19_recurrent_neural_nets/01_univariate_time_series_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "# Recurrent Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Univariate Time Series Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "This notebook demonstrates how to forecast the S&P 500 index using a Recurrent Neural Network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:30.844209Z",
     "start_time": "2021-02-23T06:36:30.842448Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:30.861089Z",
     "start_time": "2021-02-23T06:36:30.848187Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM\n",
    "from tensorflow import keras\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:30.871046Z",
     "start_time": "2021-02-23T06:36:30.862297Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using CPU\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpu_devices:\n",
    "    print('Using GPU')\n",
    "    tf.config.experimental.set_memory_growth(gpu_devices[0], True)\n",
    "else:\n",
    "    print('Using CPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:30.878498Z",
     "start_time": "2021-02-23T06:36:30.872701Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:30.886656Z",
     "start_time": "2021-02-23T06:36:30.880270Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path('results', 'univariate_time_series')\n",
    "if not results_path.exists():\n",
    "    results_path.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "We obtain data for 2010-2018 from the Federal Reserve Bank’s Data Service [FRED](https://fred.stlouisfed.org/) using the [pandas_datareader](https://pandas-datareader.readthedocs.io/) library introduced in [Chapter 2 on Market and Fundamental Data](../02_market_and_fundamental_data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.318516Z",
     "start_time": "2021-02-23T06:36:30.888059Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500 = web.DataReader('SP500', 'fred', start='2010', end='2020').dropna()\n",
    "ax = sp500.plot(title='S&P 500',\n",
    "           legend=False,\n",
    "           figsize=(14, 4),\n",
    "           rot=0)\n",
    "ax.set_xlabel('')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.322036Z",
     "start_time": "2021-02-23T06:36:31.319768Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.337265Z",
     "start_time": "2021-02-23T06:36:31.323341Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2229.000000\n",
       "mean        0.451605\n",
       "std         0.254561\n",
       "min         0.000000\n",
       "25%         0.238076\n",
       "50%         0.447456\n",
       "75%         0.659023\n",
       "max         1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500_scaled = pd.Series(scaler.fit_transform(sp500).squeeze(), \n",
    "                         index=sp500.index)\n",
    "sp500_scaled.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Generating recurrent sequences from our time series\n",
    "\n",
    "Our time series is a sequence of numbers indexed by time:\n",
    "\n",
    "$$x_{0},x_{1},x_{2},...,x_{T}$$\n",
    "\n",
    "where $\\{x_t\\}$ is the numerical value in period $t$ and  $T$ is the total length of the series. \n",
    "\n",
    "To apply a RNN for regression of classification, we use a sliding window to construct a rolling set of input/output pairs for our model to learn from as animated below.\n",
    "\n",
    "<img src=\"../assets/timeseries_windowing.gif\" width=600 height=600/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "We will generate sequences of 63 trading days, approximately three months, and use a single LSTM layer with 20 hidden units to predict the index value one timestep ahead. \n",
    "The input to every LSTM layer must have three dimensions, namely:\n",
    "- **Samples**: One sequence is one sample. A batch contains one or more samples.\n",
    "- **Time Steps**: One time step is one point of observation in the sample.\n",
    "- **Features**: One feature is one observation at a time step.\n",
    "\n",
    "Our S&P 500 sample has 2,264 observations or time steps. We will create overlapping sequences using a window of 63 observations each.\n",
    "For a simpler window of size T = 5, we obtain input-output pairs as shown in the following table:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "$$\\begin{array}{c|c}\n",
    "\\text{Input} & \\text{Output}\\\\\n",
    "\\hline  {\\langle x_1,x_2,x_3,x_4,x_5\\rangle} & { x_6} \\\\\n",
    "\\ {\\langle x_{2},x_{3},x_{4},x_{5},x_{6} \\rangle } & {x_{7} } \\\\\n",
    "  {\\vdots} & {\\vdots}\\\\\n",
    " { \\langle x_{T-5},x_{T-4},x_{T-3},x_{T-2},x_{T-1} \\rangle } &  {x_{T}}\n",
    "\\end{array}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Generally speaking, for window size S, the relationship takes the form \n",
    "\n",
    "$$x_t = f( x_{t-1}, x_{t-2}, ..., x_{t-S}) \\quad\\forall t=S, S+1, ..., T$$\n",
    "\n",
    "Each of the $T-S$ lagged input sequence or vector is of length S with a corresponding scalar output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "We can use the function create_univariate_rnn_data() to stack sequences selected using a rolling windows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.343430Z",
     "start_time": "2021-02-23T06:36:31.338826Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "def create_univariate_rnn_data(data, window_size):\n",
    "    n = len(data)\n",
    "    y = data[window_size:]\n",
    "    data = data.values.reshape(-1, 1) # make 2D\n",
    "    X = np.hstack(tuple([data[i: n-j, :] for i, j in enumerate(range(window_size, 0, -1))]))\n",
    "    return pd.DataFrame(X, index=y.index), y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "We apply this function to the rescaled stock index for a window_size=63 to obtain a two-dimensional dataset of shape number of samples x number of timesteps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.351064Z",
     "start_time": "2021-02-23T06:36:31.344858Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "window_size = 63"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.364327Z",
     "start_time": "2021-02-23T06:36:31.352204Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "X, y = create_univariate_rnn_data(sp500_scaled, window_size=window_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.386219Z",
     "start_time": "2021-02-23T06:36:31.365399Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
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       "                  0         1         2         3         4         5   \\\n",
       "DATE                                                                     \n",
       "2011-05-24  0.097240  0.096633  0.103069  0.106498  0.096740  0.097726   \n",
       "2011-05-25  0.096633  0.103069  0.106498  0.096740  0.097726  0.108250   \n",
       "2011-05-26  0.103069  0.106498  0.096740  0.097726  0.108250  0.103663   \n",
       "2011-05-27  0.106498  0.096740  0.097726  0.108250  0.103663  0.098515   \n",
       "2011-05-31  0.096740  0.097726  0.108250  0.103663  0.098515  0.103976   \n",
       "\n",
       "                  6         7         8         9   ...        53        54  \\\n",
       "DATE                                                ...                       \n",
       "2011-05-24  0.108250  0.103663  0.098515  0.103976  ...  0.120484  0.113439   \n",
       "2011-05-25  0.103663  0.098515  0.103976  0.103135  ...  0.113439  0.116508   \n",
       "2011-05-26  0.098515  0.103976  0.103135  0.091499  ...  0.116508  0.111426   \n",
       "2011-05-27  0.103976  0.103135  0.091499  0.095782  ...  0.111426  0.107549   \n",
       "2011-05-31  0.103135  0.091499  0.095782  0.092097  ...  0.107549  0.107320   \n",
       "\n",
       "                  55        56        57        58        59        60  \\\n",
       "DATE                                                                     \n",
       "2011-05-24  0.116508  0.111426  0.107549  0.107320  0.112785  0.114149   \n",
       "2011-05-25  0.111426  0.107549  0.107320  0.112785  0.114149  0.109324   \n",
       "2011-05-26  0.107549  0.107320  0.112785  0.114149  0.109324  0.101897   \n",
       "2011-05-27  0.107320  0.112785  0.114149  0.109324  0.101897  0.101388   \n",
       "2011-05-31  0.112785  0.114149  0.109324  0.101897  0.101388  0.103345   \n",
       "\n",
       "                  61        62  \n",
       "DATE                            \n",
       "2011-05-24  0.109324  0.101897  \n",
       "2011-05-25  0.101897  0.101388  \n",
       "2011-05-26  0.101388  0.103345  \n",
       "2011-05-27  0.103345  0.105783  \n",
       "2011-05-31  0.105783  0.108310  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.390988Z",
     "start_time": "2021-02-23T06:36:31.387303Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "2011-05-24    0.101388\n",
       "2011-05-25    0.103345\n",
       "2011-05-26    0.105783\n",
       "2011-05-27    0.108310\n",
       "2011-05-31    0.114897\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.399459Z",
     "start_time": "2021-02-23T06:36:31.392960Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2166, 63)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Train-test split\n",
    "\n",
    "To respect the time series nature of the data, we set aside the data at the end of the sample as hold-out or test set. More specifically, we'll use the data for 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.649966Z",
     "start_time": "2021-02-23T06:36:31.400418Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sp500_scaled.plot(lw=2, figsize=(14, 4), rot=0)\n",
    "ax.set_xlabel('')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.658244Z",
     "start_time": "2021-02-23T06:36:31.651319Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "X_train = X[:'2018'].values.reshape(-1, window_size, 1)\n",
    "y_train = y[:'2018']\n",
    "\n",
    "# keep the last year for testing\n",
    "X_test = X['2019'].values.reshape(-1, window_size, 1)\n",
    "y_test = y['2019']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.664101Z",
     "start_time": "2021-02-23T06:36:31.659546Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "n_obs, window_size, n_features = X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.674631Z",
     "start_time": "2021-02-23T06:36:31.665281Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1914,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Keras LSTM Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Keras has several built-in RNN layers with various configuration options described in detail in the [documentation](https://keras.io/layers/recurrent/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-23T18:41:32.906454Z",
     "start_time": "2020-03-23T18:41:32.655189Z"
    },
    "pycharm": {}
   },
   "source": [
    "```\n",
    "LSTM(units,\n",
    "     activation='tanh',\n",
    "     recurrent_activation='hard_sigmoid',\n",
    "     use_bias=True,\n",
    "     kernel_initializer='glorot_uniform',\n",
    "     recurrent_initializer='orthogonal',\n",
    "     bias_initializer='zeros',\n",
    "     unit_forget_bias=True,\n",
    "     kernel_regularizer=None,\n",
    "     recurrent_regularizer=None,\n",
    "     bias_regularizer=None,\n",
    "     activity_regularizer=None,\n",
    "     kernel_constraint=None,\n",
    "     recurrent_constraint=None,\n",
    "     bias_constraint=None,\n",
    "     dropout=0.0,\n",
    "     recurrent_dropout=0.0,\n",
    "     implementation=1,\n",
    "     return_sequences=False,\n",
    "     return_state=False,\n",
    "     go_backwards=False,\n",
    "     stateful=False,\n",
    "     unroll=False)\n",
    " ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "##  Define the Model Architecture\n",
    "\n",
    "Having created input/output pairs out of our time series and cut this into training/testing sets, we can now begin setting up our RNN.  We use Keras to quickly build a two hidden layer RNN of the following specifications\n",
    "\n",
    "- layer 1 uses an LSTM module with 20 hidden units (note here the input_shape = (window_size,1))\n",
    "- layer 2 uses a fully connected module with one unit\n",
    "- the 'mean_squared_error' loss should be used (remember: we are performing regression here)\n",
    "\n",
    "This can be constructed using just a few lines - see e.g., the [general Keras documentation](https://keras.io/getting-started/sequential-model-guide/) and the [LSTM documentation in particular](https://keras.io/layers/recurrent/) for examples of how to quickly use Keras to build neural network models.  Make sure you are initializing your optimizer given the [keras-recommended approach for RNNs](https://keras.io/optimizers/) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.921646Z",
     "start_time": "2021-02-23T06:36:31.676694Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "rnn = Sequential([\n",
    "    LSTM(units=10, \n",
    "         input_shape=(window_size, n_features), name='LSTM'),\n",
    "    Dense(1, name='Output')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "The summary shows that the model has 1,781 parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.927298Z",
     "start_time": "2021-02-23T06:36:31.922780Z"
    },
    "pycharm": {},
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "LSTM (LSTM)                  (None, 10)                480       \n",
      "_________________________________________________________________\n",
      "Output (Dense)               (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 491\n",
      "Trainable params: 491\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "rnn.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "We train the model using the RMSProp optimizer recommended for RNN with default settings and compile the model with mean squared error for this regression problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.936019Z",
     "start_time": "2021-02-23T06:36:31.928473Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.RMSprop(lr=0.001,\n",
    "                                     rho=0.9,\n",
    "                                     epsilon=1e-08,\n",
    "                                     decay=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.948987Z",
     "start_time": "2021-02-23T06:36:31.937213Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "rnn.compile(loss='mean_squared_error', \n",
    "            optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "We define an EarlyStopping callback and train the model for up to 100 episodes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.956938Z",
     "start_time": "2021-02-23T06:36:31.949937Z"
    }
   },
   "outputs": [],
   "source": [
    "rnn_path = (results_path / 'rnn.h5').as_posix()\n",
    "checkpointer = ModelCheckpoint(filepath=rnn_path,\n",
    "                               verbose=1,\n",
    "                               monitor='val_loss',\n",
    "                               save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:36:31.964790Z",
     "start_time": "2021-02-23T06:36:31.960370Z"
    },
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_loss', \n",
    "                              patience=20,\n",
    "                              restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:38:24.570692Z",
     "start_time": "2021-02-23T06:36:31.966149Z"
    },
    "pycharm": {},
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 0.0162\n",
      "Epoch 00001: val_loss improved from inf to 0.00766, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 12ms/step - loss: 0.0161 - val_loss: 0.0077\n",
      "Epoch 2/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 5.0726e-04\n",
      "Epoch 00002: val_loss improved from 0.00766 to 0.00135, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 5.0613e-04 - val_loss: 0.0014\n",
      "Epoch 3/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 4.2700e-04\n",
      "Epoch 00003: val_loss did not improve from 0.00135\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 4.2515e-04 - val_loss: 0.0033\n",
      "Epoch 4/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 4.0140e-04\n",
      "Epoch 00004: val_loss did not improve from 0.00135\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 3.9946e-04 - val_loss: 0.0022\n",
      "Epoch 5/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 3.7595e-04\n",
      "Epoch 00005: val_loss did not improve from 0.00135\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 3.6440e-04 - val_loss: 0.0033\n",
      "Epoch 6/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 3.4672e-04\n",
      "Epoch 00006: val_loss improved from 0.00135 to 0.00068, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 3.4672e-04 - val_loss: 6.7836e-04\n",
      "Epoch 7/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 3.1172e-04\n",
      "Epoch 00007: val_loss did not improve from 0.00068\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 3.1417e-04 - val_loss: 0.0047\n",
      "Epoch 8/150\n",
      "93/96 [============================>.] - ETA: 0s - loss: 3.2092e-04\n",
      "Epoch 00008: val_loss did not improve from 0.00068\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 3.1923e-04 - val_loss: 0.0014\n",
      "Epoch 9/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 2.9657e-04\n",
      "Epoch 00009: val_loss improved from 0.00068 to 0.00043, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 2.9568e-04 - val_loss: 4.3396e-04\n",
      "Epoch 10/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 2.8456e-04\n",
      "Epoch 00010: val_loss did not improve from 0.00043\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 2.8585e-04 - val_loss: 0.0016\n",
      "Epoch 11/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 2.5980e-04\n",
      "Epoch 00011: val_loss improved from 0.00043 to 0.00032, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 2.6074e-04 - val_loss: 3.1798e-04\n",
      "Epoch 12/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 2.6164e-04\n",
      "Epoch 00012: val_loss did not improve from 0.00032\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 2.5868e-04 - val_loss: 4.8836e-04\n",
      "Epoch 13/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 2.5184e-04\n",
      "Epoch 00013: val_loss did not improve from 0.00032\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 2.5184e-04 - val_loss: 4.2231e-04\n",
      "Epoch 14/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 2.4671e-04\n",
      "Epoch 00014: val_loss did not improve from 0.00032\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 2.4586e-04 - val_loss: 4.4436e-04\n",
      "Epoch 15/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 2.3177e-04\n",
      "Epoch 00015: val_loss did not improve from 0.00032\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 2.3762e-04 - val_loss: 4.7206e-04\n",
      "Epoch 16/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 2.2798e-04\n",
      "Epoch 00016: val_loss did not improve from 0.00032\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 2.2959e-04 - val_loss: 3.2628e-04\n",
      "Epoch 17/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 2.2682e-04\n",
      "Epoch 00017: val_loss did not improve from 0.00032\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 2.2815e-04 - val_loss: 0.0013\n",
      "Epoch 18/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 2.1929e-04\n",
      "Epoch 00018: val_loss did not improve from 0.00032\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 2.1929e-04 - val_loss: 0.0022\n",
      "Epoch 19/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 2.1801e-04\n",
      "Epoch 00019: val_loss improved from 0.00032 to 0.00024, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 2.1470e-04 - val_loss: 2.4200e-04\n",
      "Epoch 20/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 2.1644e-04\n",
      "Epoch 00020: val_loss improved from 0.00024 to 0.00023, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 2.1644e-04 - val_loss: 2.3101e-04\n",
      "Epoch 21/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 2.0451e-04\n",
      "Epoch 00021: val_loss improved from 0.00023 to 0.00021, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 2.0451e-04 - val_loss: 2.1255e-04\n",
      "Epoch 22/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 2.0134e-04\n",
      "Epoch 00022: val_loss did not improve from 0.00021\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 2.0179e-04 - val_loss: 2.2027e-04\n",
      "Epoch 23/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.9941e-04\n",
      "Epoch 00023: val_loss did not improve from 0.00021\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.9941e-04 - val_loss: 4.4025e-04\n",
      "Epoch 24/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.9096e-04\n",
      "Epoch 00024: val_loss did not improve from 0.00021\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.9096e-04 - val_loss: 5.4885e-04\n",
      "Epoch 25/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.8358e-04\n",
      "Epoch 00025: val_loss did not improve from 0.00021\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.8358e-04 - val_loss: 2.4444e-04\n",
      "Epoch 26/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 1.8497e-04\n",
      "Epoch 00026: val_loss did not improve from 0.00021\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.8471e-04 - val_loss: 4.2620e-04\n",
      "Epoch 27/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.7350e-04\n",
      "Epoch 00027: val_loss did not improve from 0.00021\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.7350e-04 - val_loss: 4.0677e-04\n",
      "Epoch 28/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 1.6671e-04\n",
      "Epoch 00028: val_loss did not improve from 0.00021\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.6921e-04 - val_loss: 3.8056e-04\n",
      "Epoch 29/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 1.6757e-04\n",
      "Epoch 00029: val_loss did not improve from 0.00021\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.7095e-04 - val_loss: 3.5113e-04\n",
      "Epoch 30/150\n",
      "93/96 [============================>.] - ETA: 0s - loss: 1.5892e-04\n",
      "Epoch 00030: val_loss improved from 0.00021 to 0.00018, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.5886e-04 - val_loss: 1.7758e-04\n",
      "Epoch 31/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 1.5301e-04\n",
      "Epoch 00031: val_loss improved from 0.00018 to 0.00016, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.5573e-04 - val_loss: 1.5858e-04\n",
      "Epoch 32/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 1.4994e-04\n",
      "Epoch 00032: val_loss improved from 0.00016 to 0.00016, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.5008e-04 - val_loss: 1.5702e-04\n",
      "Epoch 33/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 1.5448e-04\n",
      "Epoch 00033: val_loss did not improve from 0.00016\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.5528e-04 - val_loss: 3.3243e-04\n",
      "Epoch 34/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 1.5249e-04\n",
      "Epoch 00034: val_loss improved from 0.00016 to 0.00014, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.5086e-04 - val_loss: 1.4220e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 35/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.4350e-04\n",
      "Epoch 00035: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.4350e-04 - val_loss: 8.7128e-04\n",
      "Epoch 36/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 1.4087e-04\n",
      "Epoch 00036: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.4304e-04 - val_loss: 2.2118e-04\n",
      "Epoch 37/150\n",
      "93/96 [============================>.] - ETA: 0s - loss: 1.4318e-04\n",
      "Epoch 00037: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.4475e-04 - val_loss: 5.7758e-04\n",
      "Epoch 38/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 1.3891e-04\n",
      "Epoch 00038: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.4370e-04 - val_loss: 5.5123e-04\n",
      "Epoch 39/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.3512e-04\n",
      "Epoch 00039: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.3512e-04 - val_loss: 2.0821e-04\n",
      "Epoch 40/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 1.3034e-04\n",
      "Epoch 00040: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.3073e-04 - val_loss: 6.1821e-04\n",
      "Epoch 41/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 1.3208e-04\n",
      "Epoch 00041: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.3247e-04 - val_loss: 5.5452e-04\n",
      "Epoch 42/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 1.2555e-04\n",
      "Epoch 00042: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.2543e-04 - val_loss: 3.1468e-04\n",
      "Epoch 43/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 1.2727e-04\n",
      "Epoch 00043: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 12ms/step - loss: 1.2745e-04 - val_loss: 2.5483e-04\n",
      "Epoch 44/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.3116e-04\n",
      "Epoch 00044: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 11ms/step - loss: 1.3116e-04 - val_loss: 2.0916e-04\n",
      "Epoch 45/150\n",
      "93/96 [============================>.] - ETA: 0s - loss: 1.2340e-04\n",
      "Epoch 00045: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 12ms/step - loss: 1.2624e-04 - val_loss: 1.5523e-04\n",
      "Epoch 46/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 1.2741e-04\n",
      "Epoch 00046: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 16ms/step - loss: 1.2674e-04 - val_loss: 1.4887e-04\n",
      "Epoch 47/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 1.2104e-04\n",
      "Epoch 00047: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 2s 16ms/step - loss: 1.2028e-04 - val_loss: 3.1503e-04\n",
      "Epoch 48/150\n",
      "93/96 [============================>.] - ETA: 0s - loss: 1.2040e-04\n",
      "Epoch 00048: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.2169e-04 - val_loss: 3.1043e-04\n",
      "Epoch 49/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.2513e-04\n",
      "Epoch 00049: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.2513e-04 - val_loss: 3.6431e-04\n",
      "Epoch 50/150\n",
      "93/96 [============================>.] - ETA: 0s - loss: 1.1566e-04\n",
      "Epoch 00050: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 1.1806e-04 - val_loss: 2.0745e-04\n",
      "Epoch 51/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.1787e-04\n",
      "Epoch 00051: val_loss did not improve from 0.00014\n",
      "96/96 [==============================] - 1s 8ms/step - loss: 1.1787e-04 - val_loss: 3.8254e-04\n",
      "Epoch 52/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 1.1391e-04\n",
      "Epoch 00052: val_loss improved from 0.00014 to 0.00013, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.1292e-04 - val_loss: 1.3098e-04\n",
      "Epoch 53/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 1.1152e-04\n",
      "Epoch 00053: val_loss did not improve from 0.00013\n",
      "96/96 [==============================] - 1s 8ms/step - loss: 1.1118e-04 - val_loss: 3.5197e-04\n",
      "Epoch 54/150\n",
      "90/96 [===========================>..] - ETA: 0s - loss: 1.1793e-04\n",
      "Epoch 00054: val_loss did not improve from 0.00013\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.1724e-04 - val_loss: 1.9148e-04\n",
      "Epoch 55/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 1.1052e-04\n",
      "Epoch 00055: val_loss did not improve from 0.00013\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.1202e-04 - val_loss: 2.7836e-04\n",
      "Epoch 56/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 1.0805e-04\n",
      "Epoch 00056: val_loss did not improve from 0.00013\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.0802e-04 - val_loss: 1.3425e-04\n",
      "Epoch 57/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 1.1062e-04\n",
      "Epoch 00057: val_loss improved from 0.00013 to 0.00012, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.0968e-04 - val_loss: 1.1734e-04\n",
      "Epoch 58/150\n",
      "90/96 [===========================>..] - ETA: 0s - loss: 1.0471e-04\n",
      "Epoch 00058: val_loss did not improve from 0.00012\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.0535e-04 - val_loss: 2.0888e-04\n",
      "Epoch 59/150\n",
      "93/96 [============================>.] - ETA: 0s - loss: 1.0651e-04\n",
      "Epoch 00059: val_loss did not improve from 0.00012\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.0444e-04 - val_loss: 1.2834e-04\n",
      "Epoch 60/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.0868e-04\n",
      "Epoch 00060: val_loss did not improve from 0.00012\n",
      "96/96 [==============================] - 1s 8ms/step - loss: 1.0868e-04 - val_loss: 1.7766e-04\n",
      "Epoch 61/150\n",
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      "Epoch 00061: val_loss did not improve from 0.00012\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.0307e-04 - val_loss: 2.2622e-04\n",
      "Epoch 62/150\n",
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      "Epoch 00062: val_loss did not improve from 0.00012\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.0332e-04 - val_loss: 1.1764e-04\n",
      "Epoch 63/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 1.0141e-04\n",
      "Epoch 00063: val_loss did not improve from 0.00012\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.0113e-04 - val_loss: 1.7721e-04\n",
      "Epoch 64/150\n",
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      "Epoch 00064: val_loss did not improve from 0.00012\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.0589e-04 - val_loss: 2.7786e-04\n",
      "Epoch 65/150\n",
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      "Epoch 00065: val_loss did not improve from 0.00012\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.8424e-05 - val_loss: 2.5257e-04\n",
      "Epoch 66/150\n",
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      "Epoch 00066: val_loss did not improve from 0.00012\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.0111e-04 - val_loss: 1.2785e-04\n",
      "Epoch 67/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 1.0120e-04\n",
      "Epoch 00067: val_loss did not improve from 0.00012\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.0110e-04 - val_loss: 1.5218e-04\n",
      "Epoch 68/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 9.5239e-05\n",
      "Epoch 00068: val_loss improved from 0.00012 to 0.00011, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.5239e-05 - val_loss: 1.0982e-04\n",
      "Epoch 69/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 9.7534e-05\n",
      "Epoch 00069: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.7244e-05 - val_loss: 1.3820e-04\n",
      "Epoch 70/150\n",
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      "Epoch 00070: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 1.0096e-04 - val_loss: 2.6083e-04\n",
      "Epoch 71/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 9.8744e-05\n",
      "Epoch 00071: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.8619e-05 - val_loss: 1.4675e-04\n",
      "Epoch 72/150\n",
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      "Epoch 00072: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.7614e-05 - val_loss: 1.7677e-04\n",
      "Epoch 73/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 9.5597e-05\n",
      "Epoch 00073: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.5436e-05 - val_loss: 2.8398e-04\n",
      "Epoch 74/150\n",
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      "Epoch 00074: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.7079e-05 - val_loss: 1.4352e-04\n",
      "Epoch 75/150\n",
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      "Epoch 00075: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.6704e-05 - val_loss: 1.2011e-04\n",
      "Epoch 76/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 9.8459e-05\n",
      "Epoch 00076: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.8009e-05 - val_loss: 1.7817e-04\n",
      "Epoch 77/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 9.1118e-05\n",
      "Epoch 00077: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.1118e-05 - val_loss: 1.1157e-04\n",
      "Epoch 78/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 9.5011e-05\n",
      "Epoch 00078: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.5155e-05 - val_loss: 1.5538e-04\n",
      "Epoch 79/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 9.4418e-05\n",
      "Epoch 00079: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.4418e-05 - val_loss: 1.4241e-04\n",
      "Epoch 80/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 9.4359e-05\n",
      "Epoch 00080: val_loss improved from 0.00011 to 0.00011, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.4327e-05 - val_loss: 1.0896e-04\n",
      "Epoch 81/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 9.4806e-05\n",
      "Epoch 00081: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.4769e-05 - val_loss: 1.3463e-04\n",
      "Epoch 82/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 9.3654e-05\n",
      "Epoch 00082: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.4321e-05 - val_loss: 1.4250e-04\n",
      "Epoch 83/150\n",
      "93/96 [============================>.] - ETA: 0s - loss: 9.5975e-05\n",
      "Epoch 00083: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.6019e-05 - val_loss: 1.1075e-04\n",
      "Epoch 84/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 9.5794e-05\n",
      "Epoch 00084: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.6212e-05 - val_loss: 2.0834e-04\n",
      "Epoch 85/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 8.9862e-05\n",
      "Epoch 00085: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.7901e-05 - val_loss: 1.2320e-04\n",
      "Epoch 86/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 9.0540e-05\n",
      "Epoch 00086: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 8ms/step - loss: 9.0288e-05 - val_loss: 1.1778e-04\n",
      "Epoch 87/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 9.1800e-05\n",
      "Epoch 00087: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.0853e-05 - val_loss: 1.8410e-04\n",
      "Epoch 88/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 9.0630e-05\n",
      "Epoch 00088: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.1169e-05 - val_loss: 1.5173e-04\n",
      "Epoch 89/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 9.0615e-05\n",
      "Epoch 00089: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.0969e-05 - val_loss: 2.6647e-04\n",
      "Epoch 90/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 8.4236e-05\n",
      "Epoch 00090: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 8.6761e-05 - val_loss: 2.2579e-04\n",
      "Epoch 91/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 9.0738e-05\n",
      "Epoch 00091: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.0392e-05 - val_loss: 1.4102e-04\n",
      "Epoch 92/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 8.7762e-05\n",
      "Epoch 00092: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.1175e-05 - val_loss: 1.5866e-04\n",
      "Epoch 93/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 8.8039e-05\n",
      "Epoch 00093: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.9345e-05 - val_loss: 2.2237e-04\n",
      "Epoch 94/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 8.7299e-05\n",
      "Epoch 00094: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.8101e-05 - val_loss: 2.0188e-04\n",
      "Epoch 95/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 8.7247e-05\n",
      "Epoch 00095: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.7141e-05 - val_loss: 1.1806e-04\n",
      "Epoch 96/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 9.1653e-05\n",
      "Epoch 00096: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.1519e-05 - val_loss: 1.1541e-04\n",
      "Epoch 97/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 8.8514e-05\n",
      "Epoch 00097: val_loss improved from 0.00011 to 0.00011, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.6380e-05 - val_loss: 1.0814e-04\n",
      "Epoch 98/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 8.6094e-05\n",
      "Epoch 00098: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.6094e-05 - val_loss: 1.1347e-04\n",
      "Epoch 99/150\n",
      "90/96 [===========================>..] - ETA: 0s - loss: 8.7294e-05\n",
      "Epoch 00099: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.9347e-05 - val_loss: 1.3592e-04\n",
      "Epoch 100/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 8.6722e-05\n",
      "Epoch 00100: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.6722e-05 - val_loss: 7.0361e-04\n",
      "Epoch 101/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 9.0485e-05\n",
      "Epoch 00101: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.9286e-05 - val_loss: 1.1654e-04\n",
      "Epoch 102/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 8.7473e-05\n",
      "Epoch 00102: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.6557e-05 - val_loss: 1.1325e-04\n",
      "Epoch 103/150\n",
      "94/96 [============================>.] - ETA: 0s - loss: 8.9702e-05\n",
      "Epoch 00103: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.9611e-05 - val_loss: 2.9964e-04\n",
      "Epoch 104/150\n",
      "91/96 [===========================>..] - ETA: 0s - loss: 8.8178e-05\n",
      "Epoch 00104: val_loss improved from 0.00011 to 0.00011, saving model to results/univariate_time_series/rnn.h5\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.8027e-05 - val_loss: 1.0701e-04\n",
      "Epoch 105/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90/96 [===========================>..] - ETA: 0s - loss: 8.4287e-05\n",
      "Epoch 00105: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.5441e-05 - val_loss: 2.6547e-04\n",
      "Epoch 106/150\n",
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      "Epoch 00106: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.6718e-05 - val_loss: 1.4452e-04\n",
      "Epoch 107/150\n",
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      "Epoch 00107: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.8267e-05 - val_loss: 1.1976e-04\n",
      "Epoch 108/150\n",
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      "Epoch 00108: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.7117e-05 - val_loss: 1.0868e-04\n",
      "Epoch 109/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 9.0933e-05\n",
      "Epoch 00109: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.0933e-05 - val_loss: 1.1853e-04\n",
      "Epoch 110/150\n",
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      "Epoch 00110: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.6446e-05 - val_loss: 1.0818e-04\n",
      "Epoch 111/150\n",
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      "Epoch 00111: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.8124e-05 - val_loss: 2.3456e-04\n",
      "Epoch 112/150\n",
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      "Epoch 00112: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.6186e-05 - val_loss: 1.1133e-04\n",
      "Epoch 113/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 9.0423e-05\n",
      "Epoch 00113: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.0423e-05 - val_loss: 1.3993e-04\n",
      "Epoch 114/150\n",
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      "Epoch 00114: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.9644e-05 - val_loss: 1.1688e-04\n",
      "Epoch 115/150\n",
      "95/96 [============================>.] - ETA: 0s - loss: 9.0703e-05\n",
      "Epoch 00115: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 9.0592e-05 - val_loss: 1.3260e-04\n",
      "Epoch 116/150\n",
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      "Epoch 00116: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.4973e-05 - val_loss: 1.0909e-04\n",
      "Epoch 117/150\n",
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      "Epoch 00117: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.9528e-05 - val_loss: 1.5286e-04\n",
      "Epoch 118/150\n",
      "96/96 [==============================] - ETA: 0s - loss: 8.5693e-05\n",
      "Epoch 00118: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.5693e-05 - val_loss: 1.6449e-04\n",
      "Epoch 119/150\n",
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      "Epoch 00119: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.6209e-05 - val_loss: 3.1345e-04\n",
      "Epoch 120/150\n",
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      "Epoch 00120: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.5270e-05 - val_loss: 1.1120e-04\n",
      "Epoch 121/150\n",
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      "Epoch 00121: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.6331e-05 - val_loss: 1.0745e-04\n",
      "Epoch 122/150\n",
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      "Epoch 00122: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.5809e-05 - val_loss: 1.0791e-04\n",
      "Epoch 123/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 8.7627e-05\n",
      "Epoch 00123: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 10ms/step - loss: 8.7544e-05 - val_loss: 1.1710e-04\n",
      "Epoch 124/150\n",
      "92/96 [===========================>..] - ETA: 0s - loss: 8.9236e-05\n",
      "Epoch 00124: val_loss did not improve from 0.00011\n",
      "96/96 [==============================] - 1s 9ms/step - loss: 8.7432e-05 - val_loss: 1.0717e-04\n"
     ]
    }
   ],
   "source": [
    "lstm_training = rnn.fit(X_train,\n",
    "                        y_train,\n",
    "                        epochs=150,\n",
    "                        batch_size=20,\n",
    "                        shuffle=True,\n",
    "                        validation_data=(X_test, y_test),\n",
    "                        callbacks=[early_stopping, checkpointer],\n",
    "                        verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Training stops after 51 epochs; the `early_stopping` callback restores the weights for the best model (after 41 epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Evaluate model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:38:25.321655Z",
     "start_time": "2021-02-23T06:38:24.572172Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h3LZtWxYtWsTu3bsZMGAA//rXvwAKJOMlGTt2LL/88gt//vknqamp9OvXD4Bly5Zx4cIFVq1axaFDh/jqq68ASh2jV7t27QItFWazucB5evvtt9HpdKxfv55Dhw7xf//3f2Ua9wdU2udMp9MxbNgw2rdvz0cffVRovYODA3369OGbb75h9OjRJe7L2dmZ6dOnk5aWRkRExC3FVV7+/v4FWuzS0tK4dOkSzZo1K3L7nJwcy9+GPXv2cPz4cXr27EnPnj3ZtGkTK1as4LHHHrOs/9///mdZD3DPPffw008/FdhnRkYGCxcu5IUXXiAsLIw6derg7OxMmzZtCox/PHfuHHXr1i2QLAshqgdJsIQQmkhKSmLnzp2Wb8/Xr19PSEgIvXr1uul9xsXFsWLFCrKzs/n55585d+4cffv2pU6dOnTo0IGFCxeSmZnJ6dOn+eGHHxg5ciQAd999N++99x4XL17EbDZz+vTpMrcYlKRp06b07t2bpUuXlhpDRahbty6tW7fmgw8+ICsri8OHD1uSkLIwGo08/PDDRV5A63Q6PvnkExYtWlQoMUlMTOT9998nLCyM3Nxc4uLiWL16Ne3bt7+p99G3b98Su8SlpqayceNGy0Vxv379uHjxImvXriU7O5vs7GyOHj3KuXPnyMrKYv369SQnJ2Nra4uTk5OlnLmXlxcJCQkkJyeXGE9QUBAuLi7MmjWLYcOGYTQaLXHY2dlZKs59+OGHZX5/oaGh/Pbbb+Tk5LBixYoC49ZSU1NxdHTE1dWV6Oholi5dWuD1tWrVshRJ+KfK/pxNmzaNVatWWVoHbzRjxgxWrlxZZMGGjz76iKNHj5KVlUVmZiYrVqzA1dW1xLF2ZZWbm0tmZibZ2dmYzWYyMzPJysoqctuBAwcSGhrKr7/+SmZmJh999BEBAQE0bdoUUN2FY2NjATh79ixLliyhe/fuADzzzDP8+uuvrF27lrVr1xIcHMzdd9/N/PnzAfj1119Zt26dZT2oqqcDBw4sEMOiRYsYN24cPj4+1KlThwsXLnDt2jX27dtXoFDHgQMHSpxGQAhRdUmCJYTQRE5ODu+++y7dunWjW7dufPnll3z00UdFVua6UX71tPzbjYPN27ZtS1hYGN26dePdd9/l/ffft4ylWrhwIZGRkfTu3Zsnn3ySp556yvIt80MPPcTQoUN5+OGH6dixIy+//DKZmZkV8j4feeQRVq1aRWxsbIkxVJS33nqLI0eO0LVrV959990CCUFZ3HnnnURFRfHHH38UWufv74+/v3+h5ba2tkRGRvLQQw/RqVMnRo4cidFoZMGCBZZtbqzWl3/LHyP1T6NHj2b79u1kZGQU+frg4GASExN56623ANUi8tlnn7Fp0yZ69+5Nr169eOuttywX2evWrSM4OJiOHTvy7bff8r///Q9QCfDw4cMZMGAAQUFBxXbX1Ol0jBkzhsjIyALdIx944AEyMzPp1q0b99xzT5m7tHl6evLee+9ZCryEhYXRsWNHy/onn3ySkydPEhQUxLRp0xg0aFCB10+bNo1FixYRFBTEZ599Vmj/lfk5CwgIoHPnzkUe18fHp9hiFDqdjpdeeolu3brRu3dvdu/ezeLFiwt0zRs9enSBz8cbb7wBqCIX/xzTdaMDBw7Qtm1bpk2bRlRUFG3btuWRRx6xrB8+fLilm7GnpycffPAB77zzDp07d+bo0aMFSqQfOnSIkSNH0r59e6ZNm0afPn149tlnAfU5q127tuVmb2+Pg4MD7u7ugErYb1wPauzVjQVzzp8/z65du7jvvvsA1eI4depUhg8fzsqVKy3HAlWN8cYCG0KI6kNnLmu/AyGEqMLWrFnD999/zzfffKN1KFXKv/71L5o0acLTTz+tdSjlsnDhQjw9PXnwwQe1DkUIq/vjjz9Yt24d7733ntahCCFugkw0LIQQNcjRo0dxd3enXr167Nq1iy1btjBt2jStwyq3G7/JF+J2ExwcXGoFUCFE1SUJlhBC1CDXrl3jqaeeIiEhAV9fX2bPnl2o7LoQQgghKo90ERRCCCGEEEKICiJFLoQQQgghhBCigtT4BCs0NLTQspLmVxE1h5znmk/Occ0n5/j2IOe55qvsczx79uxK3b8onfweX1fjE6yiZqe/cRJOUXPJea755BzXfHKObw9ynmu+yj7HpU2ALiqf/B5fVy2LXGzevJlt27YRGxvLpEmTbmliUiGEEEIIUb1JgiWqkjK1YCUlJfH0008zZMgQhg4dyuHDh2/qYDNnzqR79+6MGDGi0LodO3YwePBgBg4cyJIlS0rcz4ABA3j99ddZsGABmzZtuqlYhBBCCCFEzVC3bl2tQxDCokwtWG+88Qa9e/fm/fffJysri4yMjALrY2NjsbOzw9nZ2bIsLCyMhg0bFthu3Lhx3HfffbzwwgsFlptMJubOncvnn3+Oj48Pd911F8HBwZhMpgIzrAPMmzcPLy8vABYtWsSkSZPK/m6FEEIIIYQQohKVmmClpKRw4MABFixYAIDRaMRoNBbYZv/+/XzzzTd8+umn2NnZsWrVKn7//Xc+/fTTAtt17tyZiIiIQsc4evQoDRs2pH79+gAMHz6cLVu2MH36dBYvXlxoe7PZzFtvvUWfPn0IDAws+7sVQgghhBCikmVnZxMREVGoUaImy87O5tSpU1qHUSns7e2pV68etra2Zdq+1AQrPDwcT09PZs6cyenTpwkMDOTll1/G0dHRss3QoUOJiIhgxowZDBkyhNWrV7Ns2bIyBx0dHY2vr6/luY+PD0ePHi12+5UrV7Jnzx6Sk5MJCwtj4sSJZT6WEEIIIYSoWaZOnap1CAVERETg4uJCo0aN0Ol0WodjFenp6Tg4OGgdRoUzm83ExsYSERFB48aNy/SaUsdg5eTkcPLkSSZOnMjatWtxcHAocozU1KlTsbOzY/bs2SxatAgnJ6dyBf5PJX0YJ0+ezJo1a5g7d64kV0IIIYQQt7nSxu9bW0ZGBl5eXrdNclWT6XQ6vLy8ytUaWWqC5evri6+vL+3atQNgyJAhnDx5stB2ISEhhIaGMnDgQD788MNyhK2OceXKFcvz6OhovL29y7UPIYQQQghxe6qKVQQluao5ynsuS02wateuja+vL+fPnwdgz549NG3atMA2J0+e5JVXXuHjjz9m/vz5JCYm8s4775Q5iDZt2nDx4kXCw8PJyspi48aNBAcHl+uNCCGEEEKI29OhQ4e0DkEIizKVaX/11Vd57rnnGDlyJKdOneLRRx8tsD49PZ333nuPBg0aoNfrWbBgQZHlMp999lkmTJjAhQsX6NOnD99//z0ANjY2zJo1iylTpjBs2DCGDh2Kv79/Bby9KiTpMrzXDnZ/oHUkQgghhBCiBouPj2f06NGMHj2anj170rt3b8vzrKysEl977NgxXn/99VKPMWHChAqJdd++fXTq1IkxY8YwZMgQ3nzzTcu6NWvWEBAQwJ49eyzLfv/9dwICAvjll18A2Lp1K2PGjGHUqFEMGzaMb7/9FoAPPvigwPsePXo0SUlJFRJzacpUpr1ly5asWbOm2PX/bJa1tbVl/Pjxhbb7Z8n1G/Xt25e+ffuWJZzqKeUKxF+EQyugx1NaRyOEEEIIUWPUqVNH6xCqFA8PD9atWweoRMPR0ZFHHnnEsj4nJwcbm6LTgDZt2tCmTZtSj5GfyFSEoKAgFi9eTEZGBmPGjGHAgAGW/KJ58+Zs2LCB7t27A7Bx40ZatGgBqMqFr776Kj/88AO+vr5kZWUVqFj+4IMPFnjf1lKmBEtUAO9AMNjBtTOQngAO7lpHJIQQQghRI0RFRWkdQpX34osv4ubmxsmTJwkMDGTYsGHMmzePjIwM7O3tmTdvHk2aNGHfvn0sW7aMxYsX88EHHxAVFUVERARRUVE88MADTJ48GYAOHTpw+PBh9u3bx4cffoirqyvnzp0jMDCQt956C51Ox/bt25k/fz4eHh4EBgYSHh5e5BRM+ezt7WnZsiXR0dGWZUFBQYSEhJCdnU1WVhaXLl2iZcuWAKSmpmIymXB3dwfUdFJNmjSpvB9iGUmCZS02RqjTFiIOQNQhaCpjzIQQQgghKsLs2bOZPXu21mEU6aHP97P176sVus/+AbX5/KEu5X7dxYsXWb58OQaDgZSUFL788ktsbGzYvXs377zzDh98UHgoy4ULF1ixYgUpKSkMHTqUiRMnFpoP6uTJk6xevZoGDRowceJEDh48SJs2bZg1axZffvkl9evX59lnny01vsTERMLCwujcubNlmU6no0ePHuzatYvk5GSCg4MtrVTu7u4EBwfTv39/unfvTr9+/RgxYgR6vRoFtXz5ctavXw+Aq6srK1euLPfP7GaUaQyWqCB1g9R9xEFt4xBCCCGEqEHmzJmjdQjVwpAhQzAYDAAkJyfzzDPPMGLECObPn09oaGiRr+nbty9GoxFPT088PT2JjY0ttE3btm3x8fFBr9fTokULIiMjOX/+PPXr16d+/foADB8+vNi4QkJCGDlyJD179qRfv37Url27wPrhw4ezceNGNm3aVGg/b7zxBsuXL6dt27YsW7aMl156ybLuwQcfZN26daxbt85qyRVIC5Z11QuCfUBkiNaRCCGEEEIIK7iZlqbKcuNEwO+99x5du3blo48+IiIiwtL175+MRqPlscFgICcnp9RtTCZTkfPcFid/DNaFCxe49957GThwoKUbIKgELjQ0FHt7+yIn+w0ICCAgIIBRo0Zxxx13sGDBgjIfuzJIC5Y11c0rBhIRAuX40AkhhBBCCFGRkpOT8fHxAeDHH3+s8P03adKE8PBwS3e+TZs2lfqaxo0bM336dD799NNC65599llmzJhRYFlqair79u2zPD99+nSRlcytTVqwrMmjETh6Qdo1VVHQs3AGLoQQQgghyickRHoHldeUKVN48cUX+fzzz+nWrVuF79/e3p7XXnuNKVOm4OHhQdu2bcv0ugkTJvDZZ58RHh5eYHlR1cbNZjNLly5l1qxZ2Nvb4+DgwPz58y3rbxyDBfDRRx9Rr169m3xHZaczl6f9rho6depUgSbG4pZZzVfjIfRXuPMzaHOXNjHcJjQ9z8Iq5BzXfHKObw9ynmu+yj7HBw8eLDRtkJZux890enp6gS6IoFqYnJycMJvNzJkzh0aNGvHggw9qE+AtKs85lS6C1lYvv9CFfNMihBBCCFERgoKCtA5BFOH7779n9OjRDB8+nOTkZO655x6tQ7IK6SJobfkJlhS6EEIIIYQQNdiDDz5YbVusboW0YFmbX0d1f/ko5GRpG4sQQgghhBCiQkmCZW0O7lCrOZgyIfpYydsmRUFmslXCEkIIIYSorl577TWtQxDCQhIsLZRlwuG4C/B+R1g2BHIyrROXEEIIIUQ1NHv2bK1DEMJCEiwt1MufD+tA8dsc+gJy0iH6OOx6xzpxCSGEEEJUQ35+flqHIISFJFhaqFtKoQtTNhz+6vrzHW9BzOnKj0sIIYQQohq6fPmy1iFUKffffz87d+4ssGz58uUltvTdf//9HDumhq9MnTqVpKSkQtt88MEHfPbZZyUee/PmzZw9e9by/L333mP37t3liL5o+/bto1OnTowZM4YhQ4bw5ptvWtatWbOGgIAA9uzZY1n2+++/ExAQwC+//ALA1q1bGTNmDKNGjWLYsGF8++23lvfUu3dvRo8ebbkV9d7LQ6oIasEnEGzsIe48pMWBo2fB9Wd+hdQYqBUADbqp1qz1T8HDv4JecmIhhBBCCFG84cOHs2nTJnr37m1ZtmnTJv7zn/+U6fWffvrpTR978+bN9OvXj2bNmgHwzDPP3PS+/ikoKIjFixeTkZHBmDFjGDBggGX+s+bNm7Nhwwa6d+8OwMaNG2nRogUA2dnZvPrqq/zwww/4+vqSlZVFRESEZb8PPvggjzzySIXFKVfrWjDYQp326nFkEeOwDn2h7jtOhoFzwdkXIvZDSMnfGAghhBBC3I46duyodQhVyuDBg9m6dStZWapidUREBDExMXTq1InXXnuNcePGMXz4cN5///0iXx8cHExcXBwAixYtYvDgwTz44INcuHDBss2qVau48847GTVqFE899RTp6ekcOnSIP/74g//973+MHj2aS5cu8eKLL1pakfbs2cOYMWMYOXIkM2fOtMQXHBzM+++/z9ixYxk5ciTnzp0r8f3Z29vTsmVLoqOjLcuCgoI4evQo2dnZpKamcunSJcvEwKmpqZhMJtzd3QEwGo00adLkJn6yZSMtWFqpFwThe9WEw/4Dry9PjICzm8FghHYTVdXB4W/Bd/fB5tkQMBTc6mkVtRBCCCFElXPwYAmFw7T21d0Q+lvF7tN/EEz6vtjVHh4etG3blh07djBgwAA2bdrE0KFD0el0zJgxA3d3d0wmEw8++CCnT5+2tPT80/Hjx9m0aRNr167FZDIxduxYAgMDARg4cCDjx48H4J133mHt2rU8/PDDBAcH069fP4YMGVJgX5mZmbz44ossX76cxo0b85///Ievv/7aMk+Wh4cHP/74I1999RXLli3jjTfeKPb9JSYmEhYWRufOnS3LdDodPXr0YNeuXSQnJxMcHGxppXJ3dyc4OJj+/fvTvXt3+vXrx4gRI9Dn9Qxbvnw569evB8DV1ZWVK1eW9NMvlbRgaaVuXqGLf47DOvwVmHOhxQhw8lLLWo5Ut6wU2PhvMJutG6sQQgghRBU2bdo0rUOocvK7CYLqLjdixAgAfv75Z8aOHcuYMWMIDQ0tsbUoJCSEAQMG4ODggLOzM8HBwZZ1oaGh3HvvvYwcOZKffvqp1FanCxcuUK9ePRo3bgzA2LFjCQm5fh08aNAgAFq3bk1kZGSx8YwcOZKePXvSr18/ateuXeg9b9y4kU2bNjF8+PAC69544w2WL19O27ZtWbZsGS+99JJl3YMPPsi6detYt27dLSdXIC1Y2qmXl3FHhKiESaeDXBMczjupHScX3H7o/8H5HXDmFzixBlrfad14hRBCCCGqqE8//ZQlS5ZoHUbRSmhpqkwDBgxgwYIFnDhxgoyMDAIDAwkPD2fZsmX88MMPuLm58eKLL5KZWfJ0QDqdrsjlL774Ih9//DEtWrRgzZo1pRayMJfSQGBrawuAXq/HZDIVuU3+GKwLFy5w7733MnDgQEs3QIC2bdsSGhqKvb29JZG7UUBAAAEBAYwaNYo77riDBQsWlBjTzZIWLK241QNnH8hIgNi8jP/cVkgMB/eG0Lhvwe1d68Cguerxpv+o4hhCCCGEEEIUwcnJiS5duvDSSy9ZWq9SU1NxcHDAxcWFa9eusWPHjhL30blzZ37//XcyMjJISUlh69atlnWpqanUrl2b7OxsfvrppwLHTU1NLbSvJk2aEBkZSVhYGADr1q0r0MWvPBo3bsz06dOLLMbx7LPPMmPGjALLUlNT2bdvn+X56dOnqVu37k0duyykBUsrOp0q1/73RtVNsFYzOLRcres4uehqgR0mw9HvIWwX/PYqjPnIqiELIYQQQojqY8SIETz55JMsXLgQgBYtWtCqVSuGDx9O/fr1Sy0OEhgYyLBhwxg9ejR169a1VOwDVR3w7rvvpm7dujRv3pzExEQAhg0bxquvvsrKlSsLFNGws7Nj/vz5PPPMM5hMJlq3bs3EiRNv+r1NmDCBzz77jPDw8ALL+/btW2hbs9nM0qVLmTVrFvb29jg4ODB//nzL+hvHYAF89NFH1Kt38zUPdObS2uuquVOnThVoOixumSZ2vg1b5kLnqdD3P7CwpeouOOOEarEqyrWz8HE3MJvg6cPg0ciqIVcnVeY8i0oj57jmk3N8e5DzXPNV9jmOioqqUpMN346f6fT0dBwcHLQOo9KU55xKF0Et3Tjh8JGvITcHmg8uPrkC1dLV+k5VCGP/zc9RIIQQQghRU1TpKoLitiMJlpb8OgA6uHIMDn6ulnV8oPTXdXtM3R9aAZnJlRaeEEIIIUR1MGrUKK1DEMJCEiwt2btC7Raq5Sr+Irj4QbMBpb/Orz007AmZSaqsuxBCCCGEqFJq+Cic20p5z6UkWFqrd32wIB3uA0MZ6450e1zd71ukyrsLIYQQQogqwd7entjYWEmyagCz2UxsbCz29vZlfo1UEdRa3SA4/CWgUwlWWQUMVQUu4i/C3z9DyxGVFKAQQgghRNW2ePFirUMooF69ekRERHD16lWtQ7Ga7Oxsy1xWNY29vX25qgpKgqW1psFg6wjNh4BHw7K/Tm+Aro/CLy/C3kWSYAkhhBDitjVt2jStQyjA1ta2yIlua7LbsXJicaSLoNY8GsK//4ZxNzH7eIf7wM5VzYt1+a+Kj00IIYQQohrQ6XRahyCEhSRYVYG9KxhuoknVzgU63K8e7/m4YmMSQgghhBBClJskWNVd12mg08Px1ZB8RetohBBCCCGEuK1JglXdeTSCFsMhNxsOLNU6GiGEEEIIqxsxQsaii6pDEqyaoNsT6j5kGWSnaxuLEEIIIYSV/fTTT1qHIISFJFg1QYNu4NcB0mLh6CqtoxFCCCGEsKqRI0dqHYIQFpJgWYnZbGbv+VjiU7Mqfuc63fWJh/d8BKacij+GEEIIIUQVtWHDBq1DEMJCEiwrORqRyIQle5m74WTlHKDVGHBvANf+hn2LKucYQgghhBBCiBJJgmUlWaZcAMJiUyvnADZGGPa2erx1HsSHVc5xhBBCCCGEEMWSBMtK3B3UPFcJ6dmVd5DmgyBwLGSnwabnwGyuvGMJIYQQQlQRZrnmEVVItUywNm/ezCuvvMJjjz3Grl27tA6nTNwdjQAkplViggUwZAHYuUHob3BybeUeSwghhBCiCliyZInWIQhhUeYEy2QyMWbMGKZPn37TB5s5cybdu3cvcq6CHTt2MHjwYAYOHFjqL8mAAQN4/fXXWbBgAZs2bbrpeKzJ7YYWrEr9lsXFFwa8ph7//AKkJ1TesYQQQgghqoBbuT4VoqKVOcFasWIFTZs2LXJdbGwsKSkpBZaFhRUeAzRu3DiWLi08Ga7JZGLu3LksXbqUjRs3smHDBs6ePcvff//N9OnTC9xiY2Mtr1u0aBGTJk0q61vQlNFGj5PRgCnXTHJmJVf56/QQ1O8KKdGwZU7lHksIIYQQQghhUaYE68qVK2zbto277rqryPX79+/n8ccfJzMzE4BVq1bx+uuvF9quc+fOuLm5FVp+9OhRGjZsSP369TEajQwfPpwtW7YQEBDA4sWLC9y8vLwwm8383//9H3369CEwMLA871dTVusmqNfDiHdBb6MmH760r3KPJ4QQQgghhADKmGDNmzeP559/Hr2+6M2HDh1K7969mTFjBuvXr2f16tW8++67ZQ4iOjoaX19fy3MfHx+io6OL3X7lypXs2bOHX375hW+++abMx9Gau2NeN8HKTrAAfFpBz2fU45+egZxKmH9LCCGEEKIKWL9+vdYhCGFRaoK1detWPD09ad26dYnbTZ06FTs7O2bPns2iRYtwcnIqcxBFjUnS6XTFbj958mTWrFnD3LlzmThxYpmPo7X8BCs+zUrJTp/nwaMxXD0Fez6wzjGFEEIIIaysU6dOWocghEWpCdahQ4f4448/CA4O5tlnn2Xv3r0899xzhbYLCQkhNDSUgQMH8uGHH5YrCF9fX65cuWJ5Hh0djbe3d7n2UR24O6gugpVaqv1Gtg4w4h31eOs81V1QCCGEEKKGqVu3rtYhCGFRaoL173//mx07dvDHH3+wcOFCunXrxltvvVVgm5MnT/LKK6/w8ccfM3/+fBITE3nnnXfKHESbNm24ePEi4eHhZGVlsXHjRoKDg8v/bqo4t7wWrERrtWABNO2vWrJyc2DDDFVZ0FTJRTaEEEIIIYS4TVXIPFjp6em89957NGjQAL1ez4IFC4r8JuHZZ59lwoQJXLhwgT59+vD9998DYGNjw6xZs5gyZQrDhg1j6NCh+Pv7V0RoVYqHpYuglVqw8gW/AqM/Ar0t7PsEvh4v5duFEEIIIYSoBDbl2bhr16507dq10PJ/9nu1tbVl/PjxhbZbuHBhsfvu27cvffv2LU841Y6li6C1EyyADveBZ1P4bhKc2wKfDYSJ34JX0aX3hRBCCCGqi6lTp2odghAWFdKCJcomv4tgQrpGFf0adoepf4B3K7h2BpbeARd2aBOLEEIIIUQFWbJkidYhCGEhCZYVeVhrHqwSg2gED/8K/oMhPR6+niDdBYUQQghRrUkVQVGVSIJlRVYv014ce1eY+A34toHsVLhyVNt4hBBCCCFuwaFDh7QOQQgLSbCsyN0hv4ughi1Y+fQGqJv3bU/0CW1jEUIIIYQQooaQBMuKrpdprwIJFoBP3uTR0ce1jUMIIYQQ4hbUqVNH6xCEsJAEy4punGjYbDZrHA3gE6jupQVLCCGEENVYVFSU1iEIYSEJlhUZbfQ4GQ2Ycs0kZ1aByX69W6n7mFMy+bAQQgghqq3Zs2drHYIQFpJgWZl7VagkmM/BHdzqQ04GxJ3XOhohhBBCiJsyZ84crUMQwkISLCtzyy90URUSLJBxWEIIIYQQQlQgSbCszMOpipRqzyfjsIQQQgghhKgwkmBZ2Y2FLqoESbCEEEIIUc2FhIRoHYIQFpJgWdn1Uu1VpQVLuggKIYQQQghRUSTBsjIPxyo2BsuzCdjYQ2I4pCdoHY0QQgghRLkFBQVpHYIQFpJgWVl+F8H4qpJgGWygdgv1OOaktrEIIYQQQghRzUmCZWX5XQQT0qtIF0EA3/xugjIOSwghhBBCiFshCZaVuTvkj8GqIi1YIOOwhBBCCFGtvfbaa1qHIISFJFhW5uGU30WwCrVgSSVBIYQQQlRjs2fP1joEISwkwbKy/BasKlOmHcA7P8E6Cbm52sYihBBCCFFOfn5+WocghIUkWFZ2vUx7FUqwnLzApQ5kp0L8Ba2jEUIIIYQol8uXL2sdghAWkmBZ2Y0TDZvNZo2juYF0ExRCCCGEEOKWSYJlZUYbPU5GA6ZcM8mZOVqHc52PVBIUQgghRPXUsWNHrUMQwkISLA24O6pWrCrVTVAqCQohhBCimjp48KDWIQhhIQmWBtzyC11UqQQrv4ugJFhCCCGEqF6mTZumdQhCWFTLBGvz5s288sorPPbYY+zatUvrcMrNvSpONlzLH/S2EH8RMpO1jkYIIYQQosw+/fRTrUMQwqLUBCszM5O77rqLUaNGMXz4cN5///2bPtjMmTPp3r07I0aMKLRux44dDB48mIEDB7JkyZIS9zNgwABef/11FixYwKZNm246Hq14OObPhVWFWrAMtlC7hXocc0rbWIQQQgghhKimbErbwGg08sUXX+Dk5ER2djb33nsvffr0oX379pZtYmNjsbOzw9nZ2bIsLCyMhg0bFtjXuHHjuO+++3jhhRcKLDeZTMydO5fPP/8cHx8f7rrrLoKDgzGZTCxcuLDAtvPmzcPLywuARYsWMWnSpHK/aa1dL9VehVqwQHUTjD6mugnW76J1NEIIIYQQQlQ7pSZYOp0OJycnAHJycsjJyUGn0xXYZv/+/XzzzTd8+umn2NnZsWrVKn7//fdCzbWdO3cmIiKi0DGOHj1Kw4YNqV+/PgDDhw9ny5YtTJ8+ncWLFxfa3mw289Zbb9GnTx8CAwPL/m6rCPeqOAYLpFS7EEIIIaqlyMhIrUMQwqLUBAtUC9O4ceO4dOkS9957L+3atSuwfujQoURERDBjxgyGDBnC6tWrWbZsWZmDiI6OxtfX1/Lcx8eHo0ePFrv9ypUr2bNnD8nJyYSFhTFx4sQyH6sqqJJdBAF88yoJXpFCF0IIIYSoPg4ePIifn5/WYQgBlDHBMhgMrFu3jqSkJJ544gnOnDlD8+bNC2wzdepUZsyYwezZs9m8ebOl1assippw95+tZDeaPHkykydPLvP+qxq3qljkAgrOhWU2QwnnQAghhBCiqhg1alSR15NCaKFcVQRdXV3p2rUrO3fuLLQuJCSE0NBQBg4cyIcffliuIHx9fbly5YrleXR0NN7e3uXaR3WS30WwSs2DBeDsDU61ISsZEi5pHY0QQgghhBDVTqkJVlxcHElJSQBkZGSwe/dumjRpUmCbkydP8sorr/Dxxx8zf/58EhMTeeedd8ocRJs2bbh48SLh4eFkZWWxceNGgoODy/lWqo/8iYYT0qtYggVVdxxWSoxqVRNCCCGEEKIKKzXBiomJYfLkyYwcOZK77rqLHj160L9//wLbpKen895779GgQQP0ej0LFiygbt26hfb17LPPMmHCBC5cuECfPn34/vvvAbCxsWHWrFlMmTKFYcOGMXToUPz9/SvoLVY9HnldBOOrWhVBKNhNsKo4uR7e8oev7oLUWK2jEUIIIUQVU1RRNCG0UuoYrBYtWrB27doSt+nUqVOB57a2towfP77Qdv8suX6jvn370rdv39LCqRGul2mvyi1YVajQxan16v7sZljcG+5aBg26aRuTEEIIIaqMadOmaR2CEBblGoMlKoZbfpn29OyqNyDT0oJVRRIssxku7FCPvVtBUiR8Pgz+fF+6DAohhBACKLk4mhDWJgmWBuxsDDgaDZhyzaRk5mgdTkG1A0BngNhzkJWmdTRw7QykRIOzD0zbDt2fBLMJfn8Vvr0X0uO1jlAIIYQQQggLSbA0kj8XVpWbbNjGTrUUYYYL27WO5nrrVeM+YGOEwW/AhK/B3g3+3gSf9IGoI5qGKIQQQgghRD5JsDRi6SZY1RIsgDZ3qfuDX2gbB1xP8hr1vr6sxXCYvgP8OkLiJVg+Ai7+qU18QgghhNDciBEjtA5BCAtJsDTiXlUnGwZoPwn0thD6KyRGaBdHbi5cyJtzrXGfgus8GsHDv0DrO9W8XV+OgzO/Wj1EIYQQQmjvp59+0joEISwkwdJIfhfB+KrYguVcG1qOAHMuHP5Suziij0FGArg1UAnVP9nYwbhPodNDkJOhxmQd/d7aUQohhBBCYyNHjtQ6BCEsJMHSyPVS7VWwBQug04Pq/tAKMGlUiOPG8VfFVQfSG2DEO9BrBuTmwJqpcGCp9WIUQgghhOY2bNigdQhCWEiCpRH3qjwGC6BRH/Bsosqin92sTQw3Jlgl0elgwGx1wwwb/w0735Yy7kIIIYQQwuokwdLI9TFYVTTB0uuh4wPq8cHl1j++KRvCdqvHjXuXvG2+XjNgxLuADrbMxW/vaxB3vrIiFEIIIYQQohBJsDTibhmDVUW7CIK2xS6iDkNWCnj5g6tf2V8X9BDc9RkYjLhd+g0+CIL1T2tbrEMIIYQQlcosvVZEFSIJlkbyuwgmVtUugqBtsYv88uyldQ8sSus74ckDJDQeAZjh0Bfwfgf4+QVIjq7QMIUQQgihvSVLlmgdghAWkmBpJL8Fq8p2EczX6SF1b+1iF2Udf1Ucj0Zc7vIKPLFfJVymLNj3CbzfHo79UGFhCiGEEEJ706dP1zoEISwkwdKIZQxWVe4iCGqCX2sXu8jOgEv7rh//VtTyh7uWwaN/QvOhkJ0Gv8+SAhhCCCGEEKJSSIKlkesJVhVvwSpQ7OJz6xwz4gCYMsGnDTh5Vcw+fVvDhK/B2Ucli9EnKma/QgghhBBC3EASLI24OVyvIljlB2Zail38Zp1iEbfaPbA4ej34D1KPz/xSsfsWQgghhGbWr1+vdQhCWEiCpRE7GwOORgOmXDMpmRpN5FtWNxa7OLSy8o9XWQkWQPPB6j70t4rftxBCCCE00alTJ61DEMLCRusAbmcejkbSstJJSMvGxd5W63BK1ukhOPGj6iZoyoLsdDWeKf/e3h2Gvgl2zrd2nMwUiAwBnR4adq+Q0Ato0g8MRgjfD6mxFdcFUQghhBCaqVu3btXvESRuG5JgacjNwZbIBJVg1ffUOppSNOoNnk0h7hzsWlj0NnU7QOcpt3acS3shNwfqdgJ7t1vbV1HsXKBhTzi/VRXtaHdPxR9DCCGEEELctiTB0pCl0EV6Fa8kCGr80vgv4OR6sLEDW0ewdVD3V47Cng/h9KZbT7BuZf6rsmo+RCVYZ36RBEsIIYQQQlQoSbA0VG0qCebzbaNu/9RsAOz9WI2dyki8tZanyhx/la/5IPjlBTi3BUzZYKji3TOFEEIIUaKpU6dqHYIQFlLkQkOWyYar+lxYpXHyggY9IDcbQn+/+f2kx8Plv1TFwvrdKi6+f/JsArWaq2QwfF/lHUcIIYQQVrFkyRKtQxDCQhIsDbk7VLMWrJK0GKbu/9508/u4+CdghvpdwOhYIWEVS8q1CyGEEDWGVBEUVYkkWBq6PgarBiRYAXkJ1pnfICfz5vZx4kd137hvxcRUkuZD1P0ZKdcuhBBCVHeHDh3SOgQhLCTB0pC7Q34XwRqQYHk2Bu9AyEqGizvL//qky3ByrSrP3v7eCg+vkAbdwM4Nrv0NcRcq/3hCCCGEEOK2IAmWhq4XuajmY7DytRiu7k/fRDfBg5+r8uwtRoB7/YqNqygGW2gWrB7LpMNCCCFEtVanTh2tQxDCQhIsDVmKXNSELoJwPcH6exPk5pb9dTmZELJMPe46veLjKo7/YHUv47CEEEKIai0qKkrrEISwkARLQzWuBatOO3CtB8mXIepw2V93Yi2kXgWf1moSYGvxHwjo4OIuyEyx3nGFEEIIUaFmz56tdQhCWEiCpaFqNw9WaXS6G6oJbizba8xm2LdIPe4yTe3DWpxqQb0gMGXB+W3WO64QQgghKtScOXO0DkEIC0mwNOTmcL2KoNls1jiaCpJfTfB0GROsiBDV2uXgAW3urry4itM8r5tg6K/WP7aoeGYzbHgWvpmo5jkTQgghhLAySbA0ZGdjwNFowJRrJiUzR+twKkajXqo639XTEHuu9O33L1b3HR+o/LmvimIZh/WbujgX1duRryDkMzUO8NtJNz9lgBBCCCHETZIES2M1arJhUNX5mudN4ltaK1bSZTX3lU4PnR+p/NiK4tsGXPwg5Qpc/kubGETFSLkKv76sHts6qekC1j5evoIrQgghqqWQkBCtQxDCQhIsjVkqCdaUBAtuKNdeSoJlKc0+HNwbVH5cRdHprieEZ6SbYLX2y4uQkQBN+sPDP4PRGY7/AJtf0zoyIYQQQtxGJMHSmKXQRXoNqSQI0GwAGIwQvg9SYoreJicTQj5Xj7tYsTR7UZoPUfdnf9c2DlFYWhzs/gCO/VDydqG/q2TKxgFGvKMqWt6zEvQ2sPt92LfYOvEKIYTQRFBQkNYhCGEhCZbGalwlQQA7F2jcFzAXP8fUibWQGgPegWrclpYadFf3V46BqYaMhavu4i/Cpv/AO4Hw2yuw+hH4ZSbkmgpvm5kCG2aox/1fAs/G6nHTYBj1oXr88wtwcp1VQhdCCCHE7U0SLI25OdSwyYbzldZNcN8n6r7rdOuWZi+Kg7vqopiTAbGh2sZyu4s6DN8/BO93UAVQstPU3Gh6W9j7MXx3H2SlFnzN1nmQGK5arbo9XnBd+4kQ/CpghtVTIWyP1d6KEEIIIW5PNloHcLvzyG/BSq1BXQQBAobChn/Bua1w7g/IyYLsVMhKg6QoiDoE9u7alGYvim9bSLikWrG8W2odze0nPQHWTIXQ39RzvQ20GQ89ngLf1moy6G8nqeqAnw+Fid+Bax3sY0+qedR0Bhj5PhiK+JPW+9+QFAkhy+CbCfDoLnCvb9W3J4QQonK99pqMtxVVhyRYGrs+BquGtWC5+EK9zhBxAFaOLXqbjpO1Kc1eFN+2cHqDqiTYdrzW0dxe0uPVZyTqMBhdoNMD0O0xcKt3fZtGvWDKZvjqLnWOlg6ACV9RJ2Q+mHNVIubXvuj963Qw7C1IjFAJ3NrHYPJ60EsDvhBC1BSzZ8/WOgQhLKplgrV582a2bdtGbGwskyZNolcvjcfw3AJ3hxpYRTBfv5mw/X+qNcLoCLaOYHQCWwdwrAXdn9A6wut826j7K8e0jeN2kx4PK8bA5SPg0Qge2FB861Itf5iyBb69VxVQ+bQ/9uZc1b2z38ySj6M3wOiP4eNuqnz7vkVV6/MnhBDilvj5+REVFaV1GEIAZUiwLl++zH/+8x+uXbuGXq9n/PjxPPDAAzd1sJkzZ7Jt2za8vLzYsGFDgXU7duzgjTfeIDc3l7vvvptp06YVu58BAwYwYMAAEhMTefPNN6t3gmUpclHDuggCNLtD3aqDOm3V/ZVjasJha40LizkF0SegUW9w8bHOMauKtDhYOUa1SHk0hgc3FGy1KopTLdX6tPYxOLFGLRvxrkrcS+NcG0Z9AN9OhM1zVBEM6Q4qhBA1wuXLl7UOQQiLUhMsg8HAiy++SGBgICkpKdx555307NmTZs2aWbaJjY3Fzs4OZ2dny7KwsDAaNmxYYF/jxo3jvvvu44UXXiiw3GQyMXfuXD7//HN8fHy46667CA4OxmQysXDhwgLbzps3Dy8vLwAWLVrEpEmTyv+uq5BaLnYAhITFczwykdZ13TSO6DblWhccPCA9To3XKe1CvyKEH1AJRlaKeu7bFvwHqjL39boUPZ6opkiLgxWj4crRvORqI7jVLdtrbe3hzs+gcR+iYq7hV54kvsUw1TX10Ao15mvKH2BjvLn3IIQQQghRhFIHIXh7exMYGAiAs7MzTZo0ITo6usA2+/fv5/HHHyczMxOAVatW8frrrxfaV+fOnXFzK5xAHD16lIYNG1K/fn2MRiPDhw9ny5YtBAQEsHjx4gI3Ly8vzGYz//d//0efPn0ssVVX7eu50y+gNonp2UxYspd952O1Dun2pNNZt5tg1BH48k6VXNUKABt7lWzsfFsVcfhfE1gzXXWhq2luTK48m5Qvucqn10PQQyQ2HlH+4w+ep7ojXjkG2+aX//VCCCGqnI4dO2odghAW5RrlHRERwalTp2jXrl2B5UOHDqV3797MmDGD9evXs3r1at59990y7zc6OhpfX1/Lcx8fn0JJ3I1WrlzJnj17+OWXX/jmm2/K8xaqHL1ex5L7gxjetg4pmTlMXrafP04X/95FJfK9oZtgZYo5pYo6ZCZCy1Hw2G544SLct0aVGa/VXK07+i18PhySr1RuPNaUnX7rydWtsnOBsYtBp4c/35XS7UIIUQMcPHhQ6xCEsChzgpWamsrTTz/NSy+9VKArYL6pU6diZ2fH7NmzWbRoEU5OZRgTkcdsNhdapithDMzkyZNZs2YNc+fOZeLEiWU+TlVltNHz/oQOTOzSgMycXKatOMi6I5Fah3X7yU+wLv9VeceIPacSjPQ48B+kuroZbFThj2Z3wJD58OQBeDJEJVoxJ2DZYIi7UHkxWdPRVSq5cm+okitXP23iaNANev5LVSD8cTpkJmsThxBCiApR0th9IaytTAlWdnY2Tz/9NCNHjmTQoEFFbhMSEkJoaCgDBw7kww8/LFcQvr6+XLly/Vv66OhovL29y7WP6s6g1zFvbGse7duUnFwz//ruCCv3hmkd1u2lsrsIxofBF6MgJRoa94HxK4sf/1PLHx76Bfw6QPxFWDYEok9WTlzWYjbDgU/V4/4va5dc5es3U53zhDBY9wQc+0ElgH99B0e+gSNfQ+hmbWMUQghRJp9++qnWIQhhUWqCZTabefnll2nSpAkPPfRQkducPHmSV155hY8//pj58+eTmJjIO++8U+Yg2rRpw8WLFwkPDycrK4uNGzcSHBxc9ndRQ+h0Ol4c2oIXhrTAbIZX1x5n/qZTpGbmaB3a7aFWczDYqQvu9ISK3XdSFKwYBUkRUL8bTPhGFWsoiZMXPPCTqjCYckWNzQo/ULFxWVP4PpW8OtaCwDFaR6OS23GfqnN+ch2sfkQVvvhxGqx9VFUq/OpOOCtJlhBCCCHKrtQE6+DBg6xbt469e/cyevRoRo8ezfbt2wtsk56eznvvvUeDBg3Q6/UsWLCAunULj6t49tlnmTBhAhcuXKBPnz58//33ANjY2DBr1iymTJnCsGHDGDp0KP7+/hX0Fqufx/o1Zd7YNuh0sHjHefq9tY3vDlzClFu4K6WoQAYb8GmlHkcfr7j9ZibDynGqJcqvA0xaBXaFu9kWyc4FJv0AAcMhI0Elaef+qLjYrGl/3reLnR4AGzttY8nn3RLuWgaB46D1ndD6LmgzHtpOgAbd1TaHVmgboxBCCCGqlVLrQAcFBfH333+XuE2nTp0KPLe1tWX8+PGFtvtnyfUb9e3bl759+5YWzm3j3q4NaO7jzH83nuKv8AReWH2Mz/+8yMvDW9Lbv7bW4dVcvm0h6rBqaWlUAfOrmc2q+9nVU6qF7L41YF/OUvy29jB+Bax/Cv76Gr66G9pNgJ4zoFaz0l9fFSRHq1YinR46Fd0SrpmWI9TtnxIj4d3W8PfPqvKho6f1YxNCCFEmkZEydl1UHeWqIiisK6iRJz8+1oP3JrSnrrsDp68kc/9n+3lg2X7ORMug/EpR0eOw/nxXJRZ2rqpb4M1epBtsYPRH0ONpVZjh8JfwYRCseqByi3JUlENfQG42BAwD9/paR1M2bnWhSX8wZcGx77WORgghRAmkiqCoSiTBquL0eh2j29dly7/78sKQFrjY2bD9zFWGvreT2etPkJiWrXWINYulkuDRW9/X2S2wZa56PG7Jrbc26fUw6L+qwmDHB0BvAyfXwuI+qgti2O5bDrlSmLIhZJl63GWqtrGUV4e8icwPf6ltHEIIIUo0atQorUMQwkISrGrC3tbAY/2asu35ftzXrQFms5nluy/S762tfLUvTMZnVRSfQEAHV09DTtbN7yf+IvzwsGpt6vsiBAytqAjBqymMeh/+dRS6Pwm2TnBuiyqCceqnijtORTm9EZIvqy6SjatZN+CA4apL55Wj1pmAWgghhBDVniRY1YyXsx2vj2nDhqd607WxJ/Fp2bz843FGfrCL/RfitA6v+rNzVglMbrYaN3UzstLg2/tUUYrmQ6DvCxUaooWrHwx+A2Ychy5583/88hJkZ1TO8W5WfnGLzlOhhPntqiRbe2hzt3p8+CttYxFCCCFEtSAJVjXVys+Vb6d146N7O1LX3YGTl5MYv3gPj315kOORiVqHV73dyjgssxl+egaij4FnUxi7WHXtq0yOnjB4PngHQuIl2PtR5R6vPKJPQtguMDqrwhzVUfu8boLHVt1aq6YQQohKs3jxYq1DEMJCEqxqTKfTMbxtHTY/25dn7vDHzkbPz8evMOKDXUxetp8952Ixm6XrYLnlj8MqS4KVmQKx5yBsD5xYC7/MVBfitk4w4StwcK/MSK8z2MCQ+erxzoWQfKXk7a0lf2LhdhPA3lXbWG6WXwfwbgVpsXDmF62jEUIIUYRp06ZpHYIQFqWWaRdVn4PRwIyBzZnQpT6f7bzA1/svsePMVXacuUr7+u481q8pA1v6oNdXs+5ZWimt0IUpG76dBBd3QXZq0duM+VjNsWRNTfqqMUN/b4Q//quqDmopIxH++k497jxF21huhU6nWrF+exmOfAWtquBA6pwsMNhWvy6YQghRQXQ6nXypLKoMacGqQeq4OfDKiFbsfjGYZwc2x8PRliPhCUxfeZAh7+1gy6lo+eNTFjd2EczNLbz+4HII/VUlVzb24N4Q6nWBFiMg6BGY8DUEjrFmxNcN+i/obdV4oagj2sSQ78g36mfUqLf1k82K1vYeVbUx9Hc1p1dVEnUY3mwEax9XXVSFEEIIoSlJsGogd0cjT9/hz58vBjN7ZCvqujtwJjqFR74I4Z4lezkSnqB1iFWbiw84+0BWMiSEFVyXngBb56nHdy+Hl6+oan5TflddAkcshBbDrR3xdV5Noet0wKy6K2p1wZ2bCweWqsfVrTR7UZxrg/9gMJvg6HdaR3Ndrgk2zFCJ7F9fV63YhBBCiNuUJFg1mKPRhgd7NuaP5/ry6ohWuDvasv9CHGM++pMnvj5EWGwx3dvEDa1Y/+gmuPMtSI+Dhj2h1Ziq2SWrz/Pg6AWXdqtJjq3JbIa/f4ElfSE2FFzrqm6LNUH+nFhHvqo6LUWHVqgWLFtH9XzjcxAfVvJrhBCiBhoxYoTWIQhhIQnWbcDOxsAjvRqz/fn+PNavKXY2ejYevcwdb2/npR+PcSwiUboO/lNRlQTjzsO+vCpFg9+omskVqMIawa+ox7+/ap2y7WYzhG6GT4Phm3tUYursq+brMtSQoZ7+g8CptpojLfKQ1tFAWhxsmaMej/4QWo5Ura4/TlctW0IIcRv56acqOA+kuG1JgnUbcXOw5YUhLdj2fD/u7lQPk9nM1/suMfLDXQx+dweLt58jOqmKzaGklaIqCW6eDaYsaDdRVZaryjpMVmXbEy7B3o8r91gXdsCywfDVnRB1SCUhg+fDM0eg2YDKPbY1GWzVWCyAI19qGwvAlrmQHg+N+0DgOBjxnkpqL+2BP9/VOjohhLCqkSNHah2CEBaSYN2G6rg58H93t+PXf/XhwR6N8HQyciY6hfk/n6b7/C1MXrafDUejyDEVUeDhdvHPSoJhe1R3OxsHCH5Vu7jKymADQ/LGiu18u/IKM4QfgC9GQfg+cPCEgXPhmb+g++Ng61A5x9SSZU6s1ZCdrl0ckYdUsRW9DQx7S7WmOnnBmLzKkVvnqa6DQghxm9iwYYPWIQhhIQnWbay5jwuzRwWyd+YdfDo5iCGBvhj0OnacucqTXx+m31vbWP7nBdKycrQO1fo8m6i5rJKjIOUq/PqSWt7zaXCrq21sZdWkHwQMg6wU2Davco5x+ifADIFjVbGPns+A0alyjlUV+LRSrZeZiXB6ozYx5ObCpucAM3R7HGoHXF/XbAB0fRRyc2D1VMhKK3ofOVlFV8gUQgghxC2TBEtgtNEzsJUPn9zfif0vDWDOqECa1HIiIj6d2T+dpMeCP1j4+xliUzK1DtV69Hrwba0eb35NdX1z9oUeT2sbV3kNnAs6gyqGcPXvit//xT/Vffv7wM6l4vdfFbW7V90fXaXN8Q+vhMiD4OIHff9TeP2A2VC7pSoy8lveWDxTNlzaC9sWwLIhMK8OfDEScm6j32khhBDCSiTBEgV4OBl5oEcjNj/bl8X3d6JDA3cS0rJ5f0soPRb8wcs/HuNIeMLtURQjv9DFka/U/R2vgp2zdvHcjFr+0OlBMOfC769V7L4zU1Q3NJ0eGnSt2H1XZa3HqaT17GZIvWbdY6fFqbGAAINfLzqptXWAOz8FgxFCPlNdON9spMbJbZuvxmjl5kDYLtj0vDWjF0KISnNbXJeIakMSLFEkvV7H4EBf1jzWg+8f7c4dLbzJzMnlq32XGPPRn/R/axvv/H6GC9dqcKn3/AQr/3G7idrFciv6vQhGZzjzM1zcVXH7Dd+n5oWq0+72ab0CcKqluuKZTXB8jXWP/cd/1TQB+YUtiuPb5vpYwQvbVTfRWs2hyzQ1EfYDG9Qk2Ye+gJBl1oldCCEq0ZIlS7QOQQiLGlI/WVQWnU5H50aedH7Qk9DoZL49EM76v6K4GJvGe1tCeW9LKO3quzO2vR8j2vlRy9lO65ArTn6hC4BBb4DeoF0st8LZW42N2voG/PYqTNmiukDeqrDd6r5hz1vfV3XTdjyE/qom9u06rfKPl3xFVYMM+bxgYYuSdH8S7F1VS1bjvoXHDo58T5V03/QfVXHydmqFFELUONOnT2faNCv8PRaiDCTBEmXm7+PCqyNa8dKwluw+d421h6P45fhl/gpP4K/wBP678RR9/GsxpkNdBrbywdFYzT9evm3V3EIejaBJX62juTXdn4ADS9VYspM/Qus7b32fYXnjrxr1uvV9VTcBw1SrYGQIxJ4Dr6aVc5xrZ2H3e/DXt2qKAIC+LxQsbFEcvV51Dy1OuwkQdQT2LYJV98O07eBap/B2l4/Cjv+Bs0/ZEjshhBDiNlfNr4CFFgx6Hb39a9Pbvzavj2nN76eiWXc4ku1nrrL1b3VzNBoYEujLqPZ+dG3shYOxGrb+GGzgniow31FFMDpB/5fgp2dg8xxoMQJsbqG1MTtdFVpABw26V1iY1YbREVqOgr++VsUu+s+s2P1HHoRd78KpvCqN6NTxev0L6naquOMM+q+a6y1sl0qyHtx4/XORGAl/vA5/fZMXA9B8KPjXoLnNhBBCiEogCZa4JQ5GA6Pa+TGqnR9xqVlsPBrFj4cjOXQpgTWHI1lzOBJbg442dd3o2sSLLo096dTQA1d7W61Dv/20vw/2LoKrp+HAZ2quqpsVcUC1qPi2AQf3CguxWmk7Pi/B+k6Nc6uolp39n+aVYUd172s3AXo8A7WaVcz+b2SwhbuXw5J+6pxueh4GvQ5/vgd7PoScDNDbQt2Oaszd9gXQ7A5pxRJCVDnr16/XOgQhLCTBEhXG08nI/d0bcX/3RoTFprLuSBS/nbzCyagkDl1K4NClBBZtO4deB638XOnSSCVcnRt54FWTxm5VVQYbGDAHvrlHdflqf+/NJ0f55dkb3obdA/M17qNK98dfgIgQqN/51vf598/wc17p9W5PQI+niu62V5Gca8OEL+GzwaroxYkfITNJrWs1Bga8proHvttGJWHntqgiH0IIUYV06lSBrftC3CJJsESlaOjlxNN3+PP0Hf4kZWRzMCye/Rfi2H8hjqMRCRyPTOJ4ZBLL/rwAgL+3M10ae9KlsSfdm3rh7WKv8TuooZoPVklR2C7YtVDNk3UzLOOvbsMCF/n0Bmhzl2rpOfrdrSdYUYfhh4dVSf1+M1WrmLX4dVBFL9Y+qpKr+l1VS1b9Lte36fG0mhNu25vQVFqxhBBVS926daVUu6gyJMESlc7V3pb+Ad70D/AGID3LxOFL8ey/qBKuQ5fiCY1JITQmha/2XQIgwMeFns1q0cvfiy6NvXC2k49qhdDpYNBc+DQY9n4CTfqpCnPlqZCYk6laMgAa9KiUMKuNtveoBOv4ahgyX3W5uxkJ4fD1PZCdpqYD6PtCxcZZFu0nqnne9LYqEf9nAtV5Cux+HyL2w7k/VFdBIYSwpt9nqR4UzQerFvbazbWOSIgiyVWrsDoHo4EezWrRo1ktALJycjkWmcj+C3HsOR/LgQtx/B2dzN/RySz78wI2eh0dGrjTo2ktejT1okMDD4w2MoXbTavbSVURPL4aVo4Fx1rQYpgqotC4L9gYS3595EE1Nse7FTh5WSfmqsq3DdRuCVdPwdktEDCk/PvISISvx0NKNDTqDSPf1651qOXI4tfZOavS71vmwPY3oWmwtGIJIazn4i41PhRUBdetb6j/h1qNgVajNQ1NiH+SBEtozmijp1NDDzo19OCxfk3JzDFx+FICf569xq6z1/grPIEDF+M5cDGe97aE4mBroHNjT3o29aJH01q0qOOCrUESrnIZ+R64N4STayHuPBxaoW52bhAwFAbOARffol9rGX91G3cPzKfTqWIXW+bA0W/Ln2CZsmHVZIg5qSYCvmdl6QmulrpMhd0fqIIX57eqJEsIISpbrgl+zus23X4SmM3w90b1tzPmJGybx9Q+9SEz+faa+F5UWZJgiSrHzsZAtyZedGvixb8HBZCUkc3ec7HsPhfLn2evERqTwo4zV9lx5ioARoOeZt7OtKjjQqs6rrTwdaVFHfkDWyI7F1W84I5Z6j+nUz/ByfUQc0IlCinRMHlt0a8N26Xub+fxVzdqc7dKsP7+WbVG2buV7XVmM2yYAee3gVNtmPQ9OHhUaqi3zM4FejwJW+aqsVhN+ksrlhCi8h1eCdHHwLWemo/P6Ag5WXBhO5xYC6d+Ykn/RNUzo6T5/4SwEkmwRJXnam/LoEBfBgWqFpWY5Az25CVbe8/HcSkujZOXkzh5OYk1RFpe5+1kQ+em6XRs4EHHBu608nPFzqYazsdVmXQ68AlUt34vwtW/YelA1TpxdnPhanGmbAjfrx5LC5biXv964ZBTP0GH+0re3pQNJ9fBvk/UWDYbB5j4nZrQujroMi2vFWuvurhp0k/riIQQNVlGImz5r3o8aK5KrkC19vsPVLeGPeg0fDIHG66SBEtUCZJgiWrH28We0e3rMrp9XQCSM7I5E53MqcvJnL6SpO4vJxGTmsPGo5fZePQyoLoitvZzJaiRJ0ENPQhq5ImnUxXujqWF2gHQ+1lVLe7311QLxY0FMKIOq0IMXv7g7K1dnFVN2/EqwTr6XfEJVuo1OPi5moMsWX0msXeDsYuhXjUqL2znosZi/fFf2LZAjduTViwhRGXZ/j9Iu6YmtQ8cV/Q2LUdy6HKuqnCbEK6++BJCQ5JgiWrPxd6WTg096dTQ07LMlGvmt71HSbTx4NCleA5dSuBsTIplPq4leds183amcyMPOjfypG09dxrXcsKgv80vFrs+CgeWQvRx+Otb6DDp+rqL0j2wSK1Gq8mBL+yEP14HnQEwq26AAInhcHwNmDLV81oB0HW6mkTY6KRZ2DetyzRVPfHSHriwA5r01ToiIURNFHsO9i0GdKpSa3Ff5ti7Xn987Hv1RaEQGpIES9RIBr2ORh5GWrZswIQuDQBITM/m8KV4DobFc+BiHIfzkq6zMSl8sz8cAHtbPQE+LrTwdaVlHRda1nGllZ8rLvY3WX67OrK1h+BX4cdpKlkIHHu9S0aYTDBcJAd3VRzk5DrY8X/FbKSD5kNUYlXdxy7Zu0L3J9Tn4+f/qEHntVuoFlC3+qCXojNCiArw68uQm616Bvh1KHHTOrU9gRw4ugp6zajef2NFtScJlrhtuDnY0i/Am35583Fl5eRyPCqRkItxHLgYz8moJCIT0vkrIpG/IhItr9Pp1LxcHRqoSocdG6iWLl1N/uPd5m7VQnHlKOxbBL3/DaYcuLRXrZcWrMKGLACfNupigLzPhk6nHtvYqRLoXk21jLBidZkO+5bA1dPw+6vXl9s6qoqITYOh/8tgqIb/zYQfgF3vQPt7oeUIraMR4vZ07g848zMYXSB4VqmbR0VdhrcD1LQZ0cfVNBpCaKQa/s8nRMUw2ujzCmB4MK2PWpaYls2pK0mcvqzGcp28nMTpK0mcvpLM6SvJfLNfTYTs4WhLtyZeDAr0IbiFD24ONayFS6+HQf+FFaNh5zvQ8QFICIOsFPBoDK5+WkdY9bj6Qd/ntY7CeuxdYfp2OL1RFUe5ehqunVEVKC8fUbesFBhWXIteFWTKVi2QO94CswnO/AJ3LYPAMVpHJkTRstPB1kHrKCqeKQd+make9/k3uPiU+pLZr89jdudxqov70e8kwRKakgRLiBu45SVO3Zpcn0A3I9vEschEDoXFc+hSPAfDEriWksnPx6/w8/Er2Oh1dG/qxeBAXwa18sHb1V7Dd1CBmvSDZgPh7O9qYlm3vEHD0nol8rn6qbmxbpQWp8bqrX4E9i9RrVn/3KYqunYW1kyFqEOATk36fHGneh8GW2gxXOsIyycnUyW+GYmQmaTuM/LuHdyh85SCBWxE9ZKZDJueh7++Ud24B/63+hZ2MJtVopiRAOkJ6j70N/WljUdj6PZ4mXYzZ84cZl/apxKsYz/AgDnyGReakQRLiFLY2xro3MiTzo1UEQ2z2Ux4XDp/nI7m1xPR7L8Yx87Qa+wMvcar647Tqo4rbeq6EVjXjdZ+rrSs44q9bTX9Iz9wLpzbAiHLoHZLtUzGX4mSOHpCq1GQ/aEax/fzC+DZBJrdoXVkRTObIeQz+PUVyElXXySM/URNQ7D5NfjzPVj1AEz8RpWDLk5OlkrEqkLXYbNZtT5f2lP8Ntnp0OtfVgtJVKCIEJX4x19Uz0/8CH//os5nz2eqXouW2Qw/PkrT87vgV72aNDg35/p9dhqYsop+7aDXVRfrsqrXWU15EX9RfUEi00gIjUiCJUQ56XQ6Gng58mDPxjzYszFxqVlsPhXNbyeusCP0GieikjgRlQQHVOEMg15Hs9rOdGjgTr+A2vRsVqv6FM3waaXGoRz+Uk3yCNKCJcqm3T1w7W/Y+TZ8/yBM2ayKYFQl6Qmq1Sr0N/W87QQY9r/rk0UPmKMSp32L4NtJcO930LT/9debzepi98BSdZFbrzPc/bn2Uxic+FElV0YX1U3K3lW9J3s3QAf7F8PWN8B/kPodF9rLzVVjXuPOqc+Re4MitjHBroWwdb7qwurTBga/Dge/gBNrYNt89bd60OuqsmlVSPZBdR0++i0lTopisFMtq/bu1+/rdy5/y7FOB23Gw47/wdHvJcESmpEES4hb5OlkZHxQfcYH1SctK4cTUUkcj0zkWGQiJyKTCI1J5u9odfv2QDg2eh1BjTzoH+BN/xbe+Hs7V+2CGf1fhmOr877db1D0f/xCFKX/K3AtFE6th6/Hw5Q/wMmr9NdZy6bnVXJl7w4j31VdrW6kyysNbcpSrVzfTIT7fgC/jnD8B5VYXf7r+vZhu2BxX7jnS+3mNsvJgi1z1ONB/4WghwpvY8pSc7L9OB2m/qFa3oT1pcfDua0Q+rua2D015vo6r2aqUEyT/tCol+rmuWba9Uqu3Z+EO2ap1p0m/aDzI/Dzi+qLsO8fUF1cx3xcNf5eRx0GIKVOd5zHfwJ6GzWVhd5GdeGzdaiQVreQkBD1oG1egnVyHQx/q+q16InbgiRYQlQgR6NNge6EAOlZJk5dSWLv+Vi2nb7KwUvx7D0fx97zccz/+TR13R0YFOjD4EBfOjfyrHrzcLn6QY8n1eD/pv20jkZUJ3q96m6XEKYSkVX3w/1rwaYKTPB95lc4tgpsHGDKFqjVrOjtdDoY9paqDnloBXx1t0pIMvIqjTp4Qsf7oeUoVVI6fC98PgSGL1TLrS1kmeoeVas5dCjm+IP+qyq0XTmqCnr0n2nVEG9757aqSboj9oM59/py17pquoOIAxB7Vt32L1GJiMEOslPByVv9Tv2zy22jXqrozMHlahLwizvhy7tgyu/XW2S1EnkIgLRa7XH2aFT5x6vlr0q6Rx2Gv3+G1sVMTixEJZIES4hK5mA0WKoVPt6vGYlp2ew8e5Wtp6+y/UwMkQnpfP7nRT7/8yKeTkYGtPRmcKAvPZvVqjpjt/q+qP7jb9K/9G2FuJHRCSZ+C58Gq2/fN/wLRn2o7VxZGUmwYYZ6HPxK8clVPr0eRrynWoeOfgvZQN1O0HmqavWyzSts88BP8MuLqrVr/ZPqAm/IAusllBmJqiANqO6NxZXIt3NRrRvLR6gvTgKGlDrHkKggl/bBNxMgJ0MlTg17QrMBanyfdyuV0JtyIPKgSoLPb1XdULNTwX8wjP4InGsXvW+9QbVkBY6F5cMh5iT88DBM/E7b6RLyWrAyPFtU6mGCgoIw50/u3vYeddxj30uCJTQhCZYQVubmaMuItn6MaOtHbq6Zw+EJ/HbiCr+euMLF2DRWhUSwKiQCOxs9gX6utK/vQfsG7nSo7049DwdtuhMabKDNXdY/rqgZXP1UkYhlQ+HIV5AYAWMXg2sdbeLZPBuSIlWS1O2xsr1Gr1dJif9AVbSjbsfC29gYYcRC8GsPG/+tEq2Yk3D3F2UqM33Ldr0L6XHQoIea+LokjXqp6mx7P4IfH4Vp268niqJyXDt7PbnqcD8MnqfGx/2TwQYadFW3/jNV4pwSo7oNluXvv6On+n37NFh1Pfz9VdXVVQumbNVSCqR7trTecVvfqVqUQ3+D1NjCXZPNZvV3KCUGUq8WvJlzoe8L6ucoxE2SBEsIDen1Ojo1VBMYvzi0BaExKfx6/Aq/nrzC8cgkDl1K4NClBMjrdu/lZKRDA3d6NqtFn+a1aVLTJzwWNYdfB7j3W/jhEbiwHT7pCWM+geaDrBvHxT9V4qO3zWtJK0crsd5Qti8aOk4G70D47j5VbGLFKJi2rXLHgiRGwt6P1eNB/y3bhfgdr14vh731DfW6fGYzXDmm5hOKOaW2lVaum5dyFb66UyXA/oNgxLtlb1WyFCgpB49GaizgF6PU56J2AHR6sJxBV4CYUyqh9GxCrrGIZLKyOHurgjRnN8PJH9W0BABXz6iuwUdXqa7LJRn6ZuXHKWosSbCEqCJ0Oh3NfVxo7uPCU3f4k5CWxV8RiRy5lMBfEQkcCU8gNjWLzadi2HxKDYau6+5An+a16ONfmx7NatW8CY9FzdKkHzy2WxVXOL8Vvr4buj0BA14rXynmm5WdDuufUo97/7tyK+jV66SSquXDVQLzx+sw+I3KO97WeepCttUYqBdUttfYOqiWxM8GwO4PVMU217qqW9XRVXD11PVtw/6EUR+oAgKifLLS4Jt71Ni4Ou3hrs+t02WvYQ8Y8Y7qrrrx3+DZFBr3rvzj3iive6A1kvPXXnut4II241WCdfhLNS/c0e8KFqVx9FLTMjjVVjfn2mpM5vYFqjJj7+eK744pRCkkwRKiinJ3NNK3eW36Nld/4PPn39p3IZadodfYdfYakQnpfLM/nG/2h2PQ6+jR1IuhreswKNCHWs5WuGAVorxcfOC+NbD7fTUYf+9HakD+XZ+XPhYqX26uqpaWnQGYVZces1k9NtipLnpFVcbbtkCVwa7dEno/W4FvqhguPtcTmD0fqQSmYY+KP070CdX1Um+jKsuVR71O0OtZ2PkWfHknZKVcX+fgqbpaZaXCX1+rkvaX/yp5fJcoKNcEq6eoMVVuDeDeVWDnbL3jd7xfJfh7PlRFZqb+obq4WkuUKnCBXxFdaivY7NmzCy5oMRxsHVWSl5/o2bmqEvZtx6vxb0W1YF/+C878rFr+BrxWeL0QZSB/IYWoJvLn32rg5cjdQfXJzTVzIiqJHaFX2XHmKiFh8ZYJj19Ze4yujb0Y1saXwYG+eLvK2ApRhej1alLURr3UIPwrR2FxH5i0Si0rSU6mmpPq7O/Fb+PgAS1HQuA4Va7aYANRR1QrDToY/aF1WsygYAKz9jF49M+Kv8D+/TXADEGPgFfT8r++7wuqqmL0MbCxV+O32t4DTe9Q48rMZvU+fn5BXahHH1cJsYxRKZnZrIqe/L1RdfG77wfrjMX7p4Fz1VxUob/B1xPg4V+sd+4i8xOsDpBeuYfy8/MjKirq+gI7Z9U1cN9iNXay7XhVKKS0sYa9/60SrANL1cTNDu6VGne1s+l5VQK/UW/V3bXZHeBUS+uoqhyd2VJypWY6deoULVu2LHWZqHlut/Mcn5rF76ei2XTsMn+evUa2Sf1q63TQ2s/N0pWwY0MPbA0aVnCrQLfbOa6RMhJh/dNwci3YOsF9q6Fhd8vqAufYlKPm+Dm9QV2w1grIG2uku36fehViQ6/v36m2KqEevk8lBt2egCHzrPkOVfXBT4NVAhP0iCqEUVHOb4MVo9Wkws8cufkLnZSr6mfUuHfx430u/gmrJkPaNXBvCBO+Bt/WNxt5ATXudzk7XU38++d7YDCq6Qm0nKQ9Iwk+G6haswxGaDZQVddrPqTyWtSyM2B+XdXC/GI4p86HV+o51ul0FHlJazaXf9LlL0bChR1qLr++z1dMgDXB1TPwUed/LNSpgkH+gwjT1aVhs1bqM2Ywqp4EBqP6m2LNltsqQBIsUWPdzuc5MS2bzaei+fn4ZXaEXiMr5/pcK05GA92b1qJP81oE+rnh7+OMq331HLt1O5/jGiXXBGsfVyXQjc7qYrS++k/cco5zc9XYrWOr1H/WD2yAOm2L3l/MKTi+Bk6sUXMJ5XNvCI/vUaXjre3KcVjST82ndd+awvMYlUdqrBpbEvqbmqQ2MxGCX4U+z1VYuMVKjFAtiJePqO5X960pkBDfrBrzu2w2w4kfVati4iW17M7PqkYV1vgwNQbxwg4g79LPxkEVmgkcBwHDKnZKgYgQWHqH6pL7xN5KP8fFJlg34/x2VZzGwRP+dey2Sw6Kte4JNaYtcBzU76L+Bl3cpSYvL4neRlVs7fuCmibiNiAJlqix5Dwr6Vkm9l2IZceZa+wIvcrZmJRC2/i62uPv44y/twvNfZzp2NADf2/nKl+hUM5xDZJrgjXT4PgPapzE5LVQt5M6xy1aqPmzDi4vlICVyGxW3Q+Pr1EXewNml+11lWXn27BlLrj4qUSvrF2PTDnqfZzdoi5oIg5guUAGaNBdJTpGx8qIurDsdFj3pDpXLn7w2J+33OWsRvwuRx1RXQIv7VHPfVqredCsXViiNMlXVBev42vUxNj5areEsYsqriDF/k9h03PQ7l4Yu6jSz3GnTp04ePBgxezMbFYtfhEHVDn97k9UzH6rs6TL8G4byM2Bpw5e746cmaKS9tBfSbt4EEejQZXnN2Xl3bIh+TJgVn8vBr+h5mqr4tcXt0oSLFFjyXkuWmRCOjvPXGXfhTjORCdzNiaFzBtauPJ5Ohnp3MiDLo296NrYk5Z1XDHoq9YfRDnHNYwpB1Y/oroL2rvB5PWcSjDS8tKXqhiGjT1M+qHqXbCWlSkHlg2GyBBoNxHGflL8dpf/UsU/wv6EsD2QlXx9vcGoBuj7D4Lmg29u3NWtMmXD50PVBWjAcJjw1S1dMFXr3+XkaPhjLhz+CjCDYy01gXXHyeWbBkALiRGqxS1kGcSdB50B+jyvWkOLKhRTHj8+poqjDHsLukytfuf4719U9UeXOvDMX9Ybt1lV/faqKk7UajSMX1HkJsWe48iDqpJlfrGRJv1h2P9BLf9KDFhbUuRCiNtMXXcHJnRpwIQuDQAw5ZoJj0sjNCaFM9HJnLqcxIGLcUQnZfLriWh+PRENgIudDW3quRHo50rrum4E+rnRuJZTlUu6RDVmsIE7l6pvSE9vgJVj8K3TB86vU/NW3fNl9U2uQL2/sZ/AJ73gr2/UXEVGJ0iLhdRrkBanxjdFnyhYzQ9U5bdGvVVC1biv9l2WDLaq69snvVURhwNLoctUbWOytux0VfRj17vqfOltoet06Puf8s9bpRW3etDjKTU28I//qsp52xfA35tUBcxbmcog6oYCF1Ywbdo0lixZUnE7bD5YtUJGH1dVOoMerrh9VzfpCRDyuXrc85nyv75uJ5iyBQ59AZvnqGk6Pu6uPnt9nrde67sVSQuWqLHkPN88s9nMpbg09l2IY9/5OPZfjCU8rnAJKAdbA638XAnwdaFZbWdLN0MfVzurdC+Uc1xD5WSpYgpnflbPdQa4ezm0GqVpWBVm7yLVlawknk1VRcVGvVVxBFc/68RWXid+hO8fVOXxp24B3zY3tZtq9bucm6vmCtsyF5Ii1LLmQ1XXJy1aEyvShZ2w7nFIuKRaSvu/BD2eLn9LXGYKzK+nXjczEmztq9cYrHzHV6tKp+4N4alDt+/0BLvegc2z1d+jBzcUu1mZznHqNbWvwyvVc49Gar62psEVFW2VcJt+UoQQJdHpdDT0cqKhlxPjg+oDEJ2UwfHIRI5HJnE8KpETkYlEJWZwMCyeg2HxBV7vYmdDU29nfF3tcXOwxd3RFjdHW/XYwYifuz1Najnj5lg9i2uISmZjhPFfwKoHMJ/9Hd3oj2pOcgXQZbq6yEgMV5Od5t+caql7j8bgWkfrKMsmcCyc26q+mf7hYTW5shZFRKwlbDf8+tL1rk6+bdQYncZ9tI2rojTurSYD/+1VOPi5uhCOOqK+4CjPl2aX/wLM4BNYeln0qqzVGPB8Q82fd3w1tLun8DY3U6WwOsnOUF8KgZpe41Y51VJTZXScDD/9C2JOwMqx0HaC+pKihpR8lwRLCFEmPq72+Ljac0fL6/O4xKZkcvJyEqHRKYTGpHA2JpnQmBQS0rI5Ep5Q6j69nIw0qe1E41pONKntTAtfF9rVc8fDqQIrWYnqycYOJn7D38cO0aJtJ62jqVh6PdzxqtZRVJwhC1SJ96un1VxZoz/UOqKKF3NadaE7nfftvbOvmtS53YSqP86qvOxcYOS70HIEfP+QGhN5cDkEPVT2fVi5e2Cl0Rug1wxY/6QqUuMTqD7n0SdUtdKYk6qAw5hFVaNSZGU4+h2kRINPGzU3XkWp3wWmb1fzE25/U1WRDf1NfWHRbkK1T1olwRJC3DQvZzt6+9emt39tyzKz2UxsahbnYlKITc0iIS2bhPQsEtOySUzPJi41i/D4dC5cU+tjU7M4cLFgC1hDL0fa1XOnXX132td3o1UdNxyMNewiRpROp8NsW/P65tc4Rke4a5ma6+vwSmjaH1rfqXVUpUuPV+WjSyobHR+m5rM6+p2az8nWUXWZ6/l0zW6pA2g2QHXdWv0I/DJTVav0blG21+a38Pl1rLz4/iEyMrJydtz2Hti2AK79DZ8UM5fZ77OgxQhtW+tiTqvP6cl1an66u5arL3NuRa5JFbYANfaqopMegy30flYVztgwAy5sh7WPqjGqdy+v1pOZS4IlhKhQOp2OWs521HIuueJSbq6ZK0kZXLiWyvmrKZy7msqJqESORSYSFptGWGwa6/+KytsnNPR0JMDXhRa+rrTwdSHA1wVTbo0eQipE9eETqLr3bPy36vYTtkclL3rD9XuDHXg2htotoFZz616M5mTClWOqXH9kiLqPvwA6Pfi2VVUZG/VUSYSjp6oMuPMtNbA/N1u9h6CHofdz1af7ZkVoc5eaHuCvr2H1FJiyuWznLdL6LVgHDx7Ez68SxiraGNUUD+ufVGMhvVvl3Vqq2+opqhDGoS9UkRNrSr4Cx35QidWVo9eXx51ThTk63n9r+z+9Uc0l6NZAdQeuLF5NYfI6+Otb1QX3wnbY8Zb1J4WvQFLkQtRYcp6rpxxTLmeiU/grIoG/whM4Ep7A2ZgUcopIpmz00LiWM41qOdGklupq2KiWEwE+LtLNsIaQ3+NqxGyG7+673o2uJDq9KuTh3QK8W3HWqRPNugyu+JiSr6g5uy5sLzwZqo29qliZm1Nwee2WkBAG2WmATrVg9HtRJYe3o8xkWNxHlXHv+hgMXVDy9unx8GYj9fOdGWEp914ti1zcqLixVqc2wHeTwNlHlXO3daiY42Umw8EvVAn99HhVdMTGqO4NdiqWmJOqZRXAzg0Cx6gkcNt8cPCAJw+Ck9fNHd9sVhNFRx6Eof8rU/JYIec48qBqDXfwhGdPVdsxfNKCJYSoUmwMelr5udLKz5WJeaXks3JyOXc1hb+vJHP6SjJ/X0ni7yvJRCVmEBqjxn/dSKeD1n5u9Glei97+tenYwAOjzS12lRBClEynU10FT65XF4T5yUtujupqlJ0GsaGqK1PcOfU4NhRO/URTvQ0kPKZKNhdX4txsVonSoZVQr7MqC1/S+KeoI/DNREiOAnSq5axuENTtCPWCVCuEKUvN5RW2Gy7+qR5fPaVe32IE9H/51kqV1wR2Lmr6hM8Gwb5Fqtpb80HFb28pANL21ufSqkqK6x7XYrh6r1eOqmToViclTomBfZ+oqQ8yEkveVm8LAcOg7XjwH6ySEbNZfZ4vbIfNs2D0RzcXR9ifKtlx8IQO993cPm6GX8frP8/TG6rt2DZJsIQQVZ7RRk/LOq60rONaYPmhoyewq1WPC9dSuXA1lQuxqZy/msrJy0kci1TdDT/aeg5Ho4HuTbzo5V+Lzo08aeHrgo1BEi4hKpyNHbS9u/TtsjPykq1Tqgva0e/UYPe/vlXFI9rfd338iClHFVrY/X5edTrg+A+qVProD1U3rX86sRZ+fBRy0qFBDzWew8Wn8HYGW2jST91AdSWMOqySvKL2e7uq2wmCX4XNr8Hax1SlwaJ+nqBJ90BN6XQqEf/mHlXOvNODxY/PizgIP05TLVAeDVX5d4+GqlS5g6fqinnkGzBlqu0b9FBjn+p1VstMWWoaC1PezbOxaqn6ZzzDF8Ki7nD4S/W71LB72d9PcjScWg/7FqvnXadbd7yhTqd+hhufVcVVJMESQgjrcrDV09JPTXp8o/QsE/suxLIz9Bo7zlwlNCaFLadj2HI6BgAno4GODT0IauhJ50YeBNZ1w8XOBr1MmiyEddjaqxLnvm2g7Xgu+gym8elPVDXC9U/Bgc9g4BzV2rXnI0i8pF7nVFt12Tu+Ro2l+qS3avXqNUN1nzKbYfv/YFve2I0O98Hwd9S6srCxgwbdKuc9V3c9noZzf1wvRDBpddFFFPJbsOpar8AFwOLFi616vAKaD1ZJaORB2P9p0eXMY8/B13ericVBlScvTosRKrGq3+Xm4qnVDHr+C3b8TyUq03eU3JqYn1SdWKtarsjraulaFzprMIF4m7vht1fg4k64dla9n2pGEiwhRI3jYDTQL8CbfgHeAFxOTGfnmWvsvRBLyMV4LsWlsTP0GjtDr1leo9OBs50Nrva2uNjb5N3U3F1uDra4OuTP46Xm9fJ0MlLL2Q4vZyOORvlTKsStyPBsCQ//qgbs/z4LLh+BFaOvb+DVDLo/qco32zpA3//A76+puZq2zVMtXMPeUt2qTqxRY7wG/ld116rm5Z6rDL0exi6GRT1UovXnO9D734W3s1QQtG4L1rRp06x6vAJ0OjUp85d3wp/vQedHClanTImBL8ep5KrpHRD8iprMOSEM4i+qapVJUarrao+noXbzW4+p97OqlTfmJOz9WCVs/xR3QRWV+PtnLEmVwahiDBwDAUOL77JbmexdofU41QJ36AsY9F/rx3CL5KpACFHj1XFzYHzn+ozvrCZNjknKICQsngMX4wi5GM+5qymkZZlIzsghOSOnlL0V5mBrwMvZiJeTEU8nIx5O6rGHkxFPR3WvKiuqeyc7+dMrRCE6nepe2GIY7HoX9nwIPq1VSfSAYQXHW9m7qbmaWt+pWrxiTsLyYWqd0UWNBStpnJC4Oa51YMzH8M0E2DIXslJV18H8JDY5GpIi1Tnw8rdqaJVe5KI0Te+A+l1VK+y+xdDnObU8MwW+Hq8SqTrtYfwKsHOu/BY+WwcY/pZK+rYtUFUA3dW4ZnKyVJfbHf8HORlVI6n6p04PqQTryNfqM1bWVugqQv6XF0Lcdrxd7RnWpg7D2lwvt5xjyiUlUyVYSRnZpGTkkJSRQ2J6tuWWlHcfn5ZFXGoWsSlZXE3JJD3bRER8OhHx6WU6fn5Cll/OvraLkdrOdtRysaO2sx21Xezwc3fAx9Ueg3RbFLcboxMEv6wq95U2iW/j3mo80LZ5qiuhW3249zsZP1WZAobCqA9UOf6db0NiBIz6UF0AW1qv2t/6HEzVTX4r1orRajxhl6lq3rTvH1Q/F/eGMOl7lVxZS7MBKrE68SP8/CJM/FpNobDhX2rCZIA242HQ68WPqdNK3U7gHai6Uv69sXLLxFcCSbCEEAJVvdDd0Yi7Y/m+JTObzaRmmYhNyeRaShbxqVnEpeXd33C7lprFteRMrpUjIbM16Kjr7kB9T0fqeThS39OBhp5ONPRypIGXI672NahClxD/VFpylc/oqC4Quz2hBvxX07LO1UrHyeBSB1Y9oAqUJF+Be1ZCVH6Bi/aahqeZxn2hYS8I2wV7F0FCOJz9HRy94P4fwdnb+jENng+hm1WSsnIcnNuilns2UcUwmva3fkxlkV/s4ufnVbELSbCEEOL2odPpcLazwdnOhoZepVdayk/I8pOtaymZXE3J4mre86vJmcQkZxIZn861lEwuxqZxMTatyH15Ohlp6OVIIy8n6nk4UNfdAT93B+rmPba3LeMFqhA1we00AXBV4D8QHtoIX41XhS+WDb0+B5SfdQtcAIwYMcLqxyxEp4P+M2H5cNj+ppqjysYB7l2lJtPVgmsd1SL8y4squTIYVVGYXs9W/S8j2o6H31+F89vUeLFqNBedJFhCCGFFNyZkjWqVnJClZ5mIiE8jIj6d8Pg0LsWmcSkujbDYNMLiUi2tY4cvJRT5+lrORhrXcqK5jwsBvi7qXiZhFkJUFL8OMGUzfHVXwap4GpRo/+mnn6x+zCI16qVasi5sV8VW7l6uildoqfNUVeEwK01V56xl3fFxN83BXbVc/fUNHFoBA17TOqIykwRLCCGqKAejAX8fF/x9XAqty801E5OcSVhsKmGxaUTEpxGZkEFkQhqRCelcTsjgWkoW11KyOHAxvsBra7vY0biWE/Xzuh2qe/XY20XGfQkhysGjoaoA+e29cGmPms/Jo5HVwxg5cmTVSbIGvwE/PgY9noSAIVpHAwYbNVl0ddTpQZVgHflKjXGrJpNXS4IlhBDVkF6vw9fNHl83e7o28Sq03pRrJjopg3NXU/j7SjJnopP5OzqF0Ohkriarroj7L8QVep1Br6OWsxEfV3u8XezxcbXD28We+p4O+Hu70MzbGQejdD0UQtzA0RPuX6uKXvi21qQ0/oYNG6x+zGL5toHHdmkdRc1QvyvUbqGKcpz5BVqO1DqiMpEESwghaiCDXodf3pis3v61Lctzc81ExKcTFpequh7GpREen05EfBrhcWrcV3SSukFiof3qdFDfw5HmPs74+7hQz8MBl/y5w+xscLZX3R89HI1Sjl6I24mtvRrrI0RF0umg4wPw60xV7EISLCGEEFWNXq+jQV4VwqJk5eTmJVkZRCdlEpOcwZXEDMJi0zgTncyFa6lcilNjwTafiinxWM52Nni72uGT1xLm42pPPU9HOtR3p4WvCzaG26yMsxBCiPJrNwE2z4azW9QEzfnzeVVhkmAJIYSwMNroLS1fRcnKyeVibCpnopM5E51CTFIGyZk5pGTk5M0jpuYQi0vLIiUzh5SrOZy/mlpoPw62BtrWc6NjQw86NvCgdV1X3B2M2Nvq0WnQvUgIUb1pOsmwqFyOntBqNBxbBWd+VXOMVXGSYAkhhCgzo42e5j6qImFJzGYzSRk5xOS1hF1JyiA6KYOzMSkcuhRPWGwa+y7Ese8f48Bs9DrV3dDeFlcHG2xys2lwJMMyMbOakNlIbWd76rjb4+VklIRMCMGSJUuYNm2a1mGIynLHLDUNgP9ArSMpE0mwhBBCVDidToebgy1uDrZFVkG8lpLJ4UsJHLoUz8GweM5fTSEpI4esnFzi07KJT8u2bHvkcvETMhtt9Pi52VPHTbW61XGzx8vZiKeTEQ/HvHsnI24OtphyzWTl5JJlylX3ObmYcs009HKU8WJCVHPTp0+XBKsmc68Po97XOooyq1b/o2zevJlt27YRGxvLpEmT6NWrl9YhCSGEuAm1nO0Y2MqHga18CizPzDGRnJFDckYOSenZHPv7HI6ePlxLySQ2JYurKZlcS8kiJimDy4kZJKZnlzgZc1nodNDYy4mWfq4E+rkS6OdGqzqueDoZpWS9EEKIcrNagjVz5ky2bduGl5dXgVKaO3bs4I033iA3N5e77767xG8fBgwYwIABA0hMTOTNN9+UBEsIIWoYOxsDds4GajnbAWBMcaRly3rFbp+amcPlxHSiEjKISkjncmIG8WlqAmZ1n018ahaJ6dnYGHQYDXqMNupmm1dkIyw2lfPX1G3j0csF9m806LGz1WNva8DB1oC9rR4HWwMORvXc0WiDg9GAo9GAl5MdddztqZvXkubn7oC9rZS0F0KI243VEqxx48Zx33338cILL1iWmUwm5s6dy+eff46Pjw933XUXwcHBmEwmFi5cWOD18+bNw8tLzfWyaNEiJk2aZK3QhRBCVFFOdjY083ahmXfJY8JKkpWTS2hMMieikjiZdzt9JYnkzBzVndCUS3JGzk3t28PRloZeTqqsvbcLzXycae7jgp+bvWXsWG6umbRsE6mZOaRmquM429ngaGeDo60BvbSiCVGq9evXax2CEBY6sxXLrkRERPDoo49aWrAOHz7Mhx9+yGeffQbA4sWLAdWPtihms5m33nqLnj170qNHjzId88iRI9jZ2RVYlpGRgb29/c2+DVFNyHmu+eQc13xanmOz2Ux2rpnMHDNZJnWfmZNLRt7jjJxcy31Gjpn4dBMxqTlcTc3hWmoO19JyyMktet8ONjrsbfSk5722JPY2Ohxs9Dga9Tgb9bjY5d0bDTjb6bG30WPGjNkMZoD8+zy6vH/y0zRbgw5XOwNu9gbc7Q242etxszdgb6Nd2Xz5Xa75Kvscx8TE4O3tXWn7F6W7HX+PW7ZsWeRyTcdgRUdH4+vra3nu4+PD0aNHi91+5cqV7Nmzh+TkZMLCwpg4cWKpx7Czsyv05k+dOlXsD0TUHHKeaz45xzVfdT7HublmrqVkcv5aKqExKYRGJxManUJoTArXUjJJzzFZtnU0GnCys8HJqLoUpmapFq20LBMZOWYyckzEZ5iKO1SFMBr0GPQ6DHodeh2Wx3Y2Bup6OFDfw5EGno7U93SggacjddwdsNXr0Ol06PISOL1Oh16vw9FosHTBLIvqfJ5F2VT2OW7VqpWUateY/B5fp2mCVdQvQknldidPnszkyZMrMyQhhBCiQuj1Orxd7fF2tadbE68C6xLSssjKycXJzgaHEroB5uaaSc/rPpiUkUNiejaJ6WpMWWJaNgnp2aRnm9BxPclR9+q5atW6oXULyMzOJS41k9hUNVYtNkXdZ5lyoZgcLjIhnf3/KKlfGntbPc52Nupmr+6djKrro7OdGr/mZDTgaGdDakIS57Mv4+pgY6k+6WJvS0a2Ke89Z5OUf5+RQ2aOiRyTmZxcMzkmVQ0y22Qmy2QiPSuXjBwTmdkmMrJzycg24WJvk5ccqiSxgZcj9T2keqQQonJo+pfF19eXK1euWJ5HR0dL864QQogaz93RWKbt9Hqdatmys8HbtfLiMZtV98ecXDOmXDO5uWZMZrMlwYuIT+dSXBrhcWnqPj6dK4npmHLVa815+8g1q6QwNSsnL7nJ4lpKVtmC2HOt8t5gMdwdbS3zq9V2sVOPXezwcjbi7mCLh5O6d3O0xd3BiFHDbpRCiOpD0wSrTZs2XLx4kfDwcHx8fNi4cSNvv/22liEJIYQQtx2dTldixcOGXk70LMf+zGaVmKVk5pCSkWO5T80ykZalnqdlmkjNUsvDo6+hMzrltVCplqrkjBzsbfW42ttaWrXcHGxxdbDFzlaPrV51abQ16DDo9djodZaKj/a2akyZg9GAnY2BhLQsS4IYlpckRsSlk5CWTUJaNmdjUsr0vlzsbfB2scPH1R4fV3u8XezwdrXHx9WOOnnzsXm72GFTju6RomJMnTpV6xCEsLBagvXss8+yf/9+4uPj6dOnD0899RR33303s2bNYsqUKZhMJu688078/f2tFZIQQgghKoFOp8PRaIOj0YayFHjUYuxGbq6Z+DQ1t9rV5Eyu5d1fTc4kLjWbhLQsEtKziU/LsnTHzJ+j7dzV1GL3q9dBbRc7fN0cqOVktEwJYLk36CwJWP5ICXNeB06z+XqLYG5ei2D+cAo7m+vTAzjYGrA3GnC0NeBib4OLvS2uDja42qsE1NnOBt0N+8g1q26iJrPqUpllyiXbZCY7J/9xLu6ORrxd7Mo1dq4qWbJkidYhCGFhtQTrn2XX8/Xt25e+fftaKwwhhBBCCPR6HV7Odng529HCt/TtzWYzCWnZxCRnEp2UQXRSBjHJmZZJr6Pz7q+mZBKdpG7VjU4HXk52+Lja4Zs3frCWsxE3B1vcHVV3SXdHdQMdWXkJWlaOStKycnIxY0an06HX6TDoVMEUnU7H5asZ6D2ScbIz5I3FU62Lxck25ea1MBac1y4zx6TmsstLWu1sDNjZ6Hn0roF8uWErdnnz3OWvt7c14GxnUylz0uV3rc3OT1jzfgY5uWb1s8nJJTPHRGbevXqei52NXo1BtDPgYJt/byA920R83nvOf+8J6dkAlvdkeX82elIycriWksW1lMy8m3qsA9zyzpdb/jlzsMUMxKdl/eMY2eh05LXK2uHtYo+vm3pc29ked0fVVdbT0YiDUeb1KysZ3SmEEEIIUQqdToeHkxEPJyMBvsU3y2WbcolJzuRKYjpxqdmWi+78lqKsnFxyTGb+WdMrv8iXXpdXjTEvMdHrdJgxk5mdS3q2iYxsE+lZJtKzTaRlmUjOUIU/kvJa2JLSs0nOzEFXaD/qua3hekuabV6rmo1eR1zqjRfqmZyISqr4H+KmqAJPbQ0qnkLFWMyooivlEHbiKHd/sqfY9UaDHmd7G1zyCq7Y2xrIzFGFUNKz1M81I9tEtsmMo53BUqDlxu1Ts0wk5RVcScrIJik9p9xxWk1sWrk2j4hPL3UbOxs9Ho5GvJyN+Lk7UNfdAT93Nam6n7sD8UnZ2EQnWxLKzOxcMk25mM1m3B2NeDkZ8XQyqhbWIoramc2qWE16tqlA8p5/M9roae7jXGJBvKpCEiwhhBBCiApia9BTN+/iUytms/mmLkJzTLlcS8niSl4LXXRSBvGp2SSkX+8maWlVMWNpSTEarneD1OuwdEvMNZvJzVVdExOTU8nV26oJtfOmIVCtPkWXrtTpwMPRiLujLZ6OKrH1cLTFwdZAlkm1BOW3CGXl5LIC6NjAvcBFeWaOSkpTMlQiFJdXObM0WWmq9aws8luVbAzXk1fbvG6gdjb5NwN2tuqxrUFPVl5c+VMxpGblkJ5lwsFowN3BmNdKeL3FUAdk/iPZyMzJxcnOQC1nuxtuRmq5qLlfr1caLdhSlf8zvfHelGtWXwokZRCTd96vJGVyLTkzr8VLtXpl5uRyJSmDK0kZJSTg4aX/zGz0eDoacXWwITMnV01HkWUiLduEKbfkUvtv3d2OuzrVK9O50ZIkWEIIIYQQNcjNfsNvY9Dj66a6iFW0osbZ5Zfbh4LTC4BKVA3FTF9QlN9fqcOax4suxZLflS85r+BKckY2Gdm52Nvq1Xi2vJuD0YCNXkdalkrKkjOzLUVa0rNNONnZWIqu5I95q4yuh1poVMupxPX5hWviUrO4mpzJ5cQMohLSiUxIJyohnaiEDK4mpuLsaK+STtv87ozq55OQdn1KiPRsU16iVvg4Br0OB1tDga6Q+d093R1taVfPrTLefoWTBEsIIYQQQlidGj9VMfuKiooqdl1+lUx7WwO181p4SmJva8DTqWxTKdwubixcU8/DkQ5FbFPWYjXpWSZiUzPzKoXmFW7JK+BSU6ZCqBnvQgghhBBC3LZmz56tdQiijByMBup5ONKyjiuNaznh62aPm4NtjUmuQBIsIYQQQghRzc2ZM0frEISwkARLCCGEEEIIISqIJFhCCCGEEEIIUUEkwRJCCCGEENVaSEiI1iEIYSEJlhBCCCGEEEJUEEmwhBBCCCFEtRYUFKR1CEJYSIIlhBBCCCGEEBVEEiwhhBBCCCGEqCA6s9ls1jqIynTkyBHs7EqftVsIIYQQQgghysrGxgZ/f/9Cy2t8giWEEEIIIYQQ1iJdBIUQQgghhBCigkiCJYQQQgghhBAVRBIsIYQQQgghhKggkmAJIYQQQgghRAWRBEsIIYQQQgghKogkWEIIIYQQQghRQW67BGvHjh0MHjyYgQMHsmTJEq3DERXg8uXL3H///QwdOpThw4fzxRdfAJCQkMBDDz3EoEGDeOihh0hMTNQ4UnGrTCYTY8aMYfr06YCc45ooKSmJp59+miFDhjB06FAOHz4s57mGWb58OcOHD2fEiBE8++yzZGZmyjmu5mbOnEn37t0ZMWKEZVlJ53Tx4sUMHDiQwYMHs3PnTi1CFjehqPP85ptvMmTIEEaOHMkTTzxBUlKSZd3tfJ5vqwTLZDIxd+5cli5dysaNG9mwYQNnz57VOixxiwwGAy+++CI///wz3333HV9//TVnz55lyZIldO/end9++43u3btLQl0DrFixgqZNm1qeyzmued544w169+7NL7/8wrp162jatKmc5xokOjqaFStWsHr1ajZs2IDJZGLjxo1yjqu5cePGsXTp0gLLijunZ8+eZePGjWzcuJGlS5cyZ84cTCaTFmGLcirqPPfs2ZMNGzbw008/0ahRIxYvXgzIeb6tEqyjR4/SsGFD6tevj9FoZPjw4WzZskXrsMQt8vb2JjAwEABnZ2eaNGlCdHQ0W7ZsYcyYMQCMGTOGzZs3axiluFVXrlxh27Zt3HXXXZZlco5rlpSUFA4cOGA5x0ajEVdXVznPNYzJZCIjI4OcnBwyMjLw9vaWc1zNde7cGTc3twLLijunW7ZsYfjw4RiNRurXr0/Dhg05evSotUMWN6Go89yrVy9sbGwAaN++PVeuXAHkPN9WCVZ0dDS+vr6W5z4+PkRHR2sYkahoERERnDp1inbt2hEbG4u3tzegkrC4uDiNoxO3Yt68eTz//PPo9df/bMk5rlnCw8Px9PRk5syZjBkzhpdffpm0tDQ5zzWIj48PDz/8MP3796dXr144OzvTq1cvOcc1UHHnVK7Faq7Vq1fTp08fQM7zbZVgmc3mQst0Op0GkYjKkJqaytNPP81LL72Es7Oz1uGICrR161Y8PT1p3bq11qGISpSTk8PJkyeZOHEia9euxcHBQbqK1TCJiYls2bKFLVu2sHPnTtLT01m3bp3WYQkrkmuxmmnRokUYDAZGjRoFyHm20ToAa/L19bU0XYLKrvO/XRHVW3Z2Nk8//TQjR45k0KBBAHh5eRETE4O3tzcxMTF4enpqHKW4WYcOHeKPP/5gx44dZGZmkpKSwnPPPSfnuIbx9fXF19eXdu3aATBkyBCWLFki57kG2b17N/Xq1bOcw0GDBnH48GE5xzVQcedUrsVqnh9//JFt27axfPlySxJ1u5/n26oFq02bNly8+P/t2zHLcQEchvH77bHYlBTKQikLq00dvoHRZDQosbEpLFIsQvkGysKoWEQWk03KYDRJHXHezfQ89RZPerl+45n+p2u6O+y03+9lmqZGo5EMw3j1WXiQZVkqlUry+/1Kp9P354ZhaDgcSpKGw6Hi8fiLLsSjCoWCZrOZJpOJGo2GotGo6vU6jd+My+WS2+3WdruVJM3ncwUCATq/Ea/Xq/V6rfP5LMuyaPzGfmpqGIZGo5FM09R+v9dut1M4HH7hpXjEbDZTr9dTu92W3W6/P//0zn+s777hvbHpdKpqtarr9apkMqlMJvPqk/Cg1WqlVCqlYDB4/39OPp9XOBxWLpfT4XCQx+NRs9mUw+F47bF42GKxUL/fV6fT0fF4pPGb2Ww2KpVKulwu8vl8qtVqut1udH4jrVZL4/FYNptNoVBIlUpFp9OJxv+xfD6v5XKp4/Eop9OpbDarRCLxY9N2u63BYKCvry8Vi0XFYrHXvgD+yXedu92uTNO8t41EIiqXy5I+u/PHDSwAAAAA+C0f9RNBAAAAAPhNDCwAAAAAeBIGFgAAAAA8CQMLAAAAAJ6EgQUAAAAAT8LAAgAAAIAnYWABAAAAwJP8Ba3mc3rsaa7pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "\n",
    "loss_history = pd.DataFrame(lstm_training.history).pow(.5)\n",
    "loss_history.index += 1\n",
    "best_rmse = loss_history.val_loss.min()\n",
    "\n",
    "best_epoch = loss_history.val_loss.idxmin()\n",
    "\n",
    "title = f'5-Epoch Rolling RMSE (Best Validation RMSE: {best_rmse:.4%})'\n",
    "loss_history.columns=['Training RMSE', 'Validation RMSE']\n",
    "loss_history.rolling(5).mean().plot(logy=True, lw=2, title=title, ax=ax)\n",
    "\n",
    "ax.axvline(best_epoch, ls='--', lw=1, c='k')\n",
    "\n",
    "sns.despine()\n",
    "fig.tight_layout()\n",
    "fig.savefig(results_path / 'rnn_sp500_error', dpi=300);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:38:25.532463Z",
     "start_time": "2021-02-23T06:38:25.322584Z"
    },
    "pycharm": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 0.0085 | Test RMSE: 0.0103\n"
     ]
    }
   ],
   "source": [
    "train_rmse_scaled = np.sqrt(rnn.evaluate(X_train, y_train, verbose=0))\n",
    "test_rmse_scaled = np.sqrt(rnn.evaluate(X_test, y_test, verbose=0))\n",
    "print(f'Train RMSE: {train_rmse_scaled:.4f} | Test RMSE: {test_rmse_scaled:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:38:25.963989Z",
     "start_time": "2021-02-23T06:38:25.533710Z"
    }
   },
   "outputs": [],
   "source": [
    "train_predict_scaled = rnn.predict(X_train)\n",
    "test_predict_scaled = rnn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:38:25.971051Z",
     "start_time": "2021-02-23T06:38:25.964993Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train IC: 0.9986 | Test IC: 0.9817\n"
     ]
    }
   ],
   "source": [
    "train_ic = spearmanr(y_train, train_predict_scaled)[0]\n",
    "test_ic = spearmanr(y_test, test_predict_scaled)[0]\n",
    "print(f'Train IC: {train_ic:.4f} | Test IC: {test_ic:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rescale predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:38:25.979481Z",
     "start_time": "2021-02-23T06:38:25.972762Z"
    }
   },
   "outputs": [],
   "source": [
    "train_predict = pd.Series(scaler.inverse_transform(train_predict_scaled).squeeze(), index=y_train.index)\n",
    "test_predict = (pd.Series(scaler.inverse_transform(test_predict_scaled)\n",
    "                          .squeeze(), \n",
    "                          index=y_test.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:38:25.991927Z",
     "start_time": "2021-02-23T06:38:25.981050Z"
    }
   },
   "outputs": [],
   "source": [
    "y_train_rescaled = scaler.inverse_transform(y_train.to_frame()).squeeze()\n",
    "y_test_rescaled = scaler.inverse_transform(y_test.to_frame()).squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:38:26.008431Z",
     "start_time": "2021-02-23T06:38:26.002713Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Train RMSE: 18.18 | Test RMSE: 22.15'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_rmse = np.sqrt(mean_squared_error(train_predict, y_train_rescaled))\n",
    "test_rmse = np.sqrt(mean_squared_error(test_predict, y_test_rescaled))\n",
    "f'Train RMSE: {train_rmse:.2f} | Test RMSE: {test_rmse:.2f}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:38:26.024417Z",
     "start_time": "2021-02-23T06:38:26.010162Z"
    }
   },
   "outputs": [],
   "source": [
    "sp500['Train Predictions'] = train_predict\n",
    "sp500['Test Predictions'] = test_predict\n",
    "sp500 = sp500.join(train_predict.to_frame('predictions').assign(data='Train')\n",
    "                        .append(test_predict.to_frame('predictions').assign(data='Test')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:38:27.512613Z",
     "start_time": "2021-02-23T06:38:26.026008Z"
    },
    "pycharm": {},
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ffvopUVFR3Hvvvbzxxhu8/fbbNGjQoFxjFOJ2pVJuRl8YIYQoQeFYaUkohRBCCCGEkBZ0IYQQQgghhBCiSpAEXQghhBBCCCGEqAKki7sQQgghhBBCCFEFSAu6EEIIIYQQQghRBdy2CfqNLv30X7GxseV6vjuNPL8bJ8/wxskzvDHy/G6cPMOyefrppys7BCGEEKJS3LYJutlsLtfz5efnl+v57jTy/G6cPMMbJ8/wxsjzu3HyDMsmPT29skMQQgghKsVtm6ALIYQQQgghhBC3EknQhRBCCCGEEEKIKkASdCGEEEIIIYQQogrQVnYAN5PJZCI+Ph6DwXBdx8bExFRAVHeGinp+jo6O1K5dGwcHh3I/txBCCCGEEELcTHdUgh4fH4+bmxt169ZFpVJd07H5+fk4OTlVUGS3v4p4foqikJqaSnx8PPXq1SvXcwshRHnJzc3l0KFDBAUFVXYoQgghhKji7qgu7gaDgerVq19zci6qJpVKRfXq1a+rR4QQQtwMR48epVatWtx33304OjqSkpJS2SEJIYQQogq7oxJ0QJLz24x8nkKIqmzChAlkZmbaf+7atSu5ubmVGNGVRUVF0bNnT0JDQ1m8eHGx/YqiMHv2bEJDQwkPD+fIkSMAFBQU8OCDD9KvXz/CwsJYsGCB/ZiFCxfSpUsX+vfvT//+/dm6dWuFxJ6enm6/RqdOnYpc02g0XvHYQ4cOMXv27Gu6XkhICOHh4YSHh/P4449z4cIF+74mTZowefJk+89ms5l7772XMWPGAJCSksKYMWPo168fffr0YdSoUYCtp1/Lli3tcffv35+1a9deU1wAp0+f5uGHH+buu+/myy+/LLJv6dKlhIWF0bdvX1544QUKCgrK9fgvv/ySJk2akJaWBsD69euL3E/Tpk1lyKAQQlzBHdXFXQghhLhZ1qxZQ2RkJKi11Br7BUkrpmFOv8jvv/9O//79Kzu8YiwWCzNnzmTJkiX4+Pjw4IMPEhISQsOGDe1loqKiiI2NZePGjRw8eJAZM2bw448/otPpWLZsGS4uLphMJh599FG6du1K69atAXjyySd5+umnKzR+T09P1q1bB9gqBZydnYtc02w2o9WW/NrTokULWrRocc3XXLZsGV5eXixYsIBPP/3UnuQ7Oztz8uRJDAYDjo6ObN++HR8fH/txCxYsoGPHjgwfPhyAY8eO2ffVqVPHfh/Xy8PDg1deecX2/9+/JCUl8fXXX/PLL7/g6OjI888/T0REBIMGDSqX4xMSEtixYwf+/v72Y/r160e/fv0AOH78OM888wzNmjW7ofsTpcvMM5JdYC5xn5teSzVn3U2OSAhxrSqsBb202vS33nqLXr16ER4ezvjx48nKyrIfs2jRIkJDQ+nZsyfbtm2zbz98+DDh4eGEhoYye/ZsFEWpqLAr1I3U7gPs2rWLffv2lbhv9erV3HvvvfTv358+ffqwcuXKG4q1cKxkUlISEyZMuGLZpUuXkp+fb/951KhRRT5XIYS40yiKwjPPPAOAa4sH0LrVwOfhWQBkZ2dXZmilio6OJjAwkICAAHQ6HWFhYcUStMjISAYMGIBKpaJ169ZkZWWRnJyMSqXCxcUFsCXCZrO5SvRwmjJlCnPnzmXYsGG88847REdH88gjjzBgwAAeeeQRzpw5A9i+XwtbtxcuXMjUqVMZNmwYDzzwAF9//fVVr9O6dWuSkpKKbOvatStbtmwBICIigrCwMPu+5ORkfH197T83bdr0Rm+1iOrVq9OyZcsSKyQsFgsGgwGz2YzBYMDb27vcjp87dy6TJ08u9bOPiIigb9++N3Bn4mqyC8xEnUgp8U9pibsQomqpsBb00mrTO3XqxIsvvohWq2X+/PksWrSIyZMnc+rUKSIiIoiIiCApKYkRI0bw22+/odFomDFjBjNnzqR169aMGjWKqKgogoODKyr0CnO12v2r2b17N87OzrRp06bE/X369GH69OmkpqYSFhZGSEgINWrUsO+/UutBaXx8fIp0VSzJ119/Tb9+/eyTwH3++efXdA0hhLjdpKSkkJiYCGoN1To/RsovH5B3YieOjo507969ssMrUVJSUpGk0cfHh+jo6CuW8fX1JSkpCW9vbywWC4MGDeL8+fM8+uijtGrVyl7u22+/Ze3atdx9991MmTKFatWqVfwN/SM2NpalS5ei0WjIyclh+fLlaLVaduzYwfvvv8/ChQuLHXP27Fm+/vprcnJy6N27N0OHDr3iaiHbtm0r9rn26dOHTz75hG7dunH8+HEGDx7M3r17AXjssceYNGkSy5cvp2PHjgwaNMjewn7+/PkiPSxee+012rVrV+Tcc+bMYdeuXcXiCAsLY/To0aXG6ePjw1NPPUW3bt3Q6/V06tSJzp07l1r+Wo6PjIzE29v7ipUNv/zyC5988kmZryfKl9liJT49r8R90rouRNVRYQl6abXp//4iaN26Nf/73/8A2y/2sLAwdDodAQEBBAYGEh0dTa1atcjJybG36A4YMIDIyMhbMkEvyeHDh5k3bx55eXl4enoyd+5cvL29+frrr/n+++/RaDQ0bNiQF198ke+//x61Ws369etL/MIuVL16derUqcPFixd55513qFatGkePHqV58+Y8+uijvPHGG6Snp+Po6MisWbNo0KABcXFxvPTSS5jNZrp06WI/V3x8PGPHjmXDhg1YLBbeeecd/vzzTwAeeughFEUhOTmZ4cOH4+HhwTfffENISAirVq3Cy8uLJUuW8NNPP2G1WnnooYd48skniY+PZ9SoUbRt25b9+/fj4+PDJ598gqOjY7H7fv/992/K5yCEEOXp5MmTtr8oCinr3qIg/gh16tRhwoQJRRLcqqSk3mn/bQm9UhmNRsO6devIyspi/PjxnDhxgsaNGzN06FCeeeYZVCoVH374IfPmzWPu3LkVcxMl6NWrFxqNBrD1Xnj55Zc5d+4cKpUKk8lU4jHBwcHodDq8vLzw8vIiNTW1xM9t+PDhpKSkUL16dSZOnFhkX9OmTYmPj2fDhg3F3lm6dOnC77//zrZt24iKimLgwIFs2LABKFsX92nTppX19ovIzMwkMjKSyMhI3NzceP7551m3bl2Zh1yUdnyPHj347LPP+Oqrr0o99uDBgzg5OdG4cePril3cuHyTlf2n00rc17VxDUnQhagiKnQM+pVq0wF++uknevfuDdhq5f+938fHh6SkJLRabYm19VdTUFBQbBISk8lUpCv2tVAU5bqPLYnJZMJoNPLGG2/wwQcf4OXlxW+//cY777zDG2+8weLFi4mIiECn05GVlYW7uzuDBw/G2dnZPmbt3/EYjUbMZjP5+fnEx8dz/vx5vL29MZvNnD59mk8//RSNRsPo0aN55ZVXCAwM5NChQ7z++ut8/vnnzJo1i8GDBxMeHs73339vv1+DwYDVaiU/P5+VK1dy7tw5vvvuO7RaLZmZmVSrVo2vvvqKRYsW4enpSX5+PlarFYPBwN69e1m1ahXffPMNVquVYcOG0apVK9zc3Dh37hxz5szhlVdeYfLkyWzYsIGwsLBi912WZ36nrFFvMBjuiPusSPIMb4w8v7IrHHPrUD0AU2oc999/P5988km5P8PyHMvr6+tra/X/R2HL+JXKJCYmFivj7u5Ohw4d2LZtG40bNy7Sk2vIkCGMHTu23GIui38v8fnhhx/SoUMHPv74Y+Lj43niiSdKPEanu5yoaDQazOaSuwYvW7YMZ2dnpkyZwoIFC5g6dWqR/SEhIbz99tt8/fXXZGRkFNnn4eFhn2RuzJgx7Nmzh+bNm5fpnq63BX3Hjh3Url0bLy8vAHr06MH+/fvLnKCXdnxhZUTheRITExk0aBA//vgjNWvWBIp38xdCCFGyCk3QS6tNB+wJY+FLTGm18mWp0S+JXq8v9uISExNT5Iv6/U0n+DDypP3nn5+1te6Hf/SnfdvzDzRiUmhj2s/exKUc2zjxu2u5s+G5LkxdHc2K3XH2srumPYCPu+NVYwNwcHBAURROnz5tH6dotVqpWbMmTk5ONGnShNdee40HHniA7t274+TkhIODAw4ODiWuJ67T6eyT9uh0OmbNmoWvry9arZawsDBcXV3Jzc3l4MGDvPzyy/bjjEYjTk5OHDx4kI8//hgHBweGDBnCggULcHJywtHREbVajZOTE3///TePPfYYbm5uwOWXHrVajaOjY7GfDx8+TI8ePfDy8iI/P5+ePXty6NAhQkJCqF27tr1XRMuWLUlOTi71vsvyLO+ECWdiYmLuiPusSPIMb4w8v7KxWCykpqaicfXC/+mPSfrhNVq2bEmzZs2q9DNs0aIFsbGxxMXF4ePjQ0REBO+++26RMiEhISxfvpywsDAOHjyIm5sb3t7epKWlodVqcXd3x2AwsGPHDvvM5MnJyfYk/vfff6dRo0Y3/d4KZWdn27uSr1mzplzO6ejoyLRp0wgPD2fcuHF4eHjY9z344IO4ubnRpEmTIgn1zp07ad26NU5OTuTk5HD+/Hn8/PzKfM3rbUH39/fn4MGD5Ofn4+joyM6dO7n77rtv+PgmTZqwc+dOe7l/96QD2/vN//73P7799tvrilsIIe4kN2UW9//Wpq9Zs4YtW7awdOlSe7JdWs19WWrrr9ek0MZMCi3e1Sp2XvEa3qgXOxdLFucOasncQS2v+/qKotCoUSN++OGHYvsWL17Mnj17+OOPP/jkk0+IiIi46vkKx6D/V2HciqLg7u5eate5q1V8KIpyTZP+XGkyv/+2ThQu01LSfV/ruHkhhKhMf/zxBwBaDz8KEk5iiN3PI498XMlRXZ1Wq2X69OmMHDkSi8XC4MGDadSoEStWrABg6NChBAcHs3XrVkJDQ3FycmLOnDmALQmfMmUKFosFRVHo1asX3bp1A2D+/Pn2Wcpr1arFzJkzK+cGgZEjRzJlyhSWLFnCvffeW27n9fb2pm/fvnz77beMHz/evt3X19fe6+3fjhw5wqxZs9BoNCiKwpAhQ2jZsqW9B9y/W7QHDx5cakt/aS5dusTgwYPJyclBrVazbNkyfvnlF1q1akXPnj0ZOHAgWq2WZs2a8fDDDwMU+Zyv5/gr2bNnD76+vgQEBFzTfQghxJ2owjKf0mrTo6Ki+Pzzz1m+fHmRhDckJIQXX3yRESNGkJSURGxsLC1btkSj0eDi4sKBAwdo1aoVa9euZdiwYRUV9k2l0+lIS0tj//79BAUFYTKZiI2NpUGDBiQkJHDvvffStm1bNmzYQF5eHi4uLuTk5Fz39VxdXalduza//vorvXv3RlEUjh8/TtOmTQkKCiIiIoL+/fuzfv36Eo/v1KkT33//Pffccw9arZaMjAw8PDxwcXEhNzfXXlNeqH379kyZMoXRo0eTn5/P77//zttvv11qfFartcT7dnd3v+57FkKIm8VkMqHVau1Lbal0TpjTL1C/fn3uu+++So6ubIKDg4uNlx46dKj97yqVitdff73YcU2bNi11ve758+eXa4xl8dxzz5W4PSgoiN9++83+c+G48Q4dOtChQ4cSjy0cG/5fhRUxhV577TX73/fv31+s/L+vMXLkSEaOHFmsTO3atYtNzHc9atasSVRUVIn7JkyYUOLqLP/+nK/n+H/777Pp0KHDDa8uI4QQd4oKS9BLq00PDQ3FaDQyYsQIAFq1asXMmTNp1KgRvXv3pk+fPmg0GqZPn26f1GXGjBlMnToVg8FA165d6dq1a0WFfVOp1WoWLFjA7Nmzyc7OxmKxMHz4cOrWrcvkyZPJyclBURSefPJJ3N3d6datGxMmTCAyMvKKk8Rdyfz585kxYwaffvopZrOZPn360LRpU1555RVeeuklvv76a3r27FnisUOGDCE2NpZ+/fqh1Wp56KGHePzxx3nooYcYNWoUNWvW5JtvvrGXb968OYMGDWLIkCH2SeLuuusu4uPjSzy/xWIp8b6FEKKq++abb5g4cSLOzs6233FqDebUOFJ+fofR19kdWQghhBB3HpVyqy4qfhUljfO7kbF/+fn5ZRoPLUpWkc+vKo/pLE93yn1WJHmGN0aeX8ny8/OpVasWGdm5ODfpTO6RP9B6+OH90EwuLh7FoUOH7ON85RmWzaBBg1i9enVlhyHELSc+PY+oEykl7guq48H+8xkl7uvauAa1PZ0rMDIhRFmpKzsAIYQQ4lZ28uRJ29KV9dpSo+8LONSsi9rJDashG6DKLqsmhBBCiKpHZt8SQgghrlNubi7vvPMOAJbsS+Sd2o1ri+64tx+AIe4IQJFZvYUQ4mZTFIU8owWj2VrZoQghykASdCGEEOI6HDx4kLCwMC5cuACAMfEUl36aiWP9dri3H0DWrlW0bNlSVqIQQlSaP44l8eepFAwmKz7uep7uXB9XvfxOEqIqky7uQgghxHV47733uHDhAvrazUGlptYzS9HXaopX9zFk/rWK/NN7mDx5cmWHKYS4Q51NyeX3mGQCPJ0JvcuHtFwj3+85j8V6W04/JcRtQxJ0IYQQ4jocP34cAI/Oj+LaqicaVy9QaXDw9CPv5F80bNiQxx9/vJKjFELciUwWK2v2x+Pp7MBjHQLp1sSbZ7s15MylXH6PSars8IQQVyAJuhBCCHGNPvnkE3bt2gVqLTrfRnh1H03+yV1YclIxZyZhvHiM/v37V3aYQog71NYTl0jJMTKgdS10Wtvrfo/mvgQFeLD9VAp5RnMlRyiEKI0MQrmJ0tPTefLJJwFISUlBrVbj5eUFwI8//ohOpyv12EOHDrFu3TpeffXVMl8vJCQEFxcXVCoVNWrU4K233qJmzZrXFfvChQtxdnbm6aef5sMPP6R9+/Z07NixxLIxMTEkJycTHBwMQGRkJMeOHWP8+PHXdW0hhKhKTpw4Yf995tykE6bUOJJWTEMxF6DSOaGt5gNAtWrVKjNMIcQdymJV2HU2jWZ+7jTycSuyr1PDGuyPy+BgfCb31a9eSREKIa5EEvSbyNPTk3Xr1gFFE95CZrO51MmEWrRoQYsWLa75msuWLcPLy4v33nuPRYsWFUnwFUVBURTU6mvrSPH8889fcX9MTAyHDx+2J+gPPPBAqcm8EELcalq2bAlAtS6PY8m6RErEeyjmAgAUYz7xHw0D4LHHHqu0GIUQd67Tl3LILTDTto5HsX3+Hk74VXNk37l0SdCFqKIkQa9kU6ZMoVq1ahw9epTmzZvTp08f5syZg8FgwNHRkTlz5lC/fn127drFV199xaJFi1i4cCEXL14kPj6eixcvMnz4cJ544okrXqddu3Z88803xMfHM2rUKDp06MCBAwf4+OOP+fXXX/n1118xGo2EhoYyYcIEAD799FPWrl2Ln58fXl5eNG/e3B7z/fffT69evYiOjmbOnDnk5eWh0+lYsmQJCxYswGAwsHfvXsaMGYPBYODAgQPMnDmTCxcuMG3aNNLS0vDy8mLu3Ln4+/szZcoUXF1dOXz4MJcuXWLy5Mn06tWL5ORkJk2aRE5ODhaLhRkzZtCuXbsK/1yEEKIkf/75JwUFBej8m+DR8RHiP30KS1YyUVFR+Pr68vDDD3Py5ElenjWL+vXrV3a4Qog70MG4DBwd1DT+T+t5obaBnmyITiAhMx+/ak43OTohxNVIgl4FxMbGsnTpUjQaDTk5OSxfvhytVsuOHTt4//33WbhwYbFjzp49y9dff01OTg69e/dm6NChODg4lHqNLVu20LhxY/uxc+fOZcaMGfz555+cO3eOVatWoSgK48aNY8+ePTg5OfHLL7+wdu1aLBYLAwcOtCfohYxGI5MmTeL999+nZcuW5OTk4OjoyIQJEzh8+DDTp08HYPXq1fZjZs2axYABAxg4cCCrVq1i9uzZfPLJJwAkJyfz3XffcebMGcaNG0evXr3YsGEDnTt3Zty4cVgsFvLz82/4eQshxPVasGABAO7tB2A15GDJSua1116jS5cuAOzbt68ywxNC3OEMJgtHErJoWasaWk3JPSRb1fbg10OJ7DuXTlhLSdCFqGru7AR981zYOu/yz6O32P67+P7L24KnQLepOH7cCnL+mfXSrxWMiYL1E2DfsstlXzgG7n7XHEavXr3QaDQAZGdn8/LLL3Pu3DlUKhUmk6nEY4KDg9HpdHh5eeHl5UVqaiq+vr7Fyg0fPhy1Wk2TJk2YOHEiWVlZ+Pv707p1awC2b9/O9u3bGTBgAAB5eXnExsaSm5tL9+7dcXKy/eIOCQkpdu6zZ89Ss2ZNe3dPV1fXq97r/v377RUO/fv3Z/78+fZ93bt3R61W07BhQ1JSUgBb1/5p06ZhNpvp3r07zZo1u+o1hBCiomzbtg2AlPXzUWm03H333cycObOSoxJCCJvtp1Ixmq20CvAotYyLXktTPzcOxmfSp4UfKpXq5gUohLiqOztB7zbV9ue/ZmQW22QYf9CerNr1W2D7c4P+fd4PP/yQDh068PHHHxMfH19q1/V/Tyin0Wgwm0uejbNwDHqhrKwsnJ2d7T8risLo0aN55JFHihy3dOnSq/7CVhTlhn+p//v4kibJa9++PcuXL2fr1q383//9H08//bS9MkEIIW4mo9FIYmIiaLS4tuxBzv5f+N///lfZYQkhhF3ksSTcHLXUq+FyxXJNfNw4cjGL5OwCfNwdb1J0QoiykGXWqpjs7Gx8fGwzAK9Zs6bCr9e5c2d++ukncnNzAUhKSiI1NZX27duzadMmDAYDOTk5bN68udix9evXJzk5mejoaABycnIwm824uLjYz/dfQUFBREREAPDzzz/Ttm3bK8Z34cIFqlevzkMPPcTgwYM5cuTIjdyuEEJct4EDBwJQ+9nlVO/xDAAeHh6VGJEQQlxmtljZG5tOU1831FdpQGlQ09br8fSlnJsRmhDiGkiCXsWMHDmS9957j0ceeQSLxVLh1+vcuTN9+/blkUceITw8nAkTJpCbm2ufsK5///5MmDChxERap9Px/vvvM3v2bPr168dTTz1FQUEBHTp04NSpU/Tv359ffvmlyDGvvvoqq1evJjw8nHXr1vHKK69cMb7du3czYMAABgwYwMaNG686GZ4QQgCYTCZCQ0NRqVT069ePhISEGzqfoij232cZW5ZgSr8IUKRHkhBCVKYjF7PINVqoX/PqQw49XXR4ueg4fankBhUhROVRKYqiVHYQFSEmJqbYeOWStpVVfn5+8S7uoswq8vndyOd6K7lT7rMiyTO8MbfC89uxYwfLly/n008/BUDn3xS3NmHUTdjCrl277PN9/JfFYkGlUpW67OThw4dp0aIFGreaeA9+jYSlE1Cr1ddckVrVn2FUVBRvvvkmVquVIUOGMHr06CL7FUXhzTffZOvWrTg6OjJv3jyaN29OQUEBjz32GEajEYvFQs+ePe0rgmRkZDBp0iQuXLhArVq1+OCDD666RvygQYOKTDAqhLi6T7ec5q3/HWNq76a4ORafODiojgf7z2fYf169L57DFzN5Newu7m9Sk9qeUuEoRFVQYS3oBQUFPPjgg/Tr14+wsDD7zLcZGRmMGDGCHj16MGLECDIzL4/3XrRoEaGhofTs2dM+EQ/YXozCw8MJDQ1l9uzZ3KZ1CkIIIW7A3r176dSpE599/iUO3vXR+TTAb9g7uDbvxt4D0WzZsqXE43bs2IG/vz8ajQaVSsX06dMxGAxFysyePRsAp3qtMWfbJrHMybm9uoZaLBZmzpzJF198QUREBBs2bODUqVNFykRFRREbG8vGjRuZNWsWM2bMAGw9qpYtW8b69etZu3Yt27Zt48CBAwAsXryY++67j40bN3LfffexePHim3xnQtwZdpxOoV4NlxKT85I0qOmKwWTlYoaskCNEVVJhCXppX9alfVGfOnWKiIgIIiIi+OKLL3jjjTfsLRMzZsxg5syZbNy4kdjYWKKioioqbCGEELegkydPEhwcDIBH8HD8RyxAU80bgKy/16PW6uzzZfzX5MmTSU5OBsC5cUfmfvQlH3zwgX3/okWL+OGHH9B6+uP5wGiy/17HO++8c9v1qoqOjiYwMJCAgAB0Oh1hYWFERkYWKRMZGcmAAQNQqVS0bt2arKwskpOTUalUuLjYJqUym82YzWb7JKCFxwAMGDCA33///abelxB3AqPZyp7YNNoGepT5mPo1bf9mz0g3dyGqlApL0Ev7si7tizoyMpKwsDB0Oh0BAQEEBgYSHR1NcnIyOTk5BAUFoVKpGDBgQLEXhmshre+3F/k8hRAxMTG0bNnSPjllQfxRAPT+Tck++BvpkYuxFuSSnp5e7FiDwcCuXbvQevjh1eMZag6chkeXx5k6dSpZWVns3LmTsWPHonH1onqfiah1ThjORXPPPffc1Hu8GZKSkoos1+nj40NSUtIVy/j6+trLWCwW+vfvT8eOHenYsSOtWrUCIDU1FW9vW2WJt7c3aWlpFX0rQtxxDsRlYDBZaVPHs8zHuDk64O2ml4nihKhiKnSZNYvFwqBBgzh//jyPPvoorVq1KvWLOikpyf5lDpdfDLRabakvA1dSUFBATExMkW1ms5mEhAQ8PDyueXkwRVHIz5cuQNerIp6foihkZGRgNpuLfda3I4PBcEfcZ0WSZ3hjqurzW7hwob1LumurnuQctlXiVuswmAufPU2N/i+Te3QrZ86cKRb/8ePHsVgsuNZtjcbZg9SNn2K8eAywfUe1adMGgNrjvwYgZcO7gIKzs/N1PYvyfoblOZ69pArP/35XXqmMRqNh3bp1ZGVlMX78eE6cOEHjxo3LLT4hROl2nk5FpYLWdTw4cL74csGlqV/ThX3nMrBYpcFDiKqiQhP0kr6sS1Pal35ZXhhKotfri724mEwm4uPjOXfuXBmiL8pkMuHgULYxPaK4inp+jo6ONG3a9I74bKr65FK3AnmGN6aqPr/CseDVOj6CR5fHcQvqw8Uvx+P/9MdY8rNQTAWoHV1Rq9VF4i8oKOCuu+4CQOPqhTEllpz9ETg37YLWwxdD/BF27txZ5FrmjAQaNGhw1SUiS1NVnyHYKsATExPtPyclJdkr1Esrk5iYWKyMu7s7HTp0YNu2bTRu3Jjq1auTnJyMt7c3ycnJeHl5VeyNCHEH2nkmheb+7riXcfx5odqezvx1Jo3zaXkEVr/y2ulCiJujQhP0Qv/+si7ti7q0F4OyvAyUlYODA/Xq1buuY6vyS9WtQJ6fEKKiFE426n7PIKxGA3mndmFKOYclNwONW3WshhzUjq5kZGQUOW7Tpk0AqJ09cGsTRsr6twGo3nM8akfbMkXn3uoLqFCsFs6/MxAUK4dv095ULVq0IDY2lri4OHx8fIiIiODdd98tUiYkJITly5cTFhbGwYMHcXNzs/eG02q1uLu7YzAY2LFjB6NGjbIfs3btWkaPHs3atWt54IEHKuP2hLhtmS1WDsZl8nD7gGs+tpaHbS6NY4nZdGlUs7xDE0Jchwobg56WlkZWVhaA/cu6fv369i9qoMgXdUhICBERERiNRuLi4oiNjaVly5Z4e3vj4uLCgQMHUBRFvtyFEEIAcPr0ad566y3Wr1+P1tMfqyGHuA8eIvPP7wCI/+hxzKnxGJPOYM3NKJagnz59GgBrXgYXPhmBIfYAAHkn/ypSTuvpx6XVb4JiZenSpTg6Olb4vVUGrVbL9OnTGTlyJH369KF37940atSIFStWsGLFCgCCg4MJCAggNDSU1157jddffx2A5ORknnjiCcLDw3nwwQfp2LEj3bp1A2D06NFs376dHj16sH379mJLtwkhbsyJpBzyTRaC6nhc87E13fToNGqOJ2aVf2BCiOtSYS3oycnJTJkyBYvFgqIo9OrVi27dutG6dWsmTpzIqlWr8PPz48MPPwSgUaNG9O7dmz59+qDRaJg+fbp9vdoZM2YwdepUDAYDXbt2pWvXrhUVthBCiEq0bt06Jk6cyNChQ3nttddKnSn90qVLtGjRAoNFBWotWk9/TGnxoFjp0aMHHh4eREdHc+zYMXKP/AFARosWRc5RWIlcvffzZO1ZS51qWk6fPk3qLx+Q+dcqfB55EwDXlqEoRgP5p3fTo0ePCrz7yhccHGyfDb/Q0KFD7X9XqVT2pPzfmjZtaq98/y9PT0+WLVtWrnEKIS7bH2ebADMooOwTxBVSq1T4ezhyLCG7vMMSQlynCkvQS/uyvtIX9bhx4xg3blyx7S1atGDDhg3lHaIQQogqwmw2M3ToUFatWoXOvylz585l586dbN68ucTyP//8M/n5+fg89jaOte/i/HsPculCDB07duS3334DIDY2lnr16qFx96bafUM49NvHbN261Z6AZmfbXkh1fraJzEaOHElSUhIffPAB5owENC4e6Hwb4VSvDRl/fktgYCB+fn434WkIIUTZ7T+fgZeLjgAvJy5cx5rmtTyc2HMuHbPFilZTYZ1rhRBlJP8KhRDiNlBQUMCbb77JgAEDWLJkCZmZmVy6dKmywyqzwMDAf5LzJvgNewe39gPZsjWKXbt2lVj+/PnzACSvegPjpXO4temLWu9Mly5d7GVq1rSNp7TkpuPSLBj/0Z/TfeCj7N27F6vVap8wVK13xlqQh7u7O++//z7VqlUDqwWVWoPf8PfR+TTAdOkc8+bNq+CnIIQQ1+5AXAZBAde+QlGhWp7OGM1WTibLcmtCVAWSoAshxG1g6dKlvPrqqwRl/o/V80bj4eFBbT/vEnslVTUXLlzg4sWLAHgGP0nmrp/wCnka56ad+eabb0o8pnDZRr1fY4yJJ/G8/0kcPP1p166dvYyLiwvOzs5gMWFMPouDpx/Vez5L+3s7odFoWLlyJSqtHpVWh7UgF3d3d4AiS34CFFw8gTnrEtWrV6+I2xdCiOuWmW/iVHLOdY0/L1T7n4niDsWXfXk2IUTFkQRdCCFuMVarlbfffpuQkBDmz5+P1Wq1d+vuUV/Dgl6OzLhfT8Gr7nz22WecOnWqkiO+sqNHjwLg1PAetNW8capvW8JM4+xhbyn/r6SkJABq9H2B7P2/YMnLxHD+EHXq1ClS7nIrehrmrEs4BrbEf8znBL68Aa2nP04N2lGQcALFmIebmxtwednP5NW2Mej5Z/aAYpXlwYQQVc7BuAwAgupc+/jzQl6uOlx0GqIvZJRPUEKIG3JTllkTQghRfn755RdefvllADZv3oxGo2HNmjUAdFmSx/lJrky4RweARgXHjx+nYcOGlRZvaS5dusSQIUPYunUrAM6N7yNzx/eg0uARPByVg568vLQSjz106BBqFw9QqXEMaI4lLwMUa7GlND09PTl37hwp694CIPDlDRgTTqJt1AHHukFo3Wvi3KA9gL0FvTBBd2/XDwCPzo+RuX2FtKALIaqc/eczUKmgZe1q130OtUpFE183aUEXooqQBF0IIW4xTz/9NACvddURnWRl6v+9CEDHAA3PttcR+k0eTlqITrJiUSAxMbEywy2R2WymY8eO9tZ9nX9THANakHs0CkPsflQaLajU5OXllXj8qVOn0Dh7YclNJ2vvBnKPbKFOnTr2FvNCHh4eADg1uhfvQa8CYC3ItW2r3xath4+9bGELutVqBUClcyI96mucAm1d3iVBF0JUNQfi0mnk7Yqbo8MNnaeJrxs/7b0gE8UJUQVIgi6EELeY5ORkPB1hZjfbetyTfjNQTa9ixv16Np81c/SSLcF87h4d5zOtxMbGVmK0JXvooYfsybnnA6PR+zchJ3oThtj9AGTvs63ckfef8eAAI0aMICsrC7WjQvqWJWAxYclNZ+7ij4qVLUzQXZp3s29zucs2i7s1PxtjfhYJXz6Lo6MjDRo0AP41vt23IXrfyz0PXF1db/S2hRCi3CiKQnR8Jt2aet/wuerXdMVosRKbmktDb7dyiE4Icb0kQRdCiFtIcnIyAPfU0vDneTMf/GVkWEsH6nrYWjxOp1vtZVPyrLzUUUfX2bOZMmUKLi4ulRLzf506dYo1a9agdq6GzyNz0NUM5Px7g1FMBfj4+JCUlITOrzFaD1+ij+4iKiqKrl27ArBmzRqWLl2Ka1AfvEJGErfgUcDWXb5GjRrFruXpaRuXqdZfTq5Vag0pP7+D2skdracfoDB37lx72dxcWwu78VIsWbt+wnD+kO2465whWQghKkJCpoHUXCMtal1/9/ZC9WvYvh+OJ+ZIgi5EJZM+LEKIO9KuXbto1aoVzZo1Y9OmTZUdTplt2bIFAB9XNWfTrUSeNfPhLiONqqtxnJ3FmA0GtFpb3etPMWZqOqu4L0DDd999V4lRF1U4KZx7237oagZizk5FMRXw2muv8ffffwO2JLpmv/+jRvhkejw+ngULFqAoCpMnTwbAq/sYVFodDl61aNq0aYnJOWBbMg1QaR0wpSdgTD4LgEONQLy6jyb/xE50Oh0TJ060H/Pss88CkPDVs+Qe2YwlO4WRI0dWyLMQQojrdfiCbcz43eWQoNet7oxaBccTs274XEKIGyMt6EKIO9LYsWOJjo6mnoeKJ594nLPn4tDpdJUdVoksFguHDh0iICCA1157DYDIM2b2JVjIMEBclsKqoyYKLLbyf+/eTZs2bTBaYPUxM2YrxMTEVOIdFDVy5EhQa3Bt1ZOUXz6k4IIttpkzZ9rHfxdcPA6A1t0bn4dn8/zzA9Dr9Zw+fRo0Wi4uHg1qDeb0iyz4ZmOp1yr8TFMj3sNqMuDefiCG84cwpdpmh1fpXTAajUWOGTZsGB9//DHHjx+3x7tgwYLyfQhCCHGDDl/IRK2Cu/zcb/hcegcNdWu4cCwxuxwiE0LcCGlBF0LccRRF4cCBAwDsHuWCJfuSvWW6qjGZTHTs2JGgoCBq1KjBiRMnGNHagfmhjhxOtiWzp9KsjPrZQNOmTTGZTAQFBTFlyhQApkUWsPuCpVgSerNZrVbeeecd2rVrx6VLl3CoHkD+mT3kHtqEOS2e9evXA6BWq9Hr9aBYSVz+Ejqf+mT8+S1gq1QBcG7YAc+QkZjTL7Jy5UpCQ0NLvW5hbwLnxp2odu9DZGxbTtaetZizLgFQcDEGf3//Isd4eHiwd+9edu/eTVpaGp9//jlOTk7l/kyEEOJGHLqQSSNvN5x0mnI5X1NfN04kSYIuRGWTBF0IcccpnBncx0VFDWc1d9VUs3Fj6a2wlSEyMpKwsDB8fX3ZvXs3s7rpudvb9iv7re56hrYoOmPvXS1a8+uvv9oT0sLlxiZ00NEpQENBQcHNvYH/iIyMZPLkyezduxcA06VYUn/5EICdO3cSHh5uL/vhh7btVpOtUsEzeDiBL2+w73fwrofxUiyDBw9myJAhV7xu4fPw7DYCt7Z98ej4MLXHfYUlLwNzdirW3Aw++eSTYse5uLjQvn17+7h0IYSoShRF4dCFrHLp3l6oiY8759LyyDOay+2cQohrJ13chRB3nMJlx1r42BLetv4aDh85UpkhFZGZmUnfvn0JrWPmfh9YnQYj2ziQZ1LIKjBxOt2K37s5AKSlpZGXl4evry8azeVWlMKu3W391KTnK5Xegj5u3DgAaoRPJmP7CvS1mqHWOVEr9wT33ntvkbKdO3e2/cViwpQah0P1gH/2qAAFXc265B7ZTL1777vqdQsT9Iwd34PZhFtbW0WAOTWeC58MB6B///43foNCCHETJWUVkJJTwN21brx7e6Emvq4oCpxMyqFVgEe5nVcIcW0kQRdC3HHmzZsHQHKuQnSSBQ9HFRkZGZUb1L9s3rwZg8HAkv6uVHdW0/qzHIwW6NVQi5eTivu+tPUA+Omnn/D09CyxlbcwQTdaQK+FnEpM0BVFsY0dx7bEmSUvA8VoQLGYeLjvw8XKF65lbi3IxaF6AMk/zsB7yAxUOkcUYz4FCScwJp6iRo3wYsf+V+Hs75nblgOQd2oXivVy69Cjjz56w/cnhBA326F/JogrjxncCzXxtSX7xxOzJUEXohJJF3chxB1nz549eDrCXTXVLN5rxNNRxV9//YWiKJUdGgAXL14EYPkhE1tizYQ20PLJHiMxKVZe6qinhbeaatWqMWjQoFLPodfrASiwgE5DpXZxtw8fUNm+ctQ6J3Q+DTClxdO8efNi5b28vACw5KRhzk7FeOkseSd2ota7ACqydq7EnJlU6szt/9a1a1d7kg62rvXm1HgAmjdvzuuvv36DdyeEEDeffYI4//JpQTdbrKhVoNeq2Xsujfj0PPufzLzK7YElxJ2mwhL0hIQEhg0bRu/evQkLC2PZsmWAbSbhhx56iP79+zNo0CCio6PtxyxatIjQ0FB69uzJtm3b7NsPHz5MeHg4oaGhzJ49u8q8RAshbj2XLl3i4MGDNPRS88K9er7ab2LyJgMAW7dureToYNu2bYwfPx4HNUz8XwEhy/LYcMLMW9uNRJ2ztfy66lT2VubSFLagT/qfgUV/m1izZg0pKSkArF+/nsDAQBo3blzkd21FGT9+PACe3Z4CwKnRvTh41yP/zF4CAwOLlS/slq7SOaN1q44lN5NLa97Eb/j7aNyq4zvsHYCrPgOwrV0eGRnJH3/8wfz587n//vt56qmnSEtLIzo6msaNG5fXbQohxE1z+EImDWq64qwrn86w+SYr20+lUsNVz95zGUSdSLH/yS6QMelC3EwVlqBrNBqmTJnCr7/+yg8//MB3333HqVOnmD9/PuPHj2fdunU8//zzzJ8/H4BTp04RERFBREQEX3zxBW+88QYWi23NoBkzZjBz5kw2btxIbGwsUVFRFRW2EOI2t27dOsC2jnhyrpV8Myzt78RdNdWcOXOmUmOzWCx07doVjQrSX3ZjYW9HDj/jQsx4VwC+O2QmaFEOO+MtV+2aXZig31VTTe9GWpTX3enQrg25ubmMGTOGWtYLPNvgPCNHjqyQSs+EhASefvpp+vTpw+nTp3FpHoJ7+wEkfjcVjZM7ptTzKMZ8/Pz8Sj+J1YwpNQ7+6ZJuyc3AoWZdNG62xLwsLehgS/i7devGSy+9xObNm/nyyy/x9PRErZZOZP8VFRVFz549CQ0NZfHixcX2K4rC7NmzCQ0NJTw8nCP/zN1QWqU8wMKFC+nSpQv9+/enf//+VaIiTIhb3aELmeXavb2Qr7sjiVmGcj+vEKLsKuztxNvb29510dXVlfr165OUlIRKpSI3NxeA7OxsvL29gcszFut0OgICAggMDCQ6Oprk5GRycnIICgpCpVIxYMAAIiMjKypsIcRtbtSoUQCENdKy56JtmbJqjioC3FUYDJX7UlK7dm1UQEg9DflmhWfv0RFzyUqB+XICfSDRFvOzzz57xXMVdnEf2MyBwc3+mSgtOZ4VK1aQmJhIcF0Nz7TXceLECc6dOwfYkq8TJ06QlZV1Q/exb98+/P39+eqrr9hlrY9DzXq4NOtK/tl9FMQdAsCcZuvGX1qCHhwcjGI2cvGLcfZtlrwMdDXrYs2zjb0sSwu6KDuLxcLMmTP54osviIiIYMOGDZw6dapImaioKGJjY9m4cSOzZs1ixowZQOmV8oWefPJJ1q1bx7p16wgODr6ZtyXEbSc5y0BydkG5zuBeyNtdT06BWWZyF6IS3ZTmg/j4eGJiYmjVqhXTpk3j7bffJjg4mLfeeosXXngBgKSkJHx9fe3H+Pj4kJSUVGy7r68vSUlJNyNsIcRtJj7eNvbY303F1nNmFuyyjctOyLbi56YulqAbjUamTJlCnz59WLJkCVar9YZjKCgoYOLEiXh4ePD555/bt69bt47ExESiRjizcZgLc/800m1ZLo+tzsf7Hdu6tNHR0Xz11VdcuHChzF3cjRaFi9kK5zOtuOqw9xJIyVNYHm3CQW1r/bRarfTu3ZsmTZrQoEF99u/ff133d/z4cdq2bYvKwRGHGoG4te6NU4N2pG36lJQN7wEQ/8mTpP1hu/fC7uz/NWPGDNzdi46tLIg7gtbTD3NWMoAsgVbOoqOjCQwMJCAgAJ1OR1hYWLEK8cjISAYMGIBKpaJ169ZkZWWRnJxcaqW8EKL82SeIq10BCbqbIwCXsit3aU4h7mQVPot7bm4uEyZMYNq0abi6uvLBBx8wdepUevbsyS+//MIrr7zC0qVLS+xiqVKpSt1+NQUFBcTExJTLPQAYDIZyPd+dRp7fjZNneGMURWH06NEAvBmip3F1Nd8ftrUQxKRY0ajg/PnzxMTE8Pvvv/Pee+8RGxsL2GZP3zB/E3/++ScvvfTSdceQn59P3759uZSUQC03Fa+/MIakpCQGDx5sS3qAznW0HE628N7Oy5PymBQ1c+bMRqvVcu+995KZmUlmZuYVr3XhwgUACswQ3ljDxtNm4jIVzp8/D0BijsKTrXX8fsbCgQMH2LVrF7/99hubhzvz5/kc5s6dyxtvvFHknFf7f9BkMtGqVSvQOFAj7AV0/k0wpcaRtXs1Op8GmBNOAmDJto2FnzdvXqnn8/Hx4bfffiMrK4udO3cyY8YMDOcPUb3Xs/ZW9fPnz3Pp0qUrPoeqprz/HTdr1qzczlVSRfm/54kpqUxhpXlhbzgoWilf6Ntvv2Xt2rXcfffdTJkyhWrVyj+xEOJOcehCJioV3OVXfkusFfJ2s/W+Ss4qILC6S7mfXwhxdRWaoJtMJiZMmEB4eDg9evQAYM2aNbzyyisA9O7dm1dffRWwfckXrk0M2L/w/7s9MTGxyItAafR6fbm+uMTExJTr+e408vxunDzDG5OcnGxPuJ9srSuy763ttmR4eng16tSpw/Tp04ssu7ZsgCPeLmpUb3zFF198UaZKwpLcfffdJCQkMLmjjrdDba0Udd+ZzsCBAwGo7mw779w/bS0X27Zto1q1ari4uFC/fv1rupbZbKt8WH/cjJtexUv36Zi/w4iDgwMAG06YmfK7gda+apydnVm6dCkAexMsZBgUfvzxR1auXFnknFf7f3DEiBEAqDRanJt0JG3TZxiTzuDU4B5qDpjC+XcG4lGtGm3atOGxxx7jqaeeuqZ7MqWcx6F6AC533U/u0S20atWqyNrvt4Kq/O+4LBXiVyvz30p5gKFDh/LMM8+gUqn48MMPmTdvHnPnzi3n6IW4cxy+kEn9Gi646Mv/Nb6aswMOGhXJ2TIOXYjKUmFd3BVF4ZVXXqF+/fr2lzawjU3fvXs3AH/99Rd169YFICQkhIiICIxGI3FxccTGxtKyZUu8vb1xcXHhwIEDKIrC2rVreeCBByoqbCHEbSohIQGAdv5qei3PJWhRjn1fsxpqRrVxwGAw8Pvvv5ORkcHYdg7M6mZrSYhOsjJ9s+1lpXAm9GtlNps5cuQIA5pqeTvUkfofZvNttInQBlruvvtuWxmrwgu/GfjukC257ty5My1atLjm5Bwud3GPzbDyerAeF52KzcMvJ+IPN9fSu6EWF52KxMREjh49CsCoNjpa+177V4OiKHz//fcA6GvfReaun8g++BumtHhqhL+EYjGDYmXjxo1ERkZeU3JeOGbZarB19Xdr2w/glkvOq7rSKsqvVObfleYlVcqDbTI/jUaDWq1myJAhHDp0qILvRIjb2+ELWRUyQRyAWqXC282RZOniLkSlqbAW9L1797Ju3ToaN25M//79AXjhhReYNWsWc+bMwWw2o9frmTlzJgCNGjWid+/e9OnTB41Gw/Tp0+0vXzNmzGDq1KkYDIZia9oKIURZFK4tPrKNjugkC7+dNtGsWTNiYmLwcVXxWAsHVhsM9jHaI4N0tPXX8NrmAkK/yaNONVsr4ZEjR7j//vuv+fqFXbF1Gnh3ZwFnMxR2X7Tg56qihrOKlDyFDAO8/5etNX/hwoU3dL+FCfqjLWwt5hezrbjpVHg4QoYBWvlqMFrgQKIFx4sXycmxVVi461XkGMHZ2fmarpebm2sbw69SUyPsBRKWTgCLCWu+GbWDHmtBHgANGza85nspnPAOwJh8FsVquuZziKtr0aIFsbGxxMXF4ePjQ0REBO+++26RMiEhISxfvpywsDAOHjyIm5sb3t7epVbKA/Yx6gC///47jRo1umn3JMTt5kxyNolZBgK8nIlPzyu2v8BkueFr1HTTczYl94bPI4S4PhWWoLdr147jx4+XuG/16tUlbh83bhzjxo0rtr1FixZs2LChXOMTQtw5FEVh8ODBqFUwpq2OIT/m4efnx8SJExkzZgwmC2jVtiTz9OnTRY5Vq2BqZx2zQxzZfNbMc8+O59DhI9ccQ+GEWSuPmFl5xNZC/vleI3mvuDOgqQNtF+fyUkcdeg28uc1Y4u/Ca/HvSeIA7v4kh68HOlHTWU2GwYq7XsXPJ8ws3mtiYJ04AFwcIMeo8OJGA03uDrqm6/3666+2czTrikqjxZKdSoMGDTh9+jTWglxyj2wBrn9it2rVqpGZmUn2vg3ofCXBqwharZbp06czcuRILBYLgwcPplGjRqxYsQKwdVUPDg5m69athIaG4uTkxJw5c4DSK+WDg4OZP38+x44dA6BWrVr2inkhxLXbez4DAIPJStSJ4j26gup43PA1vN30HIjLwGCy4OggPZWEuNkqfJI4IYSobCtWrCA/P5/qTiryTQp/xVt4bPRjuLm5AWCyglatIi4ujo0bNwJwOt1KW38N/m4qnmmvY8aWAmbcryf9l/gyX9dsNrNnzx6mTJli72L/bg897+wwkpCjkG+G6ZsNdAyw/SoOqavl831GZsyYccPdtwtbnf/Jz0k3QPiKfAAGNNUSWl9DdoHCnAf0TFuzBoD7AjS46lQ8HaRjFw5lvtbnn39un4BPX/su8k7ZhjE1btyY06dPc2ndWxhiD9zQ/VSvXp3MzEwKLp7AkpOGl5fXDZ1PlCw4OLjYMmhDhw61/12lUvH6668XO+5KlfLz588v3yCFuIMdT8xGBfhXc6ywa/x7JvcAr2vrTSWEuHE3ZZk1IYSoTL/99hsA2UYF5znZxGcp9O7d256gH0y08OCPeWzcuJEnWjnwdqieSb8Z+Gi3kQaeas6kW3ljawG138smITXrquuEJycn079/fxwcHOjYsSMdzH/RiLNo1TC2nY4Mw+WJtj7728SsKNtYv3tqqdkeZymX9b0LW9BPplqZ+D/b+Pmhd2vpUEvDmoedcdKq2HrOwoAmWjT/zPHVt7GWxXuNdKmjwWQqWzfy5ORkJkyYgFv7AXh2H4vG1YvUXz4EYPjw4QAYzu4DxUrfvn2v+36mT58OgOnSWfJP7+Guu+667nMJIcSt6nhSNtVd9egrsGW7cCZ3WWpNiMohCboQ4rZ3/vx5tGqIfd6V6k4qmjVrRrdu3eyzTGvV4O9m+3X4ahcdL96n4/NwR6ZGGhjZxoHkXFtCXddDjYsDnDx58orXe/7551m/fj2ejtAxQMPboY6831PP68F6ErJtLedbt24FIDVf4bEWDuwd7UJ1ZzWXchX8/Pxu+J4LE/T9iVY+3GUb135/XS0tfdQM+D6PgGpqsgoUmtXUcOEFV3xcVAT5akg3KLjoKHOCvmvXLgwGAxoXT9zb9sWpQXuwmpk/fz4DBgygadOmgG1Me+EKHtfjiSee4P/+7/8AcHd3t68AIoQQd5LjidnU8qi41nMATxcdGrXM5C5EZZEEXQhx2zt37hzNaqjJMCik5iusWrUKlUplb0GvU03Nkv62F572n+fy1nYjfRo5sO4RZ55YY2DwSlvX8D+fcmFed0eef/55+7nNZjOLFi3izTffJCUlBavVyvfff0/XQA1RI1zY/pQLW2LNvLixgFe76qnpYmuuvvfeewGo9U8X+jZ+Gr4+aEQBe1J7I/R6PQEBAUW2GS22SeqOpVgJXprL7gu2yYR8XNX4uKpw1KpIzVNwcVBhNBpLOm0xe/fuBSAnehOWvEz7ZHD33nsver2evXv3smnTJo4dO2a/5+uhUql46623SElJIT4+np49e173uYQQ4laUklNAcnYB/h5OFXodjVpFTVe9zOQuRCWRBF0IcVuzWCzEx8fTpIaamBQrAHXq1AGwt6CbreDwz2/D0AZavo02cSzFwj21NHzUxxHHf2briDpnpmughu3bt9vPP2rUKMaOHcurr75Kt27d7OunN/BUU7hkdLdleaTn237otdyWwBa2cMdlKWQXKIyLyGf4WltrxfXMdP5fKpWKDz74gGrVLi/FY7Qo6DQqIp9w5nSaFbMVfjtlm7DO2UFFgVnht9Nmun+TV+YW9B9//BFQUSNsEjmHfseSkwZgr/xwdname/fuxSoLrlf16tXt5xZCiDvJ4QuZANSq4AQdbDO5S4IuROWQBF0IcVs6d+4cq1at4tixY5hMJtLybcmnl5eXPTG/PEmcQqPqGmo4q1g+0Inu9bUk5tgS52fa6zDb8noe/SmfCb9e7vKXnZ3N0qVLcdPB2eddOXz4MDt37gSghrOK/502o3rDNl49w6BwPMXCzvjiS+C0XZzLNwdtCXFISAgODmWfoO1KBg0aREZGhn2G9S2xFp5o5YC7XkVmga3CwKIoXMq14uIAXZfmcSrNykPNtZw9exaL5crL9Rw9epSjR49SrePDoNaQsW05Sd9NAcDf379c7kEIIYRNYYJe0S3oYBuHnp5rxGSxVvi1hBBFSYIuhLjtHDp0iLp16/LRS4/QuuXdeDpC1DkLi/ea7K3ncDlBT82zJauDm2nJLFBYsMtIt2V5mP55LylM0C9mK+yIsyWtVqvVvnRa70Za6nqoqe6k4tChQwDotZCYc3kyuGyjQpMatkl9+vXrB8Abb7wBwMk0K7n/NFh/9tln5f047K31tdxVtPbV4KZXkftPD/aw7/Jp9nEu285bGN/eAY0KPuzliK+rij/++OOK5z169CgAWg8/svf+bFv33JDN2LFjy2WiOyGEEJcdupBJbU+nm7L0mbe7IwoyUZwQlUESdCHEbadwGagtT7pw5BkXZnZzxPSaOw/U0xSZ/dvFxQWAbCOcy7ByOt1KVgEUptWzogpYcciWOev1eu4L0PDHcNuSMwUFBaSnpwPQ1s/2stSpjobDhw8DMDvKyHs7jXTv3h2A85kKLT7NAWDy5MkAjB071t4FXavVsmPHDho1Kv81vgtb5J8OsiXqSw8YUf61P6CaCh8XFTO7OaLXqriQpeDjorIvDVcSk8lESoptDd7cY9souGhb5/rnn3/mk08+Kfd7EEKIO93hC1k08b05Q3xkJnchKo8k6EKI20pqaipr1qxBBcRnWVm428gbW20vGM4OKgYNGmQvq1bbfgV6OakI9FDTxk/DpN8ud2H/cp+JcRG2CeIcHR0xmBX0/6xJlpqaak/Gazir2BlnxlELW3/+Do0K5nXXU89DRY0aNZg2bRoAh5Ot1K5d2z5Zmre3N4cOHeLLL79k79693HfffRXyTJR/BsMn5di6Aiw9YKt0ePLJJwF47h4dw1s7YLEqpOQpJOUq+LqqKCgo+cVszpw56PV6xo0bB4DhzN+Y0y4wYcIE+vbti0qlqpD7EEKIO1VarpELGfk08bk5CXp1Vx1qFTKTuxCVQBJ0IcRt5amnnkKngf5NtQS8n8NHu02k5Ck0WphDxEkzffr0KXaMxWpLYB218OtJs327AmQWwAcffICjoyMFZlvXdYCAgABWzxtNPQ8VwYFa5m03kpijEDfJjUfuduDlTnpyjLZJzaZPn85HH33E1KlT+fPPP9FqtfZrBAQE8NRTT9GyZcsKeyZms+2eIs/auudvedLWcyAwMBCAXBMEVlOT8E+X/Jd/N7A3wYrBUPzFLDU1lenTp6MoChpXLzTuNan1zDIcatYtMiGdEEKI8nPon/HnTW9SC7pWrcbLRSaKE6IySIIuhLgtpKam8v7777N+/XruqaVhzcPOzOqmt+8/lWZlzNhxODkVn1zH8k9/70n36pnzgO2Yvn374uDgQI8ePRgxYgR6vZ7UfIUCMxhfdcPFAX4e6sysbo78FW/h5+Nmtj7pgtGi8MjdWnbFW7iUp9CjRw/0ej3jx49nzpw59qT4ZipM0M+kW1l5xMSOONvPPj4+AOQaFbKNCkN/yreXA3ht8gT7rPSFjh49aps8TqXG+8HXqT1uCVq36ijGfEnQq5DnnnuOLVu2YLXKBE9C3A4KJ4hr5ON6067p7aYnOUsSdCFuNu3ViwghRNUWGxtLy5Ytyc7OBuDP8xb2JVjY/s+EbgsXLsTHx4dmzZqVeHzePxO0eTiqSMlTqF69Oj///HORMo6OjpzPUXhxo4E37tfj7aIiLV/hsZYOTN9ssI/pXnLAxJi2Oj792zYLW9++fcv/hq9RjRo1AFh33ExzbzUPNbet+e7r6wvATzEm9oxy5YfDtsR9VBsdIfU0XMhWGD9+PN9++639XIUzu+t8G6LzaWDfbi3IlQS9Chk6dCg//fQTs2fPplevXgwcOJAGDRpc/UAhRJV0+EImgdWdcXMsn1U+ysLbTc+xxCyZyV2Im0wSdCHELW/JkiVkZ2fj56pi0n063PUq2i7OBeD999/n2WefBSAmJqbE460KqN7I4tMwR7IKFO6+++7iZaxWHNSXu4efed6NPJMtLU/Lv/zflUdMhDXS8kyEgQcffNA+zr0yBQUF2f++OsZMQratpbxwFvu/L1oxWxX+GumC6o0sUvMV+jRy4O+LFkZ//l2RBD0ry7ZsnN6/KQCmjETMGQlYC/Ju1u2IMujYsSMdO3YkOzubDRs28NRTT+Hn58eQIUPo169fuS3lJ4S4OQ5dyKRVgMdNvaa3ux6rAvHp+dSrcfNa7oW401X+m6MQQtygmTNnAnBqgiuTO+rpUsc2q3rz5s0ZPnz4FY8tTN7b+atxdrBN5FZSgp6Tk2Nfdq3Qn+dtrckf77E1wZ9Ks5KWr1DbXU2daqpi3cMri0qlYt++fQAcS7Gy5ICJGjVq0LhxY3uZJ9ca+HiPrdX/XIbtRgPci0/2Vpig63wbkn/6b1I3vEPyD6+BYpUW2iomPT2d1atX8+OPP9KsWTOeeOIJjh49ylNPPVXZoQkhrkFqTgHx6fm0qn1zeyl5u9l6W8Wm5N7U6wpxp5MEXQhxW+gYoMHZQcWjP+Xx3K8GHBwcOHToEJ6enlc8rjCBv7+uluRche1xlhIT9MLu84XJq8mi0KNB0U5IHb7I5UCilTaLcojPUuyztVcFQUFBPPPMM/afn3322SJrwn97yMSzv9gmhdsca0E/OwuDudhp7EurGc4fIn3zlxRcsC2vFhAQQMeOHSvwDsS1ePbZZ3nssccwGAx89tlnfPbZZ/Tp04fXXnuN3Fx52RbiVhL9z/jzlrU9bup1a7jqUQHnUqWHlBA3U4Ul6AkJCQwbNozevXsTFhbGsmXL7Pu++eYbevbsSVhYGG+//bZ9+6JFiwgNDaVnz55s27bNvv3w4cOEh4cTGhrK7Nmz7UsGCSFE4VJgAe4qVh4xseKwmT/OWnj66afLtNxX4YzqJgu81FGPr6uqxAS9MKlx09vO6fDPcmsJ2baE/d9dhvcnWrEqEB4efgN3Vv4++ugj/vzzT3bt2mVfK/7UqVNFyuj1tknyjBao+6Ft3XaTyWTfX7g2eu6hTZhS4wAYMmQIu3fvth8rKt+QIUP45ZdfGDNmDN7e3gAYjbYeEqtXr67M0IQQ1yg6LhOVCu6udXNb0HVaNR7ODpxNlUo9IW6mCkvQNRoNU6ZM4ddff+WHH37gu+++49SpU/z1119ERkby888/ExERwdNPPw3YXhIjIiKIiIjgiy++4I033rBPRjRjxgxmzpzJxo0biY2NJSoqqqLCFkLcIvLz81EUhVGjRgG27uZz/7Ql68899xzz5s0r03kKE2v1P7l8Sp5C3bp1Sy0/eZOBdcdsCWtCtpVe39paFqKjo8nMzOSLL77g0UcfZeXKldxzzz3Xc2sVRqVS0alTpyJxNWjQgISEBNatW0diYiLJycn2fU+2dqC+p4r8/Hz7to8//hiV3gW/pz8BbMMLVq5caZ9wTlQNH3zwQbFtDz/88FWPi4qKomfPnoSGhrJ48eJi+xVFYfbs2YSGhhIeHs6RI0eAK1fKZ2RkMGLECPuKCJmZmdd/Y0LcgaLjM2hQ0xVX/c2fOsrbzVG6uAtxk1XYv3Rvb297rb2rqyv169cnKSmJlStXMnr0aHQ6HWBbIxggMjKSsLAwdDodAQEBBAYGEh0dTa1atcjJybFPcjRgwAAiIyMJDg6uqNCFEFXYmTNnaNCgAbXcVCTlKpit8P1gJywKPLY6nyZNmrBgwYIyn68wQT+cbKsQNFsvT55WkuBADY2r2+o2o5OsRCfZWtAbNWqERqPh6aeftlc83ip8fX3p16+f/eeaNWty6dIlhtyl5VKuQn5+Pu7u7nz22Wfk5htwa93b3jth4MCBlRW2KMGlS5dISkrCYDBw9OhRe4+znJycIhUtJbFYLMycOZMlS5bg4+PDgw8+SEhICA0bNrSXiYqKIjY2lo0bN3Lw4EFmzJjBjz/+aK+Ub968OTk5OQwePJhOnTrRsGFDFi9ezH333cfo0aNZvHgxixcvZvLkyRX6HIS4XSiKwsH4TLo2rlEp1/d207PrbBoWq4JGffVeaUKIG3dTquLi4+OJiYmhVatWvP322/z999+8//776PV6/u///o+WLVuSlJREq1at7Mf4+PiQlJSEVqst0jLj6+tLUlLSVa9ZUFBQ6ozN18NgMJTr+e408vxu3J3+DAsKCvjoo4/48ssvaeOnZu9oV55en89X+008fLcDnb+y1fAHBASU+pxKeobnz58HbF3W18TYWsbj4uK4ePFiiefYEWchLksh8W+jfZK4+fPnc+LEiXK5z6rA0dE2MVC+CZwc4O+//6ZevXp8/fXXOAbcjVfoWAzxRwHIzMy8o/+/vFbl/e/4v0sH/vnnn6xevZrExETmzp1r3+7i4sILL7xwxXNFR0cTGBhIQEAAAGFhYURGRhZJ0CMjIxkwYAAqlYrWrVuTlZVFcnJyqZXyDRs2JDIykm+++QawVbIPGzZMEnQhyigh00BKTgGtbvL480Le7nqMFitxaXnUreFSKTEIcaep8AQ9NzeXCRMmMG3aNFxdXbFYLGRlZbFy5UoOHTrExIkTiYyMLHFcuUqlKnX71ej1+lLXPL4eMTEx5Xq+O408vxt3pz5Do9HIjBkz7MmGmw5+eNCZYWvyic2wMuleHUaLwo5/1jx/7733Sn1OV3qGO+LMnEi1tYaXNAa9UaNGnDx5kkV7TcX2vfTSS9d1b1VVnTp1iIuLI8+k4KRV4eTkRLVq1cjIyMCtYTCm1HisebZuyvfcc48s2XUNKvrf8cCBAxk4cCC//fYbPXv2vKZjk5KSilSI+/j4EB0dfcUyhZXmhck5FK2UB0hNTbXv9/b2Ji0t7ZrvS4g7VXR8BgAtb/IM7oUKZ3I/mZwjCboQN0mFJugmk4kJEyYQHh5Ojx49ANsXfmhoKCqVipYtW6JWq0lPT8fX15fExET7sYVf+P/dnpiYWORFQAhxe/v666+ZO3cu07rocNOp6N9Eyyd7jCyPNjGgqZb3ejrSZUkuCrBjxw5atGhxTef38vICIMMAGQZrqeXef/99+vbtW2z7448/fk3XuxXUqlULgFc3F5BdoBB64QJms21Kd4cadcjeH0He8e3Url1bkvMqZt26dfTv358LFy6wZMmSYvtHjBhR6rFlqRC/Wpn/VsoLIa5dZp6R7ALb79w/T6WgUatwd9ISn55HgclyU2Op6Wab/PNkcjahd/nc1GsLcaeqsEniFEXhlVdeoX79+kVeCLp3785ff/0FwNmzZzGZTHh6ehISEkJERARGo5G4uDhiY2Np2bIl3t7euLi4cODAARRFYe3atTzwwAMVFbYQooooKChgz549jBo1imp6GN9ex5TOei5kK7z/l2026pP/tHgXdjW/nknZfHx8ioy/fuWVV0osFxYWxvr163nuuefs21xdXZk0adI1X7Oq8/f3B0BRwMlBxcWLFzl37hwAWXvWkBuzDUtOGtOmTavMMEUJCseZ5+XlkZubW+zPlZRWUX6lMv+uNC+pUh5sc80UTj6YnJxsrxQTQpQsu8BM1IkUok6ksPN0Kj7uenadSSfqRApGy81dycjRQUNNVz2nknNu6nWFuJNVWAv63r17WbduHY0bN6Z///4AvPDCCwwePJhp06bRt29fHBwcmDdvHiqVikaNGtG7d2/69OmDRqNh+vTpaDQawDaL+9SpUzEYDHTt2pWuXbtWVNhCiCpg48aN9OzZk3oeKtQq2DjMhRWHTQxu5sA30Ze7mB+5ZEX1RhZgGz9b+DvjWq1atYrVq1fj6upKnz59Si0XHh5OeHg4c+bM4c8//+Suu+4qspb47aKwBf2Z9jpyTQrx8fGXW04tZqyGHIYMGcK4ceMqMUpRkkceeQSwrYN+rVq0aEFsbCxxcXH4+PgQERHBu+++W6RMSEgIy5cvJywsjIMHD+Lm5oa3t3eplfKFx6xdu5bRo0dLJbsQ18CqKFzIyKdlLY9KjaNuDWdJ0IW4iSosQW/Xrh3Hjx8vcd8777xT4vZx48aV+MLXokULNmzYUK7xCSGqrilTptDOX82eUa4sjzayOsbE/B1GXtpoW0bt/vvv56GHHuKZZ54BoHfv3tfctf3fHBwcyrQEVSFXV1d69ep13der6gpb0GMzrIQ31tL3o4/sE4x5PzSL5JWv0aZNm8oMUVzF22+/zTPPPINer2fkyJEcO3aMadOm2SvMS6LVapk+fTojR47EYrEwePBgGjVqxIoVKwAYOnQowcHBbN26ldDQUJycnJgzZw5QeqV8cHAwo0ePZuLEiaxatQo/Pz8+/PDDin8AQtwG0nKNGExWans6VWocdau7EHEoAatVQS0zuQtR4W7+gopCCHEFZrOZ/fv380QrB6LOmfnhiJkNJ8z2/YGBgSxdupTAwEDatm3LpUuXrnkyLHFltWvXBuBQspUnWtlexrZu3QqAxtkdS362vZVdVE3bt2/n//7v/9i0aRO+vr58+OGHPPHEE1dM0AGCg4OLLWM6dOhQ+99VKhWvv/56seOuVCnv6elZZF10IUTZxKfbhqzUquQEPbCGM3lGCwlZBmp5VG4sQtwJJEEXQlQpkZGRAKyOMbE/wcKhZNs489OnT5ORkUGjRo3s65Rfz5hzcXUdOnQA4FiKldlRtvH+e/fuBbUWlVYPxjxCQ0MrM0RxFYWT+m3dupWwsDA8PDwqNyAhxDW7kJ6Hg0Zln0m9stSrbpu9/WRStiToQtwEFTZJnBBCXCur1cqUKVPQquGeWhp7ch4fH0/9+vVp06aNPTkXFUevt83am5avsPXc5d4LKrWGzJ0/0KZNmyJLbYmqp1u3bvTq1YvDhw9z3333kZaWZv9chRC3hvj0fPyrOaGp5G7lhcuryTh0IW4OSdCFEFXCF198gU6r4Uj0AZ67R8fkjrZkYvfu3dKduhLUqVOHGs4qzk28vFSWW/v+ZG7/XmbhvgW89NJL/PDDD/z00084ODjg5OTEJ598UtlhCSHKyGJVuJiZX+nd2wGqOTlQ3UUnCboQN4l0cRdCVKqMjAw8PT0B2DvahQ93GXmtq557v8ylZ8+etG/fvpIjvDO5uLiQfFHBVXe55caz6xPo/Zpw8M9FlRiZKKvTp09z4cIFLJbL6yYPGDCg8gISQpRZcrYBk0Wp9AniCjX0duWkJOhC3BSSoAshKlVwcDCNq6s5/qwrO+LMXMiyklmgcCLVynN9+1Z2eHcsFxcXDGbQqqGaHtr6aziNglrvTNI/a1qLqmvy5MnExcXRtGlT+/KDKpVKEnQhbhEX/pkgrraHcyVHYtPIx5X1By6iKAoqlczkLkRFkgRdCFEhYmNjOXPmDBcvXmTFihUEBgbywgsv0LBhQwAUReHIkSNER0dTz0PFa5sNzN9uxKJA689stfRhYWGVeQt3NBcX25jDbw6aCG/iwDcDnahrUGE15ldyZKIsDh8+zC+//CIv0kLcouLT83F0UOPlqqvsUABo7ONGlsFMYpYBv2pVo1VfiNuVjEEXQpSryMhIVCoV9evVI+LV3nwz40nqXdrEk6avadyoEbGxsZhMJrp27co9QS3o21jL2QyF2VFGCixgtkJmATz88MPUq1evsm/njlWYoD+13oCrDj7720j2vg0UxB+t5MhEWTRq1IhLly5VdhhCiOsUn5FHLQ8n1FWkku0uP3cAYhKyKjkSIW5/0oIuhCg3BoOB7t274+2iws9Vxbs9bEvDDPg+D19XFb0baalfrx6tWrfmwIED7BrpgrMDPHSXls2xFv4qaMisWbNwdnaWtc0rWWGC/kU/R/JMcCbdStbRdVhyUis5MlEW6enphIWF0bJlSxwcHOzbP/vss0qMSghRFgaThcRMA10a1azsUOya/pOgH72YRUhTn0qORojbmyToQohrpigKmzdv5uTJkwwePJgaNWoA0LlzZ1TAp2GO7E+00OzjHAxmhfOZCk8FWfjpISdG/2zgmwMHcNVB85pq1h03M6yVjrXH83j22WcZPHhw5d6cAC4n6LXd1Hy4y8ivp8wEvvw5qf/7iJyD/6vk6MTVPPfcc5UdghDiOh1LzMaqQKBX1Rh/DuCq1xJY3Zmj0oIuRIWTLu5CiGu2bNkyuj/wAA6/TOSee9pjNBo5evQoe/fuZUBTLe38NSw9YOJYipXYDAWrAidSrThqVXzRz5FZ3fQ0rq5m23kLf543c//SXCJOmLnnnnsq+9bEPwoT9LgsK6PbOtApwDbRmMa9RmWGJcronnvuoVatWpjNtn9XLVq04K677qrssIQQZXD4QiYAAVUoQQdbN/ejFyVBF6KiSQu6EKJMPvvsM9avX09cXBxHDh8moJqKp4J0TN50Dke9ngb/TP42uaOOZ38xEJ+lAODk5ER+fj5TIwt4qaMenUbFq1315BgVen+bV+Qabdq0uen3JUpWmKCfSbcyso0jW2It/L1iKsbEU5UcmSiLlStX8sMPP5CZmcnvv/9OUlISr7/+OsuWLavs0IQQV3H4QhY1XHW46KvWa/pdfu7870giOQVmXKtYbELcTqQFXQhxVRs3bmTcuHH8+uuvFFw8yrIBjqx80Faz36uhhk/CHMlKOI23i4ouS/LYcMKMXq/HYrGwYsUKwDb528LdRgCScqyk5itFrpGUlIRaLb+SqorCBH3un0Y+2m3EbFUoOH8IxZjPrFmzKjk6cTXffvstK1aswNXVFYC6deuSlpZWyVEJIa5GURQOX8ykThVrPQdo5ueOosDxRGlFF6IiyduwEOKqVq1ahQqIeNSJE8+5MqyVjg0nTajeyKKhl5qx7XREPenMpHt1zO+hRwHWrFmDWq2mdu3a9vNM+NXAllgzQYty+WKfCYAZM2agKAre3t6Vc3OiRIUJur+bijFtHTBbbdvbtm3L2LFjKzEyURY6nQ6d7vLyTGazuRKjEUKU1bnUPDLyTFWuezvAXf6XJ4oTQlQc6Z8ihLiiBQsW8Pnnn9O3sZaQelocZmVhtkLhwi9n0m0t4ZkF8HwHHZ/vs7WS161bF7B1Ww8JCeGPP/4AoNuyy93ae/XqxfTp02/avYiyK2x59XBUYTDDj0fNhISE8Ntvv6HVyldHVde+fXs+++wzDAYD27dv57vvviMkJKSywxJCXMW+8+kABHq5VHIkxflVc8TD2YGjCdmVHYoQt7UKa0FPSEhg2LBh9O7dm7CwsGLj3r788kuaNGlSpMvdokWLCA0NpWfPnmzbts2+/fDhw4SHhxMaGsrs2bNRlKJdY4UQ5Ss5OZldu3Yxffp0nn/+eQDGtXPAUauyt6QW/itcHm3C6c0s3tpegJODikm/FQDQ8J8x6SqVik2bNjFp0qQi12jWrBm//PILqiqyxqsoqrAFPdOg4KZXoQJ8fHwkOb9FvPTSS3h5edG4cWN++OEHgoODmThxYmWHJYS4in3n03HWafB211d2KMWoVCrbRHEyk7sQFarC3rQ0Gg1TpkyhefPm5OTkMHjwYDp16kTDhg1JSEhgx44d+Pv728ufOnWKiIgIIiIiSEpKYsSIEfz2229oNBpmzJjBzJkzad26NaNGjSIqKorg4OCKCl2IW15SUhJz5syx/zssa/fxxMREmjVrhs6USb8mWrILFNQqGNXGgSE/5qNV5wOwdOlSgoODGTJkCH///TcGM6w9Zqb627alYf74448iay+r1WreffddAgIC2LBhAx06dODll1+W5LwK8/T0BCCzwFYV07OhBod/dZkWVZtaraZ79+50794dLy+vyg5HCFFGe89l0NzfHXUV/X5s5ufO8r/OYbZY0WpkpKwQFaHC/mV5e3vTvHlzwNZVsn79+iQlJQEwd+5cJk+eXOTlPDIykrCwMHQ6HQEBAQQGBhIdHU1ycjI5OTkEBQWhUqkYMGAAkZGRFRW2ELeFwYMH8+ePHxFw9BMee+wxNm3axM8//4zJZCqxfFxcHM2bN8fPzw93JRN3vYrPw534/kFnutfX8HqwHoMZsgrgscceY/jw4dStW5cdO3bw8ssvA2BVIC1fISQkhG7duhW7hkqlYtKkSURGRjJnzhyqVatWoc9A3JiaNWsCkGOEs+lWTBZwdHSs5KjE1SiKwsKFC+nQoQO9e/emV69e3HvvvXz00UdlOj4qKoqePXsSGhrK4sWLSzz/7NmzCQ0NJTw8nCNHjtj3TZ06lfvuu4++ffsWOWbhwoV06dKF/v37079/f7Zu3XpjNynEbSoz38SxxCzurlV1vx9b1KpGgdnKyeScyg5FiNvWTemrGB8fT0xMDK1atSIyMhJvb2+aNm1apExSUhKtWrWy/+zj40NSUhJarRZfX1/7dl9fX3uifyUFBQXExMSU2z0YDIZyPd+dRp7fjSvrMywcc9q7oZZJ9+r56tM/6NHjdwB6dH+A9z9cUKzletKkSeRdjEF53TYBTIcvcvF9J5ufHnLihXv17L5gwarAc889x5gxY4rEMXz4cAYOHMiXX36JoiiMGDGiyn7W8v9h2WVlXe7CaLbaelJoNBp5fjeovP8fbNasWZGfly1bxr59+1i1ahUBAQGArQJuxowZLF26lCeffLLUc1ksFmbOnMmSJUvw8fHhwQcfJCQkxD5cBWwJfGxsLBs3buTgwYPMmDGDH3/8EYBBgwbx+OOP2yvt/u3JJ5/k6aefLoc7FuL29XdsGooCQQEe5BRYKjucEgXV8QBg//kMmvm5V24wQtymKjxBz83NZcKECUybNg2NRsNnn33GV199VaxcSePKVSpVqduvRq/XF3txuRExMTHler47jTy/G1fWZ5iQkADA7gsWtp0zMz/UkQ93GelSR0M99x3MmjWLBx98kPvvv5/q1aujKArbtm2jlqOKt7cXsPuChd0XbC8Gyw+Z+DTMide3GOjQoQMLFiwo9bodOnQonxutQPL/YdnVqVPH/vdG1TVkFkBtJyd5fjeoov8fXLt2LV999VWRbu0BAQHMnz+fp5566ooJenR0NIGBgfbEPiwsjMjIyCIJemRkJAMGDEClUtG6dWuysrJITk7G29ub9u3bEx8fX2H3JsTtbtfZNHQaNc393dl1Nr2ywylRHS9nvFx07D+fzqMd6lz9ACHENavQwSMmk4kJEyYQHh5Ojx49OH/+PPHx8fTv35+QkBASExMZNGgQly5dwtfXl8TERPuxSUlJeHt7F9uemJgoyzEJUYqYmBj8/f1xcYCU/3MjzwQzthTw62POTOuiJ7SBhu+//55HHnqQGjVqsGbNGlJTU8nLy+NkmpWXfy/gp5jLyzHlGOHXk2Y+3m1i+PDhlXhn4mYrnCQO4GCihYRsa6lDJETVYTabSxxz7uXlddWl1pKSkor0WCvsyXalMmXt1fbtt98SHh7O1KlTyczMvGp5Ie5Eu86k0jrAA72DprJDKZVKpaJ1gAcH4jIqOxQhblsVlqArisIrr7xC/fr1GTFiBABNmjRh586d/PHHH/zxxx/4+vqyevVqatasSUhICBERERiNRuLi4oiNjaVly5Z4e3vj4uLCgQMHUBSFtWvX8sADD1RU2ELcsqxWK127dgVgahfb7K9bz5k5nGxh/C/5dPwyFw9HFa191Rwa58JnfR05/elQujX3QQVM6axjVBvbxG67du2ynVNR0GshNV8pNixF3Dla+WroVEdDbm5uZYciruLfkzNeyz4ovSfbtZb5r6FDh7Jp0ybWrVuHt7c38+bNu2J5Ie5EOQVmDl/MokP9qj+pY1CAByeTc8jMl0pbISpChXVx37t3L+vWraNx48b0798fgBdeeKHU2dcbNWpE79696dOnDxqNhunTp6PR2GoQZ8yYwdSpUzEYDHTt2tWehAghLmvevDkFWSk82kLLd4dMvPpHgX3fJ3tsX6J/nrfQp5GWrecs9KivpZ6nmo4BGvYmWHnuHh1vbC1g1KhR9gke18SY2X3Btq5a48aNb/5NiSqh3ofZxGcpDK4vkwJVdceOHaNNmzbFtiuKgtFovOKxpfVku1KZsvRqq1Gjhv3vQ4YMYezYsVcsL8Sd6O/YNCxWhQ71qld2KFcVVMe2ykd0fAZdGtWs5GiEuP1UWILerl07jh8/fsUyf/zxR5Gfx40bx7hx44qVa9GiBRs2bCjX+IS4naSkpHDs2DGmdtYx5wFHenyTy9FLxct1W5aH8ro7b28voP6CHArbvayvu/PaZgNL9puY+mArnJ2dadq0KceOHeNUmpWWLVtSq1atm3pPouqIzbC1mtavX7+SIxFXcyMT0LVo0YLY2Fji4uLw8fEhIiKCd999t0iZkJAQli9fTlhYGAcPHsTNze2qCXrhGHWA33//nUaNGl13jELcrnadTUOrVtEm0IO03CtXplW2lgHVUKlsE8VJgi5E+bsps7gLISrWgQMHAGhcXc3I9flsOmNh3759tG7d2r4m+sKFCwGo8XY2BRZbwqUAPRrYeqrMjrK9EAwcOBCVSsXy5ct58cUXUavVvPfeezf9nkTl++abbxg2bBgAbm5uDBgwoHIDEhVKq9Uyffp0Ro4cicViYfDgwTRq1IgVK1YAtq7qwcHBbN26ldDQUJycnJgzZ479+BdeeIHdu3eTnp5O165dee655xgyZAjz58/n2LFjANSqVYuZM2dWyv0JUZXtOpNKy9rVcNZpq3yC7u7oQMOarjIOXYgKIgm6ELeB/fv3A/DDETMxlyw4ODgQFBQE2LqkPvnkk/YEPTW/6BjS4ylWvtx/+WXA398fgLZt27Jly5abEL2oqh577DFcXFw4dOgQQ4cOveokY+LWFxwcXGwo2tChQ+1/V6lUvP766yUeW1pF3vz588svQCFuQ9kGE9HxmYzueuv0Ugqq48Gmo0koilKm1ZWEEGUnCboQtzir1coXX3yBVg2bz5opsMAHHxR9IW7Tpg2xsbG88MILmM1mOnfujNlsZtq0aZzLVBi53gDAvn37KuMWRBWlUqkYOHAgAwcOBG6s+7QQQoiSbT+VitmqENz41uku3jbQk5V/x3MqOYdGPm6VHY4QtxVJ0IW4ReXm5pKRkUFiYiInTpxgamcdHWppePAnE4MGDSpWPjAwkJ9++sn+c0ZGBt999x2HDx+mZcuWREVFUa1atZt5C0IIIcQdb+uJZNz0WtoEelZ2KGXWsYFt8sftp1IkQReinEmCLsQtaPfu3YSFhaHOT8VoUajprOLxlg6MXG+gV6/eBAQEXPUcHh4eHDx4kAsXLlC7dm3poiaEEELcZIqisPX4JTo1rIGDpsJWPy53AV7O1PFyZvvpVJ7sVK+ywxHitnLr/CYQQti9/fbbpKSkEN5YS9STLnQJ1HBXTQ1HLlnsY8/LQq1WExAQIMm5EEIIUQlOJudwMdNAcJNbp3t7oU4Na/DX6VTMFmtlhyLEbUVa0IW4BaSnp7NgwQL+97//Ua9ePTZv3oyfq4rUfIUWPhoyDArV384mq4BrStCFEEIIUXm2HretiVqVx5+bLVbi0/OKbW/q68aK3WZ2nk6hS+MrL7cohCg7SdCFqGLmzp3LtGnTim2vpgcHjYoJHZIIfUDPxWwrYY0c+GSPkeY1Nfxx1kLt2rXp1atXJUQthBBCiGu15UQyjX1c8fdwquxQSpVvsrL/dFqx7UazreU86qQk6EKUJ0nQhahC9uzZw7Rp0+gUoCGzQKFLHQ13e2tw1YGXk4oazirura1lwD/lL2RZ6f3t5VrtEydO4ORUdb/khRBCCGGTmW9i99k0RtyiY7hd9Fr8qjny97n0yg5FiNuKJOhCVBGKonDPPfegUcGfT7kAcCTZgkoFWjXkGmHUz/nsTbCiVkEtNxWFQ8ebNm3K119/Lcm5EEIIcYv4/WgSJotC77t9KzuU69awpit/nU0lz2jGWSdphRDlQSaJE6KKOHHiBACDml3+gntoVT6Pr84nPR8CPdScy1QAsCoQl6VwPlNh9OjRxMTE0L59+0qJWwghhBDXLuJQArU8nGgd4FHZoVy3xr5umCyKfSy9EOLGSYIuRBVx6NAhAFQqCP0mF9UbWRy9ZGV/opUey3OJPGMmJU/h0UcftR8zaNAg3nvvvcoKWQghhBDXITPPxLaTlwhr6XdLr6RSt7oLHk4O/HI4sbJDEeK2IX1RhKgidu7cCcDKI+Zi+7IKbK3pAN9++y3ffvvtTY1NCCGEEOVn3YELmCwK7et6FpshvcBkqaSorp1GraJLoxr8EZOEwWTB0UFT2SEJccuTFnQhqoDU1FQSE221zwfGuODsAAsXLuSXX34pUu7rr7+ujPCEEEIIUY5+OZyAp7MDyVkFRJ1IKfLHaFEqO7xrcn+TmuQaLfx5MqWyQxHitlBhCXpCQgLDhg2jd+/ehIWFsWzZMgDeeustevXqRXh4OOPHjycrK8t+zKJFiwgNDaVnz55s27bNvv3w4cOEh4cTGhrK7NmzUZRb6xeXEKVRFIWJEydSs2ZNojasQKuG5t5q8k1Qq1YtevXqxfvvv09wcDAvvvgijz/+eGWHLIQQQogbkJxtYE9sOi1qVbulu7cXahPoibujll8OJ1R2KELcFiosQddoNEyZMoVff/2VH374ge+++45Tp07RqVMnNmzYwM8//0zdunVZtGgRAKdOnSIiIoKIiAi++OIL3njjDSwWWxefGTNmMHPmTDZu3EhsbCxRUVEVFbYQN9XGjRv58MMPqVsN4ia50cJbTVKOggJ4eHigUqmYOHEiW7Zs4emnn74tvsiFEEKIO9nKPXFYrArtAr0qO5Ry4aBRE3qXL78fTbKvjS6EuH4VlqB7e3vTvHlzAFxdXalfvz5JSUl07twZrdY29L1169b2br2RkZGEhYWh0+kICAggMDCQ6OhokpOTycnJISgoCJVKxYABA4iMjKyosIUo1f79+1mxYgU5OTnl1ovj9ddfx1ELZzNs51s6wInfTtvGoAcEBJTLNYQQQghRNVisCit2x9E20JMabvrKDqfc9G3lR5bBTGRMUmWHIsQt76ZMEhcfH09MTAytWrUqsv2nn36id+/eACQlJRXZ7+PjQ1JSElqtFl/fy+tD+vr6kpR09X/8BQUFxMTElNMdgMFgKNfz3Wlu1ednMpn47rvveOuttwB4p4eeds+ZOJ5qpX379qSmpuBNGu36DOOZZ565pnMvXbqUXbt2MbmjjhrOKtosyqGNn4Yv95vw8/PDaDQWeWa36jOsSuQZ3hh5fjeuvJ9hs2bNyu1cAFFRUbz55ptYrVaGDBnC6NGji+xXFIU333yTrVu34ujoyLx58+yV8VOnTmXLli1Ur16dDRs22I/JyMhg0qRJXLhwgVq1avHBBx9QrVq1co1biFvFluPJXMjIZ9z99Ss7lHLVtVFNank48d3u8/Ru4VfZ4QhxS6vwBD03N5cJEyYwbdo0XF1d7ds//fRTNBoN/fr1AyixRVKlUpW6/Wr0en25vrjExMSU+4vQneRWen6nT59m4MCB+Pj48PvvvwMwO0SPv6uKEUE6XrxPT6evcqmVu5+cLCs7x7vS/vNPiI+PJ/7cWWr6+rFw4Uc0aNCg1Gvk5+fz9ttv80A9DW+HOvLqHwb2J9qWVAOYOHEid911V5FjbqVnWFXJM7wx8vxuXFV+hhaLhZkzZ7JkyRJ8fHx48MEHCQkJoWHDhvYyUVFRxMbGsnHjRg4ePMiMGTP48ccfAduyj48//jgvv/xykfMuXryY++67j9GjR7N48WIWL17M5MmTb+q9CVFVLP/rHN5uejo3rMGO02mVHU65MFusJGTm0/tuX7748yy7zqZSy8MJADe9lmrOukqOUIhbS4XO4m4ymZgwYQLh4eH06NHDvn3NmjVs2bKFd955x55s+/r62ru7g61F3dvbu9j2xMREvL29KzJscQfbtGkTDRs2xCPzKAd2RDK8lQNzHtDzShc9I4J0PL0+n4YLsnk6yIGVQ5zp1VDLsgNG7vbWsHbtWha1P8Wk6lH0u68xDRrUZ+/evaSmpha7Tvv27QE4mWZlzIZ8Fu012ffVq1ePMWPG3LR7FkIIgOjoaAIDAwkICECn0xEWFlZsSFlkZCQDBgxApVLRunVrsrKySE5OBmy/10pqGS88BmDAgAH2ik8h7jSnknPYcuISj7QPQKu5fRZSyjdZiTqRQnVXPSrgk82n7TPSZxcUXzpWCHFlFfbbQVEUXnnlFerXr8+IESPs26Oiovj888/59NNPcXJysm8PCQkhIiICo9FIXFwcsbGxtGzZEm9vb1xcXDhw4ACKorB27VoeeOCBigpb3MHWr19Pjx49GNhUS9QIF3aPdGHpACemdtYz/pd8VG9k8dV+E6fTFb4/bOKTPUY+3GUkLkuhtrutomnRXiOhDbQcecaVzzomMeWhDrRtUJNDhw7Zr/PDDz9w5MgR4ia5cm6iG6tjzKTkKaxcuZLvvvuOffv2SfdPIcRNl5SUVGRIWeFQsyuVKcuws9TUVHvFure3N2lpt0eroRDX6pPNp9Br1QzvWLeyQ6kQ1ZwcaOrrxt5z6ZitMlmcENerwrq47927l3Xr1tG4cWP69+8PwAsvvMDs2bMxGo32pL1Vq1bMnDmTRo0a0bt3b/r06YNGo2H69OloNBrANov71KlTMRgMdO3ala5du1ZU2OIO1r9/f9Qq6FxHg9GicO+XuaTnK/RooLVP3FZo0xkLm87YVhmY92cBQX4a3PXw5T4Ti/raKp5CG2gJbaBlyX4jvXv3pnHjxjRu3JgjR45QTQ9+rioeW51Hap5Cr169GDJkyE2/ZyGEKFSWIWXXO+xMiDvdudRc1h28yJMd61LdVU98el5lh1QhOtSvTsyOWKLjMmkT6FnZ4QhxS6qwBL1du3YcP3682Pbg4OBSjxk3bhzjxo0rtr1FixZFJpwRorwVtgA1rq5m3p9GXtxYYN8XcfJycm61/j979x5fc/0HcPx1Lju7b7axi5m538cwJEzG3G8RUSkK5RJCckvCEP1CUpFCJRVlLlPJKpNC5LbcL2PDNrvYfTs753x/f3zbYZm7mcv7+XjIOd/v5/v9fr6fZue8v5/P5/2xMHr0aObPn2/dZqfXsG2AIz+dMPG4n44GizNJyVE49qoTH+82kpCl4JBzgcyjFzgTu43fT5hwMsCwTbl8dVA9d8Fyg0IIUVKuNdXsemVuZtqZh4cHiYmJeHp6kpiYiLv7w7G0lBC34qPfTqLTahgc/HAlh/uvqp5O+Lja8duxiwSWL1XS1RHigfTwTIB5CEVFRbFgwQLOnj17S8dlZWWRn59/44LCatu2bQDMDLGlZQV15IaHh0ehMrGxsWg0GubNm4fRaLQ+NPL9d3h7uyp6nG017I23cCZNwXZGBn9fsJCepwbrH3S057226pIqWUas884jIiIoX778PblPIYS4loCAAGJiYoiNjcVoNBIREUFISEihMiEhIYSHh6MoCvv27cPZ2fmGAXrBMYBMUxOPpNNJWXz3dxxPB/nh5WJX0tUpVhqNhieqe5KUmcc/59NLujpCPJAkQL8Pmc1mWrRoQcuWLZkwdhTPtqjEW2+9ZR1amJ+fz7Jly3j22WeZN28eJpPaCxsdHU2jRo2o5OVMYI1KHDhwoCRv476kKEqRQzQ//fRTAOp66TiYYKF8+fIkJSVx5MgRPvnkE86cOUO5cuWs5W1sbLCxsQHgfIZ6vkEbcnCZVfjDaMX+fL6JVv//BC/Lwt5Gw1O19LzUwIaFHexwdna2LjUohBAlSa/XM2XKFAYOHEjHjh3p0KEDVatWZdWqVaxatQpQR8H5+fkRGhrKm2++yVtvvWU9fvTo0fTp04fTp08THBxsze4+ePBgtm/fTtu2bdm+fftVS7cJ8TBKyzYSl5pNXGo2b4ZHo9dpeaqhr3VbXr65pKtYbGqXdaG0ky1bjyYW+Z1LCHF992Qd9EfJtm3bmDBhArt376Z3797MmjULX1/fa5ZXFIWZM2cyefJk9FoIbBDE7t27cTLAki52/BFrZlk3e0b+OBvbWbM4f/48derUwc18ETc7DQe3fE1iYiJhYWH06NGD48ePE/60PTFpF5kyZYq110LAmTNn6NmzJ8ePH+e1115j9OjR7Nixg7Jly7J582YcbcDbScOJFAvL5s0AoHr16lSvXr3I8xWMUkjKVhiwLofzGRYyjOq+gQMHsnTpUgCy8xXe3ppHnhme+jabT7vas+6oiUu5Cv7+/jJ/Uwhx32jZsuVVU9H69u1rfa3RaAoF5Vd67733itzu5ubGihUr7l4lhXgAZOSZiDqWxInETH4/kUS7Wl78cz4DyACg/kM8/Fur0fBEtTKs+TuOqGNJPPuYY0lXSYgHivSg3yWKotCqVSuCg4PZvn0745oo9DCuYcyYMezfv58XXniBiRMncu7cOesxe/bsISAggMmTJ/PDsw78NciReub9vP64gYwJLtQsreXbf/I5dNHMgvZ2GCfaU6FsGbJSE/jxWQf+eMmRMU0NbNy4kSNHjnD8+HH8XTV8ti+fIB8dmzasY8jzvdi/fz979uwhJycHs/n2n9ieOnWK9u3bU69evasCf4vFckfnvlMFowiut79x48bs37uHzIx03n77bVxdXZk+qCMtG9fFYrGg1cDLG3OxoKFPnz43vGazZs2sr5fvy2fzSfX+z507xyeffMK4ceMAyMqHqb+pc9r3XLAQuDgLWz1k5SsytF0IIYR4SJktChEHz+PmYMPjVUqXdHXuqXp+pfB0tuWjrSfJN0tGdyFuhQTod4GiKLz66qv89ttvNC2no19dG6a1sqN7DRu++eYbAgMD+fKLz2kVu4DWjzfg+PHj1KlTh8aNguhc6jhP19bTtJyOT/42srSrPXNC1flJey6Yyc4HD/vLPax+rloyJrgQn6mQZVRo7Ksj/tQ/TJ06FYCYUc74u2oJ9NYxPcSWjypt5vl2DXi1exMcHBwoXbr0ba9B++qrr3Lyr5/Z/2QMPXr0IC4uTr1mTAz16tVDr9czcODAezqcKTs7m3bt2lGhjAPt2rXj/PnzV5X59ddfsbGxQZd9kU3POGCe4sKkFgYAtg1wZEaIOi883wIrD+YTFBRkHb5+PaVKlWLlypWFtn3//feULVsWgHfeecc6dL5gRQKAAYE2+LtqOZpkwd/f//ZuXAghhBD3tb9iUkhIz6NDHR9sHqJ1z2+GTquhfR1v4lJzWLXr1nIpCfGoe7R+WxSTt956i0WLFtHAR8sfLzmi08JjS7Mo914GzmocSN5kZ/xcNVSxTSEoKIh//vmH73rbM7uNHV8/5cCRJAtV3bWcTFGfMv5+1sTJFDXQbbI0C8eZ6djOSCfQW8vaw/nEpluo9kEm5zIUhgTZsHr1apwNkGlU+GCXkWe/z6Gym5bfYky0raxncWc7gspqeS0whxdffBHLLa5PeeTIETZt2kQdT/VHZt/LDrz22ihmz55NxYoViY6Opr63ls+Xfcq333579xr3BqZOncqOrZuJG2mP6Xgk7ev78fHHH1v3//PPP9YkR91qqMueLd5jpFt1NQCPS7eQlK1Q10tLziQXJgcbcHFxuenrP/PMMyiKwsWLF8nKyuLJJ58stL+grfPz81myZAkAldy0HE4ys/aISQJ0IYQQ4iGUnpvPlsMJVCztSO2yN/+94mFS3cuZ+uVLMX/LcdJzJXmxEDdLAvQ7ZLFY+OCDD7DVwXe9HXhnex5f7M9n5zkzI5oYeLWJGqHrtRpstBoquWlJT0/H2QBPVNATtCSTPJNC92+yqeSmpbK7+r9k/JY8PtilTmg+k6aQnQ/u9hpW9XSglJ0GBxs1OVnoF9lMj1LLeTtpuZBhQQE2HTexYKeRnt/m8GpjAwFeOv4a5MSUlrbExsZy/Pjxm75Hs9lMzZo1qVlai6+zBtsZ6fRancOaNd8xYcIEnqig469Bjvz9shN+rurw8IIHALm5uRw9epS8vLwbXOXWRUdHM3fuXBr66DhzyUKvWjYcGOLEkCFD8PH2JKRlc+rUqYOzAYY1smHXOTPu76Qz7udcapbR4umoIT1P4UiSmeXd1LXL399pJDMz85brUrp0aRwcHIrcp9Fo0Gg0VKtWDYD9CWbefsKOpuV0MsRdCCGEeAgt3x5DjtFMpwCfRzbXjEajYXirKqRmG3lv87GSro4QDwwJ0O9QbGwsqamp5JnVnu7xW/Iw/zvCe/d5M2Ehdng6asg1Kaw8mI+Xk4YnKugIKqvj0EUzey5YyLeAXgsjf8zlsaVZ6Kal82ecGQUYOnSodc3Y+Ez1xCdSLaw9os65ru6hZUQTAz/3c2BhBztarcgGIKSijvV9HMjIU9h7QZ0bveZQPul5Cu72GmJjY2/6HidOnAjAs3Vt+KCjPV2r63murg09aupZ2MGOwxctRCeq1zg4xIlaZbQ0a9aMTz75hIYNG9L58Vo81aM7Fy9evOP2VhSFr7/+mmnTphEQEABAhVJa1h8zMfcP9SHAuj72TG6YwXs19wFQ0U3LK0EG/r5gITUX0vPg7wtmFne2o1YZHbkmSMlRMExPJz2v8Nzyu6kg2dx3h0z0Xp3NyVQZ4i6EEEI8bE5ezOS7v88RVMGNsqXsS7o6Jaq6tzPPNfHn8z9jiD6XVtLVEeKBIAH6TYqJiWHnzp0oisKOHTtYtWoVWVlZrF+/HoDO1fSU+3c97Bkz1Azgf51Xg9ZAby1DInJ567c8/jpn5tcXHEnIUnjzVzWgdJ6VQVy6QswlhZ3nzFgUsCgwZ84cFi1aRHJyMg0aNACg7P8yeHVTLkv/VocKlXbQ0KuWnjaV9LSrosfeRq3D3ngLbvYaXO00PPN9Dl1WZdNrdQ5J2QqutnDhwoWbuu9z584xZ84c3OzglYY2TPk1lx1xZso4aHguwIau1fXY6mHAulzSchUcbDSEVtKzY8cOBg8ezKFDhzj+qhMdypy3DvG+E2FhYfTt25cDX0+nsa+O5uV1JGYpTIzMJTZNfYDRtboNSdkK4f8+xPB20nAho/C8+C6rsulew4ZDF82s7mXPpVyF/H9H/c+aNeuO61kUb29vGjZsiAKsPmQiR+tEnTp1iuVaQgghhCgZYRGHsdNrCa3lXdJVuS+MbVcdd0dbJq09iNkiy64JcSMSoN/Ar7/+ik6r4dkeHXnzmWZotVqaNm3KhpnP4+zkxIgRIwipqGN2a1vKOGjo1KkTTZo0ASDmksKT32Tz03OO1CqjpbKbhiFB6pD39X0c2H72ctZzPz+/QtetW7cuo0aNsr6fOXMmABcyFfKuSJaena/QvLyexz/NYs2hfDpVVVfOS8xSWLzHSHK2Ojx+47HLa3HHpitkZWXd1P3v3r0bUIfj/3PRwvQoI3HpCuuPmrDTayhlp+FSrvrLduVB9aFBjdJaPu1qx8gmBr58Un1y7O2kZfLkyTd1zWsxm828+eab6LWwprcDOwc6MiDQhhqltWQa1SRv2rfTSc9TqOquZeoTtrjYgpejlvhMNfr+8MMPAbUX3T4sndd+ykWr0fB3vNqoFy5cwGAw3FE9r2fNmjW0aNGCatWq8emnn97SfHchhBBC3N+2HrvIL0cSeeHxCjjZymrGAK72NrzZuSb749L4ShLGCXFDEqBfR35+Pl26dKFmaS0pb7iwuZ8jWo06F/yrng74OGvQaeDz7vZUctPyR6yZixcv0rx5c+s5hjVSgz2LAvY2GtpV0fPBLiMtl2eRZ4aFCxeyYcMGYmJiMJlMrFu3jvnz5/PTTz8VyiTerl071q5dy5AhQ/jmm2+sa3AfTrIweEMOFzItPFXLhm1nLy839srGXBTA09PTus3HWYOzAXJycm6qDVJTUwEI9tex6fjlc1/ItJCSo+BoAxn/Ti//56KZuHQL5V01vFjfwKjHDLTw19FqRRb/JN7+EmwXLlyge/fu6PXqB52Pk4Zhm9T6B5XVcSHzcsI7Bfh4t5Hd/45eSBvvQoZRYfZ2I15eXjz//PPWsrkmdR5/dKKZmduM+Pr64u1dvE+7K1SoQFRUFEePHqVXr17Fei0hhBBC3Dv5ZgvTNx6igocDvYLKlXR17itd65WlWRUP5vx4hIsZdz8vkRAPEwnQr+PChQtkZWXxz0U1ADyZYsGiQCk7yDIq7H3ZkWmtbPF10WJvoyHDCMOGDcPOzo7+/fsD0Ly8juhEM5Mi84hOtGA3I51Xf8jlXIbCqlWrGD58OJ07d0ar1aLT6ejatSsjR44sMlDs3r07H374Ib1790av1xMYGEiuCT75O58sI2yNMbE/3sLUqVNp3rw5pUqV4u233yYhIYE33ngDgFmt7Wjgo7vlAL1RWR074i4H2fviLTy3Nofy8zMpGKx0IMHCiB9yGfez+ovX01FDzCULv8WYWRV9/XXKr2f06NGsW7eOiS0MlHPRsKijHcnZCocumqldRkt8pkJoaKi1/Btb8pi3w8iEyFwA1j7twKlUC56enjg6OnL27OWnt+fSLbjba+haXY+rq+tt11EIIYQQj7aVO85wIjGTSZ1qPXLLqt2IRqNhWrc65OVbmLnpcElXR4j7mvz2uA5vb2+02stNVNldy9xQW9zsNJxKteBmpyH8SD7rj+bjOTcDgJo1awLg6+sLwJjNuXx5IN+aOK5gePrIkSN5+umn76h+06ZNw9nZGYCL2QpPrMjGrEBwcDDbtm0jNTWVKVOmAGBvrw41zzQqONtqbipAj42NZfTo0QB0+zqHP2LNjB8/3rp/Sz8HetW63Mv/+1l16bAF7dV13I8lW/jygNrTv7izHYMaO93y8m4AW7ZsoZmfjsENDPQPtKF5eT3RiRYCP84i4KMsdp0zExQUxFdffVXouNm/Gxm0IYfl+4zkmi5PI/Dz8+P3338HIDUXVh/Kp2IprQw3F0IIIcRtuZRtZN6W4zSr4kGbmp43PuARYTJbiEvNJi41G1u9lr6N/Vi79xzr9p0jLdtY0tUT4r4kAfp1GAwGypVThyiN+CGX4ZtyaOan43CShadW51izfz/7fQ4XsxXKly9Pw4YNAWjfvj0AH/6VzzvbjWg0Gvbt28fmzZvJy8tj/vz5d7zsRpcuXYiNjWX48OHWbW3btqVly5ZXlS0I0JOyFco4aMjNzb3h+ceNGwfAO21sSRqnPgjw8vKy7m9dSc/kYHUI/8iRI63bj6dYSMi00HBJFp/8m8wu16Rgh5FDhw7d0j3+9ttvJCUl0ay8ju8O55OcrfDpXiNHktTs94eTLGQaISAggN69ezN06NBCxy/9O58B69R7HTZsmHW7ra2t9fXIJrbkmBTpQRdCCCHEbZm/5TgZufm82bnWI7usWlFy8i1EHUuy/qlUxgl3RwPTNhwiKUuGugtRFAnQb6AgIN1wLJ8dcWYCvXU0KqsjPtPCjjgzW553pG8dtRc5Ojra2uPerFkzBg8eDICDgwPh4eHUq1eP0NDQu5qEzNXVlffff5/w8HC++uor1qxZU6jXv0BBgL7i3zXab9SD/vnnn/P1118D6trgADotZGdnW8u8uC6H13/Oo3r16syfP986rP9YsoXVh0yYp7gwtJHaNpdy1akBb7311k3fW2JiIq1atQKgnIuWfy5a+Gh3Pq//fHkpO4AOHTrw5JNPotPpWLRoEYqiEB0dXehc7777Lh07drS+/29SPrMF62gEIYQQQoibdTQ+gy92nOGZJuWp4S2j8a7HRqelW2BZkrOMfPmnJIwToijFFqBfuHCBfv360aFDBzp16sSKFSsAuHTpEgMGDKBt27YMGDCAtLTLayIuXryY0NBQ2rVrx7Zt26zbo6Oj6dKlC6GhocyYMQNFuXdLNLi5uQFweqQz3/V2IOqMmd/6O1LGQUMdTy0VSmlJzlHo0aNHoQBPo9Hw0Ucfcfr0aU6dOkXXrl2LrY4ajYZu3brRt2/fawaZBQH672fNHEywXDdA37BhAy+88AIv1rch7jUnWlXU0z9cHTHg7u7OkCFDAFi2L5/l+/KtPe0F67Xvizdj/+/Idy9H9UcsIUvt6f7+++8xm28uYVxERAQADXy0bI0x8dletTf+hRdeYPfu3ZhMJiwWC5s2bcLOzq7QsbVr10ZRFI4dO0ZiYiJjxowptP/KxHkJmRa2njGTmZl5U/USQgghhEjLNhKbksW4NftxstXTt3F563DuvPzbT477sKvq6UygXym++HfOvhCisGIL0HU6HePHj+eHH37gm2++4auvvuLEiRMsWbKEpk2bsnnzZpo2bWpdG/vEiRNEREQQERHB0qVLefvtt62B3NSpU5k2bRqbN28mJiaGqKio4qr2VQrmJafnKRxLtjDxl1yOJJk5marg46w2X0qOQq1ata46VqvVUqFChULDwktKQQDbs6aeVT3tCwXoycnJrFq1ipMnTwJY13Z/N9SOqDNmGpXVsmJ/Pnq9np49ezJ48GDrg4v69evz3HPPAVC2bFkAfo0xM3B9Lq/+kMPiPWrv+5I9+USeVhPFJSUl3bC+O3bs4MUXX8TRBnrWtKGRrw5Qs94vX76chg0botPpbjiMrGrVqpQpU+aq7Vcep9WAnf7m14YXQgghhMjIM7Eg8gT749JoXcOT/bFp1qHcRrOs9309HQN8sDfomLj24D3teBPiQVBsAbqnpye1a9cGwMnJiUqVKpGQkEBkZCTdu3cH1KzkW7ZsASAyMpJOnTphMBjw8/PD39+fAwcOkJiYSGZmJvXr10ej0dC9e3ciIyOLq9pXadCgAQD+8zPo/k02E5vbciJFTXRW5f0Mxm/J5WSKxbr2+f2qoAc9OUfB20nDqlWriIqKIikpicqVK/PR+H6EPFaPgwcPcujQIVxtQa+FvgE2tKusLm/2ww8/UKZMGQIDAzl27Bg7d+5k165d1iH73bt3L7Q03Ae78jmfof7StdfD7wMc1TokJ9+wvoMHD8bdXsOuQY5MbGHL/n/XKX/xxRfvXqP867FPs4hOtFCqVKm7fm4hhBBCPJzSc/P5ITqe8u4ONPB3K+nqPFCcbPUMfaIyu06nsHpPXElXR4j7iv5eXCQuLo7Dhw9Tr149kpOTrcOLPT09SUlJASAhIYF69epZj/Hy8iIhIQG9Xl9oyTFvb28SEhJueM28vDwOH77zZRyeeOIJ7OzsuPRvUrWxP+eS+W/SyZOpCu9sV9+4ubndlesVl4sXLwKQkKkQ4KVjbqgtrz3bnlpP9CQtLY1vXnLi2e9zeP7559m3bx9lHDTM32nk97MmUnPUINvX17fQPTo7O3P8+PFC1/nuu+94+eWXrb3Rffv2ZefOnZw6dQoHG9Bp4O+//75uz/f58+c5ePAg45sb8HZSnyH9fcHC2LFjOXPmzF1rk+nTp/Pmm29yKlW9v969e9/X/w9zc3Pv6/o9CKQN74y03527221YsHKIEOLe+yTqNNl5JgY8XgGtJIa7ZZ3q+vDLkURmbjpM6xqeeDjZ3vggIR4BxR6gZ2VlMWLECCZOnIiTk9M1yxU1vEWj0Vxz+43Y2tretS8uW7duZeTIkezYsYOYS1fXp3379jRr1uyuXKu4xMfHA3A+w0LMJQtjH7elZul8On/5JQAWBextNPy6bx8Aox4zEHPJwuaTas91WFjYTbVnzZo16dKlC9HR0eTn51O3bl06d+7MqVOnyM4HBxv1Ycb1zrVz504A5mw38sEuI1oNpOfBlPr17+qX0ddff53Y2FgiIyPp2rUrgwcPRq+/J8+sbsvhw4fly/gdkja8M9J+d+5+b8OoqCjCwsKwWCz06tXLmuy0gKIohIWFsXXrVuzs7Jg9e7Z1tNy1jl24cCHffvutNU/J6NGji1xtRIgHycG4NML3nuOxyh6ULWVf0tV5IGk1GmY+GUDH97cRtukw7/UOLOkqCXFfKNZoJD8/nxEjRtClSxfatm0LgIeHB4mJiXh6epKYmGj9wPb29rYGkaD2qHt6el61PT4+vlCCr3uhcePGfPbZZ+zZs4cPPviA2rVrM2PGDFauXInFYuGVV165p/W5HQVD3NPyYPCGHHYPdsLV7vKDDl8XLev62GMfloHJAl5OGk6mqvvefffdQsuo3Yw6depYXzs6qkPbp0ep2devzARflD179gDqfPmI4ybriIWCqRF3i62tLYsXL76r5xRCiAeV2Wxm2rRpLFu2DC8vL5566ilCQkKoUqWKtUxUVBQxMTFs3ryZ/fv3M3XqVFavXn3DY/v3789LL71UUrcmxF1ltihMDj+Iu6OB0Joln2foQVbVy5lXWlZm4S8neKpBOR6vUrqkqyREiSu2OeiKojBp0iQqVarEgAEDrNtDQkIIDw8HIDw8nNatW1u3R0REYDQaiY2NJSYmhrp16+Lp6YmjoyP79u1DUZRCx9xrzz33HDt27ODTTz/Fx8eHsWPHMm7cOGsiufuZg4OD9fXmfo6YLAr2eg39A214t60tq//JR6/VML+9mkzOxaAhI09Bp9MxZsyYO+pZLrj23D+MZOffOEA/ffo0AB90tMPJoD5EePXVVwvdgxBCiLvrwIED+Pv74+fnh8FgoFOnTlflfCnII6PRaAgMDCQ9PZ3ExMSbOlaIh8Vnv59mf1waw0KqYGejK+nqPPCGtapCBQ8HJoVHkyvZ74UovgB9z549rFu3jh07dtCtWze6devG1q1bGTx4MNu3b6dt27Zs377dOgSuatWqdOjQgY4dOzJw4ECmTJmCTqf+0ps6dSqTJ08mNDSU8uXLExwcXFzVfmgVZF0HcLfX8MTybPbGmxneyMCYprYcSbYwcH0Ob/+WB8DZdAvnMhTWrFlzx9cuCKwjn3egVhntdQP0VatWERERgUEHTgYNmUaF5557jvnz599xPYQQQlxbQkJCoZwvBblgrlemIC/MjY5duXIlXbp0YcKECYWWVxXiQXM8IYO5m48SWsuL0Jr3dkTnw8rORseM7gGcTspi3pZjJV0dIUpcsQ1xDwoK4ujRo0XuK1gT/b+GDBliXWP7SgEBAWzcuPGu1u9RUzCVoIBGA/3q2mCrV3uop/yah7eTBi8nDRezFcZuVgP1uzFXsiBA12og0FvLOzOn06dPH15++WV27NhBvboBzH5nDp6enjzzzDNU99ByZLiaryAnH55++mm02mJ7liSEEIJr54K5mTLXO7Zv374MHToUjUbDggULmD17NrNmzbpLtRbi3sk3WxizWl3zfOaTAeSZpLf3TpjMFuJS1U6bCqUd6FLPh0+iThFYrhQB5VxxttXj6mAo4VoKce9J1POIcHBwsI5IAPisq501OB+/Rc1Q7++q5bve9gR6a/mokzrUvVy5cnfl2gDbY82s7OFA/8opNGnShNWrV/OsfwKLq0fx0ksvMXHiRABrcP73BTMKyNB2IYS4B66VC+Z6ZQrywlzv2NKlS6PT6dBqtfTq1YuDBw8W850IUTze/ekoB+LSCOtehzLOknH8TuXkW6zrxkcdS6KBnxuu9jZMDo9my6FEMvJMJV1FIUqEBOiPCI1GY03W9vzaHI7/u5Z7xLF8Fv2lZmHbec7M/ngLe192opKbFoPBcFeC44Le+x9PqL9ok3Ms1uXZKrtpWfSXkZ07d7J06VKal7/8EKHhkixATeYmhBCieAUEBBATE0NsbCxGo5GIiAhCQkIKlSnII6MoCvv27cPZ2RlPT8/rHpuYmGg9fsuWLVStWvWe3pcQd8OWQwksjjrFc4+Vp0OAT0lX56Fka6OjZ4NypGQZWbfvXJEjc4R4FNy/a0qJu87BwYH09HQGBNqQla/+0uu8KgdQs7zn5OTwzvY8etW24fezJtzc3G5qSbsb8fJSM5z+ftbMiRQLCzvY88GufACqumsxWS6X3TbAkS8PGBm+Kde6rXr16ndcByGEENen1+uZMmUKAwcOxGw207NnT6pWrcqqVasAdah6y5Yt2bp1K6Ghodjb2zNz5szrHgswd+5cjhw5AoCvry/Tpk0rmRsU4jadTc5mzOr91C7rwuROtUq6Og+1SmWcCKnpSeThRNbuPc+I1vJATzx6JEB/hBT0oLeqqOfVH3Lo8m9wDuoXqOHDh7PngoWvDuZzOMmCv7//XblutWrVrK8/32+kW3Ub6/uvovNZ3NmecxkWZkQZic+0sGRPPmnqFHg++OADSpeWJTeEEOJeaNmy5VVrlPft29f6WqPR8NZbb930saB+vgjxoErLyefFFX8B8OGzDSRr+z3Qqron51JzeD/yOEEV3Hi8snwPFI8WGeL+CCkYrr4/3sy2M5cTm7z66qs0adIEAE9HDadSLaw5ZKJu3bp35bpBQUHW+e/Z+dCwrI5GZbWMeszAzyfVYe8bj6l/bzhmsg6///zzzxk2bNhdqYMQQgghxK0wmS0M/+pvYpKy+Pi5hvh7OJZ0lR4JWo2GXg398HN34KXlu9l1OqWkqyTEPSUB+iOkoAe9nreOd9qoSeBat27N+++/j5+fHwB2epgcbEsZBw2lSpW6K9fVaDTMmTMHgByTOrS+V20b5rWz49RIZzRvpxOfqW4fvCGX+EyFQYMG0a9fv7tyfSGEEEKIW2GxKIz77gDbjicxtm01/NztiUvNLvQnT9bsLjb2Bh3zn66HTyk7BizbRdSxiyVdJSHuGQnQHyHW9chPmYi+qH6obNmyBYAyZcoAkJarBspu9pq7Mv+8QEEP+od/5dM/PIfXH7+c+K2UHRwb7oROAxv62gPQuXPnu3ZtIYQQQoibpSgK0zYe4vu/zzGweUVc7A2Fso0X/DGaJYlZcfJwsmXVoMfwdbPnhWW7WBh5HItF2lw8/CRAf4QU9KCfTbOQkVd4n1arpXTp0qTlQZX3MziWbLmra483a9bM+rpgODtA4MeZXMqFXJNCJTctLcqraRGCgoLu2rWFEEIIIW6GoijM+ekoy/+I4cVmFXnh8buTj0fcHi8XO8KHNaNbvbL87+djPPnhdvacSS3paglRrCRAf4QUBOipuQpn0yxX7W/Xrh0AJ1PVp5MDBgy4q9e2sVGTwyXnKPx0wkS3r7PZn6DWIyVHoaKbhrQ8hSpVqlC2bNm7dm0hhBBCiBspCM4/+u0kzzQpz+RONe/qaEJxexwMeuY9Hcj8pwOJT8+l50d/MGDZLn4/niRLsYmHkgToj5B69eoBMGZzHsv25V+1f/r06bRq1YoqVaqwbNmyu7682Z49e6yvp0Xl8evpyz3pn+3Lx1an4UKGQnBw8F29rhBCCCHE9ZgtCm+t/4ePfjvJs03KM6NbHbRaCc7vFxqNhu71ffllzBO81qYaB8+l8dynO2k/fxvf/HWWXMkHIB4isszaI2To0KGEhYWRmZkJwMcff1xof8WKFfnll1+K7foBAQE0btyYXbt28Uds4V+k8/40UsZRQ/NlWbw3P7DY6iCEEEIIcaXcfDOvfbOPH6LjGRxcifHta0hwfh8wmS3EpWZftb1nQ1+61/fmp38S+eavWN747iDTNx6mbW0vutT1oUF5N1wdDCVQYyHuDgnQHyEuLi789ddfLFu2jNq1a5dIlvSxY8fSu3dvAOzs7Bg5ciTvvPMOdb207B7sRNn/ZViH4gshhBBCFKe0nHxeXLaLPWcv8WpIFZ5u5Mf5tBzrfsnUXnJy8i3sPVn0Emv1y5fCydaGF5tV5HRSFn/FpLB+33m+//scNbyd6dfUn671yuJsZ3OPay3EnZMA/RFTo0YN3nnnnRK7fq9evfj+++/566+/6NOnD+XKleOdd94hIUudQ2SygNFoLLH6CSGEEOLREJeazUvLd3PyYiZPN/LDx9WeqGNJhcrUL1+qZConbopGo6FSGScqlXEi22hiX+wlDl9IZ9LaaGZsPEznuj70aexHg/Jukk9APDAkQBf33JNPPsmTTz5ZaNu5dIV+a3O4mK2Qm5tbQjUTQgghxKMg6thFRny9F7NZ4X+965GVJz3lDzoHg57HK5fmjfbVSc7K55u/zrJ+33lW74mjnJs9bWp6EVrLi8YV3bHRSRoucf8qtp/OCRMm0LRp00LrWR8+fJjevXvTrVs3evTowYEDB6z7Fi9eTGhoKO3atWPbtm3W7dHR0XTp0oXQ0FBmzJgh2RofQq1bt0YBvjygJq7r2rVryVZICCGEEA+l3HwzszYd5oVlu/B2sWP9q81p6O9W0tUSd5HZolDaycCwVlVYO+xxJnSogb+HA1/tOsuzS3fSYNrPDP58N5/9fpp/zqfJ2urivlNsPeg9evTgueee44033rBumzt3LsOGDaNly5Zs3bqVuXPn8sUXX3DixAkiIiKIiIggISGBAQMG8NNPP6HT6Zg6dSrTpk0jMDCQQYMGERUVRcuWLYur2qIEzJgxg4MHD5KYmMiwYcOoVKlSSVdJCCGEEA+Z7SeSmLIumpMXs+jTyI8pXWrhYNAXmYhMPLj+O3fd2c6GTgFlCa3pzcmLmaRmG9kfd4nNhxIAcLHT07iiO00qetCkkju1fFzQSw+7KEHFFqA3atSIuLi4Qts0Gg1ZWVkAZGRk4OnpCUBkZCSdOnXCYDDg5+eHv78/Bw4cwNfXl8zMTOrXrw9A9+7diYyMlAD9IfPYY49x+vRpcnNzcXd3L+nqCCGEEOIhYbEo/H4iiSVRp/j9RBK+pexZ8WJjWlYrU9JVE/eYQa+lpo8Lj1d2R6/TkpCey77YS+w7e4m9sZfYcjgRACdbPY0quNGkkgdNKrpTx9dVhsSLe+qezkGfOHEiL730Eu+88w4Wi4Wvv/4agISEBOsa3QBeXl4kJCSg1+vx9va2bvf29iYhIeFeVlncIw4ODjg4OJR0NYQQQgjxALNYFM6n5XDofDp/nEzm50MJnLuUQykHG0aEVKFb/bLY6nWFes0lU/uj5coednsbPU0rl6Zp5dKk5+Rjo9dwMC6NfbGX+PXoxX/L6AjwdaFJRQ+aVvagiqcTZZxtJemcKDb3NEBftWoVEyZMoF27dmzatIlJkyaxfPnyIueVazSaa26/GXl5eRw+fPiO61wgNzf3rp7vUSPtd+ekDe+ctOGdkfa7c3e7DWvWrHnXziXEg0JRFBIz8jh1MYuY5CxikrI4naS+PpOcTZ7JAoCdjZbHKnkwOLgSGkCv07LzVOpV55NM7QLAxd6G+uVLYW+jp3FFDzJy84lJzuZ0Uiank7LYFZPKwl9PAOBsp6eKpxPl3R1wdzRQ2skWd0cDLnY26HUa9FoNep0Wm3//zss3Y1YUDHottnotjrZ6HA06NBoNzrZ6WbddFHJPA/S1a9cyadIkADp06MDkyZMBtWc8Pj7eWi4hIQFPT8+rtsfHx1uHxd+Ira3tXf3icvjwYfkidAek/e6ctOGdkza8M9J+d+5+b8OoqCjCwsKwWCz06tWLwYMHF9qvKAphYWFs3boVOzs7Zs+eTe3ata977KVLl3jttdc4d+4cvr6+zJ8/H1dX13t+b+LBZDJbOJ2UxaEL6fx9JpXo82kcS8gkI9dkLWPQafF1s6OcmwMN/d0o5+ZAhdIO1PR2waBXg6Odp68OzIW4Hmc7GwJ8XQnwVX9f1fJxtj4EiknOJiZZXX/9UnY+2cZbH4Vh0GlxsbfB38OBiqUdqeDhgL+HIxU8HKlQ2kHWcH+E3dMA3dPTk127dtGkSRN27NhBhQoVAAgJCWHMmDEMGDCAhIQEYmJiqFu3LjqdDkdHR/bt20e9evUIDw+nX79+97LKQgghxCPBbDYzbdo0li1bhpeXF0899RQhISFUqVLFWiYqKoqYmBg2b97M/v37mTp1KqtXr77usUuWLKFp06YMHjyYJUuWsGTJEl5//fUSvFNRHBRFwaJAvtmC2aJgsij//m3BZFZfA1gUBUVR/1Y3qX9n5Zm4mJFHUqaRixl5nEnJ4siFDE4kZmI0qz3iBp0WTxdbani74O1qR2kntefS1d6Ghv5u7D17yVqftGwTO06pw5ilh1zcDbY2OrKNFso421HG2Y5GFS7nTWroX4rMPBNZeWbMFgsmi4LJ/O/Pv0UhJ89M9Pl0TBYL+WaF3Hwz6Tn5pOWayMs3s+34RdbsySt0PQ9HA37uDpRxtrX+rLs7GnAw6LA36LG30eFg0GFno7O+tjfosNPrsLVRe+pvduSxoijkmSzkGM3Ep+VwMdNITr6J3HwLGg3YaLXY6LXY6DR4OBjwLmWPk63+lq4hbl6xBeijR49m165dpKamEhwczKuvvsr06dOZOXMmJpMJW1tbpk2bBkDVqlXp0KEDHTt2RKfTMWXKFHQ6HQBTp05lwoQJ5ObmEhwcTHBwcHFVWQghhHhkHThwAH9/f/z8/ADo1KkTkZGRhQL0yMhIunfvjkajITAwkPT0dBITEzl37tw1j42MjOSLL74A1GSv/fr1u6cBemJGLsfiMzErihoUWtSA0GxRUBTl3+3q3GWzNahUrF+yCwWbZnW7RQGtVoNWAzqN5t/XGnRa0Go0//4BnVaDRqNB92/Zgn06rabQ8YXKFJzr33MUTPYrmPWnoFzxGut0QMX6n/+UUa48h3LFcQUtpJY1mtUv57n5ZnJNFnLzzeTkm8k1mskymskxmsk2msg2mv/9YyLnin0FQfTd4vVvIN6iWmlqertQw8cZO72WP67Izi3E/cKiwNH4zGvur+/vBtcIZAuS1uUYzZxPyyEuJYe4S9nEpeYQn5bLqYuZ7I4xkpaTz62uCGfQa7HTa7GzKQjadViu+J2Wb7aQk6/+mzbfxnJzOq0GR4MOJ1u9OmzfVv/vax22el2Rw/3V9+rvOKt/X2sKv0Xz75bL76865KoHBNc6tuB4jQb0Wu2/dbuiPloNNjrtv39r0Gm11vIaNHg4Gajp43LLbXQ7NMpDurD4vn37sLW1LelqCCGEEMVGr9dTtWrVu3KuH3/8kW3bthEWFgZAeHg4Bw4cYMqUKdYyL7/8MoMGDSIoKAiAF154gbFjx3Lu3LlrHhsUFMTu3but52jUqBF//fXXdevSpEkTfH1978p9CSGEEPcjNzc3Pv3006u239Mh7vdSYGBgSVdBCCGEeGDcTGLW4kjqWpSdO3fe9rFCCCHEg0wW9RNCCCHENRO2Xq9MQfLW6x3r4eFBYqK6vnBiYiLu7u4IIYQQomgSoAshhBCCgIAAYmJiiI2NxWg0EhERQUhISKEyISEhhIeHoygK+/btw9nZGU9Pz+seW3AMqEPfW7dufa9vTQghhHhgPLRz0IUQQghxa7Zu3crMmTMxm8307NmTIUOGsGrVKgD69u2LoihMmzaNbdu2YW9vz8yZMwkICLjmsQCpqamMGjWKCxcu4OPjw4IFCyhVqlRJ3aIQQghxX5MAXQghhBBCCCGEuA/IEHchhBBCCCGEEOI+IAG6EEIIIYQQQghxH5AAXQghhJXMeroz0n4Pnx9++IFOnTpRo0YNDh48WGjf4sWLCQ0NpV27dmzbts26PTo6mi5duhAaGsqMGTPk56IICxcupEWLFnTr1o1u3bqxdetW675rtasoWlRUFO3atSM0NJQlS5aUdHUeOCEhIXTp0oVu3brRo0cPAC5dusSAAQNo27YtAwYMIC0trYRref+ZMGECTZs2pXPnztZt12s3+Xd98yRA/1dcXFxJV+GBd/DgQZKTk0u6Gg+0P/74g+jo6JKuxgMrIyPD+lq+EN8ek8lU0lV4oOXn55d0FcRdVq1aNRYuXEijRo0KbT9x4gQRERFERESwdOlS3n77bcxmMwBTp05l2rRpbN68mZiYGKKiokqi6ve9/v37s27dOtatW0fLli2B67eruJrZbGbatGksXbqUiIgINm7cyIkTJ0q6Wg+cFStWsG7dOr7//nsAlixZQtOmTdm8eTNNmzaVBx9F6NGjB0uXLi207VrtJv+ub80jH6D/888/9O/fn/fff19+UG7T8ePH6dOnD4sWLSI9Pb2kq/NAOnToEAMHDmTYsGGcPXu2pKvzwNm/fz9Dhgxh0qRJrFmzBqPRiEajKelqPVD27dvHmDFjeP/994mJiZHfh7do7969jBgxgjlz5nDixAlpv4dI5cqVqVSp0lXbIyMj6dSpEwaDAT8/P/z9/Tlw4ACJiYlkZmZSv359NBoN3bt3JzIysgRq/mC6VruKoh04cAB/f3/8/PwwGAx06tRJft7ugsjISLp37w5A9+7d2bJlS8lW6D7UqFEjXF1dC227VrvJv+tb88gG6Iqi8NFHHzF69Gg6derEnDlz0Ol01n3i5n3++ee0adOGjz/+mIoVKwLShjfLbDbz5ptvMnnyZPr06UOXLl04efIkABaLpYRr92A4cuQIb7/9Nu3ataN9+/bs2LGDM2fOlHS1HijHjh1j+vTptGrVCg8PD7755hvrutXyb/nGkpOTmT59Oi1btqRUqVJ8/vnnfPfdd4C038MsISEBb29v63svLy8SEhKu2u7t7U1CQkJJVPG+t3LlSrp06cKECROsQ2Gv1a6iaNJed8dLL71Ejx49+OabbwD197qnpycAnp6epKSklGT1HhjXajf5Ob01+pKuQEnRaDSYTCYaNmxIr169ALUXs1q1auj1j2yz3BKz2UxGRgYajYbnnnsOgJ9//pmAgABcXV2xt7dHURTpybwOnU5HixYtmDRpEnZ2dtjZ2fHxxx8zePBgbG1tS7p6D4R//vkHf39/unfvTlpaGj/++CM+Pj7W/fIzeGP79++nUqVKdO7cmezsbJYuXcqGDRto0qQJ5cqVkza8gSNHjlChQgV69uxJdnY2e/bs4csvv6RRo0ZUrFhR2u8B0L9/f5KSkq7aPmrUKNq0aVPkMUU9fNFoNNfc/ii6Xrv27duXoUOHotFoWLBgAbNnz2bWrFnSfrdI2uvOrVq1Ci8vL5KTkxkwYECRI2bEnZGf01vzSEWiu3btwtbWlnr16gHq07JXX32Vd955h127dlG6dGmcnJwIDQ2lffv2JVzb+9OVbajT6bC3t+evv/5ix44dbNy4kdTUVCIjI7GxsWH69Onyj68I//05bNu2LaD+8tJqtVSoUIGcnBwJ0K/hv+33xBNP8NZbbzFv3jzWrl2Lt7c3YWFhVKxYkcGDB8vPYBH+24YBAQGsX7+es2fPUr58ebRaLc7OzqxevZrXXntN2vA/tmzZwokTJ6hRowZPPPEENWvWJDo62tp+AQEB1KlTh2+++Ybx48dL+z0Ali9ffsvHeHt7Ex8fb32fkJCAp6fnVdvj4+OtPUqPmptt1169evHKK68A125XUTRprzvn5eUFgIeHB6GhoRw4cAAPDw8SExPx9PQkMTERd3f3Eq7lg+Fa7SY/p7fmkRjinpmZyfDhwxk+fDhff/21dRiVg4MD3bp14/Dhw4wfP57FixfTqFEjtm3bxunTp0u41veXa7Whra0tPXr0YNq0aTRv3pxPP/2U1157jePHjxfKyCqu3YaKolh72CpVqsSff/5JXl6edZ9QXav9PDw8WLduHSaTidGjR/Ptt9/y5JNP8vfff7N3794SrvX95b9teOnSJQD8/PwICAhgwoQJDB06lOjoaNq3b4/JZLL+LApISUlh6NChLFu2DFdXVyZMmMCPP/6Iu7s7bdu25YsvvgDAxcWFpk2bkpOTQ2JiYgnXWhSXkJAQIiIiMBqNxMbGEhMTQ926dfH09MTR0ZF9+/ahKArh4eG0bt26pKt737ny38aWLVuoWrUqcO12FUULCAggJiaG2NhYjEYjERERhISElHS1HhjZ2dlkZmZaX2/fvp2qVasSEhJineol/4Zv3rXaTf5d35pHogfdYDDw2GOP0atXL/7++29+/PFHnn76aQC6du1KcHAwpUqVAuDxxx9n69atODg4lGCN7z/Xa8NnnnmGZcuWkZqaCqhPIhs0aIBW+0g8/7lp12rDgt41i8WCt7c39erV46effuL555+XnrcrXO9nsHLlypw6dYoWLVoAULt2bdzd3TEYDCVZ5ftOUW3Yp08fHB0dGTduHEeOHCEuLo42bdpw8OBB1q5dKyM5rnD27FkaNGjAwIEDAbC3t2f58uW0b9+ezp07M3fuXP7880+aNm2Km5sbCQkJODs7l3CtxZ36+eefmT59OikpKbz88svUrFmTTz/9lKpVq9KhQwc6duyITqdjypQp1lw2U6dOZcKECeTm5hIcHExwcHAJ38X9Z+7cuRw5cgQAX19fpk2bBnDddhVX0+v1TJkyhYEDB2I2m+nZs6f1YYe4seTkZIYNGwaoUzc7d+5McHAwAQEBjBo1ijVr1uDj48OCBQtKuKb3n9GjR7Nr1y5SU1MJDg7m1VdfZfDgwUW2m/y7vjUa5SHtogsPD6ds2bLUqFEDFxcX8vLy0Gg0bNq0iT179vDiiy8WOTdw1apVbNu2jTlz5uDk5FSCd1DybrYNATZt2sTHH3/MO++8w969e1m9ejX/+9//Hvl5PDfbhhaLBa1Wi8lkYtGiRZQtW9aaG+FRdis/gytXruSHH37giy++YM2aNaxevZp58+bh6+tbwndRsm6lDa/0ySefkJeXx7Bhwx7pB0Xh4eH4+PhQt25d9Ho9aWlplC5dGrPZzOHDh/n666+ZMWMGFouF8PBwPvvsMxYtWsS2bdv4/fffmTNnDi4uLiV9G0IIIYR4QDxUAbqiKFy8eJExY8ag1WopX7482dnZTJo0yToHIiYmxtorNHToUEDtudyzZw9hYWH4+PgwduxYKleuXJK3UmJutw1BDdKPHDnC8ePHGT169CP7BPdOfg61Wi0zZ87EwcGBUaNGleBdlJxbaT+DwWB98p2bm8ubb75JSkoKZrOZyZMnU6VKlZK8lRJzJ/+Oo6Ojeeedd9BqtUyfPp3y5cuX1G2UmBu1n9lsRqfTsW7dOn755ZdCPSuffPIJMTExnDp1ihkzZjyynyVCCCGEuD0PTYBe8IXp9OnTLFq0iHfffReTycSsWbNITExk4cKF1rI///wz27dvZ8CAAXh7e2MwGIiNjeX48eOP9ByT223DMmXKYGNjg42NzSOfrfhOfg7NZjMODg6PdBveTvv1798fLy8v7O3tMZlMpKamUqZMmRK8i5J1uz+DXl5e2NnZkZqayvHjx2ncuHEJ3kXJuV77JSQk8MEHH1jLjBs3jieeeIKOHTty8eJF68+d0WiU6RVCCCGEuC0P/Bx0k8nE+++/j9lspmXLlmRmZlrnNOj1eiZPnkyLFi3YtWuX9QtnaGgoJ0+eZODAgWRnZ7NixQqqVKnySPYUwd1pw88//5zKlSs/soGltOGduZP2GzRoUKH2e1SD87v5u/BRDM5vp/0cHBwoV64cCxYs4Oeff2bp0qXWh75CCCGEELfjgc7itWvXLnr06EFaWhr+/v4sWLAAvV7Pzp07OXDgAKCusTd06NBCvUY//PADH3/8MU2aNGH9+vWP7DBYuHtt+CgP45Q2vDPSfndOfhfemdtpP7PZzHfffcfIkSPJzMzk888/x9vbuyRvQwghhBAPgQd6iPvu3buJi4uje/fugJo1tVq1atjZ2fHll1/y/fffY7FYSE5OZsaMGYwdOxY/Pz92794NQFBQUAnW/v4gbXjnpA3vjLTfnZM2vDO32n7jxo3DbDbzxRdf0L17d2rXrl2yNyCEEEKIh8YD3YNeu3ZtOnbsiNlsBqBBgwZcuHCBHj16WL88abVa4uPj0el0+Pn5AeqX0Uf9C2kBacM7J214Z6T97py04Z25lfbTarX4+vpSvnx5Jk2aJMG5EEIIIe6qBzpAt7e3x2AwWOcJ/vHHH9YMxbNmzeLkyZO8/PLLjBkzhlq1apVkVe9b0oZ3Ttrwzkj73TlpwztzK+1XEJA/wIPPhBBCCHEfe+CTxIE6F1Cj0ZCUlMSQIUMAcHR0ZPTo0Rw7dgw/Pz+8vLxKuJb3N2nDOydteGek/e6ctOGduZX2exSTOQohhBCi+D3QPegFtFotJpMJNzc3jh49yssvv8yHH36IVqslKChIvpDeBGnDOydteGek/e6ctOGdkfYTQgghREl7KHrQNRoNhw4dYsOGDcTFxdGjRw969epV0tV6oEgb3jlpwzsj7XfnpA3vjLSfEEIIIUraA53F/Urx8fGsW7eOAQMGyBq0t0na8M5JG94Zab87J214Z6T9hBBCCFGSHpoAXQghhBBCCCGEeJA9FHPQhRBCCCGEEEKIB50E6EIIIYQQQgghxH1AAnQhhBBCCCGEEOI+IAG6EEIIIYQQQghxH5AAXQghhBBCCCGEuA88FOugCyHuvpo1a1KtWjVMJhM6nY4nn3ySF154Aa328nO9IUOGkJKSwjfffMO2bdt49913ATh79iyenp7Y2dlRvXp1evbsydChQylXrpz12DfeeIPHH3/8nt+XEEIIIYQQ9ysJ0IUQRbKzs2PdunUAJCcnM2bMGDIyMhgxYgQA6enpHDp0CAcHB2JjY2nRogUtWrQAoF+/fowbN46AgAAAdu7cSVBQEIsXLy6ZmxFCCCGEEOIBIEPchRA35OHhwfTp01m5ciWKogDw008/0apVKzp16sSmTZtKuIZCCCGEEEI8+CRAF0LcFD8/PywWC8nJyQBERETQuXNnOnXqxMaNG294/O7du+nWrZv1z9mzZ4u7ykIIIYQQQjxQZIi7EOKmFfSeJyUlcfbsWRo2bIhGo0Gv13Ps2DGqVat2zWNliLsQQgghhBDXJwG6EOKmxMbGotPp8PDw4IsvviAtLY3WrVsDkJmZSURExHUDdCGEEEIIIcT1yRB3IcQNpaSk8NZbb/Hss8+i0WiIiIhg6dKl/PLLL/zyyy989913Mg9dCCGEEEKIOyQ96EKIIuXm5tKtWzfrMmvdunVjwIABxMXFcf78eQIDA61l/fz8cHJyYv/+/dSrV6/I8xXMQS8wZMgQ2rdvX9y3IYQQQgghxANDoxRMKhVCCCGEEEIIIUSJkSHuQgghhBBCCCHEfUACdCGEEEIIIYQQ4j4gAboQQgghhBBCCHEfkABdCCGEEEIIIYS4D0iALoQQQgghhBBC3AckQBdCCCGEEEIIIe4DEqALIYQQQgghhBD3AQnQhRBCCCGEEEKI+4AE6EIIIYQQQgghxH1AAnQhhBBCCCGEEOI+IAG6EEIIIYQQQghxH5AAXQghhBBCCCGEuA9IgC6EuCu+//57+vbte9vHDxw4kLVr197FGgkhhBBCCPFg0Zd0BYQQd9eGDRtYtmwZp0+fxtHRkRo1avDKK68QFBRU0lWzWrhwIWfOnOHdd9+1blu6dGkJ1kgIIYS4f9SvX9/6OicnB4PBgE6nA+Dtt9+ma9eut3S+fv360bVrV3r16lXk/ri4OFq3bo2Dg0Oh7WFhYXTs2PEWay+EuBMSoAvxEFm2bBlLlizh7bffpnnz5tjY2LBt2zYiIyNvKUA3mUzo9fobbhNCCCHE3bd3717r65CQEGbMmMHjjz9e7Nf966+/buqz3mw2Wx8YwK1/R5DvFEJcmwxxF+IhkZGRwfvvv8+UKVNo27YtDg4O2NjYEBISwhtvvIHRaCQsLIzmzZvTvHlzwsLCMBqNAOzcuZPg4GCWLFlCs2bNmDBhAgsXLmTEiBGMHTuWBg0asHbtWjIyMpg4cSLNmzenRYsWzJs3D7PZXGR9ZsyYQcuWLWnQoAE9evRg9+7dAERFRbF48WJ++OEH6tevb+0F6NevH6tXrwbAYrHw4Ycf0qpVK5o2bcq4cePIyMgA1Kf81atXZ+3atTzxxBM0adKEjz76qLibVwghhChxFouFJUuW0KZNG5o0acLIkSO5dOkSAHl5eYwdO5YmTZoQFBREz549SUpKYt68eezevZtp06ZRv359pk2bdsvXHT9+PG+99RaDBg0iMDCQnTt3EhISwpIlS+jSpQuBgYGYTCYiIyPp1KkTQUFB9OvXj5MnT1rPUVR5IcTVJEAX4iGxd+9e8vLyCA0NLXL/Rx99xP79+1m3bh3r16/n4MGDfPjhh9b9SUlJpKWl8euvvzJ9+nQAIiMjad++Pbt376ZLly688cYb6PV6Nm/eTHh4ONu3b7cG1f8VEBBAeHg4u3btonPnzowcOZK8vDyCg4N5+eWX6dChA3v37mX9+vVXHfv999+zdu1aPv/8c7Zs2UJ2dvZVXyj27NnDjz/+yIoVK1i0aFGhLwFCCCHEw6jgc/HLL79k27ZtuLq6Wj8f165dS2ZmJr/99hs7d+7k7bffxs7Ojtdee42goCCmTJnC3r17mTJlym1de+PGjbzyyiv8/fffNGzYEICIiAiWLFnC7t27iY2NZcyYMUycOJE///yT4OBgXnnlFWtnwH/LSw+6EEWTAF2Ih8SlS5dwc3O75gfehg0bGDZsGB4eHri7uzNs2LBCwbFWq2XEiBEYDAbs7OwACAwMpE2bNmi1WjIzM4mKimLixIk4ODjg4eFB//79iYiIKPJ63bp1s9bnxRdfxGg0cvr06Zu6lw0bNtC/f3/8/PxwdHRk9OjRbNq0qdDT9uHDh2NnZ0eNGjWoUaMGR44cudmmEkIIIR5I33zzDa+99hre3t4YDAaGDx/OTz/9ZB0yfunSJc6cOYNOp6NOnTo4OTnd0vkfe+wxgoKCrH+ufPjdunVrGjZsiFarxdbWFlBHv/n4+GBnZ8emTZto2bIlzZo1w8bGhpdeeonc3NxCw/WvLC+EKJo8uhLiIVGqVClSU1OvOa8rMTGRsmXLWt+XLVuWxMRE63s3NzfrB24Bb29v6+vz589jMplo3ry5dZvFYsHHx6fI+nz22WesXr2axMRENBoNmZmZpKam3tS9JCYm4uvra33v6+uLyWQiOTnZuq106dLW1/b29mRnZ9/UuYUQQogH1fnz5xk2bBha7eU+Nq1WS3JyMt26dSM+Pp7Ro0eTnp5O165dee2117Cxsbnp8+/YseOaD/qL+ry/ctt/v2dotVp8fHxISEi47jmEEIVJgC7EQ6J+/frY2tqyZcsW2rdvf9V+T09Pzp8/T9WqVQG4cOECnp6e1v0ajeaqY67cVvC0/nof3gV2797NJ598wvLly6latSparZZGjRqhKMo1r/Xfup47d876/vz58+j1ejw8PIiPj7/usUIIIcTDytvbm5kzZ1qHmP/X8OHDGT58OHFxcQwePJiKFSteM3P73XDl57mnpyfHjh2zvlcUhQsXLuDl5VVkeSFE0WSIuxAPCWdnZ0aMGMG0adPYsmULOTk55Ofns3XrVubMmUOnTp346KOPSElJISUlhUWLFtGlS5ebPr+npyfNmjVj9uzZZGZmYrFYOHv2LLt27bqqbFZWFjqdDnd3d0wmEx988AGZmZnW/R4eHpw7dw6LxVLktTp37syKFSuIjY0lKyuLefPm0aFDB5mvJoQQ4pHWt29f5s+fb32InZKSwpYtWwC19/vo0aOYzWacnJzQ6/XWTOulS5cmNja2WOvWoUMHtm7dyp9//kl+fj6fffYZBoOh0JJxQogbk2+7QjxEBgwYgIeHBx9++CFjx47F0dGR2rVr88orr1C7dm2ysrKsWdPbt2/P0KFDb+n8c+bM4d1336Vjx45kZWXh5+fHoEGDrirXvHlzgoODadeuHQ4ODrzwwguFhrW1b9+e9evX06RJE8qVK8fatWsLHd+zZ08SEhJ47rnnyMvLo3nz5rz55pu30SJCCCHEw+P5559HURRefPFFEhMT8fDwoGPHjrRp04akpCTeeustEhIScHBwoGPHjtbP/Oeff57x48ezatUqunXrxuTJk4s8f6NGjQq9HzFiBAMGDLipulWqVIm5c+cyffp0EhISqFmzJh9//DEGg+HOblqIR4xGKRhzKoQQQgghhBBCiBIjQ9yFEEIIIYQQQoj7gAToQgghhBBCCCHEfUACdCGEEEIIIYQQ4j4gAboQQgghhBBCCHEfeGgD9JdeeqmkqyCEEEKI2yCf4UIIIR5VD22AnpqaWtJVEEIIIcRtkM9wIYQQj6pHdh301NRU+vfvD0BSUhJarRZ3d3cAVq9efcM1G3fu3ImNjQ0NGjS4at/3339PdHQ0U6ZMASA8PJylS5eiKAqKotCzZ8/b6h2YO3cuW7duBWDo0KF07NgRgD///JM5c+ZgsVhwcHBg9uzZ+Pv7X3X8Sy+9xP79+2nYsCGLFy+2bn/mmWfIysoCIDk5mbp16/Lhhx/y008/8f777+Pq6sqiRYtwc3Pj7NmzzJs3j3nz5t1y/YUQQgghhBBCXNsjG6C7ubmxbt06ABYuXIiDg8MtBc27du3CwcGhyAD9Slu3bmXFihV8+umneHl5kZeXZ73urfjtt984dOgQ4eHhGI1GnnvuOYKDg3FycmLq1Kl8+OGHVK5cmZUrV/LRRx8xe/bsq84xcOBAcnJy+Oabbwpt/+qrr6yvX331VVq3bg3AsmXL+Oabb9i0aRMbN26kX79+zJ8/n5EjR95y/YUQQgghhBBCXN9DO8T9dkRHR/Pcc8/Ro0cPXnrpJRITEwH4/PPP6dixI126dOG1114jLi6Or7/+muXLl9OtWzd27959zXMuWbKEcePG4eXlBYCtrS29e/e+5bqdOHGCRo0aodfrcXBwoEaNGkRFRVn3Z2ZmWv/29PQs8hxNmzbF0dHxmtfIzMxkx44dtGnTBgCNRoPRaCQnJwe9Xs/u3bspU6YMFSpUuOX6CyGEEEIIIYS4vke2B/2/FEVhxowZfPjhh7i7u7Np0ybmzZvHrFmzWLJkCb/88gsGg4H09HRcXFzo06fPTfW6Hz9+nDp16tzw+uvXr+fTTz+9aru/vz/vv/8+NWrU4IMPPmDAgAHk5OSwc+dOqlSpAkBYWBiDBw/G1tYWJycnvv3229tqgy1bttC0aVOcnJwAGD58OAMHDsTT05O5c+cyatQo3nvvvds6txBCPDLycyB2JxxYDbYuUKMjJJ2Ak5FQtS1UaQ2uviVdSyGEEELchyRA/5fRaOTYsWMMGDAAAIvFQpkyZQCoXr06Y8eOpXXr1tbe5buta9eudO3a9Zr7mzdvzsGDB+nTpw/u7u4EBgai0+kAWL58OUuWLKFevXosXbqUWbNmERYWdst12LhxI7169bK+b9asGc2aNQNg7dq1BAcHc/r0aT777DNcXFyYNGkS9vb2t3wdIYR4qMVsg5W9wMkTGvaHmCgoXR2MGbDhVQjoDV0WgMGhpGsqhBBCiPuMBOj/UhSFqlWrXjU/G9Rh6n/99Re//PILH374IRERETd93ipVqhAdHU3Tpk2vW+5GPegAQ4YMYciQIQCMGTOGChUqkJKSwpEjR6hXrx4AHTt2ZODAgTddvwKpqakcPHiQRYsWXbUvJyeHtWvX8umnn/Liiy/y0UcfsWHDBjZs2HBbw/WFEOKhlZ8Lv88HgyM0GwW/TFd71AECeqk96Ae/hcdfBZ+6JVnTh05xJ3+dM2eONZdMnz59rNdauHAhH3zwAZs3b7YmaF2+fDmzZs1izZo1BAQEsGbNGlasWAGo3zdGjRpFmzZtGD9+PLt27cLZ2RkAe3t7vv7661u675ycHEaOHMnZs2fR6XS0atWKsWPHAmoumdWrV6PT6XB3d2fmzJn4+l49emPevHmEh4eTnp7O3r17r9r/448/MnLkSOv9ANSsWZNq1aoB4OPjw8cff1zomOnTp/P9998XeT4hhBDXJgH6vwwGAykpKezdu5f69euTn59PTEwMlStX5sKFCzz22GM0bNiQjRs3kp2djaOjo3Xe9/W8/PLLzJ07l8WLF1OmTBmMRiNff/01zz//fKFyN+pBN5vNpKen4+bmxpEjRzh69Ki1dzsjI4PTp09TsWJFtm/fTuXKlW/5/n/88UeeeOIJbG1tr9q3dOlSnn/+eWxsbMjLywNAq9WSk5Nzy9cRQoiHTvIpyMsAl3JgzgGzEWo/CbuWXA7OAQ6uhlaT4PhmMGaVXH2vIyoqirCwMCwWC7169WLw4MGF9iuKQlhYGFu3bsXOzo7Zs2dTu3Zt8vLyePbZZzEajZjNZtq1a8eIESMANYD99ttvrcHy6NGjadmy5V2ve3Enf+3YsSNTpkwhNTWV9u3b065dO3x8fACoVq0aERERDB06FFA/UwumocXHx/Pxxx+zdu1anJ2dycrKIiUlxXrecePG0b59+9u65wIvvvgijz32GEajkf79+7N161ZatmxJzZo1+e6777C3t+err75i7ty5zJ8//6rjW7VqxbPPPku7du2u2peZmckXX3xh7QgoYGdnd82ktwcPHiQ9Pf2O7kkIIR5VEqD/S6vV8v777zNjxgwyMjIwm8288MILVKhQgddff53MzEwURaF///64uLjQqlUrRowYQWRkJG+++SZBQUFFnrdly5YkJSUxYMAAFEVBo9HQs2fPW66fyWTi2WefBcDJyYm5c+ei16v/+2bMmMGIESPQaDS4uroyc+ZMQP2A/Prrr63D3Z955hlOnTpFdnY2wcHBhIWF0aJFCwA2bdrEoEGDrrpuQkIC0dHRvPrqqwAMGDCAp59+GmdnZz788MNbvg8hhHho5OfAwTWwZYraM27vBid/g6bD4dJpSI25+hhzHlRuDbZO97q2N2Q2m5k2bRrLli3Dy8uLp556ipCQEGugCWoAHxMTw+bNm9m/fz9Tp0619k6vWLECR0dH8vPzeeaZZwgODiYwMBCA/v3739byoncqOjqa2bNnk52djZubG7NmzcLT05PPP/+cr7/+Gp1OR5UqVRgzZgxff/01Wq2W9evXX/dz3c3NDX9/fy5evGgN0Nu0aUNkZCRDhw4lNjYWZ2dnbGxsAHX5UkdHRxwc1CkNjo6O103Yeqvs7e157LHHALWzoVatWiQkJABYtwMEBgayfv36Is9R8P+pKAsWLGDgwIF89tlnN1Ufs9nMnDlz+N///seWLVtu8i6EEEIUkAAdrMEnwMqVK6/av2rVqqu2VaxYkQ0bNhR5vh49etCjRw/r+549e95WUH4lW1tbNm3aVOS+0NBQQkNDr9oeEBBgHYoGhZdT+68vvviiyO1eXl6F1kzv0KEDHTp0uNlqCyHEwyU7BZKOgWIBkxHWD1cDcycv2L4AanUDZy/Q20LZBnD+78LHe1QFrQGcfUqm/tdx4MAB/P398fPzA6BTp05ERkYWCtAjIyPp3r07Go2GwMBA0tPTSUxMxNPT0xp0mkwmTCYTGo2mRO6jQHElfz1//jx5eXlUr17dus3JyQkfHx+OHTtGZGQkHTt25PvvvwegRo0alC5dmtatW9O0aVNCQ0MJCQmxHjtnzhw++ugjQJ0W97///a/Q9U6dOsVrr71WZF2++OILXFxcrO/T09P59ddfeeGFF64qu2bNGoKDg2/QaoUdOnSI+Ph4WrVqdVWAnpeXR48ePdDr9QwePNiao+fLL7+kdevW11xRRgghxPVJgC6EEEIUJS8DLsWCjR2UqgBpZ2HdcDUJHEC72erf5R+Dk7+or9Ni4cyfalK4x16BqHfVgN7gCM1HQ9weaPg8OJYukVu6noSEBLy9va3vvby8OHDgwHXLeHt7k5CQgKenJ2azmR49enD27FmeeeaZQkOiV65cSXh4OHXq1GH8+PG4uroW+/3c7eSvmzZtYufOnZw+fZrp06dfNSWsY8eORERE8Pvvv7NixQprgK7T6Vi6dCkHDx7kzz//ZNasWfzzzz/WzoEbDXGvVKnSNYeSX8lkMjF69Gj69etnfchSYN26dURHR/Pll1/e1L2C2l6zZs1i1qxZRe7/9ddf8fLyIjY2lhdeeIFq1apha2vLjz/+eM2H/kIIIW5MAnQhhBAiK0kNrm2dwbU8XNgPiYcgOxlQwK6UOre8IDgHcHBT/85MVHvE4w/Cub+hTk/Y8yk0Hgwd5qrzzV3Kgr07OLqr17gPKYpy1bb/9oJfr4xOp2PdunWkp6czbNgwjh07RrVq1ejbty9Dhw5Fo9GwYMECZs+efc2g726628lfC+ag7927l5dffpng4GBrwA8QEhLC3LlzqVOnjnW50gIajYa6detSt25dHn/8cSZOnFho9N713GwP+ptvvkmFChWsyesK/PHHH3z88cd8+eWXN0yUd6WsrCyOHTtmzZlz8eJFhgwZwkcffURAQABeXl4A+Pn50bhxYw4dOoSdnR1nz56lbdu2gJrALjQ0lJ9//vmmryuEEI86CdCFEEI82uIPwncD4eIRtae84xw4HAFl66rzzLU6cK8If/1nDq6NA/g3gzPbod1MiPkd8rPht3eg+Si1jDkPygY+EOuee3t7Ex8fb31f0DN+vTLx8fFXlXFxcaFJkyZs27aNatWqUbr05dECvXr14pVXXimmOyisuJK/1q9fn65du/L5558zZswY63Y7OzvGjh1LhQoVCpVPSEggKSmJ2rVrA3DkyBHKli170/dxMz3o8+bNIzMz86olVg8dOsSUKVNYunQpHh4eN31NAGdnZ3bu3Gl9369fP8aNG0dAQABpaWnY29tb2/jvv/9m4MCBVKlShe3bt1uPqV+/vgTnQghxiyRAF0II8ejJSYNLZwCNOnc84wIEvQRVQ2F1f2j9JvzwBlhMannPWlCvDxy5IvdI0jGo1AoCn1HL9f5C3ebkpQblHre+okZJCggIICYmhtjYWLy8vIiIiLhqPnRISAhffvklnTp1Yv/+/Tg7O+Pp6UlKSgp6vR4XFxdyc3P5448/rIlHC+aoA2zZsoWqVavek/spruSvAIMGDaJHjx68/PLLhbZ36tTpqrImk4l33nmHxMREbG1tcXd35+2337buv3IOOtzcknBXKsgSX6lSJZ588kkAnnvuOXr16sWcOXPIzs5m5MiRQOHl0Lp162YN/OfMmcPGjRvJyckhODiYXr16XbeH/+TJk7z11ltoNBoURWHQoEGFchUIIYS4fRqlqPFqD4EePXpY538JIYQQVknHYP1IOPsH6AzQbKQ6BH3rHGjyMhz9AQxOcDLy8jF+TaBaB3WIe8H2hv3VYHzbu2Axg7M39PkKfBuWyG3dDVu3bmXmzJmYzWZ69uzJkCFDrIlS+/bti6IoTJs2jW3btmFvb8/MmTMJCAjgyJEjjB8/HrPZjKIotG/fnuHDhwPw+uuvc+TIEQB8fX2ZNm3aDROIyWe4EEKIR9UjG6CnpqZa52klJSWh1Wqta7Te6On1wYMHWbduHZMnT77p+oSEhLBmzRrc3d25ePEiM2fO5ODBgxgMBnx9fZk4cSIVK1a84Xn+/PNP5syZQ35+PrVr1yYsLAy9Xk9aWhoTJ07k7Nmz2NraMnPmTKpVq3bN4y0WCw4ODsyePRt/f38Adu7cycyZMzGZTLi5ufHll1+SkpLCsGHDyMjIYNSoUdZkOkOGDGHq1KnWOWhCCPFAyM+BiLGw74pkWd0WwcZRYM6HJyZC3E7IuQTn9lwu4+Cu9rCf3wvlgkBRIPEw1OgI3nUhNx1KlX8ghrI/CCRAF0II8ah6ZIe4u7m5WYd2LVy48KqlVUwmk3Wd8f/67/Jlt0JRFIYPH0737t2ZN28eAIcPHyY5OfmGAbrFYmH8+PEsX76cihUrsmDBAtauXUuvXr34+OOPqVmzJosWLeLkyZNMmzaNFStWXHWOqVOn8uGHH1K5cmVWrlzJRx99xOzZs0lPT+ftt99m6dKllC1bluTkZAA2btzIk08+SceOHRk4cCBt2rThl19+oXbt2hKcCyEeHGYTXDqr/jn2w+XtlZ6A7FQ1OAdQTGrg3eCFwgF6dooaiMfuhN/+zd5eJRTKPw5u/vfsNoQQQgjxcHtkA/SiFCz9cujQIWrXrk3Hjh2ZOXMmubm52NnZMXPmTCpVqsTOnTv57LPPWLx4MQsXLuT8+fPExcVx/vx5XnjhBWvG06Ls2LEDvV5P3759rdtq1qx5U/W7dOkSBoPBGsg3a9aMxYsX06tXL06ePMngwYMBqFy5MufOnSMpKalQcp4CBUlwMjMzrcMMN2zYQGhoqDVxTUEyGb1eT25uLkajEa1Wi8lkYsWKFdY5bEIIcd8zZsPp39Re7rwsNdA+9au6r3xTMGVfLvvnInU5NFsXaDMV9n4JejsI6AW/vwft34HWetBowL0y2Bf/cmFCCCGEeHRIgP4fMTExLF++HJ1OR2ZmJl9++SV6vZ4//viDefPmsXDhwquOOX36NJ9//jmZmZl06NCBvn37YmNjU+T5jx8/bs3k+l+ZmZk8++yzRe773//+R+XKlTGZTBw8eJCAgAB+/PFHazbdGjVq8PPPPxMUFMSBAwc4f/488fHxVwXoYWFhDB48GFtbW5ycnPj222+t920ymejXrx9ZWVk8//zzdO/enS5dujBmzBjCw8N5/fXX+eqrr+jevTv29vY33aZCCHHPmE1qJnU7l8vbko7DvlVQuwfo8qDRQLiwD3JSQTFD7G51abTo79Ql0X6dAV0XqUun1eoOSUdhxyJo8zb41AUb+f0nhBBCiOIhAfp/tG/fHp1OB0BGRgZvvPEGZ86cQaPRkJ+fX+QxLVu2xGAw4O7ujru7O8nJyXh7e9/ytZ2cnG64lMp7773HrFmzMBqNNGvWzFrXwYMHExYWRrdu3ahWrRo1a9Yscoj+8uXLWbJkCfXq1WPp0qXMmjWLsLAwzGYz//zzD8uXLyc3N5c+ffpQr149KlasyJIlSwBIS0vjk08+YeHChUyePJn09HQGDBhA/fr1b/lehRDirju3Vw2kL+yDWk9Czc5QpibkpsLRCKjWXh3KbsqDJq+oa5uXqQH/rAWNFlpNUpdFs/dQ9x37EWp3hxqdwLG0OsdcCCGEEKIYSYD+H1f2DC9YsIAmTZqwaNEi4uLirjl0/cqEcjqdDpPJdM3zV61alZ9++qnIfTfqQa9SpQr169fnq6++AuD3338nJiYGUIP7WbNmAeo899atW1OuXLlC50hJSeHIkSPUq1cPwDqvHNS1bd3c3HBwcMDBwYGgoCCOHDlSaF78okWLeOWVV4iIiKB27dp06dKFIUOG8MUXX1zzfoUQolhYzOrfGfGQnaQOY//mGXWuOEDUHEg4CPWeUXu8HT0hMx6cfdUg3K0C7P4ULsVC5/lwaD38/bk6p9y9qpoUrvN7oCt6NJQQQgghRHGQAP06MjIyrInQ1q5de1fO+dhjj/Hee+/x7bff0rt3bwAOHDhAbm4ujRs3vmEPenJyMh4eHhiNRj755BNeeeUVANLT07Gzs8NgMLB69WqCgoJwcnIqdKyLiwsZGRmcPn2aihUrsn37dipXVtfpbd26NdOmTcNkMpGfn8+BAwesWe5BHQKfmJhI48aNOXz4MHZ2dgAYjca70i5CCFEki1ldFu3SWTXI9qgCGecg6TTYOcHRH0GrBc+al4PzAhePwumtkB4PLd+AwxvA2Qt8g6CUPzwxSQ3AHUpD1wWQmwZ2rhKUCyGEEKLEaIvrxHl5eTz11FN07dqVTp068f777wNqorMBAwbQtm1bBgwYQFpamvWYxYsXExoaSrt27di2bZt1e3R0NF26dCE0NJQZM2Zwr1aGGzhwIO+99x59+vTBbDbflXNqNBo++OADtm/fTps2bejUqRMffPDBDdeELbB06VI6dOhAly5daNWqFU2bNgXg5MmTdO7cmfbt2xMVFcWkSZOsxwwaNIiEhAT0ej0zZsxgxIgRdO3alfXr1zNu3DhATSzXokULunbtSq9evXjqqacKLdM2b948Ro0aBUDnzp1Zu3YtTz/9NC+++OJdaRchhCjSqd9gSUs1o3peJvy9DLZMA71eDdzLN4Yzf1zuUb+SX2M4EQlHN6rz0hsOgAot1YA8KwlcfKBCM/CsrgbljqUlOBdCCCFEiSq2ddAVRSE7OxtHR0fy8/N55plnmDRpEps3b6ZUqVIMHjyYJUuWkJaWxuuvv86JEycYPXo0a9asISEhgQEDBvDTTz+h0+l46qmnmDRpEoGBgQwaNIh+/frRsmXL615f1lAVQogHkKJAThokH1fngOttofxjcOwnSDwE6echZDL8NBGqtlXLpMXBM6thyxR1ibQCIVPg/B44EqG+96kHVUPB7zGo2BL0hqLrIEqcfIYLIYR4VBVbD7pGo8HR0RFQ1xQ3mUxoNBoiIyPp3r07AN27d2fLli0AREZG0qlTJwwGA35+fvj7+3PgwAESExPJzMykfv36aDQaunfvTmRkZHFVWwghxL0WHw27PoHtC9SEbed2Q0wUeNaACs0hai7YOsHJX6BeX0j4Rx3ybl9KDc4Bkk9Ak6FqpvXAZ9WEb+d2Q4UW4PTvCKUL++HMTihdXYJzIYQQQtyXinUOutlspkePHpw9e5ZnnnmGevXqkZycbB3O7enpSUqKOmcwISHBmrwMwMvLyzos+8qM6N7e3iQkJBRntYUQQtwr5/fB8k5g7wZ5GZCfBT2Wqj3lUe9C89fgdBT4P66WST+nzhMHNfO6Vg8WE2ybC63fUpdNq95JTR5Xvin4t1DfJx1Te+M9a4BjmRK9ZSGEEEKIaynWAF2n07Fu3TrS09MZNmwYx44du2bZokbaazSaa24XQgjxALCY1aBZZyi8LTMBctPh9DYIfh0uHgYHD7W3++IR+GupWjb9PLj4qr3fvkFg4wB6O3Wu+OGN6prmOz9WE8T9tVRdPi3trLokmk8guPqq53GTJdKEEEIIcf+7J1ncXVxcaNKkCdu2bcPDw4PExEQ8PT1JTEzE3d0dUHvG4+PjrcckJCTg6el51fb4+PibTqgmhBCiGBmz1QA6Lx3cKoKD2+V9FgvE7lSHnl88qiZpq94RPCpD7G44ux1cyoHBHn6afPm4Ok+p888LRH8HbWdAxGvQ+GUoW1/tWQ+dpg6Hz4iHJxer58/LUgP4qm0vD2sXQgghhHiAFNsc9JSUFNLT0wHIzc3ljz/+oFKlSoSEhBAeHg5AeHg4rVu3BiAkJISIiAiMRiOxsbHExMRQt25dPD09cXR0ZN++fSiKUugYIYQQJeTcXnXO+OmtcGEfnIyEpBOX98dsU+eR//wm/LlQXXN8ZU+U01shJwWqt1eD7b1fQNPhEDxWXQrNnA/ulS6fJy8dfn8PnvkWylQHRQOhb0NOKtTpBbW7gUYH1TpB/eegTg8JzoUQQgjxwCq2HvTExETGjx+P2WxGURTat29Pq1atCAwMZNSoUaxZswYfHx8WLFgAQNWqVenQoQMdO3ZEp9MxZcoUdDodAFOnTmXChAnk5uYSHBxMcHBwcVVbCCHEjaSchg2joFZn+GGcOgdco4HWU6HJEMi6CHF/gcmoBtJX0PyxEMoGwv5V6vD0oJfgt1mQnazOJ39sCFRqBcc3gzFLPciUq55n5ydqsrganaBMTXWpNAdPKBcENnb3uhWEEEIIIe66YltmraTJEi1CCFFMjkSo65Lv+EgdWl5Aq4OBv6iB9clIda759vmFj3XxhTbTIPMCeNaGiNGQerpwmRc2wNkdlwN/kxF2fgRPLVMTxsX8DlVaQ90+atI38dCRz3AhhBCPqnsyB10IIcSDIc9k5mxKNhrAz90BW70OspIh5aSamM29ilpQoykcnIMakBuzUCwmNO6V1F5xnY06bL1AyBQ4tBaObFSHtf83OAdIOgnxB+Dwhsvb9LaQfgECnobWb6pzzcVdFxUVRVhYGBaLhV69ejF48OBC+xVFISwsjK1bt2JnZ8fs2bOpXbs2eXl5PPvssxiNRsxmM+3atWPEiBEAXLp0iddee41z587h6+vL/PnzcXV1LYnbE0IIIe57xTYHXQghxIPlwqUcpq4/RLt5UbSbv42wiMPkXDgCK5+CT0NhyROwYSS4VQA06rJnV2o1CWX3p2hWPgV7loF3AHT/GKqGgk89aDVRnX9+ZKNaPiPh33P9R06SOg+9Xl81s7tvA2gbBo6eao+5BOfFwmw2M23aNJYuXUpERAQbN27kxIkThcpERUURExPD5s2bmT59OlOnTgXAYDCwYsUK1q9fT3h4ONu2bWPfvn0ALFmyhKZNm7J582aaNm3KkiVL7vGdCSGEEA8OCdCFEEIA8OvRRFbtOotFAbNF4cj5dGz2fwnn/1bXHK/XF0pXRkmLw1ytA0r7dy6vKV69I5zbgyb6OzAbwdZFHQq/5W31dcibsG+VuoRagejv1GXRCgJ9rQ4eGwrHflQT0LlVgEG/QY9PoUZndc67zDUvNgcOHMDf3x8/Pz8MBgOdOnUiMjKyUJnIyEi6d++ORqMhMDCQ9PR0EhMT0Wg0ODo6AmAymTCZTNYlUQuOAejevTtbtmy5p/clhBBCPEhkiLsQQggANuw/b309spEjL5Y7hX7Pv8FUq4kQ/T3sX4UG0NZ5Co1reei1AtJiwaE0rOx5+WT+zeCX6errtLNQupr6t0vZy2Xys2HrO9D6LTBmqgH54Qh16bSWb0Dgs1DKr9jvW6gSEhLw9va2vvfy8uLAgQPXLePt7W1dFtVsNtOjRw/Onj3LM888Q7169QBITk62Lo/q6elJSkrKPbgbIYQQ4sEkPehCCCEAaFTBHQDfUvY8a7sN178/hAb91WA5Ix4SD4HOAO6V0Jz8Bco1hPChsPZlOPWbOhwdip6ffm43VA5R57LX7Hp5u6MnOJYGgyNkXwJnb3hmtfpAQILze6qonLEFveA3U0an07Fu3Tq2bt3KgQMHOHbsWPFUVAghhHiISQ+6EEI8orLyTBy6kE5sSjYVSzvSua4P3+89x5NVbfBM3g0BveGvTyA3TX3dbqb6Ouk41O0NP02CSzHqyQ6sUoer/xoGigI29oUvdvxnaDkeSvmh5FxCU7c3ijELTcppyE4FW1f4cxG0mwFete55Wwi1Nzw+Pt76vqBn/Hpl4uPjryrj4uJCkyZN2LZtG9WqVcPDw4PExEQ8PT1JTEzE3d29eG9ECCGEeIBJD7oQQjyCTGYLq3ad5X+bj5KQnsvHW0+x9fhFvu5bkcGBtiiNB0HUHEg+oa5rvmOROgw9L10NoPNz1N7wAllJatb13l9A8OvgUg5qXzHk3cEDHNzILR1Afr3noNITaKp3VNdCD+gJ5ZtAv7Xg//i9bwwBQEBAADExMcTGxmI0GomIiCAkJKRQmZCQEMLDw1EUhX379uHs7Gwdtp6eng5Abm4uf/zxB5UqVSp0DEB4eDitW7e+p/clhBBCPEikB10IIR4y6bn56LUaHAyXf8VfyjaSk2/G09kOnVZDctJFfE1nGPNYaV7dGMPrLb1p53YSpzWjICsRfBtBm7fhlxmQe0nNnO7oCXtXQrkgMJvUBHFZFy9fOP6A2nv+9wrITITKrdWh6np7MDhiST2DXcNaoLe5utIGx2JvF3F9er2eKVOmMHDgQMxmMz179qRq1aqsWrUKgL59+9KyZUu2bt1KaGgo9vb2zJw5E4DExETGjx+P2WxGURTat29Pq1atABg8eDCjRo1izZo1+Pj4sGDBghK7RyGEEOJ+p1GKmlD2EOjRowfff/99SVdDCCHumZSsPH7+J4El207jaq9nZOuqNKrgxsFz6ew5m4rFouBo0NO7fDr2P41Ge243OJbG2CYMm/wMNL9MV5dE86iiDmN3qwilq8DaV6Bhfzj5C1w6C9U7QF4WVGsLkdPUrO0ATYdB6RqAAsc3w/m9UKkVil8QGq0eKoeCi/f1bkEIQD7DhRBCPLqkB10IIR4QqdlGcoxmyjjbYqO7eobSlkOJvPH9Qev7/sv/4rMXGjFlfTSxKTkAvNulIg4/jUFzbrdaKCsJw/oh0OMTNTDXaOG32ZdP6hukLq/mWEYNzgFORELrKbDzY2j+GihmsHGEck0g9TTYOmIJfBZzi3EYHXxwdCs8R1kIIYQQQhRNAnQhhLjPmS0K208k8faGf4hLzeHJ+r4MeaIy/h6Xh4Vn5ubz6e+nCx2nKLD12EVyjRbrtjZeGWjy20DlJ8CUC7uXqXPLs5OhShuImlv44hnnIXis2ktuV0od7m42wr6voPFgyEhAcfRQl0/78300jw0BJ28UVz9sbB0pYjC7EEIIIYS4BgnQhRDiPnfoQjoDlv9FOTd7hrWqgtFsYcepZFzsbXBzMACg12op5XB1OGyj05BnMuNkq2fD8/64Rn8Ae79Qdzp4QMhk+Gmiunxa6Wqg/BvM6+3UZG9pcbBnOVRors4n/2kCWMyQeAglO4X8Wj2Ita1KGVszLjU6ga0zALp70TBCCCGEEA8ZCdCFEOI+dyIhg1L2NvRp5Me7m49htqipQ05ezGJESFVsbbTsPptKt8Cy7D6TwtAgZ5qVSkGvBYeyOuq4+9Ha7iiOWQfQFATnoPaaR69BeWIiGmMmGJygSiic+BkeGwq7FqvJ3gCO/QhtpqH0WYWSlYzJtQLppWpQ2t2dyiXQJkIIIYQQDyMJ0IUQ4j5Xyt7A2HbVmP3DUcwWBTcHGwx6LUuiTtG6hidGk4UXlu2igocjEc+WpcrWV9EfOAgaDWaPGlRsNRX7nYuhShHLW104gCb030zt3zwHbWeAZw2wdbkcnBfYOpvozhvoucbCT6/Vo6K7ZF4XQgghhLibJEAXQoj7mMlswd5Wy6WEfADGtatOYkYeOflmKpV2JCYlkzNJOVgUcLDVUfZCJPryjaDFKEg7h87OGXtzBuhswKH01RfwbwbZSZB+ASwm+HE8eNeFRi9dXVaxsP98NjW8nXF3lNnlQgghhBB3mwToQghRAhRFISffjL2NDo1GU2SZo/HprN9/nmMJGQRX9WR2jzocvpDBql1nyTNZ0GhgyXMNidPm0rJaGVpWL4Oj8Q91Lvl3VwTYZRtCnSehVHloNBD2LFPnkbtXgsdHwMUjkHwCXP0gLVZdz/xSrDpHPTvZepqLdV9hfYyW6d1r42pvKO4mEkIIIYR45EiALoQQ99ipi5ms3hPH1qMXaVXDk54NfKlUxqlQmTPJWTz36S4uZuQB8POhRAY0q8DfZ1IZ2qoK834+hqLA8j/P8EpwRcq52TNr0xH6P9cI1r5c+ILn90CDfpAQDf6PQ4VgMOeCkxfsWU6eb1MyPQLx8KoFe7+E83uxnPyN3O7LMJzcjC5hP7m1epNWphkLgsviU8r+XjWVEEIIIcQjRQJ0IYS4h5Iz8xj19T4OnEsD1Aztvx9P4uN+DUjJNKLRQmlHWw6dT6d3dRvaeaTipMtH5+bP6UvJ1Pf05O8EIx6OBvJMFjrX9WHhryfxd3egbCk7NfDOSiriyhr1z5oXwcYBqrVVe9pLV+O8Z0v2pDlSziaTmp2fICcvjySzI3bOHlTp0BIAe6DKPWslIYQQQohHkwToQghxD51OyrIG5+Xd7anvV4pOdX344WA8aTn5XMrJJzPXyAsBdrRMnYNDfALUewa2zMQ/PQ5zuSY0Dp5FQrob3q72/G/zUbLyzAT5u/FkZS1ajRaqd4QjGy9fVGcDBgdw9VWTv+Wlwz/hAKQ1GIbJ0ZsOfo7Y2+jRajW4At73vmmEEEIIIR55EqALIcQ9pNdqeLyyBx0DfDiXmkOgnysLfznJwX+D9nJu9jzdyI9K+UdwOPc7dPsAMhKgZhewL4Uu9QyloyYzo/lkDLnn6OXvQ3p6BmVtT6K3d4a4v6D+s2ov+dFNUMoPpeUbaH5+CwyO5PT4HMu+r3C8dJS4Cj35TduYDs52ONpK0jchhBBCiJImAboQQtxDlco40qiCO5PDo3G21WPQV7QG5wBxqTmcSc5G63QJPGtD8in4/b3LJ6jYEr1zaTzORULcbmqVKg9/f67uC3wO5fxeNNkXIeApCHwGTEZizB44dF5BusaZ3l+ewtftOco569mxM4t3elTEw8nu3jaCEEIIIYQokrakKyCEEI+SSzkmPtl2CgBvVzuOJWRcVSbfbCbXtTI0HwV/Liy88/RWKNsAslOgQvPLwTnAwW+g8SCUmD/hq96wZgBJaWnE4E2cjT+O7mX5rH8jAsu7ozPYMf/pQFpUK2LpNSGEEEIIUSKkB10IIYqBoij8cz6dwxfSMei1BPi6UqmME0aThZx8M3qtBmc7PQ383dgfewlfN3syc4zMapxDrbiF6I84odTqisacf/XJHTzUXvXGgwpvN+ej+WEc8c9sYfepRFLN9qzboyHsSReqe7sAUNbNgfr+bvegBYQQQgghxK2SAF0IIYrBnrOpPLNkJ0azBYAyTrZ8NagJ5dwcmBRSlmDDUcrHfok+ozwDOjdBe2Ax+c2exXbtAHWN8nKNwNQBfBvBub8un9jeDTITIDcN9FcPTTeVrk6W3o1Eexec7fTMblCKKp7O9+q2hRBCCCHEHZAAXQgh7rI8k5nFv520BucAFzPz2HwogQru9jzrsg/7H0ZdPmD/lxA6A9vzf6nBOYBnLYjfrw5zP7gaYqLAszZK4LNYjNnoDI5w9AcIfh22LwCzEVzLkdd+Hgbn0vR/3B6tVnNP71sIIYQQQtwZCdCFEOIuM5kVzqbkXLX9dFIWzX0U7CPnFt6RlwGOHhB/4PK2hGi1l9zGQc3gXrsHAGaHMmzPq0StpwIoZTwPOWkovVeBzoDBqxqOzt44FufNiYdaVFQUYWFhWCwWevXqxeDBgwvtVxSFsLAwtm7dip2dHbNnz6Z27dpcuHCBcePGkZSUhFarpXfv3rzwwgsALFy4kG+//RZ3d3cARo8eTcuWLe/5vQkhhBAPAgnQhRDiNlksCieTMrlwKZfSTrZU8XTEoNfhaKun/+MViEnOws5Gh0YDqdn5+LvbY6M1g2IpfCLPWvDPOvAJAJ1B7Q0/twdqdUPZswKNVgelypPrXoPlSmfm/bmXJ6qXoU2NhlxIy+blwCrY2uhKphHEQ8NsNjNt2jSWLVuGl5cXTz31FCEhIVSpUsVaJioqipiYGDZv3sz+/fuZOnUqq1evRqfTMX78eGrXrk1mZiY9e/akWbNm1mP79+/PSy+9VFK3JoQQQjwwJEAXQojb9PPhBEas2kueyYJWA1O71ubpID9sbXRUKuPAez8fI8toolIZRww6Dc387Kh46Q9o8jJsmXr5RAYnsBhhx4fQegrEHwRjFhY7N850XoVTTjwmnR0XDX6Uz4SXbdwxWRQ++O0Eb7SvIcG5uCsOHDiAv78/fn5+AHTq1InIyMhCAXpkZCTdu3dHo9EQGBhIeno6iYmJeHp64unpCYCTkxOVKlUiISGh0LFCCCGEuDEJ0IUQ4jacTc7m9dX7yTOpveEWBaau/4d65UqRbzbz3d/neLqGnp6lTuFz4RdS3OvjYhuI/boR0PUDaDUJYn6HUuWhUkswZsOhdbB5MrhXAht7Ys3u7Etz5PFKjfFxtcfDZCEjJoX9cZfIyjMzoUMNmleRZdLE3ZGQkIC3t7f1vZeXFwcOHLhuGW9vbxISEqzBOUBcXByHDx+mXr161m0rV64kPDycOnXqMH78eFxdXYvxToQQQogHlwToQghxpfw8SDoKGfHgWo40xwocu5hLrslM5TJOlC1lD0BKtpH0XBN1fRx4qZaCncbElnh7Tl3MxGSxoLMYGaRZg+vvXwBQ9uQPWBwnqtc4+ydcigVzHjh7Q/oFzA6eKE+tQLv/KxRFIbv+ICjdkM6l3bDRaQEw6LU0q1KaRhXcUBSk51zcVYqiXLVNo9HcUpmsrCxGjBjBxIkTcXJyAqBv374MHToUjUbDggULmD17NrNmzbrLtRdCCCEeDhKgCyFEAZMR9n0Jm8aq88S1evJaz2dklA/n0/LwdrXlixebUNrJgA6FF+u7MMx+Mx5/fggWE0/4NiXJZQ6fHNLRvwa4rl1Z6PTagkBm30qo+zR4VMai0XHOtRGKT13KezhCra6AgrNWx7UWRzPoJTAXd5+3tzfx8fHW9//tGS+qTHx8vLVMfn4+I0aMoEuXLrRt29ZapnTpy6M8evXqxSuvvFJctyDEQyst20hGnummyzvb6nF1MBRjjYQQxUVbXCe+cOEC/fr1o0OHDnTq1IkVK1YAajbXFi1a0K1bN7p168bWrVutxyxevJjQ0FDatWvHtm3brNujo6Pp0qULoaGhzJgxo8gn+EIIcceSj8MPr19O4mYx4fnb64xqoAbEXer6snpPHP0+28XKXbGMqZWOx9/vg0X90mR77k88Dq1g75lkXB30wH9+VyUewlK7p/r6wDewfQG5ZZvgWqWhGpwDaLWglQBc3HsBAQHExMQQGxuL0WgkIiKCkJCQQmVCQkIIDw9HURT27duHs7Mznp6eKIrCpEmTqFSpEgMGDCh0TGJiovX1li1bqFq16j25HyEeJhl5JqKOJd30n1sJ5oUQ95di60G/VkZXKDqb64kTJ4iIiCAiIoKEhAQGDBjATz/9hE6nY+rUqUybNo3AwEAGDRpEVFSULNEihLgjZovCqYuZnLuUQxknWzycDDgnn8exYB3yAvk5lNFm8kR1Lw5dSGP7iWQAfFzsMMXtveq8did+4Lmgfkzbns37dZ5Gf/Br6z7l/D7ynv4W+8YD1SXUPKrgUFqCFXF/0Ov1TJkyhYEDB2I2m+nZsydVq1Zl1apVgDpUvWXLlmzdupXQ0FDs7e2ZOXMmAHv27GHdunVUq1aNbt26AZeXU5s7dy5HjhwBwNfXl2nTppXMDQohhBAPgGIL0K+V0fVaIiMj6dSpEwaDAT8/P/z9/Tlw4AC+vr5kZmZSv359ALp3705kZKQE6EKIO7LlUAKvrtqL0WxBo4GXW1Ti8VIOBOvtwJR7uaC9GzFGF+qVK8WCyOPWzQHlXEnUlOe/qa7yfYJwLeVGk6qu7HcaRm2fBtgdWUt++WZQ5ynsvasB1e7JPQpxq1q2bHnV52vfvn2trzUaDW+99dZVxwUFBXH06NEizzl37ty7W0khhBDiIVZsQ9yv9N+MritXrqRLly5MmDCBtLQ0oOjssQkJCdfMGCuEELcrNiWb19fsx2hWh7IrCizedoocl4pkd/8U7N3Ugo5lON92Me/vyQPgynxZZgv8cKk8WeWvGALs5E1+k6EEZfzC0znfUMcuGbuGz0H/Tdi0eRMb75r36haFEEIIIcQD6KYC9BUrVpCZmYmiKEycOJEnn3yS33///aYu8N+Mrn379uXnn39m3bp1eHp6Mnv2bODamWFvJqusEELcipQsNQP7lRQFcvIVvkyuybFuEfzTMZzFNT5jzG4X+jX1p7y7PU/W97WWP5qQwdYEA7PsXuPP4C/Y3WIpJ9p9jv3G4bj/PAq7bbOw/ao7HF6nzisXQgghhBDiBm7qW+N3332Hk5MTv//+OykpKcyaNYv//e9/NzyuqIyupUuXRqfTodVq6dWrFwcPHgSunT32ehljhRCiQFJGHtuOXyTiwHkOX0jHYik6mWSO0URyZh6ezraFtuu1Gpxs9cz84QiJOk8WHnVh1vYM/jyZwvuRJ/h8xxmeqFaaV0Oq0K62N+XdHej3mD86R3emHSzFxqyalFPi0aQcL3zBn9+E9AvFddtCCCGEEOIhclMBekEv9tatW+nZsyc1atS4YSb1a2V0vVY215CQECIiIjAajcTGxhITE0PdunXx9PTE0dGRffv2oSgK4eHhtG7d+pZvVAjx8EpIz+X17/bT79NdDPtqL10/+J0/TiYVWfbwhQxGfL2PV1pWpoyTGqQ72eqZ1aMOB8+pU24OnkujSz2fQscdjc/AVq9jy+FETl7MZNXOs7zx3QFCa3nx3ZDHmdK5NnYYr75gbjpY8u/uDQshhBBCiIfSTSWJq1OnDi+++CJx/2/vvsOjqtIHjn+nJpNMejKZ9EISaigCUhTQQACJCIKorOKCorvY1rqKFVnFtq6iv7WwKjZ0XSygoIJEBQQFpYVe08uk9+lzf39ER5EWlJAE3s/z5CFz5px733u5mbnvveeeU1TEnXfeSWNjI+oTdNk81oiuy5YtO+porqmpqVx00UWMGzcOjUbDQw89hEbTMtXQnDlzmD17NjabjeHDhzN8+PDfvcFCiDPPzpI6vt5T4X3tdCs8uHQH71w3CHOQgaKaZpwuN7VWJ/vLm2i0u3j2y328ck1/NufXEGTQEeavp7yhJcF2eRTeWJ/Hc1f0YeG6PDwKXNY/Bj+9lhlDEyirt+PyeEgM9adLhD9++p8+Sk3d4beDzA24DgKiT+fuEEIIIYQQnVSrEvTHHnuM3bt3ExcXh8FgoKamxju1yrEca0TX442+PmvWLGbNmnVEeXp6OsuWLWtNqEKIs1B105F3qPOrmtlaVEsXm5MGm4vtxXWE+Ptg9Gm58NfocOFye3hjfR61zU6en9qPrpFGekYH8v2hKlJNATy2fDf3Z3XHHGTA4XRT0WinV0wg/RO0eFCID/XDR/urOcsje8I1S+GbJ6D6IPSbBn2mgqbNJswQQgghhBBnkFadNarVasLDwzlw4ABut/vEDYQQ4jSKDPQ5omx4agTvfJfP3aO7srusjhB/PQXVzaRG+JOVHsWq3RYMOi1XDIzj318f5P6Pt/Pk5HTuGduV0job/notY3tFsrWwFo1ajdvtpld0MGnmgGMHolJB/GC48l1wWsE/rA23WgghhBBCnGlalaA//fTTfP7553Tp0sXb7Rxg4MCBbRaYEEK0xpaCGl5ZfZB5l/bi6RV7qWl2cn5KOFPPjaey0c6+8kZUqNmcX0uwn45FGwroHx9C2oUpVDXZGZ4aQbi/D0u2FrN0awmTz4klwuhDQVUTmwqsZHQz4XR5cLhVtHoCCb1fy48QQgghhBAnoVUJ+qpVq/jiiy/Q6/VtHY8QGyxX8AAAYehJREFUQrRKVaOdfZYGFBRuvjCVvKpGrhgYR3pMEF/usnDTe5u5Y1QaT63Yyy0jUwj116PTqPk+t5qrBsXz3aEqNudXk9nDzJ7SOmYOS8LmchNo0OLxwNj0KDwK3PbfrWzMqwbAoNPwzsxz6Z8Q2s5bL4QQQgghzkStStDj4uJwOp2SoAshOoTCmmbufH8bo3tGcrCikTqrk6/3VPDAxd3ZXFDDkq0l9IwOZFNBDQB6jZryejuWOiv3XtSNxz/fwxUD4xjb04xeq6J3bCBatZpAXx3mIF+0mpZBMD/bXupNzgGsTjdPf7GX16YPxN9HnisXHdctt9zC5MmTGT58+AkHdRVCCCFEx9GqM0yDwcDEiRMZMmTIYUn6Aw880GaBCSHEsXx3sIo6q5N9lpapz749UInV6Wbxj0UE/JQ419ucdI8KBGBTXg2BBh0BBh1Ol5s7R6cRE2zAR6uhd1zwMddTVm87omyPpYFGu0sSdNGhTZ06lQ8//JBHH32UsWPHcumll9KlS5f2DksIIYQQJ9CqM8yMjAwyMjLaOhYhhDihoupmthXW0i8+mFW7LaTHBJMSYWRzQS37LA1MH5rI2gOVFFZbuXqQET+9hm/2VTAsNZxBSaGYgwxEBvry7Jd7efTS3sddV7ejDAg3oU80Yf7Sm0h0bEOHDmXo0KE0NDSwbNkyrr32WqKiopgyZQqXXHIJOp2uvUMUQvxBiqJQb3PRZHfho1UT6q9H1erBUoQQHVWrEvRLL70Uh8NBXl4eAElJSfLlLoRoM5WNdvZbGjHoVDhcCpVNdqKDfKlsdPDAkh3ceGEKK3daSDUFsHpfOQ+N78HesgaaHG7KG+xcOTCOxZuKeGXNIR65pCflDXY8HoV+8cFY6u0s2VrMfVk9SY4wHjeO3rHBPDqxF098vodGu4sxPSKZcV6Stwu8EB1ZTU0Nn3zyCUuXLqV79+5ccsklbNq0iSVLlvD222+3d3hCiN+p2e5i3cFKthbWUtP8yzSjvjo1feOCGZFmasfohBB/VKsS9A0bNnDvvfcSExODoiiUlpby5JNPyijuQohT7lBFAwfLmwkzaimts/PQ0p1k9ojEHOTLB5uKSAo3YqmzERdiIM0cwI7iOp5btZ+np/Rmn6URlUqFw+HmuSv64nJ7WL69hGCDnmvPT6JHdBB2p5uJfWNQq098l8Hoo+XqwQlc0DUCu9NDdLABg15zwnZCtLebb76ZQ4cOMWHCBF5++WVMppYT9nHjxjFp0qR2jk4I8XsoisKm/Bo+31GGzemmi8nIeSnhBPrqsDrc5FY18UNuDT/k1eD2eLjpwhS5oy5EJ9SqBP3JJ5/ktddeIzk5GYDc3FzuvPNOPvroozYNTghxZmt2uNhZXE+jzYm/r5biGivRwQa+3lvOuHQzT3yxh1qrg/AAPQ63h4LqZvz0Gg5VNtHscDEsLYKnLuuNw+XB6nCTagrgu0NVNNtd9IkPZmBiMBd0M+Gv16DXtiTWPrqTT7BjQ2TKNNG5TJkyhREjRhxW5nA40Ov18t0tRCdkd7n5cHMRmwtqSQzz55K+0ZgDfQ+rMzAplMwekSzdWsw/V+6jqMbKY5emo2nFBWkhRMfRqn6aTqfTm5xDSxd3p9N5nBZCCHF89VYni38o5I31uajVKm5/fxs1zQ5yiup4d2MBjXYX+VXNBPvpsdTbKahqJtVkZE9ZA/0TQtiQW81b3+WRV9XElsIaQv31gMK4Xmbuz+rO6J5mzEF+hPjpvcm5EGeL55577oiyK6644oTt1qxZw5gxY8jMzGTBggVHvK8oCo8++iiZmZmMHz+enTt3AlBaWsq0adO46KKLyMrK4s033/S2qa2tZcaMGYwePZoZM2ZQV1f3+zdMiLNQk93Fnf/bxuaCWkZ2MzFzWNIRyfnPQvz0XDMkkWlDEvjvD4U8sGQHiqKc5oiFEH9Eq+6g9+rVi/vuu48JEyYA8Omnn9KrV682DUwIcWZSFIXNBbW8uT6XigY704Yk8EN+DcW1VlJNAbz1fT4A/j5aooN8Kau3ER1s4N9fHWD2uG4sWHOIF785yK0ZqfjoNAT6ahnd00yXEzxPLsTZoKKiAovFgs1mY9euXd4T88bGRqxW63Hbut1u5s6dy8KFC4mMjOSyyy4jIyODlJQUb501a9aQl5fHypUr2bZtG3PmzGHx4sVoNBruvfdeevbsSWNjI5MnT+a8884jJSWFBQsWMGTIEG644QYWLFjAggULuPvuu9t0Pwhxpmiyu5ix8Ae2F9VzxYA4+hxn5pGfqVUq/jI8mQAfLS9+c5AIo547Rndt+2CFEKdEqxL0Rx55hEWLFvH222+jKAoDBw7kT3/6U1vHJoQ4A20vrmPqgu9xuD0ATB+ahKWuZTqzBruLLuH+fAks21bC3WO78cDH29lbVs8lfaP554q9XHpOLIlhfgxIDCE9Jli67gnxK99++y0fffQRZWVlPP74495yf39/7rjjjuO2zcnJISEhgbi4OACysrLIzs4+LEHPzs5m4sSJqFQq+vbtS319PeXl5ZhMJu9z7kajkeTkZCwWCykpKWRnZ3sHpZs4cSLTpk2TBF2IVnC5Pdy4aDM/5lfz8Pge6DQn1xvs7jFdqWiw8/xXBzgnIYQLusrgcUJ0Bq1K0PV6PTNmzGDGjBltHY8Q4gzicntweRR8f/Xc98bcam9yPjAxhOzdZQxIDGXxpiLe+i6Pmy9MYcUuC+//WERyhD8vTzuH8noHEUY9AxNDiArypU9cCEEGmUlCiN+69NJLufTSS1mxYgVjxow5qbYWiwWz2ex9HRkZSU5OznHrmM1mLBaLNzkHKCoqYvfu3fTp0weAqqoq7/smk4nq6uqT3i4hzjaKovDwJztZva+CeZemMzwtnDX7Kk9qGSqVin9M7EVOUR13/G8bn/9tGJHH6BovhOg4jpug/+1vf2P+/PmMHz/+qO9/+umnbRKUEKJz83gU9lsa2FFS5z0ZUICkUL/D7nifmxTGi98cINkUwANZ3XnhqwOU1tl+mhrNBgro1GrCjHoaHW7SY4LoZg5s1QjsQpyNli5dyoQJEyguLmbhwoVHvH+8C+1He071tyNAn6hOU1MTt956K/fddx9Gozx2IsTvtWhDAYs2FPCXEcn8aVA8RTXNv2s5vjoN/77qHC5+YS33f7yD/1zTX0Z2F6KDO26Cfv/99wPw8ssvn5ZghBCdV32zg0a7k/IGB/vKG7A63HQzB/LJtmLe3ViIokDP6EAevLg7Bp0Gq9NNbbMDU4APT3y+h5nnJ/DmjIE02l3YXR48HvDz0RAX6k9MiKG9N0+ITuHn58ybm0/+ZN5sNlNWVuZ9/ds740erU1ZW5q3jdDq59dZbGT9+PKNHj/bWCQsL83aDLy8vJzQ09KRjE+JssrWwlrmf7uKCrhHcM6bbH15eisnI7aPSePzzPazYWcbYXlGnIEohRFs5boL+85fuu+++e8TzYk8//bQ8QybEWcBSb2NXSR11ViddIox0jwpEq/llAoiimmaqmxw4XR5qrQ4259eycH0efx/TlR/za1i0odBbd2dJPSW1Nu4b1419lkZsTjd/H9OVuz7I4dVv81m1u4LpQxNJjw2ie1QA0cEyvZkQJ+PKK68EWuZBP1np6enk5eVRWFhIZGQky5cv55lnnjmsTkZGBu+88w5ZWVls27aNgIAATCYTiqJw//33k5ycfMRd+oyMDJYsWcINN9zAkiVLGDly5O/fQCHOcHVWJzct2kxEgA/PXdH3lPUYu+78JJZuLeGhpTs5LyWcAF95TEyIjqpVz6CvX7/+iLI1a9ZIgi7EGc5Sb+WO97exMa+apDB/MntE4lYU7E4PHkUhxE9HvdWJ06Pw9Iq9/HlIIv/+5iBjekaycpeFxDD/I5ZZWmfl2S/3My49iiHJYTTYXbx01TlUNzkI8dfTNzaYqGC5Yy7EH/HUU09x44034uPjw8yZM9mzZ89hs7EcjVar5aGHHmLmzJm43W4mT55Mamoq7733HgBTp05lxIgRrF69mszMTAwGA/PmzQNg06ZNLF26lLS0NO867rjjDkaMGMENN9zAbbfdxgcffEBUVBTz589v+x0gRCf10NIdlNXb+HDWUIL99L97OS6354hu8beNSuWGtzfx1Iq9/GV48hFtAny0BP2BdQohTo3jJujvvvsu7733HgUFBYc9h97U1ES/fv3aPDghRPvaVVLPgMRQJvaLYW9ZA8u2l6JSQa+YIPx0GpZsLUGjUuGj07C7tIHaZicAKaYAXl59kHOTjuzKWtnoYMZ5ifxnbS6fbCsBoGdUIAtnDMQkg9cIcUqsW7eOv//973z55ZeYzWbmz5/PNddcc9wEHWDEiBGMGDHisLKpU6d6f1epVDz88MNHtBswYAB79+496jJDQkIOmxddCHF0n2wrYenWEm4flUbfVkyndjxWp4ctB48ckLFvXDDvbSggKtD3iAsAw9PCJUEXogM4boI+fvx4hg8fzr/+9S/uvPNOb7m/vz/BwcFtHZsQot2p+HhLMQ9ldWdcbzMDE0OwOt3kVTURbvTh5dWHuDkjBbdHwe7yEOLf8sV+sKKRbuYAimutDE8NZ83+lpFnY4J8yeweSVpkAP3iQlizv4JeMUGcnxouybkQp5DL5QJg9erVZGVlyXe2EB1caZ2VBz7eTr/4YG66sEubrSezRyQ7iuv4cpeFKQPi2mw9Qojf77gJekBAAAEBAVxzzTUEBQV5R2RtbGxk27Zt3ilUhBBnDrvTTWWjjZpmJx5F4YWpfdlf3ojT40GjVlNaZ6dndCArd1kA0GtUWOrspEUa+TSnhL8MT+a1b3O5P6s7L359kD5xQdw+KhVToC99YoPoHhWISqViXO8oxvWWgWqEaAsXXnghY8eOxdfXl4cffpjq6mp8fHzaOywhxFF4PAp3Ld6G063w7OV9Dxvn5VQL8dMztEsYa/dXMjwtQqZdE6IDatUz6HPmzOHjjz/2vvbz8zuiTAjRMdU0OWh2ujEF+KA7xpe+3eWmpMZKg91Jo91Ng83Jw0t3UVZvo09cEFcOiOXH0gZe+zYXgKQwP64enADAki3FXDUogR7RAeRWNWN3efjPNf3Jr2pm3qW9qLE6iAvxo198yGHzoQsh2s5dd93F9ddfj9FoRKPRYDAYePHFF9s7LCHEUbz1XR7rDlQx79J0EsOPHLvlVBueGsH3udV8taecqefGt/n6hBAnp1UJuqIoh82ZqFarvd3nhBAdk8vtYd3BKuZ+upOiGiuTz4ll5rAk3B4Ft6IQ6qen0dbyzLjD5aHe5mJTfg0L1+ehKDBzWBLvbMhnW2EdU/rHsXBdrnfZieFGwow+pEUa2WdpxOn24FYU0mOCMPpoOVTRSPeoIPx9tPSNDyEiQO7cCXG6HTx4kOLiYtxut7ds4sSJ7ReQEOIIRTXNPLViLyPSIph67unpcu7no2Vochir91VgqbfJXXQhOphWJehxcXG89dZb3oFi3n33XeLi5LkVITqy3aX1XPvGD7g9CgDvbiyg0e4kv6qZigY7C6b1p7jWSpCfjqJqK4U1zbg8Cn86N54f8qt5asVebh2ZyrNf7qO22cFPiwFanjFff6CSqefGEeKnx+FSiA/1o6i2mfIGOz1jgugRFUigQQabEaI93H333RQWFtKtWzc0mpaeKyqVShJ0IToQRVG47+MdADx2aa/Dboa1tfNTwvnuUJXcRReiA2pVgv7II4/w6KOP8tJLL6FSqRgyZAj/+Mc/2jo2IcQfsL+80Zuc/2z59jIW/2Uw6w5W0mB3Eeav58PNxbz3Q8tc5aH+em7JSOG8lHDWH6zC6fZwce8oYkMMBPhoabC39JwpqG5m6rnxvP19AYcqmlCp4NzEUB4a34Oe0UGnfVuFEIfbsWMHn3322Wk94RfibFXX7PB+P7bGz9OZfbylmDX7KpgzvgexIX5tGOGR/Hy0DEoKZe3+SqqbHIT6ywV1ITqKViXoYWFhPPvss20dixDiFNKqjzwxNwX44FEUEsKMXPP6Rp6f2s+bnANUNzn4eEsxWelmYoIN+Os1JEf488inu7gtM5XX1uZSUmcjPtRAt0gjT05Kp6rJgUdR6GYOIDki4HRuohDiGFJTU6moqMBkMrV3KEKc8RrsLtbsq2x1/eFp4Tg9CnOX7eKc+GCmDUlsu+COY2iXcNYdqOLbA5Vc0ie6XWIQQhzpuAn6f/7zH66//nr+8Y9/HPUq/AMPPNBmgQkhTo7L7aG01kZBdROoIMhPR9+4YLYW1gKgUsGdmWm43AqPfLITg05Deb39iOXsKK5j5vnJnNcljP4JITz9xV5qmp38a+U+JvSNIdSoJ9RPjznYF3+9ltgQPyIDfVEf5YKAEKJ91NTUkJWVRe/evdHpdN7yl19+uR2jEkL8bO6nu2i2u3lycm807fT9GWjQ0ScumE351YzqJhfzhOgojpugd+nSMg9jr169TkswQojjK6m1kl/VTICvluQIf/z0WkpqrWw4VEVhTTNxIX7sLqtHo1YzKDGU+8Z141BFE7VWJ26PwitrDnFLRgpVTQ6mD00gMvDIwdsGJIQQ6q/F30eL3eUmPsyfH/JraHK4eXdjAQC3jkyhe5R0ZReio7rlllvaOwQhxDFsOFTFJ9tKuPa8RAx6DUU1zSdsY3e6T1jn9xiWGs7mghq+z61ibLq5TdYhhDg5x03QMzIyALj00ktPSzBCiGPLKarlujd+pKKx5a73zGFJTBucwB3vb2VTQa233s0XprCntJ6RXSP4aEsxUcEGnlm5z/t+k83FA1nd2VPWwPLtpdzw07zlbo9CfKiBmy5MYVtRHeFGHxwuhaFdQlm5s8z7fF2ov55x6TJ/uRAd2bnnnktxcTH5+fkMHToUq9V62GjuQoj24fYoPJd9gFB/PQlh/q3uGt8vPrhN4okM9KVrZADfHaxqs4sAQoiTc9wE/a9//etxG0tXOSFOj0a7k38s2+VNzgEKq61sKag9LDkHeHN9Hs9e0Qe3Ah9uLuYvI5IPe397SR3bi+vZXlwHQPeoAO4f150QPx3VTQ52lNThUWBncS1JEf4EGXS8dd25FNZYUQM9ogNJjjC29SYLIf6A//3vf7z//vvU1dWxatUqLBYLDz/8MG+++WZ7hybEWW39wUoKq61cMzgBnUbd3uEAMCwtnFfX5vL5jjJuzpCxZIRob8dN0K+99loAVq5cSWVlJZdccgkAy5cvJyYmpu2jE0IAUNvsZPNPifj5KWFM6BuD1eGmuslxRN1Ghwu7y4Ojyc7tmWn46zX8bWQqKhW8tjaX8AAfb3IOsLu0gbnLdrFw+gCqmx18tLmEcKOev4xIZmhSGL4+LR8T/eJDTsu2CiH+uEWLFrF48WIuv/xyABITE6murm7nqIQ4u9XbnGTvKefcxFC6RQW2dzheSWH+xIYY+O8Phcy6IKXdnokXQrQ4boJ+7rnnAjB//nwWLVrkLc/IyOCqq6467oJLS0v5+9//TmVlJWq1mssvv5w///nP1NbWcvvtt1NcXExMTAzPPfccQUEtz7K+8sorfPDBB6jVah544AGGDRsGtEwXM3v2bGw2GyNGjOD++++XqWPEGa2iwcbm/Bp2FNeTFhlAeICee8Z2JdUUwP7yBh5cuoOIAB/uu6g7Bp0G66+6pY3qHskXO8u4pE80L68+SG2zE4BAXy03Z6Sg0DJgnHL4DGxUNTlIMxm5IC2MUT3MDOkSfhq3WAhxKun1evT6X6ZNcrlaPwWUEKJtfLGjDLdH4S8jkrEcZZDW9qJSqRiWGsF7Gwv4cpeFsb3kWXQh2lOrplmrrq6msLCQuLg4AAoLC094JV6j0XDvvffSs2dPGhsbmTx5Mueddx4fffQRQ4YM4YYbbmDBggUsWLCAu+++mwMHDrB8+XKWL1+OxWJhxowZrFixAo1Gw5w5c5g7dy59+/bl+uuvZ82aNYwYMeKPb70Q7aC6ycF+SwM2p5vkCCMeRWF3aT0eBbqbAwg3+vDsl/u9A7IBXNA1AhUqPIrCE5/vpV98MJndI5m7bBd/H9uVZdtKya1qYnhqBKN6mJi3fDfmQF9vcg5Qb3NxqLKR3jFBTOoXw4ebi73vjexuors5gNpmJ1cPSSQxzP+07hMhxKk1cOBAXn75ZWw2G+vWrePdd9/1jisjhDj9ciub2FpYywVdI4gONnSoBB2gR1QgkYE+LFyXKwm6EO2sVQn67NmzmTZtmjdBLy4u5pFHHjluG5PJ5J1/1Wg0kpycjMViITs7m7fffhuAiRMnMm3aNO6++26ys7PJyspCr9cTFxdHQkICOTk5xMTE0NjYSL9+/bxtsrOzJUEXnVJprZV7Psxhzf6WQWHuvagrr3+bR3lDyxd1sJ+OZ6b0OSw5B/hmbwWzL+pGgG/Ln+y0wQnc8b9tAPxj2S6Gp0bwz8t688+Ve0mO8CfcqGd/eeMR6y+stvKnc+Ppbg7k/NRwdpfUk2YO4LyUcKKCDG256UKI0+iuu+7igw8+IC0tjffff58RI0YwZcqU9g5LiLOS26Pw6bYSggw6LkjrmNOZadQqJp8Ty4vfHGRHcR29YmSmFiHaS6sS9OHDh7Ny5UoOHToEQHJy8mFd506kqKiI3bt306dPH6qqqryJu8lk8t6Jt1gs9OnTx9smMjISi8WCVqvFbP7lSp7ZbMZisbR63UJ0JJvya1izvxKNWkWYv56iaivlDXZGpIUzZUAsOrWaMH8dt41KZV9ZAyF+ekKMeiz1NiICfpkSreZXz557FPhmXwU7Sup46rLefLnLwqRzYmm0u/lmb8Vh67+kTzQHypsI9dcRoNeQGObPBV1NhBuPnG5NCNF5qdVqRo0axahRowgNDW3vcIQ4q23Mq6as3sbUc+PRazvGwHBHc3GfKN5Yn8fCdXk8c3mfEzcQQrSJVn1KWK1WXn31Vd555x26detGSUkJX3/9datW0NTUxK233sp9992H0XjskZ+V3z4QS8szMccqF6Iz0mlUzJ3Qkxsv6MLcCT3pHh3I/VndmXVBCoXVVmwuD3OX7eb57P1UNtnpHh3Iq2sPsTG3miCDDo8Hpg9J8N5J/7VgPz1Ol4fcyiZsLjcJYQbuzEwj2E9HoEHL/eO6M7JbBAMTQ/AoEGDQM653lCTnQpxBFEXhhRdeYNCgQVx00UWMHTuWwYMH83//93+tar9mzRrGjBlDZmYmCxYsOOryH330UTIzMxk/fjw7d+70vjd79myGDBnCxRdffFibF154gWHDhjFhwgQmTJjA6tWr/9hGCtGJNNpdfLmrjJQII72iO87AcEcT6Kvjsv6xfLqthIqGjtUFX4izSau7uPfs2ZOtW7cCLXex//a3v3HhhRcet53T6eTWW29l/PjxjB49GoCwsDDKy8sxmUyUl5d7r+ybzWbKysq8bS0WCyaT6YjysrIy7x14ITqLJruLXSV1NNpdlNXZCDPqeWjpTsob7EzpH8tXe8rpZg7g9W/zvFOpbcytobDaymX943jn+3zu/Wg7fx+dypheZhwuD2N6mlmxs+Vvw6DTcEtGCjq1ijE9zMz7fDdOt0JSuD8vXdWfpHB/zEG+3nji5RlzIc5Ib775Jps3b+aDDz44bNyYOXPm8MYbbzB9+vRjtnW73cydO5eFCxcSGRnJZZddRkZGBikpKd46a9asIS8vj5UrV7Jt2zbmzJnD4sWLAZg0aRJXX30199xzzxHLnj59Otddd92p3VghOoGVO8twuDxc3DuqU9xgmj40kbe+y2fRhnxuG5XW3uEIcVZq1R30goICrr/+erTalnze19f3qHe2f01RFO6//36Sk5OZMWOGtzwjI4MlS5YAsGTJEkaOHOktX758OQ6Hg8LCQvLy8ujduzcmkwl/f3+2bt2KoiiHtRGis9hVUkdeZTPlDXbCjXr0ajXlDXY0ahUju5n47mAV8aF+h81zDlBaZyPETwdARYOdUKMvNy7aTFGNlax0Mw9kdef2Uak8MTmd+BA/An21bC+uZd7EdF68qh//uaY/Q7qEHZacCyHOXEuWLOGZZ57xJucAcXFxPP30097v3mPJyckhISGBuLg49Ho9WVlZZGdnH1YnOzubiRMnolKp6Nu3L/X19ZSXlwMtA9P9PCuLEAIKq5vZlF/DeV3CMQV2ju/h5AgjGd1MvPN9PnaX+8QNhBCnXKvuoOv1emw2m/fKX0FBwQmfQd+0aRNLly4lLS2NCRMmAHDHHXdwww03cNttt/HBBx8QFRXF/PnzAUhNTeWiiy5i3LhxaDQaHnroITQaDQBz5szxTrM2fPhwhg8f/rs3WIjTqbbZgaXeRm5VE/d8tN07tVlGNxPj0s1kdjNR1exAr1ET7n/k35RaBeqf/u5C/XTsKK6jptnJK2sOccXAOEwBPsQEG1ChUN5op29cMH8alECQQUdSuD9aTcd91k0Iceq5XK6jPnMeGhp6wqnWLBbLYWO+REZGkpOTc9w6P48Lc6KebYsWLWLJkiX06tWLe++9VxJ5ccbzKAqf5pRg9NVyYbfO1fPz2vOSuPq1DXy6rZTL+se2dzhCnHValaDfcsstzJw5k9LSUu688062bNnC448/ftw2AwYMYO/evUd978033zxq+axZs5g1a9YR5enp6Sxbtqw1oQrRIThdHvKrmsitaiLAV8eTn+89bN7xr/aUc8/YrsSG+rNqt4UhyWFsKazlsv6xfLCpyFvvioFxZO9pGRTx6iGJ/LyIgupmnl7R8vfVPSqAqwbFEx/qR2SgL5Gd5Cq9EOLU0+l0v+s9OPZYMCdb57emTp3KjTfeiEqlYv78+TzxxBMnPIcQorPblF9DUY2VywfE4qvTtHc4J+W8lDDSIo28/m0uk8+J6RRd84U4k5wwQfd4PNTV1fHCCy+wbds2b9d1GRVWiF94PAqWeht6rZp6m5MXvz7IZ9tLSQz3528jU713wX8twFdHvc3J59vLmDk8ifyqZiob7dw1uis2l5uukUasDjdhRh/G9DTz+fYybrwgmRA/HTW/mt/82vOSaHa46SlToghx1tuzZw/nnHPOEeWKouBwOI7S4hfHGgvmeHVaMy5MeHi49/cpU6bw17/+9bj1hejsmh0uVuwsIyHMjz6xwe0dzklTqVRce14S9360nQ251QxODmvvkIQ4q5wwQVer1SxatIhx48ZxwQUXnIaQhOhcSmqtvP9jAYt/LKJ/fAhNdheVTXbG94nm+0NV3PLeFu4b152HP2kZ7bhvXDCZPSLxKApNDhfXj0jiwSU7mTO+J59sK/FOjTYsNYxR3c28v7GQepuTKwfG4QGmn5dEo82JWqViaJcw4kP9iAv1k+7sQgh27979u9ump6eTl5dHYWEhkZGRLF++nGeeeeawOhkZGbzzzjtkZWWxbds2AgICTpig/zwwLMCqVatITU393TEK0Rms2m3B6nBzSZ/oTnv3eWK/GJ78Yg+vf5srCboQp1mrurgPHTqU1157jXHjxmEwGLzlwcHBbRWXEJ3G1sJaGm1uMrpFcm5iKP56Nfsrmiisbuba85PQqVVo1CrOTQqltM7K+Snh3u7pALdmdOHFq/rxY14Nj4zvSY3VQZ3VSZcII6t2lvGXEcmkmQLoFmXE6vQQE+xHZKAPsSF+7bjVQogzjVar5aGHHmLmzJm43W4mT55Mamoq7733HtDSVX3EiBGsXr2azMxMDAYD8+bN87a/44472LhxIzU1NQwfPpxbbrmFKVOm8PTTT7Nnzx4AYmJimDt3brtsnxCnQ0mtlQ2HqhmUHEZUkOHEDToQl9tDUU2z9/XFfaJ557t8NuRWERN85LYE+GgJ8jv+mFRCiJOnUk40HDstV8yPdgXwt6O7diSTJk3io48+au8wxBnE4XKjUavRqFU4XG425dfQ7HDzt/9updHeMviSWgX/mNCLB5fuwKPAgIQQpg1OIDbEF5vDg6KCmW/9iM3pOWzZb84YyMqdZSzdVupd1sR+0eg1avZbGpkyIJYpA+LQyV1yIcRZQL7DRWdSVNPMmn2VKIrCgjWHqGy0c0dmVwz6oz973i8+mC0FtSe1jpNtcyrWUWd18vSKPQxJDiOrd/QR9YenhcvNAiHaQKvuoH/22We8++67bNq0CZVKxYABA7jyyivbOjYhOoSqRjtf7Sln0YYC4kMN3DAsmZ2l9Ty9Yi/j0qO8CTWAR4Fl20s5NymUc+JD2Gtp4Lb/bSXIoOO685JIMxuPSM4BdpTUMyLNRO+4YPKrm0mJMFLVaGdfeSNje5mptzolORdCCCE6sK2FteRXNzOpX8wxk/POJMigIz0miB/zaxjZPbLTDXYnRGfVqjP+e+65h4MHDzJt2jSuvvpqDh48yD333NPWsQnRISzZWszdH+SwtbCWT7aV8vmOMuZ8sgsfrYZmx5FzhDbYnKSYjBTXWsneXY6iQG2zk2e+3IfLrdA10nhYfT+9huRwf1buLiUp3MjEPlEs/rGQN9bns25/JR9sKmJUD/MR6xFCCCFEx2BzuvliRxmxIQbOSQhp73BOmaFdwrG7PGzKr2nvUIQ4a7TqDnpubi6ffPKJ9/XgwYO55JJL2iwoITqKg5Z6ooN8eWFqP9bur0Cv1ZAcYcTqdFNc2/I8+a+nRQMY08NMTbODr/eWH7G80jor156fxAebivghr4ZUk5G/j+1GgK+G2Rf1IMzoA8D//ekcDpQ34vYopJiMmGTqNCGEEKLD+mpPOY12F9OGJBx15pbOKi7Uj/hQP747VMWQLmFn1LYJ0VG1KkHv0aMHW7dupW/fvgBs27btqNO4CNEZHapopKC6mWA/HSmmAIw+Wiz1NrJ3laGg4vV1eVQ02Hh4fA+eXrEPAHOgL2X1Nmqa7Nw3rjufbS+h2eFmXHoUvWICySmqIz7Ujx3F9YetK9BXR0qEkX//qR82p4cAXx0h/kcOsBJm9PEm60IIIYTouHIrm1h/sJIBiSFn5DPZ56WE897GAvaUNtAjOrC9wxHijNeqBH3btm0sWbKE6OiWASJKSkro0qUL48ePB+DTTz9tuwiFaEPfHazkujd/9HZVv/b8RP46PJlFGwrQqFS8+M1B7C4PVw9O4OkV+yirt/HR5iKeu7Ivjy7bTb3dzf99tZ9L+kTTPzGEecv30Gh3cV6XUG4bmcZN727G7mp55rxvXDDdogJJNRkx6Fv1pyeEEEKIDkxRFJ5btQ8frYbRZ+jjaD2iAgky6Fh3sFISdCFOg1ZlCa+++mpbxyHEaVfVaOfej7bT7HCjUsH5KeHo1GoOljfx5ro8/npBF4Ykh1FQ3UzfuCDe+T4fgGaHG4fTw5iekfSOCaRvXDAfbi6musnJX0YkU1DVTFK4P2H+Oj6+cSiHKprw99HSIzqQSOmqLoQQQpwxPttexqb8Wi7pE42/z5l58V2jVjEkOYwvdpZRUmsl+ihTrgkhTp1WfZLExMS0dRxCnHZ1Vif5Vc34aNXcPaYr2XvKeX1dLpsLavj3VeewMa+ayiY7g5LC8NNpCPTVUm9rGbH9UGUjeo2aLQW1zLqgCxNqbRTXWEkxGRnX00yQn47wgJZkvEd0UHtuphBCCCHaQLPDxaPLd5FqMnJuUmh7h9OmBiaGkr3HwvqDVVzWP7a9wxHijHZmXuoTohXCjD70jgmid2wQb6zPo6jGCoBapeKt7/JZucsCgN3pweVxM+uCLjyzch8uj8Lb3xfwQFZ3Xl+by7sbCxieGs6tI1NJDDceb5VCCCGEOEP8++sDlNbZePGqftQ2u07coBMz6DX0Twjhh9waMntEEmTQtXdIQpyxJEEXZ438qiaKaqz46TV4PB4UVMwe140tBbXe5BxgUHIoL3x1wPu6oLqZi9LNLNpQwM0ZKTjdCnqtmuomO93MAVw9JIHM7pEEH2WwNyGEEEKceXIrm/jPmlwm9Yuhd2wwa/ZVtndIbW5YSgQbc6tZs7+C8b2j2zscIc5YkqCLM1qDzUlOUR2ldVb8dBo8KDyzMpcJfWMpqm5mYFII6TFBqFXgUVraeBTQqFS4lJYCu8tDUY2V87qE8Xz2fjwKjO4RydRB8XQ1B9IjKpBgP0nOhRBCiLOBoig88ulO9Fo1917UDYfb094hnRYh/nr6xYXwQ241F6RFtHc4QpyxJEEXZ5wGm4OiGitut8Kq3eU8l70fAJUK5lzcgz8PTaTR5ubtDfmckxhCrdXOzPOTWLA2F4Cv95Rz+YA43t1Y4F3mrpJ6nruiD9cMTUQNxIf74y8jsQshhBBnnezd5Xyzt4IHsrpjCvSlqKa5vUM6bUakRbC5oIZ1ByrJ6h3V3uEIcUaSDEN0ejVNdg5WNFFaayU8wIcPNhWRW9nE9KGJzP9qv7eeRqXCrSi8vDqXuBADDpcHt1vBUu/gvJQwUiMDyK9qRq9VYwrwoXtUT9YdrKJHVACjupvpFiVTiwghhBBnM5vTzSPLdpJqMvLnoYntHc5pFx7gQ3psEN/nVlNndRIb0t4RCXHmkQRddGq7Sup4afVBthTUMiItguhgAx9tKUZRYGhKIz/1UgdaplFbllNKeb2NQUmheBTYVlRLqL+eohobaWYj5kAfNGo1EQF6jD46MntEEhHgi0atar+NFEIIIUSH8MrqQxRWW3l35iB0GnV7h9MuLuhqIqeojg9+LKLnJTJTjRCn2tn5ySI6rQPlDXyxo5T1ByvZVVLHfR9uJzncyMR+MUQG+pJb0cRFvcwAGHQaDDqNt22gQUd1k4OqJgfBfjqignx59dtcSuts1FkdFFZbCTP6MKRLGKmRgUQFGzAHGSQ5F0IIIQSF1c28+M0BsnpHMTQlvL3DaTfmQF96RAWyeFMR9TZne4cjxBlH7qCLTmP13nL++s5mIgN9CPHTc9WgeMb2juKZlXtxultulU8bnEDvuCD2ljWgU8Pjk3rxxOd7Kau3kV/dxOT+sTyzch///voAfx6aiNFHiynAh75xwTJfuRBCCCGO6dHlu1CrVNw/rnt7h9LuLugawYvfHOT1b3O5bVRae4cjxBlF7qCLDqW83sYPedXsKqnH5nSTV9nEj3nV7CyuY372fh68uDuXD4hj0jkxRIcYeGX1QW9yDvD29/kE+eq4pG8MT3yxl+1FdUzpH8vcS3py3flJ6LVqbh2ZQqi/ntV7K4gNMTC0S5gk50IIAaxZs4YxY8aQmZnJggULjnhfURQeffRRMjMzGT9+PDt37vS+N3v2bIYMGcLFF198WJva2lpmzJjB6NGjmTFjBnV1dW2+HUKcaqv3VbBip4WbM1KIDja0dzjtLjbEjxFpESxYc4iKBnt7hyPEGUUSdNFh7CqpZ/LL65ny8ndkvbCWp77YQ35lI2v3leNweRiXHsVHm4soqbPxyKe7sDrc1DQf2bWq2enGqNfgUWDh+jxUathnaeD9jYWE+OkYEB/Ce9cP5oNZQ7isfxyJ4cZ22FohhOhY3G43c+fO5dVXX2X58uUsW7aMAwcOHFZnzZo15OXlsXLlSv7xj38wZ84c73uTJk3i1VdfPWK5CxYsYMiQIaxcuZIhQ4YcNfEXoiNzuDw88slOEsP8mDksqb3D6TD+MjwZu8vD89n7T1xZCNFqkqCLDsHqcPPMyr0UVlsBCDf6EB1swOZSMPjoeHdjPmqVimGpESzakE96TBA+WjWxIYdfxdaoVYQb9YT661H9NLf589kH+HK3Bb1WTd+4YIamhJMcYSTIIHOXCyHEz3JyckhISCAuLg69Xk9WVhbZ2dmH1cnOzmbixImoVCr69u1LfX095eXlAAwcOJCgoCN7I/3cBmDixImsWrWqzbdFiFPp9XW5HKps4uFLeuKj1Zy4wVkiPsyPqefG8d7GAnIrm9o7HCHOGJKgi3bRZHfR8NPAIjani32WBnrFBDEoKYTrhyXz8Pge/JhbxaHKRp74fA+f5JSg06hweRQUBZodbmqaHdw1uivRQb4ABPhouX1UGk99sYeiWiuzx3bzDvBWb3VxzZBE0iID0Z6lo64KIcTxWCwWzGaz93VkZCQWi+W4dcxm8xF1fquqqgqTyQSAyWSiurr6FEYtRNsqq7PxfPZ+RnWP5MKupvYOp8P528g09Fo1T6/Y096hCHHGkEHixGlldbhYe6CSF7L343Qr3Du2G0u3lbBkazEalYprz0siKsiHwupmJp0Ty/yvWrpXdosMJNzoQ2mdjRA/HXstDZQ32PHVanh8Ujp1VicRRh8AIox61GoVKRH+fHLzedQ2OYgKNpAU7t+emy6EEB2a8ut5KX+iUqlOuo4QZ5J5n+3G5VF4eHyP9g6lQ4oI8OH6YcnMz97PloIa+sXLxOhC/FFyK1GcVlsKatleVEdGt0gu7RfD5sJaPv5p3vIJfaNRqVrmJgfQatQE+LZcQ6q1OvnuUBWDk0J55JKedIkw8srqQ7g9HnYU11HZaMelKPSKDmDKgDimDIijX0IoPaODOC81guQIo5xECiHEcZjNZsrKyryvLRaL9873seqUlZUdUee3wsLCvN3gy8vLCQ0NPYVRC9F2vj9UxSfbSpg1ogtxoX7tHU6Hdf3wZMKNeuZ9tvuoF/GEECdHEnRx2thdbuwuN3qtmvUHKymttfLN3paTtvhQPwx6La+sOURcqD9Ot8L7PxYw9dx41CrIr2omJtjATe9upqTWyvNX9mX+lX3pHhXIyG4mRnYzMSw1ggA/H9Qyb7kQQpy09PR08vLyKCwsxOFwsHz5cjIyMg6rk5GRwZIlS1AUha1btxIQEHDCBP3nNgBLlixh5MiRbbUJQpwyTreHh5fuJDbEwKwLurR3OB2a0UfLnaO78kNeDR9uLm7vcITo9KSLuzgtLHVWnv/qAO9tLCDM34d5k3rxY241XSMDyCmqY3TPSN7bUOCt/+raQ9wzthuh/nr+fdU55BTWAvDS1f3JrWziu0NVJIb5YQ4ykGwyopdBW4QQ4g/RarU89NBDzJw5E7fbzeTJk0lNTeW9994DYOrUqYwYMYLVq1eTmZmJwWBg3rx53vZ33HEHGzdupKamhuHDh3PLLbcwZcoUbrjhBm677TY++OADoqKimD9/fnttohAnVNfsoMHu4n8/FrLX0sC8S3tR2Xj8acTsTvdpiq7jumJAHB9sKmLeZ7sZ2c1EiL8MxCvE7yUJumgTRTXNVDc5MAX6YA408M2+ChZtKCDYT8ctI1P4Ia+GBWtzeWJSOmv2V2BzejDoNTQ53Kw7UMnwtAjuX7KDKQNiubBrOP0TgjHodPjq1VzWPxaDXg5dIYQ41UaMGMGIESMOK5s6dar3d5VKxcMPP3zUtv/617+OWh4SEsKbb7556oIUog012F0szynlldWHSDUZURRYs6/yuG36xQefnuA6MLVaxWOX9iLr+W954vM9PHlZ7/YOSYhOS7q4i1OivN5GdZMdm8PF94eqeHN9LtuLa9lb1sC6/RWs2tUyyu/kc2JZf7CSjbkto/i+/X0eT07qzeDkUG66MAWAzQW1GH203HRhF0pqrOwuaSTEz4foEF/6xYVIci6EEEKINrNipwWXW+Hi3tEyfs1J6GYOZOb5Sbz/Y6H3PE8IcfIk0xF/SGWjnY83F/PKmoP46jRMH5pIVaOdAQlhlNZZeWDJDoanRtAjOogvd5dj0GvYW9ZIv/hgthbWsrOkgbzqJuqanZwTH8y//9SPDbnVRAcZGJAQwoyhiYQH+Lb3ZgohhBDiLLCjuI7NBTUMT40gIsCnvcPpdP42KpVlOaXc//F2lt86DL1W7gUKcbLa7K9m9uzZDBkyhIsvvthb9sILLzBs2DAmTJjAhAkTWL16tfe9V155hczMTMaMGcPatWu95Tt27GD8+PFkZmby6KOPyuiQHcyXuyw89tluHG4PY3qaabS7OCchhEUb8tlRUk9htZUuEf70jg2iS4Q/jTYXjTYXqSaj94vvrfX5hPjrWfxjEcu3lTC6RyQXdA2nZ0ygJOdCCCGEOC3cHoV/fbmPQF8tF3aLaO9wOiU/vZZHLunJ/vJG/v31gfYOR4hOqc3uoE+aNImrr76ae+6557Dy6dOnc9111x1WduDAAZYvX87y5cuxWCzMmDGDFStWoNFomDNnDnPnzqVv375cf/31rFmz5ojn40T7aHa4eOf7fPz1Gm4bmcbzX+2nuzmQQF8tarWKPWX1AJQ32Anw0XLzhSn46TUMTg7lgSU7mHROLH56DbEhfqSZ/BmcHIo50ECgQdfOWyaEEEKIs81/fyhgn6WRKwbG4SODz/5uo3q0TKX7f18f4IKuETI3uhAnqc3uoA8cOJCgoKBW1c3OziYrKwu9Xk9cXBwJCQnk5ORQXl5OY2Mj/fr1Q6VSMXHiRLKzs9sqZNEKFXVWDljqWbu/gg2HqogK8mVivxheXXuI2mYnNc0OfHUaCqubOeenD+QvdpSRX23F4fLQYHeh06j4v6n9GJocyqjuJi7tF0PvuBDSIgMlORdCCCHEaVfT5ODpFXvpFx9M75jWnb+KY3tkQk/Mgb7c/v5Wmuyu9g5HiE7ltD8YsmjRIsaPH8/s2bOpq6sDwGKxYDabvXUiIyOxWCxHlJvNZiwWy+kO+axU1+zk6z3l/HPFXt7+Lo/1+yv4ereFb/ZVUlhr418r92GptzO+dzSh/npK6mwA7C9vJCLAh0nnxOKj1XBeShh5Vc3sL2+g2ekmp7CW4hobEQG+XNAtkl4xwWhk3nIhhBBCtKN/rtxLg83F7aNSZWC4UyDQV8czl/chv7qZR5fvbu9whOhUTusgcVOnTuXGG29EpVIxf/58nnjiCR5//PGjPleuUqmOWS7alsejsHZ/Bc1ON4lhfoQZ9djcHvKrmwnx0/H1nnLOSw2nuNaKzeliVHcz//uxEEu9HbdHobC6mQAfLfGhfvSNC2bG0EQAukYGYB6cgE4jA4YIIf44p9NJUVERNputvUNpc76+vsTGxqLTSS8jIU617UV1vLuxgBlDk0iOMFJUc+Z/ppwOg5PDuGF4Mq+sPsTIbiZG9Yhs75CE6BROa4IeHh7u/X3KlCn89a9/BVrujJeVlXnfs1gsmEymI8rLysowmUynL+AznKIo5Fc1U2t1EmHUY6mz4gJ0KjU+WjWNdhfljQ7WH6pkUGIYK3aWcUdmGl/vrSDYT4fV4eGrPRWcExfCA1k9mP3RdhrtLt5Yn8ddY7rx5rpcLA12Lh8Qx5QBsZiDDO29yUKIM0hRUREBAQEkJiae0RdvFUWhqqqKoqIikpKS2jscIc4oHo/CQ5/sIMxfz22ZqdRbne0d0hnljsw01uyr5O8f5rA85nyi5FxQiBM6rbcyy8vLvb+vWrWK1NRUADIyMli+fDkOh4PCwkLy8vLo3bs3JpMJf39/tm7diqIoLFmyhJEjR57OkM9YDpebjzYXM+21DazeV8GKnWUU1thosrl474cCNuRV4/R4cLjcmAMNGH21lNbZWLnLQkY3Ez/m1VBWb+WKgfGs2G1Bo1bxytXn8OJV/Xhycm8GJQbznz8PYOnN53HLyFRJzoUQp5zNZiMsLOyMTs6hpedYWFjYWdFTQIjT7cPNRWwpqOXei7oT6Cs9VE41H62G//tTP+xONzct2ozD5WnvkITo8NrsDvodd9zBxo0bqampYfjw4dxyyy1s3LiRPXv2ABATE8PcuXMBSE1N5aKLLmLcuHFoNBoeeughNJqW0TPnzJnD7NmzsdlsDB8+nOHDh7dVyGeNklorZXVW3v+hgL+P7cZdi7dh/+kDMzncn5nnJ1HZ5KCy0UHv2CDe+i6P4akRjOxm4o11efz3hkFc2i+GigY7DVYHPcyB1FsdJIT5MSAxBB/dae2YIYQ4i53pyfnPzpbtFOJ0qm128MTnezgnPphJ/WLaO5wzVpcII09d1oeb3t3M45/v5uHxPds7JCE6tDbLpP71r38dUTZlypRj1p81axazZs06ojw9PZ1ly5ad0tjORlWNNgqqrWwpqOH/vj6ASgUvXdWfZ1bu8ybnAF3NAS131He1DManVsHcCb2oszoZ3TMSRYF/rtjLzGGJpEaGolGrUKGiS4QRg14ScyGEEEJ0Dk+v2EtNs4O3rxuEWgasPWkut4eimuZW1e0TF8SU/rEsXJdHYpg/I7u37pHVAB8tQX76PxKmEJ2OZFRnuNyKRvaUNeCrU2P00eKv13JXZldiQ/0oq7NRWH34B2u3qECe/XKf97VHgfnZ+3n6st5k766g2eFi2pBEGmxu1h0oweby8EBWD0nOhRBnlZqaGqZPnw5AZWUlarWa0NBQABYvXoxef+wTyu3bt7N06VIeeOCB0xGqEOIothbWegeG6xEd2N7hdEpWp4ctB6tbXT89NogdJXU89tluapocmAJ9T9hmeFq4JOjirCNZ1RmqoLqZ6kY7H24uQlHAUm/D6nLT3RxEekwgz63ax6juJsb1juLVtbnedk73kc8GVTTYabA56RcfzL0f5vD+j0Xe9xbOGIi/jxxGQoiOb8mWYp5esZeSWivRwQbuHtOVib+zW2tISAhLly4F4IUXXsDPz4/rrrvO+77L5UKrPfpnY3p6Ounp6b9rvUKIP87tUXhgyXYijD7cnpna3uGcNbRqNbMv6s6sRZt5+/t8Zl3QBT+5wSPEEeSv4gxR0WBjR3E91Y124sL8qG1ygEqFSgVxoX4EGnSE+euJDTFgc3rYXFDLqO6R+Go1TD03jsU/FhHgq6VnVCBqVcud85+dmxiKOdCXZqebN689l035NdQ2OxmeFk6f2OB222YhhGitJVuKmf3RdqxONwDFtVZmf7Qd4Hcn6b917733EhQUxK5du+jZsyfjxo1j3rx52Gw2fH19mTdvHsnJyWzYsIHXX3+dV155hRdeeIGSkhKKioooKSnhz3/+M9dcc80piUcIAXXNDhrsrsPKPtxcxI7ieh65pAd1Vid1vxq53f7TZ4RoGxEBPlw9KJ5Xv83lvY0FTB+ahEYeLxDiMJKgd3JldVYqG+18saOM19bl8uLUc9iYW01KpJE31+Uysnsk8z7b7U24+8QGcc3QBJLD/flwczFXDoyjX3ww43tH4XR7CDLomH9lP+Yu20VFg53ByaHMGtEFtVrFiLSW54X6xYe04xYLIcTJe3rFXm9y/jOr083TK/aesgQdIC8vjzfeeAONRkNjYyPvvPMOWq2W9evX8+yzz/LCCy8c0SY3N5e33nqLxsZGLrroIqZOnSrznQtxijTYXazZV/nLa5uTl745SIrJiFatPuw9gH7xwac5wrNPQpg/E/vG8OHmIpZvL+WSPtHtHZIQHYok6J1UXmUjpTXN2D3w8eZi6qxO3rnuXPZbmvj31we58YIu+PloWbq15LC74SH+euxOD9OGJPDPFXv558q93JyRwoCEYDRqNW5FITHUwMLpA3C6FAx6DaH++lY9JySEEB1VSa31pMp/r7Fjx3pnIWloaOCee+4hPz8flUqF03n0+ZVHjBiBXq8nNDSU0NBQqqqqMJvNpzQuIUSLz3eU4XQrXNI7WmZHaEf9E0Kw1Nv49kAlkYE+DEoKa++QhOgwTus86OL3K6uzsquknvIGG5X1NvIqm6mzu/nL25tQqVRceW4czQ4Pr6w5hNXpxup0E+qno6bZcdhyapocbMqvQaWCRyb05J6LupEY5k+jzY2/XoOPRkNShJFeMcH0SwihW1SgJOdCiE4vOthwUuW/l8Hwy/Lmz5/PoEGDWLZsGS+99BIOh+OobX49oJxGo8Hlch213umwZs0axowZQ2ZmJgsWLDjifUVRePTRR8nMzGT8+PHs3LnzhG1feOEFhg0bxoQJE5gwYQKrV68+LdsixG8drGhka2Etw1MjCA/wae9wznpje5npGhnAp9tK2FvW0N7hCNFhyB30Dk5RFNbur+TOxduoaLATG2Lg9lFpON0eKhrtZPaIxOlxs724jl7RQeRWNgEtV4gvOyeGID/9YYPA5RTXcXtmGn95exMhfnp8dWrqbS6evqw3wX56kiKM7bWpQgjRZu4e0/WwZ9ABDDoNd4/p2mbrbGhoIDIyEoCPP/64zdZzqrjdbubOncvChQuJjIzksssuIyMjg5SUFG+dNWvWkJeXx8qVK9m2bRtz5sxh8eLFJ2w7ffr0wwbRE+J0c7o9fLK1hBA/HRd0jWjvcASgVqm4cmAc/1l7iPc2FjBzWBKxIX7tHZYQ7U4S9A6orN5KSY21peuVAn99ZxMDE0NJ6xtAflUTz325j7+M6IJWpSI1MoBl20rQqtR0iTAyMCGEH/JryK1soqimmRFdTfjpNCzeVESIn46J/WLRa1Q8d0VfCmua8dVp6GYO4FzpWiSEOIP9/Jz5qRrFvTVmzpzJvffey8KFCxk8eHCbredUycnJISEhgbi4OACysrLIzs4+LEHPzs5m4sSJqFQq+vbtS319PeXl5RQXF5+wrRDt6as95VQ02pk+NBGdRjqQdhQ+Og3XDE3k5dUHefO7fGaN6EKov0yrJs5ukqB3AIXVzTQ73EQH+1JY3cxn28tYsbOMcKMPVwyI5c7RaWwrrCUiQI9K1fLcTqhRR4hRx76yRg5WNHL9sGSWbi3m2vOTqLU62V/eyJKtpWR0j2Tt/krOTwmnwe7i+0OVBBu0XNovFqvTjY9OjY9W0967QAgh2tzEfjFtkpDfcsstRy3v168fK1as8L6+7bbbABg0aBCDBg06attly5ad8vhay2KxHPbse2RkJDk5OcetYzabsVgsJ2y7aNEilixZQq9evbyj3QtxuhTVNLN2fwX9E0JIiwxo73DEbwT66pg+JJFX1hzijfW5/GV4F5nCV5zV5Og/jSob7ORXN+Ov15AU4U+TzcWKXRb+9eU+xqWbGZQQSmSQL6FGHXeNTsPoq8VXq+bplXuZcV4Sj322G+WnAd8m9o1mYGII6TFBaNVq9lkaGNolnDfX53LThV0IMujRaVSEGLTcPiqV0nobeq0aFIX+iWHotGp0WrmCLIQQooWiKEeU/XYQrWPVOV7bqVOncuONN6JSqZg/fz5PPPEEjz/++CmKWojjc7o9fLS5GKOPlnG9oto7HHEMpkBfpg1O4PV1uSxcl8t15ydj0MsNJHF2kgztNNlTWs/lr6xn8kvrmfnWj3y+vYwVOy28sS6X+8d1IzrIgAeFv3+wDUWB+dkHuOrVjVQ0OuhqDuTTbSX8+vxnydYS4kL90Knh+al9+XpfOQ02J5f1jyOvsoktBTVo1SqqmpwkhvsxrlcUQ7qEcUnfWOJD5fkeIYQQhzObzZSVlXlfWywWTCbTceuUlZVhMpmO2zY8PByNRoNarWbKlCls3769jbdEiF+8tT6fsnobE/vGSMLXwSWG+3P14AQsDXYWrs/FJnPSi7OUJOingc3p4tkv93GoshmAWSOSKauz0WBzcO9F3Wi0u1EUhcIaK33iQ3h3QyG7SuuBlkGMjD5a6qxHTs9T1eTgP9/mcaiyiYezejAsLYIuJn/Gpkfxp0FxpMcGMbyrifgwIwEGHeZAAxq1TCkihBDiSOnp6eTl5VFYWIjD4WD58uVkZGQcVicjI4MlS5agKApbt24lICAAk8l03Lbl5eXe9qtWrSI1NfW0bpc4e+0sqeOt7/PpGxdMt6jA9g5HtEJaZABTB8ZTUmvlze/ysDokSRdnH+nifhrUNDnZVlRL//hgLkgLJ9UUQFm9jXqrk9yKJv7vmwNcMySR8gY70cEGPtpc7G1r8NEQYdQztpeZl1cf8parVBBi0BMbYuCpL/Z6y+de0pOrByeglkRcCCHESdBqtTz00EPMnDkTt9vN5MmTSU1N5b333gNauqqPGDGC1atXk5mZicFgYN68ecdtC/D000+zZ88eAGJiYpg7d277bKA4qzTZXdzy7haC/XRcnC5d2zuTHtGBXD4gjvd/KOTvH+bw1rXnEuCra++whDhtJEFvQ/U2J1q1CqvTzTVDEvHVabDU25i1aDORgT78dUQXDlY08tDFPcitbKZrpJHiWhsBPloa7C3z4BZWNTO6p5naZid3j+7K/zYVEuKn54bhyYT6a7lyYCxZ6Waqm5zEh/nRzRwgybkQQojfZcSIEYwYMeKwsqlTp3p/V6lUPPzww61uCy0JuhCn24NLd5BX1cRzV/al0SZ3YTub3rHBKAp8sLmIKxd8zxszziVC5q4XZwlJ0E9CRYON9QerWLXbQp/YYEZ2MxFo0FHeYCfQoCUm2I/KRjt7y+r5Ma8avVZLbIgvlY0OdFoVRTXNvL4uD2jpnn7b+1u5c3RXPvixCFSQFO5PYrg/t2Wm8ujylgHhdpc18GN+DWN7RDIkOZTzUkPRqtSs3l+B2+NhfJ+2myJICCGEEKKz+XBTER9tLuZvI1M5Jz6ENfsq2zsk8Tv0iQtmQGIIDy3dyWUvr+ftawcRHybjKIkznyToreR0eViwJpf/rG3pZr5ypwU/vYbX1+VyoLyJa89LZFT3SLYX17Exr5rEMH+CDGqsTjcL1+Vy70XdeOGrA4ct06NAs8NNn/hg7E4Pr6/LZe6EXtRa7SycPpCdJXUoCqREGDlQ0YSl3kZqZABLtxaz/mAVn9x8fnvsCiGEOOvV1NQwffp0ACorK1Gr1YSGhgKwePFi9Prjz+O7YcMGdDod55xzTluHKsRZ5WBFIw8u3cGgpFBuHZlKaZ21vUMSf8CQLmG8e/0gZrzxA5NeWs+Ca/pzTnxIe4clRJuSBL2VCmqaWbgul9gQA5P7x9IzKpC9lgbG944myKAj2E/HV3vK8SgKiWH+fL69lECDlnMTQ5k+NIns3eWE+ftQb3Udtly9RoXTAxq1ivSYIBauy+OKgXEszymhf0II8aF++Gg1FNVYabK7uPuDHMyBviyYNoBuZpnLUwghWi3nf5A9F+qKICgWRj4EvS//XYsKCQlh6dKlALzwwgv4+flx3XXXtbr9xo0b8fPzkwRdiFOoye7ipkWb8dVpmH9lPxkY9wzRLz6ED/46hBlv/MDlL3/HfeO6M+O8xCOmgRTiTCEJeispisLEfjGYA30w+mjYWliLVqPCoNUQHWzA5VFABR/8WISiwOUD4yiutdIrJoiPthSzrbCO20al8sQXe7zTpcWH+tFod6PTqHjzuzxG9zCTHOFPs8NFgEFHmNEHu0vB30fFuN5RON0exveNwV+vwRTo2747RAghOpOc/8Gnt4Lzp7tpdYUtr+F3J+m/tWPHDp544gmam5sJCQnh8ccfx2Qy8dZbb/Hf//4XjUZDSkoKd955J//9739Rq9V88sknPPjggwwYMOCUxCDE2crjUfjbf7eyv7yRN2YMxBwk50lnkhRTAMtuHsZdH2xj7rJd/JBXzZOX9SZQBo8TZyBJ0FspLtSPmCBfupiMVDTY+e8PhVQ3OQC4enA8PaOCeHVtrrf+a9/mctuoVEyBPpTX27E63XywqYi/j+mK26MQ4tfS/fHVtblc1CuSB7J68ODSHd7k/drzEnlu1X4em5hOn7hgAHQaNUnh8l8mhBAnLXvuL8n5z5zWlvJTkKArisKjjz7Kiy++SGhoKJ999hnPPvssjz/+OAsWLOCrr75Cr9dTX19PYGAgV1555UnfdRdCHJ2iKMz7bDerdluYM74Hw1Ij2jsk0QaC/HQsmNaf/6w9xJNf7CXnubXMuaQnmT0i2zs0IU4pyfaOw+50s6WwlnUHKgnx05MaGYDN5ea7Q1Xe5BygV0wQX+woO6L9xtxqzokPJqO7iUNrc9lf3siTX+zFR6vm1WkD8PPR8MTkdFbsKMXjcfPanwewOb8WvVbNV3vKGd0jkrRI4+ncZCGEODPVFZ1c+UlyOBzs27ePGTNmAODxeIiIaEkSunbtyl133cXIkSMZNWrUKVmfEOIXL68+xKvf5vLnIQn8eWhie4cj2pBKpeKG4V3onxDK7I9yuP6tHxnVPZI5l/QgNkQGkBNnBknQj2PN/kquf+tH7+tQfz0v/qkfB8obvWWDkkIxaNWYAo7sSpUSYaSq0UFNk4PpQxNZurWYAF8dVwyMQ1F52FnSRGKEL1eeG09SmD9qjZqYYD+KapoZkRZOiikAPx/5LxJCiD8sKLalW/vRyk8BRVFITU3l/fffP+K9BQsW8MMPP/DVV1/x4osvsnz58lOyTiEEvLEulye/2MMlfaJ5eHxPeS75LNE/IYTltw5j4bpcnlu1n1H/Ws20wQlcPyxZHgMVnZ5kf8dQZ3XyzxV7DiurbnKwp7SB2zPT2F/eSEywLz/k1nCwsokuJn+C/XTUNjsBCDRo6RcfjN3lZvn2UsL8fbi4dzTNDhff7C2na6QRg06NTq0lxfTL3OVdzQF0lcHfhBDi1Br50OHPoAPoDC3lp4Ber6e6upotW7bQr18/nE4neXl5dOnShdLSUgYPHkz//v1ZtmwZzc3N+Pv709jYeOIFCyGO6eXVB3ni8z2M7hHJP6f08Z5LibODTqPmhuFduLh3NE+v2Mtr3+by5nf5XD4glhuGdZEp2USnJQn6MbjcHhpsh4+4Hh/qh6KCuxfn4HB7uHt0Gh9tKSbC6MMtGSn8dUQyNqcHvUZNF5ORez/MISHUj+ev7MdTK/byzoZ8hiSHccXAOMyBvpyTEEqo//Gn4hFCCHEK/Pyc+Skaxf231Go1zz//PI8++igNDQ243W7+/Oc/k5iYyN13301jYyOKojB9+nQCAwO58MILufXWW8nOzpZB4oQ4SS63h38s28Wb3+Uzvk80/7q8DzqNur3DEu0kOtjAs1f05bZRqby8+iDv/1DIO98XMDg5lCn947go3YyfXlIe0XnI0XoMYUYfZg5PZu6nu7xll/aL5vHP9uBwewCwulr+rWi08/jne3hiUjp51U1sK6rlmS/34fYo3D0mDoNOzbNT+mBzefDVqokL9yPYIIm5EEKcVr0vP2UJ+a/dcsst3t8XLVp0xPvvvffeEWVJSUl8+umnpzwWIc50FQ127vjfVtbur+SG4cncM7abTKcmAEgI8+fesd24fEAcn20v5fMdZdy5eBsPLNnBuUmhnJcSxpDkMEJ+c3MswEdLkJ+cl4uOQxL047ikTzQ+GjVvrM/DFOhDV3OgNzkH8NVqvL9bnW7u+3g7s8d1Jy7Ej3G9zIzoGoFBq6G8wcGg5HD0Wrm6K4QQQgjxe6zaZeHej3JosLl4anJvLh8Y194hiQ6mwe5id2kDSeFGZo3oQn5VM1sLa9lSUMPqfRWoaLnjnhjmR2K4Pwlh/oxLN0uCLjoUSdCPI9zow1WDE7ikTzQ6rZriGis+WjX2n+6crztYyfShibzzfT4uj0KYUU90kC9Ol5tu5gBiQwxEBxmID/Nv5y0RQgghhOic8quamPfZblbstNDNHMC71w8mLVLG6xHHp1KpSAz3JzHcH0WJprTOxp6yeg5WNLEht5p1B6sAeO3bQ/SODaZHVCA9owPpER1ITLBBBhwU7UYS9FYIMOgASAr359kr+nLn/7ZhdbrZXlTHTRd04U+D4mm0uYgLNRBu9KG6yYGfXotBrznBkoUQQvwRiqKcFSdRiqK0dwhCnHYHyht4dW0uizcVodeouWdsN647P0l6JIqTplKpiA42EB1sIKNbyzgGxbVW8quacXo8HKpoZNVuCz9/1PrpNSSF+5McYSQ53J/kCH+6RBhJCvfHX2ZYEm1MjrCToFaruKiXme5RAVQ02IkM9CXhKHfHw4w+7RCdEEKcXXx9famqqiIsLOyMTtIVRaGqqgpfX5k6SJz5yuttrNhZxrKcUjbkVqPXqpk2OIEbL+xy1Clthfg9tBo1CWEtXdyHp4UTG+JHs8PFnrIGdpXUc6C8kUOVTWwpqGFZTgm/vkZqDvQlOaIlaU8KN5IQ6kdiuB+xIX746uTmnPjjJEE/SSqViqRwI0nhxvYORQghzmqxsbEUFRVRUVHR3qG0OV9fX2JjT82c7UJ0FHXNDiwNNnaW1JNTVMem/Bq2F9WhAAmhftwwPInxfaIJ8dPjcHmoa3bIs8JnGZfbQ1FNc6vr253uP7QOU4APpq4RXNA14rBlFtdaKahupqC6maJqK3lVzSzZUkKj/ZcZn1RARIAPMSEGYoINmIN8CffXE2rUExfsR1KEkTCjvlUzDtQ1O2iwu44oVxQFt6Lg8bT8rtGo0KhUqFSqkx7s7ljrOB4ZUO/0kARdCCFEp6TT6UhKSmrvMIQQreDxKBTXWtlb1sBeSwP7LA3sKK4jt7IJj9KS3EQF+ZLR3USv6CAiA1vulm8vqvcuY3hauCQHZxmr08OWg9Wtrt8vPrjN1qFWqUkMM3Jpv1i2FNSiKArNDjfVTQ6qmuxUNTmobnRQ1ehgv6XxsOT917RqFb46DT5adcuPToOiKLg8Cm6PgtOt4HR7sLvceDzgURQ8ioKiwLEedtKoVeh/Wp5Oo0avUaPTtJTpNGrvvz7an99T4/J4qG5yoFGr0ahVaNUq1CoVGrXK+/rn3320agw6DYO7hJJqCiDIoCPEv3UXG8TJkwRdCCGEEACsWbOGxx57DI/Hw5QpU7jhhhsOe19RFB577DFWr16Nr68vTzzxBD179jxu29raWm6//XaKi4uJiYnhueeeIygo6LRv29mstslOvd2FClr9OMjJ3ilrdrioanRQ0WinosFOYXUzRTUtdx0Lq5sprGnG5vxlJpyYYAPxoQbiQ/1IDPMnLlS6B4vORaVS4e+jxd9HS1yo3xHvO90emuwuGmwu4sMMeBSobnRgdbqxuzzYnG5szpZEXK1qSYi1GhUatRqb001ZvQ2NSoVa1bKuX/+rVqlQAW4F3D8l9uZAH9zKLwm+0+3B5VZw/PS7w9UST8t7Cg6Xmya723thwK0o3mUdy1vf5x/2OtyoJzLQl6ggXyIDfTEH+hIZ1PKvOciXyABfAg3aM/oxtLYgCboQQgghcLvdzJ07l4ULFxIZGclll11GRkYGKSkp3jpr1qwhLy+PlStXsm3bNubMmcPixYuP23bBggUMGTKEG264gQULFrBgwQLuvvvudtzS00tRfjn59XjwngT/utzpVrA53VgdbuwuN1ZHy8m71en+6STeTbPj5x+X9/cmuwurs+Xf377vcHla7rrBYc/PalQqNJqfkgG1Cq1G7f295W6ZGrUawv31+Pto0ahb3tdoVLjdCjaX25tY2JxuGu0tibn1KF2LA3y0xIb6kRTuz/C0CFJMRtIiA0iLNBLgq6Ooppk1+ypP33+GEKeRTqMm2E9PsJ+eYaktz7m31u/52+gXH8yWgto/XF9RFDy/SvxdHg92lwerw02KyR+dVkOd1Ullgx1LvY2yehtFNVY25ddQ0+w8Ynl6jZqIAB/CA3yIMPoQEeBDhFFPkJ+eAB8tRl8tAb5ajD5a/PRadBqV966/XqNGp23pDaBTq1GpWn+RsTOTBF0IIYQQ5OTkkJCQQFxcy9zSWVlZZGdnH5agZ2dnM3HiRFQqFX379qW+vp7y8nKKi4uP2TY7O5u3334bgIkTJzJt2rQzOkGf/dF2PtxU1JKQ/9Qt9VQy6DT4+2jw02vx02t++tESbvRp+d1Hi79eg06jRv3T3bYGu4uCqmYUWk66XW4PLs8vXWp/+9rtUWhyuLG5Wu7A/XySrlapMOg1+Go1BPhqiQjwwV+vIdzoQ5jRh3CjnvCfTsBjQwwEGXRnxcm0EGcSlUqFRtXSbb6Fhp8nNRyUHHbcCw02p5vyejtlPyXuFQ32X34a7RTXWtlaWEtVk/13fzaqfupBoFbBBV1N/OeaAb9vQR3YGZugFxcXM2nSpPYOQwghhGgzISEhvPbaa6dkWRaLBbPZ7H0dGRlJTk7OceuYzWYsFstx21ZVVWEymQAwmUxUV5/4Wc/O/h3e6zSvr/mnn99DDeh/+jmao50oun76aQKqgILfue6T9UEHXcfJtpF1dJz6HXUdv0dn2A4DEP/Tz6lQtRsmLTlFC2sHx/oOP2MT9A0bNrR3CEIIIUSncbS51n979/NYdVrT9mTId7gQQoizlQy9J4QQQgjMZjNlZWXe1xaLxXvn+1h1ysrKMJlMx20bFhZGeXk5AOXl5YSGhrblZgghhBCdmiToQgghhCA9PZ28vDwKCwtxOBwsX76cjIyMw+pkZGSwZMkSFEVh69atBAQEYDKZjtv25zYAS5YsYeTIkad704QQQohOQ6UcrV+aEEIIIc46q1evZt68ebjdbiZPnsysWbN47733AJg6dSqKojB37lzWrl2LwWBg3rx5pKenH7MtQE1NDbfddhulpaVERUUxf/58goOD22sThRBCiA5NEnQhhBBCCCGEEKIDkC7uQgghhBBCCCFEByAJuhBCCCGEEEII0QF0ugR99uzZDBkyhIsvvthbVltby4wZMxg9ejQzZsygrq7O+94rr7xCZmYmY8aMYe3atd7yHTt2MH78eDIzM3n00UePOkWM+MXR9vsLL7zAsGHDmDBhAhMmTGD16tXe92S//3GlpaVMmzaNiy66iKysLN58801Ajve2dqz9Lsd727Lb7Vx22WVccsklZGVl8fzzzwNyvIv29/nnn5OVlUW3bt3Yvn37Ye/JMfj7/Z7PVHF0a9asYcyYMWRmZrJgwYL2DqfTycjIYPz48UyYMIFJkyYBx//uES1OVU4mjkLpZDZu3Kjs2LFDycrK8pY9+eSTyiuvvKIoiqK88sorylNPPaUoiqLs379fGT9+vGK325WCggJl5MiRisvlUhRFUSZPnqxs3rxZ8Xg8ynXXXad88803p39jOpGj7ffnn39eefXVV4+oK/v91LBYLMqOHTsURVGUhoYGZfTo0cr+/fvleG9jx9rvcry3LY/HozQ2NiqKoigOh0O57LLLlC1btsjxLtrdgQMHlIMHDypXX321kpOT4y2XY/CP+T2fqeJILpdLGTlypFJQUKDY7XZl/Pjxyv79+9s7rE7lwgsvVKqqqg4rO9Z3j/jFqcrJxJE63R30gQMHEhQUdFhZdnY2EydOBGDixImsWrXKW56VlYVerycuLo6EhARycnIoLy+nsbGRfv36oVKpmDhxItnZ2ad7UzqVo+33Y5H9fmqYTCZ69uwJgNFoJDk5GYvFIsd7GzvWfj8W2e+nhkqlwt/fHwCXy4XL5UKlUsnxLtpdly5dSE5OPqJcjsG2caz9Ko4uJyeHhIQE4uLi0Ov1ZGVlyfF2Chzru0f84lTkZOLoOl2CfjRVVVWYTCag5eS6uroaAIvFgtls9taLjIzEYrEcUW42m497Ai6ObdGiRYwfP57Zs2d7u7HIfj/1ioqK2L17N3369JHj/TT69X4HOd7bmtvtZsKECQwdOpShQ4fK8S46NDkG/7iT+UwVRyf769S47rrrmDRpEu+//z5w7NxCHN/JfmeLo9O2dwBtSTnKM18qleqY5eLkTJ06lRtvvBGVSsX8+fN54oknePzxx2W/n2JNTU3ceuut3HfffRiNxmPWk/1+av12v8vx3vY0Gg1Lly6lvr6em266iX379h2zrux3cSpNnz6dysrKI8pvu+02Ro0addQ2cgye2PH268l+poqjk/31x7333ntERkZSVVXFjBkzjtpjRvwxcpyenDMiQQ8LC6O8vByTyUR5eTmhoaFAy1XrsrIybz2LxYLJZDqivKyszHu1R7ReeHi49/cpU6bw17/+FZD9fio5nU5uvfVWxo8fz+jRowE53k+Ho+13Od5Pn8DAQAYNGsTatWvleBenxRtvvHHSbeQYPLHW7tfWfKaKo5P99cdFRkYCLedXmZmZ5OTkHPO7RxzfyX5ni6M7I7q4Z2RksGTJEgCWLFnCyJEjveXLly/H4XBQWFhIXl4evXv3xmQy4e/vz9atW1EU5bA2ovXKy8u9v69atYrU1FRA9vupoigK999/P8nJycyYMcNbLsd72zrWfpfjvW1VV1dTX18PgM1mY/369SQnJ8vxLjosOQb/mJP9TBVHl56eTl5eHoWFhTgcDpYvX05GRkZ7h9VpNDc309jY6P193bp1pKamHvO7RxzfyX5ni6NTKUfrc9CB3XHHHWzcuJGamhrCwsK45ZZbGDVqFLfddhulpaVERUUxf/58goODAXjppZf48MMP0Wg03HfffYwYMQKA7du3M3v2bGw2G8OHD+fBBx+UrhbHcbT9vnHjRvbs2QNATEwMc+fO9V4Nk/3+x/34449cddVVpKWloVa3XEu744476N27txzvbehY+33ZsmVyvLehPXv2cO+99+J2u1EUhbFjx3LzzTdTU1Mjx7toV19++SX/+Mc/qK6uJjAwkO7du/Paa68Bcgz+EXffffdJf6aKo1u9ejXz5s3D7XYzefJkZs2a1d4hdRqFhYXcdNNNQMs4KBdffDGzZs067nePaHGqcjJxpE6XoAshhBBCCCGEEGeiM6KLuxBCCCGEEEII0dlJgi6EEEIIIYQQQnQAkqALIYQQQgghhBAdgCToQgghhBBCCCFEByAJuhBCCCGEEEII0QFo2zsAIUTH8tJLL7Fs2TLUajVqtZq5c+fyz3/+k/Lycnx8fPDz82PevHkkJydz7733snHjRgICAgB44okn6N69O4qi8Nhjj7F69Wp8fX154okn6NmzJwBr1qzhsccew+PxMGXKFG644Yb23FwhhBBCCCE6DEnQhRBeW7Zs4ZtvvuHjjz9Gr9dTXV2N0+kE4J///Cfp6em8//77PPXUU7z88ssA/P3vf2fs2LGHLWfNmjXk5eWxcuVKtm3bxpw5c1i8eDFut5u5c+eycOFCIiMjueyyy8jIyCAlJeW0b6sQQgghhBAdjXRxF0J4VVRUEBISgl6vByA0NJTIyMjD6gwYMICCgoLjLic7O5uJEyeiUqno27cv9fX1lJeXk5OTQ0JCAnFxcej1erKyssjOzm6z7RFCCCGEEKIzkQRdCOF13nnnUVpaypgxY5gzZw4bN248os7XX39NWlqa9/Wzzz7L+PHjmTdvHg6HAwCLxYLZbPbWMZvNWCyWI8ojIyOxWCxtuEVCCCGEEEJ0HtLFXQjh5e/vz0cffcSPP/7Ihg0buP3227nzzjsBuOuuu/D19SUmJoYHH3wQgDvuuIOIiAicTicPPvggCxYs4Oabb0ZRlCOWrVKpjlkuhBBCCCGEkARdCPEbGo2GQYMGMWjQINLS0liyZAnwyzPov2YymQDQ6/VMmjSJ119/HWi5Y15WVuatV1ZWhslkwul0HlZusVi8yxBCCCGEEOJsJ13chRBehw4dIi8vz/t69+7dREdHH7N+eXk5AIqisGrVKlJTUwHIyMhgyZIlKIrC1q1bCQgIwGQykZ6eTl5eHoWFhTgcDpYvX05GRkabbpMQQgghhBCdhdxBF0J4NTc38+ijj1JfX49GoyEhIYG5c+fyt7/97aj177rrLmpqalAUhW7duvHII48AMGLECFavXk1mZiYGg4F58+YBoNVqeeihh5g5cyZut5vJkyd7k3ohhBBCCCHOdirlaA+FCiGEEEIIIYQQ4rSSLu5CCCGEEEIIIUQHIAm6EEIIIYQQQgjRAUiCLoQQQgghhBBCdACSoAshhBBCCCGEEB2AJOhCCCGEEEIIIUQHIAm6EEIIIYQQQgjRAUiCLoQQQgghhBBCdAD/D9r1PAzohe85AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x504 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(14,7))\n",
    "ax1 = plt.subplot(221)\n",
    "\n",
    "sp500.loc['2015':, 'SP500'].plot(lw=4, ax=ax1, c='k')\n",
    "sp500.loc['2015':, ['Test Predictions', 'Train Predictions']].plot(lw=1, ax=ax1, ls='--')\n",
    "ax1.set_title('In- and Out-of-sample Predictions')\n",
    "\n",
    "\n",
    "with sns.axes_style(\"white\"):\n",
    "    ax3 = plt.subplot(223)\n",
    "    sns.scatterplot(x='SP500', y='predictions', data=sp500, hue='data', ax=ax3)\n",
    "    ax3.text(x=.02, y=.95, s=f'Test IC ={test_ic:.2%}', transform=ax3.transAxes)\n",
    "    ax3.text(x=.02, y=.87, s=f'Train IC={train_ic:.2%}', transform=ax3.transAxes)\n",
    "    ax3.set_title('Correlation')\n",
    "    ax3.legend(loc='lower right')\n",
    "    \n",
    "    ax2 = plt.subplot(222)\n",
    "    ax4 = plt.subplot(224, sharex = ax2, sharey=ax2)\n",
    "    sns.distplot(train_predict.squeeze()- y_train_rescaled, ax=ax2)\n",
    "    ax2.set_title('Train Error')\n",
    "    ax2.text(x=.03, y=.92, s=f'Train RMSE ={train_rmse:.4f}', transform=ax2.transAxes)\n",
    "    sns.distplot(test_predict.squeeze()-y_test_rescaled, ax=ax4)\n",
    "    ax4.set_title('Test Error')\n",
    "    ax4.text(x=.03, y=.92, s=f'Test RMSE ={test_rmse:.4f}', transform=ax4.transAxes)\n",
    "\n",
    "sns.despine()\n",
    "fig.tight_layout()\n",
    "fig.savefig(results_path / 'rnn_sp500_regression', dpi=300);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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