multiple_stock.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import build\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import time\n",
    "import warnings\n",
    "import keras\n",
    "from numpy import newaxis\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.models import Sequential\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(threshold=np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(predicted_data, true_data):\n",
    "    fig = plt.figure(facecolor='white')\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.plot(true_data, label='True Data')\n",
    "    plt.plot(predicted_data, label='Prediction')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AAPL.csv', 'AMZN.csv', 'BA.csv', 'BAC.csv', 'BHP.csv', 'BRK-B.csv', 'CHL.csv', 'CMCSA.csv', 'CVX.csv', 'D.csv', 'DCM.csv', 'DIS.csv', 'DOW.csv', 'DUK.csv', 'EXC.csv', 'GE.csv', 'GOOGL.csv', 'HD.csv', 'INTC.csv', 'JNJ.csv', 'JPM.csv', 'KO.csv', 'MA.csv', 'MMM.csv', 'MO.csv', 'MRK.csv', 'MSFT.csv', 'NGG.csv', 'NTT.csv', 'NVS.csv', 'ORCL.csv', 'PEP.csv', 'PFE.csv', 'PG.csv', 'PTR.csv', 'RDS-B.csv', 'RIO.csv', 'SO.csv', 'SPY.csv', 'SYT.csv', 'T.csv', 'TM.csv', 'TOT.csv', 'UNH.csv', 'UPS.csv', 'VALE.csv', 'VZ.csv', 'WFC.csv', 'WMT.csv', 'XOM.csv']\n",
      "(2518, 50)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "directory = '../dataset/price_long_50'\n",
    "filenames = os.listdir(directory)\n",
    "print(filenames)\n",
    "\n",
    "all_data = np.zeros((len(filenames),2518),dtype = np.float32)\n",
    "for i in range(len(filenames)):\n",
    "    filename = filenames[i]\n",
    "    #print(i)\n",
    "    #print(filename)\n",
    "  \n",
    "    data=pd.read_csv(directory+'/'+filename)\n",
    "    vars = ['Open']\n",
    "    data = data[vars]\n",
    "    data = np.array(data)\n",
    "    data = np.transpose(data)\n",
    "    data = data[0]\n",
    "    data = data[::-1]\n",
    "  \n",
    "    all_data[i] = data\n",
    "all_data=all_data.T\n",
    "print(all_data.shape)  \n",
    "#np.save('data',all_data)\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "data50=scaler.fit_transform(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2518, 50)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data50.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(filename, step):\n",
    "    #load data from the data file\n",
    "    day = step\n",
    "    data = np.array(filename).T\n",
    "    print data.shape\n",
    "    data = data[:, :]\n",
    "    gt_test = data[:,day:]\n",
    "    #data normalization\n",
    "    max_data = np.max(data, axis = 1)\n",
    "    min_data = np.min(data, axis = 1)\n",
    "    max_data = np.reshape(max_data, (max_data.shape[0],1))\n",
    "    min_data = np.reshape(min_data, (min_data.shape[0],1))\n",
    "    data = (2 * data - (max_data + min_data)) / (max_data - min_data)\n",
    "    #dataset split\n",
    "    train_split = int(round(0.8 * data.shape[1]))\n",
    "    val_split = int(round(0.9 * data.shape[1]))\n",
    "    x_train=[]\n",
    "    for i in range(step):\n",
    "        tmp=data[:,day:day+train_split].T\n",
    "        x_train.append( np.reshape(tmp,(tmp.shape[0],1,tmp.shape[1])) )\n",
    "    x_train=np.concatenate(x_train,axis=1)\n",
    "    y_train = data[:,day:train_split+day].T\n",
    "    \n",
    "    x_test=[]\n",
    "    for i in range(step):\n",
    "        tmp=data[:,day+train_split:day+val_split].T\n",
    "        x_test.append( np.reshape(tmp,(tmp.shape[0],1,tmp.shape[1])) )\n",
    "    x_test=np.concatenate(x_test,axis=1)\n",
    "    y_test = data[:,day+train_split:day+val_split].T\n",
    "    \n",
    "    \n",
    "\n",
    "    return [x_train, y_train, x_test, y_test, gt_test, max_data, min_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 2518)\n"
     ]
    }
   ],
   "source": [
    "step=7\n",
    "X_train, y_train, X_test, y_test, gt_test, max_data, min_data = load_data(data50, step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2014, 7, 50) (2014, 50)\n"
     ]
    }
   ],
   "source": [
    "print X_train.shape,y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a=np.ones((14,1,5))\n",
    "# b=np.ones((14,1,5))*2\n",
    "# c=np.ones((14,1,5))*3\n",
    "# np.concatenate([a,b,c],axis=1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train=np.random.random((2014,7,50))\n",
    "#y_train=np.random.random((2014,50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LSTM50(timestep=7,input_n_stock=50,outputdim=50):\n",
    "    model = Sequential()\n",
    "    # model.add(LSTM(units=4,input_shape=(input_dim,step)))\n",
    "    model.add(LSTM(\n",
    "            outputdim,input_dim=input_n_stock,input_length=timestep,stateful=False,return_sequences=True))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(LSTM(\n",
    "            outputdim,stateful=False,return_sequences=False))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(outputdim,kernel_initializer=\"uniform\",activation='relu')) \n",
    "    model.add(Dense(outputdim))\n",
    "    \n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1812 samples, validate on 202 samples\n",
      "Epoch 1/100\n",
      "1812/1812 [==============================] - 0s 221us/step - loss: 0.1815 - val_loss: 0.2210\n",
      "Epoch 2/100\n",
      "1812/1812 [==============================] - 0s 199us/step - loss: 0.0684 - val_loss: 0.1046\n",
      "Epoch 3/100\n",
      "1812/1812 [==============================] - 1s 300us/step - loss: 0.0306 - val_loss: 0.0738\n",
      "Epoch 4/100\n",
      "1812/1812 [==============================] - 1s 314us/step - loss: 0.0218 - val_loss: 0.0691\n",
      "Epoch 5/100\n",
      "1812/1812 [==============================] - 0s 253us/step - loss: 0.0182 - val_loss: 0.0657\n",
      "Epoch 6/100\n",
      "1812/1812 [==============================] - 0s 207us/step - loss: 0.0155 - val_loss: 0.0648\n",
      "Epoch 7/100\n",
      "1812/1812 [==============================] - 0s 246us/step - loss: 0.0142 - val_loss: 0.0640\n",
      "Epoch 8/100\n",
      "1812/1812 [==============================] - 0s 240us/step - loss: 0.0131 - val_loss: 0.0619\n",
      "Epoch 9/100\n",
      "1812/1812 [==============================] - 0s 214us/step - loss: 0.0120 - val_loss: 0.0616\n",
      "Epoch 10/100\n",
      "1812/1812 [==============================] - 0s 218us/step - loss: 0.0114 - val_loss: 0.0573\n",
      "Epoch 11/100\n",
      "1812/1812 [==============================] - 0s 230us/step - loss: 0.0107 - val_loss: 0.0556\n",
      "Epoch 12/100\n",
      "1812/1812 [==============================] - 0s 214us/step - loss: 0.0103 - val_loss: 0.0523\n",
      "Epoch 13/100\n",
      "1812/1812 [==============================] - 0s 211us/step - loss: 0.0099 - val_loss: 0.0523\n",
      "Epoch 14/100\n",
      "1812/1812 [==============================] - 0s 268us/step - loss: 0.0094 - val_loss: 0.0508\n",
      "Epoch 15/100\n",
      "1812/1812 [==============================] - 0s 220us/step - loss: 0.0092 - val_loss: 0.0511\n",
      "Epoch 16/100\n",
      "1812/1812 [==============================] - 0s 217us/step - loss: 0.0089 - val_loss: 0.0505\n",
      "Epoch 17/100\n",
      "1812/1812 [==============================] - 0s 252us/step - loss: 0.0086 - val_loss: 0.0516\n",
      "Epoch 18/100\n",
      "1812/1812 [==============================] - 0s 223us/step - loss: 0.0084 - val_loss: 0.0504\n",
      "Epoch 19/100\n",
      "1812/1812 [==============================] - 0s 239us/step - loss: 0.0082 - val_loss: 0.0502\n",
      "Epoch 20/100\n",
      "1812/1812 [==============================] - 0s 231us/step - loss: 0.0077 - val_loss: 0.0490\n",
      "Epoch 21/100\n",
      "1812/1812 [==============================] - 0s 216us/step - loss: 0.0076 - val_loss: 0.0493\n",
      "Epoch 22/100\n",
      "1812/1812 [==============================] - 0s 233us/step - loss: 0.0074 - val_loss: 0.0478\n",
      "Epoch 23/100\n",
      "1812/1812 [==============================] - 0s 255us/step - loss: 0.0073 - val_loss: 0.0474\n",
      "Epoch 24/100\n",
      "1812/1812 [==============================] - 0s 215us/step - loss: 0.0072 - val_loss: 0.0483\n",
      "Epoch 25/100\n",
      "1812/1812 [==============================] - 0s 222us/step - loss: 0.0071 - val_loss: 0.0469\n",
      "Epoch 26/100\n",
      "1812/1812 [==============================] - 0s 234us/step - loss: 0.0068 - val_loss: 0.0469\n",
      "Epoch 27/100\n",
      "1812/1812 [==============================] - 0s 223us/step - loss: 0.0069 - val_loss: 0.0475\n",
      "Epoch 28/100\n",
      "1812/1812 [==============================] - 0s 252us/step - loss: 0.0066 - val_loss: 0.0493\n",
      "Epoch 29/100\n",
      "1812/1812 [==============================] - 0s 246us/step - loss: 0.0067 - val_loss: 0.0462\n",
      "Epoch 30/100\n",
      "1812/1812 [==============================] - 1s 306us/step - loss: 0.0066 - val_loss: 0.0470\n",
      "Epoch 31/100\n",
      "1812/1812 [==============================] - 0s 265us/step - loss: 0.0064 - val_loss: 0.0471\n",
      "Epoch 32/100\n",
      "1812/1812 [==============================] - 0s 220us/step - loss: 0.0065 - val_loss: 0.0461\n",
      "Epoch 33/100\n",
      "1812/1812 [==============================] - 0s 213us/step - loss: 0.0062 - val_loss: 0.0470\n",
      "Epoch 34/100\n",
      "1812/1812 [==============================] - 0s 215us/step - loss: 0.0063 - val_loss: 0.0455\n",
      "Epoch 35/100\n",
      "1812/1812 [==============================] - 0s 220us/step - loss: 0.0061 - val_loss: 0.0457\n",
      "Epoch 36/100\n",
      "1812/1812 [==============================] - 0s 223us/step - loss: 0.0061 - val_loss: 0.0469\n",
      "Epoch 37/100\n",
      "1812/1812 [==============================] - 1s 309us/step - loss: 0.0060 - val_loss: 0.0451\n",
      "Epoch 38/100\n",
      "1812/1812 [==============================] - 0s 220us/step - loss: 0.0059 - val_loss: 0.0453\n",
      "Epoch 39/100\n",
      "1812/1812 [==============================] - 0s 253us/step - loss: 0.0058 - val_loss: 0.0453\n",
      "Epoch 40/100\n",
      "1812/1812 [==============================] - 0s 204us/step - loss: 0.0058 - val_loss: 0.0455\n",
      "Epoch 41/100\n",
      "1812/1812 [==============================] - 0s 252us/step - loss: 0.0057 - val_loss: 0.0445\n",
      "Epoch 42/100\n",
      "1812/1812 [==============================] - 0s 212us/step - loss: 0.0057 - val_loss: 0.0444\n",
      "Epoch 43/100\n",
      "1812/1812 [==============================] - 0s 210us/step - loss: 0.0054 - val_loss: 0.0442\n",
      "Epoch 44/100\n",
      "1812/1812 [==============================] - 0s 219us/step - loss: 0.0055 - val_loss: 0.0436\n",
      "Epoch 45/100\n",
      "1812/1812 [==============================] - 0s 265us/step - loss: 0.0054 - val_loss: 0.0447\n",
      "Epoch 46/100\n",
      "1812/1812 [==============================] - 0s 216us/step - loss: 0.0053 - val_loss: 0.0446\n",
      "Epoch 47/100\n",
      "1812/1812 [==============================] - 0s 209us/step - loss: 0.0054 - val_loss: 0.0458\n",
      "Epoch 48/100\n",
      "1812/1812 [==============================] - 0s 216us/step - loss: 0.0052 - val_loss: 0.0454\n",
      "Epoch 49/100\n",
      "1812/1812 [==============================] - 0s 196us/step - loss: 0.0052 - val_loss: 0.0446\n",
      "Epoch 50/100\n",
      "1812/1812 [==============================] - 0s 202us/step - loss: 0.0052 - val_loss: 0.0442\n",
      "Epoch 51/100\n",
      "1812/1812 [==============================] - 0s 206us/step - loss: 0.0051 - val_loss: 0.0427\n",
      "Epoch 52/100\n",
      "1812/1812 [==============================] - 0s 225us/step - loss: 0.0050 - val_loss: 0.0442\n",
      "Epoch 53/100\n",
      "1812/1812 [==============================] - 0s 230us/step - loss: 0.0051 - val_loss: 0.0433\n",
      "Epoch 54/100\n",
      "1812/1812 [==============================] - 0s 212us/step - loss: 0.0050 - val_loss: 0.0445\n",
      "Epoch 55/100\n",
      "1812/1812 [==============================] - 0s 244us/step - loss: 0.0050 - val_loss: 0.0424\n",
      "Epoch 56/100\n",
      "1812/1812 [==============================] - 0s 198us/step - loss: 0.0050 - val_loss: 0.0452\n",
      "Epoch 57/100\n",
      "1812/1812 [==============================] - 0s 216us/step - loss: 0.0049 - val_loss: 0.0422\n",
      "Epoch 58/100\n",
      "1812/1812 [==============================] - 0s 200us/step - loss: 0.0048 - val_loss: 0.0433\n",
      "Epoch 59/100\n",
      "1812/1812 [==============================] - 0s 206us/step - loss: 0.0048 - val_loss: 0.0440\n",
      "Epoch 60/100\n",
      "1812/1812 [==============================] - 0s 217us/step - loss: 0.0046 - val_loss: 0.0413\n",
      "Epoch 61/100\n",
      "1812/1812 [==============================] - 0s 209us/step - loss: 0.0047 - val_loss: 0.0408\n",
      "Epoch 62/100\n",
      "1812/1812 [==============================] - 0s 198us/step - loss: 0.0048 - val_loss: 0.0409\n",
      "Epoch 63/100\n",
      "1812/1812 [==============================] - 0s 213us/step - loss: 0.0047 - val_loss: 0.0408\n",
      "Epoch 64/100\n",
      "1812/1812 [==============================] - 0s 185us/step - loss: 0.0046 - val_loss: 0.0426\n",
      "Epoch 65/100\n",
      "1812/1812 [==============================] - 0s 184us/step - loss: 0.0046 - val_loss: 0.0405\n",
      "Epoch 66/100\n",
      "1812/1812 [==============================] - 0s 208us/step - loss: 0.0047 - val_loss: 0.0405\n",
      "Epoch 67/100\n",
      "1812/1812 [==============================] - 0s 223us/step - loss: 0.0045 - val_loss: 0.0390\n",
      "Epoch 68/100\n",
      "1812/1812 [==============================] - 0s 201us/step - loss: 0.0045 - val_loss: 0.0400\n",
      "Epoch 69/100\n",
      "1812/1812 [==============================] - 0s 210us/step - loss: 0.0044 - val_loss: 0.0390\n",
      "Epoch 70/100\n",
      "1812/1812 [==============================] - 0s 196us/step - loss: 0.0044 - val_loss: 0.0409\n",
      "Epoch 71/100\n",
      "1812/1812 [==============================] - 0s 194us/step - loss: 0.0044 - val_loss: 0.0421\n",
      "Epoch 72/100\n",
      "1812/1812 [==============================] - 0s 200us/step - loss: 0.0043 - val_loss: 0.0396\n",
      "Epoch 73/100\n",
      "1812/1812 [==============================] - 0s 202us/step - loss: 0.0044 - val_loss: 0.0449\n",
      "Epoch 74/100\n",
      "1812/1812 [==============================] - 0s 190us/step - loss: 0.0043 - val_loss: 0.0405\n",
      "Epoch 75/100\n",
      "1812/1812 [==============================] - 0s 214us/step - loss: 0.0042 - val_loss: 0.0424\n",
      "Epoch 76/100\n",
      "1812/1812 [==============================] - 0s 230us/step - loss: 0.0043 - val_loss: 0.0408\n",
      "Epoch 77/100\n",
      "1812/1812 [==============================] - 0s 217us/step - loss: 0.0044 - val_loss: 0.0415\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 78/100\n",
      "1812/1812 [==============================] - 0s 211us/step - loss: 0.0043 - val_loss: 0.0412\n",
      "Epoch 79/100\n",
      "1812/1812 [==============================] - 0s 214us/step - loss: 0.0042 - val_loss: 0.0435\n",
      "Epoch 80/100\n",
      "1812/1812 [==============================] - 0s 229us/step - loss: 0.0042 - val_loss: 0.0430\n",
      "Epoch 81/100\n",
      "1812/1812 [==============================] - 0s 222us/step - loss: 0.0042 - val_loss: 0.0407\n",
      "Epoch 82/100\n",
      "1812/1812 [==============================] - 0s 223us/step - loss: 0.0041 - val_loss: 0.0400\n",
      "Epoch 83/100\n",
      "1812/1812 [==============================] - 1s 284us/step - loss: 0.0041 - val_loss: 0.0401\n",
      "Epoch 84/100\n",
      "1812/1812 [==============================] - 0s 218us/step - loss: 0.0041 - val_loss: 0.0412\n",
      "Epoch 85/100\n",
      "1812/1812 [==============================] - 0s 212us/step - loss: 0.0040 - val_loss: 0.0394\n",
      "Epoch 86/100\n",
      "1812/1812 [==============================] - 0s 197us/step - loss: 0.0040 - val_loss: 0.0412\n",
      "Epoch 87/100\n",
      "1812/1812 [==============================] - 0s 185us/step - loss: 0.0039 - val_loss: 0.0395\n",
      "Epoch 88/100\n",
      "1812/1812 [==============================] - 0s 221us/step - loss: 0.0040 - val_loss: 0.0402\n",
      "Epoch 89/100\n",
      "1812/1812 [==============================] - 0s 225us/step - loss: 0.0039 - val_loss: 0.0397\n",
      "Epoch 90/100\n",
      "1812/1812 [==============================] - 0s 198us/step - loss: 0.0040 - val_loss: 0.0398\n",
      "Epoch 91/100\n",
      "1812/1812 [==============================] - 0s 187us/step - loss: 0.0039 - val_loss: 0.0421\n",
      "Epoch 92/100\n",
      "1812/1812 [==============================] - 0s 195us/step - loss: 0.0039 - val_loss: 0.0413\n",
      "Epoch 93/100\n",
      "1812/1812 [==============================] - 0s 190us/step - loss: 0.0038 - val_loss: 0.0410\n",
      "Epoch 94/100\n",
      "1812/1812 [==============================] - 0s 209us/step - loss: 0.0038 - val_loss: 0.0434\n",
      "Epoch 95/100\n",
      "1812/1812 [==============================] - 0s 205us/step - loss: 0.0038 - val_loss: 0.0404\n",
      "Epoch 96/100\n",
      "1812/1812 [==============================] - 0s 228us/step - loss: 0.0039 - val_loss: 0.0419\n",
      "Epoch 97/100\n",
      "1812/1812 [==============================] - 0s 231us/step - loss: 0.0038 - val_loss: 0.0407\n",
      "Epoch 98/100\n",
      "1812/1812 [==============================] - 0s 234us/step - loss: 0.0039 - val_loss: 0.0392\n",
      "Epoch 99/100\n",
      "1812/1812 [==============================] - 1s 299us/step - loss: 0.0038 - val_loss: 0.0389\n",
      "Epoch 100/100\n",
      "1812/1812 [==============================] - 0s 224us/step - loss: 0.0038 - val_loss: 0.0412\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1c26a85950>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "step=7;input_n_stock=50;outputdim=50\n",
    "model=LSTM50(step,input_n_stock,outputdim)\n",
    "model.fit(X_train,\n",
    "          y_train,\n",
    "          batch_size=60,\n",
    "          validation_split=0.1,\n",
    "          epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainPredict = model.predict(X_train)\n",
    "testPredict = model.predict(X_test)\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "y_train = scaler.inverse_transform(y_train)\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "y_test = scaler.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k4BtG/Ns7TX26TLRlQ3Y7UxHb15G/9FBt2KuaDUAOyy6VlHkYoF9Ftn3ruz/u\nb18BEG2Ew8/pzzcOCgUxERGRahNrwuCwzp1Mielc+yUzR6xQJugNREUsmb5GOD386e5XSSztvJu6\nP97TEkgVQkFMRESk2sRaAGhxa/E/4Njnhq4+/vYVfpkHAZz+hKa0TEUsc5/9umL9hO6P+5dDqmAK\nYiIiItUm7gWxVmrznnAM9fGpye2n35i7ZBc93q10EMuEqP7ILG8UDg5ARaywP1ihkIKYiIiIDIZY\nMwCtbg3+Gliqj482BkO5Jw+7qohZA1gRSzqZilgmiPXjYoX9wQqpIiYiIiKDwlcR80/W72sQi0Ry\n86h6CmL2APSvyFbEMkGsPxfraekizRETERGRQZGeI9bq5gexvoalaCRXEXO6iASZINbXkFdKMjWA\nFbGehiZVERMREZFBkTdHLLf7mrMO79NlQsFcFayrz1pm4IJY5snLYHayfj+u2dPjogpiIiIiMihi\nLbgY2ojm5ZFPnnxI364TyE3Q7+qzmYrYL59az5amzj7fqp/dy4rYu7//BJ+5/W/dX6zHoUkFMRER\nERkMyQ5SgQguAQLGcMHiqbz/yCl9v06g54W8M0HsrW0tfOSWF/r+HT7JwjliXQSx9bvb+eOr27q/\n2DAZmhyYpdRFRERk6Dg2TsAbVjQGfvCBhft3nYIgdszM0SyePipvX9DXqGxPe2L/vifNdgr7iBUn\nsV63tOjpqckqaV+hICYiIlJtUkkc4/0JD/SntX5BELvrH44rPsUXxJJ2/7rr5/qIedWsUpmrI5Hq\n3cV6Gpq09NSkiIiIDAYnmVcR2289De+RXxFL9nOZo2RhZ/0SQWybb+Hybh8Q6OnereqoNSmIiYiI\nVBtfRcwwcBWxUqKhXODpb3f9zFOT2c76BUOTsWSKm55Yl33f0pm/AHmenoYmMw49vW83OcSqIy6K\niIgcqDqbwApBuC63L5XECWSGJvtx7b4GsX62sOipj9j3H17F3X/bnH3f3JlkVF2YknoTxL60AUJ1\nPZ5WTqqIiYiIVLLrD4IfL8jf5yRJDcIcscGWKlziqOD49pZY3vtu54v1YliVmlEQ7CLIVQgFMRER\nkUrXsTv/fcrODU32pyIWqe/Hh/suM1k/M++s8AnJwlAZt7sJYr0dmqxww+OnEBEROZCkEtmhSdOf\nJFY3foBuqHeSjkvIMtnwWFgRmz66Nu99vLunNHt6arJKKIiJiIhUGyeJY7ynJvs3RywA04+H077R\n7WnvWzS5H1+SY6ccgoFANjwWVsSSjkM4GODuTx0PeJP3u9TF0GRnIkVnb1tgVABN1hcREak2KTs7\nR6xfFTGAjz7Y4ylzJzdy74ruhv+WAAAgAElEQVSt/fqaHS0x1u5sI2jlnvMsnKzfmUhRG7aIpJ+q\n7L4iVrqWdMy1j9KesHn7u+f0636HioKYiIhINXDd3IQwx9/QdfC/+mMnHsz/PPdOv65x7HceA2B0\nXbjLocmORIrakEU0tP9BrDVu9+s+h5qGJkVERKpB0rfgdrIDO+B1ju/XU5O9FAgYlh48mkQfOuu/\nsqmJ5o7iPmDBgMn2PvNXxB59cwe/e2kzHckUkXTn/W6HJofg5x4KCmIiIiLVwB/EOpuJBRuG9Osj\nQav7pxgLvO+GZzjyW4/w0sa9eftDViBXEUsnMTvl8LHfLAegqSNJpDcVsRJufGJtn86vBApiIiIi\n1SDZntuONREPNgJDUxEDrxt+XypiAI4LF9z0V5o6couF580RS7+u3N6a97lMRSxeqiI2YX6X3/fv\nD63q0/1VAs0RExERqQaJDu/VSUG8hZjlVcSGYo4YeEOKqVKLQ5ZQuEZkayw3b8syBrIVMe91S1Nn\n3vndzhH7yP3QsqVot9PPrv/looqYiIhINUimg1g6hLSGxwED8NRkLwUCJhuwdrTE+MvqXV2em0zl\nByj/Qt6242areJm1JgvnkoXTw5clK2I1I2HC3KLd/V1+qVwUxERERCqVvwKVCWJ73wagKToVGLqK\nmGVyQeysnzzFh295oagPWEaiIIj5ly6yU05R+wr/3LNPnnwIxhgiwUCf5oj5q3DjGiK9/ly5aWhS\nRESkUrm+IJIZmoy3AdCZHpocqoqYFTA4rjfBfm+7N+erM5miNlwcJXY0568Z2RrLVbxc8DV09fZl\nAteLXz0tG6IiQav7pyYL2E7ud2Wn+jaXrZxUERMREalUzZtz23/9ufeaigPw8taOIb0VK116292W\nm3jfFivds+vDt7yQ994/R8zfDi0zNJkJYiNqQtnz4naKX/91Y9F8s65kzjMG7FT1DFMqiImIiFSq\nG4/Lba//i9fCwvaC2AubyxPEjr720ey+rpqnbu2mIgYUD00mUxgDIStX3YslvXD23Nt7enV/yXT4\nigQDVTVfTEFMRESkUvlbVoA3P8z2Qk7CDZX4wOCxSkxG66oi1t15Lm5RZ/14yklP0C/+jrpI72ZR\nZSpi0ZCVN0xZ6RTEREREqkUyBrY3NJgY4mneVomQVPh0ZFfueyW3TqVXBctf9DuedLLrS2Z86pRD\nAHhm7e6iilopmfAVDVqqiImIiMgAmp1ewNpJZiticcpfEett4Nnna0/h5M0R87THbSIhK+8zJxwy\nFoDvPbyK+d94pMeJ+7mKWADXLe5lVqkUxERERCrd9KXeayrh/QMShDh7/sQhu4VSQayrsDNzbF23\n1woUJLGXNzUxoTG/5UTQyv++B1/f1u01X9q4D/CGJoE+rwJQLgpiIiIilWrOuRBugGnHeu9TCbBj\nOMYihcU/nT57yG6lLxWx+VNGdHMlNztZ30kPTbbGksydlP+ZUEEQC1ndR5Z/uusVABqi3pDt7rZ4\nt+dXCgUxERGRSuWkYNRBYKWHIVM2xFtJWTVAcVgZTKUrYqWrTt1Vo/LaV6RzXDLlEgrmXz8YCHT7\nPv+auUA4oiYM5Hfzr2T9CmLGmO8ZY1YaY141xvzeGDPSd+zLxpi1xphVxpgzffuXpfetNcZc05/v\nFxERGdYcGwJBsLxwQSoBe9+mrXYaUDocDZZSk/W7mqvv75RfyAUyy35n4lPSdooqXoVDk+Fg1z+r\nvzKXOa8t3vME/0rQ34rYn4B5rusuAFYDXwYwxhwBXALMBZYBNxpjLGOMBdwAnAUcAXwwfa6IiIgU\ncpLpIJauiG1/Fdp20hEZD3RfJRpofamI2Y7L1FE1HDSmNrvvvEWTAZjYGM1WxB56fTsAScdrX+FX\nGMy6e0DT//RmwnbT+w6Ayfqu6z7ium6mOchzwNT09vuAO13Xjbuuux5YCxyT/rfWdd23XddNAHem\nzxUREZFCju2FsEwQe/J7sP1V7IBXISusGg2mvswRc1yXCY1R/vKFd2f3/fiSI/n+RQv5wQcWZvfd\n/Tdv5YBkyi36WYIF3/fYWzv4xn1vlPw+f+g6Y+4E794OhCBW4KPAg+ntKcAm37HN6X1d7RcREZFC\nqfTQZCC/VYVtvPeFYWUwlVrSsqunJlOOmx3KnD9lBN94rzf4deFRU5kzqTHvWo7jknLc4qHJgmrf\nnS9u4tZnN5RcaDyztuQ3z53LUQeN8vZVSVPXHrvBGWMeBUo9H/tV13XvTZ/zVcAGbhuoGzPGfAL4\nBMD06dMH6rIiIiLVw7EhGM7NEUvLBLGhnCPmlAxAXVTEHMjkqP/77IlFxwO+JJZMB6bCIOZS+tpt\ncZuGaEEwTQfCoGWy4XTYVMRc1z3Ndd15Jf5lQthHgPcAH3JzMXULMM13manpfV3tL/W9N7uuu8R1\n3SXjxo3r8w8mIiJS1VwXNr8A7XtyQ5NpSeMFs55aOgyk7c3F7SBSJcJZZn93IdFfEdvZ4l238AnQ\nTLGtNpzf6HXpdx4rul5mjlgoECCY/p1US0Wsv09NLgO+CJzruq5/9dH7gEuMMRFjzExgFvAC8CIw\nyxgz0xgTxpvQf19/7kFERGRYavbmT7HjNagZhT01twC4bbwBraGsiB1/yJiifd0NTQZKjWWm+atV\na3a2AsWhctKIKBMaI3zn/Pl5+9sTxU9kZuaIhYKGUPp3ckBM1gd+DjQAfzLGrDDG/ALAdd03gLuA\nN4GHgM+4rptKT+y/CngYeAu4K32uiIiI+Lm+wGEM/zbq2uzbTEVsKOeILZw2kusvyA9F3U3W7y4k\n+p9yfHuXt7B5sCCIRUMWz3/lNM47cgrvXTi523vLzBEL+itivVwHs9z6tWKo67qHdnPsWuDaEvsf\nAB7oz/eKiIgMe8l0Q9JZZwDQlAiQcC3CJkWSEFbAYLqpOg2GwipXqouw45+sX4q/WtXc6fX7Cnfz\nBOhV7z6U//MtHN7V9UKWyQbAaln4W531RUREKlF6cW+O+gjgVZHCxquSJdJBbKgVBrGuK2IQ6GVF\n7Pn1ewGYPrrr9Smjoe7jSmY+WDAQyM41UxATERGR/ZcJYsEokL9sUNKEhnRYMqPwyclST1KC15Ki\nu4pYwhfEtqeXIjpkXHdBzOryGOQqYt5Tk9U1NKkgJiIiUol8QezxVTt5bOXO7KEkwbJUxNrjdt77\nrqpOPT01eeS0kTREvNlRLbH00GSw60gS6eYY5CpsYSuQDajff2R1t5+pFApiIiIileiBL3qvoSj3\nrfDmR/0ltQCAlGuGtHVFRkfSGxqdMtJbdDzVZR8xt2QD2IyRtWFe++aZhCxDS3qOWCTYddVrZG2Y\n/7ny2Ox713VxfCEwlr6vaNjqdki0EimIiYiIVKLdq7xXJ5UdAnzH9daYTLlD27oiI5ZuHXHhUd6K\nhvtbEcuIBq1sv7DuKmIAJ84ay6XHTmdsfZhv3f8WB3/lgWyX/WwQ6ybMVSoFMRERkUp0+HsAcKcc\nxb0r8p8YdBy3LHPEOtOBpzZsYQVM0Ryx37+8mYXffISNezq6nSOWEfHN/epNcAtbARK2wy3PrAeg\nNT1UGkt6Q5M16eavJx46llG1odIXqTAKYiIiIpUoVAujZnDYvz5cdMhxixfJHgqzxjcAMGdSI1bA\nFFXEPv/bV7LtKHozRFjYTb8nkWCAuO3QEPXml+1u9bryZwJi5unKmWO7nvhfafrVR0xEREQGiZOE\nQCiv55ZJr7+YcosXxR4KFy2ZysJpI5k9sYFgwHTZWR/oVUVsW/qJyd6qCVvEbYcRNSFaYzb7OhJA\nbmiyJl1hi4YC2SpZxutbmjliUmPFzSFTRUxERKQSOXbeGpPnHzmFg8fVe4ec8swRM8Ywe6JXFbOM\n6XZh7cEIPJl1JzNPWmZyYK4iZmVfO5MpNuz2uvY/u3Y37/nZ0/z3cxsH/J76S0FMRESkTF7f0pyr\nKqWS3kLfGSkbAhZj68OMqAnxnfPnZ5+UdNzyzBHzsyxDqpuFtQfjoc7asDeQl6l2ZX5de9sSRIKB\nbJuLTCA75ftPAPDypiYAtjR1DvxN9ZOCmIiISBm8ta2F9/zsaX7y6Gpvge9vjYUVt+VOSA9NBozh\nrHkTqQlbLJ9yGW+6M3ix9qSyzBHzCwYMb2xtYdX21pLHezM0mfHBY6b16ry6SP5TkZmHBdbvbmfm\n2Lrskk/hghSYaRo7viHS63saKgpiIiIiZbC33Zvf9OKGfbD9NW/nm/flTkgPTdpObmJ+R900zo5/\nhz+sjhfNgRpqVsCwfOM+zvzxkyWP92Vo8l/OOaJX52UqYhmZINbUmWRMfbjL797R0re5aENJQUxE\nRGSIPb1mNx/6z+cBiNkpiDV7B6KNuZNSNgSCJFNOdmK+v4nr2p1tQ3a/pfgfFvjzyh1Fx/tSEauL\n9O7ZwRE1BS0p0kOTsWQqr4eY/5tTjpt9kjNuV96yRwpiIiIiQ+xb97+Z3e5M+IJYxBfEnCQEgtgp\nN9vmoRzd9Lvif2hzw+4OgLzeXb15mODoGaP69J0TGqN57x1/EPP1JPNnwF8+9TZt6X5jnemGtJVE\n7StERESG0N72BKt25OZVxW0Hdq303vgrYtmhSYeglamIVU7rBX9FLDN3K+BLQD2tDwlw5yeO67YF\nRqFxBXO8nGxnfYdIqPT3bdzTTlOHVxHLPF1ZSRTEREREhtC/3vt63vtYwoblt3hvgjW5A6kkbsAi\nmXIJpatLvQk3Q8Vf8crM3UqkckN/M8f13FTVCpg+teGoC+dP1s9EuLidyvYQK3THC5uy27EKDGKV\n87+oiIjIAWBvWyLvfV1yT+5Nx2745mhY8yhsfxU37vXBylXEcn+2H/zcSYN/s93wt8/IDAv6+4od\nku55NpBMwbyze/62mQtuepZY0skfmuzi86qIiYiIHOAKF7c+zF4NmQyx8VlwU/CnfwXA7F4NkH1q\nMhPEJjZGmTOpkXLyV7Jc1yWZcojbuaBz8CAEsUL+NTijXQxN+lViRUxBTEREZAglU/lP7t1ofT/3\npmWL92q8UBFf+jn4I4TS87Ey4acwzJWDP4ilHJdXNjXhuPCTSxZx3MFjqO/lk5ADxf/UZFfK3fKj\nFAUxERGRIdTdskB07st7mxw1C7CzFbFMP6xjZo4erNvrtbxFAFyXPem+aIeMq2d8wdONQyH/qcn8\nwck5kxqpj1gV+dRk+SO1iIjIASSRcpiYDiphkqVPintPVSZC3vBepgI2tt57avDMuRMH+S575viS\nWMpxaU4/mVjU62uI+IcmC1uYjagJEg1ZXs+2CqOKmIiIyBBKphxmT2xge0uMKWZ36ZMS3iT9hFUH\nNBFJD7u9f/EUDpvQwPypI4bobrvmr4h97s4V2e0RteUJYhFfRWzZ3Inc8PhadrTEAa9aFrYC7GqN\nl+XeuqOKmIiIyBCyU2621cIYmnMHQrW57ZQ3zBfHCzWZipgxpiJCGORXxPzqw+Wp8fiHJsc3Rnn+\nK6dl34esADVhi5XbW3lja3Opj5eNgpiIiMgQSqac7Jyv0cYbgnwjeAQsujR3UrwFgDd3ehWcSuof\n1p1IMNCnNSYHUrSb31HA5J44fe/Pnh6qW+qV6vhfVkREZJhIpBzCVoDPWH/gXOuvAHwu8WniU48v\nOvcr93ntKyoxiJWqiNWEe35ycbBEu2joCt4TnrvbvFDbh0b+Q0JzxERERIaQne6U/4XQXdl9+xIW\nGyKHM7vg3ETB0GQlKTUy2ZsWEoOlu4XDA8bQEfcm6ocraL1OUEVMRERk4Nx4PPz8mG5PSaYcRrj5\n85QShNgSCxefm66XmC57xZdPpVXExtVHujxmBUy22WygwpKPKmIiIiIDZecbPZ7SkUgxgfynJRME\n2d5ZnBCS6Zb7bXF7YO5vAJVarLucQ6hj6ouDbIZljLe4Oj30cSuDCsuFIiIiw1fKcelMphjv7Mrb\nnyRIU7w4IDREvaHJ+VMq40lJv30dxT3QQmUc9qvtphoXCBjOmT8JgIZoZdWgKutuREREhrFMZWu0\nsze7zzUWgYDFO3s6is4/d+Fkrj1//pDdX1/UhS2aO/PDWF2kfEOThd30/f5+6UHMnzKC846cQmO0\nPH3OuqKKmIiIyEDbs67k7vZ0EDt092PZfSYYwXZc7nxxE064Ie/8iWVYKqi3bv/4Uv79ggV5+yaU\n6X5f+doZJfffesXR/PSDR7Jw2kgCAcO00bVlazjbFQUxERGRgfYfJ5fc7QUxlwl7X8zttEJceux0\nAF664BmYtBCAdc4kJoyo3CA2Y2wdFy2ZmrdvqBf6zugqXJ0yezznLpw8xHfTNwpiIiIiA8FxctuJ\n1pKntMZtxtCSv9OK8NETZgKwpSMI0ZEA3JU6paIrYlA8HFjOOWLVSr8xERGRgRBv6fGU9rjNdLMz\nf6cVpjE9gbwtbkOoBvAm8I9v7LolQyUaiqcmP3XKIXnvf/iBhYP+nYNJQUxERGQgdO7r8ZS2mM20\nwiAWDGcXrI4lU1AzGgCbAHVlWrdxf80YWzfo3/GlZYfzwldPzb7/u8PHD/p3DiYFMRERkYHQmyDm\nr4gt+pD3Gh2ZrSTFbQdqM0EsWNYGqX11/QXzueToaUPyXf4Gt9U+HFrddy8iIlIpehHENuxpZ7rZ\niVM/AUZ6E/SJ1BMJBjAGnlqzi2Men8Nz4y/m7tRJ3a6fWGnOnj+p2xYSA8m/rnhmAfVqpSAmIiIy\nEHoRxNZt2c0Hgn8hEAhC3Vhvp+NgjCESDPDc23vZySg+vvMC4oSJVuAak4XGN3jz2IZyGDXgC3yh\nSluzqI+qa/BZRESkUsWaejxlfMdqb6NlC9Sl5za53hqI0ZBFLOk9edkaswlZhmAVDLv96Z9O5u1d\nbQQCQ1eZ8hfehvJ7B0Pl/y8sIiJSDTp6roi12+k/u6d8Jft0JJa3RmI0mD8MWfi+Uo2oCXHk9FFD\n+p1DNQQ6FBTEREREBkLr1h5PsZMJb2PKYhg7y9te+EHACzR+kSqaHzbUqrwIlkdDkyIiIv3VvBmW\n39LjabadXpsxYMGoGfDVHRDymrZOGBFl1Y5cI9j6Mq7bWOlUERMREZGcuz/Wq9PsZCaIpatfoVzn\n/IkFzVvryrRcUDUYThUxBTEREZH+atrkvU6cn9vXvsd7bd4MiXZSjkssEff2BYpDVm3BU4flWrex\nGgRUERMREZGsRBsc/XEI+taG/N7B3uuP5sLtF7OtuRPj2N6+EkGsMFsoiB0YFMREROSAlbAdUo7b\nv4skO73WFQ0T4aJb4bircsfs9OT8DU+xqzWOhdeqgkDx/K//d8bsvPfjK3zB73Ky0mOTmR5m1UxB\nTEREDliH/cuDXHHri/27SOt277VhEoyYCkdeljsWa05vGFpiNkG8PmFY+U9IglcB23DdOdn3Sw8e\n3b/7GsZCVoCfXLKI+646sdy30m+qe4qIyAHtydW72NMWZ0z9flZXskFsovfqH57MBLFIIy2dSYLZ\niljPf34L21lIvvctmlLuWxgQqoiJiMgBb2tTbP8/3OariEGuUSvkuu1H6mnuTPqGJnsOYg1RBbED\ngYKYiIgc8Pr1EF5hRaxEEOughg2724la6flovaqIadDqQKAgJiIiB7x+tUNo3wXGgpr0Mj/BXBBz\n7/Um7q9qgnW72pjckA5XvQhiU0fV7v89SdVQEBMRkQOS/2nJQH/+GibaIVKfK6v5JuKb1m0AtLlR\n9nYkyfZs7SaIfff98zltzgSiWuLogKAgJiIiB6Rkysluu/3pYJFog1Bd7r0xMGlh3imt1PLKpiZq\nAj0PTX7wmOn854eX9OOGpJooiImIyAHJ9lXE+tVLLNEB4br8fYsuy3ubSv+5HZncDlYkN4wpBzwF\nMREROSAl7VxFzO5XEGuHcMF8rlB+M1aDd/1JqW0w+mCwNBFfPApiIiJyQEo6uSCW8m33WaI9f2gS\nvKqX/226kWvUjXnzyUTSFMREROSAlEzlqmB2Kr8i1pGw+d1Lm3F7M3mscx/UFnTBz6wpmZYJYg1B\nO7/hqxzwFMREROSA5B+ajOxdCZtySx1d9+BK/vl/X2Hmlx/g9S3NpT6e074L6sbm70sl8t4G0kFs\ncq0LIbWlkJx+BTFjzLeMMa8aY1YYYx4xxkxO7zfGmJ8aY9amjy/2febDxpg16X8f7u8PICIisj8y\nT03OM2+z6P6z4VenZY9t3teZ3f7n/32l64s4DnTshtrug5iFw9RRNRg7lt/wVQ54/a2Ifc913QWu\n6y4C7ge+lt5/FjAr/e8TwE0AxpjRwNeBY4FjgK8bY/ToiIiIDLlY0gti90f+Jbdz30YAIkHvz2MI\nG1PY7HX3WrDTQcuOgesUPzU58115by0c4rYDyU4FMcnTryDmum6L720dkBlMfx/wG9fzHDDSGDMJ\nOBP4k+u6e13X3Qf8CVjWn3sQERHZH3E7Re7PVtpPFsALv6QuEuTEwGusiV7OYak13jE74QW1nx8F\n/3e1ty9T+QoWLBg+fg5Lw/fwycQ/stMdSQCHmpAFtoKY5Ov3HDFjzLXGmE3Ah8hVxKYAm3ynbU7v\n62p/qet+whiz3BizfNeuXf29TRERkTyxpEMDncUHHvhnlm27if8JfxeAD3T+r7f/rsu9oAbwyh3e\nayaIWeGiy1gBw0POMax3J2LhUhe2INYCYT01KTk9BjFjzKPGmNdL/HsfgOu6X3VddxpwG3DVQN2Y\n67o3u667xHXdJePGjRuoy4qIiLBiUxOX/ep5RppWAF5e/J2846ftvSO7Pcte5W2sfjC7L1Uzlv97\nZSuf/e/nvB2FFTHATrfEcNwAAeMwvd6BVLx4Yr8c0HoMYq7rnua67rwS/+4tOPU24IL09hZgmu/Y\n1PS+rvaLiIgMmfNueAaAUbQB0Gk1wqGnlTx3lNvsdc/3ebs9wmfveJnX3tnp7ShREUvYDgEDc6aM\npMZy+acTxngHascM0E8hw0F/n5qc5Xv7PmBlevs+4PL005NLgWbXdbcBDwNnGGNGpSfpn5HeJyIi\nMuRGGi+I7XLq4IJfFR2/3X43IWy4blre/jHGa2lRS9zb0UUQ++gJMxlZF2XB5AZmj0h5B6IjB/An\nkGrX3zUWrjPGzAYcYCPwyfT+B4CzgbVAB3AFgOu6e40x3wIyzVr+zXXdvf28BxERkV7zJul7RqYr\nYtsTtVBTHJB2NRwBnY8XNWgdbdo4xGzhgchXvB3BCHE7xW3PvcMHjp7G82/voT2RojZsgbHATcHW\nl71zC5dDkgNav4KY67oXdLHfBT7TxbFbgFv6870iIiL764u/exWAr5x5CFc+9RFwYHPSm0D/G/t0\nLg/+CYDnoyfSFhpDqfn8AFPM7twbY/Hi+n382/1v8srmJu5dsRWAcQ0R2BOClA33/6N3rhq6io9W\nHRURkQNGynGzIemst7+N5XhPPbZQx8rtLXzNvoJ7UifxvjHv8JeR7ydmu5Bp1HT29+GBf85e6zfh\n63MXbt9FaygJwIvrcwM94xqi3kT+pG+OmYKY+GiJIxERGd7adsLPjoLda4j/5kI+ad1HAIdpm/4P\ngO1mPPGkw6W/fB6AFe6hfGffqQSCYSLhML+u/TBu7Vg+9vJMAF5lVtFXuJMW8qnb/gbA1uZYdv/4\nxoi3tmTr9tzJCmLioyAmIiLD2xt/gD1r4ZmfULvhUa4J3ckrkY97x+a8l0+O/k/idoqORG4eWDLl\nEgwYoqEAt4Uu4OVLlvPo+jifaLyJz9v5nZpePPMPtI6aU/Krx9Wng1iyPbczWDyxXw5cCmIiIjK8\n2ekKVUduyLDBpCd+HXQioVCIx1ftYsaY/GWKQsEANSGLWNJhZ4v3dGRi1KHssHPnvSv+I1pGHkFT\ne7LkV49riBR30o+O6OcPJMOJ5oiJiMjwtXUF/OlfvW07Vny8ZiTrd3vzt1Zub807VB8OYgzEkqns\nk5YJ26GNWl5zZrDencQ77gRSjsu+jvxFvg8eV8c9nzqeaMjKNXuNNML/W6WnJiWPgpiIiAxfT/8o\nt22Fio+PORRv6WPPsTNH83x6sv3YhjDt8RSdyRQJ2+uS39zpVb7OTXwbF28xcMd12dPuVcxG14XZ\n257gsmMPYmRteggymK6IRRoVwqSIhiZFRGT46vS1qkxXxK5IfIHOqSd6+ybOz4YsgIAx2e2x9REi\noQDxpEMi5Z2zt92rfLkEIB3EUg7ZocvxDV71KxLy/XnNVMRKLIMkoiAmIiLDj5OC534BG5+Fued7\nVan0HLEEQdrO/zV8YR0EIzium/3Yhj25SfXjG6JEgxaJlEMs6QWxbc3Fw5u247Cz1Qti49JBLGz5\n/rxm5ogVzhUTQUFMREQq3Dt7Ovjhn1ZzwnV/pqkjQXvcpjOR6v5Df/sNPPQlryP+vAuJ1U3Gbd4M\nQAqLaN2I7OLbly09KPuxmrCV3R7fGPHmeAGtsdKT8cEbmtzREmNUbYhQOoCFg/6KWNR7LbEMkoiC\nmIiIVLTrHnqLnz62hi1NnbyyuZm5X3+Yk/79z91/qOmd7OYLzuG8uLcGkx6mtN1ANmABXHPW4dnt\n3MCkN8wYTQ8xtnTmL3Hkr3ilHNjZGmdCYzS7LxLMXT8bxIK54yIZCmIiIlLRVrzTlN2uj3gBZ3db\noqvTPb4g9k4syl4as++NZWUrVwDGGO759PEAeQFtfEM0+z4zST+jNpI7z3FcdrbEGNcQIRjwolws\n6avYhTJBTHPEpJiCmIiIVKy2uE2TLwTFk043Z/s/uCO7GQkGmG52Zt8Hg8VPT9opb56YP4jVhC1q\n0u9bCoYm68K5pgMp181WxD5ywgwADh1fnzvZpP/UqiImJah9hYiIVKzL/vN5OnzzweKpXBBr7kwy\noqZESwoA25s8z6wzCFkBGslNwi8VxGZPaMAYuPrUWYQsw5odbUDuKcg/vbkj73z/nLFkymFXa5zx\nDRGOP2Qs675zNlbAN8iZuRdVxKQEVcRERKRirdjUlPfeXxF7du3u0h9KdMCO1+GwZfCh/8V2HP6Q\nOiF7OBgqDmIjakOs/8HpUUcAACAASURBVO45nHzYOI4/ZCwfPn4GAAumjSz5FfOn5rrjf+3eN7Ad\nNztHLC+EgS+IqSImxRTERESkYuW1gYBsh3uAxq6qYQ9+EZIdEGsGIJZ0+HnqvOzhkXW9byNRHwlm\nq2J+N112FHd/6ri8faXOA2DmSd7rko/2+nvlwKEgJiIiFWt8Y364+dydK7LbeRPi/VY/7L3ufAvw\nwpvr+3N39MHj+3QPmcB32ITcvK/GaIhZExryzps2uouu+SOnwzeaYfqxffpeOTAoiImISMXyt4Qo\nFOtq4n5mKDCVLHnepFH1hZ/oVmPUm079mXcfCpBtaREsGIKcO7kRkb7SZH0REalKXVbE3PT+lBfI\nMsOZthsgaBzCob796cs8EFAXDvL19x7ByYeNA/KXQ/rK2YdjjCn5eZHuqCImIiIVKxO27v7UcYyL\neNuftu7lUusxYnZXQSxdAXNs/v5Xz7N+l/fEZBwvUBmrb0HsgqOmAnDwuDquOGEmB4/zKmr++WuF\nc9lEeksVMRERqVixZIqz50/kqNbHedF8lNPNv/PF0G8B+FXiM7kTfetFkvKave4csYCn1uSerEwQ\noo44ltXFJP8uvGfBZM44YmL+skVAwDc0GQoqiMn+0X85IiJSsWJJh2jQgrfuB2BxYE322MxNf8id\n+PtPws2nQKIdHBt77oWctuOzeddKpGsPQXpYp7KEwhAmMlD0X5aIiFSsuJ0iErLgjXsAuD70y+yx\npW//DO75B69v2Kt3wrYV0LIVgL+m5tBCXfbc2rDF9+yLAXBqRg/4fTqO2/NJIiVoaFJERCpWLOlk\nn1L0a3VraLD3eQHM8S0/tHsVAO2B/CcjOxIpfsfJ/C51MneHe99HrLdSCmKyn1QRExGRihVLpmg0\nnXn73qhZwkvmiNyO1+/Obe9eDUBnIL/Hl19h24mBYCuIyX5SEBMRkYpkpxxsx+Xv3/xE3v4jTjqf\n74av5q7JXyr+UMs278XUFR9LK1qCaAA4roKY7B8FMRERqUgx22tDMbZjXd5+M+ZQkuGRvB49svhD\nL3pzyLYnavC39XroH0/KbgetgQ9itWHN9JH9o/9yRESkIn329r+VPtA4mUiomU1tVpef3RyLMHtC\nDXHbYfO+Dg6fmOt6P5BDk6u/fRb//dxGLjl62oBdUw4sqoiJiEjFcRyXx1ftAqCz8eDcgVAdTJhH\nTSjAX96J5/Z/7M95n98VD9EYDfGnz7+LN765LO+YFRi4P33hYIArT5xJUA1dZT+pIiYiIhXnqbVe\nI9bLjzuImo45UFMLn3ome7wmbOH4awm1+S0pYiloiAZKBqTBmKwvsr8U4UVEpOL8yx9eA9ILbafi\nYIXzjteEvGHJf0j8Ix2HvgdC+S0pYskU0VDpocvBmKwvsr8UxEREpOJs2uu1rBhZG/KWLCoIYpmQ\n9bBzDOtOubHoeMJ2ugxiqohJJVEQExGpUs+u2813Hnir3LcxsFwX2nZm30aCFqx/EpIdeacl0k9U\nAnQmUxCM5h2PJVNECpYlyjSGDSiISQVREBMRqVKX/vJ5bn7ybeJ239dOrCidTfD9w+COS+GbI+H7\nszjEbPGO7U6vLbn91byPtMbs7HbCdiAYyTu+tTlWFMSWHOTNIwsYBTGpHApiIiJVbmdLvOeTKtl9\nn4W2HbDqj9ldH7Ce4LN/dyjs21DyI3WR3LBjIpWCgAUffTjvnDteeCfv/U2XLebXHz2G0XX5w5gi\n5aQgJiJSpWrDXhjZ0RIr85300843i3YdaraSSDnZJYtYeGne8esvWMC7Z48DfMOU05fCxx7j6VPu\nBGDJjPwnKRuiIU4+bNwA37xI/yiIiYhUic5E/hBkXcTrQLS92oOYnYDpx8Onn6PtSztZ60zmVOtl\nTt3xX97QZHQknHdj3kfG1Ef46jneepNx33wxpi5ha/08AL534YIh+xFE9peCmIhIFbh3xRbmfO0h\n1u5sze6rTwexHdU0NLntVbh2Mrx+T25fxx6YshjGz+G59U2MNG0AHLPhP7wgNvYwKDGvKzMHzD9x\nH6ClMwnAyFoNQUrlUxATEakCD762HYDVO9qKjnUm7KJ9Feul/4JkO/zuCtjyN9i6wnsf8ZYg2tbc\nyZeSH8+dv/FpGDur5KXCmSCWyg9ir2xuZnRdmIaIepZL5dN/pSIiVSCZDhshX6f4WNIbqtzWHCOZ\ncvKOVSzHFxp/+e7cdnQEAM2dSfa6jfmfGXNoyUuFrdIVsR3NMQ6bUK82FVIVquD/tSIiknRcAIJW\nLly0xb1Qc9vz7/CN+94oy331WbKz9P6oF76aO5O8xsEkZ52TO9ZDRSxZUBGL21131RepNApiIiJV\nIJmp+rjpF9elPZ6rLj2+cmeJT1WgttL36dRPBqCl02ZMQy2hD92eOzhuTsnPhLuYIxa3naIeYiKV\nSv+liohUgVS6IpZp3rq9JUZ6FwCNNaFy3Fbfte+CyYuLdl/zeAsAHckUteH0rJkP/hZO/DyMLT00\nmVmqaF9HMi+UekFMFTGpDgpiIiJVIPPQYKZVw2ubm/OOFw7PVay2nTC+uMJ1/7okzZ3J/KWJZi+D\n077R5aVM+pfyq6fXM/frD/PWNi/MxUssbyRSqfRfqohIFcgsyxNPeoErXjAcd/oRE4f8nvZLog3q\nxhbt7iDCwm8+wqubm/Z7ftdZP3kKx3G9ilhIf96kOuipSRGRKmClh+H+sGILsybUFwWxqqgAOQ7Y\nMQjWeMOOiTa4+8r0Qe/n29ES71dftI5kSkOTUlWq4P+5IiKSGZp8dt0ezr/x2aKFvt0Sn6k4dnoF\ngFDUG3acf+GAf0VrLEnc1tCkVA/9lyoiUoUyQ5QZrlsFUSwbxGpz+z72GB9P/NOAfcXe9gTJlJtd\nh1Ok0mloUkSkCv1/9u48Pq6qbOD4784+kz1N2nTf6EJboEDLLjsUBAFBfQVBQRREQFEEQV9FZFUU\nEeQFWZWdgohYlkLZy9YF6L7vadM2TbPPPnPfP86duXNnJmm2Jpnp8/188pl7z7n3zs0n7eTJc859\nTvrQZC7EYUT86tXhMduGTeOt+I4ee4vEAugFUlVf5AjJiAkhRA5ID7QSVfWvOH6s6u/Pg5Otu+B3\nJfDGjWo/JSMWT6nBMbqiAJfDxtM/OLzLb1XbrOaXSSAmcoUEYkIIkQNicWugFYrGcdlt/PK0iTjt\nGvF+HIfx/EXqdcUr6tXpTXZFU258WJmX1beeztH7ZT5VuSeJOXS7W9WC3wUuCcREbpBATAghckA0\nbh2KDEZiyRINmqb1+dDk2yt2JLNRGXamLb9UODC5mRpg/uSk7EsZteexi6cBZsZwd2siIyZzxERu\nkEBMCCFyQCRmjbSag1EKjeE3jb6drH/X7JVc+s8FfP8f87MfMNw61Hj97J1EjQK0iQDzf8/Yn+mj\nyjv93uMGFln2tzaotSwHFLg7fS0h+oIEYkIIkQPSM2LNwUjyyUBN69vyFfe/uw6AJVsbsx9QPtay\n+++1MeZt3A2YGbHEckWdVepTSzuVF7gA2LBLPRAwuNTT5jlC9CcSiAkhRA6IZsmIJdZktGla/y5f\nkShbYYjgYMOuVtVlBGJ2e9d+HRV5nNx/wSE8danKum1vVBmxcp+rq3crRK+SQEwIIXJAermK5lBK\nRgz6dLL+WQcNAeCQEaU0+MPJYUcANn8KCx8HZ4HlnB2NKjjrbkYM4IwDBzNigHoSs94fwWW3YevG\n9YToTRKICSFEDmgJRS37zcFoskSDrY8n68eNN99U52fq79/illnLzc6Z3zM2rDd47ztricbi/OCf\nCwBzCaeuSq2kH86VBdCFQAIxIYToF+paQu0OL7ZmCcSS1eM1MxjqC0Gjyn9daxiAf36yyex0G5Pp\nI37iZ9zDOdE7kl0LNtUn55V1JyMG4Ozi0KYQfa1H/uVqmnatpmm6pmkVxr6madq9mqat1TRtsaZp\nh6Qc+z1N09YYX99r+6pCCLFv+O+ibRx66xye+nRT1v5YXMcftq4t2RyMJGtl9eUgXDyuM2dFZmX8\nZKFWbxkALZUHc+688XwZHZk85jcvL01uO3ogkDpufGW3ryFEb+v2v3xN04YDpwKbU5pPB8YZX5cB\nDxjHlgM3AYcDhwE3aZpW1t17EEKIXHb1s18A8P7q2qz9reFoRlskpuMzamXZbH03Wb+tYcDknLbq\neQBM33I1X25psBwzaUhxctvr7H7dr1vPmdLtawjR23oiI/YX4HqsEwDOBp7QlU+BUk3TBgMzgLd0\nXd+t63o98BZwWg/cgxBC5Dyblj23lT4smZCaEetvlfUTSzAlBDDLSfz2zEkZx3ic3f91VOSRavoi\n93TrX62maWcDW3VdX6RZP0CGAltS9quNtrbas137MlQ2jREjRnTnNoUQIie0NWG9JZg9EPMac8Rs\nmtZna022NTct1FIHevZfMcPL1ROOjYFIss3TAxkxnyxrJHLQHv/Vapo2B6jK0vVr4FeoYckep+v6\nQ8BDANOmTetnf+sJIUTPS19PMiH9icmEMqNWlqb1XUYsEYeNrihI1gYDqHpgYpvnuIwnHBsD5vfl\ncXQ/EHM5ZMK+yD17/Fer6/rJuq5PSf8C1gOjgUWapm0EhgGfa5pWBWwFhqdcZpjR1la7EELs89xt\nZIVaQ7Gs7QMKE0VL+658RSIjdvqUKhxEKaNpj+c47Srz1+gPJ9t6YmgSwKaZVfaFyAVd/pev6/oS\nXdcH6ro+Stf1UahhxkN0Xd8OvAJ813h68gigUdf1GmA2cKqmaWXGJP1TjTYhhNjnxOI6f35zVXI/\nsXZkukRGzJX2ZOEAI+BQI5p9NTSpXkt9Tn7v+AdfeH5EKc2WYwJHXZfcdjlsyZpfqUOTPXX3S2+e\nwdxfntBDVxNi79tbedzXUBmztcDDwI8BdF3fDdwCzDe+fm+0CSHEPueD1bXc987a5H44mv0JxMZA\nGBcRFk58mr8eZ35sJwq6ahrE47C9Mciby7bv3ZtOZ0RQDpuN8z2fADDHfZ3lkCdXq/s8bnwlb15z\nLC67yvy1GiU5KovcDCvz9sjt+FwOmSsmckqP/Ws1smKJbR24so3jHgMe66n3FUKInJU2Nz8UzRyC\n/OfHG7nplWU87rybonWLOKqlHuNv2+ScqMRk/fMe+JitDQHW3f7Vbleq76jE0KQ72og2cH/Y9jkV\nmnV48vNtav3Hsw4awqiKAlZtNzNmR4wp57nLjuyVexWiP5KZjUII0UfcaUON2TJiN72yDIAT7IsA\nCBUMS/YlhioT5Su2NqiA58DfzWZjysT5vSkxpHjBe8fDts+JeDOLqoaNv/ndxjywUp8z2dcT9cOE\nyGUSiAkhRB/Y2RxMDs0lZCuOWlnktux7SgYktxPL+miaZilx0RqO8ey8zfSGuK6jEUczQrJQ1aEZ\nx2zTKwAzcEw87Qnw7qrsRWyF2FfIQLoQQvSBw257O6MtW0bM57KjYbYXFhYltxNPH2oabKyzZsAC\nkexPWvY0XYdrHP8y33f6lSxft4HDbOZDCOv0IYD5NKOUmRDCJIGYEEL0smgbywKlB2KxuM6mOj/F\n+JNtDt3MfCUCGl2HldutTypGe6mwmK7rnGhTSzTxky+wuYZyQfjXlNLKAs8VAESMXzXp2T2AS48Z\n3Sv3KUR/JYGYEEL0ssc+2pC1PX1o8r531gBQprUk2+z1G7BzEDHsyaHJbEOavTRXH12PU6XVs3nQ\nKYwoH4MnFCWKg12UcEXFP1i2tR5QdcIGFZvLHD144SHUNAa55GgJxMS+TfLDQgjRy56fv4WJ2mb+\n7HwAO2oI8Ygx5YQi1oBq3gZV3ac8pS6Xtuxf3OJQD54nArFsC377w700NBlsoFJrZFf5wQDJGmEA\nr1e72KwP4tHvTeO9X5xgWcbotCmDJQgTAgnEhBCi150wYSD3O//KefYPGaWpul8Vhe6MzNaQwBrO\nsH1KaUpGDOACx7uAuTZltmFIfxvV+HuaFmgAIOwqBcBht3HKpEGWSvlHjBlAVYkn6/lC7OskEBNC\niF5W0rCMsbYaAG5yPMGG3x6J22HPmCP2p91Xcb/rXktGLJtYLDMQaw1nX5+yxxmBWMRVkmx6+LvT\n+Pb0Ecl9n0tKVAjRFgnEhBCil31r0++S28fal6B9+GdcDhuhNirr/8r5dJZWM/hKzYhde8p4oPeG\nJu3NarngkKvc0p46MV/TemnCmhA5SAIxIYToZe6Y39qwez3H7n6RaNRcezF1Fe8BWmZGzEsouR1L\nCcSuPmkcx0+oJNLGk5k9zbn9S8K6nabSiZb2gVmekBRCZJJATAghepldD6uNC2aq11WvcXr1PXw/\nbtbj4tnz271GIUHY8CFULyCWNlnfZbe1uW5lT7MFdrGbYrC7LO0Di2VOmBAdIYGYEEL0pmiYIr2F\n/5ZfAuNnwORzk10H6KvNJyBXv97uZQq0APzzTHjkJEtGDFR9sd4KxOz+XdTrhdjShh8lIyZEx0gg\nJoQQPa1uHSx4HJa/Ag1brH0BVZIi5i1T+ymZJA2IRsLw8pV7fIs/njkmZS8zEGtrvlmPiobwbZpD\nkRYgfRqYBGJCdIwUdBVCiJ5Stw6euwBqV5ptJSPgZ0vMfb8RiLmNQKx0eLLLSZTops9wfvnUHt/q\nMMxrFuOnCR9/c94HM2fitv+8d+aINaj1LN+OHUxZWiSWup6kEKJtkhETQoie8v4frUEYQKN18e1Y\n/SYAwoVDVcOx14NN/U1coAXaXP4IAI9ZIoK3fpvcrNQaqKSRM+2fwvKX+d6mG5kaXdT176OjNs4F\n4OXYMaQ/F2nrrdL+QuQ4CcSEEKKnhFv2eEhk52p1aMko1eBwwdfuBaBBLyIaaGr75Cs+gTP/ktFc\nRjNTbOaySRObPuLm+P0dv+9O2N0aNne2LiDmKeNLfWzGHDGAxy+ZzkMXHbpX7kOIfCGBmBBC9IR5\nD8PKWdn7YmZx1fiudTToBTgKK8z+g79DY+kkYtgINO3Kfo1TboGSoTBuRkZXkRbgO/Y51rfMyFF1\n378WVnPILW+xfJsRLDZuJVw8Eh1bxhwxUCsInDq5qsfvQ4h8IoGYEEJ0V7ARXvtFcvdXkUvhpgaY\ncbtqCKfUAWvYwha9kgKXdYqu5immSPMTaKrL/h5H/0S9ugvNtoKBgBqaPNn+hWobc4K6HjrxLEsf\ndcfbK3cA8Pj7K1RDYzWRgiFA7y0yLkS+kUBMCCG6q8GcBxbTNZ6JnQSaBu4i1TjrZ1C7Cv68P77N\n71Cnl2Qs+2P3llBEgEhLG4FYgislELv8AwD+6HxYvfekc+CCmaweeBoDaSAWi1oKw3ZXMBLnYG0N\nd606FV78PtStIVw4xOiVSEyIrpBATAghusuYgF971lNMCz1gtieCpmX/hr8fC83b1OEUUuBOe2i9\noJJB2m7crTWW5mUDzyB+yi1mgy0lgEudvA/Yj78BHC52lE7FqcVg4T/h5lJY/373vj9DIBxjvK1a\n7SxVxWcDlVPVbUkcJkSXSCAmhBDdoetQtwaANY7x1FNs9kUC5nY0mNys14syMmLxygmUay0M32YW\ncr05chHzD7oNW2JYMp3Ta90vHwtAyD1Adb9xrWpf8FhnvqM2xXWdPxjZNwBm3E7DmLMAsk7WF0Ls\nmQRiQgjRHXP/AnN+B8DqJqe1r3xM5vFAABeFaRkxW0ElAI64uYbk47HT8LnaKfeYHvw4VO0uv3eI\ntT3c2vY1OsESbN2wGQ6/grgx9ClxmBBdI4GYEEJ0hzFE13TAxSzY3ABgBlkjj4Sjrs44xUcIX1og\nZvcUZhwHGvZsY36uIqg60Np28IXJzcbSSdY+Pdb+99BBh7aqIc65sclqWNRmS9b0l4yYEF0jgZgQ\nQnRVw2bYsRSGH87U+Scza7Ga3xWMpAQ+p94K5zxoOa2JAnxO69CkLeVpyPXxKtYceB0ADnuWAOeG\nzXBZ2ryvs826YXa7g+sil5l9oWZ6wtmh/wKwUTdLUiQyYjJXX4iukUBMCCG66ulvqtfiIcRTPk6j\n8bTSEQd+K7n5q8ilLBh+MWUF1iWAHJ6i5Pbb8UNYOOy7ANkzYjab+gK48F9w8avWa9k0FsQnEHcV\nwoBxUL0AQnsuNrsnjbZSAJ4s+kGyLRGHSUZMiK6RQEwIIboqUK9e0xf2BiLxlKWKbHYYfzrhcx7m\nmdhJHDF+aMbxqRmxKHZ2tai5Yo49PY6438kw6hhLk92msUEfTPVlq1k0+lJAh6Zt7V9n11p44hz1\npCVA8w5r8KbrTPN/SLVeQWPMndJszBFr/+pCiDZIICaEEF3RWA0tqsApwYaM7rveWGVtuOA5QhO/\nDoDbkeWj11WQ3FSBmFpKyGHr/Md0YjgzGItz18eNqvH+6e3XFHvvdlj/Lrz6c1j5Gtw9Ee4YCjWL\nVb8RyA3TdrG9KUhrSK0WIHPEhOgeCcSEEKKzmmrgL5PN/ZR6Xsfsp5YuemTuBiJpC3iHo2rftadA\nTLcnAx17tjlie5AYzqxpDLIqPszs8LdTLDaRMYtH4bnzQTfufcGjlv6Ho18F4PPNKhuYGIKVOEyI\nrpFATAghOuvuiZZd/Zv/RNPg6hP347Qp5kT2m15ZZjkubARmTnu2QMwcmoxgx29M+N/j0GQWiXO2\nNQSopYy34tNUR8tO9RrMsrB4c01mm9MHnz+hsmINqmjtovLTANi4S5XESGTEJBATomskEBNCiO4o\nGsJnu33ouipb4UoJsp75bDOPfLg+uR+JqrDFlS0Qc5iT92PYCYRVIJZ1sv4e2I3hzKZABIBHIqer\njgeOhMUvwJ3DYdFz5gm6Ds3bMy90zM9UZuzvX4F/XaquXaoybPV+de1kHTGZJSZEl0ggJoQQnZE+\nz6p5G99+6FMACtyOjHITt766IrkdjqngypltaBLQNdUeTQnEsmbP9iCREattVhP+V+rDzU6j+CwL\n/wnv/xF+VwJr37ZU/gdAs6tALI3dV47HaaMlMUcs+dRkp29TCIEEYkII0TlBY/L7YZdndIWj8XYD\np1Bijlgbx2h29TRiFDufrFfzubqWEVPnPDJ3AwCNFKrACqDJWCuycQu8e5vafvo89eqrMC9y3iNg\nd8JNDfCNx5PNXreDIo+T5qDKiCUDMYnEhOiSdtbOEEIIkaFunXodfSxUTgBvKcfPr+S9VbWcceBg\nFmysb/PUM+6dC8CIcl/2AxxuiAbQbQ4wasJ2Z45YqtCPF+C+/2CzoTGz5EbLlYso3D4Pxp5oNmoa\njDsluetz2SlyO2gOqoxYXMpXCNEtkhETQojOaNioXsvHwPRLYcp5DC/zUeZzMqjYk3zasT1jBxZk\n77AZfxtrZtX97mTEUj24KAbXb2j3vKaI3RqEJbiL0Kd8kzsi5+N1OSj0OJi1uIbF1Q3cM2c10LUh\nVCGEBGJCCNE5iSKuvgHJppiumxPkjSG7VLG4TjSllEVbQ5P4dwEQt5sT9+Px7Ie2J1t5jF0tIfCV\nw41bYdRXVGPF+GT/zZGLzOWKsvhx8Ar+HvsaPpedxdVqePasv33E58b6muVpKwUIITpGAjEhhOgM\nvxGIeUuTTbGYnhwOHJ5l2LG63p+cHwag7aHWw/v2o5LbreE9Z9jSuR32jLZkkOUuhPGqBAWFg5L9\nj8dOIx6H5duaeHfVzozzX1+qnqr0Ou2cvP/AjP70JZuEEB0jgZgQYp925B1v86fZq/Z8YEKgXtXX\ncpjL/ETjenI4cMbkKmZfcyw/P8XMNq3f1WoJxPZEd3qT26Mr2hjGbIfbaX60F7hUUBZOff+JZ4C7\nhGVjf8jXQzdzefhngEZc1/nqvR9yyePz27y2x2nj+tMmZrQn3kcI0TkSiAkh9lm6rlPTGORv7661\ntH+yro71tW0skh3xW6rgA8TicUvZiglVRfzkpHG8c+1xADz96WZagh3IbF21AC5+NZld++6RIxlU\n7OnEd6SkDn1+fMNJ2DSoNdauBKB8NNy4mU84gC/0ccyOTwdod2hyaKkKDo8ZV0mRJ/M5rz1l+YQQ\n2clTk0KIfVbAqF6fau3OZs5/WNUF23jnGZknRYPg8Fqb4jr2LIFIidcJwJwVO3gvy3BfhopxUDEO\nh/19oOvrN6ZmxEp8Tk6fMphl2xozjkuPu9oLxA4cVkKB287QUi/+LgyXCiGyk4yYEGKf1RjInFj/\n6Nz2nyxUgZjb0hTX9axPKiYCMVDBGsANp2cO66VLZMS6UroCMueIjRzgY0t9wPLAAJhLLiUEI20P\nn6bWSPO5rH/DF7nlb3ohukr+9wgh9km/fHExbyzLXNZnRLk57KjreuaQWzQETutwYTSWPRBzZHk6\ncv/BxXu8t8QwZ1dKVwC4056aLPO5iMV1ApEYRSn3FI1ZM2DZnvhMCMfi2RcrB2b+6Mgu3acQQjJi\nQoh91PMLtmTNiKXWAVu6Ncvi2NEgOKyBWCyuZyxt1JYBHXi6MFEKo6vV6tPLYyTuLT3wiqbVxmgK\nZA45rt3ZwjsrdxCJWVcNeNuY/3bc+MoOBZdCiOwkIyaE2Ods3NVq2fekzKlqSQnEYtnmTEWCRDQX\nd/x3Ob88fQJ2TePtlR2Y/2XoSL0tpxGAZZt31hE2m8YVx4/llEmqPEUiMxeJtz80+aOnFmZc6+S7\n1Xy1Q0aUUpAyBDm2spDnLzuC/YdIECZEd0ggJoTYZ8TjOi3hKFc/+4Wl3es051SlBmKJhbctokE2\nNLt4bM0GovE435o2PPOYdlQUuvd4TGJIsqtDkwC/TCkxkQjsMjJisbYn56cLRuKU+qyZtsPHDGjj\naCFER0kgJoTYZzzw/jrumr2K4rTyC6lzn6rr/cntlmzLFYVbCWlFADzxySbGDypq9z0/vP4EYnEd\nfzjG0FJvm/OsUiUSYd0JxFIlMmLpgVck1vHaZrUtobbXyBRCdJnMERNC7DNeWKAWum5Kq+nVGjIz\nX+tqW5k+qgyAUEsd/O0wWDvHOLAOdq3C7yxLHr96R3O77zm83MeoigImDSmmxOds99iERNmKngrE\nnMYcsfShyUhMKVZ/VwAAIABJREFUx6bBredMyThnR1OQW2ctT+7XNocYWLznbJ4QonMkEBNC7DMS\nAc5F9je53vFcskhpSyhKMBKjMRChtjnE1OFq+aLpn1wFu1bBKz9VF1jwKADbvClV82ut8816QmJq\nWlfriKVzGJP/U4dad7eGicbiVBV7GDUgs3r/r15awiNppTyyHSeE6B4JxIQQ+wy3MRfsFuc/+LHj\nFd6+5ihuNOp6NQYi1BnV5/er8DJB28ygemPyurEYN41bwF3MFxVnJa+5zqjAf/8Fh/TYfX6yvg6A\nTXU9E+Qlnpo88765fLimltU7mjnklrd4YWE1ToeNbIm3bA8gnDp5UOaBQohukUBMCLHPmB6Zz7PO\nW5P7nl1LqSh0M1Vbi7ZyVnI9yEM3PMhs9w3midEg7N4AoWb0woE88enmZFdNY5Dpo8o448DBPX6/\n9f5wj1zHmVJa47+LtrGtIZDSZ7OUyUhdIzNdIoMohOg5MllfCJH36lpCDCh08/uWmyG16Pz2JXjd\nQ3nZ/Vt4DQLH3cOV9o8ZvvG9zItsXQihFqKOwoyuIk/H5n51VraCsN3lcdotdcYGFLgsQ6DpxWAB\n/vTNgzhiTLmsJynEXiAZMSFE3lq0pYG/v7+OQ2+dw+zF1cn26LE3oju8ULeWceufSLaPfP8arnPO\nxB1MG5azOWH7YqieT9iu5kmdMKEy2V2wl5b4CWVZC7MrAmFzkn44GressVlV4iERl/lcdpqzLE4+\nqNjNsDJ5YlKIvUECMSFE3jr7/o+44/WVACxevQaA/w7/BY4Tb0ArHgy71jDui9vbv8j3ZkHlBPjo\nrxBswFGvrvOj48Zy09cm7ZX7TqxH2d7aj52Rukj3f77cxpwVZqBZ4nUmM10+lyPrMkfpa0sKIXqO\nBGJCiPwSaoF4ZgDjq1+lNoqMuVyFVbBmdsZxc2IHUzP9l5wVuoUl8VFsK5gIA82Aa9HEawGoLHJT\napSjiMc7Xhi1I06YMBCAURU9k4VKzWYFIjGenWfOcWsJRZNDk26HLeuyT6krDwghepb87xJC5I83\nfwN3DIU3brA0a8S5cusvASgdfahqHHF4sv+S8HXJ7R9ErqPx0KtYrI/la+Hb2ea3g6882f+ZV62x\nOLTMmyxwOm2UWVesJ0yoKuLZHx7B/57RMxm3I8cOYPY1x2bt03VzKSWXw0ZTWiB2/mEjGDew/aK1\nQoiuk0BMCJE/Pr5Xvc77e7KpED8vum429weOVBtf+UWybWFcPSnYUDAGALfDnNHvsNvAYRQy9ZRQ\nG4hR4nXidtg5dGQ5C//3ZC4+alSPfytHjh2Ax2nf84EdNKEqezAV1/VkJX+nXeMnJ41LBphjKgu4\n49wDOrQagBCia2TgXwiRl6K71gMww7aAQ21qXtfdkW9wjs9YdNtdCGfdR2T3FprmFHBU8F7OmjoJ\n6nbicdrwOG0EI3GCkRg4POocbznhaNzyZOGADqwd2Z+ljqo67TYOHlHGB9efQH1rGLcMSQqx18n/\nMiFE/vBVJDf1D+4CwKOZtbjujZ1LiTel1MQh38V58q8B2EYF728KMqTEQ1Wxh6cuVUOXgUgM7Ebw\n1lpLOBrPqwxRRaGLsLHmpDOlrEVZgUsm6QvRC7r1aaJp2u80TduqadqXxtdXU/pu1DRtraZpqzRN\nm5HSfprRtlbTtBuyX1kIITopUK8q4I84EoCYTWWqful4DoDbI+cDWAOxNNX1foaWedE0LVmSIhSJ\nQWutOiDcQiiWH4HYHecewB/OO4DrZ0wkYhSyde2FumVCiPb1xP+6v+i6PtX4eg1A07RJwLeBycBp\nwP9pmmbXNM0O3A+cDkwCzjeOFUKIrtv8Kbz3B7V93C9hwH54vnycjZ4LKNb8+CsO5KHY14DsRVL/\nccl0AJqDUSqMocbE/Kzq+oCqrA8w+VyVEcvRgOWtn5kT9qcMKeF/po/A67ITianxSadDCrYK0dv2\n1qfJ2cBzuq6HdF3fAKwFDjO+1uq6vl7X9TDwnHGsEEJ0TSQAj82Azx4AZwGMPhaaaiyH+A6/uN1L\npE7OH1OpCraWF6jhyFtfXUFjY6PqHD8jp4cmxw0yJ+wXecxhx+Hlaumi0yZX9fo9CbGv64kJAFdp\nmvZdYAFwra7r9cBQ4NOUY6qNNoAtae2HI4QQXTXvYXM70sp1/1rKXZG0xbJ96unGaBv1vlLrZB08\nXJWiSB3CXBkbrD6oykYTieVuRixVaiA2ckABi246lWKPzAkTorft8dNE07Q5mqYtzfJ1NvAAMBaY\nCtQAf+6pG9M07TJN0xZomragtra2py4rhMgnK2bBW7+xNL2wsDrzOG8ZAwrdDCr2ZL1Makassijz\nKcjn3d+AS+cQHDyNj9fVUdsS6t599wPp62OmVtgXQvSePf75o+v6yR25kKZpDwOzjN2twPCU7mFG\nG+20p7/vQ8BDANOmTevZstVCiPyw+vXk5oL4eEq/cS88s5sW3UOhFjSP85ZnOTml29V+IFbnj8Hw\nw3h3iRry3FTn7+aN971cHV4VIt9096nJwSm7XweWGtuvAN/WNM2tadpoYBwwD5gPjNM0bbSmaS7U\nhP5XunMPQoh9mG7+jfaN8E2ss48G4Jvhm1g8/irzOF/7gdjgEk/W7YTWkFqrsTXcM4twCyFEQncn\nBPxR07SpgA5sBC4H0HV9maZpM4HlQBS4Utf1GICmaVcBswE78Jiu68u6eQ9CiH1RzSJYqZLwf42e\nC2hc/uRCAFboI3nKdTSXx//FWFvNHjNiHqedw0aVM3FwUdbhuQZj2Z+AsXj2u784vue+DyHEPq1b\ngZiu6xe103cbcFuW9teA17rzvkIIwd+NUgxFQ/hL7Tcyunc2h7gg/GvmfMNJkWvPi2fP/NGRGW0O\nm0Y0rtPgV0VhAxGVERuYZfgyVzx44aHsbg3v+UAhRK+QSQJCiNwWbLTsjqlQ5Sd2tYTYQTnuqd/q\n8qU/uP4EjtmvggZ/BF3X8RtDk94eXAOyt502pYoLDh/R17chhDBIICaEyD3BJnP71N9bugYUqvpf\ntc0h3A5btyalDyn1cuz4CqJxndqWEIFIDLfDhs0mTxcKIXqGBGJCiNzTtE29nvcoTP+BpavMWNR7\nV0s4o0RDVySyYIfd9jaBcAyfK3ezYUKI/kcCMSFE7mkyaoUVqzrRqYVIE4FYLK73SIHS1GG8Jz7Z\nlNPDkkKI/kcCMSFE7klkxIqH8NAH62gKRpNdZcbSRGCtHt9VA4us5Sw8khETQvQgCcSEELmncSug\nsds+gNtfW2npSqybCLRZSb+zzj14aHJbl/LSQogeJIGYECK3xGOwZCYMmkJMy5wDVup1kZhLP3Zg\nYY+8pTNlbckNu1rbOVIIITpHVngVQuSWncth93o46z7CsXiy+ftHj2a/gYXMmDyIxNreA1KGKbsj\nkvI+QgjRkyQQE0Lklvf/qF4HTyUcNQOkuK5n1Mcq7oGnJgEicRmPFELsHTI0KYTILTuXQ9loGHyg\nJRA7/7DMIqXF3p75W9OTUovsPVneSAjRgyQQE0LklHhTDf4xp3LVM58zf+PuZHupLzP71VMZsV+f\nsT/lBS4OH13OKKNyvxBC9AQZmhRC5I5QM7ZIK/d82sSsWA2zFtcku7IVWi329kwgVupz8flvTumR\nawkhRCrJiAkhckfzdgB26GUZXQWuzL8reyojJoQQe4sEYkKI3PHRPQDsJDMQy7b+Y0/NERNCiL1F\nAjEhRO5Y/z4Aa+LDLM2zrznWsn/wiFIACt0SiAkh+jf5lBJC5AzdN4D368vZRYmlPT3z9dSlh7Ox\nrhWHXf7WFEL0b/IpJYTIDfE4Ws2XbIlXZnQVpc0FK3A7mDykJOM4IYTobyQQE0L0fy074d6pADTj\ny+gukIW4hRA5SgIxIUT/98Fd0LAJgGdiJ2V0a1rmRH0hhMgFEogJIXKGf/DhVOvWockJg4r66G6E\nEKL7JBATQvR/8x4CYM1XZwJw81mT+c2ZkwAIRmN9dltCCNFdEogJIXKGP6yCrnGDCjln6hC1PbCw\nL29JCCG6RcpXCCH6t2hYvX7lFwQiUQB8LgcDCt08+8MjmDK0uA9vTgghukcCMSFE//bSD9Wrt5S7\n31oNmIVajxw7oK/uSggheoQMTQoh+rflL6vXEUeydGsTAMPKvH14Q0II0XMkEBNC9B/xGDRssbYV\nDYGpFxIdfEiyyeOUumFCiPwggZgQov/46B64Zwp88n8wVy3wTaQVXAW8tnQ7AJcfN6YPb1AIIXqW\nzBETQvQfmz5Rr7NvVK/HXANhP7h8NAcjAJx90NA+ujkhhOh5khETQnTLzqYgVzy1kCYjUOqWggrr\nvn83xCPgLMAfUqUrhpfL/DAhRP6QQEwI0S1/mbOG15du55Uvt3X/Yq4C6/72JUa7j+aQKl1R4JJE\nvhAif0ggJoTolpBR2f5/X17avQsFm2D+I9a2J85Sr04fLcEohW4HNpusKymEyB8SiAkhumzuml28\n9PnW5H40Fu/6xda82WaX7vQxd20tYysL2jxGCCFykQRiQuQ5XddZurWReFzv8Wvf9toKy35TMNr1\ni22Z12bX2gad1TtauPCIkV2/vhBC9EMSiAmR55ZubeLM++byh9kre/zaLof1I6TBH+7ahVa+BvP+\nDt7yrN01LWr4UyrpCyHyjQRiQuS5utYQAJ+t393j13bZNY62LeEQTS091BDo4pOTH/xRvQay32Ni\nse8ij7Nr1xdCiH5KAjEh8lwwEstoa/CHCUe7MZ/LYNM0nnbdwUvu3wGwsqa5axeKpQxpXjkPLnrZ\n0u2PqHtNrDEphBD5QgIxIfJcIpukGQ8b6rrO1N+/xVXPfN6t666rbaF6w6rk/t3O/+NX/17StYvF\nUoY0KyfA2BOg2Czcuk2rosBlxy5PTAoh8owEYkLkuUQg9vXWF+B3JTQ0tQDw5vIdXb/oilnsuPcU\nPvL8NNl0rn0uz7t+37XrxUKZbV+7Fw78NlyzlHd3FTNUFvoWQuQhyfMLkecCiUDM/wIAO7Zvbe/w\njnn+OxyVZd3tw20r1cLdtk4syq3rEGzMbB93svoCNtWt5JRJVV28WSGE6L8kIyZEnmsxKtLHUcFR\na30NAE57J4f5ti+BR2dAw2Zr+zcehxu2mPvZgqr2NNdAoL7Nbl3XqfdHKC+QifpCiPwjgZgQeW53\nq5p/FcIFgL5T1f6Kdrau2CtXw5ZP4Z4DrO1TzgVPMS8M/7Xa72wg1pAI4jQ47Q8Z3U2BKLG4TpnP\n1bnrCiFEDpBATIg8lyhfsU0bBICnVk2o1/XsT1S2yeFptzvuLlEbL/0QXri449dt3aleL3sPjvhR\nRvdH63YBsP/g4o5fUwghcoQEYkLkufrWCDc4nmFqXK0FGQ+1JPvW7mxp67RMNuuU0vWOscx1fyW5\n7yosUxvV82HZvyES7Nh1m7er18JBWbs/XLOLEq+TI8ZIMVchRP6RyfpC5LlYuJUfOWaZDSmB2KY6\nP1OGlrR/gUADfPkMtNZamk9suQWAjcZ+aVmF9bxQEzjbz6IBULcWnAVtBmL+cJRSn1NKVwgh8pJk\nxITIc8XB7ZZ9LdLCXZ7H2Oi5gO1rv4CWne1f4LXrYPaNULsSpn2/zcN8JWkZq47OFWvaCqXDwZb9\n4ygQjuFxdOIpTCGEyCESiAmR5w4KLbDsuyNNfJM5AEz58mb40zj4/MnMExc8Dhs+hN3rk01+ZxkX\n+h5kevD+jMNt3lJrwxNnd+wGo+F2558Fo3E8LgnEhBD5SYYmhchjDz//b66OPGZpGxNZk9w+QNug\nNl65Cg6+0Cy/DzDrGvVaOjLZdNsH9cyNmZPmH7zwkOS23Zs2mb6pg/XKYiFwuNvsDkZieBzyN6MQ\nIj/Jp5sQeeyS5ZlDiQ7MJyV9WkpF+5TMl0XTtuTm+/GDLF0ep5mpctk78HFStw5CaetRRkNgb7s0\nRSgSs7yPEELkEwnEhMhDK2qaGHXDqzg068Le78SmJrcbCsdaT2rdZW7HU86LR5KbW3XrPDBbSgbN\nlZK18utuogVpk+91He47BJ4819oebT8jFojE8Djlo0oIkZ/k002IPPTNBz+x7G/2TeHVc1dyW/Q7\nybaAd7D1pJaUSf1ha1mL+QPO5vP4fuhpHxmpdchSM2Jz41OIa2mV8CN+9Vo9z9oeC4O9vaHJOF7J\niAkh8pTMERMizwQjseSyRglRh49RFT7W6UM5LHg/t31tHJPWPAipFSnCfnP7k79Zzn9z8I94eKu5\nDNGlx4ymwGXnxIkDk23OlIxYMz5iqdcD8O/OvNntS9QSRxXj2v1+ZGhSCJGvJCMmRJ5JLPKtYQ4v\nVg0ZzuQhql7YTsrwDhzL4KMvtJz3xpcbuOjRz9TO+9alhh5bsMsy9Fjmc/LzUyfgSMmCpa5d2aT7\niIYC1hvz11n3m7bBg8eo9rSMWGsoysZdrYAEYkKI/CaBmBB5pjWssmHFDjMr5jvse5Zjygqc2Cqt\nWaj5a7by4ZpdpIthI4adcDTOFcereWWBLEsjue12duuFqh83bsLWA7Yvse4ba14CYFfDmPG4zq6W\nEDe+tITj//QeO5uDBCNx3DJHTAiRp2RoUog8k8iI7VdqhxZ4d/S1nDDmOEBNqA9H45QXuKBkBNc4\nfk3U38TfXPdRQJAf21+GtW4oqExW0rcbmbW7v3UQQ0u9PPDeOs48cEjG+zrsGmeEb2Wqto4xWg0u\nLQbxGNiMbNbqN8yDYxHrU5rGgwLPzd/Cr/5tBmzzN9QTjskcMSFE/pI/M4XIM9X1akjwV+7nARhU\nYS499KvTJwIwoEANBR4143xmxY8grmv83Pki1ztnwlPnQdmo5Dm1tkoAvn7wUA4fM4D1t3816wLc\nXqedan0gs+JHEsSYqB8xhic3zoWVKcssNdfAqtfAUwLDDoOTfgPAkq3WavxXPvM5gAxNCiHylgRi\nQuSZ+95ZA+gcWqcCn0kjzTISFx89mo13npGc7/Wt6cMBDZumWy8S9oNNBVN1tgq+Mq4CzShVYWtj\nzUebTWPjnWew8c4zmDrGeCIzGmRFTRM7tm6wHrx7A9Qshv3Pgh+8BYMmA/DsvM1Zry0FXYUQ+UqG\nJoXIMxOqitmyeaPZEA21eSzAwSNKIX25yYgfCgdC01YicZ1ijzPruW2xOb0AXPvMp1Rt/A/FziiX\no+qYHWtfimPdO6pEhq/ccp6mqXJj6byyxJEQIk91+89MTdOu1jRtpaZpyzRN+2NK+42apq3VNG2V\npmkzUtpPM9rWapp2Q3ffXwhhpekx5nt+bDYUVLZ7/MzLj2SFPsraGAmA0wdAKBpXwVon2N0qELt4\ny6+5zjmTy3kJgN9HL6LOPRTq1kI0aFljMh7PEoEZCt2dCwSFECJXdCsQ0zTtBOBs4CBd1ycDfzLa\nJwHfBiYDpwH/p2maXdM0O3A/cDowCTjfOFYI0QNaQ1H0YJPZcPb9MO6Uds9x2m085/mWtTHihyGq\nCv8/ojM4ef9BWc5sm8OlArEDbBut96d7qLUPUoEYWCrq+yOxrNkwAJ9bMmJCiPzU3YzYFcCduq6H\nAHRdTwxwnA08p+t6SNf1DcBa4DDja62u6+t1XQ8DzxnHCiG66b1VO5l802xWbdhkNg49tEPnLiw8\nlo3xlGAr4ofSETx9+mJmxY/sdCDkcPuytgdws902CGpXGQeaGbGWYDTrOWBdSkkIIfJJdwOx8cBX\nNE37TNO09zVNm260DwW2pBxXbbS11S6E6KaLH58PQLw1pYK9q6BD5/pcDn4auVKdb3dDPApOL6GI\nKl3htncuEHN6sgdiLXip0QYBKvV151sb+PObKihrDqo1LS8/bgzDy728+4vjsbfxYIAQQuSLPQZi\nmqbN0TRtaZavs1GT/cuBI4DrgJma1jN/umqadpmmaQs0TVtQW1u75xOEEAAUa63mjquwQ+fYNFik\n78cj0dOxxYzJ/U4foagRiHWyoKozS0bs0ejpTBpcwrIWs/RFLNTKfe+oYcpmY1mmI8YM4MPrT2R0\nRQEfXH8C354+nCPHDMi4nhBC5IM9PjWp6/rJbfVpmnYF8JKu6zowT9O0OFABbAWGpxw6zGijnfb0\n930IeAhg2rRpbc/iFUJYeEl5SrKDgZjboTJeYVImxTt9hFpVcdjUBb07dD2vmYmLOItwRppp0n2c\nNXUIs98oAWNq2ACtGQBd12k2hiaL3ObH0tBSL3eed2Cn3lsIIXJJd4cmXwZOANA0bTzgAnYBrwDf\n1jTNrWnaaGAcMA+YD4zTNG20pmku1IT+V7p5D0KIFN7E0kKDpoDD1aFzijwq+Amn/m3mKiAcjeO0\na23WDmtLaiCme1U2K4CLSYOL2aqbBWarje1rX1iUnCNW6JGqOkKIfUd3A7HHgDGapi1FTbz/nq4s\nA2YCy4E3gCt1XY/puh4FrgJmAyuAmcaxQohuShRp9WlGRuw7L3T43KtO3I9RA3yEdTMj9vDCRkLR\neDJb1hnulDlitkJVPiOAm6oSD7WUJPuejp0EwEufb6UxoOaIFXWyZpkQQuSybgViuq6HdV2/UNf1\nKbquH6Lr+jspfbfpuj5W1/UJuq6/ntL+mq7r442+27rz/kIIJRKLEzbmcyWHJp3ZJ8xnM7GqmP/7\nzqEUaoFk2+urWwhFY7i7UNXe60t5SKBAZcS+f/QoynwudGw8MfwW5p05Bz3lI2h5TSMFLjtVxZ70\nywkhRN6SdUOEyAOJSfXQtUAMoMBtZz/NnLJZrVfy1Keb8RuLiHeG12fOTbMVqOHH0UU6pT6V7frt\nmrHUOKwLhz/16WaGlHrlSUkhxD5FAjEh8kAkGqeKOgC8WoiIbu/w/LCEQcUe3oqrumMnhv7ETsoA\nmFBV1On78WUJxAi34EyZ9F/fGs44b83Olk6/lxBC5DIJxITIA46Fj/Cp52quPyiMjxCaUdm+MzxO\nOy/Ejmds8EnW62a26pHvTev0tWypdceOuAJGHg3TfwjAZceOwe2wUV0fwOO0cee5B3T6+kIIkS/k\n8SQh8oBn4UMATCkJcuxhg2B1x8pWpPO57PhTElX/uuIoKgrdbZ/Qnus3wM7lUFQFl7yWbC71OQlF\n46za0cyIch+OTpbGEEKIfCKfgELkumgIZ+MGADx6AMJ+cHY+IwZwz/+o9SUTWarRFR2rzJ+VrxxG\nHZPR7HWqbNnqHc2MKC/AkTInLNEnhBD7CsmICZHrmrcnN90xP0QCHV7aKN2pk6vYeOcZAHz7sBE9\ncnvpEvPEdjSFOH2KOTm/otDNf646eq+8pxBC9FeSERMi17XsSG66Yy0Qae1yRqw3uFLKYfhc9mQg\ndujIUoaW9t/7FkKIvUECMSFy3Zq3kpsTF90BLbWdLl3Rm1KXS/I47UixCiHEvkwCMSFyVTQED34F\nPvgjMWchq+NDVfvOZeApbv/cPpRawkLmhAkh9nUSiAmRq9a9A9sXA7D5gKs4J3yL2ecuaeOkvue0\nmzkwj1M+goQQ+zb5FBQiV/nrkpvbh56CHw9xu1FqwtN/A7HUOWJupx1NxiaFEPswCcSEyFXBJvV6\n/Qb8vuEA6MlArP8OTabOEdvWEGjnSCGEyH8SiAmRq0LN6tVdTCyuA2ALG22Fg/ropvbMmZIRO2ni\nIHS9D29GCCH6mARiQuSqYCM4C8DuIG5EMxpGVFM8tA9vrH2JAq4HDS/lgGH9dwhVCCF6gwRi+Wrx\nTHjsNCTdkKfmPwqf3g+lakgyFk/rL+m/gdikIcVcesxoHrroUEu7JoUshBD7IKmsn69eUgss01id\n/GUt8sirP1evZaMBiKUH3MVD6K/cDju/OXNSX9+GEEL0C5IRy3e716vXWBRqV8OTX4f6TX17T6Ln\nGEsZ6UYgtvvwX6rhSk9pX96VEEKIDpJALB+FW83tYIN6ffQUuH+6qj31xZOqrXaVDF32N+vfM4Pn\ntsRTxyHVzy8xWb9p+k/h19vIpZoQ4wYVAnD8hMo+vhMhhOh9MjSZj3auMLdba1Wwte1zs+2Du2Dg\nJHjxEvjmP2Dy13v9FkUbnjhbvf6use1jEsE1QMtOwAzEEus25pL9Bhax6KZTKfbIx5EQYt8jn3z5\nqLXW3H71Wti1NvOYt3+vXrd9KYFYfxGPdey4XavN7YhfnWpkNm05GIgBlHidfX0LQgjRJ2RoMh9F\ng9b9zx5Qr4VVcOkctV2/Qb227Oi9+xLtSw2gE4FyuvmPwGMzzP1YRL0Yo5X2HBqSFEIIIYFYfoqG\nsrcfdx1Ujre2NVbv/fsRe9a8Hf48wdz/8M+wfan1mKYaleFMFY8C5lOTNvkfLYQQOUU+tvNRekYs\nwVlgXYPQ4YGaxRAN9859ibbtWpPZ9uDR1v2mrep1whlwjpHlNDJiiacmbZIRE0KInCKBWD5qKyPm\n9Fr3D7sMQo1QPW/v35NoX2K5IoCvP5T9mM2fqNfjfwljTlDbA/cHUibrSyAmhBA5RQKxfBRpYyFl\np8+6P/pY9dq4de/ej9izwG5z2+XL7A81w5v/q7aLBkPxYLj41WRmLLnWZI5O1hdCiH2VBGL5aE8Z\nsaHG0jIVxnyx1CBA9A1/nXq9dE6ySCsADxwD1Qvhvz8123wV6nXUMeBWNbgST03mYvkKIYTYl0n5\ninwUDYDNCT/5Ata8aS6Hk8iIXfACVM+HkmGABn4JxPrU7g3w1m/V9rBpoMfBVQjhFtixBB45EbSU\nv5myzMiXpyaFECI3SUYsH4WawV2k1piceIbZnsiIFQyACaeBzQ7eUgjU9819CmXjXHNb09TP5bQ7\nrMfoRqR1yHezXiIuT00KIUROko/tfBRsNJ+OLBxktqdP1gfwlsvQZF8Lt2S2DTk4+7Gn3Zm1OR6X\npyaFECIXSSCW40LRGMfd9S6zl203G1MDMU0DmzECnT5ZHwi5StBlaLJvGcsUcckbZlvZqMzjxp5k\nnT8G7G4Nc/+7a2kIqDIWMjQphBC5ReaI5bgdjSE21fm5+pkvWHrzDALhGCWpgRiYw1ppGbFZi7fh\n2qpxgmcykCZIAAAgAElEQVQhzlgE7LLMTJ9orVWrHow80mxzFWYet+7tjKYT/vQejUYQBvLUpBBC\n5BrJiOW4hoAqxhqOxbnymc856PdvWjNiAMXDAPjFf1bz7LzNyearnvmC+fEJOCNN8OL3IR7v1Xvf\np+i6mgtmzOWyaK2FwkprWyKzlXjCFWDkMRmnpgZhQgghco9kxHJUgz/M3z9YT4PfrIr/1nK1bqQe\nbERLDcQueY345k958ZkdvPjFDqaPKmd4ucqO7dDL1DErXoGW7VA8pNe+h33Kh3+Gd26BA74F5z1s\n7WuthYLKzHNu2Ax2Nzx3gcqGHXW1pbu+VVZEEEKIXCcZsRz15vIdPPDeOp6dtyWzM9ionoZMKB1O\nYMLXk7ufb6qn0a8yKS14reeJnhePqyAMYMlM9brhQ1j9ptpuqYWCgZnneUrA6YFDLlL7VVMs3U1B\nyYYJIUSuk4xYjmorG+IkihbxW4cmgUAkltx+d9VODhquArUWPSUQCzT0/I0KWPWadX/xC/DSD9T2\n9RtUMVffgLbPn/x19WX4xQuLcDtsyZIVt5w9mRlTqlhf29rTdy6EEGIvk0AsR+1szl49vxjjl7Gn\n1NIeCJuB2IqaJlbtUGsbbtJTylsE+1kgpuuqJpqnuK/vpHue/451PxGEAWxfApFWawZzD15cWG3Z\n329gEQOLPAws8nTnLoUQQvQBGZrMUV9uaWDq8FKe/sHhXHrM6GT7TNfv1UZaRiwUVYFYVbGHbY1B\nfvLsFwCEfYO4c+Sj6qBocO/feGcseg7uHA61q/v6TvaeXcb3lvbzasv2xsyfUZFH/p4SQohcJYFY\nDorHdVbUNDF1eClH71eBI6VkwVhbjdpI+8X+638vBeDQUWWEo+bTkYOKPbTE7MaFY/Qr695Rr6tf\n79v76Cln/S2z7Yun1GsHA7Gj//BORtuAQld37koIIUQfkkAsB72/uhZ/OMZ+A1WtKX84M4DaFTSD\nM13X+WyDKtp62uSqZPtjF0/D57LjjxoNsX40+TvYaE5s37nSbP/s77Div31zT10RCajXw6+AKedm\n9td8CcA/v6jnf/7+yR4vFzMq6N97vll5v7xAAjEhhMhVEojloEv+MR+AEeWqUv4MI7iaqKkaYWvj\nQ1jqPCB5fFNQRVrnTB2SPBagxOvC53KYgVg8Sr+xY7m5HW42t1+/Hp6/sPfvp6N0Hd6/ywweW3ep\n14H7Z13ZIOG/q/zJYLkjZkwexDgjEHc77F2+XSGEEH1LArEcs6nOfDJu+qhyAI6pinJghc7hXjWJ\n+0eRa3ho7qbkcbuNJyyPHV+Jy2H+yIs9Doo8Dur8xlBlTwdifz9WfXVFgwoqIzaPyoDVrobm7Xs4\nqR9o3ALv3moGi63G8kUFlapI60k3qf1BB1hOa0ItXZTIeLXF57Lzw6+Mxu2w8+pPvsKim07t0dsX\nQgjRu2SWb445+e73AThx4kC8Ljs8fBJsXcArk89FrzoA3oategVr19Wh6zqapiUDsbK0Iaxir5PR\nFQXMWxoCD10LxDbOVUVgy8dk9tUs6vz1Epfduo1RgDNuTE6ffSOsf888IBoCh7vL199rlv1bvdat\ngeoFsEbVCtscLuDU37zOK1f+gPGeYhh9PHz5FMz9CwBNusqW3fv2GvzhKL8+Y1LGpXVdJxCJ4XWq\nDJjLYbME1kIIIXKPfIrnmEhMZUyGl3nh1Wth6wLVsewltOoFUDSE84/eH4Cv/W0uAC0hFWAVpz1d\nV+xxctTYCiIkJut3MhDb9DH84wyY9XO179+t5nYt/w+secs8rgtLJz39UdqTkq5C6/19+Uynr9nj\nlr2sCrOmqt9obj9yErz/BwDu/rieYCTOG8t2wPQfQMV+5mLsQANqmPGvb6/h4Q83ZH27UDSOroPH\nJUORQgiRLyQjlqO+e+gAeOQRs8FdAjuXw8gjGV2hsitLtzYRjcWTNcS8TuuP2+uyM25QIbFEINbZ\nyfqPn65e178LjVvhL5lZHADCLZ2uBeZBZfHq9CIGaM2w/GXrAbOugWmXdO5+e9oL31Ov33jcnIjf\nRlHcdzarYLQ1lBJMlgxLbgaxZvdeX1LDuEGF7DewyDwmkvg5SiAmhBD5QjJi/UgwEuPet9e0WTU/\nsa7kRUeMZOzcX1g7o0E1MbxgIAVuM+DyR2IEIuqXv9fIpNxy9mSuPWU8AAVuB9GuZMRiacfeO7Xt\nY0PNULuq49cGhhVCRLdzdvjWTp3Xa4JN5vaLl0CLMRcsUJ/18Ka4CrTWpVa/n3ROcrPM57Qcf8XT\nn3Py3R9w+ZMLeG1JDdFYnDrj30Vp2rFCCCFylwRi/ciLC6u5+63VPDJ3fdb+M+9TQ43+ui2wcpbZ\nUTkRYiH1dGFhJZ6UjEkgHCMQVtmYRCbloiNHcfVJ4wDwOe0pgVgn6og1bLLux9pZgHrx83D/YWrI\nsiOWv8L/hF7EqcWo1iu5JHxdx++rtyTKUiT8aRy6rqMH6mHsiZau6lEqWzakxMPWBnXewk31/Ow/\n5hBk4sGLdLOX7eDHT3/Owx9u4NqZas7d4BJv1mOFEELkHgnE+pGQUWj1/nfXEYllzquqrle/xH/B\nE9aOySn1qQoGcujIsuSuPxzDH7ZmxFLZbBqxxD+DeBtDk6EWM+OTULdOvX5/Npz027a+JcWYL9a0\nYQG6rlsKymY18yLL7rvxg9s4sI8Em2Du3RnN4258Ba3mS/BVWNptzarIbmWRmxU1TWxtCHDeAx/z\n7y+30Tr1Ui6PXMv4QUUZ10u1ebefL7eoYc8xFQU99I0IIYToaxKI9SPxlNIFtWlrScbiOpoGPzlp\nHIOLU+YTffcVKDJrg1FQyaBiDw9eeCig5iT5w+3PLTp+wkAiuj1zaDIagk2fqAn5fxqnamQlhIyh\nOW8ZDG5nWBJg88cAPPnJRs6+/yPG/+/rnP7XD9s/J811kctoLJsCP/7UbPzPVZ26Ro+5czh89mBG\n81rPdwFoXf6Gpb2BIhw2jcVbGwE44a73kn3z9r+B2bFDGV7u5ZkfHs6H15/Aiz86MtlfYATPiWWM\nJg8pZmCxrCkphBD5QgKxfiSaEoilZ43q/WF0HQYUuACzaj5jjgN3oblfUKle3OoXeCASY8nWRoaX\ne9ssdTB9VDlOLYae/iTiGzfC46clq79jZHYAcxjT5lDlKwAcKQHChf+i6cyHLJc7x/4RS6rVHKoV\nNU20yVnAZtswbndcmWx6IXY8B9X8ig8bU7JNXzzZ9jX6UCwaYY13KlQdAD98h/u8VzB+UBEzL1cB\nVjgl27nMCM6Gl/s4amwFw8t9TBtVzk+NoeNBJR7KC1ysr20B4ILDR/TydyOEEGJvkkCsL1QvUKUe\n0sRTMk7BqHW+Vl2LmoNVXuDKnI/lKTW3jaDMZ2RSWkNRtuz2M7Gq7acWq4wMi5YaaAFsX2LdD7Wk\n3KyRPUsJxOJTvmH273cyj+62ZsqGanV82/6ueYm2ipfGo3zsOIwVg8/O6PrNy0vb/D76i/8J/5b7\nR94Dl38IQw+lJuRmQKGL6aPKuW7GBMuxy7apgHR4mbXqfiJorq4PUFHoYokRsA0skmyYEELkEwnE\neltjtaovNesalVX64mkI+wGIxlICsYiZNflkXR1/e3ctYGTEoiFwFcHPjWV0EhkpAKeayO1zqaGs\n2cu2U9capqKdhaEHl3Twl3s0aG4n5pPZHGrB6qs/58Wqn1sO97ozq6NU0pjc/mJLlicMdR1iYYJx\nO5WFbm762iTOmWp+fzubQ2zTs09s7zGxaNtPeaaX+Dj/OTjvUUvTCn0kNpumKukD/nCUAuPnce4h\nQy3HbtilnqKsLLKWrzhomAquB5d4GFtZyI4mNVQ9sKgfFrEVQgjRZRKI9bbEJPf178HimfCfH8O7\ntwEQSyl8esdrK5KTs89/+FP+u2gbAOWFLhUQDZoMxYPVwUWDzesb6xkmMmLPzttCbXOIAQVt/wIf\nXu7j37Gj2RKvVA8JbJyrAo7qedYDUzNxqRkx4J+r7Fz/srk497srd+Kwafwtas1qBTFLL5z3gHWR\n6427Wlm8ZRegE9QdOO02Ljl6NPd8+2AOGFoCqIcPvhH6XZvfS494+2b1lGdqcdaEp86z7K6qDTJz\nbeZ/o6aAOd/OH44lfx6FacHptoYATruGO23Y+JhxFcz5+bG8+KOjkou7A4ySifpCCJFXpKBrb3rv\nD6AbQ47BRlj6L7W9WQUksZShyc827Oac+z9i/e1ftVxiv8pCFRClLu/jKTG3jUAs/QnJQe1kvYaV\neflEd2C3xdi4ZhnjnjuD8ISvkZFDi6Y8QJCYI2ZXgdXK7WqI7aTQXYRx8MN6P02BCHX6AMslgplX\nTTr+T+/hJcgKD7RGNApTVgJw2M15cduo4PP4fhxc4k+dLddzNqhlpGitg7JR2fsMv3ttDbV6Cd9K\n+XEcMLSENTvNhcr94Rg+Y85eIjOW0BSMUuZzommZ30mimGtqIFbilRpiQgiRTyQQ6y0ttfDe7da2\nOjXcyNaF8PS38BSaTwF+3/46K/QRvLNyWrLtK+MqcHzxT9jyGex3inmd1F/ixoT59F/43zx0GG3R\nNI2DR1dSurmVp568l+udYF/1WuaBsdRALJERUwFG4snMdboaerNpGo2BCM/GTqRZ93Ht/vWMXPc0\nYZwUuR0cMKwkuQZmKhfqui0xO8Up2SOnzZoxWhYfxdToF3snENONzGQ00P5xhmY9ZX7Xd15kyaNq\n+PXIO96mplEN5yaGim22zDve00LfYytVIJZe9FUIIUTuk6HJ3pJtAez6DWppIoA1szl8q/kU4G+d\nT/Ks67ZkAVAwyk/MukbttFVA1QhYfGkZMc8elsVxOJz4tBDXO2cCYCflYQGXUeMqmvKexlyp99fW\n0xyM0BK0lr5oDkZpCESIY6NmxBl4T7pBvQ8xmkNRqko8NCfO2fgRPDoDJ9FkIBbBkSzZAOB0WAMY\nP25sgTp4/YZ2v68uSWQmsy1XNMkYajWGZD2EaUYFYv+KHQPjzAA5EYQBDC01i7BWFXsssXNTsP0V\nDSZWFXH21CHc+NX9O/NdCCGEyAESiPUGXYenz8veV3VAclPTIxS5HZaCnallLFKH6qhPWxj6x5/B\nuQ+b19I03r72OABGDbA+kZeNZm8n2+IwhhOzZMT+v737jpOzqvc4/vntzO7O9t000isBElpCFhIg\nEJoQigYjhFwUYoFY8CIKV1REsV0RERXlqohwaVINEL0IkkgR6WgSOimEFNJI22zfmTn3j+eZtjuz\nJVtmd/N9v177mmfOPM/MOfPMZH455zy/c9Fdyznm2r+zx19D8XcXTCOUm8OOmgZ21TZx2MgyHvjC\nMQwZ4AWcP8y9jXMDT1GSH4wvRs6fL4X1LzDatpCHF+A1kFqfeZWjUu7XxdZmfPE3bbatw2JDve89\nA9eUwfeThlejERhyMBzoDRkX0EAtIWY13MDm43+a8SmTJ+kvvXwWy797ajw4+/KJ+7danWAgh1/O\nn9riPRARkb5PgVhPWPdC5seGHBTfLK/fRCBgPHLJMfGyd7Z4c40OH1XOVWdMgopx3gPHXd7yeQ6b\nl1I0YXAxN51/BPcuPJq25LQaiPnzy/w5Yk2RKO9t84bfwuSwpz7MnvowJx80hNMOHsrAonx21DSx\nu64pMacpkJhE9dPcm5m3/gdUN4RxzkHAC/TKqCHX/B4xF2TWAYPjx8yZMoJTJ+8Xv9/ounFU3Z9n\nx0u/826jYdizxduONEEgCLOv5ZHIMfzdz/r/vhvKl07xeqxu/XRlytPNGD+AklDi/S3KD1IayuUj\nfnsuOm5c97VFRER6NQVi3W3jq15S1EzyEr1fY6r/TRGNlEQSKR4efHUDAI9cciwDi/O9OVmT58C0\nT7fr5c88bBhD25GeIifYjkBs0cVQ9QE3Ll3J+mVePjDnf4Q27qz1cpzh5Tpbta2adzbvobzQ700L\npl61efCHjxGJOnbVNsUfG5VfzbHDvOe7/GNHMbHZsj/JedaKrZ5uE07z3A3+5PtIIy4nj0jJcK7J\n/Sr1JNoVm3B/0kH7pVwFmbLQd5KrzpzEs1eemHiPRERkn9OpQMzM7jOzZf7fWjNblvTYN81slZm9\nY2anJZXP9stWmVk3TPDpZR74TMuy466ACSd72/ml7Dn919wZnEuea+Ce6OXwswNbHhNTtxMKB2Z+\nfG/ltBKIFSTWrmTts1RvfJvjA6nJXqvqw15qDeDY/QexfP0u6poirPXzZJHmqsAQDVz18Gts9afB\nDYju4OvV1wIwauzEFvt/84xJ8WWaykgf3HSJ5gt6Q2JYNtLEyxuqOfe3z1HfFOWimeO49KT9uf7c\nw1N2b0gaUk7Og5YsN5DDyIq2h41FRKT/6tT4jnPuvNi2mf0MvGydZjYZmA8cDAwHlpjZAf6uNwEf\nATYAL5vZYufcm52pR6+WvH7jZx7z5oTlF3tzjV65FaZewKtrqvhzzRYuyF/EaDZDpovo3nsGaren\nZtLvIg3RzNcfPlt4MjN5xbuz6GK+m/TYcRMH8Y+VHwIwuNjrHTpwaCLdQk1j5onog6yKR1/bzIV5\n9QzJgZkso6zRHwJsnjYCb6j13oUzmHPTP3ndJT2+9e2UId5OSxuI+RcqRJtoiAb41zpvIn9ZQS7/\neXLLoDHmx3MP5TzN7RIRkQy6ZGjSvDGZecA9ftEc4F7nXINz7j1gFXCU/7fKObfGOdcI3Ovv248Z\nBAvg0mV87skgJ/3qFf744jqanMFRF0NuiK1VDbzuMs8T+vX5U72g7faPegUFXR+IDSxNnyj0qPqb\n+NRrh2U8bsb4RO/c+MHecyT38vzOX3w87Wv6WfYL8YYCh+MFdBx3eer6mUkCfvqHeyInsad4vFfY\nfGmmzkqXtiKWUT/SSFPS/1+GJV0Nmewbpx/EyIoC5h4xIm3KChEREei6OWLHAVuccyv9+yOA9UmP\nb/DLMpW3YGYLzewVM3tl27ZtXVTNHuYc1GyF6QupKx7N0re3subDGr710Gv85K+JLPSbdtdTS4gj\n629q8RSF1DN2YBH85auJwrLMOcH2Vnlx+oBiByWA0TRlQdrHK5LmN00d5Q1hjqxIPFfzeV7J9rOd\nGFFGmXd+J+Ws8w86NeMxk4aVMvvgoYDxzJSfeYV1Ldft7JTGWigbBWfekCjzL1RwkUbCJFKBnHjg\n4OZHA/CFWRN49sqTyA+2njZERET2bW0GYma2xMxeT/OX3JP1HyR6w7qEc+5m51ylc65y8OD0P3a9\nXv0ub0iraAg7alPzfj31rhd87K5t4udL3gVgGxUtnuK7wTuY8NzXEwVDJsOkbuhEzDBHLOz3/jyW\nf1qLx37aNC8lyWiFP1l/v5IQx00cxM0XNOsNm3cHzPgSnO/lKru66CG+HryPCqtO3S8v8zI+gRzj\nF/O9xcS/s2SzV/j+c4lVCjor3ADVW2DKJ+HIzyXK/aHJhvo6GpN6xAYWa+1HERHZe23OEXPOndLa\n42YWBOYCyb+6G4HkiTEj/TJaKe9/qv2evOIh/O7p1SkPbdvj9bA8vTK1t+/G8NlcGnw4fn9e3rPY\nG/48q5FHwUVPdE9dW0tfAWyqbTm89j+Rj3F3QS63f/aolOSrOTnGnZ+b3vJJJs9JJEQN5DOqaS0L\ncze0nBOXl35YMiaUG2BoaYjNVY7drpCyl2+Bl2+BQzLkauuIqo2Ag4oxfp3Phjcf9oYmww2Edq+h\n2o3u/OuIiIjQNUOTpwBvO+c2JJUtBuabWb6ZjQMmAi8BLwMTzWycmeXhTehf3AV16J1qtgKw1ZVx\nx/Pvpzy0u66J7dUNXPngCgBOOHAwT11xAicvvB6mfzGeL8ySJ/s3T+LalXISQ2hhl/hYnDJpCAD/\nXFvd4hBHDvm5AWYdMJgjRrfszWvVubcBEHBpJvMXtP1cowcWAsYG18W9pQ1+O/NLvdtZfm9kpIHw\n4q8AMNIfSi3XkkMiItJJXRGIzafZsKRz7g3gfuBN4DHgEudcxDkXBr4MPA68Bdzv79s/VXuB2Kfu\nTQRQf7xoOted401+n/bDJdQ1eUsJXXfOYYwdVMTBowfD6dfCeXe1fL7Z13ZfXZOGJje6QfHt319Y\nyQUzxvDunpZBxw3zDueI0Xt54cCACWmL3xt+VrsuRhgzwLsgoClpvhau9TUb2yV2xWSuP88tEFtV\noIltbz4DwKCQ48VvncxTV5zQ+dcTEZF9WqfTkzvnPp2h/EfAj9KUPwqkWVG6H6rxek4+dGXxoknD\nShk/uOXQ26CiZnONhh6Sev/Lr8CgzGkSOi1paPIb4Yu5J887dWZGRWEum+rzICkv7JrT72buEZ24\naGDIQd7VpM2uUNxTdkCGA1LtP8R7DyPJgVg03OYQa5uaar3bWHb92PNFGtnUWMCwHKgtHsNBpW0n\nyRUREWmLMut3p51riViAnSQCr9KCXIaWhfjOWZPjZX/64jHpUxwM8hO7LvhL9wZhEF/E2uWV8IlZ\n3hI9rxdUxusM0Dj5nPjuwf1P6vxrjj02vnlb2LsYICenfVcZjvPX40y+gjHjQugd0aJHzA+Qww2s\nc94w7S0lX+r864iIiNAFPWKSxubX4LczAQgAJaE8ln3nVBrC0XgerFii+fOnj2bamAxzohYshuX3\nwtiZ3V9nPxCznBwmHXIElz79ZSYdcx6HQHy9yPUn/pIJbz4IQFlXzI8aOBFWLeEX4bkU4l28ELT2\nDS/GgsMmlxSIhRtaveKyXfwesQbL9xYvShqaDBJldXQYY4cP6dxriIiI+NQj1h3+dnV8c23hoYRy\nA+TkGAV5iaDh7CkjOHb/gXzphPRzpQAoGQozL0u7PFCX8wMxcoIcPKKcL116JZ8/5VAgEYh9+6HX\nOb3hx1zSeCkl+V0Qwx8+H4AXo5Oocd5Q38Ty9gVihf57GU7+v0Qs6WrN9tYXWs8k0gR/8lJWnPjL\nl1i5ZU98aDIabiBIhFAoxGWntG/4VEREpC0KxLpDfiKJ6bKimSkBWExFUR53XzSj96w1GJsLZV5d\nDxpaGh8ujQViz6/ZzltuDP8XndE12eKHT4Grt/NOwVS24U3QD/hXmrZlSIkXuIWTP8KxocmfT4Zb\nW+Y9a9O/74xv1pLPMys/jC9Ivm1XFQEi5OflkRfU10ZERLqGhia7Q/2u+ObyvKkUNPWB7OpJPWLN\nNR+GPHxkWYt99logyNKvzaKp9nB47D04/op2HTa0LMQtF1Zywn3LE4WxQCzsLZmEcx3rTdydyMBS\nRRHRqOPOlzZyAfDH51YzNSdCTmcvBhAREUmiQKw7VG+Dg86C+Xez6g8vEsrNvPB1rxEPxFoGjbEe\nsZgHv3hMl750RVEeFA2BCx7q0HGHjiwjaNFEQWxoMvl+MI92q070xkXJ4UePvgXA/PwAuRYmSBjr\nyPOJiIi0QWMs3aFmKxQNxjnHW5v2xOcz9WrxocmWH4nkQGx4WYjcQO/42AwpaZbyo/lVk21dRekc\nrHgAVi3x7lenHxZtJEgeYYIWJRBUj5iIiHQd9Yh1JefgyR9B7Xbuf7uBRZte4MPqhpTlf3qtWELX\nQMsen4LcRCBZ2BWT9LuINR92bN4jFm12v7m1/4BFF3nbH70RVj4OwA+aPpmyWxNBcglzsK0lJ+ew\nzlRZREQkRe/o2ugv3loMz/wUgOW7QrywZgdA61dG9haxoKVwQIuHkgOe8ypHtXi812jeA7asjXXo\nk/f/86UAbBw7lz9EzkzZrZFcxtpmSqyOok0vdkVNRUREAAViXev+C+ObsXQMAGcdNjwbtemY+t3e\nbeGgtA/PqxzJlbMP4qLjxvVgpTomGm5IXebo8W+2cUCkRdGuUMtAs5EgE4rb6F0TERHZC71nnKmv\nS/pRXxSZyf9FZwBw7rSRadNX9DrjT4Qpn4KTvp324evOObyHK9Q+EWcE/CSw4XADebElitqjYU+L\nonUhL0fYg184mtEDCrnj+fepeLOcgjwHdS12FxER6RQFYs2tXAKjjoRQB1M01HrDkItKP8Xt+eez\naM4hjB9cRFEvmlPVqtwQnH1TtmvRYXMbv8cj+d8BINLUGF/fs10aa1LvT/0Ur+VXEshZw7QxFZgZ\nV5x2IGwcDNve6cJai4iIeDQ0mWzLm3D3J2DRwo4fW+cFYmvdUMoKcjl0ZFnfCcL6sF1J63hGGht4\n/d2V7T+4eY/Y4eezs7aRisK81AsBCiri55fRR3eitiIiIqkUiMWsWgp3fcLbXvd8x4+/5RQAtjUV\nUNoXrpLsJyJJi34H336E3zzyTPsP3rMJggWJ+4UD2Ly7nsHN02IkX8Bw/H/tZU1FRERaUiAWc9dc\n2POBt93YgXlGMQ1VANQ1NrVIgCrdZ4MbxMMRL8Fs6N3FLAg+3v6Dd28gWjqCnQec690vHMiaD2sY\nP7jZwuEDxie2g82CNBERkU5QIJZOtAme+3X792+ojm8+WX+AArEeZfx3Ut6v4bY99eFolIyqNvLS\njgKmrziL3QuehOIh7K5rYmBRs1xq5WMS22nyrImIiOwtBWKZ/O2q9u+7ez0A9XN+z+5oiFIFYj2q\nKWl4srj5pY2tXEXZtHM968MVNJLLzpIDAWgMR8lrvnJAqDyxrbUmRUSkCykQa+5zTyS2N77avmN2\neYFYdWgY0HJtRuk+l3/kAJqSLv4ttxo+dKWJHdIFYqv/Dn88j9yazXzAQACq6r08YU2RKLnBZl+L\nguRATEOTIiLSdRSINTd8Khx7mbe9/L72HVO3E4DdOV4AUBpSINZTzq0cRS2hlLKNbhDvR4d4d157\nMPUA5+DOj8O7jwGwy3lXXVbVhYlGHU0Rl6ZHLCmViYYmRUSkCykQi8krhhmXeENPsaSm+cWtHxPj\nT9TfFfYCAvWI9RwziDb7GNe4ENeF53t3Hv8mrHshnuctfut7NeolcK2qb6LJn0+W17xHrHx0Yjuv\nsOsqLyIi+zzlWYj52ltg/g9wIBdKR8Keze079tErAKhyXiqEYqWv6DEteq+A+pxCaiNJQ4i3ngYj\nKkccAMsAAA4cSURBVOHipYR3bSAIPBY5ksciR7LCeeuAVtU10RiOpn/O5Csl89oZnIuIiLSDesRi\nQqWpPWAlQ708U21JWtqoPuoFYPnNe1Sk21QU5XH1WZNTysKBQna6ktQdNy3DOcfG1a8D8Kvwx3k4\nOpNBxd5Q46/+vopdtd48sRY9YsnyijI/JiIi0kGKGDIpGdq+HrEV98c3G6Pemoe5aXpppPtUjqng\ntvBp8fuR3ELWuGGpO1mAXy5dyX2PPw3AGjcUSPR+bdxVx9WPeEFaq+cvpw+sGyoiIn2GIoZMCiqg\nblfb+/3rdqgYB1dvJxzxArF0w2XSfXIDOXwvvICV0RFeQV4RVRSxZ+j0xE6hMm56chUDrIoal0+d\nP8F/SGliov9T73jrVLbaIyYiItKF9IuTSagsPgk/o/ef95ZDqvwsBII0Rbw5RrlBa/046VJ5/vtd\ngzeXy/x5XFetq0zsVLOV6W4FFVbNThLDliPKk5Y48oVy03wtFj4Fn/hDl9VZREQEFIhllhOAxmp4\n4DOw9Aew8omW+2z3F5g++GyAeCAWzNHb2pNi77fzP86hYi+NyHZKU/ablbOcAVSx0xXzl/+cCcBn\nZ45rcZXrrAMGt3yR4VPh0HO6uuoiIrKPU8SQyeq/e7dvLIJ/XB+/MjLFjve8Wz/zeqOGJrOieQLW\n0tIKAF6MTvIKRk3HFQ1mtG1ljG1hkxvIISPKWHvtmUwbU8HHp3pDmoNL8nngC0dTojxwIiLSQ5Rn\nIZMzrvfSHsTUfJj6+HvPwLM3eNv53lBXWEOTWZEbSH2/Kyq8QCxMEL61CSyH6E3TOS3wCgD/dFNT\n9r/6rMlcfPz4tMOUIiIi3UldN5mMmp56v2hQ6v3bP5rYNi8QiM8RU49Yj0oMTXqKi8s481Dvqslo\nsAByQ0Qs0ct12rFHpRwfyDEFYSIikhWKGDKxpF6WiadCuCH18WIv/QEXLY0XxYYmgznqEetJhXle\nSolGvGArFMpn6mhvuHhPQxiA2qZofP8hoyb2cA1FRETSUyDWms//Ay54GMaf4CV33bMl8Vi4Ho68\nGEZ6V+Zds/gNblzqTd43UyDWk0K5AVb/9xn8JTLDu18ykFJ/An5VnZektdaSErEmL1kkIiKSRZoj\n1pphh3m3zs+ev2MNlOwH0SjU74aC8viu//vc2p6vn8QFcoy7IqfwfHQyS8cdTVmtl4x3d10TQ8IR\nNtpQhrPC27lsVBZrKiIikqBArD3Kx3q3t82Gw86DoYcBLn61ZEM4sczR52eN7/n6ic9Y7bwrIGMp\nKe59eR01DRHyto/hyFxg9DFQOCCLdRQREUlQINYexUl5pVbc5/0BDDoAgA921QNw/bmHc860kT1d\nO/ENLMqjxF9wPRaI3fXCOgrzAtRGTmCrK+e2T38rm1UUERFJoUCsPfJL05ePn4Vzjiff3gqkz9Iu\nPefFb50c305O0lrbGAGMJ6NTQcl2RUSkF9GvUhu2VzfwtfuXJwoK/GGtys9CMJ/fPL2a7//lTUK5\nORw8IkPAJj0iGMgh6KcOaZ4tX0REpDdSj1gbHnh1A4v+vZH8Ad/lxwtmw36TIRKGnADOOe57eT0A\n9y08mlJlZO81YiktREREejP1iLViS1U91/71bQDu2XEgm0P+RPxAkJVbq7nj+fd5f3st1849lMNH\nlbfyTNLTzEwXToiISK+nHjGfc44dNY0MLM4HYNPuOmb+5EkApowqZ9n6XSy49SV+e8E0ivIDfOTn\nzwDevLDT/Szu0rtMGZkIjn949iHM3H9QK3uLiIj0PPWI+b798OtM++ESXnpvB5t31/OLJ1YSiTpG\nlBdwywIvaes7W/Zw4vVPcdSPEtn0b5h3uOYj9VKHjiyLbx8yooyxg4pa2VtERKTnqUfMd/eL6wCY\n97vn42X7Dynm4UuOpTg/yJwpw3lk2Qfxx0ryg/zjyhMpL8zr8bpK+wwpCcW3FSyLiEhvpB4xIBJ1\nacuPGjeA4nwvVp0wuBiAK2cfxKRhpTz6leMUhPVyecHEx1uBmIiI9EbqEcNbHmfFNady2DV/i5ct\nPH48l5ywf/z+52eN58ixAzh6wkC+eMKEbFRTOqE0pI+6iIj0Pvp18pWGcll77Zls3FXH8LJQi4W7\n84MBjp4wMEu1k86K5RcTERHpTRSINaPs+P3LVWdMojESzXY1RERE0lIgJv3axccrl5iIiPReGq8R\nERERyRIFYiIiIiJZokBMREREJEsUiImIiIhkiQIxERERkSxRICYiIiKSJQrERERERLJEgZiIiIhI\nligQExEREckSBWIiIiIiWaJATERERCRLOhWImdkUM3vBzJaZ2StmdpRfbmZ2o5mtMrMVZnZE0jEL\nzGyl/7egsw0QERER6as6u+j3dcD3nHN/NbMz/PsnAKcDE/2/6cBvgOlmNgD4LlAJOOBVM1vsnNvZ\nyXqIiIiI9DmdHZp0QKm/XQZ84G/PAe5wnheAcjMbBpwGPOGc2+EHX08AsztZBxEREZE+qbM9YpcB\nj5vZ9XhB3TF++QhgfdJ+G/yyTOUiIiIi+5w2AzEzWwIMTfPQVcDJwFedc38ys3nAH4BTuqJiZrYQ\nWAgwevTornhKERERkV6lzUDMOZcxsDKzO4Cv+HcfAG7xtzcCo5J2HemXbcSbQ5Zc/lSG170ZuBmg\nsrLStVVPERERkb6ms3PEPgBm+dsnASv97cXAhf7VkzOA3c65TcDjwKlmVmFmFcCpfpmIiIjIPqez\nc8QuBn5pZkGgHn8oEXgUOANYBdQCnwFwzu0wsx8AL/v7fd85t6OTdRARERHpkzoViDnnngWmpSl3\nwCUZjrkVuLUzrysiIiLSHyizvoiIiEiWKBATERERyRIFYiIiIiJZYt50rt7NzLYB7/fASw0CPuyB\n1+mN1PZ9k9q+b1Lb901qe88Z45wb3J4d+0Qg1lPM7BXnXGW265ENarvavq9R29X2fY3a3jvbrqFJ\nERERkSxRICYiIiKSJQrEUt2c7Qpkkdq+b1Lb901q+75Jbe+FNEdMREREJEvUIyYiIiKSJQrEADOb\nbWbvmNkqM/tGtuvT1cxslJk9aWZvmtkbZvYVv/waM9toZsv8vzOSjvmm/368Y2anZa/2nWdma83s\nNb+Nr/hlA8zsCTNb6d9W+OVmZjf6bV9hZkdkt/Z7z8wOTDq3y8ysyswu66/n3cxuNbOtZvZ6UlmH\nz7OZLfD3X2lmC7LRlo7K0PafmtnbfvseMrNyv3ysmdUlnf/fJh0zzf+urPLfH8tGezoiQ9s7/Bnv\ni78DGdp+X1K715rZMr+8v533TL9rfe8775zbp/+AALAaGA/kAcuBydmuVxe3cRhwhL9dArwLTAau\nAa5Is/9k/33IB8b5708g2+3oRPvXAoOalV0HfMPf/gbwE3/7DOCvgAEzgBezXf8ueg8CwGZgTH89\n78DxwBHA63t7noEBwBr/tsLfrsh22/ay7acCQX/7J0ltH5u8X7Pnecl/P8x/f07Pdtv2su0d+oz3\n1d+BdG1v9vjPgO/00/Oe6Xetz33n1SMGRwGrnHNrnHONwL3AnCzXqUs55zY55/7lb+8B3gJGtHLI\nHOBe51yDc+49YBXe+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c27bf61d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10)) \n",
    "l1,=plt.plot(y_train[:,1])\n",
    "l2,=plt.plot(trainPredict[:,1])\n",
    "plt.legend(handles = [l1, l2,], labels = ['Reality', 'Prediction'], loc = 'best')\n",
    "#plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pQgghJE8FQgst81EBejwomCKEEELylBg0zVvmo8xUXCiYIoQQQvKU1GeKCtCXhYIpQggh\nJE/5gyEoZAzh+btSUEUF6PGhYIoQQgjJU4Egl+qlgJnMFC3zxWfJQceEEBKNw+PH/3ziBBgDagp1\n+MLljSgpUK/0ZRFC4uALhqQ6KWBmNx8t88WHgilCSEJ+f7gfL50bQVNpAV5tHcW404sff3TrSl8W\nISQOgSCPCKaoAD0xtMxHCIlbKMTxq4Pd2LbKjL9+9VJ87tIGPH1iECf7bSt9aYSQOPiDIalOCqAC\n9ERRMEUIidvrbWPonnDhzr11AIDPXdqIIr0K9z/fStPmCckivmBIGnIMzGSmvJSZigsFU4SQuD12\noBsWgxrXbqwAABg1SnzpfatxoHMCH/rpAfzd746hb9K1wldJSG440mNN2fMpEORSNgoA1NS0MyEU\nTBFC4jJoc+P1tjF8fNeqiBfhj++qxV376qFWyPDCmWF857lzK3iVhOSGYIjjU//xLr7yu2MpOb/Y\nGkGkpAL0hFAwRQiJi1gXdfna0ojbVQoZvn79evzu7j24a189/nJ2GJ1jzpW4REJyxtnBKUx5Ajja\na8PRXmvSz++fU4BOrRESQ8EUISQurcMOMAasKTMseMwn99ZDJZfhZ290pfHKCMk9h7omAAA6lRy/\neOtCxNd+ebAbz50cWtb55xagix9TZio+FEwRQuLSOuRAXbEeWpV8wWMsBjVu3VGNPx4dwOiUJ41X\nR0huOdQ1gYYSPW7fXYvnTw1F1E49/Fonnjjct6zzB0KhqK0RqAA9PhRMEULicn7EgebyhbNSos/u\nb4AvGMIfjw2k4aoIyT3BEMe7Fyaxu7EYd+6tA2MMv36nF4CQORqe8sDu9i/rMfwBTk07k4CCKUJI\nzFy+ALonprE2hmCqtliPumIdjvYkv86DkHxwdnAKDm8AuxuKUWnWYlOVCacGhJrFYbsHIQ5MLTeY\nCoUixsnIZAwKGaNgKk4UTBFCYtY+4gTnQHO5Mabjt64qxLE+G/WeIiQBB7vGAQC764sAALXFOvRM\nCMt8AzY3gCQEU8GQlI0SqRQyKkCPEwVThJCYnR92AEBMy3wA0FJjxpjDi0E71U0REq9DXZNosOhR\natQAAFYV6TBoc8MXCGHAKgRTdrd/wTcr/3moB4/OKVoHhKxWV3in7dxBx4BQN0V9puJDwRQhJGbn\nhqegVcqxqkgX0/FbV5kBAMd7acwMIfE62W/H9lWF0uerinQIcSErNRjOTAVCHC5fcN59Oef4v690\n4JEoO2q//dxZfPE3Qt+quYOOASEzRQXo8aFgihASs/PDDqwpN0AmY0sfDGE5UK2Q4VgK+uMQkss4\n57C6fCg1qqXbaov1AIDeSZe0zAcgahF6z4QLw1MeDE95MObwRnxtwObGxLRwmz9aMCWXUc1UnCiY\nIoTEhHOO1mEHmhfpLzWXSiHDxioTjvVRZoqQeEx5AgiGOAp1Kum22mIhI9w7Mb1kMCX2pwKA0wP2\niK+NObyY9grZrECQR/SZAoReU1QzFR8KpgghMRlzejE57UNzRezBFABsrTHj9IA9pS/Oz50cwiut\nIyk7PyHpZnP5AADmWcFUqUENtUKGngkXBqxumHVKANGL0A91TcCsU4Ix4NSsYIpzjjGHF05vAKEQ\nF8bJRFnmo8xUfCiYIoTE5MLYNACg0VIQ1/22riqENxBC6/BUKi4LAPDAX8/jgRfbUnZ+QtLN5hIC\nJLNWKd3GGMOqIh26J4RlvnXhXbVzM1OccxzsmsC+1SWoL9FHBFNOb0Cqh5r2BeAP8nm7+ZS0zBc3\nCqYIyVHnhqbwhV8fkd7hLpe4rFBdqI3rfi3hIvQTKVrq45xj0ObG+WEHPP75hbiEZCNr+HlbqFdG\n3F5brMPxPiu8gRDWV0YPpronXBiZ8mJ3QzE2VZkilvlm1085vYF5g44BKkBPBAVThOSoV8+P4s+n\nhvG//+skOOfgnMMdZddPrMTdQ5Xm+IKpSpMGaoUMfVb30gcnwOryw+MPIRASaroIyQVSZmrWMh8A\nrCrSY9wpBFrrK6IHU2K91J5GIZgasnsw7hSCqIhgyhMQaqYUqclM+QKhlL2JyjQUTBGSo8Q+NC+d\nG8E3nj6DD//0AFq+9SKGE+z5NGBzo6RABY1y4Zl80TDGUGHSYChFvaYGZxXinurPjxdukvukzNSc\nYEosQgeAteUGMDa/ZupQ1wQsBjUaSvTYWGUCMFM3NeacCaYc3oDQGmFOZkqdpKadfzjSj5t+8nZE\nsXyuomCKkBw1aHNjXYURV28owy8P9qBj1AlvIIQTCQYcAzZP3FkpUblJg2F7al5QZwdTJ/vtixxJ\nSPawhjNTJm3kMt+qWcFUTZEOBrUCU55AxDGnB+xoqTGDMYYN4aXA0+HnxuzMlJjRmtsaIVlNO1uH\np8A5cD6F9ZKZgoIpQnLUoM2DKrMWP/hIC/799u14/Z7LwRjQOpTYUtiA1YWqBIOpCpM25ZmpzdUm\nCqZIzrC5fDBqFJDPyRqJDXMNagVMWiVMOmXEMp8/GELPhAtNpcJGEYNGiYZZReizgynrtJD9mr/M\nl5zZfF3hTSttI85lnyvTUTBFSI4atLlRXaiFXq3A1RvKUahXobZIl9CuOqHIe3mZqZEpD0Kh5I+o\nGLR7oFLIcNkaC9pHHXD5AkvfiZAMZ3P5UahXzbu9ulALxoCq8EYQoyYymOqZmEYgxLG6dGbX7fpK\nI86Fn/fjTi9YOD4Ts1/zC9DlSVnm6wyPrGmnYIoQko2mPH44vAFUmjURtzeXGxMq0ra6/HD7gwln\npipNGviDHOPT3qUPjtOAzY1Kkwabq80IceDsYO4vKZDcZ3X55hWfA4BaIUeVWSvtqjVpI4OpjlEh\ncJkdTK0pM6Bv0g2XL4Axh1e6r5iZUkXJTE37AvjRS+34znNnE7p+pzcgZaPbRyNfc07123Hx/a9g\nwpn814OVQsEUITlooZ13zRUGdE9Mx529SXQnn6jcJNwv0eL3xQzZ3Kg0a7GpWii0paU+kgtsLj8K\ndcqoX3vg1i245+pmAAsHU7P7wa0pEz5uH3FizOlFXXgsjVjkrpBFhgJqhQwjU148+FIbfv7WhYRa\njoh96SpMGnSMOiOy0u9cmMCAzZ1Tu28pmCIkBy0YTJUbwXn8NQz91sR6TIkqTEKGLBV1U+LyY5lR\ngzKjGidpRx/JAVaXb95OPtGuhmKsLRcmEZi0yojdfB2jTlSaNNCrFdJtTeERUG0jDow5vKgItysR\n2y/MHSezbVUhWmrMuH13LTgH+q2uuK+/a1x4jbl6QzlcvmDEjr6+SeF8gzm0y4+CKUJy0IBNCFrm\nLsutC4+CaR2KbylsYNmZKSGYSnZmyh8MYcQxU8u1s64Ib3WMI5iC2ixC0snm8s/byRfNvMzUmBON\npZFTCmqLdFDJZTg/7MC40weLQQ2DRoFJsQB9zm6+W3fU4E9/ezE+vL0aAHBhPP5gqnPUCRkDrlxf\nJlzX6MwbuF4pmErNppSVQMEUITlo0OaGUs5gKVBH3F5TqINOJY87vT5oc0OrlC+47LCUIp0KKrks\n6ZmpYbsHnANV4dqwqzaUY9zpw7Fea1Ifh5B08gdDcHoDC2amZjNqlfAGQvD4gwiFODpHpyPqpQBA\nIZehwaLHu92TCIY4LAVqFKgV0jLf3GBKVBduw9AzMR3399A5No2aIp3UmmF23VQPZaYIIdlgwOpG\nhUkL2ZxdOjIZw9pyA87Fm5myulFVqAVjbOmDo5DJGMpM6qT3mpq7nHn5WgtUchn+cmY4qY9DSDqJ\ny29zR8lEY9TODDsetLvh9gfnBVOA0OBTHCtjMWhQoFFIj6OQR39em3UqmLRKdCcUTDnRaCmAWaeC\nxaCWSgtCIY7+SeF5O5ii3nMrIeZgijEmZ4wdY4w9G/78McbYBcbY8fB/Lam7TEJIPAZt7nk7+UTN\n5QacH3GA89iXwgbt7oSX+EQVxuT3mhLPVxEucDdolNi7uhh/OTMS1/dHSCYR52lG2803l7gUaHf7\nZ3byRRlGvqbMAHH122KIzEzNHXQ8W12xDt1xLvMFQxwXxqfRaNGHH7sA7eFrG3F44Av3sMqlzujx\nZKa+AuDcnNvu4Zy3hP87nsTrIoTEiXMOp1fYpTdoWzj4aS43wubyY2Rq/rbkfqsLBzrH8UrrSMTu\nmwGrO+G2CKLyFIyUmanlmgkcr95Qjt5JV07tFCL5Rez/FMuyuhhMTXn8UdsiiJpm3VZSoEKBWikN\nM14oMwUAdSX6uDNTgzY3vIEQGsJBXVOpAR3hN3A9E0Jg1lxuwJDNkzNvemIKphhj1QCuA/Dz1F4O\nISRRvz/ch53ffgkdo04MT3kWDH6aw7uAzs1p3vnC6WHs/9dX8bGfvYNPP3YYBzqFYakefxAT0z6p\nLilRFSZNuMYpeS+egzY3CnVK6FQzO5fev64MjIGW+kjWWmguXzRGjfC7b3f70TnmRKFOieI5tZKA\nkJkSiQXoooVqpgCgtlgfDo5ib4/QMRbZnqGprADTviB6J11S8fmu+iK4/UFpqTHbxZqZ+iGAewHM\nbYn6HcbYScbYg4yx+f96hJC0Odg5Abc/iK/+/jhCfP5OPlFzuVAQOnusjMcfxL88exZrywx49JM7\nAACnB4X6CjH7U5VgWwRRhUkDXzAk7SBKhmgZOItBjS3VZhzomEja4xCymI5RR1J3kIrLfLHu5gOE\nYOr0wBSaSg1Rj6sp0kGtkEGjlKFArYBePTOwfG5rhNnqinUIcaBvMvYlObHHVEN4mW9XfTEA4K2O\ncfROuCCXMeyoKwKQO0t9SwZTjLHrAYxyzo/M+dLXADQD2AmgCMDfL3D/uxljhxljh8fGxpZ7vYSQ\nBZwcsEOtkElNKxda5jPplKg0aSLGyvz8zS4M2Nz4pxvW433NZagya6VO4m3h5bL6kvlLB/EQG3cm\na6mPc462ESdqCnXzvlZXrMup4laSufomXbjqwTfw7MnBpJ1zpgA99pqp97qtODVgx1UbyqIeJ5cx\nrC4tgMWgBmMMBeqZQG2xzFRdiRAQxbOjr3fSBZ1KjuLw9Tda9Kgya/H6+TH0TrpQadagNrxTMFd2\n9MWSmboYwI2MsW4AvwPwPsbYf3LOh7jAC+A/AFwU7c6c80c45zs45zssFkvSLpwQMsPh8aNrbBqf\n3d+AmiIhaFmsYLy5wihlpkanPHjo1U5cvaEMextLAAj9qMQdf8f6bFApZFhfYVzWNVYkudfUsT4b\nBmxuvH/9/D8eZSYNRqe8OVOPQTLXmcEpYYxRnDtkF2N1+aGUM+hV8iWPFXfz/feRfijlDDdvrVrw\n2L+5aBVu2VYDABHLfHM7oM8mdkvvnoi9CL3f6kZNoU7a/csYwyVrLDjQOYGucSdWFemk16e8CaY4\n51/jnFdzzusA/A2AVzjnn2CMVQAAE35aNwE4ndIrJSRDnOy34d9f78S/PHsWb3eMr/TlAABODwgv\n5DvqCvHdmzdj3+oSabp8NM3lBnSOOeENBPHMySG4/UHce02z9PV1FUZ0jU/D4w/iWK8VGyqN8+Z3\nxUvqgj6VnGDq6eODUClkuDrKO/FyY/KXFAmJpiPcP6lzNP72AQuxhefyxdKKRCmXQa+SwxsI4ar1\n5VHrpUS3767FV97fBAAomNUhXaVY+HEKdUoYNAp0j8f+/fVbXdKbOtGlayxwegM4PTCFVUU6FOtV\nUCmS33tupSiWPmRBv2aMWQAwAMcB/I/kXBIhme1vf3MUfZNuyGUMvzrUg99/bg9aaszzjvuXZ8/C\n5Qvgux/anPJrOjUgjFDZXG1GkV6FfU0lix7fXGFEINzg77Xzo2i06CNmea2rMCIY4jg3NIWT/XZ8\nfFftsq+xpEANjVIm1VMsRyAYwrMnh3BFcykMmvl1JbPH1yz2x4WQ5RL7J3WNxTeiKZpT/XZUFWrD\no2Rib5Br1Cox7Qvitp01Md9ndjC12DIfYwx1xbHv6OOco2/Shd0NxRG3711dDLmMIRjiWFWkB2MM\nlSZN/tRMzcY5f41zfn344/dxzjdxzjdyzj/BOV/+bxIhGc7pDaBv0o2vXrkGh//h/SgzqnH3Lw9j\nJEq25cWzw/jDkf6IUQ+pcrLfjupCLYpiqLEAgHXhHX1He614p2sSl68tjfi6uKT35LEBeAMhbF01\nP1iMl0zG0FxuxNmh5Q8iPtQ1iXGnFzduqYz69TKjEExF+3chJJnE/km9ky74g3P3aEX67yP9GFqg\nlo9zjo/9/BA+9rNDGLR5YNYvV3FXAAAgAElEQVTG9lwGhH5UVWYt9q1e/E3UbAWzl/kWCaYAoW6q\nJ8ZlPqvLj2lfEDVzMuNGjRLbVxUCgJQ1rzRr82eZjxAyo31ESOk3lxtQqFfhZ3fsgNMbwHf/HNmC\nzeMPot/qhj/I8fK5kZRf18l+OzZXm2I+vr5ED5VchscOdMMXDOGyOcHUqiId9Co5njw6AABRM2+J\nWF9pxNnBqWXXMj19YgAGtQKXN5dG/bo0C5CCKZJCwRBH55gTpQY1AiE+L+A4P+yQAqwjPVb8r/86\ngf881BP1XJPTPjg8AbQOO3BqwA5zHJmp/+8DzXjwtpZ5Ew8WE5mZWvx+tUU6DNjcSwaLwMwQ45oo\nu38vWSMEe5HBVG48RymYIiQO7eGUvtizpbnciO21hbgw50W0c8wJMV7486nU9juyuXzonXRhU1Xs\nAY9CLkNTWQE6Rp3QqeTYWV8Y8XVx7IzDG0BJgRrVy2yLIFpfYcSUJyCl9h96tQNPvNcb04v0bG93\nTOCStRZolNELdC0FasgYMJIj9RgkM/VOuuALhKQddJ2zlvpGpzz4wI/fxD8/cwYA8Iu3ugBgwW7i\n/VbhObE/vEQfS48p0f4mCy6qL4rr2iOCqUUK0AGgpkiLYIjHtHmkzxoOpqLUbH58Vy3+/ppmaV5f\npUmDEYcn7ud/JqJgipA4tI04oFbIIl4oLAY1xh2R3cTFTsR7G4vxRvuY1Jk8FU6F523Fk5kCZvpN\nXby6BGrF/KBkXXipr6XGnPBMvrnWh19Ezw5OoXt8Gv/2l/P4+/8+hSseeB1nBmNb/vP4gxiwubG2\nLHo/HUAIFksK1JSZIiklZqqv3lAOIDKYeueCMFT41+/04tmTg3jhtPCmaqHaI/ENxn3XNuPr16/H\nx3atSuWlRyzzKZfYXFIdbj8iBkqLEftRRXsDVqhX4fOXNUoZtEqzFpznxnI8BVOELMHu9sPjF7r/\nto06sbq0APJZ6XSLQY0xR+Q2/M5RJ2QM+PxljfAFQnildTRl13e8Vyg+31gZXzC1rkIIRi5bG71l\niRhMJaNeStRcbgBjwjZy8WfynZs3Ytzpxa/f6Y3pHOIfo/pw/5uFVJg0GI4yMoeQ5Zjy+HHXY+/h\n/LBDqpdqqTGjzKhG16zNFe91T0KnksNSoMaXf3sMMsZw9YYy9Ey4oi5zD1jFIESHu/bVY0uSltYX\nYlDPbo2w+JslsZebmD1bTJ/VBbNOGXVjyFwztY3Z/zylYIqQRfiDIXzgR2/in54SOn+0jzgixjIA\nwpKSLxjClGcm+9Qx5kRNkQ57G0tgMajxwumhBR/jZL9NeteaiINdE1hXYYQpjhoLQAiiWmrMuGp9\nedSv76ovgkouwyVNyesPp1MpUF+ix9lBIZhaXVqAj++qRX2JHkMxFqKKuwGXCqbKjBoMU+NOkmQH\nOibwcusovvXsGbSNOFBp0sCgUaLRUhCRmXr3wiS2rSrE169fjxAHrttcgd0NxXB6Axh3zm/Z0W91\nwaBWxNT1PBkKYhwnAwAVZg1kDOifjCUz5YraSDcasS7M7s7+FibLaY1ASM57tXUUAzY3nj05hP99\n1VoM2T1oKovsBG4xCFvvxxxe6YWwY9SJ1RYhg3VxYzHeuTC54GN857lzODc0has3lMW9nObxB3G4\nx4rbd8ffumB1qQF/+tuLF/x6U5kBZ7519ZIvtPFaX2HEoa5J2N0+fPriegBCur83xt1CXeOxBVPl\nJg0OddFIGZJcx3qtAIS6Pb1Kju3hsSgNFj2ePj4IzjmmPAGcH3Hg2o0VuH5zBVy+AC5ZY5Ea5fZM\nTEuvG6IBm3vZI5vioVXKISak5EtkppRyGSpM2pgyU/1Wt5T1Xoo5XBdmnc7++XyUmSIkrG/SNa+2\n6Yn3+qCSy+DyBfHT1zsBYF6tjqVgJpgChB5IF8anpcnta8uNGLJ7YI8y0HPaG8DRXiumPAGMOuJP\ndR/ttcIXCGFvY/HSBycg2YEUINRNjTu98Ac53hfejVcVxxbpC+PTKDOqoVcv/l6wzKjBlCcAty/2\nAa2ELOVorxUbq4yoKdJi2hfEmvDzvNFSgCmPkHU60jMJzoGd9YVgjOG2natQYdJKI1SidRPvt7qT\nttEjFsJIGUXMz/GqQu2SNVOhEMdAuPt5LMReWrY0tI9JNQqmCIFQF3XND9/Ajf/nLakQdNjuwavn\nR/GpfXUoM6rx60NCTc+8ZT4xM+UUgiGh3wxHY/hFtjnc0+n8iANzvXthEv6gUD/RFuXrSznYOQG5\njMW9k2cliT2sjBoFttcKuwgrzRo4vAFMeZZ+Ue0en5ZGXCym3EjtEUhy+QIhnOy3Y1d9Me65WpgY\nsCb8/Bab3naNOfHuBSuUcoatNZG7ZKsLdZDLWNQ5dwNW94LDyVMlnmCqplC3ZGZq1OGFLxhC9SLT\nF2YzapRgbGawczajYIoQAE+fGMS0L4ghuwe3/PQAnj81hEffvoAQBz66cxWu31wJXzAErVI+7wVv\n9jIfMLOTbyYztXAw9Ub7mFT8KXZSjseBzglsqjLFVOyZKcQdfZetLZWaBcYzp+vC+LQ0jX4xyZ4F\nSMi5oSl4AyFsW1WIGzZX4NFP7pAax64tN0AuY/iX587i5XMj2FRlgnbObD2VQoYqsxYX5oxmsbv9\ncHgDaV3mA4S6qaV6TImqC7UYnvLAG1g40yu1RYjx+5DJGExaJawUTBGSG554rxfrK4z44xf2IsQ5\nPv/ro3jkjS7saShGXYleesFsKiuY1xjPpFVCKWczwdRYZDBVYdLAoFHg/PD8QahvtY9jT2MxivUq\ntA3Hl5lyegM40WfDnhQt8aVKqUGDe69Zi89f1ijdVmESXnyHlmjgZ3f5MTHtW7JeChCGHQO5se2a\nZIaj4XqpbbVCu5D3NZdJvc7KjBr89OPb0G91o33UiZ0LZItri3XzmnuKO/mqzLFldJKlQK1Ysvu5\nqLpQaGOw2HNUatgZY2YKEPpp2aKUQGQbKkAnee/0gB2nB6bwrQ9uwLoKI16/53KcHZrCmcEpqRZp\nc7UJGyqN2Fk3/wWSMQZLgRrjzpnMVJlRDWM4W8QYw9oyA87PCZaG7MKL7q07quEPhtA2Gl8w9V73\nJAIhnrJ6qVT6wmWrIz4Xs31Lzem6ILVFKFj0OICW+UjyHe21odyokYL/ua7aUI7N1WY8/HonPn5R\n9E0h9SV6PHlsAJxzacOJ+HufzpopACjQKKGSx1arKQZIfVYX6hZ4MyM+1yoX+PlEY9YpKZgiJBc8\n8V4f1AoZPrilCgCgUcqxbVUhtq2aqXdgjOGpv714wV0vJeFeU0B4J19p5B/7teUGPH1iMOIF9K32\ncQDAvtUW9FvdePJo5AusyO7yY9ThQdOcWq2jPVbIZQw7arOnXmohFoMaChlbcpnvwriQ9YslM6VX\nK2BQK2iZjyTN0R4rttUu3v+p3KTBN2/csODXa4v1cHgCmJz2SUO4+8PLY+le5jNqFFAv0bBTJAZ6\ni9VN2V1+qBWyecubizFrlQltvsk0tMxH8po3EMSfjg/g2o3lS/ZpUshlC7YusBQIwVQgGML5YQfW\nhbuLi5rLDXB4Ahie8mB0yoPvPn8O//+fz6HUoEZzuQFNZcLolqE5f/h7J1y44f++het+/JaUQhe1\njzhRW6SL64UrU8llDOUmzdLB1Ng0ZGxmttdSyk0aCqZIUgza3BiwuSPeZCWiLsqOvgGrGxqlDMUx\nDipPli9ctnrRwG+2cqMGChmTAr9obC5/XDMFgdxZ5qNgiuS1tzvG4fAE8MGWqmWdx2JQY8zpxYXx\naXgDIal7uGhtOLg61W/HJ37xDn7+5gXsqi/GL+7cCZmMSdurZ+/oaxtx4MMPH8CUxw+ZDPj+i+cj\nztkx5pR2DOaCWIaedo1Po6ZIB1WM76bLTRoMUuNOskycc3zz6TNQyWW4Yl3Zss5VG96JOntH34DN\njUqzNmljm2K1vtKIS9bE1pRXIZehwqyRxsVEY3X54popCAi9pmg3HyFZ7vlTwzBoFNi7enl1RxaD\nGhNOL06H58vNC6bCS3T//MxZtI048bM7tuPh27djU3ienthuoX3Wjr6v/+k0QiGOP/yPPbhrXz2e\nOj6I0+E5fP5gCN2zelnlgsoYAp8L49MxLfGJGi0F6Bx1Rh3fQUis/utwP148O4J7rl4b1+9fNDVF\nWsgYInb0CT2m0lt8ngihPcIimSm3P+4O7madEtO+IHyB7B52TMEUyVv+YAgvnh3B+9eVRR30Gw+L\nQY0QF7oiK+VsXpBj0ilRbtRgwObGzVur8L7myHe3hXoVLAa1lJk62mvFOxcm8fnLGrG61IDPXdqI\nQp0S33uhFQDQM+FCIMSx2pJDwZRZi2G7B8FQ9MAnFOLoHHNK/Xxi0VRWILW8ICReE04vHn69E//8\nzBnsaSjGXfvql31OtUKOuhJ9RBZ6wJb+HlOJqC7UomfCBX8weuBjc/kSWOYTG3dmd3aKgimStw51\nTcDu9uPajdFn08VD7IL+ZvsYVpcaoi5DbawyoaRAhX+6fn3Uc6wpK8CpATsCwRAefq0TJq0SH71I\nmBxv1Chx1756vNk+jtEpz7xeVrmg0qxFIMSlQv65+qwuePwhrCmL/XsWg01xIC0hseq3unDJv76K\n+59vxcYqE35w25Z5bVEStbbMIPWVG3V4MDntQ2MMvdNW2r4mCyamffj8fx6Rhr/PZnP5E1rmE++b\nzSiYInnrz6eGoVPJY64ZWExJuHHnyJR3wblU3/3QJjz1xX0oXKDI9IrmMrQOO/Chnx7AX8+N4I49\ntREjUy5dI4xeOdg1IQ1UzaWaqaXaI4h/fObualxMk7R8Gn93eZLfTvTZMe0L4vFPX4QnPrdnwXYI\niVhTZkD3xDQ8/iDODAr95zZWmZJ2/lS5cUsl/uWmjXi5dRSfefxwxPI551xY5oszMyVmsqzT2Z2Z\notYIJC9xzvHXsyO4vLlUarq3HGJmCpgZlzLvmDmDTef69L56FOlV+Mc/nYZaIcOde+sivr6+0gij\nRoGDnRPwBkKoMGlQsMR8umwidkEfsrsBzN8xJS6LNMURQBbpVSjWq6RMHiGxGrAJtUEtNYu3QkjE\n2nIDOBfaqJwJ10GKkwEy3e27azHu8OJHL7djYtqHkvBrn9sv1D3Fm5kSj8/2+Xy580pMSBw8/hDG\nnV5srEzOu8HZgdJCwVQsbtpahV0NRbC5/NKLlEguY9jVUIwDnRMwaZU5tcQHCPP5gIVHyrSPOFBp\n0sQ9OqextICCKRK3QZsHBrUi7oLqWIgbTs4PO3B6YAp1xTqpyW82aFklBJhdY9PS65S4TGdOoABd\nuH92Z6ZomY/kJXEWVGGcKemF6NUK6ML9nubu5ItXhUm74Dn2Nhajd9KFc0NTcRViZwODRolCnRKn\nBuaP3QGA8yPOuJb4RE2lBWinHX0kTv1Wt5QtTba6Yh1UchnaRhw4PWjHhixY4putsWRmqLNIfE01\nJ5iZslLNFCHZR3oXlaRgChCyU+VGzYI1Ucmwt7EEAISdfDmWmQKAD7ZU4YXTQ/OK0IPhnXzi0Oh4\nNJUWwO72Y8yZ/V2WSfIFgiG80joyL9getLlT1pFcIZehwaLHOxcm0W91Jy1Dni5VhVqoFDJ0zWrv\nYE/wNVWnkkMpZ1SATkg2siX4Lmox6yuMuHh1SdLOF82asgKpS3IuBlN37KmFP8jx23d7I27vmZiG\nLxCKq15KtLpUCMA6Rmipj8z317Mj+PRjh/HOhcmI24VGmpqUPe7acgOO99kAAJuyLDMllzHUF+sj\nMlNizVO8wRRjLCcad1IwRfKSNQWZqZ98fBu+f+vmpJ0vGsYY9oQHG+diMNVgKcAlayz49Ts9Eb1s\nxJ18axJZ5gu3UugYo2CKzNc6PNPbTeT0BmB3+1FlTl0jzdm/yxuypPh8tgaLHl1jM5mpmdKJ+N+g\nFuqU0v2zFQVTJC+JDeISeeIvhDGWlnEQn7q4Dp/ZV5/2OV7pcueeWoxMefGXM8PSbWJrg0QCyFKD\nGgaNIqK7PCEicXPCsV6bdJu4CSKlmalwMFVl1qa0NCBVGix69E7ONPAUl+kSKdg3a7N/Ph8FUyQv\nLeeJv9K21xbhH69fn/Y5Xuly+dpSVJo0ePr4oHRb26gT1YXaiL5bsWKMoam0IKLjNCEiMZg63meT\n6qbEXmfVKaqZAiDV/22syr6sFAA0lBQgEOLoDQ9gt7v90CrlCbWaMeuUFEwRko1sLl/CT3ySWjIZ\nw6VrLTjYOYFA+F3v+eGphJb4RBurTFJ3eUJEgWAIXeNOmHVKjDm8UhA1YBX+n8plviqzsGv3iubl\nDU5eKQ3hju2d4WDUOh3/KBlRoU5Fy3yEZCOry5/UeimSXPubLHB4AzjRb0PPxDTaRpzYVV+U8Pl2\n1BXB5QtK9TGEAEDPpAv+IMdNLVUAZpb6Bm1uKGRsyUa7yyGTMTz/lf34yM6alD1GKjWEW7OIO/oS\nGXIsMuuUsLn9Wd2+hIIpkpdsLn9Sd/KR5NrbWAzGgDfbx6Xlvuu3VCZ8vh21Qkf197onlziS5BNx\nie+GLRVQK2TS7roBmxsVZg3kSZrFl4tMWiVKCtTSjj6by5dwDapZp4IvEII7yry/bEHBFMlLwhOf\nMlOZyqxTYXO1GW+2j+OpE4O4qK5Imt2XiEqzFlVmLQ73WJc+mOQNMZhaW27E5moTjoV39A3a3KhM\n4iy+XDV7R59tGdl+8bU4mxt3UjBF8pLNTct8mW7/6hIc6bGiY9SJG1oSz0qJttcW4nD3ZFYvJZDk\nEkcUFagVaKkx4/TgFHyBEAasqWvYmUsaLfqIZb5Es/3i/Y71Zu+bHQqmSF6yuXy0zJfh9jcJDVAV\nMobrNlUs+3w76woxMuVFvzX67D+Sf9pHnVgd3tiwbVUhfIEQfvNOD4anPMvKhOaL5nIjJqd96Jt0\nhV9TE3uDevHqYqyrMOJ/PnEcz54cXPoOGYiCKZJ3OOdCSjoL2yLkk62rClGgVmBfUwmKktCHZ3ut\nUMB+uIfqpggQCo8oErvqX7GuDJeuseCbz5xFiIOCqRiIb3ieOzUEf5An/Jpq0Cjxu7t3Y2tNIb70\n22PozMIGuxRMkbwxOuWBPxiC0xtAIMST2rCTJJ9KIcPjn74I37l5U1LOt7bcAINagfe6s3cpgSTP\ngM0Njz8kNYJVKWT499u3Y194JFR1YeraIuSK+hI9aot1ePLoAIDlNUE2aZX4h+vWgXPgwqzO6tmC\ngimSF/omXdj/r6/iVwd7Zhp2Us1UxtteW5i0DIFcxtCyyhzR6ZrkL3G80Oyu+hqlHD+7Ywd+8JEt\n2N2QeCuOfMEYw+VrS3E+3BB3ua+pYgZ6cjr7ek5RMEXywo9eboc3EMLJfpsUTFFmKv80lxvQNeZE\nKERF6PnOHn4dmLuErFXJ8aFt1VDI6c9jLC5da5E+Xu5ranGBcP8JCqYIyTydY0788Wg/AKHgVOy0\nS7v58k+jpQDeQEjqdE3yl8sn9DTSqWgKwnLsaSiGWiGEEst9TdUq5VArZJic9ibj0tKKgimSc+Zm\nHX74UjvUCjlu3lqFjlGnlEKmPlP5R+zanI0FriS5xAaRWhoptSwapRx7GosBYNmbehhjKNarKDNF\n0mfU4YHdnb0NzlLF4fGj5Vsv4pkTwvbaAZsbz54cxJ1767C7oQjeQAinB+wAQK0R8lCjOE8sCwtc\nSXK5fQEAwrIeWZ5bt9dgdWkBCpOw67aoQEU1UyQ9bC4fPvCjt/Cl3x5b6UvJOL2TLkx5AvjVwR4A\nwDMnBsE58NGLatAU7icjjhRJdI4UyV5FehXMOiVlpgjc/iBkDFBRbdSyXbe5Ai999VIok/CzLNKr\nYaVgiqTDt549i3GnF2+0jaFv0hXTfb7z3Nm8CL6G7R4AwLvdk+ibdOHp44NoqTGjtlgv7do5PTgF\ng1qRlCc+yS6MMTRaCqR5YiR/uXxB6FQKMEbz9zIJLfORtHj1/Cj+eHQAt26vBmPAfx3ui+l+b7SN\n44XTQ3CFU9u5aigcTAHAD/7ahrNDU7gxPCDXqFGiwqRBMMSpLUIeayjR0zIfgccfhIbqpTJOkZ6W\n+UgafO/5VjRa9Pj2zRtx6RoLfn+4H8EltnmHQhzdE9PwBzkO53jDwpEpD+Qyhu21hXjy2ABkDLh+\n88woEjE7RW0R8ldjaQHGHF6qOcxzQmaKgqlMU6RXweULwhPeIJAtYg6mGGNyxtgxxtiz4c/rGWPv\nMMY6GGNPMMbor1OKOTx+tA478MGWKqgVcvzNzhoMT3nwRtvYovcbcXjgDYQAAAc6J9JxqStmyO5B\nqUGNW7ZXAwD2NBaj1KiRvr4mXDdFbRHyV2N4Rx8t9eU3NwVTGUns+5VtS33xZKa+AuDcrM+/B+BB\nzvlqAFYAdyXzwohgZMqDaa+wNHduSOgyu7HKCAB4X3MZSgpU+MOR/kXPcSE81VujlOFg53gKr3bl\nDds9KDdp8IFNFVhVpMPtu+sivi7O4aKdfPlL3NHXRUt9ec1Ny3wZSeqC7szBYIoxVg3gOgA/D3/O\nALwPwB/ChzwO4KZUXGC+u/Xhg/jWM2cBQNrSv7HSBECYJXVFcxnebB9DIBha8Bzd40KR+vWbK3Fq\nwJ7TyxtDdjcqTBqYtEq8ce/luGZjecTXxR19NOQ4f9UU6aCQMdrRl+coM5WZisVgypWDwRSAHwK4\nF4D4F7sYgI1zLlYz9wOoSvK15T2Hx4/eSRdebh0F5xynB+2wGNQRy1b7mkow5QngZDjQiqZ7Yhoq\nhQwf2laFEAfevTCZjstfEcN2D8qNC89yayorAGMzYwtI/lHKZagt1lEwledcviA17MxAM/P5sqsL\n+pLBFGPsegCjnPMjiTwAY+xuxthhxtjhsbHFa3tIJHF5btzpReuwA2cGprCx0hhxzMWrS8AY8Fb7\nwst33ePTWFWkw/baQqgVMhzI0aU+h8ePaV8Q5Sb1gscYNUr84s4d+Piu2jReGck09SUFUsaW5CeP\nP0gNOzNQsV54/Z7IwWW+iwHcyBjrBvA7CMt7PwJgZowpwsdUAxiIdmfO+SOc8x2c8x0WiyXaIWQB\ns2s6/np2BO2jDmysMkUcU6RXYVOVCW+2Lxyodk9Mo65YD7VCju21hTjSk5s7+sQeU+WmhTNTgFBr\nZjEsHHCR3FdSoJJmNJL8RJmpzGTUKiCXsaxrj7BkMMU5/xrnvJpzXgfgbwC8wjn/OIBXAdwSPuxO\nAE+l7CrzVNf4NGQMqCvW4ZcHexDiwIZK07zj9q0uwbFeGxye+bVQoRBHz4QL9SU6AECVWYtxR3al\nT2Ml9piqMGmWOJLkO5NWmdO1g2Rpbj/VTGUixhgKddnXa2o5fab+HsBXGWMdEGqofpGcSyKirjEn\nqgt1uLy5FONOIQASd/LNtr/JgkCI41DX/FqooSmhLUJdibCDyaxTwurKzT8iUmbKSMEUWZxRq4Q3\nEMq6XjYkedy+IDQUTGWkbOyCHlcwxTl/jXN+ffjjLs75RZzz1ZzzWznnuZnuWEEXxqdRX6LHJU3C\n8mihTokq8/wlrG21ZmiVcjzw4nn83e+O4anjMyuu3eG6q/piMZhSwe3PvoZosRAzU2UUTJEliHMZ\npyg7lZcCwRB8wRB0SsXSB5O0K9Krsm4+H3VAz1Ccc1wYn0aDRY9dDUVQyhk2VpmizpFSK+S4Y28t\n3P4g3u6cwFd+dxxPnxgEINRLAYjITAHIySWO4Sk3SgrUUCno15osTgymbDn4PCBLc4ffTGpV9FqR\niYoKsm+Zj8LyDOPw+KFVyjHu9MHlC6KhRA+dSoF/vG496sMBUTRfu3YdvnbtOnj8Qdzx6Lv4X78/\njkAwhNMDdqgVMmnpy6wVtp3aXP6cy+AIDTupsJwsTQymcvFNBVnaTDBFfwIzUTYu89FvUgbhnOOa\nH76JK9aVSs0mG8KjL+7cWxfTOTRKOX52+w585N8P4qu/PwEAWFNWAJlMyGgVhjNTthzcyTRk96C6\nULfSl0GygBRM5Wj9IFmc2xcOpmg3X0Yq0qtgd/vhD4aglGdH9pCCqRXEOccn/+M93LqjGtdvrsS4\n04cBmxu/e7dPGneyWDZqISadEk998WIc77PhVL8d62f1pjKFg6lcLEIfnvJgZ13RSl8GyQKUmcpv\nYmaKdvNlJrELutXlQ6khO1ZQsiPky1EDNjdebxvDC6eHAQAdo0JHZl8whH9/vRNapTzhnWkapRy7\nG4rx2UsacPHqEul2MUizu1cmMzU65cG3njmb9AJ4ty8Im8uPcmqLQGJAwVR+c1FmKqMVSl3Qs2cF\nhYKpFXRmcAoA0DosDDDuCI+32FlXKLUzEJfnkqVwhTNTfz41hEffvoA32pLbDb/fKnSzjrbbkZC5\njBRM5TWPGExRZiojFVEwReIhBlMXxqfh8QfRMeKAXiXHN27YAABosMS/xLcUrVIOlVwG2woFU23h\n7NtrSQ6m2kaE864uLUjqeUlukssYDGoFBVN5ijJTma1QXEHJonIUqplaQWcHheHEwRBHx6gTHWNO\nNJYWYGOVCV+7thlbasxJf0zGGMw65Yot83WEg57XwsObo7V6SETbiAMyRsEUiZ1Rq6Q+U3mKaqYy\nm9jCJ5tal1BmagWdGZySOpqfH3agY9SJ1eHde5+7tBG7G4pT8rhmnRLW6fT/knLO0TbqgEGjwKDd\nI2WT4nW4exLBEI+4rW3EgdpiPTT0TpPEiEbK5C9xNx+9XmSm2S18sgUFUytkctqHIbsH122qhEoh\nw+GeSYxMedGYhsyKWauCbQUyU+NOH2wuPz520SoAwGvnR+M+x4k+G255+CB+dbA74va2EQeaKCtF\n4kDBVP6izFRm0yhlUClkK/J3KlEUTK2QM+Elvi3VJjSVFkg7+tKxTGXWKVck4m8fEQrt9zdZ0Fxu\nwKsJBFPvXJgAAGHwczg75Q0E0T3hwpoyQ/IuluQ8Cqbyl4sK0DMaYwxmrTKraqYomFohpweE4vP1\nlUY0lxul3XU5HUyFi/TNjVUAACAASURBVM/XlBXg8uZSHO62YsoT33Uc7raCMaBrfBpvdowDEAr4\ngyGOpjLKTJHYUTCVv8TMlEZBwVSmWqm/U4miYGqFnBm0o8qshVmnQnO5kFFRyhlqi1LfwdusU8G6\nAh3Q20YcMGoUsBjU2N1QjECI42x4R2MsOOc40mPFDZsrUVKgxuMHusPnFYM0ykyR2Jl0FEzlK7cv\nAI1SlvTWMyR5VqocJVEUTK2Qs4NTUmfy5gohCKgv0UORhtb5Zp0S3kAo6Y0zl9I+6kRTmQGMMSkD\n1zkWvQi9a8yJPd99Gf/6QqtULHphfBoT0z7saSzGx3atwqvnR9E9Po22YQfkMpaSVhIkd5m0K/M8\nICvP7Q9CR3P5MpqJMlMkmpP9Nrh8AQBC8fmFiWlsrjIBAJrLhaAqXdv6V2qnRMeoE2vCS3EVRg20\nSjk6R6ejHvvCmWEM2T34yWuduPLB19EzMY3D3VYAQlPTT+xaBY1Cjn9+5kx4J58OakrZkziIjTup\nPUL+cfmC1GMqw5mzbBmegqk0GLC5cdNDb+P/vNIBAHijbQycA/vXWAAAFoMal6yx4IrmsrRcz0wX\n9PSlUMedXkxO+7C6VMjCyWQM9SV6dI1Hz0y92TaOdRVG/O7u3bC7/bjnDyfxbvckCnVKNFoKUGrU\n4J6r1+LV82N49fwo1tISH4kTjZTJXx5/kIrPMxzVTJF5/nxyCCEOPHNiEJxzvHZ+FMV6lZSZAoBf\nfvoifHh7dVquRxx2nM5f1PZwXdPs9gWNpQVRl/lcvgAO90zikqYS7G4oxtevW493L0ziT8cGsL22\nUGr0eefeOmxbZYY/yNFEwRSJEwVT+YsyU5nPrFPB7Q9mzTI8BVNp8OypIShkDP1WN4712fB62xgu\nXWNZseLHmWW+9GWm+sKz8+qKZ+qaGi169Fvd854s71yYhD/Isa9JGNB8645q7G8qQSDEsaOuSDpO\nLmP411s2o9Sgxp4UNTgluYuCqfzl9lFmKtOZsmwZnoKpFOubdOFEnw13X9IApZzh/j+3wury49K1\nlhW7pkJ9+lv1i08I8QkCAI2WAnAuFJbP9mbbONQKGXaGAyfGGL77oU3Y21iMazaURxy7utSAd//h\n/djTSMEUiQ8FU/lLKECnYCqTZdtIGQqmUuy5U0MAgI9etAr7myx4t3sSMgZc0rRywdRKFKA7PELx\nfYFmZgeNuPuua2xOMNU+hovqiyJGPVQX6vCbz+5GXQnt2CPJQcFU/nLTMl/Gy7aRMhRMpdhzJ4ew\npcaMmiIdrt9cAQDYuqoQhXrVil2T1Ko/jct8Dk8AepUc8llLmw0l89sjDNs9aB91Yn94iY+QVDGG\nA3sKpvKPi5b5Mp6UmVqBnoiJoGAqhU702XBqwI4bwkHU+9eXwahR4NqN5UvcM7UYYyhM804Jh8cP\ng0YZcZtWJUeVWRsRTB3vE9of7KqnZTuSWgq5DAa1goKpPOTxU2Yq04mZ42xZ5qOuZSn0wF/bUKhT\n4m/Cg32NGiUOfO0K6DLgSWzWprcLusMTgEEz/9etsbQgYplP2vVHo2FIGhizrJcNSQ6Xj2qmMp2Y\nmcqW+XyUmUqRdy9M4o22MXz+skYUqGeCiAK1IiNGGJh1yrRG/A6vP2ow1VCiR+eYE5wLQ4s7xpyo\nMmupOzFJC5NWmTW7hUhycM7hpsxUxitQKyCXsawZKUPBVApwzvHAi+dhMahx++66lb6cqMw6JazT\n6fsldXoC85b5ACEz5fIFMTzlASB0SW9MUyd4QmjYcf7x+EMAAC29YctojDGYtdnTuJOCqRQYd/rw\nzoVJfHJvXcYWOVaZdei3uqWMUKottMwnDnk+PTCFUIijc8yJ1RYKpkh6UDCVfewuP471WhO+vzvc\n106rpD9/mc6U5hWU5aDfphQQdx/UFOlW+EoWVl+ig9sfxMiUNy2PN7VAMLWpygSVXIbDPZMYsLnh\n8YfSNqOQkAKNAtPe7OiwTASPvNmJ2x45BH8wlND9xRmpVEqQ+cxaJdVM5TMxkjZr5y9rZQqxX9Pc\nhpmpEm03HwBolHJsqjbhcLcVHeFdfRRMkXTRKuVSpoJkh/YRJ3yBECYTLFMQJy5oMnTVgMww61RU\nM5XPxEjalMnBVHisS/dE6oMpXyAEbyAEgzr6O8EdtYU41W/H2cEpABRMkfTRquRw+yiYyibiG8Ax\nR2JZdVf43zsTdlWTxVHNVJ6TMlO6zA2mKs1aqOSytARTDo/w84i2zAcAO+qK4AuG8Mej/SjSq1C0\ngg1NSX7RhDNT6aodJMsTDHH0TApzPpcKpoIhjp4or29i8Jyp9axkhklHy3x5zR5lDl2mkcsYVhXr\n0J2GZT5xlEy0ZT4A2F5bCADoHJum4nOSVuL2eG8gsfobkl6DNjd84X+rpYKpJ97rwxUPvI5Rhyfi\ndpefgqlsYdaq4PAGEq6PSycKplLA7vKBsYWDh0xRV6xD97gr5Y8zE0xFz0wV6VVoDM/po7YIJJ3E\nHV1LLfWdHrDjV4d60nFJZBGzM+ljzsWDqdfbRhEIcakRsMjlFXfzUTCV6cTVnWzoBUfBVArY3X4Y\nwg3HMlldsR7dE9MIhVK7xDGzzLdwcLmzrggA1UuR9BKzE4sVoY85vPjUY+/hG0+dzop3yLlMzKTL\n2OKZqVCI450LkwCArrHIYOporxUquSyjd1sTgTSfj4Kp/GR3+2HWZX7dT12JHt5ASGqYmSpTS2Sm\ngJmlPgqmSDpplIsHU8EQx989cQxjDi9CHBhNsOiZJEfX+DS0SjnqSvSLBlPnRxxS4XLnrHFVnHP8\n5cww9jWVREymIJlJms+XBXVTFEylgM3tz+h6KVF9SXp29Dm9QjBlXCQzdf3mSvzjdeuwt5EGHJP0\nEZd6Flrm+89DPXi7YwLXhYeVD9vdabs2Ml/3+DTqSvQoNagXDaYOdk4AACwGNbpm1YWeHZpCv9WN\nq9aXpfxayfKVGjQAhFq5TEfBVAoImanMD6bEXlOpqJtyePx4/tSQ9DGweGZKq5LjM/sboJTTryRJ\nH3GZz7NAZupg5wTq/x975x3eZnnu/8+rYXlvO7ZjO46dTXZCCIEywoa2UFZpoVBKW1oKdO/T9vS0\ndLccelrOj5Ye6IBS9ixlrwIJhJAdZznDSbynJFv7/f1x65VkW7JlW/J8PteVS7b8SnrsSO/7fe7x\nvQszuGX9HAAaupIbxVUMzqG2HmYXplOUlTpozdSGujYq89NZW13QJ8333M4mTBqcrcTUpKCmOAOL\nSWN3Q/d4L2VI1JUrCXT1eMmeBJGp0uxUbJbk2CP8+a1DfP6+zRzr7A0VoGcOIqYUivHAiEz1xIhM\n1bU6mFOcSWlOGgANnUpMjRdef4Aj7T3MLsygKDN2ZMqol1pbnU91YUZwsoL8/z6/s5HVs/IpzLSN\n5dIVI8RmMTOnOFOJqelKV693QrufG5hMGrMK0pPigv52nYTZD7c5sbu8pFpNKuqkmHAMVoDu8wc4\n1NpDdVEG2akWMlLMKjI1jhzt6MUf0KkqyKAoy4bD7QuNhomkttFOV6+XtdUFVBdloOtSynCkrYfa\nRjvnnqCiUpOJhaXZ7G6wj/cyhkSFChKMruuTpmYKpKNvX7Nj6AOHgdvnZ9MhGUR6tL03OOR4cvw9\nFNMLIzIVLc13tKMXjz9ATVEmmqZRkpNKg6qZGnP2Nzt4bW8LBUEz39mFGWiadEq32j1UFvS9jL0X\nHIK8ZnZ+qHC5rsUZ2jSed0LJWC1dkQAWlmbx2PvHaHd6JrShsxJTCcbp8eMP6JOiZgrg5JoCnt/V\nxL4mO3NnZCXkObfWd4VMEOs7eoJiSr3VFBOPUGQqSpqvrlU2GTVBI9my3LQBkSld13H7AqGuQEXi\nuefNg9y38Ujo+9mFGTiD/18tDheVBX0tDo539mI1a5TlpJEX7Kqua3Hw6OZjrKnKV5YIk4yFpdkA\n7G7o5pQ5heO8mtiovEuC6eyRoYyTJTJ10dJSTBo8ufX4qJ7n1T3NfOTON+nq9bKhrg1Ng7x0K/Xt\nPXTHGHKsUIw3aYNYIxxolkiGYShbkj0wMvX3d+pZ+9OXQp97ReJp6HJRnpfGuYtmsLY6n/yMFIqC\nNU/R6qaaulwUZ6ViMmlk2CyU5qTy6PvHqGt1cvnq8rFevmKURIqpiYwSUwkmPEpm4oYjIynOSmVd\nTSFPbj0+qvlkz+1s4v0jnfzPS/vYUNfGwpJs5pdkUd8hab5sFZlSTEAG85k60OKgICMl5BlXmptG\ns93dx7hzS30HnT1eHtl8bGwWPA1p7HIxf0YWf7h2NQ989mQ0TaMoaxAxZXdRnB0uMK8uyqCuRfyp\nLlxSOmbrViSGwkwbxVk2dikxNb0whjJOlsgUwIeXlXG4rYetR7tC9x3v7GVDsIg8HnYdl8fe+9Yh\nNh3uYG11ARV56dS392B3eVWaTzEhsVlMaBq4oqX5WpyhFB9AaU4qej/jTsNW5L6Nh9Ww5CTR2O2i\nJCe1z335GSkxXdCbut2UZIePry6U/8MLlpQoo85JymQoQh9STGmalqpp2juapm3VNG2npmk/DN5/\nr6ZpBzVN2xL8tzz5y534GJGpyVIzBXDe4hJSzCae3CKpvnanhyvvepuP/3EDW+s7h3y8zx+gttHO\nJcvLSLWa8fgCnFxTQEV+Os12N60OD1m2yfP3UEwfNE0jzWqOGZmqKc4IfW9c0CONOw+2OclLt1LX\n4gx1sCoSh8vrp93p6SOOQAa1F2TaojrSN3W5mBFxvDFV4fJVKsU3WVlYms3+ZntoyPVEJJ7IlBtY\nr+v6MmA5cL6maWuDP/u6ruvLg/+2JG2Vk4jO3skXmcpJs3LWwmL+tvEwd7y4j5vue49mu5v8DBvf\nfGRb1Dfwu4fa+dYj2wgEdA60OHH7Apw+v4ivnjuPLJuFNbPzqcgXb56uXhWZUkxcoompDqeHNqcn\nFNUAKAt6TR0Pek3ZXV5a7G6uPbmK3HQr9204giKxNAVHXfWPTAFRvaacbh92t69Pmu/SlTP5n4+t\n4ORqNV1hsrKoLBuvX2d/gjvPE8mQYkoXjN/AGvyn4tkxmIyRKYAfXbKYcxfN4PYX97Khrp2fX7aE\nn1+2hNpGO3e+un/A8c9sa+CBd+vZdqyLncEU3wllOVx/ymze+9455KRZqcgLd82oAnTFRCXVaqbX\n03fDEOrkixqZkgv84TZJ8S0szeLSFeU8t7Mxqu+RYuQY3ZOGaWokRVm2AS7oRqQqMpKVlWrlQ8vK\nQnYKisnH/GCn+b7miZvqi6tmStM0s6ZpW4Bm4AVd1zcGf3SbpmnbNE27XdO0qJaymqZ9VtO0TZqm\nbWppaUnQsicunT1erGYt1CU0WSjMtPG7j6/kvk+fxB1XLecjK8o5a+EMPrSsjDtfPTCgW+loh6Q6\nntvZyM7j3dgsJqqD42lSLPK2imxBVpEpxUQlLcVMr7evCDKG40bWTPU37jRmvlUVZrCiMhdfQKe+\nXflQJZJBI1NR5vMZQndG9sDjFZOXWUH7iyNtiR99lijiElO6rvt1XV8OlANrNE1bDHwbWACcCOQD\n34zx2D/our5a1/XVRUVFCVr2xKWr10tOWsqk3QWdMqeQi5fPDH3/+dNr8PgCPP5+326lox3yphYx\n1cWC0mws/RzOizJtIWGlRskoJippVvMAn6kDLQ5SzCbKI6Kr/Y07DwXF1Kz8DCqDG4cj7RP3ZD8Z\nMYTrYGIqEAgnSprthphS42KmEqlWMyXZqRya7GLKQNf1TuAV4Hxd1xuCKUA3cA+wJhkLnGx09XrI\nSZs6wmFRWTZLy3N44N36ULeSrusc6+glK9VCXYuT9w53cEJZ9oDHmkwa5XkSnlfWCIqJikSm+oqp\nxi7pIDOb+m6KIo07D7U6KctJJS3FHIrCKjGVWBq7XGTZLFG78EqyU/EFdNqc4ai5EclSkampx6yC\ndA4nYY5sooinm69I07Tc4NdpwDlAraZppcH7NOASYEcyFzpZ6Or1hnxppgofPbGC2kY724LWCd29\nUuT50dUVAHj9elQxBYTqplTNlGKiIgXofWumWh1uCjMHfo4jjTvrWp1UBVPbeelWMm0W6pWYSigN\nXb1Ro1IAM3ONhoBwarWxy016illZIExBqgoyJn1kqhR4RdO0bcC7SM3U08B9mqZtB7YDhcCPk7fM\nyUNnz+SZyxcvH15WRprVzAPv1gMyIgZgdVUeyypyASk+j4bR0adqphQTlTSreYDPVKvdQ2HmwFRR\ndVEmTd1u9jfbOdTmZHZQTGmaRkV+uhJTCcaIEEajLCimjkWIqSa7i5Ls1ElbZqGIzazCdFodbhzu\nidnkEU833zZd11four5U1/XFuq7/V/D+9bquLwned01Ex9+0pqvXS+4UE1NZqVYuXFLK01uP4/MH\nQsXn5XnpXLpiJnnpVhaURJ/rpyJTiolOtDRfq8NNQRQxdeXqctKsZm57ZjedPd6QmAKozE9Tab4E\n09DlonQYkanm7r7u54qpw6x8+axN1CJ05YCeYLp6vORMMluEePjA3ELsbh/7Wxyh4vPyvDSuPXkW\nb3/7rJiDXs9fXMLH1lRSkTewtVmhmAik9vOZ8vkDtPd4KIqS5ivItHHN2kpe2SOdyVUFkWIqnSPt\nPcoJPUF4/QFaHG5KotgiAGSnSXdlZGSqsdul6qWmKEZH30Stm1JiKoH4/AHsbt+US/MBoXTeliOd\nHO3oJdNmISfNiqZpMYUUwKyCDH566ZIBnX4KxUShf5qvvceDrkNhVvQIx2dOq8YW7FKdXRQWUxX5\n6bh9gagjThTDp8XuRteJGZnSNI2y3LRQZErX9QGjZBRTB0NMTdS6KXWFSyAv1TYDk8v9PF6qCtLJ\nSbOy9WgnRzt6KM9LU3UJiilBWoqpT2SqzSHdYdFqpkCGg1+zdhaZNksfY1rV0ZdYQrYIg4gjEVNy\nXFevF48vQLESU1OSrFQrhZkpKjI1lfEHdG79+/vc+Nf3qCnK4PzFJeO9pISjaRrLKnJ5PxiZivTf\nUSgmM2lWM76AjtcvHX2tQVftWGIK4NsXLODFr5we8lEDlNdUgmkcxGPKYGZeODLV2D2OHlNtB0Cl\nd5NOZX56aPLAREOJqQRQ29jNk1uP88l1VTz7xdOijj6YCiwvz2Fvk3QxlasaKMUUwUhTG9GpsJiK\nbXFiMZsGXORn5qahaUpMJQrDgiJWmg/kb97m9NDr8dPUPXCUzJjQcQh+txo2/O/Yvu40pKogQ0Wm\npjJ1wdETV66u6LNTnWosr8wloIPLG1BiSjFlSEsJiqlg3VSrPZjmi1EzFQvDpVmNlEkMTd0ubBbT\noGUTZbkinI539Y6fYefRTaAH4K3fgk/VyyWTWQUZHO9y4erXfTsRmLpX/jHkYHCsRGSb9FRkaXlu\n6GuV5lNMFYw5miEx5XCTYjGRNQLjR+U1lTi6er3kpQ8+mqssJ2yPsCs4I3TMrREatsqtvQG2PpCc\n13j3T3D47eQ89ySiqlCuOxPxM6bEVAKoa3EwMzcttMOdqhRm2kIRKRWZUkwV0vql+VocbooybSNq\nsKjIS1dpvgThcPuGnOkZMu7s6OWl2iZOnVOIzTLG5+GGrVC6DEqXw5t3QCDBUZNN98AzX4FXbkvs\n805CZgWtSIxB5BMJJaYSwMFW55SPShksD1okVKjIlGKKEErzhWqmPIPWSw1GZX46jd2uAYOTFcPH\n7vINORamJCcVkwZv7Gulvr2X9QuLx2h1QXQ9KKaWw6lfhvYDsOefiXv+Q2/CP78GZhvUbwTP9Bbq\nC0qySDGb2HykY7yXMgAlpkaJruvUtTipLpoeYuryVeV8ZMXMKWlMqpgi+Nzw5K3w6GfB0TLk4UZk\nyhWqmXIP2sk3GMsrZbOxoa5tRI9XhHG4fUOOobKaTczITuW5nY0ArF8wxmKq8wi4OiUytfBDkDkD\ntv0jcc//xBcgrwouuRP8HjgyvVN9qVYzS8tzeOdg+3gvZQBKTI2SVocHu9s3bSJTZ8wv5vaPLh/v\nZSgmAt5eqRF55afwr29Dc+3AY3o7x7ZlvLcD/nopbP4z7HgU7jwJ9j4/6EMGRqZGLqbWVueTkWLm\n+V1NI3q8IowjjsgUSKrPF9BZWJo99p3URr1U6XIwmWHRJbDvBXB1j/65e9qh4yCsvA7mXwAmK9S9\nOvrnneScODufHce66PFMrBl9SkyNkroWGUlYXZQ5zitRKEaJvRGe+66cxAfD54HNf4H/WQWP3Qiv\n/Qze+SP8cT3sfFyO0XVpFf9lDTz7jeSvHSQi9ddLJR1y6d3wuTckUvD458DrivmwyJqpQECnzemh\nYIRpPpvFzOnzi3hpdxOBgPIdGg1Od/xiCuCssY5KgYgpzQwzFsn3iy8Fnwv2PDv6524Jbk6KF0FK\nBlScpMQUsKYqH19AZ8uRzvFeSh+UmBolRidf9TSJTCkmMAG/7Iqf/SY8eC00bh/e4//1LXj7d1JE\nG/X5A/DW7+COZfDkLZBVAtc+Cd9rhS9tkwvKQ9fB79bAPRfI8+WUwzt/gE3/N/rfbyie/w84vhku\n/z9YegUUL4TzfwY9bbD9oZgPS43o5uvs9eIP6COOTAGcvXAGzXY32491jfg5FGB3+8iIS0yJFcKY\n10uBiKmiBWANRsTK10B2Oex8dPTP3bxLbosXym31GdC4DZzTO4W8clYemgbvHppYdVNKTI2SulYn\nKRZTaHekUCSMf98Od50uRahD4feKkLnvcnjvz1D3mjz2pf+KL8128HXY+Rik5kqUKdoJe/O98Px3\noaAGPv4QfPolqD4dzFbILoNPPgPn/AjyZ4OjCc76Ptz8Hsw5B/75dfHjSRY7HxfRtvYLsOjD4ftn\nnyY7+43/L+bfwUjzubz+sGHnMD2mIjlzfjEmDV7crVJ9I0XX9bhqpgAuXFzKx9ZUsizCumVM0HVo\n2CL1UgYmE5xwCex/SVLOkQQC8MIP4t/kNO0CW458tkDEFMDB10a78klNTpqVBSXZvHtoYtVNKTE1\nSupanFQVpGM2qTl1UxK/F7Y/DM27x/Z1D/0bXvyhvO69F0pNUiwCfkm37X4Kzv4hfPMQ3Po+LLkC\n3vg17Hpi8Nfye+Gf34DcSrj2cfD2wIY7+x7T0w4v/QhmnQrXPQXzzoX+1gEWG5xyK3z8H/L6H/gq\nmC1w+Z/Amg7v/21Ef4oh8fvk71O2As7+z74/0zRY+3lo2iF/0yhEpvla7UO7nw9FXkYKq6vyeUHV\nTY2YHo8fXSeuNN+yilx+eumSsT8Hdx8HZwuULu17/+LLIOCFO9fBg9dBx2G5v34DvPnfsOXv8T1/\n826JShmfs7IVYMue9mIKYE1VHpuPdOALjoCaCCgxNUrqWh1UF6p6qSnJjkekLuiRG+Avl4CjOfwz\nvxfevw+2PSRfJ5LeDulEy6+GL++EFZ8QcRMrsvPcd2StZ/8QTv0SWFMhPV86gIoWwMs/FsER8/d8\nFFp2w7m3yQn7hEtg4119a6de/al0LV3w84EiaihSc6Dy5JhiZtTsfwHsx0W8WaKIoCVXQFq+RKei\nLS8opno8flqCkamiyDSfxwmv/0rSp3EW05+7aAa1jXb2NzuG97soAOnkA4b0mRpXDrwkt1Wn9r1/\n5kq49I8w62TY9zy88H253+jya90z9HPruqT5jFoskI1J+Wo4+t7o1z7JOXF2Pj0ePzuPJ6DQP0Eo\nMTUKfP4AR9p6mD1NbBGmFTsfg4c/BWm5cNGvRUg8cgO4uiQC9P8+AE/cBI9+Gv5nJez518Dn6O0A\nZ+vwXtfrgoeulzTZZX+EzCI4/6cS7o9Wy7T5ryIS1t4kQioSkxnWfw/a9sHWQXbDm/4P8muktRvg\ntK+Dxx4WH0074d27YfUNULJ4eL+PQdWpsg5748gePxib7oHMEph3fvSfW9Ng5SekKDhSEAcxmzRS\nLCaJTDmCo2QMMVX/Lvx2Jbz8I/l7xJmiuXj5TKxmjb+/c2REv9J0JySmRuBCP2w66+H+q6C7YXiP\n2/MvqY+aEeUzsfRKqd1b8xnY/SS07JVzCkDr3qGf294o55ziRX3vn7lKRNY095taU5VPYWYKzfaJ\nM75HialRcKS9B19AV8XnU42jm+Cxz0HFWvjU83Dip0VQHXwdfjYL/nENeJ3w0b/Bxx8EawY8+hmw\nR6R1PE64+2z4zSLpkDN+FvBLPdOOR6H9YN9Ih98LD30S6l6BD90hJ04AWxac+CkRcW0Hwscfe0+c\nkavPkFqlaCy4CGaulshS1zG5z9kqkayAX+oy6jfAqk+GI04zThBhteH/ibXBs9+U6NKZ3xn539TY\nvSc6OtVZL5GpFddI7VYsll8Duj+mB1Ca1YzLIzVTFpMWngf33j1iAfGxB8BkgR0Px7Wsoiwb551Q\nwsPvHZ2Qc8QmOg6XiKl4aqZGzaE3YO+z8OJ/xv8Yr0s+p/PPHzxSu+ZG0Ezw4CdkI1axVt6zQ4mh\n/sXnBmUr5X3cuC2+dbbsjW5ZMskpzk7l3e+ezTmLZoz3UkJM4BjqxGdvkx2AeTOyxnklioTgc0v0\n4fVfSafaVfdJygzkYt3TJqmvmjMlbWUJRi/ya+DOtfDiD+AjwWjOC9+Htv0w/yJJ0b39O0nbeZwS\ndTIw2yA1W064vZ3gd8OFv5LXi+Skz8Hbv5d/H/yNpO2evBUyiuHyeyQFEA1Nk8jWXy6B/3cKrP6U\nzPlydcLKV+X1zSmw/Oq+jzvtGyLe/n6VGAVe9GtJHY6U0mVS73Ho37Dk8pE/T3/e/6sI0pXXDn5c\n0TzptHr/b3DyzQMugGlWM71eP812t7hqG/U3HYck1TL/AqhZLyL4rP+UQuMhuPqkWTy9rYGntzVw\n+arykf1+0xQjMpWRMgaXKGOTse0B2ThVnDj0Yw6+LrWF8y4Y/LicmVJDte0f8lld8xnZvLTt61u4\n3h9DTBX1E1MzV8rtsfegcq0UtGfOgJNviv57/ekcsVX40naJVE8hRjLuKZkoMTUKahvtaJoSU5Oe\nt34He/8lRcq9h/WFnQAAIABJREFUHTD3PLjwF5BR2Pe4U74Y/fGFc2DdzdJ9N/t0iVq9e7d0lp3/\nE9kd7vknHH1XTmgnfATyZksbf/tBcNtlt5maCxVrwum2SLJKYNlVYkY55yzZ3TbtgCv/MrTIqVgD\nN74OD18vBelVH5Baqnf/CGhyss8o6PuY0qUiBPc8I2mMVdfH/eeMiskMs9YlNjLV2yl/5zlnQ96s\noY9fcTU89UU4thnKV/X5UXqKme5eH2/ub+WDy0rDP+g4JB2BAIsvh8c+C0ffkQvZEKytzqemKIP7\nNh5WYmqY2F1jWDPVVS+fPUuq2Hnc8MLQYnnvsxKR7l8vFY2TvyBiaskV4bRd61BiareIpP6fy6wS\nyJ4p72F7E7z1W1n30o/2PTYQkKYUV6f8O/AyzD1n6LUqRowSU6Ngb5OdWfnpU37A8ZRm/4vS7j9j\nsaTEFl8ukafhctrXpRj98c/J98UniDUASFSkaN7Ax5QN00n+3B9L/dKD18oJtPpMWPjhoR8HIvg+\n/aLU/BjpQ3SJUp346eiPOfPbYhx40a8Ts6utOlVEq71RLgqj5bVfSKTwrO/Fd/wJl8Kz34JNfxog\nplKtZt6ua8Pu9rF+QTB14HVJx1ZelXy/4EL5u29/OCymNt4lUcesUrnvrB+E/laapvH12Ycp23I7\nx5uep2zGOPggTVKMyFSWbQzGVnUdFTuPEz8t41sOvipRyP50N8D9V0LJUkktz1kfjlwPRukyuPYJ\nae6wpEoUumWQInRdl89p/xSfQdkK2Yhtfwj0gETI3rkrnIbXdUnrH3oDLvqNDEje/BclppKMqpka\nBbWNduaXTJKo1IFXYPfT472KiUXAD89/H3JnwWdehot/PzIhBRJK/9S/4BOPw6dfFuESz4l2OKTm\nwCcek9EVfg9c+MvhddZZbNINpGny78JfwZd3SNdRNEqWwK2b44rCxIWxi0+Ei3PLXrmArLx28B1+\nJKnZkj7dcp+YjvrCxatpKWa6er2kWEycMie4w++qB/SwmLJlwbzzYNuDku57715xd88IiqQ375Dn\nNujtYP2+H7HUdJCOnS8Gn/MY/Os7Eo1UxMQ5lt18XUfFXHbxZWLhUftM9OO23i+1SruekFT9gg/G\n/xrVZ8jn12KT91Osjj5nq9RkNm4TG5JozFwF7XWyKShbKRHkjXeB2yEblfs/Cq//ApZcKWn9ZR+T\nyHgccyoVI0eJqRHi8vo51Opk/mRI8bm6pTPt0c8MPSpkOrH179C8U7yJLCM3aQyRWyFirHwVpKSP\n/vmikZoj5pi3vAeFc0f3XJomF5GxomQpFMyB578nF7CR4vdK4b01Ixz9i5cLfg6nfkV26o/cELrb\n8JpaV1NAulGnY/gDGWIKpDsyr1JSpk99UVKM1z8Ln3pOxn289KPwXLbn/wOrqx23bkE79AYAvW//\nETb8nsBTX+7bfOC2S1PAYBYW04hQzZQtyVF/XYfuY5BTIV2fNeuh9p8DLTB0XSLPFSfB1/fDDS+K\nWBkJhfNlMxCNez8odgrn/lisPqJh1E2114lQOvXLksr7wxnS8FL3Kpz/c/jIXfIZX/EJCPikJkyR\nNJSYGiH7mx0EdJhfkj3eS4nOgZfD86He/h30tks4+N27x3ddEwW/D16+TTrdTvjIeK9meFhTxWBz\nsmEyw1X3S3fc3z8mxfjDRddFxBx6Q+rR+te1xbOGs38gNg/7XpToJGGvqfWR8906DsptpJgqnAuf\nfU3SJ6s+CVf+VbytjEJ/Z7PU3Tz7TSl2X3cr77GIgua3AXDteIpePQXTjodE0IFEDO69SDY8W5Jk\nbDrJsLt8pFhM2CxJFlOuTvA4pA4JJNpkPw7H35fvjc1n0w7xYlt6pXz+Kk6MqwkhKkXzoP3AQOHc\n0y6vceZ3YN0tsZ+/NFgeYLJINK3iRFh0saQPT/kifP4tWPu58OOLF0g0y5ibqUgKqmZqhOxplDB9\nUtN8ui41JrmV0q4e6xhd7/vBa90nFyufSz5c79wt08y9vdKttu6W8CypRNO6H7Y/KBerrHFoW9V1\nSbXMO3/wC+2Rt+Wkef5Phm9CqRg5RfPFf+f+K6Vr8uwfDO/xr/5M/n/P+PbAjsfhMHOVpEnaD0Lh\nnFDd45nzI8XUIalxyez3PjaZ4cQbGMDMVbD0KlmfOQVOuBTtjG+y9/0O1vXcC/XvkOfYz499V3P9\njAPMfOYrkip0toqTdu4sSRWu+MSU67waLg63d2w8powIqRGhnXeeDC6ufUYE1TNfkXpIn0vEy6IE\nbLwK50mavvOwjGYyaK8L/nz+4I9Py5XUdl5VuOj8yr8M/piyFVLrN5Hw+2J3IU9Cps5vMsbsabKT\nYjFRVZCkdI7HCc98VVJRZpvUx6y6ru8xjha4/wrp/MirkpTDqV+WLg5rmrRzv3mHnBzW/4cYFt57\noZzsYxUdjwZnG9x3mVyE3v69XPDW3Zz41xmMw29JEeniy+SiDTLbzt4gNS8168WPaPdTcqGco4oy\nx5x550qx/+Y/wxnfij/F6nNL99KiS+D0b45uDcbmpGkHFM5hZWUubq+fivyIz3PHIflcDUdsX/hL\n+d1mnyYXPaCtaC0cvjc0Eui5wGrchVfxo/nPSW1MwCcGrY4maS7Y9QQsvnR0v98kx+HyjZGYCtoi\n5FTIbXq+dJ2+d6909maWwOu/FCE15+yB3XUjwRBLLXv6iqm2/XJbMGfo57juKTANozg/b7ZE4Xra\nR2dxkgh8bonebnsQbnp7ckbZo6DE1AipbbQzpygTiznOUG8gMDBsa9R+6Lr4FhXOkzf63udg4/9K\n+/sHviaeIk/dKgNoS5ZIzrxsBTxxs5zwV14nKYkNd8qwV79HhMQJl0qBosUm6YmCOZLW2viHxIsp\nn0cuBN0N8tpb7pcuuTlnxe5KSQZGAfCOR8QVvG2/iEuDldeJIWbtMyKsbGoU0Liw+lNQ+zTsehKW\nXhHfYw6/Janq5R8ffTSxaL6kRZp2wgmXcP0ps7n+lNl9j+k43DfFFw+p2X0HLQPW8hV0H0on+9gm\navUK6vUZvNsMfPy2vo8NBKBgrlhsnPCRaR0xdbjHSkzVy21k7eCCiySNXLJE6uFe+i85ry77WGJe\n0+jsbdktHaIGbQfkPRnPey41Z3ivmV8tt+0Hx1dM9XbAfVeITQyIVcryj4/fehKIElMjZG+jnXU1\nce5Suhvgrg9IrvuCn4d3I6/8ROombDliPhhJ5clw8Z0w+wNS17Hhf6WNf/+L0lUCYEmDqx8M++A0\n7hAX37wqicyADJ410DTxKvrn12RXVDREOHk4vPErOPxvmUm1+DIxSPzvxVIMOVZiyu2QuoDFl8HB\nN0RstteJr9JFv5Zo2ft/lb9t91FY/92xWZdiINVnym5505/iF1P7X5T0WTzePkNhTZPNhWGO2B9d\nl41K1SmjfqnZxdlsCCzkXPN7PO9fRVVBOvuaHbi8/lCtFiCbrVNulU7D+neg8qRRv/ZkxeH2jV0n\nn8kKGUXh+5ZdJfef/AWJZl/wCzjxM9HtTUZCao5snI9s6Ht/236J0kSbLzla8oMbhY6DA2xBxpTN\nfxEhdfn/wVNfkmkTSkxNX7p6vDR2u+Kvl3rh+zJKoH4j/P4kMQ8sWyk70JXXwgfvkHlNnYcl1F+y\ntK8Hkcks6bJ1NwdP8gfh8NuSN4+clVayGK4ZIi++4CIRU7ufSpyY6j4Ob/5WImFLgx0uuRWyG6p7\nDdZ+PjGvMxS7HhfDzDU3ioB6+kuQUwlX3Cv1U2f9QOZjPXWrpD5jzXJTJB+TSaJTL3xPxtZ4nZK+\ni0x79GffCzDrFLGhSAQzTggXGvenp13mEw43MhWF2YUZ/COwJCimVnPF6gp++dweahvtLK/I7Xvw\n/IuAW+DIW9NeTM3ISrC1SDS6jopLeWTWIC0PzouIGmpa4oSUwezTYOsDkp0wxiC1H5BpCsnAeB8b\ndVnjxb4XxINv8WVhYTVFUN18I+Bgm3Qh1RTFSBH1tMPtS+DxL8gwzO0PSiH4zZtESG19QC7ohfOk\nhdVkko6LeeeJuBrMzFHTRKSsuHpkQ2ezy6RQtjaBnlMv3yYO3v2LiWefLmHcsWr3fv8+SZNUrJEi\n3jO/C1c/FC5EzyiQYcB+j0Qcxrt2YLqz/GqJrv7rm5JKeeW22Md2HhFvnkQaD844QaJP0TyfOg7J\nbYLE1AP+9Vzt+x77LXP40NIyAHYc6xp4cEaBRMzq3xn1605mHC4fGWOR5jNsEcaa2adJF+GxzfK9\nrkuaL556qZFgTYOsMknzJQJ7k5SjDAe3XaJxc86S72euljT7FBnarMTUCDje2QvAzLwYHXG7n4Su\nI1K/8/ePyof11K9Id9uH7oCv7BbDxI//I3l+RIOx4IOyIx+N149B43b5PU+6ceCFp/p02d0f3zz6\n1xmKlj2ymzfqacwWOP0bIlIjOenzYoZ34meSvybF4GQUwOffhJs2iGfP/pdCVgUD2B80vUxkw8CM\n4GakeffAn0WzRRghGTYLBdmZvOlbyIKSbCry08hNt7LzeBQxBeJlVL9xoNfRNGJM03yGLcJYUvUB\nuT34utw6mkVcDRaZHS351UNHpmqfkS7boXj8czK309s7+HF1r4r/laNFSi8C3vCGqHy1bMIbtsSz\n+gmPElNBvP5A3McaYqosJ4aY2v6wREg+/aLsQD50R1/RlJ4vAy/zZ0d/fLIxZr/FcvodDi/fJjUA\n0Qzmqk4DNEn1GRzbDH+7PGxsmCje+q1EOYYaeJuSDtc/M6BIWDFOFNRITd2CC6Xb6Oim6Mfte1FS\ntqM1Ko0ksqOvP0ZkKjeOmX9xUF0kqclFZdlomsbishx2HAt/Bg62Ornot2/QbHdJZLWnbfxTMuOI\n3eUjK9mRqYBfShTG0rjWID1fCtwPBs+NoU6+ZIqpqvB7as+/wj5nBvYmeOxz8MZvBhfy+18UH0M9\nIBHjWOi61PAefx9e/Yk8zpoBFcGJCjNXy+0USfUpMQW8uKuJE37wHIda4zMRPNbZS0aKmey0KB/2\nrmOS2lpyhSjv654KhzUnCoVzpT1391Oje57j78vAz3U3S51BfzIK+p4wvL3iwr7/BfmXKLobYOs/\nxHdouCaOiolB9ZlSx7YvSuogEJDuqjnrE9vhllMBtmxJNfSnvU78pRIUOZ5dGBRTpWLye8LMbPY0\n2vH4ZBP32p5mdh7vZmt9l0SmQKJT0xCvP4DbF0h+N5+9USIj4yGmQMog6t+R8+JwbBFGSn61mMq6\nHfDct+HlH/f9+XPfAXe31C/2dkR/DmMElyUYSDCmBETjwMtyjcivEauJnY9JtsIosM8sks1KrA3U\nJGPaiymX188Pn96Jxxdg5/H4oiXHO3spy01Di3Zi3/kooMOSyxO70EQz/3zJX7sdI3+OV38m09bX\n3Bj7mOrT5aJQ+0/58LbtF9+sAy8P/tyBAOx9Pr56q43/KyfFk78wvPUrJg5puTIDcN/zA3/WUisn\n+coYMwRHiqZJdKoxSmSqaWdCu1Crg/WVJ5SJmFpcloPHH2Bvk9RrGeeeox09stGx5UxbMTVmc/lC\nhp3jUDMFIqb8bhFU7QekUzWZa8kLZkJ2PSGbBUdTuF7wwMuw4+HwnMtYJSDbH5YRXOf8UL43IrjR\neOM3Uqd1/T9l09LbPjCwUH6iElNThf978yD17ZK2O9IeXyHc8U4XZbmDpPjKViQ3XJsIqs+U/PXh\nN0f2+GObxZ193c3irROLlZ+ErFJ44GMy1mbV9SLkDrwyeCh53/NiSPrqTwZfh6MZNt0jnWDjlTZV\nJIa550oNXvfxvvcboqJiTeJfs2jBwKGzAb8IuOIYUwdGwIeWlXLL+jksmSn+QEYX3+YjEgHY1SBi\nqr69VxpSKk6ctkXodldQTI02MuXpkSxBLIwN3Xidq2edLALq9V9CwzYRO8l0vje8pv59e/i+tgNy\nu+keOU+f/zP5vvtY9Oc48LIYmZ74GYlOdcaITNW/I1Y5626BrBIxjTanwNzz+h5XvlomUfT/zE9C\nppWY2nakhS/87H/p6vEC0GJ38/uX93POohkUZKRwpD2+NF9DVy9luakyBuK3K2WmVu0/pRaoYYuM\nlJjoVJ4sDuAHXhnZ43c+Jh+OwaJSAIVz4JbNMnRzxSfg3B+JWWb3MSkaj0VdcF3/vn2gH4uBzwMP\nXiftxad/Y2S/h2LiMPdcud3XLwVc/474AOUlQSwXzJGURuQA8I5DMj5kxqKEvUxxVipfPXd+yOS3\nPC+Noiwbmw934PEF2NckEeKjHcENXcVJUhjf25mwNUwWjCHHoxZTW+6TmYcNWwf+zNkmvnMLPzx+\nmzBbFnz4f2RDW/dK8kWd8Xu27ZP6Q5CIGEgktnx12JohVmSqead0kZtM4okVKzK1/0UxIDVqWNd8\nBr62VyxzIjGaQFpqR/QrTSSmlZhybvoHv3d9i+YtUnj91oFWnB4/t6yfQ0V+Oofbho5Mubx+Wh0e\nKT7f+5y8Gfc8K5GX+nfgvJ/IG2eiY02VsQlDpdti0bJHrB0Gi0oZmC1ihHfx78IjXWDw1657TS4o\nORXw6GejpyOf+4508F38u7F1WVckh+KFsnt+796+Ucv6jfJeSIYjuHEBiyz2NmqoihMnpvqjaRor\nK3PZfKSTAy0OPP4AZpNGfUewO2rmKkCXcTPTDEei0nxG1GXbgwN/9ubtUht05jgb9y67Cq74sxiH\nxpq/mihScyA9aDRtmDm3HZCarfY6icRmFMkm2XCGj8TvlfO+sc68WbEjU827ZfMTOWEiWl2tIfAS\nZdkwjkwtMbXrSbjnwph1Nlnt2wEofO+/QddpsbsBmJWfwayC+MRUQ5cLQNJ8+1+UItWv7oGP3ge3\nvCd1O5NlSGn1mZLi6IoR0h2MltqRm37mVkq344GXov/c3iijFuZfKEKp87BEwiLZ/Fd4948SRp7o\n9WmK+NA0+f88vllaqkGiv+0HkpPig/BO3CgAhqAruiYpwCSysjKPI+09vLGvBYC11fnhyJQxr8ze\nmNQ1ABxqdYbqlCYCQ0amXN3R69z6Y1zotz/c13Kj+7iM5lp61UDrlPFg0Yfhi1tkdFiyyZstwm3J\n5ZBdLu/7llpAl82MySRWEdGuCW0HxKPPSH/nzoKOGN18LXvi+/xklUkN7WC1V5OEqSWmvD0SMm3b\nF/XHud21+HQTeW3vw8HX8bYc4AcpfyP7nzdyS9P3udxxH95Dgxd9hmwRslMksjLnbInOLPygdCdM\nJmrOlFvjwhUvnh5piR1quvmgr71eBhBH8ykxvFeqTxc/lqyyvoXJRzfJTMPqM+Cs/xz5GhQTj+VX\nS+3GG7+W7426oYokuYHnVUk6wohigESm8quT7gG3cpbs1O/feIRUq4kPzC3C7vJJGUJmsRzkaErq\nGgAu/v2b3P3GxIkMOII1U1mxIlNv3gF3nwVe1+BP1HFIWvEdjeFzCgRNhgNwxiiHZSeSnHLJFiSb\nFVfDqV+WKFFBtbzvm4IjlYyIU0559DSfYSESikxVgbtrYOefzyMboHiEqskkEa6OifP+GylTS0yV\nLJXbhiihcV2n0LmfR/0fwJFSCE99kU9tv4aPm15AO76FYu9Rvmh+BOu958K2h2K+xLGgmKry1Iov\nzkSzPRgORli3bph1U237AH1042gWfRh8vfDIpyV8HEnda9IlWLJUohVzz5HaLr9XPrj/uEYuuJff\nIylExdTBYpPo1KE3ROTXb5SddOkgUwFG9XopEgXqH5lKYL1ULJbMzMFi0jjU1sOCkmyqCkS81Xf0\nSPeTJTXpYsrt89PV6w1tEuOhw+lJaiQrHJmyRj+gaYfUtLXujf0kui5t+0uvlL/l9uA5vWFrbJPh\n6cDqT4VnkhbMkfd98y55rxkF6rHEVPMuMFmkvANEBMFAe4S2/RDwQVGcpRd5VdB+aLi/yYRjaomp\nwnnypohWZ9B5hFS/gy36HF4tvBo6DlJrW8JN+X+CWzez5/KXWO6+i66C5TLewtkmj+vXcdbQKbuh\nosY3ZEdbfWayf6vkYTJBzVkS9RmOpX9L8CQ2GjFVdaoMEK19Gh65IZya1XXxpZp9WjhdOvdccVI/\nsgE2/gHsDTJvT42DmZqs+qR0DP3lYth4l4xXSuauPb8mXIgbWT+SZFKt5pBVwqKybMrzREwd7eiR\nTUTmDDFSTCJGFKi9xxP3Y6675x1+9HSMAdEJwFhTzJopo1h5sKLlnjapiSqaL0Xmu56Q2qnnvitR\nmbFIqU108mskIHDoDfk7GefbnHI5x/Yvl2naGbzGBn2iDEPb/im6luBEgXhTqHmz5TkmueP/1BJT\nZosUjRrdG72dEmXSdWm5BnYHKnnS9iH43L/5Rsr3MOXKKIHK/HS6yeS1+f8hQ4mfvEWiJj8u7uOD\ncbyzl6IsG5aDr0iR6GS/oK+8Vn7fHUMMSI6kpVYMFkc7lPOkG+Hc2+REt+H34efuqpcUn0H16RKd\n2PmYeErNvxBmrhzdaysmLikZcONrcNb3Zbe85Mrkvl7BHGirk/NES62kgMaooWFFpaT6FpVmUxES\nU8EoUeaMpEemnG6pJeochpg62OLkYJwGxyPBHoxMpVuj1J56e8ORkGhjgAwiHezX3SJmvo9+RoTD\nmd8RX7PpjmEQ2rC1b7NF9kzx7XP0q9dr2tX3OCMy1b8IvblWAg0FcU4ryJ8tm+WetuGtf4IxpJjS\nNC1V07R3NE3bqmnaTk3Tfhi8f7amaRs1Tduvado/NE1LSf5y46B0qUSmdF1aXx/9tHiNNO0ggEat\nXkGL0wMlS2hxeijOsgFQnGUj1Wpim2em5JT3PCN2B2jw/l9DT3+8q5f52R449p7US012Zq2TXfg7\nf4h/Z9BSKxc5SwL+y0/+Asy/CF75ibjlPvwpMSycf2H4GFuWrHPTnyTNd+pXRv+6iolNVomMKLrp\nLTjps8l9rYIaOZk7mgfWjySZNbNlM7asPJecdCtZqRbqDb+7zGJZUxIxUmrtzvjElMvrx+720epw\nJ21NTrePTJsFkylK92ZrsMQABo9MRQ6qLl4At7wv0yjOvU0in4q+VgyRIskwDo1M9bm6ZN5s5Oci\nNUfKMfqn+VqCnXzxRpPzpkZHXzyRKTewXtf1ZcBy4HxN09YCPwdu13V9DtAB3JC8ZQ6DkqXyH995\nODwu5b17oXE7x81l9JJKi92N1x+g3emhKCimNE2jMj+dw+09cNo34NI/wpe2yRy7XU+G6nqOdfZy\nCa/J7tWYcTeZ0TSxcmjcHr9JYOve0aX4+r/+Rb+Wjo67z5bn/uhfILus73GGB9Hs08TQUKFIFCF7\nhAOyS4+sH0ky559QwmM3rWNJuZh5luelj21kyiNiqrPHO8SRgiGiWh3xR7KGi8PlI8MWoyPaqJMq\nnB/suoyBES0xuiJNJjl3rLsZzDFqsaYbubMkwwB9awSN8TqRYsqIAvbfZESzR2iuHV5k17BHmORF\n6EOKKV0wTH6swX86sB4wckN/Bi5JygqHgdcfoDM3+KbY8ago5Ixi2P0k1L/Dfq0KELNO46RgiCmA\nyvwMjrT1SMRl6ZUSGl58mdjg172Grus0djpZ73gKKteN2e416Sy9UqJB/759YDF4f3we6QBJlJgC\nyC6FC34m7csX/Ua69Pqz8EPS1XfGdxL3ugoFhNPVxzbD1gek4WGM7E1MJi2U6gOoyEuTAnSQ6Fxv\nu3zmkoRRn9TZ6yUQGDoybdjJdPV6cfv8Qxw9wjV5fGTEskVo2SMppIUflIhIrFrPjkOQXtjX50jR\nF6P5AvrWCOZI6UsfMdW/k88gd1bfyJTPLTWHw7EVMdYwye0R4qqZ0jTNrGnaFqAZeAE4AHTqum5U\nqB0FZiZnifFzz5sHOelPjeiaGd76rdz5kf8VbwxnMzsDkuN1+wIcbJGcf3FWOBRZmZ/OkfYe9Mh0\n15yzRGjseISOHi8n+d8n331schhzxktKhuzY9j4Ld502+Kyk9jrJpyfag2f5x+Gbh2DVddF/njcL\nvrpbRjAoFIkkp0Jq8l77hbR6nz5+LfNGZErX9bA9gjN5qT4jzecP6KExLoMRGZFqS1J0ykjzhV90\nn3hFgaT28mYHZ8jpA0cBGXQcnp7desOlYI6k6rJKwvfZsiSFFymmGnfIfdn9LvN5VSKCtvw9uNHe\nP/zrgzVNNsrTIM2Hrut+XdeXA+XAGiDuv5SmaZ/VNG2TpmmbWlpaRrjM+MhOteImBV9+cETEzFVS\n11SxFoBt3vJQJMqYhRUZmZpVkE6v109LZD2AxSZRkdqnOVZfx3Xm53GlFk2NFF8kp38DrrpfDPHu\nuyJ2hMqoUzDaYxOJKgpVjAdmi6Qa3F2w4INQsmTcllKRn0aPxy81TJkz5M4kpvoiLQ464ihCNyJT\n/b9O9JoyUiLE1IY7peO3dV+wxGBBuO2+OUbdVOfhcIG0IjanfQ0+ePvA6QI5FX3F1PHNMnO2/3Er\nrxVB9vjn4Fdz4aHr5f7hmqHmzw6n+SZpV9+wuvl0Xe8EXgFOBnI1TTPe8eVAVBttXdf/oOv6al3X\nVxcVJdfUMidNcuHOvGAo0hA8J9+EnpLJJu9sZhdmALDr+EAxVRn0eRnghL7kMnB3s+SBkzjDvBXf\nik9Ozbz7govg/J9KaiHaxPr2g9KqrpmgMM5ODYViMmCk+sYxKgWE7BGOtPeEI1NJtEdwRIipeOwR\nIgvPk1WE7nD7+6b5jIv627+XyEfRPKlpM6dEr5vy+6CzPty6r4hN5VpYfOnA+3PKoTv4d/e6pDGj\nbMXA4wrnwk1vw9WPiHdgWq4EL4Zr6GzYI2x/GH5R3ddEd5IQTzdfkaZpucGv04BzgN2IqDLmeFwH\nPJGsRcZLdlBMdeQvBTTxFwFYdDFdt+6jjRxqikRM7QyKqcLMcEdaTaHk1+ta+s2Bqz4TPv4Qfy+4\nmbutHyPz9FuS+4uMJzVnSspj77/63r/nWbhzrXRKfvB2SQ0qFFOFtZ+D834q3cDjyNxiOQftbbKL\n1xYkNTIZs9XeAAAgAElEQVQVKabisUcYCzHV4/GRGVmAbow22fznoBnkAokmFs6L3tHXfUxSTSrN\nN3LyqsQuxOeR4cYBb3QxBUFj5bNlaPMNz8MNzw2/0zuvSrytHv1scDMfZzPUBCKeyFQp8IqmaduA\nd4EXdF1/Gvgm8BVN0/YDBcCfkrfM+DAiU/vKL4MbX+/T+mkPnieMyNT+Fge56VZslvCHdmZeGikW\nEwda+nmoaBr63HP4ZecZ7J77eckdT1VsWWKouTdifIuuywiG3Er4wjuqtVgx9ag+A06+abxXQWV+\nOhkpZnY32GU6ASTVHqFPms85dEdfi91NRX5a6OtkrSk9MjLVfRTK10gHNYRLDIoWRE/zGd1lKs03\ncqrPENPTI29JYwbEFlOJwOjoK10mm/nBbC8mKPF0823TdX2FrutLdV1frOv6fwXvr9N1fY2u63N0\nXb9C1/XkGY/ESXaqiKkujzZgh2kUV1bkpWMxafgDOkWZtj7HmE0a1YUZHGgOR6Z2N3Sj6zoHWhy0\nOz2cNHuSm3TGw7zzpbCzvU6+P7oJmrbD2s+HOz0UCkXCMZk05pdkSU2nJQXS8pMcmfJjs8hlIJ6a\nqVaHm5m5aWSlWpJmj+CILEB3O8TqZv4FUBlsPjHEVPEC8T5y2/s+QaRhp2JkzD5dLEL2/AuOb5HO\nSMN/KhnMOVssia55RGqwBhsVNEGZUg7oRmSqq3fgDsvukvuy06wUBkVUcbZtwHHVRRnUBd19Nx1q\n54I73uC+jUd456AMczxxWoipoKeTEZ3a9H+QkgVLrhi/NSkU04RFZdmhTVyyvaacbh8lOamYTVrc\nBeiFmTaKMm1JiUz5/AFc3kC4AL07mOLLKZfxUxf8Imx3YBSht/Tr6Dv+vpyvjJZ7xfBJSRdfrr3P\nxi4+TyRpuTIzMD1fbHemYmRqMmHMcuqO0uIbHp5poTBL8rn9I1MANUWZHGnvwe3zs/FgOwC/fn4P\nL+1uojDTFhpGOqXJr5YCwh0Pi1nbzkeDXlRZ470yhWLKs7A0G7vLJ+adWckXU5k2C3npVtrjSPO1\nOsTouDDL1rfrOVHr8Yh3Vci00yg+zymXbMNJN4YPNowh+4+VObJRjH3HyCtsyjLvfInyNccoPk8W\nRQvkdb3xD9+eCEwpMWU2aWSlWuiOEpkyxFRWqiUkoiI7+QxqijLxB3SOtPWw+XAH+RkpdPZ6eam2\nmTWz89CSqc4nEis/AUfflaJznwtWXz/eK1IopgULS2X48e6G7qRHpuxuMcjMS08ZsgC91+PH4faF\nIlOtSYhMOSM2vUA4MtXf3wikaNmS2jeK0dspF/+gHY5iFMw7L/z1WM5CLZov9XFt+8fuNRNADJvZ\nyUt2qjWqmOqOmEReFJrHN3B2UE2RhJD3Nzt4v76TsxYUYzFr/P2dek6smgYpPoN1t8Ccc2DX42KF\nMI7eOwrFdGJBSRaaBrsb7JxrzOfT9aSkWZxuHyXZqaAPXTMVOTWiKMvG6/uSJ6ZC1ghdRwFt4Hgp\nkMhT4by+kamjmwAdKk9K+NqmHTnlMGOJ1MuWLh+71zUMP1v2TKrrzpQTUzlpVrpdUSJTQTGVZbOG\nxFS0yNTsoHXCy7XNtDs9rJyVx7mLZuD2BbhoSWkSVz4BKV4Axd8a71UoFNOK9BQLVQUZEpmqmSGR\nYVdXUkxtncHIlMWscag1xmiWIEZaryjTRmFmCnaXD5fXT6o1cem0gWm+YxKdi+XrV7wQDr4R/r5+\ng8ybm7k6YWua1qz+JOx6QkZ+jRUFNfJ/OMnqpqZUmg8gO80StQDd4fZiMWmkWk2DpvkybRZKslN5\nZnsDACsr8yjItPGbK5dTnB3nFGyFQqEYBQtLgx19Ia+p5NgjGAaZeekpQ0amjILzwkxbqIkn0V5T\nochUqAD96OAdxEULwH5c0nsARzZAyWI1ky9RnPhpuO6psX1Ni03qdpWYGl9y0qx09w4sQLe7fGSm\nWtA0jeqiTMwmjcr86MXkNcUZ9Hj8ZNksIRM9hUKhGCsWlmRzpL2HnpRgaUGS6qYcbi+ZNjO5QTGl\nDzLKo3+aT+5LrD2CY0Ca71j0eimD4oiOPr8Xjr2n6qWmAkXzB3ZpTnCmnJjKTrVGj0y5wt4lH5hb\nyFvfWk9FLDEVrJtaVpGLyTRNCs4VCsWEwShCP2wPnn+8g6fgRoJhQ5Bps5KfYcXr10Nptmi0Bp2P\nCzJTQpGpRNsj9ClA13UpQM8pj/2AkJjaDY3b5e+k6qUmP0ULZKSMLzleZslg6ompiJopXddlYChS\ngJ4VNPXUNI0Zg6TsqoMu6Ssr1eBdhUIx9sybITYkR7qCUXZ/4i8qkfVJueliF9PhjP06LQ4XeelW\nrGYThVlJTvPZLDKs3tszeGQqpxKs6VKEvuMRuU9FpiY/RQtkJFD75JnRN+XEVE6alR6PH68/wDPb\nGzj5py/RYnfjcHvJssVXb2/sCtfMLkjmUhUKhSIqxmirw8kUUxFRoHxDTA1SN9Vq94QiUsZM00RH\nphzuiAL0SMPOWJhMkhLa9QS8/TtY8Qk1pWEqUDhXblv3je86hsGUE1PZhnFnr5fdDd24fQF2HO/C\n4faRlRqfmFozO5+nbj6VU+YoMaVQKMYes0ljdkEGB9uD4sY/sA50tETWJ+VlBIfE94RLJNw+P4FA\nuIaqxeEOiSmbxUx2qoXDbT0c7ejB5w8kZE09Hh8mDdKs5vCA48HEFIgTur0Bik+AC3+ZkHUoxhlj\nrq6KTI0fOelyUuh2+TjWIQ6quxu6QwXo8aBpGkvKc6aPQadCoZhw1BRnUNcRFDdJiEyFpkKkWgak\n+XRd54I73uD2F8Mz0prtrj4d0KU5aTyy+Sin/vwVvvrQ1tD9gYA+aCH7UGvKSJFGIbqD7ueDpfkA\nZp0sw+evuBesaSN6XcUEw5YFGcVSNzVJmHJiKjTsuNfLsU4RU7UN9j4F6AqFQjHRqSnK5FDn8MSU\nrus8ve141Cac/gyW5qtttFPX4mTTIZlJ6vL6OdbRy+xgPSnAf1+1nF9cvpR1NQW8sa81JKBuum9z\nH3E1HAzfK0AiUyYLZBYP/qCV18LX9kPRvBG9pmKCUlAD7XXjvYq4mXJiyhh23N3rldlWQG2jRKaM\nAnSFQqGY6NQUZeIKBM0r/UOLI4CHNh3l5vvf58ktx4Y8NtLTKTvNiqaFI1Ov7W0BYF+zA4C6FicB\nHeZEWMUsLM3mytUVfHhZGe1OD3WtTno9fl6ubWZfkyPu37PvmvwRhp314nwez4w9S8qIXk8xgcmv\nUZGp8SQ7KKbanG6aul2kmE3sb3bg8QfirplSKBSK8aa6KAMfhpgaOjLV1O3iR8/sAuLzf7K7wpEp\ns0mjpiiT1/e1AvB6UEy1Otx09njY3yLiaO6Mgb57q4NjtjYdaufdQ+14/IG4ImPRcLgjMgidRyB3\n1oieRzEFKKgGRyO4RybMx5opJ6aMyNSeRgcBHdbWFGDUUCoxpVAoJgvVRZl4jYlfQ4gpXdf5j8d3\n4PEFsFlMQw4thog0X/C8ePVJlWyp7+TtA228e6ideTPCc0r3N9kxafRJ8xnUFGWQl25l06EO3jwg\nYmykYsrp9pGeosSUAolMwaRJ9U05MWXUTO1q6AbgnIXhfLuqmVIoFJOFTJuFgqygeBkizXe8y8UL\nu5q46Yw5lOak9unKi0X/OXiXryonI8XM1x7aitevc8OpswFJ9e1rdjCrIAObZWDKTdM0Vs3KZ9Ph\nDt7cL2Kq2+Xt0wkYL06PjLfB55YOvdzKYT+HYopgdPS17R/fdcTJlBNTqVYTKWYTu46LmFo3pxCb\nRX5NJaYUCsVkonpGlqT6hohMtQb9nhbPzA6NhhkKh9uH1ayFBFJWqpXLVpVzrLOXNKuZi5fPJNVq\nYl+TiKk5g4zWWl2Vx8FWJzuPd5OfkYKug8MzfDsHp9tHps0MXcFOPiWmpi/51XI7SewRppyY0jSN\n7DRLyJm3PC+N+SXiJqwK0BUKxWSiujATj25BHyIy1R4UT3kZKeSlW+MSU6HOucc+D3ufB+Dak6sA\nWFdTQKrVzJziTGobuznU6hx0TumJVXmATIA574QZAHTFER2LuabOw3KHElPTl5QMyCqFNpXmGzeM\nIvQZ2TZsFjMLQmJKRaYUCsXkoaYoAy9mel29gx5ndOHlpaeQl5FCh3NoIeNw+ShI8cPW+2Hfc4B0\n6/34ksV88WxxoJ5bnMU7B9vxBfRBI1OLZ+aQYjGRabNw6pwiYGR1U6EC9M4jcocSU9Ob/JpJE5ma\nkurCqJuamSsGbouC42GM4nSFQqGYDFQXZeLBgsvZQ/Sx7IIxgzQ/PYW8YaT5Zlm7wAU4W0P3X7M2\nXPQ9pzgTX7D2aW5xVsznslnMnDm/KDg0WWwKuocppnz+AG5fIBiZOiIeU1mlw3oOxRSjoBpq/zne\nq4iLKSmmDNE0M09OP1eeWEFBpo2K/MFORwqFQjGxyEmz4sWC7h1cHHX2eDGbNLJSLeRnpNDj8ePy\n+km1xvZocnp8VFs65ZuetqjHREajaooHdvJFctcnVgOE6lWHG5lyBufypaeYofmIOJ+bp+QlShEv\n+TXQ0wquLnG5n8BM6TRfeZ5EptJTLHxoWdl4LkmhUCiGTYrFhE/vW4C+tb5zQKdce4+HvHQrJpNG\nbnCkVucQNUsOt58yU1BMRUSmIjHqpGbmpoUtC4bAGOk1XDFlFKyH0nwqxacwOvqe/jK8+VvwDp7u\nHk+mpJjKSZMPvZHmUygUislIisUkkSmfiKmDrU4u/v2bvL6vpc9xHQ4311hehtZ95PUbDRMLh8tL\nidYu3zhboh5TmZ9OitkU1awzFkZmYLhiqidi8LISUwoAKtbKvwOvwAvfg30vjPeKYjIlxVSoZipP\niSmFQjF5STGb8GAJRaYMM87+Qim7azdfct0Jd53G/GOPAvqQYsrp9lOoB8VUbzsEAgOOsZhN3Hh6\nNVedWBH3mjNSzJhNWkhMPbezkSfiGG9jDF7OsviVx5RCyCyCG56DWzfL90aX5wRkSiakjZ1RhRJT\nCoViEmOzmOjAAgERJh6fCJ5eT1/hU+TcG/xiPjUbvsO5pi/T4Vw16HM73T7yM4K1UnoAejsgo2DA\ncV89d/6w1qxpGjlp1pCYuuu1A/R6A1y8fOYQ65GaqTxfs9yhxJTCIDUXUrKgs368VxKTKRmZWldT\nyNkLZ1CZP3jBpEKhUExkjDSfFvSZ8vhFRLm8/j7HzXTtx21Kg+ueAqBaaxg0MuUP6Dg8PnJ9EbVS\nPdHrpkZCTpqV7uDsv4YuV1ydfUZkKsd9XO5QYkphoGmQWyHDrycoU1JMLSnP4e7rVpNimZK/nkKh\nmCYYYsoYJ+P2BsWULyymdF2n2l9Ha/ocSMlEN9vI1Rwh76lodPZ40HXI9rZCpphsxipCH8Dz34PN\nfxn0kOxgZMrnD9Bsd8clpkKzAnsb5A4lphSR5FSoyJRCoVAohk+K2YRHt6AF+kWmPGEx1d3rZaF2\nhK6cBaBpaGl5FJqdA+bz9Xr86Lp0AbY6PIBOuqcFSpbIAfFEpnwe2HgXbH940MOMNF+Lw40/oGN3\n+/APMavPGezmS+s5BpoZslQHtiKC3AroOjLeq4iJElMKhUIxQbGYTfiwYApIlMmomXL5wjVT9qY6\nsrUeXAWL5I70fIrMPaFidZDOurU/fYkHN8nOvs3hJh875oA3LKbiiUw17wS/Ozw7LwY5aVa6e700\ndLlC9zlcg8/qM2qmbK4WyCxWHlOKvuRUiN+Uq3u8VxIVJaYUCoViAuMzhSNT7mB6L7JmynN0CwCB\nGUFRlJZHvqknNK8P4NU9zXT1eqlttAPQ4nAzQ+uQH85YLLfxiKlj78lt19Go3X8G2akWunq9NHSG\nxdRQVglOtw+TBmavA2yx3dYV0xQj7TtB66aUmFIoFIoJTECzYBrQzRcWU1rjdvy6hrUsKIrS8sjV\n7H3SfM/tbASguVsGwLc5PMwwPKZyK8VdOp4037H35dbvjulNBeE0X0NX2GSx2zWUiagMOdbcSkwp\nomCIqQlaN6XElEKhUExgApoVs25Epgam+VJad1Gnl5GXHRy3kZZLlh4uQHd5/by6R4RPU7dEilod\nbmaagpGprFJIL4w/MmVJla8HSfXlpFnxB3QOtDhC9w0WmWpzuHl9XwuFmTZw2yElfpNQxTQhJ+h1\npiJTCoVCoRgufpMVU0DqjTz+AOtMO3B7wim87K7d7NJnkZcRHOSelk+GvztkjfDm/lZ6PH6Ksmw0\n2cNiqiolWHuSVQIZhUNHptx2aKmFuefI94MUAxtef7WNdjRN7ovV0dfh9HD13Rs51tHLbR9ZDB4V\nmVJEIaMIzDZxx5+AKDGlUCgUE5iAZsWki5hK76rj/pSfsMD+tvywt4MsVwN7mCUz7QDS8rDqHjyu\nHrz+AM/tbCTLZuGDS0tp6naj6zptDg/llq7gBcoajExFH3Yc4vgWQIdFl8j3Q0SmAPY02qkqEL+/\nWJGp37ywl7oWJ3+8djXragpFtCkxpeiPyQQ55SoypVAoFIrhEzCF03xmTxcAaZ5gvVO3GFy2p8xE\nM0JAaXkA5GGn1eHmxd3NnLmgmAWpneg+D129XlodbkpNHZLiA3E+HyoyZRSf16wHW/agtSuGmOrx\n+Jk/Q4RRrJqpY529zCnO5LR5RXKHElOKWORWqMiUQqFQKIaPbrJiCUamdK+k6Ww+6crDJeKK1Nzw\nA9LzAcjVnPzx9YO0Oz1csTiTy9/+CB8zv0RTt5tWh4civR2yg15ORs3UIB16HHsP8mbL8w8RIcgO\niimAmuKMPrP6+tPV6w2JL3Rd1UwpYhNp3Ln/xcHfr2OMElMKhUIxgdHN4cgUPumOs/mChd29nQBo\naTnhBwQjU7magz+/fYgVlbmcmnIAc8DNLK2Zxm4XrQ43uf62iMhUEeh+cHXGXkjDFihbIV/nDD7a\nIydCTJXlppGdaqG7N7rPVB8x5XPJOlRkShGN3EpwNsO7d8PfLoPNfx7vFYVQYkqhUCgmMLopBQt+\n2YUHI1Op/r6RKWtGRGQqKKZycOAP6Hzt3Plo9RsBKNS6ONjiIODzkOGLTPMVym1PjLopT4+kV4qD\nxqA55YOn+dLDYqo0JzU0XiYafcSUO/h7KTGliIZhj/DMV6H6DFhxzXiupg9KTCkUCsUERjcFhUbA\ni+YTMZUWcMp9QTGVklkQfoBRM6U5WFudz7qaAgiKqQK62Hm8m0KC6cGs4Fy+9ODjY9kjtO2T28K5\ncptbIVEsQ/z0IzPFgilYwlWSnRYcfDyImEpXYkoRB4Y9QuF8uOLP0jwxQVB+/QqFQjGRMS4Yfg+a\nX8RURkDSfIHeDkxAenZkZEpqps6uSqHyw4vR/N5Q8fgMczc7j3dTpAXTecaQ41BkKoaYag2KqaL5\nchvy/DkKxQsHHG4yaWSnWens8VKWm0p2avTIlMvrx+MLDIxMqZopRTRmroKTb4Y1n4W03KGPH0NU\nZEqhUCgmMiEx5UXziYN5hi6RKY+jA7ueRm5Gevh4axqYbZxdZWXejCxo2Cq1SOmFFGrd7Gu2U9xf\nTKUHxVSsyFTrXtBMkF8t3xtiapBUX3aqlVSriZw0a2hWX38MgRUqWPcEa8FUZEoRDWsqnHcb5M0a\n75UMQIkphUKhmMiYU+TW78UUjExl4cTrD+B1dtBFBrkRNUpomqT6eoL2CfUb5HbBheTodvx+f+zI\nVCwx1bIH8qrAYpPvc4d2o85Js1Kak4amaWSnWeiOMujYEFMDa6ZUZEoxuRhSTGmaVqFp2iuapu3S\nNG2npmlfDN7/n5qmHdM0bUvw34XJX65CoVBML7SQmPJg8ktkKlvrweX1E+jtxK6nk53ar3YkPR96\ng+NijmwQS4MZSzARIB87xRhiqlhuLTY55s3/hnf/NLDlvHWf1KkYZM4Ak2VQMbVqVp7Ua/H/27vb\nGLmu+o7j3//cO087++j1rkk2dmIcQ2L6wkEudpVSHgKFEGjgDYVKIaJIpmpoCUWiFKmC9kWLKh6k\nSiUSiEAqUSgSSQmUQqMobcWLhIQosZNYgTTBiTd+Xu+M98HztKcvzp2d2YdZrzuzO3dmfx8pmjt3\n7tjHObrrn//n3HNoOsy3MkzVKlODa/wfEYmf9cyZqgCfds49aWYDwC/N7KHos6865760cc0TEdna\nLKyHqaAWpphlrrxAaj5PgT76M8t+lGdH/LIJzvkwtfed0O8XxRy1PBPJgp903jiB9yM/hB/9Ofz7\nX/gK1Vv/0p9fqML5F+D6W+rXJgIYnIDz/9u03V/4gzcsHg9mk5QqC1wqV8kkg8Xz+bnlYSra4kZz\npqTLXLYy5Zw76Zx7Mjq+CBwDJja6YSIiAhbW50wlFqI5U1bk0qVLWDFP3uXqW8nUZEd8ZWrqRT+p\nfOdBv5YUfnmEibAA/a9Z+p2Ra+GOf/OPnD/9XR/EAKaPQ7UI21+39Po9b4df/WxdGyTX5kQtnze1\nojKlOVPSpa5ozpSZXQfcBDwWnfqEmR0xs3vNbKTNbRMR2fKsNk+pWiKMwhRAaXaaoJSnQG7lMF92\nGOan4OVoD79dhyDnh/RGKTBu+foQ35LfzPzeexdegjPP+XO1J/mWh6mDf+JD1hPfuuyfoRaWli+P\nsPqcKYNU7rK/pkicrDtMmVk/8APgbudcAbgH2APsB04CX27yvcNm9oSZPXH27Nk2NFlEZOtIRHOm\nqpUS4UJp8Xxldppk+SIFt9owXzRn6uVH/VYz21+/OMw3ZnlG3QUYWFaZqrnhNsDg2I/8+7PP+9fa\nGlM14zfAnlvg8W9ApcRaBqP2LZ83tfg0X639xRlflartMyjSJdYVpswsiQ9S33HO3Q/gnDvtnKs6\n5xaAbwBvWu27zrmvO+cOOOcOjI2NtavdIiJbQiKaM1UpFZdUpqpz50hVZsi7HLl0sPRL2RG/HMKL\n/+2H+BIJyAzjEknGLM9gdWr1yhT48zsPwrEf+/fnfuWHCKM9/5Y49KcwcxqefWDNP8NiZWrZljL5\n+TID6ZAwiP4q0r580qXW8zSfAd8EjjnnvtJw/qqGyz4APNP+5omIbG2JpA9T5VKRpGuoAEVrPM0m\ncqTDVcIUQP5l2HXQH5tBbozXhScJXXnlnKlGN74PTh/1ValTR1YO8dVcf4tfMuEyYao2Z2p5Zaow\nX16yKTKli5ovJV1pPZWpm4E7gLcvWwbhH8zsqJkdAd4GfGojGyoishUlojlTlUqRpCtyyTIAhAUf\npsrhKssINFaRdv3O4qH1j/GWwTP+TbPKFMCN7/WvXzvkF/3c/ZbVrzOD0eth5tSaf4a15kw1bopM\n8aLWmJKudNmlEZxzPwdWG8D+SfubIyIijcJkfZgv5Urkw1Ey5UlSM1GYSq0SpmqVqUQSrr6pfj43\nRnDyaX/cbM4U+GrTgT/2Gxzv/yO47s3Nr82N1edVNVGbIF9bCqFmZZiaUWVKupL25hMRibEg6StT\n5dIlUpSYTY5CeZL0zCQA1dUWuKyFqav3++1lanIN1aja6ufNvPer62tgbjvMnvVLKTSZOJ4KE2ST\nwaqVqT1jDZWo4sW1K2YiMaXtZEREYiyIKlPFYpEMZUqpESouQd/cCQDcWmFq58Gl52vbxsDlw9R6\n5cb9ZPfaGlFNDGbDVSegL6lMlWa0+rl0JYUpEZEYC6I5U8XiJdKUsGSWAn3k5l8FwGVWWeJv4Gq4\n+W4/VNdocfuYbPuG06LFQJlde+mboezKLWXy82WGGvcVLBY0Z0q6ksKUiEiMhSkfpkrFS2SsTCKV\noeByi2tOBdnhlV9KJOCdfwOje5aerw3zDexo31pOtTA1s3aYGswklwzzXSpXKVYWeOPMf8Ejf++H\nCTVnSrqUwpSISIzVwlS5VCRNCcIsF+kDoEKCVN8VhI/aMN9ayyJcqdqvuY7K1HTDBPTa1jJvOPdT\nv8FyaQZcVetMSVdSmBIRibFkNGeqVPJzpiyZZtZ84PCrnyfX+vpStWG+dk7yXucw30guxYW5+jpZ\ntSG//vJ5P+fq1FH/gSpT0oUUpkREYixM+XWlKqVLi5Wp2YTfu67gciu3klnL4jDfRlSm1t7weFsu\nxdRsCRdtoFwLU9liFMJeibZ8VZiSLqQwJSISY6lomM+VZgnMYckM84moMkUfA+krCFN9o76SNL6v\nfQ0M05AZumxlalsuRbGywFypCvgwZSyQno9C2Cu/8K8KU9KFtM6UiEiMpZJJFpyRKF4EwJJZ5oN+\nqPphvoErGeYLQrj7KATp9jYyN3b5MNXnhyunZkvk0iH5+TIjzGAuWi6hVpnSnCnpQqpMiYjEWCoZ\nUCYkKPt1nBLJLMXQV2/y5Oi/ksoU+EU8E23+0b+eMJWrhynwlalxm/YfBimYO++PVZmSLqQwJSIS\nY6kgQYmQsOwrU4lUhmLgA8cVz5naKLVV0NewrT8KU3ONYeqC/7Bh/0CFKelGClMiIjGWChOUCUhV\nZwEIUllKyfqcqSuuTG2EtSpTZ47Byafrw3wz9TC1M+kDInveXr9eYUq6kMKUiEiM+TAVko7CVCKV\npZL0W67kXY6BWFSmxmBuCqqVlZ/9x2fgwT9brExdmGsMUwV/zZ631a/XnCnpQgpTIiIxlgp8mMos\nzAEQprJUUkMAFMhd2QT0jZIbAxzMT638bPplmH6ZgXRIMjDOR3OmzhSKTIQFvxffjt+KJsUbpHKb\n2nSRdlCYEhGJMTOjQkif85WpMJ2llBkF4JwbIpcOOtk8r9nCnQsLUHgV5i9g5TlG+lKLw3yvTs9z\ndZj3Gy4nAhh7vR/ia9c2NyKbKAb1YRERWUuFkJybB4NkOsds/zh/WPxrjgQ3kA5jFqbuP+xD0W1f\nhrlzUI1WPc9P+oU75/zCnZPT84wNT9cXEL3mt+vXinQZhSkRkZirWEgfRcBXprLJgMfcjYymUx1u\nWaQWpk48Dkf+FbZFGyznT9Svyb/CtlyOqdkS52ZKFCsLDFenoH+v//ydfwvluc1tt0ibaJhPRCTm\nqog9WtAAAAnPSURBVFafF5VM95FO+mpULJZFgPqWMo/e41+nj/vJ6IXJ+jWFSb8/32yJyel5wJEr\nnatXptL97d0zUGQTKUyJiMRcxeqhKUhlyST9j+5YPMkHkBmGROgX3gzSsFCB/CuQbwhT+ROM5lKc\nny0xeWGefuYJqvN+zpRIl1OYEhGJucbKFGGGbK0yFYc1psCvqN4XVacOfty/XngJCid8uOp/DeRP\nMNKXIj9f5vjUbH3183ZuuizSIQpTIiIxt9AYppIZMothKgbLItT0j8PQTnjTYf9+6kVfmRq8GoZ3\n+spUtNbUs68WuDYVLdipypT0gJj8s0ZERJqpJpZWpmI3zAfwrr+DIAmDExBmYeolP2dq6Bo/p+rk\nEUaiVdCfmcxza24W5lCYkp6gypSISMy5hA9NZUJIBPEb5gPY/WbYdcgP+W3b7StThVd9uBqcgMIk\no30+FB4/P8futN+4mQGFKel+MboTRURkNQtRZapEkiQsPs0Xq8pUo22vhXO/8mFqaAJy41C5xPZg\nZvGSiTDv51NlhjvYUJH2UGVKRCTmXDRnqmRpADJhzJZGWG7bbh+mXNVXpYauAWB79cziJeOJvK9K\nacVz6QExvRNFRKRmIfBhqhyFqmwqqkzFaZiv0cju+vHgxOJQ3mDpDLV/w48sTPmn/ER6gCpTIiIx\n5xK1MOUrU321MBWHTY5Xs+219eOhCf+UHxBenFwcmuwvn9d8KekZClMiInEX+KfgKgn/une8n8+/\nbx/v2BfTMNIYpgYnoG8UwgwU/MKdAOn5s6pMSc9QmBIRiblaZaqS8JUpM+OjN++O19N8jYaugUQS\nkn2QHfHzogYn/MKduRT9QYVEcVqVKekZMb0TRURkUVirTKU73JB1SgQwcp0/rk0wH7oG8icYH0iT\nGb4Es6gyJT1DYUpEJOYsmoBe7ZYwBXDj+2ChXH8/vBN+/RCfufMGFo7n4cdoKxnpGQpTIiIxZ9Ew\nXzXIdLglV+Adn1/6fvg6mDnNnuEAzs76c/3jm94skY2gOVMiInEXDfMtRBPRu9LItf51+mWYOe2P\nNcwnPUJhSkQk5mwxTHVRZWq54ShMXTgOF0+BJfyefSI9QGFKRCTmLKiFqS6aM7XcYmXqOMyc8lvM\nJILOtkmkTRSmRERiLkj6EOXCLq5M9e/wa01d+A3MnNGyCNJTFKZERGKuNsxH2MWVKTO/Evp0NMyn\n+VLSQxSmRERiLghrlalsh1vSopFr/ZypmdOqTElPUZgSEYm5RNJXpizZ5WFq+Fo/zDerrWSkt2id\nKRGRmAuiYT7r5mE+8JWpYsEfqzIlPUSVKRGRmAt6qTJVo8qU9BCFKRGRmAujp/kSyS5+mg/qyyOA\nf7pPpEcoTImIxNxALgdAX66/wy1pUWNlSsN80kMuG6bMbKeZPWJmz5nZs2b2yej8NjN7yMx+Hb2O\nbHxzRUS2nokbDzJ14FPsPXRbp5vSmuwIpAb8sSpT0kPWU5mqAJ92zu0DDgF3mdk+4LPAw865vcDD\n0XsREWm3IMm2934BSw90uiWtMfNDfdmR7l4zS2SZy4Yp59xJ59yT0fFF4BgwAdwO3Bdddh/w/o1q\npIiI9IjxfTB6fadbIdJWV7Q0gpldB9wEPAbscM6djD46BaxaszWzw8BhgF27dv1/2ykiIr3gti9B\ntdzpVoi01bonoJtZP/AD4G7nXKHxM+ecA9xq33POfd05d8A5d2BsbKylxoqISJfLDEFue6dbIdJW\n6wpTZpbEB6nvOOfuj06fNrOros+vAs5sTBNFRERE4ms9T/MZ8E3gmHPuKw0fPQjcGR3fCfyw/c0T\nERERibf1zJm6GbgDOGpmT0XnPgd8Efi+mX0MOA58cGOaKCIiIhJflw1TzrmfA9bk41va2xwRERGR\n7qIV0EVERERaoDAlIiIi0gKFKREREZEWKEyJiIiItEBhSkRERKQFClMiIiIiLVCYEhEREWmBwpSI\niIhICxSmRERERFqgMCUiIiLSAoUpERERkRaYc27zfjOzs/hNkTfSduDcBv8ecmXUJ/GjPokf9Uk8\nqV/iZzP75Frn3NjlLtrUMLUZzOwJ59yBTrdD6tQn8aM+iR/1STypX+Injn2iYT4RERGRFihMiYiI\niLSgF8PU1zvdAFlBfRI/6pP4UZ/Ek/olfmLXJz03Z0pERERkM/ViZUpERERk0/RUmDKzd5vZ82b2\ngpl9ttPt2arM7DdmdtTMnjKzJ6Jz28zsITP7dfQ60ul29jIzu9fMzpjZMw3nVu0D8/4xum+OmNkb\nO9fy3tWkT75gZpPRvfKUmb2n4bO/ivrkeTN7V2da3dvMbKeZPWJmz5nZs2b2yei87pUOWaNPYn2v\n9EyYMrMA+CfgVmAf8GEz29fZVm1pb3PO7W94fPWzwMPOub3Aw9F72TjfBt697FyzPrgV2Bv9dxi4\nZ5PauNV8m5V9AvDV6F7Z75z7CUD0s+tDwBui73wt+hkn7VUBPu2c2wccAu6K/t/rXumcZn0CMb5X\neiZMAW8CXnDOveicKwHfA27vcJuk7nbgvuj4PuD9HWxLz3PO/Q8wtex0sz64Hfhn5z0KDJvZVZvT\n0q2jSZ80czvwPedc0Tn3EvAC/mectJFz7qRz7sno+CJwDJhA90rHrNEnzcTiXumlMDUBvNLw/gRr\nd4BsHAf8p5n90swOR+d2OOdORsengB2dadqW1qwPdO901ieiIaN7G4a/1SebzMyuA24CHkP3Siws\n6xOI8b3SS2FK4uN3nXNvxJfE7zKz32v80PlHSPUYaQepD2LjHmAPsB84CXy5s83ZmsysH/gBcLdz\nrtD4me6VzlilT2J9r/RSmJoEdja8vyY6J5vMOTcZvZ4BHsCXXE/XyuHR65nOtXDLatYHunc6xDl3\n2jlXdc4tAN+gPjyhPtkkZpbE/6X9Hefc/dFp3SsdtFqfxP1e6aUw9Tiw18x2m1kKPyHtwQ63acsx\ns5yZDdSOgd8HnsH3xZ3RZXcCP+xMC7e0Zn3wIPCR6EmlQ0C+YYhDNtCy+TYfwN8r4PvkQ2aWNrPd\n+AnPv9js9vU6MzPgm8Ax59xXGj7SvdIhzfok7vdKuNm/4UZxzlXM7BPAz4AAuNc592yHm7UV7QAe\n8PcDIfAvzrmfmtnjwPfN7GPAceCDHWxjzzOz7wJvBbab2Qng88AXWb0PfgK8Bz9xcw746KY3eAto\n0idvNbP9+GGk3wAfB3DOPWtm3weewz/ddJdzrtqJdve4m4E7gKNm9lR07nPoXumkZn3y4TjfK1oB\nXURERKQFvTTMJyIiIrLpFKZEREREWqAwJSIiItIChSkRERGRFihMiYiIiLRAYUpERESkBQpTIiIi\nIi1QmBIRERFpwf8BfT0u4R6tFNsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c27d80c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,7)) \n",
    "n=12\n",
    "l1,=plt.plot(y_test[:,n])\n",
    "l2,=plt.plot(testPredict[:,n])\n",
    "plt.legend(handles = [l1, l2,], labels = ['Reality', 'Prediction'], loc = 'best')\n",
    "#plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}