Lecture24_03 - Neural Network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import theano\n",
    "import pymc3 as pm\n",
    "import theano.tensor as T\n",
    "import sklearn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from warnings import filterwarnings\n",
    "from sklearn import datasets\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_moons\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, Y = make_moons(noise=0.2, random_state=0, n_samples=1000)\n",
    "X = scale(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = Y.astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1172e04e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(X[Y==0, 0], X[Y==0, 1], label='Class 0')\n",
    "ax.scatter(X[Y==1, 0], X[Y==1, 1], color='r', label='Class 1')\n",
    "sns.despine(); ax.legend()\n",
    "ax.set(xlabel='X', ylabel='Y', title='Toy binary classification data set');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def construct_nn(ann_input, ann_output):\n",
    "    n_hidden = 5\n",
    "\n",
    "    # Initialize random weights between each layer\n",
    "    init_1 = np.random.randn(X.shape[1], n_hidden)\n",
    "    init_2 = np.random.randn(n_hidden, n_hidden)\n",
    "    init_out = np.random.randn(n_hidden)\n",
    "\n",
    "    with pm.Model() as neural_network:\n",
    "        # Weights from input to hidden layer\n",
    "        weights_in_1 = pm.Normal('w_in_1', 0, sd=1,\n",
    "                                 shape=(X.shape[1], n_hidden),\n",
    "                                 testval=init_1)\n",
    "\n",
    "        # Weights from 1st to 2nd layer\n",
    "        weights_1_2 = pm.Normal('w_1_2', 0, sd=1,\n",
    "                                shape=(n_hidden, n_hidden),\n",
    "                                testval=init_2)\n",
    "\n",
    "        # Weights from hidden layer to output\n",
    "        weights_2_out = pm.Normal('w_2_out', 0, sd=1,\n",
    "                                  shape=(n_hidden,),\n",
    "                                  testval=init_out)\n",
    "\n",
    "        # Build neural-network using tanh activation function\n",
    "        act_1 = pm.math.tanh(pm.math.dot(ann_input,\n",
    "                                         weights_in_1))\n",
    "        act_2 = pm.math.tanh(pm.math.dot(act_1,\n",
    "                                         weights_1_2))\n",
    "        act_out = pm.math.sigmoid(pm.math.dot(act_2,\n",
    "                                              weights_2_out))\n",
    "\n",
    "        # Binary classification -> Bernoulli likelihood\n",
    "        out = pm.Bernoulli('out',\n",
    "                           act_out,\n",
    "                           observed=ann_output,\n",
    "                           total_size=Y_train.shape[0] # IMPORTANT for minibatches\n",
    "                          )\n",
    "    return neural_network\n",
    "\n",
    "# Trick: Turn inputs and outputs into shared variables.\n",
    "# It's still the same thing, but we can later change the values of the shared variable\n",
    "# (to switch in the test-data later) and pymc3 will just use the new data.\n",
    "# Kind-of like a pointer we can redirect.\n",
    "# For more info, see: http://deeplearning.net/software/theano/library/compile/shared.html\n",
    "ann_input = theano.shared(X_train)\n",
    "ann_output = theano.shared(Y_train)\n",
    "neural_network = construct_nn(ann_input, ann_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [w_2_out, w_1_2, w_in_1]\n",
      "100%|██████████| 3000/3000 [03:52<00:00, 12.92it/s]\n",
      "There were 232 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.651692949328, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 120 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.682274841411, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The gelman-rubin statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    }
   ],
   "source": [
    "with neural_network:\n",
    "    nutstrace = pm.sample(2000, tune=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>mc_error</th>\n",
       "      <th>hpd_2.5</th>\n",
       "      <th>hpd_97.5</th>\n",
       "      <th>n_eff</th>\n",
       "      <th>Rhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>w_in_1__0_0</th>\n",
       "      <td>0.123930</td>\n",
       "      <td>1.330869</td>\n",
       "      <td>0.116547</td>\n",
       "      <td>-2.460931</td>\n",
       "      <td>2.930068</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.137070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__0_1</th>\n",
       "      <td>-0.493261</td>\n",
       "      <td>1.431399</td>\n",
       "      <td>0.130625</td>\n",
       "      <td>-3.050534</td>\n",
       "      <td>2.414883</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.107352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__0_2</th>\n",
       "      <td>-0.391219</td>\n",
       "      <td>1.359743</td>\n",
       "      <td>0.118412</td>\n",
       "      <td>-2.887345</td>\n",
       "      <td>2.457370</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.999820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__0_3</th>\n",
       "      <td>0.265870</td>\n",
       "      <td>1.427571</td>\n",
       "      <td>0.128061</td>\n",
       "      <td>-2.439405</td>\n",
       "      <td>3.031381</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.018467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__0_4</th>\n",
       "      <td>-0.135690</td>\n",
       "      <td>1.161102</td>\n",
       "      <td>0.101015</td>\n",
       "      <td>-2.902407</td>\n",
       "      <td>2.339875</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.051387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__1_0</th>\n",
       "      <td>0.071497</td>\n",
       "      <td>0.948036</td>\n",
       "      <td>0.068116</td>\n",
       "      <td>-2.057060</td>\n",
       "      <td>2.555026</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1.013077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__1_1</th>\n",
       "      <td>-0.023531</td>\n",
       "      <td>0.901718</td>\n",
       "      <td>0.072519</td>\n",
       "      <td>-2.280693</td>\n",
       "      <td>2.076487</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.000335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__1_2</th>\n",
       "      <td>0.053893</td>\n",
       "      <td>1.127319</td>\n",
       "      <td>0.087329</td>\n",
       "      <td>-2.456869</td>\n",
       "      <td>2.609779</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1.008521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__1_3</th>\n",
       "      <td>0.047101</td>\n",
       "      <td>0.900319</td>\n",
       "      <td>0.075818</td>\n",
       "      <td>-2.516146</td>\n",
       "      <td>1.791953</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.055726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__1_4</th>\n",
       "      <td>-0.035805</td>\n",
       "      <td>0.962708</td>\n",
       "      <td>0.082209</td>\n",
       "      <td>-2.360810</td>\n",
       "      <td>2.205260</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.000899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__0_0</th>\n",
       "      <td>0.045259</td>\n",
       "      <td>1.230785</td>\n",
       "      <td>0.044207</td>\n",
       "      <td>-2.419610</td>\n",
       "      <td>2.257352</td>\n",
       "      <td>564.0</td>\n",
       "      <td>1.003747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__0_1</th>\n",
       "      <td>0.041488</td>\n",
       "      <td>1.301054</td>\n",
       "      <td>0.055290</td>\n",
       "      <td>-2.277383</td>\n",
       "      <td>2.743031</td>\n",
       "      <td>446.0</td>\n",
       "      <td>0.999899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__0_2</th>\n",
       "      <td>0.125124</td>\n",
       "      <td>1.280967</td>\n",
       "      <td>0.046201</td>\n",
       "      <td>-2.312125</td>\n",
       "      <td>2.697887</td>\n",
       "      <td>566.0</td>\n",
       "      <td>0.999813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__0_3</th>\n",
       "      <td>-0.028414</td>\n",
       "      <td>1.304735</td>\n",
       "      <td>0.050443</td>\n",
       "      <td>-2.587809</td>\n",
       "      <td>2.428393</td>\n",
       "      <td>538.0</td>\n",
       "      <td>1.000262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__0_4</th>\n",
       "      <td>0.001644</td>\n",
       "      <td>1.287256</td>\n",
       "      <td>0.051705</td>\n",
       "      <td>-2.583537</td>\n",
       "      <td>2.315840</td>\n",
       "      <td>448.0</td>\n",
       "      <td>1.000342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__1_0</th>\n",
       "      <td>-0.004406</td>\n",
       "      <td>1.205375</td>\n",
       "      <td>0.041829</td>\n",
       "      <td>-2.514428</td>\n",
       "      <td>2.244782</td>\n",
       "      <td>746.0</td>\n",
       "      <td>0.999793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__1_1</th>\n",
       "      <td>0.121821</td>\n",
       "      <td>1.251840</td>\n",
       "      <td>0.050935</td>\n",
       "      <td>-2.261252</td>\n",
       "      <td>2.517510</td>\n",
       "      <td>518.0</td>\n",
       "      <td>1.000542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__1_2</th>\n",
       "      <td>-0.008304</td>\n",
       "      <td>1.254002</td>\n",
       "      <td>0.051344</td>\n",
       "      <td>-2.213697</td>\n",
       "      <td>2.390261</td>\n",
       "      <td>498.0</td>\n",
       "      <td>1.000063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__1_3</th>\n",
       "      <td>-0.005456</td>\n",
       "      <td>1.291233</td>\n",
       "      <td>0.048944</td>\n",
       "      <td>-2.536668</td>\n",
       "      <td>2.415580</td>\n",
       "      <td>638.0</td>\n",
       "      <td>0.999986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__1_4</th>\n",
       "      <td>-0.057718</td>\n",
       "      <td>1.277334</td>\n",
       "      <td>0.052935</td>\n",
       "      <td>-2.423612</td>\n",
       "      <td>2.380413</td>\n",
       "      <td>473.0</td>\n",
       "      <td>1.006059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__2_0</th>\n",
       "      <td>-0.019780</td>\n",
       "      <td>1.189632</td>\n",
       "      <td>0.047975</td>\n",
       "      <td>-2.274943</td>\n",
       "      <td>2.288784</td>\n",
       "      <td>561.0</td>\n",
       "      <td>0.999921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__2_1</th>\n",
       "      <td>-0.064628</td>\n",
       "      <td>1.254670</td>\n",
       "      <td>0.054349</td>\n",
       "      <td>-2.402784</td>\n",
       "      <td>2.431458</td>\n",
       "      <td>416.0</td>\n",
       "      <td>1.000838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__2_2</th>\n",
       "      <td>0.078829</td>\n",
       "      <td>1.258498</td>\n",
       "      <td>0.055981</td>\n",
       "      <td>-2.252705</td>\n",
       "      <td>2.592491</td>\n",
       "      <td>371.0</td>\n",
       "      <td>1.004758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__2_3</th>\n",
       "      <td>-0.048391</td>\n",
       "      <td>1.272118</td>\n",
       "      <td>0.044732</td>\n",
       "      <td>-2.434831</td>\n",
       "      <td>2.407843</td>\n",
       "      <td>625.0</td>\n",
       "      <td>1.001006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__2_4</th>\n",
       "      <td>-0.018019</td>\n",
       "      <td>1.240764</td>\n",
       "      <td>0.056564</td>\n",
       "      <td>-2.332373</td>\n",
       "      <td>2.334165</td>\n",
       "      <td>389.0</td>\n",
       "      <td>1.000648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__3_0</th>\n",
       "      <td>-0.011157</td>\n",
       "      <td>1.239160</td>\n",
       "      <td>0.038526</td>\n",
       "      <td>-2.365355</td>\n",
       "      <td>2.310785</td>\n",
       "      <td>902.0</td>\n",
       "      <td>0.999805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__3_1</th>\n",
       "      <td>0.046428</td>\n",
       "      <td>1.260380</td>\n",
       "      <td>0.050470</td>\n",
       "      <td>-2.314920</td>\n",
       "      <td>2.407944</td>\n",
       "      <td>536.0</td>\n",
       "      <td>0.999840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__3_2</th>\n",
       "      <td>-0.078094</td>\n",
       "      <td>1.292863</td>\n",
       "      <td>0.051612</td>\n",
       "      <td>-2.612755</td>\n",
       "      <td>2.369275</td>\n",
       "      <td>488.0</td>\n",
       "      <td>1.002045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__3_3</th>\n",
       "      <td>0.018767</td>\n",
       "      <td>1.302577</td>\n",
       "      <td>0.049790</td>\n",
       "      <td>-2.517321</td>\n",
       "      <td>2.450889</td>\n",
       "      <td>381.0</td>\n",
       "      <td>1.005646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__3_4</th>\n",
       "      <td>0.047618</td>\n",
       "      <td>1.268120</td>\n",
       "      <td>0.052708</td>\n",
       "      <td>-2.293177</td>\n",
       "      <td>2.450042</td>\n",
       "      <td>499.0</td>\n",
       "      <td>1.000193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__4_0</th>\n",
       "      <td>-0.024577</td>\n",
       "      <td>1.280421</td>\n",
       "      <td>0.051886</td>\n",
       "      <td>-2.530172</td>\n",
       "      <td>2.461077</td>\n",
       "      <td>634.0</td>\n",
       "      <td>1.000493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__4_1</th>\n",
       "      <td>-0.015574</td>\n",
       "      <td>1.292227</td>\n",
       "      <td>0.051797</td>\n",
       "      <td>-2.667592</td>\n",
       "      <td>2.328790</td>\n",
       "      <td>455.0</td>\n",
       "      <td>1.000200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__4_2</th>\n",
       "      <td>-0.020678</td>\n",
       "      <td>1.314246</td>\n",
       "      <td>0.051867</td>\n",
       "      <td>-2.589785</td>\n",
       "      <td>2.605920</td>\n",
       "      <td>494.0</td>\n",
       "      <td>1.004190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__4_3</th>\n",
       "      <td>-0.024291</td>\n",
       "      <td>1.278493</td>\n",
       "      <td>0.047599</td>\n",
       "      <td>-2.407167</td>\n",
       "      <td>2.504326</td>\n",
       "      <td>666.0</td>\n",
       "      <td>0.999903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__4_4</th>\n",
       "      <td>-0.005184</td>\n",
       "      <td>1.287306</td>\n",
       "      <td>0.053009</td>\n",
       "      <td>-2.486049</td>\n",
       "      <td>2.611267</td>\n",
       "      <td>414.0</td>\n",
       "      <td>0.999980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_2_out__0</th>\n",
       "      <td>0.156560</td>\n",
       "      <td>2.219129</td>\n",
       "      <td>0.105795</td>\n",
       "      <td>-4.386398</td>\n",
       "      <td>4.350683</td>\n",
       "      <td>437.0</td>\n",
       "      <td>1.000577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_2_out__1</th>\n",
       "      <td>0.223283</td>\n",
       "      <td>2.321585</td>\n",
       "      <td>0.117838</td>\n",
       "      <td>-3.916271</td>\n",
       "      <td>5.231669</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.002508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_2_out__2</th>\n",
       "      <td>-0.082721</td>\n",
       "      <td>2.400420</td>\n",
       "      <td>0.118276</td>\n",
       "      <td>-4.787125</td>\n",
       "      <td>4.424813</td>\n",
       "      <td>283.0</td>\n",
       "      <td>0.999791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_2_out__3</th>\n",
       "      <td>-0.388788</td>\n",
       "      <td>2.341725</td>\n",
       "      <td>0.110378</td>\n",
       "      <td>-5.037902</td>\n",
       "      <td>4.127480</td>\n",
       "      <td>332.0</td>\n",
       "      <td>1.009560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_2_out__4</th>\n",
       "      <td>-0.067230</td>\n",
       "      <td>2.405774</td>\n",
       "      <td>0.133776</td>\n",
       "      <td>-4.656133</td>\n",
       "      <td>4.642663</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1.000985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean        sd  mc_error   hpd_2.5  hpd_97.5  n_eff      Rhat\n",
       "w_in_1__0_0  0.123930  1.330869  0.116547 -2.460931  2.930068    9.0  1.137070\n",
       "w_in_1__0_1 -0.493261  1.431399  0.130625 -3.050534  2.414883   23.0  1.107352\n",
       "w_in_1__0_2 -0.391219  1.359743  0.118412 -2.887345  2.457370   51.0  0.999820\n",
       "w_in_1__0_3  0.265870  1.427571  0.128061 -2.439405  3.031381   24.0  1.018467\n",
       "w_in_1__0_4 -0.135690  1.161102  0.101015 -2.902407  2.339875   18.0  1.051387\n",
       "w_in_1__1_0  0.071497  0.948036  0.068116 -2.057060  2.555026   67.0  1.013077\n",
       "w_in_1__1_1 -0.023531  0.901718  0.072519 -2.280693  2.076487   81.0  1.000335\n",
       "w_in_1__1_2  0.053893  1.127319  0.087329 -2.456869  2.609779   67.0  1.008521\n",
       "w_in_1__1_3  0.047101  0.900319  0.075818 -2.516146  1.791953   12.0  1.055726\n",
       "w_in_1__1_4 -0.035805  0.962708  0.082209 -2.360810  2.205260   47.0  1.000899\n",
       "w_1_2__0_0   0.045259  1.230785  0.044207 -2.419610  2.257352  564.0  1.003747\n",
       "w_1_2__0_1   0.041488  1.301054  0.055290 -2.277383  2.743031  446.0  0.999899\n",
       "w_1_2__0_2   0.125124  1.280967  0.046201 -2.312125  2.697887  566.0  0.999813\n",
       "w_1_2__0_3  -0.028414  1.304735  0.050443 -2.587809  2.428393  538.0  1.000262\n",
       "w_1_2__0_4   0.001644  1.287256  0.051705 -2.583537  2.315840  448.0  1.000342\n",
       "w_1_2__1_0  -0.004406  1.205375  0.041829 -2.514428  2.244782  746.0  0.999793\n",
       "w_1_2__1_1   0.121821  1.251840  0.050935 -2.261252  2.517510  518.0  1.000542\n",
       "w_1_2__1_2  -0.008304  1.254002  0.051344 -2.213697  2.390261  498.0  1.000063\n",
       "w_1_2__1_3  -0.005456  1.291233  0.048944 -2.536668  2.415580  638.0  0.999986\n",
       "w_1_2__1_4  -0.057718  1.277334  0.052935 -2.423612  2.380413  473.0  1.006059\n",
       "w_1_2__2_0  -0.019780  1.189632  0.047975 -2.274943  2.288784  561.0  0.999921\n",
       "w_1_2__2_1  -0.064628  1.254670  0.054349 -2.402784  2.431458  416.0  1.000838\n",
       "w_1_2__2_2   0.078829  1.258498  0.055981 -2.252705  2.592491  371.0  1.004758\n",
       "w_1_2__2_3  -0.048391  1.272118  0.044732 -2.434831  2.407843  625.0  1.001006\n",
       "w_1_2__2_4  -0.018019  1.240764  0.056564 -2.332373  2.334165  389.0  1.000648\n",
       "w_1_2__3_0  -0.011157  1.239160  0.038526 -2.365355  2.310785  902.0  0.999805\n",
       "w_1_2__3_1   0.046428  1.260380  0.050470 -2.314920  2.407944  536.0  0.999840\n",
       "w_1_2__3_2  -0.078094  1.292863  0.051612 -2.612755  2.369275  488.0  1.002045\n",
       "w_1_2__3_3   0.018767  1.302577  0.049790 -2.517321  2.450889  381.0  1.005646\n",
       "w_1_2__3_4   0.047618  1.268120  0.052708 -2.293177  2.450042  499.0  1.000193\n",
       "w_1_2__4_0  -0.024577  1.280421  0.051886 -2.530172  2.461077  634.0  1.000493\n",
       "w_1_2__4_1  -0.015574  1.292227  0.051797 -2.667592  2.328790  455.0  1.000200\n",
       "w_1_2__4_2  -0.020678  1.314246  0.051867 -2.589785  2.605920  494.0  1.004190\n",
       "w_1_2__4_3  -0.024291  1.278493  0.047599 -2.407167  2.504326  666.0  0.999903\n",
       "w_1_2__4_4  -0.005184  1.287306  0.053009 -2.486049  2.611267  414.0  0.999980\n",
       "w_2_out__0   0.156560  2.219129  0.105795 -4.386398  4.350683  437.0  1.000577\n",
       "w_2_out__1   0.223283  2.321585  0.117838 -3.916271  5.231669  206.0  1.002508\n",
       "w_2_out__2  -0.082721  2.400420  0.118276 -4.787125  4.424813  283.0  0.999791\n",
       "w_2_out__3  -0.388788  2.341725  0.110378 -5.037902  4.127480  332.0  1.009560\n",
       "w_2_out__4  -0.067230  2.405774  0.133776 -4.656133  4.642663  240.0  1.000985"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm.summary(nutstrace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Average Loss = 151.75: 100%|██████████| 30000/30000 [00:28<00:00, 1057.53it/s]\n",
      "Finished [100%]: Average Loss = 151.75\n"
     ]
    }
   ],
   "source": [
    "with neural_network:\n",
    "    inference = pm.ADVI()\n",
    "    approx = pm.fit(n=30000, method=inference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "advitrace = approx.sample(draws=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>mc_error</th>\n",
       "      <th>hpd_2.5</th>\n",
       "      <th>hpd_97.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>w_in_1__0_0</th>\n",
       "      <td>-0.521386</td>\n",
       "      <td>0.112502</td>\n",
       "      <td>0.001786</td>\n",
       "      <td>-0.741013</td>\n",
       "      <td>-0.297054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__0_1</th>\n",
       "      <td>0.122723</td>\n",
       "      <td>0.319251</td>\n",
       "      <td>0.004560</td>\n",
       "      <td>-0.504744</td>\n",
       "      <td>0.743053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__0_2</th>\n",
       "      <td>-0.543037</td>\n",
       "      <td>0.113075</td>\n",
       "      <td>0.001731</td>\n",
       "      <td>-0.769624</td>\n",
       "      <td>-0.328908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__0_3</th>\n",
       "      <td>3.255195</td>\n",
       "      <td>0.487644</td>\n",
       "      <td>0.006504</td>\n",
       "      <td>2.279137</td>\n",
       "      <td>4.182791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__0_4</th>\n",
       "      <td>-0.464174</td>\n",
       "      <td>0.162645</td>\n",
       "      <td>0.002485</td>\n",
       "      <td>-0.789237</td>\n",
       "      <td>-0.154705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__1_0</th>\n",
       "      <td>0.362184</td>\n",
       "      <td>0.177318</td>\n",
       "      <td>0.002432</td>\n",
       "      <td>0.008508</td>\n",
       "      <td>0.706695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__1_1</th>\n",
       "      <td>-0.153209</td>\n",
       "      <td>0.440645</td>\n",
       "      <td>0.006077</td>\n",
       "      <td>-0.998433</td>\n",
       "      <td>0.710622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__1_2</th>\n",
       "      <td>0.328326</td>\n",
       "      <td>0.182822</td>\n",
       "      <td>0.002778</td>\n",
       "      <td>-0.036471</td>\n",
       "      <td>0.671428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__1_3</th>\n",
       "      <td>0.814828</td>\n",
       "      <td>0.332562</td>\n",
       "      <td>0.004819</td>\n",
       "      <td>0.187650</td>\n",
       "      <td>1.495967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_in_1__1_4</th>\n",
       "      <td>0.262358</td>\n",
       "      <td>0.236041</td>\n",
       "      <td>0.003509</td>\n",
       "      <td>-0.200934</td>\n",
       "      <td>0.738899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__0_0</th>\n",
       "      <td>0.430823</td>\n",
       "      <td>0.900179</td>\n",
       "      <td>0.012896</td>\n",
       "      <td>-1.306313</td>\n",
       "      <td>2.182824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__0_1</th>\n",
       "      <td>0.159472</td>\n",
       "      <td>1.001399</td>\n",
       "      <td>0.015379</td>\n",
       "      <td>-1.865583</td>\n",
       "      <td>2.044132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__0_2</th>\n",
       "      <td>1.769050</td>\n",
       "      <td>0.488465</td>\n",
       "      <td>0.006968</td>\n",
       "      <td>0.793287</td>\n",
       "      <td>2.708693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__0_3</th>\n",
       "      <td>-0.798432</td>\n",
       "      <td>0.219366</td>\n",
       "      <td>0.002937</td>\n",
       "      <td>-1.225897</td>\n",
       "      <td>-0.375460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__0_4</th>\n",
       "      <td>-0.138738</td>\n",
       "      <td>1.058936</td>\n",
       "      <td>0.015063</td>\n",
       "      <td>-2.161605</td>\n",
       "      <td>1.937199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__1_0</th>\n",
       "      <td>-0.014915</td>\n",
       "      <td>0.998757</td>\n",
       "      <td>0.015708</td>\n",
       "      <td>-1.996569</td>\n",
       "      <td>1.954171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__1_1</th>\n",
       "      <td>-0.112639</td>\n",
       "      <td>1.036376</td>\n",
       "      <td>0.014671</td>\n",
       "      <td>-2.166837</td>\n",
       "      <td>1.896193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__1_2</th>\n",
       "      <td>-0.279527</td>\n",
       "      <td>0.607058</td>\n",
       "      <td>0.007500</td>\n",
       "      <td>-1.462639</td>\n",
       "      <td>0.905531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__1_3</th>\n",
       "      <td>0.030542</td>\n",
       "      <td>0.262629</td>\n",
       "      <td>0.003622</td>\n",
       "      <td>-0.449362</td>\n",
       "      <td>0.566730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__1_4</th>\n",
       "      <td>0.033081</td>\n",
       "      <td>1.031896</td>\n",
       "      <td>0.014412</td>\n",
       "      <td>-1.996529</td>\n",
       "      <td>2.026740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__2_0</th>\n",
       "      <td>0.698759</td>\n",
       "      <td>0.894654</td>\n",
       "      <td>0.013301</td>\n",
       "      <td>-1.058448</td>\n",
       "      <td>2.480351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__2_1</th>\n",
       "      <td>0.075845</td>\n",
       "      <td>0.993218</td>\n",
       "      <td>0.014997</td>\n",
       "      <td>-1.837229</td>\n",
       "      <td>2.087505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__2_2</th>\n",
       "      <td>0.227468</td>\n",
       "      <td>0.470235</td>\n",
       "      <td>0.006694</td>\n",
       "      <td>-0.692072</td>\n",
       "      <td>1.136107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__2_3</th>\n",
       "      <td>-1.266305</td>\n",
       "      <td>0.213226</td>\n",
       "      <td>0.003163</td>\n",
       "      <td>-1.653136</td>\n",
       "      <td>-0.825669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__2_4</th>\n",
       "      <td>-0.140237</td>\n",
       "      <td>1.001324</td>\n",
       "      <td>0.014957</td>\n",
       "      <td>-2.069265</td>\n",
       "      <td>1.830847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__3_0</th>\n",
       "      <td>0.697528</td>\n",
       "      <td>0.698237</td>\n",
       "      <td>0.009634</td>\n",
       "      <td>-0.619907</td>\n",
       "      <td>2.115031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__3_1</th>\n",
       "      <td>0.260246</td>\n",
       "      <td>0.865400</td>\n",
       "      <td>0.012721</td>\n",
       "      <td>-1.346997</td>\n",
       "      <td>1.989603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__3_2</th>\n",
       "      <td>1.164568</td>\n",
       "      <td>0.346030</td>\n",
       "      <td>0.004961</td>\n",
       "      <td>0.499555</td>\n",
       "      <td>1.842212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__3_3</th>\n",
       "      <td>-1.060768</td>\n",
       "      <td>0.125721</td>\n",
       "      <td>0.001927</td>\n",
       "      <td>-1.293945</td>\n",
       "      <td>-0.800757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__3_4</th>\n",
       "      <td>-0.395368</td>\n",
       "      <td>0.913273</td>\n",
       "      <td>0.013130</td>\n",
       "      <td>-2.218135</td>\n",
       "      <td>1.333269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__4_0</th>\n",
       "      <td>0.021826</td>\n",
       "      <td>0.933852</td>\n",
       "      <td>0.013489</td>\n",
       "      <td>-1.863657</td>\n",
       "      <td>1.834411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__4_1</th>\n",
       "      <td>0.042679</td>\n",
       "      <td>1.048042</td>\n",
       "      <td>0.015151</td>\n",
       "      <td>-2.087779</td>\n",
       "      <td>2.004247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__4_2</th>\n",
       "      <td>0.582934</td>\n",
       "      <td>0.556188</td>\n",
       "      <td>0.007582</td>\n",
       "      <td>-0.457234</td>\n",
       "      <td>1.709272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__4_3</th>\n",
       "      <td>-0.659373</td>\n",
       "      <td>0.240979</td>\n",
       "      <td>0.003342</td>\n",
       "      <td>-1.121006</td>\n",
       "      <td>-0.194694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_1_2__4_4</th>\n",
       "      <td>-0.121137</td>\n",
       "      <td>1.025468</td>\n",
       "      <td>0.013284</td>\n",
       "      <td>-2.159029</td>\n",
       "      <td>1.859368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_2_out__0</th>\n",
       "      <td>-0.312281</td>\n",
       "      <td>0.265692</td>\n",
       "      <td>0.003796</td>\n",
       "      <td>-0.832638</td>\n",
       "      <td>0.205428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_2_out__1</th>\n",
       "      <td>-0.076431</td>\n",
       "      <td>0.253095</td>\n",
       "      <td>0.003764</td>\n",
       "      <td>-0.557819</td>\n",
       "      <td>0.428869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_2_out__2</th>\n",
       "      <td>-1.139659</td>\n",
       "      <td>0.268075</td>\n",
       "      <td>0.003511</td>\n",
       "      <td>-1.672406</td>\n",
       "      <td>-0.614689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_2_out__3</th>\n",
       "      <td>3.192447</td>\n",
       "      <td>0.274850</td>\n",
       "      <td>0.004289</td>\n",
       "      <td>2.655092</td>\n",
       "      <td>3.718204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_2_out__4</th>\n",
       "      <td>0.112249</td>\n",
       "      <td>0.257997</td>\n",
       "      <td>0.003784</td>\n",
       "      <td>-0.414541</td>\n",
       "      <td>0.584599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean        sd  mc_error   hpd_2.5  hpd_97.5\n",
       "w_in_1__0_0 -0.521386  0.112502  0.001786 -0.741013 -0.297054\n",
       "w_in_1__0_1  0.122723  0.319251  0.004560 -0.504744  0.743053\n",
       "w_in_1__0_2 -0.543037  0.113075  0.001731 -0.769624 -0.328908\n",
       "w_in_1__0_3  3.255195  0.487644  0.006504  2.279137  4.182791\n",
       "w_in_1__0_4 -0.464174  0.162645  0.002485 -0.789237 -0.154705\n",
       "w_in_1__1_0  0.362184  0.177318  0.002432  0.008508  0.706695\n",
       "w_in_1__1_1 -0.153209  0.440645  0.006077 -0.998433  0.710622\n",
       "w_in_1__1_2  0.328326  0.182822  0.002778 -0.036471  0.671428\n",
       "w_in_1__1_3  0.814828  0.332562  0.004819  0.187650  1.495967\n",
       "w_in_1__1_4  0.262358  0.236041  0.003509 -0.200934  0.738899\n",
       "w_1_2__0_0   0.430823  0.900179  0.012896 -1.306313  2.182824\n",
       "w_1_2__0_1   0.159472  1.001399  0.015379 -1.865583  2.044132\n",
       "w_1_2__0_2   1.769050  0.488465  0.006968  0.793287  2.708693\n",
       "w_1_2__0_3  -0.798432  0.219366  0.002937 -1.225897 -0.375460\n",
       "w_1_2__0_4  -0.138738  1.058936  0.015063 -2.161605  1.937199\n",
       "w_1_2__1_0  -0.014915  0.998757  0.015708 -1.996569  1.954171\n",
       "w_1_2__1_1  -0.112639  1.036376  0.014671 -2.166837  1.896193\n",
       "w_1_2__1_2  -0.279527  0.607058  0.007500 -1.462639  0.905531\n",
       "w_1_2__1_3   0.030542  0.262629  0.003622 -0.449362  0.566730\n",
       "w_1_2__1_4   0.033081  1.031896  0.014412 -1.996529  2.026740\n",
       "w_1_2__2_0   0.698759  0.894654  0.013301 -1.058448  2.480351\n",
       "w_1_2__2_1   0.075845  0.993218  0.014997 -1.837229  2.087505\n",
       "w_1_2__2_2   0.227468  0.470235  0.006694 -0.692072  1.136107\n",
       "w_1_2__2_3  -1.266305  0.213226  0.003163 -1.653136 -0.825669\n",
       "w_1_2__2_4  -0.140237  1.001324  0.014957 -2.069265  1.830847\n",
       "w_1_2__3_0   0.697528  0.698237  0.009634 -0.619907  2.115031\n",
       "w_1_2__3_1   0.260246  0.865400  0.012721 -1.346997  1.989603\n",
       "w_1_2__3_2   1.164568  0.346030  0.004961  0.499555  1.842212\n",
       "w_1_2__3_3  -1.060768  0.125721  0.001927 -1.293945 -0.800757\n",
       "w_1_2__3_4  -0.395368  0.913273  0.013130 -2.218135  1.333269\n",
       "w_1_2__4_0   0.021826  0.933852  0.013489 -1.863657  1.834411\n",
       "w_1_2__4_1   0.042679  1.048042  0.015151 -2.087779  2.004247\n",
       "w_1_2__4_2   0.582934  0.556188  0.007582 -0.457234  1.709272\n",
       "w_1_2__4_3  -0.659373  0.240979  0.003342 -1.121006 -0.194694\n",
       "w_1_2__4_4  -0.121137  1.025468  0.013284 -2.159029  1.859368\n",
       "w_2_out__0  -0.312281  0.265692  0.003796 -0.832638  0.205428\n",
       "w_2_out__1  -0.076431  0.253095  0.003764 -0.557819  0.428869\n",
       "w_2_out__2  -1.139659  0.268075  0.003511 -1.672406 -0.614689\n",
       "w_2_out__3   3.192447  0.274850  0.004289  2.655092  3.718204\n",
       "w_2_out__4   0.112249  0.257997  0.003784 -0.414541  0.584599"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm.summary(advitrace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x121ced550>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1219ae518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(-inference.hist, alpha=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:00<00:00, 1593.05it/s]\n"
     ]
    }
   ],
   "source": [
    "ann_input.set_value(X_test)\n",
    "ann_output.set_value(Y_test)\n",
    "with neural_network:\n",
    "    ppc = pm.sample_ppc(advitrace, samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = ppc['out'].mean(axis=0) > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121995978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(X_test[pred==0, 0], X_test[pred==0, 1])\n",
    "ax.scatter(X_test[pred==1, 0], X_test[pred==1, 1], color='r')\n",
    "sns.despine()\n",
    "ax.set(title='Predicted labels in testing set', xlabel='X', ylabel='Y');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 95.19999999999999%\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy = {}%'.format((Y_test == pred).mean() * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = pm.floatX(np.mgrid[-3:3:100j,-3:3:100j])\n",
    "grid_2d = grid.reshape(2, -1).T\n",
    "dummy_out = np.ones(grid.shape[1], dtype=np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:02<00:00, 227.28it/s]\n"
     ]
    }
   ],
   "source": [
    "ann_input.set_value(grid_2d)\n",
    "ann_output.set_value(dummy_out)\n",
    "with neural_network:\n",
    "    ppc_grid = pm.sample_ppc(advitrace ,500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12234d828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = sns.diverging_palette(250, 12, s=85, l=25, as_cmap=True)\n",
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "contour = ax.contourf(grid[0], grid[1], ppc_grid['out'].mean(axis=0).reshape(100, 100), cmap=cmap)\n",
    "ax.scatter(X_test[pred==0, 0], X_test[pred==0, 1])\n",
    "ax.scatter(X_test[pred==1, 0], X_test[pred==1, 1], color='r')\n",
    "cbar = plt.colorbar(contour, ax=ax)\n",
    "_ = ax.set(xlim=(-3, 3), ylim=(-3, 3), xlabel='X', ylabel='Y');\n",
    "cbar.ax.set_ylabel('Posterior predictive mean probability of class label = 0');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z7jVcuiaZJFfm4X2WVP9tbI+UyVLUZzppOwiSseSYKFUq8KpNJhEkf4XKLGKV5LVZKgJn2abK8+HhxYPKo2a2CdkoQ1KWDJKy0aM0YiZLmrvkJE+jB/3Nw9ExsSLMxlQhU5lCmGY7eQlZGpLcePCLVx43LFSeQ0jKiLJCUjaalErMkq7dGuWS7aYVZyDMoyxr4jxs6EFmGkmqIJoqb2VD12c47+9GVjkjIduP6evLKFomBwnZaFMqMQP2T76SVLEC9PXxMkGEolK5ih4bYRb+CVNw8nR48aC1sma6nJmy7ihOskRjU7X2KikmFt4wkaz9z3SmdycRtKKEzATxshmSMjlIyojSiVmZUClOefV6IgjTMVXOgkKTdyl2GWTG4l97pZs8mmPnsR/dRGWMAPrasIhu6oig9OjsmBwtIymTg6SMAEooZmFRoTSFAVSKigqxyrKNuMkoSRkRh61RMhEy62k6fRfrbWB6HBirOnkNbQ9FCUEw+pV0HLrlrIhm0kXsNy9Ury8LkyyRqCUVsmMLDQD5R85OHJk2MmpmspABJGUykJARfkojZmkjQnE9XgBzpCXreFSU6CdGg+CEyQQp0xHVFd2w6A9cPPh0D6eXB1jdBmYngFvnK3jny2qoVvITNBMkIGtxC5uFJqxfGWDn8cig4vuVRLSyRMmOLTQKkTPAjLRG04UMICmTgaSMCFIKMUvTvyhtxSoTUBU9M+mYCPMwQcaAvd9vHTdLgildDz7dw2eWBru/W9nGzs893MfqyvY7CtgqMHHRPpsELa/zfBGpiKMaPSMpsx9VQkbvc/mwWszSpl2keZ2uHPy0qJCzskIRwfIQ9r3TdWPh4pk1fPXCBIDKvsdOLw9w70k3l7RGGyb8BBUe8Sh6fVgRglZU9IykzH6yShm9v+XGWjHTvXhfdtJnYhEBlYiOT8eEOO1EWyaFFSBBI+TY7Dlo9sTitboNrLeBw5N69p1n0QwinKR/A5PlLI/zXtFSNkrYIGVEOJS2SMhgpZhlkSEda1R0IUq5zFswRGPQtdZHJ5S6aSdhace6/pZTNRczNRdrAjmbnRgWAtFJlgm+KaX2bSWtFJv6Xo/a+a6IMvomrDUzDYrm6IcEr9xYJWYmCVle2DpuEyE5sxN/0Rrdf7+xCnBLo49Tzf2pjLfOVwqrzpgU09dAmSKRMjJm6nsYRZnXlYkwrdm0DihaRhBqYIxVAPwegNsAtAH8KOf8GcHzPghglXP+84yxOoAPAzgBoA/gxzjnT+oY3/7Zh6GklbLm2TWajGvAhBROXWsFCTPJ63v89vku7lqsYG4CcADMTQB3LQ6rMppKmGBEiUfjxNHd//JCtD+VY/AfU9R7IrtPG6Vs1KCG0+ZA0TLCEu4FMME5fyOAnwfwG8EnMMZ+AsBrfL96O4Aa5/xNAD4A4Nd0Dc7cmYaPNJNpkjF1hKWT2RqBEo276JRR0zCtVH6ezN1wCPcBuPek+j5mQRnIY+IvIyB5rJPSWekwjZjGYZuU5X3OShItizqHZIm6jYqUmR4tIyEjLONOAJ8AAM75w4yxb/Q/yBh7E4A7APwhgFfs/PopALWdaNtBAF1dg7NCzJIw6hNqYj8yn4mkLRTKjtfHbNSkzM9Y1dFW6MM0dEpIWjmSETXV0T7bZKxIZJpDx50/TEmFNBkTpYxEjFBBgWvlDgLw313pM8ZqnPMeY2wBwK8AeAeA+3zPaWGYxvgkgHkA36VrcKUQM5Ix/ZhQiCSI6sIQRR+PaYyqlOmUdF0T/yQVHfOUj6TiJBpbkm2QWOWLTCN6nfJVRJPpPDFNykjIsvHM6jkq3GEGGwCmfD9XOOe9nX9/H4bi9RCAowAmGWNPArgVwN9yzn+BMXY9gL9jjL2Gc76tenDWihlNostF2rRI/ySaPhPJGYWomK0pt0nx5MzE6JcMwXEnbR9AUlYMZT9/FIVJUkZCRpSMLwK4B8ADjLE3AHjMe4Bz/jsAfgcAGGM/COAVnPMPMcbej6vpi6sA6gCqOgZnpZiZOMnKeoc97THlOenUJUHeNrPIGZEc7042ydl+bE1t1SEneRQHIakqJ5SqmA6SsvJCUTMj+BiAtzLGTmFY4+uHGGP3A2hwzj8Y8prfAvAnjLHPAxgD8Iuc88s6BmedmKmYhPsnXFm3p2rylkZK/ELjYZuk2Dj5LQNZJ0y2yJz/8zUqkTNV5FWtUYWUjbrYjfLnumzpjKZIGQkZUVY45wMA7w38el/pe875h3z/bmHvmjNtWFMuXxWiZsmmMHP80Mj1arNxzIQ9+D9f9FmTJ88S+kQ2TP1clzVaNgpl8knKCKI4RkrMdEiYjj5psuP09ltErzaV76X/OIh8yBrtsiFa5pHk++HdHDHphk3e5C1lWfuYJYmWlU046Zw55NhCA8cWGtr3MwpSRuiF0hiJOKxLZVSNqgubt528UxvzvDDrnKzSBIMoklEWMT9pKzuqEJ5geXzVDaC97fm3a2sKpOnny6KiZZ6c6UhtzEPKTEhjpGiZPkjKCBmsE7O060RUllWPQmXRAJPWxNDElSgjZf5ci8Qmq4iEvT5pBcUodES1ZBpQ2yJpplwTTEb1urNRiZSRlOmBhIxIgnViZgOq5czbpmmYJI5EemxKS1SJzHdUdSRcN3FSE3zcLyNhr5UVliSRLhXIjkt2PDINrYuEzrXJUCFnoyJkAElZEk7OHpN+v0jKiKRYJ2YmR8t0UmTlxbBJqW3vIbGXYHNYQowtUgYkTzHUIVEqo2dR+5AhzThsjKKZRF7nlHPnW7msKyMIP55o+YVLJGkkZERarBEzVUKWdlsq9qty+3lKka09nYh4RjVaBshFw2z/3GddC5ZWTLLKmQq5K1uhD8pQGOKPgnn/FglamUroE2bjSZgnaCRlRBasEDObpCwJwbEkmQQWneJo0vtIEFkYpRsPeUSzsuLJoAnRqsaJo0aMw2PU5SxMtqIELS2jlMZIqIGEjFCBFWKWhKgJVh4XtKSl7kW/M13QRnliEIeXxjPKkSgb8X+myy5peclZmv2okiDT5TMLRd+UKwLZ6JftUbKb5uaNqMxIEERxlErMbJCyJCXwTRK0UZoEpMW/tsL/71GTNNvldBQkTUaaVEhSEjkzVcpMi5p5jIqgFSFbFC0jCKIoSiFmcZMnW9eUJRW0sl+gbcRWORHRGQCbPQdTNRdjI9KafhQkLQyRkERVdSQI2ylayIqKllFFRoIwByvELO+8epn9BSdpMhKV9hhEryuySiMhJljlsCxS1neBh5breKJVRbPnYKbm4pZGH2+f76Lq7H9+WY47SJY1obYSFX2SkbIk0StVkam4bZQ5zZFIT5FSRkJmPrR+jMgLK8QMCE/bUD058raXdrthd9hVyxPJmJmUUUoeWq7jVLO++/Naz8Gp5jBkds/hblHDKhzbepwR6TE1nZFQA0kZEQYJGZE31oiZR9JJkKzAZJlche3DRHmiSBuRhM4AeKJVFT72RKuKu+e6I5PWGIYpVR2TyIPuqJFo+zKRrFESoDKKvY19EfOSMlOKepCQyUFCRhSFdWKmgzJdGMMQHeOol142heBkxqSo22bPQbMnyFcE0Ow52Ow5mBtzcx6VeRQpZ34J8v4dJzc6KzOmXYcmmxqZVdxMaBngP+9m+ezQ+XsvQck6cWQ60fN1YIqQASRlspCUEUVCYgZz7njrRHR3li7qZuBfm2aSlAHAVM3FTM3FmkDOZmoupmokZUURJRf+x8JExv97VaIi2k5SmZJZ15alCElRJfz9mHhTzMRzUNKKjCeOTCeSraTPT4pJUkbIQVJGFA2J2Q5Z5MzEi2wYtoxz1DBpMuRnrALc0ujvrinzc0ujvy+NsYzFT2Qw+caOTBRNRtLavQGaW33MTFYxXqvsEa442Ykbg4ws+V8bjHplLVKi4jVJMfUzY4qgpS2T78lWXLRMN6ZJGUXL4iEpI0yAxMxHlpz/UekpQ5SfYGrlN7nAleoUnu2PYxNVTKGPm6ttfNP2Ji4tRW+n6MldmdGVkheUkmuOX4s/+9Iavnx2CyutPuYaVbzu+CTuv+NQ4v2nHa9IlGSPP8k+bVrfpvJaI1obVuT3N2vvsiKlzDQhI+QgKSNMgcRMAAmaPdgUrbQB0QSt4gB3jW3iTncTl90qDjh91MXLzkK3V2ZBMzXyESTt+qwPf2oJn1ka7P683Orjk1/bBAC8+42zysYXhm5ZsknGdBBVsCPJ91dV4Y+8G0qrTGc0VcooWlZObpqbL3oIhAZyrafGGKszxj7CGPs8Y+wfGWPfLfO65tm1QibfWfY7c/yQNRM2W8na2qAsXFraUDIpittG3QFmKvJSlmTbNlPkjYGkUpE0YtXpuzi9PBA+9uWzW2j3xI/lRVqpap25sPufTnR9NvL+zOX1/c1bylRCUmYvtkXLbpqbJykrMXlHzN4FYIVz/m7G2CyArwL4G9kXJ1kHZkoUhSI6+qD39SoqIlLBBtkqKXPEDIhvAq8THUU8PNbbwOq2+LGVVh8vPvUSbnzlgtJ96qIskTHV5z2Z733c91fVeePYQiNXOSuyf1kekJSVDxKy8pO3mP0FgL/c+bcDoJd0A1FphiZO1E0ck05IRPNHpfR425Ip4lHmKJgKiqj2qnrd2fQ4MDsBrAjkbHZi+LhsARAdJC0akjcq//55n1fzTF8sApVSZmK0jKRMDluiZSRko0OuYsY5bwEAY2wKQ0H7pbTb8iY9JAF7CU4E8nx//KmF9HexG5lJmWyErezRsiiKaCKsUpTGqg5una/sWWPmcet8BWPVq3mtuoqRZOldZnuULK/zaJrvqC4p0x01K3uUDCApk4WkjDCR3It/MMauB/AxAL/HOf+zLNvKsv6rbOIQNvHL61iD+y/je0zsR2f6Y5kwJXqWRlTe+bIagB5OLw+wuj2MlN06X9n5/f59AsVEz0TjsBXTz50qC30cW2js+71qORsFGQNIyOKwRcT8kJSNHrmKGWPsWgCfBPCvOeefznPfHmmiOmkmVUkurGklRnZMuiWpKCnU1TOLKmsmI0rO8oqWUeROjIooVrXi4D5Wx70nXay3h+mL/kiZrv36kY2aeb3W6n03doymYvJ5R8dNGL+A+SUtq5yNiowBJGRh2ChifkjKRpO8I2a/COAQgPczxt6/87vv4JxfyWsAui96SYUs+O8kr/c/N0rSirzQ65QzXRNtkydGplJ05KxM0tXpywtQUkSCI7u/saqDw5Py+1FNcOz+ffQH7p5ea/6oXrVil6CZmm2g8/stipqp6GU2CnJGUibGZikjIRtt8l5j9j4A78tzn6qIipqpjHaljdaESZqJF3hidOkMgM2eg6mai7Fcm3UUh0xku9N3sbrt4rMv9PH4yv6UQZVy4QlOf+DiwafFKYre/pKmQupOYQzb/p99aW23txowLFYyXBfXw32srnVMOjBJzvIWMsDusvl5QlJWPkjKCGsbTBchH345073PLBdmUy7oBHFpaQOHFw+i7wIPLdfxRKuKZs/BTM3FLY0+3j7fRZEZZ7obYMdJmV+OgpUPdcvFg0/39hT1CO5PJEH+35mynqzdG+DLZ7eEjz3WdHCvpWmNRaZUl2XdaF5Rs5vm5nOvzEhSFo6t0TKSMgKwVMxEhSY8TEpVzIpJd02jKKKwgU0Uvf7KBB5aruNU86pcrPUcnGoOQ2b3HO4WMiadk0/Z70NQjkScXh7g3pPp5CJMmuqLR3D6iy8IH3us6eAHFo+k3nZWkgpfc6uPlVZf+Nhqq4/u4WsxtrqibHx5o+o6INOrzAQho2gZMYqQlBEe1iUTxU14Zo4fKpUklOlYRo1LSxuRE524x9M+V/S6tK9Psp8wOgPgiVZV+NgTrSo60V5iFUnOP52+i9PL8Qe/uj1s9JyGsNTD5lY/tHH0aquP5tZV0Tlw/Q3pdp4R2cqKM5NVzDXEn69DE0D9knk9poDhDS1Z4crjOkBSRhD5c9PcPEkZsQerImZJLk6mR5tMHptKRuU4RcjcgU4SNVMRYQuOR9RQOsl+ZSZzX1+6jGbvGuFjzZ4EqMmlAAAgAElEQVSDzZ6DuTFXe1phEJURgjQT5/U2QuXIj9fIOS2iComezCwLIk2zjSpmJq+KzuUXvp5+5xmRqe44Xqvgdccn96wx8wj2WjOJpJ8Z069pWdEhZXkV/zCxwfSoYlsa43MryyRmxB6siZilmfhQtCk/RBOGMk8iZImSjLylTIRMJC0YdUsagTvg9DEFcarZTM1F+6X1PdvK88794cWDhaWUTo8PpSsOFXIRjD55MiPidccnMV4r5tIgipLFRc5aZy7gu69t467FCuYmAAfA3ARw16K415rNZLmmmZw6nUbK4qSLpGz0sE3KPOgzRPix4qqV5WKUZAF1lsXW1PtqL/Q+XCUYlcpzgmTCupG6A9xcbeMr/f2nmxPuFdQFzuEVDbGBtOensaqDW+croWvM5iIaOachuHbr/juG437k65exenmA2QMV3H7Dgd3fy25Phqzr0cIiZ94Y0vRaI8wgqZT5hSsoXyeOTAt/r4siJ9QnZ49RARAftkqZB0XOCA8rxMyPaMIvMzEKSwMRvTZpykjWfmRlY5SPPQpbREMHb64P08zOONeg2XMwhT5urrZ3fy/CBjnLGpUfStfekvWvmnVw1/EaZiccLXLhycw1x68d/sJx9v4/5Pkq9qmqYEjYmJL0WrOVLCmNRdyoCWsgnQZTomQARTlMwnYp8yA5IwCLxCzqQiTbaNkvTTJFROIufrL7GhVG6VjDKCIqZgMVB7hrbBNddxOXq1UccPrCSFkQG+QsC0VGej78qaW95fJb/d11Wu9Y6GjZZ9SaMZmURSI7ecpZMBqWZR2ZSc2iScrMoSxS5kFyRlgjZrLISFqUUKmuklX2BdvEXkyUCJkCH3lRd4AZR7zezDbiziNJoml5R3qiKkI+8twmvvPImDZBDMoZCVdybLmuHFtolK7SYp5S5k9VLJuAZMW29yNOtkjICA9rxCzNhSjp5EhX6WJbLqJEufFLo0pJ67rAZXcYBQOu/lsmIiaDbVEz77tucn+/qIqQXnl+naJIMlYseUbNVMmZ7mbRYcLlnywXJWWin0eVsglZ0ucR5ccaMdNNVinzXh8VpcsiZyqifAThoULSBi7wue4Unu2PYwNVjGEAFw66cHDQt4asIiFofrlTJXQ6kRUuU+XMqwi5IpCzYHn+YOohSZV+4q4nKjChMJAJxMlWEWmLJGF7sU3GABItIj1WiZmuqFlasYkqEa+iqEjYdlRslxCTtp+X6n3mXbkxaixhfK47ha/0D+z+3MHV3lcbqO1WYbxrLLzAR1DukgqdaYSdE0yTs6iKkP7y/KL1YJSGqBf/Z0j19SRIXnKWZ9QsSWTNxLViJGV7sUnKSMYIFVglZmmJmhilrb4Y97qwC2qSi6lpk7myI9PPKy9h8o/FREnrusCz/fiux8/2x3GnuxkaBQvKXZjQmZ7OKHM+MO37LKoImbQ8f+PEUZIzRRRxc62METOSsnJgspCRgBE6sUbMsl60RBOjtFKWdb9ZKz6K9kFkJ+7ucVFikGelR9mJ2mW3ig1fhCyMTVRx2a0KC35EyZ1I6IJjK1LU0nzn8khP8+j046s8xlWElCltLyNlMmMZZWQ+SyrFvggZM7EAyE1z88bIGQnZXkjKiFHGCjFTJR7+i1vWbSaJfCWRs6QXX5IytYjkzJRIjS5BSzNRO+D0cRB9bMScQqbQ3y0KEiRK7qKEziPpuE35O2aZZMdJTn/g4sGnxVGwakhuqKgipAopSzMWGcoienmfu4uKjqmUMpNK5quCpGwvJksZQeSBFWKmEpUXQxVyFvZY3LYIfXhyZlKESvSaJOPrDIDNnoOpmouxSvZJWt0Bbq62d9MOw7i52g5NY4ySuyihKwMy32H/OUFWch58ure3N9k2dn7u4T5WlxpbnJTJpi6qGIsfXaJXdopMVzQxUuan6KgZSdleSMoIYgTFLA1xjaSTyJloezJSlkbGdBU9GQVMia5EISNofRd4aLmOx5tOoLgGMhfXeHN9uAZsb1VGoIcKpnxFPMKIkrsooUuL6evUgvhv2MhITlRvstPLA9x70o2NMKmSMhVjCaJa9FQQl54atgYvj3Nv0evHVEuZrmhZ0XJGDLFByiiNkcgDEjMFJK2OpXutiS7RI8wkSjgeWq7jVPPqpFW2WqIMFWe4jTvdzdR9zPxyt4mqlNBlwTY5A6Il59T5Ae65aYBr6hWtvcmSFvjIOpagILZ7A5z+4gvC56YVPdswsYCMCNOjZKZA0bKr2CBlBJEXJGYFIitoqiWKpMwcVJWrFkXPOgPgiZZ4DVdctURZRP3HotaFBRHJne4+ZnkWU1FBlOS0+8ADT/XwnleNJepNFoaqCotZx9I6c2GPnDW3+lKiZ9r6M9XRMpXrpHWgU8p0NJguKlJGUmYfFC0j8oLELIY8ok95Vmsz8WJOqMMfEdrsOWj2xJNTmeIaUajuP1Z3woVOV/NpndGz4Jq+LEyPAzPjwFpb/PhTay46fVe6N1keqBiLX85mJquYa1Sx3Nr/GZmdABp1Fw/w/NefFRHBkj2H59lA2sYoGTWOJmQgISsfjLEKgN8DcBuANoAf5Zw/I3jeBwGscs5/njFWB/AnAE4AGAfwq5zzv9ExPhIzg9ApaCRko4MnHFM1FzM1F2sCOctaXEO2/1gW8mg+rTp65q3pe6JVRbPnYKbm4pZGH2+f7yKtF1UdYDyiM8Fa+2rESEVvMlWoHMt4rYLXHZ/EJ7+2/7N163wFH3+ub9z6MxF5n4fzkLM8pUxV1IykzCyeWT1nZDojSVlpuRfABOf8jYyxNwD4DQDf438CY+wnALwGwOd2fvUuACuc83czxmYBfBUAidmooFrQRlXKkqT8hE1ebEl3C+LJ2S2NPk4194dsXj3j4rrD6SZtSfuPpSUP+fNQJWjBNX1rPWf3/b/ncDfVNh98uocLW+GP+1MD43qTAfml+8mMJQ5/1Oz+O4bf5y+f3cJqq4/ZRhWvmXFxz01V/No/it/bUVl/FoUuObMxSgaQlBHxkJCVnjsBfAIAOOcPM8a+0f8gY+xNAO4A8IcAXrHz678A8Jc7/3YA9HQNrnRi5pcZ24XE9vHbQtHVy3RxaWkD3+QCmJkTRnCAqyKS5D1I0n8sbRpiXvIXJEt6Y9SavidaVdw9102c1hhV+MNDlBoo6k1WVLl50ViS4MlZteLg3W+cxX2vn0Fzq4+ZySq6SxdxacvVVvREJXQ+zwZFygjdmCxkJ45MFz2EMnEQgP+E0meM1TjnPcbYAoBfAfAOAPd5T+CctwCAMTaFoaD9kq7BlU7MVFeusqUSVhRJq0aWhSRrMcpKxQHe0F7B7TXgcnVHkNrA6rn0d9Jl+o9lTUPM2ny6CKLW9DV7DjZ7DubGXOntzRw/FCkdAPCGo450aqDKcvN5F9nwR87GaxVce7CyW1hDRdGTMqPrxtOxhYb2qJnKYh8kZWZiSgqjyVJWVgp8zzcATPl+rnDOvQjY9wGYB/AQgKMAJhljT3LOP8QYux7AxwD8Huf8z3QNrnRiBuirYhgmaDaIjw1jJMLxy2OaiZaouEbaCZtM/7HPdLKlIRbZfDpt1CxqTd9MzcVULZmUATHSMQ58/yvqUtEuVX3Fimzy7Jczf7VDk4qehFHEudfmTABdPcvyhKQsHFNkzMMGKTtzcZ2iZur4IoB7ADyws8bsMe8BzvnvAPgdAGCM/SCAV+xI2bUAPgngX3POP61zcKUUM13YHj0jObOToCSkST9UTVT/MRVpiHk3nw6SRs7GKghd03dLoy+Vxhg8v0RJx22H5aVDVY+zops8h5XzT1NoRNRMW1W7gKLJ69ygI2qmQ8qogXTxmCZjNkJypoyPAXgrY+wUhuvFfogxdj+ABuf8gyGv+UUAhwC8nzH2/p3ffQfn/IrqwZGYJcQvNv5JlC3SY3IPHGIvcWJQpKBF9R/bGKhJQ8y7+XSQNHLmrd0LW9MXRdhNHxXVDVWk+6mKuulAttCISMZEj6sUtDzPtbrOBUH5OrbQ2PNvFXKmK0pGKYzFYYOM2RAt80Nylh3O+QDAewO/flLwvA/5/v0+AO/TO7IhJGYZsFlu8m5qTSQjiRDk2a8oiChFUiYNUaYoSBHNp7NSdYbVF++e6ybqYxb1fVRR3VBFup+qqJtOogqNxEkZIc+58609cpaVMqQueoy6lNkgYx62SZkHyVm5ITEbQUi6zCdptEaHnKXdZlQa4k2VNr6QsChIVPNpGdJWhsxSFGasgkSFPqS2mbG6YdbIm+1FNrwoWJyg2RotywuVQgaQlJUJm6TMVkjIyg+J2QhRxkkCcRWVcuZJSdpthqUhukBuvclkK0MGxU1GyDoDJIqIxZHH+tW0kbfGiaNonblgRZENGYLipSN9ESjX+Va1jOUFrS3LD9ukzLZoGQnZ6EBiNiKUaZIwKqRZ41RkWqMfURoiAHx4W3wx1NGbLK5BdVDcDnnrwdwuqo5YvvrusIm0aA2ZJV6SKPLmSYsnZyrWu5mGjoIfo1SFMcv6MoqWEUQ8JGWjhb1XUw0E71iXRWbKchy2oDqakpSsciaqApmltL6XhthUVBREBpnKkF8IiNtaz8GpZgWuCziOuIDHQ8t1nGrW970GGK4ty4JpVV+DKX/ez/dVLuyLunX6Lla33Mx9zfz7tLVCIp1v5SiTlBGELkjKRg8Ssx1EEyJbKi0G8U/wbBy/6YRFspJGU2Rkx/+cNGvO0qyv8vapOvqWZ2+yqAbVG6hifVANFbcvb1TRdq8atSdfgwHAt8TbfKJVxd1z3UJEXAdx67C8qFt/4OIB3s3U1yxsX16UTgd5N8guI6ZHyiiNMT8ojVEPJGWjCYnZDv4m0iQzRBRhghQWTem7wL1H9kZT0ghPktTGvjts8pykyIZof0HSFtIAsvUmS7rfMfThABCV33AA9FyEilvbFe/ga5er2OiLH2v2HGz2nMwFP0yJmvmbOQd/70d3XzPVcpZHg+yyX0NU9y3TAUlZfpCU6YGkzH4YY9MAbgYwAPA851zqjlRpxcw/uUlykSzLBTXLcZR9YqGDzmAYNRHxT+s1OC7wXUe6WD2XLQIlK2cPLdfxlf7ViXHWIhuyhTTiSNqbLO1+O6gKpQwYylrNQWj0LozNvoOpqotNgZzN1FxM1dRWYYxDd9THX8VQJEd59TVTWZwjrwbZeZ9D81hfZoOQASRleWKTlNkiZABJme0wxr4DwL8D8EoASwC6AK5njD0B4Nc55/8j6vWlFLPgHWfT1o6ZNh4PGxtmm8Jmz0GzJ56EDuDg4Y062pc7uGss+77i5CxKEtMW2YgrpCFL0t5kafd7wOljCn1shpziHu1P4qZKG18diB4XD8gF0A7Jtryl0VeWxhgXNcsj6uMnTIjy7muWVdCiRPKxpoN7+2obZJflHGqLkBH5QlKWnBNHpmPTeEnK7IYx9iEAFwD8FOf8nwOPvQrAjzDGfoBz/q6wbZRSzOIo4oIZNdEyYT1Y2Bo7QM24sqZnqXxvRHeXs/SsAoCpmoupmAiMysqDUXIWJYlpimzIFNJIk9YYN4Ys+607wMmQtEnAwaP9A/iG6mXcVrmM04NJuCEyFnxdZ+d5446Ljos96wiB/Z+ttJ+rqM/7xy/tTZmVifroSJGM62t23cuvxXitonydWFpBixTJVh/dw9dibHVl32P+v0XS99B2ObNNyihalg+2SJkJQhYUreDPflEjKSsF/yfnXFiidUfU/nfG2GLUBkonZjIXTlMvlKZexLOOy4Q1M7pZf3EDN1enQkRgiOrKg2FyFiWJaYpsRBXSUH1MKvf75vqwJH6YeD3XH8e9Y2t4tBMW1vFSE/e/dszt438bW8N0pY96G8irUnZUNFSUPuj/3qqWs7i+ZuO1YQgxqtJiXJGRKJKuP4sUyUYVM5NVdFejt5H0PTTxfF40ugp/FCllo1Iq3wYhM0HGAHnJIhkrF34p24mQzcI3ieCc/z3nfClqG6UTs6gLZ1EXSdkLeZEX8bgJRxY5yzohVP2+ZI2OhfEttU280K9jGXWIJvOqKw8C++XMi9iESWJckQ0ReVZTVLnfigPcXt/Co22xeG3uSF/YPg5ggMsQ5ye2UEXNgdK+ax5R35VLWy6az3aEj8mkD6qWs6i+ZqICIllELCtRIvm645MYr1UganjgnfuSvm95nc91ri87ttCwImpGUqYPG2TMwxQpIwjG2O8CuAfAc7h6l9cF8G1xry2dmAH7Jx+mpQiKMOHOqolyZsL7Isvne1NYRvgisjRSJIMnZ94EbeACrjusTNjZEYs6BnhVdTu0yEYUUdUUxzEIiWllJ0sVR484uZuu9EP3cbK6jef746nFMOkNAJnvR1z64LQ481Mb1YqD+1h9X18zj7DqjipIkyIZJpL333EocnumSlkeHFtoAFCT1qgjWkZSph6bZMyDpIwwjLcBYJzzK0lfWEox87Dl4mjSOE2SM5Pelyi8fmFh66EcuLi1upVKipKMweNz3Sl8dXBgz+NdVOEAiSoo+kvUv7m+iaV+HZcC4nkJY/hcdypVpUeZEvhJqzgGkZG7qH1UAGWRxzCSfCfi0gdlilfoWG/m9TXz8FeMTIq/GqRqwkTyytmXlO+rbJgYPSMpU49tUkZCRhjKcwirJBZDacXMhEm9revdipYzE9+TOKLWQ7kAbq9tRUpRMLoSlZ4UJTQqCnWIStTfWG1jOyStL2kBkCQl8JNWcRQRJ3dR+0grhjLRsrRyFJU+WDTCipHPvID3vHVRqmKkP2qlU9CCIqkSG89fspgkZyRlo4dNEnbm4jqtHxttVgF8jTF2CsBujgvn/IfjXlj8lbyk2F7wQkbOvOep3q+NRKXMHZRIe/OvFQuTMhmhUVGoQ1Si/tF+DeJ2zckLgKQpgR9VxTEu8iYrd6J9qBDDMPxN7ZMQlz7opzMYVumcqrnKyvlHEdon7FNL+OG7r498bVgqoUjQVFd6VIWt5y9ZskiZyjTGoqSs7EJmYrTMJhkLQnI20nxi57/ElFbMiqxwaNO6sihkJo5J3+eobZn+fkShYj1U3CJ+GaHJWjAjOiVTrGZJCoCoLL2ftPm0TIn+MJK8NunaMtHnPup74k8TPDwpfrOuRq4msbrt7inrP3eDnptGcQ2n273BbqXGIDKiZaqMedh8/gpiSlRMRBFSVnYhM5nnVpZJzgjr4Jx/mDH2agBvwdC1Pss5/6rMa0srZgAKKQBie6RMRNroWRnXk4nwCm9kXQ8VRZTQPNMfx2v6W8PS7RkFMS4lU4TMdr2oVs+FstL7qppeq0JltU9R76wkjaX3Rq4crPUcnGpWMN4Yx32BfflFL0uD5biG082tPq49qD5sp2r8WbD5/OVhsowBFCXTjYnRMg+SM8I2GGPvBvB/AfhrABUAf8UY+1XO+Z/EvbaUYqa7WXLUfmSx7UIuszYs76qLqpr5qkJn2ltciuKfduZ3I0bfUksviHEpmTdW2nh+IL/dYFRrCn2MYYCO4FjqGOAa5B95U4HOz573HfnklYYwTbDduox7Dl8t9D55bCYycuX1OksieiKCqYVxFSPrl5aBgwv7HksbCcs6flnCzlG60rmLQLeUZU1jpCgZYYqcnTgynerzTHI2cvwsgG/inK8AAGPs1wB8FsBoilkUQXmQuaiWMQqWBtXV3NJOaMJS/vy/D5soJ+n5k3ayLUp7E/UaS0KUMHmFf4IRozBBjFqTFRdxu2tsE90E4hmMam1GnHI6qOJUT67Co+xauuCE0yv9rZI8bgh0BsBXL/QAQQGWJ1pV3D3X3V1D9sLz61jdnoCoIJS/11noejD0cB+rR44nrD/Zrc+8kLhiZNqIV5bxyxB3fjJFyPytMtJAkbK9jKKQFR0t84Qr7m/tPZ6HoEVJlP+xJJJGcjZSVD0pAwDO+TJjTHzHNMDIiVmQuDVSJohIGkzp4ybCtPGoIGxyJJq0p5lIRQlTEH/EyC+IsmuyRCmZN1bbuK26ha4rv94qKqo1TIzcPwmXjXYV1fQakCvzr5rNnoNmT7yzZs/BZs/B3Ngw2XSqNlxTtiZ4vtfrLG49mBdVS8p73roIfGoptGKkv69Zf+DiwyHPjYt46Rq/bWRtLk1SRhRJULBkBU0nScUpraSpQsfNRkIJjzLG/iOAP975+UcAPCrzwlKKmWxkR0YQRGs90oxnFJBZY6bivYiqXhgXvYiTorzSIeMqMIrwhOmZHWEKa5ERtlZLdk2WPyVzc1DFV3qTeL4/jkf7k7EFNvxERbXCkF1nJruWTuVFK1RsX9jY817IfIaSVkuMkq2Zmoup2tUVgGMV4BuO1iIjV5e23Mj1YF5ULYywxtHVioMfvvt6rD57PjQK5qUuPsC7qSNecevZ4sYvg+mpimWOkhUFRcvMwC9spkTJ8nh9Gs6db6HYRRxECD8G4N9jmLpYAfB3AP6VzAtLKWZhZL3AJi1vbUpVSFUVKsOOO2zbuo/fL1lJhCrt65Iis+0kguYJ02v6W/jTTvRF6pHuJO4auypPWwPg6f6E8LlhUaq6Azzan8Sjg3QFNqKiWmEVHq/p97D+0gZmFg4IHt2LqmIr5863pAROVmyjUmr7LvDQch1PtKpo9pw91RKjAjxjFeCWRh+nmvst7pZGf4/czRw/hHcOXET1OotbDybTGDpMzoD4PmFZI14qxi9LkRV+w0grZTYJGaUw6sVEIRNhwroygkgK5/wKgJ9L89rSipnOC6nJTZLDxqVrclH0hCVKfqLkS4eQZVnrkeS105Wo9WaACwePDg6g0h3Ky+e6U3i6P45WSIPosChV1gIbUVGtOXSxjLF9v1/stlCDKyVLWYutJJmkpn0v/P3pgKGUnWpejQZ51RIB7CngIeLt88PH/VLHJvt4w8EeOoOhvHnf/7heZ2NVB7fOVxKvBwsSLNzhiVrjxNHIoh5ZI16qxi9L2uiZ6vOurVGytAUT8oKkjKC1X4QKGGNf5py/bmc9mf/+swPA5ZzHphGVVsx0ExY9K1JUdBYpMek4k5JHdCxImn3JRs9k15s92x/HAMCj/ejoU9iaLBXNqvelX+6cpi4PHMwMrqBdqeFKpYbJQQ+L3RZu295dK7s7kYwTtLoDXL6wjpmI5wXXhQUnqXEimOW98P6e09cdxBMt8TYebzq4fXtjV+5En5+qM5S3u+e6aPYcnGrWwC9X8aWNGmYnhqLyzoG7Z31WVORqGD0Lj6qlQbbCooqIl47xxyErWqrPxbYKWVryjJaNmpQRBKEPzvnrdv6/7044Y0wql4PELCOqKxVmIa8ooQ1SJprIBKMXOvcpUyEyDFH0LCgWnvA83Z/YiYTtjxBsoIpnQ9IX/YT1IVNRYMOLag06O4K4s58r1TquVOs4ud0E6zQxMeijFtIpLUyaRHIVZOFoQ7gu7GRo/FCMivfi60uXsda7RvhYkh5uYxXgS+s1fGn9auQtTUXCuKhaVrzomUjWVES8dI8/jCg5C2vVYsM5UwcUJTMPipYRhH4YY//AOX+j7+cKgP8J4DVxryUxU8CoXHRtOc6slcpUk2UdXFQlxbvGNnHHYBP/tT2PluCr3EAfrdDiGy4aGOBl1e3QNVlZm1V7dF3g+ZA0wBfrB3Db9kqolHmkvev/UHMcT00I1oXNLSRqQq3ivYiSu5maixsWD0gVAukMEBp5S1ORMG49mCxh682C/c48VEW8VI0/CXnKVtbz2bGFRq5RM9N7lo2qkAEkZSIohZFQCWPs7wC8Zeff/juPPQB/I7MNK8QsqvS7quqLRDmIm8ToiprJTJ6SCtrhxYN44Dk3suDEZAV4WZgw7DSDFolAA328a3wZkyEi4E3k3rwzp85SYCMqDXCrUsN2pYrGoCe9PVl6cLBUF6cnyqyR809mjy00MhcbiZK7YAGPKKJK56uqSChLmIzJPL915kIhES9VBOXMlMyJIjA5MuYxykIGkJSJsEHKolLsbUxTLjuc828DAMbYb3PO35dmG1aImZ80F7+k1QSJcqNazpLe0Zbdf2cAnHHEqW9+sYgShkoXQhF4WbUdK2UAcP5CCy9HC3cebaTu3RUVKZoc9DAx0NNzbLtSxVZFfIqLSx0MS5O8awGpi40A4r/Vq2fc3cIeMsj0KdNNUiEL20brzIVCIl6qMV3KdEXNbBAygKTMRCkrstqi6UIm2+aFepgZzb9jjL0DQAPDRRxVADdyzn857oXWiZlK0lxMyy5zWdZGFU0SQVIlZ2nTjGT2HxUZ8YtFVHXCpFGesMnbxQv713nJNlqOihR5VRh1MDHoY3LQw1Z1/5qrqHVhURNY2YIkYYj+Vtcd3v85iOpzFlU6X0dFQj8qhCy4PdmCIUURFRWzKWNDtZzpkDLVaYyjLmSAmVJWFDJC5v9c5y1wJFql4kEAkwBOAvg8gG8F8A8yLxwpMVORdmJTAYws2CZlQLKS86qOL22JfJn9R0VGRGJRd7AvApS1pLyIqHVvYU2n31zfxOXLHSzVG9gKqcKomhpcLHZbeKq6/7ueZI2cDkR/K2DY5+zjl4Z9zjb6Dg6F9DnzImx8u55bRUIguneZ/zlhBF9rupQBxUbDTFsv62GDlI06JGR7KUuUjLAGBuBlAH4bwybT/xbAX8q80AoxSyJUssKUZq1a8PlllDMbhcxPWMn5PBpJq5xEeds64bpYw/5y90nFIkwEPLzo15GjjX3l5IMXDNlGy34qDvDa7RW8ZnsV25VqZBVGlXjiFxTCk9srQMiFMCqyoOPi6UVP+y7wu2fHcb5zdT1eWJ+z3dL5gy42ew6uv3E6t/VZnkylkSwbRCwpo7bOjKTMLGwUsCLSGM9cXJeSs6IETqZ3J2EVL3HOXcbYkwBu5Zz/aWnL5Qf7h6mSozQX11GJntlIEYIZtk8ZSQyTumAfsCn0cTJh8Y0oQqNfR4fry0Tpi2mbTnvCEyz00YOzT9ZEv0tDBeqEUPdF879frO+RMj9PtKq4e64rTGucG3OxdegxMp4AACAASURBVK6JsZyloHXmAuqLR/DiUy9ZV7gjKZ2+uEBJ8Nx/8czabgrqkRPZbt6pjpapSGO0TcpOzh4rdTojSVkyZOWsKKK+oyRt1vHPjLH/BOD3AXyUMXYdAKleNtaJmUdePbtkKGv0jFBDlCTKTL4GAAaCPmUqiIx+Lex/voqm0x4DAI9OzO2JZl3XbcEBcE6Q8pik71iQGtx9Qhh1hzIYNUt6UUx697MzAL52Oay1wTByttlzMDcWLpVhTe910B+4ePDpHk5/8YV9qZTVsHzWBJiy9mz3OAUl/f3H2XeBh5aHKajNnoOZmotbVlp4+zxQFl+1TcrKDklZOkyXszCyrm8mcucnAbyJc/41xtgvA/hfANwv80Jrxcw0KHoWTZpeXjLY+r5LCZkLfHR7Fpcwtvu7TYm0QVnSRL+yNlr2X1Q+05nCUz4p3KrW8UxgPdhWtb67Ruy1EevRvElj0guujJylkbK4bQfZ7DnY7IfP4A9WXUzV5CJ9eTS9f/Dp3p7m0GkaXIfhpUiaIGdRx/m2a66K+0PLdZxqXj3usBRUWUyLlpGUEWkxQcaC2CpnAAma6TDGXsc5/zKAb975+VsBrGNYDGRWZhskZiHkefdZFtFYbBESXamFthy/H9lJ12c6U3ukzI9MH6440kS/VDadDpNCEUv1Bl6zvaplXVqcnOVB+6V1zNTGhYVegKt9zqKqNeZFp+/i9PJA+FiaBtdhFC1lUcf51Qs9vOUG7P5Nwhp+h6Wg5oWJfY7yFLKypjGaHC0zUcRE2CxnJGVG814APw7g3wsecwF8W9wGSMxikBG0IuXA1ojRqCIrZV0XeHYQLi5J0wZFpI1+ZWm07E0UW5UaNqaqkM3Q1NmI2j8uFRe8NGXJ6w5wwr0iLPSyMDasyuhVa9xNlRNUa8yD9fawkbUIFQ2uixYyj6jjbPpSS6PaWjQlUlCDqIqWqZIyVdEyEjI1mCpltgiZqcheN0jKzIZz/uM7//wZzvlX02yDxEwSv6AVIUFxkTsStGSkTa3Mq3z1ZbeKVkg0CwAOYBCbNhhH2uhX2hL8/otOVI8xETobUftRJWhpXr9PeKsuXnmgj+860lWeKpeF6fHhWqsVgbTk1eA6D6KOc6Z2NbU0qq2F/3kyqDi/mNSrLO90xTILGWCmlBUtZCeOTKf+nBYdNQteJ0TXjSxrnYlC+SPG2ASAjwL4KOf8BdkXkpglxHTxGSVBS9sM2/86WUHLu59QVDQLAG6ubivpw5Ul+hVXgt9PcLIY1WNMhEwjapUX2CLy+IXNp689aFyq3FjVwa3zlT1rrzxuv2kKszdfTaM3JfqVhqjj9FJLgeiG3/7n6UBnqmIWKctLyMouYqZjgpR5/7dBztJcT0jG7IRz/nrG2MsA/AsADzHGVgB8hHP+x3GvJTGzhDSVIv2vLQNRciQraWHbiBK0Ipq8RkWzDqODuxSVy9fRgDpI2ORR1GMsqipjERQhaH7hvbS0gcqRaaWpcip458tqqB8cx5fPbmG11cdso4rXHZ/E/XfsPUd5hTxsFLTm2TV8+wTQnhGnkPrxfo57XhSy5xkT1435yUPKRlHITIqWFS1kwP4bcd7POgrVpIWkarThnD/NGPtNAM8C+FkAPw+AxKxMxAlWUNzKImRJiIt8HV48GDoBMq25tj+atYEqGujj5kobd41tIklFcq959Bj66EAsX0miX0mImkRG9Ri7NWHfMd13PGXTSYLHq+LCrDJVThXVioN3LHTwnUeqWG9Xh2l/N+8vOBUmZGF9wUzCS1u/53AXd891I4uu7Db8jnmeLaSd3FKlxfRE9VwzRcpMEDIg+nyfJXqmApIxAgAYY98L4PsB3AHgvwP4ac75KZnXkpiViLKLWJRUZdlOFpnTSdZoltc82mtO7WBYEuigL10xbcspGVHxnhPXLFrUY0z0O1NIErFIU24/SJGpcnGMVZ3dQh8yUTHZvmCm4E8Nl4lKeg2/dZKmuEwSTIo4jBLPrJ4zRsBE2CBl/ufEfY513MwjKSN8/ACAPwVwP+c80UJwEjPCKjyJEolSkohX0uhY1H5F21QpcmmjWcHm0d50cSNjL7TgpFCU7nfufEvYQFpFs2jbUJEOqSJVTgbdkaywvmBXej18/ytqRkfPgPLf/CIpI4KYImSq0JVdQVJG+OGcv5Mx9s0AfoQx9l8A3ME5/3uZ15KY+RiVi28ZSBr10rHfqH3qjrJ56YlhUTSZPmEqeqH5CQrboxNzeGrianqt1yx6AOD2gtaMqSIuCigiS/RMd6pcmkiW/zwpWv8alLyovmAPXxjgqbUObjs83GffhVJBVCWcOqvyJjln6Iia2SJlZV5fZlLUzEQhyyJVtvYsI+yEMfY+APcCOAbgLwD8IWPsjznnvx732pEXM9GEggTNDryJTFCQgpMb1dImuz2ZKFtSvPREb91ZWFpiVPNojw23iucubqMx6EkLg+xksAcHS3XxNp8ZnwHg4LXby9ojZ2kEKoqsUcCgnMUJdhDZVLmkxYLCIllAD/ex/S0NgudGfzuRK90BHniqD742QLN9VfK+dbEa2hcMAFbbw30+3ezgSg9KUh11pE7quj4UkS4N2CNkRH6UQcryFDGKlhECfhDD9WVf4pyvMMZeD+AfAZCYxSG660tCZg8iSdIVPUsrfCqjZ8H0xLC0xLhy+4De3mDblSq2KiH7dhw8MzGDCly8VlPkTFcaZVgUEID0sZw738LC0YaUYAMQ3nxQSVQk67Gmgx9YPILu0sXY7fRd4AHexT+cH2Db97HyJG/gAnONKpZb0Z+5JZ/7xwliHEmFswiKEjLVUOGPcmCilBGEhfQ55x3GmPfzNgCpCVchSz0YY3cwxj5bxL6jaJ5d0yZlSe5e54Fp4zGRS0sbe/4TPe7RGQArHQcdwfxW1cQrKj3x2f44ur5AilduPwo21sENCwcS3e2TfW5tIJ7o+1mqN9CDnjVFnkBtVeuA4wwFauIQHp2YS73NqChg0mPxBHsozs6OYB/AZ7pTwuen+QzJnMs6fRfPr7uhkazVVh/Nrf3XEtH549PbDXxmaa+U+Xl8ZYDbFq+JHZOI08sDdPrJIp5Rwplme35UXCfCziuymHSXPk8pMyXVLyvjgx6u61zG+E6Ro5Ozxwo/NpIyYhRgjFUYY3/AGPsHxthnGWMnA4+/kzH2T4yxf9xJSfR+/ws7r3mEMfYjMbv5HGPs1wEcYIzdC+BvAHxaZny5R8wYYz8H4N0ALue976LwJjGmROTSjMeUsedBksnSSy9s4HPdKZxxrtlXlEF1HYOo9MRNVHHZre4pEuKV24+qypgGmfUtvUr8PZ+tSg3blary6otxAvWa7dVUaY1RUcCtSg2PXDOP11+5FHu3qwcnVLBP9yeBNoQtEdJEzsJSGv0pfivbwzt0ondktlHFzGQV3dX9j/nXW00em8HphzuRY1ndBu5+1RSqFQePnN3CSkzkLPja9TZ2K0DKsN5GuHD6thfMmohbj5b1HFiWCBmRjqo7wE9e/Bq+pXUeh3tXsDbewFcO3YgHDi1g4IxSWSSCKIx7AUxwzt/IGHsDgN8A8D0AwBirAvh/AXwjgBaArzHGPgrg1QDeBOCbAUwC+Lcx+/g/APwYgEcB/EsADwH4A5nBFZHK+CyA7wXwkQL2nTtha9iKEpyoNXXA/kkHRdaiCaYWrvWc3bLm9xxWWzEvKj1xCn0cCFRuDJbbj+pjlpQ4OZsY9DE26KFTDU8Xm9CUShknUGllcGLQx+SgN4zCBXEcnBmfxpg7iE1p3K6EC7YLB48ODqDSFVfMVCVnwRS/sPjm645PYrxWwfiJo8Jy+N52L22FR908ZieA2UYN737jLO57/Qw+9MVVfOEZuftzsxNDUUrC9PjwdSuCcc3UBnAvNuHvQNB3gY880tqpfFkRrkfLct4uq5BRCmMyfmHj63hb87ndnw+3W3jbhccAAH9+451FDYuiZRlQ0RaFyJU7AXwCADjnDzPGvtF7gHPeZ4zdwjnvMcaOAKgC6AC4G8BjAD4G4CCG4rUPxthx34//Y+c/j+sAnI0bXO63ZzjnDwJQO2O1kJnjh4yUHm9cacYXTP2zcSKSZMxRqYVPtKroDNSXzQ9LT7y52g6VrboDzFT6mKwM/6+qCmMYAwCPTcyiH3P3d7HbUlKQI4gnUCKyrKurwcViNzpSKJPSODEYCnYUwdRUP1nTGqNS/CrO8KIw36jiba+cwv13DM8BcT3KPAmK4tb5CsZrw8/EeK2CH/2WObztlVOYbwyjuXMTwGLI3ObW+UriaopjVQe3zos/g6L+bw8t13GqWcdarwIXV9ejPfj08LOkWsrOnW9p7UcmAxX+yI+Ts8fwyukjeO3a88LHX7t2BmP9kZ8aEUQeHATgP/n1GWO7d3N3pOx7MYx2fRbDDL95DKNo3wfgvQA+yhgTXZQ+t/OahwE8j2H64icBPAfgUzKDG/niH6NGXpG6vMrXqyZJoY6o1MJmz8HXly5jRvGtDy/98Nmd9MSpjGmJOggWyBAx072irfCHJ1BeUQ4/WWXwtu0VdJwKzowdBJz952SZiFwNLm6utneLtogQpab6kWnZEMSLnEWl+A1c4N+8to5jDcA5OoXewMWVsy/FbtuTIH8UzmO8CrxpYRh5ap25gMaJowCAasXBOxY6+M4jVay3q5geH7YFCKuimIbh60TbO4DNpebu8zqD4c0UEaeXB3jL+JryRt5Z77CrkDqZRrxRFBEt01Uuf3zQw1yvjZXaONphhYsyMt3dwmxb/Hc71G5huruFS9X8y7qbHi0zudQ9RcrSU+DfdQOAf0F3hXO+56LNOf8rxthfA/gQhqmIKwCe5Jx3AHDG2DaAwwAuBl53IwAwxv4cwO9yzj+/8/PrAfyczOBIzAhl2CpjaYlKLZypuftSC1UQTE9UkZaokqj1XXBdTAx6WOxe1l4q/7btFWxc6WCtMY12bRzjvTYOtdYxc2kJZ5D+glABcPuVZbxUm8QVQUqjbETu5Mp5DOYWcLo/CVcQYQumpoaV1U+T2hiV4jc7DnzlYh8feWKA1e0XE5WXD0rQoXHg5Ycc3PfyGq6pX/1rB6NvY1Vnz9qx+1gd955U03esWnFCt+fPCLi05aL5rHiN3Oq2i82eI9WmIAzdfQ2LoCxSFlzzdal2DT7fWMDvH3llbNQ/Kev1SayON3BYIGdr4w2s1xMsolSEaVJmioSRcJWaLwK4B8ADO2vMHvMeYIwdBPBxAG/jnLcZY5cxTAT6AoD3McZ+E8ACgAMYyloYt3hSBgCc839ijL1CZnBWi1mRa7XyIGrtF6EP2UmUl1ooinyccK9oFaa6g9BoSh50XaBVqe3rDxZZJh/AWy6/iOmBunSdqLv9N2Adi8vn0K3VUe91UXXdyNfJTgiWLjYx7TZw5dC1+x6TjchVALxi5Twwu4BHBwf2Pe6lpsr0rUsiZ17ULCy6NVkHPncuXXn5oAS5F5s4ciJdunZQ1rISt73o9WgupmrZU2519DVUQZqoWVmkDAB+8uLXcJ9vzddC78ruz//52lcr3VenWsdXDt24u6bMz1cOnYhck6sak4TMFBkDSMhGhI8BeCtj7BQAB8APMcbuB9DgnH9wp9jH3zPGugBOA/ivO2vPvhXDXmQVAD/FOY+ahC0xxj4A4L/tPP9dAJ6SGVwhYsY5PwPgDSq2lVe1wLwlKbi+q+wSqhr/5CdNJC8oZ2ERi2Bq4UzNxQn3ilGphSrZIwpT1X39waIKZEwOejiQoQJjmpSrquui2o2uFhjcftQkwXvO8UtLALAbkfO/D0m4a2wTle7VyplT6OOkLzVVtm9dktTG5tk1vPNlMwim+L1q1sHjK2IBOb08wL0nXano1VjVQX15Dagkb3SdBVFPSlmiUjFF69GyoCJ6pnptWtaURp3oEjJgmL74La3zwsfubJ3HHx1+hfK0xgdOvAnAcE3ZoXZrpyrjid3f54EpUmaSkAEkZaMC53yA4ToxP0/6Hv8ggA8KXieVirjDuwB8AMCfY1j0+FMYNp2OxeqImZ8ocRFdpJNIThZJCpuYJBlrmv2OKirvRh9ePLhbDj8sYqEytTBqUm3KXfY9ouDsb7Csen1XEZPFMEHzj8UBcMOlpT0RuZsPJ78BcO5CC5cnxoGJ/UVk4vrW3elupv6sbS41cR87tCfFb70NfP7FsHQ++XL1wXNUnnKWZZ/BVMyZ2mC39YVq4uQsTLxMmDjmGS3TKWUAMNdr43DvivCxI70rmOu18eKYmmnSM6vncHL2GAZOBX9+4534q+N3YLq7hfX6ZGkiZbKCb5qQAWZ8t4jywDlfA/DTaV5rrZglKUPv/U7l5CCtnMW9JuyuLwmZHKrXcshGLFSkFpoiX8D+ieGxhUakKPj7g3lRo6V6A1uVWqJokkl37c9cXI+daPgjcv7nyU48goVSNn2fr9fWthL1rUuKd17yZGt63A1fe7ZTrj6ux1eR+M/Jced60XGIUjFVF/zIio6y3Em+c3lJmW4h81ipjeNS7RosCOTsYu0arNQS9miIINg8ulOt517oI49IWdQNLdHjJkBSRpiEtWKWBtVyk1TOklImGcuaWpgEVWs5OgN9EQvbOHe+hValho2pKkTV4P3VCCsYRs9es72K7Up13zq0MEySMo+0Y5JJi4wqlPJsfxx31DYT9a1Lg/+mUVQ636vnHPz1M+JqicGiIP6bUN6/8xK6OCHzN9YOOw5vPVpTk5SZkMaY5nNdNikDgHalhs83FvasMfP4QmMhcxpjUMaKogghi/s9QRBirBQzk9Ji8lrjZjPBiUiaUt9pyLr9zZ6jNWJhKmETv7j1Y8FqhDW40o2ck6S/pJlU9h1HWAREN17UTURUoZRNVNFBNbS4TFTfuqT4z29h5eVdYI+wyRQFmTl+SEqE8qJ5dg0fvzTsVeaRpLiJCmSkTGdvs7Q3GvKQsjyFzM/vH3klgOGasiO9K7hYuwZf2KnKmAZTZAwwZy1ZEXhRsKJ7BRJEUqwUMxMZlfVfSctzR01E8hK0tEzVXO0RC914FyXZVI2oi5iu/mBJJ4tJBM0FcPbwIlYb0+jUxjHWa2O2tY7jl5Zi2kCrI0zOokT3mn4PB5x+bn3r/Oeut10DvOUY4ByZwfROwPgDD4vXnsUVBXnw6V5ioQOAxomjsU2tk9A8uxbbqyx4HCrP6SakKZsYkfYoSsoAoO9U8J+vfTX+6PArUvcxM0nGAPuELOoGVlKC17pjCw2SMyI3GGMDYM9kqIthuf1xABuc89jIkmEZ9PHkHS3zkLlAzxw/VNj48sCbXKieZIRtrzMAVjoOOvszq3JhrDKMTIhIE7E4vHgwVwlNejGSef5t2yt4+fYaJvtdwHUx2e/i5dtriasRAsNUvlalhr6gUbMMMhfys4cXceHQtejUJwDHQac+gQuHrsXZw4up9pkW0aTYE10Ri90WLl5o7RaXec/EMn5ofBnvmVgeVnIMecuivptJvk9jFaC+3MTWuSZeeH4dq9ti6faKggj313dxelm8s9PLA3T64m16DagbJ47u/lsFmz0HzZ74jRMdh8w5P+49vbS0Yb2U6Y6WFSllftqVGl4cO5BIyk7OHjNGym6am9/9L2+ySJWqGwbHFhqhNyDjbkySuBGq4JxXOOdVDKs6vgfANZzzSQD3AfhLmW2UKmKmO2KVpRxzGVBRdl5mm30XeGi5jidaVTR7DmZq7m51NBXLU4LjiTqupBGLsLL6ov3qJsmdQtnnpV0/5meAYdELrzjI2DVHpaJYSS/gfcfBakM8YVhrTGNx+VzhaY1xhVL8EU/ZtNlgJDrr92mq5mKm5mJNIDVeURAR6+2h8IiIqvKoMlIGXD1vJzmOuGuJzHtqgpABZkuZzZgkZEViyhqyuMI4UamNVPyD0MAdnPOf9H7gnD/IGHu/zAutE7Ok5ed1jgPYL2ijkM6YlKCcxQneQ8t714Gs9Ryc2lmJf8/hbKWrk06WZMvhyzQCLgJddwqTrB8LEqxEOIxiTQAYlqBXRbdWRyekqlq7No5urS7d40wVQTmTFd001fi8z/rD43OZvk9jlWEvr1OCahi3zldC0xijmjZHCZ0KvGIj/sqKUcfBJroYq45Ln7+jzlFvaCePHuuCpIzQiSlS5iGTuh+8YUlSRmjiMmPshwA8gOGl/t0ApC4O1qUyiihShppn13b3T1IWjpfGFydlUetAnmhVM6U1hkmZTESv7gAzlfAeZV5Z/eF6NGenrP4BfK47ted5XRdoDqroSgZqkj5flnPnW4Wkb0RVIlxrTKdOaxRR73Ux1hPn2Y332qj31PenSosnulHRxzR/r64LPN4Uv6dJvk9vn+/iTTNdHKoN4ACYmwDuWqzsFAsR41V5FPGyGT13K/oDFw/wLj7wcAe/8g8d/MevT+Djl+rwsib3HoeLQ7UB3jTTxdvnu9Ln76hz1ONNJ/N3VcVE8czFdZKyklNU2iIwFDJVUqZD7uKub953jKSM0Mi7AHwvgAsAlgB8O4ZyFot1ETMgWV+wvDBlHLYTtQ6k2XOw2XMwN5Z85hMXKYsqahKXjhnV3+uZnbL6VeyPqN1YaeO1tS1MCYRPVwSu6Fz6qEqEqqNYVdfFbGt9Nxrn51BrPdc0Rj9ZFronjZxddquhlUWTfJ+qzjC6dvdcd7coiEzZe3+Vx5VtYKI6XBX9pQsunm52lFdoDBYbWetV9kQH/cex2XMwVXMT9yqLOkeZUK0165odkjLzkRGy51aWlYubaRGyOKIiaCRlhGZ+jHN+T5oXWhsx80eqbEfXejUbi5G0X1rHFMSTmin00X4p+aRDNn0x7ZqQqMnvJqr4dOcgPiuIqD06OIAPdebx4e15fKYzhYFvfiwbgUtC0VIGXK1EKEJHFOv4pSUcXXsJ491twHUx3t3G0bWXcFxhymQaskyek/wdDzh9HAz5Ps3UXEzVksmpVxREtheZ17T5l98whjccrWC7D7T7QznzKjQ++HS6lNggUcVGgtHBsQowN5ZcyoCra9WEjxVcrdU2KTOl8IctyETJnltZVvp39KJjOqVMt/CZcO0jRo57GGOp7jhaGTErG2URTBVcd/1BvPqSi1PN/Y/dXG3juuvVFyDxPy/NNrzJr6isPuDga4NJ1BGWM+ZJ1/C1d41tRkbg0jS2NumiFFVyX0cUy8Fw3dri8jlhHzNvAlNESpBME+qs1B2E9kK7pdFPJSZpb/Y83Qyv0BhVcl+WqGIjWaLtQaLWqmXtL2fSd1U3JGXJkD1HqTyX2RYhC4OiY0QBrAB4kjH2ZQBXvF9yzn847oUkZiXGVuF7+/wwarK/4ln6GY8nXWFylaWMfdTk16Mr0TXLk66oCNyGW8VzF7fBrt2fnifCxImeqBLhwY1VrVGsqutGpkjqSPuRRWUPHxHByqL+CoJ5kbZCYxKiio2kiQ5G4b13jzcdZf3lVHxXTxyZNrpfGWC/kOVdjbHIqou6z00yiKTKxOsaQQT4cNoXkpgRxqFiHUgYosiXit5ib65vou06+NrgGiBl62JvfcoBp4/Jvrjx8OSgh4lBPzR33oQLVlwkSFSJcOmSIESqkb7joFurw61U4AyGkZyyRs/8lUXHr53O9H0KRsu86odxa87yqNA4VnXAJro4tb3/exMXHUzaTLrqDKsv3j4R3h5DFhO+s3lhs5SNkpCZgEzp+yBlLIUvmp+Y0oqDCIdz/mHG2CyAAxhOCqsAbpR5LYkZYSzeOhDVpBWxqHTGigN8+9gGXtgew6bgazWGATohUTCPKfSx/tIGLsPF4sS4MN1vsdvaU7nPtEmd/259nGxkKbmfFhfDptOrjWl0amOoLNyMsdWLaHz9KTg772vR0TNAj6DVHXXfp/7AxYNPD4t6rG4P5SqqkIdXodFfmMMjquR+UsKj7Wqjg955oO4gU6GPtN9fEyIZSSEpk8M0Icv7s5ZFpGyXsCBRBckAEjSTYYz9BwA/BaAOYBnAMQD/E8Adca8lMcuJpHdkCTmCzXSLpO4AJ0NSGl9Z2YbjYLfKoiiqdnR7c1e64hoP++nBSd3sWSWmp1ABQym7cOja3Z8HE5PYvu4EAGDq63z390VGzwDzJ97B6odeIQ+gh/vYMGIVjKb5KzQGZU4VaaPtRZyb00iZ6MaHCnQX/bBVyvISMtNkrAjKJlVZkZnTeM8RN4UhCub7AVwP4LcB/CqA4wB+VuaFJGY5QVI2GgTX8/jXnlx7/UG8+MIyNgdVfKU/ieeDz5lp4/yF4XZkGg8PMGzWLJI3m8qt5iV0fcfBakMsO53ZI3BfeHo3rdFDZsKqa1IVJWdpGk6ruHnRPLuGyWMzodUPH700wBsX+vjiiwM8vrJfwO5jddx7Ui79MQu6ou0esgWFogg2ui2CPKowkpTZTR43iEjK9hPVwoewgvOc8w3G2OMAbuOc/xVj7P+TeSGJGUEkpOuGryvxr+cRPafuALPVPr69uomu4DneBcqbsEWl+z06MYenJq6mO25V67vpj68VRNZ0Iyo8YFLUp1uro1MTL2YajE1gUB9HtX1F+HgUOlMf4+QMyH9S88Lz61jdnoAo6rvaBv7DP+39vAajaWNVJ3OhjzBM629pKiRk0Yxy6mLekJSFEyVn/htDB1+f14iIBKwzxt4N4BEAP80YexGAVEljm26sE0ShvPTCBj7TmcKHt+fxJ21x/zGPugPMCBpH+0+yYc8BhherqAtWDw4uTIh7mi3VG+ilLEBSZuq9LsZ64qSPSmcbla6dCSF5R12i+nhFcXp5gE4/n1TbPPo3qribnWRSqiKyrLrHVRgkZeXAhBtr5863dv8bRS4tbexKmPdvWltmBT8C4Ajn/LMAzgD4QwC/JPNCipglhNaKjS5e02ePYP+xMLwI2/QAiavh+SNo/klcc1DFRltcTORKtYbpaw9iptLffW1e+KNmJlzU/VRdF7OtdVw4tL/VwNjqxX1pMXoC2gAAIABJREFUjEkoKmrmkSa1MUhnAKl1WVF9vKJY2QZWt10cPZDPTYPJYzNYbwPuxaayqq5RREXSo8grpTGv5tG2SlkRQmZytKzoFEbRdyLsezIKUTeSMbvgnL8I4Dd2/i21tsyDxCwhJGXJkZ3wJSWvE9WlpY2dps/ii2hY0+eBO5Q5r+DHwWe99WbDlMckBC88UU2tp9DHgQyV4lQRJ2hZIwBeyftg0+goek8+iokbXo7O7BEMxiZQ6WzvVmXMis6CISrkLCwtpu8CDy3XhZUMw5aAvX2+C9cFvrxRRdv1nhT/of7sC338i1fotaT9FSMnwSa6kceTlsOLB/HSCxt7v+e+daVJv+e6ICkLhyJk+zFRygjCBhhjA2BPQYAuhiUBxgFscM5j0zlIzAhtpJnwySASMtULZYP7iGr6vNt/DP09d8zTRthkiGpqfXO1nbqnkg5UFffwJpc3zs37St6PY6zXxmxrHccvLe1Tg+CE1MGw+qL7wtMY1MdR6bYzRcrCxmmynAF70/AeWq7jVPNq76+1nrMbDbvnsLjMfNUBHAdou8kk6/GVYTqjrqIfgLhipNfbLOx4khC80aTqe66zuupNc/NUeVGAKikb63cx3d3Cen0SHUH/yTCKbM0hwrQshzIhs1aMsB/OeQUAGGO/D+CLAD7KOXcZY+8E8L/KbIPEjNBGmglfFHlGyIJERaga6OOR3rDKonfH/MZqG8/1xYUmwiJsSYmqAOmR953HPCosnm7MYdtX8r5Tn9hNT+w/+VWpbTiDQapCH7IUWc1RJq3R+4xPX3cQT7TENxyeaFVx91xXGOXuDBD6uihWt4H1/5+9d4+S4zzvM5+qvs5MzxWYAYYYAgOQQgG8CrxINANKpkyRNiPFjHkCK75Llr2inexxspuT+GSdjRPnbJyNs8lajmzL68TeyFGQlZwTKo4syqIl0oxEEgIhmiALxG1AXAfAzPRMz0xfq/aPnmpUV3917aq+zNRzDg7Z01PVNTP9Vb+/733f31uC1I165UGQXjC7qoWyBgtlieNXM4jap9Viiqc08c/jBdFG025tnXNae+v8vSsFT+6qInMdP0QpzraqKJN1jSPnX+HQ4jkmSgUWMjmOj+/l6OyjaJK3N1qviLNeEmVey3s3QwmjWbDFIm1T8UFVVZ8zHqiq+iVFUX7Fy4GbUpjFfWDRYL5puGWnnAI3p4DP6TW9fG87WTOn13LKUGXROGHZMT9RS4LNrreRYWtnMC3ccoDcd+Vq0077lbbO2tvoskx5Ykr43PzAMBOyHHoGLCqClD56nW/mteds7uIqi9UB4XNLVYmVqiS0nV+pSixV7RewhPjdP5Gt93wZimPpwqJncWZ3TzcLpsWqvQpaKII0NQY3ljy9nhXRRtMiQ7S7zv24q/aiOOs3URZm6eKR86/w5NU3G48nS4XG4y/uPez5PN0UZ70kyMw4ibPNIMhExIOjNxWriqJ8EjhK/RPvJwFPdtmbypVxbPd440O+E65cWwnrjcLNGageuImDJCPgc3u9Tt6cvLzWh1MrHEqsMkIVCZ0Rqtwvr1KyWUZ2P6GoB6wd1ynDUr+bw6Wj5sziArXMALX0AFqm1bwDblne9xt+nfK8BuZe3k9GJljEWFJn2MZ98fa9o0yI/wyMZeCv3CZeE0q2dUPGbRNt6cKi4/cYgmmxKlNfdeKVN5Gtz04L8tngtNHkZ51bqeh1F1URdu6qvRJIn1641Fei7M6JXaGKsnStwqHFc8LnDi2eJ11rv2w2SmanRnvmveSHzSrKzEzOjMQzzPqfnwB+BLgKXAJ+gLo4c2VTZczMu69BMmbmD+w449Yew0mdYQdzitK1PNelcCynzQTJmnkVgKIZZat6ghMl8VAmO5mUQWsqbzIH0H5nU/VKk3RUJYw61PvJdh9Ey2SRS0Wo1iDVGvj3s+U9+Ns1dzJWMfcqueGUCT6Yq9lmtdMJifu2y029XAaHJuvDpFOy2YCjPmT6B7LiYNUuc+Z2H/ZTUnnfdpl0Qgp0b3faaLJb5156PU/PF1kbFn8Mr8lJinLCdo5hUMLImvWbIIuC0coaEyXx/Xe8VGC0ssb1RG8Jn34TYuas2VYQZFbiDFr/oqrqHPDxIMduKmEG7QmqWIzZMzkz0nJzcBJAaRnuGdN5RVA1ZAQsTseLXs/rdbZ7jNvrpiQa5UlD2Pee2ZVzFZGp6DgGbV5L0TpltW1H1D1lFyZnuGrqJ9Oy9pOJ27W87wX8ljSZBZoGLb1KSrns6g5o9CSelwZaTHrsWLqwyA9kgZlci/h69n1JErLEESXFM3fq5Ev1TNXaJecSQuP+62dzzUkwgY4EjZ/nB7IVli64nlKIMbtNVCo5TI19iRLnHHo9rRhr9t3s9rqLioBBrdokrsM20QlCLMhukU8NspDJMSkQZ4uZHPlURFPUfdJvYszKVhRkVoLGQzHdQ1GUp4BfAyYwFVaoqrrP7dhNJ8xiosOv6Hl6e4X1lbKjOYXX13O7Kfm5NrdeOT+7VE4ZB7ud9IKp98RJVPW6OItalNUkiYWcTVBRrSBVK+ghW973AkH6Tc7P55mbnOGqpVfpeK3eE+XkDihLcGSfRFkr+hprkZDgyYECzzwy1hBfVrfFdEJictDfppef73USTGMJnZ/eVWIi1f6YDqfZbXcmSjyeXqFiyqS7bbpAPbN5OTVk+33DywtcvB6sH05EvwuyjFZlW7XEzWSGkuwcunTCAr+cSHF8fG9Tj5nB8fFZX+6MUfWX9aoo24piq90e+Fic9R2/Cfxd4C+xDweFxMIsJjISgtK/oE6EYZU8RnFjE7kjGq6MK23OGQtjcHCYdMJ10aCSTFFO2vSMyQnG3vkOkqZFYnnfbfwagziJWDd3QGNtpWWERh9uAcXapSVSwBqQ7nBvr5Ngumu4xs5MsL5L0exFI4NouDJaN5rMmXQ7zBsoRTnBmp3A0HV2LM0HuvZOzSzrFAld47n5kzxWuMJkdZ3ryQFeyk3zuam7qHl0PoyKo7OPAvWesonSCkupId6YmG183Y0oBFmvirEoiKrMsVdFUK9eV4yQG6qqfiXIgbEwi4kcLwFL1NjdzJyCTq83QVHvWUoCuYyvOWN2c4y8fPhEnTXrpCAzSFUrpKslyqlWlwm5XCRRWt90gsyK1+yZk4h1cgd0Elzm977X3V5ztstvv2/QQfRWweSlFNPutdxmL358ssJT2yrMXVz1vdFkXZ9ZrcagVmVNkFnJVEukq/7MIzabIDN4bv4kR5bONh5PV9cbjz+7456W7+/kwGhNkjk6+yiyrnFo8Rxj5VXuW5qjdl52tMzfyoIsLBEVdZVIFCJINEfSL7E46xteUhTlXwFfBYrGF1VV/ZbbgbEwCwFr03q/96qFOW6gmzcRr6/rZwyAE1YB6jRnzPyhYvQGvZfKsS4nGdCq3C6YY9QtuiHKABK6zkQh35hRZmYz9JN5xUv2zEnEOmVozYIr7HXq9R7iZxC9WeyZxZUhmNyEndtreZm9mL+8zJjPhSkKIpPozFQKDWt8M+OFPAnde7YvSlHWzTLGjFblsYJ4AMjhwhU+P3nAtawxao6cf4Unrr3VeOxmmR+2KOsXQdYvmGOAIPGLl/up3XNe449YnPUFH9j47yHT13TgI24HxsKsTTajLX8nhGWUVrDt3LDCvNnZZdKsQdrx7DZOm3qD1jfmGGnAgxtzjMy7jBWdltLQqHYPuyXKDHZfv8jS+jrliSm0TdhP5gcngeYkYncWV5hfsi+J9fKej3K9+h1EX9N0nr8uFleiUkyvr/XUtorr7MX85XCDofs31rdh2JKplhgv5Nl9/WKorxOUbveWbauWmKyKB8FPVdfZVi1xOX0rjOlktgycLfMfzl/gy7VKU6/ZZhdl5nuM6DMpzJLDKEr87XrO/c5TDYqfPjTz9/WvF/HmRVXVx4MeGwuzmMgx39iins3Ri7tIRiZN9EFVReJ8Wvw7OZ8e4f7iAnum6wYBmg7frAxzppZhmQQj1NhZXOmZzFoUSMDwnIr+3rtoqcym7Cfzi115oxHML+ZGKSUzDGpVZjYyrxC8XzHKNet3EP3ShUW+tp7zJeS8vtbDI1XH2YtzF1d9Z8rAedNEpj5E+t7iAqcX10hVK74yZRBdtqzbogzgZjLD9eQA0wJxNp8c4KapfLfTogycLfNH1pe5dyjD4tBEh6+qs9jdU3qpN9oL8dywmDBRFOUw8PeAHPVQJgHsUVV11u3YWJiFTL+XMUZFOzc9r6KuU6JMlLEKSkFOUrXpQ6jKMkNTI7AxAPiblWGO1265uC2TZHkj03ao6GmgvC+6nS0zI2kaiZJ453wrIhJnErDn+kVmblyikkyRqla4Y7J5zVhFglvwFHWw4mUQvTkLVtbgjatVEGxFiISc19darEqg42iJ79Wwx4zXTHYSnWyl7Pv8UdELogygJCd5KTfd1GNm8HJuulHG2A1RBs6W+csDI6xkhyN77W5ly/pNcPUb7bo3xvQMvwf8OvAzwP8N/BDwXS8HxsKsDTZjGWMvYRVadnPUggiyIOJKlLEyesac5kRBs0W22eDDq66r6PVeNREXUznuLS40GYa0Sy+JsjAG4m5G7DJnCV0nsRHkn5/POwZwZuHQjYDL0e4+qTOcbH5P+xVyXl9LAv78ssYefZ1FWi3svQyLtjJ3ZZWinGwx8xERdL1FsS56RZQZfG7qLqDeUzZVXWc+OcDLG66M3RJkBk6W+ad2KFR9WOb3A5tVlMVCKCYC1lVV/XeKoswCi8DPAce8HLglhZlftzA7ggxEjfGGF7EVZGepHXElylgZrotOc6IAykgcH9jOteQg6xvDf2cqBe4uLpDUNapSa4lVGo1Rub5Lv6onWEZchrUmJynKCXJa1fkH6DNiMeaOIc5qktTIkvkthesmTnb3B3O1luyXXyHn9bV0JE7Uhnh/YpVDidXAsxfh1j1GHd7WGPQ9E4GZT9jro9cEmUFNkvnsjnv4/OSBpjlm3RZlBkdnH2U0O8D+ayoj68ssD4xwaofC1+/6aLcvLXR6bXxLGHiJIWKzjZgAFBVFmQBU4BFVVb+hKIr94EoTW0qYWTNcYbkPxoIsPPzc/Iwbqp/MWVBx5ZSxcpoTZQRpb45MUpVvCau1DYMPgL3lZd7NtmZf9xSXSQ3U/39IqjFCjWXBkh3UqmS17o4jCIt+FWN2QW3UwaOOxNzkDAu5UcrJDOlqiYkN8wjj7eiWNXMi7LIaL/PBnOzu/Qo5K09vr1DT4bV8Ek2Qrz5by/DT2RscTjnPXnTKuDfuMRvL3bzWoyg5bpdeFWRWSnKSy+neEWQGs9uneGH7U7y0/zF2LF/j2sgOiulBz8cnaxWGiyusZIc9Zdi6bfixGcWZFzolzuLs3abhXwH/CfgR4DVFUX4ceN3LgVtCmMUlh72Bky19GDc88zlF5wsqrsA5Y+U0J6oRpNkEjBdTOZ5auYAEQrv8Syan6J3ZTKOnzMxMpRBqGSPUP/w7Wc7YS4IszEDVfK4oAsrCnv0Ux3c0HpdT2YYz456QnP3CEGde54N5mWMWZG6ZQUKCx8aqvJoXf/Q11rJcE65nt4x7p0qOw1gv/SLIDHpNkEG9zFrSNJ44+QL7r6mMrufJD4w2Mma6bP9G9nNct8VYTJ2oxVksyjYVXwf+P1VVdUVRHgT2A0teDtzUwsyLIAtzZlc/0ekGU7d+Mb+Irt28Gy/KogUVV+CcsbIzBnAK0gzW5CRlOdFwZhMNmDawWmtbXfdEnJ/PN5W5Wc0gnGhHnPkpresVURZ1oBq2SNNlmfLElPC5xdwoMzcuNX73blkztx3wdu8XXizx0zKudveAbyFnxakc0s3kwy7jvrpa5mPjpb4pOe4nUdYpQZauVRitrJFPDTZZ3Nth9Hc+cfIFPnj+1cbXx9fzjccv3PNU4+vWzJjdcSODaY498kxYP5Zvdk3nXE1rNkvWLMg9LSpxFouyzYGiKLdTb1v+E+CHFEUxPmjywH8HDridY9MKszhLZo9xUwlrsLLX14sKp9148000iLgySEl1AwCj7NGMnTGAU5BmYC5DTKI7Bm1ma20nAWegQUuZ20VTmVsUu7A6cMGltM5MN0VZN4NTt9f2EoxqqQxapnVuGUApmaGSTDVMQKC9kkbw7o5qxa8lvle8CjnR/WdW132bfLhlw+auLAAwMjYeaclxO2umnwQZdEaUybrGkfOvcGjxHBOlAguZHMfH93J09lE0G8dcg2Stwv5rqvC5/ddUXjz4EWpSoiUzdnryTu68flp43MyFt3jjoaepJdOOr21smoV5HzfE1lYSZ0Fwq84Jch6/mF935OHAp4kJn18FHgduA75l+noV+IqXE2w6YeZFkG3FDJnBZmhgtd7MvA6oDSKuzHw4tcLqarkpY6Wky7bGAE5C0MBLGaLVydFNwBm8pA9z1aHMzS1QD5ItuzA54/iaZrolytoOTuUEcnYQrbgGEfX2nV645BqUypUScqmIlm3tZ8lUS6SqraV9dn9zP0GW3+xZO06K7WJ3vzPWrB+TD6/ZsJ3FFV8lx37WWa9klztBpzJlR86/0uSqOFkqNB5/ce9h4TFGtmy4uMLouvjvN7K+zHBxhYfOvdaSGXv4wjHbu/5QYYmBtWUKI+EOo/aLcT/wOvKh3whrMzqIO3ScHdu8qKr6KQBFUf6+qqq/HuQcm06YubGVRRlsvhuC2278g8WbTYLrw6kVNB3OaBkKlh4RN2QJPjZeoqKXPFntOwnBpFZjX3nZsQxRA05ktwlLF90SDKevLzO/b4/wOWuZm1X4tVO+uJATCz3ra3YjwGxbkEkS2QMPk9y5Bzk7hFZcpXp1juI7r0EXnBAlTSO9ME/xttnW565ftS0hDUucgfP9xCgtzsjBnRTbxa7sSJbqZj+HdWeTDzNeDXi8lhzHgswZL5sT7ZKuVTi0eE743KHF83x59webyhqt4ylWssPkB0YZF4iz5YER1lNZ24yaJskkdK3l66u5MdYH3T+no6h4EN0D7ARav2fLujUvbLPFYDG2fGnD8OOPgN8GHgD+jqqqL7sduKmEmVu2bKuLsm7gZRfJT1mA9abmthu/mrjVN2Y07p/bEGU5NPZ6tMo3k5Kw7UWzYt2Zz1HjdrmMsnSFtEum7ER2G6dMO+9+3N3mpm5Hs+mTMMrc5EqZ4xbhN7K8wG7ynuermakkU5ST4lIvc2ldu0GmLstoqQxypYSktQY2VsIq38oeeJjMvnsajxODwyQ2HhffftXusMB4CUxzc6cAKE9MoaWzyOUi6YV5cnOnOLvx/hLNOnMSZ+A96BIFN6LS4qwsfq97cVJsF6eeED9r2WmjxZwNsys5DrLhsRUFmZmoxdloZY0JwXBogPFSgdHKGtcTo8I1BFBNpDi1Q2nKiBmc2qEwUCnaZtQkgSgDuLj7btcyxiix26DpdyEmolPtHDFbkt8HfhP4YUAB/i7wL4FH3A7cNMLMSZTFgqx3sd4MncoCRDdOP4381sb9AglO1IaQcZ9DFhS7nflLHsoXL6bEH4Ru7m5nri+zPHu77bnTG2VuS7v3tQi/tY0yxCCOfqlqhXS1RDnV2veUqZa4NH/Vk5CyQ0eisGd/XYRkssilWyJECtmVsgk5gTw4THKnOAOZ3LkH1GORlDW6BaYSOsNzKvp779qKVSO4twaXInFmZE/nrqyyZ9rTyJWW4EZUWgwwna5R1CTfTopuiCz4rYTVsG/daBmo2RvwJNG5cTW4Pf5WmVPmRpTiLJ8aZCGTY1IgzhYzOfKpQVtRZmDMKxPNMUvoNduMWj47wukd+zlw8wxDhSVWc2Nc3H033/3Ax8P54VxIVMsMrC2zPjjSIgS3Yv9YJ0RaWOfdDC0pW4Csqqr/WVGU3wO+oKrqS4qieJo4v2mEmR2xKOtN3G5Q1iya3fc7zTUy9421Y5UfBuadeS81+0U5wZosXp5O7m7n5/NUUmnKDjuuI2v117cTftayQ68kdJ2JQr7RU2ZGut6eKIMNa3hT2Z6WHWw8Hp4Tlwu1FYxaSheRxG8QOTtU7zlbi0bce0HSNBKldcfvMYZRmzHEmbBsdrHA02MlX9nky+8t85dFcSC7Wtb5scx1yomNDYoSLFwKHqy4WfCbKWuwpHkrWXRCtNEyf7V1PbczamKrZ8nMZLQq26ol3rsxx+3bxRsj7VBOpDg+vrepx8zg7PRBZqamXc+hyzIv3PMULx78SMs8siqyfUZt5wFeuOcp/qxW4cAQQoEUBZJW44FXn2fmwlstglA3zdrsZ3Emuqf4ETN+jY78VgbFbAlqiqI8C3wM+BVFUZ4BPO3ebhphZggwc+YsFmW9hXkoq1fMDpJ2NzZj1/0vlyTbRv52rPLDxGsjdVarMahVWROUI9q5uxnBoFPmSq5V2TN/gUoyZSv8RI5+Xtm9kWlbzI1SSqSbSuvawckavjwxhf7euy3Cr90MgbV00Q6tuFo3AokI4+cwsgZ+SznN2ImzRUH29FRiHJYW+dh4yfP53dZZmfqMMDNBez28mP4Y4u0vlySWGWiZOxYUY6NlsxojdJuErvHc/EkeK1xhsrrO9eQAb67c6ckp0S9HZx8F6j1l46UCi5kcZ6cPNjJhXqkmUiwOTbR83SmjZhxXGAm/X8wuI/bAq89z8OStNpfhwmLjcTdt+sPC7l4SxKQjLGJRtiX5eeDvAL+oquoVRVE+AXzay4GbRpgZxGKsdzBugMKhrGdLPLtXatnZth7rhVtzjWDu4qpwV7wdq3wRZpHpdQfeTxCXRGemUmj0lJkRubuZd+idMldTyzdJ6jqSS9mhyNHPCxL1MsiZG5c4u1IIJB5EOFnDa+ksWirTyBiFUrIlJ2xLF61Ur85F5s5o5vTiZXa8//HQSzlrksT5xIDwuYupHBXd3bHUwGmdjSV19swMkZaDW+8beLXg/9I5neO1W+LNmDsG7uXLQdZ4GOzbtj3aMsYOuIq2y3PzJzmydLbxeLq6zrSLU2JQNEnmi3sP8+XdH2S0ssb4zt2NjFcYOGXUojDwcMqIyVqNmQtvCY/zatPf7ziVNQe9H9mdsxMlkXFZY2+iquqbwKdMjz/h9dhNJ8xi6rQb+ISJ3VBWzq3yeHrF803GbWc9LdOyG2/QrlW+gVBketiBD7Kz3o672+7rF9GBxdwY5WSaTLXE+MY8MXAWb+OFvO8yRitzN667THDzh5M1vFwuIldKofbQyNnBevmiAF3XQdebXRk7QPbAw75LOUVYs2ZOpi1rcpKz80XH8QzmciendWY2+gh6XzLuD0tagsWqWEwuVaXGBs2Zmris0ql82esad1rT7Qxnj4wecxW1I6NVeaxwRficyCkxLMqJFNcTowxHcG5ozahFIcrAOSOm3nWYocKS8DirTX+/ljF6ycCHMT9M1B8ftE+tnXhtcmYE7zUNMQCKosjAvwXuB0rAp1VVPW16/lngH1AfzfoFVVX/jdsxYRILs02GVdh0y3XIeF0vvV1+a7+D/hxBZhdZsRWZiHfg2yl18jJQWhT8GUOeF3OjlJNpUtUyo4Ihz01lh8lMi3jrJZys4dML85y58V6or6cV19CKqyQGh1ufWy+w9toL9Z6yTmUcHDJ4q2PbuHbiKmg1zyYJZnEm16qkqmUqqdZ16mUosrUXxbrOwjD6sN4jvGTAg5Yve1njXtZ1O+IszKyZsWHRaVfRoGyrlpiqivslzU6J/UpUggzq5YtOGbE33/8Eq7kxhgutlUVebfq3Mub7kEhMBRFjoq/1wob6JucZ6uYc36coyiPAb1B3T0RRlATwz4GHgAJwUlGULwAfsjsmbGJhtgnwKmy6seij6O1yEmdOZQpBZheZ8WsgElb/iWigtFPAZx3yXEllmB/fgUyz26K57LCSTJGqVtrOlEF05gV21vDX3njR34m8lHJpNapX5xqBq5nq1Tk0m13nqHDK4JnNR/w42J25eZPkgftZyI1SsSlf8jIAHZrFmWid3TbZ/g61GS8Z8CH8ly97WeMisw87ekWcOQn7KF1F/XLnxC7StQoLl52dEvuVKEUZwMDasmNGLF0ucnH33U0ZNYNu2/SHSdgzytwqeSBco5BYoEXOYeCrAKqqfltRlIeMJ1RVrSmKclBV1aqiKFNAAig7HWOHoiizwN0bx+1WVVU8NNFCLMxiIiXs3q4w8DO7yEyvGIg44WfIs0FC1wMZfYiI0lHOzhr+mlcx6bOUyyhRFH5/h3HM4FnMR7yKs8Ke/RRNAr6BrpOplthTW3ccgO6GeZ0FDZScghi3DHiQ8mW3NX52vki/FHiZS3u9CvtuYX6/OjklHh+fjaSM0cDNGj8IUYsxM+uDI64ZMcOOX9SDZtCvZYxREPdwRUcX32cjgHnHrKYoSlJV1SrAhij7EeC3gP8GrLodY0VRlB8F/jdgEPg+4H8oivK/qqr6H9wuLhZmmwCvPVqd3H0xOyCF0dslOreIKG+ifkWmcdOJwrnNbife65DnsOmkxbfZGt5PX5ltKZckUTz5ndYDdL1e4qUe675ZgksGz3pdbuLMyeUyVS1z19zbpDUN2UdQGdRe224Omdta9pIB91u+7LTGB2ruZZ0GYfSXBV1TojXhR9h3Cqf3p8gp8fj4bOPrYWMnyJK1SothhxOdFGEiasm0p4zYsUee4Y2Hnm5xbdwMgqxTmbKYvmYZMN8MZavAUlX1y4qi/Bfg3wM/5eUYC38feBT4lqqq84qiHAK+DsTCbKthtYTtdip8cmaEZ3UYuFFxtLP3cz4r5sAuSie1oAYiVoFmDPEV9Yz5QSTO3IY8B3VbdKJbc5d8mX04lHKl9xwANvpsRNk3rdbVbIKB3wyekzhzcrmsJNNoiSRo4Qp4a9bMaQ7ZwiXvAZFRtriqJxiied37LV92WuNeyjrDMvwIU5QBvoV9lHjJ5lqdEvOpwdAzZU7ZMUnTeOLkC+y/pjK6nic/MNqwuNflVrv+bgs4J8ENAAAgAElEQVQyM14yYlAXcYWR7ZtCjBl0O97xQljD7mPa4i+AjwNHN/rFGul5RVFGgOeBJ1VVLSmKsgpoTsfYUFNVdUVRFAA2LPM9WVTHwmyT0q7rWTvnsOLFzt4L1uuxBnZpCXQ9Qxk5tHlFVtoxEJnemeNPljJCl8Wgk3ms4ixqt0Uz/TQI16mUS5ITZPbefStD1qsEyODZiTMnl0uzgDcGUIeFWZzZzSFbXynzuMdWF68Oin7Klz+cWmFltcKl1BDrDm6oBmG6L0a5prpdmuu199GM4ZQYBulaxbMd/hMnX2gaCj2+nm88fuGep5q+t5dEGcBtu0a5+td/gvlKmXQhTzk3ipZKc1u3LyxCohBkUYqnWJx1nT8GPqooyivU2+0/qSjKjwE5VVV/d8Ps41uKolSA71HPcunWY1xe4y1FUf4WkFIU5f3ALwBveLm4vhVmY7vH45llIdIJh6C0DO/bPRTohiS6BmtgV9KBjf4QP/OK/NCOgcg3K8OcMomDxhBf6u6LQagisX3nNq5dW2iIrqjdFntBkPm1xncq5TLoJRMER3xm8ETizMnl0irgoxBno7eN2M4hc7Kyt+LXJdWJS1cKaMCJ7DYub4iyrFblNofNk14SZa5rokuluUEEWZjIusaR86/wcP6Cp+xXslZh/zXx+In911RePPgRqolUKGvC/P5p93zWzJeWSlMcn2zrnP1A2KIsrPgkpndRVVUDPmP58jum538X+F3BodZjnPhF6j1m68DvA98A/hcvB/atMIsJl17ewbErX7QL7Mz4CfL84NdAxMnt7WIqx73FBV9ljUYA2ci+De1iZHmhYYkfhdsi9KcoAxxLuQzcTBAyWpVt1RI3kxlKcn/dOo3fmTlIFrlcTq2vCAW8V3FmlOu6lUfNXVy1nUPm1UjHr0uqE8Z1n8hu41T21lD3YiLF6cR4Y3SFGSdRVpMkx7Vnfn7uxnVvF+mArzXRwdLcbosygJ+7epwPmoxEnLJfAMPFFUbXxX/bkfVlhosrTfPIus1mKkX0Q5TDmzsRC/VyzBUTCj8H/GtVVX/Z74H9FV3EREo7AxI7zUpVYqnqHnUFcUuMok/Nye1tTU5SlBOOQ3ytWAPItUSKtQ2HvT2mIdJhGn30gihrh+I7r4Ekkd5zAElu/VvYmSAkdI3n5k/yWOEKk9V1ricHeCk3zeem7qImBS1C7T4il8uapiHZ9N74yZy5CbQw3FrDcEk1G/NUkbiYEl+v180TY4bgQm6UcjJDulpiwjRD0Pq8XFonPTRBbu4UUsB+0zAHq2820h6zX2ZWssPkB0YZF4iz5YERVrL2WXe/9FoZZL8QdWxid/6w4yM7cdbLsVeMZ3YB31YURaVeCvllVVU9uSz1b1Sxibh+cdnxXyeZnBlp/OsVRL+D4aTOWNI9kPFjya/p8GJ5mD8obuf3S9v5g+J2XiwPo4WQbBqSagzWxMLLyxBfM04B5GJulJoUbnrw7M0bPSPK2gpCdZ3iye9QnntH+LSdCcJz8yc5snSW6eo6SWC6us6RpbM8N38y+LV0CdHvz3C5lLR6X7LT3/r8fN5X+d6lKwWhK6lhsiHCq1urIe5EuK170XUV5QRrNplQY/PEwO53YMwQLKeyIEmUU1muju/gwuSM8HktO0jxtlkKe/Y7/qx2NP095QTy4DAINh22KvcOZVyzX1aqiRSndijCY07tUBpCLswy1naIwvW3l+lmbBJFfGQ9Vy/FXjHBUVX176mquhf4Z8AjwBuKovy/Xo6NM2ZdZqsNGwxLaKZlOJir8cqS896CH0v+MPtVoPUDcyabafSUNX3d4xBfA6cAMmxL/F4RZBBeZsBwX/RigpDRqjxWuCI8z+HCFT4/eaAvyxrdSszO3rzh6Fpnlz2zcxw1rwUji9aOkY6R1d4rlzih+XNJtXNHzWo1BrUqawJjCC+bJ24zBKdvXrZ9vjwxhf7euw1x7IXGevA5n69TdLuMcd+27azUKoGyX1+/66NAPas2sr7M8sBIoy/NTNj9lzHObIYYSMRm/bm2OoqiSEAKSFPvQBHvRlror4jCQr8bgMT1xd4xflejt400rPGf3l53jjNcGZNoSNDiyugFp34VtZxm381V9kyLXf3MOO1eGq5uIldGPzgFkGFa4m9GUQb4MkHYVi0xWV0XPjdVXWdbtcTldHi30V7qY/MizqBejtXS8+jgOGouczQb6eSvLZNEx5DBojJIkQvjJGWKyBRcxJ3xuk7XOlMpuG6e2GVK3GYIni1WKCfFdpNaOouWyjTm87lhXg+28/mgt11GQ8JwWxRZ6hvZL7PDooE5+2VFl2VeuOcpXjz4Edc5ZrE46wxRzCeLBVFMVCiK8pvAM8Bx4AvA/6yqatHLsX0rzPpZkG1VggrRig4rWoLjtUHOnUlbbLFv8mASVhOJRulSkP4wLz1gTgN0vZSTGAYC9xYXAs0xM+/w2wWQYVni94ooi7R/xoMJws1khuvJAaYF4mw+OcBNm0DcDXM24fTCpY73sYnMQES4iTMDUc+jm+Oodc1YP4xEfWqirDbA/YlVHkyu2a5782s5XavT5olb6ZrTDEG5tE5yfcV2RIFcLiJXPG2mtpQv2s3n6xuX0YAYbouHFs8xUSqwkMlxfHwvR2cfZXb7reHpXrNfIqqJlCejj1icRUtUAso6WzEmJkROAQ+oqurb3alvhdlmYCvdEIKIMuvueH10RB1rmaG5yd+P0YeBkxmBuYzJLM6C1vYn0X0ZfYh2+HdVCtxZXOTyxtfCtsTvJr1kZlCSk7yUm+bI0tmW517OTfvOaomE0J0Tu/jEuZd50vQaRh8bwGd32DtJtovXskawH8h75voyF/eJxUEQx1Erxjqb2pmzzWqfq2X4UKrVhdG6Rr0YfFg3Ty7OL3HBw3U6zRBML8wjV6u2IwrSC/Oeyhita8NpPp+by2iUdKKM8cj5V3jS5LY4WSrw5NU3Gc0O8ML2W26LfrJf7dANcXb79nR9VlkljZbyOPyvT4hSjFkfb6VYLCZaFEX5+Q27/QngOWPAtIGqqv/E7RyxMIuJnKCZMuvuuIggdvgi10XDjMAQe2asPWCdbrYW7fC/mxhnf3GRH1q5QFFONM0xa5duZct6SZCZ+dzUXUC9p2yqus58coCXN7JZfrALVtO1CocWzwmfe3z9Ot8YneJkft7fRfvAizgD++xZJZlyNc3wsxFh+/rzRZZHvLkw2q1RLwYfN64GmykIt2YIzg8MN0YQpBfmG6MJRCMKzM87IVofTvP57FxGo6RTfWVOa8bObdFr9qsdOiXOJK3Gh976U7afOkEmv0BpdIIb++/nzBPPovep+UsnxFHcPhLTASSb/we87VDGwiymCS83rk7cQJ16vsz4scMX9acY/Siy1GxGsKwnAveA2RkgBMFth3/kvXN1W/y2XuUWsShrpSbJfHbHPXx+8kCg/i+3YHW0ssZESSwkxksFRitrjXNE9XtqR5w5lfD5dRx1IqvVnU1FvZVmF0anjRO3/sxr1xbaWksSUHvnDSZkuTGCwJwJE40oCJIpa+Awn8/OZTQqOmn24bRmuj1rrBPi7ENv/Sm3v/aNxuOB/M3G49NPHon0tcOkk5kqp9gmzprFhIWqqr+z8b/nVVX9A/NziqL8opdzxMIsJPq9mdTPTpKfm1jQHSqnni8zA7Uq+WvLrDoIIKP00M11UZZomBGcnS/6FlZ+DBC84rbDH6YDY7foZVFmpiQnm4w+wgpE86lBFjI5JgWB5mImRz51qycpSoEWtO/MqYTPr+OoE0l0297KncUV5pfcM9lO5wijP9PY2DBGENjh9rwZt7+14SbqxWU0CrrhvphPDVKSUwxqrUZHlUQq1FljvcSu6Rxypcz2UyeEz28/dYKzjz8TaVmjnxgnntEVs9VQFOWXgBHgM4qimGv8k8CPA7/ldo5NJ8zGdt/6wO2kQUg/32yiSO+3e06nni8zXgM/pwyctRxy/moBcY7KmSAGCG5sdgfGfhFlVtyCUSenOCvlRIrj43ub+mUMjo/PCo+PWqD57TszSvgWc6OUkpnA2WY3wnA2tZ4jrP7MKNaQp7+vD5fRsOmuJb74vh/lgIDZqdHQ55eZzW2csr3G96ULeTL5BeH3ZJYXSBfyFMcnQ71GA79xTj/HRTExATkNPEi9gMJcylgCfsbLCTadMIvxTi8KMgOnni/Qb5UhjpWQJXcZ5ZSBM5dDBu0f82IqECRzEPUOP4QbUOo2JVybBbdA1MkpTnNwVzw6+ygAhxbPM15aIZ8e4vj4bOPrbtcTtkDzW9ooAXuuX2TmxiUqyRSpaoU7JsMPytp1NhWdI4z+zJokUcsMhPq+9/039eAyGibdFGWjlTUyNn2L6Vo5klJGozxRVKZoHh1hJlEtM7C2zPrgCDWbUQlm7IylzOKtnBulNDrBQL51M6I0MkHZZlZeGMQlfzExzqiq+hXgK4qiHAWyqqoeVxRlFHhQVdWXvJwjFmZblHYElOjGHIXIEw2g3SuXOJRcY1j2Z4fvlIEz+lPaMfXwYioQ1ABh7MJZdk7ONLIRvbjDryNR2LO/bmqQySKXbpkaSDaBc79ly7wEonZOcQBf3HvY9jhNkjk6+ygJXePQwnnGyqvcvzSHdl52FXXWawvr9xqk7yyh643S2ih7bfw6mzqd44auU5OkhqC0ijSn53TgwuRM3ezD4/t+M9Dt4dFO5b/LA6MdL2W0vs8lrcYDrz7PzIW3GCossZob4+Luu/nuBz7eMOdIVMvM5mpCR0W7sSwAWirNjf33N/WYGdzYf3/k7oxhizNr7BALv5hNwk8DDwBPAoPAP1IU5UOqqv5jtwNjYbYFCVNERelyZO75Es0mE7kr2uGUgbsjUWL+antOi04lh+0aIIiyEWH1woRFYc/+JhtwLTvYeDw8p7Z8fz+JMq9BqJNT3KGFc3x59wcdyxqPnH+FH7j2VuOxV1HndL3t/p7bnXdml0noFTRgcfc+zicGKCczpKslJkybHhcmZ1jIjbY8Z9xuLkzOcHV8x63zubzvvdLL66OTosxpht7Z6buY9DE4OlmrBLbL9/v+feDV5zl48uXG4+HCYuPxdz/w8SbR5tdR8dKVApfvfooHVsstwu/aE8/6us6ghCHO7GKHWKjFbBI+BtwPoKrqFUVRnqA+bPofux0YC7M+pJ0bV1hCqpO2sympeTaZm7uiHaIMnHHclTav0anksB0DBHM/gzkbEZQo+mB0WaY8MSV8rjwxhf7eu03lXb0cdBoECT6dnOK2lwv8+Llv8Qd3PC7MfqVrFR5YsBF1i+ddRZ0dYYm0dued9apAs/aFllPZJhMTs+gyP7fn+kVqksT8gDgzI3rfe6VX10evCDIDr4OjJU3jiZMvsP+ayuh6nvzAaOP7dDn84e2Jal0wiZi58BaSVuPAO/+j8TWvjormig5dTnDskWd446Gnm0slr90ylHHKuoVBUHHmN3bohFALYmYWC8YYF5LAAGAs3DRb1S6/k4Yf3cK4IXSj3rsXbkZu7op22GXgwppLFoYxgZmwm8yjQktl0DKtjnwAWjqLlsqQKK33bMBppp3g06m8SgIeu36K9USmKfuVrlUYLxf42Huvs63sbJl/PdGeqLlzYlfk4gzss2fQWwKtisT5xIDwuYXcqO0n6GJutJG99vK+73d6TZAZeB0c/cTJF/igKbM2vp5vPH7hnqdavt+K3/fqwNoyQ4Ul4XNDhSVb0bZdfcPRUXHXdK7ls6qWTFMYaf2dRS3KDDrh0BwWkzMjbbtE2p0jJkbA7wDHFEV5fuPxDwGf9XLgphNm/UY7Nvu9IJI6jR93RTvMGbgwh0WHYUxg0C+iDECulJBLRbTsYOtz5SLnrp3r6DylIIQRfDq5KxoY2a+qnGiYhGwrFRzHKVgt89vhrtEplq6f8z2LzSAMcQbdFWjGaxdTacqj4ntJOWk/Q7GUzFBJprg0fxV5+g7b971cKfm+tl7bvOhVUWbGaXB0slZh/zVxSeldN97lxVrrIGozQd6f64MjrObGGC60bhKvDw4zuCYO7LPLC7zvv/8Rpz72k7YljSIHx06JsKD0kpAJI2bainFXjH9UVf2/FEV5GfgQUAF+QlXV416OjYVZB+n3AYe9cH1e3RW9EKYoMxOGMUEURGWJL2ka6YX5ph4zg/XLZ7eEKDM4OvsoA9USh2+cQrQ/YGS/fuDKm44CzoydZb4frG6R88kBXspN87mpu6i5GItY8dN3Bs4Bt9MGRJiiTfQ6ToOx09USOlARPGeMqXB636cX5j2VMfayi2k/iDI3hosrjK6LP3eHCkscGIK/LIqPDfr+qyXTXNx9d1OPmcF7u+9h18W3haJNAm5789vUsoOehkTHgiwmpvdQFOVjqqp+RVGUn9r40vWN/96rKMq9qqr+ods5YmHWAbzeoPp9SHXUVHSo6nUXxRUHd8V+p5+yZQa5uVMArI5t68qQWysZrcq2ask1MxR28KlJMkf3PMrd+YtMVNZanl/M5FiT0zycv+B6Lh04MXMfr933cfZZemH8imyrW+R0dZ0jS2cB+OyOe3ydy8BP9gz8B99hZNWc1lJC1xkv5LkmGIw9UagfJxqaPV7IM3ej/llrvO/LE1No6Sxy+ZYroxMiF9P1y2dh8TKEMAKjXbrtuhgWE7fvss1erebGWB8cYXakXjpofq+0uynw3Q98HEDsyviqLBRtBp0YEh0m1k3lzSDI+mGjPKZneRj4CvC4zfPBhZmiKEOqqq4GvDC7c8rAv6XuVFICPq2q6ukg5+oXEdNLN6kwr6WTtdZWs4+UTXngHYmSJwv9qDJlYRCVKIt6gLSEzrXj3wA50fEht2YSusZz8yd5rHCFyeo61x0yQ2EHn+as1JhAlEE9+3VgZIjRdfe/81J2hK/e+7TQoMAQOV7+rk5ukYcLV/j85IFAZY3gXZyBe3mjHUEEmnUdWS3vDZv7xdzohhDSAanFlRFoGVNRfedEIxsqoTM8p6K/966vzJfIxTSzry6Qi2+3Og12kk6LsjCzZYbz4sTtu6gl09TANnt1cffdTbPFjOHRYWRqbc05qIu2nZdOMZ6fFx7rZUi0XCmTLuQp50Z7SsD1UrzTLr0izsZ2NxuKbQUfhX5GVdX/feO/nwx6DqdP4xOKonzS60A0jzxDfeDa9ymK8gjwG8APBz1ZLywaN3qhWbSXB0l7wWr2UdkIi9LUqCA3uSv2M/0qypro8JBbK8/Nn2xkgiCczJBXrFkpAw24mRnm7PRBXrvroyT0GvmBUcZdxNmpnQdcbb3NQa3d39nJLXKqWuSB3Bj/Yy34ZoXX0kbzNbYj0AzsAmjz9xkCzGp5rwPXTI6LbNxTxgp59phEmTGm4uxKAblSoqZpwhJVSdM8G33osszq2DZhQXZy5x5Qj3Wt/LdfM2XNzovLTRkqp+yVlbB7HkXmHLJWI1m1d9ctDY83hkRbBZik1bjj619i+6kTZPILvq32o6YT8U6vCKZOEQux/kJRlHPYuy/qqqre4XYOJ2H2C8C/UxTlvwD/UFVV/53MrRwGvgqgquq3FUV5KOiJ+mlh2l1r1DawYd8gjRtiJ0WZk9lHBp1PpG8w6mPYtN9sWRWpbSMPL0QhyjoqyHqAjFblsYJ48IE1MxR2AOqUlVpMD/EHH/55ium6SUQVmVM7lCanOAMNmuy8/WAn0pzcIg1jkTsnRkOZeRZ19syMlzVjnTNmWN7LNXEPaD43Su3GJRK6ztmbN0LvATu9cAl5cJhcdkj4vJwdQh4YQlvdPJmHTmB1XjTPDTv2yDO22atuMLC2zNCq/Xt3cXY/eiLBnV872iLA0DVuf/3Pb53Lo9V+lFjjlk6JM9Frh0k/xZgxPcX3U9/p+0fAWeDfA1Xgx4G9Xk5gK8xUVf2aoij3Af8UeFVRlL8FzJmed2+SaGUEMN+RaoqiJFVV7T2nhA7gZeH7uQF1OzMXBU5mHwUSJCU8izInrAJMoz7jSGR9H2TyjZ3A2yyCrBfc5KbLq0xVxVmLqeo626olLqeTkYiyvYVrtlmpsfIaA5ViQ5iBeAbT6an38drswywPjvoegGvFKnrs3CLNxiLt2ulD57JnXqhJEgs5cQZEs8kulBLpenasVKSwR2nqATN6x6SAGzTG70YrrqEVV0kMts5Ak2SZ9J6DFE9+J9BrtEO3smXtinQn58WZC2/xxkNP18sabazlO42Ta2MlneXdJ3+UO77+pYbgglsCrJIWb1J2qy/NLi7xK86CirmosmexKIsJiqqqcwCKotynquqnTE/9hqIox7ycw7GxQFXVNUVRfgWYAf4rsEhdCerAvgDXvAyYP43krSrKvGC+UfWK6Or0dQxJNUaosRyC2YcoW2YnwHTgXdPg2bVEqjE8+pCPuWROAu9CyKJsq2XIDBp9ZSuXbUXzfHKAm8lMqMGn1enw1q2xmeWBEVayzUG40wwmo5wqTNH+2gN/jdGTA+y78jbjpQKLmRzHx2c5Ovto0/cZv5/NINAqyZSj7b0Iw+Ze1ANmPB6eE4sAO1p+l1qN6rULJPbeLfz+5I7d8M7rPe9m2ivUnRfFayVXWGSwsMTK2FTT1xPVMgNry5TTWdLlYkezaE6ujVfvfxTkBNtPnRAemyyLC5e89KWFjZt4Mc9b9XK82/fbsVlKG/vFNyHGM5KiKI+rqvoigKIoP0Q9c+aKozBTFOWvAr8F/CmwW1XVdptH/gL4OHB0o8fMm190TM8TlStTSqqbehgDpM14Nftw4kR2G6cEAixpExRdTOW4t7jguazR7vzL62X2EE7gvVUFmYG1r0zEy7lpbt++J9TXbe0pE78nTu1QbDNg5hlM1v4WLz1UXjGEYHJDCL65WnK04A8jewb+yxshPIHmZIdPtQqp1p8/vVA3ZChPTLU8Z3xdf+9dT2WNTr+/8tzbpGfvQpJab2BydqhuoNPBXs1+7S0DWMkO2/ZsSoBy8iVef/TZ+mOtxgOvPs/M3F8ytLqELsnIukYhN37LNbEDvVpNfW+rS5RGbvWKZfILZPILvs5XHhqlmhEPS4+CdoYytyvoRPS6ODMbeIh6xqyb8L38s8R45tPAHyiKctvG4zngJ70c6OTK+J+BB4CfVVX1z9q+xDp/DHxUUZRXqN8zA7uWxPQWUWbSDFOPM7UMKyRCM/uoInExJZ4FU7WZ7bQmJynKCU9zypzOv5gbZWajlyUmOE59ZTpwdcOV8WsH/PVrueHUU1bbyJwte+wV82s4YP1+P0LNEIIzQ+6CvhviDJqvqx2RltB1Jgp5oeV95volJMQ291omi5YRiDlAS2fRUhlXow+335u2voq2XhCWM2rF1bqraYwnqokUp6fex8Nzrwuf33XxHY5Xy9SSaR549fnmTJVeF9jWnjTheSxzw9px9zVcG+c/dqTFXbGcG6U0OsFAvrUyo5rOkiq3Dl7LFJZ46Pf/j44YgfgVDUFjg6AZtF7Ej4FHLMo2BxvDpO9TFGUbddMPz7stThmzq8B9YVrmq6qqAZ8J63xbmc02N8QJWYLH0ysc1ldY1RMMSd7NPpwoygnWfNqED2pVsh5LjJzOX0pmqCRTJCr27lxe2OrZsm3VEpM2fWU14O/v+iDJ2w6G/rqjlTW22fSUAfzRIz/BpfEZ116xMFzgzOfwI9L2bdvuSZxBZ0sbzVivz49QO3vzBvrNm2SNeWEWASahC23u5UoJuVREyw62nNModXTC0+9Kq1G9OkdiX6tTaPXq3JYqYwwjQ/ra7MM8OPe6sJR5qLDUMP2YufCW43nMPWlumIVaEJG2azqHBi3lh1oqzY399zf1mBlcve8RkOQNU5Bb/c4SvWEEYuAUk/jNCHkRaP0uZvr9+mNaURRlD/B7wCzwmKIo3wA+parqebdjncw//nZYF9hPeBE5nVpEXksAesGSvxOkJBgLcYB0VqsxqFVZEwTPSV2jKrXuOs5UCp7LGJ3On6mWSFUr/i+6B+mm8cfNZIbryQGmBeLseiLL7tEpbtQqjmV7ftm3bTvJ2qht+dTywIirKAvbltt6Xq8CzYs4g+5lz6z43YhwmzMmsrmXNI30wnxTj5lBemHesYzRz+/IGL6e3Lmnq0PZu1nGGFbZ6vKg/Xo0BkkPrC0zVFhyPI8h4qwmIdZsmRU/Is3tXABnnqiXXm4/dYLM8kJTqaMuJzh/+Gke/vyvkRX8PFEagbjFPl7jkCD9VJspgxazJfgd4P8Efh24BvxH6sOlP+R2YLCpopuQbi52px0krzeuzXTTqug0MmPzV299yHn5QHNi13Su6UMzic5MpdAw9TAzW15GBqFph1eS6IwsL7DWNC+pznghH5cxhkBJTvJSblrYYzaq1/jV7x1lIZPj+Phejs4+imZToupEulZhtLJGPjXIzNQ0UC+fsrO8F/WURSXE7PAj0PpNnAXBz5wxgNzcKUBc6miH79+NrteHSavHujqUvRuEbfTitB6NQdJObogGhojzimjQs5NI8/oZpssJTj95hLOPPyMcJJ0srZMuiNd2VEYgTrFI0LgjSD/VVqoWiulrtm+42/+6qqo68HlFUX7Ry4GxMKN7i9vOdbGdjFw/Z880vT5M+kwtwzIJBmtVZrKZhkX9pSuFtsWZFUNo2dni31tcCDTHzAiId28YfCzmRiklM2SqJcYLeXabhtgGpRfKGHvBJv9zU3cB9VllU9V11qUEw3qNQa2ekZwsFRomHV/ce9jzec2ui9tKhab5YrosCy3vzT1lQcSY8b4JS8jNTo32rDgzzteLuGXazLT9++jyUPZOE9V4hK/f9VFGBtO2g6Sd3BANDBFnRvSZ43XQc7ufV1oqLRRYTn1opZGJxoDqsIhClJmPDxrzRFW91E8GHGO7x7nW7YuIEbGuKMoMG65giqIcBjzNg5b0Ht+1P3bsmJ55zX6XMiz8ztyI+vWCvka/ijKAF8vDHK+1Dl7dX1xsWNS380HnVGL/wScAACAASURBVGIS5iBpUSBckyQqyRSpaqXtTFkvCDLoDVFmJqNVmS6v8euXviMubcwM8yv3/6jnssZPnHtZOPvrO7Mf4IV7nmo8TtYqtpb3vYTX8kav76+w/v69Ks680GtrwC+d/N1HJcgMjDVnWOGLLPD9uDKaz7Pz9omm89z5taPCHrD3Hv5Ix/q7OnUNnZih2i8iqBcZ2z3OtR2TPPjggyF03neOY8eO6ct/LDbsCYORv/5QV38niqI8RL3H7A7gDDAB/A1VVV2HVMYZsw28psc7dQMJsmPTz6KsotddF0UYFvV7pltFW1gk0T05LbphF/wmdH1TGH30ciBakpNMT9zG1Jy4ZG28VGC0ssb1hLtocnJd3H9N5cWDH2mIMLPlPUQnypwCTi94NQmJs2fe6OW10Gu0K8pEmx9mzO9tu0HSxvp546GneeOhp23nmDXEmyXzdu2H/ya6nECulG3njHVy0LNTH1oYiOIPu7iknUqdWJS1x9KFRdjRufl1MZ7ZATwM7AcSwDuqqnoKAmNhJqDfbhT9IMjMfWMiR8VVPcEyYovfNTnJ6I4R6j57/s7bScIcCGylW6KsX4JPI6jP1yosZHJMChwTFzM58qlWpz0RTq6LI+vLDBdXmsSYQRSizC5QbGfmklsPmhFId8pSH9rrPdNl2bXkMCz6ZU240Qkh3K4gkzSNJ06+wP5rKqPr+ZZyYnBfc27rp5xtrsKwWuobVvq5oTSnn6zb29vNGevkoGe3PrR2sBNl5v+2OyC63+KsmBif/AtVVf8b4GwFKyAWZj2Ml6xZr4sya9/YiGkGmWwSUvlry4yMjbMseEuOSDWGLG6M710pcCK7rak3TEmXW87bCTaTIOvHoNMcYJYTKY6P7xWWIB4fn/Vcxji+c7ej6+JKtnX+VFQ4BYrfvPfpts7tRaB1WpwZ5/SCjkTBsMPPZJFLzXb4YdOP60NEP4gygCdOvtBk6DG+nm88fuGepzxthNitH2idWZaolm0t9cfePo78+DMd7+9yw64PLQh28YYoznDKntkd4/Y6MTGbiDOKovw+8B2gUcajquofuh3o36Ispm38TrQ3/7/1X6/zzUq9b6wuuCSWSXK8NsQ3K7cC20tX6hb0O4viBvg7EiVSUv37jH8nsts4lR2vW9FLEmuJFMdrQ/zJUqbxPebztzMM1IkoRVmn6begM6NV+b7BHOla89iBo7OP8rWd93I9M0wVieuZYb62816Ozj7q6bz7tm1vuLyJELkuQjTZskS1zOylt4XPbT91gtu3p9k1nWv8C4rTtXsJsO+c2BVqsH964ZKn92Nhz36Kt83WZ45JMlp2kOJtsxT27A/tWvxcT0ydMERZslZh/zVV+Nz+ayrJmvu4ESehNXPhLRLV5soiJ0v9ocISi2cvN+aMibix/37fWavJmZGeECpBrsFNfDkJt63I2O7xxr+YTc9N6iMGHwEe3/j3/V4OjDNmPgjLqcdvPXY/CDARTn1jZ2oZDusrTeWH9xdvMjSU5kwtwwoJhk3ZNbOwqiJxMSUOQo1+tCR6qGLMag6ymQRZv5HQNZ6bP8nj6/NMlAotdviaJPPFvYf58u4PNmzuvWbKjIAyWavw+p6HkLUad14/LXRdNBOFKNs1nSO7eN1z2VS773en7Fk3ShvBOYOmyzLliSnhceWJKfT33m2rrHGzCrGos2VBRJmoh2y4uMKoIGMNt8qJwfm13ISWdWaZk6W+YaU/SvT9XZ0mSlfErSzErCxdsB/VELO5UFX1k4qiJIH7gCrw5oZtviuxMPOItb4atq6tvVec+sZWSLCqJxiTak07/bezwmF9hVU9QZoaZRLUaBZGRTnBmix+667JSYpyIhQjDwANmkom09USE4U8u8nTVxZIHgg7qI6K5+ZPNs0ts7PDLydSnow+4FYwKeppOT31Pl6bfZjlwdGOZcqMNeGlbCrsbLCTxb5XY5CwMb8vDWGhpTJomazw+7V0Fi2V8TW7zPo6m5FeE2VOPWQr2WHXcmK3ledFaJlxstQ3W+mH2d/VD3GAtTwxFlsxMc4oivIE9YHSl6mbf4wpinJEVdXX3I6NhZkH7G6c7d6kNrs4G5JqjFAT9o0N09o3ZpAAjlcHG31pSU2DkUmqksygVuW2SoFBrVovY7QwqFXJhjik1SiZNCinslwdrweDe0KYRWYmTEv9oPS6OMtoVR5fnxc+d2jxPF/e/UHP2TEDczAp6ml5eO51NElussiH8ASZUwmiUTYlssU+v+sg791oz+nTDjdxBvbZs6jfQ41zywly64dIDLb2+8nlInJFPDKml9/fUdGrjpduPWR+hriL8Cq0zBhzz+zmoZnnaYbZ39Ut/MQvsSCLifHMvwZ+SFXVE9Cwz/9t4CG3A2Nh5oIX4dSOQPPrZNRPpKR6f9jxWuvbzOgbE2H0pRlUTc5za4kUpxPjjFXWhcJsplJoew5Z43UdSiYXc6PM3LgUioDSgQuTMyzkRiknM7eyciELv83AA7kxJmzcEv3Y4UPr7r5bT8uLBz/CzHQ4s5jc+sHkSrmxEy8qmzq/62AjULQjDHv9oLb6HRH4Wo3q1TkS++5peWr98lnO3Hiv/kBOIGcH0YprEOKmTT/Qy3PKvKw3tyHuXnATWlZ0OcGxR55pWOoHXT8xm5OwqqZiNj0lQ5QBqKr6uqIongqtYmHmgF+xFNYE+80k0j6cqht6iPrGRDj1pZkpyUnuLC5xOTXUcGWcqRS4v9ha8hUUp5LJUjJDJZlqezYZ1EXZ1fEdjcdGVm5pfZ3hHphd1ivcObErNDt8URDp1NMyur7MgSEIo2jQSZRJWo07vv6lugjLL1AavdW7YpRNnS8kHANFkT340sFDnHniWS5e81fa50WcgTh71glxVnynXhWS3LkHOTuEVlylenWu/nVJInvgYfFzXcpId4qwBZmT6Apa2uqlh2xxaIIX7nmKFw9+RDjH7Px83jVzHVRo2c1D6xe8VOTEwqI9wvIdcGNs93jcn9Z/fEdRlN8DPk+9x+wTwHlFUT4EoKrqt+wOjIWZA90qNdxMJY6yBI+nb/WNuc0bW9UTLOsJ3Bq41uUkSnmJ+4s3m0w5wiSr1UhXS5RTrX0smWqJVNXdFcyNmiSxYGOvHIaBgR96ucTLCDTDsMO3CzKdelpE/ShRcMfXv9RUtjiQv8ntr32DRGmNd3/wxyiOT7Jzo7LWrrdMZA8+vHFO/ckjvnvS3MQZ2GfPjL9bZO8tXaf49qugHmvJimUPfoCMKZuWGBxuZNeKb7eWx8UEI6gDo5ceMgPrEHcDP+XE/S60/LJZYoheplPCNhZlfcnBjf/+c8vXf5V6odRH7A6MhZkLTqWG8W6Td1ISjNn0lJnJX1tmcHhMWKZoxuglS6KHZvRhxghEJyZzjZ4yM+OFfChljJVkinJSnCEMamDgl34QZGYM2/tDi+cZLxVYzOQ4Pj7ryQ7fKYg0LPJFPS12/ShhIlfKbD91Qvjc9Pe+zcTZt7mx//2cfuoIupwQZt7kirO9/tnHn2k6zqtIc5t35oYoe5bRqmyrlriZzFCyyUx7RquhrZmy8HKC5M49wm9N7twD6rEtV9bYDmdv3gjFAt+M03rz0kMWhemOF9oZS9FLxPFLMOLfW4wXVFV9POixsTDzSLwYo8UIEJPU+8ROJZznfLTbS+Y1wDT6vBZzo5SSGTLVEuMh9n9dmr+KPH1HfQ6TBScDgzDoN0FmEMQO32tQae1pWXPpRwmC2TzATLqQt7XGl4BsIc/Md7/JyMUzfPdnfxldbnU8dTpHZnmB7OJ11qZu/W6N6whDoLk5NhrizBh38FjhCpPVda4nB3gpN83npu6iJoUzWlPODiJnh2yeG6pn19bE5dTuJ/fWsxaq8OwBjL9tWDPKhosr/Lny/YC/HrJOCzJzr+bO21uzdjExMTFh0hefFrFFq5jNUPIoCgiNPjHDoj6p10v5DFfGdnvJ/Oz6S9TdF2duXArNMdEcvEpAemGe4m2zLd+XXpiPpIyxXwWZFa92+H4CSV2Wm3paJm7fFUmmTCTOnKzxzYzMX+SOrx3l9A/+zZbnnM4h6Tr3/aff4obyfs488WyTsAsi0IKKsx98+0+bxh1MV9cbjz+7o9XIIwhacQ2tuCp0bNSKq3VR5YRIfHnsWeuE8Owm7Qi0VKXEU299ldmb5xlZX27Y4//uh/4ncuXVlh4yK2E7oTq93yWtxofe+lNhv6doUyQmJiYmDPpCmIXFZnLT6ffrB/sPRRk4VLzJvcWFRv8YEFkvmRcSuh6K0YcoaM3NnQLqPWVaOotcLpJemG98PUw2iyjzQjs7+9VEitG9e+lksZuTNb6VnWfeZHnqZ5ifb86oOp1DAgaWFxrPnX7ySMv3+BFoQcRZulaxHXdwuHCFz08eCCe75ODYWL06Z5/pchBf2QMPu/asZbQqv3T1Tf7qynuN7wtbeIrWSTfWtZ/yRmNe2X3vvcFA7dZ91GqPb0eYWTLzhohTWeKdXzsq7PcE8dqJErNLa9B5aVEhlUuk8ktURsfQ0+7GXTExWwFFUT6jqupvBzl2SwkzM51y0wmCV4v+Xr1+L3gJ/Kz9Y1H0klnpxiwxCZ3hORX9vXfRUhnkSmnLZcqgfn29Om/JL0H7UBrW+OobZJcXbD1wkvklUvklJmfqbp7me4aXcxj9ZnZBnleB5lecjVbWbMcdTFXX2VYtcTkdzseSo2OjDbbiS5JI7tgtPCa5cw+Jd17juatv8ljhCjur4p7QMISn3froxrrxs/FhnVdmxbDHj3qAu9u6NN7vt29P2/Z7uq0dP7gJLieX1nazdm1XItVq7PzSFxj53uukFm5SmdjG8n0PcfXZH4dEnFGM2fL8Lepzy3zTF8IsDAHSDyV/Qez5Y/wjCiadZol5GTzhVdA5ZRMkTWsYfeiyHKpI63VRtlkIIsiswdnpJ4+w/BM/yfR/+D0mXvsL4THliW1URseEz+lygtNPHuHy+/8KD3/+nwrfv5nlBdKFvOtw3F3TuUawajcXzY84y6cGbccdzCcHuGljhBMIB8dGIS6GIXJGPIpBzg7xCzff4W+YyjNFtCs8e2nTwo8oc5pXZtAYRzHSnslMO5g3Idx6Nb2sHSe8Ci47l1YIL2sXVKDt/NIX2P7iVxuP0zdvNB5fPfJToVxbTEwUKIoiA/8WuB8oAZ9WVfW06fm/CfwSdZv7N4FfUFVV23huCjgGfFRV1XccXuY9RVG+AXwHaOzWqar6T9yury+EWVT0UtYpFlmdxRpM2s0Sg3qPmR1BBJ3T7CcdicKe/fWyxkwWuXSrrFEKWMIZi7LO4FeU2QVn+Z/+JHoiweWf/gzZKxcZvDjXcuzKfQ82lQ2J3GOL45OURrcJ+81KIxOUbcY0WJnZMcAdX/8SY28fbxnQawSRXsWZ07iDl3PT0ZhkWB0bbXA0DMkMopXWSAy0/o1TKwscXrzgev52hGeviLIg5cFO88oMrOMoOm3wYc0Mny8keL9Nr6aftWOHF8Hl5NIaZtbOwI9Ak8olRr73uvC5oe++hvTMj3oua4w9BOpzyiC2xe8gzwBZVVW/T1GUR4DfAH4YQFGUAeDXgHtVVV1TFOU/Ah8D/quiKCngdzAJLQe+bfp/T4OlDfq/E9kDTqLHjyC6fnG58S9Mtpoo8ztHKWqcZokt5kapSfZryhB05VQWJGlD0O3gwuSM6+uKgpzCnv0Ub5utuzRKMlp2kOJtsxT27Pf+A21weuFS34myMK836OBbv+yazjVEmVwpk128juyhH9EIzgbyN5HRG8HZzi99of4NiQRn/8GvcePDH6UyNoEuyZS2TXLj8R+slwsJmJwZaQQ4Rr+ZiBv77/cc1BnXOVxYREZnuLDIwZMv88Crzzd9n10wbX2fH519lK/tvJfrmWGqSFzPDPO1nffyuam7PF1PVBiGIeLn6mWQIkYuvMWkTfmimaDC006UpWsVJot50rX25ym6sW/b9rbnlTnRiXEUfqgl05zfdVD4nJ+1I8JNcBn3Di9Zuygwxzl2sUkqv0TyptikKLO8wLJ6seU8dv+sr7uVGNs93hBlxuOYjnAY+CqAqqrfBh4yPVcCHlVV1XCISgLFjf//l9TLEy+7vYCqqr9KPSt3DDgB/PbG11zZ0hkzP1hvGGHNNdtqN6JexGmWWCmZoZJMCY0/3ATdzI1LvvrUdFmmPDElfM7vsOl+E2T9iiHI/PaCOAVnw987xjVjxzmR4OonPsm1H/kxXw32hmNro9/s1AkyywuURm5dlxecrnPmwlu88dDTLWWN0FqKZs4S24072EuX37cuhiGG+6K1Z+3y3CmuJweYFogzHbiayPLS8G2BhKdIlMm6xpHzr3Bo8RwTpQILmRzHx/dydPZRtJBdH8OwxneaV1ZOZjiz/+FQx1GExXc/8HFyQ+nAa8cOLyMttFSaambA1mHVKWtnjUPajTFEx8uVBLdHlFGMupKpl0zgli4sNomxOGPWMUYA84dUTVGUpKqq1Y2SxWsAiqL8bSAHvKAoys8A11VV/VNFUX7Z7QUURXkK+H3qmTMZ+B1FUX5WVdWvuB27JYSZk628l4Xp9cYWp+Td6YVsmTVoTFUrpKuletbLQqZaIlUV70gHFXQgzuZoqQxapvUawN+w6SiC207NZAqrZKvdgNKtlMpatui3F8QpOEvfvEEqv0R58lZprZ7OND32gnEfyn/q0yyXSyyrF327ujld51BhiYG1ZQojwX7XonEHxt+/WwLN0TDErmdNSvBSbrppBIDBn4zM8K933Bfqmjly/pWmUtDJUqHx+It7D7d9/rAHSUPrfMDl7DA3Z97H6488QzU9EPrr2WGMqbD2dZp7KQ1u2zXK6V1HOPv4M6E6IjqNtKilMtz3n36LzPIipdEJKpkBRL8dUdbOLu6IYqyOk/trOxnFTsROvRafbWUxFuXfwmUC7DJgnqUiq6racJfb6EH7F8B+4FlVVXVFUT4F6IqiPAG8H/hDRVH+mqqqV21e458Bh1VVPbdxzn3Al4FYmBmIbk5hijLrMVGd20y/zTHrtiizayhP6DoThXyjp8zMeCFvm/UKKujsSuzkSgm5VGxr2HTYAW2nZjL1giDz2tdiFWVBekGcgjNdlpj4sz/h6t/4qdDczfR0huF772j6mpd7h9N1lkYnmvqCvOA258zAGEbdcbwYhgh61oxs2OHCFaaq68wnB3i5zXUiWhPpWoVDi+eE339o8Txf3v1B12HrTkQhyuDWfMCX9j/GjuVrXBvZQTE9yGwHRRnUM9t3fu2oMLNt1yOqpdJtGX2IzmcnalLlIqlyvWpqIH+TAWB5aoZUad02a+cl1hD1oLZLu9n4XhNIMVuKvwA+Dhzd6DGzNj3/DnVt94xh+qGq6oeMJxVF+XPgMw6iDCBliLKN489uCD5Xtowwg2YhE7VwijJ7Zj5nv4izbokyr+5euzcMPhZzo5SSGTLVEuMbJh52+BV0bgGppGltDZuOIpB9bv5k5MOAwxBlnRBkIDb4COLg5hScyZrG9m++AHIiUncz833E7h7itjO+8/YJ4dq2MwPxQ9fEGXg2DDGoSTKf3XEPn588EGlm2WncwHipwGhlzdPQdStRCTIDY47Z/msqo+v5xmDpr9/1Ufbs7FxfzQOvPs/tJ19uPO7WbLIWUTM8TrK41hBlZlKldV7/1C+TLK03Ze2CxBZhxguG+6uRURxRZtDTGaJ9J8XEhMIfAx9VFOUV6sYcn1QU5ceoly2+Dvws8BLwDUVRAP6Nqqp/7PM1LiiK8kvA/7Px+NOAuFHZwpYSZuD9ZhbWzSvMeulOliqESa+LMqivzD3XLzJz45KvOWZugs6vAUXQYdNRlS8+VrgifC6sYcD9IsqcHBcds0oO/RZnnngWqVbjtuMvIeutotuvu1k7ON1D3HbGRWVgTnjNmkH3Sxv9UpKTocxhs1sXTuMGFjM58imxpb8TUYsyaJ1j1jRYWn6qbRdGu1EO1u+ZufCW8LkoXA6dsIoauVLm4c//mvB7M8sLJEvrTZs77cQUYWfPtFSa4XvvCOgZHBPTeTayYJ+xfNlsfe+Y2VJV9fs9vMzPAr8J/MON8/0Z8PNerm/LCTMvROG6GLQht5/T/d0sXQy6W5/Qddu+MBFOgi6IK2Cnhk17YVu1ZOs21wszmbqZJTMTtN9ClxO898gT7Prut4TPG+5m1hLEqLATZ9YgUtRrE6U4gy5nzyLCrm/TaW04jRs4Pj7ru4yxE6LMaY6ZMVg6KJJW44FXn2fmwlu2oxwMBtaWGSosCc8TxmyyIBhlknKlHGhzpx3C2tDt5RhlbPf4lu7hiukeqqrOAz8a5NhYmFmIOvPU7hBp0U2w17JlvdpLFiVmQReGTbt52LQbUQWsN5MZW7e5bs9k6kRACd5nkwXttyjnRinaBGSVie318iD/lx0Yp910t14bqzhzK2fsNXHWKYMbc9/mVHXdt6vi0dlHgXpP2XipwGImx/Hx2cbXvdKpNXRgqD5AWsSIMVg64LkfePV5DppKE41RDgDHHnmm8fVEtUyiWmFtaIzcamugHpX48YrfzZ2wWiXaEWe9LMgMYlEW02kURfmKqqofUxTlHLR+fKuqus/tHLEwM9Froiyqc0RFtwUZdEaUdWo+lheiDFRLctLWbS7smUx+iNpx0cDPwGgvWSURTgGZdYh0JwkasHVCnEG47/tOGdwYWPs2/boq2o0b6FXWB0dYzY0xXGgNktcsg6X94FSaaIxy0OREU0atYlPm2O5sMqDF6dEvQTZ3whBoftd6pwVZ7HYd02f83MZ/jwDzQU4QCzM6I3Z6WVC1SyzINi9ObnN+Cct5sZcJ4uCW/+lPMpBLM/y9Y6QXblKe2MbKfQ/aDpHuFL0qzqD5vdSuSOuEwY1BRqvy+Lr4s9qvq6Jo3IBXOpUtg/qw5ou7727KbBm0M1jaqTTRGOWgnHy56XUz1bqzbTmZIVn7/9l79zA5rvrO+9vd05eZ6Zme6bloZI1kWbZ1fJVtWcKyMdgywgZvHAQmMmBsMJgQnvAskCd+syTZkGXJm7zhzctmQ7IGGzZAzIIXE29MEnzBV7Dli3yRbypZkiVZI821RzPTo+l7v3/0VE919alTp+5V3efzPHrs7rqd7umqOt/6/X7fX8GW3mRGexhqQXu4M3DGIKoc56BV8cJ7rnslyuT/F+JM4HckSZIL838oSRK9S70ObS/MWlkwOY0fBBnQvqLMjZobu9zm/GCH7zeUk4zxXbdiYudNhppIuwGPcyMNmjgDtM9VM+JMxopIc8PgRsnmZJ8jropGcPMckv/ucgNpWi2YWViRuMVkHwqxhGZErdzVjZdu+r+Q6x+yHCkz2sOQxcr5Ntjwnte9VL0QRLTPLMSZIEC8Qgi5BcBzAOr1IJIkHdXbsK2FmRBl5vCLIAO8qSfzA24bIdjlNmcFNyeURtIYjaI1sTDTRFpJqJB3VNgZjaDRDEFY0TMr4kzGaKqjkwY3tLHNlYu2uyrq4YeHGdVwBHu27cTLW67XdU9UwnJbZEXiYrlT2PzcA9pmHwuzqERjtqQvGu1hSENPbBg998wIGLO9Xp1AzM0ELcBly/+UVAGIGjMWfreZ9yNeizKrQqwcChmyw/cjQXOn80NdmQxvfdnYiawj4syRiU65jJH77kHv3hcQzcygmB7A/KYttVRImxpUA/ZNlpwWZwC/UYhTBjfAiplI39AZ9fREs66KsXLRVD2ZnaKso1xET24BC4kelEzWtJU7Ysj26o+J121Rjriduf95xJbTFIFayuJZB/ag0BFveF/GLrMPMz0MlTglfOxIafQyMqVlQiSiZYKgIEnSGWa3bWthBtjf08Pr4zhJEPqRaVEFcHRoFJlkqn6z7s/OYdXJScQ4RNqGgUEcnM14amEfNEEGWBdlfnjabxdOTSpG7rsHg4/9sv46NjNdf21Hg2qz1yzW9cKOBtR68ETPnDC4kc1Eti9NIp3PInO80XXRiKtiuFrBrsNP45LZt2v7MujgaAes5tDVsPYYrPQm43VbrIYjeHnL9Rg98hpVgGld1e0w+wDM9zAEjF8P3KoD85P4kT+zn8bkFMLav7UghPQD+GsAZwL4HQDfBPAHkiTRw/gK2l6Yyehd9Ozu+RE0gea2KLN70nZ0aBTj/StpYoVoAhP9CUz0DSNWyiO93BQ6RNm2LurWnYtKPIFwfqXpc8glM/OgiTI/RclkrDaxNYuTk4pQIY/evS9Ql/Xs3YMJiw2q5euUVcc5GlrizK6omYxe9Mxug5uPvf1rXMtwXTTiqrjr8NMN0TWjDo52cO3rD2LrkZXfWENz6Auuo25j5VzjcVtUpjV2nppH9yL9fhEt5XHgrC1YM/W2oTYWvJjtYeiUSUcrCphW/ExATYhpvScEWktwF4CHALwLwAKAEwDuAfAf9DZsOWGmddHiObntzvPmOVbQBJqTOPUEvRwKIaP15DIUQiGawHh/AkCtWbQatairJLqQO209AKDnCL1xqp20myhzIkrWiqIMAKJzJxHNND+tB4BYZgbRuZNcdWu0+rSpY/O2Oc5p4bQ4k1MAY6lhvDFHd0O0anCj/L3HykVcMvs2dT2166Keq6KRfTlBqFLBta/9EpuPvkhdLjeHNpvWqAWP26IyHZJlApJPDWDsI7diDLD9wYLMwR03ApUyhva/glh2jin+nLweeClggphy6EUkjibG9NYTIi2wnCFJ0ncJIV+QJKkA4E8IIfSCVBUtJcxYIseP4fCgiDI3omVOpjUVO6IocNSKzCZTGJ0ea0hrZIm6QnoY1XfecjStMUiirNUEmdU6MzeuN8VUH4rpAcQoIqaQHkAx1cfeAas+DeYd54xcM5wQZ0ZSAOVzzKjBDe33niqess110c59mWHHGw9j69E9mst7l+bRk1vAbHe64X2rD0H03BbVfc9YJiDKqJXRNhY81B9cHHgNsYWTKCT7MHPmunOIUAAAIABJREFUBbY9uKDhJ5MOQLgnOo2IogWWEiEkheWMakLI2QC4JostJcxYES2ei4STQikoIswrnKw5iZaKiJXyKEQTzPXyHXEUO6KIFAsAahb55XgnChruYZVYApVoHJE83dXNKu0kyuzGqwiZEjcmJ9VYHPObtjTUmMnwNKhm1aeFdt5k2nGO5shoBrPizEgKoJ2/3blol22ui1b3ZeVBR0e5iI0T7GyA+c5eLCR6Gt6z47wz0/dMy45/wqaURS3UDy4S2ZMYffEJVCMR6oMLJ64JXgsgWvaP12PiwYsxyuKKN3ImCDRfA/A4gHWEkPsBXA7gMzwbtpQwA8y7Cgnh5D084syMq2KkWkU6O1dPV9QiXsojWio2TATDxTzC+RwqieZJULiQQ7jYXHAu8AY/iDEvkKNbrAbVtKeuevVpmSu3IzFv3nFOjjbyCDQ9p0aAv5eglymAZl0X7dyXHZHnntwCUkvsa/H+VcT2NEYZo33PtOz41zgUtQLMW+UbEWdacxkrZRWt1OssqJw8OivEWYsjSdIvCSEvoGaZHwHweQBcYc/ACjPWxcXoBUKIMv9Dc1VkGXaoWbdcOzabTCHfEQdCzVuFpsZxZHqq8b1KBbHMZL2mTEksM+lYGqOIlhnDKVFmNZ3RlZSeSIS7QbXS+UuvPi1UhWaapBG7cV6Bpvdghjd65nUKoBHXRTv3ZWcq8EKiB3OdKfRTxFkZIbx4+qV45Lz3N7xv5zlotu+Z2o7fqbYXgDWrfNb8RSs1UL2+1SygoKQbtqpboZHoWSt+/laHEPKMJEmXA/jX5ddhAK8AuFBv20AKM+XFxurTHyHK/IXW5Izmqsgy7FATWl5vdHoMhY4oxvuGMbcs0sL5pbrLIg35/UJ6GJVYAuFCDtHZKXSOv4NqOGy7OAuSKPMDfo+UOTUBUht2aDWo1rrx69WnFYaGNdMkFzdvxcAZg4YbTgPWbPR5omd2phOawYjrol37srPP3+HJOZQiUexfRerui0peXLcZD174wYZtnIK37xkLp8SZFat8GeW1gedcYqULmpnL+KFnGQv52tWq4gzQF2it+rlbFULIowCuXv7/CmpxhRCAEoB/4dlHIIUZDTPpAQJ9vOhdpp6csQw4aIYdLCLVKjqLBZwxdQzl6TEUO6IYmxxniqsQqug5IqH6zlsoR+NYWn06Cv1DmB1Za7t1vhBlxrB7UhgpFQw9ofcEAw2llTf7UC6H8GwG/cNpzE4ucdWn6aVJmkmp0hNoPCnNLIFmZzqhFfRcF+3Yl51RMvW5JEfENk5I6F2ax3xnb71/GW19Fqzzyq56RLcxa5VPQ8xJGtGykvdapKjH5eR4vP6sAnNIknQNABBC/laSpC+Z2UcghZleHZm4yNmDX26WLFdFtWGHESLVKo6MH+dKhQRqaY1LI+uQW316/T07rPODLMb8kMZolTWrk2xbeI3teJ9Y2/00mrehdH0SUS4jdfddSOx+Bh1TUyimBxBfFnK69WkcaZJ2thExilZ6Iy0F8JW+dXhs5HzEykXXxJlTOOFgqhbD1XAYD19wHR479xr05BawkOip15TxirJQpYzNzz1ArRWrhiN1kR5UcSZb4g/uf8V0nzQz545WCYdd+/KSoESO7B6Psu7Mb59VYIqvE0J2SJL0CCHkqwA2A/gzSZLe1NswkMIM8N/FpJXwww1S+dSc5aooG3aYwajbWzUcRiE9TF3Ga50fZBHmJGYmmnZEy+SJoVlbeCXyNSlUyGNeOoZeMsp0RaT1DtODt6G0cnKTuvsu9PzL/6m/Vgs5nvo0rTRJGbORMytRMxbKFMD+QhY7TryKi04ewfaJ15nW+UHACVHGohSJ1i3xjZ5zm597oMFdsSc7W3+9Z9tO+wbpEdVwBHOfuR3zy+fyiWzE9j5palhzn6D3Rw2KKHOKdvmcbcKPATxACAGA3wHwLQDfAfBevQ0DK8y0COoFyWn8ILbMwnJV7M/OcacxKjFjwV2JxlGJ050d9azzW1GQ2RUtc3uiKSOLMrPuak3IKYYvP4/oyQyKfWnMX7y1OcXQQCqimnR3RduwY3YG6e4Kyqsb0xcTu5+hrq8UcnrCiwe7J4W84oxlClKIRLF9/HW8b+L1lXEyrPPdRG5+zVuD5tV5ImNUlEVKBYwefZ26bPTo65j8rV0NTX2CGDWTf/Py+TMw5OwchPeBdNAFmozfhIpwUxQYoF+SpG8TQv4OwD9KkvQjQghXamNLCTM7LkKsC1+QLnJBu8HpoXZVjJfy6F92ZdTDbJNaNWat81tJlPklddFqtExpBmDGXY12nRj52Y8w+PhDK/udnalFpqoVjN/06ZX1OFMR1fSt60cl14nS0BCik5NNy0tDQ6j0Nzb7Dc9m0DE11bQuUHNejM6dtCzI1BiJnjk9GffSOl8LI82vZbwWZWboPDWP7uxJ6rLu7EnqeeU3cRYuFhDLzqGQTDU8nNGLWjkxVzCTJaQn0PyUeeSHGjJegjJOgaeECSGXAtgJ4CpCyMXg1FwtJcycxstaCh78dEOzAu0pudJVkaePmV1iTKYaDqMSjSM2O9VQYyajZZ3fCqLMSTHmxYRT7dBmh7taaOkU0k/9irqs/+knMfHhj6Mai3OnIqqRn9JWEwnktl2OqCI1USZ32TZUE40R3Up/WlPIFdIDKKb6dD+bGey6VlqNmnltnU8bl5Hm1/I+gshSVy8Wk33oyTZPYheTfZrnlR/EmVbN6dynbtONajs1R7DioOj3uQsQHLETlHEKPOePAHwTwP8rSdIhQshuAF/h2bClhJmTFx8/X9S8vonZBc8ELFKtMo0+bBdkCCF7+saaVX48gVA+h8jCHKrRaN06X8tqvxVEmZOYnXCajZZpWWbb4a62+n/9T4TLZeqycCGH0+65G2O3/p5u7zBaBEudOjN3++cAAIlnd6NjagqloSHkLttWf18JS8jJzotOwXs9tsOlEaD/nhKFLmTjSaQ8ss5XX4+MRvD8JMoOT84ZOvfKHTEcW3d+Q42ZzLF15zPPKzvFmRmrfK2a085kjBnVdmOewLLMZ+Gn6JhA0OpIkvQrQsirAN5FCNkJ4EOSJE3wbBsIYWbECt9ucSYEmTNYKezXwm5RBgDZ0zc2NJeuJrpQTgDxE0fQfeIIwsV8y0bKnMTtCafe5Myou5rymhQq5JHcT6+lAWrR3v7nfoNydw8mdt7E7B3GFcGKRDD3+d/D/Kc+jfBsBpX+dFOkTIks2GK/eZruvOggZtIaadbqRs1AQpUKdrzxcM3qXSNi5qZ1voyRCJ6fRJmMUXH24rtuAACqK+NpOtvaIc6UdaS0lEQarJpTdVTb6/mB3/uQCQTtCiHkOgDfB7AbQBjAdwghn5Uk6Rd62wZCmAHuiTOvL7Q8BFGQOSHEZJwQZADbhbHYP4Tw0f1ClJnAyoTTTLSM54l5NRzBgWt34dD2ndwTOJno3ElE5+i1NErkSZ1e7zBeqokEyqv1preoC7nQpz6NxdcPG3KBVEbrzKbw8F6PQ5Uyrnr139D35ktUa3Uj4mzHGw9TmyNXAMx1prB/FcG9I5cY/SiW4W1+7UdRJmNEnFXDEezZthMvb7m+QWzzRrF4mpKztmW2wQjTUxJZNafRzDTmpWNNtXFeIwRaM2YjiwKBTfwFgCslSXobAAghGwD8HEDrCDPA28iZHwiaIHNSjAHOCTIZMy6MQpSx8VukTE0lGjM86Sqm+lAcGKRGwZTIqYq6vcMU2OkAVk0k0HXpOdwCS31sp4vz1eljNGt1WRCwri0d5SI2TtB7Ci7Ee/C999yOXKwL65ffc/o6ooSn+TXrHFGP1SsBZyatMdtrfqxGomfKc95MGww7ak69QogROkbmjgKBTURlUQYAy3VmXP1ZAiXMAHGCBQWnRZkbmHVhFNCxOok0Gi0zU1tihmosrhkFU1JPVeRo2uwkegKrb10/QrkcwieON6VJmm2AqvegjJU+Nnr0dby85fp6WiPATm3syS0gtURflsxn0VnMIRdbOaeVv0s3RBqt+fVL/etx7/orfB0psxNZZPGeo+FiAWcmlnA4G2n4HShR78tsGww7ak4FAkHbc5QQ8mUA31t+fTuAIzwbBk6YAe0Ztg9atMwNWP2L7CBUqSCWmWyoMZOhuTC2YrTMD73KnEpfZMGqSaFddxqiYDNTCFH2qU5VtKN3mFloAqtvXT9QLiP1nTuR2P3MirHItstrdWoKNzoz0TOWOGOlj3VnT6Lz1HxTxIUmzjrKRXSUi5jv7EXfUvOx5jt7sZDo0Ryj+ndq5fqi1adM2fxaXj46vLoewWNhh3Cz2rzbjsbuMmMnssxzVZ2KePFyKuKT519XT0XU2p7ZBmNuhmrXL6NVczr3qduAE4tGPqKrtNOcSA/xXQg85rMA/g7An6BWav4ogN/l2TCQwkyGJdBaKY1RiDJtaOJMtrbXMuYwguy2WEgP67ow+oF4pYSBUh4zHXHkw9ZO76CKMiuYtslWRsEyM0g//iB6XnuZ22wjVMh7Fj1Tkrr7LvQoHByjk5N1R8e5z/9e07Z2iTNW+thisg9LXexJltLsI7U0h3yEHtXYv4qgZMDww4xQ4+1TVohE0TN8JrRlonPwpISytrMTljjTSkV8L7BSD1qMUaNYrN8UQmGMPvsIDl67i1prJtecHr7yenRPjmFxeA36N9bqOe0okzBiRqKHECACgf+QJGmSEHIzgIsAFAG8KkmSdo8nBYEWZjLqi6S4ULUXsjhTW9uH8ysiKgSu86GJEKroOSKh+s5bTLHndbQsUq3gC5Nv4D3ZExgqLWGqoxNPJVfjfwyfh7JG01ot7Oxb5rYlPmAtWnbB0/dj0IRNtkw1Fkdh5DSMf+w2TPCIrXIZI/fdg969LyCamUExPYD5TVswfuPN6DvD3bS2UC6HxO5nqMsSz+7G/Kc+3eT+aCa1kTaxZaWPHVt3vmb6mhz9UZt9dJZrLTVykRiiyxG0/asIHjnv/dzjpCH/nlkCjadPmV9SFnmiZ248HKGJM1Yq4sgrT2NQehnx+VlNQw/WbypcrWDtnieAZQGmpukBTW8/cuddgOO7bkW1s6s+x5h5e9qQwKI9+FncvBXjN96MKYORODHPaR3MpogL/Ash5P0AfgDgOIAIgD5CyC5Jkp7X27YlhJkaES1zBq0buNtRDRobBgbxSnKgIe2wkuiqv+45QjcD4CVUqTQZffiJL0y+gV0nD9Vfry4t1V9/e9UF3PvxWpQ5/VvSnMyUyxi594dI/5reJJrV/FkLnlTFkfvuaahNi81M11/n/vBLDeuGcjkue3yzhGcz6Jiaoi7rmJpCeDaj6QJpNHpGE2e09LHDa86tW64DoFrps8w+lqIJ/OO7b8PJ7n5DkTI9tOrSWH3Kts4dxYt9KVvHYQe06JkX13S1OGO6IxZyiBZyANiGHgd33IhQuYzTXnoK4WrzAzWtWrOmSN18Bp27n0Tvy89h9vKrMb7zYxi5/yfYuPcFdMzMcLk9Uvc7N4NO+fzfdSvX3EUIMuvomSq5KZCUY3HaYEngKt8C8EFJkl4BAELIFgB3Atiit2FLCrNWwE+CzAiHJ+dQDoVQjMYQLRURqZqLVBnl4GwGhXXnUpcV0sOovvOW5bRGGl5HyoBa+uJ7sieoy67MnsBdQ+fopjXaKcjM4pkow7JAevJhzeVazZ+tECrk0bv3Beqynr17kM/lagKsXEbq7rt0676sUulPozQ0hOjkZNOy0tAQKv1p5vZGn/qqxRmtZcE707XIV6hSxubnHqD2wzqnG5pmH725BZQjHY6KIaVI61/MYECjT1nv0jx6cguY7WZ/j15h9vyjiWUlSrFl5L7GTEWkQBNZ1XAE72zbgTUvPkndJj6faao1Y0XqIrkcBh/7JbreehNdx1bq+HncHnn6o7HSJIUgswcep1u3BBJtLEKctQx5WZQBgCRJLxBCaOXnTQhh5gLqmxEr1crPgkzvxv325ByODo0ik0yh0BFHrJRHOjuHdVPHqGYIVlCnElXinYat7a3iB1EGAAOlPIZK9M82XFrCQCmP4zHtU70dRBmgXZPKEkgy3M2fDRCdO4lohj7pjGVm6hEqI3VfVqgmEshtu7y+byW5y7ZxR+msTiyULQvWrI5h7EQWm597oG6dDzRa6b+85XosJvvRk20+pp7Zh90sJHow15lCP0Uouj0WGnaeZyyxrBU1Ytneq++LrFREGkqRpazhKiRTyBmwv2dF6mQSx9+hvs9ye2TtV/ngR3l9akejM6ehXZu0BBJrG6cQoqxleJYQcjeAuwCUAHwMwGFCyHsBQJIk+tMiCGHmCcobk5UGmn7i8LIoG+9fiSgUogmM99cmc6dPHTO1X15XNLet7f0iygBgpiOOqY5OrKaIs8mOTsx0aKff+UGUuY16ssMSSDJGmz/zUEz1oZgeoPY/Kw3XIlRm6r6sMHf75+r7rkfnLttWf98uZLOTcDGiW5uzdjCG9WNvUpfJVvrH1p3fINxkjJp9WKUUiWL/KkJtbu32WJyGJZblvnM0aPc8rYeV9fRW6WUk5jPMB3z53jQKXUmc9dC9TeY902dfiLUvPN60Dc3+nidSp5V9QYvAKfdbGqCf71oPfoQgc4cmd1oVTom0k0dnbe1VKfAVcgrXX6ne/y8AqgCu0dowEMIsyM2i9QSXWUHmJ0Enpy9mNBpvziZTGJ0eM5zWaMSq2qi1vVn8JMhk8uEOPJVc3VBjJvPr5GrNNEa/iDK7o2V6Ftwy8jVleFhbIFXCYWTe8z6mo6JZWP3P5AhV5MRx03VfpohEMPf538P8pz5tqZ5NM2qmMjtZ26tfm8NjpS/XocnRmzmbzD7MIB9z44SE3qV524xH/ESkVMDo0depy9R957TOR55zVE5vPX7xu/Guu74BMEycpjdehDMfvR+jLz5Rf09OMXxny9V4Z+s1Tfb3svBTwhOpq4bD1HuKVgNqWWBpne9OPPgRmEO+bmkJJruNOpTiTETLWgdJkrab3TYQwgxYubAFVaDZifKGxkoNcQO5YLzYEUVBIzKT74ij2BFFpFjg2qfZ3kFOWtv7UZAp+R/D5wGo1ZQNl5Yw2dGJXy+7MtKwKsrcbsrrJJOTefRu2IS1M80TscyV78P4x24zvE/eJ6wN/c8yMygNDSJ/4SbM33ILAOt1X2apJhKmBJ/SoIQmztRmJzy1OaV4JwrJFBLZk03LZCv9ajiCPdt24uUt19frnQ5mvDHrqYbDePiC6/DYudegJ7eAhURP4CNlahE1Jx1GN+XvAWj3nbNCrn9IMx2xEgrj+MVX4tTCKZwlNUcqAWDwrVfx3Oe/1lC/yIrUyoJt5JWn6yYjDeM5bW1DjZmMOgKnjnipz3eeVhpu43aNk3JO56cIoV40y04xJSJnrQMh5GcAviNJErVonRByPYDPSpLU/FRomcAIMxkh0BqfQHolytQOjdFSEbFSHoVo89P1eCmPaKmou0+rE3xea/tWpBwK49urLsBdQ+fo9jGzO1JmRaT5wdETqE3EOpMxSxMmOT2v+/z1Dc/1mRMduf/ZDR/F6f/2EyRe2YvuR3+F+Kt76wYfdtR9OY6GQcnJaz9aNyhh1fIpa3Pq9UFdSWx44oFalENDBKit9MsdsbogWD9ce99KM2UrlCJRR4w+/NCDbKmrF4vJPmpdH63vHG8UWwtWFGts83uAcARkn2bJRkOKoVZTaSVypO7tq27AWQ/+FANjBxCbzaxcF5ZdGbtffJ4agdMUGMp+hx70LeRBRG1W0IueOXEsQeD5NICvEUL+O4BXABxDrcZsPWqOjPcDYD7tDZwwE9TwiyCTiVSrSGfn6jVlSvqzc7ppjHZGXfxube8k+XCHp0YfXvdnMjP5kydR4+tMTphU6XlmXBNXPfAzJB97pP66weDDpbovwLwlv5ZBychCvt7/jVXLl5ifRWw+g9E9T9brg8qxODVaUQGQTw1geuNFmNhxIzDRXuf64ck5rB9OGRJoVkWZLKzq952OmGZdH6vvnBVo7RSmN16Et6+6AVvv+gZzW60UQz3K8U5Iv/1pDA/Hm64L47tuRWj5enEiW6uV5I348LTSaBf8FCXTgibQhJAS0JAkKQvgDkLI11GrIzsbtdvWM6hFynQbFgph5jBepxraBc/Nf92ywcdsMoV8RxzxUh79y66MLPyeCuf3NEYe/FJP5iRWRJmMmQmTOj3PqGsiK5IkG3zYUffFxIIlP8ugJPXGS5go1Pq/scxOCukBnP3mbxqae4cpogwACj19eOEzX0Wpq/b31sse4GmiHGRYAs2OKJlshz9eKgAKwaWu65NdGV/ZfB2S89NN9vlWo2a0dgqVaAyJ2SldF0WayYehY2tcF+T3B/SDcIIWQIgxAS+SJC0AaE514SAQwmzq2HwgnqoYxWx/F7fhndSEUHNfHJ0eQ7EjytXHzO+irBVwQpQdmpn2PDomY2WyZxUeUaUnoliRJKXBh9m6Lx6sWPLrNaaWbcBZZicL51+M3tde4hprLDuHjvxSXZjJWH0IFnQBpxy/HYJMzw5fXdeXS3TjohcfxH+4///jts83g7KdAsB2UayEwjh+yXuoJh9G8GoO4tf6K4FxhMGHgJew1wPgZerYfMO/IF+k1qxONk0mvZxcsjAzUYlUq0gUC641lxZo08qRMtp5ZAQ7riE8okoPOZJEw0mDDxk9S/5Qjh65kpENSmiUhobQff76+uvxG2/G9PYPID8whGoojEJ6ELPb3ovMldt1WxbIsNLStH4PWkJFTgmUlytfq5cFATvHK9vh92RnEUa1boe/+bkHGtaT6/ouevFB3fWdeAAp15/RGNv8Hrz1wY/bKgzNYKYmXr2Neg7kFO1cv+8UyhRIYfIh0CMwwiyo0G5Efo6OKQny02M7OJAZC3QaY6uLMivY9WDHiqgKFfKITU0AqNlo03DD4EM34iXtY4ozuTE1jdxl2zA7qagBWzY/OPAnf4nZy64EUEXfs09h/Z1/gwpnTZ9eWhpLnJkVXEETaGaJlApIzk8jlssy7fAjpULTdrzrO3H/O7jjRryz9RospQZQCYWwlBrAO1uvwUENp08z8AoW+bwOFaz1zmRdo4L8YFogaAcIIX9PCNlqZttApDJq4ff+ZqwbkLzMa3dFLYQoC64gcwM70hhbYaLL04usCZVZSDE9gPkLNmP66mvR8+pLiM3OOGrwoYZlyQ8AQ3/8VZSGh5k1ZzSDkrnzLsH4tR+l7nPVAz9DeveKi16MES0rxzsRKuSYvaeUOHktDXq6oxbqtMVTXT3oPkW/t9Ls8DtPzTPs82eb1rdabyZTd/BMpqj1Z3bDzNahndebtmD8xptNCykvBJgQffYjepW1Jc8C+CtCyDCAHwL4kSRJ4zwbBlKYKS8cfhRnRiYGfhNkgLuibMPAoG/qzIQYcw8roswv0TKZ8RtvRrwnznRNVFrmq81CYjPTGHziIUxv/wAO/NlfI91dccbgQwM54kWz5JfbTejWnKkaU2cWw5qulqy6vFIigUpXElGFNfnEDR9FNLtQd77zGrN29XYeXzbkUBtsmEVOW5RJaogygG6Hz7LPr4bCOOe1J7Fn24caUgqV4kwpsGh/Y/XyUKWMMx+5r+7gmU+tiHYeO3yeY2qhNd8466F7G8xrYjPT9fNcdiYVuIffHBT9MAaBe0iS9EMAPySErAXwcQBPE0LeAHC3JEn3s7YNnDCjTar8JM78KLRotOJTXzMIMWYcq9EyLyNljjwNVokSlqhiiZKevXswsfMmzBQ70WdSlJm1u1dHvABQewDqGZpUEwnMFDsBxjyXVZcXyRfw9h/egWos1mBNXujswsCQfjqZ8vprRbzwbOuFQDtjMInNu+/XNOQwAysNkQbNDr/MsM+PVCs4Z9/TqIbD2LNtZ8OyiSNTeO/ef0X/4f2Iz882CKxqOKIpwFCtYO0Lj9f3w9OoXIYl6sx+h+FiAYP7X6Euk89rv/Ura2XUdVxuN80WCACAEHIGgE+iJswOAPhnALsIIR+RJEnzaU2ghJmfQ+x+F2R+FmJuR82EGDOPEGXa8LgmskRJLDODrrcP4NQZZxk/uAW7ewAN4jIq7cPQH3+VuprSJZIGz+RHzza/MDSsOYnleQin5yZo97ZupjeqI1uywQaAJtHDCysNsYJaNKzz1ELDd0HjxXfdgFCljLOlZxGpNov60aOv4+Ut16PcEat/zxv2P494aaUWSy2wznzkvoam0vLyosbvQ9moXAutfcrHNEMsO6dp2R/LzNSdSQXOo2WuIcSZwE0IIb8BsArADwB8QJKko8vv/wAAcxIaKGEmaC38ksLYivjN+MMuQeZX91JlDYEWfev6ERru1KznqoaA9f/tL1AcGET+3e821KDait19wxgSCRTJOSgND1PHWEkkUOm19rdk2uZvulQ3sqAnzqyIF7PbuhE9OzPdidHHtQ02ZNFjFFYa4mKyH//+219CrJDTjTxWwxHsu+AqbNy3m7pcWZum/p7VDO5/BYevvF4zCtWhYawRn88glp3TTGdkRbZ4RJ0WLMv+QnoAxVSf4X0KBIJA8zeSJP1c+QYh5HRJko6gJtg08cSVkRDyYULIj41u55d0RTUiWsbPoZnp+j+v8JtosRunPp+XfcusWuP7BZaDYbhSQQi12pSef/k/SN19V9M6yfUjTe9Ztbs3MsbIqVPo/acfaW4ri1M9Zzq1bX5+YAjT2z+A8Rtv1h0f6z6wdjCG9WNvUpfR3ASVGHEW1ILH8dGoM6S8HttgoyZ6zCCnIdKYGDmzbofPI/pkkUcjn0qjf8NpzL+RTHw+g+7JMd3G0U3HULRSCBcLSMxOIVxc+buxIluyqDMDy7Kf52GDwHlEtEwgQwgJE0LuJIQ8Qwh5nBDSlKZCCOkihPyGEHLO8usoIeTHhJCnCSFPye9TtltLCFkH4Ovy/y//2wDgQZ7xuR4xI4T8LYDrALxs1z7tFGx2uUUJVhCcYu6oAAAgAElEQVSRMXdoNUFm93nodP9DnqgZYF89l4ye3T0t9VCvFm3+llvQ9fBDiCwtNS1jjqtcxun//tN6SqXSma4h+rdsmz+x8yZE50421JNZgTXxprkJKuERPlrb0uCNEtPWozWKZke2mg05jCCnJ8opnKWOGKoANhx4AavGD+LYuvMx8aFaPzDWg0hWrZnc5iAxO6UruPK9aSwOr9GMQpViCUQLzQ8cpjdehGokUjPioNSQsSJbrP54Mqzrx9ynbkNnMoaevXsQy8yg2J/G4sbzMHED3ZlU4Ay067AQZQIVOwEkJEm6nBCyDcDfAPiQvJAQsgXAnQBGFdtcD6BDkqQrCCHvB/AXAGgWwf8FwHYApwF4UvF+CcAveAbnRSrj0wDuB/B5Mxs7NblS3myU/683ORTRMm2EIHMHJyOAXtjiB/nBCJc4s1jPlVw/guzhFdddlt19Uy81zlq08NwcwhqRNladmTqlUs+ZrhqLM2tvQoW8IeHGmnjriZdCLIFTXT1UR8K5zl7sWwRKucbrqVM1k7T9skQPzZDDCNVwBHu27cTLW67H1qfvw1kH9tSXyemcye4YDly7q+H8VN//1qxOYuJDH0eyO1YTRvOZpjYHrL+RzPTGi1DqSmJ640UN9WAy45u2AaEw9Rh6NWRa+9Trj6c775AfNtzwUay+94dI7n8dfc8+he633qA/nODA6O/fz7hZ4yXs6QU6XAnglwAgSdLuZSGmJA7gwwCU6SH7AXQQQsIAegEUaTuWJOkzAEAI+SNJkv4fM4NzTJgRQj4L4Cuqt2+TJOmnhJCrrexbKc7siJYZ6TfGs41f8EKU+UmMtYPJh99FGS92WuD7NeVZjV49l16DauV+tOzu1b3UeGvRDIm9ZVgplak3XkJ++PONzaZZaPSEem3bDQDDwENOKaNNvLXES4Phh0Y64P5VBKVItOl9WmTLSdSRLT1DDjOsGj9EfZ9Wg0U7b6vhCLOnGOtvVIwlcOCsLXjx/OtQPZHF8fOvw3uXj60WYNVwpOkYPDVkskDUEo40jDwMburRZ8Y2n9ETzai4cwv5usv6rsyIM7MCSwgyAYNeAMoJcpkQ0iFJUgkAJEn6DQAQQpTbZAGsB7APwCCA39I5xncJIb8PIA0gJL8pSdLX9QbnmDCTJOl7AL7n1P7dnnz5UYj5qXZMxk99yVoZp+vkgiTKlARFlMkYEVUAGiJlSmgNntW91PRq0ZTpiUbHBfClVPatq0XZ9CZN1F5vj/0SFwB49Qq2gYfWxPvF86+jrq9lRFFBzfzijcGz8ch576du67bLqDKyZWcfMxlWOqeesYaaSjSmuW7T36inH7Onb8SB625COd6J01C75/KIPOUxeGrIcv1DjjWj5mmHoY580bKAtH7/gH97oumJV7NCSQis1oUn9d8sE+zF8wB6FK/Dsihj8BUAD0qS9NXl3mSPEkIulCRJq4j7XtTE32sAqlyDXka4MgYQPwoyv9HK0bIgiTK3Jq48gsyNdhtmbjQ8okoXjl5qRmvRjI7LSJSN9RRcb3I7vPMmTE7STUUA7YhNlfJwjWX4kUsk8dD1X8BSMo3TNY+2sh8nhJIWsiGH3bDq2HhqsHjRE1xA4wMblshTYqSGjGefRq8Zeu0w1Lb58nVLKc7MiDuBQGCI3wC4AcC9yzVmr3JsM4uV9MUMgCgAVvh6RJIk+hM9HYQwCxhClLU3QRJlvLhhuONXUQbAUINqPVi91FjCqTw4COTzCOVyK8c2OC4zUTaaQOOZ3A6NrtIV48qJt1bGAytC1JnL4gO/+DaOrt+k2cfMSs80P8Jj3mEnvILLyP7M1pCpMXrNmDo2j+Fhdo8+2Taf9ds1Ku4EAoFh/hnA+wkhT6OWZngbIeQTAJKSJH1XY5tvAfg+IeQpADEAfyxJ0iLjGC8RQjZJkrTX6OA8EWaSJD0O4HEvjh1UgiTIvE5nbNVoWavUk7Ujei6IAF+DaivHZAmn8OIiRr74+1QzEK1xydb9yvTKuds/B5TL6Nz9DCKzs9zRP6VA02tALU9ueZpNA+w0dFaEKASg+9Q8s4+ZEw2ftXArKtdQx7Z4kqsGy0+YqSFTY0aUAcDkZB69GzZh7UyzMJRt8/V+s7y/f4FAYA5JkioA1A0+91HWu1rx/1kARjrQX4CaOJsAkEPtllKVJGmD3oYtGzFrJdt7O0WZMrXMzH6VgktM5t0hqFEyu9IYjbik0vA0WsbpgmgrjGOq0xOrnZ0ILy4ivFh78KfXmDqUy6GnK4LK0PJvZmkJkRPHa+IvGq0d9/nnEJmZQSmdRu7SLYY+q/w98jag5hVnWpQ7Yjh57iXooURYlNAaOOv1PTPb8FmN21E5ZR3b+mTZ1hosO9H62/OkSbIYHo4jOjXB5YRIO75aGBbTg1jYdCnGb7yZ67dqtQF7OyGcFwU+5sNmN2xJYeZHow6/YEXkqaNgfjT5aLVoGY8oi5WLSBVPYS7ahQLFOU6PQzPTvhbZVh6weJ3CyOuCaBVldKz3B//IPKacntgTKaL79/4jsNicjdHUq4wi9pDuQ3h+AakT47UasmQS8UMrjn7RTAbRf/tXoKPD8GeVG03LPaEK6YH65FaNnjhbszpJvSfIv6uDq2oT6aE3X0Q8e3LFPksBrY9ZLQ2SPiE00/dMC72onFORtHJHDLl+/z3c1Dqnw8VCgxAzmiY5tLrbkBMi7Tcnj+HQ9p3NwvAEK+upESO//3aGt3ekQOAWhJDfkiTpFwCu0ljlh3r7aElh1mqsH04FKpXRd4QjCCe6UMmdAiplr0djG+FqBbsOP41LZt9GOp9FJp7ES/1n4N71V6ASCnsyJjNRsiBGtpX9hTTXMeCCaBq1YBocRDhLfzClPGY1kcDiiQx6KPVmQLMZCE1gQrGt+rXWcWlQn3YbbEDNI860kCMsh6+8Hlvv+gYSlJozdQ+0UKWMc157EtVQGKg2Nwe32vBZRi8qF6qUsebYPsciaX7KPNESZKFKGWc+ch+1obTe96Dc58i9P+RyQqT9zqyMgYpDDdhbkXaIlrnZA05gma2oNZLeTllWRbsLMz/dVLRQCi7WpNZrcebH6JiapmhZKITEOVvRMXI6woluVHKLKI0fQW7f80DVkHupJ+hFy3YdfhrXjq+YCQ3ls/XXPznjSkPHMhs1c9su3AiORMso/YXy7343NV3PqAuiGZoEk8bxaMes9KdRWT2CyNjxpnWVLoosgckD7bPyTjL0GlArUUdMjFLqSmLq3M1cPdA2P/cAztn3tOa+eBs+60W7WOYkyewsztm38ndxor4tUiogMTvlaTrj0Ghv7UGIKr1QFkh6DaVp+1PD64SoJf6NjoFnTICx37/A31hNuxRpm8FAkqSvLf/3NvUyQkgnzz5aWpj5HbXQOjw550txFkhRBiBxzlbEN1xQfx3p6kFk+XXuzedcG5sTxMpFXDL7NnXZJbOH8fN1lxlOazQqzuwQZX5/cKKG1l8oppGaaKZJsxGMCib1MauJBIrbr0bkn37ctK7SRZElMM0c13aWxfLGvS+gY2bGUrTi4I4bkV0sNNRzjY2eg/3nXIFIqYByR4wZxSqHwniLXKbb8Jm3boxlTlIJhRGhROvsqG9Tj8/sdyqnkBo9z+tCpVzGyL0/1Gw0ztNQWikotQQQywkxmpnGvHRMMy3S6BjUuJFu7QbtEtUxI5CspFuqt22X7znoEEJuBPBnAJKoGX9EAHQCGNbb1pt8JwdR1xLo1Zt5VY+mJbAOT84xxdf64ZSrUYqgijKEI+gYoXcf6hg5HQiglbWSVPEU0nn6b7c/n0WqeMrR4/s5UgY4M9lhPVVPPLsboVxjn0nZBZGGln28EYwKJvmYoVwOkRPHEcrlML3rE8h98hMorlqFajiM4qpVWPjtDzW4KGYWw7WaMpOoP6vdkwpZLMdmphFGtR6tOPOR+wzvSza++MVH7sADH7kDY6PnYs2xfbjh59/Eb/38m7h09/3oyp7UjGKFqlXsu+AqXfEi1431ZGcRRrUe7dr83AMN68n29fRjNYsyYKW+zQrq8Zn5TpX3Vt777NBob2N6oeJvG6pW6+mF8jh4Gkor962F7IRIQ69/m5ExqGkFUda3rr8uHlq53kv5OWmvWduxXhvZ1sw+BJ7x1wC+DOBNADcD+J+oNZ3WpaWEmdbFf+xElrrMS5MQvciY1xyamfa9KDuQGdM0+wgnuhBOdGss60Y40eXk0Cyjl8Y4F+1CJk5/Cj0bT2Iu6u/P5yROTXZYT9XldD01c7d/Dgu//SGm8DGLHJHjIb9hA+Zu+wxS37kTw1/4PEY+dzuGv/B5pO6+C9O7PoHJf7gT49+9C5P/cGct8qdIy0ydNaIpMGnHceKzasESy4P7X0G4WDC0P/meUO6IYeO+p3HOvqebxBN549dYTNJrC3lqy/TqxiKlxjG/+K4b8OZ5V2Ih2Y8KQlhI9mPfOVdgsdv8GMyOz8h3qoyS8UbMlKmCPH9buaE0DVlQqcUeDdkJkYa6/1m4WEvvlL8HnjFoYcVR1C+cPDpbf9jSjpEclkCjvd+O31GbMitJ0mMAdgNISZL05wC4bqQtk8rII7L85taoTk3kFWROpzMGQZDpUcmdQiW3iEhXD2XZYs0IxIfwWuMXIlG81H9GQ42ZzEv96w2nMfrZldEvsPoLaabr2dg8Wg2rL5ma8OJirR7tX39Rf0/t1siqd1Pb7JcGB1FJJhHOZtExPd3QryxULDI/q52pOCyxLEcreJ35lPcHljhZc+xNjI2e01DfJcNTW8aqG6O5OSrt65X1aNXdYWojaN76NjPjM/qdmklVlsXKmvgSOmb0/7ashtIDZ/Bf19ROiLne/ob+ZyyDDytNraeOzbdE5KzVBYf8+cxGsexMfWz177pFWCKEbEQtYnY1IeRRAFyT/MALM7+ILVksGY12+SE6JtMKgqxOpYzS+JF6TZmS0vgR37kzmulVdu/6KwDUasr681nMxpN4qX99/X0ezAoyvXpIr3BygsPqL6SXmmh382iZuds/h9hrrzbY1NPomJxEpxWHSA2BSWucXY1EHPmsNFhiWS9awUJPPEnnvQfVcIRaI6YHq26MFe0qd8QaBFtDI2iDYzA7PivfqVFOZCNYm0qjc65ZnCnHodVQeu5TTbX3bDScEOVvPPX9u6kGH53JGMY/dRs6kzH07N2DaGbacFPrVhFn7YDTFv0iTbFl+FMA3wBwC4D/BODzAO7m2TDQwswPooxm4CHjxMTViWiZnwWZlb5kuX3PAwDdldFHmG0gXQmF8ZMzrsTP111muI+ZHyJkdht/6E1slBb3Zq2ntfoL5RxM12MRKhY17fGVlNJpdGTodTBGHCJlgSnXqVX604ZEmCzk+ofTmJ1c4t5OczwMscwTrZBRR8sipSJOdfchuUgXT6eSfdQoFg9y3ZjVaJdWJM0qrPEZ+U6tUonGuCJR6obSvWTUkrU8zQkxVMhj1aG91PVl10aWqONJWRTiLDgYFWe8ES6efYpoWWCYliRJtmTdSgjpB0B4NgyEMPODAKOhJ5LsjCo4lb7YqqIMAFCt1twXpT2+7GNmVpCpKUSimIro/878IMacgjmhoVjcsxrHMtF4qt5ndD82wWsAUn7/+xB+6te61vj6O2rsm8ZqF8DarjQ0hN5tl+PItR81/jdQQRPLkxsu5I5WyKidCIsaIkcpntRRLF6sRLsipQLWJ8s4nI2g3BEzPQaZWC6LvswJnEyvRiGRbB7f4knDESBeWK6NoUoZqFZQjCXQUagZ65RiCYxv2kYdRy1tcRBONEJhpczGMjOIzp1EYWiVpr29fH1qhZoyQQ0ecWanIBMEA0LIu1FzYLybEPJZ1BwZgZreuhPARr19BEKY+RUe+3oz4sxNS/wNA4O+E2eWBZmaShmVUwv27tMidokyXuwWZX5JY+R5wkyzuKc1jjWCX/oLsSz5qwAqIyMo7rgGuTu+AnR06Frj66Hum8ZqF8DaTq5vO315O0tPgSlieXIyb2gXa1YnMfLP/9QQJYqXavsodMTRUSo0iSeamOB9iGgm2qUWjhen0ji85lzTDaXDpQKu+8W30Tc7jnC1gkoojJP9I3jwt76ISkesYXz9G05zLFKmFTk/85H7sPaFxxveixZyQCjc8HntjDJpRa1YKbOF9ACz0bwSvUboImoWLFh1Z06JMtHPzPe8H8BVAFYD+Lri/RKA7/DsILDCTL6Y+zWapkRPnHnZOBpYmbT7QaDZLsoEtmC3ELMjjZFnAsPbONYKXj7tZBmALO7YgdJffR3orPW0zN3xFQBA9NHHER4fbzDs4IHVN41Vp8aznR2TjUaxbEyYhYsFrB97k7osH+/Egzd8EdmeAZQ7Yram4BqJdskW9jKdczM4d+7XSHbH8MSF1xs+9nW/+DYGMisR1Ei1goHMcVz3i2/j33f+QcP4stMFrFltvzDT+i6t9gbjZebt6aam5LJwUl5fWCmzC+dfbDlFWokQZ8FDKdC86G82YXovArtZdl8EIeRWSZJ+aGYfgRVmMmtWJz23vTcirLwWYSy8FGhCkDmH2WiZX6JiaoxMWnhTkIJMk2OiUnBNzCG5vibM0NGB3FfvQO7LX8TSy28adohkpU2y6tR4tqv0p+u1Z9VEwvWnwaxeVN2LcyhHohhZy073dPI+pGdhv3ZZqPCOQU5fpNGXOYFYLltPa5QZO5F1rSE8T2+wXP8Q9VrAky4oOyyeSXFYlKNxaoHWlDLbn0a5qxu9r72EgacesZYiTfkMQpwFDxHFEij4IwDtI8zUN4cgiDM/CzI1bqY3CkHmT5wSZVYndkYnK3alIGnhi9oAo5b8nZ2mXBNZaZOsOjXmdoODSP7850i88PyKqNx2OaCoWTMz2dFLGVMj96KiOgCm0rVUPo79REoFW404ZHgt7GnnV7hYaIgKjZ2oibKwRjVWGFX0ZU5g8rSzbRu/UVh/j2J6EL1kFD2K6JTR2q0zH7mP6rAIAAeu3dWwbl0kqVJm07/6Nww+8XB9PTtSpAXeo7ymOym0fHHvEDjJQULI9wE8C6DudMUTRQukMFM+uQtCKmMQcUOcCVHmPEajZa0iyGSYKUibLkXqrBEAwXnSmVw/guzhceoyLUv+7OFxJNePaO6TZnmvtX+ttElWnRpru0oyiZ5/+9f6a3VvNcCdiRKvA6AWoUoZl+6+n2rmYab+S40ZC3utvluhHTci2nU683gLvQPU992KmlWiMSxu3opOjfNWThk0Y6ZhJk1SGT2rxuIopvrQ+9pL1H3YlSLdSlGzINRF0YSSnT0XBW3HDGrGH9sU71XBEUULpDAD/CPIghQJk1ELLtrk3Q/1ZgJ3cUKU2TWJo9V98KJlcT9+481AgG66LHGlh1qcJdePIHtwrMkpMbftcqbDIjNtkgF1uy1bkXjuWer6XL3VdDAaNdPqh8XjRHjmI/dhraL+qyc7W68H27Ntp8GRN2PGwp4VFRq79L3M40UqK/FBZRRQL53TTtTnbbE/jenRs/DWpdehYsHdkDdNkoYslqykSPP+LltNlPkdmsuiU6IsKN+JwDySJN0GAISQfkmSDP2QAivMvCSIYkyGJriECGtNWtke3xAaFvd24HSzUScZvPfHSFCcEgGGw6LRtEnGduHZDJKKaJkSWs2a0ymN6n5YSkMIFqwIzOjR1/HylusNpTUqH2YoH0BqWuyffx3UcVK9qFC4WKh7OKvJ9dZSN/siEWrETVmH5SRTJxYxdcVORC69Dmc9+FP0H9mPkb3Pov/IW5bGwUxb5WigPXVsHsPD1lKkWb/LVhFkQcSsiYcRzNwvgnyfaVcIIRcB+CmALkLINgBPAtglSdKLetsKYWaAIAsyQAgwv+C2VX4rYSW9xymL+6DcNBuiZktLiD76OHU9nmiVVtqkHsrtjNSsWZkkGe0hVYnGNCMmNJjGIdmT6Dw1z+W8SIsuK+unWRb76n5gelGhgQOvaY5j+uwLUYnGcNZD93LXYTnJGU88gNNe3W3bOKymrQLA5GQeqxgp0jwPftS/y3YQZEFJDXRblPEcT2udINx72pS/A/BhAD+WJOk4IeQLqPUxe5fehmGnR9YqCFEmCBKtHC1zq0lr37p+zZteqJBHbGoCoYIxW3avyR4eR/bwOMJT0wiP02vVOqan0dMVQXL9SP2fE8i1ZzQq3d2oRqM4eXTWtknS0GivI5NfOQJDYzHZh6Uu9jHXrE4yU37Vy2QLe1oUThZorDEVkinENIxEqgAihRwi+SXdiJsb8ET+zHBwx414Z+s1WEoNoBIKYSk1gHe2XmOogfZr227A9PYPID8whGoojPzAEKa3f6CegsmLU7/LVkK+Fqv/BQmtXmdWr29BELptSpckSfUeLJIkPQyAK1VHRMzaAD+KsnY1/nAjWuYXUeaWtbYTKG+iDYXr5TJG7rsHvXtfQDQz02CR3XeGse+d13RDRsv0wwwLp8roXj2CyNjxpmWVVatQGWr8LEpxZuc45m7/HGKvvYr4oUMN78cPHULq7rtw8oM32XYsGaMRND1YEZiT515iS12WEedh2aBDa0xTGy/C4IHXqKl8IQCnvfosgJBmxC0xN4P4fAZLA84IdiVW6sFYaKWtGk19ffWKnRh2IEXar5iJ7lnNKGBtGwRTEUBEtdqUzHI6YxUACCE3A6BfzFQIYSZwlXYVZEB7ibKgo55MyDf+kfvuaUhfki2y4z1x7bosNeUyl+mGUeFmhGoigVOXbkXPWLNTYvGaq+tNqWXsFGNKQsUiwlm64Eg8uxuh9+10ZLJrd9TVjHGI2sZeD6NtYZhjCkeook2m/7CEfG8/Oueb5xEhAKPPPYa3Pvhx7rGYxWo9mB7qtFWjv4uh0V5UgcD3QuTFbGRPvp46mSLoV3FmJX1REGi+AOAHAM4nhJwE8BaAT/JsKIQZJ0YbSQsaEYLMeayIMr82k6bhVtqP+sYZKuTRu/cF6rpGXARTd9+FHpbpBqdws4rslNj1wvMIT0ygsmoVitdcjdwdXwHgnBhTotd82okG4E6kwtIiMAAQn8s0CS8tG3seMwsj4oxlZnJwx42I5E5h9au7qSYg8YVZTFzwLnS+9hx13wMHX8PBYoFLUFrBjnowowKYB5F6aByzQoQn4uZXkSMiZe2LJEkHCSE3AsgCiAAYliTpAM+2QpgZQJ68Bkmg+SGNUYgy5/FbpMxsGqPdaWZ2wrLIprkI0gjlckjsfoa6TBZ3vT/4R7ZwswuFU2JPVwQLp8o1YXnMvWuGngGI1QbgapS/Kycm7JVoDPlUmim8jDQ3tmtM6nS/ajiCgzs+ipHXnkOoSmudHUKoXEYVoAs3C2mERjETjRwa7cX00VlHXCWFKHOfoBgsCQQyhJD/CODTkiRtJoScDuABQsi3JEn6rt62QpgpUAouVgQhiALNK4QocxY7BJnd0TKrokz9/4A/nMuKKW2LbNlFUP3kVj2Z0IsQRcZP6Ao3J9Ia5ysAEtH6e0bSKFnmIHpRN92m1SbTGEOFfFPdj/wbshKx4oElvA5t38nV3FgdFVOeU/L/W+3l2ZFf0hBlQAhVjLy5R3NbO9IIeTHSxkB5fbhg9wMYtFEAC0HmLcpraxBEWhDGKHCU3wVwGQBIknSEEHIpgGcBCGFmFl6R5me8jpYJUeYcfhRkgD2izMxyN6jG4pjXsMieO+8SzE4u6e5DL0JUBZjCjRWVS64fsZ5+aKL+zShq0cdqWt0XiRhLU9IwZ3lt2w3AsugyGrEyYgih5yJ4/OJ3M80sErNTODFbRERhhQ+sGHoo0ep3xkshmUKuN02tI6uGwpqiDeBPI7QTvTYGymsEK+1YKYB58cP1R7CCiKAJAkAUgNK2uYBlIxA9hDDjIIiRMa9FWbsSlB5lfnvYYKU/mZvIVtipN15qEBG52z8HjOlP3vUiRJWR1dy9vZTYZWlvpf4teWbjb79JJDK2ZTWtNlLYr2XOckG5jLcueC9K8U5N4TR86FXMD99CjdJpiTN1OqSeiyAATTOLcjSOTT/9e2ydn11pHv2uG3DaGv1zlfZARC9VsxKNYZpcTK3f0hJlVQAnLtxmyFbeLZTXEFbasZE0zCBck9oVpw1FtPCz0YjAV9wP4FFCyL3Lrz8C4F94NhTCrAXwowhrx2hZUESZXwmEOItEkPvDLyFPSfXjvWGzIkSIRNipfZS0QrtEmdP1b3qij9W0mue7ZUVJ0r/+FbY99QhKqT50zNH3E8vMMM1GlPWPWumQb191A9NFMNc/pGlmES3kEC3kAAA92Vmc+8avkeyO4cAaY2l3RlI1afVbM2degIEDr1IjabneNN764CdsSfl0Alk8Dw9rpx0X04PoJaPITQarB6GgGTcFktEInYjqtTeSJP0RIeSjAK4CUATw3yVJup9nWyHMAoAfhRcLIcoEZvG7OJNvtFoiQtlXR/OmHImg/Jd/jsl9h6kRIqZwU2Fn82e9+rewhfo3HtGnV8um17OIFSUJV2oRoOhJ7YlcIT3AZTYyNNqL1Pfv1kyH1HMRbBJDPf3oyJ2qizIlZtLujKRqatVvVR+i2+lPk4sNjcUJgxUeJifz6N2wCWtnmj/DwqZLUY3FMTTaWHuoxs/XIYE7aF3DeR/C2SXORJQusLwJYALLHkqEkPdKkvSk3kZCmPmUoImxdsUrQXZoZtp0nZnf0hjV+MHoQ43RmytrfVlMdZ+zHtnDFDGicEvUMuCwU5DJsOrfyoODiExNma5/0xV9HI6WMlqTFJY5Cw/yhF2PUCGPVYf2UpcN7n8Fz33uT+v/T3MRVIuhcLGArXd9g7o/o+6HejVuWiJPXb9lxglRCW/Uzkj9nlHUn6GYHsTCpkvr6cjKMQCNAs0v1x5aL0U/ov4b+uX7s4LeNd8NcabcLijNtAU1CCF/D+AGAAcVb1cBXKO3bdsLMz/WjwVVlLVbpExEydoDr9JRWKl9TvQYY9W/hRYWMPTnXwPCYaDaXBwgWHUAACAASURBVL/Mqn8D9E1PjJqI0CZFLHMWNVUAhb40YnMnUUgPUCfsWujWL53KcrkIymIoXCzY1kRZr8aNV+QZcUKkoRe103JgtVOk0T7DwBnaD7P8JibU1x0xMXcPu6/5anHG8ze0Gq0TeM61AIgkSfquYCraTpj5UYi1AkKUuYvf+pa1Kk6KMjcaOBtFnUZZiccRWVpCZGn53lKhm0Jo1b/J6NriG2gBwJqUjO/8GLreehOJ4+8gtDxWWh+uwsAQDv6n/4qOpaUGS30eWJE5pZDScxGUsaOJskwhmbJN5MljM9qrTC9qN//JWzStyZzoY1iJxtBz4Zm27c9L/DohV0Y+/SZwncLo38LM+rT7j19/A4ImDoF++9GlrYSZEGXO0E6izGtBBvhXlJm1yqfh9c3daUFmdyqikb5jTBRplJHxExj486+tiDIF1XAYAJj1b2qM1M7R4JmQjNz/E3QdO6K73sKmS1FJ9qKQNP47Y0XmzNrIW00dlLFT5MkYrRPTi9qxDFZk7Ehx9PoawotWXa2ZKIuXePF9exVFdOt4QfsNCBrIAHiDEPI0gHoBsSRJn9HbsC2EmRBkziFEmbu0gyizGyM3NrfSFm2LlnH2HTNKNZFANR5Hx7RGWnW1iqm/+L9RJOfwC0GO2jkavJMRlitjJRxGqArDaYtayNv37N2DWGYGhfQAJjdcaNpG3mrqoBK7RJ7ZRtx6UbsT2QgGOIJwZsVZkASZ8v9Z4izIE3KnhIW6/squfbPuAV78HYL8t29zfrn8zzAtL8yCKMo2DAz6vs5MCDL3aRdRZtfEinaDVb8n3/SCamus23fMAsy6sOFhY6JMAat2TonRCQmr9itUqeLwl/8Yp844y1DaoiaRCMZ33YqJnTchOncSxVQfJm2wXzeTOqjGLpFntBG3jJ1RO6PiLIiiTPmeljizC63rn1PwXleNuvFqXdud/DxCIAmMIEnSD8xu29LCLIiizO+CDBCizG38KsgA/0bKeCcEQRVkgD0W9CzsrAsziplJEKv2qzAwaJ8oU1CNxRVpef7qi0UTebxCx6y7o4xdUTtegiLIAHvr54zgtpgxem31Q6sUrTELUSbghRBSARrKaKsAZgE8AuD3JUmi53kraGlhFjT8LsraSZABQpTp4YQos+PGHGSxZQQ7Lei1sFoXpoa3Fs5M7Qir9ovXCr/V4RUFVt0dWVE7I+e4VyLGSZxsEcDCqzosnuux0eu+et+tEilzO6IpsB9JksLq9wghqwB8DsDfA/i43j6EMPMBQpAJ1DglyOzqYRb0SFkrYLcFPRWTdWFNmKyFM/pEn1b7pVVTFirk62mIQrQ1YsbdkWYSoo7atbsok6GJM6+jRU7RKqLJaYyk3guChSRJEwC+QQh5nWf9lhVmfktj9Lv4UiPEmHf4OUoGOCfKWnVi4hRuphry1oVpYaUWztBTcUrtV5PoKpcxct896N37AqKZGRTTA5jftKUm3igi0Y+Nh53GSJ2YWZMQLcwIMjfS4Oy2g/cqcibgx29CKOhGMAIUeFZqWWHmF4IkyIQYW8GLNEanBZnVaJmTUTKRwmgO3lRDpT2/XY6QvGmJdtXCGRFojbVfjYzcd09DumNsZrr+enzXrQ3rqifO7dSribdOjNckhPWdKb9no/b8buCUgGqH31G7YUY8Gbl3iUbjwYQQ8hEAdHcqFS0pzPwSLROiTNAq+F2UtS0cqYbqnmnJ9SPWxJnBtES7a+GsPDVmWer37N2DiZ03caU18kRo/CgwjMDj7shrEsJzjofzSzj7oZ+i//B+xOdnLUfeBAIr+F34iOiZPyGEvI1G8w8ASAF4C8AnefbRcsLML6IsaCgjREKktRZ21ZUFFZ6bV1CibVqiipVqKK9vWZAtYzQt0YlaOLOTEpalfiwzw9X8WEYpzpQRFbtT++zGqGBkWfjrmYSsTpZRGNIRZeUyLnz6fqR+8ziihXofVm57fjcQaYfth9lrjNltjNyDhCDzNVerXlcAzEqSlOXdQUsJMz+JsiBFy9SclV7T1uLMD26MdhAUow+760P8ctOySwjR9gsYT0lkre9kWqKXtvtqmJb66QEUU32G9kebrJvt/+U0TghGlklIMT3I9X2qU0vV8NjzuxF1F5F9gdf45d4m0EaSpCNW99ESwsxPgqxVaFdxJkRZI351X6Rh5aZl9ImlHrJ4UqcRAvqCincstgg/l9IS1bVwlZERnNqy1bTtvlmcttS32v/LSZwQjCyTEJ7vk5VaKqNlz6+M/AkEQYd23RdCrD0JvDDzoygLcrRMEGzaPW3RCnaJM5oYUy/XElRGb8Rmo2cyrqUlLtfClf/sjxCemkZlaBDo7ETSwtjNphoZsdQ3itX+XwCo6ZFWcVIwqk1CiulB7u+TlVoqo7bnp0X+Fjdv1XTVDCKtZKXPc031gwBxowG33v788D0IvCfQwkyIMoGdeBkts+rIKARZe2MmeuZJWmJnJyrr1hoap+3wWOqbxEz/L4A+8bazrskOwaiF0iRkdbJs6PtkpZbKLG7e2iAaaZG/Tg1XTYG38D7oagcjC/m7EFExgR6BFWZClDlLO6YxeoUdNvmHJ+eEODOBE6YfSrMN5Wur42FF4sxEnexKS9Sy6NcaZ/1zLC1h6eU3ETLbtBrWJnQsS30Zo+LISP8vJVp1lnaJM7OC0QgDZwzyNelRwEotLcc7MXvFVRi/8WYMRSKYOjbPjPwZcdX0O0GOkClxWnTYKejsTmfnPaZAoCZwwsyPggwItigTIsy7aJnfm0mPncg6XmfmxiTEC9dFO40/7BZlgPW0RJZFP4vswbF6XVuKo65ND7/19eHt/6XGSXFmVjA6zdSxeUCdWtqfxuLG83Bi162odnbV1x0a7UVsakIz8mfUVVMQPNxIN3QKpfCzOz1ywra9tQeEkDCAfwBwEYA8gNslSTqgWqcLwMMAPitJ0j5CSBTA9wGsBxAH8A1Jkv7FifEFRpgJQWYvQoyt0CqiLIhRM6dFWVBs8Fno1awZ2YdSxFlNS2RZ9LMwWtfGi1MCTfkb5RFIPP2/tHBSnJkVjFbRG/fUiUWAM7W0mOpDaYCe+pjr7ceJbASVvPbxrFxvWqnuK0jwXMPtEmduRM3sFmUCU+wEkJAk6XJCyDYAfwPgQ/JCQsgWAHcCGFVs80kAM5Ik3UIISQN4GUD7CjMhyuxFiDLv8XukTIlTUTMhyqzDEy1TCju1QLOSlmiG0KlFdD38EHWZVl2bUZyMoNF+s1rCg9X/i4VT4syKYGRhVLjSkD+zXrSLlfrIE/lTjs/I9Yf2uczuS8CPkWt4ECJndo2vHe5tDnMlgF8CgCRJu5eFmJI4gA8D+JHivf8N4GfL/x8CUHJqcIEQZgJ7EIKsGS+iZX4SZbLgGjvB3fvQ9xi5aVl1NXSDhrosxXs8aEXblO9bSUs0SurOOxFZWqIuY9W10dDrvebWRM2J5sNazavtwKxgVGJEoNqN0lUzmpk2Hfmzu3+iwFvsrjXzq8gToswWegEoIz5lQkiHJEklAJAk6TcAQAipryA3iCaE9KAm0P7UqcEJYWaSoEXLhChrpt1FmRKWQHOqxsyJiZHRm5afBZkSpTgzMmaaqFMjOzqaSUs0QiiXQ3wv3bgBAMqDg9p1bQ0r8vdec6v+zAkR5ZbQMQLrfLUjcsbFsqvm61uvtxz5k8epdx1Sim8h5txDPm/1rut2n99+FWWthh1p+lpMLJVZi+cB9Cheh2VRxoIQshbAPwP4B0mSfmxthNoIYdbiCEHWTKvUlKmxo75szepkgzgLUoPpVn+SaFZEGhFnThKezaBjWvuBVm7TJq5onZkatSBHz1gomyx7ZeBBw6hIMyp2po7NAzZE/pT74xFnQcFvhjhWaZXPIfANvwFwA4B7l2vMXtXbgBCyCsBDAL4oSdKvnBycEGYmCEK0TAgyOq0oyuw2/HBTjNkVNWt1UWYVXnEmr8uD0fRKlgtkuasLc7+rb/xhpveajFtP3d0QZ7Qmy3I6XzXsTJNls+ep3ZE0p77bVkltVP7O/ZyS16o4+Z2L+5xt/DOA9xNCnkatXuw2QsgnACQlSfquxjZ/DKAfwH8mhPzn5fc+KEkSPTffAkKYqVCKLr+mnWkhxBibVkld9LvzottRAz/erLREkJepkzziDNCPnvHUrcnHU8JygTy14/2odnfrjo3Ze21yCouvH64bSBj9XdgZZXD6HKA1WZZfH7h2l+n9Oh2Bs5Ly6cY1xW/izGj6ZJDt5N3CyQbP6ibSPPum/c3E38tZJEmqAFA/CdxHWe9qxf9/CcCXnB1ZDSHMlqFFwYIQGZMRokybVomS+V2Qybglyngn3m5YIPPmyivX80KkqZtfAwCWlhCemkZlaBDo7DS3Dwq05eX/+qfI9XYj8vCvTLlAsqJuhfQAiqk+rv2wsEug0cSZeoJt5lxhNVke3P8KDm3faVhU8UTgnKyxsksQuVbnJggcWvcAu4SrligG6NcS1j2p1VJRBcZoe2EWJPFFQwgyfxKEaKvX9WNuPJmWb2xuiDMjeG06kj08DpTLGLz3x4g+9jjCJ8ZRWT2CU5du5W70zCvQGujowPTHb0How79jygWSFXUrvPsKpM5yrpgcAEKFvG6vLRpav3UzEaRYdk6zyXJ8PoNYds5w7ZWRCJxVEWVWMBk9ppnv1k/RMsD4eGjXOTG5dx4z9xaee5KIdrYnbS3MhChrbVolUiZjZwNpL0WZGzVltJuZX8SZ16JMJnX3XUgoBE5k7Dh6xow3ejYi0OR1zTanBtzpvdb0+ymXMXLfPejd+wKimRkU0wOY37SlZt3OELG8v3UjkZ5CMoV8Ko3OuZmmZfneNApJY9cIMxE4s+KM9dmcSiPUE2h+E2NWUV7nxKS+hpPXfT/cUwStRVsKMyHIWh+vRFkQCLoo08PtyYiRiJFfRJkVEw0t9ASabZ89EnG19xoAjNx3T0NT49jMdP31+K5bbT2WnpCoRGOY3nhRQ4RLhqfJshqzETgjqY28USsna7zUqaWtJsiUCEHGj5XvikeUWf1byFEzIQDbh7YSZkEXZIAQZXp4Lcj8nsLolSjziyAL+g3OLlt7pomGwUbPamgCzQlBaiXqxkL9OwoV8ujd+wJ13Z69ezCx8yZUY3HTaY5asAxE5GbKg/tfQXw+g3xPP2ZP34i3r7rB8HGsRuC0xJTZlEWnxZmXaF17giSk3DSr8LMxhh33Ed77ke7vZpU9bSME/qCthJmgtRGijE0riTK/3JwBfrdDKyj3b6bRtBqWiUZpaIiv0TMnfokSmiU6dxLRTLNoAYBYZgbRzAzSTz5iKM2RV8RpibNqOIID1+7CoatuwNkP/RT9h/dj5NVn0X/0LcO2+XZH4AR0WkGUaaH8bE4ZaaiX8R7HqQdxeqLKrCOj2X0JWgchzASBx2tBBrgjyqzUl7WSKLOCl9EyMwKFJfiM9hFTwjLRyF22zfHUQC+QJzdGfwPFVB+K6QHEKBkXhfQA0o8/iMEnHq6/x0xzNFGrxoqcbXjiAZy2d3f9tVnb/KYIXO+KKyML1vltpmWA364XdtIuk2srIs3IualljOFmmwyn0yAF7UlbCbMNA4OBTmcUaYzN+EGU+R2v3Rf9gpM3QrujZmb2ZTSS5oaJhl+iZcoJlFGBVo3FMb9pS0ONmczC+Rej97WXqNsp0xxlzNaq0erOWKYdQ/texOErr0epi+/cr0fgtu/U7WNmRDwZMTVpZVHWrjgtPuzcv5sOiEbG3S6CXrBCWwkzQevgJ0Hm92iZV/hpouXG00m7xJnVfXALNA9MNLxAa2JDE2ha647feDOAmtiKZWZQSA9gYdOlyLx3BwaeeoS6TSwzg+jcyXrDa95aNRbKKBTTtGPhJLbe9Q1MnbvZcFqjltW+1fOZ1cPNT9cKP2FFLLjlzKjef5AjQU5/Z0a/G950yAmzAxL4EiHMBIFDiDJ+RAqjuxMFLXHGEzmyu06NV6CxTDTsqGfzEtsMYSIRjO+6FRM7b2qoDQsV8sw0R2XDa91aNYWIYyGLs14yitIA/dghAInsSax9/lF0JmMY33Wra/3DvN6vHk6aiziBGXGm/D3bLTbcjODYkapoBieiZ06JMkHrEfZ6AG5waGa6/k8QbPwkytzAaqRs7ETWppEEEy9uXEoRkz08ritqkutHHDUPkfdv5Bjq9Z02N3EKs/UmWlRjcRSGVtUjW3KaI42FTZc2RMDkWjUaahGnhywssmefp7tuz949CBXyGBrtrf/jPYZ63VAhj9jUBEKFPPdYp47NN/0TOIuWoJH/OX1s+Z/VfRhdZid2f09Gxsy7rlvfhcBdWjZi1moiTNSX1TiQGfOFOPN7pMxrvH4q7fWTRN4Ik9uCR8vCPpTLITybQefF5wKdnU3bBTViBrCfftN+J0YturXSHOX3ZZi1aioRx0Q2EHn5eURnMyjHE0C1inAhjxBldVo0znDTZZMNtrX2r37fzWtFUKJl6qiX0Qk4KxLsVj2VkXpOOz+fHeNy4vvx6riCYNFSwqzVxJjAn/jdFr/d8VqUeYFSOPGKveT6EaBUQuKb30L0sccRPjGOyuoRFLdfjdwdXwE6Opr2HVSUE1Ezvw/mRFYjzZEGr4hjNW8e+dmPMPj4QyuHz+cAAOVYDJFCoWl9VjSO1znR6QbbRppVtytOiDN5Hadx6hhW9+uVCNKqyxOiTAC0gDATYkzgJm6JMjujZWMnsp7UmrkdNQuaILMjWkYTTUZMSBLf/BYS//Tj+uvI2HFEll/nvnpHS4gyGau/D72JsZzmyIRDxCmFkvocChXy6H/mSequw8Ui9X29aJxanKnP2Zm3p3Hmi89Tt2WZlphJWRQCrYadkS69yJKbboQCOuL7FygJpDATYqy98SqdMYiiTMYrceY0QRNjMk6JMuUy3WMsLSH66OPURdFHH8fMh38HaEGXRivYNYmliTgeIROdnkR4OUKmJlStNr13avT0pmgcDZZjYoLh/qhlWmK1jkwINHvhEWfyegI2TqRlCgRKAinM2g1RX9ZIK4uyoKBXo+I0QRVkMnb3PTNDeGoa4XG6uAtPTCA8m9F0a+ShlaJtMk5MuIycQ6Fm7cUksnQKoXIJ1eU6MNqx9HqNFZIp5FNpdM41O0pqpUmaaS4tWIEmpOxM2xNCzDxWatsEAh4CKczkSXKrR86EIPMHrSDKnIqWedGbSNwUa7Cs7HlEX2VoEJXVI4iMHW9etmpV3QikFQWWHzAjXApDw6gkEojk6FEzNcqIFq8Rh5pKNIbpjRdh7fOPNi0zZFpikHaPljkpmoQgs4b4/gRO0hZ2+UHiQGas/k/QjB8cGdsd1oTJqCW3UbQc89rBNljLel8twrgjcZ2dKG6/mrqoeM3VdXdGryN7fsHO35dSDIWLBSRmpxAuNht3qM+jaiyO2cvey30cOaJlNXp1cMeNeGfrNVhKDaASCmEpNYDp7R9gpklauQa0uyjzEvHgSyDwlkBGzGQ2DAy2RNRMiDA+RAqjOVqxtozV40aJnycZRtIZ1b3RAHD3GVOLOeW6uTu+AqBWUxaemEBl1SoUr7m6/r56m3aNnjkh+kOVMs585D4M7n8F8bkM8qk0pjdehIM7bkQ1HNEUJ+O/cwsQDqPnlRfqzo7lrm50HTvStO5KRIu/7xiNajiCA9fuwqHtOxHLzqGQTKESjWGIYZUvCC7CEEQg8A5XhRkhJAXgnwD0AogB+ANJkp6xss8gizMhyPgRoswcdosyr3uTmXEkk7dtNXiEnZZrI7Astjo6kPvqHch9+YsIT02jMjRI7WMmk1w/0lbizMmasjMfua8hPbBzbqb++sC1u7R3QHN2jHRg5L57qDb8dtZ6VaIx5PqHGj6LXgTd6PFFtEwfN1LGzcB7nW1H0af8btrx8wv4cTti9gcAfiVJ0n8jhBAA/wvAZis7FKKsNRApivbTipEyJwiq2LCaYtgg7Do7UVm3lvu4Qfy+/ES4WMDg/leoywb3v4L5T94CPZ8PtbMjby81t9EzF9FaV+ANVkQDrzGGMB8RCLRxW5h9Cys5FR0A+CqYNQiqKBP4nyBHy9wy+nAbszdxvYmCFyLDL3VbtLRI3m0E5hga7cXCqwc1LegT87NUC3oeaDb8TjgkhosFxLJz6CWjugJSYD9eX4tZ8GYp+EGUscZo9/ic+rx96/ox4cieBV7hmDAjhHwWwFdUb98mSdLzhJAR1FIav2x0v60gxkS0zN94Kcqs9DBzMkLm54kAC69TGO0SYOooFe9+eaNbvAJNiDJ76CWjKA0MIEa5n2lZ0FvBLnGmrItLzGdQTA9gftOWmgkIR70ZaxxBvcYI6LCiZ34XZcrlQRiroLVwTJhJkvQ9AN9Tv08IuRDATwD8oSRJT/DsqxXEmIwQZf4mqJEykbbYjBc3MycjYbLIcvIYZiJoAuNUY3HMb9qCwcd+2bTMigW9WvTwih3edEN1XVxsZrr+GcZ33WpkqIJl9Or0ggwteman0DGzX6P3Ba8FmhBl7Yfb5h/nAfjfAG6SJImeYC9oO/xSXxZEUeaGIAvipMHNm5mdIkbPDl99LJ4IlpmaMJpAE9Eye5Gt5mmGHWagCSozJhFazeNZdXE9e/dgYudNXIKSFjUL4jXGDuTvoZXFGbASPXNS3PDs38p9QUugabVwsQMhytoTt2vM/hJAAsDf1rw/MCdJ0odcHoNniGiZf3FLlFlJVaQxdiILQETMlLh1M7M7qqQlfNQiyYhAsjJGWn80K+KMNha/iz1HJ5M0d0UfNWtWC6hYdk6zLu7/b+/eY+y46juAf73rNdvGzw0bO3Vw3BI4hIpEwkEkQEOKCGqrpoRSQIJCiAiPqkIFRIBUgFSpFaqAGqQSqaRxhRqiQpumQkJNKzWkFYHwsNQmleAgY5nUW9ux1/ZuTP2Ine0fu+OMx/M4Z+Y8fufM9yNZye7dO/fce2fuzPf+zqO8kHWfbY/VmF4H18dR3ed81/FqMjlJ3f3L9wnVPZOhbLyCBrMxhbAyBrJ6UiplobgOZWVzB054C2cpfZvr+2Tmq4ufzTiwLkPb2HZ/m3CWS3dI39/0103Y0Uf1It/FMVve5pm1G3B6wwx+YWH+or/rMy4ulc8U3/g6hNVn7FvobowMZeOW9ALTkjB82ZEUynxXy3wGsjKf1bOUwplrvgOGy6rRkKqUzYQiTdvNJYxVpbLgrs9j9NmpNTjy4msvGGNWGDIujsglk2O1Gs4kHdsMZcRgNhADmT2GMr98Vc+khzPXJ7TUQkbfUNb6PE+ebFx42sU6ailJJZz59NPXvxkAcNneJ5yMiyOKJZVjuatrJeWHwWwAhjI7kgKZbzECWVlq4ax6orE9aaYaylyFkz7tbb3P2bOY/uxOTH3rEUwcOIhnL9+CZ379Jpy688PAanmnjab9xfV+EXuGttiWJiax5w1vxeJl7xS3kDVRIdUvUUw/Xy66ffOsryZRBPLOsJQdqYHMV7UsdigD0llk2sWMVjG+PXTVjW/ohBp9Hxdonxp/+rM7MX3f/ed/npz7X0yu/HzqrjsvuH+fNriullUvxHzvE6le+PVRHmdWHP9LgJNxcUR9dS1kndoxyioYFRjMyCupocyX2KEsldkZXc1sFfpk1hYoqre5GLNlet8h274ooJ08iamHH6n926mHH8H8m96Cpenpi7Zh+nx9dWHkhY0/krsw07i1TeaRQjjrM7sk5W0idgMoX5JDmY9qWcxQtvXytaMLZT7VhYc+a4HZTm1f/DP9e5ttdynae/I/f4SJg/Xtnjh4EBPH6qdMT23MGBG5s3HbpvP/QpN07rDBL5OoDoMZeTG2UBZTSoHMZSjzfVIrgoZtwGrajo2ukOZzDNyzm2Zw9vn1x8jZ2Vk8u2mm8b5dz5XhjchcKhfu1XaaBrRymBv6XEONM3Wlq11S203+MZj1xIk/mo0xlMWoluVQJQNkhrKCqyAxdGHmckjrG8pM77c0PY1T199Qe9upV15/UTfGqqYgGzuUHX/yWLLfrFPa+nxeFfdx+Vnno6rVtq22LoZNXfiGSCGcxaoqUjo4xswSA1k7hrIwYgQyCeNMUj6h2YzDajLk/jbBaOGO9wIApr/3GFYfPoyzs7M49crrz//e9eOF4GrfYbgjW332mWLslMv9zce+2zXpjs1x5/PYSnHcVoptpuEYzCwwlLUbYygLLVaFbGgoGzpAO+VAlqTJSSy8/wNYvO3dmDh2FM9umumslI0BL5IopNT2t7bP+a77hSAh6Ni+RhLaTGGxK6MhhrJ0MZQN46pS1nZyse3ykqpYVaS+j7s0PY1zl/8SQxnSu0gmisH2OHF9XKXwJV+f2YdjTq5CYbFi1oGBzIzEalkugQwIF8p8d1c0/bYw55OPiy6Nto9HwzCUEZkz/Zw3Pa5cV42K7ZmeZ6Qd/9V2H4rUDvKDwawFQ1macgpkgN9QFmPcWNPCoDmHsVi6Qlk5IIYOcF3hVEKglHZB1qZYBLpge2xXF5EOYSyPOUZdwcfk2Crfv/h/k/t1LT7ddVvb37r6TOjb7ZPyx2DWgKHMnKRqGUMZjZ1NoIkVfkJWDIewuRiMbfaK9cmFjhjtTOW1SZ3rmXir2zUNaK7Dj8vPBIYzqsNgVsFARrb2PbUQdXHpIarfsgPhLlzGelJyHUokVJZck/acUhmA7+LYPbx/UUR4KX82SWgPDddn/NmQSaO6tkEkEYMZGMaGkFQtA4C980eyq5qFFjOs5c4klEkLJa6l0IVxbKrHfOxwVtcegJ9DqagLQn2/2IgRzky6QrrC4EhVowxmDGJEdnhhNBxDWTpdGOukUjWTwNeXO/wcks/HeKyQym3uWqMtZFtitoPCGtV0+XuOzjGUOSStWkb+1V1wUTeGsjxeA9uLImkXUavOnMaaw4ew6sxpL9s/vH+x8TPC9LPD5O/4OSRT8eVF8c+VPkut1G2jbTsmt4eUYqglN0ZR7ipsIAAAGItJREFUMWMYI59SHV8mwRi6caRaJXI5Y2NO3RdtF0QXUWk7dw5bHvgq1j/+Q0wdncczM5di8ZrrcPDN7wAmJ51VoIrtDKmYlScx6XocksX1fu7j3FA950Q/NlF/HhTxuUFRZB3MGMj8kVotCzm+zGcgkzgbo6+LoZzDmU0oW7t9i9eAYhK0fITItm2mFMjK6mZm65qaO+ZF1pYHvornf+uh8z+vmT9y/ueDb31X5/1tJ+KoBjTXwY/y1Oc80Oe46nOfGOeoXM+LsSmlJgDcDeBaAKcB3KG13lO6/RYAnwZwFsAurfU9K7+/C8DvAFgD4G6t9b0+2pdlMGMg80tqKAvBd3UsZCCrXuS0fUstZeHplEiqlFXbYts216Ex1UBWlcI+u+rMaax//Ie1t617fDcO3fo2LK15nvH2bCYG6fu5Ua2aMZDlLYXjiLJyK4BprfUNSqnrAXwewBsBQCk1BWAngFcA+DmAR5VS3wBwNYBXAXg1gF8E8FFfjcsumDGU+SU5lPmsloXorhgqlDVd5JR/H2OQvZRw5mLAtU2VqPy3rsdhuQyHfcJZ8fi5BLEUTS0cx9TR+drb1hydx9TCcZyZ3dx4/7ovbEJ8PjCMjcOQz/xQVWgJ5yVy6jUAHgIArfVjSqnrSrddDWCP1voYACilvg3gRgAvB/AEgAcBrAdwp6/GZRXMGMrIpZBjx0KEMpsLHUkXRaFmxnJ1krftulf8zjREmQYkCRU7BrJlMbsxPrNhI56ZuRRr5o9cdNuZmUvxzIaNEVpFLvj6PGTg6Ra7ezINsh7AQunnc0qp1VrrszW3PQ1gA4DnA7gSwG8D+GUA31BKvURrveS6cVkFs6tmtjKceTTWaplv0kJZLCYnaZ9VNRfb9R2GTIOOj3YwZPUT++Jtac3zsHjNdReMMSs8fc0Oq26MZSl8puTOZhIaV9scm1DhUUqPkZAuecGV/jb+k71tty4CWFf6eWIllNXdtg7AcQDzAH6stT4DQCulTgGYBfCUszavyCqYkT9jDGX7nlrgjIsBdJ2Mcvpmsq7aZRqiYgUjBrL+pOy3B9/8DgDLY8rWHJ3HmZlL8fQ1O87/vk3dLIkMZWnI/WK/bhIeH9uP9fjV7eb8Xgb2KIBbAHx9ZYzZE6XbfgTgRUqpGQAnsNyN8XMATgH4I6XUXwC4HMAlWA5rzmUXzFg1c09qKEu5SkbLxn6i8RHKXFbLGMr6kxLKAACTkzj41nfh0K1vw9TCcTyzYaNVpawIZwxk6Uk5nJkGk7rfDz3+bF4zV18edm2juD3V91OQBwHcrJT6DoBVAG5XSr0dwFqt9ZeVUh8B8C9YXut5l9Z6DsCcUupGAN9f+f0faq3P+WjcqqUl590jndq9e/fSlz5oPyMlw5kb0kJZjDCWwkyMqV0w2Z5YfI0zc3mi89WNMUYoYyDrT1QgI1ph2lU8tL6hykc3TRfngZCvYdHeQ5tnsWPHjlXBHtiB3bt3L13z4l/xtv3Hf7I3udekkF3FrMDK2XCSQlnO1bG5AydErlvmQ98TX6zujD7WFrOZ7IMBKS0MZfnKqUt1HQnPzaYNfb5UC1Fp8lG9a3J+u5tnvWyf4sg2mDGU9ScpkAFxQ1moMWZzB04A6F89izG9fR9DKlQ+Tqout3li30GrGRm7/r5vG4DhlTPfi10TpSjlcNbWpTHmcxrS1bLtfpIWkZZarSSZkg1mDF7uSQtkscWY+GNo9SyVRVlTGfdgG06awlbTdnyEM1fb5RpkdnhhNQ65hTMJz8VliBryfGJOtpHyfkVuTcRuQB8MZW5dNbNVbCgLWS3bftmGC/7FUlTPhjq8f7F2cVjfTE9mx588NooTUVew8RV8XG137fYtItZEk2wM+3Eu+l5sh1pP0bfieaT8+dvUXdD187HtWln+10fK+xW5k1TFjIHMLalhLBTJU+EP7dpY5nM2tbZZskxPTqlUz0wV1SqbYOSqC2JTW1zIuXtjjNnOyo+V6gWy6Rc/Uqr3Lt/flCscqbYbCDuGq9h2ny6Tfc9rKe9X5EYywYyhzC2GMrmhTDrTk82YTzB9A0w5SEkMQZLb1kd1/+xzMeWiG1YOIa2NlKn2h34JlON7k5LQoaz8GLEem8YniWDGUOZWCqHMdxfGmItHb718bWd3RcmzNDadjHK/uAwll9CTAhcVFNsvIHJaUF1C2LKVymtLZkIdL9VwFmpRaRqfJIIZuSM9lOU8LX5ZNXi57LoYC/vVjwOD48VcXhzywozITnEO8X3spL59SgOD2UgwkMmWciAbQlooq47HSj2EpDppR4pdJlOqdFEeQgUSCUzOFaavh8TXbcgXm4cct4XiYjAbAcmhTGogi9nVcSykhLK28CJtwovY7fH52HXvg83SAxKYhDOp60lRWsr7ELuRX6h8HLadZ3x+mWJzfmNvEypLcrp8MiN9GnxJoWzfUwsX/PPJ1XT4NmJMm98mpROKlKpT0Y7cpq/P7fn43rdTOnaom+v3k/vHso3bNllV2WJhKKMqBrOMcdIUc9U1zHxWy2J1W5QSzqSdUCRXYJqYttn1c3MdonIKZGVty0h0fYPfhRWRvPR9P+vul/LaZG18njN8vF4hznG5vtfErowUyd75I6IqZr5JGUNWhLNYs6lJC2WmYnchHMLHGmldY8BSHCPmWtFNKtV9ntKQ+8V57qFsaHfK408eAzbP9r4/ycNgRqOUY0XMRox1hVK/QI0dzvosXF29f8HlotM2t48tqKW+z5NsDGWypNZekonBLHN7js6JHWeWoxRCWSFkOOMJa5mUSlJbSOuqgg2RYtdFKe/ZGHFSi3SEfq/qHiPEhBt1eH4jlxjMKJpY3Rk52+JzQoQzFyctkxm2hvDZ3a/8GNIv7tm+51Tfv9gV0zbV48L1hbGE6cXrLvzbPg8Y5Jq5fj99fC73mbm0fFuooDj0uac4tT/5xWCWOenVstDhbOxdGFMV6hvJortg022223KpPCujr4Dgatuuuk3G7joKhK+Y2Y45qTs2clpTrc+FODXz8Tr52NfqxmfaPE6I/T9UKCv+P5djmtoxmGVMeigrhAhnrJLFYzoBgu+qmKlYF+RNmtb3ctEu022bhqu6NrWFXdNtxCStPWMi7UI8dbE/W22Vzx2S3l/fgaztcSW9DuQHg1mGUglkZT7D2VhDWbWLopTp8uuUTzZSZrKLfUFuMrHGkDZ2LawNmL0Gpn9jEs5iv+Y5cX0RxwvC/KRwoR+jfRJmgpRwDqQ4GMwykmIg8ylkIJPWjbFu3FjxO2kBzfeJV0rly5RNdcn3uCeXsyp2hbNU3p8+bLsmSrtgltQW6ocX+t2GvEahjhFpnw3kHheYzsBVM1sZykp8LxDty+wV64PMkhjqccpinkhSmHRjiD5juPrcJ+fXUJJyl14uOE2uVBck5gLFF5IUXLvaIqmt5B4rZonLIZC57MKYYiAruKxkmcy2GDKctZ1I+A3ghWzHZNnqu22fXSfLt+cYAE338b4TeVTHZ+Y4KyO5wfew3tAxznXHaZ9ZQxm6iMEsYSmHMh/jyWKFMmndGAsxFpGuY3Ki4cnoQr7C2dBt9g1PkrpnxtIVblwuK+ESj00akyEBzeY+Q48rfqGZLwYzCibGmmUUV6iLOl8X8zGrOK7Dmettdb0mQx4v9+oZYHcBGOMiLPdp+HPH92oYm+qXb02fFXyP88Rglqiuatmeo3NGfxdCzoFMarWsUHSPjFE5CxnKiv+6upCvW1y4EDIsuAhnpuuy2T6Oz+6W5cfIMZwB9sdHyIswdj0OJ4XFnl0Isd/0WZTaVIygVn3MunX7Nm7bhEPeW0IhMZglSELYMpHLwtGUDhcX8r6nqbflunLW1PY+j+N7PByQdziTqukbegYyd+qqH4Uh61xVf879PevTVT7314TSxmCWGNNQlkp4I/9ijTMLtR5ZORz4nJxi6PaHGBKAbNrcZ72x6oLcrqUcylwfA6EvtHkB60/TvuEqlA3Zlks+zwFDJuoo9H2NpKy3SfnhdPkJYdiqx0k/msWe/CPUhcHQKfG7QoWEKffbHj9ERa3rdtPXp3gtY7+evhX7vutpyXkxmI+2rmo2JIayuuUeQu27xTFncuyZLksRQ1O7pLaX3GDFLBEMZfmrC1HSFoPuQ/I3iyaBTJJye0KM8Wp7/Lbb69pWd9+uapu0199E04Wg5OOA4hkaoKTtU7HbU/d6mhx7fapoPKbJBwazBKQayvbOH/E+ziyXsWVNla3i930CmotqWZ91WJr+VtIJzHe3xer2fQQMn6Glrktjny6RNlW0GEHTNdv1xvoaw9gh6iZp7JTpPu1q37V97jbnIMnHl6TzKPnBYEZJ2/fUQpRw5robY9uaY7FCWZcUF501vfh33S0yxPTv0ibJsG2LpLanQPLFI4Uh6f03DT6u2hxqhkeXga9vOyS9z+Qfgxl5E2pWxpDhzOe4MondFk2+4Q+xcK4rJpWZoQGh7TF8r7fmc1bKnNcWcyX0seBiEgMahxDj0LrOF7nuo6F6hEg6l5I/DGYJ2HN0LrnujDmuXZbCZB+An2qZTR99V921UhYjnA1lWk2U2n5JTI6FujWJXD4mjZvNNPK+AtqY+AxnYz6XjhGDGWXBZ9UslUAG+O3CaBq4pJ9E2kJTqMDhezFs2233XVyaAa2daeXAZYUtlSoag6QcdftMblWvIVPrD50MxPWXMJQ3BrNEpFQ1y6VallIgC0naRB6uuA4ZXTMo+qw8mW576IQbYwhorivAtpPmDHlsaSGt7nlwDI17Q/ZVky/e+H41CzUBEOWLwYys1IWuvfNHIrSEqmKvWZYSKbMADg1nQyt/Ll6HnEOZFC6+DIkZ0lK62JfUFnJHWiAyaU/dfiil/eQPg1kiYlfL2qpguVTIUsZQNlxq1R9X3TG71hLrul/ufEzY0efCP+VKtekY1diBqGijhLZIlMNr0ieg+dwfbLdb+/ebZx21hiSYiN0A6iY5lOVs7sCJ2E1IzvEnj53/J11TsOgKKGu3bzn/b+hjmTyezX2GhKUT+w5aVdqIbKTwmVC2cdumpINw3e+q/4ZuM2WpnKdofFgxE46hzIyviT/mDpwQP9YsdrWs6eQmreuIjXL1TEKXxzLfE5e0VdAkBrIQi3lL4apqFqsilGLVL+VJSlyNd0rxuZtKcZ+kvLFiJhhDmV+mgYuVs3qm3zhKPql3XcSbVM9cPJbJdpqqdDaVLhvVbUoKPG1Vy75BWloAb9L2RciQikgoUtuV+myzVX0mman7fcz3q6ha+n7tpe6TNE6smAkUO5ABaYWyEItLS62cxaiWpXaB0kVKVaxuIpDY7SpeG9ehbOg26+7L9dUuJPk4rVYpJFwYS6+c2LSt7xjG8uOM7T1pqh5KeB1oXBjMBJEQyAp754+IDWeugljKlTAJgczkwkryhU5Z3wkwyvdxMY7M1+MPkUrYGdrOFJ5n2/GUyrFWkHjBKzmc2bRtSLiS9r6Ebo+050/jw2AmgKRAViYlnIWoiJmIXTWLNZbM5GLAZoFSyYZUz5oqNqGqXqnNKknmUjyWUpXThbmk6tcYdC0uTWSCY8wiumpmq9hQJsH2yzaICWWFriqbjyrc7BXrW0PZ4f2LOLx/0fnjtvXtbzvZpDybGTBszFZ13FOMroi2M0YOfSyyZ1P58HUspXyMkp0Q7/XY96em5x9qnBzlgxWzCFIKY7GqZtICWZlJOHNVWeuqkpUD2eH9i0GqamP5BrCrenbJC67Ez//nZ7W3SQgsvipo1eeWSqUulXbyAo6aDOlq6XMmzrGv/2bzRYvr12fjtk045HSLFBuDWUApBbJYJAcyUy5CWewp8PtUynLTFa6aQpk0LgNJ22tiM/mG9HAUEoMY2ZA2Dk5SW0Lr89xdh7PjTx7jAtOZCRrMlFKXALgfwCYAZwDcprWeC9kGMhe6UpZSKPM51swmlM1esf581cxlmJM4a5pvEqpcqZNclZLWpjFf0NIwfcbx+vgMH/MMhkOOX479i0spNQHgbgDXAjgN4A6t9Z7S7bcA+DSAswB2aa3v6bqPS6HHmL0XwG6t9Y0A7gPwscCPH01q1TKGsottvXzt+X++9AlXXWPQhupaQDp1Icdk+VKMi/O1ppmt1F9PGqfUxgLFWksyldfJVztdbdN0Yq0UXuvE3ApgWmt9A4BPAPh8cYNSagrATgBvAPBaAO9TSm1uu49rQStmWusvKKUmV37cBuB4yMcnMwxlz5Gwdll5HFnILo65BK82fQOEpPXG6oJY+Xcu22YzayXXFaOUlC9+UxsrVW6r754OqXRzr76fhSHtjBnyUtsnhXsNgIcAQGv9mFLqutJtVwPYo7U+BgBKqW8DuBHADS33ccpbMFNKvQfAhyu/vl1r/QOl1MMAXgbgZl+PL0lK1TJpoWzfUwvGf+tKjDBWDVxNsyy67LY45m4oQL/A0hQ0hqyDNoRJ8IkZjsYQzqSN+TE1tuO9SdtsekD3DLRVsV9Xn48fOpT5OEf16UZoenz7XiqG4cyZ9QAWSj+fU0qt1lqfrbntaQAbOu7jlLdgprW+F8C9Dbe9Tin1EgDfBPDCrm3t/OqnHbeOUnF17AYEcrry8/pX2P19HxfN5NRjAHHKs0EdOnnO/k4dr1GvbQ4RYdC39XPMfWD65tk0j4Pc3xdDne9dy+tUe9+MX9fG18rTcx5yjhryvlpvq7JNr58H+exfP3v8J3uv9Ln9ltsWAawr/TxRCljV29ZhuXdf232cCj35x10A9mut/xbACQCdZ/gdO3as8t4wIiIiIiLybseOHdsjPvyjAG4B8HWl1PUAnijd9iMAL1JKzWA5p9wI4HMAllru49SqpaUlX9u+yMoAuq8AmAYwCeATWutHgzWAiIiIiIhGqTTD4jUAVgG4HcDLAazVWn+5NCvjBJZnZfxS3X201j/20b6gwYyIiIiIiIguFnq6fCIiIiIiIqpgMCMiIiIiIoqMwYyIiIiIiCiyoLMy2lJKXQLgfgCbAJwBcJvWei5uq8gVpdQGAPdheX2INQA+orX+btxWkUtKqTcBeIvW+u2x20L9lQY+X4vl1Rru0Frvidsqckkp9UoAf661vil2W8gNpdQUgF0AtgN4HoA/1Vp/I2qjyBml1CSAewAoLM8a+AGt9X/HbRUNJb1i9l4Au7XWN2L5Av5jkdtDbn0EwL9prV8L4N0AvhS3OeSSUuqLAD4D+Z8z1O1WANNa6xsAfALA5yO3hxxSSn0MwF9jecZkysfvA5jXWv8agN8A8JeR20Nu3QIAWutXA/gkgD+L2xxyQfQFk9b6C3huR9uG5UXeKB87AfzVyv+vBnAqYlvIve8A+IPYjSAnXgPgIQDQWj8G4Lq4zSHHfgrgd2M3gpz7ewCfWvn/VQC8LIhLcWit/wnA+1Z+vBK8Rs6CmK6MSqn3APhw5de3a61/oJR6GMDLANwcvmXkQsf7uwXLFdEPhW8ZDdXy3n5NKXVThCaRe+sBLJR+PqeUWq215oVeBrTWDyiltsduB7mltT4BAEqpdQD+ActVFcqI1vqsUuorAN4E4Pdit4eGExPMtNb3Ari34bbXKaVeAuCbAF4YtGHkRNP7q5R6GYC/A/BRrfW/B28YDdZ27FI2FgGsK/08wVBGJJ9S6gUAHgRwt9b6/tjtIfe01rcppT4O4HtKqZdqrX8eu03Un+iujEqpu5RS71z58QSAczHbQ24ppV6K5a4Wb9da/3Ps9hBRo0cB/BYAKKWuB/BE3OYQURel1GYA/wrg41rrXbHbQ24ppd6plLpr5cf/A/Dsyj9KmJiKWYNdAL6y0lVqEsDtkdtDbn0Gy4PNv6iUAoAFrfUb4zaJiGo8COBmpdR3sDxWhZ/FRPL9MZZntf6UUqoYa/abWuuTEdtE7vwjgL9RSv0HgCkAH+J7m75VS0tLsdtAREREREQ0aqK7MhIREREREY0BgxkREREREVFkDGZERERERESRMZgRERERERFFxmBGREREREQUGYMZERE1UkrdpJQ6oJS6rPS7jyqlHojZLiIiotwwmBERUSOt9SMA7gNwD3B+gen3A3hPxGYRERFlh+uYERFRK6XUGgDfB7ALwAcBvEtr/d24rSIiIsoLgxkREXVSSv0qgP8C8Bmt9adit4eIiCg37MpIREQmXg3gCIDXK6VWx24MERFRbhjMiIiolVLqpQD+BMCrAJwG8Mm4LSIiIsoPgxkRETVSSk0D+BqAO7XWewHcBuCDK5OAEBERkSMMZkRE1GYngCe01vcBgNb6ZwA+BOA+pdTaqC0jIiLKCCf/ICIiIiIiiowVMyIiIiIiosgYzIiIiIiIiCJjMCMiIiIiIoqMwYyIiIiIiCgyBjMiIiIiIqLIGMyIiIiIiIgiYzAjIiIiIiKKjMGMiIiIiIgosv8HXiGZ7E8qU2UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12234d0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = sns.cubehelix_palette(light=1, as_cmap=True)\n",
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "contour = ax.contourf(grid[0], grid[1], ppc_grid['out'].std(axis=0).reshape(100, 100), cmap=cmap)\n",
    "ax.scatter(X_test[pred==0, 0], X_test[pred==0, 1])\n",
    "ax.scatter(X_test[pred==1, 0], X_test[pred==1, 1], color='r')\n",
    "cbar = plt.colorbar(contour, ax=ax)\n",
    "_ = ax.set(xlim=(-3, 3), ylim=(-3, 3), xlabel='X', ylabel='Y');\n",
    "cbar.ax.set_ylabel('Uncertainty (posterior predictive standard deviation)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Average Loss = 162.17: 100%|██████████| 40000/40000 [00:28<00:00, 1425.03it/s]\n",
      "Finished [100%]: Average Loss = 162.06\n"
     ]
    }
   ],
   "source": [
    "minibatch_x = pm.Minibatch(X_train, batch_size=50)\n",
    "minibatch_y = pm.Minibatch(Y_train, batch_size=50)\n",
    "neural_network_minibatch = construct_nn(minibatch_x, minibatch_y)\n",
    "with neural_network_minibatch:\n",
    "    approx_mb = pm.fit(40000, method=pm.ADVI())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x120b2fa58>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lwmnCmW09vtesUptzueTEUxXtkJ7N6N81dFcj9eJjAejcuoFLeY8OWV7XzQhhk0iXNqf237pJesXPr9x3DucPa+/Xtho3SCY58dR34aKzvCcUdy3J/l4ETRjSrkpZg/REn9adNq6xn3vzTZ1PAKr2apqVwo0X94x0GC4uGO5fZRIql47uxOszx5JZTYWQ4KiMncVWuvFYPmb8k38Z5tN+05ITqn3f+az26ZuHV/zctU0DZt/j/5l04wZVm2+umtDV63qP/3koz94ygsvHdaZr21PJoGFGEtec391l2TEDW3OGDwmqYv8Tu5GSGN57HH3EXoFXd6P4oevPdFvevX1DmmRW/3sKlCYAFTEDujVh7KA2Ydn23y73b9yUC0d04PE/DaVRZlJI42jeyP1Zu7NXZ4zh8rFdqJ8W+BnzbX/oy+AzmtEq295U0cmHNukWjVO9nv06J4D2LepXVGAxsRYaZ1bfFj/1vO5VEupD01wruZTEOPp1za6ybkpS1eajts0yuHSUeJ0YpVV2Gg9dP5h7r/a9Kcz5BH9wz2Y8cN0gn9f1RUK8vap99+EJHpfp0b5q0kqqF8efL+kV0lic1fkEUFbHewH9629n1di+Kt8I9JXz5buzyaPFp/UDOSt3d6ZVLyGWDi0yKl53a9eQ34/tzLO3jGDKud3o0tZ9nMH49+0jvS6T6WPFX/5dHtSjKZeM7Ojy3tBezbnjyn5+dUXs3LqBS2X67C0jqjQzVe798+gNQ+gjjZkw2LU544rxXSp+vmxMZz5+8nz6dslm6nndmXpet4r3mjRMcfm8HVtm0sqpSafc/945yufPAbg9g+/fzXNTWMMMz4n+jiv60auT5yaXF+8YySv3neNXfOXNYe6u4qrzzsMTaNYo1a91/FHnE8CxE4WRDiGswjFNnDudW2dy06W9A1o3LjaGWXe5/kG3a55RcZb3/G1n8xenbbdrnsGcB8ZVvA6kLbmszF7BO7NYLDx184iK1/XiY5k8WmjbLMNpxarb8rWd1l1zlrffz1M3D/f5d9izUyPAnlDPsrb0uM9y5wxo7cNWT33gts0yePHOUbw2cwz1U+uRkhjHrZf3dVm6S9sG3P/HQaRWOkO/ZOSpm9eJCa6VXOXukc6/z1s9XKl563Z611X9+J8/njpL//tN7pu8PPXOua/SjXJv54mj+p06+WnWKLXaBOLOldU0c826axSP/2moy/fg1t9befFO7ycPwarzCaCuq3xGFQ7d2zfksT8NDXh9i8Uep7N6TmdCLbPTGNW/Fe0dZ+cNMxK9tk2Xa9c8gzM6ZPHsLSNc3yiDh68fXPHSXVNH66ZVzzzd+fufh3KhDz1Pxg5qwx/GdfG6XLlnbxlBhxb1q5S/NtN9z5CrJnTjwWlnct7QdrTMTmP+E+cxuppK/kLHZ54y8VTlk+V0bD3lncy0RF6/fyxvPzSB5l7OPh+5YTCP3DDYpazydju1tlfCZ/dt6VI+sHsTj8k9Pq76qmlQj2b0llNn6S2z3ffS+ftNrt/b8We2Aap+H70NGjOkV7Nq36/O78d2dvk+X3N+d4b3bsFbjpOcJg1Tqlx9ZtVPollW+M78y+nTHae5+LgYXps5lnNv+Sio7bz/2EQuvH2B2/cS4mODesrRXZfBypWBOyOsLejQon61I7pmZSR57PboHLPzvYY37h/Lf1fvYMLgqj1ugm0wnDSiA02zUjh0tIDUJPuf1y2/t7Jt91HmfbnZZVlPZ7mZaaeuOJo3SuG3AycA+++6Z8dGFe95u3Jo3iiV+yY3p1+/jry8YH1FfIePFTDvy8307NiIgzn5/n9IJ92d2q1nXjuQNxZuYGQ/16bCZlmpvPXg+IBvtF58dkfvCzlx/r1bLJaKY/jG/WO9Xk0O6uy+0rW46/Pjw5Vby+zUKk2d/vZaCqc6nwBqqonkdBcb49vF4DsPT+CyexdSXFK1Up4+uTdPv722Svk4x1lXOV/+EAFuuczePPDeF7/4FFt1nD9fRmo9LvKzUvE1McTHxVTpSjqiTwvo06JKAvDFsN4teGux8Xu9cu4S9xXjuzKyXytaNE7l/QBi8sTaORtr56o3dIEqTUa+ePIvw/hm3V4uH9vZp+Uf+9MQ5n2x2aW5BuDpv47g6PHCar9zYwe15YMlm2nXxLfmPl/VS6jdVaw2AZ3GHg+gWWb2PaNpmFH1S17dGX4Lp2aApHpxTBxS9cx59t2jq5z5gb13yplnuF4+e/pDLH+0/qw+nq8O2jXx8YZppSo72POAcHQmqK7XT3lzWFb9JJ+bw3wVE2OhZXYaFoulVo+W26lVJn8Y18Xnk7iubRty79QBJFa6skpMiCPbTffTaZN6cMNFZwD2ZrKX7jmHjs1CmwACVVPnrbU7PYVAMF3rartAeq00zkxm9j3ncMHf5vu8Tlys9/MEd/27wXtbrrOxg9owpFfzKmeLw3o159VP7E0Yvv5hlPenL7/89/UMtCbGvLluUg/OGdC62h4hM6YOZMVPexjZtyVnWVtS4uaKKxQ6tbLfgxjSM/A2bn91bduAbXuOub3/UZOcH8yyWCw0ykxih9P7t/2hL4+//h0dW9YnqZ733junY1tDnU8AKYnheRz9dBIbY6GktMzl9ex7RnMwJ5/j+UUej1FCfCyFRSVVzhJ9SQiBcldRN26QTFb9JA7m5JNSzR9i/bR65OSetMfoaPL5561ncTyvqMpZoSdJ9eL49+1ns2nHEZ5661RzVigvABLiY712B8xMT6y4RxGLf4nUnS5tGrBh2+EqXQrP6NCIf956ltebvaF09XndSYvN5cKzqjbDZTdIDtsQEv4a2qs5Z/ZoSkyMBYvFwoPTzqwyAmi41POzu2ig6nwCOF11aFmfzTtzwrb9xpnJXh/k8XS2XbncuQfObZf3pV69WB6Y/U2QEbp65IbBLPv+N0ry9vPjtryKcuemnlfuG1PlyiY+LpbMdP/+mFo0TqNF4zTXBOCmsaQ8QfrqH9OH8cnyrfZ7AjXsvmsGsnHbYaydq/Zvb+Njb6hQqRcfS5eWSW6T2pN/GUZ6Snieeg1ErNPJjvMN+Oq0aZpOg4xE1mzc7/f+npo+HJvZR7vmGd4XDoGQJQDHBPC7gPI7diuNMXeKyEDgGaAYWGyMud+x/AxggqN8ujFmdahicVYX7gH7W9FU1qZZOlt2HfV7vUvO7sgbn25kQDUP1PztcqtLP/rKg4Y5u+l3vThRUOR3HGDvKnfJyE7MXXDA4zKVh0UIVnJiHHkFxSQnxVe5C9wyO5V7rh7AdY987vP2OrbMZPrkyIwamZoUT98u7m/Q1gbd2jVk3a+HSE6MP206bniKsmPL+hxxXIn6q0PL+nRoWXNNY6G8AmgPrDHGnFup/AXgIuBX4BMR6Y392A0HBgAtgXlAvxDGUme0a5ZBq6ZpLLHt8nvdPtKYNWY/fbtkB5QALh0tnDu0nctgY87iYi0M6+372Wx1fdZDLRR1yKy7RnPoaL7bZqlpF55BeohvzkazR24YTGFxadBNXbXB6TT4QCgTgBVoLiJfAvnAzcAeoJ4xZguAiCwCRgEnsV8NlAE7RCRORBoZYzyf3kUZaZXJ5p059O2azf7Ded5XcOPKCV254eKexMZYmPvZJp9viDdIT2RCX3ubsKfKv7YIZ/NFekpCRXNE+d90SmIcr80cS0J8LMfz6vZT5jXJYrHUWLt3qJR3wqjoFXeaXLk4CygBiMhU7BW8sxuBR4wx74rIEOANYBJwzGmZXKAdUAAcqlSeAXhNADabza9YDx8v9mv5cDuzSyorNhwHIDUxhuMFVXt3TOxfnzOanyRpcAM6NTzBhs1H3G7L3bEY2i2NZetyAdiwYX3FKILXnNOYBmmxPh2/sb1TaN8k0eOye/bYryZKy8q8bm/Lli3En9ztdZ++cv4Tu/DMBnTKynOJoU/7FNZsOcHBPVuwHdoasv1mxtuHFe/XMZmffvwegJNFrr87f7+bNUXj8o8/cd1zaXPiYouw2WwUFtpPCA4eOsgJx9913okTIfuc4TheASUAY8xsYLZzmYgkY2/PxxjztYg0w16xOz+jnQbkAIUeyr2yWv0b5XHvoRMwf69f64RT0yZNYIP94Zv4+HgoONVWeJa1BZPPkYpHwMtHOlm2yQbbXK8Cpp7XHavV6YnCOfYmImuPDixbZ7952bVr14r2eZ+OmmMbHTt2hLxdHo/1z3vXw/pcYiwxnn8fjm21b98ea/emvuzdJ5sXLK/4ecpFVZ+DsFqhpKTU5eZdKFiBsWcVVumTf03hFto1y6Dw6Ha/v5s1wWazaVx+CCauhP8shrx8shpmEXf8JOwuIDklJSSfM5i4qkscoWwCmoH9rP5xEekJ7DTGHBWRQhFpj/0ewBjgfuyJ4nEReQJoAcQYYw6GMJZay90Nrr5dsrnjyn4+XwI3bZgSsXHr/RWJibJDXfmXc/dAVvlj/Tbb9rDsU6lwCuVfyqPAcBFZCvwDuMpRPg14E1gNrDXGfGOMsQHLgJXYbwDfGMI4arXyAciG9GxW0b7eLCul2sq/Q8tKXcJOg6bGB64bxJCezSomwgiV0+CjqyjnrstwbRWyKwBjzBHs3Torl68CBropnwnMDNX+TxdNGibzzsMTSEyIZdf+4zw/dyWXehkX/9wh7Zg9f10NRRgavTo1rnZMdaXqmtPwHrCOBVSTrhjfBWmVSVK9OCwW+3gsFw9u6PXBF1+aNMr76tfUAyRKqdOfPglcQ84Z0Npl0oxgZLhJGHdc2Y+DOfluxjn3X20+k0lNsjeVuZsyUKnaooljbKxmIfh7DKc6nwBqy1OFsbGhi+NvlWZpAvv4PJUr/3B99qz69tmQ2jar2SEEALLrx3P3lP50rMGnJZXy1x/Gd6FRZpKPs7JFTp1PAHWRp5E3y903dQCrft5L6ybuZ0nyxkL1wwSPGdiaktJShvb0POxDOA0MYbdSpUKtrMz+AKW7we5qG00AdVC/rk3o19Xz+D3exMVZqG7EnrjYGM4benp0Q1WqptSOtgb/6E1gVeHRG4cwcXBburfL8r6wUuq0p1cANeR0ODvo1q4h3do1jHQYSqkaolcASikVpTQBKKVUlNIEEEaj+1edJF0pVUfVki7n/tAEECIpiVVvp9Smqe2UUqoyTQAh8vr943jv0YmRDkMppXxW53sB1dRFmbup7BqkJ9bQ3pVSyn96BRBG485sG/Jt3j2lf8i3qZQKXjvH0ChNs2r3+D/O6vwVQCTFx8WQnpLAsROhmzs2nHPgKqUC95dLe2PtvJuz+7aMdCg+0wSglFIhkJqcwNhBbSIdhl+0CUgppaKUJgCllIpSIWsCEpE7gLGOl/WBJsaYJiIyCXgC2Ol4b4YxZqmIzMA+hWQxMN0YszpUsSillPIulHMCP4p9YnhEZAFwm+MtK3CbMWZe+bIi0gcYDgwAWmKfGL5fqGJRSinlXchvAovIhcARY8xiR5EV6C0i04HVwO3AEGCxMaYM2CEicSLSyBhzINTxKKWUci+gBCAiU4GbKxVPMcZ8C9wJ/D+n8s+AD4GtwAvANCAdOOS0TC6QAXhNADabza9Yc04U+7V8oNzFZbPZKC627//AgQMeY/fnM/3888/8lloznbf8PdY1RePyj8bln2iKK6CaxBgzG5hduVxEugI5xpjNTsUvGWNyHO9/BFwE/AA4z1eYBuT4sm+r1epXrPuP5MFHe/1aJxAVcc3Z5VIW99F+OFlIo0aNsFp7VlnPZrP59pkc2+3evXtIJn73xue4apjG5R+Nyz91Ma7qEkeoewGNAhaWvxARC/CjiLRwFI0EbMByYIyIxIhIKyDGGHMwxLHUCs0bpQKhGxYi5jQccVApVTuFOgEI8Gv5C0cb/zXA+yKyFEgGZhljbMAyYCX2G8A3hjiOWuP2K/py+djOXDCiQ1DbuXtKf8ad2YZGmUkhikwpFe1C2phsjKlSkTtuBi92Uz4TmBnK/ddGDTOSuHS0BL2dgd2bMrB70xBEpJRSdvogmFJKRSlNAEopFaU0AQQoKyORS0Z2JDkxjovOCq6mJzRXAAAQcElEQVR9XymlIkFHAw3CFeO7csX4rpEOQymlAqJXAEopFaXqfAKw1NikkEopdXqp8wlAKaWUe5oAlFIqSmkCCFBWfX0iVyl1etNeQH66+OyOlJWVMXFIu0iHopRSQdErAD8l1YvjqondPF4BtG+RAUBGakJNhqWUUn7TKwAfjO7fis9W7wDA22CcT00fzpI1u+jeLqsGIlNKqcDpFYAb/bpmkxAfW/H6pkt7+7yuxWLhLGtLHbVTKVXr6RVAJR88fi6xMRaW/7ibx177LtLhKKVU2GgCqCQu1n5RlJFaL8KRKKVUeNX5JiCdQEsppdyr8wkgVB69cQhndMhi3JltIx2KUkqFhDYB+ahbu4Y8dP3gSIehlFIhE1QCEJFJwCXGmMscrwcCzwDFwGJjzP2O8hnABEf5dGPMahHJAuYAScBuYIoxJi+YeEKqLNIBKKVUeAXcBCQizwCPVNrGC8BlwBBggIj0FpE+wHBgADAZ+Jdj2fuAOcaYocBa4LpAY1FKKeW/YO4BrACuL38hIulAPWPMFmNMGbAIGIU9GSw2xpQZY3YAcSLSyFH+qWP1hY5llVJK1RCvTUAiMhW4uVLxFGPMXBEZ4VSWDhxzep0LtAMKgEOVyjMcyx+tVOaVzWbzZbEKx/JK/Fq+fPtb9xUEvE9/hXv7gdK4/KNx+Ufj8k844vKaAIwxs4HZPmzrGJDm9DoNyAEKPZSXL5/vVOaV1Wr1ZbEKh47mw4d7fF6+fPsJWw7C5wcD2qc/bDZbWLcfKI3LPxqXfzQu/wQTV3WJI2TdQI0xx4BCEWkvIhZgDLAMWA6MEZEYEWkFxBhjDjrKxztWH+dYVimlVA0JdTfQacCbQCz2dv9vAERkGbASe8K50bHsg8CrInItcBD7zeOISk2KP/VCewEppeq4oBKAMWYJsMTp9SpgoJvlZgIzK5XtA8YGs3+llFKB0yeBneiwEUqpaKIJQCmlopQmACdl2u6vlIoimgCUUipKaQJwovcAlFLRRBOAUkpFKU0ASikVpTQBKKVUlNIE4OSeqwdEOgSllKoxmgCcdG3bMNIhKKVUjdEE4EGZDgaklKrjNAEopVSU0gSglFJRShOAUkpFKU0AHljQx4KVUnWbJgCllIpSdT4BxMfFBrSe9gJSStV1dT4BpKckRDoEpZSqlYKaElJEJgGXGGMuc7weiX2u3yJgP3CFMSZPRD4Cshzl+caYcSKSBcwBkoDdwBRjTF4w8SillPJdwFcAIvIM8EilbTwPXGCMGQb8AlzjKO8IDDHGjDDGjHOU3QfMMcYMBdYC1wUai1JKKf8F0wS0Ari+UtkIx2TvYL+6KBCRbKA+8LGIfC0iEx3vDwE+dfy8EBgVRCxKKaX85LUJSESmAjdXKp5ijJkrIiOcC40xexzrXAicBdwLNAKeBJ4BGgDLRWQ1kA4cdayaC2T4ErDNZvNlsYA4b3vrvoIa2WdNbD9QGpd/NC7/aFz+CUdcXhOAMWY2MNvXDYrIzcDFwFhjTIGI7AVeMMYUA/tFZC0gwDEgDch3/J/jy/atVquvoZwyZ5dPizlvu+mB47z6+ec0zkwKbJ8+stlsYd1+oDQu/2hc/tG4/BNMXNUljqBuAlcmIncDVmCUMSbfUTwK+DMwXkRSge7ABmA5MB54BRgHLAtlLMFq1iiVx/80lBbZqZEORSmlwiJk3UAdbf0zgGbAQhFZIiLXG2MWAptEZBWwGLjLGHMQe2+hySKyHBgEPBeqWEKlS9sGpCVrN1KlVN0U1BWAMWYJsMTx8z7AbW1pjJnupmwfMDaY/SullApcnX8QTCmllHuaAJRSKkppAlBKqSilCUAppaKUJgCllIpSmgCUUipKaQJQSqkopQlAKaWilCYApZSKUlGfAHp1bBTpEJRSKiKiPgEM7d080iEopVRERH0CUEqpaKUJQCmlopQmAKWUilKaAJRSKkppAlBKqSilCUAppaKUJgCllIpSQU0JKSKTgEuMMZc5vX4C2OlYZIYxZqmIzAAmAMXAdGPMahHJAuYAScBuYIoxJi+YeJRSSvku4AQgIs8AY4DvnYqtwG3GmHlOy/UBhgMDgJbAPKAfcB8wxxjziojcAVwHPBVoPEoppfwTTBPQCuD6SmVW4GoRWSYiT4pIHDAEWGyMKTPG7ADiRKSRo/xTx3oLgVFBxOK3xplJ/GP6sJrcpVJK1SperwBEZCpwc6XiKcaYuSIyolL5Z8CHwFbgBWAakA4cclomF8hwlB+tVOaVzWbzZTGvxvVJ4dj+X9m+/UTIt+2vSO3XG43LPxqXfzQu/4QjLq8JwBgzG5jt4/ZeMsbkAIjIR8BFwA9AmtMyaUAOcMzxc75TmVdWq9XHUJzM2VWlSDoJPTpkUZa8j/nfrKJhRmJg2w6SzWaLyH690bj8o3H5R+PyTzBxVZc4QtYLSEQswI8i0sJRNBKwAcuBMSISIyKtgBhjzEFH+XjHsuOAZaGKxR/Wzo350yU9+fuftTlIKRVdQpYAjDFlwDXA+yKyFEgGZhljbNgr95XYbwDf6FjlQWCyiCwHBgHPhSoWf1gsFsYMbEOjzKRI7F4ppSImqG6gxpglwBKn14uBxW6WmwnMrFS2DxgbzP6VUkoFTh8EU0qpKKUJQCmlopQmAKWUilKaAJRSKkppAlBKqSgVtQmgjLJIh6CUUhEVtQlAKaWiXdQmAAuWSIeglFIRFbUJQCmlop0mAKWUilKaAJRSKkppAlBKqSilCUAppaKUJgCllIpSmgCUUipKRW0C0CeBlVLRLmoTgFJKRTtNAEopFaWCmhJSRCYBlxhjLnO8XuL0dmfgFWPMHSLyEZAFFAH5xphxIpIFzAGSgN3AFGNMXjDxKKWU8l3ACUBEngHGAN+XlxljRjjeawe8g33id4COQDfHxPHl7gPmGGNeEZE7gOuApwKNRymllH+CaQJaAVzv4b2ngduNMcdFJBuoD3wsIl+LyETHMkOATx0/LwRGBRGLUkopP3m9AhCRqcDNlYqnGGPmisgIN8ufAaQbYz53FCUATwLPAA2A5SKyGkgHjjqWyQUyfAnYZrP5sphXmzZtoujojpBsK1ih+kyhpnH5R+Pyj8bln3DE5TUBGGNmA7P92OblwCyn13uBF4wxxcB+EVkLCHAMSAPyHf/n+LJxq9XqRygOc3ZVKZJOQo8OWf5vK8RsNltgnynMNC7/aFz+0bj8E0xc1SWOcPQCGsmpph2wN+28CyAiqUB3YAOwHBjvWGYcsCwMsSillPIgHAmgiTHmUPkLY8xCYJOIrAIWA3cZYw5iv0E8WUSWA4OA58IQi1JKKQ+C6gZqjFkCLKlU1tzNctPdlO0Dxgaz/2Dok8BKqWinD4IppVSU0gSglFJRShOAUkpFqahIAA9fP5iWWQmRDkMppWqVqEgAPTpk0SQzPtJhKKVUrRIVCQDQPj9KKVVJ1CQApZRSrjQBKKVUlNIEoJRSUSpqE0BmWmKkQ1BKqYiKygQw89qBtMxOi3QYSikVUdGTABzdgDJSE7B2zo5sLEopVQtETwJwsGCJdAhKKVUrRF0CUEopZacJQCmlopQmAKWUilJRkwBiY+1t/wnxUfORlVKqWkHNCHY6GdYtDeJSuXxcl0iHopRStUJACUBEMoA3gHQgAfirMWaliAwEngGKgcXGmPsdy88AJjjKpxtjVotIFjAHSAJ2A1OMMXnBfiBPUhJjuedqa7g2r5RSp51A20P+CnxujBkOXAX8y1H+AnAZMAQYICK9RaQPMBwYAEx2WvY+YI4xZiiwFrguwFiUUkoFINAE8BTwouPnOKBARNKBesaYLcaYMmARMAp7MlhsjCkzxuwA4kSkkaP8U8c2FjqWVUopVUO8NgGJyFTg5krFU4wx34pIE+xNQdOxNwcdc1omF2gHFACHKpVnOJY/WqnMK5vN5stiIV83nDQu/2hc/tG4/BNNcXlNAMaY2cDsyuUi0gN4G7jVGLPUcQXgPMBOGpADFHooP+b4Od+pzCurNbB2fJvNFvC64aRx+Ufj8o/G5Z+6GFd1iSOgJiAR6Qq8C1xmjFkIYIw5BhSKSHsRsQBjgGXAcmCMiMSISCsgxhhz0FE+3rHJcY5llVJK1ZBAu4E+AiQCz4gIwFFjzPnANOBNIBZ7u/83ACKyDFiJPeHc6NjGg8CrInItcBD7zWOllFI1JKAE4Kjs3ZWvAga6KZ8JzKxUtg8YG8j+lVJKBU8fi1VKqShlKSsri3QMPrPZbKdPsEopVUtYrVa34+CfVglAKaVU6GgTkFJKRSlNAEopFaU0ASilVJTSBKCUUlFKE4BSSkUpTQBKKRWl6vSMYCISAzwP9AROAtcYYzbX0L7XcGp01K3AQ8ArQBnwM3CjMabUMRTGddgny3nQGLNARJKwj7LaGPtIqVcaYw4EGc8A4DFjzAgR6RBsLJ4m/wkyrt7AAuAXx9v/NsbMrcm4RCQeeAloA9TDPmTJeiJ8vDzEtbMWHK9YYBYgjuMzDfsIwJE+Xu7iio/08XKKrzFgA0Y7tvUKEThedf0K4AIg0RgzCLgDeLImdioiiYDFGDPC8W8K8A/gHscEOBbgfMdw2jcBg7EPnveIiNQDrgd+ciz7GnBPkPHcBvwf9vGbCFEsVSb/CUFcVuAfTsdtbgTiuhw45NjuWOA5asfxchdXbThe5wIYYwY7tvkQteN4uYurNhyv8mT+IvaRkCGCx6uuJ4CKSWcc4xT1raH99gSSRWSxiHzhyM5WYKnj/fIJcPoDy40xJ40xR4HNwBmEfrKcLcCFTq+DiqWayX9CEdcEEflKRGaLSFoE4noXuNfxswX7GVVtOF6e4oro8TLGfAj80fGyNfZh3SN+vKqJK9LfL4AnsFfYux2vI3a86noCcJ50BqBERGqi2SsP+y95DKdGSLU4fjngflIcT+U+T5bjiTFmHlDkVBRsLO4m//E7RjdxrQb+ZowZBvwKzKjpuIwxx40xuY7K4T3sZ1gRP14e4or48XLEViwirwL/JDTf9XDFFfHjJSJXAQeMMYuciiN2vOp6AiifdKZcjDGmuAb2uwl4w9inwdyEfUa0bKf3K0+KU125z5Pl+KE0yFg8LRusD4wx5bNXfAD0jkRcItIS+BJ43Rgzh1pyvNzEVSuOF4Ax5kqgE/Z29yQv+49UXItrwfG6GhgtIkuAXtibcRp72X/Y4qrrCaBi0hlHM8xPNbTfq3HcbxCRZtgz9GIRGeF4v3wCnNXAUBFJFJEMoAv2m0DhnixnbTCxGM+T/wRrkYj0d/w8EvtNshqNS0SygcXA7caYlxzFET9eHuKqDcfrDyJyp+NlHvZk+V0tOF7u4no/0sfLGDPMGDPcGDMC+B64AlgYqeNVp3sBYc/yo0VkBfZ20yk1tN/ZwCsi8jX2O/tXY5/0ZpaIJAAbgPeMMSUi8iz2X1YMcLcxpkBE/o19spyvsU+pGerJcm4JQSxuJ/8J0vXAP0WkCNgL/NEYc6yG47oLyATuFZHyNve/AM9G+Hi5i+uvwFMRPl7vAy+LyFfYe9lMx36MIv39chfXTiL//XInYn+POhqoUkpFqbreBKSUUsoDTQBKKRWlNAEopVSU0gSglFJRShOAUkpFKU0ASikVpTQBKKVUlPr/yoo3B09KR7IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122348630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(-approx_mb.hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}