lstm_electricity_forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    " \n",
    "df = pd.read_csv('electricity.csv')\n",
    "\n",
    "# \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>Exogenous1</th>\n",
       "      <th>Exogenous2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BE</td>\n",
       "      <td>10/22/2016 0:00</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>49593.0</td>\n",
       "      <td>57253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BE</td>\n",
       "      <td>10/22/2016 1:00</td>\n",
       "      <td>37.100000</td>\n",
       "      <td>46073.0</td>\n",
       "      <td>51887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>10/22/2016 2:00</td>\n",
       "      <td>37.100000</td>\n",
       "      <td>44927.0</td>\n",
       "      <td>51896.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BE</td>\n",
       "      <td>10/22/2016 3:00</td>\n",
       "      <td>44.750000</td>\n",
       "      <td>44483.0</td>\n",
       "      <td>48428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BE</td>\n",
       "      <td>10/22/2016 4:00</td>\n",
       "      <td>37.100000</td>\n",
       "      <td>44338.0</td>\n",
       "      <td>46721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8395</th>\n",
       "      <td>PJM</td>\n",
       "      <td>12/23/2018 19:00</td>\n",
       "      <td>32.461970</td>\n",
       "      <td>98288.0</td>\n",
       "      <td>11711.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8396</th>\n",
       "      <td>PJM</td>\n",
       "      <td>12/23/2018 20:00</td>\n",
       "      <td>32.052179</td>\n",
       "      <td>97194.0</td>\n",
       "      <td>11637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8397</th>\n",
       "      <td>PJM</td>\n",
       "      <td>12/23/2018 21:00</td>\n",
       "      <td>27.632347</td>\n",
       "      <td>94319.0</td>\n",
       "      <td>11433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8398</th>\n",
       "      <td>PJM</td>\n",
       "      <td>12/23/2018 22:00</td>\n",
       "      <td>24.746053</td>\n",
       "      <td>90106.0</td>\n",
       "      <td>11108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8399</th>\n",
       "      <td>PJM</td>\n",
       "      <td>12/23/2018 23:00</td>\n",
       "      <td>23.333499</td>\n",
       "      <td>85547.0</td>\n",
       "      <td>10581.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     unique_id                ds          y  Exogenous1  Exogenous2\n",
       "0           BE   10/22/2016 0:00  70.000000     49593.0     57253.0\n",
       "1           BE   10/22/2016 1:00  37.100000     46073.0     51887.0\n",
       "2           BE   10/22/2016 2:00  37.100000     44927.0     51896.0\n",
       "3           BE   10/22/2016 3:00  44.750000     44483.0     48428.0\n",
       "4           BE   10/22/2016 4:00  37.100000     44338.0     46721.0\n",
       "...        ...               ...        ...         ...         ...\n",
       "8395       PJM  12/23/2018 19:00  32.461970     98288.0     11711.0\n",
       "8396       PJM  12/23/2018 20:00  32.052179     97194.0     11637.0\n",
       "8397       PJM  12/23/2018 21:00  27.632347     94319.0     11433.0\n",
       "8398       PJM  12/23/2018 22:00  24.746053     90106.0     11108.0\n",
       "8399       PJM  12/23/2018 23:00  23.333499     85547.0     10581.0\n",
       "\n",
       "[8400 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>Exogenous1</th>\n",
       "      <th>Exogenous2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-10-22 00:00:00</td>\n",
       "      <td>19.10</td>\n",
       "      <td>587.25</td>\n",
       "      <td>16972.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-10-22 01:00:00</td>\n",
       "      <td>19.03</td>\n",
       "      <td>623.00</td>\n",
       "      <td>16254.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-10-22 02:00:00</td>\n",
       "      <td>16.90</td>\n",
       "      <td>650.00</td>\n",
       "      <td>15940.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-10-22 03:00:00</td>\n",
       "      <td>12.98</td>\n",
       "      <td>687.25</td>\n",
       "      <td>15959.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-10-22 04:00:00</td>\n",
       "      <td>9.24</td>\n",
       "      <td>717.25</td>\n",
       "      <td>16071.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3355</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-12-30 19:00:00</td>\n",
       "      <td>16.18</td>\n",
       "      <td>1519.50</td>\n",
       "      <td>19326.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-12-30 20:00:00</td>\n",
       "      <td>11.13</td>\n",
       "      <td>1526.75</td>\n",
       "      <td>18135.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-12-30 21:00:00</td>\n",
       "      <td>4.62</td>\n",
       "      <td>1514.75</td>\n",
       "      <td>17631.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-12-30 22:00:00</td>\n",
       "      <td>4.71</td>\n",
       "      <td>1484.75</td>\n",
       "      <td>17553.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3359</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-12-30 23:00:00</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>1440.75</td>\n",
       "      <td>16754.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1680 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     unique_id                  ds      y  Exogenous1  Exogenous2\n",
       "1680        DE 2017-10-22 00:00:00  19.10      587.25    16972.75\n",
       "1681        DE 2017-10-22 01:00:00  19.03      623.00    16254.50\n",
       "1682        DE 2017-10-22 02:00:00  16.90      650.00    15940.25\n",
       "1683        DE 2017-10-22 03:00:00  12.98      687.25    15959.50\n",
       "1684        DE 2017-10-22 04:00:00   9.24      717.25    16071.50\n",
       "...        ...                 ...    ...         ...         ...\n",
       "3355        DE 2017-12-30 19:00:00  16.18     1519.50    19326.00\n",
       "3356        DE 2017-12-30 20:00:00  11.13     1526.75    18135.25\n",
       "3357        DE 2017-12-30 21:00:00   4.62     1514.75    17631.50\n",
       "3358        DE 2017-12-30 22:00:00   4.71     1484.75    17553.75\n",
       "3359        DE 2017-12-30 23:00:00  -0.27     1440.75    16754.50\n",
       "\n",
       "[1680 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the dataframe to include only rows where the 'unique_id' is \"DE\"\n",
    "df = df[df['unique_id'] == \"DE\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ds'] = pd.to_datetime(df['ds'])\n",
    "data = df[['y', 'Exogenous1', 'Exogenous2']].values\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "data = scaler.fit_transform(data)\n",
    "\n",
    " \n",
    "X = data[:, 1:]   \n",
    "y = data[:, 0]   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1680, 2)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1680,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    " \n",
    "def create_sequences(X, y, seq_length):\n",
    "    sequences = []\n",
    "    labels = []\n",
    "    for i in range(len(X) - seq_length):\n",
    "        sequences.append(X[i:i+seq_length])\n",
    "        labels.append(y[i+seq_length])\n",
    "    return torch.FloatTensor(sequences), torch.FloatTensor(labels)\n",
    "\n",
    "seq_length = 3 # 3 saatlik geçmiş veriyle tahmin yapalım \n",
    "X_seq, y_seq = create_sequences(X, y, seq_length)\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_seq, y_seq, test_size=0.2, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.2797, 0.2934],\n",
      "        [0.2970, 0.2493],\n",
      "        [0.3101, 0.2300]])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor(0.4631)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train[0])\n",
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.2508, 0.5198],\n",
      "        [0.2489, 0.4945],\n",
      "        [0.2468, 0.4313]])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor(0.5399)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_test[0])\n",
    "y_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "train_dataset = TensorDataset(X_train, y_train)\n",
    "test_dataset = TensorDataset(X_test, y_test)\n",
    "\n",
    "batch_size = 32\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([29, 3, 2])\n",
      "torch.Size([29])\n"
     ]
    }
   ],
   "source": [
    "for x,y in train_loader :\n",
    "    print(x.shape)\n",
    "    print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTMModel(\n",
       "  (lstm): LSTM(2, 64, batch_first=True)\n",
       "  (fc): Linear(in_features=64, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(LSTMModel, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        lstm_out, _ = self.lstm(x)\n",
    "        last_hidden = lstm_out[:, -1, :]\n",
    "        return self.fc(last_hidden)\n",
    "\n",
    "input_size = 2  \n",
    "hidden_size = 64  \n",
    "output_size = 1  \n",
    "\n",
    "\n",
    "model = LSTMModel(input_size, hidden_size, output_size)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 Loss: 0.001072293845936656\n",
      "Epoch 10 Loss: 0.0008260656613856554\n",
      "Epoch 20 Loss: 0.0011018023360520601\n",
      "Epoch 30 Loss: 0.002729017985984683\n",
      "Epoch 40 Loss: 0.0012613561702892184\n",
      "Epoch 50 Loss: 0.0012085555354133248\n",
      "Epoch 60 Loss: 0.0010828716913238168\n",
      "Epoch 70 Loss: 0.0009871887741610408\n",
      "Epoch 80 Loss: 0.0011298106983304024\n",
      "Epoch 90 Loss: 0.006951858289539814\n",
      "Epoch 100 Loss: 0.002168843988329172\n",
      "Epoch 110 Loss: 0.0009374896762892604\n",
      "Epoch 120 Loss: 0.0017449704464524984\n",
      "Epoch 130 Loss: 0.0003500503080431372\n",
      "Epoch 140 Loss: 0.0005973627557978034\n",
      "Epoch 150 Loss: 0.0011760455090552568\n",
      "Epoch 160 Loss: 0.0008092409698292613\n",
      "Epoch 170 Loss: 0.0008461173274554312\n",
      "Epoch 180 Loss: 0.0013634199276566505\n",
      "Epoch 190 Loss: 0.001078780391253531\n",
      "Epoch 200 Loss: 0.0010440327459946275\n",
      "Epoch 210 Loss: 0.0008326669922098517\n",
      "Epoch 220 Loss: 0.0026944123674184084\n",
      "Epoch 230 Loss: 0.0011375475442036986\n",
      "Epoch 240 Loss: 0.0008262909832410514\n",
      "Epoch 250 Loss: 0.0018403230933472514\n",
      "Epoch 260 Loss: 0.0018299834337085485\n",
      "Epoch 270 Loss: 0.0007097800844348967\n",
      "Epoch 280 Loss: 0.0007838095771148801\n",
      "Epoch 290 Loss: 0.001023101038299501\n",
      "Epoch 300 Loss: 0.0012960175517946482\n",
      "Epoch 310 Loss: 0.001134083024226129\n",
      "Epoch 320 Loss: 0.0010667054448276758\n",
      "Epoch 330 Loss: 0.0011918222298845649\n",
      "Epoch 340 Loss: 0.002306772395968437\n",
      "Epoch 350 Loss: 0.0009880104335024953\n",
      "Epoch 360 Loss: 0.0032561051193624735\n",
      "Epoch 370 Loss: 0.0012034765677526593\n",
      "Epoch 380 Loss: 0.0013330401852726936\n",
      "Epoch 390 Loss: 0.001291138119995594\n",
      "Epoch 400 Loss: 0.0008560096612200141\n",
      "Epoch 410 Loss: 0.0031260072719305754\n",
      "Epoch 420 Loss: 0.0023684357292950153\n",
      "Epoch 430 Loss: 0.0007487473776564002\n",
      "Epoch 440 Loss: 0.0020314485300332308\n",
      "Epoch 450 Loss: 0.0019182577962055802\n",
      "Epoch 460 Loss: 0.0034074571449309587\n",
      "Epoch 470 Loss: 0.0011867412831634283\n",
      "Epoch 480 Loss: 0.0012045432813465595\n",
      "Epoch 490 Loss: 0.0012905971379950643\n",
      "Epoch 500 Loss: 0.0009142477065324783\n",
      "Epoch 510 Loss: 0.0008634544792585075\n",
      "Epoch 520 Loss: 0.0017317496240139008\n",
      "Epoch 530 Loss: 0.001063609030097723\n",
      "Epoch 540 Loss: 0.001182577689178288\n",
      "Epoch 550 Loss: 0.0008981775026768446\n",
      "Epoch 560 Loss: 0.006951375398784876\n",
      "Epoch 570 Loss: 0.0011536752572283149\n",
      "Epoch 580 Loss: 0.0008913304191082716\n",
      "Epoch 590 Loss: 0.0010887986281886697\n"
     ]
    }
   ],
   "source": [
    "\n",
    "loss_function = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "model.train()\n",
    "train_losses = []\n",
    "mse_values = []\n",
    "\n",
    "epochs = 600\n",
    "for epoch in range(epochs):\n",
    "    epoch_loss = 0\n",
    "    mse_epoch = 0\n",
    "    for seq, labels in train_loader:\n",
    "        seq, labels = seq.to(device), labels.to(device)\n",
    "\n",
    "        optimizer.zero_grad()  \n",
    "        y_pred = model(seq)  \n",
    "        loss = loss_function(y_pred.squeeze(), labels)  \n",
    "        mse = torch.mean((y_pred - labels) ** 2).item()\n",
    "        mse_epoch += mse\n",
    "        loss.backward()  \n",
    "        optimizer.step()  \n",
    "        epoch_loss += loss.item()\n",
    "        avg_loss = epoch_loss / len(X_train)\n",
    "        avg_mse = mse_epoch / len(X_train)\n",
    "        train_losses.append(avg_loss)\n",
    "        mse_values.append(avg_mse)\n",
    "\n",
    "\n",
    "    if epoch % 10 == 0:\n",
    "        print(f'Epoch {epoch} Loss: {loss.item()}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(train_losses, label=\"Loss\")\n",
    "plt.plot(mse_values, label=\"MSE\")\n",
    "plt.title(\"Loss and MSE over epochs\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss / MSE\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model.eval()\n",
    "test_predictions = []\n",
    "with torch.no_grad():\n",
    "    for seq in X_test:\n",
    "        seq = seq.to(device)\n",
    "        y_pred = model(seq.unsqueeze(0))  \n",
    "        test_predictions.append(y_pred.item())\n",
    "\n",
    "test_predictions_np = np.array(test_predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 0.0324842594563961\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model.eval()\n",
    "test_predictions = []\n",
    "test_labels = []\n",
    "mse_loss = 0.0\n",
    "\n",
    "with torch.no_grad():\n",
    "    for seq, labels in DataLoader(TensorDataset(X_test, y_test), batch_size=32):\n",
    "        seq = seq.to(device)\n",
    "        labels = labels.to(device)\n",
    "        y_pred = model(seq) \n",
    "        test_predictions.append(y_pred.cpu().numpy())  \n",
    "        test_labels.append(labels.cpu().numpy())  \n",
    "        \n",
    "# Numpy dizisine çevir\n",
    "test_predictions_np = np.concatenate(test_predictions)\n",
    "test_labels_np = np.concatenate(test_labels)\n",
    "\n",
    "\n",
    "mse_loss = np.mean((test_predictions_np - test_labels_np) ** 2)\n",
    "print(f'Test MSE: {mse_loss}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IutiMGDECgLfeesthe2WfjzFjxnD8+HE+/vjjYtfl5OSUOjOYqxw+fJgJEybg7+/Po48+6jC21atX8+effxa7TWpqKoWFhQAMGzaMgoICZs2aZb1e0zTef/99h9tU13k6bNgwwsPDmTFjBgUFBS47rhBCCOFOkmEkhBBCVFCLFi34+uuvufHGG2nVqhXjx4+nU6dOaJpGUlISX3/9NUaj0aEnybvvvkvfvn3p0KEDd955J02bNuX06dOsXr2aY8eOsXXr1lLvMzw8nNdff5077riDHj16MG7cOGrVqsXWrVvJzs7m888/x2g08sknnzBixAjatWvHrbfeSv369Tl+/Dj//PMP4eHh/Pbbb8WObTQa+fLLL7nqqqsYM2YMCxYsKLH3is7Pz49rrrmGb7/9lqysLGbOnFlsn6o+5tKei/79+/Pyyy9TUFBA/fr1WbRoEUlJSZU6nrdbvnx5sSAhqObSHTt2pHPnztx444289957pKWl0bt3b5YsWVIsm6a8brrpJr777jvuuece/vnnH/r06YPJZGLPnj189913/Pnnn3Tv3r2qD8tq06ZNfPnll5jNZlJTU1m/fj0//vgjBoOBOXPm0LFjR+u+jz76KL/++iuXX345EyZMoFu3bmRlZbF9+3Z++OEHDh06RHR0NFdddRU9e/bk4Ycf5uDBg7Ru3ZpffvnF2uPIPnOqOs7T8PBw3n//fW666Sa6du3KDTfcQExMDEeOHGH+/Pn06dOHd955p+pPnhBCCFGNJGAkhBBCVMKVV17J9u3befXVV1m0aBGffvopBoOBRo0aMWrUKO655x46depk3b9t27Zs2LCBZ599ls8++4xz585Rp04dunTp4lBWVZrbb7+dOnXq8OKLL/LEE08AWD8U6wYOHMjq1at5/vnneeedd8jMzCQ2NpaEhIRSexT5+fnxww8/MGLECK688kr++usva0+mkowdO5ZPPvkEg8HAmDFjil3visdckq+//pr777+fd999F03TGDp0KH/88Qf16tWr0nG9UdHMId306dOtwZRPP/2UmJgYvvrqK+bNm8dll13G/Pnzi/UbKg+j0ci8efN4/fXX+eKLL/j5558JDg6madOmPPjggxXuE1SWb775hm+++QZfX1/Cw8Np0aIFDz30EPfcc0+xhtrBwcH8+++/zJgxg++//54vvviC8PBwWrZsybPPPmttRu7j48P8+fN58MEHmTVrFkajkSuuuIInn3ySvn37Osw0WF3n6bhx46hXrx4vvvgir7zyCnl5edSvX59+/fpx6623Vvq4QgghhLsYNG/tuCiEEEKIEmVkZNC+fXs2btxIdHS0p4cjRI3wyy+/cNVVV7FixQr69Onj6eEIIYQQXk16GAkhhBA1UFhYGF27di13zyEhLjY5OTkOl00mE2+99Rbh4eF07drVQ6MSQgghag4pSRNCCCFqmJkzZxIWFsaaNWu49NJLPT0cIbzS/fffT05ODr169SIvL4+ffvqJVatWMWPGDIKCgjw9PCGEEMLrSUmaEEIIUcPofYq6dOnC77//LiVpQjjx9ddf8+qrr5KYmEhubi7Nmzdn4sSJTJo0ydNDE0IIIWoECRgJIYQQQgghhBBCCAfSw0gIIYQQQgghhBBCOJCAkRBCCCGEEEIIIYRwcNE1vTabzZw4cYKwsDAMBoOnhyOEEEIIIYQQQgjhEpqmkZGRQb169TAaq5YjdNEFjE6cOEF8fLynhyGEEEIIIYQQQghRLY4ePUqDBg2qdIyLLmAUFhYGqCcvPDzcw6OpvIKCAhYtWsTQoUPx8/Pz9HBEDSbnknAVOZeEq8i5JFxFziXhSnI+CVeRc0m4irNzKT09nfj4eGvsoyouuoCRXoYWHh5e4wNGwcHBhIeHy4uMqBI5l4SryLkkXEXOJeEqci4JV5LzSbiKnEvCVUo7l1zRgkeaXgshhBBCCCGEEEIIBxIwEkIIIYQQQgghhBAOJGAkhBBCCCGEEEIIIRxcdD2MhBBCCCGEEEJ4jslkoqCgwNPD8JiCggJ8fX3Jzc3FZDJ5ejiiBvLz88PHx6fa70cCRkIIIYQQQggh3CIzM5Njx46haZqnh+IxmqYRGxvL0aNHXdKYWFx8DAYDDRo0ICAgoFrvRwJGQgghhBBCCCGqnclk4tixYwQHBxMTE3PRBkvMZjOZmZmEhoZiNEqXGFExmqaRnJzMsWPHaNy4cbXelwSMhBBCCCGEEEJUu4KCAjRNIyYmhqCgIE8Px2PMZjP5+fkEBgZKwEhUSkxMDIcOHaKwsLBa70fOTiGEEEIIIYQQbnOxZhYJ4Sr631B1l3ZKwEgIIYQQQgghhBBCOJCAkRBCCCGEEEIIIYRwIAEjIYQQQgghhBCiBmvcuDFvvPGGR8fQrl07jhw5wokTJ2jatCkZGRkuOa7BYGDevHkuOZaoGAkYCSGEEEIIIYQQpTh16hQPPvggzZs3JzAwkLp169KnTx/ef/99srOzPT28Clu6dCkGgwGDwYDRaCQiIoIuXbowZcoUTp48Walj3nPPPTRv3pz4+HjGjh1LWFiYi0ct3E1mSRNCCCGEEEIIIUpw8OBB+vTpQ2RkJDNmzKBDhw4EBASwfft2PvroI+rXr88VV1xRqWPn5+fj7+/v4hGX3969ewkPDyc9PZ1Nmzbx8ssvM2vWLJYuXUqHDh0qdKz777+fu+++G7PZTGBgYJXH5qrnxtPPcU0mGUZCCCGEEEIIIdxO0yAryzM/FZlc6t5778XX15cNGzYwZswY2rRpQ9OmTbnyyiuZP38+o0ePtu6bmprKHXfcQUxMDOHh4Vx22WVs3brVev0zzzxD165d+eKLL2jWrJk1sJKamsrdd99N3bp1CQwMpH379vz+++/W261YsYJ+/foRFBREfHw8DzzwAFlZWSWO+ZNPPiEyMpIlS5aU+tjq1KlDbGwsLVu25IYbbmDlypXExMQwceLEYsdr06YNgYGBtG7dmvfee8/h+lWrVtG5c2fCw8Pp27cv8+bNw2AwsGXLFus+O3bsYMSIEYSGhlK3bl1uuukmzp49a71+4MCBTJo0iYceeojo6GiGDRvmdMxHjx5lzJgxREZGEhUVxZVXXsmhQ4es10+YMIGrrrqK//3vf9SrV49WrVqV+hyIknk8YPTuu+/SuHFjAgMDSUhIYN26daXu/8Ybb9CqVSvrH8rDDz9Mbm6um0YrhBBCCCGEEMIVsrMhNNQzP+WtIjt37hyLFi3ivvvuIyQkxOk++hTnANdffz1nzpzhjz/+YOPGjXTt2pVBgwaRkpJi3ScxMZFff/2VH374gS1btmA2mxkxYgQrV67kyy+/ZNeuXbz44ov4+PgAcODAAYYPH861117Ltm3bmDt3LitWrGDSpElOx/Pyyy/z+OOPs2jRIgYNGlTO34YSFBTEPffcw8qVKzlz5gwAX331FdOmTeN///sfu3fvZsaMGTz99NN8/vnnAKSnpzN69Gg6dOjApk2beP7553nsscccjpuamspll11Gly5d2LBhAwsXLuT06dOMGTPGYb/PP/8cf39/Vq5cyQcffFBsfAUFBQwbNoywsDCWL1/OypUrCQ0NZfjw4eTn51v3W7JkCXv37mXx4sUOgTdRMR4tSZs7dy6TJ0/mgw8+ICEhgTfeeINhw4axd+9e6tSpU2z/r7/+mscff5xPP/2U3r17s2/fPiZMmIDBYOC1117zwCMQQgghhBBCCHGhSkxMRNO0Ylkq0dHR1sSF++67j5deeokVK1awbt06zpw5Q0BAAAAzZ85k3rx5/PDDD9x1112AKpH64IMPaNq0KUajkUWLFrFu3Tp2795Ny5YtAWjatKn1vl544QXGjx/PQw89BECLFi146623GDBgAO+//75D+ddjjz3GnDlz+Pfff2nXrl2lHnPr1q0BOHToEHXq1GH69Om8+uqrXHPNNQA0adKEXbt28eGHH3LLLbfw9ddfYzAY+PjjjwkMDKRt27YcP36cO++803rMd955hy5dujBjxgzrtk8//ZT4+Hj27dtnfdwtWrTg5ZdfLnFsc+fOxWw288knn1gDdbNnzyYyMpKlS5cydOhQAEJCQvjkk0+kFK2KPBoweu2117jzzju59dZbAfjggw+YP38+n376KY8//nix/VetWkWfPn0YN24coDrB33jjjaxdu9at4xZCCCGEEEIIUTXBwZCZ6bn7rop169ZhNpsZP348eXl5AGzdupXMzExq167tsG9OTg4HDhywXm7UqBHR0dHWy1u2bKFBgwbWoElRW7duZdu2bXz11VfWbZqmYTabSUpKok2bNgC8+uqrZGVlsWHDBoeAU0Vplno9g8FAVlYWBw4c4Pbbb3cIABUWFhIREQGoPkgdO3Z0CFz17Nmz2GP4559/CA0NLXZ/Bw4csD72bt26lTq2rVu3kpiYWKyhdm5ursNz3KFDBwkWuYDHAkb5+fls3LiRqVOnWrcZjUYGDx7M6tWrnd6md+/efPnll6xbt46ePXty8OBBFixYwE033VTi/eTl5Vn/gEGly4FKZSsoKHDRo3E/few1+TEI7yDnknAVOZeEq8i5JFxFziXhSnI+VV1BQYE10GE2mwEICvLMWDStfH2MmjZtisFgYM+ePdYxg0peAFXCpT+mjIwM4uLi+Pvvv4sdJzIyErPZjKZp1tI2/XZ6oMX++PYyMzO56667uP/++4td17BhQ+vt+vbty4IFC5g7d26xkrCi9NvY/y50u3btsh5b//z84YcfkpCQ4LCfj4+P9TEVHX/R42dkZHD55Zfz4osvFhtLXFycdf/g4GCnz4P9cbp168acOXOK7RMTE2MdT0nHuVDoj7OwsBBwfF1y5WuUxwJGZ8+exWQyUbduXYftdevWZc+ePU5vM27cOM6ePUvfvn2tT84999zDE088UeL9vPDCCzz77LPFti9atIjgqoaVvcDixYs9PQRxgZBzSbiKnEvCVeRcEq4i55JwJTmfKs/X15fY2FgyMzMd+s14Mz8/Py699FLeeecdbr755mJ9jAoLC8nPzyc9PZ1WrVpx6tQpcnNzadiwYbFjpaenk5eXh8lkAiAjIwOAZs2acezYMTZt2kTz5s2L3a59+/Zs377daduW3NxccnNzMZvNdOzYkQkTJnD99ddTWFjoNMCky7Y0ccrIyMBotLU2zsnJ4cMPP6R3797Wsrq4uDj27Nnj0Nzb/jE1bNiQL7/8kuTkZOttli9fDkBWVhbp6em0a9eO3377jaioKHx9HcMQJpOJ9PR0h+eyqJycHNLT02nTpg1z584lMDCQ8PBwp+MpKCigsLDQ6XEuFPn5+eTk5LBq1SrA8XUpu7wNusrBoyVpFbV06VJmzJjBe++9R0JCAomJiTz44IM8//zzPP30005vM3XqVCZPnmy9nJ6eTnx8PEOHDnV6gtUUBQUFLF68mCFDhuDn5+fp4YgaTM4l4SpyLglXkXNJuIqcS8KV5HyqutzcXI4ePUpoaKhLpl13lw8++IB+/foxePBgpk2bRseOHTEajaxfv57ExER69uxJeHg4V1xxBb169eLmm2/mxRdfpGXLlpw4cYIFCxZw1VVX0b17dwICAqzNrMPCwjAYDIwYMYL+/ftz6623MnPmTJo3b86ePXswGAwMHz6cJ598kt69e/Pkk09y++23ExISwq5du/jrr794++23AVWtExgYyJAhQ/j9998ZNWoUoaGhPPjgg04fk548kZOTg5+fHxkZGWzcuJGZM2eSkpLCTz/9ZP28/Mwzz/DQQw9Rp04dhg0bRl5eHhs2bCA1NZWHH36Y2267jf/9739MmTKFqVOncuTIEd59910AQkNDCQ8P5+GHH2bOnDncc889PProo0RFRZGYmMjcuXP5+OOP8fHxwdfXF39/f6ef04OCgggPD+f222/n3Xff5ZZbbuGZZ56hQYMGHD58mJ9//plHH32UBg0a4Ofnh6+vb43+vF+W3NxcgoKC6N27N8uWLXN4XXJloMxjAaPo6Gh8fHw4ffq0w/bTp08TGxvr9DZPP/00N910E3fccQeg6hKzsrK46667ePLJJx0io7qAgABrlNOen5/fBfFCf6E8DuF5ci55AZMJ7r8fGjSAUjInvZ2cS8JV5FwSriLnknAlOZ8qz2QyYTAYMBqNTj+7easWLVqwefNmZsyYwZNPPsmxY8cICAigbdu2PPLII9x7773Wx7NgwQJrYCc5OZnY2Fj69+9PXFwcRqPRYUY1/bkA+PHHH3nkkUe48cYbOX/+PO3bt+fFF1/EaDTSuXNn/v33X5588kkGDBiApmk0a9aMsWPHOjyP+vH69+/P/PnzGTlyJL6+vk4zjfTbtWnTBoPBQGhoKE2bNmXo0KFMnjzZ4TP5XXfdRWhoKK+88gpTpkwhJCSEDh068NBDD2E0GomMjOS3335j4sSJdOrUiQ4dOjBt2jTGjx9PcHAwRqORBg0asHLlSh577DGGDx9OXl4ejRo1Yvjw4fj6+lqfF/vnpOh4jUYjoaGhLFu2jMcee4zrrruOjIwM6tevz6BBg4iMjLQ+xyUd50KhP049W8v+dcmVr08GTStP5Wb1SEhIoGfPntaoqNlspmHDhkyaNMlp0+tu3boxePBgXnrpJeu2b775httvv52MjAxrpLY06enpREREkJaWVqMjjgUFBSxYsICRI0fKPyxRJXIueZElS2DwYDAYIC0NijTz83ZyLglXkXNJuIqcS8KV5HyqutzcXJKSkmjSpEmNyjByNbPZTHp6OuHh4U6DGpdffjkzZ860zlZWE3311VfceuutpKWlEeSpRlUXMP1vqUGDBvz9998Or0uujHl4tCRt8uTJ3HLLLXTv3p2ePXvyxhtvkJWVZZ017eabb6Z+/fq88MILAIwePZrXXnuNLl26WEvSnn76aUaPHl2uYJEQQni1n39WS02DLVugXz+PDkcIIYQQQrjPmTNnyMrKwt/fnz/++KNGBYy++OILmjZtSv369dm6dSuPPfYYY8aMkWBRDefRgNHYsWNJTk5m2rRpnDp1is6dO7Nw4UJrI+wjR444RFyfeuopDAYDTz31FMePHycmJobRo0fzv//9z1MPQQghXMNshnnzbJc3bpSAkRBCCCHERWTr1q1ceeWVREdHM336dE8Pp0JOnTpl/VwfFxfH9ddfL5/TLwAeb3o9adIkJk2a5PS6pUuXOlz29fVl+vTpNe6PRwghyrRhAxw/bru8aZPnxiKEEEIIIdxuyJAhLp3hyp2mTJnClClTPD0M4WIXbhcoIYSoSfRytJgYtdy40XNjEUIIIYQQQlz0JGAkhBCepmnw009qfepUtdyzB7KyPDcmIYQQQgghxEVNAkZCeKF//oHbb4eMDE+PRLjF7t2wbx/4+cFtt0FcnOpptHWrp0cmhBBCCCGEuEhJwEgILzRtGnz6Kcye7emRCLfQy9EGD4aICOjWTV2uYFnaunWQk+PisQkhhBBCCCEuShIwEsIL7d+vlqtXe3YcwsV27ICVK4tv1wNGV1+tll27qmUFGl//9BMkJKgEJUBlKAkhhBBCCCFEJUnASAgvk5kJp0+rdQkYXUAKCuCyy+DSS+HUKdv21FRbJtEVV6hlJTKM9BZI330Hp7/9B8LC4OOPqz5uIYQQQgghxEVJAkZCeJkDB2zrhw/DyZOeG4twodWrITlZBY4SE23bDx9Wy+hoqFtXresZRrt2lavGTNPgr7/UutkMR1/4ErKz4e+/XfgAhBBCCCFERTVu3Jg33njD5cedMGECV111lcuPWx5FH5PBYGDevHkAHDp0CIPBwJYtWzwytsrasWMHQ4cOJSMjg1WrVpGQkOCS4y5duhSDwUBqaqpLjuduEjASwsvYB4wA1qzxzDi8kabBAw/AY495eiSVsGiRbf3oUdv6sWNq2aCBbVv9+lCnDphMsG1bmYfeudOWlQZQe8dStZKZWfnxCiGEEEIIDAZDqT/PPPOMR8b15ptv8tlnn1XpGM8884zTx9S6desKHefkyZOMGDGiSmOpKPux+/r6Eh0dTf/+/XnjjTfIy8ur8PHat29P7dq1CQ8PZ9iwYTz++OPVMOqax9fTAxBCOLJPPgGVmKK3trnYrV4Nb7+t1u+8E5o39+x4KsQ+YKQHiezX7QNGBoPKMlq4UJWllfENh55dNGgQZO46QpOTB9UGCRgJIYQQQlTJSbt0/7lz5zJt2jT27t1r3RYaGuqJYREREeGS47Rr146/9DeTFr6+FQsTxMbGumQsFaWP3Ww2c+7cOZYuXcr//d//MWfOHJYuXUpYWFiFjvfNN9/wySefEBgYiI+PT5XHV1BQUOVjAOTn5+Pv7++SY1WUZBgJ4WX0DKNGjdRS+hjZfP21bd0+/uL1zp6FDRtsl50FjOLjHW9TgT5G+v/44cPhyb7/2q6QgJEQQgghvJmmQVaWZ340rVxDjI2Ntf5ERERgMBisl7Oyshg/fjx169YlNDSUHj16FAu+AGRnZ3PbbbcRFhZGw4YN+eijj6zX6SVc3333Hf369SMoKIgePXqwb98+1q9fT/fu3QkNDWXEiBEkJydbb1e0JG3gwIE88MADTJkyhaioKGJjY8uV/eTr6+vwGGNjY4mOjrZef+bMGUaPHk1QUBBNmjThq6++KnYM+5I0Z3bs2MGIESMIDQ2lbt263HTTTZw9e9ZlY69Xrx4dOnTg/vvv599//2XHjh289NJL1v3y8vJ45JFHqF+/PiEhISQkJLB06VKHY3388cfEx8cTExPDddddx2uvvUZkZKTDPr/88gtdu3YlMDCQpk2b8uyzz1JYWOjwPLz//vtcccUVhISE8L///c/puFesWGH9XcfHx/PAAw+QlZVlvb5x48Y8//zz3HzzzYSHh3PXXXeV+VxUFwkYCeFl9IDRuHFquWGDantzsSssVA2ddX/+6bmxVNiSJY5vSsoqSYNyB4wKCkD/fzd4MAz2W2q9LuesBIyEEEII4cWysyE01DM/2dlVHn5mZiYjR45kyZIlbN68meHDhzN69GiOHDnisN+rr75K9+7d2bx5M/feey/33Xcf+/VpkS2mT5/OU089xaZNm/D19WXcuHFMmTKFN998k+XLl5OYmMi0adNKHc/nn39OSEgIa9eu5eWXX+a5555j8eLFVXqMEyZM4OjRo/zzzz/88MMPvPfee5w5c6bct09NTeWyyy6jS5cubNiwgYULF3L69GnGjBlTLWNv3bo1I0aM4Cd9Rhhg0qRJrF69mm+//ZZt27Zx/fXXM3z4cOvvYOXKldxzzz08+OCDbNmyhSFDhhQL9ixfvpybb76ZBx98kF27dvHhhx/y2WefFdvvmWee4eqrr2b79u3cZp2+2ObAgQMMHz6ca6+9lm3btjF37lxWrFjBpEmTHPabOXMmnTp1YvPmzTz99NMVfh5cRrvIpKWlaYCWlpbm6aFUSX5+vjZv3jwtPz/f00MRLtaokaaBpi1bpmm1aqn19eur7/5cdS7t3atpc+dq2lNPadrEiZp28qSLBmixcKF6Lvz81DI0VNNqzOl/221q0M2bq2WPHrbrBg1S2z7/3PE2Bw6o7f7+mlZYWOKhly9Xu0VHa5rJpGla06ZqA2hnAhtoZnP1PCRn5HVJuIqcS8JV5FwSriTnU9Xl5ORou3bt0nJyctSGzEzr+xa3/2RmVnj8s2fP1iIiIkrdp127dtrbb79tvdyoUSPtP//5j/Wy2WzW6tSpo7366quayWTSkpKSNED75JNPrPt88803GqAtWbLEuu2FF17QWrVqZb18yy23aFdeeaX18oABA7S+ffs6jKVHjx7aY489VuJYp0+frhmNRi0kJMTh5+6779Y0TdP27t2rAdq6deust9m9e7cGaK+//rp1G6D9/PPPmqZp1sezefNmTdM07fnnn9eGDh3qcL9Hjx7VAG3v3r1VGnunTp2cXvfYY49pQUFBmqZp2uHDhzUfHx/t+PHjDvsMGjRImzp1qqZpmjZ27Fht1KhRDtePHz/e4Xc9aNAgbcaMGQ77zJkzR4uLi7NeBrSHHnrIYZ9//vlHA7Tz589rmqZpt99+u3bXXXc57LN8+XLNaDRa/y4aNWqkXXXVVSU+dk2z/S2lp6cXe11yZcxDehgJ4UXy8mzJJy1awCWXwB9/qLK07t09O7bSfPKJ6ilkLyMD5sxx3X3o5Wi33w4//qgmHFu9Gvr3d919VIddOzUa/vQnocDaDreTkDiV3APH2LYOateGxkeO4QPFM4waNYKAAHVSHDoEzZo5Pb59/yLjsSNw8KD1Op/cLL78Em66qToemRBCCCFEFQUHe66EPji4yofIzMzkmWeeYf78+Zw8eZLCwkJycnKKZRh17NjRuq6XtNmXZBXdp65l5twOHTo4bCsrs8f+GABxcXFl3qZVq1b8+uuvDtvCw8MB2L17N76+vnTTM99RGTxFS7VKs3XrVv755x+nvZ4OHDhAy5YtKz32kmiahsFgAGD79u2YTCbr/ejy8vKoXbs2AHv37uXqIk1je/bsye+//+7wOFauXOmQUWQymcjNzSU7O5tgy/nUvYwPbVu3bmXbtm0OpX2apmE2m0lKSqJNmzblOo67SMBICC9y6JCaFj0kRM2w3quXLWB0//2eHl3Jli9Xy+bNVZDryy9VgGfaNBX4qqqcHPj5Z7U+fjykp6vj//mndweMvvgCXr1jN1sLjpNDIGN+vpHDTMU/5RR9E/IpwI8MjhEKDL09nkaD1eMZMgRiY33Uk7djB+zdW2bAaPBg4F9L/6L69eH4cULJ5IEH1HVxcW55yEIIIYQQ5WcwqDe+NdQjjzzC4sWLmTlzJs2bNycoKIjrrruO/Px8h/38/PwcLhsMBsxmc4n76MGOotuK3qao8txPUf7+/jSvxplkMjMzGT16tENPIV2c3RvUyoy9JLt376ZJkybW+/fx8WHjxo3FGllXpGF5ZmYmzz77LNdcc02x6wIDA63rIWWcz5mZmdx999088MADxa5r2LBhuY/jLhIwEsKL6P2LmjVT/z8vuURd9vbG1/rkEU89BbfcAikpsGABvPACfPpp1Y8/f77KWGrYEHr3Vkk0X3+tGl+X0EvOo8xmePJJePFFeBDVnXtvnf50vSSe/N/88dfy6V7vJIfORxCaoxrcrThUn8WfqGwtoxGGDYMPQ1oRjyVgNHJksftJT4c1a9T64MHA80vVhcsvhw8/xJ8CslLzuecef+bNU+eUEEIIIYRwjZUrVzJhwgRrdkpmZiaHDh3y7KBcqHXr1hQWFrJx40Z69OgBqGyc1NTUch+ja9eu/PjjjzRu3LjCs69Vxp49e1i4cCFTp04FoEuXLphMJs6cOUO/fv2c3qZVq1asX7/eYVvRy127dmXv3r1VDq517dqVXbt2VWuQzpWk6bUQXiQxUS3114+EBPUh/9AhOHXKY8Mqkx4w0r8k0PuyzZkDSUlVP/4336jlDTeoYMqQIeryxo1qAjJv8+ijKlgEMLGZChh1njKUn38x4t+4PgCrvjvGibWq/tAUGcV3vwXzyCOq17XZrDLLvljbCoCCnXuL3wkqs8tkUgHGxo2xdb8eNcq6Ty3fTH79Fez6/gkhhBBCCBdo0aIFP/30E1u2bGHr1q2MGzeu0lkxnlJYWMipU6ccfk6fPg2oQMrw4cO5++67Wbt2LRs3buSOO+4gKCio3Me/7777SElJ4cYbb2T9+vUcOHCAP//8k1tvvRWTyeSSsZ84cYLt27fz9ttvM2DAADp37syjjz4KQMuWLRk/fjw333wzP/30E0lJSaxbt44XXniB+fPnA3D//fezYMEC3njjDZKSkvj4449ZsGCBNdMLYNq0aXzxxRc8++yz7Ny5k927d/Ptt9/y1FNPVWjMjz32GKtWrWLSpEls2bKF/fv388svvxRreu0tJGAkhBexzzACCA+Htm3VejlmV/eYogGjSy5RQZ3CQlvgpLIKClSGEcCNN9rup2NH1bHQycylHqfPKvrWW9AqdZ26cOmlahkfr5ZHj1pnSPNpFM/ll8Mrr6hZ8fbuVRlKx0NUwChxvvOAkZ551q8fcMTSv8jHBwYMUP2PgHtvVn0BFi506UMUQgghhLjovfbaa9SqVYvevXszevRohg0bRteuXT09rArZuXMncXFxDj+NGjWyXj979mzq1avHgAEDuOaaa7jrrruoU6dOuY9fr149Vq5ciclkYujQoXTo0IGHHnqIyMhIjMaqhSP0sTds2JCBAwfy3XffMXXqVJYvX+5QbjZ79mxuvvlm/vvf/9KqVSuuuuoq1q9fby0B69OnDx988AEzZ86kXbt2LFiwgIcfftih1GzYsGH8/vvvLFq0iB49enDJJZfw+uuvOzxX5dGxY0f+/fdf9u3bR79+/ejSpQvTpk2jXr16VXouqk2V22bXMDJLmvBmI0eqSRs+/NC27f3O72vL6aN9/OLZarnPqp5Lubm2ySbO2g1x2TLbrGZHjlR+fLt3q+OEhGgOM3498ojaftNNltnBvEidOmps29fn2J6cc+fUlePGqcuvvKJpH32k1ovMyqDb8O4aTQPtOHHaZ58Vv37wYHXz99/XNO3LL9WFnj3VlbVraxpoXz+1UwNNu+aa6nms9uR1SbiKnEvCVeRcEq4k51PVFZsl7SJlMpm08+fPayZvexMrrO64445iM7d5E3fNkiYZRkJ4kaIZRgBXH36DvqzEb+VSj4ypLHqpnL8/REXZtvfrBwMHqgyhBx9UUZPK2LNHLVu3duzBM2yYWs6ZA0FB6jl75ZXK3Yer6ZN9RGRbUq8CA6FWLbWuZxgdO2bNMCo2Q5pFt3Eqw6geJ5lyTzo7d9quM5thnSV5KSEB2LpVXejZUy0t36rUDlCDOXeuSg9JCCGEEEKIC9bMmTPZunUriYmJvP3223z++efccsstnh6Wx0nASAgvYTLZ+v1YA0aFhUSnqShSzvEUzwysDHo5Wmxs8abKb7wBvr5qhrPvvqvc8e0DRvb691c/RiPk56tqrP/7v8rdhyuZzZCdrdZD046rlXr1bE+OHhw6elT92G8rKjISzZLyG5+7jzvvtF21d69qeh0UBB06oGZTA2jf3nLnKmAU5S8BIyGEEEIIIUqzbt06hgwZQocOHfjggw946623uOOOOzw9LI+TgJEQXuLYMRX48POzJaFw+DA+5kIACk975yf+ov2L7HXqpHrxANx3H5w5U/HjlxQw8vdXs8jn5sKuXWpberotWOMp9vcfknZCrdjXJOvBIfsMI+svvDhDK5Vl1Jq9rF5tu8natWrZrZsKypUUMKrlr2ZhS/HOeKMQQgghhBAe991333HmzBlycnLYuXMn99xzj6eH5BUkYCSEl9DL0Zo0UX2LAdi/33q95qWf+PWAUUl92p54QjWoPncOKtP8v6SAkc7PT12nT9agj8dT9HI0gwH8ki0Bo/r1bTtUoCQNAEvAaFAD1fj6t9/UZj1glJAApKXZspXatVNLS8Ao3GjLMKpsWaAQQgghhBDi4iMBIyG8RGKiWjZvbrdx3z7ralBOCllZ7h1TeZywxEScZRiBygSaPVsFwb7/Hv7+u/zH1rSyA0aggjN6wEofj6foAaPQUDCcsCtJ0+nBoZMn4fBhx23OWAJGl0SVEjDSmxs1aACRkWo9JASAMIMaUF6e57OvhBBCCCEANPkWS4gq0f+GDEV7griYBIyE8BLOGl7bZxhFkWKNL3iT0krSdF27gt4z7pdfyn/s06dV8ozRWCSQ5oR+/96SYRQaii16ZZ9hFBOj0qI0zRbBKUfAqEmeChgtWQLJybBtm7o6IQFbOVqHDrbbWTKMAgoy8fNTm7w0SU0IIYQQFwkfSxp9fn6+h0ciRM2m/w35WEtTqodvtR5dCFFuGzaopUNgpEjA6NAhaNvWrcMqU3kCRgCjRsGnn8KiReU/9u7datmkiZporDReGTA67iTDyGhUASK9w3mtWtZsIKcsAaOAI/to3tRM4kEjL76omqTHxloq3LZvV/vq/YusAwBDViZRUSr4du5cqe2ShBBCCCGqla+vL8HBwSQnJ+Pn54fReHHmL5jNZvLz88nNzb1onwNReWazmeTkZIKDgyVgJMTFICnJVqp1+eV2V9iVpEWRwu4ammEEcNllKlayZw8cOQING5Z97PKUo+m8JWCklw06ZBgVbfBkHzAqLbsIVLTM1xdDTg7/GXiMZw425L331FUJCZbJ14o2vLYOAMjMpHZtW8BICCGEEMJTDAYDcXFxJCUlcdgbU+fdRNM0cnJyCAoKqvaSInFhMhqNNGzYsNrPHwkYCeEFZs1Sy8GDoWlTy8a8POxr0GpzjkOH3D60MpUUEykqMlIFOFavhsWL4fbbyz52TQwY6RlGIcEa7HNSkgaOQaKyAkZ+fqpOce9ermy9l2doSG6uuiohAVXaVkqGkR4wAilJE0IIIYTn+fv706JFi4u6LK2goIBly5bRv39//PTeAUJUgL+/P0ajkYKCgmq9HwkYCeFhhYWqKTTAXXfZXXHwIJjN1ove2MOosFD104GyM4wAhg5VAaNFiyoWMGrTpux9vS1gVCcw3ZZuVPTJsa8LKytgBKosbe9eOvjvJSJiCGlpanNCAnDmjEodMhgcnyi7gFFUlFqVDCMhhBBCeAOj0UhgWf0GLmA+Pj4UFhYSGBgoASPh1aRgUggPW7BAZenExMCVV9pdofcvsjQ1CiKXkwdz3D/AUpw+rRJcfHzU+MsydKha/vWX6sFTlpqcYRTvY8kuiogo3qPIPkhUnqZClj5GPol7GTlSbTIYoHt3bOVozZtDUJDtNnrAKCvLmmEkASMhhBBCCCFEeUnASAgP+/hjtbzlFjUFvZUeMOraFc1HJQOmH/KumiI9OFO3rupPVJaePSE8XJVGbdpU+r5ZWarXEdTMgFF9LA2vi5ajQcVK0sAaMGLvXq66Sq127KieS6f9i8AWpJKSNCGEEEIIIUQlSMBICA86dkxlGAHccUeRK/WG1y1bollqigqTU8jxoiQjPThTVv8ina8vDBqk1hcvLn1f/eFHR2MNeJRGDxidO6faP3mKXoUWay6luVNFS9L0iNnOnVx3Hbzzjq2MscSAkZSkCSGEEEIIIapAAkZCeNCnn6o2Rf3725JIrPQMoxYtMNRWn/ijSLFm3XgDveF1efoX6fSytEWLSt+vIuVooIJKegn4qVPlH4+r6RlGMQUuzDDq2FHVoJ04gTH5NPfdB126WK4rR8BIStKEEEIIIYQQFSUBIyE8JDtbZYoA3HOPkx30gFHLlhgsKSK1OedVja/1DKPKBIxWriw9sFPRgJHB4B1laXrAqHZuKRlGdeqoLKOoKGjcuOyDhoVBy5ZqffNm23ZNq1DASErShBBCCCGEEOUlASMhPGT2bDXDWOPGcP31Ra7Mzlb1agAtWqDXFEWRwqFD7hxl6SoTMGraVM0SX1iokmuGDFF9nPRAi66iASP7cXhDwCgyp5SAkdEIW7bA7t2OjapL07WrWm7caNt25Ii6Qz8/dZ7Yk5I0IYQQQgghRBVIwEgIDygogFdeUeuPPqp6+zhITFTLWrVUrZUlRSSKlBqfYQTw5pvQqZOaKe2vv+Cuu1TwaPJk1dvo009h9Wq1b00NGEVklFKSBioIWKdO+Q/crZta2ncL37ZNLVu3ttXj6aQkTQghhBBCCFEFEjASwg0KC+H3320f2OfOhcOHVbzg1lud3MCu4TXgtRlGJ0pJoinNqFEqwWb/fnjxRZUck5YGr7+uStZuvx2OHlX7duhQ/uN6U8AoJL2ST05J9Awj+4DR33+rZUJC8f31gFF2NrVrmQFVkmY2u2Y4QgghhBBCiAubBIyEcCFNU5/n8/Mdtz/xBIwerUqxZs6El15S2x96qISKJLuG14DXBowqm2Gka94cHntMlZ8tWAAjRqhtQ4bAxInw/ffQsGH5j+cNAaOsLDBgJii1glPIlUXvcn3okK0Z0cKFajl8ePH9Q0LUUtOIClJT65nNkJ7umuEIIYQQQgghLmxFC2GEEFXwxx8qe2bUKPjtN9WI+fRpW3PrtDRVggaqj/HEiSUcqISAkTc1vTaZ1GODygeMdEajChaNGFG143hDwCgzE2JIxmgqVCdAbKxrDhwZqSKOBw6oqGSLFirS5uMDgwYV3z8oSN2/phFYmElwcAjZ2SrLLTLSNUMSQgghhBBCXLgkw0gIF1q2TC3nz4efflLrM2dCTg707AmzZtmCGg8+WMoH9y1b1LJNG7W0yzA6caJ4BpMnnD2rgkYGA9St6+nRKN4SMKqHpRytTp3ivYWqwr4s7c8/1follzg/kYxGW5aRzJQmhBBCCCGEqCAJGAnhQrt329YfegiSkuC999Tl6dPhtttU8tCyZfDssyUc5Px5W8Cob1+11DOMDClomq2/jyfpQZk6dZw07fYQbwkY1aeMhteVZR8wKq0cTSczpQkhhBBCCCEqyUs+5glxYdADRgEBcOyYivdkZ0P37rZyq5AQ6NevlIMsX66aIbVqZYuAWNJDYnxSoFBVJTVrVn2Pozz0htdVLUdzJb1d0OnTqtG4JwJZDhlGrupfpNNnSlu71pYqNGxYyfvLTGlCCCGEEEKISpIMIyFcJC9PBXIA3npLLfWgyrRpqnSrXP75Ry0vvdS2zZIeUktTQQJ9EjVPqmrD6+oQE6MqsTQNzpxx//0XFKhyQWuGkasDRvaNr9PTITraFkRyxknASErShBBCCCGEEOUhASMhXGT/fjULVXg43HknXHGF2t6lC1x+eQUOtHSpWg4caNtmCRgFmrIJIJc9e1wx4qrxxoCRj4+tn5InytKystTSmmHk6pK06GjHaeOGDlURspJISZoQQgghhBCikqQkTQgX0cvR2rRR2UQff6zKxm6/vQLZRSkpsHWrWh8wwLY9PFxFQ0wmokhhzx4XZ65Ugp6pomeueIu4OBUs8kTAKDNTLesbToCG6zOMQGUUHTmi1ksrRwNb0+usLClJE0IIIYQQQlSIZBgJ4SL2ASNQzaBfew3atavAQZYtU/VUbdo4TsduMECtWoCaKW3vXteMuSrS0tTS26Zo92Tjaz1g1MBYTT2MwNb4GlSGUWmkJE0IIYQQQghRSRIwEsJFigaMKsVZOZrOUlMURQrHjkFGRhXuxwX0gFFEhGfHUZQ3BIxqcV6tVEf6ld4x/ZJLHIOKzkhJmhBCCCGEEKKSJGAkhItUe8DIEnxoGuEdja+rFDDaswc++0w1fXIxbwgYhWBpZqSXhLnSgAHwxx/w3Xdl7yuzpAkhhBBCCCEqSXoYCeECJhPWMrFKB4zOnbP1Lyolw6h1nRRIU/dX2gRZ1a3SAaOCAhg5EpKSVJrU/fe7dFyeDBjpTa+DzZbIkR6wcbXhw8u3n5SkCSGEEEIIISpJMoyEcIHDhyE3FwICoEmTEnY6ebL0jJply9SybVvVAKkoS8CoWZT6xO/pmdJSU9WywgGjb75RwSKA555T08O7kKczjIyYCNRy1YbqyDCqCClJE0IIIYQQQlSSBIyEcAG9HK1lSzWZWTGffKIaILdtC7NmQV5e8X3++UctnWUXgTVg1ChEfeL3dMCoUhlGJhPMmKHWfXzg7Fl45RWXjqtuXbU8c8alhy2XzEy7cjSovgyj8nKSYZSWBoWFnhuSEEIIIYQQomaQgJEQLlBq/6LCQnj+ebW+dy/ccQc0bQpbttj2MZth3jy1PmSI8zuxBIzq+qdYD+VJlZol7ccf1cBr1VKBM4BXX4UTJyo3iJwcVeJmRx+PPj53ysyEUCzlaEYjBAa6fxD29AynzEx9kj0Azp/3zHCEEEIIIYQQNYcEjIRwgVIDRj//DEeOQHQ0vPwy1K+vAiRPPGHbZ80aOHoUwsJK7k9jCRjVxtb0uhp6RpdLXp4tSarcGUZmM/zf/6n1Bx+Em2+G3r1V0OeZZyo+iKwsFXjr29dhs33ASNMqftiqcMgwCgkBg8G9AyhKzzDKysLX1/a7krI0IYQQQgghRFkkYCSEC5QaMHrtNbW891549FHbTGgLF6pAEsC336rllVeWnJViCRiF5KXg7696Juk3dzf77J2wMCA/H1avLr3W6fffYft2dYP771fBlJdfVtfNmmXra1ReW7bAqVOwbp0KOlnoQRGz2TZrmbtkZdllGHm6HM1+DJYnQmZKE0IIIYQQQpSXBIyEqCJNg1271HqxgNGaNerH318FjACaN4dLL1U3/PRT1dfn++/VdTfcUPIdWT7tG86n0KKF2uSpPkZ6w+uwMEvPppdfVtlCV1/tvD8TwEcfqeW991qDX/TpA716qejOihUVG4T+pINDSVtQEPj5OY7TXYplGHlakYCR/rTLTGlCCCGEEEKIskjASIhyMplgyRLYsMGx1OnUKZVxYzSqptcOXn9dLceNs3VjBrjrLrWcNUs1uz51SvX1Kal/EWA/zVXr1mrVUwGjYg2vFy9Wy99/h+uucx400psujRjhuL17d7W07+lUHiUEjAwG27jc3cfIoYeRZBgJIYQQQgghajBfTw9ACG+naap6bMoU2LFDbevYEW6/XQWR/vxTbWvSpEg12eHD8MMPav3hhx0PevXV6tP7sWMwaZJtm79/yQOxSw9p1UqteqrxtUPD68JCWL9ebfD1tQWNfvgBAgLUdpMJDh1S602bOh6sUye13Lq1YoPYudO2fvy4w1WRkWoCNk9kGEnASAghhBBCCHEhkAwjIUpRUACjRsHIkSpYFB6uYiDbtqm+zZMn2wJGgwYVufG336pSq8suUxEmewEBqukz2KI+pZWjgS1glJVF22Yqg8crMoy2b1c9hCIjYcECFTX7/XeYPdt2g6NHVWDJ3x/q1XM8WOfOarllS8W6VJeQYWQdF57JMPKqkjS7WdJAStKEEEIIIYQQ5ScBIyFKsXs3/PGH6tPzyCOqL/PJk/DWW2pyrlGj4IUXYNkyeO+9Ijdeu1YtR450fvA777Stx8SovkaliYiwzrrVNk7Ni+4VAaM1a9SFhARVUvff/6rLGzfabnDwoFo2aWJpemSnXTu17dy5YoGfUgdgn1VUJMPIUwEjr216nZ8PBQWqQTnubwYuhBBCCCGEqHmkJE2IUqSnq2XTpvDKK7bt99+vfkq1YYNa9ujh/Po2bVTT55Ur4dprVTlXaYxG1ecoJYUWtVOAWGv/pHJPbe8ieqmXQ8DokkvUsl07tbSvl9MDRkXL0UBlJLVurUrMtmyB+vXLHoA+LZ2uSKApMtJxnO7idRlG9kGrrCzCwiIBCRgJIYQQQgghyiYZRkKUIrOyySKnT6syLIMBunQpeb/33oMJE+Dpp8t33OhoNZ6048TGqk379lVwbC7gNMNIDxg568hdWsAIKt7HSO9fZMm48pYMI6/rYeTvb5syLjOzaEsjIYQQQgghhCiRVwSM3n33XRo3bkxgYCAJCQmsW7euxH0HDhyIwWAo9jNq1Cg3jlhcLDIy1DIsDJWu0q4d3HNP2TfUm0C3aYO1DsiZjh1Vr5+ifX1KkpCglosXW2Mvei9pd9IDMXH+52wRq5491VKfKi452dYsp6yAkX0fo/LQ+xd166aWTppeg2QYAQ6NryVgJIQQQgghhCgvjweM5s6dy+TJk5k+fTqbNm2iU6dODBs2jDNnzjjd/6effuLkyZPWnx07duDj48P111/v5pGLi4FDhtHff6tAxYcf2qaRL4keMCqpHK2y9H5ICxbQpIlaTUpy7V2Uhx4wap1uCe62amXrqBwSAvHxal0vSytvhlFFA0aDB6vliRMODbMlw8iOk4CRHggVQgghhBBCiJJ4PGD02muvceedd3LrrbfStm1bPvjgA4KDg/n000+d7h8VFUVsbKz1Z/HixQQHB0vASFQLhwwj+2nc//tfNVV8SaorYDRsmOpltHMnnWsdBjwbMGp6pkg5mq5VK7XUy9LKGzBKTCxf+kvRgFFuLpw/b71azzByZ8BI02pOwEgyjIQQQgghhBBl8WjT6/z8fDZu3MjUqVOt24xGI4MHD2b16tXlOsasWbO44YYbCCmh/CMvL4+8vDzr5XRLF+OCggIKCgqqMHrP0sdekx9DTZCWZgR8CA42Y962zRZh3b6dwo8/Rrv99uI30jR8N2zAABR26YLmyt9RaCg+vXphXLmShJTfgEkcPGimoKCU4FUZKnMupab6AEbqHVF/p6YePTDb3d7YsiU+f/2FaedOzGfP4nfunLqPBg3A2f1EReEbG4vh1CkKN29GKxqAspeRgd+RI+p47dvjGxWFISWFgsOHreV/oaEGwJfz56v23FREbi6YzX7WkjRTYKDDc+IpPsHBGIHC1FQCIwoBXzIyNAoKCl1+X/K6JFxFziXhKnIuCVeS80m4ipxLwlWcnUuuPK88GjA6e/YsJpOJunXrOmyvW7cue8oxX/i6devYsWMHs2bNKnGfF154gWeffbbY9kWLFhEcHFzxQXuZxWWVRokq2bq1DdCS5OQkMretIxw43bUrdTdtovDxx1kSEUFhkfMo6MwZhiYnY/bx4Y/jxzEnJ7t0TC2aNqXtypXU3fANMImdO7NYsODvKh+3IufS4cMDMBBOrf0qYLQsP5/0BQus1zcpLKQjcGb5cvbWr89AIC8igoXLl5d4zEvq1aPuqVPs/PprDum9j5yI3LePAUBurVr8uWYNA8PCiEhJYf0vv5BsCSQlJsYBPTl0KJUFC0q+T1dKT/cHRlgzjLYkJnLM7jnxlN55ecQAW1asYEv9COBSzp3LY8GCP6vtPuV1SbiKnEvCVeRcEq4k55NwFTmXhKvYn0vZ2dkuO65HA0ZVNWvWLDp06EBPvdmuE1OnTmXy5MnWy+np6cTHxzN06FDCw8PdMcxqUVBQwOLFixkyZAh++ixIwuUWLVI5RZ3b1iNsoZq6Peqrr9BGjyYwMZHhW7difv55h9sYfvxRLTt2ZPhVV7l+UA0awJw5ND+ymUByOHs2lOHDR2KsZIFpZc6l//7Xl1bsISgvEy04mL733AO+tpcTQ0AAfPQRsampxFgaevu1bs1IvQeTE8YVK2DTJtqbTLQtZT/D2bMA+HfpwsiRI/F57z04fJieDRqgWW4XHGzgxRfBYKhV6n26kt58PMyQBRp06t2bjm6679L4fPQR7NhB5xYtCOnfl8mTobAwoFqeF3ldEq4i55JwFTmXhCvJ+SRcRc4l4SrOziW9qsoVPBowio6OxsfHh9OnTztsP336NLH6nOElyMrK4ttvv+W5554rdb+AgAACAgKKbffz87sg/jgvlMfhrfTgbOOCgxgKCyEsDL9WrWDGDBgzBp/vvsPnxRcdb7R5MwCGnj2r53fTtSs0aIDx2DEuMyxlQd4Izp3zK/dEayWpyLmUlgaXo/oXGbp3xy8oyHGH9u3VdQcP4nvgAADGZs0wlnZ8y4xnPtu341PafpZG2sZ27dTxGjQAwPfUKesU8rVrq13T0w1u+/vQK1/DjJlgAt/ISNuU9p5kCYz75uZSq5YaT2amAV9fPwyG6rlLeV0SriLnknAVOZeEK8n5JFxFziXhKvbnkivPKY82vfb396dbt24sWbLEus1sNrNkyRJ69epV6m2///578vLy+M9//lPdwxQXMb3pdYM0S8Prtm3BYLA1W05Kcmi2DMCGDWrp6obXOoPBOlva9aGq5EnPbnEHTVMBoxbsVxv0htX26tdXs6UVFsJff6ltJTW81unH2bat9IbiO+1+F4A1UnbihHUXvel1amrpd+lK1hn1DFmWFe9teq1pkJPjuSEJIYQQQgghvJ/HZ0mbPHkyH3/8MZ9//jm7d+9m4sSJZGVlceuttwJw8803OzTF1s2aNYurrrqK2noqgRDVQA8CxJ6zBCksmTPUqoV1XntLRhEAZrMtYNS9e/UNbNQoAIYWzAc0t86Ulpur+laHY0l11KMz9gwG20xpet+isgJGLVtCUJBK67JkJTmlz5CmB4zq11fL48etu0REqGV2tvMe29UhS48TaZaTpoRG/G5nFzCyb7elB0OFEEIIIYQQwhmPB4zGjh3LzJkzmTZtGp07d2bLli0sXLjQ2gj7yJEjnDx50uE2e/fuZcWKFdzubIYqIVxIDxhFn9qhVtq1s13Ztatabtpk27Z/P6Snq8CH/b6udtll4O9PvdwkmnLQrQEjPWvHGjAqqReYHjAqtMzGVVbAyMfHFpDbssX5PpmZtnSqNm3U0kmGkR4wAvXrcAf9XAnWA0bekmGkPxmnT2M02uJY+niFEEIIIYQQwhmPB4wAJk2axOHDh8nLy2Pt2rUkJCRYr1u6dCmfffaZw/6tWrVC0zSGDBni5pGKi42ehRF53JJhVFbAaO1atezSxaEJtMuFhkJ8PAB1Oe3WgFFamlrW9i0jYNS6tePlsgJGAJ07q+XWrc6v17O34uMhJkatO8kw8vW1BUbcVZaWmQlGTASaLbVe3pJhZOkNxapVgEPCkRBCCCGEEEKUyCsCRkJ4q8xMCCCX4JOJaoOeAQO2D+L2ASN9OsN+/ap/cGFhgMr0cWcPIz1gFOmT4TCOYuwDRv7+lKsrt97HqKQMozWq0TaXXGLbpgeMTp+2ZTNhq5TTx1vdMjMhGLspLL0lw6hvX1UiuHcvnDpl/XVJwEgIIYQQQghRGgkYCVGKjAxoxV4MJpOKQMTF2a7s0kUt9+1TO5rNsGiR2jZiRPUPzvLJP4wMj2QYRRjLWZIG0LixKjkrS1kZRs4CRjEx6thmswoaWeiVWO7MMArB0sjIYFBlid6gVi3o2FGtL1smGUZCCCGEEEKIcpGAkRClyMyEdtiVo9nPQ16njprSXdNUgGPLFjhzRmWWlDHLn0tYAjVhZHDkiENyTbXSA0Zl9jBq0cL2fDVrVr6D64GN48fh7FnH6zQNVq9W6/YBIx8fWyDPSR8jd2YYhWLX8Lq65qyvjAED1FICRkIIIYQQQohykoCRECUoLFRTjzsEjIqy72O0cKFaHzRIlWBVN0uGUZRPOiYTHDtW/XcJtoydEHMZJWnBwdCwoVovT/8i/Vh6cKloltGhQyog5+dne951ermbXR8jd5ekZWXZZRh5Szmarn9/tfz3XwkYCSGEEEIIIcpFAkZClECfJr09lhnS7PsX6fTAxcaNtoDR8OHVPziwBmoaRKjAjbv6GOkBmBBTGRlGYJvJrLwZRlByHyO9HK1LFwgMdLxO72PkJMPInSVpDhlG3kQPGO3YQZyfytzSG7oLIYQQQgghhDMSMBKiBHoGRrkyjJYts5VLDRtW/YMDa6CmXpj65O+uPkZpaZbZwAqzHMbh1BNPwLhxcOON5b+DkvoYOetfpPOCDCOHgJG3ZRjFxEDbtgB0zlwBSIaREEIIIYQQonQSMBKiBBkZEEQ2TTmoNjgLGOkzpR06pGrYWraEJk3cM0BLhlHdYJXp486AkTUwYjcOp/r1g6++gtjY8t9BWRlGzgJGXpJhZC1J87YMI7BmGXU4/y8gASMhhBBCCCFE6SRgJEQJMjOhCUkY0VS6Sp06xXeKi4O6dW2X3ZVdBNZATbS/+0vSrA2v/f0hIMC1d6BnGO3eDXl5aj03FzZvVuuSYVQ5lsbXLU9KwEgIIYTwNtOmQYcOkJzs6ZEIIYSNBIyEKEFGhpqBDFBTkzub9cpgcGzA7K7+RWAtBYv0dW9JWmpqOWZIq4r4eBXtKSyEXbvUts2boaBABe0aNy5+Gz3D6OhR6yaPZhh5Y8DIkmFU78wWwkmTgJEQQgjhJdauheefhx07YPFiT49GCCFsJGAkRAnKnTGiB4wCAmzTl7uDJcMozOz+kjRrIK20crTKMhiK9zHS+0NdconzwF2rVmr7nj3w0kuALWDkrgyj8+e9uOk1qCys5s0xaBp9WSEBIyGEEMILmM3wwAO2ywcOeG4sQghRlASMhChBuQNGgwap5ciR7g0UWII1QSYVvDlxwlbBVZ0cStKqI8MIivcxKq1/EaispFdeUeuPPw4ffuj2krRz57w8wwhUTymgJ+skYCSEEEJ4gTlzYN0622UJGAkhvIkEjIQoQUZGOTNGLr1U/af/9FP3DExnCdb45mQQFASaBseOVf/duiVgZJ9htG4dLF2qLvfqVfJt/vtfNSsbwMSJNN30A+CekjRNg5QUL88wAmvz8Vqcl4CRNzt2DJ57zqHEUgghxIUnI0N9zwWQkKCWiYmeG48QQhQlASMhSlChnjQ9eti6LLuLJcPIkJ5OTIzadPZs9d9ttZekgS3DaNky9Q4qOVmVVPXoUfrt/u//YOJE0DQafzbdOt7qlpYGJpOXN70G6+8rlEwyMjw8lgoqKIC5c1Um1wXvhRdg+nTo3VuVWQohhLggvfginDoFzZvDa6+pbZJhJITwJhIwEqIEDhlG3hgA0IM1GRnUrq1Wq/vDtKa5KcOobVvw81OF/UYjTJgAK1aUnbljMMD99wPgf/YEoDKMNK16hqnTn/cIHy8vSdP7XpFR4zKMHnkEbrhBzSJzwdu4US2PHVPNyvUZAoUQQlxQfvtNLZ99Ftq0UeunTkFWlufGJIQQ9iRgJEQJvH6adD1glJ1NTJQJqP6AUVaWyqSp9oBRQAB88gk89JDKsJg9G5o0Kd9tLdEzY1oqPhRSWAg5OdUzTJ3+vNfy9/KSNMt5HEpmjQoY7dwJ776r1vW2Vhcskwm2bVPrzZqp7LpLL5VMIyGEuMAUFsLevWq9Vy81IW9UlLosWUZCCG8hASMhSpCRYVeS5o0BALtgTf1wVV9U3QEjvbwrwlDNJWkAN98Mr78OLVpU7Hb6uy0g2ngeqP6yNGuGka9kGLmapqm4oUnFRNm/36PDqX6JiSrCGRQE69erMsy0NPjqK0+PTAghhAsdOAD5+RAcDI0aqW3NmtmuE0IIbyABIyFK4PUZRgEBqmwLiAt1b8Cotl81ZxhVha8vREQA0DhMPSHV3fhaf97DjV6eYVQkYFTdpXqu8Msv8Ndf4O+vLicnu2/mO4/QU6g6dFBfN48dqy5f8JEyIYS4uOzapZZt2qjqe5CAkRDC+0jASIgSeH3ACKwBgNgQ9waMorw5YATWsrT4YPWEuCvDKLS8TdI9xa4kzWyG3FwPj6cMubkwebJaf+QRqFtXrVcpdjJ9uuouevJklcdXLbZuVUt9psCWLdVy3z6PDEcIIUT12LlTLdu2tW1r3lwtZaY0IYS3kICRECXw+pI0sAZs6gSqAI67AkaRPm4oSasKS8CofqB7M4yCtZoRYNRnufP2srRvvoGkJDVB3tSpturESgeMzGZ4+2311e0ff7hsnC6lZxjpMwXqD3rfvpqREiaEEKJc9Awj+4CRZBgJIbyNBIyEKEFNyjCq7e+eDCM98BJhqBkZRnH+7skwOntWLYNMNackDVRQ1Jv99Zda3n67+hOscsBo5044r/paWTuNepuiGUZNm6pahaws782KEkIIUWF6hlG7drZtEjASQngbCRgJUYKMjJoTMIryc29JWrhWMwJGdXzdm2HkX1AzStKCyMWHQq/OMNI0WLpUrV96qVpWOWC0bJlt3RsDRsnJcOKEWu/QQS39/W0zBEofIyGEuCDYz5DmrCTt8GHVEFsIITxNAkZClMAhw8hbM0YsAZtIo3tL0kK0mlGSFmNwXw8jA2b88728hNHu9xVKplcHjA4cULETf3+45BK17YIPGOnZRc2bO/5tSR8jIYS4oCQlQV6emhCzcWPb9thYNWua2ayCRkII4WkSMBKiBJmZdj2MvDVjxPKhMtzgngyjlBS1DC6sGRlGUZr7AkZB5Ng2eOv54u+vZpHD+wNGenZRQoJ6Qw22gFGlmoFqmmPA6MAB9RWvN9H7F+nlaDr7PkZCCCFqPL0crXVr8PGxbTcYVCUySFmaEMI7SMBICCc0rWaVpIVaMn6ysyEnp7QbVI3e/iUov2YEjCJN7itJs54rYItweBuDwaGPUU0IGA0caNump+qfO2c7F8stMRFOnVJBs8BAKChQX/F6Ez3DSG94rdMzjKQkTQghLgh6w2v7/kU6vY+RzJQmhPAGEjASwon8fJV84PUBI0vAJiA/Q08cqdYso/PnwY98fE15aoOXl6SFFbgvw8hhRj2jF7+01oCAkX3/IvuAUUiImjENKhE70bOLEhJsARhvK0srKcNIStKEEOKComcY2fcv0ulfjkiGkRDCG3jxpxohPCcjQ/WkCSZbbfDynjSGjHSiotSm6gwYpaTYZtiyv3+vYwkYheRWf4ZRbq7K7PL64KLOMj5vLkk7cACOH3fsX6SrdB8jPWDUvz+0aqXWvSlglJsLe/ao9aIZRva1eCaTe8clhBDC5cqTYSQBIyGEN5CAkRBOZGaqnjRGNLXBW4MAesAmI0OPkVR7hlE4lnK04GBrPxyvY3kygrKrP2CkP9/hRi9veK3zpgwjTVOdPYuw718UHOx4XXkDRps3w+TJqgoN8P6A0a5dKq0xKgoaNHC8Lj4eAgJUGZ10QRVCiBrNZLJ9P+Asw0gCRkIIbyIBIyGccOhfZDB4b08avYeQmwJGKSl2ASNvzS4Ca8AoIPMcoLFzZ/UlZujPd1xYDckwsgsYZWSUsW91OnEC6taFsWNV4MjOv/+qpX05mq68AaOnn4bXX4cRIyBz91E4dEh1Fu3VyzsDRvb9iwwGx+t8fGw1CtLHSAgharSkJJVUGhgITZoUv96+JM3JdypCCOFWEjASwgmHGdKCg723J40etElPd1uGkbUkzVsbXoM1YGQsyKd+RBapqbBhQ/Xclf58x4ZaAkbenmHkLSVpP/8Mycnwww/w++/Wzfb9iwYMKH6z8sZNtm1Tyy1b4J2xy9WFrl3V34w3Boz0/kTO6hNA+hgJIcQFoqQZ0nQNG6oE7rw89d2KEEJ4kpd+ChbCszIza0hPGjeWpJlMqnm0NcPImwNGISGqAQ5weS/1hCxaVD13pT/fdUIsAUZvPl/Ae0rSFi+2rT/6qCq3Ag4ehGPHwM9PJQMVZZ9hVCQxySo9HY4eVetBQVBru0pZ2hTWnyefhNfmWwJGp09Xf0f08rKeSHWcX68/cAkYCSFEjVZa/yJQwaKGDdW6t03mKYS4+EjASAgnHErSvDkA4MaSNL0PUI0IGBkM1iyjId3cEzCKDqwB5wtYA0YezTAqLIR//lHrgYEq0+ejjzCb4b//VZt79SrevwhsvR1SU0s+1/Vvb+vVg+++NTOK+QBM+3sgM2bAf58NJ7dWrNrJW7KM9Aejd68vqrozjHJyVErXrbdWz/GFEEIApc+QptNnBD15svrHI4QQpZGAkRBOOJSkeXOJkRtL0s6fV8to/wzH+/ZWliekd0v1hKxerTJPXM36OT+gBpwvYA1oeTTDaN069cuIioJXXlHbnnmG5/+byi+/qP7OL73k/KbBwbae0CWVpe3YoZbt28PltVbSgONk+oQTdvUQ6xv0UxFeVpaWkqKW+h9yUXrAqLp6GP31l2oM/tlnKngkhBCiWmzZopYdOpS8T1ycWkrASAjhaRIwEsKJGpNh5MaSND1gVDeoBmQYgfWDd5z/OVq0UCV1elKLK+nPdy2/GnC+gHeUpOnlaIMGwT33QJs2cPYsAW+8CMCsWXDJJSXfvKzG1/q3t+3aAXPnAhD6n6v55qcAawJNotHLAkalZBiZzdge9KFDqrGFqy1YYFs/eND1xxdCCEF2Nuzerda7dSt5Pz1gZJ3pUwghPEQCRkI4UWN6GOlBm8xMatdSU2lUV8BIT4CICahZASPOnWPoULVaHWVpZ8+qZYRvDckw8oaSNEvAaM7JwXTt6cuEY/8HwI18w5NPwvjxpd+8rICRnmHUoU0hfP+9ujB2LKCyjgA2ZXlZwKiEDKO331YvQXMW1VW/O01z/VzLmuYYMJK5nIUQolps366+BKhTxxYUcibWUjUtGUZCCE+TgJEQTjgEjLw5AKBnGGmatelydWcY1a5hJWnVHTDSn+8wYw0IMIJDSVpGhgfuPz0d1qwBYNqKIWzeDD9mDMGMgUYc4bmJZb87tgZ9Njm/Xs8wuiTvXzhzRmXtDB7scNsVyV4WMHKSYZSXB88/ryrEbr/DQEacpSztyishIQFuuAGOH6/6fe/aBUeO2C5LwEgIIaqF/n+ra1fVbrEkUpImhPAWEjASwomMDLseRt4cAAgKAqP6M44JVJ/+qzvDqJZPzcswGjhQzTqSmOj6ahv9+Q7VakjAyNMlaUuXgsnEuVrNOEQTLr8cVm4NQ2ujposxrl9b5iH0crU1ayzlWnbOnbOl8DfbqMrRuPZaNe0aUL8+RETALrMlYLR/f/GDuFtODuTmqnW7DKO5cyE5Wa0XFMDXh3urC4mJqg/U3Llw9dW221bW/PmOlxMTq3Y8IYQQTtkHjEojJWlCCG8hASMhnKgxJWkGgzVwE+WnAkbnz6t+Pa6mZxhFGGpewCg83DZFu/1s7q6gB4yCtBpSkmY5nz1Wkmb5BawKHgLAqFHQsSP49E5Q168tO2DUubOKlaakFJ80TM8uatawAP9ff1QXLOVooP5k2rWDQzTG5OOngi2HD1fpIVWZfhL5+loDepoGb72lNj/9tAqSTcp7lRvqLSPj+4Wq1K5WLVi/Hh54oGr3r5ejde6slpJhJIQQ1aK8ASMpSRNCeAsJGAnhRI1peg3WD5iRRhXI0TQ15bir6QGjMGpeSRpgLUv76y/X3o01YFRYs84XPcNI09x8/5aA0dwUFTCyNrfWV0oLGO3cCWfO4OcH3burTatXO+6i9y8aX/cvFVGqWxcGDnTYp317MOHLqRjLFDUlTcnmLnr6XlSUtUZh9WrYuBECA1U86JdfoEFjP+ae6Mfru4bBddfBN9+o/T/+WP1URloarFih1u+/Xy0lYCSEEC6Xn696GEH5M4ySk1WGqRBCeIoEjIRwIjPTriTN2zNGLAEA35wMa9KP3ojZlfTPtCHmmpdhBLYAgytb1phMtkCaf0HNOl9CycRkqp4Jt0p04gTs3YtmNPJ7zmUEB9t6CpFgyTBav955itxrr6md69aFuDg+OHEFrdhTLGCkZxhdnfGFWrnuOvDxcdhHv8/3m7yiAi4ffqgiMp7ipH+Rnl00fjxER6sGqc88o7Z9/bUl0DdsGPzvf2rjpEm2qXcqYtEi9Xy3bg1DVBCPQ4egsLAyj0QIIUQJdu1SwZ/ISGjcuPR9o6Nt/7rOnKnukQkhRMkkYCSEEzUxw4iMjKIxEpfSAyPBhTUzYBQfry4eO+a6u0hNtWXo+J2xNB+OiXHdHVQHu6bXgHvL0pKS1H1GNSKNSLp3V1VYALRtq8aWmaneVdvbsweeeMJ2+dQp2h74jZ+4hvUrHCNeO3ZAK/bQce93asNttxUbhh4w+ub0ZfDf/6oLt9/uudz/IjOkHTsGP/ygNulJP6DaFQUGqqDnli2WjY8/rgJH+fnw+usVv2+9HG3kSNXgKSBABYuOHq3UQxFCCOGcXo7WpUvpDa9BtaesW1etS1maEMKTJGAkhBM1pocR2AI36eluCRgF5tfMkrQGDdTF8+chK8s1d2FNDAnNx3DUMstU8+auOXh1sZakZWLA7N6AkSX17axBBdWs5Wigvkrt0UOt25elmUwq6JOXpwIjGRmwciXm6Dq0ZTejd79MWpraVdNUhtE0nsOomdVsYk7y/vWA0cGDkDX1/1TvnnPnYMIED9To4ZBhtGSJ6utkMsGAAdCpk2238HAYPVqtf/WVZaPBwIoBTwKgffllxf74zWb44w+1PnKk+oTStKm6LI2vhRDCpcrbv0gnM6UJIbyBBIyEcKImlqRVd4aRngThn1tDMoz08p60NCgsJCLC9lS5KnlCf547RRxSH76Dg22dKr2VXaAvmGwyMtx435Ypv47mOgkYga0sbc0a27a33lINfcLCVJ+e0FDo3Rvj228C8CT/x/YfVJ3h6dNQ59wubuBbdVu9hquImBhV4gWw60CAqvEKCFDlWXqDCXey/HEt3lSbwYNh2zY1k9sLLxTfddw4tfzmGxVU2rMHBj/Tl010wZCTQ/Y7s8p/v7t3qyctOBj69lXbmjVTS+ljJIQQLlXZgJHMlCaE8CQJGAnhhJSkFacyjDR8c2pYwAisH8j1sjRXB4zaB1k+XDdrVnaeuacFBalMEmyNr93GkmGUlBEN2OJDVglFZkrbuxeeVNkzzJxp+wUCjB3L1nojCCCfBs/fBWYzO3fCdJ7FiKbqt/RZv5zQs4x27ADatLGl8ngiUGI5kbYdj8LXFx58UA1Dn9nP3ogRKph04gQsXWpJvso38BZqprT0Ge+Qcb58/Yfy/1bNrneEXoLmH6A2SsBICCFczmSylRKXN2AkM6UJIbyBBIyEx6SkqM+CGzZ4eiTFSUlacSkpEEguBr0hsbeXpPn6qk/WUKwszVV9jPTnuZWvXcDI2xkMDn2MPBEwOkMM8fFQr16R6/WA0c6d6pd05ZWQkwODBsGddzruazCw7Z73yCKYxoeXQbt21Hv8Jq7ne3V9CdlFOoeAEUCjRmp5+HClHlqVWAKa56jN00/DG2/YKiqLCghQfbwBbr7Zlnx147wbSDbEEJt/lFf6/uK0b3hR295TAaMfz/TVk79sJZUSMBJCCJfZu1f9OwsOhhYtyncbKUkTQngDCRgJj9A09WFnxgwYPNi1M1dVldlcJGAkJWnk50N2NoRjyS4yGLz/eYESG1+7OsOoqdnS78Xb+xfp7GZKc2vAyBKVOEt08XI0UO+OGzZULxADB6oXhgYN4MsvnWZudRjdmAd4i0J8YM8e2mz4EiMaO1pfBx07ljoUrwoYWU6kFKKsQc3SjB+vlidOqOUrr8CwKwMpvO1uAAbteosVK0o/xrJlUHuP2mkFfdm3z3KFZBgJIYTLbd6slp07F5u40+b116FfPzWjBlKSJoTwDhIwEh7xwQcwf75aT0uDK66w/n/0uOxstbT2MPL2DCM3BIz0htcResAoLMxa1uTV3BQwapBfgzKMwOMZRmeJLl6OptOvOHBAlc/98kuJfaHat4e5IbdTjxOMCfqV//EE3wXeTNgnZc8WVmLA6MiRcj4YF7LLMNI/IJSmf39bdtall9qSr+Kem0ihwZcBLCNt9a4Sb5+ZCVNvOkYTDmHCyBouYf9+y5X2ASNPNAAXQogLQE4OfPih+lK0TRu4W8XzSy5Hy82FadNgxQrrZARSkiaE8Aa+Ze8ihGvt2WObyfqpp+Dzz2HfPrjxRvj991K+eXET/QO0lKTZ6A2v40IzIBPvL0fTlTBTmqtL0mIzJcOoPLSzZzEAycQ4zzAC1Qn7e0tZ2WefldrswdcXevaEf/6pw/c5o0nsMppffnFsdVSSdu3U8sQJdX5HeUmGUXkCRj4+qqXTF1+o4Ls1dluvHntjB9Du5BJ8160E2jq9/eOPQ4MjKwE4EtWZzJQwW4ZR48bqgFlZqiG2tzdxF0IIDztyBCZPBn9/W5Lsp59avyOx8vNTldZO/fWX7Q2oJYIvJWlCCG9QA1IExIUkP1+VU+TkwNCh8OyzKoEgKAgWLlSlFZ6WmgoGzIRgSTXy9oCRXYZRtOolXG0ZRvXDakjDa519wCgpiSvfG8o4vnJphpEBM7VSk9SGmpJhZDlnwsgo9oa2OplOqpK0VJ/okuNAN9ygsozeeAPGjCnzmPo089dfD8uXly9YBOoU1vfdswePlqRpFcwwAhVg/+MP27B1Z5v0ACB8r/PmcLm56lvvvqhytLT2anY0a8DI39/2xEhZmhBClOntt+HHH9XslS+9BC+/rIJFjRvDq6/CP//Arl3qPcPgwSUc5McfbeuJ6kso+5I0SfgUQniKZBgJt5o3T00rGhUFs2erL7K7dFFl2/fco1qVPP64Z8d4/ryabtzK23v1uKEkTc8wig2uoQGjkydhzBiiN23g/9hP5yPjgKrPZnbiBNTnOL6FeSrdpbzRCk+zK0nTp/l1i3MqOuUXF01QUAn71KsHa9aU+5APPQTXXque+opOUNeggSpPPHUKGGSJvJw9q7Jr3PV3r2nWP9hUQ5Q16FtZue26wyqIPe48YHToEBQWQn/jCjCD1qcvLLMLGIHKlDt8WAWM+vSp2oCEEOICt3y5Wv7nP+r9bUqK+jLjmmvUW4MyFRTAr7/aLlsyjOrWVRfz89V7U/vJX4UQwl0kw0i4lT5b9o03Os6QdEOHnayjB412zvdICxF7KSl25WgGAyV/svUSevCmSMDIld9G6RlGdYIy1EpNK0n74APrdHxNOES9jD2kp1ft0JqmJvNqjqUcrUmTcr4z9AJ2JWlr17rpm8u8PHyz1fljrBvjssMaDKoEoKLBIoAYyzCSk1Ez6umz6rnzRSgrC0NBAQA+dWpXuSTXJ6E7AI3St6t0oiKSkiCcNNqbtwFQ63IVEEpMVA3/AWl8LYQQ5ZSdDRs3qvXnnoM334Q5c1SCbLnfEixbpt586vXFloBRYCDUqqU2SVmaEMJTJGAk3MrymZ3u3R23R3z0Cj3YwN18qPf68xiHgFFoaOU+ibqTHryx62GUl6eSJFxFDxhF+9fQDKOcHLW0NDEayYIq9zE6elQ1bG9prGENr8F6zkQYMjh50nU9nUplqX0rxIeg2Ag33GHZ6tRRyzNnLBs8UZZmyS7Kw5/IesFVPlztLg1JJho/rQC2by92fVISXMIafDBD06Y06FkPPz8VW7KeB/q5bCmLEEII4dy6dSprs359VYJWKXo5ml6Gfe6c9Y2XzJQmhPA0CRgJtzGZsJa/OASM8vNVIyOgKQe9ImBknSHN28vRwKEkLSQEgi2fOV355kIvSatjVD1orF95eTs9YATwwAPw6KOAChjZ9zHKy6t4lo0+u1aPqBrW8BqsJWlNY1TGj575V63sZkirHeMd/3qKBYwaNlRLdwaM7PsX1at6cLp+AwMbUC+whWs3Frs+KcnWv4i+ffH1haZN1UVrWZoEjIQQolz0crS+fSv5/aLZDD//rNZvvtkWIbK8/spMaUIIT/OOd+3iorBvn5oAIjgYWre2u+Lvv1WnaVTAaMlfGnl5HhkioL7UqTEzpIFDSZohO4uZgU/yHE9z7KhjBOT559WsHZWhZxg1yLV8gKwp2TSdOql3cD16qC6UI0cC0J9lnN6vsqUWL1ZVh2+9VbFD68kb7YNqboZRo2h1nq9b54b7TFbBxrNEW0vBPM2hJA08mmGUQpRLJiSLjoYtxm4A5Kwo3scoKQn6oGZIo69qeN2ypbpoDRjpU8itWwdPP21XqyaEEMLeCkv8vV+/Sh5gzRr1DV94OAwaZPvySWZKE0J4CQkYCbfRy9G6dClS1203M0QwOYRlnbT+A/aEYiVp3k7PMDKZoGNHJqbM4Gn+j8xV26y7HDgA06bBxIkqdbqi9IBRbLp6A0OLFlUctJu0aaO6/K5YAQEB0Lw5p8Nb4EchAcv/AtTM7ZoG331XsUPrGUaNC2tgwMhyXjcI90yGUVUbO7uKV5SkVWKGtNIYjXAoWmUYGTYWDxidSszkEixNxYsEjCyfT1RE/+mn1fr//R+MG2cr6xRCCAGo91OrVql1y8tpxX3/vVqOHq1mqdTfXzmZKU0IITxBAkaiUo4fVzNEVYTT/kWFhbZUXEuzv2Yc8GhZWo0rSbMf48GD1tWwpb8V25yfX7l+vnpJWq2UGlh+1bChehNmcaityjKK27IATVMJbgBbtqiYW3mpDCON2qk18DmxBBljglVgdMOGygUSK8SSxpNMjNdkGOkBI2/JMHJFwAgguZF6kQ0+uKNYoKfr/rkEk0New+bWVE/984nDTGnPPaemsvTzg7lz4fbbXTM4IYS4QGzbpjLnw8OhfftKHGDLFnjvPbU+dqxa6i/Ilgi+lKQJITxNAkaiwnJyVJZQ586q6W95OQ0Y/fuv+sAUHQ39+wOe72NU40rSjEZb75X//Ic/Bs8EoPHWX6y72H/+rczER+fPQwSpBGWqLJEaFRwpIrW3Chi1O7yAXTs167d22dlFPjCXorAQdu+GaM7il5Ohyt6aNKmmEVcDS8AojAzCw9Vj37mzmu/TCzOM9MCVt2QYuaIkDSCgaX1OURej2QRbt1q3p6XBf7I/VBfuvMvacKNYSZpuwgT44Qe1vmCBm6bTE0KImkHPhu/Th4rPcJmdraYMzs+HK66Ayy9X2/X3V0UyjCRgJITwFAkYiQrbs0d9I5+crKYOLY/CQti8Wa07BIz0DyNXX2391NKcA+za5d7PbPZqXEkaqEY8q1bBnDkkD/0PZgw0St6gUsFwfC4r08c2JcVu+vjY2JrzvDjhN6g/WQRTO+8kW7/Y6nDdxuI9gp3av1+9x+sQaHlOGjRQ89/WFJbfnyEjgx491KZqL0uzCxh5W4bR2bOWNj16wOjECbBMdV/tqiHDqEG8rfG1NVIPnPpjMz1ZTx7+BNw9wbpdDxglJTl52EOHqsBSWppdZE0IIYR9w+sKe/hh9YY6Lg5mzbJ1zC6SYXSxlaRpGqSnw+nTqqNAtWc/CyHKJAEjUWF79tjW33uvfF86796tMpNCQ20fTjCZ4Kef1Pp111l7wCTEqBQYT2UZ1ciAUcuW0KsXANHt6rKWBLX9998B12QYtaCG9S8qQf1mgSxhEACm3xYAquk12GbxK4ve8Lp//RrYvwhsfa8yM0mwnCrVHjCyK0nzlgwjfRxmsyXRp04d1evKbLabY756aS7uYQRqemdnASP/2Sq7aGnUNdhH7erVU5MRmEwqaGQvyxTImWAVSNP2ljMFTwghLnCaZsswqnDA6Oef4aOPVJBozhwc/inq7yfOnYPz5y+qkjSzWb2VjYhQ3002aaLeclr76wkhPEICRqLC7ANGu3fD0qVl30b/zNKtm7VVEaxcqb6xrlULLr3U+k+yrb/6EL5mjevGXBHnz9ewHkZFNGgAv3KFuvDrr0DVAkaapp4Ta4ZRDS5HA/X8/MkwAGL3LQPgttvUdeXNMNIbXncJr+EBo4wMa8CoumdKM53xvgwjPz/18gOW5Bn78k43pTgWnHLtLGmgzvFiAaPMTOr/+xUAazre7bC/wVBCHyPUZ5nNWSrK//3/9klVmhBCoHpDnjqlWiT27FnBGz/7rFo++qiaGc1eaKgtrSgx0bqalnbhzz2wYYPjl1c+PirLaMAA2LvXY8MS4qInASNRYfqLtp58o/frK43+QdyhHG3xYrUcNUp9crN86I7OUB2ai37T7Q6aVkMzjOzEx9sCRtqSJZCZyaFDtusP789Xz/njj5freNnZqvzqQskwCgmBg2GdAGhp3k1kJNxxh7pu8+byzSCuZxi19KmhQTT9vM7IsL7R3bkTMjKq7y5Np1SG0XmfGMLDq+9+KsrTja8LT6sMo9zg2i6ramzQADbSTV3YvVuVO7z5Jv55meylJTk9BxS7TUl9jD79FPahrkxatI/Jk6WVkRBCrF6tSsi6datgRfrWrerH3x8ee8z5PnZlaRERtuNbugxYLV8O//xTsXF7s/nz1fKaa9R7sRMnoEMHlV01YID6dyaEcD+PB4zeffddGjduTGBgIAkJCawr42vu1NRU7rvvPuLi4ggICKBly5YsWLDATaMVYMswevJJtfz55+L/xIpy2vBaL/4eYPnw0rQpAIHpyYSSYT/hl9tkZKiyjJocMIqMhENBbTlAUwx5eZgWLnb4/UQe2Kga2L78srWvTGnOn1fLFhdIhhFAVnwbABpxhOF9M2nfXr0hy8goXwbWjh1Ql1O02GVpLN6hQzWOthrYlaTF1tVo2FAFAeyql1zOcE6da6Za0dZWDd7A042vNUsPI2N0lMuOWb8+nCKORJqpd9133AFPPQXAR9xFk6bFfwF6wMg+9X/7dli/HhKNrdQ+7OONN+D//s9lQxVCiBopMVG9jlb437/e/PPyyyGqhNd9/X3W/v0YDNCxo7r455+2XU6cUMlJI0eqmdouBHrA6PLLVeZrnTpqJtuOHVVPo+uu8+z4hLhYeTRgNHfuXCZPnsz06dPZtGkTnTp1YtiwYZwpobFmfn4+Q4YM4dChQ/zwww/s3buXjz/+mPr167t55Bcvs9mWYXTdddCvnwqwfPxxybcpKFAzh4JdwCg/35Z3qhd/h4db67ibcYDjxyE31+UPoVR6cCTcWHNL0gwG1fRWzzLKmfsrJpNK4jIaITr3qNpR08rVKMoaMDJcGBlGAGGNa3MGFSm4qs1efH2hk0o6KrMsLStLBZVe5HF8szOgRw8YMaKaR+xiesDIZILcXGtZ2vr11XR/mobPeRUw0mp7SQMjCz3DyFMBI59UlWHkF1vbZceMjVV/6yNZQOZDT0Hv3uDjw1nfunzOLU4n9HNWkjZ7tlrW7qWiSf1j1ZWzZrlsqEIIUSMdPaoCRvq/jBLZp2QWFsJXqjSYm28u+Tb6C7JllpJx49TFL7+07fLFF+r9dW6uCqbUdCdP2t5/2b+lio62vVXdtUtlvQsh3MujAaPXXnuNO++8k1tvvZW2bdvywQcfEBwczKeffup0/08//ZSUlBTmzZtHnz59aNy4MQMGDKCT/klPVLsjR9Q/J39/aNwYJk008TKPUuuVJ8hKdv4qvmMH5OWpJnbWVi8bN6oDxcRAq1a2nS07tAs8gKa5f6Y0S/9ZavnV3AwjcCxL81/8OwbMNGqktjfArpmv/nVOKVJSIIJUojVLNlJN69fjRHw87EZlGfWLUSlzXbuq68pqfL1rF1yirWICn6sN77xj15irhrAPhGZmWh+7PpOhy6WlYTSpqU58Y70zYOSRkjRNwz9LvegE1XddhpGfnwoa7acle8Y/DytXop1PpbXfQc4R7TRgpGcYbd+uemXk59s+nPS9TV0ZlZKIEROHD0NqqsuGK4QQNc6RI2pZYsCosBCmTFHvcz/7TG1bskQ1Pqpdu/QvmorMlDZ2rHqbsWaNiiFpmioX1ln/f9VgCxeqZffuFOvnV68e1lJ2/XkXQriPr6fuOD8/n40bNzJ16lTrNqPRyODBg1m9erXT2/z666/06tWL++67j19++YWYmBjGjRvHY489ho+Pj9Pb5OXlkZeXZ72cnp4OQEFBAQXumja5Guhjd/dj2LHDAPjSvLmGphVyReEPBDITsiG57Tz8F86x5c5a/PSTEfChd28zhYUmAIz//osPYO7dG5PdnJk+TZpgXLuWrhEH+DoX9u0rpGlT9zXMOHNGPb4IHxUwKgwMRKuB50m9ej58Sx/MBiP+aWepy2kaNqyLpkH84aPW/bQ//6TA8nVNSedScrLB2vBaq1uXwsBA9005Xk3q1TOyh9YMYBl1U3ZQUFBA587qd79xo5mCAlOJt9222czb3A+AecIETF261MjnwzckBENWFgUpKXToUAvwZdMmjYKCys9hW+Lr0smT+AGZhBAaE+BVr71RUer16dQpEwUFZgz16+MLaIcPU1jd40xLw8+szrXgBhEufV7q1fPhxAkjhw4V0qmTxpn0AM7l+GEwaMTFFRY7Zdu0gZgYX5KTDQwZYubOO80kJ/sSF6fRa0ws2r0BGPLy6FXvMCtPNGXjxkL696++12ZP/Y8TFx45l4Qr6eeR/p1C/fqFFBQUeS08exaf8eMxWhoMaXfcgal2bYxff40RMI0di9lgKPm9Q6NG+AHa/v0UFhRQuzYMHuzDokVGvvjCxGWXaezfb/sId+qUkzHUML/95gMYGT5c/S8uKj7el507DRw8WEizZjX7serktUm4irNzyZXnlccCRmfPnsVkMlG3bl2H7XXr1mWP/TRcdg4ePMjff//N+PHjWbBgAYmJidx7770UFBQwffp0p7d54YUXeFafjcDOokWLCA4OrvoD8bDFeuNoN/n116ZAByIiTrJg/joGPPs0gUAhPsSc3Y3pkl5svn8Sxy19iTQNZs0aBITSqtVmFixQ2S09f/qJOGBXVBQH7HpQtTaZaAU0MatpqH7/fSdm8yG3Pb5Vq+KAngRpaQBs3LOHUzWwR1ZubmsKaEVyQCx1c0/QmEMYDPn4+GgOGUaG1FQ2vfMOtG9f4rm0aFETa8AoJSqKFTXw+SjKzy/KmmF0eulS1i9YQFZWBDCQdesKmT//jxL77OS+u51ubCLTN4zlAweSX0Ofj2H+/gRmZbHijz84W6sVMIL9++HHH/8kKKjkgFl5FD2Xau3dS3/UDGnZ2YdYsGB7lY7vSsnJTYCObN9+igULNhB0+jRDAfOhQyz4/fdqzR4LPnWKIUA2QZzJOMSCBRWcwrAUvr49gHosXrwLP78k9u2rBfQnKiqXJUsWOb3N1KkRTJ/ei/XrA1i/Xj3u3r33s2jJbi6tW5fwI0foGrqWlTTl2293k5lZ/Y3m3P0/Tly45FwSrmIywVHLd2+JiUtIS7P1Twg+dYo+Tz+NX3IyhYGBnG/Zkpht22DMGDTLrBormjQhtZT3Dj65uVwOGFJSWDx3LgVhYbRt24BFi7oxa1YOK1emAA2t+//99zYMhqMlHs/bFRQYWLhwBGAkPHwFCxakFtsnKCgBiGX+/B0UFLi5/KCayWuTcBX7cynbhfWbHgsYVYbZbKZOnTp89NFH+Pj40K1bN44fP84rr7xSYsBo6tSpTJ482Xo5PT2d+Ph4hg4dSrg3TdVTQQUFBSxevJghQ4bg5+fntvudP199iBgwoC6jgoLwPXAALSiI8c3Xc9P2x7i8cD7dZs2i04wZ4OPD+vUGTp70JThYY9q0joSGdgSzGd9bbwWg9Z130squE7bh7Fn4/nvaBZ4AICSkPSNHtnXb4ztxQkUJIn3VP/9uAwagXXaZ2+7fVY4fN/L993AmuLk1YNSyT08CAiB+sXpToUVFYUhJIeHsWRZAiefSjBk+DEU1d67VowcjR45050OpFiNHQlrDdPgPxKWlMXLkSPLz4fHHNTIz/WnbdqTTsh1yckjbMxGA7dc+w2C9sUAN5Fu7Npw/T78uXdB69+bJJzWOHzcQFzec3r0r9+1dSa9LBsub5GRi6Nq1ESNHxrvkMbhCZqaBjz8GH584dW4XFKBNnIhPYSGjQkPRBg6stvs2WBo2nKM2l17ampEjW5Vxi/JbvNjImjUQEdGOkSPbkJGhXtvatAko9W94wAAYPlwjOVnt/+yzTWjZsgk+s2fDkSMMaXSGt/dBYWE7Ro5s7bLxFuWp/3HiwiPnknClgoICvv12OSaTER8fjfHjL8PX7tOU8b778ElORmveHO3774ls0QLzFVfg+/ffAGitWtH7gQcoa/YHrV49DCdOMLRhQ7RevejfHz76SOPEiVBOn1Zl5c2aaRw4YKB+/U6MHFnDJt+ws3SpgZwcX+rU0Xjggd5Ov6f54w8jGzZAREQHRo5s5/5BVgN5bRKu4uxc0quqXMFjAaPo6Gh8fHw4XaRT2+nTp4ktWrxqERcXh5+fn0P5WZs2bTh16hT5+fn4+/sXu01AQAABAQHFtvv5+V0Qf5zufhz6DDrt2vngO3MmAIbbb+eBG9rRv+8vpBJJWHo6fomJ0L49336r9r/ySgO1alnGuXOn6qQcHIxv9+6q4YbO0kgjNlt9c33okA9+fs7LDatDmkosIkRTJWm+kZGO46shGjdWy0NaYzqwjMYcomlTH0JDIR4VMDLceiu8+iq+f/4JAwc6PZc2b4Z162CSIRE0MLZqhbEGPh/ORPdtD4AhMRE/gwG/EF86dFDttbZt87P2dLFX+NZ7ROef5DANqf3UpJr9GmJpfO2bkwN+fnTpomY73LbN1zpxYWUVO5csDW/OEk1srHv/pstSr55aJicb8fMzqr/3sWPhm2/wHTNGzebYvn313LnlBSeFKBo08HXpS028JSZ38qR6vvVvw5s2tTzOEnTtCv/+q6Y1TkiAdu0sg2rdGn75hbY+6p/A9u2lH8dVLpT/1cLz5FwSrpKcHARAgwYGgoKKnFN//QWA4Y038OvcWW376Sc1wcuOHRhuuw0/J59XiunZE+bNw3f1aujfn1q14OqrVc9sk8lAq1YwfLiBN9+E8+c9/3/19GlVpteiBdSqpbZlZanJNHJyYPjwkmNk+uxvI0YYCAhw/jeqf4l37JjnH6uryWuTcBX7c8mV55THOrX6+/vTrVs3lixZYt1mNptZsmQJvXr1cnqbPn36kJiYiNlsq23dt28fcXFxToNFwvX0asGu2kb1T9HHB/77X/r0gauv9WEDKlvItGothYUwd67af/x4u4MsX66WvXoVD8ZYGiqHnT+CLwUcrP6KBwf6jGCBJsssaTW06XWDBmq5J7cxAI05ROPG0KxhAXGcVFfecQf4+GDYvZvgEqbY+PBDtUyIskQK9aleLwTx8RAcrHoIHFClQHrzZ30CPwfZ2ZhnvADAW2FP0aJdDX/N0c/tjAyA6m18benImUyMPhGi1yjW9Brgk0/U61NqqnqXe7SaUv0tXfbPUZu4ONceWn8NOH5cLZOS1FIPJpemTRvYvdvWpxWwBvPjMtRMaTt2qJ6uQghxsTlzRrW0KNbw+uBB9WLr64vDNy8RESoS/8038PDD5buTSy9VS0tmEsB//mO7+rbbVD9tgLNnK/gAXOTgQTWmpk1Vo+qEBIiKUv9/OnRQD/vSS1VW95NPlnwcvTqvtAT2hpYKPGl6LYT7eXRqn8mTJ/Pxxx/z+eefs3v3biZOnEhWVha3WsqVbr75Zoem2BMnTiQlJYUHH3yQffv2MX/+fGbMmMF9993nqYdwUUlNVZM7ALT48UW1csMN1k8gL74IG33U/NwrX13D4sVqquroaBg61O5AK1aoZd++xe8kLg4CAzGaTTTkCAcPOs5IWt30WdICCy2zpNnPJlWD6NkFu3IaAypg1KgRNA85iRGNfPxIj20JffoAUHfDhmLHyMiwzf7a2KR6GFln7rgQGI0qawKskdDBg9XFuXNVjwIHH3yAf8ppkmjM8SETysom936WDCOOHIG//mJU7o8YMFdPwMjybvYs0dY3uN5CH8+5c3YBkOBg+O03FTk5fly9i62G6Ej+yXOAyjCqroDRMUvLMj1g5LTUsjwsAaOgY/sIDVUzX+7dW7UxCiFETZScXELASO8f0qtX8S8co6LUe+byfuuvB4xWrFDTVqLeozRvDpGRcPPNWL+A8UTAyGSyZTwlJansIb0t7fHj6ksFk8mWxfvCC/DKK8WPc/Kk+oLCYIAhQ0q+P3dOYCqEcOTRgNHYsWOZOXMm06ZNo3PnzmzZsoWFCxdaG2EfOXKEkydPWvePj4/nzz//ZP369XTs2JEHHniABx98kMcff9xTD+GisncvRHKen4NuxO+XH9TGKVOs1zdvDv0fVQGjyH1rueUWtX3MmCL/H/UMo379it+JwaC+qgCacYCMDPVBzl1SUsCICb+CHLWhhmYYRUaqz7yHaAyogFH9+hCaqj49Hqc+B5KMMGoU4Dxg9NVXkJkJ3Vuk4Z9qSb+4kDKMwBYw2r0bgCuuUM/d0aMOX+qpvOoXVZD0/3iKPgMvgNRhPWD0yCMwZAg9X76OW5nNjh0qGOBSdgEjb8swql3blibv8FpTu7aa57dWLfXO1zLbjSvl7D4EwBmfekREuPbY9eur5bFj6ny2nOKVDxi1Uv2VDEeO0L2den3curWKgxRCiBpIL0lr2LDIFXrAqLTIR3m1a6e+0cjOVr0BUIlL69ap1/PYWM9mGH3yCWzbpv5FLlyoMvRPnVKV1qtWwa+/qv89x4/DSy+p20yZAh9/7Hgc/Tvkjh1tpWzO6M/1sWNOvtATQlQrjwaMACZNmsThw4fJy8tj7dq1JCQkWK9bunQpnznkxEOvXr1Ys2YNubm5HDhwgCeeeMKhp5GoPufn/cs2OnJVzreqFO3ll9UrvJ2e96vfXzt2kp2ssnQcytGOHFE/Pj4qd9UZS1latwhVJuTOsrTz5yGELNuGGhowMhhUhoEeMGrEYfx8zNbSmqPEk5gIXHklADFbt9pSEVBZXR98oNYnX2HJLqpb1xZkuFC0UTOl6Z+mAwNB72M9e7Zln/x8lUKenEySoSlfcLPTWGeNY/+3a3mXNtbvJwoLVZsxV9LOeG9Jmo+Pig2Byoh00LChiniDrb7Whcw71BN9LLK9yzPW9IBRTo4KEh0/rl4XWlW2r3Z0tIqmahqXNVKvzRIwEkJcjPSAkUOGkclk+6bJFQEjoxH0SRfsvrCoVUsFi8BzGUZpafDUU2r9mWdg2DCsX3qEh6sEq9GjbZmuU6aA/t3+PffYvsCA0r9DthcXpwJmhYUqK0kI4T4eDxiJGuLUKS57dSTxHONMRHNYuRIefbT4fvXqQXw8PpjpzgZatlT/OKy+/FItu3YtORhjCRj1CFYfptwZMEpJsQsYGY3gpGF6TREfD8dogAkjgeSpjoSWgNExGqi2Pa1aYe7bF6PZjNEaIVE9fLZuhcAAjatMlmyyCy27CIoFjAAsFbH8/DOkb0pUZXuWr8Smav8jJMKPDjV3MhKbxx5TjzstTfVWAAaY/iaIbDZtcu1dmU57b4YR2PoYFQsYAXs6jVUrP/1kLQtwlYD9OwA4F+v6GV+CgmyPy2RS1Q1//GH7oFFhBoO1LC2hlupjtGVL1ccphBA1jdOStI0b1beOERFgN/tvlehlaSVkuHoqYPT88+o+27SBiRPLd5sZM1QczWyGH36wbS9vwMjHxxaAkj5GQriXBIxE+cyciX9BNuvpzk9PbS45Owis1826Yw1//mk3K8KRI/C//6n1Bx4o+faWaeyHnvuaYLLcHjCqjaUupVatMqc99WYNGkAhfhzD8h/20CFrFtFR4vU+z5jvvBMA46efWvu0fPEF+JPHnw1uI+gNS7+qa6915/DdQw8Y7dljbZbVrZuaFKtf7iICe3eBDRugVi1+u+1n5nIDffqoNy41no+PKskLD1cPOD6eAHMul/KPy/sYmS0ZRjnB0Xjj/AROG1+jXg+6T+7PSWLVBwG93MAV0tIIPqf+HrObVM8UwW++qZLjtm1TX3wPG1bFA1oCRq19VMBIMoyEEBcbTSuh6bX+/+Gyy1QqjCvoAaNVqyA3t9jVesAoJcV9ZVr798Nbb6n1114rf0smg0FNQAqqXA3U91X6/5HyZG7rZWnSx0gI95KAkSjbmTPw/vsATOM5mnUqo0zrkksAaHZ2reOMPP/9r6rF7tevSJ1aESNHQrNmhOaf5xY+d3tJWh0saQZ6974aSm98rZelkZTkUJKmB4y0a64hLzwcw/HjMH8++fmw6Jtz/M1l9D/wmcq0evNNeOghdz+E6te8uQqcZGTAiROAelPz8OX7+Y4x+OdlQv/+sHUrs89fBaiLFxyDwdrPahTzXR4wMqaorz/Ntb2s47WF3geiaIbRjz9CVq4P33O92uDKsrRduwA4Rn2imka67rh2brhBvaF3WUacJWBUf/083mESH56+kvNfLXDRwYUQwvudPw+5uSogpL/PAtTMwWCbPcMVWrVStVh5ebB6dbGro6LUUtNss/xWtxdfVJPLjhypJhGtiMsvV283NmxQZdKrVqmxN2tGuSZ+0AN0kmEkhHtJwEiU7dVXITubDXRnIcPL7oGhZx+tWWOb4uyvv1QOqtEI77xTeuaOjw88+CAAD/EGSQfMVX8M5ZCfr5o8WwNGetpBDaWn7loDRocOOZSkrV6tUoRzzAEcGTRI7fPBByz5PoXvUwfTh1VokZGqm+EDD9TobKsS+ftbSyCtZWkZGdz881VEksZKerPrzcVoDeLLnTZdY11+OaACRlu3aK77trKgAN/MNAAMMV5Yj0bJGUZff62Wc7F8LTpvntNveSvF0ihqJ+0q34ja3SwBI5/1a7mPd7mSXwn8772qxkAIIS4CerAiJkYjONiyMStLtWoA1/Qv0hkMpZal+fraGkUX/f9VHXJzbeVkU6agWh28/365Z8qoW9f2EeH338tfjqaTDCMhPEMCRqJ0Z8/Cu+8C8AzTqVfP4PiNijNdu6qgz6lTKkCRmwv336+uu+++Yo2ynbr1VgpDI2jJfhrtdM832Pq3MxdKwEj/PSVh+TRqV5JWr2c8ubnw5JPQvr0vv8VZOj3/+Sft7u1PF7aQEVwHw8qVrn3z4430srQVK1Su9S234Lt3F+cC63EdPzBklD+vv67+FAIDXdeawOtceilaYCCNOELTnB3s2+ei41rexZoxEBhXyhQoHuSsh9Hx49bWTqymF2cCGqhMtIULXXOnO1T/ohoVMLr8crj+erjySn5pNYU0wgk6fRiWLfP0yIQQwi2OHFFfnjVsqNk2Llum0m4aNXJ9v0c9YOQwdauNO/sY/fEHpKerLyT79UM1MLr3Xpg5s9zHuOIKtfz114oHjCTDSAjPkICRKN3rr0NWFidiuzCfUVx6aTkSTYKDoVMntf7HHyrgsGeP+lT23HPlu9/QUPJuUr11xiW/4epes07pAaNGAafVSg0PGBXLMNq3TwXxgHd+ieerr9S3NUePGnjso6s533MIaBoN03dyhhiOf/E3tG3rmcG7U+vWavnssyqD4uefwd+fzM9+JLR5HCdOqGpKUNWW3tiDxyWCgzFY+oeNYr7rGl9bAiMHaUpUjHc2f3JWkjZ3rkqQrF8fNIx8a7LMlvbtt665U0uG0Q7a15yAUUgIfPcdzJvHzptf4jssz8kXX3h2XEII4Sa2gJHdxjVr1LJcb5IryPJ/mbVrVSZTEe4MGOlZtzfeCMbUFJUmBI5drMugB4yWLIF169R6uQJGO3bQMlBFiiTDSAj3koCRKJmmwYcfAvB2xNOAwfp/q0x6zunEiSpzIyJCfdCIjCz33Qc/dj+F+DCYJZxatK1CQ6+MlBS1rO93YfQwatRIpSuf8GusNqxfr36n/v4Y60QzbpyK440ebaaw0IfJByYBkEw0dzdbQqtrqqcRr9cZM0Y9WRERqgF0vXrw2Wc0GnsJ27aptGuj5ZVywADPDrXaVUcfo/XrAVhHT2tgxts4K0nT3xg/9pg6Lb4stJSl/fab+ia5isw7amBJmp127eALblYXvv/e6QcZIYS40OjZLY0a2WUY7d+vltXxJVuTJuqbi8JCnP1jdlfAKD1d/fsDGDcO9bqv/y/cskX1ySyHtm2haVNVxZafr95ql5mUdfIkdOtGrycGApJhJIS7ScBIlCwlBc6pGcPeSVSd7fTM2DLpASNNU1/DrFxZ4U/bhkYNWRJ2NQCmL76s0G0rQw8YxflcGCVpEREqWeapTxqrDdnZatmggTUCEhQEs2ebqFcvk8/OXcEofqcbG0m4o8MF2bLIqa5dVbleaqqasuP4cfX1Ger5eekl9S3YtGmlT+53QbAEjHqzisR1Ka45puUrxPX0sL6x9TZFM4z27VMzJPv4qFldEhJgA90pCAhRf0eJiVW7w5QUjKdOAnA6qi1hYVU7nCc0aQIr6EuSsalq/jZvnqeHJIQQ1e7wYfXmyKE9g17D3aKF6+/QYFARerD1WrTjroDRzz+rIE+bNpYigq++Ulfo36iV83+AwWDLMgKVXVTm+81VqyA/H/9jSYSSQXq6essmhHAPCRiJklmmJ8utXY9MUxCNGlH+b8KHDlWd+BISVBptu8plq+xtogJVfttcVR9TMr0kLeYC6WEEquVI/3ENHOeB12vVLMLDYerUdYSGaixgFEdpqL49ElbduqmKNX1GkgtWo0bkNGuHD2YiNy6x9qyvEkuG0Xp61JgMo2++UcshQ9R1vXqpsrSjoZZvjy1ldpVmKUc7RCNimtbAaBFYZsA08JnZkmX0+eceHI0QQriHZe4QWw8jTbNlGFkmBnA5PXPJgwEjPet23DgwHDmsGhAZDLaa/Z9/LvexigaMyqTXrgEdI9UvQLKMhHAfCRiJklkCRicDVZSo3OVooObHPHVKTQMaG1vpIRS07wJArUObcM2n15LpGUa1Cy+MHkZWvr6OQSInXcvj4zP45BMTRqMKMjnU5ouLin//SwBolr2t6n0Cjh+HkycpxIfNdPHaDCP9Tz01FWbNsvb51xPN6NVLLTcVtFcrloBPpdXEGdKKCA9XAdQ53KQ2/PWX+n0LIcQFrFjT6+RkVa9lMKhaq+qgB4x27Sp2lTsCRqdPq55DYPm/qH+rMmCAmswGVPsJ+0aApejb15bZW67PFpYvngC61lZvTKSPkRDuIwEjUTJLPfLuXPUPsNzlaDp//yo3/2s0sh35+BGSd77av07QA0YReRdGDyMHKh1AKWGau2uu0Th8WLWaEhcvn44qKNKOnVXvY2T5VnC/XzuyCfHaDKPISFsS3h13qPf/zZvD1aoi1lphuzrdkilZ1YCRJUOpRjW8dqJJE0iiKefa9lMB/S+rv3RYCCE8JScHzpxR72v1Gbus5WgNG6qpVKuDhwNGP/4IJpP6X9isGbZytPHj1RPRtav6H/Drr+U6np8f/Pmn6onUvn0ZO5vNsGGD9WK7MPVZQDKMhHAfCRiJklkyjNafq2TAyAX6DgpgJ+pDWuay6i1LO38egskisNDSvPVCyTACx4BRkZI0ew0aqL494iJmKR9tz46qz5Rm+VZwrdYDwGszjIxGWxw1Olr1rdq8GWtvoVq11GR6+muRZBgp+svKtnaWVKzFiz02FiGEqG56kCIwsJBatSwbq7scDVTjIFD1cBkZDle5I2CkV4SNHAls26a+9PD3h+uuU1fo365UoJddly4qo71Me/c6POamfjJTmhDuJgEjUTJLwOgATWnRotQ4Q7WJjYWD4aos7fjvrpq2ybmUFKij9y8KDITQ0Gq9P7cqR4aREID1675mHGDnhhzr5n//hVdfrVhlqHmtChitKuwJ4LUZRqC+MP3oI5VYOWVK8T//Xr1URhCgvlHOy6v8ndkFjOz/NGsaPdi1xdhVrTj59lsIIS4EJhPMn6/WY2KybQn01dnwWhcVZct637PH4Sp3BIz0l/b27YG5c9WFUaNsMx/rAaPFi4sFtKrs/9k77/im6v2NP0maTloKlJZNmQKylKGoOMGBuL2Oe1X0qteF4+IeV696vV5/br1O3HtdBwoiCKIgyN4CAmUU2lIKpXukSX5/fPLNOUmzmzSjz/v16uucJCcnJ+nJyfk+5/k8H11+EQD0sNFhREhrQ8GIeMdRkrYDfaLiLlI0DpXBiHV5ZB1GLoJRbm6Ly+liCgpGJFC6dIElswNMsKF6hZyY1tcD558P3HEH8P33/ldhsQAX/8mGivliI1+OMUhJkdybWOWYY4Brr/WuE48bB+xFd1SbsmTkoAYJwVJaCuzfDxsM2ITBCeEwWl7tuPpdXKx1DyCEkAThrbeAww7T8p179dKJIsphFEnBCPAafB1pwchu1wSjIUMgWUWAqz1oyBB5/42NwEMPhTdzVOUXde8OAOhcR4dRXR3w1FPOYRohEYeCEfFMU5PzaFyAvsEFXoeZThPEYZSzJ7IOo/JynWCUSPlFQMAlaYTAYIBxmDhpcss2Yt8+4MsvtYwvFXzpi5deMmL1F9vQAYdQh1SkjR6KZ5+Nbw1Wgq8N2GhvYVma43kF6Is6pGs5GHGIErt+35OlCdF0GRFCEogffgCuvhrYvl3Kk++5x4obblirLdAaJWmAVpbmdoxVglFlpeg14aawEKipkdyhfr2btDwhFe4HyI/73XfL/LPPAldeKVeOwoFyGF1wAQAgq1zGJm3ZYfTYY8Cdd8rHTEhrQMGIeKawELBaUY8UFKMrTjwxepsy9C8jYIMBuZYiVG7dF7HXaeYwSiQGDpQf9Ozs2A2SITGDabiWY7R6NTB9uvbYTz/5fu7Bgyl49FEjxkCuCqaOOwKLl5txww2R2trWYdAgCcZea2thp7SlSwEA6zEM3bpFLiO1NVA69M6d8BnKSggh8cr//ifTP/1JTo0fecSGdu0cYojN1voOI7djrL5pw4ED4X9Z9XIDBwLmPzYCtbUS8DdokOuCV18tViyTCXjvPeDss2XZltDQAKx1iHOOvKTk0j0wworS0og3T45J6uqAV1+V+V9+aVahSEhEoGBEPKMrRxs8xBhVw023ge2wwyxXbjZ/HDmX0cGDQB4cglSiCUbduslZz4wZ8W3zIK3DUK1T2iefAAsWaLvNmjWa28gT7713OKqqDDgrTwQjw5gxkd3WViIpSRzxzuBrR6ezoHGEgv6A0+K6HA3QBKOKCqC+X5gCwQkhJEaw24HvvpP5a64BMjLcFigqkhG8yYSIB9J5KUkzGoFOnWR+//7wv6wSjA4/HJrbZ8wYTaXSc9VVwDffAOnpwOzZwG23tezF160T21SnTlI3npQEg9WKrihGUxNw6FDLVh+PfPihqzCov6BHSKSgYEQ84wi83oE+US1HU+zvKTlGB+ZFRjCy291K0hJNMAIklHD8+GhvBYkHHJ3SDsdGvPuu3DVpklxQtNvlqpYnfv3VgAULesJgsGNyruPEcuzYVtjg1iE/v4Wd0vbuBZYuhd1gwDc4J+4Fo/R07VC5L4cOI0JIYrF6tUSzZWQAJ5zgYQHlLurbV2q2IokqSSsoEJFKRyRzjNRP3ZAhcDpkXcrR3DnzTLkwYjCImvH556G/uMovUgKVI1JhSIbUo+2LXNFBTGK3A88/L/MTJsj03XclZ5KQSELBiHjGIRgVoG9UA68V5jGSY5S8ITLB14cOSY5twmYYERIMDsGoL3YgA9UAJBBaHQs8laVt2AD87W9yxfHGyyrQbqtD3E0QhxEA9O6tE4y2bw/+LM3hLirIPRol6Br3ghGg5RjtSKPDiBCSWKiuaBMnAikpHhZojQ5pirw8CVGy2Zo1XYikYOQSeB2IYATIB3bPPTJ/7bWOuuUQ0DuaAKBXLwDA4ZmSY1RaGtpq45X58+VcKyMD+OQT0c8OHAC++iraW0YSHQpGxCP1vyuHUV/PV1VamR5ni8Oo98HVqK4O//pVDXCvlAQtSSMkGDp3hq2zfAcGYxO6dpWLhp4Eo8ZG4OGHgSOPBLZuNaB9+3o8YbtDxJT+/eUvQejdGyhBF1SndJST9mDDAxxndfOzpAVxIghGqgpjg9Vx9buoqG3WCRBCEg5VjqZvCOZCa+UXAeLYaeVOafoOaUN7V2kXBAJxDj/8MHD00VKz/Oc/SzOdYFEOI/V6DsFoYIo4jNqaYKTcRVdeKVV611wjt19/PWqbRNoIFIyIR2o2SIaRPb+PszY6muSdLg6j/tiOZXMrwr5+FUfS3ZzAJWmEBIHqlDYUG3D11ZLho8Lv16+XrISGBhGR/vlPaYgyebINn171NDI+fFMWfPNNCVhIEKSjmQE700PIMTp4UMKgAHzSkDiCkXoPf+xr72x7zLI0Qki8s2+fZnCZNMnLQq3VIU3hp1NauAWjoiLpvmYyAf0rVoqC1LMn0LWr/yebzcBHHwFZWcCSJRKIHQxlZZow5uYwyje1vZK0bds0AfPmm2X617/KKdaCBc1MZ4SElcQ5kydhJXmvOIy6Hdc3ylvioGNHlGVI/+kNH6wJ++rVuC/HxpI0QgA4y9LO6LkRU6fKXZ07O/OwsWABcNddwOLF0qXl44+B/715AMe//6wscOutwPHHt/pmRxIRjIANoeQYffcdYLXCPnQoFhaL6yoRBCPlMNqxA859hoIRISTe+f57mY4a5UMfac2SNMBrp7RICUbqZQYMAMyrAixH09OnjziNAODf/xZLcqC8/bYIVKNGaefkjh/h7ta25zB68UX5OCZNAg47TO7r2VMTM994I3rbRhIfCkakOVVVyKyXX52hZ8XOiKZpuMNl9O2zaKhsCOu6N24EjLAio87xa0uHEWnrOJShi4ZscNFPVVnao48CL7wg8x98AFxyCZB0+zSkHTgAe//+cnKYYCjBaFmNQzULRjBylKNVnnIeLBZxbDnyO+MaJXrt3AmvgxlCCIk3/JajWa3OvM9WF4zWrQPeeQc4+WTgiiuQ09EGIHKCUVD5Re5cdx3QpQuwaxecXTT8YbMBr70m8zfcoN3vcBjl1rctwaiyUvQzQK7F6bnqKpl++qkISoREAgpGpBn7fpNytP3IwbjTsqK8NRqdH74ZjTBjkuUblB9/NlBTE7Z1b9gAdMIBGO3yo+u8XENIW+Vwzy4aJRitXy/TO+6QfCO89x6MH3wAu8EA61tvSQutBMNxropVjUGWpNXWAj/8AADYNux857o8dSWON/QOI/sQBl8TQuKfxkZgzhyZ9yoY7d4tC6akiNWjNVAlaVu3ilLw00/A+++jf738FsWkYJSWpgVgP/ZYYC6juXOlsUT79nI1SuH4Ee5QKaHXbaUk7a23gKoq+fdPnOj62BlnAO3aye6oSigJCTcUjEgzNs2UKyalGX3Qvn2UN0aHaeLJ+OCSmahGBrqsnSNHzT17Wrze/fvlR8fZIa1TJ7n8T0hbRglGe/a4hBifcIJkbwJy3vjYYxD16PrrAQCbL7kE9qOPbt1tbSVSU8UZvw7D5Y4dOwI7Q//+e2mDnJ+P9cYRABKjHA3QXFc1NUBFdzqMCCHxz6+/ygA9L08aOnjCoPKL+vVrPfW/Z0/tykXfvs4fkr57FwKInGA0Km+PBBqZTN4/EF/87W/BuYxeeUWmU6ZISzCFQ5hLqatAFirahMPIapVyNEDcRer8S5GWBlw2cR8MsOGzz1p/+0jbgIIRaUbJYhGMmnrGSH6RjvGPTMQE/IiD6CAhegMHSn10bW3I61QXw0d2ZX4RIU6ys7UQ48WLnXd37Aj87YIDOLvbCnz62iEk11cCF1wA1NXBduqp+ONPf4rO9rYSvXsD5eiIyh6OK726z8Yr770n04suwtp1cranKgvinZQUoFs3mS9IdbypPXukMw4hhMQh8+fLdMIE730bDFu2yExrBV4Dohb88ouc/yqXEYCuW38BEF7ByG7XnR83OqwrQ4e6CjiBoncZ/etfvl1GhYXAt9/K/HXXuT6WmQl06AAA6InCNiEYffedVD526ABcfrmHBd57D6981QV/x7P4/HOp5iMk3FAwIi40NQG1v0tJWuaI2BOMBgwAzMcdjWPxKwp7HydX7f/5T2DQILlyEQKqqmSEEoyYX0SIoNIUr7tOrHgAsG0bXl08HN8UjUHvkR0kDXTrVqBnT1jfeSehuqJ5QpVg7epxnMwsWuT7Cfv3A7NmyfyUKVi5UmZHjYrI5kUF5ZbafiBbU4/c2j4TQki88NNPMlUl2J4wrF4tMyNHRnx7XOjdW9rVG43OxhJZ6xYCsGP//vDl2JSWAuXl8jLdSxw/XKq9fSj87W9yQXb3bmD2bO/LTZ8uqscJJ3i+suJwWPXC7jZRkvb88zL92988VPpbLMA//gEAOD5pMQoLtcpBQsJJYp/Zk6D56isgr0YcRj2Pj82aiauuAjZjME4x/wL7p5/JD1BhodQ8h4ASjAZ3dPzyUDAiRHjqKbl6umcP8Oc/S1/Xk08Wa3pqqixTWwskJwOffdYmsr9UCda6rAAFo48+EiV+9GjYBg2BGmMkkmDk0ilNneAzx4gQEofU1GiD7pNP9r6cYdUqmQmlRCtcjB0LmM1IKi3GsLTtqK8Pn2CgytH69gXMu7fLDdWeKxTS0oCLLpJ55SByp6lJa/elD7vW4/gR7oXdqKwE6utD36RYZ8ECES9NJuCmmzws8NFHIsABGNRBLnqzLI1EAgpGxIXnngP6QgQj82Gx5zACgD/9SRyxW7cZ8Gu3P2kuiNJS2O3B2zGVYJSfQYcRIS5kZQH/+59c1vrxR2D4cBFnBw2StliVlcCKFeImSdDcIneUYLTQ7hCMVqwQp6M3VF7DlCnYuhWorpbz5kGDIrudrYlLpzQvYemEEBIPLFokukXv3t6z5kwNDcDmzXIjmup/WprT9XPzSMkx+uCD8KxaXdwYMgSOqwEQ9aglnHWWTL/91vPJ+i+/AMXFUvt+3nme1+FwGPUxSFWBMj8nGps2AedLjwxMmeIhV91mA554wnmze7KMYViWRiIBBSPiZMUKYPFiO3pB1GrnyCjGyMzULlK89ho0gae0FDfeKN0CHnzQ9xhOYbdrglFXEzOMCGnG0KHA66/LfF2d1IXOny/fk8xMOVlu6UlkHKEOi8v295FyPIsFWL7c88Lr1slZt9kMXHqpsxxtxIjEytXv10+mmzeDghEhJK4JpBwta8cOGGw2+R3s2rV1Nswb48cDACa1kxyjTz6Rn6WWYLUCr74q8yefDE0wamm3hhNOkPOGffs8/24qe8wFF4hz2RMOwWhAqoxVYrUsreizRdjx/IyQnltSIt3PysvlWtx//+thoW++EVXJEbieUb0PWVnA3r0ScUVIOKFgRJw8/zzQHhXIgCNAWgXexiDKqfrZZ0BVughGFdtK8eqrMqZ99FFg2DCtLao3iookm9VkArIbWJJGiEf+8hfgySeBs88WsSjaJ8hRRAlGu3YbgOP8lKUpd9FZZwGdOiVkfhEAHHGETFetAuyHD5Ub69dHb4MIISREAhGMsrc7SrRGjWretqq1ceQYdStYiLw84MAB4IcfdI//+GPQmXJffy3RhB06AFdfXK3ZeFoqGCUnixICNC9La2oSRzOgXRX2hHIYGUUwirXg66oq4O5pFrS7eBL63HYO/jZ4Id57D2ho8PNER/jU3r3AmWdKLGv//sCMGWIka7bsv/8t845gcENFBS48S17kzTfD+IYIAQUj4qC4GPj0U6AHHG3qO3XycISKHcaMkb/GRmDBRhF49q6SX41Ro0Tr2r4dOP10aY/qDeUuGjAAMJWxJI0Qr9xxh1zR6tEj2lsSVZRgdPAg0DDGh2BksQAffijzU6YAEEEFSDzBaMgQ6ZZWUQEUpDsEo+JiGbkQQkicUFEhbnvAt2DUvkCiG2LiYH7MMYDBAMP27bjurCIA2k8Ptm0DTj1VohsCTMO227VKp5tuAtrtd7iLOnYE2rdv+faqsrQZbu6bBQukzVtODnDiid6f7xCMejbJ/yCWBKNvv5WYpx+e3YgsVAEALt98H6ZMsaNnT+D++yUS0h3DV1/B3qMHth/9Zxx+uJwr5OQA338PdO7s4YU+/VR21LQ04KGHxMUM4MYL5cN4/31HiTghYYKCEQEAvPyyjG9OG+I4ksXBoPDGG2X62QIReKzFcqD873/lYso558gP38MPe1+HEoyGDoX2q0PBiBDihawsIDtb5vfkOwSjxYvFww+IxfHll+Wscd8+Ods74wzYbIkrGJnNWqOg5ZsztRRsdYAlhJA4YOFCyX/p399DZowOp8MomoHXivbtnQfgKX0lx+jrryViEJs3y4nwzp3Smz0AFiyQarHUVODmmxG+cjTFpEli61+/3lXV+PRTmV5wge+a7cMPBwwG5DQUIQ8lMSUYXX+9XCs5I3el877xWITLcn7A/v1iCvpb7x/wyvBX8P29v6B87W4MefIlJF18MQxFRei39GOkVOzD2LGyL/bv7+FFvv0WuOIKmb/5ZhmzOMYto3qWYuJEMWs9/ngrvGHSZqBgFKeEM+Tt44+1A8ulx8ePYHTxxXLBY32pHCg7oxTHHy/1vpmZEuCdlCTN0377zfM6PApGzDAihPhAuYy2pAyX0LSKCsnsWbpUAn1uuklOsnNyJGjNbMb27XICn5ICDB4c3e2PBEoEW7kSUg8MsCyNEBJXBFKOhro6ZDo6U8WM+u/IMepT+AsGDZLOYV99BWlSofDX0dOBchf99a8OHUIJTeESjDp21Mq5VVmaxQJ8+aXM+ypHA+SqjeNHdAyWx0yGkd2uDSPuO9UhGLVrBwB4r8d9+N+nTfisx98xy3Y6blh/I874zwnIHdMfA36dCyuMOIgOAIC3//Q9fv3VS2OM2bOBCy+Uz+uSS7SyNHWhe98+PPigzL79trOBGiEthoJRHLJkCTBwYBJmzuwTqMPUK2++KfEkVqsI1qNy40cwSkuTH7RSKMFoP+65S2sNkJ+vifCPPup5HUowGtmvSnqpAnQYEUJ8ogSjnXuSgHHj5MZjj0k6aHGxLPDiixJC4Oj0og+8drjHEwoXwWiooyyNDiNCSBwRiGBk2LABRpsN9s6dY+dc2ZFjZFi0EJddJne98QZg262rfwpAMFq7VvKPjEbg9tsdd4bbYQQ0L0ubP1/qvHNzne/FJ47OcGOwPGYcRrW14uwBgPTNjh/8//wHyMyEYc1qnP/4GPxpz3MAgB35J6HILCcS29APNwz+CRtOvBkAMMn2nWeD1apVwLnnShbHhRdK3Zkj8Np5obu0FMcdJ6ciFgtdRiR8UDCKQz77DKipMWD69OG44QaT/yA1HU1N0rTn1VeByy4DrrlGVPEbbhA12rDX8eMSw4HXeq6/HjiAHACACTacPvagy+P33SfH01mztLp0RVmZ1sjnCONamenWTexJhBDiBVVxtWsXtCuln30mZ4ynnSZCydSpQHq68zmJWo6mGD1apitXArbD6TAihMQX5eXAmjUy7ytCx+A4mNuPOCL6gdeKY4+V6fr1uOyCOiQliT604AOdw8hXoKeDt9+W6QUX6JqfKsEonN1Qzz5bpj/9JOKHyo7wV46mcAhGY7EsZgSjQ4dkmmqywLjeMaY49VRNeVuzRq4Wffgh+uyYj26NO7FrYzmWvPsCXlo7Dsf/32RZbs4cEYX02O3ALbdIcvakScBHH7l+Trpu0QCcLqM333Q1mRESKhSM4pBnngEef9wKg8GOt94y4qSTpIHRk09KGdbKlVq2XWOjlGTdcYc4VrOypOT6hhu0ULw77wReekmuKGDvXrkzVq6a+KFfP2Dy+clOK6dhf2mzx//yF5l3dxnddpvEjQwdCvQoWip3HnVUhLeYEBLvODul6QUjQFT4GTOcNnQ9idohTTFkiGReVFYChdkOwWjDhoCDVgkhJJps2iSHq969fTcCNaxeDcAhGMUKeXnOi529sQuffCLHY5eE5U2b5EqpF2w24PPPZf7yy3UPRMJhNGCAuHOtVumMpvrA+ytHU4wZA0AEo30lsfEbowSjozJ/h6GhQQZc/foBf/+7iG3Z2WLf+vOfnc/pNiADWe0tcmPUKPk/VlVJgJGe//1PBL+0NOD115vblJXDyFGfd8IJ8mexyJiRkJYSgIxLYg2DAbj9dhvq6pbh+eePxpIlBuexVtGtmwhDCxdKvIae9u1FnD/qKLEtnnii7iLJnvgpSVO89x5gWpUL7CwXdX3IEJfH77tPnJszZsiBc9o0YOZMEcyMRuCttwDjU8tkYcdVC0II8YaLYHTCCdLWNj8fuOsuh/Luit2uOYxiISM1EiQlSbnd0qXA0vKB6J2U5FCPCp1dbQghJFYpkgZjfk9/nQ6jWDqYGwwi6KxbBxQU4IILBqFbNyDvuELABlhhhAk2adCg3D1uLF4sn0FWlhhjAMiPV7gzjBQ//SQBo0uXAsuWyQcfSDkaAAwfDps5GR0t5Ugv3g7AUzp066IEo3HJjqtDRx4p5wNZWVLrZzZLiKE3jEZxD739NvDdd8App8j9DQ1ybgHI1FMFiJvDCADuvhv4+WcZ4zzyCIsnSMugYBTHHHlkKRYvbsIrr5hRJd0bcfCglAIXFWk/frm5Ui48fryIRAMHehzTCHEoGGVkAOiZC+zc4rG/5mGHAffcI7W8t98uv31ffy2PTZvmuFCxjIIRISQwlGBUUABYYYLp1Vd9Ll9QICeTycnS4CVRGT3ace6/JhkXDRokDqP16ykYkTaL3S6nVWvWSBa8KmclsYc6Z+7WzcdCDQ3OLIOYchgBmmDkcASNO9oOm3kP0AD8hJMwAfNQ/+MipHoRjD77TKbnnqvTNfbvl1Jrg0H74QsXKSmaFSZYkpNhGXoEUlYvRd8Dy2Gz9fc+rmkllGB0pN2DndiD69gjkyeLYDRzJvDss3Lfiy/K/7RrVykJ8YQu9Fpx2mky/tmyBXjnHUfHO0JChCVpcc6AAXIseecd+ZsxAzhwQIL0/+//xMFYVCThd1OmSOq+14NqdbV2xIsjwQiAR3Vdz2OPSckeIOV3e/dKu8qHH3Y8Z+dO+UFM1HoRQkjYGDRILhqWlgL//a//5dWJ+PDhIholKuyURohQXw/87W8yxuvVS0wd48c3jyYhsUNxsUx9CkYbNsBgsaAxMzP8AkpLUQ4gVUJ24ACMDfUAgNlZFwMAtr69yNnfRY/VCnzxhcy7VIWpdXXv7tsdEwVM4+QC7yjbMpSXR3ljoA2fhja0oP584kRxIm3dCvzxh1zM/te/5LF//9txhdwDutBrhdEosUcA8MILUnJISKhQMEpAUlJEWb7zTuCYY7QQfb+o/KLMzPjzLvoRjAwGyXH6+GMZsBkMIqKlpwNYvlwWGjRI6vUIIcQH7dqJIA8A994LbN/ufdm1a4GHHpL5G26I/LZFE71gZBtKwYi0TaxWyU6cPl0u+JtMcl62Zw/w6afR3jriDeUw8pVfpDqNHerXL3YCrxUqlFqJPKpiIDcX13wi5U2HVa/A5RfWNRMPfv1VBLP27UWzcBKJ/KIwkTQutoKvDx0CTGhCv2pH4HUoglFmpua4Ov98KQupqJB1qbbPnvDgMALkKe3bA9u2SfMfQkKFghHRiLPAaxf8CEaKSy6RKonly3UuWJajEUKC5NprpfVyXZ3Me7p6V18vA0eLBTjnHOCqq1p/O1sTFXxdVQUUdxwqd27YEN2NIqQVsduBW28FvvxSLk598YVEeamuRc8+yxz4WCWgkrTvvwcAlI4cGfHtCRol6qjMIdUeq2dPDDq9DxpzuiIZFuyfvQIvvuj6VCVknneemws2UvlF4cBxzn4kVmF/kSXKGyOC0RD8jmRbvQg//UPMVZrs6Jam2jhPmSJqj6+aO+Uw2r/f5WSkXTvphg1IU6Q5cySfqnNnuZhFSKBQMCIacZhf5CRAwQiQMj4X4Z+CESEkSIxGcRCkp0t257/+5dpgwGaT7LSNG+Vcbvr02LsgHW6SkgA1jlrZ6HAYbdokihkhbYAnnpCyd4NBmm1ccIEcI667ThocrV4N/PJLtLeSeMJvSVpNDbBgAQBgXyzGF7iXpOnP6Q0GJJ8kHT2PwyLcdZcmGHgtR9OvS7mXYon+/VFlao801KN+5cZobw0OHQJGwS3wOhQuvljCh44/XkIB33lHG+N4IydHplarhNnqmDpVNmXePKk+mTtXmuX9/HNom0faJhSMiEYbEYxcsNspGBFCQqJfP4kVAKTsLCdHnIvjx0sH3eefl8fefFOu6LUF1Djq55295fKmxSJZDIQkOBUVwD/+IfPPPus6+O7USasoUVm2JLbwW5L2009AQwPs+fmojsXzZJWoXlEBlJe7OIwAAMeJYHRB7kI0NgKXXgp8842EXJeWAh06ABMmuK0zhkvSYDSioNMYAIB59bIob4x85E7BaPTo0FfUpQuwebMoOoGOS5KT5R8INBsH5ecDF14o8xkZ2r/STVcixCcUjIhGogpGdrsEGE2ahGZpfwUFctRMTpZEWkIICYKpUyVUf9AgoKlJ3AOLFklZVkqKtLM988xob2XrMWSITAt2GIChLEsjbYfCQjkGdOwoZWnu3HabTGfM8J17Rlqfujo4g5O9OowcITC200+PTbtoRoZWmrRjR/NzekfL+lHVv6B3Xj02bRKx6Lvv5OG775a8ZRdiuSQNQHEPEVSyNkdfMHJxGEXDgeYh+FrxxhvA//4nx6hLL5X7DhxoxW0jcQ8FI6Khfly6d4/udoSCL8HosceAp5+W2vPPP3d9TLmLjjgisdsXEUIigskE3HefVF5t3w68/jrwwQeikVRXa46DtoIabBUXQ+uUtmZNtDaHkFZDxUB6O4UaNAg44wy5hvXCC623XcQ/JSUyTU310vvEbncKRvbTT2+9DQsWfVmachgpwWjECKB7dxhqa/DVrQuQnCyu2GnT5Pfq7rvd1tXUBOze7breGOPQAHEY5RauiPKWAIfK7RgGR5OHaGRceQm+BiRS6fzzxYTUqZPcR8GIBAMFI6KRCA6jigqgoUG7//PPXUdsH3zg+rylS2XKcjRCSAvp21cCsP/yF+DwwyXTp62hBKOiIgDHHis3Zs6M2vYQ0loEEpqs2lx/8gnDr1vKZ5+JW6K2tuXr0v/vPJqHNm0Cdu0CUlJgP/HElr9gpNAHX6tzelWSZjA4A5WP2PMtSkpE5Hz6afm9asaePZKJk5zsJwk8etgPGwQA6FRREPUvlKlsH9qhBnajMTqZTz4cRno6dpQpS9JIMFAwIhrx3CUtO1sbne3fL9MVK6S7ACDt0QBg/nztRxRgfhEhhIQRlf9RUgLYzjxLjsvr1wNbtkR3wwiJMIEIRiedJKWqpaWM9mopjz8uwtu8eS1fl9//nepJftJJkmIeq3gSjPTn9GedJdNvv0WHbLtvY73KL8rPDz3AOcKkHtYbAJDeVCU1YVGkfZnUmTbk9pQveWvjw2Gkhw4jEgqxeQQgfjHMmgVDODvPNDRoqnQ8CkZGo5Yqq97HDTdIYfqkSeIsGj9erkB8/LE8vns3sNJRb0zBiBBCWkyXLjK1WIAD9o7AKafIHf/7X/Q2ipBWoJnoUFvbrENgSgpw9NEyz25pLUNdG/QzPg4I1SHNa+C1EowmTWr5i0USJRitWAHU18u8vkby5JOlXV9hIbBune91xXh+EQDk5adhHxxCyc6dUd2WnEoRjKy9+0VnAwJs/kOHEQkFCkbxyKOPIuncczHi1VfDZ8FUZzqpqdrRJN7QHywPHdLEoDfekKCRyy6T2++/L5/bjTcCjY1SNjFgQFQ2mRBCEgmzWTsUFxdDa8+i+jYTkqC4CEbV1TLQdnSm0uPIHqZg1EKUQyLY5rie8OkwqqwEFi6U+TPOaPmLRRJVCrV6tUxzc13dLmlpwMSJMv/tt77XpR5XzQtikMGDgZ3IBwDUbtoVte2w24FuNdvkRv/+0dkIVZJGhxGJABSM4pFRo2A3GtF73jwYn3wyPOvUB17HYveHQNALRkuWyBG8f3/tktGf/iS12OvXAw88ILkaycmSUhuv75kQQmIMdcgtKoK04TGZZADD1lAkgXERHdavl3ORZcukZaIOCkYtp65OM9Aop1FL8CkYLVggAdADBkRPDAgU5QayWmWq8ov06MrSvLJzp/b41VeHbfPCTYcOQGmqlKXtW7YzattRUwP0scvvm3lQbDuMlGBUUSG7NSGBQMEoHpk0CbZnnwUAmB54QJL/AmX2bODvfwfKylzvj+fAa4X+YPnrrzKvQlcB+WVxBP7h3/+W6f33a32gCSGEtBiX4OucHMn9AOgyIgmNi+igF0dVaY+DceMk2mv3bslRJsGjd0eEQzDyWZL2228yVUpfLNOzpwj0Ck/n9GeeKdNly7T2cO68/DJgs4kbafDg8G9nGKnPE8GoemP0vkyHDgH9IQ4j8+AoO4z8CEYdOmjz5eUR3B6SUFAwilNsN9yA7eoqwRVXaN2+fPH881J//dxz0l+xsVF7LJ4DrxV6wWjRIpl3t4Nffrk2P2QIcM89rbNthBDSRlCDLjUIY1kaSXRsNm1/byYYuTnrMjKA0aNlvs26jGy2Fj1dn78S8ZI0JRip8KlYJinJ1VXk6Zy+a1dgjLSj99jBsrZWohwA4Oabw7+NYcbYNx8AYNsRXcGoH+R7bugfZYeRn5K0pCSgfXuZZ1kaCRQKRnHMhiuvhG3yZAmsvu46zYLqjs0GTJsG3HablGklJUk99tSpWgZSIjmM9u7Vup/pHUaA1J936SIh2dOnw3eLCEIIIcHi4jACpCzNaJQg1igHkxISCfbvl1Mwg8FxoV8vEm3b1mz5Nl2WtmCBjFjffDPkVYTbYeRVMLJageXLZf6oo1r+Qq2BvqW7p5I0QCtLe/NNGUPo+fBDsZ706RP7Id8AMoc6OqWV7ozaNlQVHkIOHDul/vNvTZTDqKZG/nzA4GsSLBSM4hmTCdbp06Wl/Nq1wLvvNl9myxZgwgTAUcKGxx8HZsyQs5rp08VtVFkp3mggMQSjuXOlwL1jR+Cww1yXSUmRcrWVK4Fjjmn9bSSEkASnmcMoL08bIX/+eVS2iZBIogSH3FwJfvflMALauGC0cKGEgs+YEfIqwukwqqvTOrI3K0n7/XfZ1nbt4ie+QN/VzNs5/aWXSgD2kiUyr8Js7HbghRdkfupU1/K2GKXL0fkAgE7V0XMYNW2R7/iBpDwgMzM6G9GunTQuAgLOMaLDiAQKBaN4p1Mn4B//kPn775cfNkDSAB96CBg+HPjpJzmIfPCBlGCdcQbwf/8ny02bJld6vvpKbieCYKQuNx1zjFzVdqdvX2DkyFbbLEIIaUs0cxgBwCWXyPSdd8LX3ZOQGEHt684O5n4Eo2OPlet2f/zhPUYmYVHnqS0IcHJ3GLXkkKKE7bQ0rVTHiYp7GDMmLsQTAK6CkTeHUf/+wDffiMv+q6+AKVNkeumlwIYNQHo68Ne/ts72tpA+J4rDqKP9IA7uqvKzdGSwO77jJRlRKkcD5IASZPA1HUYkUCgYJQI33SQiSEmJCEFz5wLDhgGPPCI5RaefDmzcCPzlL9pzbr9dxKKkJO2+rCxg7NjW3/5woQ6UCvdyNEIIIRHHq2CUliZX7APJ3CMkjnApaaqpcc0R8SAYZWcDI0bIvOrY3mZQglELylP1A12LRTo+hYr633Xt6qFhbjzlFykCcRgBEmj9xRcyDvjoI8k2/fRTeWzaNNlJ44DMbpkoN0qN1Y4F0XEZmXfJd7wsO4qCEaCVpfnJMVIlaXQYkUChYJQIpKRojqF//xs49VSpme/WTez/s2Y1r6k1GICnnxZBqbZWLtGUlHhJ/IsT3AUj98BrQgghEUeVdZSU6LJt27fXwq/feisq2xUMdrt0Rletu0liU1cHbN4c+vNdBCPVFc1slunu3a5NRhyosrRHHpHreqNGSdzXI4/IaVvCtrxWglFFhVYL5oOaGoml1LuI3Ae6Lckxcgkrd0eJ2/GSXwS4CkZOy5sXzjoL+PhjqULIzxehaOFC2QnjiAPtxGW0b+nOqLx+2l7JKTuUE6UOaYogHUYUjEigUDBKFM4/XwQSq1XKsG65Bdi0SU7Qm10y0WEwyFXfnByZxjOdO2vzyclaGxJCCCGtRpcuMrVY3E5IVYnDJ5/4DeWMNt99JxXd06ZFe0tIa3D55dI9PNRMIRfBSDmKhg+X0h6bzaOb5qSTZLphA/DDD8CqVVIl9NBD0vn8zjtD25aYRwlGQEBlabfeKnrNt99q97mX0rQkx8hr4HVlpbjzgfgSjIYNkzczfrxcUPbHhReKeFdQIBeSjzvO97ghBqnPywcAVG+MjsMos1S+87VdYsRh5OcLwdBrEiwUjBIFg0E6G9x1l1yKef55KTFrS2RkyB8gl+pU+BshhJBWw2zW9Htn8DUAnHAC0K8fUFUlpRAxzLp1Mv3hh+huB4k8BQXAl1/KvF6UCAaPglH//pq720NZ2tlnAy++CPznP8Dbb4tY9OyzwIknyuMtcTzFNEEKRnPmyHTDBu2+cDqM9CVpLqxYIbam3r01FTweaNdOduqffgr8OcnJcScS6TH2EYeRrWBnVF6/w0FxGDX0jLLDSO2nfr5XdBiRYIkJweill15Cfn4+UlNTcdRRR2GZaonugXfeeQcGg8HlL5XCgNCrF/DEEyKWtFWUHZP5RYQQEjU85hgZDMBVV8l8jJelqSDigoKWd2Eisc3rr2vlTosWhbYOj4JRv34iGgEeBSOjURpR3X03cOWVIiDddpvcB4iumpDoBSM/OUZFRUBhoTavUM4IpXFEpCQtHvOLFCkp8RPSHQYyh4lglFq6q/V7KtTVoWPtXgCArU+UHUaqoc/KlT4XY+g1CZaoC0affvoppk2bhoceegirVq3CiBEjcNppp6HUxxlaVlYWiouLnX+7WtBpgSQY/RwH65NPju52EEJIG0ZdrXdxGAHSjcdolNqfrVtbfbsCRd+5ihndiUtDg6t2uXKlxDoGi1fBSJ2TeBCMvKG6crcJwcjP+bv+u6cXjJQzorfoBJEpSYvH/KI2Su6YfABAt8adrS/w79gBAKhAFlK7d2rlF3djzBiZrl3rM4CPodckWKIuGD3zzDO49tprcdVVV2HIkCF49dVXkZ6ejrd8XH00GAzo0qWL8y9P1WwS8vrr0uXh9NOjvSWEENJm8egwAqRrz2mnyfwHH7TqNgWDXjBSRgOSeHz5pbhTuneXfdZikar+YGhq0poSUTAKgCAcRvrvnl58Vs6IQYNkGvaSNLs9vh1GbYyUgaIc9sYuZ+xUq7FNytG2oT+yO0S5rK93b6kHb2oC1qzxuhhL0kiwJPlfJHI0NjZi5cqVuPfee533GY1GTJgwAUuWLPH6vOrqavTu3Rs2mw1HHnkk/v3vf+Pwww/3uGxDQwMaGhqctysrKwEAFosFFoslTO+k9VHbHs/vISL06CF/CdteJPxwXyLhgvsSUeTlGQGYsGePFRaLzeUxwwUXIOn772H/+ms0PfCAx+dHe18qLk4CICf/S5bYYLFYo7IdpOX42pdeecUEwIi//tWKzZsN+PxzI37+2Ypjj7U1W9Ybe/cCdrsZJpMd2e3qYd+1CwYAll69YKipQRIA+7ZtaApwX5aUBTOqquywWBLvXCapuhpqWG3buRNWH5/Lb7/J/wcAiork87DbgQMH5Ps5cKAVs2ebUFIS+ndUfdc7d7bAuSk7dsBcWgq72YymoUMB3TZG+9hEPNC9O8wA8lCKz1dUYfz41osqMf7xB0wAtqMfumY2wWIJvCYuEvuSacwYGGfNgnXJEti8RJSIKG3GwYOJeYxpi3jal8K5X0VVMCorK4PVam3mEMrLy8NmL2l/hx12GN566y0MHz4cFRUVeOqpp3DMMcdg48aN6NGjR7PlH3/8cTz88MPN7p8zZw7S09PD80aiyNy5c6O9CSRB4L5EwgX3JXLgQD6AEVizZh9mzVru8pg5ORlnGI0wrFuHBW+/jVofLuFA96Xvv8+HzWbAmWfuaMFWa+zdeybUKdKSJTZ8++2sthQJkpC470u7d2di4cKTYTTakJ//I0pKugIYjm++KcPIkYHbyrZuzQZwArKz6/HLB+9hYlMTrGYzZq1Zg/R9+zARgG3bNsz67jspx/RDaWkagFNRUWHDrFmzgnmLccGZFRXOwYdl2zbM9vIerVYDli6dBL1g9N13s9DQYILFMlmeb1kPYCQ2by7DrFneLzR7o6HBhEOHZF0bNszBjh0yeB78/vsYCODAgAH4df58j8/l71wMYbdjYlIG0ptqsGbGMsw6rNr/c8LEsJ9+Ql+IYFS69mccPBj8a4dzXxrYvj0GAyj65husUqH7blRXmwFMQk2NAd98Mxtmc+ACOYlt9PtSbSj11V4w2O2tHg/mpKioCN27d8fixYsxbtw45/133XUXfv75ZywNIDjAYrFg8ODBuPTSS/Hoo482e9yTw6hnz54oKytDVhx3EbNYLJg7dy4mTpwIs9kc7c0hcQz3JRIuuC8RxTffGPCnPyVh7FgbFi1qfuXfNHEijD//DOvTT8N2883NHg9mX1KvBQA7d1qaZ5EESXU10LGjvGZqqh319QasWGHB8OEtWy+JDt72pTvvNOL550045xwbPv/cijVrgLFjzcjMtKO0tClggXDGDAMuvDAJY8bYsPjhH5A0aRLsgwahad06wGJBUlYWDFYrLAUF4oD2w8GDQJcusp01NRYk1KHUaoU5Lc3lLkt5udbhVsfatcCYMWa0a2dHdbV4kvbutaC2FhgwwIyUFDu++MKKs85KwrBhdqxcGbxTYts2YMgQM9LS7Dh0qElCtA8cQNKAATBUV6Ppyy9hnzzZdXv5OxeTVPc7Eh0KN+DirFmYNmcCjjyydV7XdOZkGOfOwdV4Aw/tuqJ5tz0fRGJfMvzwA5LOOgv2AQPQ5KU+z2YD0tOTYLMZsGuXJahtJrGJp32psrISOTk5qKioaLHmEVWHUU5ODkwmE/ap4m8H+/btQ5cAW1iazWYcccQR2OaoIXUnJSUFKSkpHp+XCAf6RHkfJPpwXyLhgvsS6dlTpiUlRpjNHlwV554L/PwzTN9+C9O0aV7X43Nf+v13VOypwtSpWijt1q1mZxBuqKhch3btgLFjDZg/H1i50tymG5AmAu77kmrTfv75so8eeSSQlQVUVhqwebPZ2XDIHypkt3t3I5IcIc6G/v3ltcxmyRUpKIB5926gTx+/61OBtABQX29GApjhNfRBvGlpQF0dzEVFwJAhzRZVjZ6OPtqAtWslp6iszAyrQ3/u2NGAbt1kGFNWZgjpN2evNLdCfr4BycmO57/0kqjGI0ci6dxzvbab5+9cbJE1NB8o3IDsyt2YMMGM//0POPXUyL+urUBcrQXoi86dzSEJvGHdlxwGDMPWrTBXVwMdOnhcrEMH+a2rrDSjV6/wvDSJPvp9KZzHp6iGXicnJ2PUqFGYN2+e8z6bzYZ58+a5OI58YbVasX79enSlPEoIIYTEBMrlU1wsVzObcc45Mv3ll9CSN202YMIEpJ9xPJL27XHe/fvvwa/KHRV43aWLlnfL4OvEQwk96vTRZAKOOUbmFy0KfD1eO6Qpggy+NpulKzqQgMHXKvDaZAIGDJB5L8HX6jt31FGuIfrqcNGpE5CbK/P79yOkduqOBlfIz3fcUV4OvPCCzD/4oFexiMQepn75AIATe+9EdTVw5pnAd9+1wguXSBp7qbmHI38synTqpB1zVqzwuRigBcgT4ouod0mbNm0apk+fjnfffRebNm3CDTfcgJqaGlx11VUAgCuuuMIlFPuRRx7BnDlzUFBQgFWrVuGyyy7Drl27cM0110TrLRBCCCFEhzIJWyxe9KA+fYARI0T4CeWsvrAQKC6G2daIYw1LnFeSN20KeZOdqG5MFIwSGyUYKdEBAI47TqYLFwa+Ho+CkT47hJ3SNJRg1K6dptI4XFnffit3ffSR3K1SKY4+2lUwUgPcjh2lIRQgfU4OHQp+c5RW5TR+vfACUFkJDB2qidokPnBYSy9J+wZLul+ARU1HYc29n0b2NaurYayRfbqufZfY0RfHjJGpj5aPyskY7U5p69YBYYzaIREi6oLRxRdfjKeeegoPPvggRo4ciTVr1mD27NnOIOzdu3ejWNdLs7y8HNdeey0GDx6MSZMmobKyEosXL8YQD3ZWQgghhLQ+ZrM2mNO3w3bh3HNl+vXXQa/f/rumDN04djkuu0zmwyEYeXIYbdoU2oCUxCZWq9aK3ZNgtGhR4I4Vvw6j/v1lSsHIVTBStaMO1eaLL0Q7uuIK4MMPte+y3mFUXOzqMEpJkTJCQPt/BoNyGPXp49i2556TO/7xj4ACykkM4RBpDZs34ei9X+IoLMNftv4zsq/piFSpQTrMHdpF9rWCYexYmS5f7nUR5TCKpmD0/ffAVSNW4qoLKqO3ESQgYuJoOHXqVOzatQsNDQ1YunQpjjpKyyNYsGAB3nnnHeftZ5991rlsSUkJZs6ciSOOOCIKW00IIYQQb+hdAR5RV/B/+CHoS4wHFmnK0HEpyzF4sMyHWzDq3Fkb+/u4WEvijIMHtVLJnBzt/rFjRewsKtLEBH84BaOudqCgQG54KknzkrXpiYQXjDIymjmM9jgqS61WOAXgvn3lO6jKBt0dRoAmTCvHWDC4lKStXy+qcJcuwAUXBL8yEl3OPBO4+Wbghhuwc+pTsMKIPg2btR0rEjh+LErQBdkdYsVeBE0wWrrUq/IdCyVpex58HSsxGmfMvoW/rzFOTAhGhBBCCEks1CDPq8No5EigVy+grg748ceg1n1oiRZWZFq9EoMGyui/tLTlV0yVYKS2//jRtbgSb2Pt3BBGpCQmUeJCx45wCalNSxNHC+C7UnLFCuCJJ4BHH9U0ol6ppSKIGAy6UBzAqWauW+fjy+BKwgtGHhxGhYVyUxmyAO1/4SnDSAlG+hyjYHEpSVMNeHr3RsAt8kjskJIiJYUvv4z6m27HCoyW+3U5uWHHsc+UoAuysyP3MkFzxBGyD5eUaMnubkS7JK1hVwn+tOIuAMAYLMdjj0VnO0hgUDAihBBCSNjx6zAyGEIuSzNu1lmJqqrQrugPZ2e2lrqM9A4jALi84Q28jb9i2FePtGzFJGZQgpEj/cCFiy+W6XvveX6uzQacfjpwzz2Si1xdLWOzHnVbZYFeveCSfnvYYZKmbbEAL78c0Pa1CcFI5zCy2zUjyFdfAcceK/MTJ8rUU4aRckiE6jCqq9O+6336wHOoFYlLsrOBHzEBAGCfG9zFiKDQO4yyI/cyQZOeDgwbJvNerDvRdhiVXXk7slEBAMjHTsyYYcfatfJYdbWcErgbj+12MSSGEnBPWgYFI0IIIYSEHTXIU84BjyjB6Ntv4eyX7Q+7HTml4jBqaOe4TLp8ubMzd0sFI33oNQD0K5dOM5llAdYokZjHlzZwySVAUpK0dfe0L+3cKVflk5OBv/0NmDpVMncySxyCker+pefvf5fpq6+KUuGHNiEYKYdRSQnKi+udH0v//mIKWbRI8owAzxlG7iVpwTqMlLsoM9PReZyCUcLgIhj9+GPkFAaHYLQPebElGAFa8PXKlR4fjmqG0Y8/ovuCj2CFETYYkIFadMZ+PPaYGDFHjwbOOw+44QbXpz3+uOjMH3wQhW1u41AwIoQQQkjYUaUlPqNbxo+X0VpZGbB4cUDrbdxTiixrOWwwoH7yn+TO5eHLMXJ3GOWUbAAAZNSwJK0Z33wDXH11QCJILKGqjzxpAzk5wBlnyPz77zd/fIPsDjj8cOC114AXX3S4krY6BKOBA5s/6dxzRSApKwtotNMmBKNOnSTLCEDpit0ARPxJTZXqomOP1SrD9OWtZWUyrwa86n8YrMNIX45mMMC37YzEFampwOqUcahFGoz7SoDff/f/pFCIVYcRIJ3+AK8/iFErSauvh/3GGwEAL+EmNHbuDgDogx344gspQ92yRRb95BNt+xoagGeekfmZM1t5mwkFI0IIIYSEH2W0UONojyQlAZMny3yAZWk7ZsnJ/05jX2SdOV7uDJNgZLVq48YuXeSOtJ3yeh2aSlFfH/q6E5IHHgDeeguYMyfaWxIU/swkytnywQdaOLZCCUZqPObkjz9k6slhlJQE3HKLzD/3nF/HQ5sQjAwGp8uoct1OAECPHp6flpcnizc1aYPJljqMXDqkAXQYJRjpHVOxEI7fhyAz8gImlgUjZbn1IpZFrSRt/nwYtm7FPuTimexHkXKYfAEvGr0DdjtQXw9MmiQVdY2NWmnwF19o4tGqVd5XX1oqfTTcj9ukZVAwIoQQQkjYUePmwkI/BhR9jlEApQMlP4kiVNppCAxjHbb7NWswZIAFQMsuJh84IKKRweAYN27fDkNDAwAgF6XYU8jwBCd2XVcwL8GqsYo/bWDyZKB9e9l3f/7Z9TGvgtFWHyVpgDix2rWTHdSPwNYmBCPAmWNUt1k6pXkTjMxm7X9VXi7TljqMXDqk6VdAwSgh0JeltUnBSF1B2bZNlBc33B1GdnvgVeEtwvE9W4UjcfJ57WHokw8AuPLEnTjtNODZZ6VC/aabZPHXX5dte+UVbRVbtwKVlZ5Xf/nlkjF32WXiSiLhgYIRIYQQQsJOTo4Muu12YPt2HwuedprUEBQUaKNxH1jWiCLUNGCw1L21bw/U12OoYSMAYPdubVwaLKocrXNnMYXotycN9di7JcQVJyJlZVoqqddk89jEX/VRaipw0UUy7x5+vX69TF0EI7tdq730Jhi1by+iESDdnHzQZgQjt05pKrjeEyrHSBGuDCOnw8hXnSKJOzp00AlGCxZI6Hy4iWXBqFs3ICtLVCAPNl+9w8huF4GlUyftGkCksB8QS1M5OuC88+D8Anaq3IHZs4HbbgOMRuDSS6VidfNmEYt+/VV+k9X3fs2a5us+cEDTBj/+WJxKlZVywWrpUjhDtUnwUDAihBBCSNgxGAIsS8vIACY4Tuy/+cbvejMKxWHU7qghcmY5ahQAIHvrcufgUZWtBIt74LW7gFW6MYTe3YmKGnEDAbeLjxUC0QYuv1ymX3yh6WKNjTKAAdwEo6IiWchk0ikQHpgyRaaLF/t007UZwWjQIABA+13rAHh3GAFajpFCDRzV/5AlaURPdjawFiNQ366T7HfLl4f3Bez22BaMDAbNZeTBdqsEo4YGqSr+6COgogL47LPIblbx72IRrE7qIF0Q1Rdwh2tTiawsaUAAALfeKtNzz5XYQ8BzWdp330kpWo8ecoiZP19Wn5kJHH00cOSRoZ8btHUoGBFCCCEkIqj8X5+CEeBaluaDQ4eA/Fo5+e19muNkWHWDCUOOkXvgtdNO4qBiK4OvnehP8OPUYeRLGzj2WClXqq7WKsi2bpUcncxMNzeMyi/q00fqp7wxaJAM5A4d8qlwtBnBaPRoAEDPUunk5Esw0juM0tLkD3B1GAWTW+JSktbUpNXmMPQ6IejQAbDDiF39TpE7wl2WVl7udC3FZJc0AL5ah2ZkaIcqFa8GALNnR3aTdq0RwShnQAekpsKrYARIF0pAvp6AdE078kiZ9yQYqdOHa66RUuLcXHFQqVI7mw1YsiQ876OtQcGIEEIIIREhIIcRAJx1lgykV66U4BgvrPmpHF0hqk6HY5oLRn5yPv3STDByOIzsBgMAoHYnBSMneodRAgpGRqOWx/799zLV5xc5dgnBX36RIi1NC81RViUPtBnBaORIwGhEp8ZidEVRwIKRckcAmmBktYoOFwiVlVrYb58+ELHIbpd/qn7lJG5RAs6WLifIzLJl4X0Bx49FuaEDGpHidLzFFD4cRvpdvbZWqw799Vfv+UAtxW4HyraKYNTnyA5ypzoe7trVTPEdMwYYMULmBw4ETjrJu2BUWyth1wBwzjmy3ObNwNy5ckoxdao8tnFjmN9UGyFowaipqQnvvfce9ik/LyGEEEKIB9T4WRkwvJKbC4wbJ/M+rgTvnC1XSg+k9dBG1UowWr8ew/pJ7VBLHUZdu0LatTiEgIO9RgIALHspGDmJ05K02lpNt/BnJjnjDJl+/70MdkIOvNZz2GEypWAEpKfD7lB5R2FlwIKRfnCenCzxUEDgwddq1+3UyfFZqyfm5EhZIYl7Ojj0iMLkfjKza1d4X8DxY1Fsl6sLMSkY+XAYAdo2GwzAp5/K4aupSUq5IsGmTYCpSgSjQeMc/6AePSScyGJpduHBYAAeeghITwceflhuOyrQsWkTUFOjLTt3rmQV9e6tiUwdOki1e48e2jE7gJhE4oGgBaOkpCRcf/31qGdvWUIIIYT4IGCHEQCMHStTH8mUVUvlxLeq1xDtzp495c9qxbiGBQDkYvKTTwKPPgosWhT49ro4jLZsEdtCdjYah4yUB4JtxZTI6EsI9u/32IknFlGVYCkpmjDjjRNPlOUKC+UivV/BSNVg+sKR2+MrTCNhBSM1wlOCEQDLMBkBjsYKvxlGI7AGD+BR5HZwDTDu1Uum77wT2GY065DGwOuEQzmMdtgc1plduwLqwhkwuvwiQBOoYgrlMFK/ZW507y7TW24BjjpK+k8AmlMn3Hz5JdABIhildXN8YCaT9gX2UJZ23nly2FB5Rl27yu+zzQasW6ctp8rRzj3Xzf3pQB2z6TAKjZBK0saOHYs1nuLJCSGEEEIcKMGouDiAzmXqsqAXwchuB8xbxVqfPHyw9oDB4LSCDNgutUNFRcBddwEPPihlRSoDwR8ugpFOHUjLFytKckVpUDkpCY3eYQRoH16Mo9cGPA0s9KSni2gEiMvIq2CkLHSBOIyUYESHEQCgLF9yjMaZVzpziTzRLc+K/+ECPIoHcXr91y6PPfigTJ94IrDBbrMOaQy8TjiUgFPQ5BAjqqoCr1kMBJ1glJnp6KoZa/TuLWWwDQ2aGGO1Og+CTz0lF1Yef1weOv10mc6eHV5tTaEXjFwsWUq5df9N8YJ7WVpTE/DttzKv4hDdOfxwmRYWSrg3CY6QBKMbb7wR06ZNw3//+18sWbIE69atc/kjhBBCCOnQQctJUF3HvTJ8uEzXrfN4tlpYCPSqFYdRzvFDXB90CEbtFs7G008DF14IXHGFBHtWVLheifSFS5c0nTqQ2VcGkp2spTQZAfL/USf3aqQUJ2VpwWoDqiztf/8Dtm+XeRfByGrVHohASVokBm5Rw4NgtLuzOIyOtK/w+Wb7bpiBfpCe3/nY6fLYhRdKIC4g3e387YrskJb4KIdRSWW6FnQVzrI0nWAUk+VogLh31PFG5RjdeqvYdGbMwPDhwB13aAHyJ54oJZ47dwZQRh4kO3cCq1frBCO9JctH8LUn3AWjxYslhqxjR+C44zw/Jztbc1SFmnHYlglJMLrkkkuwY8cO3HLLLTj22GMxcuRIHHHEEc4pIYQQQgigVen4PQEdMkROcA8eBPbubfbw0qXAYIhglDxisOuDp5wiLV+2bcO0s7fh88+Bd9/VTh5/+y2wbfXmMDJ1lYFkLkqxe3dg60po9u2TjCejURP64iT4OlTB6LffRM/o3NntuYWFUo6XnKyVVvhCOYx27pTP0ANKMLJavS4Sn3gQjH43j0ATTMhp2udzH+rwzrPO+S7W5seHZ56RXXH/filf8WV4a1aSpnYKdkhLGJQecegQtETncApGDpdOTAtGAFw6QRQXA6+/Lgeyu+9uVqaWkaG1rQ93WdpXXwGAHR0N4RWM7HZg+nS5PXmyb6cXc4xCJyTBaMeOHc3+CgoKnFNCCCGEECCIHKPUVOdg2uDBErT8Nyt6wtFBrW9f1wczMzV1SLW0AnD00TINpJVuXZ1mVe/aFa71R7maYBTu7NS4RLmLunfXBmNxJhgFqg0MGOC6u3nNL+rbN7DA5Lw8SWm22bza7nR6SuKUpdntHgWjXfvT8Tscg9oVKzw/d8UKGBYtdN7MaWy+r6WmAp99JoPeX34B+vUD7rtPup+7w5K0xEc5jCImGDkUyX3Ii23BSOUYbdoEvPqqhEsD4nD86KNmi+vL0sLJl18CGaiB2e54fb1gpJTbAAUjFXy9YQNwzTXABx/I7csv9/08VZZGwSh4QhKMevfu7fOPEEIIIQQIMvja4VYxrF/f7KHti4qRBCtspiRd33sd6kxXJxipxmuBOIxUtk1qKpBlqNJGlW6CER1GcK3pUe2rErQkTReRBcBHflEggddqhX7K0oxGET6ABBKMGhqcjob7H2/nPB7s2QOsgOQYYeVKz899VtxFB5OktCi7trnDCJCPdd48CfCtrZVsloEDpVW4wm73UJLG0OuEQ+kR5eWIqGAUNw6j1atFMAK0iyv//KcmIDlQP6M//QT83/8B773X8vK0oiL5DjrL0ZKStAMcoH0RA8ww6tlTSt2bmoC33pLj5fTp0hHNFwy+Dp2QBCMAeP/993HssceiW7du2OX4Aj733HP45ptvwrZxhBBCCIlvghKMHMHX7g6jpibg4FpxFzXldffs5FCj+p9+ErsQtMZr27Zp3bG8oS9HM2xyhBx07Spnpo6BZGfsR+Eupl47T+zz8zXBKE4cRqFoAz4FI7VjB5JfpGiLndJ0qff/eTEd110n83v2ACvhsAx4chjt3SvWIQBLJj4EAOjoRTACRCxasgSYMUPGymVlUrH6ySdSPXj22fKZJiVpOgIdRomHchjV1wOWro5S0QgJRjHZIU2hHEbr18t+3qMH8N13sq8XFEjtto7DD5fK2vp6qVqbMkXu+/nn0DfhzjtFqD1phK4cTd9xQAlGhYXNBCxPGAxaWZrZDHz6qTiN/EGHUeiEJBi98sormDZtGiZNmoRDhw7B6rhikJ2djeeeey6c20cIIYSQOEaNowO6SulFMNq4EejcIIKRuW9Pz88dOlRKpOrrnWe3HTpo58tLl/p+aRWb1KULtEuQ6gwzJwcAYIINB7YeDOCNJDhxLBiFog2cdBKQkiLzYRWM2lKnNIdgVGtIhw0m/PSTfHSFhW4OI/fg65dfFsX4+ONx5iuTAQDm/UU+A7INBuCss4Bly4BzzhFz06WXysf+3XcyyHz+eS3sl4JR4pGVpWkS1Z0cymC47KFNTc4rEDHvMOrf3zXYZ+pUKYm99165/cgj8gVxYDBIwP8dd0iJ1/Dh8nYvvzy0JnOzZknlm9EI3HeDh/wiQMp0U1KkTLewMKD1Tp0KjBkDzJwpofeBoMxW+/aJkEwCJyTB6MUXX8T06dNx//33w6S7yjd69Gis92AjJ4QQQkjbRI2jy8qan3BWVMgJn7NVvUMwwtatMOpOYpcuhTO/yNDTi2Ckrx3SBTAEmmO0YIFMhw2DVrPSv79MzWY0ZsqooHYn26S51PR07SrzCVqSBgDp6cBrr8kVd1Xm6CQUwSjITmkJgUMwqrJr+UVvvCEOo3UYDrvJJP+cPXtcnzdrlkyvu07b1yyWgEZ8GRky+J02TW7X1gLHHAOsWQPceKNuQYZeJxxGo+giAHCofZhL0vbvB+x22AxGlCEntgUjs1krl01LA669Vuavv17E/sJC7TvmYPRo4MknpRzt11/lZ7CwUJ4STNfGqip5DgDcdhswKM+LYGQ0ajlGAZalnX22CMITJwa+Pe3aaWYmlqUFR8ih1566oaWkpKCmpqbFG0UIIYSQxCAzU4sc0pel1dZKG9/Jk4GPP3bc2aULkJMDg82GLN2VxmXLNMEI3gQjQBOMvv1WOlchsBwju12eAogzwTmw0OUy2jpJfkrjXj+1bW2BBHAYBasNTJkC/Oc/rpUUaGzUxLNQS9K8jMASVTCqRjvnZ//66/L+6pEG2xCHdUufY1RVBSi34fHHSyc61SI9wP3NZAKeflpCdz/8EFi4UHMaAABqauRgBNBhlGAoXaIsw3EcLy11liu3CEc52qHkXNhgim3BCNBskVdcAefGpqZKrSbgU7hu106+NyaTlH6pgOlAuP9+EZry88XI5Eyg91TDF2SntFBhWVpohCQY9enTB2vWrGl2/+zZszFYeb8JIYQQQtA8x8huB264Qa70A8D8+Y4FDQanyyhLd+K4dCnQC45yAl+C0YQJMtIuKJAaFIvF6TBatqxZF2EnGzeKRuQ8h1aCiE4wSuoqg8nUqlJ9HEvbw2bTBDW9YFRW5hTpYhWbTcuyCos2sGWL1GtkZUk2SKD06ycjsKoqr86sRBWMapCB//xHzELKcZidDZjGOsrS9FbA5cvln9arl/b5du8u073ec4w8cd55wJ//LGYGF1SoVVqaaxAviXtUjlGZtYPWmS8cZWkOwagsSa6ExLxg9NBDwN//Djz2mOv9ykHrpVujYuxY4OGHZf6668Rt6c9ptHkz8N//yvxrrzm+WjEgGDH4OjRCEoymTZuGm266CZ9++insdjuWLVuGxx57DPfeey/uuuuucG8jIYQQQuIYVYFz773AF19Is5b33tMed8kXUoKRQ5SoqpKTu4AcRllZ8gLJyWIpuOIKDDnMisxMGa96u6qo3EWnnCLlRy6CiIOkbuyUBkAEjsZGETx69JDRUnKyPKaSw2OUgwc10dARS9UylPtl2DA365EfUlKAvn1lfvNmyRB56y2pi3RsYKIJRnu3KMGoHc49F/jrX7XHevaE2A0BYM4c7YHFi2V6zDHafSEKRl7R1ygG8z8kMY+zU9ohQ3g7pekCr/WvE7MMGQI884w0cNAToGAEAPfcA5x5phi0rr8emDTJt8nvzTdFVJo8GTj1VMedSjDypLCp39qWtmTzgxKM6DAKjpAEo2uuuQZPPPEEHnjgAdTW1uLPf/4zXnnlFTz//PO45JJLwr2NhBBCCIljbr9dBoW7dwN/+pOWH3L77TL9/XegstKxsEMwau+40qhycHsbHYJRr16+X+zUUyW4xGwGPvkEpptvdHZL81aW5lKO1tSkDUZ1DiNlSWnzgpFyX/XsKWGqBoOWLRPjZWlKG+jQQdO4WoTK7Rw+PPjnKhV14ULg5JOBq6+WdO2ePYHbbkMPo3yWiSIYrfpFBKPkTu2QnS1vV+kzPXpAG1WuWaO5fpRgpA+OCncJJAOvExblMDp0CBERjPZa48Rh5I0gBCOTSToPPvus6N2zZ4uOW1/ffFmLRbsgpCKTAPh2GKnv+I8/BtQpLVRUSdrGjcHlMbV1QhKMAOAvf/kLtm7diurqapSUlGDPnj24+uqrw7lthBBCCEkABg0SI8WDD0rZFyCdTZ58UvQfu13XUdsx+M7auROw27FsGZCMBuTaHINIXw4jxeTJErhgMACvv47JA+Wqpafg69JSTUiaPBkiFlmtoiio8CXARTAKZ3fmeOOXd0XIa+iWr90ZJzlGYc82Vg6jUAQjlWP0z3+KMJKVJSPc4mLg+edx8aq7ASSOYLRxqQhGHXtKaVCfPppG1KMH5PulemXPmSOlaOqL2RoOIwZeJxxOh1E5IiIY7W5IEMGoqEjL8fKB0Sjh1atXS5TYrl3AL780X27mTPla5eVpsYIAfAtGxxwjts/ychHRw8Err4gYX1HhvGvQIHkfBw/GvCE2pghZMFKkp6cjl6o8IYQQQnyQni45CJs3A++/L1cgDQbgqKPkcWdZ2uDBsCclIbmmBigsxNKlQHc4Boepqc1t9d447zyHAgScXfQKAM8Oo++/F8HqiCMcY1G9g0YfeELBCACw5pudAIBdhj7anXHSKS0gM8m8ecBdd+ksbz4Ih2AESMjXsmXirHHkjHSuLgCQGILR7t1A2S4RjLr017qkPfmkiEaqkxJOO02mP/wg+VDl5ZItpLonAnQYkYCJtMOoyB7nglHHjtqHVFAQ8NMGDwbOOUfm3RqsAZDqWkAaBZjNugcOHpSpJ8HIZHJYfAF8803A2+KTF18EfvpJjukOUlO1uCR9Ew7im5AEoyOOOAJHHnlks79Ro0bh2GOPxZQpU/DTTz+Fe1sJIYQQEuf07g1cdpmMAwEPglFKinMwvf79dZgxwy3wOpicEUftW/5PbyMdNdi6tXmTHJdyNMBjfhEAF8GorV6ZrKkBMkp3AgCKkvO1B+LMYeRVG7DbgWuuESXjnHM811soDhzQXC4qGCMYTjlFBmuTJsnOf9hh4mpzZPlk1Yj4lgiC0ezZQDuIYJTWWROMhg0TbUgZi5yC0Zw50s8bAMaMcR11htthpMrfKBglHJF2GJWgC1JStN+yuCSIsjQ9kybJdOZM1/uLizUR6aqr3J7ky2EEAOeeK9Ovvw5PvZg64LsdK1R8XBAaWZsnYMHooosuwrJlywAAp59+OgoKCpCRkYGTTjoJJ510Etq1a4dt27ZhzJgxKC4uxoQJE/BNuBRCQgghhCQkqovZ0qXaOaJ91CgAwLz/rERTE3DR0QEEXnvi1FOBvn1hrKzA1emfwGZzzdRsaJABK+BBMNLnFwEugpHqtNXWWLsW6I2dAIAdyNceiBPByK82sHmz5jBbsEA67TU1eV5W5Rf16SPlZMGSny+d5WbOdB1AOcog21UVA7AnhGC0fr0mGDm7VXli3Dh5fP9+4OWX5T59ORoQ2dBrklBExGFkt4v7DcBu9EKHDnGelR6iYDRhgui427a5OnXee08quo85xtVECcC/YDRhgqhvu3fLj01LaGoSUR+gYBQGAhaMLrjgApzlOJsqKyvD7bffjoULF+Lpp5/G008/jV9++QV33303ampqMGfOHDzwwAN49NFHI7bhhBBCCIl/jjxSspNLSoBChy5UMVBabA9rWIHjjgOuPSNEwchoBG64AQBwk+ElAHb8/rv28K+/Sve0rl11LgclGFAwasbKlZrb6486Xfh4opSkff+9TAcMELfP119LvZSnq90tKUdTmEzN73N8lkmWerRHRUIIRhs3BigYJSdL5gggQSlAc8FIiZP790u3vpZCwShhcXEYOZol2PfuhbXBiwgcCEVFwL59sBuNWIsR8VuOplCC0fbtQT0tMxM4/niZVy4ju10rR/MYa+xPMEpP11yGX38d1PY0o6xMm6dg1GICFozOOecclJWVobq6Gp999hkuvfTSZsv8+c9/xmeffQYAuPTSS7HFocASQgghhHgiLU0bcy9dKlm3d34udWpHG5fhqy/tMBcH2CHNE1ddBaSm4rCa1TgKS10EI4dxGscfr4sr8lOS1gGHcKg0DAPVOGTlCjt6Qv4XGyt14l2cOIz85hsrweiGG4BPPpGd4s03dYnsOlrSIc0XaWlA+/YAgK4oTgjB6PffAxSMAG3AqNB3SAMkGFeVqIVDoKRglLC4OIy6doXVZIbBasULd7fAnbZyJQCgsvsQ1CE9/gWjfv1kGqTDCADOPFOmqgTtvffEwZuRId1QXbDb/QtGgBaO1NIqJfW9BrwKRo5GrCQAAhaMbrzxRpxwwglo164dUlNTsVi1utSxaNEipDran9hsNuc8IYQQQog39DlGL78MvLdmBOqRgmxbOXIqtmvWo2AdRoCEZF9yCQDgJrzkIhitWiVTp7sI8F6Slp0Ne1ISAMBe2jYtRgXLypAGyfVZvb+H9kCcCUYetYHqaq3lzxlnSGi6auOldhQ94XAYecPhMkoEwejAASkFDEkwGjBABCI9BkN49zd2SUtYlGBUXg7AaERZqvx+7F/RgrI0h2C0r4eUTce9YBRiSRqg5Rj9/LO4df7+d7n9j3+IA8mFmhqtvNeXYDR5sgj1a9Zobt9Q0NuA3QQjFXpNh1HgBFWSNnv2bADAzTffjOuvvx633norPvjgA3zwwQe49dZbcf311+OWW24BAPzwww8YOXJkRDaaEEIIIYmDEoy++w645x7AgmQU5Q6UO5cvl0wDIDTBCACuvRYAcCZmYuNG7e5mgpHNpr2Wu2BkNMLeSQav6TWlaGgIbVPildpaoHaLCHclyMOe/SmwWBwPqgH8gQOI5Q/GZ4bRTz9JiVN+vgRQA8Dhh8tUv9MAEtKxYYPMDxsW/g1NIMFIfXQ5qQEKRv36aa4H93I0RbhyjKxWrXSFDqOEQ+kShw7JdHuTHNOTi1suGO3KGeXyGnGLEox27w66xHPgQHHrNDZK/FB5uXQbvf12Dwsrd5HZLBYkb+TkAMcdJ/MtcRm5O4x0ZcXKYVRSIr9rxD8BC0ZnnnkmkpOTAQAPPPAApk+fjmXLluGWW27BLbfcgmXLluH111/H/fffDwC4/vrr8a1qPUIIIYQQ4gUlGG3ZIhcix4+3wT7aUX62bFnLHEaAsy13R5SjbGs5GhpkEKFiG444wrFcSYmc/RqN2qBUhyEvQXKMioul5/GaNQE/Zd06oLtd/g97DD2dqwEgo6aUFJmP4RZyPh1GqhztjDO0FFtvglFBgYw0UlO1AVc4cQhGXVAS94KRcvQFLBgBsm8CwIUXen48XA6jAwdEJAaaO5lI3KMvSduzB9jSIIJRxoHdoa/UIRhtzUoQh1Fengg4NlvQjh6DQStL27FDItneeEMyCZuhL0fzlxJ+3nkyff/9oLbHBb1gVFPj0m6yQwdt32BZWmAELBi585e//AVLlizBwYMHcfDgQSxZsgR//vOfnY+npaWxJI0QQgghfhk40BnbgrQ04LXXrDg0cIDcMX++dok4VMEoIwN2R8lJb1sBtm7VtJLevaVqDYBWjtajh2srbwcGh9LQGfvjWzD6978lcOLOOwN+ysqVcOYXqdIOp8FDXyYUo2fgdXXamKFZ9ZHd7ioYKYYOlam7YKTK0YYO9Rxc3VJ0DqPqak3TiEfUR5edFIRgdP/9IjxOnuz58XA5jJSa1bOnl1EuiWeU+8duB+bOBXZBBKOOVbtC69peVCT7pdGIjeaRsq54F4wMhhblGKmyNACYNs2tvFtPIPlFissukwD8lSudAl3QuP9AM/i6RYQsGB06dAhvvPEG7rvvPhw8eBAAsGrVKuwNV5tLQgghhLQJjEatOdK//iWmjUMDHIKRGpxnZYXWvtyBwXFS3BcF+P33IPOLFLpOafoLmHGFzQZ89ZXM//wzUFER0NP0glFltptgBABjx8p0/vzAtuPFF4Hp0wNbNgyo8UNysofdaMsWubqu79IFAIMHy3TfPteuO5HMLwJcBCNALpDHK0qTaWcPQjAyGn1nCoVLMPr1V5m6B2uThCA1VTM+fv89sBviWu1u2+28BhEUSrwYMgQllekAEkAwAlqUY3TiicCQIcCoUcA//+ljQYdWEJBglJMDXHCBzL/2WtDbBADNfqApGLWIkASjdevWYeDAgXjiiSfw5JNP4pDjW/fll1/i3nvvDef2EUIIIaQN8PrrojWo4Mzqrl1hV7YjILQOaXocZ4jugtGoUbpllCXfj2DUFcXx6zBavlw7ebZYAEc+pT/0glFdZ/lfuJyDq7DiH37wv7Jdu4BbbpFuZHV1gW55i9i/X8ogcnM9VEQod9Hxx7vma7Rrp3XL07uMIi0YdekCQBOM4rksTX1sKU1BCEb+CFdJmmrgc+yxLVsPiVmUPjFnDlAIEbp7ojC0BntKMBo1ymmYaeuCUWqqxLktWwakp/tYMBiHEQBcd51MP/oIqKwMerv8CUYMvg6OkASjadOm4corr8TWrVtdys4mTZqEX1SHCUIIIYSQAMnJAU46STeYNxphHz1aWyDUcjSFF8EoKIeRQyA4GzOwvzSUmoYY4MsvZWp0nAIGECxaVycD/16Q7A97d/lfuIzXJ06U6fLl2tVkbyxZIlOrVcJFWgGfgdczZshUX46m8JRj1EoOo+7G+BaMVIc0AEiqD6NgFA6Hkc2m7YfewrVJ3KOyaioqXAWjkKLWdIJRMIaZmKcFghEgv9lGf4pCsILR8ccDgwaJvfLDD4PfKCUYqYtOXhxGMVpBHXOEJBgtX74c1ynlT0f37t1REsNhh4QQQgiJH+x6+0+YBKN+2I7ly4HNm+Vuj4KRcpW4c+GFaEhKx2H4A2lrFrdse6KB3a4JRjffLNNZs6C1O/PMunWi7fQ2isPI3NdDSVqPHiKu2O3Ajz/63g41UAe0rnQRRjnCmglGO3YACxbIqEeVQehxF4wKC+WytNEIRKobsAq9tse3YKTK0fr1ssCguufFisNo82YZxKanO0PxSeKh1ydqOshxKwtVKNseWCmuCx4Eo4RwGKkMI9UFIhIEKxgZDJrL6LXXEHTolDrgq2M0S9JaREiCUUpKCio92MP++OMPdO7cucUbRQghhBBiHzNGu9FSwUiXYbRzp5x/duvmFpXiz2GUmYlNwy4CAAxd+mbLticK/PHlBrmKnJICPPywqCcVFcDChT6ft3IlYIQV3exy0p1+mAfBCAi8LO2337R51QEvwpSWinWtWTTOu+/K9JRTPP/f3QWjmTNlOm5c5OwFDsGovb0CqaiLW8FIfWRHHqYLYQqnw6i6OrRyFUArRxs71mPAPUkMlMMIAIaPy0B1snxna7cEedwpKpK2kA6hOKEEI+Uw2rEDaGqKzGsEKxgBwBVXyG/V2rVS8xYMymGkWqD6EIxCCkBvY4QkGJ199tl45JFHYHFckTIYDNi9ezfuvvtuXODp6gwhhBBCSJBEoiStF3YjCXL+4uIustv9ZxgB2Hny1QCAUds/iyvrxzvvAB9e6HAXnXqqWPVVFyo/ZWmrVkmLd5PdCphM6DBEBI1mgtGpp8r0hx+8n4XX1wOrV2u3o+kwstnkgwGAv/7V8xPdBaPvvpOptw5e4aB9ewkHgeQYxdFu5oJyGA3v6yhHM5slWLylZGR4LTUJGCUYsRwtodHrE+PGAZXt5XekaUeQgpFyFw0ejMakdGcQfUIIRj16iDBjsUROwA9FMOrYEfjTn2T+iy8Cf159vSYkexGMevUS7a+uTiubJd4JSTB6+umnUV1djc6dO6Ourg4nnHAC+vfvj8zMTDz22GPh3kZCCCGEtEW6d5eTWUBLqQyVLl2A1FQkweoMb3YRjA4cAGprZd5HwLb16GOxBQORZq0BPvusZdvUSjQ1AY88ApwPh2B0/vkyPeccmc6Y4fMy68aNWuA1undH917SSr6oyO1pxx8vQsfevcCmTZ5XtmqVawlcKwlGymHkIhj99JO4yrKzgXPP9fzEwYOlPKKsTJadN0/uj6RgZDC4dEqLV8FIaWyH9w5jfpFClaUpkTdYVIc0Bl4nNHqH0bhxQF2OCEaGPSEKRrrAa4NB0y3jGqNRs9xs3RqZ1whFMAI0wScYIUtdHUhK0gR/N8EoOVm7BsWyNP+EJBi1b98ec+fOxcyZM/HCCy9g6tSpmDVrFn7++Wdk6LtLEEIIIYS0hOnTgQceaPnAzmh0ik79IFkNLh3SVDmaQ1jyRudcA96Cw43y1lst26ZW4uOPAcOO7RiBdWiCCXUTzpIHJkyQ97pzJ7B+vcfn2u3SdV4FXqNnT2dFUE2NW0VQWpqIRoD3sjSVX2QS0an1StJk6iIYqf/fpZfKtnsiPV0bTL3wgly97t1bG4hECpVjhJK4FYyUw2hgtwgIRkcdJdNAuvK5U1YG/PGHzB99dPi2icQcSp8wGqX60NpNVILk0iCPO8uXy3T0aGc5WnZ2AGHP8cLgwTLdsCEy6w+1rZzjOBhUWzslGHXurF1w2revWbkdO6UFTtC7uc1mw1tvvYXJkyfjuuuuwyuvvIJFixahqKgIdhYBEkIIISScnH468OijmsDQEnSd0gA3h5E6a/RRjgbIOeh7uAJNMElZizcnTYxgswGPPw6cAnHGLMR4bCjuJA+mp2vdzb791uPzy8rkXN/pMOrZE+np2pX7oHOMVH7RKafINFoOo0OHtABwb+VoCiUOvfaaTM86S9fOL0LEucPo4EE4O1H1zY2AYKQcYV9/7Wpzq6nRBqfeUOVogwcnSE0R8YY6Tg0dCmRmAsbeIhhllgchGNXXSzA+ABx3XGLlFymGDZNppAWjYB1GSjAKpqmW/upA585SCmuzNVsHg68DJyjByG634+yzz8Y111yDvXv3YtiwYTj88MOxa9cuXHnllTjvvPMitZ2EEEIIIS1DF3zdubOWnQtAc7746XzVuTNQgq6YhUlyRzDZClHgq69E0xqYIsLM7xiCNWt0C4wbJ1MvLZW3bJHpkExNMAK0iiCvOUY//ywDLXeUYKSyKQoLWyV1VF10doZef/KJbN+wYW5WMw8owUgFl0SyHE3RpQuA+BWMVDla795AmtUhGIWzCmHiRHGF7dolobiAtPI7/ngZKD79tPf9SglGLEdLeI47TnTxyy6T22kD5PjVsSYIwWjBAilX7t498QKvFUow8uI0bTEtFYyCcRjpBSOjUVsHO6WFTFCC0TvvvINffvkF8+bNw+rVq/Hxxx/jk08+wdq1a/Hjjz9i/vz5eO+99yK1rYQQQgghoeM4QzylTwGeecbNJPLLLzI94QSfq+jYUc5Bl8JRErNjRwQ2tGUsXy4Gn19/BVS05MmDpAV5Ebo5x9cANAVFnWS7oQSjgWmugpES25oJRocfLmpSfb2WE6PYs0f+TCZAXWQMxBHSQmw2D6HXygF12WX+3UL68rOMDL/7SFjQOYw2bgTmzAEWLYpcE6Nws3mzTAcPhnQzA8LrMEpP19xsX38t06++koyspibgjjsko0uN7vUw8LrNcPTR0gjyzjvldubhkk/XpakQDQ0BrkS5LydPBgyGxBSMhg6V6caNIryGE7td+x4GKxg5hHNUVWmCvT/0JWmA16sbSjCKwZ/wmCMowejjjz/Gfffdh5NOOqnZYyeffDLuuecefPjhh2HbOEIIIYSQsOE4QxzdocB5xRmABPGozl3jx/tchdEIdOoEFMKRmNlKGTyB8uuvktVx+ulydX31atE4hnaUk+W96O4qGCkFxY9g1EuVpDkCwZVgVFTk9gSDAVDniT//7PqYchcNHy4fojqhj3BZWk2NGU1NIgqpl3QOYAIJU9cLRhMn+sy4Chs6wejLL0UbGT8eeOaZyL90OFDZuQMHIjKCEdC8LO3//k9un3SSdH369ltxj+mDfAsKtDwaOozaBElJ2nzmEDlu90Qh9pUE4Gy025t1RgzVLBPT9O8v35m6uuAVlOpq+FTfqqs1ESrYDy0rS8uXC9Rl5B5Y5+XqhrvDqL6+VcyucUlQgtG6detw+umne338jDPOwFqXsxBCCCGEkBhBnSFu3+56Zvjrr2JD6dtXC8n0QefOsSsYff65THNzZQzQo4dkGCXvd3UY2WzQFgS89hZWglFOnRZ6DfhwGAHAiSfK9KefXO9XZX8qaFh1o4vwZ3joUAoAyTNxdnU/dAjOO/0xaJCWbtsa5WiAUzAa0K4YI0dqHX2WLWudl28pSqMZMACaMyDcjXEmT5b/y9q1wDvviBCUlgZ8+qmIk/36SaD7+PHAunVSmzl+vIwMhw93bBxpSxh6yIErDfXYv/mA/yds2CCCdlqaM3ctIR1GJhMwZIjMB1OWVlICHHaYdDPz5kxSCpvZLM7AYNB1jAw4xyhIwWjPHvlfpqUBI0bEj4uzNQlKMDp48CDynMXfzcnLy0N5hG3FhBBCCCEhodwkFRWuZVABlqMpcnPdBCNvlyWrqyMXIuqFmTNl+uqrMmgvLARuvhnOk+XSpO6oqtJ1I9c7jDy8jz/+AJLRgIwqh6DkL8MI0BxGS5dK9odCOYxUbpJSQSLsMKqoEMHI5RS2okKmgQhGqanA2WfLwOOcc8K+fR5xDJL6pxdj9WrgpZfk7njJ21CCUf/+iJxg1KmT1pXvxhtl+te/iqI7cqSUno0YIWLoiSfKskVF4hibPTvyweUk9khJwQGzHAiqfg9AqFbuolNOcTpdElIwAkLLMbrzTvlObdok5aCe0FuyQvnOBZtjFKBglJOj9bhQm7h+vdZAkWgEJRhZrVYk6X19bphMJjRRliOEEEJILJKerp186kfeqnRKDT790LkzsAcOJ1JtrfcMnuuvl5PwhQtD3ODg+OMPya42m4EJE3QP1NU5t7H9YFF6nMHXqkaroQHu6cpNTWLG6g7HiXZqqpxlw4/DSDm1LBbNVVRZCaxcKfOt7DBSgpEzvwgIzmEESD7O7t3O9x9x1H66fz/Q1OTVHBeL2GyynYDDxKNEw3ALRoBWllZfL26jadO0x3JzxeV29NGy/5eVSYnazz9rny9pcxzMEKG67o8ADe7XdAAAZ5JJREFUjjv6/CL1/EQXjAK9yLFgAfDBB9rtuXM9LxdqfpFC5RgFKhi5Zxh5+bEyGERTnjNHhKLRo+X+Vr7GExd4V388YLfbceWVVyIlJcXj4w0Bp4cRQgghhESBvn3lxLOgQM4Qa2u1TJMAHUadOwMNSEVNeg4yastE8HAfPdhs2tXpBQv8ZiOFA+UuOv54aSHtRAUNpaWh36hs/LJeqnjOPx8yiM/IEBdIaalkRjjYsUM0n/7JhUAjRARyXCH2KRgZDOLo+OADGbCfcgrw4YciSg0a5LCdQBOMWslh5BSMbDYRsACgffvAV2QM6jpry+jcWcpErFagtBR9+ojQV1kp2kcsD1b37BH9JinJcQVfOYyCLUcJhHPOAW67TeYvukirM1F06CAD2RtuABobgddfD+5/ThKO6uyewKEVsO3yIxjt36+5Is8803l3wgpGKvg6EIdRY6Pm6uveXX4I5s4F7ruv+bLK8eOjSsknEXIYAeKUVW7ZkSOBFSvk7V90UWibmqgE9cs3ZcoU5Obmon379h7/cnNzccUVV0RqWwkhhBBCWobeqgHIgKCpScSQ/PyAVqEuXB5I95FjtHGjVvbUSpcslWCkG9sI6kS5e3eMGCmCTyDB1yq/aHSea+C1Y1UApNrHo7lclaUtWCCWmNdek9vXXaeVJbRaSZoEFzkFo8pKzaYTq+KB0agNsIqLkZ6uXWiP9bI0VY7Wt68jcDiSDqP8fNnXUlOBe+7xvEy7dsD770u2Uaz+v0mr0Zgnxx1TkR/B6Pvv5TgxcqQz227ZMhEVAKmITCiUw2jrVlF8ffHcc1KG1rkz8OWXct+vv3ruZKayh1oqGIUjw8iHPVPpZXQYNScoh9Hbb78dqe0ghBBCCIk8/frJVI26VTnaCScEnK+gBKMSc0/0wmrPgpG+pfzGjSFubOBUVWlRTM0EI+Uw6tYNI0bIrLMkDZAT+R07vApGw9rvBgqhCTyQc3FlgNm3Tzsnd6KCr5ctE5fR2rUyqNdfWGzl0GunYKTK0dLSpDNQrNKli/zvHFfW+/aVMdOOHVr5RCziEngNRNZhBAAzZogIqKwChPjA1l2OY2llfoRqVW511lkAgLfflirjxkbJh3ZkYCcO3bqJI6+8HNi8WYQyT2zfDjz8sMw/9RQwZoxYCXftkh+hM85wXV41VFCKd7AE4zCqqZESbKC5YFRTI8cJL6JxsBV5bYlW9NYSQgghhEQZ5TBas0acD0plCTC/CNAEoz0q+HrPnuYL6QWjP/6Q2q4IMneuo3ysv6OVuR69w8ghGO3aJbrJiy8Cs1f7dhj1S3YIOjrByGTSzv89vX306SPLWyziKgLE56+v41Dr27vXe4edMNAs9DrY/KJo4TZQUpntse4w2rZNpk7BKJIOI0AcRBSLSICY+8pxp32FD6H6hx/koGo2A1Om4IknJE+9sVFis5Ys0bq9JwwGg/+yNJsNuPpq+U6fdBJw+eXyvFNPlcc95RgpwShUh1EwGUbqNyw1VTvepKdrx3qPNdSCeuvbt7v2aiAUjAghhBDSllCtg1esELfR4sVyO8D8IkATjHY0+ShJ0wtGFotmu4gQXsvRABeHUYcOmrHnkkuAW24BCht8C0a96hwzbiV7gwbJ9B//8FCWZjBoZWlKQbj+etdlunSRmiWrNfB8ihBolmEUp4KR0jpjXTBqdYcRIUGQfpgctzvVeRGMmpqA22+X+ZtvRnnHfnj0Ubn5j38A//ufS9RbYuGvU9orr4grNz0deOMNzZU7caJMPQlG4SpJC0Ywys11dQyrH71du7w+NTdXftvtduD330Pb1ESFghEhhBBC2g5HHgm8956IHyUlcsk4L8+DLcc7SjD6o86LYFRcLHVDBgMweLDcF0Gfu80GzJol8x4FI53DCNAqDX74QaalECWlfndzwSgZDcgtcHQ6O+YYl8efekrGDXPnAnfd5eF1VVkaIAMR1R1NYTI5s0EimWNEwah1UYKRyjZ3CkaRchgREgTth8pxu4t1L+xNHpyNb70lZcQdOwIPPIDXXpNdePhwqcRqzez7Vse9LquuTgT/6mo58Nx9t9z/xBOuAfMnnyy/dxs2NBd2wlWSVlbm36nrnl+kcM8u9AJzjDyTyLs8IYQQQogrBoPY6LdskY5J48bJZeMA84sATTDaVO1FMFKupWHDZP1ARHOMNmwQ7Ssjw0tlnc5hBGiCkdksWcCmLnJyXbJeE4wqKuQ8fyyWwdhQLyfgylLkYORI0d4A4NlnJePDBb1gdP31nj/jVgi+bpZhpMLIYz0AWV0V37kTQHwIRlarNiZrtZI0QoIgZ1hXNMEEM5pQvnmf64OVlfJ7AAD//CcaMzrghRfk5u23B/UzEZ8oxWTdOnET9eolX+TMTLmoUlMjPzKqQ5qiUydg1CiZd3cZtdRhlJMjFxfs9mYu2Gbs3y9Td8FIZRdSMAoJCkaEEEIIaXskJwPXXivizk03BfVU1R1ntz7DSN99RZWjHXusdgYaQcFI6VWDBnnJcHZzGF13HXDNNcCPPwKXXQb0OUpOrmu2a4MnVY52VrsFMnPiiR5HSxdcADz0kMxff72bdtanD3DaacBhh8kLeSLCwdf19UBtrRlAHGYYuQ1ylGC0a5eXznQxQGGhmPaSk3VN9ViSRmKIlIwklBhFPD+03u2489RTIkoMHAhcfz0+/lgMM926SQlvwqN+r/buFVGorEyuLACiBmdlAW++6dlm5aksTS/yhCoYmUyaAOSvLE29lrqqowhQMGLwtWcoGBFCCCGEBEFSklQrFKEb7AYD0NCgXdkEXAWjww+X+QgKRgcPylSfJ+3Ebm/mMOrWDZg+XXMjDZ8gJ+PGA6VobJT7lGB0StICmdG7hdx48EE50W5s1FpOO5k9WzrueAv9iLDDSP1bzGa7ZiiKN8Fo507AakW3biLEWK0RbywXMqocrW9fGecBoMOIxBz7U+S4U71J90WqrxdXDQA89hjsSWY8/bTcvOUW+e4lPNnZWlZdx47Af/+rdRfbskXK05y1pm4owWj+fO2+8nKtjCxUwQgIPMfIW0kaHUYtgoIRIYQQQkiQ5OYCFiSjsYPjJFiN4GtrgVWrZF4vGG3dKsJSBPApGJWXy0AI8NpJ6rDxcnKdYyvFzz/LfZs3S37RsCpHfpEPwcho1LLEd+wIcuOVDSXCgpFLBmq8CEbdu8so1WIBCgthNMZ+p7RmgdcAHUYk5jiUKYJR43adYPTZZ0BZGQ6264V7fjsX06ZJ9nNGBvC3v0VpQ6PBO+8Ajz8u4tBNN4nDSJWkuTt39KiStKIiyTwCtHK07Gwv9tcAUYKRWp83/AlGBQWubmA31M/13r3y00kECkaEEEIIIUGizptrOujK0gBg+XKpF+rWDejdW6bt24stRNl2wsyBAzJVpXIuKHdRx47SatgDRkeGUSccwHdfN+H334GXXgLGYDmSrXUe84vcURelHXE7gRPhkrTSUlGJXMY58SIYmUzaB+tWlha0MNdKeBSM6DAiMUZ153wAQKcVszUB4b//BQD8X/UNeOLpJDz3nNx9zTVAhw6tv41R44QTgHvuCf5NZ2VpVy3UD0FLA68VwTqM3IWtXr3keFpf73MdWVnaTxJdRhoxIRi99NJLyM/PR2pqKo466igsW7YsoOd98sknMBgMOPfccyO7gYQQQgghOtT5aHk7t+BrfTmawSB/Ec4x8ukwcssv8kinTrAbDDDCjgX/O4BJkyQX+oqeC+TxE07wm/aqnC9BCxkRLknTLjjrrirHi2AEuF4ZR+wHXzcTjKxWzVlHhxGJEbYcfSXqkYI+f8yRrmjLlgHLl6MeKXjXdDVuvVVi1664Arj//mhvbRyhBG71Q6AEo5aUowGa4ORPMNq2zXU7FGazXMABWJYWAlEXjD799FNMmzYNDz30EFatWoURI0bgtNNOQ6mfFPSdO3fijjvuwPjx41tpSwkhhBBCBHX+O2+rCB6WHQ7B6McfZXrssdrCEc4xarFglJQEdMoBANj2lWLXLhnwX9lngTzuoxxNEYhgVF4uGeMrVkgTnro66JS3cp+lAqGiHEYuFQrxKBi5OYziRjBS5WgAHUYkZjjx+kF4AP8CAFhv/TvwwAMAgE9wCU66qDOee046SL77ru8qLOKG+w9BSzukKQJxGNXUaM4mVSOthzlGIZMU7Q145plncO211+Kqq64CALz66quYOXMm3nrrLdxzzz0en2O1WvGXv/wFDz/8MBYuXIhD6offAw0NDWjQZQZUVlYCACwWCywqhCsOUdsez++BxAbcl0i44L5EwkU87EtTpgDff5+ELTtFMPrmv4V4Z95uzFi7AEYAZcdOQnvH9hsHDYIJgG39elgj8J4OHDABMCIrqwkWi6voYiwslNfu0sXnayfldgbK9iMXpcjJsWPG5zUwj1sMALAce6wWXOoF0aPM2LnTjsbGpmaGJKsVGDEiCYWF2gP9+9uxYWkaUgDAZoOloiLsooKML0zo2NEKi8UKAEgqL4cBQFNGBuwxvI8BgDE/X/5/W7fCarGgVy8DgCRs325zvp9wcM01JhQUAHPmWJEU4uigqQnYsSMJgAG9e1tkl6mogBmA3WBAk8nkdz+KdeLh2ET8M2IE8O+zb8XiGV/imJolzs5eL+EmPHdT8+NoJEjEfcnYuzdMAKwFBbBZLDAWFcnt3FzYWvA+DZ07IwmArbjY++/Yxo0w2+2w5+SgKTu72bFGHUutf/zhc1sGD5Zj7Pr14T3GRhJP+1I496uoCkaNjY1YuXIl7r33Xud9RqMREyZMwJIlS7w+75FHHkFubi6uvvpqLFy40OdrPP7443j44Yeb3T9nzhykJ4A1dq6+dSEhLYD7EgkX3JdIuIj1fenJJw2omN4EzAHyGgsxeO2nMMKOXzAen75dg1NPnQUAyKmqwrEAaleswLxZs8K+HQUFxwPogB07VmDWrH0ujw3/7Tf0AbC1thabfbz2MSYTOgM4pt8WnHF9EvbPWoxBdXVoaN8es3fs8BtOZLEYYTBMRk2NAZ988iPat290eXz//lQUFp4Gg8GOTp3qcOBAGrZtM+Cdz5bhb0YjDDYb5n31FRo82qRCZ8mSowB0QX3975g1S97DhJISZABY/PvvKLfG9oAg7+BBHA2gcs0a/DxrFvbuzQJwErZssWDWrNlheQ2r1YD33z8LdrsB7747D1271oa0npKSdFgsE2E2W7F+/Sxs3AikFxdjIgBrSgpmff99WLY3Foj1YxPxz0kTMnHVjLexGkcgHXVYirGoGNAfZWUzEYHDtFcSaV/qU12N4QBKf/sNy2bNwshVq9AbwJbycmxtwYfaYccOHA+gfscOzPWynh4//4xRAA7k5uJXD8v0b2zE4QCKFy3CSh/bUl4ux9jVq5swc+b3/qqxYwr9vlRbG9px3BNRFYzKyspgtVqR52ZTy8vLw+bNmz0+Z9GiRXjzzTexZs2agF7j3nvvxbRp05y3Kysr0bNnT5x66qnI8tbiNQ6wWCyYO3cuJk6cCLPZHO3NIXEM9yUSLrgvkXART/uSITcbmAOM6VKI4SnvA7uAD3AZeuQNw6RJjlK0UaOAhx5CRnExJp10EpCWFtZtmDZNTudOPXU0xo1zvTJueuMNAED/449H30mTvK7D9OGHwPr1+OeN9bDdfAyMj0u7NPOECZh05pkBbUe3blIB17//RIwZ47odCxfKWXffvsCmTWaMGQOsXQvk5p0sSaOHDuGUMWOAww4L7E0HyK23Sm/3Cy44DCedJGUKSY0iZo074wy/Yd5RJz8f+Pe/0b6sDJPOOANV1QbcdhtQVZWCY4+dhPbtW/4SJSWA3S7/n6OOOtFZkhEsP/4o6+jXz4jJkx372vr1AABTVhYm+dj/4oV4OjYR//z2mwk3fvQynsSdeBCP4P77M1ttP03EfclgMACvv44udXWYNGkSTK++CgAYePzxGNCSz3XIEODuu5FWUYFJZ5zhMVPP6DCbdDj2WI//Q0NDA/Duu+hWX488H9tSXw/ccYcdVqsZY8dOiouSRE/7kqqqCgdRL0kLhqqqKlx++eWYPn06cnJyAnpOSkoKUjy08TObzQnx5UyU90GiD/clEi64L5FwERf7kiOzIbVkF1IBNBnN+MJ2Ia48ZILZLGIFuncHOnWC4cABmLdvB444IqyboDKMcnOT0OzjcmQ+mHr1gsnXZ+kIFTUdOCDLORzcxpNPhjHA/0F+vghGe/Yk4ZhjXB9TTeTy8w0wm80YOlQEo82bk2DIzAQOHYK5rg7N30DoVFcDu3bJ/PDhJtmXbDZJ9AZg7tw5rK8XEQYOBAAYKithrqxEx5wc5OQAZWXAnj1mBHg67BN9++imJnPIH4kan+TlGbTvrUOcM6Snx/53OQji4thE/PLII8Cgz67Eu01XoksX4NtLW/+QkFD7kiO8zLBzJ8xJSc6uA0ndu7fsg3U0RzA0NsJcVeW5JaijC6lp6FDPv3WOixHGggKfv2lms8Qc9ewJmEzx9X/R70vh3KeiGnqdk5MDk8mEfftc7dP79u1DFw/t97Zv346dO3firLPOQlJSEpKSkvDee+9hxowZSEpKwnY/IVaEEEIIIWGla1fAqJ1OFQw6E+Xo6BRxAMjVUOVkUV1cwoTVqmU4e6zmKiqSabduvlekUqH37ZNBvur2dsIJAW+Lr+BrVdGmmte4BIsqx3cYr4gCwO+/y7RDh3ptfFFdLaIREB+h12lp2v8uQp3S9KfhdXWhr6eqSqbt2unuVGURDLwmMUi/fsANN8j8bbcByclR3Zz4R3Uiq6yUH6ZwhV6npgIdOsi8Wqc76oA/eLDnx9WB88AB50UDb+TnAyZT8JuZqERVMEpOTsaoUaMwb9485302mw3z5s3DuHHjmi0/aNAgrF+/HmvWrHH+nX322TjppJOwZs0a9FStWQkhhBBCWoOkJBcxZvfxlwGAq2AEAD16yFR1LQsTendIM8GoqUlTA3x1SQM0wai0FFi+XJSDnBzP3Wa8EIhgpJZRgtHGjYiYYKS63PTqpVuvUtdSUmQQEg9EuFOavjFxSwSj6mqZZmbq7lRd0hIgN5QkJs88I90b77wz2luSAKSna+JQQYF2cGmpYAT47pTW0KB1P/P2m5WZqf3O0WQSFFEVjABg2rRpmD59Ot59911s2rQJN9xwA2pqapxd06644gpnKHZqaiqGDh3q8pednY3MzEwMHToUyZSFCSGEENLaqAtW7duj+gTJ+zlwwG0ZJdiEWTBSwlRWFpp3t9q3T9w0JpP/3tB6wWjBApk/8USPWRHeUGKQp3xsJSIph9HhjninTZsAW6ZDMFIWlTChCUa69SrBKBzhP62Fm2DUv7/cVC3sW0pEHUZKMKLDiMQoSUnAuHEuRlHSEtQPwcqVctEC0H5fWoKqPvIkGG3dKnbbrCzfblp1LA2X2t5GiHqG0cUXX4z9+/fjwQcfRElJCUaOHInZs2c7g7B3794NI7/BhBBCCIlVevUCliwBLrwQ2V3EtdLMYaQEIxXmEybU63gsR1PiVNeu/v313gSjIFBiUCAlab17i4ZQUwNUG7OQBYTdYbRxo0w9OozioRxN4SYYqepGL/1hgiaiDiNVkkaHESFtgz59gN9+kz9ASsk85AkHjXIYqTJrPaocbcgQ3xc5+vWT32o6jIIi6oIRAEydOhVTp071+NgCddLihXfeeSf8G0QIIYQQEijTpomT58EH0ckRjeBVMIqQw6hF+UWAJhiVlGiWkyAFI73DyGbTrtg3NQGFha7LGI3iMlq2DDjQkBkRwcijw0hlV1AwchJuh5HHkjQ6jAhpG6irAkuXytRDLnFIKFvqL78Ad9/t+pi//CKF27GUBAatO4QQQgghLWHsWOCzz4BevZzCzcGDgF3fWT5CGUaq9M1T0xinmhRIKy0lGNXViSskyPwiQN6iySSZ2fpc0j17pFogOdl17KDO//fVhT/D6OBBTS/r2dNDSVocC0aOZj8oLfUgTIaA3mGkDEGhwNBrQojzqoASccKRXwQAZ58t03nzNDujYtMmmfr7zVIBcBSMgoKCESGEEEJImFCCUVOTWySP3mHkoiS1DJ8OI7UBKlTaF5mZriHQQeYXAZIFouKc9GVpqhytd2/XnBAVfF1YEX7BSJWj9e5tR3p6k/ZAPAtGRUVAXR3atdP0R0cn6RYRLocRQ68JIU7BSBEuwWjIEDkWNjQAP/zg+pi+JM0XdBiFBAUjQgghhJAwkZYmf4Cb+0OVhTU0eEjEDh2fgpESYFxG8F4wGFyDSU84IaTt8dQpzT2/SKEEo4Ky8IdeK8FoyBA3cS4eBaOOHTXRz/HBhlqW9tRTwMSJrk6iiIZe02FESNvC/UAfrpI0gwE45xyZ/+Yb7f6mJk05D7QkrbBQrLAkICgYEUIIIYSEESXeuOhCycmaIBPGsrSwOYwAV8EoyPwihadOaWre/cKzKklzCkZhdBip/KKEEIwMhrDlGD33HPDjj8DPP8ttuz3Codd0GBHStujVy9WdGi6HEaAJRt99B1gsMr99u8ynpYmN1Rd5eSJe22ye23kSj1AwIoQQQggJI/ocIxciEHztM8MoGIcRoAlGIeQXKTx1SlPz7heeu3UT3eaQPXKC0eGHexGM2rcP22u1CmEQjKxWLVtK/U8qKlwvtIfdYcTQa0LaFsnJWs0sEF7B6Jhj5MeuvBxYtEjuU/lFgwe71jx7wmBgjlEIUDAihBBCCAkjSrzxKhjt2RO214qIw+iEE/yfeHshmJI0g0HK0qrgELTCJBjZ7QEIRvHkMAI0wWjbNgCaYKTGSoGwf7+IRgBQUCBTvbsIiIDDSJWk0WFESNtBf7APV0kaIEF5Z50l86osLdD8IgVzjIKGghEhhBBCSBjxWJIGRMRhFLYMIwA49VQJvr788pC3J5iSNEDK0ioRXodRaal89gaDJqw4iVfBqH9/mToGOSqqo6BAYrECobhYm1eCkT6/CAiPw8hjSRodRoS0HfQH+3A6jADXHKP584F33pHb/vKLFBSMgiYp2htACCGEEJJIeC1JUzb9WM0wuvRS4KKLAJMp5O1RF5Z375YsUrtdM1S5O4wAcRgtDlIwslols3TLFhE8zjgD6NxZe1y5i/r31wLInVRUyDTeBKMBA2TqcBh17SrCTFWVjHsCubheVKTNKwdYJBxGDL0mpI2jF4zC6TACJLU/NVWuRJxyityXlSW/XYGgBCOlmhO/0GFECCGEEBJG/GYYhbEkLawZRkCLxCJAhIyUFBF1duwQYcdmk/N7Txeahw7VOYwC6JK2dau8Rp8+wOmnA1OmAA884LqMVo7mYQXx7jDasQOwWFzcU4HmGLk7jOz28DmMGhq0DFqGXhPSxtELRvpmCuEgI0MO/gBgNgM33yw/DOoY6Q86jIKGghEhhBBCSBhR4k2kS9KsVk3/CIvDKAwYjcBRR8n8G2+45hfpG+co+vTRCUZ1dZrq4IW5cyWLx2yWbG5AC3JWKBGkVy8PK4hXwahrV7FLWa3Arl0AgheM9A6jykoRNJXDKEv3LwgFvdZHhxEhbRxlJ+3YUQ7W4eall4Ann5SD3wsvBCdK6R1GdrvvZQkACkaEEEIIIWGltUrSlPYBAB06eFggFIdRGLjrLpm+/DKwapXMeypHA0QDcYZeA35dRkqAuvFG4P/+T+abmlyXUbeTk92ebLfHb5c0o9Fr8HUoDiNAzEpKXFP/n1AFI1WOlpoqubRO6DAipO1x9NHApEnAtGmRWX+3bsAdd2gdz4KhVy9x0tbVNT8oEo9QMCKEEEIICSN+S9LKyzXnRQtQ68/M9HIRNwoOI0DGCcOHi4jw+ONynzfBKDUVaIIZdUiVO/zkGDnMNcjP14QJb4JRs+q6mhqtTVi8OYyAZjlGLXEYAXKBXTmMWioYqV3NxV0EMPSakLZISgowcyZw//3R3pLmmM2a/ZRlaQFBwYgQQgghJIx4LUnLytIGzmFwGan1eyxHa2rSRv+t7DAyGID77pN5JWp56pAGiGAEBN4pTV/i5k8wSnJv7aLcRUlJ8el4URkdW7cCcBWMAqmsUBfTlVZWUBB+h1GzXU0Jo/H4eRNCEhPmGAUFBSNCCCGEkDDi1WFkMIQ1x0it32Pgtb60q5UFIwC48ELNEAN4dxiZzfKxBBp8HRbBKDvbc6BSrKMEI4fDqF8/cVFVVQVWWaGWGTdOpjt2aA6j3r1l2lKHkcuuZrNpK6TDiBASK1AwCgoKRoQQQgghYUQvGDVzfoQxx0gJRh4dRsqpk5LiIcwn8phMwD33aLe9CUYGg7iMAnEY1da6ChwtEoziEbeStJQULcLDX1mazaaFgx97rEwj4TByKUnTr4yCESEkVqBgFBQUjAghhBBCwogScKxWD/qHchjt2dPi1/EpGEUpv0jPZZcBQ4dKA5shQ7wvF6hgtHu3TLOyRPNRgpCKJVJ4FYwqKmQar4KRchjt2OF8k6osbcMG308tK5OnGAyaw+j337WPOyIOI5VfBEi6OSGExAIUjIKCghEhhBBCSBhJS9PGx16DryOdYRSlDml6kpOB334TJ0uzMGQdqam6Tmk+BCN9OZrBoIVatxmHUffuYiuyWJzq2THHyEOzZvl+qgq8zs0FBg50vS85GejSRebD6jBS+UVpadLljRBCYgElGBUURHc74gQevQkhhBBCwozXHKMIlKT5zDCKosMIkEokf9VIgTqM9IIR4L0kTTmOvApG7dv73qBYxWjUBjqOsrTzzpOb8+drBipPqPyirl2lI7W+SjE3V8uktlpFjwoWnw4jBl4TQmIJVctbVua30QKhYEQIIYQQEna8dkprrZK0GHAYBUpKSmCh17t2yVSVT7W5DCOgWY7RYYcBgweLyDNzpvenKTdRt26iO+kzpXJzXSvGQnEZqX+bi8NICUbMLyKExBKZmXLgA1iWFgAUjAghhBBCwoxXh1EEuqTFaoZRoITbYZTQgpFbpzQAOP98mX75pfen6R1GgHaBHQDy8kS0U43jQhGMVEmaiz6pStLoMCKExBrMMQoYCkaEEEIIIWHGr2BUUtJc6QiSRHEYUTAKAiUYbd3qvEuVpX3/vXexR+8wAlwFo9xcrVsd0DKHkceSNDqMCCGxhjoIUjDyCwUjQgghhJAw47UkLS9P0pqtVq2neYiodcdyhlEgeBWM7HaX5VosGJWWyjQnpyWbG13cHUa//IIjn7gIR3UrRG0tMHeu56e5O4z69NEey8uTqSpLU8agYPAZek3BiBASa9BhFDAUjAghhBBCwoxXh5HJpI3aW5hjlEgOo2Zd0g4cEFXj1lsBAPX1YsoCAs8wUl3UnLirJvGIyjAqKBAF7dxzYfj8c/w773kA3svS/DmMAE0wCrvDiCVphJBYg4JRwFAwIoQQQggJM14FIyAsVnirVauwSsgMo4ULJeX6o48AOLvIo1077f0G7TBKBMGoRw9pcdbYCEyaBJSXAwDGVM0DAHz7redKR38ZRkDLBCM6jAghcQUFo4ChYEQIIYQQEma8lqQBwMCBMv3jj5DXX1GhVWx16OBhgThzGDXrkqZKrhxtj/XlaCqcWQlCVqvr+jwKRjabZlGKZ8HIZNLUnk2bnApN5rY1OKzjfhw8KCHYb72liUQ2mzavHEa+StLoMCKEJDxKMCosFAGeeIWCESGEEEJImPHpMAqDYKSEqMxMMZw0I94dRvqrvtu3N8svArSSs4AcRgcOaA8ohSReUWVpAPDGG8CwYQCA+8b9BEBcRldfLYstX669dYNBe+vt22vzPXrINOwOI4ZeE0Jilbw8+X202YCNG6O9NTENBSNCCCGEkDATacHIo6NDT7w6jLwIRrt2yazKLwKCLElTFpucHC8KWxwxdqxMr7kGuPhi4JRTAACXd5uHxYuBhx4CBg0Svebf/9beeufOgNmsreb994EXXwQGD5bbYXcYqZI0OowIIbGGwQAcd5zM//xzdLclxqFgRAghhBASZpRg5Lckza0TWKA0NMg0JcXLAvHsMLLbA3IYeROMVImaXjAyJEI5muLOO4FFi4DXXpPbDsHIMO9HjBsH/POfWvj1N98Av/4q8+5vfeJEYOpU7XbEStLoMCKExCInnCDTBQuiuhmxDgUjQgghhJAwozKMDh4Ux7sLffsCRqOMsvftC2n9KnLBq2AUZw4jZ5c0m00CmpSlCAhaMPLoMEokwSglBTj2WNmHABn0mExa5zSIa+jUU0V7+9e/ZDGVX+SNUAUjq1V7jsfQazqMCCGxyIknyvSXXzz8UBMFBSNCCCGEkDCjgqhtNs194SQlRVM+QixLSzSHUQ0yYIMjzXrDBtck6wAEI71Ry5NgZEiEDmneyMwEjjpK5ufNc959660yLSqSqb+3HqpgpPKL1KY4ocOIEBLLHHmkqNzl5cC6ddHempiFghEhhBBCSJhJS9MG4B7L0lRwcQsFI69xPHHmMAIMqDc7xK3Vq2XqcNDYt2135vB4yjACXC8OJ7zDyBOOsjT8+KPzrtNPd83H9ucwUkagUAUjk8lNwFQOIwpGhJBYJCkJGD9e5lmW5hUKRoQQQgghEUBfltaMFgZf+yxJs1iA+nqZjxOHEQDUugtGY8bIdE8hkuyNSE+XzGqFXhDSl6W1OYcRAEyYINP58512K6MRuPlmbZFIOYz0+UUGg+4B5TBiSRohJFZRZWkUjLxCwYgQQgghJAJEslOaz5I0fQ1c3DiMgFqTQzBatUqmxxwDZGTAYLMhHzvRu7erIBGMYJTwDqOjjxZhprRUSvocTJmi7QLdu/teRUtL0lzyiwCWpBFCYh/mGPmFghEhhBBCSARQOUaRFIw8lqQpwSg11bWPeoyiBKMao0PZ2LhRpv37S0A4gH7Y7lKOBrgKQvrIIyUYmUzafQnVJc0TycnA8cfL/Ny5zruzsoA33gCuuAI47TTfqwiHw8gFhl4TQmId5hj5hYIRIYQQQkgEUONkVR3mghKMtm1zVTsCxGdJWhzlFwGaYFRtdDiMlOLTr5/8QQSjHj1cnxeww8huBxK9JA0AJk6U6Q8/uNx90UXAu+9qn7M3Wuowara70WFECIl1kpKA446TeZaleYSCESGEEEJIBFADdI+CUc+eovZYLK4t5AMkoJK0OMgvArTPqcrgtr1ugpF7SZVRdxbrSzBKqq2FQakgiSwYKQvRL78Er/qg5Q6jZiVpdBgRQuIBVZb2889R3YxYhYIRIYQQQkgEUGKOR8HIZJKSKyCksjSfJWlx6jCqhE4wMpmkJZoPh5HBoJWd+RKMUsvLZSYrK7HFiyFDgB49ZIf75Zegnx72kjQ6jAgh8YBeMGKOUTMoGBFCCCGERAAlhChxpxktyDHyWZIWpw6jCrtue3v3lvwlH4IRoIlCAQlGiewuAkRBUy4jt7K0QAhr6LXdrjmMKBgRQmKZI4+U42d5ObBvX7S3JuagYEQIIYQQEgF8lqQBmmC0dWvQ6/ZZkhanDqNDNp1g5BCK1LQvCtCjm9uV37VrsaFxAK7Dqy6CkYqEUoJRikodT3TBCIiKYOTRYVRfL6IRkNiuLkJI/GM2Azk5Mr9/f3S3JQahYEQIIYQQEgF8lqQBLXIYBdQlLc4cRodsOsXBIRTVde6FJpiQhnr0TCp2feI//oH+9m14Fn8Hduxw3t1mHUYAMGGChDv9/jtQWBjUU8PqMFLlaAAFI0JI7JOXJ1M6jJpBwYgQQgghJAJErSQtTh1G5U3NHUZ7S83YjV4AgKz927XHt2wBvv0WAJCGenR+7DYAYmpp04JRhw7A2LEyP2dOUE8Nq8NIlaMlJ7u2syOEkFgkN1empaXR3Y4YhIIRIYQQQkgECLgkbdcuHwt5JhG7pB3UC0aOQPA9e4DtEPHIUKATjJ55BgCwJmk0LEhC5k8zgFmzXPJKnSVpbUkwAkIuS1OCkdJ6AkU5jFwEIwZeE0LiCQpGXqFgRAghhBASAfyWpHXuLKKO3Q4UFAS17kTsklZmae4w0gtG2O4QjEpLgffeAwA8kv0Mnsetcv+tt6KpRrNztUmHEaAJRj/+qAU6BUBLHUYuJWlKdWI5GiEkHqBg5BUKRoQQQgghEcBvSZrBoOUmHDgQ1LoTsUtaJXTb27cvADfBaP16EUBefllUuDFjsCr9ODyMh9CY0xXYtg146b/OVZhMjvW3NcFozBggO1s6/ixfHvDTWpphRIcRISRuYYaRVygYEUIIIYREAL8laYBkzgAyuA+CROySVoRuMtOnj1No2LMH+AOO0r0ZM4AePYBnn5Xbd9wBU5IB1chE0Z/vBAAYf17gXK/TYdSWuqQB8saPPlrmN24M+GkRcRhRMCKExAN0GHmFghEhhBBCSATwW5IGiBMECFkwSoQuaeo97EBfHHrrS+DLL52P7dkD/IDTsP7EqSKulZSIIJafD5x/vlMUquo9FABg2KHlHCUlAairg1mJF21FMAKAbg7xraQk4KcowchiCaqSzXPotXIYsSSNEBIPUDDyCgUjQgghhJAI4LckDQjZYZRIXdIMBu2zqjj5PGDkSOdje/cCDUjFrttfFPHj22+BW24BPvoISEpyCkbVXSQk27RzO4wQtcNoBFBcDACwp6YC7du31luKPl26yNTx/gNBCUZAcC4jjyVpah90sR0RQkiMokrSKBg1g30uCSGEEEIiQNRK0uLMYQTIZ1Vf3/yz2rNHpt27Q6xIkyfLnwMlGNV07AmYzTA0NqI79qI4qRcMBsCgHDZdu4oy1VZQbqoQHEaACEaBaj0eS9L275dp584Bvz4hhEQN5TDat08aUbSl3ws/0GFECCGEEBIBAipJY4YRAM/iWmOjlj/ao4fn5ynByGJPkuwjAP2xzXm/02GkHDdtBfV+gxCMjEatPDBQh5Hd7sVhRMGIEBJPKMGovl47qBEAFIwIIYQQQiJCUCVphw4Fte5EyjACPAtGxcUiSCQnAzk5np+nhKGmJgD9pJtaP2x33u90GFEwCohgg6/r6gCbTeY9OozUIIwQQmKZjAwtc41laS5QMCKEEEIIiQCRLEnzmmFksWgvGOcOI1WO1qOH9+oAF8Gov+QYeXQYtaXAayBkwUiNlwIVjPQX4l0aotFhRAiJN5hj5BEKRoQQQgghESAqJWnKXQQklGDkDSUMWa3wKBgZNm2Smfz8sG1rXKAEo5oa133CD8E6jNSqMzIcIeMKCkaEkHhDn2NEnFAwIoQQQgiJAJHskua1JE3lF6WmAmZzUOuMJi0VjPQOI5eStJUrAQD2MWPCuLVxQLt2Wo1YCMHXwQpGzbRJdYWeghEhJF5QghEdRi5QMCKEEEIIiQABlaRlZ8s0XCVpcZhfBIQuGJlMMtVnGPXHNiSZ7MDevTAUFcFmNMI+cmTYtznmCaEsLVjBSJWkNeuoRocRISTeoGDkEQpGhBBCCCERIColaXHYIQ0Ik8MoPx92oxHtUIMuhn3AsmUAgKpevdwCdtoIrSAYqf1Q/f8AALW18gdQMCKExA/MMPIIBSNCCCGEkAigBtEWi9ZJqhlKMKqv96MsueK3JK0tCkYpKWjI6wUA6Gvb5hSMygcMCPPWxgmtIBgpp5vLfqjcRWZz3DndCCFtGGYYeYSCESGEEEJIBNC7LrzmGGVlaS3AgnAZeS1Ji9NSoLAIRgDquklZWh/bdmD5cgDAobYqGKnOcI5OcYEQqmDksh+qfTA313t7O0IIiTVYkuYRCkaEEEIIIRFAP4j2ah4yGrUco0OHAlqvzSauJffXAKC5SZS7JE5wF4yamjSdIxjBqLarBF/3bfrDKRjRYRT5kjSPDqM4Ey0JIW0clqR5hIIRIYQQQkgEMJs1g0U4O6UpVwfgoSRNWenjXDDatw+wWkUQUhd9PeEuGFV3EcFofM33QGUl7GlpkmHUFol2SRoFI0JIPEGHkUcoGBFCCCGERACDIcBOaS0QjLw6jNSV0jjB/XPau1emXbtqndA84U0wGly3GgBgP+II2H2tIJGJlmCkBlsUjAgh8YQSjA4c0H5USGwIRi+99BLy8/ORmpqKo446CsscIYWe+PLLLzF69GhkZ2cjIyMDI0eOxPvvv9+KW0sIIYQQEhiR6JSmdys1cxglSElaWZlMfbmLAE0wslplWpXbz+Vx+5gxYdrCOKQVBSOPGUYUjAgh8USnTlImbrdrP0Ik+oLRp59+imnTpuGhhx7CqlWrMGLECJx22mko9WIF69ixI+6//34sWbIE69atw1VXXYWrrroKP/zwQytvOSGEEEKIb5QQ4rMkTWUYBSkYJSXJua0LqiQtzh1GBw/KVGlp3nB3GFXm9HV53D56dJi2MA5Rodeqvi8AmGFECGmzmExATo7MsyzNSVK0N+CZZ57Btddei6uuugoA8Oqrr2LmzJl46623cM899zRb/sQTT3S5feutt+Ldd9/FokWLcNpppzVbvqGhAQ26s7RKR7tZi8UCi0qMjEPUtsfzeyCxAfclEi64L5FwkUj7UmpqEgADqqubYLHYPS5jat8eRgDWsjLYAnjPNTUAYEZKih0Wi6ttPqmkBAYAlk6dtGTsOMBsNgIwobbWBovFirIyud2+vdz2htEoyzU0WGGx2FCDFOxFN3RHEQDAMnIksH17QuxLQZOdjSSDAQabDZbi4oBExORk+Tyrq31/7oq6Olk+KUlb3rRvH4wAmjp1gj3BPvdEOjaR6MJ9KTZJ6twZhtJSNO3dC/vgwdHenIDwtC+Fc7+KqmDU2NiIlStX4t5773XeZzQaMWHCBCxZssTv8+12O+bPn48tW7bgiSee8LjM448/jocffrjZ/XPmzEF6enroGx8jzJ07N9qbQBIE7kskXHBfIuEiEfYli+VkAJlYsOA3lJUd8LjMkIMHMQDAztWrsWHWLL/rLCxsB+AUGI0WzJr1vfN+Q1MTzj4gr/Hj+vVo3L07DO+gdSgo6AtgGHbsKMKsWSuxbNlAAINRVbUbs2at9fq8wsJhAPpi8+ZtmDVrM1as6I5u6I/uKEJjZibmbNsGGAwJsS+FwmlZWUitqMCiL75AZZ8+fpffuVP+D9u3y//BHxs2yP9p3z7t/zR+2zZ0BLBy1y6UBLA/xyNtdX8i4Yf7UmxxjNGIzgDWzp2LPfrAwDhAvy/V1taGbb1RFYzKyspgtVqR53bFIy8vD5s3b/b6vIqKCnTv3h0NDQ0wmUx4+eWXMXHiRI/L3nvvvZg2bZrzdmVlJXr27IlTTz0VWVlZ4XkjUcBisWDu3LmYOHEizGZztDeHxDHcl0i44L5EwkUi7UudOiVh717giCOOxsSJnh1Gxg0bgK++Qp/sbPSaNMnvOteskWlGhhmT9Ms7kqLtJhMmXHyxh3q12GXvXiPefBPo0KEbJk3Kw/z5su3Dh/fEpEndvT5PLZef3x+TJvXFwYMGbEc/nIBfkHTMMZh46qkJsy+FQlKvXsD69Rg/YADsp57qd/k9e4x46y3t/+CP5cvl8+/XT/s/JTnOu0edfjrsxxzTgq2PPRLp2ESiC/el2MT00UfA+vUY2a0bhgfwexwLeNqXVFVVOIh6SVooZGZmYs2aNaiursa8efMwbdo09O3bt1m5GgCkpKQgpVkLEcBsNifElzNR3geJPtyXSLjgvkTCRSLsSyoTpqkpCV7fiiMzwVhZCWMA79fu0J1SUgyun4/DXWTIy4PZw7lPLKNM342NRpjNRlRUyO3OnU0wm713OVPZOXa7ttxMnIm/4m0YL7nE+fkkwr4UEl27AuvXI6msDN53QI127WTa0GB0lAn6RkUjpaXp/k+O7I+krl0Des14pM3uTyTscF+KMRzZb6YDB2CKs/+Lfl8K5z4VVcEoJycHJpMJ+1RAo4N9+/ahi4/uHkajEf37S9vUkSNHYtOmTXj88cc9CkaEEEIIIdEikl3SmmlCcRp4DYQv9LqpCfgSF+CiSdX4bEo6WyOr4Ovi4oAWb3HodX09UF0t8wy9JoTEG6o1p5s+0ZaJqlc5OTkZo0aNwrx585z32Ww2zJs3D+PGjQt4PTabzSXYmhBCCCEkFgioS1qIgpFLZypAa5/u46JbrOIuGKmPIhTBCACsqRmAwRDejYxH1L6g9g0/BCsYqYgP576oOqQlJWnd/wghJF5QghG7pDmJeknatGnTMGXKFIwePRpjx47Fc889h5qaGmfXtCuuuALdu3fH448/DkBCrEePHo1+/fqhoaEBs2bNwvvvv49XXnklmm+DEEIIIaQZ7kKIR4IUjNQgPZEdRi0VjJKifoYbI0RLMOrcmYIdIST+UL+fFIycRP3n9OKLL8b+/fvx4IMPoqSkBCNHjsTs2bOdQdi7d+92tEwVampqcOONN2LPnj1IS0vDoEGD8MEHH+Diiy+O1lsghBBCCPFIQCVpyonR0pK0NuwwUlk6akrByEErCUbOfVEvGBFCSLxBh1EzYuLndOrUqZg6darHxxYsWOBy+1//+hf+9a9/tcJWEUIIIYS0jKBK0mpqAIvFb1Cw15K0BHIYqQyjjh19P48OIz8EmWGkwsdDzjCiYEQIiWf0GUZ2O52SiHKGESGEEEJIIhNQSZo+6yUAl5HXkrQEcRipP4AlaS0mmiVphBASbyjBKCkJqK2N7rbECBSMCCGEEEIiREAlaSYTkJUl84cO+V1nopekKc3MaAQyM30/z+To5E7ByAtqX6isDGjw02LBSJVxUDAihMQjGRni9q2qknlCwYgQQgghJFIEVJIGBBV87VUwSpCSNPURZGeLaOQLOoz8kJWlfbgBtIlWglF9vVRj+IMOI0JIwqFqcwkACkaEEEIIIREjoJI0ICTByCXDqL5ecyfFucMo0PwigIKRXwyGoMrSlGAEBLDPwkfotSrrIIQQEtdQMCKEEEIIiRABlaQBQQlGHjOMVClQcrJrJlKcoAQjQNM1/OUXAd4FI1WqRqCJN0rM8UF6upbxWlHhf9UMvSaEkMSGghEhhBBCSIQIuCRNiTyhlqQplSUvLy67uugFI9XQqyWCER1GOlQ+VmWl30VNJqBbN5kvLPS/apakEUJIYkPBiBBCCCEkQrRaSZpeMIpD9OIXBaMwowSjqqqAFu/VS6a7d/tfloIRIYQkNhSMCCGEEEIihBJCwhl67bEkTQUax2F+ESCmKPV+QhGMrFaZUjDyQBAOIyA0wSglBbKTqzo2CkaEEJIQUDAihBBCCIkQkXQYeSxJi1PBCNA+KyUYMfQ6TLSCYJScDKCsTG6YTIGpfYQQQmIeCkaEEEIIIREiaMFIdTrzgceSNOUwitOSNKC5YBRKSZpyGlEw0hGiYBRIhpHLvqjK0XJyACOHGIQQkgjwaE4IIYQQEiFarSQtAR1GgQhGqhsaHUY+yMyUaaQdRqpTH8vRCCEkYaBgRAghhBASIVqtJC2BHEbKqMLQ6zARpMOoZ0+ZBi0YLVsmNwYNCm77CCGExCwUjAghhBBCIkSrd0lLAIeRgoJRmAixS9q+ff73Wxe32/z5cuOUU4LfRkIIITEJBSNCCCGEkAgRcEladrZMW1qSlgAOIwVDr8NEkA6jjh2B9HSZ37PH+3J2u068tNYBixfLjZNPDnFDCSGExBoUjAghhBBCIkTQDqPKSi252QvNStJqaoDqapmnw8jlfoKgBSODIbAcI6tVRCMASF+7RHbM7t2BAQNasLGEEEJiCQpGhBBCCCERImjBCPDbKa1ZSZrKL0pL0wKO45BwCkYqDJsgaMEICEwwUk43AEhd7ChHO/lkUZwIIYQkBBSMCCGEEEIiRMAlaWazppD46WferCRNX44Wx4N1vWBkMgHt2vl/jhKMlCmLDiMPtIJglLRQJxgRQghJGCgYEUIIIYRECL3DSJXveOWII2S6cqXPxZqVpG3bJtM+fULaxlhBLxh17BiY9sWStABQrrOaGr/ljopgBKN2qIJhuaND2kknhbiRhBBCYhEKRoQQQgghEUKJIHY7YLH4WXj0aJkuX+5zsWYlaX/8IdOBA0PaxlhBLxgFUo4GUDAKCOUwArSsKz8EIhip/fDkpIUwWK1Av35A794hbiQhhJBYhIIRIYQQQkiE0Hcy81uWNmaMTFes8LlYs5I0CkZOoUgZaCgY6UhJ0dTFAMvSgnEYnWJgORohhCQqFIwIIYQQQiKEXjDyG3ytHEbr1vlUl5qVpLVhwUiFW9Nh5Icgc4z0gpG3UkolGJ1go2BECCGJCgUjQgghhJAIYTRq5g6/glHv3kBOjtSurVvndTGXkjSbDdi6Ve5og4IRS9ICJEjBqEcPmdbVAQcOuD1YWwuMHYv+47tgPYZimHWN3M/8IkIISTgoGBFCCCGERJCAO6UZDL5zjIqLgb/9DddWPaOtt6hIBvBJSQkXeh0IFIwCJEjBKCUF6NJF5puVpS1eDCxfDvOBfRiKjTDCLvttXl74tpcQQkhMQMGIEEIIISSC6Dul+cVTjpHdDrz3HnD44cD06Xik4S5koFoEI1WO1rcvYDaHc7NbHTqMIojqlBagYAT4yDHavBkAcOjIk3AKfsTNXT4Hvv665dtICCEk5qBgRAghhBASQYISjNwdRjYb8Kc/AVOmAOXlAIAkWDEaK6QkLUHyi4CWCUZWq+hqFIy8EKTDCPAhGG3aBAA4dNhRmI9T8FOnC4Hu3cOwkYQQQmINCkaEEEIIIREk4JI0QBOMfv8dqKkBPvoI+N//JLDo8cdhO+c8AMA4LHF1GLVxwQgQbU0JRioMmzhQglFVVcBP8ecwquw6CIBrsDshhJDEgoIRIYQQQkgECcph1K2b/NlswG+/AQ88IPf/85/APffAcvR4ABSMFHrBqKmJDiOvtMBhVFjo9oByGHUdDEALdSeEEJJ4UDAihBBCCIkgQQlGgOYymjoV2LVLBKRbb5V1HDEOgEMwSrYnrGAUbOg1QMHIJyEIRj17ytTFYVRRIeHrAA50PgwABSNCCElkKBgRQgghhESQoErSAC342lH6g4cfBtLTAQA1A49AA5LRGWUwbd0MFBTIMgkmGNFhFGbClWGk9smuXVGb1B4ABSNCCElkKBgRQgghhESQkB1GADBoEHDllc6bjYYUrMQoufHRR5L2nJ4uLqQ4JxTBSJ9VRMHIByF0SVMOo+Ji2c0AaILR4MFobJRZCkaEEJK4UDAihBBCCIkgLRKM/vMfF/WjoQH4DUfLjfffl+nAgYDB0PINjTKhCEZG3ZlsU5MmbFAwciMEh5F6it0O1NY67nTkF2HQIKdgxNBrQghJXPhzSgghhBASQYIuScvJAZ5/Xgb3Z5/t8lBDA7AE4wA8K/lGQEKUowGaYJSc7KzA84vBIOKQchfRYeSFELqk6QW82lqHSUnnMFL7Mx1GhBCSuPDnlBBCCCEkggTtMAKAW27xeHdjoxKMdCSIYJSdLdO8vOAMUxSMAiAEh5HBIMJdbS1QU+O4U+8wWiOzFIwIISRxYUkaIYQQQkgECUkw8kJDA7AXPVCc1EO7M0EEo8MOA555Bpg+PbjnKXGIgpEPQhCMACAjQ6a1tRC1cvt2uYMZRoQQ0ibgzykhhBBCSAQJuiTNB2oda9PHoWvl53IjQQQjgwH4+9+Df54Sh6xWCkZeCVEwUqWBtbUAtm2TDzkzE+jWjRlGhBDSBqDDiBBCCCEkgoTTYaQG6RszdWVpAwa0fMVxjBKHGhsBm831PuJA3yXNbg/4acphVFMDLb9o0CDAYKDDiBBC2gAUjAghhBBCIki4S9IAYEOH8TLTrRvQsWPLVxzHKHFI7+AymaKzLTGLchg1NQW1I7o4jHT5RQAYek0IIW0AXn8hhBBCCIkgkShJK+g4GnjnHaBPn5avNM5RgpFeB6HDyI127bT5qiogLS2gp3l1GAF0GBFCSBuAP6eEEEIIIREkEiVpKSkApkxp+QoTAOUmomDkA6NRytKqqqQsLTc3oKd5dBgNHgyAghEhhLQFWJJGCCGEEBJBIlGSxqBhDTqMAiSE4GslGNVU2706jLgvEkJI4kLBiBBCCCEkgkSiJI2uDg1mGAVICIKRKkkzFu+VujSTCejXDwD3RUIIaQtQMCKEEEIIiSARK0kjAJo7jIxG+SNu6DulBYhyGKWU7JKZnj2dChFL0gghJPHhzykhhBBCSARhSVpkcReMWI7mhRY4jFJLd8tMr17OxygYEUJI4kPBiBBCCCEkgrS0JO3dd4FPPnFdBwfpGu4laRSMvNCCDKP0A94FI4qXhBCSuPAnlRBCCCEkgrTEYVRUBFx5JWA2A+ecw0G6J+gwChAlGFVVBfwUJRhllhfKDB1GhBDSpqDDiBBCCCEkgrREMFq6VKYWC7B7N0vSPEHBKEBaUJLWvtLhMOrZ0/kY3W6EEJL4UDAihBBCCIkgLSlJW7ZMm9+5k4N0T7gLRuyQ5oUWlKR1rGaGESGEtEUoGBFCCCGERJCWOIzcBSOWpDVHCUR0GPmhBQ6jTrUsSSOEkLYIBSNCCCGEkAgSqmBkswHLl2u39Q4jCkYaLEkLkMxMmQbpMMpANdo3HZQ7GHpNyP+3d//RUdV3/sdfk5CExBASDOSHAcIvofI7gDH6VWmNgFQaa+uisgqstYpkd5ViLXaBgl1hlcOKPai7upR2D0dQjuhZlrJENKASUJCAglJJgagQfgoBAmTIfL5/jDPJTSYwSS5zM5Pn45w5M3PvzeQz8e0d8sr787lAm8JHKgAAwBXU3Clpe/ZY1yfev19yubyP6eqoRWAUpGZ2GHXV17Vf73sNMT0SANoCOowAAACuoOZ2GPmmo/lCIqakBUZgFKRmXiWtmxquXyQxJQ0A2gICIwAAgCvIFxhdvCjV1AT/db7paLfe6r1nSlpgvoDI97MhMGpEMxe99ncYERgBQJtDYAQAAHAF1Q13mjItzddh9Hd/572vqJBOnWr4mm0dgVGQmjkl7XIdRtQiAEQuAiMAAIAryNdhJAU/Le3CBam01Pt49Oja9Yr/+lfvPV0dtZiSFqRmdhj5AiOT1dWyjw4jAIh8BEYAAABXULt2DS/9fjk7dkhut5SaKvXoIWVne7cfPuy9p6ujFoFRkHyp49mzQc+NrLvo9cVMa4cRi14DQOQjMAIAALjCmnqlNN90tOuv9y567QuM6r8eCIyCVucKZ8EufF23w+h8l9rAqKZG8ni8jwmMACByERgBAABcYU29UlrdwEhqGBjxS3otAqMgxcXVFk6QgVFMO+PvMDrTqTYw8k1Hk6hFAIhkBEYAAABXmN2BER1GtepP9/M9RwBNXcfo6FG11wV55NLppGv8m+sGRtQiAEQuAiMAAIArrClT0i5ckPbs8T7OyfHeExg1jg6jJvAFRidPBnd8uXc62iFl6Gx1jH9z3TqOian/RQCASNEqAqPFixcrOztb7du3V25urj72/VktgFdffVU333yzUlJSlJKSovz8/EseDwAA4LSmdBgdOeK9j4mRunTxPmZKWuMIjJogLc1771s9/XK+D4zK1U1VVbWbfR1GMTHeNbYAAJHJ8cBoxYoVmjZtmmbPnq1PP/1UgwcP1ujRo3XE96+leoqLi3Xffffp/fffV0lJibp27apRo0bp22+/DfHIAQAAguMLjIKZCeT7Xb5Ll9pfxukwapwvIPJ1vRAYXUJGhvf+4MHgjq8TGJ09W7vZFxgRXAJAZHM8MFq4cKEefvhhTZ48Wdddd51eeeUVJSQkaMmSJQGPX7ZsmR577DENGTJE/fr102uvvSaPx6P169eHeOQAAADBGTrUe7969eWP9f3NzNddJEkpKbVXRZcIjOoiMGqCzEzvfbCB0dfeBa+/VteAHUbUIQBENkc/Uqurq7Vt2zbNmDHDvy0qKkr5+fkqKSkJ6jWqqqrkdrvVqVOngPsvXLigC3UmWld+/6c9t9stt9vdgtE7yzf2cH4PaB2oJdiFWoJdIrGW7r/fpSVL2umNN4wWLLio+PjGjz140CWpnbp08cjtrvFv7969nT7/3Nty5HK5FUE/nhZxuaIkRevcOSPJpaio2p9bJNZSS0SlpSlakuebb1QTxM8kev9+RcnbYdSl8qLcbiNJ33cbxSg21sjtvnglh9yqUE+wC7UEuwSqJTvrytHA6NixY6qpqVGabz7199LS0vTll18G9RpPPfWUMjMzlZ+fH3D/vHnzNGfOnAbb161bp4SEhKYPupUpKipyegiIENQS7EItwS6RVEsej9S58+06ejRBzzxTqv/3/xrv8Pjgg96S+qu6+hutWbPdvz0+/npJ3ilFGzcWKSmJXzQkad++ayX9QMZ4w7SjRw9qzZptlmMiqZZaouvRo8qRdGznTpWsWXPZ42/57DOlyBsY1Wz5XB07HpAkffVVsqRbVVNzTmvWtL2fLfUEu1BLsEvdWqqq2xLaQmHdtDt//nwtX75cxcXFau9bHKCeGTNmaNq0af7nlZWV/nWPknxXighDbrdbRUVFuv322xXD5SnQAtQS7EItwS6RWku/+EWU5s2Tdu0apmefHdLoccXF3hUDhg69RmPHZvi3FxVF6ZNPvI/vvPN2JSZeydGGj507rSssdO2aqbFjvX+MjNRaai5XbKz04ovq7HZr7Nixlz2+3ZQpkryB0S29Bmrs2P6SpE2bvOFcx47xQb1OpKCeYBdqCXYJVEuVwSyYGCRHA6PU1FRFR0frcL0rNRw+fFjp6emX/NoFCxZo/vz5evfddzVo0KBGj4uLi1NcgAnWMTExEfE/Z6S8DziPWoJdqCXYJdJqadIkad48ad26KB0/HqXG/qlz9Kj3PiMjWjEx0f7tPXvWHnPVVTFczvx79f+ZFxsbpZgYa4gUabXUbN26SZJchw5d/udRXS1VVEjyrmF0/nxtPXo83kNiY11t8udKPcEu1BLsUreW7KwpRxe9jo2N1bBhwywLVvsWsM7Ly2v065577jk988wzWrt2rYYPHx6KoQIAALTItddKN9wg1dRIr7/e+HGBFr2WrFdK4/eLWtHR1ucsen0JvkWvv/tOOnfu0sfu2SMZo3OxSTqmVBa9BoA2yPGrpE2bNk2vvvqq/vSnP+mLL77QlClTdPbsWU2ePFmS9OCDD1oWxf63f/s3zZw5U0uWLFF2drYqKipUUVGhM2fOOPUWAAAAgvLgg977P/+58WN8gVG9JR79gVFcnORy2T60sFU/ICIwuoTkZMm3jMOhQ5c+9tNPvYelDZXk+n6hay/f9WRiY20fIQCgFXE8MBo/frwWLFigWbNmaciQISotLdXatWv9C2GXl5frUJ0PtJdfflnV1dX6+c9/royMDP9twYIFTr0FAACAoIwf7+0OKi2VGru+h2+mfv0Oo/79pYEDpTa0ZExQ6gdE9TuOUIfLVdtldLDxhdcl+QOjI1k5khSww4jACAAiW6v4G0xhYaEKCwsD7isuLrY8379//5UfEAAAwBXQqZM3+Cktlfbvl/r1s+73eGrXMKofGMXFSTt2hGKU4YUOoybKzJT+9regO4xOZA+VSgiMAKAt4iMVAAAghFJSvPcnTjTc99133jWOJKlz54b7mYrWEIFREwXTYeTxeFNNSZW9vB1GdaeksYYRALQNjk9JAwAAaEt8gdF33zXc55uOlpJC90awCIyaKJjAaO9e6cwZKT5e7p59JdFhBABtEYERAABACF0qMGrsCmloHIFREwUTGH0/HU2DBim+g/cHyqLXAND2EBgBAACEUDCBUf0rpKFxBEZNFExgtH279z4nR1dd5X1IhxEAtD0ERgAAACHUqZP3/lJT0ugwCh6BURM1pcMoJ0cJCd6HgdYwIjACgMjGRyoAAEAIXWrRa6akNV10tPU5gdFlXC4wMqY2MBo6VFcZ78NAHUYseg0AkY0OIwAAgBAKZtFrpqQFjw6jJvIFRpWV3oWt6ysv96aZ7dpJAwb4O4yYkgYAbQ+BEQAAQAix6LW9CIyaqEMHKTHR+/jQoYb7fesXDRggxcUFnJLGotcA0DYQGAEAAIQQi17bq35AVH+KGgK41LS0OtPRJPkXvT5/XvJ4vI/pMAKAtoHACAAAIIRY9NpedBg1QzCBUU6OJPk7jKTaaWmsYQQAbQOBEQAAQAj5OozOnq39xduHKWlNR2DUDE0IjOLja3fVD4zoMAKAyEZgBAAAEEIdO9Y+rttlVFVVuwYxU9KCR2DUDI0FRnv2eNc1iomRBg+WJEVF1YZGvnWMWMMIANoGAiMAAIAQio6uDY3qBka+7qK4OO+6xAgOgVEzZGR47+sHRu+8470fObJ28SKpwZXS6DACgLaBwAgAACDEAi18XXc6mssV+jGFKwKjZmisw8gXGBUUWDb7siMCIwBoWwiMAAAAQizQwtdcIa15CIyaIVBgdOSIVFLiffyTn1gO93UY+aakseg1ALQNBEYAAAAh5uswOnGidhtXSGue6GjrcwKjINQNjIzxPl692vs4J0fq2tVyOB1GANA2ERgBAACE2OWmpCF4dBg1g28No6oqqbLS+7iR6WhSww4jFr0GgLaBwAgAACDEAgVGvg4jpqQ1DYFRM1x1lZSc7H28fLk3OCoq8j4PEBjRYQQAbROBEQAAQIjRYWQfAqNm+sd/9N5PmSJNnSqdOyd17y4NGtTgUNYwAoC2icAIAAAgxAKtYcSi181TPyCqv6YRGjFnjvTII951i5Yu9W4rKAh4iT5fYESHEQC0LQRGAAAAIRboKmkset08dBg1k8slvfSS9MADtdsCTEeTGk5JYw0jAGgb+EgFAAAIMaak2YfAqAWioqQlS7wFefKkdMstAQ9rbEoagREARDY+UgEAAEKsfmB0/rx09Kj3se+K5wgOgVELtWsnLVp0yUNY9BoA2iampAEAAIRY/cCorMy7lEzHjlJqqnPjCkcERlcei14DQNtEYAQAABBi9Re9/uor733v3gHXHMYl1F/kmsDIfix6DQBtE4ERAABAiPkWvT5/3nvbu9f7vE8f58YUrugwuvJ8U9J8HUYseg0AbQOBEQAAQIglJdV2En33XW2HEYFR00VFWbuyCIzsV7fDqKbGe5MIjAAg0hEYAQAAhFhUlJSc7H1cNzDq3duxIYW1uiERgZH96i567XbXbmcNIwCIbARGAAAADqi78DVT0lqGwOjKqrvotW/9IokOIwCIdARGAAAADvAFRgcPSl9/7X1MYNQ8dUOi+otgo+Xqdhj51i+SpJgYZ8YDAAgNAiMAAAAH+Ba+3rrVe5+cLF19tWPDCWt0GF1ZgTqM2rXzTq0EAEQuTvMAAAAO8HUYffyx9753b+vizQgegdGV5eswOnZMWrbM+5jpaAAQ+QiMAAAAHOALjLZt894zHa35CIyurN69pR/9SLp4UXrqKe82FrwGgMhHYAQAAOAAX2B0+rT3nsCo+equW0RgZD+XS1q7Vpo5s3YaGh1GABD5CIwAAAAc4AuMfHr3dmYckYAOoysvJkaaO1f68EMpN1eaONHpEQEArjQ+UgEAABzgW/Tahw6j5iMwCp28PGnzZqdHAQAIBTqMAAAAHFC/w4jAqPnqhkRcuQsAAHvwkQoAAOCAuoFRSop09dXOjSXc+QKjdu240hwAAHYhMAIAAHBA3cCI9Ytapm5gBAAA7EFgBAAA4IC6gRHT0VqGwAgAAPsRGAEAADiAwMg+vqAoOtrZcQAAEEkIjAAAAByQlFQbcBAYtQwdRgAA2I/ACAAAwAEuV+1C1wRGLUNgBACA/fhYBQAAcMiCBdKOHdKIEU6PJLz5OrUIjAAAsA8fqwAAAA554AHvDS1DhxEAAPZjShoAAADCGoERAAD2IzACAABAWCMwAgDAfgRGAAAACGsERgAA2I/ACAAAAGGNwAgAAPsRGAEAACCsERgBAGA/AiMAAACENQIjAADsR2AEAACAsOYLiqKjnR0HAACRhMAIAAAAYc0XFNFhBACAfQiMAAAAENaYkgYAgP0IjAAAABDWCIwAALAfgREAAADCGoERAAD2IzACAABAWCMwAgDAfgRGAAAACGsERgAA2I/ACAAAAGGNwAgAAPsRGAEAACCsERgBAGA/xwOjxYsXKzs7W+3bt1dubq4+/vjjRo/dtWuXfvaznyk7O1sul0svvPBC6AYKAACAVonACAAA+zkaGK1YsULTpk3T7Nmz9emnn2rw4MEaPXq0jhw5EvD4qqoq9ezZU/Pnz1d6enqIRwsAAIDWqFcv732PHs6OAwCASOJoYLRw4UI9/PDDmjx5sq677jq98sorSkhI0JIlSwIeP2LECD3//PO69957FRcXF+LRAgAAoDW65x5p925p5kynRwIAQORwrHG3urpa27Zt04wZM/zboqKilJ+fr5KSEtu+z4ULF3ThwgX/88rKSkmS2+2W2+227fuEmm/s4fwe0DpQS7ALtQS7UEtojt69JY/He/OhlmAn6gl2oZZgl0C1ZGddORYYHTt2TDU1NUpLS7NsT0tL05dffmnb95k3b57mzJnTYPu6deuUkJBg2/dxSlFRkdNDQISglmAXagl2oZZgF2oJdqKeYBdqCXapW0tVVVW2vW7ELw04Y8YMTZs2zf+8srJSXbt21ahRo5SUlOTgyFrG7XarqKhIt99+u2JiYpweDsIYtQS7UEuwC7UEu1BLsBP1BLtQS7BLoFryzaqyg2OBUWpqqqKjo3X48GHL9sOHD9u6oHVcXFzA9Y5iYmIi4n/OSHkfcB61BLtQS7ALtQS7UEuwE/UEu1BLsEvdWrKzphxb9Do2NlbDhg3T+vXr/ds8Ho/Wr1+vvLw8p4YFAAAAAADQ5jk6JW3atGmaOHGihg8fruuvv14vvPCCzp49q8mTJ0uSHnzwQV1zzTWaN2+eJO9C2bt37/Y//vbbb1VaWqrExET17t3bsfcBAAAAAAAQSRwNjMaPH6+jR49q1qxZqqio0JAhQ7R27Vr/Qtjl5eWKiqptgjp48KCGDh3qf75gwQItWLBAt956q4qLi0M9fAAAAAAAgIjk+KLXhYWFKiwsDLivfgiUnZ0tY0wIRgUAAAAAANB2ObaGEQAAAAAAAFonAiMAAAAAAABYEBgBAAAAAADAgsAIAAAAAAAAFgRGAAAAAAAAsCAwAgAAAAAAgAWBEQAAAAAAACwIjAAAAAAAAGBBYAQAAAAAAAALAiMAAAAAAABYEBgBAAAAAADAgsAIAAAAAAAAFgRGAAAAAAAAsCAwAgAAAAAAgEU7pwcQasYYSVJlZaXDI2kZt9utqqoqVVZWKiYmxunhIIxRS7ALtQS7UEuwC7UEO1FPsAu1BLsEqiVf1uHLPlqizQVGp0+fliR17drV4ZEAAAAAAADY7/Tp0+rYsWOLXsNl7IidwojH49HBgwfVoUMHuVwup4fTbJWVleratau+/vprJSUlOT0chDFqCXahlmAXagl2oZZgJ+oJdqGWYJdAtWSM0enTp5WZmamoqJatQtTmOoyioqKUlZXl9DBsk5SUxEkGtqCWYBdqCXahlmAXagl2op5gF2oJdqlfSy3tLPJh0WsAAAAAAABYEBgBAAAAAADAgsAoTMXFxWn27NmKi4tzeigIc9QS7EItwS7UEuxCLcFO1BPsQi3BLle6ltrcotcAAAAAAAC4NDqMAAAAAAAAYEFgBAAAAAAAAAsCIwAAAAAAAFgQGAEAAAAAAMCCwCgMLV68WNnZ2Wrfvr1yc3P18ccfOz0ktHK/+93v5HK5LLd+/fr5958/f15Tp07V1VdfrcTERP3sZz/T4cOHHRwxWouNGzdq3LhxyszMlMvl0ttvv23Zb4zRrFmzlJGRofj4eOXn5+urr76yHHPixAlNmDBBSUlJSk5O1kMPPaQzZ86E8F2gtbhcPU2aNKnBuWrMmDGWY6gnzJs3TyNGjFCHDh3UpUsX3XXXXdqzZ4/lmGA+18rLy/XjH/9YCQkJ6tKli5588kldvHgxlG8FrUAw9TRy5MgG56ZHH33Ucgz1hJdfflmDBg1SUlKSkpKSlJeXp7/85S/+/ZyXEKzL1VIoz0kERmFmxYoVmjZtmmbPnq1PP/1UgwcP1ujRo3XkyBGnh4ZWrn///jp06JD/9uGHH/r3PfHEE/qf//kfvfnmm9qwYYMOHjyou+++28HRorU4e/asBg8erMWLFwfc/9xzz+nFF1/UK6+8oi1btuiqq67S6NGjdf78ef8xEyZM0K5du1RUVKTVq1dr48aN+uUvfxmqt4BW5HL1JEljxoyxnKtef/11y37qCRs2bNDUqVO1efNmFRUVye12a9SoUTp79qz/mMt9rtXU1OjHP/6xqqurtWnTJv3pT3/S0qVLNWvWLCfeEhwUTD1J0sMPP2w5Nz333HP+fdQTJCkrK0vz58/Xtm3btHXrVv3oRz9SQUGBdu3aJYnzEoJ3uVqSQnhOMggr119/vZk6dar/eU1NjcnMzDTz5s1zcFRo7WbPnm0GDx4ccN/JkydNTEyMefPNN/3bvvjiCyPJlJSUhGiECAeSzKpVq/zPPR6PSU9PN88//7x/28mTJ01cXJx5/fXXjTHG7N6920gyn3zyif+Yv/zlL8blcplvv/02ZGNH61O/nowxZuLEiaagoKDRr6GeEMiRI0eMJLNhwwZjTHCfa2vWrDFRUVGmoqLCf8zLL79skpKSzIULF0L7BtCq1K8nY4y59dZbzT//8z83+jXUExqTkpJiXnvtNc5LaDFfLRkT2nMSHUZhpLq6Wtu2bVN+fr5/W1RUlPLz81VSUuLgyBAOvvrqK2VmZqpnz56aMGGCysvLJUnbtm2T2+221FW/fv3UrVs36gqXtG/fPlVUVFhqp2PHjsrNzfXXTklJiZKTkzV8+HD/Mfn5+YqKitKWLVtCPma0fsXFxerSpYv69u2rKVOm6Pjx4/591BMCOXXqlCSpU6dOkoL7XCspKdHAgQOVlpbmP2b06NGqrKy0/AUXbU/9evJZtmyZUlNTNWDAAM2YMUNVVVX+fdQT6qupqdHy5ct19uxZ5eXlcV5Cs9WvJZ9QnZPatfwtIFSOHTummpoay394SUpLS9OXX37p0KgQDnJzc7V06VL17dtXhw4d0pw5c3TzzTfr888/V0VFhWJjY5WcnGz5mrS0NFVUVDgzYIQFX30EOif59lVUVKhLly6W/e3atVOnTp2oLzQwZswY3X333erRo4fKysr09NNP64477lBJSYmio6OpJzTg8Xj0+OOP66abbtKAAQMkKajPtYqKioDnLt8+tE2B6kmS7r//fnXv3l2ZmZnauXOnnnrqKe3Zs0dvvfWWJOoJtT777DPl5eXp/PnzSkxM1KpVq3TdddeptLSU8xKapLFakkJ7TiIwAtqAO+64w/940KBBys3NVffu3fXGG28oPj7ewZEBQK17773X/3jgwIEaNGiQevXqpeLiYt12220Ojgyt1dSpU/X5559b1uUDmquxeqq7TtrAgQOVkZGh2267TWVlZerVq1eoh4lWrG/fviotLdWpU6e0cuVKTZw4URs2bHB6WAhDjdXSddddF9JzElPSwkhqaqqio6MbrKZ/+PBhpaenOzQqhKPk5GRde+212rt3r9LT01VdXa2TJ09ajqGucDm++rjUOSk9Pb3BovwXL17UiRMnqC9cVs+ePZWamqq9e/dKop5gVVhYqNWrV+v9999XVlaWf3swn2vp6ekBz12+fWh7GqunQHJzcyXJcm6iniBJsbGx6t27t4YNG6Z58+Zp8ODBWrRoEeclNFljtRTIlTwnERiFkdjYWA0bNkzr16/3b/N4PFq/fr1lPiNwOWfOnFFZWZkyMjI0bNgwxcTEWOpqz549Ki8vp65wST169FB6erqldiorK7VlyxZ/7eTl5enkyZPatm2b/5j33ntPHo/H/+EGNOabb77R8ePHlZGRIYl6gpcxRoWFhVq1apXee+899ejRw7I/mM+1vLw8ffbZZ5YAsqioSElJSf6Wf7QNl6unQEpLSyXJcm6inhCIx+PRhQsXOC+hxXy1FMgVPSc1Y4FuOGj58uUmLi7OLF261Ozevdv88pe/NMnJyZYV0IH6fvWrX5ni4mKzb98+89FHH5n8/HyTmppqjhw5Yowx5tFHHzXdunUz7733ntm6davJy8szeXl5Do8arcHp06fN9u3bzfbt240ks3DhQrN9+3Zz4MABY4wx8+fPN8nJyeadd94xO3fuNAUFBaZHjx7m3Llz/tcYM2aMGTp0qNmyZYv58MMPTZ8+fcx9993n1FuCgy5VT6dPnzbTp083JSUlZt++febdd981OTk5pk+fPub8+fP+16CeMGXKFNOxY0dTXFxsDh065L9VVVX5j7nc59rFixfNgAEDzKhRo0xpaalZu3at6dy5s5kxY4YTbwkOulw97d2718ydO9ds3brV7Nu3z7zzzjumZ8+e5pZbbvG/BvUEY4z5zW9+YzZs2GD27dtndu7caX7zm98Yl8tl1q1bZ4zhvITgXaqWQn1OIjAKQ3/4wx9Mt27dTGxsrLn++uvN5s2bnR4SWrnx48ebjIwMExsba6655hozfvx4s3fvXv/+c+fOmccee8ykpKSYhIQE89Of/tQcOnTIwRGjtXj//feNpAa3iRMnGmOM8Xg8ZubMmSYtLc3ExcWZ2267zezZs8fyGsePHzf33XefSUx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      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "test_predictions_np = np.concatenate(test_predictions)  \n",
    "test_labels_np = np.concatenate(test_labels)  \n",
    "\n",
    "\n",
    "plt.figure(figsize=(14, 7))\n",
    "plt.plot(test_labels_np, label='Gerçek Değerler', color='blue')\n",
    "plt.plot(test_predictions_np, label='Tahmin Edilen Değerler', color='red')\n",
    "plt.title('Gerçek ve Tahmin Edilen Değerler')\n",
    "plt.xlabel('Zaman (Saat)')\n",
    "plt.ylabel('Değer')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}