LSTM GITHUB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STOCK MARKET -  LSTM\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem Description\n",
    "The problem we are going to look at in this post is the stock market prices prediction problem.\n",
    "\n",
    "This is a problem where, given a day, month, and a year, the task is to predict the price of next days stocks . The data ranges from 1996 to 2010,  with 4800 observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Name of the Dataset **  “TATAMOTORS.xlsx'“"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import pandas\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "\n",
    "import os\n",
    "\n",
    "\n",
    "from keras.callbacks import ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = pandas.read_excel('TATAMOTORS.xlsx', usecols=[0,4], skipfooter=3)\n",
    "dataset.fillna(method='ffill', inplace=True)\n",
    "\n",
    "\n",
    "plt.plot(dataset['Close'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***The Long Short-Term Memory network, or LSTM network, is a recurrent neural network that is trained using Backpropagation Through Time and overcomes the vanishing gradient problem.***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are three types of gates within a unit:\n",
    "- **Forget Gate**: conditionally decides what information to throw away from the block.\n",
    "- **Input Gate**: conditionally decides which values from the input to update the memory state.\n",
    "- **Output Gate**: conditionally decides what to output based on input and the memory of the block."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict next 10 days(forward_days) depending on the previous 40 days (look_back) using LSTM Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we do anything, it is a good idea to fix the random number seed to ensure our results are reproducible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load this dataset easily using the Pandas library. <br>\n",
    "We are not interested in the date, open price ... etc\n",
    "given that each observation is separated by the same interval of one day. <br>\n",
    "Therefore, when we load the dataset **we can exclude the other columns and keep only the close price**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>93.709801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94.188904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>95.626099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>96.967598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.033997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Close\n",
       "0   93.709801\n",
       "1   94.188904\n",
       "2   95.626099\n",
       "3   96.967598\n",
       "4  100.033997"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fix random seed for reproducibility\n",
    "numpy.random.seed(7)\n",
    "# load the dataset sales-of-soaps.csv\n",
    "dataframe = pandas.read_excel('TATAMOTORS.xlsx', usecols=[4])\n",
    "#dataframe = pandas.read_csv('international-airline-passengers.csv', usecols=[1], engine='python')\n",
    "dataframe.fillna(method='ffill', inplace=True)\n",
    "\n",
    "dataframe.head()\n",
    "#print(type(dataframe.dtypes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ** extract the NumPy array from the dataframe** <br>\n",
    "- Then **convert the integer values to floating point values**, which are more suitable for modeling with a neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataframe.values      ##   convert the data-frame to its Numpy-array\n",
    "dataset = dataset.astype('float32')\n",
    "# normalize the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Important:\n",
    "**LSTMs** are sensitive to the scale of the input data, specifically when the ***sigmoid (default)*** or ***tanh*** activation functions are used. <br>\n",
    "It can be a good practice to rescale the data to the range of 0-to-1, also called ** normalizing**.<br>\n",
    "We can easily normalize the dataset using the MinMaxScaler preprocessing class from the scikit-learn library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing a Train-Test Split<br>\n",
    "Train: 67%<br>\n",
    "Test:  33%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3240 1596\n"
     ]
    }
   ],
   "source": [
    "# split into train and test sets\n",
    "train_size = int(len(dataset) * 0.67)\n",
    "test_size = len(dataset) - train_size\n",
    "\n",
    "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]\n",
    "print(len(train), len(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross Validation\n",
    "\n",
    "##### When using time series data, the sequence of values is important. <br>\n",
    "\n",
    "Now we can define a function to create a new dataset.\n",
    "\n",
    "The function takes two arguments: the ***dataset***, which is a NumPy array that we want to convert into a dataset, and the ***look_back***, which is the number of previous time steps to use as input variables to predict the next time period — in this case defaulted to 40.\n",
    "\n",
    "This default will create a dataset where X is the  price of stocks last 40 days and Y is the price at the next 10 days.\n",
    "\n",
    "It can be configured, and we will be constructing a differently shaped dataset in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "forward_days = 10\n",
    "# convert an array of values into a dataset matrix\n",
    "def create_dataset(dataset, look_back=40):\n",
    "\tdataX, dataY = [], []\n",
    "\tfor i in range(len(dataset)-look_back-forward_days+1):\n",
    "\t\ta = dataset[i:(i+look_back), 0]\n",
    "\t\tdataX.append(a)\n",
    "\t\tdataY.append(dataset[i + look_back, 0])\n",
    "\treturn numpy.array(dataX), numpy.array(dataY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Let’s use this function to prepare the train and test datasets for modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape into X=t and Y=t+1\n",
    "look_back = 40\n",
    "\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3191, 40)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "\n",
    "trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))\n",
    "testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3191, 40, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to design and fit our LSTM network for this problem.\n",
    "\n",
    "The network has :<br>\n",
    "- a visible layer with 1 input<br>\n",
    "- a hidden layer with 4 LSTM blocks or neurons<br>\n",
    "- an output layer that makes a single value prediction. <br>\n",
    "\n",
    "The default sigmoid activation function is used for the LSTM blocks. <br>\n",
    "The network is trained for 40 epochs and a batch size of 40 is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the LSTM network\n",
    "model = Sequential()\n",
    "model.add(LSTM(8, input_shape=(look_back, 1), return_sequences=True))\n",
    "model.add(LSTM(8, return_sequences=True))\n",
    "model.add(LSTM(8))\n",
    "model.add(Dense(1))\n",
    "from keras import optimizers\n",
    "adam = optimizers. Adam(lr=0.001, decay=1e-6)\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer=adam, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 40, 8)             320       \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 40, 8)             544       \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 8)                 544       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 1,417\n",
      "Trainable params: 1,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#!pip install GraphViz \n",
    "import os\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:/ProgramData/Anaconda3/Library/bin/graphviz/'\n",
    "from keras.utils.vis_utils import plot_model\n",
    "plot_model(model, to_file='LSTM_model_plot2.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      " - 4s - loss: 0.0038 - accuracy: 3.1338e-04\n",
      "Epoch 2/20\n",
      " - 3s - loss: 1.7322e-04 - accuracy: 3.1338e-04\n",
      "Epoch 3/20\n",
      " - 3s - loss: 1.5638e-04 - accuracy: 3.1338e-04\n",
      "Epoch 4/20\n",
      " - 2s - loss: 1.4683e-04 - accuracy: 3.1338e-04\n",
      "Epoch 5/20\n",
      " - 3s - loss: 1.4005e-04 - accuracy: 3.1338e-04\n",
      "Epoch 6/20\n",
      " - 2s - loss: 1.4309e-04 - accuracy: 3.1338e-04\n",
      "Epoch 7/20\n",
      " - 3s - loss: 1.3251e-04 - accuracy: 3.1338e-04\n",
      "Epoch 8/20\n",
      " - 3s - loss: 1.2462e-04 - accuracy: 3.1338e-04\n",
      "Epoch 9/20\n",
      " - 3s - loss: 1.2283e-04 - accuracy: 3.1338e-04\n",
      "Epoch 10/20\n",
      " - 3s - loss: 1.1684e-04 - accuracy: 3.1338e-04\n",
      "Epoch 11/20\n",
      " - 3s - loss: 1.1779e-04 - accuracy: 3.1338e-04\n",
      "Epoch 12/20\n",
      " - 3s - loss: 1.0907e-04 - accuracy: 3.1338e-04\n",
      "Epoch 13/20\n",
      " - 3s - loss: 1.0036e-04 - accuracy: 3.1338e-04\n",
      "Epoch 14/20\n",
      " - 3s - loss: 9.7895e-05 - accuracy: 3.1338e-04\n",
      "Epoch 15/20\n",
      " - 3s - loss: 9.6277e-05 - accuracy: 3.1338e-04\n",
      "Epoch 16/20\n",
      " - 3s - loss: 9.2609e-05 - accuracy: 3.1338e-04\n",
      "Epoch 17/20\n",
      " - 3s - loss: 8.9838e-05 - accuracy: 3.1338e-04\n",
      "Epoch 18/20\n",
      " - 3s - loss: 8.4894e-05 - accuracy: 3.1338e-04\n",
      "Epoch 19/20\n",
      " - 3s - loss: 8.1990e-05 - accuracy: 3.1338e-04\n",
      "Epoch 20/20\n",
      " - 3s - loss: 7.6857e-05 - accuracy: 3.1338e-04\n",
      "Wall time: 57 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1de4f0098c8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Fit the LSTM network\n",
    "model.fit(trainX, trainY, epochs=20, batch_size=40, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the model is fit, we can estimate the performance of the model on the train and test datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.786234160789886e-05, 0.0003133813734166324]\n",
      "['loss', 'accuracy']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "scores = model.evaluate(trainX, trainY, verbose=0)\n",
    "print(scores)\n",
    "print(model.metrics_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions\n",
    "trainPredict = model.predict(trainX)\n",
    "testPredict = model.predict(testX)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Note that we invert the predictions before calculating error scores \n",
    "to ensure that performance is reported in the same units as the original data (thousands of passengers per month).***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# invert predictions\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "trainY = scaler.inverse_transform([trainY])\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "testY = scaler.inverse_transform([testY])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 23.42 MSE\n",
      "Test Score: 672.04 MSE\n"
     ]
    }
   ],
   "source": [
    "# calculate root mean squared error\n",
    "trainScore = mean_squared_error(trainY[0], trainPredict[:,0])\n",
    "print('Train Score: %.2f MSE' % (trainScore))\n",
    "testScore = mean_squared_error(testY[0], testPredict[:,0])\n",
    "print('Test Score: %.2f MSE' % (testScore))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MSE of LSTM, when it was trained with 200 epochs, was 53\n",
    "## MEa Squared Error (MSE) = 53"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of how the dataset was prepared, we must shift the predictions so that they align on the x-axis with the original dataset. Once prepared, the data is plotted, showing the original dataset in blue, the predictions for the training dataset in green, and the predictions on the unseen test dataset in red."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\1864012\\Miniconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZNdW2bRZSeQoL7QmAV15p4dc55/iClFtvlfr1q/i0wVC9mwKfuhes+iuUe+6RPvzQ9zoagVZmpi8gC8ujj0p33RV5F/woOe88Gzt1skArJ8dCRi/QOuww6dBD7TgjQ/rlF99nI/l7aurYcggAAAAAQAMXH2/VWZs2WcBUXGxN2t95R7r4YmnnTgu5JF/D+MsuszElRerTR5oxw7YTNisrbQk30Aq3QiuY9HRrFl+dpvBeoBVR76yvvpIeeMCO8/Oll16K4MPRMXGiZWmOY4GWJJ12moWNkpSU5Jsb+N283xBVo0ILAAAAAIAGzntq4KZNvtCjXTvpooukv//dqn9WrCj/mZ9+sjE1Verd20IWr7+TZCGYZIGTv2gGWo5jzeS9Ru9Vcd0aBlqvv24p0m23SePGSYsWRbTeaEhI8P1eGRk2TpsmXXutHfsHWocfXv6zVGiFj0ALAAAAAIAGrmVLGx97zLd1sH17G73AZ/586dhjpdGj7fWsWTZ6FVqStHy575pehZb3REJPYKCVklKztbdq5QupqlJYaM3TpfJbDsOyf7/0ySfS2WdLDz1kpWx//3vE642mnj19x1645TXMl6S+fcvPJ9AKX4MLtBzHyXIcxw3x58qAuS0cx3nWcZxfHMcpdBxnluM4p4a47iGO47zlOE624zh5juNMcxxnUN18KwAAAAAAqi8hwcbZs6Xzz7djr4eW91S8//xH6tpVeuEF6bjjbK5kFVZdutjx+PG+816FVmCPLP9AKyXFt0WxuvyfxlgV/3mbN0txcb4wr0oLFljjsDPPlFq3tmRv4kRpzZqI1xwtgwf7jr2qrVAVWh07EmhFosEFWn5ekPSbgD/fe286juNI+ljSdZJekXRH2VufOo5zov+FHMdJlTRd0n9J+oek+yR1kDTdcZzetfs1AAAAAAComby8isdeGOLfHL5zZxvbtfM9ETElxVfl9M47VsW1dKkv0ArcUugfaNVku6GnVSvps8+kX/+66rn+gdaOHRa2hd0Q3mseNnKkjXfeac3Hxo6NaL3R5DjS6tUWzHlbRf0DrXi/zubt2tFDKxINOdD6wXXdtwL+rPV7/3xJIyXd5LruA67rviQpS9J6SU8EXMaB5ygAACAASURBVGu0pN6SLnBd9xHXdZ8t++wBSQ/X+jcBAAAAAKAG9u61fOb//s93zgtD/AMtL+Rq1cp3LjXVQhT/Sqwff/SFJ65b/l7RDrS8CqvJk6ue6x9o5eZGeP/vvrO/gI4d7XXHjtI110ivvVb+UYJ1rEcP6dRTpYICe+2/5VCS5syxVl/t2lGhFYmGHGjJcZw0x3ESQrx9saQcSW95J1zXLZJVaw1yHOfwgLlLXded7jd3h6R3JJ3tOE4NdwQDAAAAAFA7Skst0EpPl44/vuL7/lvyBg6seM7rgZXg93/XW7da5ZAk7dtX/nr+gVbgdsTq8A/XAvtzBQrcmhhRoDVnjpWf+bv3XmvK9cADVrJWXFzxC5eU2GdDPfYxCvz/DvwrtCRp0CDpyCMJtCLVkAOt5yXtlVTkOM6PQXpjHSNpvuu6+wPO/+T3vhzHaSbpKL/zgXMTJfWL2qoBAAAAAIgiLwRKT7fG4o88Is2YEXzukUfa6B+geIHWfr//e/7lF2nVKjuuLNDyr/6qLv9wzWv0Hkrg0xDDbki/c6dd/Jhjyp8//HDprrukV1+1xzkmJlpKd+WV0pdfSn/6kzUYGzLE9mtOnBjmDSNTWaDlad+eQCsS8VVPqXMlkj6Q9KmkbZJ6SLpL0meO45zruu7HZfM6KHhItbVsLKsxVBtJSX7nK5sLAAAAAECDsnevjd7TCP/wh9BzvYoq/xDJO/YPtNatsz9S7Qda/gHOpk1S70o6WVe7Qmv+fBuPPrrie48/Lp1wgjRzpv0FbdsmvfSSNGGCNbk680x7GuKLL9oWxV69fKVuUeL/ewRuOfS0a2fbEvPyolMZ19g1uEDLdd3v5df8XZIcx3lD0nJJT8oawUtSsqSA/+wkSUV+7/uP4cytwHGcGyTdIEnt27fXjFAxeJm8vLwq5yA28ds2fvzGjRe/bePG79t48ds2fvzGjRe/bXRt2JAsaag2blymGTOCl/C8/Xai9u2L04wZ1qjpl186SOolSfrppxmSpJKSkZKaqU2bfZo2LU6uG682bfapoCBOM2Z8d/Bay5Z1k2SPRSwuXqsZM9ZXuF8kv/GqVYdLsm71X365QvHxoftZzZvXWZKvg1Bx8S7NmLGoynt0njRJh0v6Lj9f+4Otq0UL6fTTD76M/9WvlL5ihQoOO0z7yh4TmXDPPRp8ww0qPesszX3pJe2PYqq0a9dhkrpLkubPn6mtWytGFLt2HSKptz76aJY6diyq8H5diZX/fhtcoBWM67rZjuO8Kun3juMc7rruGkmFsu2CgbzstzBgDGdusHuPkzROkgYPHuxmZWVVutYZM2aoqjmITfy2jR+/cePFb9u48fs2Xvy2jR+/cePFbxtdc+bYOHRoX2Vl9Q3rM94TDCUd/C2aNfOuk6gpU+z4zDMT9e67Kvd7ffih/2e7KSurW4XrR/Ib++/iS07urays0CVaX31lRVMtW9r2w06d2oR3n5dekrp00YhRo8JakyQp2NxDDpFGjtSI8eOld9+N4BGLlVuxwnd80knD1L59xTkFBdJjj0ndux+n446Lym2rJVb++23IPbQCbSgbM8rGrQq+VbBD2bilbNwlq84KZy4AAAAAAA2KtwUwkoIhb4ubfw+q6dOlm2+W+pZlYiNH2hP4iovLP+kwP9/Cr9tvly6+uGZrl6RTTvEdL6qi2Co314qpvHWHveVw3rzg2w0jNWyY9PDD0nvvSdddZ385UeDfQ6uyLYeS7+mTqFxMVGiV8WoOd5SN8ySd5jhOfEBj+KFl43xJcl231HGchZKGBLnmUFnYtawW1gsAAAAAQI0V2C7C8Bukyxeg+Idgw4fbn40brQH5WWdJ//d/9l5xsS9oycuzXupPPVXztUvShRdKu3dLo0dLP/5Y+dzcXFt7XJy9Dus7FxTYIxsvv7zGa5VkT0YsLJQeekhav16aPFlKDtmpKCxt2/qOQzWF936zPXtqdKsmo8FVaDmO0y7Iuc6SrpO0wnXdtWWnJ0lqJelKv3lJkq6VPf3wZ79LTJLUz3GcLL+5mZIukvSp67pVPDgUAAAAAID6UZ1AywuyglU4de4s3XOPVWp5IZZ/Y/itWxV0S1xNtGplBVTr1lm4FUpOjlWXeRVmYVVorV5tJWZ9+kRjqbbNcMwYafx4ado06b//u8aXzMz0HTdvHnxOsN8CoTXECq23HcfZJ2sMv01WmXWDrHH7zX7z3pP0naQXHMfpIWmjpGskdZV0asA1X5D0O0nvO44zVlKupFskJUj6Y219EQAAAAAAaqo6gZYXoFx7beXzvBClqMi2+knS5s3S4MGRrTEc/fvbuHKlQvaIys31BVrz54cZaK1caWOvXlFZ50FXXy2tWiU9+qg1lL/ggmpfyr9Cq1mI0iKvcquo/vrBx5QGV6El6UNZ5dUdkp6XVVxNk3Sc67rTvUmu65ZKOlvSa7Kw6klJcZLO8p9XNjdPUpakzyT9j6THZWFZluu6bDcEAAAAADRY1Qm02ra1SqgHHqh8XmBVkOtKmzZJhx4a+Tqr0sUenKj1FR+aeNDmzVKbNr4AKKxAa9UqG3v0qNH6gnroIStlGzOmfKOxQLt2SV9/HbLnln+gFQqBVmQaXKDluu7TrusOc123reu6Ca7rtnNd90LXdecHmZvruu7Nruu2d1032XXdY13X/TzEdbe4rnu567ptXNdNdV33JNd159T+NwIAAAAAoPq8QCvsBullWrWq+iF9Xojy7rs27t5tgUptBlrr1gV/f/Fi6eefrRiqdWs75/XSqtSqVVKnTpH/BYUjPl667z5pyRLpk0+Cz1m2zKrDsrKkIUOszCxAqEbwweaw5TA8DS7QAgAAAAAAPt5TDmvYlzwoL0S5+24bN2+2sTYCrfR0q74KFmitXSv9/e+WH110ka9Bek5OGBdeuTL62w39XXqppXFjxwZ///bbbXz8cQu3vNcRSkiwAJIKrfAQaAEAAAAA0IAVFFjYkZAQ/Wv7Vw4VFEhbtthxbQRaktS1q2/L4bJl1qaqpEQaNkz617/sKYxt2/oqtKoMtFzXKrR69qydBUv2F3/DDbalcM2a8u/NmiV99ZX0+99bp/2775befNO+XIQcx34PKrTCQ6AFAAAAAEADVlAQWf+sSPi3fNq2zRcgeYFStHXuLG3caMfXXSe98YY0d67dW5LuvNPGUaNsvPrqKi64c6ctujYDLUm66irr5v766+XPP/KIlZ2NHm2v77nHHmP47LPVuk1SEhVa4SLQAgAAAACgAavNQCs+3nf8yy/Va0AfiTZtrE+X5OvvtWuXPWHx1lulc8+1c126WPHV0KFVXLC2nnAYqFMn6dRTLdAqLbVzCxZIkydLd9whpaXZuYwM6eKLpQkTKjSIX7ZM+umnym9DoBU+Ai0AAAAAABqw2gy0zjnHeldJ5QOt2uivLlnll1cF5m13/Plnac8eqV27alzQe8JhbVdoSdI110gbNkjTp9vrRx+1xmC33lp+3kUX2Rf6+utyp/v0sZ7xlWHLYfgItAAAAAAAaMBqM9ByHNtNJ1mg5TWgr637tWpl9ygp8VVozZ9vY9u21bjgqlXW48p7hGJtOvdc+wIvvWSLnjTJwqzA/ZmnnGId/D/+OOJbUKEVPgItAAAAAAAasIKC2quYkqTMTBv/9CffdsDaeKKiVP7phTt22PG8eTZWK9BauVLq0aP83snakpQk3XKL9O670uDBVlJ2zz0V56WkSL/6lfTRR7ZvMsJbvPNOGL3DQKAFAAAAAEBDlp9fexVTkmVBPXtK2dmWwSQnW//z2uD/9MJffrHjRYtsrHaFVl1sN/Tcf7905pm22EmTQnfPHzXKHue4eHFEl/e2Yb7xhrR3bw3X2sgRaAEAAAAAUMdcV8rLk5YskQoLK59bm1sOPbNm2bhsWe1Wg3kVWlu32gMK/b9XRkaEFztwwBpw1XZDeH+pqdKnn1oaN2JE6HlnnWXjJ59EdPmkJN/xtGnVWF8TQqAFAAAAAEAd++AD6yd+5JHWW7wydRFotW4tHXqoHdfmvbyCpjvusPGJJ3zvRRxorV9vTxKsywotj9cALJQOHaRjjrGnIEbAvzLOq2BDcHWwyRQAAAAAAPhbsMB3XNXWsry82q2a8vTvL23eXDcVWvPnSyecII0eLXXsaNsOO3aM8GJ1+YTD6jj/fOmPf5T+8x+pe/ewPuLfEH7nzlpaVyNBhRYAAAAAAHXMv8AnPb3yuTk5viCoNvXoYWNtVmj5fw8vwBo1ynKfqoqeKvACrbrcchiJq6+2L/Xaa2F/xH/7qdc0H8ERaAEAAAAAUMd27bLtd8nJ5atyApWUWIVWqN7j0dShg41xcbV3D//v4T1dsdpWrpRatozChWpJp07S6adL48dbv68w+AdaVGhVjkALAAAAAIA6Fm6glZtrY11UaB1yiI21+XS9lBQpLc2Oa5xDLVsm9e5djdKuOnTdddKmTdKXX4Y13f/fAhValSPQAgAAAACgFmzaJM2cGfy93bulNm3sqXaVBVo5OTbWRYWWF2jt2VO79/G2WNYo0HJdafFi6aijorKmWvPrX1u3+zC3HXr/Frp2JdCqCoEWAAAAAAC1oE8fafjw4O/t2uULtPy3mQXavdvGuqjQ8rYcRhxobdggXXml1K6dNGKENHdupdO9pvM1CrS2bpWys+0xkQ1ZYqL93Xz4oa23Ct6/hR492HJYFQItAAAAAABqQV6ejaWlFd/zAq2qthx6FVoNdsvhjh3SsGHSBx9Yv6i1a+3xhV9+aVVUQXiBVtu2NVjsokU2NvRAS5J++1upuFiaMKHKqV6g1bOntH178H87MARaAAAAAADUIq8PlufHH6XVq20bYVVbDr0KrbrYctiunY3JyWF+oLTUnuSXnS1995305pvSvHm2X+6006xh+6GHSp07SzfccDCt6dbNPp6UVIPFzp5tvbOOProGF6kjAwZIgwZJr7wSMuTzfP65dOmlUv/+0r590ubNdbTGGBRf3wsAAAAAAKAx8xrAS9K0adIpp9hxXFzVgdYPP9hYFxVa8fHS88/brsGwPP64NGWK9NxzvmCpfXvpp5+k11+XVq2S8vMt0Xv5ZfvCL7ygf/5TOv54aejQGix21izb09myZQ0uUoeuu066+WZp/nzpmGNCTjv5ZPszfbq9XrnS8kBURIUWAAAAAAC1aNcu3/E339g4fLh0/vmV99BavVp66ik7rotAS5Juuim8XXztP/9cuv9+6eKL7UP+0tKkW26xxf/zn9K770p33im9+KI0a5YyMqT/+Z8aPJywtNTK3GqUiNWxyy6zH/vVV8Oa3quXjStX1uKaYhyBFgAAAAAAUea/s8zbNihJS5daw+/vv5dOOqnyHlr+QVhKSu2ss1q2btURTz0lZWXZNsNwkqm//MX2NP71rzW//9Klts1x5MiaX6uutGolnXOONHGidOBAldM7dLBckEArNAItAAAAAACizL+xun8wtWSJ1K+f73VlWw737bPxuedqUM1UG554QnH79tk2wubNw/tMWpptufv0U9uKWBPefryTTqrZderaeefZowtnzapyquNIhx0mbdlSB+uKUQRaAAAAAABEWXa279gLtPbvt22Effv63gsn0BowoHbWWC3Z2dKLL2r7ySdbqVkkRo+2AMzbR1ld06ZJ3btLXbrU7Dp17cwzpcREq9IKQ0aG5V8IjkALAAAAAIAoO/dc37EXaP3yi+02889hKgu0vPOJiRHc2HWlnBypuDii9Ybtn/+U8vO1/vLLI/9s+/bWc2vCBF9aF6kDB6Svv7bO6bGmRQtrnPbmm9KePXZu9Wp7ImRGhr0/bJg0d64kqW3b8sEoyiPQAgAAAAAgyhYt8h17gdamTTZ26uR7Lzk5dFN4L/MJK9AqLpZuu01q08YeqZiYKB1xhDR5su9id95pj8w79VTpiy8i+j4HTZwoDR2qgm7dqvf5K66wpx5+9ln1Pj9/vgV2sbbd0HPHHbYf9ayzpDFjpCFDLMC66CLpt7+VNm6UfvUrKSeHCq0qEGgBAAAAABBFpaXWA+lPf7I+SF6gtXmzjYce6psbToVWUlIYN73zTunZZy0oGTvWwpLERCsVe/RR2+725JPSwIFWFXT66VZtFYn16y1QuvDCyD7n75RTpJYtrZdWdbz/vhQXZ6FcLDr2WGn8eGnePOmhhyzQmjPHngD51FPSRx9ZYPfKKwcrtPwfMACf+PpeAAAAAAAAjUl+voUQLVpYwVRlFVpeoOW6FRu/h12htW6d9NJL0i23WKjluesuC58eeMAu/vLL0vXX2w3PO896WnXvHv72valTbTzjjOqXDiUkSCeeKH31VeSfLS2V3n7bKpgyM6t3/4bgyiulCy6wH7hVq/LvHXOMdNxx0htvKOM3d6ukxAq6WrSon6U2ZFRoAQAAAAAQRV57pJYtKwZaiYnWLsmTlGRhVklJxet4gVaVFVpvvmlhz+9/X/58erpt7Vu2TFq50sIs74ITJ0o9e9pWtxUrwvtiX31lfbD8H9NYHSefLK1ZYxVfkfj2W2ntWtu2GOuSkyuGWZ6LL5YWLVLG/m2S6KMVCoEWAAAAAABR5AVawSq0OnUqX4mVnGxjsD5aYTeFf/99afhw648VyHGkPn2sn5a/Fi1se5vr2jbE99+v/B6ua4HWKadULCWLlFcRNn16ZJ975RVb9wUX1Oz+Dd3550uS2i7/VhJ9tEIh0AIAAAAAIIpCBVqbN5fvnyX5qq+CBVphVWitXSstWGBbCCPVo4d99uijpcsus15OoSxZIm3fboFWTfXrZ1sGI9l2mJsrTZpk60xJqfkaGrIuXaTBg5X50yeS7K8dFRFoAQAAAAAQRcECLdf1VWj5a9PGRi/08ucFWs2bV3KzDz6wsTqBlmRd6z/+WGrbVrr11tAdyL3+WdEItJo1s6cUzpgRfsfziRMt9bv22prfPxace666r7BAa82ael5LA0WgBQAAAABAFOXm2ugFWiUlUl6eVWgFBlrt2tm4bVvF6xQVWZhV6Q6/KVOk/v2tuXt1tW1rT9z78Ufpk0+Cz/n8c6lXL6seioaTTrKEL9y05tVXrbJryJDo3L+hO/lkZWqHWqYUa9Wq+l5Mw0SgBQAAAABAFAU2hZekVauk4uLQgVawbWX79lWx3bCkRJo5U8rKqumSpauvlg4/XHrwwYpVU4WF0tdf29MNo8Vb84wZVc9dutTCtmuvrXn/rlgxeLCc1FT1TNtCoBUCgRYAAAAAAFEUuOVQkhYtsjEw0Grf3kYv0Nq503YASlahVWlD+Pnzpfx8aeTImi86IUG65x5p3jxp1qzy7337rS3m9NNrfh9Pr1725cMJtF55RYqPl668Mnr3b+gSEqQTTlDPosVaubK+F9MwEWgBAAAAABBFXqCVnu578OC39sA6dexYfm6bNtZSyttyeN550qhRUk5OGBVaP/5o47Bh0Vn4FVfYoseNK3/+888tWTvxxOjcR7JKq6ysqvtoFRRIr71mT/7zytmaipNO0mF7FmvzZjfsVmNNCYEWAAAAAABRtHu3lJoqxcVZ26e4OF9P9czM8nObNbNzXoXW0qU27txpgValFVpz51qVU+CjE6srLU26+GLp3Xet6ZcklZZK771nPa+i/XTBk06yxmJLloSe8+9/W7p3663RvXcsyMpSW+3UgQPOwb5s8CHQAgAAAAAgipYtsx11kpScLPXuLW3caK8zMirOb9/eF2h5LaJ27LBdfpVWaM2ZIw0aFN2+UtdcY9sY33/fXk+fLq1fL/3mN9G7h+fccy3Re/vt4O+XlkpPPSUdeaQ0YkT079/QDRigzLjdkizgRHkEWgAAAAAAVOHAASsS8nphheK61tpq4EDfuQEDbIyLs75agXr0kH76yXq8+wdalVZo5edLy5dLgwdH/F0qdfzx1hx+3DgLlB5+2BK3886L7n0ku+4pp1igFWxP3euvS4sXS/ff33SawftLTFTbbumS7N8DyiPQAgAAAACgCkuWSM89J/32t5XP27rVwgf/QKtLFxtbtgyey1x3nX3u448rVmiFDLQWLrTAadCgiL9LpRxHuvNO6fvvLdiaMcOefJicHN37eC6/XPrPfyzR85efLz3wgHTccdKll9bOvWNA26Os6drO7aW68UbfAwNAoAUAAAAAaKJKSqRHHvG1i6rMwoU2LltmFVihrFtn4xFH+M55La4OHAj+mdNOk5o3t4cLBlZohdxyOGeOjdEOtCRp9Gjpd7+TWrWSnnnGXteW886zhmPPPFP+/PjxlvL9v//XNKuzymQe202StGXRDo0bZw8MgCHQAgAAAAA0SbNmSX/8o/TRR1XPnTfPxqIi6ZhjQs8rKLAxLc13znuyYVFR8M/Ex0t9+lgVmPf5Krcczp9vW/YCH5sYDXFxtuVw/nzbZ1mbgVLLlhaY/fvf0s8/2zmvd9bQoU2zd5aftiP7SpLmz9hTzytpeAi0AAAAAABNktdoe+XKqucuWxbeNb1Ayv+BgF6F1r59oT/Xv78VXeXn2+sqm8IvX26PUGwM1Ut33y0lJEh/+pP10po8WVq9WrrjjvpeWb1LHdxHiSrSnCX2DyFYD7amikALAAAAANAkZWfbGE6gFW5T7soCrcr061f+Hps3S4WFIVpXua60YoU9PrEx6NDBGr+//bZ05pnWqKxnT+mCC+p7ZfXOSYhXu+Y5mru9s6TgT8lsquLrewEAAAAAANSl77+X2rSRdu2y1+EEWl74VRUv0PIPotq3t7Gy6hqvcbxkratWr7aKrvT0IJO3bZNycxtPoCX5qrNeecW+1yuvWNUW1KlVnjZut+NWrep3LQ0JFVoAAAAAgCZlxAipb19fSLVqlbVtqszOndL111sllVSxH1ZxsV0jWIVWfLz0+uvSjz+Gvr5/FVdWlrRxo7R9e4gQbMUKG/v0qXzRsaRZM2nMGPvi33/fuMK6GurU0T14XFxcjwtpYAi0AAAAAABNktfovaDAtvitXGm737wnFXoKCmz7X48e0i232LmcHN/7RUUWPI0eHTzQkqSrrqo8owkMtDxBA63ly20k9GkSOh3hK/cL54mcTQWBFgAAAACgSZo61Xe8cqW0eLH0yy/2cD9/XvP4tm19W7527/a9/8ortj3w5Zct+JJC9L6qhP/DCo8/3nccdMvhihVSamp4zbkQ8zr0a3PwmEDLh0ALAAAAANBkuG7w8ytX+kKqTZvKv+cfaLVubcePPy698YYdL15sY5s2VqGVlGQ76CLhX9HVubPvOOSWw969G8cTDlGlA4m+fxwEWj4EWgAAAACAJiNYD6K0NGnZMt82wlCBVkaGr0Jr/Hjp6qvteONGG3ftkrZsqbjdMFL+T7ILWaHFdsMm45xzbLywxefat08qKanf9TQUBFoAAAAAgCYjP9/G//1f6YILpOefl4YPl6ZNC12htWOHjf4VWp4zz7Sm8p7Fi6sfaP3zn9Jbb1kTeU+FCq28PGnDhsbVEB6V6tNHch/4o4bv/UKS/RseM0a67LKqH2bQmMVXPQUAAAAAgMbBC7RSU6VJk+y4uFi64w5fk/jAQGvJEikhQerWTcrNLf/eZ5/ZOHKk9M03Fmgdfnj11nbddRXPVQi0vPSMCq2m5aijlOZ+KckqCceNkwYNinxra2PShL86AAAAAKCp8QKttDTfOa8J+9df2xgYaM2ZIx15pJSYaFVawXhPJiwtrfmWQ38VthyuWGEjgVbTMmCA0mQNtCZPlrZula69tp7XVM8ItAAAAAAATYbXVDs11XfusMNsLCqycdMmX/N417VAa/Bgex2sIiYxURoxQmrXzl5HHGi5rrR+vbR/f4W3KlRorVhhi+jRI8KbIKb16KG0BGsAN2uWnfJ/GmZTRKAFAAAAAGgy/LccejIzLZTyFBf7GsHv2WNbvI44wve+/9xf/9qawZ96qi9jijjQ+s1vpK5dpRNP9KVqZYJWaHXvXn4RaPzi4tSmuz2RYPZsqzDMzKznNdUzAi0AAAAAQKOxalWFTKicYIGW40idO9uxlxN5Ty70emZ5TzeUpORkGy++WHr1VV+A5fXOiijQ+vxzacIEacgQ6YcfpKeekiSdcYa97b81UpK0fDkN4Zuodke2l2T/xg8/3P7dNmUEWgAAAACAmOW60qJFdpyTI/XqJV1/fej5wQItSTrkEBv79rXR66PlBVotW/rmJiXZ+PDD5XtqHXecjYsXR/AFnn/ebv7dd9Jpp0nPPCOVluq996wYy/+JhzpwwNIM+mc1Se0GH3bwuLoPHmhMCLQAAAAAADHrpZekAQOk6dOlmTPt3IQJoecHawov+XpjjR5tY2WB1p132ti+fflr3HCDbUH8/e/DXPzmzdInn0hXXSU1by5dc42d++YbpaRYOFfOunW2H5JAq0lqMbSP4lUiiUBLItACAAAAAMSw+fNtXLbMipw8e/YEnx+sKbwkPfecBWHXXWdVUZUFWvfcY7lSYMP2+Hjpo498oViVXnzRHot44432etQoW1ioRI4nHDZpzoCjtF8JknyVhE0ZgRYAAAAAIGY1b25jcbG0dKnv/E8/BZ8fasth//7S5ZdLcXFSx46+QMsLxvwDLceREhJquPB9+6Rx46Szz7Ym796izjtPmjTJ3g+0fLmNBFpNU+vWBw8JtAi0AAAAAAAxLC7Oxpwcae9e3//ohwq0du2yMMpr7B5M586VV2hFxRtvSNu3S7feWv78FVfYl5kypeJnVqyQ2rWT2rSJ8mIQa3guAIEWAAAAACCG5eTYuGWLVV916iT17Ck98IA0d27F+WvWWEFUs0r+b7hTp1oOtHJybIHHHy+demr59371KykzM/i2wxUrqM5q4o7ruEGSlN48SAVfE0OgBQAAAACIWTt32ugFWqmp0u2327lgzdl//lnq0aPya3qBlutaoBUfX3lFV8Tuu0/Kzpaefdb2L/qLj5cuuUT6+OPyjcBc1xqFEWg1OYMGnQAAIABJREFUaV/97SftVIZv+2kTRqAFAAAAAIhZXqC1ebMv0LrlFunhh6WvvpK2bvXNdV2r0KrqCXGdOkmFhbY9MTfXqrMCc6dq2b9fuv9+ezTjXXdJAwcGn3fFFdZD6/33fec2b5Z275aOOioKC0GsShnSTxnaJS1aVN9LqXcEWgAAAACAmOUFWjt32hMMvWbvAwbYuGGDb+727TYnnAotyaq0vEArKgvNypL+/nd7lOKjj4aeO3SopW7+2w69AINAq2k74ggpKUlauLC+V1Lv4ut7AQAAAAAAVJcXaO3aZVVUXqDVsaONmzf75q5ZY2M4FVqSr4AqsM1VxFzXHqE4Z470r39Jl11W+XzHsfmPPGIlZh06+AKMI4+s4WIQ0+LjpX79qNASFVoAAAAAgBjlutZmKi7OtggWFEhpafbeoYfauGWLb/7PP9sYboWWp7JiqrDMmiV9+aX0t79VHWZ5rrhCKi2V3n7bXn/3nVXntGpVw8Ug5g0YYAGn69b3SuoVgRYAAAAAICYVFtr/03fu7DvnVWhlZkoJCb4KrXXrpDvvtKcbdu1a+XUPOcR3PHmyNHhwDRf68stSixbS734X/md69bIbv/WW9dOaMUM6/fQaLgSNwlFHSTt2SNu21fdK6hWBFgAAAACgzi1ebEVHNZGfb2OwQKtZM9up5wVat95q2xJLS6XmzSu/brxfc56ePWu2RpWWSp9+Kv3Xf/nKx8J1xRXSvHnSPfdY+dkZZ9RwMWgUvAZxTXzbIYEWAAAAAKDOHXWUdMIJNbtGZYGWZH20vC2HOTk2xkfYSbpLl+qvT5KFDtu2VS+MuuIKKzV75hlp0CDpzDNruBg0Cl4ftSbeGJ5ACwAAAABQb0pLq//ZYIGWfxHUoYdKa9fawwK//17q21eaPz+8aycl2VhVNVeVpkyxsTrbBTMzrYztySeljz+2sjMgI8P+cTfxCi2ecggAAAAAqDfbt5fvWRWOn3+2P23a2OtQFVqHHiq995505ZX2+tZbpf79w7vHmjXWcL7GPvtMOvroyL+kp2fPKOx7RKPjNYZvwgi0AAAAAAD1pkMH623VunX4nznzTAu03n/fXlcWaHk+/lg69dTw79Gxo/2pkdxcKw27994aXggIcMMNUnZ2fa+iXhFoAQAAAADq1ZdfShdfHP78ggIb//1vG/2LnwJ7aElSy5bS2WfXbI3V8tVX0oED9L5C9J1zTn2voN7FxAZcx3FOcBzHLfvTKeC9Fo7jPOs4zi+O4xQ6jjPLcZygubvjOIc4jvOW4zjZjuPkOY4zzXGcQXXzLQAAAAAAnrg433GkTzs8/HAbP/vMxtRU6bXXLLjyb+LuVWj5h1x16qOPpBYtpOOOq6cFAI1Xgw+0HMeJl/ScpPwg7zmSPpZ0naRXJN1R9tanjuOcGDA3VdJ0Sf8l6R+S7pPUQdJ0x3F619oXAAAAAABUEBcnHXGENHCg9Nxz0t/+JrlueJ/dtcvGvXttTE2VrrlG2r1batvWNy8z08aWLaO27PDl51sDrwsvlBIS6mEBQOPW4AMtSbdJOkTSP4O8d76kkZJucl33Add1X5KUJWm9pCcC5o6W1FvSBa7rPuK67rNlnz0g6eFaWjsAAAAAIEBJiVRcLF11lbWXKi2V/vAHacOG8D7vBVoerwLLccqf793bgq533qnxkkMrLLRHKe7aJW3dKq1bZ8nciy9KeXnSddfV4s2BpqtBB1qO43SQNEbSHyTlBJlycdn5t7wTrusWyaq1BjmOc3jA3KWu6073m7tD0juSznYcJyXqXwAAAAAAUEF+2f6b1FTp/PN9gVROsP/rC+C6FXthh9pSGB9vWxHDfbKhtm6VbrnFmnINHSotXhx6bnGxdOedUkaG1L27jR07St26ST16SPffb72zhg8P8+YAItHQm8KPlbRa0quS/hzk/WMkzXddd3/A+Z/83l/jOE4zSUdJ+neQa/wk6QZJ/STNjsaiAQAAAACh5eXZmJYmJSZKH3wgnXaaPRSwKvn5liV162aFUZKUnByFRa1fLw0ZYvsWzztP+vZbW9SKFRX3LLqudP310ptvWgnYCSfY4hMTrQn8Bx/YubFjo7AwAME02ECrrAfWZZJGuK5b6gTWjpoO8oVX/raWjd5DVttISvI7X9lcAAAAAEAt8g+0JF9eFE6g5W03PPZYX6AV/H8XI3T77dK+fdK8edKRR0pz5thNHn1Ueuyx8nPfecfCrDFjpAcfrHit226LwoIAVCbiQMtxnK6STpR0uCwokqRsSWskzXBdN8xdz5Xew2sEP8F13R8qmZosaV+Q80V+7/9/9u47PsoqbeP479B770gTlCKCCIooIooI6uoqih0VFdvu6tr2dd3VVde1rK6uZe2IXWwoFhBEQUFpAiKg9KrUhBA6hOS8f9zzMCUzSSYkgQzX9/PJ+8w8c56Zk0zCu3N5n/tEHgsyNnYu12IVXDRs2JAJEybkMR3YunVrvmOkdNJ7m/r0HqcuvbepTe9v6tJ7m/r0Hqeu/N7bBQuqAd1YtmwOEyaks3JlZaA7U6b8QvXq6/J87kWL7Nq2bedhC23Y59+jKsuWcewnn7DsyitZkZ4Ooedrd+qp1H/qKaZ0705WndDHX+/pdtdduJYtmd6z596xBxv9/aau0vLeFjjQcs6dADwI9Mxn3DfAXd77Kfswr5uBFsBp+YzbAVSMc75SxOORx4KMjeK9fxF4EaBbt26+d+/eeU5owoQJ5DdGSie9t6lP73Hq0nub2vT+pi69t6lP73Hqyu+9DSqqevQ4kt69Ye1au9+kSXt6926f53MH/bf69TuC5s1h3jz2/fdozBgoW5ZWjzxCqwYNwuebNoX27Tnhu+/gP6F9x8aOhaVLYdgwevfps2+vW4rp7zd1lZb3tkCBVqhK6RmgLJBfMedJwLfOueu9968kOyHnXE3gH1jfrAqhijCAWqHjIc45vPe/YssF4y0VbBw6rg4dN2LVWQUZKyIiIiIiIsUoWHIYNHMvyJLDM86ALl2gdm27365dEfVb9x6GD4dTT4XIMAvgsMNg0CB49lm4/XZo3NiWHzZuDBdfXAQvLiKFle8uh865XsBzWPi1HVsKeAbQFFumVzl0+4zQY9tDY19wzhXmn5faQHXgJmBZxNfNoccnA5NCt2cCR4WWKEbqHjrOAvDe5wCzgWPivF53LOz6uRBzFREREREROWDk5Fhv87lz4dJLYcGC/T2j+JYvt2Owiq9SJShfHjZvTnzN6NHWzurnn20TwuDafTZ1qk0oUUB1992QlQUPPwxff21fd9xhDeBFZL8pSIXWc1hV1mzgHO/9ijhj1oS+vnDOPQZ8AhwZurZzknNaD5wb5/xFwIXANcCq0LkPgAuAy4BXAZxzlYCrsN0PF0dc/wHwb+dcb+/9hNDY+sBAYJT3fluS8xQRERERETmgfPKJbdAXWLECJk1KPH5/GTYMOnWC1q3tvnNWpTVnDuzZA+ViPqnm5IRvz5sHHToU4WTeecfCqXPOif/4oYfC4MHwzDPw8su2veL11xfhBESkMPIMtEJ9s9oDmcDZ3vtVeY0H8N6vcM6dhQVgHZ1zPbz3kws6Ie/9duDjOHM5KnRzTGi5IcCHWLXWc865NljQdSXQEugb8xTPAUOAEaHQLRP4A1Ae+HtB5yciIiIiInKgWrIkfLtBA5g/f//NJZEZM+zrmWeidydMS4NRo+C//7XVfZE2bQrfnjYNbrutiCaTnW07Fp5xRnjdYzwPPGB9s9atg1degcpx9xQTkRKU35LDs0LHlwsSZgVCOx2+jFV2nV3IuRXkdXKA3wHDsLDqv1ifrzO99+Njxm4FegNfALcD/wbWAb2991puKCIiIiIipV7Qg2rePLj5ZkhPDzdRPxDs2gV33WV50KWXxh/zyy+5z23cGH3/yiuLaEJffWUd6fPrh9WwoY2dOxeOPbaIXlxE9kV+gVYXwGOVUMkKrjm6ENfm4r2/13vvIqqzgvOZ3vsbvfcNvfeVvffHeu/HJHiO1d77S7z3dbz3Vb33J3vvfyiK+YmIiIiIiBSXnTvhhhvCuwEmsn69VWZ16AAtWti5FfGaxuwnn3ximwQ+/DDUqhX92DGhjsfxip/S0+04YIBd27FjAV9w3Tq48Ub4y1+sBCzWSy9B3bpwdrHVYYhIMcmvh9bhoeOsQjx3cM1hhbhWREREREREQsaNg+eft8DqwzzKDdavh/r17XZkoFWkPaf2wdy5UKYMXHtt7se++842FdywwQK8ihXDSxKDQOuOO+C44wr4Yjt3Qp8+Vq5WpgyMGAHffgtNmtjj69fDyJHwxz+qwbtIKZRfhVZNYLv3fleyTxy6ZitQK7+xIiIiIiIikliZ0Ce3yB5ZYJvzbd8evh9UaEE40Fq82Jb5xV67P/zyi/VYr1Qp92Ply8Mhh8DMmValNWxY+LFgyWHdukm82COPWJg1erR1xl+3Dk491X5IAEOH2u6FQ4YU+vsRkf0nv0CrOrBlH55/G1BtH64XERERERE56AWBzq+/Rp9v1QqOPDJ8PzLQatrUWj/dcgs89ND+3Zhv2za49154/31o3z7xuPr1LYADK6gKTJ9uxzp1CviC6enw6KNw/vnQvz/06AGffWYJ4KmnwpQp9nj//nlPSEQOWPkFWmWL4DWK4jlERERERERS0oQJULZs3hVUQaAVLL2D8M5/S5eGr92wIbzksEwZW3GXnW33Z8yAPXuKdOoFNno03Hef3c6rp3ow98jbkyfDU0/Z7di+Wwm9+KKlaPfcEz530knw6af2w+rRA3bvtlBLREql/AItERERERERKUbDh0NODjzxROIxkUHWjh12XLkyfO6HH2DLFgu5ghZRANddB506weDBkJERXm1X0hYutOOyZXDnnYnHRQZa1apZtdaAAXb/ySct+MvX7t3wzDNWiRVZvgaW8M2ZY0HWxIlJdJcXkQNNfk3hAeo4574u5PMXtCBURERERETkoJSVZcfJkxOPCSq0gttNm0bvXjh/vvWmAmjXLny+Vy+YPdtCs2HDIDMzOvAqCePHw9/+ZnNu2TLvsc2bh29v3mxzXrvWCq1uuqmAL/jBB7B6te1gGM+hh8LttxfwyUTkQFWQQKs80LsQz+0BFzqKiIiIiIhIHEuX2nFLHt2LIwOt9PToQKtSJViwwPppQfwdDWvWtGOwTLEkXXyxHXcVYKuxyy+HMWPgo4+soqx8eWjcOLxcMV/ew+OPQ9u21h9LRFJWfoHWtyiQEhERERERKTZB/6u8Aq3IJYdBuLVyJVSsaFVYCxbYrobly0Pr1rmvDwKtzMyimXMwn4LsOnj44bbBYNeu+Y+tXNmawZ98soVvzoWb3BfIpEnWLOz558NbQ4pISsoz0PLe9y6heYiIiIiIiBx0srLCOxdu3Zp43MaN0KiRLb87+WTb9W/jRguUOne2/lKHHGJL+srF+ZQXNFMvikBr4UIrgOrWLbz7YF7KlrXdCd9+u+CvUbu2VWkB9O2bxOQef9xebNCgJC4SkdJIkbWIiIiIiMh+kplpq+Rq17ZN+XJy4o9btQqOOCJ8f/hwaw5fpQr07Gl90MeOTVwxVZRLDkeNsuMPP+S9M2Ng0yY4/njLmQoqmC9EN4rP05IlMHIk3HCD/WBEJKUp0BIREREREdlPgoqppk0t2Nq+Pfrx9eutImrtWujdO3y+enUbGwRaADt3hiuxYhVlhVbk8scvv8x//KZNieeVS1YWpKWxdm34VIEDrTfesOMNNxTwAhEpzYos0HLOdXLODXTOne2ca1ZUzysiIiIiIpKqNm+2Y9OmdoxddtiwoS3vAzjuuPD5jAwLtCpXtsqnIPRJFBxVqWJL/4qiQistDWrUsNuR4VYiBQ60VqywhlsNGrBn4dK9p4NdIPP1/vvWUCz4YYpISssz0HLOVXTO9Qp9lU8wppVzbiowCxgOfAQsd84Nd85VK/opi4iIiIiIpIbICi2Al16yXQFnzoze2RCgU6fw7TVrwhVakH+g5Zwt4yuqCq0mTWx3xfyeLyfHxhQo0LriCkvqevXi5aUnc3KXDMAqz/I1bx78/DMMHFiAwSKSCvKr0DoZmAC87L3PlYs756oD44BugIv5Ggi8V5STFRERERERSSWxFVr33GP9sbp2je6HNWCA7fb37LN2f82acA8tyD/QCh4rikArLQ3q1bPny6/ia8sWW0qZb6A1fTp88w3cey+MGkWLFo4vcvpx0588//xnASb1/vuW2p13XgG/CxEp7fILtHqFjm8kePwWoFXo9njgUuD3wAgs1OrnnOu3r5MUERERERFJRbEVWvGMHw8ffGC3b7gBLrwwd4VWtdDamLyCo5o1i2bJYXq6hW0Feb7g8XwDrVdftfWTV11l39Q//kGF2dN58rxvadKkAJMKlhs2alSAwSKSCvILtI4DPDAqweODQ49P8t738d6/473/1Ht/PvBZaMxFRTNVERERERGR1BIEWpGhTdAzK9CzpxUfBRo1gnXrwj20AMqHGsTkFRxVr24VU4ls3gx33mmr/iJlZ8PUqdCxI4wYYRVadesWrOJr0iQ71q6dx6DsbPjwQzjzzHBzrgsvtMTs5ZfzfgHQckORg1R+gVZTIAf4KfYB51wboEXo7qNxrn0Eq9Lqti8TFBERERERSVWxSw4B9uwJ377/fihXLvqa6tWteXxkhVYQaFWvnvi1qlWDbduiz6Wnw5QpdvvJJ+GRR+Bvf4se8+ST1pB+3jwroCroksPMTLjsMrudZ5XVxImW0F1wQfhclSpw6aVWmhabsMX63/+gQgU4//y8x4lISskv0GoIZMbrnwV0Dx2zga/jPP5D6KgdD0VEREREROLIzISKFS0gCnQLlQTMnAl//3vua6pVs75U6em5A63s7MSvVbVq7l0U+/aFHj0spHrzTTv35pvROwvOnx++7T3s3l2wJYevv27Hl16C7t0Tj+O99+wbOeOM6PNXX20d4YcPT3xterotV7zsMtsSUkQOGvkFWpWAqgke6xo6LvTeb4990Hu/C8jI43oREREREZGDWmamrbILKqtOOskCoHHjoEuX6KWGgaqhT1g5OeElh0HrqIoVE79WbIXWxo0wa5bdfvFFWLjQwq0tW2DGjOjrAkFFWevW+S85/Okna2R/zTXxvw/AErLPP4fTTgt/Y4EuXeCII+CttxK/yHPPWXf8W29NPEZEUlJ+gVYaUME5F69FYXesf9YPcR4LVAZ2FHJuIiIiIiIiKW3zZqt0ql0bRo+GkSMt3OrTJ/E1kblPUKH1z3/CY4/lvclf1arRgdbkyeHbwTLD4Ph1xBqc9etzP1fbtvlXaKWnh3dfTGjBAli5EvrF2UvMOVt2+N13sHx57sd37oRnnoH+/S34EpGDSn6B1uzQ8fLIk865JsCxobsT413onGuEVXit2ZcJioiIiIiIpKrNm8PVWf37W0iUn8iKqSDQqlIFbrsNypZNcFF6OlWXziEjw5YyAvz4ox2vvjo8rG9f6+e1eLEtX5w40XZUjNWmjVVo7dpluVKCl6Ru3Xy+mbFj7Rgv0AK45BI7xqvSeust6711++35vIiIpKL8Aq0PscbudznnBjrnKjrnWgOvAWWBLGBkgmt7ho7zimSmIiIiIiIiKWbbttwr7fITr0IrT+PGQZs2VBv1LgBdu1pR1KRJtnTwuuts2H33WW/1Bg1gwwZ47TXo1cuqtQYMgGeftXG1a0OlSuEgLtHOiQUOtNq0gVat4j/eogX07m3rMCO75Wdnw3/+A507wymnFOCHICKpplw+j78O3AIcAcR24vPAS977tATXnh8a8/0+zVBERERERCRFbdsGdeokd01khVbQQyuhxYvh3HOhZUuqDroanrbTZ5/lmf2T44or4JhjbCPBWrXssfr1LdBatiz8NI0bW6g1Z4712Yp87UJXaO3aBePHw+DBeX8PN99s38M778CgQXbujTfgl1+sYXzCBl0iksryDLS893ucc6cDnwBHxTz8JfCXeNeFliSeE7r7xb5OUkREREREJBVt2wbNktwXPqkKrb/+1QKfUaOoOjr8QrN/crRuDU+HAq4gzAILtL780lbzgVVinXiibSIYVGlBONDaEadrsvfWdD7PQOv772H7dmsIn5ezz7aysjvvtKWJs2fDH/8Ixx8PAwfmfa2IpKz8KrTw3v/qnOsGnEI41Jrmvf82j8tqA7cBWd77ufs+TRERERERkdRTrEsOf/oJPvgA7r4bmjWLquwCuOee8LLBSPXrWyC1fDkceaQ9TTx5BVo7d5Zl9+58Aq2xY6FcOVtSmJcyZWzJ4fHHwyGHQFYWdOhg31uZ/LroiEiqyjfQAvDe5wDjQl8FGT8P9c4SERERERHJU2ECrchgKs8m8vfeawNuuQUIv06DypuZ5brSeOAcbB+vaJE7E7ZsmfjpK4UujbfkMDPTPmrmG2gdfzzUqJHHoJAuXWDCBFtq2KYNDBmS/A9ORFJKgQItERERERERKXr7WqF16KEJBv34I3z0EfzjH9bFnXAQVrl6eZqsXwxfjYPf/S7h83fsCEOHJp5HXhVamzZVAPIItNavt+0WH3gg8QvE6t7dvkREyH+XQxERERERESkGOTkWBiUdaC2ctfd2w4YJBgXVWX/+c/i60OtUrl3JHhsxIu6lWVl2HDQoulorVlChFS/QWrDA1jIeeWSCi8eFFv/065f4BURE8pBnhZZz7usknmsHsAGYAXzkvf91XyYmIiIiIiKSyrZvt2NSgdZTT1HhttsAS53cuC+hb9/oMV9+CSNHwv33R3V7Lxf69Fe5ioOzzoJPPoE9e8IPhNxwA2zeDH/6U95TidzlcPRoWzl4wgl27qefatK0KbRqleDisWOtfKtLlwJ80yIiueW35LA34IGC7IPqQ8dBwGPOuSeAu0L9t0RERERERCTCtm12jG3WHldaGjzxBDz4oO3690no/NVXw9y5li5NmQI//wx33QVt28Idd0Q9RdBAvksX4HcD4M03YeJEOPnkqHFVq1oWlp/IJYcDBtht7+3rp59qceqptsFiLt5boHXqqVC2bAG+eRGR3PILtL4lHFTlpwrQFGgClAfuwHY7vK7QsxMREREREUlRQaCVZ4XW1Knwl79Y8OQ9XHEFvPwy510EPQ9ZDk//BscdZyVVv/1m17RrB59+Gl4TGNKhg1VSnXQS4PtZIjViRK5Aq6ASNYVfuhTS0irSq1eCCxctgjVroE+fQr2uiAjkE2h573sn+4TOuZbAX4EhwDXOude8998XZnIiIiIiIiKpKt9A67PPrPSpYUO45x5r4N61KzjHBx8AtITj34FHHrG1fU8+aU2r2rSBMvHbJffvH9yqYnc++siuSzA+L0GFVvB9BCZOtGPCQCsYcOKJSb+miEigyHc59N4vB65zzpUBrgauAhRoiYiIiIiIRMgz0Nqwwbqyd+pkPbFCOxXmcsEF9lUYAwZYoDV9eqF2DwwCrY8/jj7//vtQt+4u2revGP/CiROt23zbtkm/pohIoDh3Ofx36HhCMb6GiIiIiIhIqZRnoPWf/0BmJrzxRuIwa1/97nfWED7Bbof5CZYcfh2xldjq1bas8cwz1yQu+po4EXr2TNBgS0SkYIot0PLeLwK2Yz21REREREREJELCQGvXLnjxRTjvPGjfvvgmUKsWnHIKfPgh5CS/l1f58rnPzZtnrb46dcqMf9Hq1dZkS8sNRWQfFWeFFsBOoFK+o0RERERERA4yq1fbsW7dmAc++wwyMuCaa4p/EldcAUuW2NLDZKWn5zq1eLEda9bMin+N+meJSBEptkDLOVcZqAVkFNdriIiIiIiIlFaTJ0ODBtCiRcwDo0db9dSppxb/JC680KrAbr3VljgWlPdw9dW5TocDrd3xr5s4EapVg6OOKsRkRUTCirNC64zQ888rxtcQEREREREplSZPhh494rSS+uorOPlkKFu2+CdRtiwMHWrlYueeC5s2wfbt8MMPsGZN4uuefhpGjsx1ukAVWj16WO8uEZF9UCyBlnOuFfA44IHPiuM1REREREREDhQ7d4bDnPz89hv06gWLFllv9CjLlsHy5dbbqqT06AGvvmphU6NGVh12zDHQpAmcfjp8H7Fp/dSpcMklcPPNcNZZe09PGvgkAIt+zqJ6dahQwed+nU2bYM4cLTcUkSKRZyzunOuVxHNVBpoCPYGBQFVgDfByoWcnIiIiIiJSCgwbZqv20tOhShU79+CDtprv3HOjx77+eriVVP/+MU8UbBlYkoEWwKWXQocONrkKFeDYY+Hnn60S64QTbIngnj0wdy7UqAG33w4PPLC3Y3KtIQPhfVi6zNGkeYLX+O47W6qoQEtEikB+dZ4TsCqrZDmsd9Z53vsthbheRERERESk1EhLsyqtNWugdWs797e/2dH73GMDRxwR80Rff21VUsW5u2EiXbrYV+C88yyle/ll+Phj29bw+uutkXy1alGX1upgm9vvyi5H/foJPkJOnGjPceyxxfUdiMhBpCALl2NXdOcnA3gf+Jf3flXyUxIRERERESlddod6oK9dGw60Epk/H5o1s4KlXP2zJk+2dYi5HthPqla15YU33xz34V9+sXZYtWqFz9WrkKCmYcIE6NYtXMImIrIP8gu0Tk7iuXYAad77pfswHxERERERkVJn1y47rl1rxx07Eo9dsMDaVjVrFvPAxo3WQ+u664pljsWhXTs7eg/lynn27HHU27Y898AtW6zR/P/9X4nOT0RSV56Blvf+m5KaiIiIiIiISGkVWaEF1v880q5dtiSxenXr+X7BBXGeZOZMO3btWlzTLDbOQa1ajrQ0qL9uLtAkesCkSZCdbbs3iogUgWLZ5VBERERERORgEhtoZWSEH9uwAerVs77qGRmW6zRoEOdJfvjBjkcfXaxzLS7BssN6a+ZQLjMz+sEJE6x/1vHHl/i8RCQ1KdASERERERHZR3kFWp+BTwhrAAAgAElEQVR/Dlu3WmXW5Ml2rn79OE8yYwa0agV16hTnVItNEGjV9+upM3169IPjx0P37uqfJSJFRoGWiIiIiIjIPspryWGwkhDgk0/smDDQ6tatWOZXEvZWaNXIou6UKeEH1q+36rM+ffbPxEQkJSnQEhERERER2UexTeEjK7RmzICKFW3DwNGj7VyuQCtoCF9KlxtCRIXW8YdRZ9q08A/lk0+sa/w55+y/yYlIylGgJSIiIiIiso/yWnI4daqtJGzRAn791c7lCrRmzbJjKWwIH9hboXXeSZTfsgVGjrQg6+WXoU0b6Nx5/05QRFKKAi0REREREZF9FARa69ZBTk50oJWdbWFWk4iN/3IFWsG6xC5dinWexalmTTvWO6cnO5o0gQcegM8+s0Tvz3+2rRBFRIqIAi0REREREZF9FARaWVkWZo0eDe3a2e6GAIceGg60qle3JYhRZs2CZs3CF5RC3bpBp05Qq04ZltxwA8yZA2efDe3bw9VX7+/piUiKUaAlIiIiIiKyj4J2UWAb+k2ZAtdfD+npdq5v33Cg1axZnCeYObNU988CuOgimD0bypSBtJ49Yfhwq8z6/HOoVGl/T09EUowCLRERERGRA9Drr1vrISkddu8OZzYDB9qxb1/o0SN8u2FDu92uXczFW7fCwoWlPtDK5cIL4YknrIGYiEgRU6AlIiIiInIAuuIKGDLEemrLgW/37txB1eGHW1/0GTOgWjXrrQVw5JExF8+ebW90qgVaIiLFqFxhL3TO9QA6AXWA8nmN9d7fX9jXERERERE5mI0YYcFIrhBEDihBoDV0aHijwnLlrCVW0Bbrmmtg9Wq4/faYi7/7zo6leIdDEZGSlnSg5ZzrC7wAtEjiMgVaIiIiIiIFtGNH+Pb559tRlVoHtt27oUIFK7K66SYLIWPVqAGPPRbn4tGjrZt648bFPk8RkVSRVKDlnDsW+IxwRdYyYDWwp4jnJSIiIiJy0Pr119znHngAbrwR6tQp+flI/nbtskAL4Mknk7hw3TqYNAluvbVY5iUikqqSrdC6Gwuz5gMXeO/nFv2UREREREQObqtW2bFOHdi40W7ffTfMnWsbx8mBZ/duqFixEBc+8QRkZ8NVVxX5nEREUlmyTeF7AB4YpDBLRERERKR4BBVaX30FRx0VPj979v6Zj+QvWHKYlOHD4dFH4bLLoG3bYpmXiEiqSrZCqwqw3Xs/ozgmIyIiIiIikJZmx5YtYeBA+PFHu790qRXzlC2736YmCRQo0EpPh3vvhZ9/hq1bYdo0OOEEePbZkpiiiEhKSbZCa0UhrhERERERkSRkZoJz1kS8bt3w+d27w8sR5cCSb6C1ZQuccgo8/zxs22ZbID76qJXhVatWYvMUEUkVyVZofQj81TnXy3v/bXFMSERERETkYLdpk4VZZcrkbgK/eLFVbsmBw/sC9ND64x9h3jwYNQpOO63E5iYikqqSrbZ6GFgK/M85Vze/wSIiIiIikrzMTKhZ024HgVa9enZctGj/zEkSy8qyY8IKra+/htdfhzvvVJglIlJEkq3QOhrb6fB/wDzn3IvAVGBLXhepmktEREREpOA2bYJatex2lSp2bNHCVqotXrz/5iXx7d5tx7iBVk6OVWe1bg1/+1uJzktEJJUlG2hNwHY5DBTkX2RfiNcRERERkQKYORNGj9bn5FSTmRkOtJo0seOAAbBrlyq0StLkybBwIVxxRd7j8gy0xo2DX36BN9+EypWLfI4iIgerwgRNrpjHi4iIiEgBXXyxfeC+8EJo02Z/z0b2xaxZMH06XH65VWg1a2bnW7SA336DRo3ghx9g/vz9O8+DyfHH2/Hyy61JfyJBoBW3h9abb9q60fPPL/L5iYgczJLqoeW9L1OYr+KavIiIiMjBLtgBb/To8LmJE7UTXmmTlWV5x3XXwd13Ry85BKvSKlPGQsslSyA7e//NtbitXw9PPWWN1g8UGRl5Pz5hgh3rxnYZzsmxP87TT8+nY7yIiCRLYZOIiIhIKRY0Dv/uOztmZ0OvXnDiiftvTpK8UaNg6VK7/d57uQOtwGGHWTXQr7+W7PxK0l/+AjffDOPH7++ZhC1blvfjjz8ORx4J55wT88APP0BaGpxxRrHNTUTkYKVAS0RERKQUS0uzY1CRFTQMX7Fi/8xHCmfqVChXDl54AVautEArCCsjBctKU7mPVtCH6scf9+88ABo3tmNegZb3sGABnHSSvYdRRo2ytYr9+hXbHEVEDlYKtERERERKschAq39/GDTI7tevv//mJMmbNQs6dICBA8PnGjTIPa5NK1truHjezhKaWckLKtNuuw0mTdq/cylIoJWWBps3J+hhN3o0dO8eZy2iiIjsq4RN4Z1zl4duZnrvR8acS4r3/vXCXCciIiIieYsMtCL7ZgU748mBz3sLtPr3h9q1bdnalClxdtbbsIGmF5xFJcaz6K7X4PRT4PDD98uci9PmzeHb48dDz577by6VKtnxt98SjwmqInMFWuvXW5f/++4rlrmJiBzs8trl8FXAAwuAkTHnkuEBBVoiIiIiRWznTti61cKr1aujH9u1a//MSZK3cCGsW2eFPADvvmu90CpXjhi0Zw+ccw5l5symRb3trMhoBPfcA8OH75c5F6fMTOsVlpGR+/e6pAV/R5s2JR6TMNAaM8bSSvXPEhEpFnktOVwZ+lod51wyX9pjR0RERKQYpKfb8aij7HjJJVaxde21+e/KJgcG7+G//7XbQZulChViwiyAhx+G77+HV16hesu6bGvWDt5/36qAUkxmpvUPa9Ik78qokhAEWnn9Pa1da8dcVZHffAN16kCXLsUyNxGRg13CQMt739J738p7f2qcc0l9JTsp51x759y7zrklzrltzrkM59xU59wg55yLGVvDOfeMc26tc26Hc26Kc65vgudt5Jx70zmX7pzb6pz72jnXNdn5iYiIiBwINmyw46mnQo0acOON1qqnVq1wRUkK5h0pZfRoeP55aN0aDj00waDFi23Z2sUXw8UXU7UqbKtzCDNzOrNy6Jf79PobNljP8k8/3aenKbA1a2wVXmZm9BLZSEGg1bTpgRNo5VWhtXmz/QyrVYt5YNo0OPZYKKO2xSIixeFA/de1GVALeBP4M3A3sA5buvhIMCgUbn0KXA0MDY0FGOWcOynyCZ1zVYHxwBnA48CdQGNgvHOuXXF+MyIiIiLFIagM6d7dPnCfcILdr1XLPogPHQoNG1qAIAeOefPgvffs9rRpdsyz+fkjj0DZsvD44wBUrQrbXVXOLzeSvzy5b83Sfv7Zjn/5yz49TYF17GgZT48e0Lx5/DGRFVpz5+YdJhW33bvtmFeF1ubNFihH/Wf3bdvsjT7mmGKdn4jIwSyvHlr7jfd+LDA25vQzzrlPgZucc3d773cBA4BewGDv/asAzrnXgLnAf4BuEddfD7QDTvHejw+NfRdYCDwAnF9835GIiIhI0QsCrUaNoj9MB7vEvfGGHRcs0OfqA8kZZ8DKlVatdN990K6dvYdx/forvPYaDBmyd1CVKrBli2NldhMqr9ts60zr1Uv4eqNHwzXXwI8/5t79MgiLFi0qgm+sADZutOMvv9hxx46yucYEgVb9+hYoNW8OkyfDEUeUzBwj5bXkcPt2WxoaBFpRZs6EnBxL70REpFgcqBVaiawAKgJBV4ELgE1YJRcA3vudWLVWV+dc64hrLwDmBWFWaOwG4D3gd865KsU8dxEREZEiFQRaDRtGn69d246//mrHChVKbk6Sv7KhDOfPf46+H9fDD1ujrTvu2HuqalULxLJ9WRZxGHs+/izP1xs0yJqrf/FF7sfWrbNjdvb+qYT67bfYZmHhQKt/f2jRArZsKbklkbESLTncssXeh/vvTxBoBWWRSpJFRIrNAR1oOeeqOOfqOedaOeeuAgYD0733wf9LORqY5b3fE3PptIjHcc6VATpFnI8dWxHYD//NR0RERKTw1q6F6tXtg3WkINBatsyOmzeX7Lwkb40bR99/7LEEA1euhJdegsGDoWXLvaerVrUdLgGyqMCS92fmutR7q9o77rjw5gHffJP7JSJ7rN1yC9x0U8G/j6KwalV0oJWdbWFRzZpw0kmwfLkVpi1cWLLzCgSB1tatkJUVPr9mjR2ffz5BoDVtmpWWxabNIiJSZA7oQAu4H9gALMWqriYDAyMebwysiXNdcC5oKlAHqFTAsSIiIiKlwtq18Zeqdexox5wcO+7PHkSSW1pa+PaVV1olUlz33mvHv/896nSVmHUFv3y7IdzsKSSovJo61RrOn3aa3Y61bp2FR82bw6uvwtNPhyv7SsL99x8R1eMtM9OOwbJZgMMP37+BVhBWRf4dBYFWTk4egZaWG4qIFKsDsodWhBeAL4D6QD/gECBy/5DKwK441+2MeDzyWJCxUZxz1wLXAjRs2JAJEybkOeGtW7fmO0ZKJ723qU/vcerSe5vaDub3d/78o6hcGSZM+DHXY40adWftWvufNz/+uIIJE5aV9PT2Waq+t2vXngCUB2DPnvjvTb1vv6XjsGGsvPhili5dCkuX7n1sw4aWQMu993/Z2ZJWTz9NRtfw5t0//1wdsPsNG6ZRrdpOli5txPjxk6L6rc2Z04EaNapx7LHprFzZDIChQ+dy0kkRqVsR2bGjDNb+1tSvv5OMjApcdtk2XnhhBgDLl1cBjmXjxp+ZMMHKx6pXP5zvvqvHhAnfF/mc8pKdDdnZvalZczubN1dhzJipHHLIDgC+/roB0IFdu3azdm0WlStvY8IE67BfPjOTE5YtY0nfvqxKwd/fgkrVv18J03ucukrLe3tAB1re+0VA0KLyHefcg8C3zrm23vs0YAe2XDBWpdBxR8yxIGNj5/Ai8CJAt27dfO/evfOc84QJE8hvjJROem9Tn97j1KX3NrUdrO/v8uWWcQwYQNzv//TeOxg23G7XLFuX3r1blOj8ikIqvrd79tiSukDXri1yvzfr18O558Ixx9D81VdpHtMELahocg4a1Pf8ktaRv66aDrfdFvUUgc6d63HYYTBiBHTu3Js6daJfrmVLuOWWKnzwgd2/996OLFoEbdrs4zcb49Zbo++//HIlhg37jW++abr3ff7qK3vslFM60Lt3B8C+388/h6OO6h1VuVXctm+3Y8uWVVi1Cg4/vDszZ9rfXbBstGzZCuzZU4E2barSu3cDOzl6NACtL76Y1in2+5uMVPz7lWh6j1NXaXlvD/Qlh7GGY8sHzw3dX0P8pYJBZ4LVoeNGrDqrIGNFREREDnivv259fYJVaVGys/nvojOZxAk0YyWb3htrXcFlv8vIsP5WgT2xnWAB7rnH3tzXX4/b0T9YctigARzZyfFL9WPgs8+innjFivD4Jk3CLbhmzoxe8rhhg+0mePzxds2gQXb+hx8K9/1F2r3bmqZv3Ai//QZPPBH9eKtWUKvWbjIywv2pgo0OIvuMHX64HYOdGKdMgX/8Y9/mlp1tz3PIITBjRvwxQf+sYGfILVvghhvg0UdhyRI7t3Gj/WlFLTmcPt3SxoiKORERKXqlLdAKlgWGWp0yEzjKORdbadY9dJwF4L3PAWYD8bYZ6Y6FXT8X7VRFREREik9amvU+iugVHvbRR9SYMZ4TXr+ehkfUI2N31fCWerJfBWHS3XdbUDJwYMyA1avhlVdgyBBo1y7ucwSbADRpAp06wU/bWjNvScWoRlOxgVaLUBFY377hgAYskKlb1243bx5uUB80kt8XI0da8HTXXfDuu3bu4ovDjx9xBNSsmRX1ekFvqsjecEGgFXx7ffpYUDZ/fuHndvnl0KOHBW2vvRZ/TGygFbm5QvD9BCpVirgzbRq0b287NoiISLE5IAMt51yDBA9dHzoGrSM/AGoBl0VcWwm4Ctv9cHHEtR8ARzjnekeMrY81mR/lvd9WNLMXERERKX4ZGeHdDHMZNgyaNoVLLqFW4ypsatKeZ9+vx53XFH1fJElOUIHUq5ctC2wRuxL0hResbCti+WCsoEKrSRO4/XaoUNHxNH+CMWP2jols7F65cvzg03sLtCKXIAa304rgV2XuXDsuXAhz5th8337bzpUvD2XKQK1aFmgFSyTXrLH5RlY8HXqojV240EKmYCngxx8Xbl6//hqeB1ilWzzxAq0go0pLsyWZxx9v94MdRfHeKrSOifff0UVEpCgVOtByzpVxzh3jnDvfOXd5UU4KeME5941z7n7n3DXOuf9zzn0HXAmM8N6PD437EJgEPOece8A5dx0wHuuSGfu/Ap7D+nGNcM7d5Zz7A/AN1pHz74iIiIiUIhs3Jgi01qyxYOPyy6FsWWrXhvSKTfgDz/LI0HolPk+JNnu2HY84Is6D2dlWnXXaabY1YQKRFVqNG0Oz5mVIr9YCxo7dOyZyhWnbthZU1awZ/Tzbt9uywMhAq1w5+73a10Br1CirogKYNcvm07Sp3Z84MRwABYHWhg12P9i5M7JxfcWKFvzNnx9dlTV5cuLXX7wYfv97W7kZ6+uv7fjPf9rxkUfgiy9yjwsCrXqhP5uMjOjn69TJnuvyy60KDYCVKy2d0w6HIiLFrlCBlnPuT1j/qinAu8CwmMdrO+fmOufmO+fqxHuOfAzHmrRfAzwL/C001z8AFwSDQksJfxd6/SHAf4GywJkRoVcwdivQG9s18Xbg38A6oLf3XssNRUREpFTJyCBXc2/ASk+ys+GKKwBbtbZ4Wbg7w54dWSU0Q4ln5kwLoSJ7RO01ZoyVDw0ZkudzRAZaYEFVZq2WMGGCJVRYrnnlldbr6eijbVzkCrjsbAtFIffvUb16+x5oDR1qx9atYdMmm0cQaPXsGb5ds6bNd8MG61H15Zfxw76uXS3Aysiw+zVqWGiVyJAh8MknMGlS7scmTLDv+a67rI/X1q1w+um5x8VWaC1bFt3/rHFjC9teew2OPDJ0MujYrwotEZFil3Sg5Zx7BguO6gNbAB87xnufAcwADsMCp6R479/13vf33jfx3lfw3tfw3vfw3j/rvc+OGZvpvb/Re9/Qe1/Ze3+s935Mgudd7b2/xHtfx3tf1Xt/sve+CFpeioiIiJSshEsOP/8cOne2shzghBMgJyf88Lq3xpXMBCWu6dPz6BU+dKitfzvrrDyfI3LJIVigtalSI9i2DaZMISfHKp2aNLHleoHKlcO3N20qvkArJ8dCoyuvhDvusHNLloTnG6l27XCF1kcf2fGvf8097qRjd7BqFbzzQiZgP8MlS6J/tyMFFWo74uxjPmsWdO9uyxirVUv8fQSBVvXqFlzFBmhxQ8lp06yRf6dOiZ9YRESKRFKBlnOuH3AjsBU413tfC9iQYPjbgAPO3qcZioiIiEgucZccbt8O331nnb9DjjsuevnWb69+WTITlFx++smWzEW8PWEbN9pOhZdeGndnw0ht2lgVU9C/qWZNyKQGOWXKwZdfkpZmbbhiA5cePcK309OLL9BautSeu2fP6N/ReIFW9epZVKhg/baWLLHf1W7dYgatWcOpT1rI9+JwWzd59NEWOEX2Cou5BAj3LMvICIdfv/4KzZrZ7aAfVzxBoFWxolWEBbsslgl9gkoYaHXubBeJiEixSrZC63qsIuse7/3IfMYGq9qPSnpWIiIiIpKQ9wmWHH73nS0569Nn76maNSOWQwG/fb8i/GlfStQ771iPqksuifPg++/be3fZZXEejFanjgVAHTva/Zo1YeHispTNyYKxY/dWJ8UGLs89F97sMi0tHGgFuxwG6tXbt10OgzCscePo39F4gVblzI0MajiG117cyaiXV9Okic+d5914I+02fs91fcIlUl07WBK1YEHu59y925Yvgv2qb91q8yhfHv71L5tfsOQxXo+tQBBoVagQvcSxYUM7Ru7ECFg52JQpluSJiEixSzbQOi50fCW/gd77zcBmIN5/uxARERGRQtq+HbKy4lRojRtnn9pPPDHq9AknhG+f799j15vvF/8kJZdZsyyEqhevN/+bb0KHDtClS9LPG9nsPXP6QlYvtDQnNtCqUiWcl0UGWrHBaMOGVtm0Z0/SUwFsOSPY72fk72gQIu2VkUHnW2/l3g1/pG6FLfywugktyv0WPeaLL2w7w3vuofc1bfae7vXDEwD8ENM8JD09umpr7VpYscJu5+TA3/8ePZfIQKt8eSuuCoKroHqrShULtIL+WUEwlyt4+/57S8FOPRURESl+yQZadYBM7/2WAo7PwZq0i4iIiEgcGRm2dKx//4JfEwQRuQKtr76yNYZB1/CQiIItcijLsjfidMqWYjdvXoLdDZcts+7ll10WvT60gCIDra/8yXz4gpVIxdsoMQjTNmywHvQVK+au0Grb1gLTpUuTngoQbtxeu3Z0WBbZzwvv4ZprqPzrrxwy+iVue9AmVmbl8vBWhrt2wU03weGHwy23RM2zyUv30bbV7qidDr237y8ywH3pJXj33dxzDAKtP/whfG7PHlsWGmwWGVSp1a1rgRZYhd0771iVXUxuDMOHQ6VKcR4QEZHikGygtRmo4Zwrn99A51w9oBawj3ukiIiIiKSuL7+03dvGjIHffst/PIR7+bRoEXFy2zYrATrppFzjBwywaz76yO7vmLM4nDpIidi0ySqHgmWCUd56y45x1yLmLzLQOo8RvPJ1K6pXt/7ysZo0sYqjp56CDz+Ee+6xDCZS+8wpAPzS548we3bS8wkqtGrVig5dmzePGDR0KIwYwbJrroHevel/ugV52WUq2BaFOTnwxBP2i/vUU1CxYlSg5SpV5Pg93zJxYvhXOVhmGPTNCvzzn7nneMghdnz4YXj88ejH5syx1YPB0snIQKtBAzjsMHvLotpkLV8Or78OgwdHbycpIiLFJtlAax7W6L0g+9AOCh1nJPkaIiIiIqXWokXx+/okEiyHAiuwKogZof91FbVb3owZFgJ0755rvHPWSDzY5W4HlWwbOikxQQjZrl2cBz/80Mr0ohLKgovcvTCQlRW/2Kt82Rx6NFvFTz/BYS12cfvtMQNmzaLdnecA8EtGQzj77LwbTcURBEy1aoWDIIhYord6Ndx2G/TuzaoLLgCgfXt49FF49Z+rrFrtvPMsiTrnHOjXD4ipJLv7bv646i9s2ZzDQw/Zqdgg68MPw7edswwtEIRrzoV7Yh19tDWLf/55C4HT06FsWQsMg9cOxkbZvt22dCxfPv4WjSIiUiySDbRGYIHWvc65hNc6544H7scayL9X+OmJiIiIlC6HH54gtEhg+XL7wFyvXsEDrR9+gJYtYz7gT5tmx2OPTXjd3kCrUp2Cv5gUiaDaJ1cgsmoV/PijBUeFtG1b7nM7dyYY/Mc/ctKCFwF4Yt2lVPhxWvixzZvhgguoWbccTRpl89dtd3Phyn/Df/5T4LmkpVmhUqVK9lUm3ieGm2+2zu0vvbR3gHNw++1w+J0D4IorrG/WIYfA//6397Ko3/ebb+boI/dwUrnv+HqsNfuKDbTOOgsuvthuN2sGZ55pt/v1iw7agts1a4ary774wgKtunVtbsHGCrlCwqlTLQn79lt44YXw9okiIlLskg20XgAWA32AL5xz/YPncM7Vc86d4px7ERgPVAVmA+8U4XxFREREUsqKFdCqFZx8smVMQePpRLy3JYq5cqupUy3lirfOLCQItLZ36KZAqxCysmzZZn7vUTxB1VKunSk/+8yOZ51V6Hm1CfVKf+st+HnUcobwIqP+8HnugSNGwHPP8ac/OT4Zls4ZjWZaBdTq1ZCdDZdfbo2z3nmH9kdYG9z3uNBKlrKyCjSXc8+1CsXIQK1VK3tqwH5PP/gA7rorPPFIzsGwYbBkiTW0itgaMao1XMWK8PrrHLfnO2b/ZEVS69ZFP1X58uHG+G3bWpj444/w6afR44K51qoVXkbYsmU40AI4KrRv+94NQpctsyqz44+3Fx87Fi69tAA/IRERKSpJBVre+13AmcBK4FTgcyDYp2Ud8CVwNVAeWAKc673PKbLZioiIiKSQV1+Fzz+3D819+lgPrSVLEo/fvNkKWlatsgAsytSpcZcbRtpbodWhqzXeji1pkTy9+KItRXvjjeSvTbSjIJ9+at3S27cv9LzOPBPmzrUWXO37t+DF1v/m9AX/jR6UnQ1/+xsccQS1Hr+Hs66sixv5MWRm2hMMHgwjR1rfqhNPjJrO7rXptoNmAUyKs9/A0qXw2muhO/fea7/wf/5z4idxzn4mUU2q4lRHHXUUx11yKHt8OSY/Pnnvr/Mdd4SbvZ91llVgBX2yOne2oCtS/1P3cG6nJTzh/8zYK6yfmffxA60a1XNs7oceak86eLD98LWzoYhIiUu2Qgvv/SLgKOA/QAa2BDHyawvwX+BY7/2KRM8jIiIikspyCvCf9F5+2Y6nnWaNpiHvxvCLF4dvn3JKxANr1ljKlU+gVaWKHXc0b2s3ZqjVaTL22Mq2pNqPffut5R1xd6bctg2+/tpSl0Lsbhhp7+6Jzlmy9fXXEeVE2A588+dboFSunJ3r1MnWB86ebSndnXfaroJEF/plVmoEo0blO4fMzHwGTJ5sa/nuuKPQjdOPPNJWJAZOefpcapTZwuuPrmPt6hzKlrVG7888Y4/37m3LCOM24wfIyaHaTVcx4qc2tJg8nC7/uYzbKv+PdWtzSEsL7wpZpw4MezyDz8oPgCeftMRs+XL7I45cvygiIiUm6UALwHuf6b2/w3tfD+gInAacDnQB6nrvb/Xea+scEREROWjl++EeW7U0aJB9vg+Cjrw2HwzyicsuCwdgQIH6Z0FEhVaD5hZ8HKSB1ubN9pWsIAf67DNrK5VfaLlxo206eeSRVu1To0b4OQDb4nLXrn1abhjXpZfa5IYPt/t79sD991uANWBA9NjzzrOO9b/8wt7u6kC3buEhGcedbqWE+ay1zHeXzvvus4QoKJ8qhJ9+ssrGQNVa5Rlw0kY+23wi6yYtpEGD3H278swK//EPC/Puv98qFhTwaWQAACAASURBVKdPp2HlLezcVYZlS7LDfbuys7ny7dNos+IrW3f6zDOFbuIvIiJFo1CBViTv/c/e+3He+zHe+9ne++yimJiIiIhIaZaenv/jq1dbxuCc9e+BcFPqSFu2WC+goDf3Aw/EfEifNs22Yzv66Dxfc2+glVPJmgodpIFW/fpxlv4VQBCCbdhgDcx//DHv8XuX2QGffJKgf1aNGnDiiclPJi9t29oWmK+8YksNX3oJFi606qx4Xdpbt861k8Hpp1shEsCm7v0sfc1n+86gCu2ZZ6J37wQsMBszBm65BapVK9z3lcChvZuzkbqsnbWWOnWSaHA2bhw8+KDtUPj3v9u5bt1oeO8NAGzbUZYj2+628++/b7sxvPCC9R0TEZH9bp8DLRERERExkQUs+QVa8+fbMVgqllegNX26FY+MH2/3GzWKGTB1qiVjQWKVwN6m8NuxwGPmzLwnmaJ277acJ1lbtlhu+P33dn/16txjfv7ZsiOwaqLata0P0/LlMYGW9zB6tK03rVAh+cnk5/bbba3j6afDrbdaj6ckg5iuXe24qWNPu/F57kbzu3aFf42C3/njjoPmzWMGPvusfZ9DhiQ1h4Jo2MjS3QXbmlK7TAFKI8ECtoEDoUMHeOqpqIS4Uduae2+ft+wxq3b717+sz9lFFxXp3EVEpPCSCrScc0c450Y45x4owNiHQ2OT2LhaREREpPSK3NktqFZJJC3Njg0b2rFmTftMHW/J4fTp0fejemXn5NiAfPpngeUJzsGOHVg116+/wvr1QOF27jtQvfCCraSLJ+iDVRhbtljrp6ZN7X5ki6rAEUdYgRTYSr4jj4Rrr7X7UYHWnDmWiJ1+euEnlJcLL7Tm5ZMm2U58b7+ddJ+uYBnspgoN7Bv74otcYx56KJyNJmx8v3WrlatdcIGVxxWx4G9oMYdRa9PyvAdv3gx3321/LxUqWOlcTD+vnj2tkOyfR3/EIS/9w6q35s61nRnjVbiJiMh+key/yJcBvweWF2DsutDYy5J8DREREZFSadu28O38KrSC4CoIDcqUsdVn999vK5sCWVlWyFO2bIInWrjQPqTn0z8LLM+oUiUUaAXlNzNn8uGH1gQ8Njgrra6/HkaMsAAqVmRV1datyT1vEGgFFXLxAq1AdrYFWocdBjfcYO9fVEP40aPt2L9/cpMoKOdsx8Lt2+GrrwoVJAVVgxkZQL9+MHFiqLwvLOibNW5cONDa23cqMHas/fCuvjrpORREEGgB1F77S+gXPI5vvrGE8YEHrGJt6lRo1SrXsCpVbAPDv4/tZT+Ehx6ypmIXX1ws8xcRkcJJNtDqGzqOLcDYEdiuh6cl+RoiIiIipVLkZ/05c/IeG6+aJVgG16dP+Nwbb9jn8KB/Vq7P31On2rEAFVpgyw537AC6dLETM2YwYYJVjD34YIGeotS48MLoFWKbNkWHhWvXJvd8QaBVoYKFNnkFWjNmWPHbYYdBs2a2Gd7NN0cM+OILWybapElykyhBkctgB8+5lU939bVtGyMExU3ffmu/02XLxtnA8KuvrG/WCScUyzwjA61aWRus6iqS9/Dvf8PJJ9ubN3myJZ4tW+b9xHXrWmP9226DDz/MI1UWEZH9IdlAqzmQBawqwNhVobHNkp2UiIiISGkUWaH1xhuJl7fNnm0f/suUif7wH1QMRT7P99/b5+qbbrJKmVyNyCdOtPWKwTq3fOwNtGrUsLRlxoy9yx8jX7c0C5Zkjh4N775rvazAVvdFLkVcvRoeeQTWrSvY827ebD82sCb9sYFW5LLNYcPs2L69Ha+8MiLP2bzZlgIW13LDIlK5MpQvD0uWwKtfNuVsPrXG7hGCYHbhQrtdp06clY3jxkGvXvZkxSAq0KqJVaYFb0ZOjgVS//d/tuTxxx+tyVdB9ekDjz0WpymYiIjsb8kGWjWA7d7n32XBe58DbANq5zdWRERS2/DhUKlSeIcwkVQVVGhdeqmFHUHx1MKFtoTJe1t9ddRRVnFVu3b8ljzZ2TB4sH2OnjwZjjkmvBNiEKgA9oRffAF9+xa4eqRy5YhKsq5dYcaMvcvGdu8u1Ld9QNmzx5ZpRnrvPTtOmWLHIFcZOxbuvNPer4IIKrQgfqAVWaH3/PN2POaYOE/01Vc20QM80HLO+oV99lnEyZhAK1hau2KF7f6Yq3/WqlX2BxBZdljEqlQJ367dt5v94b39tv0iXHGFBVx/+pOdq1q12OYhIiIlK9lAKw2o6ZyLXRmfS2hMTSBOa1MREUk1775rO8LHc9ttthNW8GFSJFUFFU7nnmtBVdBD+5Zb7O9g8uRwtdDOnTE9lWK8+irccYftmtetW4JBs2dbE6Mk+jBVrmxz2LULC7RWruS3lbbWMRUCrXXrrChn0CBbnlm9enj5Z926ttJyyRK7/9VXdixo77D8Aq2gL1pkZtK4cZwnGjnSksnjjy/YC+9Hxx0X833+8ouFVCFBhdbu3Vb8lCvQCn7Ip55arPMM+prV6tfdSuGuvtqWdL75pvXMevJJNXQXEUkxyf6rHvy/+ysLMHYw1kNrRpKvISIipdBFF8F998XfKS34kDx5csnOSaSkBYFW06bW0mrcOLsfLIl6+eXoKp7YD//v/Xt5ruds13o311+f4AXffhvKlYPf/77Ac1y7FubPt79XunXDA7+FGqWnQqAVNH0fOBCWLrWsb948C/DS0y1sbNbMgq0gZN+82d6XceOs91WsGTNgwYLcgdbatdH/5gWB1r//DZ07x/TMCmRmWsnYRRcV2xK8ohSZuTnnyaJcVJVWerq1xwILCnv1inmCoCF9x47FOs/DDrNjjdplLTA8/XQrMXvrLfjb35Le4VFERA58yQZa72Ah1T+dc/0SDXLO9QfuBzzwVuGnJyIipc369eHbX31lnyGC/jzB8iuRVBWEVVWrWpHIzJkWpAS9tL76Kqq4JVeF1sAvr2V27d5R5+Y17EPTRtm5X2zXLmvUdfrpUK9egecY9Itatgzo0oV06rIry5YrpkKgFXx/QYh4xBEWbAVVWU2b2vF3v4u+7qGHbOVmt27Rux/u2mXn2rWz9y4y0MrKit7NctMmOx5+uFUrPfFEnAm+/bY1MRsyZJ++z5JyzjnWS71TJ/DesbLhsbkCrWB/AYjZWMB7SwlPOaXYq6OCKsasLKwU76OPrLzxkkuK9XVFRGT/Sfb/s7wPTAQqAZ8750Y654Y4505zzvUN3f4E+Cw0ZqL3/p0inrOIiBzAFi0K3w5WmrRtax8Ugz49IqkqqNCqUgV69LCAqFKl8JKtlSvhlVfC46MCrXnz4Msv6XRHP1assFNVK2ZR5vtJlrbEGjbMSoRuuimpOd5wgx1r1rT/M6HRxYAV0aRCoBUsgasbapDRoYPlKuPH2/1gU8HIvlk9e4aXh4It9wzELpVu08aOwVLCyOV4QYVWsDtgrqKgnBx47jlrota1a0G/pf2qWTP4+utwT7C5HS+ykGrPHnJy7OfdvTuccYbtTxCVW/3yi/2OFvNyQ7BVhQ8+CAMGFPtLiYjIASKpQCvUDH4A8H3o2t8BzwOjgS9Ct88MPTYJOC/+M4mISKo68URblgP24b1JE6tUaNmy4DuJiZRWwcYH1avb30Jg3DgLQCpUsEwjcPjhERc/9ZSlX9deS7NmcM01MGZcObj4YmtQ9/334bHz59uubSedlHSz7WeftWqjIPgZUeEi6pdJo0+f1Ai0glApCAtbt7bjpEl2DCq02ra1nOXGG6FFC/jhh/BzvBWxvmD8+OiQ5pxz7BgEWr/9Fl52GFRoJeyN9uqr1tDrjjtK3RK4zp1t34ErJl/H/E0NYfp0Nm603+emTeHzzy0YjBL8V41ibAgfqFIF/vpX+xsTEZGDQ9K1v977dOAkYAgwGdiDLUN0odvfA1cBJ4fGiohIiguqUgLz59txyRL74Fypki3/2bDBdm8TSVVpaZZT1KljFU8ZGeEw5NhjrYf7O++Ee2cNGhS6MD3dlg9edhnUrYtz8NJLcEJPZxU9LVpYn6xhw2DoUOjd2/6wXn21UMFI7drh4Gf69g6clDOeKmV2pkSgtXGj/Uhq1rT7hx5qx4kT7RhUaAF8+U4a/7t2NofUyNx77oILrCorWKK4fLkFNkOH2irBFi3sfBBonX66tYdaty5crVU33vZJmzbZloonnGAhZSlTpYr9+525vQJ/5SEYM2bvzygIDXMZN87egFatSmyeIiJy8CjUYnbvfbb3fqj3vidQBWgU+qrivT/Re/+q914fWUREDhLTptkx2GgtqFJYvDj8QadhQ/sv+en6Tx2SwtLSLCwqV87u16plq8sAam5eSbs573PRib/xzTfWID5YvsYLL1hfpXjLB2vWtPVw9erBVVdZ6Vb9+lY61LJloeZZp45VFvXuDYvTanM4C6mQuT4lAq2MDPuRlbW2YNSqFf5+K1YMhYlbttjPulEjOOoomj73973X3313uJc42L9ntWvbj/7FF8OvExmM/fwzDHs8g8mT7T0NlhxGuf9++wV56qlSV50VuOsuO1atWwnGjNm7xHzv73Gk3bttrWIJLDcUEZGD0z53ZwyFW+tDXwqxREQOMt5bv1+wD8dgm3ilp9tnt2DnqQYN7Khlh5LK0tJy92dv18TWIdYa/5GV/7RoQcfHruTqngtswJo1ti3eGWfAkUfGf+LDDoO5c2397o8/WqlXhw6FnmedOtbe6Jtv7H4bt5QK638r9YHWnj3WuD1298igSqtJ4xzcvx6wMqtnnrGSqw8+4JCrwnsddZzxGsccE96hMjMzXO0VqUoVGDIgnNAvf+wDJo3fnXvZHdgP++mn7fWOPnofv8v951//suln1G4N06ax6KcdOBf++UaZONG66595ZonPU0REDg7Fu92IiIikvMiKq6DH8aZNMHmy3e7e3Y7BjmMKtCSVbdiQO9A6dPZHAFS+8PcwY4ZVBr33HrRvD+eea32wsrLgP//J+8nLlrVGRp077/OOcbGBz2Hty1Hh16WlPtC67DL4+OPcPayCYL3puplWgnXiibbt6nPPwXnn0eAq2/Kwc7UlMHgwJ9SYw/TpVmS0aVOCiivveWHVGayr35EubTbzaZnfk765Ar2O2pxrHDfeCNWqWefyUq5+fdhQvjHk5LB48nqaN7fKt1w+/9weKIH+WSIicnBK+L+GnHPNQ1+N45xL6qtkvhURESlqWVlW8ZCXYDe255+3HkFgFQ2TJkH58nDMMXYuCLSC8TfdZEuxguWKIqkgLc0+8O/1/fc0WmW/5Bk1W1p5y+OPW2Om//s/+O47Czw++8wazpWQ2IDmsL4tqbB2Jbt3+xKbQ3F49107BhtTBI47zo45u7JsGdzIkeF/nLAw/tJL4YPJTaFfP04Ydy87d8KsWXkEWhMn4qZPo8G/bqZZhxqs3tOAMmTzu/G3RY974w2YMAEefjjml6N0ql8f1m+vBjVrsmhBzt6wMJfPP7ey3apVS3J6IiJyEMnrP+8tC32Ni3Muma+lRT5rEREpEY0b57+z/MqVdjzmGCtAKFPGPgDOmGGFJJUr2+OtW9uObvffD8uW2eqb2bNh8ODwDmEipV2uJYdvvUXjitZ9fefOiPMNGsBDD8H69bBoEZx8conOc+tWOz76qIU2DQefQQW/k+xsV6o3bmjUyI5Bs/LA8Q0WA7CiWse4P+tKleDNN6FNx0rw8cd07WfB04//+jxxoPXyy1CjBlxyCYccYqd6tviV+iNfhk8+sRPTpsGf/gQ9ethywxTQoAFs2ODwp/RhUVodDmsT5x/w+fNh4UItNxQRkWKVV6DlIr7inSvol5Y1ioiUUunp8NNPeY8JKq5atLAwq2ZNq9BavNgCrED58nDbTVmsXAlPnD8JgNtvt2bKwXMcqD7+2AoNli/f3zOR/2fvvsOjKLcHjn8nhU5CaIFA6L33plJFRFGULiLIFUXEa0FQFO+1oV75gSggRUVBBBVQaSpI71WaIKFDEgiQBAgQSkIyvz/OzpYUkkDKbjif58nzzs7M7r5LMoE5nHNed2aaKZQcLltGlw7XeOUViV+5i5deglGj4OWXbU3r69cnb5BMPP7StZyd3B2wViC0MkIt9XdMpxJH+Wx8OqLnefNSbtEkCvlcY9/iY1y6ZCbvoXXxIsybB337QsGC9lLNviPKQL160Lu3lJK2aiX1jz/9dMdlou6iRAm4ehXCmnbjoulPFb9zyU/65htZGaFnz+yfoFJKqbvGrf5mrWj7uj+FfRn9Ukop5WGuOd3T3qrsMDRUmiNbPXn8/SXpJDQ0yVLuiYlUmP0hAIt3lqGMVwRPtJU17t297HD8eGme/c47OT0T5a7i4qSSMD4eypSx7Tx6FI4eJU/nDowf77oqXk4rV07aOfn6Ovbl6doZgLjPp+TQrO7c9esSR1m3znW/7x+LOPrAC/T4l1+6Xscrjw+1G+VlS4H2JCYaFCmU5Jfg7NnyZrasqxEjYMgQeHqQDyxfDj16yC/RYcNg504IDs6Mj+cWrKrJFp/1BqBq9BbXE+LiYOZMeOQRR8qcUkoplQVSDWiZpnnS9nU6hX0Z+sqej6KUUiozWaWEcOsMqjNn5EbdWoW+SBEpJUxMTLKU+7RplNv8IwAnqEhNrxDqvvYAefOabN2a+fPPLDdvSkkWSKmkUimZP18yDkuUgAEDbDuXLZOxU6dUn+dO8tSSCzbu8ylJ6iM9R0wM9OmTJJh++rSkgt5/f6rPS0ntOl7suFobgCJ/r3ccME346ito2NC+YmG1ajB5sq05esmS0jdr2zZZvTJpB34PZ1VsRpzzAaBayCLXExYtkv/VeOaZbJ6ZUkqpu02Gcp8Nw+hv+wpM+2yllFKezDmIdfBg6udFRUGxYo7H/v7SPgWcbiqPHoURIwhu5+geXKtrNXxPHKauTwh7d7tv0559+6TBtGHAqVM5PRvlrg4flvHAAWmrBEhAq2LFJJFd95Unj4xxF2MdwTgPExND8vLANWtkbN8+Q69VzmlZI//ff3Ckra5aJVH7IUNue56erFIlxyq2ANW2fCd/EYD8T8aHH8pJHhLIVUop5bkyWsw/A/gauJzGeUoppTzcypWO7WO3WN4jOto1oOXcPLlyZaT8ZMAA8PGh4MzJ9mM17y8Ds2dTK3Yb/2y6mPZyijnECuY98IAEtLSBvUpJaKgsomC/FuLiJPDRqZMjfdHN2QNaAaWkP5SHMU24dCmFgNa2bbI6Rf36GXq9kiUd20UuHJNVCq9elVS8oCDo3//OJ+2hmjWDf/0L1n59GCPhJsyZIwc++AB275Z6Vh+fnJ2kUkqpXC+jAa3zwCXTNK9mxWSUUkq5hzNnYOxYqajJk+fWzdCTBrQqVJCxQAEIDIiDXr1g40aYMgWCg6lo66zYsyfQvTu1Hq3K6evFuPj8yCz6NHfm0CEZ27SBGzfg/Pnk51y4kL1zUu4nNNTRkByQFJYrVzwqS8Ue0GrZRrKaPCx6e/UqJCSkENDavl1KAzMYYLF6RQEEPdxIlmgNCJCAjb2+8O7k5QXTp0Prf1WRxvdjxkjA7733JNDXp09OT1EppdRdIKMBrRDA3zCMQlkxGaWUUu7h2DGpHBk9WipHMhLQqllTxjx5TIwn+sDChTBpEjzxBADr10t7Fes5tQa1AuCf6Ztwx2Zahw5J6ZFVNZa07HDJEmmR41yCo+4uiYlSoutcosayZRJAyWCZW06yB7QaNpcfdA9b1jMmRkaXgFZ8vDTBa9o0w6/nnKEVPHM0TJwIgwfD6tXQteudTTa3MAz45BP5i+DNN6F1a/nPCw/JSlRKKeXZbqfk0BsYlPlTUUop5S7Cw2UsV06yTn7+GTZtSn5eXJwkoTgHtGrUkDH+yg349VeYMAGGDrUfL1PGNfPBymqJ8KsBH398x3P/6y/J0sgsBw5Iw2dr5bojR+QeOSFBeh8vWSL7Fy7MvPdUnmPdOvD2lh5ayQJaLVs6NdRyf/aAVt3GsrFxY85N5jakGNDav196X91hQKtwsTzw4ovy+6xt2zuaZ65z773SbHDFCvkqUCCnZ6SUUuoukaGAlmmaXwPzgU8Mw3jBMAwtjldKqVzICmiVLSvBG4DOnZOfFx0tY/Hijn1WQKvEzQh45x34979v+V7W/f6llp3gt98kfes2/fYbNGkCM2bc9kvYffON9In56y9JOrACWt27Q5cu8PvvkqQxbZrs37Dhzt9TeZ7582X097cnIcLZs7Bzp0eVG4JTQKtMRSmn27MnZyeUQVZjfpcY4vbtMjZrluHXcw5oqTRUrgwdOmjfLKWUUtkqQ3/rGIbxDRAL3AAmAu8bhrEdOAek9v/hpmmaum6vUkp5kPBwKFhQbtL79pXe1gEByc+zAlrOGVolS5iMKfM5j/gshbeXpPle9oBW/Xth2U3J6ho8OMNzNk1pawOOBbduV2goPP+8I5jXs6c0/Lb8+Sf06+f6nK1bpYePJifcHUJC4NFHITJSAp7LlzsCQixfLqOnBrQSfaBOHekVlYpt26TMsmfP7JnbmjWSZFWwYOrnDBsmi0q2auW0c/t2+eVlX3I1/ZwXuFBKKaWU+8nof6M8DZiAVRhfFEjtX2vWeSagAS2llPIg4eGSnWUY8MwzUkWyZUvy86zAkXNAy/hnPyNOvQpffJGu/60vXFjGSwWDoGJFPv40Ly2rZ7yqZ9o0yZoCKYW8E7NmyaKLa9ZIg3wr68zZjh0yvvWWZKgNGyY3+VqNdHeYMcOREfTAA07BLJByw+LFpRG5B7EHtOKABg2kjtY0U+yH9Mor8jshTx7JirQyGJ0lJkrz8Du1dSu0ayeVy5MmpXzO1atw9Kj0/XMJRG3bJhO8jZ5O1tyrVcv4nJVSSimV9TIa0PoOCVAppZTKxU6dcr1BLV1a+kO3awdvvAEdO0rfoJQytPj1V7l57NYtXe/l6wv588OlywaxD/firUlPQ7uML7C2c6dj+04ztLZvh+rVZWXD1Cz79hQ+3qUZPdqLixdh+HD5+vBDj0vMUbfhqtN6zwMGOB24fh0WL4bHHsucaE42ShbQmj4dTp9OFq06e1aCWaYpH7NGDek1l/ScUqWkdHfgwDubl5V5+cUXUL8+PPts8nOOHZPRJRHr6lXp7TTy9ldQPXkyhVUTlVJKKeUWMtpD62nTNAdm9CurJq+UUiprREa69o+xmrivWSO9tN57Tx6nGNBasABatJC72XTy84NLl2B39d4A+PokZnjOx47J21apcucBrR07oHHjW59z8HIZiiecxRj+GgF+CUyeLP22Xnnlzt5beYaDB2WcPFmyGe3++EO6k/ftmyPzuhMuAa369eVBCmWHGzdKMMvKlgoJSf5a1p/PZ5/d+by2bXNsP/dcyuccPSqjS0Br925ZveE2GsJbypXTgJZSSinlrjzrvw6VUkpli8hI10bvSZsjf/CBBH2SBbRCQyVV6vHHM/R+VkDrr/h6AFQqcCbDcz52DCpVknnfSUArMlIy1FIKaOXLJ2ljAT6XAChe1IRPP4VHHmFwx2MMGgQXL97+eyvPERIifdSGDElyYM4cuWDat8+Red0Jl4BWPbkWrYDWtWuO86xFI3r1kozEpMdBriGQhLU7kZAg1/aLL0omWN68yVcxTUyEjz6S7SpVnA5s3SrjbTSEV0oppZT7S3dAyzAML8MwahmG0cIwDO0moJRSuVR8vARlrKwscN229OoSS3S0lAvmz2/buWCBjI89lqH3tAJaBw55A5AvNlqaWKXTzZsSS6tY8c4DWqGhMlaqlPzYX899yQwGULO2zLNE/SCYMgVWroSqVfFfvYCYCwlyh+1k1y7JalG5Q2KiBHUqVEhy4PJlWLJEOqV74Gpv1nUcG4ukJVWqBLt2sXmzLHawcqUcP3VKgl/Fi0NQkOzbutXRUwykVA+SB7oyKjIyH3FxkjD22mtw44bjGrVs3y5ZXL6+SRav2LYNgoNdV3RQSimlVK6RZkDLMAxfwzA+Ac4DfwMbgQOGYUQahjHKMG6jy6ZSSim3ZWVdOWdoJf1NXzXPSY6fLciuJeEu57FgAdSuDVWrZug9rYDW+fPy+EKCH6xdm+7nh4dL1kZmBLQiImRM6R641sqJDLjvOFXqy1JrPj7IcojHj8Prr+MfEcK1G97E93kKEhKIjZXKy0aN4N57ZXVE5fkuXJCgVrJA77JlkpKUXUv/ZTLrWrZfPw0awJ499mCsFa8+dUoCWYbhCGi1a+faPP34cRkjIhyrhabHP//A/fc7fg+dOiVRtipVHIszJC1xPHtWxmS/MrZuhebN0//mSimllPIo6cnQWgAMB/yQVQutr2LA+8C3WTY7pZRS2c66mXW+We8QdIAOrLA/nrMyEIBVB8tSLN8V2Xn6tNxRZrDcEBwBrQsX5PF5irL+0+2cSWfloRUIK15cviIjM95U3nL6tIzJAlohIbB/P/TsyfPPyy57r+ygIPj4Y/w/GA7AxXl/MqTNP8yc6bjZBscqjMpzxcU5yuySBbQWLYKiReGee7J9XpmhUCEp6YuMtO1o0ACOHMGIk7rBiAjprx4S4vjZtwJalkuXoH9/+PJLeXzzpqO/VXqMHi2ZYF9/LY/DwyWgVbUq1Kol+/bscX2OFfxyadsXGSlRNQ1oKaWUUrnWLQNahmH0BDojAawjwMfAUOD/gNO2/U8ZhnGLdaCUUkq5M9OUlcimT5fH1s2sc+ZVge+/ZJlPF/vjoEr57NvFwvdIqdU330jaisuSb+nj7++aoXWFwrT+fSSlS8MzzzgaUB85kvLzY2Icr1OypJQ5XbmS4WkAjoBWsp72P/8sY7dutGwJf/8N48Yl+RzFpMzsSNO+TN1Yl6FDXY/v2HF7c1LuY/JkGD9etl2yE2/ehN9+gy5dPLLcECTjygoIA9CwIVtpxoi38wJyCXzyiSx+YAWykgZ+v/8eZs2CunXhpZdkX0pN41Ozf7+MixbJGB6en/z55X2KFpVMLas1KbjI8wAAIABJREFUliXFxSmskzSgpZRSSuVaaf2Lq59t/BPoaprmDeuAYRgfAquAhsCTQPprQ5RSSrmNL76Af/9btosUcbR/smef3LgB332Hd7euMFd2lSwpN7+mCcWuhUOnEbB3Lzz0UJKuzOnj5yd9u1IqYv/mG8nc+vVXqF495Ztj54CWdYMdEQGFC2d4KkREyGf39U1yYP58aNnSnppSp07y51qroYU9OhS2ux7r2BE2bJC4h4fGOxQSeLW4ZGht2CAR2UcfzfY5ZaYSJVwztP7L+5hm8guzXDkZixaFV1+VS6NPH+xB3J9+kktlwoT0B7Ti4+XXCDiC16dP56dKFfCy/Rds8+awapX87rF+X0RHyzXlcr1v3Qre3mkvV6qUUkopj5VWyWEjwARedQ5mAZimeQl4A8nSapg101NKKZWZfv/dNasoMVFWLGzTBvLlg82b4dw5OWbPPlmwQG7UBw1i7lzYskVuHq3jJZpXkicWLSrRsdtQp44EtI4fl5exHGw1kHLlJJgFrsEEZ9bKgkWKODKr0luumNTp08nLqDh+XFZ769btls+1Alqh+RzNhMqVMxk3Tp567Zrjz1d5JudgpEuG1sKFUq/XqVO2zykzlSjhKDs+n78Mq0i+WuOQIfDGG7JtGLLQZ8+erpd/tWoSqA4KkmzG9LAyNEuWlOskLk4ytJxb8jVvLkFnLy9YO2gWnD5NdLRkZ7kExFetgoYNpZu9UkoppXKltAJaxYHrpmkeSOX4DqfzlFJKubGYGHj4YRg+3NFfKiJCbhx795YV206cgLAwyU4qWdL2xOnToXx56NCBnj0dFTxxcTLWebqpRIEOHkxh2bf06dbNcTPqnOBVddMM1k75x/7Yzy/1zwbJM7QsCQlSDZaehRPDwpx6Y1msiFoa/cHsAS2nVdhGtt3KsGEpz0t5HucFB+wBLdOUGrkOHaQRlQdzztBatNjgJklTFSUontLKp4Nb7CEwfwzNSp7A+/fFEB9P584wd640e0+LVTpYr56Mu3ZBaGhBl98JzRvG2bffmR5MbMN7iT5x2bXc8Px5ibx37pz2myqllFLKY6UV0MoLxKR20DTNGKfzlFJKubFlyxzbY8dKtpYVeClfXr5+/hnmzJFyIi8v5IQVK6QvlpfrXxlWEKlRIyRakz//bc+tRAnHCmndusFHH8H+zZcwChWi3Pcf2c9LrYTQmoufnyNDyzlwtH17Ubp0gRdfdOy7cCF5cMk0pdQp2SKNv/4qTYEqV77l57ACWidPyvhw4bU8s+VZuHnTPq+wMNeAl/Iszhl2+axWcseOydfDD+fInDKTc0Dr55+hnN8Fxni/yeOPJtjPyZcvhScuW4bRvBnHvaqw9kpjKb0sU4Z3Dvfj5k1Y9e66NJc7tIKFVkCrRw8Zu3Z1nNNg1af27bW0pdC5Y/yyvDDFijjmx8KFkn6aC74fSimllEpdelY5VEoplQvs3OnYfv11uddr1UoeWwEtkICLtc1330mU5+mnU33dunUzZ35WEKlECXjzTajVwg+GDsXrh9n2c65dS/m5MTFQsKCUgxUtKhlmziWHkZHy/y7Tpkm21rJlcl5QkOt5EREQG5skoHX2LGzcmK7VG4sUkdEKWE0cc508h/bB1Kn2DK3HH5c/37i4lF9DuTcroOWykOGaNTK2a5fd08l0xYvLGg83bkiGVLvGlxmR8D96NzxkPydZr7vQUEnzrFWL/CdDyHchAhYvho4dKXv1EPm4xol526B7d0eTvhQkzdAKD4dWraLsv6e4do08X4znnoD9yZ5b7Mw+x4Ovv5aGe82a3cafgFJKKaU8hQa0lFLqLrFzp2t/KmfBwXITaylQANkxeTK0bw8VKyZ7ztatMGXKHSVmuahVS0bnefDOO1C9OlMD3gQcGRymKYEpy8WLjuwow5AsLWu1QnAEtKz3efBBx7HSpaVaEuDwYRmrOVpgyY25aaYroOXvL0Gt3bvlcZFeD0hPpREjCDy4zuXcM2fgvvvgv/9N82WVGzl3Dh55RHrA261ZIzW6NWrk1LQyjVVKGBkp11XR6rKj2MmdqT/p7bclAvbzz9LMKk8eWe1x9myM7dsoVy0/J+t3lWvp669TfZmkAS2AUqWuOx589x2cO8eGX6NYv971uWWObYClS+HPP2HTJmn0ldIqE0oppZTKNdIT0Ao0DCMhtS+kafytzklHxxKllFJZ6eJFCUA99pg8ThrY8vOT3lp580pzrQuHI+GJJyRl6aOPSEmzZvD885k3x7fegsGDpbrRLn9++P57Bt+YwKhiU4mKMjFNePJJqFTJkewRE+PIjgLpw2UFpwCiohwBrUOHZDFGZ1b22iFbEopLhtavv0pAr379ND+Dj48E+Sx+/obchJcrR94H2rice/qUyYYN0pRfeY5z55L0jzJNWLsW2rbNFQEU67OdOSPZikVK54f69Sm6Z3XKT/jnH/j+e6nnrVQpxVMqVICTeapIA74PP0w1PdEKWFev7tgXGGgLaCUkSK1006bQunWy5Ku6Zc/Lhd25s5QGDx6czk+slFJKKU+VnoCWkQlfSimlctAXX0jm04svSvbQkSPwww9y31elChAfT6MVY7jkFUAffmDCwU6S7fD5544u8FnM3x+mTnUNTAHQpAn88QfFLh8nIcHgs+dD+OEHqXIaM0ZOiYlxZGiB3BAf+MckoX1H6NCB6FOuLzlvnjSqtuzdK+OuXfI65crZDkRFScZHjx7pDla0bOnY9vZGMne2bYPRo13O2/Xc5HS9nnIfpimZS/YFE0BWwAwLk6VCcwEroHX0qIz+/kCXLqkHtN59V+p9X3891dcsXx5OnDAk4zI0FGbOTPG86GiJYRcoAIGBsi8w0LbI9sKF8ovr9dfBMMiTB/7v/xzPrTvt3/Dqq5KZtW5dKo2+lFJKKZWb+KRx/L1smYVSSqlMd+aMLLhWqJAkUVSsKKvYW/r0gV49TYwli6HOCDh0iDxdu/LDyIpQeJbcURZ3k0VsW7em2ucVYQi89mU1Aotc5+zFfLz1FgwaJBlozlOtUQNiLhn4rF6OicGlPFcoXtwkKkqCUgUKuLY72rVLxh07oHFjp/73c+fK0oj9+qV7qsHBKez094dRo1jaBKLPJfBkf2/W7wvI2J+BynEXL8qPg0tAy+qf1bZtDswo81kBrT59ZCxSBLj3cYp+OCH5ybt2SXT47bdTXvbQpkoVCQRGN32QYo0awYQJcuEmCRJHRWFfrbBUKWlf5+8fJ5HEMWMkAu9U+jt8OIwfL+XFde4tAg+Nu5OPrpRSSikPc8uAlmmaGtBSSikPVbOm3IA/+6yUSbnchAPcuIHXU0/JDWmNGrBkiZTsuGnZ1EODg2n/402ObjpH+MWS1K6ZwP4D3pw4IZ/P6sEFUCVfOFAWAHPW95x9qhhNSp9heVRp+znFi0vm2uzZsH+//DFs354k0eT776XrvXNTnzR43SL3uVMnuHnTm34DYEPhh+AS5PFJALzT/foq51gN4ZMFtEqUkAsuF0gal/L3Bxo3pnCLOrAlycn/+Y9EvF577ZavaSV5btlq8PDgwVIOuG2b/cDixRASIgEsKzNr1iwYMQKqV78Mq1dLzfTkyba0R4dVq6SfmZ/fbX5gpZRSSnksbQqvlFK5UEyMBLMAvvoKVqxIchOemChldPPmSY+svXtl2UM3DWaBTK1aTR9OJZYiAR8a55FVzcLCJBvNuhEGaH/iG/v21a5PcJEi1Az7M9lrvvCCxPDCw2HoULlXtjJTCAmBzZulYVcGzZoFkyalfMzHR74XYZektjIgIQpOnszwe6jslyygZfXPatPGra+djAhIkjhoX2xh3FgAhlb4TdLUvvsOfvtNliRNVifsqmlTubY2b0YusAIF7M3hL12CRx+VQHJoKPbVQOvWlarn/PkSpKwxKAgGDkz22tWrwzPP3MknVkoppZSn0oCWUkrlQn//LePnn4Ovr2y7BLQmTJCMrAkT5IbUOsnNFS4MNxPkr67GB74HYN8+6TFdqpTtJNMk//xZTKgmEaXTZ7ww8aLCRVl6MGkiTY2qslxiZCRMHHvdUZY5Zoz04UnhJjot/fpJgCw1zovhXTPzSXMz5faSBbROnJAoTC4pN4TkGYb2WFWrVpjjP2PSiS7yBzBgALRuDcOGpfmaBQpIkuO6dXA82g969YIff4Rr1/jTKc68f78joGV//z17YP16+T2lfbGUUkop5UQDWkoplQtZTc67dXP0zbKXEh06JEsKdunicYGUQoUc27Vv7iGPVzw7dshje4bWX3/BkSMUbCVlguHhsjugvD87i97P+m+POF5k+XKqj3T05On8ZkNJ2xo3Dr79VqJSyWo179wbb8gYHAyXDT/MJUtg06ZMfx+VuZIFtHJZ/6yUOC+2wMsvw/z50LWrZHYuXSoph+lQr57EpSpVgr0tnoMrV2DpUvvvKktQkOvjcnPmyMU9aNCdfRCllFJK5Toa0FJKqVxo714pHSpTxrFiX4kSQEKCZBzlzQvTpnlcmZRzQKvUMw9TNjGUHeti5bGVofXjj+DrS6H7GgFwyrbCYcCwgTT03kuxrvdKCVPnzvDAA1Q1jlCrbAyfDD5Ghb6tYPp06TbdqhW8/36WfI7OneHYMYknmqZBbNFy8OGHWfJeKvNYAS37AgRr1sgD5wZuucClS45tl2pCw4Du3SXY++absiRhOtWt69geu765dH+fO5e9e6FqVccxlwyt3bspun07vPKKZmcppZRSKhkNaCmlVC6zYIHEqurVk/tPK3Pp5k2kBnHTJpg4MXkqhAcoXNixXfLdFyhbJJbTFwsCts+ZmAg//QSdO1MwUKJfCxbI+UXql5fm0mXKwHvvSV3mxx+Td/9O9of58/rUShLMCguT7vBr10qtVBapWNHRyPrys8Pg998dyy0qtxQWJvErX1+kf9bKldC+vccFhtPifJ1lVrP1SpUc26HhXpI+ungxe/ck0qiRXJbgFJgG+PRTbubPD88/nzmTUEoppVSuogEtpZTKZaxV7evUkfHhh22Pi4TDqFHSgfk2Gp27AytDy8sLipXyJbhjdfuxwOsn4Y8/pMawb18KSpyLn3+WMSAAqF1bShJjYyU6MXJk8syPkiWhSZN0l1LdCStYcKn7QAmepdZJXrmFTZugWTPbg5AQSf+7//4cnVNW2bJFFjHMrMugUydp3n7vvbasyZ49SYy9SlgYVK4ML70k51mBLa5cgZ9/5tz996fZdF4ppZRSdycNaCmlVC5y7Zpj20rA6twZwk8m0PnbXlIiNHWqx2aUWAGtEiUkqFW2Ul4AChBL8R5tpedVxYrQrZtLeSIkWb2tQAG3+DOwMmHOXPUjvm9/mDMHzp/P2UmpZG7ckDUCDhyQPuiALB0KuTag1bx55lbcWgsbNmsmAS2zdRvOFyrPzQQvAgNhxAhJmmzSxPaEBQvg6lXOduyYeZNQSimlVK6iAS2llMpFjh2TsU0bR8YDQJm542HzZskASrqMmAexAkBWGWVwsIxFivlgnImAM2fgyy/B19eeoWVxxyQPK0OrbVvoeeR/cP06zJiRk1NSScTFSXuzN96QZD4r45EVK6SOrmLFHJ2fpwkKksB7zLU8nG0l6aSlAk0Mw5FVCsDs2VC+PDG1a+fMRJVSSinl9jSgpZRSuUR8PAwYINv/939ODdSPHIG334bHHoMnnsix+WUG6zNZq8xZAa38RfJCRAScPm3PmEka0EqaseUO7OVVwMI1/iQ2aSZZWsptFCoEH3wA1arB1au2oMuNG9KPLZdmZ2Ul62c+PBzONugEQODlI64nnTsHy5fL7ysv/aeqUkoppVKm/0pQSikP9+efcu+3YoW0hwLpSWM3apQ0wpk82S3K7O5E0oBWiRIyFiiA1BQWLZrsXIs7fvQqVVwf77nnBfkmWql2KkdduSKBYoDGjZ1+hlauhMuXpR+dyhArCN2iBUSUbwFA4O5lrifNnSsrsnporz+llFJKZQ8NaCmllIfr1AkeeAAeekgejx/vFNfZtUtuDl991aNLDS1JSw7r1JGMj08/TX6uc4bWb7+tz/rJ3aaFC+G++2T7QIXOsjF/fs5NSNnt3+/Ytn7mAPn++PlphtZtaN5cSmxjY2Htbn8AApd9J6tGgozffAP16yepQVRKKaWUcqUBLaWU8mDOTeABevWCV15x2vGf/0jzqNdey9Z5ZRV/f/D2dpQtFS4spUspxRXy5HFsFyiQkD0TvA2PPgpLl8r28VjbCovz5t3x60ZESIaRun179zq2q1a1bcTHSxTykUcgb94cmZcn8/GB6dNl+6efII9PAkWObId162Tnhg0SiH/++ZybpFJKKaU8gga0lFLKg+3bJ+Ps2dLfZ9Ysp4ObNsFvv0k3a3fsiH4bChWCVavg2WfTPtcdSwxTU6CAZACdOAH07Ak7dsDx47f9eqYJLVvCm29m2hTvSnv3StB0xQqn+Mry5bISZffuOTo3T1axovy8X7oEgaUMjKAgySJdvx6GDJGIdb9+OT1NpZRSSrk5DWgppZSHMU0YO1baLO3ZI/uaNYP8+Z2ykhITYeRIuWv8979zbK5ZoXVrx+qA6dGwYdbNJTNVqOAU0IIMlx2aJvzyi3zb58yBkyddM4xUxu3dC3XrQocOTr3Jp0yRJm5Wja/KMMNw9NIKLOUlf6b79snFffy4lBy64yoOSimllHIrPjk9AaWUUrBxozQ4r1Yt7XPDw2HECMnGat1a7vsqVUpy0tSpku3w5ZfJl/u7i0RHS6Bv69acnknaKlSQxCwqVoSmTaUea8SIdD9/505H0lC+fDIePZrp07xrmKYEtPr0cdp5/LhkPb71lpYb3qFSpZzGRx+VgNauXdCqlSPapZRSSil1C5qhpZRSbuDee6F6dUmsSotViXb2rCODxGVl+2PHJBDSsSMMGpQl8/UURYtKQMsTVKkiGVpxcUgU5a+/4PDhdD/fWuGyalW4fl22T51K3mdNpU9YGFy8CPXqOe2cOlXSiwYPzrF55RbWGhX2ZvvVqkHv3hrMUkoppVS6uV1AyzCMpoZhTDAM42/DMK4YhnHKMIwlhmE0SeFcP8MwJhmGccYwjGuGYWwxDKNjKq9byjCM7w3DiLa97irDMBpn/SdSSqlbsxb3Ali5MuVzEhPhv/+F4cMdWTeGISWH9es7nRgfDwMGODove1IjqbtcjRqQkABHjuAoO/zpp3Q/f/duKcXs3Nl1/x204rqrLV8uY6tWth3Xr8s11bWrBl0yQcmSMhYvnrPzUEoppZTncruAFvAG0BtYB7wKfA7UBLYahmFvWGEYhgEsBp4BpgPWul6/G4bRxvkFDcMoCKwGHgI+BUYCpYHVhmHUyNJPo5RSabh40bH9998pn7NoEXzwAYwbB5Mmyb4zZyAmRvpn2b3+uqwSNnmy3nR7mJo1ZQwJgfMFg7l5TxtphuUc8byFXbugQQOpWARH+eqBA1kw2Sw0Ywb8809OzwIWL5ZLyJ6h9euvUsP6wgs5Oq/cwur35+2ds/NQSimllOdyx4DWp0CwaZpDTdP8yjTNMUAzIAoY7XReN6A1MMQ0zVGmaU4D2gIngXFJXvN5oAbQ3TTND03TnGR7bkKS11RKqWxz/jysWSOlg5aDB1M+1yonK1RIeiVZypSBvn1tD+bMgc8+g5degiefzIopqyxUvbqM3btDsWLwWr4vJBq1YUOaz712TX4umjVzBLSeekqCBbt3Z+GkM5lpwsCBULt2zs7h9ddh4UJJlLMnOc6YAeXLQ/v2OTe5XCid8VqllFJKqWTcLqBlmuYm0zTjkuyLBtYAtZx29wIuAt87nXcdydZqbBhG5STn7jdNc7XTuZHAXKCLYRgFMvtzKKVUWl59Fdq1gyVLHPtSCmhdvy5N42vUgPvuk30BAeDrC3Pn2npTr14tkYD77pMlEJXHKVQIunRxlGBNWFmbsflGSd+mNGzdKr232rSRfvKVK8Pjj8vPzK7FYdJ0+8cfs/gT3LkbN3J6BhI8/r//k+1XX7XtPHUKVqyA/v2TNKxTt+uRR2R87LGcnYdSSimlPJcn/assCIh2etwI2GWa5s0k521zOo5hGF5APaf9Sc/NC+Tg/wUrpe5We/fKaC1k17QprF0rCxMC0lDp2jX69pV4VcmSjtUMv/pKAl2tasfAu+9KJKRqVViwQCJdyiMtXgyRkRARAU2awIjro9kx9xicO5fqc0wTpk2TbKx774WgIOnDVbs2NPQ/xu49hjSEeuIJ+P77VF/HHVy+nHPvffq0rLjXtKk8XrECypa1HZw1SxrZ9e+fY/PLbRo2lJ9dl5JppZRSSqkM8IiAlmEY9wH3AM7/vVwaiEjhdGtfkG0sCuRL57lKKZUt4uOTZ2O9+640eB88GHZ/uQ3KluWLAiP49Vc53qsXvP++VBU+dv8VvD75WOrL3nsPHnwQ/vxTlvVTHq9UKVkgIF/eRH642RO++CLVc/ftk+SrUaOgSBGnA9HRVN35E6coy43T0dC6tfR/Onky6z/Abbp0Kefee+xY1/Lftm1tG6YJM2dKtLBKlZyYmlJKKaWUSoFPTk8gLYZhlAZ+AEKB950O5QdSKk647nTceUzPuSm9/3PAcwCBgYGsWbPmlvO9cuVKmucoz6Tf29wvO7/HoaH5uXatOY0aXWDnzgAA8uVbw3//60v37vew4sWfKVGqBP9mAuWNk2wzm8K8WlzcXZW+ISGYo3ZBbCxRLVtyYuBArlStCocOyZdKxlOv38BSTTlwuQnxnz3C5hYtSMyf/K+rRYtKA9WpXn0La9Zct++v+PXXBF+XbL35v/9NTJv/Mn59eRZ1eZazn490y9K5I0cKAbKo8erVa9K9SOedfn+PHCnE5583pn37c6xaFQjA+vXyeoX/+YfGISEcHD6cCA/8GfJ0nnrtqvTT73Hupd/b3E+/x7mXp3xv3TqgZRiGP/A7UAi4zzTNGKfD15BywaTyOR13HtNzbjKmaX4JfAnQpEkTs639v2xTtmbNGtI6R3km/d7mftn5Pbb6fL/6agDjx0sT6vbt5b1LFYhhxNVP2Fr7P5hhXnz7awAltz0LkydTcu1aqTvs0wcGDaJ4ixboqvdp89Trt25dOLy3KXGhN2l96BC8/LL92OXL0n9o1SooUQKeeKKFIwB0/jwsWkSFNq/BWihdujlPPSXJRof25afr4cOSCuhmnGNsdeu2tfcTS8udfn83bpSKwrlzA5k4Uf487a83dy7ky0f1t9+mur//bb+Huj2eeu2q9NPvce6l39vcT7/HuZenfG/dNqBla9S+BKgOPGCaZtLF7CNIuVSwtG08bRvPI9lZ6TlXKaWyRVSUjLVqOVYwBODyZQKuRXAGf+YvLYRhQPOOftD1Q6k3vHZNuoeru0KlSrBkSX4q+YYR8Uk9vAYPhnzyfzErV0owC6QZvEs20xdfwOXLlBvRG9ZKvzZrNbnwym3hv/+VlTDd7GfJueTw9GnSHdC6U6GhEsQqVkxKf+2uX5d6zm7dQINZSimllFJuxf3qDQDDMPIAvwAtgJ6maaa0ZvlOoIFhGEmDcs1t4y4A0zQTgT1A0xReozkS7PonM+atlFLpZQW0kt2w//47M8z+vNzjFG3awOjRUMBah9Xb2+0CECpr2WJXnIsvyuKIxvD11/ZjK1fKuGSJtHiyi4+XlRE7daLs/TUwDNeVNE807y1N5j/9NOs/QAY5N4U/ejT73jc0FMqVS+HA4sVw4QIMGJB9k1FKKaWUUunidgEtwzC8gTlAR2CAaZq/pXLqfKAI0M/pufmAfyGrHx5Jcm5twzDaOp1bAugJ/G6aZmymfgillEpDqgGtP/6gWdGjfPZjKdasgbfeyu6ZKXfSpw+ULg1585r08/6BCx9OhuvXOXsW5syBRx+Fhx92CnqCRK9On4YhQ8ibFypUcAS/ChSAE3FBknE0dixER6f0tjnGOUMrJCT73jfVgNbMmVCmDHTokH2TUUoppZRS6eJ2AS1gLNAdWAl4GYbRL8lXQdt5PwMbgCmGYYw2DGMwsBqoALyW5DWnAIeBXwzDeMswjKHAWsAXeDvrP5JSSrmKioL8+ZMEIhITYelSeOABycZSd72GDSU29eOPBlcSCnD8TD6YPp0pUyRx6H//S+FJU6ZA2bIS6UIWNwRZAfGee+DECWRlzCtXYNy47Poo6WJlaPn5JV8FNKuYpgS0goOTHDh7Vq7Hp57S61EppZRSyg25Y0CroW3sCMxK4asE2EsJuwDfAs8CnwHewMOmaa52fkHTNK8AbYGlwHBgDHAWaGuappYbKqWyXXR0CtlZu3fLTXTnzjkyJ+W+rJ+VqNpt4eOPWbQwkVatoGbNJCf+8w8sXy4N332kIr9FCznUrZv0bNu/H+Kr14HevWHCBIiMzLbPkZZLl6QXWOPG8lGyQ0yMxPaSBbR++QUSEqTXmFJKKaWUcjtuF9AyTbOtaZrGLb5OOJ0bY5rmC6ZpBpqmmd80zWamaS5L5XVPm6bZ1zTNoqZpFjRNs51pmjuy7YMppZSTqChpQO1i6VIZO3XK9vko92b9rEQ/8jTnTsWxa7cXXbqkcOKECZA3r8sKhv36wQcfwOefw733yroCu3bB0vZjCL9aFD75JHs+RDpcvixt4tq3h+3bkyyYkE6JiRk7/9w5GQMDkxyYPx9q1IDatTM+CaWUUkopleXcLqCllFJ3gxQDWuvXy81zsjtrdbezMrSig+ryV4UeALRqabqeFB0N330nGUUlSth3FyoEb78t4z33yL6vvoLOzwUTbIYSO+lbOHUqOz5Gmi5ckMUEX3oJChaEb74B9uyRpuxvvunaZCsFo0aBr2/GerhbbcRcMiYjI2HNGujRI8nykUoppZRSyl1oQEsppXLAmTPS7NsuMRE2b4ZWrXJsTsp9BQTIGBVtsLPxswA0iFzuetLo0XDjBryWtI2kQ+nS0lrLabFE/r5ZU1K43MAQ/XiVAAAgAElEQVTJk9Kc3c8PWraETauu8XfL54id+5s0DOveXcoAU/Hll3IpffcdHDqUvve0FmhwCTAvWCAv1KPH7X8YpZRSSimVpTSgpZRS2cw0pdG3S0ArJESa+bRsmWPzUu7Lx0eaukdHw/a4+lT1OY7fh284gjtbtki54aBB0ijrFqZPhzx5HI+PtR8kEa7jx7PwE6TP8eNQsaJst2ppsjskP/WubWVknxMwbRqsWAHffpvic+Pj5c/n6afl8aJF6XvPFDO05s+HKlWgXr3b+RhKKaWUUiobaEBLKaWy2YULEBcHQUFOOzdvllEztFQqihaFSZNg4WIvOnW4KYsIDBsmQZ7HH5fUpjFj0nydwEC4eBHCw+XxsfqPg5cXjB+fxZ8guf/9zxHYjY+HsDBHQOvRUtvs5+07UQiefVZqJt96S4K/SURGSrC4WTOoXFlifOlhBbTsGVrnz8OqVZINpuWGSimllFJuSwNaSimVzU6fltElQ2vTJolYVKuWI3NS7u/YMcf28+OqwtChkpXVsaM0jvrtN2lAlQ7580OZMhJUPRbtD716SZ3e9etZNPuUvfmmlN+2aQNbt0qVnxXQarx6LJv9HyRfPpPz55HgkrUq42efJXuts2dlDAyUy+jo0dTfd+ZMCZ6BlBz6+EiZIyCpXTdvarmhUkoppZSb04CWUkpls4gIGZNlaLVsqRkhKlXt28t46JBt4b2JEyU7a/Zs2LcvzVLDlFSqBEeOAP37S9bTb79l6pzTa906eOYZ2a5YEUkhW7iQFgNrMnSowcGDturKRo2kCdjkydIvzIkV0CpVSjK0jh6VjK2kNm6UssShQ+VxdLRkZ9kvvXnzoHx5aNw4Cz6pUkoppZTKLBrQUkqpbGZlaNkDWufPw4EDWm6obmnJElnkr2pV2w7DgA4doG9fp/SijGncGHbsgOut2kt21x9/ZN6EM8hq4l6mDLB4sdQg9ulDrVoSuzpyxHbiK6/AuXPw448uzz9zRsbAQGl/dfmyJHM5i4uD11+X7YsXZYyKcuqfFR0Nf/4pGWsaXFZKKaWUcmsa0FJKqWxmlTrZSw63bpVRG8KrW8ifHwoXztzXvP9+uHYNNm/3kRSw5ctTTmvKRqVLI03Zg4OhWTP7ZbFune2EDh0kRW3KFJfnOZccVqki20nLDhcvlupecPTAP3sWSpa0nfDzz1Ju+MQTmfmRlFJKKaVUFtCAllJKZbODB+VevUAB247Nm8HbG5o2zdF5qbtP69YybtiA9OIKDYXDh7P8fa9edWRkOfPxgULmZVi2zN6UvUYNKSNctgweegheGGrAk09KIPjkSftzz52Ta6pQISk5BKesLhurD9lbb0lT/OhoOcc6nzlzoEYNaNAg8z+0UkoppZTKVBrQUkqpbBYSIvfMdps2Qb16cieuVDby85N2UQcOIAEtkL5cWaxHD6hePfn+mzfBWLJYagy7dwek8q91a0me+uMPW2JWz57yhPnzmTkTunS5l7NnHSsVVqwozztyBImcff01hIYSGiqVld26yXkTJ0ogrEoVJNq1bp1kZ2m5oVJKKaWU29OAllJKZSPTTBLQSkiQTBPtn6VySM2atoBW5cpQoYKUHWYx51Zd77wDCxc6HZw9W1IYna6JJk0chwsUALNyFcmimjePkSMhNtaHDRtkoVCAvHnlJTYsPs+1us3Y8OwMjPLlWL7gCsHB0jusfXv48EM5v0oV5EGePDBoUFZ9bKWUUkoplYk0oKWUUtkoIgKuXIFq1Ww7/v5bdmj/LJVDataUMthE05CmWqtWSapUNundG+67z2nHsmXS6N7L8U8U54DW1auyICM9e8LWrVQoLasdnjzpyNACyJ83kVW7ivJsvln81G0uAAfDC1EunzTb+uYbCAiQc6ts+V52vPhikuVHlVJKKaWUu9KAllJKZaPQUBkrVrTtWLtWRquZkVLZrHp1aQx/+jRSdnjpkix9mEViY10fV63qCCw92fSgZC0++aTLOY0ayWj1ujp1CnvZoV9shP0854BWg3whAMy+9AheZR1BquCdi2D8eMqv/IbF1YczwGsWNcf+Cx54AEaPvvMPqJRSSimlsoUGtJRSKhtZKxwGB9t2rFkDlSo57VAqexUvLuP588gKgoaRpX20kjaD9/GR8fp1mJl3sKxgWLeuyzn+/tLA/dtv5XF4OBIJa9CAyPAb9vPsAa34eKZGdmdKrQkALFrkeK3ala/DsGHwzDM0D53HjJd2kmftCqmDzJcvEz+pUkoppZTKShrQUkp5rEWLbDfhHsTK0AoOBhITpQl1mzY5Oid1d7Oyoy5cQCJC9evD6tVZ9n4nTji2n3vOsZ03+jTeG9dBr14pPq9oUUfc1woM07MnkVcLuJwDwIIFFDkTQp//VEv2nj3Xvgi7d8O+fXJg/HjJkPTSfxIppZRSSnkS/debUsojRUVB167w6KM5PZO0hYZCfLxsh4XJYoZFiiA31OfPQ9u2OTk9dZcrUkTGCxdsO9q3h40bJWUqC5w8KWNUFEyb5nTg559l1QRrBcMUlC0rfdsPH5bHkR36EI4juzEx0bYxaRJUrEiRnh0pX97x/NGjoVRpQ4J2tWvraoZKKaWUUh5MA1pKKY907JiMGzfm7DzScu4clC8Pb74pj8PCJMvEMJDm26AZWipHuWRogQS0btyAzZuz5P1OnICCBZ2yqSzz5kmQqWbNVJ/r4yOVhnv3Qv/+ULJFJQCCvcIBiIxEDq5bB0OHgrc39evLc/v0gVGjMv/zKKWUUkqpnKEBLaWUR3IuIcrGBdkyzGpF9MsvMh4+7NQQfu5cqFMHlxQSpbJZsoDWffeBt7cj4JrJTpyAChWSJEfFxEh0+rHH0nx+zZqwdCnMmuXYNzhxCm0rHJfA8YcfQv78MHAgAK+/DoGBEqdTSimllFK5hwa0lFIeZeFC+PRTOH7cse+vv3JuPmlZskTG69elxGr/fmjSBCk33LwZ+vXL0fkp5ecnwSV7QMvPD5o2zbKA1smTKcRw162TesH770/z+UkTuHr0gCfb7Wb1iUpU7n+PBIpHjrSngN1zD5w5A88+m0kfQCmllFJKuQWfnJ6AUkqlV1iYI4Ejb14ZDUMWJ2vePOfmlZqjR+XeGiAiAkqUkO0WzRJh+HAJHDzzTM5NUCmkF3qRIhJoGjgQWrSAwe3bw5gxcPkyFC6cqe8XFQUNGiTZuWqVrDDYokWaz7dnONoMHw4nL71ChfrVpGzx+ecdNb5KKaWUUirX0gwtpZTHGDtWeuiAtPhp2lTuf+fMgbi4nJ1bUnv2yHwTEqSSyur7XqCASYvvX4Rly+Cjj6B48Rydp1IgZYezZsGMGfJzS/v2Usu7YUOmv9eFC44yR7vVq6FVKwlqpaFkSdfHNWqA6esrqxWGh8OUKeDrm3kTVkoppZRSbkkDWkopj5CYCN98I42d771X9tWpI02eDx+GH9p9KRGu6dPZs8dptbNstHmzNIFfu1YyUKZOlRUNW7aU+/X1C6I5WPlhAn6cAu+8Ay+8kP2TVCoF1kqHYOtJ16qVLCeYwbJD05TyvtTEx0NsbJKAVlSURIDT2eTKOaC1cyf4+2doikoppZRSKpfQgJZSd5GpU2HNmpyexe0JC4MrV6B1a8fqaHXrwkOdEvD3ucKWzSZcucKBQWNp0AD+85/snV98vMQAgoJg/nzH/oAAW/PryEjufbUpZY+uhUWL4N13k3TFVirnFCvm2I6MRJqqt2qV4YDWjz9C6dKp/56x+nS5BLSsk9u1S9d7OAe06tbN0PSUUkoppVQuogEtpe4iQ4ak+57R7Rw5ImOVKhI8AmksbXw5jXo3d7K38mOwfz/H2vwLgDnTLmXr/A4dkjEhASZNclQ8Xb5s29mvH5w+DStXwiOPZOvclEpLUJBjOzZWvmjfHnbtgvPn0/06mzfL+NFHKR9PMaC1ejUULCgZlulg9aIDRwmyUkoppZS6+2hAS6m7xCWn+M7Nmzk3j9vlHNAaOxYeeAA6NoiEUaOoX/Y8e8+UZMdOLw51GQZAaHRBzG9nZNv89u51fWxliN1/P3J3/+efMGFCuppeK5XdypZ1fRwZiQS0TFNqaNMpNFRG51VInVkBLecSR1aulNTLdPa9KlAg3dNRSimllFK5mAa0lLpLnDrl2C5XzvVYXBz89lvm9Z0KC4N69WDmzNt7fkICfPeda+Dt8GHpF12mDNSqJT3VC49+A2JjqTmwBVeuGDRtCsNGeAOQiDeRz4x0LDOYQbGxcp+dXn//LdkigYHy+OWXJcg1Y+BaKS988kl49tnbmotSWa1MGdfH584hGVMFC6a77PDYMVi4ULajo1M+5+JFGe0ZWidPwsGD0LFjhueslFJKKaXubhrQUuouER7u2I6IkH5UlilToEsXeOghR4bFnXjpJQnwTJ16e8+fPRsGDIDPPnPs+/tvqF4dvKzfWps2wbffwrBhFK9TKsXXOVL3cXjqKTh6NMNzGDhQsqv27En73PXrYfJkaNIEtm+XKi0/P6hb4gwF/9UbqlWTPwztmaXcVNKA1rp1SFP4++5Ld0BrmCRHYhiSiWUFpCdNgi+/lO1kJYd//CFj5863PXellFJKKXV30oCWUncJK6D1v//J6BzjWbBAxmXLYOLEO3ufqChYskS2t2yR4E5GWTe9VplhQoK8VsuWthNu3oShQ6VO6u23XRpag2MVxMMDP5Kb8lGjMvT+r74K8+bJ9owZtz732jWploqJgV69IDhYVjjENOG55+TAvHmy3KFSbqqULSZsXUsjRtjKlNu3h3/+ufXShTZnz8r44YcyWq23/v1vGDxYtlMMaFWoINHqDDhzxpZFppRSSiml7loa0FLqLnHsmIxt2si4f7/EXC6ei2PdOpNRbTfSsOhJ9n27XSIz8+fLCRm0ebMj3gTw4IMZe/6KFfDKK7J99apjrpcuwT332E6aOhV274ZPP4VChVwCWn37SkDN2xsORwXA88/LZ8lA6tkvv8jYpAl8/bWtn1AqNmyQMTgY+vd3OvD997B4sdzd16mT7vdWKidYGVp9+8Lw4bIdEoIEtEAat6chJga6d4dKleRxVBTcuOE4npCQJKAVFyd1vQ8+mOHsxcBA1+bwSimllFLq7qMBLaXuAlOnSiP1++6T/lMgLZ3q1Yyjf5WNJCYadFozkjrXtrHvcjmJSvXsCaNHZ/i9Ll+W8YUXJAB17pzct4Jkgs2ZIwGv69dTfr5zK52wMAkmTZ8uj1u1QtJA3n5bTuzRA8AloPXpp+DvL0kfBw8ikbWEBJg1K13zv3RJYl+jR8tTrl2Tz9K1Kxw4kPz8lSull/WBA07zOHVK6i7vuUeaaSnl5sqUkZ/hsWMdrd4OHEDSDYsUSbHs8Omn5VqznD8v14B1HURFuWaCnjghAa38+SFvXuTiiY2VWmellFJKKaUySANaSt0F3n9fKt5+/FF6O5UtC76+JhFHYll8uR0ALc7/QZ13exIeF8jOX05IxOvdd2HHjgy9V2ysjP7+khwFkqgE8Pjj8rK+vvBU7ziXztGmKcEoZ/v3y6KAEyZIb+qKFUx48UVJ3Zo40Z7VUbSo4znWdqNGtqlXqCA1iD/8kK75798vY926UKOG9AWaPx8WLYIn250i5qEnYORIaWaNxP4aNZL5AdJZ/+mnJTXl228lVUwpD1CjhlToVqwo12hICPLz26ZNigGtmTMlAHz9uly/0dFy/RUvLsejolzXZDhwQAJa9nLDmTPlCZ06ZflnU0oppZRSuY8GtJTK5RISJKlp8GAICpJ9//wD1155i/CE0iz+YDeLFoFvQCG6dpVAVJ8nvTEnfSGZGR99lKH3s5rNFywovdBBEqmsoJZl/qI8UjP05JNw9Srz5sFrr7meExnpKJX09U7E6PekRJdGj3bpuVOggOM5vr4ytmghGSFnzgBPPCGRqr//dnn9+HjX5vjgaAJft66MvXs7ju06W4bWq96FceOgcmVu/uc9duwwad7c6QU+/1zqJj/7DKpWTe2PSSm35esLVarYAlogZYfHjskFZeO8AumRI3Id3bwp2VlWQOvQIXjvPcd5K36M4sKJiwQEmBIQ/uUX6NdPomhKKaWUUkplkAa0lMrlIiMlaah0ace+wlci8J74Gfn69aTL2w145BHZX726xGMOH4aN+/wlCrZggSOqlA5WhpZzQAvg0UdlXMqDjPX/AICo59+WzKk+fVi0IBFwPGfkSHjsMejSOYF/1djE/NjO0lz9vfekY7WTlNrvWEGmHTuQ8klvb6l3tImLk+StZs1cn7d9u9yQV6ggjxsER7sc33ujOut+OMUPLSewe/Rirl41aFbN1hhoyxaZeNeujrotpTxQzZpJAlrg0kfLOXHz0CFHsmXRoo7eVjt3yjizxRS6513MD7MTiF61h4CTu+XiBkfDLqWUUkoppTJIA1pK5XIRETI6B7SYMkVK4t55J9n5jz8u47p1SP8pb2+p+UunK1ck4cLXVxK8Zs2CIUMcx5t3KESdmRKQ2t/7ffjiC1i8mN1Lz9Cli8nBg1K+9PHH8Ov/HWFxeEOmH7yXDs9VloY8//1vuhpIBwfLePYscofdrp1khNga3f/2G5w+LWVQpikZZP37N+ObbyTIZb2F97CXGes1gkdax9hfu03PkvTd8ALPV/wTgPvebit/cO3ayRt/9VWGm1wr5U5q1JDMq/h4oHZtuYZsZYcJCdCnjyOx6uBBx4qGxYpJf6xixWD3atlZ/sBS7q8fxTkC+adQMwISouVanjnTcaEqpZRSSimVQRrQUiqXSxbQiouDL7+Ehx+WuqIk/PykhO/8eaRTdM+eMGOGY8nBNMTGOvWTQiqK3v1PAgCj/CdRZMEM6jTJB8C+fcCQISzv/TX7LwTR4NJ6x8qKixbJMoOnTsHvv8PkyVCuXKrv+/jjUllosfr0WKuq8dhjkkoSEsK5c7B3r+PcM2fgqacgLExqF+0lhIsWwezZvPZ2ARat9ScqSmJWtl70/HW8KFXL36Bc6wpS0tinD6xfr8uvKY9Xo4aUEB49igRn27eXgJZp8uWXUjE4c6asNnjsmGuGFkBQwFUOR8mDwLVzKffOQACiruQnoGcHWRLR+YJVSimllFIqgzSgpVQuZwW0rP5Z/PyzpC0NHZrqc4oWderXPniw3HzOm5eu94uNlQb0zkpuXUwMfnwwtQQUKkRQkGRv7dsnJX49l/4LgNbrPoC2bSVq1LUrVK4Mf/0FDz6Y5vv+8otLRSGFCklymUtAC5g68gSBgfD9945zt2yRj9iv30lmzZIFCrlwQbra160Lo0YBknWyapX8UVjtsR5+PC8sXCjBsm+/TZIKp5RnqllTRpeyw9OnWTMrjBdekF0PPSQx5tBQx+8La4XDMhf22V+rZHBel0SsgABDMxiVUkoppdQd04CWUrnYhQswaJBslypl2zlhgmRmPfBAqs8rVswpoNW6tTTXmjYtXe955YprhhYAkyfjVy4Ao0d3QO5la9eWIFSzZhATY/D+u4l0HNdZUsOioqTx+8aNjmZWGWQYkqVllUJRpgzRDe9nyKLOgJRTWQlf69bJWLfuRfr1gyJ+iTBgAJw7J0GqFJpWv/66JJC9++5tTU8pt1a+vIxhYbYdtj5aC2dKhHjxYsnmLFdOzjl7Vk4rWRJYv56gaFmAwcdHrkPXgFY2fACllFJKKZXraUBLqVxs0yYZBw6UvjZs2ybpSC+9BF6pX/4uAS3DgOeeg82bk60SmJKkJYecOwcrV0pNn4+PfXedOnLI8uhjXjBsmLzH339LVlS+fOn/sCkICHDK0ALGFP7A5fhDD8loaw1EqVLXpeTx9dfljv2zz6Bx4xRfe9AgyS7z97+jKSrllooVk8s1PNy2omHlyphlg/l9Wwk6dYIuXeQ8K0Pr7FnJiCxaJBHeeIOggjH21zEM1+tEA1pKKaWUUiozaEBLqVxqwQKJQQGMH2/b+fnnklbx9NO3fK5LQAskWylPHum9lYYrV5KUHP7yiyyz2KuXy3kNGzq2W7WSAFdmSxrQOuRbm9o4SqE6dpTSKqufVpDfRejbF8aNk072tyjLVCo38/KS/lhjxkCbNoBhsLPBQA5dCaJb1wT7eeXKSRA7JARKFk/Ea9grsHkzlfvI8qFWSzzDkJZ8YMviUkoppZRS6g75pH2KUsrdJCbCPffIivfduyc/vm+fY7XC8uVt2RF798KPP0oWVOHCt3z9okWdSvVAIlw9esiShZ98Il3jUxEbKzfCdj/9JB2m69Z1Oa9jR8f2xo23nM5tc+kFBoTHFCa4cAz7L8vjJk1g+XIoW1Yet3rpWUlJ+fhjeOMN7fOj7molSsiaDJs2ya+PX/L2xYd4egZuANoBjrLd7cuiCbwaBhMnwtCh9B13D+EVHL3mQHrPHT7s+N2klFJKKaXUndAMLaU8UESEVA726CFlb23bykpjFucV/Jo1Q1Y2HDRIUpbefDPN1y9WTAJapgkJCfD113D+iaHSOX3u3Fs+16Up/JkzsHatZGclCQ5VqiSjTxaG1ZNmaIWFQdlGJWnALkD6+pQpncgnXdYzzOszvK9dk/rDkSM1mKXues4Lm27aBIfNKlT0OknAL9Pt+8uVkWyt8KvFKFkuH+zeDZMmkSevwdtvQ+/ejtdo2RL697eVPyullFJKKXWHNKCllAdyDl799JPEjB5+2NbrBti/X/rZbNwI096NcES+pk2TtKU0FCsmgaxnnpGA07PPwsiFLSXTKo3m8C5N4efPl6hYknJDy+HDrp8lszk3hY+Lkz4/ZdtUZk2zNziZvwbG5C+gY0deX9KacV1Ws+PrryU6qJRyCQZv2wYHDnlTvkyC/NI5eRKAcr98Zj8nsE0NqF8/u6eplFJKKaXuUhrQUsoDOQeBvvpKxpAQaNdOYkhz5phUL3OZVp90JaBeMCxdCpMmpVyfmIImTWT89lvHvhtxtubwW7bALslw+uMP6NNH3tvi0hR+7lxZzrB27RTfp0oV19XPMlvFihLQGjJEgmcAweW98F8wk3LV88OLL8qd+rRpsGAB8drhXSk7az2EoCD5XbBvH5RvVUb66T31FIwbR4kxI/D1kki6S6mxUkoppZRSWUwDWkp5IOeA1qVLkp01Zgxs2AA9e8KJEwaPhH4hwafhw+HAgQw1OG/dGkaMcN135QqyXGKBAjBxIomJ8PLLkqzRtKk0nk9IkKrEIkWQ5jsbNqSanZUdXnwRXntN4lVW0/myZYHSpWHHDvjrLzh+XAJ1WmKolIsffpBSQ19fx75ytQrJ4hCbN8Pw4RhtWhOfKHXDzgs9KKWUUkopldW0KbxSHsgKaN1zD2zdKosW9ugBZ/+JZtyMYgR7n+J/cyrBY2GSTXEbrJvTQoWksfzJk0ikasAA+OYbtvYYx+HDAQQGSinfsGHSGycx0ZapMXu2lBs6N9HJZvnywdixEoP74APZZzWAx9sbGjXKsbkp5e6KFJG+V85rSMTHA08+Kb98zpyRJn3ecqxDhxyZ5v+3d99hUlV3A8e/ZwHpIAgBUQED9mgw1igqGrvRqLFFwa6viS2214aKvcSCxteSxGjE1waKRiXmjRE0VizYQI1gEKTYKIrSOe8f5y47rAvsLrs7OzPfz/PMc2fOPfeec+fHmWR/nnuuJEmSSpQztKQCNGUKbLddmgC1cGFKZrFgAT969hYA2q/3gzQzqpbJLICNN07bH/84/e2aLZkDp54KCxYw5ubngXQrUvkMjosuStsunRanWRw77AAbbFDrPtSV3C7U5y2OUjF69NE0A3SvvdJ6egD07AnbbgtlZVxwQbpN2VsOJUmS1JBMaEkFaPr0dNfcMgYPZr1JzwDQZvVm3z+ohjbdNCWo/vKX9Lfrl1+m2xvZaCNe2e0iTv6/X9Cu7RL23BPmz091yxdg7/LiozBhApxxxir3oy50717xPne2iaSVW2+9dAvyiBHLjqVyV16ZnjkhSZIkNSQTWlIBmjYNunbNKZg9G665hq327MSAAXDXXaveRlkZXHYZ9OpVcWde+R+tF3x9HgBh/jxCSMtP7bNPxbFdb70Q9t4b9t9/1TtSB6r6I1ySJEmSVLhcQ0sqMPPmwaxZlWZoDR4MM2ey2hUXc+8Wdd/mNtuk7bBh6cmGI19pCcC9Cw6DK7aGvn3pM/h24CEAuvz30TDojEaz0Hq3bvnugSRJkiSpLpnQkgrM9Olpu3SG1owZcOONcMABsEU9ZLNIi0NvthnccUdF2XnnLGa/j1dbunBW85x7+dpdcwE0jlwWULHG1/bb57cfkiRJkqS6YUJLKjDlCa2lM7Suvx6++QYuvbRe233sMfjwQ2jZEi6+GPof1QQ2HgovvwwzZ0Lfvgw4FZ56qtFMzFrGjBmp75IkSZKkwmdCSyowU6embdeupOzWzTfDYYelVdzr0brrphfAc8+Vl4b0uMXMvffWaxdWSYcO+e6BJEmSJKmuuCi8VCDmzIElS2DSpPS5e3dg0CBYsCCt3i5JkiRJUolwhpbUiL3zDrz9NqyzDuy3Hxx0ELRvD61awRrTx8Kf/gS/+Q307p3vrkqSJEmS1GBMaEmN2O67w2efpfetW8Pdd6dbDXuus4gwoH9arT1blF2SJEmSpFLhLYdSI/XssxXJLICxY1P+avp06DH5RRg3Li1a1blz/jopSZIkSVIemNCSGqEFC+BnP0vv27dPya0ePeC0I2cBEBbOh3/+E/beO4+9lCRJkiQpP7zlUGpkJkyA116r+DxkCOy8M7B4MRe+uh+fNDuJAXdsD3175K2PkiRJkiTlkwktaRXMnQtNm0KzZnV3zr59022FIcA//gG77JLtuO02Vnv1X9wz5ETobzJLkiRJklS6vOVQWgWbbQb9+tXd+ebPT8ksSDOzfvazlNji88/hwgthjz3giCPqrkFJkiRJkgqQCS2pFp59NuWWxo+Hl16Cv/61bs47cWLa3ntvpbzVlVfCt9KnjKUAACAASURBVN/C4MFZhkuSJEmSpNJlQkuqhZNOgv/7v4rPxx0H8+ZlH6ZMgZkza3Xe8ePTtnfvnMKJE+H22+HYY2HDDWt1XkmSJEmSiokJLamGYoRPP03ve/WCv/8dvvwSbr3uO2bufiisvTZ06QIXXACLF9fo3BMmVJx3qYsvhiZNYNCgOum/JEmSJEmFzoSWVEMzZ6bF4G+8ET76CHbdFTbZeAnnXNKKTf5xEwsvvpwFh/RnyNWTWfKLA2DBgmqfe/Ro6Nw5vQB491247z449VRYa636uSBJkiRJkgqMCS2phqZMSdu1107LWZWVwQu7XcbBPMw0uvHPnw7kmvX/zJEMYfhTzWDAgGrN1Fq8GJ5+GvbaK2eZrIEDoV07OO+8+rsgSZIkSZIKjAktqYbKbzdcOmHq449Z/X+uZMiRz9C2bVog/qOP0q7ZB58ADz/MiIPvpn17OOSQqs/5+edw/vnw1Vewzz5ZYflq8//939CxY31ekiRJkiRJBaVpvjsgFZrcGVoAXHopNG1K86sHsdWn8Npr0LNn2jWn755c+slTDBq+NwCPPx6ZPz/QvHnF+WKEnXeGcePS5332AZYsSbOyunSB009viMuSJEmSJKlgOENLWom33kozq8aPb8PTT8PIkdCqFay5JvD++2mNq1NOgW7d2GILeOedNNMKYNo0+OvCvQC4nrNYsCDwxnkPw5QpzJuX6h54YEUy69hjoXVr4I474F//giuuyAokSZIkSVI5Z2hJK/G3v8HQoTB06JZLy845B5o1Iz2BsHVrOPdcALbcMq0BP3JkqjdlCox7P3Dmb5fQv9NGnD0QXho8mu1uP5JdOn3Iy1N6LD3nRx9lTzd8553UwB57wHHHNeCVSpIkSZJUGJyhJa3E119/v+yUU4AxY2DYMDjjDOjUCYB9981ZA4u0yPu8efCjzcrocuHxrL02vLXvxXzzi/7LJLMAerWcSrjtf2CnnaBDB/jTn3JWh5ckSZIkSeVMaEkrMXNmylcNHfoSkHJN3buTnkDYoQOceebSui1bwpNPwowZsPvu8MUXqXyzzdK2Tx946+N2PDfgTwDs1eZfrMZ8duZZwtprpUzZppvC88/nLNIlSZIkSZJyecuhtBIzZ6a8VadOC5g+HZo2JT2BcMQIuOYaaN/+e8d06JDWct9gA9hxR/jJT1J5nz7pFsY770x1hk/tS9nroyl7aQysdlOandWnjzOzJEmSJElagUaZ0AohtAHOBrYGtgI6AZfGGAdVUbcdcBVwENAeeBu4KMb4jyrqdgWuB/YCmgOjgXNijG/Uz5WoGJQntCA9dBBIs7O6dMnuPaza3nunV66f/hQWL06zuM46C5q3CNB3m/SSJEmSJEnV0lhvOewEXAJsBry5vEohhAA8ARwH3AX8Nts1IoSwU6W6rYGRwN7AjcB5wJrAyBDChnV9ASoeuQktAJ59Nq36fsEFNX4C4c47pyckhgC//nXd9lOSJEmSpFLRKGdoAdOAtWKMU0MIawOTl1PvQGBH4JgY4z0AIYS/AO8BNwBb5tQ9CdgQ2CXGODKr+xDwb+AK0gwv6XtmzoTevXMKBg2CtdaCE0+s8blatkwPLpwzJ3uioSRJkiRJqrFGmdCKMc4Hplaj6iHALOC+nGPnhRDuAq4KIfSKMU7IqTu2PJmV1f0ihPAwcFQIoVWM8bu6uwoVusGD4fPP08LuS2doPfcc/OtfcMst0KJFrc57yy1110dJkiRJkkpRo0xo1cBPgDExxkWVykfn7J8QQigj3b74QBXnGA2cCGwCvFZfHVVh+ewzOOOMis9LE1qXXw5du8Lxx+elX5IkSZIkqfGuoVVda5JuT6ysvKxbtu0ItKhmXYkJE5b93LEjtH3/ffjnP9Nq7i1b5qdjkiRJkiSp4GdotQTmV1E+L2d/7rY6dZcRQjiRNIOLLl26MGrUqBV2aM6cOSuto8ZrzpymLFhQxhtvdAA24tpr32HcuHZ07z6NbjcNYWGbNryy8cYsNsZFyfFbvIxtcTO+xcvYFj9jXLyMbfEzxsWrUGJb6AmtuUDzKspb5OzP3Van7jJijH8A/gCw5ZZbxn79+q2wQ6NGjWJlddR4desG06aldd9DgNNP34zmzYH358LLL8PAgeyw99757qbqieO3eBnb4mZ8i5exLX7GuHgZ2+JnjItXocS20G85nEbVtwqumW3LF5afQZqdVZ26KlFjxqRkFqSEVrdupGQWwDXXsLh5czjttHx1T5IkSZIkZQo9ofUm0CeEUHmm2TbZdgxAjHEJ8DawVRXn2IaU7BpXX51UYXit0iMBjjkme/PKK3DvvUzZf3/o3LnB+yVJkiRJkpZV6AmtYcDqQP/yghBCC+BY0tMPx1equ0kIoV9O3c7AwcCIGOO3DdJjNVqTJkGTJjBqFEyfnh5oyOLFcPLJ0K0bnxx5ZL67KEmSJEmSaMRraIUQTiElq9plRTuGEAZm74fEGD8BHgFeAG4PIfQGJgNHAz2B3Sqd8nbgBODREML1wGzgZKAZMBCVlLvvhgcfhCefhGbNUtmkSbD22rDTTjkV77gD3nwTHnqIxa1a5aWvkiRJkiRpWY02oQWcDfTI+bxz9oKUxPokxrgkhPBz4GpSsqod8C6wT4xxZO7JYoxzstlZ12fnbg6MBo6MMXq7YYm58MK0XtYf/wi/+U0qmzQJunfPqfTZZ6nirrvCwQfDc8/lpa+SJEmSJGlZjfaWwxhjzxhjWM5rVE692THG38QYu8QYW8YYt44x/n0555waYzw8xtgxxtg6xrhzjPH1BrsoNRodO6bt889XlC2T0FqyBI47DubOhd//Pj3yUJIkSZIkNQqNeYaWVC+WLIHx2epqn3yStosWweTJOQmt3/0OnnoqJbM23DAv/ZQkSZIkSVVrtDO0pLr0179C+ZrukyfD/Pnp/cSJFdtFi2D99YG77oLzzoNDDkkLwkuSJEmSpEbFhJaK3jffwC9+AUOGwKxZMC5bMe1nP0tPM/z6a/jwuWkArH/HmXD88bDHHnDvvd5qKEmSJElSI2RCS0Xv8ccr3k+cCG+8kd7vv3/aduywhHEn3ATABl++mBaCf+wxaN68YTsqSZIkSZKqxTW0VPQeeaTi/SefwOuvwwYbQL9+qWzxkjL+m+tYo8Ni1hj/al76KEmSJEmSqs8ZWipqX3yR1nbv3z99njgRxoyBLbaAH037B3NpydqtvqKsLHLJpU3y2ldJkiRJklQ9ztBSUXvoIVi4MK3x/uij8MEHMGkSbLjmbDj8cFps0ouPnmvBwtUCbdvmu7eSJEmSJKk6TGipKI0ZA3fckZ5c2LkzbLIJbLllKgPoPeIWmDsXhg2jxRqtaZHf7kqSJEmSpBowoaWiNGAAjB0La6wBG26YyoYMgR490vve7/8V/vz7ip2SJEmSJKlguIaWilKrVmn71Vew3nrpfffuFft77bUBHH10g/dLkiRJkiStOhNaKkodOlS8X3/9iveXb/83ejGejrcMghAavF+SJEmSJGnVmdBSXrz3Hrz1Vv2d/8svK97vumv2Zt48Br7fn/EHXwC9e9df45IkSZIkqV6Z0FJebLopbL55/Z1/+nTYd1+YNg222iorfOopmDEDTjih/hqWJEmSJEn1zoSWGtzChRXvlyyp+/MvXgyffQabbQZdu+bsePhh+MEPYOed675RSZIkSZLUYExoqcF98EHF+0mT4JNP6jaxdemlKanVrVtO4bffwpNPwi9/CU19uKckSZIkSYXMhJbqxZIl8N13Ve8bNari/brrQs+ecNNNddf2I4+kmVmHHZZTOGJE6tDBB9ddQ5IkSZIkKS9MaKlenH02tG4N33yzbPnChXDddbDBBsuWD//LbDjiCNh9d/j975e9L7EGYkyzvg45BDp2zNkxdGi63XDHHWt1XkmSJEmS1HiY0FK9eOSRtL3xxmXLn38ePv0Urroq3Wp4ycURgHfeDSz8xyiYOhVOO41JG+/JumvN5733atbu7NkwZw50755TmHu7YZMmtb4mSZIkSZLUOJjQUr1Yf/20vffetJ5VueHDoWVL2HNP6L5OZNCM03iUA/iGdmzYZjLjh78LTzzBPZ/vxcSpzbn1mDdg/vxqtzt5ctqus05O4RNPwNy5adqWJEmSJEkqeCa0VC9mzkzbjz+GH/4Qzj8/LWE1fHhKZrVqBVxwAdx6KzufvEmq+58yrro6sMm5P+cPrc8EYPbr/yZuvAk89li6n3AlJk1K22VmaN1/P6y1FuywQx1eoSRJkiRJyhcTWqoXM2fCAQekxNKkSXDNNdCrV7qj8MADgeuvT4UnncTqv7+cPfdMx91/P4wbB1OmpX+aD/IrBn19ZjrZ8cev9HGI//lP2i5NaM2YAU8/DYce6u2GkiRJkiQVCRNaqhczZ0K3bnDzzRVl06fDVlvBfl/fB+eck24BvPVWCIERI2CbbZa9u/Dii9P2yXV+naZ4/fnPcNZZK2z35ZdhzTXTC4BHH00LzP/qV3V7gZIkSZIkKW+a5rsDKj5LlqTF2Tt0gD59KsqvuALO3+oZyvY+Oj3NcMiQpbOmQoAuXVK97t3hoovShKxPP4Wnngpw5ZVpcffBg2HzzeHII5dp85lnoH9/+OwzOPjgdD4AHngA1lsPttii/i9ckiRJkiQ1CGdoqc59801KanXoAD16pFzSXXfBhUdMpOywQ2CjjWDYMFhttWWO22WXtH3ggZTMAthww5SkmjkrwA03QL9+cNJJMHbsMsf+6U+pHsD++2eFU6bAyJFpdtbSDJckSZIkSSp0JrRU58oXhO/QIeWRXn8djj0WGDgw3VP4+OPQtu33jjv11LTk1XbbVZRtuGHafvgh0LRpyna1aQNHHLH0/sQFC+Cpp+CEE1LR4YdnB991V1pIvtJsLkmSJEmSVNhMaKnOlSe0Vl89p/D999OK76eckh57WIWyspQEy7XBBmn7wQdZQdeuKVH19tvpvkTSIvJz5qQZXksnfS1enKZt7bZbWo1ekiRJkiQVDRNaqnO335625WtiAXDppdC6dVoMvgbWXReaNUv5sDlz0ppa7Lsv/Nd/pScljhzJmDGp7uab5xw4YgRMnpzqSZIkSZKkomJCS3UqxrTW+y67wE9/mhW++y48/DCcfjp06lSj8zVrlhaJv+66dJfiOuukNrjhBujdG448kjdfnkebNmntdyAt4HXZZenA/fary8uTJEmSJEmNgAkt1alZs2DePPj5z3PWYb/00pSNOvPMWp2zfLH3ch9/TJrt9b//y9xps3hkyHdsv32krPxf8x/+kBbuuvzylBGTJEmSJElFxYSW6tTUqWnbrVtW8NZb8MgjcMYZ0LFjrc55113Qvz/cd1/6/PLL2Y6ttmLY/vcxbV5Hzu12X5q69be/pZlgu+0GAwas0rVIkiRJkqTGyYSWqvTmm9CnD8yeXbPjvpfQGjQorQ7/29/Wui+HHJJuYzzssHTH4oABcOCBKX/1TKv96NT8a3a6+yjo2RP23hs22ggefDBnipgkSZIkSSomJrRUpWeeSQ8SXPp0wSq88kp6vfhiRdkyCa3Ro+Hxx+Gssyo98rB2mjSBJ56ANm1g+HB49lkYOSrQ7+dtKbv9NvjJT9LtjS+8UOvZYJIkSZIkqfFrmu8OqHGaMCFtyxNUlcWYs+g7cOGFqe7Ysenzmh3nw6G/ga5d4bTT6qxf224L06dDr16w666pbODAACeeBCedVGftSJIkSZKkxsuElqq0soTWhx8u+/nKKyvet2u9iFZ794M33kjrZ7VrV6d9a906zQrr3Tt9PuCAOj29JEmSJElq5ExoqUorS2iNHJm2556bHmA4cCB0+8FCpn7ejKu/PQ0++SQtfHXggfXSv169YPBgmDwZOneulyYkSZIkSVIjZUJL37NgAUyalN7nJrQOPRRmzYJhw1JCa+214eqr4ev3JjHh7olcPuFw2rWDtuedDKePh1at6rWfp59er6eXJEmSJEmNlAktfc+kSbBkSXr/4IPw7rtpjayHH05ll1wCQ4fCgH6TCf3Po/2wYfw5BDj7VDj/fBdklyRJkiRJ9cqElr6n/HbDnj1h4sS0FNYRR6SlsNq1XcJNN6WHY+4x6jzo+DQcd1xKZK2zTt76LEmSJEmSSkdZvjugxqc8oXXnnRVlc+fCHv3mscbM8QCc13sYv3pqQHrk4G23mcySJEmSJEkNxhla+p4JE6BFC9h1V/joI3j6afjddUs4+p2zaLXwI677yb1c8uJBlLXId08lSZIkSVIpMqGlZYwbB3/8I/z4x1BWBr17wyknR04ZdQgMHw4jRtBvj6757qYkSZIkSSphJrS0jCFD0u2FDzyQU/jII+l17bWwxx5565skSZIkSRK4hpZyxAhPPgl9+8K662aF8+bBOefAppvCmWfmtX+SJEmSJEngDC3leOwxeO+9tMb7Urfckh51+M9/QlP/uUiSJEmSpPxzhpaWuuce6NEDTjghK5g1C665BvbaC3bZJZ9dkyRJkiRJWsqElli8OG3HjoWtt86ZiPW738HMmXDVVXnrmyRJkiRJUmUmtErciy+mBNbzz8PHH8PGG2c7pk+HwYPhsMOgT5+89lGSJEmSJCmXiyKVuAcfTNuddkrbTTYhTdk68URYtAguuyxvfZMkSZIkSaqKM7RK0Hvvwfz56f2//73svu22WZwW0XriCbjhBlhvvYbvoCRJkiRJ0gqY0Coxzz0Hm24KDz0E336bbjks9/X9T7LW3j+Gu++Giy+GU07JX0clSZIkSZKWw4RWidl++7Q96iho0yYltR4a+A7T+uxF28P3TbcZDh0KgwbltZ+SJEmSJEnLY0KrxDRtCmefnfM5LGLfK7ah61dj4a670v2IBx0EIeSvk5IkSZIkSStgQqsEXXvlImZddAOTmv6QMavvQsubr4WPPoJjj00ZL0mSJEmSpEbM7EWp+fhjyo44gvavvEL7Qw9lndtug44d890rSZIkSZKkajOhVYqmToUHHoDDDst3TyRJkiRJkmrMhFap+eEPYfx4aNYs3z2RJEmSJEmqFdfQKkUmsyRJkiRJUgEzoSVJkiRJkqSCYkJLkiRJkiRJBcWEliRJkiRJkgqKCS1JkiRJkiQVFBNakiRJkiRJKigmtCRJkiRJklRQTGhJkiRJkiSpoJjQkiRJkiRJUkExoSVJkiRJkqSCYkJLkiRJkiRJBcWEliRJkiRJkgqKCS1JkiRJkiQVFBNakiRJkiRJKigmtCRJkiRJklRQTGhJkiRJkiSpoJjQkiRJkiRJUkExoSVJkiRJkqSCYkJLkiRJkiRJBcWEliRJkiRJkgqKCS1JkiRJkiQVFBNakiRJkiRJKigmtCRJkiRJklRQQowx330oGCGEL4BPVlKtE/BlA3RHDc/YFj9jXLyMbXEzvsXL2BY/Y1y8jG3xM8bFKx+x7RFj7FyTA0xo1bEQwusxxi3z3Q/VPWNb/Ixx8TK2xc34Fi9jW/yMcfEytsXPGBevQomttxxKkiRJkiSpoJjQkiRJkiRJUkExoVX3/pDvDqjeGNviZ4yLl7Etbsa3eBnb4meMi5exLX7GuHgVRGxdQ0uSJEmSJEkFxRlakiRJkiRJKigmtCRJkiRJklRQiiahFULYKoRwSwjh3RDCnBDClBDCkyGE7z1qMoTQLoRwawhheghhbgjhlRDCbss5b9cQwn0hhK+y8z4bQtiiinr3hBBiFa9FNbyO6ra3dXYNo0MI87O2etakrUJQgnGtqq3y18KatFko8h3jrG4IIRyTjadvQwgzQwgvhRD2qMF1OHYrKcHYOn4b/je6Tr5zx+/3lWBsS2r85ju+Wd3dQggjQwhfhBBmhxDGhBBODiE0qcF1OHarUILxdfw2fHwPCiG8lp1zRgjhwRDCOjW8DsdvJSUY21Ueu0WzhlYIYRiwAzAMeAvoAPwX0BPYN8Y4IqsXgFHA1sCNwCTgGGALYNcY43M552wNvA50AW4AZgMnA2sBW8cYP8ipew8wADiqUteWxBjvr+Y11KS9QcCFwFggAJsB68YYJ1anrUJRgnHtX8UpugK/A56MMe5bnTYLSb5jnNW/DTgRuB/4F9AC2Bh4PcZ4VzWuwbFbhRKMreO34X+jV/k7d/xWrQRjW1LjtxHE9yBgKPAK6fd5EbAfsCdwc4zxt9W4BsfucpRgfB2/DRvfE4E7gReAh4COwGnAt8AWMcYvq3ENjt8qlGBsV33sxhiL4gVsB6xWqWwN4DPgzZyyXwIRODqnrAUwnvQHTu7xZ2V1d84p6wzMBIZVqnsPsGgVr6Em7XUBWmbvB2bH9cx3HIzrqsV1JccfnO94FGmMD1jV79exa2yrcbzjtx5iXBffuePX2NZVe4X0ynd8geeBKUDznLIAvArMruv4ltLYLcX4ruR4x28dxhdYDZgBjAbKcsp/DCwGflfX8S2l8VtqsV3J8dX73/p8B60B/lE8BMyr9Hkm0LRSvfOzL65XTtmrwHtVnPNOYB7QKqfsHtJ/fSgD2pHNfqthX6vdXqX9RT2wSzWuOfXeyq6teU3bLuRXA8b4ReC17H0A2jRUjEtx7JZKbHPqOX7rMcZ18Z07fo1tXbVXDK8G/H1+azl1Hwem1Wd8S3Xslkp8K/XB8VvH8QU2z449q4q67wGf1md8S3X8lkJsc+rVaOwWzRpaK9AN+Crn80+AMTHGymsgjc7ZTwihjDSdcTTfNxpoDmxSqbwJaUrdbODrEMK9IYQu1elkLdsrZSUR1xDCpqSs+MMxxvnVabOI1HuMQwhtgW2BV0MIlwGzgG+y+9VPqU4nHbu1UhKxdfw22G/0UjX9zh2/tVISsS3h8dtQ8R0FbBJCuDqEsF4IYd0Qwm+BfYArV9ZJx26tlUR8Hb9L1Ud8m2fb76qo+x2wVgih64o66fitlZKIbW3GblEntEIIOwDbAw/mFK8JTKuienlZt2zbkTRtrzp1y8uuB04ADgH+AhwOvBBCaF+N7ta0vZJVYnE9MtsOqUZbRaMBY9yL9Dt4KPBr0v35h5L+K8TvQwinVqO7jt0aKLHYOn4r1NdvdGU1/c4dvzVQYrEtufHbwPG9iLRGzLnAv4GPgeuAE2OMt1aju47dGiqx+Dp+k/qI70fAEtI6T7ntr0FaoxTSWkkr4vitgRKLbY3HbtPqViw0IYQ1gQdIC6RdlrOrJVBVtm9ezv7cbXXqEmM8v1KdoSGEV4F7gdMr9aEqNWqvVJVSXLMM9+HAxzHGF1bSTtFo4Bi3ybadgO1jjC9lfXiENN31ohDCbTHGxSvosmO3mkopto7fhvmNrtR2bb5zx281lVJsS3H85iG+C0hrvfwv8BTpP0D0B+4MIcyKMT66ki47dmuglOLr+K3f+MYYvwohPAj8KoQwlpRk6QhcS1qDKfd8y+P4raZSim1tx25RztDKZs6MIP1Bs2+McXbO7rlUTKfL1SJnf+62OnWrFGMcAkwHlj4+M4TQPqTHWJa/OtdVe8WuBOO6KymDXUr/damhY1y+/U95wgMgS3I8SFrAcIPyvjl2a68EY+v4bfjf6OV+547fVVOCsS2p8Zun+D4AbBtjHBBjfDCmJ0fvQ3oq3m0hhOblfXPsrpoSjK/jt0J9xfck4FHgClLicjQpgfHnbP835X1z/NZeCca2VmO36BJaIYRWwJOkP1R+HmN8t1KVaVQ9zW3NbDs1284gBa86dVdkMunJBOVuzvpQ/nqtjtsrSiUa15KaLp2nGJdvP6uibnlZh2zr2K2lEo2t43dZDfEbvaLv3PFbSyUa25IZv/mIbwihB+kptMNzK8W0GvBw0hPN1suKHburoETj6/itUC+/zzHGb2KMBwFrAzsB68cY9wLak25Zm5BVdfzWUonGtlZjt6huOQwhrEbKKG4L7L+cqWpvAruHEJpWWkRtm2w7BiDGuCSE8DawVRXn2IYUqHEr6U8A1q1U7zrgvpzPc+uqvWJVinENIbQB9gdejDFOqLy/2OQrxjHGaSGEqaQf7crKy77Ito7dWijF2Dp+G/43uhrfueO3FkoxtqU0fvMY3/I/dJpUUbdppa1jt5ZKMb6O3++p19/nGOMUYErWn6bAzsDLMcY5WRXHby2UYmxXaezGRvAYyrp4kX40hwGLgcNXUO9g0uMoj84pa0GaUvdmpbrnZHX75ZR1Jj1G8tFKx7eroq1Ts+PPreY1VKu9Ko4r2seXlmpcgaOyY07MdwyKOcZZ+Q1Z3b0qnXcCaRHTUB8xzuoU7dgt5dg6fhsuxnXxnTt+jW1dtVdIr3zGl7S24WLSrSxNcsqbkv5I+xZoUR/xzeoU9dgt5fg6fus/vito68Ls+P2reQ2OX2Obe0ytx27ITlDwQgg3Ab8F/kFasLuy4THGb0NabOw5YEvSHzuTgaNJWcTdYowjc87ZhvTD24n0pLvZwMlAd2DrGOO4rF5P4G3S/eIfAIuAHUn/0N4C+sYYv63GNVSrvaxuD2BA9nEn0j2nN5AeTT8rVu8JIo1eqcU155hngL5A1xjjrJW1UcjyGeOsbuesbgfgJtKsnSOBzYFfxhgfq8Y1OHarUGqxzTnG8VuhXmOcc0ytv3PHb9VKLbZ10V4hyXd8Qwi3kZ5AO5r0/7MCaTHgLYGLY4yXV+MaHLvLUWrxzTnG8ZvUd3zPBrYgrYk2D9iDdJvp7THG31TzGhy/VSi12OYcU/uxW1fZxHy/gFGkrN7yXj1z6rYHbiOtozKX9GO7x3LO2w24n3Q/6LfASGDLSnVWJ/2D+xCYQ5pK9yFwFdCmhtex0vayev1WcK0T8x0P41q7uGZ11yJl5R/O9/df7DHOqdsDeIj0Xw7mkX7E96rhdTh2Szy2WV3Hb8PHeJW/c8evsa2r9grlle/4kmYhHEdad2Vmdt43gRPqI76lNHZLMb5ZXcdvw8V3T+BlUkLpuyzOx1CNme+1bK9kxm+pxTaru0pjt2hmaEmSJEmSJKk0vmwVDAAABXpJREFUFN1TDiVJkiRJklTcTGhJkiRJkiSpoJjQkiRJkiRJUkExoSVJkiRJkqSCYkJLkiRJkiRJBcWEliRJkiRJkgqKCS1JkiRJkiQVFBNakiRJkiRJKigmtCRJkhpACGFQCCFWei0JIXwdQvg0hPBSCOF/QggHhRBWq6c+rJ71Y1AIYfX6aEOSJKkhhBhjvvsgSZJU9EIIg4BLso+f5exqCbQFQk7ZV8BFwB2xDv/PWgihJ/Cf7OO6McaJdXVuSZKkhuQMLUmSpAYWY+ya82oPNAM2A84iJZzWAG4D7gshhBWcSpIkqSSZ0JIkScqzGOPiGOO7McYbgR8BD2a7DgfOy1/PJEmSGicTWpIkSY1IjPE74ChgTFZ0XgihY/n+EEJZCGH7EMI1IYRXsvW3FoQQvgohPBdCOCmE0KzyeUMIo6i43RDgP5XW8xpVxTFNQghHhxD+HkL4LGvni+zzYc4ekyRJ+dI03x2QJEnSsmKMC0IIVwFDgXbA/sCfs93dgRdyqi8CvgM6Ajtmr8NDCHvEGOfm1JsBfAl0yj5/CSyutH+pEEIX4HFgm5zi2dnxu2evX4UQDo4xLqjlpUqSJNWKM7QkSZIap6epSDjtlFO+iJRoOhRYC2iercPVFjgGmArsAFyZe7IY44HAVjlFW1Vay+vA8h3ZUxafICWz3gT2AVrHGFcH2pBmkH0O7AdcWzeXK0mSVH0+5VCSJKkB5D7lMMZYrVv1Qgj/BtYDXowx9q3mMVsCrwHfAp1ijPNy9vWkGk85DCGcDNwKjAV+GmP8poo6W2TtLATWiTF+Xp3+SZIk1QVnaEmSJDVe5bcBdlxhrRwxxtdJs6daA31q2e7x2fa2qpJZWTtvkBJeqwE717IdSZKkWnENLUmSpMaryplc2S2BxwIHkp6K2BFoXkXVtWvcYAhtgc2yj5eHEC5eQfXyRFuPmrYjSZK0KkxoSZIkNV4dsu1X5QUhhB8AzwCb5tSbx7KLvHcmzcRvXYs2u1Ixi7+6M8Na1aIdSZKkWjOhJUmS1AiFENoAP8w+TsjZdRMpmfUVcA7wtxjj9ErHTibNzqrWWl2VNMl5v22M8dVanEOSJKleuYaWJElS47QnFcmlUQAhhGak2wwBTokx3l1FMqsJ0GkV2v0s5/2my60lSZKURya0JEmSGplsjawLso+zgcey952BFtn7Mcs5vG9OncqW5DZTVYUY40xgXPbxsOr0V5IkqaGZ0JIkSWpEQggtgXuAzbOiq2OMs7L3XwMxe//jKo5tCly5gtN/nfN+9RXU+0O2/VkIYYVJrRBCtZ/AKEmSVFdMaEmSJOVZCKEshPCjEMKZwFjgV9muIcB15fVijHOAF7OPN4YQdgkhlGXn+BEwAtgS+LaqdrLE2JTs4zFZAqwqdwDla2cNCSFcEUJYJ6e/rUII/UIIt7Ls+l6SJEkNIsQYV15LkiRJqySEMAi4JPuYu05Vc6Ady/6Hxi+BgTHGO6s4zxbAc1Q8wXA+sABoCywCjgUuB3oAx8QY76l0/MBsf/mxn5NuRXwlxnhYTr1OwEPALjmHf53VbU/FLYuLYozNVnjxkiRJdcwZWpIkSQ2vS/b6Aemp09OBV4DbgYOAtapKZgHEGN8AtgYeJiW+yoBvss/bxRiHrKTtq4DTgdeBhaSnIfYAulZq50tgV+AXwDBgMin51pI0y+tvwClAz2pftSRJUh1xhpYkSZIkSZIKijO0JEmSJEmSVFBMaEmSJEmSJKmgmNCSJEmSJElSQTGhJUmSJEmSpIJiQkuSJEmSJEkFxYSWJEmSJEmSCooJLUmSJEmSJBUUE1qSJEmSJEkqKCa0JEmSJEmSVFBMaEmSJEmSJKmgmNCSJEmSJElSQfl/7gNK6mdC5LQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datedate = pandas.read_excel('TATAMOTORS.xlsx', usecols=[0], skipfooter=3)\n",
    "\n",
    "# plot baseline and predictions\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.grid()\n",
    "plt.plot(datedate.tail(1547),testPredict, label=' predicted', color='red')\n",
    "plt.plot(datedate.tail(1547),testY[0], label=' actual value', color='blue')\n",
    "\n",
    "plt.xlabel('Date', fontsize=25)\n",
    "plt.ylabel('Price in USD', fontsize=25)\n",
    "plt.xticks(fontsize=17)\n",
    "plt.yticks(fontsize=17)\n",
    "\n",
    "plt.legend()\n",
    "\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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}