Vu1WnbtRUX3HEm7Xsklub+adGvjD17Ep6S2FRc92O6MGXxA3Wa/55CY4fqfgIZWIksvEaxQQgANrAfDRlTo4J5bYEdUI1NpjVEG2ROh8KriT3AB55XEam3ItGRJePA+z4QQuodEOn3o6VGW6IJLQty5oP/C8V8sXRAWntB/vHx7hLp+xgtZQhkv4ssewL8ybgzmSHROWpBFEIg0u5Eptygnky0Zgi9qoAvZRACK4AAWHtH7eSF3gJyPoXg9xDOB9shlXx9o3gUyBKimsXCAWTpI4j0KhEtIaxI14XgeYvoAO8E9w31uOe9AyEEtz1zA/8ZfgY/ffELadmpONx2HrhoCqGA8im1O224M9xMWzEJu8tOOBQmNbM+T2jqesOevo7bjxtXaW+njLKtDJ06uPI4b5mXYX1H88+GPHxlPiw2nWAgxMZf/iYcCrP+hw0sfuebKBMTM7hSnXHZOimZ7nrdw76Exp37XoSUBjK/n2pfj4ITkTYO4TqvbuN530OW3BdN0UMD/QC03I8wth2I+Y5ZoDVbi1EwGALfErX7Fk5E9hyEpb3JedEwSqdA+TMm19DAdQNa2m3Vjn00IqmbqPBZc+ergaUrhP4kpvCp5SBylyBE4kyjDPyILLwe1SsgVBE1fQKa88zE5xkeZF4vzJ8a0tGaLo8+XgYjC+UHqtNWGpByPcJ90x4RGdud2Pz7Vt57ch5b1//Docd357TrTqx3QDfDlvX/8Nb/5rLuhw0ccEg7LrzrLFofWEVPfG3CO8z87+wYQ5KaaNauCa/8MTXu5y2l5MpOt7BtQ37U04jdZWfcu3fSOyIlvK+hUThsP4AyyL7MvBvS2hMte2byY0k/MrgGyl8E/0LFI0eASEVkvYawtMHYcR6Efok92dIdkfGY6kqNSavo4DwfLb32R1QZ+gu54wyTMSKKjyIN3Fcj3EMQQsfYcQ6EVpsPpncB4y+VFiGI0qVxQOYrUHiVYt9U3QBkvoxmT9xDJ6UPmXd0hIZZHQ5EzlzTBUxKf0TUbYPitps9OYgstKbfmF/TKFHMJb1lpYhbI3YNg7vfxl+ra2d4WWwW3twynbTs1LjHbP59KyNOvJ+y4nKEEIQCIS6++1wuH3tBQ065QdGYltkvkGhhTX7RNTzvQukE1E40BHobcF2IsHQE25GVKR2RNgZZcBUq+BqoXLwdkTZGdV0KG8QUCMMQ+B7peROsPRLS+ISlLdI1GDwVXaYV91CRxihRDkdGMSLtHki5EYriNP6kDkVYuiA9r0JovapDuAYptklMcNZVGqWW4I7/S8w/1xDS+16MFo4MbUYWXKQW35hGrwrYlal5HAgtDbS0xPMywdoVfzB16AzWLl+PM8XBmTeewlX3X4TF2viTtdViRlIBIai1INyqcwte2ziNX5b8RvGOUrof0yXKrm9/RuPOfS9CyjAy/xgTZoUTkTYG4aq9mUIGflQFwKgUhg56G0T2bIQWnTuUwXXI8qch+BtYu6hdtLUzMrwdmX8C5q3/VtQ+QIL9KETGVISJvooM/oosuh3Cm1G5fQ3TNAZ2RJNvQLiQhVdBoIYxtvWoiIyBepyWUirGTdk0kNvNPwitCVqT+PomAEbZsxH9erPH+TRIvRnhulTdb/B7pZYZ/pPYBcEC6OrpyNIZkfVyg+7KN6/7hxsPH4GvvOpvanfaOPqcPtzz+rAGu87+inkzPmfa8BdjZBCqw2q3csx5fehwcDvmPPkx5SVeDu7flRsmX0mb/bwDtTEts59ABlZElBsNwKeYJ9beiMynk7JvM4qGRxyZzP6ONnBfg0i5Lak8r1F0t5LXTShxYFedmXKHcnZyDwbH2SCLkfkDTHbVJhBuxTCJuEIZ/sVQ/qp6z3U5mqN/9LzKnlMiZvEajCJjak1Xmt9XuARKx4PvE8wXrwo4wHqQMk4x8hNQSlMQqbeBtQtYe+1yDn39yg1s+m0Lbbu14oBD2jHlhmf55MWFMU06VruVV/6YSk6LrDgjRcMwDH784lc2rdlMm66tOPT4g6I8TvdXGIbBw1c9xVfvfqP0YYRAGgZGyMDmtBH0Bznk2INIb5LGV+9+U9kkJQQ4U508t+pRmrTO2ct3UX80pmX2EwhbL8j9IqJZXqBa7OsSMMLbiZ/CCYDnZaSWC/b+yNIpEPwWtGyE+3pwnBl1HZH+INLSDjyvReSEzSiLfgivUf9r5COLx0Foo5LJlUkKQ8kwaFUNLpq9P9j7mx8qg1A+jYSBHQ1sR8eeaxQrvRr/IpLTmPFB8IeKs+MfJqyVvPddgbfMyz2n/Zd1P2xA0wVG2KBLn06UF3tMuy9tDitb1v2TVHAvLSzjjuPGsW1DHuFQWHmFdmjCo1/eT0rG/s0E0TSNu18ZyiWjzmX1srVkNc+k18mHUFpYxl+rN9OkTQ42h40rDrg5qugqJQR8Ad6d8iE3PnrV3ruBPYTG4L4PQGgZ4BpUv5Ptx0HwF+J6k0ovlD+DLHssUrg1lIRuyRgIb0Kk3FJ1bOBb8H+uztHbRlIStfmNeqF8BjjPJzlRMye4LqvVe1SGdyDLpoDvU/OCcyVsSrQrdUTsGIWDIbiauomHJWHsETEw2VU8c8fLrF3+R5R585pvfqf5AU3RLVpMgA/4g7RM0jlo2vAX+Xvt1mryuUH+/m0r04a/yIiXbkl47v6Ctl1b0bZrVSd1Rm46GceqfPnKhauw2q0xjJpQIMxv3+7bGj8Nhf3/Ge3/OYTrEtCyids2D+qJoLKjMwLpjRQ3VRpF+hchC4dA8CfVYRlei3m+3GwSVtCbVhMtqw6dqq+ZBvZTEal3JBzOCOcjd5ymbOxkcYLrpij2Tc7HCEt0w4oMroXg7yRthp0MhAssbREpw2s/thZIKVnw6uKowA4Q8AXZvjEfa43uS7vTRr/zjkw6JbPora9jdNFDgRCL3qpNx+ffgeYdmuL3mm94dEsSPSP/AjQG9/0cQktD5MwF93WowqcZrJjzsy0QVtrVsuQhYnfeSdZjZBDsp4LWrMYc9MgYFYuKAf55EIgfYAzvR5B/HMg40r+VcCAypqKl3oHQm8S+Hd4aoYM2BHSVKkt/BJE9F6HFp9bVBfFUFoOBEI98cR/djuqMpmu4M1ycO+x07nox1mIuHuI155ile/6NaNauCc4Uc8XPjb/+jWH8+z+HxrTMvwBCS0ekDkPaDkMWVrefU1RHLN3MDTZkIBKQqZvxdRQixtaFl4FRjEqBOEDkgMzD1I6u7DFEjL8qyNAmKB5FrbttvTWkjELYY/PslbB2VfeXEBFLPaMkMs84P3gtQxW4tdopcjK0Hlk2TWnI6O0QKTchbIfHHCeE4OBju/Hzl79GeYAKITj8hIPp3PMAHl/6YMx5yaL3qYfy3cc/YBhVg2uaoPfAw+o95v6CL2Yt5dXxb1FaYJ7O85b5KCsqJy2rYRbpfRWNO/d/EYS9PyLrVbAdpwKg/WRE9tuItLswVX60H1vVlq/Vkz2gpSsJAiOPKv68AZamxBUTC5o73UjvbBIGduGCzFfQcj9Hc56YcFpCbwbOs0is1x4G6+GInA/AeTlgpjtjgYxnkwvswdXIneer4nh4EwQWIwuuQvoWmh5/61PX4U53Y4uoItqcNlIy3dz0+NW1Xqs2DJ16Lem5aZX67A63nfTcdIY+ec0uj70v44NnPuGRwU/z91pz0xFQzU2u1H9/Q1njzv1fBmE7BJE1HQAZXIP0zIDQZrAfrwqm0gMY4DgpSg8F901Q+hDRrBSHOjYRfdAoIHbHG4gUeS2YF3rLkaE/EZYautlGCQmLn8KNsCVvQC3SJiAtXZQ0sGHGKpIQ3qDy9SlDkL73Irv9iuPsYOuJZkvOPk+W/s+EPulDloxXnz9h8L6P9L4HQqd12/N5Yc0U5s1YyPqVG+l0eHtOu+7ESnu6XUGTNrm8vO5Jvpi5lD9/3kiHg9tx/CV9cab8e4NaOBzmxdGzEhpx2102zh068P9FM9i//w7/n0L5r95OVbrBrgqQWTMRlnYxzU3CdTESL5Q9pbpUhV2JXFkPhqIbIzRHsx9NnGAsbOA4HbxvYhZUZelkROa06Jct7RLflN6xTpxyITSE+wqk8xxkXl9i528Ba091rJ4N2W8hSx5Qi6CwK0OP1DuTvh7Bn8xfN/KV8mfx7RBYTsUCKgMrSXecxKB7Jid/jTrAmeLktOsSP+H8m1BaUJawsQnggEPbc+X9F+2hGe1dNAb3PQwpg0jPbPDNVXxp54XgOK1BxaSkDCOLRxNdIPUrfrl3NiJ9TI3jDWT5C+CZoVg0lk6QOrpSq0U2+VopOZa/inmAr+a+VDloAFxXg/dtYhcAqRyQZBh885G+DwAb6J3Mx6qApUVyH0ANCC0N6bo8YupR8WQiFIXSXaU2KCwdEFkv1usagDIJCZs1cVkg9BMEVxD9ZOQF36fI4Op91p1pf0JKhhvdqscwkKrj79+21Gri8W9BY859D0JKA1lwLZT+V/3QA18ji+9Bloxq2AuF/66hDFmBkJLprTmv0slQ9mREBiEEoTVQeC0yqES9hHAg3DeDqeKiI0KBrP5ehMtuaYFizJhApCOLbkKW3KOEzvzzwfMc8Rk6OsJ1WfS8w9swSp/EKBqB9LyLjKv/AiL1Lkgdpfj7Ih3sJ0S6ZOM5TtUDruuJNQlxgOsCtWM37XgNx8ovNKJesFgtnDf89ISmH2ba7f9WNAb3PYnAVxD6mZjdm/fjhjVPFqnETZfUKAxKoxw8rxLbAepHlj1VNaTmRmQ8gQpeLlQ+3g4p1yFyPlQSBFqu2vWnjUGkjlDGGM4ziC1UOsF+bKQGUP26PtRXsiZnX0DqWIT1oKp5B1Ygd5yiZIN9c5Al9yN3nKlUGM0+EiHQ3Bej5X6G1nQ5Wua02Jx/HSGNAmS4yixcuC4E97WoBc+NEhU7HZE6MmLmYRZ0rAlsARtRV1x534VceNdZSpbABN2O6ryHZ7T30JiW2YOQ/qVxdm8SAt+AtVODXEfo2Uhbz0h+tzqX2olw12BihLdG9MZrplskhH6LHtd+LDRZrOzu8IKtP8LSWr2XMQkziLRxSKMY/EsiqpMBcF0MhON8FlZwnAGhdRDeAZZmarctS5HhfISeq4TEiu6qsTB4IfwPsvwZ027VhoQMbVL1jNAaQCD1toiMRxDWLsr0xD0YwltAb1LJspGOM6BsSuyDiRBgP3m3zvf/EzRN44pxF9Lr5EMYcdL9BP1BjLBEt+rY7NYGYSLtL2gM7nsSlZ2kNdgnwtLguzeRMQVZcD2Efo9YzQWUxIGjhimF3jyOJowAvWPsq1o61MFERAgnIvNpZHgbhP8BSweElo5RMhmVsqnxhCF0xV9PH4PcOUgF0OD3gB1ZPg0ynwO9RRyP0gB458FuDO5SBpAFl0SuH6kNhNchCy6F3C9UU5nmAi16oRZ6DmRMQxYNp3LBFQ5ExjREvbxlG5EI3Y46kGdWTuadR97nj5/+onPPDpx/+5k079B0b09tj6ExuO9BCOfZUamOKmjgOKHO40n/UmT5c0o8zHY0IuV6hK6+vELLQuS8o9I9xnawdovy+ayck5YSsYR7m+gCrB2RenOd5xQPQm8GumqYkr7PwfMScVNH9gHI8peUjntlAdcPErVjzppN3KKrSMRrTw4ytB7p/RAIIRynIKw9qt70f1FFJ40+CXwfJtQIEva+0GQZBFehul67J2ef2Ih6oVWn5gx/Zv+xNWxoNAb3PQihN4PMp5BFt6ECmwSRgsh8ps564Eb5zGheuvcvxTrJeV9dp+Ka1k5A4nSPSL0HKdKVobQsVZTDtDEIa3L87rpAGoWR+zfhzotUROYqm+5uAAAT/0lEQVR0hOaKBFcTZo4sQVCMtHaLUA9r5Dms8T0zk4FR/gKUPoZqppLI8leQrkFoaXerA8Jb43S+epGhv+O1bVVCCCuYdKzuC5AyoFhT3reVpIRjICLlZgitRZZOUlo9elNwD0VznbW3p9uIWtAY3PcwhL0fNPlaNfkIC1gOqtX3syak9EPZw0QXQUMgy5Bl0xHpY+s2J6EjUm+F1FuR0qjzfBLONbQJjEKwHogQDvB9pvLMMaQYDZxXIWw9I5OKo5MjJUqn/hYoui72fd+HSONupbRZ17mGt0LpFKIXFR943kA6z1QFXWuPSJqrBt1OuBC2fdNzM1nIwpsg8B2VT3Ce15G++ZFGtchnEv4LSsZgyBI092XxhmpENRiGwbrv/8TvDdClT0dsjuScpHYVjcF9L0Dt3nZB4yNk3r4PIQgsrf+40GCBXYZ3IotuVI5PwgKEkSkjEYQjxiQxZ4CoFlSdF0PpRKIXMAF6K4SlDdL7ZpwrWxS10hm/LiClVLrtxk6wHlolPOb/AnPJhADS92kkuPcES/fIU0PFfG2gtwL7gLjX3Nchg79ECvDVU3OBSGdvzb+XF8oeR7ouaUwr1YI/ftrI6DMmUl7sQWgCKSV3zriJ/ucftduv3Rjc90doWbE7xwro9S8YSVnhWLPrOwsV2H9BPVFEXix9CJk2Ic4ZDoT9pMp/CdcFyMCyKt9TYVEFyMypkQvE64yV8d8D5YtaeJVyW4p4zkrX5YoHT8RUPAYaFXx9IQRkzVC1Ds9sIAzOMxDuG02tB/cbBFdh3mMQp7YhfWAUgZ69O2e1XyMYCDLipPsp2VEa9frDV06lwyHtaJWkNn99Ues2TQjxghAiTwjxS5z3LxVC/Bz5b5kQYteSno2oFUJvArY+xEr8OqM6LpOFDG/DKLgGuf0Q5PZDMAouR4b+rvf8ZOgvtWOPkRn2qaJjyvUonryG6hR1gvMshK3qqyOEjpb5BCL7TUTaKET6w4jcRZXcdOE4FVNRMGkoAxNAhvMUH74aF10WDVEer9ITMQHxg/d18H8K9pMwD2Y6wnl6tbnZ0VJuQWuyEK3JIrTUu/Z/xovePI5EcpwqgtDrZfz9/wkrPvmJkD+WiRYKhZk/4/Pdfv1kdu4vAVOBV+K8vwE4VkpZKIQYCEwHkld3akS9IDIeU7S6wPJI2kNCyp2IOHZ18SBlALnzoujH78ByZMGFkLuwToVeKUOKDVT+InFdmYw8tJShSFt/pPd9FCPltMhiFQth7arke2u+bjs0wvJ5C5UeieyuU+8GLR2j6Hbl4iTsIP1I55nguhZCm4hluniR5a+iZZ+CTJ+oZIeFFsnvS0i9o9Lv9V8L2zEg0mJNXbCjdvTV6xBOcF29fz+p7AGUFpRFSS5XIBwMU5SXwISmgVBrcJdSLhZCtEvwfnXnhW+ABuznbkQ8CC0NkfUCMrxd5Y4tHVTBsq7wfwGyhOgftKF+5L754Dw36aFkyVjwfkh8uz2b6kwlol5pq/9DnjTKoimJeitIG49mPwqj5CFVuCVQxWzxvg9Y1I7TLPsg1aOz5jwDaT8afAuAENgHRLGPdgVSBqu6cm1HIPahna8QFsieqaimwVVU1jcyJiNDGxUzy9ipnrLc1yLcN+7tKe/zOPjYbhjh2BShI8VBn9N2P2OqoXPu1wLzGnjMRiSA0JvuUp6d0F8m3amA9CBDG2ul9lUebhREAmg8eWCbMr1wX1nPiVa7lpTIgishtLbqeuFNUDQcI+cz8MwklkYZBO/cOCwcAVo2UgYQwqb6AVwX7vI8o+Yc+El5ulZw+2UQmXovmvviBr3OrkDoLRDZs5BGIchgZaFZWLsjHaerRUk4GpRN9W9Gs3ZNOPPGk/lo+oJKtUq7y84BB7el7znmT6oNiQYL7kKI41HBPdZip+qY64HrAdq0adNQl27ErsDaJZK6qJEbFC6VEkkWoU1V8gIxsCuvU/dVpo1UdUZwJYT/IHohMVSRzzubWJ2cCvgg9QEoGU108JcQWIEsuAqyXmvw4CVlAFl4beQJqRpK/4u0HYqwdmnQ6+0qhEm3tBACc4/cRiTCDZOv5JDjuvPhs5/h9/g57qK+nHL1cXvEx7VBgrsQ4mDgeWCglNKsLxwAKeV0VE6eXr16JWnQ2YjdCtsxKqUR2kBVsLQoETB7HbpmLW3iBHYNHAPRUm+v1/SkDKgOXD2nKv8f+oMob7pKeCH8GypPbC5NLCxtkWnjoGQs0QVfH4RWK4VGe996zTUu/Esw78YNIL3vIKyjG/Z6jdhnIITgqDN7cdSZvfb4tXd5iyKEaAPMBi6XUv6+61NqxJ6EEBoi6w1wXqAKaiIVnOcqe746FMyElhXH1s6OSLm+zvOSUmKUPYPM64PceQZy+xEYJQ8pDXhLB9UIFQMnWLpANfXIaCjPVGHswDTxLr3qqaChIcvjLEZGxHe2EY1oeNS6cxdCzASOA3KEEJuBcUQ4eFLKZ4CxQDYwLWI4EZJS7vllqhH1htBSEenjIH3cro2Tdj9SywXPayDLVPdt2liEJVaArDZIz1v8X3v3HiRXWeZx/Pv0ZaYnmcltuEgIFwWyhGVZoQIGo5By8UKgDFKsBVvsoru1F4sCtKiyiJbi/uEF2aIWSlTcIBixQEUNlIUIArtUeQEHuZqsitYKUyQkBJLMJZO5nGf/eE/CJNMz3UmfS3fP71PVNdPd53Q/7/TU06fffs/zMPg19ptiGb4bty6s+2oovi0URdv3aaMQ5oO7LobSyfgb/8z+R++lUMuldCxeOIJwdH9gVcpK+MSStI4VTF0WSpj6qqgipKTDvOoRRfqWL1/ufX19uTy3pM/dG+ouFW1dBVGVJsc2FzviKfAhfOAL8eqc8VA4bd71WCl8lxMN3Q2DNwCFcMJX+VRs4a1YYREeDeHbzp06B249YS19CmvWo8HbYPCrhJVEHuavy6djC9fpLE85KGb2VD0H0Eru0pSiLX/FtPPmRz4bGoHEpnsjcR+JSx4vmtJxycc24TuuDvP5AMXDsQW37NcQJGk++ht89/cgGsIq50PlfWEJoshBqDe56z9LmlP55OoNp4uLObBT03SfEMwqocF3tfvKy+Cwh0JLQhyKxybax7bqc3acgaVYEdLH/wjRAJSX7ffmJ7OTkrs0JetZG5Ym7ndCVAW6P5VYEjazsMqnxfnEK/gb/xrOWYjPVvae6ynMuSjv0CRHOhtBmpJ1nIH13gUd54QvOctnYgv/i0LXeXmH1lTCCV3/GNoSMhK+yPYh2PVZfOz5vMOTHOnIXZqWlU/DFq3LO4yD4tEg4FihJ5snHH8Boi1MLXg2ig/dNW1vW0lG30PPcs+XfsTWl17j1HedzOWfuYTFJyRTrqJRSu4iMZ/YEtr7jT0LpaXY3I9ipePr23e8H9/5SRh7JlwvnYIt+PK+KpapiV6n+gfwKC4GJ2l58I5H+cpVt7NnOCzHffXP2/j5hif5at8NHH1iuuV866FpGRHCl5H+2moY/nZoyL37+/j2NfjoM7X39VH89UtDAxDGw2X8eXz7pXg0lG7g5dOmqe1f2Vf6WJI3PjbObdeu35fYAaKJiJHBEdZ/7ns5RvYmJXcRwHd9Ma7vvjdRjodSwLvqaFm459F438lTIx4Kso08kHywk1hhIXT/W6jWuE8nFI/Eui5J9blns20vb2d8bOqJaVHkPP/4phwimkrTMiIQ9w6tcs7H+O/3VYuc1kR/9cqa7MYnajfNblSh+0q8/Jf40PrQr7byPmzO37d+A5EmNq+3m4nx6l2qehdPLbyWByV3EYDCXIiq1aEvs7fF3rRKp8QVMatV1kzvpKj9nqpzFaZpmMzMnT+XlR86i19seJLRkTenxTrndHLZ2un792ZJ0zIiAHMup1rRM7ouql0eoGMFFE8I2795IxSOaumm2TKza9d9jLM/uJxypUxXT4Wu7gr/9MW/451rzsw7NEDlB0SAuEXgzrUw8pO4vv0odJyNLby5rlaDHg3jQ7fC7g1ABJULsO6ra3Zb8mgYRh7AxzZi5aVQuVDTKS1m1/YB3ti6k6PeegQdlcaby9ei2jIih8AntoR68cVj9hUhS/O5fPslEA0SKlR2gXVhvfdOqYUjsle9yV3TMiKTWPEtWOfK1BM7gO/6fOhLuq/08G7wHfWt0BGpQV+oiiTExzbCnsdDOd/K+VixRm34Pf/N1A5NEYz+AvdIvUqlIUruIg1yd3zXv8f9W8eAEgzciM+/iULXe6ff0YpVV1+GD9RpL6CUdqdDA5FGjf4SRn5EqGA5QahDvwd2XjvzGaqVC4ibmk1Siuu8K7lLY5TcRRrkI/eF/qsHshKM/nza/aznOiidGKZx6ASbC8XjsHmNtTsUAU3LiOTGCj3QuwFGnwgdo0onhOWXGcy1e7QTxv8PiotrfzcgLUnJXaRBVlmDjzw49ejdJ6Bj5cz7mkHninDJgHuED3wJhu+Oz6odxSvnYfNvmLnEgrQcTcuINKrjbKhcTDjDtUg4U7UT5v8HVpibb2wH8OH1MPxdYA/4QPg58kgonCZtRUfuIg0yM2z+9ficD8Oe/6l/KWQehu4ADvx+YAR2/wCf92k17G4jeiVFEmLlZVBelncYM/Od09wxFipbKrm3DU3LiMwm5dOr315c0nRTSNIYJXeRWcR61sZLL/dWuiwAFWze5/ILSlJRM7mb2TfNbKuZvTDN/WZmt5jZi2b2nJmdkXyYIpIEKy/Feu+DrouhtBQ634/13oN1zryqR1pPPRNsdwJfAdZPc//5wEnx5R3A1+KfItKErHQcNv/zeYchKat55O7ujwOvz7DJGmC9B78CFphZ/q2/RURmsSTm3I8GXp50vT++bQoz+xcz6zOzvm3btiXw1CIiUk0Syb1ahaOqte7c/Rvuvtzdlx9+eBOuARYRaRNJJPd+4JhJ15cAryTwuCIicoiSSO73A/8Qr5pZAex0980JPK6IiByimqtlzOxuYBVwmJn1A9cTF6F2968DDwCrgRcJ/cI+mlawIiJSn5rJ3d0vq3G/A1cmFpGIiDRMZ6iKiLQhJXcRkTak5C4i0oaU3EVE2pCSu0iT8ZHHiF67iOjVM4m2X46PPp13SNKClNxFmkg0fD++4xoY3xgaa4w9ib9+BT7al3do0mKU3EWahLvD4A3AyAH3jOADN+YRkrQwJXeRZuFDEL1R/b7x32Ubi7Q8JXeRZmFdYJ3V7yscmW0s0vKU3EWahFkR5nwE6Drgni7oviqHiKSVqdW5SBOx7qtwIhi+EzwCq0D3xyl0XZh3aNJilNxFmohZAev5BN59JUQDUFgQjuhFDpKSu0gTMuuAYm/eYUgL05y7iEgbUnIXEWlDSu4iIm1IyV1EpA0puYuItCEldxGRNqTkLiLShiz0t87hic22AX+usdlhwGsZhNMsZtN4Z9NYQeNtZ1mP9Th3P7zWRrkl93qYWZ+7L887jqzMpvHOprGCxtvOmnWsmpYREWlDSu4iIm2o2ZP7N/IOIGOzabyzaayg8bazphxrU8+5i4jIoWn2I3cRETkETZXczexvzey3ZhaZ2bTfPpvZAjO718z+18w2mdnZWcaZlHrHG29bNLOnzezHWcWXpHrGambHmNlj8Wv6WzO7Jus4k3IQ/8sfMLPfmdmLZnZdljEmycwWmdnDZvaH+OfCabb7cvx32WRmt5iZZR1row5irMea2UPxWDea2fFZxtlUyR14AbgYeLzGdjcDD7r7ycBfA5vSDiwl9Y4X4Bpad5xQ31jHgWvdfRmwArjSzE7JIrgU1ByvhS4ctwLnA6cAl7XweK8DHnH3k4BH4uv7MbN3AiuB04BTgTOBc7MMMiE1xxpbD9wY/z+fBWzNKD6gyZK7u29y9xnbvJvZPOAc4PZ4n1F335FFfEmrZ7wAZrYEuABYl35U6ahnrO6+2d1/E/8+QHgzOzqL+JJW52t7FvCiu//J3UeBe4A16UeXijXAt+LfvwVcVGUbBypAB9AJlIFXM4kuWTXHGr9Jl9z9YQB3H3T34exCbLLkXqe3AduAO+JpinVmNjfvoFL2n8AngSjvQLISf4Q9HXgi30hSdTTw8qTr/bTomxlwpLtvhvAmDRxx4Abu/kvgMWBzfPmpu7fip9GaYwWWAjvM7IdxnrrRMu6XmHmbPTP7GfCWKnd92t3vq+MhSsAZwFXu/oSZ3Uz4WPSZBMNMTKPjNbMLga3u/pSZrUo6viQl8NrufZxu4AfAx919V1LxJS2B8Vabb27a5WszjbfO/U8ElgFL4pseNrNz3L2eaclMNTpWQp56N+EA5SXgu8BHiGccspB5cnf38xp8iH6g3933HtHdy/RzXrlLYLwrgQ+a2WrCR9p5ZnaXu1/eeHTJSmCsmFmZkNi/4+4/bDyq9CT0v3zMpOtLgFcafMzUzDReM3vVzI5y981mdhTV55c/BPzK3QfjfX5C+G6l6ZJ7AmPtB5529z/F+2wgjDWz5N5y0zLuvgV42cz+Ir7pb4CNOYaUKndf6+5L3P144FLg0WZM7EmIV07cDmxy95vyjicDvwZOMrO3mlkH4fW9P+eYDtX9wBXx71cA1T65vASca2al+E38XFpzkUA9Y/01sNDM9hb4eg9Z5yl3b5oL4Z29H9hD+KLlp/Hti4EHJm33dqAPeA7YACzMO/Y0xztp+1XAj/OOO62xAu8iTEs8BzwTX1bnHXuary2wGvg98EfCdE7usR/ieHsJK0f+EP9cFN++HFgX/14EbiMk9I3ATXnHndZY4+vvjf+XnwfuBDqyjFNnqIqItKGWm5YREZHalNxFRNqQkruISBtSchcRaUNK7iIibUjJXUSkDSm5i4i0ISV3EZE29P9k3lKzQ46YTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(layer_output[:,0], layer_output[:,1],c=data_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Another way to visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_layer = Model(inputs=model.input, outputs=model.get_layer(index=1).output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_output = hidden_layer.predict(scaled_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 2)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hidden_output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2db960deeb8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(hidden_output[:,0], hidden_output[:,1],c=data_y)"
   ]
  },
  {
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