QLSTM2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96963d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import time\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.set_printoptions(suppress = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efc40d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33a08936",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyqpanda import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01c40c9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d48811",
   "metadata": {},
   "source": [
    "# 1. Prepare Dadaset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "757bbfef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.kaggle.com/datasets/sumanthvrao/daily-climate-time-series-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "91a53ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = './data/DailyDelhiClimateTrain.csv'\n",
    "test_path = './data/DailyDelhiClimateTest.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef5a3d26",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [1,2,3,4]\n",
    "\n",
    "train = pd.read_csv(train_path, usecols=cols, engine=\"python\")\n",
    "test = pd.read_csv(test_path, usecols=cols, engine=\"python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c112f41d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len(train)=1462\n",
      "len(test)=114\n"
     ]
    }
   ],
   "source": [
    "print(f'len(train)={len(train)}')\n",
    "print(f'len(test)={len(test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ca701d",
   "metadata": {},
   "source": [
    "## 1.1 Outlier Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5b3dc6f",
   "metadata": {},
   "source": [
    "### 1.1.1 outlier detection for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d076311",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b92a5f9bb0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(train)), train[\"meanpressure\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6f7435",
   "metadata": {},
   "source": [
    "### - remove outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb83d8fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "remove outliers num: 9\n"
     ]
    }
   ],
   "source": [
    "unnormal_num = 0\n",
    "for i in range(len(train)):\n",
    "    mp = train.iloc[i][3]\n",
    "    if mp > 1200 or mp < 950:\n",
    "        unnormal_num += 1\n",
    "        train.iloc[i][3] = train.iloc[i + 1][3]\n",
    "print(f'remove outliers num: {unnormal_num}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c182752f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b92a6bfeb0>,\n",
       " <matplotlib.lines.Line2D at 0x1b92a6bfa30>,\n",
       " <matplotlib.lines.Line2D at 0x1b92a6bfb50>,\n",
       " <matplotlib.lines.Line2D at 0x1b92a6bf9a0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a2a352d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>meantemp</th>\n",
       "      <td>1462.0</td>\n",
       "      <td>25.495521</td>\n",
       "      <td>7.348103</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>18.857143</td>\n",
       "      <td>27.714286</td>\n",
       "      <td>31.305804</td>\n",
       "      <td>38.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>1462.0</td>\n",
       "      <td>60.771702</td>\n",
       "      <td>16.769652</td>\n",
       "      <td>13.428571</td>\n",
       "      <td>50.375000</td>\n",
       "      <td>62.625000</td>\n",
       "      <td>72.218750</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wind_speed</th>\n",
       "      <td>1462.0</td>\n",
       "      <td>6.802209</td>\n",
       "      <td>4.561602</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.475000</td>\n",
       "      <td>6.221667</td>\n",
       "      <td>9.238235</td>\n",
       "      <td>42.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meanpressure</th>\n",
       "      <td>1462.0</td>\n",
       "      <td>1008.247674</td>\n",
       "      <td>7.437992</td>\n",
       "      <td>991.375000</td>\n",
       "      <td>1001.625000</td>\n",
       "      <td>1008.563492</td>\n",
       "      <td>1014.875000</td>\n",
       "      <td>1023.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count         mean        std         min          25%  \\\n",
       "meantemp      1462.0    25.495521   7.348103    6.000000    18.857143   \n",
       "humidity      1462.0    60.771702  16.769652   13.428571    50.375000   \n",
       "wind_speed    1462.0     6.802209   4.561602    0.000000     3.475000   \n",
       "meanpressure  1462.0  1008.247674   7.437992  991.375000  1001.625000   \n",
       "\n",
       "                      50%          75%          max  \n",
       "meantemp        27.714286    31.305804    38.714286  \n",
       "humidity        62.625000    72.218750   100.000000  \n",
       "wind_speed       6.221667     9.238235    42.220000  \n",
       "meanpressure  1008.563492  1014.875000  1023.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "52776605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meantemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>meanpressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>84.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   meantemp  humidity  wind_speed  meanpressure\n",
       "0      10.0      84.5         0.0   1015.666667"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6e536f",
   "metadata": {},
   "source": [
    "### 1.1.2 outlier detection for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6fa0149b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b92a7cc490>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(test)), test[\"meanpressure\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09b1874",
   "metadata": {},
   "source": [
    "### - remove outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d62ae65e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test.iloc[0][3] = test.iloc[1][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6cf7014f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b92a820400>,\n",
       " <matplotlib.lines.Line2D at 0x1b92a820520>,\n",
       " <matplotlib.lines.Line2D at 0x1b92a8205e0>,\n",
       " <matplotlib.lines.Line2D at 0x1b92a8206a0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bddd4f67",
   "metadata": {},
   "source": [
    "## 1.2 Transfer data to LSTM representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8d1fe2e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d28fd514",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_process(data, window_size, predict_size):\n",
    "#     scaler = StandardScaler()\n",
    "    scaler = MinMaxScaler()\n",
    "    data = scaler.fit_transform(np.array(data).reshape(-1, 1))\n",
    "    \n",
    "    data_in = []\n",
    "    data_out = []\n",
    "    \n",
    "    for i in range(data.shape[0] - window_size - predict_size):\n",
    "        data_in.append(data[i:i + window_size].reshape(1, window_size)[0])\n",
    "        data_out.append(data[i + window_size:i + window_size + predict_size].reshape(1, predict_size)[0])\n",
    "        \n",
    "    data_in = np.array(data_in).reshape(-1, window_size)\n",
    "    data_out = np.array(data_out).reshape(-1, predict_size)\n",
    "    \n",
    "    data_process = {'datain': data_in, 'dataout': data_out}\n",
    "    \n",
    "    return data_process, scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b642d83",
   "metadata": {},
   "source": [
    "## 1.3 prepare train/test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b1b5ca0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_size = 4\n",
    "window_size = features_size * 3 # features num * time steps\n",
    "predict_size = features_size # features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "da56bdd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_processed, train_scaler = data_process(train, window_size, predict_size)\n",
    "X_train, y_train = train_processed['datain'], train_processed['dataout']\n",
    "\n",
    "test_processed, test_scaler = data_process(test, window_size, predict_size)\n",
    "X_test, y_test = test_processed['datain'], test_processed['dataout']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "89c5ab59",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = torch.from_numpy(X_train.astype(np.float32))\n",
    "X_test = torch.from_numpy(X_test.astype(np.float32))\n",
    "\n",
    "y_train = torch.from_numpy(y_train.astype(np.float32))\n",
    "y_test = torch.from_numpy(y_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81da8b10",
   "metadata": {},
   "source": [
    "### - data loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "881c39bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.utils.data as Data\n",
    "\n",
    "train_data = Data.TensorDataset(X_train, y_train)\n",
    "test_data = Data.TensorDataset(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "000e5717",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of train_data: 5832\n",
      "size of test_data: 440\n"
     ]
    }
   ],
   "source": [
    "print(f'size of train_data: {len(train_data)}')\n",
    "print(f'size of test_data: {len(test_data)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "fd5abd4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([0.0098, 0.0826, 0.0000, 0.9928, 0.0072, 0.0899, 0.0029, 0.9949, 0.0070,\n",
       "         0.0850, 0.0045, 0.9958]),\n",
       " tensor([0.0085, 0.0697, 0.0012, 0.9943]))"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3f77a830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([0.0142, 0.0827, 0.0013, 0.9956, 0.0168, 0.0742, 0.0015, 0.9956, 0.0154,\n",
       "         0.0788, 0.0026, 0.9956]),\n",
       " tensor([0.0169, 0.0672, 0.0031, 0.9930]))"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a99ad9fa",
   "metadata": {},
   "source": [
    "# 2. Quantum Enhanced LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee1c6bb",
   "metadata": {},
   "source": [
    "## 2.1 initiate quantum environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "7f83f918",
   "metadata": {},
   "outputs": [],
   "source": [
    "class InitQMachine:\n",
    "    def __init__(self, qubitsCount, cbitsCount = 0, machineType = QMachineType.CPU):\n",
    "        self.machine = init_quantum_machine(machineType)\n",
    "        \n",
    "        self.qubits = self.machine.qAlloc_many(qubitsCount)\n",
    "        self.cbits = self.machine.cAlloc_many(cbitsCount)\n",
    "        \n",
    "        print(f'Init Quantum Machine with qubits:[{qubitsCount}] / cbits:[{cbitsCount}] Successfully')\n",
    "    \n",
    "    def __del__(self):\n",
    "        destroy_quantum_machine(self.machine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1b73cfac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init Quantum Machine with qubits:[5] / cbits:[0] Successfully\n"
     ]
    }
   ],
   "source": [
    "# maximum qubits size\n",
    "ctx = InitQMachine(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64fd176a",
   "metadata": {},
   "source": [
    "## 2.2 Quantum Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993c3c69",
   "metadata": {},
   "source": [
    "### - Tool Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "467e68dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(prog, filename=''):\n",
    "    dir_path = './images/'\n",
    "    \n",
    "    if filename != '':\n",
    "        draw_qprog(prog, 'pic', filename=f'{dir_path}{filename}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d1db12c",
   "metadata": {},
   "source": [
    "### 2.2.1 Quantum Layer Base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a9372635",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "from torch import Tensor\n",
    "from torch.nn import Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0ef35c60",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QuantumLayerBase(nn.Module):\n",
    "    def __init__(self, input_size, output_size, *, n_qubits, n_layers = 1, ctx = None):\n",
    "        super(QuantumLayerBase, self).__init__()\n",
    "        \n",
    "        self.data = None # need to input during forward\n",
    "    \n",
    "        self.input_size = input_size\n",
    "        self.output_size = output_size # hidden size, not n_qubits\n",
    "        \n",
    "        # quantum infos\n",
    "        self.n_qubits = n_qubits\n",
    "        self.n_layers = n_layers\n",
    "        \n",
    "        self.ctx = ctx\n",
    "        self.qubits = ctx.qubits\n",
    "        self.machine = ctx.machine\n",
    "        \n",
    "        # convert quantum input/output to match classical computation\n",
    "        self.qin = nn.Linear(self.input_size, self.n_qubits)\n",
    "        self.qout = nn.Linear(self.n_qubits, self.output_size)\n",
    "        \n",
    "    @property\n",
    "    def circuit(self):\n",
    "        raise NotImplementedError('Should init circuit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "21d0b1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure(self):\n",
    "    HamiZ = [ PauliOperator({f'Z{i}': 1}) for i in range(len(self.qubits)) ]\n",
    "    res = [ eval(qop(self.circuit, Hami, self.machine, self.qubits))[0,0] for Hami in HamiZ ]\n",
    "    \n",
    "    return Parameter(Tensor(res[:self.n_qubits]))\n",
    "\n",
    "QuantumLayerBase.measure = measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "8dd09ad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward(self, inputs):\n",
    "    y_t = self.qin(Parameter(inputs))\n",
    "    self.data = y_t[0]\n",
    "    \n",
    "    return self.qout(self.measure())\n",
    "\n",
    "QuantumLayerBase.forward = forward"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73293db3",
   "metadata": {},
   "source": [
    "### 2.2.2 Quantum Layer Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "3f45cef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QuantumLayer(QuantumLayerBase):\n",
    "    def __init__(self, input_size, output_size, *, n_qubits, degree = 1, n_layers = 1, ctx = None):\n",
    "        super(QuantumLayer, self).__init__(input_size, output_size, \n",
    "                                         n_qubits = n_qubits, n_layers = n_layers, ctx = ctx)\n",
    "        \n",
    "        self.degree = degree\n",
    "        self.angles = Parameter(torch.rand(n_layers + 1, degree, self.n_qubits))\n",
    "        \n",
    "    @property\n",
    "    def qparameters_size(self):\n",
    "        return self.angles.flatten().size()[0]\n",
    "        \n",
    "    @property\n",
    "    def circuit(self):\n",
    "        if self.data == None:\n",
    "            raise ValueError('Need to feed a input data!')\n",
    "        \n",
    "        n = self.n_qubits\n",
    "        q = self.qubits\n",
    "        x = self.data\n",
    "        p = self.angles\n",
    "        degree = self.degree\n",
    "        \n",
    "        # quantum gates - must use small case!\n",
    "        identity = VariationalQuantumGate_I\n",
    "        h = VariationalQuantumGate_H\n",
    "        ry = VariationalQuantumGate_RY\n",
    "        cz = VariationalQuantumGate_CZ\n",
    "        u = [\n",
    "            None,\n",
    "            VariationalQuantumGate_U1,\n",
    "            VariationalQuantumGate_U2,\n",
    "            VariationalQuantumGate_U3\n",
    "        ]\n",
    "        \n",
    "        # init variational quantum circuit\n",
    "        vqc = VariationalQuantumCircuit()\n",
    "\n",
    "        # in order to use each qubits => when n_qubits < len(ctx.qubits)\n",
    "        [ vqc.insert(identity(q[i])) for i in range(len(q)) ]\n",
    "        \n",
    "        [ vqc.insert( h(q[i]) ) for i in range(n) ]\n",
    "        [ vqc.insert( ry(q[i], var(x[i])) ) for i in range(n) ]\n",
    "        [ vqc.insert( u[degree](q[i], *[ var(p[0][d][i]) for d in range(degree) ]) ) \n",
    "                 for i in range(n) ]\n",
    "        \n",
    "        for layer in range(self.n_layers):\n",
    "            for i in range(n - 1):\n",
    "                vqc.insert(cz(q[i], q[i + 1]))\n",
    "            vqc.insert(cz(q[n - 1], q[0]))\n",
    "            \n",
    "            [ vqc.insert( u[degree](q[i], *[ var(p[layer + 1][d][i]) for d in range(degree) ]) ) \n",
    "                 for i in range(n) ]\n",
    "        \n",
    "        return vqc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d956e7",
   "metadata": {},
   "source": [
    "## 2.3 Quantum-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "0d437f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLSTMBase(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.ctx = ctx\n",
    "        \n",
    "    @property\n",
    "    def qparameters_size(self):\n",
    "        num = 0\n",
    "        for attr in dir(self):\n",
    "            if attr.endswith('_circuit'):\n",
    "                num += getattr(self, attr).qparameters_size\n",
    "        return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "be3c3cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward(self, inputs, init_states = None):\n",
    "    sequence_size, batch_size, _ = inputs.size()\n",
    "    hidden_sequence = []\n",
    "    \n",
    "    if init_states == None:\n",
    "        h_t, c_t = (\n",
    "            torch.zeros(1, batch_size, self.hidden_size).to(inputs.device),\n",
    "            torch.zeros(1, batch_size, self.hidden_size).to(inputs.device),\n",
    "        )\n",
    "    else:\n",
    "        h_t, c_t = init_states\n",
    "    \n",
    "    return hidden_sequence, (h_t, c_t)\n",
    "\n",
    "QLSTMBase.forward = forward"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce36bcc1",
   "metadata": {},
   "source": [
    "### 2.3.1 classical Quantum-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "1539d83e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLSTM(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # Parameters: angles\n",
    "        #  => Q * (n + 1) * degree\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # input gate:     5       2       3\n",
    "        # forget gate:    5       2       3\n",
    "        # candidate:      5       2       3\n",
    "        # output gate:    5       2       3\n",
    "        \n",
    "        # input gates\n",
    "        self.input_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                        n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # forget gates\n",
    "        self.forget_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # candidate\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # output gates\n",
    "        self.output_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(QLSTM, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((h_t[0], x_t), dim = 1)\n",
    "\n",
    "            # input gates\n",
    "            i_t = torch.sigmoid(self.input_circuit(v_t))\n",
    "            # forget gates\n",
    "            f_t = torch.sigmoid(self.forget_circuit(v_t))\n",
    "            # candidate for cell state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_t))\n",
    "            c_t = (f_t * c_t) + (i_t * g_t)\n",
    "\n",
    "            # output gates\n",
    "            o_t = torch.sigmoid(self.output_circuit(v_t))\n",
    "            # update output ht\n",
    "            h_t = o_t * (torch.tanh(c_t))\n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        # reshape hidden_seq p/ retornar\n",
    "        #\n",
    "        # [tensor([[[0.0444, ...]]] => tensor([[[0.0444, ...]]]\n",
    "        # \n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6caa3435",
   "metadata": {},
   "source": [
    "### 2.3.2 adjusted classical QLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "d152d2ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AdjustedQLSTM(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # input gate:     4       2       3\n",
    "        # forget gate:    5       2       3\n",
    "        # candidate:      4       1       3\n",
    "        # output gate:    3       2       2\n",
    "        \n",
    "        # input gates\n",
    "        self.input_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                        n_qubits = 4, n_layers = 2, degree = 3, ctx = ctx) # 36\n",
    "        # forget gates\n",
    "        self.forget_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # candidate\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 4, n_layers = 1, degree = 3, ctx = ctx) # 24\n",
    "        # output gates\n",
    "        self.output_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 3, n_layers = 2, degree = 2, ctx = ctx) # 18\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(AdjustedQLSTM, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((h_t[0], x_t), dim = 1)\n",
    "\n",
    "            # input gates\n",
    "            i_t = torch.sigmoid(self.input_circuit(v_t))\n",
    "            # forget gates\n",
    "            f_t = torch.sigmoid(self.forget_circuit(v_t))\n",
    "            # candidate for cell state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_t))\n",
    "            c_t = (f_t * c_t) + (i_t * g_t)\n",
    "\n",
    "            # output gates\n",
    "            o_t = torch.sigmoid(self.output_circuit(v_t))\n",
    "            # update output ht\n",
    "            h_t = o_t * (torch.tanh(c_t))\n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3bda938",
   "metadata": {},
   "source": [
    "### 2.3.3 peephole QLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "66ed785c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PeepholeQLSTM(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # input gate:     4       2       3\n",
    "        # forget gate:    5       2       3\n",
    "        # candidate:      4       1       3\n",
    "        # output gate:    3       2       2\n",
    "        \n",
    "        # input gates\n",
    "        self.input_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                        n_qubits = 4, n_layers = 2, degree = 3, ctx = ctx) # 36\n",
    "        # forget gates\n",
    "        self.forget_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 2, degree = 3, ctx = ctx) # 45\n",
    "        # candidate\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 4, n_layers = 1, degree = 2, ctx = ctx) # 24\n",
    "        # output gates\n",
    "        self.output_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 3, n_layers = 2, degree = 2, ctx = ctx) # 18\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(PeepholeQLSTM, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((c_t[0], x_t), dim = 1)\n",
    "            \n",
    "            # input gates\n",
    "            i_t = torch.sigmoid(self.input_circuit(v_t))\n",
    "            # forget gates\n",
    "            f_t = torch.sigmoid(self.forget_circuit(v_t))\n",
    "            # candidate for cell state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_t))\n",
    "            c_t = (f_t * c_t) + (i_t * g_t)\n",
    "            \n",
    "            # output gates\n",
    "            o_t = torch.sigmoid(self.output_circuit(v_t))\n",
    "            # update output ht\n",
    "            h_t = o_t * (torch.tanh(c_t))\n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e631aeb",
   "metadata": {},
   "source": [
    "### 2.3.4 Coupled Input and Forget gates QLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f77c5ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CIFGQLSTM(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # coupled IF:     5       2       3\n",
    "        # candidate:      4       2       3\n",
    "        # output gate:    3       2       2\n",
    "        \n",
    "        # Coupled Input and Forget gate\n",
    "        self.coupled_IF_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 1, degree = 3, ctx = ctx) # 30\n",
    "        # candidate for cell state update\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 3, n_layers = 1, degree = 2, ctx = ctx) # 24\n",
    "        # output gates\n",
    "        self.output_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 4, n_layers = 1, degree = 3, ctx = ctx) # 12\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(CIFGQLSTM, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((h_t[0], x_t), dim = 1)\n",
    "            \n",
    "            # coupled input and forget gate\n",
    "            f_t = torch.sigmoid(self.coupled_IF_circuit(v_t))\n",
    "            # candidate for cell state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_t))\n",
    "            c_t = (f_t * c_t) + ((1 - f_t) * g_t)\n",
    "            \n",
    "            # output gates\n",
    "            o_t = torch.sigmoid(self.output_circuit(v_t))\n",
    "            # update output ht\n",
    "            h_t = o_t * (torch.tanh(c_t))\n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fded5be6",
   "metadata": {},
   "source": [
    "### 2.3.5 Recurrent Gate Units - QGRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "62d3eb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QGRU(QLSTMBase):\n",
    "    def __init__(self, input_size, hidden_size, *, ctx):\n",
    "        super().__init__(input_size, hidden_size, ctx = ctx)\n",
    "        \n",
    "        # gates names:  qubits  layers  degree\n",
    "        # update gate:     5       1       3\n",
    "        # candidate:       4       1       3\n",
    "        # reset gate:      3       1       2\n",
    "        \n",
    "        # update gates\n",
    "        self.update_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 5, n_layers = 1, degree = 1, ctx = ctx) # 45\n",
    "        # candidate for hidden state update\n",
    "        self.candidate_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                       n_qubits = 3, n_layers = 1, degree = 1, ctx = ctx) # 36\n",
    "        # reset gates\n",
    "        self.reset_circuit = QuantumLayer(input_size + hidden_size, hidden_size, \n",
    "                                         n_qubits = 4, n_layers = 1, degree = 1, ctx = ctx) # 18\n",
    "        \n",
    "    def forward(self, inputs, init_states = None):\n",
    "        hidden_sequence, (h_t, c_t) = super(QGRU, self).forward(inputs, init_states)\n",
    "\n",
    "        for t in range(inputs.size()[0]):\n",
    "            x_t = inputs[t, :, :]\n",
    "            v_t = torch.cat((h_t[0], x_t), dim = 1)\n",
    "            \n",
    "            # update gates\n",
    "            z_t = torch.sigmoid(self.update_circuit(v_t))\n",
    "            # reset gates\n",
    "            r_t = torch.sigmoid(self.reset_circuit(v_t))\n",
    "        \n",
    "            v_hat_t = torch.cat(((r_t * h_t)[0], x_t), dim = 1)\n",
    "            # candidate for hidden state update\n",
    "            g_t = torch.tanh(self.candidate_circuit(v_hat_t))\n",
    "            h_t = (z_t * g_t) + (1 - z_t) * h_t \n",
    "\n",
    "            hidden_sequence.append(h_t)\n",
    "\n",
    "        hidden_sequence = torch.cat(hidden_sequence, dim = 0)\n",
    "\n",
    "        return hidden_sequence, (h_t, c_t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63514807",
   "metadata": {},
   "source": [
    "### - QLSTMs Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "5ab09833",
   "metadata": {},
   "outputs": [],
   "source": [
    "QLSTMMap = {\n",
    "    'classical': ('QLSTM', QLSTM),\n",
    "    'adjusted': ('QLSTM(adjusted)', AdjustedQLSTM),\n",
    "    'peephole': ('peephole QLSTM', PeepholeQLSTM),\n",
    "    'CIFG': ('CIFG-QLSTM', CIFGQLSTM),\n",
    "    'GRU': ('QGRU', QGRU)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6497332",
   "metadata": {},
   "source": [
    "## 2.4 Stacked QLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "2c6cb0fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "class StackedQLSTM(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, *, num_layers = 1, ctx = None, mode = 'classical'):\n",
    "        super().__init__()\n",
    "        \n",
    "        label, qlstm = QLSTMMap.get(mode)\n",
    "        self.qlstms = nn.Sequential(OrderedDict([\n",
    "            (f'{label} {i + 1}', qlstm(input_size if i == 0 else hidden_size , hidden_size, ctx = ctx)) \n",
    "                for i in range(num_layers)\n",
    "        ]))\n",
    "\n",
    "    def forward(self, inputs, parameters = None):\n",
    "        outputs = None\n",
    "        \n",
    "        for i, qlstm in enumerate(self.qlstms):\n",
    "            if i != 0:\n",
    "                inputs = outputs\n",
    "            \n",
    "            outputs, parameters = qlstm(inputs, parameters)\n",
    "        \n",
    "        return outputs, parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55dbbb2f",
   "metadata": {},
   "source": [
    "# 3. Quantum Model and Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "8313baa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QModel(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_output, *, num_layers = 1, ctx = None, mode = 'classical'):\n",
    "        super(QModel, self).__init__()\n",
    "        \n",
    "        self.hidden_size = hidden_size\n",
    "        self.num_layers = num_layers\n",
    "        self.mode = mode\n",
    "        \n",
    "        self.qlstm = StackedQLSTM(input_size, hidden_size, \n",
    "                                  num_layers = num_layers, ctx = ctx, mode = mode)\n",
    "        self.predict = nn.Linear(hidden_size, num_output)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.unsqueeze(0)\n",
    "        \n",
    "        # sequence lenth , batch_size, features length\n",
    "        # \n",
    "        h0 = torch.zeros(1, x.size(1), self.hidden_size)\n",
    "        c0 = torch.zeros(1, x.size(1), self.hidden_size)\n",
    "        \n",
    "        out, _ = self.qlstm(x, (h0, c0))\n",
    "        out = self.predict(out[0])\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf519363",
   "metadata": {},
   "source": [
    "## 3.1 train QModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "1abf2ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import RandomSampler\n",
    "\n",
    "def train_model(model, datas, batch_size, *, loss_func, optimizer, epoch = 50):\n",
    "    losses = []\n",
    "    sampler = RandomSampler(datas, num_samples = batch_size)\n",
    "    \n",
    "    for step in range(epoch):\n",
    "        train_loss = 0.0\n",
    "        \n",
    "        for index in sampler:\n",
    "            batch_x, batch_y = datas[index][0], datas[index][1]\n",
    "            b_x = batch_x.unsqueeze(0)\n",
    "            b_y = batch_y.unsqueeze(0)\n",
    "            \n",
    "            output = model(b_x)\n",
    "\n",
    "            loss = loss_func(output, b_y)\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            train_loss += loss.item()\n",
    "\n",
    "        print(f'Epoch {step + 1}/{epoch}: Loss: {train_loss / batch_size}')\n",
    "        losses.append(train_loss / batch_size)\n",
    "    \n",
    "    return losses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5cdd42a",
   "metadata": {},
   "source": [
    "## 3.2 Evaluate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "d7ba3d94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "84475602",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MAE_naive(actuals, predicteds):\n",
    "    n = len(actuals)\n",
    "    err = 0.0\n",
    "    \n",
    "    for i in range(1, n):\n",
    "        err += np.abs(actuals[i] - actuals[i - 1])\n",
    "    return err / (n - 1)\n",
    "\n",
    "def calculate_accuarcy(model, X_test, y_test, scaler=test_scaler):\n",
    "    n = len(X_test)\n",
    "    \n",
    "    actuals = []\n",
    "    predicteds = []\n",
    "    \n",
    "    for i in range(0, n, predict_size):\n",
    "        actual = scaler.inverse_transform(y_test[i:i+1].data)\n",
    "        actuals.append(np.array(actual[0]))\n",
    "        predicted = scaler.inverse_transform(model(X_test[i:i+1]).data)\n",
    "        predicteds.append(np.array(predicted[0]))\n",
    "    \n",
    "    actuals = np.array(actuals)\n",
    "    predicteds = np.array(predicteds)\n",
    "    \n",
    "    mae = mean_absolute_error(actuals, predicteds)\n",
    "    mase = mae / MAE_naive(actuals.flatten(), predicteds.flatten())\n",
    "    mape = mean_absolute_percentage_error(actuals, predicteds)\n",
    "    mse = mean_squared_error(actuals, predicteds)\n",
    "    rmse = mse ** 0.5\n",
    "    \n",
    "    return np.array([(1 - mase) * 100, rmse, mse, mae, mape])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "cdcdb169",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_accuarcies(model, X_test, y_test, scaler=test_scaler):\n",
    "    n = len(X_test)\n",
    "    \n",
    "    actuals = []\n",
    "    predicteds = []\n",
    "    \n",
    "    for i in range(0, n, predict_size):\n",
    "        actual = scaler.inverse_transform(y_test[i:i+1].data)\n",
    "        actuals.append(np.array(actual[0]))\n",
    "        predicted = scaler.inverse_transform(model(X_test[i:i+1]).data)\n",
    "        predicteds.append(np.array(predicted[0]))\n",
    "    \n",
    "    accuarcies = []\n",
    "    for i in range(predict_size):\n",
    "        a = [row[i] for row in actuals]\n",
    "        p = [row[i] for row in predicteds]\n",
    "    \n",
    "        mae = mean_absolute_error(a, p)\n",
    "        mase = mae / MAE_naive(a, p)\n",
    "        mape = mean_absolute_percentage_error(a, p)\n",
    "        mse = mean_squared_error(a, p)\n",
    "        rmse = mse ** 0.5\n",
    "        \n",
    "        accuarcies.append([rmse, mse, mae, mape, mase])\n",
    "    \n",
    "    return np.array(accuarcies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec9e103",
   "metadata": {},
   "source": [
    "## 3.3 Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "38531f1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_size = 4\n",
    "window_size = features_size * 3 # \n",
    "predict_size = features_size # features\n",
    "\n",
    "input_size = window_size\n",
    "num_output = predict_size\n",
    "\n",
    "hidden_size = 32\n",
    "num_layers = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "bd365761",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100: Loss: 0.18295134902000426\n",
      "Epoch 2/100: Loss: 0.1907977845519781\n",
      "Epoch 3/100: Loss: 0.15977448970079422\n",
      "Epoch 4/100: Loss: 0.16031863056123258\n",
      "Epoch 5/100: Loss: 0.16675194278359412\n",
      "Epoch 6/100: Loss: 0.15248086377978326\n",
      "Epoch 7/100: Loss: 0.15096076317131518\n",
      "Epoch 8/100: Loss: 0.14667084589600562\n",
      "Epoch 9/100: Loss: 0.13043245859444141\n",
      "Epoch 10/100: Loss: 0.11824187114834786\n",
      "Epoch 11/100: Loss: 0.1021526325494051\n",
      "Epoch 12/100: Loss: 0.10789438970386982\n",
      "Epoch 13/100: Loss: 0.09250117298215628\n",
      "Epoch 14/100: Loss: 0.09248494673520327\n",
      "Epoch 15/100: Loss: 0.08944254145026206\n",
      "Epoch 16/100: Loss: 0.07105323448777198\n",
      "Epoch 17/100: Loss: 0.07192575968801976\n",
      "Epoch 18/100: Loss: 0.07115800119936466\n",
      "Epoch 19/100: Loss: 0.054493614751845595\n",
      "Epoch 20/100: Loss: 0.05371464202180505\n",
      "Epoch 21/100: Loss: 0.039062231127172706\n",
      "Epoch 22/100: Loss: 0.03944674078375101\n",
      "Epoch 23/100: Loss: 0.030052760243415834\n",
      "Epoch 24/100: Loss: 0.02287146947346628\n",
      "Epoch 25/100: Loss: 0.019310889206826685\n",
      "Epoch 26/100: Loss: 0.012181370938196778\n",
      "Epoch 27/100: Loss: 0.00826314160367474\n",
      "Epoch 28/100: Loss: 0.006857211620081216\n",
      "Epoch 29/100: Loss: 0.004060924297664315\n",
      "Epoch 30/100: Loss: 0.003007330644322792\n",
      "Epoch 31/100: Loss: 0.0024970446189399808\n",
      "Epoch 32/100: Loss: 0.00131607645744225\n",
      "Epoch 33/100: Loss: 0.0006479800858869567\n",
      "Epoch 34/100: Loss: 0.0004451996503121336\n",
      "Epoch 35/100: Loss: 0.0002185681718401611\n",
      "Epoch 36/100: Loss: 0.00012160342753304576\n",
      "Epoch 37/100: Loss: 0.00016951771631283917\n",
      "Epoch 38/100: Loss: 0.00011412204576117801\n",
      "Epoch 39/100: Loss: 6.292227337780786e-05\n",
      "Epoch 40/100: Loss: 7.230530363528942e-05\n",
      "Epoch 41/100: Loss: 5.5532055421281255e-05\n",
      "Epoch 42/100: Loss: 4.619709629878343e-05\n",
      "Epoch 43/100: Loss: 5.065925299732044e-05\n",
      "Epoch 44/100: Loss: 7.175714454206172e-05\n",
      "Epoch 45/100: Loss: 6.714002738590352e-05\n",
      "Epoch 46/100: Loss: 4.6079513731456244e-05\n",
      "Epoch 47/100: Loss: 6.15101249422878e-05\n",
      "Epoch 48/100: Loss: 7.913257497875748e-05\n",
      "Epoch 49/100: Loss: 7.912523972208874e-05\n",
      "Epoch 50/100: Loss: 4.812062052224064e-05\n",
      "Epoch 51/100: Loss: 6.4896779576884e-05\n",
      "Epoch 52/100: Loss: 9.226445233707636e-05\n",
      "Epoch 53/100: Loss: 7.007984081610629e-05\n",
      "Epoch 54/100: Loss: 6.94077638854651e-05\n",
      "Epoch 55/100: Loss: 4.937257774599857e-05\n",
      "Epoch 56/100: Loss: 7.15922765493815e-05\n",
      "Epoch 57/100: Loss: 6.328973968265928e-05\n",
      "Epoch 58/100: Loss: 8.358247259820928e-05\n",
      "Epoch 59/100: Loss: 5.9192390176576735e-05\n",
      "Epoch 60/100: Loss: 4.789815548065235e-05\n",
      "Epoch 61/100: Loss: 7.691969185543712e-05\n",
      "Epoch 62/100: Loss: 5.698587515325926e-05\n",
      "Epoch 63/100: Loss: 7.268794811352564e-05\n",
      "Epoch 64/100: Loss: 7.258981038376078e-05\n",
      "Epoch 65/100: Loss: 6.643873784923926e-05\n",
      "Epoch 66/100: Loss: 6.27068564426736e-05\n",
      "Epoch 67/100: Loss: 5.806143703921407e-05\n",
      "Epoch 68/100: Loss: 5.77076297304302e-05\n",
      "Epoch 69/100: Loss: 6.966438761537574e-05\n",
      "Epoch 70/100: Loss: 7.644729430467123e-05\n",
      "Epoch 71/100: Loss: 6.578711311249208e-05\n",
      "Epoch 72/100: Loss: 6.954646571557532e-05\n",
      "Epoch 73/100: Loss: 6.725297637331096e-05\n",
      "Epoch 74/100: Loss: 7.687216348131187e-05\n",
      "Epoch 75/100: Loss: 6.713912132454425e-05\n",
      "Epoch 76/100: Loss: 4.784628295055882e-05\n",
      "Epoch 77/100: Loss: 6.914614332345081e-05\n",
      "Epoch 78/100: Loss: 6.160009565974179e-05\n",
      "Epoch 79/100: Loss: 5.442204225118985e-05\n",
      "Epoch 80/100: Loss: 0.00011979386258644808\n",
      "Epoch 81/100: Loss: 5.827707584558084e-05\n",
      "Epoch 82/100: Loss: 8.77887517617637e-05\n",
      "Epoch 83/100: Loss: 8.151785923473653e-05\n",
      "Epoch 84/100: Loss: 8.077327547653112e-05\n",
      "Epoch 85/100: Loss: 6.409706306840235e-05\n",
      "Epoch 86/100: Loss: 6.141048143035733e-05\n",
      "Epoch 87/100: Loss: 7.305723152057908e-05\n",
      "Epoch 88/100: Loss: 8.717441446606244e-05\n",
      "Epoch 89/100: Loss: 8.286996810511483e-05\n",
      "Epoch 90/100: Loss: 8.73154050168523e-05\n",
      "Epoch 91/100: Loss: 0.00015569954134662112\n",
      "Epoch 92/100: Loss: 0.00010621875389915658\n",
      "Epoch 93/100: Loss: 0.0001156106354756048\n",
      "Epoch 94/100: Loss: 7.17368098321458e-05\n",
      "Epoch 95/100: Loss: 5.941390411408065e-05\n",
      "Epoch 96/100: Loss: 8.760455384617671e-05\n",
      "Epoch 97/100: Loss: 7.967442861627206e-05\n",
      "Epoch 98/100: Loss: 0.00010608782464487377\n",
      "Epoch 99/100: Loss: 7.15508907376261e-05\n",
      "Epoch 100/100: Loss: 7.470662295645525e-05\n",
      "time costs: 347.53884291648865\n"
     ]
    }
   ],
   "source": [
    "qmodel = QModel(input_size, hidden_size, num_output, \n",
    "                num_layers = num_layers, ctx = ctx, mode='GRU')\n",
    "optimizer = torch.optim.Adam(qmodel.parameters(), lr = 0.0033)\n",
    "loss_func = nn.MSELoss()\n",
    "\n",
    "start = time.time()\n",
    "losses = train_model(qmodel, train_data, batch_size=20,          \n",
    "               loss_func = loss_func, optimizer = optimizer, epoch = 100)\n",
    "end = time.time()\n",
    "\n",
    "print(f'time costs: {end - start}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "e44ed256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b93feb5250>]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(losses, color=\"#FF6666\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "4ca046fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.87326056e+01, 8.33223036e+00, 6.94260627e+01, 6.57960597e+00,\n",
       "       2.98091780e+14])"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_accuarcy(qmodel, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "7270a8a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([98.73260555,  8.3193779 , 69.21204868,  6.56945694,  0.45391727])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_accuarcy(qmodel, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "b29b7304",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "\n",
    "for i in range(0, len(X_test), features_size):\n",
    "    input_data = X_test[i:i+1]\n",
    "    result = qmodel(input_data)\n",
    "    result = train_scaler.inverse_transform(result.data)\n",
    "    \n",
    "    results.append(result[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "695e4bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = pd.DataFrame(test, columns=('meantemp', 'humidity', 'wind_speed'))\n",
    "test2 = pd.DataFrame(test, columns=('meanpressure',))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "bce46ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meantemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>meanpressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.715864</td>\n",
       "      <td>76.605415</td>\n",
       "      <td>-10.409098</td>\n",
       "      <td>1011.128548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.079337</td>\n",
       "      <td>65.248566</td>\n",
       "      <td>1.163055</td>\n",
       "      <td>1011.593304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.733489</td>\n",
       "      <td>69.583379</td>\n",
       "      <td>-8.061158</td>\n",
       "      <td>1010.848670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.333531</td>\n",
       "      <td>69.072099</td>\n",
       "      <td>4.377625</td>\n",
       "      <td>1010.363793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.029692</td>\n",
       "      <td>77.412426</td>\n",
       "      <td>8.189100</td>\n",
       "      <td>1005.534224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>38.941099</td>\n",
       "      <td>36.056094</td>\n",
       "      <td>16.972751</td>\n",
       "      <td>1006.345382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>47.865328</td>\n",
       "      <td>34.298489</td>\n",
       "      <td>11.055500</td>\n",
       "      <td>1005.152822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>43.838563</td>\n",
       "      <td>26.321255</td>\n",
       "      <td>20.344546</td>\n",
       "      <td>1007.957088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>35.349490</td>\n",
       "      <td>31.300382</td>\n",
       "      <td>22.822105</td>\n",
       "      <td>1009.034038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>30.554906</td>\n",
       "      <td>36.018594</td>\n",
       "      <td>26.435424</td>\n",
       "      <td>1005.428919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      meantemp   humidity  wind_speed  meanpressure\n",
       "0    20.715864  76.605415  -10.409098   1011.128548\n",
       "1    20.079337  65.248566    1.163055   1011.593304\n",
       "2    25.733489  69.583379   -8.061158   1010.848670\n",
       "3    14.333531  69.072099    4.377625   1010.363793\n",
       "4     7.029692  77.412426    8.189100   1005.534224\n",
       "..         ...        ...         ...           ...\n",
       "105  38.941099  36.056094   16.972751   1006.345382\n",
       "106  47.865328  34.298489   11.055500   1005.152822\n",
       "107  43.838563  26.321255   20.344546   1007.957088\n",
       "108  35.349490  31.300382   22.822105   1009.034038\n",
       "109  30.554906  36.018594   26.435424   1005.428919\n",
       "\n",
       "[110 rows x 4 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(results)\n",
    "results.columns = ('meantemp', 'humidity', 'wind_speed', 'meanpressure')\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "9d1455a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "result1 = pd.DataFrame(results, columns=('meantemp', 'humidity', 'wind_speed'))\n",
    "result2 = pd.DataFrame(results, columns=('meanpressure',))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "752af190",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b9412ce370>,\n",
       " <matplotlib.lines.Line2D at 0x1b9412ce490>,\n",
       " <matplotlib.lines.Line2D at 0x1b9412ce550>]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nMGYZYXWHgYpv9e9PHPLuXJ3tegDHUyAMAKb+UHqUVe8Gdrzl3XsE07EtQFs9kJgB5JXIY0VaQOzQF5Et2TTLCEt3yghT/cFyR+mN9AH5WXKKpffZzv+G5XKJiIh6i1614ElERER9UtADYZ9//jkuu+wyXHTRRRg6dCiuuuoqzJ49G+vXrwcgN0eLFy/Ggw8+iDlz5qCkpARLly5Fc3Mzli1bFuzLCUz/MbKt2Q90tMj+zncdj6kr8+5czdWytcQCyf08H5+UAUy4SvbLN3r3HsF0UMv6KjwdiImV/YJSaeTfVAnU7Av/NSlmGWGqWb7KCFP9wYae6fhaiwUomSP7W14L3TUSERH1Qr1qwZOIiIj6pKAHws4880x8+OGH2LVrFwDgm2++wZo1a3DhhRcCAPbv34+KigrMnj3b/prExERMnz4da9euNT1nW1sb6uvrHb7CIm2AFrSyAVXa5EhVFhmbKNsTXgbC7P3BcqXfljdURlrVLu+ODybVKH/IGfpj8UlAwana8xHsE+YuI6ypSqZaGvuDORuvBcL2fgS01IbuOomIiHqZXrXgSURERH1S0ANh999/P66//nqMGTMG8fHxKC0txfz583H99dcDACoqJGMnLy/P4XV5eXn255w98sgjyMzMtH8VFhYG+7LNWSx6VtjxHUBrnZ5ppLK1vM0Ia/KyP5hRrotm/aFms+mN8ovOcHwuGvqEmWWEpeRKth1sEjh07g9mNGAMMGAcYO0Atr8d8sslIiLqLXrVgmc4VGz2vo0GERERhUXQA2Evv/wynn/+eSxbtgwbN27E0qVL8cc//hFLly51OM5isTh8b7PZuj2mPPDAA6irq7N/lZV5GXwKBmMgbM8HEjzJHQUMP0ce9zojrEq2qbm+v3ftQb00Mxyq90oGW2wiMOhUx+eMfcIixSwjLCZGMvgAreTRpD+YkcoK28rySCIiIm/1qgXPUGuqAp6cATx7UWR7qxIREZGDoAfCfvazn+HnP/85rrvuOkyYMAE333wz7r77bjzyyCMAgPx8KWFzvhmqrKzsdtOkJCYmIiMjw+ErbFQwqnKH3h9s9AVAlnaTVuflKt+RTbLNKvL+vVNzu5dmhoMqixx0GhCX6Phc4emyrd6tB/fCzSwjDND7hG35j2zNyiIV1Sds3+rI/RxEREQ9TK9b8Aylmv2AtVMywpgVRkREFDWCHghrbm5GjFMPrNjYWPs0oWHDhiE/Px8rV660P9/e3o7Vq1fjjDOcyvCiwQAtEHZsM7B7heyPvgjI1AJh9UcAa5f7c1itwPY3tdde6P17G0szw9knTDXKH2Ly90jJBvqPlf1IZYWZZYQBQPpA2dbul627QFjOCCD/JMDWBWz3MJVz43PAew/I35GIiKgP63ULnqGk2mIAwJGvI3cdRERE5CDogbBLLrkEv/vd7/DOO+/gwIEDWL58ORYtWoQrrrgCgKwQzp8/HwsXLsTy5cuxZcsW3HLLLUhJScENN9wQ7MsJnApEnTgkPcJScoHBE6U5e0ycrPQ1HHV/jvINQH05kJCul1R6K3eUbI/v9P3a/XXwM9kOmWr+vHr8oHmvj5BrrZNtUqbj485lkGb9wYxKvCiPrNwOvPU/wBd/k7+jGZtNhigc2+b+/YiIiHq4XrfgGUqNDIQRERFFo7hgn/Avf/kLfvnLX2LevHmorKxEQUEB7rjjDjz00EP2Y+677z60tLRg3rx5qK2txeTJk7FixQqkp6cH+3ICl5YnJXiqHG/0d4CYWNnPGAScOCh9wjIHuz7Httf118Yn+fb+/cPcML+uXH4mSwww+HTzY4ZNB9Y/DWx5FTjvV93LJ0OpowXoapP9bqWR+fq+u/5gyvgrgA8WyACExkq9x5jRBwsAm5YJVncIKJzU/ZjtbwL//q7sF04BJn4fGHeZ739rIiKiKKcWPIuKijB+/Hh8/fXXWLRoEb7//e8DcFzwLC4uRnFxMRYuXBi9C56hZGy9wEAYERFR1Ah6ICw9PR2LFy/G4sWLXR5jsViwYMECLFiwINhvH3yqPLFMKwMcfZH+XFaRBI3qygC4yJ6y2YBtWlnkuMt8f38VCAtXaaTK8hp4MpDkojRhzEVAegHQcATY/ApQelN4rg3QyyItsUCiU+DUGPhyVxap9BsqfdDKNwBbXwcm3+74/IE1wK739O/rDpufp2Kzvl/2hXy9dz8w8BTJGOzqALragezhwBVPALHxnq+NiIgoCvW6Bc9QciiN3CQtFmKCXoxBREREPuKnsTdUn7C4ZGD4DP1x1SfMXQPUI19LJlF8KjDyPN/fO1cLhFXvkYBKqB1cI9sh01wfExsPTL5D9j//a3gnIdkb5WdKkNLImBHmTSAMAEqulO2q30kZpGKzASt+KftxybJ1NSFUPT5lHnDuL+S/i5ZaYN8q4MCnEhg7slEy6FTZKRERUQ+kFjwPHjyIlpYW7N27Fw8//DASEhLsx6gFz6NHj6K1tRWrV69GSUlJBK86RNqbgeYa188bSyPb6vQepuQfm02GV3W2R/pKiIioh2MgzBsFpbItPg9ISNEft0+OdDPdSDXJHzUbiE/2/b0zBwMJaZJZVBOGGyiVEeYuEAYAp90i11W5Ddj7Ycgvy85Vo3zAMSPMU38w5bRbpQS09QTwryuA2oPy+NblErxKSAPOvFsec5URpgKhg04Dzv4Z8D/fADcvB654ErjqaeDa54EirS8K+4gRERH1Dk+dB/z5FKCtwfx556nULI8MzK73gb9NBlb+MtJXQkREPRwDYd445Sbgsr8CF/3J8XF7RpiLQJjNBmx7Q/b9KYsEJOspt1j2Q90nrLFSK8G0AEVT3B+bnAWU3iz7a5eE9rqM7BlhJoGwAeOlHLHkKs/9wZSEFOCGl2USZsNR4F+XS5+0D38tz5/xE2DQqbLvKuCpAmFZRbKNiQVGnAucfK1knI29BBh2tjx3bKt310VERETRq/E4ULlVBvhU7zE/RpVGDhgn2/KN4bm23qriW9m6Gl5ERETkJQbCvBEbJ32w0vo7Pu4pI+zYFqBmn5TWjZzl//ur8siqEE+OVNlgeeOBlGzPx0+5U5rq71sFVGzx7b1aTgBv3w28/yCw8V/A4fWuV1SdXweYZ4TFJwF3rAau+qdv15KSDdz8mgSyavYBf58K1B6QQQlTf6gHPM3+zl0d0isN0ANhZvLGy/aYj78nIiIiCq6v/gE8dzmwxc3UaE8qDRneDRXmx6jSyGLtHpAZYYFRU9pV9j4REZGfGAgLhDEjzKxPlsoGGzkTSEzz/33skyND3DDfXhbp5XjzfkOBsZfK/ud/9e291j0lkyc/XwK8+SPgqZnAI4OB5Xe5f527jLBAZBQAN78OpPaX1V0AmPGA/N3URNDWOqC13vF19eUyVTI2EUg1mTqpqEDY8R1AV2dwr52IiIi8V7VLW8T71v9zGPuK1h/p/nxnu37PUny+bI9+A1i7/H/Pvq7hmGybKqU/GxERkZ8YCAuECpB0tgDN1Y7P2WwyiRAAxl0e2PvYA2EhLo1Ujdy9DYQBwBk/lu3mV4D6o96/bt/Hsh02Xb7StFLGb19yf3PjLiMsUDkjgJv+AyRnS184VfqZmAYk95N95z5hqiw2c7D7SVD9hgHxKUBnK1CzN/jXTkRERN5RGdzuhh154ikjrFnrD2aJBQony9Ckjiagarf/79nXNRjuMwP52xERUZ/HQFgg4hL1SYXOH8jHdwDVu4HYBGDU+YG9T39tamXVbhm9HQrNNXr/Kk+N8o0GTwQKpwDWDuCrJ717TXsTUPal7F+0CPjem8C9O4GUXMmuOr7d9WtDlRGmDDwZuGcbMPcDKYlVVNCzWyDMqT+YKzExeo8QlkcSERFFTlACYYZ7lQaTjDBVFpmaK/cTA0+W749EcZ8waxfwzIXAC1eHdyK4t4wBRwbCiIgoAAyEBcpVnzBVFjliJpCUEeB7DJGAWmcLUBeiD/5DXwCwATnFQJqbEj8zZ/xItl8/792N06HPga52KS3NGSGPWSxAvjZa3V2/sVBmhCnxyY5BMMB1nzD1vfrvwB17nzA2zCciIoqYrCGy9TeYYrM5BcJMMsLUxEjVNkEN3onmPmE1+6Q6YPcKx+yraGC1Ao3H9O9PsE8YERH5j4GwQLmaHLnjHdmOvSTw94iNA3JGyr6nPmFf/QN47wHfM8f8KYtURswEYJGeDc6jws3sXSXb4dMlAKbkaYEwd4GiUGeEueIqEOZtRhjg3c9HREREoaU+sxuPAR0tvr++7jDQbhjwY9YaQk2MVIOWCkplG82BsGpD64Zou1dprgJshv5qtQcidilERNTzMRAWKLOMsPqjWgNWS+BlkYo3fcIObwD++1Pgi78Bh78yP6ZmP/DP2cCmFx0fV4GwoWf6fm0JKUA/bXXVXVmjsm+1bIef4/i4PVDkJiNMNbIPZUaYGU+lkZleBMLyGQgjIiKKuOR+QEK67DsvZHpDZYPFJsrWLHvKXhrpFAir2CwTp6ORsYdptLVxcP4dMyOMiIgCwEBYoMwywnavkO2g06Q3RDDkaoGwqp3mz1utwLv36d9XbDY/7puXpD/XG/OAPR/KY20NMskI8C8jDAD6j5XtcRfXpzRWAse0axs23fG5fEMgzFWJZUukMsIC7BEG6D3C6sr0n4OIiIjCy2LRF/D8KY9Ui37qnqmlBuhsczym6bhsVSCs3zAgMVOG5lR6sWgYCdGcEeZcflrLQBgREfmPgbBAqQCIsXeXCoQFKxsMMGSEuSiN3PxvoHy9/r0KbDk7ukm2Nivwyq0SuDr0pXyfVaQHfPy+Pg+TLfd/Itu8CXq5gJI7GoiJk6wv54CTYi+NzPTvOv1lb6xrCHhau4D6cu15L3qEJWfpgVPjtCkiIiIKL/vnuh8BlUpDIMxVVpgKhKm+qzExQMEpsh+t5ZE1PSAQlhnA342IiEjDQFignDPCOtv0HljFs4P3PvZA087u2VJtDcDKX8l+4WTZVnxrfh4VIMssBNrqgGXXAjvekseG+FEWab8+bbJlpYdA2D7tdzNiRvfn4hL0zDdXKfnhaJZvRgUIG44AXZ3a/lHA2inBu/SB3p2HDfOJiIgiL6BAmLaYNWAckKF9/jtnLDmXRgLR3yesep++X7Wre5ZbJKnfb+Hpsm2tY3Y9ERH5jYGwQKlMoNYTEpA6+BnQ0QSk5eujsoMhZyRgiZHglXFqDgB8+jjQWCFp95f8WR6r3N69B0XDMQneWGKAW96Rm8Da/cCGZ+V5f8siAWCAFghzlxFmswF7P5b94TPMj8l30yess00mZwLhL41MHSCTO21WfUy6vT/YYCAm1rvz2ANhUdZ7g4iIqC+xB8J8LI20dultIAaM1RfC6o84HmcvjTRM4rYHwjb69p7h0Nmm97uNTZCFvioPA5rCSWXc5YwAUrS2I/5O/SQioj6PgbBAJabrQZkTZcAurSyyeJbjRMRAxSVKoAtwDDbV7AM+/6vsn78QyB0FJGYAXe3dg1KqLDJ3lPTGuP5lICFNfz6QQFjuKNk2V7meHFm9F6g/LDdYRS7eSzXMrzAJFNlX/izyM4ZTTAyQMUj2VdmmygLM9KIsUmFGGBERUeRl+dkjrPaA9PmKSwb6DdUDYc4ZYfbSSJOMsGPbgI5WX684tGoPALDJfeGgifJYOO9VWk4A/zwf+Oh35s+rReD0fEN/N5ZHEhGRfxgICwbj5Mjd78t+MPuDKao88sjXkvG1eyXw9t0S9BpxLjD6AgnY5E+Q45wb5h/RAmEDtR4VeeOAq56WDLHcUUD2cP+vLSFVX111lRWmyiILJ8ukSTPuMqaM/cFiIvCfrnPDfHuj/CHen8M+GXObDDggIiKi8PM3I0yVRfYfLdng9kCYISPMatUXBY2lkVlFQHI2YO2IvgUx1Sg/e7j77PxQ2f4WUPYF8NX/mQ9MUhlh6QP1+y42zCciIj8xEBYMqnHnvo8lQysm3nXpXyBUIOyDBcDfpgAvXCXvaYkFvvN7PQMt/yTZHnXqE6Yywowlm6POB364TkolA81gs0+OdBUI+1i2w2e4PocK4lXvBdqbHZ+LVH8wxfmmWQ1I8KZRvpI9QhrrdjRJWWogWuuAza9G7xh2IiKiaKU+05uOA+1N3r9ONapVlrcAACAASURBVMpXk6DNeoS11AC2Ltk3BsIsFqBoiuxvfNbnS/ZJVwfw/oPA3o+8O141ys8ZEZns9T0fyLa1Dmiu6f68+v2m5TEjjIiIAsZAWDCoQMjXL8h26DQpmQy2ETMBaMGqpCyZvDjqO8C1z+tBMgAYqAXCnBvmq4wwNbVIyR2pTzUKhLGhv7OuTmD/p7I/4hzX50gboPXTsHUfL27PCItQIMxlRliR9+eIjZOeIkDgN5hv/Aj4z1xg49LAzkNERNTXJGcBidoEauNEaE/sjfK1z3J7jzDD1EhVFpncD4iNd3z9tPmy/fp58/ulYNn1HvD5EuDV7wNtjZ6Pt2eEjTBkr4cpENbVqVcNALKobGTtMpRGRklGWEcr8O/vAasWRu4aiIjIbwyEBYPqEdVWJ9viEJRFAsCws4D79wMPHAZ+fhC4aw1ww8vAmAsdjzOWRqryu8ZKLW3fomeMBZt9cuT27s8d+Vp+P0mZemmmK/aVSKfSzkhnhKm/cyA9woDg3GCeOATseFv2y77y/zxERER9VT8/yiOdM8LspZGGQJjZxEilaDIw5mIZvvPhb3y7Xl9U75FtS613C2bGjLD+YwBYJPjUeDxkl2hXvkEywZyvRWmqkt+XJUZ+p9GQEbbjbWDb6zKwqrM9ctdBRER+YSAsGJxL40LRH0xJ7uc526z/GGlI31av3ySobLDcYiAxzfVrA2GfHGmywqnKIoed7XnCYr6LQFHUZISVSYBRTVfyJSMMCM7kyPVPy00hYD5YIJzaGoBd77PnGRER9SxZPgZUOtv0AJM9Iyxftg1H9d5WZhMjjWY+JEGdHW8Dh77w/bq9YcyqWrtErt2dau347BFyn5itDWgKR58wVRZpvxanQJgKMqYOkMx646ADs35i4fDNS7K1dur/TRARUY/BQFgwGDOCskfIalokxcbrN2iqPPLoN7L1lI0VCDU5sqmye3+HXe/JdribskjF1eTIaMoIazwmQwosMUBGgW/nCbT3RkcrsPE5/fuqna5vcE8cCv1q7oe/AZZdIw1uiYiIegp7708vA2HVeyTwkZipf/arjLCOZlmABMwnRhr1Hw2U3iT7K38VmmBOjaEPacMR4NuXXR/b0SJTvQH9HjacfcJUIEz1mnXOCFP9wdLzZJs5GIBFfueuJpWHUsMxYO+H+vfHTSohiIgoqjEQFgzGjKBQZoP5wrlhvlmj/GBLTNeDRcaG+XWHgfL1ACzAmIs8n8dYOmi8OVRp8xHLCBsk2/ZGPcCYMah7/w9P1M1l7X7v+nY427ocaK4GMgZLqam10zwLr/E48LepwFPnel4JDoRqxLvltdC9BxERUbD5OjnSXhY5Vh8wlJAin8WA3ifMXWmkMuMBIC5ZJiXufNe36/aGCoSNu0y2axZLry13xyZmAik5sh+uPmFNVdI+AwBOv022zhlhjSoQpgUd4xL1QGQkyiM3v6Jn5QNApYshUUREFLUYCAuGlBwgQSs3LJ4d2WtRVMBLBWxcNcoPNtUnzBgI2/6WbIum6iUE7uSOksmbbXV6+SGg3+xEKiMsPlm/qT34mWx9LYsEgNRcIE37PZj1U/PkqydlO2muDEwAzEsXDn4mQbsTh4Ctr/v+Pt5oqtZLAg6v02/+iYiIop2xxM4bzo3ylXQtKKNK+JpUIMzNIKKMAmDKXbL/wQJpGB8sxgyvWb+RBcSavcD2N82Pt/cHG64H+ILRxsEbe1cBsMn9zJBp2vXsc1wItWeEGe4h7Q3zD4Tmuj76HfDaHeYLiaosMlcNiWJGGBFRT8NAWDBYLMB3fg+c8RPpgRUNVEZYxWZZbVM3RKFqlK+YTY7c9oZsx13q3TniEvTzqPLIza/qzeELpwR+nf5SGW8HP3f83lfqBtN5sqcnhzcARzYCsYnAqd/V+6lVbDY5dp2+H6qyReN7wKaXwHpyfGdoVsCJiIi8FUhGmJG9T5gWsGn0UBqpnDlfer9W7QQ2veDdNXhDTVNMzJSA0eQ75PtPF5mXYRonRirqPuX4juAG6ZypssiRM4F+QwFYpMTUWPKoAowqIwwIrGH+Ny8BL97g2KDfqLUO+OQx4NuXgDV/cnyuYrMMc4pNAGbcL48xI4yIqMdhICxYTr0ZmP1bz43gwyVvPACL3DzsXimP5YwEkjJC+77q5lDdLDZU6I1gx17i/XnsKflbgGPbgDd/LN+feQ8wZGpwrtUfqmH+kY2y9ScjDAAGnSpbXyc+qmywkjmSWZbnJhBW9qW+X75BgmjBpt4jRisP3fFf71738s3Ai9f5lxHXG1XvBXatiFzTXyKivkh9hjdXm7cqcB4C4yojTJXpNRyRrb1ZvodAWFImcNa9sr/hWa8u2SuqUX72UFmsnXwnEJ8ii2+qnYHD8YaJkUrWUCA+VfqhhqoZvNWq99oaeR4Qn6TfZxn7hKkAY1qe4fpURpiPgbCuDuC9nwM739EXap2pvrqATIU0Lu6qbLBR35FKB0B+36FsQUFEREHHQFhvlZim39B8/S/ZhrJRvtLfaXLk9rcA2IBBE/WbG2+oTKeDa4GXb5KGqMNnAOf+IogX6weVAWbVVkedJ4Z6a8gZsj241vvXNB4Htmp9uFQfjXxDaaQxiNLRqpfDqhu1UGSFqUDepLmy3bcKaG9y/5qOFqBql+yrbV/3n7nAsquBd+5x3cOFiIiCKylDMrKA7llh794PPNwfeOFq4NtXJENJleENGOd4rHNGmKepkUZjtWz5is3y2R0M9kDYcNmmZAOn3SL7zhlOgGFi5HD9sZgYIE/7OUNVHlnxrfyuEtKAwsmO11BtEggLRkbY3lVAS63sH15vfozqWQZIIPCt+RK06+qU/mAAcPL1cj2JmYCtC6ja7dt1EBFRRDEQ1pupMkjVzyqUjfIVNTmysUJuNFQ/CtWs1VsqJX/fKlkVzCwErnw68hl3zoEvfzPCBp8OWGKBukOuSzL2rgK+/D9g3VPAhqXAyofkhmzQafIFSODREiu/6/oj+muPfgNYO2Q1+vzfyWNblwe3h1dXh2SaAcBpt8rvorNV6/fhRvVeAFrQru5w8K6nJ1P/CFn/tATFOtsjez1ERH2FWXlkW6NkaFk7gd0rgNd+ACzSssBS+0tGtpEK0NQfkUUp9VnrqTRSvX9qf/nM9rVdgiu1WvN7Y2Br6g8le/vAp0D5Rsfj7YEzp6nnoZ4cqcoih02XthiAvohrlhFm2iPMx0DYlv/o++UuMuVVIGzi9yUr7tBa4OvngH0fy9TwlBzJYLNYgAEmvXGJiCjqMRDWmw106gcW6kb5gKyuZmiZXwfXAge0IJy3/cEU1QQekD4M1ywFUnOCc42BcM5q87dHWGKa/vdQ/caMqvYA/7oCePc+4J17gbd+AnyzTJ47/Xb9uPgkPfhoXLFVJYuDT9cCZxMliLZhqX/Xa6ZiM9DZIqUduaOA0dpEUE+9v4xZYAyESTlFm9anxBIrAcsXr/VvoigREfnGLBC2e4Us7GQNAabfL72rurQFCrNeqyoQ1lABtDUAXVqZnKfSSECCKYMnyb5D380AOGeEAXL/Mv5y2d/wjP54e7Ne0pnjHAgL8eTIPaoscqb+mArGqYywrk59+IAxI0z93eoOe59J3dEC7HhH/75ym3kWuwqEjb1Ur0RY8RDw+V9kv+QqPXCnKiHY6oGIqEdhIKw3c75ZC0dGGKA3uv/0cUkXH3iy1gDVB2n99dW+C/+oZ0BFmkPgy+Jbuacze3nkZ92f26GVlGYWSW+1URcAI2cBE+cC4+c4HmtvmG9YST6slSwWni5b1Sh3/T8lk8sVmw3Y/YFMg/RE3bAPPl1KKMZcKN/ves/9Tamx14hxKmhfpUpoYuKBG/8tfVz2fgQ8d5nrRr5ERBQcWSYldtu0Scslc4Bz/hf4ySZg7gfAub+U4UjOMlQg7Kj+/+nxqUBCqnfXoO5xfAmEbXvDdQa2WSAMkAwnQAYQqc8XdWxSlpRQGoUyI6y1Tl+0G3me/rhzRljTccBmBSwxjpl4GQXyuWnt0Jvpe7J7JdDeIPdy6QPlvKqNhNJco5fAFpwi908FpbJgte9jefzk6/TjVb84ZoQREfUoDIT1ZsbAV/ZwydwJB7U6plLOx/qYDabc8DJw43+A074XnOsKBmMgLD0fiEv0/1xqTLhZnzCVVTXtJ8C1zwM3vATc9Cpw8SJ9FVJRfcLUhE2bTe/dpXpujLtMVqYbjmp921z45A/AC1cC7z/g+frVDax6j6Kp8t9Yc5X7IQDMCHNkbKo88jzgu29Kz5ry9VIWS0REoeMcCGtv0ocMqbYOFgtQOAk4+6dA/1Hdz2HMCLM3dvciG0yxZ4R5OdTmRBnw7+8BL17fva9YZ7ue3dZvmONzRVOB3NHSd/Xbf8tjZo3yFdULrf6w3lcrWPatlsXS3FF6vy/AkBG2TyszNTTKN7bHiInVFyO9LY9UZZHjr9CDj87lkUe1wFi/YfJZHBMLXPL/JGMbkN9fQal+PDPCiIh6JAbCerPUXCBdm2QUjkb5iuqXoIy73M/zjAWKz/N8XDilZANxybLvb38wpWgKAAtQvduxd1djpR5IGn2h5/MYJ2wCcgPceAyIidPLL+MSpY8XAHz1D/PzVO4AVj8m+2ZTKJ2VOWWdxcYDxefL/s53zF8DODaUrSv3/D69nRoRr1a6CycBZ/9M9kMx6ZOIiHTOpZG7V0qgKGuI9/dOqQMkY8nWpU+W9KZRvlJQCsAifUMbjnk+/ugmADZpT2Bs7A5IprXNKvcqxp5agAT0Jmr3AhuelUCTKkF07g8GAMlZ+gJgsLPC1PTKETMdH+83VH6XHU1yL2PWH8x+rA8N89sagF3vy37JlYZAmFPDfPX7NAa7Bp4MnHWP7E++XX6PisoIq90fvGEHREQUcgyE9Xbqg3zQqeF7z/6GQNiAcUDuyPC9d6hZLHrD/EADYcn99CCWMSts13sAbHIDnjnI83lURlj1XlnJVgGqgScD8cn6cRO/L8GxQ2uBTS86nsPaBbz5IykxAGR11TiF0lldudxsW2Icy1ZVeeSO/5q/3mZzLI1squSNo72psuEfTeofX8YR7kREFHzOgbBtb8h23GWOAQ93YuP0wJdqU+BNfzAlKUMPqDgHZswYF6tUdrZiLIs0u/6TrwPikmTx7PB69xlhgN5m480fB3dxRmVeDZnq+Hhcgh58q96rlz0a+4MpvjTM3/muBA5zRsr90eCJ8rjz4ACzQBgAnPMgMH+LtKgwSsuTslKblZOwiYh6EAbCertZv5FGr84f3KGUaygb8HVaZE+gUvH9bZRvZO8TZgiE7fivbMdc5N050gZoN+A2Sc03Nso3yhgoU6MA4I15+s0+IFlih9cBCekALLISqzKVzKgeZHnjpfG/MvI8GW5Qs9f8hrDhKNDeKCUGKrOuvo9nhRlLIxUV3Kw/7P7vQEREgVGBsJZaWZhQWUPjfcxmV33CjmqBMF9KIwE9MONNn7AK43Acp1YENWpipFNZpJLcT+81uv5pfWqxWUYYAJz7oAxBqtkH/HMWsPoP3jend6WrEzimZc7lT+j+vLFPmMqQS8vrfpwvGWGqLLLkSgkQ2rPwyhyz8FTPMOdAmFoIdQ4uWizsE0ZE1AMxENbb5Y6URq8JKeF7z+QsYMB4aWLq3Ni9Nxh6FgALMHRa4OdyDoS1NwH7tOa33pRFKsaG+c6N8o1mLgBKb5KVy1fnArtWyErqh7+W52f9GsjQstDc3Vg69yBTEtO13w+Anf/t/joVHOs3VM+s6+t9wswCYUkZ+j9KmBVGRBQ6iWlAijaVev3TshCUWQQU+JhJrzKW/CmNBGS6MyBZWp4cc8oIM2Zg2zPCXATCAL08cutr+vXmDDc/Nm88cNdncj9n6wJWPQw8c6H0KfNX1S6ZrJmQDmQN7f68cXJkMDLCmmv0CZXqvjQxvXtP28bj+hAfXwZMsU8YEVGPw0AYhcaNrwC3f2zeVLanO+se4OcHHacc+UsFwo5tkdXovau0ke1F+rQmb6gV1bKv9JVi5yAVINMdL/mzrIhaO4CXbwJevlH6oQyZJn3E1AqrmppkxlUgDABGXyBb1WzYSPUHyx2lB9wYCJOtcxmNugk3TgMlIqLgU1lhXz0p23GXel8WqageVl3tsvWlNBLQG+Yf+dp9xlXLCb2MMyZeBtSo4BfgemKk83vllcj9RusJ7XgXGWGALHBe9TRwxf9J8KrsC+D1uzz/TK6o0s78CXJf4syeEbbPQ4+wobL1lBG2422558krcexjO9ipT5gq18wplgUpbwU7I6z2oHe94oiIyG8MhFFoZA7Ss5R6o2BN4EwbIDdcsAGHvtSzqEZf5NtNeJ4WCNv6uqzYZgxy3V8sJlZuZkdfJCuyFZuB2EQJkMXE6DeWrgJhHS16lpJZ1tnwGbI9vB7obHN8zh4IG6mXmPb1QJhZjzBAD4QxI4yIKLRUIKy5Wrb+DPlRw4kUX0sj+48GEtKkfYC7gIpqWp9ZqPfoNPYJ8yYQZrEAp92if5+SI8EudywW6S926zv6e3Z1uH+NK2qBx6wsEtCvvWafh4ww7e9Wf6T79Ecje1mkU5WC8+RIV/3BPAlmRlhTNfDEWcCzF7rv1UpERAFhIIwo0lRW2IFPtUb50JvOe0sFHTtbZGsWoDKKjQeufkaf1jTzIX2oQZaHjLAjm2RlNS1PP9YoZ6SshHe1dZ9mVW3ICFM91uoCKK/oDZynRioMhBERhYdx+E3GYL1fly+cM5Z8LY2MidUHG7nrE6YmROdP0D/rVSDM2qV/drsLhAHASdcA8VrbDHfZYM7yJkhWWFe74xRoX3gMhJllhJn0CEvtrzXztwFPnQes/JXjAJ6GCuCDBcD+T+R753YdgwwN861W/wNh9smRB4D2Zt9e6+zQ50BbnQwWaq4J7FxEROQSA2FEkTZE6zW28TlZjU7KAoqmun+Ns5xiyepSnBvlm4lLBG76D/A/3wJn/Eh/3FOpgb0Z/yTzrDWLRb/+g585PmcsjVQZYX2+Wb6WEeb8jyYVCKvZB7TWhfeaiIj6EuOijj9lkYDeLF/xtTQS8K5PmAoi5ZXo7QlUu4L6clmoik3Q2w+4kpQJTLhK9nN9aGMRE6O3blDZab6w2fTSyIEnmR/Tb4gM1elo1j8jzTLCLBbgu28AE66W3qefLQb+7ywZBvTGD4HFE4A1f5Lnxl3evW/agHEyuKetXgJP/gbCUvsDydkAbIFPjjQGQd21qPDkyNfAP2YC+z4O7HqIiHopBsKIIk1lhLXVy7Z4tmRs+SI2Tl+RBMx7d5mxWPSeYIqnHmHu+oMp9iEAn+uPtTfr2V85xSyNBGQF2p4R5vSPppRsadgM6P9oICKi4HMIhPlRFgl0D9T4WhoJ6H3C3AbCVEZYiZ4RVrldeoepssisIZJh5snMBcAZPwHOvte367QHwvz4bKo7LD1RY+L0kkJnsfGOWXqWWCAl1/zYlGzgyqeAa1+QBaWqXcC/vwt8/bxkrRVOAa5bBlz1jMn7xAEFp8j+jrelDNMS4zpTzRXj5MhAyyONf/va/f6dw2YD3r1fep+tXRLY9RAR9VIMhBFFWlahHvAAfC+LVFR5ZFyS7zdxRiojrK7cvP+HairrrvxSBcLKvtSb/lbvkW1yNpCa4xgI66t9MFpqpacb0L00EtBXy1keSUQUOnnjJYsqe4QejPKVMRAWEy/Z3b5SJZnHdwCt9d2f7+rUAy35E6S3ZL9hAGwSQPGmP5hRag4w+7feH6/YJ1Vv8e11gL6w03+sZKa7kmMo10zPN2+qbzT2YuCHXwInXStBtrGXAHNXAnPfB8Zc5Pr1qk/Yei1QljtaJon6SgX1jgcQCOvqBI5s1L/3NyPswBo9e//QF3JeIiJywEAYUTRQgaPYBP+nUeZrpXQFpwJxCf5fS1qeBNNsXd2ztRqOAY3HAFjcB9vySoDEDMlyU/1MVLlAbrFsVdlGR7MEhPoiNTEyuZ95FuBAbaWagTAiotDJHATcvhq45W3PARdXkvvpLQpS+/tXXpk2QMuEsjkGRJTq3dJ/MyENyBoqjxVNkW3Zl74HwvylBvQcCyAQ5mnBzti3LM2kP5iZlGxgzpPALyqBa5/33C8V0ANhddokTl/LIhV7RlgAkyMrt8k9keJvIOzTx/X99gb/MveIiHo5BsKIosFIrWn9yFlAYrp/5yi9EZh8F3D+7wK7FotFLxNx7hOmbqZyRgIJqa7PEROrl04eXCtblRGmAmHxSXpfrL7aMN/eH8xFCQ0b5hMRhUfeOCCjwPNxrlgsep8wf8oiFXufMJOG+SoDK69ED9gZG+bXaKV0IQ+EjQNgkYWxxuO+vdZTo3zFISPMpD+YO96UhSoqEKb4GwgLRkaYyriHFkT1JxBWvgHYt0rKSdXv+MBn7l9DRNQHMRBGFA0mXC39LS79s//nSEgFLvi9PnUqEK76hHm7kgsAQ1TDfC0QpjLCcor1YzK1rLC+2idMZYS5mi6mAmFVu4D2pvBcExER+UcFbHydGGlk7xO2oftz9iBSif6YWnQq36APpAl1ICwhVX8PX7PC1M/gqlG+ku1UGhkqWUWOi1GBZoSdOAS0Nfp3DtUfbNhZsq11MbTInU+0bLCTrpV7S6D74CIiImIgjCgqWCzS38KsT1QkqD5hzjdhPgXCtGmYB9dKDzDjxEilrzfMtzfKd/F3T88D0vJl4pU/07mIiCh87IGwADLCBhsywpz7Zx4zZIQp/cdIK4L2RqBqpzzmPB0xFOwN830IhLWckEAR4PgzmMkxBPN8zQjzhcWiZ4VZYh2DjL5IzdUb+vv7ea2yAFUAq/4w0Nnu/euPbQV2vgPAApx5t+N9mNXq3zUREfVSDIQRUXdZnjLCPKzkArKqGpsINFdJEMy5NBIAMgtl21cDYY1aaWSam+wBlkcSEfUMKkvKeRqzL/JPkmb7zVXdg0z2iZGGz+CYWD14BkgwR322hpJaEPOlYb66h8gqApI9DBPILJKm94AsCoWSKkcdMA6ITw7gPFpA7eUbgf2f+Pballo9c370hUB8iiyC+dI64tNFsh13KdB/lNw/xKcCrSeASi6mEREZMRBGRN2pjDBjj7D2Jj2ry5uMsLhEvcRjy6vSADYmTj83wIwwe2mkm+wB++TITaG/HiIi8t/UHwIXLQIm3+H/OeKTZMohAKx+VH+84Zj0lbTE6GV4SuEUfT+rMLCBOd5SGV2+ZIT5spgWG6cHFtVwnVA5+VoJGk2dF9h5LnhUfi9Nx4HnLgM++aP3mVjlWils9nDJLrNn5u/37vXVe4Gtr8n+WffKNjYeKNJKZ8PZJ6y9Gdj3MbPQiCiqMRBGRN2Z9Qir3A7AJr1PvF2dVX3CNv5LO+8wx+mIDITJ1m0gjBlhREQ9Qko2MGmuTJAMxIwHAFiA7W8BR76WxxyG1aQ4Hm+cjhjq/mCKKiE8vtP78j1fAmEAcP4jwNQfAcPO9v36fJFVBNzxCXDKDYGdJ3sYMHclcMpNks310W+BF68Fmms8v1b1B1MLiPZA2AHPr22pBVY+JO9ZPFu/bwAM5ZFrvP0pArfiQQkEbn4lfO9JROQjBsKIqDtVGtlcDbQ1yL63k56Mhpwh24YjsjX2BwMYCPMlEFa5HehsC/01ERFRZA0YA5x0jex/pE2CrjDpD6YMOk0yxYDwBcIyC4HETMDaofcm88TX+4ji82QStnEBLdolpACX/xW4dAkQlwTsXgEsv9Pz61R/MF8CYU3VwAe/Bv40Adjxtjx21k8djxl6pmxVv1ZPDn0JNFR4Ps4VqxXYrl2LCuISEUWhkATCysvLcdNNNyEnJwcpKSk45ZRTsGGDPv3GZrNhwYIFKCgoQHJyMmbMmIGtW1m7ThQ1kjKA5GzZVw3zfWmUrww+XfqVKLkjHZ9XfUwajgJdHf5da0/mTY+wzELJLrB2ApXbwnNdREQUWdPvl8/PPSuBQ18YPoNNAmFJGcAArXl9vzA0ygekyby9Yb4X9/CdbcDxHbLvaWJkb3DqzcCt7wKwALvfB6r2uD7WajVkhGn9ytwFwmw24MPfAotLgDWLgPYGCZBe/5JeCqkUnArEJcvC5nEPActDXwJPzwZeu82LH9CFY1ukhBdwbK9BRBRlgh4Iq62txbRp0xAfH493330X27Ztw+OPP46sLL0p5mOPPYZFixZhyZIlWLduHfLz8zFr1iw0NDQE+3KIyF+qPPJEAIGwxDTHFH3njLCUXGmoDxtQf8TvS+2xPE2NBOQfGyyPJCLqW3JGAKU3yv5HD3suKzzrHqBwMlAyJzzXB+hBOXVt7hzfIQs6yf1C3/MrWgw6FRh1vuyve8r1cTV7paF9XJKe8ecuELbjHeDTP0rv1YGnANctA+74FBh9Qfdj4xKAQi3LzFN5pMoqO7ze//5eez/U99WEUCKiKBT0QNijjz6KwsJCPPPMMzj99NMxdOhQzJw5EyNGjAAg2WCLFy/Ggw8+iDlz5qCkpARLly5Fc3Mzli1bFuzLISJ/GW/CrF36iq+3vT0UVR4JADnFjs/FxAAZBbJfX+7PVfZc7U1AR5Psp7rJCAMYCCMi6ovOvg+ITQAOfKqXH5qVRgISAJu7Qv9MDQdfGuYfVWWRJ8kCT18xScuu2rRMPvfNqLLIglK9DFTdg9Uc6F7SeEALaJ1yI3D7xzJcIcbNP+mGaOWRnhrm71sl245moN7PlhV7DIGw2oPelWMSEUVA0ANhb775JiZOnIirr74aAwYMQGlpKf7xj3/Yn9+/fz8qKiowe/Zs+2OJiYmYPn061q5dG+zLISJ/qT5htQeBmn1yYxSXLKvUvjAGwnKLuz/fV/uEqf5gcclAQqr7Y9VI9j0fcgoTEUUcW2CESVYhcNqt+vcpuUB6fuSux5k9I2yL54CHP1nlvcGIc6VvW1sd8O2/zY+x9webqD+m7sHaG7o32y/7UrbDz/EuqKjuww5+5vrv1FjpmNlX+glXywAAIABJREFUtcvzeZ21NUoZr9LeII38iYiiUNADYfv27cPf//53FBcX4/3338edd96Jn/zkJ3juuecAABUV0oAxL89x6lxeXp79OWdtbW2or693+CKiEDNmhKkGt3njgZhYV68wN2QakD5Qgjkp2d2fV33C6sr8vdKeqdHQKN/TjezIWUBCupSpHgzhCPQ9HwKrFjLYRkQusQVGmJ11jyyYABJ4iqZsqgHjpEl/cxXQeEx//EQZsPwu4L0HgPXPSKP2ci1Q6mtWeU8XEwNM+oHsr3vKPBDl3CgfAOKTgHQtu89YHtnerN+TOfcDc2XwRMksbDwGVO81P2bfx47fH/cjEHbwMxmekFkEpGkBW2+mXhIRRUDQA2FWqxWnnnoqFi5ciNLSUtxxxx247bbb8Pe//93hOIvTB7nNZuv2mPLII48gMzPT/lVYWBjsyyYiZ8YeYYGs5CZnAT9arzWNNdHXM8LS3EyMVBJS9L4vm14I3TX996fA6keBQ5+H7j2IqEdjC4wwS88Hps6TfTUBMFrEJwM52hAcVR7Z3gQsuxb4Zhnwxd+At+cDz1wAlGvN4PtCo3xnp9wgwcxjWxwzpgD5fanWE8ZAGGBYkNyvP1a+QXqtpQ/UFxI9iU8GBmnZZq76hO39SLaxibL1JyNMlUWOPLd7n1kioigT9EDYwIEDMW7cOIfHxo4di0OHpGFifr6sEDhnf1VWVnbLElMeeOAB1NXV2b/KyvpY5ghRJNhvwA4aenv4WdKQmAbEJZo/F6xAWP1RYM1i4MimwM4TLmqqUqoXgTAAKL1JttveAFpDkBXb2a6v3Pa17Dwi8looWmAw89+Dc34BfH8FcMZPIn0l3anJkao88vV5QOVW6X05ZR4wYqYesOk/pnuv0L4guR9w0tWyv+4fjs8d+RqwWWWAgHN/N7OG+aossnCyb9mBQ6fJ9qDJ/wZtNj0QNuEq2Vbt9v7cimqUP2KmY3sNIqIoFPRA2LRp07Bzp+N43l27dmHIEPk/xGHDhiE/Px8rV660P9/e3o7Vq1fjjDPOgJnExERkZGQ4fBFRiGUWSslDZ4t+4xSKkoZgBcJWPQx88CvgyenA0xdIwKirM/DrC5UmQ2mkNwZPkn9AdDQDW5cH/3rqyuRmHOibEzyJyCuhaIHBzH8PYmKkDM7VglIkGRvmr1kEbHsdiIkHrn0e+M4jwM2vAXdvAf73CHDX50BsXGSvN1JU0/xtbwAN2v8OjnwNfPgb2Tf2B1PcBcKKpvj2/kO0QNiBNd3bH1Ruk7LJuGTg1O/KY1WO/5bzqPYgUL0HsMQCw6cDWUXyODPCiChKBT0Qdvfdd+OLL77AwoULsWfPHixbtgxPPvkkfvjDHwKQksj58+dj4cKFWL58ObZs2YJbbrkFKSkpuOGGG4J9OUTkr9h4IEMLUnW2ALAAeePcvsQv9h5hAQbCVNYaABxaC/z7u8CfS4E9HwR23lBpqpKtt4Ewi0XPCgtFeWSNofSCgTAiciEULTCY+d+DqUzx3SuAD38r+xf9sXv/qoRU95MNe7uBJ0kWl7VTWhC8Ohd4coYEtmITgInf7/4a50CY1QqUfSX7haf79v6Fk6XXaH05sOMtx+dUNtjQaXpgs+l49yb97qhzDJ4EJGUaSiMP+XadRERhEvRPpEmTJmH58uV48cUXUVJSgt/+9rdYvHgxbrzxRvsx9913H+bPn4958+Zh4sSJKC8vx4oVK5Cenh7syyGiQKgbGUD6gHiabuiPzEGybasHWuv8O4e1S+9nccs7wFk/BZKzgbpDwMe/D851BlujVhqZNsD715x8nay2ln3pX9mCO8YeJA1Hg3tuIuo1QtECg5n/PZgKnLTWAbBJQOe0WyJ5RdFLZYWtfxrY8ioAC3DStdJHdfiM7sdnD5OtCoRV7QJaT0jmlq8Z+gkpwJS7ZP/j3ztmhe1dJdsR50oriwztvsyX+wxVFjlypmxZGklEUS4kSzMXX3wxNm/ejNbWVmzfvh233Xabw/MWiwULFizA0aNH0draitWrV6OkpCQUl0JEgTAGwkI18jwhVfpnAEBduX/nqD0AdLYCcUlA0VRg5i+BuVr59dFvpf9VtPG1NBKQpskjz5P9YGeFOWSE+fl3IKJeLxQtMKgHyyjQP8OLpgLfeTSy1xPNxl2mZ8GPOBe44xNgzpOO91pGKiOs7rDcx6iyyEGnSda+r6bOAxIzpRRy2+vyWEerPo16xLmyzR0lW28b5nd1Avs+0c6hBcKMGWGcRE1EUagP5ygTkUdZQ/X9UAXCgMD7hB3fIdvcYiAmVvZzRsjNeVebPs0qmvgTCAP08shvXgpuDzRjRlg9M8KIyBxbYJADiwU4+z6geDZwzXNAXEKkryh6xSUAc1cAd34G3Lzc8wTN1P5AfAoAm/TxtPcHm+z2ZS4l9wOmyv9OJSusS6ZEd7bKFMr+Y+Q5eyDMyz5h5euBtjo5f8Ep8ljGYMlg72qT/mNERFGGgTAick2tRgKhaZSv2PuE+dlLQgXC+o/VH7NYZNUUkHHj0cbfQNio7wApOVK+qHpyBIMxI6zxGNDVEbxzE1GvwRYY1M3UecCNr/hW6t9XZRQA+V5WwVgshj5h+x0nRvpryp3Sw6tqJ7DlNf0+Yvg5+hTKXG2yp7elkXu0ssjhM/TFyNg4vfUFG+YTURRiIIyIXAtHaSSgjwz3NxOpUgXCRjs+PkibwnR4vX/nDZWuTr0Jra+BsLgEYMI1sr/p+eBcj83mOJUKNq7gEpFLbIFBFCYqEHZ4g0xlBKQhvb+SMoEzfiz7q3+vDxRSZZGAfi/lbWmkCqapskiFfcKIKIoxEEZErvUfAyRlSTPcdPMmx0GRLs2V0Vjh/jhXjm+X7YCxjo+rceTlURYIa64GYAMsMUBKtu+vL9UyL3a+C3S2mR9TtRt4937vbkAbKmQyqCUWSNP+ziyPJCIiiiwVCPv2Zdn2H+PffYPR5DuljLF6j/QLAxyb9avSyNoD0kPMncodeta9MZgG6IEwZoQRURRiIIyIXEvKAP5nE/CDD0L7PukDZdvgRyDM2qWn76v+FkrBqbKt3gO01Pp/fcHWpE2MTMnRywh8kVciAcqudqByu/kxqx8DvnwCeO5SoMFDdpfqD5Y5WL9xZcN8IiKiyOqnTY6s2SvbwtMDP2diOjDtf/Tv808C0gzZ6Wl50lTfZgVq9rk/14oHAdiA0RfppZBKP2aEEVH0YiCMiNxL7gfEJ4f2PVRGmD+BMOPESGNPMwBIzdFvIss3BnKFwWXvD+ZnPxWLRW9Ie3ST+TFqhbb2APDClUBrvevzqf5g2cOADBWUZEYYERFRRDnf1xROCc55J90GpOTKvnMml8Vi6BPmpmH+7pVSWhkTD8z+bffnmREWHDYbJ28ShQADYUQUeWkqEOZH8EVlROWOMs+uspdHRlHD/EYVCMv1/xwDT5bt0W+6P9dyQl89TskBKjYDL93guoxSZYT1GwZkaCu69Uf8vzYiIiIKXLdAWACN8o0S04BL/wwMPQuYNLf78/bJkS4a5nd1AO//r+xPuVMmdTvrx0BYwGoPAv+cBSwaA+xdFemrIepVGAgjoshTpZHN1UBnu2+vVRMjnfuDKdHYMF9lhAUyYctdIExliWUNAW56DUhIAw58Crx2m5SSOjNmhKm/BQNhREREkZVVpO+n5JgHnPw15iLglrcd30PprwJhLhrmr39ankvJBc7+mfkxKiOsrlyGBJFv9nwIPDkdOLxOBhg9PwdY+xfJECOigDEQRkSRl5ItqfWA79MKVSDMuT+YMug02ZZviJ6bB9UjzNeJkUYDtdLIii2yMmt05GvZDjpVSiive0F+v9veAFY/2v1cDhlh2gRPb7LzrFZg1ULgq3/49zMQERGRa/FJQLr2uVw4WcoWw0FlhB03KY1srpHPfgA490GZRGkmLQ+ITQRsXUD94dBcZ29ktQKf/AF4/krpb1twKnDSddKzbcUvZFGzvTnw94mWe2KiCGEgjIgiz2Lxv2F+pYdAWP4ECQI1V3lOz285AWxa5ntWmq+aqmQbSCCs3zAgMQPoaut+o6r6oRWUynb4DODiRbK/aVn3mx+HHmHaDbc3zfK/eVECa//9KdBU7c9PQURERO5ka71Og1UW6Q0VCKve070/1epHgdYTwIDxQOl3XZ8jJkbPNnNumN/RwunUZjpagZdvAj56GIANOPV7wK3vAlc8AVz4RyAmDtj8CvD07MB/f6/dDjw+RgKbRH0QA2FEFB3S/egTZu3S0/YHuAiExSdJMAzwXB75wQLg9buADc96fw3+aAxCRlhMjEx6ArqXRx7RSiPV1EwAKLlSAoJ1ZXoGGAC01gEt2k1Qv6GG0sij7lcLW04AKx/Svz+4xq8fg4iIiNw46x5g7KXAqW6CTsHWb6jcM3Q0O2ZzHd+pZ4F/ZyEQG+f+PCoQ5rwQ+eL1wJ/GAZteDNol9wpr/gTsfEcy6S79i/Rxi0+SBePTbwO++4aUo1ZslntWf7XWAVtelXvuaOqhSxRGDIQRUXRIz5OtL6WRNfslIyouGcga6vo4e8N8D5Mj96+WbeU276/BH8HoEQaYT45sqgLqDsm+6iMGAAmpwOBJsr//E/1xlQ2W2l9GqqtAWFebpOS7sup3kmWnHGAgjIiIKOhGngdc+y9pIxEusfFA9nDZVwuOXR3A8jul1HH0hZJt7olqmG/MCDu2Ddi3Skr9Xr8L+PbfwbzynutEGfDZYtm/4gnzwOfQM4Frn5f9HW/7XyJ5cK38/gGgjmWr1DcxEEZE0cFeGulDRthxbWJk/1GSIeWKvU+Ym4ywxuNAzT7Zryvz/hr8YS+NDGBqJGDeMF/1B8spBpIyHI8fdrZsjYEwY38wQFYeU3Jk31V55NFvgXVPyf6kH2jn/NT36yciIqLopBrmH9cCYasfBY5sBJKypEzPG1kmkyM3LpVtYiYAG7D8DmDzq0G55B5t5UNAZysw5Exg/BWujyuaIpl27Y3A7vf9ey/jPRsDYdRHMRBGRNHBXhrpQ48we38wFxMjFTU58ug33RvLK4e/0vdPHPL+GnxlswWnWT6gB8IqNuvTII2N8p0ZA2Gq7NHYH0yx9wkzCUpardITzGYFxs8BZmjj049vl2AiERER9Xy5hsmRBz8HPn1cvr9kMZA5yLtzqIwwdV/V0Qp885LsX/mUZD3ZrNIAfstrwbv2nubgWmDrawAswHcecT8UwWKRdheA/wFE44IoA2HURzEQRkTRwa+MMC0Q5qo/mJIzQlYwO1uBY1vMjyn7Ut8/URa6aTpt9UCX1ow/0EBYzkggPlV6eFTtlsecG+UbDZ4kZaRNx/XfnT0jbKh+XLqbhvnfvCi/q/hUYPbDQGoOkFcizx1gVhgREVGvoAJhRzYCy2+XgNXJ17vPVnKW5VQauf1NabSfWQiMnAlc/P+AU26Sc//nB8CeD4L7M/QE1i7g3ftl/7TvAQNP8vyakqtku3ul9PvyRXMNcGyz/j0DYdRHMRBGRNEhTesR1uBDj7DjXmaEWSyG8kgXTUHLDBlhnS1Ac4imINYfkW1SJhCfHNi5YmL1QQCqPFJlhBWYZITFJQBDpsq+Wg2scSqNBIAMF0FJY4P8GffrK8JDz5QtA2FERES9gwqEHf1GMrqyioALHvPtHGqRrbFCJkVu0MoiS2+We5iYGGkIX3KV9B776qmgXX6PsekFoOJbmQR+zi+8e03eeJmW3tUGbH/bt/dTPV1jtEEHoW4HQhSlGAgjoujga0ZYV6fewLX/aM/Hq0DYYZNAWGe7nkkVEy/bUJVHqlVRtUoaKGOfsPojcrNpMQTInKnyyH3aYIDaA7J1KI3UAlzOGWHbXpcG+TnFwOS79MeHniVbNswnIiLqHXKL9X1LDDDnH917j3qS3A9ISJf9vatkwrQlBii9ST8mJlYyzYDQtqaIRq31wIe/kf3p9+P/s3fe4XFUZ/u+d1e992K523LvNjamGVzAQAADoYQWSCEJhB+EEAiQfCENEvKFEEJIQvkg9N57tTHGDfcuuchW772tdnd+f5yZnV1pV1r1lfze1+VrZmfOFK3h0vFznvd5iQmwUsBiMV1hu7tZHmkshGafqbZ1RSr2wheVh8z8XEEYZogQJghCcGBkhDVXgaO16/HVR1SJYWhUYKKSu3Okj8D8kp1qVS0yyRSW+msyZgTGJvaREObZOdJwg6VNhbAo3+MNISzvK9VtyLDEezrCDFGyfUbY0a/VdvqFyl1mMPZkwKKEye5kvAmCIAiCEJyExyoXGMCpP1ch7d3FYjHv8fkf1Hbiio4ZY/Ej1Xa4l+nZm1Te2oZ/w5s3wGNnqLiK5Imw8Pru3WvGRWp7eE33MloN9/7MS5Qo6Wozs2vbv+vjy+DRM5RgJwjDDBHCBEEIDiITwRau9gMRU8r0jpEpXXSMNBh5gnJKVeSYeVoGRj7YqEXmhK2/rOL95gjbaZZ9GuKYLzJmq05NrbWw/z1AU3lfMWnmGCMsv707zxDCxpzkfTwy0XSgiStMEARBEIYH5/8Dlv9WuZV6irHwV7ZHbed/t+MYQwhrre1+5tVQobUB/jEPnlwJH96hSiIrD0JIBJzzF+8FxkBInqBiMDSncuwHQn2pHitigfGnm5mwvgTIyoPQXK0y3XJ62J1SEIIYEcIEQQgOLBaINXLCAhDC3EH5XeSDGUQlmTbwbc96n3MLYQtNIaymn4Swmj4WwlImq0mUvd7suOQrH8zAFqI7uDBbmCeO9e5QFOcjLL/mmBIHLTYlKrbHKI/07EQkCIIgCMLQZfzpcMotYAvt+T085zsxGZB9Vscx4TFqUQ36b/412ORvVAuMIZEwaaUSFy97Dm7eAROW9uyeM/XyyEC7RxpusIwZal7sduL5+M6rDpn7gQptgjCEECFMEITgwSjJa+iGEJbaRcdIT4xMih0vqIwxUN0hjaD8UYsgYZTa7y9HWF+XRtpCzK6NRgdIXx0jPXGXR+oTIs98MDD/Hlpqwd6o9o+u1+89R01YO9xTcsIEQRAEQWiH53xn7lVq3uKLeGP+1YflkRW58Ojp8NHdKqx/MCnQozmmfguueAnOuEvtG9EgPWH6RYAF8jcEJiAa876x+jyws5LUyoPm/sFPlaNNEIYRIoQJghA8GJOBgEoju+kIA5h0FkSnQkOp2aK7Nl+t0FlDlIAUbzjC+issX79vXznCwCyPBLCFqW5CnTFuifdno6uTQUS8KpcEMyfsmF4WOXqx73uOOUllTVQdMjtjCoIgCIJwfOM535l3dSfj+iGa4os/qvzU9Q+rrKuS3X137+5iZNT6ctX3lLhMs3P37te6Hm+49o3FSyOrrbaw49hKj5B8RwvkSnmkMLwQIUwQhOAh0M6RLpdp2U6eGPj9baEw6zK1v+0ZtTXcYBmzVMB8f5ZGNteo/AswnWd9gWcmWPp0CAnvfHzaVIhKMT+3d4RZLGpyBVCvi1qGI2zMyb7vGRFvCnJH1nb+/LZm5cQTBEEQBGF4M2axmqst+F7HhTdPDHdSXy1EVh+FvW+p/cgkKN+nwunX/9N/l8T+QtOgYLPaN5o39RVGaP6uVzufW9UWqA6QFquZ9RrfSRWE4QhLmqC2xncpCMMEEcIEQQgeYgLMCKsvVqtT1pDuO6uM8sicD1WXHc+gfDAFqv4IbDXKIqNTISy67+7r6QjrLB/MwGIxyyPBu2OkgTsnrBgaK6DigPrcWdcoIycsr5OcsOId8Oex8PGvun5PQRAEQRCGNpGJcNMW+NbfOh/X16WRG/8NmkvlnN24SeVyOe3w0V3wyncHdkGu6rAKnreFQ/rMvr33tFWqGqB0Fxxb73+csUiZOUctXkLnpZHGgvMpt6ht7idmXIYgDANECBMEIXhwO8K6EMKMX84JY/xnTfgjbSpkLQCXA3a+5B2UD0qgikxS+33tCuvrjpEGqVPVJAi6zgcz8BTC2jvCwOwkVFdoTqzSpqlwVX+MDSAnbNtzSsQ89Hlg7ykIgiAIwvCnLzNam2tg69Nq/6SbICYVvvMinPuAWkTd9zZUHur8Hn2JkQ+WObv73SG7IioJ5lyh9r/qRGw08sGMskjwL4Q1V0NTpdqffpGat7Y1KTFMEIYJIoQJghA8BJoRVqXnFiSN79lzDFfYN0+YeRGGIwzMyVhf54S5O0aO7tv7hoSplc6wWJhwRmDXjF8CWCA0ylyF9STOo0z1aBf5YAZjFquuktV5vr87TYOcD9R+X4bhCoIgCIIwtHGXRvaBELblKbA3qAW8CcvUMYsFTvg+JGfrzzna++cEirsssg/zwTw56f+pksfcj33noGmaRz6Yx0Ko8Z03VXg3EzDywWIyVIOkaReoz1Ie2Ss0iQUJKkQIEwQheAg0I8xYxUue0LPnzLhIta+uOgyaE+JGmoGh0D+BrWCKQ33VMdKTi5+An+83JzVdkTQeLv0vXPas77bocfr3UVdkCmFGpoQ/wmPNSd6+dzqeL9tnfgetdX1feioIgiAIwtDEaFbUUAKO1p7fx2GHjf9R+4tvVAKYJwn93BTJF/2VD2aQPEGVSAKse7Dj+cqDak5rDYFRHhEXEQlmcyTPwPz2ObzT9XvnfDT43TeHKPlVTcz/w6f87ZOcwX4VQUeEMEEQgodYPSOspabzX7S9dYRFxJurW2CWRRr0V+fI/iqNBOUKC4/p3jXTLoCJy3yfM0TJihwo2an2uxLCAGZ+W213vtTx3IH3vT/3R0MCQRAEQRCGHtEpEBKh9ut8dDEMlD1vqEY/Mekw85KO5wdaCGtrhlLdpdVfQhiYWV67X4OqI97Pf/16tT/mZO+5osViLqDWeTj1jaD8ZH2ePWKeqh5oazS7rgvdYuORKqoa7Xy8t3SwX0XQESFMEITgISLBnAQ1dPKLwi2E9dARBmZ5JHiXRULf5lR4Ytjw+8MR1tcYYfkVOSpsNnGseawzpl+kVhyLd0D5Ae9zOR96f5bySEEQBEEQwFuU6elCmabB+n+o/YXX++6ibczBBkoIK96hcmlj0n1HUfQVmbNVGajmgq/170DT4J2boWiralpwng+3mK+csMp2jjCLRcoje0l5favXVhh8RAgTBCF4sFi6zglzucyVLl8h74Ey5mRInaIyFcYv8T4X3w8ZYZpm3q8/HGF9TXvRa3QAbjCA6GSYuFzt73zZPN5QbobFGp0t+1poFARBEARh6NLbzpFH1kDJLpV/uuB7vse4HWEDlBHmmQ/WvkyzrznlZ2q77VloKFOC2M6XVH7rJf/1XUnhUwjTHWGeC85G6eWBD6Gtpe/ffZhT0aAEsKrGVpwuyQoLBkQIEwQhuOgqJ6y+GBzNynXUG0HJaoVr3oLrV6tOkp64J0l9KNQ0VqiOO1gCz/EaTKJT1cTJIJCySINZl6rtrpeVcAmQ+xGgqbbdRimqCGGCIAiCIBj01pFvZIPNudJ/l+uBLo00FgGz5vf/s8aeogQ3Zyu8/kP49Dfq+Mo/dVz0NYhv951rmll5YTjCQL1/XBbY61Uov9AtDCHMpUFlo7jCggERwgRBCC5i9Jwwf44wI8AzYTTYQnr3rNgMZSVvjzERa6oAe1PvnmFgrDzGjfBt1Q82rDZTlITuCWGTzlYdLGuOQf5GdeyA3i1y8tm9X/EVBEEQBGH44Xbk90AIa2uBQ5+r/QXX+R9nLKI2lA5M8LshhPVXx0hPLBbTFXZ4tSqTnHcNLPyh/2uMZlHGnKyxQjU0wqJiMQysVnOh84t7weno45cf3hhCGEh5ZLAgQpggCMGF2xHmTwjrg3ywrohIUEIO9J1YU52ntsZK5FAgTv+7iEnvXmOCsCiYdr7a3/mS9+R00sq+bZEuCIIgCMLwoL07qTsc+xocLWoemTbN/7jIRAjTA+P7e0GurliF0FusMGJu/z7LYNLZkDJZ7Y9eDOf8tfOSTHdppN6gwCiLjB8FoRHeY0++WX1/5ftg61N9+trDnYp6u3t/OAlhlQ2taNrQLPUUIUwQhOCiq4wwI8Czpx0jA8FiMV1hfWWdH0r5YAZGTtjoxd3PtTBWDfe8AYc+U2WhsSOUAy9hGDvCNE39EQRBEAShe7hFmR4IYcaC24Slnc9ZLJaBywkr1N1gadO739m7p1itcNGjsPincNmzqqt4Z3hmhGmaWXmR7GPBOTIRzrhb7X/+R2iu6Zt3ri+Frx/uuyqMIKR8GDrCvswpZ/4fPuVvn+QM9qv0CBHCBEEILrrKCHPnFvSjIwzMSVJtXwlhQ6hjpMGEpWo7+/LuXzv2VPV32VIDH+mTpskr9a5QuhBWXwzOtr5512DA6YDHlsIzq0QMEwRBEITu4l4oKzQzRgPl0Bdqa8xdOn3OAOWEuYPyByAfzJMRc+CsP0J0Stdj4/TSSEczNFWZjjB/8+z516lmU81V8OVfOp7XtO6XnH7xB/j4btjwSPeuGyI4nC6qmzwcYQ3DQwhbk1MOwNMbjmJ3dPP/1yBAhDBBEIKL2K4ywozSyH50hEHvcip8Ua0LYUOpNHLed+HuEpXr1V2sNphxsdqv1rt8TtLvE5UCtnBAg7rCPnnVoKA2X7UoP7wa7I2D/TaCIAiCMLSIywIsKuy9sTzw6+pLoHS3unb8GV2PHzAhbADzwXpKSLiZz1ubb1ZeeAble2ILUSIbwMZ/Q8VB81zBN/Cvk+EvE815byAU71Tbwi3de/chQlWj3Wt9dLg4wg6WNQBQ09TGlznd+P81SBAhTBCE4MJwhDX4EMJcLqjSRZX+FsJ627moPUOxNNJigdDInl8/6zJzPzQKxp2m9q1W3+26hzqe4m1z9eC9hyAIgiAMRWyh5jywO/Mvww2WORuik7seb8zF+lMIczqgaJvaD2YhDExXWG2BRwRJJ5UXE5dD9pngcsAnv1aLfx/eBY8vh7I9YG+AfW8H9myXCyr00rriHT3/GYKY9g6w4SaEAby1o2gQ36RniBAmCEJwYWSEtdR2zAqoL1YrH4jaAAAgAElEQVTWbYut/51V8X2YEeZymRO6oVQa2VsyZkLqVLU/Yal36OqwFMI8ynmbqwbvPQRBEARhqNKThUjPfLCAnjEAjrCyvSofNTwekrP77zl9gWc2W6ARJGf+Uc3HD7wP/5gPG/4JaJAySZ0/+Glgz67NV98TqCqBhqHnLOqKiga71+fhIIQ12R0U1pglsJ/sLaGhdWh1EhUhTBCE4CI8TrmHoKMrzPjlnDhGrRr2J+7Vwj5whNUXg9MO1hAVGH+8YLHAabepcNWF13uf6+vS02BAHGGCIAiC0Du6Oz9wuUwhbOKywK4xhDB/5Xt1xb0PgjfywbLmKSd8MGN85wWb1YKzNaTrBefUSbDwh2q/vhjiRsKVr8Llz6tjR7+G1gb/1xuU7/f+XDL8XGGG8BURqv47GA4ZYYfKVARIUnQY41OiaWlz8fEeP7E2QUqQ/18pCMJxh8Xiv3Nk1QB0jDRI8Ah0d9g7H9sVRlB+XJbKVjiemPltuCMPxi/xPt7XpafBgJcjTIQwQRAEQeg23e0cWboLmiogNBpGLgzsGkPkaSzrGOxeXwoPnwBPndu7xjcleu5V1rye32OgML7zw2vUNiHABefTfwlTz1cdKm/cANkrVLZYwmi1AJz3Vdf3aC+EFW3v3rsPASp04WtyeiwwPBxhB8vrAZiYGsMFc1Rp7Zvbh1Z5pAhhgiAEHzGGENauc6Q7KL+fO0YCRKdCSAR9EuhuWO+Pp7LIrhiWpZEewm2TlEYKgiAIQrdxL5QFOD8w3GDjToWQsMCuiUyEMCVKdHCe5a0Fe70K3y/cGtj9fFG6V23Tp/f8HgOFMSdrqlBbf0H57YlMhMueUeH54fr3abHAxBVq/+AnXd+j/IB+ryS1HYY5YRW68DU1Mw6A+hYHLW3OwXylXmPkg01Ii+GCOara5avc8iEl8okQJghC8OF2hJV6H68cQEeYxWJODHqbIeHuGClCmJvurvgOBcQRJgiCIAi9I97I7wpwfuDOBwuwLBLUHM9fTlj+RnN/75uB39MTTYOyfWo/bVrP7jGQGHMyg67ywbpi4nK1zf2ka1ed8T3NuEhth6MQpjvCxqVEEx6il0cOIcHIF4YQNjEthrEp0cwelYBLg3d3Dh1XmAhhgiAEH0bHoA6OsAHqGGlgTJIMscbeCNtfgCNfdu8+NSKEdSDeY8W3N6UHwYRkhAmCIAhC73AvlAWwCGlvhGMb1H6gQfkGbiGsXU5Y/iZzf++bPZuj1BYoV5k1dGCqGHpLXwth405VP3vNUbOawxeaZjrCjE7jNUeH3RzKCMtPiQknNTYcgLJhJIQBrNJdYUOpPFKEMEEQgo9YH6WRLlfgnWz6CkOsyd8EH/8aHpgKb/4Ynl4FR9cHfh8pjeyI0aq7rWn4lBGKECYIgiAIvcMQZVpqoaWu87F561QWVfzo7s8NfTnC7I1QskvtW0PVueIeZFYZLqeU7MDLNQeTqBSwhZufeyvehcfCmMVqP7eT8sjaAmhrVOH8I+ZC4lh1vHhn754fZBiOsNRYUwgbyo6wNqeLo5Wq06chhH1r1ghsVgs78ms4UtE4mK8XMCKECYIQfBiTmdxPTJGkoUR1srHYuu5k01cYORVb/wtfP6QmZaHRoDnh1et8t3iuK4Jv/g8aK81jUhrZkdAIiE5T+8OhPLK1Qa3+GogQJgiCIAjdJyIOIuLVflc5Ye5ukUtVuWN3MBYnPYWwwq1qjheXBVPOUcf2vtW9+wKU6flgaVO7f+1gYLVCfJb5uS8WnI3yyIOf+h9jBOUnT1Th/Jmz1eeeiI9BjCF6pcSEkxqjC2FDuHPk0cpGHC6NqDAbI+IjACXynTwxBYC3tvcyW3mAECFMEITgI/ssSJsOLTWw+j51zMgHSxgdWCebviBjlrk/bgl85yX4+X5Imazcaq99H1weYZf5m+A/S+Ddn8E/F8KeN8DZBnUF5rsLJt0NxA1mGtrl2YkQJgiCIAg9I75dNIU/Dn2mtt0tiwTfjrACvSxy5AkwbZXa3/tW98sj3flgQ0QIA9OJZwuHuJGdjw0EIzA/76uOnTkNDCEsdYraZs5R22GUE+Zwuqhq0ksjY8OGhSPMHZSfGoPFQ4A2yiPf2l6ENgRiT0QIEwQh+LCFwEpdANv8BJTt9+gYOUD5YADZZyrx6ydfw3ffhskr1UrlpU9DaBQcWQOr/6TG7nhJtdpuLANbmOq888q18OzFoLnUxCImfeDefSgwnALz2+fZDZdyT0EQBEEYaNwLZZ3MD6qPQkUOWKww7rQePMOHEGbkg41apOaAIRFq/mmUSwaK2xE2BILyDYw4kKTxyiHWW9KmQuwIVc1xdJ3vMR2EMMMRNnyEsKomO5qmDItJUcNLCDPKIg3Omp5BiNXCkYpGimtbBuPVuoUIYYIgBCfjl8Dkc5VF/aO7oEp3hA1UPhio31qTV3ZsfZ02Bc57SO1/eT+8ch28cb3KqZjyLfj5AVhyh8o8OLJGjUsY3TcTi+FE/DByhBn5YBEJaiuOMEEQBEHoGe6u3Z0IYTteUNsxJ0NkYvefYQhhjWVgb1KuL08hLDzGLO/rTnmky2kGwA9FR1hfzbMtFpiod/I8+JnvMcb3lDpZbQ0hrPJg1/lwQ4SKeuUGS4oKI8RmJS1WlRIORyEsOjyEsSnRAOSU1ne4LtiQf5UJghC8nPl7FVZ66DPY+bI6NpCOsM6YdQks+L7a3/O62p5yK1z6DEQlwRl3wQ+/gIyZ6lz6EFoVHCjiA1jxHSoYjjBj9be5avh0wxQEQRCEgaSr+YHLCVufUfvzrunZMyISIDzOfE7lIfW7OyTCnLu5yyPbdY+szoPXr/cdBF91GJytqnIgYWzP3m0wmH4RjF4MC77Xd/fM1ssjfX1Pnh0jDcEwOsUsy+yuCy9I8QzK99wO5Yywg+VmaWR7JqfHApBb2jCg79QT+l0Iu++++7BYLNxyyy3uY5qmcc899zBixAgiIyM5/fTT2bNnT3+/iiAIQ43kCXDiT9S+ITQEUxvqlfepSUNIJFz4H1j+G2/XV+YsJYZd/gKc89fBe89gJZAV30Bpqhrc1UPDEWZM5lwOsAf/JGBA0TT49Lfw+HJorhnstxEEQRCCla4yRA99rvJXIxJg6vk9e4bF4l0emb9R7Y+Ya3Z6nHSWiraoPGjmfhVuVb/Hdr4EH93d8b5GWWTqlKFVCZA2Bb73oeni6gvGLVFNripzlXjoSV0RtNap855z+xFDMCdM02DDv2HfOx1OeQblgymEVQxRR5jLpXGoTHWFbO8IA8hOV8eOe0fY5s2befTRR5k1a5bX8fvvv58HHniAhx9+mM2bN5ORkcGKFSuorw/+L0wQhAHmtF9AdKr5OVgcYQAh4XDte3D7YZh9ue8xtlDVeSgm1ff545m+CsuvOgwPzYX/nObdvGAgcQu1480W5FIeaaJp8Mmv4asHoGAzHPlysN9IEARBCFYMR5i/hbItT6nt7O+oLtQ9xS2EHTWFsJEnmOcj4kxhaO+bkPORngerdw2vOOCdMQYeQflSCUBkAoxaqPbbl0e6O0ZOMIVHGJo5Yfmb4MM74M0bO5wyHGEpMepn9MwIGwqB8u0pqm2muc1JiNXCmOSoDucn6Y6wnLLgXwzuNyGsoaGBK6+8kscee4zERLNuW9M0HnzwQe6++24uuugiZsyYwX//+1+ampp4/vnn++t1BEEYqkTEwdJfq31rSPB1XrTaIKzjLwIhAIyJbmMZtPUwVNPpUOUJLTVQfcRciR1oDEdYXKaZVSKB+SZr/xe+/of5uX1zAUEQBEEwMOYH9cXQ2s4oUV8COR+q/fnf7d1zEsaobc0xtUgDKh/Mk2kXqO2mR+GFy6GtSXWpHDFXHW9f9ucOyh9C+WD9iZGztvs17+Ptg/INhqIQZmTItdaqvDkPTCEsXN8qQczudFHX7Bi4d+yCFzYdY+7vPmZXQW2n44x8sLEp0YTaOkpJk3RHWG5pPS5XcAt9/SaE3XjjjZx77rksX77c6/iRI0coKSnhzDPPdB8LDw9nyZIlfP311z7v1draSl1dndcfQRCOI+ZepfK3zr7fe9VIGNpEJqoMDYC6wp7dY+1fzckrwLENvX+vnmAIO7GZKiMOxBFmsPFR+PwPat8Qsnv69y0IgiAMf6JTIX40oMEHv/Q+t/05FT8wcmHvxSbjd1LJLtPJZTiYDCatVHm1zdWqC/icq+CKl2HKuer8wU+9x7sdYSKEAapiwhqiOkcWbjGPdyWEVRzoICoFJZrm3Uyh2XsRtKJBheWn6E6w8BAb8ZGhAJQ3BE9nxRc2HaO6qY13dhZ1Os4dlO8jHwxgTHI0oTYLTXYnhTXNff6efUm/CGEvvvgiW7Zs4b777utwrqRErZqnp6d7HU9PT3efa899991HfHy8+8+oUaP6/qUFQQherDaVv3XC9wf7TYS+xGLxHYhbkx9Y6VzBFljzZ7VvrMwOhhCmaaYjLDbDdISJEAbbX4APfqH2l9wBJ/xA7deJI0wQBEHwg9UKqx4BixW2P2s2THK5YOvTan/+tb1/jiGEHV4DaJA4FmLSvMdEJsA0PYfs9DvhgodV7MXEFea1DiV20NaiQvdBSiMN4kfCjG+r/XUPmcfbd4w0iM2AmHQlOpbuHph37A2FW1VenUFTeyFMD8vXHWFglkeWBUlOWEubk33Fymi0u7BzR9ihct8dIw1CbVbGp+iusLLgjr3qcyEsPz+fm2++meeee46ICP812xaLxeuzpmkdjhnceeed1NbWuv/k5w+DDmOCIAiCGZhv5ITVHIPHzoD/nge7X/d/XWsDvP4D0Jww42JY/lt13Mj4GEha61SpBECMpxB2nJdG2hvh3Z+p/UU/Uf+AiMtSn6U0ctggTZEEQegXxp0Kp92u9t/9mRKY8r5UoevhcTB9Ve+fYQhhmp4v2r4s0mDVv+CWXXD6L9UiHkDGLIhOg7ZGOLZeHavMVfeKSFCCjqA46Sa13fc2VB3RO0b6cYQBZA6hwPx9b3l/bqr0+ugOy4/1EMJiwr3ODTZ7iupoc6oyxt2FtZ1mlxmOMCMU3xdmYH5w54T1uRC2ZcsWysrKmD9/PiEhIYSEhLBmzRoeeughQkJC3E6w9u6vsrKyDi4xg/DwcOLi4rz+CIIgCMMAz86RrfXw/OVmCO0Hd/h3VX18twrJj8uCc/8KIxeozkO1+b0P3+8uhhssIl7lxYkjTFF9FBzN6ns56171j4fYTHVOSiOHBdIUSRCEfuW0X8CYk1UX5levg02PqeMzL4Gw6N7fv33urGdQvich4R3HWq1m/tVBPSes1MgHm2YKZgJkzIAJy5TLa/0/1byppVY5/lKyO45354RtH9j39MPmvCo+2uOjcs2zLNJiU9sOpZHeYfngHZgfDGzPNzt517U4KKj2XdKoaRq5uhA2wU9pJHgE5gd558g+F8KWLVvGrl272L59u/vPggULuPLKK9m+fTvjx48nIyODTz4xgwXtdjtr1qzhpJNO6uvXEQRBEIIZo3NkzVF47QdQtketsCZNUCH6n97T8Zptz+kdoyxw4b+V8BQWDZn6P8YHujzSMx8MPISwGt/jhwo7XuzcldcVRrlrwmizhXzcCLWtK1YTSGHIIk2RBEHod2whcNFjEJmk3EH731XHexuSbxCZAOHx5md/jjB/GB0lc/WcMAnK98/J/09ttz2r8sJAddoOCe841hDCcj6GY4Pg9Pcgp7SeKx/byI+e2cKeonZlgyU7lUMxJBImnKGOeZRGOl0aVY2qbNZXaWR5Q/AJYeC/PLKy0U5NUxsWS2BCWO7x5giLjY1lxowZXn+io6NJTk5mxowZbvv8vffeyxtvvMHu3bu59tpriYqK4oorrujr1xEEQRCCGSMjbNcrqguULRy+8wKcr3cY3PIUHF1vjt/7Nrz9U7V/6q0w7jTz3KgT1XbAhTCPfDAYHl0jK3LhjR/B6z9UJY49wWgpH++xim6IhY5mccwNcaQpkiAIA0J8lipNNMicYwolfYHh9AqL6X6u14SlytVUvk+50SUo3z/jlqi/N0ezucjpqywS1NwuYbRaEP2/s+DDO3s+F+kFDqeL217Zgd3pAuDVLe0qDva+rbbZy835rEdpZFWjHZemzIFJ0cHrCNt2TM3HRiepBla72wt+OkZZZFZCJJFhNr/3c3eOLAvuzpH91jWyM26//XZuueUWbrjhBhYsWEBhYSEff/wxsbGxg/E6giAIwmBhTBxcegvpVY+oMsexJ8Pcq9Wxd24GRysc/Axe/Z6y1s+9Gpb+2vteo3UhLD9AIay+BJx90Lq6vSMsGLtGOuzdex/DCeZymIJWd3E7wjwa3IRGqJV9kJywIYw0RRIEYUCZvBJO0TMnT765b+9tCGFZ85QDrTtEJUHWArV/8FMPIUyC8jtgscBJuivMmB/4E8Ii4uBHX8KcKwENNjwC/zopsEZKfcijaw+zs6AWm1WVub69vYg2XRRTZZFvqv2pF5hzP49FUKMsMikqjBCbKbsEU0ZYRUMrBdXNWCzwnYXq/4Xdhb4Xo9wdI/0E5RuMSY4mLMRKS5uL/Org7fw5IELY6tWrefDBB92fLRYL99xzD8XFxbS0tLBmzRpmzJgxEK8iCIIgBBOeIsmSX8LMb5ufV/xOtVCvOABv/gRevBJcbTBtFZz39475G4YQVroHWjpxlJTuVVlkf50Mr/RBeYU/R1gwCWFv/gT+OtXsZtUZmga7XzM/Vx/t2TONrDYjB87ACMyXzpFDEmmKJAjCoLD8HrjjKMy4qG/va7jLJizt2fXZevfIPW9Arb5wJI4w30xb5Z215k8IAzWXWvUIXPkaxI1UJYjPXAjlOf3+mqBKIh/8JBeAey+cQUpMOJWNdlYf0HNsy/ZB5UGwhcGksyAqWR33cISZ+WDe5Z/B5AjbfkyVRU5MjeHE8UrM8xeY7xbCOimLBLBZLe7SyWAOzB8UR5ggCIIgAGpCdMrPYMkdqhuTJ1FJsPJPan/3a8pOP3GFygux+rBkx2ao1ueaCwo2dzxfkw9v3qBWFXM+UMf2v6syKHqD34ywICmN1DTI/Vh9fwc/63p82V4lPhrU9FAIq9HFjPh27p44CcwfykhTJEEQBo3IhL6/50k3wRUvw+Kbena9EZh/eLXaxmaa7iDBG1sInHij+Tl1ctfXZC+HG9arkliXw8wX60ccThe/0Esil05J49IFo1g1R2WcvmaUR+7TyyInLFMONsPt7jH3MztGmmWRYAphFUGQEWbkg80ZlcDUzDhsVguVjXZK6zq+26HywBxhYJZHBnNgvghhgiAIwuCy/B444y7fHZZmXGxOMkefBJc+DSFhHccZ+MsJ2/sW/GM+bH8O0GDqeTBbz6X86C5wtvX8/Q1HWIz+j/zIICuNrCuEVt0hF0gHJk83GPTCEeajNBLMwHwpjRySSFMkQRCGFWFRytHT3bJIg8w5yr1uIG6wzpl3tVoEjRsZmBAGSmgao//+qDzYf++m8+jaw+woqCUuIoT7LpqJxWLh4vnK3f7Z/lKqG+1mt8hp56ttDxxhlY12HEap5SCxLV/NVeeMTiAi1OZ2e7VvDGB3uNihi2aTM7qOszID84NXCOvh//GCIAiCMABYLEr8OvipEsTCojofP/pE2Pmid05YUxW8cws4W1Ub9hW/UzlkLbXKKVWZq1qyL76hZ+/o1xFWrdxYg91CvWy/uV/UhRCmaWY+2OiT4NjXqhyhuzjspkAY367lfKzROVIcYUMRoymSJ55NkQB3U6Ts7Gyys7O59957pSmSIAjDE6tVuYJ2vqg+Sz5Y54RFw4/XqSYDvjpG+iN5gtr2sxC2Pb/GXRL5P+dNJz1ORQBMzYxjWmYce4vrWLP+a1aV7QVrCEw+W13oFsLMRdCKBtUxsr0QlhgVhs1qwenSqGy0u58x0LhcGjvzleA1Z5RyW07PiuNAaT27C+tYNtV0ca87WEFdi4PU2HBmjezamZmdJqWRgiAIgtA7wqJh2gVq2xVGTljBN6bL69N7lFU9bRpc85YSwQAi4mHpr9T+mj9BY2WH23WJpvnPCHM5oDUIVsLK93ns74e2Zv9ji7ZB9REIjYJF16tjPSmNrCsANNVSPDrF+5y7NFIcYcMVaYokCMJxhZETBuIIC4SIOAg3y+sqG1p5ct0Rmu1O/9ckZ6ttRW6/vdb2/BqufmIjdqeLZVPSuHheltd5wxVWu/UNdWD86eacL8roGO7hCNNLIw0HmIHNaiFZ7yLZk5wwl0vjQEk9z6zP46YXtnHG/67mn190XyA8VN5AfauDyFAbk3UH14wR8UDHzpHv7VJztrNnZLibB3SG4Ro7VN6AM0g7R4ojTBAEQRg+pEyGiARoqYGSXeBywtan1blzHwBbqPf4edfA5iegdBesvhfO/Wv3ntdcDU614ucWwsKiICQCHC3qfMQg5x15OsI0J5TshlEn+B67R3eDTTrLXNWuPtp9Z5s7H2xkx+ukNHLYsXr1aq/PRlOke+65Z1DeRxAEYUAZfwZgATRIFSGsu/zm7T28u7OYVoeLHy+Z4HtQii6EVecp13lnMRk6z288xu/e3cP9357N+bNHdDp2e34NVz++kfpWBwvHJvHQd+Z2aPBywZwR3Pf+PlLr94INJYQZGI6wtkZoa4HQCMr9lEaCEsfK6lsDEsJa2pzsyK9hy7FqtuRVs+VYNTVN3pEeD32Wy1UnjiE+MtTPXTqyTS91nDky3t3VckaWEsL2FJpCmN3h4uM9atH3nJmZAd17VGIUEaGqc+SxqibGpQSwmD3AiCNMEARBGD5YrTBqkdo/ug7euxXQVB7YmMU+xttg5X1q/5v/Ux0lu4Mh5kQmeVv8gykw33CE2fRJo7+cMJcLduurnDMuNjs72eu7n3fmLx8MpDRSEARBGF5EJ8Oy/4F534URcwf7bYYULW1OPt9fBsCuglr/A2MzITRaLegF4FTPr2ri9+/upaXNxd1v7KK41r8bftuxalMEG5fEk9edQHR4R79QSkw4p09OZYpF7w6aPt08GR6nSiXBPfdzh+XHdBTtAukc2epwcuPzW5l5z0dc9ugG7v/wAJ/tL6OmqY3IUBunTEzhZ8snMT41mlaHi7e3d29etU3vGDl3lFnqOG2EWrwtqm2hUhfy1h1SZZEpMeGcMDawRhBWq8Udqh+sgfkihAmCIAjDC6M8cs1foGSnKoFc8Tv/48edqsLzNRe8/4vuBee3zwczCJbAfE2Dcr0DpJFj4S8nrGCzKmkMi1XdOUMjzQYA3c0J89cxEszSyObqzss0BUEQBGGocOqtcP5DakFOCJi1uRU06SWR+0rq/A+0WMycsC7KIzVN49dv7cbR1spp1h00t7Rw5+u70LSOJXpbj1VzzRObTBHsWt8imMEls5IZaykFwJnqIYRZLObcTy+P9JcRBpCqHyvvpHPkI18c4r2dxbQ5NVJjwzl7Rga/Oncqb954MjvvOZNnf7CIm5dnc9WiMQC8uDm/k2+lI0bHyLmjTSEsJjyE8bp7a0+R+vt4f2f3yiINJqWp8sicEhHCBEEQBKH/MYSwVn1lcdlvICbV/3iAFb9X5YxHv4KXrla29kBonw9m4BmYP5jU5oO9AayhMP0idcyfI8zoFjnlXAjVg1sT1OSq2zlhnTnCIhJUBhlAXVH37isIgiAIwrDhw90l7v28isbOc8KM8sjKzoWw93YVs/pAOfeHPc7TYX/m+6GfsPpAOa9uKfAa91VuBVfpTrBF45J4yo8TzJMzkiuxWjTKtTi+Lm0npbgD86twujSqGn1nhHke8+cIyymt55HVKvfrb5fNZtNdy/jXVfP5wanjmTMqgVCb+ewL52YRZrOyp6iO3YWduOo8aLI7OKALj3NGJYLToRZO7U1MzzJzwuwOFx91syzSIFvPHcspC87AfBHCBEEQhOHFiHlmGeCIeTD/2q6vSRoHlz2nxLCcD+CFy8He1PV1fh1h+upa0yCXRhr5YCnZZpOAsn0dnVguJ+x9U+3PuMg8nqgLYdVdC2Gf7C1l5YNfsvLBL8nJUeWYe5riqW1u57CzWMzvS3LCBEEQBOG4pM3p4tN9yl1lsYBLg9yyTtxDRmB+J50ja5vb+O07e5lpOcyF1i8BuDL1MAC/e3cvJbVqofPD3cV876nNNNmdnJqdwpPXnUBUWNfx6WEVal51wDXKXdLpJsp0hFU32XHp8apJ0d0rjXS6NG5/dSdtTo3lU9NYNSerQ16ZJ4nRYZw5XTn4XwrQFbaroBaXBhlxEWTER8Dnv4d/LoT7RvKbgh9yX8hjJOx9jt3r3yeutYiMaCsLxwVWFmkwKV2VRuZKaaQgCIIgDAChEaoMMCwGvvWAygELhOzlcOUrKoPi8Bfw3Le77vpYryZwHRxhxmSouaZ7797XlOmZZ6lTIC4LolPNwHxP8r6ChlLl1hp/hnncwxF2sKyBV7cUUNHOxt/Y6uCXr+3kh09/w/6SevaX1BPWoHIq7llTz+l/+YId+e2+ByMwXxxhgiAIgnBcsvFwFbXNbSRHh7FIF1n2d1ZGlzxRbSv8C2F//nA/5fUt/D7qRfexUc37mD0ynvoWB3e+vpOXNh/jhue2Yne6OGdmBo9/d0FAIhgApXvUe2qjOVLR6H3OQwgz5kqJUWFe7i2DzoSwp9fnsT2/hpjwEH6/akanIpjB5SeoXNc3txfS0taJq07HKIucY+SDHfhAbTUnKY25fCfkC64oe4B5n13JV+G3sMF5Oba/ToIXvgONFV3eH2CS7gg7XN6Iw+lSB+2N8MhJ8NHdgVdf9BMihAmCIAjDj28/CT/f3/3Q2nGnwdVvqNDTo+vg6VWd51i5HWFBWhpZrjvC0qaqZcnMOepz+/LIHS+o7bQLvDsx6Y6wuuJDXPjPddz2yg4W3fsZVz+xkVe3FLDuYAXnPLSWFzfnY7HA9aeN5/Gr5zHKppxwzriRVDe1ccVjG1h/yM87Yw8AACAASURBVGwpLkKYIAjC8UtLmzOgbnnC8ObDPWoOdeb0dKZlqnK8/cWdCGEpuhDmpzTym7wqnt94jBXWLcxx7lYuf1sYluYq/n5mPGE2K18cKOeO13bh0uCyBaP4x3fmER4S4IIpQKlaSDygjeoohHnkw3YWlA/+M8IKqpv4y0cq2/WOs6eQGR8Z0GudNCGZkYmR1Lc4+GB31257d1D+6AQ1V63Q82R//BUNFzzFPx3n86VzJke1DFo0vRNlYxkceB9e/m5AebpZCZFEhdmwO13kVepVFoe+gLI9sO8d7yZTg4AIYYIgCMLww2qD8NieXTt6EXz3bSVmFX5jZmf5wp0R1r40Mki6RpbpHSNTp6jtCF0I8wzMb62HvW+p/blXeV+vO8IqC3Oob3WQGBWK06WxNreC217ZwZWPb+RoZRNZCZE8/4MTueucqSwfBTatDSw2nr7lAhaPT6bR7uTaJzfx+X7DQSelkYIgCMcrt768nVP+/DkHgjREW+h/XC6Nj/aoOcFZ0zOYkqnmbPs7C8w3HGGN5R0c90crG7ntlR2E4ODemFfUwRNvgIxZAIxt2c8tK7Ld43+0ZDx/unhmt8Lf0TS3I2yfazT5VU3YHS7zvDsjzHSE+QrKB0iLU1msnoKwpmnc/cZumuxOFo5N4sqFowN+NavVwqULVC7ri5u8yyOdLo2Nhyt5a3shT647wgMfH2DDEbU4OWdUAhRsUQOTxkPGTGLmXsjzMddxTdudLGl9gFNCXsB52yG45m3VUOnoV/DhLwN6p+y0duWRObrzbPLZaoF2EBEhTBAEQRDaM2IuLP6p2t/5kv9xfoWwIOga6XJBRY7aT5uqtr4cYXvegLYmlb0x8gSvW+xrVYLeCK2chWPi+eqOpay+7XRuXTHJ3Rb7wrlZfHDLqSyeoE8AjaD8uBHEREbw5HUnsHxqGq0OF9c/vYWXN+dTZlFjmyqOudtzC4IgCMcHm45U0epw8drWgq4HC8OSbfnKNRUbHsJJE1KYmhEHqNJIX90dAbXAGaM78CsPuQ9/lVvB+Q+vI6+yiR/HrCXVng9RKXDKz8x81MJvuP7U8fx8xST+esls7jx7akAlh140lEJzFZrFSlHoGFwaHKvyyJN1l0ZWUVHvv2MkmKWRDa0Onlx3hNtf3cHKB9eyJqecMJuV+y6eibU7Ih3w7fkjsVhg45Eqt1ttR34NFz6yjsse3cDNL27nt+/s5aHPD1LT1EZ4iJWZI+OhYJO6wciF7nvNyIpz76+cmYEtJgXGL4GLHwMssPlx+ObJLt9pcoYSOLfn16g82gMf6ifO7tbP1h8EWAwrCIIgCMcZMy9R4aFH1kJtIcRneZ93uaAhiLtG1hxVApctDBLHqWOGI8wIzA+NhO3Pq2NzrvBandtdWMvVLxWwWbMSbnHw5CWjiQ4PITo8hP+3LJublk6krsVBfGRou+ceU9t4tTIZEWrjX1fN57ZXdvDW9iJuf20nZ1mr+E8YHMjN4cI/fMrK6Rn8z3nTGJEQWAmAIAiCMDRpdTipaFAiwXs7i7nz7CndFyT6mS/2l5EaG84MvXue0PcY3SKXTU0jLMRKdnoMVgtUNdopb2glLTbC94Up2WruVZmLljWPJ9fl8cf39+F0aSzOCuXWptfBAZxxJ0TEQZYuhBV8Q4jNyk3Lsr3vl/sJbHlKiWyRiWohMzoZppzXseO47gazJE0gU0ukqqiOvIpG98KgL0eYr46RANFhNiJDbTS3OfntO3qeKxoTrMVcs/J0JqTGBPhNmoxIiGTJpFRWHyjnia8Oo2nw/KZjaBrEhocwIyuepOgwEqNDSYoK46SJKSobLV8XwkaZi6EzRsS7HXte3SInnw1L74bP/wDv/0JVHIxZ7PedTslO5eVvCvhsfxl3zqyHpgoIj4cxJ3f75+trRAgTBEEQBF8kjoHRi+HYetj9Kpx8s/f5pkpwOQALxKR5nzOEsMHsGmnkg6VMApv+6z4uS62SNlWoCV1kovr5LFaYfbn70pzSeq56YiM1LRqV0WmkO0uIbiyEFNOmb7FYOopgYDrCEka5D4XarPzt0jlkxkfy5rZCGh3p4IJMSxUWC3y4p4Qvc8u5ZXk21508zmewrCAIgjD0Ka01XcCFNc3sKKg1A7uDgNzSeq57ajNpseFsvGtZ0Il0wwFN0/hwjxLCVs5QC4kRoTbGpkRzuLyR/cX1/oWw5ImQtxZneS53vLqTV7coV+HF80by5/jXsK6vVPOeedeq8SPnq23JTnC0eudSaRq8d6u5gOfJ/vfhqle9j+lCGOnTGeeIZk9RnXdOmCGENVe5s7/8OcIsFguXLhjJ+7tLmJoZx6yseM62f8T0Lb+GHZMh7Xcw6axulw9efsIoVh8o59kN5s900dws7jxnqm9RzuWCQr000sMRNkv/fzIlJoxF45K9rzn1NtV0ae+b8PLV8MMvvOZ8niyZlIrNauFgWQO12z8hHmDiMrD5mD8OMDLTFARBEAR/zLpUbXe+0vGckW8VndrxF3pUEJRGGvlgRlkkqAmV0UCgaJvpBpuw1B1gn1/VxNVPbKSmqY05oxJIHqmvntYcDey5NboQFu89KbJaLfzy7ClsuGsZz96yCoAMay0f3HQSC8Yk0mR3cu/7+zn3obVsOjLI2WqCIAhCv1BU692A5v1dwZUV+bXe2KWsvpXCmk6a5RwHvL+r2O3cCpSGVgc7C2qoabL7HbO3uI78qmYiQq2cNsl0XZnlkV3nhO3dtYVXtxRgtcCvvzWN/71gPCGbH1VjVvzeXABMHKcEKqe9Y8fskp1KBAuJhOW/VQues69Q5w5/0XEO5xbCZjA+JVoN8xTCIs2ukWV1nYflA/z2ghlsvns5T39vIbedNZnp9l3qRMUBeOEy+O95aq7WDZZOSXcLXtlpMbx4/Yk8cNkcv840yvdDa53qmJ42zX341Ikp3LFyCg9fMa9jjprFAqsegYyZKq9tzZ/8vk98ZCgnjFWLw67976uDk8/p1s/UX4gQJgiCIAj+mLYKrKFQusucABkY+VvtyyLBuzTSX9ZFf2M4woygfAN3YP42s1vknCvVJfWtXP3ERkrrWpmUHsNT151ASNJYNaY6QCHMhyOsAzFpYLGB5mRKTAsv/2gxf/n2LJKiw8gpbeCG57YG1P5bEARBGFoU60JYmO78fW9nsf9MqEFgU565ELO3qBNBZphT02Tnphe2cdMLW2myOwK+7kfPfMP5D69jzu8+YcEfPuXyR9fzqzd38cz6PDYdqaK2qY2PdHFtyaRUVZqnMyXDCMzvrHOkWpwLqT6ExQL/uXoB3z9lHJbcj8HRrALfJ51ljrdYIEt3hRV+432vfe+q7cRlcMotsOJ3cOG/lCDkckDOR97jywwhbBrjUpUQdqSiwTyvL4JqTVXsK1b/7WSnd6NxU7m+gDlxOdjCIW8tPHo6fNB1ML1BWIiV536wiH9eMY/3bz6VE8cnd36BkQ+WNc8UD1GLlz85fYL/68Oi4fS71H5h52Ld8qnpjLKUkth4SM39spcH+uP0KyKECYIgCMc9FQ2t/PXjAzy17oj3hDwqyZxQ7XzZPN5SC5/8Ru2PX9LxhoYQpjkpqyjvn5fuCl+OMHAH5rt2vQp1hRARD5PPoa6ljWuf3ESe3gXy6e8tIiEqTJWIQuCOsFo9/Di+EyHMajMFxLoirFYLlywYxec/X8IVi0bzq3OnEhHajXbmgiAIwpCgqKYFgBXT0okKs7nLI4MBTdP4xkMI21d8/Ha1PFBSj9Ol0ebUKKwOzBlX29zGet1RB2puteFwFc9uOMav39rDpf9Zz+zffcw/V6uge6Ms0sAIVt/fyfee41DXjLWU8LNlE1kxLV2dMLpfT1vVsZzQIyfMi/26EDb1PO/jU76ltvveMY8526D8gNpPn87YZCWE5VV0DMu32Buob2zEZrW4xb0ucTmhXF9gPft+uGkLzLpMfd74L1XWGSCT0mM5d1ZmYDET7nywhZ2P80XGDLWtOAAO/w7ApVPSWG7dCoBj1InmHHmQESFMEARBGHZsOFzJM+vzcDhdnY6rbW7jfz86wGn3f8E/Pj/IPe/s5dEvD3sPMsojd72qshQAPrwL6gpoihlN5YJbO944NBItRAW/X/bg+wPfIt7lNB1rHo4wp0vjo2o1ibQ61aTq5dYTueSJrVz67/XsKaojOTqMZ3+wiIx4PZ8jYazaBuII0zSzNDKhi7bfRqfN+iL3oYSoMO69cCar5mb5uUgQBEEYypTUKiFsXEo0y6YqEeO9nUWdXTJg5Fc1U1pnCg57i4NDoBsMckrNeUtBgCWim49U4dJgfEo0u397Fm/deDJ/vWQ2P14ygaVT0sjSG+I4XRqxESEsnZLudf3UTFUaebCsgTYf87fqRjs/fLsMu2Yj0mLnp/P1Bjv2Rsj5WO1Pu6Dji4304QirPARle8Ea4u0gA5hyrtoe/AzsutBVeVCVV4bFQPxoxumlkSV1LTS26o658HjleAISaCA7LSbwRb2qI+BsVWWaiWOVq/7C/6iGR2B2Ke9r8jt2jAyY+FEQHqfcc5W5foeNT43hvIgdAOQknNqTt+wXJCxfEARBGDY4XRoPfprDPz4/CKjJ1G8vmNFhnMPp4rG1R/jX6oPUtagJzNjkKPIqm7jvg/1kxEdwwRxdjMk+S01u6goo3f05Ow8VsmLHs7g0C9dUXkvFEzt444aTSYz2zoGot8QQRzPRrnpe3ZLP3edOa/8a/Ud1HjhaICQCEsficmm8t6uYhz7LJbesnm/C40ixKNv+sy2nsDNP5WDEhofw3+8tdE/wANMRVp3X9XNbasCuT57jR3Y+Ni4TCoG64PgHkCAIgtD/GKWRmQkRzMiK550dRby/q4S7zpk66MH0RllkRKiVljYXe4uP39LInFKz5K8gQEfY+sPKDXbihGRiwkOYPSqB2e0aIdS3tJFT2kBabHiHhjtZCZHEhIfQ0OrgSEUjkzzKCp0ujZtf2s7RGjvFkZmM0QqwVh2ExNGq86OjWQlImbM7vtiIeWpbdVg1MYpKMt1eY0/p6FDKnA3xo6H2GBz6HKZ+y4zHSJsGVisJUWEkRYdR1Wgnr7KR6SPiwWpV924sJ9FSz7QRcQF9b4BZFpk6SbnmQTnbYjOVI7++2JyP9RVNVaaANfKEzsf6wmKB9Omq6VLpHrXvi+YaZrvU9/dm0ywGcDbcKeIIEwRBEIYFVY12rn1yk1sEA/jv+qM8sz7Pa1xLm5MbntvKnz/cT12Lg0npMfzn6vl8cdvp/OCUcQDc9soOvj5UoS4IjaB67NkAbHnlz8ze/j8APO48h53WaeRVNvGT57Zgd5irl6sPlFHYqlYqEy0NvLezGJdrADNQjLLIlElsOlrLeQ9/xU0vbCO3rIG4iFAakpQ46EqZwr03XsPfL5/Dz1dM4oXrT+zYLj5Bn3jVFXZqfQdMN1h0KoRGdj42ThcaRQgTBEE4bjBKIzPjIzh9cirRennk9vyafnleVaMdZ4C/fzfrjVpW6Qth+VXN1Da39ct7BTuejrBASyM3GEJYJ7lUsRGhzB+TyKikqA7nrFaLWR7Zzkn/8OcH+TKnnIhQK4mjdSmlQp/vucsiL/DdZTEqCZIm6D+M3iHRKIs0yiA9sVhMV5gxzqNjpMHYZPUzeJVH6oH5SZZ6ZoxoN5/qjDIj17VdnIXeyIi6wsDvFShGqWjSBIjuIkvMH8b3Ubrb/5iDn2LTnOS6snj1SFjA/z/2NyKECYIgCEMaTdPYcLiSbz20lrW5FUSEWnnwsjncvnIyAPe8s5e1uSqnq6HVwfee2szHe0sJC7Fy/8Wz+ODm0zhregYWi4W7zpnKuTMzaXNq/OiZLXyyt5Qbn9/KT3apLkXn2DaRZqmhJmocZ/30Id6+6WSiw2xsOFzFr9/cjaZplNe3ctsrO6jRYgDICG2mqLaFbfnVqrSy8hA4Aw+e7RH6yuKmpnQu/Y8qeYwND+GW5dl89culjD1ZlXtaT76JGSMTuGBOFjcty+4ogoEKtg+JBDQzCN8ftb47RvrEXRoZXB3DBEEQhP7D7QiLjyQi1OYuj+yP7pGf7SvlhD9+yh/f2xfQ+M1HlRC2fGq6u4xv/3HoCtM0zbs0srqpk9GKmia720F34rikHj/bzAkzv/f8qib++YUSvf64aiZxWXrkQ+VBaGs2Q+19lUUajPTICasrgoLN6rMvIQyUCwzgwAcqH6xsr/rsIYSNS1HzPO/AfCUoJVLve07lD8MRltauwZFbCOuHuVJBL/LBDNxC2B7/Yw58AMCX1gVUNdr7TfTuLiKECYIgCEOOsvoWXt9awM9e2s7Cez/j8kc3UFTbwriUaN688WRWzc3iJ0smcNHcLJwujRue28qWo9Vc9fhGvj5USXSYjaeuO4FLTxjl1RbaarXw10tnc8LYROpbHPzw6W94b2cxm7QpVIfoLb4tNhKueIIxGSlMyYjjH1fMxWqBl77J57G1h/nFqzuoaLDjDFcToEUZ6v7v7CiGdQ/CP+bBA1Pg/V/AsY192lXS6dLYeqyaPTvU5OaLymQsFrhi0WhW/+J0blk+ibiIUJh/Ldx+BOZe1fVNLRYz76urwPyaADpGGogjTBAE4biipc1JdZNyWI2IV0LTOTPVosj7u0r6tHuk3eHi9+/uxenSeOWbfFodnXcirmho5XB5IwALxia686qOx/LIiga7++8JoDCAjLBNR6rQNJiQGk1aXESPnz3VhyPs/o8OYHe6OHliMhfNy3J3jqQyV+V4tTWqBTijBNIXRmB+4RbY/57aH3mCimnwxejFStRqqYGj63w6wsbrnSMPVzS6j9nD1NzPGlHM+qoXaHYE5qYzGxy1Kxzsz0VDdz5YD8oiDdL1+BF/QpizDQ5+AkDtqGWAEqiDAckIEwRBEIKeVoeTLXnVrMkt58ucCndbaoPIUBvnzMzknvOnERuhMicsFgv3XTyTo1VNbDlazcX/+hqAhKhQnrpuIXPa5VYYRITaeOyaBVzy7/XkljWwfGoaPz9zMom5P4LP/wBLbjeDV4GlU9L51bnT+N27e7n3fWVtDwuxMiN7HOxbz9xUDY7ChzsL+E3U41gAGsth06PqT/xoWHlvx65Ffmhpc/LF/jKa7Oak3u50selIFWtyyqlqtPNB2H611JU6hXcvPUVlV3hisbi7GwVE4hjVFairwPzuOMKMyacIYYIgCMcFxXpQflSYjbhI9c/Q9uWRc0f3TUe5FzYdI69SOZnqWx2szalg+bR0v+ONbpGT02NJiApj2og4Pt1Xyt6i408Iy9XdYFYLuLTAMsLWB1AWGQhTdAHScIRtO1bNOzuKsFgwc+SSdSGs4mDXZZEG7sD8LSr0Hvy7wUDldE0+G7Y9C9ufN+c3HkKVkad6xEMIq9RiyQQOp+9nze6NTE6ewMqxKzv/oZ1tUKFndaX6c4T18VzJ5TTLRHvjCDM6k9cXQ2NlxxLLY+tVp/WoZMbPPQNyd/H5/jJuXzml470GGBHCBEEQhEFlV0EtUeE2JqTG+Dz/6d5SbnlpOw2tZjmhxQIzRsRzanYKp2SnMH9MIuEhHTvzhIfY+M/V87ng4XUU1jSTHhfOM99f5BXA6ouEqDDe/ukplNe3MlrPgCD9Vph+ESRP6DD+upPHcqi8gec2HgPgV+dOJaFhAwBjo+zERYQwoWkbFkchRCSoTkB73lDZE7XHYM2fAxLCapvauObJTezoxFaeGAETLcWgwe3XrMKS1A1rvj+MnLAuHWHq5+9WaWRdkXLFDXJIsiAIgtC/FOvOooz4CHcwvlEe+faOIt7fVdwnQlh9Sxt//0wJC1kJkRTWNPP+ruJOhbBNR1TTmAVj1fOnHceOMKMsct7oRL45Wk15fSstbc5OOyBuOKyExMUTeieEGaWRRbUt1Da1uctaL5430lzUS1ZxFdTmwwH13E7LIgHSZ4ItHJqr4MgadayredeU85QQtusV9TluJESai6hjk5UQluchhBXbI8kEqsPUscrmys6fASrE39UGodEd50+x/bRoWLYP7A2qC2Z7F1p3CI9VTQqq86BsD4w7zfu8XhZJ9lksmZyB1bKL/SX1FFQ3MTKxY07cQCJCmCAIgjAoVDS08rt39vL2jiKiwmx8eusSRiR4B6w3tjr45eu7aGh1kBITzmmTUlgyKZVTJqaQHBMe0HNSYsJ5/oeLeG1rIZcuGBnwL97IMJspgoFaHfQhgoFyn91z/nRiwkOIDLNx9YljYJ2aTNtaqjlregaLd65Vg2dcDJNXqj+Vh1SpZHmOyg2z+f+1XN1o56onNrKnqI74yFAvR5vFoiaPSyenMb/2I0LeaoOYdCwJfdRhyN05MkBHWEClkfoqp6NZlR6079okCIIgDCuKdEeYURZpsHRKGm/vKGLrsb7JDvrPmsNUNdoZnxrNH1fN5DuPbeCTvaW0Opw+F80AvtHzwRbq+VbT9Y5/uaUNtDldhNqOn0ShA3rHyEXjk9hTVEdzm5OimmbG+1mwrG60u536i8b1TgiLiwh1i5d//yyXb45WExFq5bYzJ5uDolMgIl45jewNEDvCLH30R0gYZM4ys8HSpvmd07kZf7oSiux6Bli7rohjU9QcsbqpjepGO4nRYeQ1RzLBaqXBqhZv6+3eof8+McoiUyerzpOeGDES9X0shBn5YFnzzC6VPSV9hhLCSn0IYbkfq+2ks0iMDmPBmCQ25VXx+f4yrlk8tnfP7SUihAmCIAgDiqZpvL61kN+/t5caPYOiye7kj+/v459XeOc7PPrlYSoaWhmTHMUnP1tCWEjPJqJjkqO5dcWkXr97Z4TarNx5jke3H6P0sLmaC+bEMW+3mnw5Zl1u/vJNHKdWANsa1Ypgqu93rGho5arHN7K/pJ6UmDCe/cEipmT4aMutafCvf6j9RT/uOKHqKYE6wmoL1DYQR1hopBK/mqvVSqcIYYIgCMMawxGWGe+dITUlU7mAckrq0TTN7RbrCSW1LTz+1WEA7lg5hUXjkkiPC6e0rpWvcivc4fyeNLY62KOXQJ4wVv3uHpkYSWx4CPWtDg6VN/j+nTtMMUojJ6XHMjIxktyyBgo7EcI26t02s9NiSI0NbJGyM6ZkxFJY08z/rTsCwPWnjifD878ZozyyUO96OO38wOY7WQu6Dsn3JDQCJi6HvW+qz+nezqmosBAy4yMorm3hSGUjidFh5NSFMj7UlFi6JYT5cmYZMRL1JX3rns/Xv4eRvSiLNEifrioc2neOrDqsGhpYQ2DCGQAsnZrGprwqPts3+ELY8SNtC4IgCIPO/pI6rvm/Tfz8lR3UNLUxNTOO/71kNlYLvLezmK8PVrjHltW18OiXajJ7+1lTeiyCDRqGsNNczYmt64iytHLIlcmGlnHmGKvVzFco8x00WlbXwuWPbmB/ST1pseG8eP2J/ifkBz9VnY3CYmDB9/ruZwnEEdbWrLLPIDBHGKhVXOifbkiCIAhCv+J0aVz4yDoufGQdTlfXQffFdcoRltnO/T0+JYYQq4X6VofbNdZT/vZJDi1tLhaMSeTMaelYrRbOnqHEhPd2+v5ds/VYNU6XRlZCpNuZbrFYmKq7wo6nnDDPjpGT0mPJSlTfR2c5YRv6KB/MwBBGQbn6r1/iw7lllEcCTFsV2I1HerjGpgYghIF3+aQRDO+BOyesvJG6ljZyG8I4HBrqPu8lhBVugWYfrkd/HSMBYjLU1mmHpgDKLAOlLzpGGvjrHJn7qf6ME5WDD1g+NQ1QzRVa2jpvYNHfDLF/VQiCIAhDkbyKRm5+cRtn/30ta3MrCA+xcsfKKbz905P59vyRqpQQ+M3be2hzugD426c5NLc5mTs6gXNmZgzm6/cMtxBWRcjOFwF4zXkq77ZvEe8Wwrzbu9e3tPHQZ7kse2ANB8sayIyP4KUfLWZiWif5Zuv+rrbzr/XKseg1hiOsqQJaG3yPMdxgYbEqBy0QjPLIvrb8C4IgCP3O0cpGth2rYduxGnLLuna++HOEhYVY3TmhB0p6LjodKKnnlS2qRP9OI1gdszOlUR7Zns1HvMsiDdw5YceREFZa10pdiwOb1cL41GhG6kJYYQBCWG/zwQwmeyz23bpiEjHhPorYUnQhLCYDRi0K7MZjT1FzlIxZ6k8gZK8AW5jaz5jZ8Za6EJZX2cjeojqqtViO+BLC8r6Cx5bCq9d1fEaZarRE6tSO50LCIFrvWl5XGNg7d8WRL5VTC0vvOkYapOlCWNk+FcJvoHeLJHu5+9CE1Bj+deU81t+5tNPMuYFAhDBBEASh36hpsnPn6ztZ9sAa3tpehKbBubMy+eiW0/jJ6RPcmRu3rphMUnQYuWUN/PfrPHJK63lps5rM3u0xmR1SROoT6pp8yFuLhoU3nafw4Z4St9gHdFhJa2x18Mjqg5x6/xc88EkO9S0OpmTE8vKPFrtXHn1SsAXy1ioL+ok39PHPkuBezXMH4ren8pDaJowK3LpvWP4/vBOe/TZ89aD6OZyOzq8TBEEQBp3cMnNhpLMmLgZG18j2QhiYIen7SwIoJfPDQ5/l4tLg7BkZzB9jltsvGJNIWmw49a0Ovsqt6HDd5jzvoHyD7gTm7ymqZd3BjvfuLWX1LW6H1kBgPGtMchThITayElQOVmGNbyGssqHV/Xe2aFw3ulF3wsKxSUSG2pg98v+zd57hTZxp2z7VLDe5N1zANjY2oXcIJUAIpPe+KaSyCWGTzaZsNu9usu9mk02+Ta+bN1lSSU+ANELvYMB044p775ZcZLX5foxG7rZsywWY8zh82JZmRo9lSfPM9Vz3dfty4/TIzjdKvEIUiOY94nwMhC4MViXD8p+cn6e4+8INH8Flr4gZXu2Itc/LsisbSCnWU4OObLdOhLBMuyh0emtbd73FBNX2+VNnjjBo1TnSBe75xmr4foX485TbetdBvCsCYkDtARajWA4JYpVAzk7x5/iljk0VCgWXTBiBn6dbAbbwfwAAIABJREFU/x+3n8hCmIyMjIzMgCAIAn/48ihfHCjAahNYnBjCT6vm8fatUx0raBK+nhqeWCZOMF7bnMn/rD2JTYBl40KZHu2aidWgIznCrM0ACNHzMXmHU9toZnt6Rct2rRxhBdWNLH11Jy9tSKe20czoYC/evGUKv/xhPlEBPYT877W7wSbcCL4RLv5jgCB7fln2ts7vP/q5+H3kbOePOf568Xky1Ysrh5ufgQ8Ww1s9hN7KyMjIyAw5ma0EmqNOCGHFdjGlfWMcaBHC0vshhCXniYLW3fNi2tyuVCocrrCf27myTRYbRwrE/Wa2m2+cF94ihAlC16WfqSV6rn1nL7d9mEROqw6C/aXcYOSyN3Zzyeu7OJJf47LjdockhCXYu2u3lEY2drr9AbubLiFU53QTo54I83Vnz58X8+X9c1B31aQgJBEez4LZD/Tu4LrQloU9Z0m8DGbc0+ldrUsjU4rqqBZ05LTKCNOb7CJqQVLLTlIXShCdWTYLaH1agvE7jLl37vnMmky25m/teIcgwLqHxOMExsElLzp1vB5RqlrmslJOWO4eURjziehfV8oBRBbCZGRkZGQGhLVHi9iZUYGbWskX983mv8tnMD6i68nHjdOjmBTpS32zhQM51aiVCp68uIvVsTOBduHvysm3cvlEcTLz2DfHWi4a7JZyoTqb5e/voKi2iQg/D169aRIb/3gBV0wKR6nsYeWy6jScWi/+fP4ql/4ZDib/Tvye9F5Hx1ZdIaT9LP484z7njxl7ATx+GlbsgmUvQMJlYlnliEmuGbOMjIyMzIDR2hF2pIeOjw3NFvRG8dzRqSMstH9CWEOzhVJ7BtmYTiIEuiqPPFlch9Fsw99TQ1xI2zD4uBAxu6y20exws7VHbzTzwGfJNFtsCAJsTy/v0/jbY7UJ/PGro1QYmrHaBJ5dn4LNiRy2/pJp7xgZb/9/9FQauc+RD+baRcsALzc83Ia2dM4ZWpdGniiqQ6/QUKRuF5ZvMUHR4Zadjn8lilIg5roCBCd27VKT3PNOOMLSq9P53S+/4+FtD5NZk9n2zkMfQvrPoNTAdR+CWzdVBr3FUd1g/3ukbpFxS1wX8O9iZCFMRkZGRsblVDeY+MdPYubVwxfGO5UboVQq+PtVLUGkv5s1sssORWcEGnfQ2F1cGi8YewWPLh3D1JF+1DWZue2DJA7mVoN3MDaPIBQIeNRlER3oyQ8Pns81UyJR9SSASex7CxAgflmHrkYuY9LNYrlnbb7YHag1Bz8EwQrR83v/+EqV2NJ8zoNwyxp4IgeueM1145aRkZGRGRAk0QREJ1GjqeuydklI0mnV6Nw1He6XHGGnK+rbxgc4ieTECvRyw9ez4/Ed5ZFGi6OEMbuinne2ZYn3Rwd0iGFw16gc4lhnOWGCIPDY18fIrWpxS+3MqOiwXV94a2sWe7Kq8NCo8NaqOVZYx7fJhX06lsVq41+/pjk1tnRHUL74d0fa3XulemOn/xdX54OdaUT5e6JSKmg0Wcksr0fhVoOt1evIYDZAyTGxOsDdF9TuUJkBJUfFDSrs+WBdlUVCq8ZC3TvCKpsqWbV1FU0WUbRMq05rubPsFPz2tPjzkmchfHIv/konkBoJSIH5jnywpZ1vPwyQhTAZGRkZGZfz3E+nqG4wkRim4/4FsU7vNznKj8eXJTA/PoiHl4wZwBEOEpIr7LwrQeuNj7uGT++ZxZzYQOqbLdzx4QF+Pl7CcbM4yZnjVcbn980mxKfjanmX1FfAEXtZ4tyHXfwHtELjATPuFX/e91bL7WYjHP5Y/Hnm/f1/HKWyg5tORkZGRmZ4YbUJnK4QhTA3tRKbACcK67rcvqROvDgP68QNBqLzyFurxmwV+lRemG3fp6ssTbF7pNh457+7c7n/k0Nc+MoONqeKDq5rpnReliblhKV2khP2f7uy2XiqDDeVkhevE4PU92dXdxrI3xv2nq7k9S0ZADx39XgeWRIPwIsb0qhrMvf6eFvSynlvx2ke/fpot909BUEgy+7yG2N3hAV5ax3/39J2rrjK+mYy7GLozJhzUwhzUyuJ8m8p9fX1EUtFR1hEUdhgMiDk7xfvHHk+JFwq/nz8a/G71Cips6B8iVaNhcw2M6UNpR1KdZutzTy87WFKGlpcY7n6XPEHcxN8d49Yqjj6QtfnyEIrR9hJsUqhOlt0nsVe4PrHchGyECYjIyMj41J2ZlTw/ZEiFAp44doJjkB8Z1m5KI5P75lFgNfQB2n2m+BEQAFT73Tc5KVVs/quGSxMCKbJbGXlmsMcMYq294fGm4joJDulW7I2iyuNYRNh1PkuHHwnzLxP7J5UeBAK7K23U74XW3r7RLZM8GRkZGRkzmoKaxppttjQqpVcMEbsanessOvyyJJae1B+F+c4hULhcCH1JTA/2y7KxQZ3Xe4llUfuzqpk46kyBAGWjA3h6xVzHPe1p3VOWGuSsqt4cUM6AH+94jxunB5FsE5Lk9lKcm7f87wqDM08/OVRbALcMC2S66ZFcuf50cSFeFPVYOL1zZk9H6QdaSXi81lZbyLJ7uDqjOI6I/XNFjQqBdGB4vOoVCoc85KCdjlhSdmi6JMYpjs75mx9pLX4GhwgisETjWI+rE2w0ViwT7wzaqborgc48a0YM+GMI6xVaeSbR97kom8v4qafbuLXnF+x2CwIgsBf9/yV4xXH8XHz4YYxNwCQp7eH8h/5TCzB9AqGa95zvrlAb5CEsNo8OPm9+POoOaDtptP5ECMLYTIyMjIyLqPRZOHptScAuHNONFNGnuPOnmvfhxU7xMlAK9w1Kv5z+zSWjQsFoEATDYCvofcTXMrtNvSRswc+h8E7BCbeKP687y0x4yLpP+LvM+4BVSctzmVkZGRkzjokJ9DoYG+m2s/13QXmS6WR4V04wgASwkTRKb205y6N7cmuEB1h3UUqTI8OYOwIH9xUSm6aHsXmRxfwwZ0zmNlNt8P2nSPNVhu/nCjhoS+OYLUJXDMlgttmjUShUDA/PgiAHZl9K4+02QQe/VrMBRsT6s3/2uMiNColz1whxg58vC+3TZMCZ2jddfLH412X12XYBciYIC/c1C0yQVc5YQdyRFHNVd0iz1RaN4DSeoplt2NNJtQK8Tk0FB8S7xw5G0YvBs9AaCiHjA0tXRa7C5RvFZZ/oERchEytTuWJnU9w+Q+X8/jOx/k151fUCjWvLnyVCyJFF1ZuXa64n+Q6m3qHOI8bCDwDWsaZ9J74fRiXRYIshMnIyMjIuJCXN2ZQUN1EuK87jy3r2Gb6nMMrqMvgd61axdu3TuWNW6aw4vorxBulyUpvkPYZrK48s1eK31N/FFc0S46CStvG9SYjIyMjc3aTWS6KJvGh3kyO8gPgaDeB+VJp5Ajfrl3Pif3oHJnTQ2kkgEqp4IcHz+fYM0t58fqJxHUSqt+esXYhLK+qkRd+TeX8f23lwc8PO8Sqf14z3pEttiBedMbtzKjs9fgB9pyuZFdmJR4acX7QOix+fnwwS88LFYPzf0zptotle9JaCYu/niztMoNNEsykoHwJyRFW2E4IS7J3jJwVe26WRUrEtnrNmRSl4m1mCz4K0SWnb6oWywTDp4BKA+OvEzfe9jwINrFJkHdo1w8glUYa6yg0FABwXfx1+Gv9Kaov4rfc3wD4y+y/MHPETKJ9owHREWYTbFCTK+7vH8OAIrnCGu2v/7iLBvbx+om8dCsjI3POYrLYMBjNLmv3fLaxK7OC535KxWKzoVEpUSkVuGtU3DwjihumR3XY/tvkQj7cnQPAc9eMx1srn2J6Qq1ScuWkcGi2TzoNJdBYLa6sOYvUoWewhLDQ88SMidNbYJ09Z2LC9eB1bk+EZWRkZM4lsqTugiHeTIj0RaEQS+vK9cZOcy6L7Y6wzjpGSkiB+b0tjRQEwVEaObqb0kgQHdm9wd/LjRG+7pTUGfnPDtG9E+St5eYZUdw9LwZPt5a5zjy7Iyy1RE+5wUiIrhd5n+AI3Z8XH9RBjAL46+XnsT2jgj1ZVXy0N5fl50d3CPhvj9FsdRzXy01FbaOZPVmVLEzo6AySXH7tu246HGG1LUJYbaPJEaw/I/rcdoTFBEkuRBtVzWJDg1iTGR+FimrAoFRCyAQxaxVg4s1w4P0WR3/I2O4d/e4+4OaN3tJAnUkUNR+f8ThPznyS9VnrWZu1loVRCx0lkeHe4agVaoxWI2UNZYxwCGHRLv27OxA6riUk33ckBA/vBXHZESYjI3NOklqi56JXdzDjn5t55MsjjnBQmRb+/Vs66WUGTlc0kFZqIKVYT3JeDY9/e5w3t2S2WY3ce7qSp74/DsDKRaNZnNjNypZMR7Q68Bsp/twbV1hTDRjsZQ4h3QStupo5dleY1SR+d0VIvoyMjMxZRm2jCUsfOiCeCWTa501xITq8tWqHeNJVeWSJXUQZ4deNEGYXfwprmqhv7roDZXvKDc00mKyolApGBnQvhPWFi+0h+/Pignjnd1PZ99RiHluW0CEXK8hby/gI0UG2O7P3rrAyu1gY1kXDnKgATx64YDQAf//xFPd9kkyFobnbY2ZXNGC1Cfi4q7l2aiQAPx4r6XRbyeWXENa2vDTCX3KEtWSEHcytQRBE4TFYd24vKCeE6dCoFIQFNNFsa0aDkgiLBZ0gilsGpRKiZrXsEDEVAka3/B7cTT6YhG4ERWpRdA1wD8BL44WH2oObEm/ii8u/YMWkFY5NNUoNkTrxf51Te1rs9g2DIIS1dH4nfkmX4t6W/C00mHvfEMPVyEKYjIzMOce6o0Vc884e8qoasQmw9mgxF726gz98cYSs8t7b8c9EbDaBZ9en8MrG9E7vzygzcKywDrVSweq7ZvDZPbP46K4ZrLB3gHx5Uwb/+CkVm00gq9zA7z9NxmwVuHziCP500fBeARq2SI6u8lPO7yOJZr5R4orhYDF6cct4o2a5vg23jIyMzBnO0YJapj23mb/8cGKoh+JyxHO/3RFmD7h3lEd2JYQ5HGFdl0b6e7kRYhdVelMeKXWvjPL3aJNt5Sr+etl5pP7vxXx27ywunTCi2yZA8x3lkb3PCSvTi89RqE/XwtIfLoznyYsTcVMp2ZxaxrLXdvLric6FLWgpd0wM8+HyiWLo+sZTpR06W9psApmSyy+0vSPME2jrCJNC98/VbpGtCdZp+eHBuTxxheiMG6UNQA3o7AvGBpUCRrYSwhQKmHhTy+/OOPp9RlBoF8IivSN73NxRHll+HGxmsTRTKrEcKKTSSOgyHyy7NptHtj3CRd9cNORimCyEycjInDOYrTb+/mMKD395FKPZxvz4INbcN4slY0MRBFh/rJilr+5kW1r5UA91wNmfI9rq39iaxZH8jt2Nvk0Wrd2LEkNYlBDCvPggFiaE8NSlY/nb5eIJ+797cnj066Pc9dFB9EYL00b58+8bJqFUDnBg+9lKn4SwQS6LlFAoYNnz4uMu+fvgPraMjIzMGcAPhwux2gS+O1xEqV0EOlsoqm2iyWzFTaVkVIAokkyyC2GddY40GM0Oh1d3pZHQUh6Z0YtAeGfywfqDUqlok9fVHVJO2O6sSmw253O8AEodQljXz5FKqeCBhaNZv2ouY0f4UN1g4oHPD/O/P3Y+d5DKTMeEeTMjOoBQHy0Go6VDjllhTcf/qYSUEVZSa8Rq/5sO5Ir5YLNjz+2ySInxEb7U20SHfoyn6CDUmUThUN/eEQYtjYeg+46REj4RFGpEISxCF9Hj5jE+Yh5YbpV9wdRvJCh7Vxbca4LixZJI3QiIWdDpJt9lfgfAtNBpeGkG5v3qLLIQJiMjM6AcL6zlqrf3cP8nh2gyWXveYYCwWG0sX32A1XtyAXhoURwf3TWT80cH8cGd0/lp1TzmxQVhE+CVTRm9CiE9E2lti5cyLyTMVhvfHy4CxNbd7bl7Xgwv3zAJlVLB2qPFFFQ3MSrQk/dvn9br7A2ZVkhiVlkvhDBHPtgglkVKjF4ED+7r0BFTRuZs4PXNmcx7cetZJ2DIDA6CILDFvqhmtQl8cSB/iEfkWqQSuthgL9R2d5TkCDteUNdBAJLcYD7uarx6yA/tS2C+Mx0jB4tpo/zxdFNRWW9ydJp0FskRFtaDWAiiw2vdyrmsXCSW2P13Tw7lho6fV5KgmBCqQ6lUcNkE0RX047HiTrdr/T+VCPVxR61UYLEJlOmNGIxmThbVAXTbcfNcI7tOnE/H+IwCQNckPkcGzwDQhbXdOCBGjJWIng+RM3o+uK5vjrBcfa54w0CXRYLYCGDFDvj9HnDrKHKZrCbWn14PwPVjrh/48fSALITJyMgMCFabwFtbM7n2nb0cK6hl46kyVq453GWnmoFmc2oZe7Kq8HJT8f7t03hsWQKqVs6l8RG+vHHLFLRqJSeK6jiY29EldbZgstj49WSLEPbbqVJHWQHAjvQKKuubCfRyY1Fi522Wr5sWybu/m4qbWom/p4b/Lp8hNx3oL6GSIywVnBVipdLI1nZ0GRmZfmGzCazem0NhTRM7M3tf3iQjk1le36bD3pcH84ds/jMQSCV0cSEtwtOYUG88NCoMzRayK9vmrhbbS+rC/boui5RICBPL/Ft3OuwJKSg/toeg/MHATa1kjr2LYm8/P8r0Yt5Xd46w9o/1+LJEzrN3tkzKru6wjSQojrGXO14+SSyP3Jxa5ligzq1s4PlfxfmEdKzWqJQKR7ZbYU0TyXk12AQYGeDZbanruUZOndgwKsY/DgCdXRA2+IzofIdL/x8s/6klRL87fMIdQliUrmPDqvaMsotxuU32KpfBEMJAbPbURfOkLflbqG2uJcQzhLkRcwdnPN0gC2EyMjIup6C6kZv+s49/b8zAYhNYmBCMu0bJ1rRyHvvmWK+t4q7g0/15ACyfG83ScWGdbhPg5ca1U0W78Ye7szvdprfUNJj4yw8nWHe0yCXHcwW7syqobTQTrNOyKCEYQYAPdrX8vd8ki62Zr54S0W0OxtJxYex5cjHbHlvI6GGwCnvGExgPSjU014HeideLILTtOCQjI+MS0koN1DaaAdqIGTIyzrI5tQwQw9WDvN0o0zezxX7bcEUQBDacLHGISt0hBeXHt+ouqFYpmRDhC8CR/LblkSVOdIyUaO0Ic9adP9Clkb1lwRixPHJXhvOB+Uazlbom8XPHWSFMYrZdeNtvz+2SMBjNjlwvqeR0SpQfEX4eNJqsbEsvJym7iqvf2UN2RQPhvu48tDiu08eI9JNywhpJyhEFt1myG6wNkhAWGySGxvvYRPHb4Onf/4PrRjhKI6Ug/O6I9okGoMTaiFGhGDwhrBu+yxDLIq+Juwa1cug7y8tCmIyMjEvZmlbGJa/v4lBeDd5aNS/fMInVy2fw7u+moVYqWHe0mGfWpwxq6WFWeT17sqpQKuCWmSO73fbuuWJN/cZTZeRXNXa7rTO89Fsaa5LyefjLo/zp62M0mpzvgjRQrD8q2uEvmzCCBxeJE57vkoso1xupqm9mS6q4enTD9J5PtME6LX6ebj1uJ+MEajdRDAPnOkcaSsBYBwoVBI0Z2LHJyJxDtL6YbN0lrS9klRscbhiZc4et9vPoxePDuHG66N74bP/wLo9cf6yY3392mEe/PtbjtpntgvIlJo/sPDDfIYQ54QiLC/FGqYCaRnOPHRFBdLkX2AXr4bIoJwlhh/KqaXCy+6VUFumuUeLj3juRQMrpai+EZdide6E+LXM1hULhcIX9e2M6t32YRG2jmUmRvqxdObfL8lJH58jqJg7YhTC5LLKFGmMNNc1iNUl00HhAgU4SwrT9F2itujCKe1EaGeAegM5NhwDkq9VDLoTl6/NJKk1CgYJr468d0rFIyEKYjIyMy/hkXy73fnyI+mYxOP3Xh+dz3bRIFAoFixJDePnGSSgUojvr5Y0Zgzauz5NEN9jixFBH55uuiA/VsWCM6JJavTenX4+bWWbgq4Oiu0qpgO8OF3LVW3vI7EUArKtpMlnZeEpclb5ycjjTR/kzdaQfJquN1XtzWXe0GItNYEKEL4lhg9iFUEZEcnaVpfS8rZQPFhgHarksVUbGVexrLYRV913Eqqxv5vI3d3Pz+/uHxAktMzRUN5g4bG9CszgxhFtmjkShEMPTJefScMNqE3hjSyYAp4r1WLop4xQEgawyqdyurWgyKbLzwPwSuxg8wgmnk7tGRbTd2ZXWKidMEARMVlOH7fOrG7DaBLzcVI6Ok0NNdKAnkf4emK1CB3GqK6Q8wjAfdxSK3jUdmhkTgEIBpysa2uSEZZS1LYuUuGKimBOWXdGA2Spw6YQwvrx/DiHd/H8i7UJYVkU9x+3/X8mJJtPiBhvhNQJPrTd4+LcIYS5oIlWuccOiUKAWBEK0PQuQCoWiJTBfM/RCmBSSf37E+YR7D3D3SieRhTAZGZl+Y7UJ/OOnU/xtXQo2AW6cHsmX988mql3XmasmR/C/V4l24be2ZbEny3nLeF9pNFkcHRBvnzPKqX3umSeeOL4+WIDeaO7zY7+4IQ2bAMvGhbLmvtmE6LRkltdz5Vt7+OJAvqPzzmCyJa2MRpOVqAAPpkT5oVAo+P0FYtDqZ/vzWGMP9HXGDSYzALTOCesJqWNk6CB3jJSROYux2gSSXOQIyyqvx2i2kV/dSJYT5WYyZwc7MsqxCTB2hA/hfh5EBXiy0O4Q+twe0zDc+OVECaftgfMmq43cbhzxxXVGGkxW1EoFowLbOl0kR1haiQGjuaVBUm8cYSAGu0PbwPxvMr5h2mfT2Fm4s822UlB+TLBXrwWkgUKhUDDf3j1SKiPsiTJD7/LBWuPn6cbYsI45YdLzl9BOCBsX7uMoQX1oURxv3TK1x66YUufIzafKMFsFRvi6O8QxmVb5YL7iNQSeAQ4hTG/q/wJ4oVV8T0ZYLKianBNXo73FuJdcjQb8nbsGGgjMVjNrs9YCcEP8DUM2jvbIQpiMjIzTNFus/Pu3dG76zz4e/vIIL21I4/OkPB74LJkPd4sngMeXJfDidRO7zJa6ffYobp8tfhi/tCFtwEsk1x0txmC0MCrQk/lxQU7tsyA+iPgQbxpMVr62O7p6y/7sKjanlqNSKnji4kRmxwby8x/mMy8uiCazlae+P8Elr+9kY0rpoJaJSmWRV0wMd0wYl4wNJTbYC4PRQlZ5PW4qJVdOGh6rNeccUufIciccYZJYFiILYTIyriK1RI/eaEGjEj8fS/VGTJa+hZy3Lol09mJY5sxns70s8sJWzWZus897vkkubCMQDQdsNoE3t2a2uS2jG+e65GqPCfLqMNcL93UnWKfFYhM4Ye8qCFBc1+S43xmkPKv0VuP4NedXAH46/VObbbPtLrvYoOFRFimRYHfL5VU55wIss4uFfRHCoPOcMIcjLKytEKZQKPj0nln8/Id5PLYsAaUTjiWpoqLBHrA/KyZg2AiPwwGpY2Ssb6x4g2dgiyPMFUJYg9jkKsJsAX1JD1uLRGtEcTTXwwvcffs9hr6yvXA71cZqAt0DWRC1YMjG0R6XC2EvvPACM2bMQKfTERISwtVXX016enqbbQRB4NlnnyU8PBwPDw8WLlxISooTk36Zc5ofjxVzyeu7hrSs7Fwmu6Kea9/Zy1vbskjKqWbd0WLe2X6ap384ycZTZbiplLx+82RWLorr8cT48JJ4PN1UHCus47eU0gEbsyAIfLpPXH29bdYop070IE4Q7ra7wlbvye22RKCrx33hF1GkuGVmlCOzIlin5eO7Z/L0pWPxcVeTUVbP/Z8mc+27e522zveHuiYz29PFDkZXTm4RupRKBSsWxDp+v2hcqJz7NVQ4hLC0ltLHrnAE5ctCmIyMq9h3WvwsnhcXhFatxCa0lCz1lpJW+yUNwmf8mYDNJnAgp5pn1p1k+eoDjiDvswWz1cZO+3l28dgWIWxhQggRfh7UNZn56bhzF7HdkZRd5VR+ljP8llJKRlk9Oq2aZeNCge6FsKwu8sFAnD9NiRJdYQ98lsyn+3IxW22O95CzjrDWgfkAVpuVU1XiOfFQ2aE2C4jDqWNka6SqiAIny6tL7RlhYU6Khe3pLCdMev4S2wlhIM5Jx4U7L460d3/NjJHLIlvTwREWvxQftfiaNJj7f+1aYBAX5iMtFjAUO7XPKEF0+eVqh9a5J4XkXx13NRqlZkjH0hqXC2E7duxg5cqV7N+/n02bNmGxWFi6dCkNDS1q+EsvvcQrr7zCW2+9xcGDBwkLC+Oiiy7CYJAFDpnOqWs08z9rT5JaoufjfblDPZxzju+SC7n8zd2kFOvx99Tw9yvH8dQlidwxZxQXJoZwwZhg1tw3i6smRzh1vCBvLffahab/91t6r4UmZzmcX8upEj1atZLrp/Wu1O+aKREEeLlRVNvkyNRylp9PlHCssA5PNxUPX9g2xFylVHDfglh2PbGYBxeOxkOj4kh+LTe/v58HP08e0IuC31JKMVltjAn17pD/dfWUCMLsq5A3Te+5LbPMAOEfDTEXgM0Ma26C+i5ar9usUGFfZJI7RsrIuAzpInLO6EDHhV9fyyNbO8IO5FQPqvt3uJFWqufZ9SnM+dcWbvzPPj7el8f29Ap+OFw41ENzKQdzqzE0Wwj0cnPkZYF47r91ltis57N+lkf+dLyYm97fz2Pf9Bxq3xOCIPDG1iwA7pobzfRRopiSWdZ1Ka90X1xIR3EF4LFlCUQHelJZb+Kv61JY8soOGu0uojAn3U4J9jlKRpkBq00gV59Lo0V8H1Y0VThEAWgpjewq5H2okISwrj4/zDYzR8uPsrtoN4IgOMLy+5pz1j4nrLK+maoGEwqF2ICgv4T5utN6PXlWrByU3xrJEeYQwhY8hu6BJEB0hPX387+wXvysjLRYQO+cEBZtEjP1cpXCkJ1/iuqL2Fu8F4Dr4q8bkjF0hcv7Vm7YsKHN76tXryYkJITk5GQWLFiAIAi89tprPP3001x7rdgx4OOPPyY0NJQ1a9awYsUKVw9J5izgnR1ZjpYXyHAlAAAgAElEQVTCW1PLEa4SZDvuIGCzCTz53XG+sWdszY4N4LWbpvR5tao19y6I5ZP9eZyuaOD7I0WOrkquRJpsXjEpHH+v3jmc3DUqbps1kje2ZvHM+hTiQ7yJD+180tcak8XGSxtEgWLFgtEEdzGh8fXU8MTFiSyfG80bWzJZk5TPLydK2ZpWzsqFcdy3IBZ3Tfd5Db3lx2MtZZHt0apVfHbvTE5XNDi6HckMAQoF3PARfLAEqk/Dl7fCnT+Cpt17rjoHLEZQe4B/zJAMVUbmbMNitTm6oc2JDWJPVhWnKxoorOnbAkVrIazc0ExeVaMjBPxcwmi2ct07ex0lVTp3NUHeWnIqG1zmanI129LL0TeZnV7gk5C6RS5KDEHVzoV+4/QoXtucwdGCWnIrG/r0WrDaBF7bLJYxHs6rQRD6Nx/enFpOaokeLzcVd8+LcXR7TO/GEZZR3nlQvsSYUB2bHr2ALw7k8/rmTPLseWP+npoec6gkRgZ44ummotFkJaeynhT9yTb3J5clM9JHFBZzHKWRw+u9JWVq6Y0W6prM+HpoyNfns61gG0klSSSXJTvEvXcufIcyvfjc9HWOLeWEnSrRk5RdTaB93is+l/2/5NeolIT5uFNcZyTIWzvsnu+hxGgxUlwvzrEdQhigs5cj2gQbjZZGvDR9f86KDEUARJqdF8JGNtSiEAQMCqtYmugx+C6+r9O/RkBg1ohZRPkMr4X2Ac8Iq6sT68MDAkTVOCcnh9LSUpYuXerYRqvVcsEFF7B3795Oj9Hc3Ixer2/zJXPuUFzbxOo9uYB4jVhcZ2zTRUZm4PjxeDHfJBeiVMCfLhrD5/fOdokIBuDjrmHlwjgAXtuU4fLMjHK9kZ/t5QdSJllvuWd+LIlhOioMzdz0/n5Otsq7kDAYzezIqOD9naf509fHuOLN3eRXNxKs03Lv/J4FihCdO89dPYGf/zCfmTEBGM02Xt6UwdJXd3KisOPj9ZUKQ7OjOcEVXeR/xYXoWDYuzGWPKdNHPAPg1q/B3Q8KD8C6ldB+JU8Kyg9JBKUc9ykj4wpSivUYmi34uKs5L9yn344wqTTSzZ6jlJRzbpZHnirR02Cy4uuh4YM7pnPof5Zwp715TUX98BPCvjiQz12rD/Lwl0d7XRa7Ja1jPphEsE7L+Ajxwrh9V0Vn+flEiaM00dBs6ZeLXBBaOkXecX40fp5ujmyu3MoGmi0d52Vix0h7aWQXjjAQRZM75kSz/fGFrFoch4dG1atFNpVSwXkjRFfYiaI6TlaKQphaKQo6h8oOAWLFSFWD6HqJGWbCjJdW7RCjCqobqTXWcu36a/n3oX+zq2iXQwQDyKzNpEwvvhecdc11RuucsPQuOkb2ByknTM4Ha0uuPhcBAR83HwLdW8QmrUrrKAXsb05YG0eYwbnyavfaAsLt7+NcfW6/Hr8vFNcX83nq5wDcknjLoD9+Twzo7FkQBB599FHmzZvH+PFip7jSUjEPKDQ0tM22oaGhjvva88ILL+Dr6+v4iooaXmqizMDy2uYMTBYbM2MCWJwgTiy22icaMgOHyWLj5Y0ZADyyZAyrLozvsLrZX26fM8qxuvR5Ur7LjptSXMd17+3FZLUxMdKXSVF+Pe/UCb4eGr68fzYTI32pbjBx6//t54i9JXpuZQPPrk9h9vNbuPO/B3j+lzS+O1xIepkBlVLB/1w2Fi+t8ytwY0f48NX9s3n95smE+mjJr27k1g9aHq8/CILY1dMmwKQov3PSkXDGERQHN34CSjWc/BZ2vNT2focQNm7wxyYjc5ayz14WOTMmEJVS4bjo66sjTBIpFiX2rnvc2Ya0iDRlpB9LzgtFq1YRrBMv9gfaEWa1Cby7/TRb05yLOPjmUAF/+eGE4/eCXoig2RX15FQ2oFEpmBffeXOeCXYhrC8LXTabwJtb2obap/djYXh7egUniurw0KgccRVhPu7otGosNsHhtGpNmb4ZQ7MFlVJBdJBnh/vbo3PX8KelCRx/dimv3TS5V+Mb73iu9KRUiZmYl8ZcCoiOMIDTlfWOcfdmzjVYRLYqjzxVdYpmazP+Wn8em/4Y31zxDXeNuwuA8sZyR0ZYX8PyoW1OWHf5YH1lYqT4P1ncidB7LpNVK5YXx/m1zUlWKBTo3MTnX2/qu5GnwdxAtVE8f/TGEUZNLtFmsaIqty63z4/fV15JfoVmazPTQ6ezOGrxoD9+TwyoEPbQQw9x/Phxvvjiiw73tVeRu7P2PvXUU9TV1Tm+Cgr61sVtqLDZBNYk5XPZG7v49UT/AzLPJTLKDHxrL8v78yWJXDhWFFA3p/Yus0mm93x5MJ/86kYxz8sJZ1NfcNeoeGRJPABvb8uiqouVYYvVxv7sKjadKuNwfg35VY00miydbvtdciHXvrOXguomogI8+PcNk/o1Rj9PNz67dxbTR/mjN1q47YMklq8+wKKXt/PR3lwaTFYi/T24dEIYf1wyhvdum8aOxxf2upwCxM/FqyZHsPnRC5gR7Y/BaOH2Dw9wKLd/F0+fJeWz/lixQ6CTOUOIvQAue4XVvjqWp31AzZFPWu5zCGHy/1NGxlVIQfnSxaTkCOuNGCJR32zBYBTPU9dMEc8HSdnnphB23C76TIxoCeaWYgMGWgj7eG8uL25I4w9fHO2x++e6o0U88d1xBAHU9oW/kl44wqRF2lkxgejcOw+Edog7nTjMe+KXkyVkltfj465mUYIorva1QsJmE/j3RjHG4fY5owj0Fv8fCoXCEYKf0UlOWKa9LHJUoCdatfPxDRqVstcOIodoWFRFerU41tvG3oZSoaSovojShlJy7Plgw80NJhElfYZUN5FeI/4NM8JmcOe4O0kMSCTUS7yuKdKXOl6fIT59ywgD8bUn5YTttX+eudIR9seLxvDt7+dw7dTez3HPZrJqWoSw9vi4ic7G/jjCCg3itbCv2gudIDgnhNlsUJtHtFk8Dw22Iyy5LJnfcn9DqVDy5Mwnh6WDcMCk81WrVrF+/Xp27txJZGRLSHVYmFh2U1payogRIxy3l5eXd3CJSWi1WrTavn8oDAZWm4DZauuQ6ZNb2cCfvz/Ofvvk55VNGVwyYURnh5DphJc2pGET4JLxYUwd6U+4r3hCOVpQS2V9M0Hew/N1YbLYaDJZ8dSqOrSWPhNoaLY47PIPXxjnkmyBrrh+WiTv78wmu7KBOS9s5YKEYK6cFM6FY0NILTHw47FifjpeTGW9qcO+Onc1Y0J1JITpSAzTkV5qcDjLFiUE89pNU/D17H93Eh93DZ/cM5P7PjnEnqwqR+fFRQnB3D0vhnlxQS79gNe5a/jorpnc8/FB9mdXc8d/D7B6+Qxmxfa+tv94YS3/+FEUTZ68OIEZ0XK46ZmEecqt/CflNRoEC9/t/Bv3Bo6BkbNbOkqGyh0jZWRcgdlqcyw6zBktfta2lEb23hFWYneD+birmR8fjEqpoKi2icKaRofT7FxBcoSNd4EQll1RT1SAp1Nzq+LaJl62iz31zRYO5FR36dT6+XgJj359DEGAW2eNpN5oYf2xYkrrnP/fS4u03bllJHEnpViPzSY43c3aZmspY7x7XgwalZJt6RV9doT9cKSIlGI9Oq26TddogIQwHYfzazvt0p5WIt4W74Lw9Z6YYHcfpVSmo/Iw4ePmQ2JAImMDxpJSlUJyWTLZlaOB4dcxUqJ1YL5JIVZZjPFvaaIU4im+VoobxNeOv6emVwJje3w9NZw3woeUYj351aKAn+BCR5iXVs10eR7ZAYcjzL+jECY5wjoTwvYV76OwvpDr46/v9jrCURbpNQJIFUsjBUHMDOoKQwlYTYyyiA7D1kKYTbDx35P/RafRcVPiTT39eb3GarPy4oEXATEgPzEg0eWP4QpcfnUrCAKrVq3ihx9+YPv27cTEtHWSxMTEEBYWxqZNm5gyZQoAJpOJHTt28OKLL7p6OC7BZLHx47Fizo8LZIRvx/ajdU1mbvsgiZTiOsaE6pgy0p8pUX5UNZh4bXMGzRYbHhoVJquNzHLRNj1cVy4GC0EQOJRXQ4CXG7FBXp2++Q/kVLM5tRyVUsFjyxIAMUByXLj4Ab89vaLXnQAHg+oGExe/tpNy++TOTa3Ey03FuHBfXrt58rAV71rz4e4cKutNjAr05OaZIwf0sdQqJa/eNJknvj1OepmBTafK2HSqDKUCbK1ikfw8NYwM8KSq3kRlfTPNFhsGo4XkvBqS89qWDz58YTwPXxjv9ATTGTzd1Hx45wxe3JAGwG2zRzF6ADsUeWnVrF4uim+7sypZvvog798xjfnxzmds1DaaeOCzw5isNpaeF8p982N73klmWHG84jgNgriat9bLnXu+vBXF8p/FIH2AEFkIkxlcXnjhBb7//nvS0tLw8PDg/PPP58UXXyQhIcGxjSAI/P3vf+f999+npqaGWbNm8fbbbzNu3PAt5T1RVEeDyYqfp4ax9o51kmBVqjdisthwUzu/sCWVRYb7eeClVTM+wpdjBbUczK0+p4SwJpOVTHumlSRsQIsQ1mCy0tBscaqsbVtaOXd9dJC75kbzzBXdv5YEQeBv61IcAf0AW9LKOhXCjhXU8vCXR7DaBG6YFslzV43nxd/Ec72zjrC6RjMHc8W5yJKxnS/sgyggadVK6pst5FY1ON3pcENKKRll9ejc1dw1N8Yh2vZFCGsyWfl/v4kC4YOL4hxusJYx6ro89m571qjUXXIgGR3sjYdGhVmdjwoYFzgOhULBtNBpDiGspEI0WAy3jpESLa7SJuq6EcIqGkU3YX/KIiVmxwaSUiyW4WlUinP+mnMwaF0a2Z7uhLCndj1FlbGKaJ9oZoTN6PL4jqB832jxBnMjGOvAo5vol1qxYVi01h8Q2pRGvnfsPd499i4KFFwae6ljjK5ibdZaUqtT0Wl0PDTlIZce25W4XAhbuXIla9asYd26deh0Okful6+vLx4eHigUCh555BGef/554uPjiY+P5/nnn8fT05Nbb73V1cNxCS9tSOOD3TkE67SsuXdWm85xRrOV+z455LA4p5UaSCs18MWBlryjuXGBvHDNRP7ywwl2Z1Wy6VQp9y8YPeh/x3Diq4MF/Pl7MYPBz1PD1JH+TB0pvplPVzSQVV7vsF/fNCOqjeBwYWIIKcV6tqaVDUsh7KO9uQ4RDEQh1WSxsTurkt9/msya+2b3ajI92FTVN/P+TrEF8J+WJgyKo21SlB+//XEB6aUG1h8rYv2xYgqqm/B0U7FsXBhXTgpnXnyQYyyCINBgslJQ3UhGmYH0UvHL0Gzh9xfEsjix60lof3DXqHqcfLsSDzcVH9w5nRWfJrMjo4Llqw/y9KVjuWtudI8ONJtN4E9fH6OotomRAZ78vxsmDUtbskz37Cna4/g5T6PhmLWKyR9dBoINPPzBe2Be6zIyXbFjxw5WrlzJjBkzsFgsPP300yxdupRTp07h5SVecL300ku88sorfPTRR4wZM4bnnnuOiy66iPT0dHQ61064+8L+7CrWJOVz7/wYJkaKcw+pLHJWTIBjESXI2w13jRKj2UZJXROjAp2/oJQElBH2BjOzYwI4VlBLUnY110wZfnOXgeJUiR6rTSDIW9smBNzLTYWHRkWT2UplfbNTQpjkLHOmxPS3lFI2p5ahUSn4w+J4Xt6Uwda0cv52+XkdzoWf7c/DYhNYMjaEf103EaVSwQj7WJ0Ny9+eUY7VJjAm1JuRgV0LnWqVkrEjfDhaUMuJojqnBBybTeB1e6fIu+bG4Ouhcbh8TlfU91qk/WBXNqV6IxF+Htw1N7rD/VIpnSRgShjNVkfDh8HoLq1SKjgv3IeTJjESZ3yQmDc9LXQan5z6hOSyZIwVC4Hh1zFSIsoueudX66lSiXPrhICWRYNQT/EcXmeqBmwuaUg1OzaQD3fnAKKYeCZWppxJNJobKaoXharuhLD2GWGN5kaqjOL76decX7sVwhyOMJ9R4tyvqUZ0fHUnhNXkAhDjHQXkU2goxGwzsyVvC+8eexcAAYE8fZ7jveUKDCYDbxx5A4DfT/o9Ae7D10HociHs3XfFJ3bhwoVtbl+9ejXLly8H4IknnqCpqYkHH3zQsVK4cePGYTE5ak9+VSMf78sFcHSO++yeWZwX7oPVJvDIl0c5kFONTqvmP7dPQ2+0cLSgliP5NdQ2mrlnXgw3TI9EoVBw0Xmh7M6qZGNK2TkthFmsNt7eLirnSgXUNprZmlbeaQB+bJAXj1wY3+a2xWNDeWNrFjszKnt98h9oGpotfLw3F4DXb57MBWOCaTBZKapp4p6PD3Ior4a/rj3Jv66bMGxFibe3naa+2cK4cB8uH+Qy3oQwHY+HJfLY0gTyqhoJ9XHvtNW2QqHAW6tm7Agfxtq7Cp2tuGtUvH/HNJ767gTfHynif386RUqxnn9eM75DKTaIE9WNp8pYk5TH/uxq3NRK3vndVHw9+l8iKjP47C0WuykHuAdQbaxmbUAIk0vECREh47q3xcvIDAAbNmxo8/vq1asJCQkhOTmZBQsWIAgCr732Gk8//TTXXnstAB9//DGhoaGsWbOGFStWDMWw2/DBrmw2p5az/lgxM6MDuGd+DHtPi06X2a1K0BUKMTA/q7yewpreCWHFrRxhADNjAvjPzmwOnGOB+ZJ4NSHCp0OIdLBObAxTYWh26rmVOkyerqjHahO6bOBjMJp5Zr0Yrr5iwWjumhfDm1uzyKtqJLuyoc3iqtFs5deT4qL9ffNjHccMs1eAOOsI25xq7xbZjRtMYkKEL0cLajlZVOdUnuhvKaWklxnQadXcM1estInw80CnVWNotpBdWU9imHNzoXKDkXd3iI7iJy5O6HQeMSZMfH5yqxowmq2ObQ7mVmM02wjzcWdM6OA4sCZE+JJqP+eNCxIXIqeGTAUguy6b5toywHPYl0YWNeSjDrTgrfFmhFfL3DrQIxAFCmxYUKgaCdX1XwibGR2AQiFWzrkyH0ymc07Xiu+nQPdA/N39O9zflRBW2tDSJHBz3maemvWUo8Nke6SMsEhdJOjCRSFMX9R9TqxdCAvxH42HoYImSxMbczfyzN5nAFApVFgFKzl1OS4Vwv5z7D9UG6uJ9okelp0iWzMgpZE9oVAoePbZZ3n22Wdd/fAu58Xf0jBbBWbHBtBosnK8sI5b/m8/n94zk68OFrAhpRQ3lZL375juyJS4eHxYp8e66LxQnlmfQnJ+zbDOtxpofj5RQkF1EwFebmx/fCE5FQ0k59VwtKAWlVJBXIi342tUgCfqdisZEyN8CfLWUlnfzMHcaubGdZ73MBR8cSCfuiYzMUFeXD4xHJVSgZ+nOGF569ap3LX6AF8dKiAhTMfd8wYmgL6vCILA5tRyPtsvWmmfvDjRpaWFvUGhUMidDVuhVat4+cZJnBfuw/O/pPLd4UKyyg387YrzsFgFGk1W6pvFMtG1R4uobRQ7xCgV8M+rx7fJZTkTSalK4b2j7/Gn6X8iWrKFnwPUGGs4VSVmgf155p95YucTbPDy4gmtN57N9XJQvsywoK5OFDoCAsRV35ycHEpLS1m6dKljG61WywUXXMDevXuHhRD2x4vG4OOh4cdjxRzIreZAq4Yk0lxOItLfwy6E9S4wv7hWFFAkIWz6KPHiNLuygXK9kRAXlECdCZxwCGEdz0OthTBnKNeL2zVbbORXN3ZZ8vXv39Ip0zcTHejJQ4vjcNeomBUbwK7MSramlrcRwjanllHfbCHCz6NNhqbk5HPGEWay2NieLgph3ZVFSkglos4G5ksL8svnRjtyTxUKBWPCdCTn1ZBeanBaCHt1UyaNJiuTovy4clJ4p9sEe2vx89RQ22gmq7zeMYfYmSHmo86Pd20uanckjNCirBaf23GBohDm5+5HnF8cWbVZ2NyycbNMHLblxuF+7igUYFYVoUYsi2z93GmUGgLcA6gyVqFQ1xHqAkdY65wwV+aDDTcEQeBI+RESAhLw0gzdNUN3+WDQdWlkayGsprmGAyUHmBsxt9NjOBxhukjwjYDyFKjIgLglXQ/MLoQpA2IYpSghrTqNp3c/jVWwMj9iPsGewXyf+b1LQ/SNFiNr0tYA8PiMx9Gohvci/PCx0gxDkvNq+Pl4CQoFPHPFOD67dxZTR/pR12Tmunf38nlSPgoFvHbz5A4Tp84I9/NgQoQvggBbztGuh4IgtrEGuOv8aHzcNUyK8uPueTG8ccsUXr1pMisXxbFsXBijg707iGAASqWCxfZW5FtSO7rIBhqz1cbxwlpstrair8li44NdohV5xYLYDiuVF4wJ5i+Xiheuz/18yjGhGGpsNoFfTpRw6Ru7ue+TQ5isNubHBzG/i0BZmaFBoVBw7/xYPrl7Fr4eGo4V1nHdu/u46f393PXRQVZ9cYSP9uZS22hmhK87qxbHsf2xRdwwPWqoh95vVp9czfbC7bx99O2hHsqgsr9kPwIC8f7xLIteRqR3JA3WJrYs+qM4+Zl+91APUeYcRxAEHn30UebNm8f48eKKshSJ0b4BUmhoqOO+9jQ3N6PX69t8DSTjwn155cbJ7HpiMQ8uHO1wzIb5uDMmpO2FY18D80vqJEeYeGHr66lxiBWthbek7CoufX2Xw01+tnGisGNQvkSwfUG4oouO0e1pvV1GJ0HuIDZT+sS+oPfPayY43ExSgP2WtLbz77VHxJKmqyaHt1n8k4SwcoMRi7X7bpMHc6sxGC0EebsxOaqbUiU7kih4skjfYS7ZntpGkyN77MZ253NJ5Oisc+TB3GpmP7+F332wn4/25FBU20R6qYGvDorRLX+9bGyXYpZCoWhVHtly7J0ZomtyMMoiJTy9y1AobAgWHcHuLU0IpoVOA0DlmUPiCF2X7sChRqtWEebjjsq9BIB4//gO20g5YQqNntB+dIxszarFcUwb5c/VU87e7o6fnPqEOzfcyZ93/rlX++Xp86hsqnTZOCQhLN6v4/8Wuu4aWdJQ0ub3X3N+7XR/m2BzZIRFeEdA7CLxjuNfdj8wuxCGfzSjfEYBYBWsjPYdzUsLXmK0r1id1jo7rL/k6fMw28zo3HTMj5jvsuMOFLIQ1gWCIPD8L6kA3DAtkrEjfOyd42YxMyYAs1U8cf39ynFc2ovysYvOEyeGm06dm0LYtvRy0koNeLmpuGNOdJ+PI2VAbUkrc8qF6CosVhvLVx/gyrf28Ng3x7C2msCsO1pEqd5IiE7LNV20Fb5nXgzXT4vEJsBDaw6TXdGxNfVgUddo5rP9eSx7bScPfn6Y1BI9Xm4qVlwQy1u3Th12pZuD+X8ezsyLD+LHh+YxJzaQUB8tsUFeTIjwZVZMAFdPDmf1XTPY/eRi/rQ0oduckjOJlEqxxGVbwbZ+tZ8+05DyweaGz0WpUHJl3JUArKtLg9u+kztGygw5Dz30EMePH+eLL77ocF/7c4ggCF2eV1544QV8fX0dX1FRgyPgh/m688TFiex7ajFv3jKFj+6e0cEJLTlNCqp76wgThbDWTZZmxYiOI6k88osD+fzugyROleh5e1vWWXeeE4Pyxc9sKYutNb3tHFluaHFnddbREOCTfbkIAlw9ObxNxYAkhB3MraGuSXRNVzeYHF2gr2knGAR6a1ErFdiEnoU6qVvkooQQpwSZ9oH53bE9vQKrTSAhVOcos5NIDOs61H71nhxK9Ub2ZFXx7I+nmPuvrVz/3l5HJ/aeOv9JpY/ppeI8tbTOSHqZAYUC5g1iJUatVVw8tzZFklPV8h6cHjodEIWwripxhguR/h4oteIiQOugfAkpJ0yp1rfJ0esPF48fwXcPnE+EX8cmb2cDBYYC3jryFgDbC7dzouKEU/vl6/O5dt21LN+wHKvN2vMOTiAJYaP9Oo890mm6cIQ1iq8JKVdsa/5WTFZTh/0rGisw2UyoFCrCvMJg4k2gcoOSY+JXV7QSwmJ9xWZZflo/3rzwTbzdvB0VFq50hEnHivGNGXbXkZ0hC2FdsOFkKcl5NXhoVDx6UUuoobdWzcd3zWTFglhevG5Cr8WcpePED7udmZU0NFtcOeQ+c7SglrweTsSuQnKD/W72KIe9uy/Miw/CTaV05D3UNZrZll7O65sz2dwLkdFotrLhZAlJ2VU0mXr+QHx5UwZ7ssRgw++PFPHkd8ex2QRsNoH37JkL986P6bL1sUKh4J/XjGfqSD/0Rgv3fXIIg9Hs9Hj7gyAINJms7MioYNUXR5jx/Gb+Z+1JMsvFLkR/uDCe3U8u5qlLxg67PKkaYw1Xrr2SFZtWnHUXCn1hZKAnX9w/m6S/LGHrYwv5cdU8vloxh9dunuL0RPxMoa65zmEJb7Y2szlv8xCPaHAQBIF9xfsAOD/8fACuGn0VAEmlSRTXFw/Z2GRkAFatWsX69evZtm0bkZEt4e9hYeJFaXv3V3l5eQeXmMRTTz1FXV2d46ugoGDgBt4Jnm5qrpgU3ml5WV8cYYIgUGwvqWt9ISoJYftOV/Hs+hSe+v4EFvuCWrmhmdSSs0voP1WixyZAkLe2U6eLFBHijBAmCEKb7TLKOl9IPG53oLXP3hoV6EVciDdWm8CuTFH8+vl4MRabwLhwnzaNsEAMapc6+HWXEybGSojzziXnOde8RArMh57LIzc5jh3S4b6E0M6FMJPF5nBv3TMvhpnRASgVYDBa0KgUPHlxYo9jdDjC7ILjTvtzNjHSD38vtx73dxWpNWI8gNUY6cibA4jxFh2oSm0JCxOHd/lflL8nSq3o/mkdlC/hcISp9S7pGtlXKpsqeSHpBbJqsoZsDM4gCAL/2PcPjFYjKoV4vSWFv/fEz9k/Y7KZyNPnsb9kv0vGIz1fnQXlQ9elkSX14mtiWfQyQjxDMJgNbRokSUhz4DCvMDFDzCsQEi8T7zz8aeeDMjVCvf162D+aG8bcwA1jbuC9i94jSicuNMX4iBE9efo8bEL3rldnyakTK6OifaJdcryBxuUZYWcDJouNf20Q2ybftyC2QwcPDzcVT13at1unLxsAACAASURBVGyWhFAdIwM8ya9uZFdmBRePH9ww8vacKtZz7Tt78NKqWf/QvAFtsXswt5qDuTW4qZTc0898LG+t2pH3cON7+6hqaFHQFQr4z23TWDqu6xUii9XGd4cLeXVTJqV6cYKjVioYO8KHqSP9uHj8iA7lrr+llLYIebNG8uXBAr5NLkSlULAoMZjTFQ34uKu5ZebIbseuVat47/ZpXPnmHk5XNPDIl0f5vzumuzSPy2oT2JJaxudJ+WRX1mMwWqg3WhwTbonEMB3XT4vkxhlR+LgPL/GrNa8kv0KuPpdcfS7ZddldrrrInH2kVqe2+X396fVcE3/NEI1m8MiqzaK8qRx3lTtTQ8Vg4HDvcGaFzSKpNIl1p9fxwKQHhniUMucigiCwatUqfvjhB7Zv305MTNvzeUxMDGFhYWzatIkpU6YAYDKZ2LFjBy+++GKnx9RqtWi1wzM3VXKE9UYIq2owYbLYUChoc2E7wy6EZZbXOzry/emiMRwpqGVrWjk7Mio4L3x4NYCpbjCx6VQpzRYbgtDi7FuUENKj67iroHyJ3jjCDM0WjOaWi7XOSiPrmy2ctjvtOyvFvDAxhKzyeramlnP5xHB+sJdFtneDSYT5ulNU29RtTlhmeT0F1U24qZW9ipRwJjDfZLGxw+5Y6yx7TBJui2qb0BvNjnlcUk4V9c0WgnVanr50LEqlgqr6ZnZmVhDl7+lUBqskhGXYHX1SnMcFgxybITnCrU2RnCiqc5T6JWfbsJkCUbpVUWlJB4ZvB+UgXzNKo/g8dlY+F+guPqcKTd2QCmFrs9ayJm0NDeYGnpv33JCNoyd+yv6JfSX7cFO68fri13loy0PsKtrFiYoTTAie0OV+giDwS84vjt/XZq3tMpPLWeqa6yhvEmN6eiuESY6wCO8IlkUv49NTn/Jr7q8sGrmozXZtgvIlptwOKT/Aia9h6T9A0875VyuWh+PuCx7+BAN/m/O3NpuEe4ejUWpotjZT0lAill32k9aOsDMB2RHWCZ8n5ZFX1UiQt5YVC2JdemypeyTAxnbOJbPVRv0gu8Q+2J2NTRBXie775NCAPr4kIl03LcIlH/TL7EKXJILFBHkxdaQfggB/+PIIR/JrOuwjCAKbT5Vxyeu7ePK7E5TqjYT6aAnRabHYBE4U1fHxvjxu+b/9rFxzmDK7SJZT2cBjX4v207vnxvDPaybw2k2TUSrgq0MFPPLVUQBunzMKnROCUojOnffvmIZWrWRLWjkvb0rvsE2FoZlKJ3MzJAxGMx/uzmHRv7dz/6fJ7MiooKC6idpGs0ME8/PUcOecUfy0ah6/Pjyfe+fHDmsR7FDpIdZmrXX8vq1g2xCORmawkcLiJwZPRIGCQ2WHzgk3lNQtclrYNLSqFoHgqjjRFbYua53LVvBkZHrDypUr+eyzz1izZg06nY7S0lJKS0tpahKFIoVCwSOPPMLzzz/PDz/8wMmTJ1m+fDmenp7ceuutQzz63iM5wsoMRpotzpXSlNiD8oO8tW06Wwd5a4kLEUvOPDQq3rttKqsujGdhgpi5JAWu94TJYuOLA/kDnjWaVW7g8jfE+dLf1qXwzPoUnv3xFM+sT2HFZ8k97i+5syZ0UhYJLUKYM3MdKShf0tOyKxo6ZHedLKpDECDc191x7NYsspdHbksvJ7uinsP5tSgVcEUXofHSQnh3jjAp6mTu6EA83Zz3F0g5Yd05wiRBK8hby6ROnkNfT42jlC6jlStMys9dnBDiWGQN9NZyzZTIHksiJSQhrKC6CYPRzO6swc8H05v0jgtrmzGyzXP184liLI3ixfahskODNqa+oHIXBQ83IRhPTUfx2EMl/k9Uaj2Bg+i2a48kuFQZq4ZsDD1RbazmpYMvAfDA5AeYFzGPy2JFd9R7x9/rdt/0mnRy9bkOF9nW/K3UNTvXsEIQBCy2jtfIUsfIMK8wvN0676TaVdfIsoYyx76XRF8CwPaC7TSa25bhO4LyvVsJYbGLwHckGOsg9ceOD9qqLLIrVEoVI3WiecNVOWGSI0xymw13ZEdYOwRB4OtD4gvu0YvG4KV1/VO09LxQPtydw9a0cixWG2qVkl2ZFTzx7XFqG808f+14rpkS2fOB+km53siPx8QLSj9PDVnl9Tz61VHeu22ay9xJRrOVqgYTqcV6tqaVo1SIraxdwc0zotCqlfh5ujF1pB+B3losVhv3f5rM1rRy7vn4ED88eL6jJfeJwjqe+/kUSfZsDj9PDQ8tiuO22aPQqpUU1TZxOL+W3ZkVfJtcyM/HS9iRXsHDF8bzbXIhhmYLM6L9eepS0VJ+xaRwbILAH786itFsQ6tWsvx859/4EyP9+Nd1E/jjV8d4e9tpEsN8uHh8GFtSy8UJbmYFggAjAzyZNsqfqaP8OX90YJtuRxI2m8DnB/J56dc0DHYx09dDwy0zR7JkbAg+Hhp07mp07hq83FRnRN02gNlq5h/7/wGIGQpljWXsKNjBvRPuHeKRyQwW0mrwoqhFuKvcOVB6gJ+zf+a+ifcN8cgGltb5YK1ZMmoJ/0z6J0X1RSSXJTMjbMZQDE/mHObdd8USlIULF7a5ffXq1SxfvhyAJ554gqamJh588EFqamqYNWsWGzduRKcb3iVMnRHo5Ya7RonRbKOk1uiUm6bYEZTfMZ/nkSXxfH2okCcvTmBcuCiGLBwTAqSQnFeDwWjudkEtpbiOP319jLRSA24qJQf/Z8mAxBkk51Vz90eHqGsyExUgNntSIM4dfj1ZQmqJnqLapm4ziE520zESeucIk7aJCfSipM5Ik9lKXnVjmzmRFMzfWR4Z8P/ZO+/wqMq8Dd9nSia9J6RCqAmEKr1XAQELutjXtjYUe6+Lrt21rO1z10Wxi6sCgopK70iHhA4JhFDS+2Tq+f44eU9mkplkJo3i3NflJZApZ0pmzvu8z/P86N8hglB/HcVVFmYvUjZZhneJdrs5m6BOjnTvBvQ2FikQjrXMmsJ8V+fdQtAanxbr9rw8NS6EU2XV7DtVzoCUSGRZVgcCjO9eP07pKZFBfuqE9vnblWnUIf46j4YBtBRiIyw2IIFyWxB7TijPVXGVmQ2HC9GEdoTwLWw93bgoeyYxaxXnISbXKSCNTXlO9YaKMzatHWrL2z0Vh84Er29+nRJTCV0junJj+o0A3NH7Dn468hOrj68moyCDntE9XV5XlNGPTR7LsfJjHCg+wC9Zv3B12tVu70+WZZYeW8rrm19HI2mYN20eYYbazzN1YqQbNxhAqKF+Wb4sy+rzHRcUR1JwEonBieRW5LI6dzWTUyarl3XpCNNooN91sPJl2PYZ9L7S+U49EMIAUsJSOFx6mOyy7Ga742RZVgW1c2XCu88RVgdJkvhh5jCevzSdKwe0jhjVv0MEkUF+lFRZWHUgn6cX7Oavc/5Qv9gfmLeTZxZkeLzz2FQ+23AUi01mQIcI5t48CD+tht/2nObd5U3Phsuy0r1w0yd/0Gv2r6Q9s4Thryzn1s+U3ZopveI9Oon0BJ1Ww4wByVzYox1RNT0TOq2Gd6/pR6/EMIoqzdz0yWYyT5Ty4LwdXPzeWjZlFWHQaZg5pjOrHhnLrSM74a9XhKGkiEAu6ZPAa3/pw4+zRtA3OZwKk5UXf97L/tPlxIQYeP/aC9A7TLK8tG8i/5zRB4NOw20jO7ncgWyI6f2SuL3GdfjIdzsZ/spy7vxCcXLJsrLzeayoivnbc3lmQQbj31jFrZ9uUU/2QCnwvX7OJp5ZkEG5yUqX2GBenN6TjU+M5/GL0hiQEkm3diHEhwUQbNCdMyIYwNzMuRwpPUKkfyQfTPgAgJ35Oyk0nr27VT5aFnEinB6VzsWdLwZg0ZFF53VXnNFqVE/s6wphAboAJqVMAvjT9KX5OLuQZdnlf0IEA+Vcavbs2Zw8eZLq6mpWrVqlTpU81xDnB+B5PFIU5SeE1RdYpvVO4LNbBqkiGCi9jx2jg7DaZbWHtC4Wm523lx7g0vfWqVMCzTY7K/a1/PTs3zJPce1Hmyg1WujXPpyFd4/gg+v68/51F/D+dRfQr30EgBrbc4VjUX6jQliFqdHPdFGUHxta66qrW5i/SwhvSa7vT6/VMDpVEYeEm+4yN7FEgLiaQQfuHGH55SZ25JQAMD7NOyGsa7tg/HQayk1WjroYxCDLsuo2a0hkq1uY7xjVHNHMGKMozP94reLyGN452uU0d0/YX7SfkuoSr66TUZABQJ+YnvjrleECWYWV/Jp5GrsMnUOVjWnhQmkq1dZqNp7ciMXWOp29xZZsAKoqY52GbAlka80Gge7MClDCbX82CmGyLPP70d9ZfGQxEhKzh85W+rKA9qHtVVeYu64wWZZZkrUEgIs6XsRlXS4DcEqc1OVY2TFmLpvJgysf5GTlSXIrcvk1+1enyxwsPgi4nxgJtVMjKywVqpO/xFSCyaaI++0C2yFJkip+/ZrlfB8uhTCAvtcBEmSvgaIjzj8TQlh4B7fHBbVdXs39HQLIq8qjylqFVtKqPWRnOz4hzAUBNRMNm/ph3xg6rUadXnPbZ1v4YqMyyvjGoR24Z5yiKH++8ShX/XujejLV0hjNNr7cpOSHbx3Zkb7J4bwwXTlJfWvpAa+nWpqtdr7bepyL/rWGv875g5X78ymvVpxJeq1EfJg/A1MieHhi/ZLIlibIoGPOTQNIigggq6CSqe+s5QeHHogVD4/hsclpDe6g9kwM44eZw3j58l6EBejx02l4/9oLiHWxa3j5BUlkPDeJhyc17bE9NjmNkV2jqbbYySs3ER3sx52jO7PqkTHs/PtEPrtlEPeN78rwLlFoJGX38eL31nLL3M18sPIQk99ezfrDhQTotcy+uAe/3T+K6wZ3IMDPdWH/uUJOWQ7/3vVvAB4d+CjdIrrRPbI7MjJrctec4aPz0RY4FuX3iOrBhR0uxF/rT1ZpliqQnY9sO70Ns91Mu8B2LnsWhsYPVS6Xt62tD82Hjz8lyWphvmeTI4Vw4soR5o7RNZGzVQfqC1u5JUYufW8dby89iNUuMzk9jusGK5GWXzNP1bt8c/jmj2Pc+cVWTFY749Ni+erWIUTWiWuNaeBYBXtOlmKXFbHLVVE+QHSwcrsWm6xOcnSHcITFhvjTtUagqVuYv+u4IrT0diOEgeKuEvjrNUxqYOJgvOoIcy2ErdiXhywrQl/dPuHG0DdSmL/vVDm5JUYMOk2DUxpT6whhwqE2zMuopitEPDK7ZlpjU2OR2aXZzFg0g/tX3u/V9YQjvFdMT/W5ysgt5afdimBzUXdFCCs3l2O0Nn299EnGJ9z22218va/+9NuW4HilIlJYjHFq5Yoj1dXK82yXKlVxpK2RZZlTlcpnSYnJO8GyNcmryuPjjI+5bOFlPLjyQQCu7X4tvWN6O13u9t63o5E0rD6+Wn3fOLIzfycnKk8QqAtkVNIopnaaik7SkVmYqYpZAqvdygc7PmD6wumsy12HXqPnglilq3XxkcVOlz1cqkQju0S4d4SJaKRdtquxR+EGi/KPwk+rfA5e1FGJR64+vpoKc+1nmzgPTg6uIy6FJ0Pnccqft3/h/DMvHGHQMpMjxW0kBieqj+lsxxeNPENM7NGO77Yex17TZfD6jD7qmOcL2kdw/7wd7MgpYdq7a/nm9iHql1FL8cP24xRXKXb3C3soJwFXDkgms6Yj64F5O1j+0GiXwk9dcoqquPa/G8kpUr6EAv20XDkgmRkDkkgKDyQ0oO1cSCXVJei1emJDgph780Cu+L8NlBotDEqJ5Kmp3enjhaVbo5G4ZlB7LumTQKXZSmyI++dC3wzRVKuReP+6C/h4bRZdY0O4sEc7p06RUd1i1JOPw/kVvL/8EAt25LJ8Xx7La3aCB6VE8vqM3moM9FxHlmVe3PQiJpuJIfFDmNJxCgBjksewt2gvK3NWqrs5Ps5fRFF+YnCiakUf234sv2T9wqIji0iPTj+ThwdApaWSQmMhVdYqjFYjVZYqOoR2qL9z5wXrTtTEIhOHu/zsFOX5+4v2U24uV0+yfPjw0Tp46wjLrdnEjPdCHBmdGsPc9dms2p+vFtILnlmQwZ6TZYQH6nn+0p5c3DuezBNlfLnpGCv351NtseGvb/7m18lSI08tyMAuw1UDknlxek+Xm8KjU2PUKdpmq93pnEUgnOu9EsPcngMadFrCAvSUGi3kl5sID3RePJ2oOMGhkkOMTBypCmExIQbVSeZYmF9aZeFojWDjzoEGiuCokcAuw8QecQQ3UIHSWEeYGot0UWTvCb0Tw9hZU5h/SZ2eMjEBfWTX6AY3NoUQtu9UmRKLFHHKJh6TI3XXHqO6Nc1htq94HzKy166TjELFEdYzuidZiQFsP1bCqv35bDisuCan9+7MVycDMFqN5Ffl0z604WFV7thTpGys7S+u39fbXKx2q9ojZauOJ6eoqp5AXlKhQ7brkDRW8qryzoibpthUTLVNeZ+Xm8ux2W1oNWduQ91is/Do6kdZnrNcdVEZtAYu6ngR9/a7t97lO4R2YGrHqSw6sogPdn7A++Pfd/r5kmzFDTau/Tj8df746/wZlTSK5TnLWXBoAY8MfARQxKpn1j2jCl7DEobxxKAnCNQHcuF3F7I9bzs55TkkhyQjy7IqojU0xMugNeCn8cNsN1NuLifYL1gVHeODauOy3SK60TGsI1mlWVzz0zVIkkSVpYoCo9LP5/K88oK/wuFlsOMrGPMkWI2wdS4cVXpmGxPCxGZrSzjCRCzyXCnKB58j7IwxJjWWKwckcfPwFJY8MEoVwUAp81x8zwjSE0IpqjTz9PyMFo0B2e2yanO+eVhHtA559Ken9aBPUhgVJiv/Xn3E3U2oFFSYuOHjP8gpMhIbYuCxyWlseHw8sy9JJz0hjLBAfZuJYEXVRUydP5Wbl9yMLMt0iQ3hp3tH8O0dQ5l3xxCvRDBHggy6BkWwliDUX8/9E7oxtXe8yxNKQeeYYN68qi/LHxrDX/onkRDmz7PTevDN7UPOGxEMYMOJDaw7sQ4/jR9PD3lafQ+NSR4DKEXiZ2rXzEfbIXb1ekT1UP/t4k5KPPKXrF+w2FsnxuAph4oPMeqbUUydP5UZi2Zwwy83cOfSO5k2fxovbHyhyRHe9bnKCcywhGEufx4bGEtScBIyMjvzdzb5+H348OEZojA/x1NHWI0Q1lB/Vl2GdorCoNNworRanSgJ8EdWEcv35aHVSHx35zAu6ZOAJEmkJ4SSGB6A0WJrsdL8X3afwmaX6dde6TB1l4zomRBGVJAfFSYrW4/WH0wEsDtXKYZ2Nb3RkYZ6wp5Y8wR3L7ubPUV7HBxhBjWy5yiECVdVh6jAeoKaIxFBfozsqmwuXj2wYcFBCJmny6qx14m0VVtsrDmoLFAn9GhaF5cQ7ISTzZGl+zwTtLrEBqPVSJRVW9lzsoxtNYOiHJ1vTUU8zwCdYoJUQdhbTlYo7pcyU5nH65kqS5UqFqRGpqrvowU7crHXuPA6RAcRE6C8lnlVTY8Ii+iZ+H9TsMt2Xt70Mvctv8/JnXa07ChmuxmNbEC2RJDjQkzPKzchW5XHl1/VugMw3CFeIwAZuV6xe1uzq2AXS48txS7buSD2AmYPnc2KK1fwj+H/cDlwAJxdYfP2zVP/3Wa3qZFG4boC1A31xUcWY7FbkGWZ1ze/zuIji9FKWl4a8RIfTviQlLAUYgNjGRw3GICfjvwEKEMFSkwlSEh0Cmt4uF7dwnzx3o4LqnWkSpLEJZ0vARR3VVZpFqerFEG8e2R3NWLpROoUCIyC8pPww23wVjr89jSYypRYZGL/Bo9LRCPzqvLqlfQ7cqTkCO9uf5epP0xl4ncTVXHOkayyLKfbPBfwCWFnCD+dhtf+0oe/X5zucmJfcmQgH90wAH+9hj+yi1i866SLW2kaqw7kczi/khCDjivrnATotRoerIkvfrnpaIOTfCpMVm7+ZDNZBZUkhgew6J4RzBzTmbDA+o/HZrdxpPRIq/b6bDq5iTJzGXuL9rKvaB+g7OIO6hh5TvVieUJKdBD/nNGH9U+M55YRHc9ouWZrsOnUJgCmdppKh9DafHv3yO7EBsZitBr54+QfZ+rwfLQRjv1ggqEJQ4nyj6KoukgVjM4Ua3LXYLab0Wv0xATE0D6kPZ3COmGTbczbP49p86cxZ/ccr0TbvKo8DpceRkJiSPwQt5cTrrBtp33xSB8+WhvvO8IUZ0W8F0KYv17L4E5RQG33lizLvLZEOZ+5amCy2o0FyqJJTM/+NdO7Ogt3/LxbOde8uHdCg+dNGo2kOtVXuRHhducq4k5D7iyAmODanrC6HCxR3BanKk6R5+AI6xqrLCqzCiqx1EyO3Hncs/sDeOeafvx870iGNRA5FMemkcBqlymodD6+3bmlGC02YkIM9Ih3sUD1gLqF+YK8smp2qt1jDQtaBp2WjjXdu/9edQRZhh7xoV7Fct3R1cERNqpr06dFiu4pq2ylwlLRyKUVhAAQqAsk1C9UfV3F0zS1t+KkiQlUjivf2DQBSZZlciuU+hQRQXPF1tNblaJ2Nz1nH2d8zFf7vmJ5znI+3Fk7vfBA8QEAQrXJgMZlvPp0WTV2i/Ieao6g1xxEVE9wpuORQpQcHD+YTy/6lCu6XdGo+z0lLIW7+twFwIubXmTZ0WWAMlW0wFhAmCFMrZYAGJE0gkj/SIqqi1hzfA3/3vVvvtirRAxfGPECF3e+2OlzUPTULj6yGFmWVadfckgyAbqGf9/EsYvCfFdCGMCN6Tfy/vj3eX/8+3w86WO+mfYNP172I19O/dL1Z7LOAL1ryv4zf1CmSEZ1gUveg1mbIaBhE0iYIYxIf2Vqqat45HcHvuMvP/6FSxdeyn92/Ydj5cc4WXmS1cdX17uszxHmo0VJCA/grjFK5vjln/dSZa4/trUpzKlxg109KNmlJXxU12j6JIdTbbHz0RrXrjCT1cadn29ld24pkUF+fP63QW6n7gA8u/5ZLl1wKStzVrbIY3CF49SY1rwfH62PcALV7QCQJIkxSWMA32v8Z0AIYY6OMJ1Gp+7o1e1qaGtEdPOuvnex/Mrl/HT5Tyy8bCEfT/qY7pHdqbBU8Pa2t5mxaAaVlkqPbnPLKWWwSFpkmtNkorr0b6fs8p3t07J8+DgfSPKiI8xqs6vF7q7K8htCdG+trOneWrY3jy1Hi/HXa7hvfP0y5knpilto6d7TqiDUVE6VVrOlxt11US/3vVnqsabWHOv++gv3KrOVQzWutob6usC9I6zMXKYuGsst5bVl+SH+JIYHEOinxWKTyS5QPltrJ0Y2LoSFBejpkdC4eKXTatREQN2eMDERs0+S++hnY7grzF9W4wbrkxzuUUWJiEcu3qUIThOaMS3SkbAAvepqHJ3adCHMUWTxtDBfCEKxgcpj6RobjMEhMTG1V7zTz5sqIBVWF6oOrvyqfMw2s8vLvbX1LT7b8xm3/nZrvcew/sR63t3+rvr3TzM/ZX+RErMUQlh8oOIYEjUyjpwqrUa2Ku9HIQC2NUKsFJzpwnxxPEnB3tVM3N77dq7oegUyMo+teYztedvVaZET2k9Ar601a+g1ejVl8NKml3h/hxKnfHzQ40zrNK3ebY9vP54AXQBHy46yu2C3RxMjBcLN1ZgQptfoGZU0ilFJoxgYN5D0qHQ6hnVUBwO4ZPDtEJqkuL+u/Bzu/kOJTOo8G+AmHFxCyBKsy13HcxueY3/xfnQaHWOSxjA6aTTg+txTxCvPlYmR4BPCznpuH9WJpIgATpRW8+HKw15f/4uNRxn7z5WMem0F499YyeS3V7P2UAEaCW4cluLyOpIkca8o7d9wlKJK5y8Fu13moW93svZQAYF+Wj65aSCdHMZX12Xr6a38ePhHADac3OD1Y/AUsYAEWJGzotXux0frYpftqgDiagSyiEeuPL7yvJ4c+GenblG+I6Izbk3uGrdTnhYcWsCTa550KhxtafYWKkJY98juTv8+MG4g30z7hhdHvEiYIYys0izW5q716Da3nFY+xwbEDWjwcqK4NaMgw+2Juw8fPloGIYSdLjM1OtH7dLkJu6wMCooO9m6StBAbNmcVU15t4fVflcX0zcM7utxsHJASSVSQH6VGC39kFXl1X3VZkqGIFf07RBAf1ribaESXaCRJKXWvWwC+92SZQ1F+w0KOOyHMMapVaamsjUaGGtBoJLrGOhfmi2hkr8Sm1WC4w11PWEZN9NNx+qe3OBbmL9yRy+7jpeSWGNUBCBd6KGiltRNl3Mrfx7VAP5jg9Rm9eXpqd1WkbQrCcQWeO42EsNUuUHksOofnqndSGMmRikszNkB5jpoaKXSMQ8rI9QQhUFxjR0oVY8D+4v3c9vttqhh2ouIEj61+DLts5/KulzOh/QRsso3nNzyPXbarQliXMEXIdhWvziszqULY2e4Im39wPqO+GcX2vO2tejziPZMQnNDIJZ2RJImnhzzNmKQxmGwmZi2bxW9HfwNqzx0dEfFIIUDO7DOT67pf5/K2A/WBjGuvlNMvPrLYo34wgeoIsyhCmHi+6wphTSIiBR7MhNuWQ49LwMtuN3eF+aJXbWKHiay8ciXvjn+Xa7tfC9QXwoxWo/qYfI4wHy2Gv17LU1OURda/Vx8hx8WIZXfMWZvF0wsyyCqo5FhRFYfzK9WR25f0SWgw6z8uLZb0hFCqzDa1TwyU8d0PfLuDxbtOotdKfHh9/wa7t2x2G6/+8ar6d7FD0tIUVRepkzs0koa9RXudTqJ8nDscKztGuaUcg9bg8stlUPwgAnQB5FXlqY4cH+cfQgx1LMoXpEenE+UfRaWlkq159XelzDYzr/zxCouOLOK/u//bKsdXZaniaJkyeTctMq3ezzWShks6X6I6GMVJdGNsPrUZgIHtBjZ4uQ6hHYj0j8RsN6sj5n348NE6RAb5EVBTRi9ij+4Q077jwvy9ri3oFB1EcmQAZpudx7/fzf7T5YT667hzlOuFllYjcWEPAhzvyQAAIABJREFURSho7vTIn3cr17+ogSmKjkQFG+hdE1cTUU7BkgzltjyJKboTwhwFidLqcoqrlE0PEaUURe4HTpdTUGEit8SIJEHPxKbFFN3hbnJk5glFeGusA60xxHP49tKDXPzeWoa/spyVNc+np4X3whEGyvPZu5nH5MiwztHcOrJTk11vsiw7O8K8FMKE4wuUxArAjP61LiERjcwzNk1AyinPcfq7q3hkialEdfJE+keyr2gft/9+O3lVeTyw8gFKTCWkR6Xz5OAneXzQ4wTpg9hVsItv93+rCmG9YpS13PE667hKk5VykxV7jRB2xjrCPBTClh1bRrGpmI92fdSqxyOEsMTgRK+vq9PoeG30a/SO7q06S2MCYlQnvSNdIrrQN6YvANekXcPMPjMbvG3hIFuStUSt4ekaUd+tW5d60ciq+mX5ZwpXjjCr3aqaSq5KvUo9D+8b0xetpCW3ItdpnX2s7BgyMqF+oUQYItrs2JuLTwg7B5jcM46hnaIwWe289LNnC/+P12bxj8XKQvLO0Z35fuYwvrl9CJ/dMohPbxnEy5f3bvD6kiRxzzjlF/vT9dmUGi1Uma3c9tkWFu44gU4j8dZVfRsdpTz/0Hz2Fu1Fp1EimAeLD7aKi2f7aWVnokt47QfayuMrW/x+fLQMVZYqt1/2YkpQWmSaSyuwQWtQS8RX5axqvYP0cUZx1Q8m0EgaRiWNAly/B9afWK9GEb/Y+4VqQW9J9hfvR0YmNjCWqIAot5frFK7EIbJKGp/IU2AsILssGwlJ7QBzhyRJ6kndtrzzqyfMZm/YcePDR1sjSZLH8UghhCV44KpydT9juikL/59q+rruGtvFZfeqoLYn7FS9QndPySurZvNRxVE2pZfnC7PRqcqxOvaEbT1aXFvB0UgZPaC65up2hJ2orBXC8qsU0UmvlQiveS6EEHYwr1yNRXaKDiLERe9uc3DlCKu22NSBBs0V3m4ansL4tFi6x4cSF+qPX82Agv4dIkiL82wicFpc7TGMS409q3pjy8xlTtUAzRHC7h7XhR9nDef6IbXdseLnLeEIA8gtz613GbHp1S6wHR9P+phI/0j2Fu1l2vxp7CncQ7ghnDfHvIlBa6BdUDt1quFbW99Szz8GJyvnMifLqjFba2PMwk1pQBEPzlQ0UghhQXqlb85dNFIMAVp3Yl2rnFsJhBDeFCEMIEAXwHvj31N7hielTHI7BfONMW/w/vj3eXzQ440KvoPjBxPlH0WxqZjMQqXGxZNopGNZvtVuVd+vLeIIayZCCBNl96CkE0pNpUQYIpzORwP1gWpKw3EjWi3KD0s5p3q5fULYOYAkSfz9kh5oNRK/ZJxqdDrQ3HVZPF8jgs0a24XHJqfSv0MEQzpFMapbDKO7xTQ4ilkwsUc7UtuFUG6y8q+lB7n+v5tYuT8ff72Gj24cwLTeDdtVy8xlvLPtHQDu7XcvOo2Ocku508lNSyHiRP3b9Wds8lgAVhzzxSPPVu5edjdTfphCTllOvZ+JfjBXsUiByKj7IrDnL676wRwZnay8B1bm1I/I/n70dwAkJEw2k1N3R4sfX6Tr4xOISUKeOMJEvDs1MrXBfjBBv9h+wPnVE1ZmLuPyHy/nfwf+54s++zirEFGsxgrzhWOsqWXlox02GNuFGrhxaEqDlx/WJYpgg47TZSa1MN5blmSeQpahX/twr45bHOuag/lYbXaMZhsP/28ndhmm90tkYnrjizx3jjDHOF1hlRJDjAk2qIusru1qo5G71H6wlo1FgqMjrPZ133eqHJtdJirIjzgPOrwaonNMMHNuGsgv941k45Pj2f/CZDKem8R3dw71eEGZFBFAUM15/fgW6gdrKeo6jTztnnIlhBl0WnonhTs9L82dGikcYVpJef4c33eCY+XHAEUw6BzemTkT5xDpH4nRakQjaXht1GtOEb6rUq+id3RvqqyKaJ4QlEDHiCj89RpkGU46vJdO1QhhEYboZj2O5iLcPcLh7u51KqpWBHO7bGfhoYWtcixWu1UVBL2NRjoS4R/Bx5M+5qH+DzGzr3unV2xgLKOSRqGRGpdFdBodUzrVRix1ks6jKYmOjrACYwE22YZO0hHl734jta0Q0cijZUexy4pIu/ToUgDGtR+nmlkEoprD8dxT9IN1DD13YpHgE8LOGdLiQrl+cHsAbvtsC99uqS8gWGx2Plh5iNmLlAXaXWM689DEbk1WZjUaiXvGKyr3x+uy2HashFB/HV/eOpixqY1/0X6480OKTcV0CuvE9T2up3OYYu1vjXik+GUc0G6A2iG1+fRm1YLq4+whvyqfLae3UG2rZumxpfV+LmJerpxAglFJo5CQ2Fu0l6/2foXV3jKDJHycPTQmhA2NH4peo+d4xXGnXSyLzaKK4A8NeAiARYcXtfjnjtoPFtW9wcsJISy7LLtRp5PaD9au4X4wgdil25G347xxUb2x5Q2OlB7h08xPMdt93Wc+zh48dYSJRW5CeNMEkqGdo1RX0P0TujW6cWnQaRlbM1mwqdMjf6qZTD7VCzcYQN/kcMIC9JRVW9mRU8KrS/aRVVBJXKg/sy92/x3uiDo1soGOsGJjjRDmIDoJR1h2QSVbjykl/54U5XtLXI2zz9ERJory0xObXpTvDkmSCDbovLpdjUbiqak9uH5Ie/W9cLZQt3OrqR1h7lAdYcb8Jm2eiChkr+heTn93RETG2ocq67AuEV2YM3EOwxOH89yw5xiaMNTp8lqNlmeHPquKa90iutW4ShUx3bEwP69Med+3cyj9b+tNIKPVSLFJ+R0SnaeuXidZlimsLlT/Pv/QfFU4aUlOV53GJtvw0/gRHdDwZNfGiA2M5aaeN6ll9S2BiEeCUlPhWMDvDkchTDjp2gW1c+tSa0uSQpLQSTqMViN5VXnYZTvLjikTN8e3H1/v8q6GNYnfkXOpKB98Qtg5xaOT0xiXFovJaufR73bxxA+7qLbYkGWZ3zJPMent1by2RFns3Tm6M49MSm32F/RFPePpHKPYZGNDDHx751D6d4hs9HpHSo7w9d6vleMe+Ch6jZ7UyFRAiRS1JOXmcjWnfUG7C0gJS6FjWEesdivrcte16H35aD4bT25U/1x3/K7VblVfy/Ro9yfRUQFRTEqZBMDLf7zMX378y3n1WueU5/DmljfbfGrPsmPLeGPLG1jsrgvo24qGivIFgfpABsUNApzjkRtPbqTcovRB/LXHX7ko5SJkZN7c+maLHqPop3PVD+ZIYnAifho/TDZTo25Y0Q/WWFG+IDUilUBdIBWWCg6WHPToOmcz60+s54eDPwDw3LDnMGi9Kxr34aM1EUKYq6lvjghHmCeF864IMuh4YXpP7hzd2akLqSHE9MhfM095vYjOK6/mj2zF5THZw34wgVYjMbKmt+nN3w8wd302AK/+pXeDcU5HhCOsqMqM1WHypaMzp6xmUzPGYfhAfJg/IQYdVrvMukMFQOsIYaojzGEggOgH69XCfWTN4drB7Xnhsl7otWfX0s7T7qm6CEeQoyPMFaIjzGg1UmHxfjiOcIQNjh8M1I9KQq0jTMTsQBHDPpzwoVq2XpfUyFT+1utvAAxJGAI4fIY4iOnifZUUovzume1mysxlXj+O5uAYi0wKUT5zXL1OVdYqTDZFuAvQBZBbkauet7QkIp6aEJzgkUurrUmLTFPNHV0iGo9FgvPUSPF8NybythV6jV593bNKs9iZv5MCYwEh+hCGxA+pd3mxCZtVmqVGZVVH2DlUlA8+IeycIsig4783DODhid2QJPj6jxz+8uF6rvrPRm7/fCtH8iuJDPLjhct68tjk5otgoJzkfHBdf24Z3pHvZw5z6iFwx7GyYzy59kmsspUxSWMYnjgcUHZEAHXKRkuxPW87MjLtQ9qrX5jCFbY8Z3mL3peP5uMohG3P2+7k2jtccphqWzXB+uBGrcYvj3yZpwc/TbghnMOlh7lz6Z3ctfSuVp0S2FY8s+4ZPsn8hI8zPm6z+5Rlmec3PM/czLn8lv1bm92vK4QbLCk4qcGIoIhHrjpeK4SJ6UDj249HI2m454J70Gl0rD+xnvW561vk+Ew2E0dKlKijO6FOoNVo6RCmnDyLEwVXFBoL1fhk/9j6ha6u0Gl09I1VOhHP9XhklaWK59Y/B8DVqVe7LLX14eNM0iFK2RQ8lNfwd4zoCEtsYjQS4MoByTx+URo6D0WNMamx+Ok0ZBVUcucXW3n0u53M/jGTf/66nwOnG3bG/5p5GlmGPsnhDQ5Raui+AdYfVhZE1w5u7xTvbIzIID80EsgyTlPKHTcOxPd6bGitECZJEl1q4pE2u4xGgh7xreAIC63tCBMio5gY2bMZEyP/LAhHmNjY8GSDz2a3UWBUxM3GhLAAXYDqtvG2J8xoNar3Ixb8LqORZYoQ1j6kvVe3P6vvLBZPX8zVqVcDkKw6whyEsBqnYVxYiFoy3tY9Yacqaovbww1KvNjV6yREjwBdANM6TQPg+4Pft/jxNHViZFshSRI3pt8I1Fa1NIYrR1h88Jkvyhc4To4U9SKjk0e7dLuFGcLUAQHb8rYhy7Lao+eLRvpoVTQaiVnjuvLZLYOIDPIjI7eMP7KKMOg03DWmMysfGcP1Qzq0qFU7NS6EZy/uofZjuMNsM/Phzg+ZvnA6mYWZBOoCeWTgI7W3IxxhLRxRUuNEDi6KccnKeNu1x9eecXeLj1pkWVaFMJ1Gh022sf5ErTghYpE9ono0uguk0+i4Ku0qfrr8J27ocQM6jY41uWv4dM+nrfcA2oBd+btUUUO8t9uCrNIstftBdAOcKRqLRQpEYf6OvB2Umkqx2C0sP6aI3xNTJgKQHJKsnoS+ufXNFokQHio+hFW2EmGI8GhHT+wcHi457PYy4jXvFtGNcH/Pe25EV8O20+d2Yf6/tv2LE5UnSAhK4P7+95/pw/Hhox5iOuCB0+VUW9x/johoZHwTo5FNIdigY1xqbTzy2y3Hmbs+m/dWHOKBeTsavO7PaiyyaaXNo7rVRpeSIwPUSeeeotVIRNU4vfJq4pGVlkqnhbjRpggHsSHOLtFusbVl8t3ahXjUf+st7WqEMLPVTnGVBbPVzv6aCezNnRj5Z0AIYWIz3BNHWFF1ETbZhkbSNDiMRhAbUBMr9HJypHB/hfiFqOcbYsqgwHGRLza1PEWSJDqEdlDjb8mRwhFmxG6XWbrntDpoIi7UUDsBs417woTo7CiEuXqdxDlipH8kV3S9AoBlR5e1eHpBHE9Ti/Lbguldp7Phmg2qINgYroSwuMAzX5QvEAJWVmkWy44qscgJ7Se4vbzYsN16eit5VXlUWavQSlqSQxofkHI24RPCzlFGdo1h8T0jmNijHdcMSmbFw2N4dHIaoS0wLSerNMtrq+uWU1u44screH/H+5jtZobED+Hbi79V8/SgxHhAsSFXWRru2PAGsYB0dBD0iu5FpH8k5ZZyJ6eEzW5r87jZuc63+7/lpyM/tUhnQVZZFnlVefhp/NQv0TXH16g/FxMjG4pF1iXUL5RHBj7CE4OeAGoLx89mZFl2K8jMzZyr/nlPwZ4W/V1pCEfRbU3umja737rklOew7oQSc21MCEsMTqRLeBdsso21uWvZfHIzZeYyIv0jVYEI4I7edxCiD2F/8X5+yf6l2ce4p0gR6rpHdfdo08GTwnw1FulhP5hAWNTFrty5yPa87Xy9T4nS/33o39WpVT58nE0khPkTEajHapfduqyMZhvFVcrmW1OjkU3llSt68caMPsy+uAePTEpl5hhFgM88UUZhnYmMgoIKE5uyFJfHRT2b5k6IDfFneBel1+yff+lDkEHX+JXqEFNncmTdXimTXfk+iqkjhInCfGidWCSAn06jTrY8WWrkYF45ZpudUH+dGnXz4R4haojvc0/OwYUQFO0fXa+o2xVCQPLWESaEsKTgJAL1gUT6K9Uvjq6wAmMBVdYqNJKG5ODmLfKFI2zTkULGvrGSWz/bQlZBJX5aDRd0iFDdb20thImoXkJwQoNCmHCERQVE0SOqB90iumG2m/npyE9e32epqZSSateiqGM08mwm2C/YY+OJ49RI8XyfDRMjBcIRtvToUk5UniBAF8CwxGFuL98/rlYIEz29SSFJHvWlnU34hLAzyMaTGz2aJOaOhPAA/nPDAF6+vHeTpxPVRZZlZi6dyS2/3sLa3LUeXSe3Ipc7fr+D7LJsovyjeHXkq/znwv84ZelBmd4RGxCLjMyB4gMtcrxVlir2FCiLUkchTKvR1k4WPLaC4upi5uyew5QfpjDimxHnVZ9Ua7K/aD//2PgPHl/zOH9f/3fMtuaVV288objB+sX248IOFwKwNnetWrapToyMcj8x0h3CEbi7YHezj9Mbqq3V5FbkelwYWmWpYur8qVzz0zVOI8VBmdgi3FjB+mCsspVdBbta/Jhd4SgYm2wm1uSuaeDSCja7jYWHFrIka4nLx2+z2/h8z+dc//P1arm8K7ad3sYLG19gyg9TmPLDFFUU6h3Tu9FjEDHoVTmr1FjkhPYTnApIw/3DVRv7N/u+afQ2G2NfodJj11g/mKBjuLLT1tDnvStnqyf0iu6FTqOjwFigdp2cS5hsJp5d9ywyMpd1uazBEy8fPs4kkiSpDiARjatLTkkFmoCjBBskQv29F4SaQ3igH1f0T+Km4R25e2wXHpucRlqcsvjacKTQ5XV+zTyFXVZEpMZc/w3x3xsGsvaxsQzu1LQJaHUnRwohLECnnNtaZeEIc3bZicJ8gF6tMDFSUDs5sppMEYtshaL88xEx9ECUsItS9obwtB9MIC7nbaRQdJEKF4twIDn2hAk3WHxQfLMX+eJ3LK/cxNHCKkL9ddwxqhPLHx5N76Rw1WHe5kJYzWsUHxSv1lG4jEbWFOVH+kciSRKXd70cgB8O/uDVRlyZuYzLF17O9B+nU22trvdzIUSezY4wb3EZjQw6i6KRNXU0+UZFTB6ROEL9/HWFcITtL9rP7vzdwLkXiwSfEHZGkGWZd7a9w22/3cZdS+9q0m2YbCbW567n7a1vq1GgluBk5Un1A+jFjS+6/ICqy5KsJZjtZnpG9eTH6T8ypdMUtycHXSOVTHFLCWE783dila3EB8XX+8AcmzwWgAWHFjDhfxN4e9vb6s7UkuwlLXL/rpBlmVf+eIX/7v7vOevQEDh2L80/NJ+bf725WV/QIhY5JGEIF8ReQJA+iMLqQvYW7sVkM6n9cT2jvRfCOoZ2JNI/EpPNpEbrWptSUynX/HQNk7+fzIivR3Drb7fy9ta3WXFshVvH18qcleSU57C3aC//2PgPp/fIZ5mfISMzMnGk2n/VFt1PsiyrQoyYnCQ6Atyxv2g/1/98PU+ve5pHVj/CNT9d43SsB4sP8tdf/sprm19jZ/5Otz0SxdXF/O23vzFv/zxyynPQSTr6t+vPIwMe8agnSgjea3PXqp+FF6ZcWO9yV3S7Ap2kY2f+Tg4VH2r0dhtCFOU3NjFSIBxhWSVZLj8TiquLOVSiHJO33Vj+On9VOD7XesKKq4u5f8X9ZJdlEx0QzcMDHj7Th+TDR4Ok13RCZZxw7WqZmzGHoJT/Iyx261khkgzrrMQW1x1yLYQt3aMIB5PSm+dMCPDTEhva9ChoPSGs5lxN9NDYqQbkeo4wRyGsdyvGFOPCanvCxGv/Z4lFbj29la/3fd2k89kqS1XtNMKa70tvHGGeCmExAU1zhInNI1EULtYRjo4wUZTfWG+tJ3RrF8KglEjS4kJ44bKebHxyPE9M6a52851pR5hjNNJkM2G0Og8GEUJYlL8ieE/rNA0/jR/7i/erTnlP+GLPF+QZ8ygwFqgDshw5F6KR3iLK8issFWe1I0zQUCwSFBdmh9AOyMjMPzTf5W2cC/iEsDbGLtt5dfOrfLT7I0D5sBVWU0+uO//gfGYtm8XIb0Zyx9I7mJMxh0dWPaKWPTYXR/fJ8YrjzMmY0+h1fs3+FYDLu13e6HhaEY9sqZ4wV7FIwZCEIfhr/amyVmG2m+ke2Z1r0q5xul5rsLdoL1/u/ZJ/bfsXn+35rNXupy0QQtglnS8h1C+UXfm7uHrx1ezM3+n1bVntVtXpMyR+CHqtnqHxysjp1bmr2Ve0D6tsJdI/skm7JJIk0S+2H9A2goDJZuLe5feqAka5pZxNJzcxJ2MO9664ly/3funyeo4i7E9HfmLBoQWAYr8Xf765581qRK4top65FbnkVeWhk3Q82P9BQJnoWfckCJTH/c62d7h68dVkFGYQrA8mWB/MnsI93LTkJh5c+SDvbHuHKxdfye6C3WrX26581862nfk7sdqtxAXF8e64d1l7zVrmTp7LDek3eDQtqFd0LyIMEZRbyik2FRNuCHcZL4wOiFbFRU/LXc02c73nwGK3qJ9fPSIbjm4KUkJT0Egayi3lLj+rxfu1S3gXNZrhDY7xyHOF7XnbmbFoBmtz1+Kn8eMfw//R4GAEHz7OBnrWTAnMzHW9mF9/WtlAMAS2zDlZcxnWWVmwbjhc/3iqzFbW1RTcX9jjzE4vc+cIE+eMSDJozPU6wtqFGhjdLYZ+7cPpHt96ExwdHWEZNa99esLZMzGyNZm9fjYvbXrJI5d4XYTzJVgfrIpNRqtRnTzoDq+FMBGNNDYxGllzbOL/jo6w7LJsAKeql6bip9Pw7Z1DWXL/KK4f0oFAP2fXaEt2hJlspgYH9DjiGI0M0gehk5TjqitaFhlrO8JAKU0f3348AD8c+MGj+yo1lfL5ns/Vv4uNRYHFZuF05Wn1eM4XhCPMLtvV2OnZJIRFGCLUNbxeo1c7eBtCrL2FoNwSYnFb4xPC2hCb3cbs9bPVBbKwHHo69v6TjE94dv2zrDq+CqPVSGxALLGBsZjtZrVfpblk5CsdTeLNPGf3HLJLs91eXjhbtJJW/TBsCFUIK24ZIUy4WFwJYQG6AJ4f/jxXp17NF1O+YN60eczqNwsJiZzynFbbcXF0u7259U1WH19d7zInKk6w4NCCZvUwHSs7xqeZn7Zal1NRdZFqd72n3z18M/UbuoR3Id+Yz99+/VuD7wtX7CncQ4WlghC/ENUiPzJpJKD0hIlYZHpUepN30tXi8FYWBOyynSfWPMG2vG0E64OZN20e3077lmeHPqs6Eb898G29HdQKc4UaOb6408UAvLTpJQ4VH+LrfV9jtpvpFd2LAe0GqO/pXfm7PIp67incw6LDi5q0ayuEmPTodPq3609CUAJGq7HelMWTFSf5y49/4aPdH2GVrUxoP4GFly1k8fTFzOg2A42k4fejvys/t1sZmzyWTycrwwsOFh906TAVAtnQ+KGMSR7jdT+UVqNV30egTIt01ykieukWHVnk8kR86+mtvLHlDWYtm8XUH6Yy8MuBjJ432mnHMqs0C7Pd7DRmvDH8tH4kBSuXdRWPVGORXvaDCcR7Zf2J9R5HdFuCCnMFv2X/5pFzWGCX7czZPYebl9zM6arTpISm8NXUrxiROKIVj9SHj5ahV40LaO+pciw259+1rNIsCs3KAtrgd3YM6RncKRKtRiK7sIrcEmdRf83BAsxWO+0jA+kaG+zmFtqGuh1hwpHTMawjWkmJuUuaarWrSyBJEp/eMoj5dw3HT9d6SxrhCDteXMWek7XRyPMdWZbV+GBT0idqCXtwPCH6EPW1dNcNJRARx3ZBngm0TY0UigV83WikkyOsZmJk3bqX1qAlo5Fvb32bSxZc4nIN4ojNblOFp7igOCRJItSgCCJ1hTDVEeYwwOCyrpcBsOzYMo/OPz/N/JQKS+3k3boJjlOVp5CR8df6q86z8wGD1oCfxk/9e4AuoFHzSFsiSRIdw5Ro47CEYQT7Nf6dUHftLa5/LuETwtoIi93C42seZ/6h+WgkDS+NeIlhCUoXyoGixmOC5eZyPs74GIDru1/Pdxd/x9IZS3l80OMAzNs/r0UEkd0FivDxt15/Y3jicCx2Cy9uetHth9tv2Uonz8C4gR45GcTkyAPFB7xesGWVZvHGljeYt28eGQUZlJvLVaHG3QLyoo4X8dSQp+gT00f5cPcLVY+htaasCSEsSB+EXbbz6OpH1SiWXbbz9b6vuWzhZTyz7hlmLp1ZryfKE9blruPqxVfzzy3/dOs8ai7rctchI5MakUpcUBzJocl8MeULLoi9AJPNpL4fPUXEIgfHDVb7m8TiN6MgQ91tbEosUiA+lLfnbW81QUCWZV7b/Bq/H/0dvUbPv8b+ix5RPege1Z0Z3WbwyshXCNQFcrTsaL2pjytyVmCxW+gY1pEXRrzA8IThVNuqeWjVQ2p31c09b0aSJFJCU4jyj8JsN6vTNN2xNnctf/35rzy59kk2ndrk9WNydFZKkqT2t4nOLVBOlh5f8zjZZdnEBMTw1pi3eGvsW8QGxhIVEMWzQ5/lfxf/j+GJw0kMTuSN0W/wr7H/ok9MH6L8o7DKVpcWeCGEedIH5g7HnStx7K4YljCM+KB4Sk2l9SZjbj61mZuX3MzczLmsOr6KY+XHsMt2jFYjz294Xo26iseQFpnmkWNN0FBhvlqU72U/mGBw/GACdAHkVeW1WSw4pyyHa3++lodWPcT7O9736DqyLPPU2qd4e9vb2GQbUztN5Ztp36ifyT58nO20jwwkxF+H2Wrn4OkKp5+tyFmh/tlgODuEsBB/vVoiv/6QsytMxCLHd4894zHOuo4w0VmUEJxAoE5ZkIUF2lpV7GoI4Qhbf7iQaoudID8tHaPO/6EeJaYSrHYroLy/vZ26LJx9CUEJSJKkun4bmxwphCAReWyMppTl22W7KniJjSq1I6yifkdY+5DmO8IaoyWjkeKcurEp4PnGfKyyFZ2kU59vd4X5Ymqko0DVv11//DR+FFYXqs+VO0qqS9Q1yyWdLwGo1x+bW1lblH+mP5daGuEKg1rR8WxiaIKS0hGbxo3hOJQKfNFIHw2w8NBClmQvQafR8cboN7i488Vq94EnjrCcUYstAAAgAElEQVQv9n5BmbmMTmGdeHjAw6RGpiJJEuOSx9E+pD2lplI1o9tUrHaralHtHd2bpwY9hUFrYOPJjW47tcRCeWLKRI/uo0NoB/w0fhitRifrsSe8seUN5mbO5YVNL3DNT9cw4psRmO1movyjvNqpEb+4rRWfE8Lmg/0fZEC7AVRaKpm1fBY783dyy6+38NKmlzBajUhIbMvbxu2/3+40qrkhZFnmiz1fcNeyuyi3KNdpil3dE8QukqPIEKQP4oH+DwCKq0bY3j1B7QeLH6L+W2xgLN0juyMjq06p5ghhqZGpBOoCKTeXq31jLc0nmZ+oX+QvjniRQfGDnH4eqA9kSqcpAHx34Dunn4kY8eSUyWgkDS+OeJGYgBiOlB6hzFxG+5D2jEseByi7M0LYa+i9uvr4au5dfi9mu+Ia+z274W4vVwgHnbg/0bG16vgq1Tn1SeYnbMvbRqAukE8v+pQJHer3B3SL6MaHEz5kyRVLmJgyEUmSkCRJ7R2rG4+02W2q+N4cIWx4wnBiAmJoH9K+3uvhiFajZXqX6YBzPLLCXMEz655BRmZo/FCeHvw0cybO4buLvyNIH8Tugt3q5cVJm3A1eooozD9cctjp30tNpep7tamOMIPWoIrKy44ta9JteMPW01u59udr1djFDwd/cBmjrcu3+79l8ZHF6CQdzw17jpdHvOybEOnjnEKSJDUSV7cnbNnRWiEs2N87waA1EfHI9Ydrazhsdpnl+5TF9oXdz2wsElCdXgV1OsISgxPx0yj9SRHBbed2rUtcqJLgyKs5vh4JoWg0Z9citjVwFGSKqovU72tPUYWwmoibEFga6wnzNhoZG1AjIBnzPHbF51XlYbFb0Ek6NaImBLETFSeQZRm7bG/T2Jd4vEXVRVjsTRfTy83lqihVd0O2LiIW2S6onbpJ7U4Ic5waKTBoDfSKUc7xGltXzc2cS5W1iu6R3bm7792Ack7k6NA/VyZGNgVHIexsKsoX3NH7DpbNWMbY9mM9unxicKLqYgwzhBFhiGjNw2sVfEJYGyFEhTt636EuILtFdANodMFeairl80wlTz2z70ynaWhajZYbetwAwOd7Pld3bprC4ZLDGK1GgvXBpISlkByazK29bgXgtc2v1RNrcspz2FO4x+NYJIBOo6NLRBfAu3ikxWbhj1N/AIqQFWGIUB0/o5JGeaWqi8V+a8TnZFlWH1d6dDpvjnmTpOAkcityuf7n69l6eisBugCeHPwkX039Su3duv232xs9MbDYLDy34Tle3fwqdtmuPue78ndRZnY9waqpWOwWdbJm3Zx439i+DIwbiNVu5dPMTz26vSpLFTvydgBKd5sjjrE2UKKRTUWn0dEnpg/Q8q9vgbGAB1Y8wFtb3wLg4QEPc1HHi1xeVuymLD26VH1dS02lrDuhPKeTUiYBysnEq6NeVZ1FN6bf6PT73ZgQtipnFfevuB+L3UKXcOX3akXOCq/ccPlV+RwtO4pEbcdar+hetAtsR6Wlkg0nNrCncA/vb1dcP08MfkKNEXiKOEmqexJ9uPQwVdYqgvRBdA7r7NVtOhLsF8yCyxYwb9o89JqGpzpN7zodjaRh86nN6oni61teJ7cil8TgRN4a+xZXpV3FoPhBpEamck+/ewAlZlBgLFAdV54W5QvE46vb2bHhxAZkZDqFdXI6ufQW8XnQ2kLYj4d/5NbfbqXEVEJ6VDoJQQmUmctYktXwAJJ9Rft4bfNrADzQ/wEu73r5Wbcb6sOHJ/SsKcx37AkrNBayu6C2O9MueR4Xbm2Gq4X5BapIsCOnhMJKMyH+OgZ29L6XsKVxdIRVWapU50l8UDx6SRGhQoPOnLiYEO48CEAMTTjfqdu55W08UgiaCUHOQlhjjjDh7BKL7MaIDlDe41a7tdHbFgiBKz44Xq1TiAuOQyNpMNlMFBgLOF15GpPNhE7SER/c+sJFuCEcnUaHjExBVdN7Bh3d9znlOWr00RWOEyMF7iZHit/LugkgTzZtC42FfLXvKwDu6nsX8UHxRBgisMpWp3Xw+TgxUuAYhTyb+sEEOo3OY/EZnDfsU0JTzslzOp8Q1gY4loSPTKxd9HcNVxxhh0oONWg3/mzPZ5RbyukS3oWJHeo7ry7pcgkRhghyK3JZeqxhC2xDiKL89Oh0dWF+S89bSAlNocBYwKt/vOp0eRGLHBA3wKuCZ9ET5s3kyJ35OzFajUT6R/LJ5E9YddUqfr3iVz6a+JEaD/UUUSx9sPigR9NrvKHAWECJqQSNpKFzWGci/CN4b/x7BOsVa/+Q+CHMv3Q+16RdQ8/onsyZNIdwQzgZhRnc9tttDfYmPLH2Cb4/+D0SEg8PeJi3xrxFp7BO2GQbm056H4driB15Oyi3lBNhiFDdPI7c2lMRSL8/+D3F1Y2Pwt6etx2L3UJ8UHw9e7nj70R8UHyzxABwKA5voeirLMv8ePhHLl1wKUuPLUUn6bjvgvu4Mf1Gt9dJj0onNSIVs93MosOLAEWgstqtdAnvQufwWtFnYNxAXhrxEjf0uEF1Kwkco551Re7lx5Zz/0pFBLuww4V8OeVLgvRB5Bvz3RbTu2JrnnLikhaZpu5WaSSNGjH88fCPPL7mcayylQs7XMilnS/1+LYF4j1UVwgTQxd6Rvd0EgCbQqhfqEedBnFBcQxPGA4o79+VOSv54eAPSEi8MPyFeg6lq1Ovpntkd8ot5by++XVV6PbWEeYuGikGJHi6meCOUUmj0Gl0ZJVmuYxftgSfZHzCU2ufwmpX3gufTP6Eq9KuApR4vjsqLZU8vOphzHYzY5LG8Ncef22V4/Phoy0Q3VAZJ2o3oFYfX42MjCwr506t1d3ZFC7oEIGfTkNeuYnD+UoVw7K9ysJ4TGoseu2ZXwoIIazcZCW7RFkIB+uDCfULRSMrQlhwwJmLm7arMxHzz9APBrWClOg28rQHSqCKLDUikifRyCpLlZp48HRRrtfq1XWIp7FCkUpx3NjTa/TEBSoCxfGK4xwtVzbLkkKS3HaPtiQaSaO620RPWlOoW5HQkCtM7XFzEMJcCZYWm0XddK/b3eWJEDY3cy5Gq5H0qHRGJ41GkiR1Q9HxeM/HiZECp2hk4NknhDUFsVYYFOc+jXE2c+a//f4EZBZmUmGpINQvlLTINPXfk0OS8df6Y7KZ1J2JupRUl/DFni8AuLvv3S47aQJ0AVyddjUAczPmNqksG1B7iByFDz+tH7OHzUYjaVh4eKFTRFKNRboQ5xpC9MF4MzlS7ZeKH4xG0iBJEgnBCQyJH0KgPtCr+48OiCYlNAUZWXUptRRC3OsQ2gF/nXLi1Dm8M19P/Zr/m/B//OfC/zh9uKdFpjFn0hwi/SPZW7SXf2z8h8vbzSnL4dfsX5GQeHfcu9yYfiOSJKk9c8K91VKsOa7ELYcnDncpUAxNGEr3yO4YrUZ1h6chHGORdXcMekX3Ur90m+MGE6iOv9Pbmvy7ICg1lXLXsrt4au1TlJnL6B7ZnW+mfaM6Jd0hSRJXdFNcYd8f/B5ZltXfHeEGc2Rqp6k8MvAR9FpnN1PXiK6E+oVSZa1y2uHbX7Sfh1c9jNVuZVLKJF4d9SqB+kBGJSruveU5nu/abj3levKq+HL7/ejvZJVmERMQw7NDnm3Sjk/P6J5ISORW5Ko7iuDQDxbd9FhkUxCvzcJDC5m9fjaguPFcdXRpNVqeHfosEhI/Z/1MpaUSg9bgdSmouHyBsUA9mTxRcYL1J5SBBNO7Tnd7XU8I8QthcNxgoGmlxo1xpOQI72x7B4Bbe93KP0f/kwBdANO7TMdP40dmYabLLjtZlnl+w/McLTtKXFAcL4x44ZzcNfThQyAmR+45UYbNrnzHiH4wW4VyfuNYBn2m8ddrGdBBiaysr5keubRGCJvQ3fPd/9Yk1F+n9n/tK1DEB9ERJNsVkSzA0PTEQ3Px12uJDKotuu71JxHChKg0tv1Y9Bo9x8qPebXR4s4R1tAmtLjPQF2gR5tbAtFv5akQJtZdIg4pSAyp6QkrP87RUuW92BZF+QIh/nk7AdORzEJl+JQYytaQECYqThwdb66EMFGUr5W0apm+oG9MX7SSlhOVJ9Q4rCMFxgK1B/fuvner5wA9opTJ245C2J8lGnk2OsKawoQOE1h02SJm9pl5pg+lSfiEsDZg44laEadurFE4Q9z1hIk8dVpkGuPaj3N7H1enXY1BayCzMLPRPLg7hFujrgOof7v+6sL/+Q3Pc7LipBqL1Egal11BDSEiod44wjac3AAok+VaAuEaaumeMPGYxGMUpISlMCJxhMsFYLeIbnww/gNA2W1z9SW+8PBCQCn7Hp08Wv130Qu0Nndtk0SfCnMFO/N31ovSrTq+CqgfixRIkqS+J77c+2Wjhf+u+sEEWo2WMcljgKaXhTvSK7oXOo2OPGOeU+Gpt5Sby7n999tZm7sWP40f911wH19N/crjYu+pnaZi0Bo4VHKINblr2HRCce1NTpns8TFoJE2996rJZuKJtU9gsVsYlTSKV0a+osYBx3VQPiOWH1te7/2QXZrN9IXT+c+u/zj9u3CE1RXC+sb2dSqqfWH4C4T7h3t87I6E+IWoQpAYcAG1QpiIs7YVo5JGER0QTVF1EYXVhXQJ78KsfrPcXr5ndE+uSr1K/XtqRKrXu8PBfsHqCe6REmUhsfDQQmRkBscN9jpu6grxHdHSQpgsy7y6+VWsspUxSWO474L71E2ZCP8IVdwVJ7qOzD80n5+zfkYraXlt1GuqI8CHj3OVjtHBBPppMVpsZBVUYLQaWVczYddersTLzyZHGMDwLkp0bP2hQo4VVnHgdAVajcSYbmeHECZJkjo58nCJIlAI8cRmUzYV/fwan57cmsTVuMIMOg2dY/4c3YZCjEkJTVHP3zz9frHYLKqjTIgsnkQjve0HE3grIInzw7rfvY6TI4UjrH1o6xflC1qiMF90mYpC+i2nGnCEVdR3hLmKRjrGIuuaMgL1gaqo5Wpd9e3+b6m2VdM7urfThGjhrBf91I7Hc947ws4TIQyUNW7djfxzBZ8Q1gY0JAKIwnxXopBjntqdG0wQ6R+pRpY+2vURuRW5XvWFVVmq1BJnV1G4O/vcSe/o3pSby3li7RNq6ben0yIdESJRbkWu2jtWXF3Me9vfc9kzU24uV50Grp7DpqDaePNaRwgT8U9PSY9Op19sP2yyjfkHnYce2GU7Px7+EYBLuzjH0vq3649Ba+B01el6JdwNsbdwL89teI5x/xvH9T9fr8adQNkFO1J6BK2kVR1nrhjffjwpoSmUm8v53/7/ubyM0Wrk7a1vq24md0Xmjw58lFdGvsKVqVd6/Bjc4a/zV51lTY1HVloqmbl0JnsK9xBhiOCrqV9xa69bvRI/Qv1CVbfkM+uewSpbSYtM83qqiihQFwL3e9vf42DxQSL9I3l+2PNOxzQycSR+Gj+Olh2t937455Z/cqjkEO9uf1ct+3csahf9YAKNpGFap2mAMql2WKL794IniCEIIoJdZi5Td5ZFh1hbodfouayLMvJbJ+l4acRLGLSGBq9z7wX3qj0kjs5ebxDxyKzSLGx2mzrgpLluMMG49uOQkNhdsLvBThBvWZGzgvUn1qPX6Hlk4CP1fi7ikUuylzjFu1cfX82LG18EYFa/WfXeYz58nItoNRI94hVHxO7cUjad3ITZbsJuCadnVF8AKq2VzXYktyRDawrzNxwp5Lc9igNkYEoEYYFnz+JFxCNzypwdIRaLcox6/ZkVwsTkyO7xoejOgjhpW+AoSnm70XKq6hQyMgatQY3SeRKNFJFAT/vBBN4KSCIamRTi7AgTDrHcilyOlR0DoENI2zvCmhqNrDBXkF2WDSjnbhIS2WXZFBhdd46JsnwhPIMbR5iLonxH3MUjZVlWK0Ku6X6NkyFAiGcHiw9isVkw28zkGZXX73wXws7Gsvw/I3+OT/IzSJWlih35SvzOlZupocL8z/Z85pSnbowb0m9AQmLDyQ1M/n4yA78YyNQfpnLv8nsbLeTPLMzELtuJC4pTxxA7otfoeWXkKwTqAtl6eisf7FAcTN7GIkH5IhRK+P6i/cw/OJ9LFlzCv3f9myfWPFHvS2zzqc3YZTspoSktVlYpJkfuKdjToju37hxhnjCj2wxAidI5dsZtOrmJk5UnCfELqecK9Nf5q0KJKGJviO1527nup+u4cvGVfHfgO3XS2+Iji3lyzZNY7VZ1sEPf2L4Nuje0Gi239LwFqOmxqzNMYVXOKqYvnM6cjDkAXJV6lSok1CXEL4SpnaY2WnTuKWpPWCOF+UuPLuWlTS+xNnetOqHHaDUya5ky5TPUL5SPJn7ksQusLiKCJ3bSXMUiG8Mx6vnHyT/UAQWzh86ud0ISpA9ShxE4lqZvObVFdfkBvPrHq/x+9HdVKOwY1tHlyc2sfrP4/KLPeXTgo14fd11E/FGI2hn5yv+TQ5K9FtNbguu7X8/whOE8O/RZj4rvQ/xCeGnES/SN6cuM1BlNuk/HnjDxex3qF+q1q9Yd0QHR6vRNb+KxDWGymdSS+5vSb3K5M947ujfdI7tjsplU9+ryY8u5b8V9mO1mJrSfoH5W+PBxPqD2hOWWqbFIa3l3RndV3CV22U617ewpzO+dGEaIQUe5nMWHmxUBfsJZMC3SESGEnax0njRYbVLOCzRak+srthHxNYX5YmronwHh6IoJiGFM8hgkJDIKMzyaGO7oNBLiR2s6wsTaRRxzY7jqCIM60ciagTodwtpeCGuqI0y4q+KD4kkJS1HXI67SQrIsq0JYXHCtQ8nV6+SuKF/gTgjbkb+D4xXHCdAFqFPRBYnBiYT6hWKxWzhUckh9zwToAtRjOJ9wLMtvF3R2ff7+WfEJYa3M1tNbsdqtJAYn1tt1gFpHWF2hSpZlfsn6BYC/9fqbR50qHUI7cO8F95ISmoJeo8cqWzlWfowVOSu47ufrWHrUfZG+q36wuiSHJvPE4CcAZaqgRtI0ueBZOKYeWvUQz65/lhJTCRISVtnK/w44u4s2nFBikS3lBoPaka9W2er1OGh3WGwW1eHSFCHswg4XEuoXysnKk06ilijTntJxikvXyvBEpfi7sZ4wq93K/SvuZ1fBLnQaHZNTJvPxpI95e8zb6DQ6fsn+hUdXP6ouoN3FIh2Z1mka7QLbkW/MZ9jXwxg9bzTX/3w9Ny25iVnLZ5FbkUt8UDzvjH2Hp4c87fFz0Vz6x9aKR+44Xn6cx1Y/xtf7vmbm0pmM/XYss9fP5p5l97Dl9BaC9cH8+8L/b+/Mw5sq0/d/n+zplm60paWlC1vZoYDKvsiO4CioCLiPg4oDMi6MOIPjAm4XMPP9ic6IoqMoiODO6BRGUEAFyyoga+kGpYV0hex5f38c3tOkTdokzdLl+VxXr6tNTk7evDlN3nOf+7mff/osggGi4OqYJeWLENYjtgfCFGGoNldj0Y5FYGC4pestbtsb1+8eyBjDqv1ip8tZ3Wbhtm63gYFhyfdLJGdY/bJIjkquQv+E/n7JdHLsHGlndhy61mGNCzfBJk4bhzfHv+mVG+uG5Bvw/pT3m+0IO1N5BptPbQYg/g815UbzBk+7R1rsFukiQ2O8d/Q9lNSWICEswW02niAIUunoxhMb8e25b/GnHX+C1W7FhM4T8MqoVxp1NBNEa4OLIUdKKrCjaAcAwFrTEzd2T4UA8fOyqciAYKKQy3BdZiy0KR/BFPcuZJoijGuhQthloyiycEeIwSgKYUxmCM3ArjF7SBom9EzE3UPTQzqOYMLdOQlhCU4XWvgx3xhc1HDMegqoEMYzwgxNC0i15lpUmMRGT+4cYYXVhVL5ZEgcYT66unneFndb8cgRV+WRNZYa6XPK0aHEM8CqTXUNQXhGWP2gfM6AhAEu3We8omV85/ENMp3rB+Y7lkW2xSxR7giLVkdL+W1EaKGVaYBpLCQcqOscWVRT5ORM+k3/Gy5cuQCNXONUT90UD/R5AF/+7kvsm7MPuTNz8c7Ed3Bd0nUwWA14bMdjeP3g6y5PfNzlg9VnRtYM6WR+cNJgnzv8caFIb9RDq9DiTzl/wvIRywEAm05sgsVW1x1ImsNk/wlhgiD4vbtgfnU+rHYrIpWRPtV+axQaqfSRi4E15hrphNZdtz4uhP1y8ZdG3W2Hyg9Bb9QjShWF3Jm5eHXUqxicNBjjOo/DqtGroJQpkVuQK3Wg5MHrjaGUK/H44McRoxaDePVGPQ6VH0LexTwoBAXu7X0vPpvxmVvRJlD0T+jfpB18Zd5KmO1mpEWmIVYTiypTFTaf2oyfS3+GVqHFGze+IZXz+YogCLi1q+gK6x3X26ccKIVMIZWU1ZhrkBKR0qhDa3TqaMgEGY7rj+N87XlsL9yOw+WHoVVo8VC/h/D0dU9jTOoYmO1m/FwqvtfuhDB/0jWmK9RyNWrMNSioLghZUH4oyYwWhbCjl49KgvMtXW/x63NwIeyX0l8aDST+56F/4r5v78PaI2vdblN6pVS6/085f2q0McnkjMmIVEaiqKZIbOTArJiSMQUvj3zZb05PgmgpcEfY8cu/Qm/Ug9k0iFNkI7tjlPR/0pKEMAAYlBEBmUo8mU1IOIeM+JaVc8UzwqqtdblSRosNRrMYUm9HaB12vZJ1+Nddg9AtMbLpjdsANrtNKofjbitvyiO506ip7Kn6NDcjzBMnFRe4YjWxDTpFcwG2zFAGq90KtVwdVPcOD+b3tfszD8qXhLBE90IY7+oZo45xEmZcOsIMjTvCdGqdZO7g51Umm0mK0rkp6yaXj+sZK47zuP44Sq603aB8oM4R1pbywVo7JIQFmMbywQDRlRCniQMDc8r04SdJQ5OH+qQay2VyJIUnYXDSYLw5/k3MzZ4LAHjz0JtY+N1C1JqdOxpxIaypE39BEPDc0Ofwp5w/Ydn1y7weF2ds2lhEKCMwLm0cPp/xOe7pfQ8mpk9EgjYBl42X8W2B+MFZeqUU56rPQSbIMDhpsM/P5wr+5eCvwHxeFtk1pqvPVzJmdpsJQMzWKb1Sim/OfQOTzYQsXZbb9yYjKgPJ4cmw2C2NNkrYWSSWxo3oNKJBieLo1NH4+5i/Sy2yUyJSpEYOTTEpfRK+v+N77J69GxunbcRro17DkiFL8Mn0T7A4Z7HXXT39gU6tQ5eYLgDEctD67Cvdh9yCXMgEGVaNWYX/zfof1k5Yi1ndZqFPfB+8Pu519E/o75ex3NnjTjw+6HG8OPxFn/fBhSoBAl4c/mKDhZsjsZpYSTjLLcjF3/f/HQBwV8+70CGsA+QyOV4e+bKTE4v/LwQSpUwpBaMeLj8csqD8UMIdYXqjHla7Fb3iejXLceiKtKg0dInuAhuzSWXO9WGM4euzXwMAPjn5iVtX2MpfVsJgNWBgwkBMzpjc6POGKcOcMgynZ03H8uHLg9JyniCCTZeECKgUMpjU4trJWtsdo7slQRAE6fO5pQlhGR3rHFXh0fkhHIlrOkSqAcECExNPvlPCU1BWbQK7FpZvtLWs+WzrVJgqYGM2yASZJH7w0rZ9pfuk7sfuaK4jzNuMMG9KI911jATEiAFHl3ZqZGpQHc1Z0VkQIEBv1Lu9kNsYPCifC2F8/Xim6oxT127AQaysFzvD36dqc7W0PpAcYY0YIOqXR35f/D1qzDVIDEvE4ETX53COnSPbclA+IGoB13e8Hnf1vCvUQyGuQUJYALlkuCSJI+5CwgHXgfn8aktjnSI9RSFT4KkhT+GFYS9AJVNhR9EOPLz9YZhsYt5C+dVylF4phUyQSSHjjRGmDMM9ve9BapTvXc56x/fGntl7sHrMaukDWClTSmHpHx3/CEBdWWTv+N5OtdX+gOeEHSo/5ORA41hsFvxQ/AOW7VmGv+z+i5Sn5Y7m5INxMnWZGJQ4CHZmx5ZTW6SyyJu73OxWXBMEQQoyb6w8ckfxDgDA6E6jXd4/otMI/N+4/0NaZBru6XWP12JelCoKPeN6YmL6RMzJnuOxkBYo+Pu7q2SX0+02uw2v7nsVgFgq2C2mG+QyOa7reB3+esNf8eHUD/0quirlStzd627JDeQL0zKnoWtMVzw+6HGP3FvcFfT6wddxrvocYtQxuKfXPdL9WoUW/2/s/0NOYg6mZk4N2tUpXh759dmvUW2uhlqubtb/S2sjVhPrlLvnbzcYp6nyyJMVJ6Ur4heuXHB5pfhUxSn859x/IEDAkiFLPPo8mNdzHjJ1mZjXcx6eH/a8U5dkgmhLKOUy9EiKgCJSLEOy1vbEqGsdGFuqECZTXpZ+11tPNbmmCTYdItUQlKJAEqYIg06tQ3mtEcwuihItbT6DCWMs6O8XF6TiNHHSBY10XToydZmwMit+KP6h0ce7coTxztPVpmqnLFxHeEi8t44wLpxdNl5uslkYzwfjeWCOCILgJMRwh1aw0Cq00nO6aqTWGI5B+VxgitHEoEu0eGG4/oV/Vx0jgTohzM7sUv5vUxlhgEOm7bV8Xl4WOTVzqtv1AC+NPKE/IWWytVUhLFoTjbcmvOXWHUcEHxLC/ITFZmlQlsZLzLJjsxv94JAC8yvFnLDimmKcrDgJmSDzKCTfU2Z0mYF1k9YhUhmJA2UHsHTXUtiZXXKDZUVnBdW94+rEama3mVDKlDh86TCOlB/Bjxf8nw/GyYzORLQ6GkabEcf04mL2kuESthduxzO7nsGoj0fh4e0PS4LUqrxVje7vpP6aEBbbvBN7Hpq//vh6HC4/DLkgx7SsaY0+ZniyWD7rLjC/oLoA+VX5UAgKqZTSFUOTh+LrW77GHT3u8HH0LYcRKSMAAFtObcGKn1dIC6PPz3yO4/rjiFRG4uH+D4dyiB7TMaIjtkzfgrt6eXYViQvofOH8h35/QIQqwmmbGE0M3p30Ll4a8ZJ/B9sIvAyS/1/3jOvZalsu+4IgCJIrTCPXNOmy8hUuhO0u2d3A/Qs0DNLnAfeO8Py4Gzvf6FEzAUC88v/5zZ/jyW5e7mYAACAASURBVMFPUiYY0eZJTdJDri4Hsytgv9Idw7uITutwRcsUwopqi6Tfrczi0i3tKyvzVmLmFzMbOE68IS02DDKlmNukUyZAEASUVZuAa0JYraXhZ1l74ZV9r+D6D6+X8p+CgRSUX6+BFl9f8CoDd7hyhPELQQysQYMlQLxQyV1Q3gphMeoYyAU57Mze5HHIHWHu4iochRhXDWICjbv86KbgQflJ4UlO551coKp/0cuVWAmIF3DDFOL5IHfvNdU10vF5TuhPoLC6ELuKxQvRN2W6F35SI1MRrgyH2W7GT+fFKqq2WhpJtDxopdoMGGPYf3E/nt3zLEZtHIVxm8Y5uU+aKovk1P/A4x2IchJzpKsn/qJvh75YNWYVFDIFvj33LVbvX+1RUH6wiNPGYVL6JADA+t/WS2Kiq46bzUUmyKQSsr/9+DeM/2Q8xnw8Bou+W4TPz3yOGnMN4jRxmJIxBQDw0W8fYc/5PW735w9HGCCeeMaoYyTb+fCU4W67LXKGdBwChaBAQXWB9AXvCA82zUnMcWrf25YZ2WkkHh3wKADgw98+xEPbHsL52vNSqeD8fvND0q0wGKREpEhliCkRKbit220hHpEId4Rx2lM+GIfnQk5InxCw/8UesT2QqcuE0WaUSiAd+a5Q/I7h+XW5BblOF3L0Rr3U7nxez3kBGSNBtHauqMQ1nrWmFwZ26ghdmCjqt1RHWGF1odPfey/s9ct+GWP45OQnOFFxQvrc8IUeSZEYJBpXcOGyFqfLalFeW1ca6UrUbwvojXq8cegNt9lWRdVF+Oi3j2Bnduwr3Re0cUlB+VpnQWposliBsLd0LxhjLh9rs9ukzpKOopJSpkSEUrwo56o8Um/US+WY3mYQy2Vy6TFNlUdyR5ir0sj6Yw5mUD6Hn0d46wiTgvKv5W5xpMD8i54JYUDDMlZPHGHx2nikR6WDgWH53uWwMiuyY7OlqBJXyASZtF6tsYjiaFt1hBEtDxLCfMBit+DtI29jypYpuPubu7H51GbUWGpQa6nFgu0LsOnkJjDGvBbCTlacBGOsriwytfllka64ruN1+NvQvwEA1v26Dh+f/BhAyxDCAODO7DsBAFvPbpXC9AOVI8RL4E5VnELplVIIENAlugvu6H4H3pn4DrbP2o6XR76MO7qLDqm/7P6Ly5BPvVGPcoP4xctPdH1FJVfh5i43S387/u6OSFUk+iWIc7SnpKFYt7NYvHI3OnV0s8bWmhAEAQ/2fRCrR6+GVqHFTxd+wvTPpkNv1CM9Kh2ze8wO9RADyrye8xClisLT1z3dYlxXyeHJTouoUHWMDCX397kf9/a6F4tzFgfsOQRBkPIG+fcR53zteRzXH4dMkOGPA/+IzlGdYbAasK2wrqvwphObYLab0SuuF/p38E9WHkG0Jcw2M07UiBl8lqocjOpW55ppsUJYjSiE8QuL/EJjc9Eb9ZK7hwdj+4IgCLju2vLJbIzGA+/tw6mLtWD2a0JYiB1hZpsZ+0r3uS3p85W1R9ZizcE1eGLnEy6FpbW/roWNic/JhYtg4M4R1rdDX6jlalw2XkZ+leusuXJDOazMCoWgkLo5crgrzJUQ5qoc0xu4aNdUYH5TjjDHTpLBLo0Emi+E9Yp3jrnhObCnKk5J5zBXLFekbGpXDizHxgaOLjt3XSM53BXGo1qmZ01vctz1XeckhBHBgoQwL7lkuITf//f3WL1/NYprixGmCMOMrBl4a8JbmJ41HTZmw3M/Podndj+D0iulUMqUGJA4oNF9ZumyIBNkqDRV4lTlKam2OpCd9qZnTccj/R8BUNe9paUIYb3je6Nvh75gEBcEOYk5ATuRv7XrrXigzwN4LOcxvDPxHfx454/4dManWHr9UgxOGizVtC8etBjpUekou1qGF39uGHrO3Xypkal+KS+d1W0WVDIVEsISPC6PHZYsljx+ceYLp/DrKlOV1MFlVKr/Sm1bC+M6j8O/J/8bSeFJUi7e44MebzHiUKC4Kesm7J69GyM7Nd39M1gIguD0OdMehbDkiGQsHrTY5467njI9azpUMhVOVJyQyt+BOsfxgIQBiNXESiULX5wWszzMNjM2nNgAAJjbc26bbGFOEM1lZ/FO1FqrwaxRsF3pgtHd61wzXAhrrItzKOCOMC6SH9MfazLw3BN4rg8gNl5y5Ur3lItXRRdRpKIDzl2+ivd/KgCuCWFXrVf9LkJ5w/rj63Hft/fh3aPv+nW/3OW1v2w/vjn3jdN952vPS5/NQF2Xv2DAxaT6QpharpYukPDO0/Xhgl1ieGKDbCjuNHJ1UdnXfDCOFJhvcO8Iu2q5Ko3PXdmjo1MslELYmcozsNgbZhi7Q3KExTk7whydWj9e+BHvH3sfU7ZMwenK0wCADF1Gg305OsKqTdWSGNtUJYVjjq1ckHsUAeE43nBluN8zoQnCHSSEecGBsgO47cvbkHcxD+HKcDx7w7P47rbv8MLwF3B9x+vxwrAXpMwhHhA4MGFgk10fNQoN0iLFD+O1R9bCzuzoEdsj4Ir4H/r+Ab/r8jsAYjhjqMPNHbmzx53S74Eoi+SEKcOwcOBC3Nf7PgxOGuy2G59WocXy4cshF+T4T/5/8E2+82LFX2WRnNSoVGy6aRPWT1nvsWAzPWs6whRhOHzpMD499al0++6S3bAxG7J0WW6vfrV1esT2wEdTP8LUzKm4v/f9LUocam9wISwhLIFaSAcQnVqHiekTAYiuME59xzEPbd1buhcXai/g23Pf4pLhEhK0CZjYeWKQR00QrYPPT4u5eqNTJuOv03qjd0rdiRu/GHbF2nIcYUarURIZhiQNQXpUOuzM7rJRhrfwcG5Oc1xhPFfq4eGDEaYSBRQelg+Edk75Oi+3INdv+6w2V+OE/oT092u/vOYkoL7z6zuwMqtUThhUR9g1Mal+aSRQV03hrlTTXQg7UCewVJgqGj7nNRear0IYfxw/1l1xqPwQbMyGjuEd3T4PbwYWoYxoMpokECRHJCNcGQ6L3YKCqoKmHwDR4cVF6fpCGFBXHvnU90/hlX2vQG/Uo3NUZ6wcvdLl+Z8khBkrpY6RUaqoJs9JHIWwYSnDPLro51jKmRKRQhfgiKBBQlgTWGwWlNSW4N9H/437vrkP5YZyZOmysGHqBtza7VYn948gCHio30N4cfiLUAiipff6ZM9C3nl5JBdYAlUW6YggCPjLDX/B/H7z8dyw51pUm/sJnScgKTwJckGOEZ1GhHo4AMR8o9/3/T0A4PmfnpcyBgD/C2GAGObvjVCQGJ4oCbGr9q+SbMw8H6w9usEcidfG46URL2FRziL6kg0h4zuPh1ah9cguTzQP3oX3m/xvUG2uRqWxUuoaxR3HyRHJGJI0BAwMX579Eu8fex8AcEePO9q8a5IgfOGS4ZKUB7v4hjtx3/AMp+8UfkGtJWVa8fVKpCoSOrUO13W8DoB/yiPPVZ0DIIaVA2hwodAbuIAyJLUL/n7HAAgCAKaAQhA/i0I5p3xNdezysWY1BXDkwMUDYGBIiUhBSkQKyq6W4a0jbwEALl65iC2ntgAAFgxYIN7WiMDjb9yVRgKQjp99pfucKhA4XLBrquSuPs12hF0rw2wsI4x/Bw5MHOh2m67RXfHogEfx7NBnQ7JelAkyKWbF0/LI45ePg4E1CMrnDE4UxUs7syMhLAHLbliGT2d8ivGdx7vcn/Q+mas8ygfjJEckS0aOxkLyHekc1VkyjVBQPhFMSAhzwaq8VZj15SyM2jgKAz8YiEmbJ+HVX16FlVkxKX0SPpz6IdJ16W4fPz1rOt6e+DbmZs+VTkSaggsovByQd2UJNEqZEo/0f0QKqG8pKOVKvDfpPayfst6lZTdUPNj3QfSK64VqczXmbJ0jfaHyL6ruMd1DOTzMyZ6D7jHdUWWqwspfVsJit0gL9vaUD0a0XDKjM7F3zl4sHLgw1ENp8/Tr0A9dorvAaDPiyzNf4vuS72FjNnSL6ebkDuWi5Lpf1+G4/jjUcrXUvZYgCGe+Pvs1bMyGvh36Sl1gHZFKI62+l0Yeu3wM2wq2uQ0j9xaeD5YWmQZBEDAkaQgA0QnaXLgj7M7sO6EQFDhRcQJnK896vR+zzSy5kDqGd8T4nolYfXt/3DE4FVFqsbFIKHPCeNc8BoYfz//ol33yNeT1Ha/Hk4OfBAC8d/Q9FFYX4t2j78Jit2BgwkBMyxQ7h+uNehitRr88d1Pw0khXolSv+F7QKrRipIuLzoa8PNaTEHZXz5kYlujTmPlYedC/K3j8jKNzqT48Y5a7qkOBtzlh7oLyOePTx+Pe3vfiqcFP4evffY2Z3WZCKXN/sctRsPSkY6QjL414CU8NfgoT0id4tL1cJkeP2B4AKB+MCC4khLmgpLYEv+l/kxRwpUyJ1MhULBmyBK+MfMWjDKiBiQPx1JCnPK5z5o4wQPwQ8KezqLWSHJHcIPAx1ChlSqwesxrZsdnQG/V44L8PYONvG6XAyVC/bwqZAn+54S8QIODzM59j7eG1qLHUIEYd0y479BFEe0YQBEnQ+uTkJ9hesB0AMC5tnNN23KXHTzJvyrrJ7x2LCaItwBjDZ6c/AwDMyJrhcpvmhuUzxrBg+wI8tuMxLN6xWAqibw5cmOAxHFwIO115GpcMl5q1by6E9e3QF0NTxI6C9bOuPKH0SikYGDRyjeQ8mdE/BS/d2lcqDXQ1p5cMl6QOhYHE0QXGg8CbCxfCchJzMCZ1DIYlD4PFbsGyPcvwyclPAIgxJlGqKIQpxHOPYLxWi90ivd76YfeAuBYemCA6quqLqSabSSrBd5UD6okQ1tzSSHeOMLPNjMPlhwE0LoS1BLwVwo5ePgrAdVkkIL5ni3MWY27PudAoNE3uz/F94qWRnnZa75/QH3N7zoVM8Fxm4N1IA9UcjSBcQUKYC+b1nIfXx72OTTdtws7bdyJvbh623rIVc7LnBMwi6yigjEkdQ6VbLZik8CS8N/k9TEyfCKvdihd+fgEmmwlahRYpkaG/ktGvQz8pDHfNoTUAgBGdRjQILCUIou0zLWsaNHINTleexo7iHQAaOo7DlGG4Me1G6e+52XODOUSCaDUc0x/D6crTUMvVmJTh2knfXCHswpULkjNqW+E23PHVHU45Uu7YXrAd9397Py5eaVg+x7ODePZRtCZacmDsveC7K8xqt0oiW3pUulRd8M25b7x2s/FyuqTwpAZr4AiVKITVFwXtzI65W+di5pczUWlsKKz4C8eueQCw5/welyWB3nDVclVy8eQk5kAQBDw55EkoBAV+ufgLjDYj+sT3wQ3JN0AQBMldFYycsMuGy2BgUAgKxGhiXG4zpKNrV+G2gm2oNFUiKTxJauLkSGOlkc0VwqSwfDdC2LHLx2CymRCriUVGVMupNnFFt1jPhDCzzYzLhstNCmHe4tjdU3KENdExsjk82PdBfHvrtx6F6xOEvyAhzAX9OvTDyE4j0SO2B2I1sUERpVIiUqQrXsEqiyR8R6vQ4tWRr+KPA/4o3dY1pqtXVz8CycKBC52u3FBZJEG0T6JUUdIJu53ZkRye7LKE+/Yet0MhKDC+8/gW1TiFIFoSPCR/bNpYt47/5gphx/XHAYiCUMfwjiisKcTcrXOl53bHuqPrsLd0L7ac3tLgPsfSSM51Sddywtx0/vOE87XnYbVboZFrkBSehDGpY6CSqZBfle+xk4XD86FcZaO6c4TpjXqU1JagylQldcQNBI5d87QKLS4bL3v9+upzsPwgrMyKjuEdpVykTF0m5vasuxDxh75/kM5BkiLEeQmGI4wLSfFh8W7XtdxVmFea59TN8+MTHwMQu7K7ugAb0NLIa8H+FaYKmG3mBvf/clFsDjEwYWCLNxx0ie4CQPy/qC8aHig7gEmbJ2HQB4OQ80EORn88utGgfF9w7O4pZYRpPXOE+YJMkFE+GBF0WsZZOwGZIMOKESvwxKAnMChxUKiHQ3iAIAj4fd/f4x9j/oFMXSZmdp0Z6iFJ6NQ6PD7ocQCiHZpbjgmCaH/c1q0uq3Js2liXJwD9OvRD7qxcvDzi5WAOjSBaDWabGVvztwIAbs662e124YrmCWG/6X8DIApVH0/7GMNShsFoM+KZ3c9IHcnrwxiTIhr2X9zf4P6iatG11Tmqs3SbPwLzeVlkWlQaZIIMEaoIqSvzf/L/49W+uJPNlRDGxcX6jjAunACiey5QcCEgUhUpCYg8f9VXeFlk/TX/H/r+AX079MWNaTc6dbgOpiOMZ2y56hjJ6RHbA5HKSNRYaqRj9nTFaewv2w+5IMctXW9x+Th3QthVy1XUWMT311dHmE6tk3KvuKvSEcdS1JZOpCpSysuqL7r+89A/UVJbApPNVLe9MhIzsmZ4nOPVFK5KIwPpCCOIUBBSIWzNmjXIyMiARqNBTk4Ofvjhh1AOJ+SMTh2Nu3rd1eKvUhDOjEkbg89v/hy/6/q7UA/FiWmZ0/D0dU/jtVGvSYtIgiDaH73je6Nfh34QIDRadhCvjadOkQThhh/P/4gqUxUSwhIkEckV4apmCmGXRVEhOy4b0ZporBm3Roo74NlL9bl49aKU8Xeo/BCsdqt0n9lmlsQTxyYZOYk5UAgKlNSWOHXB9gbeMdJRYOMOVG/LI7kjzJUbKFIlhuXXn1PHErgfz/8YsK6SjkIAz0Hbc35Ps/bpTpSJUEVg/ZT1WDVmldP5QDCFsMY6RnIUMoU0du4q3HRyEwDxfMadmKXTXCuNNDq7nLioGaYIk0phvUUQhLrA/KvOgfk2uw0Hyw4CaLxjZEuC50c7CmEltSXSsffBlA+we/ZuHJx3EHvu3IMXhr/gt+d2coQZRCGYhDCirREyIWzjxo1YtGgRli5digMHDmDEiBGYPHkyCgsLQzUkgmhTCIKA2T1mU6ktQbRzBEHA6+NexyfTP3EZXkwQRNPkV+UDEIWLxjI3uSPsqsW3rpG8NJJneMkEGSaniwK2u6yw05Wnpd8NVoPTdsW1xWBgCFeGO0UmhCnD0KdDHwC+d4/kjrD0qHTptpGdRkKr0KKktgS/XvrV433xkr/E8IZCGL+YV79rJBfPADHgfWfxTo+fzxscw8KHJw8HABy4eMBnsdNkM+FI+REAnruTuFMuGKWRXERyFZTvyOCkwQDE48dgNeDLM18CcHYh1ydGLWaOVZoqnYTS5uaDcfixuLPI+Vg4WXEStZZahCvDQ97h3VN4frRjZ87PTn8GBobrkq5Dvw79EKWKCkgGMBcsDVaDJL76y21GEC2FkAlhK1euxP33348HHngA2dnZWL16NVJTU/HGG2+EakgEQRAE0SbRqXUh72pLEK0ZfjLInTnucCfaeEKFsUISdxxP1vn/bnFtsUvX0+mK005/c7cRABRW1+WD1a844GV53CnjLVwIy9DVBY9rFVopl/Tr/K893ldjjjCeEVb/tXPxhOdYbSsITHmk5IjRxiE1KhWpkamwMqvPjQaOlB+B2W5GnCbOyU3XGPy4cyeE7Svdhy2nGubDNca6X9dh9MbRUmkjh5cVNiVKcWfk/ov78dXZr1BjqUGniE64Pvl6t4/hTiOz3QyD1SDd3tj77w2zuoudkj8+8bHT8bK/TCwZHpAwoNU0j6rfOdJmt+HTU58CAG7tdmtAnztSGQm5IM4TPx487RpJEK2FkAhhZrMZeXl5mDBhgtPtEyZMwJ49Da3GJpMJ1dXVTj8EQRAEQRAEEQy4ANGUEBamDAMgOpQsNotXz8HdYGmRaU7lYdGaaEmUOFV5qsHjuCOMd3rjJ/1AnRDmWBbJ4SKMr+V2BVUFTvvh3JR5EwDgi9NfeOyM4xlhLoWwa3NRX1zkQtjYVNH5vqtkl89OvMZwdIQBkHJXd5/f7bTd2cqz+PjEx07h8a6Q8sGSBnkch8IdYReuXGhQcsoYwxM7n8CyPctwoOyAR/vbf3E/Vu9fjcvGy9h8crPTfZ6URgJi6V60OhoGqwH/2P8PAMDMbjMbbRylVWilHC/HnDDeQZN3NvWVMaljkKHLQI2lBp+c/ES6vTXlg3G4EHa68jRsdht2n9+Ni1cvQqfWBbzaQxAE6fOEQ44woq0REiHs0qVLsNlsSEx0/rJLTExEaWnDKx0rVqyATqeTflJTm/chSRAEQRAEQRCewsUiV2HujjhmcnpbOsedObws0hHuEDupb9itkAthPMT/QNkBSSzhHSNdOY+aU253xXJFClVP16U73TcsZRjSItNQY6nBV2e/anJfJpsJFaYKpzE54tYRdu35R3YaiZSIFBhtxgbilD/gYfk8I2l4ilgeubuk7rn2lOzB7K9n4/mfnnfb1IDjiyiTGJYIAYLTXHHKrpZJYp0nIf5XLFfw9K6nYWd2cez18s48CcsHRCceL4+sNFVCIVPg5i7uG0kAosDiKjCfv2/Nbe4kE2S4t9e9AID3j70Ps80MxlirFMLSItOglqthsBpQXFssCZY3Zd4EtVwd8Od3FMLUcjXCFGEBf06CCCYhDcuvfxWEMebyysif//xnVFVVST9FRUXBGiJBEARBEATRzuGlW005whQyBTRyDQDgitVLIcwhKL8+3B1yosI5J8zO7DhbdRYAML3LdKjlauiNeuRXi5lmRTXimtmVI4yLThevXvQq2B6oK4uM1cQiShXldJ9MkOHO7DsBAOuPr29y32VXROFFI9c02BdQJ4TVFxYdc6XGdx4PAMgtyPXqdXjCZcO1sPxrjpghSUOgkClQXFuMwupCfHHmCzyy/RFctYputM9Of+Z2Xxa7BQfLxVJUb0QZlVyFeG08gIYOPsdjYk9J0yH+r+57FSW1JUgKT4JCUKCwplDqLAp47ggD6nLCAGB82niPXENcYOFCWEltCfKr8iEX5I02ovCUqZlTkaBNQJmhDF+f/Rrnqs9Bb9RDLVejV1yvZu8/WMhlcnSJ7gJAFCt5Bt6tXQNbFsnRqeqEsDhNHDVzI9ocIRHC4uPjIZfLG7i/ysrKGrjEAECtViMqKsrphyAIgiAIgiACjdFqlFxBTTnCgLrySG+7GNYPyneke6zoCKsvhJXUlsBgNUApUyJTl4k+8WIA/v6LYnmklBEWldZgn7wM0WA1oNrsXewIL4t0DMp3ZEbWDIQpwnC26ix+uvBTo/sqvVoXlO/qZJuXRtZYapxudxTCbux8IwDg++LvYbaZPX8hHsDfe14aGaYMw8AEsfPg0l1LsXTXUliZFWNTx0KAgP1l+yUBsj7HLx+HwWqATq2TRA5PkXLCap3Pnxy7Ch69fBSVxkq4Y0fRDmw+tRkCBCwfvhz9E/oDqHNkmW1mSaDyJLh+SNIQ6Xeez9UUjh0JgTpnHQ9/by4quQpze84FAKw7ug77SvcBAPrE94FKrmr2/oMJF8DfPPQmbMyGfh36oUuMd8eNr/D3CaB8MKJtEhIhTKVSIScnB7m5zldtcnNzMXRo8yyxBEEQBEEQBOEvuBtMq9B6dKLOyyO5Q8gTrlquoqBaFJcaK408VXFKKmkDgDOVZwAAmbpMKGQKDEwUBZr9F/fDYrPg/JXzAMQyq/poFBqpi5+35ZFSx8h6ZZGcCFUEZnSZAQD48PiHje6Lz29SmGuR0ZUjzGg1SkJKQlgC+sT3QYI2AVcsV/Dj+R89fh2eUF8IA+pK+Li7697e92LVmFW4vqMYFP/VGdclobxEb2DCwEaztFzBO2rWd4Q5lssyMPxU6lp41Bv1WLZnGQDgrp53YXDSYAxLGQagToy6ZLgEAFDJVB4d65m6TNze/XbM6jZLar7QFPVLI/lz87H4g1ndZiFSGYn8qnysPbIWQOsqi+RwIYwfg8FygwHOpZGUD0a0RUJWGrl48WKsXbsW77zzDo4fP47HHnsMhYWFmD9/fqiGRBAEQRAEQRBOOOaDeVIexIUwbzLCTlacBANDB20HqQTOkbSoNKhkKjEvqKZYup3ng2VFZwEAchLEk/39ZftRUlsCO7NDq9C63Cd/TUAzhDA3jjAAmN1jNgBgZ/FOtw4pwCEoP9x1x0CpE6eDw46X76nlakSpoiATZBjXeRwA/5dH1i+NBIARnUYAAAQIWDJkCRbnLIZMkGF6l+kAgC/OfOGyJLQ5WVXuOkdyR1imLhMA3AqBz/34HPRGPbpEd8GjAx8FAAxLFsWnn0t/hsVmkVx2HcI6eHSsC4KAZ65/Bn+94a8el845lkZabBb8XPqzOBY/CmERqgjc1v02AHX/v1wkbk04dnsOV4ZjYvrEoD03OcKItk7IhLDbb78dq1evxnPPPYf+/fvj+++/x9atW9G5s2dthAmCIAiCIAjCHxy9fNStGORpx0iOL0JYY2WRgJg9xkuiHMsjuRDWNaYrAKBfQj/IBBlKakuw76JYEpYWmeZWpODik9dCWNU5AK5D+DkZugwMSx4GBoYNv21wux13hLnqGAm47hopBbqHJUivjeeEfVf0HSx27zp2usNgNUjOPkcxoFtMN7w68lW8PfFtzMmeI90+NnUswhRhKK4tbtDBsaS2BD9fEEUfT91TjvDjz9ERZrKZJFHynl73ABAdVvVFuEPlh7C9cDsUMgVWjFghha13j+2OWE0sDFYDDpQdQLlBFBg9KYv0FcfSyIPlB3HFcgWxmlhkxzbMxmsOc7LnSB0q5YIc/Tv09+v+gwH/vwaAKRlTpLLrYBCtqRPCyBFGtEVCGpb/8MMP49y5czCZTMjLy8PIkSNDORyCIAiCIAjCT6xZswYZGRnQaDTIycnBDz/8EOohueRC7QXc+fWdmJ/ruirB046RHF+EsMY6RnKkzpEOmVCnK645wnRZ0nPzffDQdlf5YBxejshzujyBMSaVcborjeTw0PxPT32KqxbXpaKSI8ydEHatNNJkM8FiEwUux3wwzsCEgYjVxKLaXC3lQjWXCqPYoVElU0nj4EzKmOQUFg+I+WET0icAgFP3SMYYnv/peRhtRuQk5qBnXE+vx+LKEXam8gxszAadWofJGZOhkqlwtx/v9wAADv1JREFU8epF5FflOz32g2MfAACmZU5zOsZkgkxyhe0+v7vOEaZtOijfVxxLI3lZ5NDkoV6XijZFh7AOmJ4lOvSyY7ODKiL5ixhNDDJ0GZAJMszq5lkGm79wLI0kRxjRFgmpEEYQBEEQBEG0PTZu3IhFixZh6dKlOHDgAEaMGIHJkyejsLAw1ENrwKlKMXfrTNUZl2INF2o8FsIUPjjCLouOMFcdIzlS50i96Aiz2q2S4OEYoM2D3A+XHwbgumMkx5fSyHJDOa5ar0IuyJEa4X7fADA8ZTjSItNQY6nBl2e+dLmN5AhrojQSqHOFuRLC5DI5xqaNBQB8e+5bD19N4/CyyFhtrMelf1x8+fbctzBajQCArflbsbtkN1QyFZbdsMynDnxJEeJ75egI48dC95ju0Cg0UsnlnvN13SNLr5RK5aJzs+c22O/QFDHvbHfJbqnkNJCOMMfSSB7S78+ySEcWDFiASemTpFLQ1siacWvwweQPGv1sCASOpZFxGnKEEW0PEsIIgiAIgiAIv7Jy5Urcf//9eOCBB5CdnY3Vq1cjNTUVb7zxRqiH1oDzteel33mZmSOSI8xNmHt9uPPEUyHMYrdIJY6NOsJinR1hRTVFMNvN0Cq0SIlIkbarnz/lKiif44sQxssiUyJSoJQrG91WJsikrLAPf/vQZW5WU6WRCpkCWoUWQJ0Q5u4xUzKmAAByz+XCZDN58nJgsVuwrWCbFBTvCA8p90YIyEnMQXJ4Mmottfiu6DtUGCvw8t6XAQAP9n0QGboMj/flCHeElRvKpc6Y/FjgIikP8XcUwjb8tgE2ZsPgpMHSMeTI0OShECDgRMUJHLt8DIDopgoUXGA5W3kWv+l/gwBBGre/idfG49VRrwZs/8GgU2Qn9OnQJ+jP65QRpiVHGNH2UIR6AL7Av0Srq71r9UwQBEEQRPuGrx1cnZAT/sFsNiMvLw9Llixxun3ChAnYs2dPg+1NJhNMpjrRoqpK7AYYrHXe2YtnYTPYAABHi4+ik7KT0/1F5UWwGWyItEd6NCaFSQGbwQZ9hd6j7U/pT8F4xYhwRTii7FFuH5MkT4LNYEOhoRAll0pwuPQwbAYbOsV2Qm1NXX5WF00X6fUAQKwQ63afkfZI2Aw2FJcXezzfx84fg81gQ3JMskePGZMwBq8YX8EpwymcvHASHSPqstYsNgvK9GVgYNDatG73p7FoUGusRenlUuiYDsXlxS7fk67arohDHMqqy/DN8W8wJm1Mo2OrMlVh6a6l+OXiLxiXNg4vDn/R6f7iS+LzRNgivDoexyWOw7tH38Wmw5uw/eR2XKq8hExdJmamzfT5uJYzORRmBUw2E06XnkanyE44WnxUPAaUnVBdXY0+EX1gM9jwc/7PuFRxCTZmw0cHP4LNYsPNnW52+dwKKNBV2xXH9cex++xuMDCEW8MD9v+ntCjFY84gNn3Ijs2GwqxAtZnO61oScpNc+hxRm9V03k20Gjxd5wmsFa4Ei4uLkZrauBWbIAiCIAjCHUVFRejUqVPTGxJec/78eaSkpGD37t0YOrTOibF8+XK89957OHHihNP2zz77LP72t78Fe5gEQRAEQbRRmlrntUpHWHJyMoqKihAZGelTjX1TVFdXIzU1FUVFRYiKivL7/tsjNKf+h+bU/9Cc+h+aU/9Dc9o8GGOoqalBcnJyqIfS5qm/RmOMuVy3/fnPf8bixYulv+12O/R6PeLi4mid10qhOQ4sNL+BheY3sND8Bpb2Pr+ervNapRAmk8mCchU3KiqqXR48gYTm1P/QnPofmlP/Q3Pqf2hOfUen0zW9EeEz8fHxkMvlKC11zp0qKytDYmLDHCi1Wg21Wu10W3R0dIPt/A39DwUemuPAQvMbWGh+AwvNb2Bpz/PryTqPwvIJgiAIgiAIv6FSqZCTk4Pc3Fyn23Nzc51KJQmCIAiCIEJBq3SEEQRBEARBEC2XxYsXY968eRg0aBBuuOEG/Otf/0JhYSHmz58f6qERBEEQBNHOkT/77LPPhnoQLRG5XI7Ro0dDoSCt0F/QnPofmlP/Q3Pqf2hO/Q/NKdHS6d27N+Li4rB8+XK89tprMBgMeP/999GvX79QDw0A/Q8FA5rjwELzG1hofgMLzW9gofltmlbZNZIgCIIgCIIgCIIgCIIgvIUywgiCIAiCIAiCIAiCIIh2AQlhBEEQBEEQBEEQBEEQRLuAhDCCIAiCIAiCIAiCIAiiXUBCGEEQBEEQBEEQBEEQBNEuICHMBWvWrEFGRgY0Gg1ycnLwww8/hHpIrYYVK1Zg8ODBiIyMREJCAm6++WacOHHCaRvGGJ599lkkJydDq9Vi9OjROHr0aIhG3LpYsWIFBEHAokWLpNtoPn2jpKQEc+fORVxcHMLCwtC/f3/k5eVJ99O8eofVasUzzzyDjIwMaLVaZGZm4rnnnoPdbpe2oTltnO+//x433XQTkpOTIQgCPvvsM6f7PZk/k8mERx99FPHx8QgPD8f06dNRXFwczJdBEC0eWuf5B1rzBRdaA/ofWgsGDloX+hdaIwYARjixYcMGplQq2VtvvcWOHTvGFi5cyMLDw1lBQUGoh9YqmDhxIlu3bh379ddf2cGDB9nUqVNZWloaq62tlbZ56aWXWGRkJNu8eTM7cuQIu/3221nHjh1ZdXV1CEfe8tm7dy9LT09nffv2ZQsXLpRup/n0Hr1ezzp37szuuece9vPPP7P8/Hy2bds2dvr0aWkbmlfveOGFF1hcXBz76quvWH5+Ptu0aROLiIhgq1evlrahOW2crVu3sqVLl7LNmzczAOzTTz91ut+T+Zs/fz5LSUlhubm5bP/+/WzMmDGsX79+zGq1BvvlEESLhNZ5/oPWfMGD1oD+h9aCgYXWhf6F1oj+h4SwegwZMoTNnz/f6bYePXqwJUuWhGhErZuysjIGgO3cuZMxxpjdbmdJSUnspZdekrYxGo1Mp9OxN998M1TDbPHU1NSwrl27stzcXDZq1ChpEUTz6RtPPfUUGz58uNv7aV69Z+rUqey+++5zuu2WW25hc+fOZYzRnHpL/UWOJ/NXWVnJlEol27Bhg7RNSUkJk8lk7Jtvvgne4AmiBUPrvMBBa77AQGvAwEBrwcBC68LAQWtE/0ClkQ6YzWbk5eVhwoQJTrdPmDABe/bsCdGoWjdVVVUAgNjYWABAfn4+SktLneZYrVZj1KhRNMeN8Mgjj2Dq1Km48cYbnW6n+fSNL774AoMGDcKsWbOQkJCAAQMG4K233pLup3n1nuHDh2P79u04efIkAODQoUPYtWsXpkyZAoDmtLl4Mn95eXmwWCxO2yQnJ6N37940xwQBWucFGlrzBQZaAwYGWgsGFloXBg9aI/qGItQDaElcunQJNpsNiYmJTrcnJiaitLQ0RKNqvTDGsHjxYgwfPhy9e/cGAGkeXc1xQUFB0MfYGtiwYQPy8vLwyy+/NLiP5tM3zp49izfeeAOLFy/G008/jb179+KPf/wj1Go17rrrLppXH3jqqadQVVWFHj16QC6Xw2az4cUXX8Ts2bMB0LHaXDyZv9LSUqhUKsTExDTYhr7DCILWeYGE1nyBgdaAgYPWgoGF1oXBg9aIvkFCmAsEQXD6mzHW4DaiaRYsWIDDhw9j165dDe6jOfaMoqIiLFy4EP/973+h0Wjcbkfz6R12ux2DBg3C8uXLAQADBgzA0aNH8cYbb+Cuu+6StqN59ZyNGzfigw8+wIcffohevXrh4MGDWLRoEZKTk3H33XdL29GcNg9f5o/mmCCcoc8h/0NrPv9Da8DAQmvBwELrwuBDa0TvoNJIB+Lj4yGXyxuoomVlZQ0UVqJxHn30UXzxxRf47rvv0KlTJ+n2pKQkAKA59pC8vDyUlZUhJycHCoUCCoUCO3fuxD/+8Q8oFAppzmg+vaNjx47o2bOn023Z2dkoLCwEQMepLzzxxBNYsmQJ7rjjDvTp0wfz5s3DY489hhUrVgCgOW0unsxfUlISzGYzKioq3G5DEO0ZWucFBlrzBQZaAwYWWgsGFloXBg9aI/oGCWEOqFQq5OTkIDc31+n23NxcDB06NESjal0wxrBgwQJs2bIF//vf/5CRkeF0f0ZGBpKSkpzm2Gw2Y+fOnTTHLhg3bhyOHDmCgwcPSj+DBg3CnDlzcPDgQWRmZtJ8+sCwYcMatHg/efIkOnfuDICOU1+4evUqZDLnrxS5XC61yaY5bR6ezF9OTg6USqXTNhcuXMCvv/5Kc0wQoHWev6E1X2ChNWBgobVgYKF1YfCgNaKPBDudv6XD22q//fbb7NixY2zRokUsPDycnTt3LtRDaxU89NBDTKfTsR07drALFy5IP1evXpW2eemll5hOp2NbtmxhR44cYbNnz6ZWuV7g2DGIMZpPX9i7dy9TKBTsxRdfZKdOnWLr169nYWFh7IMPPpC2oXn1jrvvvpulpKRIbbK3bNnC4uPj2ZNPPiltQ3PaODU1NezAgQPswIEDDABbuXIlO3DgACsoKGCMeTZ/8+fPZ506dWLbtm1j+/fvZ2PHjm3XrbEJoj60zvMftOYLPrQG9B+0FgwstC70L7RG9D8khLng9ddfZ507d2YqlYoNHDhQagNNNA0Alz/r1q2TtrHb7WzZsmUsKSmJqdVqNnLkSHbkyJHQDbqVUX8RRPPpG19++SXr3bs3U6vVrEePHuxf//qX0/00r95RXV3NFi5cyNLS0phGo2GZmZls6dKlzGQySdvQnDbOd9995/Lz8+6772aMeTZ/BoOBLViwgMXGxjKtVsumTZvGCgsLQ/BqCKLlQus8/0BrvuBDa0D/QmvBwEHrQv9Ca0T/IzDGWPD8ZwRBEARBEARBEARBEAQRGigjjCAIgiAIgiAIgiAIgmgXkBBGEARBEARBEARBEARBtAtICCMIgiAIgiAIgiAIgiDaBSSEEQRBEARBEARBEARBEO0CEsIIgiAIgiAIgiAIgiCIdgEJYQRBEARBEARBEARBEES7gIQwgiAIgiAIgiAIgiAIol1AQhhBEARBEARBEARBEATRLiAhjCAIgiAIgiAIgiAIgmgXkBBGEARBEARBEARBEARBtAtICCMIgiAIgiAIgiAIgiDaBSSEEQRBEARBEARBEARBEO2C/w9zyd5u5U73YwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(test1)\n",
    "plt.subplot(1,2,2)\n",
    "plt.ylim(0, 100)\n",
    "plt.plot(result1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "9bed5038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b9413669d0>]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(test2)\n",
    "plt.subplot(1,2,2)\n",
    "plt.ylim(1000, 1030)\n",
    "plt.plot(result2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfa26441",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81f866f7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}